diff --git a/.dockerignore b/.dockerignore
new file mode 100644
index 0000000000000000000000000000000000000000..c82aeb1b243357d8da67c73ba19070827cc92df6
--- /dev/null
+++ b/.dockerignore
@@ -0,0 +1,24 @@
+# Git
+.git
+.gitignore
+
+# Environments and Secrets
+.env
+env/
+__pycache__/
+*.pyc
+
+# Testing and Load Testing
+locustfile.py
+tests/
+.pytest_cache/
+htmlcov/
+
+# Documentation and Metadata
+.vscode/
+.idea/
+
+# Large data files
+*.csv
+*.json
+!master_metadata.json
\ No newline at end of file
diff --git a/.gitattributes b/.gitattributes
new file mode 100644
index 0000000000000000000000000000000000000000..308ef13388f723205e88dbc77b22910efc0d8bb6
--- /dev/null
+++ b/.gitattributes
@@ -0,0 +1,3 @@
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.keras filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
diff --git a/.github/workflows/cd-ml.yml b/.github/workflows/cd-ml.yml
new file mode 100644
index 0000000000000000000000000000000000000000..15083f04e2c87e3f1663df1f46d54a820a7cbf85
--- /dev/null
+++ b/.github/workflows/cd-ml.yml
@@ -0,0 +1,49 @@
+name: SONIX-ML Continuous Deployment
+
+on:
+  push:
+    branches: [main]
+  workflow_run:
+    workflows: ["SONIX-ML Continuous Training"]
+    types: [completed]
+  # Allows for manual execution from the Actions tab
+  workflow_dispatch:
+
+jobs:
+  deploy-to-huggingface:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout Repository
+        uses: actions/checkout@v3
+        with:
+          # Fetch only the latest state to keep the runner lightweight
+          fetch-depth: 1 
+          lfs: true
+
+      - name: Initialize Clean Deployment Environment
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        run: |
+          # 1. Reset the local git state to remove historical binary conflicts
+          rm -rf .git
+          git init
+          
+          # 2. Configure Git LFS within the runner environment
+          # This converts physical binary files into LFS pointers during the push
+          git lfs install
+          git lfs track "*.pkl"
+          git lfs track "*.keras"
+          git lfs track "*.h5"
+          
+          # 3. Set deployment identity using the standard GitHub Actions bot
+          git config user.name "github-actions[bot]"
+          git config user.email "41898282+github-actions[bot]@users.noreply.github.com"
+          
+          # 4. Prepare the production-ready commit
+          git checkout -b main
+          git add .
+          git commit -m "deploy: production build for sonix-ml-api"
+          
+          # 5. Mirror the clean state to Hugging Face Spaces
+          # Note: --force is required here to overwrite the HF history with the clean version
+          git push --force https://SONIX-RUSH:$HF_TOKEN@huggingface.co/spaces/SONIX-RUSH/sonix-ml-api main
\ No newline at end of file
diff --git a/.github/workflows/ci-ml.yml b/.github/workflows/ci-ml.yml
new file mode 100644
index 0000000000000000000000000000000000000000..13f59379a135cebb4a249cb4491f54ba77e71a59
--- /dev/null
+++ b/.github/workflows/ci-ml.yml
@@ -0,0 +1,95 @@
+name: SONIX-ML Continuous Integration
+
+# This workflow ensures code quality, validates data artifacts, 
+# and performs unit tests to prevent regression in the footwear recommendation logic.
+on:
+  push:
+    branches: [ "main", "develop" ]
+    paths:
+      - "**"                       # Watch all files at the root and subfolders
+      - ".github/workflows/**"      # Watch for changes in the workflow itself
+  pull_request:
+    branches: [ "main", "develop" ]
+    paths:
+      - "**"                       # Ensure PRs also trigger for root files
+  # Allows manual triggering from the GitHub Actions tab
+  workflow_dispatch:
+
+# Security: Set global permissions to read-only for repository contents
+permissions:
+  contents: read
+
+# Optimization: Cancel in-progress builds if a new commit is pushed to the same branch
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  quality-assurance:
+    name: Code Quality & Automated Testing
+    runs-on: ubuntu-latest
+    
+    # Unified Environment Block: Prevents 'env is already defined' syntax errors
+    # Mock credentials allow the Supabase client to initialize safely during tests.
+    env:
+      SUPABASE_URL: "https://placeholder-project.supabase.co"
+      SUPABASE_KEY: "ci-mock-key-for-initialization"
+
+    steps:
+      - name: Fetch Source Code
+        uses: actions/checkout@v4
+
+      - name: Initialize Python Environment
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+          cache: "pip"
+          cache-dependency-path: "requirements.txt"
+
+      - name: Install Project Dependencies
+        run: |
+          python -m pip install --upgrade pip
+          # Prioritize the dev requirements to include testing tools like pytest
+          if [ -f requirements-dev.txt ]; then 
+            pip install -r requirements-dev.txt
+          else 
+            pip install -r requirements.txt pytest pytest-mock flake8 nbqa nbformat
+          fi
+
+      - name: Execute Static Analysis (Linting)
+        run: |
+          # Fails the build on critical syntax errors or undefined names
+          flake8 src --count --select=E9,F63,F7,F82 --show-source --statistics
+          # Soft-check for PEP8 style compliance and complexity (non-blocking)
+          flake8 src --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+
+      - name: Validate Jupyter Notebook Integrity
+        run: |
+          # Ensure .ipynb files in the notebooks directory are valid and not corrupt
+          python -c "
+          import os, nbformat, sys
+          failed = False
+          search_dir = 'notebooks' if os.path.exists('notebooks') else '.'
+          for root, _, files in os.walk(search_dir):
+              for f in files:
+                  if f.endswith('.ipynb'):
+                      try:
+                          nbformat.read(os.path.join(root, f), as_version=4)
+                      except Exception as e:
+                          print(f'Critical Error: Corrupt notebook {f} - {e}', file=sys.stderr)
+                          failed = True
+          if failed: sys.exit(1)
+          "
+
+      - name: API Smoke Test
+        run: |
+          # Verifies that the FastAPI application can be imported without runtime errors.
+          # Inject 'src' into PYTHONPATH to resolve internal module references.
+          export PYTHONPATH=$PYTHONPATH:$(pwd)/src
+          python -c "from src.main import app; print('>>> FastAPI Instance Initialized Successfully')"
+
+      - name: Execute Unit Tests
+        run: |
+          # Run automated logic tests using pytest.
+          export PYTHONPATH=$PYTHONPATH:$(pwd)/src
+          pytest tests/ --exitfirst
diff --git a/.github/workflows/ct-ml.yml b/.github/workflows/ct-ml.yml
new file mode 100644
index 0000000000000000000000000000000000000000..19860a13143a8da56df747b31e381f285fddf64c
--- /dev/null
+++ b/.github/workflows/ct-ml.yml
@@ -0,0 +1,69 @@
+name: SONIX-ML Continuous Training
+
+on:
+  # Triggered via Supabase Webhook (Database Changes)
+  repository_dispatch:
+    types: [shoe_data_changed]
+  
+  # Allows manual triggering from GitHub Actions tab (for testing)
+  workflow_dispatch:
+
+jobs:
+  train-and-deploy:
+    runs-on: ubuntu-latest
+    
+    # Standard permissions (overridden by GH_PAT for the push)
+    permissions:
+      contents: write
+
+    steps:
+      # 1. Checkout Code with PERSONAL ACCESS TOKEN
+      - name: Checkout Repository
+        uses: actions/checkout@v4
+        with:
+          token: ${{ secrets.GH_PAT }} 
+          fetch-depth: 0              # Fetch full history to ensure rebase works
+          persist-credentials: true   # Keep the token for the push step
+
+      # 2. Setup Python Environment
+      - name: Set up Python 3.11
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+          cache: "pip"
+
+      # 3. Install Dependencies
+      - name: Install Dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r requirements.txt
+
+      # 4. Execute Training Engine
+      # This script connects to Supabase, retrains the model, and saves artifacts.
+      - name: Run Training Pipeline
+        env:
+          SUPABASE_URL: ${{ secrets.SUPABASE_URL }}
+          SUPABASE_KEY: ${{ secrets.SUPABASE_KEY }}
+          PYTHONPATH: .
+        run: python -m src.training.training_engine
+
+      # 5. Commit, Rebase, and Push
+      # This step handles version control for the new model artifacts.
+      - name: Commit and Push New Artifacts
+        run: |
+          # Configure Git Identity (Bot)
+          git config --local user.email "action@github.com"
+          git config --local user.name "SONIX ML Bot"
+          
+          # Stage the new model artifacts
+          git add model_artifacts/
+          
+          # Commit changes (if any)
+          # '|| echo' prevents the workflow from failing if there are no changes
+          git commit -m "auto: model retraining complete [skip ci]" || echo "No changes to commit"
+          
+          # PULL REBASE: Critical to prevent 'race conditions' if the repo changed during training
+          git pull --rebase origin main
+          
+          # Push changes using the GH_PAT credentials from the checkout step
+          git push origin main
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..525da5fc24595fbb12dd2599e95faa440ee2fed1
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,4 @@
+env/
+.env
+__pycache__/
+.ipynb_checkpoints/
\ No newline at end of file
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000000000000000000000000000000000000..595d78a8b145c770a67fc136d9749b16cd3638da
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,29 @@
+# 1. Base Image
+FROM python:3.11-slim
+
+# 2. Environment Setup
+ENV PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1
+
+# 3. Working Directory
+WORKDIR /app
+
+# 4. Dependency Caching
+COPY requirements.txt .
+
+# 5. Installation
+RUN pip install --no-cache-dir --upgrade pip && \
+    pip install --no-cache-dir -r requirements.txt
+
+# 6. Source Code (Cukup ngopi 2 folder utama ini aja)
+COPY src/ src/
+COPY model_artifacts/ model_artifacts/
+
+# 7. Network (Udah disamain ke 7860)
+EXPOSE 7860
+
+# 8. Environment Variables (Warning PYTHONPATH udah dibenerin di sini)
+ENV PYTHONPATH="/app/src"
+
+# 9. Execution
+CMD ["gunicorn", "-w", "4", "-k", "uvicorn.workers.UvicornWorker", "src.main:app", "--bind", "0.0.0.0:7860"]
\ No newline at end of file
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..b47d617f52ae542ec0f5bd24e831b7b7843569e8
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,21 @@
+This project is licensed under the MIT License.
+
+Copyright (c) 2026 SONIX RUSH
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
\ No newline at end of file
diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..4fc1b365d8ec624c397b31fb270fd89987f1b49d
--- /dev/null
+++ b/README.md
@@ -0,0 +1,730 @@
+---
+title: Sonix ML API
+emoji: 👟
+colorFrom: blue
+colorTo: indigo
+sdk: docker
+pinned: false
+---
+
+# SONIX RUSH AI — Running Shoes Recommender Engine
+
+**SONIX RUSH AI** is a specialized machine learning inference service designed for the SONIX RUSH application. It implements a **Hybrid Recommendation System** that combines **Content-Based Filtering** (via Deep Autoencoders and K-Means Clustering) with **Collaborative Filtering** (User-Based KNN) to deliver personalized running shoe recommendations.
+
+This engine operates as a standalone inference service and communicates with the core backend and frontend via RESTful APIs.
+
+---
+
+## Table of Contents
+
+- [System Overview](#system-overview)
+- [Architecture](#architecture)
+- [Technology Stack](#technology-stack)
+- [Project Structure](#project-structure)
+- [Installation Guide](#installation-guide)
+- [Configuration](#configuration)
+- [Usage Guide](#usage-guide)
+- [API Documentation](#api-documentation)
+- [Continuous Training](#continuous-training)
+- [Error Handling](#error-handling)
+- [Docker Deployment](#docker-deployment)
+- [Testing](#testing)
+- [Contributing](#contributing)
+- [License](#license)
+
+---
+
+## System Overview
+
+The SONIX RUSH AI engine isolates high-computational ML workloads from the primary transactional backend. Running as an independent service ensures that model inference and data processing do not impact the performance of the main application.
+
+| Property | Detail |
+|---|---|
+| **Integration Method** | REST API |
+| **Methodology** | Hybrid — Content-Based + Collaborative (separate pipelines) |
+| **Optimization** | In-Memory Micro-Caching with TTL (60s) |
+| **Continuous Training** | Background CF refresh every 50 interactions |
+| **Deployment** | Docker / Hugging Face Spaces |
+
+---
+
+## Architecture
+
+The system uses two **independent** recommendation pipelines, each serving a different use case.
+```
+┌──────────────────────────────────────────────────────────────────────┐
+│                        SONIX RUSH AI Engine                          │
+│                                                                      │
+│  ┌─────────────────────────────────┐   ┌─────────────────────────┐   │
+│  │    Content-Based Pipeline       │   │  Collaborative Pipeline │   │
+│  │  POST /recommend/road           │   │  POST /interact         │   │
+│  │  POST /recommend/trail          │   │  GET  /recommend/feed   │   │
+│  │                                 │   │                         │   │
+│  │  1. Map user questionnaire      │   │  1. Receive interaction │   │
+│  │     → numerical vector          │   │  2. Real-time inject    │   │
+│  │  2. Encode → 8D Latent Space    │   │     into user vector    │   │
+│  │     (Deep Autoencoder)          │   │  3. KNN: find similar   │   │
+│  │  3. Route to nearest clusters   │   │     users (cosine)      │   │
+│  │     (K-Means, top ⌈K/3⌉)         │   │  4. Weighted score      │   │
+│  │  4. Masked Cosine Similarity    │   │     aggregation         │   │
+│  │  5. Return Top 10 shoes         │   │  5. Return Top 20 shoes │   │
+│  └─────────────────────────────────┘   └─────────────────────────┘   │
+│                                                                      │
+│                    FastAPI + Uvicorn (4 workers)                     │
+└──────────────────────────────────────────────────────────────────────┘
+              ↑                                      ↑
+      SONIX RUSH Backend                    SONIX RUSH Frontend
+        (REST calls)                           (REST calls)
+```
+
+### Content-Based: Training Pipeline (Offline)
+```
+Supabase → fetch_shoes_by_type() → MinMaxScaler
+    → Deep Autoencoder (300 epochs, batch=64)
+    → K-Means (K=5, n_init=20) on 8D latent space
+    → Save versioned artifacts to model_artifacts/{type}/v_{timestamp}/
+```
+
+### Content-Based: Inference Pipeline (Online)
+```
+User Questionnaire → Heuristic Feature Mapping → Numerical Vector
+    → Encoder → 8D Latent Vector
+    → K-Means: select top ⌈K/3⌉ nearest clusters
+    → Masked Cosine Similarity on candidate pool
+    → Top 10 shoes
+```
+
+Feature masking ensures similarity is computed **only on features the user explicitly provided**, preventing noise from neutral default values.
+
+### Collaborative Filtering Pipeline (Real-Time)
+```
+POST /interact → Real-Time Injection into user vector
+    → User-Based KNN (cosine, brute force, sparse CSR matrix)
+    → Weighted score aggregation from k neighbors
+    → Filter seen items → Enrich with shoe metadata
+    → Top 20 shoes (TTL-cached 60s per user)
+    → Background CT trigger every 50 interactions
+```
+
+### Autoencoder Architecture
+```
+Input (N-dim)
+  → Dense(32) + BatchNorm + Dropout(0.3)   ← Encoder
+  → Dense(16) + BatchNorm + Dropout(0.3)   ← Encoder
+  → Dense(8)  + BatchNorm + Dropout(0.3)   ← Latent Space (saved as shoe_encoder.h5)
+  → Dense(16) + BatchNorm + Dropout(0.3)   ← Decoder
+  → Dense(32) + BatchNorm + Dropout(0.3)   ← Decoder
+  → Dense(N, sigmoid)                      ← Reconstruction Output
+
+Loss: MSE | Optimizer: Adam (lr=0.001) | Metric: MAE
+```
+
+---
+
+## Technology Stack
+
+### Core & Backend
+| Library | Version | Purpose |
+|---|---|---|
+| Python | 3.11 | Primary language — strict version required for TensorFlow 2.15.0 |
+| FastAPI | 0.129.0 | High-performance async web framework |
+| Uvicorn | 0.40.0 | ASGI web server |
+| Gunicorn | 25.1.0 | Process manager |
+| Pydantic | 2.12.5 | Request/response schema validation |
+| ujson | 5.11.0 | High-speed JSON serialization (`UJSONResponse`) |
+
+### Machine Learning & Data Science
+| Library | Version | Purpose |
+|---|---|---|
+| TensorFlow / Keras | 2.15.0 | Deep Autoencoder for latent space projection |
+| Scikit-Learn | 1.5.2 | K-Means Clustering and User-Based KNN |
+| Pandas | 2.2.2 | Data manipulation and user-item pivot matrix |
+| NumPy | 1.26.4 | Numerical computation and vector operations |
+| SciPy | 1.13.1 | Sparse CSR matrix for memory-efficient CF |
+
+### Database & Infrastructure
+| Library | Version | Purpose |
+|---|---|---|
+| Supabase | 2.27.3 | PostgreSQL-based Backend-as-a-Service |
+| HTTPX | 0.28.1 | Async HTTP client |
+| python-dotenv | 1.0.1 | Environment variable management |
+| Docker | latest | Containerization |
+
+### Testing & Quality Assurance
+| Library | Version | Purpose |
+|---|---|---|
+| Pytest | 8.0.2 | Unit and integration testing |
+| pytest-mock | 3.12.0 | Supabase client mocking in CI |
+| pytest-asyncio | 0.23.5 | Async test support |
+| Locust | 2.24.0 | Load testing and benchmarking |
+| Flake8 | 7.0.0 | Code linting |
+
+---
+
+## Project Structure
+```
+sonix-ml/
+│
+├── src/
+│   ├── main.py                        # FastAPI app, lifespan, all endpoints
+│   ├── config.py                      # ROAD_FEATURES and TRAIL_FEATURES definitions
+│   ├── database.py                    # Supabase client, interaction aggregation, upsert logic
+│   │
+│   ├── recommender/
+│   │   ├── content_based.py           # Core pipeline: cluster routing + masked cosine similarity
+│   │   ├── road_recommender.py        # Road heuristic mapping + pipeline wrapper
+│   │   ├── trail_recommender.py       # Trail heuristic mapping + pipeline wrapper
+│   │   └── collaborative_filtering.py # UserCollaborativeRecommender (UBCF + TTL cache)
+│   │
+│   └── training/
+│       ├── architecture.py            # Deep Autoencoder definition (build_autoencoder)
+│       └── training_engine.py         # Full training orchestration (run_training)
+│
+├── model_artifacts/
+│   ├── road/
+│   │   └── v_YYYYMMDD_HHMMSS/        # Versioned — latest selected automatically on startup
+│   │       ├── shoe_encoder.h5
+│   │       ├── kmeans_model.pkl
+│   │       ├── scaler.pkl
+│   │       ├── shoe_features.pkl
+│   │       └── shoe_metadata.pkl
+│   └── trail/
+│       └── v_YYYYMMDD_HHMMSS/        # Same structure as road
+│
+├── data/
+│   ├── road_dataset.csv
+│   ├── trail_dataset.csv
+│   └── unused-data/                   # Archived previous dataset versions
+│
+├── notebooks/
+│   ├── data-preparation/
+│   ├── data-preparation-v2/
+│   ├── data-preparation-v3/
+│   └── modelling/                     # road_ml.ipynb, trail_ml.ipynb
+│
+├── tests/
+│   ├── conftest.py                    # Auto-mocks Supabase for all tests
+│   ├── test_data_processing.py        # Rating conversion + DB error handling
+│   └── test_recommender_logic.py      # Heuristic mapping + priority logic
+│
+├── .github/workflows/
+│   ├── ci-ml.yml                      # Continuous Integration (lint + test)
+│   ├── cd-ml.yml                      # Continuous Deployment
+│   └── ct-ml.yml                      # Continuous Training trigger
+│
+├── locustfile.py
+├── Dockerfile
+├── requirements.txt                   # Production dependencies (all pinned)
+├── requirements-dev.txt               # Dev/test dependencies
+└── README.md
+```
+
+---
+
+## Installation Guide
+
+### Prerequisites
+- Python **3.11** (strictly required)
+- Git
+
+### 1. Clone the Repository
+```bash
+git clone https://github.com/SONIX-Kelompok-6/sonix-ml
+cd sonix-ml
+```
+
+### 2. Set Up a Virtual Environment
+
+**Windows (PowerShell):**
+```powershell
+py -3.11 -m venv env
+.\env\Scripts\activate
+```
+
+**macOS / Linux:**
+```bash
+python3.11 -m venv env
+source env/bin/activate
+```
+
+### 3. Install Dependencies
+
+**Production:**
+```bash
+pip install -r requirements.txt
+```
+
+**Development** (includes testing and linting tools):
+```bash
+pip install -r requirements-dev.txt
+```
+
+---
+
+## Configuration
+
+Create a `.env` file in the root directory:
+```bash
+cp .env.example .env
+```
+
+Then populate it with your credentials:
+```env
+SUPABASE_URL=https://your-project-id.supabase.co
+SUPABASE_KEY=your-anon-public-key
+```
+
+### Environment Variables Reference
+
+| Variable | Required | Description |
+|---|---|---|
+| `SUPABASE_URL` | ✅ Yes | Your Supabase project URL |
+| `SUPABASE_KEY` | ✅ Yes | Supabase anon/public API key |
+
+> 🔒 Never commit `.env` to version control. Only `.env.example` (with placeholder values) should be committed.
+
+---
+
+## Usage Guide
+
+### 1. Training Pipeline (Offline)
+
+Before starting the API, model artifacts must be generated. This step fetches shoe catalog data from Supabase, trains the Deep Autoencoders, runs K-Means Clustering, and serializes all artifacts with a versioned timestamp.
+```bash
+python -m src.training.training_engine
+```
+
+The engine runs sequentially for both categories:
+```
+>>> Initializing training sequence for: ROAD
+>>> Initializing training sequence for: TRAIL
+```
+
+Artifacts are saved to versioned directories:
+```
+model_artifacts/
+├── road/v_YYYYMMDD_HHMMSS/
+└── trail/v_YYYYMMDD_HHMMSS/
+```
+
+**Generated files per category:**
+
+| File | Description |
+|---|---|
+| `shoe_encoder.h5` | Encoder model — projects features to 8D latent space |
+| `kmeans_model.pkl` | K-Means (K=5) — routes inputs to candidate clusters |
+| `scaler.pkl` | MinMaxScaler — must be used for inference normalization |
+| `shoe_features.pkl` | Scaled feature matrix — used for cosine similarity at inference |
+| `shoe_metadata.pkl` | DataFrame with cluster labels + column type definitions |
+
+> ⚠️ The API will fail to start if artifacts are missing. Always run training before launching the server.
+>
+> 💡 The API automatically selects the **latest versioned directory** on startup — no manual version management required.
+
+### 2. Starting the API (Online)
+
+Once artifacts are generated, launch the FastAPI server:
+```bash
+python -m src.main
+```
+
+The server starts at `http://0.0.0.0:7860` with **4 Uvicorn workers**. On startup, the server loads both artifact sets and initializes the CF engine from Supabase interaction data.
+
+---
+
+## API Documentation
+
+Interactive documentation is available at runtime:
+
+- **Swagger UI:** `http://127.0.0.1:7860/docs`
+- **ReDoc:** `http://127.0.0.1:7860/redoc`
+
+Visiting `/` auto-redirects to `/docs`.
+
+---
+
+### `GET /health`
+
+Returns service health and Continuous Training sync progress.
+
+**Response `200 OK`:**
+```json
+{
+  "status": "healthy",
+  "ct_sync_progress": "12/50"
+}
+```
+
+---
+
+### `POST /recommend/road`
+
+Returns Top 10 road shoe recommendations via the Content-Based pipeline. All fields are optional — unset fields are excluded from similarity calculation via feature masking.
+
+**Request Body:**
+```json
+{
+  "pace": "Fast",
+  "arch_type": "Normal",
+  "strike_pattern": "Mid",
+  "foot_width": "Regular",
+  "season": "Summer",
+  "orthotic_usage": "No",
+  "running_purpose": "Race",
+  "cushion_preferences": "Firm",
+  "stability_need": "Neutral"
+}
+```
+
+**Request Body Fields:**
+
+| Field | Accepted Values |
+|---|---|
+| `pace` | `"Easy"`, `"Steady"`, `"Fast"` |
+| `arch_type` | `"Flat"`, `"Normal"`, `"High"` |
+| `strike_pattern` | `"Heel"`, `"Mid"`, `"Forefoot"` |
+| `foot_width` | `"Narrow"`, `"Regular"`, `"Wide"` |
+| `season` | `"Summer"`, `"Spring & Fall"`, `"Winter"` |
+| `orthotic_usage` | `"Yes"`, `"No"` |
+| `running_purpose` | `"Daily"`, `"Tempo"`, `"Race"` |
+| `cushion_preferences` | `"Soft"`, `"Balanced"`, `"Firm"` |
+| `stability_need` | `"Neutral"`, `"Guided"` |
+
+**Response `200 OK`:**
+```json
+{
+  "status": "success",
+  "data": [
+    {
+      "shoe_id": "R045",
+      "name": "Nike Vaporfly 3",
+      "brand": "Nike",
+      "cluster": 2,
+      "match_score": 0.97
+    }
+  ]
+}
+```
+
+---
+
+### `POST /recommend/trail`
+
+Returns Top 10 trail shoe recommendations. Uses trail-specific heuristics for terrain, traction, lug depth, and water resistance.
+
+**Request Body:**
+```json
+{
+  "pace": "Steady",
+  "arch_type": "Normal",
+  "strike_pattern": "Mid",
+  "foot_width": "Regular",
+  "season": "Summer",
+  "orthotic_usage": "No",
+  "terrain": "Rocky",
+  "rock_sensitive": "Yes",
+  "water_resistance": "Water Repellent"
+}
+```
+
+**Request Body Fields:**
+
+| Field | Accepted Values |
+|---|---|
+| `pace` | `"Easy"`, `"Steady"`, `"Fast"` |
+| `arch_type` | `"Flat"`, `"Normal"`, `"High"` |
+| `strike_pattern` | `"Heel"`, `"Mid"`, `"Forefoot"` |
+| `foot_width` | `"Narrow"`, `"Regular"`, `"Wide"` |
+| `season` | `"Summer"`, `"Spring & Fall"`, `"Winter"` |
+| `orthotic_usage` | `"Yes"`, `"No"` |
+| `terrain` | `"Light"`, `"Mixed"`, `"Rocky"`, `"Muddy"` |
+| `rock_sensitive` | `"Yes"`, `"No"` |
+| `water_resistance` | `"Waterproof"`, `"Water Repellent"` |
+
+**Response `200 OK`:**
+```json
+{
+  "status": "success",
+  "data": [
+    {
+      "shoe_id": "T012",
+      "name": "Hoka Speedgoat 5",
+      "brand": "Hoka",
+      "cluster": 4,
+      "match_score": 0.94
+    }
+  ]
+}
+```
+
+---
+
+### `POST /interact`
+
+Records a user interaction (Like or Rating) and immediately returns personalized CF recommendations via **Real-Time Injection** — the new interaction is injected into the user's vector before running KNN, so results reflect the latest signal without waiting for a full rebuild.
+
+Also triggers a **background CT refresh** every 50 interactions (see [Continuous Training](#continuous-training)).
+
+**Request Body:**
+```json
+{
+  "user_id": 8,
+  "shoe_id": "R278",
+  "action_type": "like",
+  "value": null
+}
+```
+
+**Request Body Fields:**
+
+| Field | Type | Required | Description |
+|---|---|---|---|
+| `user_id` | `integer` | ✅ Yes | Interacting user's ID |
+| `shoe_id` | `string` | ✅ Yes | Target shoe ID (`R` prefix = road, `T` prefix = trail) |
+| `action_type` | `string` | ✅ Yes | `"like"` or `"rate"` |
+| `value` | `integer` | Conditional | Star rating `1–5`. Required when `action_type` is `"rate"` |
+
+**Interaction Score Mapping:**
+
+| Signal | Converted Score |
+|---|---|
+| Like | `+1.0` |
+| Rate 5★ | `+2.0` |
+| Rate 4★ | `+1.0` |
+| Rate 3★ | `+0.1` (neutral) |
+| Rate 2★ | `-1.0` |
+| Rate 1★ | `-2.0` |
+
+> If a user has both liked and rated the same shoe, scores are **summed** for higher confidence.
+
+**Response `200 OK`:**
+```json
+{
+  "status": "success",
+  "data": [
+    {
+      "shoe_id": "R145",
+      "name": "ASICS Gel-Kayano 31",
+      "brand": "ASICS",
+      "match_score": 1.84
+    }
+  ]
+}
+```
+
+---
+
+### `GET /recommend/feed/{user_id}`
+
+Returns a personalized shoe feed from the CF engine. Served from the **60-second TTL cache** if available, otherwise computed fresh.
+
+**Path Parameters:**
+
+| Parameter | Type | Required | Description |
+|---|---|---|---|
+| `user_id` | `integer` | ✅ Yes | Target user's ID |
+
+**Example Request:**
+```
+GET /recommend/feed/8
+```
+
+**Response `200 OK`:**
+```json
+{
+  "status": "success",
+  "data": [
+    {
+      "shoe_id": "R278",
+      "name": "Nike Pegasus 41",
+      "brand": "Nike",
+      "match_score": 2.31
+    }
+  ]
+}
+```
+
+**Response `404 Not Found`** — cold-start user with no interaction history.
+
+---
+
+## Continuous Training
+
+Every **50 new interactions** received via `POST /interact`, a non-blocking background task is dispatched that:
+
+1. Fetches fresh interaction data from Supabase (`favorites` + `reviews` tables)
+2. Rebuilds `UserCollaborativeRecommender` from the updated data
+3. Hot-swaps the global `cf_engine` with zero downtime
+
+The API response is returned immediately — the rebuild happens in the background. Progress is visible in `/health` via `ct_sync_progress`.
+
+---
+
+## Error Handling
+
+All error responses follow a consistent format:
+```json
+{
+  "detail": "Human-readable error message."
+}
+```
+
+| Status Code | Meaning | Common Cause |
+|---|---|---|
+| `200 OK` | Success | — |
+| `404 Not Found` | Resource not found | Cold-start user with no interaction history |
+| `422 Unprocessable Entity` | Validation error | Missing or invalid request fields |
+| `500 Internal Server Error` | Unexpected error | Check server logs |
+| `503 Service Unavailable` | Engine not ready | Run training pipeline first |
+
+---
+
+## Docker Deployment
+
+This service is containerized and compatible with **Hugging Face Spaces** and standard Docker environments.
+
+### Build and Run
+```bash
+docker build -t sonix-ml .
+docker run -p 7860:7860 --env-file .env sonix-ml
+```
+
+The service will be accessible at `http://localhost:7860`.
+
+### Docker Compose (Optional)
+```yaml
+version: "3.9"
+services:
+  rush-ai:
+    build: .
+    ports:
+      - "7860:7860"
+    env_file:
+      - .env
+    restart: unless-stopped
+```
+```bash
+docker compose up --build
+```
+
+---
+
+## Testing
+
+### Unit & Integration Tests
+
+`conftest.py` automatically mocks the Supabase client across all tests, preventing real network calls during CI.
+```bash
+# Run all tests
+pytest
+
+# With verbose output and coverage report
+pytest -v --tb=short --cov=src --cov-report=term-missing
+```
+
+**Test coverage:**
+
+| File | What it tests |
+|---|---|
+| `test_data_processing.py` | Rating-to-score conversion, empty DataFrame handling from DB |
+| `test_recommender_logic.py` | Heuristic priority mapping, fallback defaults, multi-source priority |
+
+### Load Testing
+```bash
+locust -f locustfile.py
+```
+
+Open `http://localhost:8089` to configure and launch. Default scenario:
+
+| Endpoint | Weight | Description |
+|---|---|---|
+| `POST /interact` | 3× | Heaviest endpoint — simulates likes and ratings |
+| `GET /recommend/feed/{user_id}` | 1× | Feed retrieval |
+| `GET /health` | 1× | Lightweight health probe |
+
+### Load Test Results
+
+> Total requests: **10,420** across all endpoints — **0 failures** (0% error rate).
+> Aggregated throughput: **65.5 RPS**.
+
+#### Request Statistics
+
+| Method | Endpoint | # Requests | Median (ms) | Avg (ms) | Min (ms) | Max (ms) | RPS |
+|---|---|---|---|---|---|---|---|
+| GET | `/health` | 2,052 | 4 | 27.6 | 2 | 3,014 | 14.3 |
+| POST | `/interact` | 6,218 | 23 | 46.93 | 10 | 3,173 | 39.1 |
+| GET | `/recommend/feed/8` | 2,150 | 7 | 37.36 | 3 | 2,146 | 12.1 |
+| | **Aggregated** | **10,420** | **19** | **41.15** | **2** | **3,173** | **65.5** |
+
+#### Response Time Percentiles
+
+| Method | Endpoint | p50 (ms) | p90 (ms) | p95 (ms) | p99 (ms) |
+|---|---|---|---|---|---|
+| GET | `/health` | 4 | 13 | 19 | 900 |
+| POST | `/interact` | 23 | 41 | 53 | 730 |
+| GET | `/recommend/feed/8` | 7 | 23 | 33 | 2,000 |
+| | **Aggregated** | **19** | **36** | **46** | **920** |
+
+**Key observations:**
+- `POST /interact` handles the highest load (39.1 RPS, 3× weight) with a p95 of **53ms** — well within real-time UX thresholds.
+- `GET /recommend/feed/8` achieves a p50 of **7ms** for cached responses thanks to the 60-second TTL cache.
+- `GET /health` spikes at p99 (900ms) and max (3,014ms) due to occasional GIL contention under high concurrency — negligible for a health probe.
+- Zero failures across 10,420 total requests confirms production-grade stability.
+
+### Code Linting
+```bash
+flake8 src/
+```
+
+---
+
+## Contributing
+
+1. Fork the repository.
+2. Create a new branch: `git checkout -b feature/your-feature-name`
+3. Commit your changes using [Conventional Commits](https://www.conventionalcommits.org/).
+4. Push to the branch: `git push origin feature/your-feature-name`
+5. Open a Pull Request.
+
+Ensure all code passes `flake8` and `pytest` before submitting.
+
+### Commit Message Convention
+
+| Prefix | Use for |
+|---|---|
+| `feat:` | New features |
+| `fix:` | Bug fixes |
+| `docs:` | Documentation changes |
+| `refactor:` | Code restructuring without behavior change |
+| `test:` | Adding or updating tests |
+| `chore:` | Maintenance tasks (deps, CI, config) |
+
+---
+
+## License
+
+This project is licensed under the MIT License.
+
+Copyright (c) 2026 SONIX RUSH
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+---
+
+*Built with ❤️ for the SONIX RUSH Application.*
\ No newline at end of file
diff --git a/data/.gitkeep b/data/.gitkeep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/data/SONIX utilities - Road.csv b/data/SONIX utilities - Road.csv
new file mode 100644
index 0000000000000000000000000000000000000000..14c3de422452fe1e8aa94ef269e7c341f6dafdf3
--- /dev/null
+++ b/data/SONIX utilities - Road.csv	
@@ -0,0 +1,2327 @@
+Brand,Name,Audience score,Price,Pace,Arch support,Weight lab Weight brand,Lightweight,Drop lab Drop brand,Strike pattern,Size,Midsole softness,Toebox durability,Heel padding durability,Outsole durability,Breathability,Width / fit,Toebox width,Stiffness,Torsional rigidity,Heel counter stiffness,Plate,Rocker,Heel lab Heel brand,Forefoot lab Forefoot brand,Widths available,Orthotic friendly,Season,Removable insole,Ranking,Popularity,Gender,Terrain
+Brooks, Launch 9,"87
+ Great!",$110,Daily runningTempo,Neutral,7.9 oz / 225g 8.1 oz / 230g,1,9.4 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,-,-,-,-,Narrow,-,Stiff,Stiff,Flexible,0,0,32.4 mm 36.0 mm,23.0 mm 26.0 mm,NormalWide,1,-,1,#301 Top 47%,#352 Bottom 45%,,
+Brooks, Levitate 6,"90
+ Superb!",$150,Daily running,Neutral,10.7 oz / 304g 10.9 oz / 309g,0,7.7 mm 8.0 mm,Mid/forefoot,True to size,Soft,Good,Good,Good,Breathable,Narrow,Medium,Stiff,Moderate,Moderate,0,0,34.3 mm 32.5 mm,26.6 mm 24.5 mm,Normal,1,SummerAll seasons,1,#72 Top 20%,#255 Bottom 30%,,
+Adidas,4DFWD,"90
+ Superb!",$200,Daily running,Neutral,11.9 oz / 336g 11.5 oz / 327g,0,8.9 mm 10.0 mm,HeelMid/forefoot,True to size,Firm,-,Good,-,Warm,Narrow,-,Stiff,Flexible,Flexible,0,0,33.3 mm 32.5 mm,24.4 mm 22.5 mm,Normal,1,All seasons,1,#104 Top 17%,#368 Bottom 42%,,
+Adidas,4DFWD 2,"90
+ Superb!",$200,Daily running,Neutral,12.6 oz / 356g 12.4 oz / 352g,0,10.6 mm 11.0 mm,Heel,Slightly small,Firm,-,-,-,Warm,Narrow,-,Stiff,Flexible,Moderate,0,0,31.8 mm 32.0 mm,21.2 mm 21.0 mm,Normal,1,All seasons,1,#126 Top 20%,#541 Bottom 16%,,
+Adidas,4DFWD 3,"88
+ Great!",$200,Daily running,Neutral,12.3 oz / 348g 12.2 oz / 345g,0,9.9 mm 10.0 mm,HeelMid/forefoot,True to size,Firm,Good,Good,Good,Warm,Narrow,Medium,Moderate,Flexible,Flexible,0,0,32.6 mm 34.0 mm,22.7 mm 24.0 mm,Normal,1,All seasons,1,#116 Top 32%,#339 Bottom 7%,,
+Adidas,4DFWD 3,"88
+ Great!",$200,Daily running,Neutral,12.3 oz / 348g 12.2 oz / 345g,0,9.9 mm 10.0 mm,HeelMid/forefoot,True to size,Firm,Good,Good,Good,Warm,Narrow,Medium,Moderate,Flexible,Flexible,0,0,32.6 mm 34.0 mm,22.7 mm 24.0 mm,Normal,1,All seasons,1,#116 Top 32%,#339 Bottom 7%,,
+Brooks,Addiction GTS 15,"85
+ Good!",$140,Daily running,Motion control,12.5 oz / 353g 12.2 oz / 346g,0,12.1 mm 12.0 mm,Heel,Slightly small,Firm,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,0,36.5 mm 36.0 mm,24.4 mm 24.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#221 Bottom 39%,#159 Top 44%,,
+Adidas,Adidas Adizero SL2,"90
+ Superb!",$130,Daily runningTempo,Neutral,8.6 oz / 245g 8.4 oz / 238g,1,8.2 mm 9.0 mm,HeelMid/forefoot,Half size small,Balanced,Bad,Good,Decent,Breathable,Wide,Medium,Moderate,Moderate,Flexible,0,0,34.9 mm 35.0 mm,26.7 mm 26.0 mm,NormalWide,1,SummerAll seasons,1,#44 Top 13%,#166 Top 46%,,
+Adidas,Adistar,"88
+ Great!",$130,Daily running,Neutral,11.5 oz / 325g 11.2 oz / 318g,0,9.6 mm 6.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,-,Moderate,Narrow,Medium,Stiff,Stiff,Flexible,0,1,34.4 mm 37.5 mm,24.8 mm 31.5 mm,Normal,1,All seasons,1,#267 Top 42%,#247 Top 39%,,
+Adidas,Adistar 2.0,"83
+ Good!",$130,Daily running,Neutral,11.6 oz / 328g 11.6 oz / 328g,0,8.0 mm 6.0 mm,HeelMid/forefoot,Half size small,Balanced,Bad,-,-,Breathable,Narrow,Wide,Stiff,Stiff,Stiff,0,1,33.8 mm 33.0 mm,25.8 mm 27.0 mm,Normal,1,SummerAll seasons,1,#506 Bottom 21%,#643 Bottom 1%,,
+Adidas,Adistar 3,"89
+ Great!",$130,Daily running,Neutral,9.7 oz / 274g 9.5 oz / 270g,0,10.5 mm 5.0 mm,Heel,True to size,Soft,Bad,Decent,Good,Breathable,Medium,Medium,Moderate,Stiff,Stiff,0,0,40.7 mm 40.0 mm,30.2 mm 35.0 mm,NormalWide,1,SummerAll seasons,1,#97 Top 27%,#241 Bottom 33%,,
+Adidas,Adistar 3,"89
+ Great!",$130,Daily running,Neutral,9.7 oz / 274g 9.5 oz / 270g,0,10.5 mm 5.0 mm,Heel,True to size,Soft,Bad,Decent,Good,Breathable,Medium,Medium,Moderate,Stiff,Stiff,0,0,40.7 mm 40.0 mm,30.2 mm 35.0 mm,NormalWide,1,SummerAll seasons,1,#96 Top 27%,#242 Bottom 34%,,
+Adidas,Adistar 3,"89
+ Great!",$130,Daily running,Neutral,9.7 oz / 274g 9.5 oz / 270g,0,10.5 mm 5.0 mm,Heel,True to size,Soft,Bad,Decent,Good,Breathable,Medium,Medium,Moderate,Stiff,Stiff,0,0,40.7 mm 40.0 mm,30.2 mm 35.0 mm,NormalWide,1,SummerAll seasons,1,#98 Top 27%,#242 Bottom 33%,,
+Adidas,Adistar 3,"89
+ Great!",$130,Daily running,Neutral,9.7 oz / 274g 9.5 oz / 270g,0,10.5 mm 5.0 mm,Heel,True to size,Soft,Bad,Decent,Good,Breathable,Medium,Medium,Moderate,Stiff,Stiff,0,0,40.7 mm 40.0 mm,30.2 mm 35.0 mm,NormalWide,1,SummerAll seasons,1,#96 Top 27%,#242 Bottom 33%,,
+Adidas,Adistar 3,"89
+ Great!",$130,Daily running,Neutral,9.7 oz / 274g 9.5 oz / 270g,0,10.5 mm 5.0 mm,Heel,True to size,Soft,Bad,Decent,Good,Breathable,Medium,Medium,Moderate,Stiff,Stiff,0,0,40.7 mm 40.0 mm,30.2 mm 35.0 mm,NormalWide,1,SummerAll seasons,1,#96 Top 27%,#242 Bottom 33%,,
+Adidas,Adistar 3,"89
+ Great!",$130,Daily running,Neutral,9.7 oz / 274g 9.5 oz / 270g,0,10.5 mm 5.0 mm,Heel,True to size,Soft,Bad,Decent,Good,Breathable,Medium,Medium,Moderate,Stiff,Stiff,0,0,40.7 mm 40.0 mm,30.2 mm 35.0 mm,NormalWide,1,SummerAll seasons,1,#98 Top 27%,#242 Bottom 33%,,
+Adidas,Adistar 3,"89
+ Great!",$130,Daily running,Neutral,9.7 oz / 274g 9.5 oz / 270g,0,10.5 mm 5.0 mm,Heel,True to size,Soft,Bad,Decent,Good,Breathable,Medium,Medium,Moderate,Stiff,Stiff,0,0,40.7 mm 40.0 mm,30.2 mm 35.0 mm,NormalWide,1,SummerAll seasons,1,#96 Top 27%,#242 Bottom 33%,,
+Adidas,Adistar 3,"89
+ Great!",$130,Daily running,Neutral,9.7 oz / 274g 9.5 oz / 270g,0,10.5 mm 5.0 mm,Heel,True to size,Soft,Bad,Decent,Good,Breathable,Medium,Medium,Moderate,Stiff,Stiff,0,0,40.7 mm 40.0 mm,30.2 mm 35.0 mm,NormalWide,1,SummerAll seasons,1,#98 Top 27%,#242 Bottom 33%,,
+Adidas,Adistar 3,"89
+ Great!",$130,Daily running,Neutral,9.7 oz / 274g 9.5 oz / 270g,0,10.5 mm 5.0 mm,Heel,True to size,Soft,Bad,Decent,Good,Breathable,Medium,Medium,Moderate,Stiff,Stiff,0,0,40.7 mm 40.0 mm,30.2 mm 35.0 mm,NormalWide,1,SummerAll seasons,1,#98 Top 27%,#242 Bottom 33%,,
+Adidas,Adistar 3,"89
+ Great!",$130,Daily running,Neutral,9.7 oz / 274g 9.5 oz / 270g,0,10.5 mm 5.0 mm,Heel,True to size,Soft,Bad,Decent,Good,Breathable,Medium,Medium,Moderate,Stiff,Stiff,0,0,40.7 mm 40.0 mm,30.2 mm 35.0 mm,NormalWide,1,SummerAll seasons,1,#98 Top 27%,#242 Bottom 33%,,
+Adidas,Adistar 3,"89
+ Great!",$130,Daily running,Neutral,9.7 oz / 274g 9.5 oz / 270g,0,10.5 mm 5.0 mm,Heel,True to size,Soft,Bad,Decent,Good,Breathable,Medium,Medium,Moderate,Stiff,Stiff,0,0,40.7 mm 40.0 mm,30.2 mm 35.0 mm,NormalWide,1,SummerAll seasons,1,#98 Top 27%,#242 Bottom 33%,,
+Adidas,Adistar 3,"89
+ Great!",$130,Daily running,Neutral,9.7 oz / 274g 9.5 oz / 270g,0,10.5 mm 5.0 mm,Heel,True to size,Soft,Bad,Decent,Good,Breathable,Medium,Medium,Moderate,Stiff,Stiff,0,0,40.7 mm 40.0 mm,30.2 mm 35.0 mm,NormalWide,1,SummerAll seasons,1,#98 Top 27%,#242 Bottom 33%,,
+Adidas,Adizero Adios 7,"81
+ Good!",$130,Tempo,Neutral,7.5 oz / 212g 7.5 oz / 212g,1,8.7 mm 8.0 mm,HeelMid/forefoot,Slightly small,Balanced,-,-,-,Breathable,Medium,Narrow,Stiff,Stiff,Flexible,0,0,31.6 mm 27.0 mm,22.9 mm 19.0 mm,NormalWide,1,SummerAll seasons,1,#535 Bottom 17%,#614 Bottom 4%,,
+Adidas,Adizero Adios 8,"90
+ Superb!",$130,Tempo,Neutral,7.4 oz / 210g 7 oz / 198g,1,7.6 mm 8.0 mm,Mid/forefoot,True to size,Soft,Bad,Decent,Good,Breathable,Medium,Wide,Flexible,Flexible,Flexible,0,0,28.0 mm 28.0 mm,20.4 mm 20.0 mm,Normal,1,SummerAll seasons,1,#129 Top 20%,#553 Bottom 14%,,
+Adidas,Adizero Adios 9,"92
+ Superb!",$140,CompetitionTempo,Neutral,6.2 oz / 176g 6.2 oz / 176g,1,6.2 mm 7.0 mm,Mid/forefoot,Slightly small,Soft,Bad,Good,Good,Warm,Medium,Narrow,Flexible,Flexible,Flexible,0,0,25.0 mm 28.0 mm,18.8 mm 21.0 mm,Normal,1,All seasons,1,#7 Top 2%,#245 Bottom 33%,,
+Adidas,Adizero Adios Pro 2.0,"91
+ Superb!",$220,Competition,Neutral,7.9 oz / 223g 7.6 oz / 215g,1,10.3 mm 10.0 mm,Heel,True to size,-,-,-,-,-,Narrow,-,Stiff,Stiff,Flexible,Carbon plate,1,40.0 mm 39.5 mm,29.7 mm 29.5 mm,Normal,0,-,0,#32 Top 5%,#569 Bottom 11%,,
+Adidas,Adizero Adios Pro 3,"91
+ Superb!",$250,Competition,Neutral,7.7 oz / 218g 7.9 oz / 223g,1,8.0 mm 6.5 mm,HeelMid/forefoot,True to size,Balanced,Bad,Good,Good,Breathable,Medium,Wide,Stiff,Stiff,Flexible,Carbon plate,1,37.8 mm 39.5 mm,29.8 mm 33.0 mm,Normal,1,SummerAll seasons,1,#41 Top 7%,#202 Top 32%,,
+Adidas,Adizero Adios Pro 4,"93
+ Superb!",$250,Competition,Neutral,7.1 oz / 200g 7.1 oz / 201g,1,8.1 mm 6.0 mm,HeelMid/forefoot,Slightly small,Soft,Bad,Good,Good,Warm,Medium,Narrow,Stiff,Stiff,Flexible,Carbon plate,1,36.6 mm 39.0 mm,28.5 mm 33.0 mm,NormalWide,1,All seasons,1,#1 Top 1%,#38 Top 11%,,
+Adidas,Adizero Adios Pro 4,"93
+ Superb!",$250,Competition,Neutral,7.1 oz / 200g 7.1 oz / 201g,1,8.1 mm 6.0 mm,HeelMid/forefoot,Slightly small,Soft,Bad,Good,Good,Warm,Medium,Narrow,Stiff,Stiff,Flexible,Carbon plate,1,36.6 mm 39.0 mm,28.5 mm 33.0 mm,NormalWide,1,All seasons,1,#1 Top 1%,#38 Top 11%,,
+Adidas,Adizero Adios Pro 4,"93
+ Superb!",$250,Competition,Neutral,7.1 oz / 200g 7.1 oz / 201g,1,8.1 mm 6.0 mm,HeelMid/forefoot,Slightly small,Soft,Bad,Good,Good,Warm,Medium,Narrow,Stiff,Stiff,Flexible,Carbon plate,1,36.6 mm 39.0 mm,28.5 mm 33.0 mm,NormalWide,1,All seasons,1,#1 Top 1%,#38 Top 11%,,
+Adidas,Adizero Boston 11,"83
+ Good!",$160,Tempo,Neutral,10.2 oz / 290g 9.6 oz / 272g,0,9.8 mm 8.5 mm,HeelMid/forefoot,True to size,Balanced,Bad,Decent,Good,Moderate,Wide,Wide,Stiff,Stiff,Moderate,Carbon plate,1,39.1 mm 39.5 mm,29.3 mm 31.0 mm,NormalWide,1,All seasons,1,#480 Bottom 25%,#579 Bottom 10%,,
+Adidas,Adizero Boston 12,"88
+ Great!",$160,Tempo,Neutral,9.2 oz / 261g 9.2 oz / 260g,0,6.1 mm 6.5 mm,Mid/forefoot,Slightly large,Balanced,Bad,Decent,Good,Breathable,Wide,Medium,Stiff,Stiff,Flexible,0,1,34.5 mm 37.0 mm,28.4 mm 30.5 mm,NormalWide,1,SummerAll seasons,1,#216 Top 34%,#338 Bottom 47%,,
+Adidas,Adizero Boston 13,"90
+ Superb!",$160,CompetitionTempo,Neutral,9 oz / 254g 9 oz / 255g,0,6.0 mm 6.0 mm,Mid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Narrow,Moderate,Stiff,Stiff,Carbon plate,1,34.3 mm 36.0 mm,28.3 mm 30.0 mm,NormalWide,1,All seasons,1,#38 Top 11%,#206 Bottom 43%,,
+Adidas,Adizero Boston 13,"90
+ Superb!",$160,CompetitionTempo,Neutral,9 oz / 254g 9 oz / 255g,0,6.0 mm 6.0 mm,Mid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Narrow,Moderate,Stiff,Stiff,Carbon plate,1,34.3 mm 36.0 mm,28.3 mm 30.0 mm,NormalWide,1,All seasons,1,#38 Top 11%,#206 Bottom 43%,,
+Adidas,Adizero Boston 13,"90
+ Superb!",$160,CompetitionTempo,Neutral,9 oz / 254g 9 oz / 255g,0,6.0 mm 6.0 mm,Mid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Narrow,Moderate,Stiff,Stiff,Carbon plate,1,34.3 mm 36.0 mm,28.3 mm 30.0 mm,NormalWide,1,All seasons,1,#38 Top 11%,#206 Bottom 43%,,
+Adidas,Adizero EVO SL,"93
+ Superb!",$150,Daily runningTempo,Neutral,7.9 oz / 223g 7.9 oz / 224g,1,8.0 mm 6.5 mm,HeelMid/forefoot,True to size,Balanced,Bad,Good,Good,Breathable,Medium,Medium,Flexible,Stiff,Moderate,0,1,36.1 mm 38.5 mm,28.1 mm 32.0 mm,NormalWide,1,SummerAll seasons,1,#2 Top 1%,#23 Top 7%,,
+Adidas,Adizero EVO SL,"93
+ Superb!",$150,Daily runningTempo,Neutral,7.9 oz / 223g 7.9 oz / 224g,1,8.0 mm 6.5 mm,HeelMid/forefoot,True to size,Balanced,Bad,Good,Good,Breathable,Medium,Medium,Flexible,Stiff,Moderate,0,1,36.1 mm 38.5 mm,28.1 mm 32.0 mm,NormalWide,1,SummerAll seasons,1,#2 Top 1%,#23 Top 7%,,
+Adidas,Adizero EVO SL,"93
+ Superb!",$150,Daily runningTempo,Neutral,7.9 oz / 223g 7.9 oz / 224g,1,8.0 mm 6.5 mm,HeelMid/forefoot,True to size,Balanced,Bad,Good,Good,Breathable,Medium,Medium,Flexible,Stiff,Moderate,0,1,36.1 mm 38.5 mm,28.1 mm 32.0 mm,NormalWide,1,SummerAll seasons,1,#2 Top 1%,#23 Top 7%,,
+Adidas,Adizero Prime X 2 Strung,"88
+ Great!",$300,CompetitionTempo,Neutral,10.8 oz / 305g 10.8 oz / 306g,0,8.8 mm 6.5 mm,HeelMid/forefoot,Slightly small,Balanced,Good,Good,Good,Moderate,Medium,Narrow,Stiff,Stiff,Flexible,Carbon plate,1,45.7 mm 50.0 mm,36.9 mm 43.5 mm,Normal,1,All seasons,1,#132 Top 37%,#52 Top 15%,,
+Adidas,Adizero Prime X 2 Strung,"88
+ Great!",$300,CompetitionTempo,Neutral,10.8 oz / 305g 10.8 oz / 306g,0,8.8 mm 6.5 mm,HeelMid/forefoot,Slightly small,Balanced,Good,Good,Good,Moderate,Medium,Narrow,Stiff,Stiff,Flexible,Carbon plate,1,45.7 mm 50.0 mm,36.9 mm 43.5 mm,Normal,1,All seasons,1,#132 Top 37%,#52 Top 15%,,
+Adidas,Adizero Prime X 2 Strung,"88
+ Great!",$300,CompetitionTempo,Neutral,10.8 oz / 305g 10.8 oz / 306g,0,8.8 mm 6.5 mm,HeelMid/forefoot,Slightly small,Balanced,Good,Good,Good,Moderate,Medium,Narrow,Stiff,Stiff,Flexible,Carbon plate,1,45.7 mm 50.0 mm,36.9 mm 43.5 mm,Normal,1,All seasons,1,#132 Top 37%,#52 Top 15%,,
+Adidas,Adizero Prime X3 STRUNG,"90
+ Superb!",$300,CompetitionTempo,Neutral,10.3 oz / 291g 10.1 oz / 285g,0,13.0 mm 7.0 mm,HeelMid/forefoot,True to size,Soft,Good,Good,Bad,Moderate,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,48.1 mm 50.0 mm,35.1 mm 43.0 mm,Normal,1,All seasons,1,#48 Top 14%,#250 Bottom 31%,,
+Adidas,Adizero SL,"88
+ Great!",$120,Daily runningTempo,Neutral,8.8 oz / 249g 8.6 oz / 244g,1,8.6 mm 10.0 mm,HeelMid/forefoot,Slightly small,Balanced,Decent,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Moderate,0,0,34.9 mm 35.0 mm,26.3 mm 25.0 mm,NormalWide,1,SummerAll seasons,1,#235 Top 37%,#278 Top 44%,,
+Adidas,Adizero SL2,"90
+ Superb!",$130,Daily runningTempo,Neutral,8.6 oz / 245g 8.4 oz / 238g,1,8.2 mm 9.0 mm,HeelMid/forefoot,Half size small,Balanced,Bad,Good,Decent,Breathable,Wide,Medium,Moderate,Moderate,Flexible,0,0,34.9 mm 35.0 mm,26.7 mm 26.0 mm,NormalWide,1,SummerAll seasons,1,#44 Top 13%,#166 Top 46%,,
+Adidas,Adizero SL2,"90
+ Superb!",$130,Daily runningTempo,Neutral,8.6 oz / 245g 8.4 oz / 238g,1,8.2 mm 9.0 mm,HeelMid/forefoot,Half size small,Balanced,Bad,Good,Decent,Breathable,Wide,Medium,Moderate,Moderate,Flexible,0,0,34.9 mm 35.0 mm,26.7 mm 26.0 mm,NormalWide,1,SummerAll seasons,1,#44 Top 13%,#166 Top 46%,,
+Adidas,Adizero SL2,"90
+ Superb!",$130,Daily runningTempo,Neutral,8.6 oz / 245g 8.4 oz / 238g,1,8.2 mm 9.0 mm,HeelMid/forefoot,Half size small,Balanced,Bad,Good,Decent,Breathable,Wide,Medium,Moderate,Moderate,Flexible,0,0,34.9 mm 35.0 mm,26.7 mm 26.0 mm,NormalWide,1,SummerAll seasons,1,#44 Top 13%,#166 Top 46%,,
+Adidas,Adizero SL2,"90
+ Superb!",$130,Daily runningTempo,Neutral,8.6 oz / 245g 8.4 oz / 238g,1,8.2 mm 9.0 mm,HeelMid/forefoot,Half size small,Balanced,Bad,Good,Decent,Breathable,Wide,Medium,Moderate,Moderate,Flexible,0,0,34.9 mm 35.0 mm,26.7 mm 26.0 mm,NormalWide,1,SummerAll seasons,1,#44 Top 13%,#166 Top 46%,,
+Adidas,Adizero SL2,"90
+ Superb!",$130,Daily runningTempo,Neutral,8.6 oz / 245g 8.4 oz / 238g,1,8.2 mm 9.0 mm,HeelMid/forefoot,Half size small,Balanced,Bad,Good,Decent,Breathable,Wide,Medium,Moderate,Moderate,Flexible,0,0,34.9 mm 35.0 mm,26.7 mm 26.0 mm,NormalWide,1,SummerAll seasons,1,#44 Top 13%,#166 Top 46%,,
+Adidas,Adizero SL2,"90
+ Superb!",$130,Daily runningTempo,Neutral,8.6 oz / 245g 8.4 oz / 238g,1,8.2 mm 9.0 mm,HeelMid/forefoot,Half size small,Balanced,Bad,Good,Decent,Breathable,Wide,Medium,Moderate,Moderate,Flexible,0,0,34.9 mm 35.0 mm,26.7 mm 26.0 mm,NormalWide,1,SummerAll seasons,1,#44 Top 13%,#166 Top 46%,,
+Adidas,Adizero SL2,"90
+ Superb!",$130,Daily runningTempo,Neutral,8.6 oz / 245g 8.4 oz / 238g,1,8.2 mm 9.0 mm,HeelMid/forefoot,Half size small,Balanced,Bad,Good,Decent,Breathable,Wide,Medium,Moderate,Moderate,Flexible,0,0,34.9 mm 35.0 mm,26.7 mm 26.0 mm,NormalWide,1,SummerAll seasons,1,#44 Top 13%,#166 Top 46%,,
+Adidas,Adizero SL2,"90
+ Superb!",$130,Daily runningTempo,Neutral,8.6 oz / 245g 8.4 oz / 238g,1,8.2 mm 9.0 mm,HeelMid/forefoot,Half size small,Balanced,Bad,Good,Decent,Breathable,Wide,Medium,Moderate,Moderate,Flexible,0,0,34.9 mm 35.0 mm,26.7 mm 26.0 mm,NormalWide,1,SummerAll seasons,1,#44 Top 13%,#166 Top 46%,,
+Adidas,Adizero SL2,"90
+ Superb!",$130,Daily runningTempo,Neutral,8.6 oz / 245g 8.4 oz / 238g,1,8.2 mm 9.0 mm,HeelMid/forefoot,Half size small,Balanced,Bad,Good,Decent,Breathable,Wide,Medium,Moderate,Moderate,Flexible,0,0,34.9 mm 35.0 mm,26.7 mm 26.0 mm,NormalWide,1,SummerAll seasons,1,#44 Top 13%,#166 Top 46%,,
+Adidas,Adizero Takumi Sen 10,"89
+ Great!",$180,CompetitionTempo,Neutral,7.1 oz / 200g 6.9 oz / 196g,1,7.8 mm 6.0 mm,Mid/forefoot,Half size small,Soft,Bad,Good,Bad,Breathable,Narrow,Medium,Moderate,Stiff,Flexible,0,0,30.6 mm 33.0 mm,22.8 mm 27.0 mm,Normal,1,SummerAll seasons,1,#88 Top 25%,#252 Bottom 31%,,
+Brooks,Adrenaline GTS 22,"89
+ Great!",$140,Daily running,Stability,10.4 oz / 294g 10.2 oz / 289g,0,14.7 mm 12.0 mm,Heel,True to size,-,-,-,-,-,Medium,-,Stiff,-,-,0,0,37.4 mm 36.0 mm,22.7 mm 24.0 mm,NarrowNormalWideX-Wide,0,-,0,#148 Top 23%,#82 Top 13%,,
+Brooks,Adrenaline GTS 23,"89
+ Great!",$140,Daily running,Stability,10.1 oz / 286g 10.4 oz / 294g,0,12.6 mm 12.0 mm,Heel,True to size,Soft,Bad,Good,Good,Breathable,Medium,Medium,Moderate,Stiff,Stiff,0,0,34.1 mm 36.0 mm,21.5 mm 24.0 mm,NarrowNormalWideX-Wide,1,SummerAll seasons,1,#211 Top 33%,#36 Top 6%,,
+Brooks,Adrenaline GTS 24,"85
+ Good!",$140,Daily running,Stability,10.3 oz / 291g 10 oz / 283g,0,13.5 mm 12.0 mm,Heel,Slightly small,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,39.0 mm 39.0 mm,25.5 mm 27.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#214 Bottom 41%,#5 Top 2%,,
+Brooks,Adrenaline GTS 24,"85
+ Good!",$140,Daily running,Stability,10.3 oz / 291g 10 oz / 283g,0,13.5 mm 12.0 mm,Heel,Slightly small,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,39.0 mm 39.0 mm,25.5 mm 27.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#216 Bottom 40%,#5 Top 2%,,
+Brooks,Adrenaline GTS 24,"85
+ Good!",$140,Daily running,Stability,10.3 oz / 291g 10 oz / 283g,0,13.5 mm 12.0 mm,Heel,Slightly small,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,39.0 mm 39.0 mm,25.5 mm 27.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#216 Bottom 40%,#5 Top 2%,,
+Brooks,Adrenaline GTS 24,"85
+ Good!",$140,Daily running,Stability,10.3 oz / 291g 10 oz / 283g,0,13.5 mm 12.0 mm,Heel,Slightly small,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,39.0 mm 39.0 mm,25.5 mm 27.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#216 Bottom 40%,#5 Top 2%,,
+Brooks,Adrenaline GTS 24,"85
+ Good!",$140,Daily running,Stability,10.3 oz / 291g 10 oz / 283g,0,13.5 mm 12.0 mm,Heel,Slightly small,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,39.0 mm 39.0 mm,25.5 mm 27.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#216 Bottom 40%,#5 Top 2%,,
+Brooks,Adrenaline GTS 24,"85
+ Good!",$140,Daily running,Stability,10.3 oz / 291g 10 oz / 283g,0,13.5 mm 12.0 mm,Heel,Slightly small,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,39.0 mm 39.0 mm,25.5 mm 27.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#216 Bottom 40%,#5 Top 2%,,
+Skechers,Aero Burst,"92
+ Superb!",$150,Daily running,Neutral,11.4 oz / 322g,0,8.8 mm 6.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Good,Good,Moderate,Medium,Narrow,Moderate,Stiff,Stiff,0,0,41.7 mm 42.0 mm,32.9 mm 36.0 mm,NormalWide,1,All seasons,1,#12 Top 4%,#24 Top 7%,,
+Skechers,Aero Burst,"92
+ Superb!",$150,Daily running,Neutral,11.4 oz / 322g,0,8.8 mm 6.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Good,Good,Moderate,Medium,Narrow,Moderate,Stiff,Stiff,0,0,41.7 mm 42.0 mm,32.9 mm 36.0 mm,NormalWide,1,All seasons,1,#12 Top 4%,#24 Top 7%,,
+Salomon,Aero Glide,"88
+ Great!",$160,Daily running,Neutral,9.3 oz / 264g 9 oz / 254g,0,11.0 mm 10.0 mm,Heel,True to size,Balanced,Decent,Decent,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,1,35.2 mm 37.0 mm,24.2 mm 27.0 mm,Normal,1,All seasons,1,#221 Top 35%,#443 Bottom 31%,,
+Salomon,Aero Glide 2,"93
+ Superb!",$160,Daily running,Neutral,9.5 oz / 268g 9.2 oz / 260g,0,11.3 mm 10.0 mm,Heel,True to size,Balanced,Good,Decent,Decent,Moderate,Medium,Medium,Moderate,Stiff,Moderate,0,1,35.5 mm 41.0 mm,24.2 mm 31.0 mm,Normal,1,All seasons,1,#9 Top 2%,#529 Bottom 17%,,
+Salomon,Aero Glide 3,"92
+ Superb!",$160,Daily running,Neutral,8.7 oz / 248g 8.6 oz / 245g,1,10.3 mm 8.0 mm,Heel,Slightly large,Balanced,Bad,Decent,Decent,Moderate,Narrow,Narrow,Moderate,Stiff,Flexible,0,0,42.2 mm 40.0 mm,31.9 mm 32.0 mm,Normal,1,All seasons,1,#7 Top 2%,#110 Top 30%,,
+Salomon,Aero Glide 3,"92
+ Superb!",$160,Daily running,Neutral,8.7 oz / 248g 8.6 oz / 245g,1,10.3 mm 8.0 mm,Heel,Slightly large,Balanced,Bad,Decent,Decent,Moderate,Narrow,Narrow,Moderate,Stiff,Flexible,0,0,42.2 mm 40.0 mm,31.9 mm 32.0 mm,Normal,1,All seasons,1,#7 Top 2%,#110 Top 30%,,
+Salomon,Aero Glide 3,"92
+ Superb!",$160,Daily running,Neutral,8.7 oz / 248g 8.6 oz / 245g,1,10.3 mm 8.0 mm,Heel,Slightly large,Balanced,Bad,Decent,Decent,Moderate,Narrow,Narrow,Moderate,Stiff,Flexible,0,0,42.2 mm 40.0 mm,31.9 mm 32.0 mm,Normal,1,All seasons,1,#9 Top 3%,#110 Top 31%,,
+Salomon,Aero Glide 3,"92
+ Superb!",$160,Daily running,Neutral,8.7 oz / 248g 8.6 oz / 245g,1,10.3 mm 8.0 mm,Heel,Slightly large,Balanced,Bad,Decent,Decent,Moderate,Narrow,Narrow,Moderate,Stiff,Flexible,0,0,42.2 mm 40.0 mm,31.9 mm 32.0 mm,Normal,1,All seasons,1,#9 Top 3%,#110 Top 31%,,
+Nike,Air Winflo 9,"85
+ Good!",$100,Daily running,Neutral,9.8 oz / 279g 9.9 oz / 280g,0,10.8 mm 10.0 mm,Heel,True to size,Soft,Decent,-,-,Moderate,Narrow,Medium,Stiff,Moderate,Stiff,0,0,35.0 mm,24.2 mm,Normal,1,All seasons,1,#430 Bottom 33%,#555 Bottom 13%,,
+Nike,Air Zoom Pegasus 38,"87
+ Great!",$120,Daily running,Neutral,10.3 oz / 291g 10 oz / 283g,0,8.7 mm 10.0 mm,HeelMid/forefoot,True to size,-,-,-,-,Moderate,Medium,-,Stiff,Flexible,Moderate,0,0,31.8 mm 27.5 mm,23.1 mm 17.5 mm,NormalWide,1,All seasons,1,#298 Top 47%,#232 Top 36%,,
+Nike,Air Zoom Pegasus 38 FlyEase,"77
+ Decent!",$120,Daily running,Neutral,9.7 oz / 275g 9.2 oz / 260g,0,8.7 mm,HeelMid/forefoot,-,-,-,-,-,-,Medium,-,Stiff,-,-,0,0,31.8 mm,23.1 mm,NormalX-Wide,0,-,0,#613 Bottom 4%,#521 Bottom 19%,,
+Nike,Air Zoom Pegasus 39,"87
+ Great!",$130,Daily running,Neutral,9.3 oz / 264g 9.2 oz / 261g,0,8.0 mm 10.0 mm,HeelMid/forefoot,True to size,Soft,-,-,-,Moderate,Narrow,-,Stiff,Flexible,Moderate,0,0,30.3 mm 33.0 mm,22.3 mm 23.0 mm,NormalX-Wide,1,All seasons,1,#289 Top 45%,#150 Top 24%,,
+NOBULL,Allday Knit,"87
+ Great!",$159,Daily running,Neutral,10.6 oz / 301g 10.6 oz / 300g,0,12.0 mm 10.0 mm,Heel,-,Firm,-,-,-,Moderate,Narrow,-,Stiff,Moderate,Flexible,0,0,31.8 mm,19.8 mm,Normal,1,All seasons,1,#154 Top 43%,#284 Bottom 22%,,
+NOBULL,Allday Knit,"87
+ Great!",$159,Daily running,Neutral,10.6 oz / 301g 10.6 oz / 300g,0,12.0 mm 10.0 mm,Heel,-,Firm,-,-,-,Moderate,Narrow,-,Stiff,Moderate,Flexible,0,0,31.8 mm,19.8 mm,Normal,1,All seasons,1,#157 Top 43%,#285 Bottom 22%,,
+NOBULL,Allday Ripstop,"61
+ Bad!",$139,Daily running,Neutral,10.5 oz / 297g 10.5 oz / 298g,0,11.4 mm,Heel,True to size,Balanced,-,-,-,Moderate,Narrow,-,Stiff,Moderate,Flexible,0,0,31.3 mm,19.9 mm,Normal,1,All seasons,1,#365 Bottom 1%,#299 Bottom 18%,,
+Adidas,Alphabounce+,"80
+ Good!",$100,Daily running,Neutral,12 oz / 340g 12.5 oz / 354g,0,11.5 mm 10.0 mm,Heel,True to size,Balanced,Bad,Bad,Good,Moderate,Medium,Narrow,Stiff,Stiff,Moderate,0,0,37.7 mm 20.0 mm,26.2 mm 10.0 mm,Normal,1,All seasons,1,#300 Bottom 17%,#202 Bottom 44%,,
+Adidas,Alphabounce+,"80
+ Good!",$100,Daily running,Neutral,12 oz / 340g 12.5 oz / 354g,0,11.5 mm 10.0 mm,Heel,True to size,Balanced,Bad,Bad,Good,Moderate,Medium,Narrow,Stiff,Stiff,Moderate,0,0,37.7 mm 20.0 mm,26.2 mm 10.0 mm,Normal,1,All seasons,1,#301 Bottom 17%,#202 Bottom 44%,,
+Adidas,Alphabounce+,"80
+ Good!",$100,Daily running,Neutral,12 oz / 340g 12.5 oz / 354g,0,11.5 mm 10.0 mm,Heel,True to size,Balanced,Bad,Bad,Good,Moderate,Medium,Narrow,Stiff,Stiff,Moderate,0,0,37.7 mm 20.0 mm,26.2 mm 10.0 mm,Normal,1,All seasons,1,#300 Bottom 18%,#202 Bottom 44%,,
+Adidas,Alphabounce+,"80
+ Good!",$100,Daily running,Neutral,12 oz / 340g 12.5 oz / 354g,0,11.5 mm 10.0 mm,Heel,True to size,Balanced,Bad,Bad,Good,Moderate,Medium,Narrow,Stiff,Stiff,Moderate,0,0,37.7 mm 20.0 mm,26.2 mm 10.0 mm,Normal,1,All seasons,1,#299 Bottom 18%,#202 Bottom 44%,,
+Adidas,Alphabounce+,"80
+ Good!",$100,Daily running,Neutral,12 oz / 340g 12.5 oz / 354g,0,11.5 mm 10.0 mm,Heel,True to size,Balanced,Bad,Bad,Good,Moderate,Medium,Narrow,Stiff,Stiff,Moderate,0,0,37.7 mm 20.0 mm,26.2 mm 10.0 mm,Normal,1,All seasons,1,#299 Bottom 18%,#202 Bottom 44%,,
+Adidas,Alphabounce+,"80
+ Good!",$100,Daily running,Neutral,12 oz / 340g 12.5 oz / 354g,0,11.5 mm 10.0 mm,Heel,True to size,Balanced,Bad,Bad,Good,Moderate,Medium,Narrow,Stiff,Stiff,Moderate,0,0,37.7 mm 20.0 mm,26.2 mm 10.0 mm,Normal,1,All seasons,1,#299 Bottom 18%,#202 Bottom 44%,,
+Nike,Alphafly 2,"84
+ Good!",$275,Competition,Neutral,8.5 oz / 240g 8.6 oz / 243g,1,4.7 mm 8.0 mm,Mid/forefoot,Slightly small,Soft,Good,-,-,Breathable,Narrow,Narrow,Stiff,Stiff,Flexible,Carbon plate,1,38.6 mm 40.0 mm,33.9 mm 32.0 mm,Normal,0,SummerAll seasons,0,#445 Bottom 31%,#216 Top 34%,,
+Nike,Alphafly 3,"88
+ Great!",$285,Competition,Neutral,7.1 oz / 201g 7 oz / 198g,1,8.5 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,Bad,Good,Bad,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,38.1 mm 40.0 mm,29.6 mm 32.0 mm,Normal,0,SummerAll seasons,0,#131 Top 36%,#20 Top 6%,,Road
+Nike,Alphafly 3,"88
+ Great!",$285,Competition,Neutral,7.1 oz / 201g 7 oz / 198g,1,8.5 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,Bad,Good,Bad,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,38.1 mm 40.0 mm,29.6 mm 32.0 mm,Normal,0,SummerAll seasons,0,#131 Top 36%,#20 Top 6%,,
+Nike,Alphafly 3,"88
+ Great!",$285,Competition,Neutral,7.1 oz / 201g 7 oz / 198g,1,8.5 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,Bad,Good,Bad,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,38.1 mm 40.0 mm,29.6 mm 32.0 mm,Normal,0,SummerAll seasons,0,#131 Top 36%,#20 Top 6%,,
+Nike,Alphafly 3,"88
+ Great!",$285,Competition,Neutral,7.1 oz / 201g 7 oz / 198g,1,8.5 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,Bad,Good,Bad,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,38.1 mm 40.0 mm,29.6 mm 32.0 mm,Normal,0,SummerAll seasons,0,#124 Top 34%,#20 Top 6%,,
+Nike,Alphafly 3,"88
+ Great!",$285,Competition,Neutral,7.1 oz / 201g 7 oz / 198g,1,8.5 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,Bad,Good,Bad,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,38.1 mm 40.0 mm,29.6 mm 32.0 mm,Normal,0,SummerAll seasons,0,#124 Top 34%,#20 Top 6%,,
+Nike,Alphafly 3,"88
+ Great!",$285,Competition,Neutral,7.1 oz / 201g 7 oz / 198g,1,8.5 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,Bad,Good,Bad,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,38.1 mm 40.0 mm,29.6 mm 32.0 mm,Normal,0,SummerAll seasons,0,#124 Top 34%,#20 Top 6%,,
+Nike,Alphafly 3,"88
+ Great!",$285,Competition,Neutral,7.1 oz / 201g 7 oz / 198g,1,8.5 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,Bad,Good,Bad,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,38.1 mm 40.0 mm,29.6 mm 32.0 mm,Normal,0,SummerAll seasons,0,#124 Top 34%,#20 Top 6%,,
+Nike,Alphafly 3,"88
+ Great!",$285,Competition,Neutral,7.1 oz / 201g 7 oz / 198g,1,8.5 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,Bad,Good,Bad,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,38.1 mm 40.0 mm,29.6 mm 32.0 mm,Normal,0,SummerAll seasons,0,#124 Top 34%,#20 Top 6%,,
+Nike,Alphafly 3,"88
+ Great!",$285,Competition,Neutral,7.1 oz / 201g 7 oz / 198g,1,8.5 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,Bad,Good,Bad,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,38.1 mm 40.0 mm,29.6 mm 32.0 mm,Normal,0,SummerAll seasons,0,#124 Top 34%,#20 Top 6%,,
+Brooks,Anthem 4,"81
+ Good!",$85,Daily running,Neutral,8.6 oz / 245g,1,11.1 mm 10.0 mm,Heel,-,-,-,-,-,Moderate,Narrow,-,Stiff,Flexible,Stiff,0,0,32.5 mm,21.4 mm,Normal,1,All seasons,1,#298 Bottom 18%,#289 Bottom 21%,,
+Hoka,Arahi 7,"82
+ Good!",$145,Daily running,Stability,9.4 oz / 266g 9.6 oz / 272g,0,6.3 mm 5.0 mm,Mid/forefoot,True to size,Balanced,Good,Good,Good,Moderate,Medium,Narrow,Stiff,Stiff,Stiff,0,1,34.2 mm 34.0 mm,27.9 mm 29.0 mm,NarrowNormalWide,1,All seasons,1,#527 Bottom 18%,#34 Top 6%,,
+HOKA,Arahi 8,"85
+ Good!",$150,Daily running,Stability,9.1 oz / 259g 9 oz / 256g,0,11.3 mm 8.0 mm,Heel,True to size,Soft,Good,Good,Good,Warm,Medium,Medium,Stiff,Stiff,Moderate,0,1,39.4 mm 39.0 mm,28.1 mm 31.0 mm,NormalWideX-Wide,1,All seasons,1,#213 Bottom 41%,#29 Top 8%,,
+HOKA,Arahi 8,"85
+ Good!",$150,Daily running,Stability,9.1 oz / 259g 9 oz / 256g,0,11.3 mm 8.0 mm,Heel,True to size,Soft,Good,Good,Good,Warm,Medium,Medium,Stiff,Stiff,Moderate,0,1,39.4 mm 39.0 mm,28.1 mm 31.0 mm,NormalWideX-Wide,1,All seasons,1,#213 Bottom 41%,#29 Top 8%,,
+Topo,Atmos,"90
+ Superb!",$160,Daily running,Neutral,9.7 oz / 275g 9.7 oz / 275g,0,5.3 mm 5.0 mm,Mid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Wide,Wide,Moderate,Stiff,Stiff,0,0,37.8 mm 38.0 mm,32.5 mm 33.0 mm,NormalWide,1,All seasons,1,#40 Top 11%,#152 Top 42%,,
+Topo,Atmos,"90
+ Superb!",$160,Daily running,Neutral,9.7 oz / 275g 9.7 oz / 275g,0,5.3 mm 5.0 mm,Mid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Wide,Wide,Moderate,Stiff,Stiff,0,0,37.8 mm 38.0 mm,32.5 mm 33.0 mm,NormalWide,1,All seasons,1,#40 Top 11%,#152 Top 42%,,
+Diadora,Atomo Star,"88
+ Great!",$240,Daily running,Neutral,9.5 oz / 268g 9.7 oz / 275g,0,7.8 mm 6.0 mm,Mid/forefoot,-,Soft,Decent,Bad,Good,Moderate,Narrow,Narrow,Moderate,Stiff,Stiff,0,1,39.9 mm 40.0 mm,32.1 mm 34.0 mm,Normal,1,All seasons,1,#118 Top 33%,#311 Bottom 14%,,
+Brooks,Aurora-BL,"90
+ Superb!",$200,Daily running,Neutral,8.7 oz / 247g 8.5 oz / 240g,1,8.7 mm 6.0 mm,HeelMid/forefoot,Slightly large,Soft,Bad,Bad,Good,Moderate,Narrow,Medium,Stiff,Flexible,Moderate,0,1,37.0 mm 37.0 mm,28.3 mm 31.0 mm,Normal,1,All seasons,1,#60 Top 17%,#235 Bottom 35%,,
+Saucony,Axon,"87
+ Great!",$100,Daily runningTempo,Neutral,9.9 oz / 281g 9.3 oz / 264g,0,5.5 mm 4.0 mm,Mid/forefoot,True to size,-,-,-,-,-,Medium,-,-,Moderate,-,0,1,35.3 mm 35.0 mm,29.8 mm 31.0 mm,Normal,1,-,1,#338 Bottom 47%,#600 Bottom 6%,,
+Saucony,Axon 2,"89
+ Great!",$100,Daily runningTempo,Neutral,9.9 oz / 282g 9.6 oz / 272g,0,7.8 mm 4.0 mm,Mid/forefoot,Half size small,Balanced,Bad,-,-,Moderate,Narrow,Narrow,Stiff,Stiff,Stiff,0,1,35.3 mm 35.0 mm,27.5 mm 31.0 mm,Normal,1,All seasons,1,#189 Top 30%,#582 Bottom 9%,,
+Saucony,Axon 3,"89
+ Great!",$100,Daily runningTempo,Neutral,8.6 oz / 244g 8.5 oz / 241g,1,5.7 mm 4.0 mm,Mid/forefoot,True to size,Soft,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,1,33.6 mm 35.0 mm,27.9 mm 31.0 mm,Normal,1,All seasons,1,#115 Top 32%,#284 Bottom 22%,,
+Brooks,Beast GTS 23,"85
+ Good!",$160,Daily running,Stability,12.4 oz / 352g 11.9 oz / 337g,0,11.9 mm 12.0 mm,Heel,True to size,Balanced,Decent,Decent,Good,Moderate,Medium,Medium,Stiff,Stiff,Stiff,0,0,36.4 mm,24.5 mm,NormalWideX-Wide,1,All seasons,1,#405 Bottom 37%,#339 Bottom 47%,,
+Brooks,Beast GTS 24,"86
+ Good!",$160,Daily running,Stability,12.6 oz / 357g 12.6 oz / 357g,0,12.7 mm 12.0 mm,Heel,Slightly small,Firm,Decent,Decent,Good,Warm,Medium,Medium,Moderate,Stiff,Stiff,0,0,38.5 mm 36.0 mm,25.8 mm 24.0 mm,NormalWideX-Wide,1,All seasons,1,#187 Bottom 48%,#98 Top 27%,,
+Hoka,Bondi 8,"84
+ Good!",$165,Daily running,Neutral,11 oz / 311g 11 oz / 311g,0,6.2 mm 4.0 mm,Mid/forefoot,True to size,Soft,-,-,-,Moderate,Narrow,Narrow,Stiff,Stiff,Moderate,0,1,36.2 mm 39.0 mm,30.0 mm 35.0 mm,NormalWideX-Wide,1,All seasons,1,#458 Bottom 28%,#11 Top 2%,,
+Hoka,Bondi 9,"90
+ Superb!",$170,Daily running,Neutral,10.7 oz / 303g 10.5 oz / 297g,0,9.1 mm 5.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Moderate,0,1,41.3 mm 43.0 mm,32.2 mm 38.0 mm,NormalWideX-Wide,1,All seasons,1,#43 Top 12%,#1 Top 1%,,
+Hoka,Bondi 9,"90
+ Superb!",$170,Daily running,Neutral,10.7 oz / 303g 10.5 oz / 297g,0,9.1 mm 5.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Moderate,0,1,41.3 mm 43.0 mm,32.2 mm 38.0 mm,NormalWideX-Wide,1,All seasons,1,#42 Top 12%,#1 Top 1%,,
+Hoka,Bondi 9,"90
+ Superb!",$170,Daily running,Neutral,10.7 oz / 303g 10.5 oz / 297g,0,9.1 mm 5.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Moderate,0,1,41.3 mm 43.0 mm,32.2 mm 38.0 mm,NormalWideX-Wide,1,All seasons,1,#42 Top 12%,#1 Top 1%,,
+Diadora,Cellula,"87
+ Great!",$170,Daily running,Neutral,9.8 oz / 278g 9.9 oz / 280g,0,7.2 mm 5.0 mm,Mid/forefoot,Slightly small,Soft,Decent,Good,Good,Moderate,Narrow,Medium,Stiff,Stiff,Stiff,0,0,41.9 mm 38.0 mm,34.7 mm 33.0 mm,Normal,1,All seasons,1,#144 Top 40%,#290 Bottom 20%,,
+Diadora,Cellula,"87
+ Great!",$170,Daily running,Neutral,9.8 oz / 278g 9.9 oz / 280g,0,7.2 mm 5.0 mm,Mid/forefoot,Slightly small,Soft,Decent,Good,Good,Moderate,Narrow,Medium,Stiff,Stiff,Stiff,0,0,41.9 mm 38.0 mm,34.7 mm 33.0 mm,Normal,1,All seasons,1,#144 Top 40%,#290 Bottom 20%,,
+Under Armour,Charged Assert 10,"81
+ Good!",$75,Daily running,Neutral,10.5 oz / 298g 9.9 oz / 280g,0,9.4 mm 10.0 mm,HeelMid/forefoot,Slightly small,Firm,Bad,Bad,-,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,32.0 mm,22.6 mm,NormalWideX-Wide,1,All seasons,1,#287 Bottom 21%,#99 Top 27%,,
+Under Armour,Charged Assert 10,"81
+ Good!",$75,Daily running,Neutral,10.5 oz / 298g 9.9 oz / 280g,0,9.4 mm 10.0 mm,HeelMid/forefoot,Slightly small,Firm,Bad,Bad,-,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,32.0 mm,22.6 mm,NormalWideX-Wide,1,All seasons,1,#287 Bottom 21%,#99 Top 27%,,
+Under Armour,Charged Assert 10,"81
+ Good!",$75,Daily running,Neutral,10.5 oz / 298g 9.9 oz / 280g,0,9.4 mm 10.0 mm,HeelMid/forefoot,Slightly small,Firm,Bad,Bad,-,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,32.0 mm,22.6 mm,NormalWideX-Wide,1,All seasons,1,#287 Bottom 21%,#99 Top 27%,,
+Under Armour,Charged Assert 10,"81
+ Good!",$75,Daily running,Neutral,10.5 oz / 298g 9.9 oz / 280g,0,9.4 mm 10.0 mm,HeelMid/forefoot,Slightly small,Firm,Bad,Bad,-,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,32.0 mm,22.6 mm,NormalWideX-Wide,1,All seasons,1,#287 Bottom 21%,#99 Top 27%,,
+Under Armour,Charged Assert 10,"81
+ Good!",$75,Daily running,Neutral,10.5 oz / 298g 9.9 oz / 280g,0,9.4 mm 10.0 mm,HeelMid/forefoot,Slightly small,Firm,Bad,Bad,-,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,32.0 mm,22.6 mm,NormalWideX-Wide,1,All seasons,1,#287 Bottom 21%,#99 Top 28%,,
+Under Armour,Charged Assert 10,"81
+ Good!",$75,Daily running,Neutral,10.5 oz / 298g 9.9 oz / 280g,0,9.4 mm 10.0 mm,HeelMid/forefoot,Slightly small,Firm,Bad,Bad,-,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,32.0 mm,22.6 mm,NormalWideX-Wide,1,All seasons,1,#287 Bottom 21%,#99 Top 28%,,
+Under Armour,Charged Assert 10,"81
+ Good!",$75,Daily running,Neutral,10.5 oz / 298g 9.9 oz / 280g,0,9.4 mm 10.0 mm,HeelMid/forefoot,Slightly small,Firm,Bad,Bad,-,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,32.0 mm,22.6 mm,NormalWideX-Wide,1,All seasons,1,#286 Bottom 21%,#99 Top 28%,,
+Under Armour,Charged Assert 10,"81
+ Good!",$75,Daily running,Neutral,10.5 oz / 298g 9.9 oz / 280g,0,9.4 mm 10.0 mm,HeelMid/forefoot,Slightly small,Firm,Bad,Bad,-,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,32.0 mm,22.6 mm,NormalWideX-Wide,1,All seasons,1,#286 Bottom 21%,#99 Top 28%,,
+Under Armour,Charged Assert 10,"81
+ Good!",$75,Daily running,Neutral,10.5 oz / 298g 9.9 oz / 280g,0,9.4 mm 10.0 mm,HeelMid/forefoot,Slightly small,Firm,Bad,Bad,-,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,32.0 mm,22.6 mm,NormalWideX-Wide,1,All seasons,1,#286 Bottom 21%,#99 Top 28%,,
+Under Armour,Charged Assert 10,"81
+ Good!",$75,Daily running,Neutral,10.5 oz / 298g 9.9 oz / 280g,0,9.4 mm 10.0 mm,HeelMid/forefoot,Slightly small,Firm,Bad,Bad,-,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,32.0 mm,22.6 mm,NormalWideX-Wide,1,All seasons,1,#286 Bottom 21%,#99 Top 28%,,
+Under Armour,Charged Assert 9,"84
+ Good!",$70,Daily running,Neutral,10.2 oz / 290g 9.9 oz / 281g,0,10.9 mm 10.0 mm,Heel,True to size,Balanced,Bad,Bad,-,Moderate,Medium,Medium,Stiff,Flexible,Flexible,0,0,34.0 mm,23.1 mm,NarrowNormalWideX-Wide,1,All seasons,1,#456 Bottom 29%,#264 Top 41%,,
+Under Armour,Charged Pursuit 3,"79
+ Good!",$70,Daily running,Neutral,9.3 oz / 264g 10 oz / 283g,0,9.6 mm 8.0 mm,HeelMid/forefoot,Slightly small,Balanced,Decent,Bad,Decent,Moderate,Medium,Wide,Moderate,Moderate,Moderate,0,0,30.8 mm 30.0 mm,21.2 mm 22.0 mm,NormalX-Wide,1,All seasons,1,#321 Bottom 12%,#265 Bottom 27%,,
+Under Armour,Charged Pursuit 3,"79
+ Good!",$70,Daily running,Neutral,9.3 oz / 264g 10 oz / 283g,0,9.6 mm 8.0 mm,HeelMid/forefoot,Slightly small,Balanced,Decent,Bad,Decent,Moderate,Medium,Wide,Moderate,Moderate,Moderate,0,0,30.8 mm 30.0 mm,21.2 mm 22.0 mm,NormalX-Wide,1,All seasons,1,#321 Bottom 12%,#265 Bottom 27%,,
+Under Armour,Charged Pursuit 3,"79
+ Good!",$70,Daily running,Neutral,9.3 oz / 264g 10 oz / 283g,0,9.6 mm 8.0 mm,HeelMid/forefoot,Slightly small,Balanced,Decent,Bad,Decent,Moderate,Medium,Wide,Moderate,Moderate,Moderate,0,0,30.8 mm 30.0 mm,21.2 mm 22.0 mm,NormalX-Wide,1,All seasons,1,#320 Bottom 12%,#265 Bottom 27%,,
+Under Armour,Charged Pursuit 3,"79
+ Good!",$70,Daily running,Neutral,9.3 oz / 264g 10 oz / 283g,0,9.6 mm 8.0 mm,HeelMid/forefoot,Slightly small,Balanced,Decent,Bad,Decent,Moderate,Medium,Wide,Moderate,Moderate,Moderate,0,0,30.8 mm 30.0 mm,21.2 mm 22.0 mm,NormalX-Wide,1,All seasons,1,#319 Bottom 12%,#265 Bottom 27%,,
+Under Armour,Charged Pursuit 3,"79
+ Good!",$70,Daily running,Neutral,9.3 oz / 264g 10 oz / 283g,0,9.6 mm 8.0 mm,HeelMid/forefoot,Slightly small,Balanced,Decent,Bad,Decent,Moderate,Medium,Wide,Moderate,Moderate,Moderate,0,0,30.8 mm 30.0 mm,21.2 mm 22.0 mm,NormalX-Wide,1,All seasons,1,#319 Bottom 12%,#265 Bottom 27%,,
+Under Armour,Charged Pursuit 3,"79
+ Good!",$70,Daily running,Neutral,9.3 oz / 264g 10 oz / 283g,0,9.6 mm 8.0 mm,HeelMid/forefoot,Slightly small,Balanced,Decent,Bad,Decent,Moderate,Medium,Wide,Moderate,Moderate,Moderate,0,0,30.8 mm 30.0 mm,21.2 mm 22.0 mm,NormalX-Wide,1,All seasons,1,#319 Bottom 12%,#265 Bottom 27%,,
+HOKA,Cielo X1 2.0,"89
+ Great!",$275,CompetitionTempo,Neutral,7.3 oz / 208g 7.4 oz / 210g,1,10.7 mm 7.0 mm,Heel,True to size,Soft,Bad,Bad,Decent,Breathable,Narrow,Medium,Stiff,Stiff,Flexible,Carbon plate,1,38.8 mm 46.0 mm,28.1 mm 39.0 mm,Normal,1,SummerAll seasons,1,#82 Top 23%,#366 Bottom 1%,,
+Hoka,Clifton 10,89 Great!,$150,Daily running,Neutral,9.7 oz / 275g 9.8 oz / 277g,0,12.4 mm 8.0 mm,Heel,True to size,Soft,Good,Good,Decent,Moderate,Medium,Medium,Moderate,Stiff,Moderate,0,1,44.4 mm 42.0 mm,32.0 mm 34.0 mm,Narrow Normal Wide X-Wide,1,All seasons,1,#92 Top 26%,#4 Top 2%,,
+Hoka,Clifton 10,"89
+ Great!",$150,Daily running,Neutral,9.7 oz / 275g 9.8 oz / 277g,0,12.4 mm 8.0 mm,Heel,True to size,Soft,Good,Good,Decent,Moderate,Medium,Medium,Moderate,Stiff,Moderate,0,1,44.4 mm 42.0 mm,32.0 mm 34.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#92 Top 26%,#4 Top 2%,,
+Hoka,Clifton 10,"88
+ Great!",$150,Daily running,Neutral,9.7 oz / 275g 9.8 oz / 277g,0,12.4 mm 8.0 mm,Heel,True to size,Soft,Good,Good,Decent,Moderate,Medium,Medium,Moderate,Stiff,Moderate,0,1,44.4 mm 42.0 mm,32.0 mm 34.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#119 Top 33%,#4 Top 2%,,
+Hoka,Clifton 10,"88
+ Great!",$150,Daily running,Neutral,9.7 oz / 275g 9.8 oz / 277g,0,12.4 mm 8.0 mm,Heel,True to size,Soft,Good,Good,Decent,Moderate,Medium,Medium,Moderate,Stiff,Moderate,0,1,44.4 mm 42.0 mm,32.0 mm 34.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#118 Top 33%,#4 Top 2%,,
+Hoka,Clifton 8,"89
+ Great!",$130,Daily running,Neutral,9 oz / 256g 8.8 oz / 250g,0,8.6 mm 5.0 mm,HeelMid/forefoot,True to size,-,-,-,-,Warm,Narrow,-,Stiff,Stiff,Stiff,0,1,33.7 mm 29.0 mm,25.1 mm 24.0 mm,NormalWide,1,All seasons,1,#175 Top 28%,#111 Top 18%,,
+Hoka,Clifton 9,"86
+ Good!",$145,Daily running,Neutral,8.8 oz / 249g 8.8 oz / 249g,1,6.1 mm 5.0 mm,Mid/forefoot,True to size,Balanced,-,-,-,Moderate,Narrow,Narrow,Stiff,Stiff,Stiff,0,1,32.7 mm 32.0 mm,26.6 mm 27.0 mm,NormalWide,1,All seasons,1,#368 Bottom 42%,#3 Top 1%,,
+Hoka,Clifton 9 GTX,"87
+ Great!",$160,Daily running,Neutral,9.6 oz / 271g 9.6 oz / 272g,0,8.6 mm 5.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Good,Good,Moderate,Narrow,Narrow,Stiff,Stiff,Stiff,0,1,37.2 mm 40.0 mm,28.6 mm 35.0 mm,Normal,1,All seasons,1,#151 Top 42%,#69 Top 19%,,
+On,Cloud X,"89
+ Great!",$140,Daily runningTempo,Neutral,8.5 oz / 240g 8.1 oz / 229g,1,10.1 mm 6.0 mm,Heel,True to size,Firm,Decent,Bad,Good,Moderate,Medium,Wide,Moderate,Flexible,Flexible,0,0,27.9 mm 28.0 mm,17.8 mm 22.0 mm,Normal,1,All seasons,1,#79 Top 22%,#101 Top 28%,,
+On,Cloudboom Echo 3,"90
+ Superb!",$290,Competition,Neutral,7.9 oz / 225g 7.5 oz / 212g,1,10.2 mm 9.0 mm,Heel,True to size,Balanced,Decent,Decent,Decent,Breathable,Narrow,Narrow,Stiff,Stiff,Flexible,Carbon plate,1,38.6 mm 37.0 mm,28.4 mm 28.0 mm,Normal,1,SummerAll seasons,1,#66 Top 19%,#223 Bottom 39%,,
+On,Cloudboom Echo 3,"90
+ Superb!",$290,Competition,Neutral,7.9 oz / 225g 7.5 oz / 212g,1,10.2 mm 9.0 mm,Heel,True to size,Balanced,Decent,Decent,Decent,Breathable,Narrow,Narrow,Stiff,Stiff,Flexible,Carbon plate,1,38.6 mm 37.0 mm,28.4 mm 28.0 mm,Normal,1,SummerAll seasons,1,#66 Top 19%,#223 Bottom 39%,,
+On,Cloudboom Echo 3,"90
+ Superb!",$290,Competition,Neutral,7.9 oz / 225g 7.5 oz / 212g,1,10.2 mm 9.0 mm,Heel,True to size,Balanced,Decent,Decent,Decent,Breathable,Narrow,Narrow,Stiff,Stiff,Flexible,Carbon plate,1,38.6 mm 37.0 mm,28.4 mm 28.0 mm,Normal,1,SummerAll seasons,1,#67 Top 19%,#223 Bottom 39%,,
+On,Cloudboom Echo 3,"90
+ Superb!",$290,Competition,Neutral,7.9 oz / 225g 7.5 oz / 212g,1,10.2 mm 9.0 mm,Heel,True to size,Balanced,Decent,Decent,Decent,Breathable,Narrow,Narrow,Stiff,Stiff,Flexible,Carbon plate,1,38.6 mm 37.0 mm,28.4 mm 28.0 mm,Normal,1,SummerAll seasons,1,#67 Top 19%,#223 Bottom 39%,#223 Bottom 39%,
+On,Cloudboom Echo 3,"90
+ Superb!",$290,Competition,Neutral,7.9 oz / 225g 7.5 oz / 212g,1,10.2 mm 9.0 mm,Heel,True to size,Balanced,Decent,Decent,Decent,Breathable,Narrow,Narrow,Stiff,Stiff,Flexible,Carbon plate,1,38.6 mm 37.0 mm,28.4 mm 28.0 mm,Normal,1,SummerAll seasons,1,#67 Top 19%,#223 Bottom 39%,,
+On,Cloudeclipse,"91
+ Superb!",$180,Daily running,Neutral,9.6 oz / 272g 9.7 oz / 275g,0,9.4 mm 6.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,1,39.9 mm 37.0 mm,30.5 mm 31.0 mm,Normal,1,All seasons,1,#34 Top 10%,#233 Bottom 36%,,
+On,Cloudeclipse,"91
+ Superb!",$180,Daily running,Neutral,9.6 oz / 272g 9.7 oz / 275g,0,9.4 mm 6.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,1,39.9 mm 37.0 mm,30.5 mm 31.0 mm,Normal,1,All seasons,1,#34 Top 10%,#233 Bottom 36%,,
+On,Cloudeclipse,"91
+ Superb!",$180,Daily running,Neutral,9.6 oz / 272g 9.7 oz / 275g,0,9.4 mm 6.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,1,39.9 mm 37.0 mm,30.5 mm 31.0 mm,Normal,1,All seasons,1,#34 Top 10%,#233 Bottom 36%,,
+On,Cloudeclipse,"90
+ Superb!",$180,Daily running,Neutral,9.6 oz / 272g 9.7 oz / 275g,0,9.4 mm 6.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,1,39.9 mm 37.0 mm,30.5 mm 31.0 mm,Normal,1,All seasons,1,#37 Top 11%,#234 Bottom 36%,,
+On,Cloudeclipse,"91
+ Superb!",$180,Daily running,Neutral,9.6 oz / 272g 9.7 oz / 275g,0,9.4 mm 6.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,1,39.9 mm 37.0 mm,30.5 mm 31.0 mm,Normal,1,All seasons,1,#32 Top 9%,#234 Bottom 36%,,
+On,Cloudeclipse,"90
+ Superb!",$180,Daily running,Neutral,9.6 oz / 272g 9.7 oz / 275g,0,9.4 mm 6.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,1,39.9 mm 37.0 mm,30.5 mm 31.0 mm,Normal,1,All seasons,1,#31 Top 9%,#235 Bottom 35%,,
+on,Cloudeclipse,"90
+ Superb!",$180,Daily running,Neutral,9.6 oz / 272g 9.7 oz / 275g,0,9.4 mm 6.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,1,39.9 mm 37.0 mm,30.5 mm 31.0 mm,Normal,1,All seasons,1,#37 Top 11%,#234 Bottom 36%,,
+On,Cloudeclipse,"90
+ Superb!",$180,Daily running,Neutral,9.6 oz / 272g 9.7 oz / 275g,0,9.4 mm 6.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,1,39.9 mm 37.0 mm,30.5 mm 31.0 mm,Normal,1,All seasons,1,#37 Top 11%,#234 Bottom 36%,,
+On,Cloudeclipse,"90
+ Superb!",$180,Daily running,Neutral,9.6 oz / 272g 9.7 oz / 275g,0,9.4 mm 6.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,1,39.9 mm 37.0 mm,30.5 mm 31.0 mm,Normal,1,All seasons,1,#37 Top 11%,#234 Bottom 36%,,
+On,Cloudeclipse,"90
+ Superb!",$180,Daily running,Neutral,9.6 oz / 272g 9.7 oz / 275g,0,9.4 mm 6.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,1,39.9 mm 37.0 mm,30.5 mm 31.0 mm,Normal,1,All seasons,1,#37 Top 11%,#234 Bottom 36%,,
+On,Cloudeclipse,"90
+ Superb!",$180,Daily running,Neutral,9.6 oz / 272g 9.7 oz / 275g,0,9.4 mm 6.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,1,39.9 mm 37.0 mm,30.5 mm 31.0 mm,Normal,1,All seasons,1,#37 Top 11%,#234 Bottom 36%,,
+On,Cloudflow 4,"90
+ Superb!",$160,Daily runningTempo,Neutral,8.6 oz / 245g 9.2 oz / 260g,1,7.9 mm 8.0 mm,Mid/forefoot,True to size,Balanced,Decent,Decent,Good,Moderate,Medium,Medium,Moderate,Stiff,Flexible,0,0,36.1 mm 31.0 mm,28.2 mm 23.0 mm,Normal,1,All seasons,1,#60 Top 17%,#225 Bottom 38%,,
+On,Cloudflow 4,"90
+ Superb!",$160,Daily runningTempo,Neutral,8.6 oz / 245g 9.2 oz / 260g,1,7.9 mm 8.0 mm,Mid/forefoot,True to size,Balanced,Decent,Decent,Good,Moderate,Medium,Medium,Moderate,Stiff,Flexible,0,0,36.1 mm 31.0 mm,28.2 mm 23.0 mm,Normal,1,All seasons,1,#61 Top 17%,#226 Bottom 38%,,
+On ,Cloudflow 4,"90
+ Superb!",$160,Daily runningTempo,Neutral,8.6 oz / 245g 9.2 oz / 260g,1,7.9 mm 8.0 mm,Mid/forefoot,True to size,Balanced,Decent,Decent,Good,Moderate,Medium,Medium,Moderate,Stiff,Flexible,0,0,36.1 mm 31.0 mm,28.2 mm 23.0 mm,Normal,1,All seasons,1,#61 Top 17%,#225 Bottom 38%,,
+On,Cloudflyer 5,"83
+ Good!",$170,Daily running,Stability,11.6 oz / 329g 11.3 oz / 320g,0,7.9 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Good,Good,Moderate,Narrow,Medium,Moderate,Stiff,Moderate,0,0,33.2 mm 31.0 mm,25.3 mm 21.0 mm,Normal,1,All seasons,1,#257 Bottom 29%,#171 Top 47%,,
+On,Cloudflyer 5,"83
+ Good!",$170,Daily running,Stability,11.6 oz / 329g 11.3 oz / 320g,0,7.9 mm 10.0 mm,Mid/forefoot,True to size,Balanced,Good,Good,Good,Moderate,Narrow,Medium,Moderate,Stiff,Moderate,0,0,33.2 mm 31.0 mm,25.3 mm 21.0 mm,Normal,1,All seasons,1,#259 Bottom 29%,#172 Top 47%,,
+On,Cloudflyer 5,"83
+ Good!",$170,Daily running,Stability,11.6 oz / 329g 11.3 oz / 320g,0,7.9 mm 10.0 mm,Mid/forefoot,True to size,Balanced,Good,Good,Good,Moderate,Narrow,Medium,Moderate,Stiff,Moderate,0,0,33.2 mm 31.0 mm,25.3 mm 21.0 mm,Normal,1,All seasons,1,#259 Bottom 29%,#172 Top 47%,,
+On,Cloudflyer 5,"83
+ Good!",$170,Daily running,Stability,11.6 oz / 329g 11.3 oz / 320g,0,7.9 mm 10.0 mm,Mid/forefoot,True to size,Balanced,Good,Good,Good,Moderate,Narrow,Medium,Moderate,Stiff,Moderate,0,0,33.2 mm 31.0 mm,25.3 mm 21.0 mm,Normal,1,All seasons,1,#249 Bottom 31%,#172 Top 47%,,
+On,Cloudflyer 5,"83
+ Good!",$170,Daily running,Stability,11.6 oz / 329g 11.3 oz / 320g,0,7.9 mm 10.0 mm,Mid/forefoot,True to size,Balanced,Good,Good,Good,Moderate,Narrow,Medium,Moderate,Stiff,Moderate,0,0,33.2 mm 31.0 mm,25.3 mm 21.0 mm,Normal,1,All seasons,1,#247 Bottom 32%,#172 Top 47%,,
+On,Cloudflyer 5,"83
+ Good!",$170,Daily running,Stability,11.6 oz / 329g 11.3 oz / 320g,0,7.9 mm 10.0 mm,Mid/forefoot,True to size,Balanced,Good,Good,Good,Moderate,Narrow,Medium,Moderate,Stiff,Moderate,0,0,33.2 mm 31.0 mm,25.3 mm 21.0 mm,Normal,1,All seasons,1,#247 Bottom 32%,#172 Top 47%,,
+On,Cloudflyer 5,"83
+ Good!",$170,Daily running,Stability,11.6 oz / 329g 11.3 oz / 320g,0,7.9 mm 10.0 mm,Mid/forefoot,True to size,Balanced,Good,Good,Good,Moderate,Narrow,Medium,Moderate,Stiff,Moderate,0,0,33.2 mm 31.0 mm,25.3 mm 21.0 mm,Normal,1,All seasons,1,#247 Bottom 32%,#172 Top 47%,,
+On,Cloudflyer 5,"83
+ Good!",$170,Daily running,Stability,11.6 oz / 329g 11.3 oz / 320g,0,7.9 mm 10.0 mm,Mid/forefoot,True to size,Balanced,Good,Good,Good,Moderate,Narrow,Medium,Moderate,Stiff,Moderate,0,0,33.2 mm 31.0 mm,25.3 mm 21.0 mm,Normal,1,All seasons,1,#247 Bottom 32%,#172 Top 47%,,
+On,Cloudflyer 5,"83
+ Good!",$170,Daily running,Stability,11.6 oz / 329g 11.3 oz / 320g,0,7.9 mm 10.0 mm,Mid/forefoot,True to size,Balanced,Good,Good,Good,Moderate,Narrow,Medium,Moderate,Stiff,Moderate,0,0,33.2 mm 31.0 mm,25.3 mm 21.0 mm,Normal,1,All seasons,1,#247 Bottom 32%,#172 Top 47%,,
+On,Cloudgo,"89
+ Great!",$150,Daily runningTempo,Neutral,9.1 oz / 259g 7.5 oz / 214g,0,11.2 mm 11.0 mm,Heel,Slightly small,Balanced,Bad,Bad,Good,Moderate,Medium,Medium,Moderate,Moderate,Flexible,0,0,33.8 mm 30.0 mm,22.6 mm 19.0 mm,NormalWide,1,All seasons,1,#112 Top 31%,#235 Bottom 35%,,
+On,Cloudgo,"89
+ Great!",$150,Daily runningTempo,Neutral,9.1 oz / 259g 7.5 oz / 214g,0,11.2 mm 11.0 mm,Heel,Slightly small,Balanced,Bad,Bad,Good,Moderate,Medium,Medium,Moderate,Moderate,Flexible,0,0,33.8 mm 30.0 mm,22.6 mm 19.0 mm,NormalWide,1,All seasons,1,#112 Top 31%,#236 Bottom 35%,,
+On,Cloudmonster,"91
+ Superb!",$170,Daily running,Neutral,9.9 oz / 280g 9.7 oz / 274g,0,6.8 mm 6.0 mm,Mid/forefoot,Slightly small,Balanced,-,-,-,Breathable,Narrow,-,Moderate,Moderate,Flexible,0,1,34.9 mm 30.0 mm,28.1 mm 24.0 mm,Normal,1,SummerAll seasons,1,#39 Top 7%,#55 Top 9%,,
+On,Cloudmonster 2,"89
+ Great!",$180,Daily running,Neutral,10.3 oz / 292g 10.4 oz / 295g,0,6.6 mm 6.0 mm,Mid/forefoot,Slightly large,Balanced,Good,Decent,Good,Warm,Wide,Medium,Stiff,Moderate,Moderate,0,1,37.9 mm 35.0 mm,31.3 mm 29.0 mm,Normal,1,All seasons,1,#96 Top 27%,#41 Top 12%,,
+On,Cloudmonster 2,"89
+ Great!",$180,Daily running,Neutral,10.3 oz / 292g 10.4 oz / 295g,0,6.6 mm 6.0 mm,Mid/forefoot,Slightly large,Balanced,Good,Decent,Good,Warm,Wide,Medium,Stiff,Moderate,Moderate,0,1,37.9 mm 35.0 mm,31.3 mm 29.0 mm,Normal,1,All seasons,1,#96 Top 27%,#41 Top 12%,,
+On,Cloudmonster 2,"89
+ Great!",$180,Daily running,Neutral,10.3 oz / 292g 10.4 oz / 295g,0,6.6 mm 6.0 mm,Mid/forefoot,Slightly large,Balanced,Good,Decent,Good,Warm,Wide,Medium,Stiff,Moderate,Moderate,0,1,37.9 mm 35.0 mm,31.3 mm 29.0 mm,Normal,1,All seasons,1,#98 Top 27%,#41 Top 12%,,
+On,Cloudmonster 2,"89
+ Great!",$180,Daily running,Neutral,10.3 oz / 292g 10.4 oz / 295g,0,6.6 mm 6.0 mm,Mid/forefoot,Slightly large,Balanced,Good,Decent,Good,Warm,Wide,Medium,Stiff,Moderate,Moderate,0,1,37.9 mm 35.0 mm,31.3 mm 29.0 mm,Normal,1,All seasons,1,#97 Top 27%,#41 Top 12%,,
+On,Cloudmonster 2,"89
+ Great!",$180,Daily running,Neutral,10.3 oz / 292g 10.4 oz / 295g,0,6.6 mm 6.0 mm,Mid/forefoot,Slightly large,Balanced,Good,Decent,Good,Warm,Wide,Medium,Stiff,Moderate,Moderate,0,1,37.9 mm 35.0 mm,31.3 mm 29.0 mm,Normal,1,All seasons,1,#99 Top 28%,#41 Top 12%,,
+On,Cloudmonster Hyper,"89
+ Great!",$220,Daily runningTempo,Neutral,9.1 oz / 258g 9 oz / 255g,0,6.7 mm 6.0 mm,Mid/forefoot,True to size,Soft,Decent,Bad,Good,Moderate,Medium,Medium,Moderate,Stiff,Moderate,0,0,39.7 mm 37.0 mm,33.0 mm 31.0 mm,Normal,1,All seasons,1,#89 Top 25%,#168 Top 47%,,
+On,Cloudmonster Hyper,"89
+ Great!",$220,Daily runningTempo,Neutral,9.1 oz / 258g 9 oz / 255g,0,6.7 mm 6.0 mm,Mid/forefoot,True to size,Soft,Decent,Bad,Good,Moderate,Medium,Medium,Moderate,Stiff,Moderate,0,0,39.7 mm 37.0 mm,33.0 mm 31.0 mm,Normal,1,All seasons,1,#93 Top 26%,#169 Top 47%,,
+On,Cloudrunner 2,"88
+ Great!",$150,Daily running,Neutral,9.7 oz / 275g 9.8 oz / 277g,0,8.5 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Decent,Decent,Moderate,Medium,Medium,Stiff,Moderate,Stiff,0,0,33.6 mm 39.0 mm,25.1 mm 29.0 mm,NormalWide,1,All seasons,1,#131 Top 36%,#77 Top 21%,,
+On,Cloudrunner 2,"88
+ Great!",$150,Daily running,Neutral,9.7 oz / 275g 9.8 oz / 277g,0,8.5 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Decent,Decent,Moderate,Medium,Medium,Stiff,Moderate,Stiff,0,0,33.6 mm 39.0 mm,25.1 mm 29.0 mm,NormalWide,1,All seasons,1,#131 Top 36%,#77 Top 22%,,
+On,Cloudrunner 2,"88
+ Great!",$150,Daily running,Neutral,9.7 oz / 275g 9.8 oz / 277g,0,8.5 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Decent,Decent,Moderate,Medium,Medium,Stiff,Moderate,Stiff,0,0,33.6 mm 39.0 mm,25.1 mm 29.0 mm,NormalWide,1,All seasons,1,#128 Top 35%,#77 Top 22%,,
+On,Cloudrunner 2 Waterproof,"71
+ Bad!",$170,Daily running,Stability,11.4 oz / 323g 11.3 oz / 320g,0,8.3 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Good,Warm,Narrow,Medium,Stiff,Stiff,Stiff,0,0,35.8 mm 39.0 mm,27.5 mm 29.0 mm,Normal,1,Winter,1,#362 Bottom 1%,#225 Bottom 38%,,
+On,Cloudspark,"86
+ Good!",$160,Daily running,Neutral,9.5 oz / 269g 9.9 oz / 282g,0,8.6 mm 8.0 mm,HeelMid/forefoot,Slightly small,Firm,Decent,Bad,Decent,Moderate,Medium,Medium,Stiff,Stiff,Flexible,0,0,34.6 mm 34.0 mm,26.0 mm 26.0 mm,Normal,1,All seasons,1,#170 Top 47%,#332 Bottom 9%,,
+On,Cloudspark,"86
+ Good!",$160,Daily running,Neutral,9.5 oz / 269g 9.9 oz / 282g,0,8.6 mm 8.0 mm,HeelMid/forefoot,Slightly small,Firm,Decent,Bad,Decent,Moderate,Medium,Medium,Stiff,Stiff,Flexible,0,0,34.6 mm 34.0 mm,26.0 mm 26.0 mm,Normal,1,All seasons,1,#170 Top 47%,#333 Bottom 9%,,
+On,Cloudspark,"86
+ Good!",$160,Daily running,Neutral,9.5 oz / 269g 9.9 oz / 282g,0,8.6 mm 8.0 mm,HeelMid/forefoot,Slightly small,Firm,Decent,Bad,Decent,Moderate,Medium,Medium,Stiff,Stiff,Flexible,0,0,34.6 mm 34.0 mm,26.0 mm 26.0 mm,Normal,1,All seasons,1,#170 Top 47%,#333 Bottom 9%,,
+On,Cloudspark,"86
+ Good!",$160,Daily running,Neutral,9.5 oz / 269g 9.9 oz / 282g,0,8.6 mm 8.0 mm,HeelMid/forefoot,Slightly small,Firm,Decent,Bad,Decent,Moderate,Medium,Medium,Stiff,Stiff,Flexible,0,0,34.6 mm 34.0 mm,26.0 mm 26.0 mm,Normal,1,All seasons,1,#169 Top 47%,#333 Bottom 9%,,
+On,Cloudstratus 3,"90
+ Superb!",$180,Daily running,Neutral,10.4 oz / 296g 10.3 oz / 292g,0,9.1 mm 6.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Wide,Medium,Stiff,Stiff,Flexible,0,0,35.3 mm 37.0 mm,26.2 mm 31.0 mm,Normal,1,All seasons,1,#65 Top 18%,#212 Bottom 42%,,
+On,Cloudstratus 3,"90
+ Superb!",$180,Daily running,Neutral,10.4 oz / 296g 10.3 oz / 292g,0,9.1 mm 6.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Wide,Medium,Stiff,Stiff,Flexible,0,0,35.3 mm 37.0 mm,26.2 mm 31.0 mm,Normal,1,All seasons,1,#66 Top 19%,#212 Bottom 42%,,
+On,Cloudsurfer 7,"86
+ Good!",$160,Daily running,Neutral,8.4 oz / 237g 8.6 oz / 245g,1,14.6 mm 10.0 mm,Heel,Slightly small,Balanced,Bad,Bad,Good,Moderate,Medium,Medium,Flexible,Flexible,Flexible,0,0,36.3 mm 32.0 mm,21.7 mm 22.0 mm,Normal,1,All seasons,1,#176 Top 49%,#305 Bottom 16%,,
+On,Cloudsurfer Max,"90
+ Superb!",$180,Daily running,Neutral,10.3 oz / 292g 10.3 oz / 292g,0,7.9 mm 6.0 mm,Mid/forefoot,-,Balanced,Decent,Decent,Decent,Warm,Medium,Medium,Stiff,Moderate,Moderate,0,0,37.3 mm 37.0 mm,29.4 mm 31.0 mm,NormalWide,1,All seasons,1,#40 Top 11%,#101 Top 28%,,
+On,Cloudsurfer Next,"89
+ Great!",$150,Daily running,Neutral,9.3 oz / 264g 9.4 oz / 266g,0,4.5 mm 6.0 mm,Mid/forefoot,True to size,Firm,Decent,Bad,Decent,Warm,Medium,Medium,Stiff,Stiff,Moderate,0,0,33.8 mm 37.0 mm,29.3 mm 31.0 mm,NormalWide,1,All seasons,1,#75 Top 21%,#146 Top 40%,,
+Topo,Cyclone 2,"89
+ Great!",$150,Daily runningTempo,Neutral,6.7 oz / 190g 6.9 oz / 196g,1,4.2 mm 5.0 mm,Mid/forefoot,True to size,Balanced,Bad,Good,Good,Breathable,Wide,Wide,Flexible,Flexible,Flexible,0,1,26.2 mm 28.0 mm,22.0 mm 23.0 mm,Normal,1,SummerAll seasons,1,#89 Top 25%,#267 Bottom 27%,,
+PUMA,Deviate Nitro 3,"89
+ Great!",$160,Tempo,Neutral,9.5 oz / 268g 9.5 oz / 269g,0,10.1 mm 10.0 mm,Heel,Slightly small,Soft,Decent,Decent,Decent,Moderate,Narrow,Narrow,Stiff,Stiff,Flexible,Carbon plate,0,37.4 mm 39.0 mm,27.3 mm 29.0 mm,NormalWide,1,All seasons,1,#113 Top 31%,#60 Top 17%,,Road
+PUMA,Deviate Nitro 3,"89
+ Great!",$160,Tempo,Neutral,9.5 oz / 268g 9.5 oz / 269g,0,10.1 mm 10.0 mm,Heel,Slightly small,Soft,Decent,Decent,Decent,Moderate,Narrow,Narrow,Stiff,Stiff,Flexible,Carbon plate,0,37.4 mm 39.0 mm,27.3 mm 29.0 mm,NormalWide,1,All seasons,1,#113 Top 31%,#60 Top 17%,,
+PUMA,Deviate Nitro 3,"89
+ Great!",$160,Tempo,Neutral,9.5 oz / 268g 9.5 oz / 269g,0,10.1 mm 10.0 mm,Heel,Slightly small,Soft,Decent,Decent,Decent,Moderate,Narrow,Narrow,Stiff,Stiff,Flexible,Carbon plate,0,37.4 mm 39.0 mm,27.3 mm 29.0 mm,NormalWide,1,All seasons,1,#113 Top 31%,#60 Top 17%,,
+PUMA,Deviate Nitro 3,"89
+ Great!",$160,Tempo,Neutral,9.5 oz / 268g 9.5 oz / 269g,0,10.1 mm 10.0 mm,Heel,Slightly small,Soft,Decent,Decent,Decent,Moderate,Narrow,Narrow,Stiff,Stiff,Flexible,Carbon plate,0,37.4 mm 39.0 mm,27.3 mm 29.0 mm,NormalWide,1,All seasons,1,#112 Top 31%,#60 Top 17%,,
+PUMA,Deviate Nitro 3,"89
+ Great!",$160,Tempo,Neutral,9.5 oz / 268g 9.5 oz / 269g,0,10.1 mm 10.0 mm,Heel,Slightly small,Soft,Decent,Decent,Decent,Moderate,Narrow,Narrow,Stiff,Stiff,Flexible,Carbon plate,0,37.4 mm 39.0 mm,27.3 mm 29.0 mm,NormalWide,1,All seasons,1,#112 Top 31%,#60 Top 17%,,
+PUMA,Deviate Nitro 3,"89
+ Great!",$160,Tempo,Neutral,9.5 oz / 268g 9.5 oz / 269g,0,10.1 mm 10.0 mm,Heel,Slightly small,Soft,Decent,Decent,Decent,Moderate,Narrow,Narrow,Stiff,Stiff,Flexible,Carbon plate,0,37.4 mm 39.0 mm,27.3 mm 29.0 mm,NormalWide,1,All seasons,1,#112 Top 31%,#60 Top 17%,,
+PUMA,Deviate Nitro 3,"89
+ Great!",$160,Tempo,Neutral,9.5 oz / 268g 9.5 oz / 269g,0,10.1 mm 10.0 mm,Heel,Slightly small,Soft,Decent,Decent,Decent,Moderate,Narrow,Narrow,Stiff,Stiff,Flexible,Carbon plate,0,37.4 mm 39.0 mm,27.3 mm 29.0 mm,NormalWide,1,All seasons,1,#112 Top 31%,#60 Top 17%,,
+PUMA,Deviate Nitro 3,"89
+ Great!",$160,Tempo,Neutral,9.5 oz / 268g 9.5 oz / 269g,0,10.1 mm 10.0 mm,Heel,Slightly small,Soft,Decent,Decent,Decent,Moderate,Narrow,Narrow,Stiff,Stiff,Flexible,Carbon plate,0,37.4 mm 39.0 mm,27.3 mm 29.0 mm,NormalWide,1,All seasons,1,#113 Top 31%,#60 Top 17%,,
+PUMA,Deviate Nitro 3,"89
+ Great!",$160,Tempo,Neutral,9.5 oz / 268g 9.5 oz / 269g,0,10.1 mm 10.0 mm,Heel,Slightly small,Soft,Decent,Decent,Decent,Moderate,Narrow,Narrow,Stiff,Stiff,Flexible,Carbon plate,0,37.4 mm 39.0 mm,27.3 mm 29.0 mm,NormalWide,1,All seasons,1,#113 Top 31%,#60 Top 17%,,
+PUMA,Deviate Nitro 3,"89
+ Great!",$160,Tempo,Neutral,9.5 oz / 268g 9.5 oz / 269g,0,10.1 mm 10.0 mm,Heel,Slightly small,Soft,Decent,Decent,Decent,Moderate,Narrow,Narrow,Stiff,Stiff,Flexible,Carbon plate,0,37.4 mm 39.0 mm,27.3 mm 29.0 mm,NormalWide,1,All seasons,1,#113 Top 31%,#60 Top 17%,,
+PUMA,Deviate Nitro Elite 3,"90
+ Superb!",$230,CompetitionTempo,Neutral,7.2 oz / 204g 7.4 oz / 209g,1,10.6 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Bad,Good,Decent,Moderate,Medium,Narrow,Stiff,Stiff,Flexible,Carbon plate,1,39.2 mm 40.0 mm,28.6 mm 32.0 mm,Normal,1,All seasons,1,#37 Top 11%,#50 Top 14%,,Road
+PUMA,Deviate Nitro Elite 3,"90
+ Superb!",$230,CompetitionTempo,Neutral,7.2 oz / 204g 7.4 oz / 209g,1,10.6 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Bad,Good,Decent,Moderate,Medium,Narrow,Stiff,Stiff,Flexible,Carbon plate,1,39.2 mm 40.0 mm,28.6 mm 32.0 mm,Normal,1,All seasons,1,#37 Top 11%,#50 Top 14%,,
+PUMA,Deviate Nitro Elite 3,"90
+ Superb!",$230,CompetitionTempo,Neutral,7.2 oz / 204g 7.4 oz / 209g,1,10.6 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Bad,Good,Decent,Moderate,Medium,Narrow,Stiff,Stiff,Flexible,Carbon plate,1,39.2 mm 40.0 mm,28.6 mm 32.0 mm,Normal,1,All seasons,1,#37 Top 11%,#50 Top 14%,,
+PUMA,Deviate Nitro Elite 3,"90
+ Superb!",$230,CompetitionTempo,Neutral,7.2 oz / 204g 7.4 oz / 209g,1,10.6 mm 8.0 mm,Heel,True to size,Soft,Bad,Good,Decent,Moderate,Medium,Narrow,Stiff,Stiff,Flexible,Carbon plate,1,39.2 mm 40.0 mm,28.6 mm 32.0 mm,Normal,1,All seasons,1,#37 Top 11%,#50 Top 14%,,
+PUMA,Deviate Nitro Elite 3,"90
+ Superb!",$230,CompetitionTempo,Neutral,7.2 oz / 204g 7.4 oz / 209g,1,10.6 mm 8.0 mm,Heel,True to size,Soft,Bad,Good,Decent,Moderate,Medium,Narrow,Stiff,Stiff,Flexible,Carbon plate,1,39.2 mm 40.0 mm,28.6 mm 32.0 mm,Normal,1,All seasons,1,#35 Top 10%,#50 Top 14%,,
+PUMA,Deviate Nitro Elite 3,"90
+ Superb!",$230,CompetitionTempo,Neutral,7.2 oz / 204g 7.4 oz / 209g,1,10.6 mm 8.0 mm,Heel,True to size,Soft,Bad,Good,Decent,Moderate,Medium,Narrow,Stiff,Stiff,Flexible,Carbon plate,1,39.2 mm 40.0 mm,28.6 mm 32.0 mm,Normal,1,All seasons,1,#35 Top 10%,#50 Top 14%,,
+PUMA,Deviate Nitro Elite 3,"90
+ Superb!",$230,CompetitionTempo,Neutral,7.2 oz / 204g 7.4 oz / 209g,1,10.6 mm 8.0 mm,Heel,True to size,Soft,Bad,Good,Decent,Moderate,Medium,Narrow,Stiff,Stiff,Flexible,Carbon plate,1,39.2 mm 40.0 mm,28.6 mm 32.0 mm,Normal,1,All seasons,1,#35 Top 10%,#50 Top 14%,,
+PUMA,Deviate Nitro Elite 3,"90
+ Superb!",$230,CompetitionTempo,Neutral,7.2 oz / 204g 7.4 oz / 209g,1,10.6 mm 8.0 mm,Heel,True to size,Soft,Bad,Good,Decent,Moderate,Medium,Narrow,Stiff,Stiff,Flexible,Carbon plate,1,39.2 mm 40.0 mm,28.6 mm 32.0 mm,Normal,1,All seasons,1,#35 Top 10%,#50 Top 14%,,
+PUMA,Deviate Nitro Elite 3,"90
+ Superb!",$230,CompetitionTempo,Neutral,7.2 oz / 204g 7.4 oz / 209g,1,10.6 mm 8.0 mm,Heel,True to size,Soft,Bad,Good,Decent,Moderate,Medium,Narrow,Stiff,Stiff,Flexible,Carbon plate,1,39.2 mm 40.0 mm,28.6 mm 32.0 mm,Normal,1,All seasons,1,#35 Top 10%,#50 Top 14%,,
+PUMA,Deviate Nitro Elite 3,"90
+ Superb!",$230,CompetitionTempo,Neutral,7.2 oz / 204g 7.4 oz / 209g,1,10.6 mm 8.0 mm,Heel,True to size,Soft,Bad,Good,Decent,Moderate,Medium,Narrow,Stiff,Stiff,Flexible,Carbon plate,1,39.2 mm 40.0 mm,28.6 mm 32.0 mm,Normal,1,All seasons,1,#35 Top 10%,#50 Top 14%,,
+PUMA,Deviate Nitro Elite 3,"90
+ Superb!",$230,CompetitionTempo,Neutral,7.2 oz / 204g 7.4 oz / 209g,1,10.6 mm 8.0 mm,Heel,True to size,Soft,Bad,Good,Decent,Moderate,Medium,Narrow,Stiff,Stiff,Flexible,Carbon plate,1,39.2 mm 40.0 mm,28.6 mm 32.0 mm,Normal,1,All seasons,1,#35 Top 10%,#50 Top 14%,,
+PUMA,Deviate Nitro Elite 3,"90
+ Superb!",$230,CompetitionTempo,Neutral,7.2 oz / 204g 7.4 oz / 209g,1,10.6 mm 8.0 mm,Heel,True to size,Soft,Bad,Good,Decent,Moderate,Medium,Narrow,Stiff,Stiff,Flexible,Carbon plate,1,39.2 mm 40.0 mm,28.6 mm 32.0 mm,Normal,1,All seasons,1,#35 Top 10%,#50 Top 14%,,
+PUMA,Deviate Nitro Elite 3,"90
+ Superb!",$230,CompetitionTempo,Neutral,7.2 oz / 204g 7.4 oz / 209g,1,10.6 mm 8.0 mm,Heel,True to size,Soft,Bad,Good,Decent,Moderate,Medium,Narrow,Stiff,Stiff,Flexible,Carbon plate,1,39.2 mm 40.0 mm,28.6 mm 32.0 mm,Normal,1,All seasons,1,#35 Top 10%,#50 Top 14%,#50 Top 14%,
+PUMA,Deviate Nitro Elite 3,"90
+ Superb!",$230,CompetitionTempo,Neutral,7.2 oz / 204g 7.4 oz / 209g,1,10.6 mm 8.0 mm,Heel,True to size,Soft,Bad,Good,Decent,Moderate,Medium,Narrow,Stiff,Stiff,Flexible,Carbon plate,1,39.2 mm 40.0 mm,28.6 mm 32.0 mm,Normal,1,All seasons,1,#35 Top 10%,#50 Top 14%,,
+PUMA,Deviate Nitro Elite 3,"90
+ Superb!",$230,CompetitionTempo,Neutral,7.2 oz / 204g 7.4 oz / 209g,1,10.6 mm 8.0 mm,Heel,True to size,Soft,Bad,Good,Decent,Moderate,Medium,Narrow,Stiff,Stiff,Flexible,Carbon plate,1,39.2 mm 40.0 mm,28.6 mm 32.0 mm,Normal,1,All seasons,1,#35 Top 10%,#50 Top 14%,,
+PUMA,Deviate Nitro Elite 3,"90
+ Superb!",$230,CompetitionTempo,Neutral,7.2 oz / 204g 7.4 oz / 209g,1,10.6 mm 8.0 mm,Heel,True to size,Soft,Bad,Good,Decent,Moderate,Medium,Narrow,Stiff,Stiff,Flexible,Carbon plate,1,39.2 mm 40.0 mm,28.6 mm 32.0 mm,Normal,1,All seasons,1,#35 Top 10%,#50 Top 14%,,
+Puma,Deviate Nitro Elite 3,"90
+ Superb!",$230,CompetitionTempo,Neutral,7.2 oz / 204g 7.4 oz / 209g,1,10.6 mm 8.0 mm,Heel,True to size,Soft,Bad,Good,Decent,Moderate,Medium,Narrow,Stiff,Stiff,Flexible,Carbon plate,1,39.2 mm 40.0 mm,28.6 mm 32.0 mm,Normal,1,All seasons,1,#35 Top 10%,#50 Top 14%,,
+PUMA,Deviate Nitro Elite 3,"90
+ Superb!",$265,CompetitionTempo,Neutral,7.2 oz / 204g 7.4 oz / 209g,1,10.6 mm 8.0 mm,Heel,True to size,Soft,Bad,Good,Decent,Moderate,Medium,Narrow,Stiff,Stiff,Flexible,Carbon plate,1,39.2 mm 40.0 mm,28.6 mm 32.0 mm,Normal,1,All seasons,1,#35 Top 10%,#50 Top 14%,,
+Nike,Downshifter 11,"83
+ Good!",$60,Daily running,Neutral,8.8 oz / 249g 10.2 oz / 288g,1,11.6 mm 10.0 mm,Heel,True to size,-,-,-,-,-,Narrow,-,Stiff,Moderate,Moderate,0,0,31.5 mm,19.9 mm,Normal,0,-,0,#488 Bottom 24%,#549 Bottom 14%,,
+Nike,Downshifter 12,"81
+ Good!",$70,Daily running,Neutral,9.9 oz / 280g 9.9 oz / 280g,0,10.0 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,-,-,-,Moderate,Medium,-,Moderate,Flexible,Flexible,0,0,31.7 mm 32.0 mm,21.7 mm 22.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#542 Bottom 15%,#243 Top 38%,,
+Nike,Downshifter 13,"77
+ Decent!",$75,Daily running,Neutral,9.3 oz / 265g 9.4 oz / 267g,0,10.1 mm 10.0 mm,Heel,Slightly small,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,32.2 mm 32.0 mm,22.1 mm 22.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#336 Bottom 8%,#96 Top 27%,,
+Nike,Downshifter 13,"77
+ Decent!",$75,Daily running,Neutral,9.3 oz / 265g 9.4 oz / 267g,0,10.1 mm 10.0 mm,Heel,Slightly small,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,32.2 mm 32.0 mm,22.1 mm 22.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#337 Bottom 7%,#96 Top 27%,,
+Nike,Downshifter 13,"77
+ Decent!",$75,Daily running,Neutral,9.3 oz / 265g 9.4 oz / 267g,0,10.1 mm 10.0 mm,Heel,Slightly small,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,32.2 mm 32.0 mm,22.1 mm 22.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#335 Bottom 8%,#96 Top 27%,,
+Nike,Downshifter 13,"77
+ Decent!",$75,Daily running,Neutral,9.3 oz / 265g 9.4 oz / 267g,0,10.1 mm 10.0 mm,Heel,Slightly small,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,32.2 mm 32.0 mm,22.1 mm 22.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#335 Bottom 8%,#96 Top 27%,,
+Nike,Downshifter 13,"77
+ Decent!",$75,Daily running,Neutral,9.3 oz / 265g 9.4 oz / 267g,0,10.1 mm 10.0 mm,Heel,Slightly small,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,32.2 mm 32.0 mm,22.1 mm 22.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#335 Bottom 8%,#96 Top 27%,,
+Nike,Downshifter 13,"77
+ Decent!",$75,Daily running,Neutral,9.3 oz / 265g 9.4 oz / 267g,0,10.1 mm 10.0 mm,Heel,Slightly small,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,32.2 mm 32.0 mm,22.1 mm 22.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#335 Bottom 8%,#96 Top 27%,,
+Nike,Downshifter 13,"77
+ Decent!",$75,Daily running,Neutral,9.3 oz / 265g 9.4 oz / 267g,0,10.1 mm 10.0 mm,Heel,Slightly small,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,32.2 mm 32.0 mm,22.1 mm 22.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#335 Bottom 8%,#96 Top 27%,,
+Nike,Downshifter 13,"77
+ Decent!",$75,Daily running,Neutral,9.3 oz / 265g 9.4 oz / 267g,0,10.1 mm 10.0 mm,Heel,Slightly small,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,32.2 mm 32.0 mm,22.1 mm 22.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#335 Bottom 8%,#96 Top 27%,,
+Nike,Downshifter 13,"77
+ Decent!",$75,Daily running,Neutral,9.3 oz / 265g 9.4 oz / 267g,0,10.1 mm 10.0 mm,Heel,Slightly small,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,32.2 mm 32.0 mm,22.1 mm 22.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#335 Bottom 8%,#96 Top 27%,,
+Nike,Downshifter 13,"77
+ Decent!",$75,Daily running,Neutral,9.3 oz / 265g 9.4 oz / 267g,0,10.1 mm 10.0 mm,Heel,Slightly small,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,32.2 mm 32.0 mm,22.1 mm 22.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#335 Bottom 8%,#96 Top 27%,,
+Nike,Downshifter 13,"77
+ Decent!",$75,Daily running,Neutral,9.3 oz / 265g 9.4 oz / 267g,0,10.1 mm 10.0 mm,Heel,Slightly small,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,32.2 mm 32.0 mm,22.1 mm 22.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#335 Bottom 8%,#96 Top 27%,,
+Nike,Downshifter 13,"77
+ Decent!",$75,Daily running,Neutral,9.3 oz / 265g 9.4 oz / 267g,0,10.1 mm 10.0 mm,Heel,Slightly small,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,32.2 mm 32.0 mm,22.1 mm 22.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#335 Bottom 8%,#96 Top 27%,,
+NIke,Downshifter 13,"77
+ Decent!",$75,Daily running,Neutral,9.3 oz / 265g 9.4 oz / 267g,0,10.1 mm 10.0 mm,Heel,Slightly small,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,32.2 mm 32.0 mm,22.1 mm 22.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#335 Bottom 8%,#96 Top 27%,,
+Nike,Downshifter 13,"77
+ Decent!",$75,Daily running,Neutral,9.3 oz / 265g 9.4 oz / 267g,0,10.1 mm 10.0 mm,Heel,Slightly small,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,32.2 mm 32.0 mm,22.1 mm 22.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#335 Bottom 8%,#96 Top 27%,,
+Nike,Downshifter 13,"77
+ Decent!",$75,Daily running,Neutral,9.3 oz / 265g 9.4 oz / 267g,0,10.1 mm 10.0 mm,Heel,Slightly small,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,32.2 mm 32.0 mm,22.1 mm 22.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#335 Bottom 8%,#96 Top 27%,,
+Nike,Downshifter 13,"77
+ Decent!",$75,Daily running,Neutral,9.3 oz / 265g 9.4 oz / 267g,0,10.1 mm 10.0 mm,Heel,Slightly small,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,32.2 mm 32.0 mm,22.1 mm 22.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#335 Bottom 8%,#96 Top 27%,,
+Nike,Downshifter 13,"77
+ Decent!",$75,Daily running,Neutral,9.3 oz / 265g 9.4 oz / 267g,0,10.1 mm 10.0 mm,Heel,Slightly small,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,32.2 mm 32.0 mm,22.1 mm 22.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#335 Bottom 8%,#96 Top 27%,,
+Nike,Downshifter 13,"77
+ Decent!",$75,Daily running,Neutral,9.3 oz / 265g 9.4 oz / 267g,0,10.1 mm 10.0 mm,Heel,Slightly small,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,32.2 mm 32.0 mm,22.1 mm 22.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#330 Bottom 10%,#97 Top 27%,,
+Adidas,Duramo 10,"84
+ Good!",$70,Daily running,Neutral,10.3 oz / 292g 9.7 oz / 275g,0,8.7 mm 9.0 mm,HeelMid/forefoot,True to size,Balanced,-,-,-,-,Narrow,-,Stiff,Stiff,Flexible,0,0,31.6 mm 32.0 mm,22.9 mm 23.0 mm,NarrowNormalWide,1,-,1,#246 Bottom 32%,#310 Bottom 15%,,
+Adidas,Duramo Speed,"88
+ Great!",$90,Daily running,Neutral,9.2 oz / 261g 9.3 oz / 263g,0,6.0 mm 6.0 mm,Mid/forefoot,True to size,Balanced,Bad,Decent,Good,Breathable,Medium,Medium,Stiff,Stiff,Moderate,0,0,32.7 mm 29.0 mm,26.7 mm 23.0 mm,Normal,1,SummerAll seasons,1,#125 Top 35%,#267 Bottom 26%,,
+Adidas,Duramo Speed,"88
+ Great!",$90,Daily running,Neutral,9.2 oz / 261g 9.3 oz / 263g,0,6.0 mm 6.0 mm,Mid/forefoot,True to size,Balanced,Bad,Decent,Good,Breathable,Medium,Medium,Stiff,Stiff,Moderate,0,0,32.7 mm 29.0 mm,26.7 mm 23.0 mm,Normal,1,SummerAll seasons,1,#134 Top 37%,#268 Bottom 26%,,
+Adidas,Duramo Speed,"88
+ Great!",$90,Daily running,Neutral,9.2 oz / 261g 9.3 oz / 263g,0,6.0 mm 6.0 mm,Mid/forefoot,True to size,Balanced,Bad,Decent,Good,Breathable,Medium,Medium,Stiff,Stiff,Moderate,0,0,32.7 mm 29.0 mm,26.7 mm 23.0 mm,Normal,1,SummerAll seasons,1,#127 Top 35%,#269 Bottom 26%,,
+Adidas,Duramo Speed,"88
+ Great!",$90,Daily running,Neutral,9.2 oz / 261g 9.3 oz / 263g,0,6.0 mm 6.0 mm,Mid/forefoot,True to size,Balanced,Bad,Decent,Good,Breathable,Medium,Medium,Stiff,Stiff,Moderate,0,0,32.7 mm 29.0 mm,26.7 mm 23.0 mm,Normal,1,SummerAll seasons,1,#134 Top 37%,#268 Bottom 26%,,
+Adidas,Duramo Speed,"88
+ Great!",$90,Daily running,Neutral,9.2 oz / 261g 9.3 oz / 263g,0,6.0 mm 6.0 mm,Mid/forefoot,True to size,Balanced,Bad,Decent,Good,Breathable,Medium,Medium,Stiff,Stiff,Moderate,0,0,32.7 mm 29.0 mm,26.7 mm 23.0 mm,Normal,1,SummerAll seasons,1,#134 Top 37%,#268 Bottom 26%,,
+Adidas,Duramo Speed,"88
+ Great!",$90,Daily running,Neutral,9.2 oz / 261g 9.3 oz / 263g,0,6.0 mm 6.0 mm,Mid/forefoot,True to size,Balanced,Bad,Decent,Good,Breathable,Medium,Medium,Stiff,Stiff,Moderate,0,0,32.7 mm 29.0 mm,26.7 mm 23.0 mm,Normal,1,SummerAll seasons,1,#134 Top 37%,#268 Bottom 26%,,
+Adidas,Duramo Speed,"88
+ Great!",$90,Daily running,Neutral,9.2 oz / 261g 9.3 oz / 263g,0,6.0 mm 6.0 mm,Mid/forefoot,True to size,Balanced,Bad,Decent,Good,Breathable,Medium,Medium,Stiff,Stiff,Moderate,0,0,32.7 mm 29.0 mm,26.7 mm 23.0 mm,Normal,1,SummerAll seasons,1,#134 Top 37%,#268 Bottom 26%,,
+ASICS,Dynablast 3,"83
+ Good!",$100,Daily running,Neutral,8.7 oz / 248g 9.1 oz / 258g,1,9.7 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Decent,Good,Warm,Medium,Wide,Stiff,Moderate,Stiff,0,0,35.7 mm 31.5 mm,26.0 mm 23.5 mm,Normal,1,All seasons,1,#503 Bottom 22%,#585 Bottom 9%,,
+ASICS,Dynablast 4,92 Superb!,$110,Daily running,Neutral,9.2 oz / 262g 9.3 oz / 264g,0,6.4 mm 8.0 mm,Mid/forefoot,True to size,Balanced,Good,Good,Good,Warm,Narrow,Medium,Moderate,Moderate,Stiff,0,0,32.6 mm 34.0 mm,26.2 mm 26.0 mm,Normal,1,Winter,1,#21 Top 4%,#539 Bottom 16%,,
+ASICS,Dynablast 5,83 Good!,$120,Daily running,Neutral,9.3 oz / 264g 9.2 oz / 260g,0,7.6 mm 8.0 mm,Mid/forefoot,True to size,Balanced,Good,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,39.4 mm 39.0 mm,31.8 mm 31.0 mm,Normal,1,All seasons,1,#237 Bottom 35%,#195 Bottom 46%,,
+ASICS,Dynablast 5,"83
+ Good!",$120,Daily running,Neutral,9.3 oz / 264g 9.2 oz / 260g,0,7.6 mm 8.0 mm,Mid/forefoot,True to size,Balanced,Good,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,39.4 mm 39.0 mm,31.8 mm 31.0 mm,Normal,1,All seasons,1,#237 Bottom 35%,#195 Bottom 46%,,
+Saucony,Endorphin Elite,"90
+ Superb!",$275,Competition,Neutral,7.2 oz / 203g 7.2 oz / 204g,1,8.0 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Bad,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,39.9 mm 40.0 mm,31.9 mm 32.0 mm,Normal,1,SummerAll seasons,1,#81 Top 13%,#180 Top 28%,,
+Saucony,Endorphin Elite 2,"86
+ Good!",$290,Competition,Neutral,6.9 oz / 197g 7 oz / 199g,1,7.5 mm 8.0 mm,Mid/forefoot,Half size small,Soft,Good,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,39.9 mm 39.5 mm,32.4 mm 31.5 mm,Normal,1,SummerAll seasons,1,#186 Bottom 49%,#85 Top 24%,,
+Saucony,Endorphin Elite 2,"86
+ Good!",$290,Competition,Neutral,6.9 oz / 197g 7 oz / 199g,1,7.5 mm 8.0 mm,Mid/forefoot,Half size small,Soft,Good,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,39.9 mm 39.5 mm,32.4 mm 31.5 mm,Normal,1,SummerAll seasons,1,#185 Bottom 49%,#85 Top 24%,,
+Saucony,Endorphin Pro 2,"91
+ Superb!",$200,Competition,Neutral,7.6 oz / 215g 7.9 oz / 223g,1,10.0 mm 8.0 mm,HeelMid/forefoot,Slightly small,-,-,-,-,Breathable,Narrow,-,Stiff,Stiff,Flexible,Carbon plate,1,35.8 mm 35.5 mm,25.8 mm 27.5 mm,Normal,1,SummerAll seasons,1,#59 Top 10%,#334 Bottom 48%,,
+Saucony,Endorphin Pro 3,"89
+ Great!",$225,Competition,Neutral,7.3 oz / 206g 7.2 oz / 204g,1,9.3 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,-,-,-,-,Narrow,-,Stiff,Stiff,Flexible,Carbon plate,1,35.0 mm 39.5 mm,25.7 mm 31.5 mm,Normal,0,-,0,#159 Top 25%,#147 Top 23%,,
+Saucony,Endorphin Pro 4,"87
+ Great!",$225,Competition,Neutral,7.8 oz / 220g 7.7 oz / 218g,1,9.5 mm 8.0 mm,HeelMid/forefoot,Slightly small,Balanced,Good,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,38.1 mm 40.0 mm,28.6 mm 32.0 mm,Normal,1,SummerAll seasons,1,#140 Top 39%,#36 Top 10%,,Road
+Saucony,Endorphin Pro 4,"87
+ Great!",$225,Competition,Neutral,7.8 oz / 220g 7.7 oz / 218g,1,9.5 mm 8.0 mm,HeelMid/forefoot,Slightly small,Balanced,Good,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,38.1 mm 40.0 mm,28.6 mm 32.0 mm,Normal,1,SummerAll seasons,1,#140 Top 39%,#36 Top 10%,,
+Saucony,Endorphin Pro 4,"87
+ Great!",$225,Competition,Neutral,7.8 oz / 220g 7.7 oz / 218g,1,9.5 mm 8.0 mm,HeelMid/forefoot,Slightly small,Balanced,Good,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,38.1 mm 40.0 mm,28.6 mm 32.0 mm,Normal,1,SummerAll seasons,1,#140 Top 39%,#36 Top 10%,,
+Saucony,Endorphin Pro 4,"87
+ Great!",$225,Competition,Neutral,7.8 oz / 220g 7.7 oz / 218g,1,9.5 mm 8.0 mm,HeelMid/forefoot,Slightly small,Balanced,Good,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,38.1 mm 40.0 mm,28.6 mm 32.0 mm,Normal,1,SummerAll seasons,1,#140 Top 39%,#36 Top 10%,,
+PUMA,Deviate Nitro 3,"89
+ Great!",$160,Tempo,Neutral,9.5 oz / 268g 9.5 oz / 269g,0,10.1 mm 10.0 mm,Heel,Slightly small,Soft,Decent,Decent,Decent,Moderate,Narrow,Narrow,Stiff,Stiff,Flexible,Carbon plate,0,37.4 mm 39.0 mm,27.3 mm 29.0 mm,NormalWide,1,All seasons,1,#115 Top 32%,#60 Top 17%,,
+Saucony,Endorphin Pro 4,"87
+ Great!",$225,Competition,Neutral,7.8 oz / 220g 7.7 oz / 218g,1,9.5 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,38.1 mm 40.0 mm,28.6 mm 32.0 mm,Normal,1,SummerAll seasons,1,#142 Top 39%,#36 Top 10%,,
+ASICS,Magic Speed 4,"92
+ Superb!",$170,CompetitionTempo,Neutral,8.4 oz / 237g 8.5 oz / 242g,1,9.6 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Good,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,42.5 mm 43.5 mm,32.9 mm 35.5 mm,NormalWide,1,SummerAll seasons,1,#6 Top 2%,#82 Top 23%,,
+ASICS,Jolt 4,"79
+ Good!",$60,Daily running,Neutral,9.1 oz / 259g 9.5 oz / 268g,0,9.4 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Bad,Decent,Good,Breathable,Medium,Wide,Moderate,Stiff,Stiff,0,0,31.6 mm,22.2 mm,NormalX-Wide,1,SummerAll seasons,1,#314 Bottom 14%,#325 Bottom 11%,,
+Saucony,Endorphin Pro 4,"87
+ Great!",$225,Competition,Neutral,7.8 oz / 220g 7.7 oz / 218g,1,9.5 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,38.1 mm 40.0 mm,28.6 mm 32.0 mm,Normal,1,SummerAll seasons,1,#142 Top 39%,#36 Top 10%,,
+Saucony,Endorphin Pro 4,"87
+ Great!",$225,Competition,Neutral,7.8 oz / 220g 7.7 oz / 218g,1,9.5 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,38.1 mm 40.0 mm,28.6 mm 32.0 mm,Normal,1,SummerAll seasons,1,#142 Top 39%,#36 Top 10%,,
+Saucony,Endorphin Pro 4,"87
+ Great!",$225,Competition,Neutral,7.8 oz / 220g 7.7 oz / 218g,1,9.5 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,38.1 mm 40.0 mm,28.6 mm 32.0 mm,Normal,1,SummerAll seasons,1,#142 Top 39%,#36 Top 10%,,
+Saucony,Endorphin Pro 4,"87
+ Great!",$225,Competition,Neutral,7.8 oz / 220g 7.7 oz / 218g,1,9.5 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,38.1 mm 40.0 mm,28.6 mm 32.0 mm,Normal,1,SummerAll seasons,1,#142 Top 39%,#36 Top 10%,,
+Saucony,Endorphin Pro 4,"87
+ Great!",$225,Competition,Neutral,7.8 oz / 220g 7.7 oz / 218g,1,9.5 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,38.1 mm 40.0 mm,28.6 mm 32.0 mm,Normal,1,SummerAll seasons,1,#142 Top 39%,#36 Top 10%,,
+Saucony,Endorphin Pro 4,"87
+ Great!",$225,Competition,Neutral,7.8 oz / 220g 7.7 oz / 218g,1,9.5 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,38.1 mm 40.0 mm,28.6 mm 32.0 mm,Normal,1,SummerAll seasons,1,#141 Top 39%,#36 Top 10%,,
+Saucony,Endorphin Pro 4,"87
+ Great!",$225,Competition,Neutral,7.8 oz / 220g 7.7 oz / 218g,1,9.5 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,38.1 mm 40.0 mm,28.6 mm 32.0 mm,Normal,1,SummerAll seasons,1,#141 Top 39%,#36 Top 10%,,
+Saucony,Endorphin Pro 4,"87
+ Great!",$225,Competition,Neutral,7.8 oz / 220g 7.7 oz / 218g,1,9.5 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,38.1 mm 40.0 mm,28.6 mm 32.0 mm,Normal,1,SummerAll seasons,1,#141 Top 39%,#36 Top 10%,,
+Saucony,Endorphin Shift 2,"88
+ Great!",$140,Daily running,Neutral,10 oz / 284g 10.4 oz / 296g,0,4.3 mm 4.0 mm,Mid/forefoot,Half size small,-,-,-,-,-,Medium,-,Stiff,Stiff,Moderate,0,1,37.3 mm 39.0 mm,33.0 mm 35.0 mm,Normal,1,-,1,#268 Top 42%,#532 Bottom 17%,,
+Saucony,Endorphin Shift 3,"89
+ Great!",$150,Daily running,Neutral,9.6 oz / 272g 9.4 oz / 266g,0,6.5 mm 4.0 mm,Mid/forefoot,True to size,Balanced,-,-,-,Moderate,Narrow,-,Stiff,Moderate,Moderate,0,1,39.6 mm 39.0 mm,33.1 mm 35.0 mm,NormalWide,1,All seasons,1,#108 Top 30%,#207 Bottom 43%,,
+Saucony,Endorphin Shift 3,"89
+ Great!",$150,Daily running,Neutral,9.6 oz / 272g 9.4 oz / 266g,0,6.5 mm 4.0 mm,Mid/forefoot,True to size,Balanced,-,-,-,Moderate,Narrow,-,Stiff,Moderate,Moderate,0,1,39.6 mm 39.0 mm,33.1 mm 35.0 mm,NormalWide,1,All seasons,1,#109 Top 30%,#207 Bottom 43%,,
+Saucony,Endorphin Shift 3,"89
+ Great!",$150,Daily running,Neutral,9.6 oz / 272g 9.4 oz / 266g,0,6.5 mm 4.0 mm,Mid/forefoot,True to size,Balanced,-,-,-,Moderate,Narrow,-,Stiff,Moderate,Moderate,0,1,39.6 mm 39.0 mm,33.1 mm 35.0 mm,NormalWide,1,All seasons,1,#106 Top 29%,#207 Bottom 43%,,
+Saucony,Endorphin Shift 3,"89
+ Great!",$150,Daily running,Neutral,9.6 oz / 272g 9.4 oz / 266g,0,6.5 mm 4.0 mm,Mid/forefoot,True to size,Balanced,-,-,-,Moderate,Narrow,-,Stiff,Moderate,Moderate,0,1,39.6 mm 39.0 mm,33.1 mm 35.0 mm,NormalWide,1,All seasons,1,#106 Top 29%,#207 Bottom 43%,,
+Saucony,Endorphin Shift 3,"89
+ Great!",$150,Daily running,Neutral,9.6 oz / 272g 9.4 oz / 266g,0,6.5 mm 4.0 mm,Mid/forefoot,True to size,Balanced,-,-,-,Moderate,Narrow,-,Stiff,Moderate,Moderate,0,1,39.6 mm 39.0 mm,33.1 mm 35.0 mm,NormalWide,1,All seasons,1,#106 Top 29%,#207 Bottom 43%,,
+Saucony,Endorphin Shift 3,"89
+ Great!",$150,Daily running,Neutral,9.6 oz / 272g 9.4 oz / 266g,0,6.5 mm 4.0 mm,Mid/forefoot,True to size,Balanced,-,-,-,Moderate,Narrow,-,Stiff,Moderate,Moderate,0,1,39.6 mm 39.0 mm,33.1 mm 35.0 mm,NormalWide,1,All seasons,1,#107 Top 30%,#207 Bottom 43%,,
+Saucony,Endorphin Speed 2,"90
+ Superb!",$160,Tempo,Neutral,8.1 oz / 229g 8 oz / 227g,1,9.2 mm 8.0 mm,HeelMid/forefoot,Slightly small,-,-,-,-,Moderate,Medium,-,Stiff,Moderate,Moderate,0,1,35.3 mm 35.5 mm,26.1 mm 27.5 mm,Normal,1,All seasons,1,#74 Top 12%,#359 Bottom 44%,,
+Saucony,Endorphin Speed 3,"89
+ Great!",$160,Tempo,Neutral,7.9 oz / 225g 8.1 oz / 229g,1,7.4 mm 8.0 mm,Mid/forefoot,Slightly small,Soft,-,-,-,Moderate,Medium,-,Moderate,Flexible,Flexible,0,1,34.1 mm 36.0 mm,26.7 mm 28.0 mm,Normal,1,All seasons,1,#145 Top 23%,#146 Top 23%,,
+Saucony,Endorphin Speed 4,"82
+ Good!",$170,Tempo,Neutral,8.4 oz / 237g 8.3 oz / 235g,1,8.7 mm 8.0 mm,HeelMid/forefoot,Slightly small,Balanced,Decent,Good,Good,Breathable,Medium,Narrow,Moderate,Moderate,Moderate,0,1,36.2 mm 38.0 mm,27.5 mm 30.0 mm,Normal,1,SummerAll seasons,1,#530 Bottom 17%,#38 Top 6%,,
+Saucony,Endorphin Speed 5,"82
+ Good!",$175,Tempo,Neutral,8.5 oz / 241g 8.4 oz / 238g,1,10.6 mm 8.0 mm,Heel,True to size,Soft,Decent,Good,Good,Breathable,Medium,Medium,Moderate,Stiff,Stiff,0,1,37.4 mm 36.0 mm,26.8 mm 28.0 mm,NormalWide,1,SummerAll seasons,1,#272 Bottom 25%,#37 Top 11%,,
+Saucony,Endorphin Speed 5,"82
+ Good!",$175,Tempo,Neutral,8.5 oz / 241g 8.4 oz / 238g,1,10.6 mm 8.0 mm,Heel,True to size,Soft,Decent,Good,Good,Breathable,Medium,Medium,Moderate,Stiff,Stiff,0,1,37.4 mm 36.0 mm,26.8 mm 28.0 mm,NormalWide,1,SummerAll seasons,1,#272 Bottom 25%,#37 Top 11%,,
+Saucony,Endorphin Speed 5,"82
+ Good!",$175,Tempo,Neutral,8.5 oz / 241g 8.4 oz / 238g,1,10.6 mm 8.0 mm,Heel,True to size,Soft,Decent,Good,Good,Breathable,Medium,Medium,Moderate,Stiff,Stiff,0,1,37.4 mm 36.0 mm,26.8 mm 28.0 mm,NormalWide,1,SummerAll seasons,1,#274 Bottom 25%,#37 Top 11%,,
+Saucony,Endorphin Speed 5,"82
+ Good!",$175,Tempo,Neutral,8.5 oz / 241g 8.4 oz / 238g,1,10.6 mm 8.0 mm,Heel,True to size,Soft,Decent,Good,Good,Breathable,Medium,Medium,Moderate,Stiff,Stiff,0,1,37.4 mm 36.0 mm,26.8 mm 28.0 mm,NormalWide,1,SummerAll seasons,1,#271 Bottom 25%,#37 Top 11%,,
+Saucony,Endorphin Trainer,"77
+ Decent!",$180,Daily runningTempo,Neutral,10.1 oz / 285g 10.1 oz / 286g,0,6.9 mm 8.0 mm,Mid/forefoot,True to size,Soft,Decent,Decent,Good,Breathable,Narrow,Narrow,Stiff,Stiff,Flexible,Carbon plate,1,38.9 mm 42.0 mm,32.0 mm 34.0 mm,Normal,1,SummerAll seasons,1,#331 Bottom 9%,#175 Top 48%,,
+Altra,Escalante 3,"84
+ Good!",$140,Daily runningTempo,Neutral,9.5 oz / 269g 7.7 oz / 219g,0,0.2 mm 0.0 mm,Mid/forefoot,True to size,Balanced,-,-,-,Moderate,Medium,-,Stiff,Flexible,Flexible,0,0,25.0 mm 26.0 mm,24.8 mm 26.0 mm,Normal,1,All seasons,1,#459 Bottom 28%,#356 Bottom 44%,,
+Altra,Escalante 4,"85
+ Good!",$130,Daily running,Neutral,8.4 oz / 237g 9.5 oz / 269g,1,1.4 mm 0.0 mm,Mid/forefoot,True to size,Balanced,Good,Good,Good,Moderate,Wide,Wide,Flexible,Flexible,Flexible,0,0,23.8 mm 24.0 mm,22.4 mm 24.0 mm,Normal,1,All seasons,1,#212 Bottom 42%,#95 Top 26%,,
+Altra,Escalante 4,"85
+ Good!",$130,Daily running,Neutral,8.4 oz / 237g 9.5 oz / 269g,1,1.4 mm 0.0 mm,Mid/forefoot,True to size,Balanced,Good,Good,Good,Moderate,Wide,Wide,Flexible,Flexible,Flexible,0,0,23.8 mm 24.0 mm,22.4 mm 24.0 mm,Normal,1,All seasons,1,#219 Bottom 40%,#95 Top 26%,,
+Altra,Escalante 4,"85
+ Good!",$130,Daily running,Neutral,8.4 oz / 237g 9.5 oz / 269g,1,1.4 mm 0.0 mm,Mid/forefoot,True to size,Balanced,Good,Good,Good,Moderate,Wide,Wide,Flexible,Flexible,Flexible,0,0,23.8 mm 24.0 mm,22.4 mm 24.0 mm,Normal,1,All seasons,1,#219 Bottom 40%,#95 Top 26%,,
+Altra,Escalante 4,"85
+ Good!",$130,Daily running,Neutral,8.4 oz / 237g 9.5 oz / 269g,1,1.4 mm 0.0 mm,Mid/forefoot,True to size,Balanced,Good,Good,Good,Moderate,Wide,Wide,Flexible,Flexible,Flexible,0,0,23.8 mm 24.0 mm,22.4 mm 24.0 mm,Normal,1,All seasons,1,#219 Bottom 40%,#95 Top 26%,,
+Altra,Escalante 4,"85
+ Good!",$130,Daily running,Neutral,8.4 oz / 237g 9.5 oz / 269g,1,1.4 mm 0.0 mm,Mid/forefoot,True to size,Balanced,Good,Good,Good,Moderate,Wide,Wide,Flexible,Flexible,Flexible,0,0,23.8 mm 24.0 mm,22.4 mm 24.0 mm,Normal,1,All seasons,1,#219 Bottom 40%,#95 Top 26%,,
+Altra,Escalante Racer,"91
+ Superb!",$140,CompetitionTempo,Neutral,7.3 oz / 208g 6.8 oz / 193g,1,0.5 mm 0.0 mm,Mid/forefoot,True to size,Firm,Good,Good,Decent,Breathable,Medium,Wide,Moderate,Flexible,Flexible,0,0,19.0 mm 22.0 mm,18.5 mm 22.0 mm,Normal,1,SummerAll seasons,1,#52 Top 9%,#384 Bottom 40%,,
+Altra,Escalante Racer 2,"87
+ Great!",$140,CompetitionTempo,Neutral,7.9 oz / 224g 7.9 oz / 224g,1,1.1 mm 0.0 mm,Mid/forefoot,True to size,Balanced,Good,Decent,Good,Breathable,Wide,Wide,Moderate,Flexible,Flexible,0,0,22.5 mm 24.0 mm,21.4 mm 24.0 mm,Normal,1,SummerAll seasons,1,#149 Top 41%,#246 Bottom 32%,,
+Altra,Escalante Racer 2,"87
+ Great!",$140,CompetitionTempo,Neutral,7.9 oz / 224g 7.9 oz / 224g,1,1.1 mm 0.0 mm,Mid/forefoot,True to size,Balanced,Good,Decent,Good,Breathable,Wide,Wide,Moderate,Flexible,Flexible,0,0,22.5 mm 24.0 mm,21.4 mm 24.0 mm,Normal,1,SummerAll seasons,1,#149 Top 41%,#246 Bottom 32%,,
+Altra,Escalante Racer 2,"87
+ Great!",$140,CompetitionTempo,Neutral,7.9 oz / 224g 7.9 oz / 224g,1,1.1 mm 0.0 mm,Mid/forefoot,True to size,Balanced,Good,Decent,Good,Breathable,Wide,Wide,Moderate,Flexible,Flexible,0,0,22.5 mm 24.0 mm,21.4 mm 24.0 mm,Normal,1,SummerAll seasons,1,#150 Top 41%,#246 Bottom 32%,,
+Altra,Escalante Racer 2,"87
+ Great!",$140,CompetitionTempo,Neutral,7.9 oz / 224g 7.9 oz / 224g,1,1.1 mm 0.0 mm,Mid/forefoot,True to size,Balanced,Good,Decent,Good,Breathable,Wide,Wide,Moderate,Flexible,Flexible,0,0,22.5 mm 24.0 mm,21.4 mm 24.0 mm,Normal,1,SummerAll seasons,1,#150 Top 41%,#246 Bottom 32%,,
+Altra,Escalante Racer 2,"87
+ Great!",$140,CompetitionTempo,Neutral,7.9 oz / 224g 7.9 oz / 224g,1,1.1 mm 0.0 mm,Mid/forefoot,True to size,Balanced,Good,Decent,Good,Breathable,Wide,Wide,Moderate,Flexible,Flexible,0,0,22.5 mm 24.0 mm,21.4 mm 24.0 mm,Normal,1,SummerAll seasons,1,#149 Top 41%,#246 Bottom 32%,,
+Altra,Experience Flow,"88
+ Great!",$140,Daily running,Neutral,8.3 oz / 235g 8.4 oz / 238g,1,4.1 mm 4.0 mm,Mid/forefoot,True to size,Soft,Good,Good,Decent,Moderate,Wide,Wide,Moderate,Moderate,Moderate,0,0,30.7 mm 32.0 mm,26.6 mm 28.0 mm,Normal,1,All seasons,1,#255 Top 40%,#138 Top 22%,,
+Altra,Experience Flow 2,"84
+ Good!",$140,Daily running,Neutral,8.3 oz / 235g 8.1 oz / 231g,1,4.4 mm 4.0 mm,Mid/forefoot,-,Soft,Decent,Good,Decent,Moderate,Wide,Wide,Moderate,Moderate,Moderate,0,0,30.3 mm 32.0 mm,25.9 mm 28.0 mm,Normal,1,All seasons,1,#232 Bottom 36%,#87 Top 24%,,
+Altra,Experience Form,"91
+ Superb!",$145,Daily running,Stability,9.2 oz / 261g 9.6 oz / 272g,0,4.0 mm 4.0 mm,Mid/forefoot,Slightly small,Balanced,Decent,Decent,Decent,Moderate,Medium,Wide,Stiff,Moderate,Moderate,0,0,29.9 mm 30.0 mm,25.9 mm 26.0 mm,Normal,1,All seasons,1,#26 Top 8%,#211 Bottom 42%,,
+Altra,Experience Form,"91
+ Superb!",$145,Daily running,Stability,9.2 oz / 261g 9.6 oz / 272g,0,4.0 mm 4.0 mm,Mid/forefoot,Slightly small,Balanced,Decent,Decent,Decent,Moderate,Medium,Wide,Stiff,Moderate,Moderate,0,0,29.9 mm 30.0 mm,25.9 mm 26.0 mm,Normal,1,All seasons,1,#27 Top 8%,#211 Bottom 42%,,
+Nike,Flex Experience Run 10,"79
+ Good!",$65,Daily running,Neutral,7.1 oz / 201g 8 oz / 227g,1,10.4 mm,-,Slightly small,-,-,-,-,-,Narrow,-,Flexible,-,-,0,0,24.1 mm,13.7 mm,Normal,0,-,0,#582 Bottom 9%,#523 Bottom 18%,,
+Nike,Flex Experience Run 11,"77
+ Decent!",$70,Daily running,Neutral,8.2 oz / 232g 8.2 oz / 232g,1,6.2 mm,Mid/forefoot,True to size,Firm,Decent,-,-,Moderate,Medium,Narrow,Flexible,Flexible,Flexible,0,0,24.1 mm,17.9 mm,NormalWideX-Wide,1,All seasons,1,#611 Bottom 5%,#265 Top 42%,,
+Nike,Flex Experience Run 12,"75
+ Bad!",$75,Daily running,Neutral,8.5 oz / 241g 8.5 oz / 240g,1,6.0 mm 6.0 mm,Mid/forefoot,True to size,Firm,Decent,Good,Bad,Moderate,Medium,Medium,Flexible,Flexible,Flexible,0,0,25.9 mm,19.9 mm,NormalWideX-Wide,1,All seasons,1,#345 Bottom 5%,#61 Top 17%,,
+Nike,Flex Experience Run 12,"75
+ Bad!",$75,Daily running,Neutral,8.5 oz / 241g 8.5 oz / 240g,1,6.0 mm 6.0 mm,Mid/forefoot,True to size,Firm,Decent,Good,Bad,Moderate,Medium,Medium,Flexible,Flexible,Flexible,0,0,25.9 mm,19.9 mm,NormalWideX-Wide,1,All seasons,1,#345 Bottom 5%,#61 Top 17%,,
+Nike,Flex Experience Run 12,"75
+ Bad!",$75,Daily running,Neutral,8.5 oz / 241g 8.5 oz / 240g,1,6.0 mm 6.0 mm,Mid/forefoot,True to size,Firm,Decent,Good,Bad,Moderate,Medium,Medium,Flexible,Flexible,Flexible,0,0,25.9 mm,19.9 mm,NormalWideX-Wide,1,All seasons,1,#345 Bottom 5%,#61 Top 17%,,
+Nike,Flex Experience Run 12,"75
+ Bad!",$75,Daily running,Neutral,8.5 oz / 241g 8.5 oz / 240g,1,6.0 mm 6.0 mm,Mid/forefoot,True to size,Firm,Decent,Good,Bad,Moderate,Medium,Medium,Flexible,Flexible,Flexible,0,0,25.9 mm,19.9 mm,NormalWideX-Wide,1,All seasons,1,#345 Bottom 5%,#61 Top 17%,,
+Nike,Flex Experience Run 12,"75
+ Bad!",$75,Daily running,Neutral,8.5 oz / 241g 8.5 oz / 240g,1,6.0 mm 6.0 mm,Mid/forefoot,True to size,Firm,Decent,Good,Bad,Moderate,Medium,Medium,Flexible,Flexible,Flexible,0,0,25.9 mm,19.9 mm,NormalWideX-Wide,1,All seasons,1,#345 Bottom 5%,#61 Top 17%,,
+Nike,Flex Run 2021,"82
+ Good!",$80,Daily running,Neutral,7.9 oz / 223g,1,7.2 mm,Mid/forefoot,True to size,-,-,-,-,-,Medium,-,Flexible,-,-,0,0,32.3 mm,25.1 mm,Normal,0,-,0,#276 Bottom 24%,#337 Bottom 7%,,
+Reebok,Floatride Energy 3,"91
+ Superb!",$100,Daily running,Neutral,9 oz / 256g 8.5 oz / 241g,0,7.1 mm 9.0 mm,Mid/forefoot,True to size,-,-,-,-,-,Medium,-,Stiff,Moderate,Moderate,0,0,30.2 mm 26.0 mm,23.1 mm 17.0 mm,Normal,1,-,1,#38 Top 6%,#592 Bottom 8%,,
+Reebok,Floatride Energy 5,"87
+ Great!",$110,Daily running,Neutral,9 oz / 254g 9.4 oz / 266g,0,6.0 mm 8.0 mm,Mid/forefoot,True to size,Balanced,Bad,Decent,Good,Warm,Medium,Medium,Stiff,Flexible,Flexible,0,0,30.2 mm 27.0 mm,24.2 mm 19.0 mm,Normal,1,All seasons,1,#158 Top 44%,#273 Bottom 25%,,
+Reebok,Floatride Energy 5,"87
+ Great!",$110,Daily running,Neutral,9 oz / 254g 9.4 oz / 266g,0,6.0 mm 8.0 mm,Mid/forefoot,True to size,Balanced,Bad,Decent,Good,Warm,Medium,Medium,Stiff,Flexible,Flexible,0,0,30.2 mm 27.0 mm,24.2 mm 19.0 mm,Normal,1,All seasons,1,#160 Top 44%,#274 Bottom 25%,,
+Reebok,Floatride Energy 5,"87
+ Great!",$110,Daily running,Neutral,9 oz / 254g 9.4 oz / 266g,0,6.0 mm 8.0 mm,Mid/forefoot,True to size,Balanced,Bad,Decent,Good,Warm,Medium,Medium,Stiff,Flexible,Flexible,0,0,30.2 mm 27.0 mm,24.2 mm 19.0 mm,Normal,1,All seasons,1,#160 Top 44%,#274 Bottom 25%,,
+Reebok,Floatride Energy 5,"87
+ Great!",$110,Daily running,Neutral,9 oz / 254g 9.4 oz / 266g,0,6.0 mm 8.0 mm,Mid/forefoot,True to size,Balanced,Bad,Decent,Good,Warm,Medium,Medium,Stiff,Flexible,Flexible,0,0,30.2 mm 27.0 mm,24.2 mm 19.0 mm,Normal,1,All seasons,1,#158 Top 44%,#273 Bottom 25%,,
+Reebok,Floatride Energy 5,"87
+ Great!",$110,Daily running,Neutral,9 oz / 254g 9.4 oz / 266g,0,6.0 mm 8.0 mm,Mid/forefoot,True to size,Balanced,Bad,Decent,Good,Warm,Medium,Medium,Stiff,Flexible,Flexible,0,0,30.2 mm 27.0 mm,24.2 mm 19.0 mm,Normal,1,All seasons,1,#163 Top 45%,#274 Bottom 25%,,
+Reebok,Floatride Energy 5,"87
+ Great!",$110,Daily running,Neutral,9 oz / 254g 9.4 oz / 266g,0,6.0 mm 8.0 mm,Mid/forefoot,True to size,Balanced,Bad,Decent,Good,Warm,Medium,Medium,Stiff,Flexible,Flexible,0,0,30.2 mm 27.0 mm,24.2 mm 19.0 mm,Normal,1,All seasons,1,#162 Top 45%,#274 Bottom 25%,,
+Reebok,Floatride Energy 5,"87
+ Great!",$110,Daily running,Neutral,9 oz / 254g 9.4 oz / 266g,0,6.0 mm 8.0 mm,Mid/forefoot,True to size,Balanced,Bad,Decent,Good,Warm,Medium,Medium,Stiff,Flexible,Flexible,0,0,30.2 mm 27.0 mm,24.2 mm 19.0 mm,Normal,1,All seasons,1,#163 Top 45%,#274 Bottom 25%,,
+Reebok,Floatride Energy 5,"87
+ Great!",$110,Daily running,Neutral,9 oz / 254g 9.4 oz / 266g,0,6.0 mm 8.0 mm,Mid/forefoot,True to size,Balanced,Bad,Decent,Good,Warm,Medium,Medium,Stiff,Flexible,Flexible,0,0,30.2 mm 27.0 mm,24.2 mm 19.0 mm,Normal,1,All seasons,1,#162 Top 45%,#274 Bottom 25%,,
+Reebok,Floatride Energy 5,"87
+ Great!",$110,Daily running,Neutral,9 oz / 254g 9.4 oz / 266g,0,6.0 mm 8.0 mm,Mid/forefoot,True to size,Balanced,Bad,Decent,Good,Warm,Medium,Medium,Stiff,Flexible,Flexible,0,0,30.2 mm 27.0 mm,24.2 mm 19.0 mm,Normal,1,All seasons,1,#162 Top 45%,#274 Bottom 25%,,
+Reebok,FloatZig 1,"89
+ Great!",$130,Daily running,Neutral,10.1 oz / 285g 9.8 oz / 277g,0,7.0 mm 6.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Decent,Bad,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,1,36.8 mm 31.0 mm,29.8 mm 25.0 mm,Normal,1,All seasons,1,#79 Top 22%,#255 Bottom 30%,,Road
+Reebok,FloatZig 1,"89
+ Great!",$130,Daily running,Neutral,10.1 oz / 285g 9.8 oz / 277g,0,7.0 mm 6.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Decent,Bad,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,1,36.8 mm 31.0 mm,29.8 mm 25.0 mm,Normal,1,All seasons,1,#80 Top 22%,#256 Bottom 30%,,
+Reebok,FloatZig 1,"89
+ Great!",$130,Daily running,Neutral,10.1 oz / 285g 9.8 oz / 277g,0,7.0 mm 6.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Decent,Bad,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,1,36.8 mm 31.0 mm,29.8 mm 25.0 mm,Normal,1,All seasons,1,#83 Top 23%,#256 Bottom 30%,,
+Adidas,Fluidflow 2.0,"80
+ Good!",$80,Daily running,Neutral,11.1 oz / 316g 11 oz / 312g,0,8.1 mm,HeelMid/forefoot,Slightly large,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Flexible,Flexible,0,0,26.4 mm,18.3 mm,Normal,1,All seasons,1,#301 Bottom 17%,#198 Bottom 45%,,
+Adidas,Fluidflow 2.0,"80
+ Good!",$80,Daily running,Neutral,11.1 oz / 316g 11 oz / 312g,0,8.1 mm,HeelMid/forefoot,Slightly large,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Flexible,Flexible,0,0,26.4 mm,18.3 mm,Normal,1,All seasons,1,#301 Bottom 17%,#198 Bottom 45%,,
+Adidas,Fluidflow 2.0,"80
+ Good!",$80,Daily running,Neutral,11.1 oz / 316g 11 oz / 312g,0,8.1 mm,HeelMid/forefoot,Slightly large,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Flexible,Flexible,0,0,26.4 mm,18.3 mm,Normal,1,All seasons,1,#300 Bottom 18%,#198 Bottom 45%,,
+New Balance,Foam Arishi v4,"70
+ Bad!",$70,Daily running,Neutral,8.5 oz / 242g 8.7 oz / 246g,1,7.7 mm 8.0 mm,Mid/forefoot,True to size,Soft,Bad,Bad,Good,Moderate,Medium,Medium,Flexible,Flexible,Flexible,0,0,28.1 mm,20.4 mm,NormalWideX-Wide,1,All seasons,1,#362 Bottom 1%,#12 Top 4%,,
+Nike,Free RN NN,"74
+ Bad!",$100,Daily running,Neutral,6.9 oz / 197g,1,6.9 mm,Mid/forefoot,True to size,Balanced,Good,Good,Good,Warm,Wide,Wide,Flexible,Flexible,Flexible,0,0,25.6 mm,18.7 mm,Normal,1,All seasons,1,#355 Bottom 3%,#278 Bottom 24%,,
+Nike,Free RN NN,"74
+ Bad!",$100,Daily running,Neutral,6.9 oz / 197g,1,6.9 mm,Mid/forefoot,True to size,Balanced,Good,Good,Good,Warm,Wide,Wide,Flexible,Flexible,Flexible,0,0,25.6 mm,18.7 mm,Normal,1,All seasons,1,#355 Bottom 3%,#278 Bottom 24%,,
+Nike,Free RN NN,"74
+ Bad!",$100,Daily running,Neutral,6.9 oz / 197g,1,6.9 mm,Mid/forefoot,True to size,Balanced,Good,Good,Good,Warm,Wide,Wide,Flexible,Flexible,Flexible,0,0,25.6 mm,18.7 mm,Normal,1,All seasons,1,#355 Bottom 3%,#278 Bottom 24%,,
+Nike,Free RN NN,"74
+ Bad!",$100,Daily running,Neutral,6.9 oz / 197g,1,6.9 mm,Mid/forefoot,True to size,Balanced,Good,Good,Good,Warm,Wide,Wide,Flexible,Flexible,Flexible,0,0,25.6 mm,18.7 mm,Normal,1,All seasons,1,#355 Bottom 3%,#278 Bottom 24%,,
+Nike,Free Run 5.0,"80
+ Good!",$100,Daily running,Neutral,6.2 oz / 175g 8.2 oz / 232g,1,8.1 mm,Mid/forefoot,True to size,-,-,-,-,-,Wide,-,Flexible,Flexible,Flexible,0,0,23.4 mm,15.3 mm,Normal,0,-,0,#557 Bottom 13%,#626 Bottom 2%,,
+Saucony,Freedom 4,"86
+ Good!",$150,Daily running,Neutral,8 oz / 227g 9.5 oz / 269g,1,5.2 mm 4.0 mm,Mid/forefoot,-,-,-,-,-,-,Medium,-,-,Flexible,-,0,0,26.4 mm 28.0 mm,21.2 mm 24.0 mm,Normal,1,-,1,#193 Bottom 47%,#331 Bottom 9%,,
+New Balance,Fresh Foam 1080 v11,"89
+ Great!",$150,Daily running,Neutral,9.2 oz / 261g 10.1 oz / 285g,0,8.0 mm 8.0 mm,HeelMid/forefoot,True to size,-,-,-,-,-,Narrow,-,-,Moderate,-,0,1,34.2 mm 30.0 mm,26.2 mm 22.0 mm,NarrowNormalWideX-Wide,1,-,1,#190 Top 30%,#88 Top 14%,,
+New Balance,Fresh Foam 680 v8,"84
+ Good!",$80,Daily running,Neutral,9.2 oz / 261g 9.5 oz / 268g,0,7.8 mm,Mid/forefoot,Slightly small,Soft,Good,Good,Good,Breathable,Medium,Medium,Flexible,Flexible,Moderate,0,0,35.4 mm,27.6 mm,NormalWideX-Wide,1,SummerAll seasons,1,#241 Bottom 34%,#32 Top 9%,,
+New Balance,Fresh Foam 680 v8,"84
+ Good!",$80,Daily running,Neutral,9.2 oz / 261g 9.5 oz / 268g,0,7.8 mm,Mid/forefoot,Slightly small,Soft,Good,Good,Good,Breathable,Medium,Medium,Flexible,Flexible,Moderate,0,0,35.4 mm,27.6 mm,NormalWideX-Wide,1,SummerAll seasons,1,#240 Bottom 34%,#32 Top 9%,,
+New Balance,Fresh Foam 680 v8,"84
+ Good!",$80,Daily running,Neutral,9.2 oz / 261g 9.5 oz / 268g,0,7.8 mm,Mid/forefoot,Slightly small,Soft,Good,Good,Good,Breathable,Medium,Medium,Flexible,Flexible,Moderate,0,0,35.4 mm,27.6 mm,NormalWideX-Wide,1,SummerAll seasons,1,#240 Bottom 34%,#32 Top 9%,,
+New Balance,Fresh Foam 860 v11,"91
+ Superb!",$130,Daily running,Stability,10.6 oz / 300g 9.7 oz / 275g,0,12.4 mm 10.0 mm,Heel,Slightly large,-,-,-,-,-,Medium,-,Stiff,-,-,0,0,34.2 mm,21.8 mm,NarrowNormalWideX-Wide,0,-,0,#61 Top 10%,#67 Top 11%,,
+New Balance,Fresh Foam 860 v12,"87
+ Great!",$130,Daily running,Stability,11 oz / 311g 10.8 oz / 306g,0,13.3 mm,Heel,Slightly small,-,-,-,-,-,Medium,-,Stiff,-,-,0,0,33.6 mm,20.3 mm,NarrowNormalWideX-Wide,0,-,0,#330 Bottom 48%,#435 Bottom 32%,,
+New Balance,Fresh Foam Arishi v4,"70
+ Bad!",$70,Daily running,Neutral,8.5 oz / 242g 8.7 oz / 246g,1,7.7 mm 8.0 mm,Mid/forefoot,True to size,Soft,Bad,Bad,Good,Moderate,Medium,Medium,Flexible,Flexible,Flexible,0,0,28.1 mm,20.4 mm,NormalWideX-Wide,1,All seasons,1,#361 Bottom 1%,#12 Top 4%,,
+New Balance,Fresh Foam Arishi v4,"70
+ Bad!",$70,Daily running,Neutral,8.5 oz / 242g 8.7 oz / 246g,1,7.7 mm 8.0 mm,Mid/forefoot,True to size,Soft,Bad,Bad,Good,Moderate,Medium,Medium,Flexible,Flexible,Flexible,0,0,28.1 mm,20.4 mm,NormalWideX-Wide,1,All seasons,1,#362 Bottom 1%,#12 Top 4%,,
+New Balance,Fresh Foam Arishi v4,"70
+ Bad!",$70,Daily running,Neutral,8.5 oz / 242g 8.7 oz / 246g,1,7.7 mm 8.0 mm,Mid/forefoot,True to size,Soft,Bad,Bad,Good,Moderate,Medium,Medium,Flexible,Flexible,Flexible,0,0,28.1 mm,20.4 mm,NormalWideX-Wide,1,All seasons,1,#362 Bottom 1%,#12 Top 4%,,
+New Balance,Fresh Foam More v3,"90
+ Superb!",$165,Daily running,Neutral,10.4 oz / 296g 10.9 oz / 309g,0,7.8 mm 4.0 mm,Mid/forefoot,True to size,Soft,-,-,-,Warm,Medium,-,Stiff,Moderate,Moderate,0,0,37.5 mm 33.0 mm,29.7 mm 29.0 mm,Normal,1,Winter,1,#117 Top 19%,#261 Top 41%,,
+New Balance,Fresh Foam Roav V2,"79
+ Good!",$85,Daily running,Neutral,8.4 oz / 238g 9.1 oz / 258g,1,7.2 mm 8.0 mm,Mid/forefoot,Slightly small,Balanced,-,-,-,Warm,Narrow,-,Moderate,Flexible,Flexible,0,0,32.2 mm,25.0 mm,NormalWideX-Wide,1,Winter,1,#320 Bottom 12%,#113 Top 31%,,
+New Balance,Fresh Foam Roav V2,"79
+ Good!",$85,Daily running,Neutral,8.4 oz / 238g 9.1 oz / 258g,1,7.2 mm 8.0 mm,Mid/forefoot,Slightly small,Balanced,-,-,-,Warm,Narrow,-,Moderate,Flexible,Flexible,0,0,32.2 mm,25.0 mm,NormalWideX-Wide,1,Winter,1,#319 Bottom 12%,#113 Top 31%,,
+New Balance,Fresh Foam Roav V2,"79
+ Good!",$85,Daily running,Neutral,8.4 oz / 238g 9.1 oz / 258g,1,7.2 mm 8.0 mm,Mid/forefoot,Slightly small,Balanced,-,-,-,Warm,Narrow,-,Moderate,Flexible,Flexible,0,0,32.2 mm,25.0 mm,NormalWideX-Wide,1,Winter,1,#319 Bottom 12%,#113 Top 31%,,
+New Balance,Fresh Foam Roav V2,"79
+ Good!",$85,Daily running,Neutral,8.4 oz / 238g 9.1 oz / 258g,1,7.2 mm 8.0 mm,Mid/forefoot,Slightly small,Balanced,-,-,-,Warm,Narrow,-,Moderate,Flexible,Flexible,0,0,32.2 mm,25.0 mm,NormalWideX-Wide,1,Winter,1,#319 Bottom 12%,#113 Top 31%,,
+New Balance,Fresh Foam Roav V2,"79
+ Good!",$85,Daily running,Neutral,8.4 oz / 238g 9.1 oz / 258g,1,7.2 mm 8.0 mm,Mid/forefoot,Slightly small,Balanced,-,-,-,Warm,Narrow,-,Moderate,Flexible,Flexible,0,0,32.2 mm,25.0 mm,NormalWideX-Wide,1,Winter,1,#318 Bottom 13%,#113 Top 31%,,
+New Balance,Fresh Foam X 1080 v12,"88
+ Great!",$160,Daily running,Neutral,10.1 oz / 286g 10.3 oz / 292g,0,3.3 mm 8.0 mm,Mid/forefoot,True to size,Soft,-,-,-,-,Narrow,-,Moderate,Moderate,Flexible,0,0,26.9 mm 34.0 mm,23.6 mm 26.0 mm,NarrowNormalWide,1,-,1,#283 Top 44%,#170 Top 27%,,
+New Balance,Fresh Foam X 1080 v13,"89
+ Great!",$165,Daily running,Neutral,9.3 oz / 264g 9.2 oz / 261g,0,5.6 mm 6.0 mm,Mid/forefoot,True to size,Soft,Decent,Good,Good,Breathable,Medium,Medium,Moderate,Flexible,Stiff,0,1,34.1 mm 37.0 mm,28.5 mm 31.0 mm,NarrowNormalWideX-Wide,1,SummerAll seasons,1,#206 Top 33%,#50 Top 8%,,
+New Balance,Fresh Foam X 1080 v14,"86
+ Good!",$165,Daily running,Neutral,10.1 oz / 285g 10.5 oz / 298g,0,4.2 mm 6.0 mm,HeelMid/forefoot,True to size,Soft,Good,Decent,Good,Moderate,Medium,Narrow,Moderate,Stiff,Stiff,0,1,37.0 mm 38.0 mm,32.8 mm 32.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#172 Top 48%,#3 Top 1%,,
+New Balance,Fresh Foam X 1080 v14,"86
+ Good!",$165,Daily running,Neutral,10.1 oz / 285g 10.5 oz / 298g,0,4.2 mm 6.0 mm,HeelMid/forefoot,True to size,Soft,Good,Decent,Good,Moderate,Medium,Narrow,Moderate,Stiff,Stiff,0,1,37.0 mm 38.0 mm,32.8 mm 32.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#172 Top 48%,#3 Top 1%,,
+New Balance,Fresh Foam X 1080 v14,"86
+ Good!",$165,Daily running,Neutral,10.1 oz / 285g 10.5 oz / 298g,0,4.2 mm 6.0 mm,Mid/forefoot,True to size,Soft,Good,Decent,Good,Moderate,Medium,Narrow,Moderate,Stiff,Stiff,0,1,37.0 mm 38.0 mm,32.8 mm 32.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#172 Top 47%,#3 Top 1%,,
+New Balance,Fresh Foam X 860 v14,"85
+ Good!",$140,Daily running,Stability,10.4 oz / 295g 10.5 oz / 298g,0,9.3 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,Decent,Bad,Decent,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,36.8 mm 38.0 mm,27.5 mm 30.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#217 Bottom 40%,#35 Top 10%,,
+New Balance,Fresh Foam X 860 v14,"85
+ Good!",$140,Daily running,Stability,10.4 oz / 295g 10.5 oz / 298g,0,9.3 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,Decent,Bad,Decent,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,36.8 mm 38.0 mm,27.5 mm 30.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#217 Bottom 40%,#35 Top 10%,,
+New Balance,Fresh Foam X 860 v14,"85
+ Good!",$140,Daily running,Stability,10.4 oz / 295g 10.5 oz / 298g,0,9.3 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,Decent,Bad,Decent,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,36.8 mm 38.0 mm,27.5 mm 30.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#217 Bottom 40%,#35 Top 10%,,
+New Balance,Fresh Foam X 860 v14,"85
+ Good!",$140,Daily running,Stability,10.4 oz / 295g 10.5 oz / 298g,0,9.3 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,Decent,Bad,Decent,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,36.8 mm 38.0 mm,27.5 mm 30.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#218 Bottom 40%,#35 Top 10%,,
+New Balance,Fresh Foam X 860 v14,"85
+ Good!",$140,Daily running,Stability,10.4 oz / 295g 10.5 oz / 298g,0,9.3 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,Decent,Bad,Decent,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,36.8 mm 38.0 mm,27.5 mm 30.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#216 Bottom 40%,#35 Top 10%,,
+new Balance,Fresh Foam X 860 v14,"85
+ Good!",$140,Daily running,Stability,10.4 oz / 295g 10.5 oz / 298g,0,9.3 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,Decent,Bad,Decent,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,36.8 mm 38.0 mm,27.5 mm 30.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#215 Bottom 41%,#35 Top 10%,,
+New Balance,Fresh Foam X 860 v14,"85
+ Good!",$140,Daily running,Stability,10.4 oz / 295g 10.5 oz / 298g,0,9.3 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,Decent,Bad,Decent,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,36.8 mm 38.0 mm,27.5 mm 30.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#215 Bottom 41%,#35 Top 10%,,
+New Balance,Fresh Foam X 860 v14,"85
+ Good!",$140,Daily running,Stability,10.4 oz / 295g 10.5 oz / 298g,0,9.3 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,Decent,Bad,Decent,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,36.8 mm 38.0 mm,27.5 mm 30.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#215 Bottom 41%,#35 Top 10%,,
+New Balance,Fresh Foam X 860 v14,"85
+ Good!",$140,Daily running,Stability,10.4 oz / 295g 10.5 oz / 298g,0,9.3 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,Decent,Bad,Decent,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,36.8 mm 38.0 mm,27.5 mm 30.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#215 Bottom 41%,#35 Top 10%,,
+New Balance,Fresh Foam X 860 v14,"85
+ Good!",$140,Daily running,Stability,10.4 oz / 295g 10.5 oz / 298g,0,9.3 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,Decent,Bad,Decent,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,36.8 mm 38.0 mm,27.5 mm 30.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#215 Bottom 41%,#35 Top 10%,,
+New Balance,Fresh Foam X 860 v14,"85
+ Good!",$140,Daily running,Stability,10.4 oz / 295g 10.5 oz / 298g,0,9.3 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,Decent,Bad,Decent,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,36.8 mm 38.0 mm,27.5 mm 30.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#215 Bottom 41%,#35 Top 10%,,
+New Balance,Fresh Foam X 860 v14,"85
+ Good!",$140,Daily running,Stability,10.4 oz / 295g 10.5 oz / 298g,0,9.3 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,Decent,Bad,Decent,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,36.8 mm 38.0 mm,27.5 mm 30.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#215 Bottom 41%,#35 Top 10%,,
+New Balance,Fresh Foam X 860 v14,"85
+ Good!",$140,Daily running,Stability,10.4 oz / 295g 10.5 oz / 298g,0,9.3 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,Decent,Bad,Decent,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,36.8 mm 38.0 mm,27.5 mm 30.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#215 Bottom 41%,#35 Top 10%,,
+New Balance,Fresh Foam X 860 v14,"85
+ Good!",$140,Daily running,Stability,10.4 oz / 295g 10.5 oz / 298g,0,9.3 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,Decent,Bad,Decent,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,36.8 mm 38.0 mm,27.5 mm 30.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#215 Bottom 41%,#35 Top 10%,,
+New Balance,Fresh Foam X 860 v14,"85
+ Good!",$140,Daily running,Stability,10.4 oz / 295g 10.5 oz / 298g,0,9.3 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,Decent,Bad,Decent,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,36.8 mm 38.0 mm,27.5 mm 30.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#215 Bottom 41%,#35 Top 10%,,
+New Balance,Fresh Foam X 860 v14,"85
+ Good!",$140,Daily running,Stability,10.4 oz / 295g 10.5 oz / 298g,0,9.3 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,Decent,Bad,Decent,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,36.8 mm 38.0 mm,27.5 mm 30.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#215 Bottom 41%,#35 Top 10%,,
+New Balance,Fresh Foam X 860 v14,"85
+ Good!",$140,Daily running,Stability,10.4 oz / 295g 10.5 oz / 298g,0,9.3 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,Decent,Bad,Decent,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,36.8 mm 38.0 mm,27.5 mm 30.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#215 Bottom 41%,#35 Top 10%,,
+New Balance,Fresh Foam X 880 v14,"88
+ Great!",$140,Daily running,Neutral,8.9 oz / 251g 8.7 oz / 247g,0,8.0 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,Decent,Bad,Decent,Moderate,Medium,Wide,Moderate,Moderate,Moderate,0,0,33.0 mm 31.0 mm,25.0 mm 23.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#266 Top 42%,#96 Top 15%,,
+New Balance,Fresh Foam X 880 v14 GTX,"73
+ Bad!",$160,Daily running,Neutral,9.2 oz / 261g 9.1 oz / 257g,0,11.3 mm 8.0 mm,Heel,True to size,Soft,Good,Decent,Good,Warm,Medium,Medium,Moderate,Stiff,Stiff,0,0,35.4 mm 31.0 mm,24.1 mm 23.0 mm,NormalWide,1,Winter,1,#357 Bottom 2%,#305 Bottom 16%,,
+New Balance,Fresh Foam X 880 v15,"85
+ Good!",$140,Daily running,Neutral,10.1 oz / 286g 9.9 oz / 281g,0,4.3 mm 6.0 mm,Mid/forefoot,Slightly small,Soft,Decent,Good,Decent,Moderate,Medium,Medium,Stiff,Stiff,Stiff,0,1,39.7 mm 40.5 mm,35.4 mm 34.5 mm,NarrowNormalWideX-Wide,1,All seasons,1,#201 Bottom 45%,#31 Top 9%,,
+New Balance,Fresh Foam X 880 v15,"85
+ Good!",$140,Daily running,Neutral,10.1 oz / 286g 9.9 oz / 281g,0,4.3 mm 6.0 mm,Mid/forefoot,Slightly small,Soft,Decent,Good,Decent,Moderate,Medium,Medium,Stiff,Stiff,Stiff,0,1,39.7 mm 40.5 mm,35.4 mm 34.5 mm,NarrowNormalWideX-Wide,1,All seasons,1,#201 Bottom 45%,#31 Top 9%,,
+New Balance,Fresh Foam X Balos,"90
+ Superb!",$200,Daily running,Neutral,8.7 oz / 247g 9.2 oz / 261g,1,5.9 mm 6.0 mm,Mid/forefoot,True to size,Soft,Bad,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,1,37.8 mm 38.5 mm,31.9 mm 32.5 mm,NormalWide,1,All seasons,1,#65 Top 18%,#51 Top 14%,,
+New Balance,Fresh Foam X Balos,"90
+ Superb!",$200,Daily running,Neutral,8.7 oz / 247g 9.2 oz / 261g,1,5.9 mm 6.0 mm,Mid/forefoot,True to size,Soft,Bad,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,1,37.8 mm 38.5 mm,31.9 mm 32.5 mm,NormalWide,1,All seasons,1,#65 Top 18%,#51 Top 14%,,
+New Balance,Fresh Foam X Evoz v3,"79
+ Good!",$100,Daily running,Neutral,9.1 oz / 257g 9.5 oz / 270g,0,7.9 mm 6.0 mm,Mid/forefoot,True to size,Balanced,Decent,Decent,Good,Warm,Narrow,Medium,Moderate,Moderate,Stiff,0,0,34.3 mm 32.0 mm,26.4 mm 26.0 mm,NormalWideX-Wide,1,All seasons,1,#584 Bottom 9%,#260 Top 41%,,
+New Balance,Fresh Foam X Evoz v4,"82
+ Good!",$100,Daily running,Neutral,10.1 oz / 286g 10.6 oz / 301g,0,5.5 mm 8.0 mm,Mid/forefoot,True to size,Soft,Decent,Decent,Decent,Moderate,Medium,Medium,Stiff,Stiff,Stiff,0,0,31.4 mm 32.0 mm,25.9 mm 24.0 mm,NormalWideX-Wide,1,All seasons,1,#285 Bottom 22%,#81 Top 23%,,
+New Balance,Fresh Foam X Evoz v4,"82
+ Good!",$100,Daily running,Neutral,10.1 oz / 286g 10.6 oz / 301g,0,5.5 mm 8.0 mm,Mid/forefoot,True to size,Soft,Decent,Decent,Decent,Moderate,Medium,Medium,Stiff,Stiff,Stiff,0,0,31.4 mm 32.0 mm,25.9 mm 24.0 mm,NormalWideX-Wide,1,All seasons,1,#285 Bottom 22%,#81 Top 23%,,
+New Balance,Fresh Foam X Kaiha Road,"81
+ Good!",$100,Daily running,Neutral,9.9 oz / 281g 10.6 oz / 300g,0,3.8 mm 4.0 mm,Mid/forefoot,True to size,Soft,Bad,Bad,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,0,35.8 mm,32.0 mm,NormalWideX-Wide,1,All seasons,1,#293 Bottom 19%,#17 Top 5%,,
+New Balance,Fresh Foam X More v4,"87
+ Great!",$150,Daily running,Neutral,10.6 oz / 301g 10.6 oz / 301g,0,4.6 mm 4.0 mm,Mid/forefoot,True to size,Soft,Bad,-,-,Warm,Narrow,Narrow,Moderate,Flexible,Flexible,0,0,32.5 mm 35.0 mm,27.9 mm 31.0 mm,NormalWide,1,All seasons,1,#315 Top 49%,#22 Top 4%,,
+New Balance,Fresh Foam X More v5,"87
+ Great!",$155,Daily running,Neutral,10.9 oz / 308g 10.7 oz / 303g,0,7.8 mm 4.0 mm,Mid/forefoot,Slightly small,Soft,Decent,Decent,Good,Breathable,Medium,Narrow,Stiff,Stiff,Stiff,0,1,42.1 mm 43.0 mm,34.3 mm 39.0 mm,NormalWideX-Wide,1,SummerAll seasons,1,#292 Top 46%,#2 Top 1%,,
+New Balance,Fresh Foam X More v6,"88
+ Great!",$155,Daily running,Neutral,10.7 oz / 302g 10.8 oz / 306g,0,3.3 mm 4.0 mm,Mid/forefoot,True to size,Soft,Decent,Good,Good,Warm,Medium,Medium,Stiff,Moderate,Moderate,0,0,41.8 mm 44.0 mm,38.5 mm 40.0 mm,NormalWideX-Wide,1,All seasons,1,#130 Top 36%,#9 Top 3%,,
+New Balance,Fresh Foam X More v6,"88
+ Great!",$155,Daily running,Neutral,10.7 oz / 302g 10.8 oz / 306g,0,3.3 mm 4.0 mm,Mid/forefoot,True to size,Soft,Decent,Good,Good,Warm,Medium,Medium,Stiff,Moderate,Moderate,0,0,41.8 mm 44.0 mm,38.5 mm 40.0 mm,NormalWideX-Wide,1,All seasons,1,#130 Top 36%,#9 Top 3%,,
+New Balance,Fresh Foam X Tempo v2,"77
+ Decent!",$120,Daily runningTempo,Neutral,8.6 oz / 244g 9.2 oz / 260g,1,7.0 mm 6.0 mm,Mid/forefoot,True to size,Soft,Good,Decent,Good,Warm,Narrow,Medium,Flexible,Flexible,Moderate,0,0,29.2 mm 28.0 mm,22.2 mm 22.0 mm,NormalWide,1,All seasons,1,#333 Bottom 9%,#187 Bottom 48%,,
+New Balance,Fresh Foam X Vongo v6,"88
+ Great!",$150,Daily running,Stability,11 oz / 312g 10.9 oz / 309g,0,5.6 mm 6.0 mm,Mid/forefoot,True to size,Soft,Decent,Good,Good,Warm,Medium,Medium,Moderate,Moderate,Stiff,0,1,36.1 mm 35.5 mm,30.5 mm 29.5 mm,NormalWideX-Wide,1,Winter,1,#123 Top 34%,#34 Top 10%,,
+New Balance,Fresh Foam X Vongo v6,"88
+ Great!",$150,Daily running,Stability,11 oz / 312g 10.9 oz / 309g,0,5.6 mm 6.0 mm,Mid/forefoot,True to size,Soft,Decent,Good,Good,Warm,Medium,Medium,Moderate,Moderate,Stiff,0,1,36.1 mm 35.5 mm,30.5 mm 29.5 mm,NormalWideX-Wide,1,Winter,1,#123 Top 34%,#34 Top 10%,,
+New Balance,FuelCell Propel v5,"80
+ Good!",$120,Daily runningTempo,Neutral,9.5 oz / 269g 9.7 oz / 275g,0,6.7 mm 6.0 mm,Mid/forefoot,Slightly small,Soft,Bad,Bad,Decent,Breathable,Medium,Medium,Moderate,Moderate,Moderate,0,0,35.2 mm 37.0 mm,28.5 mm 31.0 mm,NormalWide,1,SummerAll seasons,1,#310 Bottom 15%,#185 Bottom 49%,,
+New Balance,FuelCell Propel v5,"80
+ Good!",$120,Daily runningTempo,Neutral,9.5 oz / 269g 9.7 oz / 275g,0,6.7 mm 6.0 mm,Mid/forefoot,Slightly small,Soft,Bad,Bad,Decent,Breathable,Medium,Medium,Moderate,Moderate,Moderate,0,0,35.2 mm 37.0 mm,28.5 mm 31.0 mm,NormalWide,1,SummerAll seasons,1,#310 Bottom 15%,#185 Bottom 49%,,
+New Balance,FuelCell RC Elite v2,"89
+ Great!",$225,Competition,Neutral,7.7 oz / 217g 7.8 oz / 221g,1,8.3 mm 8.0 mm,HeelMid/forefoot,Slightly small,-,-,-,-,Moderate,Narrow,-,Stiff,Stiff,Flexible,Carbon plate,1,34.1 mm 39.0 mm,25.8 mm 31.0 mm,Normal,1,All seasons,1,#76 Top 21%,#222 Bottom 39%,,
+New Balance,FuelCell Rebel v2,"89
+ Great!",$130,Tempo,Neutral,7.1 oz / 201g 7.4 oz / 210g,1,8.9 mm 6.0 mm,HeelMid/forefoot,Half size small,-,-,-,-,Breathable,Narrow,-,Moderate,Flexible,Flexible,0,1,26.2 mm 26.0 mm,17.3 mm 20.0 mm,Normal,0,SummerAll seasons,0,#203 Top 32%,#522 Bottom 19%,,
+New Balance,FuelCell Rebel v3,"89
+ Great!",$130,Tempo,Neutral,7.4 oz / 211g 7.4 oz / 211g,1,9.0 mm 6.0 mm,HeelMid/forefoot,Half size small,Soft,Decent,Bad,-,Breathable,Narrow,Narrow,Moderate,Flexible,Moderate,0,0,31.7 mm 29.5 mm,22.7 mm 23.5 mm,NormalWide,1,SummerAll seasons,1,#138 Top 22%,#371 Bottom 42%,,
+New Balance,FuelCell Rebel v4,"86
+ Good!",$140,Daily runningTempo,Neutral,7.5 oz / 213g 7.7 oz / 218g,1,6.5 mm 6.0 mm,Mid/forefoot,Slightly small,Soft,Bad,Good,Good,Moderate,Medium,Wide,Flexible,Flexible,Flexible,0,1,28.0 mm 30.0 mm,21.5 mm 24.0 mm,NormalWide,1,All seasons,1,#386 Bottom 39%,#45 Top 8%,,
+New Balance,FuelCell Rebel v5,"91
+ Superb!",$140,Daily runningTempo,Neutral,7.8 oz / 220g 7.9 oz / 225g,1,6.3 mm 6.0 mm,Mid/forefoot,True to size,Soft,Bad,Good,Good,Moderate,Medium,Medium,Moderate,Flexible,Moderate,0,1,33.0 mm 35.0 mm,26.7 mm 29.0 mm,NormalWide,1,All seasons,1,#26 Top 8%,#39 Top 11%,,
+New Balance,FuelCell Rebel v5,"91
+ Superb!",$140,Daily runningTempo,Neutral,7.8 oz / 220g 7.9 oz / 225g,1,6.3 mm 6.0 mm,Mid/forefoot,True to size,Soft,Bad,Good,Good,Moderate,Medium,Medium,Moderate,Flexible,Moderate,0,1,33.0 mm 35.0 mm,26.7 mm 29.0 mm,NormalWide,1,All seasons,1,#26 Top 8%,#39 Top 11%,,
+New Balance,FuelCell Rebel v5,"91
+ Superb!",$140,Daily runningTempo,Neutral,7.8 oz / 220g 7.9 oz / 225g,1,6.3 mm 6.0 mm,Mid/forefoot,True to size,Soft,Bad,Good,Good,Moderate,Medium,Medium,Moderate,Flexible,Moderate,0,1,33.0 mm 35.0 mm,26.7 mm 29.0 mm,NormalWide,1,All seasons,1,#26 Top 8%,#39 Top 11%,,
+New Balance,FuelCell SuperComp Elite v3,"87
+ Great!",$230,CompetitionTempo,Neutral,7.7 oz / 217g 7.6 oz / 215g,1,14.2 mm 4.0 mm,Heel,Slightly small,Soft,Bad,Good,Good,Breathable,Narrow,Medium,Stiff,Stiff,Moderate,Carbon plate,1,36.4 mm 40.0 mm,22.2 mm 36.0 mm,NarrowNormal,1,SummerAll seasons,1,#336 Bottom 47%,#108 Top 17%,,
+New Balance,FuelCell SuperComp Elite v4,"88
+ Great!",$250,Competition,Neutral,8.2 oz / 232g 8.1 oz / 230g,1,9.3 mm 4.0 mm,HeelMid/forefoot,Half size small,Soft,Decent,Decent,Good,Moderate,Medium,Wide,Stiff,Stiff,Flexible,Carbon plate,1,38.2 mm 40.0 mm,28.9 mm 36.0 mm,NormalWide,1,All seasons,1,#236 Top 37%,#26 Top 5%,,
+New Balance,FuelCell SuperComp Elite v5,"92
+ Superb!",$265,Competition,Neutral,7 oz / 198g 7.5 oz / 213g,1,10.7 mm 8.0 mm,Heel,True to size,Soft,Bad,Decent,Decent,Moderate,Medium,Medium,Moderate,Stiff,Flexible,Carbon plate,1,39.3 mm 40.0 mm,28.6 mm 32.0 mm,NormalWide,1,All seasons,1,#9 Top 3%,#45 Top 13%,,Road
+New Balance,FuelCell SuperComp Elite v5,"92
+ Superb!",$265,Competition,Neutral,7 oz / 198g 7.5 oz / 213g,1,10.7 mm 8.0 mm,Heel,True to size,Soft,Bad,Decent,Decent,Moderate,Medium,Medium,Moderate,Stiff,Flexible,Carbon plate,1,39.3 mm 40.0 mm,28.6 mm 32.0 mm,NormalWide,1,All seasons,1,#9 Top 3%,#45 Top 13%,,
+New Balance,FuelCell SuperComp Elite v5,"92
+ Superb!",$265,Competition,Neutral,7 oz / 198g 7.5 oz / 213g,1,10.7 mm 8.0 mm,Heel,True to size,Soft,Bad,Decent,Decent,Moderate,Medium,Medium,Moderate,Stiff,Flexible,Carbon plate,1,39.3 mm 40.0 mm,28.6 mm 32.0 mm,NormalWide,1,All seasons,1,#12 Top 4%,#45 Top 13%,,
+New Balance,FuelCell SuperComp Elite v5,"92
+ Superb!",$265,Competition,Neutral,7 oz / 198g 7.5 oz / 213g,1,10.7 mm 8.0 mm,Heel,True to size,Soft,Bad,Decent,Decent,Moderate,Medium,Medium,Moderate,Stiff,Flexible,Carbon plate,1,39.3 mm 40.0 mm,28.6 mm 32.0 mm,NormalWide,1,All seasons,1,#12 Top 4%,#45 Top 13%,,
+New Balance,FuelCell SuperComp Pacer v2,"81
+ Good!",$200,CompetitionTempo,Neutral,7.1 oz / 200g 7.4 oz / 209g,1,7.5 mm 8.0 mm,HeelMid/forefoot,Half size small,Soft,Decent,Decent,Good,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,0,32.9 mm 35.0 mm,25.4 mm 27.0 mm,Normal,1,SummerAll seasons,1,#289 Bottom 20%,#128 Top 36%,,
+New Balance,FuelCell SuperComp Pacer v2,"81
+ Good!",$200,CompetitionTempo,Neutral,7.1 oz / 200g 7.4 oz / 209g,1,7.5 mm 8.0 mm,Mid/forefoot,Half size small,Soft,Decent,Decent,Good,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,0,32.9 mm 35.0 mm,25.4 mm 27.0 mm,Normal,1,SummerAll seasons,1,#290 Bottom 20%,#129 Top 36%,,
+New Balance,FuelCell SuperComp Pacer v2,"81
+ Good!",$200,CompetitionTempo,Neutral,7.1 oz / 200g 7.4 oz / 209g,1,7.5 mm 8.0 mm,Mid/forefoot,Half size small,Soft,Decent,Decent,Good,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,0,32.9 mm 35.0 mm,25.4 mm 27.0 mm,Normal,1,SummerAll seasons,1,#289 Bottom 21%,#129 Top 36%,,
+New Balance,Fuelcell Supercomp Trainer,"88
+ Great!",$180,Tempo,Neutral,10.5 oz / 298g 11.3 oz / 320g,0,10.3 mm 8.0 mm,Heel,Slightly small,Soft,-,-,-,Breathable,Narrow,-,Stiff,Stiff,Flexible,Carbon plate,1,40.2 mm 47.0 mm,29.9 mm 39.0 mm,Normal,1,SummerAll seasons,1,#269 Top 42%,#203 Top 32%,,
+New Balance,FuelCell SuperComp Trainer v2,"89
+ Great!",$180,Tempo,Neutral,9.3 oz / 264g 9.7 oz / 275g,0,8.4 mm 6.0 mm,HeelMid/forefoot,True to size,Soft,Bad,Good,Good,Moderate,Medium,Wide,Stiff,Stiff,Moderate,Carbon plate,1,39.3 mm 40.0 mm,30.9 mm 34.0 mm,NormalWide,1,All seasons,1,#133 Top 21%,#131 Top 21%,,
+New Balance,FuelCell SuperComp Trainer v3,"85
+ Good!",$180,Tempo,Neutral,9.8 oz / 278g 9.8 oz / 278g,0,7.3 mm 6.0 mm,Mid/forefoot,True to size,Soft,Bad,Good,Decent,Moderate,Medium,Medium,Stiff,Stiff,Moderate,Carbon plate,1,36.8 mm 41.0 mm,29.5 mm 35.0 mm,NormalWide,1,All seasons,1,#211 Bottom 42%,#40 Top 11%,,
+New Balance,FuelCell SuperComp Trainer v3,"85
+ Good!",$180,Tempo,Neutral,9.8 oz / 278g 9.8 oz / 278g,0,7.3 mm 6.0 mm,Mid/forefoot,True to size,Soft,Bad,Good,Decent,Moderate,Medium,Medium,Stiff,Stiff,Moderate,Carbon plate,1,36.8 mm 41.0 mm,29.5 mm 35.0 mm,NormalWide,1,All seasons,1,#211 Bottom 42%,#40 Top 11%,,
+New Balance,FuelCell SuperComp Trainer v3,"85
+ Good!",$180,Tempo,Neutral,9.8 oz / 278g 9.8 oz / 278g,0,7.3 mm 6.0 mm,Mid/forefoot,True to size,Soft,Bad,Good,Decent,Moderate,Medium,Medium,Stiff,Stiff,Moderate,Carbon plate,1,36.8 mm 41.0 mm,29.5 mm 35.0 mm,NormalWide,1,All seasons,1,#211 Bottom 42%,#40 Top 11%,,
+New Balance,FuelCell SuperComp Trainer v3,"85
+ Good!",$180,Tempo,Neutral,9.8 oz / 278g 9.8 oz / 278g,0,7.3 mm 6.0 mm,Mid/forefoot,True to size,Soft,Bad,Good,Decent,Moderate,Medium,Medium,Stiff,Stiff,Moderate,Carbon plate,1,36.8 mm 41.0 mm,29.5 mm 35.0 mm,NormalWide,1,All seasons,1,#211 Bottom 42%,#40 Top 11%,,
+New Balance,FuelCell SuperComp Trainer v3,"85
+ Good!",$180,Tempo,Neutral,9.8 oz / 278g 9.8 oz / 278g,0,7.3 mm 6.0 mm,Mid/forefoot,True to size,Soft,Bad,Good,Decent,Moderate,Medium,Medium,Stiff,Stiff,Moderate,Carbon plate,1,36.8 mm 41.0 mm,29.5 mm 35.0 mm,NormalWide,1,All seasons,1,#211 Bottom 42%,#40 Top 11%,,
+New Balance,FuelCell SuperComp Trainer v3,"85
+ Good!",$180,Tempo,Neutral,9.8 oz / 278g 9.8 oz / 278g,0,7.3 mm 6.0 mm,Mid/forefoot,True to size,Soft,Bad,Good,Decent,Moderate,Medium,Medium,Stiff,Stiff,Moderate,Carbon plate,1,36.8 mm 41.0 mm,29.5 mm 35.0 mm,NormalWide,1,All seasons,1,#211 Bottom 42%,#40 Top 11%,,
+Altra,FWD VIA,"85
+ Good!",$160,Daily running,Neutral,9 oz / 254g 9.5 oz / 269g,0,6.5 mm 4.0 mm,Mid/forefoot,Slightly small,Balanced,Decent,Decent,Decent,Moderate,Wide,Wide,Moderate,Stiff,Moderate,0,1,35.9 mm 37.0 mm,29.4 mm 33.0 mm,Normal,1,All seasons,1,#207 Bottom 43%,#134 Top 37%,,
+Adidas,Galaxy 6,"82
+ Good!",$60,Daily running,Neutral,11.7 oz / 332g 11.6 oz / 330g,0,11.0 mm 10.0 mm,Heel,True to size,Balanced,Bad,Bad,Decent,Warm,Wide,Wide,Stiff,Stiff,Flexible,0,0,33.9 mm,22.9 mm,Normal,1,All seasons,1,#286 Bottom 21%,#287 Bottom 21%,,
+Hoka,Gaviota 5,"83
+ Good!",$175,Daily running,Stability,10.5 oz / 299g 10.9 oz / 310g,0,2.2 mm 6.0 mm,Mid/forefoot,True to size,Soft,Decent,Bad,Good,Breathable,Wide,Wide,Stiff,Stiff,Moderate,0,0,34.9 mm 36.0 mm,32.7 mm 30.0 mm,NormalWide,1,SummerAll seasons,1,#258 Bottom 29%,#21 Top 6%,,
+ASICS,Gel Contend 7,"84
+ Good!",$65,Daily running,Neutral,9.5 oz / 268g 9.5 oz / 268g,0,9.6 mm 10.0 mm,HeelMid/forefoot,True to size,-,-,-,-,-,Medium,-,Stiff,-,Flexible,0,0,33.3 mm,23.7 mm,NormalX-Wide,1,-,1,#468 Bottom 27%,#364 Bottom 43%,,
+ASICS,Gel Contend 8,"84
+ Good!",$70,Daily running,Neutral,9.2 oz / 260g 10.3 oz / 293g,0,9.1 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,-,-,-,Breathable,Narrow,-,Stiff,Flexible,Stiff,0,0,31.1 mm,22.0 mm,NormalWideX-Wide,1,SummerAll seasons,1,#470 Bottom 27%,#341 Bottom 47%,,
+ASICS,Gel Contend 9,"79
+ Good!",$70,Daily running,Neutral,9.7 oz / 275g 9.7 oz / 275g,0,7.8 mm 8.0 mm,Mid/forefoot,True to size,Balanced,Bad,Good,Decent,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,31.2 mm 31.0 mm,23.4 mm 23.0 mm,NormalWideX-Wide,1,All seasons,1,#318 Bottom 13%,#88 Top 24%,,
+ASICS,Gel Contend 9,"79
+ Good!",$70,Daily running,Neutral,9.7 oz / 275g 9.7 oz / 275g,0,7.8 mm 8.0 mm,Mid/forefoot,True to size,Balanced,Bad,Good,Decent,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,31.2 mm 31.0 mm,23.4 mm 23.0 mm,NormalWideX-Wide,1,All seasons,1,#317 Bottom 13%,#88 Top 25%,,
+ASICS,Gel Contend 9,"79
+ Good!",$70,Daily running,Neutral,9.7 oz / 275g 9.7 oz / 275g,0,7.8 mm 8.0 mm,Mid/forefoot,True to size,Balanced,Bad,Good,Decent,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,31.2 mm 31.0 mm,23.4 mm 23.0 mm,NormalWideX-Wide,1,All seasons,1,#318 Bottom 13%,#88 Top 25%,,
+ASICS,Gel Contend 9,"79
+ Good!",$70,Daily running,Neutral,9.7 oz / 275g 9.7 oz / 275g,0,7.8 mm 8.0 mm,Mid/forefoot,True to size,Balanced,Bad,Good,Decent,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,31.2 mm 31.0 mm,23.4 mm 23.0 mm,NormalWideX-Wide,1,All seasons,1,#318 Bottom 13%,#88 Top 25%,,
+ASICS,Gel Contend 9,79 Good!,$70,Daily running,Neutral,9.7 oz / 275g 9.7 oz / 275g,0,7.8 mm 8.0 mm,Mid/forefoot,True to size,Balanced,Bad,Good,Decent,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,31.2 mm 31.0 mm,23.4 mm 23.0 mm,NormalWideX-Wide,1,All seasons,1,#317 Bottom 13%,#88 Top 25%,,
+ASICS,Gel Contend 9,"79
+ Good!",$70,Daily running,Neutral,9.7 oz / 275g 9.7 oz / 275g,0,7.8 mm 8.0 mm,Mid/forefoot,True to size,Balanced,Bad,Good,Decent,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,31.2 mm 31.0 mm,23.4 mm 23.0 mm,NormalWideX-Wide,1,All seasons,1,#317 Bottom 13%,#88 Top 25%,,
+ASICS,Gel Contend 9,"79
+ Good!",$70,Daily running,Neutral,9.7 oz / 275g 9.7 oz / 275g,0,7.8 mm 8.0 mm,Mid/forefoot,True to size,Balanced,Bad,Good,Decent,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,31.2 mm 31.0 mm,23.4 mm 23.0 mm,NormalWideX-Wide,1,All seasons,1,#317 Bottom 13%,#88 Top 25%,,
+ASICS,Gel Contend 9,"79
+ Good!",$70,Daily running,Neutral,9.7 oz / 275g 9.7 oz / 275g,0,7.8 mm 8.0 mm,Mid/forefoot,True to size,Balanced,Bad,Good,Decent,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,31.2 mm 31.0 mm,23.4 mm 23.0 mm,NormalWideX-Wide,1,All seasons,1,#318 Bottom 13%,#89 Top 25%,,
+ASICS,Gel Cumulus 23,"89
+ Great!",$120,Daily running,Neutral,9.8 oz / 277g 9.9 oz / 280g,0,10.8 mm 10.0 mm,Heel,True to size,-,-,-,-,Moderate,Narrow,-,Stiff,Moderate,Stiff,0,0,35.9 mm 23.0 mm,25.1 mm 13.0 mm,Normal,1,All seasons,1,#214 Top 34%,#512 Bottom 20%,,
+ASICS,Gel Cumulus 24,"89
+ Great!",$130,Daily running,Neutral,9.7 oz / 274g 10.1 oz / 286g,0,8.6 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,-,-,-,-,Narrow,-,Stiff,Flexible,Flexible,0,0,33.5 mm 24.0 mm,24.9 mm 16.0 mm,NormalWideX-Wide,1,-,1,#163 Top 26%,#453 Bottom 29%,,
+ASICS,Gel Cumulus 25,"88
+ Great!",$140,Daily running,Neutral,9.5 oz / 269g 9.5 oz / 269g,0,11.2 mm 8.0 mm,Heel,True to size,Soft,Decent,Good,-,Moderate,Narrow,Narrow,Moderate,Moderate,Moderate,0,0,38.4 mm 37.5 mm,27.2 mm 29.5 mm,NormalWideX-Wide,1,All seasons,1,#216 Top 34%,#291 Top 46%,,
+ASICS,Gel Cumulus 27,"89
+ Great!",$140,Daily running,Neutral,9.2 oz / 261g 9.3 oz / 265g,0,11.6 mm 8.0 mm,Heel,True to size,Soft,Good,Good,Bad,Warm,Medium,Medium,Moderate,Stiff,Stiff,0,0,40.9 mm 38.5 mm,29.3 mm 30.5 mm,NormalWideX-Wide,1,All seasons,1,#110 Top 31%,#68 Top 19%,,
+ASICS,Gel Cumulus 27,"89
+ Great!",$140,Daily running,Neutral,9.2 oz / 261g 9.3 oz / 265g,0,11.6 mm 8.0 mm,Heel,True to size,Soft,Good,Good,Bad,Warm,Medium,Medium,Moderate,Stiff,Stiff,0,0,40.9 mm 38.5 mm,29.3 mm 30.5 mm,NormalWideX-Wide,1,All seasons,1,#110 Top 31%,#68 Top 19%,,
+ASICS,Gel Cumulus 27,"89
+ Great!",$140,Daily running,Neutral,9.2 oz / 261g 9.3 oz / 265g,0,11.6 mm 8.0 mm,Heel,True to size,Soft,Good,Good,Bad,Warm,Medium,Medium,Moderate,Stiff,Stiff,0,0,40.9 mm 38.5 mm,29.3 mm 30.5 mm,NormalWideX-Wide,1,All seasons,1,#110 Top 31%,#68 Top 19%,,
+ASICS,Gel Cumulus 27,"89
+ Great!",$140,Daily running,Neutral,9.2 oz / 261g 9.3 oz / 265g,0,11.6 mm 8.0 mm,Heel,True to size,Soft,Good,Good,Bad,Warm,Medium,Medium,Moderate,Stiff,Stiff,0,0,40.9 mm 38.5 mm,29.3 mm 30.5 mm,NormalWideX-Wide,1,All seasons,1,#110 Top 31%,#68 Top 19%,,
+ASICS,Gel Excite 10,"86
+ Good!",$85,Daily running,Neutral,9.5 oz / 268g 9.2 oz / 260g,0,11.8 mm 8.0 mm,Heel,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,35.2 mm,23.4 mm,NormalWideX-Wide,1,All seasons,1,#178 Top 49%,#85 Top 24%,,
+ASICS,Gel Excite 10,"86
+ Good!",$85,Daily running,Neutral,9.5 oz / 268g 9.2 oz / 260g,0,11.8 mm 8.0 mm,Heel,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,35.2 mm,23.4 mm,NormalWideX-Wide,1,All seasons,1,#180 Top 50%,#84 Top 23%,,
+ASICS,Gel Excite 10,"86
+ Good!",$85,Daily running,Neutral,9.5 oz / 268g 9.2 oz / 260g,0,11.8 mm 8.0 mm,Heel,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,35.2 mm,23.4 mm,NormalWideX-Wide,1,All seasons,1,#180 Top 50%,#84 Top 23%,,
+ASICS,Gel Excite 10,"86
+ Good!",$85,Daily running,Neutral,9.5 oz / 268g 9.2 oz / 260g,0,11.8 mm 8.0 mm,Heel,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,35.2 mm,23.4 mm,NormalWideX-Wide,1,All seasons,1,#180 Top 50%,#84 Top 23%,,
+ASICS,Gel Excite 10,"86
+ Good!",$85,Daily running,Neutral,9.5 oz / 268g 9.2 oz / 260g,0,11.8 mm 8.0 mm,Heel,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,35.2 mm,23.4 mm,NormalWideX-Wide,1,All seasons,1,#180 Top 50%,#84 Top 23%,,
+ASICS,Gel Excite 10,"86
+ Good!",$85,Daily running,Neutral,9.5 oz / 268g 9.2 oz / 260g,0,11.8 mm 8.0 mm,Heel,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,35.2 mm,23.4 mm,NormalWideX-Wide,1,All seasons,1,#180 Top 50%,#84 Top 23%,,
+ASICS,Gel Excite 10,"86
+ Good!",$85,Daily running,Neutral,9.5 oz / 268g 9.2 oz / 260g,0,11.8 mm 8.0 mm,Heel,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,35.2 mm,23.4 mm,NormalWideX-Wide,1,All seasons,1,#180 Top 50%,#84 Top 23%,,
+ASICS,Gel Excite 10,"86
+ Good!",$85,Daily running,Neutral,9.5 oz / 268g 9.2 oz / 260g,0,11.8 mm 8.0 mm,Heel,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,35.2 mm,23.4 mm,NormalWideX-Wide,1,All seasons,1,#180 Top 50%,#84 Top 23%,,
+ASICS,Gel Excite 10,"86
+ Good!",$85,Daily running,Neutral,9.5 oz / 268g 9.2 oz / 260g,0,11.8 mm 8.0 mm,Heel,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,35.2 mm,23.4 mm,NormalWideX-Wide,1,All seasons,1,#180 Top 50%,#84 Top 23%,,
+ASICS,Gel Excite 10,"86
+ Good!",$85,Daily running,Neutral,9.5 oz / 268g 9.2 oz / 260g,0,11.8 mm 8.0 mm,Heel,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,35.2 mm,23.4 mm,NormalWideX-Wide,1,All seasons,1,#180 Top 50%,#84 Top 23%,,
+ASICS,Gel Excite 10,"86
+ Good!",$85,Daily running,Neutral,9.5 oz / 268g 9.2 oz / 260g,0,11.8 mm 8.0 mm,Heel,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,35.2 mm,23.4 mm,NormalWideX-Wide,1,All seasons,1,#180 Top 50%,#84 Top 23%,,
+ASICS,Gel Excite 10,"86
+ Good!",$85,Daily running,Neutral,9.5 oz / 268g 9.2 oz / 260g,0,11.8 mm 8.0 mm,Heel,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,35.2 mm,23.4 mm,NormalWideX-Wide,1,All seasons,1,#180 Top 50%,#84 Top 23%,,
+ASICS,Gel Excite 10,"86
+ Good!",$85,Daily running,Neutral,9.5 oz / 268g 9.2 oz / 260g,0,11.8 mm 8.0 mm,Heel,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,35.2 mm,23.4 mm,NormalWideX-Wide,1,All seasons,1,#180 Top 50%,#84 Top 23%,,
+ASICS,Gel Excite 10,"86
+ Good!",$85,Daily running,Neutral,9.5 oz / 268g 9.2 oz / 260g,0,11.8 mm 8.0 mm,Heel,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,35.2 mm,23.4 mm,NormalWideX-Wide,1,All seasons,1,#180 Top 50%,#84 Top 23%,,
+ASICS,Gel Excite 10,"86
+ Good!",$85,Daily running,Neutral,9.5 oz / 268g 9.2 oz / 260g,0,11.8 mm 8.0 mm,Heel,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,35.2 mm,23.4 mm,NormalWideX-Wide,1,All seasons,1,#180 Top 50%,#84 Top 23%,,
+ASICS,Gel Excite 8,"83
+ Good!",$75,Daily running,Neutral,9.5 oz / 268g 9.9 oz / 280g,0,12.3 mm 10.0 mm,Heel,True to size,-,-,-,-,-,Narrow,-,Stiff,-,-,0,0,35.8 mm,23.5 mm,NormalX-Wide,0,-,0,#491 Bottom 23%,#379 Bottom 41%,,
+ASICS,Gel Kayano 28,"90
+ Superb!",$160,Daily running,Stability,10.7 oz / 302g 10.8 oz / 305g,0,8.7 mm 10.0 mm,HeelMid/forefoot,Slightly large,-,-,-,-,-,Medium,-,-,Moderate,-,0,0,31.8 mm 23.0 mm,23.1 mm 13.0 mm,NormalWide,1,-,1,#85 Top 14%,#336 Bottom 47%,,
+ASICS,Gel Kayano 30,"87
+ Great!",$160,Daily running,Stability,10.7 oz / 303g 10.7 oz / 303g,0,12.0 mm 10.0 mm,Heel,Slightly small,Soft,Good,Bad,Good,Breathable,Medium,Medium,Moderate,Stiff,Stiff,0,1,39.7 mm 40.0 mm,27.7 mm 30.0 mm,NormalWideX-Wide,1,SummerAll seasons,1,#295 Top 46%,#86 Top 14%,,
+ASICS,Gel Kayano 31,"89
+ Great!",$165,Daily running,Stability,10.4 oz / 295g 11 oz / 311g,0,11.5 mm 10.0 mm,Heel,True to size,Soft,Good,Good,Decent,Moderate,Wide,Medium,Moderate,Stiff,Stiff,0,0,39.3 mm 40.0 mm,27.8 mm 30.0 mm,NormalWideX-Wide,1,All seasons,1,#155 Top 25%,#44 Top 7%,,
+ASICS,Gel Kayano 32,"83
+ Good!",$165,Daily running,Stability,10.4 oz / 295g 10.7 oz / 304g,0,9.3 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,39.9 mm 40.0 mm,30.6 mm 32.0 mm,NormalWideX-Wide,1,All seasons,1,#259 Bottom 29%,#19 Top 6%,,
+ASICS,Gel Kayano 32,"82
+ Good!",$165,Daily running,Stability,10.4 oz / 295g 10.7 oz / 304g,0,9.3 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,39.9 mm 40.0 mm,30.6 mm 32.0 mm,NormalWideX-Wide,1,All seasons,1,#278 Bottom 24%,#19 Top 6%,,
+ASICS,Gel Kayano 32,"82
+ Good!",$165,Daily running,Stability,10.4 oz / 295g 10.7 oz / 304g,0,9.3 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,39.9 mm 40.0 mm,30.6 mm 32.0 mm,NormalWideX-Wide,1,All seasons,1,#278 Bottom 24%,#19 Top 6%,,
+ASICS,Gel Kayano Lite 2,"87
+ Great!",$160,Daily running,Stability,10.1 oz / 286g 10.1 oz / 286g,0,11.1 mm 10.0 mm,Heel,-,-,-,-,-,-,Medium,-,Stiff,Stiff,Stiff,0,1,35.9 mm 35.0 mm,24.8 mm 25.0 mm,Normal,1,-,1,#287 Top 45%,#581 Bottom 9%,,
+ASICS,Gel Kayano Lite 3,"84
+ Good!",$160,Daily running,Stability,9.8 oz / 278g 9.9 oz / 281g,0,8.5 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,-,-,-,Breathable,Medium,-,Stiff,Moderate,Moderate,0,1,31.8 mm 22.0 mm,23.3 mm 14.0 mm,Normal,1,SummerAll seasons,1,#230 Bottom 37%,#334 Bottom 8%,,
+ASICS,Gel Kayano Lite 3,"84
+ Good!",$160,Daily running,Stability,9.8 oz / 278g 9.9 oz / 281g,0,8.5 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,-,-,-,Breathable,Medium,-,Stiff,Moderate,Moderate,0,1,31.8 mm 22.0 mm,23.3 mm 14.0 mm,Normal,1,SummerAll seasons,1,#230 Bottom 37%,#334 Bottom 8%,,
+ASICS,Gel Kinsei Max,"83
+ Good!",$180,Daily running,Neutral,11.4 oz / 322g 11.7 oz / 333g,0,8.6 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Decent,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,1,38.8 mm 38.0 mm,30.2 mm 30.0 mm,Normal,1,All seasons,1,#263 Bottom 27%,#295 Bottom 19%,,Road
+ASICS,Gel Kinsei Max,"83
+ Good!",$180,Daily running,Neutral,11.4 oz / 322g 11.7 oz / 333g,0,8.6 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Decent,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,1,38.8 mm 38.0 mm,30.2 mm 30.0 mm,Normal,1,All seasons,1,#263 Bottom 27%,#295 Bottom 19%,,
+ASICS,Gel Kinsei Max,"83
+ Good!",$180,Daily running,Neutral,11.4 oz / 322g 11.7 oz / 333g,0,8.6 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Decent,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,1,38.8 mm 38.0 mm,30.2 mm 30.0 mm,Normal,1,All seasons,1,#263 Bottom 27%,#295 Bottom 19%,,
+ASICS,Gel Kinsei Max,"83
+ Good!",$180,Daily running,Neutral,11.4 oz / 322g 11.7 oz / 333g,0,8.6 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Decent,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,1,38.8 mm 38.0 mm,30.2 mm 30.0 mm,Normal,1,All seasons,1,#264 Bottom 27%,#296 Bottom 19%,,
+ASICS,Gel Nimbus 24,"88
+ Great!",$160,Daily running,Neutral,9.3 oz / 265g 10.2 oz / 289g,0,8.7 mm 10.0 mm,HeelMid/forefoot,True to size,-,-,-,-,-,Medium,-,Stiff,-,-,0,1,38.7 mm 26.0 mm,30.0 mm 16.0 mm,NormalWideX-Wide,0,-,0,#223 Top 35%,#292 Top 46%,,
+ASICS,Gel Nimbus 25,"89
+ Great!",$160,Daily running,Neutral,10.2 oz / 289g 10.5 oz / 299g,0,7.8 mm 8.0 mm,Mid/forefoot,True to size,Soft,-,-,-,Moderate,Narrow,-,Moderate,Stiff,Moderate,0,1,38.0 mm 41.5 mm,30.2 mm 33.5 mm,NormalWideX-Wide,1,All seasons,1,#166 Top 26%,#83 Top 13%,,
+ASICS,Gel Nimbus 26,"89
+ Great!",$160,Daily running,Neutral,10.7 oz / 303g 10.7 oz / 304g,0,8.4 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Good,Good,Good,Moderate,Wide,Medium,Moderate,Stiff,Moderate,0,1,40.4 mm 42.0 mm,32.0 mm 34.0 mm,NormalWideX-Wide,1,All seasons,1,#148 Top 24%,#43 Top 7%,,
+ASICS,Gel Nimbus 27,"87
+ Great!",$165,Daily running,Neutral,10.5 oz / 299g 10.8 oz / 305g,0,8.3 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Stiff,0,1,42.7 mm 44.0 mm,34.4 mm 36.0 mm,NormalWideX-Wide,1,All seasons,1,#162 Top 45%,#18 Top 5%,#18 Top 5%,
+ASICS,Gel Nimbus 27,"87
+ Great!",$165,Daily running,Neutral,10.5 oz / 299g 10.8 oz / 305g,0,8.3 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Stiff,0,1,42.7 mm 44.0 mm,34.4 mm 36.0 mm,NormalWideX-Wide,1,All seasons,1,#162 Top 45%,#18 Top 5%,,Road
+ASICS,Gel Nimbus 27,"87
+ Great!",$165,Daily running,Neutral,10.5 oz / 299g 10.8 oz / 305g,0,8.3 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Stiff,0,1,42.7 mm 44.0 mm,34.4 mm 36.0 mm,NormalWideX-Wide,1,All seasons,1,#162 Top 45%,#18 Top 5%,,
+ASICS,Gel Nimbus 27,"87
+ Great!",$165,Daily running,Neutral,10.5 oz / 299g 10.8 oz / 305g,0,8.3 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Stiff,0,1,42.7 mm 44.0 mm,34.4 mm 36.0 mm,NormalWideX-Wide,1,All seasons,1,#162 Top 45%,#18 Top 5%,,
+ASICS,Gel Nimbus 27,"87
+ Great!",$165,Daily running,Neutral,10.5 oz / 299g 10.8 oz / 305g,0,8.3 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Stiff,0,1,42.7 mm 44.0 mm,34.4 mm 36.0 mm,NormalWideX-Wide,1,All seasons,1,#162 Top 45%,#18 Top 5%,,
+ASICS,Gel Nimbus 27,"87
+ Great!",$165,Daily running,Neutral,10.5 oz / 299g 10.8 oz / 305g,0,8.3 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Stiff,0,1,42.7 mm 44.0 mm,34.4 mm 36.0 mm,NormalWideX-Wide,1,All seasons,1,#162 Top 45%,#18 Top 5%,,
+ASICS,Gel Nimbus 27,"87
+ Great!",$165,Daily running,Neutral,10.5 oz / 299g 10.8 oz / 305g,0,8.3 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Stiff,0,1,42.7 mm 44.0 mm,34.4 mm 36.0 mm,NormalWideX-Wide,1,All seasons,1,#164 Top 45%,#18 Top 5%,,
+ASICS,Gel Nimbus 27,"87
+ Great!",$165,Daily running,Neutral,10.5 oz / 299g 10.8 oz / 305g,0,8.3 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Stiff,0,1,42.7 mm 44.0 mm,34.4 mm 36.0 mm,NormalWideX-Wide,1,All seasons,1,#162 Top 45%,#18 Top 5%,,
+ASICS,Gel Nimbus 27,"86
+ Good!",$165,Daily running,Neutral,10.5 oz / 299g 10.8 oz / 305g,0,8.3 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Stiff,0,1,42.7 mm 44.0 mm,34.4 mm 36.0 mm,NormalWideX-Wide,1,All seasons,1,#164 Top 45%,#18 Top 5%,,
+ASICS,Gel Nimbus 27,"86
+ Good!",$165,Daily running,Neutral,10.5 oz / 299g 10.8 oz / 305g,0,8.3 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Stiff,0,1,42.7 mm 44.0 mm,34.4 mm 36.0 mm,NormalWideX-Wide,1,All seasons,1,#164 Top 45%,#18 Top 5%,,
+ASICS,Gel Nimbus Lite 3,"89
+ Great!",$160,Daily running,Neutral,9 oz / 254g 9.1 oz / 259g,0,9.9 mm 10.0 mm,HeelMid/forefoot,-,-,-,-,-,-,Medium,-,Stiff,-,-,0,1,34.9 mm 25.0 mm,25.0 mm 15.0 mm,Normal,0,-,0,#108 Top 30%,#305 Bottom 16%,,
+ASICS,Gel Pulse 11,"85
+ Good!",$90,Daily running,Neutral,11.1 oz / 314g 11.4 oz / 322g,0,8.7 mm 8.0 mm,HeelMid/forefoot,Slightly large,-,-,-,-,-,Medium,-,Stiff,-,-,0,0,34.0 mm 31.0 mm,25.3 mm 13.0 mm,Normal,0,-,0,#420 Bottom 34%,#604 Bottom 6%,,
+ASICS,Gel Pulse 13,"80
+ Good!",$90,Daily running,Neutral,10.3 oz / 291g 10.6 oz / 300g,0,11.0 mm 10.0 mm,Heel,True to size,Balanced,Bad,-,-,Breathable,Narrow,Medium,Moderate,Flexible,Moderate,0,0,32.6 mm 23.0 mm,21.6 mm 13.0 mm,Normal,1,SummerAll seasons,1,#563 Bottom 12%,#590 Bottom 8%,,
+ASICS,Gel Pulse 13,"80
+ Good!",$90,Daily running,Neutral,10.3 oz / 291g 10.6 oz / 300g,0,11.0 mm 10.0 mm,Heel,True to size,Balanced,Bad,-,-,Breathable,Narrow,Medium,Moderate,Flexible,Moderate,0,0,32.6 mm 23.0 mm,21.6 mm 13.0 mm,Normal,1,SummerAll seasons,1,#563 Bottom 12%,#590 Bottom 8%,,
+ASICS,Gel Pulse 14,"80
+ Good!",$100,Daily running,Neutral,10.4 oz / 296g 10.5 oz / 298g,0,9.7 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Bad,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,32.0 mm,22.3 mm,Normal,1,All seasons,1,#562 Bottom 13%,#597 Bottom 7%,,
+ASICS,Gel Pulse 15,"83
+ Good!",$100,Daily running,Neutral,8.4 oz / 237g 9.2 oz / 260g,1,8.1 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,35.3 mm 35.0 mm,27.2 mm 27.0 mm,Normal,1,All seasons,1,#262 Bottom 28%,#181 Top 50%,,Road
+ASICS,Gel Pulse 15,"83
+ Good!",$100,Daily running,Neutral,8.4 oz / 237g 9.2 oz / 260g,1,8.1 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,35.3 mm 35.0 mm,27.2 mm 27.0 mm,Normal,1,All seasons,1,#266 Bottom 27%,#182 Top 50%,,
+ASICS,Gel Pulse 15,"83
+ Good!",$100,Daily running,Neutral,8.4 oz / 237g 9.2 oz / 260g,1,8.1 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,35.3 mm 35.0 mm,27.2 mm 27.0 mm,Normal,1,All seasons,1,#266 Bottom 27%,#182 Top 50%,,
+ASICS,Gel Pulse 15,"83
+ Good!",$100,Daily running,Neutral,8.4 oz / 237g 9.2 oz / 260g,1,8.1 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,35.3 mm 35.0 mm,27.2 mm 27.0 mm,Normal,1,All seasons,1,#262 Bottom 28%,#181 Top 50%,,
+ASICS,Gel Pulse 15,"83
+ Good!",$100,Daily running,Neutral,8.4 oz / 237g 9.2 oz / 260g,1,8.1 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,35.3 mm 35.0 mm,27.2 mm 27.0 mm,Normal,1,All seasons,1,#262 Bottom 28%,#181 Top 50%,,
+ASICS,Gel Pulse 15,"83
+ Good!",$100,Daily running,Neutral,8.4 oz / 237g 9.2 oz / 260g,1,8.1 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,35.3 mm 35.0 mm,27.2 mm 27.0 mm,Normal,1,All seasons,1,#266 Bottom 27%,#182 Top 50%,,
+ASICS,Gel Pulse 15,"83
+ Good!",$100,Daily running,Neutral,8.4 oz / 237g 9.2 oz / 260g,1,8.1 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,35.3 mm 35.0 mm,27.2 mm 27.0 mm,Normal,1,All seasons,1,#262 Bottom 28%,#182 Top 50%,,
+ASICS,Gel Pulse 15,"83
+ Good!",$100,Daily running,Neutral,8.4 oz / 237g 9.2 oz / 260g,1,8.1 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,35.3 mm 35.0 mm,27.2 mm 27.0 mm,Normal,1,All seasons,1,#262 Bottom 28%,#182 Top 50%,,
+ASICS,Gel Pulse 15,"83
+ Good!",$100,Daily running,Neutral,8.4 oz / 237g 9.2 oz / 260g,1,8.1 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,35.3 mm 35.0 mm,27.2 mm 27.0 mm,Normal,1,All seasons,1,#262 Bottom 28%,#182 Top 50%,,
+ASICS,Gel Pulse 15,"83
+ Good!",$100,Daily running,Neutral,8.4 oz / 237g 9.2 oz / 260g,1,8.1 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,35.3 mm 35.0 mm,27.2 mm 27.0 mm,Normal,1,All seasons,1,#262 Bottom 28%,#182 Top 50%,,
+ASICS,Gel Pulse 15,"83
+ Good!",$100,Daily running,Neutral,8.4 oz / 237g 9.2 oz / 260g,1,8.1 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,35.3 mm 35.0 mm,27.2 mm 27.0 mm,Normal,1,All seasons,1,#262 Bottom 28%,#182 Top 50%,,
+ASICS,Gel Pulse 15,"83
+ Good!",$100,Daily running,Neutral,8.4 oz / 237g 9.2 oz / 260g,1,8.1 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,35.3 mm 35.0 mm,27.2 mm 27.0 mm,Normal,1,All seasons,1,#263 Bottom 28%,#183 Top 50%,,
+Brooks,Ghost 14,"91
+ Superb!",$140,Daily running,Neutral,10.1 oz / 287g 9.9 oz / 280g,0,12.4 mm 12.0 mm,Heel,True to size,-,-,-,-,Moderate,Narrow,-,Stiff,Moderate,Stiff,0,0,33.8 mm 36.0 mm,21.4 mm 24.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#53 Top 9%,#153 Top 24%,,
+Brooks,Ghost 15,"88
+ Great!",$140,Daily running,Neutral,9.8 oz / 279g 10.1 oz / 286g,0,13.2 mm 12.0 mm,Heel,True to size,Soft,Bad,Decent,Good,Moderate,Narrow,Medium,Flexible,Moderate,Stiff,0,0,36.3 mm 35.0 mm,23.1 mm 23.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#224 Top 35%,#60 Top 10%,,
+Brooks,Ghost 16,"81
+ Good!",$140,Daily running,Neutral,9.4 oz / 266g 9.5 oz / 269g,0,12.4 mm 12.0 mm,Heel,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Flexible,Moderate,Stiff,0,0,35.1 mm 36.0 mm,22.7 mm 24.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#550 Bottom 14%,#20 Top 4%,,
+Brooks,Ghost 17,"76
+ Bad!",$150,Daily running,Neutral,10.2 oz / 289g 10.1 oz / 286g,0,10.4 mm 10.0 mm,Heel,Slightly large,Soft,Decent,Good,Good,Moderate,Medium,Narrow,Moderate,Moderate,Stiff,0,0,36.2 mm 36.5 mm,25.8 mm 26.5 mm,NarrowNormalWideX-Wide,1,All seasons,1,#342 Bottom 6%,#16 Top 5%,,
+Brooks,Ghost 17,"76
+ Decent!",$150,Daily running,Neutral,10.2 oz / 289g 10.1 oz / 286g,0,10.4 mm 10.0 mm,Heel,Slightly large,Soft,Decent,Good,Good,Moderate,Medium,Narrow,Moderate,Moderate,Stiff,0,0,36.2 mm 36.5 mm,25.8 mm 26.5 mm,NarrowNormalWideX-Wide,1,All seasons,1,#342 Bottom 6%,#16 Top 5%,,
+Brooks,Ghost 17,"76
+ Decent!",$150,Daily running,Neutral,10.2 oz / 289g 10.1 oz / 286g,0,10.4 mm 10.0 mm,Heel,Slightly large,Soft,Decent,Good,Good,Moderate,Medium,Narrow,Moderate,Moderate,Stiff,0,0,36.2 mm 36.5 mm,25.8 mm 26.5 mm,NarrowNormalWideX-Wide,1,All seasons,1,#342 Bottom 6%,#16 Top 5%,,
+Brooks,Ghost 17,"76
+ Decent!",$150,Daily running,Neutral,10.2 oz / 289g 10.1 oz / 286g,0,10.4 mm 10.0 mm,Heel,Slightly large,Soft,Decent,Good,Good,Moderate,Medium,Narrow,Moderate,Moderate,Stiff,0,0,36.2 mm 36.5 mm,25.8 mm 26.5 mm,NarrowNormalWideX-Wide,1,All seasons,1,#342 Bottom 6%,#16 Top 5%,,
+Brooks,Ghost Max,"91
+ Superb!",$150,Daily running,Neutral,10.3 oz / 291g 10.1 oz / 286g,0,9.5 mm 6.0 mm,HeelMid/forefoot,True to size,Soft,Bad,Good,Good,Moderate,Narrow,Medium,Stiff,Stiff,Stiff,0,1,39.8 mm 38.0 mm,30.3 mm 32.0 mm,NormalWideX-Wide,1,All seasons,1,#29 Top 5%,#27 Top 5%,,
+Brooks,Ghost Max 3,"84
+ Good!",$160,Daily running,Neutral,10.7 oz / 303g 10.8 oz / 306g,0,7.3 mm 6.0 mm,Mid/forefoot,True to size,Soft,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Stiff,0,0,38.5 mm 39.0 mm,31.2 mm 33.0 mm,NormalWideX-Wide,1,All seasons,1,#230 Bottom 36%,#27 Top 8%,,
+Brooks,Ghost Max 3,"84
+ Good!",$160,Daily running,Neutral,10.7 oz / 303g 10.8 oz / 306g,0,7.3 mm 6.0 mm,Mid/forefoot,True to size,Soft,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Stiff,0,0,38.5 mm 39.0 mm,31.2 mm 33.0 mm,NormalWideX-Wide,1,All seasons,1,#230 Bottom 36%,#27 Top 8%,,
+Brooks,Ghost Max 3,"84
+ Good!",$160,Daily running,Neutral,10.7 oz / 303g 10.8 oz / 306g,0,7.3 mm 6.0 mm,Mid/forefoot,True to size,Soft,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Stiff,0,0,38.5 mm 39.0 mm,31.2 mm 33.0 mm,NormalWideX-Wide,1,All seasons,1,#230 Bottom 37%,#27 Top 8%,,
+Brooks,Ghost Max 3,"84
+ Good!",$160,Daily running,Neutral,10.7 oz / 303g 10.8 oz / 306g,0,7.3 mm 6.0 mm,Mid/forefoot,True to size,Soft,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Stiff,0,0,38.5 mm 39.0 mm,31.2 mm 33.0 mm,NormalWideX-Wide,1,All seasons,1,#230 Bottom 37%,#27 Top 8%,,
+Brooks,Ghost Max 3,"84
+ Good!",$160,Daily running,Neutral,10.7 oz / 303g 10.8 oz / 306g,0,7.3 mm 6.0 mm,Mid/forefoot,True to size,Soft,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Stiff,0,0,38.5 mm 39.0 mm,31.2 mm 33.0 mm,NormalWideX-Wide,1,All seasons,1,#228 Bottom 37%,#27 Top 8%,,
+Brooks,GhostMax 2,"85
+ Good!",$150,Daily running,Neutral,10.8 oz / 305g 10.9 oz / 309g,0,9.9 mm 6.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Stiff,0,0,39.0 mm 39.0 mm,29.1 mm 33.0 mm,NormalWideX-Wide,1,SummerAll seasons,1,#418 Bottom 35%,#15 Top 3%,,
+ASICS,GlideRide 3,"90
+ Superb!",$150,Daily running,Neutral,9.5 oz / 270g 9.4 oz / 266g,0,11.1 mm 5.0 mm,Heel,Slightly small,Soft,-,Bad,-,Moderate,Narrow,-,Stiff,Stiff,Flexible,0,1,42.7 mm 40.0 mm,31.6 mm 35.0 mm,Normal,1,All seasons,1,#32 Top 9%,#282 Bottom 22%,,
+ASICS,Glideride Max,"92
+ Superb!",$170,Daily running,Neutral,9.9 oz / 281g 10.2 oz / 289g,0,12.7 mm 6.0 mm,Heel,Slightly small,Soft,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,1,44.1 mm 44.0 mm,31.4 mm 38.0 mm,NormalWide,1,All seasons,1,#10 Top 3%,#131 Top 36%,#131 Top 36%,
+ASICS,Glideride Max,92 Superb!,$170,Daily running,Neutral,9.9 oz / 281g 10.2 oz / 289g,0,12.7 mm 6.0 mm,Heel,Slightly small,Soft,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,1,44.1 mm 44.0 mm,31.4 mm 38.0 mm,Normal Wide,1,All seasons,1,#8 Top 3%,#129 Top 36%,,
+ASICS,Glideride Max,"92
+ Superb!",$170,Daily running,Neutral,9.9 oz / 281g 10.2 oz / 289g,0,12.7 mm 6.0 mm,Heel,Slightly small,Soft,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,1,44.1 mm 44.0 mm,31.4 mm 38.0 mm,NormalWide,1,All seasons,1,#8 Top 3%,#129 Top 36%,,
+ASICS,Glideride Max,"92
+ Superb!",$170,Daily running,Neutral,9.9 oz / 281g 10.2 oz / 289g,0,12.7 mm 6.0 mm,Heel,Slightly small,Soft,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,1,44.1 mm 44.0 mm,31.4 mm 38.0 mm,NormalWide,1,All seasons,1,#10 Top 3%,#131 Top 36%,,
+Brooks,Glycerin 19,"89
+ Great!",$150,Daily running,Neutral,10.4 oz / 294g 10.2 oz / 289g,0,11.8 mm 10.0 mm,Heel,Slightly small,-,-,-,-,-,Narrow,-,-,Moderate,-,0,0,38.1 mm 36.0 mm,26.3 mm 26.0 mm,NormalWide,1,-,1,#202 Top 32%,#417 Bottom 35%,,
+Brooks,Glycerin 20,"89
+ Great!",$160,Daily running,Neutral,10.5 oz / 297g 10.1 oz / 286g,0,12.8 mm 10.0 mm,Heel,True to size,Balanced,-,-,-,Moderate,Narrow,-,Moderate,Moderate,Moderate,0,0,37.1 mm 34.0 mm,24.3 mm 24.0 mm,NormalWide,1,All seasons,1,#210 Top 33%,#113 Top 18%,,
+Brooks,Glycerin 21,"86
+ Good!",$160,Daily running,Neutral,9.8 oz / 278g 9.8 oz / 277g,0,10.6 mm 10.0 mm,Heel,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Flexible,Stiff,0,0,37.2 mm 38.0 mm,26.6 mm 28.0 mm,NarrowNormalWide,1,All seasons,1,#374 Bottom 42%,#49 Top 8%,,
+Brooks,Glycerin 22,"86
+ Good!",$165,Daily running,Neutral,10.3 oz / 293g 10.2 oz / 289g,0,10.3 mm 10.0 mm,Heel,True to size,Soft,Decent,Good,Good,Breathable,Medium,Medium,Moderate,Stiff,Moderate,0,0,38.5 mm 38.0 mm,28.2 mm 28.0 mm,NormalWideX-Wide,1,SummerAll seasons,1,#175 Top 48%,#14 Top 4%,,
+Brooks,Glycerin 22,"86
+ Good!",$165,Daily running,Neutral,10.3 oz / 293g 10.2 oz / 289g,0,10.3 mm 10.0 mm,Heel,True to size,Soft,Decent,Good,Good,Breathable,Medium,Medium,Moderate,Stiff,Moderate,0,0,38.5 mm 38.0 mm,28.2 mm 28.0 mm,NormalWideX-Wide,1,SummerAll seasons,1,#172 Top 47%,#14 Top 4%,,
+Brooks,Glycerin 22,"86
+ Good!",$165,Daily running,Neutral,10.3 oz / 293g 10.2 oz / 289g,0,10.3 mm 10.0 mm,Heel,True to size,Soft,Decent,Good,Good,Breathable,Medium,Medium,Moderate,Stiff,Moderate,0,0,38.5 mm 38.0 mm,28.2 mm 28.0 mm,NormalWideX-Wide,1,SummerAll seasons,1,#172 Top 47%,#14 Top 4%,,
+Brooks,Glycerin 22,"86
+ Good!",$165,Daily running,Neutral,10.3 oz / 293g 10.2 oz / 289g,0,10.3 mm 10.0 mm,Heel,True to size,Soft,Decent,Good,Good,Breathable,Medium,Medium,Moderate,Stiff,Moderate,0,0,38.5 mm 38.0 mm,28.2 mm 28.0 mm,NormalWideX-Wide,1,SummerAll seasons,1,#172 Top 47%,#14 Top 4%,,
+Brooks,Glycerin 22,"86
+ Good!",$165,Daily running,Neutral,10.3 oz / 293g 10.2 oz / 289g,0,10.3 mm 10.0 mm,Heel,True to size,Soft,Decent,Good,Good,Breathable,Medium,Medium,Moderate,Stiff,Moderate,0,0,38.5 mm 38.0 mm,28.2 mm 28.0 mm,NormalWideX-Wide,1,SummerAll seasons,1,#172 Top 47%,#14 Top 4%,,
+Brooks,Glycerin GTS 20,"90
+ Superb!",$160,Daily running,Stability,10.9 oz / 309g 10.5 oz / 298g,0,11.0 mm 10.0 mm,Heel,True to size,Balanced,Bad,Bad,Good,Moderate,Narrow,Medium,Stiff,Stiff,Stiff,0,0,36.5 mm 38.0 mm,25.5 mm 28.0 mm,NormalWide,1,All seasons,1,#79 Top 13%,#288 Top 45%,,
+Brooks,Glycerin GTS 21,"88
+ Great!",$160,Daily running,Stability,10.6 oz / 301g 10.7 oz / 303g,0,10.7 mm 10.0 mm,Heel,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,37.2 mm 38.0 mm,26.5 mm 28.0 mm,NormalWide,1,All seasons,1,#233 Top 37%,#173 Top 27%,,
+Brooks,Glycerin GTS 22,"78
+ Decent!",$165,Daily running,Stability,10.8 oz / 305g 10.7 oz / 303g,0,10.1 mm 10.0 mm,Heel,True to size,Balanced,Decent,Good,Good,Breathable,Medium,Medium,Moderate,Stiff,Moderate,0,0,37.8 mm 39.0 mm,27.7 mm 29.0 mm,NormalWideX-Wide,1,SummerAll seasons,1,#324 Bottom 11%,#44 Top 13%,,
+Brooks,Glycerin GTS 22,"78
+ Decent!",$165,Daily running,Stability,10.8 oz / 305g 10.7 oz / 303g,0,10.1 mm 10.0 mm,Heel,True to size,Balanced,Decent,Good,Good,Breathable,Medium,Medium,Moderate,Stiff,Moderate,0,0,37.8 mm 39.0 mm,27.7 mm 29.0 mm,NormalWideX-Wide,1,SummerAll seasons,1,#321 Bottom 12%,#44 Top 13%,,
+Brooks,Glycerin GTS 22,"78
+ Decent!",$165,Daily running,Stability,10.8 oz / 305g 10.7 oz / 303g,0,10.1 mm 10.0 mm,Heel,True to size,Balanced,Decent,Good,Good,Breathable,Medium,Medium,Moderate,Stiff,Moderate,0,0,37.8 mm 39.0 mm,27.7 mm 29.0 mm,NormalWideX-Wide,1,SummerAll seasons,1,#321 Bottom 12%,#44 Top 13%,,
+Brooks,Glycerin GTS 22,"78
+ Decent!",$165,Daily running,Stability,10.8 oz / 305g 10.7 oz / 303g,0,10.1 mm 10.0 mm,Heel,True to size,Balanced,Decent,Good,Good,Breathable,Medium,Medium,Moderate,Stiff,Moderate,0,0,37.8 mm 39.0 mm,27.7 mm 29.0 mm,NormalWideX-Wide,1,SummerAll seasons,1,#321 Bottom 12%,#44 Top 13%,,
+Brooks,Glycerin Max,"89
+ Great!",$200,Daily running,Neutral,10.8 oz / 305g 10.9 oz / 309g,0,6.6 mm 6.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Good,Decent,Breathable,Medium,Medium,Stiff,Stiff,Moderate,0,1,42.3 mm 47.0 mm,35.7 mm 41.0 mm,Normal,1,SummerAll seasons,1,#100 Top 28%,#10 Top 3%,,Road
+Brooks,Glycerin Max,"89
+ Great!",$200,Daily running,Neutral,10.8 oz / 305g 10.9 oz / 309g,0,6.6 mm 6.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Good,Decent,Breathable,Medium,Medium,Stiff,Stiff,Moderate,0,1,42.3 mm 47.0 mm,35.7 mm 41.0 mm,Normal,1,SummerAll seasons,1,#100 Top 28%,#10 Top 3%,,
+Brooks,Glycerin Stealthfit 20,"86
+ Good!",$160,Daily running,Neutral,9.9 oz / 281g 9.4 oz / 266.5g,0,12.5 mm 10.0 mm,Heel,True to size,Balanced,Decent,Good,Good,Breathable,Narrow,Medium,Stiff,Stiff,Moderate,0,0,38.8 mm 34.0 mm,26.3 mm 24.0 mm,NarrowNormal,1,SummerAll seasons,1,#364 Bottom 43%,#478 Bottom 25%,,
+Brooks,Glycerin StealthFit 21,"84
+ Good!",$160,Daily running,Neutral,9.1 oz / 257g 10 oz / 283g,0,10.5 mm 10.0 mm,Heel,True to size,Balanced,Decent,Decent,Good,Warm,Medium,Medium,Moderate,Moderate,Moderate,0,0,36.9 mm 38.0 mm,26.4 mm 28.0 mm,Normal,1,All seasons,1,#235 Bottom 35%,#142 Top 39%,,
+Brooks,Glycerin StealthFit 21,"84
+ Good!",$160,Daily running,Neutral,9.1 oz / 257g 10 oz / 283g,0,10.5 mm 10.0 mm,Heel,True to size,Balanced,Decent,Decent,Good,Warm,Medium,Medium,Moderate,Moderate,Moderate,0,0,36.9 mm 38.0 mm,26.4 mm 28.0 mm,Normal,1,All seasons,1,#235 Bottom 35%,#142 Top 39%,,
+Brooks,Glycerin StealthFit 21,"84
+ Good!",$160,Daily running,Neutral,9.1 oz / 257g 10 oz / 283g,0,10.5 mm 10.0 mm,Heel,True to size,Balanced,Decent,Decent,Good,Warm,Medium,Medium,Moderate,Moderate,Moderate,0,0,36.9 mm 38.0 mm,26.4 mm 28.0 mm,Normal,1,All seasons,1,#235 Bottom 35%,#142 Top 39%,,
+Brooks,Glycerin StealthFit 21,"84
+ Good!",$160,Daily running,Neutral,9.1 oz / 257g 10 oz / 283g,0,10.5 mm 10.0 mm,Heel,True to size,Balanced,Decent,Decent,Good,Warm,Medium,Medium,Moderate,Moderate,Moderate,0,0,36.9 mm 38.0 mm,26.4 mm 28.0 mm,Normal,1,All seasons,1,#235 Bottom 35%,#142 Top 39%,,
+Brooks,Glycerin StealthFit 21,"84
+ Good!",$160,Daily running,Neutral,9.1 oz / 257g 10 oz / 283g,0,10.5 mm 10.0 mm,Heel,True to size,Balanced,Decent,Decent,Good,Warm,Medium,Medium,Moderate,Moderate,Moderate,0,0,36.9 mm 38.0 mm,26.4 mm 28.0 mm,Normal,1,All seasons,1,#235 Bottom 35%,#142 Top 39%,,
+Brooks,Glycerin StealthFit 21,"84
+ Good!",$160,Daily running,Neutral,9.1 oz / 257g 10 oz / 283g,0,10.5 mm 10.0 mm,Heel,True to size,Balanced,Decent,Decent,Good,Warm,Medium,Medium,Moderate,Moderate,Moderate,0,0,36.9 mm 38.0 mm,26.4 mm 28.0 mm,Normal,1,All seasons,1,#235 Bottom 35%,#142 Top 39%,,
+Brooks,Glycerin StealthFit 21,"84
+ Good!",$160,Daily running,Neutral,9.1 oz / 257g 10 oz / 283g,0,10.5 mm 10.0 mm,Heel,True to size,Balanced,Decent,Decent,Good,Warm,Medium,Medium,Moderate,Moderate,Moderate,0,0,36.9 mm 38.0 mm,26.4 mm 28.0 mm,Normal,1,All seasons,1,#235 Bottom 35%,#142 Top 39%,,
+Brooks,Glycerin StealthFit 21,"84
+ Good!",$160,Daily running,Neutral,9.1 oz / 257g 10 oz / 283g,0,10.5 mm 10.0 mm,Heel,True to size,Balanced,Decent,Decent,Good,Warm,Medium,Medium,Moderate,Moderate,Moderate,0,0,36.9 mm 38.0 mm,26.4 mm 28.0 mm,Normal,1,All seasons,1,#235 Bottom 35%,#142 Top 39%,,
+Brooks,Glycerin StealthFit 21,"84
+ Good!",$160,Daily running,Neutral,9.1 oz / 257g 10 oz / 283g,0,10.5 mm 10.0 mm,Heel,True to size,Balanced,Decent,Decent,Good,Warm,Medium,Medium,Moderate,Moderate,Moderate,0,0,36.9 mm 38.0 mm,26.4 mm 28.0 mm,Normal,1,All seasons,1,#235 Bottom 35%,#142 Top 39%,,
+Skechers,GO RUN Max Road 6,"91
+ Superb!",$145,Daily running,Neutral,11.3 oz / 319g 11 oz / 312g,0,8.1 mm 6.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Good,Good,Moderate,Medium,Narrow,Stiff,Moderate,Moderate,Carbon plate,0,39.7 mm 40.0 mm,31.6 mm 34.0 mm,Normal,1,All seasons,1,#21 Top 6%,#145 Top 40%,,
+Skechers,GO RUN Ride 11,"92
+ Superb!",$125,Daily running,Neutral,10.1 oz / 285g 9.7 oz / 275g,0,6.5 mm 6.0 mm,Mid/forefoot,True to size,Soft,Decent,Bad,Good,Moderate,Narrow,Medium,Moderate,Stiff,Moderate,0,1,34.1 mm 38.0 mm,27.6 mm 32.0 mm,Normal,1,All seasons,1,#5 Top 2%,#263 Bottom 27%,,
+Skechers,GO RUN Ride 11,"92
+ Superb!",$125,Daily running,Neutral,10.1 oz / 285g 9.7 oz / 275g,0,6.5 mm 6.0 mm,Mid/forefoot,True to size,Soft,Decent,Bad,Good,Moderate,Narrow,Medium,Moderate,Stiff,Moderate,0,1,34.1 mm 38.0 mm,27.6 mm 32.0 mm,Normal,1,All seasons,1,#5 Top 2%,#263 Bottom 27%,,
+Skechers,GO RUN Ride 11,"92
+ Superb!",$125,Daily running,Neutral,10.1 oz / 285g 9.7 oz / 275g,0,6.5 mm 6.0 mm,Mid/forefoot,True to size,Soft,Decent,Bad,Good,Moderate,Narrow,Medium,Moderate,Stiff,Moderate,0,1,34.1 mm 38.0 mm,27.6 mm 32.0 mm,Normal,1,All seasons,1,#5 Top 2%,#263 Bottom 27%,,
+Skechers,GO RUN Ride 11,"92
+ Superb!",$125,Daily running,Neutral,10.1 oz / 285g 9.7 oz / 275g,0,6.5 mm 6.0 mm,Mid/forefoot,True to size,Soft,Decent,Bad,Good,Moderate,Narrow,Medium,Moderate,Stiff,Moderate,0,1,34.1 mm 38.0 mm,27.6 mm 32.0 mm,Normal,1,All seasons,1,#5 Top 2%,#264 Bottom 28%,,
+Skechers,GO RUN Ride 11,"92
+ Superb!",$125,Daily running,Neutral,10.1 oz / 285g 9.7 oz / 275g,0,6.5 mm 6.0 mm,Mid/forefoot,True to size,Soft,Decent,Bad,Good,Moderate,Narrow,Medium,Moderate,Stiff,Moderate,0,1,34.1 mm 38.0 mm,27.6 mm 32.0 mm,Normal,1,All seasons,1,#5 Top 2%,#264 Bottom 28%,,
+Skechers,GO RUN Ride 11,"92
+ Superb!",$125,Daily running,Neutral,10.1 oz / 285g 9.7 oz / 275g,0,6.5 mm 6.0 mm,Mid/forefoot,True to size,Soft,Decent,Bad,Good,Moderate,Narrow,Medium,Moderate,Stiff,Moderate,0,1,34.1 mm 38.0 mm,27.6 mm 32.0 mm,Normal,1,All seasons,1,#5 Top 2%,#264 Bottom 28%,,
+Skechers,GO RUN Ride 11,"92
+ Superb!",$125,Daily running,Neutral,10.1 oz / 285g 9.7 oz / 275g,0,6.5 mm 6.0 mm,Mid/forefoot,True to size,Soft,Decent,Bad,Good,Moderate,Narrow,Medium,Moderate,Stiff,Moderate,0,1,34.1 mm 38.0 mm,27.6 mm 32.0 mm,Normal,1,All seasons,1,#5 Top 2%,#265 Bottom 27%,,
+Skechers,GO RUN Ride 11,"92
+ Superb!",$125,Daily running,Neutral,10.1 oz / 285g 9.7 oz / 275g,0,6.5 mm 6.0 mm,Mid/forefoot,True to size,Soft,Decent,Bad,Good,Moderate,Narrow,Medium,Moderate,Stiff,Moderate,0,1,34.1 mm 38.0 mm,27.6 mm 32.0 mm,Normal,1,All seasons,1,#5 Top 2%,#264 Bottom 27%,,
+Skechers,GO RUN Ride 11,"92
+ Superb!",$125,Daily running,Neutral,10.1 oz / 285g 9.7 oz / 275g,0,6.5 mm 6.0 mm,Mid/forefoot,True to size,Soft,Decent,Bad,Good,Moderate,Narrow,Medium,Moderate,Stiff,Moderate,0,1,34.1 mm 38.0 mm,27.6 mm 32.0 mm,Normal,1,All seasons,1,#5 Top 2%,#264 Bottom 27%,,
+Skechers,GO RUN Ride 11,92 Superb!,$125,Daily running,Neutral,10.1 oz / 285g 9.7 oz / 275g,0,6.5 mm 6.0 mm,Mid/forefoot,True to size,Soft,Decent,Bad,Good,Moderate,Narrow,Medium,Moderate,Stiff,Moderate,0,1,34.1 mm 38.0 mm,27.6 mm 32.0 mm,Normal,1,All seasons,1,#5 Top 2%,#264 Bottom 27%,,
+Skechers,GOrun Razor Excess,"82
+ Good!",$140,Daily runningTempo,Neutral,7.1 oz / 202g 7.5 oz / 213g,1,6.4 mm 4.0 mm,Mid/forefoot,Slightly large,-,-,-,-,-,Narrow,-,Stiff,Stiff,Moderate,0,1,27.4 mm 30.0 mm,21.0 mm 26.0 mm,NormalWide,0,-,0,#276 Bottom 24%,#351 Bottom 4%,,
+ASICS,GT 1000 10,"88
+ Great!",$100,Daily running,Stability,9.8 oz / 277g 9.9 oz / 281g,0,7.8 mm 9.0 mm,Mid/forefoot,Slightly small,-,-,-,-,-,Medium,-,Stiff,-,Moderate,0,0,31.0 mm,23.2 mm,NormalX-Wide,0,-,0,#276 Top 43%,#588 Bottom 8%,,
+ASICS,GT 1000 11,"85
+ Good!",$100,Daily running,Stability,9.9 oz / 281g 9.5 oz / 270g,0,9.0 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,-,-,-,-,Narrow,-,Stiff,Moderate,Flexible,0,0,31.5 mm 20.0 mm,22.5 mm 12.0 mm,NormalWideX-Wide,1,-,1,#402 Bottom 37%,#479 Bottom 25%,,
+ASICS,GT 1000 12,"84
+ Good!",$100,Daily running,Stability,9.6 oz / 271g 9.5 oz / 269g,0,7.2 mm 8.0 mm,Mid/forefoot,True to size,Soft,Bad,Good,Good,Breathable,Medium,Wide,Moderate,Moderate,Moderate,0,0,30.2 mm 30.0 mm,23.0 mm 22.0 mm,NormalWideX-Wide,1,SummerAll seasons,1,#473 Bottom 26%,#365 Bottom 43%,,
+ASICS,GT 1000 13,"79
+ Good!",$110,Daily running,Stability,9.7 oz / 276g 9.7 oz / 274g,0,8.7 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,33.7 mm 36.0 mm,25.0 mm 28.0 mm,NormalWideX-Wide,1,All seasons,1,#584 Bottom 9%,#258 Top 40%,,
+ASICS,GT 1000 14,"80
+ Good!",$110,Daily running,Stability,9.6 oz / 272g 9.3 oz / 265g,0,9.6 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,Decent,Good,Good,Warm,Medium,Medium,Moderate,Stiff,Moderate,0,0,35.4 mm 34.5 mm,25.8 mm 26.5 mm,NormalWideX-Wide,1,All seasons,1,#304 Bottom 17%,#144 Top 40%,,
+ASICS,GT 1000 14,"80
+ Good!",$110,Daily running,Stability,9.6 oz / 272g 9.3 oz / 265g,0,9.6 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,Decent,Good,Good,Warm,Medium,Medium,Moderate,Stiff,Moderate,0,0,35.4 mm 34.5 mm,25.8 mm 26.5 mm,NormalWideX-Wide,1,All seasons,1,#304 Bottom 16%,#144 Top 40%,,
+ASICS,GT 1000 14,"80
+ Good!",$110,Daily running,Stability,9.6 oz / 272g 9.3 oz / 265g,0,9.6 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,Decent,Good,Good,Warm,Medium,Medium,Moderate,Stiff,Moderate,0,0,35.4 mm 34.5 mm,25.8 mm 26.5 mm,NormalWideX-Wide,1,All seasons,1,#304 Bottom 16%,#144 Top 40%,,
+ASICS,GT 1000 14,"80
+ Good!",$110,Daily running,Stability,9.6 oz / 272g 9.3 oz / 265g,0,9.6 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,Decent,Good,Good,Warm,Medium,Medium,Moderate,Stiff,Moderate,0,0,35.4 mm 34.5 mm,25.8 mm 26.5 mm,NormalWideX-Wide,1,All seasons,1,#305 Bottom 16%,#145 Top 40%,,
+ASICS,GT 1000 9,"88
+ Great!",$100,Daily running,Stability,10.1 oz / 286g 9.8 oz / 278g,0,6.0 mm 10.0 mm,Mid/forefoot,-,-,-,-,-,-,Medium,-,Stiff,Stiff,Stiff,0,0,31.6 mm 31.0 mm,25.6 mm 21.0 mm,Normal,1,-,1,#278 Top 44%,#625 Bottom 3%,,
+ASICS,GT 2000 10,"89
+ Great!",$100,Daily running,Stability,9.9 oz / 281g 9.9 oz / 280g,0,7.5 mm 8.0 mm,Mid/forefoot,True to size,-,-,-,-,-,Medium,-,Stiff,Moderate,Stiff,0,0,31.5 mm 22.0 mm,24.0 mm 14.0 mm,NormalWideX-Wide,1,-,1,#169 Top 27%,#454 Bottom 29%,,
+ASICS,GT 2000 11,"89
+ Great!",$140,Daily running,Stability,9.9 oz / 282g 9.7 oz / 275g,0,6.0 mm 8.0 mm,Mid/forefoot,True to size,Balanced,-,-,-,Moderate,Narrow,-,Stiff,Moderate,Moderate,0,0,30.7 mm 35.0 mm,24.7 mm 27.0 mm,NormalWideX-Wide,1,All seasons,1,#187 Top 29%,#361 Bottom 44%,,
+ASICS,GT 2000 12,"90
+ Superb!",$140,Daily running,Stability,9.7 oz / 275g 9.4 oz / 266g,0,10.0 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,36.6 mm 34.5 mm,26.6 mm 26.5 mm,NarrowNormalWideX-Wide,1,All seasons,1,#80 Top 13%,#191 Top 30%,,
+ASICS,GT 2000 13,"89
+ Great!",$140,Daily running,Stability,9.3 oz / 264g 9.4 oz / 266g,0,9.4 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Good,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,36.6 mm 36.0 mm,27.2 mm 28.0 mm,NormalWideX-Wide,1,All seasons,1,#186 Top 29%,#100 Top 16%,,
+ASICS,GT 2000 14,"89
+ Great!",$140,Daily running,Stability,9.5 oz / 269g 9.4 oz / 266g,0,8.7 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Good,Good,Warm,Medium,Narrow,Moderate,Stiff,Stiff,0,0,36.9 mm 36.5 mm,28.2 mm 28.5 mm,NormalWideX-Wide,1,All seasons,1,#107 Top 30%,#83 Top 23%,,
+ASICS,GT 2000 14,"89
+ Great!",$140,Daily running,Stability,9.5 oz / 269g 9.4 oz / 266g,0,8.7 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Good,Good,Warm,Medium,Narrow,Moderate,Stiff,Stiff,0,0,36.9 mm 36.5 mm,28.2 mm 28.5 mm,NormalWideX-Wide,1,All seasons,1,#108 Top 30%,#83 Top 23%,,
+ASICS,GT 2000 14,"85
+ Good!",$140,Daily running,Stability,9.5 oz / 269g 9.4 oz / 266g,0,8.7 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Good,Good,Warm,Medium,Narrow,Moderate,Stiff,Stiff,0,0,36.9 mm 36.5 mm,28.2 mm 28.5 mm,NormalWideX-Wide,1,All seasons,1,#204 Bottom 44%,#86 Top 24%,,
+ASICS,GT 2000 14,"85
+ Good!",$140,Daily running,Stability,9.5 oz / 269g 9.4 oz / 266g,0,8.7 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Good,Good,Warm,Medium,Narrow,Moderate,Stiff,Stiff,0,0,36.9 mm 36.5 mm,28.2 mm 28.5 mm,NormalWideX-Wide,1,All seasons,1,#204 Bottom 44%,#86 Top 24%,,
+ASICS,GT 2000 14,"85
+ Good!",$140,Daily running,Stability,9.5 oz / 269g 9.4 oz / 266g,0,8.7 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Good,Good,Warm,Medium,Narrow,Moderate,Stiff,Stiff,0,0,36.9 mm 36.5 mm,28.2 mm 28.5 mm,NormalWideX-Wide,1,All seasons,1,#204 Bottom 44%,#86 Top 24%,,
+Saucony,Guide 14,"87
+ Great!",$130,Daily running,Stability,10.8 oz / 306g 10.5 oz / 298g,0,9.4 mm 8.0 mm,HeelMid/forefoot,True to size,-,-,-,-,-,Medium,-,-,Flexible,-,0,0,33.8 mm 32.5 mm,24.4 mm 24.5 mm,NormalWide,1,-,1,#304 Top 48%,#558 Bottom 13%,,
+Saucony,Guide 15,"88
+ Great!",$140,Daily running,Stability,9.8 oz / 279g 9.5 oz / 269g,0,7.1 mm 8.0 mm,Mid/forefoot,True to size,Balanced,-,-,-,-,Narrow,-,Stiff,Moderate,Stiff,0,0,31.6 mm 35.0 mm,24.5 mm 27.0 mm,NormalWide,1,-,1,#218 Top 34%,#442 Bottom 31%,,
+Saucony,Guide 17,"90
+ Superb!",$140,Daily running,Stability,9.7 oz / 275g 9.5 oz / 269g,0,7.0 mm 6.0 mm,Mid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,34.9 mm 35.0 mm,27.9 mm 29.0 mm,NormalWideX-Wide,1,All seasons,1,#96 Top 15%,#154 Top 24%,,
+Saucony,Guide 18,"80
+ Good!",$140,Daily running,Stability,9.8 oz / 278g 9.6 oz / 272g,0,8.3 mm 6.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Decent,Good,Breathable,Medium,Medium,Moderate,Stiff,Flexible,0,0,36.0 mm 35.0 mm,27.7 mm 29.0 mm,NormalWideX-Wide,1,SummerAll seasons,1,#303 Bottom 17%,#57 Top 16%,,
+Saucony,Guide 18,"80
+ Good!",$140,Daily running,Stability,9.8 oz / 278g 9.6 oz / 272g,0,8.3 mm 6.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Decent,Good,Breathable,Medium,Medium,Moderate,Stiff,Flexible,0,0,36.0 mm 35.0 mm,27.7 mm 29.0 mm,NormalWideX-Wide,1,SummerAll seasons,1,#302 Bottom 17%,#57 Top 16%,,
+Saucony,Guide 18,"80
+ Good!",$140,Daily running,Stability,9.8 oz / 278g 9.6 oz / 272g,0,8.3 mm 6.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Decent,Good,Breathable,Medium,Medium,Moderate,Stiff,Flexible,0,0,36.0 mm 35.0 mm,27.7 mm 29.0 mm,NormalWideX-Wide,1,SummerAll seasons,1,#302 Bottom 17%,#57 Top 16%,,
+Under Armour,HOVR Phantom 3,"77
+ Decent!",$140,Daily running,Neutral,11.9 oz / 338g 11.1 oz / 315g,0,12.7 mm 8.0 mm,Heel,Slightly small,Balanced,Good,Decent,-,Warm,Medium,Medium,Moderate,Stiff,Stiff,0,0,32.8 mm 25.0 mm,20.1 mm 17.0 mm,Normal,0,Winter,0,#335 Bottom 8%,#238 Bottom 34%,,
+Under Armour,HOVR Sonic 6,"82
+ Good!",$100,Daily running,Neutral,10.1 oz / 286g 9.9 oz / 281g,0,7.0 mm 8.0 mm,Mid/forefoot,Half size small,Balanced,Decent,Decent,Good,Warm,Medium,Medium,Flexible,Stiff,Moderate,0,0,30.2 mm 32.0 mm,23.2 mm 24.0 mm,NormalWide,1,All seasons,1,#282 Bottom 23%,#253 Bottom 31%,,
+Saucony,Hurricane 24,"87
+ Great!",$160,Daily running,Stability,11.1 oz / 315g 11.2 oz / 317g,0,6.3 mm 6.0 mm,Mid/forefoot,Slightly small,Soft,Bad,Decent,Decent,Breathable,Medium,Medium,Stiff,Stiff,Moderate,0,0,40.5 mm 42.0 mm,34.2 mm 36.0 mm,NormalWide,1,SummerAll seasons,1,#319 Top 50%,#158 Top 25%,,
+Saucony,Hurricane 25,"76
+ Decent!",$170,Daily running,Stability,10.1 oz / 286g 10 oz / 283g,0,7.1 mm 6.0 mm,Mid/forefoot,-,Soft,Decent,Decent,Good,Warm,Medium,Medium,Moderate,Stiff,Stiff,0,0,40.2 mm 38.0 mm,33.1 mm 32.0 mm,NormalWide,1,All seasons,1,#341 Bottom 6%,#120 Top 33%,,
+Brooks,Hyperion,"88
+ Great!",$130,Tempo,Neutral,7.4 oz / 211g 7.6 oz / 215g,1,12.3 mm 8.0 mm,Heel,True to size,Balanced,Bad,Decent,Good,Moderate,Narrow,Wide,Moderate,Flexible,Moderate,0,0,30.0 mm 22.0 mm,17.7 mm 14.0 mm,Normal,1,All seasons,1,#263 Top 41%,#143 Top 23%,,
+Brooks,Hyperion 2,"88
+ Great!",$140,CompetitionTempo,Neutral,7.2 oz / 203g 7.1 oz / 201g,1,9.8 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Decent,Breathable,Medium,Medium,Moderate,Moderate,Moderate,0,0,32.2 mm 31.5 mm,22.4 mm 23.5 mm,Normal,1,SummerAll seasons,1,#127 Top 35%,#115 Top 32%,,
+Brooks,Hyperion 2,"88
+ Great!",$140,CompetitionTempo,Neutral,7.2 oz / 203g 7.1 oz / 201g,1,9.8 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Decent,Breathable,Medium,Medium,Moderate,Moderate,Moderate,0,0,32.2 mm 31.5 mm,22.4 mm 23.5 mm,Normal,1,SummerAll seasons,1,#127 Top 35%,#115 Top 32%,,
+Brooks,Hyperion 2,"88
+ Great!",$140,CompetitionTempo,Neutral,7.2 oz / 203g 7.1 oz / 201g,1,9.8 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Decent,Breathable,Medium,Medium,Moderate,Moderate,Moderate,0,0,32.2 mm 31.5 mm,22.4 mm 23.5 mm,Normal,1,SummerAll seasons,1,#126 Top 35%,#115 Top 32%,,
+Brooks,Hyperion 2,"88
+ Great!",$140,CompetitionTempo,Neutral,7.2 oz / 203g 7.1 oz / 201g,1,9.8 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Decent,Breathable,Medium,Medium,Moderate,Moderate,Moderate,0,0,32.2 mm 31.5 mm,22.4 mm 23.5 mm,Normal,1,SummerAll seasons,1,#126 Top 35%,#115 Top 32%,,
+Brooks,Hyperion 2,"88
+ Great!",$140,CompetitionTempo,Neutral,7.2 oz / 203g 7.1 oz / 201g,1,9.8 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Decent,Breathable,Medium,Medium,Moderate,Moderate,Moderate,0,0,32.2 mm 31.5 mm,22.4 mm 23.5 mm,Normal,1,SummerAll seasons,1,#126 Top 35%,#115 Top 32%,,
+Brooks,Hyperion 2,"88
+ Great!",$140,CompetitionTempo,Neutral,7.2 oz / 203g 7.1 oz / 201g,1,9.8 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Decent,Breathable,Medium,Medium,Moderate,Moderate,Moderate,0,0,32.2 mm 31.5 mm,22.4 mm 23.5 mm,Normal,1,SummerAll seasons,1,#126 Top 35%,#115 Top 32%,,
+Brooks,Hyperion Elite 4,"87
+ Great!",$250,CompetitionTempo,Neutral,7.8 oz / 220g 7.8 oz / 221g,1,11.8 mm 8.0 mm,Heel,True to size,Balanced,Decent,Good,Good,Breathable,Narrow,Medium,Stiff,Stiff,Flexible,Carbon plate,1,39.1 mm 40.0 mm,27.3 mm 32.0 mm,Normal,1,SummerAll seasons,1,#290 Top 45%,#293 Top 46%,,
+Brooks,Hyperion Elite 4 PB,"89
+ Great!",$250,Competition,Neutral,6.9 oz / 197g 7.3 oz / 207g,1,11.7 mm 8.0 mm,Heel,True to size,Soft,Decent,Good,Decent,Breathable,Narrow,Medium,Stiff,Stiff,Flexible,Carbon plate,0,38.5 mm 40.0 mm,26.8 mm 32.0 mm,Normal,1,SummerAll seasons,1,#83 Top 23%,#237 Bottom 35%,,
+Brooks,Hyperion Elite 5,"91
+ Superb!",$275,Competition,Neutral,7.2 oz / 204g 7.1 oz / 201g,1,11.2 mm 8.0 mm,Heel,-,Soft,Decent,Good,Bad,Breathable,Narrow,Medium,Stiff,Stiff,Flexible,Carbon plate,1,37.6 mm 40.0 mm,26.4 mm 32.0 mm,Normal,1,SummerAll seasons,1,#28 Top 8%,#111 Top 31%,,
+Brooks,Hyperion Elite 5,"91
+ Superb!",$275,Competition,Neutral,7.2 oz / 204g 7.1 oz / 201g,1,11.2 mm 8.0 mm,Heel,-,Soft,Decent,Good,Bad,Breathable,Narrow,Medium,Stiff,Stiff,Flexible,Carbon plate,1,37.6 mm 40.0 mm,26.4 mm 32.0 mm,Normal,1,SummerAll seasons,1,#28 Top 8%,#111 Top 31%,,
+Brooks,Hyperion Elite 5,"91
+ Superb!",$275,Competition,Neutral,7.2 oz / 204g 7.1 oz / 201g,1,11.2 mm 8.0 mm,Heel,-,Soft,Decent,Good,Bad,Breathable,Narrow,Medium,Stiff,Stiff,Flexible,Carbon plate,1,37.6 mm 40.0 mm,26.4 mm 32.0 mm,Normal,1,SummerAll seasons,1,#28 Top 8%,#111 Top 31%,,
+Brooks,Hyperion GTS,"90
+ Superb!",$150,Tempo,Stability,8 oz / 228g 8 oz / 228g,1,9.9 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Good,Good,Breathable,Narrow,Medium,Moderate,Moderate,Moderate,0,0,28.6 mm 28.0 mm,18.7 mm 22.0 mm,Normal,1,SummerAll seasons,1,#64 Top 10%,#346 Bottom 46%,,
+Brooks,Hyperion GTS 2,"89
+ Great!",$140,Daily runningTempo,Stability,7.8 oz / 220g 7.6 oz / 215g,1,10.7 mm 8.0 mm,Heel,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Moderate,0,0,33.0 mm 34.0 mm,22.3 mm 26.0 mm,Normal,1,All seasons,1,#103 Top 29%,#177 Top 49%,,
+Brooks,Hyperion Max 2,"88
+ Great!",$180,Tempo,Neutral,9.2 oz / 262g 9.2 oz / 261g,0,6.8 mm 6.0 mm,Mid/forefoot,True to size,Balanced,Decent,Decent,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,1,33.2 mm 37.0 mm,26.4 mm 31.0 mm,Normal,1,All seasons,1,#250 Top 39%,#102 Top 16%,,
+Brooks,Hyperion Max 3,"80
+ Good!",$200,Daily runningTempo,Neutral,10 oz / 283g 9.9 oz / 281g,0,10.6 mm 6.0 mm,Heel,True to size,Soft,Good,Decent,Good,Moderate,Medium,Medium,Moderate,Stiff,Moderate,0,1,45.6 mm 46.0 mm,35.0 mm 40.0 mm,Normal,1,All seasons,1,#308 Bottom 15%,#43 Top 12%,,
+Brooks,Hyperion Max 3,"80
+ Good!",$200,Daily runningTempo,Neutral,10 oz / 283g 9.9 oz / 281g,0,10.6 mm 6.0 mm,Heel,True to size,Soft,Good,Decent,Good,Moderate,Medium,Medium,Moderate,Stiff,Moderate,0,1,45.6 mm 46.0 mm,35.0 mm 40.0 mm,Normal,1,All seasons,1,#308 Bottom 15%,#43 Top 12%,,
+Brooks,Hyperion Tempo,"89
+ Great!",$150,Tempo,Neutral,7 oz / 198g 7 oz / 199g,1,9.0 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Decent,Good,Moderate,Medium,Medium,Flexible,Flexible,Moderate,0,0,28.7 mm 28.0 mm,19.7 mm 20.0 mm,Normal,1,All seasons,1,#94 Top 26%,#171 Top 47%,,
+Brooks,Hyperion Tempo,"89
+ Great!",$150,Tempo,Neutral,7 oz / 198g 7 oz / 199g,1,9.0 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Decent,Good,Moderate,Medium,Medium,Flexible,Flexible,Moderate,0,0,28.7 mm 28.0 mm,19.7 mm 20.0 mm,Normal,1,All seasons,1,#93 Top 26%,#171 Top 47%,,
+Brooks,Hyperion Tempo,"89
+ Great!",$150,Tempo,Neutral,7 oz / 198g 7 oz / 199g,1,9.0 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Decent,Good,Moderate,Medium,Medium,Flexible,Flexible,Moderate,0,0,28.7 mm 28.0 mm,19.7 mm 20.0 mm,Normal,1,All seasons,1,#93 Top 26%,#170 Top 47%,,
+Brooks,Hyperion Tempo,"89
+ Great!",$150,Tempo,Neutral,7 oz / 198g 7 oz / 199g,1,9.0 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Decent,Good,Moderate,Medium,Medium,Flexible,Flexible,Moderate,0,0,28.7 mm 28.0 mm,19.7 mm 20.0 mm,Normal,1,All seasons,1,#93 Top 26%,#171 Top 47%,,
+Brooks,Hyperion Tempo,"89
+ Great!",$150,Tempo,Neutral,7 oz / 198g 7 oz / 199g,1,9.0 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Decent,Good,Moderate,Medium,Medium,Flexible,Flexible,Moderate,0,0,28.7 mm 28.0 mm,19.7 mm 20.0 mm,Normal,1,All seasons,1,#93 Top 26%,#171 Top 47%,,
+Brooks,Hyperion Tempo,"89
+ Great!",$150,Tempo,Neutral,7 oz / 198g 7 oz / 199g,1,9.0 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Decent,Good,Moderate,Medium,Medium,Flexible,Flexible,Moderate,0,0,28.7 mm 28.0 mm,19.7 mm 20.0 mm,Normal,1,All seasons,1,#93 Top 26%,#171 Top 47%,,
+Brooks,Hyperion Tempo,"89
+ Great!",$150,Tempo,Neutral,7 oz / 198g 7 oz / 199g,1,9.0 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Decent,Good,Moderate,Medium,Medium,Flexible,Flexible,Moderate,0,0,28.7 mm 28.0 mm,19.7 mm 20.0 mm,Normal,1,All seasons,1,#94 Top 26%,#172 Top 47%,,
+Brooks,Hyperion Tempo,"89
+ Great!",$150,Tempo,Neutral,7 oz / 198g 7 oz / 199g,1,9.0 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Decent,Good,Moderate,Medium,Medium,Flexible,Flexible,Moderate,0,0,28.7 mm 28.0 mm,19.7 mm 20.0 mm,Normal,1,All seasons,1,#93 Top 26%,#171 Top 47%,,
+Brooks,Hyperion Tempo,"89
+ Great!",$150,Tempo,Neutral,7 oz / 198g 7 oz / 199g,1,9.0 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Decent,Good,Moderate,Medium,Medium,Flexible,Flexible,Moderate,0,0,28.7 mm 28.0 mm,19.7 mm 20.0 mm,Normal,1,All seasons,1,#93 Top 26%,#171 Top 47%,,
+Brooks,Hyperion Tempo,"89
+ Great!",$150,Tempo,Neutral,7 oz / 198g 7 oz / 199g,1,9.0 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Decent,Good,Moderate,Medium,Medium,Flexible,Flexible,Moderate,0,0,28.7 mm 28.0 mm,19.7 mm 20.0 mm,Normal,1,All seasons,1,#95 Top 26%,#171 Top 47%,,
+Brooks,Hyperion Tempo,"89
+ Great!",$150,Tempo,Neutral,7 oz / 198g 7 oz / 199g,1,9.0 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Decent,Good,Moderate,Medium,Medium,Flexible,Flexible,Moderate,0,0,28.7 mm 28.0 mm,19.7 mm 20.0 mm,Normal,1,All seasons,1,#95 Top 26%,#171 Top 47%,,
+Brooks,Hyperion Tempo,"89
+ Great!",$150,Tempo,Neutral,7 oz / 198g 7 oz / 199g,1,9.0 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Decent,Good,Moderate,Medium,Medium,Flexible,Flexible,Moderate,0,0,28.7 mm 28.0 mm,19.7 mm 20.0 mm,Normal,1,All seasons,1,#93 Top 26%,#171 Top 47%,,
+Brooks,Hyperion Tempo,"89
+ Great!",$150,Tempo,Neutral,7 oz / 198g 7 oz / 199g,1,9.0 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Decent,Good,Moderate,Medium,Medium,Flexible,Flexible,Moderate,0,0,28.7 mm 28.0 mm,19.7 mm 20.0 mm,Normal,1,All seasons,1,#93 Top 26%,#171 Top 47%,,
+Brooks,Hyperion Tempo,"89
+ Great!",$150,Tempo,Neutral,7 oz / 198g 7 oz / 199g,1,9.0 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Decent,Good,Moderate,Medium,Medium,Flexible,Flexible,Moderate,0,0,28.7 mm 28.0 mm,19.7 mm 20.0 mm,Normal,1,All seasons,1,#93 Top 26%,#171 Top 47%,,
+Brooks,Hyperion Tempo,"89
+ Great!",$150,Tempo,Neutral,7 oz / 198g 7 oz / 199g,1,9.0 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Decent,Good,Moderate,Medium,Medium,Flexible,Flexible,Moderate,0,0,28.7 mm 28.0 mm,19.7 mm 20.0 mm,Normal,1,All seasons,1,#95 Top 26%,#171 Top 47%,,
+Brooks,Hyperion Tempo,"89
+ Great!",$150,Tempo,Neutral,7 oz / 198g 7 oz / 199g,1,9.0 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Decent,Good,Moderate,Medium,Medium,Flexible,Flexible,Moderate,0,0,28.7 mm 28.0 mm,19.7 mm 20.0 mm,Normal,1,All seasons,1,#95 Top 26%,#171 Top 47%,,
+Brooks,Hyperion Tempo,"89
+ Great!",$150,Tempo,Neutral,7 oz / 198g 7 oz / 199g,1,9.0 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Decent,Good,Moderate,Medium,Medium,Flexible,Flexible,Moderate,0,0,28.7 mm 28.0 mm,19.7 mm 20.0 mm,Normal,1,All seasons,1,#95 Top 26%,#171 Top 47%,,
+Under Armour,Infinite Elite,"88
+ Great!",$160,Daily running,Neutral,11.1 oz / 315g 11.6 oz / 329g,0,8.1 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Good,Decent,Good,Warm,Medium,Medium,Stiff,Stiff,Stiff,0,1,39.9 mm 40.0 mm,31.8 mm 32.0 mm,Normal,1,All seasons,1,#236 Top 37%,#513 Bottom 20%,,
+Under Armour,Infinite Elite 2,"90
+ Superb!",$160,Daily running,Neutral,10.2 oz / 288g 10.2 oz / 290g,0,7.0 mm 8.0 mm,Mid/forefoot,Half size small,Soft,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,0,40.1 mm 36.0 mm,33.1 mm 28.0 mm,Normal,1,All seasons,1,#62 Top 17%,#251 Bottom 31%,,
+Under Armour,Infinite Elite 2,"90
+ Superb!",$160,Daily running,Neutral,10.2 oz / 288g 10.2 oz / 290g,0,7.0 mm 8.0 mm,Mid/forefoot,Half size small,Soft,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,0,40.1 mm 36.0 mm,33.1 mm 28.0 mm,Normal,1,All seasons,1,#62 Top 17%,#251 Bottom 31%,,
+Under Armour,Infinite Elite 2,"90
+ Superb!",$160,Daily running,Neutral,10.2 oz / 288g 10.2 oz / 290g,0,7.0 mm 8.0 mm,Mid/forefoot,Half size small,Soft,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,0,40.1 mm 36.0 mm,33.1 mm 28.0 mm,Normal,1,All seasons,1,#62 Top 17%,#251 Bottom 31%,,
+Under Armour,Infinite Pro,"85
+ Good!",$130,Daily running,Neutral,10.8 oz / 305g 10.8 oz / 306g,0,8.3 mm 8.0 mm,HeelMid/forefoot,-,Soft,Decent,Decent,Good,Moderate,Medium,Medium,Stiff,Stiff,Stiff,0,0,41.3 mm,33.0 mm,Normal,1,All seasons,1,#215 Bottom 41%,#276 Bottom 24%,,
+Nike,InfinityRN 4,"85
+ Good!",$160,Daily running,Neutral,11.1 oz / 316g 11.1 oz / 316g,0,9.8 mm 9.0 mm,HeelMid/forefoot,Half size small,Soft,Good,Decent,Good,Moderate,Medium,Medium,Moderate,Moderate,Flexible,0,0,34.2 mm 39.0 mm,24.4 mm 30.0 mm,NormalWideX-Wide,1,All seasons,1,#197 Bottom 46%,#191 Bottom 47%,,
+Nike,InfinityRN 4,"85
+ Good!",$160,Daily running,Neutral,11.1 oz / 316g 11.1 oz / 316g,0,9.8 mm 9.0 mm,HeelMid/forefoot,Half size small,Soft,Good,Decent,Good,Moderate,Medium,Medium,Moderate,Moderate,Flexible,0,0,34.2 mm 39.0 mm,24.4 mm 30.0 mm,NormalWideX-Wide,1,All seasons,1,#197 Bottom 46%,#191 Bottom 47%,,
+Nike,InfinityRN 4,"85
+ Good!",$160,Daily running,Neutral,11.1 oz / 316g 11.1 oz / 316g,0,9.8 mm 9.0 mm,HeelMid/forefoot,Half size small,Soft,Good,Decent,Good,Moderate,Medium,Medium,Moderate,Moderate,Flexible,0,0,34.2 mm 39.0 mm,24.4 mm 30.0 mm,NormalWideX-Wide,1,All seasons,1,#197 Bottom 46%,#191 Bottom 47%,,
+Nike,InfinityRN 4,"85
+ Good!",$160,Daily running,Neutral,11.1 oz / 316g 11.1 oz / 316g,0,9.8 mm 9.0 mm,HeelMid/forefoot,Half size small,Soft,Good,Decent,Good,Moderate,Medium,Medium,Moderate,Moderate,Flexible,0,0,34.2 mm 39.0 mm,24.4 mm 30.0 mm,NormalWideX-Wide,1,All seasons,1,#199 Bottom 45%,#193 Bottom 47%,,
+Nike,InfinityRN 4,"85
+ Good!",$160,Daily running,Neutral,11.1 oz / 316g 11.1 oz / 316g,0,9.8 mm 9.0 mm,HeelMid/forefoot,Half size small,Soft,Good,Decent,Good,Moderate,Medium,Medium,Moderate,Moderate,Flexible,0,0,34.2 mm 39.0 mm,24.4 mm 30.0 mm,NormalWideX-Wide,1,All seasons,1,#199 Bottom 45%,#193 Bottom 47%,,
+Nike,InfinityRN 4,"85
+ Good!",$160,Daily running,Neutral,11.1 oz / 316g 11.1 oz / 316g,0,9.8 mm 9.0 mm,HeelMid/forefoot,Half size small,Soft,Good,Decent,Good,Moderate,Medium,Medium,Moderate,Moderate,Flexible,0,0,34.2 mm 39.0 mm,24.4 mm 30.0 mm,NormalWideX-Wide,1,All seasons,1,#199 Bottom 45%,#193 Bottom 47%,,
+Nike,InfinityRN 4,"85
+ Good!",$160,Daily running,Neutral,11.1 oz / 316g 11.1 oz / 316g,0,9.8 mm 9.0 mm,HeelMid/forefoot,Half size small,Soft,Good,Decent,Good,Moderate,Medium,Medium,Moderate,Moderate,Flexible,0,0,34.2 mm 39.0 mm,24.4 mm 30.0 mm,NormalWideX-Wide,1,All seasons,1,#199 Bottom 45%,#193 Bottom 47%,,
+Nike,InfinityRN 4,"85
+ Good!",$160,Daily running,Neutral,11.1 oz / 316g 11.1 oz / 316g,0,9.8 mm 9.0 mm,HeelMid/forefoot,Half size small,Soft,Good,Decent,Good,Moderate,Medium,Medium,Moderate,Moderate,Flexible,0,0,34.2 mm 39.0 mm,24.4 mm 30.0 mm,NormalWideX-Wide,1,All seasons,1,#199 Bottom 45%,#193 Bottom 47%,,
+Nike,Interact Run,"88
+ Great!",$85,Daily running,Neutral,8.5 oz / 241g 9.2 oz / 260g,1,9.3 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Good,Decent,Breathable,Medium,Medium,Moderate,Moderate,Moderate,0,0,29.7 mm 30.0 mm,20.4 mm 20.0 mm,Normal,1,SummerAll seasons,1,#122 Top 34%,#114 Top 32%,,
+Nike,Interact Run,"88
+ Great!",$85,Daily running,Neutral,8.5 oz / 241g 9.2 oz / 260g,1,9.3 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Good,Decent,Breathable,Medium,Medium,Moderate,Moderate,Moderate,0,0,29.7 mm 30.0 mm,20.4 mm 20.0 mm,Normal,1,SummerAll seasons,1,#109 Top 30%,#114 Top 32%,,
+Nike,Interact Run,"88
+ Great!",$85,Daily running,Neutral,8.5 oz / 241g 9.2 oz / 260g,1,9.3 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Good,Decent,Breathable,Medium,Medium,Moderate,Moderate,Moderate,0,0,29.7 mm 30.0 mm,20.4 mm 20.0 mm,Normal,1,SummerAll seasons,1,#109 Top 30%,#114 Top 32%,,
+Nike,Interact Run,"88
+ Great!",$85,Daily running,Neutral,8.5 oz / 241g 9.2 oz / 260g,1,9.3 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Good,Decent,Breathable,Medium,Medium,Moderate,Moderate,Moderate,0,0,29.7 mm 30.0 mm,20.4 mm 20.0 mm,Normal,1,SummerAll seasons,1,#109 Top 30%,#114 Top 32%,,
+Nike,Invincible 3,"81
+ Good!",$180,Daily running,Neutral,10 oz / 284g 10 oz / 284g,0,9.6 mm 9.0 mm,HeelMid/forefoot,True to size,Soft,-,-,-,Moderate,Narrow,-,Moderate,Stiff,Moderate,0,0,35.2 mm 40.0 mm,25.6 mm 31.0 mm,NormalWideX-Wide,1,All seasons,1,#293 Bottom 19%,#30 Top 9%,,
+Nike,Invincible 3,"81
+ Good!",$180,Daily running,Neutral,10 oz / 284g 10 oz / 284g,0,9.6 mm 9.0 mm,HeelMid/forefoot,True to size,Soft,-,-,-,Moderate,Narrow,-,Moderate,Stiff,Moderate,0,0,35.2 mm 40.0 mm,25.6 mm 31.0 mm,NormalWideX-Wide,1,All seasons,1,#293 Bottom 19%,#30 Top 9%,,
+ASICS,Jolt 4,"79
+ Good!",$60,Daily running,Neutral,9.1 oz / 259g 9.5 oz / 268g,0,9.4 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Bad,Decent,Good,Breathable,Medium,Wide,Moderate,Stiff,Stiff,0,0,31.6 mm,22.2 mm,NormalX-Wide,1,SummerAll seasons,1,#314 Bottom 14%,#324 Bottom 11%,,
+ASICS,Jolt 4,"79
+ Good!",$60,Daily running,Neutral,9.1 oz / 259g 9.5 oz / 268g,0,9.4 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Bad,Decent,Good,Breathable,Medium,Wide,Moderate,Stiff,Stiff,0,0,31.6 mm,22.2 mm,NormalX-Wide,1,SummerAll seasons,1,#313 Bottom 14%,#324 Bottom 11%,,
+ASICS,Jolt 4,"79
+ Good!",$60,Daily running,Neutral,9.1 oz / 259g 9.5 oz / 268g,0,9.4 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Bad,Decent,Good,Breathable,Medium,Wide,Moderate,Stiff,Stiff,0,0,31.6 mm,22.2 mm,NormalX-Wide,1,SummerAll seasons,1,#313 Bottom 14%,#324 Bottom 11%,,
+ASICS,Jolt 4,"79
+ Good!",$60,Daily running,Neutral,9.1 oz / 259g 9.5 oz / 268g,0,9.4 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Bad,Decent,Good,Breathable,Medium,Wide,Moderate,Stiff,Stiff,0,0,31.6 mm,22.2 mm,NormalX-Wide,1,SummerAll seasons,1,#313 Bottom 14%,#324 Bottom 11%,,
+ASICS,Jolt 4,"79
+ Good!",$60,Daily running,Neutral,9.1 oz / 259g 9.5 oz / 268g,0,9.4 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Bad,Decent,Good,Breathable,Medium,Wide,Moderate,Stiff,Stiff,0,0,31.6 mm,22.2 mm,NormalX-Wide,1,SummerAll seasons,1,#313 Bottom 14%,#324 Bottom 11%,,
+NOBULL,Journey,"88
+ Great!",$179,Daily running,Neutral,9.4 oz / 266g 7.6 oz / 215g,0,9.4 mm 10.0 mm,HeelMid/forefoot,-,Soft,-,-,-,Breathable,Narrow,-,Stiff,Moderate,Flexible,0,0,35.2 mm 35.0 mm,25.8 mm 25.0 mm,Normal,1,SummerAll seasons,1,#129 Top 36%,#340 Bottom 6%,,
+NOBULL,Journey,"88
+ Great!",$179,Daily running,Neutral,9.4 oz / 266g 7.6 oz / 215g,0,9.4 mm 10.0 mm,HeelMid/forefoot,-,Soft,-,-,-,Breathable,Narrow,-,Stiff,Moderate,Flexible,0,0,35.2 mm 35.0 mm,25.8 mm 25.0 mm,Normal,1,SummerAll seasons,1,#129 Top 36%,#341 Bottom 6%,,
+NOBULL,Journey,"88
+ Great!",$179,Daily running,Neutral,9.4 oz / 266g 7.6 oz / 215g,0,9.4 mm 10.0 mm,HeelMid/forefoot,-,Soft,-,-,-,Breathable,Narrow,-,Stiff,Moderate,Flexible,0,0,35.2 mm 35.0 mm,25.8 mm 25.0 mm,Normal,1,SummerAll seasons,1,#132 Top 37%,#342 Bottom 6%,,
+NOBULL,Journey,"88
+ Great!",$179,Daily running,Neutral,9.4 oz / 266g 7.6 oz / 215g,0,9.4 mm 10.0 mm,HeelMid/forefoot,-,Soft,-,-,-,Breathable,Narrow,-,Stiff,Moderate,Flexible,0,0,35.2 mm 35.0 mm,25.8 mm 25.0 mm,Normal,1,SummerAll seasons,1,#128 Top 35%,#341 Bottom 6%,,
+Nike,Journey Run,"81
+ Good!",$90,Daily running,Neutral,10.5 oz / 298g 10.8 oz / 305g,0,8.6 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Decent,Decent,Moderate,Medium,Narrow,Moderate,Stiff,Stiff,0,0,33.0 mm 34.0 mm,24.4 mm 24.0 mm,Normal,1,All seasons,1,#291 Bottom 20%,#106 Top 29%,,
+Nike,Journey Run,"81
+ Good!",$90,Daily running,Neutral,10.5 oz / 298g 10.8 oz / 305g,0,8.6 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Decent,Decent,Moderate,Medium,Narrow,Moderate,Stiff,Stiff,0,0,33.0 mm 34.0 mm,24.4 mm 24.0 mm,Normal,1,All seasons,1,#290 Bottom 20%,#106 Top 29%,,
+Nike,Journey Run,"81
+ Good!",$90,Daily running,Neutral,10.5 oz / 298g 10.8 oz / 305g,0,8.6 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Decent,Decent,Moderate,Medium,Narrow,Moderate,Stiff,Stiff,0,0,33.0 mm 34.0 mm,24.4 mm 24.0 mm,Normal,1,All seasons,1,#290 Bottom 20%,#106 Top 29%,,
+Hoka,Kawana 2,"85
+ Good!",$140,Daily running,Neutral,10.5 oz / 298g 11.1 oz / 314g,0,5.2 mm 5.0 mm,Mid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Narrow,Narrow,Stiff,Moderate,Stiff,0,0,33.2 mm 30.0 mm,28.0 mm 25.0 mm,Normal,1,All seasons,1,#224 Bottom 38%,#56 Top 16%,,
+Hoka,Kawana 2,"85
+ Good!",$140,Daily running,Neutral,10.5 oz / 298g 11.1 oz / 314g,0,5.2 mm 5.0 mm,Mid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Narrow,Narrow,Stiff,Moderate,Stiff,0,0,33.2 mm 30.0 mm,28.0 mm 25.0 mm,Normal,1,All seasons,1,#224 Bottom 38%,#56 Top 16%,,
+Hoka,Kawana 2,"85
+ Good!",$140,Daily running,Neutral,10.5 oz / 298g 11.1 oz / 314g,0,5.2 mm 5.0 mm,Mid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Narrow,Narrow,Stiff,Moderate,Stiff,0,0,33.2 mm 30.0 mm,28.0 mm 25.0 mm,Normal,1,All seasons,1,#224 Bottom 38%,#56 Top 16%,,
+Hoka,Kawana 2,"85
+ Good!",$140,Daily running,Neutral,10.5 oz / 298g 11.1 oz / 314g,0,5.2 mm 5.0 mm,Mid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Narrow,Narrow,Stiff,Moderate,Stiff,0,0,33.2 mm 30.0 mm,28.0 mm 25.0 mm,Normal,1,All seasons,1,#225 Bottom 38%,#55 Top 15%,,
+Hoka,Kawana 2,"85
+ Good!",$140,Daily running,Neutral,10.5 oz / 298g 11.1 oz / 314g,0,5.2 mm 5.0 mm,Mid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Narrow,Narrow,Stiff,Moderate,Stiff,0,0,33.2 mm 30.0 mm,28.0 mm 25.0 mm,Normal,1,All seasons,1,#224 Bottom 38%,#55 Top 16%,,
+Hoka,Kawana 2,"85
+ Good!",$140,Daily running,Neutral,10.5 oz / 298g 11.1 oz / 314g,0,5.2 mm 5.0 mm,Mid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Narrow,Narrow,Stiff,Moderate,Stiff,0,0,33.2 mm 30.0 mm,28.0 mm 25.0 mm,Normal,1,All seasons,1,#225 Bottom 38%,#55 Top 16%,,
+Hoka,Kawana 2,"85
+ Good!",$140,Daily running,Neutral,10.5 oz / 298g 11.1 oz / 314g,0,5.2 mm 5.0 mm,Mid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Narrow,Narrow,Stiff,Moderate,Stiff,0,0,33.2 mm 30.0 mm,28.0 mm 25.0 mm,Normal,1,All seasons,1,#224 Bottom 38%,#55 Top 16%,,
+Hoka,Kawana 2,"85
+ Good!",$140,Daily running,Neutral,10.5 oz / 298g 11.1 oz / 314g,0,5.2 mm 5.0 mm,Mid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Narrow,Narrow,Stiff,Moderate,Stiff,0,0,33.2 mm 30.0 mm,28.0 mm 25.0 mm,Normal,1,All seasons,1,#224 Bottom 38%,#55 Top 16%,,
+Hoka,Kawana 2,"85
+ Good!",$140,Daily running,Neutral,10.5 oz / 298g 11.1 oz / 314g,0,5.2 mm 5.0 mm,Mid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Narrow,Narrow,Stiff,Moderate,Stiff,0,0,33.2 mm 30.0 mm,28.0 mm 25.0 mm,Normal,1,All seasons,1,#224 Bottom 38%,#55 Top 16%,,
+Saucony,Kinvara 12,"90
+ Superb!",$110,Tempo,Neutral,7.7 oz / 218g 7.5 oz / 213g,1,4.6 mm 4.0 mm,Mid/forefoot,True to size,-,-,-,-,-,Medium,-,-,Flexible,-,0,0,26.1 mm 28.5 mm,21.5 mm 24.5 mm,Normal,1,-,1,#114 Top 18%,#540 Bottom 16%,,
+Saucony,Kinvara 13,"89
+ Great!",$120,Tempo,Neutral,7.2 oz / 204g 7.2 oz / 204g,1,4.5 mm 4.0 mm,Mid/forefoot,Slightly small,Balanced,-,-,-,-,Narrow,-,Moderate,Flexible,Moderate,0,0,26.9 mm 28.5 mm,22.4 mm 24.5 mm,NormalWide,1,-,1,#162 Top 26%,#439 Bottom 31%,,
+Saucony,Kinvara 14,"83
+ Good!",$120,Tempo,Neutral,6.8 oz / 194g 6.8 oz / 194g,1,4.1 mm 4.0 mm,Mid/forefoot,True to size,Balanced,Bad,-,-,Breathable,Medium,Narrow,Flexible,Flexible,Moderate,0,0,30.3 mm 31.0 mm,26.2 mm 27.0 mm,NormalWide,1,SummerAll seasons,1,#512 Bottom 20%,#358 Bottom 44%,,
+Saucony,Kinvara 15,"84
+ Good!",$120,Daily runningTempo,Neutral,6.8 oz / 194g 6.7 oz / 190g,1,4.4 mm 4.0 mm,Mid/forefoot,Slightly small,Balanced,Bad,Good,Good,Breathable,Medium,Medium,Flexible,Flexible,Flexible,0,0,27.9 mm 30.0 mm,23.5 mm 26.0 mm,NormalWide,1,SummerAll seasons,1,#243 Bottom 33%,#149 Top 41%,,
+Saucony,Kinvara 15,"84
+ Good!",$120,Daily runningTempo,Neutral,6.8 oz / 194g 6.7 oz / 190g,1,4.4 mm 4.0 mm,Mid/forefoot,Slightly small,Balanced,Bad,Good,Good,Breathable,Medium,Medium,Flexible,Flexible,Flexible,0,0,27.9 mm 30.0 mm,23.5 mm 26.0 mm,NormalWide,1,SummerAll seasons,1,#244 Bottom 33%,#149 Top 41%,,
+Saucony,Kinvara 15,"84
+ Good!",$120,Daily runningTempo,Neutral,6.8 oz / 194g 6.7 oz / 190g,1,4.4 mm 4.0 mm,Mid/forefoot,Slightly small,Balanced,Bad,Good,Good,Breathable,Medium,Medium,Flexible,Flexible,Flexible,0,0,27.9 mm 30.0 mm,23.5 mm 26.0 mm,NormalWide,1,SummerAll seasons,1,#245 Bottom 33%,#150 Top 41%,,
+Saucony,Kinvara 15,"84
+ Good!",$120,Daily runningTempo,Neutral,6.8 oz / 194g 6.7 oz / 190g,1,4.4 mm 4.0 mm,Mid/forefoot,Slightly small,Balanced,Bad,Good,Good,Breathable,Medium,Medium,Flexible,Flexible,Flexible,0,0,27.9 mm 30.0 mm,23.5 mm 26.0 mm,NormalWide,1,SummerAll seasons,1,#244 Bottom 33%,#149 Top 41%,,
+Saucony,Kinvara 15,"84
+ Good!",$120,Daily runningTempo,Neutral,6.8 oz / 194g 6.7 oz / 190g,1,4.4 mm 4.0 mm,Mid/forefoot,Slightly small,Balanced,Bad,Good,Good,Breathable,Medium,Medium,Flexible,Flexible,Flexible,0,0,27.9 mm 30.0 mm,23.5 mm 26.0 mm,NormalWide,1,SummerAll seasons,1,#244 Bottom 33%,#149 Top 41%,,
+Saucony,Kinvara 15,"84
+ Good!",$120,Daily runningTempo,Neutral,6.8 oz / 194g 6.7 oz / 190g,1,4.4 mm 4.0 mm,Mid/forefoot,Slightly small,Balanced,Bad,Good,Good,Breathable,Medium,Medium,Flexible,Flexible,Flexible,0,0,27.9 mm 30.0 mm,23.5 mm 26.0 mm,NormalWide,1,SummerAll seasons,1,#244 Bottom 33%,#149 Top 41%,,
+Saucony,Kinvara Pro,"89
+ Great!",$180,Daily runningTempo,Neutral,9.9 oz / 281g 9.5 oz / 269g,0,10.3 mm 8.0 mm,Heel,True to size,Balanced,Good,Good,Good,Warm,Medium,Narrow,Stiff,Stiff,Moderate,Carbon plate,0,45.6 mm 42.0 mm,35.3 mm 34.0 mm,NormalWide,1,All seasons,1,#72 Top 20%,#213 Bottom 41%,,
+Brooks,Launch 10,"87
+ Great!",$110,Daily runningTempo,Neutral,8.1 oz / 230g 8.2 oz / 232g,1,10.0 mm 10.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,33.8 mm 34.0 mm,23.8 mm 24.0 mm,Normal,1,All seasons,1,#338 Bottom 47%,#280 Top 44%,,
+Brooks,Launch 11,"83
+ Good!",$120,Daily runningTempo,Neutral,8.4 oz / 237g 7.7 oz / 218g,1,9.5 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,33.5 mm 35.5 mm,24.0 mm 27.5 mm,NormalWide,1,All seasons,1,#249 Bottom 31%,#103 Top 29%,,
+Brooks,Launch 11,"83
+ Good!",$120,Daily runningTempo,Neutral,8.4 oz / 237g 7.7 oz / 218g,1,9.5 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,33.5 mm 35.5 mm,24.0 mm 27.5 mm,NormalWide,1,All seasons,1,#249 Bottom 31%,#104 Top 29%,,
+Brooks,Launch 11,"83
+ Good!",$120,Daily runningTempo,Neutral,8.4 oz / 237g 7.7 oz / 218g,1,9.5 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,33.5 mm 35.5 mm,24.0 mm 27.5 mm,NormalWide,1,All seasons,1,#249 Bottom 31%,#104 Top 29%,,
+Brooks,Launch 11,"83
+ Good!",$120,Daily runningTempo,Neutral,8.4 oz / 237g 7.7 oz / 218g,1,9.5 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,33.5 mm 35.5 mm,24.0 mm 27.5 mm,NormalWide,1,All seasons,1,#249 Bottom 31%,#104 Top 29%,,
+Brooks,Launch 8,"90
+ Superb!",$100,Daily runningTempo,Neutral,8.5 oz / 240g 8.8 oz / 249g,1,9.6 mm 10.0 mm,HeelMid/forefoot,True to size,-,-,-,-,-,Narrow,-,-,Moderate,-,0,0,30.5 mm 26.0 mm,20.9 mm 16.0 mm,Normal,1,-,1,#69 Top 11%,#411 Bottom 36%,,
+Brooks,Launch GTS 10,"84
+ Good!",$110,Daily runningTempo,Stability,8.5 oz / 241g 8.4 oz / 238g,1,12.2 mm 10.0 mm,Heel,True to size,Soft,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,0,35.4 mm 34.0 mm,23.2 mm 24.0 mm,NormalWide,1,All seasons,1,#239 Bottom 34%,#295 Bottom 19%,,
+Brooks,Launch GTS 9,"87
+ Great!",$110,Daily runningTempo,Stability,8.6 oz / 245g 8.7 oz / 246g,1,10.4 mm 10.0 mm,Heel,True to size,Balanced,Decent,Good,Good,Warm,Narrow,Medium,Stiff,Stiff,Stiff,0,0,33.8 mm 36.0 mm,23.4 mm 26.0 mm,Normal,1,All seasons,1,#306 Top 48%,#566 Bottom 12%,,
+Brooks,Levitate 5,"88
+ Great!",$150,Daily running,Neutral,10.7 oz / 304g 11 oz / 311g,0,9.1 mm 8.0 mm,HeelMid/forefoot,-,-,-,-,-,-,Medium,-,Stiff,Moderate,Stiff,0,0,31.4 mm 29.0 mm,22.3 mm 21.0 mm,Normal,1,-,1,#285 Top 45%,#495 Bottom 23%,,
+Brooks,Levitate 6,"90
+ Superb!",$150,Daily running,Neutral,10.7 oz / 304g 10.9 oz / 309g,0,7.7 mm 8.0 mm,Mid/forefoot,True to size,Soft,Good,Good,Good,Breathable,Narrow,Medium,Stiff,Moderate,Moderate,0,0,34.3 mm 32.5 mm,26.6 mm 24.5 mm,Normal,1,SummerAll seasons,1,#71 Top 20%,#256 Bottom 30%,,
+Brooks,Levitate 6,"90
+ Superb!",$150,Daily running,Neutral,10.7 oz / 304g 10.9 oz / 309g,0,7.7 mm 8.0 mm,Mid/forefoot,True to size,Soft,Good,Good,Good,Breathable,Narrow,Medium,Stiff,Moderate,Moderate,0,0,34.3 mm 32.5 mm,26.6 mm 24.5 mm,Normal,1,SummerAll seasons,1,#71 Top 20%,#256 Bottom 30%,,
+Brooks,Levitate 6,"90
+ Superb!",$150,Daily running,Neutral,10.7 oz / 304g 10.9 oz / 309g,0,7.7 mm 8.0 mm,Mid/forefoot,True to size,Soft,Good,Good,Good,Breathable,Narrow,Medium,Stiff,Moderate,Moderate,0,0,34.3 mm 32.5 mm,26.6 mm 24.5 mm,Normal,1,SummerAll seasons,1,#71 Top 20%,#255 Bottom 30%,,
+Brooks,Levitate 6,"90
+ Superb!",$150,Daily running,Neutral,10.7 oz / 304g 10.9 oz / 309g,0,7.7 mm 8.0 mm,Mid/forefoot,True to size,Soft,Good,Good,Good,Breathable,Narrow,Medium,Stiff,Moderate,Moderate,0,0,34.3 mm 32.5 mm,26.6 mm 24.5 mm,Normal,1,SummerAll seasons,1,#71 Top 20%,#255 Bottom 30%,,
+Brooks,Levitate 6,"90
+ Superb!",$150,Daily running,Neutral,10.7 oz / 304g 10.9 oz / 309g,0,7.7 mm 8.0 mm,Mid/forefoot,True to size,Soft,Good,Good,Good,Breathable,Narrow,Medium,Stiff,Moderate,Moderate,0,0,34.3 mm 32.5 mm,26.6 mm 24.5 mm,Normal,1,SummerAll seasons,1,#71 Top 20%,#255 Bottom 30%,,
+Brooks,Levitate 6,"90
+ Superb!",$150,Daily running,Neutral,10.7 oz / 304g 10.9 oz / 309g,0,7.7 mm 8.0 mm,Mid/forefoot,True to size,Soft,Good,Good,Good,Breathable,Narrow,Medium,Stiff,Moderate,Moderate,0,0,34.3 mm 32.5 mm,26.6 mm 24.5 mm,Normal,1,SummerAll seasons,1,#71 Top 20%,#255 Bottom 30%,,
+Brooks,Levitate 6,"90
+ Superb!",$150,Daily running,Neutral,10.7 oz / 304g 10.9 oz / 309g,0,7.7 mm 8.0 mm,Mid/forefoot,True to size,Soft,Good,Good,Good,Breathable,Narrow,Medium,Stiff,Moderate,Moderate,0,0,34.3 mm 32.5 mm,26.6 mm 24.5 mm,Normal,1,SummerAll seasons,1,#71 Top 20%,#255 Bottom 30%,,
+Brooks,Levitate 6,"90
+ Superb!",$150,Daily running,Neutral,10.7 oz / 304g 10.9 oz / 309g,0,7.7 mm 8.0 mm,Mid/forefoot,True to size,Soft,Good,Good,Good,Breathable,Narrow,Medium,Stiff,Moderate,Moderate,0,0,34.3 mm 32.5 mm,26.6 mm 24.5 mm,Normal,1,SummerAll seasons,1,#72 Top 20%,#255 Bottom 30%,,
+Brooks,Levitate 6,"90
+ Superb!",$150,Daily running,Neutral,10.7 oz / 304g 10.9 oz / 309g,0,7.7 mm 8.0 mm,Mid/forefoot,True to size,Soft,Good,Good,Good,Breathable,Narrow,Medium,Stiff,Moderate,Moderate,0,0,34.3 mm 32.5 mm,26.6 mm 24.5 mm,Normal,1,SummerAll seasons,1,#72 Top 20%,#255 Bottom 30%,,
+Brooks,Levitate 6,"90
+ Superb!",$150,Daily running,Neutral,10.7 oz / 304g 10.9 oz / 309g,0,7.7 mm 8.0 mm,Mid/forefoot,True to size,Soft,Good,Good,Good,Breathable,Narrow,Medium,Stiff,Moderate,Moderate,0,0,34.3 mm 32.5 mm,26.6 mm 24.5 mm,Normal,1,SummerAll seasons,1,#72 Top 20%,#255 Bottom 30%,,
+Brooks,Levitate 6,"90
+ Superb!",$150,Daily running,Neutral,10.7 oz / 304g 10.9 oz / 309g,0,7.7 mm 8.0 mm,Mid/forefoot,True to size,Soft,Good,Good,Good,Breathable,Narrow,Medium,Stiff,Moderate,Moderate,0,0,34.3 mm 32.5 mm,26.6 mm 24.5 mm,Normal,1,SummerAll seasons,1,#72 Top 20%,#255 Bottom 30%,,
+Brooks,Levitate Stealthfit 5,"83
+ Good!",$150,Daily running,Neutral,10.1 oz / 285g 9.8 oz / 278g,0,9.5 mm 8.0 mm,HeelMid/forefoot,-,Soft,Good,Good,Good,Warm,Narrow,Wide,Stiff,Flexible,Moderate,0,0,28.0 mm 29.0 mm,18.5 mm 21.0 mm,Normal,1,Winter,1,#496 Bottom 23%,#589 Bottom 8%,,
+Brooks,Levitate Stealthfit 6,"83
+ Good!",$150,Daily running,Neutral,9.9 oz / 281g 9.9 oz / 280g,0,7.5 mm 8.0 mm,Mid/forefoot,-,Balanced,Good,Good,Good,Warm,Narrow,Medium,Stiff,Moderate,Moderate,0,0,32.9 mm 33.0 mm,25.4 mm 25.0 mm,Normal,1,All seasons,1,#266 Bottom 27%,#277 Bottom 24%,,
+Reebok,Lite 3,"80
+ Good!",$65,Daily running,Neutral,8.7 oz / 248g 10.2 oz / 289g,1,13.4 mm,Heel,True to size,Balanced,-,-,-,Warm,Medium,-,Stiff,Flexible,Flexible,0,0,32.7 mm,19.3 mm,Normal,0,All seasons,0,#305 Bottom 16%,#355 Bottom 3%,,
+Reebok,Lite 3,"80
+ Good!",$65,Daily running,Neutral,8.7 oz / 248g 10.2 oz / 289g,1,13.4 mm,Heel,True to size,Balanced,-,-,-,Warm,Medium,-,Stiff,Flexible,Flexible,0,0,32.7 mm,19.3 mm,Normal,0,All seasons,0,#305 Bottom 16%,#354 Bottom 3%,,
+Hoka,Mach 4,"91
+ Superb!",$130,Daily runningTempo,Neutral,7.9 oz / 223g 8.2 oz / 232g,1,4.9 mm 5.0 mm,Mid/forefoot,True to size,-,-,-,-,-,Narrow,-,-,Moderate,-,0,1,30.6 mm 29.0 mm,25.7 mm 24.0 mm,Normal,1,-,1,#48 Top 8%,#209 Top 33%,,
+Hoka,Mach 5,"90
+ Superb!",$140,Daily runningTempo,Neutral,7.9 oz / 225g 8.2 oz / 232g,1,5.7 mm 5.0 mm,Mid/forefoot,True to size,Soft,-,-,-,Moderate,Narrow,-,Flexible,Flexible,Moderate,0,1,30.7 mm 29.0 mm,25.0 mm 24.0 mm,NormalWide,1,All seasons,1,#121 Top 19%,#134 Top 21%,,
+Hoka,Mach 6,"87
+ Great!",$140,Daily running,Neutral,8.2 oz / 232g 8.3 oz / 235g,1,9.6 mm 5.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Good,Breathable,Medium,Narrow,Moderate,Stiff,Stiff,0,1,36.0 mm 37.0 mm,26.4 mm 32.0 mm,NormalWide,1,SummerAll seasons,1,#153 Top 42%,#13 Top 4%,,
+Hoka,Mach 6,"87
+ Great!",$140,Daily running,Neutral,8.2 oz / 232g 8.3 oz / 235g,1,9.6 mm 5.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Good,Breathable,Medium,Narrow,Moderate,Stiff,Stiff,0,1,36.0 mm 37.0 mm,26.4 mm 32.0 mm,NormalWide,1,SummerAll seasons,1,#153 Top 42%,#13 Top 4%,,Road
+Hoka,Mach 6,"87
+ Great!",$140,Daily running,Neutral,8.2 oz / 232g 8.3 oz / 235g,1,9.6 mm 5.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Good,Breathable,Medium,Narrow,Moderate,Stiff,Stiff,0,1,36.0 mm 37.0 mm,26.4 mm 32.0 mm,NormalWide,1,SummerAll seasons,1,#153 Top 42%,#13 Top 4%,,
+Hoka,Mach 6,"87
+ Great!",$140,Daily running,Neutral,8.2 oz / 232g 8.3 oz / 235g,1,9.6 mm 5.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Good,Breathable,Medium,Narrow,Moderate,Stiff,Stiff,0,1,36.0 mm 37.0 mm,26.4 mm 32.0 mm,NormalWide,1,SummerAll seasons,1,#155 Top 43%,#13 Top 4%,,
+Hoka,Mach 6,"87
+ Great!",$140,Daily running,Neutral,8.2 oz / 232g 8.3 oz / 235g,1,9.6 mm 5.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Good,Breathable,Medium,Narrow,Moderate,Stiff,Stiff,0,1,36.0 mm 37.0 mm,26.4 mm 32.0 mm,NormalWide,1,SummerAll seasons,1,#155 Top 43%,#13 Top 4%,,
+Hoka,Mach 6,"87
+ Great!",$140,Daily running,Neutral,8.2 oz / 232g 8.3 oz / 235g,1,9.6 mm 5.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Good,Breathable,Medium,Narrow,Moderate,Stiff,Stiff,0,1,36.0 mm 37.0 mm,26.4 mm 32.0 mm,NormalWide,1,SummerAll seasons,1,#154 Top 43%,#13 Top 4%,,
+HOKA,Mach X 3,"74
+ Bad!",$190,Daily runningTempo,Neutral,9.3 oz / 264g 8.9 oz / 252g,0,9.5 mm 5.0 mm,HeelMid/forefoot,-,Soft,Decent,Good,Good,Moderate,Narrow,Narrow,Stiff,Stiff,Flexible,0,1,42.9 mm 44.0 mm,33.4 mm 39.0 mm,NormalWide,1,All seasons,1,#351 Bottom 4%,#73 Top 20%,,
+ASICS,Magic Speed,"90
+ Superb!",$150,Tempo,Neutral,8.2 oz / 233g 7.9 oz / 224g,1,8.3 mm 5.0 mm,HeelMid/forefoot,True to size,Balanced,-,-,-,Breathable,Narrow,-,Stiff,Stiff,Flexible,Carbon plate,1,32.5 mm 29.0 mm,24.2 mm 24.0 mm,NarrowNormal,0,SummerAll seasons,0,#122 Top 19%,#273 Top 43%,,
+ASICS,Magic Speed 2,"89
+ Great!",$150,Tempo,Neutral,8 oz / 228g 8.1 oz / 230g,1,8.7 mm 7.0 mm,HeelMid/forefoot,True to size,-,-,-,-,Breathable,Narrow,-,Stiff,Stiff,Flexible,Carbon plate,1,35.1 mm 31.0 mm,26.4 mm 24.0 mm,Normal,0,SummerAll seasons,0,#176 Top 28%,#565 Bottom 12%,,
+ASICS,Magic Speed 3,"91
+ Superb!",$160,Tempo,Neutral,7.4 oz / 211g 7.8 oz / 220g,1,9.8 mm 7.0 mm,HeelMid/forefoot,Slightly small,Soft,Bad,Decent,Good,Moderate,Narrow,Medium,Stiff,Stiff,Flexible,Carbon plate,1,36.3 mm 36.0 mm,26.5 mm 29.0 mm,Normal,1,All seasons,1,#31 Top 5%,#332 Bottom 48%,,
+ASICS,Magic Speed 4,"92
+ Superb!",$170,CompetitionTempo,Neutral,8.4 oz / 237g 8.5 oz / 242g,1,9.6 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Good,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,42.5 mm 43.5 mm,32.9 mm 35.5 mm,NormalWide,1,SummerAll seasons,1,#6 Top 2%,#82 Top 23%,,
+ASICS,Magic Speed 4,"92
+ Superb!",$170,CompetitionTempo,Neutral,8.4 oz / 237g 8.5 oz / 242g,1,9.6 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Good,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,42.5 mm 43.5 mm,32.9 mm 35.5 mm,NormalWide,1,SummerAll seasons,1,#6 Top 2%,#82 Top 23%,,
+ASICS,Magic Speed 4,"92
+ Superb!",$170,CompetitionTempo,Neutral,8.4 oz / 237g 8.5 oz / 242g,1,9.6 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Good,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,42.5 mm 43.5 mm,32.9 mm 35.5 mm,NormalWide,1,SummerAll seasons,1,#6 Top 2%,#82 Top 23%,,
+ASICS,Magic Speed 4,"92
+ Superb!",$170,CompetitionTempo,Neutral,8.4 oz / 237g 8.5 oz / 242g,1,9.6 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Good,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,42.5 mm 43.5 mm,32.9 mm 35.5 mm,NormalWide,1,SummerAll seasons,1,#6 Top 2%,#82 Top 23%,,
+ASICS,Magic Speed 4,"92
+ Superb!",$170,CompetitionTempo,Neutral,8.4 oz / 237g 8.5 oz / 242g,1,9.6 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Good,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,42.5 mm 43.5 mm,32.9 mm 35.5 mm,NormalWide,1,SummerAll seasons,1,#6 Top 2%,#82 Top 23%,,
+ASICS,Magic Speed 4,"92
+ Superb!",$170,CompetitionTempo,Neutral,8.4 oz / 237g 8.5 oz / 242g,1,9.6 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Good,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,42.5 mm 43.5 mm,32.9 mm 35.5 mm,NormalWide,1,SummerAll seasons,1,#6 Top 2%,#82 Top 23%,,
+ASICS,Magic Speed 4,"92
+ Superb!",$170,CompetitionTempo,Neutral,8.4 oz / 237g 8.5 oz / 242g,1,9.6 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Good,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,42.5 mm 43.5 mm,32.9 mm 35.5 mm,NormalWide,1,SummerAll seasons,1,#6 Top 2%,#82 Top 23%,,
+ASICS,Magic Speed 4,"92
+ Superb!",$170,CompetitionTempo,Neutral,8.4 oz / 237g 8.5 oz / 242g,1,9.6 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Good,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,42.5 mm 43.5 mm,32.9 mm 35.5 mm,NormalWide,1,SummerAll seasons,1,#6 Top 2%,#82 Top 23%,,
+ASICS,Magic Speed 4,"92
+ Superb!",$170,CompetitionTempo,Neutral,8.4 oz / 237g 8.5 oz / 242g,1,9.6 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Good,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,42.5 mm 43.5 mm,32.9 mm 35.5 mm,NormalWide,1,SummerAll seasons,1,#6 Top 2%,#82 Top 23%,#82 Top 23%,
+ASICS,Magic Speed 4,"92
+ Superb!",$170,CompetitionTempo,Neutral,8.4 oz / 237g 8.5 oz / 242g,1,9.6 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Good,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,42.5 mm 43.5 mm,32.9 mm 35.5 mm,NormalWide,1,SummerAll seasons,1,#6 Top 2%,#82 Top 23%,,
+ASICS,Magic Speed 4,"92
+ Superb!",$170,CompetitionTempo,Neutral,8.4 oz / 237g 8.5 oz / 242g,1,9.6 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Good,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,42.5 mm 43.5 mm,32.9 mm 35.5 mm,NormalWide,1,SummerAll seasons,1,#6 Top 2%,#82 Top 23%,,
+ASICS,Magic Speed 4,"92
+ Superb!",$170,CompetitionTempo,Neutral,8.4 oz / 237g 8.5 oz / 242g,1,9.6 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Good,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,42.5 mm 43.5 mm,32.9 mm 35.5 mm,NormalWide,1,SummerAll seasons,1,#6 Top 2%,#82 Top 23%,,
+PUMA,MagMax Nitro,"91
+ Superb!",$180,Daily running,Neutral,10.3 oz / 291g 10.3 oz / 292g,0,9.6 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Decent,Decent,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,1,42.9 mm 47.0 mm,33.3 mm 39.0 mm,Normal,1,All seasons,1,#31 Top 9%,#164 Top 45%,,
+PUMA,MagMax Nitro,"91
+ Superb!",$180,Daily running,Neutral,10.3 oz / 291g 10.3 oz / 292g,0,9.6 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Decent,Decent,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,1,42.9 mm 47.0 mm,33.3 mm 39.0 mm,Normal,1,All seasons,1,#31 Top 9%,#164 Top 45%,#165 Top 46%,
+PUMA,MagMax Nitro,"91
+ Superb!",$180,Daily running,Neutral,10.3 oz / 291g 10.3 oz / 292g,0,9.6 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Decent,Decent,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,1,42.9 mm 47.0 mm,33.3 mm 39.0 mm,Normal,1,All seasons,1,#31 Top 9%,#164 Top 45%,,Road
+PUMA,MagMax Nitro,"91
+ Superb!",$180,Daily running,Neutral,10.3 oz / 291g 10.3 oz / 292g,0,9.6 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Decent,Decent,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,1,42.9 mm 47.0 mm,33.3 mm 39.0 mm,Normal,1,All seasons,1,#31 Top 9%,#164 Top 45%,,
+Puma,MagMax Nitro,"90
+ Superb!",$180,Daily running,Neutral,10.3 oz / 291g 10.3 oz / 292g,0,9.6 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Decent,Decent,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,1,42.9 mm 47.0 mm,33.3 mm 39.0 mm,Normal,1,All seasons,1,#36 Top 10%,#165 Top 46%,,
+PUMA,MagMax Nitro,"90
+ Superb!",$180,Daily running,Neutral,10.3 oz / 291g 10.3 oz / 292g,0,9.6 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Decent,Decent,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,1,42.9 mm 47.0 mm,33.3 mm 39.0 mm,Normal,1,All seasons,1,#36 Top 10%,#165 Top 46%,,
+PUMA,Magnify Nitro 2,"83
+ Good!",$150,Daily running,Neutral,9.9 oz / 281g 10.1 oz / 286g,0,9.3 mm 10.0 mm,HeelMid/forefoot,Half size large,Soft,Good,Bad,Good,Warm,Medium,Narrow,Moderate,Stiff,Moderate,0,0,37.1 mm 39.0 mm,27.8 mm 29.0 mm,Normal,1,All seasons,1,#256 Bottom 29%,#246 Bottom 32%,,
+PUMA,Magnify Nitro 2,"83
+ Good!",$150,Daily running,Neutral,9.9 oz / 281g 10.1 oz / 286g,0,9.3 mm 10.0 mm,HeelMid/forefoot,Half size large,Soft,Good,Bad,Good,Warm,Medium,Narrow,Moderate,Stiff,Moderate,0,0,37.1 mm 39.0 mm,27.8 mm 29.0 mm,Normal,1,All seasons,1,#257 Bottom 29%,#247 Bottom 32%,,
+PUMA,Magnify Nitro 2,"83
+ Good!",$150,Daily running,Neutral,9.9 oz / 281g 10.1 oz / 286g,0,9.3 mm 10.0 mm,HeelMid/forefoot,Half size large,Soft,Good,Bad,Good,Warm,Medium,Narrow,Moderate,Stiff,Moderate,0,0,37.1 mm 39.0 mm,27.8 mm 29.0 mm,Normal,1,All seasons,1,#257 Bottom 29%,#247 Bottom 32%,,
+PUMA,Magnify Nitro 2,"83
+ Good!",$150,Daily running,Neutral,9.9 oz / 281g 10.1 oz / 286g,0,9.3 mm 10.0 mm,HeelMid/forefoot,Half size large,Soft,Good,Bad,Good,Warm,Medium,Narrow,Moderate,Stiff,Moderate,0,0,37.1 mm 39.0 mm,27.8 mm 29.0 mm,Normal,1,All seasons,1,#257 Bottom 29%,#247 Bottom 32%,,
+PUMA,Magnify Nitro 2,"83
+ Good!",$150,Daily running,Neutral,9.9 oz / 281g 10.1 oz / 286g,0,9.3 mm 10.0 mm,HeelMid/forefoot,Half size large,Soft,Good,Bad,Good,Warm,Medium,Narrow,Moderate,Stiff,Moderate,0,0,37.1 mm 39.0 mm,27.8 mm 29.0 mm,Normal,1,All seasons,1,#257 Bottom 29%,#247 Bottom 32%,,
+PUMA,Magnify Nitro 2,"83
+ Good!",$150,Daily running,Neutral,9.9 oz / 281g 10.1 oz / 286g,0,9.3 mm 10.0 mm,HeelMid/forefoot,Half size large,Soft,Good,Bad,Good,Warm,Medium,Narrow,Moderate,Stiff,Moderate,0,0,37.1 mm 39.0 mm,27.8 mm 29.0 mm,Normal,1,All seasons,1,#258 Bottom 29%,#248 Bottom 32%,,
+PUMA,Magnify Nitro 2,"83
+ Good!",$150,Daily running,Neutral,9.9 oz / 281g 10.1 oz / 286g,0,9.3 mm 10.0 mm,HeelMid/forefoot,Half size large,Soft,Good,Bad,Good,Warm,Medium,Narrow,Moderate,Stiff,Moderate,0,0,37.1 mm 39.0 mm,27.8 mm 29.0 mm,Normal,1,All seasons,1,#255 Bottom 30%,#247 Bottom 32%,,
+Skechers,Max Cushioning Elite,"82
+ Good!",$90,Daily running,Neutral,11.9 oz / 336g 11.5 oz / 326g,0,16.1 mm 6.0 mm,Heel,True to size,-,-,-,-,-,Narrow,-,Stiff,-,-,0,0,42.3 mm,26.2 mm,NarrowNormalX-Wide,0,-,0,#520 Bottom 19%,#229 Top 36%,,
+Skechers,Max Cushioning Elite 2.0,"84
+ Good!",$100,Daily running,Neutral,9.5 oz / 269g 9.5 oz / 270g,0,8.8 mm 6.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Decent,Decent,Warm,Narrow,Medium,Moderate,Stiff,Moderate,0,1,36.4 mm 39.0 mm,27.6 mm 33.0 mm,NormalWide,1,All seasons,1,#242 Bottom 34%,#367 Bottom 1%,,
+ASICS,Megablast,"81
+ Good!",$225,CompetitionTempo,Neutral,7.7 oz / 218g 7.9 oz / 224g,1,9.9 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Bad,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,1,45.1 mm 45.0 mm,35.2 mm 37.0 mm,Normal,1,All seasons,1,#296 Bottom 19%,#49 Top 14%,,
+ASICS,Metaspeed Edge,"90
+ Superb!",$250,Competition,Neutral,6.2 oz / 176g 6.7 oz / 190g,1,8.0 mm 8.0 mm,HeelMid/forefoot,Slightly small,-,-,-,-,-,Narrow,-,Stiff,Stiff,Flexible,Carbon plate,1,31.6 mm 29.0 mm,23.6 mm 21.0 mm,Normal,0,-,0,#55 Top 16%,#266 Bottom 27%,,
+ASICS,Metaspeed Edge Tokyo,"86
+ Good!",$270,Competition,Neutral,5.6 oz / 159g 6 oz / 170g,1,6.9 mm 5.0 mm,Mid/forefoot,True to size,Soft,Bad,Good,Good,Breathable,Narrow,Medium,Moderate,Stiff,Flexible,Carbon plate,1,38.9 mm 39.5 mm,32.0 mm 34.5 mm,NormalWide,1,SummerAll seasons,1,#189 Bottom 48%,#157 Top 43%,,
+ASICS,Metaspeed Edge+,"92
+ Superb!",$250,Competition,Neutral,7.3 oz / 208g 7.4 oz / 210g,1,8.1 mm 8.0 mm,HeelMid/forefoot,True to size,Firm,-,-,-,Breathable,Narrow,-,Stiff,Stiff,Flexible,Carbon plate,1,33.3 mm 39.0 mm,25.2 mm 31.0 mm,NormalWide,0,SummerAll seasons,0,#17 Top 5%,#188 Bottom 48%,,
+ASICS,Metaspeed Edge+,"92
+ Superb!",$250,Competition,Neutral,7.3 oz / 208g 7.4 oz / 210g,1,8.1 mm 8.0 mm,HeelMid/forefoot,True to size,Firm,-,-,-,Breathable,Narrow,-,Stiff,Stiff,Flexible,Carbon plate,1,33.3 mm 39.0 mm,25.2 mm 31.0 mm,NormalWide,0,SummerAll seasons,0,#17 Top 5%,#188 Bottom 48%,,
+ASICS,Metaspeed Edge+,"92
+ Superb!",$250,Competition,Neutral,7.3 oz / 208g 7.4 oz / 210g,1,8.1 mm 8.0 mm,HeelMid/forefoot,True to size,Firm,-,-,-,Breathable,Narrow,-,Stiff,Stiff,Flexible,Carbon plate,1,33.3 mm 39.0 mm,25.2 mm 31.0 mm,NormalWide,0,SummerAll seasons,0,#17 Top 5%,#188 Bottom 48%,,
+ASICS,Metaspeed Edge+,"92
+ Superb!",$250,Competition,Neutral,7.3 oz / 208g 7.4 oz / 210g,1,8.1 mm 8.0 mm,HeelMid/forefoot,True to size,Firm,-,-,-,Breathable,Narrow,-,Stiff,Stiff,Flexible,Carbon plate,1,33.3 mm 39.0 mm,25.2 mm 31.0 mm,NormalWide,0,SummerAll seasons,0,#18 Top 5%,#189 Bottom 48%,,
+ASICS,Metaspeed Edge+,"92
+ Superb!",$250,Competition,Neutral,7.3 oz / 208g 7.4 oz / 210g,1,8.1 mm 8.0 mm,HeelMid/forefoot,True to size,Firm,-,-,-,Breathable,Narrow,-,Stiff,Stiff,Flexible,Carbon plate,1,33.3 mm 39.0 mm,25.2 mm 31.0 mm,NormalWide,0,SummerAll seasons,0,#18 Top 5%,#189 Bottom 48%,,
+ASICS,Metaspeed Edge+,"92
+ Superb!",$250,Competition,Neutral,7.3 oz / 208g 7.4 oz / 210g,1,8.1 mm 8.0 mm,HeelMid/forefoot,True to size,Firm,-,-,-,Breathable,Narrow,-,Stiff,Stiff,Flexible,Carbon plate,1,33.3 mm 39.0 mm,25.2 mm 31.0 mm,NormalWide,0,SummerAll seasons,0,#18 Top 5%,#189 Bottom 48%,,
+ASICS,Metaspeed Edge+,"92
+ Superb!",$250,Competition,Neutral,7.3 oz / 208g 7.4 oz / 210g,1,8.1 mm 8.0 mm,HeelMid/forefoot,True to size,Firm,-,-,-,Breathable,Narrow,-,Stiff,Stiff,Flexible,Carbon plate,1,33.3 mm 39.0 mm,25.2 mm 31.0 mm,NormalWide,0,SummerAll seasons,0,#18 Top 5%,#189 Bottom 48%,,
+ASICS,Metaspeed Edge+,"92
+ Superb!",$250,Competition,Neutral,7.3 oz / 208g 7.4 oz / 210g,1,8.1 mm 8.0 mm,HeelMid/forefoot,True to size,Firm,-,-,-,Breathable,Narrow,-,Stiff,Stiff,Flexible,Carbon plate,1,33.3 mm 39.0 mm,25.2 mm 31.0 mm,NormalWide,0,SummerAll seasons,0,#18 Top 5%,#189 Bottom 48%,,
+ASICS,Metaspeed Ray,"84
+ Good!",$300,Competition,Neutral,4.6 oz / 129g 4.6 oz / 129g,1,9.8 mm 5.0 mm,HeelMid/forefoot,Slightly large,Soft,Bad,Good,Decent,Moderate,Narrow,Narrow,Moderate,Moderate,Flexible,Carbon plate,1,39.8 mm 39.5 mm,30.0 mm 34.5 mm,Normal,1,All seasons,1,#236 Bottom 35%,#118 Top 33%,,
+ASICS,Metaspeed Sky,"92
+ Superb!",$250,Competition,Neutral,6.7 oz / 191g 7 oz / 198g,1,2.5 mm 5.0 mm,Mid/forefoot,True to size,-,-,-,-,Breathable,Narrow,-,-,Stiff,-,Carbon plate,1,33.7 mm 33.0 mm,31.2 mm 28.0 mm,Normal,0,SummerAll seasons,0,#15 Top 5%,#62 Top 17%,#118 Top 33%,
+ASICS,Metaspeed Sky,"92
+ Superb!",$250,Competition,Neutral,6.7 oz / 191g 7 oz / 198g,1,2.5 mm 5.0 mm,Mid/forefoot,True to size,-,-,-,-,Breathable,Narrow,-,-,Stiff,-,Carbon plate,1,33.7 mm 33.0 mm,31.2 mm 28.0 mm,Normal,0,SummerAll seasons,0,#15 Top 5%,#62 Top 17%,,
+ASICS,Metaspeed Sky,"92
+ Superb!",$250,Competition,Neutral,6.7 oz / 191g 7 oz / 198g,1,2.5 mm 5.0 mm,Mid/forefoot,True to size,-,-,-,-,Breathable,Narrow,-,-,Stiff,-,Carbon plate,1,33.7 mm 33.0 mm,31.2 mm 28.0 mm,Normal,0,SummerAll seasons,0,#16 Top 5%,#62 Top 17%,,
+ASICS,Metaspeed Sky,"92
+ Superb!",$250,Competition,Neutral,6.7 oz / 191g 7 oz / 198g,1,2.5 mm 5.0 mm,Mid/forefoot,True to size,-,-,-,-,Breathable,Narrow,-,-,Stiff,-,Carbon plate,1,33.7 mm 33.0 mm,31.2 mm 28.0 mm,Normal,0,SummerAll seasons,0,#16 Top 5%,#62 Top 17%,,
+ASICS,Metaspeed Sky Paris,"89
+ Great!",$250,Competition,Neutral,6.5 oz / 183g 6.4 oz / 181g,1,6.5 mm 5.0 mm,Mid/forefoot,Slightly small,Balanced,Good,Good,Good,Breathable,Narrow,Medium,Stiff,Stiff,Flexible,Carbon plate,1,39.1 mm 39.5 mm,32.6 mm 34.5 mm,NormalWide,1,SummerAll seasons,1,#80 Top 22%,#97 Top 27%,,
+ASICS,Metaspeed Sky Paris,"90
+ Superb!",$250,Competition,Neutral,6.5 oz / 183g 6.4 oz / 181g,1,6.5 mm 5.0 mm,Mid/forefoot,Slightly small,Balanced,Good,Good,Good,Breathable,Narrow,Medium,Stiff,Stiff,Flexible,Carbon plate,1,39.1 mm 39.5 mm,32.6 mm 34.5 mm,NormalWide,1,SummerAll seasons,1,#69 Top 19%,#97 Top 27%,,
+ASICS,Metaspeed Sky Paris,"90
+ Superb!",$250,Competition,Neutral,6.5 oz / 183g 6.4 oz / 181g,1,6.5 mm 5.0 mm,Mid/forefoot,Slightly small,Balanced,Good,Good,Good,Breathable,Narrow,Medium,Stiff,Stiff,Flexible,Carbon plate,1,39.1 mm 39.5 mm,32.6 mm 34.5 mm,NormalWide,1,SummerAll seasons,1,#69 Top 19%,#97 Top 27%,,
+ASICS,Metaspeed Sky Paris,"90
+ Superb!",$250,Competition,Neutral,6.5 oz / 183g 6.4 oz / 181g,1,6.5 mm 5.0 mm,Mid/forefoot,Slightly small,Balanced,Good,Good,Good,Breathable,Narrow,Medium,Stiff,Stiff,Flexible,Carbon plate,1,39.1 mm 39.5 mm,32.6 mm 34.5 mm,NormalWide,1,SummerAll seasons,1,#70 Top 20%,#97 Top 27%,,
+ASICS,Metaspeed Sky Paris,"90
+ Superb!",$250,Competition,Neutral,6.5 oz / 183g 6.4 oz / 181g,1,6.5 mm 5.0 mm,Mid/forefoot,Slightly small,Balanced,Good,Good,Good,Breathable,Narrow,Medium,Stiff,Stiff,Flexible,Carbon plate,1,39.1 mm 39.5 mm,32.6 mm 34.5 mm,NormalWide,1,SummerAll seasons,1,#70 Top 20%,#97 Top 27%,#97 Top 27%,
+ASICS,Metaspeed Sky Paris,"90
+ Superb!",$250,Competition,Neutral,6.5 oz / 183g 6.4 oz / 181g,1,6.5 mm 5.0 mm,Mid/forefoot,Slightly small,Balanced,Good,Good,Good,Breathable,Narrow,Medium,Stiff,Stiff,Flexible,Carbon plate,1,39.1 mm 39.5 mm,32.6 mm 34.5 mm,NormalWide,1,SummerAll seasons,1,#70 Top 20%,#97 Top 27%,,
+ASICS,Metaspeed Sky Paris,"90
+ Superb!",$250,Competition,Neutral,6.5 oz / 183g 6.4 oz / 181g,1,6.5 mm 5.0 mm,Mid/forefoot,Slightly small,Balanced,Good,Good,Good,Breathable,Narrow,Medium,Stiff,Stiff,Flexible,Carbon plate,1,39.1 mm 39.5 mm,32.6 mm 34.5 mm,NormalWide,1,SummerAll seasons,1,#70 Top 20%,#97 Top 27%,,
+ASICS,Metaspeed Sky Tokyo,"88
+ Great!",$270,Competition,Neutral,5.7 oz / 163g 6 oz / 170g,1,6.0 mm 5.0 mm,Mid/forefoot,True to size,Soft,Bad,Good,Decent,Breathable,Narrow,Narrow,Stiff,Stiff,Flexible,Carbon plate,1,38.7 mm 39.5 mm,32.7 mm 34.5 mm,NormalWide,1,SummerAll seasons,1,#122 Top 34%,#122 Top 34%,,
+ASICS,Metaspeed Sky+,"91
+ Superb!",$250,Competition,Neutral,7.2 oz / 205g 7.2 oz / 205g,1,2.7 mm 5.0 mm,Mid/forefoot,True to size,-,-,Good,-,Breathable,Narrow,-,Stiff,Stiff,Flexible,Carbon plate,1,33.5 mm 39.0 mm,30.8 mm 34.0 mm,NormalWide,0,SummerAll seasons,0,#23 Top 7%,#90 Top 25%,,
+ASICS,Metaspeed Sky+,"91
+ Superb!",$250,Competition,Neutral,7.2 oz / 205g 7.2 oz / 205g,1,2.7 mm 5.0 mm,Mid/forefoot,True to size,-,-,Good,-,Breathable,Narrow,-,Stiff,Stiff,Flexible,Carbon plate,1,33.5 mm 39.0 mm,30.8 mm 34.0 mm,NormalWide,0,SummerAll seasons,0,#23 Top 7%,#90 Top 25%,,
+ASICS,Metaspeed Sky+,"91
+ Superb!",$250,Competition,Neutral,7.2 oz / 205g 7.2 oz / 205g,1,2.7 mm 5.0 mm,Mid/forefoot,True to size,-,-,Good,-,Breathable,Narrow,-,Stiff,Stiff,Flexible,Carbon plate,1,33.5 mm 39.0 mm,30.8 mm 34.0 mm,NormalWide,0,SummerAll seasons,0,#23 Top 7%,#90 Top 25%,,
+ASICS,Metaspeed Sky+,"91
+ Superb!",$250,Competition,Neutral,7.2 oz / 205g 7.2 oz / 205g,1,2.7 mm 5.0 mm,Mid/forefoot,True to size,-,-,Good,-,Breathable,Narrow,-,Stiff,Stiff,Flexible,Carbon plate,1,33.5 mm 39.0 mm,30.8 mm 34.0 mm,NormalWide,0,SummerAll seasons,0,#23 Top 7%,#90 Top 25%,,
+ASICS,Metaspeed Sky+,"91
+ Superb!",$250,Competition,Neutral,7.2 oz / 205g 7.2 oz / 205g,1,2.7 mm 5.0 mm,Mid/forefoot,True to size,-,-,Good,-,Breathable,Narrow,-,Stiff,Stiff,Flexible,Carbon plate,1,33.5 mm 39.0 mm,30.8 mm 34.0 mm,NormalWide,0,SummerAll seasons,0,#23 Top 7%,#90 Top 25%,,
+Mizuno,Mizuno Wave Horizon 7,"88
+ Great!",$170,Daily running,Stability,11.6 oz / 329g 11.8 oz / 334g,0,7.2 mm 8.0 mm,HeelMid/forefoot,Slightly small,Balanced,Decent,Bad,Good,Moderate,Medium,Wide,Stiff,Stiff,Stiff,0,0,39.1 mm 41.0 mm,31.9 mm 33.0 mm,NormalWide,1,All seasons,1,#134 Top 37%,#294 Bottom 19%,,
+Mizuno,Neo Vista,"91
+ Superb!",$180,Daily runningTempo,Neutral,9.1 oz / 259g 9.4 oz / 266g,0,9.6 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Good,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Flexible,0,1,44.9 mm 44.5 mm,35.3 mm 36.5 mm,Normal,1,All seasons,1,#34 Top 6%,#179 Top 28%,,
+Mizuno,Neo Vista 2,"82
+ Good!",$200,Daily runningTempo,Neutral,9.3 oz / 264g 9.4 oz / 266g,0,8.5 mm 8.0 mm,HeelMid/forefoot,Half size large,Soft,Good,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Flexible,0,1,46.0 mm 44.5 mm,37.5 mm 36.5 mm,NormalWide,1,All seasons,1,#280 Bottom 23%,#72 Top 20%,,
+Mizuno,Neo Zen,"93
+ Superb!",$150,Daily runningTempo,Neutral,8.3 oz / 234g 8.5 oz / 240g,1,7.0 mm 6.0 mm,Mid/forefoot,True to size,Soft,Good,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Flexible,0,0,41.0 mm 40.0 mm,34.0 mm 34.0 mm,NormalWide,1,All seasons,1,#4 Top 2%,#74 Top 21%,,
+Nike,Nike Alphafly 3,"88
+ Great!",$285,Competition,Neutral,7.1 oz / 201g 7 oz / 198g,1,8.5 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,Bad,Good,Bad,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,38.1 mm 40.0 mm,29.6 mm 32.0 mm,Normal,0,SummerAll seasons,0,#124 Top 34%,#20 Top 6%,,
+ASICS,Noosa Tri 14,"93
+ Superb!",$130,Daily runningTempo,Neutral,7.5 oz / 213g 9.2 oz / 260g,1,8.0 mm 5.0 mm,HeelMid/forefoot,Slightly small,Soft,-,-,-,Moderate,Narrow,-,Stiff,Moderate,Flexible,0,1,30.2 mm 26.0 mm,22.2 mm 21.0 mm,Normal,1,All seasons,1,#2 Top 1%,#373 Bottom 42%,,
+ASICS,Noosa Tri 15,"92
+ Superb!",$130,Daily runningTempo,Neutral,7.7 oz / 218g 7.8 oz / 221g,1,7.7 mm 5.0 mm,Mid/forefoot,Slightly small,Soft,Bad,Good,Good,Breathable,Wide,Medium,Moderate,Stiff,Moderate,0,1,34.6 mm 34.0 mm,26.9 mm 29.0 mm,Normal,1,SummerAll seasons,1,#24 Top 4%,#331 Bottom 48%,,
+ASICS,Noosa Tri 16,"84
+ Good!",$135,Daily runningTempo,Neutral,7.7 oz / 217g 7.6 oz / 215g,1,5.9 mm 5.0 mm,Mid/forefoot,Half size small,Soft,Bad,Good,Decent,Breathable,Wide,Wide,Moderate,Stiff,Moderate,0,0,32.8 mm 34.5 mm,26.9 mm 29.5 mm,Normal,1,SummerAll seasons,1,#244 Bottom 33%,#125 Top 35%,#331 Bottom 48%,
+ASICS,Noosa Tri 16,"84
+ Good!",$135,Daily runningTempo,Neutral,7.7 oz / 217g 7.6 oz / 215g,1,5.9 mm 5.0 mm,Mid/forefoot,Half size small,Soft,Bad,Good,Decent,Breathable,Wide,Wide,Moderate,Stiff,Moderate,0,0,32.8 mm 34.5 mm,26.9 mm 29.5 mm,Normal,1,SummerAll seasons,1,#244 Bottom 33%,#125 Top 35%,,
+ASICS,Noosa Tri 16,"84
+ Good!",$135,Daily runningTempo,Neutral,7.7 oz / 217g 7.6 oz / 215g,1,5.9 mm 5.0 mm,Mid/forefoot,Half size small,Soft,Bad,Good,Decent,Breathable,Wide,Wide,Moderate,Stiff,Moderate,0,0,32.8 mm 34.5 mm,26.9 mm 29.5 mm,Normal,1,SummerAll seasons,1,#244 Bottom 33%,#125 Top 35%,,
+ASICS,Noosa Tri 16,"84
+ Good!",$135,Daily runningTempo,Neutral,7.7 oz / 217g 7.6 oz / 215g,1,5.9 mm 5.0 mm,Mid/forefoot,Half size small,Soft,Bad,Good,Decent,Breathable,Wide,Wide,Moderate,Stiff,Moderate,0,0,32.8 mm 34.5 mm,26.9 mm 29.5 mm,Normal,1,SummerAll seasons,1,#244 Bottom 33%,#125 Top 35%,,
+ASICS,Noosa Tri 16,"84
+ Good!",$135,Daily runningTempo,Neutral,7.7 oz / 217g 7.6 oz / 215g,1,5.9 mm 5.0 mm,Mid/forefoot,Half size small,Soft,Bad,Good,Decent,Breathable,Wide,Wide,Moderate,Stiff,Moderate,0,0,32.8 mm 34.5 mm,26.9 mm 29.5 mm,Normal,1,SummerAll seasons,1,#246 Bottom 32%,#126 Top 35%,,
+ASICS,Noosa Tri 16,"84
+ Good!",$135,Daily runningTempo,Neutral,7.7 oz / 217g 7.6 oz / 215g,1,5.9 mm 5.0 mm,Mid/forefoot,Half size small,Soft,Bad,Good,Decent,Breathable,Wide,Wide,Moderate,Stiff,Moderate,0,0,32.8 mm 34.5 mm,26.9 mm 29.5 mm,Normal,1,SummerAll seasons,1,#245 Bottom 33%,#125 Top 35%,,
+ASICS,Noosa Tri 16,"84
+ Good!",$135,Daily runningTempo,Neutral,7.7 oz / 217g 7.6 oz / 215g,1,5.9 mm 5.0 mm,Mid/forefoot,Half size small,Soft,Bad,Good,Decent,Breathable,Wide,Wide,Moderate,Stiff,Moderate,0,0,32.8 mm 34.5 mm,26.9 mm 29.5 mm,Normal,1,SummerAll seasons,1,#245 Bottom 33%,#125 Top 35%,,
+ASICS,Noosa Tri 16,"84
+ Good!",$135,Daily runningTempo,Neutral,7.7 oz / 217g 7.6 oz / 215g,1,5.9 mm 5.0 mm,Mid/forefoot,Half size small,Soft,Bad,Good,Decent,Breathable,Wide,Wide,Moderate,Stiff,Moderate,0,0,32.8 mm 34.5 mm,26.9 mm 29.5 mm,Normal,1,SummerAll seasons,1,#245 Bottom 33%,#125 Top 35%,,
+ASICS,Noosa Tri 16,"84
+ Good!",$135,Daily runningTempo,Neutral,7.7 oz / 217g 7.6 oz / 215g,1,5.9 mm 5.0 mm,Mid/forefoot,Half size small,Soft,Bad,Good,Decent,Breathable,Wide,Wide,Moderate,Stiff,Moderate,0,0,32.8 mm 34.5 mm,26.9 mm 29.5 mm,Normal,1,SummerAll seasons,1,#244 Bottom 33%,#125 Top 35%,,
+ASICS,Noosa Tri 16,"84
+ Good!",$135,Daily runningTempo,Neutral,7.7 oz / 217g 7.6 oz / 215g,1,5.9 mm 5.0 mm,Mid/forefoot,Half size small,Soft,Bad,Good,Decent,Breathable,Wide,Wide,Moderate,Stiff,Moderate,0,0,32.8 mm 34.5 mm,26.9 mm 29.5 mm,Normal,1,SummerAll seasons,1,#244 Bottom 33%,#125 Top 35%,,
+ASICS,Noosa Tri 16,"84
+ Good!",$135,Daily runningTempo,Neutral,7.7 oz / 217g 7.6 oz / 215g,1,5.9 mm 5.0 mm,Mid/forefoot,Half size small,Soft,Bad,Good,Decent,Breathable,Wide,Wide,Moderate,Stiff,Moderate,0,0,32.8 mm 34.5 mm,26.9 mm 29.5 mm,Normal,1,SummerAll seasons,1,#244 Bottom 33%,#125 Top 35%,,
+ASICS,Novablast 2,"89
+ Great!",$130,Daily runningTempo,Neutral,9.6 oz / 272g 9.9 oz / 280g,0,13.7 mm 8.0 mm,Heel,True to size,-,-,-,-,-,Medium,-,-,Flexible,-,0,1,39.3 mm 30.0 mm,25.6 mm 22.0 mm,Normal,1,-,1,#184 Top 29%,#465 Bottom 27%,,
+ASICS,Novablast 4,"92
+ Superb!",$140,Daily running,Neutral,9.1 oz / 259g 9 oz / 255g,0,9.0 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Good,Good,Moderate,Narrow,Medium,Stiff,Stiff,Moderate,0,1,39.2 mm 41.5 mm,30.2 mm 33.5 mm,NormalWide,1,All seasons,1,#22 Top 4%,#76 Top 12%,,
+ASICS,Novablast 5,"92
+ Superb!",$140,Daily runningTempo,Neutral,9 oz / 254g 9 oz / 255g,0,7.4 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Bad,Decent,Good,Moderate,Medium,Medium,Moderate,Stiff,Moderate,0,1,40.9 mm 41.5 mm,33.5 mm 33.5 mm,NormalWide,1,All seasons,1,#13 Top 4%,#6 Top 2%,,
+ASICS,Novablast 5,"92
+ Superb!",$140,Daily runningTempo,Neutral,9 oz / 254g 9 oz / 255g,0,7.4 mm 8.0 mm,Mid/forefoot,True to size,Soft,Bad,Decent,Good,Moderate,Medium,Medium,Moderate,Stiff,Moderate,0,1,40.9 mm 41.5 mm,33.5 mm 33.5 mm,NormalWide,1,All seasons,1,#13 Top 4%,#6 Top 2%,,Road
+ASICS,Novablast 5,"92
+ Superb!",$140,Daily runningTempo,Neutral,9 oz / 254g 9 oz / 255g,0,7.4 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Bad,Decent,Good,Moderate,Medium,Medium,Moderate,Stiff,Moderate,0,1,40.9 mm 41.5 mm,33.5 mm 33.5 mm,NormalWide,1,All seasons,1,#13 Top 4%,#6 Top 2%,,
+ASICS,Novablast 5,"92
+ Superb!",$140,Daily runningTempo,Neutral,9 oz / 254g 9 oz / 255g,0,7.4 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Bad,Decent,Good,Moderate,Medium,Medium,Moderate,Stiff,Moderate,0,1,40.9 mm 41.5 mm,33.5 mm 33.5 mm,NormalWide,1,All seasons,1,#13 Top 4%,#6 Top 2%,,
+ASICS,Novablast 5,"92
+ Superb!",$140,Daily runningTempo,Neutral,9 oz / 254g 9 oz / 255g,0,7.4 mm 8.0 mm,Mid/forefoot,True to size,Soft,Bad,Decent,Good,Moderate,Medium,Medium,Moderate,Stiff,Moderate,0,1,40.9 mm 41.5 mm,33.5 mm 33.5 mm,NormalWide,1,All seasons,1,#13 Top 4%,#6 Top 2%,,
+ASICS,Novablast 5,"92
+ Superb!",$140,Daily runningTempo,Neutral,9 oz / 254g 9 oz / 255g,0,7.4 mm 8.0 mm,Mid/forefoot,True to size,Soft,Bad,Decent,Good,Moderate,Medium,Medium,Moderate,Stiff,Moderate,0,1,40.9 mm 41.5 mm,33.5 mm 33.5 mm,NormalWide,1,All seasons,1,#15 Top 5%,#6 Top 2%,,
+ASICS,Novablast 5,"92
+ Superb!",$140,Daily runningTempo,Neutral,9 oz / 254g 9 oz / 255g,0,7.4 mm 8.0 mm,Mid/forefoot,True to size,Soft,Bad,Decent,Good,Moderate,Medium,Medium,Moderate,Stiff,Moderate,0,1,40.9 mm 41.5 mm,33.5 mm 33.5 mm,NormalWide,1,All seasons,1,#15 Top 5%,#6 Top 2%,,
+ASICS,Novablast 5,"92
+ Superb!",$140,Daily runningTempo,Neutral,9 oz / 254g 9 oz / 255g,0,7.4 mm 8.0 mm,Mid/forefoot,True to size,Soft,Bad,Decent,Good,Moderate,Medium,Medium,Moderate,Stiff,Moderate,0,1,40.9 mm 41.5 mm,33.5 mm 33.5 mm,NormalWide,1,All seasons,1,#15 Top 5%,#6 Top 2%,,
+Diadora,Nucleo 2,"81
+ Good!",$160,Daily running,Neutral,9.7 oz / 276g 9.7 oz / 275g,0,8.0 mm 5.0 mm,HeelMid/forefoot,True to size,Soft,Bad,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Moderate,0,0,38.7 mm 38.0 mm,30.7 mm 33.0 mm,NormalWide,1,All seasons,1,#294 Bottom 19%,#195 Bottom 46%,,
+Diadora,Nucleo 2,"81
+ Good!",$160,Daily running,Neutral,9.7 oz / 276g 9.7 oz / 275g,0,8.0 mm 5.0 mm,HeelMid/forefoot,True to size,Soft,Bad,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Moderate,0,0,38.7 mm 38.0 mm,30.7 mm 33.0 mm,NormalWide,1,All seasons,1,#296 Bottom 19%,#197 Bottom 46%,,
+Diadora,Nucleo 2,"81
+ Good!",$160,Daily running,Neutral,9.7 oz / 276g 9.7 oz / 275g,0,8.0 mm 5.0 mm,HeelMid/forefoot,True to size,Soft,Bad,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Moderate,0,0,38.7 mm 38.0 mm,30.7 mm 33.0 mm,NormalWide,1,All seasons,1,#296 Bottom 19%,#197 Bottom 46%,,
+Saucony,Omni 22,"77
+ Decent!",$140,Daily running,Stability,10.1 oz / 285g 10.1 oz / 286g,0,7.3 mm 8.0 mm,Mid/forefoot,True to size,Balanced,Bad,Good,Good,Moderate,Medium,Medium,Flexible,Moderate,Flexible,0,0,33.0 mm 35.0 mm,25.7 mm 27.0 mm,NormalWide,1,All seasons,1,#330 Bottom 9%,#273 Bottom 25%,,
+Altra,Paradigm 7,"82
+ Good!",$170,Daily running,Stability,9.3 oz / 264g 9.8 oz / 279g,0,0.1 mm 0.0 mm,Mid/forefoot,True to size,Soft,Decent,Decent,Good,Breathable,Wide,Wide,Moderate,Moderate,Flexible,0,0,27.6 mm 30.0 mm,27.5 mm 30.0 mm,NormalWide,1,SummerAll seasons,1,#283 Bottom 22%,#150 Top 41%,,
+Altra,Paradigm 7,"82
+ Good!",$170,Daily running,Stability,9.3 oz / 264g 9.8 oz / 279g,0,0.1 mm 0.0 mm,Mid/forefoot,True to size,Soft,Decent,Decent,Good,Breathable,Wide,Wide,Moderate,Moderate,Flexible,0,0,27.6 mm 30.0 mm,27.5 mm 30.0 mm,NormalWide,1,SummerAll seasons,1,#283 Bottom 22%,#150 Top 41%,,
+Nike,Pegasus 40,"86
+ Good!",$130,Daily running,Neutral,9.7 oz / 275g 9.7 oz / 275g,0,9.7 mm 10.0 mm,HeelMid/forefoot,True to size,Soft,Decent,-,-,Moderate,Medium,Medium,Moderate,Flexible,Flexible,0,0,30.2 mm 33.0 mm,20.5 mm 23.0 mm,NormalWideX-Wide,1,All seasons,1,#387 Bottom 40%,#93 Top 15%,,
+Nike,Pegasus 41,"88
+ Great!",$140,Daily running,Neutral,9.9 oz / 281g 10 oz / 283g,0,11.4 mm 10.0 mm,Heel,True to size,Soft,Decent,Good,Good,Moderate,Medium,Medium,Flexible,Flexible,Stiff,0,0,33.6 mm 37.0 mm,22.2 mm 27.0 mm,NormalWideX-Wide,1,All seasons,1,#130 Top 36%,#11 Top 4%,,
+Nike,Pegasus 41,"88
+ Great!",$140,Daily running,Neutral,9.9 oz / 281g 10 oz / 283g,0,11.4 mm 10.0 mm,Heel,True to size,Soft,Decent,Good,Good,Moderate,Medium,Medium,Flexible,Flexible,Stiff,0,0,33.6 mm 37.0 mm,22.2 mm 27.0 mm,NormalWideX-Wide,1,All seasons,1,#130 Top 36%,#11 Top 4%,,
+Nike,Pegasus 41,"88
+ Great!",$140,Daily running,Neutral,9.9 oz / 281g 10 oz / 283g,0,11.4 mm 10.0 mm,Heel,True to size,Soft,Decent,Good,Good,Moderate,Medium,Medium,Flexible,Flexible,Stiff,0,0,33.6 mm 37.0 mm,22.2 mm 27.0 mm,NormalWideX-Wide,1,All seasons,1,#130 Top 36%,#11 Top 4%,,
+Nike,Pegasus 41,"88
+ Great!",$140,Daily running,Neutral,9.9 oz / 281g 10 oz / 283g,0,11.4 mm 10.0 mm,Heel,True to size,Soft,Decent,Good,Good,Moderate,Medium,Medium,Flexible,Flexible,Stiff,0,0,33.6 mm 37.0 mm,22.2 mm 27.0 mm,NormalWideX-Wide,1,All seasons,1,#129 Top 36%,#11 Top 4%,,
+Nike,Pegasus 41,"88
+ Great!",$140,Daily running,Neutral,9.9 oz / 281g 10 oz / 283g,0,11.4 mm 10.0 mm,Heel,True to size,Soft,Decent,Good,Good,Moderate,Medium,Medium,Flexible,Flexible,Stiff,0,0,33.6 mm 37.0 mm,22.2 mm 27.0 mm,NormalWideX-Wide,1,All seasons,1,#129 Top 36%,#11 Top 4%,,
+Nike,Pegasus 41,"88
+ Great!",$140,Daily running,Neutral,9.9 oz / 281g 10 oz / 283g,0,11.4 mm 10.0 mm,Heel,True to size,Soft,Decent,Good,Good,Moderate,Medium,Medium,Flexible,Flexible,Stiff,0,0,33.6 mm 37.0 mm,22.2 mm 27.0 mm,NormalWideX-Wide,1,All seasons,1,#129 Top 36%,#11 Top 4%,,
+Nike,Pegasus 41,"88
+ Great!",$140,Daily running,Neutral,9.9 oz / 281g 10 oz / 283g,0,11.4 mm 10.0 mm,Heel,True to size,Soft,Decent,Good,Good,Moderate,Medium,Medium,Flexible,Flexible,Stiff,0,0,33.6 mm 37.0 mm,22.2 mm 27.0 mm,NormalWideX-Wide,1,All seasons,1,#129 Top 36%,#11 Top 4%,,
+Nike,Pegasus 41,"88
+ Great!",$140,Daily running,Neutral,9.9 oz / 281g 10 oz / 283g,0,11.4 mm 10.0 mm,Heel,True to size,Soft,Decent,Good,Good,Moderate,Medium,Medium,Flexible,Flexible,Stiff,0,0,33.6 mm 37.0 mm,22.2 mm 27.0 mm,NormalWideX-Wide,1,All seasons,1,#129 Top 36%,#11 Top 4%,,
+NIke,Pegasus 41,"88
+ Great!",$140,Daily running,Neutral,9.9 oz / 281g 10 oz / 283g,0,11.4 mm 10.0 mm,Heel,True to size,Soft,Decent,Good,Good,Moderate,Medium,Medium,Flexible,Flexible,Stiff,0,0,33.6 mm 37.0 mm,22.2 mm 27.0 mm,NormalWideX-Wide,1,All seasons,1,#129 Top 36%,#11 Top 4%,,
+Nike ,Pegasus 41,"88
+ Great!",$140,Daily running,Neutral,9.9 oz / 281g 10 oz / 283g,0,11.4 mm 10.0 mm,Heel,True to size,Soft,Decent,Good,Good,Moderate,Medium,Medium,Flexible,Flexible,Stiff,0,0,33.6 mm 37.0 mm,22.2 mm 27.0 mm,NormalWideX-Wide,1,All seasons,1,#129 Top 36%,#11 Top 4%,,
+Nike,Pegasus 41 GTX,"85
+ Good!",$160,Daily running,Neutral,11.1 oz / 315g 10 oz / 283g,0,11.9 mm 10.0 mm,Heel,True to size,Soft,Decent,Good,Bad,Warm,Medium,Medium,Moderate,Moderate,Moderate,0,1,36.3 mm 37.0 mm,24.4 mm 27.0 mm,Normal,1,Winter,1,#216 Bottom 40%,#159 Top 44%,,
+Nike,Pegasus EasyOn,"83
+ Good!",$140,Daily running,Neutral,10.1 oz / 286g 10.4 oz / 295g,0,11.6 mm 10.0 mm,Heel,True to size,Soft,Bad,Good,Decent,Breathable,Medium,Medium,Stiff,Moderate,Stiff,0,0,33.6 mm 34.0 mm,22.0 mm 24.0 mm,Normal,1,SummerAll seasons,1,#265 Bottom 27%,#197 Bottom 46%,,
+Nike,Pegasus EasyOn,"83
+ Good!",$140,Daily running,Neutral,10.1 oz / 286g 10.4 oz / 295g,0,11.6 mm 10.0 mm,Heel,True to size,Soft,Bad,Good,Decent,Breathable,Medium,Medium,Stiff,Moderate,Stiff,0,0,33.6 mm 34.0 mm,22.0 mm 24.0 mm,Normal,1,SummerAll seasons,1,#266 Bottom 27%,#197 Bottom 46%,,
+Nike,Pegasus Plus,"90
+ Superb!",$180,Daily runningTempo,Neutral,8.6 oz / 244g 8.6 oz / 244g,1,9.4 mm 10.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Good,Decent,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,33.0 mm 34.0 mm,23.6 mm 24.0 mm,Normal,1,All seasons,1,#58 Top 16%,#46 Top 13%,,
+Nike,Pegasus Plus,"90
+ Superb!",$180,Daily runningTempo,Neutral,8.6 oz / 244g 8.6 oz / 244g,1,9.4 mm 10.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Good,Decent,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,33.0 mm 34.0 mm,23.6 mm 24.0 mm,Normal,1,All seasons,1,#58 Top 16%,#46 Top 13%,,
+Nike,Pegasus Plus,"90
+ Superb!",$180,Daily runningTempo,Neutral,8.6 oz / 244g 8.6 oz / 244g,1,9.4 mm 10.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Good,Decent,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,33.0 mm 34.0 mm,23.6 mm 24.0 mm,Normal,1,All seasons,1,#58 Top 16%,#46 Top 13%,,
+Nike,Pegasus Plus,"90
+ Superb!",$180,Daily runningTempo,Neutral,8.6 oz / 244g 8.6 oz / 244g,1,9.4 mm 10.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Good,Decent,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,33.0 mm 34.0 mm,23.6 mm 24.0 mm,NarrowNormal,1,All seasons,1,#41 Top 12%,#46 Top 13%,,
+Nike,Pegasus Premium,"84
+ Good!",$210,Daily runningTempo,Neutral,10.9 oz / 308g 10.9 oz / 309g,0,11.8 mm 10.0 mm,Heel,True to size,Soft,Bad,Good,Decent,Breathable,Narrow,Narrow,Stiff,Stiff,Stiff,0,1,42.8 mm 45.0 mm,31.0 mm 35.0 mm,Normal,1,SummerAll seasons,1,#243 Bottom 33%,#15 Top 5%,,
+Nike,Pegasus Turbo,"78
+ Decent!",$150,Tempo,Neutral,9.7 oz / 275g 9.7 oz / 275g,0,10.0 mm 10.0 mm,HeelMid/forefoot,Slightly small,Soft,-,-,-,Moderate,Narrow,Narrow,Stiff,Moderate,Flexible,0,0,32.0 mm 32.0 mm,22.0 mm 22.0 mm,Normal,1,All seasons,1,#328 Bottom 10%,#150 Top 42%,,
+Nike,Pegasus Turbo,"78
+ Decent!",$150,Tempo,Neutral,9.7 oz / 275g 9.7 oz / 275g,0,10.0 mm 10.0 mm,HeelMid/forefoot,Slightly small,Soft,-,-,-,Moderate,Narrow,Narrow,Stiff,Moderate,Flexible,0,0,32.0 mm 32.0 mm,22.0 mm 22.0 mm,Normal,1,All seasons,1,#327 Bottom 10%,#151 Top 42%,,
+Salomon,Phantasm 2,"75
+ Bad!",$170,Daily runningTempo,Neutral,9.2 oz / 261g 9 oz / 255g,0,11.2 mm 9.0 mm,Heel,Slightly small,Balanced,Bad,Good,Decent,Moderate,Medium,Wide,Stiff,Stiff,Moderate,0,0,34.4 mm 35.0 mm,23.2 mm 26.0 mm,Normal,1,All seasons,1,#196 Bottom 46%,#327 Bottom 10%,,
+Topo,Phantom 3,"89
+ Great!",$145,Daily running,Neutral,9.5 oz / 269g 9.2 oz / 261g,0,5.8 mm 5.0 mm,Mid/forefoot,True to size,Soft,Decent,Good,Good,Moderate,Medium,Wide,Moderate,Stiff,Stiff,0,0,32.6 mm 32.0 mm,26.8 mm 27.0 mm,NormalWide,1,All seasons,1,#90 Top 25%,#169 Top 47%,,
+Topo,Phantom 3,"89
+ Great!",$145,Daily running,Neutral,9.5 oz / 269g 9.2 oz / 261g,0,5.8 mm 5.0 mm,Mid/forefoot,True to size,Soft,Decent,Good,Good,Moderate,Medium,Wide,Moderate,Stiff,Stiff,0,0,32.6 mm 32.0 mm,26.8 mm 27.0 mm,NormalWide,1,All seasons,1,#92 Top 26%,#170 Top 47%,,
+New Balance,Propel v4,"86
+ Good!",$110,Daily running,Neutral,9.7 oz / 276g 10.7 oz / 302g,0,4.5 mm 6.0 mm,Mid/forefoot,True to size,Soft,Decent,Good,Good,Moderate,Narrow,Medium,Moderate,Stiff,Moderate,0,0,33.7 mm 33.5 mm,29.2 mm 27.5 mm,NormalWide,1,All seasons,1,#396 Bottom 38%,#432 Bottom 33%,,
+Altra,Provision 6,"86
+ Good!",$140,Daily running,Stability,9.1 oz / 259g 10.8 oz / 307g,0,0.0 mm,Mid/forefoot,Slightly small,-,-,-,-,-,Medium,-,Stiff,-,-,0,0,28.0 mm,28.0 mm,Normal,0,-,0,#352 Bottom 45%,#572 Bottom 11%,,
+Altra,Provision 7,"83
+ Good!",$140,Daily running,Stability,9.1 oz / 259g 9.7 oz / 274g,0,4.8 mm 0.0 mm,Mid/forefoot,True to size,Balanced,Decent,Good,Good,Breathable,Narrow,Wide,Stiff,Stiff,Flexible,0,0,32.6 mm 28.0 mm,27.8 mm 28.0 mm,Normal,1,SummerAll seasons,1,#483 Bottom 25%,#472 Bottom 26%,,
+Altra,Provision 8,"84
+ Good!",$140,Daily running,Stability,9.6 oz / 273g 10.2 oz / 289g,0,0.2 mm 0.0 mm,Mid/forefoot,True to size,Balanced,Decent,Decent,Good,Moderate,Medium,Wide,Stiff,Moderate,Flexible,0,0,26.0 mm 28.0 mm,25.8 mm 28.0 mm,Normal,1,All seasons,1,#246 Bottom 32%,#229 Bottom 37%,,
+Altra,Provision 8,"84
+ Good!",$140,Daily running,Stability,9.6 oz / 273g 10.2 oz / 289g,0,0.2 mm 0.0 mm,Mid/forefoot,True to size,Balanced,Decent,Decent,Good,Moderate,Medium,Wide,Stiff,Moderate,Flexible,0,0,26.0 mm 28.0 mm,25.8 mm 28.0 mm,Normal,1,All seasons,1,#246 Bottom 32%,#229 Bottom 37%,,
+Altra,Provision 8,"84
+ Good!",$140,Daily running,Stability,9.6 oz / 273g 10.2 oz / 289g,0,0.2 mm 0.0 mm,Mid/forefoot,True to size,Balanced,Decent,Decent,Good,Moderate,Medium,Wide,Stiff,Moderate,Flexible,0,0,26.0 mm 28.0 mm,25.8 mm 28.0 mm,Normal,1,All seasons,1,#246 Bottom 32%,#229 Bottom 37%,,
+Altra,Provision 8,"84
+ Good!",$140,Daily running,Stability,9.6 oz / 273g 10.2 oz / 289g,0,0.2 mm 0.0 mm,Mid/forefoot,True to size,Balanced,Decent,Decent,Good,Moderate,Medium,Wide,Stiff,Moderate,Flexible,0,0,26.0 mm 28.0 mm,25.8 mm 28.0 mm,Normal,1,All seasons,1,#246 Bottom 32%,#229 Bottom 37%,,
+Adidas,Pureboost 23,"81
+ Good!",$140,Daily running,Neutral,10.8 oz / 305g 10.8 oz / 307g,0,11.5 mm 10.0 mm,Heel,True to size,Soft,Decent,Good,Good,Breathable,Medium,Wide,Flexible,Flexible,Stiff,0,0,27.6 mm 22.0 mm,16.1 mm 12.0 mm,NormalWide,1,SummerAll seasons,1,#291 Bottom 20%,#288 Bottom 21%,,
+Adidas,Pureboost 23,"81
+ Good!",$140,Daily running,Neutral,10.8 oz / 305g 10.8 oz / 307g,0,11.5 mm 10.0 mm,Heel,True to size,Soft,Decent,Good,Good,Breathable,Medium,Wide,Flexible,Flexible,Stiff,0,0,27.6 mm 22.0 mm,16.1 mm 12.0 mm,NormalWide,1,SummerAll seasons,1,#292 Bottom 20%,#288 Bottom 21%,,
+Adidas,Pureboost 5,"84
+ Good!",$130,Daily running,Neutral,9 oz / 254g 9.5 oz / 270g,0,9.2 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Good,Decent,Moderate,Medium,Wide,Moderate,Moderate,Moderate,0,0,31.2 mm 33.0 mm,22.0 mm 23.0 mm,Normal,1,All seasons,1,#474 Bottom 26%,#471 Bottom 26%,,
+Nike,Quest 4,"83
+ Good!",$75,Daily running,Neutral,9.5 oz / 268g 9.5 oz / 268g,0,13.7 mm,Heel,Slightly small,-,-,-,-,-,Medium,-,Stiff,Stiff,Stiff,0,0,32.1 mm,18.4 mm,Normal,0,-,0,#494 Bottom 23%,#550 Bottom 14%,,
+Nike,Quest 5,"81
+ Good!",$80,Daily running,Neutral,9.6 oz / 273g 10.4 oz / 295g,0,9.1 mm,HeelMid/forefoot,True to size,Balanced,-,-,-,Moderate,Narrow,Narrow,Moderate,Moderate,Flexible,0,0,29.8 mm,20.7 mm,NarrowNormal,1,All seasons,1,#297 Bottom 18%,#173 Top 48%,,
+Nike,Quest 5,"81
+ Good!",$80,Daily running,Neutral,9.6 oz / 273g 10.4 oz / 295g,0,9.1 mm,HeelMid/forefoot,True to size,Balanced,-,-,-,Moderate,Narrow,Narrow,Moderate,Moderate,Flexible,0,0,29.8 mm,20.7 mm,NarrowNormal,1,All seasons,1,#298 Bottom 18%,#174 Top 48%,,
+Nike,Quest 5,"81
+ Good!",$80,Daily running,Neutral,9.6 oz / 273g 10.4 oz / 295g,0,9.1 mm,HeelMid/forefoot,True to size,Balanced,-,-,-,Moderate,Narrow,Narrow,Moderate,Moderate,Flexible,0,0,29.8 mm,20.7 mm,NarrowNormal,1,All seasons,1,#296 Bottom 19%,#174 Top 48%,,
+Nike,Quest 5,"81
+ Good!",$80,Daily running,Neutral,9.6 oz / 273g 10.4 oz / 295g,0,9.1 mm,HeelMid/forefoot,True to size,Balanced,-,-,-,Moderate,Narrow,Narrow,Moderate,Moderate,Flexible,0,0,29.8 mm,20.7 mm,NarrowNormal,1,All seasons,1,#295 Bottom 19%,#174 Top 48%,,
+Nike,Quest 5,"81
+ Good!",$80,Daily running,Neutral,9.6 oz / 273g 10.4 oz / 295g,0,9.1 mm,HeelMid/forefoot,True to size,Balanced,-,-,-,Moderate,Narrow,Narrow,Moderate,Moderate,Flexible,0,0,29.8 mm,20.7 mm,NarrowNormal,1,All seasons,1,#295 Bottom 19%,#174 Top 48%,,
+Nike,Quest 5,"81
+ Good!",$80,Daily running,Neutral,9.6 oz / 273g 10.4 oz / 295g,0,9.1 mm,HeelMid/forefoot,True to size,Balanced,-,-,-,Moderate,Narrow,Narrow,Moderate,Moderate,Flexible,0,0,29.8 mm,20.7 mm,NarrowNormal,1,All seasons,1,#295 Bottom 19%,#174 Top 48%,,
+Nike,Quest 5,"81
+ Good!",$80,Daily running,Neutral,9.6 oz / 273g 10.4 oz / 295g,0,9.1 mm,HeelMid/forefoot,True to size,Balanced,-,-,-,Moderate,Narrow,Narrow,Moderate,Moderate,Flexible,0,0,29.8 mm,20.7 mm,NarrowNormal,1,All seasons,1,#295 Bottom 19%,#174 Top 48%,,
+Nike,Quest 5,"81
+ Good!",$80,Daily running,Neutral,9.6 oz / 273g 10.4 oz / 295g,0,9.1 mm,HeelMid/forefoot,True to size,Balanced,-,-,-,Moderate,Narrow,Narrow,Moderate,Moderate,Flexible,0,0,29.8 mm,20.7 mm,NarrowNormal,1,All seasons,1,#295 Bottom 19%,#174 Top 48%,,
+Nike,Quest 5,"81
+ Good!",$80,Daily running,Neutral,9.6 oz / 273g 10.4 oz / 295g,0,9.1 mm,HeelMid/forefoot,True to size,Balanced,-,-,-,Moderate,Narrow,Narrow,Moderate,Moderate,Flexible,0,0,29.8 mm,20.7 mm,NarrowNormal,1,All seasons,1,#295 Bottom 19%,#174 Top 48%,,
+Adidas,Questar,"82
+ Good!",$75,Daily running,Neutral,10.9 oz / 310g 10.9 oz / 310g,0,10.5 mm 10.0 mm,Heel,True to size,Balanced,-,-,-,Breathable,Narrow,-,Stiff,Stiff,Moderate,0,0,32.6 mm 32.0 mm,22.1 mm 22.0 mm,Normal,1,SummerAll seasons,1,#527 Bottom 18%,#528 Bottom 18%,,
+Adidas,Questar 3,"83
+ Good!",$75,Daily running,Neutral,10.4 oz / 295g 10.8 oz / 306g,0,8.5 mm 5.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,33.5 mm 29.0 mm,25.0 mm 24.0 mm,Normal,1,All seasons,1,#270 Bottom 26%,#173 Top 48%,,
+Adidas,Questar 3,"83
+ Good!",$75,Daily running,Neutral,10.4 oz / 295g 10.8 oz / 306g,0,8.5 mm 5.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,33.5 mm 29.0 mm,25.0 mm 24.0 mm,Normal,1,All seasons,1,#270 Bottom 26%,#173 Top 48%,,
+Adidas,Questar 3,"83
+ Good!",$75,Daily running,Neutral,10.4 oz / 295g 10.8 oz / 306g,0,8.5 mm 5.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,33.5 mm 29.0 mm,25.0 mm 24.0 mm,Normal,1,All seasons,1,#270 Bottom 26%,#173 Top 48%,,
+Adidas,Racer TR21,"84
+ Good!",$75,Daily running,Neutral,11.2 oz / 318g 10.1 oz / 285g,0,12.9 mm 8.0 mm,Heel,True to size,Balanced,Bad,Bad,Decent,Moderate,Medium,Medium,Moderate,Moderate,Flexible,0,0,31.9 mm 34.0 mm,19.0 mm 26.0 mm,NormalWide,1,All seasons,1,#239 Bottom 34%,#313 Bottom 14%,,
+Adidas,Racer TR21,"84
+ Good!",$75,Daily running,Neutral,11.2 oz / 318g 10.1 oz / 285g,0,12.9 mm 8.0 mm,Heel,True to size,Balanced,Bad,Bad,Decent,Moderate,Medium,Medium,Moderate,Moderate,Flexible,0,0,31.9 mm 34.0 mm,19.0 mm 26.0 mm,NormalWide,1,All seasons,1,#239 Bottom 34%,#313 Bottom 14%,,
+Adidas,Racer TR21,"84
+ Good!",$75,Daily running,Neutral,11.2 oz / 318g 10.1 oz / 285g,0,12.9 mm 8.0 mm,Heel,True to size,Balanced,Bad,Bad,Decent,Moderate,Medium,Medium,Moderate,Moderate,Flexible,0,0,31.9 mm 34.0 mm,19.0 mm 26.0 mm,NormalWide,1,All seasons,1,#239 Bottom 34%,#313 Bottom 14%,,
+Adidas,Racer TR21,"84
+ Good!",$75,Daily running,Neutral,11.2 oz / 318g 10.1 oz / 285g,0,12.9 mm 8.0 mm,Heel,True to size,Balanced,Bad,Bad,Decent,Moderate,Medium,Medium,Moderate,Moderate,Flexible,0,0,31.9 mm 34.0 mm,19.0 mm 26.0 mm,NormalWide,1,All seasons,1,#239 Bottom 34%,#313 Bottom 14%,,
+Adidas,Racer TR21,"84
+ Good!",$75,Daily running,Neutral,11.2 oz / 318g 10.1 oz / 285g,0,12.9 mm 8.0 mm,Heel,True to size,Balanced,Bad,Bad,Decent,Moderate,Medium,Medium,Moderate,Moderate,Flexible,0,0,31.9 mm 34.0 mm,19.0 mm 26.0 mm,NormalWide,1,All seasons,1,#239 Bottom 34%,#313 Bottom 14%,,
+Jordan,React Havoc,"83
+ Good!",$130,Daily running,Neutral,9.5 oz / 268g 10.8 oz / 306g,0,10.2 mm 9.0 mm,Heel,-,-,-,-,-,-,Narrow,-,Stiff,-,-,0,0,32.3 mm 28.0 mm,22.1 mm 19.0 mm,Normal,0,-,0,#258 Bottom 29%,#339 Bottom 7%,,
+Nike,React Infinity Run Flyknit 3,"87
+ Great!",$160,Daily running,Neutral,10.5 oz / 297g 10.9 oz / 310g,0,6.7 mm 8.0 mm,Mid/forefoot,Slightly small,Soft,-,-,-,Moderate,Medium,-,Stiff,Moderate,Flexible,0,0,30.0 mm 34.0 mm,23.3 mm 26.0 mm,Normal,1,All seasons,1,#341 Bottom 46%,#46 Top 8%,,
+Nike,React Miler 3,"74
+ Bad!",$120,Daily running,Neutral,10.5 oz / 297g 11.3 oz / 321g,0,9.8 mm 10.0 mm,HeelMid/forefoot,True to size,Soft,-,-,-,-,Narrow,-,Stiff,Moderate,Stiff,0,0,31.4 mm,21.6 mm,Normal,1,-,1,#351 Bottom 3%,#343 Bottom 6%,,
+Nike,React Miler 3,"74
+ Bad!",$120,Daily running,Neutral,10.5 oz / 297g 11.3 oz / 321g,0,9.8 mm 10.0 mm,HeelMid/forefoot,True to size,Soft,-,-,-,-,Narrow,-,Stiff,Moderate,Stiff,0,0,31.4 mm,21.6 mm,Normal,1,-,1,#352 Bottom 3%,#344 Bottom 6%,,
+Nike,React Miler 3,"74
+ Bad!",$120,Daily running,Neutral,10.5 oz / 297g 11.3 oz / 321g,0,9.8 mm 10.0 mm,HeelMid/forefoot,True to size,Soft,-,-,-,-,Narrow,-,Stiff,Moderate,Stiff,0,0,31.4 mm,21.6 mm,Normal,1,-,1,#352 Bottom 3%,#344 Bottom 6%,,
+Nike,Renew Ride 2,"76
+ Decent!",$75,Daily running,Neutral,10.1 oz / 286g 12.3 oz / 350g,0,8.3 mm 9.0 mm,HeelMid/forefoot,True to size,-,-,-,-,-,Medium,-,Stiff,-,-,0,0,37.6 mm,29.3 mm,Normal,0,-,0,#623 Bottom 3%,#623 Bottom 3%,,
+Nike,Renew Ride 3,"75
+ Bad!",$80,Daily running,Neutral,10.2 oz / 290g 10.2 oz / 289g,0,10.2 mm 10.0 mm,Heel,Slightly small,Balanced,Bad,Bad,-,Moderate,Narrow,Medium,Stiff,Moderate,Stiff,0,0,36.4 mm,26.2 mm,Normal,1,All seasons,1,#350 Bottom 4%,#303 Bottom 17%,,
+Nike,Renew Ride 3,"75
+ Bad!",$80,Daily running,Neutral,10.2 oz / 290g 10.2 oz / 289g,0,10.2 mm 10.0 mm,Heel,Slightly small,Balanced,Bad,Bad,-,Moderate,Narrow,Medium,Stiff,Moderate,Stiff,0,0,36.4 mm,26.2 mm,Normal,1,All seasons,1,#349 Bottom 4%,#303 Bottom 17%,,
+Nike,Renew Ride 3,"75
+ Bad!",$80,Daily running,Neutral,10.2 oz / 290g 10.2 oz / 289g,0,10.2 mm 10.0 mm,Heel,Slightly small,Balanced,Bad,Bad,-,Moderate,Narrow,Medium,Stiff,Moderate,Stiff,0,0,36.4 mm,26.2 mm,Normal,1,All seasons,1,#349 Bottom 4%,#303 Bottom 17%,,
+Nike,Renew Ride 3,"75
+ Bad!",$80,Daily running,Neutral,10.2 oz / 290g 10.2 oz / 289g,0,10.2 mm 10.0 mm,Heel,Slightly small,Balanced,Bad,Bad,-,Moderate,Narrow,Medium,Stiff,Moderate,Stiff,0,0,36.4 mm,26.2 mm,Normal,1,All seasons,1,#349 Bottom 4%,#303 Bottom 17%,,
+Brooks,Revel 5,"87
+ Great!",$100,Daily running,Neutral,8.7 oz / 247g 8.8 oz / 249g,1,9.6 mm 8.0 mm,HeelMid/forefoot,Slightly large,-,-,-,-,Moderate,Medium,-,Stiff,Moderate,Moderate,0,0,28.5 mm 20.0 mm,18.9 mm 12.0 mm,Normal,1,All seasons,1,#297 Top 47%,#446 Bottom 30%,,
+Brooks,Revel 6,"86
+ Good!",$100,Daily running,Neutral,9.2 oz / 261g 8.8 oz / 249g,0,13.2 mm 10.0 mm,Heel,True to size,Balanced,Bad,Bad,Good,Moderate,Narrow,Medium,Stiff,Stiff,Stiff,0,0,32.9 mm,19.7 mm,Normal,1,All seasons,1,#395 Bottom 38%,#313 Top 49%,,
+Brooks,Revel 7,"83
+ Good!",$100,Daily running,Neutral,9.1 oz / 258g 9.1 oz / 258g,0,9.8 mm 10.0 mm,HeelMid/forefoot,True to size,Soft,Good,Decent,Good,Breathable,Medium,Medium,Moderate,Stiff,Stiff,0,0,32.0 mm,22.2 mm,Normal,1,SummerAll seasons,1,#254 Bottom 30%,#65 Top 18%,,
+Brooks,Revel 7,"83
+ Good!",$100,Daily running,Neutral,9.1 oz / 258g 9.1 oz / 258g,0,9.8 mm 10.0 mm,HeelMid/forefoot,True to size,Soft,Good,Decent,Good,Breathable,Medium,Medium,Moderate,Stiff,Stiff,0,0,32.0 mm,22.2 mm,Normal,1,SummerAll seasons,1,#255 Bottom 30%,#65 Top 18%,,
+Brooks,Revel 7,"83
+ Good!",$100,Daily running,Neutral,9.1 oz / 258g 9.1 oz / 258g,0,9.8 mm 10.0 mm,HeelMid/forefoot,True to size,Soft,Good,Decent,Good,Breathable,Medium,Medium,Moderate,Stiff,Stiff,0,0,32.0 mm,22.2 mm,Normal,1,SummerAll seasons,1,#253 Bottom 30%,#65 Top 18%,,
+Brooks,Revel 7,"83
+ Good!",$100,Daily running,Neutral,9.1 oz / 258g 9.1 oz / 258g,0,9.8 mm 10.0 mm,HeelMid/forefoot,True to size,Soft,Good,Decent,Good,Breathable,Medium,Medium,Moderate,Stiff,Stiff,0,0,32.0 mm,22.2 mm,Normal,1,SummerAll seasons,1,#253 Bottom 30%,#65 Top 18%,,
+Brooks,Revel 7,"83
+ Good!",$100,Daily running,Neutral,9.1 oz / 258g 9.1 oz / 258g,0,9.8 mm 10.0 mm,HeelMid/forefoot,True to size,Soft,Good,Decent,Good,Breathable,Medium,Medium,Moderate,Stiff,Stiff,0,0,32.0 mm,22.2 mm,Normal,1,SummerAll seasons,1,#253 Bottom 30%,#65 Top 18%,,
+Brooks,Revel 7,"83
+ Good!",$100,Daily running,Neutral,9.1 oz / 258g 9.1 oz / 258g,0,9.8 mm 10.0 mm,HeelMid/forefoot,True to size,Soft,Good,Decent,Good,Breathable,Medium,Medium,Moderate,Stiff,Stiff,0,0,32.0 mm,22.2 mm,Normal,1,SummerAll seasons,1,#253 Bottom 30%,#65 Top 18%,,
+Brooks,Revel 7,"83
+ Good!",$100,Daily running,Neutral,9.1 oz / 258g 9.1 oz / 258g,0,9.8 mm 10.0 mm,HeelMid/forefoot,True to size,Soft,Good,Decent,Good,Breathable,Medium,Medium,Moderate,Stiff,Stiff,0,0,32.0 mm,22.2 mm,Normal,1,SummerAll seasons,1,#253 Bottom 30%,#65 Top 18%,,
+Brooks,Revel 7,"83
+ Good!",$100,Daily running,Neutral,9.1 oz / 258g 9.1 oz / 258g,0,9.8 mm 10.0 mm,HeelMid/forefoot,True to size,Soft,Good,Decent,Good,Breathable,Medium,Medium,Moderate,Stiff,Stiff,0,0,32.0 mm,22.2 mm,Normal,1,SummerAll seasons,1,#253 Bottom 30%,#65 Top 18%,,
+Brooks,Revel 7,"83
+ Good!",$100,Daily running,Neutral,9.1 oz / 258g 9.1 oz / 258g,0,9.8 mm 10.0 mm,HeelMid/forefoot,True to size,Soft,Good,Decent,Good,Breathable,Medium,Medium,Moderate,Stiff,Stiff,0,0,32.0 mm,22.2 mm,Normal,1,SummerAll seasons,1,#253 Bottom 30%,#65 Top 18%,,
+Brooks,Revel 7,"83
+ Good!",$100,Daily running,Neutral,9.1 oz / 258g 9.1 oz / 258g,0,9.8 mm 10.0 mm,HeelMid/forefoot,True to size,Soft,Good,Decent,Good,Breathable,Medium,Medium,Moderate,Stiff,Stiff,0,0,32.0 mm,22.2 mm,Normal,1,SummerAll seasons,1,#253 Bottom 30%,#65 Top 18%,,
+Nike,Revolution 6,"83
+ Good!",$65,Daily running,Neutral,9.2 oz / 262g 9.2 oz / 262g,0,10.6 mm 10.0 mm,Heel,Slightly small,-,-,-,-,-,Wide,-,Stiff,-,-,0,0,33.8 mm 24.0 mm,23.1 mm 14.0 mm,NormalWideX-Wide,0,-,0,#500 Bottom 22%,#318 Top 50%,,
+Nike,Revolution 7,"80
+ Good!",$70,Daily running,Neutral,9.9 oz / 281g 10 oz / 283g,0,10.5 mm 10.0 mm,Heel,True to size,Balanced,Bad,Good,Decent,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,31.5 mm 31.0 mm,21.0 mm 21.0 mm,NormalWideX-Wide,1,All seasons,1,#576 Bottom 10%,#121 Top 19%,,
+Nike,Revolution 7 EasyOn,"80
+ Good!",$70,Daily running,Neutral,9.7 oz / 275g,0,9.9 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Decent,Breathable,Narrow,Narrow,Moderate,Moderate,Stiff,0,0,31.7 mm,21.8 mm,Normal,1,SummerAll seasons,1,#306 Bottom 16%,#311 Bottom 15%,,
+Nike,Revolution 8,"78
+ Decent!",$70,Daily running,Neutral,9.3 oz / 264g 9.5 oz / 270g,0,8.8 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Good,Decent,Moderate,Medium,Medium,Flexible,Moderate,Stiff,0,0,31.8 mm 33.0 mm,23.0 mm 23.0 mm,NormalWideX-Wide,1,All seasons,1,#322 Bottom 12%,#66 Top 19%,,
+Nike,Revolution 8,"78
+ Decent!",$70,Daily running,Neutral,9.3 oz / 264g 9.5 oz / 270g,0,8.8 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Good,Decent,Moderate,Medium,Medium,Flexible,Moderate,Stiff,0,0,31.8 mm 33.0 mm,23.0 mm 23.0 mm,NormalWideX-Wide,1,All seasons,1,#322 Bottom 12%,#66 Top 19%,,
+Brooks,Ricochet 3,"85
+ Good!",$120,Daily running,Neutral,9.1 oz / 258g 9.4 oz / 266g,0,6.8 mm 8.0 mm,Mid/forefoot,-,-,-,-,-,-,Medium,-,-,Moderate,-,0,0,27.6 mm 24.0 mm,20.8 mm 16.0 mm,Normal,1,-,1,#205 Bottom 43%,#321 Bottom 12%,,
+Saucony,Ride 14,"89
+ Great!",$130,Daily running,Neutral,10.1 oz / 286g 9.9 oz / 281g,0,9.5 mm 8.0 mm,HeelMid/forefoot,Slightly large,-,-,-,-,Moderate,Medium,-,Stiff,Flexible,Stiff,0,0,33.9 mm 32.0 mm,24.4 mm 24.0 mm,NormalWide,1,All seasons,1,#181 Top 29%,#464 Bottom 28%,,
+Saucony,Ride 15,"89
+ Great!",$130,Daily running,Neutral,8.9 oz / 253g 9 oz / 255g,0,6.9 mm 8.0 mm,Mid/forefoot,True to size,Balanced,-,-,-,-,Narrow,-,Stiff,Moderate,Stiff,0,0,31.4 mm 35.0 mm,24.5 mm 27.0 mm,NormalWide,1,-,1,#207 Top 33%,#387 Bottom 40%,,
+Saucony,Ride 16,"87
+ Great!",$140,Daily running,Neutral,9.3 oz / 264g 8.8 oz / 250g,0,7.9 mm 8.0 mm,Mid/forefoot,Slightly small,Balanced,Bad,Bad,Good,Breathable,Narrow,Medium,Moderate,Flexible,Stiff,0,0,33.3 mm 35.0 mm,25.4 mm 27.0 mm,NormalWide,1,SummerAll seasons,1,#296 Top 46%,#351 Bottom 45%,,
+Saucony,Ride 17,"89
+ Great!",$140,Daily running,Neutral,10.2 oz / 288g 9.9 oz / 282g,0,8.5 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Good,Good,Warm,Medium,Medium,Moderate,Moderate,Moderate,0,1,35.1 mm 35.0 mm,26.6 mm 27.0 mm,NormalWide,1,All seasons,1,#134 Top 21%,#155 Top 25%,,
+Saucony,Ride 18,"89
+ Great!",$140,Daily running,Neutral,9 oz / 255g 9.5 oz / 269g,0,8.4 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Good,Breathable,Medium,Medium,Moderate,Flexible,Flexible,0,0,35.0 mm 37.0 mm,26.6 mm 29.0 mm,NormalWide,1,SummerAll seasons,1,#98 Top 27%,#54 Top 15%,,
+Saucony,Ride 18,"89
+ Great!",$140,Daily running,Neutral,9 oz / 255g 9.5 oz / 269g,0,8.4 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Good,Breathable,Medium,Medium,Moderate,Flexible,Flexible,0,0,35.0 mm 37.0 mm,26.6 mm 29.0 mm,NormalWide,1,SummerAll seasons,1,#99 Top 28%,#54 Top 15%,,
+Saucony,Ride 18,"89
+ Great!",$140,Daily running,Neutral,9 oz / 255g 9.5 oz / 269g,0,8.4 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Good,Breathable,Medium,Medium,Moderate,Flexible,Flexible,0,0,35.0 mm 37.0 mm,26.6 mm 29.0 mm,NormalWide,1,SummerAll seasons,1,#98 Top 27%,#54 Top 15%,,
+Saucony,Ride 18,"89
+ Great!",$140,Daily running,Neutral,9 oz / 255g 9.5 oz / 269g,0,8.4 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Good,Breathable,Medium,Medium,Moderate,Flexible,Flexible,0,0,35.0 mm 37.0 mm,26.6 mm 29.0 mm,NormalWide,1,SummerAll seasons,1,#99 Top 28%,#54 Top 15%,,
+Saucony,Ride 18,"89
+ Great!",$140,Daily running,Neutral,9 oz / 255g 9.5 oz / 269g,0,8.4 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Good,Breathable,Medium,Medium,Moderate,Flexible,Flexible,0,0,35.0 mm 37.0 mm,26.6 mm 29.0 mm,NormalWide,1,SummerAll seasons,1,#98 Top 27%,#54 Top 15%,,
+Hoka,Rincon 4,"80
+ Good!",$125,Daily running,Stability,8.1 oz / 231g 8 oz / 228g,1,9.4 mm 5.0 mm,HeelMid/forefoot,Slightly small,Soft,Bad,Decent,Bad,Breathable,Narrow,Narrow,Moderate,Stiff,Stiff,0,1,36.0 mm 33.0 mm,26.6 mm 28.0 mm,NormalWide,1,SummerAll seasons,1,#305 Bottom 16%,#80 Top 22%,,
+Hoka,Rincon 4,"80
+ Good!",$125,Daily running,Stability,8.1 oz / 231g 8 oz / 228g,1,9.4 mm 5.0 mm,HeelMid/forefoot,Slightly small,Soft,Bad,Decent,Bad,Breathable,Narrow,Narrow,Moderate,Stiff,Stiff,0,1,36.0 mm 33.0 mm,26.6 mm 28.0 mm,NormalWide,1,SummerAll seasons,1,#305 Bottom 16%,#80 Top 22%,,
+Hoka,Rincon 4,"80
+ Good!",$125,Daily running,Stability,8.1 oz / 231g 8 oz / 228g,1,9.4 mm 5.0 mm,HeelMid/forefoot,Slightly small,Soft,Bad,Decent,Bad,Breathable,Narrow,Narrow,Moderate,Stiff,Stiff,0,1,36.0 mm 33.0 mm,26.6 mm 28.0 mm,NormalWide,1,SummerAll seasons,1,#301 Bottom 17%,#80 Top 22%,,
+Hoka,Rincon 4,"80
+ Good!",$125,Daily running,Stability,8.1 oz / 231g 8 oz / 228g,1,9.4 mm 5.0 mm,HeelMid/forefoot,Slightly small,Soft,Bad,Decent,Bad,Breathable,Narrow,Narrow,Moderate,Stiff,Stiff,0,1,36.0 mm 33.0 mm,26.6 mm 28.0 mm,NormalWide,1,SummerAll seasons,1,#301 Bottom 17%,#80 Top 22%,,
+Hoka,Rincon 4,"80
+ Good!",$125,Daily running,Stability,8.1 oz / 231g 8 oz / 228g,1,9.4 mm 5.0 mm,HeelMid/forefoot,Slightly small,Soft,Bad,Decent,Bad,Breathable,Narrow,Narrow,Moderate,Stiff,Stiff,0,1,36.0 mm 33.0 mm,26.6 mm 28.0 mm,NormalWide,1,SummerAll seasons,1,#301 Bottom 17%,#80 Top 22%,,
+Hoka,Rincon 4,"80
+ Good!",$125,Daily running,Stability,8.1 oz / 231g 8 oz / 228g,1,9.4 mm 5.0 mm,HeelMid/forefoot,Slightly small,Soft,Bad,Decent,Bad,Breathable,Narrow,Narrow,Moderate,Stiff,Stiff,0,1,36.0 mm 33.0 mm,26.6 mm 28.0 mm,NormalWide,1,SummerAll seasons,1,#301 Bottom 17%,#80 Top 22%,,
+Hoka,Rincon 4,"80
+ Good!",$125,Daily running,Stability,8.1 oz / 231g 8 oz / 228g,1,9.4 mm 5.0 mm,HeelMid/forefoot,Slightly small,Soft,Bad,Decent,Bad,Breathable,Narrow,Narrow,Moderate,Stiff,Stiff,0,1,36.0 mm 33.0 mm,26.6 mm 28.0 mm,NormalWide,1,SummerAll seasons,1,#301 Bottom 17%,#80 Top 22%,,
+Hoka,Rincon 4,"80
+ Good!",$125,Daily running,Stability,8.1 oz / 231g 8 oz / 228g,1,9.4 mm 5.0 mm,HeelMid/forefoot,Slightly small,Soft,Bad,Decent,Bad,Breathable,Narrow,Narrow,Moderate,Stiff,Stiff,0,1,36.0 mm 33.0 mm,26.6 mm 28.0 mm,NormalWide,1,SummerAll seasons,1,#301 Bottom 17%,#80 Top 22%,,
+Hoka,Rincon 4,"80
+ Good!",$125,Daily running,Stability,8.1 oz / 231g 8 oz / 228g,1,9.4 mm 5.0 mm,HeelMid/forefoot,Slightly small,Soft,Bad,Decent,Bad,Breathable,Narrow,Narrow,Moderate,Stiff,Stiff,0,1,36.0 mm 33.0 mm,26.6 mm 28.0 mm,NormalWide,1,SummerAll seasons,1,#301 Bottom 17%,#80 Top 22%,,
+Altra,Rivera 2,"85
+ Good!",$130,Daily runningTempo,Neutral,8.6 oz / 243g 8.5 oz / 240g,1,2.8 mm 0.0 mm,Mid/forefoot,Half size small,Soft,-,-,-,-,Medium,-,Moderate,Flexible,Flexible,0,0,24.6 mm 26.0 mm,21.8 mm 26.0 mm,Normal,1,-,1,#431 Bottom 33%,#561 Bottom 13%,,
+Altra,Rivera 3,"88
+ Great!",$140,Daily running,Neutral,9.1 oz / 257g 9.8 oz / 278g,0,1.7 mm 0.0 mm,Mid/forefoot,Slightly small,Soft,Bad,Good,Good,Moderate,Medium,Wide,Stiff,Moderate,Moderate,0,0,28.4 mm 28.0 mm,26.7 mm 28.0 mm,Normal,1,All seasons,1,#275 Top 43%,#494 Bottom 23%,,
+Altra,Rivera 4,"85
+ Good!",$130,Daily runningTempo,Neutral,8.3 oz / 235g 10 oz / 283g,1,0.4 mm 0.0 mm,Mid/forefoot,Slightly small,Soft,Decent,Bad,Good,Moderate,Narrow,Wide,Stiff,Flexible,Flexible,0,0,28.4 mm 28.0 mm,28.0 mm 28.0 mm,Normal,1,All seasons,1,#212 Bottom 42%,#270 Bottom 26%,,
+Altra,Rivera 4,"85
+ Good!",$130,Daily runningTempo,Neutral,8.3 oz / 235g 10 oz / 283g,1,0.4 mm 0.0 mm,Mid/forefoot,Slightly small,Soft,Decent,Bad,Good,Moderate,Narrow,Wide,Stiff,Flexible,Flexible,0,0,28.4 mm 28.0 mm,28.0 mm 28.0 mm,Normal,1,All seasons,1,#212 Bottom 42%,#270 Bottom 26%,,
+Altra,Rivera 4,"85
+ Good!",$130,Daily runningTempo,Neutral,8.3 oz / 235g 10 oz / 283g,1,0.4 mm 0.0 mm,Mid/forefoot,Slightly small,Soft,Decent,Bad,Good,Moderate,Narrow,Wide,Stiff,Flexible,Flexible,0,0,28.4 mm 28.0 mm,28.0 mm 28.0 mm,Normal,1,All seasons,1,#212 Bottom 42%,#270 Bottom 26%,,
+Altra,Rivera 4,"85
+ Good!",$130,Daily runningTempo,Neutral,8.3 oz / 235g 10 oz / 283g,1,0.4 mm 0.0 mm,Mid/forefoot,Slightly small,Soft,Decent,Bad,Good,Moderate,Narrow,Wide,Stiff,Flexible,Flexible,0,0,28.4 mm 28.0 mm,28.0 mm 28.0 mm,Normal,1,All seasons,1,#212 Bottom 42%,#270 Bottom 26%,,
+Altra,Rivera 4,"85
+ Good!",$130,Daily runningTempo,Neutral,8.3 oz / 235g 10 oz / 283g,1,0.4 mm 0.0 mm,Mid/forefoot,Slightly small,Soft,Decent,Bad,Good,Moderate,Narrow,Wide,Stiff,Flexible,Flexible,0,0,28.4 mm 28.0 mm,28.0 mm 28.0 mm,Normal,1,All seasons,1,#212 Bottom 42%,#270 Bottom 26%,,
+Altra,Rivera 4,"85
+ Good!",$130,Daily runningTempo,Neutral,8.3 oz / 235g 10 oz / 283g,1,0.4 mm 0.0 mm,Mid/forefoot,Slightly small,Soft,Decent,Bad,Good,Moderate,Narrow,Wide,Stiff,Flexible,Flexible,0,0,28.4 mm 28.0 mm,28.0 mm 28.0 mm,Normal,1,All seasons,1,#212 Bottom 42%,#270 Bottom 26%,,
+Inov8,Roadfly,"88
+ Great!",$130,Daily running,Neutral,8.9 oz / 251g 9.3 oz / 265g,0,9.4 mm 6.0 mm,HeelMid/forefoot,Half size small,Balanced,Bad,Decent,Decent,Breathable,Medium,Wide,Stiff,Moderate,Flexible,0,0,31.5 mm 27.0 mm,22.1 mm 21.0 mm,NormalWide,1,SummerAll seasons,1,#119 Top 33%,#341 Bottom 6%,,
+Inov8,Roadfly,"88
+ Great!",$130,Daily running,Neutral,8.9 oz / 251g 9.3 oz / 265g,0,9.4 mm 6.0 mm,HeelMid/forefoot,Half size small,Balanced,Bad,Decent,Decent,Breathable,Medium,Wide,Stiff,Moderate,Flexible,0,0,31.5 mm 27.0 mm,22.1 mm 21.0 mm,NormalWide,1,SummerAll seasons,1,#120 Top 33%,#342 Bottom 6%,,
+Inov8,Roadfly,"88
+ Great!",$130,Daily running,Neutral,8.9 oz / 251g 9.3 oz / 265g,0,9.4 mm 6.0 mm,HeelMid/forefoot,Half size small,Balanced,Bad,Decent,Decent,Breathable,Medium,Wide,Stiff,Moderate,Flexible,0,0,31.5 mm 27.0 mm,22.1 mm 21.0 mm,NormalWide,1,SummerAll seasons,1,#120 Top 33%,#342 Bottom 6%,,
+Inov8,Roadfly,"88
+ Great!",$130,Daily running,Neutral,8.9 oz / 251g 9.3 oz / 265g,0,9.4 mm 6.0 mm,HeelMid/forefoot,Half size small,Balanced,Bad,Decent,Decent,Breathable,Medium,Wide,Stiff,Moderate,Flexible,0,0,31.5 mm 27.0 mm,22.1 mm 21.0 mm,NormalWide,1,SummerAll seasons,1,#120 Top 33%,#342 Bottom 6%,,
+HOKA,Rocket X 3,"89
+ Great!",$250,Competition,Neutral,7.8 oz / 220g 7.4 oz / 210g,1,10.0 mm 7.0 mm,HeelMid/forefoot,Slightly small,Soft,Decent,Good,Good,Breathable,Narrow,Narrow,Stiff,Stiff,Flexible,Carbon plate,1,39.6 mm 40.0 mm,29.6 mm 33.0 mm,Normal,1,SummerAll seasons,1,#95 Top 26%,#59 Top 17%,,
+Hoka,Rocket X 3,"89
+ Great!",$250,Competition,Neutral,7.8 oz / 220g 7.4 oz / 210g,1,10.0 mm 7.0 mm,HeelMid/forefoot,Slightly small,Soft,Decent,Good,Good,Breathable,Narrow,Narrow,Stiff,Stiff,Flexible,Carbon plate,1,39.6 mm 40.0 mm,29.6 mm 33.0 mm,Normal,1,SummerAll seasons,1,#97 Top 27%,#59 Top 17%,,
+Adidas,Runfalcon,"80
+ Good!",$60,Daily running,Neutral,9.3 oz / 264g 9.5 oz / 269g,0,8.9 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,-,-,-,Warm,Narrow,-,Stiff,Flexible,Moderate,0,0,26.5 mm 28.0 mm,17.6 mm 18.0 mm,Normal,1,Winter,1,#565 Bottom 12%,#487 Bottom 24%,,
+Adidas,Runfalcon 2.0,"80
+ Good!",$60,Daily running,Neutral,9.9 oz / 280g 9.9 oz / 280g,0,10.9 mm 10.0 mm,Heel,True to size,Balanced,-,-,-,Warm,Medium,Wide,Stiff,Moderate,Moderate,0,0,28.7 mm,17.8 mm,Normal,1,All seasons,1,#566 Bottom 12%,#645 Bottom 1%,,
+Adidas,Runfalcon 3,"81
+ Good!",$65,Daily running,Neutral,10 oz / 283g 9.7 oz / 275g,0,13.6 mm 9.0 mm,Heel,True to size,Balanced,Bad,Bad,Good,Moderate,Medium,Wide,Stiff,Moderate,Flexible,0,0,31.6 mm 25.0 mm,18.0 mm 16.0 mm,NormalWide,1,All seasons,1,#536 Bottom 16%,#586 Bottom 9%,,
+Adidas,Runfalcon 5,"85
+ Good!",$65,Daily running,Neutral,9.7 oz / 275g 10.7 oz / 303g,0,9.4 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Bad,Bad,Decent,Moderate,Wide,Medium,Moderate,Flexible,Moderate,0,0,31.2 mm 33.0 mm,21.8 mm 23.0 mm,NormalWide,1,All seasons,1,#203 Bottom 44%,#148 Top 41%,,
+Adidas,Runfalcon 5,"85
+ Good!",$65,Daily running,Neutral,9.7 oz / 275g 10.7 oz / 303g,0,9.4 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Bad,Bad,Decent,Moderate,Wide,Medium,Moderate,Flexible,Moderate,0,0,31.2 mm 33.0 mm,21.8 mm 23.0 mm,NormalWide,1,All seasons,1,#203 Bottom 44%,#148 Top 41%,,
+Adidas,Runfalcon 5,"85
+ Good!",$65,Daily running,Neutral,9.7 oz / 275g 10.7 oz / 303g,0,9.4 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Bad,Bad,Decent,Moderate,Wide,Medium,Moderate,Flexible,Moderate,0,0,31.2 mm 33.0 mm,21.8 mm 23.0 mm,NormalWide,1,All seasons,1,#205 Bottom 43%,#148 Top 41%,,
+Adidas,Runfalcon 5,"85
+ Good!",$65,Daily running,Neutral,9.7 oz / 275g 10.7 oz / 303g,0,9.4 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Bad,Bad,Decent,Moderate,Wide,Medium,Moderate,Flexible,Moderate,0,0,31.2 mm 33.0 mm,21.8 mm 23.0 mm,NormalWide,1,All seasons,1,#205 Bottom 43%,#148 Top 41%,,
+Adidas,Runfalcon 5,"85
+ Good!",$65,Daily running,Neutral,9.7 oz / 275g 10.7 oz / 303g,0,9.4 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Bad,Bad,Decent,Moderate,Wide,Medium,Moderate,Flexible,Moderate,0,0,31.2 mm 33.0 mm,21.8 mm 23.0 mm,NormalWide,1,All seasons,1,#205 Bottom 43%,#148 Top 41%,,
+Adidas,Runfalcon 5,"85
+ Good!",$65,Daily running,Neutral,9.7 oz / 275g 10.7 oz / 303g,0,9.4 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Bad,Bad,Decent,Moderate,Wide,Medium,Moderate,Flexible,Moderate,0,0,31.2 mm 33.0 mm,21.8 mm 23.0 mm,NormalWide,1,All seasons,1,#205 Bottom 43%,#148 Top 41%,,
+Adidas,Runfalcon 5,"85
+ Good!",$65,Daily running,Neutral,9.7 oz / 275g 10.7 oz / 303g,0,9.4 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Bad,Bad,Decent,Moderate,Wide,Medium,Moderate,Flexible,Moderate,0,0,31.2 mm 33.0 mm,21.8 mm 23.0 mm,NormalWide,1,All seasons,1,#205 Bottom 43%,#148 Top 41%,,
+Adidas,Runfalcon 5,"85
+ Good!",$65,Daily running,Neutral,9.7 oz / 275g 10.7 oz / 303g,0,9.4 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Bad,Bad,Decent,Moderate,Wide,Medium,Moderate,Flexible,Moderate,0,0,31.2 mm 33.0 mm,21.8 mm 23.0 mm,NormalWide,1,All seasons,1,#205 Bottom 43%,#148 Top 41%,,
+Adidas,Runfalcon 5,"85
+ Good!",$65,Daily running,Neutral,9.7 oz / 275g 10.7 oz / 303g,0,9.4 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Bad,Bad,Decent,Moderate,Wide,Medium,Moderate,Flexible,Moderate,0,0,31.2 mm 33.0 mm,21.8 mm 23.0 mm,NormalWide,1,All seasons,1,#205 Bottom 43%,#148 Top 41%,,
+Adidas,Runfalcon 5,"85
+ Good!",$65,Daily running,Neutral,9.7 oz / 275g 10.7 oz / 303g,0,9.4 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Bad,Bad,Decent,Moderate,Wide,Medium,Moderate,Flexible,Moderate,0,0,31.2 mm 33.0 mm,21.8 mm 23.0 mm,NormalWide,1,All seasons,1,#205 Bottom 43%,#148 Top 41%,,
+Adidas,Runfalcon 5,"85
+ Good!",$65,Daily running,Neutral,9.7 oz / 275g 10.7 oz / 303g,0,9.4 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Bad,Bad,Decent,Moderate,Wide,Medium,Moderate,Flexible,Moderate,0,0,31.2 mm 33.0 mm,21.8 mm 23.0 mm,NormalWide,1,All seasons,1,#203 Bottom 44%,#148 Top 41%,,
+Adidas,Runfalcon 5,"85
+ Good!",$65,Daily running,Neutral,9.7 oz / 275g 10.7 oz / 303g,0,9.4 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Bad,Bad,Decent,Moderate,Wide,Medium,Moderate,Flexible,Moderate,0,0,31.2 mm 33.0 mm,21.8 mm 23.0 mm,NormalWide,1,All seasons,1,#203 Bottom 44%,#148 Top 41%,,
+Adidas,Runfalcon 5,"85
+ Good!",$65,Daily running,Neutral,9.7 oz / 275g 10.7 oz / 303g,0,9.4 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Bad,Bad,Decent,Moderate,Wide,Medium,Moderate,Flexible,Moderate,0,0,31.2 mm 33.0 mm,21.8 mm 23.0 mm,NormalWide,1,All seasons,1,#203 Bottom 44%,#148 Top 41%,,
+Salomon,S/Lab Phantasm 2,N/A,$275,CompetitionTempo,Neutral,7.5 oz / 213g 7.4 oz / 210g,1,5.1 mm 9.0 mm,Mid/forefoot,-,Soft,Bad,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Flexible,Carbon plate,0,31.5 mm 37.0 mm,26.4 mm 28.0 mm,Normal,1,All seasons,1,#252 Bottom 30%,#358 Bottom 1%,,
+SAlomon,S/Lab Spectur,"80
+ Good!",$220,CompetitionTempo,Neutral,9.1 oz / 258g,0,10.7 mm 8.0 mm,Heel,-,Balanced,Decent,Decent,Good,Moderate,Medium,Medium,Stiff,Stiff,Flexible,0,1,36.9 mm,26.2 mm,Normal,1,All seasons,1,#312 Bottom 14%,#332 Bottom 9%,,
+Xero,Shoes HFS II,"85
+ Good!",$120,Daily running,Neutral,8.6 oz / 244g 8.3 oz / 235g,1,1.0 mm 0.0 mm,Mid/forefoot,True to size,Firm,Decent,Bad,Decent,Breathable,Narrow,Wide,Moderate,Flexible,Moderate,0,0,13.1 mm 12.0 mm,12.1 mm 12.0 mm,NormalWide,1,SummerAll seasons,1,#206 Bottom 43%,#275 Bottom 24%,,
+Xero,Shoes HFS II,"85
+ Good!",$120,Daily running,Neutral,8.6 oz / 244g 8.3 oz / 235g,1,1.0 mm 0.0 mm,Mid/forefoot,True to size,Firm,Decent,Bad,Decent,Breathable,Narrow,Wide,Moderate,Flexible,Moderate,0,0,13.1 mm 12.0 mm,12.1 mm 12.0 mm,NormalWide,1,SummerAll seasons,1,#206 Bottom 43%,#275 Bottom 25%,,
+Xero,Shoes Prio,"91
+ Superb!",$90,Daily running,Neutral,9.8 oz / 279g 7.4 oz / 210g,0,0.4 mm 0.0 mm,Mid/forefoot,True to size,-,Good,Bad,Decent,Warm,Medium,Wide,Flexible,Flexible,Flexible,0,0,12.2 mm 7.0 mm,11.8 mm 7.0 mm,NormalWide,1,All seasons,1,#21 Top 6%,#238 Bottom 34%,,
+Xero,Shoes Prio,"91
+ Superb!",$90,Daily running,Neutral,9.8 oz / 279g 7.4 oz / 210g,0,0.4 mm 0.0 mm,Mid/forefoot,True to size,-,Good,Bad,Decent,Warm,Medium,Wide,Flexible,Flexible,Flexible,0,0,12.2 mm 7.0 mm,11.8 mm 7.0 mm,NormalWide,1,All seasons,1,#22 Top 6%,#239 Bottom 34%,,
+Xero,Shoes Speed Force II,"88
+ Great!",$110,Daily runningTempo,Neutral,6.7 oz / 189g 6 oz / 170g,1,0.1 mm 0.0 mm,Mid/forefoot,True to size,-,Decent,Decent,Decent,Breathable,Medium,Wide,Flexible,Flexible,Flexible,0,0,10.6 mm 7.0 mm,10.5 mm 7.0 mm,Normal,1,SummerAll seasons,1,#115 Top 32%,#318 Bottom 12%,,
+Xero,Shoes Speed Force II,"88
+ Great!",$110,Daily runningTempo,Neutral,6.7 oz / 189g 6 oz / 170g,1,0.1 mm 0.0 mm,Mid/forefoot,True to size,-,Decent,Decent,Decent,Breathable,Medium,Wide,Flexible,Flexible,Flexible,0,0,10.6 mm 7.0 mm,10.5 mm 7.0 mm,Normal,1,SummerAll seasons,1,#117 Top 32%,#319 Bottom 13%,,
+Xero,Shoes Speed Force II,"88
+ Great!",$110,Daily runningTempo,Neutral,6.7 oz / 189g 6 oz / 170g,1,0.1 mm 0.0 mm,Mid/forefoot,True to size,-,Decent,Decent,Decent,Breathable,Medium,Wide,Flexible,Flexible,Flexible,0,0,10.6 mm 7.0 mm,10.5 mm 7.0 mm,Normal,1,SummerAll seasons,1,#115 Top 32%,#319 Bottom 12%,,
+Saucony,Sinister,"87
+ Great!",$150,Tempo,Neutral,5.3 oz / 149g 4.9 oz / 139g,1,7.8 mm 6.0 mm,Mid/forefoot,Slightly small,Balanced,Bad,Good,Decent,Moderate,Narrow,Medium,Moderate,Flexible,Flexible,0,0,25.2 mm 25.0 mm,17.4 mm 19.0 mm,Normal,1,All seasons,1,#151 Top 42%,#315 Bottom 13%,,
+Hoka,Skyflow,"89
+ Great!",$160,Daily running,Neutral,9.9 oz / 282g 10 oz / 283g,0,8.7 mm 5.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Good,Good,Breathable,Medium,Narrow,Stiff,Stiff,Stiff,0,0,39.3 mm 39.0 mm,30.6 mm 34.0 mm,NormalWide,1,SummerAll seasons,1,#111 Top 31%,#138 Top 38%,,
+Hoka,Skyflow,"89
+ Great!",$160,Daily running,Neutral,9.9 oz / 282g 10 oz / 283g,0,8.7 mm 5.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Good,Good,Breathable,Medium,Narrow,Stiff,Stiff,Stiff,0,0,39.3 mm 39.0 mm,30.6 mm 34.0 mm,NormalWide,1,SummerAll seasons,1,#111 Top 31%,#138 Top 38%,,Road
+Hoka,Skyflow,"89
+ Great!",$160,Daily running,Neutral,9.9 oz / 282g 10 oz / 283g,0,8.7 mm 5.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Good,Good,Breathable,Medium,Narrow,Stiff,Stiff,Stiff,0,0,39.3 mm 39.0 mm,30.6 mm 34.0 mm,NormalWide,1,SummerAll seasons,1,#111 Top 31%,#138 Top 38%,,
+Hoka,Skyflow,"89
+ Great!",$160,Daily running,Neutral,9.9 oz / 282g 10 oz / 283g,0,8.7 mm 5.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Good,Good,Breathable,Medium,Narrow,Stiff,Stiff,Stiff,0,0,39.3 mm 39.0 mm,30.6 mm 34.0 mm,NormalWide,1,SummerAll seasons,1,#111 Top 31%,#138 Top 38%,,
+Hoka,Skyflow,"89
+ Great!",$160,Daily running,Neutral,9.9 oz / 282g 10 oz / 283g,0,8.7 mm 5.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Good,Good,Breathable,Medium,Narrow,Stiff,Stiff,Stiff,0,0,39.3 mm 39.0 mm,30.6 mm 34.0 mm,NormalWide,1,SummerAll seasons,1,#113 Top 31%,#139 Top 38%,,
+Hoka,Skyflow,"89
+ Great!",$160,Daily running,Neutral,9.9 oz / 282g 10 oz / 283g,0,8.7 mm 5.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Good,Good,Breathable,Medium,Narrow,Stiff,Stiff,Stiff,0,0,39.3 mm 39.0 mm,30.6 mm 34.0 mm,NormalWide,1,SummerAll seasons,1,#111 Top 31%,#139 Top 38%,,
+Hoka,Skyflow,"89
+ Great!",$160,Daily running,Neutral,9.9 oz / 282g 10 oz / 283g,0,8.7 mm 5.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Good,Good,Breathable,Medium,Narrow,Stiff,Stiff,Stiff,0,0,39.3 mm 39.0 mm,30.6 mm 34.0 mm,NormalWide,1,SummerAll seasons,1,#111 Top 31%,#139 Top 38%,,
+Hoka,Skyflow,"89
+ Great!",$160,Daily running,Neutral,9.9 oz / 282g 10 oz / 283g,0,8.7 mm 5.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Good,Good,Breathable,Medium,Narrow,Stiff,Stiff,Stiff,0,0,39.3 mm 39.0 mm,30.6 mm 34.0 mm,NormalWide,1,SummerAll seasons,1,#111 Top 31%,#139 Top 38%,,
+Hoka,Skyflow,"89
+ Great!",$160,Daily running,Neutral,9.9 oz / 282g 10 oz / 283g,0,8.7 mm 5.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Good,Good,Breathable,Medium,Narrow,Stiff,Stiff,Stiff,0,0,39.3 mm 39.0 mm,30.6 mm 34.0 mm,NormalWide,1,SummerAll seasons,1,#111 Top 31%,#139 Top 38%,,
+Hoka,Skyflow,"89
+ Great!",$160,Daily running,Neutral,9.9 oz / 282g 10 oz / 283g,0,8.7 mm 5.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Good,Good,Breathable,Medium,Narrow,Stiff,Stiff,Stiff,0,0,39.3 mm 39.0 mm,30.6 mm 34.0 mm,NormalWide,1,SummerAll seasons,1,#111 Top 31%,#139 Top 38%,,
+Hoka,Skyflow,"89
+ Great!",$160,Daily running,Neutral,9.9 oz / 282g 10 oz / 283g,0,8.7 mm 5.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Good,Good,Breathable,Medium,Narrow,Stiff,Stiff,Stiff,0,0,39.3 mm 39.0 mm,30.6 mm 34.0 mm,NormalWide,1,SummerAll seasons,1,#111 Top 31%,#139 Top 38%,,
+Hoka,Skyflow,"89
+ Great!",$160,Daily running,Neutral,9.9 oz / 282g 10 oz / 283g,0,8.7 mm 5.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Good,Good,Breathable,Medium,Narrow,Stiff,Stiff,Stiff,0,0,39.3 mm 39.0 mm,30.6 mm 34.0 mm,NormalWide,1,SummerAll seasons,1,#111 Top 31%,#139 Top 38%,,
+Hoka,Skyflow,"89
+ Great!",$160,Daily running,Neutral,9.9 oz / 282g 10 oz / 283g,0,8.7 mm 5.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Good,Good,Breathable,Medium,Narrow,Stiff,Stiff,Stiff,0,0,39.3 mm 39.0 mm,30.6 mm 34.0 mm,NormalWide,1,SummerAll seasons,1,#111 Top 31%,#139 Top 38%,,
+Hoka,Skyward X,"86
+ Good!",$225,Daily runningTempo,Neutral,11.1 oz / 315g 10.8 oz / 306g,0,9.2 mm 5.0 mm,HeelMid/forefoot,True to size,Soft,Good,Good,Good,Breathable,Narrow,Narrow,Stiff,Stiff,Stiff,Carbon plate,1,46.3 mm 49.0 mm,37.1 mm 44.0 mm,Normal,1,SummerAll seasons,1,#170 Top 47%,#64 Top 18%,,
+Under Armour,SlipSpeed Mega,"90
+ Superb!",$140,Daily running,Neutral,11.7 oz / 332g 11.3 oz / 320g,0,11.9 mm 10.0 mm,Heel,True to size,Balanced,Good,Bad,Bad,Warm,Narrow,Medium,Stiff,Stiff,Flexible,0,0,40.7 mm 40.0 mm,28.8 mm 30.0 mm,Normal,1,Winter,1,#58 Top 16%,#260 Bottom 29%,,
+Adidas,Solarboost 5,"87
+ Great!",$120,Daily running,Stability,10.3 oz / 293g 10.5 oz / 297g,0,9.8 mm 10.0 mm,HeelMid/forefoot,True to size,Firm,Decent,Good,Bad,Warm,Medium,Wide,Stiff,Moderate,Moderate,0,0,32.9 mm 31.0 mm,23.1 mm 21.0 mm,Normal,1,All seasons,1,#165 Top 46%,#345 Bottom 5%,,
+Adidas,Solarboost 5,"87
+ Great!",$120,Daily running,Stability,10.3 oz / 293g 10.5 oz / 297g,0,9.8 mm 10.0 mm,HeelMid/forefoot,True to size,Firm,Decent,Good,Bad,Warm,Medium,Wide,Stiff,Moderate,Moderate,0,0,32.9 mm 31.0 mm,23.1 mm 21.0 mm,Normal,1,All seasons,1,#167 Top 46%,#346 Bottom 5%,,
+Adidas,Solarboost 5,"87
+ Great!",$120,Daily running,Stability,10.3 oz / 293g 10.5 oz / 297g,0,9.8 mm 10.0 mm,HeelMid/forefoot,True to size,Firm,Decent,Good,Bad,Warm,Medium,Wide,Stiff,Moderate,Moderate,0,0,32.9 mm 31.0 mm,23.1 mm 21.0 mm,Normal,1,All seasons,1,#167 Top 46%,#346 Bottom 5%,,
+Adidas,Solarboost 5,"87
+ Great!",$120,Daily running,Stability,10.3 oz / 293g 10.5 oz / 297g,0,9.8 mm 10.0 mm,HeelMid/forefoot,True to size,Firm,Decent,Good,Bad,Warm,Medium,Wide,Stiff,Moderate,Moderate,0,0,32.9 mm 31.0 mm,23.1 mm 21.0 mm,Normal,1,All seasons,1,#166 Top 46%,#346 Bottom 5%,,
+Adidas,Solarboost 5,"87
+ Great!",$120,Daily running,Stability,10.3 oz / 293g 10.5 oz / 297g,0,9.8 mm 10.0 mm,HeelMid/forefoot,True to size,Firm,Decent,Good,Bad,Warm,Medium,Wide,Stiff,Moderate,Moderate,0,0,32.9 mm 31.0 mm,23.1 mm 21.0 mm,Normal,1,All seasons,1,#166 Top 46%,#346 Bottom 5%,,
+Adidas,Solarboost 5,"87
+ Great!",$120,Daily running,Stability,10.3 oz / 293g 10.5 oz / 297g,0,9.8 mm 10.0 mm,HeelMid/forefoot,True to size,Firm,Decent,Good,Bad,Warm,Medium,Wide,Stiff,Moderate,Moderate,0,0,32.9 mm 31.0 mm,23.1 mm 21.0 mm,Normal,1,All seasons,1,#166 Top 46%,#346 Bottom 5%,,
+Adidas,Solarboost 5,"87
+ Great!",$120,Daily running,Stability,10.3 oz / 293g 10.5 oz / 297g,0,9.8 mm 10.0 mm,HeelMid/forefoot,True to size,Firm,Decent,Good,Bad,Warm,Medium,Wide,Stiff,Moderate,Moderate,0,0,32.9 mm 31.0 mm,23.1 mm 21.0 mm,Normal,1,All seasons,1,#166 Top 46%,#346 Bottom 5%,,
+Adidas,Solarboost 5,"87
+ Great!",$120,Daily running,Stability,10.3 oz / 293g 10.5 oz / 297g,0,9.8 mm 10.0 mm,HeelMid/forefoot,True to size,Firm,Decent,Good,Bad,Warm,Medium,Wide,Stiff,Moderate,Moderate,0,0,32.9 mm 31.0 mm,23.1 mm 21.0 mm,Normal,1,All seasons,1,#167 Top 46%,#347 Bottom 5%,,
+Hoka,Solimar,"87
+ Great!",$125,Daily running,Neutral,8.2 oz / 232g 8.5 oz / 241g,1,6.4 mm 6.0 mm,Mid/forefoot,True to size,Balanced,Good,Decent,-,Moderate,Narrow,Narrow,Moderate,Moderate,Flexible,0,0,30.4 mm 26.0 mm,24.0 mm 20.0 mm,NormalWide,1,All seasons,1,#150 Top 42%,#109 Top 30%,,
+Hoka,Solimar,"87
+ Great!",$125,Daily running,Neutral,8.2 oz / 232g 8.5 oz / 241g,1,6.4 mm 6.0 mm,Mid/forefoot,True to size,Balanced,Good,Decent,-,Moderate,Narrow,Narrow,Moderate,Moderate,Flexible,0,0,30.4 mm 26.0 mm,24.0 mm 20.0 mm,NormalWide,1,All seasons,1,#154 Top 42%,#109 Top 30%,,
+Hoka,Solimar,"87
+ Great!",$125,Daily running,Neutral,8.2 oz / 232g 8.5 oz / 241g,1,6.4 mm 6.0 mm,Mid/forefoot,True to size,Balanced,Good,Decent,-,Moderate,Narrow,Narrow,Moderate,Moderate,Flexible,0,0,30.4 mm 26.0 mm,24.0 mm 20.0 mm,NormalWide,1,All seasons,1,#154 Top 43%,#109 Top 30%,,
+Hoka,Solimar,"87
+ Great!",$125,Daily running,Neutral,8.2 oz / 232g 8.5 oz / 241g,1,6.4 mm 6.0 mm,Mid/forefoot,True to size,Balanced,Good,Decent,-,Moderate,Narrow,Narrow,Moderate,Moderate,Flexible,0,0,30.4 mm 26.0 mm,24.0 mm 20.0 mm,NormalWide,1,All seasons,1,#154 Top 43%,#109 Top 30%,,
+Hoka,Solimar,"87
+ Great!",$125,Daily running,Neutral,8.2 oz / 232g 8.5 oz / 241g,1,6.4 mm 6.0 mm,Mid/forefoot,True to size,Balanced,Good,Decent,-,Moderate,Narrow,Narrow,Moderate,Moderate,Flexible,0,0,30.4 mm 26.0 mm,24.0 mm 20.0 mm,NormalWide,1,All seasons,1,#153 Top 42%,#109 Top 30%,,
+Hoka,Solimar,"87
+ Great!",$125,Daily running,Neutral,8.2 oz / 232g 8.5 oz / 241g,1,6.4 mm 6.0 mm,Mid/forefoot,True to size,Balanced,Good,Decent,-,Moderate,Narrow,Narrow,Moderate,Moderate,Flexible,0,0,30.4 mm 26.0 mm,24.0 mm 20.0 mm,NormalWide,1,All seasons,1,#153 Top 42%,#109 Top 30%,,
+ASICS,Sonicblast,"76
+ Decent!",$180,Daily runningTempo,Neutral,9 oz / 255g 9 oz / 255g,0,9.0 mm 8.0 mm,HeelMid/forefoot,-,Soft,Bad,Good,Decent,Breathable,Narrow,Narrow,Stiff,Stiff,Flexible,0,1,45.4 mm 46.0 mm,36.4 mm 38.0 mm,Normal,1,SummerAll seasons,1,#341 Bottom 6%,#140 Top 39%,,
+ASICS,Sonicblast,"76
+ Decent!",$180,Daily runningTempo,Neutral,9 oz / 255g 9 oz / 255g,0,9.0 mm 8.0 mm,HeelMid/forefoot,-,Soft,Bad,Good,Decent,Breathable,Narrow,Narrow,Stiff,Stiff,Flexible,0,1,45.4 mm 46.0 mm,36.4 mm 38.0 mm,Normal,1,SummerAll seasons,1,#341 Bottom 6%,#140 Top 39%,,
+Salomon,Spectur 2,"80
+ Good!",$170,Tempo,Neutral,9.1 oz / 258g,0,11.0 mm 8.0 mm,Heel,-,Balanced,Decent,Decent,Good,Warm,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,35.9 mm,24.9 mm,Normal,1,All seasons,1,#310 Bottom 15%,#324 Bottom 11%,,
+Nike,Streakfly 2,"83
+ Good!",$180,CompetitionTempo,Neutral,4.5 oz / 128g 5.1 oz / 144g,1,3.7 mm 4.0 mm,Mid/forefoot,Slightly small,Soft,Bad,Good,Decent,Breathable,Narrow,Narrow,Flexible,Flexible,Flexible,Carbon plate,0,27.0 mm 27.0 mm,23.3 mm 23.0 mm,Normal,0,SummerAll seasons,0,#248 Bottom 32%,#79 Top 22%,,
+APL,Streamline,"90
+ Superb!",$300,Daily runningTempo,Neutral,9.6 oz / 272g 9.2 oz / 261g,0,10.8 mm 8.0 mm,Heel,Half size small,-,-,-,-,-,Narrow,-,Stiff,Stiff,Flexible,0,0,36.7 mm 30.0 mm,25.9 mm 22.0 mm,Normal,0,-,0,#47 Top 13%,#328 Bottom 10%,,
+Nike,Structure 25,"83
+ Good!",$140,Daily running,Stability,10.7 oz / 302g 11.4 oz / 322g,0,12.1 mm 10.0 mm,Heel,True to size,Soft,Good,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,36.7 mm 37.0 mm,24.6 mm 27.0 mm,NarrowNormalWideX-Wide,1,All seasons,1,#514 Bottom 20%,#119 Top 19%,,
+Nike,Structure 26,"84
+ Good!",$145,Daily running,Stability,10.4 oz / 296g 10.5 oz / 298g,0,10.1 mm 10.0 mm,Heel,Slightly small,Soft,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Moderate,0,0,36.1 mm 38.0 mm,26.0 mm 28.0 mm,NormalWideX-Wide,1,All seasons,1,#236 Bottom 35%,#56 Top 16%,,
+Nike,Structure 26,"83
+ Good!",$145,Daily running,Stability,10.4 oz / 296g 10.5 oz / 298g,0,10.1 mm 10.0 mm,Heel,Slightly small,Soft,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Moderate,0,0,36.1 mm 38.0 mm,26.0 mm 28.0 mm,NormalWideX-Wide,1,All seasons,1,#256 Bottom 30%,#56 Top 16%,,
+Nike,Structure 26,"83
+ Good!",$145,Daily running,Stability,10.4 oz / 296g 10.5 oz / 298g,0,10.1 mm 10.0 mm,Heel,Slightly small,Soft,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Moderate,0,0,36.1 mm 38.0 mm,26.0 mm 28.0 mm,NormalWideX-Wide,1,All seasons,1,#256 Bottom 30%,#56 Top 16%,,
+ASICS,Superblast,"92
+ Superb!",$200,Daily runningTempo,Neutral,8.6 oz / 244g 8.4 oz / 239g,1,7.9 mm 8.0 mm,Mid/forefoot,True to size,Balanced,Bad,Decent,Good,Moderate,Narrow,Medium,Stiff,Stiff,Moderate,0,1,42.7 mm 45.5 mm,34.8 mm 37.5 mm,NormalWide,1,All seasons,1,#26 Top 5%,#98 Top 16%,,
+ASICS,Superblast 2,"90
+ Superb!",$200,Daily runningTempo,Neutral,8.9 oz / 252g 8.8 oz / 250g,0,8.2 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Good,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Moderate,0,1,42.8 mm 45.0 mm,34.6 mm 37.0 mm,NormalWide,1,SummerAll seasons,1,#50 Top 14%,#25 Top 7%,,
+ASICS,Superblast 2,"90
+ Superb!",$200,Daily runningTempo,Neutral,8.9 oz / 252g 8.8 oz / 250g,0,8.2 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Good,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Moderate,0,1,42.8 mm 45.0 mm,34.6 mm 37.0 mm,NormalWide,1,SummerAll seasons,1,#50 Top 14%,#25 Top 7%,#25 Top 7%,
+ASICS,Superblast 2,"90
+ Superb!",$200,Daily runningTempo,Neutral,8.9 oz / 252g 8.8 oz / 250g,0,8.2 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Good,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Moderate,0,1,42.8 mm 45.0 mm,34.6 mm 37.0 mm,NormalWide,1,SummerAll seasons,1,#50 Top 14%,#25 Top 7%,,Road
+ASICS,Superblast 2,"90
+ Superb!",$200,Daily runningTempo,Neutral,8.9 oz / 252g 8.8 oz / 250g,0,8.2 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Good,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Moderate,0,1,42.8 mm 45.0 mm,34.6 mm 37.0 mm,NormalWide,1,SummerAll seasons,1,#50 Top 14%,#25 Top 7%,,
+ASICS,Superblast 2,"90
+ Superb!",$200,Daily runningTempo,Neutral,8.9 oz / 252g 8.8 oz / 250g,0,8.2 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Good,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Moderate,0,1,42.8 mm 45.0 mm,34.6 mm 37.0 mm,NormalWide,1,SummerAll seasons,1,#50 Top 14%,#25 Top 7%,,
+ASICS,Superblast 2,"90
+ Superb!",$200,Daily runningTempo,Neutral,8.9 oz / 252g 8.8 oz / 250g,0,8.2 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Good,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Moderate,0,1,42.8 mm 45.0 mm,34.6 mm 37.0 mm,NormalWide,1,SummerAll seasons,1,#50 Top 14%,#25 Top 7%,,
+ASICS,Superblast 2,"90
+ Superb!",$200,Daily runningTempo,Neutral,8.9 oz / 252g 8.8 oz / 250g,0,8.2 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Good,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Moderate,0,1,42.8 mm 45.0 mm,34.6 mm 37.0 mm,NormalWide,1,SummerAll seasons,1,#56 Top 16%,#25 Top 7%,,
+ASICS,Superblast 2,"90
+ Superb!",$200,Daily runningTempo,Neutral,8.9 oz / 252g 8.8 oz / 250g,0,8.2 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Good,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Moderate,0,1,42.8 mm 45.0 mm,34.6 mm 37.0 mm,NormalWide,1,SummerAll seasons,1,#56 Top 16%,#25 Top 7%,,
+ASICS,Superblast 2,"90
+ Superb!",$200,Daily runningTempo,Neutral,8.9 oz / 252g 8.8 oz / 250g,0,8.2 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Good,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Moderate,0,1,42.8 mm 45.0 mm,34.6 mm 37.0 mm,NormalWide,1,SummerAll seasons,1,#56 Top 16%,#25 Top 7%,,
+Adidas,Supernova 2,"87
+ Great!",$100,Daily running,Neutral,9.7 oz / 276g 9.8 oz / 278g,0,14.2 mm 9.0 mm,Heel,True to size,Balanced,Bad,Bad,Bad,Breathable,Medium,Wide,Flexible,Moderate,Moderate,0,0,32.4 mm 32.0 mm,18.2 mm 23.0 mm,Normal,1,SummerAll seasons,1,#308 Top 48%,#467 Bottom 27%,,
+Adidas,Supernova 3,"81
+ Good!",$100,Daily running,Neutral,9.7 oz / 274g 10 oz / 283g,0,12.5 mm 9.0 mm,Heel,True to size,Balanced,Good,Good,Bad,Breathable,Medium,Medium,Stiff,Moderate,Moderate,0,0,30.7 mm 25.0 mm,18.2 mm 16.0 mm,Normal,1,SummerAll seasons,1,#288 Bottom 21%,#322 Bottom 12%,,
+Adidas,Supernova 3,"81
+ Good!",$100,Daily running,Neutral,9.7 oz / 274g 10 oz / 283g,0,12.5 mm 9.0 mm,Heel,True to size,Balanced,Good,Good,Bad,Breathable,Medium,Medium,Stiff,Moderate,Moderate,0,0,30.7 mm 25.0 mm,18.2 mm 16.0 mm,Normal,1,SummerAll seasons,1,#287 Bottom 21%,#322 Bottom 12%,,
+Adidas,Supernova Prima,"89
+ Great!",$160,Daily running,Neutral,9.9 oz / 281g 10 oz / 284g,0,8.9 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Bad,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,1,36.1 mm 37.0 mm,27.2 mm 29.0 mm,Normal,1,All seasons,1,#87 Top 24%,#226 Bottom 38%,,
+Adidas,Supernova Prima,"89
+ Great!",$160,Daily running,Neutral,9.9 oz / 281g 10 oz / 284g,0,8.9 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Bad,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,1,36.1 mm 37.0 mm,27.2 mm 29.0 mm,Normal,1,All seasons,1,#89 Top 25%,#226 Bottom 38%,,
+Adidas,Supernova Rise,"91
+ Superb!",$140,Daily running,Neutral,9.8 oz / 278g 9.8 oz / 278g,0,9.7 mm 10.0 mm,HeelMid/forefoot,Slightly small,Balanced,Decent,Good,Good,Moderate,Wide,Wide,Moderate,Flexible,Stiff,0,1,32.5 mm 36.0 mm,22.8 mm 26.0 mm,NormalWide,1,All seasons,1,#60 Top 10%,#234 Top 37%,,
+Adidas,Supernova Rise 2,"91
+ Superb!",$140,Daily running,Neutral,9.1 oz / 257g 9.4 oz / 266g,0,9.5 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Decent,Moderate,Wide,Wide,Flexible,Moderate,Stiff,0,0,33.5 mm 33.0 mm,24.0 mm 23.0 mm,NormalWide,1,All seasons,1,#29 Top 8%,#67 Top 19%,,
+Adidas,Supernova Rise 2,"91
+ Superb!",$140,Daily running,Neutral,9.1 oz / 257g 9.4 oz / 266g,0,9.5 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Decent,Moderate,Wide,Wide,Flexible,Moderate,Stiff,0,0,33.5 mm 33.0 mm,24.0 mm 23.0 mm,NormalWide,1,All seasons,1,#29 Top 8%,#67 Top 19%,,
+Adidas,Supernova Rise 2,"91
+ Superb!",$140,Daily running,Neutral,9.1 oz / 257g 9.4 oz / 266g,0,9.5 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Decent,Moderate,Wide,Wide,Flexible,Moderate,Stiff,0,0,33.5 mm 33.0 mm,24.0 mm 23.0 mm,NormalWide,1,All seasons,1,#29 Top 8%,#67 Top 19%,,
+Adidas,Supernova Rise 2,"91
+ Superb!",$140,Daily running,Neutral,9.1 oz / 257g 9.4 oz / 266g,0,9.5 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Decent,Moderate,Wide,Wide,Flexible,Moderate,Stiff,0,0,33.5 mm 33.0 mm,24.0 mm 23.0 mm,NormalWide,1,All seasons,1,#29 Top 8%,#67 Top 19%,,
+Adidas,Supernova Rise 2,"91
+ Superb!",$140,Daily running,Neutral,9.1 oz / 257g 9.4 oz / 266g,0,9.5 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Decent,Moderate,Wide,Wide,Flexible,Moderate,Stiff,0,0,33.5 mm 33.0 mm,24.0 mm 23.0 mm,NormalWide,1,All seasons,1,#29 Top 8%,#67 Top 19%,,
+Adidas,Supernova Solution,"89
+ Great!",$140,Daily running,Stability,9.8 oz / 279g 9.8 oz / 278g,0,10.0 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Decent,Moderate,Medium,Wide,Flexible,Moderate,Stiff,0,0,33.1 mm 36.0 mm,23.1 mm 26.0 mm,Normal,1,All seasons,1,#106 Top 30%,#256 Bottom 29%,,
+Adidas,Supernova Solution,"89
+ Great!",$140,Daily running,Stability,9.8 oz / 279g 9.8 oz / 278g,0,10.0 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Decent,Moderate,Medium,Wide,Flexible,Moderate,Stiff,0,0,33.1 mm 36.0 mm,23.1 mm 26.0 mm,Normal,1,All seasons,1,#106 Top 29%,#258 Bottom 29%,,
+Adidas,Supernova Solution,"89
+ Great!",$140,Daily running,Stability,9.8 oz / 279g 9.8 oz / 278g,0,10.0 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Decent,Moderate,Medium,Wide,Flexible,Moderate,Stiff,0,0,33.1 mm 36.0 mm,23.1 mm 26.0 mm,Normal,1,All seasons,1,#105 Top 29%,#257 Bottom 29%,,
+Adidas,Supernova Solution,"89
+ Great!",$140,Daily running,Stability,9.8 oz / 279g 9.8 oz / 278g,0,10.0 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Decent,Moderate,Medium,Wide,Flexible,Moderate,Stiff,0,0,33.1 mm 36.0 mm,23.1 mm 26.0 mm,Normal,1,All seasons,1,#105 Top 29%,#257 Bottom 29%,,
+Adidas,Supernova Solution,"89
+ Great!",$140,Daily running,Stability,9.8 oz / 279g 9.8 oz / 278g,0,10.0 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Decent,Moderate,Medium,Wide,Flexible,Moderate,Stiff,0,0,33.1 mm 36.0 mm,23.1 mm 26.0 mm,Normal,1,All seasons,1,#105 Top 29%,#257 Bottom 29%,,
+Adidas,Supernova Solution,"89
+ Great!",$140,Daily running,Stability,9.8 oz / 279g 9.8 oz / 278g,0,10.0 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Decent,Moderate,Medium,Wide,Flexible,Moderate,Stiff,0,0,33.1 mm 36.0 mm,23.1 mm 26.0 mm,Normal,1,All seasons,1,#105 Top 29%,#257 Bottom 29%,,
+Adidas,Supernova Solution,"89
+ Great!",$140,Daily running,Stability,9.8 oz / 279g 9.8 oz / 278g,0,10.0 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Decent,Moderate,Medium,Wide,Flexible,Moderate,Stiff,0,0,33.1 mm 36.0 mm,23.1 mm 26.0 mm,Normal,1,All seasons,1,#106 Top 29%,#257 Bottom 29%,,
+Adidas,Supernova Solution,"89
+ Great!",$140,Daily running,Stability,9.8 oz / 279g 9.8 oz / 278g,0,10.0 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Decent,Moderate,Medium,Wide,Flexible,Moderate,Stiff,0,0,33.1 mm 36.0 mm,23.1 mm 26.0 mm,Normal,1,All seasons,1,#106 Top 29%,#257 Bottom 29%,,
+Adidas,Supernova Solution,"89
+ Great!",$140,Daily running,Stability,9.8 oz / 279g 9.8 oz / 278g,0,10.0 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Decent,Moderate,Medium,Wide,Flexible,Moderate,Stiff,0,0,33.1 mm 36.0 mm,23.1 mm 26.0 mm,Normal,1,All seasons,1,#106 Top 29%,#257 Bottom 29%,,
+Adidas,Supernova Solution,"89
+ Great!",$140,Daily running,Stability,9.8 oz / 279g 9.8 oz / 278g,0,10.0 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Decent,Moderate,Medium,Wide,Flexible,Moderate,Stiff,0,0,33.1 mm 36.0 mm,23.1 mm 26.0 mm,Normal,1,All seasons,1,#106 Top 29%,#257 Bottom 29%,,
+Adidas,Supernova Solution,"89
+ Great!",$140,Daily running,Stability,9.8 oz / 279g 9.8 oz / 278g,0,10.0 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Decent,Moderate,Medium,Wide,Flexible,Moderate,Stiff,0,0,33.1 mm 36.0 mm,23.1 mm 26.0 mm,Normal,1,All seasons,1,#106 Top 29%,#257 Bottom 29%,,
+Adidas,Supernova Solution,"89
+ Great!",$140,Daily running,Stability,9.8 oz / 279g 9.8 oz / 278g,0,10.0 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Decent,Moderate,Medium,Wide,Flexible,Moderate,Stiff,0,0,33.1 mm 36.0 mm,23.1 mm 26.0 mm,Normal,1,All seasons,1,#106 Top 29%,#257 Bottom 29%,,
+Adidas,Supernova Solution,"89
+ Great!",$140,Daily running,Stability,9.8 oz / 279g 9.8 oz / 278g,0,10.0 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Decent,Moderate,Medium,Wide,Flexible,Moderate,Stiff,0,0,33.1 mm 36.0 mm,23.1 mm 26.0 mm,Normal,1,All seasons,1,#106 Top 29%,#257 Bottom 29%,,
+Adidas,Supernova Solution,"89
+ Great!",$140,Daily running,Stability,9.8 oz / 279g 9.8 oz / 278g,0,10.0 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Decent,Moderate,Medium,Wide,Flexible,Moderate,Stiff,0,0,33.1 mm 36.0 mm,23.1 mm 26.0 mm,Normal,1,All seasons,1,#106 Top 29%,#257 Bottom 29%,,
+Adidas,Supernova Solution,"89
+ Great!",$140,Daily running,Stability,9.8 oz / 279g 9.8 oz / 278g,0,10.0 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Decent,Moderate,Medium,Wide,Flexible,Moderate,Stiff,0,0,33.1 mm 36.0 mm,23.1 mm 26.0 mm,Normal,1,All seasons,1,#106 Top 29%,#257 Bottom 29%,,
+Adidas,Supernova Solution,"89
+ Great!",$140,Daily running,Stability,9.8 oz / 279g 9.8 oz / 278g,0,10.0 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Decent,Moderate,Medium,Wide,Flexible,Moderate,Stiff,0,0,33.1 mm 36.0 mm,23.1 mm 26.0 mm,Normal,1,All seasons,1,#106 Top 29%,#257 Bottom 29%,,
+Adidas,Supernova Solution,"89
+ Great!",$140,Daily running,Stability,9.8 oz / 279g 9.8 oz / 278g,0,10.0 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Decent,Moderate,Medium,Wide,Flexible,Moderate,Stiff,0,0,33.1 mm 36.0 mm,23.1 mm 26.0 mm,Normal,1,All seasons,1,#106 Top 29%,#257 Bottom 29%,,
+Adidas,Supernova Solution,"89
+ Great!",$140,Daily running,Stability,9.8 oz / 279g 9.8 oz / 278g,0,10.0 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Decent,Moderate,Medium,Wide,Flexible,Moderate,Stiff,0,0,33.1 mm 36.0 mm,23.1 mm 26.0 mm,Normal,1,All seasons,1,#107 Top 30%,#258 Bottom 29%,,
+Adidas,Supernova+,"86
+ Good!",$120,Daily running,Neutral,11.8 oz / 335g 11.2 oz / 318g,0,9.3 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,-,-,-,-,Medium,-,Moderate,Moderate,Moderate,0,0,30.7 mm 32.0 mm,21.4 mm 22.0 mm,Normal,1,-,1,#184 Bottom 49%,#154 Top 43%,,
+Adidas,Supernova+,"86
+ Good!",$120,Daily running,Neutral,11.8 oz / 335g 11.2 oz / 318g,0,9.3 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,-,-,-,-,Medium,-,Moderate,Moderate,Moderate,0,0,30.7 mm 32.0 mm,21.4 mm 22.0 mm,Normal,1,-,1,#183 Top 50%,#154 Top 43%,,
+Adidas,Supernova+,"86
+ Good!",$120,Daily running,Neutral,11.8 oz / 335g 11.2 oz / 318g,0,9.3 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,-,-,-,-,Medium,-,Moderate,Moderate,Moderate,0,0,30.7 mm 32.0 mm,21.4 mm 22.0 mm,Normal,1,-,1,#183 Top 50%,#154 Top 43%,,
+Adidas,Supernova+,"86
+ Good!",$120,Daily running,Neutral,11.8 oz / 335g 11.2 oz / 318g,0,9.3 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,-,-,-,-,Medium,-,Moderate,Moderate,Moderate,0,0,30.7 mm 32.0 mm,21.4 mm 22.0 mm,Normal,1,-,1,#183 Top 50%,#154 Top 43%,,
+Under Armour,Surge 4,"82
+ Good!",$65,Daily running,Neutral,10.4 oz / 295g 10 oz / 284g,0,9.0 mm 8.0 mm,HeelMid/forefoot,-,Balanced,Decent,Decent,Good,Moderate,Wide,Medium,Moderate,Stiff,Moderate,0,0,33.5 mm,24.5 mm,NormalWideX-Wide,1,All seasons,1,#275 Bottom 25%,#248 Bottom 32%,,
+Adidas,Switch FWD,"85
+ Good!",$140,Daily running,Neutral,11.4 oz / 323g 11.8 oz / 334.5g,0,9.1 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Bad,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,0,39.6 mm 45.0 mm,30.5 mm 35.0 mm,Normal,1,All seasons,1,#426 Bottom 33%,#560 Bottom 13%,,
+Adidas,Switch FWD 2,"85
+ Good!",$140,Daily running,Neutral,10.2 oz / 288g 10.4 oz / 294g,0,11.7 mm 10.0 mm,Heel,True to size,Balanced,Decent,Good,Decent,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,0,44.0 mm 46.0 mm,32.3 mm 36.0 mm,Normal,1,All seasons,1,#218 Bottom 40%,#263 Bottom 28%,,
+Saucony,Tempus,"89
+ Great!",$160,Daily runningTempo,Stability,9.4 oz / 266g 8.9 oz / 252g,0,8.5 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,-,-,-,Breathable,Narrow,-,Moderate,Moderate,Stiff,0,1,33.9 mm 36.5 mm,25.4 mm 28.5 mm,NormalWide,1,SummerAll seasons,1,#76 Top 21%,#121 Top 33%,,
+Saucony,Tempus,"89
+ Great!",$160,Daily runningTempo,Stability,9.4 oz / 266g 8.9 oz / 252g,0,8.5 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,-,-,-,Breathable,Narrow,-,Moderate,Moderate,Stiff,0,1,33.9 mm 36.5 mm,25.4 mm 28.5 mm,NormalWide,1,SummerAll seasons,1,#76 Top 21%,#121 Top 33%,,
+Saucony,Tempus,"89
+ Great!",$160,Daily runningTempo,Stability,9.4 oz / 266g 8.9 oz / 252g,0,8.5 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,-,-,-,Breathable,Narrow,-,Moderate,Moderate,Stiff,0,1,33.9 mm 36.5 mm,25.4 mm 28.5 mm,NormalWide,1,SummerAll seasons,1,#79 Top 22%,#122 Top 34%,,
+Saucony,Tempus,"89
+ Great!",$160,Daily runningTempo,Stability,9.4 oz / 266g 8.9 oz / 252g,0,8.5 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,-,-,-,Breathable,Narrow,-,Moderate,Moderate,Stiff,0,1,33.9 mm 36.5 mm,25.4 mm 28.5 mm,NormalWide,1,SummerAll seasons,1,#80 Top 22%,#121 Top 34%,,
+Saucony,Tempus,"89
+ Great!",$160,Daily runningTempo,Stability,9.4 oz / 266g 8.9 oz / 252g,0,8.5 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,-,-,-,Breathable,Narrow,-,Moderate,Moderate,Stiff,0,1,33.9 mm 36.5 mm,25.4 mm 28.5 mm,NormalWide,1,SummerAll seasons,1,#80 Top 22%,#121 Top 34%,,
+Saucony,Tempus,"89
+ Great!",$160,Daily runningTempo,Stability,9.4 oz / 266g 8.9 oz / 252g,0,8.5 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,-,-,-,Breathable,Narrow,-,Moderate,Moderate,Stiff,0,1,33.9 mm 36.5 mm,25.4 mm 28.5 mm,NormalWide,1,SummerAll seasons,1,#82 Top 23%,#121 Top 34%,,
+Saucony,Tempus,"89
+ Great!",$160,Daily runningTempo,Stability,9.4 oz / 266g 8.9 oz / 252g,0,8.5 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,-,-,-,Breathable,Narrow,-,Moderate,Moderate,Stiff,0,1,33.9 mm 36.5 mm,25.4 mm 28.5 mm,NormalWide,1,SummerAll seasons,1,#82 Top 23%,#121 Top 34%,,
+Saucony,Tempus,"89
+ Great!",$160,Daily runningTempo,Stability,9.4 oz / 266g 8.9 oz / 252g,0,8.5 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,-,-,-,Breathable,Narrow,-,Moderate,Moderate,Stiff,0,1,33.9 mm 36.5 mm,25.4 mm 28.5 mm,NormalWide,1,SummerAll seasons,1,#82 Top 23%,#121 Top 34%,,
+Saucony,Tempus,"89
+ Great!",$160,Daily runningTempo,Stability,9.4 oz / 266g 8.9 oz / 252g,0,8.5 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,-,-,-,Breathable,Narrow,-,Moderate,Moderate,Stiff,0,1,33.9 mm 36.5 mm,25.4 mm 28.5 mm,NormalWide,1,SummerAll seasons,1,#82 Top 23%,#121 Top 34%,,
+Saucony,Tempus,"89
+ Great!",$160,Daily runningTempo,Stability,9.4 oz / 266g 8.9 oz / 252g,0,8.5 mm 8.0 mm,HeelMid/forefoot,Slightly small,Soft,-,-,-,Breathable,Narrow,-,Moderate,Moderate,Stiff,0,1,33.9 mm 36.5 mm,25.4 mm 28.5 mm,NormalWide,1,SummerAll seasons,1,#82 Top 23%,#121 Top 34%,,
+Altra,Torin 6,"80
+ Good!",$150,Daily running,Neutral,9 oz / 254g 9.9 oz / 280g,0,0.0 mm,Mid/forefoot,Slightly small,Balanced,-,-,-,Breathable,Medium,-,Stiff,Moderate,Flexible,0,0,25.1 mm 28.0 mm,25.1 mm 28.0 mm,NormalWide,1,SummerAll seasons,1,#558 Bottom 13%,#449 Bottom 30%,,
+Altra,Torin 7,"80
+ Good!",$150,Daily runningTempo,Neutral,9 oz / 255g 9.8 oz / 278g,0,-0.8 mm 0.0 mm,Mid/forefoot,True to size,Soft,Decent,Decent,Decent,Breathable,Wide,Wide,Moderate,Moderate,Moderate,0,0,27.6 mm 30.0 mm,28.4 mm 30.0 mm,NormalWide,1,SummerAll seasons,1,#577 Bottom 10%,#219 Top 34%,,
+Altra,Torin 8,"86
+ Good!",$150,Daily running,Neutral,9.7 oz / 275g 10.1 oz / 287g,0,-0.1 mm 0.0 mm,Mid/forefoot,True to size,Balanced,Good,Good,Good,Moderate,Medium,Wide,Moderate,Moderate,Stiff,0,0,27.6 mm 30.0 mm,27.7 mm 30.0 mm,NormalWide,1,All seasons,1,#173 Top 48%,#64 Top 18%,,
+Altra,Torin 8,"86
+ Good!",$150,Daily running,Neutral,9.7 oz / 275g 10.1 oz / 287g,0,-0.1 mm 0.0 mm,Mid/forefoot,True to size,Balanced,Good,Good,Good,Moderate,Medium,Wide,Moderate,Moderate,Stiff,0,0,27.6 mm 30.0 mm,27.7 mm 30.0 mm,NormalWide,1,All seasons,1,#173 Top 48%,#64 Top 18%,,
+Altra,Torin 8,"86
+ Good!",$150,Daily running,Neutral,9.7 oz / 275g 10.1 oz / 287g,0,-0.1 mm 0.0 mm,Mid/forefoot,True to size,Balanced,Good,Good,Good,Moderate,Medium,Wide,Moderate,Moderate,Stiff,0,0,27.6 mm 30.0 mm,27.7 mm 30.0 mm,NormalWide,1,All seasons,1,#173 Top 48%,#64 Top 18%,,
+Altra,Torin 8,"86
+ Good!",$150,Daily running,Neutral,9.7 oz / 275g 10.1 oz / 287g,0,-0.1 mm 0.0 mm,Mid/forefoot,True to size,Balanced,Good,Good,Good,Moderate,Medium,Wide,Moderate,Moderate,Stiff,0,0,27.6 mm 30.0 mm,27.7 mm 30.0 mm,NormalWide,1,All seasons,1,#173 Top 48%,#64 Top 18%,,
+Brooks,Trace 2,"88
+ Great!",$100,Daily running,Neutral,8.8 oz / 249g 8.6 oz / 243g,1,12.3 mm 12.0 mm,Heel,True to size,Balanced,Decent,Decent,Good,Breathable,Narrow,Medium,Stiff,Moderate,Moderate,0,0,34.2 mm,21.9 mm,Normal,1,SummerAll seasons,1,#239 Top 38%,#444 Bottom 31%,,
+Brooks,Trace 3,"76
+ Decent!",$100,Daily running,Neutral,9.1 oz / 257g 9 oz / 255g,0,11.9 mm 12.0 mm,Heel,True to size,Balanced,Bad,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,34.1 mm 34.0 mm,22.2 mm 22.0 mm,NormalWide,1,All seasons,1,#340 Bottom 7%,#152 Top 42%,,
+Brooks,Trace 3,"76
+ Decent!",$100,Daily running,Neutral,9.1 oz / 257g 9 oz / 255g,0,11.9 mm 12.0 mm,Heel,True to size,Balanced,Bad,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,34.1 mm 34.0 mm,22.2 mm 22.0 mm,NormalWide,1,All seasons,1,#340 Bottom 7%,#152 Top 42%,,
+Brooks,Trace 3,"76
+ Decent!",$100,Daily running,Neutral,9.1 oz / 257g 9 oz / 255g,0,11.9 mm 12.0 mm,Heel,True to size,Balanced,Bad,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,34.1 mm 34.0 mm,22.2 mm 22.0 mm,NormalWide,1,All seasons,1,#340 Bottom 7%,#152 Top 42%,,
+Brooks,Trace 3,"76
+ Decent!",$100,Daily running,Neutral,9.1 oz / 257g 9 oz / 255g,0,11.9 mm 12.0 mm,Heel,True to size,Balanced,Bad,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,34.1 mm 34.0 mm,22.2 mm 22.0 mm,NormalWide,1,All seasons,1,#340 Bottom 7%,#152 Top 42%,,
+Brooks,Trace 3,"76
+ Decent!",$100,Daily running,Neutral,9.1 oz / 257g 9 oz / 255g,0,11.9 mm 12.0 mm,Heel,True to size,Balanced,Bad,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,34.1 mm 34.0 mm,22.2 mm 22.0 mm,NormalWide,1,All seasons,1,#340 Bottom 7%,#152 Top 42%,,
+Hoka,Transport X,"84
+ Good!",$200,Daily running,Neutral,9.7 oz / 274g 8.8 oz / 250g,0,9.4 mm 5.0 mm,HeelMid/forefoot,True to size,Balanced,Bad,Bad,Good,Breathable,Medium,Medium,Stiff,Stiff,Stiff,Carbon plate,1,40.2 mm 35.0 mm,30.8 mm 30.0 mm,NormalWide,1,SummerAll seasons,1,#233 Bottom 36%,#244 Bottom 33%,,
+Hoka,Transport X,"84
+ Good!",$200,Daily running,Neutral,9.7 oz / 274g 8.8 oz / 250g,0,9.4 mm 5.0 mm,HeelMid/forefoot,True to size,Balanced,Bad,Bad,Good,Breathable,Medium,Medium,Stiff,Stiff,Stiff,Carbon plate,1,40.2 mm 35.0 mm,30.8 mm 30.0 mm,NormalWide,1,SummerAll seasons,1,#233 Bottom 36%,#244 Bottom 33%,,
+Allbirds,Tree Dasher,"91
+ Superb!",$125,Daily running,Neutral,10.6 oz / 301g 10.2 oz / 289g,0,6.0 mm 7.0 mm,Mid/forefoot,True to size,Balanced,-,-,-,Breathable,Medium,-,Stiff,Flexible,Flexible,0,0,29.4 mm 22.5 mm,23.4 mm 15.5 mm,Normal,1,SummerAll seasons,1,#36 Top 6%,#382 Bottom 40%,,
+Allbirds,Tree Dasher 2,"87
+ Great!",$135,Daily running,Neutral,10.3 oz / 291g 10.3 oz / 292g,0,9.3 mm 7.0 mm,HeelMid/forefoot,Slightly small,Balanced,Good,Good,Bad,Breathable,Wide,Wide,Stiff,Flexible,Flexible,0,0,31.6 mm 22.5 mm,22.3 mm 15.5 mm,Normal,1,SummerAll seasons,1,#152 Top 42%,#178 Top 49%,,
+Allbirds,Tree Flyer 2,"86
+ Good!",$160,Daily running,Neutral,10.3 oz / 293g 10.6 oz / 300.5g,0,8.1 mm 8.5 mm,HeelMid/forefoot,True to size,Balanced,Good,Decent,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,0,32.1 mm 30.5 mm,24.0 mm 22.0 mm,Normal,1,All seasons,1,#191 Bottom 47%,#315 Bottom 13%,,
+Allbirds,Tree Flyer 2,"86
+ Good!",$160,Daily running,Neutral,10.3 oz / 293g 10.6 oz / 300.5g,0,8.1 mm 8.5 mm,HeelMid/forefoot,True to size,Balanced,Good,Decent,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,0,32.1 mm 30.5 mm,24.0 mm 22.0 mm,Normal,1,All seasons,1,#194 Bottom 46%,#316 Bottom 13%,,
+Allbirds,Tree Flyer 2,"86
+ Good!",$160,Daily running,Neutral,10.3 oz / 293g 10.6 oz / 300.5g,0,8.1 mm 8.5 mm,HeelMid/forefoot,True to size,Balanced,Good,Decent,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,0,32.1 mm 30.5 mm,24.0 mm 22.0 mm,Normal,1,All seasons,1,#194 Bottom 46%,#316 Bottom 13%,,
+Allbirds,Tree Flyer 2,"86
+ Good!",$160,Daily running,Neutral,10.3 oz / 293g 10.6 oz / 300.5g,0,8.1 mm 8.5 mm,HeelMid/forefoot,True to size,Balanced,Good,Decent,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,0,32.1 mm 30.5 mm,24.0 mm 22.0 mm,Normal,1,All seasons,1,#194 Bottom 46%,#316 Bottom 13%,,
+Allbirds,Tree Flyer 2,"86
+ Good!",$160,Daily running,Neutral,10.3 oz / 293g 10.6 oz / 300.5g,0,8.1 mm 8.5 mm,HeelMid/forefoot,True to size,Balanced,Good,Decent,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,0,32.1 mm 30.5 mm,24.0 mm 22.0 mm,Normal,1,All seasons,1,#194 Bottom 46%,#316 Bottom 13%,,
+Allbirds,Tree Flyer 2,"86
+ Good!",$160,Daily running,Neutral,10.3 oz / 293g 10.6 oz / 300.5g,0,8.1 mm 8.5 mm,HeelMid/forefoot,True to size,Balanced,Good,Decent,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,0,32.1 mm 30.5 mm,24.0 mm 22.0 mm,Normal,1,All seasons,1,#194 Bottom 46%,#316 Bottom 13%,,
+Allbirds,Tree Flyer 2,"86
+ Good!",$160,Daily running,Neutral,10.3 oz / 293g 10.6 oz / 300.5g,0,8.1 mm 8.5 mm,HeelMid/forefoot,True to size,Balanced,Good,Decent,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,0,32.1 mm 30.5 mm,24.0 mm 22.0 mm,Normal,1,All seasons,1,#194 Bottom 46%,#316 Bottom 13%,,
+Saucony,Triumph 20,"87
+ Great!",$160,Daily running,Neutral,9.9 oz / 282g 9.7 oz / 275g,0,10.4 mm 10.0 mm,Heel,Slightly small,Soft,-,-,-,Breathable,Narrow,-,Stiff,Flexible,Moderate,0,1,35.4 mm 37.0 mm,25.0 mm 27.0 mm,NormalWide,1,SummerAll seasons,1,#292 Top 46%,#310 Top 49%,,
+Saucony,Triumph 21,"87
+ Great!",$160,Daily running,Neutral,9.9 oz / 282g 10 oz / 283g,0,10.5 mm 10.0 mm,Heel,True to size,Soft,Decent,Decent,Good,Moderate,Medium,Wide,Moderate,Stiff,Stiff,0,1,39.1 mm 39.0 mm,28.6 mm 29.0 mm,NormalWide,1,All seasons,1,#331 Bottom 48%,#279 Top 44%,,
+Saucony,Triumph 22,"88
+ Great!",$160,Daily running,Neutral,10.1 oz / 286g 10.1 oz / 286g,0,9.7 mm 10.0 mm,HeelMid/forefoot,True to size,Soft,Good,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,38.6 mm 37.0 mm,28.9 mm 27.0 mm,NormalWide,1,All seasons,1,#241 Top 38%,#137 Top 22%,,
+Saucony,Triumph 23,"89
+ Great!",$170,Daily running,Neutral,9.6 oz / 272g 9.3 oz / 263g,0,10.0 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Moderate,Stiff,0,0,42.3 mm 37.0 mm,32.3 mm 27.0 mm,NormalWide,1,All seasons,1,#77 Top 22%,#92 Top 26%,,
+Saucony,Triumph 23,"89
+ Great!",$170,Daily running,Neutral,9.6 oz / 272g 9.3 oz / 263g,0,10.0 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Moderate,Stiff,0,0,42.3 mm 37.0 mm,32.3 mm 27.0 mm,NormalWide,1,All seasons,1,#77 Top 22%,#92 Top 26%,,
+Saucony,Triumph 23,"89
+ Great!",$170,Daily running,Neutral,9.6 oz / 272g 9.3 oz / 263g,0,10.0 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Moderate,Stiff,0,0,42.3 mm 37.0 mm,32.3 mm 27.0 mm,NormalWide,1,All seasons,1,#77 Top 22%,#92 Top 26%,,
+Saucony,Triumph 23,"89
+ Great!",$170,Daily running,Neutral,9.6 oz / 272g 9.3 oz / 263g,0,10.0 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Moderate,Stiff,0,0,42.3 mm 37.0 mm,32.3 mm 27.0 mm,NormalWide,1,All seasons,1,#80 Top 22%,#92 Top 26%,,
+Adidas,Ultraboost 1.0,"89
+ Great!",$180,Daily running,Neutral,11.3 oz / 320g 10.7 oz / 303g,0,14.2 mm 10.0 mm,Heel,True to size,Soft,Bad,Decent,Good,Breathable,Narrow,Medium,Moderate,Flexible,Moderate,0,0,34.4 mm 22.0 mm,20.2 mm 12.0 mm,NormalX-Wide,1,SummerAll seasons,1,#182 Top 29%,#628 Bottom 2%,,
+Adidas,Ultraboost 21,"89
+ Great!",$180,Daily running,Neutral,12.5 oz / 355g 12 oz / 340g,0,12.1 mm 10.0 mm,Heel,True to size,-,-,-,-,-,Medium,-,-,Moderate,-,0,0,32.8 mm 30.5 mm,20.7 mm 20.5 mm,Normal,1,-,1,#169 Top 27%,#463 Bottom 28%,,
+Adidas,Ultraboost 22,"90
+ Superb!",$190,Daily running,Neutral,10.6 oz / 301g 11.7 oz / 332g,0,12.7 mm 10.0 mm,Heel,True to size,Firm,-,-,-,-,Wide,-,Stiff,-,-,0,0,33.9 mm 31.0 mm,21.2 mm 21.0 mm,Normal,0,-,0,#111 Top 18%,#306 Top 48%,,
+Adidas,Ultraboost 5,"90
+ Superb!",$180,Daily running,Neutral,10.3 oz / 292g 11.4 oz / 323g,0,10.6 mm 10.0 mm,Heel,True to size,Soft,Decent,Good,Good,Breathable,Wide,Wide,Stiff,Stiff,Moderate,0,0,35.2 mm 39.0 mm,24.6 mm 29.0 mm,NarrowNormal,1,SummerAll seasons,1,#49 Top 14%,#167 Top 46%,,
+Adidas,Ultraboost 5,"90
+ Superb!",$180,Daily running,Neutral,10.3 oz / 292g 11.4 oz / 323g,0,10.6 mm 10.0 mm,Heel,True to size,Soft,Decent,Good,Good,Breathable,Wide,Wide,Stiff,Stiff,Moderate,0,0,35.2 mm 39.0 mm,24.6 mm 29.0 mm,NarrowNormal,1,SummerAll seasons,1,#49 Top 14%,#167 Top 46%,,
+Adidas,Ultraboost 5,"90
+ Superb!",$180,Daily running,Neutral,10.3 oz / 292g 11.4 oz / 323g,0,10.6 mm 10.0 mm,Heel,True to size,Soft,Decent,Good,Good,Breathable,Wide,Wide,Stiff,Stiff,Moderate,0,0,35.2 mm 39.0 mm,24.6 mm 29.0 mm,NarrowNormal,1,SummerAll seasons,1,#49 Top 14%,#167 Top 46%,,
+Adidas,Ultraboost 5,"90
+ Superb!",$180,Daily running,Neutral,10.3 oz / 292g 11.4 oz / 323g,0,10.6 mm 10.0 mm,Heel,True to size,Soft,Decent,Good,Good,Breathable,Wide,Wide,Stiff,Stiff,Moderate,0,0,35.2 mm 39.0 mm,24.6 mm 29.0 mm,NarrowNormal,1,SummerAll seasons,1,#49 Top 14%,#167 Top 46%,,
+Adidas,Ultraboost 5,"90
+ Superb!",$180,Daily running,Neutral,10.3 oz / 292g 11.4 oz / 323g,0,10.6 mm 10.0 mm,Heel,True to size,Soft,Decent,Good,Good,Breathable,Wide,Wide,Stiff,Stiff,Moderate,0,0,35.2 mm 39.0 mm,24.6 mm 29.0 mm,NarrowNormal,1,SummerAll seasons,1,#49 Top 14%,#167 Top 46%,,
+Adidas,Ultraboost 5,"90
+ Superb!",$180,Daily running,Neutral,10.3 oz / 292g 11.4 oz / 323g,0,10.6 mm 10.0 mm,Heel,True to size,Soft,Decent,Good,Good,Breathable,Wide,Wide,Stiff,Stiff,Moderate,0,0,35.2 mm 39.0 mm,24.6 mm 29.0 mm,NarrowNormal,1,SummerAll seasons,1,#49 Top 14%,#167 Top 46%,,
+Adidas,Ultraboost 5X,"91
+ Superb!",$180,Daily running,Neutral,9.4 oz / 266g 9.7 oz / 274g,0,10.4 mm 10.0 mm,Heel,True to size,Soft,Good,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Moderate,0,0,34.6 mm 39.0 mm,24.2 mm 29.0 mm,NormalWide,1,All seasons,1,#33 Top 10%,#75 Top 21%,,
+Adidas,Ultraboost 5X,"91
+ Superb!",$180,Daily running,Neutral,9.4 oz / 266g 9.7 oz / 274g,0,10.4 mm 10.0 mm,Heel,True to size,Soft,Good,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Moderate,0,0,34.6 mm 39.0 mm,24.2 mm 29.0 mm,NormalWide,1,All seasons,1,#31 Top 9%,#75 Top 21%,,
+Adidas,Ultraboost Light,"89
+ Great!",$190,Daily running,Neutral,10.8 oz / 305g 10.5 oz / 299g,0,11.9 mm 10.0 mm,Heel,Half size small,Soft,Good,Good,Good,Moderate,Medium,Wide,Moderate,Moderate,Moderate,0,0,30.1 mm 30.0 mm,18.2 mm 20.0 mm,Normal,1,All seasons,1,#208 Top 33%,#210 Top 33%,,
+Adidas,Ultrabounce,"82
+ Good!",$80,Daily running,Neutral,11.5 oz / 326g 12.1 oz / 343g,0,11.3 mm 9.0 mm,Heel,True to size,Balanced,Decent,Decent,Decent,Breathable,Medium,Medium,Moderate,Moderate,Flexible,0,0,30.3 mm 25.0 mm,19.0 mm 16.0 mm,NormalWide,1,SummerAll seasons,1,#281 Bottom 23%,#201 Bottom 45%,,
+Adidas,Ultrabounce,"82
+ Good!",$80,Daily running,Neutral,11.5 oz / 326g 12.1 oz / 343g,0,11.3 mm 9.0 mm,Heel,True to size,Balanced,Decent,Decent,Decent,Breathable,Medium,Medium,Moderate,Moderate,Flexible,0,0,30.3 mm 25.0 mm,19.0 mm 16.0 mm,NormalWide,1,SummerAll seasons,1,#281 Bottom 23%,#201 Bottom 45%,,
+Adidas,Ultrabounce,"82
+ Good!",$80,Daily running,Neutral,11.5 oz / 326g 12.1 oz / 343g,0,11.3 mm 9.0 mm,Heel,True to size,Balanced,Decent,Decent,Decent,Breathable,Medium,Medium,Moderate,Moderate,Flexible,0,0,30.3 mm 25.0 mm,19.0 mm 16.0 mm,NormalWide,1,SummerAll seasons,1,#281 Bottom 23%,#201 Bottom 45%,,
+Adidas,Ultrabounce,"82
+ Good!",$80,Daily running,Neutral,11.5 oz / 326g 12.1 oz / 343g,0,11.3 mm 9.0 mm,Heel,True to size,Balanced,Decent,Decent,Decent,Breathable,Medium,Medium,Moderate,Moderate,Flexible,0,0,30.3 mm 25.0 mm,19.0 mm 16.0 mm,NormalWide,1,SummerAll seasons,1,#281 Bottom 23%,#201 Bottom 45%,,
+Adidas,Ultrabounce,"82
+ Good!",$80,Daily running,Neutral,11.5 oz / 326g 12.1 oz / 343g,0,11.3 mm 9.0 mm,Heel,True to size,Balanced,Decent,Decent,Decent,Breathable,Medium,Medium,Moderate,Moderate,Flexible,0,0,30.3 mm 25.0 mm,19.0 mm 16.0 mm,NormalWide,1,SummerAll seasons,1,#281 Bottom 23%,#201 Bottom 45%,,
+Adidas,Ultrarun 5,"84
+ Good!",$80,Daily running,Neutral,10.4 oz / 295g 11.4 oz / 323g,0,9.5 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Good,Good,Decent,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,34.3 mm 35.0 mm,24.8 mm 25.0 mm,Normal,1,All seasons,1,#243 Bottom 33%,#195 Bottom 46%,,
+Altra,Vanish Carbon 2,"90
+ Superb!",$260,CompetitionTempo,Neutral,7.4 oz / 210g 8.1 oz / 229g,1,3.7 mm 0.0 mm,Mid/forefoot,Half size small,Balanced,Bad,Good,Decent,Breathable,Medium,Wide,Stiff,Stiff,Flexible,Carbon plate,1,33.1 mm 36.0 mm,29.4 mm 36.0 mm,Normal,1,SummerAll seasons,1,#61 Top 17%,#218 Bottom 40%,,
+Altra,Vanish Carbon 2,"90
+ Superb!",$260,CompetitionTempo,Neutral,7.4 oz / 210g 8.1 oz / 229g,1,3.7 mm 0.0 mm,Mid/forefoot,Half size small,Balanced,Bad,Good,Decent,Breathable,Medium,Wide,Stiff,Stiff,Flexible,Carbon plate,1,33.1 mm 36.0 mm,29.4 mm 36.0 mm,Normal,1,SummerAll seasons,1,#63 Top 18%,#218 Bottom 40%,,
+Altra,Vanish Carbon 2,"90
+ Superb!",$260,CompetitionTempo,Neutral,7.4 oz / 210g 8.1 oz / 229g,1,3.7 mm 0.0 mm,Mid/forefoot,Half size small,Balanced,Bad,Good,Decent,Breathable,Medium,Wide,Stiff,Stiff,Flexible,Carbon plate,1,33.1 mm 36.0 mm,29.4 mm 36.0 mm,Normal,1,SummerAll seasons,1,#63 Top 18%,#218 Bottom 40%,,
+Altra,Vanish Carbon 2,"90
+ Superb!",$260,CompetitionTempo,Neutral,7.4 oz / 210g 8.1 oz / 229g,1,3.7 mm 0.0 mm,Mid/forefoot,Half size small,Balanced,Bad,Good,Decent,Breathable,Medium,Wide,Stiff,Stiff,Flexible,Carbon plate,1,33.1 mm 36.0 mm,29.4 mm 36.0 mm,Normal,1,SummerAll seasons,1,#63 Top 18%,#218 Bottom 40%,,
+Merrell,Vapor Glove 6,"86
+ Good!",$90,Daily running,Neutral,5.6 oz / 159g 5.3 oz / 150g,1,0.0 mm 0.0 mm,Mid/forefoot,True to size,-,Decent,Decent,Decent,Moderate,Medium,Wide,Flexible,Flexible,Flexible,0,0,7.6 mm 6.0 mm,7.6 mm 6.0 mm,Normal,0,All seasons,0,#171 Top 47%,#155 Top 43%,,
+Merrell,Vapor Glove 6,"86
+ Good!",$90,Daily running,Neutral,5.6 oz / 159g 5.3 oz / 150g,1,0.0 mm 0.0 mm,Mid/forefoot,True to size,-,Decent,Decent,Decent,Moderate,Medium,Wide,Flexible,Flexible,Flexible,0,0,7.6 mm 6.0 mm,7.6 mm 6.0 mm,Normal,0,All seasons,0,#173 Top 48%,#156 Top 43%,,
+Nike,Vaporfly 3,"86
+ Good!",$250,Competition,Neutral,6.7 oz / 190g 6.5 oz / 184g,1,11.1 mm 8.0 mm,Heel,True to size,Soft,Bad,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Moderate,Carbon plate,1,37.1 mm 40.0 mm,26.0 mm 32.0 mm,Normal,0,SummerAll seasons,0,#362 Bottom 43%,#61 Top 10%,,
+Nike,Vaporfly 4,"89
+ Great!",$260,Competition,Neutral,5.9 oz / 166g 6.5 oz / 184g,1,8.6 mm 6.0 mm,HeelMid/forefoot,True to size,Soft,Good,Decent,Bad,Moderate,Narrow,Medium,Stiff,Stiff,Flexible,Carbon plate,0,34.1 mm 35.0 mm,25.5 mm 29.0 mm,Normal,1,All seasons,1,#105 Top 29%,#33 Top 10%,,
+Nike,Vaporfly 4,"89
+ Great!",$260,Competition,Neutral,5.9 oz / 166g 6.5 oz / 184g,1,8.6 mm 6.0 mm,HeelMid/forefoot,True to size,Soft,Good,Decent,Bad,Moderate,Narrow,Medium,Stiff,Stiff,Flexible,Carbon plate,0,34.1 mm 35.0 mm,25.5 mm 29.0 mm,Normal,1,All seasons,1,#99 Top 28%,#33 Top 10%,,
+Nike,Vaporfly 4,"89
+ Great!",$260,Competition,Neutral,5.9 oz / 166g 6.5 oz / 184g,1,8.6 mm 6.0 mm,HeelMid/forefoot,True to size,Soft,Good,Decent,Bad,Moderate,Narrow,Medium,Stiff,Stiff,Flexible,Carbon plate,0,34.1 mm 35.0 mm,25.5 mm 29.0 mm,Normal,1,All seasons,1,#79 Top 22%,#33 Top 10%,,
+Nike,Vaporfly 4,"89
+ Great!",$260,Competition,Neutral,5.9 oz / 166g 6.5 oz / 184g,1,8.6 mm 6.0 mm,HeelMid/forefoot,True to size,Soft,Good,Decent,Bad,Moderate,Narrow,Medium,Stiff,Stiff,Flexible,Carbon plate,0,34.1 mm 35.0 mm,25.5 mm 29.0 mm,Normal,1,All seasons,1,#79 Top 22%,#33 Top 10%,,
+ASICS,Versablast 4,"82
+ Good!",$80,Daily running,Neutral,10.2 oz / 288g 9.2 oz / 260g,0,9.4 mm 10.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Decent,Good,Warm,Narrow,Narrow,Moderate,Moderate,Moderate,0,0,36.1 mm 36.0 mm,26.7 mm 26.0 mm,NormalWide,1,All seasons,1,#281 Bottom 23%,#136 Top 38%,,
+Altra,VIA Olympus,"85
+ Good!",$170,Daily running,Neutral,10.5 oz / 299g 11 oz / 312g,0,1.6 mm 0.0 mm,Mid/forefoot,Slightly small,Balanced,-,Bad,-,Moderate,Medium,-,Stiff,Stiff,Flexible,0,0,34.2 mm 33.0 mm,32.6 mm 33.0 mm,Normal,1,All seasons,1,#428 Bottom 33%,#281 Top 44%,,
+Altra,VIA Olympus 2,"85
+ Good!",$165,Daily running,Neutral,10.4 oz / 295g 10.5 oz / 297g,0,-0.2 mm 0.0 mm,Mid/forefoot,True to size,Soft,Good,Good,Good,Warm,Wide,Wide,Stiff,Stiff,Moderate,0,0,34.8 mm 33.0 mm,35.0 mm 33.0 mm,Normal,1,All seasons,1,#223 Bottom 39%,#124 Top 34%,,
+Altra,VIA Olympus 2,"85
+ Good!",$165,Daily running,Neutral,10.4 oz / 295g 10.5 oz / 297g,0,-0.2 mm 0.0 mm,Mid/forefoot,True to size,Soft,Good,Good,Good,Warm,Wide,Wide,Stiff,Stiff,Moderate,0,0,34.8 mm 33.0 mm,35.0 mm 33.0 mm,Normal,1,All seasons,1,#223 Bottom 39%,#124 Top 34%,,
+Altra,VIA Olympus 2,"85
+ Good!",$165,Daily running,Neutral,10.4 oz / 295g 10.5 oz / 297g,0,-0.2 mm 0.0 mm,Mid/forefoot,True to size,Soft,Good,Good,Good,Warm,Wide,Wide,Stiff,Stiff,Moderate,0,0,34.8 mm 33.0 mm,35.0 mm 33.0 mm,Normal,1,All seasons,1,#223 Bottom 39%,#124 Top 34%,,
+Altra,VIA Olympus 2,"85
+ Good!",$165,Daily running,Neutral,10.4 oz / 295g 10.5 oz / 297g,0,-0.2 mm 0.0 mm,Mid/forefoot,True to size,Soft,Good,Good,Good,Warm,Wide,Wide,Stiff,Stiff,Moderate,0,0,34.8 mm 33.0 mm,35.0 mm 33.0 mm,Normal,1,All seasons,1,#223 Bottom 39%,#124 Top 34%,,
+Altra,VIA Olympus 2,"85
+ Good!",$165,Daily running,Neutral,10.4 oz / 295g 10.5 oz / 297g,0,-0.2 mm 0.0 mm,Mid/forefoot,True to size,Soft,Good,Good,Good,Warm,Wide,Wide,Stiff,Stiff,Moderate,0,0,34.8 mm 33.0 mm,35.0 mm 33.0 mm,Normal,1,All seasons,1,#223 Bottom 39%,#124 Top 34%,,
+Altra,VIA Olympus 2,"85
+ Good!",$165,Daily running,Neutral,10.4 oz / 295g 10.5 oz / 297g,0,-0.2 mm 0.0 mm,Mid/forefoot,True to size,Soft,Good,Good,Good,Warm,Wide,Wide,Stiff,Stiff,Moderate,0,0,34.8 mm 33.0 mm,35.0 mm 33.0 mm,Normal,1,All seasons,1,#223 Bottom 39%,#124 Top 34%,,
+Nike,Vomero 17,"86
+ Good!",$160,Daily running,Neutral,9.9 oz / 282g 10.1 oz / 286g,0,7.7 mm 10.0 mm,Mid/forefoot,True to size,Soft,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,33.9 mm 39.0 mm,26.2 mm 29.0 mm,NormalWideX-Wide,1,All seasons,1,#354 Bottom 45%,#160 Top 25%,,
+Nike,Vomero 18,91 Superb!,$150,Daily running,Neutral,10.5 oz / 298g 11.5 oz / 325g,0,13.9 mm 10.0 mm,Heel,True to size,Soft,Decent,Good,Decent,Moderate,Medium,Narrow,Moderate,Stiff,Moderate,0,1,42.5 mm 45.0 mm,28.6 mm 35.0 mm,Normal Wide X-Wide,1,All seasons,1,#24 Top 7%,#8 Top 3%,,
+Nike,Vomero 18,"91
+ Superb!",$150,Daily running,Neutral,10.5 oz / 298g 11.5 oz / 325g,0,13.9 mm 10.0 mm,Heel,True to size,Soft,Decent,Good,Decent,Moderate,Medium,Narrow,Moderate,Stiff,Moderate,0,1,42.5 mm 45.0 mm,28.6 mm 35.0 mm,NormalWideX-Wide,1,All seasons,1,#24 Top 7%,#8 Top 3%,,
+Nike,Vomero 18,"91
+ Superb!",$150,Daily running,Neutral,10.5 oz / 298g 11.5 oz / 325g,0,13.9 mm 10.0 mm,Heel,True to size,Soft,Decent,Good,Decent,Moderate,Medium,Narrow,Moderate,Stiff,Moderate,0,1,42.5 mm 45.0 mm,28.6 mm 35.0 mm,NormalWideX-Wide,1,All seasons,1,#20 Top 6%,#8 Top 3%,,
+Nike,Vomero Plus,91 Superb!,$180,Daily running,Neutral,10.2 oz / 289g 10.1 oz / 285g,0,9.6 mm 10.0 mm,Heel Mid/forefoot,True to size,Soft,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,0,42.3 mm 45.0 mm,32.7 mm 35.0 mm,Normal Wide X-Wide,1,All seasons,1,#18 Top 5%,#7 Top 2%,,
+Nike,Vomero Plus,"91
+ Superb!",$180,Daily running,Neutral,10.2 oz / 289g 10.1 oz / 285g,0,9.6 mm 10.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,0,42.3 mm 45.0 mm,32.7 mm 35.0 mm,NormalWideX-Wide,1,All seasons,1,#18 Top 5%,#7 Top 2%,,
+Nike,Vomero Plus,"91
+ Superb!",$180,Daily running,Neutral,10.2 oz / 289g 10.1 oz / 285g,0,9.6 mm 10.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,0,42.3 mm 45.0 mm,32.7 mm 35.0 mm,NormalWideX-Wide,1,All seasons,1,#18 Top 5%,#7 Top 2%,,
+Nike,Vomero Plus,"92
+ Superb!",$180,Daily running,Neutral,10.2 oz / 289g 10.1 oz / 285g,0,9.6 mm 10.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,0,42.3 mm 45.0 mm,32.7 mm 35.0 mm,NormalWideX-Wide,1,All seasons,1,#19 Top 6%,#7 Top 2%,,
+Nike,Vomero Plus,"92
+ Superb!",$180,Daily running,Neutral,10.2 oz / 289g 10.1 oz / 285g,0,9.6 mm 10.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,0,42.3 mm 45.0 mm,32.7 mm 35.0 mm,NormalWideX-Wide,1,All seasons,1,#19 Top 6%,#7 Top 2%,,
+Nike,Vomero Premium,"78
+ Decent!",$230,Daily running,Neutral,11.5 oz / 326g 12.4 oz / 351g,0,8.8 mm 10.0 mm,HeelMid/forefoot,-,Soft,Decent,Good,Good,Moderate,Medium,Narrow,Stiff,Stiff,Stiff,0,1,50.1 mm 55.0 mm,41.3 mm 45.0 mm,Normal,1,All seasons,1,#329 Bottom 9%,#2 Top 1%,,
+Mizuno,Wave Horizon 6,"89
+ Great!",$170,Daily running,Stability,11 oz / 313g 11.2 oz / 318g,0,6.5 mm 8.0 mm,Mid/forefoot,True to size,Balanced,-,-,-,-,Medium,-,Stiff,Moderate,Stiff,0,0,37.1 mm 38.0 mm,30.6 mm 30.0 mm,NormalWide,1,-,1,#165 Top 26%,#531 Bottom 17%,,
+Mizuno,Wave Horizon 7,"88
+ Great!",$170,Daily running,Stability,11.6 oz / 329g 11.8 oz / 334g,0,7.2 mm 8.0 mm,HeelMid/forefoot,Slightly small,Balanced,Decent,Bad,Good,Moderate,Medium,Wide,Stiff,Stiff,Stiff,0,0,39.1 mm 41.0 mm,31.9 mm 33.0 mm,NormalWide,1,All seasons,1,#134 Top 37%,#293 Bottom 19%,,
+Mizuno,Wave Horizon 7,"88
+ Great!",$170,Daily running,Stability,11.6 oz / 329g 11.8 oz / 334g,0,7.2 mm 8.0 mm,HeelMid/forefoot,Slightly small,Balanced,Decent,Bad,Good,Moderate,Medium,Wide,Stiff,Stiff,Stiff,0,0,39.1 mm 41.0 mm,31.9 mm 33.0 mm,NormalWide,1,All seasons,1,#134 Top 37%,#293 Bottom 19%,,
+Mizuno,Wave Horizon 7,"88
+ Great!",$170,Daily running,Stability,11.6 oz / 329g 11.8 oz / 334g,0,7.2 mm 8.0 mm,HeelMid/forefoot,Slightly small,Balanced,Decent,Bad,Good,Moderate,Medium,Wide,Stiff,Stiff,Stiff,0,0,39.1 mm 41.0 mm,31.9 mm 33.0 mm,NormalWide,1,All seasons,1,#134 Top 37%,#293 Bottom 19%,,
+Mizuno,Wave Horizon 7,"88
+ Great!",$170,Daily running,Stability,11.6 oz / 329g 11.8 oz / 334g,0,7.2 mm 8.0 mm,HeelMid/forefoot,Slightly small,Balanced,Decent,Bad,Good,Moderate,Medium,Wide,Stiff,Stiff,Stiff,0,0,39.1 mm 41.0 mm,31.9 mm 33.0 mm,NormalWide,1,All seasons,1,#137 Top 38%,#295 Bottom 19%,,
+Mizuno,Wave Horizon 7,"88
+ Great!",$170,Daily running,Stability,11.6 oz / 329g 11.8 oz / 334g,0,7.2 mm 8.0 mm,HeelMid/forefoot,Slightly small,Balanced,Decent,Bad,Good,Moderate,Medium,Wide,Stiff,Stiff,Stiff,0,0,39.1 mm 41.0 mm,31.9 mm 33.0 mm,NormalWide,1,All seasons,1,#134 Top 37%,#294 Bottom 19%,,
+Mizuno,Wave Horizon 7,"88
+ Great!",$170,Daily running,Stability,11.6 oz / 329g 11.8 oz / 334g,0,7.2 mm 8.0 mm,HeelMid/forefoot,Slightly small,Balanced,Decent,Bad,Good,Moderate,Medium,Wide,Stiff,Stiff,Stiff,0,0,39.1 mm 41.0 mm,31.9 mm 33.0 mm,NormalWide,1,All seasons,1,#134 Top 37%,#294 Bottom 19%,,
+Mizuno,Wave Horizon 7,"88
+ Great!",$170,Daily running,Stability,11.6 oz / 329g 11.8 oz / 334g,0,7.2 mm 8.0 mm,HeelMid/forefoot,Slightly small,Balanced,Decent,Bad,Good,Moderate,Medium,Wide,Stiff,Stiff,Stiff,0,0,39.1 mm 41.0 mm,31.9 mm 33.0 mm,NormalWide,1,All seasons,1,#134 Top 37%,#294 Bottom 19%,,
+Mizuno,Wave Horizon 7,"88
+ Great!",$170,Daily running,Stability,11.6 oz / 329g 11.8 oz / 334g,0,7.2 mm 8.0 mm,HeelMid/forefoot,Slightly small,Balanced,Decent,Bad,Good,Moderate,Medium,Wide,Stiff,Stiff,Stiff,0,0,39.1 mm 41.0 mm,31.9 mm 33.0 mm,NormalWide,1,All seasons,1,#134 Top 37%,#294 Bottom 19%,,
+Mizuno,Wave Horizon 7,"88
+ Great!",$170,Daily running,Stability,11.6 oz / 329g 11.8 oz / 334g,0,7.2 mm 8.0 mm,HeelMid/forefoot,Slightly small,Balanced,Decent,Bad,Good,Moderate,Medium,Wide,Stiff,Stiff,Stiff,0,0,39.1 mm 41.0 mm,31.9 mm 33.0 mm,NormalWide,1,All seasons,1,#134 Top 37%,#294 Bottom 19%,,
+Mizuno,Wave Horizon 7,"88
+ Great!",$170,Daily running,Stability,11.6 oz / 329g 11.8 oz / 334g,0,7.2 mm 8.0 mm,HeelMid/forefoot,Slightly small,Balanced,Decent,Bad,Good,Moderate,Medium,Wide,Stiff,Stiff,Stiff,0,0,39.1 mm 41.0 mm,31.9 mm 33.0 mm,NormalWide,1,All seasons,1,#134 Top 37%,#294 Bottom 19%,,
+Mizuno,Wave Horizon 7,"88
+ Great!",$170,Daily running,Stability,11.6 oz / 329g 11.8 oz / 334g,0,7.2 mm 8.0 mm,HeelMid/forefoot,Slightly small,Balanced,Decent,Bad,Good,Moderate,Medium,Wide,Stiff,Stiff,Stiff,0,0,39.1 mm 41.0 mm,31.9 mm 33.0 mm,NormalWide,1,All seasons,1,#134 Top 37%,#294 Bottom 19%,,
+Mizuno,Wave Horizon 7,"88
+ Great!",$170,Daily running,Stability,11.6 oz / 329g 11.8 oz / 334g,0,7.2 mm 8.0 mm,HeelMid/forefoot,Slightly small,Balanced,Decent,Bad,Good,Moderate,Medium,Wide,Stiff,Stiff,Stiff,0,0,39.1 mm 41.0 mm,31.9 mm 33.0 mm,NormalWide,1,All seasons,1,#134 Top 37%,#294 Bottom 19%,,
+Mizuno,Wave Inspire 19,"88
+ Great!",$140,Daily running,Stability,10.3 oz / 293g 10.7 oz / 303g,0,12.4 mm 12.0 mm,Heel,True to size,Soft,Decent,Decent,Good,Moderate,Medium,Wide,Moderate,Moderate,Stiff,0,0,38.2 mm 36.0 mm,25.8 mm 24.0 mm,NormalWide,1,All seasons,1,#228 Top 36%,#455 Bottom 29%,,
+Mizuno,Wave Inspire 20,"86
+ Good!",$140,Daily running,Stability,10.7 oz / 302g 10.8 oz / 305g,0,12.6 mm 12.0 mm,Heel,True to size,Balanced,Good,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,37.7 mm 37.5 mm,25.1 mm 25.5 mm,NormalWide,1,All seasons,1,#398 Bottom 38%,#378 Bottom 41%,,
+Mizuno,Wave Inspire 21,"87
+ Great!",$140,Daily running,Stability,10.1 oz / 286g 4.9 oz / 140g,0,12.9 mm 12.0 mm,Heel,True to size,Balanced,Decent,Good,Good,Moderate,Wide,Medium,Moderate,Moderate,Stiff,0,0,38.0 mm 38.0 mm,25.1 mm 26.0 mm,NormalWide,1,All seasons,1,#167 Top 46%,#179 Top 49%,,
+Mizuno,Wave Inspire 21,"87
+ Great!",$140,Daily running,Stability,10.1 oz / 286g 4.9 oz / 140g,0,12.9 mm 12.0 mm,Heel,True to size,Balanced,Decent,Good,Good,Moderate,Wide,Medium,Moderate,Moderate,Stiff,0,0,38.0 mm 38.0 mm,25.1 mm 26.0 mm,NormalWide,1,All seasons,1,#167 Top 46%,#179 Top 49%,,
+Mizuno,Wave Inspire 21,"87
+ Great!",$140,Daily running,Stability,10.1 oz / 286g 4.9 oz / 140g,0,12.9 mm 12.0 mm,Heel,True to size,Balanced,Decent,Good,Good,Moderate,Wide,Medium,Moderate,Moderate,Stiff,0,0,38.0 mm 38.0 mm,25.1 mm 26.0 mm,NormalWide,1,All seasons,1,#167 Top 46%,#179 Top 49%,,
+Mizuno,Wave Rebellion,"87
+ Great!",$180,Tempo,Neutral,8.7 oz / 247g 9.1 oz / 259g,1,7.7 mm 8.0 mm,Mid/forefoot,Slightly large,Balanced,Decent,Decent,Bad,Moderate,Medium,Medium,Stiff,Stiff,Stiff,0,1,36.4 mm 36.0 mm,28.7 mm 28.0 mm,Normal,1,All seasons,1,#163 Top 45%,#268 Bottom 26%,,
+Mizuno,Wave Rebellion,"87
+ Great!",$180,Tempo,Neutral,8.7 oz / 247g 9.1 oz / 259g,1,7.7 mm 8.0 mm,Mid/forefoot,Slightly large,Balanced,Decent,Decent,Bad,Moderate,Medium,Medium,Stiff,Stiff,Stiff,0,1,36.4 mm 36.0 mm,28.7 mm 28.0 mm,Normal,1,All seasons,1,#160 Top 44%,#269 Bottom 26%,,
+Mizuno,Wave Rebellion,"87
+ Great!",$180,Tempo,Neutral,8.7 oz / 247g 9.1 oz / 259g,1,7.7 mm 8.0 mm,Mid/forefoot,Slightly large,Balanced,Decent,Decent,Bad,Moderate,Medium,Medium,Stiff,Stiff,Stiff,0,1,36.4 mm 36.0 mm,28.7 mm 28.0 mm,Normal,1,All seasons,1,#161 Top 44%,#269 Bottom 26%,,
+Mizuno,Wave Rebellion,"87
+ Great!",$180,Tempo,Neutral,8.7 oz / 247g 9.1 oz / 259g,1,7.7 mm 8.0 mm,Mid/forefoot,Slightly large,Balanced,Decent,Decent,Bad,Moderate,Medium,Medium,Stiff,Stiff,Stiff,0,1,36.4 mm 36.0 mm,28.7 mm 28.0 mm,Normal,1,All seasons,1,#167 Top 46%,#270 Bottom 26%,,
+Mizuno,Wave Rebellion,"87
+ Great!",$180,Tempo,Neutral,8.7 oz / 247g 9.1 oz / 259g,1,7.7 mm 8.0 mm,Mid/forefoot,Slightly large,Balanced,Decent,Decent,Bad,Moderate,Medium,Medium,Stiff,Stiff,Stiff,0,1,36.4 mm 36.0 mm,28.7 mm 28.0 mm,Normal,1,All seasons,1,#161 Top 44%,#269 Bottom 26%,,
+Mizuno,Wave Rebellion,"87
+ Great!",$180,Tempo,Neutral,8.7 oz / 247g 9.1 oz / 259g,1,7.7 mm 8.0 mm,Mid/forefoot,Slightly large,Balanced,Decent,Decent,Bad,Moderate,Medium,Medium,Stiff,Stiff,Stiff,0,1,36.4 mm 36.0 mm,28.7 mm 28.0 mm,Normal,1,All seasons,1,#160 Top 44%,#269 Bottom 26%,,
+Mizuno,Wave Rebellion Flash 2,"90
+ Superb!",$170,Tempo,Neutral,8.4 oz / 239g 8.6 oz / 243g,1,2.9 mm 0.5 mm,Mid/forefoot,True to size,Soft,Decent,Bad,Good,Moderate,Medium,Medium,Moderate,Stiff,Moderate,0,1,35.2 mm 35.0 mm,32.3 mm 34.5 mm,Normal,0,All seasons,0,#58 Top 16%,#184 Bottom 49%,,
+Mizuno,Wave Rebellion Pro,"90
+ Superb!",$250,Competition,Neutral,7.5 oz / 214g 7.7 oz / 218g,1,4.9 mm 4.5 mm,Mid/forefoot,Half size small,Firm,Bad,Decent,Bad,Moderate,Narrow,Medium,Stiff,Stiff,Flexible,Carbon plate,1,38.2 mm 39.0 mm,33.3 mm 34.5 mm,Normal,1,All seasons,1,#97 Top 16%,#422 Bottom 34%,,
+Mizuno,Wave Rebellion Pro 2,"90
+ Superb!",$250,Competition,Neutral,7.4 oz / 209g 7.6 oz / 215g,1,2.1 mm 2.5 mm,Mid/forefoot,True to size,Soft,Bad,Good,Bad,Moderate,Narrow,Medium,Stiff,Stiff,Flexible,Carbon plate,1,37.9 mm 38.0 mm,35.8 mm 36.5 mm,Normal,1,All seasons,1,#61 Top 17%,#203 Bottom 44%,,
+Mizuno,Wave Rebellion Pro 2,"90
+ Superb!",$250,Competition,Neutral,7.4 oz / 209g 7.6 oz / 215g,1,2.1 mm 2.5 mm,Mid/forefoot,True to size,Soft,Bad,Good,Bad,Moderate,Narrow,Medium,Stiff,Stiff,Flexible,Carbon plate,1,37.9 mm 38.0 mm,35.8 mm 36.5 mm,Normal,1,All seasons,1,#61 Top 17%,#203 Bottom 44%,,
+Mizuno,Wave Rider 25,"90
+ Superb!",$135,Daily running,Neutral,9.5 oz / 269g 9.8 oz / 277g,0,12.3 mm 12.0 mm,Heel,Slightly small,-,-,-,-,-,Medium,-,Stiff,Moderate,Stiff,0,0,38.4 mm 36.0 mm,26.1 mm 24.0 mm,NormalWide,1,-,1,#126 Top 20%,#530 Bottom 17%,,
+Mizuno,Wave Rider 26,"87
+ Great!",$140,Daily running,Neutral,10.3 oz / 291g 10 oz / 283g,0,11.8 mm 12.0 mm,Heel,True to size,Soft,-,-,-,Moderate,Medium,-,Stiff,Moderate,Stiff,0,0,39.2 mm 38.5 mm,27.4 mm 26.5 mm,NormalWide,1,All seasons,1,#293 Top 46%,#516 Bottom 20%,,
+Mizuno,Wave Rider 27,"91
+ Superb!",$140,Daily running,Neutral,9.8 oz / 279g 9.9 oz / 280g,0,13.2 mm 12.0 mm,Heel,True to size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,38.3 mm 38.5 mm,25.1 mm 26.5 mm,NormalWide,1,All seasons,1,#49 Top 8%,#374 Bottom 42%,,
+Mizuno,Wave Rider 28,"91
+ Superb!",$140,Daily running,Neutral,9.7 oz / 276g 9.5 oz / 269g,0,14.7 mm 12.0 mm,Heel,Half size small,Balanced,Decent,Good,Decent,Moderate,Medium,Wide,Moderate,Stiff,Moderate,0,0,39.3 mm 41.0 mm,24.6 mm 29.0 mm,NormalWide,1,All seasons,1,#54 Top 9%,#223 Top 35%,,
+Mizuno,Wave Rider 28,"91
+ Superb!",$140,Daily running,Neutral,9.7 oz / 276g 9.5 oz / 269g,0,14.7 mm 12.0 mm,Heel,Half size small,Balanced,Decent,Good,Decent,Moderate,Medium,Wide,Moderate,Stiff,Moderate,0,0,39.3 mm 41.0 mm,24.6 mm 29.0 mm,NormalWide,1,All seasons,1,#54 Top 9%,#223 Top 35%,,
+Mizuno,Wave Rider 29,N/A,$150,Daily running,Neutral,9.1 oz / 258g 9.3 oz / 265g,0,8.3 mm 10.0 mm,HeelMid/forefoot,-,Soft,Decent,Good,Decent,Moderate,Medium,Wide,Moderate,Stiff,Stiff,0,0,37.5 mm 39.0 mm,29.2 mm 29.0 mm,NormalWide,1,All seasons,1,#192 Bottom 47%,#123 Top 34%,,
+Mizuno,Wave Rider 29,N/A,$150,Daily running,Neutral,9.1 oz / 258g 9.3 oz / 265g,0,8.3 mm 10.0 mm,HeelMid/forefoot,-,Soft,Decent,Good,Decent,Moderate,Medium,Wide,Moderate,Stiff,Stiff,0,0,37.5 mm 39.0 mm,29.2 mm 29.0 mm,NormalWide,1,All seasons,1,#194 Bottom 47%,#123 Top 34%,,
+Mizuno,Wave Rider 29,N/A,$150,Daily running,Neutral,9.1 oz / 258g 9.3 oz / 265g,0,8.3 mm 10.0 mm,HeelMid/forefoot,-,Soft,Decent,Good,Decent,Moderate,Medium,Wide,Moderate,Stiff,Stiff,0,0,37.5 mm 39.0 mm,29.2 mm 29.0 mm,NormalWide,1,All seasons,1,#192 Bottom 47%,#123 Top 34%,,
+Mizuno,Wave Rider 29,N/A,$150,Daily running,Neutral,9.1 oz / 258g 9.3 oz / 265g,0,8.3 mm 10.0 mm,HeelMid/forefoot,-,Soft,Decent,Good,Decent,Moderate,Medium,Wide,Moderate,Stiff,Stiff,0,0,37.5 mm 39.0 mm,29.2 mm 29.0 mm,NormalWide,1,All seasons,1,#194 Bottom 47%,#123 Top 34%,,
+Mizuno,Wave Rider 29,N/A,$150,Daily running,Neutral,9.1 oz / 258g 9.3 oz / 265g,0,8.3 mm 10.0 mm,HeelMid/forefoot,-,Soft,Decent,Good,Decent,Moderate,Medium,Wide,Moderate,Stiff,Stiff,0,0,37.5 mm 39.0 mm,29.2 mm 29.0 mm,NormalWide,1,All seasons,1,#194 Bottom 47%,#123 Top 34%,,
+Mizuno,Wave Rider 29,"86
+ Good!",$150,Daily running,Neutral,9.1 oz / 258g 9.3 oz / 265g,0,8.3 mm 10.0 mm,HeelMid/forefoot,-,Soft,Decent,Good,Decent,Moderate,Medium,Wide,Moderate,Stiff,Stiff,0,0,37.5 mm 39.0 mm,29.2 mm 29.0 mm,NormalWide,1,All seasons,1,#195 Bottom 46%,#123 Top 34%,,
+Mizuno,Wave Rider 29,"86
+ Good!",$150,Daily running,Neutral,9.1 oz / 258g 9.3 oz / 265g,0,8.3 mm 10.0 mm,HeelMid/forefoot,-,Soft,Decent,Good,Decent,Moderate,Medium,Wide,Moderate,Stiff,Stiff,0,0,37.5 mm 39.0 mm,29.2 mm 29.0 mm,NormalWide,1,All seasons,1,#195 Bottom 46%,#123 Top 34%,,
+Mizuno,Wave Rider 29,"86
+ Good!",$150,Daily running,Neutral,9.1 oz / 258g 9.3 oz / 265g,0,8.3 mm 10.0 mm,HeelMid/forefoot,-,Soft,Decent,Good,Decent,Moderate,Medium,Wide,Moderate,Stiff,Stiff,0,0,37.5 mm 39.0 mm,29.2 mm 29.0 mm,NormalWide,1,All seasons,1,#195 Bottom 46%,#123 Top 34%,,
+Mizuno,Wave Rider 29,"86
+ Good!",$150,Daily running,Neutral,9.1 oz / 258g 9.3 oz / 265g,0,8.3 mm 10.0 mm,HeelMid/forefoot,-,Soft,Decent,Good,Decent,Moderate,Medium,Wide,Moderate,Stiff,Stiff,0,0,37.5 mm 39.0 mm,29.2 mm 29.0 mm,NormalWide,1,All seasons,1,#195 Bottom 46%,#123 Top 34%,,
+Mizuno,Wave Sky 7,"88
+ Great!",$170,Daily running,Neutral,10.4 oz / 296g 10.7 oz / 303g,0,10.9 mm 8.0 mm,Heel,True to size,Balanced,Good,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,0,40.9 mm 40.0 mm,30.0 mm 32.0 mm,NormalWide,1,All seasons,1,#279 Top 44%,#515 Bottom 20%,,
+Mizuno,Wave Sky 8,"91
+ Superb!",$170,Daily running,Neutral,9.6 oz / 271g 9.8 oz / 277g,0,10.0 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Good,Good,Bad,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,40.7 mm 42.0 mm,30.7 mm 34.0 mm,NormalWide,1,All seasons,1,#25 Top 7%,#205 Bottom 43%,,
+Mizuno,Wave Sky 8,"91
+ Superb!",$170,Daily running,Neutral,9.6 oz / 271g 9.8 oz / 277g,0,10.0 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Good,Good,Bad,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,40.7 mm 42.0 mm,30.7 mm 34.0 mm,NormalWide,1,All seasons,1,#25 Top 7%,#205 Bottom 43%,,Road
+Mizuno,Wave Sky 8,"91
+ Superb!",$170,Daily running,Neutral,9.6 oz / 271g 9.8 oz / 277g,0,10.0 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Good,Good,Bad,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,40.7 mm 42.0 mm,30.7 mm 34.0 mm,NormalWide,1,All seasons,1,#25 Top 7%,#205 Bottom 43%,,
+Mizuno,Wave Sky 8,"91
+ Superb!",$170,Daily running,Neutral,9.6 oz / 271g 9.8 oz / 277g,0,10.0 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Good,Good,Bad,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,40.7 mm 42.0 mm,30.7 mm 34.0 mm,NormalWide,1,All seasons,1,#23 Top 7%,#205 Bottom 44%,,
+Mizuno,Wave Sky 8,"91
+ Superb!",$170,Daily running,Neutral,9.6 oz / 271g 9.8 oz / 277g,0,10.0 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Good,Good,Bad,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,40.7 mm 42.0 mm,30.7 mm 34.0 mm,NormalWide,1,All seasons,1,#24 Top 7%,#205 Bottom 43%,,
+Mizuno,Wave Sky 8,"91
+ Superb!",$170,Daily running,Neutral,9.6 oz / 271g 9.8 oz / 277g,0,10.0 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Good,Good,Bad,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,40.7 mm 42.0 mm,30.7 mm 34.0 mm,NormalWide,1,All seasons,1,#25 Top 7%,#205 Bottom 43%,,
+Mizuno,Wave Sky 8,"91
+ Superb!",$170,Daily running,Neutral,9.6 oz / 271g 9.8 oz / 277g,0,10.0 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Good,Good,Bad,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,40.7 mm 42.0 mm,30.7 mm 34.0 mm,NormalWide,1,All seasons,1,#25 Top 7%,#205 Bottom 43%,,
+Mizuno,Wave Sky 8,"91
+ Superb!",$170,Daily running,Neutral,9.6 oz / 271g 9.8 oz / 277g,0,10.0 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Good,Good,Bad,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,40.7 mm 42.0 mm,30.7 mm 34.0 mm,NormalWide,1,All seasons,1,#25 Top 7%,#205 Bottom 43%,,
+Mizuno,Wave Sky 8,"91
+ Superb!",$170,Daily running,Neutral,9.6 oz / 271g 9.8 oz / 277g,0,10.0 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Good,Good,Bad,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,40.7 mm 42.0 mm,30.7 mm 34.0 mm,NormalWide,1,All seasons,1,#25 Top 7%,#205 Bottom 43%,,
+Nike,Winflo 10,"86
+ Good!",$100,Daily running,Neutral,9.5 oz / 269g 9.9 oz / 280g,0,9.7 mm 10.0 mm,HeelMid/forefoot,True to size,Soft,Decent,Decent,Good,Moderate,Medium,Medium,Moderate,Moderate,Flexible,0,0,33.5 mm 33.0 mm,23.8 mm 23.0 mm,NormalWide,1,All seasons,1,#365 Bottom 43%,#237 Top 37%,,
+Nike,Winflo 11,"86
+ Good!",$105,Daily running,Neutral,10.4 oz / 295g 10 oz / 283g,0,12.3 mm 10.0 mm,Heel,True to size,Soft,Decent,Good,Good,Moderate,Narrow,Medium,Moderate,Stiff,Moderate,0,0,37.6 mm 35.0 mm,25.3 mm 25.0 mm,NormalWideX-Wide,1,All seasons,1,#176 Top 49%,#78 Top 22%,,
+Nike,Winflo 11,"86
+ Good!",$105,Daily running,Neutral,10.4 oz / 295g 10 oz / 283g,0,12.3 mm 10.0 mm,Heel,True to size,Soft,Decent,Good,Good,Moderate,Narrow,Medium,Moderate,Stiff,Moderate,0,0,37.6 mm 35.0 mm,25.3 mm 25.0 mm,NormalWideX-Wide,1,All seasons,1,#176 Top 49%,#78 Top 22%,,
+Nike,Winflo 11 GTX,"77
+ Decent!",$130,Daily running,Neutral,10.9 oz / 310g 10.9 oz / 310g,0,13.3 mm 10.0 mm,Heel,True to size,Soft,Good,Good,Bad,Warm,Medium,Medium,Moderate,Stiff,Stiff,0,0,39.0 mm 35.0 mm,25.7 mm 25.0 mm,Normal,1,Winter,1,#335 Bottom 8%,#248 Bottom 32%,,
+Nike,Winflo 11 GTX,"77
+ Decent!",$130,Daily running,Neutral,10.9 oz / 310g 10.9 oz / 310g,0,13.3 mm 10.0 mm,Heel,True to size,Soft,Good,Good,Bad,Warm,Medium,Medium,Moderate,Stiff,Stiff,0,0,39.0 mm 35.0 mm,25.7 mm 25.0 mm,Normal,1,Winter,1,#340 Bottom 7%,#249 Bottom 32%,,
+Nike,Winflo 11 GTX,"77
+ Decent!",$130,Daily running,Neutral,10.9 oz / 310g 10.9 oz / 310g,0,13.3 mm 10.0 mm,Heel,True to size,Soft,Good,Good,Bad,Warm,Medium,Medium,Moderate,Stiff,Stiff,0,0,39.0 mm 35.0 mm,25.7 mm 25.0 mm,Normal,1,Winter,1,#337 Bottom 7%,#249 Bottom 31%,,
+Mizuno,WWave Horizon 7,"88
+ Great!",$170,Daily running,Stability,11.6 oz / 329g 11.8 oz / 334g,0,7.2 mm 8.0 mm,HeelMid/forefoot,Slightly small,Balanced,Decent,Bad,Good,Moderate,Medium,Wide,Stiff,Stiff,Stiff,0,0,39.1 mm 41.0 mm,31.9 mm 33.0 mm,NormalWide,1,All seasons,1,#134 Top 37%,#294 Bottom 19%,,
+Reebok,Zig Dynamica 4,"80
+ Good!",$85,Daily running,Neutral,12.4 oz / 352g 12.3 oz / 350g,0,8.5 mm 9.0 mm,HeelMid/forefoot,True to size,Balanced,Decent,Bad,Good,Moderate,Medium,Narrow,Stiff,Moderate,Moderate,0,0,32.0 mm 32.0 mm,23.5 mm 23.0 mm,Normal,1,All seasons,1,#577 Bottom 10%,#533 Bottom 17%,,
+Reebok,Zig Dynamica 5,"80
+ Good!",$90,Daily running,Neutral,10.5 oz / 298g 10.5 oz / 299g,0,6.3 mm 6.0 mm,Mid/forefoot,-,Balanced,Decent,Bad,Good,Moderate,Medium,Narrow,Stiff,Stiff,Moderate,0,0,34.1 mm,27.8 mm,Normal,1,All seasons,1,#311 Bottom 15%,#297 Bottom 19%,,
+Reebok,Zig Dynamica 5,"80
+ Good!",$90,Daily running,Neutral,10.5 oz / 298g 10.5 oz / 299g,0,6.3 mm 6.0 mm,Mid/forefoot,-,Balanced,Decent,Bad,Good,Moderate,Medium,Narrow,Stiff,Stiff,Moderate,0,0,34.1 mm,27.8 mm,Normal,1,All seasons,1,#311 Bottom 15%,#297 Bottom 19%,,
+Nike,Zoom Fly 4,"86
+ Good!",$160,Tempo,Neutral,9.6 oz / 271g 8.8 oz / 249g,0,7.1 mm 8.0 mm,Mid/forefoot,True to size,-,-,-,-,-,Narrow,-,Stiff,Stiff,Flexible,Carbon plateRock plate,1,38.4 mm 36.0 mm,31.3 mm,Normal,1,-,1,#353 Bottom 45%,#225 Top 35%,,
+Nike,Zoom Fly 5,"80
+ Good!",$160,Daily runningTempo,Neutral,9.8 oz / 279g 10.1 oz / 286g,0,7.5 mm 8.0 mm,Mid/forefoot,True to size,Soft,-,-,-,Warm,Narrow,Narrow,Stiff,Stiff,Moderate,Carbon plate,1,36.9 mm 41.0 mm,29.4 mm 33.0 mm,Normal,1,All seasons,1,#560 Bottom 13%,#177 Top 28%,,
+Nike,Zoom Fly 6,"92
+ Superb!",$170,CompetitionTempo,Neutral,8.7 oz / 248g 8.6 oz / 244g,1,9.6 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Good,Good,Good,Moderate,Narrow,Medium,Stiff,Stiff,Stiff,Carbon plate,1,39.7 mm 40.0 mm,30.1 mm 32.0 mm,Normal,1,All seasons,1,#14 Top 4%,#26 Top 8%,,
+Nike,Zoom Fly 6,"92
+ Superb!",$170,CompetitionTempo,Neutral,8.7 oz / 248g 8.6 oz / 244g,1,9.6 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Good,Good,Good,Moderate,Narrow,Medium,Stiff,Stiff,Stiff,Carbon plate,1,39.7 mm 40.0 mm,30.1 mm 32.0 mm,Normal,1,All seasons,1,#14 Top 4%,#26 Top 8%,,
+Nike,Zoom Fly 6,"92
+ Superb!",$170,CompetitionTempo,Neutral,8.7 oz / 248g 8.6 oz / 244g,1,9.6 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Good,Good,Good,Moderate,Narrow,Medium,Stiff,Stiff,Stiff,Carbon plate,1,39.7 mm 40.0 mm,30.1 mm 32.0 mm,Normal,1,All seasons,1,#14 Top 4%,#26 Top 8%,,
+Nike,ZoomX Invincible Run Flyknit 2,"86
+ Good!",$180,Daily running,Neutral,10.3 oz / 291g 9.7 oz / 274g,0,12.0 mm 9.0 mm,Heel,True to size,Soft,-,-,-,Warm,Medium,Medium,Stiff,Flexible,Moderate,0,0,35.5 mm 37.0 mm,23.5 mm 28.0 mm,Normal,1,All seasons,1,#385 Bottom 40%,#262 Top 41%,,
+Nike,ZoomX Streakfly,"87
+ Great!",$160,Tempo,Neutral,6 oz / 171g 6 oz / 171g,1,6.3 mm 6.0 mm,Mid/forefoot,True to size,Soft,-,-,-,Breathable,Narrow,Medium,Flexible,Flexible,Flexible,0,1,31.7 mm 32.0 mm,25.4 mm 26.0 mm,Normal,1,SummerAll seasons,1,#146 Top 40%,#191 Bottom 47%,,
+Nike,ZoomX Streakfly,"87
+ Great!",$160,Tempo,Neutral,6 oz / 171g 6 oz / 171g,1,6.3 mm 6.0 mm,Mid/forefoot,True to size,Soft,-,-,-,Breathable,Narrow,Medium,Flexible,Flexible,Flexible,0,1,31.7 mm 32.0 mm,25.4 mm 26.0 mm,Normal,1,SummerAll seasons,1,#146 Top 40%,#191 Bottom 47%,,
+Nike,ZoomX Streakfly,"87
+ Great!",$160,Tempo,Neutral,6 oz / 171g 6 oz / 171g,1,6.3 mm 6.0 mm,Mid/forefoot,True to size,Soft,-,-,-,Breathable,Narrow,Medium,Flexible,Flexible,Flexible,0,1,31.7 mm 32.0 mm,25.4 mm 26.0 mm,Normal,1,SummerAll seasons,1,#146 Top 40%,#191 Bottom 47%,,
+Nike,ZoomX Streakfly,"87
+ Great!",$160,Tempo,Neutral,6 oz / 171g 6 oz / 171g,1,6.3 mm 6.0 mm,Mid/forefoot,True to size,Soft,-,-,-,Breathable,Narrow,Medium,Flexible,Flexible,Flexible,0,1,31.7 mm 32.0 mm,25.4 mm 26.0 mm,Normal,1,SummerAll seasons,1,#145 Top 40%,#190 Bottom 48%,,
+Nike,ZoomX Streakfly,"87
+ Great!",$160,Tempo,Neutral,6 oz / 171g 6 oz / 171g,1,6.3 mm 6.0 mm,Mid/forefoot,True to size,Soft,-,-,-,Breathable,Narrow,Medium,Flexible,Flexible,Flexible,0,1,31.7 mm 32.0 mm,25.4 mm 26.0 mm,Normal,1,SummerAll seasons,1,#145 Top 40%,#190 Bottom 48%,,
+Nike,ZoomX Vaporfly NEXT% 2,"91
+ Superb!",$225,Competition,Neutral,6.9 oz / 196g 6.9 oz / 196g,1,7.7 mm 7.7 mm,Mid/forefoot,Slightly small,Soft,-,-,-,Breathable,Narrow,-,Stiff,Stiff,Flexible,Carbon plate,1,38.6 mm 38.6 mm,30.9 mm 30.9 mm,Normal,0,SummerAll seasons,0,#47 Top 8%,#327 Bottom 49%,,
\ No newline at end of file
diff --git a/data/SONIX utilities - Trail.csv b/data/SONIX utilities - Trail.csv
new file mode 100644
index 0000000000000000000000000000000000000000..b7b301b40d29cffebcf44b3ccc4191048e24dc49
--- /dev/null
+++ b/data/SONIX utilities - Trail.csv	
@@ -0,0 +1,204 @@
+Brand-Name,Audience score,Price,Trail terrain,Shock absorption,Energy return,Traction,Arch support,Weight lab Weight brand,Lightweight,Drop lab Drop brand,Strike pattern,Size,Midsole softness,Difference in midsole softness in cold,Plate,Toebox durability,Heel padding durability,Outsole durability,Breathability,Width / fit,Toebox width,Stiffness,Torsional rigidity,Heel counter stiffness,Lug depth,Heel stack lab Heel stack brand,Forefoot lab Forefoot brand,Widths available,For heavy runners,Season,Removable insole,Orthotic friendly,Waterproofing,Ranking,Popularity
+Adidas Terrex Agravic Speed Ultra,90 Great!,$220,Light,Moderate,High,-,Neutral,9.1 oz / 259g 9.5 oz / 270g,0,0.3 mm 8.0 mm,Mid/forefoot,Slightly large,Balanced,Small,0,Good,Good,Decent,Moderate,Medium,Narrow,Moderate,Stiff,Flexible,2.5 mm,30.6 mm 38.0 mm,30.3 mm 30.0 mm,Normal,0,All seasons,1,1,-,#76 Top 21%,#177 Top 47%
+Adidas Terrex Speed Ultra,90 Great!,3559500 Rp,Light,-,-,-,Neutral,9.1 oz / 258g 9 oz / 255g,0,8.2 mm 8.0 mm,Heel Mid/forefoot,True to size,-,-,0,-,-,-,-,Narrow,-,Stiff,Flexible,Flexible,2.6 mm,32.8 mm 26.0 mm,24.6 mm 18.0 mm,Normal,0,-,1,1,-,#49 Top 13%,#298 Bottom 21%
+Altra Experience Wild,88 Great!,2966250 Rp,Light Moderate,Moderate,Low,-,Neutral,10.1 oz / 285g 9.6 oz / 273g,0,4.3 mm 4.0 mm,Mid/forefoot,True to size,Soft,Normal,0,Decent,Decent,Good,Moderate,Wide,Wide,Moderate,Stiff,Moderate,3.6 mm,34.5 mm 34.0 mm,30.2 mm 30.0 mm,Normal,0,All seasons,1,1,-,#263 Top 40%,#326 Top 49%
+Altra Experience Wild 2,84 Good!,2966250 Rp,Light,Moderate,Low,High,Neutral,9.4 oz / 266g 10.3 oz / 293g,0,6.1 mm 4.0 mm,Mid/forefoot,-,Balanced,Normal,0,Decent,Good,Good,Warm,Wide,Wide,Moderate,Moderate,Flexible,3.5 mm,32.3 mm 32.0 mm,26.2 mm 28.0 mm,Normal,0,All seasons,1,1,-,#245 Bottom 35%,#154 Top 41%
+Altra Lone Peak 5.0,91 Superb!,$130,Light Moderate,-,-,-,Neutral,10.7 oz / 302g 10.6 oz / 301g,0,0.2 mm 0.0 mm,Mid/forefoot,True to size,-,-,Rock plate,-,-,-,-,Narrow,-,Stiff,Flexible,-,3.7 mm,24.5 mm 25.0 mm,24.3 mm 25.0 mm,Normal,0,-,1,1,-,#68 Top 11%,#55 Top 9%
+Altra Lone Peak 6,89 Great!,$140,Moderate Technical,-,-,-,Neutral,9.8 oz / 278g 9.7 oz / 275g,0,0.6 mm 0.0 mm,Mid/forefoot,True to size,-,-,Rock plate,-,-,-,-,Wide,-,Stiff,-,-,4.4 mm,25.1 mm 25.0 mm,24.5 mm 25.0 mm,Normal Wide,0,-,0,0,-,#147 Top 22%,#341 Bottom 49%
+Altra Lone Peak 7,86 Good!,3164000 Rp,Moderate,-,-,-,Neutral,10.4 oz / 294g 11 oz / 312g,0,0.2 mm 0.0 mm,Mid/forefoot,True to size,Balanced,Normal,0,-,-,-,Moderate,Narrow,-,Stiff,Flexible,Flexible,3.4 mm,23.3 mm 25.0 mm,23.1 mm 25.0 mm,Normal Wide,0,All seasons,1,1,-,#403 Bottom 40%,#273 Top 41%
+Altra Lone Peak 8,81 Good!,$140,Light Moderate,-,-,-,Neutral,10.2 oz / 288g 10.7 oz / 303g,0,1.4 mm 0.0 mm,Mid/forefoot,True to size,Balanced,Small,0,Good,Decent,Decent,Moderate,Medium,Wide,Stiff,Flexible,Flexible,3.0 mm,22.7 mm 25.0 mm,21.3 mm 25.0 mm,Normal Wide,0,All seasons,1,1,-,#567 Bottom 15%,#177 Top 27%
+Altra Lone Peak 9,91 Superb!,$140,Light Moderate,Low,Moderate,-,Neutral,10.9 oz / 309g 10.4 oz / 295g,0,0.0 mm 0.0 mm,Mid/forefoot,True to size,Balanced,Normal,0,Decent,Decent,Good,Warm,Wide,Wide,Moderate,Moderate,Flexible,3.8 mm,23.3 mm 25.0 mm,23.3 mm 25.0 mm,Normal Wide,0,All seasons,1,1,-,#25 Top 7%,#41 Top 11%
+Altra Mont Blanc,79 Good!,$180,Light Moderate,-,-,-,Neutral,9.6 oz / 272g 9.9 oz / 280g,0,0.0 mm,Mid/forefoot,True to size,-,-,0,-,-,-,-,Medium,-,Stiff,-,-,2.8 mm,33.8 mm 30.0 mm,33.8 mm,Normal Wide,0,-,0,0,-,#332 Bottom 12%,#241 Bottom 36%
+Altra Mont Blanc Carbon,86 Good!,4943750 Rp,Moderate,-,-,-,Neutral,8.9 oz / 251g 9.3 oz / 264g,0,0.3 mm 0.0 mm,Mid/forefoot,True to size,Soft,Normal,Carbon plate,Decent,Bad,Good,Moderate,Medium,Wide,Stiff,Flexible,Flexible,3.5 mm,27.2 mm 29.0 mm,26.9 mm 29.0 mm,Normal,0,All seasons,1,1,-,#189 Top 50%,#273 Bottom 27%
+Altra Olympus 275,82 Good!,3856130 Rp,Light,Moderate,Low,High,Neutral,10.7 oz / 303g 10.8 oz / 305g,0,0.3 mm 0.0 mm,Mid/forefoot,-,Balanced,Small,0,Good,Decent,Good,Moderate,Wide,Wide,Moderate,Stiff,Flexible,3.5 mm,30.8 mm 33.0 mm,30.5 mm 33.0 mm,Normal,0,All seasons,1,1,-,#298 Bottom 21%,#240 Bottom 36%
+Altra Olympus 5,83 Good!,3558800 Rp,Light Moderate,-,-,-,Neutral,11.5 oz / 325g 12.3 oz / 350g,0,2.0 mm 0.0 mm,Mid/forefoot,Slightly small,Balanced,Normal,0,-,-,-,Moderate,Wide,-,Stiff,Moderate,Moderate,3.0 mm,33.0 mm 33.0 mm,31.0 mm 33.0 mm,Normal,0,All seasons,1,1,-,#526 Bottom 21%,#302 Top 45%
+Altra Olympus 6,83 Good!,$175,Light Moderate,Moderate,Moderate,-,Neutral,12.6 oz / 357g 12.5 oz / 354g,0,0.7 mm 0.0 mm,Mid/forefoot,Half size small,Balanced,Normal,0,Very good,Good,Good,Breathable,Medium,Wide,Stiff,Stiff,Moderate,3.5 mm,32.2 mm 35.0 mm,31.5 mm 35.0 mm,Normal,1,Summer All seasons,1,1,-,#273 Bottom 27%,#116 Top 31%
+Altra Outroad,81 Good!,2966250 Rp,Light,-,-,-,Neutral,10.1 oz / 287g 10.7 oz / 303g,0,0.1 mm 0.0 mm,Mid/forefoot,Slightly small,Balanced,Normal,0,-,-,-,Moderate,Medium,Medium,Stiff,Moderate,Flexible,2.3 mm,25.1 mm 27.0 mm,25.0 mm 27.0 mm,Normal,0,All seasons,1,1,-,#578 Bottom 14%,#527 Bottom 21%
+Altra Outroad 2,79 Good!,2570750 Rp,Light,-,-,-,Neutral,10.3 oz / 291g 10.1 oz / 286g,0,1.4 mm 0.0 mm,Mid/forefoot,Slightly small,Balanced,Small,0,Bad,Decent,Good,Moderate,Medium,Wide,Stiff,Moderate,Moderate,2.2 mm,26.9 mm 27.5 mm,25.5 mm 27.5 mm,Normal,0,All seasons,1,1,-,#610 Bottom 9%,#564 Bottom 16%
+Altra Outroad 3,81 Good!,2570750 Rp,Light,-,-,-,Neutral,9.2 oz / 261g 10.7 oz / 303g,0,0.6 mm 0.0 mm,Mid/forefoot,Slightly small,Balanced,Normal,0,Decent,Bad,Bad,Warm,Medium,Wide,Stiff,Moderate,Flexible,1.5 mm,23.8 mm 27.0 mm,23.2 mm 27.0 mm,Normal,0,All seasons,1,1,-,#313 Bottom 17%,#283 Bottom 25%
+Altra Superior 6,78 Decent!,$130,Light Moderate,-,-,-,Neutral,9.6 oz / 272g 9.1 oz / 258g,0,0.1 mm 0.0 mm,Mid/forefoot,Slightly small,Balanced,Normal,0,Very bad,Decent,Good,Breathable,Medium,Wide,Stiff,Moderate,Flexible,3.3 mm,22.1 mm 20.5 mm,22.0 mm 20.5 mm,Normal,0,Summer All seasons,1,1,-,#631 Bottom 6%,#524 Bottom 22%
+Altra Superior 7,82 Good!,2373000 Rp,Light,Low,Low,High,Neutral,8.3 oz / 235g 9.3 oz / 263g,1,0.6 mm 0.0 mm,Mid/forefoot,-,Balanced,Small,0,Decent,Good,Good,Moderate,Medium,Wide,Flexible,Flexible,Flexible,3.5 mm,20.6 mm 21.0 mm,20.0 mm 21.0 mm,Normal,0,All seasons,1,1,-,#293 Bottom 22%,#271 Bottom 28%
+Altra Timp 4,78 Decent!,3164000 Rp,Light Moderate,-,-,-,Neutral,11.1 oz / 316g 10.6 oz / 300g,0,0.1 mm 0.0 mm,Mid/forefoot,True to size,Balanced,Big,0,-,-,-,Moderate,Medium,-,Stiff,Flexible,Flexible,2.9 mm,29.0 mm 30.0 mm,28.9 mm 30.0 mm,Normal,0,All seasons,1,1,-,#626 Bottom 6%,#523 Bottom 22%
+Altra Timp 5,80 Good!,$155,Light Moderate,Moderate,Low,-,Neutral,9.5 oz / 269g 9.8 oz / 277g,0,-0.1 mm 0.0 mm,Mid/forefoot,Half size small,Balanced,Small,0,Good,Decent,Good,Moderate,Wide,Wide,Stiff,Stiff,Moderate,3.0 mm,28.6 mm 29.0 mm,28.7 mm 29.0 mm,Normal,0,All seasons,1,1,-,#319 Bottom 15%,#146 Top 39%
+Altra Timp 5 GTX,72 Bad!,3559500 Rp,Light Moderate,Moderate,Low,High,Neutral,11 oz / 312g 11.7 oz / 331g,0,0.3 mm 0.0 mm,Mid/forefoot,-,Soft,Normal,0,Decent,Decent,Good,Warm,Wide,Wide,Stiff,Stiff,Flexible,3.5 mm,28.9 mm 29.0 mm,28.6 mm 29.0 mm,Normal,0,Winter,1,1,Waterproof,#373 Bottom 1%,#261 Bottom 31%
+ASICS Gel Excite Trail 2,"81
+ Good!",$85,Light,Moderate,Low,-,Neutral,10.3 oz / 292g 10.4 oz / 296g,0,10.1 mm 8.0 mm,Heel,True to size,Balanced,Normal,0,Very bad,Bad,Decent,Moderate,Medium,Medium,Flexible,Stiff,Stiff,4.0 mm,37.7 mm 36.0 mm,27.6 mm 28.0 mm,Normal,1,All seasons,1,1,-,#310 Bottom 18%,#233 Bottom 38%
+ASICS Gel Trabuco 12,"90
+ Great!",$140,ModerateTechnical,Moderate,Moderate,-,Neutral,10.5 oz / 299g 10.9 oz / 309g,0,7.8 mm 8.0 mm,Mid/forefoot,True to size,Soft,Small,Rock plate,Good,Good,Good,Moderate,Medium,Wide,Moderate,Moderate,Moderate,4.5 mm,35.4 mm 36.0 mm,27.6 mm 28.0 mm,Normal,0,All seasons,1,1,-,#120 Top 18%,#153 Top 23%
+ASICS Gel Trabuco 13,"88
+ Great!",$140,Light,Moderate,Moderate,-,Neutral,10.2 oz / 288g 10 oz / 283g,0,7.0 mm 8.0 mm,Mid/forefoot,True to size,Balanced,Normal,Rock plate,Good,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Stiff,3.3 mm,33.8 mm 34.0 mm,26.8 mm 26.0 mm,Normal,0,All seasons,1,1,-,#144 Top 38%,#109 Top 29%
+ASICS Gel Venture 10,83 Good!,$80,Light Moderate,Low,Low,-,Neutral,11.4 oz / 322g 11.4 oz / 323g,0,12.0 mm 10.0 mm,Heel,True to size,Soft,Big,0,Good,Good,Good,Moderate,Narrow,Narrow,Moderate,Stiff,Moderate,3.7 mm,35.3 mm 33.5 mm,23.3 mm 23.5 mm,Normal Wide X-Wide,0,All seasons,1,1,-,#262 Bottom 30%,#82 Top 22%
+ASICS Gel Venture 8,88 Great!,$70,Light Moderate,-,-,-,Neutral,10.4 oz / 295g 12.5 oz / 354g,0,13.2 mm 10.0 mm,Heel,True to size,Balanced,Normal,0,Bad,Decent,Good,Breathable,Narrow,Medium,Moderate,Moderate,Moderate,3.1 mm,34.2 mm,21.0 mm,Normal X-Wide,0,Summer All seasons,1,1,-,#258 Top 39%,#238 Top 36%
+ASICS Gel Venture 9,84 Good!,$80,Light Moderate,Low,Low,-,Neutral,11.1 oz / 314g 10.6 oz / 300g,0,10.4 mm,Heel,True to size,Balanced,Normal,0,Very bad,Decent,-,Moderate,Narrow,Narrow,Moderate,Moderate,Stiff,3.0 mm,33.3 mm,22.9 mm,Normal Wide X-Wide,0,All seasons,1,1,-,#498 Bottom 26%,#207 Top 31%
+ASICS Metafuji Trail,90 Great!,$250,Light,High,Moderate,High,Neutral,9.1 oz / 258g 9.2 oz / 261g,0,10.3 mm 5.0 mm,Heel,True to size,Soft,Normal,Carbon plate,Very bad,Good,Decent,Breathable,Medium,Narrow,Stiff,Stiff,Moderate,2.7 mm,44.7 mm 44.0 mm,34.4 mm 39.0 mm,Normal Wide,0,Summer All seasons,1,1,-,#69 Top 19%,#291 Bottom 23%
+ASICS Trabuco Max 2,93 Superb!,$150,Moderate Technical,-,-,-,Neutral,10.3 oz / 292g 10.7 oz / 303g,0,8.5 mm 5.0 mm,Heel Mid/forefoot,True to size,Soft,Big,0,Decent,Good,Decent,Breathable,Medium,Wide,Stiff,Stiff,Stiff,4.2 mm,39.7 mm 43.0 mm,31.2 mm 38.0 mm,Normal,0,Summer All seasons,1,1,-,#6 Top 1%,#312 Top 47%
+ASICS Trabuco Max 3,90 Great!,$160,Moderate Technical,High,Moderate,-,Neutral,10.9 oz / 308g 10.5 oz / 298g,0,8.5 mm 5.0 mm,Heel Mid/forefoot,Slightly small,Soft,Small,0,Good,Good,Decent,Warm,Medium,Medium,Stiff,Stiff,Stiff,4.0 mm,42.4 mm 43.0 mm,33.9 mm 38.0 mm,Normal,0,All seasons,1,1,-,#85 Top 13%,#151 Top 23%
+ASICS Trabuco Max 4,88 Great!,$160,Light,Moderate,Moderate,High,Neutral,11 oz / 312g 0.2 oz / 5g,0,6.1 mm 5.0 mm,Mid/forefoot,Half size small,Balanced,Small,0,Decent,Good,Decent,Moderate,Medium,Medium,Stiff,Stiff,Stiff,3.1 mm,39.3 mm 41.0 mm,33.2 mm 36.0 mm,Normal,0,All seasons,1,1,-,#149 Top 40%,#109 Top 29%
+ASICS Trail Scout 2,"83
+ Good!",$60,Moderate,-,-,-,Neutral,11.4 oz / 323g 11.4 oz / 323g,0,10.3 mm 10.0 mm,Heel,True to size,Balanced,Small,0,-,-,-,Moderate,Narrow,Medium,Stiff,Stiff,Stiff,4.2 mm,32.8 mm,22.5 mm,Normal,0,All seasons,1,1,-,#274 Bottom 27%,#335 Bottom 11%
+Brooks Caldera 6,88 Great!,2966250 Rp,Light Moderate,-,-,-,Neutral,11.1 oz / 315g 11.1 oz / 314g,0,12.1 mm 6.0 mm,Heel,True to size,Soft,Small,0,Very good,Decent,Good,Moderate,Medium,Medium,Stiff,Stiff,Stiff,3.5 mm,38.4 mm 36.0 mm,26.3 mm 30.0 mm,Normal,0,All seasons,1,1,-,#255 Top 38%,#488 Bottom 27%
+Brooks Caldera 7,88 Great!,$150,Light Moderate,High,Moderate,-,Neutral,10.8 oz / 305g 10.6 oz / 300g,0,8.9 mm 6.0 mm,Heel Mid/forefoot,True to size,Soft,Small,0,Decent,Good,Decent,Breathable,Medium,Medium,Stiff,Stiff,Stiff,4.0 mm,36.7 mm 39.0 mm,27.8 mm 33.0 mm,Normal,0,Summer All seasons,1,1,-,#273 Top 41%,#460 Bottom 31%
+Brooks Caldera 8,89 Great!,$150,Light Moderate,Moderate,Moderate,High,Neutral,10.9 oz / 309g 10.9 oz / 309g,0,8.8 mm 6.0 mm,Heel Mid/forefoot,True to size,Soft,Big,0,Bad,Good,Decent,Moderate,Medium,Medium,Moderate,Stiff,Stiff,3.6 mm,36.7 mm 39.0 mm,27.9 mm 33.0 mm,Normal,0,All seasons,1,1,-,#91 Top 25%,#160 Top 43%
+Brooks Cascadia 16,87 Great!,2966250 Rp,Technical,-,-,-,Neutral,10.9 oz / 310g 10.5 oz / 298g,0,10.3 mm 8.0 mm,Heel,Slightly small,-,-,Rock plate,-,-,-,-,Narrow,-,Stiff,Stiff,Stiff,4.3 mm,32.3 mm 29.0 mm,22.0 mm 21.0 mm,Normal Wide,0,-,1,1,-,#306 Top 46%,#436 Bottom 35%
+Brooks Cascadia 17,86 Good!,2966250 Rp,Moderate Technical,-,-,-,Neutral,11.6 oz / 329g 11 oz / 312g,0,9.2 mm 8.0 mm,Heel Mid/forefoot,True to size,Balanced,Normal,Rock plate,Bad,Bad,Good,Breathable,Medium,Wide,Stiff,Stiff,Stiff,3.9 mm,33.1 mm,23.9 mm,Normal Wide,0,Summer All seasons,1,1,-,#378 Bottom 43%,#401 Bottom 40%
+Brooks Cascadia 18,86 Good!,2966250 Rp,Light Moderate,Low,Low,-,Neutral,10.9 oz / 310g 11.1 oz / 314g,0,8.8 mm 8.0 mm,Heel Mid/forefoot,True to size,Balanced,Small,Rock plate,Very bad,Decent,Decent,Moderate,Medium,Medium,Stiff,Stiff,Stiff,4.0 mm,32.6 mm 33.0 mm,23.8 mm 25.0 mm,Normal Wide,0,All seasons,1,1,-,#414 Bottom 38%,#259 Top 39%
+Brooks Cascadia 19,84 Good!,2966250 Rp,Light Moderate,Moderate,Moderate,High,Neutral,10.8 oz / 306g 10.7 oz / 303g,0,7.8 mm 6.0 mm,Mid/forefoot,True to size,Balanced,Normal,Rock plate,Good,Decent,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,3.8 mm,34.8 mm 35.0 mm,27.0 mm 29.0 mm,Normal Wide,1,All seasons,1,1,-,#224 Bottom 40%,#107 Top 29%
+Brooks Catamount 2,87 Great!,$170,Light Moderate,-,-,-,Neutral,9.7 oz / 275g 9.7 oz / 275g,0,6.4 mm 6.0 mm,Mid/forefoot,True to size,Balanced,Small,Rock plate,Bad,Good,Good,Moderate,Narrow,Medium,Stiff,Stiff,Moderate,3.0 mm,29.0 mm 29.0 mm,22.6 mm 23.0 mm,Normal,0,All seasons,1,1,Water repellent,#333 Top 50%,#593 Bottom 11%
+Brooks Catamount 3,89 Great!,3361750 Rp,Light Moderate,-,-,-,Neutral,9 oz / 255g 9.4 oz / 266g,0,6.8 mm 6.0 mm,Mid/forefoot,True to size,Balanced,Normal,Rock plate,Very bad,Good,Decent,Warm,Narrow,Wide,Stiff,Moderate,Flexible,2.9 mm,28.3 mm 30.0 mm,21.5 mm 24.0 mm,Normal,0,All seasons,1,1,-,#85 Top 23%,#267 Bottom 29%
+Brooks Divide 3,90 Great!,2966250 Rp,Light Moderate,-,-,-,Neutral,10.3 oz / 291g 10.1 oz / 285g,0,8.4 mm 8.0 mm,Heel Mid/forefoot,Half size small,Firm,Small,0,Very bad,Bad,Good,Moderate,Medium,Medium,Stiff,Stiff,Stiff,3.1 mm,31.1 mm 30.0 mm,22.7 mm 22.0 mm,Normal,0,All seasons,1,1,-,#131 Top 20%,#637 Bottom 5%
+Brooks Divide 4,86 Good!,2175250 Rp,Light,-,-,-,Neutral,9.9 oz / 282g 10.4 oz / 294.8g,0,9.2 mm 8.0 mm,Heel Mid/forefoot,True to size,Balanced,Big,0,Bad,Bad,Decent,Moderate,Medium,Wide,Stiff,Moderate,Stiff,2.7 mm,32.1 mm 30.0 mm,22.9 mm 22.0 mm,Normal,0,All seasons,1,1,-,#191 Bottom 49%,#314 Bottom 17%
+Brooks Divide 5 GTX,78 Decent!,2570750 Rp,Light,Moderate,Low,High,Neutral,10.1 oz / 286g 10.4 oz / 295g,0,10.0 mm 8.0 mm,Heel Mid/forefoot,True to size,Balanced,Normal,0,Good,Good,Good,Warm,Medium,Medium,Moderate,Stiff,Stiff,3.0 mm,35.5 mm 24.0 mm,25.5 mm 16.0 mm,Normal,0,Winter,1,1,Waterproof,#337 Bottom 11%,#318 Bottom 16%
+Hoka Challenger 7,85 Good!,$145,Light Moderate,High,Low,-,Neutral,8.8 oz / 250g 8.8 oz / 250g,0,8.8 mm 5.0 mm,Heel Mid/forefoot,True to size,Soft,Normal,0,Good,Decent,-,Warm,Narrow,Narrow,Moderate,Moderate,Stiff,3.1 mm,32.9 mm 31.0 mm,24.1 mm 26.0 mm,Normal Wide,0,All seasons,1,1,-,#452 Bottom 32%,#166 Top 25%
+Hoka Challenger 7 GTX,78 Decent!,3361750 Rp,Moderate Technical,-,-,-,Neutral,9.9 oz / 281g 9 oz / 255g,0,11.1 mm 5.0 mm,Heel,True to size,Soft,Small,0,Very good,Decent,Good,Warm,Medium,Narrow,Stiff,Stiff,Stiff,3.8 mm,39.2 mm 31.0 mm,28.1 mm 26.0 mm,Normal,0,Winter,1,1,Waterproof Water repellent,#342 Bottom 9%,#136 Top 36%
+HOKA Challenger 8,77 Decent!,$155,Light Moderate,High,Low,High,Neutral,9.5 oz / 269g 9.7 oz / 275g,0,10.1 mm 8.0 mm,Heel,-,Soft,Normal,0,Good,Good,Good,Breathable,Medium,Medium,Moderate,Stiff,Stiff,3.7 mm,40.2 mm 42.0 mm,30.1 mm 34.0 mm,Normal Wide,0,Summer All seasons,1,1,-,#339 Bottom 10%,#66 Top 18%
+HOKA Mafate 5,75 Bad!,3757250 Rp,Light Moderate,High,Moderate,High,Neutral,11.1 oz / 315g 11.7 oz / 332g,0,9.0 mm 8.0 mm,Heel Mid/forefoot,-,Soft,Small,0,Decent,Decent,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,4.4 mm,42.9 mm 45.0 mm,33.9 mm 37.0 mm,Normal,0,All seasons,1,1,-,#360 Bottom 5%,#130 Top 35%
+Hoka Mafate Speed 4,88 Great!,3559500 Rp,Moderate Technical,Moderate,Moderate,-,Neutral,10.3 oz / 293g 10.4 oz / 295g,0,7.2 mm 4.0 mm,Mid/forefoot,True to size,Soft,Normal,0,Good,Good,Good,Moderate,Medium,Narrow,Moderate,Stiff,Flexible,3.9 mm,38.0 mm 33.0 mm,30.8 mm 29.0 mm,Normal,0,All seasons,1,1,-,#140 Top 37%,#119 Top 32%
+Hoka Mafate Three2,"77
+ Decent!",3955000Rp,Light,-,-,-,Neutral,11.7 oz / 332g 11.6 oz / 329g,0,3.9 mm 4.0 mm,Mid/forefoot,Slightly small,Soft,Normal,0,Very good,Decent,Good,Moderate,Narrow,Narrow,Stiff,Stiff,Moderate,4.0 mm,35.6 mm 35.0 mm,31.7 mm 31.0 mm,Normal,0,All seasons,1,1,-,#346 Bottom 8%,#206 Bottom 45%
+HOKA Mafate X,89 Great!,$225,Light Moderate,High,Moderate,High,Neutral,11.8 oz / 335g 12.1 oz / 343g,0,10.6 mm 8.0 mm,Heel,-,Balanced,Small,Carbon plate,Bad,Good,Good,Moderate,Wide,Medium,Stiff,Stiff,Stiff,3.0 mm,47.3 mm 49.0 mm,36.7 mm 41.0 mm,Normal,1,All seasons,1,1,-,#86 Top 23%,#181 Top 48%
+HOKA Mafate X,89 Great!,4449380 Rp,Light Moderate,High,Moderate,High,Neutral,11.8 oz / 335g 12.1 oz / 343g,0,10.6 mm 8.0 mm,Heel,-,Balanced,Small,Carbon plate,Bad,Good,Good,Moderate,Wide,Medium,Stiff,Stiff,Stiff,3.0 mm,47.3 mm 49.0 mm,36.7 mm 41.0 mm,Normal,1,All seasons,1,1,-,#87 Top 23%,#182 Top 49%
+Hoka Speedgoat 5,88 Great!,$155,Light Moderate,Moderate,Low,-,Neutral,9.8 oz / 277g 9.7 oz / 276g,0,3.8 mm 4.0 mm,Mid/forefoot,True to size,Soft,Big,0,-,-,-,Moderate,Narrow,-,Moderate,Flexible,Flexible,3.0 mm,27.5 mm 33.0 mm,23.7 mm 29.0 mm,Normal Wide,0,All seasons,1,1,-,#248 Top 37%,#121 Top 18%
+Hoka Speedgoat 5 GTX,82 Good!,3559500 Rp,Moderate,-,-,-,Neutral,11.3 oz / 319g 11.5 oz / 326g,0,7.0 mm 4.0 mm,Mid/forefoot,True to size,Soft,Big,0,Good,Good,Decent,Moderate,Medium,Medium,Stiff,Stiff,Moderate,3.5 mm,34.6 mm,27.6 mm,Normal,0,Winter,1,1,Waterproof,#553 Bottom 17%,#371 Bottom 44%
+Hoka Speedgoat 6,78 Decent!,$155,Moderate,Moderate,Low,-,Neutral,9.8 oz / 278g 9.8 oz / 277g,0,4.9 mm 5.0 mm,Mid/forefoot,Slightly small,Balanced,Small,0,Good,Good,Decent,Warm,Medium,Medium,Stiff,Stiff,Moderate,4.0 mm,32.1 mm 40.0 mm,27.2 mm 35.0 mm,Normal Wide,1,All seasons,1,1,-,#341 Bottom 10%,#37 Top 10%
+Hoka Speedgoat 6 GTX,"77
+ Decent!",3558800Rp,Moderate,-,-,-,Neutral,10.2 oz / 289g 10.4 oz / 295g,0,5.0 mm 5.0 mm,Mid/forefoot,Half size small,Balanced,Small,0,Very good,Good,Good,Warm,Medium,Narrow,Stiff,Stiff,Moderate,3.9 mm,32.9 mm 37.0 mm,27.9 mm 32.0 mm,NormalWide,0,Winter,1,1,Waterproof,#352 Bottom 7%,#113 Top 30%
+Hoka Stinson 7,85 Good!,$170,Light Moderate,Moderate,Moderate,-,Neutral,12.1 oz / 342g 12.9 oz / 365g,0,7.0 mm 5.0 mm,Mid/forefoot,True to size,Soft,Big,0,Very bad,Bad,Decent,Moderate,Medium,Medium,Stiff,Stiff,Stiff,3.0 mm,40.0 mm 42.0 mm,33.0 mm 37.0 mm,Normal,0,All seasons,1,1,-,#217 Bottom 42%,#105 Top 28%
+Hoka Tecton X,90 Great!,4152750 Rp,Moderate,-,-,-,Neutral,8.6 oz / 245g 8.9 oz / 252g,1,8.0 mm 5.0 mm,Heel Mid/forefoot,True to size,Firm,Small,Carbon plate,-,-,-,-,Narrow,-,Stiff,Stiff,Flexible,3.5 mm,35.3 mm 33.0 mm,27.3 mm 29.0 mm,Normal,0,-,1,1,-,#140 Top 21%,#481 Bottom 28%
+Hoka Tecton X 2,88 Great!,4350500 Rp,Moderate,-,-,-,Neutral,9.1 oz / 257g 8.8 oz / 249g,0,5.6 mm 5.0 mm,Mid/forefoot,Slightly small,Balanced,Small,Carbon plate,Good,Bad,Good,Moderate,Narrow,Narrow,Stiff,Stiff,Moderate,3.6 mm,37.4 mm 32.0 mm,31.8 mm 27.0 mm,Normal,0,All seasons,1,1,-,#301 Top 45%,#445 Bottom 33%
+HOKA Tecton X 3,84 Good!,4943750 Rp,Light Moderate,High,High,-,Neutral,9.7 oz / 275g 10.3 oz / 292g,0,6.9 mm 5.0 mm,Mid/forefoot,Slightly small,Soft,Small,Carbon plate,Good,Decent,Good,Moderate,Narrow,Medium,Stiff,Stiff,Flexible,4.0 mm,37.8 mm 40.0 mm,30.9 mm 35.0 mm,Normal,0,All seasons,1,1,Water repellent,#242 Bottom 36%,#153 Top 41%
+Hoka Torrent 3,84 Good!,2966250 Rp,Moderate,-,-,-,Neutral,9.1 oz / 258g 8.7 oz / 247g,0,7.1 mm 5.0 mm,Mid/forefoot,True to size,Balanced,Small,0,Decent,Decent,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,3.6 mm,29.5 mm 23.0 mm,22.4 mm 18.0 mm,Normal,0,All seasons,1,1,-,#237 Bottom 37%,#306 Bottom 19%
+Hoka Zinal,88 Great!,3361750 Rp,Light Moderate,-,-,-,Neutral,8.4 oz / 239g 8.5 oz / 241g,1,3.9 mm 4.0 mm,Mid/forefoot,True to size,-,-,0,-,-,-,-,Narrow,-,Stiff,Stiff,Moderate,3.4 mm,29.3 mm 22.0 mm,25.4 mm 18.0 mm,Normal,0,-,1,1,-,#256 Top 38%,#607 Bottom 9%
+Hoka Zinal 2,84 Good!,3559500 Rp,Moderate,-,-,-,Neutral,7.5 oz / 213g 8 oz / 227g,1,7.2 mm 5.0 mm,Mid/forefoot,Slightly small,Soft,Big,0,Very bad,Good,Decent,Moderate,Narrow,Medium,Stiff,Moderate,Flexible,3.7 mm,29.8 mm 30.0 mm,22.6 mm 25.0 mm,Normal,0,All seasons,1,1,-,#240 Bottom 36%,#226 Bottom 40%
+Icebug Järv RB9X,90 Great!,$180,Moderate Technical,Moderate,Moderate,High,Neutral,11.6 oz / 329g 12 oz / 340g,0,6.0 mm 4.0 mm,Mid/forefoot,-,Balanced,Small,0,Good,Decent,Decent,Warm,Medium,Medium,Stiff,Stiff,Moderate,5.0 mm,37.0 mm 29.0 mm,31.0 mm 25.0 mm,Normal,0,All seasons,1,1,-,#61 Top 17%,#378 Bottom 1%
+Inov8 Trailfly,88 Great!,$150,Light Moderate,-,-,-,Neutral,9.9 oz / 282g 9.9 oz / 280g,0,6.0 mm 6.0 mm,Mid/forefoot,Slightly small,Soft,Small,Rock plate,Decent,Good,Good,Moderate,Medium,Wide,Stiff,Flexible,Flexible,3.9 mm,30.1 mm 29.0 mm,24.1 mm 23.0 mm,Normal Wide,0,All seasons,1,1,-,#153 Top 41%,#326 Bottom 13%
+Inov8 Trailfly,88 Great!,2966250 Rp,Light Moderate,-,-,-,Neutral,9.9 oz / 282g 9.9 oz / 280g,0,6.0 mm 6.0 mm,Mid/forefoot,Slightly small,Soft,Small,Rock plate,Decent,Good,Good,Moderate,Medium,Wide,Stiff,Flexible,Flexible,3.9 mm,30.1 mm 29.0 mm,24.1 mm 23.0 mm,Normal Wide,0,All seasons,1,1,-,#153 Top 41%,#326 Bottom 13%
+Inov8 Trailfly Max,69 Bad!,3361750 Rp,Light Moderate,Moderate,Low,High,Neutral,10.2 oz / 289g,0,7.6 mm 6.0 mm,Mid/forefoot,-,Balanced,Small,0,Decent,Decent,Good,Moderate,Medium,Wide,Moderate,Moderate,Flexible,3.4 mm,37.1 mm,29.5 mm,Normal Wide,0,All seasons,1,1,-,#376 Bottom 1%,#363 Bottom 4%
+Inov8 Trailfly Zero,87 Great!,3164000 Rp,Light Moderate,Low,Low,High,Neutral,9.4 oz / 266g,0,0.5 mm 0.0 mm,Mid/forefoot,-,Balanced,Small,0,Decent,Decent,Good,Moderate,Medium,Wide,Flexible,Flexible,Flexible,3.4 mm,24.9 mm,24.4 mm,Normal Wide,0,All seasons,1,1,-,#179 Top 48%,#345 Bottom 8%
+Inov8 Trailtalon,90 Great!,2966250 Rp,Moderate Technical,-,-,-,Neutral,10.2 oz / 289g 10.2 oz / 290g,0,7.8 mm 6.0 mm,Mid/forefoot,Half size small,Soft,Small,0,Very bad,Good,Good,Warm,Wide,Wide,Stiff,Moderate,Flexible,5.4 mm,34.2 mm 31.0 mm,26.4 mm 25.0 mm,Normal Wide,0,0,1,1,-,#75 Top 20%,#365 Bottom 3%
+Kailas Flythorn Air 2.0,90 Great!,3164000 Rp,Light Moderate,-,-,-,Neutral,10.6 oz / 301g 10.3 oz / 291g,0,10.3 mm 10.0 mm,Heel,-,-,-,0,-,-,-,-,Narrow,-,Stiff,Moderate,Stiff,3.0 mm,30.6 mm 29.0 mm,20.3 mm 19.0 mm,Normal,0,-,1,1,-,#59 Top 16%,#376 Bottom 1%
+Kailas Fuga DU,81 Good!,3559500 Rp,Moderate,-,-,-,Neutral,10.8 oz / 306g 10.3 oz / 293g,0,11.6 mm 8.0 mm,Heel,-,Balanced,Normal,0,-,-,-,Moderate,Narrow,-,Stiff,Stiff,Stiff,3.2 mm,36.9 mm 36.0 mm,25.3 mm,Wide,0,All seasons,1,1,-,#304 Bottom 19%,#367 Bottom 3%
+Kailas Fuga Elite 2,83 Good!,7910000 Rp,Light,-,-,-,Neutral,10.9 oz / 310g 10 oz / 284g,0,12.5 mm 10.0 mm,Heel,-,-,-,Carbon plate,-,-,-,-,Narrow,-,Stiff,Stiff,Moderate,2.5 mm,40.4 mm 41.0 mm,27.9 mm 31.0 mm,Normal,0,-,1,1,-,#264 Bottom 30%,#371 Bottom 2%
+Kailas Fuga EX 2,85 Good!,$160,Moderate Technical,-,-,-,Neutral,10.4 oz / 295g 9.5 oz / 270g,0,10.6 mm 8.0 mm,Heel,-,-,-,0,-,-,-,-,Narrow,-,Stiff,Stiff,Stiff,4.0 mm,38.0 mm 36.0 mm,27.4 mm 28.0 mm,Normal,0,-,1,1,-,#456 Bottom 32%,#661 Bottom 1%
+Kailas Fuga EX 3,86 Good!,$180,Light Moderate,Moderate,Low,High,Neutral,10.3 oz / 293g 10.1 oz / 285g,0,13.7 mm 8.0 mm,Heel,-,Balanced,Normal,0,Decent,Decent,Good,Moderate,Narrow,Medium,Stiff,Stiff,Stiff,3.4 mm,38.4 mm 36.0 mm,24.7 mm 28.0 mm,Normal,0,All seasons,1,1,-,#200 Bottom 47%,#357 Bottom 5%
+Kailas Fuga EX BOA,88 Great!,3361750 Rp,Moderate,-,-,-,Neutral,9.9 oz / 281g 9.6 oz / 272g,0,10.9 mm 8.0 mm,Heel,-,-,-,0,-,-,-,-,Narrow,-,Stiff,Stiff,Stiff,3.7 mm,38.5 mm 36.0 mm,27.6 mm 28.0 mm,Normal Wide,0,-,1,1,-,#135 Top 36%,#368 Bottom 2%
+Kailas Fuga EX Pro,81 Good!,6478290 Rp,Light Moderate,Moderate,High,High,Neutral,9.6 oz / 272g 9.7 oz / 275g,0,7.2 mm 5.0 mm,Mid/forefoot,-,Balanced,Small,0,Bad,Decent,Good,Moderate,Narrow,Narrow,Stiff,Stiff,Moderate,3.7 mm,37.9 mm 35.0 mm,30.7 mm 30.0 mm,Normal,0,All seasons,1,1,-,#307 Bottom 18%,#359 Bottom 5%
+Kailas Fuga Pro 4,86 Good!,3955000 Rp,Moderate,-,-,-,Neutral,9.8 oz / 279g 9.5 oz / 270g,0,10.0 mm 10.0 mm,Heel Mid/forefoot,-,-,-,0,-,-,-,-,Narrow,-,Stiff,Stiff,Stiff,3.4 mm,31.7 mm 32.0 mm,21.8 mm 22.0 mm,Normal,0,-,1,1,-,#181 Top 48%,#370 Bottom 2%
+Kailas Fuga YAO,90 Great!,3164000 Rp,Light,-,-,-,Neutral,10.5 oz / 299g 10.1 oz / 285g,0,10.7 mm,Heel,-,Balanced,Big,Carbon plate,-,-,-,Moderate,Narrow,-,Stiff,Stiff,Stiff,1.7 mm,38.6 mm,27.9 mm,Normal,0,All seasons,1,1,-,#58 Top 16%,#374 Bottom 1%
+Kailas Phantom 3.0,90 Great!,2768500 Rp,Light,-,-,-,Neutral,8.9 oz / 253g 8.3 oz / 235g,0,10.5 mm,Heel,-,Balanced,Small,0,-,-,-,Moderate,Narrow,-,Stiff,Moderate,Stiff,1.8 mm,35.5 mm,25.0 mm,Normal,0,All seasons,1,1,-,#60 Top 16%,#377 Bottom 1%
+KEEN Seek,90 Great!,$185,Light Moderate,High,Low,High,Neutral,10.8 oz / 305g 10.9 oz / 309g,0,8.6 mm 6.0 mm,Heel Mid/forefoot,-,Soft,Small,0,Good,Good,Good,Moderate,Medium,Wide,Moderate,Moderate,Flexible,4.1 mm,36.3 mm 38.5 mm,27.7 mm 32.5 mm,Normal,0,All seasons,1,1,-,#74 Top 20%,#325 Bottom 14%
+La Sportiva Mutant,87 Great!,$165,Technical,-,-,-,Neutral,11.4 oz / 323g 10.7 oz / 303g,0,11.3 mm 10.0 mm,Heel,Half size small,Firm,Small,0,Good,Good,Decent,Moderate,Narrow,Medium,Stiff,Stiff,Moderate,5.0 mm,33.2 mm 26.0 mm,21.9 mm 16.0 mm,Normal,0,All seasons,1,1,-,#163 Top 44%,#269 Bottom 29%
+La Sportiva Prodigio,"83
+ Good!",$155,Moderate,-,-,-,Neutral,9.6 oz / 271g 9.5 oz / 270g,0,8.9 mm 6.0 mm,HeelMid/forefoot,Half size small,Soft,Normal,0,Very bad,Bad,Decent,Moderate,Medium,Medium,Stiff,Stiff,Stiff,3.4 mm,29.3 mm 34.0 mm,20.4 mm 28.0 mm,Normal,0,All seasons,1,1,-,#275 Bottom 27%,#204 Bottom 46%
+La Sportiva Prodigio,83 Good!,$155,Moderate,-,-,-,Neutral,9.6 oz / 271g 9.5 oz / 270g,0,8.9 mm 6.0 mm,Heel Mid/forefoot,Half size small,Soft,Normal,0,Very bad,Bad,Decent,Moderate,Medium,Medium,Stiff,Stiff,Stiff,3.4 mm,29.3 mm 34.0 mm,20.4 mm 28.0 mm,Normal,0,All seasons,1,1,-,#274 Bottom 27%,#203 Bottom 46%
+La Sportiva Prodigio,83 Good!,$155,Moderate,-,-,-,Neutral,9.6 oz / 271g 9.5 oz / 270g,0,8.9 mm 6.0 mm,Heel Mid/forefoot,Half size small,Soft,Normal,0,Very bad,Bad,Decent,Moderate,Medium,Medium,Stiff,Stiff,Stiff,3.4 mm,29.3 mm 34.0 mm,20.4 mm 28.0 mm,Normal,0,All seasons,1,1,-,#275 Bottom 27%,#204 Bottom 46%
+La Sportiva Prodigio Max,84 Good!,3559500 Rp,Moderate Technical,Moderate,Moderate,High,Neutral,10.8 oz / 306g 10.4 oz / 295g,0,7.1 mm 6.0 mm,Mid/forefoot,-,Balanced,Normal,0,Good,Good,Good,Moderate,Narrow,Narrow,Stiff,Stiff,Moderate,5.5 mm,35.1 mm 37.0 mm,28.0 mm 31.0 mm,Normal,0,All seasons,1,1,-,#246 Bottom 35%,#217 Bottom 42%
+Merrell Agility Peak 4,86 Good!,$130,Technical,-,-,-,Neutral,10.3 oz / 292g 10.8 oz / 305g,0,9.3 mm 6.0 mm,Heel Mid/forefoot,Slightly large,Balanced,Small,Rock plate,Decent,Decent,Decent,Moderate,Narrow,Wide,Stiff,Stiff,Stiff,4.4 mm,34.4 mm 30.0 mm,25.1 mm 24.0 mm,Normal,0,All seasons,1,1,-,#380 Bottom 43%,#619 Bottom 8%
+Merrell Agility Peak 5,88 Great!,$140,Moderate Technical,Moderate,Low,-,Neutral,10.2 oz / 289g 10.6 oz / 300g,0,13.4 mm 6.0 mm,Heel,True to size,Balanced,Small,Rock plate,Very good,Decent,Good,Moderate,Medium,Narrow,Moderate,Stiff,Stiff,4.5 mm,39.2 mm 39.0 mm,25.8 mm 33.0 mm,Normal,1,All seasons,1,1,-,#152 Top 41%,#96 Top 26%
+Merrell Agility Peak 5 GTX,81 Good!,3757250 Rp,Moderate Technical,-,-,-,Neutral,10.4 oz / 296g 10.7 oz / 303g,0,11.6 mm 6.0 mm,Heel,True to size,Balanced,Small,Rock plate,Good,Bad,Decent,Warm,Narrow,Medium,Stiff,Stiff,Stiff,4.4 mm,37.3 mm 39.0 mm,25.7 mm 33.0 mm,Normal,0,Winter,1,1,Waterproof,#315 Bottom 16%,#180 Top 48%
+Merrell Antora 3,83 Good!,$125,Light,Moderate,Low,-,Neutral,10.1 oz / 285g,0,9.1 mm,Heel Mid/forefoot,True to size,Balanced,Normal,Rock plate,Decent,Bad,Good,Breathable,Narrow,Medium,Moderate,Stiff,Moderate,3.4 mm,33.5 mm,24.4 mm,Normal,0,Summer All seasons,1,1,-,#280 Bottom 26%,#227 Bottom 40%
+Merrell Fly Strike,79 Good!,2373000 Rp,Light,-,-,-,Neutral,10.3 oz / 292g 10.4 oz / 295g,0,17.3 mm 10.0 mm,Heel,True to size,Balanced,Small,0,Good,Decent,Decent,Moderate,Narrow,Wide,Stiff,Stiff,Moderate,3.5 mm,34.3 mm 27.0 mm,17.0 mm 17.0 mm,Normal Wide,0,All seasons,1,1,-,#331 Bottom 12%,#308 Bottom 18%
+Merrell Moab Flight,89 Great!,2570750 Rp,Light,-,-,-,Neutral,9.6 oz / 271g 10.6 oz / 300g,0,13.5 mm 10.0 mm,Heel,True to size,Balanced,Normal,0,Good,Good,Good,Warm,Medium,Medium,Moderate,Stiff,Moderate,2.9 mm,32.6 mm 29.0 mm,19.1 mm 19.0 mm,Normal,0,All seasons,1,1,-,#94 Top 25%,#321 Bottom 15%
+Merrell Morphlite,87 Great!,2175250 Rp,Light,Moderate,Low,-,Neutral,8.4 oz / 237g 8.6 oz / 243g,1,11.0 mm 6.0 mm,Heel,True to size,Soft,Small,0,Bad,Decent,Decent,Moderate,Narrow,Medium,Moderate,Stiff,Flexible,2.0 mm,32.3 mm 26.0 mm,21.3 mm 20.0 mm,Normal Wide,0,All seasons,1,1,-,#157 Top 42%,#297 Bottom 21%
+Merrell Nova 2,85 Good!,2570750 Rp,Moderate Technical,-,-,-,Neutral,10.3 oz / 293g 9.9 oz / 280g,0,9.3 mm 8.0 mm,Heel Mid/forefoot,-,-,-,Rock plate,-,-,-,-,Narrow,-,Stiff,-,-,4.2 mm,35.1 mm 29.0 mm,25.8 mm 21.0 mm,Normal,0,-,0,0,-,#441 Bottom 34%,#464 Bottom 31%
+Merrell Nova 3,80 Good!,2669630 Rp,Light Moderate,Moderate,Moderate,-,Neutral,10.8 oz / 305g 10.4 oz / 295g,0,9.9 mm 8.0 mm,Heel Mid/forefoot,True to size,Balanced,Small,Rock plate,Decent,Decent,Good,Moderate,Narrow,Medium,Moderate,Stiff,Flexible,3.5 mm,34.1 mm 29.0 mm,24.2 mm 21.0 mm,Normal Wide,0,All seasons,1,1,-,#605 Bottom 10%,#306 Top 46%
+Merrell Nova 4,86 Good!,2966250 Rp,Light Moderate,Moderate,Low,Moderate,Neutral,9.6 oz / 272g 9.7 oz / 275g,0,12.1 mm 8.0 mm,Heel,-,Balanced,Small,0,Decent,Decent,Decent,Moderate,Medium,Medium,Moderate,Stiff,Flexible,4.0 mm,37.7 mm 29.0 mm,25.6 mm 21.0 mm,Normal Wide,0,All seasons,1,1,-,#199 Bottom 47%,#164 Top 44%
+Merrell Trail Glove 7,84 Good!,2669630 Rp,Light,-,-,-,Neutral,7.8 oz / 221g 9 oz / 255g,1,0.1 mm 0.0 mm,Mid/forefoot,True to size,Balanced,Normal,0,Decent,Bad,Decent,Moderate,Medium,Wide,Moderate,Flexible,Flexible,2.5 mm,16.1 mm 14.0 mm,16.0 mm 14.0 mm,Normal,0,All seasons,0,0,-,#236 Bottom 37%,#174 Top 46%
+New Balance 510 v6,74 Bad!,$90,Light,-,-,-,Neutral,11 oz / 312g 11.5 oz / 326g,0,8.2 mm,Heel Mid/forefoot,True to size,Balanced,Small,0,Decent,Bad,Good,Moderate,Narrow,Medium,Stiff,Moderate,Moderate,2.9 mm,33.8 mm,25.6 mm,Normal Wide X-Wide,0,All seasons,1,1,-,#368 Bottom 2%,#319 Bottom 15%
+New Balance DynaSoft Nitrel v6,75 Bad!,1977500 Rp,Light,Low,Low,-,Neutral,10.3 oz / 291g 10.1 oz / 285g,0,2.5 mm 6.0 mm,Mid/forefoot,True to size,Balanced,Small,0,Decent,Decent,Good,Moderate,Medium,Medium,Moderate,Flexible,Moderate,2.7 mm,22.7 mm 27.0 mm,20.2 mm 21.0 mm,Normal Wide X-Wide,0,All seasons,1,1,-,#365 Bottom 3%,#193 Bottom 49%
+New Balance Fresh Foam 680 v8,84 Good!,2274130 Rp,-,Moderate,Moderate,High,Neutral,9.2 oz / 261g 9.5 oz / 268g,0,7.8 mm,Mid/forefoot,Slightly small,Soft,Normal,0,Good,Good,Good,Breathable,Medium,Medium,Flexible,Flexible,Moderate,‐,35.4 mm,27.6 mm,Normal Wide X-Wide,0,Summer All seasons,1,1,-,#251 Bottom 33%,#28 Top 8%
+New Balance Fresh Foam X 1080 v14,86 Good!,3757250 Rp,-,High,Moderate,High,Neutral,10.1 oz / 285g 10.5 oz / 298g,0,4.2 mm 6.0 mm,Mid/forefoot,True to size,Soft,Normal,0,Good,Decent,Good,Moderate,Medium,Narrow,Moderate,Stiff,Stiff,‐,37.0 mm 38.0 mm,32.8 mm 32.0 mm,Narrow Normal Wide X-Wide,0,All seasons,1,1,-,#185 Top 49%,#5 Top 2%
+New Balance Fresh Foam X 880 v15,85 Good!,3164000 Rp,-,Moderate,Moderate,High,Neutral,10.1 oz / 286g 9.9 oz / 281g,0,4.3 mm 6.0 mm,Mid/forefoot,Slightly small,Soft,Small,0,Decent,Good,Decent,Moderate,Medium,Medium,Stiff,Stiff,Stiff,‐,39.7 mm 40.5 mm,35.4 mm 34.5 mm,Narrow Normal Wide X-Wide,0,All seasons,1,1,-,#207 Bottom 45%,#39 Top 11%
+New Balance Fresh Foam X Garoe v2,90 Great!,$110,Light,High,Moderate,-,Neutral,9.5 oz / 269g 10.5 oz / 298g,0,11.0 mm 8.0 mm,Heel,-,Soft,Small,0,Decent,Decent,Good,Moderate,Narrow,Medium,Stiff,Stiff,Stiff,3.2 mm,38.4 mm,27.4 mm,Normal,0,All seasons,1,1,-,#54 Top 15%,#120 Top 32%
+New Balance Fresh Foam X Hierro v9,84 Good!,$155,Light,High,Moderate,-,Neutral,10.9 oz / 309g 10.5 oz / 297g,0,4.2 mm 4.0 mm,Mid/forefoot,Half size small,Soft,Small,0,Good,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,3.3 mm,37.3 mm 33.0 mm,33.1 mm 29.0 mm,Normal Wide X-Wide,0,All seasons,1,1,-,#238 Bottom 37%,#24 Top 7%
+New Balance Fresh Foam X More Trail v3,87 Great!,$160,Moderate Technical,High,Moderate,-,Neutral,10.6 oz / 301g 10.6 oz / 301g,0,7.1 mm 4.0 mm,Mid/forefoot,True to size,Soft,Normal,0,Very bad,Decent,-,Moderate,Medium,Narrow,Moderate,Moderate,Moderate,5.0 mm,38.6 mm 39.4 mm,31.5 mm 35.4 mm,Normal Wide,0,All seasons,1,1,-,#169 Top 45%,#30 Top 8%
+New Balance Fresh Foam X More v6,89 Great!,3361750 Rp,-,High,Moderate,Moderate,Neutral,10.7 oz / 302g 10.8 oz / 306g,0,3.3 mm 4.0 mm,Mid/forefoot,True to size,Soft,Big,0,Decent,Good,Good,Warm,Medium,Medium,Stiff,Moderate,Moderate,‐,41.8 mm 44.0 mm,38.5 mm 40.0 mm,Narrow Normal Wide X-Wide,0,All seasons,1,1,-,#123 Top 33%,#3 Top 1%
+New Balance FuelCell Rebel v4,86 Good!,3164000 Rp,-,High,Moderate,High,Neutral,7.5 oz / 213g 7.7 oz / 218g,1,6.5 mm 6.0 mm,Mid/forefoot,Slightly small,Soft,Big,0,Very bad,Good,Good,Moderate,Medium,Wide,Flexible,Flexible,Flexible,‐,28.0 mm 30.0 mm,21.5 mm 24.0 mm,Normal Wide,0,All seasons,1,1,-,#406 Bottom 39%,#62 Top 10%
+New Balance FuelCell Rebel v5,90 Great!,3164000 Rp,-,High,Moderate,High,Neutral,7.8 oz / 220g 7.9 oz / 225g,1,6.3 mm 6.0 mm,Mid/forefoot,True to size,Soft,Small,0,Very bad,Good,Good,Moderate,Medium,Medium,Moderate,Flexible,Moderate,‐,33.0 mm 35.0 mm,26.7 mm 29.0 mm,Normal Wide,0,All seasons,1,1,-,#39 Top 11%,#32 Top 9%
+New Balance FuelCell SuperComp Elite v5,92 Superb!,5537000 Rp,-,High,High,High,Neutral,7 oz / 198g 7.5 oz / 213g,1,10.7 mm 8.0 mm,Heel,True to size,Soft,Small,Carbon plate,Very bad,Decent,Decent,Moderate,Medium,Medium,Moderate,Stiff,Flexible,‐,39.3 mm 40.0 mm,28.6 mm 32.0 mm,Normal Wide,0,All seasons,1,1,-,#12 Top 4%,#33 Top 9%
+New Balance FuelCell SuperComp Trail,90 Great!,$200,Light Moderate,-,-,-,Neutral,8.7 oz / 248g 8.8 oz / 249g,1,13.0 mm 10.0 mm,Heel,Half size small,Soft,Normal,Carbon plate,Decent,Decent,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,2.9 mm,34.7 mm 36.5 mm,21.7 mm 26.5 mm,Normal,0,All seasons,1,1,-,#70 Top 19%,#319 Bottom 15%
+New Balance MT10,85 Good!,2373000 Rp,Light,Low,Low,-,Neutral,7.1 oz / 200g 7.2 oz / 204g,1,5.0 mm 4.0 mm,Mid/forefoot,Slightly small,Firm,Small,0,Very good,Decent,Good,Moderate,Narrow,Medium,Flexible,Flexible,Flexible,‐,15.6 mm 14.0 mm,10.6 mm 10.0 mm,Normal,0,All seasons,0,0,-,#212 Bottom 44%,#142 Top 38%
+New Balance Tektrel,76 Bad!,$90,Light,-,-,-,Neutral,10.7 oz / 302g 9.9 oz / 282g,0,8.0 mm 8.0 mm,Heel Mid/forefoot,Slightly small,Balanced,Normal,0,Good,Decent,Decent,Breathable,Medium,Wide,Stiff,Moderate,Moderate,2.4 mm,32.2 mm 32.0 mm,24.2 mm 24.0 mm,Normal Wide,0,Summer All seasons,1,1,-,#358 Bottom 5%,#166 Top 44%
+Nike Air Zoom Terra Kiger 6,87 Great!,2768500 Rp,Moderate,-,-,-,Neutral,11.2 oz / 317g 10.3 oz / 291g,0,4.4 mm 4.0 mm,Mid/forefoot,-,-,-,Rock plate,-,-,-,-,Wide,-,Stiff,-,-,4.8 mm,19.6 mm 15.0 mm,15.2 mm 11.0 mm,Normal,0,-,0,0,-,#359 Bottom 46%,#600 Bottom 10%
+Nike Alphafly 3,88 Great!,6129030 Rp,-,High,High,Moderate,Neutral,7.1 oz / 201g 7 oz / 198g,1,8.5 mm 8.0 mm,Heel Mid/forefoot,Slightly small,Soft,Small,Carbon plate,Very bad,Good,Bad,Breathable,Medium,Medium,Stiff,Stiff,Flexible,‐,38.1 mm 40.0 mm,29.6 mm 32.0 mm,Normal,0,Summer All seasons,0,0,,#136 Top 36%,#23 Top 7%
+Nike Juniper Trail 2 GTX,"75
+ Bad!",2273680Rp,Light,-,-,-,Neutral,10.3 oz / 293g 11.4 oz / 323g,0,10.2 mm 9.0 mm,Heel,True to size,Firm,Small,0,Very good,Good,Good,Warm,Wide,Wide,Stiff,Stiff,Moderate,2.7 mm,34.5 mm 35.0 mm,24.3 mm 26.0 mm,Normal,0,Winter,1,1,Waterproof,#362 Bottom 4%,#248 Bottom 34%
+Nike Pegasus 41,88 Great!,2767950 Rp,-,Moderate,Moderate,Low,Neutral,9.9 oz / 281g 10 oz / 283g,0,11.4 mm 10.0 mm,Heel,True to size,Soft,Normal,0,Decent,Good,Good,Moderate,Medium,Medium,Flexible,Flexible,Stiff,‐,33.6 mm 37.0 mm,22.2 mm 27.0 mm,Normal Wide X-Wide,0,All seasons,1,1,-,#132 Top 35%,#11 Top 3%
+Nike Pegasus Premium,83 Good!,4178310 Rp,-,High,High,High,Neutral,10.9 oz / 308g 10.9 oz / 309g,0,11.8 mm 10.0 mm,Heel,Slightly small,Soft,Small,0,Very bad,Good,Decent,Breathable,Narrow,Narrow,Stiff,Stiff,Stiff,‐,42.8 mm 45.0 mm,31.0 mm 35.0 mm,Normal,0,Summer All seasons,1,1,,#267 Bottom 29%,#14 Top 4%
+Nike Pegasus Trail 3,90 Great!,$130,Light Moderate,-,-,-,Neutral,10.8 oz / 306g 11.3 oz / 320g,0,10.3 mm 10.0 mm,Heel,True to size,Soft,Big,0,-,-,-,Warm,Medium,-,Stiff,Moderate,Moderate,3.3 mm,35.3 mm 36.0 mm,25.0 mm 26.0 mm,Normal,0,All seasons,1,1,,#92 Top 14%,#413 Bottom 38%
+Nike Pegasus Trail 4,88 Great!,$140,Light,High,Low,-,Neutral,9.6 oz / 272g 10.4 oz / 295g,0,12.7 mm 10.0 mm,Heel,True to size,-,-,0,-,-,-,-,Medium,-,Moderate,Flexible,Moderate,3.4 mm,35.5 mm 36.0 mm,22.8 mm 26.0 mm,Normal,0,-,1,1,-,#253 Top 38%,#201 Top 30%
+Nike Pegasus Trail 4 GTX,"86
+ Good!",$160,LightModerate,-,-,-,Neutral,9.6 oz / 271g 9.6 oz / 272g,0,12.8 mm 10.0 mm,Heel,Slightly small,Soft,Small,0,Good,Good,Good,Warm,Medium,Wide,Stiff,Stiff,Flexible,3.5 mm,37.7 mm 37.0 mm,24.9 mm 27.0 mm,Normal,0,Winter,1,1,Waterproof,#388 Bottom 42%,#388 Bottom 42%
+Nike Pegasus Trail 5,89 Great!,$150,Light,High,Moderate,-,Neutral,10.1 oz / 286g 10.1 oz / 286g,0,9.6 mm 9.5 mm,Heel Mid/forefoot,True to size,Soft,Normal,0,Very good,Good,Good,Warm,Medium,Medium,Moderate,Moderate,Stiff,3.2 mm,34.6 mm 37.0 mm,25.0 mm 27.5 mm,Narrow Normal Wide,0,All seasons,1,1,-,#92 Top 25%,#49 Top 13%
+Nike Pegasus Trail 5 GTX,"76
+ Bad!",$170,Light,Moderate,Moderate,-,Neutral,9.9 oz / 281g 10.6 oz / 300g,0,8.3 mm 9.5 mm,HeelMid/forefoot,Slightly small,Soft,Normal,0,Decent,Bad,Good,Warm,Medium,Wide,Moderate,Stiff,Flexible,3.6 mm,32.1 mm 37.0 mm,23.8 mm 27.5 mm,Normal,0,Winter,1,1,Waterproof,#355 Bottom 6%,#122 Top 33%
+Nike Quest 6,"84
+ Good!",1591740Rp,-,Low,Low,Moderate,Neutral,10 oz / 283g 10.4 oz / 295g,0,12.0 mm 10.0 mm,Heel,True to size,Balanced,Small,0,Decent,Good,Decent,Moderate,Medium,Medium,Moderate,Flexible,Moderate,‐,35.2 mm,23.2 mm,Normal,0,All seasons,1,1,-,#256 Bottom 32%,#91 Top 25%
+Nike Terra Kiger 9,90 Great!,$150,Moderate Technical,-,-,-,Neutral,10.2 oz / 288g 10.1 oz / 286g,,4.4 mm 3.0 mm,Mid/forefoot,True to size,Soft,Normal,0,Very good,Decent,-,Moderate,Medium,Medium,Stiff,Moderate,Moderate,4.4 mm,30.1 mm 31.0 mm,25.7 mm 28.0 mm,Normal,0,All seasons,1,1,-,#41 Top 11%,#317 Bottom 16%
+Nike Terra Kiger 9,90 Great!,2966250 Rp,Moderate Technical,-,-,-,Neutral,10.2 oz / 288g 10.1 oz / 286g,0,4.4 mm 3.0 mm,Mid/forefoot,True to size,Soft,Normal,0,Very good,Decent,-,Moderate,Medium,Medium,Stiff,Moderate,Moderate,4.4 mm,30.1 mm 31.0 mm,25.7 mm 28.0 mm,Normal,0,All seasons,1,1,-,#41 Top 11%,#317 Bottom 16%
+Nike Ultrafly,89 Great!,$260,Light Moderate,High,High,-,Neutral,10.5 oz / 299g 10.1 oz / 286g,0,11.8 mm 8.5 mm,Heel,True to size,Soft,Small,Carbon plate,Very bad,Good,Good,Moderate,Medium,Wide,Stiff,Stiff,Moderate,3.0 mm,36.6 mm 38.0 mm,24.8 mm 29.5 mm,Normal,0,All seasons,1,1,-,#89 Top 24%,#229 Bottom 39%
+Nike Vaporfly 4,91 Superb!,5140480 Rp,-,High,High,High,Neutral,5.9 oz / 166g 6.5 oz / 184g,1,8.6 mm 6.0 mm,Heel Mid/forefoot,True to size,Soft,Small,Carbon plate,Good,Decent,Bad,Moderate,Narrow,Medium,Stiff,Stiff,Flexible,‐,34.1 mm 35.0 mm,25.5 mm 29.0 mm,Normal,0,All seasons,1,1,,#34 Top 9%,#22 Top 6%
+Nike Vomero 18,91 Superb!,2984510 Rp,-,High,Moderate,Moderate,Neutral,10.5 oz / 298g 11.5 oz / 325g,0,13.9 mm 10.0 mm,Heel,True to size,Soft,Small,0,Decent,Good,Decent,Moderate,Medium,Narrow,Moderate,Stiff,Moderate,‐,42.5 mm 45.0 mm,28.6 mm 35.0 mm,Normal Wide X-Wide,0,All seasons,1,1,-,#28 Top 8%,#8 Top 3%
+Nike Vomero Plus,92 Superb!,3361090 Rp,-,High,High,Moderate,Neutral,10.2 oz / 289g 10.1 oz / 285g,,9.6 mm 10.0 mm,Heel Mid/forefoot,True to size,Soft,Small,0,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,‐,42.3 mm 45.0 mm,32.7 mm 35.0 mm,Normal Wide X-Wide,0,All seasons,1,1,-,#20 Top 6%,#4 Top 2%
+Nike Vomero Premium,79 Good!,4547350 Rp,-,High,Moderate,High,Neutral,11.5 oz / 326g 12.4 oz / 351g,0,8.8 mm 10.0 mm,Heel Mid/forefoot,-,Soft,Small,0,Decent,Good,Good,Moderate,Medium,Narrow,Stiff,Stiff,Stiff,‐,50.1 mm 55.0 mm,41.3 mm 45.0 mm,Normal,0,All seasons,1,1,-,#129 Top 35%,#10 Top 3%
+Nike Wildhorse 10,"91
+ Superb!",$165,LightModerate,High,Moderate,-,Neutral,11 oz / 312g 11 oz / 311g,0,10.9 mm 9.5 mm,Heel,True to size,Soft,Normal,Rock plate,Very bad,Good,Decent,Moderate,Medium,Medium,Moderate,Moderate,Moderate,3.4 mm,38.3 mm 38.0 mm,27.4 mm 28.5 mm,Normal,0,All seasons,1,1,-,#37 Top 10%,#126 Top 34%
+Nike Winflo 11,86 Good!,2174820 Rp,-,Moderate,Low,Moderate,Neutral,10.4 oz / 295g 10 oz / 283g,0,12.3 mm 10.0 mm,Heel,True to size,Soft,Normal,0,Decent,Good,Good,Moderate,Narrow,Medium,Moderate,Stiff,Moderate,‐,37.6 mm 35.0 mm,25.3 mm 25.0 mm,Normal Wide X-Wide,0,All seasons,1,1,,#198 Bottom 47%,#75 Top 20%
+Nike Zegama 2,87 Great!,$180,Moderate,Moderate,Moderate,-,Neutral,10.7 oz / 302g 10.7 oz / 303g,0,4.0 mm 4.0 mm,Mid/forefoot,True to size,Soft,Small,0,Good,Good,Good,Moderate,Medium,Wide,Moderate,Stiff,Flexible,4.0 mm,30.3 mm 36.0 mm,26.3 mm 32.0 mm,Normal,0,All seasons,1,1,-,#154 Top 41%,#105 Top 28%
+Nike Zoom Fly 6,92 Superb!,3361090 Rp,-,High,Moderate,Moderate,Neutral,8.7 oz / 248g 8.6 oz / 244g,1,9.6 mm 8.0 mm,Heel Mid/forefoot,True to size,Soft,Small,Carbon plate,Very good,Good,Good,Moderate,Narrow,Medium,Stiff,Stiff,Stiff,‐,39.7 mm 40.0 mm,30.1 mm 32.0 mm,Normal,0,All seasons,1,1,-,#15 Top 4%,#27 Top 8%
+NNormal Kjerag,93 Superb!,$195,Light,Moderate,Moderate,,Neutral,7.5 oz / 214g 7.1 oz / 200g,1,8.6 mm 6.0 mm,Mid/forefoot,True to size,Balanced,Normal,0,Decent,Decent,Decent,Warm,Medium,Medium,Flexible,Moderate,Flexible,3.0 mm,25.0 mm 23.5 mm,16.4 mm 17.5 mm,Normal,0,All seasons,0,0,-,#7 Top 2%,#262 Bottom 30%
+On Cloudsurfer Trail,86 Good!,3382440 Rp,Light,High,Low,-,Neutral,9.6 oz / 272g 9.6 oz / 272g,0,10.7 mm 7.0 mm,Heel,True to size,Balanced,Small,0,Decent,Bad,Good,Moderate,Medium,Medium,Moderate,Stiff,Moderate,2.5 mm,37.4 mm 41.0 mm,26.7 mm 34.0 mm,Normal,1,All seasons,1,1,,#409 Bottom 39%,#377 Bottom 44%
+On Cloudsurfer Trail 2,N/A,3361750 Rp,Light,Moderate,Low,High,Neutral,10 oz / 283g 10.1 oz / 287g,0,14.0 mm 8.0 mm,Heel,-,Balanced,Small,0,Decent,Good,Good,Moderate,Medium,Narrow,Moderate,Stiff,Stiff,2.0 mm,40.8 mm 34.0 mm,26.8 mm 26.0 mm,Normal,0,All seasons,1,1,-,#204 Bottom 46%,#243 Bottom 35%
+On Cloudsurfer Trail 2,N/A,3361750 Rp,Light,Moderate,Low,High,Neutral,10 oz / 283g 10.1 oz / 287g,0,14.0 mm 8.0 mm,Heel,-,Balanced,Small,0,Decent,Good,Good,Moderate,Medium,Narrow,Moderate,Stiff,Stiff,2.0 mm,40.8 mm 34.0 mm,26.8 mm 26.0 mm,Normal,0,All seasons,1,1,,#204 Bottom 46%,#243 Bottom 35%
+On Cloudultra 2,89 Great!,$180,Light,Moderate,Moderate,-,Neutral,10.4 oz / 296g 10.4 oz / 295g,0,10.2 mm 6.0 mm,Heel,True to size,Firm,Small,Rock plate,Bad,Decent,Good,Breathable,Medium,Medium,Flexible,Flexible,Flexible,2.5 mm,30.2 mm 27.0 mm,20.0 mm 21.0 mm,Normal,0,Summer All seasons,1,1,-,#108 Top 29%,#268 Bottom 29%
+On Cloudvista,89 Great!,$150,Light,-,-,-,Neutral,10.1 oz / 285g 9.9 oz / 280g,0,10.3 mm 10.3 mm,Heel,Half size small,Balanced,Big,Rock plate,Very bad,Bad,Good,Breathable,Medium,Medium,Stiff,Moderate,Flexible,2.5 mm,32.3 mm 32.3 mm,22.0 mm 22.0 mm,Normal,0,Summer All seasons,1,1,-,#153 Top 23%,#475 Bottom 29%
+On Cloudvista 2,89 Great!,$150,Light,Moderate,Low,-,Neutral,10.3 oz / 292g 10.9 oz / 309g,0,6.0 mm 5.0 mm,Mid/forefoot,True to size,Balanced,Small,0,Decent,Bad,Good,Warm,Narrow,Medium,Moderate,Stiff,Moderate,3.1 mm,31.7 mm 29.0 mm,25.7 mm 24.0 mm,Narrow Normal,0,All seasons,1,1,-,#114 Top 31%,#184 Top 49%
+On Cloudvista 2,89 Great!,$150,Light,Moderate,Low,-,Neutral,10.3 oz / 292g 10.9 oz / 309g,0,6.0 mm 5.0 mm,Mid/forefoot,True to size,Balanced,Small,0,Decent,Bad,Good,Warm,Narrow,Medium,Moderate,Stiff,Moderate,3.1 mm,31.7 mm 29.0 mm,25.7 mm 24.0 mm,Narrow Normal,0,All seasons,1,1,,#114 Top 31%,#184 Top 49%
+Salomon Genesis,92 Superb!,2965660 Rp,Moderate Technical,Moderate,Moderate,-,Neutral,9.9 oz / 282g 9.7 oz / 275g,0,9.0 mm 8.0 mm,Heel Mid/forefoot,True to size,Balanced,Small,0,Good,Good,Decent,Moderate,Medium,Medium,Moderate,Moderate,Stiff,4.0 mm,33.5 mm 33.0 mm,24.5 mm 25.0 mm,Normal,0,All seasons,1,1,Water repellent,#14 Top 4%,#227 Bottom 40%
+Salomon Pulsar Trail,87 Great!,2966250 Rp,Light,-,-,-,Neutral,9.9 oz / 281g 9.9 oz / 280g,,7.2 mm 6.0 mm,Mid/forefoot,True to size,Soft,Small,Rock plate,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,2.5 mm,31.0 mm 32.6 mm,23.8 mm 26.6 mm,Normal,0,All seasons,1,1,-,#155 Top 41%,#305 Bottom 19%
+Salomon S/Lab Genesis,92 Superb!,3955000 Rp,Moderate,Moderate,Moderate,High,Neutral,8.8 oz / 249g 9.1 oz / 258g,1,7.8 mm 8.0 mm,Mid/forefoot,True to size,Soft,Small,Rock plate,Bad,Decent,Decent,Moderate,Medium,Medium,Moderate,Moderate,Flexible,4.3 mm,31.9 mm 33.0 mm,24.1 mm 25.0 mm,Normal,0,All seasons,1,1,-,#17 Top 5%,#351 Bottom 7%
+Salomon S/Lab Pulsar 4,78 Decent!,4350500 Rp,Light,High,Low,High,Neutral,8.7 oz / 247g 8.8 oz / 250g,1,7.1 mm 6.0 mm,Mid/forefoot,-,Soft,Normal,0,Decent,Good,Good,Warm,Medium,Medium,Moderate,Stiff,Moderate,3.0 mm,32.6 mm 31.0 mm,25.5 mm 25.0 mm,Normal,0,All seasons,0,0,-,#340 Bottom 10%,#373 Bottom 1%
+Salomon S/Lab Ultra,82 Good!,4746000 Rp,Light Moderate,High,Moderate,High,Neutral,10.2 oz / 290g 10.3 oz / 293g,0,10.2 mm 8.0 mm,Heel,-,Soft,Normal,0,Bad,Good,Good,Moderate,Narrow,Narrow,Moderate,Stiff,Flexible,3.5 mm,36.3 mm 40.0 mm,26.1 mm 32.0 mm,Normal,0,All seasons,1,1,-,#285 Bottom 24%,#366 Bottom 3%
+Salomon S/Lab Ultra Glide,77 Decent!,4943750 Rp,Light Moderate,Moderate,Low,High,Neutral,10.8 oz / 305g 10.2 oz / 290g,0,7.2 mm 6.0 mm,Mid/forefoot,-,Balanced,Small,0,Good,Good,Good,Warm,Narrow,Medium,Stiff,Stiff,Moderate,3.2 mm,41.0 mm 41.0 mm,33.8 mm 35.0 mm,Normal,0,All seasons,1,1,-,#350 Bottom 7%,#360 Bottom 5%
+Salomon Sense Pro 4,87 Great!,2965660 Rp,Moderate Technical,-,-,-,Neutral,9.6 oz / 272g 9 oz / 255g,,4.0 mm 4.0 mm,Mid/forefoot,-,-,-,Rock plate,-,-,-,-,Medium,-,Stiff,-,Flexible,4.3 mm,24.4 mm 25.0 mm,20.5 mm 21.0 mm,Normal,0,-,0,0,Water repellent,#156 Top 42%,#369 Bottom 2%
+Salomon Sense Ride 4,88 Great!,2373000 Rp,Moderate,-,-,-,Neutral,10.5 oz / 297g 10.2 oz / 290g,0,7.3 mm 8.0 mm,Mid/forefoot,True to size,-,-,0,-,-,-,Moderate,Medium,-,Stiff,Moderate,-,3.6 mm,26.5 mm 27.0 mm,19.2 mm 19.0 mm,Normal,0,All seasons,1,1,-,#244 Top 37%,#503 Bottom 25%
+Salomon Sense Ride 5,89 Great!,$140,Light Moderate,Low,Moderate,-,Neutral,10.3 oz / 291g 10.3 oz / 291g,0,8.7 mm 8.3 mm,Heel Mid/forefoot,Slightly small,Balanced,Small,0,-,-,-,Moderate,Medium,Medium,Moderate,Moderate,Flexible,3.5 mm,27.2 mm 29.6 mm,18.5 mm 21.3 mm,Narrow Normal,0,All seasons,1,1,-,#111 Top 30%,#168 Top 45%
+Salomon Speedcross 6,89 Great!,$145,Technical,Low,Low,-,Neutral,10.4 oz / 296g 10.5 oz / 298g,0,14.1 mm 10.0 mm,Heel,True to size,Firm,Small,0,Good,Decent,Decent,Warm,Medium,Medium,Moderate,Stiff,Stiff,5.8 mm,36.5 mm 32.0 mm,22.4 mm 22.0 mm,Normal Wide,0,Winter,1,1,-,#109 Top 29%,#71 Top 19%
+Salomon Speedcross 6 GTX,87 Great!,3361750 Rp,Technical,-,-,-,Neutral,11.5 oz / 325g 11.6 oz / 328g,,11.2 mm 10.0 mm,Heel,Slightly small,Firm,Small,0,Good,Good,Decent,Warm,Medium,Medium,Stiff,Stiff,Stiff,5.0 mm,37.0 mm 32.0 mm,25.8 mm 22.0 mm,Normal,0,Winter,1,1,Waterproof,#168 Top 45%,#120 Top 32%
+Salomon Supercross 4,85 Good!,$120,Moderate Technical,-,-,-,Neutral,11.1 oz / 315g 10.7 oz / 303g,0,15.2 mm 11.0 mm,Heel,True to size,Soft,Small,0,Good,Good,Good,Moderate,Medium,Narrow,Stiff,Flexible,Flexible,4.2 mm,35.1 mm 32.0 mm,19.9 mm 21.0 mm,Normal,0,All seasons,1,1,0,#221 Bottom 41%,#254 Bottom 32%
+Salomon Thundercross,89 Great!,2768500 Rp,Moderate Technical,Low,Low,-,Neutral,9.6 oz / 271g 10.2 oz / 290g,0,3.0 mm 4.0 mm,Mid/forefoot,True to size,Soft,Normal,0,Good,Decent,Good,Warm,Medium,Medium,Moderate,Stiff,Flexible,4.0 mm,27.6 mm 31.0 mm,24.6 mm 27.0 mm,Normal,0,Winter,1,1,-,#103 Top 28%,#231 Bottom 39%
+Salomon Ultra Flow,83 Good!,2570750 Rp,Light,Moderate,Moderate,-,Neutral,9.1 oz / 258g 8.6 oz / 244g,0,12.5 mm 6.0 mm,Heel,Slightly small,Soft,Normal,0,Good,Decent,Decent,Moderate,Medium,Medium,Moderate,Stiff,Stiff,2.8 mm,34.9 mm 32.0 mm,22.4 mm 26.0 mm,Normal,0,All seasons,1,1,-,#281 Bottom 25%,#307 Bottom 18%
+Salomon Ultra Glide,89 Great!,$140,Light Moderate,-,-,-,Neutral,9.7 oz / 275g 10 oz / 283g,,8.8 mm 6.0 mm,Heel Mid/forefoot,True to size,Balanced,Normal,0,Good,-,-,Breathable,Narrow,Medium,Stiff,Flexible,Moderate,3.0 mm,31.2 mm 32.0 mm,22.4 mm 26.0 mm,Normal Wide,0,Summer All seasons,1,1,-,#186 Top 28%,#396 Bottom 41%
+Salomon Ultra Glide 2,86 Good!,$150,Light Moderate,Moderate,Moderate,-,Neutral,10.1 oz / 286g 9.2 oz / 260g,0,7.2 mm 6.0 mm,Mid/forefoot,True to size,Soft,Big,0,Bad,-,-,Moderate,Narrow,Narrow,Moderate,Flexible,Moderate,2.8 mm,30.6 mm 32.0 mm,23.4 mm 26.0 mm,Normal,0,All seasons,0,0,-,#187 Top 50%,#208 Bottom 45%
+Salomon XA Pro 3D GTX,87 Great!,3361750 Rp,Light Moderate,-,-,-,Stability,13.4 oz / 379g 14.3 oz / 405g,0,13.1 mm 12.0 mm,Heel,True to size,Firm,Small,0,-,-,-,Warm,Medium,-,Stiff,Stiff,Stiff,2.9 mm,31.9 mm 27.0 mm,18.8 mm 15.0 mm,Normal,0,Winter,1,1,Waterproof,#362 Bottom 46%,#354 Bottom 47%
+Salomon XA Pro 3D V8,89 Great!,2966250 Rp,Light Moderate,-,-,-,Stability,12.3 oz / 350g 12 oz / 340g,,14.6 mm 11.0 mm,Heel,True to size,Firm,Small,0,-,-,-,Moderate,Medium,-,Stiff,Stiff,Stiff,2.9 mm,35.0 mm 28.0 mm,20.4 mm 17.0 mm,Normal Wide,0,All seasons,1,1,-,#190 Top 29%,#534 Bottom 20%
+Salomon XA Pro 3D v9,75 Bad!,2966250 Rp,Light Moderate,-,-,-,Stability,12.2 oz / 346g 11.4 oz / 323g,0,12.5 mm 11.0 mm,Heel,True to size,Firm,Small,0,Very good,Decent,Good,Moderate,Wide,Medium,Stiff,Stiff,Stiff,2.8 mm,31.7 mm 28.0 mm,19.2 mm 17.0 mm,Normal Wide,0,All seasons,1,1,-,#361 Bottom 4%,#123 Top 33%
+Salomon XA Pro 3D v9 GTX,83 Good!,3361750 Rp,Light Moderate,Low,Low,-,Stability,12.7 oz / 359g 12.7 oz / 360g,0,13.5 mm 11.0 mm,Heel,True to size,Firm,Small,Rock plate,Very good,Decent,Good,Warm,Medium,Medium,Stiff,Stiff,Stiff,2.8 mm,33.5 mm,20.0 mm,Normal Wide,0,Winter,1,1,Waterproof,#263 Bottom 30%,#45 Top 12%
+Saucony Endorphin Edge,"87
+ Great!",$200,Moderate,Moderate,High,-,Neutral,9.5 oz / 269g 9.1 oz / 258g,0,7.1 mm 6.0 mm,Mid/forefoot,True to size,Soft,Small,Carbon plate,-,-,-,Moderate,Narrow,-,Stiff,Stiff,Moderate,3.4 mm,33.4 mm 36.0 mm,26.3 mm 30.0 mm,Normal,0,All seasons,1,1,-,#175 Top 47%,#270 Bottom 28%
+Saucony Endorphin Rift,"85
+ Good!",$170,Technical,-,-,-,Neutral,9 oz / 255g 9.1 oz / 258g,0,7.9 mm 6.0 mm,Mid/forefoot,True to size,Soft,Small,Rock plate,Good,Good,Good,Breathable,Medium,Medium,Stiff,Moderate,Moderate,4.5 mm,33.0 mm 34.0 mm,25.1 mm 28.0 mm,Normal,0,SummerAll seasons,1,1,-,#331 Bottom 12%,#292 Bottom 22%
+Saucony Endorphin Trail,85 Good!,3559500 Rp,Technical,-,-,,Neutral,11 oz / 312g 10.4 oz / 295g,0,5.2 mm 4.0 mm,Mid/forefoot,True to size,-,-,0,-,-,-,-,Medium,-,Stiff,Stiff,Stiff,4.5 mm,36.3 mm 36.5 mm,31.1 mm 32.5 mm,Normal,,-,1,1,-,#205 Bottom 45%,#315 Bottom 16%
+Saucony Peregrine 11,"88
+ Great!",2768500Rp,Technical,-,-,-,Neutral,11.2 oz / 318g 10.9 oz / 310g,0,5.1 mm 4.0 mm,Mid/forefoot,Slightly large,-,-,Rock plate,-,-,-,-,Medium,-,Stiff,Moderate,-,4.4 mm,27.5 mm 27.0 mm,22.4 mm 23.0 mm,Normal,0,-,1,1,-,#275 Top 41%,#624 Bottom 7%
+Saucony Peregrine 12,"86
+ Good!",3065130Rp,Technical,-,-,-,Neutral,10.1 oz / 285g 9.6 oz / 272g,0,6.9 mm 4.0 mm,Mid/forefoot,True to size,-,-,Rock plate,-,-,-,-,Wide,-,Stiff,-,-,4.6 mm,30.2 mm 26.5 mm,23.2 mm 22.5 mm,NormalWide,0,-,0,0,-,#369 Bottom 45%,#560 Bottom 16%
+Saucony Peregrine 13,"89
+ Great!",3065130Rp,Technical,-,-,-,Neutral,9.6 oz / 271g 9.6 oz / 271g,0,3.9 mm 4.0 mm,Mid/forefoot,Slightly small,Balanced,Small,Rock plate,-,-,-,Moderate,Medium,Narrow,Stiff,Flexible,Moderate,4.8 mm,27.5 mm 28.0 mm,23.6 mm 24.0 mm,Normal,0,All seasons,1,1,-,#196 Top 30%,#486 Bottom 27%
+Saucony Peregrine 14,"88
+ Great!",3064520Rp,ModerateTechnical,Moderate,Moderate,-,Neutral,9.4 oz / 266g 9.4 oz / 267g,0,2.2 mm 4.0 mm,Mid/forefoot,True to size,Balanced,Small,Rock plate,Decent,Decent,Good,Moderate,Wide,Medium,Moderate,Moderate,Moderate,4.7 mm,27.3 mm 31.0 mm,25.1 mm 27.0 mm,NormalWide,0,All seasons,1,1,-,#233 Top 35%,#487 Bottom 27%
+Saucony Peregrine 15,"85
+ Good!",$140,LightModerate,Moderate,Moderate,-,Neutral,9.4 oz / 266g 9.7 oz / 275g,0,3.7 mm 4.0 mm,Mid/forefoot,True to size,Soft,Small,Rock plate,Good,Decent,Good,Moderate,Medium,Narrow,Moderate,Flexible,Moderate,4.7 mm,29.5 mm 28.0 mm,25.8 mm 24.0 mm,NormalWide,0,All seasons,1,1,-,#231 Bottom 39%,#114 Top 31%
+Saucony Xodus Ultra,87 Great!,$150,Moderate Technical,-,-,,Neutral,10.1 oz / 286g 10.3 oz / 292g,0,7.2 mm 6.0 mm,Mid/forefoot,True to size,Balanced,Normal,Rock plate,-,-,-,Moderate,Narrow,-,Stiff,Moderate,Moderate,3.8 mm,33.9 mm 32.0 mm,26.7 mm 26.0 mm,Normal,,All seasons,1,1,-,#331 Top 50%,#491 Bottom 27%
+Saucony Xodus Ultra 2,88 Great!,3361750 Rp,Moderate Technical,-,-,,Neutral,10.3 oz / 293g 9.2 oz / 262g,0,7.3 mm 6.0 mm,Mid/forefoot,True to size,Balanced,Small,Rock plate,Bad,Good,Decent,Moderate,Medium,Medium,Stiff,Stiff,Stiff,4.6 mm,34.1 mm 32.5 mm,26.8 mm 26.5 mm,Normal,,All seasons,1,1,-,#240 Top 36%,#586 Bottom 13%
+Saucony Xodus Ultra 3,87 Great!,3361750 Rp,Technical,Moderate,Moderate,,Neutral,10.7 oz / 302g 10.2 oz / 288g,0,5.9 mm 6.0 mm,Mid/forefoot,True to size,Soft,Small,Rock plate,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Flexible,4.3 mm,35.1 mm 36.0 mm,29.2 mm 30.0 mm,Normal,,All seasons,1,1,-,#363 Bottom 46%,#505 Bottom 25%
+Saucony Xodus Ultra 4,"88
+ Great!",$170,Light,Moderate,Moderate,High,Neutral,11 oz / 312g 10.9 oz / 309g,0,6.5 mm 6.0 mm,Mid/forefoot,-,Soft,Normal,0,Decent,Bad,Decent,Moderate,Medium,Medium,Moderate,Stiff,Stiff,3.5 mm,37.6 mm 36.0 mm,31.1 mm 30.0 mm,Normal,0,All seasons,1,1,-,#134 Top 36%,#195 Bottom 48%
+Scarpa Spin Planet,94 Superb!,$160,Light Moderate,-,-,-,Neutral,11.4 oz / 322g 10.2 oz / 290g,0,6.2 mm 4.0 mm,Mid/forefoot,True to size,Balanced,Normal,0,Decent,Good,Decent,Breathable,Medium,Medium,Stiff,Stiff,Moderate,3.2 mm,32.8 mm 28.5 mm,26.6 mm 24.5 mm,Normal,0,Summer All seasons,1,1,-,#2 Top 1%,#355 Bottom 6%
+The North Face Vectiv Enduris 3,90 Great!,$150,Light,-,-,-,Neutral,9.7 oz / 275g 10.8 oz / 307g,0,11.6 mm 6.0 mm,Heel,True to size,Balanced,Normal,0,Good,Good,Good,Warm,Medium,Medium,Moderate,Stiff,Stiff,3.3 mm,35.8 mm 30.0 mm,24.2 mm 24.0 mm,Normal,0,All seasons,1,1,-,#50 Top 14%,#264 Bottom 30%
+Topo MTN Racer 3,87 Great!,3460630 Rp,Light Moderate,-,-,-,Neutral,10.1 oz / 286g 9.8 oz / 278g,0,6.9 mm 5.0 mm,Mid/forefoot,Slightly small,Balanced,Small,0,Bad,Decent,Good,Moderate,Wide,Wide,Stiff,Stiff,Moderate,4.2 mm,33.5 mm 33.5 mm,26.6 mm 28.5 mm,Normal,0,All seasons,1,1,-,#177 Top 47%,#258 Bottom 31%
+Topo Traverse,90 Great!,3460630 Rp,Light Moderate,-,-,-,Neutral,10.9 oz / 308g 10.6 oz / 300g,0,4.8 mm 5.0 mm,Mid/forefoot,Slightly small,Balanced,Small,Rock plate,Bad,Good,Good,Moderate,Wide,Wide,Stiff,Stiff,Moderate,4.1 mm,30.8 mm 30.0 mm,26.0 mm 25.0 mm,Normal Wide,0,All seasons,1,1,Water repellent,#45 Top 12%,#275 Bottom 27%
+Topo Ultraventure 3,88 Great!,$150,Light,High,Low,-,Neutral,9.7 oz / 276g 10.2 oz / 289g,0,6.3 mm 5.0 mm,Mid/forefoot,True to size,Soft,Small,0,Decent,Decent,Good,Moderate,Wide,Wide,Moderate,Stiff,Moderate,3.2 mm,37.2 mm 35.0 mm,30.9 mm 30.0 mm,Normal,0,All seasons,1,1,-,#270 Top 41%,#185 Top 28%
+Topo Ultraventure 4,86 Good!,$150,Light,Moderate,Low,-,Neutral,10.1 oz / 286g 10.1 oz / 285g,0,6.6 mm 5.0 mm,Mid/forefoot,True to size,Soft,Small,0,Bad,Decent,Good,Moderate,Wide,Wide,Moderate,Moderate,Flexible,3.2 mm,35.1 mm 35.0 mm,28.5 mm 30.0 mm,Normal Wide,0,All seasons,1,1,-,#182 Top 49%,#162 Top 43%
+Topo Ultraventure 4,86 Good!,$150,Light,Moderate,Low,-,Neutral,10.1 oz / 286g 10.1 oz / 285g,0,6.6 mm 5.0 mm,Mid/forefoot,True to size,Soft,Small,0,Bad,Decent,Good,Moderate,Wide,Wide,Moderate,Moderate,Flexible,3.2 mm,35.1 mm 35.0 mm,28.5 mm 30.0 mm,Normal Wide,0,All seasons,1,1,-,#183 Top 49%,#163 Top 44%
+Xero Shoes Mesa Trail WP,66 Bad!,2966250 Rp,Light,-,-,-,Neutral,9.7 oz / 274g 9.6 oz / 272g,0,1.2 mm 0.0 mm,Mid/forefoot,Slightly small,Balanced,Small,0,Decent,Bad,Decent,Warm,Medium,Wide,Moderate,Flexible,Flexible,3.8 mm,14.6 mm 8.0 mm,13.4 mm 8.0 mm,Normal Wide,0,Winter,1,1,Waterproof,#378 Bottom 1%,#364 Bottom 3%
+Xero Shoes Scrambler Low,88 Great!,3164000 Rp,Light,-,-,-,Neutral,9.2 oz / 261g 9.2 oz / 260g,0,-0.1 mm 0.0 mm,Mid/forefoot,True to size,Firm,Small,0,Decent,Decent,Decent,Breathable,Medium,Wide,Moderate,Flexible,Flexible,2.7 mm,16.3 mm 15.0 mm,16.4 mm 15.0 mm,Normal Wide,0,Summer All seasons,1,1,-,#126 Top 34%,#354 Bottom 6%
\ No newline at end of file
diff --git a/data/road_dataset.csv b/data/road_dataset.csv
new file mode 100644
index 0000000000000000000000000000000000000000..60bd624e13f2232ee1aa3b4e4ca2ba60fe03749d
--- /dev/null
+++ b/data/road_dataset.csv
@@ -0,0 +1,429 @@
+brand,name,lightweight,rocker,removable_insole,pace_daily_running,pace_tempo,pace_competition,arch_neutral,arch_stability,weight_lab_oz,drop_lab_mm,strike_heel,strike_mid,strike_forefoot,midsole_softness,toebox_durability,heel_durability,outsole_durability,breathability_scaled,width_fit,toebox_width,stiffness_scaled,torsional_rigidity,heel_stiff,plate_rock_plate,plate_carbon_plate,heel_lab_mm,forefoot_lab_mm,season_summer,season_winter,season_all
+brooks,launch 9,1,0,1,1,1,0,1,0,7.9,9.4,1,1,1,3,0,0,0,0,1,0,5,5,1,0,0,32.4,23.0,0,0,0
+brooks,levitate 6,0,0,1,1,0,0,1,0,10.7,7.7,0,1,1,5,4,4,4,5,1,3,5,3,3,0,0,34.3,26.6,1,0,1
+adidas,4dfwd,0,0,1,1,0,0,1,0,11.9,8.9,1,1,1,1,0,4,0,1,1,0,5,1,1,0,0,33.3,24.4,0,0,1
+adidas,4dfwd 2,0,0,1,1,0,0,1,0,12.6,10.6,1,0,0,1,0,0,0,1,1,0,5,1,3,0,0,31.8,21.2,0,0,1
+adidas,4dfwd 3,0,0,1,1,0,0,1,0,12.3,9.9,1,1,1,1,4,4,4,1,1,3,3,1,1,0,0,32.6,22.7,0,0,1
+brooks,addiction gts 15,0,0,1,1,0,0,0,1,12.5,12.1,1,0,0,1,3,4,4,3,3,3,5,5,3,0,0,36.5,24.4,0,0,1
+adidas,adizero sl2,1,0,1,1,1,0,1,0,8.6,8.2,1,1,1,3,2,4,3,5,5,3,3,3,1,0,0,34.9,26.7,1,0,1
+adidas,adistar,0,1,1,1,0,0,1,0,11.5,9.6,1,1,1,3,3,4,0,3,1,3,5,5,1,0,0,34.4,24.8,0,0,1
+adidas,adistar 2.0,0,1,1,1,0,0,1,0,11.6,8.0,1,1,1,3,2,0,0,5,1,5,5,5,5,0,0,33.8,25.8,1,0,1
+adidas,adistar 3,0,0,1,1,0,0,1,0,9.7,10.5,1,0,0,5,2,3,4,5,3,3,3,5,5,0,0,40.7,30.2,1,0,1
+adidas,wave horizon 7,1,0,1,0,1,0,1,0,7.5,8.7,1,1,1,3,0,0,0,5,3,1,5,5,1,0,0,31.6,22.9,1,0,1
+adidas,adizero adios 8,1,0,1,0,1,0,1,0,7.4,7.6,0,1,1,5,2,3,4,5,3,5,1,1,1,0,0,28.0,20.4,1,0,1
+adidas,adizero adios 9,1,0,1,0,1,1,1,0,6.2,6.2,0,1,1,5,2,4,4,1,3,1,1,1,1,0,0,25.0,18.8,0,0,1
+adidas,adizero adios pro 2.0,1,1,0,0,0,1,1,0,7.9,10.3,1,0,0,0,0,0,0,0,1,0,5,5,1,0,1,40.0,29.7,0,0,0
+adidas,adizero adios pro 3,1,1,1,0,0,1,1,0,7.7,8.0,1,1,1,3,2,4,4,5,3,5,5,5,1,0,1,37.8,29.8,1,0,1
+adidas,adizero adios pro 4,1,1,1,0,0,1,1,0,7.1,8.1,1,1,1,5,2,4,4,1,3,1,5,5,1,0,1,36.6,28.5,0,0,1
+adidas,adizero boston 11,0,1,1,0,1,0,1,0,10.2,9.8,1,1,1,3,2,3,4,3,5,5,5,5,3,0,1,39.1,29.3,0,0,1
+adidas,adizero boston 12,0,1,1,0,1,0,1,0,9.2,6.1,0,1,1,3,2,3,4,5,5,3,5,5,1,0,0,34.5,28.4,1,0,1
+adidas,adizero boston 13,0,1,1,0,1,1,1,0,9.0,6.0,0,1,1,3,3,4,4,3,3,1,3,5,5,0,1,34.3,28.3,0,0,1
+adidas,adizero evo sl,1,1,1,1,1,0,1,0,7.9,8.0,1,1,1,3,2,4,4,5,3,3,1,5,3,0,0,36.1,28.1,1,0,1
+adidas,adizero prime x 2 strung,0,1,1,0,1,1,1,0,10.8,8.8,1,1,1,3,4,4,4,3,3,1,5,5,1,0,1,45.7,36.9,0,0,1
+adidas,adizero prime x3 strung,0,1,1,0,1,1,1,0,10.3,13.0,1,1,1,5,4,4,2,3,3,3,5,5,1,0,1,48.1,35.1,0,0,1
+adidas,adizero sl,1,0,1,1,1,0,1,0,8.8,8.6,1,1,1,3,3,4,4,5,3,3,5,5,3,0,0,34.9,26.3,1,0,1
+adidas,adizero takumi sen 10,1,0,1,0,1,1,1,0,7.1,7.8,0,1,1,5,2,4,2,5,1,3,3,5,1,0,0,30.6,22.8,1,0,1
+brooks,adrenaline gts 22,0,0,0,1,0,0,0,1,10.4,14.7,1,0,0,0,0,0,0,0,3,0,5,0,0,0,0,37.4,22.7,0,0,0
+brooks,adrenaline gts 23,0,0,1,1,0,0,0,1,10.1,12.6,1,0,0,5,2,4,4,5,3,3,3,5,5,0,0,34.1,21.5,1,0,1
+brooks,adrenaline gts 24,0,0,1,1,0,0,0,1,10.3,13.5,1,0,0,3,3,4,4,3,3,3,3,5,5,0,0,39.0,25.5,0,0,1
+skechers,aero burst,0,0,1,1,0,0,1,0,11.4,8.8,1,1,1,5,3,4,4,3,3,1,3,5,5,0,0,41.7,32.9,0,0,1
+salomon,aero glide,0,1,1,1,0,0,1,0,9.3,11.0,1,0,0,3,3,3,4,3,3,3,5,5,3,0,0,35.2,24.2,0,0,1
+salomon,aero glide 2,0,1,1,1,0,0,1,0,9.5,11.3,1,0,0,3,4,3,3,3,3,3,3,5,3,0,0,35.5,24.2,0,0,1
+salomon,aero glide 3,1,0,1,1,0,0,1,0,8.7,10.3,1,0,0,3,2,3,3,3,1,1,3,5,1,0,0,42.2,31.9,0,0,1
+nike,air winflo 9,0,0,1,1,0,0,1,0,9.8,10.8,1,0,0,5,3,0,0,3,1,3,5,3,5,0,0,35.0,24.2,0,0,1
+nike,air zoom pegasus 38,0,0,1,1,0,0,1,0,10.3,8.7,1,1,1,0,0,0,0,3,3,0,5,1,3,0,0,31.8,23.1,0,0,1
+nike,air zoom pegasus 38 flyease,0,0,0,1,0,0,1,0,9.7,8.7,1,1,1,0,0,0,0,0,3,0,5,0,0,0,0,31.8,23.1,0,0,0
+nike,air zoom pegasus 39,0,0,1,1,0,0,1,0,9.3,8.0,1,1,1,5,0,0,0,3,1,0,5,1,3,0,0,30.3,22.3,0,0,1
+nobull,allday knit,0,0,1,1,0,0,1,0,10.6,12.0,1,0,0,1,0,0,0,3,1,0,5,3,1,0,0,31.8,19.8,0,0,1
+nobull,allday ripstop,0,0,1,1,0,0,1,0,10.5,11.4,1,0,0,3,0,0,0,3,1,0,5,3,1,0,0,31.3,19.9,0,0,1
+adidas,alphabounce+,0,0,1,1,0,0,1,0,12.0,11.5,1,0,0,3,2,2,4,3,3,1,5,5,3,0,0,37.7,26.2,0,0,1
+nike,alphafly 2,1,1,0,0,0,1,1,0,8.5,4.7,0,1,1,5,4,0,0,5,1,1,5,5,1,0,1,38.6,33.9,1,0,1
+nike,alphafly 3,1,1,0,0,0,1,1,0,7.1,8.5,1,1,1,5,2,4,2,5,3,3,5,5,1,0,1,38.1,29.6,1,0,1
+brooks,anthem 4,1,0,1,1,0,0,1,0,8.6,11.1,1,0,0,0,0,0,0,3,1,0,5,1,5,0,0,32.5,21.4,0,0,1
+hoka,arahi 7,0,1,1,1,0,0,0,1,9.4,6.3,0,1,1,3,4,4,4,3,3,1,5,5,5,0,0,34.2,27.9,0,0,1
+hoka,arahi 8,0,1,1,1,0,0,0,1,9.1,11.3,1,0,0,5,4,4,4,1,3,3,5,5,3,0,0,39.4,28.1,0,0,1
+topo,atmos,0,0,1,1,0,0,1,0,9.7,5.3,0,1,1,3,3,4,4,3,5,5,3,5,5,0,0,37.8,32.5,0,0,1
+diadora,atomo star,0,1,1,1,0,0,1,0,9.5,7.8,0,1,1,5,3,2,4,3,1,1,3,5,5,0,0,39.9,32.1,0,0,1
+brooks,aurora-bl,1,1,1,1,0,0,1,0,8.7,8.7,1,1,1,5,2,2,4,3,1,3,5,1,3,0,0,37.0,28.3,0,0,1
+saucony,axon,0,1,1,1,1,0,1,0,9.9,5.5,0,1,1,0,0,0,0,0,3,0,0,3,0,0,0,35.3,29.8,0,0,0
+saucony,axon 2,0,1,1,1,1,0,1,0,9.9,7.8,0,1,1,3,2,0,0,3,1,1,5,5,5,0,0,35.3,27.5,0,0,1
+saucony,axon 3,1,1,1,1,1,0,1,0,8.6,5.7,0,1,1,5,3,4,4,3,3,3,5,5,3,0,0,33.6,27.9,0,0,1
+brooks,beast gts 23,0,0,1,1,0,0,0,1,12.4,11.9,1,0,0,3,3,3,4,3,3,3,5,5,5,0,0,36.4,24.5,0,0,1
+brooks,beast gts 24,0,0,1,1,0,0,0,1,12.6,12.7,1,0,0,1,3,3,4,1,3,3,3,5,5,0,0,38.5,25.8,0,0,1
+hoka,bondi 8,0,1,1,1,0,0,1,0,11.0,6.2,0,1,1,5,0,0,0,3,1,1,5,5,3,0,0,36.2,30.0,0,0,1
+hoka,bondi 9,0,1,1,1,0,0,1,0,10.7,9.1,1,1,1,3,4,4,4,3,3,3,3,5,3,0,0,41.3,32.2,0,0,1
+diadora,cellula,0,0,1,1,0,0,1,0,9.8,7.2,0,1,1,5,3,4,4,3,1,3,5,5,5,0,0,41.9,34.7,0,0,1
+under armour,charged assert 10,0,0,1,1,0,0,1,0,10.5,9.4,1,1,1,1,2,2,0,3,3,3,3,3,3,0,0,32.0,22.6,0,0,1
+under armour,charged assert 9,0,0,1,1,0,0,1,0,10.2,10.9,1,0,0,3,2,2,0,3,3,3,5,1,1,0,0,34.0,23.1,0,0,1
+under armour,charged pursuit 3,0,0,1,1,0,0,1,0,9.3,9.6,1,1,1,3,3,2,3,3,3,5,3,3,3,0,0,30.8,21.2,0,0,1
+hoka,cielo x1 2.0,1,1,1,0,1,1,1,0,7.3,10.7,1,0,0,5,2,2,3,5,1,3,5,5,1,0,1,38.8,28.1,1,0,1
+hoka,clifton 10,0,1,1,1,0,0,1,0,9.7,12.4,1,0,0,5,4,4,3,3,3,3,3,5,3,0,0,44.4,32.0,0,0,1
+hoka,clifton 8,0,1,1,1,0,0,1,0,9.0,8.6,1,1,1,0,0,0,0,1,1,0,5,5,5,0,0,33.7,25.1,0,0,1
+hoka,clifton 9,1,1,1,1,0,0,1,0,8.8,6.1,0,1,1,3,0,0,0,3,1,1,5,5,5,0,0,32.7,26.6,0,0,1
+hoka,clifton 9 gtx,0,1,1,1,0,0,1,0,9.6,8.6,1,1,1,3,4,4,4,3,1,1,5,5,5,0,0,37.2,28.6,0,0,1
+on,cloud x,1,0,1,1,1,0,1,0,8.5,10.1,1,0,0,1,3,2,4,3,3,5,3,1,1,0,0,27.9,17.8,0,0,1
+on,cloudboom echo 3,1,1,1,0,0,1,1,0,7.9,10.2,1,0,0,3,3,3,3,5,1,1,5,5,1,0,1,38.6,28.4,1,0,1
+on,cloudeclipse,0,1,1,1,0,0,1,0,9.6,9.4,1,1,1,3,3,2,4,3,3,3,5,5,3,0,0,39.9,30.5,0,0,1
+on,cloudflow 4,1,0,1,1,1,0,1,0,8.6,7.9,0,1,1,3,3,3,4,3,3,3,3,5,1,0,0,36.1,28.2,0,0,1
+on,cloudflyer 5,0,0,1,1,0,0,0,1,11.6,7.9,1,1,1,3,4,4,4,3,1,3,3,5,3,0,0,33.2,25.3,0,0,1
+on,cloudgo,0,0,1,1,1,0,1,0,9.1,11.2,1,0,0,3,2,2,4,3,3,3,3,3,1,0,0,33.8,22.6,0,0,1
+on,cloudmonster,0,1,1,1,0,0,1,0,9.9,6.8,0,1,1,3,0,0,0,5,1,0,3,3,1,0,0,34.9,28.1,1,0,1
+on,cloudmonster 2,0,1,1,1,0,0,1,0,10.3,6.6,0,1,1,3,4,3,4,1,5,3,5,3,3,0,0,37.9,31.3,0,0,1
+on,cloudmonster hyper,0,0,1,1,1,0,1,0,9.1,6.7,0,1,1,5,3,2,4,3,3,3,3,5,3,0,0,39.7,33.0,0,0,1
+on,cloudrunner 2,0,0,1,1,0,0,1,0,9.7,8.5,1,1,1,3,3,3,3,3,3,3,5,3,5,0,0,33.6,25.1,0,0,1
+on,cloudrunner 2 waterproof,0,0,1,1,0,0,0,1,11.4,8.3,1,1,1,3,3,2,4,1,1,3,5,5,5,0,0,35.8,27.5,0,1,0
+on,cloudspark,0,0,1,1,0,0,1,0,9.5,8.6,1,1,1,1,3,2,3,3,3,3,5,5,1,0,0,34.6,26.0,0,0,1
+on,cloudstratus 3,0,0,1,1,0,0,1,0,10.4,9.1,1,1,1,3,3,4,4,3,5,3,5,5,1,0,0,35.3,26.2,0,0,1
+on,cloudsurfer 7,1,0,1,1,0,0,1,0,8.4,14.6,1,0,0,3,2,2,4,3,3,3,1,1,1,0,0,36.3,21.7,0,0,1
+on,cloudsurfer max,0,0,1,1,0,0,1,0,10.3,7.9,0,1,1,3,3,3,3,1,3,3,5,3,3,0,0,37.3,29.4,0,0,1
+on,cloudsurfer next,0,0,1,1,0,0,1,0,9.3,4.5,0,1,1,1,3,2,3,1,3,3,5,5,3,0,0,33.8,29.3,0,0,1
+topo,cyclone 2,1,1,1,1,1,0,1,0,6.7,4.2,0,1,1,3,2,4,4,5,5,5,1,1,1,0,0,26.2,22.0,1,0,1
+puma,deviate nitro 3,0,0,1,0,1,0,1,0,9.5,10.1,1,0,0,5,3,3,3,3,1,1,5,5,1,0,1,37.4,27.3,0,0,1
+puma,deviate nitro elite 3,1,1,1,0,1,1,1,0,7.2,10.6,1,1,1,5,2,4,3,3,3,1,5,5,1,0,1,39.2,28.6,0,0,1
+nike,downshifter 11,1,0,0,1,0,0,1,0,8.8,11.6,1,0,0,0,0,0,0,0,1,0,5,3,3,0,0,31.5,19.9,0,0,0
+nike,downshifter 12,0,0,1,1,0,0,1,0,9.9,10.0,1,1,1,3,0,0,0,3,3,0,3,1,1,0,0,31.7,21.7,0,0,1
+nike,downshifter 13,0,0,1,1,0,0,1,0,9.3,10.1,1,0,0,3,3,4,4,3,3,3,3,3,3,0,0,32.2,22.1,0,0,1
+adidas,duramo 10,0,0,1,1,0,0,1,0,10.3,8.7,1,1,1,3,0,0,0,0,1,0,5,5,1,0,0,31.6,22.9,0,0,0
+adidas,duramo speed,0,0,1,1,0,0,1,0,9.2,6.0,0,1,1,3,2,3,4,5,3,3,5,5,3,0,0,32.7,26.7,1,0,1
+asics,dynablast 3,1,0,1,1,0,0,1,0,8.7,9.7,1,1,1,3,4,3,4,1,3,5,5,3,5,0,0,35.7,26.0,0,0,1
+asics,dynablast 4,0,0,1,1,0,0,1,0,9.2,6.4,0,1,1,3,4,4,4,1,1,3,3,3,5,0,0,32.6,26.2,0,1,0
+asics,dynablast 5,0,0,1,1,0,0,1,0,9.3,7.6,0,1,1,3,4,4,4,3,3,3,3,5,5,0,0,39.4,31.8,0,0,1
+saucony,endorphin elite,1,1,1,0,0,1,1,0,7.2,8.0,1,1,1,5,2,4,4,5,3,3,5,5,1,0,1,39.9,31.9,1,0,1
+saucony,endorphin elite 2,1,1,1,0,0,1,1,0,6.9,7.5,0,1,1,5,4,4,4,5,3,3,5,5,1,0,1,39.9,32.4,1,0,1
+saucony,endorphin pro 2,1,1,1,0,0,1,1,0,7.6,10.0,1,1,1,0,0,0,0,5,1,0,5,5,1,0,1,35.8,25.8,1,0,1
+saucony,endorphin pro 3,1,1,0,0,0,1,1,0,7.3,9.3,1,1,1,5,0,0,0,0,1,0,5,5,1,0,1,35.0,25.7,0,0,0
+saucony,endorphin pro 4,1,1,1,0,0,1,1,0,7.8,9.5,1,1,1,3,4,4,4,5,3,3,5,5,1,0,1,38.1,28.6,1,0,1
+asics,magic speed 4,1,1,1,0,1,1,1,0,8.4,9.6,1,1,1,5,4,4,4,5,3,3,5,5,1,0,1,42.5,32.9,1,0,1
+asics,jolt 4,0,0,1,1,0,0,1,0,9.1,9.4,1,1,1,3,2,3,4,5,3,5,3,5,5,0,0,31.6,22.2,1,0,1
+saucony,endorphin shift 2,0,1,1,1,0,0,1,0,10.0,4.3,0,1,1,0,0,0,0,0,3,0,5,5,3,0,0,37.3,33.0,0,0,0
+saucony,endorphin shift 3,0,1,1,1,0,0,1,0,9.6,6.5,0,1,1,3,0,0,0,3,1,0,5,3,3,0,0,39.6,33.1,0,0,1
+saucony,endorphin speed 2,1,1,1,0,1,0,1,0,8.1,9.2,1,1,1,0,0,0,0,3,3,0,5,3,3,0,0,35.3,26.1,0,0,1
+saucony,endorphin speed 3,1,1,1,0,1,0,1,0,7.9,7.4,0,1,1,5,0,0,0,3,3,0,3,1,1,0,0,34.1,26.7,0,0,1
+saucony,endorphin speed 4,1,1,1,0,1,0,1,0,8.4,8.7,1,1,1,3,3,4,4,5,3,1,3,3,3,0,0,36.2,27.5,1,0,1
+saucony,endorphin speed 5,1,1,1,0,1,0,1,0,8.5,10.6,1,0,0,5,3,4,4,5,3,3,3,5,5,0,0,37.4,26.8,1,0,1
+saucony,endorphin trainer,0,1,1,1,1,0,1,0,10.1,6.9,0,1,1,5,3,3,4,5,1,1,5,5,1,0,1,38.9,32.0,1,0,1
+altra,escalante 3,0,0,1,1,1,0,1,0,9.5,0.2,0,1,1,3,0,0,0,3,3,0,5,1,1,0,0,25.0,24.8,0,0,1
+altra,escalante 4,1,0,1,1,0,0,1,0,8.4,1.4,0,1,1,3,4,4,4,3,5,5,1,1,1,0,0,23.8,22.4,0,0,1
+altra,escalante racer,1,0,1,0,1,1,1,0,7.3,0.5,0,1,1,1,4,4,3,5,3,5,3,1,1,0,0,19.0,18.5,1,0,1
+altra,escalante racer 2,1,0,1,0,1,1,1,0,7.9,1.1,0,1,1,3,4,3,4,5,5,5,3,1,1,0,0,22.5,21.4,1,0,1
+altra,experience flow,1,0,1,1,0,0,1,0,8.3,4.1,0,1,1,5,4,4,3,3,5,5,3,3,3,0,0,30.7,26.6,0,0,1
+altra,experience flow 2,1,0,1,1,0,0,1,0,8.3,4.4,0,1,1,5,3,4,3,3,5,5,3,3,3,0,0,30.3,25.9,0,0,1
+altra,experience form,0,0,1,1,0,0,0,1,9.2,4.0,0,1,1,3,3,3,3,3,3,5,5,3,3,0,0,29.9,25.9,0,0,1
+nike,flex experience run 10,1,0,0,1,0,0,1,0,7.1,10.4,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,24.1,13.7,0,0,0
+nike,flex experience run 11,1,0,1,1,0,0,1,0,8.2,6.2,0,1,1,1,3,0,0,3,3,1,1,1,1,0,0,24.1,17.9,0,0,1
+nike,flex experience run 12,1,0,1,1,0,0,1,0,8.5,6.0,0,1,1,1,3,4,2,3,3,3,1,1,1,0,0,25.9,19.9,0,0,1
+nike,flex run 2021,1,0,0,1,0,0,1,0,7.9,7.2,0,1,1,0,0,0,0,0,3,0,1,0,0,0,0,32.3,25.1,0,0,0
+reebok,floatride energy 3,0,0,1,1,0,0,1,0,9.0,7.1,0,1,1,0,0,0,0,0,3,0,5,3,3,0,0,30.2,23.1,0,0,0
+reebok,floatride energy 5,0,0,1,1,0,0,1,0,9.0,6.0,0,1,1,3,2,3,4,1,3,3,5,1,1,0,0,30.2,24.2,0,0,1
+reebok,floatzig 1,0,1,1,1,0,0,1,0,10.1,7.0,1,1,1,3,3,3,2,3,3,3,3,3,3,0,0,36.8,29.8,0,0,1
+adidas,fluidflow 2.0,0,0,1,1,0,0,1,0,11.1,8.1,1,1,1,3,3,4,4,3,3,3,5,1,1,0,0,26.4,18.3,0,0,1
+new balance,foam arishi v4,1,0,1,1,0,0,1,0,8.5,7.7,0,1,1,5,2,2,4,3,3,3,1,1,1,0,0,28.1,20.4,0,0,1
+nike,free rn nn,1,0,1,1,0,0,1,0,6.9,6.9,0,1,1,3,4,4,4,1,5,5,1,1,1,0,0,25.6,18.7,0,0,1
+nike,free run 5.0,1,0,0,1,0,0,1,0,6.2,8.1,0,1,1,0,0,0,0,0,5,0,1,1,1,0,0,23.4,15.3,0,0,0
+saucony,freedom 4,1,0,1,1,0,0,1,0,8.0,5.2,0,1,1,0,0,0,0,0,3,0,0,1,0,0,0,26.4,21.2,0,0,0
+new balance,fresh foam 1080 v11,0,1,1,1,0,0,1,0,9.2,8.0,1,1,1,0,0,0,0,0,1,0,0,3,0,0,0,34.2,26.2,0,0,0
+new balance,fresh foam 680 v8,0,0,1,1,0,0,1,0,9.2,7.8,0,1,1,5,4,4,4,5,3,3,1,1,3,0,0,35.4,27.6,1,0,1
+new balance,fresh foam 860 v11,0,0,0,1,0,0,0,1,10.6,12.4,1,0,0,0,0,0,0,0,3,0,5,0,0,0,0,34.2,21.8,0,0,0
+new balance,fresh foam 860 v12,0,0,0,1,0,0,0,1,11.0,13.3,1,0,0,0,0,0,0,0,3,0,5,0,0,0,0,33.6,20.3,0,0,0
+new balance,fresh foam more v3,0,0,1,1,0,0,1,0,10.4,7.8,0,1,1,5,0,0,0,1,3,0,5,3,3,0,0,37.5,29.7,0,1,0
+new balance,fresh foam roav v2,1,0,1,1,0,0,1,0,8.4,7.2,0,1,1,3,0,0,0,1,1,0,3,1,1,0,0,32.2,25.0,0,1,0
+new balance,fresh foam x 1080 v12,0,0,1,1,0,0,1,0,10.1,3.3,0,1,1,5,0,0,0,0,1,0,3,3,1,0,0,26.9,23.6,0,0,0
+new balance,fresh foam x 1080 v13,0,1,1,1,0,0,1,0,9.3,5.6,0,1,1,5,3,4,4,5,3,3,3,1,5,0,0,34.1,28.5,1,0,1
+new balance,fresh foam x 1080 v14,0,1,1,1,0,0,1,0,10.1,4.2,1,1,1,5,4,3,4,3,3,1,3,5,5,0,0,37.0,32.8,0,0,1
+new balance,fresh foam x 860 v14,0,0,1,1,0,0,0,1,10.4,9.3,1,1,1,5,3,2,3,3,3,3,3,3,5,0,0,36.8,27.5,0,0,1
+new balance,fresh foam x 880 v14,0,0,1,1,0,0,1,0,8.9,8.0,1,1,1,5,3,2,3,3,3,5,3,3,3,0,0,33.0,25.0,0,0,1
+new balance,fresh foam x 880 v14 gtx,0,0,1,1,0,0,1,0,9.2,11.3,1,0,0,5,4,3,4,1,3,3,3,5,5,0,0,35.4,24.1,0,1,0
+new balance,fresh foam x 880 v15,0,1,1,1,0,0,1,0,10.1,4.3,0,1,1,5,3,4,3,3,3,3,5,5,5,0,0,39.7,35.4,0,0,1
+new balance,fresh foam x balos,1,1,1,1,0,0,1,0,8.7,5.9,0,1,1,5,2,4,4,3,3,3,3,5,5,0,0,37.8,31.9,0,0,1
+new balance,fresh foam x evoz v3,0,0,1,1,0,0,1,0,9.1,7.9,0,1,1,3,3,3,4,1,1,3,3,3,5,0,0,34.3,26.4,0,0,1
+new balance,fresh foam x evoz v4,0,0,1,1,0,0,1,0,10.1,5.5,0,1,1,5,3,3,3,3,3,3,5,5,5,0,0,31.4,25.9,0,0,1
+new balance,fresh foam x kaiha road,0,0,1,1,0,0,1,0,9.9,3.8,0,1,1,5,2,2,4,3,3,3,5,5,3,0,0,35.8,32.0,0,0,1
+new balance,fresh foam x more v4,0,0,1,1,0,0,1,0,10.6,4.6,0,1,1,5,2,0,0,1,1,1,3,1,1,0,0,32.5,27.9,0,0,1
+new balance,fresh foam x more v5,0,1,1,1,0,0,1,0,10.9,7.8,0,1,1,5,3,3,4,5,3,1,5,5,5,0,0,42.1,34.3,1,0,1
+new balance,fresh foam x more v6,0,0,1,1,0,0,1,0,10.7,3.3,0,1,1,5,3,4,4,1,3,3,5,3,3,0,0,41.8,38.5,0,0,1
+new balance,fresh foam x tempo v2,1,0,1,1,1,0,1,0,8.6,7.0,0,1,1,5,4,3,4,1,1,3,1,1,3,0,0,29.2,22.2,0,0,1
+new balance,fresh foam x vongo v6,0,1,1,1,0,0,0,1,11.0,5.6,0,1,1,5,3,4,4,1,3,3,3,3,5,0,0,36.1,30.5,0,1,0
+new balance,fuelcell propel v5,0,0,1,1,1,0,1,0,9.5,6.7,0,1,1,5,2,2,3,5,3,3,3,3,3,0,0,35.2,28.5,1,0,1
+new balance,fuelcell rc elite v2,1,1,1,0,0,1,1,0,7.7,8.3,1,1,1,0,0,0,0,3,1,0,5,5,1,0,1,34.1,25.8,0,0,1
+new balance,fuelcell rebel v2,1,1,0,0,1,0,1,0,7.1,8.9,1,1,1,0,0,0,0,5,1,0,3,1,1,0,0,26.2,17.3,1,0,1
+new balance,fuelcell rebel v3,1,0,1,0,1,0,1,0,7.4,9.0,1,1,1,5,3,2,0,5,1,1,3,1,3,0,0,31.7,22.7,1,0,1
+new balance,fuelcell rebel v4,1,1,1,1,1,0,1,0,7.5,6.5,0,1,1,5,2,4,4,3,3,5,1,1,1,0,0,28.0,21.5,0,0,1
+new balance,fuelcell rebel v5,1,1,1,1,1,0,1,0,7.8,6.3,0,1,1,5,2,4,4,3,3,3,3,1,3,0,0,33.0,26.7,0,0,1
+new balance,fuelcell supercomp elite v3,1,1,1,0,1,1,1,0,7.7,14.2,1,0,0,5,2,4,4,5,1,3,5,5,3,0,1,36.4,22.2,1,0,1
+new balance,fuelcell supercomp elite v4,1,1,1,0,0,1,1,0,8.2,9.3,1,1,1,5,3,3,4,3,3,5,5,5,1,0,1,38.2,28.9,0,0,1
+new balance,fuelcell supercomp elite v5,1,1,1,0,0,1,1,0,7.0,10.7,1,0,0,5,2,3,3,3,3,3,3,5,1,0,1,39.3,28.6,0,0,1
+new balance,fuelcell supercomp pacer v2,1,0,1,0,1,1,1,0,7.1,7.5,1,1,1,5,3,3,4,5,3,3,5,5,1,0,1,32.9,25.4,1,0,1
+new balance,fuelcell supercomp trainer,0,1,1,0,1,0,1,0,10.5,10.3,1,0,0,5,0,0,0,5,1,0,5,5,1,0,1,40.2,29.9,1,0,1
+new balance,fuelcell supercomp trainer v2,0,1,1,0,1,0,1,0,9.3,8.4,1,1,1,5,2,4,4,3,3,5,5,5,3,0,1,39.3,30.9,0,0,1
+new balance,fuelcell supercomp trainer v3,0,1,1,0,1,0,1,0,9.8,7.3,0,1,1,5,2,4,3,3,3,3,5,5,3,0,1,36.8,29.5,0,0,1
+altra,fwd via,0,1,1,1,0,0,1,0,9.0,6.5,0,1,1,3,3,3,3,3,5,5,3,5,3,0,0,35.9,29.4,0,0,1
+adidas,galaxy 6,0,0,1,1,0,0,1,0,11.7,11.0,1,0,0,3,2,2,3,1,5,5,5,5,1,0,0,33.9,22.9,0,0,1
+hoka,gaviota 5,0,0,1,1,0,0,0,1,10.5,2.2,0,1,1,5,3,2,4,5,5,5,5,5,3,0,0,34.9,32.7,1,0,1
+asics,gel contend 7,0,0,1,1,0,0,1,0,9.5,9.6,1,1,1,0,0,0,0,0,3,0,5,0,1,0,0,33.3,23.7,0,0,0
+asics,gel contend 8,0,0,1,1,0,0,1,0,9.2,9.1,1,1,1,3,0,0,0,5,1,0,5,1,5,0,0,31.1,22.0,1,0,1
+asics,gel contend 9,0,0,1,1,0,0,1,0,9.7,7.8,0,1,1,3,2,4,3,3,3,3,3,3,5,0,0,31.2,23.4,0,0,1
+asics,gel cumulus 23,0,0,1,1,0,0,1,0,9.8,10.8,1,0,0,0,0,0,0,3,1,0,5,3,5,0,0,35.9,25.1,0,0,1
+asics,gel cumulus 24,0,0,1,1,0,0,1,0,9.7,8.6,1,1,1,5,0,0,0,0,1,0,5,1,1,0,0,33.5,24.9,0,0,0
+asics,gel cumulus 25,0,0,1,1,0,0,1,0,9.5,11.2,1,0,0,5,3,4,0,3,1,1,3,3,3,0,0,38.4,27.2,0,0,1
+asics,gel cumulus 27,0,0,1,1,0,0,1,0,9.2,11.6,1,0,0,5,4,4,2,1,3,3,3,5,5,0,0,40.9,29.3,0,0,1
+asics,gel excite 10,0,0,1,1,0,0,1,0,9.5,11.8,1,0,0,3,3,4,4,3,3,3,3,3,3,0,0,35.2,23.4,0,0,1
+asics,gel excite 8,0,0,0,1,0,0,1,0,9.5,12.3,1,0,0,0,0,0,0,0,1,0,5,0,0,0,0,35.8,23.5,0,0,0
+asics,gel kayano 28,0,0,1,1,0,0,0,1,10.7,8.7,1,1,1,0,0,0,0,0,3,0,0,3,0,0,0,31.8,23.1,0,0,0
+asics,gel kayano 30,0,1,1,1,0,0,0,1,10.7,12.0,1,0,0,5,4,2,4,5,3,3,3,5,5,0,0,39.7,27.7,1,0,1
+asics,gel kayano 31,0,0,1,1,0,0,0,1,10.4,11.5,1,0,0,5,4,4,3,3,5,3,3,5,5,0,0,39.3,27.8,0,0,1
+asics,gel kayano 32,0,0,1,1,0,0,0,1,10.4,9.3,1,1,1,3,4,4,4,3,3,3,3,5,5,0,0,39.9,30.6,0,0,1
+asics,gel kayano lite 2,0,1,1,1,0,0,0,1,10.1,11.1,1,0,0,0,0,0,0,0,3,0,5,5,5,0,0,35.9,24.8,0,0,0
+asics,gel kayano lite 3,0,1,1,1,0,0,0,1,9.8,8.5,1,1,1,3,0,0,0,5,3,0,5,3,3,0,0,31.8,23.3,1,0,1
+asics,gel kinsei max,0,1,1,1,0,0,1,0,11.4,8.6,1,1,1,5,3,3,4,3,3,3,3,5,5,0,0,38.8,30.2,0,0,1
+asics,gel nimbus 24,0,1,0,1,0,0,1,0,9.3,8.7,1,1,1,0,0,0,0,0,3,0,5,0,0,0,0,38.7,30.0,0,0,0
+asics,gel nimbus 25,0,1,1,1,0,0,1,0,10.2,7.8,0,1,1,5,0,0,0,3,1,0,3,5,3,0,0,38.0,30.2,0,0,1
+asics,gel nimbus 26,0,1,1,1,0,0,1,0,10.7,8.4,1,1,1,5,4,4,4,3,5,3,3,5,3,0,0,40.4,32.0,0,0,1
+asics,gel nimbus 27,0,1,1,1,0,0,1,0,10.5,8.3,1,1,1,3,3,4,4,3,3,3,5,5,5,0,0,42.7,34.4,0,0,1
+asics,gel nimbus lite 3,0,1,0,1,0,0,1,0,9.0,9.9,1,1,1,0,0,0,0,0,3,0,5,0,0,0,0,34.9,25.0,0,0,0
+asics,gel pulse 11,0,0,0,1,0,0,1,0,11.1,8.7,1,1,1,0,0,0,0,0,3,0,5,0,0,0,0,34.0,25.3,0,0,0
+asics,gel pulse 13,0,0,1,1,0,0,1,0,10.3,11.0,1,0,0,3,2,0,0,5,1,3,3,1,3,0,0,32.6,21.6,1,0,1
+asics,gel pulse 14,0,0,1,1,0,0,1,0,10.4,9.7,1,1,1,3,2,4,4,3,3,3,3,3,5,0,0,32.0,22.3,0,0,1
+asics,gel pulse 15,1,0,1,1,0,0,1,0,8.4,8.1,1,1,1,3,3,4,4,3,3,3,3,5,5,0,0,35.3,27.2,0,0,1
+brooks,ghost 14,0,0,1,1,0,0,1,0,10.1,12.4,1,0,0,0,0,0,0,3,1,0,5,3,5,0,0,33.8,21.4,0,0,1
+brooks,ghost 15,0,0,1,1,0,0,1,0,9.8,13.2,1,0,0,5,2,3,4,3,1,3,1,3,5,0,0,36.3,23.1,0,0,1
+brooks,ghost 16,0,0,1,1,0,0,1,0,9.4,12.4,1,0,0,3,3,4,4,3,3,3,1,3,5,0,0,35.1,22.7,0,0,1
+brooks,ghost 17,0,0,1,1,0,0,1,0,10.2,10.4,1,0,0,5,3,4,4,3,3,1,3,3,5,0,0,36.2,25.8,0,0,1
+brooks,ghost max,0,1,1,1,0,0,1,0,10.3,9.5,1,1,1,5,2,4,4,3,1,3,5,5,5,0,0,39.8,30.3,0,0,1
+brooks,ghost max 3,0,0,1,1,0,0,1,0,10.7,7.3,0,1,1,5,3,4,4,3,3,3,5,5,5,0,0,38.5,31.2,0,0,1
+brooks,ghostmax 2,0,0,1,1,0,0,1,0,10.8,9.9,1,1,1,3,3,4,4,5,3,3,5,5,5,0,0,39.0,29.1,1,0,1
+asics,glideride 3,0,1,1,1,0,0,1,0,9.5,11.1,1,0,0,5,0,2,0,3,1,0,5,5,1,0,0,42.7,31.6,0,0,1
+asics,glideride max,0,1,1,1,0,0,1,0,9.9,12.7,1,0,0,5,3,4,4,3,3,3,3,5,5,0,0,44.1,31.4,0,0,1
+brooks,glycerin 19,0,0,1,1,0,0,1,0,10.4,11.8,1,0,0,0,0,0,0,0,1,0,0,3,0,0,0,38.1,26.3,0,0,0
+brooks,glycerin 20,0,0,1,1,0,0,1,0,10.5,12.8,1,0,0,3,0,0,0,3,1,0,3,3,3,0,0,37.1,24.3,0,0,1
+brooks,glycerin 21,0,0,1,1,0,0,1,0,9.8,10.6,1,0,0,3,3,4,4,3,3,3,3,1,5,0,0,37.2,26.6,0,0,1
+brooks,glycerin 22,0,0,1,1,0,0,1,0,10.3,10.3,1,0,0,5,3,4,4,5,3,3,3,5,3,0,0,38.5,28.2,1,0,1
+brooks,glycerin gts 20,0,0,1,1,0,0,0,1,10.9,11.0,1,0,0,3,2,2,4,3,1,3,5,5,5,0,0,36.5,25.5,0,0,1
+brooks,glycerin gts 21,0,0,1,1,0,0,0,1,10.6,10.7,1,0,0,3,3,4,4,3,3,3,3,5,5,0,0,37.2,26.5,0,0,1
+brooks,glycerin gts 22,0,0,1,1,0,0,0,1,10.8,10.1,1,0,0,3,3,4,4,5,3,3,3,5,3,0,0,37.8,27.7,1,0,1
+brooks,glycerin max,0,1,1,1,0,0,1,0,10.8,6.6,1,1,1,5,3,4,3,5,3,3,5,5,3,0,0,42.3,35.7,1,0,1
+brooks,glycerin stealthfit 20,0,0,1,1,0,0,1,0,9.9,12.5,1,0,0,3,3,4,4,5,1,3,5,5,3,0,0,38.8,26.3,1,0,1
+brooks,glycerin stealthfit 21,0,0,1,1,0,0,1,0,9.1,10.5,1,0,0,3,3,3,4,1,3,3,3,3,3,0,0,36.9,26.4,0,0,1
+skechers,go run max road 6,0,0,1,1,0,0,1,0,11.3,8.1,1,1,1,5,3,4,4,3,3,1,5,3,3,0,1,39.7,31.6,0,0,1
+skechers,go run ride 11,0,1,1,1,0,0,1,0,10.1,6.5,0,1,1,5,3,2,4,3,1,3,3,5,3,0,0,34.1,27.6,0,0,1
+skechers,gorun razor excess,1,1,0,1,1,0,1,0,7.1,6.4,0,1,1,0,0,0,0,0,1,0,5,5,3,0,0,27.4,21.0,0,0,0
+asics,gt 1000 10,0,0,0,1,0,0,0,1,9.8,7.8,0,1,1,0,0,0,0,0,3,0,5,0,3,0,0,31.0,23.2,0,0,0
+asics,gt 1000 11,0,0,1,1,0,0,0,1,9.9,9.0,1,1,1,5,0,0,0,0,1,0,5,3,1,0,0,31.5,22.5,0,0,0
+asics,gt 1000 12,0,0,1,1,0,0,0,1,9.6,7.2,0,1,1,5,2,4,4,5,3,5,3,3,3,0,0,30.2,23.0,1,0,1
+asics,gt 1000 13,0,0,1,1,0,0,0,1,9.7,8.7,1,1,1,3,3,4,4,3,3,3,3,5,5,0,0,33.7,25.0,0,0,1
+asics,gt 1000 14,0,0,1,1,0,0,0,1,9.6,9.6,1,1,1,5,3,4,4,1,3,3,3,5,3,0,0,35.4,25.8,0,0,1
+asics,gt 1000 9,0,0,1,1,0,0,0,1,10.1,6.0,0,1,1,0,0,0,0,0,3,0,5,5,5,0,0,31.6,25.6,0,0,0
+asics,gt 2000 10,0,0,1,1,0,0,0,1,9.9,7.5,0,1,1,0,0,0,0,0,3,0,5,3,5,0,0,31.5,24.0,0,0,0
+asics,gt 2000 11,0,0,1,1,0,0,0,1,9.9,6.0,0,1,1,3,0,0,0,3,1,0,5,3,3,0,0,30.7,24.7,0,0,1
+asics,gt 2000 12,0,0,1,1,0,0,0,1,9.7,10.0,1,1,1,3,3,4,4,3,3,3,3,5,5,0,0,36.6,26.6,0,0,1
+asics,gt 2000 13,0,0,1,1,0,0,0,1,9.3,9.4,1,1,1,5,4,4,4,3,3,3,3,5,5,0,0,36.6,27.2,0,0,1
+asics,gt 2000 14,0,0,1,1,0,0,0,1,9.5,8.7,1,1,1,5,3,4,4,1,3,1,3,5,5,0,0,36.9,28.2,0,0,1
+saucony,guide 14,0,0,1,1,0,0,0,1,10.8,9.4,1,1,1,0,0,0,0,0,3,0,0,1,0,0,0,33.8,24.4,0,0,0
+saucony,guide 15,0,0,1,1,0,0,0,1,9.8,7.1,0,1,1,3,0,0,0,0,1,0,5,3,5,0,0,31.6,24.5,0,0,0
+saucony,guide 17,0,0,1,1,0,0,0,1,9.7,7.0,0,1,1,3,3,4,4,3,3,3,3,3,5,0,0,34.9,27.9,0,0,1
+saucony,guide 18,0,0,1,1,0,0,0,1,9.8,8.3,1,1,1,3,4,3,4,5,3,3,3,5,1,0,0,36.0,27.7,1,0,1
+under armour,hovr phantom 3,0,0,0,1,0,0,1,0,11.9,12.7,1,0,0,3,4,3,0,1,3,3,3,5,5,0,0,32.8,20.1,0,1,0
+under armour,hovr sonic 6,0,0,1,1,0,0,1,0,10.1,7.0,0,1,1,3,3,3,4,1,3,3,1,5,3,0,0,30.2,23.2,0,0,1
+saucony,hurricane 24,0,0,1,1,0,0,0,1,11.1,6.3,0,1,1,5,2,3,3,5,3,3,5,5,3,0,0,40.5,34.2,1,0,1
+saucony,hurricane 25,0,0,1,1,0,0,0,1,10.1,7.1,0,1,1,5,3,3,4,1,3,3,3,5,5,0,0,40.2,33.1,0,0,1
+brooks,hyperion,1,0,1,0,1,0,1,0,7.4,12.3,1,0,0,3,2,3,4,3,1,5,3,1,3,0,0,30.0,17.7,0,0,1
+brooks,hyperion 2,1,0,1,0,1,1,1,0,7.2,9.8,1,1,1,3,3,2,3,5,3,3,3,3,3,0,0,32.2,22.4,1,0,1
+brooks,hyperion elite 4,1,1,1,0,1,1,1,0,7.8,11.8,1,0,0,3,3,4,4,5,1,3,5,5,1,0,1,39.1,27.3,1,0,1
+brooks,hyperion elite 4 pb,1,0,1,0,0,1,1,0,6.9,11.7,1,0,0,5,3,4,3,5,1,3,5,5,1,0,1,38.5,26.8,1,0,1
+brooks,hyperion elite 5,1,1,1,0,0,1,1,0,7.2,11.2,1,0,0,5,3,4,2,5,1,3,5,5,1,0,1,37.6,26.4,1,0,1
+brooks,hyperion gts,1,0,1,0,1,0,0,1,8.0,9.9,1,1,1,5,3,4,4,5,1,3,3,3,3,0,0,28.6,18.7,1,0,1
+brooks,hyperion gts 2,1,0,1,1,1,0,0,1,7.8,10.7,1,0,0,3,3,4,4,3,3,3,3,5,3,0,0,33.0,22.3,0,0,1
+brooks,hyperion max 2,0,1,1,0,1,0,1,0,9.2,6.8,0,1,1,3,3,3,4,3,3,3,5,5,3,0,0,33.2,26.4,0,0,1
+brooks,hyperion max 3,0,1,1,1,1,0,1,0,10.0,10.6,1,0,0,5,4,3,4,3,3,3,3,5,3,0,0,45.6,35.0,0,0,1
+brooks,hyperion tempo,1,0,1,0,1,0,1,0,7.0,9.0,1,1,1,3,4,3,4,3,3,3,1,1,3,0,0,28.7,19.7,0,0,1
+under armour,infinite elite,0,1,1,1,0,0,1,0,11.1,8.1,1,1,1,5,4,3,4,1,3,3,5,5,5,0,0,39.9,31.8,0,0,1
+under armour,infinite elite 2,0,0,1,1,0,0,1,0,10.2,7.0,0,1,1,5,3,4,4,3,3,3,5,5,3,0,0,40.1,33.1,0,0,1
+under armour,infinite pro,0,0,1,1,0,0,1,0,10.8,8.3,1,1,1,5,3,3,4,3,3,3,5,5,5,0,0,41.3,33.0,0,0,1
+nike,infinityrn 4,0,0,1,1,0,0,1,0,11.1,9.8,1,1,1,5,4,3,4,3,3,3,3,3,1,0,0,34.2,24.4,0,0,1
+nike,interact run,1,0,1,1,0,0,1,0,8.5,9.3,1,1,1,3,4,4,3,5,3,3,3,3,3,0,0,29.7,20.4,1,0,1
+nike,invincible 3,0,0,1,1,0,0,1,0,10.0,9.6,1,1,1,5,0,0,0,3,1,0,3,5,3,0,0,35.2,25.6,0,0,1
+nobull,journey,0,0,1,1,0,0,1,0,9.4,9.4,1,1,1,5,0,0,0,5,1,0,5,3,1,0,0,35.2,25.8,1,0,1
+nike,journey run,0,0,1,1,0,0,1,0,10.5,8.6,1,1,1,3,3,3,3,3,3,1,3,5,5,0,0,33.0,24.4,0,0,1
+hoka,kawana 2,0,0,1,1,0,0,1,0,10.5,5.2,0,1,1,3,3,4,4,3,1,1,5,3,5,0,0,33.2,28.0,0,0,1
+saucony,kinvara 12,1,0,1,0,1,0,1,0,7.7,4.6,0,1,1,0,0,0,0,0,3,0,0,1,0,0,0,26.1,21.5,0,0,0
+saucony,kinvara 13,1,0,1,0,1,0,1,0,7.2,4.5,0,1,1,3,0,0,0,0,1,0,3,1,3,0,0,26.9,22.4,0,0,0
+saucony,kinvara 14,1,0,1,0,1,0,1,0,6.8,4.1,0,1,1,3,2,0,0,5,3,1,1,1,3,0,0,30.3,26.2,1,0,1
+saucony,kinvara 15,1,0,1,1,1,0,1,0,6.8,4.4,0,1,1,3,2,4,4,5,3,3,1,1,1,0,0,27.9,23.5,1,0,1
+saucony,kinvara pro,0,0,1,1,1,0,1,0,9.9,10.3,1,0,0,3,4,4,4,1,3,1,5,5,3,0,1,45.6,35.3,0,0,1
+brooks,launch 10,1,0,1,1,1,0,1,0,8.1,10.0,1,1,1,5,3,4,4,3,3,3,3,3,3,0,0,33.8,23.8,0,0,1
+brooks,launch 11,1,0,1,1,1,0,1,0,8.4,9.5,1,1,1,3,3,2,4,3,3,3,3,5,5,0,0,33.5,24.0,0,0,1
+brooks,launch 8,1,0,1,1,1,0,1,0,8.5,9.6,1,1,1,0,0,0,0,0,1,0,0,3,0,0,0,30.5,20.9,0,0,0
+brooks,launch gts 10,1,0,1,1,1,0,0,1,8.5,12.2,1,0,0,5,3,4,4,3,3,3,5,5,3,0,0,35.4,23.2,0,0,1
+brooks,launch gts 9,1,0,1,1,1,0,0,1,8.6,10.4,1,0,0,3,3,4,4,1,1,3,5,5,5,0,0,33.8,23.4,0,0,1
+brooks,levitate 5,0,0,1,1,0,0,1,0,10.7,9.1,1,1,1,0,0,0,0,0,3,0,5,3,5,0,0,31.4,22.3,0,0,0
+brooks,levitate stealthfit 5,0,0,1,1,0,0,1,0,10.1,9.5,1,1,1,5,4,4,4,1,1,5,5,1,3,0,0,28.0,18.5,0,1,0
+brooks,levitate stealthfit 6,0,0,1,1,0,0,1,0,9.9,7.5,0,1,1,3,4,4,4,1,1,3,5,3,3,0,0,32.9,25.4,0,0,1
+reebok,lite 3,1,0,0,1,0,0,1,0,8.7,13.4,1,0,0,3,0,0,0,1,3,0,5,1,1,0,0,32.7,19.3,0,0,1
+hoka,mach 4,1,1,1,1,1,0,1,0,7.9,4.9,0,1,1,0,0,0,0,0,1,0,0,3,0,0,0,30.6,25.7,0,0,0
+hoka,mach 5,1,1,1,1,1,0,1,0,7.9,5.7,0,1,1,5,0,0,0,3,1,0,1,1,3,0,0,30.7,25.0,0,0,1
+hoka,mach 6,1,1,1,1,0,0,1,0,8.2,9.6,1,1,1,3,3,2,4,5,3,1,3,5,5,0,0,36.0,26.4,1,0,1
+hoka,mach x 3,0,1,1,1,1,0,1,0,9.3,9.5,1,1,1,5,3,4,4,3,1,1,5,5,1,0,0,42.9,33.4,0,0,1
+asics,magic speed,1,1,0,0,1,0,1,0,8.2,8.3,1,1,1,3,0,0,0,5,1,0,5,5,1,0,1,32.5,24.2,1,0,1
+asics,magic speed 2,1,1,0,0,1,0,1,0,8.0,8.7,1,1,1,0,0,0,0,5,1,0,5,5,1,0,1,35.1,26.4,1,0,1
+asics,magic speed 3,1,1,1,0,1,0,1,0,7.4,9.8,1,1,1,5,2,3,4,3,1,3,5,5,1,0,1,36.3,26.5,0,0,1
+puma,magmax nitro,0,1,1,1,0,0,1,0,10.3,9.6,1,1,1,5,3,3,3,3,3,3,5,5,3,0,0,42.9,33.3,0,0,1
+puma,magnify nitro 2,0,0,1,1,0,0,1,0,9.9,9.3,1,1,1,5,4,2,4,1,3,1,3,5,3,0,0,37.1,27.8,0,0,1
+skechers,max cushioning elite,0,0,0,1,0,0,1,0,11.9,16.1,1,0,0,0,0,0,0,0,1,0,5,0,0,0,0,42.3,26.2,0,0,0
+skechers,max cushioning elite 2.0,0,1,1,1,0,0,1,0,9.5,8.8,1,1,1,3,3,3,3,1,1,3,3,5,3,0,0,36.4,27.6,0,0,1
+asics,megablast,1,1,1,0,1,1,1,0,7.7,9.9,1,1,1,5,2,4,4,3,3,3,5,5,3,0,0,45.1,35.2,0,0,1
+asics,metaspeed edge,1,1,0,0,0,1,1,0,6.2,8.0,1,1,1,0,0,0,0,0,1,0,5,5,1,0,1,31.6,23.6,0,0,0
+asics,metaspeed edge tokyo,1,1,1,0,0,1,1,0,5.6,6.9,0,1,1,5,2,4,4,5,1,3,3,5,1,0,1,38.9,32.0,1,0,1
+asics,metaspeed edge+,1,1,0,0,0,1,1,0,7.3,8.1,1,1,1,1,0,0,0,5,1,0,5,5,1,0,1,33.3,25.2,1,0,1
+asics,metaspeed ray,1,1,1,0,0,1,1,0,4.6,9.8,1,1,1,5,2,4,3,3,1,1,3,3,1,0,1,39.8,30.0,0,0,1
+asics,metaspeed sky,1,1,0,0,0,1,1,0,6.7,2.5,0,1,1,0,0,0,0,5,1,0,0,5,0,0,1,33.7,31.2,1,0,1
+asics,metaspeed sky paris,1,1,1,0,0,1,1,0,6.5,6.5,0,1,1,3,4,4,4,5,1,3,5,5,1,0,1,39.1,32.6,1,0,1
+asics,metaspeed sky tokyo,1,1,1,0,0,1,1,0,5.7,6.0,0,1,1,5,2,4,3,5,1,1,5,5,1,0,1,38.7,32.7,1,0,1
+asics,metaspeed sky+,1,1,0,0,0,1,1,0,7.2,2.7,0,1,1,0,0,4,0,5,1,0,5,5,1,0,1,33.5,30.8,1,0,1
+mizuno,mizuno wave horizon 7,0,0,1,1,0,0,0,1,11.6,7.2,1,1,1,3,3,2,4,3,3,5,5,5,5,0,0,39.1,31.9,0,0,1
+mizuno,neo vista,0,1,1,1,1,0,1,0,9.1,9.6,1,1,1,5,4,4,4,3,3,3,3,5,1,0,0,44.9,35.3,0,0,1
+mizuno,neo vista 2,0,1,1,1,1,0,1,0,9.3,8.5,1,1,1,5,4,4,4,3,3,3,3,5,1,0,0,46.0,37.5,0,0,1
+mizuno,neo zen,1,0,1,1,1,0,1,0,8.3,7.0,0,1,1,5,4,4,4,3,3,3,3,3,1,0,0,41.0,34.0,0,0,1
+asics,noosa tri 14,1,1,1,1,1,0,1,0,7.5,8.0,1,1,1,5,0,0,0,3,1,0,5,3,1,0,0,30.2,22.2,0,0,1
+asics,noosa tri 15,1,1,1,1,1,0,1,0,7.7,7.7,0,1,1,5,2,4,4,5,5,3,3,5,3,0,0,34.6,26.9,1,0,1
+asics,noosa tri 16,1,0,1,1,1,0,1,0,7.7,5.9,0,1,1,5,2,4,3,5,5,5,3,5,3,0,0,32.8,26.9,1,0,1
+asics,novablast 2,0,1,1,1,1,0,1,0,9.6,13.7,1,0,0,0,0,0,0,0,3,0,0,1,0,0,0,39.3,25.6,0,0,0
+asics,novablast 4,0,1,1,1,0,0,1,0,9.1,9.0,1,1,1,5,3,4,4,3,1,3,5,5,3,0,0,39.2,30.2,0,0,1
+asics,novablast 5,0,1,1,1,1,0,1,0,9.0,7.4,1,1,1,5,2,3,4,3,3,3,3,5,3,0,0,40.9,33.5,0,0,1
+diadora,nucleo 2,0,0,1,1,0,0,1,0,9.7,8.0,1,1,1,5,2,4,4,3,3,3,3,5,3,0,0,38.7,30.7,0,0,1
+saucony,omni 22,0,0,1,1,0,0,0,1,10.1,7.3,0,1,1,3,2,4,4,3,3,3,1,3,1,0,0,33.0,25.7,0,0,1
+altra,paradigm 7,0,0,1,1,0,0,0,1,9.3,0.1,0,1,1,5,3,3,4,5,5,5,3,3,1,0,0,27.6,27.5,1,0,1
+nike,pegasus 40,0,0,1,1,0,0,1,0,9.7,9.7,1,1,1,5,3,0,0,3,3,3,3,1,1,0,0,30.2,20.5,0,0,1
+nike,pegasus 41,0,0,1,1,0,0,1,0,9.9,11.4,1,0,0,5,3,4,4,3,3,3,1,1,5,0,0,33.6,22.2,0,0,1
+nike,pegasus 41 gtx,0,1,1,1,0,0,1,0,11.1,11.9,1,0,0,5,3,4,2,1,3,3,3,3,3,0,0,36.3,24.4,0,1,0
+nike,pegasus easyon,0,0,1,1,0,0,1,0,10.1,11.6,1,0,0,5,2,4,3,5,3,3,5,3,5,0,0,33.6,22.0,1,0,1
+nike,pegasus plus,1,0,1,1,1,0,1,0,8.6,9.4,1,1,1,5,3,4,3,3,3,3,3,3,5,0,0,33.0,23.6,0,0,1
+nike,pegasus premium,0,1,1,1,1,0,1,0,10.9,11.8,1,0,0,5,2,4,3,5,1,1,5,5,5,0,0,42.8,31.0,1,0,1
+nike,pegasus turbo,0,0,1,0,1,0,1,0,9.7,10.0,1,1,1,5,0,0,0,3,1,1,5,3,1,0,0,32.0,22.0,0,0,1
+salomon,phantasm 2,0,0,1,1,1,0,1,0,9.2,11.2,1,0,0,3,2,4,3,3,3,5,5,5,3,0,0,34.4,23.2,0,0,1
+topo,phantom 3,0,0,1,1,0,0,1,0,9.5,5.8,0,1,1,5,3,4,4,3,3,5,3,5,5,0,0,32.6,26.8,0,0,1
+new balance,propel v4,0,0,1,1,0,0,1,0,9.7,4.5,0,1,1,5,3,4,4,3,1,3,3,5,3,0,0,33.7,29.2,0,0,1
+altra,provision 6,0,0,0,1,0,0,0,1,9.1,0.0,0,1,1,0,0,0,0,0,3,0,5,0,0,0,0,28.0,28.0,0,0,0
+altra,provision 7,0,0,1,1,0,0,0,1,9.1,4.8,0,1,1,3,3,4,4,5,1,5,5,5,1,0,0,32.6,27.8,1,0,1
+altra,provision 8,0,0,1,1,0,0,0,1,9.6,0.2,0,1,1,3,3,3,4,3,3,5,5,3,1,0,0,26.0,25.8,0,0,1
+adidas,pureboost 23,0,0,1,1,0,0,1,0,10.8,11.5,1,0,0,5,3,4,4,5,3,5,1,1,5,0,0,27.6,16.1,1,0,1
+adidas,pureboost 5,0,0,1,1,0,0,1,0,9.0,9.2,1,1,1,3,4,4,3,3,3,5,3,3,3,0,0,31.2,22.0,0,0,1
+nike,quest 4,0,0,0,1,0,0,1,0,9.5,13.7,1,0,0,0,0,0,0,0,3,0,5,5,5,0,0,32.1,18.4,0,0,0
+nike,quest 5,0,0,1,1,0,0,1,0,9.6,9.1,1,1,1,3,0,0,0,3,1,1,3,3,1,0,0,29.8,20.7,0,0,1
+adidas,questar,0,0,1,1,0,0,1,0,10.9,10.5,1,0,0,3,0,0,0,5,1,0,5,5,3,0,0,32.6,22.1,1,0,1
+adidas,questar 3,0,0,1,1,0,0,1,0,10.4,8.5,1,1,1,3,3,4,4,3,3,3,3,5,5,0,0,33.5,25.0,0,0,1
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+mizuno,wave sky 8,0,0,1,1,0,0,1,0,9.6,10.0,1,1,1,5,4,4,2,3,3,3,3,3,3,0,0,40.7,30.7,0,0,1
+nike,winflo 10,0,0,1,1,0,0,1,0,9.5,9.7,1,1,1,5,3,3,4,3,3,3,3,3,1,0,0,33.5,23.8,0,0,1
+nike,winflo 11,0,0,1,1,0,0,1,0,10.4,12.3,1,0,0,5,3,4,4,3,1,3,3,5,3,0,0,37.6,25.3,0,0,1
+nike,winflo 11 gtx,0,0,1,1,0,0,1,0,10.9,13.3,1,0,0,5,4,4,2,1,3,3,3,5,5,0,0,39.0,25.7,0,1,0
+reebok,zig dynamica 4,0,0,1,1,0,0,1,0,12.4,8.5,1,1,1,3,3,2,4,3,3,1,5,3,3,0,0,32.0,23.5,0,0,1
+reebok,zig dynamica 5,0,0,1,1,0,0,1,0,10.5,6.3,0,1,1,3,3,2,4,3,3,1,5,5,3,0,0,34.1,27.8,0,0,1
+nike,zoom fly 4,0,1,1,0,1,0,1,0,9.6,7.1,0,1,1,0,0,0,0,0,1,0,5,5,1,1,1,38.4,31.3,0,0,0
+nike,zoom fly 5,0,1,1,1,1,0,1,0,9.8,7.5,0,1,1,5,0,0,0,1,1,1,5,5,3,0,1,36.9,29.4,0,0,1
+nike,zoom fly 6,1,1,1,0,1,1,1,0,8.7,9.6,1,1,1,5,4,4,4,3,1,3,5,5,5,0,1,39.7,30.1,0,0,1
+nike,zoomx invincible run flyknit 2,0,0,1,1,0,0,1,0,10.3,12.0,1,0,0,5,0,0,0,1,3,3,5,1,3,0,0,35.5,23.5,0,0,1
+nike,zoomx streakfly,1,1,1,0,1,0,1,0,6.0,6.3,0,1,1,5,0,0,0,5,1,3,1,1,1,0,0,31.7,25.4,1,0,1
+nike,zoomx vaporfly next% 2,1,1,0,0,0,1,1,0,6.9,7.7,0,1,1,5,0,0,0,5,1,0,5,5,1,0,1,38.6,30.9,1,0,1
diff --git a/data/trail_dataset.csv b/data/trail_dataset.csv
new file mode 100644
index 0000000000000000000000000000000000000000..2f45dedb2e13b209ad71b1436c00ac893bc46331
--- /dev/null
+++ b/data/trail_dataset.csv
@@ -0,0 +1,159 @@
+brand,name,lightweight,terrain_light,terrain_moderate,terrain_technical,shock_absorption,energy_return,traction_scaled,arch_neutral,arch_stability,weight_lab_oz,drop_lab_mm,strike_heel,strike_mid,strike_forefoot,midsole_softness,toebox_durability,heel_durability,outsole_durability,breathability_scaled,plate_rock_plate,plate_carbon_plate,width_fit,toebox_width,stiffness_scaled,torsional_rigidity,heel_stiff,lug_dept_mm,heel_lab_mm,forefoot_lab_mm,season_summer,season_winter,season_all,removable_insole,waterproof,water_repellent
+adidas,terrex agravic speed ultra,0,1,0,0,3,5,0,1,0,9.1,0.3,0,1,1,3,4,4,3,3,0,0,3,1,3,5,1,2.5,30.6,30.3,0,0,1,1,0,0
+adidas,terrex speed ultra,0,1,0,0,0,0,0,1,0,9.1,8.2,1,1,1,0,0,0,0,0,0,0,1,0,5,1,1,2.6,32.8,24.6,0,0,0,1,0,0
+altra,experience wild,0,1,1,0,3,1,0,1,0,10.1,4.3,0,1,1,5,3,3,4,3,0,0,5,5,3,5,3,3.6,34.5,30.2,0,0,1,1,0,0
+altra,experience wild 2,0,1,0,0,3,1,5,1,0,9.4,6.1,0,1,1,3,3,4,4,1,0,0,5,5,3,3,1,3.5,32.3,26.2,0,0,1,1,0,0
+altra,lone peak 5.0,0,1,1,0,0,0,0,1,0,10.7,0.2,0,1,1,0,0,0,0,0,1,0,1,0,5,1,0,3.7,24.5,24.3,0,0,0,1,0,0
+altra,lone peak 6,0,0,1,1,0,0,0,1,0,9.8,0.6,0,1,1,0,0,0,0,0,1,0,5,0,5,0,0,4.4,25.1,24.5,0,0,0,0,0,0
+altra,lone peak 7,0,0,1,0,0,0,0,1,0,10.4,0.2,0,1,1,3,0,0,0,3,0,0,1,0,5,1,1,3.4,23.3,23.1,0,0,1,1,0,0
+altra,lone peak 8,0,1,1,0,0,0,0,1,0,10.2,1.4,0,1,1,3,4,3,3,3,0,0,3,5,5,1,1,3.0,22.7,21.3,0,0,1,1,0,0
+altra,lone peak 9,0,1,1,0,1,3,0,1,0,10.9,0.0,0,1,1,3,3,3,4,1,0,0,5,5,3,3,1,3.8,23.3,23.3,0,0,1,1,0,0
+altra,mont blanc,0,1,1,0,0,0,0,1,0,9.6,0.0,0,1,1,0,0,0,0,0,0,0,3,0,5,0,0,2.8,33.8,33.8,0,0,0,0,0,0
+altra,mont blanc carbon,0,0,1,0,0,0,0,1,0,8.9,0.3,0,1,1,5,3,2,4,3,0,1,3,5,5,1,1,3.5,27.2,26.9,0,0,1,1,0,0
+altra,olympus 275,0,1,0,0,3,1,5,1,0,10.7,0.3,0,1,1,3,4,3,4,3,0,0,5,5,3,5,1,3.5,30.8,30.5,0,0,1,1,0,0
+altra,olympus 5,0,1,1,0,0,0,0,1,0,11.5,2.0,0,1,1,3,0,0,0,3,0,0,5,0,5,3,3,3.0,33.0,31.0,0,0,1,1,0,0
+altra,olympus 6,0,1,1,0,3,3,0,1,0,12.6,0.7,0,1,1,3,5,4,4,5,0,0,3,5,5,5,3,3.5,32.2,31.5,1,0,1,1,0,0
+altra,outroad,0,1,0,0,0,0,0,1,0,10.1,0.1,0,1,1,3,0,0,0,3,0,0,3,3,5,3,1,2.3,25.1,25.0,0,0,1,1,0,0
+altra,outroad 2,0,1,0,0,0,0,0,1,0,10.3,1.4,0,1,1,3,2,3,4,3,0,0,3,5,5,3,3,2.2,26.9,25.5,0,0,1,1,0,0
+altra,outroad 3,0,1,0,0,0,0,0,1,0,9.2,0.6,0,1,1,3,3,2,2,1,0,0,3,5,5,3,1,1.5,23.8,23.2,0,0,1,1,0,0
+altra,superior 6,0,1,1,0,0,0,0,1,0,9.6,0.1,0,1,1,3,1,3,4,5,0,0,3,5,5,3,1,3.3,22.1,22.0,1,0,1,1,0,0
+altra,superior 7,1,1,0,0,1,1,5,1,0,8.3,0.6,0,1,1,3,3,4,4,3,0,0,3,5,1,1,1,3.5,20.6,20.0,0,0,1,1,0,0
+altra,timp 4,0,1,1,0,0,0,0,1,0,11.1,0.1,0,1,1,3,0,0,0,3,0,0,3,0,5,1,1,2.9,29.0,28.9,0,0,1,1,0,0
+altra,timp 5,0,1,1,0,3,1,0,1,0,9.5,0.1,0,1,1,3,4,3,4,3,0,0,5,5,5,5,3,3.0,28.6,28.7,0,0,1,1,0,0
+altra,timp 5 gtx,0,1,1,0,3,1,5,1,0,11.0,0.3,0,1,1,5,3,3,4,1,0,0,5,5,5,5,1,3.5,28.9,28.6,0,1,0,1,1,0
+asics,gel excite trail 2,0,1,0,0,3,1,0,1,0,10.3,10.1,1,0,0,3,1,2,3,3,0,0,3,3,1,5,5,4.0,37.7,27.6,0,0,1,1,0,0
+asics,gel trabuco 12,0,0,1,1,3,3,0,1,0,10.5,7.8,0,1,1,5,4,4,4,3,1,0,3,5,3,3,3,4.5,35.4,27.6,0,0,1,1,0,0
+asics,gel trabuco 13,0,1,0,0,3,3,0,1,0,10.2,7.0,0,1,1,3,4,4,4,3,1,0,3,3,5,5,5,3.3,33.8,26.8,0,0,1,1,0,0
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+asics,metafuji trail,0,1,0,0,5,3,5,1,0,9.1,10.3,1,0,0,5,1,4,3,5,0,1,3,1,5,5,3,2.7,44.7,34.4,1,0,1,1,0,0
+asics,trabuco max 2,0,0,1,1,0,0,0,1,0,10.3,8.5,1,1,1,5,3,4,3,5,0,0,3,5,5,5,5,4.2,39.7,31.2,1,0,1,1,0,0
+asics,trabuco max 3,0,0,1,1,5,3,0,1,0,10.9,8.5,1,1,1,5,4,4,3,1,0,0,3,3,5,5,5,4.0,42.4,33.9,0,0,1,1,0,0
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+asics,trail scout 2,0,0,1,0,0,0,0,1,0,11.4,10.3,1,0,0,3,0,0,0,3,0,0,1,3,5,5,5,4.2,32.8,22.5,0,0,1,1,0,0
+brooks,caldera 6,0,1,1,0,0,0,0,1,0,11.1,12.1,1,0,0,5,5,3,4,3,0,0,3,3,5,5,5,3.5,38.4,26.3,0,0,1,1,0,0
+brooks,caldera 7,0,1,1,0,5,3,0,1,0,10.8,8.9,1,1,1,5,3,4,3,5,0,0,3,3,5,5,5,4.0,36.7,27.8,1,0,1,1,0,0
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+brooks,cascadia 18,0,1,1,0,1,1,0,1,0,10.9,8.8,1,1,1,3,1,3,3,3,1,0,3,3,5,5,5,4.0,32.6,23.8,0,0,1,1,0,0
+brooks,cascadia 19,0,1,1,0,3,3,5,1,0,10.8,7.8,0,1,1,3,4,3,4,3,1,0,3,3,3,5,5,3.8,34.8,27.0,0,0,1,1,0,0
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+kailas,fuga ex pro,0,1,1,0,3,5,5,1,0,9.6,7.2,0,1,1,3,2,3,4,3,0,0,1,1,5,5,3,3.7,37.9,30.7,0,0,1,1,0,0
+kailas,fuga pro 4,0,0,1,0,0,0,0,1,0,9.8,10.0,1,1,1,0,0,0,0,0,0,0,1,0,5,5,5,3.4,31.7,21.8,0,0,0,1,0,0
+kailas,fuga yao,0,1,0,0,0,0,0,1,0,10.5,10.7,1,0,0,3,0,0,0,3,0,1,1,0,5,5,5,1.7,38.6,27.9,0,0,1,1,0,0
+kailas,phantom 3.0,0,1,0,0,0,0,0,1,0,8.9,10.5,1,0,0,3,0,0,0,3,0,0,1,0,5,3,5,1.8,35.5,25.0,0,0,1,1,0,0
+keen,seek,0,1,1,0,5,1,5,1,0,10.8,8.6,1,1,1,5,4,4,4,3,0,0,3,5,3,3,1,4.1,36.3,27.7,0,0,1,1,0,0
+la sportiva,mutant,0,0,0,1,0,0,0,1,0,11.4,11.3,1,0,0,1,4,4,3,3,0,0,1,3,5,5,3,5.0,33.2,21.9,0,0,1,1,0,0
+la sportiva,prodigio,0,0,1,0,0,0,0,1,0,9.6,8.9,1,1,1,5,1,2,3,3,0,0,3,3,5,5,5,3.4,29.3,20.4,0,0,1,1,0,0
+la sportiva,prodigio max,0,0,1,1,3,3,5,1,0,10.8,7.1,0,1,1,3,4,4,4,3,0,0,1,1,5,5,3,5.5,35.1,28.0,0,0,1,1,0,0
+merrell,agility peak 4,0,0,0,1,0,0,0,1,0,10.3,9.3,1,1,1,3,3,3,3,3,1,0,1,5,5,5,5,4.4,34.4,25.1,0,0,1,1,0,0
+merrell,agility peak 5,0,0,1,1,3,1,0,1,0,10.2,13.4,1,0,0,3,5,3,4,3,1,0,3,1,3,5,5,4.5,39.2,25.8,0,0,1,1,0,0
+merrell,agility peak 5 gtx,0,0,1,1,0,0,0,1,0,10.4,11.6,1,0,0,3,4,2,3,1,1,0,1,3,5,5,5,4.4,37.3,25.7,0,1,0,1,1,0
+merrell,antora 3,0,1,0,0,3,1,0,1,0,10.1,9.1,1,1,1,3,3,2,4,5,1,0,1,3,3,5,3,3.4,33.5,24.4,1,0,1,1,0,0
+merrell,fly strike,0,1,0,0,0,0,0,1,0,10.3,17.3,1,0,0,3,4,3,3,3,0,0,1,5,5,5,3,3.5,34.3,17.0,0,0,1,1,0,0
+merrell,moab flight,0,1,0,0,0,0,0,1,0,9.6,13.5,1,0,0,3,4,4,4,1,0,0,3,3,3,5,3,2.9,32.6,19.1,0,0,1,1,0,0
+merrell,morphlite,1,1,0,0,3,1,0,1,0,8.4,11.0,1,0,0,5,2,3,3,3,0,0,1,3,3,5,1,2.0,32.3,21.3,0,0,1,1,0,0
+merrell,nova 2,0,0,1,1,0,0,0,1,0,10.3,9.3,1,1,1,0,0,0,0,0,1,0,1,0,5,0,0,4.2,35.1,25.8,0,0,0,0,0,0
+merrell,nova 3,0,1,1,0,3,3,0,1,0,10.8,9.9,1,1,1,3,3,3,4,3,1,0,1,3,3,5,1,3.5,34.1,24.2,0,0,1,1,0,0
+merrell,nova 4,0,1,1,0,3,1,3,1,0,9.6,12.1,1,0,0,3,3,3,3,3,0,0,3,3,3,5,1,4.0,37.7,25.6,0,0,1,1,0,0
+merrell,trail glove 7,1,1,0,0,0,0,0,1,0,7.8,0.1,0,1,1,3,3,2,3,3,0,0,3,5,3,1,1,2.5,16.1,16.0,0,0,1,0,0,0
+new balance,510 v6,0,1,0,0,0,0,0,1,0,11.0,8.2,1,1,1,3,3,2,4,3,0,0,1,3,5,3,3,2.9,33.8,25.6,0,0,1,1,0,0
+new balance,dynasoft nitrel v6,0,1,0,0,1,1,0,1,0,10.3,2.5,0,1,1,3,3,3,4,3,0,0,3,3,3,1,3,2.7,22.7,20.2,0,0,1,1,0,0
+new balance,fresh foam x garoe v2,0,1,0,0,5,3,0,1,0,9.5,11.0,1,0,0,5,3,3,4,3,0,0,1,3,5,5,5,3.2,38.4,27.4,0,0,1,1,0,0
+new balance,fresh foam x hierro v9,0,1,0,0,5,3,0,1,0,10.9,4.2,0,1,1,5,4,4,4,3,0,0,3,3,5,5,3,3.3,37.3,33.1,0,0,1,1,0,0
+new balance,fresh foam x more trail v3,0,0,1,1,5,3,0,1,0,10.6,7.1,0,1,1,5,1,3,0,3,0,0,3,1,3,3,3,5.0,38.6,31.5,0,0,1,1,0,0
+new balance,fuelcell supercomp trail,1,1,1,0,0,0,0,1,0,8.7,13.0,1,0,0,5,3,3,4,3,0,1,3,3,5,5,3,2.9,34.7,21.7,0,0,1,1,0,0
+new balance,mt10,1,1,0,0,1,1,0,1,0,7.1,5.0,0,1,1,1,5,3,4,3,0,0,1,3,1,1,1,0.0,15.6,10.6,0,0,1,0,0,0
+new balance,tektrel,0,1,0,0,0,0,0,1,0,10.7,8.0,1,1,1,3,4,3,3,5,0,0,3,5,5,3,3,2.4,32.2,24.2,1,0,1,1,0,0
+nike,air zoom terra kiger 6,0,0,1,0,0,0,0,1,0,11.2,4.4,0,1,1,0,0,0,0,0,1,0,5,0,5,0,0,4.8,19.6,15.2,0,0,0,0,0,0
+nike,juniper trail 2 gtx,0,1,0,0,0,0,0,1,0,10.3,10.2,1,0,0,1,5,4,4,1,0,0,5,5,5,5,3,2.7,34.5,24.3,0,1,0,1,1,0
+nike,pegasus trail 3,0,1,1,0,0,0,0,1,0,10.8,10.3,1,0,0,5,0,0,0,1,0,0,3,0,5,3,3,3.3,35.3,25.0,0,0,1,1,0,0
+nike,pegasus trail 4,0,1,0,0,5,1,0,1,0,9.6,12.7,1,0,0,0,0,0,0,0,0,0,3,0,3,1,3,3.4,35.5,22.8,0,0,0,1,0,0
+nike,pegasus trail 4 gtx,0,1,1,0,0,0,0,1,0,9.6,12.8,1,0,0,5,4,4,4,1,0,0,3,5,5,5,1,3.5,37.7,24.9,0,1,0,1,1,0
+nike,pegasus trail 5,0,1,0,0,5,3,0,1,0,10.1,9.6,1,1,1,5,5,4,4,1,0,0,3,3,3,3,5,3.2,34.6,25.0,0,0,1,1,0,0
+nike,pegasus trail 5 gtx,0,1,0,0,3,3,0,1,0,9.9,8.3,1,1,1,5,3,2,4,1,0,0,3,5,3,5,1,3.6,32.1,23.8,0,1,0,1,1,0
+nike,terra kiger 9,0,0,1,1,0,0,0,1,0,10.2,4.4,0,1,1,5,5,3,0,3,0,0,3,3,5,3,3,4.4,30.1,25.7,0,0,1,1,0,0
+nike,ultrafly,0,1,1,0,5,5,0,1,0,10.5,11.8,1,0,0,5,1,4,4,3,0,1,3,5,5,5,3,3.0,36.6,24.8,0,0,1,1,0,0
+nike,wildhorse 10,0,1,1,0,5,3,0,1,0,11.0,10.9,1,0,0,5,1,4,3,3,1,0,3,3,3,3,3,3.4,38.3,27.4,0,0,1,1,0,0
+nike,zegama 2,0,0,1,0,3,3,0,1,0,10.7,4.0,0,1,1,5,4,4,4,3,0,0,3,5,3,5,1,4.0,30.3,26.3,0,0,1,1,0,0
+nnormal,kjerag,1,1,0,0,3,3,0,1,0,7.5,8.6,0,1,1,3,3,3,3,1,0,0,3,3,1,3,1,3.0,25.0,16.4,0,0,1,0,0,0
+on,cloudsurfer trail,0,1,0,0,5,1,0,1,0,9.6,10.7,1,0,0,3,3,2,4,3,0,0,3,3,3,5,3,2.5,37.4,26.7,0,0,1,1,0,0
+on,cloudsurfer trail 2,0,1,0,0,3,1,5,1,0,10.0,14.0,1,0,0,3,3,4,4,3,0,0,3,1,3,5,5,2.0,40.8,26.8,0,0,1,1,0,0
+on,cloudultra 2,0,1,0,0,3,3,0,1,0,10.4,10.2,1,0,0,1,2,3,4,5,1,0,3,3,1,1,1,2.5,30.2,20.0,1,0,1,1,0,0
+on,cloudvista,0,1,0,0,0,0,0,1,0,10.1,10.3,1,0,0,3,1,2,4,5,1,0,3,3,5,3,1,2.5,32.3,22.0,1,0,1,1,0,0
+on,cloudvista 2,0,1,0,0,3,1,0,1,0,10.3,6.0,0,1,1,3,3,2,4,1,0,0,1,3,3,5,3,3.1,31.7,25.7,0,0,1,1,0,0
+salomon,genesis,0,0,1,1,3,3,0,1,0,9.9,9.0,1,1,1,3,4,4,3,3,0,0,3,3,3,3,5,4.0,33.5,24.5,0,0,1,1,0,1
+salomon,pulsar trail,0,1,0,0,0,0,0,1,0,9.9,7.2,0,1,1,5,3,4,4,3,1,0,3,3,5,5,3,2.5,31.0,23.8,0,0,1,1,0,0
+salomon,s/lab genesis,1,0,1,0,3,3,5,1,0,8.8,7.8,0,1,1,5,2,3,3,3,1,0,3,3,3,3,1,4.3,31.9,24.1,0,0,1,1,0,0
+salomon,s/lab pulsar 4,1,1,0,0,5,1,5,1,0,8.7,7.1,0,1,1,5,3,4,4,1,0,0,3,3,3,5,3,3.0,32.6,25.5,0,0,1,0,0,0
+salomon,s/lab ultra,0,1,1,0,5,3,5,1,0,10.2,10.2,1,0,0,5,2,4,4,3,0,0,1,1,3,5,1,3.5,36.3,26.1,0,0,1,1,0,0
+salomon,s/lab ultra glide,0,1,1,0,3,1,5,1,0,10.8,7.2,0,1,1,3,4,4,4,1,0,0,1,3,5,5,3,3.2,41.0,33.8,0,0,1,1,0,0
+salomon,sense pro 4,0,0,1,1,0,0,0,1,0,9.6,4.0,0,1,1,0,0,0,0,0,1,0,3,0,5,0,1,4.3,24.4,20.5,0,0,0,0,0,1
+salomon,sense ride 4,0,0,1,0,0,0,0,1,0,10.5,7.3,0,1,1,0,0,0,0,3,0,0,3,0,5,3,0,3.6,26.5,19.2,0,0,1,1,0,0
+salomon,sense ride 5,0,1,1,0,1,3,0,1,0,10.3,8.7,1,1,1,3,0,0,0,3,0,0,3,3,3,3,1,3.5,27.2,18.5,0,0,1,1,0,0
+salomon,speedcross 6,0,0,0,1,1,1,0,1,0,10.4,14.1,1,0,0,1,4,3,3,1,0,0,3,3,3,5,5,5.8,36.5,22.4,0,1,0,1,0,0
+salomon,speedcross 6 gtx,0,0,0,1,0,0,0,1,0,11.5,11.2,1,0,0,1,4,4,3,1,0,0,3,3,5,5,5,5.0,37.0,25.8,0,1,0,1,1,0
+salomon,supercross 4,0,0,1,1,0,0,0,1,0,11.1,15.2,1,0,0,5,4,4,4,3,0,0,3,1,5,1,1,4.2,35.1,19.9,0,0,1,1,0,0
+salomon,thundercross,0,0,1,1,1,1,0,1,0,9.6,3.0,0,1,1,5,4,3,4,1,0,0,3,3,3,5,1,4.0,27.6,24.6,0,1,0,1,0,0
+salomon,ultra flow,0,1,0,0,3,3,0,1,0,9.1,12.5,1,0,0,5,4,3,3,3,0,0,3,3,3,5,5,2.8,34.9,22.4,0,0,1,1,0,0
+salomon,ultra glide,0,1,1,0,0,0,0,1,0,9.7,8.8,1,1,1,3,4,0,0,5,0,0,1,3,5,1,3,3.0,31.2,22.4,1,0,1,1,0,0
+salomon,ultra glide 2,0,1,1,0,3,3,0,1,0,10.1,7.2,0,1,1,5,2,0,0,3,0,0,1,1,3,1,3,2.8,30.6,23.4,0,0,1,0,0,0
+salomon,xa pro 3d gtx,0,1,1,0,0,0,0,0,1,13.4,13.1,1,0,0,1,0,0,0,1,0,0,3,0,5,5,5,2.9,31.9,18.8,0,1,0,1,1,0
+salomon,xa pro 3d v8,0,1,1,0,0,0,0,0,1,12.3,14.6,1,0,0,1,0,0,0,3,0,0,3,0,5,5,5,2.9,35.0,20.4,0,0,1,1,0,0
+salomon,xa pro 3d v9,0,1,1,0,0,0,0,0,1,12.2,12.5,1,0,0,1,5,3,4,3,0,0,5,3,5,5,5,2.8,31.7,19.2,0,0,1,1,0,0
+salomon,xa pro 3d v9 gtx,0,1,1,0,1,1,0,0,1,12.7,13.5,1,0,0,1,5,3,4,1,1,0,3,3,5,5,5,2.8,33.5,20.0,0,1,0,1,1,0
+saucony,endorphin edge,0,0,1,0,3,5,0,1,0,9.5,7.1,0,1,1,5,0,0,0,3,0,1,1,0,5,5,3,3.4,33.4,26.3,0,0,1,1,0,0
+saucony,endorphin rift,0,0,0,1,0,0,0,1,0,9.0,7.9,0,1,1,5,4,4,4,5,1,0,3,3,5,3,3,4.5,33.0,25.1,1,0,1,1,0,0
+saucony,endorphin trail,0,0,0,1,0,0,0,1,0,11.0,5.2,0,1,1,0,0,0,0,0,0,0,3,0,5,5,5,4.5,36.3,31.1,0,0,0,1,0,0
+saucony,peregrine 11,0,0,0,1,0,0,0,1,0,11.2,5.1,0,1,1,0,0,0,0,0,1,0,3,0,5,3,0,4.4,27.5,22.4,0,0,0,1,0,0
+saucony,peregrine 12,0,0,0,1,0,0,0,1,0,10.1,6.9,0,1,1,0,0,0,0,0,1,0,5,0,5,0,0,4.6,30.2,23.2,0,0,0,0,0,0
+saucony,peregrine 13,0,0,0,1,0,0,0,1,0,9.6,3.9,0,1,1,3,0,0,0,3,1,0,3,1,5,1,3,4.8,27.5,23.6,0,0,1,1,0,0
+saucony,peregrine 14,0,0,1,1,3,3,0,1,0,9.4,2.2,0,1,1,3,3,3,4,3,1,0,5,3,3,3,3,4.7,27.3,25.1,0,0,1,1,0,0
+saucony,peregrine 15,0,1,1,0,3,3,0,1,0,9.4,3.7,0,1,1,5,4,3,4,3,1,0,3,1,3,1,3,4.7,29.5,25.8,0,0,1,1,0,0
+saucony,xodus ultra,0,0,1,1,0,0,0,1,0,10.1,7.2,0,1,1,3,0,0,0,3,1,0,1,0,5,3,3,3.8,33.9,26.7,0,0,1,1,0,0
+saucony,xodus ultra 2,0,0,1,1,0,0,0,1,0,10.3,7.3,0,1,1,3,2,4,3,3,1,0,3,3,5,5,5,4.6,34.1,26.8,0,0,1,1,0,0
+saucony,xodus ultra 3,0,0,0,1,3,3,0,1,0,10.7,5.9,0,1,1,5,3,4,4,3,1,0,3,3,3,5,1,4.3,35.1,29.2,0,0,1,1,0,0
+saucony,xodus ultra 4,0,1,0,0,3,3,5,1,0,11.0,6.5,0,1,1,5,3,2,3,3,0,0,3,3,3,5,5,3.5,37.6,31.1,0,0,1,1,0,0
+scarpa,spin planet,0,1,1,0,0,0,0,1,0,11.4,6.2,0,1,1,3,3,4,3,5,0,0,3,3,5,5,3,3.2,32.8,26.6,1,0,1,1,0,0
+the north face,vectiv enduris 3,0,1,0,0,0,0,0,1,0,9.7,11.6,1,0,0,3,4,4,4,1,0,0,3,3,3,5,5,3.3,35.8,24.2,0,0,1,1,0,0
+topo,mtn racer 3,0,1,1,0,0,0,0,1,0,10.1,6.9,0,1,1,3,2,3,4,3,0,0,5,5,5,5,3,4.2,33.5,26.6,0,0,1,1,0,0
+topo,traverse,0,1,1,0,0,0,0,1,0,10.9,4.8,0,1,1,3,2,4,4,3,1,0,5,5,5,5,3,4.1,30.8,26.0,0,0,1,1,0,1
+topo,ultraventure 3,0,1,0,0,5,1,0,1,0,9.7,6.3,0,1,1,5,3,3,4,3,0,0,5,5,3,5,3,3.2,37.2,30.9,0,0,1,1,0,0
+topo,ultraventure 4,0,1,0,0,3,1,0,1,0,10.1,6.6,0,1,1,5,2,3,4,3,0,0,5,5,3,3,1,3.2,35.1,28.5,0,0,1,1,0,0
+xero,shoes mesa trail wp,0,1,0,0,0,0,0,1,0,9.7,1.2,0,1,1,3,3,2,3,1,0,0,3,5,3,1,1,3.8,14.6,13.4,0,1,0,1,1,0
+xero,shoes scrambler low,0,1,0,0,0,0,0,1,0,9.2,0.1,0,1,1,1,3,3,3,5,0,0,3,5,3,1,1,2.7,16.3,16.4,1,0,1,1,0,0
diff --git a/data/unused-data/(old) SONIX utilities - Trail.csv b/data/unused-data/(old) SONIX utilities - Trail.csv
new file mode 100644
index 0000000000000000000000000000000000000000..a9beb16e0d953dd41255ff6628d595b3768bc222
--- /dev/null
+++ b/data/unused-data/(old) SONIX utilities - Trail.csv	
@@ -0,0 +1,952 @@
+Brand,Name,Audience score,Price,Trail terrain,Arch support,Weight lab Weight brand,Lightweight,Drop lab Drop brand,Strike pattern,Size,Midsole softness,Plate,Toebox durability,Heel padding durability,Outsole durability,Breathability,Width / fit,Toebox width,Stiffness,Torsional rigidity,Heel counter stiffness,Lug depth,Heel stack lab Heel stack brand,Forefoot lab Forefoot brand,Widths available,For heavy runners,Season,Removable insole,Orthotic friendly,Waterproofing,Ranking,Popularity
+Adidas,Terrex Agravic Speed Ultra,"90
+ Superb!",$220,Light,Neutral,9.1 oz / 259g 9.5 oz / 270g,0,0.3 mm 8.0 mm,Mid/forefoot,Slightly large,Balanced,0,Good,Good,Decent,Moderate,Medium,Narrow,Moderate,Stiff,Flexible,2.5 mm,30.6 mm 38.0 mm,30.3 mm 30.0 mm,Normal,0,All seasons,1,1,-,#67 Top 19%,#163 Top 45%
+Adidas,Terrex Speed Ultra,"90
+ Superb!",$160,Light,Neutral,9.1 oz / 258g 9 oz / 255g,0,8.2 mm 8.0 mm,HeelMid/forefoot,True to size,-,0,-,-,-,-,Narrow,-,Stiff,Flexible,Flexible,2.6 mm,32.8 mm 26.0 mm,24.6 mm 18.0 mm,Normal,0,-,1,1,-,#45 Top 13%,#294 Bottom 20%
+Altra,Experience Wild,"88
+ Great!",$145,LightModerate,Neutral,10.1 oz / 285g 9.6 oz / 273g,0,4.3 mm 4.0 mm,Mid/forefoot,True to size,Soft,0,Decent,Decent,Good,Moderate,Wide,Wide,Moderate,Stiff,Moderate,3.6 mm,34.5 mm 34.0 mm,30.2 mm 30.0 mm,Normal,0,All seasons,1,1,-,#251 Top 39%,#308 Top 48%
+Altra,Experience Wild 2,"79
+ Good!",$140,Light,Neutral,9.4 oz / 266g 10.3 oz / 293g,0,6.1 mm 4.0 mm,Mid/forefoot,-,Balanced,0,Decent,Good,Good,Warm,Wide,Wide,Moderate,Moderate,Flexible,3.5 mm,32.3 mm 32.0 mm,26.2 mm 28.0 mm,Normal,0,All seasons,1,1,-,#315 Bottom 14%,#211 Bottom 42%
+Altra,Lone Peak 5.0,"91
+ Superb!",$130,LightModerate,Neutral,10.7 oz / 302g 10.6 oz / 301g,0,0.2 mm 0.0 mm,Mid/forefoot,True to size,-,Rock plate,-,-,-,-,Narrow,-,Stiff,Flexible,-,3.7 mm,24.5 mm 25.0 mm,24.3 mm 25.0 mm,Normal,0,-,1,1,-,#62 Top 10%,#63 Top 10%
+Altra,Lone Peak 6,"89
+ Great!",$140,ModerateTechnical,Neutral,9.8 oz / 278g 9.7 oz / 275g,0,0.6 mm 0.0 mm,Mid/forefoot,True to size,-,Rock plate,-,-,-,-,Wide,-,Stiff,-,-,4.4 mm,25.1 mm 25.0 mm,24.5 mm 25.0 mm,NormalWide,0,-,0,0,-,#139 Top 22%,#290 Top 45%
+Altra,Lone Peak 7,"86
+ Good!",$150,Moderate,Neutral,10.4 oz / 294g 11 oz / 312g,0,0.2 mm 0.0 mm,Mid/forefoot,True to size,Balanced,0,-,-,-,Moderate,Narrow,-,Stiff,Flexible,Flexible,3.4 mm,23.3 mm 25.0 mm,23.1 mm 25.0 mm,NormalWide,0,All seasons,1,1,-,#387 Bottom 40%,#227 Top 36%
+Altra,Lone Peak 8,"81
+ Good!",$140,LightModerate,Neutral,10.2 oz / 288g 10.7 oz / 303g,0,1.4 mm 0.0 mm,Mid/forefoot,True to size,Balanced,0,Good,Decent,Decent,Moderate,Medium,Wide,Stiff,Flexible,Flexible,3.0 mm,22.7 mm 25.0 mm,21.3 mm 25.0 mm,NormalWide,0,All seasons,1,1,-,#540 Bottom 16%,#177 Top 28%
+Altra,Lone Peak 9,"91
+ Superb!",$140,LightModerate,Neutral,10.9 oz / 309g 10.4 oz / 295g,0,0.0 mm 0.0 mm,Mid/forefoot,True to size,Balanced,0,Decent,Decent,Good,Warm,Wide,Wide,Moderate,Moderate,Flexible,3.8 mm,23.3 mm 25.0 mm,23.3 mm 25.0 mm,NormalWide,0,All seasons,1,1,-,#30 Top 9%,#42 Top 12%
+Altra,Lone Peak 9,"91
+ Superb!",$140,LightModerate,Neutral,10.9 oz / 309g 10.4 oz / 295g,0,0.0 mm 0.0 mm,Mid/forefoot,True to size,Balanced,0,Decent,Decent,Good,Warm,Wide,Wide,Moderate,Moderate,Flexible,3.8 mm,23.3 mm 25.0 mm,23.3 mm 25.0 mm,NormalWide,0,All seasons,1,1,-,#30 Top 9%,#42 Top 12%
+Altra,Lone Peak 9,"91
+ Superb!",$140,LightModerate,Neutral,10.9 oz / 309g 10.4 oz / 295g,0,0.0 mm 0.0 mm,Mid/forefoot,True to size,Balanced,0,Decent,Decent,Good,Warm,Wide,Wide,Moderate,Moderate,Flexible,3.8 mm,23.3 mm 25.0 mm,23.3 mm 25.0 mm,NormalWide,0,All seasons,1,1,-,#30 Top 9%,#42 Top 12%
+Altra,Lone Peak 9,"91
+ Superb!",$140,LightModerate,Neutral,10.9 oz / 309g 10.4 oz / 295g,0,0.0 mm 0.0 mm,Mid/forefoot,True to size,Balanced,0,Decent,Decent,Good,Warm,Wide,Wide,Moderate,Moderate,Flexible,3.8 mm,23.3 mm 25.0 mm,23.3 mm 25.0 mm,NormalWide,0,All seasons,1,1,-,#29 Top 8%,#42 Top 12%
+Altra,Lone Peak 9,"91
+ Superb!",$140,LightModerate,Neutral,10.9 oz / 309g 10.4 oz / 295g,0,0.0 mm 0.0 mm,Mid/forefoot,True to size,Balanced,0,Decent,Decent,Good,Warm,Wide,Wide,Moderate,Moderate,Flexible,3.8 mm,23.3 mm 25.0 mm,23.3 mm 25.0 mm,NormalWide,0,All seasons,1,1,-,#29 Top 8%,#42 Top 12%
+Altra,Lone Peak 9,"91
+ Superb!",$140,LightModerate,Neutral,10.9 oz / 309g 10.4 oz / 295g,0,0.0 mm 0.0 mm,Mid/forefoot,True to size,Balanced,0,Decent,Decent,Good,Warm,Wide,Wide,Moderate,Moderate,Flexible,3.8 mm,23.3 mm 25.0 mm,23.3 mm 25.0 mm,NormalWide,0,All seasons,1,1,-,#30 Top 9%,#42 Top 12%
+Altra,Mont Blanc,"79
+ Good!",$180,LightModerate,Neutral,9.6 oz / 272g 9.9 oz / 280g,0,0.0 mm,Mid/forefoot,True to size,-,0,-,-,-,-,Medium,-,Stiff,-,-,2.8 mm,33.8 mm 30.0 mm,33.8 mm,NormalWide,0,-,0,0,-,#317 Bottom 13%,#232 Bottom 36%
+Altra,Mont Blanc Carbon,"86
+ Good!",$260,Moderate,Neutral,8.9 oz / 251g 9.3 oz / 264g,0,0.3 mm 0.0 mm,Mid/forefoot,True to size,Soft,Carbon plate,Decent,Bad,Good,Moderate,Medium,Wide,Stiff,Flexible,Flexible,3.5 mm,27.2 mm 29.0 mm,26.9 mm 29.0 mm,Normal,0,All seasons,1,1,-,#180 Top 49%,#263 Bottom 28%
+Altra,Olympus 5,"83
+ Good!",$170,LightModerate,Neutral,11.5 oz / 325g 12.3 oz / 350g,0,2.0 mm 0.0 mm,Mid/forefoot,Slightly small,Balanced,0,-,-,-,Moderate,Wide,-,Stiff,Moderate,Moderate,3.0 mm,33.0 mm 33.0 mm,31.0 mm 33.0 mm,Normal,0,All seasons,1,1,-,#503 Bottom 22%,#284 Top 44%
+Altra,Olympus 6,"82
+ Good!",$175,LightModerate,Neutral,12.6 oz / 357g 12.5 oz / 354g,0,0.7 mm 0.0 mm,Mid/forefoot,Half size small,Balanced,0,Very good,Good,Good,Breathable,Medium,Wide,Stiff,Stiff,Moderate,3.5 mm,32.2 mm 35.0 mm,31.5 mm 35.0 mm,Normal,1,SummerAll seasons,1,1,-,#274 Bottom 25%,#106 Top 29%
+Altra,Olympus 6,"82
+ Good!",$175,LightModerate,Neutral,12.6 oz / 357g 12.5 oz / 354g,0,0.7 mm 0.0 mm,Mid/forefoot,Half size small,Balanced,0,Very good,Good,Good,Breathable,Medium,Wide,Stiff,Stiff,Moderate,3.5 mm,32.2 mm 35.0 mm,31.5 mm 35.0 mm,Normal,1,SummerAll seasons,1,1,-,#274 Bottom 25%,#106 Top 29%
+Altra,Olympus 6,"82
+ Good!",$175,LightModerate,Neutral,12.6 oz / 357g 12.5 oz / 354g,0,0.7 mm 0.0 mm,Mid/forefoot,Half size small,Balanced,0,Very good,Good,Good,Breathable,Medium,Wide,Stiff,Stiff,Moderate,3.5 mm,32.2 mm 35.0 mm,31.5 mm 35.0 mm,Normal,1,SummerAll seasons,1,1,-,#274 Bottom 25%,#106 Top 29%
+Altra,Olympus 6,"82
+ Good!",$175,LightModerate,Neutral,12.6 oz / 357g 12.5 oz / 354g,0,0.7 mm 0.0 mm,Mid/forefoot,Half size small,Balanced,0,Very good,Good,Good,Breathable,Medium,Wide,Stiff,Stiff,Moderate,3.5 mm,32.2 mm 35.0 mm,31.5 mm 35.0 mm,Normal,1,SummerAll seasons,1,1,-,#274 Bottom 25%,#106 Top 29%
+Altra,Olympus 6,"82
+ Good!",$175,LightModerate,Neutral,12.6 oz / 357g 12.5 oz / 354g,0,0.7 mm 0.0 mm,Mid/forefoot,Half size small,Balanced,0,Very good,Good,Good,Breathable,Medium,Wide,Stiff,Stiff,Moderate,3.5 mm,32.2 mm 35.0 mm,31.5 mm 35.0 mm,Normal,1,SummerAll seasons,1,1,-,#274 Bottom 25%,#106 Top 29%
+Altra,Outroad,"81
+ Good!",$140,Light,Neutral,10.1 oz / 287g 10.7 oz / 303g,0,0.1 mm 0.0 mm,Mid/forefoot,Slightly small,Balanced,0,-,-,-,Moderate,Medium,Medium,Stiff,Moderate,Flexible,2.3 mm,25.1 mm 27.0 mm,25.0 mm 27.0 mm,Normal,0,All seasons,1,1,-,#550 Bottom 14%,#510 Bottom 21%
+Altra,Outroad 2,"79
+ Good!",$120,Light,Neutral,10.3 oz / 291g 10.1 oz / 286g,0,1.4 mm 0.0 mm,Mid/forefoot,Slightly small,Balanced,0,Bad,Decent,Good,Moderate,Medium,Wide,Stiff,Moderate,Moderate,2.2 mm,26.9 mm 27.5 mm,25.5 mm 27.5 mm,Normal,0,All seasons,1,1,-,#581 Bottom 10%,#552 Bottom 14%
+Altra,Outroad 3,"81
+ Good!",$130,Light,Neutral,9.2 oz / 261g 10.7 oz / 303g,0,0.6 mm 0.0 mm,Mid/forefoot,Slightly small,Balanced,0,Decent,Bad,Bad,Warm,Medium,Wide,Stiff,Moderate,Flexible,1.5 mm,23.8 mm 27.0 mm,23.2 mm 27.0 mm,Normal,0,All seasons,1,1,-,#289 Bottom 21%,#272 Bottom 26%
+Altra,Outroad 3,"81
+ Good!",$130,Light,Neutral,9.2 oz / 261g 10.7 oz / 303g,0,0.6 mm 0.0 mm,Mid/forefoot,Slightly small,Balanced,0,Decent,Bad,Bad,Warm,Medium,Wide,Stiff,Moderate,Flexible,1.5 mm,23.8 mm 27.0 mm,23.2 mm 27.0 mm,Normal,0,All seasons,1,1,-,#289 Bottom 21%,#272 Bottom 26%
+Altra,Outroad 3,"81
+ Good!",$130,Light,Neutral,9.2 oz / 261g 10.7 oz / 303g,0,0.6 mm 0.0 mm,Mid/forefoot,Slightly small,Balanced,0,Decent,Bad,Bad,Warm,Medium,Wide,Stiff,Moderate,Flexible,1.5 mm,23.8 mm 27.0 mm,23.2 mm 27.0 mm,Normal,0,All seasons,1,1,-,#289 Bottom 21%,#272 Bottom 26%
+Altra,Outroad 3,"81
+ Good!",$130,Light,Neutral,9.2 oz / 261g 10.7 oz / 303g,0,0.6 mm 0.0 mm,Mid/forefoot,Slightly small,Balanced,0,Decent,Bad,Bad,Warm,Medium,Wide,Stiff,Moderate,Flexible,1.5 mm,23.8 mm 27.0 mm,23.2 mm 27.0 mm,Normal,0,All seasons,1,1,-,#289 Bottom 21%,#272 Bottom 26%
+Altra,Superior 6,"78
+ Decent!",$130,LightModerate,Neutral,9.6 oz / 272g 9.1 oz / 258g,0,0.1 mm 0.0 mm,Mid/forefoot,Slightly small,Balanced,0,Very bad,Decent,Good,Breathable,Medium,Wide,Stiff,Moderate,Flexible,3.3 mm,22.1 mm 20.5 mm,22.0 mm 20.5 mm,Normal,0,SummerAll seasons,1,1,-,#324 Bottom 11%,#260 Bottom 29%
+Altra,Superior 6,"78
+ Decent!",$130,LightModerate,Neutral,9.6 oz / 272g 9.1 oz / 258g,0,0.1 mm 0.0 mm,Mid/forefoot,Slightly small,Balanced,0,Very bad,Decent,Good,Breathable,Medium,Wide,Stiff,Moderate,Flexible,3.3 mm,22.1 mm 20.5 mm,22.0 mm 20.5 mm,Normal,0,SummerAll seasons,1,1,-,#324 Bottom 11%,#260 Bottom 29%
+Altra,Superior 6,"78
+ Decent!",$130,LightModerate,Neutral,9.6 oz / 272g 9.1 oz / 258g,0,0.1 mm 0.0 mm,Mid/forefoot,Slightly small,Balanced,0,Very bad,Decent,Good,Breathable,Medium,Wide,Stiff,Moderate,Flexible,3.3 mm,22.1 mm 20.5 mm,22.0 mm 20.5 mm,Normal,0,SummerAll seasons,1,1,-,#324 Bottom 11%,#260 Bottom 29%
+Altra,Superior 6,"78
+ Decent!",$130,LightModerate,Neutral,9.6 oz / 272g 9.1 oz / 258g,0,0.1 mm 0.0 mm,Mid/forefoot,Slightly small,Balanced,0,Very bad,Decent,Good,Breathable,Medium,Wide,Stiff,Moderate,Flexible,3.3 mm,22.1 mm 20.5 mm,22.0 mm 20.5 mm,Normal,0,SummerAll seasons,1,1,-,#324 Bottom 11%,#260 Bottom 29%
+Altra,Superior 6,"78
+ Decent!",$130,LightModerate,Neutral,9.6 oz / 272g 9.1 oz / 258g,0,0.1 mm 0.0 mm,Mid/forefoot,Slightly small,Balanced,0,Very bad,Decent,Good,Breathable,Medium,Wide,Stiff,Moderate,Flexible,3.3 mm,22.1 mm 20.5 mm,22.0 mm 20.5 mm,Normal,0,SummerAll seasons,1,1,-,#324 Bottom 11%,#260 Bottom 29%
+Altra,Superior 6,"78
+ Decent!",$130,LightModerate,Neutral,9.6 oz / 272g 9.1 oz / 258g,0,0.1 mm 0.0 mm,Mid/forefoot,Slightly small,Balanced,0,Very bad,Decent,Good,Breathable,Medium,Wide,Stiff,Moderate,Flexible,3.3 mm,22.1 mm 20.5 mm,22.0 mm 20.5 mm,Normal,0,SummerAll seasons,1,1,-,#324 Bottom 11%,#260 Bottom 29%
+Altra,Superior 6,"78
+ Decent!",$130,LightModerate,Neutral,9.6 oz / 272g 9.1 oz / 258g,0,0.1 mm 0.0 mm,Mid/forefoot,Slightly small,Balanced,0,Very bad,Decent,Good,Breathable,Medium,Wide,Stiff,Moderate,Flexible,3.3 mm,22.1 mm 20.5 mm,22.0 mm 20.5 mm,Normal,0,SummerAll seasons,1,1,-,#324 Bottom 11%,#260 Bottom 29%
+Altra,Timp 4,"78
+ Decent!",$160,LightModerate,Neutral,11.1 oz / 316g 10.6 oz / 300g,0,0.1 mm 0.0 mm,Mid/forefoot,True to size,Balanced,0,-,-,-,Moderate,Medium,-,Stiff,Flexible,Flexible,2.9 mm,29.0 mm 30.0 mm,28.9 mm 30.0 mm,Normal,0,All seasons,1,1,-,#595 Bottom 8%,#507 Bottom 21%
+Altra,Timp 5,"80
+ Good!",$155,LightModerate,Neutral,9.5 oz / 269g 9.8 oz / 277g,0,-0.1 mm 0.0 mm,Mid/forefoot,Slightly small,Balanced,0,Good,Decent,Good,Moderate,Wide,Wide,Stiff,Stiff,Moderate,3.0 mm,28.6 mm 29.0 mm,28.7 mm 29.0 mm,Normal,0,All seasons,1,1,-,#304 Bottom 17%,#138 Top 38%
+Altra,Timp 5,"80
+ Good!",$155,LightModerate,Neutral,9.5 oz / 269g 9.8 oz / 277g,0,-0.1 mm 0.0 mm,Mid/forefoot,Slightly small,Balanced,0,Good,Decent,Good,Moderate,Wide,Wide,Stiff,Stiff,Moderate,3.0 mm,28.6 mm 29.0 mm,28.7 mm 29.0 mm,Normal,0,All seasons,1,1,-,#304 Bottom 17%,#138 Top 38%
+Altra,Timp 5,"80
+ Good!",$155,LightModerate,Neutral,9.5 oz / 269g 9.8 oz / 277g,0,-0.1 mm 0.0 mm,Mid/forefoot,Slightly small,Balanced,0,Good,Decent,Good,Moderate,Wide,Wide,Stiff,Stiff,Moderate,3.0 mm,28.6 mm 29.0 mm,28.7 mm 29.0 mm,Normal,0,All seasons,1,1,-,#304 Bottom 17%,#138 Top 38%
+Altra,Timp 5,"80
+ Good!",$155,LightModerate,Neutral,9.5 oz / 269g 9.8 oz / 277g,0,-0.1 mm 0.0 mm,Mid/forefoot,Slightly small,Balanced,0,Good,Decent,Good,Moderate,Wide,Wide,Stiff,Stiff,Moderate,3.0 mm,28.6 mm 29.0 mm,28.7 mm 29.0 mm,Normal,0,All seasons,1,1,-,#304 Bottom 17%,#138 Top 38%
+Altra,Timp 5,"80
+ Good!",$155,LightModerate,Neutral,9.5 oz / 269g 9.8 oz / 277g,0,-0.1 mm 0.0 mm,Mid/forefoot,Slightly small,Balanced,0,Good,Decent,Good,Moderate,Wide,Wide,Stiff,Stiff,Moderate,3.0 mm,28.6 mm 29.0 mm,28.7 mm 29.0 mm,Normal,0,All seasons,1,1,-,#304 Bottom 17%,#138 Top 38%
+Altra,Timp 5,"80
+ Good!",$155,LightModerate,Neutral,9.5 oz / 269g 9.8 oz / 277g,0,-0.1 mm 0.0 mm,Mid/forefoot,Slightly small,Balanced,0,Good,Decent,Good,Moderate,Wide,Wide,Stiff,Stiff,Moderate,3.0 mm,28.6 mm 29.0 mm,28.7 mm 29.0 mm,Normal,0,All seasons,1,1,-,#304 Bottom 17%,#138 Top 38%
+Altra,Timp 5,"80
+ Good!",$155,LightModerate,Neutral,9.5 oz / 269g 9.8 oz / 277g,0,-0.1 mm 0.0 mm,Mid/forefoot,Slightly small,Balanced,0,Good,Decent,Good,Moderate,Wide,Wide,Stiff,Stiff,Moderate,3.0 mm,28.6 mm 29.0 mm,28.7 mm 29.0 mm,Normal,0,All seasons,1,1,-,#304 Bottom 17%,#138 Top 38%
+Altra,Timp 5,"80
+ Good!",$155,LightModerate,Neutral,9.5 oz / 269g 9.8 oz / 277g,0,-0.1 mm 0.0 mm,Mid/forefoot,Slightly small,Balanced,0,Good,Decent,Good,Moderate,Wide,Wide,Stiff,Stiff,Moderate,3.0 mm,28.6 mm 29.0 mm,28.7 mm 29.0 mm,Normal,0,All seasons,1,1,-,#304 Bottom 17%,#138 Top 38%
+Altra,Timp 5,"80
+ Good!",$155,LightModerate,Neutral,9.5 oz / 269g 9.8 oz / 277g,0,-0.1 mm 0.0 mm,Mid/forefoot,Slightly small,Balanced,0,Good,Decent,Good,Moderate,Wide,Wide,Stiff,Stiff,Moderate,3.0 mm,28.6 mm 29.0 mm,28.7 mm 29.0 mm,Normal,0,All seasons,1,1,-,#304 Bottom 17%,#138 Top 38%
+Altra,Timp 5 GTX,"67
+ Bad!",$175,LightModerate,Neutral,11 oz / 312g 11.7 oz / 331g,0,0.3 mm 0.0 mm,Mid/forefoot,-,Soft,0,Decent,Decent,Good,Warm,Wide,Wide,Stiff,Stiff,Flexible,3.5 mm,28.9 mm 29.0 mm,28.6 mm 29.0 mm,Normal,0,Winter,1,1,Waterproof,#365 Bottom 1%,#280 Bottom 23%
+ASICS,Gel Excite Trail 2,"81
+ Good!",$85,Light,Neutral,10.3 oz / 292g 10.4 oz / 296g,0,10.1 mm 8.0 mm,Heel,True to size,Balanced,0,Very bad,Bad,Decent,Moderate,Medium,Medium,Flexible,Stiff,Stiff,4.0 mm,37.7 mm 36.0 mm,27.6 mm 28.0 mm,Normal,1,All seasons,1,1,-,#293 Bottom 20%,#205 Bottom 44%
+ASICS,Gel Excite Trail 2,"81
+ Good!",1750820Rp,Light,Neutral,10.3 oz / 292g 10.4 oz / 296g,0,10.1 mm 8.0 mm,Heel,True to size,Balanced,0,Very bad,Bad,Decent,Moderate,Medium,Medium,Flexible,Stiff,Stiff,4.0 mm,37.7 mm 36.0 mm,27.6 mm 28.0 mm,Normal,1,All seasons,1,1,-,#293 Bottom 20%,#205 Bottom 44%
+ASICS,Gel Excite Trail 2,"81
+ Good!",$85,Light,Neutral,10.3 oz / 292g 10.4 oz / 296g,0,10.1 mm 8.0 mm,Heel,True to size,Balanced,0,Very bad,Bad,Decent,Moderate,Medium,Medium,Flexible,Stiff,Stiff,4.0 mm,37.7 mm 36.0 mm,27.6 mm 28.0 mm,Normal,1,All seasons,1,1,-,#293 Bottom 20%,#205 Bottom 44%
+ASICS,Gel Excite Trail 2,"81
+ Good!",$85,Light,Neutral,10.3 oz / 292g 10.4 oz / 296g,0,10.1 mm 8.0 mm,Heel,True to size,Balanced,0,Very bad,Bad,Decent,Moderate,Medium,Medium,Flexible,Stiff,Stiff,4.0 mm,37.7 mm 36.0 mm,27.6 mm 28.0 mm,Normal,1,All seasons,1,1,-,#293 Bottom 20%,#205 Bottom 44%
+ASICS,Gel Excite Trail 2,"81
+ Good!",$85,Light,Neutral,10.3 oz / 292g 10.4 oz / 296g,0,10.1 mm 8.0 mm,Heel,True to size,Balanced,0,Very bad,Bad,Decent,Moderate,Medium,Medium,Flexible,Stiff,Stiff,4.0 mm,37.7 mm 36.0 mm,27.6 mm 28.0 mm,Normal,1,All seasons,1,1,-,#293 Bottom 20%,#205 Bottom 44%
+ASICS,Gel Excite Trail 2,"81
+ Good!",$85,Light,Neutral,10.3 oz / 292g 10.4 oz / 296g,0,10.1 mm 8.0 mm,Heel,True to size,Balanced,0,Very bad,Bad,Decent,Moderate,Medium,Medium,Flexible,Stiff,Stiff,4.0 mm,37.7 mm 36.0 mm,27.6 mm 28.0 mm,Normal,1,All seasons,1,1,-,#293 Bottom 20%,#205 Bottom 44%
+ASICS,Gel Excite Trail 2,"81
+ Good!",$85,Light,Neutral,10.3 oz / 292g 10.4 oz / 296g,0,10.1 mm 8.0 mm,Heel,True to size,Balanced,0,Very bad,Bad,Decent,Moderate,Medium,Medium,Flexible,Stiff,Stiff,4.0 mm,37.7 mm 36.0 mm,27.6 mm 28.0 mm,Normal,1,All seasons,1,1,-,#293 Bottom 20%,#205 Bottom 44%
+ASICS,Gel Excite Trail 2,"81
+ Good!",$85,Light,Neutral,10.3 oz / 292g 10.4 oz / 296g,0,10.1 mm 8.0 mm,Heel,True to size,Balanced,0,Very bad,Bad,Decent,Moderate,Medium,Medium,Flexible,Stiff,Stiff,4.0 mm,37.7 mm 36.0 mm,27.6 mm 28.0 mm,Normal,1,All seasons,1,1,-,#293 Bottom 20%,#205 Bottom 44%
+ASICS,Gel Excite Trail 2,"81
+ Good!",$85,Light,Neutral,10.3 oz / 292g 10.4 oz / 296g,0,10.1 mm 8.0 mm,Heel,True to size,Balanced,0,Very bad,Bad,Decent,Moderate,Medium,Medium,Flexible,Stiff,Stiff,4.0 mm,37.7 mm 36.0 mm,27.6 mm 28.0 mm,Normal,1,All seasons,1,1,-,#293 Bottom 20%,#205 Bottom 44%
+ASICS,Gel Trabuco 12,"90
+ Superb!",$140,ModerateTechnical,Neutral,10.5 oz / 299g 10.9 oz / 309g,0,7.8 mm 8.0 mm,Mid/forefoot,True to size,Soft,Rock plate,Good,Good,Good,Moderate,Medium,Wide,Moderate,Moderate,Moderate,4.5 mm,35.4 mm 36.0 mm,27.6 mm 28.0 mm,Normal,0,All seasons,1,1,-,#111 Top 18%,#166 Top 26%
+ASICS,Gel Trabuco 13,"88
+ Great!",$140,Light,Neutral,10.2 oz / 288g 10 oz / 283g,0,7.0 mm 8.0 mm,Mid/forefoot,True to size,Balanced,Rock plate,Good,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Stiff,3.3 mm,33.8 mm 34.0 mm,26.8 mm 26.0 mm,Normal,0,All seasons,1,1,-,#135 Top 37%,#108 Top 30%
+ASICS,Gel Trabuco 13,"88
+ Great!",$140,Light,Neutral,10.2 oz / 288g 10 oz / 283g,0,7.0 mm 8.0 mm,Mid/forefoot,True to size,Balanced,Rock plate,Good,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Stiff,3.3 mm,33.8 mm 34.0 mm,26.8 mm 26.0 mm,Normal,0,All seasons,1,1,-,#135 Top 37%,#108 Top 30%
+ASICS,Metafuji Trail,"90
+ Superb!",$250,Light,Neutral,9.1 oz / 258g 9.2 oz / 261g,0,10.3 mm 5.0 mm,Heel,True to size,Soft,Carbon plate,Very bad,Good,Decent,Breathable,Medium,Narrow,Stiff,Stiff,Moderate,2.7 mm,44.7 mm 44.0 mm,34.4 mm 39.0 mm,NormalWide,0,SummerAll seasons,1,1,-,#61 Top 17%,#300 Bottom 18%
+ASICS,Trabuco Max 3,"90
+ Superb!",$160,ModerateTechnical,Neutral,10.9 oz / 308g 10.5 oz / 298g,0,8.5 mm 5.0 mm,HeelMid/forefoot,Slightly small,Soft,0,Good,Good,Decent,Warm,Medium,Medium,Stiff,Stiff,Stiff,4.0 mm,42.4 mm 43.0 mm,33.9 mm 38.0 mm,Normal,0,All seasons,1,1,-,#76 Top 12%,#165 Top 26%
+ASICS,Trabuco Max 4,"88
+ Great!",$160,Light,Neutral,11 oz / 312g 0.2 oz / 5g,0,6.1 mm 5.0 mm,Mid/forefoot,Half size small,Balanced,0,Decent,Good,Decent,Moderate,Medium,Medium,Stiff,Stiff,Stiff,3.1 mm,39.3 mm 41.0 mm,33.2 mm 36.0 mm,Normal,0,All seasons,1,1,-,#134 Top 37%,#107 Top 30%
+ASICS,Trabuco Max 4,"88
+ Great!",$160,Light,Neutral,11 oz / 312g 0.2 oz / 5g,0,6.1 mm 5.0 mm,Mid/forefoot,Half size small,Balanced,0,Decent,Good,Decent,Moderate,Medium,Medium,Stiff,Stiff,Stiff,3.1 mm,39.3 mm 41.0 mm,33.2 mm 36.0 mm,Normal,0,All seasons,1,1,-,#134 Top 37%,#107 Top 30%
+ASICS,Trabuco Max 4,"88
+ Great!",$160,Light,Neutral,11 oz / 312g 0.2 oz / 5g,0,6.1 mm 5.0 mm,Mid/forefoot,Half size small,Balanced,0,Decent,Good,Decent,Moderate,Medium,Medium,Stiff,Stiff,Stiff,3.1 mm,39.3 mm 41.0 mm,33.2 mm 36.0 mm,Normal,0,All seasons,1,1,-,#136 Top 37%,#108 Top 30%
+Brooks,Caldera 6,"88
+ Great!",$150,LightModerate,Neutral,11.1 oz / 315g 11.1 oz / 314g,0,12.1 mm 6.0 mm,Heel,True to size,Soft,0,Very good,Decent,Good,Moderate,Medium,Medium,Stiff,Stiff,Stiff,3.5 mm,38.4 mm 36.0 mm,26.3 mm 30.0 mm,Normal,0,All seasons,1,1,-,#245 Top 38%,#446 Bottom 31%
+Brooks,Caldera 7,"88
+ Great!",$150,LightModerate,Neutral,10.8 oz / 305g 10.6 oz / 300g,0,8.9 mm 6.0 mm,HeelMid/forefoot,True to size,Soft,0,Decent,Good,Decent,Breathable,Medium,Medium,Stiff,Stiff,Stiff,4.0 mm,36.7 mm 39.0 mm,27.8 mm 33.0 mm,Normal,0,SummerAll seasons,1,1,-,#260 Top 41%,#389 Bottom 39%
+Brooks,Caldera 8,"89
+ Great!",$150,LightModerate,Neutral,10.9 oz / 309g 10.9 oz / 309g,0,8.8 mm 6.0 mm,HeelMid/forefoot,True to size,Soft,0,Bad,Good,Decent,Moderate,Medium,Medium,Moderate,Stiff,Stiff,3.6 mm,36.7 mm 39.0 mm,27.9 mm 33.0 mm,Normal,0,All seasons,1,1,-,#87 Top 24%,#168 Top 46%
+Brooks,Caldera 8,"89
+ Great!",$150,LightModerate,Neutral,10.9 oz / 309g 10.9 oz / 309g,0,8.8 mm 6.0 mm,HeelMid/forefoot,True to size,Soft,0,Bad,Good,Decent,Moderate,Medium,Medium,Moderate,Stiff,Stiff,3.6 mm,36.7 mm 39.0 mm,27.9 mm 33.0 mm,Normal,0,All seasons,1,1,-,#88 Top 24%,#169 Top 46%
+Brooks,Caldera 8,"89
+ Great!",$150,LightModerate,Neutral,10.9 oz / 309g 10.9 oz / 309g,0,8.8 mm 6.0 mm,HeelMid/forefoot,True to size,Soft,0,Bad,Good,Decent,Moderate,Medium,Medium,Moderate,Stiff,Stiff,3.6 mm,36.7 mm 39.0 mm,27.9 mm 33.0 mm,Normal,0,All seasons,1,1,-,#89 Top 25%,#169 Top 46%
+Brooks,Caldera 8,"89
+ Great!",$150,LightModerate,Neutral,10.9 oz / 309g 10.9 oz / 309g,0,8.8 mm 6.0 mm,HeelMid/forefoot,True to size,Soft,0,Bad,Good,Decent,Moderate,Medium,Medium,Moderate,Stiff,Stiff,3.6 mm,36.7 mm 39.0 mm,27.9 mm 33.0 mm,Normal,0,All seasons,1,1,-,#89 Top 25%,#169 Top 46%
+Brooks,Caldera 8,"89
+ Great!",$150,LightModerate,Neutral,10.9 oz / 309g 10.9 oz / 309g,0,8.8 mm 6.0 mm,HeelMid/forefoot,True to size,Soft,0,Bad,Good,Decent,Moderate,Medium,Medium,Moderate,Stiff,Stiff,3.6 mm,36.7 mm 39.0 mm,27.9 mm 33.0 mm,Normal,0,All seasons,1,1,-,#89 Top 25%,#169 Top 46%
+Brooks,Cascadia 16,"87
+ Great!",$130,Technical,Neutral,10.9 oz / 310g 10.5 oz / 298g,0,10.3 mm 8.0 mm,Heel,Slightly small,-,Rock plate,-,-,-,-,Narrow,-,Stiff,Stiff,Stiff,4.3 mm,32.3 mm 29.0 mm,22.0 mm 21.0 mm,NormalWide,0,-,1,1,-,#290 Top 45%,#391 Bottom 39%
+Brooks,Cascadia 17,"86
+ Good!",$140,ModerateTechnical,Neutral,11.6 oz / 329g 11 oz / 312g,0,9.2 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Rock plate,Bad,Bad,Good,Breathable,Medium,Wide,Stiff,Stiff,Stiff,3.9 mm,33.1 mm,23.9 mm,NormalWide,0,SummerAll seasons,1,1,-,#360 Bottom 44%,#394 Bottom 39%
+Brooks,Cascadia 18,"86
+ Good!",$140,LightModerate,Neutral,10.9 oz / 310g 11.1 oz / 314g,0,8.8 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Rock plate,Very bad,Decent,Decent,Moderate,Medium,Medium,Stiff,Stiff,Stiff,4.0 mm,32.6 mm 33.0 mm,23.8 mm 25.0 mm,NormalWide,0,All seasons,1,1,-,#391 Bottom 39%,#214 Top 34%
+Brooks,Cascadia 19,"86
+ Good!",$150,LightModerate,Neutral,10.8 oz / 306g 10.7 oz / 303g,0,7.8 mm 6.0 mm,Mid/forefoot,True to size,Balanced,Rock plate,Good,Decent,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,3.8 mm,34.8 mm 35.0 mm,27.0 mm 29.0 mm,NormalWide,1,All seasons,1,1,-,#184 Top 50%,#117 Top 32%
+Brooks,Cascadia 19,"86
+ Good!",$150,LightModerate,Neutral,10.8 oz / 306g 10.7 oz / 303g,0,7.8 mm 6.0 mm,Mid/forefoot,True to size,Balanced,Rock plate,Good,Decent,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,3.8 mm,34.8 mm 35.0 mm,27.0 mm 29.0 mm,NormalWide,1,All seasons,1,1,-,#184 Top 50%,#117 Top 32%
+Brooks,Cascadia 19,"86
+ Good!",$150,LightModerate,Neutral,10.8 oz / 306g 10.7 oz / 303g,0,7.8 mm 6.0 mm,Mid/forefoot,True to size,Balanced,Rock plate,Good,Decent,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,3.8 mm,34.8 mm 35.0 mm,27.0 mm 29.0 mm,NormalWide,1,All seasons,1,1,-,#184 Top 50%,#117 Top 32%
+Brooks,Catamount 2,"87
+ Great!",$170,LightModerate,Neutral,9.7 oz / 275g 9.7 oz / 275g,0,6.4 mm 6.0 mm,Mid/forefoot,True to size,Balanced,Rock plate,Bad,Good,Good,Moderate,Narrow,Medium,Stiff,Stiff,Moderate,3.0 mm,29.0 mm 29.0 mm,22.6 mm 23.0 mm,Normal,0,All seasons,1,1,Water repellent,#316 Top 49%,#560 Bottom 13%
+Brooks,Catamount 3,"89
+ Great!",$170,LightModerate,Neutral,9 oz / 255g 9.4 oz / 266g,0,6.8 mm 6.0 mm,Mid/forefoot,True to size,Balanced,Rock plate,Very bad,Good,Decent,Warm,Narrow,Wide,Stiff,Moderate,Flexible,2.9 mm,28.3 mm 30.0 mm,21.5 mm 24.0 mm,Normal,0,All seasons,1,1,-,#81 Top 23%,#242 Bottom 34%
+Brooks,Catamount 3,"89
+ Great!",$170,LightModerate,Neutral,9 oz / 255g 9.4 oz / 266g,0,6.8 mm 6.0 mm,Mid/forefoot,True to size,Balanced,Rock plate,Very bad,Good,Decent,Warm,Narrow,Wide,Stiff,Moderate,Flexible,2.9 mm,28.3 mm 30.0 mm,21.5 mm 24.0 mm,Normal,0,All seasons,1,1,-,#81 Top 23%,#242 Bottom 34%
+Brooks,Catamount 3,"89
+ Great!",$170,LightModerate,Neutral,9 oz / 255g 9.4 oz / 266g,0,6.8 mm 6.0 mm,Mid/forefoot,True to size,Balanced,Rock plate,Very bad,Good,Decent,Warm,Narrow,Wide,Stiff,Moderate,Flexible,2.9 mm,28.3 mm 30.0 mm,21.5 mm 24.0 mm,Normal,0,All seasons,1,1,-,#81 Top 23%,#242 Bottom 34%
+Brooks,Catamount 3,"89
+ Great!",$170,LightModerate,Neutral,9 oz / 255g 9.4 oz / 266g,0,6.8 mm 6.0 mm,Mid/forefoot,True to size,Balanced,Rock plate,Very bad,Good,Decent,Warm,Narrow,Wide,Stiff,Moderate,Flexible,2.9 mm,28.3 mm 30.0 mm,21.5 mm 24.0 mm,Normal,0,All seasons,1,1,-,#81 Top 23%,#242 Bottom 34%
+Brooks,Catamount 3,"89
+ Great!",$170,LightModerate,Neutral,9 oz / 255g 9.4 oz / 266g,0,6.8 mm 6.0 mm,Mid/forefoot,True to size,Balanced,Rock plate,Very bad,Good,Decent,Warm,Narrow,Wide,Stiff,Moderate,Flexible,2.9 mm,28.3 mm 30.0 mm,21.5 mm 24.0 mm,Normal,0,All seasons,1,1,-,#81 Top 23%,#242 Bottom 34%
+Brooks,Divide 3,"90
+ Superb!",$100,LightModerate,Neutral,10.3 oz / 291g 10.1 oz / 285g,0,8.4 mm 8.0 mm,HeelMid/forefoot,Half size small,Firm,0,Very bad,Bad,Good,Moderate,Medium,Medium,Stiff,Stiff,Stiff,3.1 mm,31.1 mm 30.0 mm,22.7 mm 22.0 mm,Normal,0,All seasons,1,1,-,#124 Top 20%,#609 Bottom 5%
+Brooks,Divide 4,"86
+ Good!",$100,Light,Neutral,9.9 oz / 282g 10.4 oz / 294.8g,0,9.2 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,0,Bad,Bad,Decent,Moderate,Medium,Wide,Stiff,Moderate,Stiff,2.7 mm,32.1 mm 30.0 mm,22.9 mm 22.0 mm,Normal,0,All seasons,1,1,-,#183 Top 50%,#308 Bottom 16%
+Brooks,Divide 5 GTX,N/A,$140,Light,Neutral,10.1 oz / 286g 10.4 oz / 295g,0,10.0 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,0,Good,Good,Good,Warm,Medium,Medium,Moderate,Stiff,Stiff,3.0 mm,35.5 mm 24.0 mm,25.5 mm 16.0 mm,Normal,0,Winter,1,1,Waterproof,#321 Bottom 12%,#299 Bottom 18%
+Hoka,Challenger 7,"85
+ Good!",$145,LightModerate,Neutral,8.8 oz / 250g 8.8 oz / 250g,0,8.8 mm 5.0 mm,HeelMid/forefoot,True to size,Soft,0,Good,Decent,-,Warm,Narrow,Narrow,Moderate,Moderate,Stiff,3.1 mm,32.9 mm 31.0 mm,24.1 mm 26.0 mm,NormalWide,0,All seasons,1,1,-,#436 Bottom 32%,#141 Top 22%
+Hoka,Challenger 7 GTX,"78
+ Decent!",$160,ModerateTechnical,Neutral,9.9 oz / 281g 9 oz / 255g,0,11.1 mm 5.0 mm,Heel,True to size,Soft,0,Very good,Decent,Good,Warm,Medium,Narrow,Stiff,Stiff,Stiff,3.8 mm,39.2 mm 31.0 mm,28.1 mm 26.0 mm,Normal,0,Winter,1,1,WaterproofWater repellent,#325 Bottom 11%,#127 Top 35%
+Hoka,Challenger 8,"72
+ Bad!",$155,LightModerate,Neutral,9.5 oz / 269g 9.7 oz / 275g,0,10.1 mm 8.0 mm,Heel,-,Soft,0,Good,Good,Good,Breathable,Medium,Medium,Moderate,Stiff,Stiff,3.7 mm,40.2 mm 42.0 mm,30.1 mm 34.0 mm,NormalWide,0,SummerAll seasons,1,1,-,#360 Bottom 2%,#58 Top 16%
+Hoka,Challenger 8,"72
+ Bad!",$155,LightModerate,Neutral,9.5 oz / 269g 9.7 oz / 275g,0,10.1 mm 8.0 mm,Heel,-,Soft,0,Good,Good,Good,Breathable,Medium,Medium,Moderate,Stiff,Stiff,3.7 mm,40.2 mm 42.0 mm,30.1 mm 34.0 mm,NormalWide,0,SummerAll seasons,1,1,-,#360 Bottom 2%,#58 Top 16%
+Hoka,Mafate 5,"71
+ Bad!",$185,LightModerate,Neutral,11.1 oz / 315g 11.7 oz / 332g,0,9.0 mm 8.0 mm,HeelMid/forefoot,-,Soft,0,Decent,Decent,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,4.4 mm,42.9 mm 45.0 mm,33.9 mm 37.0 mm,Normal,0,All seasons,1,1,-,#361 Bottom 1%,#121 Top 33%
+Hoka,Mafate Speed 4,"88
+ Great!",$185,ModerateTechnical,Neutral,10.3 oz / 293g 10.4 oz / 295g,0,7.2 mm 4.0 mm,Mid/forefoot,True to size,Soft,0,Good,Good,Good,Moderate,Medium,Narrow,Moderate,Stiff,Flexible,3.9 mm,38.0 mm 33.0 mm,30.8 mm 29.0 mm,Normal,0,All seasons,1,1,-,#132 Top 36%,#84 Top 23%
+Hoka,Mafate Speed 4,"88
+ Great!",$185,ModerateTechnical,Neutral,10.3 oz / 293g 10.4 oz / 295g,0,7.2 mm 4.0 mm,Mid/forefoot,True to size,Soft,0,Good,Good,Good,Moderate,Medium,Narrow,Moderate,Stiff,Flexible,3.9 mm,38.0 mm 33.0 mm,30.8 mm 29.0 mm,Normal,0,All seasons,1,1,-,#132 Top 36%,#84 Top 23%
+Hoka,Mafate Speed 4,"88
+ Great!",$185,ModerateTechnical,Neutral,10.3 oz / 293g 10.4 oz / 295g,0,7.2 mm 4.0 mm,Mid/forefoot,True to size,Soft,0,Good,Good,Good,Moderate,Medium,Narrow,Moderate,Stiff,Flexible,3.9 mm,38.0 mm 33.0 mm,30.8 mm 29.0 mm,Normal,0,All seasons,1,1,-,#132 Top 36%,#84 Top 23%
+Hoka,Mafate Speed 4,"88
+ Great!",$185,ModerateTechnical,Neutral,10.3 oz / 293g 10.4 oz / 295g,0,7.2 mm 4.0 mm,Mid/forefoot,True to size,Soft,0,Good,Good,Good,Moderate,Medium,Narrow,Moderate,Stiff,Flexible,3.9 mm,38.0 mm 33.0 mm,30.8 mm 29.0 mm,Normal,0,All seasons,1,1,-,#133 Top 37%,#84 Top 23%
+Hoka,Mafate Speed 4,"88
+ Great!",$185,ModerateTechnical,Neutral,10.3 oz / 293g 10.4 oz / 295g,0,7.2 mm 4.0 mm,Mid/forefoot,True to size,Soft,0,Good,Good,Good,Moderate,Medium,Narrow,Moderate,Stiff,Flexible,3.9 mm,38.0 mm 33.0 mm,30.8 mm 29.0 mm,Normal,0,All seasons,1,1,-,#133 Top 37%,#84 Top 23%
+Hoka,Mafate Speed 4,"88
+ Great!",$185,ModerateTechnical,Neutral,10.3 oz / 293g 10.4 oz / 295g,0,7.2 mm 4.0 mm,Mid/forefoot,True to size,Soft,0,Good,Good,Good,Moderate,Medium,Narrow,Moderate,Stiff,Flexible,3.9 mm,38.0 mm 33.0 mm,30.8 mm 29.0 mm,Normal,0,All seasons,1,1,-,#132 Top 36%,#84 Top 23%
+Hoka,Mafate Speed 4,"88
+ Great!",$185,ModerateTechnical,Neutral,10.3 oz / 293g 10.4 oz / 295g,0,7.2 mm 4.0 mm,Mid/forefoot,True to size,Soft,0,Good,Good,Good,Moderate,Medium,Narrow,Moderate,Stiff,Flexible,3.9 mm,38.0 mm 33.0 mm,30.8 mm 29.0 mm,Normal,0,All seasons,1,1,-,#133 Top 37%,#84 Top 23%
+HOKA,Mafate Speed 4,"88
+ Great!",$185,ModerateTechnical,Neutral,10.3 oz / 293g 10.4 oz / 295g,0,7.2 mm 4.0 mm,Mid/forefoot,True to size,Soft,0,Good,Good,Good,Moderate,Medium,Narrow,Moderate,Stiff,Flexible,3.9 mm,38.0 mm 33.0 mm,30.8 mm 29.0 mm,Normal,0,All seasons,1,1,-,#133 Top 37%,#84 Top 23%
+Hoka,Mafate Speed 4,"88
+ Great!",$185,ModerateTechnical,Neutral,10.3 oz / 293g 10.4 oz / 295g,0,7.2 mm 4.0 mm,Mid/forefoot,True to size,Soft,0,Good,Good,Good,Moderate,Medium,Narrow,Moderate,Stiff,Flexible,3.9 mm,38.0 mm 33.0 mm,30.8 mm 29.0 mm,Normal,0,All seasons,1,1,-,#133 Top 37%,#84 Top 23%
+Hoka,Mafate Speed 4,"88
+ Great!",$185,ModerateTechnical,Neutral,10.3 oz / 293g 10.4 oz / 295g,0,7.2 mm 4.0 mm,Mid/forefoot,True to size,Soft,0,Good,Good,Good,Moderate,Medium,Narrow,Moderate,Stiff,Flexible,3.9 mm,38.0 mm 33.0 mm,30.8 mm 29.0 mm,Normal,0,All seasons,1,1,-,#133 Top 37%,#84 Top 23%
+hoka,Mafate Speed 4,"88
+ Great!",$185,ModerateTechnical,Neutral,10.3 oz / 293g 10.4 oz / 295g,0,7.2 mm 4.0 mm,Mid/forefoot,True to size,Soft,0,Good,Good,Good,Moderate,Medium,Narrow,Moderate,Stiff,Flexible,3.9 mm,38.0 mm 33.0 mm,30.8 mm 29.0 mm,Normal,0,All seasons,1,1,-,#132 Top 36%,#84 Top 23%
+Hoka,Mafate Three2,"77
+ Decent!",$185,Light,Neutral,11.7 oz / 332g 11.6 oz / 329g,0,3.9 mm 4.0 mm,Mid/forefoot,Slightly small,Soft,0,Very good,Decent,Good,Moderate,Narrow,Narrow,Stiff,Stiff,Moderate,4.0 mm,35.6 mm 35.0 mm,31.7 mm 31.0 mm,Normal,0,All seasons,1,1,-,#329 Bottom 10%,#200 Bottom 45%
+Hoka,Mafate Three2,"77
+ Decent!",$185,Light,Neutral,11.7 oz / 332g 11.6 oz / 329g,0,3.9 mm 4.0 mm,Mid/forefoot,Slightly small,Soft,0,Very good,Decent,Good,Moderate,Narrow,Narrow,Stiff,Stiff,Moderate,4.0 mm,35.6 mm 35.0 mm,31.7 mm 31.0 mm,Normal,0,All seasons,1,1,-,#329 Bottom 10%,#200 Bottom 45%
+Hoka,Mafate X,"86
+ Good!",$225,LightModerate,Neutral,11.8 oz / 335g 12.1 oz / 343g,0,10.6 mm 8.0 mm,Heel,-,Balanced,Carbon plate,Bad,Good,Good,Moderate,Wide,Medium,Stiff,Stiff,Stiff,3.0 mm,47.3 mm 49.0 mm,36.7 mm 41.0 mm,Normal,1,All seasons,1,1,-,#185 Bottom 49%,#192 Bottom 47%
+Hoka,Speedgoat 5,"88
+ Great!",$155,LightModerate,Neutral,9.8 oz / 277g 9.7 oz / 276g,0,3.8 mm 4.0 mm,Mid/forefoot,True to size,Soft,0,-,-,-,Moderate,Narrow,-,Moderate,Flexible,Flexible,3.0 mm,27.5 mm 33.0 mm,23.7 mm 29.0 mm,NormalWide,0,All seasons,1,1,-,#237 Top 37%,#115 Top 18%
+Hoka,Speedgoat 5 GTX,"82
+ Good!",$170,Moderate,Neutral,11.3 oz / 319g 11.5 oz / 326g,0,7.0 mm 4.0 mm,Mid/forefoot,True to size,Soft,0,Good,Good,Decent,Moderate,Medium,Medium,Stiff,Stiff,Moderate,3.5 mm,34.6 mm,27.6 mm,Normal,0,Winter,1,1,Waterproof,#531 Bottom 17%,#381 Bottom 41%
+Hoka,Speedgoat 6,"78
+ Decent!",$155,Moderate,Neutral,9.8 oz / 278g 9.8 oz / 277g,0,4.9 mm 5.0 mm,Mid/forefoot,Slightly small,Balanced,0,Good,Good,Decent,Warm,Medium,Medium,Stiff,Stiff,Moderate,4.0 mm,32.1 mm 40.0 mm,27.2 mm 35.0 mm,NormalWide,1,All seasons,1,1,-,#327 Bottom 10%,#47 Top 13%
+Hoka,Speedgoat 6,"78
+ Decent!",$155,Moderate,Neutral,9.8 oz / 278g 9.8 oz / 277g,0,4.9 mm 5.0 mm,Mid/forefoot,Slightly small,Balanced,0,Good,Good,Decent,Warm,Medium,Medium,Stiff,Stiff,Moderate,4.0 mm,32.1 mm 40.0 mm,27.2 mm 35.0 mm,NormalWide,1,All seasons,1,1,-,#327 Bottom 11%,#47 Top 13%
+Hoka,Speedgoat 6,"78
+ Decent!",$155,Moderate,Neutral,9.8 oz / 278g 9.8 oz / 277g,0,4.9 mm 5.0 mm,Mid/forefoot,Slightly small,Balanced,0,Good,Good,Decent,Warm,Medium,Medium,Stiff,Stiff,Moderate,4.0 mm,32.1 mm 40.0 mm,27.2 mm 35.0 mm,NormalWide,1,All seasons,1,1,-,#327 Bottom 11%,#47 Top 13%
+Hoka,Speedgoat 6,"78
+ Decent!",$155,Moderate,Neutral,9.8 oz / 278g 9.8 oz / 277g,0,4.9 mm 5.0 mm,Mid/forefoot,Slightly small,Balanced,0,Good,Good,Decent,Warm,Medium,Medium,Stiff,Stiff,Moderate,4.0 mm,32.1 mm 40.0 mm,27.2 mm 35.0 mm,NormalWide,1,All seasons,1,1,-,#327 Bottom 11%,#47 Top 13%
+Hoka,Speedgoat 6,"78
+ Decent!",$155,Moderate,Neutral,9.8 oz / 278g 9.8 oz / 277g,0,4.9 mm 5.0 mm,Mid/forefoot,Slightly small,Balanced,0,Good,Good,Decent,Warm,Medium,Medium,Stiff,Stiff,Moderate,4.0 mm,32.1 mm 40.0 mm,27.2 mm 35.0 mm,NormalWide,1,All seasons,1,1,-,#327 Bottom 11%,#47 Top 13%
+Hoka,Speedgoat 6,"78
+ Decent!",$155,Moderate,Neutral,9.8 oz / 278g 9.8 oz / 277g,0,4.9 mm 5.0 mm,Mid/forefoot,Slightly small,Balanced,0,Good,Good,Decent,Warm,Medium,Medium,Stiff,Stiff,Moderate,4.0 mm,32.1 mm 40.0 mm,27.2 mm 35.0 mm,NormalWide,1,All seasons,1,1,-,#327 Bottom 11%,#47 Top 13%
+Hoka,Speedgoat 6,"78
+ Decent!",$155,Moderate,Neutral,9.8 oz / 278g 9.8 oz / 277g,0,4.9 mm 5.0 mm,Mid/forefoot,Slightly small,Balanced,0,Good,Good,Decent,Warm,Medium,Medium,Stiff,Stiff,Moderate,4.0 mm,32.1 mm 40.0 mm,27.2 mm 35.0 mm,NormalWide,1,All seasons,1,1,-,#327 Bottom 11%,#47 Top 13%
+hoka,Speedgoat 6,"78
+ Decent!",$155,Moderate,Neutral,9.8 oz / 278g 9.8 oz / 277g,0,4.9 mm 5.0 mm,Mid/forefoot,Slightly small,Balanced,0,Good,Good,Decent,Warm,Medium,Medium,Stiff,Stiff,Moderate,4.0 mm,32.1 mm 40.0 mm,27.2 mm 35.0 mm,NormalWide,1,All seasons,1,1,-,#327 Bottom 11%,#47 Top 13%
+Hoka,Speedgoat 6 GTX,"74
+ Bad!",$170,Moderate,Neutral,10.2 oz / 289g 10.4 oz / 295g,0,5.0 mm 5.0 mm,Mid/forefoot,Half size small,Balanced,Rock plate,Very good,Good,Good,Warm,Medium,Narrow,Stiff,Stiff,Moderate,3.9 mm,32.9 mm 37.0 mm,27.9 mm 32.0 mm,NormalWide,0,Winter,1,1,Waterproof,#355 Bottom 3%,#128 Top 35%
+Hoka,Speedgoat 6 GTX,"74
+ Bad!",$170,Moderate,Neutral,10.2 oz / 289g 10.4 oz / 295g,0,5.0 mm 5.0 mm,Mid/forefoot,Half size small,Balanced,0,Very good,Good,Good,Warm,Medium,Narrow,Stiff,Stiff,Moderate,3.9 mm,32.9 mm 37.0 mm,27.9 mm 32.0 mm,NormalWide,0,Winter,1,1,Waterproof,#355 Bottom 3%,#128 Top 35%
+Hoka,Speedgoat 6 GTX,"74
+ Bad!",$170,Moderate,Neutral,10.2 oz / 289g 10.4 oz / 295g,0,5.0 mm 5.0 mm,Mid/forefoot,Half size small,Balanced,0,Very good,Good,Good,Warm,Medium,Narrow,Stiff,Stiff,Moderate,3.9 mm,32.9 mm 37.0 mm,27.9 mm 32.0 mm,NormalWide,0,Winter,1,1,Waterproof,#355 Bottom 3%,#128 Top 35%
+Hoka,Stinson 7,"85
+ Good!",$170,LightModerate,Neutral,12.1 oz / 342g 12.9 oz / 365g,0,7.0 mm 5.0 mm,Mid/forefoot,True to size,Soft,0,Very bad,Bad,Decent,Moderate,Medium,Medium,Stiff,Stiff,Stiff,3.0 mm,40.0 mm 42.0 mm,33.0 mm 37.0 mm,Normal,0,All seasons,1,1,-,#209 Bottom 43%,#117 Top 32%
+Hoka,Stinson 7,"85
+ Good!",$170,LightModerate,Neutral,12.1 oz / 342g 12.9 oz / 365g,0,7.0 mm 5.0 mm,Mid/forefoot,True to size,Soft,0,Very bad,Bad,Decent,Moderate,Medium,Medium,Stiff,Stiff,Stiff,3.0 mm,40.0 mm 42.0 mm,33.0 mm 37.0 mm,Normal,0,All seasons,1,1,-,#209 Bottom 43%,#117 Top 32%
+Hoka,Stinson 7,"85
+ Good!",$170,LightModerate,Neutral,12.1 oz / 342g 12.9 oz / 365g,0,7.0 mm 5.0 mm,Mid/forefoot,True to size,Soft,0,Very bad,Bad,Decent,Moderate,Medium,Medium,Stiff,Stiff,Stiff,3.0 mm,40.0 mm 42.0 mm,33.0 mm 37.0 mm,Normal,0,All seasons,1,1,-,#210 Bottom 42%,#118 Top 33%
+Hoka,Stinson 7,"85
+ Good!",$170,LightModerate,Neutral,12.1 oz / 342g 12.9 oz / 365g,0,7.0 mm 5.0 mm,Mid/forefoot,True to size,Soft,0,Very bad,Bad,Decent,Moderate,Medium,Medium,Stiff,Stiff,Stiff,3.0 mm,40.0 mm 42.0 mm,33.0 mm 37.0 mm,Normal,0,All seasons,1,1,-,#209 Bottom 43%,#118 Top 33%
+Hoka,Tecton X,"90
+ Superb!",$200,Moderate,Neutral,8.6 oz / 245g 8.9 oz / 252g,1,8.0 mm 5.0 mm,HeelMid/forefoot,True to size,Firm,Carbon plate,-,-,-,-,Narrow,-,Stiff,Stiff,Flexible,3.5 mm,35.3 mm 33.0 mm,27.3 mm 29.0 mm,Normal,0,-,1,1,-,#134 Top 21%,#492 Bottom 23%
+Hoka,Tecton X 2,"88
+ Great!",$225,Moderate,Neutral,9.1 oz / 257g 8.8 oz / 249g,0,5.6 mm 5.0 mm,Mid/forefoot,Slightly small,Balanced,Carbon plate,Good,Bad,Good,Moderate,Narrow,Narrow,Stiff,Stiff,Moderate,3.6 mm,37.4 mm 32.0 mm,31.8 mm 27.0 mm,Normal,0,All seasons,1,1,-,#282 Top 44%,#363 Bottom 43%
+Hoka,Tecton X 3,"84
+ Good!",$275,LightModerate,Neutral,9.7 oz / 275g 10.3 oz / 292g,0,6.9 mm 5.0 mm,Mid/forefoot,Slightly small,Soft,Carbon plate,Good,Decent,Good,Moderate,Narrow,Medium,Stiff,Stiff,Flexible,4.0 mm,37.8 mm 40.0 mm,30.9 mm 35.0 mm,Normal,0,All seasons,1,1,Water repellent,#229 Bottom 37%,#162 Top 45%
+Hoka,Tecton X 3,"84
+ Good!",$275,LightModerate,Neutral,9.7 oz / 275g 10.3 oz / 292g,0,6.9 mm 5.0 mm,Mid/forefoot,Slightly small,Soft,Carbon plate,Good,Decent,Good,Moderate,Narrow,Medium,Stiff,Stiff,Flexible,4.0 mm,37.8 mm 40.0 mm,30.9 mm 35.0 mm,Normal,0,All seasons,1,1,Water repellent,#228 Bottom 38%,#162 Top 45%
+Hoka,Torrent 3,"84
+ Good!",$130,Moderate,Neutral,9.1 oz / 258g 8.7 oz / 247g,0,7.1 mm 5.0 mm,Mid/forefoot,True to size,Balanced,0,Decent,Decent,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,3.6 mm,29.5 mm 23.0 mm,22.4 mm 18.0 mm,Normal,0,All seasons,1,1,-,#226 Bottom 38%,#301 Bottom 18%
+Hoka,Zinal,"88
+ Great!",$160,LightModerate,Neutral,8.4 oz / 239g 8.5 oz / 241g,1,3.9 mm 4.0 mm,Mid/forefoot,True to size,-,0,-,-,-,-,Narrow,-,Stiff,Stiff,Moderate,3.4 mm,29.3 mm 22.0 mm,25.4 mm 18.0 mm,Normal,0,-,1,1,-,#246 Top 39%,#588 Bottom 9%
+Hoka,Zinal 2,"84
+ Good!",$180,Moderate,Neutral,7.5 oz / 213g 8 oz / 227g,1,7.2 mm 5.0 mm,Mid/forefoot,Slightly small,Soft,0,Very bad,Good,Decent,Moderate,Narrow,Medium,Stiff,Moderate,Flexible,3.7 mm,29.8 mm 30.0 mm,22.6 mm 25.0 mm,Normal,0,All seasons,1,1,-,#227 Bottom 38%,#310 Bottom 15%
+Hoka,Zinal 2,"84
+ Good!",$180,Moderate,Neutral,7.5 oz / 213g 8 oz / 227g,1,7.2 mm 5.0 mm,Mid/forefoot,Slightly small,Soft,0,Very bad,Good,Decent,Moderate,Narrow,Medium,Stiff,Moderate,Flexible,3.7 mm,29.8 mm 30.0 mm,22.6 mm 25.0 mm,Normal,0,All seasons,1,1,-,#227 Bottom 38%,#310 Bottom 15%
+Hoka,Zinal 2,"84
+ Good!",$180,Moderate,Neutral,7.5 oz / 213g 8 oz / 227g,1,7.2 mm 5.0 mm,Mid/forefoot,Slightly small,Soft,0,Very bad,Good,Decent,Moderate,Narrow,Medium,Stiff,Moderate,Flexible,3.7 mm,29.8 mm 30.0 mm,22.6 mm 25.0 mm,Normal,0,All seasons,1,1,-,#227 Bottom 38%,#310 Bottom 15%
+Inov8,Trailfly,"88
+ Great!",$150,LightModerate,Neutral,9.9 oz / 282g 9.9 oz / 280g,0,6.0 mm 6.0 mm,Mid/forefoot,Slightly small,Soft,Rock plate,Decent,Good,Good,Moderate,Medium,Wide,Stiff,Flexible,Flexible,3.9 mm,30.1 mm 29.0 mm,24.1 mm 23.0 mm,NormalWide,0,All seasons,1,1,-,#141 Top 39%,#326 Bottom 11%
+Inov8,Trailfly,"88
+ Great!",$150,LightModerate,Neutral,9.9 oz / 282g 9.9 oz / 280g,0,6.0 mm 6.0 mm,Mid/forefoot,Slightly small,Soft,Rock plate,Decent,Good,Good,Moderate,Medium,Wide,Stiff,Flexible,Flexible,3.9 mm,30.1 mm 29.0 mm,24.1 mm 23.0 mm,NormalWide,0,All seasons,1,1,-,#141 Top 39%,#326 Bottom 11%
+Inov8,Trailfly,"88
+ Great!",$150,LightModerate,Neutral,9.9 oz / 282g 9.9 oz / 280g,0,6.0 mm 6.0 mm,Mid/forefoot,Slightly small,Soft,Rock plate,Decent,Good,Good,Moderate,Medium,Wide,Stiff,Flexible,Flexible,3.9 mm,30.1 mm 29.0 mm,24.1 mm 23.0 mm,NormalWide,0,All seasons,1,1,-,#141 Top 39%,#326 Bottom 11%
+Inov8,Trailfly,"88
+ Great!",$150,LightModerate,Neutral,9.9 oz / 282g 9.9 oz / 280g,0,6.0 mm 6.0 mm,Mid/forefoot,Slightly small,Soft,Rock plate,Decent,Good,Good,Moderate,Medium,Wide,Stiff,Flexible,Flexible,3.9 mm,30.1 mm 29.0 mm,24.1 mm 23.0 mm,NormalWide,0,All seasons,1,1,-,#140 Top 39%,#326 Bottom 11%
+Inov8,Trailfly,"88
+ Great!",$150,LightModerate,Neutral,9.9 oz / 282g 9.9 oz / 280g,0,6.0 mm 6.0 mm,Mid/forefoot,Slightly small,Soft,Rock plate,Decent,Good,Good,Moderate,Medium,Wide,Stiff,Flexible,Flexible,3.9 mm,30.1 mm 29.0 mm,24.1 mm 23.0 mm,NormalWide,0,All seasons,1,1,-,#141 Top 39%,#326 Bottom 11%
+Inov8,Trailfly,"88
+ Great!",$150,LightModerate,Neutral,9.9 oz / 282g 9.9 oz / 280g,0,6.0 mm 6.0 mm,Mid/forefoot,Slightly small,Soft,Rock plate,Decent,Good,Good,Moderate,Medium,Wide,Stiff,Flexible,Flexible,3.9 mm,30.1 mm 29.0 mm,24.1 mm 23.0 mm,NormalWide,0,All seasons,1,1,-,#141 Top 39%,#326 Bottom 11%
+Inov8,Trailfly,"88
+ Great!",$150,LightModerate,Neutral,9.9 oz / 282g 9.9 oz / 280g,0,6.0 mm 6.0 mm,Mid/forefoot,Slightly small,Soft,Rock plate,Decent,Good,Good,Moderate,Medium,Wide,Stiff,Flexible,Flexible,3.9 mm,30.1 mm 29.0 mm,24.1 mm 23.0 mm,NormalWide,0,All seasons,1,1,-,#141 Top 39%,#326 Bottom 11%
+Inov8,Trailfly,"88
+ Great!",$150,LightModerate,Neutral,9.9 oz / 282g 9.9 oz / 280g,0,6.0 mm 6.0 mm,Mid/forefoot,Slightly small,Soft,Rock plate,Decent,Good,Good,Moderate,Medium,Wide,Stiff,Flexible,Flexible,3.9 mm,30.1 mm 29.0 mm,24.1 mm 23.0 mm,NormalWide,0,All seasons,1,1,-,#141 Top 39%,#326 Bottom 11%
+KEEN,Seek,"87
+ Great!",$185,LightModerate,Neutral,10.8 oz / 305g 10.9 oz / 309g,0,8.6 mm 6.0 mm,HeelMid/forefoot,-,Soft,0,Good,Good,Good,Moderate,Medium,Wide,Moderate,Moderate,Flexible,4.1 mm,36.3 mm 38.5 mm,27.7 mm 32.5 mm,Normal,0,All seasons,1,1,-,#146 Top 40%,#315 Bottom 14%
+La Sportiva,Mutant,"87
+ Great!",$165,Technical,Neutral,11.4 oz / 323g 10.7 oz / 303g,0,11.3 mm 10.0 mm,Heel,Half size small,Firm,0,Good,Good,Decent,Moderate,Narrow,Medium,Stiff,Stiff,Moderate,5.0 mm,33.2 mm 26.0 mm,21.9 mm 16.0 mm,Normal,0,All seasons,1,1,-,#149 Top 41%,#228 Bottom 38%
+La Sportiva,Mutant,"87
+ Great!",$165,Technical,Neutral,11.4 oz / 323g 10.7 oz / 303g,0,11.3 mm 10.0 mm,Heel,Half size small,Firm,0,Good,Good,Decent,Moderate,Narrow,Medium,Stiff,Stiff,Moderate,5.0 mm,33.2 mm 26.0 mm,21.9 mm 16.0 mm,Normal,0,All seasons,1,1,-,#149 Top 41%,#228 Bottom 38%
+La Sportiva,Prodigio,"83
+ Good!",$155,Moderate,Neutral,9.6 oz / 271g 9.5 oz / 270g,0,8.9 mm 6.0 mm,HeelMid/forefoot,Half size small,Soft,0,Very bad,Bad,Decent,Moderate,Medium,Medium,Stiff,Stiff,Stiff,3.4 mm,29.3 mm 34.0 mm,20.4 mm 28.0 mm,Normal,0,All seasons,1,1,-,#269 Bottom 26%,#217 Bottom 40%
+La Sportiva,Prodigio,"83
+ Good!",$155,Moderate,Neutral,9.6 oz / 271g 9.5 oz / 270g,0,8.9 mm 6.0 mm,HeelMid/forefoot,Half size small,Soft,0,Very bad,Bad,Decent,Moderate,Medium,Medium,Stiff,Stiff,Stiff,3.4 mm,29.3 mm 34.0 mm,20.4 mm 28.0 mm,Normal,0,All seasons,1,1,-,#269 Bottom 26%,#218 Bottom 40%
+La Sportiva,Prodigio,"83
+ Good!",$155,Moderate,Neutral,9.6 oz / 271g 9.5 oz / 270g,0,8.9 mm 6.0 mm,HeelMid/forefoot,Half size small,Soft,0,Very bad,Bad,Decent,Moderate,Medium,Medium,Stiff,Stiff,Stiff,3.4 mm,29.3 mm 34.0 mm,20.4 mm 28.0 mm,Normal,0,All seasons,1,1,-,#269 Bottom 26%,#218 Bottom 40%
+La Sportiva,Prodigio,"83
+ Good!",$155,Moderate,Neutral,9.6 oz / 271g 9.5 oz / 270g,0,8.9 mm 6.0 mm,HeelMid/forefoot,Half size small,Soft,0,Very bad,Bad,Decent,Moderate,Medium,Medium,Stiff,Stiff,Stiff,3.4 mm,29.3 mm 34.0 mm,20.4 mm 28.0 mm,Normal,0,All seasons,1,1,-,#269 Bottom 26%,#218 Bottom 40%
+La Sportiva,Prodigio,"83
+ Good!",$155,Moderate,Neutral,9.6 oz / 271g 9.5 oz / 270g,0,8.9 mm 6.0 mm,HeelMid/forefoot,Half size small,Soft,0,Very bad,Bad,Decent,Moderate,Medium,Medium,Stiff,Stiff,Stiff,3.4 mm,29.3 mm 34.0 mm,20.4 mm 28.0 mm,Normal,0,All seasons,1,1,-,#269 Bottom 26%,#218 Bottom 40%
+La Sportiva,Prodigio,"83
+ Good!",$155,Moderate,Neutral,9.6 oz / 271g 9.5 oz / 270g,0,8.9 mm 6.0 mm,HeelMid/forefoot,Half size small,Soft,0,Very bad,Bad,Decent,Moderate,Medium,Medium,Stiff,Stiff,Stiff,3.4 mm,29.3 mm 34.0 mm,20.4 mm 28.0 mm,Normal,0,All seasons,1,1,-,#269 Bottom 26%,#218 Bottom 40%
+Merrell,Agility Peak 4,"86
+ Good!",$130,Technical,Neutral,10.3 oz / 292g 10.8 oz / 305g,0,9.3 mm 6.0 mm,HeelMid/forefoot,Slightly large,Balanced,Rock plate,Decent,Decent,Decent,Moderate,Narrow,Wide,Stiff,Stiff,Stiff,4.4 mm,34.4 mm 30.0 mm,25.1 mm 24.0 mm,Normal,0,All seasons,1,1,-,#364 Bottom 43%,#595 Bottom 8%
+Merrell,Agility Peak 5,"88
+ Great!",$140,ModerateTechnical,Neutral,10.2 oz / 289g 10.6 oz / 300g,0,13.4 mm 6.0 mm,Heel,True to size,Balanced,Rock plate,Very good,Decent,Good,Moderate,Medium,Narrow,Moderate,Stiff,Stiff,4.5 mm,39.2 mm 39.0 mm,25.8 mm 33.0 mm,Normal,1,All seasons,1,1,-,#139 Top 38%,#113 Top 31%
+Merrell,Agility Peak 5,"88
+ Great!",$140,ModerateTechnical,Neutral,10.2 oz / 289g 10.6 oz / 300g,0,13.4 mm 6.0 mm,Heel,True to size,Balanced,Rock plate,Very good,Decent,Good,Moderate,Medium,Narrow,Moderate,Stiff,Stiff,4.5 mm,39.2 mm 39.0 mm,25.8 mm 33.0 mm,Normal,1,All seasons,1,1,-,#140 Top 39%,#113 Top 31%
+Merrell,Agility Peak 5,"88
+ Great!",$140,ModerateTechnical,Neutral,10.2 oz / 289g 10.6 oz / 300g,0,13.4 mm 6.0 mm,Heel,True to size,Balanced,Rock plate,Very good,Decent,Good,Moderate,Medium,Narrow,Moderate,Stiff,Stiff,4.5 mm,39.2 mm 39.0 mm,25.8 mm 33.0 mm,Normal,1,All seasons,1,1,-,#140 Top 39%,#113 Top 31%
+Merrell,Agility Peak 5,"88
+ Great!",$140,ModerateTechnical,Neutral,10.2 oz / 289g 10.6 oz / 300g,0,13.4 mm 6.0 mm,Heel,True to size,Balanced,Rock plate,Very good,Decent,Good,Moderate,Medium,Narrow,Moderate,Stiff,Stiff,4.5 mm,39.2 mm 39.0 mm,25.8 mm 33.0 mm,Normal,1,All seasons,1,1,-,#140 Top 39%,#113 Top 31%
+Merrell,Agility Peak 5,"88
+ Great!",$140,ModerateTechnical,Neutral,10.2 oz / 289g 10.6 oz / 300g,0,13.4 mm 6.0 mm,Heel,True to size,Balanced,Rock plate,Very good,Decent,Good,Moderate,Medium,Narrow,Moderate,Stiff,Stiff,4.5 mm,39.2 mm 39.0 mm,25.8 mm 33.0 mm,Normal,1,All seasons,1,1,-,#140 Top 39%,#113 Top 31%
+Merrell,Agility Peak 5 GTX,"81
+ Good!",$170,ModerateTechnical,Neutral,10.4 oz / 296g 10.7 oz / 303g,0,11.6 mm 6.0 mm,Heel,True to size,Balanced,Rock plate,Good,Bad,Decent,Warm,Narrow,Medium,Stiff,Stiff,Stiff,4.4 mm,37.3 mm 39.0 mm,25.7 mm 33.0 mm,Normal,0,Winter,1,1,Waterproof,#298 Bottom 19%,#255 Bottom 30%
+Merrell,Agility Peak 5 GTX,"81
+ Good!",$170,ModerateTechnical,Neutral,10.4 oz / 296g 10.7 oz / 303g,0,11.6 mm 6.0 mm,Heel,True to size,Balanced,Rock plate,Good,Bad,Decent,Warm,Narrow,Medium,Stiff,Stiff,Stiff,4.4 mm,37.3 mm 39.0 mm,25.7 mm 33.0 mm,Normal,0,Winter,1,1,Waterproof,#298 Bottom 19%,#255 Bottom 30%
+Merrell,Agility Peak 5 GTX,"81
+ Good!",$170,ModerateTechnical,Neutral,10.4 oz / 296g 10.7 oz / 303g,0,11.6 mm 6.0 mm,Heel,True to size,Balanced,Rock plate,Good,Bad,Decent,Warm,Narrow,Medium,Stiff,Stiff,Stiff,4.4 mm,37.3 mm 39.0 mm,25.7 mm 33.0 mm,Normal,0,Winter,1,1,Waterproof,#298 Bottom 19%,#255 Bottom 30%
+Merrell,Agility Peak 5 GTX,"81
+ Good!",$170,ModerateTechnical,Neutral,10.4 oz / 296g 10.7 oz / 303g,0,11.6 mm 6.0 mm,Heel,True to size,Balanced,Rock plate,Good,Bad,Decent,Warm,Narrow,Medium,Stiff,Stiff,Stiff,4.4 mm,37.3 mm 39.0 mm,25.7 mm 33.0 mm,Normal,0,Winter,1,1,Waterproof,#298 Bottom 19%,#255 Bottom 30%
+Merrell,Antora 3,"83
+ Good!",$125,Light,Neutral,10.1 oz / 285g,0,9.1 mm,HeelMid/forefoot,True to size,Balanced,Rock plate,Decent,Bad,Good,Breathable,Narrow,Medium,Moderate,Stiff,Moderate,3.4 mm,33.5 mm,24.4 mm,Normal,0,SummerAll seasons,1,1,-,#269 Bottom 26%,#231 Bottom 37%
+Merrell,Fly Strike,"79
+ Good!",$90,Light,Neutral,10.3 oz / 292g 10.4 oz / 295g,0,17.3 mm 10.0 mm,Heel,True to size,Balanced,0,Good,Decent,Decent,Moderate,Narrow,Wide,Stiff,Stiff,Moderate,3.5 mm,34.3 mm 27.0 mm,17.0 mm 17.0 mm,NormalWide,0,All seasons,1,1,-,#316 Bottom 14%,#273 Bottom 25%
+Merrell,Moab Flight,"89
+ Great!",$110,Light,Neutral,9.6 oz / 271g 10.6 oz / 300g,0,13.5 mm 10.0 mm,Heel,True to size,Balanced,0,Good,Good,Good,Warm,Medium,Medium,Moderate,Stiff,Moderate,2.9 mm,32.6 mm 29.0 mm,19.1 mm 19.0 mm,NormalWide,0,All seasons,1,1,-,#91 Top 25%,#307 Bottom 16%
+Merrell,Morphlite,"87
+ Great!",$100,Light,Neutral,8.4 oz / 237g 8.6 oz / 243g,1,11.0 mm 6.0 mm,Heel,True to size,Soft,0,Bad,Decent,Decent,Moderate,Narrow,Medium,Moderate,Stiff,Flexible,2.0 mm,32.3 mm 26.0 mm,21.3 mm 20.0 mm,NormalWide,0,All seasons,1,1,-,#148 Top 41%,#292 Bottom 20%
+Merrell,Nova 2,"85
+ Good!",$110,ModerateTechnical,Neutral,10.3 oz / 293g 9.9 oz / 280g,0,9.3 mm 8.0 mm,HeelMid/forefoot,-,-,Rock plate,-,-,-,-,Narrow,-,Stiff,-,-,4.2 mm,35.1 mm 29.0 mm,25.8 mm 21.0 mm,NormalWide,0,-,0,0,-,#424 Bottom 34%,#470 Bottom 27%
+Merrell,Nova 3,"80
+ Good!",$125,LightModerate,Neutral,10.8 oz / 305g 10.4 oz / 295g,0,9.9 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,Rock plate,Decent,Decent,Good,Moderate,Narrow,Medium,Moderate,Stiff,Flexible,3.5 mm,34.1 mm 29.0 mm,24.2 mm 21.0 mm,NormalWide,0,All seasons,1,1,-,#574 Bottom 11%,#322 Top 50%
+Merrell,Nova 4,"86
+ Good!",$130,LightModerate,Neutral,9.6 oz / 272g 9.7 oz / 275g,0,12.1 mm 8.0 mm,Heel,-,Balanced,0,Decent,Decent,Decent,Moderate,Medium,Medium,Moderate,Stiff,Flexible,4.0 mm,37.7 mm 29.0 mm,25.6 mm 21.0 mm,NormalWide,0,All seasons,1,1,-,#192 Bottom 47%,#193 Bottom 47%
+Merrell,Nova 4,"86
+ Good!",$130,LightModerate,Neutral,9.6 oz / 272g 9.7 oz / 275g,0,12.1 mm 8.0 mm,Heel,-,Balanced,0,Decent,Decent,Decent,Moderate,Medium,Medium,Moderate,Stiff,Flexible,4.0 mm,37.7 mm 29.0 mm,25.6 mm 21.0 mm,NormalWide,0,All seasons,1,1,-,#193 Bottom 47%,#193 Bottom 47%
+Merrell,Trail Glove 7,"84
+ Good!",$120,Light,Neutral,7.8 oz / 221g 9 oz / 255g,1,0.1 mm 0.0 mm,Mid/forefoot,True to size,Balanced,0,Decent,Bad,Decent,Moderate,Medium,Wide,Moderate,Flexible,Flexible,2.5 mm,16.1 mm 14.0 mm,16.0 mm 14.0 mm,Normal,0,All seasons,0,0,-,#225 Bottom 38%,#161 Top 44%
+new Balance, Foam X Hierro v8,"82
+ Good!",$150,LightModerate,Neutral,10.5 oz / 298g 11 oz / 311g,0,8.1 mm 6.0 mm,HeelMid/forefoot,Slightly small,Soft,0,Very bad,Decent,Good,Warm,Medium,Medium,Moderate,Flexible,Moderate,4.0 mm,32.2 mm 37.0 mm,24.1 mm 31.0 mm,NormalWideX-Wide,0,All seasons,1,1,-,#513 Bottom 20%,#97 Top 15%
+New Balance,510 v6,"74
+ Bad!",$90,Light,Neutral,11 oz / 312g 11.5 oz / 326g,0,8.2 mm,HeelMid/forefoot,True to size,Balanced,0,Decent,Bad,Good,Moderate,Narrow,Medium,Stiff,Moderate,Moderate,2.9 mm,33.8 mm,25.6 mm,NormalWideX-Wide,0,All seasons,1,1,-,#355 Bottom 3%,#318 Bottom 13%
+New Balance,510 v6,"74
+ Bad!",$90,Light,Neutral,11 oz / 312g 11.5 oz / 326g,0,8.2 mm,HeelMid/forefoot,True to size,Balanced,0,Decent,Bad,Good,Moderate,Narrow,Medium,Stiff,Moderate,Moderate,2.9 mm,33.8 mm,25.6 mm,NormalWideX-Wide,0,All seasons,1,1,-,#355 Bottom 3%,#318 Bottom 13%
+New Balance,510 v6,"74
+ Bad!",$90,Light,Neutral,11 oz / 312g 11.5 oz / 326g,0,8.2 mm,HeelMid/forefoot,True to size,Balanced,0,Decent,Bad,Good,Moderate,Narrow,Medium,Stiff,Moderate,Moderate,2.9 mm,33.8 mm,25.6 mm,NormalWideX-Wide,0,All seasons,1,1,-,#355 Bottom 3%,#318 Bottom 13%
+New Balance,510 v6,"74
+ Bad!",$90,Light,Neutral,11 oz / 312g 11.5 oz / 326g,0,8.2 mm,HeelMid/forefoot,True to size,Balanced,0,Decent,Bad,Good,Moderate,Narrow,Medium,Stiff,Moderate,Moderate,2.9 mm,33.8 mm,25.6 mm,NormalWideX-Wide,0,All seasons,1,1,-,#355 Bottom 3%,#318 Bottom 13%
+New Balance,510 v6,"74
+ Bad!",$90,Light,Neutral,11 oz / 312g 11.5 oz / 326g,0,8.2 mm,HeelMid/forefoot,True to size,Balanced,0,Decent,Bad,Good,Moderate,Narrow,Medium,Stiff,Moderate,Moderate,2.9 mm,33.8 mm,25.6 mm,NormalWideX-Wide,0,All seasons,1,1,-,#355 Bottom 3%,#318 Bottom 13%
+New Balance,510 v6,"74
+ Bad!",$90,Light,Neutral,11 oz / 312g 11.5 oz / 326g,0,8.2 mm,HeelMid/forefoot,True to size,Balanced,0,Decent,Bad,Good,Moderate,Narrow,Medium,Stiff,Moderate,Moderate,2.9 mm,33.8 mm,25.6 mm,NormalWideX-Wide,0,All seasons,1,1,-,#355 Bottom 3%,#318 Bottom 13%
+New Balance,510 v6,"74
+ Bad!",$90,Light,Neutral,11 oz / 312g 11.5 oz / 326g,0,8.2 mm,HeelMid/forefoot,True to size,Balanced,0,Decent,Bad,Good,Moderate,Narrow,Medium,Stiff,Moderate,Moderate,2.9 mm,33.8 mm,25.6 mm,NormalWideX-Wide,0,All seasons,1,1,-,#354 Bottom 3%,#318 Bottom 13%
+New Balance,510 v6,"74
+ Bad!",$90,Light,Neutral,11 oz / 312g 11.5 oz / 326g,0,8.2 mm,HeelMid/forefoot,True to size,Balanced,0,Decent,Bad,Good,Moderate,Narrow,Medium,Stiff,Moderate,Moderate,2.9 mm,33.8 mm,25.6 mm,NormalWideX-Wide,0,All seasons,1,1,-,#355 Bottom 3%,#318 Bottom 13%
+New Balance,510 v6,"74
+ Bad!",$90,Light,Neutral,11 oz / 312g 11.5 oz / 326g,0,8.2 mm,HeelMid/forefoot,True to size,Balanced,0,Decent,Bad,Good,Moderate,Narrow,Medium,Stiff,Moderate,Moderate,2.9 mm,33.8 mm,25.6 mm,NormalWideX-Wide,0,All seasons,1,1,-,#355 Bottom 3%,#318 Bottom 13%
+New Balance,510 v6,"74
+ Bad!",$90,Light,Neutral,11 oz / 312g 11.5 oz / 326g,0,8.2 mm,HeelMid/forefoot,True to size,Balanced,0,Decent,Bad,Good,Moderate,Narrow,Medium,Stiff,Moderate,Moderate,2.9 mm,33.8 mm,25.6 mm,NormalWideX-Wide,0,All seasons,1,1,-,#354 Bottom 3%,#318 Bottom 13%
+New Balance,DynaSoft Nitrel v5,"77
+ Decent!",$75,Light,Neutral,9.8 oz / 279g 10.3 oz / 292g,0,6.7 mm 6.0 mm,Mid/forefoot,Slightly small,Balanced,0,Very bad,Bad,Good,Moderate,Narrow,Wide,Stiff,Moderate,Flexible,2.9 mm,29.8 mm 29.0 mm,23.1 mm 23.0 mm,NormalWideX-Wide,0,All seasons,1,1,-,#602 Bottom 6%,#490 Bottom 24%
+New Balance,DynaSoft Nitrel v6,"75
+ Bad!",$75,Light,Neutral,10.3 oz / 291g 10.1 oz / 285g,0,2.5 mm 6.0 mm,Mid/forefoot,True to size,Balanced,0,Decent,Decent,Good,Moderate,Medium,Medium,Moderate,Flexible,Moderate,2.7 mm,22.7 mm 27.0 mm,20.2 mm 21.0 mm,NormalWideX-Wide,0,All seasons,1,1,-,#351 Bottom 4%,#187 Bottom 49%
+New Balance,DynaSoft Nitrel v6,"75
+ Bad!",$75,Light,Neutral,10.3 oz / 291g 10.1 oz / 285g,0,2.5 mm 6.0 mm,Mid/forefoot,True to size,Balanced,0,Decent,Decent,Good,Moderate,Medium,Medium,Moderate,Flexible,Moderate,2.7 mm,22.7 mm 27.0 mm,20.2 mm 21.0 mm,NormalWideX-Wide,0,All seasons,1,1,-,#351 Bottom 4%,#187 Bottom 49%
+New Balance,DynaSoft Nitrel v6,"75
+ Bad!",$75,Light,Neutral,10.3 oz / 291g 10.1 oz / 285g,0,2.5 mm 6.0 mm,Mid/forefoot,True to size,Balanced,0,Decent,Decent,Good,Moderate,Medium,Medium,Moderate,Flexible,Moderate,2.7 mm,22.7 mm 27.0 mm,20.2 mm 21.0 mm,NormalWideX-Wide,0,All seasons,1,1,-,#351 Bottom 4%,#187 Bottom 49%
+New Balance,DynaSoft Nitrel v6,"75
+ Bad!",$75,Light,Neutral,10.3 oz / 291g 10.1 oz / 285g,0,2.5 mm 6.0 mm,Mid/forefoot,True to size,Balanced,0,Decent,Decent,Good,Moderate,Medium,Medium,Moderate,Flexible,Moderate,2.7 mm,22.7 mm 27.0 mm,20.2 mm 21.0 mm,NormalWideX-Wide,0,All seasons,1,1,-,#352 Bottom 4%,#187 Bottom 49%
+new Balance,Fresh Foam Hierro v6,"89
+ Great!",$135,LightModerate,Neutral,11.4 oz / 323g 11.9 oz / 337g,0,9.2 mm 8.0 mm,HeelMid/forefoot,True to size,-,0,-,-,-,Warm,Medium,-,Stiff,Stiff,Stiff,3.3 mm,26.9 mm 28.0 mm,17.7 mm 20.0 mm,Normal,0,All seasons,1,1,-,#197 Top 31%,#345 Bottom 46%
+New Balance,Fresh Foam X Garoe v2,"90
+ Superb!",$110,Light,Neutral,9.5 oz / 269g 10.5 oz / 298g,0,11.0 mm 8.0 mm,Heel,-,Soft,0,Decent,Decent,Good,Moderate,Narrow,Medium,Stiff,Stiff,Stiff,3.2 mm,38.4 mm,27.4 mm,NormalWideX-Wide,0,All seasons,1,1,-,#50 Top 14%,#129 Top 36%
+New Balance,Fresh Foam X Hierro v7,"86
+ Good!",$140,LightModerate,Neutral,10.5 oz / 297g 10.5 oz / 297g,0,9.9 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,0,-,-,-,Moderate,Narrow,Wide,Moderate,Flexible,Flexible,3.0 mm,32.4 mm 29.0 mm,22.5 mm 21.0 mm,NormalWideX-Wide,0,All seasons,1,1,-,#370 Bottom 42%,#150 Top 24%
+New Balance,Fresh Foam X Hierro v9,"84
+ Good!",$155,Light,Neutral,10.9 oz / 309g 10.5 oz / 297g,0,4.2 mm 4.0 mm,Mid/forefoot,Half size small,Soft,0,Good,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,3.3 mm,37.3 mm 33.0 mm,33.1 mm 29.0 mm,NormalWideX-Wide,0,All seasons,1,1,-,#234 Bottom 36%,#28 Top 8%
+New Balance,Fresh Foam X Hierro v9,"84
+ Good!",$155,Light,Neutral,10.9 oz / 309g 10.5 oz / 297g,0,4.2 mm 4.0 mm,Mid/forefoot,Half size small,Soft,0,Good,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,3.3 mm,37.3 mm 33.0 mm,33.1 mm 29.0 mm,NormalWideX-Wide,0,All seasons,1,1,-,#234 Bottom 36%,#28 Top 8%
+new Balance,Fresh Foam X Hierro v9,"84
+ Good!",$155,Light,Neutral,10.9 oz / 309g 10.5 oz / 297g,0,4.2 mm 4.0 mm,Mid/forefoot,Half size small,Soft,0,Good,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,3.3 mm,37.3 mm 33.0 mm,33.1 mm 29.0 mm,NormalWideX-Wide,0,All seasons,1,1,-,#234 Bottom 36%,#28 Top 8%
+New Balance,Fresh Foam X More Trail v3,"87
+ Great!",$160,ModerateTechnical,Neutral,10.6 oz / 301g 10.6 oz / 301g,0,7.1 mm 4.0 mm,Mid/forefoot,True to size,Soft,0,Very bad,Decent,-,Moderate,Medium,Narrow,Moderate,Moderate,Moderate,5.0 mm,38.6 mm 39.4 mm,31.5 mm 35.4 mm,NormalWide,0,All seasons,1,1,-,#159 Top 44%,#22 Top 6%
+New Balance,Fresh Foam X More Trail v3,"87
+ Great!",$160,ModerateTechnical,Neutral,10.6 oz / 301g 10.6 oz / 301g,0,7.1 mm 4.0 mm,Mid/forefoot,True to size,Soft,0,Very bad,Decent,-,Moderate,Medium,Narrow,Moderate,Moderate,Moderate,5.0 mm,38.6 mm 39.4 mm,31.5 mm 35.4 mm,NormalWide,0,All seasons,1,1,-,#159 Top 44%,#22 Top 6%
+New Balance,Fresh Foam X More Trail v3,"87
+ Great!",$160,ModerateTechnical,Neutral,10.6 oz / 301g 10.6 oz / 301g,0,7.1 mm 4.0 mm,Mid/forefoot,True to size,Soft,0,Very bad,Decent,-,Moderate,Medium,Narrow,Moderate,Moderate,Moderate,5.0 mm,38.6 mm 39.4 mm,31.5 mm 35.4 mm,NormalWide,0,All seasons,1,1,-,#159 Top 44%,#22 Top 6%
+New Balance,FuelCell SuperComp Trail,"90
+ Superb!",$200,LightModerate,Neutral,8.7 oz / 248g 8.8 oz / 249g,1,13.0 mm 10.0 mm,Heel,Half size small,Soft,Carbon plate,Decent,Decent,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,2.9 mm,34.7 mm 36.5 mm,21.7 mm 26.5 mm,Normal,0,All seasons,1,1,-,#65 Top 18%,#319 Bottom 13%
+New Balance,FuelCell SuperComp Trail,"90
+ Superb!",$200,LightModerate,Neutral,8.7 oz / 248g 8.8 oz / 249g,1,13.0 mm 10.0 mm,Heel,Half size small,Soft,Carbon plate,Decent,Decent,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,2.9 mm,34.7 mm 36.5 mm,21.7 mm 26.5 mm,Normal,0,All seasons,1,1,-,#65 Top 18%,#319 Bottom 13%
+New Balance,Minimus Trail,"73
+ Bad!",$120,Light,Neutral,7.5 oz / 213g 7.9 oz / 224g,1,5.2 mm 4.0 mm,Mid/forefoot,Slightly small,Soft,0,Very good,Good,Good,Moderate,Narrow,Medium,Flexible,Flexible,Flexible,3.3 mm,19.5 mm,14.3 mm,NormalWide,0,All seasons,0,0,-,#359 Bottom 2%,#220 Bottom 40%
+New Balance,MT10,"85
+ Good!",$110,Light,Neutral,7.1 oz / 200g 7.2 oz / 204g,1,5.0 mm 4.0 mm,Mid/forefoot,Slightly small,Firm,0,Very good,Decent,Good,Moderate,Narrow,Medium,Flexible,Flexible,Flexible,0,15.6 mm 14.0 mm,10.6 mm 10.0 mm,Normal,0,All seasons,0,0,-,#206 Bottom 44%,#104 Top 29%
+new Balance,Shando,"76
+ Bad!",$90,Moderate,Neutral,12.5 oz / 354g 11 oz / 312g,0,7.3 mm,Mid/forefoot,True to size,-,0,-,-,-,-,Medium,-,Stiff,Flexible,Flexible,4.9 mm,34.7 mm,27.4 mm,Normal,0,-,1,1,-,#345 Bottom 6%,#348 Bottom 5%
+New Balance,Tektrel,"76
+ Decent!",$90,Light,Neutral,10.7 oz / 302g 9.9 oz / 282g,0,8.0 mm 8.0 mm,HeelMid/forefoot,Half size small,Balanced,0,Good,Decent,Decent,Breathable,Medium,Wide,Stiff,Moderate,Moderate,2.4 mm,32.2 mm 32.0 mm,24.2 mm 24.0 mm,NormalWide,0,SummerAll seasons,1,1,-,#340 Bottom 7%,#177 Top 49%
+New Balance,Tektrel,"76
+ Bad!",$90,Light,Neutral,10.7 oz / 302g 9.9 oz / 282g,0,8.0 mm 8.0 mm,HeelMid/forefoot,Half size small,Balanced,0,Good,Decent,Decent,Breathable,Medium,Wide,Stiff,Moderate,Moderate,2.4 mm,32.2 mm 32.0 mm,24.2 mm 24.0 mm,NormalWide,0,SummerAll seasons,1,1,-,#341 Bottom 7%,#177 Top 49%
+New Balance,Tektrel,"76
+ Bad!",$90,Light,Neutral,10.7 oz / 302g 9.9 oz / 282g,0,8.0 mm 8.0 mm,HeelMid/forefoot,Half size small,Balanced,0,Good,Decent,Decent,Breathable,Medium,Wide,Stiff,Moderate,Moderate,2.4 mm,32.2 mm 32.0 mm,24.2 mm 24.0 mm,NormalWide,0,SummerAll seasons,1,1,-,#341 Bottom 7%,#177 Top 49%
+New Balance,Tektrel,"76
+ Bad!",$90,Light,Neutral,10.7 oz / 302g 9.9 oz / 282g,0,8.0 mm 8.0 mm,HeelMid/forefoot,Half size small,Balanced,0,Good,Decent,Decent,Breathable,Medium,Wide,Stiff,Moderate,Moderate,2.4 mm,32.2 mm 32.0 mm,24.2 mm 24.0 mm,NormalWide,0,SummerAll seasons,1,1,-,#341 Bottom 7%,#177 Top 49%
+New Balance,Tektrel,"76
+ Bad!",$90,Light,Neutral,10.7 oz / 302g 9.9 oz / 282g,0,8.0 mm 8.0 mm,HeelMid/forefoot,Half size small,Balanced,0,Good,Decent,Decent,Breathable,Medium,Wide,Stiff,Moderate,Moderate,2.4 mm,32.2 mm 32.0 mm,24.2 mm 24.0 mm,NormalWide,0,SummerAll seasons,1,1,-,#341 Bottom 7%,#177 Top 49%
+New Balance,Tektrel,"76
+ Bad!",$90,Light,Neutral,10.7 oz / 302g 9.9 oz / 282g,0,8.0 mm 8.0 mm,HeelMid/forefoot,Half size small,Balanced,0,Good,Decent,Decent,Breathable,Medium,Wide,Stiff,Moderate,Moderate,2.4 mm,32.2 mm 32.0 mm,24.2 mm 24.0 mm,NormalWide,0,SummerAll seasons,1,1,-,#341 Bottom 7%,#177 Top 49%
+New Balance,Tektrel,"76
+ Bad!",$90,Light,Neutral,10.7 oz / 302g 9.9 oz / 282g,0,8.0 mm 8.0 mm,HeelMid/forefoot,Half size small,Balanced,0,Good,Decent,Decent,Breathable,Medium,Wide,Stiff,Moderate,Moderate,2.4 mm,32.2 mm 32.0 mm,24.2 mm 24.0 mm,NormalWide,0,SummerAll seasons,1,1,-,#341 Bottom 7%,#177 Top 49%
+Nike,Air Zoom Terra Kiger 6,"87
+ Great!",$130,Moderate,Neutral,11.2 oz / 317g 10.3 oz / 291g,0,4.4 mm 4.0 mm,Mid/forefoot,-,-,Rock plate,-,-,-,-,Wide,-,Stiff,-,-,4.8 mm,19.6 mm 15.0 mm,15.2 mm 11.0 mm,Normal,0,-,0,0,-,#342 Bottom 47%,#553 Bottom 14%
+Nike,Juniper Trail,"79
+ Good!",$70,Moderate,Neutral,9.6 oz / 273g 8 oz / 227g,0,7.6 mm 6.0 mm,Mid/forefoot,Slightly small,-,0,-,-,-,-,Medium,-,Stiff,Moderate,Moderate,4.8 mm,26.6 mm 29.0 mm,19.0 mm 23.0 mm,Normal,0,-,1,1,-,#588 Bottom 9%,#397 Bottom 38%
+Nike,Juniper Trail 2,"77
+ Decent!",$85,Light,Neutral,10.8 oz / 306g 10.4 oz / 295g,0,9.4 mm 9.0 mm,HeelMid/forefoot,True to size,Balanced,0,Decent,Good,Decent,Moderate,Medium,Wide,Stiff,Stiff,Stiff,3.1 mm,34.7 mm 35.0 mm,25.3 mm 26.0 mm,Normal,0,All seasons,1,1,-,#615 Bottom 4%,#260 Top 41%
+Nike,Juniper Trail 2 GTX,"75
+ Bad!",$130,Light,Neutral,10.3 oz / 293g 11.4 oz / 323g,0,10.2 mm 9.0 mm,Heel,True to size,Firm,0,Very good,Good,Good,Warm,Wide,Wide,Stiff,Stiff,Moderate,2.7 mm,34.5 mm 35.0 mm,24.3 mm 26.0 mm,Normal,0,Winter,1,1,Waterproof,#347 Bottom 5%,#164 Top 45%
+Nike,Juniper Trail 2 GTX,"75
+ Bad!",$130,Light,Neutral,10.3 oz / 293g 11.4 oz / 323g,0,10.2 mm 9.0 mm,Heel,True to size,Firm,0,Very good,Good,Good,Warm,Wide,Wide,Stiff,Stiff,Moderate,2.7 mm,34.5 mm 35.0 mm,24.3 mm 26.0 mm,Normal,0,Winter,1,1,Waterproof,#347 Bottom 5%,#164 Top 45%
+Nike,Juniper Trail 2 GTX,"75
+ Bad!",$130,Light,Neutral,10.3 oz / 293g 11.4 oz / 323g,0,10.2 mm 9.0 mm,Heel,True to size,Firm,0,Very good,Good,Good,Warm,Wide,Wide,Stiff,Stiff,Moderate,2.7 mm,34.5 mm 35.0 mm,24.3 mm 26.0 mm,Normal,0,Winter,1,1,Waterproof,#347 Bottom 5%,#164 Top 45%
+Nike,Juniper Trail 2 GTX,"75
+ Bad!",$130,Light,Neutral,10.3 oz / 293g 11.4 oz / 323g,0,10.2 mm 9.0 mm,Heel,True to size,Firm,0,Very good,Good,Good,Warm,Wide,Wide,Stiff,Stiff,Moderate,2.7 mm,34.5 mm 35.0 mm,24.3 mm 26.0 mm,Normal,0,Winter,1,1,Waterproof,#347 Bottom 5%,#164 Top 45%
+Nike,Juniper Trail 2 GTX,"75
+ Bad!",$130,Light,Neutral,10.3 oz / 293g 11.4 oz / 323g,0,10.2 mm 9.0 mm,Heel,True to size,Firm,0,Very good,Good,Good,Warm,Wide,Wide,Stiff,Stiff,Moderate,2.7 mm,34.5 mm 35.0 mm,24.3 mm 26.0 mm,Normal,0,Winter,1,1,Waterproof,#347 Bottom 5%,#164 Top 45%
+Nike,Juniper Trail 2 GTX,"75
+ Bad!",$130,Light,Neutral,10.3 oz / 293g 11.4 oz / 323g,0,10.2 mm 9.0 mm,Heel,True to size,Firm,0,Very good,Good,Good,Warm,Wide,Wide,Stiff,Stiff,Moderate,2.7 mm,34.5 mm 35.0 mm,24.3 mm 26.0 mm,Normal,0,Winter,1,1,Waterproof,#347 Bottom 5%,#164 Top 45%
+Nike,Juniper Trail 3,"73
+ Bad!",$90,Light,Neutral,10.2 oz / 288g 10.8 oz / 305g,0,10.9 mm 10.0 mm,Heel,True to size,Balanced,0,Decent,Bad,Good,Warm,Medium,Wide,Moderate,Stiff,Stiff,2.6 mm,32.6 mm 32.0 mm,21.7 mm 22.0 mm,Normal,0,All seasons,1,1,-,#358 Bottom 2%,#184 Top 50%
+Nike,Kiger 10,"77
+ Decent!",$160,LightModerate,Neutral,9.2 oz / 261g 9.3 oz / 263g,0,3.7 mm 5.0 mm,Mid/forefoot,Slightly small,Soft,Rock plate,Very good,Good,Good,Breathable,Medium,Wide,Moderate,Flexible,Moderate,3.3 mm,28.3 mm 29.0 mm,24.6 mm 24.0 mm,Normal,0,SummerAll seasons,1,1,-,#333 Bottom 9%,#233 Bottom 36%
+Nike,Pegasus Trail 3,"90
+ Superb!",$130,LightModerate,Neutral,10.8 oz / 306g 11.3 oz / 320g,0,10.3 mm 10.0 mm,Heel,True to size,Soft,0,-,-,-,Warm,Medium,-,Stiff,Moderate,Moderate,3.3 mm,35.3 mm 36.0 mm,25.0 mm 26.0 mm,Normal,0,All seasons,1,1,-,#87 Top 14%,#375 Bottom 41%
+Nike,Pegasus Trail 3 GTX,"87
+ Great!",$160,LightModerate,Neutral,11 oz / 311g 10.9 oz / 309g,0,9.8 mm 10.0 mm,HeelMid/forefoot,True to size,-,0,-,-,-,Warm,Narrow,-,Stiff,Moderate,Moderate,3.3 mm,32.5 mm 36.0 mm,22.7 mm 26.0 mm,Normal,0,Winter,1,1,Waterproof,#338 Bottom 47%,#432 Bottom 33%
+Nike,Pegasus Trail 4 GTX,"86
+ Good!",$160,LightModerate,Neutral,9.6 oz / 271g 9.6 oz / 272g,0,12.8 mm 10.0 mm,Heel,Slightly small,Soft,0,Good,Good,Good,Warm,Medium,Wide,Stiff,Stiff,Flexible,3.5 mm,37.7 mm 37.0 mm,24.9 mm 27.0 mm,Normal,0,Winter,1,1,Waterproof,#371 Bottom 42%,#407 Bottom 37%
+Nike,Pegasus Trail 5,"90
+ Superb!",$150,Light,Neutral,10.1 oz / 286g 10.1 oz / 286g,0,9.6 mm 9.5 mm,HeelMid/forefoot,True to size,Soft,0,Very good,Good,Good,Warm,Medium,Medium,Moderate,Moderate,Stiff,3.2 mm,34.6 mm 37.0 mm,25.0 mm 27.5 mm,NormalWideX-Wide,0,All seasons,1,1,-,#72 Top 20%,#48 Top 14%
+Nike,Pegasus Trail 5,"90
+ Superb!",$150,Light,Neutral,10.1 oz / 286g 10.1 oz / 286g,0,9.6 mm 9.5 mm,HeelMid/forefoot,True to size,Soft,0,Very good,Good,Good,Warm,Medium,Medium,Moderate,Moderate,Stiff,3.2 mm,34.6 mm 37.0 mm,25.0 mm 27.5 mm,NormalWideX-Wide,0,All seasons,1,1,-,#72 Top 20%,#48 Top 14%
+Nike,Pegasus Trail 5,"90
+ Superb!",$150,Light,Neutral,10.1 oz / 286g 10.1 oz / 286g,0,9.6 mm 9.5 mm,HeelMid/forefoot,True to size,Soft,0,Very good,Good,Good,Warm,Medium,Medium,Moderate,Moderate,Stiff,3.2 mm,34.6 mm 37.0 mm,25.0 mm 27.5 mm,NormalWideX-Wide,0,All seasons,1,1,-,#73 Top 20%,#48 Top 14%
+Nike,Pegasus Trail 5,"90
+ Superb!",$150,Light,Neutral,10.1 oz / 286g 10.1 oz / 286g,0,9.6 mm 9.5 mm,HeelMid/forefoot,True to size,Soft,0,Very good,Good,Good,Warm,Medium,Medium,Moderate,Moderate,Stiff,3.2 mm,34.6 mm 37.0 mm,25.0 mm 27.5 mm,NormalWideX-Wide,0,All seasons,1,1,-,#73 Top 20%,#48 Top 14%
+Nike,Pegasus Trail 5,"90
+ Superb!",$150,Light,Neutral,10.1 oz / 286g 10.1 oz / 286g,0,9.6 mm 9.5 mm,HeelMid/forefoot,True to size,Soft,0,Very good,Good,Good,Warm,Medium,Medium,Moderate,Moderate,Stiff,3.2 mm,34.6 mm 37.0 mm,25.0 mm 27.5 mm,NormalWideX-Wide,0,All seasons,1,1,-,#73 Top 20%,#48 Top 14%
+Nike,Pegasus Trail 5,"90
+ Superb!",$150,Light,Neutral,10.1 oz / 286g 10.1 oz / 286g,0,9.6 mm 9.5 mm,HeelMid/forefoot,True to size,Soft,0,Very good,Good,Good,Warm,Medium,Medium,Moderate,Moderate,Stiff,3.2 mm,34.6 mm 37.0 mm,25.0 mm 27.5 mm,NormalWideX-Wide,0,All seasons,1,1,-,#73 Top 20%,#48 Top 14%
+Nike,Pegasus Trail 5,"90
+ Superb!",$150,Light,Neutral,10.1 oz / 286g 10.1 oz / 286g,0,9.6 mm 9.5 mm,HeelMid/forefoot,True to size,Soft,0,Very good,Good,Good,Warm,Medium,Medium,Moderate,Moderate,Stiff,3.2 mm,34.6 mm 37.0 mm,25.0 mm 27.5 mm,NormalWideX-Wide,0,All seasons,1,1,-,#73 Top 20%,#48 Top 14%
+nike,Pegasus Trail 5,"90
+ Superb!",$150,Light,Neutral,10.1 oz / 286g 10.1 oz / 286g,0,9.6 mm 9.5 mm,HeelMid/forefoot,True to size,Soft,0,Very good,Good,Good,Warm,Medium,Medium,Moderate,Moderate,Stiff,3.2 mm,34.6 mm 37.0 mm,25.0 mm 27.5 mm,NormalWideX-Wide,0,All seasons,1,1,-,#73 Top 20%,#48 Top 14%
+Nike,Pegasus Trail 5,"90
+ Superb!",$150,Light,Neutral,10.1 oz / 286g 10.1 oz / 286g,0,9.6 mm 9.5 mm,HeelMid/forefoot,True to size,Soft,0,Very good,Good,Good,Warm,Medium,Medium,Moderate,Moderate,Stiff,3.2 mm,34.6 mm 37.0 mm,25.0 mm 27.5 mm,NormalWideX-Wide,0,All seasons,1,1,-,#73 Top 20%,#48 Top 14%
+Nike,Pegasus Trail 5,"90
+ Superb!",$150,Light,Neutral,10.1 oz / 286g 10.1 oz / 286g,0,9.6 mm 9.5 mm,HeelMid/forefoot,True to size,Soft,0,Very good,Good,Good,Warm,Medium,Medium,Moderate,Moderate,Stiff,3.2 mm,34.6 mm 37.0 mm,25.0 mm 27.5 mm,NormalWideX-Wide,0,All seasons,1,1,-,#73 Top 20%,#48 Top 14%
+Nike,Pegasus Trail 5,"90
+ Superb!",$150,Light,Neutral,10.1 oz / 286g 10.1 oz / 286g,0,9.6 mm 9.5 mm,HeelMid/forefoot,True to size,Soft,0,Very good,Good,Good,Warm,Medium,Medium,Moderate,Moderate,Stiff,3.2 mm,34.6 mm 37.0 mm,25.0 mm 27.5 mm,NormalWideX-Wide,0,All seasons,1,1,-,#73 Top 20%,#48 Top 14%
+Nike,Pegasus Trail 5 GTX,"75
+ Bad!",$170,Light,Neutral,9.9 oz / 281g 10.6 oz / 300g,0,8.3 mm 9.5 mm,HeelMid/forefoot,Slightly small,Soft,0,Decent,Bad,Good,Warm,Medium,Wide,Moderate,Stiff,Flexible,3.6 mm,32.1 mm 37.0 mm,23.8 mm 27.5 mm,NarrowNormal,0,Winter,1,1,Waterproof,#346 Bottom 5%,#101 Top 28%
+Nike,Pegasus Trail 5 GTX,"75
+ Bad!",$170,Light,Neutral,9.9 oz / 281g 10.6 oz / 300g,0,8.3 mm 9.5 mm,HeelMid/forefoot,Slightly small,Soft,0,Decent,Bad,Good,Warm,Medium,Wide,Moderate,Stiff,Flexible,3.6 mm,32.1 mm 37.0 mm,23.8 mm 27.5 mm,NarrowNormal,0,Winter,1,1,Waterproof,#346 Bottom 5%,#101 Top 28%
+Nike,Pegasus Trail 5 GTX,"75
+ Bad!",$170,Light,Neutral,9.9 oz / 281g 10.6 oz / 300g,0,8.3 mm 9.5 mm,HeelMid/forefoot,Slightly small,Soft,0,Decent,Bad,Good,Warm,Medium,Wide,Moderate,Stiff,Flexible,3.6 mm,32.1 mm 37.0 mm,23.8 mm 27.5 mm,NarrowNormal,0,Winter,1,1,Waterproof,#346 Bottom 5%,#101 Top 28%
+Nike,Pegasus Trail 5 GTX,"75
+ Bad!",$170,Light,Neutral,9.9 oz / 281g 10.6 oz / 300g,0,8.3 mm 9.5 mm,HeelMid/forefoot,Slightly small,Soft,0,Decent,Bad,Good,Warm,Medium,Wide,Moderate,Stiff,Flexible,3.6 mm,32.1 mm 37.0 mm,23.8 mm 27.5 mm,NarrowNormal,0,Winter,1,1,Waterproof,#346 Bottom 5%,#101 Top 28%
+Nike,Pegasus Trail 5 GTX,"75
+ Bad!",$170,Light,Neutral,9.9 oz / 281g 10.6 oz / 300g,0,8.3 mm 9.5 mm,HeelMid/forefoot,Slightly small,Soft,0,Decent,Bad,Good,Warm,Medium,Wide,Moderate,Stiff,Flexible,3.6 mm,32.1 mm 37.0 mm,23.8 mm 27.5 mm,NarrowNormal,0,Winter,1,1,Waterproof,#347 Bottom 5%,#101 Top 28%
+Nike,Pegasus Trail 5 GTX,"75
+ Bad!",$170,Light,Neutral,9.9 oz / 281g 10.6 oz / 300g,0,8.3 mm 9.5 mm,HeelMid/forefoot,Slightly small,Soft,0,Decent,Bad,Good,Warm,Medium,Wide,Moderate,Stiff,Flexible,3.6 mm,32.1 mm 37.0 mm,23.8 mm 27.5 mm,NarrowNormal,0,Winter,1,1,Waterproof,#346 Bottom 5%,#101 Top 28%
+Nike,Pegasus Trail 5 GTX,"75
+ Bad!",$170,Light,Neutral,9.9 oz / 281g 10.6 oz / 300g,0,8.3 mm 9.5 mm,HeelMid/forefoot,Slightly small,Soft,0,Decent,Bad,Good,Warm,Medium,Wide,Moderate,Stiff,Flexible,3.6 mm,32.1 mm 37.0 mm,23.8 mm 27.5 mm,NarrowNormal,0,Winter,1,1,Waterproof,#346 Bottom 5%,#101 Top 28%
+Nike,Terra Kiger 8,"85
+ Good!",$140,ModerateTechnical,Neutral,10.8 oz / 306g 10.4 oz / 295g,0,5.9 mm 6.0 mm,Mid/forefoot,True to size,Firm,Rock plate,-,-,-,-,Medium,-,Stiff,Moderate,Flexible,3.9 mm,28.1 mm 30.0 mm,22.2 mm 24.0 mm,Normal,0,-,1,1,-,#422 Bottom 34%,#596 Bottom 7%
+Nike,Terra Kiger 9,"90
+ Superb!",$150,ModerateTechnical,Neutral,10.2 oz / 288g 10.1 oz / 286g,0,4.4 mm 3.0 mm,Mid/forefoot,True to size,Soft,0,Very good,Decent,-,Moderate,Medium,Medium,Stiff,Moderate,Moderate,4.4 mm,30.1 mm 31.0 mm,25.7 mm 28.0 mm,Normal,0,All seasons,1,1,-,#34 Top 10%,#293 Bottom 20%
+Nike,Terra Kiger 9,"90
+ Superb!",$150,ModerateTechnical,Neutral,10.2 oz / 288g 10.1 oz / 286g,0,4.4 mm 3.0 mm,Mid/forefoot,True to size,Soft,0,Very good,Decent,-,Moderate,Medium,Medium,Stiff,Moderate,Moderate,4.4 mm,30.1 mm 31.0 mm,25.7 mm 28.0 mm,Normal,0,All seasons,1,1,-,#34 Top 10%,#293 Bottom 20%
+Nike,Terra Kiger 9,"90
+ Superb!",$150,ModerateTechnical,Neutral,10.2 oz / 288g 10.1 oz / 286g,0,4.4 mm 3.0 mm,Mid/forefoot,True to size,Soft,0,Very good,Decent,-,Moderate,Medium,Medium,Stiff,Moderate,Moderate,4.4 mm,30.1 mm 31.0 mm,25.7 mm 28.0 mm,Normal,0,All seasons,1,1,-,#34 Top 10%,#293 Bottom 20%
+Nike,Terra Kiger 9,"90
+ Superb!",$150,ModerateTechnical,Neutral,10.2 oz / 288g 10.1 oz / 286g,0,4.4 mm 3.0 mm,Mid/forefoot,True to size,Soft,0,Very good,Decent,-,Moderate,Medium,Medium,Stiff,Moderate,Moderate,4.4 mm,30.1 mm 31.0 mm,25.7 mm 28.0 mm,Normal,0,All seasons,1,1,-,#34 Top 10%,#293 Bottom 20%
+Nike,Terra Kiger 9,"90
+ Superb!",$150,ModerateTechnical,Neutral,10.2 oz / 288g 10.1 oz / 286g,0,4.4 mm 3.0 mm,Mid/forefoot,True to size,Soft,0,Very good,Decent,-,Moderate,Medium,Medium,Stiff,Moderate,Moderate,4.4 mm,30.1 mm 31.0 mm,25.7 mm 28.0 mm,Normal,0,All seasons,1,1,-,#34 Top 10%,#293 Bottom 20%
+Nike,Terra Kiger 9,"90
+ Superb!",$150,ModerateTechnical,Neutral,10.2 oz / 288g 10.1 oz / 286g,0,4.4 mm 3.0 mm,Mid/forefoot,True to size,Soft,0,Very good,Decent,-,Moderate,Medium,Medium,Stiff,Moderate,Moderate,4.4 mm,30.1 mm 31.0 mm,25.7 mm 28.0 mm,Normal,0,All seasons,1,1,-,#34 Top 10%,#293 Bottom 20%
+Nike,Terra Kiger 9,"90
+ Superb!",$150,ModerateTechnical,Neutral,10.2 oz / 288g 10.1 oz / 286g,0,4.4 mm 3.0 mm,Mid/forefoot,True to size,Soft,0,Very good,Decent,-,Moderate,Medium,Medium,Stiff,Moderate,Moderate,4.4 mm,30.1 mm 31.0 mm,25.7 mm 28.0 mm,Normal,0,All seasons,1,1,-,#34 Top 10%,#293 Bottom 20%
+Nike,Terra Kiger 9,"90
+ Superb!",$150,ModerateTechnical,Neutral,10.2 oz / 288g 10.1 oz / 286g,0,4.4 mm 3.0 mm,Mid/forefoot,True to size,Soft,0,Very good,Decent,-,Moderate,Medium,Medium,Stiff,Moderate,Moderate,4.4 mm,30.1 mm 31.0 mm,25.7 mm 28.0 mm,Normal,0,All seasons,1,1,-,#34 Top 10%,#293 Bottom 20%
+Nike,Terra Kiger 9,"90
+ Superb!",$150,ModerateTechnical,Neutral,10.2 oz / 288g 10.1 oz / 286g,0,4.4 mm 3.0 mm,Mid/forefoot,True to size,Soft,0,Very good,Decent,-,Moderate,Medium,Medium,Stiff,Moderate,Moderate,4.4 mm,30.1 mm 31.0 mm,25.7 mm 28.0 mm,Normal,0,All seasons,1,1,-,#34 Top 10%,#293 Bottom 20%
+Nike,Terra Kiger 9,"90
+ Superb!",$150,ModerateTechnical,Neutral,10.2 oz / 288g 10.1 oz / 286g,0,4.4 mm 3.0 mm,Mid/forefoot,True to size,Soft,0,Very good,Decent,-,Moderate,Medium,Medium,Stiff,Moderate,Moderate,4.4 mm,30.1 mm 31.0 mm,25.7 mm 28.0 mm,Normal,0,All seasons,1,1,-,#34 Top 10%,#293 Bottom 20%
+Nike,Ultrafly,"89
+ Great!",$260,LightModerate,Neutral,10.5 oz / 299g 10.1 oz / 286g,0,11.8 mm 8.5 mm,Heel,True to size,Soft,Carbon plate,Very bad,Good,Good,Moderate,Medium,Wide,Stiff,Stiff,Moderate,3.0 mm,36.6 mm 38.0 mm,24.8 mm 29.5 mm,Normal,0,All seasons,1,1,-,#85 Top 24%,#201 Bottom 45%
+Nike,Ultrafly,"89
+ Great!",$260,LightModerate,Neutral,10.5 oz / 299g 10.1 oz / 286g,0,11.8 mm 8.5 mm,Heel,True to size,Soft,Carbon plate,Very bad,Good,Good,Moderate,Medium,Wide,Stiff,Stiff,Moderate,3.0 mm,36.6 mm 38.0 mm,24.8 mm 29.5 mm,Normal,0,All seasons,1,1,-,#85 Top 24%,#201 Bottom 45%
+Nike,Ultrafly,"89
+ Great!",$260,LightModerate,Neutral,10.5 oz / 299g 10.1 oz / 286g,0,11.8 mm 8.5 mm,Heel,True to size,Soft,Carbon plate,Very bad,Good,Good,Moderate,Medium,Wide,Stiff,Stiff,Moderate,3.0 mm,36.6 mm 38.0 mm,24.8 mm 29.5 mm,Normal,0,All seasons,1,1,-,#85 Top 24%,#201 Bottom 45%
+Nike,Ultrafly,"89
+ Great!",$260,LightModerate,Neutral,10.5 oz / 299g 10.1 oz / 286g,0,11.8 mm 8.5 mm,Heel,True to size,Soft,Carbon plate,Very bad,Good,Good,Moderate,Medium,Wide,Stiff,Stiff,Moderate,3.0 mm,36.6 mm 38.0 mm,24.8 mm 29.5 mm,Normal,0,All seasons,1,1,-,#85 Top 24%,#201 Bottom 45%
+Nike,Wildhorse 10,"91
+ Superb!",$165,LightModerate,Neutral,11 oz / 312g 11 oz / 311g,0,10.9 mm 9.5 mm,Heel,True to size,Soft,Rock plate,Very bad,Good,Decent,Moderate,Medium,Medium,Moderate,Moderate,Moderate,3.4 mm,38.3 mm 38.0 mm,27.4 mm 28.5 mm,Normal,0,All seasons,1,1,-,#32 Top 9%,#132 Top 36%
+Nike,Wildhorse 10,"91
+ Superb!",$165,LightModerate,Neutral,11 oz / 312g 11 oz / 311g,0,10.9 mm 9.5 mm,Heel,True to size,Soft,Rock plate,Very bad,Good,Decent,Moderate,Medium,Medium,Moderate,Moderate,Moderate,3.4 mm,38.3 mm 38.0 mm,27.4 mm 28.5 mm,Normal,0,All seasons,1,1,-,#24 Top 7%,#133 Top 37%
+Nike,Wildhorse 10,"91
+ Superb!",$165,LightModerate,Neutral,11 oz / 312g 11 oz / 311g,0,10.9 mm 9.5 mm,Heel,True to size,Soft,Rock plate,Very bad,Good,Decent,Moderate,Medium,Medium,Moderate,Moderate,Moderate,3.4 mm,38.3 mm 38.0 mm,27.4 mm 28.5 mm,Normal,0,All seasons,1,1,-,#24 Top 7%,#133 Top 37%
+Nike,Wildhorse 7,"87
+ Great!",$130,LightModerate,Neutral,11.2 oz / 318g 11 oz / 312g,0,7.3 mm 8.0 mm,Mid/forefoot,True to size,-,Rock plate,-,-,-,-,Narrow,-,Stiff,Moderate,Flexible,4.2 mm,33.5 mm 30.0 mm,26.2 mm 22.0 mm,Normal,0,-,1,1,-,#344 Bottom 46%,#501 Bottom 22%
+Nike,Wildhorse 8,"85
+ Good!",$130,Moderate,Neutral,11.3 oz / 319g 10.5 oz / 298g,0,9.2 mm 8.0 mm,HeelMid/forefoot,True to size,Soft,Rock plate,Very bad,Bad,-,Moderate,Medium,Medium,Moderate,Moderate,Moderate,3.5 mm,34.2 mm 35.5 mm,25.0 mm 27.5 mm,Normal,0,All seasons,1,1,-,#415 Bottom 35%,#356 Bottom 44%
+Nike,Zegama 2,"87
+ Great!",$180,Moderate,Neutral,10.7 oz / 302g 10.7 oz / 303g,0,4.0 mm 4.0 mm,Mid/forefoot,True to size,Soft,0,Good,Good,Good,Moderate,Medium,Wide,Moderate,Stiff,Flexible,4.0 mm,30.3 mm 36.0 mm,26.3 mm 32.0 mm,Normal,0,All seasons,1,1,-,#143 Top 39%,#95 Top 26%
+Nike,Zegama 2,"87
+ Great!",$180,Moderate,Neutral,10.7 oz / 302g 10.7 oz / 303g,0,4.0 mm 4.0 mm,Mid/forefoot,True to size,Soft,0,Good,Good,Good,Moderate,Medium,Wide,Moderate,Stiff,Flexible,4.0 mm,30.3 mm 36.0 mm,26.3 mm 32.0 mm,Normal,0,All seasons,1,1,-,#143 Top 39%,#95 Top 26%
+Nike,Zegama 2,"87
+ Great!",$180,Moderate,Neutral,10.7 oz / 302g 10.7 oz / 303g,0,4.0 mm 4.0 mm,Mid/forefoot,True to size,Soft,0,Good,Good,Good,Moderate,Medium,Wide,Moderate,Stiff,Flexible,4.0 mm,30.3 mm 36.0 mm,26.3 mm 32.0 mm,Normal,0,All seasons,1,1,-,#143 Top 39%,#95 Top 26%
+Nike,Zegama 2,"87
+ Great!",$180,Moderate,Neutral,10.7 oz / 302g 10.7 oz / 303g,0,4.0 mm 4.0 mm,Mid/forefoot,True to size,Soft,0,Good,Good,Good,Moderate,Medium,Wide,Moderate,Stiff,Flexible,4.0 mm,30.3 mm 36.0 mm,26.3 mm 32.0 mm,Normal,0,All seasons,1,1,-,#143 Top 39%,#95 Top 26%
+Nike,Zegama 2,"87
+ Great!",$180,Moderate,Neutral,10.7 oz / 302g 10.7 oz / 303g,0,4.0 mm 4.0 mm,Mid/forefoot,True to size,Soft,0,Good,Good,Good,Moderate,Medium,Wide,Moderate,Stiff,Flexible,4.0 mm,30.3 mm 36.0 mm,26.3 mm 32.0 mm,Normal,0,All seasons,1,1,-,#143 Top 39%,#95 Top 26%
+Nike,Zegama 2,"87
+ Great!",$180,Moderate,Neutral,10.7 oz / 302g 10.7 oz / 303g,0,4.0 mm 4.0 mm,Mid/forefoot,True to size,Soft,0,Good,Good,Good,Moderate,Medium,Wide,Moderate,Stiff,Flexible,4.0 mm,30.3 mm 36.0 mm,26.3 mm 32.0 mm,Normal,0,All seasons,1,1,-,#143 Top 39%,#95 Top 26%
+Nike,Zegama 2,"87
+ Great!",$180,Moderate,Neutral,10.7 oz / 302g 10.7 oz / 303g,0,4.0 mm 4.0 mm,Mid/forefoot,True to size,Soft,0,Good,Good,Good,Moderate,Medium,Wide,Moderate,Stiff,Flexible,4.0 mm,30.3 mm 36.0 mm,26.3 mm 32.0 mm,Normal,0,All seasons,1,1,-,#143 Top 39%,#95 Top 26%
+NNormal,Kjerag,"93
+ Superb!",$195,Light,Neutral,7.5 oz / 214g 7.1 oz / 200g,1,8.6 mm 6.0 mm,Mid/forefoot,True to size,Balanced,0,Decent,Decent,Decent,Warm,Medium,Medium,Flexible,Moderate,Flexible,3.0 mm,25.0 mm 23.5 mm,16.4 mm 17.5 mm,Normal,0,All seasons,0,0,-,#3 Top 1%,#234 Bottom 36%
+NNormal,Kjerag,"93
+ Superb!",$195,Light,Neutral,7.5 oz / 214g 7.1 oz / 200g,1,8.6 mm 6.0 mm,Mid/forefoot,True to size,Balanced,0,Decent,Decent,Decent,Warm,Medium,Medium,Flexible,Moderate,Flexible,3.0 mm,25.0 mm 23.5 mm,16.4 mm 17.5 mm,Normal,0,All seasons,0,0,-,#3 Top 1%,#234 Bottom 36%
+On,Cloudsurfer Trail,"85
+ Good!",$160,Light,Neutral,9.6 oz / 272g 9.6 oz / 272g,0,10.7 mm 7.0 mm,Heel,True to size,Balanced,0,Decent,Bad,Good,Moderate,Medium,Medium,Moderate,Stiff,Moderate,2.5 mm,37.4 mm 41.0 mm,26.7 mm 34.0 mm,Normal,1,All seasons,1,1,-,#197 Bottom 46%,#217 Bottom 41%
+on,Cloudsurfer Trail,"85
+ Good!",$160,Light,Neutral,9.6 oz / 272g 9.6 oz / 272g,0,10.7 mm 7.0 mm,Heel,True to size,Balanced,0,Decent,Bad,Good,Moderate,Medium,Medium,Moderate,Stiff,Moderate,2.5 mm,37.4 mm 41.0 mm,26.7 mm 34.0 mm,Normal,1,All seasons,1,1,-,#197 Bottom 46%,#217 Bottom 41%
+On,Cloudultra 2,"89
+ Great!",$180,Light,Neutral,10.4 oz / 296g 10.4 oz / 295g,0,10.2 mm 6.0 mm,Heel,True to size,Firm,Rock plate,Bad,Decent,Good,Breathable,Medium,Medium,Flexible,Flexible,Flexible,2.5 mm,30.2 mm 27.0 mm,20.0 mm 21.0 mm,Normal,0,SummerAll seasons,1,1,-,#104 Top 29%,#244 Bottom 33%
+On,Cloudultra 2,"89
+ Great!",$180,Light,Neutral,10.4 oz / 296g 10.4 oz / 295g,0,10.2 mm 6.0 mm,Heel,True to size,Firm,Rock plate,Bad,Decent,Good,Breathable,Medium,Medium,Flexible,Flexible,Flexible,2.5 mm,30.2 mm 27.0 mm,20.0 mm 21.0 mm,Normal,0,SummerAll seasons,1,1,-,#104 Top 29%,#244 Bottom 33%
+On,Cloudultra 2,"89
+ Great!",$180,Light,Neutral,10.4 oz / 296g 10.4 oz / 295g,0,10.2 mm 6.0 mm,Heel,True to size,Firm,Rock plate,Bad,Decent,Good,Breathable,Medium,Medium,Flexible,Flexible,Flexible,2.5 mm,30.2 mm 27.0 mm,20.0 mm 21.0 mm,Normal,0,SummerAll seasons,1,1,-,#105 Top 29%,#244 Bottom 33%
+On,Cloudvista,"89
+ Great!",$150,Light,Neutral,10.1 oz / 285g 9.9 oz / 280g,0,10.3 mm 10.3 mm,Heel,Half size small,Balanced,Rock plate,Very bad,Bad,Good,Breathable,Medium,Medium,Stiff,Moderate,Flexible,2.5 mm,32.3 mm 32.3 mm,22.0 mm 22.0 mm,Normal,0,SummerAll seasons,1,1,-,#147 Top 23%,#487 Bottom 24%
+On,Cloudvista 2,"88
+ Great!",$150,Light,Neutral,10.3 oz / 292g 10.9 oz / 309g,0,6.0 mm 5.0 mm,Mid/forefoot,True to size,Balanced,0,Decent,Bad,Good,Warm,Narrow,Medium,Moderate,Stiff,Moderate,3.1 mm,31.7 mm 29.0 mm,25.7 mm 24.0 mm,NarrowNormal,0,All seasons,1,1,-,#130 Top 36%,#215 Bottom 41%
+On,Cloudvista 2,"88
+ Great!",$150,Light,Neutral,10.3 oz / 292g 10.9 oz / 309g,0,6.0 mm 5.0 mm,Mid/forefoot,True to size,Balanced,0,Decent,Bad,Good,Warm,Narrow,Medium,Moderate,Stiff,Moderate,3.1 mm,31.7 mm 29.0 mm,25.7 mm 24.0 mm,NarrowNormal,0,All seasons,1,1,-,#131 Top 36%,#215 Bottom 41%
+On,Cloudvista 2,"88
+ Great!",$150,Light,Neutral,10.3 oz / 292g 10.9 oz / 309g,0,6.0 mm 5.0 mm,Mid/forefoot,True to size,Balanced,0,Decent,Bad,Good,Warm,Narrow,Medium,Moderate,Stiff,Moderate,3.1 mm,31.7 mm 29.0 mm,25.7 mm 24.0 mm,NarrowNormal,0,All seasons,1,1,-,#131 Top 36%,#215 Bottom 41%
+salomon,Genesis,"92
+ Superb!",$150,ModerateTechnical,Neutral,9.9 oz / 282g 9.7 oz / 275g,0,9.0 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,0,Good,Good,Decent,Moderate,Medium,Medium,Moderate,Moderate,Stiff,4.0 mm,33.5 mm 33.0 mm,24.5 mm 25.0 mm,Normal,0,All seasons,1,1,Water repellent,#12 Top 4%,#221 Bottom 39%
+Salomon,Genesis,"92
+ Superb!",$150,ModerateTechnical,Neutral,9.9 oz / 282g 9.7 oz / 275g,0,9.0 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,0,Good,Good,Decent,Moderate,Medium,Medium,Moderate,Moderate,Stiff,4.0 mm,33.5 mm 33.0 mm,24.5 mm 25.0 mm,Normal,0,All seasons,1,1,Water repellent,#12 Top 4%,#221 Bottom 39%
+Salomon,Genesis,"92
+ Superb!",$150,ModerateTechnical,Neutral,9.9 oz / 282g 9.7 oz / 275g,0,9.0 mm 8.0 mm,HeelMid/forefoot,True to size,Balanced,0,Good,Good,Decent,Moderate,Medium,Medium,Moderate,Moderate,Stiff,4.0 mm,33.5 mm 33.0 mm,24.5 mm 25.0 mm,Normal,0,All seasons,1,1,Water repellent,#12 Top 4%,#221 Bottom 39%
+Salomon,Pulsar Trail,"87
+ Great!",$130,Light,Neutral,9.9 oz / 281g 9.9 oz / 280g,0,7.2 mm 6.0 mm,Mid/forefoot,True to size,Soft,Rock plate,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,2.5 mm,31.0 mm 32.6 mm,23.8 mm 26.6 mm,Normal,0,All seasons,1,1,-,#153 Top 42%,#285 Bottom 22%
+salomon,Pulsar Trail,"87
+ Great!",$130,Light,Neutral,9.9 oz / 281g 9.9 oz / 280g,0,7.2 mm 6.0 mm,Mid/forefoot,True to size,Soft,Rock plate,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,2.5 mm,31.0 mm 32.6 mm,23.8 mm 26.6 mm,Normal,0,All seasons,1,1,-,#153 Top 42%,#285 Bottom 22%
+Salomon,Pulsar Trail,"87
+ Great!",$130,Light,Neutral,9.9 oz / 281g 9.9 oz / 280g,0,7.2 mm 6.0 mm,Mid/forefoot,True to size,Soft,Rock plate,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,2.5 mm,31.0 mm 32.6 mm,23.8 mm 26.6 mm,Normal,0,All seasons,1,1,-,#153 Top 42%,#285 Bottom 22%
+Salomon,Pulsar Trail,"87
+ Great!",$130,Light,Neutral,9.9 oz / 281g 9.9 oz / 280g,0,7.2 mm 6.0 mm,Mid/forefoot,True to size,Soft,Rock plate,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,2.5 mm,31.0 mm 32.6 mm,23.8 mm 26.6 mm,Normal,0,All seasons,1,1,-,#153 Top 42%,#285 Bottom 22%
+Salomon,Pulsar Trail,"87
+ Great!",$130,Light,Neutral,9.9 oz / 281g 9.9 oz / 280g,0,7.2 mm 6.0 mm,Mid/forefoot,True to size,Soft,Rock plate,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,2.5 mm,31.0 mm 32.6 mm,23.8 mm 26.6 mm,Normal,0,All seasons,1,1,-,#153 Top 42%,#285 Bottom 22%
+Salomon,Pulsar Trail,"87
+ Great!",$130,Light,Neutral,9.9 oz / 281g 9.9 oz / 280g,0,7.2 mm 6.0 mm,Mid/forefoot,True to size,Soft,Rock plate,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,2.5 mm,31.0 mm 32.6 mm,23.8 mm 26.6 mm,Normal,0,All seasons,1,1,-,#153 Top 42%,#285 Bottom 22%
+Salomon,Pulsar Trail,"87
+ Great!",$130,Light,Neutral,9.9 oz / 281g 9.9 oz / 280g,0,7.2 mm 6.0 mm,Mid/forefoot,True to size,Soft,Rock plate,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,2.5 mm,31.0 mm 32.6 mm,23.8 mm 26.6 mm,Normal,0,All seasons,1,1,-,#153 Top 42%,#285 Bottom 22%
+Salomon,S/Lab Genesis,"92
+ Superb!",$199,LightModerate,Neutral,8.8 oz / 249g 9.1 oz / 258g,1,7.8 mm 8.0 mm,Mid/forefoot,True to size,Soft,Rock plate,Bad,Decent,Decent,Moderate,Medium,Medium,Moderate,Moderate,Flexible,4.3 mm,31.9 mm 33.0 mm,24.1 mm 25.0 mm,Normal,0,All seasons,1,1,-,#17 Top 5%,#339 Bottom 7%
+Salomon,S/Lab Pulsar 4,"77
+ Decent!",$220,Light,Neutral,8.7 oz / 247g 8.8 oz / 250g,1,7.1 mm 6.0 mm,Mid/forefoot,-,Soft,0,Decent,Good,Good,Warm,Medium,Medium,Moderate,Stiff,Moderate,3.0 mm,32.6 mm 31.0 mm,25.5 mm 25.0 mm,Normal,0,All seasons,0,0,-,#336 Bottom 8%,#361 Bottom 1%
+Salomon,S/Lab Ultra,"82
+ Good!",$240,LightModerate,Neutral,10.2 oz / 290g 10.3 oz / 293g,0,10.2 mm 8.0 mm,Heel,-,Soft,0,Bad,Good,Good,Moderate,Narrow,Narrow,Moderate,Stiff,Flexible,3.5 mm,36.3 mm 40.0 mm,26.1 mm 32.0 mm,Normal,0,All seasons,1,1,-,#271 Bottom 26%,#353 Bottom 4%
+Salomon,S/Lab Ultra Glide,"76
+ Decent!",$250,LightModerate,Neutral,10.8 oz / 305g 10.2 oz / 290g,0,7.2 mm 6.0 mm,Mid/forefoot,-,Balanced,0,Good,Good,Good,Warm,Narrow,Medium,Stiff,Stiff,Moderate,3.2 mm,41.0 mm 41.0 mm,33.8 mm 35.0 mm,Normal,0,All seasons,1,1,-,#339 Bottom 7%,#352 Bottom 4%
+Salomon,Sense Pro 4,"87
+ Great!",$140,ModerateTechnical,Neutral,9.6 oz / 272g 9 oz / 255g,0,4.0 mm 4.0 mm,Mid/forefoot,-,-,Rock plate,-,-,-,-,Medium,-,Stiff,-,Flexible,4.3 mm,24.4 mm 25.0 mm,20.5 mm 21.0 mm,Normal,0,-,0,0,Water repellent,#144 Top 40%,#356 Bottom 3%
+Salomon,Sense Ride 4,"88
+ Great!",$120,Moderate,Neutral,10.5 oz / 297g 10.2 oz / 290g,0,7.3 mm 8.0 mm,Mid/forefoot,True to size,-,0,-,-,-,Moderate,Medium,-,Stiff,Moderate,-,3.6 mm,26.5 mm 27.0 mm,19.2 mm 19.0 mm,Normal,0,All seasons,1,1,-,#231 Top 36%,#477 Bottom 26%
+Salomon,Sense Ride 5,"89
+ Great!",$140,LightModerate,Neutral,10.3 oz / 291g 10.3 oz / 291g,0,8.7 mm 8.3 mm,HeelMid/forefoot,Slightly small,Balanced,0,-,-,-,Moderate,Medium,Medium,Moderate,Moderate,Flexible,3.5 mm,27.2 mm 29.6 mm,18.5 mm 21.3 mm,NarrowNormal,0,All seasons,1,1,-,#107 Top 30%,#142 Top 39%
+salomon,Sense Ride 5,"89
+ Great!",$140,LightModerate,Neutral,10.3 oz / 291g 10.3 oz / 291g,0,8.7 mm 8.3 mm,HeelMid/forefoot,Slightly small,Balanced,0,-,-,-,Moderate,Medium,Medium,Moderate,Moderate,Flexible,3.5 mm,27.2 mm 29.6 mm,18.5 mm 21.3 mm,NarrowNormal,0,All seasons,1,1,-,#107 Top 30%,#142 Top 39%
+Salomon,Sense Ride 5,"89
+ Great!",$140,LightModerate,Neutral,10.3 oz / 291g 10.3 oz / 291g,0,8.7 mm 8.3 mm,HeelMid/forefoot,Slightly small,Balanced,0,-,-,-,Moderate,Medium,Medium,Moderate,Moderate,Flexible,3.5 mm,27.2 mm 29.6 mm,18.5 mm 21.3 mm,NarrowNormal,0,All seasons,1,1,-,#107 Top 30%,#142 Top 39%
+Salomon,Sense Ride 5,"89
+ Great!",$140,LightModerate,Neutral,10.3 oz / 291g 10.3 oz / 291g,0,8.7 mm 8.3 mm,HeelMid/forefoot,Slightly small,Balanced,0,-,-,-,Moderate,Medium,Medium,Moderate,Moderate,Flexible,3.5 mm,27.2 mm 29.6 mm,18.5 mm 21.3 mm,NarrowNormal,0,All seasons,1,1,-,#107 Top 30%,#142 Top 39%
+Salomon,Sense Ride 5,"89
+ Great!",$140,LightModerate,Neutral,10.3 oz / 291g 10.3 oz / 291g,0,8.7 mm 8.3 mm,HeelMid/forefoot,Slightly small,Balanced,0,-,-,-,Moderate,Medium,Medium,Moderate,Moderate,Flexible,3.5 mm,27.2 mm 29.6 mm,18.5 mm 21.3 mm,NarrowNormal,0,All seasons,1,1,-,#107 Top 30%,#142 Top 39%
+Salomon,Sense Ride 5,"89
+ Great!",$140,LightModerate,Neutral,10.3 oz / 291g 10.3 oz / 291g,0,8.7 mm 8.3 mm,HeelMid/forefoot,Slightly small,Balanced,0,-,-,-,Moderate,Medium,Medium,Moderate,Moderate,Flexible,3.5 mm,27.2 mm 29.6 mm,18.5 mm 21.3 mm,NarrowNormal,0,All seasons,1,1,-,#106 Top 29%,#142 Top 39%
+Salomon,Sense Ride 5,"89
+ Great!",$140,LightModerate,Neutral,10.3 oz / 291g 10.3 oz / 291g,0,8.7 mm 8.3 mm,HeelMid/forefoot,Slightly small,Balanced,0,-,-,-,Moderate,Medium,Medium,Moderate,Moderate,Flexible,3.5 mm,27.2 mm 29.6 mm,18.5 mm 21.3 mm,NarrowNormal,0,All seasons,1,1,-,#106 Top 29%,#142 Top 39%
+Salomon,Speedcross 6,"89
+ Great!",$145,Technical,Neutral,10.4 oz / 296g 10.5 oz / 298g,0,14.1 mm 10.0 mm,Heel,True to size,Firm,0,Good,Decent,Decent,Warm,Medium,Medium,Moderate,Stiff,Stiff,5.8 mm,36.5 mm 32.0 mm,22.4 mm 22.0 mm,NormalWide,0,Winter,1,1,-,#103 Top 28%,#77 Top 21%
+Salomon,Speedcross 6 GTX,"87
+ Great!",$165,Technical,Neutral,11.5 oz / 325g 11.6 oz / 328g,0,11.2 mm 10.0 mm,Heel,True to size,Firm,0,Good,Good,Decent,Warm,Medium,Medium,Stiff,Stiff,Stiff,5.0 mm,37.0 mm 32.0 mm,25.8 mm 22.0 mm,Normal,0,Winter,1,1,Waterproof,#157 Top 43%,#156 Top 43%
+salomon,Speedcross 6 GTX,"87
+ Great!",$165,Technical,Neutral,11.5 oz / 325g 11.6 oz / 328g,0,11.2 mm 10.0 mm,Heel,True to size,Firm,0,Good,Good,Decent,Warm,Medium,Medium,Stiff,Stiff,Stiff,5.0 mm,37.0 mm 32.0 mm,25.8 mm 22.0 mm,Normal,0,Winter,1,1,Waterproof,#157 Top 43%,#156 Top 43%
+Salomon,Speedcross 6 GTX,"87
+ Great!",$165,Technical,Neutral,11.5 oz / 325g 11.6 oz / 328g,0,11.2 mm 10.0 mm,Heel,True to size,Firm,0,Good,Good,Decent,Warm,Medium,Medium,Stiff,Stiff,Stiff,5.0 mm,37.0 mm 32.0 mm,25.8 mm 22.0 mm,Normal,0,Winter,1,1,Waterproof,#157 Top 43%,#156 Top 43%
+Salomon,Supercross 4,"85
+ Good!",$120,ModerateTechnical,Neutral,11.1 oz / 315g 10.7 oz / 303g,0,15.2 mm 11.0 mm,Heel,True to size,Soft,0,Good,Good,Good,Moderate,Medium,Narrow,Stiff,Flexible,Flexible,4.2 mm,35.1 mm 32.0 mm,19.9 mm 21.0 mm,Normal,0,All seasons,1,1,-,#213 Bottom 42%,#259 Bottom 29%
+Salomon,Thundercross,"89
+ Great!",$140,ModerateTechnical,Neutral,9.6 oz / 271g 10.2 oz / 290g,0,3.0 mm 4.0 mm,Mid/forefoot,True to size,Soft,0,Good,Decent,Good,Warm,Medium,Medium,Moderate,Stiff,Flexible,4.0 mm,27.6 mm 31.0 mm,24.6 mm 27.0 mm,Normal,0,Winter,1,1,-,#97 Top 27%,#222 Bottom 39%
+Salomon,Thundercross,"89
+ Great!",$140,ModerateTechnical,Neutral,9.6 oz / 271g 10.2 oz / 290g,0,3.0 mm 4.0 mm,Mid/forefoot,True to size,Soft,0,Good,Decent,Good,Warm,Medium,Medium,Moderate,Stiff,Flexible,4.0 mm,27.6 mm 31.0 mm,24.6 mm 27.0 mm,Normal,0,Winter,1,1,-,#98 Top 27%,#222 Bottom 39%
+salomon,Thundercross,"89
+ Great!",$140,ModerateTechnical,Neutral,9.6 oz / 271g 10.2 oz / 290g,0,3.0 mm 4.0 mm,Mid/forefoot,True to size,Soft,0,Good,Decent,Good,Warm,Medium,Medium,Moderate,Stiff,Flexible,4.0 mm,27.6 mm 31.0 mm,24.6 mm 27.0 mm,Normal,0,Winter,1,1,-,#98 Top 27%,#222 Bottom 39%
+Salomon,Ultra Flow,"83
+ Good!",$120,Light,Neutral,9.1 oz / 258g 8.6 oz / 244g,0,12.5 mm 6.0 mm,Heel,Slightly small,Soft,0,Good,Decent,Decent,Moderate,Medium,Medium,Moderate,Stiff,Stiff,2.8 mm,34.9 mm 32.0 mm,22.4 mm 26.0 mm,Normal,0,All seasons,1,1,-,#271 Bottom 26%,#287 Bottom 22%
+Salomon,Ultra Glide,"89
+ Great!",$140,LightModerate,Neutral,10 oz / 283g 10 oz / 283g,0,7.9 mm 6.0 mm,Mid/forefoot,True to size,Soft,0,Bad,Good,-,-,Medium,-,Stiff,Stiff,Stiff,3.5 mm,31.8 mm 32.0 mm,23.9 mm 26.0 mm,NormalWide,0,-,1,1,-,#175 Top 28%,#415 Bottom 35%
+Salomon,Ultra Glide 2,"86
+ Good!",$150,LightModerate,Neutral,10.1 oz / 286g 9.2 oz / 260g,0,7.2 mm 6.0 mm,Mid/forefoot,True to size,Soft,0,Bad,-,-,Moderate,Narrow,Narrow,Moderate,Flexible,Moderate,2.8 mm,30.6 mm 32.0 mm,23.4 mm 26.0 mm,Normal,0,All seasons,0,0,-,#177 Top 49%,#181 Top 50%
+salomon,Ultra Glide 2,"86
+ Good!",$150,LightModerate,Neutral,10.1 oz / 286g 9.2 oz / 260g,0,7.2 mm 6.0 mm,Mid/forefoot,True to size,Soft,0,Bad,-,-,Moderate,Narrow,Narrow,Moderate,Flexible,Moderate,2.8 mm,30.6 mm 32.0 mm,23.4 mm 26.0 mm,Normal,0,All seasons,0,0,-,#178 Top 49%,#181 Top 50%
+Salomon,Ultra Glide 2,"86
+ Good!",$150,LightModerate,Neutral,10.1 oz / 286g 9.2 oz / 260g,0,7.2 mm 6.0 mm,Mid/forefoot,True to size,Soft,0,Bad,-,-,Moderate,Narrow,Narrow,Moderate,Flexible,Moderate,2.8 mm,30.6 mm 32.0 mm,23.4 mm 26.0 mm,Normal,0,All seasons,0,0,-,#178 Top 49%,#181 Top 50%
+Salomon,Ultra Glide 2,"86
+ Good!",$150,LightModerate,Neutral,10.1 oz / 286g 9.2 oz / 260g,0,7.2 mm 6.0 mm,Mid/forefoot,True to size,Soft,0,Bad,-,-,Moderate,Narrow,Narrow,Moderate,Flexible,Moderate,2.8 mm,30.6 mm 32.0 mm,23.4 mm 26.0 mm,Normal,0,All seasons,0,0,-,#178 Top 49%,#181 Top 50%
+Salomon,Ultra Glide 2,"86
+ Good!",$150,LightModerate,Neutral,10.1 oz / 286g 9.2 oz / 260g,0,7.2 mm 6.0 mm,Mid/forefoot,True to size,Soft,0,Bad,-,-,Moderate,Narrow,Narrow,Moderate,Flexible,Moderate,2.8 mm,30.6 mm 32.0 mm,23.4 mm 26.0 mm,Normal,0,All seasons,0,0,-,#177 Top 49%,#181 Top 50%
+Salomon,Ultra Glide 2,"86
+ Good!",$150,LightModerate,Neutral,10.1 oz / 286g 9.2 oz / 260g,0,7.2 mm 6.0 mm,Mid/forefoot,True to size,Soft,0,Bad,-,-,Moderate,Narrow,Narrow,Moderate,Flexible,Moderate,2.8 mm,30.6 mm 32.0 mm,23.4 mm 26.0 mm,Normal,0,All seasons,0,0,-,#178 Top 49%,#181 Top 50%
+Salomon,Ultra Glide 2,"86
+ Good!",$150,LightModerate,Neutral,10.1 oz / 286g 9.2 oz / 260g,0,7.2 mm 6.0 mm,Mid/forefoot,True to size,Soft,0,Bad,-,-,Moderate,Narrow,Narrow,Moderate,Flexible,Moderate,2.8 mm,30.6 mm 32.0 mm,23.4 mm 26.0 mm,Normal,0,All seasons,0,0,-,#177 Top 49%,#181 Top 50%
+Salomon,Ultra Glide 2,"86
+ Good!",$150,LightModerate,Neutral,10.1 oz / 286g 9.2 oz / 260g,0,7.2 mm 6.0 mm,Mid/forefoot,True to size,Soft,0,Bad,-,-,Moderate,Narrow,Narrow,Moderate,Flexible,Moderate,2.8 mm,30.6 mm 32.0 mm,23.4 mm 26.0 mm,Normal,0,All seasons,0,0,-,#178 Top 49%,#181 Top 50%
+Salomon,XA Pro 3D GTX,"87
+ Great!",$160,LightModerate,Stability,13.4 oz / 379g 14.3 oz / 405g,0,13.1 mm 12.0 mm,Heel,True to size,Firm,0,-,-,-,Warm,Medium,-,Stiff,Stiff,Stiff,2.9 mm,31.9 mm 27.0 mm,18.8 mm 15.0 mm,Normal,0,Winter,1,1,Waterproof,#334 Bottom 48%,#406 Bottom 37%
+Salomon,XA Pro 3D V8,"89
+ Great!",$130,LightModerate,Stability,12.3 oz / 350g 12 oz / 340g,0,14.6 mm 11.0 mm,Heel,True to size,Firm,0,-,-,-,Moderate,Medium,-,Stiff,Stiff,Stiff,2.9 mm,35.0 mm 28.0 mm,20.4 mm 17.0 mm,NormalWide,0,All seasons,1,1,-,#180 Top 28%,#538 Bottom 16%
+Salomon,XA Pro 3D v9,"75
+ Bad!",$140,LightModerate,Stability,12.2 oz / 346g 11.4 oz / 323g,0,12.5 mm 11.0 mm,Heel,True to size,Firm,0,Very good,Decent,Good,Moderate,Wide,Medium,Stiff,Stiff,Stiff,2.8 mm,31.7 mm 28.0 mm,19.2 mm 17.0 mm,NormalWide,0,All seasons,1,1,-,#348 Bottom 5%,#165 Top 45%
+Salomon,XA Pro 3D v9,"75
+ Bad!",$140,LightModerate,Stability,12.2 oz / 346g 11.4 oz / 323g,0,12.5 mm 11.0 mm,Heel,True to size,Firm,0,Very good,Decent,Good,Moderate,Wide,Medium,Stiff,Stiff,Stiff,2.8 mm,31.7 mm 28.0 mm,19.2 mm 17.0 mm,NormalWide,0,All seasons,1,1,-,#348 Bottom 5%,#165 Top 45%
+Salomon,XA Pro 3D v9 GTX,"83
+ Good!",$160,LightModerate,Stability,12.7 oz / 359g 12.7 oz / 360g,0,13.5 mm 11.0 mm,Heel,True to size,Firm,Rock plate,Very good,Decent,Good,Warm,Medium,Medium,Stiff,Stiff,Stiff,2.8 mm,33.5 mm,20.0 mm,NormalWide,0,Winter,1,1,Waterproof,#257 Bottom 30%,#53 Top 15%
+Salomon,XA Pro 3D v9 GTX,"83
+ Good!",$160,LightModerate,Stability,12.7 oz / 359g 12.7 oz / 360g,0,13.5 mm 11.0 mm,Heel,True to size,Firm,Rock plate,Very good,Decent,Good,Warm,Medium,Medium,Stiff,Stiff,Stiff,2.8 mm,33.5 mm,20.0 mm,NormalWide,0,Winter,1,1,Waterproof,#257 Bottom 30%,#53 Top 15%
+Saucony,Endorphin Edge,"87
+ Great!",$200,Moderate,Neutral,9.5 oz / 269g 9.1 oz / 258g,0,7.1 mm 6.0 mm,Mid/forefoot,True to size,Soft,Carbon plate,-,-,-,Moderate,Narrow,-,Stiff,Stiff,Moderate,3.4 mm,33.4 mm 36.0 mm,26.3 mm 30.0 mm,Normal,0,All seasons,1,1,-,#163 Top 45%,#229 Bottom 37%
+Saucony,Endorphin Edge,"87
+ Great!",$200,Moderate,Neutral,9.5 oz / 269g 9.1 oz / 258g,0,7.1 mm 6.0 mm,Mid/forefoot,True to size,Soft,Carbon plate,-,-,-,Moderate,Narrow,-,Stiff,Stiff,Moderate,3.4 mm,33.4 mm 36.0 mm,26.3 mm 30.0 mm,Normal,0,All seasons,1,1,-,#163 Top 45%,#229 Bottom 37%
+Saucony,Endorphin Edge,"87
+ Great!",$200,Moderate,Neutral,9.5 oz / 269g 9.1 oz / 258g,0,7.1 mm 6.0 mm,Mid/forefoot,True to size,Soft,Carbon plate,-,-,-,Moderate,Narrow,-,Stiff,Stiff,Moderate,3.4 mm,33.4 mm 36.0 mm,26.3 mm 30.0 mm,Normal,0,All seasons,1,1,-,#163 Top 45%,#229 Bottom 37%
+Saucony,Endorphin Edge,"87
+ Great!",$200,Moderate,Neutral,9.5 oz / 269g 9.1 oz / 258g,0,7.1 mm 6.0 mm,Mid/forefoot,True to size,Soft,Carbon plate,-,-,-,Moderate,Narrow,-,Stiff,Stiff,Moderate,3.4 mm,33.4 mm 36.0 mm,26.3 mm 30.0 mm,Normal,0,All seasons,1,1,-,#163 Top 45%,#229 Bottom 37%
+Saucony,Endorphin Rift,"85
+ Good!",$170,Technical,Neutral,9 oz / 255g 9.1 oz / 258g,0,7.9 mm 6.0 mm,Mid/forefoot,True to size,Soft,Rock plate,Good,Good,Good,Breathable,Medium,Medium,Stiff,Moderate,Moderate,4.5 mm,33.0 mm 34.0 mm,25.1 mm 28.0 mm,Normal,0,SummerAll seasons,1,1,-,#222 Bottom 39%,#309 Bottom 15%
+Saucony,Endorphin Rift,"85
+ Good!",$170,Technical,Neutral,9 oz / 255g 9.1 oz / 258g,0,7.9 mm 6.0 mm,Mid/forefoot,True to size,Soft,Rock plate,Good,Good,Good,Breathable,Medium,Medium,Stiff,Moderate,Moderate,4.5 mm,33.0 mm 34.0 mm,25.1 mm 28.0 mm,Normal,0,SummerAll seasons,1,1,-,#221 Bottom 39%,#309 Bottom 16%
+Saucony,Endorphin Rift,"85
+ Good!",$170,Technical,Neutral,9 oz / 255g 9.1 oz / 258g,0,7.9 mm 6.0 mm,Mid/forefoot,True to size,Soft,Rock plate,Good,Good,Good,Breathable,Medium,Medium,Stiff,Moderate,Moderate,4.5 mm,33.0 mm 34.0 mm,25.1 mm 28.0 mm,Normal,0,SummerAll seasons,1,1,-,#221 Bottom 39%,#309 Bottom 16%
+Saucony,Endorphin Rift,"85
+ Good!",$170,Technical,Neutral,9 oz / 255g 9.1 oz / 258g,0,7.9 mm 6.0 mm,Mid/forefoot,True to size,Soft,Rock plate,Good,Good,Good,Breathable,Medium,Medium,Stiff,Moderate,Moderate,4.5 mm,33.0 mm 34.0 mm,25.1 mm 28.0 mm,Normal,0,SummerAll seasons,1,1,-,#221 Bottom 39%,#309 Bottom 16%
+Saucony,Endorphin Rift,"85
+ Good!",$170,Technical,Neutral,9 oz / 255g 9.1 oz / 258g,0,7.9 mm 6.0 mm,Mid/forefoot,True to size,Soft,Rock plate,Good,Good,Good,Breathable,Medium,Medium,Stiff,Moderate,Moderate,4.5 mm,33.0 mm 34.0 mm,25.1 mm 28.0 mm,Normal,0,SummerAll seasons,1,1,-,#221 Bottom 39%,#309 Bottom 16%
+Saucony,Endorphin Rift,"85
+ Good!",$170,Technical,Neutral,9 oz / 255g 9.1 oz / 258g,0,7.9 mm 6.0 mm,Mid/forefoot,True to size,Soft,Rock plate,Good,Good,Good,Breathable,Medium,Medium,Stiff,Moderate,Moderate,4.5 mm,33.0 mm 34.0 mm,25.1 mm 28.0 mm,Normal,0,SummerAll seasons,1,1,-,#221 Bottom 39%,#309 Bottom 16%
+Saucony,Endorphin Rift,"85
+ Good!",$170,Technical,Neutral,9 oz / 255g 9.1 oz / 258g,0,7.9 mm 6.0 mm,Mid/forefoot,True to size,Soft,Rock plate,Good,Good,Good,Breathable,Medium,Medium,Stiff,Moderate,Moderate,4.5 mm,33.0 mm 34.0 mm,25.1 mm 28.0 mm,Normal,0,SummerAll seasons,1,1,-,#221 Bottom 39%,#309 Bottom 16%
+Saucony,Endorphin Rift,"85
+ Good!",$170,Technical,Neutral,9 oz / 255g 9.1 oz / 258g,0,7.9 mm 6.0 mm,Mid/forefoot,True to size,Soft,Rock plate,Good,Good,Good,Breathable,Medium,Medium,Stiff,Moderate,Moderate,4.5 mm,33.0 mm 34.0 mm,25.1 mm 28.0 mm,Normal,0,SummerAll seasons,1,1,-,#221 Bottom 39%,#309 Bottom 16%
+Saucony,Endorphin Rift,"85
+ Good!",$170,Technical,Neutral,9 oz / 255g 9.1 oz / 258g,0,7.9 mm 6.0 mm,Mid/forefoot,True to size,Soft,Rock plate,Good,Good,Good,Breathable,Medium,Medium,Stiff,Moderate,Moderate,4.5 mm,33.0 mm 34.0 mm,25.1 mm 28.0 mm,Normal,0,SummerAll seasons,1,1,-,#222 Bottom 39%,#309 Bottom 16%
+Saucony,Endorphin Rift,"85
+ Good!",$170,Technical,Neutral,9 oz / 255g 9.1 oz / 258g,0,7.9 mm 6.0 mm,Mid/forefoot,True to size,Soft,Rock plate,Good,Good,Good,Breathable,Medium,Medium,Stiff,Moderate,Moderate,4.5 mm,33.0 mm 34.0 mm,25.1 mm 28.0 mm,Normal,0,SummerAll seasons,1,1,-,#221 Bottom 39%,#309 Bottom 16%
+Saucony,Endorphin Rift,"85
+ Good!",$170,Technical,Neutral,9 oz / 255g 9.1 oz / 258g,0,7.9 mm 6.0 mm,Mid/forefoot,True to size,Soft,Rock plate,Good,Good,Good,Breathable,Medium,Medium,Stiff,Moderate,Moderate,4.5 mm,33.0 mm 34.0 mm,25.1 mm 28.0 mm,Normal,0,SummerAll seasons,1,1,-,#221 Bottom 39%,#309 Bottom 16%
+Saucony,Endorphin Rift,"85
+ Good!",$170,Technical,Neutral,9 oz / 255g 9.1 oz / 258g,0,7.9 mm 6.0 mm,Mid/forefoot,True to size,Soft,Rock plate,Good,Good,Good,Breathable,Medium,Medium,Stiff,Moderate,Moderate,4.5 mm,33.0 mm 34.0 mm,25.1 mm 28.0 mm,Normal,0,SummerAll seasons,1,1,-,#221 Bottom 39%,#309 Bottom 16%
+Saucony,Endorphin Trail,"86
+ Good!",$160,Technical,Neutral,11 oz / 312g 10.4 oz / 295g,0,5.2 mm 4.0 mm,Mid/forefoot,True to size,-,0,-,-,-,-,Medium,-,Stiff,Stiff,Stiff,4.5 mm,36.3 mm 36.5 mm,31.1 mm 32.5 mm,Normal,0,-,1,1,-,#190 Bottom 48%,#321 Bottom 12%
+Saucony,Peregrine 11,"88
+ Great!",$120,Technical,Neutral,11.2 oz / 318g 10.9 oz / 310g,0,5.1 mm 4.0 mm,Mid/forefoot,Slightly large,-,Rock plate,-,-,-,-,Medium,-,Stiff,Moderate,-,4.4 mm,27.5 mm 27.0 mm,22.4 mm 23.0 mm,Normal,0,-,1,1,-,#261 Top 41%,#584 Bottom 9%
+Saucony,Peregrine 12,"86
+ Good!",$130,Technical,Neutral,10.1 oz / 285g 9.6 oz / 272g,0,6.9 mm 4.0 mm,Mid/forefoot,True to size,-,Rock plate,-,-,-,-,Wide,-,Stiff,-,-,4.6 mm,30.2 mm 26.5 mm,23.2 mm 22.5 mm,NormalWide,0,-,0,0,-,#350 Bottom 45%,#502 Bottom 22%
+Saucony,Peregrine 13,"89
+ Great!",$140,Technical,Neutral,9.6 oz / 271g 9.6 oz / 271g,0,3.9 mm 4.0 mm,Mid/forefoot,True to size,Balanced,Rock plate,-,-,-,Moderate,Medium,Narrow,Stiff,Flexible,Moderate,4.8 mm,27.5 mm 28.0 mm,23.6 mm 24.0 mm,Normal,0,All seasons,1,1,-,#186 Top 29%,#441 Bottom 31%
+Saucony,Peregrine 14,"88
+ Great!",$140,ModerateTechnical,Neutral,9.4 oz / 266g 9.4 oz / 267g,0,2.2 mm 4.0 mm,Mid/forefoot,True to size,Balanced,Rock plate,Decent,Decent,Good,Moderate,Wide,Medium,Moderate,Moderate,Moderate,4.7 mm,27.3 mm 31.0 mm,25.1 mm 27.0 mm,NormalWide,0,All seasons,1,1,-,#218 Top 34%,#350 Bottom 45%
+Saucony,Peregrine 15,"78
+ Decent!",$140,LightModerate,Neutral,9.4 oz / 266g 9.7 oz / 275g,0,3.7 mm 4.0 mm,Mid/forefoot,True to size,Soft,Rock plate,Good,Decent,Good,Moderate,Medium,Narrow,Moderate,Flexible,Moderate,4.7 mm,29.5 mm 28.0 mm,25.8 mm 24.0 mm,NormalWide,0,All seasons,1,1,-,#326 Bottom 11%,#139 Top 38%
+Saucony,Peregrine 15,"78
+ Decent!",$140,LightModerate,Neutral,9.4 oz / 266g 9.7 oz / 275g,0,3.7 mm 4.0 mm,Mid/forefoot,True to size,Soft,Rock plate,Good,Decent,Good,Moderate,Medium,Narrow,Moderate,Flexible,Moderate,4.7 mm,29.5 mm 28.0 mm,25.8 mm 24.0 mm,NormalWide,0,All seasons,1,1,-,#326 Bottom 11%,#139 Top 38%
+Saucony,Peregrine 15,"78
+ Decent!",$140,LightModerate,Neutral,9.4 oz / 266g 9.7 oz / 275g,0,3.7 mm 4.0 mm,Mid/forefoot,True to size,Soft,Rock plate,Good,Decent,Good,Moderate,Medium,Narrow,Moderate,Flexible,Moderate,4.7 mm,29.5 mm 28.0 mm,25.8 mm 24.0 mm,NormalWide,0,All seasons,1,1,-,#326 Bottom 11%,#139 Top 38%
+Saucony,Peregrine 15,"78
+ Decent!",$140,LightModerate,Neutral,9.4 oz / 266g 9.7 oz / 275g,0,3.7 mm 4.0 mm,Mid/forefoot,True to size,Soft,Rock plate,Good,Decent,Good,Moderate,Medium,Narrow,Moderate,Flexible,Moderate,4.7 mm,29.5 mm 28.0 mm,25.8 mm 24.0 mm,NormalWide,0,All seasons,1,1,-,#326 Bottom 11%,#139 Top 38%
+Saucony,Peregrine 15,"78
+ Decent!",$140,LightModerate,Neutral,9.4 oz / 266g 9.7 oz / 275g,0,3.7 mm 4.0 mm,Mid/forefoot,True to size,Soft,Rock plate,Good,Decent,Good,Moderate,Medium,Narrow,Moderate,Flexible,Moderate,4.7 mm,29.5 mm 28.0 mm,25.8 mm 24.0 mm,NormalWide,0,All seasons,1,1,-,#326 Bottom 11%,#139 Top 38%
+Saucony,Xodus Ultra,"87
+ Great!",$150,ModerateTechnical,Neutral,10.1 oz / 286g 10.3 oz / 292g,0,7.2 mm 6.0 mm,Mid/forefoot,True to size,Balanced,Rock plate,-,-,-,Moderate,Narrow,-,Stiff,Moderate,Moderate,3.8 mm,33.9 mm 32.0 mm,26.7 mm 26.0 mm,Normal,0,All seasons,1,1,-,#314 Top 49%,#498 Bottom 23%
+Saucony,Xodus Ultra 2,"88
+ Great!",$150,ModerateTechnical,Neutral,10.3 oz / 293g 9.2 oz / 262g,0,7.3 mm 6.0 mm,Mid/forefoot,True to size,Balanced,Rock plate,Bad,Good,Decent,Moderate,Medium,Medium,Stiff,Stiff,Stiff,4.6 mm,34.1 mm 32.5 mm,26.8 mm 26.5 mm,Normal,0,All seasons,1,1,-,#227 Top 36%,#555 Bottom 14%
+Saucony,Xodus Ultra 3,"87
+ Great!",$160,Technical,Neutral,10.7 oz / 302g 10.2 oz / 288g,0,5.9 mm 6.0 mm,Mid/forefoot,True to size,Soft,Rock plate,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Flexible,4.3 mm,35.1 mm 36.0 mm,29.2 mm 30.0 mm,Normal,0,All seasons,1,1,-,#341 Bottom 47%,#425 Bottom 34%
+SAucony,Xodus Ultra 4,"85
+ Good!",$170,Light,Neutral,11 oz / 312g 10.9 oz / 309g,0,6.5 mm 6.0 mm,Mid/forefoot,-,Soft,0,Decent,Bad,Decent,Moderate,Medium,Medium,Moderate,Stiff,Stiff,3.5 mm,37.6 mm 36.0 mm,31.1 mm 30.0 mm,Normal,0,All seasons,1,1,-,#201 Bottom 45%,#208 Bottom 43%
+SAucony,Xodus Ultra 4,"85
+ Good!",$170,Light,Neutral,11 oz / 312g 10.9 oz / 309g,0,6.5 mm 6.0 mm,Mid/forefoot,-,Soft,0,Decent,Bad,Decent,Moderate,Medium,Medium,Moderate,Stiff,Stiff,3.5 mm,37.6 mm 36.0 mm,31.1 mm 30.0 mm,Normal,0,All seasons,1,1,-,#201 Bottom 45%,#208 Bottom 43%
+Saucony,Xodus Ultra 4,"85
+ Good!",$170,Light,Neutral,11 oz / 312g 10.9 oz / 309g,0,6.5 mm 6.0 mm,Mid/forefoot,-,Soft,0,Decent,Bad,Decent,Moderate,Medium,Medium,Moderate,Stiff,Stiff,3.5 mm,37.6 mm 36.0 mm,31.1 mm 30.0 mm,Normal,0,All seasons,1,1,-,#202 Bottom 45%,#209 Bottom 43%
+Saucony,Xodus Ultra 4,"85
+ Good!",$170,Light,Neutral,11 oz / 312g 10.9 oz / 309g,0,6.5 mm 6.0 mm,Mid/forefoot,-,Soft,0,Decent,Bad,Decent,Moderate,Medium,Medium,Moderate,Stiff,Stiff,3.5 mm,37.6 mm 36.0 mm,31.1 mm 30.0 mm,Normal,0,All seasons,1,1,-,#202 Bottom 45%,#209 Bottom 43%
+Saucony,Xodus Ultra 4,"85
+ Good!",$170,Light,Neutral,11 oz / 312g 10.9 oz / 309g,0,6.5 mm 6.0 mm,Mid/forefoot,-,Soft,0,Decent,Bad,Decent,Moderate,Medium,Medium,Moderate,Stiff,Stiff,3.5 mm,37.6 mm 36.0 mm,31.1 mm 30.0 mm,Normal,0,All seasons,1,1,-,#202 Bottom 45%,#209 Bottom 43%
+Saucony,Xodus Ultra 4,"85
+ Good!",$170,Light,Neutral,11 oz / 312g 10.9 oz / 309g,0,6.5 mm 6.0 mm,Mid/forefoot,-,Soft,0,Decent,Bad,Decent,Moderate,Medium,Medium,Moderate,Stiff,Stiff,3.5 mm,37.6 mm 36.0 mm,31.1 mm 30.0 mm,Normal,0,All seasons,1,1,-,#202 Bottom 45%,#209 Bottom 43%
+Scarpa,Spin Planet,"88
+ Great!",$160,LightModerate,Neutral,11.4 oz / 322g 10.2 oz / 290g,0,6.2 mm 4.0 mm,Mid/forefoot,True to size,Balanced,0,Decent,Good,Decent,Breathable,Medium,Medium,Stiff,Stiff,Moderate,3.2 mm,32.8 mm 28.5 mm,26.6 mm 24.5 mm,Normal,0,SummerAll seasons,1,1,-,#138 Top 38%,#329 Bottom 10%
+Scarpa,Spin Planet,"88
+ Great!",$160,LightModerate,Neutral,11.4 oz / 322g 10.2 oz / 290g,0,6.2 mm 4.0 mm,Mid/forefoot,True to size,Balanced,0,Decent,Good,Decent,Breathable,Medium,Medium,Stiff,Stiff,Moderate,3.2 mm,32.8 mm 28.5 mm,26.6 mm 24.5 mm,Normal,0,SummerAll seasons,1,1,-,#139 Top 38%,#329 Bottom 10%
+Scarpa,Spin Planet,"88
+ Great!",$160,LightModerate,Neutral,11.4 oz / 322g 10.2 oz / 290g,0,6.2 mm 4.0 mm,Mid/forefoot,True to size,Balanced,0,Decent,Good,Decent,Breathable,Medium,Medium,Stiff,Stiff,Moderate,3.2 mm,32.8 mm 28.5 mm,26.6 mm 24.5 mm,Normal,0,SummerAll seasons,1,1,-,#139 Top 38%,#329 Bottom 10%
+Scarpa,Spin Planet,"88
+ Great!",$160,LightModerate,Neutral,11.4 oz / 322g 10.2 oz / 290g,0,6.2 mm 4.0 mm,Mid/forefoot,True to size,Balanced,0,Decent,Good,Decent,Breathable,Medium,Medium,Stiff,Stiff,Moderate,3.2 mm,32.8 mm 28.5 mm,26.6 mm 24.5 mm,Normal,0,SummerAll seasons,1,1,-,#139 Top 38%,#329 Bottom 10%
+The North Face,Vectiv Enduris 3,"90
+ Superb!",$150,Light,Neutral,9.7 oz / 275g 10.8 oz / 307g,0,11.6 mm 6.0 mm,Heel,True to size,Balanced,0,Good,Good,Good,Warm,Medium,Medium,Moderate,Stiff,Stiff,3.3 mm,35.8 mm 30.0 mm,24.2 mm 24.0 mm,Normal,0,All seasons,1,1,-,#46 Top 13%,#188 Bottom 48%
+The North Face,Vectiv Enduris 3,"90
+ Superb!",$150,Light,Neutral,9.7 oz / 275g 10.8 oz / 307g,0,11.6 mm 6.0 mm,Heel,True to size,Balanced,0,Good,Good,Good,Warm,Medium,Medium,Moderate,Stiff,Stiff,3.3 mm,35.8 mm 30.0 mm,24.2 mm 24.0 mm,Normal,0,All seasons,1,1,-,#46 Top 13%,#189 Bottom 48%
+The North Face,Vectiv Enduris 3,"90
+ Superb!",$150,Light,Neutral,9.7 oz / 275g 10.8 oz / 307g,0,11.6 mm 6.0 mm,Heel,True to size,Balanced,0,Good,Good,Good,Warm,Medium,Medium,Moderate,Stiff,Stiff,3.3 mm,35.8 mm 30.0 mm,24.2 mm 24.0 mm,Normal,0,All seasons,1,1,-,#46 Top 13%,#189 Bottom 48%
+The North Face,Vectiv Enduris 3,"90
+ Superb!",$150,Light,Neutral,9.7 oz / 275g 10.8 oz / 307g,0,11.6 mm 6.0 mm,Heel,True to size,Balanced,0,Good,Good,Good,Warm,Medium,Medium,Moderate,Stiff,Stiff,3.3 mm,35.8 mm 30.0 mm,24.2 mm 24.0 mm,Normal,0,All seasons,1,1,-,#46 Top 13%,#189 Bottom 48%
+Topo,MTN Racer 3,"87
+ Great!",$150,LightModerate,Neutral,10.1 oz / 286g 9.8 oz / 278g,0,6.9 mm 5.0 mm,Mid/forefoot,Slightly small,Balanced,0,Bad,Decent,Good,Moderate,Wide,Wide,Stiff,Stiff,Moderate,4.2 mm,33.5 mm 33.5 mm,26.6 mm 28.5 mm,Normal,0,All seasons,1,1,-,#164 Top 45%,#261 Bottom 28%
+Topo,Traverse,"90
+ Superb!",$150,LightModerate,Neutral,10.9 oz / 308g 10.6 oz / 300g,0,4.8 mm 5.0 mm,Mid/forefoot,Slightly small,Balanced,Rock plate,Bad,Good,Good,Moderate,Wide,Wide,Stiff,Stiff,Moderate,4.1 mm,30.8 mm 30.0 mm,26.0 mm 25.0 mm,NormalWide,0,All seasons,1,1,Water repellent,#39 Top 11%,#281 Bottom 23%
+Topo,Traverse,"90
+ Superb!",$150,LightModerate,Neutral,10.9 oz / 308g 10.6 oz / 300g,0,4.8 mm 5.0 mm,Mid/forefoot,Slightly small,Balanced,Rock plate,Bad,Good,Good,Moderate,Wide,Wide,Stiff,Stiff,Moderate,4.1 mm,30.8 mm 30.0 mm,26.0 mm 25.0 mm,NormalWide,0,All seasons,1,1,Water repellent,#39 Top 11%,#281 Bottom 23%
+Topo,Ultraventure 4,"86
+ Good!",$150,Light,Neutral,10.1 oz / 286g 10.1 oz / 285g,0,6.6 mm 5.0 mm,Mid/forefoot,True to size,Soft,0,Bad,Decent,Good,Moderate,Wide,Wide,Moderate,Moderate,Flexible,3.2 mm,35.1 mm 35.0 mm,28.5 mm 30.0 mm,NormalWide,0,All seasons,1,1,-,#188 Bottom 48%,#147 Top 41%
+Topo,Ultraventure 4,"85
+ Good!",$150,Light,Neutral,10.1 oz / 286g 10.1 oz / 285g,0,6.6 mm 5.0 mm,Mid/forefoot,True to size,Soft,0,Bad,Decent,Good,Moderate,Wide,Wide,Moderate,Moderate,Flexible,3.2 mm,35.1 mm 35.0 mm,28.5 mm 30.0 mm,NormalWide,0,All seasons,1,1,-,#190 Bottom 48%,#148 Top 41%
+Xero Shoes,Scrambler Low,"88
+ Great!",$150,Light,Neutral,9.2 oz / 261g 9.2 oz / 260g,0,-0.1 mm 0.0 mm,Mid/forefoot,True to size,Firm,0,Decent,Decent,Decent,Breathable,Medium,Wide,Moderate,Flexible,Flexible,2.7 mm,16.3 mm 15.0 mm,16.4 mm 15.0 mm,NormalWide,0,SummerAll seasons,1,1,-,#116 Top 32%,#336 Bottom 8%
+Topo,Ultraventure 3,"88
+ Great!",$150,Light,Neutral,9.7 oz / 276g 10.2 oz / 289g,0,6.3 mm 5.0 mm,Mid/forefoot,True to size,Soft,0,Decent,Decent,Good,Moderate,Wide,Wide,Moderate,Stiff,Moderate,3.2 mm,37.2 mm 35.0 mm,30.9 mm 30.0 mm,Normal,0,All seasons,1,1,-,#259 Top 41%,#206 Top 32%
+Topo,Ultraventure 4,"85
+ Good!",$150,Light,Neutral,10.1 oz / 286g 10.1 oz / 285g,0,6.6 mm 5.0 mm,Mid/forefoot,True to size,Soft,0,Bad,Decent,Good,Moderate,Wide,Wide,Moderate,Moderate,Flexible,3.2 mm,35.1 mm 35.0 mm,28.5 mm 30.0 mm,NormalWide,0,All seasons,1,1,-,#190 Bottom 48%,#148 Top 41%
+On,Cloudsurfer Trail,"85
+ Good!",$160,Light,Neutral,9.6 oz / 272g 9.6 oz / 272g,0,10.7 mm 7.0 mm,Heel,True to size,Balanced,0,Decent,Bad,Good,Moderate,Medium,Medium,Moderate,Stiff,Moderate,2.5 mm,37.4 mm 41.0 mm,26.7 mm 34.0 mm,Normal,1,All seasons,1,1,-,#197 Bottom 46%,#217 Bottom 41%
+Hoka,Mafate Speed 4,"88
+ Great!",$185,ModerateTechnical,Neutral,10.3 oz / 293g 10.4 oz / 295g,0,7.2 mm 4.0 mm,Mid/forefoot,True to size,Soft,0,Good,Good,Good,Moderate,Medium,Narrow,Moderate,Stiff,Flexible,3.9 mm,38.0 mm 33.0 mm,30.8 mm 29.0 mm,Normal,0,All seasons,1,1,-,#133 Top 37%,#84 Top 23%
+Inov8,Trailtalon,"90
+ Superb!",$150,ModerateTechnical,Neutral,10.2 oz / 289g 10.2 oz / 290g,0,7.8 mm 6.0 mm,Mid/forefoot,Half size small,Soft,0,Very bad,Good,Good,Warm,Wide,Wide,Stiff,Moderate,Flexible,5.4 mm,34.2 mm 31.0 mm,26.4 mm 25.0 mm,NormalWide,0,All seasons,1,1,-,#71 Top 20%,#350 Bottom 4%
+Saucony,Endorphin Rift,"85
+ Good!",$170,Technical,Neutral,9 oz / 255g 9.1 oz / 258g,0,7.9 mm 6.0 mm,Mid/forefoot,True to size,Soft,Rock plate,Good,Good,Good,Breathable,Medium,Medium,Stiff,Moderate,Moderate,4.5 mm,33.0 mm 34.0 mm,25.1 mm 28.0 mm,Normal,0,SummerAll seasons,1,1,-,#221 Bottom 39%,#309 Bottom 16%
+Inov8,Trailfly,"88
+ Great!",$150,LightModerate,Neutral,9.9 oz / 282g 9.9 oz / 280g,0,6.0 mm 6.0 mm,Mid/forefoot,Slightly small,Soft,Rock plate,Decent,Good,Good,Moderate,Medium,Wide,Stiff,Flexible,Flexible,3.9 mm,30.1 mm 29.0 mm,24.1 mm 23.0 mm,NormalWide,0,All seasons,1,1,-,#141 Top 39%,#326 Bottom 11%
+Nike,Terra Kiger 9,"90
+ Superb!",$150,ModerateTechnical,Neutral,10.2 oz / 288g 10.1 oz / 286g,0,4.4 mm 3.0 mm,Mid/forefoot,True to size,Soft,0,Very good,Decent,-,Moderate,Medium,Medium,Stiff,Moderate,Moderate,4.4 mm,30.1 mm 31.0 mm,25.7 mm 28.0 mm,Normal,0,All seasons,1,1,-,#34 Top 10%,#293 Bottom 20%
+Inov8,Trailfly Zero,"87
+ Great!",$160,LightModerate,Neutral,9.4 oz / 266g,0,0.5 mm 0.0 mm,Mid/forefoot,-,Balanced,0,Decent,Decent,Good,Moderate,Medium,Wide,Flexible,Flexible,Flexible,3.4 mm,24.9 mm,24.4 mm,NormalWide,0,All seasons,1,1,-,#161 Top 44%,#346 Bottom 5%
+Altra,Superior 6,"78
+ Decent!",$130,LightModerate,Neutral,9.6 oz / 272g 9.1 oz / 258g,0,0.1 mm 0.0 mm,Mid/forefoot,Slightly small,Balanced,0,Very bad,Decent,Good,Breathable,Medium,Wide,Stiff,Moderate,Flexible,3.3 mm,22.1 mm 20.5 mm,22.0 mm 20.5 mm,Normal,0,SummerAll seasons,1,1,-,#324 Bottom 11%,#260 Bottom 29%
+Altra,Timp 5,"80
+ Good!",$155,LightModerate,Neutral,9.5 oz / 269g 9.8 oz / 277g,0,-0.1 mm 0.0 mm,Mid/forefoot,Slightly small,Balanced,0,Good,Decent,Good,Moderate,Wide,Wide,Stiff,Stiff,Moderate,3.0 mm,28.6 mm 29.0 mm,28.7 mm 29.0 mm,Normal,0,All seasons,1,1,-,#304 Bottom 17%,#138 Top 38%
+Altra,Lone Peak 9,"91
+ Superb!",$140,LightModerate,Neutral,10.9 oz / 309g 10.4 oz / 295g,0,0.0 mm 0.0 mm,Mid/forefoot,True to size,Balanced,0,Decent,Decent,Good,Warm,Wide,Wide,Moderate,Moderate,Flexible,3.8 mm,23.3 mm 25.0 mm,23.3 mm 25.0 mm,NormalWide,0,All seasons,1,1,-,#30 Top 9%,#42 Top 12%
+Inov8,Trailfly Max,"69
+ Bad!",$170,LightModerate,Neutral,10.2 oz / 289g,0,7.6 mm 6.0 mm,Mid/forefoot,-,Balanced,0,Decent,Decent,Good,Moderate,Medium,Wide,Moderate,Moderate,Flexible,3.4 mm,37.1 mm,29.5 mm,NormalWide,0,All seasons,1,1,-,#365 Bottom 1%,#349 Bottom 5%
+Topo,Ultraventure 4,"85
+ Good!",$150,Light,Neutral,10.1 oz / 286g 10.1 oz / 285g,0,6.6 mm 5.0 mm,Mid/forefoot,True to size,Soft,0,Bad,Decent,Good,Moderate,Wide,Wide,Moderate,Moderate,Flexible,3.2 mm,35.1 mm 35.0 mm,28.5 mm 30.0 mm,NormalWide,0,All seasons,1,1,-,#190 Bottom 48%,#148 Top 41%
+Saucony,Xodus Ultra 4,"85
+ Good!",$170,Light,Neutral,11 oz / 312g 10.9 oz / 309g,0,6.5 mm 6.0 mm,Mid/forefoot,-,Soft,0,Decent,Bad,Decent,Moderate,Medium,Medium,Moderate,Stiff,Stiff,3.5 mm,37.6 mm 36.0 mm,31.1 mm 30.0 mm,Normal,0,All seasons,1,1,-,#202 Bottom 45%,#209 Bottom 43%
+Hoka,Mafate Speed 4,"88
+ Great!",$185,ModerateTechnical,Neutral,10.3 oz / 293g 10.4 oz / 295g,0,7.2 mm 4.0 mm,Mid/forefoot,True to size,Soft,0,Good,Good,Good,Moderate,Medium,Narrow,Moderate,Stiff,Flexible,3.9 mm,38.0 mm 33.0 mm,30.8 mm 29.0 mm,Normal,0,All seasons,1,1,-,#133 Top 37%,#84 Top 23%
+ASICS,Trail Scout 2,"83
+ Good!",$60,Moderate,Neutral,11.4 oz / 323g 11.4 oz / 323g,0,10.3 mm 10.0 mm,Heel,True to size,Balanced,0,-,-,-,Moderate,Narrow,Medium,Stiff,Stiff,Stiff,4.2 mm,32.8 mm,22.5 mm,Normal,0,All seasons,1,1,-,#262 Bottom 28%,#327 Bottom 11%
+New Balance,510 v6,"74
+ Bad!",$90,Light,Neutral,11 oz / 312g 11.5 oz / 326g,0,8.2 mm,HeelMid/forefoot,True to size,Balanced,0,Decent,Bad,Good,Moderate,Narrow,Medium,Stiff,Moderate,Moderate,2.9 mm,33.8 mm,25.6 mm,NormalWideX-Wide,0,All seasons,1,1,-,#355 Bottom 3%,#318 Bottom 13%
+Saucony,Endorphin Rift,"85
+ Good!",$170,Technical,Neutral,9 oz / 255g 9.1 oz / 258g,0,7.9 mm 6.0 mm,Mid/forefoot,True to size,Soft,Rock plate,Good,Good,Good,Breathable,Medium,Medium,Stiff,Moderate,Moderate,4.5 mm,33.0 mm 34.0 mm,25.1 mm 28.0 mm,Normal,0,SummerAll seasons,1,1,-,#221 Bottom 39%,#309 Bottom 16%
+La Sportiva,Prodigio,"83
+ Good!",$155,Moderate,Neutral,9.6 oz / 271g 9.5 oz / 270g,0,8.9 mm 6.0 mm,HeelMid/forefoot,Half size small,Soft,0,Very bad,Bad,Decent,Moderate,Medium,Medium,Stiff,Stiff,Stiff,3.4 mm,29.3 mm 34.0 mm,20.4 mm 28.0 mm,Normal,0,All seasons,1,1,-,#269 Bottom 26%,#218 Bottom 40%
+ASICS,Gel Venture 9,"84
+ Good!",$80,LightModerate,Neutral,11.1 oz / 314g 10.6 oz / 300g,0,10.4 mm,Heel,True to size,Balanced,,Very bad,Decent,-,Moderate,Narrow,Narrow,Moderate,Moderate,Stiff,3.0 mm,33.3 mm,22.9 mm,NormalWideX-Wide,0,All seasons,1,1,,#474 Bottom 26%,#176 Top 28%
+ASICS,Gel Venture 10,"84
+ Good!",$80,LightModerate,Neutral,11.4 oz / 322g 11.4 oz / 323g,0,12.0 mm 10.0 mm,Heel,True to size,Soft,0,Good,Good,Good,Moderate,Narrow,Narrow,Moderate,Stiff,Moderate,3.7 mm,35.3 mm 33.5 mm,23.3 mm 23.5 mm,NormalWideX-Wide,0,All seasons,1,1,-,#243 Bottom 33%,#71 Top 20%
+New Balance,510 v6,"74
+ Bad!",$90,Light,Neutral,11 oz / 312g 11.5 oz / 326g,0,8.2 mm,HeelMid/forefoot,True to size,Balanced,0,Decent,Bad,Good,Moderate,Narrow,Medium,Stiff,Moderate,Moderate,2.9 mm,33.8 mm,25.6 mm,NormalWideX-Wide,0,All seasons,1,1,-,#355 Bottom 3%,#318 Bottom 13%
+New Balance,Tektrel,"76
+ Bad!",$90,Light,Neutral,10.7 oz / 302g 9.9 oz / 282g,0,8.0 mm 8.0 mm,HeelMid/forefoot,Half size small,Balanced,0,Good,Decent,Decent,Breathable,Medium,Wide,Stiff,Moderate,Moderate,2.4 mm,32.2 mm 32.0 mm,24.2 mm 24.0 mm,NormalWide,0,SummerAll seasons,1,1,-,#341 Bottom 7%,#177 Top 49%
+ASICS,Gel Venture 8,"88
+ Great!",$70,LightModerate,Neutral,10.4 oz / 295g 12.5 oz / 354g,0,13.2 mm 10.0 mm,Heel,True to size,Balanced,0,Bad,Decent,Good,Breathable,Narrow,Medium,Moderate,Moderate,Moderate,3.1 mm,34.2 mm,21.0 mm,Normal,0,SummerAll seasons,1,1,-,#246 Top 39%,#251 Top 39%
+ASICS,Gel Venture 10,"84
+ Good!",$80,LightModerate,Neutral,11.4 oz / 322g 11.4 oz / 323g,0,12.0 mm 10.0 mm,Heel,True to size,Soft,0,Good,Good,Good,Moderate,Narrow,Narrow,Moderate,Stiff,Moderate,3.7 mm,35.3 mm 33.5 mm,23.3 mm 23.5 mm,NormalWideX-Wide,0,All seasons,1,1,-,#243 Bottom 33%,#71 Top 20%
+Merrell,Fly Strike,"79
+ Good!",$90,Light,Neutral,10.3 oz / 292g 10.4 oz / 295g,0,17.3 mm 10.0 mm,Heel,True to size,Balanced,0,Good,Decent,Decent,Moderate,Narrow,Wide,Stiff,Stiff,Moderate,3.5 mm,34.3 mm 27.0 mm,17.0 mm 17.0 mm,NormalWide,0,All seasons,1,1,-,#316 Bottom 14%,#273 Bottom 25%
+New Balance,510 v6,"74
+ Bad!",$90,Light,Neutral,11 oz / 312g 11.5 oz / 326g,0,8.2 mm,HeelMid/forefoot,True to size,Balanced,0,Decent,Bad,Good,Moderate,Narrow,Medium,Stiff,Moderate,Moderate,2.9 mm,33.8 mm,25.6 mm,NormalWideX-Wide,0,All seasons,1,1,-,#355 Bottom 3%,#318 Bottom 13%
+NNormal,Kjerag,"93
+ Superb!",$195,Light,Neutral,7.5 oz / 214g 7.1 oz / 200g,1,8.6 mm 6.0 mm,Mid/forefoot,True to size,Balanced,0,Decent,Decent,Decent,Warm,Medium,Medium,Flexible,Moderate,Flexible,3.0 mm,25.0 mm 23.5 mm,16.4 mm 17.5 mm,Normal,0,All seasons,0,0,-,#3 Top 1%,#234 Bottom 36%
+ASICS,Gel Venture 10,"84
+ Good!",$80,LightModerate,Neutral,11.4 oz / 322g 11.4 oz / 323g,0,12.0 mm 10.0 mm,Heel,True to size,Soft,0,Good,Good,Good,Moderate,Narrow,Narrow,Moderate,Stiff,Moderate,3.7 mm,35.3 mm 33.5 mm,23.3 mm 23.5 mm,NormalWideX-Wide,0,All seasons,1,1,-,#244 Bottom 33%,#71 Top 20%
+ASICS,Gel Excite Trail 2,"81
+ Good!",$85,Light,Neutral,10.3 oz / 292g 10.4 oz / 296g,0,10.1 mm 8.0 mm,Heel,True to size,Balanced,0,Very bad,Bad,Decent,Moderate,Medium,Medium,Flexible,Stiff,Stiff,4.0 mm,37.7 mm 36.0 mm,27.6 mm 28.0 mm,Normal,1,All seasons,1,1,-,#293 Bottom 20%,#205 Bottom 44%
+Merrell,Nova 4,"86
+ Good!",$130,LightModerate,Neutral,9.6 oz / 272g 9.7 oz / 275g,0,12.1 mm 8.0 mm,Heel,-,Balanced,0,Decent,Decent,Decent,Moderate,Medium,Medium,Moderate,Stiff,Flexible,4.0 mm,37.7 mm 29.0 mm,25.6 mm 21.0 mm,NormalWide,0,All seasons,1,1,-,#192 Bottom 47%,#193 Bottom 47%
+Brooks,Caldera 8,"89
+ Great!",$150,LightModerate,Neutral,10.9 oz / 309g 10.9 oz / 309g,0,8.8 mm 6.0 mm,HeelMid/forefoot,True to size,Soft,0,Bad,Good,Decent,Moderate,Medium,Medium,Moderate,Stiff,Stiff,3.6 mm,36.7 mm 39.0 mm,27.9 mm 33.0 mm,Normal,0,All seasons,1,1,-,#88 Top 24%,#169 Top 46%
+Saucony,Endorphin Rift,"85
+ Good!",$170,Technical,Neutral,9 oz / 255g 9.1 oz / 258g,0,7.9 mm 6.0 mm,Mid/forefoot,True to size,Soft,Rock plate,Good,Good,Good,Breathable,Medium,Medium,Stiff,Moderate,Moderate,4.5 mm,33.0 mm 34.0 mm,25.1 mm 28.0 mm,Normal,0,SummerAll seasons,1,1,-,#221 Bottom 39%,#309 Bottom 16%
+Kailas,Fuga EX 3,"87
+ Great!",$180,LightModerate,Neutral,10.3 oz / 293g 10.1 oz / 285g,0,13.7 mm 8.0 mm,Heel,-,Balanced,0,Decent,Decent,Good,Moderate,Narrow,Medium,Stiff,Stiff,Stiff,3.4 mm,38.4 mm 36.0 mm,24.7 mm 28.0 mm,Normal,0,All seasons,1,1,-,#166 Top 46%,#344 Bottom 6%
+Merrell,Nova 4,"86
+ Good!",$130,LightModerate,Neutral,9.6 oz / 272g 9.7 oz / 275g,0,12.1 mm 8.0 mm,Heel,-,Balanced,0,Decent,Decent,Decent,Moderate,Medium,Medium,Moderate,Stiff,Flexible,4.0 mm,37.7 mm 29.0 mm,25.6 mm 21.0 mm,NormalWide,0,All seasons,1,1,-,#192 Bottom 47%,#193 Bottom 47%
+Brooks,Caldera 8,"89
+ Great!",$150,LightModerate,Neutral,10.9 oz / 309g 10.9 oz / 309g,0,8.8 mm 6.0 mm,HeelMid/forefoot,True to size,Soft,0,Bad,Good,Decent,Moderate,Medium,Medium,Moderate,Stiff,Stiff,3.6 mm,36.7 mm 39.0 mm,27.9 mm 33.0 mm,Normal,0,All seasons,1,1,-,#88 Top 24%,#169 Top 46%
+The North Face,Vectiv Enduris 3,"90
+ Superb!",$150,Light,Neutral,9.7 oz / 275g 10.8 oz / 307g,0,11.6 mm 6.0 mm,Heel,True to size,Balanced,0,Good,Good,Good,Warm,Medium,Medium,Moderate,Stiff,Stiff,3.3 mm,35.8 mm 30.0 mm,24.2 mm 24.0 mm,Normal,0,All seasons,1,1,-,#46 Top 13%,#189 Bottom 48%
+Xero Shoes,Mesa Trail WP,"66
+ Bad!",$140,Light,Neutral,9.7 oz / 274g 9.6 oz / 272g,0,1.2 mm 0.0 mm,Mid/forefoot,Slightly small,Balanced,0,Decent,Bad,Decent,Warm,Medium,Wide,Moderate,Flexible,Flexible,3.8 mm,14.6 mm 8.0 mm,13.4 mm 8.0 mm,NormalWide,0,Winter,1,1,Waterproof,#366 Bottom 1%,#354 Bottom 3%
+Nike,Juniper Trail 2 GTX,"75
+ Bad!",$130,Light,Neutral,10.3 oz / 293g 11.4 oz / 323g,0,10.2 mm 9.0 mm,Heel,True to size,Firm,0,Very good,Good,Good,Warm,Wide,Wide,Stiff,Stiff,Moderate,2.7 mm,34.5 mm 35.0 mm,24.3 mm 26.0 mm,Normal,0,Winter,1,1,Waterproof,#347 Bottom 5%,#164 Top 45%
+Hoka,Speedgoat 6 GTX,"74
+ Bad!",$170,Moderate,Neutral,10.2 oz / 289g 10.4 oz / 295g,0,5.0 mm 5.0 mm,Mid/forefoot,Half size small,Balanced,0,Very good,Good,Good,Warm,Medium,Narrow,Stiff,Stiff,Moderate,3.9 mm,32.9 mm 37.0 mm,27.9 mm 32.0 mm,NormalWide,0,Winter,1,1,Waterproof,#355 Bottom 3%,#128 Top 35%
+Nike,Pegasus Trail 5 GTX,"75
+ Bad!",$170,Light,Neutral,9.9 oz / 281g 10.6 oz / 300g,0,8.3 mm 9.5 mm,HeelMid/forefoot,Slightly small,Soft,0,Decent,Bad,Good,Warm,Medium,Wide,Moderate,Stiff,Flexible,3.6 mm,32.1 mm 37.0 mm,23.8 mm 27.5 mm,NarrowNormal,0,Winter,1,1,Waterproof,#346 Bottom 5%,#101 Top 28%
+Kailas,Fuga EX Pro,"85
+ Good!",$300,LightModerate,Neutral,9.6 oz / 272g 9.7 oz / 275g,0,7.2 mm 5.0 mm,Mid/forefoot,-,Balanced,0,Bad,Decent,Good,Moderate,Narrow,Narrow,Stiff,Stiff,Moderate,3.7 mm,37.9 mm 35.0 mm,30.7 mm 30.0 mm,Normal,0,All seasons,1,1,-,#220 Bottom 40%,#337 Bottom 8%
+Hoka,Challenger 8,"72
+ Bad!",$155,LightModerate,Neutral,9.5 oz / 269g 9.7 oz / 275g,0,10.1 mm 8.0 mm,Heel,-,Soft,0,Good,Good,Good,Breathable,Medium,Medium,Moderate,Stiff,Stiff,3.7 mm,40.2 mm 42.0 mm,30.1 mm 34.0 mm,NormalWide,0,SummerAll seasons,1,1,-,#360 Bottom 2%,#58 Top 16%
+Inov8,Trailfly Max,"69
+ Bad!",$170,LightModerate,Neutral,10.2 oz / 289g,0,7.6 mm 6.0 mm,Mid/forefoot,-,Balanced,0,Decent,Decent,Good,Moderate,Medium,Wide,Moderate,Moderate,Flexible,3.4 mm,37.1 mm,29.5 mm,NormalWide,0,All seasons,1,1,-,#364 Bottom 1%,#349 Bottom 5%
+Hoka,Mafate Speed 4,"88
+ Great!",$185,ModerateTechnical,Neutral,10.3 oz / 293g 10.4 oz / 295g,0,7.2 mm 4.0 mm,Mid/forefoot,True to size,Soft,0,Good,Good,Good,Moderate,Medium,Narrow,Moderate,Stiff,Flexible,3.9 mm,38.0 mm 33.0 mm,30.8 mm 29.0 mm,Normal,0,All seasons,1,1,-,#133 Top 37%,#84 Top 23%
+Kailas,Fuga EX BOA,"90
+ Superb!",$170,Moderate,Neutral,9.9 oz / 281g 9.6 oz / 272g,0,10.9 mm 8.0 mm,Heel,-,-,0,-,-,-,-,Narrow,-,Stiff,Stiff,Stiff,3.7 mm,38.5 mm 36.0 mm,27.6 mm 28.0 mm,NormalWide,0,-,1,1,-,#51 Top 14%,#357 Bottom 2%
+ASICS,Gel Excite Trail 2,"81
+ Good!",$85,Light,Neutral,10.3 oz / 292g 10.4 oz / 296g,0,10.1 mm 8.0 mm,Heel,True to size,Balanced,0,Very bad,Bad,Decent,Moderate,Medium,Medium,Flexible,Stiff,Stiff,4.0 mm,37.7 mm 36.0 mm,27.6 mm 28.0 mm,Normal,1,All seasons,1,1,-,#293 Bottom 20%,#205 Bottom 44%
+Merrell,Agility Peak 5,"88
+ Great!",$140,ModerateTechnical,Neutral,10.2 oz / 289g 10.6 oz / 300g,0,13.4 mm 6.0 mm,Heel,True to size,Balanced,Rock plate,Very good,Decent,Good,Moderate,Medium,Narrow,Moderate,Stiff,Stiff,4.5 mm,39.2 mm 39.0 mm,25.8 mm 33.0 mm,Normal,1,All seasons,1,1,-,#140 Top 39%,#113 Top 31%
+Hoka,Mafate Speed 4,"88
+ Great!",$185,ModerateTechnical,Neutral,10.3 oz / 293g 10.4 oz / 295g,0,7.2 mm 4.0 mm,Mid/forefoot,True to size,Soft,0,Good,Good,Good,Moderate,Medium,Narrow,Moderate,Stiff,Flexible,3.9 mm,38.0 mm 33.0 mm,30.8 mm 29.0 mm,Normal,0,All seasons,1,1,-,#133 Top 37%,#84 Top 23%
+Kailas,Fuga YAO,90 Superb!,$160,Light,Neutral,10.5 oz / 299g 10.1 oz / 285g,0,10.7 mm,Heel,-,Balanced,Carbon plate,-,-,-,Moderate,Narrow,-,Stiff,Stiff,Stiff,1.7 mm,38.6 mm,27.9 mm,Normal,0,All seasons,1,1,-,#52 Top 15%,#362 Bottom 1%
+ASICS,Metafuji Trail,90 Superb!,$250,Light,Neutral,9.1 oz / 258g 9.2 oz / 261g,0,10.3 mm 5.0 mm,Heel,True to size,Soft,Carbon plate,Very bad,Good,Decent,Breathable,Medium,Narrow,Stiff,Stiff,Moderate,2.7 mm,44.7 mm 44.0 mm,34.4 mm 39.0 mm,NormalWide,0,SummerAll seasons,1,1,-,#62 Top 17%,#300 Bottom 18%
+Saucony,Endorphin Edge,87 Great!,$200,Moderate,Neutral,9.5 oz / 269g 9.1 oz / 258g,0,7.1 mm 6.0 mm,Mid/forefoot,True to size,Soft,Carbon plate,-,-,-,Moderate,Narrow,-,Stiff,Stiff,Moderate,3.4 mm,33.4 mm 36.0 mm,26.3 mm 30.0 mm,Normal,0,All seasons,1,1,-,#163 Top 45%,#229 Bottom 37%
+Nike,Ultrafly,89 Great!,$260,LightModerate,Neutral,10.5 oz / 299g 10.1 oz / 286g,0,11.8 mm 8.5 mm,Heel,True to size,Soft,Carbon plate,Very bad,Good,Good,Moderate,Medium,Wide,Stiff,Stiff,Moderate,3.0 mm,36.6 mm 38.0 mm,24.8 mm 29.5 mm,Normal,0,All seasons,1,1,-,#86 Top 24%,#201 Bottom 45%
+ASICS,Trabuco Max 2,"93
+ Superb!",$150,ModerateTechnical,Neutral,10.3 oz / 292g 10.7 oz / 303g,0,8.5 mm 5.0 mm,HeelMid/forefoot,True to size,Soft,0,Decent,Good,Decent,Breathable,Medium,Wide,Stiff,Stiff,Stiff,4.2 mm,39.7 mm 43.0 mm,31.2 mm 38.0 mm,Normal,0,SummerAll seasons,1,1,-,#4 Top 1%,#331 Bottom 48%
+ASICS,Gel Excite Trail 2,"81
+ Good!",$85,Light,Neutral,10.3 oz / 292g 10.4 oz / 296g,0,10.1 mm 8.0 mm,Heel,True to size,Balanced,0,Very bad,Bad,Decent,Moderate,Medium,Medium,Flexible,Stiff,Stiff,4.0 mm,37.7 mm 36.0 mm,27.6 mm 28.0 mm,Normal,1,All seasons,1,1,-,#293 Bottom 20%,#205 Bottom 44%
+ASICS,Trabuco Max 4,"88
+ Great!",$160,Light,Neutral,11 oz / 312g 0.2 oz / 5g,0,6.1 mm 5.0 mm,Mid/forefoot,Half size small,Balanced,0,Decent,Good,Decent,Moderate,Medium,Medium,Stiff,Stiff,Stiff,3.1 mm,39.3 mm 41.0 mm,33.2 mm 36.0 mm,Normal,0,All seasons,1,1,-,#135 Top 37%,#108 Top 30%
+Hoka,Mafate Speed 4,"88
+ Great!",$185,ModerateTechnical,Neutral,10.3 oz / 293g 10.4 oz / 295g,0,7.2 mm 4.0 mm,Mid/forefoot,True to size,Soft,0,Good,Good,Good,Moderate,Medium,Narrow,Moderate,Stiff,Flexible,3.9 mm,38.0 mm 33.0 mm,30.8 mm 29.0 mm,Normal,0,All seasons,1,1,-,#132 Top 36%,#84 Top 23%
+Icebug,Järv RB9X,N/A,$180,ModerateTechnical,Neutral,11.6 oz / 329g 12.3 oz / 348g,0,6.0 mm 4.0 mm,Mid/forefoot,-,Balanced,0,Good,Decent,Decent,Warm,Medium,Medium,Stiff,Stiff,Moderate,5.0 mm,37.0 mm 29.0 mm,31.0 mm 25.0 mm,Normal,0,-,0,0,-,#367 Bottom 1%,#367 Bottom 1%
+KEEN,Seek,"87
+ Great!",$185,LightModerate,Neutral,10.8 oz / 305g 10.9 oz / 309g,0,8.6 mm 6.0 mm,HeelMid/forefoot,-,Soft,0,Good,Good,Good,Moderate,Medium,Wide,Moderate,Moderate,Flexible,4.1 mm,36.3 mm 38.5 mm,27.7 mm 32.5 mm,Normal,0,All seasons,1,1,-,#146 Top 40%,#315 Bottom 14%
+Saucony,Xodus Ultra 4,"85
+ Good!",$170,Light,Neutral,11 oz / 312g 10.9 oz / 309g,0,6.5 mm 6.0 mm,Mid/forefoot,-,Soft,0,Decent,Bad,Decent,Moderate,Medium,Medium,Moderate,Stiff,Stiff,3.5 mm,37.6 mm 36.0 mm,31.1 mm 30.0 mm,Normal,0,All seasons,1,1,-,#202 Bottom 45%,#209 Bottom 43%
+New Balance,Fresh Foam X More Trail v3,"87
+ Great!",$160,ModerateTechnical,Neutral,10.6 oz / 301g 10.6 oz / 301g,0,7.1 mm 4.0 mm,Mid/forefoot,True to size,Soft,0,Very bad,Decent,-,Moderate,Medium,Narrow,Moderate,Moderate,Moderate,5.0 mm,38.6 mm 39.4 mm,31.5 mm 35.4 mm,NormalWide,0,All seasons,1,1,-,#159 Top 44%,#22 Top 6%
+Kailas,Fuga Pro 4,"86
+ Good!",$230,Moderate,Neutral,9.8 oz / 279g 9.5 oz / 270g,0,10.0 mm 10.0 mm,HeelMid/forefoot,-,-,0,-,-,-,-,Narrow,-,Stiff,Stiff,Stiff,3.4 mm,31.7 mm 32.0 mm,21.8 mm 22.0 mm,Normal,0,-,1,1,-,#169 Top 46%,#358 Bottom 2%
+Nike,Pegasus Trail 5,"90
+ Superb!",$150,Light,Neutral,10.1 oz / 286g 10.1 oz / 286g,0,9.6 mm 9.5 mm,HeelMid/forefoot,True to size,Soft,0,Very good,Good,Good,Warm,Medium,Medium,Moderate,Moderate,Stiff,3.2 mm,34.6 mm 37.0 mm,25.0 mm 27.5 mm,NormalWideX-Wide,0,All seasons,1,1,-,#73 Top 20%,#48 Top 14%
+La Sportiva,Prodigio,"83
+ Good!",$155,Moderate,Neutral,9.6 oz / 271g 9.5 oz / 270g,0,8.9 mm 6.0 mm,HeelMid/forefoot,Half size small,Soft,0,Very bad,Bad,Decent,Moderate,Medium,Medium,Stiff,Stiff,Stiff,3.4 mm,29.3 mm 34.0 mm,20.4 mm 28.0 mm,Normal,0,All seasons,1,1,-,#269 Bottom 26%,#218 Bottom 40%
+Salomon,Sense Ride 5,"89
+ Great!",$140,LightModerate,Neutral,10.3 oz / 291g 10.3 oz / 291g,0,8.7 mm 8.3 mm,HeelMid/forefoot,Slightly small,Balanced,0,-,-,-,Moderate,Medium,Medium,Moderate,Moderate,Flexible,3.5 mm,27.2 mm 29.6 mm,18.5 mm 21.3 mm,NarrowNormal,0,All seasons,1,1,-,#107 Top 30%,#142 Top 39%
+Kailas,Fuga EX 2,"85
+ Good!",$160,ModerateTechnical,Neutral,10.4 oz / 295g 9.5 oz / 270g,0,10.6 mm 8.0 mm,Heel,-,-,0,-,-,-,-,Narrow,-,Stiff,Stiff,Stiff,4.0 mm,38.0 mm 36.0 mm,27.4 mm 28.0 mm,Normal,0,-,1,1,-,#436 Bottom 32%,#634 Bottom 2%
+ASICS,Gel Excite Trail 2,"81
+ Good!",$85,Light,Neutral,10.3 oz / 292g 10.4 oz / 296g,0,10.1 mm 8.0 mm,Heel,True to size,Balanced,0,Very bad,Bad,Decent,Moderate,Medium,Medium,Flexible,Stiff,Stiff,4.0 mm,37.7 mm 36.0 mm,27.6 mm 28.0 mm,Normal,1,All seasons,1,1,-,#293 Bottom 20%,#205 Bottom 44%
+Merrell,Agility Peak 5,"88
+ Great!",$140,ModerateTechnical,Neutral,10.2 oz / 289g 10.6 oz / 300g,0,13.4 mm 6.0 mm,Heel,True to size,Balanced,Rock plate,Very good,Decent,Good,Moderate,Medium,Narrow,Moderate,Stiff,Stiff,4.5 mm,39.2 mm 39.0 mm,25.8 mm 33.0 mm,Normal,1,All seasons,1,1,-,#140 Top 39%,#113 Top 31%
+Hoka,Mafate Speed 4,"88
+ Great!",$185,ModerateTechnical,Neutral,10.3 oz / 293g 10.4 oz / 295g,0,7.2 mm 4.0 mm,Mid/forefoot,True to size,Soft,0,Good,Good,Good,Moderate,Medium,Narrow,Moderate,Stiff,Flexible,3.9 mm,38.0 mm 33.0 mm,30.8 mm 29.0 mm,Normal,0,All seasons,1,1,-,#133 Top 37%,#84 Top 23%
+Kailas,Fuga Elite 2,"83
+ Good!",$360,Light,Neutral,10.9 oz / 310g 10 oz / 284g,0,12.5 mm 10.0 mm,Heel,-,-,Carbon plate,-,-,-,-,Narrow,-,Stiff,Stiff,Moderate,2.5 mm,40.4 mm 41.0 mm,27.9 mm 31.0 mm,Normal,0,-,1,1,-,#249 Bottom 32%,#359 Bottom 2%
+ASICS,Metafuji Trail,"90
+ Superb!",$250,Light,Neutral,9.1 oz / 258g 9.2 oz / 261g,0,10.3 mm 5.0 mm,Heel,True to size,Soft,Carbon plate,Very bad,Good,Decent,Breathable,Medium,Narrow,Stiff,Stiff,Moderate,2.7 mm,44.7 mm 44.0 mm,34.4 mm 39.0 mm,NormalWide,0,SummerAll seasons,1,1,-,#61 Top 17%,#300 Bottom 18%
+New Balance,FuelCell SuperComp Trail,"90
+ Superb!",$200,LightModerate,Neutral,8.7 oz / 248g 8.8 oz / 249g,1,13.0 mm 10.0 mm,Heel,Half size small,Soft,Carbon plate,Decent,Decent,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,2.9 mm,34.7 mm 36.5 mm,21.7 mm 26.5 mm,Normal,0,All seasons,1,1,-,#65 Top 18%,#319 Bottom 13%
+Nike,Ultrafly,"89
+ Great!",$260,LightModerate,Neutral,10.5 oz / 299g 10.1 oz / 286g,0,11.8 mm 8.5 mm,Heel,True to size,Soft,Carbon plate,Very bad,Good,Good,Moderate,Medium,Wide,Stiff,Stiff,Moderate,3.0 mm,36.6 mm 38.0 mm,24.8 mm 29.5 mm,Normal,0,All seasons,1,1,-,#85 Top 24%,#201 Bottom 45%
+Kailas,Fuga DU,"83
+ Good!",$200,Moderate,Neutral,10.8 oz / 306g 10.3 oz / 293g,0,11.6 mm 8.0 mm,Heel,-,Balanced,0,-,-,-,Moderate,Narrow,-,Stiff,Stiff,Stiff,3.2 mm,36.9 mm 36.0 mm,25.3 mm,Wide,0,All seasons,1,1,-,#260 Bottom 29%,#363 Bottom 1%
+ASICS,Gel Excite Trail 2,"81
+ Good!",$85,Light,Neutral,10.3 oz / 292g 10.4 oz / 296g,0,10.1 mm 8.0 mm,Heel,True to size,Balanced,0,Very bad,Bad,Decent,Moderate,Medium,Medium,Flexible,Stiff,Stiff,4.0 mm,37.7 mm 36.0 mm,27.6 mm 28.0 mm,Normal,1,All seasons,1,1,-,#293 Bottom 20%,#205 Bottom 44%
+On,Cloudsurfer Trail,"85
+ Good!",$160,Light,Neutral,9.6 oz / 272g 9.6 oz / 272g,0,10.7 mm 7.0 mm,Heel,True to size,Balanced,0,Decent,Bad,Good,Moderate,Medium,Medium,Moderate,Stiff,Moderate,2.5 mm,37.4 mm 41.0 mm,26.7 mm 34.0 mm,Normal,1,All seasons,1,1,-,#197 Bottom 46%,#217 Bottom 41%
+Hoka,Stinson 7,"85
+ Good!",$170,LightModerate,Neutral,12.1 oz / 342g 12.9 oz / 365g,0,7.0 mm 5.0 mm,Mid/forefoot,True to size,Soft,0,Very bad,Bad,Decent,Moderate,Medium,Medium,Stiff,Stiff,Stiff,3.0 mm,40.0 mm 42.0 mm,33.0 mm 37.0 mm,Normal,0,All seasons,1,1,-,#209 Bottom 43%,#118 Top 33%
+Kailas,Flythorn Air 2.0,"90
+ Superb!",$140,LightModerate,Neutral,10.6 oz / 301g 10.3 oz / 291g,0,10.3 mm 10.0 mm,Heel,-,-,0,-,-,-,-,Narrow,-,Stiff,Moderate,Stiff,3.0 mm,30.6 mm 29.0 mm,20.3 mm 19.0 mm,Normal,0,-,1,1,-,#53 Top 15%,#364 Bottom 1%
+On,Cloudultra 2,"89
+ Great!",$180,Light,Neutral,10.4 oz / 296g 10.4 oz / 295g,0,10.2 mm 6.0 mm,Heel,True to size,Firm,Rock plate,Bad,Decent,Good,Breathable,Medium,Medium,Flexible,Flexible,Flexible,2.5 mm,30.2 mm 27.0 mm,20.0 mm 21.0 mm,Normal,0,SummerAll seasons,1,1,-,#104 Top 29%,#244 Bottom 33%
+Nike,Pegasus Trail 5,"90
+ Superb!",$150,Light,Neutral,10.1 oz / 286g 10.1 oz / 286g,0,9.6 mm 9.5 mm,HeelMid/forefoot,True to size,Soft,0,Very good,Good,Good,Warm,Medium,Medium,Moderate,Moderate,Stiff,3.2 mm,34.6 mm 37.0 mm,25.0 mm 27.5 mm,NormalWideX-Wide,0,All seasons,1,1,-,#73 Top 20%,#48 Top 14%
+Salomon,Ultra Glide 2,"86
+ Good!",$150,LightModerate,Neutral,10.1 oz / 286g 9.2 oz / 260g,0,7.2 mm 6.0 mm,Mid/forefoot,True to size,Soft,0,Bad,-,-,Moderate,Narrow,Narrow,Moderate,Flexible,Moderate,2.8 mm,30.6 mm 32.0 mm,23.4 mm 26.0 mm,Normal,0,All seasons,0,0,-,#178 Top 49%,#181 Top 50%
+Kailas,Phantom 3.0,"90
+ Superb!",$126,Light,Neutral,8.9 oz / 253g 8.3 oz / 235g,0,10.5 mm,Heel,-,Balanced,0,-,-,-,Moderate,Narrow,-,Stiff,Moderate,Stiff,1.8 mm,35.5 mm,25.0 mm,Normal,0,All seasons,1,1,-,#54 Top 15%,#365 Bottom 1%
+Merrell,Morphlite,"87
+ Great!",$100,Light,Neutral,8.4 oz / 237g 8.6 oz / 243g,1,11.0 mm 6.0 mm,Heel,True to size,Soft,0,Bad,Decent,Decent,Moderate,Narrow,Medium,Moderate,Stiff,Flexible,2.0 mm,32.3 mm 26.0 mm,21.3 mm 20.0 mm,NormalWide,0,All seasons,1,1,-,#148 Top 41%,#292 Bottom 20%
+On,Cloudsurfer Trail,"85
+ Good!",$160,Light,Neutral,9.6 oz / 272g 9.6 oz / 272g,0,10.7 mm 7.0 mm,Heel,True to size,Balanced,0,Decent,Bad,Good,Moderate,Medium,Medium,Moderate,Stiff,Moderate,2.5 mm,37.4 mm 41.0 mm,26.7 mm 34.0 mm,Normal,1,All seasons,1,1,-,#196 Bottom 46%,#217 Bottom 41%
+The North Face,Vectiv Enduris 3,"90
+ Superb!",$150,Light,Neutral,9.7 oz / 275g 10.8 oz / 307g,0,11.6 mm 6.0 mm,Heel,True to size,Balanced,0,Good,Good,Good,Warm,Medium,Medium,Moderate,Stiff,Stiff,3.3 mm,35.8 mm 30.0 mm,24.2 mm 24.0 mm,Normal,0,All seasons,1,1,-,#46 Top 13%,#189 Bottom 48%
+Nike,Pegasus Trail 4,"88
+ Great!",$140,Light,Neutral,9.6 oz / 272g 10.4 oz / 295g,0,12.7 mm 10.0 mm,Heel,True to size,-,0,-,-,-,-,Medium,-,Moderate,Flexible,Moderate,3.4 mm,35.5 mm 36.0 mm,22.8 mm 26.0 mm,Normal,0,-,1,1,-,#244 Top 38%,#195 Top 31%
+Nike,Pegasus Trail 5,"90
+ Superb!",$150,Light,Neutral,10.1 oz / 286g 10.1 oz / 286g,0,9.6 mm 9.5 mm,HeelMid/forefoot,True to size,Soft,0,Very good,Good,Good,Warm,Medium,Medium,Moderate,Moderate,Stiff,3.2 mm,34.6 mm 37.0 mm,25.0 mm 27.5 mm,NormalWideX-Wide,0,All seasons,1,1,-,#73 Top 20%,#48 Top 14%
+On,Cloudsurfer Trail,"85
+ Good!",$160,Light,Neutral,9.6 oz / 272g 9.6 oz / 272g,0,10.7 mm 7.0 mm,Heel,True to size,Balanced,0,Decent,Bad,Good,Moderate,Medium,Medium,Moderate,Stiff,Moderate,2.5 mm,37.4 mm 41.0 mm,26.7 mm 34.0 mm,Normal,1,All seasons,1,1,-,#197 Bottom 46%,#217 Bottom 41%
+The North Face,Vectiv Enduris 3,"90
+ Superb!",$150,Light,Neutral,9.7 oz / 275g 10.8 oz / 307g,0,11.6 mm 6.0 mm,Heel,True to size,Balanced,0,Good,Good,Good,Warm,Medium,Medium,Moderate,Stiff,Stiff,3.3 mm,35.8 mm 30.0 mm,24.2 mm 24.0 mm,Normal,0,All seasons,1,1,-,#46 Top 13%,#189 Bottom 48%
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+,,Audience score,Price,Trail terrain,Shock absorption,Energy return,Traction,,,,,,,,,,,,,,,,,,,,,,,,,
+,,,,,Arch support,Weight lab Weight brand,Lightweight,Drop lab Drop brand,Strike pattern,Size,Midsole softness,Difference in midsole softness in cold,,,,,,,,,,,,,,,,,,,,
+,,,,,,,,,,,,Plate,Toebox durability,Heel padding durability,Outsole durability,Breathability,Width / fit,Toebox width,Stiffness,Torsional rigidity,Heel counter stiffness,Lug depth,Heel stack lab Heel stack brand,Forefoot lab Forefoot brand,Widths available,For heavy runners,Season,Removable insole,Orthotic friendly,Ranking,Popularity,
\ No newline at end of file
diff --git a/data/unused-data/(old) road_dataset.csv b/data/unused-data/(old) road_dataset.csv
new file mode 100644
index 0000000000000000000000000000000000000000..368d357741613bb3d8534b29734843d66b585558
--- /dev/null
+++ b/data/unused-data/(old) road_dataset.csv	
@@ -0,0 +1,435 @@
+Brand,Name,Pace,Arch support,Weight lab Weight brand,Lightweight,Drop lab Drop brand,Strike pattern,Size,Midsole softness,Toebox durability,Heel padding durability,Outsole durability,Breathability,Width / fit,Toebox width,Stiffness,Torsional rigidity,Heel counter stiffness,Plate,Rocker,Heel lab Heel brand,Forefoot lab Forefoot brand,Widths available,Orthotic friendly,Season,Removable insole,Ranking,Popularity,Gender,Terrain,Pace_norm,Pace_lists,pace_competition,pace_daily_running,pace_tempo,arch_neutral,arch_stability,Strike_norm,Strike_lists,-,strike_forefoot,strike_heel,strike_mid,fit_category,fit_large,fit_small,fit_true,fit_missing,midsole_soft,midsole_balanced,midsole_firm,toebox_bad,toebox_decent,toebox_good,heelpad_bad,heelpad_decent,heelpad_good,outsole_bad,outsole_decent,outsole_good,breath_breathable,breath_moderate,breath_warm,width_narrow,width_medium,width_wide,toeboxwidth_narrow,toeboxwidth_medium,toeboxwidth_wide,stiff_flexible,stiff_moderate,stiff_stiff,torsion_flexible,torsion_moderate,torsion_stiff,heelcounter_flexible,heelcounter_moderate,heelcounter_stiff,widthavail_norm,widthavail_list,widthavail_narrow,widthavail_normal,widthavail_wide,widthavail_xwide,season_norm,season_list,season_all,season_summer,season_winter,weight_lab_oz,weight_lab_g,weight_brand_oz,weight_brand_g,drop_lab_mm,drop_brand_mm,heel_lab_mm,heel_brand_mm,forefoot_lab_mm,forefoot_brand_mm
+Brooks, Launch 9,Daily Runningtempo,Neutral,7.9 oz / 225g 8.1 oz / 230g,1,9.4 mm 10.0 mm,Heelmid/Forefoot,True To Size,Balanced,-,-,-,-,Narrow,-,Stiff,Stiff,Flexible,0,0,32.4 mm 36.0 mm,23.0 mm 26.0 mm,Normalwide,1,-,1,#301 Top 47%,#352 Bottom 45%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,1,0,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,,,0,0,0,7.9,225,8.1,230.0,9.4,10.0,32.4,36.0,23.0,26.0
+Brooks, Levitate 6,Daily Running,Neutral,10.7 oz / 304g 10.9 oz / 309g,0,7.7 mm 8.0 mm,Mid/Forefoot,True To Size,Soft,Good,Good,Good,Breathable,Narrow,Medium,Stiff,Moderate,Moderate,0,0,34.3 mm 32.5 mm,26.6 mm 24.5 mm,Normal,1,Summerall Seasons,1,#72 Top 20%,#255 Bottom 30%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,1,1,0,0,1,0,0,0,1,0,0,0,1,0,1,0,0,1,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,10.7,304,10.9,309.0,7.7,8.0,34.3,32.5,26.6,24.5
+Adidas,4DFWD,Daily Running,Neutral,11.9 oz / 336g 11.5 oz / 327g,0,8.9 mm 10.0 mm,Heelmid/Forefoot,True To Size,Firm,-,Good,-,Warm,Narrow,-,Stiff,Flexible,Flexible,0,0,33.3 mm 32.5 mm,24.4 mm 22.5 mm,Normal,1,All Seasons,1,#104 Top 17%,#368 Bottom 42%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,1,0,0,1,0,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,11.9,336,11.5,327.0,8.9,10.0,33.3,32.5,24.4,22.5
+Adidas,4DFWD 2,Daily Running,Neutral,12.6 oz / 356g 12.4 oz / 352g,0,10.6 mm 11.0 mm,Heel,Slightly Small,Firm,-,-,-,Warm,Narrow,-,Stiff,Flexible,Moderate,0,0,31.8 mm 32.0 mm,21.2 mm 21.0 mm,Normal,1,All Seasons,1,#126 Top 20%,#541 Bottom 16%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,small,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,1,0,0,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,12.6,356,12.4,352.0,10.6,11.0,31.8,32.0,21.2,21.0
+Adidas,4DFWD 3,Daily Running,Neutral,12.3 oz / 348g 12.2 oz / 345g,0,9.9 mm 10.0 mm,Heelmid/Forefoot,True To Size,Firm,Good,Good,Good,Warm,Narrow,Medium,Moderate,Flexible,Flexible,0,0,32.6 mm 34.0 mm,22.7 mm 24.0 mm,Normal,1,All Seasons,1,#116 Top 32%,#339 Bottom 7%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,12.3,348,12.2,345.0,9.9,10.0,32.6,34.0,22.7,24.0
+Brooks,Addiction GTS 15,Daily Running,Motion Control,12.5 oz / 353g 12.2 oz / 346g,0,12.1 mm 12.0 mm,Heel,Slightly Small,Firm,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,0,36.5 mm 36.0 mm,24.4 mm 24.0 mm,Narrownormalwidex-Wide,1,All Seasons,1,#221 Bottom 39%,#159 Top 44%,,,Daily Running,['Daily Running'],0,1,0,0,1,Heel,['Heel'],0,0,1,0,small,0,1,0,0,0,0,1,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,Narrow|Normal|Wide|X-Wide,"['Narrow', 'Normal', 'Wide', 'X-Wide']",1,1,1,1,All,['All'],1,0,0,12.5,353,12.2,346.0,12.1,12.0,36.5,36.0,24.4,24.0
+Adidas,Adidas Adizero SL2,Daily Runningtempo,Neutral,8.6 oz / 245g 8.4 oz / 238g,1,8.2 mm 9.0 mm,Heelmid/Forefoot,Half Size Small,Balanced,Bad,Good,Decent,Breathable,Wide,Medium,Moderate,Moderate,Flexible,0,0,34.9 mm 35.0 mm,26.7 mm 26.0 mm,Normalwide,1,Summerall Seasons,1,#44 Top 13%,#166 Top 46%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,small,0,1,0,0,0,1,0,1,0,0,0,0,1,0,1,0,1,0,0,0,0,1,0,1,0,0,1,0,0,1,0,1,0,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,Summer|All,"['Summer', 'All']",1,1,0,8.6,245,8.4,238.0,8.2,9.0,34.9,35.0,26.7,26.0
+Adidas,Adistar,Daily Running,Neutral,11.5 oz / 325g 11.2 oz / 318g,0,9.6 mm 6.0 mm,Heelmid/Forefoot,True To Size,Balanced,Decent,Good,-,Moderate,Narrow,Medium,Stiff,Stiff,Flexible,0,1,34.4 mm 37.5 mm,24.8 mm 31.5 mm,Normal,1,All Seasons,1,#267 Top 42%,#247 Top 39%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,11.5,325,11.2,318.0,9.6,6.0,34.4,37.5,24.8,31.5
+Adidas,Adistar 2.0,Daily Running,Neutral,11.6 oz / 328g 11.6 oz / 328g,0,8.0 mm 6.0 mm,Heelmid/Forefoot,Half Size Small,Balanced,Bad,-,-,Breathable,Narrow,Wide,Stiff,Stiff,Stiff,0,1,33.8 mm 33.0 mm,25.8 mm 27.0 mm,Normal,1,Summerall Seasons,1,#506 Bottom 21%,#643 Bottom 1%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,small,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,1,0,0,1,0,0,1,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,11.6,328,11.6,328.0,8.0,6.0,33.8,33.0,25.8,27.0
+Adidas,Adistar 3,Daily Running,Neutral,9.7 oz / 274g 9.5 oz / 270g,0,10.5 mm 5.0 mm,Heel,True To Size,Soft,Bad,Decent,Good,Breathable,Medium,Medium,Moderate,Stiff,Stiff,0,0,40.7 mm 40.0 mm,30.2 mm 35.0 mm,Normalwide,1,Summerall Seasons,1,#97 Top 27%,#241 Bottom 33%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,1,0,0,1,0,0,0,1,0,0,0,1,1,0,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,Summer|All,"['Summer', 'All']",1,1,0,9.7,274,9.5,270.0,10.5,5.0,40.7,40.0,30.2,35.0
+Adidas,Adizero Adios 7,Tempo,Neutral,7.5 oz / 212g 7.5 oz / 212g,1,8.7 mm 8.0 mm,Heelmid/Forefoot,Slightly Small,Balanced,-,-,-,Breathable,Medium,Narrow,Stiff,Stiff,Flexible,0,0,31.6 mm 27.0 mm,22.9 mm 19.0 mm,Normalwide,1,Summerall Seasons,1,#535 Bottom 17%,#614 Bottom 4%,,,Tempo,['Tempo'],0,0,1,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,small,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,1,0,0,1,1,0,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,Summer|All,"['Summer', 'All']",1,1,0,7.5,212,7.5,212.0,8.7,8.0,31.6,27.0,22.9,19.0
+Adidas,Adizero Adios 8,Tempo,Neutral,7.4 oz / 210g 7 oz / 198g,1,7.6 mm 8.0 mm,Mid/Forefoot,True To Size,Soft,Bad,Decent,Good,Breathable,Medium,Wide,Flexible,Flexible,Flexible,0,0,28.0 mm 28.0 mm,20.4 mm 20.0 mm,Normal,1,Summerall Seasons,1,#129 Top 20%,#553 Bottom 14%,,,Tempo,['Tempo'],0,0,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,1,0,0,1,0,0,0,1,0,0,0,1,1,0,0,0,1,0,0,0,1,1,0,0,1,0,0,1,0,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,7.4,210,7.0,198.0,7.6,8.0,28.0,28.0,20.4,20.0
+Adidas,Adizero Adios 9,Competitiontempo,Neutral,6.2 oz / 176g 6.2 oz / 176g,1,6.2 mm 7.0 mm,Mid/Forefoot,Slightly Small,Soft,Bad,Good,Good,Warm,Medium,Narrow,Flexible,Flexible,Flexible,0,0,25.0 mm 28.0 mm,18.8 mm 21.0 mm,Normal,1,All Seasons,1,#7 Top 2%,#245 Bottom 33%,,,Competition|Tempo,"['Competition', 'Tempo']",1,0,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,small,0,1,0,0,1,0,0,1,0,0,0,0,1,0,0,1,0,0,1,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,6.2,176,6.2,176.0,6.2,7.0,25.0,28.0,18.8,21.0
+Adidas,Adizero Adios Pro 2.0,Competition,Neutral,7.9 oz / 223g 7.6 oz / 215g,1,10.3 mm 10.0 mm,Heel,True To Size,-,-,-,-,-,Narrow,-,Stiff,Stiff,Flexible,Carbon plate,1,40.0 mm 39.5 mm,29.7 mm 29.5 mm,Normal,0,-,0,#32 Top 5%,#569 Bottom 11%,,,Competition,['Competition'],1,0,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,,,0,0,0,7.9,223,7.6,215.0,10.3,10.0,40.0,39.5,29.7,29.5
+Adidas,Adizero Adios Pro 3,Competition,Neutral,7.7 oz / 218g 7.9 oz / 223g,1,8.0 mm 6.5 mm,Heelmid/Forefoot,True To Size,Balanced,Bad,Good,Good,Breathable,Medium,Wide,Stiff,Stiff,Flexible,Carbon plate,1,37.8 mm 39.5 mm,29.8 mm 33.0 mm,Normal,1,Summerall Seasons,1,#41 Top 7%,#202 Top 32%,,,Competition,['Competition'],1,0,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,1,0,0,0,0,1,0,0,1,1,0,0,0,1,0,0,0,1,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,7.7,218,7.9,223.0,8.0,6.5,37.8,39.5,29.8,33.0
+Adidas,Adizero Adios Pro 4,Competition,Neutral,7.1 oz / 200g 7.1 oz / 201g,1,8.1 mm 6.0 mm,Heelmid/Forefoot,Slightly Small,Soft,Bad,Good,Good,Warm,Medium,Narrow,Stiff,Stiff,Flexible,Carbon plate,1,36.6 mm 39.0 mm,28.5 mm 33.0 mm,Normalwide,1,All Seasons,1,#1 Top 1%,#38 Top 11%,,,Competition,['Competition'],1,0,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,small,0,1,0,0,1,0,0,1,0,0,0,0,1,0,0,1,0,0,1,0,1,0,1,0,0,0,0,1,0,0,1,1,0,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,7.1,200,7.1,201.0,8.1,6.0,36.6,39.0,28.5,33.0
+Adidas,Adizero Boston 11,Tempo,Neutral,10.2 oz / 290g 9.6 oz / 272g,0,9.8 mm 8.5 mm,Heelmid/Forefoot,True To Size,Balanced,Bad,Decent,Good,Moderate,Wide,Wide,Stiff,Stiff,Moderate,Carbon plate,1,39.1 mm 39.5 mm,29.3 mm 31.0 mm,Normalwide,1,All Seasons,1,#480 Bottom 25%,#579 Bottom 10%,,,Tempo,['Tempo'],0,0,1,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,1,0,0,0,1,0,0,0,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,10.2,290,9.6,272.0,9.8,8.5,39.1,39.5,29.3,31.0
+Adidas,Adizero Boston 12,Tempo,Neutral,9.2 oz / 261g 9.2 oz / 260g,0,6.1 mm 6.5 mm,Mid/Forefoot,Slightly Large,Balanced,Bad,Decent,Good,Breathable,Wide,Medium,Stiff,Stiff,Flexible,0,1,34.5 mm 37.0 mm,28.4 mm 30.5 mm,Normalwide,1,Summerall Seasons,1,#216 Top 34%,#338 Bottom 47%,,,Tempo,['Tempo'],0,0,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,large,1,0,0,0,0,1,0,1,0,0,0,1,0,0,0,1,1,0,0,0,0,1,0,1,0,0,0,1,0,0,1,1,0,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,Summer|All,"['Summer', 'All']",1,1,0,9.2,261,9.2,260.0,6.1,6.5,34.5,37.0,28.4,30.5
+Adidas,Adizero Boston 13,Competitiontempo,Neutral,9 oz / 254g 9 oz / 255g,0,6.0 mm 6.0 mm,Mid/Forefoot,True To Size,Balanced,Decent,Good,Good,Moderate,Medium,Narrow,Moderate,Stiff,Stiff,Carbon plate,1,34.3 mm 36.0 mm,28.3 mm 30.0 mm,Normalwide,1,All Seasons,1,#38 Top 11%,#206 Bottom 43%,,,Competition|Tempo,"['Competition', 'Tempo']",1,0,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,9.0,254,9.0,255.0,6.0,6.0,34.3,36.0,28.3,30.0
+Adidas,Adizero EVO SL,Daily Runningtempo,Neutral,7.9 oz / 223g 7.9 oz / 224g,1,8.0 mm 6.5 mm,Heelmid/Forefoot,True To Size,Balanced,Bad,Good,Good,Breathable,Medium,Medium,Flexible,Stiff,Moderate,0,1,36.1 mm 38.5 mm,28.1 mm 32.0 mm,Normalwide,1,Summerall Seasons,1,#2 Top 1%,#23 Top 7%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,1,0,0,0,0,1,0,0,1,1,0,0,0,1,0,0,1,0,1,0,0,0,0,1,0,1,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,Summer|All,"['Summer', 'All']",1,1,0,7.9,223,7.9,224.0,8.0,6.5,36.1,38.5,28.1,32.0
+Adidas,Adizero Prime X 2 Strung,Competitiontempo,Neutral,10.8 oz / 305g 10.8 oz / 306g,0,8.8 mm 6.5 mm,Heelmid/Forefoot,Slightly Small,Balanced,Good,Good,Good,Moderate,Medium,Narrow,Stiff,Stiff,Flexible,Carbon plate,1,45.7 mm 50.0 mm,36.9 mm 43.5 mm,Normal,1,All Seasons,1,#132 Top 37%,#52 Top 15%,,,Competition|Tempo,"['Competition', 'Tempo']",1,0,1,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,small,0,1,0,0,0,1,0,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,1,0,0,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,10.8,305,10.8,306.0,8.8,6.5,45.7,50.0,36.9,43.5
+Adidas,Adizero Prime X3 STRUNG,Competitiontempo,Neutral,10.3 oz / 291g 10.1 oz / 285g,0,13.0 mm 7.0 mm,Heelmid/Forefoot,True To Size,Soft,Good,Good,Bad,Moderate,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,48.1 mm 50.0 mm,35.1 mm 43.0 mm,Normal,1,All Seasons,1,#48 Top 14%,#250 Bottom 31%,,,Competition|Tempo,"['Competition', 'Tempo']",1,0,1,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,1,0,0,0,0,1,0,0,1,1,0,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,10.3,291,10.1,285.0,13.0,7.0,48.1,50.0,35.1,43.0
+Adidas,Adizero SL,Daily Runningtempo,Neutral,8.8 oz / 249g 8.6 oz / 244g,1,8.6 mm 10.0 mm,Heelmid/Forefoot,Slightly Small,Balanced,Decent,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Moderate,0,0,34.9 mm 35.0 mm,26.3 mm 25.0 mm,Normalwide,1,Summerall Seasons,1,#235 Top 37%,#278 Top 44%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,small,0,1,0,0,0,1,0,0,1,0,0,0,1,0,0,1,1,0,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,Summer|All,"['Summer', 'All']",1,1,0,8.8,249,8.6,244.0,8.6,10.0,34.9,35.0,26.3,25.0
+Adidas,Adizero SL2,Daily Runningtempo,Neutral,8.6 oz / 245g 8.4 oz / 238g,1,8.2 mm 9.0 mm,Heelmid/Forefoot,Half Size Small,Balanced,Bad,Good,Decent,Breathable,Wide,Medium,Moderate,Moderate,Flexible,0,0,34.9 mm 35.0 mm,26.7 mm 26.0 mm,Normalwide,1,Summerall Seasons,1,#44 Top 13%,#166 Top 46%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,small,0,1,0,0,0,1,0,1,0,0,0,0,1,0,1,0,1,0,0,0,0,1,0,1,0,0,1,0,0,1,0,1,0,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,Summer|All,"['Summer', 'All']",1,1,0,8.6,245,8.4,238.0,8.2,9.0,34.9,35.0,26.7,26.0
+Adidas,Adizero Takumi Sen 10,Competitiontempo,Neutral,7.1 oz / 200g 6.9 oz / 196g,1,7.8 mm 6.0 mm,Mid/Forefoot,Half Size Small,Soft,Bad,Good,Bad,Breathable,Narrow,Medium,Moderate,Stiff,Flexible,0,0,30.6 mm 33.0 mm,22.8 mm 27.0 mm,Normal,1,Summerall Seasons,1,#88 Top 25%,#252 Bottom 31%,,,Competition|Tempo,"['Competition', 'Tempo']",1,0,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,small,0,1,0,0,1,0,0,1,0,0,0,0,1,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,7.1,200,6.9,196.0,7.8,6.0,30.6,33.0,22.8,27.0
+Brooks,Adrenaline GTS 22,Daily Running,Stability,10.4 oz / 294g 10.2 oz / 289g,0,14.7 mm 12.0 mm,Heel,True To Size,-,-,-,-,-,Medium,-,Stiff,-,-,0,0,37.4 mm 36.0 mm,22.7 mm 24.0 mm,Narrownormalwidex-Wide,0,-,0,#148 Top 23%,#82 Top 13%,,,Daily Running,['Daily Running'],0,1,0,0,1,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,Narrow|Normal|Wide|X-Wide,"['Narrow', 'Normal', 'Wide', 'X-Wide']",1,1,1,1,,,0,0,0,10.4,294,10.2,289.0,14.7,12.0,37.4,36.0,22.7,24.0
+Brooks,Adrenaline GTS 23,Daily Running,Stability,10.1 oz / 286g 10.4 oz / 294g,0,12.6 mm 12.0 mm,Heel,True To Size,Soft,Bad,Good,Good,Breathable,Medium,Medium,Moderate,Stiff,Stiff,0,0,34.1 mm 36.0 mm,21.5 mm 24.0 mm,Narrownormalwidex-Wide,1,Summerall Seasons,1,#211 Top 33%,#36 Top 6%,,,Daily Running,['Daily Running'],0,1,0,0,1,Heel,['Heel'],0,0,1,0,true,0,0,1,0,1,0,0,1,0,0,0,0,1,0,0,1,1,0,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,Narrow|Normal|Wide|X-Wide,"['Narrow', 'Normal', 'Wide', 'X-Wide']",1,1,1,1,Summer|All,"['Summer', 'All']",1,1,0,10.1,286,10.4,294.0,12.6,12.0,34.1,36.0,21.5,24.0
+Brooks,Adrenaline GTS 24,Daily Running,Stability,10.3 oz / 291g 10 oz / 283g,0,13.5 mm 12.0 mm,Heel,Slightly Small,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,39.0 mm 39.0 mm,25.5 mm 27.0 mm,Narrownormalwidex-Wide,1,All Seasons,1,#214 Bottom 41%,#5 Top 2%,,,Daily Running,['Daily Running'],0,1,0,0,1,Heel,['Heel'],0,0,1,0,small,0,1,0,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,Narrow|Normal|Wide|X-Wide,"['Narrow', 'Normal', 'Wide', 'X-Wide']",1,1,1,1,All,['All'],1,0,0,10.3,291,10.0,283.0,13.5,12.0,39.0,39.0,25.5,27.0
+Skechers,Aero Burst,Daily Running,Neutral,11.4 oz / 322g,0,8.8 mm 6.0 mm,Heelmid/Forefoot,True To Size,Soft,Decent,Good,Good,Moderate,Medium,Narrow,Moderate,Stiff,Stiff,0,0,41.7 mm 42.0 mm,32.9 mm 36.0 mm,Normalwide,1,All Seasons,1,#12 Top 4%,#24 Top 7%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,11.4,322,,,8.8,6.0,41.7,42.0,32.9,36.0
+Salomon,Aero Glide,Daily Running,Neutral,9.3 oz / 264g 9 oz / 254g,0,11.0 mm 10.0 mm,Heel,True To Size,Balanced,Decent,Decent,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,1,35.2 mm 37.0 mm,24.2 mm 27.0 mm,Normal,1,All Seasons,1,#221 Top 35%,#443 Bottom 31%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,9.3,264,9.0,254.0,11.0,10.0,35.2,37.0,24.2,27.0
+Salomon,Aero Glide 2,Daily Running,Neutral,9.5 oz / 268g 9.2 oz / 260g,0,11.3 mm 10.0 mm,Heel,True To Size,Balanced,Good,Decent,Decent,Moderate,Medium,Medium,Moderate,Stiff,Moderate,0,1,35.5 mm 41.0 mm,24.2 mm 31.0 mm,Normal,1,All Seasons,1,#9 Top 2%,#529 Bottom 17%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,9.5,268,9.2,260.0,11.3,10.0,35.5,41.0,24.2,31.0
+Salomon,Aero Glide 3,Daily Running,Neutral,8.7 oz / 248g 8.6 oz / 245g,1,10.3 mm 8.0 mm,Heel,Slightly Large,Balanced,Bad,Decent,Decent,Moderate,Narrow,Narrow,Moderate,Stiff,Flexible,0,0,42.2 mm 40.0 mm,31.9 mm 32.0 mm,Normal,1,All Seasons,1,#7 Top 2%,#110 Top 30%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,large,1,0,0,0,0,1,0,1,0,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,0,1,0,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,8.7,248,8.6,245.0,10.3,8.0,42.2,40.0,31.9,32.0
+Nike,Air Winflo 9,Daily Running,Neutral,9.8 oz / 279g 9.9 oz / 280g,0,10.8 mm 10.0 mm,Heel,True To Size,Soft,Decent,-,-,Moderate,Narrow,Medium,Stiff,Moderate,Stiff,0,0,35.0 mm,24.2 mm,Normal,1,All Seasons,1,#430 Bottom 33%,#555 Bottom 13%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,1,0,1,0,0,0,1,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,9.8,279,9.9,280.0,10.8,10.0,35.0,,24.2,
+Nike,Air Zoom Pegasus 38,Daily Running,Neutral,10.3 oz / 291g 10 oz / 283g,0,8.7 mm 10.0 mm,Heelmid/Forefoot,True To Size,-,-,-,-,Moderate,Medium,-,Stiff,Flexible,Moderate,0,0,31.8 mm 27.5 mm,23.1 mm 17.5 mm,Normalwide,1,All Seasons,1,#298 Top 47%,#232 Top 36%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,1,0,0,0,1,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,10.3,291,10.0,283.0,8.7,10.0,31.8,27.5,23.1,17.5
+Nike,Air Zoom Pegasus 38 FlyEase,Daily Running,Neutral,9.7 oz / 275g 9.2 oz / 260g,0,8.7 mm,Heelmid/Forefoot,-,-,-,-,-,-,Medium,-,Stiff,-,-,0,0,31.8 mm,23.1 mm,Normalx-Wide,0,-,0,#613 Bottom 4%,#521 Bottom 19%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,Normal|X-Wide,"['Normal', 'X-Wide']",0,1,0,1,,,0,0,0,9.7,275,9.2,260.0,8.7,,31.8,,23.1,
+Nike,Air Zoom Pegasus 39,Daily Running,Neutral,9.3 oz / 264g 9.2 oz / 261g,0,8.0 mm 10.0 mm,Heelmid/Forefoot,True To Size,Soft,-,-,-,Moderate,Narrow,-,Stiff,Flexible,Moderate,0,0,30.3 mm 33.0 mm,22.3 mm 23.0 mm,Normalx-Wide,1,All Seasons,1,#289 Top 45%,#150 Top 24%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,1,0,0,0,1,0,Normal|X-Wide,"['Normal', 'X-Wide']",0,1,0,1,All,['All'],1,0,0,9.3,264,9.2,261.0,8.0,10.0,30.3,33.0,22.3,23.0
+Nobull,Allday Knit,Daily Running,Neutral,10.6 oz / 301g 10.6 oz / 300g,0,12.0 mm 10.0 mm,Heel,-,Firm,-,-,-,Moderate,Narrow,-,Stiff,Moderate,Flexible,0,0,31.8 mm,19.8 mm,Normal,1,All Seasons,1,#154 Top 43%,#284 Bottom 22%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,1,0,1,0,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,10.6,301,10.6,300.0,12.0,10.0,31.8,,19.8,
+Nobull,Allday Ripstop,Daily Running,Neutral,10.5 oz / 297g 10.5 oz / 298g,0,11.4 mm,Heel,True To Size,Balanced,-,-,-,Moderate,Narrow,-,Stiff,Moderate,Flexible,0,0,31.3 mm,19.9 mm,Normal,1,All Seasons,1,#365 Bottom 1%,#299 Bottom 18%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,1,0,1,0,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,10.5,297,10.5,298.0,11.4,,31.3,,19.9,
+Adidas,Alphabounce+,Daily Running,Neutral,12 oz / 340g 12.5 oz / 354g,0,11.5 mm 10.0 mm,Heel,True To Size,Balanced,Bad,Bad,Good,Moderate,Medium,Narrow,Stiff,Stiff,Moderate,0,0,37.7 mm 20.0 mm,26.2 mm 10.0 mm,Normal,1,All Seasons,1,#300 Bottom 17%,#202 Bottom 44%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,1,0,1,0,0,1,0,0,0,0,1,0,1,0,0,1,0,1,0,0,0,0,1,0,0,1,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,12.0,340,12.5,354.0,11.5,10.0,37.7,20.0,26.2,10.0
+Nike,Alphafly 2,Competition,Neutral,8.5 oz / 240g 8.6 oz / 243g,1,4.7 mm 8.0 mm,Mid/Forefoot,Slightly Small,Soft,Good,-,-,Breathable,Narrow,Narrow,Stiff,Stiff,Flexible,Carbon plate,1,38.6 mm 40.0 mm,33.9 mm 32.0 mm,Normal,0,Summerall Seasons,0,#445 Bottom 31%,#216 Top 34%,,,Competition,['Competition'],1,0,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,small,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,8.5,240,8.6,243.0,4.7,8.0,38.6,40.0,33.9,32.0
+Nike,Alphafly 3,Competition,Neutral,7.1 oz / 201g 7 oz / 198g,1,8.5 mm 8.0 mm,Heelmid/Forefoot,Slightly Small,Soft,Bad,Good,Bad,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,38.1 mm 40.0 mm,29.6 mm 32.0 mm,Normal,0,Summerall Seasons,0,#131 Top 36%,#20 Top 6%,,Road,Competition,['Competition'],1,0,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,small,0,1,0,0,1,0,0,1,0,0,0,0,1,1,0,0,1,0,0,0,1,0,0,1,0,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,7.1,201,7.0,198.0,8.5,8.0,38.1,40.0,29.6,32.0
+Brooks,Anthem 4,Daily Running,Neutral,8.6 oz / 245g,1,11.1 mm 10.0 mm,Heel,-,-,-,-,-,Moderate,Narrow,-,Stiff,Flexible,Stiff,0,0,32.5 mm,21.4 mm,Normal,1,All Seasons,1,#298 Bottom 18%,#289 Bottom 21%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,1,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,8.6,245,,,11.1,10.0,32.5,,21.4,
+Hoka,Arahi 7,Daily Running,Stability,9.4 oz / 266g 9.6 oz / 272g,0,6.3 mm 5.0 mm,Mid/Forefoot,True To Size,Balanced,Good,Good,Good,Moderate,Medium,Narrow,Stiff,Stiff,Stiff,0,1,34.2 mm 34.0 mm,27.9 mm 29.0 mm,Narrownormalwide,1,All Seasons,1,#527 Bottom 18%,#34 Top 6%,,,Daily Running,['Daily Running'],0,1,0,0,1,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,1,Narrow|Normal|Wide,"['Narrow', 'Normal', 'Wide']",1,1,1,0,All,['All'],1,0,0,9.4,266,9.6,272.0,6.3,5.0,34.2,34.0,27.9,29.0
+Hoka,Arahi 8,Daily Running,Stability,9.1 oz / 259g 9 oz / 256g,0,11.3 mm 8.0 mm,Heel,True To Size,Soft,Good,Good,Good,Warm,Medium,Medium,Stiff,Stiff,Moderate,0,1,39.4 mm 39.0 mm,28.1 mm 31.0 mm,Normalwidex-Wide,1,All Seasons,1,#213 Bottom 41%,#29 Top 8%,,,Daily Running,['Daily Running'],0,1,0,0,1,Heel,['Heel'],0,0,1,0,true,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,9.1,259,9.0,256.0,11.3,8.0,39.4,39.0,28.1,31.0
+Topo,Atmos,Daily Running,Neutral,9.7 oz / 275g 9.7 oz / 275g,0,5.3 mm 5.0 mm,Mid/Forefoot,True To Size,Balanced,Decent,Good,Good,Moderate,Wide,Wide,Moderate,Stiff,Stiff,0,0,37.8 mm 38.0 mm,32.5 mm 33.0 mm,Normalwide,1,All Seasons,1,#40 Top 11%,#152 Top 42%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,0,1,0,0,1,0,1,0,0,0,1,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,9.7,275,9.7,275.0,5.3,5.0,37.8,38.0,32.5,33.0
+Diadora,Atomo Star,Daily Running,Neutral,9.5 oz / 268g 9.7 oz / 275g,0,7.8 mm 6.0 mm,Mid/Forefoot,-,Soft,Decent,Bad,Good,Moderate,Narrow,Narrow,Moderate,Stiff,Stiff,0,1,39.9 mm 40.0 mm,32.1 mm 34.0 mm,Normal,1,All Seasons,1,#118 Top 33%,#311 Bottom 14%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,,0,0,0,1,1,0,0,0,1,0,1,0,0,0,0,1,0,1,0,1,0,0,1,0,0,0,1,0,0,0,1,0,0,1,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,9.5,268,9.7,275.0,7.8,6.0,39.9,40.0,32.1,34.0
+Brooks,Aurora-BL,Daily Running,Neutral,8.7 oz / 247g 8.5 oz / 240g,1,8.7 mm 6.0 mm,Heelmid/Forefoot,Slightly Large,Soft,Bad,Bad,Good,Moderate,Narrow,Medium,Stiff,Flexible,Moderate,0,1,37.0 mm 37.0 mm,28.3 mm 31.0 mm,Normal,1,All Seasons,1,#60 Top 17%,#235 Bottom 35%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,large,1,0,0,0,1,0,0,1,0,0,1,0,0,0,0,1,0,1,0,1,0,0,0,1,0,0,0,1,1,0,0,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,8.7,247,8.5,240.0,8.7,6.0,37.0,37.0,28.3,31.0
+Saucony,Axon,Daily Runningtempo,Neutral,9.9 oz / 281g 9.3 oz / 264g,0,5.5 mm 4.0 mm,Mid/Forefoot,True To Size,-,-,-,-,-,Medium,-,-,Moderate,-,0,1,35.3 mm 35.0 mm,29.8 mm 31.0 mm,Normal,1,-,1,#338 Bottom 47%,#600 Bottom 6%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Normal,['Normal'],0,1,0,0,,,0,0,0,9.9,281,9.3,264.0,5.5,4.0,35.3,35.0,29.8,31.0
+Saucony,Axon 2,Daily Runningtempo,Neutral,9.9 oz / 282g 9.6 oz / 272g,0,7.8 mm 4.0 mm,Mid/Forefoot,Half Size Small,Balanced,Bad,-,-,Moderate,Narrow,Narrow,Stiff,Stiff,Stiff,0,1,35.3 mm 35.0 mm,27.5 mm 31.0 mm,Normal,1,All Seasons,1,#189 Top 30%,#582 Bottom 9%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,small,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,1,0,0,1,0,0,1,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,9.9,282,9.6,272.0,7.8,4.0,35.3,35.0,27.5,31.0
+Saucony,Axon 3,Daily Runningtempo,Neutral,8.6 oz / 244g 8.5 oz / 241g,1,5.7 mm 4.0 mm,Mid/Forefoot,True To Size,Soft,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,1,33.6 mm 35.0 mm,27.9 mm 31.0 mm,Normal,1,All Seasons,1,#115 Top 32%,#284 Bottom 22%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,8.6,244,8.5,241.0,5.7,4.0,33.6,35.0,27.9,31.0
+Brooks,Beast GTS 23,Daily Running,Stability,12.4 oz / 352g 11.9 oz / 337g,0,11.9 mm 12.0 mm,Heel,True To Size,Balanced,Decent,Decent,Good,Moderate,Medium,Medium,Stiff,Stiff,Stiff,0,0,36.4 mm,24.5 mm,Normalwidex-Wide,1,All Seasons,1,#405 Bottom 37%,#339 Bottom 47%,,,Daily Running,['Daily Running'],0,1,0,0,1,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,12.4,352,11.9,337.0,11.9,12.0,36.4,,24.5,
+Brooks,Beast GTS 24,Daily Running,Stability,12.6 oz / 357g 12.6 oz / 357g,0,12.7 mm 12.0 mm,Heel,Slightly Small,Firm,Decent,Decent,Good,Warm,Medium,Medium,Moderate,Stiff,Stiff,0,0,38.5 mm 36.0 mm,25.8 mm 24.0 mm,Normalwidex-Wide,1,All Seasons,1,#187 Bottom 48%,#98 Top 27%,,,Daily Running,['Daily Running'],0,1,0,0,1,Heel,['Heel'],0,0,1,0,small,0,1,0,0,0,0,1,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,12.6,357,12.6,357.0,12.7,12.0,38.5,36.0,25.8,24.0
+Hoka,Bondi 8,Daily Running,Neutral,11 oz / 311g 11 oz / 311g,0,6.2 mm 4.0 mm,Mid/Forefoot,True To Size,Soft,-,-,-,Moderate,Narrow,Narrow,Stiff,Stiff,Moderate,0,1,36.2 mm 39.0 mm,30.0 mm 35.0 mm,Normalwidex-Wide,1,All Seasons,1,#458 Bottom 28%,#11 Top 2%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,1,0,0,1,0,1,0,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,11.0,311,11.0,311.0,6.2,4.0,36.2,39.0,30.0,35.0
+Hoka,Bondi 9,Daily Running,Neutral,10.7 oz / 303g 10.5 oz / 297g,0,9.1 mm 5.0 mm,Heelmid/Forefoot,True To Size,Balanced,Good,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Moderate,0,1,41.3 mm 43.0 mm,32.2 mm 38.0 mm,Normalwidex-Wide,1,All Seasons,1,#43 Top 12%,#1 Top 1%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,10.7,303,10.5,297.0,9.1,5.0,41.3,43.0,32.2,38.0
+Diadora,Cellula,Daily Running,Neutral,9.8 oz / 278g 9.9 oz / 280g,0,7.2 mm 5.0 mm,Mid/Forefoot,Slightly Small,Soft,Decent,Good,Good,Moderate,Narrow,Medium,Stiff,Stiff,Stiff,0,0,41.9 mm 38.0 mm,34.7 mm 33.0 mm,Normal,1,All Seasons,1,#144 Top 40%,#290 Bottom 20%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,small,0,1,0,0,1,0,0,0,1,0,0,0,1,0,0,1,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,0,0,1,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,9.8,278,9.9,280.0,7.2,5.0,41.9,38.0,34.7,33.0
+Under Armour,Charged Assert 10,Daily Running,Neutral,10.5 oz / 298g 9.9 oz / 280g,0,9.4 mm 10.0 mm,Heelmid/Forefoot,Slightly Small,Firm,Bad,Bad,-,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,32.0 mm,22.6 mm,Normalwidex-Wide,1,All Seasons,1,#287 Bottom 21%,#99 Top 27%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,small,0,1,0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,10.5,298,9.9,280.0,9.4,10.0,32.0,,22.6,
+Under Armour,Charged Assert 9,Daily Running,Neutral,10.2 oz / 290g 9.9 oz / 281g,0,10.9 mm 10.0 mm,Heel,True To Size,Balanced,Bad,Bad,-,Moderate,Medium,Medium,Stiff,Flexible,Flexible,0,0,34.0 mm,23.1 mm,Narrownormalwidex-Wide,1,All Seasons,1,#456 Bottom 29%,#264 Top 41%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,1,0,1,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,1,1,0,0,1,0,0,Narrow|Normal|Wide|X-Wide,"['Narrow', 'Normal', 'Wide', 'X-Wide']",1,1,1,1,All,['All'],1,0,0,10.2,290,9.9,281.0,10.9,10.0,34.0,,23.1,
+Under Armour,Charged Pursuit 3,Daily Running,Neutral,9.3 oz / 264g 10 oz / 283g,0,9.6 mm 8.0 mm,Heelmid/Forefoot,Slightly Small,Balanced,Decent,Bad,Decent,Moderate,Medium,Wide,Moderate,Moderate,Moderate,0,0,30.8 mm 30.0 mm,21.2 mm 22.0 mm,Normalx-Wide,1,All Seasons,1,#321 Bottom 12%,#265 Bottom 27%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,small,0,1,0,0,0,1,0,0,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,Normal|X-Wide,"['Normal', 'X-Wide']",0,1,0,1,All,['All'],1,0,0,9.3,264,10.0,283.0,9.6,8.0,30.8,30.0,21.2,22.0
+Hoka,Cielo X1 2.0,Competitiontempo,Neutral,7.3 oz / 208g 7.4 oz / 210g,1,10.7 mm 7.0 mm,Heel,True To Size,Soft,Bad,Bad,Decent,Breathable,Narrow,Medium,Stiff,Stiff,Flexible,Carbon plate,1,38.8 mm 46.0 mm,28.1 mm 39.0 mm,Normal,1,Summerall Seasons,1,#82 Top 23%,#366 Bottom 1%,,,Competition|Tempo,"['Competition', 'Tempo']",1,0,1,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,0,0,1,0,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,7.3,208,7.4,210.0,10.7,7.0,38.8,46.0,28.1,39.0
+Hoka,Clifton 10,Daily Running,Neutral,9.7 oz / 275g 9.8 oz / 277g,0,12.4 mm 8.0 mm,Heel,True To Size,Soft,Good,Good,Decent,Moderate,Medium,Medium,Moderate,Stiff,Moderate,0,1,44.4 mm 42.0 mm,32.0 mm 34.0 mm,Narrow Normal Wide X-Wide,1,All Seasons,1,#92 Top 26%,#4 Top 2%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,1,0,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,Narrow|Normal|Wide|X-Wide,"['Narrow', 'Normal', 'Wide', 'X-Wide']",1,1,1,1,All,['All'],1,0,0,9.7,275,9.8,277.0,12.4,8.0,44.4,42.0,32.0,34.0
+Hoka,Clifton 8,Daily Running,Neutral,9 oz / 256g 8.8 oz / 250g,0,8.6 mm 5.0 mm,Heelmid/Forefoot,True To Size,-,-,-,-,Warm,Narrow,-,Stiff,Stiff,Stiff,0,1,33.7 mm 29.0 mm,25.1 mm 24.0 mm,Normalwide,1,All Seasons,1,#175 Top 28%,#111 Top 18%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,0,1,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,9.0,256,8.8,250.0,8.6,5.0,33.7,29.0,25.1,24.0
+Hoka,Clifton 9,Daily Running,Neutral,8.8 oz / 249g 8.8 oz / 249g,1,6.1 mm 5.0 mm,Mid/Forefoot,True To Size,Balanced,-,-,-,Moderate,Narrow,Narrow,Stiff,Stiff,Stiff,0,1,32.7 mm 32.0 mm,26.6 mm 27.0 mm,Normalwide,1,All Seasons,1,#368 Bottom 42%,#3 Top 1%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,1,0,0,1,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,8.8,249,8.8,249.0,6.1,5.0,32.7,32.0,26.6,27.0
+Hoka,Clifton 9 GTX,Daily Running,Neutral,9.6 oz / 271g 9.6 oz / 272g,0,8.6 mm 5.0 mm,Heelmid/Forefoot,True To Size,Balanced,Good,Good,Good,Moderate,Narrow,Narrow,Stiff,Stiff,Stiff,0,1,37.2 mm 40.0 mm,28.6 mm 35.0 mm,Normal,1,All Seasons,1,#151 Top 42%,#69 Top 19%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,0,1,0,1,0,0,1,0,0,0,0,1,0,0,1,0,0,1,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,9.6,271,9.6,272.0,8.6,5.0,37.2,40.0,28.6,35.0
+On,Cloud X,Daily Runningtempo,Neutral,8.5 oz / 240g 8.1 oz / 229g,1,10.1 mm 6.0 mm,Heel,True To Size,Firm,Decent,Bad,Good,Moderate,Medium,Wide,Moderate,Flexible,Flexible,0,0,27.9 mm 28.0 mm,17.8 mm 22.0 mm,Normal,1,All Seasons,1,#79 Top 22%,#101 Top 28%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,0,1,0,1,0,1,0,0,0,0,1,0,1,0,0,1,0,0,0,1,0,1,0,1,0,0,1,0,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,8.5,240,8.1,229.0,10.1,6.0,27.9,28.0,17.8,22.0
+On,Cloudboom Echo 3,Competition,Neutral,7.9 oz / 225g 7.5 oz / 212g,1,10.2 mm 9.0 mm,Heel,True To Size,Balanced,Decent,Decent,Decent,Breathable,Narrow,Narrow,Stiff,Stiff,Flexible,Carbon plate,1,38.6 mm 37.0 mm,28.4 mm 28.0 mm,Normal,1,Summerall Seasons,1,#66 Top 19%,#223 Bottom 39%,,,Competition,['Competition'],1,0,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,7.9,225,7.5,212.0,10.2,9.0,38.6,37.0,28.4,28.0
+On,Cloudeclipse,Daily Running,Neutral,9.6 oz / 272g 9.7 oz / 275g,0,9.4 mm 6.0 mm,Heelmid/Forefoot,True To Size,Balanced,Decent,Bad,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,1,39.9 mm 37.0 mm,30.5 mm 31.0 mm,Normal,1,All Seasons,1,#34 Top 10%,#233 Bottom 36%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,1,0,1,0,0,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,9.6,272,9.7,275.0,9.4,6.0,39.9,37.0,30.5,31.0
+On,Cloudflow 4,Daily Runningtempo,Neutral,8.6 oz / 245g 9.2 oz / 260g,1,7.9 mm 8.0 mm,Mid/Forefoot,True To Size,Balanced,Decent,Decent,Good,Moderate,Medium,Medium,Moderate,Stiff,Flexible,0,0,36.1 mm 31.0 mm,28.2 mm 23.0 mm,Normal,1,All Seasons,1,#60 Top 17%,#225 Bottom 38%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,8.6,245,9.2,260.0,7.9,8.0,36.1,31.0,28.2,23.0
+On,Cloudflyer 5,Daily Running,Stability,11.6 oz / 329g 11.3 oz / 320g,0,7.9 mm 10.0 mm,Heelmid/Forefoot,True To Size,Balanced,Good,Good,Good,Moderate,Narrow,Medium,Moderate,Stiff,Moderate,0,0,33.2 mm 31.0 mm,25.3 mm 21.0 mm,Normal,1,All Seasons,1,#257 Bottom 29%,#171 Top 47%,,,Daily Running,['Daily Running'],0,1,0,0,1,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,0,1,0,1,0,0,0,1,0,0,1,0,0,0,1,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,11.6,329,11.3,320.0,7.9,10.0,33.2,31.0,25.3,21.0
+On,Cloudgo,Daily Runningtempo,Neutral,9.1 oz / 259g 7.5 oz / 214g,0,11.2 mm 11.0 mm,Heel,Slightly Small,Balanced,Bad,Bad,Good,Moderate,Medium,Medium,Moderate,Moderate,Flexible,0,0,33.8 mm 30.0 mm,22.6 mm 19.0 mm,Normalwide,1,All Seasons,1,#112 Top 31%,#235 Bottom 35%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Heel,['Heel'],0,0,1,0,small,0,1,0,0,0,1,0,1,0,0,1,0,0,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,9.1,259,7.5,214.0,11.2,11.0,33.8,30.0,22.6,19.0
+On,Cloudmonster,Daily Running,Neutral,9.9 oz / 280g 9.7 oz / 274g,0,6.8 mm 6.0 mm,Mid/Forefoot,Slightly Small,Balanced,-,-,-,Breathable,Narrow,-,Moderate,Moderate,Flexible,0,1,34.9 mm 30.0 mm,28.1 mm 24.0 mm,Normal,1,Summerall Seasons,1,#39 Top 7%,#55 Top 9%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,small,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,1,0,1,0,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,9.9,280,9.7,274.0,6.8,6.0,34.9,30.0,28.1,24.0
+On,Cloudmonster 2,Daily Running,Neutral,10.3 oz / 292g 10.4 oz / 295g,0,6.6 mm 6.0 mm,Mid/Forefoot,Slightly Large,Balanced,Good,Decent,Good,Warm,Wide,Medium,Stiff,Moderate,Moderate,0,1,37.9 mm 35.0 mm,31.3 mm 29.0 mm,Normal,1,All Seasons,1,#96 Top 27%,#41 Top 12%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,large,1,0,0,0,0,1,0,0,0,1,0,1,0,0,0,1,0,0,1,0,0,1,0,1,0,0,0,1,0,1,0,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,10.3,292,10.4,295.0,6.6,6.0,37.9,35.0,31.3,29.0
+On,Cloudmonster Hyper,Daily Runningtempo,Neutral,9.1 oz / 258g 9 oz / 255g,0,6.7 mm 6.0 mm,Mid/Forefoot,True To Size,Soft,Decent,Bad,Good,Moderate,Medium,Medium,Moderate,Stiff,Moderate,0,0,39.7 mm 37.0 mm,33.0 mm 31.0 mm,Normal,1,All Seasons,1,#89 Top 25%,#168 Top 47%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,1,0,0,0,1,0,1,0,0,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,9.1,258,9.0,255.0,6.7,6.0,39.7,37.0,33.0,31.0
+On,Cloudrunner 2,Daily Running,Neutral,9.7 oz / 275g 9.8 oz / 277g,0,8.5 mm 10.0 mm,Heelmid/Forefoot,True To Size,Balanced,Decent,Decent,Decent,Moderate,Medium,Medium,Stiff,Moderate,Stiff,0,0,33.6 mm 39.0 mm,25.1 mm 29.0 mm,Normalwide,1,All Seasons,1,#131 Top 36%,#77 Top 21%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,9.7,275,9.8,277.0,8.5,10.0,33.6,39.0,25.1,29.0
+On,Cloudrunner 2 Waterproof,Daily Running,Stability,11.4 oz / 323g 11.3 oz / 320g,0,8.3 mm 10.0 mm,Heelmid/Forefoot,True To Size,Balanced,Decent,Bad,Good,Warm,Narrow,Medium,Stiff,Stiff,Stiff,0,0,35.8 mm 39.0 mm,27.5 mm 29.0 mm,Normal,1,Winter,1,#362 Bottom 1%,#225 Bottom 38%,,,Daily Running,['Daily Running'],0,1,0,0,1,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,1,0,1,0,0,0,0,1,0,0,1,1,0,0,0,1,0,0,0,1,0,0,1,0,0,1,Normal,['Normal'],0,1,0,0,Winter,['Winter'],0,0,1,11.4,323,11.3,320.0,8.3,10.0,35.8,39.0,27.5,29.0
+On,Cloudspark,Daily Running,Neutral,9.5 oz / 269g 9.9 oz / 282g,0,8.6 mm 8.0 mm,Heelmid/Forefoot,Slightly Small,Firm,Decent,Bad,Decent,Moderate,Medium,Medium,Stiff,Stiff,Flexible,0,0,34.6 mm 34.0 mm,26.0 mm 26.0 mm,Normal,1,All Seasons,1,#170 Top 47%,#332 Bottom 9%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,small,0,1,0,0,0,0,1,0,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,9.5,269,9.9,282.0,8.6,8.0,34.6,34.0,26.0,26.0
+On,Cloudstratus 3,Daily Running,Neutral,10.4 oz / 296g 10.3 oz / 292g,0,9.1 mm 6.0 mm,Heelmid/Forefoot,True To Size,Balanced,Decent,Good,Good,Moderate,Wide,Medium,Stiff,Stiff,Flexible,0,0,35.3 mm 37.0 mm,26.2 mm 31.0 mm,Normal,1,All Seasons,1,#65 Top 18%,#212 Bottom 42%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,0,1,0,1,0,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,10.4,296,10.3,292.0,9.1,6.0,35.3,37.0,26.2,31.0
+On,Cloudsurfer 7,Daily Running,Neutral,8.4 oz / 237g 8.6 oz / 245g,1,14.6 mm 10.0 mm,Heel,Slightly Small,Balanced,Bad,Bad,Good,Moderate,Medium,Medium,Flexible,Flexible,Flexible,0,0,36.3 mm 32.0 mm,21.7 mm 22.0 mm,Normal,1,All Seasons,1,#176 Top 49%,#305 Bottom 16%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,small,0,1,0,0,0,1,0,1,0,0,1,0,0,0,0,1,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,8.4,237,8.6,245.0,14.6,10.0,36.3,32.0,21.7,22.0
+On,Cloudsurfer Max,Daily Running,Neutral,10.3 oz / 292g 10.3 oz / 292g,0,7.9 mm 6.0 mm,Mid/Forefoot,-,Balanced,Decent,Decent,Decent,Warm,Medium,Medium,Stiff,Moderate,Moderate,0,0,37.3 mm 37.0 mm,29.4 mm 31.0 mm,Normalwide,1,All Seasons,1,#40 Top 11%,#101 Top 28%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,,0,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,10.3,292,10.3,292.0,7.9,6.0,37.3,37.0,29.4,31.0
+On,Cloudsurfer Next,Daily Running,Neutral,9.3 oz / 264g 9.4 oz / 266g,0,4.5 mm 6.0 mm,Mid/Forefoot,True To Size,Firm,Decent,Bad,Decent,Warm,Medium,Medium,Stiff,Stiff,Moderate,0,0,33.8 mm 37.0 mm,29.3 mm 31.0 mm,Normalwide,1,All Seasons,1,#75 Top 21%,#146 Top 40%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,0,1,0,1,0,1,0,0,0,1,0,0,0,1,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,9.3,264,9.4,266.0,4.5,6.0,33.8,37.0,29.3,31.0
+Topo,Cyclone 2,Daily Runningtempo,Neutral,6.7 oz / 190g 6.9 oz / 196g,1,4.2 mm 5.0 mm,Mid/Forefoot,True To Size,Balanced,Bad,Good,Good,Breathable,Wide,Wide,Flexible,Flexible,Flexible,0,1,26.2 mm 28.0 mm,22.0 mm 23.0 mm,Normal,1,Summerall Seasons,1,#89 Top 25%,#267 Bottom 27%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,1,0,1,0,0,0,0,1,0,0,1,1,0,0,0,0,1,0,0,1,1,0,0,1,0,0,1,0,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,6.7,190,6.9,196.0,4.2,5.0,26.2,28.0,22.0,23.0
+Puma,Deviate Nitro 3,Tempo,Neutral,9.5 oz / 268g 9.5 oz / 269g,0,10.1 mm 10.0 mm,Heel,Slightly Small,Soft,Decent,Decent,Decent,Moderate,Narrow,Narrow,Stiff,Stiff,Flexible,Carbon plate,0,37.4 mm 39.0 mm,27.3 mm 29.0 mm,Normalwide,1,All Seasons,1,#113 Top 31%,#60 Top 17%,,Road,Tempo,['Tempo'],0,0,1,1,0,Heel,['Heel'],0,0,1,0,small,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,0,0,1,0,0,1,1,0,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,9.5,268,9.5,269.0,10.1,10.0,37.4,39.0,27.3,29.0
+Puma,Deviate Nitro Elite 3,Competitiontempo,Neutral,7.2 oz / 204g 7.4 oz / 209g,1,10.6 mm 8.0 mm,Heelmid/Forefoot,True To Size,Soft,Bad,Good,Decent,Moderate,Medium,Narrow,Stiff,Stiff,Flexible,Carbon plate,1,39.2 mm 40.0 mm,28.6 mm 32.0 mm,Normal,1,All Seasons,1,#37 Top 11%,#50 Top 14%,,Road,Competition|Tempo,"['Competition', 'Tempo']",1,0,1,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,1,0,0,1,0,0,0,0,1,0,1,0,0,1,0,0,1,0,1,0,0,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,7.2,204,7.4,209.0,10.6,8.0,39.2,40.0,28.6,32.0
+Nike,Downshifter 11,Daily Running,Neutral,8.8 oz / 249g 10.2 oz / 288g,1,11.6 mm 10.0 mm,Heel,True To Size,-,-,-,-,-,Narrow,-,Stiff,Moderate,Moderate,0,0,31.5 mm,19.9 mm,Normal,0,-,0,#488 Bottom 24%,#549 Bottom 14%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,1,0,Normal,['Normal'],0,1,0,0,,,0,0,0,8.8,249,10.2,288.0,11.6,10.0,31.5,,19.9,
+Nike,Downshifter 12,Daily Running,Neutral,9.9 oz / 280g 9.9 oz / 280g,0,10.0 mm 10.0 mm,Heelmid/Forefoot,True To Size,Balanced,-,-,-,Moderate,Medium,-,Moderate,Flexible,Flexible,0,0,31.7 mm 32.0 mm,21.7 mm 22.0 mm,Narrownormalwidex-Wide,1,All Seasons,1,#542 Bottom 15%,#243 Top 38%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,1,0,0,1,0,0,Narrow|Normal|Wide|X-Wide,"['Narrow', 'Normal', 'Wide', 'X-Wide']",1,1,1,1,All,['All'],1,0,0,9.9,280,9.9,280.0,10.0,10.0,31.7,32.0,21.7,22.0
+Nike,Downshifter 13,Daily Running,Neutral,9.3 oz / 265g 9.4 oz / 267g,0,10.1 mm 10.0 mm,Heel,Slightly Small,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,32.2 mm 32.0 mm,22.1 mm 22.0 mm,Narrownormalwidex-Wide,1,All Seasons,1,#336 Bottom 8%,#96 Top 27%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,small,0,1,0,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,Narrow|Normal|Wide|X-Wide,"['Narrow', 'Normal', 'Wide', 'X-Wide']",1,1,1,1,All,['All'],1,0,0,9.3,265,9.4,267.0,10.1,10.0,32.2,32.0,22.1,22.0
+Adidas,Duramo 10,Daily Running,Neutral,10.3 oz / 292g 9.7 oz / 275g,0,8.7 mm 9.0 mm,Heelmid/Forefoot,True To Size,Balanced,-,-,-,-,Narrow,-,Stiff,Stiff,Flexible,0,0,31.6 mm 32.0 mm,22.9 mm 23.0 mm,Narrownormalwide,1,-,1,#246 Bottom 32%,#310 Bottom 15%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,1,0,0,Narrow|Normal|Wide,"['Narrow', 'Normal', 'Wide']",1,1,1,0,,,0,0,0,10.3,292,9.7,275.0,8.7,9.0,31.6,32.0,22.9,23.0
+Adidas,Duramo Speed,Daily Running,Neutral,9.2 oz / 261g 9.3 oz / 263g,0,6.0 mm 6.0 mm,Mid/Forefoot,True To Size,Balanced,Bad,Decent,Good,Breathable,Medium,Medium,Stiff,Stiff,Moderate,0,0,32.7 mm 29.0 mm,26.7 mm 23.0 mm,Normal,1,Summerall Seasons,1,#125 Top 35%,#267 Bottom 26%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,1,0,1,0,0,0,1,0,0,0,1,1,0,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,9.2,261,9.3,263.0,6.0,6.0,32.7,29.0,26.7,23.0
+Asics,Dynablast 3,Daily Running,Neutral,8.7 oz / 248g 9.1 oz / 258g,1,9.7 mm 8.0 mm,Heelmid/Forefoot,True To Size,Balanced,Good,Decent,Good,Warm,Medium,Wide,Stiff,Moderate,Stiff,0,0,35.7 mm 31.5 mm,26.0 mm 23.5 mm,Normal,1,All Seasons,1,#503 Bottom 22%,#585 Bottom 9%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,0,1,0,1,0,0,0,1,0,0,1,0,1,0,0,0,1,0,0,1,0,1,0,0,0,1,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,8.7,248,9.1,258.0,9.7,8.0,35.7,31.5,26.0,23.5
+Asics,Dynablast 4,Daily Running,Neutral,9.2 oz / 262g 9.3 oz / 264g,0,6.4 mm 8.0 mm,Mid/Forefoot,True To Size,Balanced,Good,Good,Good,Warm,Narrow,Medium,Moderate,Moderate,Stiff,0,0,32.6 mm 34.0 mm,26.2 mm 26.0 mm,Normal,1,Winter,1,#21 Top 4%,#539 Bottom 16%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,1,0,0,0,1,0,0,1,0,0,1,0,0,0,1,Normal,['Normal'],0,1,0,0,Winter,['Winter'],0,0,1,9.2,262,9.3,264.0,6.4,8.0,32.6,34.0,26.2,26.0
+Asics,Dynablast 5,Daily Running,Neutral,9.3 oz / 264g 9.2 oz / 260g,0,7.6 mm 8.0 mm,Mid/Forefoot,True To Size,Balanced,Good,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,39.4 mm 39.0 mm,31.8 mm 31.0 mm,Normal,1,All Seasons,1,#237 Bottom 35%,#195 Bottom 46%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,9.3,264,9.2,260.0,7.6,8.0,39.4,39.0,31.8,31.0
+Saucony,Endorphin Elite,Competition,Neutral,7.2 oz / 203g 7.2 oz / 204g,1,8.0 mm 8.0 mm,Heelmid/Forefoot,True To Size,Soft,Bad,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,39.9 mm 40.0 mm,31.9 mm 32.0 mm,Normal,1,Summerall Seasons,1,#81 Top 13%,#180 Top 28%,,,Competition,['Competition'],1,0,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,1,0,0,1,0,0,0,0,1,0,0,1,1,0,0,0,1,0,0,1,0,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,7.2,203,7.2,204.0,8.0,8.0,39.9,40.0,31.9,32.0
+Saucony,Endorphin Elite 2,Competition,Neutral,6.9 oz / 197g 7 oz / 199g,1,7.5 mm 8.0 mm,Mid/Forefoot,Half Size Small,Soft,Good,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,39.9 mm 39.5 mm,32.4 mm 31.5 mm,Normal,1,Summerall Seasons,1,#186 Bottom 49%,#85 Top 24%,,,Competition,['Competition'],1,0,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,small,0,1,0,0,1,0,0,0,0,1,0,0,1,0,0,1,1,0,0,0,1,0,0,1,0,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,6.9,197,7.0,199.0,7.5,8.0,39.9,39.5,32.4,31.5
+Saucony,Endorphin Pro 2,Competition,Neutral,7.6 oz / 215g 7.9 oz / 223g,1,10.0 mm 8.0 mm,Heelmid/Forefoot,Slightly Small,-,-,-,-,Breathable,Narrow,-,Stiff,Stiff,Flexible,Carbon plate,1,35.8 mm 35.5 mm,25.8 mm 27.5 mm,Normal,1,Summerall Seasons,1,#59 Top 10%,#334 Bottom 48%,,,Competition,['Competition'],1,0,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,small,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,7.6,215,7.9,223.0,10.0,8.0,35.8,35.5,25.8,27.5
+Saucony,Endorphin Pro 3,Competition,Neutral,7.3 oz / 206g 7.2 oz / 204g,1,9.3 mm 8.0 mm,Heelmid/Forefoot,Slightly Small,Soft,-,-,-,-,Narrow,-,Stiff,Stiff,Flexible,Carbon plate,1,35.0 mm 39.5 mm,25.7 mm 31.5 mm,Normal,0,-,0,#159 Top 25%,#147 Top 23%,,,Competition,['Competition'],1,0,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,small,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,,,0,0,0,7.3,206,7.2,204.0,9.3,8.0,35.0,39.5,25.7,31.5
+Saucony,Endorphin Pro 4,Competition,Neutral,7.8 oz / 220g 7.7 oz / 218g,1,9.5 mm 8.0 mm,Heelmid/Forefoot,Slightly Small,Balanced,Good,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,38.1 mm 40.0 mm,28.6 mm 32.0 mm,Normal,1,Summerall Seasons,1,#140 Top 39%,#36 Top 10%,,Road,Competition,['Competition'],1,0,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,small,0,1,0,0,0,1,0,0,0,1,0,0,1,0,0,1,1,0,0,0,1,0,0,1,0,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,7.8,220,7.7,218.0,9.5,8.0,38.1,40.0,28.6,32.0
+Asics,Magic Speed 4,Competitiontempo,Neutral,8.4 oz / 237g 8.5 oz / 242g,1,9.6 mm 8.0 mm,Heelmid/Forefoot,True To Size,Soft,Good,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,42.5 mm 43.5 mm,32.9 mm 35.5 mm,Normalwide,1,Summerall Seasons,1,#6 Top 2%,#82 Top 23%,,,Competition|Tempo,"['Competition', 'Tempo']",1,0,1,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,1,1,0,0,0,1,0,0,1,0,0,0,1,0,0,1,1,0,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,Summer|All,"['Summer', 'All']",1,1,0,8.4,237,8.5,242.0,9.6,8.0,42.5,43.5,32.9,35.5
+Asics,Jolt 4,Daily Running,Neutral,9.1 oz / 259g 9.5 oz / 268g,0,9.4 mm 8.0 mm,Heelmid/Forefoot,True To Size,Balanced,Bad,Decent,Good,Breathable,Medium,Wide,Moderate,Stiff,Stiff,0,0,31.6 mm,22.2 mm,Normalx-Wide,1,Summerall Seasons,1,#314 Bottom 14%,#325 Bottom 11%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,1,0,0,0,1,0,0,0,1,1,0,0,0,1,0,0,0,1,0,1,0,0,0,1,0,0,1,Normal|X-Wide,"['Normal', 'X-Wide']",0,1,0,1,Summer|All,"['Summer', 'All']",1,1,0,9.1,259,9.5,268.0,9.4,8.0,31.6,,22.2,
+Saucony,Endorphin Shift 2,Daily Running,Neutral,10 oz / 284g 10.4 oz / 296g,0,4.3 mm 4.0 mm,Mid/Forefoot,Half Size Small,-,-,-,-,-,Medium,-,Stiff,Stiff,Moderate,0,1,37.3 mm 39.0 mm,33.0 mm 35.0 mm,Normal,1,-,1,#268 Top 42%,#532 Bottom 17%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,small,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,1,0,Normal,['Normal'],0,1,0,0,,,0,0,0,10.0,284,10.4,296.0,4.3,4.0,37.3,39.0,33.0,35.0
+Saucony,Endorphin Shift 3,Daily Running,Neutral,9.6 oz / 272g 9.4 oz / 266g,0,6.5 mm 4.0 mm,Mid/Forefoot,True To Size,Balanced,-,-,-,Moderate,Narrow,-,Stiff,Moderate,Moderate,0,1,39.6 mm 39.0 mm,33.1 mm 35.0 mm,Normalwide,1,All Seasons,1,#108 Top 30%,#207 Bottom 43%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,1,0,0,1,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,9.6,272,9.4,266.0,6.5,4.0,39.6,39.0,33.1,35.0
+Saucony,Endorphin Speed 2,Tempo,Neutral,8.1 oz / 229g 8 oz / 227g,1,9.2 mm 8.0 mm,Heelmid/Forefoot,Slightly Small,-,-,-,-,Moderate,Medium,-,Stiff,Moderate,Moderate,0,1,35.3 mm 35.5 mm,26.1 mm 27.5 mm,Normal,1,All Seasons,1,#74 Top 12%,#359 Bottom 44%,,,Tempo,['Tempo'],0,0,1,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,small,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,1,0,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,8.1,229,8.0,227.0,9.2,8.0,35.3,35.5,26.1,27.5
+Saucony,Endorphin Speed 3,Tempo,Neutral,7.9 oz / 225g 8.1 oz / 229g,1,7.4 mm 8.0 mm,Mid/Forefoot,Slightly Small,Soft,-,-,-,Moderate,Medium,-,Moderate,Flexible,Flexible,0,1,34.1 mm 36.0 mm,26.7 mm 28.0 mm,Normal,1,All Seasons,1,#145 Top 23%,#146 Top 23%,,,Tempo,['Tempo'],0,0,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,small,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,1,0,0,1,0,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,7.9,225,8.1,229.0,7.4,8.0,34.1,36.0,26.7,28.0
+Saucony,Endorphin Speed 4,Tempo,Neutral,8.4 oz / 237g 8.3 oz / 235g,1,8.7 mm 8.0 mm,Heelmid/Forefoot,Slightly Small,Balanced,Decent,Good,Good,Breathable,Medium,Narrow,Moderate,Moderate,Moderate,0,1,36.2 mm 38.0 mm,27.5 mm 30.0 mm,Normal,1,Summerall Seasons,1,#530 Bottom 17%,#38 Top 6%,,,Tempo,['Tempo'],0,0,1,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,small,0,1,0,0,0,1,0,0,1,0,0,0,1,0,0,1,1,0,0,0,1,0,1,0,0,0,1,0,0,1,0,0,1,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,8.4,237,8.3,235.0,8.7,8.0,36.2,38.0,27.5,30.0
+Saucony,Endorphin Speed 5,Tempo,Neutral,8.5 oz / 241g 8.4 oz / 238g,1,10.6 mm 8.0 mm,Heel,True To Size,Soft,Decent,Good,Good,Breathable,Medium,Medium,Moderate,Stiff,Stiff,0,1,37.4 mm 36.0 mm,26.8 mm 28.0 mm,Normalwide,1,Summerall Seasons,1,#272 Bottom 25%,#37 Top 11%,,,Tempo,['Tempo'],0,0,1,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,1,0,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,Summer|All,"['Summer', 'All']",1,1,0,8.5,241,8.4,238.0,10.6,8.0,37.4,36.0,26.8,28.0
+Saucony,Endorphin Trainer,Daily Runningtempo,Neutral,10.1 oz / 285g 10.1 oz / 286g,0,6.9 mm 8.0 mm,Mid/Forefoot,True To Size,Soft,Decent,Decent,Good,Breathable,Narrow,Narrow,Stiff,Stiff,Flexible,Carbon plate,1,38.9 mm 42.0 mm,32.0 mm 34.0 mm,Normal,1,Summerall Seasons,1,#331 Bottom 9%,#175 Top 48%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,1,0,0,0,1,0,0,1,0,0,0,1,1,0,0,1,0,0,1,0,0,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,10.1,285,10.1,286.0,6.9,8.0,38.9,42.0,32.0,34.0
+Altra,Escalante 3,Daily Runningtempo,Neutral,9.5 oz / 269g 7.7 oz / 219g,0,0.2 mm 0.0 mm,Mid/Forefoot,True To Size,Balanced,-,-,-,Moderate,Medium,-,Stiff,Flexible,Flexible,0,0,25.0 mm 26.0 mm,24.8 mm 26.0 mm,Normal,1,All Seasons,1,#459 Bottom 28%,#356 Bottom 44%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,1,0,0,1,0,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,9.5,269,7.7,219.0,0.2,0.0,25.0,26.0,24.8,26.0
+Altra,Escalante 4,Daily Running,Neutral,8.4 oz / 237g 9.5 oz / 269g,1,1.4 mm 0.0 mm,Mid/Forefoot,True To Size,Balanced,Good,Good,Good,Moderate,Wide,Wide,Flexible,Flexible,Flexible,0,0,23.8 mm 24.0 mm,22.4 mm 24.0 mm,Normal,1,All Seasons,1,#212 Bottom 42%,#95 Top 26%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,0,1,0,0,0,1,0,0,1,1,0,0,1,0,0,1,0,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,8.4,237,9.5,269.0,1.4,0.0,23.8,24.0,22.4,24.0
+Altra,Escalante Racer,Competitiontempo,Neutral,7.3 oz / 208g 6.8 oz / 193g,1,0.5 mm 0.0 mm,Mid/Forefoot,True To Size,Firm,Good,Good,Decent,Breathable,Medium,Wide,Moderate,Flexible,Flexible,0,0,19.0 mm 22.0 mm,18.5 mm 22.0 mm,Normal,1,Summerall Seasons,1,#52 Top 9%,#384 Bottom 40%,,,Competition|Tempo,"['Competition', 'Tempo']",1,0,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,0,1,0,0,1,0,0,1,0,1,0,1,0,0,0,1,0,0,0,1,0,1,0,1,0,0,1,0,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,7.3,208,6.8,193.0,0.5,0.0,19.0,22.0,18.5,22.0
+Altra,Escalante Racer 2,Competitiontempo,Neutral,7.9 oz / 224g 7.9 oz / 224g,1,1.1 mm 0.0 mm,Mid/Forefoot,True To Size,Balanced,Good,Decent,Good,Breathable,Wide,Wide,Moderate,Flexible,Flexible,0,0,22.5 mm 24.0 mm,21.4 mm 24.0 mm,Normal,1,Summerall Seasons,1,#149 Top 41%,#246 Bottom 32%,,,Competition|Tempo,"['Competition', 'Tempo']",1,0,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,1,0,0,0,1,0,1,0,0,0,1,1,0,0,0,0,1,0,0,1,0,1,0,1,0,0,1,0,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,7.9,224,7.9,224.0,1.1,0.0,22.5,24.0,21.4,24.0
+Altra,Experience Flow,Daily Running,Neutral,8.3 oz / 235g 8.4 oz / 238g,1,4.1 mm 4.0 mm,Mid/Forefoot,True To Size,Soft,Good,Good,Decent,Moderate,Wide,Wide,Moderate,Moderate,Moderate,0,0,30.7 mm 32.0 mm,26.6 mm 28.0 mm,Normal,1,All Seasons,1,#255 Top 40%,#138 Top 22%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,1,0,0,0,0,1,0,0,1,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,8.3,235,8.4,238.0,4.1,4.0,30.7,32.0,26.6,28.0
+Altra,Experience Flow 2,Daily Running,Neutral,8.3 oz / 235g 8.1 oz / 231g,1,4.4 mm 4.0 mm,Mid/Forefoot,-,Soft,Decent,Good,Decent,Moderate,Wide,Wide,Moderate,Moderate,Moderate,0,0,30.3 mm 32.0 mm,25.9 mm 28.0 mm,Normal,1,All Seasons,1,#232 Bottom 36%,#87 Top 24%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,,0,0,0,1,1,0,0,0,1,0,0,0,1,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,8.3,235,8.1,231.0,4.4,4.0,30.3,32.0,25.9,28.0
+Altra,Experience Form,Daily Running,Stability,9.2 oz / 261g 9.6 oz / 272g,0,4.0 mm 4.0 mm,Mid/Forefoot,Slightly Small,Balanced,Decent,Decent,Decent,Moderate,Medium,Wide,Stiff,Moderate,Moderate,0,0,29.9 mm 30.0 mm,25.9 mm 26.0 mm,Normal,1,All Seasons,1,#26 Top 8%,#211 Bottom 42%,,,Daily Running,['Daily Running'],0,1,0,0,1,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,small,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,9.2,261,9.6,272.0,4.0,4.0,29.9,30.0,25.9,26.0
+Nike,Flex Experience Run 10,Daily Running,Neutral,7.1 oz / 201g 8 oz / 227g,1,10.4 mm,-,Slightly Small,-,-,-,-,-,Narrow,-,Flexible,-,-,0,0,24.1 mm,13.7 mm,Normal,0,-,0,#582 Bottom 9%,#523 Bottom 18%,,,Daily Running,['Daily Running'],0,1,0,1,0,-,['-'],1,0,0,0,small,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,Normal,['Normal'],0,1,0,0,,,0,0,0,7.1,201,8.0,227.0,10.4,,24.1,,13.7,
+Nike,Flex Experience Run 11,Daily Running,Neutral,8.2 oz / 232g 8.2 oz / 232g,1,6.2 mm,Mid/Forefoot,True To Size,Firm,Decent,-,-,Moderate,Medium,Narrow,Flexible,Flexible,Flexible,0,0,24.1 mm,17.9 mm,Normalwidex-Wide,1,All Seasons,1,#611 Bottom 5%,#265 Top 42%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,8.2,232,8.2,232.0,6.2,,24.1,,17.9,
+Nike,Flex Experience Run 12,Daily Running,Neutral,8.5 oz / 241g 8.5 oz / 240g,1,6.0 mm 6.0 mm,Mid/Forefoot,True To Size,Firm,Decent,Good,Bad,Moderate,Medium,Medium,Flexible,Flexible,Flexible,0,0,25.9 mm,19.9 mm,Normalwidex-Wide,1,All Seasons,1,#345 Bottom 5%,#61 Top 17%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,0,1,0,1,0,0,0,1,1,0,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,8.5,241,8.5,240.0,6.0,6.0,25.9,,19.9,
+Nike,Flex Run 2021,Daily Running,Neutral,7.9 oz / 223g,1,7.2 mm,Mid/Forefoot,True To Size,-,-,-,-,-,Medium,-,Flexible,-,-,0,0,32.3 mm,25.1 mm,Normal,0,-,0,#276 Bottom 24%,#337 Bottom 7%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,Normal,['Normal'],0,1,0,0,,,0,0,0,7.9,223,,,7.2,,32.3,,25.1,
+Reebok,Floatride Energy 3,Daily Running,Neutral,9 oz / 256g 8.5 oz / 241g,0,7.1 mm 9.0 mm,Mid/Forefoot,True To Size,-,-,-,-,-,Medium,-,Stiff,Moderate,Moderate,0,0,30.2 mm 26.0 mm,23.1 mm 17.0 mm,Normal,1,-,1,#38 Top 6%,#592 Bottom 8%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,1,0,Normal,['Normal'],0,1,0,0,,,0,0,0,9.0,256,8.5,241.0,7.1,9.0,30.2,26.0,23.1,17.0
+Reebok,Floatride Energy 5,Daily Running,Neutral,9 oz / 254g 9.4 oz / 266g,0,6.0 mm 8.0 mm,Mid/Forefoot,True To Size,Balanced,Bad,Decent,Good,Warm,Medium,Medium,Stiff,Flexible,Flexible,0,0,30.2 mm 27.0 mm,24.2 mm 19.0 mm,Normal,1,All Seasons,1,#158 Top 44%,#273 Bottom 25%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,0,1,1,0,0,1,0,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,9.0,254,9.4,266.0,6.0,8.0,30.2,27.0,24.2,19.0
+Reebok,FloatZig 1,Daily Running,Neutral,10.1 oz / 285g 9.8 oz / 277g,0,7.0 mm 6.0 mm,Heelmid/Forefoot,True To Size,Balanced,Decent,Decent,Bad,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,1,36.8 mm 31.0 mm,29.8 mm 25.0 mm,Normal,1,All Seasons,1,#79 Top 22%,#255 Bottom 30%,,Road,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,10.1,285,9.8,277.0,7.0,6.0,36.8,31.0,29.8,25.0
+Adidas,Fluidflow 2.0,Daily Running,Neutral,11.1 oz / 316g 11 oz / 312g,0,8.1 mm,Heelmid/Forefoot,Slightly Large,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Flexible,Flexible,0,0,26.4 mm,18.3 mm,Normal,1,All Seasons,1,#301 Bottom 17%,#198 Bottom 45%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,large,1,0,0,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,1,0,0,1,0,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,11.1,316,11.0,312.0,8.1,,26.4,,18.3,
+New Balance,Foam Arishi v4,Daily Running,Neutral,8.5 oz / 242g 8.7 oz / 246g,1,7.7 mm 8.0 mm,Mid/Forefoot,True To Size,Soft,Bad,Bad,Good,Moderate,Medium,Medium,Flexible,Flexible,Flexible,0,0,28.1 mm,20.4 mm,Normalwidex-Wide,1,All Seasons,1,#362 Bottom 1%,#12 Top 4%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,1,0,0,1,0,0,1,0,0,0,0,1,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,8.5,242,8.7,246.0,7.7,8.0,28.1,,20.4,
+Nike,Free RN NN,Daily Running,Neutral,6.9 oz / 197g,1,6.9 mm,Mid/Forefoot,True To Size,Balanced,Good,Good,Good,Warm,Wide,Wide,Flexible,Flexible,Flexible,0,0,25.6 mm,18.7 mm,Normal,1,All Seasons,1,#355 Bottom 3%,#278 Bottom 24%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,1,0,0,1,0,0,1,0,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,6.9,197,,,6.9,,25.6,,18.7,
+Nike,Free Run 5.0,Daily Running,Neutral,6.2 oz / 175g 8.2 oz / 232g,1,8.1 mm,Mid/Forefoot,True To Size,-,-,-,-,-,Wide,-,Flexible,Flexible,Flexible,0,0,23.4 mm,15.3 mm,Normal,0,-,0,#557 Bottom 13%,#626 Bottom 2%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,1,0,0,Normal,['Normal'],0,1,0,0,,,0,0,0,6.2,175,8.2,232.0,8.1,,23.4,,15.3,
+Saucony,Freedom 4,Daily Running,Neutral,8 oz / 227g 9.5 oz / 269g,1,5.2 mm 4.0 mm,Mid/Forefoot,-,-,-,-,-,-,Medium,-,-,Flexible,-,0,0,26.4 mm 28.0 mm,21.2 mm 24.0 mm,Normal,1,-,1,#193 Bottom 47%,#331 Bottom 9%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,Normal,['Normal'],0,1,0,0,,,0,0,0,8.0,227,9.5,269.0,5.2,4.0,26.4,28.0,21.2,24.0
+New Balance,Fresh Foam 1080 v11,Daily Running,Neutral,9.2 oz / 261g 10.1 oz / 285g,0,8.0 mm 8.0 mm,Heelmid/Forefoot,True To Size,-,-,-,-,-,Narrow,-,-,Moderate,-,0,1,34.2 mm 30.0 mm,26.2 mm 22.0 mm,Narrownormalwidex-Wide,1,-,1,#190 Top 30%,#88 Top 14%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Narrow|Normal|Wide|X-Wide,"['Narrow', 'Normal', 'Wide', 'X-Wide']",1,1,1,1,,,0,0,0,9.2,261,10.1,285.0,8.0,8.0,34.2,30.0,26.2,22.0
+New Balance,Fresh Foam 680 v8,Daily Running,Neutral,9.2 oz / 261g 9.5 oz / 268g,0,7.8 mm,Mid/Forefoot,Slightly Small,Soft,Good,Good,Good,Breathable,Medium,Medium,Flexible,Flexible,Moderate,0,0,35.4 mm,27.6 mm,Normalwidex-Wide,1,Summerall Seasons,1,#241 Bottom 34%,#32 Top 9%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,small,0,1,0,0,1,0,0,0,0,1,0,0,1,0,0,1,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,0,1,0,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,Summer|All,"['Summer', 'All']",1,1,0,9.2,261,9.5,268.0,7.8,,35.4,,27.6,
+New Balance,Fresh Foam 860 v11,Daily Running,Stability,10.6 oz / 300g 9.7 oz / 275g,0,12.4 mm 10.0 mm,Heel,Slightly Large,-,-,-,-,-,Medium,-,Stiff,-,-,0,0,34.2 mm,21.8 mm,Narrownormalwidex-Wide,0,-,0,#61 Top 10%,#67 Top 11%,,,Daily Running,['Daily Running'],0,1,0,0,1,Heel,['Heel'],0,0,1,0,large,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,Narrow|Normal|Wide|X-Wide,"['Narrow', 'Normal', 'Wide', 'X-Wide']",1,1,1,1,,,0,0,0,10.6,300,9.7,275.0,12.4,10.0,34.2,,21.8,
+New Balance,Fresh Foam 860 v12,Daily Running,Stability,11 oz / 311g 10.8 oz / 306g,0,13.3 mm,Heel,Slightly Small,-,-,-,-,-,Medium,-,Stiff,-,-,0,0,33.6 mm,20.3 mm,Narrownormalwidex-Wide,0,-,0,#330 Bottom 48%,#435 Bottom 32%,,,Daily Running,['Daily Running'],0,1,0,0,1,Heel,['Heel'],0,0,1,0,small,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,Narrow|Normal|Wide|X-Wide,"['Narrow', 'Normal', 'Wide', 'X-Wide']",1,1,1,1,,,0,0,0,11.0,311,10.8,306.0,13.3,,33.6,,20.3,
+New Balance,Fresh Foam Arishi v4,Daily Running,Neutral,8.5 oz / 242g 8.7 oz / 246g,1,7.7 mm 8.0 mm,Mid/Forefoot,True To Size,Soft,Bad,Bad,Good,Moderate,Medium,Medium,Flexible,Flexible,Flexible,0,0,28.1 mm,20.4 mm,Normalwidex-Wide,1,All Seasons,1,#361 Bottom 1%,#12 Top 4%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,1,0,0,1,0,0,1,0,0,0,0,1,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,8.5,242,8.7,246.0,7.7,8.0,28.1,,20.4,
+New Balance,Fresh Foam More v3,Daily Running,Neutral,10.4 oz / 296g 10.9 oz / 309g,0,7.8 mm 4.0 mm,Mid/Forefoot,True To Size,Soft,-,-,-,Warm,Medium,-,Stiff,Moderate,Moderate,0,0,37.5 mm 33.0 mm,29.7 mm 29.0 mm,Normal,1,Winter,1,#117 Top 19%,#261 Top 41%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,1,0,0,1,0,Normal,['Normal'],0,1,0,0,Winter,['Winter'],0,0,1,10.4,296,10.9,309.0,7.8,4.0,37.5,33.0,29.7,29.0
+New Balance,Fresh Foam Roav V2,Daily Running,Neutral,8.4 oz / 238g 9.1 oz / 258g,1,7.2 mm 8.0 mm,Mid/Forefoot,Slightly Small,Balanced,-,-,-,Warm,Narrow,-,Moderate,Flexible,Flexible,0,0,32.2 mm,25.0 mm,Normalwidex-Wide,1,Winter,1,#320 Bottom 12%,#113 Top 31%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,small,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,1,0,0,1,0,0,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,Winter,['Winter'],0,0,1,8.4,238,9.1,258.0,7.2,8.0,32.2,,25.0,
+New Balance,Fresh Foam X 1080 v12,Daily Running,Neutral,10.1 oz / 286g 10.3 oz / 292g,0,3.3 mm 8.0 mm,Mid/Forefoot,True To Size,Soft,-,-,-,-,Narrow,-,Moderate,Moderate,Flexible,0,0,26.9 mm 34.0 mm,23.6 mm 26.0 mm,Narrownormalwide,1,-,1,#283 Top 44%,#170 Top 27%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,1,0,0,Narrow|Normal|Wide,"['Narrow', 'Normal', 'Wide']",1,1,1,0,,,0,0,0,10.1,286,10.3,292.0,3.3,8.0,26.9,34.0,23.6,26.0
+New Balance,Fresh Foam X 1080 v13,Daily Running,Neutral,9.3 oz / 264g 9.2 oz / 261g,0,5.6 mm 6.0 mm,Mid/Forefoot,True To Size,Soft,Decent,Good,Good,Breathable,Medium,Medium,Moderate,Flexible,Stiff,0,1,34.1 mm 37.0 mm,28.5 mm 31.0 mm,Narrownormalwidex-Wide,1,Summerall Seasons,1,#206 Top 33%,#50 Top 8%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,1,0,0,0,1,0,0,1,0,0,1,0,1,0,0,0,0,1,Narrow|Normal|Wide|X-Wide,"['Narrow', 'Normal', 'Wide', 'X-Wide']",1,1,1,1,Summer|All,"['Summer', 'All']",1,1,0,9.3,264,9.2,261.0,5.6,6.0,34.1,37.0,28.5,31.0
+New Balance,Fresh Foam X 1080 v14,Daily Running,Neutral,10.1 oz / 285g 10.5 oz / 298g,0,4.2 mm 6.0 mm,Heelmid/Forefoot,True To Size,Soft,Good,Decent,Good,Moderate,Medium,Narrow,Moderate,Stiff,Stiff,0,1,37.0 mm 38.0 mm,32.8 mm 32.0 mm,Narrownormalwidex-Wide,1,All Seasons,1,#172 Top 48%,#3 Top 1%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,1,0,0,0,0,1,0,1,0,0,0,1,0,1,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,Narrow|Normal|Wide|X-Wide,"['Narrow', 'Normal', 'Wide', 'X-Wide']",1,1,1,1,All,['All'],1,0,0,10.1,285,10.5,298.0,4.2,6.0,37.0,38.0,32.8,32.0
+New Balance,Fresh Foam X 860 v14,Daily Running,Stability,10.4 oz / 295g 10.5 oz / 298g,0,9.3 mm 8.0 mm,Heelmid/Forefoot,Slightly Small,Soft,Decent,Bad,Decent,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,36.8 mm 38.0 mm,27.5 mm 30.0 mm,Narrownormalwidex-Wide,1,All Seasons,1,#217 Bottom 40%,#35 Top 10%,,,Daily Running,['Daily Running'],0,1,0,0,1,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,small,0,1,0,0,1,0,0,0,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,Narrow|Normal|Wide|X-Wide,"['Narrow', 'Normal', 'Wide', 'X-Wide']",1,1,1,1,All,['All'],1,0,0,10.4,295,10.5,298.0,9.3,8.0,36.8,38.0,27.5,30.0
+New Balance,Fresh Foam X 880 v14,Daily Running,Neutral,8.9 oz / 251g 8.7 oz / 247g,0,8.0 mm 8.0 mm,Heelmid/Forefoot,Slightly Small,Soft,Decent,Bad,Decent,Moderate,Medium,Wide,Moderate,Moderate,Moderate,0,0,33.0 mm 31.0 mm,25.0 mm 23.0 mm,Narrownormalwidex-Wide,1,All Seasons,1,#266 Top 42%,#96 Top 15%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,small,0,1,0,0,1,0,0,0,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,Narrow|Normal|Wide|X-Wide,"['Narrow', 'Normal', 'Wide', 'X-Wide']",1,1,1,1,All,['All'],1,0,0,8.9,251,8.7,247.0,8.0,8.0,33.0,31.0,25.0,23.0
+New Balance,Fresh Foam X 880 v14 GTX,Daily Running,Neutral,9.2 oz / 261g 9.1 oz / 257g,0,11.3 mm 8.0 mm,Heel,True To Size,Soft,Good,Decent,Good,Warm,Medium,Medium,Moderate,Stiff,Stiff,0,0,35.4 mm 31.0 mm,24.1 mm 23.0 mm,Normalwide,1,Winter,1,#357 Bottom 2%,#305 Bottom 16%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,1,0,0,0,0,1,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,Winter,['Winter'],0,0,1,9.2,261,9.1,257.0,11.3,8.0,35.4,31.0,24.1,23.0
+New Balance,Fresh Foam X 880 v15,Daily Running,Neutral,10.1 oz / 286g 9.9 oz / 281g,0,4.3 mm 6.0 mm,Mid/Forefoot,Slightly Small,Soft,Decent,Good,Decent,Moderate,Medium,Medium,Stiff,Stiff,Stiff,0,1,39.7 mm 40.5 mm,35.4 mm 34.5 mm,Narrownormalwidex-Wide,1,All Seasons,1,#201 Bottom 45%,#31 Top 9%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,small,0,1,0,0,1,0,0,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,Narrow|Normal|Wide|X-Wide,"['Narrow', 'Normal', 'Wide', 'X-Wide']",1,1,1,1,All,['All'],1,0,0,10.1,286,9.9,281.0,4.3,6.0,39.7,40.5,35.4,34.5
+New Balance,Fresh Foam X Balos,Daily Running,Neutral,8.7 oz / 247g 9.2 oz / 261g,1,5.9 mm 6.0 mm,Mid/Forefoot,True To Size,Soft,Bad,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,1,37.8 mm 38.5 mm,31.9 mm 32.5 mm,Normalwide,1,All Seasons,1,#65 Top 18%,#51 Top 14%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,1,0,0,1,0,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,8.7,247,9.2,261.0,5.9,6.0,37.8,38.5,31.9,32.5
+New Balance,Fresh Foam X Evoz v3,Daily Running,Neutral,9.1 oz / 257g 9.5 oz / 270g,0,7.9 mm 6.0 mm,Mid/Forefoot,True To Size,Balanced,Decent,Decent,Good,Warm,Narrow,Medium,Moderate,Moderate,Stiff,0,0,34.3 mm 32.0 mm,26.4 mm 26.0 mm,Normalwidex-Wide,1,All Seasons,1,#584 Bottom 9%,#260 Top 41%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,1,0,0,0,1,0,0,1,0,0,1,0,0,0,1,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,9.1,257,9.5,270.0,7.9,6.0,34.3,32.0,26.4,26.0
+New Balance,Fresh Foam X Evoz v4,Daily Running,Neutral,10.1 oz / 286g 10.6 oz / 301g,0,5.5 mm 8.0 mm,Mid/Forefoot,True To Size,Soft,Decent,Decent,Decent,Moderate,Medium,Medium,Stiff,Stiff,Stiff,0,0,31.4 mm 32.0 mm,25.9 mm 24.0 mm,Normalwidex-Wide,1,All Seasons,1,#285 Bottom 22%,#81 Top 23%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,10.1,286,10.6,301.0,5.5,8.0,31.4,32.0,25.9,24.0
+New Balance,Fresh Foam X Kaiha Road,Daily Running,Neutral,9.9 oz / 281g 10.6 oz / 300g,0,3.8 mm 4.0 mm,Mid/Forefoot,True To Size,Soft,Bad,Bad,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,0,35.8 mm,32.0 mm,Normalwidex-Wide,1,All Seasons,1,#293 Bottom 19%,#17 Top 5%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,1,0,0,1,0,0,1,0,0,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,9.9,281,10.6,300.0,3.8,4.0,35.8,,32.0,
+New Balance,Fresh Foam X More v4,Daily Running,Neutral,10.6 oz / 301g 10.6 oz / 301g,0,4.6 mm 4.0 mm,Mid/Forefoot,True To Size,Soft,Bad,-,-,Warm,Narrow,Narrow,Moderate,Flexible,Flexible,0,0,32.5 mm 35.0 mm,27.9 mm 31.0 mm,Normalwide,1,All Seasons,1,#315 Top 49%,#22 Top 4%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0,1,0,1,0,0,1,0,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,10.6,301,10.6,301.0,4.6,4.0,32.5,35.0,27.9,31.0
+New Balance,Fresh Foam X More v5,Daily Running,Neutral,10.9 oz / 308g 10.7 oz / 303g,0,7.8 mm 4.0 mm,Mid/Forefoot,Slightly Small,Soft,Decent,Decent,Good,Breathable,Medium,Narrow,Stiff,Stiff,Stiff,0,1,42.1 mm 43.0 mm,34.3 mm 39.0 mm,Normalwidex-Wide,1,Summerall Seasons,1,#292 Top 46%,#2 Top 1%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,small,0,1,0,0,1,0,0,0,1,0,0,1,0,0,0,1,1,0,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,1,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,Summer|All,"['Summer', 'All']",1,1,0,10.9,308,10.7,303.0,7.8,4.0,42.1,43.0,34.3,39.0
+New Balance,Fresh Foam X More v6,Daily Running,Neutral,10.7 oz / 302g 10.8 oz / 306g,0,3.3 mm 4.0 mm,Mid/Forefoot,True To Size,Soft,Decent,Good,Good,Warm,Medium,Medium,Stiff,Moderate,Moderate,0,0,41.8 mm 44.0 mm,38.5 mm 40.0 mm,Normalwidex-Wide,1,All Seasons,1,#130 Top 36%,#9 Top 3%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,10.7,302,10.8,306.0,3.3,4.0,41.8,44.0,38.5,40.0
+New Balance,Fresh Foam X Tempo v2,Daily Runningtempo,Neutral,8.6 oz / 244g 9.2 oz / 260g,1,7.0 mm 6.0 mm,Mid/Forefoot,True To Size,Soft,Good,Decent,Good,Warm,Narrow,Medium,Flexible,Flexible,Moderate,0,0,29.2 mm 28.0 mm,22.2 mm 22.0 mm,Normalwide,1,All Seasons,1,#333 Bottom 9%,#187 Bottom 48%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,1,0,0,0,0,1,0,1,0,0,0,1,0,0,1,1,0,0,0,1,0,1,0,0,1,0,0,0,1,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,8.6,244,9.2,260.0,7.0,6.0,29.2,28.0,22.2,22.0
+New Balance,Fresh Foam X Vongo v6,Daily Running,Stability,11 oz / 312g 10.9 oz / 309g,0,5.6 mm 6.0 mm,Mid/Forefoot,True To Size,Soft,Decent,Good,Good,Warm,Medium,Medium,Moderate,Moderate,Stiff,0,1,36.1 mm 35.5 mm,30.5 mm 29.5 mm,Normalwidex-Wide,1,Winter,1,#123 Top 34%,#34 Top 10%,,,Daily Running,['Daily Running'],0,1,0,0,1,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,Winter,['Winter'],0,0,1,11.0,312,10.9,309.0,5.6,6.0,36.1,35.5,30.5,29.5
+New Balance,FuelCell Propel v5,Daily Runningtempo,Neutral,9.5 oz / 269g 9.7 oz / 275g,0,6.7 mm 6.0 mm,Mid/Forefoot,Slightly Small,Soft,Bad,Bad,Decent,Breathable,Medium,Medium,Moderate,Moderate,Moderate,0,0,35.2 mm 37.0 mm,28.5 mm 31.0 mm,Normalwide,1,Summerall Seasons,1,#310 Bottom 15%,#185 Bottom 49%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,small,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,Summer|All,"['Summer', 'All']",1,1,0,9.5,269,9.7,275.0,6.7,6.0,35.2,37.0,28.5,31.0
+New Balance,FuelCell RC Elite v2,Competition,Neutral,7.7 oz / 217g 7.8 oz / 221g,1,8.3 mm 8.0 mm,Heelmid/Forefoot,Slightly Small,-,-,-,-,Moderate,Narrow,-,Stiff,Stiff,Flexible,Carbon plate,1,34.1 mm 39.0 mm,25.8 mm 31.0 mm,Normal,1,All Seasons,1,#76 Top 21%,#222 Bottom 39%,,,Competition,['Competition'],1,0,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,small,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,7.7,217,7.8,221.0,8.3,8.0,34.1,39.0,25.8,31.0
+New Balance,FuelCell Rebel v2,Tempo,Neutral,7.1 oz / 201g 7.4 oz / 210g,1,8.9 mm 6.0 mm,Heelmid/Forefoot,Half Size Small,-,-,-,-,Breathable,Narrow,-,Moderate,Flexible,Flexible,0,1,26.2 mm 26.0 mm,17.3 mm 20.0 mm,Normal,0,Summerall Seasons,0,#203 Top 32%,#522 Bottom 19%,,,Tempo,['Tempo'],0,0,1,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,small,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,1,0,0,1,0,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,7.1,201,7.4,210.0,8.9,6.0,26.2,26.0,17.3,20.0
+New Balance,FuelCell Rebel v3,Tempo,Neutral,7.4 oz / 211g 7.4 oz / 211g,1,9.0 mm 6.0 mm,Heelmid/Forefoot,Half Size Small,Soft,Decent,Bad,-,Breathable,Narrow,Narrow,Moderate,Flexible,Moderate,0,0,31.7 mm 29.5 mm,22.7 mm 23.5 mm,Normalwide,1,Summerall Seasons,1,#138 Top 22%,#371 Bottom 42%,,,Tempo,['Tempo'],0,0,1,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,small,0,1,0,0,1,0,0,0,1,0,1,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,0,0,1,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,Summer|All,"['Summer', 'All']",1,1,0,7.4,211,7.4,211.0,9.0,6.0,31.7,29.5,22.7,23.5
+New Balance,FuelCell Rebel v4,Daily Runningtempo,Neutral,7.5 oz / 213g 7.7 oz / 218g,1,6.5 mm 6.0 mm,Mid/Forefoot,Slightly Small,Soft,Bad,Good,Good,Moderate,Medium,Wide,Flexible,Flexible,Flexible,0,1,28.0 mm 30.0 mm,21.5 mm 24.0 mm,Normalwide,1,All Seasons,1,#386 Bottom 39%,#45 Top 8%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,small,0,1,0,0,1,0,0,1,0,0,0,0,1,0,0,1,0,1,0,0,1,0,0,0,1,1,0,0,1,0,0,1,0,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,7.5,213,7.7,218.0,6.5,6.0,28.0,30.0,21.5,24.0
+New Balance,FuelCell Rebel v5,Daily Runningtempo,Neutral,7.8 oz / 220g 7.9 oz / 225g,1,6.3 mm 6.0 mm,Mid/Forefoot,True To Size,Soft,Bad,Good,Good,Moderate,Medium,Medium,Moderate,Flexible,Moderate,0,1,33.0 mm 35.0 mm,26.7 mm 29.0 mm,Normalwide,1,All Seasons,1,#26 Top 8%,#39 Top 11%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,1,0,0,1,0,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,0,1,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,7.8,220,7.9,225.0,6.3,6.0,33.0,35.0,26.7,29.0
+New Balance,FuelCell SuperComp Elite v3,Competitiontempo,Neutral,7.7 oz / 217g 7.6 oz / 215g,1,14.2 mm 4.0 mm,Heel,Slightly Small,Soft,Bad,Good,Good,Breathable,Narrow,Medium,Stiff,Stiff,Moderate,Carbon plate,1,36.4 mm 40.0 mm,22.2 mm 36.0 mm,Narrownormal,1,Summerall Seasons,1,#336 Bottom 47%,#108 Top 17%,,,Competition|Tempo,"['Competition', 'Tempo']",1,0,1,1,0,Heel,['Heel'],0,0,1,0,small,0,1,0,0,1,0,0,1,0,0,0,0,1,0,0,1,1,0,0,1,0,0,0,1,0,0,0,1,0,0,1,0,1,0,Narrow|Normal,"['Narrow', 'Normal']",1,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,7.7,217,7.6,215.0,14.2,4.0,36.4,40.0,22.2,36.0
+New Balance,FuelCell SuperComp Elite v4,Competition,Neutral,8.2 oz / 232g 8.1 oz / 230g,1,9.3 mm 4.0 mm,Heelmid/Forefoot,Half Size Small,Soft,Decent,Decent,Good,Moderate,Medium,Wide,Stiff,Stiff,Flexible,Carbon plate,1,38.2 mm 40.0 mm,28.9 mm 36.0 mm,Normalwide,1,All Seasons,1,#236 Top 37%,#26 Top 5%,,,Competition,['Competition'],1,0,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,small,0,1,0,0,1,0,0,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,1,0,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,8.2,232,8.1,230.0,9.3,4.0,38.2,40.0,28.9,36.0
+New Balance,FuelCell SuperComp Elite v5,Competition,Neutral,7 oz / 198g 7.5 oz / 213g,1,10.7 mm 8.0 mm,Heel,True To Size,Soft,Bad,Decent,Decent,Moderate,Medium,Medium,Moderate,Stiff,Flexible,Carbon plate,1,39.3 mm 40.0 mm,28.6 mm 32.0 mm,Normalwide,1,All Seasons,1,#9 Top 3%,#45 Top 13%,,Road,Competition,['Competition'],1,0,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,1,0,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,7.0,198,7.5,213.0,10.7,8.0,39.3,40.0,28.6,32.0
+New Balance,FuelCell SuperComp Pacer v2,Competitiontempo,Neutral,7.1 oz / 200g 7.4 oz / 209g,1,7.5 mm 8.0 mm,Heelmid/Forefoot,Half Size Small,Soft,Decent,Decent,Good,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,0,32.9 mm 35.0 mm,25.4 mm 27.0 mm,Normal,1,Summerall Seasons,1,#289 Bottom 20%,#128 Top 36%,,,Competition|Tempo,"['Competition', 'Tempo']",1,0,1,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,small,0,1,0,0,1,0,0,0,1,0,0,1,0,0,0,1,1,0,0,0,1,0,0,1,0,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,7.1,200,7.4,209.0,7.5,8.0,32.9,35.0,25.4,27.0
+New Balance,Fuelcell Supercomp Trainer,Tempo,Neutral,10.5 oz / 298g 11.3 oz / 320g,0,10.3 mm 8.0 mm,Heel,Slightly Small,Soft,-,-,-,Breathable,Narrow,-,Stiff,Stiff,Flexible,Carbon plate,1,40.2 mm 47.0 mm,29.9 mm 39.0 mm,Normal,1,Summerall Seasons,1,#269 Top 42%,#203 Top 32%,,,Tempo,['Tempo'],0,0,1,1,0,Heel,['Heel'],0,0,1,0,small,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,10.5,298,11.3,320.0,10.3,8.0,40.2,47.0,29.9,39.0
+New Balance,FuelCell SuperComp Trainer v2,Tempo,Neutral,9.3 oz / 264g 9.7 oz / 275g,0,8.4 mm 6.0 mm,Heelmid/Forefoot,True To Size,Soft,Bad,Good,Good,Moderate,Medium,Wide,Stiff,Stiff,Moderate,Carbon plate,1,39.3 mm 40.0 mm,30.9 mm 34.0 mm,Normalwide,1,All Seasons,1,#133 Top 21%,#131 Top 21%,,,Tempo,['Tempo'],0,0,1,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,1,0,0,1,0,0,0,0,1,0,0,1,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,0,1,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,9.3,264,9.7,275.0,8.4,6.0,39.3,40.0,30.9,34.0
+New Balance,FuelCell SuperComp Trainer v3,Tempo,Neutral,9.8 oz / 278g 9.8 oz / 278g,0,7.3 mm 6.0 mm,Mid/Forefoot,True To Size,Soft,Bad,Good,Decent,Moderate,Medium,Medium,Stiff,Stiff,Moderate,Carbon plate,1,36.8 mm 41.0 mm,29.5 mm 35.0 mm,Normalwide,1,All Seasons,1,#211 Bottom 42%,#40 Top 11%,,,Tempo,['Tempo'],0,0,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,1,0,0,1,0,0,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,9.8,278,9.8,278.0,7.3,6.0,36.8,41.0,29.5,35.0
+Altra,FWD VIA,Daily Running,Neutral,9 oz / 254g 9.5 oz / 269g,0,6.5 mm 4.0 mm,Mid/Forefoot,Slightly Small,Balanced,Decent,Decent,Decent,Moderate,Wide,Wide,Moderate,Stiff,Moderate,0,1,35.9 mm 37.0 mm,29.4 mm 33.0 mm,Normal,1,All Seasons,1,#207 Bottom 43%,#134 Top 37%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,small,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,0,1,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,9.0,254,9.5,269.0,6.5,4.0,35.9,37.0,29.4,33.0
+Adidas,Galaxy 6,Daily Running,Neutral,11.7 oz / 332g 11.6 oz / 330g,0,11.0 mm 10.0 mm,Heel,True To Size,Balanced,Bad,Bad,Decent,Warm,Wide,Wide,Stiff,Stiff,Flexible,0,0,33.9 mm,22.9 mm,Normal,1,All Seasons,1,#286 Bottom 21%,#287 Bottom 21%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,1,0,1,0,0,1,0,0,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,11.7,332,11.6,330.0,11.0,10.0,33.9,,22.9,
+Hoka,Gaviota 5,Daily Running,Stability,10.5 oz / 299g 10.9 oz / 310g,0,2.2 mm 6.0 mm,Mid/Forefoot,True To Size,Soft,Decent,Bad,Good,Breathable,Wide,Wide,Stiff,Stiff,Moderate,0,0,34.9 mm 36.0 mm,32.7 mm 30.0 mm,Normalwide,1,Summerall Seasons,1,#258 Bottom 29%,#21 Top 6%,,,Daily Running,['Daily Running'],0,1,0,0,1,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,1,0,0,0,1,0,1,0,0,0,0,1,1,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,Summer|All,"['Summer', 'All']",1,1,0,10.5,299,10.9,310.0,2.2,6.0,34.9,36.0,32.7,30.0
+Asics,Gel Contend 7,Daily Running,Neutral,9.5 oz / 268g 9.5 oz / 268g,0,9.6 mm 10.0 mm,Heelmid/Forefoot,True To Size,-,-,-,-,-,Medium,-,Stiff,-,Flexible,0,0,33.3 mm,23.7 mm,Normalx-Wide,1,-,1,#468 Bottom 27%,#364 Bottom 43%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,Normal|X-Wide,"['Normal', 'X-Wide']",0,1,0,1,,,0,0,0,9.5,268,9.5,268.0,9.6,10.0,33.3,,23.7,
+Asics,Gel Contend 8,Daily Running,Neutral,9.2 oz / 260g 10.3 oz / 293g,0,9.1 mm 10.0 mm,Heelmid/Forefoot,True To Size,Balanced,-,-,-,Breathable,Narrow,-,Stiff,Flexible,Stiff,0,0,31.1 mm,22.0 mm,Normalwidex-Wide,1,Summerall Seasons,1,#470 Bottom 27%,#341 Bottom 47%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,1,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,Summer|All,"['Summer', 'All']",1,1,0,9.2,260,10.3,293.0,9.1,10.0,31.1,,22.0,
+Asics,Gel Contend 9,Daily Running,Neutral,9.7 oz / 275g 9.7 oz / 275g,0,7.8 mm 8.0 mm,Mid/Forefoot,True To Size,Balanced,Bad,Good,Decent,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,31.2 mm 31.0 mm,23.4 mm 23.0 mm,Normalwidex-Wide,1,All Seasons,1,#318 Bottom 13%,#88 Top 24%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,1,0,1,0,0,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,9.7,275,9.7,275.0,7.8,8.0,31.2,31.0,23.4,23.0
+Asics,Gel Cumulus 23,Daily Running,Neutral,9.8 oz / 277g 9.9 oz / 280g,0,10.8 mm 10.0 mm,Heel,True To Size,-,-,-,-,Moderate,Narrow,-,Stiff,Moderate,Stiff,0,0,35.9 mm 23.0 mm,25.1 mm 13.0 mm,Normal,1,All Seasons,1,#214 Top 34%,#512 Bottom 20%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,1,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,9.8,277,9.9,280.0,10.8,10.0,35.9,23.0,25.1,13.0
+Asics,Gel Cumulus 24,Daily Running,Neutral,9.7 oz / 274g 10.1 oz / 286g,0,8.6 mm 8.0 mm,Heelmid/Forefoot,True To Size,Soft,-,-,-,-,Narrow,-,Stiff,Flexible,Flexible,0,0,33.5 mm 24.0 mm,24.9 mm 16.0 mm,Normalwidex-Wide,1,-,1,#163 Top 26%,#453 Bottom 29%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,1,0,0,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,,,0,0,0,9.7,274,10.1,286.0,8.6,8.0,33.5,24.0,24.9,16.0
+Asics,Gel Cumulus 25,Daily Running,Neutral,9.5 oz / 269g 9.5 oz / 269g,0,11.2 mm 8.0 mm,Heel,True To Size,Soft,Decent,Good,-,Moderate,Narrow,Narrow,Moderate,Moderate,Moderate,0,0,38.4 mm 37.5 mm,27.2 mm 29.5 mm,Normalwidex-Wide,1,All Seasons,1,#216 Top 34%,#291 Top 46%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,0,0,1,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,0,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,9.5,269,9.5,269.0,11.2,8.0,38.4,37.5,27.2,29.5
+Asics,Gel Cumulus 27,Daily Running,Neutral,9.2 oz / 261g 9.3 oz / 265g,0,11.6 mm 8.0 mm,Heel,True To Size,Soft,Good,Good,Bad,Warm,Medium,Medium,Moderate,Stiff,Stiff,0,0,40.9 mm 38.5 mm,29.3 mm 30.5 mm,Normalwidex-Wide,1,All Seasons,1,#110 Top 31%,#68 Top 19%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,1,0,0,0,0,1,0,0,1,1,0,0,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,9.2,261,9.3,265.0,11.6,8.0,40.9,38.5,29.3,30.5
+Asics,Gel Excite 10,Daily Running,Neutral,9.5 oz / 268g 9.2 oz / 260g,0,11.8 mm 8.0 mm,Heel,True To Size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,35.2 mm,23.4 mm,Normalwidex-Wide,1,All Seasons,1,#178 Top 49%,#85 Top 24%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,9.5,268,9.2,260.0,11.8,8.0,35.2,,23.4,
+Asics,Gel Excite 8,Daily Running,Neutral,9.5 oz / 268g 9.9 oz / 280g,0,12.3 mm 10.0 mm,Heel,True To Size,-,-,-,-,-,Narrow,-,Stiff,-,-,0,0,35.8 mm,23.5 mm,Normalx-Wide,0,-,0,#491 Bottom 23%,#379 Bottom 41%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Normal|X-Wide,"['Normal', 'X-Wide']",0,1,0,1,,,0,0,0,9.5,268,9.9,280.0,12.3,10.0,35.8,,23.5,
+Asics,Gel Kayano 28,Daily Running,Stability,10.7 oz / 302g 10.8 oz / 305g,0,8.7 mm 10.0 mm,Heelmid/Forefoot,Slightly Large,-,-,-,-,-,Medium,-,-,Moderate,-,0,0,31.8 mm 23.0 mm,23.1 mm 13.0 mm,Normalwide,1,-,1,#85 Top 14%,#336 Bottom 47%,,,Daily Running,['Daily Running'],0,1,0,0,1,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,large,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,,,0,0,0,10.7,302,10.8,305.0,8.7,10.0,31.8,23.0,23.1,13.0
+Asics,Gel Kayano 30,Daily Running,Stability,10.7 oz / 303g 10.7 oz / 303g,0,12.0 mm 10.0 mm,Heel,Slightly Small,Soft,Good,Bad,Good,Breathable,Medium,Medium,Moderate,Stiff,Stiff,0,1,39.7 mm 40.0 mm,27.7 mm 30.0 mm,Normalwidex-Wide,1,Summerall Seasons,1,#295 Top 46%,#86 Top 14%,,,Daily Running,['Daily Running'],0,1,0,0,1,Heel,['Heel'],0,0,1,0,small,0,1,0,0,1,0,0,0,0,1,1,0,0,0,0,1,1,0,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,Summer|All,"['Summer', 'All']",1,1,0,10.7,303,10.7,303.0,12.0,10.0,39.7,40.0,27.7,30.0
+Asics,Gel Kayano 31,Daily Running,Stability,10.4 oz / 295g 11 oz / 311g,0,11.5 mm 10.0 mm,Heel,True To Size,Soft,Good,Good,Decent,Moderate,Wide,Medium,Moderate,Stiff,Stiff,0,0,39.3 mm 40.0 mm,27.8 mm 30.0 mm,Normalwidex-Wide,1,All Seasons,1,#155 Top 25%,#44 Top 7%,,,Daily Running,['Daily Running'],0,1,0,0,1,Heel,['Heel'],0,0,1,0,true,0,0,1,0,1,0,0,0,0,1,0,0,1,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,0,0,1,0,0,1,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,10.4,295,11.0,311.0,11.5,10.0,39.3,40.0,27.8,30.0
+Asics,Gel Kayano 32,Daily Running,Stability,10.4 oz / 295g 10.7 oz / 304g,0,9.3 mm 8.0 mm,Heelmid/Forefoot,True To Size,Balanced,Good,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,39.9 mm 40.0 mm,30.6 mm 32.0 mm,Normalwidex-Wide,1,All Seasons,1,#259 Bottom 29%,#19 Top 6%,,,Daily Running,['Daily Running'],0,1,0,0,1,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,10.4,295,10.7,304.0,9.3,8.0,39.9,40.0,30.6,32.0
+Asics,Gel Kayano Lite 2,Daily Running,Stability,10.1 oz / 286g 10.1 oz / 286g,0,11.1 mm 10.0 mm,Heel,-,-,-,-,-,-,Medium,-,Stiff,Stiff,Stiff,0,1,35.9 mm 35.0 mm,24.8 mm 25.0 mm,Normal,1,-,1,#287 Top 45%,#581 Bottom 9%,,,Daily Running,['Daily Running'],0,1,0,0,1,Heel,['Heel'],0,0,1,0,,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,1,Normal,['Normal'],0,1,0,0,,,0,0,0,10.1,286,10.1,286.0,11.1,10.0,35.9,35.0,24.8,25.0
+Asics,Gel Kayano Lite 3,Daily Running,Stability,9.8 oz / 278g 9.9 oz / 281g,0,8.5 mm 8.0 mm,Heelmid/Forefoot,True To Size,Balanced,-,-,-,Breathable,Medium,-,Stiff,Moderate,Moderate,0,1,31.8 mm 22.0 mm,23.3 mm 14.0 mm,Normal,1,Summerall Seasons,1,#230 Bottom 37%,#334 Bottom 8%,,,Daily Running,['Daily Running'],0,1,0,0,1,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,1,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,9.8,278,9.9,281.0,8.5,8.0,31.8,22.0,23.3,14.0
+Asics,Gel Kinsei Max,Daily Running,Neutral,11.4 oz / 322g 11.7 oz / 333g,0,8.6 mm 8.0 mm,Heelmid/Forefoot,True To Size,Soft,Decent,Decent,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,1,38.8 mm 38.0 mm,30.2 mm 30.0 mm,Normal,1,All Seasons,1,#263 Bottom 27%,#295 Bottom 19%,,Road,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,1,0,0,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,11.4,322,11.7,333.0,8.6,8.0,38.8,38.0,30.2,30.0
+Asics,Gel Nimbus 24,Daily Running,Neutral,9.3 oz / 265g 10.2 oz / 289g,0,8.7 mm 10.0 mm,Heelmid/Forefoot,True To Size,-,-,-,-,-,Medium,-,Stiff,-,-,0,1,38.7 mm 26.0 mm,30.0 mm 16.0 mm,Normalwidex-Wide,0,-,0,#223 Top 35%,#292 Top 46%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,,,0,0,0,9.3,265,10.2,289.0,8.7,10.0,38.7,26.0,30.0,16.0
+Asics,Gel Nimbus 25,Daily Running,Neutral,10.2 oz / 289g 10.5 oz / 299g,0,7.8 mm 8.0 mm,Mid/Forefoot,True To Size,Soft,-,-,-,Moderate,Narrow,-,Moderate,Stiff,Moderate,0,1,38.0 mm 41.5 mm,30.2 mm 33.5 mm,Normalwidex-Wide,1,All Seasons,1,#166 Top 26%,#83 Top 13%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,1,0,1,0,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,10.2,289,10.5,299.0,7.8,8.0,38.0,41.5,30.2,33.5
+Asics,Gel Nimbus 26,Daily Running,Neutral,10.7 oz / 303g 10.7 oz / 304g,0,8.4 mm 8.0 mm,Heelmid/Forefoot,True To Size,Soft,Good,Good,Good,Moderate,Wide,Medium,Moderate,Stiff,Moderate,0,1,40.4 mm 42.0 mm,32.0 mm 34.0 mm,Normalwidex-Wide,1,All Seasons,1,#148 Top 24%,#43 Top 7%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,1,0,1,0,0,0,1,0,1,0,0,1,0,0,0,1,0,1,0,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,10.7,303,10.7,304.0,8.4,8.0,40.4,42.0,32.0,34.0
+Asics,Gel Nimbus 27,Daily Running,Neutral,10.5 oz / 299g 10.8 oz / 305g,0,8.3 mm 8.0 mm,Heelmid/Forefoot,True To Size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Stiff,0,1,42.7 mm 44.0 mm,34.4 mm 36.0 mm,Normalwidex-Wide,1,All Seasons,1,#162 Top 45%,#18 Top 5%,#18 Top 5%,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,10.5,299,10.8,305.0,8.3,8.0,42.7,44.0,34.4,36.0
+Asics,Gel Nimbus Lite 3,Daily Running,Neutral,9 oz / 254g 9.1 oz / 259g,0,9.9 mm 10.0 mm,Heelmid/Forefoot,-,-,-,-,-,-,Medium,-,Stiff,-,-,0,1,34.9 mm 25.0 mm,25.0 mm 15.0 mm,Normal,0,-,0,#108 Top 30%,#305 Bottom 16%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,Normal,['Normal'],0,1,0,0,,,0,0,0,9.0,254,9.1,259.0,9.9,10.0,34.9,25.0,25.0,15.0
+Asics,Gel Pulse 11,Daily Running,Neutral,11.1 oz / 314g 11.4 oz / 322g,0,8.7 mm 8.0 mm,Heelmid/Forefoot,Slightly Large,-,-,-,-,-,Medium,-,Stiff,-,-,0,0,34.0 mm 31.0 mm,25.3 mm 13.0 mm,Normal,0,-,0,#420 Bottom 34%,#604 Bottom 6%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,large,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,Normal,['Normal'],0,1,0,0,,,0,0,0,11.1,314,11.4,322.0,8.7,8.0,34.0,31.0,25.3,13.0
+Asics,Gel Pulse 13,Daily Running,Neutral,10.3 oz / 291g 10.6 oz / 300g,0,11.0 mm 10.0 mm,Heel,True To Size,Balanced,Bad,-,-,Breathable,Narrow,Medium,Moderate,Flexible,Moderate,0,0,32.6 mm 23.0 mm,21.6 mm 13.0 mm,Normal,1,Summerall Seasons,1,#563 Bottom 12%,#590 Bottom 8%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,0,1,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,10.3,291,10.6,300.0,11.0,10.0,32.6,23.0,21.6,13.0
+Asics,Gel Pulse 14,Daily Running,Neutral,10.4 oz / 296g 10.5 oz / 298g,0,9.7 mm 10.0 mm,Heelmid/Forefoot,True To Size,Balanced,Bad,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,32.0 mm,22.3 mm,Normal,1,All Seasons,1,#562 Bottom 13%,#597 Bottom 7%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,1,0,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,10.4,296,10.5,298.0,9.7,10.0,32.0,,22.3,
+Asics,Gel Pulse 15,Daily Running,Neutral,8.4 oz / 237g 9.2 oz / 260g,1,8.1 mm 8.0 mm,Heelmid/Forefoot,True To Size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,35.3 mm 35.0 mm,27.2 mm 27.0 mm,Normal,1,All Seasons,1,#262 Bottom 28%,#181 Top 50%,,Road,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,8.4,237,9.2,260.0,8.1,8.0,35.3,35.0,27.2,27.0
+Brooks,Ghost 14,Daily Running,Neutral,10.1 oz / 287g 9.9 oz / 280g,0,12.4 mm 12.0 mm,Heel,True To Size,-,-,-,-,Moderate,Narrow,-,Stiff,Moderate,Stiff,0,0,33.8 mm 36.0 mm,21.4 mm 24.0 mm,Narrownormalwidex-Wide,1,All Seasons,1,#53 Top 9%,#153 Top 24%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,1,Narrow|Normal|Wide|X-Wide,"['Narrow', 'Normal', 'Wide', 'X-Wide']",1,1,1,1,All,['All'],1,0,0,10.1,287,9.9,280.0,12.4,12.0,33.8,36.0,21.4,24.0
+Brooks,Ghost 15,Daily Running,Neutral,9.8 oz / 279g 10.1 oz / 286g,0,13.2 mm 12.0 mm,Heel,True To Size,Soft,Bad,Decent,Good,Moderate,Narrow,Medium,Flexible,Moderate,Stiff,0,0,36.3 mm 35.0 mm,23.1 mm 23.0 mm,Narrownormalwidex-Wide,1,All Seasons,1,#224 Top 35%,#60 Top 10%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,1,0,0,1,0,0,0,1,0,0,0,1,0,1,0,1,0,0,0,1,0,1,0,0,0,1,0,0,0,1,Narrow|Normal|Wide|X-Wide,"['Narrow', 'Normal', 'Wide', 'X-Wide']",1,1,1,1,All,['All'],1,0,0,9.8,279,10.1,286.0,13.2,12.0,36.3,35.0,23.1,23.0
+Brooks,Ghost 16,Daily Running,Neutral,9.4 oz / 266g 9.5 oz / 269g,0,12.4 mm 12.0 mm,Heel,True To Size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Flexible,Moderate,Stiff,0,0,35.1 mm 36.0 mm,22.7 mm 24.0 mm,Narrownormalwidex-Wide,1,All Seasons,1,#550 Bottom 14%,#20 Top 4%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,1,0,0,0,1,0,0,0,1,Narrow|Normal|Wide|X-Wide,"['Narrow', 'Normal', 'Wide', 'X-Wide']",1,1,1,1,All,['All'],1,0,0,9.4,266,9.5,269.0,12.4,12.0,35.1,36.0,22.7,24.0
+Brooks,Ghost 17,Daily Running,Neutral,10.2 oz / 289g 10.1 oz / 286g,0,10.4 mm 10.0 mm,Heel,Slightly Large,Soft,Decent,Good,Good,Moderate,Medium,Narrow,Moderate,Moderate,Stiff,0,0,36.2 mm 36.5 mm,25.8 mm 26.5 mm,Narrownormalwidex-Wide,1,All Seasons,1,#342 Bottom 6%,#16 Top 5%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,large,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,1,0,0,0,1,0,0,1,0,0,0,1,Narrow|Normal|Wide|X-Wide,"['Narrow', 'Normal', 'Wide', 'X-Wide']",1,1,1,1,All,['All'],1,0,0,10.2,289,10.1,286.0,10.4,10.0,36.2,36.5,25.8,26.5
+Brooks,Ghost Max,Daily Running,Neutral,10.3 oz / 291g 10.1 oz / 286g,0,9.5 mm 6.0 mm,Heelmid/Forefoot,True To Size,Soft,Bad,Good,Good,Moderate,Narrow,Medium,Stiff,Stiff,Stiff,0,1,39.8 mm 38.0 mm,30.3 mm 32.0 mm,Normalwidex-Wide,1,All Seasons,1,#29 Top 5%,#27 Top 5%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,1,0,0,1,0,0,0,0,1,0,0,1,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,0,0,1,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,10.3,291,10.1,286.0,9.5,6.0,39.8,38.0,30.3,32.0
+Brooks,Ghost Max 3,Daily Running,Neutral,10.7 oz / 303g 10.8 oz / 306g,0,7.3 mm 6.0 mm,Mid/Forefoot,True To Size,Soft,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Stiff,0,0,38.5 mm 39.0 mm,31.2 mm 33.0 mm,Normalwidex-Wide,1,All Seasons,1,#230 Bottom 36%,#27 Top 8%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,10.7,303,10.8,306.0,7.3,6.0,38.5,39.0,31.2,33.0
+Brooks,GhostMax 2,Daily Running,Neutral,10.8 oz / 305g 10.9 oz / 309g,0,9.9 mm 6.0 mm,Heelmid/Forefoot,True To Size,Balanced,Decent,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Stiff,0,0,39.0 mm 39.0 mm,29.1 mm 33.0 mm,Normalwidex-Wide,1,Summerall Seasons,1,#418 Bottom 35%,#15 Top 3%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,1,0,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,Summer|All,"['Summer', 'All']",1,1,0,10.8,305,10.9,309.0,9.9,6.0,39.0,39.0,29.1,33.0
+Asics,GlideRide 3,Daily Running,Neutral,9.5 oz / 270g 9.4 oz / 266g,0,11.1 mm 5.0 mm,Heel,Slightly Small,Soft,-,Bad,-,Moderate,Narrow,-,Stiff,Stiff,Flexible,0,1,42.7 mm 40.0 mm,31.6 mm 35.0 mm,Normal,1,All Seasons,1,#32 Top 9%,#282 Bottom 22%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,small,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,9.5,270,9.4,266.0,11.1,5.0,42.7,40.0,31.6,35.0
+Asics,Glideride Max,Daily Running,Neutral,9.9 oz / 281g 10.2 oz / 289g,0,12.7 mm 6.0 mm,Heel,Slightly Small,Soft,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,1,44.1 mm 44.0 mm,31.4 mm 38.0 mm,Normalwide,1,All Seasons,1,#10 Top 3%,#131 Top 36%,#131 Top 36%,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,small,0,1,0,0,1,0,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,9.9,281,10.2,289.0,12.7,6.0,44.1,44.0,31.4,38.0
+Brooks,Glycerin 19,Daily Running,Neutral,10.4 oz / 294g 10.2 oz / 289g,0,11.8 mm 10.0 mm,Heel,Slightly Small,-,-,-,-,-,Narrow,-,-,Moderate,-,0,0,38.1 mm 36.0 mm,26.3 mm 26.0 mm,Normalwide,1,-,1,#202 Top 32%,#417 Bottom 35%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,small,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,,,0,0,0,10.4,294,10.2,289.0,11.8,10.0,38.1,36.0,26.3,26.0
+Brooks,Glycerin 20,Daily Running,Neutral,10.5 oz / 297g 10.1 oz / 286g,0,12.8 mm 10.0 mm,Heel,True To Size,Balanced,-,-,-,Moderate,Narrow,-,Moderate,Moderate,Moderate,0,0,37.1 mm 34.0 mm,24.3 mm 24.0 mm,Normalwide,1,All Seasons,1,#210 Top 33%,#113 Top 18%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,1,0,0,1,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,10.5,297,10.1,286.0,12.8,10.0,37.1,34.0,24.3,24.0
+Brooks,Glycerin 21,Daily Running,Neutral,9.8 oz / 278g 9.8 oz / 277g,0,10.6 mm 10.0 mm,Heel,True To Size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Flexible,Stiff,0,0,37.2 mm 38.0 mm,26.6 mm 28.0 mm,Narrownormalwide,1,All Seasons,1,#374 Bottom 42%,#49 Top 8%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,0,0,1,Narrow|Normal|Wide,"['Narrow', 'Normal', 'Wide']",1,1,1,0,All,['All'],1,0,0,9.8,278,9.8,277.0,10.6,10.0,37.2,38.0,26.6,28.0
+Brooks,Glycerin 22,Daily Running,Neutral,10.3 oz / 293g 10.2 oz / 289g,0,10.3 mm 10.0 mm,Heel,True To Size,Soft,Decent,Good,Good,Breathable,Medium,Medium,Moderate,Stiff,Moderate,0,0,38.5 mm 38.0 mm,28.2 mm 28.0 mm,Normalwidex-Wide,1,Summerall Seasons,1,#175 Top 48%,#14 Top 4%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,1,0,0,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,Summer|All,"['Summer', 'All']",1,1,0,10.3,293,10.2,289.0,10.3,10.0,38.5,38.0,28.2,28.0
+Brooks,Glycerin GTS 20,Daily Running,Stability,10.9 oz / 309g 10.5 oz / 298g,0,11.0 mm 10.0 mm,Heel,True To Size,Balanced,Bad,Bad,Good,Moderate,Narrow,Medium,Stiff,Stiff,Stiff,0,0,36.5 mm 38.0 mm,25.5 mm 28.0 mm,Normalwide,1,All Seasons,1,#79 Top 13%,#288 Top 45%,,,Daily Running,['Daily Running'],0,1,0,0,1,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,1,0,1,0,0,1,0,0,0,0,1,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,10.9,309,10.5,298.0,11.0,10.0,36.5,38.0,25.5,28.0
+Brooks,Glycerin GTS 21,Daily Running,Stability,10.6 oz / 301g 10.7 oz / 303g,0,10.7 mm 10.0 mm,Heel,True To Size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,37.2 mm 38.0 mm,26.5 mm 28.0 mm,Normalwide,1,All Seasons,1,#233 Top 37%,#173 Top 27%,,,Daily Running,['Daily Running'],0,1,0,0,1,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,10.6,301,10.7,303.0,10.7,10.0,37.2,38.0,26.5,28.0
+Brooks,Glycerin GTS 22,Daily Running,Stability,10.8 oz / 305g 10.7 oz / 303g,0,10.1 mm 10.0 mm,Heel,True To Size,Balanced,Decent,Good,Good,Breathable,Medium,Medium,Moderate,Stiff,Moderate,0,0,37.8 mm 39.0 mm,27.7 mm 29.0 mm,Normalwidex-Wide,1,Summerall Seasons,1,#324 Bottom 11%,#44 Top 13%,,,Daily Running,['Daily Running'],0,1,0,0,1,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,1,0,0,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,Summer|All,"['Summer', 'All']",1,1,0,10.8,305,10.7,303.0,10.1,10.0,37.8,39.0,27.7,29.0
+Brooks,Glycerin Max,Daily Running,Neutral,10.8 oz / 305g 10.9 oz / 309g,0,6.6 mm 6.0 mm,Heelmid/Forefoot,True To Size,Soft,Decent,Good,Decent,Breathable,Medium,Medium,Stiff,Stiff,Moderate,0,1,42.3 mm 47.0 mm,35.7 mm 41.0 mm,Normal,1,Summerall Seasons,1,#100 Top 28%,#10 Top 3%,,Road,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,1,0,0,0,1,0,0,0,1,0,1,0,1,0,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,10.8,305,10.9,309.0,6.6,6.0,42.3,47.0,35.7,41.0
+Brooks,Glycerin Stealthfit 20,Daily Running,Neutral,9.9 oz / 281g 9.4 oz / 266.5g,0,12.5 mm 10.0 mm,Heel,True To Size,Balanced,Decent,Good,Good,Breathable,Narrow,Medium,Stiff,Stiff,Moderate,0,0,38.8 mm 34.0 mm,26.3 mm 24.0 mm,Narrownormal,1,Summerall Seasons,1,#364 Bottom 43%,#478 Bottom 25%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,1,0,0,1,0,0,0,1,0,0,0,1,0,0,1,0,1,0,Narrow|Normal,"['Narrow', 'Normal']",1,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,9.9,281,9.4,266.5,12.5,10.0,38.8,34.0,26.3,24.0
+Brooks,Glycerin StealthFit 21,Daily Running,Neutral,9.1 oz / 257g 10 oz / 283g,0,10.5 mm 10.0 mm,Heel,True To Size,Balanced,Decent,Decent,Good,Warm,Medium,Medium,Moderate,Moderate,Moderate,0,0,36.9 mm 38.0 mm,26.4 mm 28.0 mm,Normal,1,All Seasons,1,#235 Bottom 35%,#142 Top 39%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,9.1,257,10.0,283.0,10.5,10.0,36.9,38.0,26.4,28.0
+Skechers,GO RUN Max Road 6,Daily Running,Neutral,11.3 oz / 319g 11 oz / 312g,0,8.1 mm 6.0 mm,Heelmid/Forefoot,True To Size,Soft,Decent,Good,Good,Moderate,Medium,Narrow,Stiff,Moderate,Moderate,Carbon plate,0,39.7 mm 40.0 mm,31.6 mm 34.0 mm,Normal,1,All Seasons,1,#21 Top 6%,#145 Top 40%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,1,0,0,0,0,1,0,1,0,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,11.3,319,11.0,312.0,8.1,6.0,39.7,40.0,31.6,34.0
+Skechers,GO RUN Ride 11,Daily Running,Neutral,10.1 oz / 285g 9.7 oz / 275g,0,6.5 mm 6.0 mm,Mid/Forefoot,True To Size,Soft,Decent,Bad,Good,Moderate,Narrow,Medium,Moderate,Stiff,Moderate,0,1,34.1 mm 38.0 mm,27.6 mm 32.0 mm,Normal,1,All Seasons,1,#5 Top 2%,#263 Bottom 27%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,1,0,0,0,1,0,1,0,0,0,0,1,0,1,0,1,0,0,0,1,0,0,1,0,0,0,1,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,10.1,285,9.7,275.0,6.5,6.0,34.1,38.0,27.6,32.0
+Skechers,GOrun Razor Excess,Daily Runningtempo,Neutral,7.1 oz / 202g 7.5 oz / 213g,1,6.4 mm 4.0 mm,Mid/Forefoot,Slightly Large,-,-,-,-,-,Narrow,-,Stiff,Stiff,Moderate,0,1,27.4 mm 30.0 mm,21.0 mm 26.0 mm,Normalwide,0,-,0,#276 Bottom 24%,#351 Bottom 4%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,large,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,1,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,,,0,0,0,7.1,202,7.5,213.0,6.4,4.0,27.4,30.0,21.0,26.0
+Asics,GT 1000 10,Daily Running,Stability,9.8 oz / 277g 9.9 oz / 281g,0,7.8 mm 9.0 mm,Mid/Forefoot,Slightly Small,-,-,-,-,-,Medium,-,Stiff,-,Moderate,0,0,31.0 mm,23.2 mm,Normalx-Wide,0,-,0,#276 Top 43%,#588 Bottom 8%,,,Daily Running,['Daily Running'],0,1,0,0,1,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,small,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,Normal|X-Wide,"['Normal', 'X-Wide']",0,1,0,1,,,0,0,0,9.8,277,9.9,281.0,7.8,9.0,31.0,,23.2,
+Asics,GT 1000 11,Daily Running,Stability,9.9 oz / 281g 9.5 oz / 270g,0,9.0 mm 8.0 mm,Heelmid/Forefoot,True To Size,Soft,-,-,-,-,Narrow,-,Stiff,Moderate,Flexible,0,0,31.5 mm 20.0 mm,22.5 mm 12.0 mm,Normalwidex-Wide,1,-,1,#402 Bottom 37%,#479 Bottom 25%,,,Daily Running,['Daily Running'],0,1,0,0,1,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,1,0,0,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,,,0,0,0,9.9,281,9.5,270.0,9.0,8.0,31.5,20.0,22.5,12.0
+Asics,GT 1000 12,Daily Running,Stability,9.6 oz / 271g 9.5 oz / 269g,0,7.2 mm 8.0 mm,Mid/Forefoot,True To Size,Soft,Bad,Good,Good,Breathable,Medium,Wide,Moderate,Moderate,Moderate,0,0,30.2 mm 30.0 mm,23.0 mm 22.0 mm,Normalwidex-Wide,1,Summerall Seasons,1,#473 Bottom 26%,#365 Bottom 43%,,,Daily Running,['Daily Running'],0,1,0,0,1,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,1,0,0,1,0,0,0,0,1,0,0,1,1,0,0,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,Summer|All,"['Summer', 'All']",1,1,0,9.6,271,9.5,269.0,7.2,8.0,30.2,30.0,23.0,22.0
+Asics,GT 1000 13,Daily Running,Stability,9.7 oz / 276g 9.7 oz / 274g,0,8.7 mm 8.0 mm,Heelmid/Forefoot,True To Size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,33.7 mm 36.0 mm,25.0 mm 28.0 mm,Normalwidex-Wide,1,All Seasons,1,#584 Bottom 9%,#258 Top 40%,,,Daily Running,['Daily Running'],0,1,0,0,1,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,9.7,276,9.7,274.0,8.7,8.0,33.7,36.0,25.0,28.0
+Asics,GT 1000 14,Daily Running,Stability,9.6 oz / 272g 9.3 oz / 265g,0,9.6 mm 8.0 mm,Heelmid/Forefoot,Slightly Small,Soft,Decent,Good,Good,Warm,Medium,Medium,Moderate,Stiff,Moderate,0,0,35.4 mm 34.5 mm,25.8 mm 26.5 mm,Normalwidex-Wide,1,All Seasons,1,#304 Bottom 17%,#144 Top 40%,,,Daily Running,['Daily Running'],0,1,0,0,1,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,small,0,1,0,0,1,0,0,0,1,0,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,9.6,272,9.3,265.0,9.6,8.0,35.4,34.5,25.8,26.5
+Asics,GT 1000 9,Daily Running,Stability,10.1 oz / 286g 9.8 oz / 278g,0,6.0 mm 10.0 mm,Mid/Forefoot,-,-,-,-,-,-,Medium,-,Stiff,Stiff,Stiff,0,0,31.6 mm 31.0 mm,25.6 mm 21.0 mm,Normal,1,-,1,#278 Top 44%,#625 Bottom 3%,,,Daily Running,['Daily Running'],0,1,0,0,1,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,1,Normal,['Normal'],0,1,0,0,,,0,0,0,10.1,286,9.8,278.0,6.0,10.0,31.6,31.0,25.6,21.0
+Asics,GT 2000 10,Daily Running,Stability,9.9 oz / 281g 9.9 oz / 280g,0,7.5 mm 8.0 mm,Mid/Forefoot,True To Size,-,-,-,-,-,Medium,-,Stiff,Moderate,Stiff,0,0,31.5 mm 22.0 mm,24.0 mm 14.0 mm,Normalwidex-Wide,1,-,1,#169 Top 27%,#454 Bottom 29%,,,Daily Running,['Daily Running'],0,1,0,0,1,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,1,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,,,0,0,0,9.9,281,9.9,280.0,7.5,8.0,31.5,22.0,24.0,14.0
+Asics,GT 2000 11,Daily Running,Stability,9.9 oz / 282g 9.7 oz / 275g,0,6.0 mm 8.0 mm,Mid/Forefoot,True To Size,Balanced,-,-,-,Moderate,Narrow,-,Stiff,Moderate,Moderate,0,0,30.7 mm 35.0 mm,24.7 mm 27.0 mm,Normalwidex-Wide,1,All Seasons,1,#187 Top 29%,#361 Bottom 44%,,,Daily Running,['Daily Running'],0,1,0,0,1,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,1,0,0,1,0,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,9.9,282,9.7,275.0,6.0,8.0,30.7,35.0,24.7,27.0
+Asics,GT 2000 12,Daily Running,Stability,9.7 oz / 275g 9.4 oz / 266g,0,10.0 mm 8.0 mm,Heelmid/Forefoot,True To Size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,36.6 mm 34.5 mm,26.6 mm 26.5 mm,Narrownormalwidex-Wide,1,All Seasons,1,#80 Top 13%,#191 Top 30%,,,Daily Running,['Daily Running'],0,1,0,0,1,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,Narrow|Normal|Wide|X-Wide,"['Narrow', 'Normal', 'Wide', 'X-Wide']",1,1,1,1,All,['All'],1,0,0,9.7,275,9.4,266.0,10.0,8.0,36.6,34.5,26.6,26.5
+Asics,GT 2000 13,Daily Running,Stability,9.3 oz / 264g 9.4 oz / 266g,0,9.4 mm 8.0 mm,Heelmid/Forefoot,True To Size,Soft,Good,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,36.6 mm 36.0 mm,27.2 mm 28.0 mm,Normalwidex-Wide,1,All Seasons,1,#186 Top 29%,#100 Top 16%,,,Daily Running,['Daily Running'],0,1,0,0,1,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,9.3,264,9.4,266.0,9.4,8.0,36.6,36.0,27.2,28.0
+Asics,GT 2000 14,Daily Running,Stability,9.5 oz / 269g 9.4 oz / 266g,0,8.7 mm 8.0 mm,Heelmid/Forefoot,True To Size,Soft,Decent,Good,Good,Warm,Medium,Narrow,Moderate,Stiff,Stiff,0,0,36.9 mm 36.5 mm,28.2 mm 28.5 mm,Normalwidex-Wide,1,All Seasons,1,#107 Top 30%,#83 Top 23%,,,Daily Running,['Daily Running'],0,1,0,0,1,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,0,0,1,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,9.5,269,9.4,266.0,8.7,8.0,36.9,36.5,28.2,28.5
+Saucony,Guide 14,Daily Running,Stability,10.8 oz / 306g 10.5 oz / 298g,0,9.4 mm 8.0 mm,Heelmid/Forefoot,True To Size,-,-,-,-,-,Medium,-,-,Flexible,-,0,0,33.8 mm 32.5 mm,24.4 mm 24.5 mm,Normalwide,1,-,1,#304 Top 48%,#558 Bottom 13%,,,Daily Running,['Daily Running'],0,1,0,0,1,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,,,0,0,0,10.8,306,10.5,298.0,9.4,8.0,33.8,32.5,24.4,24.5
+Saucony,Guide 15,Daily Running,Stability,9.8 oz / 279g 9.5 oz / 269g,0,7.1 mm 8.0 mm,Mid/Forefoot,True To Size,Balanced,-,-,-,-,Narrow,-,Stiff,Moderate,Stiff,0,0,31.6 mm 35.0 mm,24.5 mm 27.0 mm,Normalwide,1,-,1,#218 Top 34%,#442 Bottom 31%,,,Daily Running,['Daily Running'],0,1,0,0,1,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,,,0,0,0,9.8,279,9.5,269.0,7.1,8.0,31.6,35.0,24.5,27.0
+Saucony,Guide 17,Daily Running,Stability,9.7 oz / 275g 9.5 oz / 269g,0,7.0 mm 6.0 mm,Mid/Forefoot,True To Size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,34.9 mm 35.0 mm,27.9 mm 29.0 mm,Normalwidex-Wide,1,All Seasons,1,#96 Top 15%,#154 Top 24%,,,Daily Running,['Daily Running'],0,1,0,0,1,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,9.7,275,9.5,269.0,7.0,6.0,34.9,35.0,27.9,29.0
+Saucony,Guide 18,Daily Running,Stability,9.8 oz / 278g 9.6 oz / 272g,0,8.3 mm 6.0 mm,Heelmid/Forefoot,True To Size,Balanced,Good,Decent,Good,Breathable,Medium,Medium,Moderate,Stiff,Flexible,0,0,36.0 mm 35.0 mm,27.7 mm 29.0 mm,Normalwidex-Wide,1,Summerall Seasons,1,#303 Bottom 17%,#57 Top 16%,,,Daily Running,['Daily Running'],0,1,0,0,1,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,0,1,0,1,0,0,0,1,1,0,0,0,1,0,0,1,0,0,1,0,0,0,1,1,0,0,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,Summer|All,"['Summer', 'All']",1,1,0,9.8,278,9.6,272.0,8.3,6.0,36.0,35.0,27.7,29.0
+Under Armour,HOVR Phantom 3,Daily Running,Neutral,11.9 oz / 338g 11.1 oz / 315g,0,12.7 mm 8.0 mm,Heel,Slightly Small,Balanced,Good,Decent,-,Warm,Medium,Medium,Moderate,Stiff,Stiff,0,0,32.8 mm 25.0 mm,20.1 mm 17.0 mm,Normal,0,Winter,0,#335 Bottom 8%,#238 Bottom 34%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,small,0,1,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,Normal,['Normal'],0,1,0,0,Winter,['Winter'],0,0,1,11.9,338,11.1,315.0,12.7,8.0,32.8,25.0,20.1,17.0
+Under Armour,HOVR Sonic 6,Daily Running,Neutral,10.1 oz / 286g 9.9 oz / 281g,0,7.0 mm 8.0 mm,Mid/Forefoot,Half Size Small,Balanced,Decent,Decent,Good,Warm,Medium,Medium,Flexible,Stiff,Moderate,0,0,30.2 mm 32.0 mm,23.2 mm 24.0 mm,Normalwide,1,All Seasons,1,#282 Bottom 23%,#253 Bottom 31%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,small,0,1,0,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,1,0,0,0,0,1,0,1,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,10.1,286,9.9,281.0,7.0,8.0,30.2,32.0,23.2,24.0
+Saucony,Hurricane 24,Daily Running,Stability,11.1 oz / 315g 11.2 oz / 317g,0,6.3 mm 6.0 mm,Mid/Forefoot,Slightly Small,Soft,Bad,Decent,Decent,Breathable,Medium,Medium,Stiff,Stiff,Moderate,0,0,40.5 mm 42.0 mm,34.2 mm 36.0 mm,Normalwide,1,Summerall Seasons,1,#319 Top 50%,#158 Top 25%,,,Daily Running,['Daily Running'],0,1,0,0,1,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,small,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,Summer|All,"['Summer', 'All']",1,1,0,11.1,315,11.2,317.0,6.3,6.0,40.5,42.0,34.2,36.0
+Saucony,Hurricane 25,Daily Running,Stability,10.1 oz / 286g 10 oz / 283g,0,7.1 mm 6.0 mm,Mid/Forefoot,-,Soft,Decent,Decent,Good,Warm,Medium,Medium,Moderate,Stiff,Stiff,0,0,40.2 mm 38.0 mm,33.1 mm 32.0 mm,Normalwide,1,All Seasons,1,#341 Bottom 6%,#120 Top 33%,,,Daily Running,['Daily Running'],0,1,0,0,1,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,,0,0,0,1,1,0,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,10.1,286,10.0,283.0,7.1,6.0,40.2,38.0,33.1,32.0
+Brooks,Hyperion,Tempo,Neutral,7.4 oz / 211g 7.6 oz / 215g,1,12.3 mm 8.0 mm,Heel,True To Size,Balanced,Bad,Decent,Good,Moderate,Narrow,Wide,Moderate,Flexible,Moderate,0,0,30.0 mm 22.0 mm,17.7 mm 14.0 mm,Normal,1,All Seasons,1,#263 Top 41%,#143 Top 23%,,,Tempo,['Tempo'],0,0,1,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,1,0,1,0,0,0,1,0,0,0,1,0,1,0,1,0,0,0,0,1,0,1,0,1,0,0,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,7.4,211,7.6,215.0,12.3,8.0,30.0,22.0,17.7,14.0
+Brooks,Hyperion 2,Competitiontempo,Neutral,7.2 oz / 203g 7.1 oz / 201g,1,9.8 mm 8.0 mm,Heelmid/Forefoot,True To Size,Balanced,Decent,Bad,Decent,Breathable,Medium,Medium,Moderate,Moderate,Moderate,0,0,32.2 mm 31.5 mm,22.4 mm 23.5 mm,Normal,1,Summerall Seasons,1,#127 Top 35%,#115 Top 32%,,,Competition|Tempo,"['Competition', 'Tempo']",1,0,1,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,1,0,1,0,0,0,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,7.2,203,7.1,201.0,9.8,8.0,32.2,31.5,22.4,23.5
+Brooks,Hyperion Elite 4,Competitiontempo,Neutral,7.8 oz / 220g 7.8 oz / 221g,1,11.8 mm 8.0 mm,Heel,True To Size,Balanced,Decent,Good,Good,Breathable,Narrow,Medium,Stiff,Stiff,Flexible,Carbon plate,1,39.1 mm 40.0 mm,27.3 mm 32.0 mm,Normal,1,Summerall Seasons,1,#290 Top 45%,#293 Top 46%,,,Competition|Tempo,"['Competition', 'Tempo']",1,0,1,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,1,0,0,1,0,0,0,1,0,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,7.8,220,7.8,221.0,11.8,8.0,39.1,40.0,27.3,32.0
+Brooks,Hyperion Elite 4 PB,Competition,Neutral,6.9 oz / 197g 7.3 oz / 207g,1,11.7 mm 8.0 mm,Heel,True To Size,Soft,Decent,Good,Decent,Breathable,Narrow,Medium,Stiff,Stiff,Flexible,Carbon plate,0,38.5 mm 40.0 mm,26.8 mm 32.0 mm,Normal,1,Summerall Seasons,1,#83 Top 23%,#237 Bottom 35%,,,Competition,['Competition'],1,0,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,1,0,0,0,1,0,0,0,1,0,1,0,1,0,0,1,0,0,0,1,0,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,6.9,197,7.3,207.0,11.7,8.0,38.5,40.0,26.8,32.0
+Brooks,Hyperion Elite 5,Competition,Neutral,7.2 oz / 204g 7.1 oz / 201g,1,11.2 mm 8.0 mm,Heel,-,Soft,Decent,Good,Bad,Breathable,Narrow,Medium,Stiff,Stiff,Flexible,Carbon plate,1,37.6 mm 40.0 mm,26.4 mm 32.0 mm,Normal,1,Summerall Seasons,1,#28 Top 8%,#111 Top 31%,,,Competition,['Competition'],1,0,0,1,0,Heel,['Heel'],0,0,1,0,,0,0,0,1,1,0,0,0,1,0,0,0,1,1,0,0,1,0,0,1,0,0,0,1,0,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,7.2,204,7.1,201.0,11.2,8.0,37.6,40.0,26.4,32.0
+Brooks,Hyperion GTS,Tempo,Stability,8 oz / 228g 8 oz / 228g,1,9.9 mm 8.0 mm,Heelmid/Forefoot,True To Size,Soft,Decent,Good,Good,Breathable,Narrow,Medium,Moderate,Moderate,Moderate,0,0,28.6 mm 28.0 mm,18.7 mm 22.0 mm,Normal,1,Summerall Seasons,1,#64 Top 10%,#346 Bottom 46%,,,Tempo,['Tempo'],0,0,1,0,1,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,1,0,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,8.0,228,8.0,228.0,9.9,8.0,28.6,28.0,18.7,22.0
+Brooks,Hyperion GTS 2,Daily Runningtempo,Stability,7.8 oz / 220g 7.6 oz / 215g,1,10.7 mm 8.0 mm,Heel,True To Size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Moderate,0,0,33.0 mm 34.0 mm,22.3 mm 26.0 mm,Normal,1,All Seasons,1,#103 Top 29%,#177 Top 49%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,0,1,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,7.8,220,7.6,215.0,10.7,8.0,33.0,34.0,22.3,26.0
+Brooks,Hyperion Max 2,Tempo,Neutral,9.2 oz / 262g 9.2 oz / 261g,0,6.8 mm 6.0 mm,Mid/Forefoot,True To Size,Balanced,Decent,Decent,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,1,33.2 mm 37.0 mm,26.4 mm 31.0 mm,Normal,1,All Seasons,1,#250 Top 39%,#102 Top 16%,,,Tempo,['Tempo'],0,0,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,9.2,262,9.2,261.0,6.8,6.0,33.2,37.0,26.4,31.0
+Brooks,Hyperion Max 3,Daily Runningtempo,Neutral,10 oz / 283g 9.9 oz / 281g,0,10.6 mm 6.0 mm,Heel,True To Size,Soft,Good,Decent,Good,Moderate,Medium,Medium,Moderate,Stiff,Moderate,0,1,45.6 mm 46.0 mm,35.0 mm 40.0 mm,Normal,1,All Seasons,1,#308 Bottom 15%,#43 Top 12%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,1,0,0,0,0,1,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,10.0,283,9.9,281.0,10.6,6.0,45.6,46.0,35.0,40.0
+Brooks,Hyperion Tempo,Tempo,Neutral,7 oz / 198g 7 oz / 199g,1,9.0 mm 8.0 mm,Heelmid/Forefoot,True To Size,Balanced,Good,Decent,Good,Moderate,Medium,Medium,Flexible,Flexible,Moderate,0,0,28.7 mm 28.0 mm,19.7 mm 20.0 mm,Normal,1,All Seasons,1,#94 Top 26%,#171 Top 47%,,,Tempo,['Tempo'],0,0,1,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,0,1,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,7.0,198,7.0,199.0,9.0,8.0,28.7,28.0,19.7,20.0
+Under Armour,Infinite Elite,Daily Running,Neutral,11.1 oz / 315g 11.6 oz / 329g,0,8.1 mm 8.0 mm,Heelmid/Forefoot,True To Size,Soft,Good,Decent,Good,Warm,Medium,Medium,Stiff,Stiff,Stiff,0,1,39.9 mm 40.0 mm,31.8 mm 32.0 mm,Normal,1,All Seasons,1,#236 Top 37%,#513 Bottom 20%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,1,0,0,0,0,1,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,11.1,315,11.6,329.0,8.1,8.0,39.9,40.0,31.8,32.0
+Under Armour,Infinite Elite 2,Daily Running,Neutral,10.2 oz / 288g 10.2 oz / 290g,0,7.0 mm 8.0 mm,Mid/Forefoot,Half Size Small,Soft,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,0,40.1 mm 36.0 mm,33.1 mm 28.0 mm,Normal,1,All Seasons,1,#62 Top 17%,#251 Bottom 31%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,small,0,1,0,0,1,0,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,10.2,288,10.2,290.0,7.0,8.0,40.1,36.0,33.1,28.0
+Under Armour,Infinite Pro,Daily Running,Neutral,10.8 oz / 305g 10.8 oz / 306g,0,8.3 mm 8.0 mm,Heelmid/Forefoot,-,Soft,Decent,Decent,Good,Moderate,Medium,Medium,Stiff,Stiff,Stiff,0,0,41.3 mm,33.0 mm,Normal,1,All Seasons,1,#215 Bottom 41%,#276 Bottom 24%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,,0,0,0,1,1,0,0,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,10.8,305,10.8,306.0,8.3,8.0,41.3,,33.0,
+Nike,InfinityRN 4,Daily Running,Neutral,11.1 oz / 316g 11.1 oz / 316g,0,9.8 mm 9.0 mm,Heelmid/Forefoot,Half Size Small,Soft,Good,Decent,Good,Moderate,Medium,Medium,Moderate,Moderate,Flexible,0,0,34.2 mm 39.0 mm,24.4 mm 30.0 mm,Normalwidex-Wide,1,All Seasons,1,#197 Bottom 46%,#191 Bottom 47%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,small,0,1,0,0,1,0,0,0,0,1,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,11.1,316,11.1,316.0,9.8,9.0,34.2,39.0,24.4,30.0
+Nike,Interact Run,Daily Running,Neutral,8.5 oz / 241g 9.2 oz / 260g,1,9.3 mm 10.0 mm,Heelmid/Forefoot,True To Size,Balanced,Good,Good,Decent,Breathable,Medium,Medium,Moderate,Moderate,Moderate,0,0,29.7 mm 30.0 mm,20.4 mm 20.0 mm,Normal,1,Summerall Seasons,1,#122 Top 34%,#114 Top 32%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,8.5,241,9.2,260.0,9.3,10.0,29.7,30.0,20.4,20.0
+Nike,Invincible 3,Daily Running,Neutral,10 oz / 284g 10 oz / 284g,0,9.6 mm 9.0 mm,Heelmid/Forefoot,True To Size,Soft,-,-,-,Moderate,Narrow,-,Moderate,Stiff,Moderate,0,0,35.2 mm 40.0 mm,25.6 mm 31.0 mm,Normalwidex-Wide,1,All Seasons,1,#293 Bottom 19%,#30 Top 9%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,1,0,1,0,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,10.0,284,10.0,284.0,9.6,9.0,35.2,40.0,25.6,31.0
+Nobull,Journey,Daily Running,Neutral,9.4 oz / 266g 7.6 oz / 215g,0,9.4 mm 10.0 mm,Heelmid/Forefoot,-,Soft,-,-,-,Breathable,Narrow,-,Stiff,Moderate,Flexible,0,0,35.2 mm 35.0 mm,25.8 mm 25.0 mm,Normal,1,Summerall Seasons,1,#129 Top 36%,#340 Bottom 6%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,1,0,1,0,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,9.4,266,7.6,215.0,9.4,10.0,35.2,35.0,25.8,25.0
+Nike,Journey Run,Daily Running,Neutral,10.5 oz / 298g 10.8 oz / 305g,0,8.6 mm 10.0 mm,Heelmid/Forefoot,True To Size,Balanced,Decent,Decent,Decent,Moderate,Medium,Narrow,Moderate,Stiff,Stiff,0,0,33.0 mm 34.0 mm,24.4 mm 24.0 mm,Normal,1,All Seasons,1,#291 Bottom 20%,#106 Top 29%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,10.5,298,10.8,305.0,8.6,10.0,33.0,34.0,24.4,24.0
+Hoka,Kawana 2,Daily Running,Neutral,10.5 oz / 298g 11.1 oz / 314g,0,5.2 mm 5.0 mm,Mid/Forefoot,True To Size,Balanced,Decent,Good,Good,Moderate,Narrow,Narrow,Stiff,Moderate,Stiff,0,0,33.2 mm 30.0 mm,28.0 mm 25.0 mm,Normal,1,All Seasons,1,#224 Bottom 38%,#56 Top 16%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,1,0,0,1,0,0,0,0,1,0,1,0,0,0,1,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,10.5,298,11.1,314.0,5.2,5.0,33.2,30.0,28.0,25.0
+Saucony,Kinvara 12,Tempo,Neutral,7.7 oz / 218g 7.5 oz / 213g,1,4.6 mm 4.0 mm,Mid/Forefoot,True To Size,-,-,-,-,-,Medium,-,-,Flexible,-,0,0,26.1 mm 28.5 mm,21.5 mm 24.5 mm,Normal,1,-,1,#114 Top 18%,#540 Bottom 16%,,,Tempo,['Tempo'],0,0,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,Normal,['Normal'],0,1,0,0,,,0,0,0,7.7,218,7.5,213.0,4.6,4.0,26.1,28.5,21.5,24.5
+Saucony,Kinvara 13,Tempo,Neutral,7.2 oz / 204g 7.2 oz / 204g,1,4.5 mm 4.0 mm,Mid/Forefoot,Slightly Small,Balanced,-,-,-,-,Narrow,-,Moderate,Flexible,Moderate,0,0,26.9 mm 28.5 mm,22.4 mm 24.5 mm,Normalwide,1,-,1,#162 Top 26%,#439 Bottom 31%,,,Tempo,['Tempo'],0,0,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,small,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,1,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,,,0,0,0,7.2,204,7.2,204.0,4.5,4.0,26.9,28.5,22.4,24.5
+Saucony,Kinvara 14,Tempo,Neutral,6.8 oz / 194g 6.8 oz / 194g,1,4.1 mm 4.0 mm,Mid/Forefoot,True To Size,Balanced,Bad,-,-,Breathable,Medium,Narrow,Flexible,Flexible,Moderate,0,0,30.3 mm 31.0 mm,26.2 mm 27.0 mm,Normalwide,1,Summerall Seasons,1,#512 Bottom 20%,#358 Bottom 44%,,,Tempo,['Tempo'],0,0,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,Summer|All,"['Summer', 'All']",1,1,0,6.8,194,6.8,194.0,4.1,4.0,30.3,31.0,26.2,27.0
+Saucony,Kinvara 15,Daily Runningtempo,Neutral,6.8 oz / 194g 6.7 oz / 190g,1,4.4 mm 4.0 mm,Mid/Forefoot,Slightly Small,Balanced,Bad,Good,Good,Breathable,Medium,Medium,Flexible,Flexible,Flexible,0,0,27.9 mm 30.0 mm,23.5 mm 26.0 mm,Normalwide,1,Summerall Seasons,1,#243 Bottom 33%,#149 Top 41%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,small,0,1,0,0,0,1,0,1,0,0,0,0,1,0,0,1,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,Summer|All,"['Summer', 'All']",1,1,0,6.8,194,6.7,190.0,4.4,4.0,27.9,30.0,23.5,26.0
+Saucony,Kinvara Pro,Daily Runningtempo,Neutral,9.9 oz / 281g 9.5 oz / 269g,0,10.3 mm 8.0 mm,Heel,True To Size,Balanced,Good,Good,Good,Warm,Medium,Narrow,Stiff,Stiff,Moderate,Carbon plate,0,45.6 mm 42.0 mm,35.3 mm 34.0 mm,Normalwide,1,All Seasons,1,#72 Top 20%,#213 Bottom 41%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,1,0,0,0,0,1,0,0,1,0,1,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,9.9,281,9.5,269.0,10.3,8.0,45.6,42.0,35.3,34.0
+Brooks,Launch 10,Daily Runningtempo,Neutral,8.1 oz / 230g 8.2 oz / 232g,1,10.0 mm 10.0 mm,Heelmid/Forefoot,True To Size,Soft,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,33.8 mm 34.0 mm,23.8 mm 24.0 mm,Normal,1,All Seasons,1,#338 Bottom 47%,#280 Top 44%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,8.1,230,8.2,232.0,10.0,10.0,33.8,34.0,23.8,24.0
+Brooks,Launch 11,Daily Runningtempo,Neutral,8.4 oz / 237g 7.7 oz / 218g,1,9.5 mm 8.0 mm,Heelmid/Forefoot,True To Size,Balanced,Decent,Bad,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,33.5 mm 35.5 mm,24.0 mm 27.5 mm,Normalwide,1,All Seasons,1,#249 Bottom 31%,#103 Top 29%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,1,0,1,0,0,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,8.4,237,7.7,218.0,9.5,8.0,33.5,35.5,24.0,27.5
+Brooks,Launch 8,Daily Runningtempo,Neutral,8.5 oz / 240g 8.8 oz / 249g,1,9.6 mm 10.0 mm,Heelmid/Forefoot,True To Size,-,-,-,-,-,Narrow,-,-,Moderate,-,0,0,30.5 mm 26.0 mm,20.9 mm 16.0 mm,Normal,1,-,1,#69 Top 11%,#411 Bottom 36%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Normal,['Normal'],0,1,0,0,,,0,0,0,8.5,240,8.8,249.0,9.6,10.0,30.5,26.0,20.9,16.0
+Brooks,Launch GTS 10,Daily Runningtempo,Stability,8.5 oz / 241g 8.4 oz / 238g,1,12.2 mm 10.0 mm,Heel,True To Size,Soft,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,0,35.4 mm 34.0 mm,23.2 mm 24.0 mm,Normalwide,1,All Seasons,1,#239 Bottom 34%,#295 Bottom 19%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,0,1,Heel,['Heel'],0,0,1,0,true,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,8.5,241,8.4,238.0,12.2,10.0,35.4,34.0,23.2,24.0
+Brooks,Launch GTS 9,Daily Runningtempo,Stability,8.6 oz / 245g 8.7 oz / 246g,1,10.4 mm 10.0 mm,Heel,True To Size,Balanced,Decent,Good,Good,Warm,Narrow,Medium,Stiff,Stiff,Stiff,0,0,33.8 mm 36.0 mm,23.4 mm 26.0 mm,Normal,1,All Seasons,1,#306 Top 48%,#566 Bottom 12%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,0,1,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,1,0,0,0,1,0,0,0,1,0,0,1,0,0,1,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,8.6,245,8.7,246.0,10.4,10.0,33.8,36.0,23.4,26.0
+Brooks,Levitate 5,Daily Running,Neutral,10.7 oz / 304g 11 oz / 311g,0,9.1 mm 8.0 mm,Heelmid/Forefoot,-,-,-,-,-,-,Medium,-,Stiff,Moderate,Stiff,0,0,31.4 mm 29.0 mm,22.3 mm 21.0 mm,Normal,1,-,1,#285 Top 45%,#495 Bottom 23%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,1,Normal,['Normal'],0,1,0,0,,,0,0,0,10.7,304,11.0,311.0,9.1,8.0,31.4,29.0,22.3,21.0
+Brooks,Levitate 6,Daily Running,Neutral,10.7 oz / 304g 10.9 oz / 309g,0,7.7 mm 8.0 mm,Mid/Forefoot,True To Size,Soft,Good,Good,Good,Breathable,Narrow,Medium,Stiff,Moderate,Moderate,0,0,34.3 mm 32.5 mm,26.6 mm 24.5 mm,Normal,1,Summerall Seasons,1,#71 Top 20%,#256 Bottom 30%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,1,1,0,0,1,0,0,0,1,0,0,0,1,0,1,0,0,1,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,10.7,304,10.9,309.0,7.7,8.0,34.3,32.5,26.6,24.5
+Brooks,Levitate Stealthfit 5,Daily Running,Neutral,10.1 oz / 285g 9.8 oz / 278g,0,9.5 mm 8.0 mm,Heelmid/Forefoot,-,Soft,Good,Good,Good,Warm,Narrow,Wide,Stiff,Flexible,Moderate,0,0,28.0 mm 29.0 mm,18.5 mm 21.0 mm,Normal,1,Winter,1,#496 Bottom 23%,#589 Bottom 8%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,,0,0,0,1,1,0,0,0,0,1,0,0,1,0,0,1,0,0,1,1,0,0,0,0,1,0,0,1,1,0,0,0,1,0,Normal,['Normal'],0,1,0,0,Winter,['Winter'],0,0,1,10.1,285,9.8,278.0,9.5,8.0,28.0,29.0,18.5,21.0
+Brooks,Levitate Stealthfit 6,Daily Running,Neutral,9.9 oz / 281g 9.9 oz / 280g,0,7.5 mm 8.0 mm,Mid/Forefoot,-,Balanced,Good,Good,Good,Warm,Narrow,Medium,Stiff,Moderate,Moderate,0,0,32.9 mm 33.0 mm,25.4 mm 25.0 mm,Normal,1,All Seasons,1,#266 Bottom 27%,#277 Bottom 24%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,,0,0,0,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,1,0,0,0,1,0,0,0,1,0,1,0,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,9.9,281,9.9,280.0,7.5,8.0,32.9,33.0,25.4,25.0
+Reebok,Lite 3,Daily Running,Neutral,8.7 oz / 248g 10.2 oz / 289g,1,13.4 mm,Heel,True To Size,Balanced,-,-,-,Warm,Medium,-,Stiff,Flexible,Flexible,0,0,32.7 mm,19.3 mm,Normal,0,All Seasons,0,#305 Bottom 16%,#355 Bottom 3%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,1,0,0,1,0,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,8.7,248,10.2,289.0,13.4,,32.7,,19.3,
+Hoka,Mach 4,Daily Runningtempo,Neutral,7.9 oz / 223g 8.2 oz / 232g,1,4.9 mm 5.0 mm,Mid/Forefoot,True To Size,-,-,-,-,-,Narrow,-,-,Moderate,-,0,1,30.6 mm 29.0 mm,25.7 mm 24.0 mm,Normal,1,-,1,#48 Top 8%,#209 Top 33%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,Normal,['Normal'],0,1,0,0,,,0,0,0,7.9,223,8.2,232.0,4.9,5.0,30.6,29.0,25.7,24.0
+Hoka,Mach 5,Daily Runningtempo,Neutral,7.9 oz / 225g 8.2 oz / 232g,1,5.7 mm 5.0 mm,Mid/Forefoot,True To Size,Soft,-,-,-,Moderate,Narrow,-,Flexible,Flexible,Moderate,0,1,30.7 mm 29.0 mm,25.0 mm 24.0 mm,Normalwide,1,All Seasons,1,#121 Top 19%,#134 Top 21%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,1,0,0,0,1,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,7.9,225,8.2,232.0,5.7,5.0,30.7,29.0,25.0,24.0
+Hoka,Mach 6,Daily Running,Neutral,8.2 oz / 232g 8.3 oz / 235g,1,9.6 mm 5.0 mm,Heelmid/Forefoot,True To Size,Balanced,Decent,Bad,Good,Breathable,Medium,Narrow,Moderate,Stiff,Stiff,0,1,36.0 mm 37.0 mm,26.4 mm 32.0 mm,Normalwide,1,Summerall Seasons,1,#153 Top 42%,#13 Top 4%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,1,0,1,0,0,0,0,1,1,0,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,Summer|All,"['Summer', 'All']",1,1,0,8.2,232,8.3,235.0,9.6,5.0,36.0,37.0,26.4,32.0
+Hoka,Mach X 3,Daily Runningtempo,Neutral,9.3 oz / 264g 8.9 oz / 252g,0,9.5 mm 5.0 mm,Heelmid/Forefoot,-,Soft,Decent,Good,Good,Moderate,Narrow,Narrow,Stiff,Stiff,Flexible,0,1,42.9 mm 44.0 mm,33.4 mm 39.0 mm,Normalwide,1,All Seasons,1,#351 Bottom 4%,#73 Top 20%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,,0,0,0,1,1,0,0,0,1,0,0,0,1,0,0,1,0,1,0,1,0,0,1,0,0,0,0,1,0,0,1,1,0,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,9.3,264,8.9,252.0,9.5,5.0,42.9,44.0,33.4,39.0
+Asics,Magic Speed,Tempo,Neutral,8.2 oz / 233g 7.9 oz / 224g,1,8.3 mm 5.0 mm,Heelmid/Forefoot,True To Size,Balanced,-,-,-,Breathable,Narrow,-,Stiff,Stiff,Flexible,Carbon plate,1,32.5 mm 29.0 mm,24.2 mm 24.0 mm,Narrownormal,0,Summerall Seasons,0,#122 Top 19%,#273 Top 43%,,,Tempo,['Tempo'],0,0,1,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,1,1,0,0,Narrow|Normal,"['Narrow', 'Normal']",1,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,8.2,233,7.9,224.0,8.3,5.0,32.5,29.0,24.2,24.0
+Asics,Magic Speed 2,Tempo,Neutral,8 oz / 228g 8.1 oz / 230g,1,8.7 mm 7.0 mm,Heelmid/Forefoot,True To Size,-,-,-,-,Breathable,Narrow,-,Stiff,Stiff,Flexible,Carbon plate,1,35.1 mm 31.0 mm,26.4 mm 24.0 mm,Normal,0,Summerall Seasons,0,#176 Top 28%,#565 Bottom 12%,,,Tempo,['Tempo'],0,0,1,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,8.0,228,8.1,230.0,8.7,7.0,35.1,31.0,26.4,24.0
+Asics,Magic Speed 3,Tempo,Neutral,7.4 oz / 211g 7.8 oz / 220g,1,9.8 mm 7.0 mm,Heelmid/Forefoot,Slightly Small,Soft,Bad,Decent,Good,Moderate,Narrow,Medium,Stiff,Stiff,Flexible,Carbon plate,1,36.3 mm 36.0 mm,26.5 mm 29.0 mm,Normal,1,All Seasons,1,#31 Top 5%,#332 Bottom 48%,,,Tempo,['Tempo'],0,0,1,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,small,0,1,0,0,1,0,0,1,0,0,0,1,0,0,0,1,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,7.4,211,7.8,220.0,9.8,7.0,36.3,36.0,26.5,29.0
+Puma,MagMax Nitro,Daily Running,Neutral,10.3 oz / 291g 10.3 oz / 292g,0,9.6 mm 8.0 mm,Heelmid/Forefoot,True To Size,Soft,Decent,Decent,Decent,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,1,42.9 mm 47.0 mm,33.3 mm 39.0 mm,Normal,1,All Seasons,1,#31 Top 9%,#164 Top 45%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,10.3,291,10.3,292.0,9.6,8.0,42.9,47.0,33.3,39.0
+Puma,Magnify Nitro 2,Daily Running,Neutral,9.9 oz / 281g 10.1 oz / 286g,0,9.3 mm 10.0 mm,Heelmid/Forefoot,Half Size Large,Soft,Good,Bad,Good,Warm,Medium,Narrow,Moderate,Stiff,Moderate,0,0,37.1 mm 39.0 mm,27.8 mm 29.0 mm,Normal,1,All Seasons,1,#256 Bottom 29%,#246 Bottom 32%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,large,1,0,0,0,1,0,0,0,0,1,1,0,0,0,0,1,0,0,1,0,1,0,1,0,0,0,1,0,0,0,1,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,9.9,281,10.1,286.0,9.3,10.0,37.1,39.0,27.8,29.0
+Skechers,Max Cushioning Elite,Daily Running,Neutral,11.9 oz / 336g 11.5 oz / 326g,0,16.1 mm 6.0 mm,Heel,True To Size,-,-,-,-,-,Narrow,-,Stiff,-,-,0,0,42.3 mm,26.2 mm,Narrownormalx-Wide,0,-,0,#520 Bottom 19%,#229 Top 36%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Narrow|Normal|X-Wide,"['Narrow', 'Normal', 'X-Wide']",1,1,0,1,,,0,0,0,11.9,336,11.5,326.0,16.1,6.0,42.3,,26.2,
+Skechers,Max Cushioning Elite 2.0,Daily Running,Neutral,9.5 oz / 269g 9.5 oz / 270g,0,8.8 mm 6.0 mm,Heelmid/Forefoot,True To Size,Balanced,Decent,Decent,Decent,Warm,Narrow,Medium,Moderate,Stiff,Moderate,0,1,36.4 mm 39.0 mm,27.6 mm 33.0 mm,Normalwide,1,All Seasons,1,#242 Bottom 34%,#367 Bottom 1%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,1,0,0,0,1,0,0,1,0,0,0,1,0,1,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,9.5,269,9.5,270.0,8.8,6.0,36.4,39.0,27.6,33.0
+Asics,Megablast,Competitiontempo,Neutral,7.7 oz / 218g 7.9 oz / 224g,1,9.9 mm 8.0 mm,Heelmid/Forefoot,True To Size,Soft,Bad,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,1,45.1 mm 45.0 mm,35.2 mm 37.0 mm,Normal,1,All Seasons,1,#296 Bottom 19%,#49 Top 14%,,,Competition|Tempo,"['Competition', 'Tempo']",1,0,1,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,1,0,0,1,0,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,7.7,218,7.9,224.0,9.9,8.0,45.1,45.0,35.2,37.0
+Asics,Metaspeed Edge,Competition,Neutral,6.2 oz / 176g 6.7 oz / 190g,1,8.0 mm 8.0 mm,Heelmid/Forefoot,Slightly Small,-,-,-,-,-,Narrow,-,Stiff,Stiff,Flexible,Carbon plate,1,31.6 mm 29.0 mm,23.6 mm 21.0 mm,Normal,0,-,0,#55 Top 16%,#266 Bottom 27%,,,Competition,['Competition'],1,0,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,small,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,,,0,0,0,6.2,176,6.7,190.0,8.0,8.0,31.6,29.0,23.6,21.0
+Asics,Metaspeed Edge Tokyo,Competition,Neutral,5.6 oz / 159g 6 oz / 170g,1,6.9 mm 5.0 mm,Mid/Forefoot,True To Size,Soft,Bad,Good,Good,Breathable,Narrow,Medium,Moderate,Stiff,Flexible,Carbon plate,1,38.9 mm 39.5 mm,32.0 mm 34.5 mm,Normalwide,1,Summerall Seasons,1,#189 Bottom 48%,#157 Top 43%,,,Competition,['Competition'],1,0,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,1,0,0,1,0,0,0,0,1,0,0,1,1,0,0,1,0,0,0,1,0,0,1,0,0,0,1,1,0,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,Summer|All,"['Summer', 'All']",1,1,0,5.6,159,6.0,170.0,6.9,5.0,38.9,39.5,32.0,34.5
+Asics,Metaspeed Edge+,Competition,Neutral,7.3 oz / 208g 7.4 oz / 210g,1,8.1 mm 8.0 mm,Heelmid/Forefoot,True To Size,Firm,-,-,-,Breathable,Narrow,-,Stiff,Stiff,Flexible,Carbon plate,1,33.3 mm 39.0 mm,25.2 mm 31.0 mm,Normalwide,0,Summerall Seasons,0,#17 Top 5%,#188 Bottom 48%,,,Competition,['Competition'],1,0,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,1,1,0,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,Summer|All,"['Summer', 'All']",1,1,0,7.3,208,7.4,210.0,8.1,8.0,33.3,39.0,25.2,31.0
+Asics,Metaspeed Ray,Competition,Neutral,4.6 oz / 129g 4.6 oz / 129g,1,9.8 mm 5.0 mm,Heelmid/Forefoot,Slightly Large,Soft,Bad,Good,Decent,Moderate,Narrow,Narrow,Moderate,Moderate,Flexible,Carbon plate,1,39.8 mm 39.5 mm,30.0 mm 34.5 mm,Normal,1,All Seasons,1,#236 Bottom 35%,#118 Top 33%,,,Competition,['Competition'],1,0,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,large,1,0,0,0,1,0,0,1,0,0,0,0,1,0,1,0,0,1,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,4.6,129,4.6,129.0,9.8,5.0,39.8,39.5,30.0,34.5
+Asics,Metaspeed Sky,Competition,Neutral,6.7 oz / 191g 7 oz / 198g,1,2.5 mm 5.0 mm,Mid/Forefoot,True To Size,-,-,-,-,Breathable,Narrow,-,-,Stiff,-,Carbon plate,1,33.7 mm 33.0 mm,31.2 mm 28.0 mm,Normal,0,Summerall Seasons,0,#15 Top 5%,#62 Top 17%,#118 Top 33%,,Competition,['Competition'],1,0,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,6.7,191,7.0,198.0,2.5,5.0,33.7,33.0,31.2,28.0
+Asics,Metaspeed Sky Paris,Competition,Neutral,6.5 oz / 183g 6.4 oz / 181g,1,6.5 mm 5.0 mm,Mid/Forefoot,Slightly Small,Balanced,Good,Good,Good,Breathable,Narrow,Medium,Stiff,Stiff,Flexible,Carbon plate,1,39.1 mm 39.5 mm,32.6 mm 34.5 mm,Normalwide,1,Summerall Seasons,1,#80 Top 22%,#97 Top 27%,,,Competition,['Competition'],1,0,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,small,0,1,0,0,0,1,0,0,0,1,0,0,1,0,0,1,1,0,0,1,0,0,0,1,0,0,0,1,0,0,1,1,0,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,Summer|All,"['Summer', 'All']",1,1,0,6.5,183,6.4,181.0,6.5,5.0,39.1,39.5,32.6,34.5
+Asics,Metaspeed Sky Tokyo,Competition,Neutral,5.7 oz / 163g 6 oz / 170g,1,6.0 mm 5.0 mm,Mid/Forefoot,True To Size,Soft,Bad,Good,Decent,Breathable,Narrow,Narrow,Stiff,Stiff,Flexible,Carbon plate,1,38.7 mm 39.5 mm,32.7 mm 34.5 mm,Normalwide,1,Summerall Seasons,1,#122 Top 34%,#122 Top 34%,,,Competition,['Competition'],1,0,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,1,0,0,1,0,0,0,0,1,0,1,0,1,0,0,1,0,0,1,0,0,0,0,1,0,0,1,1,0,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,Summer|All,"['Summer', 'All']",1,1,0,5.7,163,6.0,170.0,6.0,5.0,38.7,39.5,32.7,34.5
+Asics,Metaspeed Sky+,Competition,Neutral,7.2 oz / 205g 7.2 oz / 205g,1,2.7 mm 5.0 mm,Mid/Forefoot,True To Size,-,-,Good,-,Breathable,Narrow,-,Stiff,Stiff,Flexible,Carbon plate,1,33.5 mm 39.0 mm,30.8 mm 34.0 mm,Normalwide,0,Summerall Seasons,0,#23 Top 7%,#90 Top 25%,,,Competition,['Competition'],1,0,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,1,1,0,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,Summer|All,"['Summer', 'All']",1,1,0,7.2,205,7.2,205.0,2.7,5.0,33.5,39.0,30.8,34.0
+Mizuno,Mizuno Wave Horizon 7,Daily Running,Stability,11.6 oz / 329g 11.8 oz / 334g,0,7.2 mm 8.0 mm,Heelmid/Forefoot,Slightly Small,Balanced,Decent,Bad,Good,Moderate,Medium,Wide,Stiff,Stiff,Stiff,0,0,39.1 mm 41.0 mm,31.9 mm 33.0 mm,Normalwide,1,All Seasons,1,#134 Top 37%,#294 Bottom 19%,,,Daily Running,['Daily Running'],0,1,0,0,1,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,small,0,1,0,0,0,1,0,0,1,0,1,0,0,0,0,1,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,11.6,329,11.8,334.0,7.2,8.0,39.1,41.0,31.9,33.0
+Mizuno,Neo Vista,Daily Runningtempo,Neutral,9.1 oz / 259g 9.4 oz / 266g,0,9.6 mm 8.0 mm,Heelmid/Forefoot,True To Size,Soft,Good,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Flexible,0,1,44.9 mm 44.5 mm,35.3 mm 36.5 mm,Normal,1,All Seasons,1,#34 Top 6%,#179 Top 28%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,9.1,259,9.4,266.0,9.6,8.0,44.9,44.5,35.3,36.5
+Mizuno,Neo Vista 2,Daily Runningtempo,Neutral,9.3 oz / 264g 9.4 oz / 266g,0,8.5 mm 8.0 mm,Heelmid/Forefoot,Half Size Large,Soft,Good,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Flexible,0,1,46.0 mm 44.5 mm,37.5 mm 36.5 mm,Normalwide,1,All Seasons,1,#280 Bottom 23%,#72 Top 20%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,large,1,0,0,0,1,0,0,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,1,0,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,9.3,264,9.4,266.0,8.5,8.0,46.0,44.5,37.5,36.5
+Mizuno,Neo Zen,Daily Runningtempo,Neutral,8.3 oz / 234g 8.5 oz / 240g,1,7.0 mm 6.0 mm,Mid/Forefoot,True To Size,Soft,Good,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Flexible,0,0,41.0 mm 40.0 mm,34.0 mm 34.0 mm,Normalwide,1,All Seasons,1,#4 Top 2%,#74 Top 21%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,8.3,234,8.5,240.0,7.0,6.0,41.0,40.0,34.0,34.0
+Nike,Nike Alphafly 3,Competition,Neutral,7.1 oz / 201g 7 oz / 198g,1,8.5 mm 8.0 mm,Heelmid/Forefoot,Slightly Small,Soft,Bad,Good,Bad,Breathable,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,38.1 mm 40.0 mm,29.6 mm 32.0 mm,Normal,0,Summerall Seasons,0,#124 Top 34%,#20 Top 6%,,,Competition,['Competition'],1,0,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,small,0,1,0,0,1,0,0,1,0,0,0,0,1,1,0,0,1,0,0,0,1,0,0,1,0,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,7.1,201,7.0,198.0,8.5,8.0,38.1,40.0,29.6,32.0
+Asics,Noosa Tri 14,Daily Runningtempo,Neutral,7.5 oz / 213g 9.2 oz / 260g,1,8.0 mm 5.0 mm,Heelmid/Forefoot,Slightly Small,Soft,-,-,-,Moderate,Narrow,-,Stiff,Moderate,Flexible,0,1,30.2 mm 26.0 mm,22.2 mm 21.0 mm,Normal,1,All Seasons,1,#2 Top 1%,#373 Bottom 42%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,small,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,1,0,1,0,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,7.5,213,9.2,260.0,8.0,5.0,30.2,26.0,22.2,21.0
+Asics,Noosa Tri 15,Daily Runningtempo,Neutral,7.7 oz / 218g 7.8 oz / 221g,1,7.7 mm 5.0 mm,Mid/Forefoot,Slightly Small,Soft,Bad,Good,Good,Breathable,Wide,Medium,Moderate,Stiff,Moderate,0,1,34.6 mm 34.0 mm,26.9 mm 29.0 mm,Normal,1,Summerall Seasons,1,#24 Top 4%,#331 Bottom 48%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,small,0,1,0,0,1,0,0,1,0,0,0,0,1,0,0,1,1,0,0,0,0,1,0,1,0,0,1,0,0,0,1,0,1,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,7.7,218,7.8,221.0,7.7,5.0,34.6,34.0,26.9,29.0
+Asics,Noosa Tri 16,Daily Runningtempo,Neutral,7.7 oz / 217g 7.6 oz / 215g,1,5.9 mm 5.0 mm,Mid/Forefoot,Half Size Small,Soft,Bad,Good,Decent,Breathable,Wide,Wide,Moderate,Stiff,Moderate,0,0,32.8 mm 34.5 mm,26.9 mm 29.5 mm,Normal,1,Summerall Seasons,1,#244 Bottom 33%,#125 Top 35%,#331 Bottom 48%,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,small,0,1,0,0,1,0,0,1,0,0,0,0,1,0,1,0,1,0,0,0,0,1,0,0,1,0,1,0,0,0,1,0,1,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,7.7,217,7.6,215.0,5.9,5.0,32.8,34.5,26.9,29.5
+Asics,Novablast 2,Daily Runningtempo,Neutral,9.6 oz / 272g 9.9 oz / 280g,0,13.7 mm 8.0 mm,Heel,True To Size,-,-,-,-,-,Medium,-,-,Flexible,-,0,1,39.3 mm 30.0 mm,25.6 mm 22.0 mm,Normal,1,-,1,#184 Top 29%,#465 Bottom 27%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,Normal,['Normal'],0,1,0,0,,,0,0,0,9.6,272,9.9,280.0,13.7,8.0,39.3,30.0,25.6,22.0
+Asics,Novablast 4,Daily Running,Neutral,9.1 oz / 259g 9 oz / 255g,0,9.0 mm 8.0 mm,Heelmid/Forefoot,True To Size,Soft,Decent,Good,Good,Moderate,Narrow,Medium,Stiff,Stiff,Moderate,0,1,39.2 mm 41.5 mm,30.2 mm 33.5 mm,Normalwide,1,All Seasons,1,#22 Top 4%,#76 Top 12%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,0,1,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,9.1,259,9.0,255.0,9.0,8.0,39.2,41.5,30.2,33.5
+Asics,Novablast 5,Daily Runningtempo,Neutral,9 oz / 254g 9 oz / 255g,0,7.4 mm 8.0 mm,Heelmid/Forefoot,True To Size,Soft,Bad,Decent,Good,Moderate,Medium,Medium,Moderate,Stiff,Moderate,0,1,40.9 mm 41.5 mm,33.5 mm 33.5 mm,Normalwide,1,All Seasons,1,#13 Top 4%,#6 Top 2%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,1,0,0,1,0,0,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,9.0,254,9.0,255.0,7.4,8.0,40.9,41.5,33.5,33.5
+Diadora,Nucleo 2,Daily Running,Neutral,9.7 oz / 276g 9.7 oz / 275g,0,8.0 mm 5.0 mm,Heelmid/Forefoot,True To Size,Soft,Bad,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Moderate,0,0,38.7 mm 38.0 mm,30.7 mm 33.0 mm,Normalwide,1,All Seasons,1,#294 Bottom 19%,#195 Bottom 46%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,1,0,0,1,0,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,9.7,276,9.7,275.0,8.0,5.0,38.7,38.0,30.7,33.0
+Saucony,Omni 22,Daily Running,Stability,10.1 oz / 285g 10.1 oz / 286g,0,7.3 mm 8.0 mm,Mid/Forefoot,True To Size,Balanced,Bad,Good,Good,Moderate,Medium,Medium,Flexible,Moderate,Flexible,0,0,33.0 mm 35.0 mm,25.7 mm 27.0 mm,Normalwide,1,All Seasons,1,#330 Bottom 9%,#273 Bottom 25%,,,Daily Running,['Daily Running'],0,1,0,0,1,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,1,0,1,0,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,1,0,0,0,1,0,1,0,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,10.1,285,10.1,286.0,7.3,8.0,33.0,35.0,25.7,27.0
+Altra,Paradigm 7,Daily Running,Stability,9.3 oz / 264g 9.8 oz / 279g,0,0.1 mm 0.0 mm,Mid/Forefoot,True To Size,Soft,Decent,Decent,Good,Breathable,Wide,Wide,Moderate,Moderate,Flexible,0,0,27.6 mm 30.0 mm,27.5 mm 30.0 mm,Normalwide,1,Summerall Seasons,1,#283 Bottom 22%,#150 Top 41%,,,Daily Running,['Daily Running'],0,1,0,0,1,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,1,0,0,0,1,0,0,1,0,0,0,1,1,0,0,0,0,1,0,0,1,0,1,0,0,1,0,1,0,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,Summer|All,"['Summer', 'All']",1,1,0,9.3,264,9.8,279.0,0.1,0.0,27.6,30.0,27.5,30.0
+Nike,Pegasus 40,Daily Running,Neutral,9.7 oz / 275g 9.7 oz / 275g,0,9.7 mm 10.0 mm,Heelmid/Forefoot,True To Size,Soft,Decent,-,-,Moderate,Medium,Medium,Moderate,Flexible,Flexible,0,0,30.2 mm 33.0 mm,20.5 mm 23.0 mm,Normalwidex-Wide,1,All Seasons,1,#387 Bottom 40%,#93 Top 15%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,9.7,275,9.7,275.0,9.7,10.0,30.2,33.0,20.5,23.0
+Nike,Pegasus 41,Daily Running,Neutral,9.9 oz / 281g 10 oz / 283g,0,11.4 mm 10.0 mm,Heel,True To Size,Soft,Decent,Good,Good,Moderate,Medium,Medium,Flexible,Flexible,Stiff,0,0,33.6 mm 37.0 mm,22.2 mm 27.0 mm,Normalwidex-Wide,1,All Seasons,1,#130 Top 36%,#11 Top 4%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,0,0,1,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,9.9,281,10.0,283.0,11.4,10.0,33.6,37.0,22.2,27.0
+Nike,Pegasus 41 GTX,Daily Running,Neutral,11.1 oz / 315g 10 oz / 283g,0,11.9 mm 10.0 mm,Heel,True To Size,Soft,Decent,Good,Bad,Warm,Medium,Medium,Moderate,Moderate,Moderate,0,1,36.3 mm 37.0 mm,24.4 mm 27.0 mm,Normal,1,Winter,1,#216 Bottom 40%,#159 Top 44%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,1,0,0,0,1,0,0,0,1,1,0,0,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,Normal,['Normal'],0,1,0,0,Winter,['Winter'],0,0,1,11.1,315,10.0,283.0,11.9,10.0,36.3,37.0,24.4,27.0
+Nike,Pegasus EasyOn,Daily Running,Neutral,10.1 oz / 286g 10.4 oz / 295g,0,11.6 mm 10.0 mm,Heel,True To Size,Soft,Bad,Good,Decent,Breathable,Medium,Medium,Stiff,Moderate,Stiff,0,0,33.6 mm 34.0 mm,22.0 mm 24.0 mm,Normal,1,Summerall Seasons,1,#265 Bottom 27%,#197 Bottom 46%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,1,0,0,1,0,0,0,0,1,0,1,0,1,0,0,0,1,0,0,1,0,0,0,1,0,1,0,0,0,1,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,10.1,286,10.4,295.0,11.6,10.0,33.6,34.0,22.0,24.0
+Nike,Pegasus Plus,Daily Runningtempo,Neutral,8.6 oz / 244g 8.6 oz / 244g,1,9.4 mm 10.0 mm,Heelmid/Forefoot,True To Size,Soft,Decent,Good,Decent,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,33.0 mm 34.0 mm,23.6 mm 24.0 mm,Normal,1,All Seasons,1,#58 Top 16%,#46 Top 13%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,1,0,0,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,8.6,244,8.6,244.0,9.4,10.0,33.0,34.0,23.6,24.0
+Nike,Pegasus Premium,Daily Runningtempo,Neutral,10.9 oz / 308g 10.9 oz / 309g,0,11.8 mm 10.0 mm,Heel,True To Size,Soft,Bad,Good,Decent,Breathable,Narrow,Narrow,Stiff,Stiff,Stiff,0,1,42.8 mm 45.0 mm,31.0 mm 35.0 mm,Normal,1,Summerall Seasons,1,#243 Bottom 33%,#15 Top 5%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,1,0,0,1,0,0,0,0,1,0,1,0,1,0,0,1,0,0,1,0,0,0,0,1,0,0,1,0,0,1,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,10.9,308,10.9,309.0,11.8,10.0,42.8,45.0,31.0,35.0
+Nike,Pegasus Turbo,Tempo,Neutral,9.7 oz / 275g 9.7 oz / 275g,0,10.0 mm 10.0 mm,Heelmid/Forefoot,Slightly Small,Soft,-,-,-,Moderate,Narrow,Narrow,Stiff,Moderate,Flexible,0,0,32.0 mm 32.0 mm,22.0 mm 22.0 mm,Normal,1,All Seasons,1,#328 Bottom 10%,#150 Top 42%,,,Tempo,['Tempo'],0,0,1,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,small,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,1,0,1,0,1,0,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,9.7,275,9.7,275.0,10.0,10.0,32.0,32.0,22.0,22.0
+Salomon,Phantasm 2,Daily Runningtempo,Neutral,9.2 oz / 261g 9 oz / 255g,0,11.2 mm 9.0 mm,Heel,Slightly Small,Balanced,Bad,Good,Decent,Moderate,Medium,Wide,Stiff,Stiff,Moderate,0,0,34.4 mm 35.0 mm,23.2 mm 26.0 mm,Normal,1,All Seasons,1,#196 Bottom 46%,#327 Bottom 10%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Heel,['Heel'],0,0,1,0,small,0,1,0,0,0,1,0,1,0,0,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,9.2,261,9.0,255.0,11.2,9.0,34.4,35.0,23.2,26.0
+Topo,Phantom 3,Daily Running,Neutral,9.5 oz / 269g 9.2 oz / 261g,0,5.8 mm 5.0 mm,Mid/Forefoot,True To Size,Soft,Decent,Good,Good,Moderate,Medium,Wide,Moderate,Stiff,Stiff,0,0,32.6 mm 32.0 mm,26.8 mm 27.0 mm,Normalwide,1,All Seasons,1,#90 Top 25%,#169 Top 47%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,0,1,0,1,0,0,0,1,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,9.5,269,9.2,261.0,5.8,5.0,32.6,32.0,26.8,27.0
+New Balance,Propel v4,Daily Running,Neutral,9.7 oz / 276g 10.7 oz / 302g,0,4.5 mm 6.0 mm,Mid/Forefoot,True To Size,Soft,Decent,Good,Good,Moderate,Narrow,Medium,Moderate,Stiff,Moderate,0,0,33.7 mm 33.5 mm,29.2 mm 27.5 mm,Normalwide,1,All Seasons,1,#396 Bottom 38%,#432 Bottom 33%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,0,1,0,1,0,0,0,1,0,0,1,0,0,0,1,0,1,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,9.7,276,10.7,302.0,4.5,6.0,33.7,33.5,29.2,27.5
+Altra,Provision 6,Daily Running,Stability,9.1 oz / 259g 10.8 oz / 307g,0,0.0 mm,Mid/Forefoot,Slightly Small,-,-,-,-,-,Medium,-,Stiff,-,-,0,0,28.0 mm,28.0 mm,Normal,0,-,0,#352 Bottom 45%,#572 Bottom 11%,,,Daily Running,['Daily Running'],0,1,0,0,1,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,small,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,Normal,['Normal'],0,1,0,0,,,0,0,0,9.1,259,10.8,307.0,0.0,,28.0,,28.0,
+Altra,Provision 7,Daily Running,Stability,9.1 oz / 259g 9.7 oz / 274g,0,4.8 mm 0.0 mm,Mid/Forefoot,True To Size,Balanced,Decent,Good,Good,Breathable,Narrow,Wide,Stiff,Stiff,Flexible,0,0,32.6 mm 28.0 mm,27.8 mm 28.0 mm,Normal,1,Summerall Seasons,1,#483 Bottom 25%,#472 Bottom 26%,,,Daily Running,['Daily Running'],0,1,0,0,1,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,1,0,0,1,0,0,0,0,1,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,9.1,259,9.7,274.0,4.8,0.0,32.6,28.0,27.8,28.0
+Altra,Provision 8,Daily Running,Stability,9.6 oz / 273g 10.2 oz / 289g,0,0.2 mm 0.0 mm,Mid/Forefoot,True To Size,Balanced,Decent,Decent,Good,Moderate,Medium,Wide,Stiff,Moderate,Flexible,0,0,26.0 mm 28.0 mm,25.8 mm 28.0 mm,Normal,1,All Seasons,1,#246 Bottom 32%,#229 Bottom 37%,,,Daily Running,['Daily Running'],0,1,0,0,1,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,1,0,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,9.6,273,10.2,289.0,0.2,0.0,26.0,28.0,25.8,28.0
+Adidas,Pureboost 23,Daily Running,Neutral,10.8 oz / 305g 10.8 oz / 307g,0,11.5 mm 10.0 mm,Heel,True To Size,Soft,Decent,Good,Good,Breathable,Medium,Wide,Flexible,Flexible,Stiff,0,0,27.6 mm 22.0 mm,16.1 mm 12.0 mm,Normalwide,1,Summerall Seasons,1,#291 Bottom 20%,#288 Bottom 21%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,1,0,0,0,1,0,0,0,1,1,0,0,1,0,0,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,Summer|All,"['Summer', 'All']",1,1,0,10.8,305,10.8,307.0,11.5,10.0,27.6,22.0,16.1,12.0
+Adidas,Pureboost 5,Daily Running,Neutral,9 oz / 254g 9.5 oz / 270g,0,9.2 mm 10.0 mm,Heelmid/Forefoot,True To Size,Balanced,Good,Good,Decent,Moderate,Medium,Wide,Moderate,Moderate,Moderate,0,0,31.2 mm 33.0 mm,22.0 mm 23.0 mm,Normal,1,All Seasons,1,#474 Bottom 26%,#471 Bottom 26%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,9.0,254,9.5,270.0,9.2,10.0,31.2,33.0,22.0,23.0
+Nike,Quest 4,Daily Running,Neutral,9.5 oz / 268g 9.5 oz / 268g,0,13.7 mm,Heel,Slightly Small,-,-,-,-,-,Medium,-,Stiff,Stiff,Stiff,0,0,32.1 mm,18.4 mm,Normal,0,-,0,#494 Bottom 23%,#550 Bottom 14%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,small,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,1,Normal,['Normal'],0,1,0,0,,,0,0,0,9.5,268,9.5,268.0,13.7,,32.1,,18.4,
+Nike,Quest 5,Daily Running,Neutral,9.6 oz / 273g 10.4 oz / 295g,0,9.1 mm,Heelmid/Forefoot,True To Size,Balanced,-,-,-,Moderate,Narrow,Narrow,Moderate,Moderate,Flexible,0,0,29.8 mm,20.7 mm,Narrownormal,1,All Seasons,1,#297 Bottom 18%,#173 Top 48%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,Narrow|Normal,"['Narrow', 'Normal']",1,1,0,0,All,['All'],1,0,0,9.6,273,10.4,295.0,9.1,,29.8,,20.7,
+Adidas,Questar,Daily Running,Neutral,10.9 oz / 310g 10.9 oz / 310g,0,10.5 mm 10.0 mm,Heel,True To Size,Balanced,-,-,-,Breathable,Narrow,-,Stiff,Stiff,Moderate,0,0,32.6 mm 32.0 mm,22.1 mm 22.0 mm,Normal,1,Summerall Seasons,1,#527 Bottom 18%,#528 Bottom 18%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,1,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,10.9,310,10.9,310.0,10.5,10.0,32.6,32.0,22.1,22.0
+Adidas,Questar 3,Daily Running,Neutral,10.4 oz / 295g 10.8 oz / 306g,0,8.5 mm 5.0 mm,Heelmid/Forefoot,True To Size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,33.5 mm 29.0 mm,25.0 mm 24.0 mm,Normal,1,All Seasons,1,#270 Bottom 26%,#173 Top 48%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,10.4,295,10.8,306.0,8.5,5.0,33.5,29.0,25.0,24.0
+Adidas,Racer TR21,Daily Running,Neutral,11.2 oz / 318g 10.1 oz / 285g,0,12.9 mm 8.0 mm,Heel,True To Size,Balanced,Bad,Bad,Decent,Moderate,Medium,Medium,Moderate,Moderate,Flexible,0,0,31.9 mm 34.0 mm,19.0 mm 26.0 mm,Normalwide,1,All Seasons,1,#239 Bottom 34%,#313 Bottom 14%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,1,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,11.2,318,10.1,285.0,12.9,8.0,31.9,34.0,19.0,26.0
+Jordan,React Havoc,Daily Running,Neutral,9.5 oz / 268g 10.8 oz / 306g,0,10.2 mm 9.0 mm,Heel,-,-,-,-,-,-,Narrow,-,Stiff,-,-,0,0,32.3 mm 28.0 mm,22.1 mm 19.0 mm,Normal,0,-,0,#258 Bottom 29%,#339 Bottom 7%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,Normal,['Normal'],0,1,0,0,,,0,0,0,9.5,268,10.8,306.0,10.2,9.0,32.3,28.0,22.1,19.0
+Nike,React Infinity Run Flyknit 3,Daily Running,Neutral,10.5 oz / 297g 10.9 oz / 310g,0,6.7 mm 8.0 mm,Mid/Forefoot,Slightly Small,Soft,-,-,-,Moderate,Medium,-,Stiff,Moderate,Flexible,0,0,30.0 mm 34.0 mm,23.3 mm 26.0 mm,Normal,1,All Seasons,1,#341 Bottom 46%,#46 Top 8%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,small,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,1,0,1,0,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,10.5,297,10.9,310.0,6.7,8.0,30.0,34.0,23.3,26.0
+Nike,React Miler 3,Daily Running,Neutral,10.5 oz / 297g 11.3 oz / 321g,0,9.8 mm 10.0 mm,Heelmid/Forefoot,True To Size,Soft,-,-,-,-,Narrow,-,Stiff,Moderate,Stiff,0,0,31.4 mm,21.6 mm,Normal,1,-,1,#351 Bottom 3%,#343 Bottom 6%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,1,Normal,['Normal'],0,1,0,0,,,0,0,0,10.5,297,11.3,321.0,9.8,10.0,31.4,,21.6,
+Nike,Renew Ride 2,Daily Running,Neutral,10.1 oz / 286g 12.3 oz / 350g,0,8.3 mm 9.0 mm,Heelmid/Forefoot,True To Size,-,-,-,-,-,Medium,-,Stiff,-,-,0,0,37.6 mm,29.3 mm,Normal,0,-,0,#623 Bottom 3%,#623 Bottom 3%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,Normal,['Normal'],0,1,0,0,,,0,0,0,10.1,286,12.3,350.0,8.3,9.0,37.6,,29.3,
+Nike,Renew Ride 3,Daily Running,Neutral,10.2 oz / 290g 10.2 oz / 289g,0,10.2 mm 10.0 mm,Heel,Slightly Small,Balanced,Bad,Bad,-,Moderate,Narrow,Medium,Stiff,Moderate,Stiff,0,0,36.4 mm,26.2 mm,Normal,1,All Seasons,1,#350 Bottom 4%,#303 Bottom 17%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,small,0,1,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,1,0,1,0,0,0,1,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,10.2,290,10.2,289.0,10.2,10.0,36.4,,26.2,
+Brooks,Revel 5,Daily Running,Neutral,8.7 oz / 247g 8.8 oz / 249g,1,9.6 mm 8.0 mm,Heelmid/Forefoot,Slightly Large,-,-,-,-,Moderate,Medium,-,Stiff,Moderate,Moderate,0,0,28.5 mm 20.0 mm,18.9 mm 12.0 mm,Normal,1,All Seasons,1,#297 Top 47%,#446 Bottom 30%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,large,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,1,0,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,8.7,247,8.8,249.0,9.6,8.0,28.5,20.0,18.9,12.0
+Brooks,Revel 6,Daily Running,Neutral,9.2 oz / 261g 8.8 oz / 249g,0,13.2 mm 10.0 mm,Heel,True To Size,Balanced,Bad,Bad,Good,Moderate,Narrow,Medium,Stiff,Stiff,Stiff,0,0,32.9 mm,19.7 mm,Normal,1,All Seasons,1,#395 Bottom 38%,#313 Top 49%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,1,0,1,0,0,1,0,0,0,0,1,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,0,0,1,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,9.2,261,8.8,249.0,13.2,10.0,32.9,,19.7,
+Brooks,Revel 7,Daily Running,Neutral,9.1 oz / 258g 9.1 oz / 258g,0,9.8 mm 10.0 mm,Heelmid/Forefoot,True To Size,Soft,Good,Decent,Good,Breathable,Medium,Medium,Moderate,Stiff,Stiff,0,0,32.0 mm,22.2 mm,Normal,1,Summerall Seasons,1,#254 Bottom 30%,#65 Top 18%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,1,0,0,0,0,1,0,1,0,0,0,1,1,0,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,9.1,258,9.1,258.0,9.8,10.0,32.0,,22.2,
+Nike,Revolution 6,Daily Running,Neutral,9.2 oz / 262g 9.2 oz / 262g,0,10.6 mm 10.0 mm,Heel,Slightly Small,-,-,-,-,-,Wide,-,Stiff,-,-,0,0,33.8 mm 24.0 mm,23.1 mm 14.0 mm,Normalwidex-Wide,0,-,0,#500 Bottom 22%,#318 Top 50%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,small,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,,,0,0,0,9.2,262,9.2,262.0,10.6,10.0,33.8,24.0,23.1,14.0
+Nike,Revolution 7,Daily Running,Neutral,9.9 oz / 281g 10 oz / 283g,0,10.5 mm 10.0 mm,Heel,True To Size,Balanced,Bad,Good,Decent,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,31.5 mm 31.0 mm,21.0 mm 21.0 mm,Normalwidex-Wide,1,All Seasons,1,#576 Bottom 10%,#121 Top 19%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,1,0,1,0,0,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,9.9,281,10.0,283.0,10.5,10.0,31.5,31.0,21.0,21.0
+Nike,Revolution 7 EasyOn,Daily Running,Neutral,9.7 oz / 275g,0,9.9 mm,Heelmid/Forefoot,True To Size,Balanced,Decent,Good,Decent,Breathable,Narrow,Narrow,Moderate,Moderate,Stiff,0,0,31.7 mm,21.8 mm,Normal,1,Summerall Seasons,1,#306 Bottom 16%,#311 Bottom 15%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,0,1,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,9.7,275,,,9.9,,31.7,,21.8,
+Nike,Revolution 8,Daily Running,Neutral,9.3 oz / 264g 9.5 oz / 270g,0,8.8 mm 10.0 mm,Heelmid/Forefoot,True To Size,Balanced,Good,Good,Decent,Moderate,Medium,Medium,Flexible,Moderate,Stiff,0,0,31.8 mm 33.0 mm,23.0 mm 23.0 mm,Normalwidex-Wide,1,All Seasons,1,#322 Bottom 12%,#66 Top 19%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,0,1,0,0,0,1,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,9.3,264,9.5,270.0,8.8,10.0,31.8,33.0,23.0,23.0
+Brooks,Ricochet 3,Daily Running,Neutral,9.1 oz / 258g 9.4 oz / 266g,0,6.8 mm 8.0 mm,Mid/Forefoot,-,-,-,-,-,-,Medium,-,-,Moderate,-,0,0,27.6 mm 24.0 mm,20.8 mm 16.0 mm,Normal,1,-,1,#205 Bottom 43%,#321 Bottom 12%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Normal,['Normal'],0,1,0,0,,,0,0,0,9.1,258,9.4,266.0,6.8,8.0,27.6,24.0,20.8,16.0
+Saucony,Ride 14,Daily Running,Neutral,10.1 oz / 286g 9.9 oz / 281g,0,9.5 mm 8.0 mm,Heelmid/Forefoot,Slightly Large,-,-,-,-,Moderate,Medium,-,Stiff,Flexible,Stiff,0,0,33.9 mm 32.0 mm,24.4 mm 24.0 mm,Normalwide,1,All Seasons,1,#181 Top 29%,#464 Bottom 28%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,large,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,10.1,286,9.9,281.0,9.5,8.0,33.9,32.0,24.4,24.0
+Saucony,Ride 15,Daily Running,Neutral,8.9 oz / 253g 9 oz / 255g,0,6.9 mm 8.0 mm,Mid/Forefoot,True To Size,Balanced,-,-,-,-,Narrow,-,Stiff,Moderate,Stiff,0,0,31.4 mm 35.0 mm,24.5 mm 27.0 mm,Normalwide,1,-,1,#207 Top 33%,#387 Bottom 40%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,,,0,0,0,8.9,253,9.0,255.0,6.9,8.0,31.4,35.0,24.5,27.0
+Saucony,Ride 16,Daily Running,Neutral,9.3 oz / 264g 8.8 oz / 250g,0,7.9 mm 8.0 mm,Mid/Forefoot,Slightly Small,Balanced,Bad,Bad,Good,Breathable,Narrow,Medium,Moderate,Flexible,Stiff,0,0,33.3 mm 35.0 mm,25.4 mm 27.0 mm,Normalwide,1,Summerall Seasons,1,#296 Top 46%,#351 Bottom 45%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,small,0,1,0,0,0,1,0,1,0,0,1,0,0,0,0,1,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,Summer|All,"['Summer', 'All']",1,1,0,9.3,264,8.8,250.0,7.9,8.0,33.3,35.0,25.4,27.0
+Saucony,Ride 17,Daily Running,Neutral,10.2 oz / 288g 9.9 oz / 282g,0,8.5 mm 8.0 mm,Heelmid/Forefoot,True To Size,Soft,Decent,Good,Good,Warm,Medium,Medium,Moderate,Moderate,Moderate,0,1,35.1 mm 35.0 mm,26.6 mm 27.0 mm,Normalwide,1,All Seasons,1,#134 Top 21%,#155 Top 25%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,10.2,288,9.9,282.0,8.5,8.0,35.1,35.0,26.6,27.0
+Saucony,Ride 18,Daily Running,Neutral,9 oz / 255g 9.5 oz / 269g,0,8.4 mm 8.0 mm,Heelmid/Forefoot,True To Size,Balanced,Decent,Good,Good,Breathable,Medium,Medium,Moderate,Flexible,Flexible,0,0,35.0 mm 37.0 mm,26.6 mm 29.0 mm,Normalwide,1,Summerall Seasons,1,#98 Top 27%,#54 Top 15%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,1,0,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,Summer|All,"['Summer', 'All']",1,1,0,9.0,255,9.5,269.0,8.4,8.0,35.0,37.0,26.6,29.0
+Hoka,Rincon 4,Daily Running,Stability,8.1 oz / 231g 8 oz / 228g,1,9.4 mm 5.0 mm,Heelmid/Forefoot,Slightly Small,Soft,Bad,Decent,Bad,Breathable,Narrow,Narrow,Moderate,Stiff,Stiff,0,1,36.0 mm 33.0 mm,26.6 mm 28.0 mm,Normalwide,1,Summerall Seasons,1,#305 Bottom 16%,#80 Top 22%,,,Daily Running,['Daily Running'],0,1,0,0,1,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,small,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,0,1,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,Summer|All,"['Summer', 'All']",1,1,0,8.1,231,8.0,228.0,9.4,5.0,36.0,33.0,26.6,28.0
+Altra,Rivera 2,Daily Runningtempo,Neutral,8.6 oz / 243g 8.5 oz / 240g,1,2.8 mm 0.0 mm,Mid/Forefoot,Half Size Small,Soft,-,-,-,-,Medium,-,Moderate,Flexible,Flexible,0,0,24.6 mm 26.0 mm,21.8 mm 26.0 mm,Normal,1,-,1,#431 Bottom 33%,#561 Bottom 13%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,small,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,1,0,0,Normal,['Normal'],0,1,0,0,,,0,0,0,8.6,243,8.5,240.0,2.8,0.0,24.6,26.0,21.8,26.0
+Altra,Rivera 3,Daily Running,Neutral,9.1 oz / 257g 9.8 oz / 278g,0,1.7 mm 0.0 mm,Mid/Forefoot,Slightly Small,Soft,Bad,Good,Good,Moderate,Medium,Wide,Stiff,Moderate,Moderate,0,0,28.4 mm 28.0 mm,26.7 mm 28.0 mm,Normal,1,All Seasons,1,#275 Top 43%,#494 Bottom 23%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,small,0,1,0,0,1,0,0,1,0,0,0,0,1,0,0,1,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,9.1,257,9.8,278.0,1.7,0.0,28.4,28.0,26.7,28.0
+Altra,Rivera 4,Daily Runningtempo,Neutral,8.3 oz / 235g 10 oz / 283g,1,0.4 mm 0.0 mm,Mid/Forefoot,Slightly Small,Soft,Decent,Bad,Good,Moderate,Narrow,Wide,Stiff,Flexible,Flexible,0,0,28.4 mm 28.0 mm,28.0 mm 28.0 mm,Normal,1,All Seasons,1,#212 Bottom 42%,#270 Bottom 26%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,small,0,1,0,0,1,0,0,0,1,0,1,0,0,0,0,1,0,1,0,1,0,0,0,0,1,0,0,1,1,0,0,1,0,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,8.3,235,10.0,283.0,0.4,0.0,28.4,28.0,28.0,28.0
+Inov8,Roadfly,Daily Running,Neutral,8.9 oz / 251g 9.3 oz / 265g,0,9.4 mm 6.0 mm,Heelmid/Forefoot,Half Size Small,Balanced,Bad,Decent,Decent,Breathable,Medium,Wide,Stiff,Moderate,Flexible,0,0,31.5 mm 27.0 mm,22.1 mm 21.0 mm,Normalwide,1,Summerall Seasons,1,#119 Top 33%,#341 Bottom 6%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,small,0,1,0,0,0,1,0,1,0,0,0,1,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,0,1,0,1,0,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,Summer|All,"['Summer', 'All']",1,1,0,8.9,251,9.3,265.0,9.4,6.0,31.5,27.0,22.1,21.0
+Hoka,Rocket X 3,Competition,Neutral,7.8 oz / 220g 7.4 oz / 210g,1,10.0 mm 7.0 mm,Heelmid/Forefoot,Slightly Small,Soft,Decent,Good,Good,Breathable,Narrow,Narrow,Stiff,Stiff,Flexible,Carbon plate,1,39.6 mm 40.0 mm,29.6 mm 33.0 mm,Normal,1,Summerall Seasons,1,#95 Top 26%,#59 Top 17%,,,Competition,['Competition'],1,0,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,small,0,1,0,0,1,0,0,0,1,0,0,0,1,0,0,1,1,0,0,1,0,0,1,0,0,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,7.8,220,7.4,210.0,10.0,7.0,39.6,40.0,29.6,33.0
+Adidas,Runfalcon,Daily Running,Neutral,9.3 oz / 264g 9.5 oz / 269g,0,8.9 mm 10.0 mm,Heelmid/Forefoot,True To Size,Balanced,-,-,-,Warm,Narrow,-,Stiff,Flexible,Moderate,0,0,26.5 mm 28.0 mm,17.6 mm 18.0 mm,Normal,1,Winter,1,#565 Bottom 12%,#487 Bottom 24%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,1,0,0,0,1,0,Normal,['Normal'],0,1,0,0,Winter,['Winter'],0,0,1,9.3,264,9.5,269.0,8.9,10.0,26.5,28.0,17.6,18.0
+Adidas,Runfalcon 2.0,Daily Running,Neutral,9.9 oz / 280g 9.9 oz / 280g,0,10.9 mm 10.0 mm,Heel,True To Size,Balanced,-,-,-,Warm,Medium,Wide,Stiff,Moderate,Moderate,0,0,28.7 mm,17.8 mm,Normal,1,All Seasons,1,#566 Bottom 12%,#645 Bottom 1%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,9.9,280,9.9,280.0,10.9,10.0,28.7,,17.8,
+Adidas,Runfalcon 3,Daily Running,Neutral,10 oz / 283g 9.7 oz / 275g,0,13.6 mm 9.0 mm,Heel,True To Size,Balanced,Bad,Bad,Good,Moderate,Medium,Wide,Stiff,Moderate,Flexible,0,0,31.6 mm 25.0 mm,18.0 mm 16.0 mm,Normalwide,1,All Seasons,1,#536 Bottom 16%,#586 Bottom 9%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,1,0,1,0,0,1,0,0,0,0,1,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,1,0,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,10.0,283,9.7,275.0,13.6,9.0,31.6,25.0,18.0,16.0
+Adidas,Runfalcon 5,Daily Running,Neutral,9.7 oz / 275g 10.7 oz / 303g,0,9.4 mm 10.0 mm,Heelmid/Forefoot,True To Size,Balanced,Bad,Bad,Decent,Moderate,Wide,Medium,Moderate,Flexible,Moderate,0,0,31.2 mm 33.0 mm,21.8 mm 23.0 mm,Normalwide,1,All Seasons,1,#203 Bottom 44%,#148 Top 41%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,1,0,0,1,0,0,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,1,0,0,0,1,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,9.7,275,10.7,303.0,9.4,10.0,31.2,33.0,21.8,23.0
+Salomon,S/Lab Phantasm 2,Competitiontempo,Neutral,7.5 oz / 213g 7.4 oz / 210g,1,5.1 mm 9.0 mm,Mid/Forefoot,-,Soft,Bad,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Flexible,Carbon plate,0,31.5 mm 37.0 mm,26.4 mm 28.0 mm,Normal,1,All Seasons,1,#252 Bottom 30%,#358 Bottom 1%,,,Competition|Tempo,"['Competition', 'Tempo']",1,0,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,,0,0,0,1,1,0,0,1,0,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,7.5,213,7.4,210.0,5.1,9.0,31.5,37.0,26.4,28.0
+Salomon,S/Lab Spectur,Competitiontempo,Neutral,9.1 oz / 258g,0,10.7 mm 8.0 mm,Heel,-,Balanced,Decent,Decent,Good,Moderate,Medium,Medium,Stiff,Stiff,Flexible,0,1,36.9 mm,26.2 mm,Normal,1,All Seasons,1,#312 Bottom 14%,#332 Bottom 9%,,,Competition|Tempo,"['Competition', 'Tempo']",1,0,1,1,0,Heel,['Heel'],0,0,1,0,,0,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,9.1,258,,,10.7,8.0,36.9,,26.2,
+Xero,Shoes HFS II,Daily Running,Neutral,8.6 oz / 244g 8.3 oz / 235g,1,1.0 mm 0.0 mm,Mid/Forefoot,True To Size,Firm,Decent,Bad,Decent,Breathable,Narrow,Wide,Moderate,Flexible,Moderate,0,0,13.1 mm 12.0 mm,12.1 mm 12.0 mm,Normalwide,1,Summerall Seasons,1,#206 Bottom 43%,#275 Bottom 24%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,0,1,0,1,0,1,0,0,0,1,0,1,0,0,1,0,0,0,0,1,0,1,0,1,0,0,0,1,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,Summer|All,"['Summer', 'All']",1,1,0,8.6,244,8.3,235.0,1.0,0.0,13.1,12.0,12.1,12.0
+Xero,Shoes Prio,Daily Running,Neutral,9.8 oz / 279g 7.4 oz / 210g,0,0.4 mm 0.0 mm,Mid/Forefoot,True To Size,-,Good,Bad,Decent,Warm,Medium,Wide,Flexible,Flexible,Flexible,0,0,12.2 mm 7.0 mm,11.8 mm 7.0 mm,Normalwide,1,All Seasons,1,#21 Top 6%,#238 Bottom 34%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,1,0,1,0,0,0,1,1,0,0,1,0,0,1,0,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,9.8,279,7.4,210.0,0.4,0.0,12.2,7.0,11.8,7.0
+Xero,Shoes Speed Force II,Daily Runningtempo,Neutral,6.7 oz / 189g 6 oz / 170g,1,0.1 mm 0.0 mm,Mid/Forefoot,True To Size,-,Decent,Decent,Decent,Breathable,Medium,Wide,Flexible,Flexible,Flexible,0,0,10.6 mm 7.0 mm,10.5 mm 7.0 mm,Normal,1,Summerall Seasons,1,#115 Top 32%,#318 Bottom 12%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,0,0,0,1,0,0,1,0,0,1,0,1,0,0,0,1,0,0,0,1,1,0,0,1,0,0,1,0,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,6.7,189,6.0,170.0,0.1,0.0,10.6,7.0,10.5,7.0
+Saucony,Sinister,Tempo,Neutral,5.3 oz / 149g 4.9 oz / 139g,1,7.8 mm 6.0 mm,Mid/Forefoot,Slightly Small,Balanced,Bad,Good,Decent,Moderate,Narrow,Medium,Moderate,Flexible,Flexible,0,0,25.2 mm 25.0 mm,17.4 mm 19.0 mm,Normal,1,All Seasons,1,#151 Top 42%,#315 Bottom 13%,,,Tempo,['Tempo'],0,0,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,small,0,1,0,0,0,1,0,1,0,0,0,0,1,0,1,0,0,1,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,5.3,149,4.9,139.0,7.8,6.0,25.2,25.0,17.4,19.0
+Hoka,Skyflow,Daily Running,Neutral,9.9 oz / 282g 10 oz / 283g,0,8.7 mm 5.0 mm,Heelmid/Forefoot,True To Size,Soft,Decent,Good,Good,Breathable,Medium,Narrow,Stiff,Stiff,Stiff,0,0,39.3 mm 39.0 mm,30.6 mm 34.0 mm,Normalwide,1,Summerall Seasons,1,#111 Top 31%,#138 Top 38%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,1,0,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,Summer|All,"['Summer', 'All']",1,1,0,9.9,282,10.0,283.0,8.7,5.0,39.3,39.0,30.6,34.0
+Hoka,Skyward X,Daily Runningtempo,Neutral,11.1 oz / 315g 10.8 oz / 306g,0,9.2 mm 5.0 mm,Heelmid/Forefoot,True To Size,Soft,Good,Good,Good,Breathable,Narrow,Narrow,Stiff,Stiff,Stiff,Carbon plate,1,46.3 mm 49.0 mm,37.1 mm 44.0 mm,Normal,1,Summerall Seasons,1,#170 Top 47%,#64 Top 18%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,1,1,0,0,1,0,0,1,0,0,0,0,1,0,0,1,0,0,1,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,11.1,315,10.8,306.0,9.2,5.0,46.3,49.0,37.1,44.0
+Under Armour,SlipSpeed Mega,Daily Running,Neutral,11.7 oz / 332g 11.3 oz / 320g,0,11.9 mm 10.0 mm,Heel,True To Size,Balanced,Good,Bad,Bad,Warm,Narrow,Medium,Stiff,Stiff,Flexible,0,0,40.7 mm 40.0 mm,28.8 mm 30.0 mm,Normal,1,Winter,1,#58 Top 16%,#260 Bottom 29%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,1,0,0,0,1,1,0,0,1,0,0,0,0,1,1,0,0,0,1,0,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,Winter,['Winter'],0,0,1,11.7,332,11.3,320.0,11.9,10.0,40.7,40.0,28.8,30.0
+Adidas,Solarboost 5,Daily Running,Stability,10.3 oz / 293g 10.5 oz / 297g,0,9.8 mm 10.0 mm,Heelmid/Forefoot,True To Size,Firm,Decent,Good,Bad,Warm,Medium,Wide,Stiff,Moderate,Moderate,0,0,32.9 mm 31.0 mm,23.1 mm 21.0 mm,Normal,1,All Seasons,1,#165 Top 46%,#345 Bottom 5%,,,Daily Running,['Daily Running'],0,1,0,0,1,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,0,1,0,1,0,0,0,1,1,0,0,0,0,1,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,10.3,293,10.5,297.0,9.8,10.0,32.9,31.0,23.1,21.0
+Hoka,Solimar,Daily Running,Neutral,8.2 oz / 232g 8.5 oz / 241g,1,6.4 mm 6.0 mm,Mid/Forefoot,True To Size,Balanced,Good,Decent,-,Moderate,Narrow,Narrow,Moderate,Moderate,Flexible,0,0,30.4 mm 26.0 mm,24.0 mm 20.0 mm,Normalwide,1,All Seasons,1,#150 Top 42%,#109 Top 30%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,1,0,0,0,1,0,1,0,0,0,0,0,1,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,8.2,232,8.5,241.0,6.4,6.0,30.4,26.0,24.0,20.0
+Asics,Sonicblast,Daily Runningtempo,Neutral,9 oz / 255g 9 oz / 255g,0,9.0 mm 8.0 mm,Heelmid/Forefoot,-,Soft,Bad,Good,Decent,Breathable,Narrow,Narrow,Stiff,Stiff,Flexible,0,1,45.4 mm 46.0 mm,36.4 mm 38.0 mm,Normal,1,Summerall Seasons,1,#341 Bottom 6%,#140 Top 39%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,,0,0,0,1,1,0,0,1,0,0,0,0,1,0,1,0,1,0,0,1,0,0,1,0,0,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,9.0,255,9.0,255.0,9.0,8.0,45.4,46.0,36.4,38.0
+Salomon,Spectur 2,Tempo,Neutral,9.1 oz / 258g,0,11.0 mm 8.0 mm,Heel,-,Balanced,Decent,Decent,Good,Warm,Medium,Medium,Stiff,Stiff,Flexible,Carbon plate,1,35.9 mm,24.9 mm,Normal,1,All Seasons,1,#310 Bottom 15%,#324 Bottom 11%,,,Tempo,['Tempo'],0,0,1,1,0,Heel,['Heel'],0,0,1,0,,0,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,9.1,258,,,11.0,8.0,35.9,,24.9,
+Nike,Streakfly 2,Competitiontempo,Neutral,4.5 oz / 128g 5.1 oz / 144g,1,3.7 mm 4.0 mm,Mid/Forefoot,Slightly Small,Soft,Bad,Good,Decent,Breathable,Narrow,Narrow,Flexible,Flexible,Flexible,Carbon plate,0,27.0 mm 27.0 mm,23.3 mm 23.0 mm,Normal,0,Summerall Seasons,0,#248 Bottom 32%,#79 Top 22%,,,Competition|Tempo,"['Competition', 'Tempo']",1,0,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,small,0,1,0,0,1,0,0,1,0,0,0,0,1,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,4.5,128,5.1,144.0,3.7,4.0,27.0,27.0,23.3,23.0
+Apl,Streamline,Daily Runningtempo,Neutral,9.6 oz / 272g 9.2 oz / 261g,0,10.8 mm 8.0 mm,Heel,Half Size Small,-,-,-,-,-,Narrow,-,Stiff,Stiff,Flexible,0,0,36.7 mm 30.0 mm,25.9 mm 22.0 mm,Normal,0,-,0,#47 Top 13%,#328 Bottom 10%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Heel,['Heel'],0,0,1,0,small,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,,,0,0,0,9.6,272,9.2,261.0,10.8,8.0,36.7,30.0,25.9,22.0
+Nike,Structure 25,Daily Running,Stability,10.7 oz / 302g 11.4 oz / 322g,0,12.1 mm 10.0 mm,Heel,True To Size,Soft,Good,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,36.7 mm 37.0 mm,24.6 mm 27.0 mm,Narrownormalwidex-Wide,1,All Seasons,1,#514 Bottom 20%,#119 Top 19%,,,Daily Running,['Daily Running'],0,1,0,0,1,Heel,['Heel'],0,0,1,0,true,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,Narrow|Normal|Wide|X-Wide,"['Narrow', 'Normal', 'Wide', 'X-Wide']",1,1,1,1,All,['All'],1,0,0,10.7,302,11.4,322.0,12.1,10.0,36.7,37.0,24.6,27.0
+Nike,Structure 26,Daily Running,Stability,10.4 oz / 296g 10.5 oz / 298g,0,10.1 mm 10.0 mm,Heel,Slightly Small,Soft,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Moderate,0,0,36.1 mm 38.0 mm,26.0 mm 28.0 mm,Normalwidex-Wide,1,All Seasons,1,#236 Bottom 35%,#56 Top 16%,,,Daily Running,['Daily Running'],0,1,0,0,1,Heel,['Heel'],0,0,1,0,small,0,1,0,0,1,0,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,10.4,296,10.5,298.0,10.1,10.0,36.1,38.0,26.0,28.0
+Asics,Superblast,Daily Runningtempo,Neutral,8.6 oz / 244g 8.4 oz / 239g,1,7.9 mm 8.0 mm,Mid/Forefoot,True To Size,Balanced,Bad,Decent,Good,Moderate,Narrow,Medium,Stiff,Stiff,Moderate,0,1,42.7 mm 45.5 mm,34.8 mm 37.5 mm,Normalwide,1,All Seasons,1,#26 Top 5%,#98 Top 16%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,1,0,1,0,0,0,1,0,0,0,1,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,0,1,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,8.6,244,8.4,239.0,7.9,8.0,42.7,45.5,34.8,37.5
+Asics,Superblast 2,Daily Runningtempo,Neutral,8.9 oz / 252g 8.8 oz / 250g,0,8.2 mm 8.0 mm,Heelmid/Forefoot,True To Size,Soft,Good,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Moderate,0,1,42.8 mm 45.0 mm,34.6 mm 37.0 mm,Normalwide,1,Summerall Seasons,1,#50 Top 14%,#25 Top 7%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,1,1,0,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,Summer|All,"['Summer', 'All']",1,1,0,8.9,252,8.8,250.0,8.2,8.0,42.8,45.0,34.6,37.0
+Adidas,Supernova 2,Daily Running,Neutral,9.7 oz / 276g 9.8 oz / 278g,0,14.2 mm 9.0 mm,Heel,True To Size,Balanced,Bad,Bad,Bad,Breathable,Medium,Wide,Flexible,Moderate,Moderate,0,0,32.4 mm 32.0 mm,18.2 mm 23.0 mm,Normal,1,Summerall Seasons,1,#308 Top 48%,#467 Bottom 27%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,0,1,1,0,0,0,1,0,0,1,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,9.7,276,9.8,278.0,14.2,9.0,32.4,32.0,18.2,23.0
+Adidas,Supernova 3,Daily Running,Neutral,9.7 oz / 274g 10 oz / 283g,0,12.5 mm 9.0 mm,Heel,True To Size,Balanced,Good,Good,Bad,Breathable,Medium,Medium,Stiff,Moderate,Moderate,0,0,30.7 mm 25.0 mm,18.2 mm 16.0 mm,Normal,1,Summerall Seasons,1,#288 Bottom 21%,#322 Bottom 12%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,1,0,0,0,1,0,0,1,1,0,0,1,0,0,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,9.7,274,10.0,283.0,12.5,9.0,30.7,25.0,18.2,16.0
+Adidas,Supernova Prima,Daily Running,Neutral,9.9 oz / 281g 10 oz / 284g,0,8.9 mm 8.0 mm,Heelmid/Forefoot,True To Size,Balanced,Decent,Bad,Bad,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,1,36.1 mm 37.0 mm,27.2 mm 29.0 mm,Normal,1,All Seasons,1,#87 Top 24%,#226 Bottom 38%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,9.9,281,10.0,284.0,8.9,8.0,36.1,37.0,27.2,29.0
+Adidas,Supernova Rise,Daily Running,Neutral,9.8 oz / 278g 9.8 oz / 278g,0,9.7 mm 10.0 mm,Heelmid/Forefoot,Slightly Small,Balanced,Decent,Good,Good,Moderate,Wide,Wide,Moderate,Flexible,Stiff,0,1,32.5 mm 36.0 mm,22.8 mm 26.0 mm,Normalwide,1,All Seasons,1,#60 Top 10%,#234 Top 37%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,small,0,1,0,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,0,1,0,0,1,0,1,0,1,0,0,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,9.8,278,9.8,278.0,9.7,10.0,32.5,36.0,22.8,26.0
+Adidas,Supernova Rise 2,Daily Running,Neutral,9.1 oz / 257g 9.4 oz / 266g,0,9.5 mm 10.0 mm,Heelmid/Forefoot,True To Size,Balanced,Decent,Good,Decent,Moderate,Wide,Wide,Flexible,Moderate,Stiff,0,0,33.5 mm 33.0 mm,24.0 mm 23.0 mm,Normalwide,1,All Seasons,1,#29 Top 8%,#67 Top 19%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,0,0,1,0,0,1,1,0,0,0,1,0,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,9.1,257,9.4,266.0,9.5,10.0,33.5,33.0,24.0,23.0
+Adidas,Supernova Solution,Daily Running,Stability,9.8 oz / 279g 9.8 oz / 278g,0,10.0 mm 10.0 mm,Heelmid/Forefoot,True To Size,Balanced,Decent,Bad,Decent,Moderate,Medium,Wide,Flexible,Moderate,Stiff,0,0,33.1 mm 36.0 mm,23.1 mm 26.0 mm,Normal,1,All Seasons,1,#106 Top 30%,#256 Bottom 29%,,,Daily Running,['Daily Running'],0,1,0,0,1,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,0,1,1,0,0,0,1,0,0,0,1,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,9.8,279,9.8,278.0,10.0,10.0,33.1,36.0,23.1,26.0
+Adidas,Supernova+,Daily Running,Neutral,11.8 oz / 335g 11.2 oz / 318g,0,9.3 mm 10.0 mm,Heelmid/Forefoot,True To Size,Balanced,-,-,-,-,Medium,-,Moderate,Moderate,Moderate,0,0,30.7 mm 32.0 mm,21.4 mm 22.0 mm,Normal,1,-,1,#184 Bottom 49%,#154 Top 43%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,1,0,Normal,['Normal'],0,1,0,0,,,0,0,0,11.8,335,11.2,318.0,9.3,10.0,30.7,32.0,21.4,22.0
+Under Armour,Surge 4,Daily Running,Neutral,10.4 oz / 295g 10 oz / 284g,0,9.0 mm 8.0 mm,Heelmid/Forefoot,-,Balanced,Decent,Decent,Good,Moderate,Wide,Medium,Moderate,Stiff,Moderate,0,0,33.5 mm,24.5 mm,Normalwidex-Wide,1,All Seasons,1,#275 Bottom 25%,#248 Bottom 32%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,,0,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,0,0,1,0,1,0,0,1,0,0,0,1,0,1,0,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,10.4,295,10.0,284.0,9.0,8.0,33.5,,24.5,
+Adidas,Switch FWD,Daily Running,Neutral,11.4 oz / 323g 11.8 oz / 334.5g,0,9.1 mm 10.0 mm,Heelmid/Forefoot,True To Size,Balanced,Bad,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,0,39.6 mm 45.0 mm,30.5 mm 35.0 mm,Normal,1,All Seasons,1,#426 Bottom 33%,#560 Bottom 13%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,1,0,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,11.4,323,11.8,334.5,9.1,10.0,39.6,45.0,30.5,35.0
+Adidas,Switch FWD 2,Daily Running,Neutral,10.2 oz / 288g 10.4 oz / 294g,0,11.7 mm 10.0 mm,Heel,True To Size,Balanced,Decent,Good,Decent,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,0,44.0 mm 46.0 mm,32.3 mm 36.0 mm,Normal,1,All Seasons,1,#218 Bottom 40%,#263 Bottom 28%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,10.2,288,10.4,294.0,11.7,10.0,44.0,46.0,32.3,36.0
+Saucony,Tempus,Daily Runningtempo,Stability,9.4 oz / 266g 8.9 oz / 252g,0,8.5 mm 8.0 mm,Heelmid/Forefoot,Slightly Small,Soft,-,-,-,Breathable,Narrow,-,Moderate,Moderate,Stiff,0,1,33.9 mm 36.5 mm,25.4 mm 28.5 mm,Normalwide,1,Summerall Seasons,1,#76 Top 21%,#121 Top 33%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,0,1,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,small,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,Summer|All,"['Summer', 'All']",1,1,0,9.4,266,8.9,252.0,8.5,8.0,33.9,36.5,25.4,28.5
+Altra,Torin 6,Daily Running,Neutral,9 oz / 254g 9.9 oz / 280g,0,0.0 mm,Mid/Forefoot,Slightly Small,Balanced,-,-,-,Breathable,Medium,-,Stiff,Moderate,Flexible,0,0,25.1 mm 28.0 mm,25.1 mm 28.0 mm,Normalwide,1,Summerall Seasons,1,#558 Bottom 13%,#449 Bottom 30%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,small,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,0,1,0,1,0,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,Summer|All,"['Summer', 'All']",1,1,0,9.0,254,9.9,280.0,0.0,,25.1,28.0,25.1,28.0
+Altra,Torin 7,Daily Runningtempo,Neutral,9 oz / 255g 9.8 oz / 278g,0,-0.8 mm 0.0 mm,Mid/Forefoot,True To Size,Soft,Decent,Decent,Decent,Breathable,Wide,Wide,Moderate,Moderate,Moderate,0,0,27.6 mm 30.0 mm,28.4 mm 30.0 mm,Normalwide,1,Summerall Seasons,1,#577 Bottom 10%,#219 Top 34%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,1,0,0,0,1,0,0,1,0,0,1,0,1,0,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,Summer|All,"['Summer', 'All']",1,1,0,9.0,255,9.8,278.0,0.8,0.0,27.6,30.0,28.4,30.0
+Altra,Torin 8,Daily Running,Neutral,9.7 oz / 275g 10.1 oz / 287g,0,-0.1 mm 0.0 mm,Mid/Forefoot,True To Size,Balanced,Good,Good,Good,Moderate,Medium,Wide,Moderate,Moderate,Stiff,0,0,27.6 mm 30.0 mm,27.7 mm 30.0 mm,Normalwide,1,All Seasons,1,#173 Top 48%,#64 Top 18%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,9.7,275,10.1,287.0,0.1,0.0,27.6,30.0,27.7,30.0
+Brooks,Trace 2,Daily Running,Neutral,8.8 oz / 249g 8.6 oz / 243g,1,12.3 mm 12.0 mm,Heel,True To Size,Balanced,Decent,Decent,Good,Breathable,Narrow,Medium,Stiff,Moderate,Moderate,0,0,34.2 mm,21.9 mm,Normal,1,Summerall Seasons,1,#239 Top 38%,#444 Bottom 31%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,1,0,0,1,0,0,0,1,0,0,0,1,0,1,0,0,1,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,8.8,249,8.6,243.0,12.3,12.0,34.2,,21.9,
+Brooks,Trace 3,Daily Running,Neutral,9.1 oz / 257g 9 oz / 255g,0,11.9 mm 12.0 mm,Heel,True To Size,Balanced,Bad,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,34.1 mm 34.0 mm,22.2 mm 22.0 mm,Normalwide,1,All Seasons,1,#340 Bottom 7%,#152 Top 42%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,1,0,1,0,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,9.1,257,9.0,255.0,11.9,12.0,34.1,34.0,22.2,22.0
+Hoka,Transport X,Daily Running,Neutral,9.7 oz / 274g 8.8 oz / 250g,0,9.4 mm 5.0 mm,Heelmid/Forefoot,True To Size,Balanced,Bad,Bad,Good,Breathable,Medium,Medium,Stiff,Stiff,Stiff,Carbon plate,1,40.2 mm 35.0 mm,30.8 mm 30.0 mm,Normalwide,1,Summerall Seasons,1,#233 Bottom 36%,#244 Bottom 33%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,1,0,0,1,0,0,0,0,1,1,0,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,Summer|All,"['Summer', 'All']",1,1,0,9.7,274,8.8,250.0,9.4,5.0,40.2,35.0,30.8,30.0
+Allbirds,Tree Dasher,Daily Running,Neutral,10.6 oz / 301g 10.2 oz / 289g,0,6.0 mm 7.0 mm,Mid/Forefoot,True To Size,Balanced,-,-,-,Breathable,Medium,-,Stiff,Flexible,Flexible,0,0,29.4 mm 22.5 mm,23.4 mm 15.5 mm,Normal,1,Summerall Seasons,1,#36 Top 6%,#382 Bottom 40%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,1,0,0,1,0,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,10.6,301,10.2,289.0,6.0,7.0,29.4,22.5,23.4,15.5
+Allbirds,Tree Dasher 2,Daily Running,Neutral,10.3 oz / 291g 10.3 oz / 292g,0,9.3 mm 7.0 mm,Heelmid/Forefoot,Slightly Small,Balanced,Good,Good,Bad,Breathable,Wide,Wide,Stiff,Flexible,Flexible,0,0,31.6 mm 22.5 mm,22.3 mm 15.5 mm,Normal,1,Summerall Seasons,1,#152 Top 42%,#178 Top 49%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,small,0,1,0,0,0,1,0,0,0,1,0,0,1,1,0,0,1,0,0,0,0,1,0,0,1,0,0,1,1,0,0,1,0,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,10.3,291,10.3,292.0,9.3,7.0,31.6,22.5,22.3,15.5
+Allbirds,Tree Flyer 2,Daily Running,Neutral,10.3 oz / 293g 10.6 oz / 300.5g,0,8.1 mm 8.5 mm,Heelmid/Forefoot,True To Size,Balanced,Good,Decent,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,0,32.1 mm 30.5 mm,24.0 mm 22.0 mm,Normal,1,All Seasons,1,#191 Bottom 47%,#315 Bottom 13%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,0,1,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,10.3,293,10.6,300.5,8.1,8.5,32.1,30.5,24.0,22.0
+Saucony,Triumph 20,Daily Running,Neutral,9.9 oz / 282g 9.7 oz / 275g,0,10.4 mm 10.0 mm,Heel,Slightly Small,Soft,-,-,-,Breathable,Narrow,-,Stiff,Flexible,Moderate,0,1,35.4 mm 37.0 mm,25.0 mm 27.0 mm,Normalwide,1,Summerall Seasons,1,#292 Top 46%,#310 Top 49%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,small,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,1,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,Summer|All,"['Summer', 'All']",1,1,0,9.9,282,9.7,275.0,10.4,10.0,35.4,37.0,25.0,27.0
+Saucony,Triumph 21,Daily Running,Neutral,9.9 oz / 282g 10 oz / 283g,0,10.5 mm 10.0 mm,Heel,True To Size,Soft,Decent,Decent,Good,Moderate,Medium,Wide,Moderate,Stiff,Stiff,0,1,39.1 mm 39.0 mm,28.6 mm 29.0 mm,Normalwide,1,All Seasons,1,#331 Bottom 48%,#279 Top 44%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,1,0,0,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,0,0,1,0,1,0,0,0,1,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,9.9,282,10.0,283.0,10.5,10.0,39.1,39.0,28.6,29.0
+Saucony,Triumph 22,Daily Running,Neutral,10.1 oz / 286g 10.1 oz / 286g,0,9.7 mm 10.0 mm,Heelmid/Forefoot,True To Size,Soft,Good,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,38.6 mm 37.0 mm,28.9 mm 27.0 mm,Normalwide,1,All Seasons,1,#241 Top 38%,#137 Top 22%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,10.1,286,10.1,286.0,9.7,10.0,38.6,37.0,28.9,27.0
+Saucony,Triumph 23,Daily Running,Neutral,9.6 oz / 272g 9.3 oz / 263g,0,10.0 mm 10.0 mm,Heelmid/Forefoot,True To Size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Moderate,Stiff,0,0,42.3 mm 37.0 mm,32.3 mm 27.0 mm,Normalwide,1,All Seasons,1,#77 Top 22%,#92 Top 26%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,9.6,272,9.3,263.0,10.0,10.0,42.3,37.0,32.3,27.0
+Adidas,Ultraboost 1.0,Daily Running,Neutral,11.3 oz / 320g 10.7 oz / 303g,0,14.2 mm 10.0 mm,Heel,True To Size,Soft,Bad,Decent,Good,Breathable,Narrow,Medium,Moderate,Flexible,Moderate,0,0,34.4 mm 22.0 mm,20.2 mm 12.0 mm,Normalx-Wide,1,Summerall Seasons,1,#182 Top 29%,#628 Bottom 2%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,1,0,0,1,0,0,0,1,0,0,0,1,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,0,1,0,Normal|X-Wide,"['Normal', 'X-Wide']",0,1,0,1,Summer|All,"['Summer', 'All']",1,1,0,11.3,320,10.7,303.0,14.2,10.0,34.4,22.0,20.2,12.0
+Adidas,Ultraboost 21,Daily Running,Neutral,12.5 oz / 355g 12 oz / 340g,0,12.1 mm 10.0 mm,Heel,True To Size,-,-,-,-,-,Medium,-,-,Moderate,-,0,0,32.8 mm 30.5 mm,20.7 mm 20.5 mm,Normal,1,-,1,#169 Top 27%,#463 Bottom 28%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,Normal,['Normal'],0,1,0,0,,,0,0,0,12.5,355,12.0,340.0,12.1,10.0,32.8,30.5,20.7,20.5
+Adidas,Ultraboost 22,Daily Running,Neutral,10.6 oz / 301g 11.7 oz / 332g,0,12.7 mm 10.0 mm,Heel,True To Size,Firm,-,-,-,-,Wide,-,Stiff,-,-,0,0,33.9 mm 31.0 mm,21.2 mm 21.0 mm,Normal,0,-,0,#111 Top 18%,#306 Top 48%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,Normal,['Normal'],0,1,0,0,,,0,0,0,10.6,301,11.7,332.0,12.7,10.0,33.9,31.0,21.2,21.0
+Adidas,Ultraboost 5,Daily Running,Neutral,10.3 oz / 292g 11.4 oz / 323g,0,10.6 mm 10.0 mm,Heel,True To Size,Soft,Decent,Good,Good,Breathable,Wide,Wide,Stiff,Stiff,Moderate,0,0,35.2 mm 39.0 mm,24.6 mm 29.0 mm,Narrownormal,1,Summerall Seasons,1,#49 Top 14%,#167 Top 46%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,1,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,Narrow|Normal,"['Narrow', 'Normal']",1,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,10.3,292,11.4,323.0,10.6,10.0,35.2,39.0,24.6,29.0
+Adidas,Ultraboost 5X,Daily Running,Neutral,9.4 oz / 266g 9.7 oz / 274g,0,10.4 mm 10.0 mm,Heel,True To Size,Soft,Good,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Moderate,0,0,34.6 mm 39.0 mm,24.2 mm 29.0 mm,Normalwide,1,All Seasons,1,#33 Top 10%,#75 Top 21%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,9.4,266,9.7,274.0,10.4,10.0,34.6,39.0,24.2,29.0
+Adidas,Ultraboost Light,Daily Running,Neutral,10.8 oz / 305g 10.5 oz / 299g,0,11.9 mm 10.0 mm,Heel,Half Size Small,Soft,Good,Good,Good,Moderate,Medium,Wide,Moderate,Moderate,Moderate,0,0,30.1 mm 30.0 mm,18.2 mm 20.0 mm,Normal,1,All Seasons,1,#208 Top 33%,#210 Top 33%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,small,0,1,0,0,1,0,0,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,10.8,305,10.5,299.0,11.9,10.0,30.1,30.0,18.2,20.0
+Adidas,Ultrabounce,Daily Running,Neutral,11.5 oz / 326g 12.1 oz / 343g,0,11.3 mm 9.0 mm,Heel,True To Size,Balanced,Decent,Decent,Decent,Breathable,Medium,Medium,Moderate,Moderate,Flexible,0,0,30.3 mm 25.0 mm,19.0 mm 16.0 mm,Normalwide,1,Summerall Seasons,1,#281 Bottom 23%,#201 Bottom 45%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,Summer|All,"['Summer', 'All']",1,1,0,11.5,326,12.1,343.0,11.3,9.0,30.3,25.0,19.0,16.0
+Adidas,Ultrarun 5,Daily Running,Neutral,10.4 oz / 295g 11.4 oz / 323g,0,9.5 mm 10.0 mm,Heelmid/Forefoot,True To Size,Balanced,Good,Good,Decent,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,34.3 mm 35.0 mm,24.8 mm 25.0 mm,Normal,1,All Seasons,1,#243 Bottom 33%,#195 Bottom 46%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,10.4,295,11.4,323.0,9.5,10.0,34.3,35.0,24.8,25.0
+Altra,Vanish Carbon 2,Competitiontempo,Neutral,7.4 oz / 210g 8.1 oz / 229g,1,3.7 mm 0.0 mm,Mid/Forefoot,Half Size Small,Balanced,Bad,Good,Decent,Breathable,Medium,Wide,Stiff,Stiff,Flexible,Carbon plate,1,33.1 mm 36.0 mm,29.4 mm 36.0 mm,Normal,1,Summerall Seasons,1,#61 Top 17%,#218 Bottom 40%,,,Competition|Tempo,"['Competition', 'Tempo']",1,0,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,small,0,1,0,0,0,1,0,1,0,0,0,0,1,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,7.4,210,8.1,229.0,3.7,0.0,33.1,36.0,29.4,36.0
+Merrell,Vapor Glove 6,Daily Running,Neutral,5.6 oz / 159g 5.3 oz / 150g,1,0.0 mm 0.0 mm,Mid/Forefoot,True To Size,-,Decent,Decent,Decent,Moderate,Medium,Wide,Flexible,Flexible,Flexible,0,0,7.6 mm 6.0 mm,7.6 mm 6.0 mm,Normal,0,All Seasons,0,#171 Top 47%,#155 Top 43%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,1,0,0,1,0,0,1,0,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,5.6,159,5.3,150.0,0.0,0.0,7.6,6.0,7.6,6.0
+Nike,Vaporfly 3,Competition,Neutral,6.7 oz / 190g 6.5 oz / 184g,1,11.1 mm 8.0 mm,Heel,True To Size,Soft,Bad,Good,Good,Breathable,Medium,Medium,Stiff,Stiff,Moderate,Carbon plate,1,37.1 mm 40.0 mm,26.0 mm 32.0 mm,Normal,0,Summerall Seasons,0,#362 Bottom 43%,#61 Top 10%,,,Competition,['Competition'],1,0,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,1,0,0,1,0,0,0,0,1,0,0,1,1,0,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,6.7,190,6.5,184.0,11.1,8.0,37.1,40.0,26.0,32.0
+Nike,Vaporfly 4,Competition,Neutral,5.9 oz / 166g 6.5 oz / 184g,1,8.6 mm 6.0 mm,Heelmid/Forefoot,True To Size,Soft,Good,Decent,Bad,Moderate,Narrow,Medium,Stiff,Stiff,Flexible,Carbon plate,0,34.1 mm 35.0 mm,25.5 mm 29.0 mm,Normal,1,All Seasons,1,#105 Top 29%,#33 Top 10%,,,Competition,['Competition'],1,0,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,1,0,0,0,0,1,0,1,0,1,0,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,5.9,166,6.5,184.0,8.6,6.0,34.1,35.0,25.5,29.0
+Asics,Versablast 4,Daily Running,Neutral,10.2 oz / 288g 9.2 oz / 260g,0,9.4 mm 10.0 mm,Heelmid/Forefoot,True To Size,Balanced,Decent,Decent,Good,Warm,Narrow,Narrow,Moderate,Moderate,Moderate,0,0,36.1 mm 36.0 mm,26.7 mm 26.0 mm,Normalwide,1,All Seasons,1,#281 Bottom 23%,#136 Top 38%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,1,0,0,1,0,0,0,1,0,0,1,0,0,1,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,10.2,288,9.2,260.0,9.4,10.0,36.1,36.0,26.7,26.0
+Altra,VIA Olympus,Daily Running,Neutral,10.5 oz / 299g 11 oz / 312g,0,1.6 mm 0.0 mm,Mid/Forefoot,Slightly Small,Balanced,-,Bad,-,Moderate,Medium,-,Stiff,Stiff,Flexible,0,0,34.2 mm 33.0 mm,32.6 mm 33.0 mm,Normal,1,All Seasons,1,#428 Bottom 33%,#281 Top 44%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,small,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,10.5,299,11.0,312.0,1.6,0.0,34.2,33.0,32.6,33.0
+Altra,VIA Olympus 2,Daily Running,Neutral,10.4 oz / 295g 10.5 oz / 297g,0,-0.2 mm 0.0 mm,Mid/Forefoot,True To Size,Soft,Good,Good,Good,Warm,Wide,Wide,Stiff,Stiff,Moderate,0,0,34.8 mm 33.0 mm,35.0 mm 33.0 mm,Normal,1,All Seasons,1,#223 Bottom 39%,#124 Top 34%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,10.4,295,10.5,297.0,0.2,0.0,34.8,33.0,35.0,33.0
+Nike,Vomero 17,Daily Running,Neutral,9.9 oz / 282g 10.1 oz / 286g,0,7.7 mm 10.0 mm,Mid/Forefoot,True To Size,Soft,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,33.9 mm 39.0 mm,26.2 mm 29.0 mm,Normalwidex-Wide,1,All Seasons,1,#354 Bottom 45%,#160 Top 25%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,9.9,282,10.1,286.0,7.7,10.0,33.9,39.0,26.2,29.0
+Nike,Vomero 18,Daily Running,Neutral,10.5 oz / 298g 11.5 oz / 325g,0,13.9 mm 10.0 mm,Heel,True To Size,Soft,Decent,Good,Decent,Moderate,Medium,Narrow,Moderate,Stiff,Moderate,0,1,42.5 mm 45.0 mm,28.6 mm 35.0 mm,Normal Wide X-Wide,1,All Seasons,1,#24 Top 7%,#8 Top 3%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,1,0,0,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,1,0,0,0,1,0,0,0,1,0,1,0,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,10.5,298,11.5,325.0,13.9,10.0,42.5,45.0,28.6,35.0
+Nike,Vomero Plus,Daily Running,Neutral,10.2 oz / 289g 10.1 oz / 285g,0,9.6 mm 10.0 mm,Heel Mid/Forefoot,True To Size,Soft,Decent,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,0,42.3 mm 45.0 mm,32.7 mm 35.0 mm,Normal Wide X-Wide,1,All Seasons,1,#18 Top 5%,#7 Top 2%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,10.2,289,10.1,285.0,9.6,10.0,42.3,45.0,32.7,35.0
+Nike,Vomero Premium,Daily Running,Neutral,11.5 oz / 326g 12.4 oz / 351g,0,8.8 mm 10.0 mm,Heelmid/Forefoot,-,Soft,Decent,Good,Good,Moderate,Medium,Narrow,Stiff,Stiff,Stiff,0,1,50.1 mm 55.0 mm,41.3 mm 45.0 mm,Normal,1,All Seasons,1,#329 Bottom 9%,#2 Top 1%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,,0,0,0,1,1,0,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,1,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,11.5,326,12.4,351.0,8.8,10.0,50.1,55.0,41.3,45.0
+Mizuno,Wave Horizon 6,Daily Running,Stability,11 oz / 313g 11.2 oz / 318g,0,6.5 mm 8.0 mm,Mid/Forefoot,True To Size,Balanced,-,-,-,-,Medium,-,Stiff,Moderate,Stiff,0,0,37.1 mm 38.0 mm,30.6 mm 30.0 mm,Normalwide,1,-,1,#165 Top 26%,#531 Bottom 17%,,,Daily Running,['Daily Running'],0,1,0,0,1,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,,,0,0,0,11.0,313,11.2,318.0,6.5,8.0,37.1,38.0,30.6,30.0
+Mizuno,Wave Horizon 7,Daily Running,Stability,11.6 oz / 329g 11.8 oz / 334g,0,7.2 mm 8.0 mm,Heelmid/Forefoot,Slightly Small,Balanced,Decent,Bad,Good,Moderate,Medium,Wide,Stiff,Stiff,Stiff,0,0,39.1 mm 41.0 mm,31.9 mm 33.0 mm,Normalwide,1,All Seasons,1,#134 Top 37%,#293 Bottom 19%,,,Daily Running,['Daily Running'],0,1,0,0,1,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,small,0,1,0,0,0,1,0,0,1,0,1,0,0,0,0,1,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,11.6,329,11.8,334.0,7.2,8.0,39.1,41.0,31.9,33.0
+Mizuno,Wave Inspire 19,Daily Running,Stability,10.3 oz / 293g 10.7 oz / 303g,0,12.4 mm 12.0 mm,Heel,True To Size,Soft,Decent,Decent,Good,Moderate,Medium,Wide,Moderate,Moderate,Stiff,0,0,38.2 mm 36.0 mm,25.8 mm 24.0 mm,Normalwide,1,All Seasons,1,#228 Top 36%,#455 Bottom 29%,,,Daily Running,['Daily Running'],0,1,0,0,1,Heel,['Heel'],0,0,1,0,true,0,0,1,0,1,0,0,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,10.3,293,10.7,303.0,12.4,12.0,38.2,36.0,25.8,24.0
+Mizuno,Wave Inspire 20,Daily Running,Stability,10.7 oz / 302g 10.8 oz / 305g,0,12.6 mm 12.0 mm,Heel,True To Size,Balanced,Good,Good,Good,Moderate,Medium,Medium,Moderate,Stiff,Stiff,0,0,37.7 mm 37.5 mm,25.1 mm 25.5 mm,Normalwide,1,All Seasons,1,#398 Bottom 38%,#378 Bottom 41%,,,Daily Running,['Daily Running'],0,1,0,0,1,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,10.7,302,10.8,305.0,12.6,12.0,37.7,37.5,25.1,25.5
+Mizuno,Wave Inspire 21,Daily Running,Stability,10.1 oz / 286g 4.9 oz / 140g,0,12.9 mm 12.0 mm,Heel,True To Size,Balanced,Decent,Good,Good,Moderate,Wide,Medium,Moderate,Moderate,Stiff,0,0,38.0 mm 38.0 mm,25.1 mm 26.0 mm,Normalwide,1,All Seasons,1,#167 Top 46%,#179 Top 49%,,,Daily Running,['Daily Running'],0,1,0,0,1,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,10.1,286,4.9,140.0,12.9,12.0,38.0,38.0,25.1,26.0
+Mizuno,Wave Rebellion,Tempo,Neutral,8.7 oz / 247g 9.1 oz / 259g,1,7.7 mm 8.0 mm,Mid/Forefoot,Slightly Large,Balanced,Decent,Decent,Bad,Moderate,Medium,Medium,Stiff,Stiff,Stiff,0,1,36.4 mm 36.0 mm,28.7 mm 28.0 mm,Normal,1,All Seasons,1,#163 Top 45%,#268 Bottom 26%,,,Tempo,['Tempo'],0,0,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,large,1,0,0,0,0,1,0,0,1,0,0,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,8.7,247,9.1,259.0,7.7,8.0,36.4,36.0,28.7,28.0
+Mizuno,Wave Rebellion Flash 2,Tempo,Neutral,8.4 oz / 239g 8.6 oz / 243g,1,2.9 mm 0.5 mm,Mid/Forefoot,True To Size,Soft,Decent,Bad,Good,Moderate,Medium,Medium,Moderate,Stiff,Moderate,0,1,35.2 mm 35.0 mm,32.3 mm 34.5 mm,Normal,0,All Seasons,0,#58 Top 16%,#184 Bottom 49%,,,Tempo,['Tempo'],0,0,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,1,0,0,0,1,0,1,0,0,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,8.4,239,8.6,243.0,2.9,0.5,35.2,35.0,32.3,34.5
+Mizuno,Wave Rebellion Pro,Competition,Neutral,7.5 oz / 214g 7.7 oz / 218g,1,4.9 mm 4.5 mm,Mid/Forefoot,Half Size Small,Firm,Bad,Decent,Bad,Moderate,Narrow,Medium,Stiff,Stiff,Flexible,Carbon plate,1,38.2 mm 39.0 mm,33.3 mm 34.5 mm,Normal,1,All Seasons,1,#97 Top 16%,#422 Bottom 34%,,,Competition,['Competition'],1,0,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,small,0,1,0,0,0,0,1,1,0,0,0,1,0,1,0,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,7.5,214,7.7,218.0,4.9,4.5,38.2,39.0,33.3,34.5
+Mizuno,Wave Rebellion Pro 2,Competition,Neutral,7.4 oz / 209g 7.6 oz / 215g,1,2.1 mm 2.5 mm,Mid/Forefoot,True To Size,Soft,Bad,Good,Bad,Moderate,Narrow,Medium,Stiff,Stiff,Flexible,Carbon plate,1,37.9 mm 38.0 mm,35.8 mm 36.5 mm,Normal,1,All Seasons,1,#61 Top 17%,#203 Bottom 44%,,,Competition,['Competition'],1,0,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,1,0,0,1,0,0,0,0,1,1,0,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,7.4,209,7.6,215.0,2.1,2.5,37.9,38.0,35.8,36.5
+Mizuno,Wave Rider 25,Daily Running,Neutral,9.5 oz / 269g 9.8 oz / 277g,0,12.3 mm 12.0 mm,Heel,Slightly Small,-,-,-,-,-,Medium,-,Stiff,Moderate,Stiff,0,0,38.4 mm 36.0 mm,26.1 mm 24.0 mm,Normalwide,1,-,1,#126 Top 20%,#530 Bottom 17%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,small,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,,,0,0,0,9.5,269,9.8,277.0,12.3,12.0,38.4,36.0,26.1,24.0
+Mizuno,Wave Rider 26,Daily Running,Neutral,10.3 oz / 291g 10 oz / 283g,0,11.8 mm 12.0 mm,Heel,True To Size,Soft,-,-,-,Moderate,Medium,-,Stiff,Moderate,Stiff,0,0,39.2 mm 38.5 mm,27.4 mm 26.5 mm,Normalwide,1,All Seasons,1,#293 Top 46%,#516 Bottom 20%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,10.3,291,10.0,283.0,11.8,12.0,39.2,38.5,27.4,26.5
+Mizuno,Wave Rider 27,Daily Running,Neutral,9.8 oz / 279g 9.9 oz / 280g,0,13.2 mm 12.0 mm,Heel,True To Size,Balanced,Decent,Good,Good,Moderate,Medium,Medium,Moderate,Moderate,Stiff,0,0,38.3 mm 38.5 mm,25.1 mm 26.5 mm,Normalwide,1,All Seasons,1,#49 Top 8%,#374 Bottom 42%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,9.8,279,9.9,280.0,13.2,12.0,38.3,38.5,25.1,26.5
+Mizuno,Wave Rider 28,Daily Running,Neutral,9.7 oz / 276g 9.5 oz / 269g,0,14.7 mm 12.0 mm,Heel,Half Size Small,Balanced,Decent,Good,Decent,Moderate,Medium,Wide,Moderate,Stiff,Moderate,0,0,39.3 mm 41.0 mm,24.6 mm 29.0 mm,Normalwide,1,All Seasons,1,#54 Top 9%,#223 Top 35%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,small,0,1,0,0,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,0,0,1,0,1,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,9.7,276,9.5,269.0,14.7,12.0,39.3,41.0,24.6,29.0
+Mizuno,Wave Rider 29,Daily Running,Neutral,9.1 oz / 258g 9.3 oz / 265g,0,8.3 mm 10.0 mm,Heelmid/Forefoot,-,Soft,Decent,Good,Decent,Moderate,Medium,Wide,Moderate,Stiff,Stiff,0,0,37.5 mm 39.0 mm,29.2 mm 29.0 mm,Normalwide,1,All Seasons,1,#192 Bottom 47%,#123 Top 34%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,,0,0,0,1,1,0,0,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,0,0,1,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,9.1,258,9.3,265.0,8.3,10.0,37.5,39.0,29.2,29.0
+Mizuno,Wave Sky 7,Daily Running,Neutral,10.4 oz / 296g 10.7 oz / 303g,0,10.9 mm 8.0 mm,Heel,True To Size,Balanced,Good,Good,Good,Moderate,Medium,Medium,Stiff,Stiff,Moderate,0,0,40.9 mm 40.0 mm,30.0 mm 32.0 mm,Normalwide,1,All Seasons,1,#279 Top 44%,#515 Bottom 20%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,10.4,296,10.7,303.0,10.9,8.0,40.9,40.0,30.0,32.0
+Mizuno,Wave Sky 8,Daily Running,Neutral,9.6 oz / 271g 9.8 oz / 277g,0,10.0 mm 8.0 mm,Heelmid/Forefoot,True To Size,Soft,Good,Good,Bad,Moderate,Medium,Medium,Moderate,Moderate,Moderate,0,0,40.7 mm 42.0 mm,30.7 mm 34.0 mm,Normalwide,1,All Seasons,1,#25 Top 7%,#205 Bottom 43%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,1,0,0,0,0,1,0,0,1,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,9.6,271,9.8,277.0,10.0,8.0,40.7,42.0,30.7,34.0
+Nike,Winflo 10,Daily Running,Neutral,9.5 oz / 269g 9.9 oz / 280g,0,9.7 mm 10.0 mm,Heelmid/Forefoot,True To Size,Soft,Decent,Decent,Good,Moderate,Medium,Medium,Moderate,Moderate,Flexible,0,0,33.5 mm 33.0 mm,23.8 mm 23.0 mm,Normalwide,1,All Seasons,1,#365 Bottom 43%,#237 Top 37%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,1,0,0,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,9.5,269,9.9,280.0,9.7,10.0,33.5,33.0,23.8,23.0
+Nike,Winflo 11,Daily Running,Neutral,10.4 oz / 295g 10 oz / 283g,0,12.3 mm 10.0 mm,Heel,True To Size,Soft,Decent,Good,Good,Moderate,Narrow,Medium,Moderate,Stiff,Moderate,0,0,37.6 mm 35.0 mm,25.3 mm 25.0 mm,Normalwidex-Wide,1,All Seasons,1,#176 Top 49%,#78 Top 22%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,0,1,0,1,0,0,0,1,0,0,1,0,0,0,1,0,1,0,Normal|Wide|X-Wide,"['Normal', 'Wide', 'X-Wide']",0,1,1,1,All,['All'],1,0,0,10.4,295,10.0,283.0,12.3,10.0,37.6,35.0,25.3,25.0
+Nike,Winflo 11 GTX,Daily Running,Neutral,10.9 oz / 310g 10.9 oz / 310g,0,13.3 mm 10.0 mm,Heel,True To Size,Soft,Good,Good,Bad,Warm,Medium,Medium,Moderate,Stiff,Stiff,0,0,39.0 mm 35.0 mm,25.7 mm 25.0 mm,Normal,1,Winter,1,#335 Bottom 8%,#248 Bottom 32%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,1,0,0,0,0,1,0,0,1,1,0,0,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,Normal,['Normal'],0,1,0,0,Winter,['Winter'],0,0,1,10.9,310,10.9,310.0,13.3,10.0,39.0,35.0,25.7,25.0
+Mizuno,WWave Horizon 7,Daily Running,Stability,11.6 oz / 329g 11.8 oz / 334g,0,7.2 mm 8.0 mm,Heelmid/Forefoot,Slightly Small,Balanced,Decent,Bad,Good,Moderate,Medium,Wide,Stiff,Stiff,Stiff,0,0,39.1 mm 41.0 mm,31.9 mm 33.0 mm,Normalwide,1,All Seasons,1,#134 Top 37%,#294 Bottom 19%,,,Daily Running,['Daily Running'],0,1,0,0,1,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,small,0,1,0,0,0,1,0,0,1,0,1,0,0,0,0,1,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,Normal|Wide,"['Normal', 'Wide']",0,1,1,0,All,['All'],1,0,0,11.6,329,11.8,334.0,7.2,8.0,39.1,41.0,31.9,33.0
+Reebok,Zig Dynamica 4,Daily Running,Neutral,12.4 oz / 352g 12.3 oz / 350g,0,8.5 mm 9.0 mm,Heelmid/Forefoot,True To Size,Balanced,Decent,Bad,Good,Moderate,Medium,Narrow,Stiff,Moderate,Moderate,0,0,32.0 mm 32.0 mm,23.5 mm 23.0 mm,Normal,1,All Seasons,1,#577 Bottom 10%,#533 Bottom 17%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,0,1,0,0,1,0,1,0,0,0,0,1,0,1,0,0,1,0,1,0,0,0,0,1,0,1,0,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,12.4,352,12.3,350.0,8.5,9.0,32.0,32.0,23.5,23.0
+Reebok,Zig Dynamica 5,Daily Running,Neutral,10.5 oz / 298g 10.5 oz / 299g,0,6.3 mm 6.0 mm,Mid/Forefoot,-,Balanced,Decent,Bad,Good,Moderate,Medium,Narrow,Stiff,Stiff,Moderate,0,0,34.1 mm,27.8 mm,Normal,1,All Seasons,1,#311 Bottom 15%,#297 Bottom 19%,,,Daily Running,['Daily Running'],0,1,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,,0,0,0,1,0,1,0,0,1,0,1,0,0,0,0,1,0,1,0,0,1,0,1,0,0,0,0,1,0,0,1,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,10.5,298,10.5,299.0,6.3,6.0,34.1,,27.8,
+Nike,Zoom Fly 4,Tempo,Neutral,9.6 oz / 271g 8.8 oz / 249g,0,7.1 mm 8.0 mm,Mid/Forefoot,True To Size,-,-,-,-,-,Narrow,-,Stiff,Stiff,Flexible,Carbon plateRock plate,1,38.4 mm 36.0 mm,31.3 mm,Normal,1,-,1,#353 Bottom 45%,#225 Top 35%,,,Tempo,['Tempo'],0,0,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,,,0,0,0,9.6,271,8.8,249.0,7.1,8.0,38.4,36.0,31.3,
+Nike,Zoom Fly 5,Daily Runningtempo,Neutral,9.8 oz / 279g 10.1 oz / 286g,0,7.5 mm 8.0 mm,Mid/Forefoot,True To Size,Soft,-,-,-,Warm,Narrow,Narrow,Stiff,Stiff,Moderate,Carbon plate,1,36.9 mm 41.0 mm,29.4 mm 33.0 mm,Normal,1,All Seasons,1,#560 Bottom 13%,#177 Top 28%,,,Daily Running|Tempo,"['Daily Running', 'Tempo']",0,1,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0,0,1,0,0,1,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,9.8,279,10.1,286.0,7.5,8.0,36.9,41.0,29.4,33.0
+Nike,Zoom Fly 6,Competitiontempo,Neutral,8.7 oz / 248g 8.6 oz / 244g,1,9.6 mm 8.0 mm,Heelmid/Forefoot,True To Size,Soft,Good,Good,Good,Moderate,Narrow,Medium,Stiff,Stiff,Stiff,Carbon plate,1,39.7 mm 40.0 mm,30.1 mm 32.0 mm,Normal,1,All Seasons,1,#14 Top 4%,#26 Top 8%,,,Competition|Tempo,"['Competition', 'Tempo']",1,0,1,1,0,Heel|Mid|Forefoot,"['Heel', 'Mid', 'Forefoot']",0,1,1,1,true,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,1,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,0,0,1,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,8.7,248,8.6,244.0,9.6,8.0,39.7,40.0,30.1,32.0
+Nike,ZoomX Invincible Run Flyknit 2,Daily Running,Neutral,10.3 oz / 291g 9.7 oz / 274g,0,12.0 mm 9.0 mm,Heel,True To Size,Soft,-,-,-,Warm,Medium,Medium,Stiff,Flexible,Moderate,0,0,35.5 mm 37.0 mm,23.5 mm 28.0 mm,Normal,1,All Seasons,1,#385 Bottom 40%,#262 Top 41%,,,Daily Running,['Daily Running'],0,1,0,1,0,Heel,['Heel'],0,0,1,0,true,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,1,1,0,0,0,1,0,Normal,['Normal'],0,1,0,0,All,['All'],1,0,0,10.3,291,9.7,274.0,12.0,9.0,35.5,37.0,23.5,28.0
+Nike,ZoomX Streakfly,Tempo,Neutral,6 oz / 171g 6 oz / 171g,1,6.3 mm 6.0 mm,Mid/Forefoot,True To Size,Soft,-,-,-,Breathable,Narrow,Medium,Flexible,Flexible,Flexible,0,1,31.7 mm 32.0 mm,25.4 mm 26.0 mm,Normal,1,Summerall Seasons,1,#146 Top 40%,#191 Bottom 47%,,,Tempo,['Tempo'],0,0,1,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,true,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,6.0,171,6.0,171.0,6.3,6.0,31.7,32.0,25.4,26.0
+Nike,ZoomX Vaporfly NEXT% 2,Competition,Neutral,6.9 oz / 196g 6.9 oz / 196g,1,7.7 mm 7.7 mm,Mid/Forefoot,Slightly Small,Soft,-,-,-,Breathable,Narrow,-,Stiff,Stiff,Flexible,Carbon plate,1,38.6 mm 38.6 mm,30.9 mm 30.9 mm,Normal,0,Summerall Seasons,0,#47 Top 8%,#327 Bottom 49%,,,Competition,['Competition'],1,0,0,1,0,Mid|Forefoot,"['Mid', 'Forefoot']",0,1,0,1,small,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,1,1,0,0,Normal,['Normal'],0,1,0,0,Summer|All,"['Summer', 'All']",1,1,0,6.9,196,6.9,196.0,7.7,7.7,38.6,38.6,30.9,30.9
diff --git a/data/unused-data/(old) trail_dataset.csv b/data/unused-data/(old) trail_dataset.csv
new file mode 100644
index 0000000000000000000000000000000000000000..fdfd934208e0ac3c2bc60979b6d8d1cecf08df27
--- /dev/null
+++ b/data/unused-data/(old) trail_dataset.csv	
@@ -0,0 +1,171 @@
+Brand,Name,Lightweight,Size,Lug depth,Widths available,For heavy runners,Removable insole,Orthotic friendly,Ranking,Popularity,terrain_norm,terrain_light,terrain_moderate,terrain_technical,Arch_grouped,arch_neutral,arch_stability,Strike_norm,strike_forefoot,strike_heel,strike_mid,drop_lab_mm,drop_brand_mm,midsole_soft,midsole_balanced,midsole_firm,plate_carbon,plate_rock,plate_none,toebox_bad,toebox_decent,toebox_good,heelpad_bad,heelpad_decent,heelpad_good,outsole_bad,outsole_decent,outsole_good,breath_breathable,breath_moderate,breath_warm,width_narrow,width_medium,width_wide,toeboxwidth_narrow,toeboxwidth_medium,toeboxwidth_wide,stiff_flexible,stiff_moderate,stiff_stiff,torsion_flexible,torsion_moderate,torsion_stiff,heelcounter_flexible,heelcounter_moderate,heelcounter_stiff,heel_lab_mm,heel_brand_mm,forefoot_lab_mm,forefoot_brand_mm,season_all,season_summer,season_winter,water_proof,water_repellent,water_both,water_none
+Adidas,Terrex Agravic Speed Ultra,0,Slightly large,2.5 mm,Normal,0,1,1,#67 Top 19%,#163 Top 45%,Light,1,0,0,Neutral,1,0,Mid|Forefoot,1,0,1,0.3,8.0,0,1,0,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,1,0,0,0,1,0,0,0,1,1,0,0,30.6,38.0,30.3,30.0,1,0,0,0,0,0,1
+Adidas,Terrex Speed Ultra,0,True to size,2.6 mm,Normal,0,1,1,#45 Top 13%,#294 Bottom 20%,Light,1,0,0,Neutral,1,0,Heel|Mid|Forefoot,1,1,1,8.2,8.0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,1,0,0,32.8,26.0,24.6,18.0,0,0,0,0,0,0,1
+Altra,Experience Wild,0,True to size,3.6 mm,Normal,0,1,1,#251 Top 39%,#308 Top 48%,Light|Moderate,1,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,4.3,4.0,1,0,0,0,0,1,0,1,0,0,1,0,0,0,1,0,1,0,0,0,1,0,0,1,0,1,0,0,0,1,0,1,0,34.5,34.0,30.2,30.0,1,0,0,0,0,0,1
+Altra,Experience Wild 2,0,-,3.5 mm,Normal,0,1,1,#315 Bottom 14%,#211 Bottom 42%,Light,1,0,0,Neutral,1,0,Mid|Forefoot,1,0,1,6.1,4.0,0,1,0,0,0,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,1,0,0,32.3,32.0,26.2,28.0,1,0,0,0,0,0,1
+Altra,Lone Peak 5.0,0,True to size,3.7 mm,Normal,0,1,1,#62 Top 10%,#63 Top 10%,Light|Moderate,1,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,0.2,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,24.5,25.0,24.3,25.0,0,0,0,0,0,0,1
+Altra,Lone Peak 6,0,True to size,4.4 mm,NormalWide,0,0,0,#139 Top 22%,#290 Top 45%,Moderate|Technical,0,1,1,Neutral,1,0,Mid|Forefoot,1,0,1,0.6,0.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,25.1,25.0,24.5,25.0,0,0,0,0,0,0,1
+Altra,Lone Peak 7,0,True to size,3.4 mm,NormalWide,0,1,1,#387 Bottom 40%,#227 Top 36%,Moderate,0,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,0.2,0.0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,1,0,0,1,0,0,23.3,25.0,23.1,25.0,1,0,0,0,0,0,1
+Altra,Lone Peak 8,0,True to size,3.0 mm,NormalWide,0,1,1,#540 Bottom 16%,#177 Top 28%,Light|Moderate,1,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,1.4,0.0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,1,0,0,1,0,0,22.7,25.0,21.3,25.0,1,0,0,0,0,0,1
+Altra,Lone Peak 9,0,True to size,3.8 mm,NormalWide,0,1,1,#30 Top 9%,#42 Top 12%,Light|Moderate,1,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,0.0,0.0,0,1,0,0,0,1,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,1,0,0,23.3,25.0,23.3,25.0,1,0,0,0,0,0,1
+Altra,Mont Blanc,0,True to size,2.8 mm,NormalWide,0,0,0,#317 Bottom 13%,#232 Bottom 36%,Light|Moderate,1,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,0.0,,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,33.8,30.0,33.8,,0,0,0,0,0,0,1
+Altra,Mont Blanc Carbon,0,True to size,3.5 mm,Normal,0,1,1,#180 Top 49%,#263 Bottom 28%,Moderate,0,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,0.3,0.0,1,0,0,1,0,0,0,1,0,1,0,0,0,0,1,0,1,0,0,1,0,0,0,1,0,0,1,1,0,0,1,0,0,27.2,29.0,26.9,29.0,1,0,0,0,0,0,1
+Altra,Olympus 5,0,Slightly small,3.0 mm,Normal,0,1,1,#503 Bottom 22%,#284 Top 44%,Light|Moderate,1,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,2.0,0.0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,1,0,0,1,0,33.0,33.0,31.0,33.0,1,0,0,0,0,0,1
+Altra,Olympus 6,0,Half size small,3.5 mm,Normal,1,1,1,#274 Bottom 25%,#106 Top 29%,Light|Moderate,1,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,0.7,0.0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,1,1,0,0,0,1,0,0,0,1,0,0,1,0,0,1,0,1,0,32.2,35.0,31.5,35.0,1,1,0,0,0,0,1
+Altra,Outroad,0,Slightly small,2.3 mm,Normal,0,1,1,#550 Bottom 14%,#510 Bottom 21%,Light,1,0,0,Neutral,1,0,Mid|Forefoot,1,0,1,0.1,0.0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,1,0,0,25.1,27.0,25.0,27.0,1,0,0,0,0,0,1
+Altra,Outroad 2,0,Slightly small,2.2 mm,Normal,0,1,1,#581 Bottom 10%,#552 Bottom 14%,Light,1,0,0,Neutral,1,0,Mid|Forefoot,1,0,1,1.4,0.0,0,1,0,0,0,1,1,0,0,0,1,0,0,0,1,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,26.9,27.5,25.5,27.5,1,0,0,0,0,0,1
+Altra,Outroad 3,0,Slightly small,1.5 mm,Normal,0,1,1,#289 Bottom 21%,#272 Bottom 26%,Light,1,0,0,Neutral,1,0,Mid|Forefoot,1,0,1,0.6,0.0,0,1,0,0,0,1,0,1,0,1,0,0,1,0,0,0,0,1,0,1,0,0,0,1,0,0,1,0,1,0,1,0,0,23.8,27.0,23.2,27.0,1,0,0,0,0,0,1
+Altra,Superior 6,0,Slightly small,3.3 mm,Normal,0,1,1,#324 Bottom 11%,#260 Bottom 29%,Light|Moderate,1,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,0.1,0.0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,1,1,0,0,0,1,0,0,0,1,0,0,1,0,1,0,1,0,0,22.1,20.5,22.0,20.5,1,1,0,0,0,0,1
+Altra,Timp 4,0,True to size,2.9 mm,Normal,0,1,1,#595 Bottom 8%,#507 Bottom 21%,Light|Moderate,1,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,0.1,0.0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,1,0,0,1,0,0,29.0,30.0,28.9,30.0,1,0,0,0,0,0,1
+Altra,Timp 5,0,Slightly small,3.0 mm,Normal,0,1,1,#304 Bottom 17%,#138 Top 38%,Light|Moderate,1,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,0.1,0.0,0,1,0,0,0,1,0,0,1,0,1,0,0,0,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,28.6,29.0,28.7,29.0,1,0,0,0,0,0,1
+Altra,Timp 5 GTX,0,-,3.5 mm,Normal,0,1,1,#365 Bottom 1%,#280 Bottom 23%,Light|Moderate,1,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,0.3,0.0,1,0,0,0,0,1,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,1,0,0,28.9,29.0,28.6,29.0,0,0,1,1,0,0,0
+Asics,Gel Excite Trail 2,0,True to size,4.0 mm,Normal,1,1,1,#293 Bottom 20%,#205 Bottom 44%,Light,1,0,0,Neutral,1,0,Heel,0,1,0,10.1,8.0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,0,0,1,0,0,1,37.7,36.0,27.6,28.0,1,0,0,0,0,0,1
+Asics,Gel Trabuco 12,0,True to size,4.5 mm,Normal,0,1,1,#111 Top 18%,#166 Top 26%,Moderate|Technical,0,1,1,Neutral,1,0,Mid|Forefoot,1,0,1,7.8,8.0,1,0,0,0,1,0,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,35.4,36.0,27.6,28.0,1,0,0,0,0,0,1
+Asics,Gel Trabuco 13,0,True to size,3.3 mm,Normal,0,1,1,#135 Top 37%,#108 Top 30%,Light,1,0,0,Neutral,1,0,Mid|Forefoot,1,0,1,7.0,8.0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,33.8,34.0,26.8,26.0,1,0,0,0,0,0,1
+Asics,Metafuji Trail,0,True to size,2.7 mm,NormalWide,0,1,1,#61 Top 17%,#300 Bottom 18%,Light,1,0,0,Neutral,1,0,Heel,0,1,0,10.3,5.0,1,0,0,1,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,1,0,1,0,0,0,0,1,0,0,1,0,1,0,44.7,44.0,34.4,39.0,1,1,0,0,0,0,1
+Asics,Trabuco Max 3,0,Slightly small,4.0 mm,Normal,0,1,1,#76 Top 12%,#165 Top 26%,Moderate|Technical,0,1,1,Neutral,1,0,Heel|Mid|Forefoot,1,1,1,8.5,5.0,1,0,0,0,0,1,0,0,1,0,0,1,0,1,0,0,0,1,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,42.4,43.0,33.9,38.0,1,0,0,0,0,0,1
+Asics,Trabuco Max 4,0,Half size small,3.1 mm,Normal,0,1,1,#134 Top 37%,#107 Top 30%,Light,1,0,0,Neutral,1,0,Mid|Forefoot,1,0,1,6.1,5.0,0,1,0,0,0,1,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,39.3,41.0,33.2,36.0,1,0,0,0,0,0,1
+Brooks,Caldera 6,0,True to size,3.5 mm,Normal,0,1,1,#245 Top 38%,#446 Bottom 31%,Light|Moderate,1,1,0,Neutral,1,0,Heel,0,1,0,12.1,6.0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,38.4,36.0,26.3,30.0,1,0,0,0,0,0,1
+Brooks,Caldera 7,0,True to size,4.0 mm,Normal,0,1,1,#260 Top 41%,#389 Bottom 39%,Light|Moderate,1,1,0,Neutral,1,0,Heel|Mid|Forefoot,1,1,1,8.9,6.0,1,0,0,0,0,1,0,1,0,0,0,1,0,1,0,1,0,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,36.7,39.0,27.8,33.0,1,1,0,0,0,0,1
+Brooks,Caldera 8,0,True to size,3.6 mm,Normal,0,1,1,#87 Top 24%,#168 Top 46%,Light|Moderate,1,1,0,Neutral,1,0,Heel|Mid|Forefoot,1,1,1,8.8,6.0,1,0,0,0,0,1,1,0,0,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,36.7,39.0,27.9,33.0,1,0,0,0,0,0,1
+Brooks,Cascadia 16,0,Slightly small,4.3 mm,NormalWide,0,1,1,#290 Top 45%,#391 Bottom 39%,Technical,0,0,1,Neutral,1,0,Heel,0,1,0,10.3,8.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,1,32.3,29.0,22.0,21.0,0,0,0,0,0,0,1
+Brooks,Cascadia 17,0,True to size,3.9 mm,NormalWide,0,1,1,#360 Bottom 44%,#394 Bottom 39%,Moderate|Technical,0,1,1,Neutral,1,0,Heel|Mid|Forefoot,1,1,1,9.2,8.0,0,1,0,0,1,0,1,0,0,1,0,0,0,0,1,1,0,0,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,33.1,,23.9,,1,1,0,0,0,0,1
+Brooks,Cascadia 18,0,True to size,4.0 mm,NormalWide,0,1,1,#391 Bottom 39%,#214 Top 34%,Light|Moderate,1,1,0,Neutral,1,0,Heel|Mid|Forefoot,1,1,1,8.8,8.0,0,1,0,0,1,0,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,32.6,33.0,23.8,25.0,1,0,0,0,0,0,1
+Brooks,Cascadia 19,0,True to size,3.8 mm,NormalWide,1,1,1,#184 Top 50%,#117 Top 32%,Light|Moderate,1,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,7.8,6.0,0,1,0,0,1,0,0,0,1,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,34.8,35.0,27.0,29.0,1,0,0,0,0,0,1
+Brooks,Catamount 2,0,True to size,3.0 mm,Normal,0,1,1,#316 Top 49%,#560 Bottom 13%,Light|Moderate,1,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,6.4,6.0,0,1,0,0,1,0,1,0,0,0,0,1,0,0,1,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,0,1,0,29.0,29.0,22.6,23.0,1,0,0,0,1,0,0
+Brooks,Catamount 3,0,True to size,2.9 mm,Normal,0,1,1,#81 Top 23%,#242 Bottom 34%,Light|Moderate,1,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,6.8,6.0,0,1,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,1,1,0,0,0,0,1,0,0,1,0,1,0,1,0,0,28.3,30.0,21.5,24.0,1,0,0,0,0,0,1
+Brooks,Divide 3,0,Half size small,3.1 mm,Normal,0,1,1,#124 Top 20%,#609 Bottom 5%,Light|Moderate,1,1,0,Neutral,1,0,Heel|Mid|Forefoot,1,1,1,8.4,8.0,0,0,1,0,0,1,0,0,0,1,0,0,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,31.1,30.0,22.7,22.0,1,0,0,0,0,0,1
+Brooks,Divide 4,0,True to size,2.7 mm,Normal,0,1,1,#183 Top 50%,#308 Bottom 16%,Light,1,0,0,Neutral,1,0,Heel|Mid|Forefoot,1,1,1,9.2,8.0,0,1,0,0,0,1,1,0,0,1,0,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,0,1,32.1,30.0,22.9,22.0,1,0,0,0,0,0,1
+Brooks,Divide 5 GTX,0,True to size,3.0 mm,Normal,0,1,1,#321 Bottom 12%,#299 Bottom 18%,Light,1,0,0,Neutral,1,0,Heel|Mid|Forefoot,1,1,1,10.0,8.0,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,35.5,24.0,25.5,16.0,0,0,1,1,0,0,0
+Hoka,Challenger 7,0,True to size,3.1 mm,NormalWide,0,1,1,#436 Bottom 32%,#141 Top 22%,Light|Moderate,1,1,0,Neutral,1,0,Heel|Mid|Forefoot,1,1,1,8.8,5.0,1,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,1,1,0,0,1,0,0,0,1,0,0,1,0,0,0,1,32.9,31.0,24.1,26.0,1,0,0,0,0,0,1
+Hoka,Challenger 7 GTX,0,True to size,3.8 mm,Normal,0,1,1,#325 Bottom 11%,#127 Top 35%,Moderate|Technical,0,1,1,Neutral,1,0,Heel,0,1,0,11.1,5.0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,1,0,1,0,1,0,0,0,0,1,0,0,1,0,0,1,39.2,31.0,28.1,26.0,0,0,1,0,0,1,0
+Hoka,Challenger 8,0,-,3.7 mm,NormalWide,0,1,1,#360 Bottom 2%,#58 Top 16%,Light|Moderate,1,1,0,Neutral,1,0,Heel,0,1,0,10.1,8.0,1,0,0,0,0,1,0,0,1,0,0,1,0,0,1,1,0,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,40.2,42.0,30.1,34.0,1,1,0,0,0,0,1
+Hoka,Mafate 5,0,-,4.4 mm,Normal,0,1,1,#361 Bottom 1%,#121 Top 33%,Light|Moderate,1,1,0,Neutral,1,0,Heel|Mid|Forefoot,1,1,1,9.0,8.0,1,0,0,0,0,1,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,42.9,45.0,33.9,37.0,1,0,0,0,0,0,1
+Hoka,Mafate Speed 4,0,True to size,3.9 mm,Normal,0,1,1,#132 Top 36%,#84 Top 23%,Moderate|Technical,0,1,1,Neutral,1,0,Mid|Forefoot,1,0,1,7.2,4.0,1,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,1,0,0,0,1,0,0,0,1,1,0,0,38.0,33.0,30.8,29.0,1,0,0,0,0,0,1
+Hoka,Mafate Three2,0,Slightly small,4.0 mm,Normal,0,1,1,#329 Bottom 10%,#200 Bottom 45%,Light,1,0,0,Neutral,1,0,Mid|Forefoot,1,0,1,3.9,4.0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,1,0,1,0,0,1,0,0,0,0,1,0,0,1,0,1,0,35.6,35.0,31.7,31.0,1,0,0,0,0,0,1
+Hoka,Mafate X,0,-,3.0 mm,Normal,1,1,1,#185 Bottom 49%,#192 Bottom 47%,Light|Moderate,1,1,0,Neutral,1,0,Heel,0,1,0,10.6,8.0,0,1,0,1,0,0,1,0,0,0,0,1,0,0,1,0,1,0,0,0,1,0,1,0,0,0,1,0,0,1,0,0,1,47.3,49.0,36.7,41.0,1,0,0,0,0,0,1
+Hoka,Speedgoat 5,0,True to size,3.0 mm,NormalWide,0,1,1,#237 Top 37%,#115 Top 18%,Light|Moderate,1,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,3.8,4.0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,1,0,0,1,0,0,27.5,33.0,23.7,29.0,1,0,0,0,0,0,1
+Hoka,Speedgoat 5 GTX,0,True to size,3.5 mm,Normal,0,1,1,#531 Bottom 17%,#381 Bottom 41%,Moderate,0,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,7.0,4.0,1,0,0,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,34.6,,27.6,,0,0,1,1,0,0,0
+Hoka,Speedgoat 6,0,Slightly small,4.0 mm,NormalWide,1,1,1,#327 Bottom 10%,#47 Top 13%,Moderate,0,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,4.9,5.0,0,1,0,0,0,1,0,0,1,0,0,1,0,1,0,0,0,1,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,32.1,40.0,27.2,35.0,1,0,0,0,0,0,1
+Hoka,Speedgoat 6 GTX,0,Half size small,3.9 mm,NormalWide,0,1,1,#355 Bottom 3%,#128 Top 35%,Moderate,0,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,5.0,5.0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,1,0,1,0,1,0,0,0,0,1,0,0,1,0,1,0,32.9,37.0,27.9,32.0,0,0,1,1,0,0,0
+Hoka,Stinson 7,0,True to size,3.0 mm,Normal,0,1,1,#209 Bottom 43%,#117 Top 32%,Light|Moderate,1,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,7.0,5.0,1,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,40.0,42.0,33.0,37.0,1,0,0,0,0,0,1
+Hoka,Tecton X,1,True to size,3.5 mm,Normal,0,1,1,#134 Top 21%,#492 Bottom 23%,Moderate,0,1,0,Neutral,1,0,Heel|Mid|Forefoot,1,1,1,8.0,5.0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,1,0,0,35.3,33.0,27.3,29.0,0,0,0,0,0,0,1
+Hoka,Tecton X 2,0,Slightly small,3.6 mm,Normal,0,1,1,#282 Top 44%,#363 Bottom 43%,Moderate,0,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,5.6,5.0,0,1,0,1,0,0,0,0,1,1,0,0,0,0,1,0,1,0,1,0,0,1,0,0,0,0,1,0,0,1,0,1,0,37.4,32.0,31.8,27.0,1,0,0,0,0,0,1
+Hoka,Tecton X 3,0,Slightly small,4.0 mm,Normal,0,1,1,#229 Bottom 37%,#162 Top 45%,Light|Moderate,1,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,6.9,5.0,1,0,0,1,0,0,0,0,1,0,1,0,0,0,1,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,1,0,0,37.8,40.0,30.9,35.0,1,0,0,0,1,0,0
+Hoka,Torrent 3,0,True to size,3.6 mm,Normal,0,1,1,#226 Bottom 38%,#301 Bottom 18%,Moderate,0,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,7.1,5.0,0,1,0,0,0,1,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,29.5,23.0,22.4,18.0,1,0,0,0,0,0,1
+Hoka,Zinal,1,True to size,3.4 mm,Normal,0,1,1,#246 Top 39%,#588 Bottom 9%,Light|Moderate,1,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,3.9,4.0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,1,0,29.3,22.0,25.4,18.0,0,0,0,0,0,0,1
+Hoka,Zinal 2,1,Slightly small,3.7 mm,Normal,0,1,1,#227 Bottom 38%,#310 Bottom 15%,Moderate,0,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,7.2,5.0,1,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,1,0,1,0,0,0,1,0,0,0,1,0,1,0,1,0,0,29.8,30.0,22.6,25.0,1,0,0,0,0,0,1
+Inov8,Trailfly,0,Slightly small,3.9 mm,NormalWide,0,1,1,#141 Top 39%,#326 Bottom 11%,Light|Moderate,1,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,6.0,6.0,1,0,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,0,1,0,0,1,1,0,0,1,0,0,30.1,29.0,24.1,23.0,1,0,0,0,0,0,1
+Keen,Seek,0,-,4.1 mm,Normal,0,1,1,#146 Top 40%,#315 Bottom 14%,Light|Moderate,1,1,0,Neutral,1,0,Heel|Mid|Forefoot,1,1,1,8.6,6.0,1,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,1,0,0,36.3,38.5,27.7,32.5,1,0,0,0,0,0,1
+La Sportiva,Mutant,0,Half size small,5.0 mm,Normal,0,1,1,#149 Top 41%,#228 Bottom 38%,Technical,0,0,1,Neutral,1,0,Heel,0,1,0,11.3,10.0,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,0,1,0,33.2,26.0,21.9,16.0,1,0,0,0,0,0,1
+La Sportiva,Prodigio,0,Half size small,3.4 mm,Normal,0,1,1,#269 Bottom 26%,#217 Bottom 40%,Moderate,0,1,0,Neutral,1,0,Heel|Mid|Forefoot,1,1,1,8.9,6.0,1,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,29.3,34.0,20.4,28.0,1,0,0,0,0,0,1
+Merrell,Agility Peak 4,0,Slightly large,4.4 mm,Normal,0,1,1,#364 Bottom 43%,#595 Bottom 8%,Technical,0,0,1,Neutral,1,0,Heel|Mid|Forefoot,1,1,1,9.3,6.0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,1,0,0,1,34.4,30.0,25.1,24.0,1,0,0,0,0,0,1
+Merrell,Agility Peak 5,0,True to size,4.5 mm,Normal,1,1,1,#139 Top 38%,#113 Top 31%,Moderate|Technical,0,1,1,Neutral,1,0,Heel,0,1,0,13.4,6.0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,1,0,1,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,39.2,39.0,25.8,33.0,1,0,0,0,0,0,1
+Merrell,Agility Peak 5 GTX,0,True to size,4.4 mm,Normal,0,1,1,#298 Bottom 19%,#255 Bottom 30%,Moderate|Technical,0,1,1,Neutral,1,0,Heel,0,1,0,11.6,6.0,0,1,0,0,1,0,0,0,1,1,0,0,0,1,0,0,0,1,1,0,0,0,1,0,0,0,1,0,0,1,0,0,1,37.3,39.0,25.7,33.0,0,0,1,1,0,0,0
+Merrell,Antora 3,0,True to size,3.4 mm,Normal,0,1,1,#269 Bottom 26%,#231 Bottom 37%,Light,1,0,0,Neutral,1,0,Heel|Mid|Forefoot,1,1,1,9.1,,0,1,0,0,1,0,0,1,0,1,0,0,0,0,1,1,0,0,1,0,0,0,1,0,0,1,0,0,0,1,0,1,0,33.5,,24.4,,1,1,0,0,0,0,1
+Merrell,Fly Strike,0,True to size,3.5 mm,NormalWide,0,1,1,#316 Bottom 14%,#273 Bottom 25%,Light,1,0,0,Neutral,1,0,Heel,0,1,0,17.3,10.0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,1,0,1,0,34.3,27.0,17.0,17.0,1,0,0,0,0,0,1
+Merrell,Moab Flight,0,True to size,2.9 mm,NormalWide,0,1,1,#91 Top 25%,#307 Bottom 16%,Light,1,0,0,Neutral,1,0,Heel,0,1,0,13.5,10.0,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,32.6,29.0,19.1,19.0,1,0,0,0,0,0,1
+Merrell,Morphlite,1,True to size,2.0 mm,NormalWide,0,1,1,#148 Top 41%,#292 Bottom 20%,Light,1,0,0,Neutral,1,0,Heel,0,1,0,11.0,6.0,1,0,0,0,0,1,1,0,0,0,1,0,0,1,0,0,1,0,1,0,0,0,1,0,0,1,0,0,0,1,1,0,0,32.3,26.0,21.3,20.0,1,0,0,0,0,0,1
+Merrell,Nova 2,0,-,4.2 mm,NormalWide,0,0,0,#424 Bottom 34%,#470 Bottom 27%,Moderate|Technical,0,1,1,Neutral,1,0,Heel|Mid|Forefoot,1,1,1,9.3,8.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,35.1,29.0,25.8,21.0,0,0,0,0,0,0,1
+Merrell,Nova 3,0,True to size,3.5 mm,NormalWide,0,1,1,#574 Bottom 11%,#322 Top 50%,Light|Moderate,1,1,0,Neutral,1,0,Heel|Mid|Forefoot,1,1,1,9.9,8.0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,1,0,0,0,1,0,0,1,0,0,0,1,1,0,0,34.1,29.0,24.2,21.0,1,0,0,0,0,0,1
+Merrell,Nova 4,0,-,4.0 mm,NormalWide,0,1,1,#192 Bottom 47%,#193 Bottom 47%,Light|Moderate,1,1,0,Neutral,1,0,Heel,0,1,0,12.1,8.0,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,1,0,0,37.7,29.0,25.6,21.0,1,0,0,0,0,0,1
+Merrell,Trail Glove 7,1,True to size,2.5 mm,Normal,0,0,0,#225 Bottom 38%,#161 Top 44%,Light,1,0,0,Neutral,1,0,Mid|Forefoot,1,0,1,0.1,0.0,0,1,0,0,0,1,0,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,1,0,0,1,0,0,16.1,14.0,16.0,14.0,1,0,0,0,0,0,1
+New Balance, Foam X Hierro v8,0,Slightly small,4.0 mm,NormalWideX-Wide,0,1,1,#513 Bottom 20%,#97 Top 15%,Light|Moderate,1,1,0,Neutral,1,0,Heel|Mid|Forefoot,1,1,1,8.1,6.0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,1,0,0,0,1,0,32.2,37.0,24.1,31.0,1,0,0,0,0,0,1
+New Balance,510 v6,0,True to size,2.9 mm,NormalWideX-Wide,0,1,1,#355 Bottom 3%,#318 Bottom 13%,Light,1,0,0,Neutral,1,0,Heel|Mid|Forefoot,1,1,1,8.2,,0,1,0,0,0,1,0,1,0,1,0,0,0,0,1,0,1,0,1,0,0,0,1,0,0,0,1,0,1,0,0,1,0,33.8,,25.6,,1,0,0,0,0,0,1
+New Balance,DynaSoft Nitrel v5,0,Slightly small,2.9 mm,NormalWideX-Wide,0,1,1,#602 Bottom 6%,#490 Bottom 24%,Light,1,0,0,Neutral,1,0,Mid|Forefoot,1,0,1,6.7,6.0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,1,0,1,0,1,0,0,0,0,1,0,0,1,0,1,0,1,0,0,29.8,29.0,23.1,23.0,1,0,0,0,0,0,1
+New Balance,DynaSoft Nitrel v6,0,True to size,2.7 mm,NormalWideX-Wide,0,1,1,#351 Bottom 4%,#187 Bottom 49%,Light,1,0,0,Neutral,1,0,Mid|Forefoot,1,0,1,2.5,6.0,0,1,0,0,0,1,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,0,1,0,22.7,27.0,20.2,21.0,1,0,0,0,0,0,1
+New Balance,Fresh Foam Hierro v6,0,True to size,3.3 mm,Normal,0,1,1,#197 Top 31%,#345 Bottom 46%,Light|Moderate,1,1,0,Neutral,1,0,Heel|Mid|Forefoot,1,1,1,9.2,8.0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,1,0,0,1,26.9,28.0,17.7,20.0,1,0,0,0,0,0,1
+New Balance,Fresh Foam X Garoe v2,0,-,3.2 mm,NormalWideX-Wide,0,1,1,#50 Top 14%,#129 Top 36%,Light,1,0,0,Neutral,1,0,Heel,0,1,0,11.0,8.0,1,0,0,0,0,1,0,1,0,0,1,0,0,0,1,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,0,0,1,38.4,,27.4,,1,0,0,0,0,0,1
+New Balance,Fresh Foam X Hierro v7,0,True to size,3.0 mm,NormalWideX-Wide,0,1,1,#370 Bottom 42%,#150 Top 24%,Light|Moderate,1,1,0,Neutral,1,0,Heel|Mid|Forefoot,1,1,1,9.9,8.0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,1,0,1,0,0,1,0,0,32.4,29.0,22.5,21.0,1,0,0,0,0,0,1
+New Balance,Fresh Foam X Hierro v9,0,Half size small,3.3 mm,NormalWideX-Wide,0,1,1,#234 Bottom 36%,#28 Top 8%,Light,1,0,0,Neutral,1,0,Mid|Forefoot,1,0,1,4.2,4.0,1,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,37.3,33.0,33.1,29.0,1,0,0,0,0,0,1
+New Balance,Fresh Foam X More Trail v3,0,True to size,5.0 mm,NormalWide,0,1,1,#159 Top 44%,#22 Top 6%,Moderate|Technical,0,1,1,Neutral,1,0,Mid|Forefoot,1,0,1,7.1,4.0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,1,0,0,0,1,0,0,1,0,0,1,0,38.6,39.4,31.5,35.4,1,0,0,0,0,0,1
+New Balance,FuelCell SuperComp Trail,1,Half size small,2.9 mm,Normal,0,1,1,#65 Top 18%,#319 Bottom 13%,Light|Moderate,1,1,0,Neutral,1,0,Heel,0,1,0,13.0,10.0,1,0,0,1,0,0,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,34.7,36.5,21.7,26.5,1,0,0,0,0,0,1
+New Balance,Minimus Trail,1,Slightly small,3.3 mm,NormalWide,0,0,0,#359 Bottom 2%,#220 Bottom 40%,Light,1,0,0,Neutral,1,0,Mid|Forefoot,1,0,1,5.2,4.0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,1,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,0,19.5,,14.3,,1,0,0,0,0,0,1
+New Balance,MT10,1,Slightly small,0,Normal,0,0,0,#206 Bottom 44%,#104 Top 29%,Light,1,0,0,Neutral,1,0,Mid|Forefoot,1,0,1,5.0,4.0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,1,0,1,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,0,15.6,14.0,10.6,10.0,1,0,0,0,0,0,1
+New Balance,Shando,0,True to size,4.9 mm,Normal,0,1,1,#345 Bottom 6%,#348 Bottom 5%,Moderate,0,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,7.3,,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,1,0,0,34.7,,27.4,,0,0,0,0,0,0,1
+New Balance,Tektrel,0,Half size small,2.4 mm,NormalWide,0,1,1,#340 Bottom 7%,#177 Top 49%,Light,1,0,0,Neutral,1,0,Heel|Mid|Forefoot,1,1,1,8.0,8.0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,32.2,32.0,24.2,24.0,1,1,0,0,0,0,1
+Nike,Air Zoom Terra Kiger 6,0,-,4.8 mm,Normal,0,0,0,#342 Bottom 47%,#553 Bottom 14%,Moderate,0,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,4.4,4.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,19.6,15.0,15.2,11.0,0,0,0,0,0,0,1
+Nike,Juniper Trail,0,Slightly small,4.8 mm,Normal,0,1,1,#588 Bottom 9%,#397 Bottom 38%,Moderate,0,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,7.6,6.0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,1,0,26.6,29.0,19.0,23.0,0,0,0,0,0,0,1
+Nike,Juniper Trail 2,0,True to size,3.1 mm,Normal,0,1,1,#615 Bottom 4%,#260 Top 41%,Light,1,0,0,Neutral,1,0,Heel|Mid|Forefoot,1,1,1,9.4,9.0,0,1,0,0,0,1,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,34.7,35.0,25.3,26.0,1,0,0,0,0,0,1
+Nike,Juniper Trail 2 GTX,0,True to size,2.7 mm,Normal,0,1,1,#347 Bottom 5%,#164 Top 45%,Light,1,0,0,Neutral,1,0,Heel,0,1,0,10.2,9.0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,34.5,35.0,24.3,26.0,0,0,1,1,0,0,0
+Nike,Juniper Trail 3,0,True to size,2.6 mm,Normal,0,1,1,#358 Bottom 2%,#184 Top 50%,Light,1,0,0,Neutral,1,0,Heel,0,1,0,10.9,10.0,0,1,0,0,0,1,0,1,0,1,0,0,0,0,1,0,0,1,0,1,0,0,0,1,0,1,0,0,0,1,0,0,1,32.6,32.0,21.7,22.0,1,0,0,0,0,0,1
+Nike,Kiger 10,0,Slightly small,3.3 mm,Normal,0,1,1,#333 Bottom 9%,#233 Bottom 36%,Light|Moderate,1,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,3.7,5.0,1,0,0,0,1,0,0,0,0,0,0,1,0,0,1,1,0,0,0,1,0,0,0,1,0,1,0,1,0,0,0,1,0,28.3,29.0,24.6,24.0,1,1,0,0,0,0,1
+Nike,Pegasus Trail 3,0,True to size,3.3 mm,Normal,0,1,1,#87 Top 14%,#375 Bottom 41%,Light|Moderate,1,1,0,Neutral,1,0,Heel,0,1,0,10.3,10.0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,1,0,0,1,0,35.3,36.0,25.0,26.0,1,0,0,0,0,0,1
+Nike,Pegasus Trail 3 GTX,0,True to size,3.3 mm,Normal,0,1,1,#338 Bottom 47%,#432 Bottom 33%,Light|Moderate,1,1,0,Neutral,1,0,Heel|Mid|Forefoot,1,1,1,9.8,10.0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,0,1,0,0,1,0,32.5,36.0,22.7,26.0,0,0,1,1,0,0,0
+Nike,Pegasus Trail 4 GTX,0,Slightly small,3.5 mm,Normal,0,1,1,#371 Bottom 42%,#407 Bottom 37%,Light|Moderate,1,1,0,Neutral,1,0,Heel,0,1,0,12.8,10.0,1,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,0,1,0,0,1,0,0,1,1,0,0,37.7,37.0,24.9,27.0,0,0,1,1,0,0,0
+Nike,Pegasus Trail 5,0,True to size,3.2 mm,NormalWideX-Wide,0,1,1,#72 Top 20%,#48 Top 14%,Light,1,0,0,Neutral,1,0,Heel|Mid|Forefoot,1,1,1,9.6,9.5,1,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,34.6,37.0,25.0,27.5,1,0,0,0,0,0,1
+Nike,Pegasus Trail 5 GTX,0,Slightly small,3.6 mm,NarrowNormal,0,1,1,#346 Bottom 5%,#101 Top 28%,Light,1,0,0,Neutral,1,0,Heel|Mid|Forefoot,1,1,1,8.3,9.5,1,0,0,0,0,1,0,1,0,1,0,0,0,0,1,0,0,1,0,1,0,0,0,1,0,1,0,0,0,1,1,0,0,32.1,37.0,23.8,27.5,0,0,1,1,0,0,0
+Nike,Terra Kiger 8,0,True to size,3.9 mm,Normal,0,1,1,#422 Bottom 34%,#596 Bottom 7%,Moderate|Technical,0,1,1,Neutral,1,0,Mid|Forefoot,1,0,1,5.9,6.0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,1,0,0,28.1,30.0,22.2,24.0,0,0,0,0,0,0,1
+Nike,Terra Kiger 9,0,True to size,4.4 mm,Normal,0,1,1,#34 Top 10%,#293 Bottom 20%,Moderate|Technical,0,1,1,Neutral,1,0,Mid|Forefoot,1,0,1,4.4,3.0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,30.1,31.0,25.7,28.0,1,0,0,0,0,0,1
+Nike,Ultrafly,0,True to size,3.0 mm,Normal,0,1,1,#85 Top 24%,#201 Bottom 45%,Light|Moderate,1,1,0,Neutral,1,0,Heel,0,1,0,11.8,8.5,1,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,0,1,0,36.6,38.0,24.8,29.5,1,0,0,0,0,0,1
+Nike,Wildhorse 10,0,True to size,3.4 mm,Normal,0,1,1,#32 Top 9%,#132 Top 36%,Light|Moderate,1,1,0,Neutral,1,0,Heel,0,1,0,10.9,9.5,1,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,38.3,38.0,27.4,28.5,1,0,0,0,0,0,1
+Nike,Wildhorse 7,0,True to size,4.2 mm,Normal,0,1,1,#344 Bottom 46%,#501 Bottom 22%,Light|Moderate,1,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,7.3,8.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,1,0,0,33.5,30.0,26.2,22.0,0,0,0,0,0,0,1
+Nike,Wildhorse 8,0,True to size,3.5 mm,Normal,0,1,1,#415 Bottom 35%,#356 Bottom 44%,Moderate,0,1,0,Neutral,1,0,Heel|Mid|Forefoot,1,1,1,9.2,8.0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,34.2,35.5,25.0,27.5,1,0,0,0,0,0,1
+Nike,Zegama 2,0,True to size,4.0 mm,Normal,0,1,1,#143 Top 39%,#95 Top 26%,Moderate,0,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,4.0,4.0,1,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,0,1,0,1,0,0,0,1,1,0,0,30.3,36.0,26.3,32.0,1,0,0,0,0,0,1
+Nnormal,Kjerag,1,True to size,3.0 mm,Normal,0,0,0,#3 Top 1%,#234 Bottom 36%,Light,1,0,0,Neutral,1,0,Mid|Forefoot,1,0,1,8.6,6.0,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,1,0,0,0,1,0,1,0,0,25.0,23.5,16.4,17.5,1,0,0,0,0,0,1
+On,Cloudsurfer Trail,0,True to size,2.5 mm,Normal,1,1,1,#197 Bottom 46%,#217 Bottom 41%,Light,1,0,0,Neutral,1,0,Heel,0,1,0,10.7,7.0,0,1,0,0,0,1,0,1,0,1,0,0,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,37.4,41.0,26.7,34.0,1,0,0,0,0,0,1
+On,Cloudultra 2,0,True to size,2.5 mm,Normal,0,1,1,#104 Top 29%,#244 Bottom 33%,Light,1,0,0,Neutral,1,0,Heel,0,1,0,10.2,6.0,0,0,1,0,1,0,1,0,0,0,1,0,0,0,1,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,30.2,27.0,20.0,21.0,1,1,0,0,0,0,1
+On,Cloudvista,0,Half size small,2.5 mm,Normal,0,1,1,#147 Top 23%,#487 Bottom 24%,Light,1,0,0,Neutral,1,0,Heel,0,1,0,10.3,10.3,0,1,0,0,1,0,0,0,0,1,0,0,0,0,1,1,0,0,0,1,0,0,1,0,0,0,1,0,1,0,1,0,0,32.3,32.3,22.0,22.0,1,1,0,0,0,0,1
+On,Cloudvista 2,0,True to size,3.1 mm,NarrowNormal,0,1,1,#130 Top 36%,#215 Bottom 41%,Light,1,0,0,Neutral,1,0,Mid|Forefoot,1,0,1,6.0,5.0,0,1,0,0,0,1,0,1,0,1,0,0,0,0,1,0,0,1,1,0,0,0,1,0,0,1,0,0,0,1,0,1,0,31.7,29.0,25.7,24.0,1,0,0,0,0,0,1
+Salomon,Genesis,0,True to size,4.0 mm,Normal,0,1,1,#12 Top 4%,#221 Bottom 39%,Moderate|Technical,0,1,1,Neutral,1,0,Heel|Mid|Forefoot,1,1,1,9.0,8.0,0,1,0,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,33.5,33.0,24.5,25.0,1,0,0,0,1,0,0
+Salomon,Pulsar Trail,0,True to size,2.5 mm,Normal,0,1,1,#153 Top 42%,#285 Bottom 22%,Light,1,0,0,Neutral,1,0,Mid|Forefoot,1,0,1,7.2,6.0,1,0,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,31.0,32.6,23.8,26.6,1,0,0,0,0,0,1
+Salomon,S/Lab Genesis,1,True to size,4.3 mm,Normal,0,1,1,#17 Top 5%,#339 Bottom 7%,Light|Moderate,1,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,7.8,8.0,1,0,0,0,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,31.9,33.0,24.1,25.0,1,0,0,0,0,0,1
+Salomon,S/Lab Pulsar 4,1,-,3.0 mm,Normal,0,0,0,#336 Bottom 8%,#361 Bottom 1%,Light,1,0,0,Neutral,1,0,Mid|Forefoot,1,0,1,7.1,6.0,1,0,0,0,0,1,0,1,0,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,32.6,31.0,25.5,25.0,1,0,0,0,0,0,1
+Salomon,S/Lab Ultra,0,-,3.5 mm,Normal,0,1,1,#271 Bottom 26%,#353 Bottom 4%,Light|Moderate,1,1,0,Neutral,1,0,Heel,0,1,0,10.2,8.0,1,0,0,0,0,1,1,0,0,0,0,1,0,0,1,0,1,0,1,0,0,1,0,0,0,1,0,0,0,1,1,0,0,36.3,40.0,26.1,32.0,1,0,0,0,0,0,1
+Salomon,S/Lab Ultra Glide,0,-,3.2 mm,Normal,0,1,1,#339 Bottom 7%,#352 Bottom 4%,Light|Moderate,1,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,7.2,6.0,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,1,0,0,0,1,0,0,0,1,0,0,1,0,1,0,41.0,41.0,33.8,35.0,1,0,0,0,0,0,1
+Salomon,Sense Pro 4,0,-,4.3 mm,Normal,0,0,0,#144 Top 40%,#356 Bottom 3%,Moderate|Technical,0,1,1,Neutral,1,0,Mid|Forefoot,1,0,1,4.0,4.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,24.4,25.0,20.5,21.0,0,0,0,0,1,0,0
+Salomon,Sense Ride 4,0,True to size,3.6 mm,Normal,0,1,1,#231 Top 36%,#477 Bottom 26%,Moderate,0,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,7.3,8.0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,26.5,27.0,19.2,19.0,1,0,0,0,0,0,1
+Salomon,Sense Ride 5,0,Slightly small,3.5 mm,NarrowNormal,0,1,1,#107 Top 30%,#142 Top 39%,Light|Moderate,1,1,0,Neutral,1,0,Heel|Mid|Forefoot,1,1,1,8.7,8.3,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,27.2,29.6,18.5,21.3,1,0,0,0,0,0,1
+Salomon,Speedcross 6,0,True to size,5.8 mm,NormalWide,0,1,1,#103 Top 28%,#77 Top 21%,Technical,0,0,1,Neutral,1,0,Heel,0,1,0,14.1,10.0,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,36.5,32.0,22.4,22.0,0,0,1,0,0,0,1
+Salomon,Speedcross 6 GTX,0,True to size,5.0 mm,Normal,0,1,1,#157 Top 43%,#156 Top 43%,Technical,0,0,1,Neutral,1,0,Heel,0,1,0,11.2,10.0,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,0,1,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,37.0,32.0,25.8,22.0,0,0,1,1,0,0,0
+Salomon,Supercross 4,0,True to size,4.2 mm,Normal,0,1,1,#213 Bottom 42%,#259 Bottom 29%,Moderate|Technical,0,1,1,Neutral,1,0,Heel,0,1,0,15.2,11.0,1,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,1,0,0,0,0,1,1,0,0,1,0,0,35.1,32.0,19.9,21.0,1,0,0,0,0,0,1
+Salomon,Thundercross,0,True to size,4.0 mm,Normal,0,1,1,#97 Top 27%,#222 Bottom 39%,Moderate|Technical,0,1,1,Neutral,1,0,Mid|Forefoot,1,0,1,3.0,4.0,1,0,0,0,0,1,0,0,1,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,1,0,0,27.6,31.0,24.6,27.0,0,0,1,0,0,0,1
+Salomon,Ultra Flow,0,Slightly small,2.8 mm,Normal,0,1,1,#271 Bottom 26%,#287 Bottom 22%,Light,1,0,0,Neutral,1,0,Heel,0,1,0,12.5,6.0,1,0,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,34.9,32.0,22.4,26.0,1,0,0,0,0,0,1
+Salomon,Ultra Glide,0,True to size,3.5 mm,NormalWide,0,1,1,#175 Top 28%,#415 Bottom 35%,Light|Moderate,1,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,7.9,6.0,1,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,1,31.8,32.0,23.9,26.0,0,0,0,0,0,0,1
+Salomon,Ultra Glide 2,0,True to size,2.8 mm,Normal,0,0,0,#177 Top 49%,#181 Top 50%,Light|Moderate,1,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,7.2,6.0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,1,0,1,0,0,0,1,0,30.6,32.0,23.4,26.0,1,0,0,0,0,0,1
+Salomon,XA Pro 3D GTX,0,True to size,2.9 mm,Normal,0,1,1,#334 Bottom 48%,#406 Bottom 37%,Light|Moderate,1,1,0,Stability,0,1,Heel,0,1,0,13.1,12.0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,1,0,0,1,31.9,27.0,18.8,15.0,0,0,1,1,0,0,0
+Salomon,XA Pro 3D V8,0,True to size,2.9 mm,NormalWide,0,1,1,#180 Top 28%,#538 Bottom 16%,Light|Moderate,1,1,0,Stability,0,1,Heel,0,1,0,14.6,11.0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,1,35.0,28.0,20.4,17.0,1,0,0,0,0,0,1
+Salomon,XA Pro 3D v9,0,True to size,2.8 mm,NormalWide,0,1,1,#348 Bottom 5%,#165 Top 45%,Light|Moderate,1,1,0,Stability,0,1,Heel,0,1,0,12.5,11.0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,1,0,1,0,0,0,1,0,1,0,0,0,1,0,0,1,0,0,1,31.7,28.0,19.2,17.0,1,0,0,0,0,0,1
+Salomon,XA Pro 3D v9 GTX,0,True to size,2.8 mm,NormalWide,0,1,1,#257 Bottom 30%,#53 Top 15%,Light|Moderate,1,1,0,Stability,0,1,Heel,0,1,0,13.5,11.0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,33.5,,20.0,,0,0,1,1,0,0,0
+Saucony,Endorphin Edge,0,True to size,3.4 mm,Normal,0,1,1,#163 Top 45%,#229 Bottom 37%,Moderate,0,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,7.1,6.0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,1,0,1,0,33.4,36.0,26.3,30.0,1,0,0,0,0,0,1
+Saucony,Endorphin Rift,0,True to size,4.5 mm,Normal,0,1,1,#222 Bottom 39%,#309 Bottom 15%,Technical,0,0,1,Neutral,1,0,Mid|Forefoot,1,0,1,7.9,6.0,1,0,0,0,1,0,0,0,1,0,0,1,0,0,1,1,0,0,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,33.0,34.0,25.1,28.0,1,1,0,0,0,0,1
+Saucony,Endorphin Trail,0,True to size,4.5 mm,Normal,0,1,1,#190 Bottom 48%,#321 Bottom 12%,Technical,0,0,1,Neutral,1,0,Mid|Forefoot,1,0,1,5.2,4.0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,1,36.3,36.5,31.1,32.5,0,0,0,0,0,0,1
+Saucony,Peregrine 11,0,Slightly large,4.4 mm,Normal,0,1,1,#261 Top 41%,#584 Bottom 9%,Technical,0,0,1,Neutral,1,0,Mid|Forefoot,1,0,1,5.1,4.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,27.5,27.0,22.4,23.0,0,0,0,0,0,0,1
+Saucony,Peregrine 12,0,True to size,4.6 mm,NormalWide,0,0,0,#350 Bottom 45%,#502 Bottom 22%,Technical,0,0,1,Neutral,1,0,Mid|Forefoot,1,0,1,6.9,4.0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,30.2,26.5,23.2,22.5,0,0,0,0,0,0,1
+Saucony,Peregrine 13,0,True to size,4.8 mm,Normal,0,1,1,#186 Top 29%,#441 Bottom 31%,Technical,0,0,1,Neutral,1,0,Mid|Forefoot,1,0,1,3.9,4.0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,1,1,0,0,0,1,0,27.5,28.0,23.6,24.0,1,0,0,0,0,0,1
+Saucony,Peregrine 14,0,True to size,4.7 mm,NormalWide,0,1,1,#218 Top 34%,#350 Bottom 45%,Moderate|Technical,0,1,1,Neutral,1,0,Mid|Forefoot,1,0,1,2.2,4.0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,1,0,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,27.3,31.0,25.1,27.0,1,0,0,0,0,0,1
+Saucony,Peregrine 15,0,True to size,4.7 mm,NormalWide,0,1,1,#326 Bottom 11%,#139 Top 38%,Light|Moderate,1,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,3.7,4.0,1,0,0,0,1,0,0,0,1,0,1,0,0,0,1,0,1,0,0,1,0,1,0,0,0,1,0,1,0,0,0,1,0,29.5,28.0,25.8,24.0,1,0,0,0,0,0,1
+Saucony,Xodus Ultra,0,True to size,3.8 mm,Normal,0,1,1,#314 Top 49%,#498 Bottom 23%,Moderate|Technical,0,1,1,Neutral,1,0,Mid|Forefoot,1,0,1,7.2,6.0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,1,0,0,1,0,33.9,32.0,26.7,26.0,1,0,0,0,0,0,1
+Saucony,Xodus Ultra 2,0,True to size,4.6 mm,Normal,0,1,1,#227 Top 36%,#555 Bottom 14%,Moderate|Technical,0,1,1,Neutral,1,0,Mid|Forefoot,1,0,1,7.3,6.0,0,1,0,0,1,0,1,0,0,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,1,34.1,32.5,26.8,26.5,1,0,0,0,0,0,1
+Saucony,Xodus Ultra 3,0,True to size,4.3 mm,Normal,0,1,1,#341 Bottom 47%,#425 Bottom 34%,Technical,0,0,1,Neutral,1,0,Mid|Forefoot,1,0,1,5.9,6.0,1,0,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,1,0,0,35.1,36.0,29.2,30.0,1,0,0,0,0,0,1
+Saucony,Xodus Ultra 4,0,-,3.5 mm,Normal,0,1,1,#201 Bottom 45%,#208 Bottom 43%,Light,1,0,0,Neutral,1,0,Mid|Forefoot,1,0,1,6.5,6.0,1,0,0,0,0,1,0,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,37.6,36.0,31.1,30.0,1,0,0,0,0,0,1
+Scarpa,Spin Planet,0,True to size,3.2 mm,Normal,0,1,1,#138 Top 38%,#329 Bottom 10%,Light|Moderate,1,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,6.2,4.0,0,1,0,0,0,1,0,1,0,0,0,1,0,1,0,1,0,0,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,32.8,28.5,26.6,24.5,1,1,0,0,0,0,1
+The North Face,Vectiv Enduris 3,0,True to size,3.3 mm,Normal,0,1,1,#46 Top 13%,#188 Bottom 48%,Light,1,0,0,Neutral,1,0,Heel,0,1,0,11.6,6.0,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,35.8,30.0,24.2,24.0,1,0,0,0,0,0,1
+Topo,MTN Racer 3,0,Slightly small,4.2 mm,Normal,0,1,1,#164 Top 45%,#261 Bottom 28%,Light|Moderate,1,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,6.9,5.0,0,1,0,0,0,1,1,0,0,0,1,0,0,0,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,33.5,33.5,26.6,28.5,1,0,0,0,0,0,1
+Topo,Traverse,0,Slightly small,4.1 mm,NormalWide,0,1,1,#39 Top 11%,#281 Bottom 23%,Light|Moderate,1,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,4.8,5.0,0,1,0,0,1,0,1,0,0,0,0,1,0,0,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,30.8,30.0,26.0,25.0,1,0,0,0,1,0,0
+Topo,Ultraventure 4,0,True to size,3.2 mm,NormalWide,0,1,1,#188 Bottom 48%,#147 Top 41%,Light,1,0,0,Neutral,1,0,Mid|Forefoot,1,0,1,6.6,5.0,1,0,0,0,0,1,1,0,0,0,1,0,0,0,1,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,1,0,0,35.1,35.0,28.5,30.0,1,0,0,0,0,0,1
+Xero Shoes,Scrambler Low,0,True to size,2.7 mm,NormalWide,0,1,1,#116 Top 32%,#336 Bottom 8%,Light,1,0,0,Neutral,1,0,Mid|Forefoot,1,0,1,0.1,0.0,0,0,1,0,0,1,0,1,0,0,1,0,0,1,0,1,0,0,0,1,0,0,0,1,0,1,0,1,0,0,1,0,0,16.3,15.0,16.4,15.0,1,1,0,0,0,0,1
+Topo,Ultraventure 3,0,True to size,3.2 mm,Normal,0,1,1,#259 Top 41%,#206 Top 32%,Light,1,0,0,Neutral,1,0,Mid|Forefoot,1,0,1,6.3,5.0,1,0,0,0,0,1,0,1,0,0,1,0,0,0,1,0,1,0,0,0,1,0,0,1,0,1,0,0,0,1,0,1,0,37.2,35.0,30.9,30.0,1,0,0,0,0,0,1
+Inov8,Trailtalon,0,Half size small,5.4 mm,NormalWide,0,1,1,#71 Top 20%,#350 Bottom 4%,Moderate|Technical,0,1,1,Neutral,1,0,Mid|Forefoot,1,0,1,7.8,6.0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,1,0,0,34.2,31.0,26.4,25.0,1,0,0,0,0,0,1
+Inov8,Trailfly Zero,0,-,3.4 mm,NormalWide,0,1,1,#161 Top 44%,#346 Bottom 5%,Light|Moderate,1,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,0.5,0.0,0,1,0,0,0,1,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,0,0,1,1,0,0,1,0,0,1,0,0,24.9,,24.4,,1,0,0,0,0,0,1
+Inov8,Trailfly Max,0,-,3.4 mm,NormalWide,0,1,1,#365 Bottom 1%,#349 Bottom 5%,Light|Moderate,1,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,7.6,6.0,0,1,0,0,0,1,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,1,0,0,37.1,,29.5,,1,0,0,0,0,0,1
+Asics,Trail Scout 2,0,True to size,4.2 mm,Normal,0,1,1,#262 Bottom 28%,#327 Bottom 11%,Moderate,0,1,0,Neutral,1,0,Heel,0,1,0,10.3,10.0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,0,0,1,32.8,,22.5,,1,0,0,0,0,0,1
+Asics,Gel Venture 9,0,True to size,3.0 mm,NormalWideX-Wide,0,1,1,#474 Bottom 26%,#176 Top 28%,Light|Moderate,1,1,0,Neutral,1,0,Heel,0,1,0,10.4,,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,1,0,0,0,1,0,0,1,0,0,0,1,33.3,,22.9,,1,0,0,0,0,0,0
+Asics,Gel Venture 10,0,True to size,3.7 mm,NormalWideX-Wide,0,1,1,#243 Bottom 33%,#71 Top 20%,Light|Moderate,1,1,0,Neutral,1,0,Heel,0,1,0,12.0,10.0,1,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,1,0,0,1,0,0,0,1,0,0,0,1,0,1,0,35.3,33.5,23.3,23.5,1,0,0,0,0,0,1
+Asics,Gel Venture 8,0,True to size,3.1 mm,Normal,0,1,1,#246 Top 39%,#251 Top 39%,Light|Moderate,1,1,0,Neutral,1,0,Heel,0,1,0,13.2,10.0,0,1,0,0,0,1,1,0,0,0,1,0,0,0,1,1,0,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,34.2,,21.0,,1,1,0,0,0,0,1
+Kailas,Fuga EX 3,0,-,3.4 mm,Normal,0,1,1,#166 Top 46%,#344 Bottom 6%,Light|Moderate,1,1,0,Neutral,1,0,Heel,0,1,0,13.7,8.0,0,1,0,0,0,1,0,1,0,0,1,0,0,0,1,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,0,0,1,38.4,36.0,24.7,28.0,1,0,0,0,0,0,1
+Xero Shoes,Mesa Trail WP,0,Slightly small,3.8 mm,NormalWide,0,1,1,#366 Bottom 1%,#354 Bottom 3%,Light,1,0,0,Neutral,1,0,Mid|Forefoot,1,0,1,1.2,0.0,0,1,0,0,0,1,0,1,0,1,0,0,0,1,0,0,0,1,0,1,0,0,0,1,0,1,0,1,0,0,1,0,0,14.6,8.0,13.4,8.0,0,0,1,1,0,0,0
+Kailas,Fuga EX Pro,0,-,3.7 mm,Normal,0,1,1,#220 Bottom 40%,#337 Bottom 8%,Light|Moderate,1,1,0,Neutral,1,0,Mid|Forefoot,1,0,1,7.2,5.0,0,1,0,0,0,1,1,0,0,0,1,0,0,0,1,0,1,0,1,0,0,1,0,0,0,0,1,0,0,1,0,1,0,37.9,35.0,30.7,30.0,1,0,0,0,0,0,1
+Kailas,Fuga EX BOA,0,-,3.7 mm,NormalWide,0,1,1,#51 Top 14%,#357 Bottom 2%,Moderate,0,1,0,Neutral,1,0,Heel,0,1,0,10.9,8.0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,1,38.5,36.0,27.6,28.0,0,0,0,0,0,0,1
+Kailas,Fuga YAO,0,-,1.7 mm,Normal,0,1,1,#52 Top 15%,#362 Bottom 1%,Light,1,0,0,Neutral,1,0,Heel,0,1,0,10.7,,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,1,38.6,,27.9,,1,0,0,0,0,0,1
+Asics,Trabuco Max 2,0,True to size,4.2 mm,Normal,0,1,1,#4 Top 1%,#331 Bottom 48%,Moderate|Technical,0,1,1,Neutral,1,0,Heel|Mid|Forefoot,1,1,1,8.5,5.0,1,0,0,0,0,1,0,1,0,0,0,1,0,1,0,1,0,0,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,39.7,43.0,31.2,38.0,1,1,0,0,0,0,1
+Icebug,Järv RB9X,0,-,5.0 mm,Normal,0,0,0,#367 Bottom 1%,#367 Bottom 1%,Moderate|Technical,0,1,1,Neutral,1,0,Mid|Forefoot,1,0,1,6.0,4.0,0,1,0,0,0,1,0,0,1,0,1,0,0,1,0,0,0,1,0,1,0,0,1,0,0,0,1,0,0,1,0,1,0,37.0,29.0,31.0,25.0,0,0,0,0,0,0,1
+Kailas,Fuga Pro 4,0,-,3.4 mm,Normal,0,1,1,#169 Top 46%,#358 Bottom 2%,Moderate,0,1,0,Neutral,1,0,Heel|Mid|Forefoot,1,1,1,10.0,10.0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,1,31.7,32.0,21.8,22.0,0,0,0,0,0,0,1
+Kailas,Fuga EX 2,0,-,4.0 mm,Normal,0,1,1,#436 Bottom 32%,#634 Bottom 2%,Moderate|Technical,0,1,1,Neutral,1,0,Heel,0,1,0,10.6,8.0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,1,38.0,36.0,27.4,28.0,0,0,0,0,0,0,1
+Kailas,Fuga Elite 2,0,-,2.5 mm,Normal,0,1,1,#249 Bottom 32%,#359 Bottom 2%,Light,1,0,0,Neutral,1,0,Heel,0,1,0,12.5,10.0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,1,0,40.4,41.0,27.9,31.0,0,0,0,0,0,0,1
+Kailas,Fuga DU,0,-,3.2 mm,Wide,0,1,1,#260 Bottom 29%,#363 Bottom 1%,Moderate,0,1,0,Neutral,1,0,Heel,0,1,0,11.6,8.0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,1,36.9,36.0,25.3,,1,0,0,0,0,0,1
+Kailas,Flythorn Air 2.0,0,-,3.0 mm,Normal,0,1,1,#53 Top 15%,#364 Bottom 1%,Light|Moderate,1,1,0,Neutral,1,0,Heel,0,1,0,10.3,10.0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,1,30.6,29.0,20.3,19.0,0,0,0,0,0,0,1
+Kailas,Phantom 3.0,0,-,1.8 mm,Normal,0,1,1,#54 Top 15%,#365 Bottom 1%,Light,1,0,0,Neutral,1,0,Heel,0,1,0,10.5,,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,1,35.5,,25.0,,1,0,0,0,0,0,1
+Nike,Pegasus Trail 4,0,True to size,3.4 mm,Normal,0,1,1,#244 Top 38%,#195 Top 31%,Light,1,0,0,Neutral,1,0,Heel,0,1,0,12.7,10.0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,1,0,35.5,36.0,22.8,26.0,0,0,0,0,0,0,1
+,,,,,,,,,,,,0,0,0,,0,0,,0,0,0,,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,0,0,0,0,0,0,0
+,168,,,,,,,,,,,0,0,0,,0,0,,0,0,0,,,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,,,,,0,0,0,0,0,0,0
diff --git a/locustfile.py b/locustfile.py
new file mode 100644
index 0000000000000000000000000000000000000000..7287c0e3706e0b999cc8ee34a06442575c27ccb9
--- /dev/null
+++ b/locustfile.py
@@ -0,0 +1,92 @@
+"""
+Sonix-ML Performance Testing Suite
+----------------------------------
+This module defines the load testing scenarios for the Sonix-ML API 
+hosted on Hugging Face. It simulates concurrent user behavior to 
+measure system latency (P50, P90, P95) across recommendation and 
+interaction endpoints.
+"""
+
+import random
+from locust import HttpUser, task, between
+
+class SonixMlTester(HttpUser):
+    """
+    Simulates a running shoe enthusiast interacting with the Sonix-ML ecosystem.
+    
+    Attributes:
+        wait_time (callable): Simulates user 'think time' between 1 to 3 seconds.
+        host (str): The target production environment on Hugging Face Spaces.
+    """
+    
+    wait_time = between(1, 3)
+    host = "https://sonix-ml-37851640508.asia-southeast2.run.app/"
+
+    @task(2)
+    def test_recommend_road(self):
+        """
+        Benchmarks the Road Recommendation endpoint.
+        
+        Tests the heuristic mapping logic (CC: 9) for road running preferences.
+        Payload includes pace, arch type, and stability needs.
+        """
+        payload = {
+            "pace": "Fast",
+            "arch_type": "Normal",
+            "strike_pattern": "Mid",
+            "foot_width": "Regular",
+            "season": "Summer",
+            "orthotic_usage": "No",
+            "running_purpose": "Race",
+            "cushion_preferences": "Firm",
+            "stability_need": "Neutral"
+        }
+        self.client.post("/recommend/road", json=payload, name="POST /recommend/road")
+
+    @task(2)
+    def test_recommend_trail(self):
+        """
+        Benchmarks the Trail Recommendation endpoint.
+        
+        This is the most computationally expensive task due to high cyclomatic 
+        complexity (CC: 10) in the pre-processing logic. It validates 
+        vectorization performance for complex terrain and protection features.
+        """
+        payload = {
+            "pace": "Steady",
+            "arch_type": "Flat",
+            "strike_pattern": "Heel",
+            "foot_width": "Wide",
+            "season": "Spring & Fall",
+            "orthotic_usage": "Yes",
+            "terrain": "Mixed",
+            "rock_sensitive": "Yes",
+            "water_resistance": "Waterproof"
+        }
+        self.client.post("/recommend/trail", json=payload, name="POST /recommend/trail")
+
+    @task(2)
+    def test_interact(self):
+        """
+        Benchmarks the Interaction Logging endpoint.
+        
+        Simulates real-time user feedback (likes/dislikes) to evaluate 
+        write-operation latency and database logging performance.
+        """
+        payload = {
+            "user_id": 2,
+            "shoe_id": "R050",
+            "action_type": "like",
+            "value": 1
+        }
+        self.client.post("/interact", json=payload, name="POST /interact")
+
+    @task(1)
+    def test_recommend_feed(self):
+        """
+        Benchmarks the Collaborative Filtering Home Feed.
+        
+        Evaluates the latency of the Neural Collaborative Filtering (NCF) 
+        pipeline for generating personalized shoe feeds based on User ID.
+        """
+        self.client.get("/recommend/feed/2", name="GET /recommend/feed/[id]")
\ No newline at end of file
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diff --git a/model_artifacts/trail/v_20260217_164906/scaler.pkl b/model_artifacts/trail/v_20260217_164906/scaler.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..c663b1ddafb0d0632ce09634227f4d4bdeaf9e1c
--- /dev/null
+++ b/model_artifacts/trail/v_20260217_164906/scaler.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/model_artifacts/trail/v_20260217_164906/shoe_encoder.h5 b/model_artifacts/trail/v_20260217_164906/shoe_encoder.h5
new file mode 100644
index 0000000000000000000000000000000000000000..d4cfac7366f2f50a7c33e0a9badd2cf08a014ce5
--- /dev/null
+++ b/model_artifacts/trail/v_20260217_164906/shoe_encoder.h5
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 42512
diff --git a/model_artifacts/trail/v_20260217_164906/shoe_features.pkl b/model_artifacts/trail/v_20260217_164906/shoe_features.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..2b00d2a4184227bb409c31d1318901dd7f76bcf5
--- /dev/null
+++ b/model_artifacts/trail/v_20260217_164906/shoe_features.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:09823675406d3933cfced071d82ff5722cb12409e2dd06807f83ac3f24272a82
+size 44392
diff --git a/model_artifacts/trail/v_20260217_164906/shoe_metadata.pkl b/model_artifacts/trail/v_20260217_164906/shoe_metadata.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..f13133fe56e3d174e5a184fdc8da8b639c3b373b
--- /dev/null
+++ b/model_artifacts/trail/v_20260217_164906/shoe_metadata.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 95408
diff --git a/notebooks/.gitkeep b/notebooks/.gitkeep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/notebooks/data-preparation-v2/pre-eda-road-v2.ipynb b/notebooks/data-preparation-v2/pre-eda-road-v2.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..50b64f468fafb89f54ec74e8aced8601e7b8405e
--- /dev/null
+++ b/notebooks/data-preparation-v2/pre-eda-road-v2.ipynb
@@ -0,0 +1,6061 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "12ec3e54",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Pace</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Heel lab Heel brand</th>\n",
+       "      <th>Forefoot lab Forefoot brand</th>\n",
+       "      <th>Widths available</th>\n",
+       "      <th>Orthotic friendly</th>\n",
+       "      <th>Season</th>\n",
+       "      <th>Removable insole</th>\n",
+       "      <th>Ranking</th>\n",
+       "      <th>Popularity</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Terrain</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Launch 9</td>\n",
+       "      <td>87\\n Great!</td>\n",
+       "      <td>$110</td>\n",
+       "      <td>Daily runningTempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>7.9 oz / 225g 8.1 oz / 230g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.4 mm 10.0 mm</td>\n",
+       "      <td>HeelMid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>32.4 mm 36.0 mm</td>\n",
+       "      <td>23.0 mm 26.0 mm</td>\n",
+       "      <td>NormalWide</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#301 Top 47%</td>\n",
+       "      <td>#352 Bottom 45%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Levitate 6</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$150</td>\n",
+       "      <td>Daily running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 304g 10.9 oz / 309g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.7 mm 8.0 mm</td>\n",
+       "      <td>Mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>34.3 mm 32.5 mm</td>\n",
+       "      <td>26.6 mm 24.5 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>SummerAll seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#72 Top 20%</td>\n",
+       "      <td>#255 Bottom 30%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>11.9 oz / 336g 11.5 oz / 327g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.9 mm 10.0 mm</td>\n",
+       "      <td>HeelMid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>33.3 mm 32.5 mm</td>\n",
+       "      <td>24.4 mm 22.5 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#104 Top 17%</td>\n",
+       "      <td>#368 Bottom 42%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 2</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.6 oz / 356g 12.4 oz / 352g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.6 mm 11.0 mm</td>\n",
+       "      <td>Heel</td>\n",
+       "      <td>...</td>\n",
+       "      <td>31.8 mm 32.0 mm</td>\n",
+       "      <td>21.2 mm 21.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#126 Top 20%</td>\n",
+       "      <td>#541 Bottom 16%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 3</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.3 oz / 348g 12.2 oz / 345g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.9 mm 10.0 mm</td>\n",
+       "      <td>HeelMid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>32.6 mm 34.0 mm</td>\n",
+       "      <td>22.7 mm 24.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#116 Top 32%</td>\n",
+       "      <td>#339 Bottom 7%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 33 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand         Name Audience score Price                Pace Arch support  \\\n",
+       "0  Brooks     Launch 9    87\\n Great!  $110  Daily runningTempo      Neutral   \n",
+       "1  Brooks   Levitate 6   90\\n Superb!  $150       Daily running      Neutral   \n",
+       "2  Adidas        4DFWD   90\\n Superb!  $200       Daily running      Neutral   \n",
+       "3  Adidas      4DFWD 2   90\\n Superb!  $200       Daily running      Neutral   \n",
+       "4  Adidas      4DFWD 3    88\\n Great!  $200       Daily running      Neutral   \n",
+       "\n",
+       "         Weight lab Weight brand  Lightweight Drop lab Drop brand  \\\n",
+       "0    7.9 oz / 225g 8.1 oz / 230g            1      9.4 mm 10.0 mm   \n",
+       "1  10.7 oz / 304g 10.9 oz / 309g            0       7.7 mm 8.0 mm   \n",
+       "2  11.9 oz / 336g 11.5 oz / 327g            0      8.9 mm 10.0 mm   \n",
+       "3  12.6 oz / 356g 12.4 oz / 352g            0     10.6 mm 11.0 mm   \n",
+       "4  12.3 oz / 348g 12.2 oz / 345g            0      9.9 mm 10.0 mm   \n",
+       "\n",
+       "     Strike pattern  ... Heel lab Heel brand Forefoot lab Forefoot brand  \\\n",
+       "0  HeelMid/forefoot  ...     32.4 mm 36.0 mm             23.0 mm 26.0 mm   \n",
+       "1      Mid/forefoot  ...     34.3 mm 32.5 mm             26.6 mm 24.5 mm   \n",
+       "2  HeelMid/forefoot  ...     33.3 mm 32.5 mm             24.4 mm 22.5 mm   \n",
+       "3              Heel  ...     31.8 mm 32.0 mm             21.2 mm 21.0 mm   \n",
+       "4  HeelMid/forefoot  ...     32.6 mm 34.0 mm             22.7 mm 24.0 mm   \n",
+       "\n",
+       "  Widths available Orthotic friendly             Season Removable insole  \\\n",
+       "0       NormalWide                 1                  -                1   \n",
+       "1           Normal                 1  SummerAll seasons                1   \n",
+       "2           Normal                 1        All seasons                1   \n",
+       "3           Normal                 1        All seasons                1   \n",
+       "4           Normal                 1        All seasons                1   \n",
+       "\n",
+       "        Ranking       Popularity Gender Terrain  \n",
+       "0  #301 Top 47%  #352 Bottom 45%    NaN     NaN  \n",
+       "1   #72 Top 20%  #255 Bottom 30%    NaN     NaN  \n",
+       "2  #104 Top 17%  #368 Bottom 42%    NaN     NaN  \n",
+       "3  #126 Top 20%  #541 Bottom 16%    NaN     NaN  \n",
+       "4  #116 Top 32%   #339 Bottom 7%    NaN     NaN  \n",
+       "\n",
+       "[5 rows x 33 columns]"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df_ori = pd.read_csv('../../data/SONIX utilities - Road.csv')\n",
+    "df_ori.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eb6307f4",
+   "metadata": {},
+   "source": [
+    "# Remove Duplicates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "id": "2edb9233",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1170\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Adistar 3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Adistar 3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Adistar 3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Adistar 3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166</th>\n",
+       "      <td>On</td>\n",
+       "      <td>Cloudgo</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>169</th>\n",
+       "      <td>On</td>\n",
+       "      <td>Cloudmonster 2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>170</th>\n",
+       "      <td>On</td>\n",
+       "      <td>Cloudmonster 2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>171</th>\n",
+       "      <td>On</td>\n",
+       "      <td>Cloudmonster 2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>172</th>\n",
+       "      <td>On</td>\n",
+       "      <td>Cloudmonster 2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Brand            Name\n",
+       "5    Adidas         4DFWD 3\n",
+       "11   Adidas       Adistar 3\n",
+       "12   Adidas       Adistar 3\n",
+       "13   Adidas       Adistar 3\n",
+       "14   Adidas       Adistar 3\n",
+       "..      ...             ...\n",
+       "166      On         Cloudgo\n",
+       "169      On  Cloudmonster 2\n",
+       "170      On  Cloudmonster 2\n",
+       "171      On  Cloudmonster 2\n",
+       "172      On  Cloudmonster 2\n",
+       "\n",
+       "[100 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dup_mask = df_ori.duplicated(subset=[\"Brand\", \"Name\"], keep=\"first\")\n",
+    "print(len(dup_mask))\n",
+    "df_ori.loc[dup_mask, [\"Brand\", \"Name\"]].head(100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "5e7c5b10",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Before: 1170\n",
+      "After : 443\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Pace</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Heel lab Heel brand</th>\n",
+       "      <th>Forefoot lab Forefoot brand</th>\n",
+       "      <th>Widths available</th>\n",
+       "      <th>Orthotic friendly</th>\n",
+       "      <th>Season</th>\n",
+       "      <th>Removable insole</th>\n",
+       "      <th>Ranking</th>\n",
+       "      <th>Popularity</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Terrain</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Launch 9</td>\n",
+       "      <td>87\\n Great!</td>\n",
+       "      <td>$110</td>\n",
+       "      <td>Daily runningTempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>7.9 oz / 225g 8.1 oz / 230g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.4 mm 10.0 mm</td>\n",
+       "      <td>HeelMid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>32.4 mm 36.0 mm</td>\n",
+       "      <td>23.0 mm 26.0 mm</td>\n",
+       "      <td>NormalWide</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#301 Top 47%</td>\n",
+       "      <td>#352 Bottom 45%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Levitate 6</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$150</td>\n",
+       "      <td>Daily running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 304g 10.9 oz / 309g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.7 mm 8.0 mm</td>\n",
+       "      <td>Mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>34.3 mm 32.5 mm</td>\n",
+       "      <td>26.6 mm 24.5 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>SummerAll seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#72 Top 20%</td>\n",
+       "      <td>#255 Bottom 30%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>11.9 oz / 336g 11.5 oz / 327g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.9 mm 10.0 mm</td>\n",
+       "      <td>HeelMid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>33.3 mm 32.5 mm</td>\n",
+       "      <td>24.4 mm 22.5 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#104 Top 17%</td>\n",
+       "      <td>#368 Bottom 42%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 2</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.6 oz / 356g 12.4 oz / 352g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.6 mm 11.0 mm</td>\n",
+       "      <td>Heel</td>\n",
+       "      <td>...</td>\n",
+       "      <td>31.8 mm 32.0 mm</td>\n",
+       "      <td>21.2 mm 21.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#126 Top 20%</td>\n",
+       "      <td>#541 Bottom 16%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 3</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.3 oz / 348g 12.2 oz / 345g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.9 mm 10.0 mm</td>\n",
+       "      <td>HeelMid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>32.6 mm 34.0 mm</td>\n",
+       "      <td>22.7 mm 24.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#116 Top 32%</td>\n",
+       "      <td>#339 Bottom 7%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Addiction GTS 15</td>\n",
+       "      <td>85\\n Good!</td>\n",
+       "      <td>$140</td>\n",
+       "      <td>Daily running</td>\n",
+       "      <td>Motion control</td>\n",
+       "      <td>12.5 oz / 353g 12.2 oz / 346g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12.1 mm 12.0 mm</td>\n",
+       "      <td>Heel</td>\n",
+       "      <td>...</td>\n",
+       "      <td>36.5 mm 36.0 mm</td>\n",
+       "      <td>24.4 mm 24.0 mm</td>\n",
+       "      <td>NarrowNormalWideX-Wide</td>\n",
+       "      <td>1</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#221 Bottom 39%</td>\n",
+       "      <td>#159 Top 44%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Adidas Adizero SL2</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$130</td>\n",
+       "      <td>Daily runningTempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>8.6 oz / 245g 8.4 oz / 238g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8.2 mm 9.0 mm</td>\n",
+       "      <td>HeelMid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>34.9 mm 35.0 mm</td>\n",
+       "      <td>26.7 mm 26.0 mm</td>\n",
+       "      <td>NormalWide</td>\n",
+       "      <td>1</td>\n",
+       "      <td>SummerAll seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#44 Top 13%</td>\n",
+       "      <td>#166 Top 46%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Adistar</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$130</td>\n",
+       "      <td>Daily running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>11.5 oz / 325g 11.2 oz / 318g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.6 mm 6.0 mm</td>\n",
+       "      <td>HeelMid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>34.4 mm 37.5 mm</td>\n",
+       "      <td>24.8 mm 31.5 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#267 Top 42%</td>\n",
+       "      <td>#247 Top 39%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Adistar 2.0</td>\n",
+       "      <td>83\\n Good!</td>\n",
+       "      <td>$130</td>\n",
+       "      <td>Daily running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>11.6 oz / 328g 11.6 oz / 328g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.0 mm 6.0 mm</td>\n",
+       "      <td>HeelMid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>33.8 mm 33.0 mm</td>\n",
+       "      <td>25.8 mm 27.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>SummerAll seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#506 Bottom 21%</td>\n",
+       "      <td>#643 Bottom 1%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Adistar 3</td>\n",
+       "      <td>89\\n Great!</td>\n",
+       "      <td>$130</td>\n",
+       "      <td>Daily running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.7 oz / 274g 9.5 oz / 270g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.5 mm 5.0 mm</td>\n",
+       "      <td>Heel</td>\n",
+       "      <td>...</td>\n",
+       "      <td>40.7 mm 40.0 mm</td>\n",
+       "      <td>30.2 mm 35.0 mm</td>\n",
+       "      <td>NormalWide</td>\n",
+       "      <td>1</td>\n",
+       "      <td>SummerAll seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#97 Top 27%</td>\n",
+       "      <td>#241 Bottom 33%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Adizero Adios 7</td>\n",
+       "      <td>81\\n Good!</td>\n",
+       "      <td>$130</td>\n",
+       "      <td>Tempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>7.5 oz / 212g 7.5 oz / 212g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8.7 mm 8.0 mm</td>\n",
+       "      <td>HeelMid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>31.6 mm 27.0 mm</td>\n",
+       "      <td>22.9 mm 19.0 mm</td>\n",
+       "      <td>NormalWide</td>\n",
+       "      <td>1</td>\n",
+       "      <td>SummerAll seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#535 Bottom 17%</td>\n",
+       "      <td>#614 Bottom 4%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Adizero Adios 8</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$130</td>\n",
+       "      <td>Tempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>7.4 oz / 210g 7 oz / 198g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7.6 mm 8.0 mm</td>\n",
+       "      <td>Mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>28.0 mm 28.0 mm</td>\n",
+       "      <td>20.4 mm 20.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>SummerAll seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#129 Top 20%</td>\n",
+       "      <td>#553 Bottom 14%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Adizero Adios 9</td>\n",
+       "      <td>92\\n Superb!</td>\n",
+       "      <td>$140</td>\n",
+       "      <td>CompetitionTempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>6.2 oz / 176g 6.2 oz / 176g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6.2 mm 7.0 mm</td>\n",
+       "      <td>Mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.0 mm 28.0 mm</td>\n",
+       "      <td>18.8 mm 21.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#7 Top 2%</td>\n",
+       "      <td>#245 Bottom 33%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Adizero Adios Pro 2.0</td>\n",
+       "      <td>91\\n Superb!</td>\n",
+       "      <td>$220</td>\n",
+       "      <td>Competition</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>7.9 oz / 223g 7.6 oz / 215g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10.3 mm 10.0 mm</td>\n",
+       "      <td>Heel</td>\n",
+       "      <td>...</td>\n",
+       "      <td>40.0 mm 39.5 mm</td>\n",
+       "      <td>29.7 mm 29.5 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>0</td>\n",
+       "      <td>#32 Top 5%</td>\n",
+       "      <td>#569 Bottom 11%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Adizero Adios Pro 3</td>\n",
+       "      <td>91\\n Superb!</td>\n",
+       "      <td>$250</td>\n",
+       "      <td>Competition</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>7.7 oz / 218g 7.9 oz / 223g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8.0 mm 6.5 mm</td>\n",
+       "      <td>HeelMid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>37.8 mm 39.5 mm</td>\n",
+       "      <td>29.8 mm 33.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>SummerAll seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#41 Top 7%</td>\n",
+       "      <td>#202 Top 32%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Adizero Adios Pro 4</td>\n",
+       "      <td>93\\n Superb!</td>\n",
+       "      <td>$250</td>\n",
+       "      <td>Competition</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>7.1 oz / 200g 7.1 oz / 201g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8.1 mm 6.0 mm</td>\n",
+       "      <td>HeelMid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>36.6 mm 39.0 mm</td>\n",
+       "      <td>28.5 mm 33.0 mm</td>\n",
+       "      <td>NormalWide</td>\n",
+       "      <td>1</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#1 Top 1%</td>\n",
+       "      <td>#38 Top 11%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Adizero Boston 11</td>\n",
+       "      <td>83\\n Good!</td>\n",
+       "      <td>$160</td>\n",
+       "      <td>Tempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.2 oz / 290g 9.6 oz / 272g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.8 mm 8.5 mm</td>\n",
+       "      <td>HeelMid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>39.1 mm 39.5 mm</td>\n",
+       "      <td>29.3 mm 31.0 mm</td>\n",
+       "      <td>NormalWide</td>\n",
+       "      <td>1</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#480 Bottom 25%</td>\n",
+       "      <td>#579 Bottom 10%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Adizero Boston 12</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$160</td>\n",
+       "      <td>Tempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.2 oz / 261g 9.2 oz / 260g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.1 mm 6.5 mm</td>\n",
+       "      <td>Mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>34.5 mm 37.0 mm</td>\n",
+       "      <td>28.4 mm 30.5 mm</td>\n",
+       "      <td>NormalWide</td>\n",
+       "      <td>1</td>\n",
+       "      <td>SummerAll seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#216 Top 34%</td>\n",
+       "      <td>#338 Bottom 47%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Adizero Boston 13</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$160</td>\n",
+       "      <td>CompetitionTempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9 oz / 254g 9 oz / 255g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.0 mm 6.0 mm</td>\n",
+       "      <td>Mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>34.3 mm 36.0 mm</td>\n",
+       "      <td>28.3 mm 30.0 mm</td>\n",
+       "      <td>NormalWide</td>\n",
+       "      <td>1</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#38 Top 11%</td>\n",
+       "      <td>#206 Bottom 43%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Adizero EVO SL</td>\n",
+       "      <td>93\\n Superb!</td>\n",
+       "      <td>$150</td>\n",
+       "      <td>Daily runningTempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>7.9 oz / 223g 7.9 oz / 224g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8.0 mm 6.5 mm</td>\n",
+       "      <td>HeelMid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>36.1 mm 38.5 mm</td>\n",
+       "      <td>28.1 mm 32.0 mm</td>\n",
+       "      <td>NormalWide</td>\n",
+       "      <td>1</td>\n",
+       "      <td>SummerAll seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#2 Top 1%</td>\n",
+       "      <td>#23 Top 7%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>20 rows × 33 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Brand                   Name Audience score Price                Pace  \\\n",
+       "0   Brooks               Launch 9    87\\n Great!  $110  Daily runningTempo   \n",
+       "1   Brooks             Levitate 6   90\\n Superb!  $150       Daily running   \n",
+       "2   Adidas                  4DFWD   90\\n Superb!  $200       Daily running   \n",
+       "3   Adidas                4DFWD 2   90\\n Superb!  $200       Daily running   \n",
+       "4   Adidas                4DFWD 3    88\\n Great!  $200       Daily running   \n",
+       "5   Brooks       Addiction GTS 15     85\\n Good!  $140       Daily running   \n",
+       "6   Adidas     Adidas Adizero SL2   90\\n Superb!  $130  Daily runningTempo   \n",
+       "7   Adidas                Adistar    88\\n Great!  $130       Daily running   \n",
+       "8   Adidas            Adistar 2.0     83\\n Good!  $130       Daily running   \n",
+       "9   Adidas              Adistar 3    89\\n Great!  $130       Daily running   \n",
+       "10  Adidas        Adizero Adios 7     81\\n Good!  $130               Tempo   \n",
+       "11  Adidas        Adizero Adios 8   90\\n Superb!  $130               Tempo   \n",
+       "12  Adidas        Adizero Adios 9   92\\n Superb!  $140    CompetitionTempo   \n",
+       "13  Adidas  Adizero Adios Pro 2.0   91\\n Superb!  $220         Competition   \n",
+       "14  Adidas    Adizero Adios Pro 3   91\\n Superb!  $250         Competition   \n",
+       "15  Adidas    Adizero Adios Pro 4   93\\n Superb!  $250         Competition   \n",
+       "16  Adidas      Adizero Boston 11     83\\n Good!  $160               Tempo   \n",
+       "17  Adidas      Adizero Boston 12    88\\n Great!  $160               Tempo   \n",
+       "18  Adidas      Adizero Boston 13   90\\n Superb!  $160    CompetitionTempo   \n",
+       "19  Adidas         Adizero EVO SL   93\\n Superb!  $150  Daily runningTempo   \n",
+       "\n",
+       "      Arch support        Weight lab Weight brand  Lightweight  \\\n",
+       "0          Neutral    7.9 oz / 225g 8.1 oz / 230g            1   \n",
+       "1          Neutral  10.7 oz / 304g 10.9 oz / 309g            0   \n",
+       "2          Neutral  11.9 oz / 336g 11.5 oz / 327g            0   \n",
+       "3          Neutral  12.6 oz / 356g 12.4 oz / 352g            0   \n",
+       "4          Neutral  12.3 oz / 348g 12.2 oz / 345g            0   \n",
+       "5   Motion control  12.5 oz / 353g 12.2 oz / 346g            0   \n",
+       "6          Neutral    8.6 oz / 245g 8.4 oz / 238g            1   \n",
+       "7          Neutral  11.5 oz / 325g 11.2 oz / 318g            0   \n",
+       "8          Neutral  11.6 oz / 328g 11.6 oz / 328g            0   \n",
+       "9          Neutral    9.7 oz / 274g 9.5 oz / 270g            0   \n",
+       "10         Neutral    7.5 oz / 212g 7.5 oz / 212g            1   \n",
+       "11         Neutral      7.4 oz / 210g 7 oz / 198g            1   \n",
+       "12         Neutral    6.2 oz / 176g 6.2 oz / 176g            1   \n",
+       "13         Neutral    7.9 oz / 223g 7.6 oz / 215g            1   \n",
+       "14         Neutral    7.7 oz / 218g 7.9 oz / 223g            1   \n",
+       "15         Neutral    7.1 oz / 200g 7.1 oz / 201g            1   \n",
+       "16         Neutral   10.2 oz / 290g 9.6 oz / 272g            0   \n",
+       "17         Neutral    9.2 oz / 261g 9.2 oz / 260g            0   \n",
+       "18         Neutral        9 oz / 254g 9 oz / 255g            0   \n",
+       "19         Neutral    7.9 oz / 223g 7.9 oz / 224g            1   \n",
+       "\n",
+       "   Drop lab Drop brand    Strike pattern  ... Heel lab Heel brand  \\\n",
+       "0       9.4 mm 10.0 mm  HeelMid/forefoot  ...     32.4 mm 36.0 mm   \n",
+       "1        7.7 mm 8.0 mm      Mid/forefoot  ...     34.3 mm 32.5 mm   \n",
+       "2       8.9 mm 10.0 mm  HeelMid/forefoot  ...     33.3 mm 32.5 mm   \n",
+       "3      10.6 mm 11.0 mm              Heel  ...     31.8 mm 32.0 mm   \n",
+       "4       9.9 mm 10.0 mm  HeelMid/forefoot  ...     32.6 mm 34.0 mm   \n",
+       "5      12.1 mm 12.0 mm              Heel  ...     36.5 mm 36.0 mm   \n",
+       "6        8.2 mm 9.0 mm  HeelMid/forefoot  ...     34.9 mm 35.0 mm   \n",
+       "7        9.6 mm 6.0 mm  HeelMid/forefoot  ...     34.4 mm 37.5 mm   \n",
+       "8        8.0 mm 6.0 mm  HeelMid/forefoot  ...     33.8 mm 33.0 mm   \n",
+       "9       10.5 mm 5.0 mm              Heel  ...     40.7 mm 40.0 mm   \n",
+       "10       8.7 mm 8.0 mm  HeelMid/forefoot  ...     31.6 mm 27.0 mm   \n",
+       "11       7.6 mm 8.0 mm      Mid/forefoot  ...     28.0 mm 28.0 mm   \n",
+       "12       6.2 mm 7.0 mm      Mid/forefoot  ...     25.0 mm 28.0 mm   \n",
+       "13     10.3 mm 10.0 mm              Heel  ...     40.0 mm 39.5 mm   \n",
+       "14       8.0 mm 6.5 mm  HeelMid/forefoot  ...     37.8 mm 39.5 mm   \n",
+       "15       8.1 mm 6.0 mm  HeelMid/forefoot  ...     36.6 mm 39.0 mm   \n",
+       "16       9.8 mm 8.5 mm  HeelMid/forefoot  ...     39.1 mm 39.5 mm   \n",
+       "17       6.1 mm 6.5 mm      Mid/forefoot  ...     34.5 mm 37.0 mm   \n",
+       "18       6.0 mm 6.0 mm      Mid/forefoot  ...     34.3 mm 36.0 mm   \n",
+       "19       8.0 mm 6.5 mm  HeelMid/forefoot  ...     36.1 mm 38.5 mm   \n",
+       "\n",
+       "   Forefoot lab Forefoot brand        Widths available Orthotic friendly  \\\n",
+       "0              23.0 mm 26.0 mm              NormalWide                 1   \n",
+       "1              26.6 mm 24.5 mm                  Normal                 1   \n",
+       "2              24.4 mm 22.5 mm                  Normal                 1   \n",
+       "3              21.2 mm 21.0 mm                  Normal                 1   \n",
+       "4              22.7 mm 24.0 mm                  Normal                 1   \n",
+       "5              24.4 mm 24.0 mm  NarrowNormalWideX-Wide                 1   \n",
+       "6              26.7 mm 26.0 mm              NormalWide                 1   \n",
+       "7              24.8 mm 31.5 mm                  Normal                 1   \n",
+       "8              25.8 mm 27.0 mm                  Normal                 1   \n",
+       "9              30.2 mm 35.0 mm              NormalWide                 1   \n",
+       "10             22.9 mm 19.0 mm              NormalWide                 1   \n",
+       "11             20.4 mm 20.0 mm                  Normal                 1   \n",
+       "12             18.8 mm 21.0 mm                  Normal                 1   \n",
+       "13             29.7 mm 29.5 mm                  Normal                 0   \n",
+       "14             29.8 mm 33.0 mm                  Normal                 1   \n",
+       "15             28.5 mm 33.0 mm              NormalWide                 1   \n",
+       "16             29.3 mm 31.0 mm              NormalWide                 1   \n",
+       "17             28.4 mm 30.5 mm              NormalWide                 1   \n",
+       "18             28.3 mm 30.0 mm              NormalWide                 1   \n",
+       "19             28.1 mm 32.0 mm              NormalWide                 1   \n",
+       "\n",
+       "               Season Removable insole          Ranking       Popularity  \\\n",
+       "0                   -                1     #301 Top 47%  #352 Bottom 45%   \n",
+       "1   SummerAll seasons                1      #72 Top 20%  #255 Bottom 30%   \n",
+       "2         All seasons                1     #104 Top 17%  #368 Bottom 42%   \n",
+       "3         All seasons                1     #126 Top 20%  #541 Bottom 16%   \n",
+       "4         All seasons                1     #116 Top 32%   #339 Bottom 7%   \n",
+       "5         All seasons                1  #221 Bottom 39%     #159 Top 44%   \n",
+       "6   SummerAll seasons                1      #44 Top 13%     #166 Top 46%   \n",
+       "7         All seasons                1     #267 Top 42%     #247 Top 39%   \n",
+       "8   SummerAll seasons                1  #506 Bottom 21%   #643 Bottom 1%   \n",
+       "9   SummerAll seasons                1      #97 Top 27%  #241 Bottom 33%   \n",
+       "10  SummerAll seasons                1  #535 Bottom 17%   #614 Bottom 4%   \n",
+       "11  SummerAll seasons                1     #129 Top 20%  #553 Bottom 14%   \n",
+       "12        All seasons                1        #7 Top 2%  #245 Bottom 33%   \n",
+       "13                  -                0       #32 Top 5%  #569 Bottom 11%   \n",
+       "14  SummerAll seasons                1       #41 Top 7%     #202 Top 32%   \n",
+       "15        All seasons                1        #1 Top 1%      #38 Top 11%   \n",
+       "16        All seasons                1  #480 Bottom 25%  #579 Bottom 10%   \n",
+       "17  SummerAll seasons                1     #216 Top 34%  #338 Bottom 47%   \n",
+       "18        All seasons                1      #38 Top 11%  #206 Bottom 43%   \n",
+       "19  SummerAll seasons                1        #2 Top 1%       #23 Top 7%   \n",
+       "\n",
+       "   Gender Terrain  \n",
+       "0     NaN     NaN  \n",
+       "1     NaN     NaN  \n",
+       "2     NaN     NaN  \n",
+       "3     NaN     NaN  \n",
+       "4     NaN     NaN  \n",
+       "5     NaN     NaN  \n",
+       "6     NaN     NaN  \n",
+       "7     NaN     NaN  \n",
+       "8     NaN     NaN  \n",
+       "9     NaN     NaN  \n",
+       "10    NaN     NaN  \n",
+       "11    NaN     NaN  \n",
+       "12    NaN     NaN  \n",
+       "13    NaN     NaN  \n",
+       "14    NaN     NaN  \n",
+       "15    NaN     NaN  \n",
+       "16    NaN     NaN  \n",
+       "17    NaN     NaN  \n",
+       "18    NaN     NaN  \n",
+       "19    NaN     NaN  \n",
+       "\n",
+       "[20 rows x 33 columns]"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# sebelum hapus\n",
+    "print(\"Before:\", len(df_ori))\n",
+    "\n",
+    "#hapus\n",
+    "df_ori = df_ori.drop_duplicates(subset=[\"Brand\", \"Name\"], keep=\"first\").reset_index(drop=True)\n",
+    "\n",
+    "print(\"After :\", len(df_ori))\n",
+    "df_ori.head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "id": "ba33ddcc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 443 entries, 0 to 442\n",
+      "Data columns (total 33 columns):\n",
+      " #   Column                       Non-Null Count  Dtype\n",
+      "---  ------                       --------------  -----\n",
+      " 0   Brand                        443 non-null    str  \n",
+      " 1   Name                         443 non-null    str  \n",
+      " 2   Audience score               441 non-null    str  \n",
+      " 3   Price                        443 non-null    str  \n",
+      " 4   Pace                         443 non-null    str  \n",
+      " 5   Arch support                 443 non-null    str  \n",
+      " 6   Weight lab Weight brand      443 non-null    str  \n",
+      " 7   Lightweight                  443 non-null    int64\n",
+      " 8   Drop lab Drop brand          443 non-null    str  \n",
+      " 9   Strike pattern               443 non-null    str  \n",
+      " 10  Size                         443 non-null    str  \n",
+      " 11  Midsole softness             443 non-null    str  \n",
+      " 12  Toebox durability            443 non-null    str  \n",
+      " 13  Heel padding durability      443 non-null    str  \n",
+      " 14  Outsole durability           443 non-null    str  \n",
+      " 15  Breathability                443 non-null    str  \n",
+      " 16  Width / fit                  443 non-null    str  \n",
+      " 17  Toebox width                 443 non-null    str  \n",
+      " 18  Stiffness                    443 non-null    str  \n",
+      " 19  Torsional rigidity           443 non-null    str  \n",
+      " 20  Heel counter stiffness       443 non-null    str  \n",
+      " 21  Plate                        443 non-null    str  \n",
+      " 22  Rocker                       443 non-null    int64\n",
+      " 23  Heel lab Heel brand          443 non-null    str  \n",
+      " 24  Forefoot lab Forefoot brand  443 non-null    str  \n",
+      " 25  Widths available             443 non-null    str  \n",
+      " 26  Orthotic friendly            443 non-null    int64\n",
+      " 27  Season                       443 non-null    str  \n",
+      " 28  Removable insole             443 non-null    int64\n",
+      " 29  Ranking                      443 non-null    str  \n",
+      " 30  Popularity                   443 non-null    str  \n",
+      " 31  Gender                       4 non-null      str  \n",
+      " 32  Terrain                      9 non-null      str  \n",
+      "dtypes: int64(4), str(29)\n",
+      "memory usage: 114.3 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_ori.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48eb34c8",
+   "metadata": {},
+   "source": [
+    "# Removing unused"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "af4ea6fc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Pace</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Heel lab Heel brand</th>\n",
+       "      <th>Forefoot lab Forefoot brand</th>\n",
+       "      <th>Widths available</th>\n",
+       "      <th>Orthotic friendly</th>\n",
+       "      <th>Season</th>\n",
+       "      <th>Removable insole</th>\n",
+       "      <th>Ranking</th>\n",
+       "      <th>Popularity</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Terrain</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Launch 9</td>\n",
+       "      <td>87\\n Great!</td>\n",
+       "      <td>$110</td>\n",
+       "      <td>Daily runningTempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>7.9 oz / 225g 8.1 oz / 230g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.4 mm 10.0 mm</td>\n",
+       "      <td>HeelMid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>32.4 mm 36.0 mm</td>\n",
+       "      <td>23.0 mm 26.0 mm</td>\n",
+       "      <td>NormalWide</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#301 Top 47%</td>\n",
+       "      <td>#352 Bottom 45%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Levitate 6</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$150</td>\n",
+       "      <td>Daily running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 304g 10.9 oz / 309g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.7 mm 8.0 mm</td>\n",
+       "      <td>Mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>34.3 mm 32.5 mm</td>\n",
+       "      <td>26.6 mm 24.5 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>SummerAll seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#72 Top 20%</td>\n",
+       "      <td>#255 Bottom 30%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>11.9 oz / 336g 11.5 oz / 327g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.9 mm 10.0 mm</td>\n",
+       "      <td>HeelMid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>33.3 mm 32.5 mm</td>\n",
+       "      <td>24.4 mm 22.5 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#104 Top 17%</td>\n",
+       "      <td>#368 Bottom 42%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 2</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.6 oz / 356g 12.4 oz / 352g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.6 mm 11.0 mm</td>\n",
+       "      <td>Heel</td>\n",
+       "      <td>...</td>\n",
+       "      <td>31.8 mm 32.0 mm</td>\n",
+       "      <td>21.2 mm 21.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#126 Top 20%</td>\n",
+       "      <td>#541 Bottom 16%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 3</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.3 oz / 348g 12.2 oz / 345g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.9 mm 10.0 mm</td>\n",
+       "      <td>HeelMid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>32.6 mm 34.0 mm</td>\n",
+       "      <td>22.7 mm 24.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#116 Top 32%</td>\n",
+       "      <td>#339 Bottom 7%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 33 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand         Name Audience score Price                Pace Arch support  \\\n",
+       "0  Brooks     Launch 9    87\\n Great!  $110  Daily runningTempo      Neutral   \n",
+       "1  Brooks   Levitate 6   90\\n Superb!  $150       Daily running      Neutral   \n",
+       "2  Adidas        4DFWD   90\\n Superb!  $200       Daily running      Neutral   \n",
+       "3  Adidas      4DFWD 2   90\\n Superb!  $200       Daily running      Neutral   \n",
+       "4  Adidas      4DFWD 3    88\\n Great!  $200       Daily running      Neutral   \n",
+       "\n",
+       "         Weight lab Weight brand  Lightweight Drop lab Drop brand  \\\n",
+       "0    7.9 oz / 225g 8.1 oz / 230g            1      9.4 mm 10.0 mm   \n",
+       "1  10.7 oz / 304g 10.9 oz / 309g            0       7.7 mm 8.0 mm   \n",
+       "2  11.9 oz / 336g 11.5 oz / 327g            0      8.9 mm 10.0 mm   \n",
+       "3  12.6 oz / 356g 12.4 oz / 352g            0     10.6 mm 11.0 mm   \n",
+       "4  12.3 oz / 348g 12.2 oz / 345g            0      9.9 mm 10.0 mm   \n",
+       "\n",
+       "     Strike pattern  ... Heel lab Heel brand Forefoot lab Forefoot brand  \\\n",
+       "0  HeelMid/forefoot  ...     32.4 mm 36.0 mm             23.0 mm 26.0 mm   \n",
+       "1      Mid/forefoot  ...     34.3 mm 32.5 mm             26.6 mm 24.5 mm   \n",
+       "2  HeelMid/forefoot  ...     33.3 mm 32.5 mm             24.4 mm 22.5 mm   \n",
+       "3              Heel  ...     31.8 mm 32.0 mm             21.2 mm 21.0 mm   \n",
+       "4  HeelMid/forefoot  ...     32.6 mm 34.0 mm             22.7 mm 24.0 mm   \n",
+       "\n",
+       "  Widths available Orthotic friendly             Season Removable insole  \\\n",
+       "0       NormalWide                 1                  -                1   \n",
+       "1           Normal                 1  SummerAll seasons                1   \n",
+       "2           Normal                 1        All seasons                1   \n",
+       "3           Normal                 1        All seasons                1   \n",
+       "4           Normal                 1        All seasons                1   \n",
+       "\n",
+       "        Ranking       Popularity Gender Terrain  \n",
+       "0  #301 Top 47%  #352 Bottom 45%    NaN     NaN  \n",
+       "1   #72 Top 20%  #255 Bottom 30%    NaN     NaN  \n",
+       "2  #104 Top 17%  #368 Bottom 42%    NaN     NaN  \n",
+       "3  #126 Top 20%  #541 Bottom 16%    NaN     NaN  \n",
+       "4  #116 Top 32%   #339 Bottom 7%    NaN     NaN  \n",
+       "\n",
+       "[5 rows x 33 columns]"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = df_ori.copy()\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "5babe62e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Pace</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Plate</th>\n",
+       "      <th>Rocker</th>\n",
+       "      <th>Heel lab Heel brand</th>\n",
+       "      <th>Forefoot lab Forefoot brand</th>\n",
+       "      <th>Widths available</th>\n",
+       "      <th>Orthotic friendly</th>\n",
+       "      <th>Season</th>\n",
+       "      <th>Removable insole</th>\n",
+       "      <th>Ranking</th>\n",
+       "      <th>Popularity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Launch 9</td>\n",
+       "      <td>87\\n Great!</td>\n",
+       "      <td>$110</td>\n",
+       "      <td>Daily runningTempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>7.9 oz / 225g 8.1 oz / 230g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.4 mm 10.0 mm</td>\n",
+       "      <td>HeelMid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>32.4 mm 36.0 mm</td>\n",
+       "      <td>23.0 mm 26.0 mm</td>\n",
+       "      <td>NormalWide</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#301 Top 47%</td>\n",
+       "      <td>#352 Bottom 45%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Levitate 6</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$150</td>\n",
+       "      <td>Daily running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 304g 10.9 oz / 309g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.7 mm 8.0 mm</td>\n",
+       "      <td>Mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>34.3 mm 32.5 mm</td>\n",
+       "      <td>26.6 mm 24.5 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>SummerAll seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#72 Top 20%</td>\n",
+       "      <td>#255 Bottom 30%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>11.9 oz / 336g 11.5 oz / 327g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.9 mm 10.0 mm</td>\n",
+       "      <td>HeelMid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>33.3 mm 32.5 mm</td>\n",
+       "      <td>24.4 mm 22.5 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#104 Top 17%</td>\n",
+       "      <td>#368 Bottom 42%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 2</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.6 oz / 356g 12.4 oz / 352g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.6 mm 11.0 mm</td>\n",
+       "      <td>Heel</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>31.8 mm 32.0 mm</td>\n",
+       "      <td>21.2 mm 21.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#126 Top 20%</td>\n",
+       "      <td>#541 Bottom 16%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 3</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.3 oz / 348g 12.2 oz / 345g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.9 mm 10.0 mm</td>\n",
+       "      <td>HeelMid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>32.6 mm 34.0 mm</td>\n",
+       "      <td>22.7 mm 24.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#116 Top 32%</td>\n",
+       "      <td>#339 Bottom 7%</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 31 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand         Name Audience score Price                Pace Arch support  \\\n",
+       "0  Brooks     Launch 9    87\\n Great!  $110  Daily runningTempo      Neutral   \n",
+       "1  Brooks   Levitate 6   90\\n Superb!  $150       Daily running      Neutral   \n",
+       "2  Adidas        4DFWD   90\\n Superb!  $200       Daily running      Neutral   \n",
+       "3  Adidas      4DFWD 2   90\\n Superb!  $200       Daily running      Neutral   \n",
+       "4  Adidas      4DFWD 3    88\\n Great!  $200       Daily running      Neutral   \n",
+       "\n",
+       "         Weight lab Weight brand  Lightweight Drop lab Drop brand  \\\n",
+       "0    7.9 oz / 225g 8.1 oz / 230g            1      9.4 mm 10.0 mm   \n",
+       "1  10.7 oz / 304g 10.9 oz / 309g            0       7.7 mm 8.0 mm   \n",
+       "2  11.9 oz / 336g 11.5 oz / 327g            0      8.9 mm 10.0 mm   \n",
+       "3  12.6 oz / 356g 12.4 oz / 352g            0     10.6 mm 11.0 mm   \n",
+       "4  12.3 oz / 348g 12.2 oz / 345g            0      9.9 mm 10.0 mm   \n",
+       "\n",
+       "     Strike pattern  ... Plate Rocker Heel lab Heel brand  \\\n",
+       "0  HeelMid/forefoot  ...     0      0     32.4 mm 36.0 mm   \n",
+       "1      Mid/forefoot  ...     0      0     34.3 mm 32.5 mm   \n",
+       "2  HeelMid/forefoot  ...     0      0     33.3 mm 32.5 mm   \n",
+       "3              Heel  ...     0      0     31.8 mm 32.0 mm   \n",
+       "4  HeelMid/forefoot  ...     0      0     32.6 mm 34.0 mm   \n",
+       "\n",
+       "  Forefoot lab Forefoot brand Widths available Orthotic friendly  \\\n",
+       "0             23.0 mm 26.0 mm       NormalWide                 1   \n",
+       "1             26.6 mm 24.5 mm           Normal                 1   \n",
+       "2             24.4 mm 22.5 mm           Normal                 1   \n",
+       "3             21.2 mm 21.0 mm           Normal                 1   \n",
+       "4             22.7 mm 24.0 mm           Normal                 1   \n",
+       "\n",
+       "              Season Removable insole       Ranking       Popularity  \n",
+       "0                  -                1  #301 Top 47%  #352 Bottom 45%  \n",
+       "1  SummerAll seasons                1   #72 Top 20%  #255 Bottom 30%  \n",
+       "2        All seasons                1  #104 Top 17%  #368 Bottom 42%  \n",
+       "3        All seasons                1  #126 Top 20%  #541 Bottom 16%  \n",
+       "4        All seasons                1  #116 Top 32%   #339 Bottom 7%  \n",
+       "\n",
+       "[5 rows x 31 columns]"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"Gender\", \"Terrain\"], inplace=True)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "29c40bab",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 443 entries, 0 to 442\n",
+      "Data columns (total 25 columns):\n",
+      " #   Column                       Non-Null Count  Dtype\n",
+      "---  ------                       --------------  -----\n",
+      " 0   Brand                        443 non-null    str  \n",
+      " 1   Name                         443 non-null    str  \n",
+      " 2   Pace                         443 non-null    str  \n",
+      " 3   Arch support                 443 non-null    str  \n",
+      " 4   Weight lab Weight brand      443 non-null    str  \n",
+      " 5   Lightweight                  443 non-null    int64\n",
+      " 6   Drop lab Drop brand          443 non-null    str  \n",
+      " 7   Strike pattern               443 non-null    str  \n",
+      " 8   Midsole softness             443 non-null    str  \n",
+      " 9   Toebox durability            443 non-null    str  \n",
+      " 10  Heel padding durability      443 non-null    str  \n",
+      " 11  Outsole durability           443 non-null    str  \n",
+      " 12  Breathability                443 non-null    str  \n",
+      " 13  Width / fit                  443 non-null    str  \n",
+      " 14  Toebox width                 443 non-null    str  \n",
+      " 15  Stiffness                    443 non-null    str  \n",
+      " 16  Torsional rigidity           443 non-null    str  \n",
+      " 17  Heel counter stiffness       443 non-null    str  \n",
+      " 18  Plate                        443 non-null    str  \n",
+      " 19  Rocker                       443 non-null    int64\n",
+      " 20  Heel lab Heel brand          443 non-null    str  \n",
+      " 21  Forefoot lab Forefoot brand  443 non-null    str  \n",
+      " 22  Orthotic friendly            443 non-null    int64\n",
+      " 23  Season                       443 non-null    str  \n",
+      " 24  Removable insole             443 non-null    int64\n",
+      "dtypes: int64(4), str(21)\n",
+      "memory usage: 86.7 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=['Audience score', 'Price', 'Size', \n",
+    "                 'Widths available', 'Ranking', 'Popularity'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a0dd91b",
+   "metadata": {},
+   "source": [
+    "# Pace"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "f98cc9d7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pace\n",
+      "Daily running         304\n",
+      "Daily runningTempo     55\n",
+      "Competition            32\n",
+      "Tempo                  31\n",
+      "CompetitionTempo       21\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Pace\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "9ab7badd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 443\n",
+      "NULL Value: 0\n",
+      "\n",
+      "Sample Comparison:\n",
+      "                  Pace  pace_daily_running  pace_tempo  pace_competition\n",
+      "0   daily runningtempo                   1           1                 0\n",
+      "6   daily runningtempo                   1           1                 0\n",
+      "12    competitiontempo                   0           1                 1\n",
+      "18    competitiontempo                   0           1                 1\n",
+      "19  daily runningtempo                   1           1                 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['Pace'] = df['Pace'].astype(str).str.lower()\n",
+    "base_pace = ['daily running', 'tempo', 'competition']\n",
+    "\n",
+    "for level in base_pace:\n",
+    "    column_name = f\"pace_{level.replace(' ', '_')}\"\n",
+    "    df[column_name] = df['Pace'].str.contains(level, na=False).astype(int)\n",
+    "\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "pace_cols = [f\"pace_{l.replace(' ', '_')}\" for l in base_pace]\n",
+    "zero_vector_count = (df[pace_cols].sum(axis=1) == 0).sum()\n",
+    "print(f\"NULL Value: {zero_vector_count}\")\n",
+    "\n",
+    "print(\"\\nSample Comparison:\")\n",
+    "print(df[df[pace_cols].sum(axis=1) > 1][['Pace'] + pace_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "dcc8c065",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Value Counts Kolom Asli:\n",
+      "Pace\n",
+      "daily running         304\n",
+      "daily runningtempo     55\n",
+      "competition            32\n",
+      "tempo                  31\n",
+      "competitiontempo       21\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "pace_daily_running sum: 359\n",
+      "pace_tempo sum: 107\n",
+      "pace_competition sum: 53\n",
+      "\n",
+      "                 Pace  pace_daily_running  pace_tempo  pace_competition\n",
+      "0  daily runningtempo                   1           1                 0\n",
+      "1       daily running                   1           0                 0\n",
+      "2       daily running                   1           0                 0\n",
+      "3       daily running                   1           0                 0\n",
+      "4       daily running                   1           0                 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"\\nValue Counts Kolom Asli:\")\n",
+    "print(df[\"Pace\"].value_counts())\n",
+    "\n",
+    "print()\n",
+    "for col in pace_cols:\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"Pace\"] + pace_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "id": "fd110dce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 443 entries, 0 to 442\n",
+      "Data columns (total 27 columns):\n",
+      " #   Column                       Non-Null Count  Dtype\n",
+      "---  ------                       --------------  -----\n",
+      " 0   Brand                        443 non-null    str  \n",
+      " 1   Name                         443 non-null    str  \n",
+      " 2   Arch support                 443 non-null    str  \n",
+      " 3   Weight lab Weight brand      443 non-null    str  \n",
+      " 4   Lightweight                  443 non-null    int64\n",
+      " 5   Drop lab Drop brand          443 non-null    str  \n",
+      " 6   Strike pattern               443 non-null    str  \n",
+      " 7   Midsole softness             443 non-null    str  \n",
+      " 8   Toebox durability            443 non-null    str  \n",
+      " 9   Heel padding durability      443 non-null    str  \n",
+      " 10  Outsole durability           443 non-null    str  \n",
+      " 11  Breathability                443 non-null    str  \n",
+      " 12  Width / fit                  443 non-null    str  \n",
+      " 13  Toebox width                 443 non-null    str  \n",
+      " 14  Stiffness                    443 non-null    str  \n",
+      " 15  Torsional rigidity           443 non-null    str  \n",
+      " 16  Heel counter stiffness       443 non-null    str  \n",
+      " 17  Plate                        443 non-null    str  \n",
+      " 18  Rocker                       443 non-null    int64\n",
+      " 19  Heel lab Heel brand          443 non-null    str  \n",
+      " 20  Forefoot lab Forefoot brand  443 non-null    str  \n",
+      " 21  Orthotic friendly            443 non-null    int64\n",
+      " 22  Season                       443 non-null    str  \n",
+      " 23  Removable insole             443 non-null    int64\n",
+      " 24  pace_daily_running           443 non-null    int64\n",
+      " 25  pace_tempo                   443 non-null    int64\n",
+      " 26  pace_competition             443 non-null    int64\n",
+      "dtypes: int64(7), str(20)\n",
+      "memory usage: 93.6 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"Pace\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f49287c1",
+   "metadata": {},
+   "source": [
+    "# Arch support"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "id": "cf46eaf4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Arch support\n",
+      "Neutral           378\n",
+      "Stability          64\n",
+      "Motion control      1\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Arch support\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "id": "9dda2e21",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 443\n",
+      "NULL Value: 0\n",
+      "\n",
+      "Sample Comparison:\n",
+      "  Arch support  arch_neutral  arch_stability\n",
+      "0      neutral             1               0\n",
+      "1      neutral             1               0\n",
+      "2      neutral             1               0\n",
+      "3      neutral             1               0\n",
+      "4      neutral             1               0\n",
+      "5    stability             0               1\n",
+      "6      neutral             1               0\n",
+      "7      neutral             1               0\n",
+      "8      neutral             1               0\n",
+      "9      neutral             1               0\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['Arch support'] = df['Arch support'].astype(str).str.lower().str.replace('motion control', 'stability', regex=False)\n",
+    "base_arch = ['neutral', 'stability']\n",
+    "\n",
+    "for level in base_arch:\n",
+    "    column_name = f\"arch_{level}\"\n",
+    "    df[column_name] = df['Arch support'].str.contains(level, na=False).astype(int)\n",
+    "\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "arch_cols = [f\"arch_{l}\" for l in base_arch]\n",
+    "zero_vector_count = (df[arch_cols].sum(axis=1) == 0).sum()\n",
+    "print(f\"NULL Value: {zero_vector_count}\")\n",
+    "\n",
+    "print(\"\\nSample Comparison:\")\n",
+    "print(df[[\"Arch support\"] + arch_cols].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "id": "c9d7789c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Arch support\n",
+      "neutral      378\n",
+      "stability     65\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "arch_neutral sum: 378\n",
+      "arch_stability sum: 65\n",
+      "\n",
+      "  Arch support  arch_neutral  arch_stability\n",
+      "0      neutral             1               0\n",
+      "1      neutral             1               0\n",
+      "2      neutral             1               0\n",
+      "3      neutral             1               0\n",
+      "4      neutral             1               0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Arch support\"].value_counts())\n",
+    "\n",
+    "print()\n",
+    "for level in base_arch:\n",
+    "    col = f\"arch_{level}\"\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"Arch support\"] + arch_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "id": "9da19116",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 443 entries, 0 to 442\n",
+      "Data columns (total 28 columns):\n",
+      " #   Column                       Non-Null Count  Dtype\n",
+      "---  ------                       --------------  -----\n",
+      " 0   Brand                        443 non-null    str  \n",
+      " 1   Name                         443 non-null    str  \n",
+      " 2   Weight lab Weight brand      443 non-null    str  \n",
+      " 3   Lightweight                  443 non-null    int64\n",
+      " 4   Drop lab Drop brand          443 non-null    str  \n",
+      " 5   Strike pattern               443 non-null    str  \n",
+      " 6   Midsole softness             443 non-null    str  \n",
+      " 7   Toebox durability            443 non-null    str  \n",
+      " 8   Heel padding durability      443 non-null    str  \n",
+      " 9   Outsole durability           443 non-null    str  \n",
+      " 10  Breathability                443 non-null    str  \n",
+      " 11  Width / fit                  443 non-null    str  \n",
+      " 12  Toebox width                 443 non-null    str  \n",
+      " 13  Stiffness                    443 non-null    str  \n",
+      " 14  Torsional rigidity           443 non-null    str  \n",
+      " 15  Heel counter stiffness       443 non-null    str  \n",
+      " 16  Plate                        443 non-null    str  \n",
+      " 17  Rocker                       443 non-null    int64\n",
+      " 18  Heel lab Heel brand          443 non-null    str  \n",
+      " 19  Forefoot lab Forefoot brand  443 non-null    str  \n",
+      " 20  Orthotic friendly            443 non-null    int64\n",
+      " 21  Season                       443 non-null    str  \n",
+      " 22  Removable insole             443 non-null    int64\n",
+      " 23  pace_daily_running           443 non-null    int64\n",
+      " 24  pace_tempo                   443 non-null    int64\n",
+      " 25  pace_competition             443 non-null    int64\n",
+      " 26  arch_neutral                 443 non-null    int64\n",
+      " 27  arch_stability               443 non-null    int64\n",
+      "dtypes: int64(9), str(19)\n",
+      "memory usage: 97.0 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"Arch support\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d60007d4",
+   "metadata": {},
+   "source": [
+    "# Weight lab Weight brand"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "id": "b9c254fe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(0)"
+      ]
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mask_missing = (\n",
+    "    df[\"Weight lab Weight brand\"].isna() |\n",
+    "    (df[\"Weight lab Weight brand\"].astype(str).str.strip() == \"-\")\n",
+    ")\n",
+    "mask_missing.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "id": "9295f8c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         Weight lab Weight brand  weight_lab_oz  weight_lab_g  \\\n",
+      "0    7.9 oz / 225g 8.1 oz / 230g            7.9           225   \n",
+      "1  10.7 oz / 304g 10.9 oz / 309g           10.7           304   \n",
+      "2  11.9 oz / 336g 11.5 oz / 327g           11.9           336   \n",
+      "3  12.6 oz / 356g 12.4 oz / 352g           12.6           356   \n",
+      "4  12.3 oz / 348g 12.2 oz / 345g           12.3           348   \n",
+      "\n",
+      "   weight_brand_oz  weight_brand_g  \n",
+      "0              8.1           230.0  \n",
+      "1             10.9           309.0  \n",
+      "2             11.5           327.0  \n",
+      "3             12.4           352.0  \n",
+      "4             12.2           345.0  \n"
+     ]
+    }
+   ],
+   "source": [
+    "weight = df[\"Weight lab Weight brand\"].str.findall(r\"[\\d.]+\")\n",
+    "df[[\"weight_lab_oz\", \"weight_lab_g\", \"weight_brand_oz\", \"weight_brand_g\"]] = (\n",
+    "    pd.DataFrame(weight.tolist(), index=df.index)\n",
+    ")\n",
+    "\n",
+    "for col in [\"weight_lab_oz\", \"weight_lab_g\", \"weight_brand_oz\", \"weight_brand_g\"]:\n",
+    "    df[col] = pd.to_numeric(df[col], errors=\"coerce\")\n",
+    "\n",
+    "print(df[[\"Weight lab Weight brand\", \"weight_lab_oz\", \"weight_lab_g\", \"weight_brand_oz\", \"weight_brand_g\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "id": "1f6b3a9b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 443 entries, 0 to 442\n",
+      "Data columns (total 31 columns):\n",
+      " #   Column                       Non-Null Count  Dtype  \n",
+      "---  ------                       --------------  -----  \n",
+      " 0   Brand                        443 non-null    str    \n",
+      " 1   Name                         443 non-null    str    \n",
+      " 2   Lightweight                  443 non-null    int64  \n",
+      " 3   Drop lab Drop brand          443 non-null    str    \n",
+      " 4   Strike pattern               443 non-null    str    \n",
+      " 5   Midsole softness             443 non-null    str    \n",
+      " 6   Toebox durability            443 non-null    str    \n",
+      " 7   Heel padding durability      443 non-null    str    \n",
+      " 8   Outsole durability           443 non-null    str    \n",
+      " 9   Breathability                443 non-null    str    \n",
+      " 10  Width / fit                  443 non-null    str    \n",
+      " 11  Toebox width                 443 non-null    str    \n",
+      " 12  Stiffness                    443 non-null    str    \n",
+      " 13  Torsional rigidity           443 non-null    str    \n",
+      " 14  Heel counter stiffness       443 non-null    str    \n",
+      " 15  Plate                        443 non-null    str    \n",
+      " 16  Rocker                       443 non-null    int64  \n",
+      " 17  Heel lab Heel brand          443 non-null    str    \n",
+      " 18  Forefoot lab Forefoot brand  443 non-null    str    \n",
+      " 19  Orthotic friendly            443 non-null    int64  \n",
+      " 20  Season                       443 non-null    str    \n",
+      " 21  Removable insole             443 non-null    int64  \n",
+      " 22  pace_daily_running           443 non-null    int64  \n",
+      " 23  pace_tempo                   443 non-null    int64  \n",
+      " 24  pace_competition             443 non-null    int64  \n",
+      " 25  arch_neutral                 443 non-null    int64  \n",
+      " 26  arch_stability               443 non-null    int64  \n",
+      " 27  weight_lab_oz                443 non-null    float64\n",
+      " 28  weight_lab_g                 443 non-null    int64  \n",
+      " 29  weight_brand_oz              436 non-null    float64\n",
+      " 30  weight_brand_g               436 non-null    float64\n",
+      "dtypes: float64(3), int64(10), str(18)\n",
+      "memory usage: 107.4 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"Weight lab Weight brand\",], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aef1ce17",
+   "metadata": {},
+   "source": [
+    "# Drop lab Drop brand"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "id": "eb47f25d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(0)"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mask_missing = (\n",
+    "    df[\"Drop lab Drop brand\"].isna() |\n",
+    "    (df[\"Drop lab Drop brand\"].astype(str).str.strip() == \"-\")\n",
+    ")\n",
+    "mask_missing.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "id": "7349620c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Drop lab Drop brand  drop_lab_mm  drop_brand_mm\n",
+      "0      9.4 mm 10.0 mm          9.4           10.0\n",
+      "1       7.7 mm 8.0 mm          7.7            8.0\n",
+      "2      8.9 mm 10.0 mm          8.9           10.0\n",
+      "3     10.6 mm 11.0 mm         10.6           11.0\n",
+      "4      9.9 mm 10.0 mm          9.9           10.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "drop = df[\"Drop lab Drop brand\"].str.findall(r\"[\\d.]+\")\n",
+    "\n",
+    "df[[\"drop_lab_mm\", \"drop_brand_mm\"]] = (\n",
+    "    pd.DataFrame(drop.tolist(), index=df.index)\n",
+    ")\n",
+    "\n",
+    "for col in [\"drop_lab_mm\", \"drop_brand_mm\"]:\n",
+    "    df[col] = pd.to_numeric(df[col], errors=\"coerce\")\n",
+    "\n",
+    "print(df[[\"Drop lab Drop brand\", \"drop_lab_mm\", \"drop_brand_mm\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "id": "15d7d64b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 443 entries, 0 to 442\n",
+      "Data columns (total 32 columns):\n",
+      " #   Column                       Non-Null Count  Dtype  \n",
+      "---  ------                       --------------  -----  \n",
+      " 0   Brand                        443 non-null    str    \n",
+      " 1   Name                         443 non-null    str    \n",
+      " 2   Lightweight                  443 non-null    int64  \n",
+      " 3   Strike pattern               443 non-null    str    \n",
+      " 4   Midsole softness             443 non-null    str    \n",
+      " 5   Toebox durability            443 non-null    str    \n",
+      " 6   Heel padding durability      443 non-null    str    \n",
+      " 7   Outsole durability           443 non-null    str    \n",
+      " 8   Breathability                443 non-null    str    \n",
+      " 9   Width / fit                  443 non-null    str    \n",
+      " 10  Toebox width                 443 non-null    str    \n",
+      " 11  Stiffness                    443 non-null    str    \n",
+      " 12  Torsional rigidity           443 non-null    str    \n",
+      " 13  Heel counter stiffness       443 non-null    str    \n",
+      " 14  Plate                        443 non-null    str    \n",
+      " 15  Rocker                       443 non-null    int64  \n",
+      " 16  Heel lab Heel brand          443 non-null    str    \n",
+      " 17  Forefoot lab Forefoot brand  443 non-null    str    \n",
+      " 18  Orthotic friendly            443 non-null    int64  \n",
+      " 19  Season                       443 non-null    str    \n",
+      " 20  Removable insole             443 non-null    int64  \n",
+      " 21  pace_daily_running           443 non-null    int64  \n",
+      " 22  pace_tempo                   443 non-null    int64  \n",
+      " 23  pace_competition             443 non-null    int64  \n",
+      " 24  arch_neutral                 443 non-null    int64  \n",
+      " 25  arch_stability               443 non-null    int64  \n",
+      " 26  weight_lab_oz                443 non-null    float64\n",
+      " 27  weight_lab_g                 443 non-null    int64  \n",
+      " 28  weight_brand_oz              436 non-null    float64\n",
+      " 29  weight_brand_g               436 non-null    float64\n",
+      " 30  drop_lab_mm                  443 non-null    float64\n",
+      " 31  drop_brand_mm                427 non-null    float64\n",
+      "dtypes: float64(5), int64(10), str(17)\n",
+      "memory usage: 110.9 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"Drop lab Drop brand\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "27e914f9",
+   "metadata": {},
+   "source": [
+    "# Strike pattern"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "id": "9c7f0a53",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Strike pattern\n",
+      "HeelMid/forefoot     170\n",
+      "Mid/forefoot         145\n",
+      "Heel                 126\n",
+      "-                      1\n",
+      "Heel Mid/forefoot      1\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Strike pattern\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "id": "0cbe3f37",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 443\n",
+      "\n",
+      "Unique Values in original column:\n",
+      "<StringArray>\n",
+      "['heelmid/forefoot', 'mid/forefoot', 'heel', '-', 'heel mid/forefoot']\n",
+      "Length: 5, dtype: str\n",
+      "\n",
+      "Sample Comparison (Multi-label Mapping):\n",
+      "     Strike pattern  strike_heel  strike_mid  strike_forefoot\n",
+      "0  heelmid/forefoot            1           1                1\n",
+      "1      mid/forefoot            0           1                1\n",
+      "2  heelmid/forefoot            1           1                1\n",
+      "3              heel            1           0                0\n",
+      "4  heelmid/forefoot            1           1                1\n",
+      "5              heel            1           0                0\n",
+      "6  heelmid/forefoot            1           1                1\n",
+      "7  heelmid/forefoot            1           1                1\n",
+      "8  heelmid/forefoot            1           1                1\n",
+      "9              heel            1           0                0\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['Strike pattern'] = df['Strike pattern'].astype(str).str.lower()\n",
+    "base_strikes = ['heel', 'mid', 'forefoot']\n",
+    "\n",
+    "for strike in base_strikes:\n",
+    "    column_name = f\"strike_{strike}\"\n",
+    "   \n",
+    "    df[column_name] = df['Strike pattern'].str.contains(strike, na=False).astype(int)\n",
+    "\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "print(\"\\nUnique Values in original column:\")\n",
+    "print(df[\"Strike pattern\"].unique())\n",
+    "\n",
+    "print(\"\\nSample Comparison (Multi-label Mapping):\")\n",
+    "strike_cols = [f\"strike_{s}\" for s in base_strikes]\n",
+    "print(df[[\"Strike pattern\"] + strike_cols].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "id": "4d1f9abc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Strike pattern\n",
+      "heelmid/forefoot     170\n",
+      "mid/forefoot         145\n",
+      "heel                 126\n",
+      "-                      1\n",
+      "heel mid/forefoot      1\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "strike_heel sum: 297\n",
+      "strike_mid sum: 316\n",
+      "strike_forefoot sum: 316\n",
+      "\n",
+      "     Strike pattern  strike_heel  strike_mid  strike_forefoot\n",
+      "0  heelmid/forefoot            1           1                1\n",
+      "1      mid/forefoot            0           1                1\n",
+      "2  heelmid/forefoot            1           1                1\n",
+      "3              heel            1           0                0\n",
+      "4  heelmid/forefoot            1           1                1\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Strike pattern\"].value_counts())\n",
+    "\n",
+    "print()\n",
+    "for strike in base_strikes:\n",
+    "    col = f\"strike_{strike}\"\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"Strike pattern\"] + strike_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "id": "23d3f758",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 443 entries, 0 to 442\n",
+      "Data columns (total 34 columns):\n",
+      " #   Column                       Non-Null Count  Dtype  \n",
+      "---  ------                       --------------  -----  \n",
+      " 0   Brand                        443 non-null    str    \n",
+      " 1   Name                         443 non-null    str    \n",
+      " 2   Lightweight                  443 non-null    int64  \n",
+      " 3   Midsole softness             443 non-null    str    \n",
+      " 4   Toebox durability            443 non-null    str    \n",
+      " 5   Heel padding durability      443 non-null    str    \n",
+      " 6   Outsole durability           443 non-null    str    \n",
+      " 7   Breathability                443 non-null    str    \n",
+      " 8   Width / fit                  443 non-null    str    \n",
+      " 9   Toebox width                 443 non-null    str    \n",
+      " 10  Stiffness                    443 non-null    str    \n",
+      " 11  Torsional rigidity           443 non-null    str    \n",
+      " 12  Heel counter stiffness       443 non-null    str    \n",
+      " 13  Plate                        443 non-null    str    \n",
+      " 14  Rocker                       443 non-null    int64  \n",
+      " 15  Heel lab Heel brand          443 non-null    str    \n",
+      " 16  Forefoot lab Forefoot brand  443 non-null    str    \n",
+      " 17  Orthotic friendly            443 non-null    int64  \n",
+      " 18  Season                       443 non-null    str    \n",
+      " 19  Removable insole             443 non-null    int64  \n",
+      " 20  pace_daily_running           443 non-null    int64  \n",
+      " 21  pace_tempo                   443 non-null    int64  \n",
+      " 22  pace_competition             443 non-null    int64  \n",
+      " 23  arch_neutral                 443 non-null    int64  \n",
+      " 24  arch_stability               443 non-null    int64  \n",
+      " 25  weight_lab_oz                443 non-null    float64\n",
+      " 26  weight_lab_g                 443 non-null    int64  \n",
+      " 27  weight_brand_oz              436 non-null    float64\n",
+      " 28  weight_brand_g               436 non-null    float64\n",
+      " 29  drop_lab_mm                  443 non-null    float64\n",
+      " 30  drop_brand_mm                427 non-null    float64\n",
+      " 31  strike_heel                  443 non-null    int64  \n",
+      " 32  strike_mid                   443 non-null    int64  \n",
+      " 33  strike_forefoot              443 non-null    int64  \n",
+      "dtypes: float64(5), int64(13), str(16)\n",
+      "memory usage: 117.8 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=['Strike pattern'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ecfcdc8",
+   "metadata": {},
+   "source": [
+    "# Midsole softness"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "id": "4d7135ac",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Midsole softness\n",
+      "Soft        186\n",
+      "Balanced    178\n",
+      "-            61\n",
+      "Firm         18\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Midsole softness\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "id": "ab6386cd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 443\n",
+      "Baris dengan semua OHE 0 (termasuk '-'): 61\n",
+      "\n",
+      "Sample Comparison:\n",
+      "  Midsole softness  softness_soft  softness_balanced  softness_firm\n",
+      "0         balanced              0                  1              0\n",
+      "1             soft              1                  0              0\n",
+      "2             firm              0                  0              1\n",
+      "3             firm              0                  0              1\n",
+      "4             firm              0                  0              1\n",
+      "5             firm              0                  0              1\n",
+      "6         balanced              0                  1              0\n",
+      "7         balanced              0                  1              0\n",
+      "8         balanced              0                  1              0\n",
+      "9             soft              1                  0              0\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['Midsole softness'] = df['Midsole softness'].astype(str).str.lower()\n",
+    "base_softness = ['soft', 'balanced', 'firm']\n",
+    "\n",
+    "\n",
+    "for level in base_softness:\n",
+    "    column_name = f\"softness_{level}\"\n",
+    "    df[column_name] = df['Midsole softness'].str.contains(level, na=False).astype(int)\n",
+    "\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "softness_cols = [f\"softness_{l}\" for l in base_softness]\n",
+    "zero_vector_count = (df[softness_cols].sum(axis=1) == 0).sum()\n",
+    "print(f\"Baris dengan semua OHE 0 (termasuk '-'): {zero_vector_count}\")\n",
+    "\n",
+    "print(\"\\nSample Comparison:\")\n",
+    "print(df[[\"Midsole softness\"] + softness_cols].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "id": "ff9581a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Midsole softness\n",
+      "soft        186\n",
+      "balanced    178\n",
+      "-            61\n",
+      "firm         18\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "softness_soft sum: 186\n",
+      "softness_balanced sum: 178\n",
+      "softness_firm sum: 18\n",
+      "\n",
+      "  Midsole softness  softness_soft  softness_balanced  softness_firm\n",
+      "0         balanced              0                  1              0\n",
+      "1             soft              1                  0              0\n",
+      "2             firm              0                  0              1\n",
+      "3             firm              0                  0              1\n",
+      "4             firm              0                  0              1\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Midsole softness\"].value_counts())\n",
+    "\n",
+    "print()\n",
+    "for level in base_softness:\n",
+    "    col = f\"softness_{level}\"\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"Midsole softness\"] + softness_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "id": "533e3bd4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 443 entries, 0 to 442\n",
+      "Data columns (total 36 columns):\n",
+      " #   Column                       Non-Null Count  Dtype  \n",
+      "---  ------                       --------------  -----  \n",
+      " 0   Brand                        443 non-null    str    \n",
+      " 1   Name                         443 non-null    str    \n",
+      " 2   Lightweight                  443 non-null    int64  \n",
+      " 3   Toebox durability            443 non-null    str    \n",
+      " 4   Heel padding durability      443 non-null    str    \n",
+      " 5   Outsole durability           443 non-null    str    \n",
+      " 6   Breathability                443 non-null    str    \n",
+      " 7   Width / fit                  443 non-null    str    \n",
+      " 8   Toebox width                 443 non-null    str    \n",
+      " 9   Stiffness                    443 non-null    str    \n",
+      " 10  Torsional rigidity           443 non-null    str    \n",
+      " 11  Heel counter stiffness       443 non-null    str    \n",
+      " 12  Plate                        443 non-null    str    \n",
+      " 13  Rocker                       443 non-null    int64  \n",
+      " 14  Heel lab Heel brand          443 non-null    str    \n",
+      " 15  Forefoot lab Forefoot brand  443 non-null    str    \n",
+      " 16  Orthotic friendly            443 non-null    int64  \n",
+      " 17  Season                       443 non-null    str    \n",
+      " 18  Removable insole             443 non-null    int64  \n",
+      " 19  pace_daily_running           443 non-null    int64  \n",
+      " 20  pace_tempo                   443 non-null    int64  \n",
+      " 21  pace_competition             443 non-null    int64  \n",
+      " 22  arch_neutral                 443 non-null    int64  \n",
+      " 23  arch_stability               443 non-null    int64  \n",
+      " 24  weight_lab_oz                443 non-null    float64\n",
+      " 25  weight_lab_g                 443 non-null    int64  \n",
+      " 26  weight_brand_oz              436 non-null    float64\n",
+      " 27  weight_brand_g               436 non-null    float64\n",
+      " 28  drop_lab_mm                  443 non-null    float64\n",
+      " 29  drop_brand_mm                427 non-null    float64\n",
+      " 30  strike_heel                  443 non-null    int64  \n",
+      " 31  strike_mid                   443 non-null    int64  \n",
+      " 32  strike_forefoot              443 non-null    int64  \n",
+      " 33  softness_soft                443 non-null    int64  \n",
+      " 34  softness_balanced            443 non-null    int64  \n",
+      " 35  softness_firm                443 non-null    int64  \n",
+      "dtypes: float64(5), int64(16), str(15)\n",
+      "memory usage: 124.7 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"Midsole softness\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c9c8eb5",
+   "metadata": {},
+   "source": [
+    "# Toebox durability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "id": "6ae9914b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Toebox durability\n",
+      "Decent    160\n",
+      "-         117\n",
+      "Bad        92\n",
+      "Good       74\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['Toebox durability'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "id": "33d497cf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 443\n",
+      "\n",
+      "Unique Values mapping check:\n",
+      "'-' di-encode menjadi 0 (Total: 117)\n",
+      "'very bad' di-encode menjadi 1 (Total: 0)\n",
+      "'bad' di-encode menjadi 2 (Total: 92)\n",
+      "'decent' di-encode menjadi 3 (Total: 160)\n",
+      "'good' di-encode menjadi 4 (Total: 74)\n",
+      "'very good' di-encode menjadi 5 (Total: 0)\n",
+      "\n",
+      "Sample Data:\n",
+      "  Toebox durability  toebox_durability\n",
+      "0                 -                  0\n",
+      "1              good                  4\n",
+      "2                 -                  0\n",
+      "3                 -                  0\n",
+      "4              good                  4\n",
+      "5            decent                  3\n",
+      "6               bad                  2\n",
+      "7            decent                  3\n",
+      "8               bad                  2\n",
+      "9               bad                  2\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['Toebox durability'] = df['Toebox durability'].astype(str).str.lower()\n",
+    "\n",
+    "durability_map = {\n",
+    "    \"-\": 0,\n",
+    "    \"very bad\": 1,\n",
+    "    \"bad\": 2,\n",
+    "    \"decent\": 3,\n",
+    "    \"good\": 4,\n",
+    "    \"very good\": 5\n",
+    "}\n",
+    "\n",
+    "df['toebox_durability'] = df['Toebox durability'].map(durability_map)\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "print(\"\\nUnique Values mapping check:\")\n",
+    "for label, value in durability_map.items():\n",
+    "    count = (df['Toebox durability'] == label).sum()\n",
+    "    print(f\"'{label}' di-encode menjadi {value} (Total: {count})\")\n",
+    "\n",
+    "print(\"\\nSample Data:\")\n",
+    "print(df[[\"Toebox durability\", \"toebox_durability\"]].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "id": "5f3035fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 443 entries, 0 to 442\n",
+      "Data columns (total 36 columns):\n",
+      " #   Column                       Non-Null Count  Dtype  \n",
+      "---  ------                       --------------  -----  \n",
+      " 0   Brand                        443 non-null    str    \n",
+      " 1   Name                         443 non-null    str    \n",
+      " 2   Lightweight                  443 non-null    int64  \n",
+      " 3   Heel padding durability      443 non-null    str    \n",
+      " 4   Outsole durability           443 non-null    str    \n",
+      " 5   Breathability                443 non-null    str    \n",
+      " 6   Width / fit                  443 non-null    str    \n",
+      " 7   Toebox width                 443 non-null    str    \n",
+      " 8   Stiffness                    443 non-null    str    \n",
+      " 9   Torsional rigidity           443 non-null    str    \n",
+      " 10  Heel counter stiffness       443 non-null    str    \n",
+      " 11  Plate                        443 non-null    str    \n",
+      " 12  Rocker                       443 non-null    int64  \n",
+      " 13  Heel lab Heel brand          443 non-null    str    \n",
+      " 14  Forefoot lab Forefoot brand  443 non-null    str    \n",
+      " 15  Orthotic friendly            443 non-null    int64  \n",
+      " 16  Season                       443 non-null    str    \n",
+      " 17  Removable insole             443 non-null    int64  \n",
+      " 18  pace_daily_running           443 non-null    int64  \n",
+      " 19  pace_tempo                   443 non-null    int64  \n",
+      " 20  pace_competition             443 non-null    int64  \n",
+      " 21  arch_neutral                 443 non-null    int64  \n",
+      " 22  arch_stability               443 non-null    int64  \n",
+      " 23  weight_lab_oz                443 non-null    float64\n",
+      " 24  weight_lab_g                 443 non-null    int64  \n",
+      " 25  weight_brand_oz              436 non-null    float64\n",
+      " 26  weight_brand_g               436 non-null    float64\n",
+      " 27  drop_lab_mm                  443 non-null    float64\n",
+      " 28  drop_brand_mm                427 non-null    float64\n",
+      " 29  strike_heel                  443 non-null    int64  \n",
+      " 30  strike_mid                   443 non-null    int64  \n",
+      " 31  strike_forefoot              443 non-null    int64  \n",
+      " 32  softness_soft                443 non-null    int64  \n",
+      " 33  softness_balanced            443 non-null    int64  \n",
+      " 34  softness_firm                443 non-null    int64  \n",
+      " 35  toebox_durability            443 non-null    int64  \n",
+      "dtypes: float64(5), int64(17), str(14)\n",
+      "memory usage: 124.7 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=['Toebox durability'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee9f0f7f",
+   "metadata": {},
+   "source": [
+    "# Heel padding durability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "id": "d72aec13",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Heel padding durability\n",
+      "Good      187\n",
+      "-         122\n",
+      "Decent     79\n",
+      "Bad        55\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Heel padding durability\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "id": "85e10770",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Value Counts Kolom Asli ---\n",
+      "Heel padding durability\n",
+      "good      187\n",
+      "-         122\n",
+      "decent     79\n",
+      "bad        55\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "--- Sebaran Nilai Hasil Ordinal (Scale 1-5) ---\n",
+      "Index 0: 122 baris\n",
+      "Index 1: 0 baris\n",
+      "Index 2: 55 baris\n",
+      "Index 3: 79 baris\n",
+      "Index 4: 187 baris\n",
+      "Index 5: 0 baris\n",
+      "\n",
+      "--- Perbandingan Data (Head) ---\n",
+      "  Heel padding durability  heel_durability\n",
+      "0                       -                0\n",
+      "1                    good                4\n",
+      "2                    good                4\n",
+      "3                       -                0\n",
+      "4                    good                4\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['Heel padding durability'] = df['Heel padding durability'].astype(str).str.lower()\n",
+    "\n",
+    "durability_scale_5 = {\n",
+    "    \"-\": 0,\n",
+    "    \"very bad\": 1,\n",
+    "    \"bad\": 2,\n",
+    "    \"decent\": 3,\n",
+    "    \"good\": 4,\n",
+    "    \"very good\": 5\n",
+    "}\n",
+    "\n",
+    "\n",
+    "df['heel_durability'] = df['Heel padding durability'].map(durability_scale_5)\n",
+    "\n",
+    "\n",
+    "print(\"--- Value Counts Kolom Asli ---\")\n",
+    "print(df[\"Heel padding durability\"].value_counts())\n",
+    "\n",
+    "print(\"\\n--- Sebaran Nilai Hasil Ordinal (Scale 1-5) ---\")\n",
+    "counts = df[\"heel_durability\"].value_counts().sort_index()\n",
+    "for i in range(6):\n",
+    "    print(f\"Index {i}: {counts.get(i, 0)} baris\")\n",
+    "\n",
+    "print(\"\\n--- Perbandingan Data (Head) ---\")\n",
+    "print(df[[\"Heel padding durability\", \"heel_durability\"]].head())\n",
+    "\n",
+    "# rename \"heel_durability_index\" to \"heel_durability\"\n",
+    "# df.rename(columns={'heel_durability_index': 'heel_durability'}, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "id": "37aa1023",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 443 entries, 0 to 442\n",
+      "Data columns (total 36 columns):\n",
+      " #   Column                       Non-Null Count  Dtype  \n",
+      "---  ------                       --------------  -----  \n",
+      " 0   Brand                        443 non-null    str    \n",
+      " 1   Name                         443 non-null    str    \n",
+      " 2   Lightweight                  443 non-null    int64  \n",
+      " 3   Outsole durability           443 non-null    str    \n",
+      " 4   Breathability                443 non-null    str    \n",
+      " 5   Width / fit                  443 non-null    str    \n",
+      " 6   Toebox width                 443 non-null    str    \n",
+      " 7   Stiffness                    443 non-null    str    \n",
+      " 8   Torsional rigidity           443 non-null    str    \n",
+      " 9   Heel counter stiffness       443 non-null    str    \n",
+      " 10  Plate                        443 non-null    str    \n",
+      " 11  Rocker                       443 non-null    int64  \n",
+      " 12  Heel lab Heel brand          443 non-null    str    \n",
+      " 13  Forefoot lab Forefoot brand  443 non-null    str    \n",
+      " 14  Orthotic friendly            443 non-null    int64  \n",
+      " 15  Season                       443 non-null    str    \n",
+      " 16  Removable insole             443 non-null    int64  \n",
+      " 17  pace_daily_running           443 non-null    int64  \n",
+      " 18  pace_tempo                   443 non-null    int64  \n",
+      " 19  pace_competition             443 non-null    int64  \n",
+      " 20  arch_neutral                 443 non-null    int64  \n",
+      " 21  arch_stability               443 non-null    int64  \n",
+      " 22  weight_lab_oz                443 non-null    float64\n",
+      " 23  weight_lab_g                 443 non-null    int64  \n",
+      " 24  weight_brand_oz              436 non-null    float64\n",
+      " 25  weight_brand_g               436 non-null    float64\n",
+      " 26  drop_lab_mm                  443 non-null    float64\n",
+      " 27  drop_brand_mm                427 non-null    float64\n",
+      " 28  strike_heel                  443 non-null    int64  \n",
+      " 29  strike_mid                   443 non-null    int64  \n",
+      " 30  strike_forefoot              443 non-null    int64  \n",
+      " 31  softness_soft                443 non-null    int64  \n",
+      " 32  softness_balanced            443 non-null    int64  \n",
+      " 33  softness_firm                443 non-null    int64  \n",
+      " 34  toebox_durability            443 non-null    int64  \n",
+      " 35  heel_durability              443 non-null    int64  \n",
+      "dtypes: float64(5), int64(18), str(13)\n",
+      "memory usage: 124.7 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop('Heel padding durability', axis=1, inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "31816ac8",
+   "metadata": {},
+   "source": [
+    "# Outsole durability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "id": "d5f018a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Outsole durability\n",
+      "Good      216\n",
+      "-         134\n",
+      "Decent     71\n",
+      "Bad        22\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Outsole durability\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "id": "fc4e697f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Value Counts Kolom Asli ---\n",
+      "Outsole durability\n",
+      "good      216\n",
+      "-         134\n",
+      "decent     71\n",
+      "bad        22\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "--- Sebaran Nilai Hasil Ordinal (Scale 1-5) ---\n",
+      "Index 0: 134 baris\n",
+      "Index 1: 0 baris\n",
+      "Index 2: 22 baris\n",
+      "Index 3: 71 baris\n",
+      "Index 4: 216 baris\n",
+      "Index 5: 0 baris\n",
+      "\n",
+      "--- Perbandingan Data (Head) ---\n",
+      "  Outsole durability  outsole_durability\n",
+      "0                  -                   0\n",
+      "1               good                   4\n",
+      "2                  -                   0\n",
+      "3                  -                   0\n",
+      "4               good                   4\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['Outsole durability'] = df['Outsole durability'].astype(str).str.lower()\n",
+    "\n",
+    "durability_scale_5 = {\n",
+    "    \"-\": 0,\n",
+    "    \"very bad\": 1,\n",
+    "    \"bad\": 2,\n",
+    "    \"decent\": 3,\n",
+    "    \"good\": 4,\n",
+    "    \"very good\": 5\n",
+    "}\n",
+    "\n",
+    "\n",
+    "df['outsole_durability'] = df['Outsole durability'].map(durability_scale_5)\n",
+    "print(\"--- Value Counts Kolom Asli ---\")\n",
+    "print(df[\"Outsole durability\"].value_counts())\n",
+    "\n",
+    "print(\"\\n--- Sebaran Nilai Hasil Ordinal (Scale 1-5) ---\")\n",
+    "counts = df[\"outsole_durability\"].value_counts().sort_index()\n",
+    "for i in range(6):\n",
+    "    print(f\"Index {i}: {counts.get(i, 0)} baris\")\n",
+    "\n",
+    "print(\"\\n--- Perbandingan Data (Head) ---\")\n",
+    "print(df[[\"Outsole durability\", \"outsole_durability\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "id": "8689107f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 443 entries, 0 to 442\n",
+      "Data columns (total 36 columns):\n",
+      " #   Column                       Non-Null Count  Dtype  \n",
+      "---  ------                       --------------  -----  \n",
+      " 0   Brand                        443 non-null    str    \n",
+      " 1   Name                         443 non-null    str    \n",
+      " 2   Lightweight                  443 non-null    int64  \n",
+      " 3   Breathability                443 non-null    str    \n",
+      " 4   Width / fit                  443 non-null    str    \n",
+      " 5   Toebox width                 443 non-null    str    \n",
+      " 6   Stiffness                    443 non-null    str    \n",
+      " 7   Torsional rigidity           443 non-null    str    \n",
+      " 8   Heel counter stiffness       443 non-null    str    \n",
+      " 9   Plate                        443 non-null    str    \n",
+      " 10  Rocker                       443 non-null    int64  \n",
+      " 11  Heel lab Heel brand          443 non-null    str    \n",
+      " 12  Forefoot lab Forefoot brand  443 non-null    str    \n",
+      " 13  Orthotic friendly            443 non-null    int64  \n",
+      " 14  Season                       443 non-null    str    \n",
+      " 15  Removable insole             443 non-null    int64  \n",
+      " 16  pace_daily_running           443 non-null    int64  \n",
+      " 17  pace_tempo                   443 non-null    int64  \n",
+      " 18  pace_competition             443 non-null    int64  \n",
+      " 19  arch_neutral                 443 non-null    int64  \n",
+      " 20  arch_stability               443 non-null    int64  \n",
+      " 21  weight_lab_oz                443 non-null    float64\n",
+      " 22  weight_lab_g                 443 non-null    int64  \n",
+      " 23  weight_brand_oz              436 non-null    float64\n",
+      " 24  weight_brand_g               436 non-null    float64\n",
+      " 25  drop_lab_mm                  443 non-null    float64\n",
+      " 26  drop_brand_mm                427 non-null    float64\n",
+      " 27  strike_heel                  443 non-null    int64  \n",
+      " 28  strike_mid                   443 non-null    int64  \n",
+      " 29  strike_forefoot              443 non-null    int64  \n",
+      " 30  softness_soft                443 non-null    int64  \n",
+      " 31  softness_balanced            443 non-null    int64  \n",
+      " 32  softness_firm                443 non-null    int64  \n",
+      " 33  toebox_durability            443 non-null    int64  \n",
+      " 34  heel_durability              443 non-null    int64  \n",
+      " 35  outsole_durability           443 non-null    int64  \n",
+      "dtypes: float64(5), int64(19), str(12)\n",
+      "memory usage: 124.7 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop('Outsole durability', axis=1, inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eff75f94",
+   "metadata": {},
+   "source": [
+    "# Breathability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "id": "8a3a5fcf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Breathability\n",
+      "Moderate      219\n",
+      "Breathable    113\n",
+      "-              58\n",
+      "Warm           53\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Breathability\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "id": "824952c8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Value Counts Kolom Asli ---\n",
+      "Breathability\n",
+      "moderate      219\n",
+      "breathable    113\n",
+      "-              58\n",
+      "warm           53\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "--- Sebaran Nilai Hasil Ordinal (Scale 1-5) ---\n",
+      "Index 0: 58 baris\n",
+      "Index 1: 0 baris\n",
+      "Index 2: 53 baris\n",
+      "Index 3: 219 baris\n",
+      "Index 4: 0 baris\n",
+      "Index 5: 113 baris\n",
+      "\n",
+      "--- Perbandingan Data (Head) ---\n",
+      "  Breathability  breathability\n",
+      "0             -              0\n",
+      "1    breathable              5\n",
+      "2          warm              2\n",
+      "3          warm              2\n",
+      "4          warm              2\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['Breathability'] = df['Breathability'].astype(str).str.lower()\n",
+    "\n",
+    "breathability_scale_5 = {\n",
+    "    \"-\": 0,\n",
+    "    \"suffocating\": 1,\n",
+    "    \"warm\": 2,\n",
+    "    \"moderate\": 3,\n",
+    "    \"good\": 4,\n",
+    "    \"breathable\": 5\n",
+    "}\n",
+    "\n",
+    "df['breathability'] = df['Breathability'].map(breathability_scale_5)\n",
+    "\n",
+    "print(\"--- Value Counts Kolom Asli ---\")\n",
+    "print(df[\"Breathability\"].value_counts())\n",
+    "\n",
+    "print(\"\\n--- Sebaran Nilai Hasil Ordinal (Scale 1-5) ---\")\n",
+    "counts = df[\"breathability\"].value_counts().sort_index()\n",
+    "for i in range(6):\n",
+    "    print(f\"Index {i}: {counts.get(i, 0)} baris\")\n",
+    "\n",
+    "print(\"\\n--- Perbandingan Data (Head) ---\")\n",
+    "print(df[[\"Breathability\", \"breathability\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "id": "6cafeb1e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 443 entries, 0 to 442\n",
+      "Data columns (total 36 columns):\n",
+      " #   Column                       Non-Null Count  Dtype  \n",
+      "---  ------                       --------------  -----  \n",
+      " 0   Brand                        443 non-null    str    \n",
+      " 1   Name                         443 non-null    str    \n",
+      " 2   Lightweight                  443 non-null    int64  \n",
+      " 3   Width / fit                  443 non-null    str    \n",
+      " 4   Toebox width                 443 non-null    str    \n",
+      " 5   Stiffness                    443 non-null    str    \n",
+      " 6   Torsional rigidity           443 non-null    str    \n",
+      " 7   Heel counter stiffness       443 non-null    str    \n",
+      " 8   Plate                        443 non-null    str    \n",
+      " 9   Rocker                       443 non-null    int64  \n",
+      " 10  Heel lab Heel brand          443 non-null    str    \n",
+      " 11  Forefoot lab Forefoot brand  443 non-null    str    \n",
+      " 12  Orthotic friendly            443 non-null    int64  \n",
+      " 13  Season                       443 non-null    str    \n",
+      " 14  Removable insole             443 non-null    int64  \n",
+      " 15  pace_daily_running           443 non-null    int64  \n",
+      " 16  pace_tempo                   443 non-null    int64  \n",
+      " 17  pace_competition             443 non-null    int64  \n",
+      " 18  arch_neutral                 443 non-null    int64  \n",
+      " 19  arch_stability               443 non-null    int64  \n",
+      " 20  weight_lab_oz                443 non-null    float64\n",
+      " 21  weight_lab_g                 443 non-null    int64  \n",
+      " 22  weight_brand_oz              436 non-null    float64\n",
+      " 23  weight_brand_g               436 non-null    float64\n",
+      " 24  drop_lab_mm                  443 non-null    float64\n",
+      " 25  drop_brand_mm                427 non-null    float64\n",
+      " 26  strike_heel                  443 non-null    int64  \n",
+      " 27  strike_mid                   443 non-null    int64  \n",
+      " 28  strike_forefoot              443 non-null    int64  \n",
+      " 29  softness_soft                443 non-null    int64  \n",
+      " 30  softness_balanced            443 non-null    int64  \n",
+      " 31  softness_firm                443 non-null    int64  \n",
+      " 32  toebox_durability            443 non-null    int64  \n",
+      " 33  heel_durability              443 non-null    int64  \n",
+      " 34  outsole_durability           443 non-null    int64  \n",
+      " 35  breathability                443 non-null    int64  \n",
+      "dtypes: float64(5), int64(20), str(11)\n",
+      "memory usage: 124.7 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop('Breathability', axis=1, inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f014a368",
+   "metadata": {},
+   "source": [
+    "# Width / fit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "id": "d5aa68e8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Width / fit\n",
+      "Medium    268\n",
+      "Narrow    142\n",
+      "Wide       33\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['Width / fit'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "id": "f37f93b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 443\n",
+      "NULL Value: 0\n",
+      "\n",
+      "Sample Comparison:\n",
+      "  Width / fit  width_narrow  width_medium  width_wide\n",
+      "0      narrow             1             0           0\n",
+      "1      narrow             1             0           0\n",
+      "2      narrow             1             0           0\n",
+      "3      narrow             1             0           0\n",
+      "4      narrow             1             0           0\n",
+      "5      medium             0             1           0\n",
+      "6        wide             0             0           1\n",
+      "7      narrow             1             0           0\n",
+      "8      narrow             1             0           0\n",
+      "9      medium             0             1           0\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['Width / fit'] = df['Width / fit'].astype(str).str.lower()\n",
+    "base_widths = ['narrow', 'medium', 'wide']\n",
+    "\n",
+    "for level in base_widths:\n",
+    "    column_name = f\"width_{level}\"\n",
+    "    df[column_name] = df['Width / fit'].str.contains(level, na=False).astype(int)\n",
+    "\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "width_cols = [f\"width_{l}\" for l in base_widths]\n",
+    "zero_vector_count = (df[width_cols].sum(axis=1) == 0).sum()\n",
+    "print(f\"NULL Value: {zero_vector_count}\")\n",
+    "\n",
+    "print(\"\\nSample Comparison:\")\n",
+    "print(df[[\"Width / fit\"] + width_cols].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "id": "3a7e364b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Width / fit\n",
+      "medium    268\n",
+      "narrow    142\n",
+      "wide       33\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "width_narrow sum: 142\n",
+      "width_medium sum: 268\n",
+      "width_wide sum: 33\n",
+      "\n",
+      "  Width / fit  width_narrow  width_medium  width_wide\n",
+      "0      narrow             1             0           0\n",
+      "1      narrow             1             0           0\n",
+      "2      narrow             1             0           0\n",
+      "3      narrow             1             0           0\n",
+      "4      narrow             1             0           0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Width / fit\"].value_counts())\n",
+    "\n",
+    "print()\n",
+    "for level in base_widths:\n",
+    "    col = f\"width_{level}\"\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"Width / fit\"] + width_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "id": "71ab506a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 443 entries, 0 to 442\n",
+      "Data columns (total 38 columns):\n",
+      " #   Column                       Non-Null Count  Dtype  \n",
+      "---  ------                       --------------  -----  \n",
+      " 0   Brand                        443 non-null    str    \n",
+      " 1   Name                         443 non-null    str    \n",
+      " 2   Lightweight                  443 non-null    int64  \n",
+      " 3   Toebox width                 443 non-null    str    \n",
+      " 4   Stiffness                    443 non-null    str    \n",
+      " 5   Torsional rigidity           443 non-null    str    \n",
+      " 6   Heel counter stiffness       443 non-null    str    \n",
+      " 7   Plate                        443 non-null    str    \n",
+      " 8   Rocker                       443 non-null    int64  \n",
+      " 9   Heel lab Heel brand          443 non-null    str    \n",
+      " 10  Forefoot lab Forefoot brand  443 non-null    str    \n",
+      " 11  Orthotic friendly            443 non-null    int64  \n",
+      " 12  Season                       443 non-null    str    \n",
+      " 13  Removable insole             443 non-null    int64  \n",
+      " 14  pace_daily_running           443 non-null    int64  \n",
+      " 15  pace_tempo                   443 non-null    int64  \n",
+      " 16  pace_competition             443 non-null    int64  \n",
+      " 17  arch_neutral                 443 non-null    int64  \n",
+      " 18  arch_stability               443 non-null    int64  \n",
+      " 19  weight_lab_oz                443 non-null    float64\n",
+      " 20  weight_lab_g                 443 non-null    int64  \n",
+      " 21  weight_brand_oz              436 non-null    float64\n",
+      " 22  weight_brand_g               436 non-null    float64\n",
+      " 23  drop_lab_mm                  443 non-null    float64\n",
+      " 24  drop_brand_mm                427 non-null    float64\n",
+      " 25  strike_heel                  443 non-null    int64  \n",
+      " 26  strike_mid                   443 non-null    int64  \n",
+      " 27  strike_forefoot              443 non-null    int64  \n",
+      " 28  softness_soft                443 non-null    int64  \n",
+      " 29  softness_balanced            443 non-null    int64  \n",
+      " 30  softness_firm                443 non-null    int64  \n",
+      " 31  toebox_durability            443 non-null    int64  \n",
+      " 32  heel_durability              443 non-null    int64  \n",
+      " 33  outsole_durability           443 non-null    int64  \n",
+      " 34  breathability                443 non-null    int64  \n",
+      " 35  width_narrow                 443 non-null    int64  \n",
+      " 36  width_medium                 443 non-null    int64  \n",
+      " 37  width_wide                   443 non-null    int64  \n",
+      "dtypes: float64(5), int64(23), str(10)\n",
+      "memory usage: 131.6 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"Width / fit\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "95a77d7e",
+   "metadata": {},
+   "source": [
+    "# Toebox width"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "id": "d735ec32",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Toebox width\n",
+      "Medium    215\n",
+      "-         108\n",
+      "Wide       63\n",
+      "Narrow     57\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['Toebox width'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "id": "e6261b61",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 443\n",
+      "NULL Value: 108\n",
+      "\n",
+      "Sample Comparison:\n",
+      "  Toebox width  toebox_narrow  toebox_medium  toebox_wide\n",
+      "0            -              0              0            0\n",
+      "1       medium              0              1            0\n",
+      "2            -              0              0            0\n",
+      "3            -              0              0            0\n",
+      "4       medium              0              1            0\n",
+      "5       medium              0              1            0\n",
+      "6       medium              0              1            0\n",
+      "7       medium              0              1            0\n",
+      "8         wide              0              0            1\n",
+      "9       medium              0              1            0\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['Toebox width'] = df['Toebox width'].astype(str).str.lower()\n",
+    "base_toebox = ['narrow', 'medium', 'wide']\n",
+    "\n",
+    "for level in base_toebox:\n",
+    "    column_name = f\"toebox_{level}\"\n",
+    "    df[column_name] = df['Toebox width'].str.contains(level, na=False).astype(int)\n",
+    "\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "toebox_cols = [f\"toebox_{l}\" for l in base_toebox]\n",
+    "zero_vector_count = (df[toebox_cols].sum(axis=1) == 0).sum()\n",
+    "print(f\"NULL Value: {zero_vector_count}\")\n",
+    "\n",
+    "print(\"\\nSample Comparison:\")\n",
+    "print(df[[\"Toebox width\"] + toebox_cols].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "id": "2efd1f7f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Toebox width\n",
+      "medium    215\n",
+      "-         108\n",
+      "wide       63\n",
+      "narrow     57\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "toebox_narrow sum: 57\n",
+      "toebox_medium sum: 215\n",
+      "toebox_wide sum: 63\n",
+      "\n",
+      "  Toebox width  toebox_narrow  toebox_medium  toebox_wide\n",
+      "0            -              0              0            0\n",
+      "1       medium              0              1            0\n",
+      "2            -              0              0            0\n",
+      "3            -              0              0            0\n",
+      "4       medium              0              1            0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Toebox width\"].value_counts())\n",
+    "\n",
+    "print()\n",
+    "for level in base_toebox:\n",
+    "    col = f\"toebox_{level}\"\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"Toebox width\"] + toebox_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "id": "fa22730e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 443 entries, 0 to 442\n",
+      "Data columns (total 40 columns):\n",
+      " #   Column                       Non-Null Count  Dtype  \n",
+      "---  ------                       --------------  -----  \n",
+      " 0   Brand                        443 non-null    str    \n",
+      " 1   Name                         443 non-null    str    \n",
+      " 2   Lightweight                  443 non-null    int64  \n",
+      " 3   Stiffness                    443 non-null    str    \n",
+      " 4   Torsional rigidity           443 non-null    str    \n",
+      " 5   Heel counter stiffness       443 non-null    str    \n",
+      " 6   Plate                        443 non-null    str    \n",
+      " 7   Rocker                       443 non-null    int64  \n",
+      " 8   Heel lab Heel brand          443 non-null    str    \n",
+      " 9   Forefoot lab Forefoot brand  443 non-null    str    \n",
+      " 10  Orthotic friendly            443 non-null    int64  \n",
+      " 11  Season                       443 non-null    str    \n",
+      " 12  Removable insole             443 non-null    int64  \n",
+      " 13  pace_daily_running           443 non-null    int64  \n",
+      " 14  pace_tempo                   443 non-null    int64  \n",
+      " 15  pace_competition             443 non-null    int64  \n",
+      " 16  arch_neutral                 443 non-null    int64  \n",
+      " 17  arch_stability               443 non-null    int64  \n",
+      " 18  weight_lab_oz                443 non-null    float64\n",
+      " 19  weight_lab_g                 443 non-null    int64  \n",
+      " 20  weight_brand_oz              436 non-null    float64\n",
+      " 21  weight_brand_g               436 non-null    float64\n",
+      " 22  drop_lab_mm                  443 non-null    float64\n",
+      " 23  drop_brand_mm                427 non-null    float64\n",
+      " 24  strike_heel                  443 non-null    int64  \n",
+      " 25  strike_mid                   443 non-null    int64  \n",
+      " 26  strike_forefoot              443 non-null    int64  \n",
+      " 27  softness_soft                443 non-null    int64  \n",
+      " 28  softness_balanced            443 non-null    int64  \n",
+      " 29  softness_firm                443 non-null    int64  \n",
+      " 30  toebox_durability            443 non-null    int64  \n",
+      " 31  heel_durability              443 non-null    int64  \n",
+      " 32  outsole_durability           443 non-null    int64  \n",
+      " 33  breathability                443 non-null    int64  \n",
+      " 34  width_narrow                 443 non-null    int64  \n",
+      " 35  width_medium                 443 non-null    int64  \n",
+      " 36  width_wide                   443 non-null    int64  \n",
+      " 37  toebox_narrow                443 non-null    int64  \n",
+      " 38  toebox_medium                443 non-null    int64  \n",
+      " 39  toebox_wide                  443 non-null    int64  \n",
+      "dtypes: float64(5), int64(26), str(9)\n",
+      "memory usage: 138.6 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"Toebox width\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5ed6c9ff",
+   "metadata": {},
+   "source": [
+    "# Stiffness"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "id": "79a1bd79",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Stiffness\n",
+      "Stiff       225\n",
+      "Moderate    167\n",
+      "Flexible     38\n",
+      "-            13\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['Stiffness'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "id": "6bf18813",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 443\n",
+      "NULL Value: 13\n",
+      "\n",
+      "Sample Comparison:\n",
+      "  Stiffness  stiffness_flexible  stiffness_moderate  stiffness_stiff\n",
+      "0     stiff                   0                   0                1\n",
+      "1     stiff                   0                   0                1\n",
+      "2     stiff                   0                   0                1\n",
+      "3     stiff                   0                   0                1\n",
+      "4  moderate                   0                   1                0\n",
+      "5     stiff                   0                   0                1\n",
+      "6  moderate                   0                   1                0\n",
+      "7     stiff                   0                   0                1\n",
+      "8     stiff                   0                   0                1\n",
+      "9  moderate                   0                   1                0\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['Stiffness'] = df['Stiffness'].astype(str).str.lower()\n",
+    "base_stiffness = ['flexible', 'moderate', 'stiff']\n",
+    "\n",
+    "for level in base_stiffness:\n",
+    "    column_name = f\"stiffness_{level}\"\n",
+    "    df[column_name] = df['Stiffness'].str.contains(level, na=False).astype(int)\n",
+    "\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "stiffness_cols = [f\"stiffness_{l}\" for l in base_stiffness]\n",
+    "zero_vector_count = (df[stiffness_cols].sum(axis=1) == 0).sum()\n",
+    "print(f\"NULL Value: {zero_vector_count}\")\n",
+    "\n",
+    "print(\"\\nSample Comparison:\")\n",
+    "print(df[[\"Stiffness\"] + stiffness_cols].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "id": "89248623",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Stiffness\n",
+      "stiff       225\n",
+      "moderate    167\n",
+      "flexible     38\n",
+      "-            13\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "stiffness_flexible sum: 38\n",
+      "stiffness_moderate sum: 167\n",
+      "stiffness_stiff sum: 225\n",
+      "\n",
+      "  Stiffness  stiffness_flexible  stiffness_moderate  stiffness_stiff\n",
+      "0     stiff                   0                   0                1\n",
+      "1     stiff                   0                   0                1\n",
+      "2     stiff                   0                   0                1\n",
+      "3     stiff                   0                   0                1\n",
+      "4  moderate                   0                   1                0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Stiffness\"].value_counts())\n",
+    "\n",
+    "print()\n",
+    "for level in base_stiffness:\n",
+    "    col = f\"stiffness_{level}\"\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"Stiffness\"] + stiffness_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "id": "c9dfcb1b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 443 entries, 0 to 442\n",
+      "Data columns (total 42 columns):\n",
+      " #   Column                       Non-Null Count  Dtype  \n",
+      "---  ------                       --------------  -----  \n",
+      " 0   Brand                        443 non-null    str    \n",
+      " 1   Name                         443 non-null    str    \n",
+      " 2   Lightweight                  443 non-null    int64  \n",
+      " 3   Torsional rigidity           443 non-null    str    \n",
+      " 4   Heel counter stiffness       443 non-null    str    \n",
+      " 5   Plate                        443 non-null    str    \n",
+      " 6   Rocker                       443 non-null    int64  \n",
+      " 7   Heel lab Heel brand          443 non-null    str    \n",
+      " 8   Forefoot lab Forefoot brand  443 non-null    str    \n",
+      " 9   Orthotic friendly            443 non-null    int64  \n",
+      " 10  Season                       443 non-null    str    \n",
+      " 11  Removable insole             443 non-null    int64  \n",
+      " 12  pace_daily_running           443 non-null    int64  \n",
+      " 13  pace_tempo                   443 non-null    int64  \n",
+      " 14  pace_competition             443 non-null    int64  \n",
+      " 15  arch_neutral                 443 non-null    int64  \n",
+      " 16  arch_stability               443 non-null    int64  \n",
+      " 17  weight_lab_oz                443 non-null    float64\n",
+      " 18  weight_lab_g                 443 non-null    int64  \n",
+      " 19  weight_brand_oz              436 non-null    float64\n",
+      " 20  weight_brand_g               436 non-null    float64\n",
+      " 21  drop_lab_mm                  443 non-null    float64\n",
+      " 22  drop_brand_mm                427 non-null    float64\n",
+      " 23  strike_heel                  443 non-null    int64  \n",
+      " 24  strike_mid                   443 non-null    int64  \n",
+      " 25  strike_forefoot              443 non-null    int64  \n",
+      " 26  softness_soft                443 non-null    int64  \n",
+      " 27  softness_balanced            443 non-null    int64  \n",
+      " 28  softness_firm                443 non-null    int64  \n",
+      " 29  toebox_durability            443 non-null    int64  \n",
+      " 30  heel_durability              443 non-null    int64  \n",
+      " 31  outsole_durability           443 non-null    int64  \n",
+      " 32  breathability                443 non-null    int64  \n",
+      " 33  width_narrow                 443 non-null    int64  \n",
+      " 34  width_medium                 443 non-null    int64  \n",
+      " 35  width_wide                   443 non-null    int64  \n",
+      " 36  toebox_narrow                443 non-null    int64  \n",
+      " 37  toebox_medium                443 non-null    int64  \n",
+      " 38  toebox_wide                  443 non-null    int64  \n",
+      " 39  stiffness_flexible           443 non-null    int64  \n",
+      " 40  stiffness_moderate           443 non-null    int64  \n",
+      " 41  stiffness_stiff              443 non-null    int64  \n",
+      "dtypes: float64(5), int64(29), str(8)\n",
+      "memory usage: 145.5 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"Stiffness\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0cd9df86",
+   "metadata": {},
+   "source": [
+    "# Torsional rigidity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "id": "10686d53",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Torsional rigidity\n",
+      "Stiff       221\n",
+      "Moderate    127\n",
+      "Flexible     77\n",
+      "-            18\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['Torsional rigidity'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "id": "3300eac2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 443\n",
+      "NULL Value: 18\n",
+      "\n",
+      "Sample Comparison:\n",
+      "  Torsional rigidity  torsional_flexible  torsional_moderate  torsional_stiff\n",
+      "0              stiff                   0                   0                1\n",
+      "1           moderate                   0                   1                0\n",
+      "2           flexible                   1                   0                0\n",
+      "3           flexible                   1                   0                0\n",
+      "4           flexible                   1                   0                0\n",
+      "5              stiff                   0                   0                1\n",
+      "6           moderate                   0                   1                0\n",
+      "7              stiff                   0                   0                1\n",
+      "8              stiff                   0                   0                1\n",
+      "9              stiff                   0                   0                1\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['Torsional rigidity'] = df['Torsional rigidity'].astype(str).str.lower()\n",
+    "base_torsional = ['flexible', 'moderate', 'stiff']\n",
+    "\n",
+    "for level in base_torsional:\n",
+    "    column_name = f\"torsional_{level}\"\n",
+    "    df[column_name] = df['Torsional rigidity'].str.contains(level, na=False).astype(int)\n",
+    "\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "torsional_cols = [f\"torsional_{l}\" for l in base_torsional]\n",
+    "zero_vector_count = (df[torsional_cols].sum(axis=1) == 0).sum()\n",
+    "print(f\"NULL Value: {zero_vector_count}\")\n",
+    "\n",
+    "print(\"\\nSample Comparison:\")\n",
+    "print(df[[\"Torsional rigidity\"] + torsional_cols].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "id": "aa13792d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Torsional rigidity\n",
+      "stiff       221\n",
+      "moderate    127\n",
+      "flexible     77\n",
+      "-            18\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "torsional_flexible sum: 77\n",
+      "torsional_moderate sum: 127\n",
+      "torsional_stiff sum: 221\n",
+      "\n",
+      "  Torsional rigidity  torsional_flexible  torsional_moderate  torsional_stiff\n",
+      "0              stiff                   0                   0                1\n",
+      "1           moderate                   0                   1                0\n",
+      "2           flexible                   1                   0                0\n",
+      "3           flexible                   1                   0                0\n",
+      "4           flexible                   1                   0                0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Torsional rigidity\"].value_counts())\n",
+    "\n",
+    "print()\n",
+    "for level in base_torsional:\n",
+    "    col = f\"torsional_{level}\"\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"Torsional rigidity\"] + torsional_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "id": "1f249cb5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 443 entries, 0 to 442\n",
+      "Data columns (total 44 columns):\n",
+      " #   Column                       Non-Null Count  Dtype  \n",
+      "---  ------                       --------------  -----  \n",
+      " 0   Brand                        443 non-null    str    \n",
+      " 1   Name                         443 non-null    str    \n",
+      " 2   Lightweight                  443 non-null    int64  \n",
+      " 3   Heel counter stiffness       443 non-null    str    \n",
+      " 4   Plate                        443 non-null    str    \n",
+      " 5   Rocker                       443 non-null    int64  \n",
+      " 6   Heel lab Heel brand          443 non-null    str    \n",
+      " 7   Forefoot lab Forefoot brand  443 non-null    str    \n",
+      " 8   Orthotic friendly            443 non-null    int64  \n",
+      " 9   Season                       443 non-null    str    \n",
+      " 10  Removable insole             443 non-null    int64  \n",
+      " 11  pace_daily_running           443 non-null    int64  \n",
+      " 12  pace_tempo                   443 non-null    int64  \n",
+      " 13  pace_competition             443 non-null    int64  \n",
+      " 14  arch_neutral                 443 non-null    int64  \n",
+      " 15  arch_stability               443 non-null    int64  \n",
+      " 16  weight_lab_oz                443 non-null    float64\n",
+      " 17  weight_lab_g                 443 non-null    int64  \n",
+      " 18  weight_brand_oz              436 non-null    float64\n",
+      " 19  weight_brand_g               436 non-null    float64\n",
+      " 20  drop_lab_mm                  443 non-null    float64\n",
+      " 21  drop_brand_mm                427 non-null    float64\n",
+      " 22  strike_heel                  443 non-null    int64  \n",
+      " 23  strike_mid                   443 non-null    int64  \n",
+      " 24  strike_forefoot              443 non-null    int64  \n",
+      " 25  softness_soft                443 non-null    int64  \n",
+      " 26  softness_balanced            443 non-null    int64  \n",
+      " 27  softness_firm                443 non-null    int64  \n",
+      " 28  toebox_durability            443 non-null    int64  \n",
+      " 29  heel_durability              443 non-null    int64  \n",
+      " 30  outsole_durability           443 non-null    int64  \n",
+      " 31  breathability                443 non-null    int64  \n",
+      " 32  width_narrow                 443 non-null    int64  \n",
+      " 33  width_medium                 443 non-null    int64  \n",
+      " 34  width_wide                   443 non-null    int64  \n",
+      " 35  toebox_narrow                443 non-null    int64  \n",
+      " 36  toebox_medium                443 non-null    int64  \n",
+      " 37  toebox_wide                  443 non-null    int64  \n",
+      " 38  stiffness_flexible           443 non-null    int64  \n",
+      " 39  stiffness_moderate           443 non-null    int64  \n",
+      " 40  stiffness_stiff              443 non-null    int64  \n",
+      " 41  torsional_flexible           443 non-null    int64  \n",
+      " 42  torsional_moderate           443 non-null    int64  \n",
+      " 43  torsional_stiff              443 non-null    int64  \n",
+      "dtypes: float64(5), int64(32), str(7)\n",
+      "memory usage: 152.4 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"Torsional rigidity\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "200b1b5e",
+   "metadata": {},
+   "source": [
+    "# Heel counter stiffness"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "id": "e783ea7c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Heel counter stiffness\n",
+      "Moderate    150\n",
+      "Flexible    138\n",
+      "Stiff       126\n",
+      "-            29\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['Heel counter stiffness'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "id": "755bbf13",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 443\n",
+      "NULL Value: 29\n",
+      "\n",
+      "Sample Comparison:\n",
+      "  Heel counter stiffness  heel_stiff_flexible  heel_stiff_moderate  \\\n",
+      "0               flexible                    1                    0   \n",
+      "1               moderate                    0                    1   \n",
+      "2               flexible                    1                    0   \n",
+      "3               moderate                    0                    1   \n",
+      "4               flexible                    1                    0   \n",
+      "5               moderate                    0                    1   \n",
+      "6               flexible                    1                    0   \n",
+      "7               flexible                    1                    0   \n",
+      "8                  stiff                    0                    0   \n",
+      "9                  stiff                    0                    0   \n",
+      "\n",
+      "   heel_stiff_stiff  \n",
+      "0                 0  \n",
+      "1                 0  \n",
+      "2                 0  \n",
+      "3                 0  \n",
+      "4                 0  \n",
+      "5                 0  \n",
+      "6                 0  \n",
+      "7                 0  \n",
+      "8                 1  \n",
+      "9                 1  \n"
+     ]
+    }
+   ],
+   "source": [
+    "df['Heel counter stiffness'] = df['Heel counter stiffness'].astype(str).str.lower()\n",
+    "base_heel_stiff = ['flexible', 'moderate', 'stiff']\n",
+    "\n",
+    "for level in base_heel_stiff:\n",
+    "    column_name = f\"heel_stiff_{level}\"\n",
+    "    df[column_name] = df['Heel counter stiffness'].str.contains(level, na=False).astype(int)\n",
+    "\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "heel_stiff_cols = [f\"heel_stiff_{l}\" for l in base_heel_stiff]\n",
+    "zero_vector_count = (df[heel_stiff_cols].sum(axis=1) == 0).sum()\n",
+    "print(f\"NULL Value: {zero_vector_count}\")\n",
+    "\n",
+    "print(\"\\nSample Comparison:\")\n",
+    "print(df[[\"Heel counter stiffness\"] + heel_stiff_cols].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "id": "5e1b18b1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Heel counter stiffness\n",
+      "moderate    150\n",
+      "flexible    138\n",
+      "stiff       126\n",
+      "-            29\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "heel_stiff_flexible sum: 138\n",
+      "heel_stiff_moderate sum: 150\n",
+      "heel_stiff_stiff sum: 126\n",
+      "\n",
+      "  Heel counter stiffness  heel_stiff_flexible  heel_stiff_moderate  \\\n",
+      "0               flexible                    1                    0   \n",
+      "1               moderate                    0                    1   \n",
+      "2               flexible                    1                    0   \n",
+      "3               moderate                    0                    1   \n",
+      "4               flexible                    1                    0   \n",
+      "\n",
+      "   heel_stiff_stiff  \n",
+      "0                 0  \n",
+      "1                 0  \n",
+      "2                 0  \n",
+      "3                 0  \n",
+      "4                 0  \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Heel counter stiffness\"].value_counts())\n",
+    "\n",
+    "print()\n",
+    "for level in base_heel_stiff:\n",
+    "    col = f\"heel_stiff_{level}\"\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"Heel counter stiffness\"] + heel_stiff_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "id": "8a249daf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 443 entries, 0 to 442\n",
+      "Data columns (total 46 columns):\n",
+      " #   Column                       Non-Null Count  Dtype  \n",
+      "---  ------                       --------------  -----  \n",
+      " 0   Brand                        443 non-null    str    \n",
+      " 1   Name                         443 non-null    str    \n",
+      " 2   Lightweight                  443 non-null    int64  \n",
+      " 3   Plate                        443 non-null    str    \n",
+      " 4   Rocker                       443 non-null    int64  \n",
+      " 5   Heel lab Heel brand          443 non-null    str    \n",
+      " 6   Forefoot lab Forefoot brand  443 non-null    str    \n",
+      " 7   Orthotic friendly            443 non-null    int64  \n",
+      " 8   Season                       443 non-null    str    \n",
+      " 9   Removable insole             443 non-null    int64  \n",
+      " 10  pace_daily_running           443 non-null    int64  \n",
+      " 11  pace_tempo                   443 non-null    int64  \n",
+      " 12  pace_competition             443 non-null    int64  \n",
+      " 13  arch_neutral                 443 non-null    int64  \n",
+      " 14  arch_stability               443 non-null    int64  \n",
+      " 15  weight_lab_oz                443 non-null    float64\n",
+      " 16  weight_lab_g                 443 non-null    int64  \n",
+      " 17  weight_brand_oz              436 non-null    float64\n",
+      " 18  weight_brand_g               436 non-null    float64\n",
+      " 19  drop_lab_mm                  443 non-null    float64\n",
+      " 20  drop_brand_mm                427 non-null    float64\n",
+      " 21  strike_heel                  443 non-null    int64  \n",
+      " 22  strike_mid                   443 non-null    int64  \n",
+      " 23  strike_forefoot              443 non-null    int64  \n",
+      " 24  softness_soft                443 non-null    int64  \n",
+      " 25  softness_balanced            443 non-null    int64  \n",
+      " 26  softness_firm                443 non-null    int64  \n",
+      " 27  toebox_durability            443 non-null    int64  \n",
+      " 28  heel_durability              443 non-null    int64  \n",
+      " 29  outsole_durability           443 non-null    int64  \n",
+      " 30  breathability                443 non-null    int64  \n",
+      " 31  width_narrow                 443 non-null    int64  \n",
+      " 32  width_medium                 443 non-null    int64  \n",
+      " 33  width_wide                   443 non-null    int64  \n",
+      " 34  toebox_narrow                443 non-null    int64  \n",
+      " 35  toebox_medium                443 non-null    int64  \n",
+      " 36  toebox_wide                  443 non-null    int64  \n",
+      " 37  stiffness_flexible           443 non-null    int64  \n",
+      " 38  stiffness_moderate           443 non-null    int64  \n",
+      " 39  stiffness_stiff              443 non-null    int64  \n",
+      " 40  torsional_flexible           443 non-null    int64  \n",
+      " 41  torsional_moderate           443 non-null    int64  \n",
+      " 42  torsional_stiff              443 non-null    int64  \n",
+      " 43  heel_stiff_flexible          443 non-null    int64  \n",
+      " 44  heel_stiff_moderate          443 non-null    int64  \n",
+      " 45  heel_stiff_stiff             443 non-null    int64  \n",
+      "dtypes: float64(5), int64(35), str(6)\n",
+      "memory usage: 159.3 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"Heel counter stiffness\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b144b6c",
+   "metadata": {},
+   "source": [
+    "# Plate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "id": "dcc5dba9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plate\n",
+      "0                         381\n",
+      "Carbon plate               61\n",
+      "Carbon plateRock plate      1\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Plate\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "id": "7486d183",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 443\n",
+      "NULL Value: 0\n",
+      "\n",
+      "Sample Comparison:\n",
+      "  Plate  plate_0  plate_rock_plate  plate_carbon_plate\n",
+      "0     0        1                 0                   0\n",
+      "1     0        1                 0                   0\n",
+      "2     0        1                 0                   0\n",
+      "3     0        1                 0                   0\n",
+      "4     0        1                 0                   0\n",
+      "5     0        1                 0                   0\n",
+      "6     0        1                 0                   0\n",
+      "7     0        1                 0                   0\n",
+      "8     0        1                 0                   0\n",
+      "9     0        1                 0                   0\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['Plate'] = df['Plate'].astype(str).str.lower()\n",
+    "base_plate = ['0', 'rock plate', 'carbon plate']\n",
+    "\n",
+    "for level in base_plate:\n",
+    "    column_name = f\"plate_{level.replace(' ', '_')}\"\n",
+    "    if level == '0':\n",
+    "        df[column_name] = (df['Plate'] == '0').astype(int)\n",
+    "    else:\n",
+    "        df[column_name] = df['Plate'].str.contains(level, na=False).astype(int)\n",
+    "\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "plate_cols = [f\"plate_{l.replace(' ', '_')}\" for l in base_plate]\n",
+    "zero_vector_count = (df[plate_cols].sum(axis=1) == 0).sum()\n",
+    "print(f\"NULL Value: {zero_vector_count}\")\n",
+    "\n",
+    "print(\"\\nSample Comparison:\")\n",
+    "print(df[[\"Plate\"] + plate_cols].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "id": "1cb4e776",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plate\n",
+      "0                         381\n",
+      "carbon plate               61\n",
+      "carbon platerock plate      1\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "plate_0 sum: 381\n",
+      "plate_rock_plate sum: 1\n",
+      "plate_carbon_plate sum: 62\n",
+      "\n",
+      "  Plate  plate_0  plate_rock_plate  plate_carbon_plate\n",
+      "0     0        1                 0                   0\n",
+      "1     0        1                 0                   0\n",
+      "2     0        1                 0                   0\n",
+      "3     0        1                 0                   0\n",
+      "4     0        1                 0                   0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Plate\"].value_counts())\n",
+    "\n",
+    "print()\n",
+    "for level in base_plate:\n",
+    "    col = f\"plate_{level.replace(' ', '_')}\"\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"Plate\"] + plate_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "id": "2664b9dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 443 entries, 0 to 442\n",
+      "Data columns (total 48 columns):\n",
+      " #   Column                       Non-Null Count  Dtype  \n",
+      "---  ------                       --------------  -----  \n",
+      " 0   Brand                        443 non-null    str    \n",
+      " 1   Name                         443 non-null    str    \n",
+      " 2   Lightweight                  443 non-null    int64  \n",
+      " 3   Rocker                       443 non-null    int64  \n",
+      " 4   Heel lab Heel brand          443 non-null    str    \n",
+      " 5   Forefoot lab Forefoot brand  443 non-null    str    \n",
+      " 6   Orthotic friendly            443 non-null    int64  \n",
+      " 7   Season                       443 non-null    str    \n",
+      " 8   Removable insole             443 non-null    int64  \n",
+      " 9   pace_daily_running           443 non-null    int64  \n",
+      " 10  pace_tempo                   443 non-null    int64  \n",
+      " 11  pace_competition             443 non-null    int64  \n",
+      " 12  arch_neutral                 443 non-null    int64  \n",
+      " 13  arch_stability               443 non-null    int64  \n",
+      " 14  weight_lab_oz                443 non-null    float64\n",
+      " 15  weight_lab_g                 443 non-null    int64  \n",
+      " 16  weight_brand_oz              436 non-null    float64\n",
+      " 17  weight_brand_g               436 non-null    float64\n",
+      " 18  drop_lab_mm                  443 non-null    float64\n",
+      " 19  drop_brand_mm                427 non-null    float64\n",
+      " 20  strike_heel                  443 non-null    int64  \n",
+      " 21  strike_mid                   443 non-null    int64  \n",
+      " 22  strike_forefoot              443 non-null    int64  \n",
+      " 23  softness_soft                443 non-null    int64  \n",
+      " 24  softness_balanced            443 non-null    int64  \n",
+      " 25  softness_firm                443 non-null    int64  \n",
+      " 26  toebox_durability            443 non-null    int64  \n",
+      " 27  heel_durability              443 non-null    int64  \n",
+      " 28  outsole_durability           443 non-null    int64  \n",
+      " 29  breathability                443 non-null    int64  \n",
+      " 30  width_narrow                 443 non-null    int64  \n",
+      " 31  width_medium                 443 non-null    int64  \n",
+      " 32  width_wide                   443 non-null    int64  \n",
+      " 33  toebox_narrow                443 non-null    int64  \n",
+      " 34  toebox_medium                443 non-null    int64  \n",
+      " 35  toebox_wide                  443 non-null    int64  \n",
+      " 36  stiffness_flexible           443 non-null    int64  \n",
+      " 37  stiffness_moderate           443 non-null    int64  \n",
+      " 38  stiffness_stiff              443 non-null    int64  \n",
+      " 39  torsional_flexible           443 non-null    int64  \n",
+      " 40  torsional_moderate           443 non-null    int64  \n",
+      " 41  torsional_stiff              443 non-null    int64  \n",
+      " 42  heel_stiff_flexible          443 non-null    int64  \n",
+      " 43  heel_stiff_moderate          443 non-null    int64  \n",
+      " 44  heel_stiff_stiff             443 non-null    int64  \n",
+      " 45  plate_0                      443 non-null    int64  \n",
+      " 46  plate_rock_plate             443 non-null    int64  \n",
+      " 47  plate_carbon_plate           443 non-null    int64  \n",
+      "dtypes: float64(5), int64(38), str(5)\n",
+      "memory usage: 166.3 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"Plate\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02a46d95",
+   "metadata": {},
+   "source": [
+    "# Rocker"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "id": "28f4c008",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rocker\n",
+      "0    296\n",
+      "1    147\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Rocker\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4e919551",
+   "metadata": {},
+   "source": [
+    "# Heel lab Heel brand"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "id": "62e355de",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Heel lab Heel brand\n",
+      "34.9 mm 35.0 mm    4\n",
+      "39.9 mm 40.0 mm    4\n",
+      "38.1 mm 40.0 mm    3\n",
+      "35.3 mm 35.0 mm    3\n",
+      "32.0 mm            3\n",
+      "                  ..\n",
+      "39.0 mm 35.0 mm    1\n",
+      "34.1 mm            1\n",
+      "36.9 mm 41.0 mm    1\n",
+      "35.5 mm 37.0 mm    1\n",
+      "38.6 mm 38.6 mm    1\n",
+      "Name: count, Length: 380, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Heel lab Heel brand\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "id": "857ec390",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(0)"
+      ]
+     },
+     "execution_count": 164,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mask_missing = (\n",
+    "    df[\"Heel lab Heel brand\"].isna() |\n",
+    "    (df[\"Heel lab Heel brand\"].astype(str).str.strip() == \"-\")\n",
+    ")\n",
+    "mask_missing.sum()  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "id": "bf4fc059",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Heel lab Heel brand  heel_lab_mm  heel_brand_mm\n",
+      "0     32.4 mm 36.0 mm         32.4           36.0\n",
+      "1     34.3 mm 32.5 mm         34.3           32.5\n",
+      "2     33.3 mm 32.5 mm         33.3           32.5\n",
+      "3     31.8 mm 32.0 mm         31.8           32.0\n",
+      "4     32.6 mm 34.0 mm         32.6           34.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "Heel = df[\"Heel lab Heel brand\"].str.findall(r\"[\\d.]+\")\n",
+    "\n",
+    "df[[\"heel_lab_mm\", \"heel_brand_mm\"]] = (\n",
+    "    pd.DataFrame(Heel.tolist(), index=df.index)\n",
+    ")\n",
+    "\n",
+    "for col in [\"heel_lab_mm\", \"heel_brand_mm\"]:\n",
+    "    df[col] = pd.to_numeric(df[col], errors=\"coerce\")\n",
+    "\n",
+    "print(df[[\"Heel lab Heel brand\", \"heel_lab_mm\", \"heel_brand_mm\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "id": "5b5e9be9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 443 entries, 0 to 442\n",
+      "Data columns (total 49 columns):\n",
+      " #   Column                       Non-Null Count  Dtype  \n",
+      "---  ------                       --------------  -----  \n",
+      " 0   Brand                        443 non-null    str    \n",
+      " 1   Name                         443 non-null    str    \n",
+      " 2   Lightweight                  443 non-null    int64  \n",
+      " 3   Rocker                       443 non-null    int64  \n",
+      " 4   Forefoot lab Forefoot brand  443 non-null    str    \n",
+      " 5   Orthotic friendly            443 non-null    int64  \n",
+      " 6   Season                       443 non-null    str    \n",
+      " 7   Removable insole             443 non-null    int64  \n",
+      " 8   pace_daily_running           443 non-null    int64  \n",
+      " 9   pace_tempo                   443 non-null    int64  \n",
+      " 10  pace_competition             443 non-null    int64  \n",
+      " 11  arch_neutral                 443 non-null    int64  \n",
+      " 12  arch_stability               443 non-null    int64  \n",
+      " 13  weight_lab_oz                443 non-null    float64\n",
+      " 14  weight_lab_g                 443 non-null    int64  \n",
+      " 15  weight_brand_oz              436 non-null    float64\n",
+      " 16  weight_brand_g               436 non-null    float64\n",
+      " 17  drop_lab_mm                  443 non-null    float64\n",
+      " 18  drop_brand_mm                427 non-null    float64\n",
+      " 19  strike_heel                  443 non-null    int64  \n",
+      " 20  strike_mid                   443 non-null    int64  \n",
+      " 21  strike_forefoot              443 non-null    int64  \n",
+      " 22  softness_soft                443 non-null    int64  \n",
+      " 23  softness_balanced            443 non-null    int64  \n",
+      " 24  softness_firm                443 non-null    int64  \n",
+      " 25  toebox_durability            443 non-null    int64  \n",
+      " 26  heel_durability              443 non-null    int64  \n",
+      " 27  outsole_durability           443 non-null    int64  \n",
+      " 28  breathability                443 non-null    int64  \n",
+      " 29  width_narrow                 443 non-null    int64  \n",
+      " 30  width_medium                 443 non-null    int64  \n",
+      " 31  width_wide                   443 non-null    int64  \n",
+      " 32  toebox_narrow                443 non-null    int64  \n",
+      " 33  toebox_medium                443 non-null    int64  \n",
+      " 34  toebox_wide                  443 non-null    int64  \n",
+      " 35  stiffness_flexible           443 non-null    int64  \n",
+      " 36  stiffness_moderate           443 non-null    int64  \n",
+      " 37  stiffness_stiff              443 non-null    int64  \n",
+      " 38  torsional_flexible           443 non-null    int64  \n",
+      " 39  torsional_moderate           443 non-null    int64  \n",
+      " 40  torsional_stiff              443 non-null    int64  \n",
+      " 41  heel_stiff_flexible          443 non-null    int64  \n",
+      " 42  heel_stiff_moderate          443 non-null    int64  \n",
+      " 43  heel_stiff_stiff             443 non-null    int64  \n",
+      " 44  plate_0                      443 non-null    int64  \n",
+      " 45  plate_rock_plate             443 non-null    int64  \n",
+      " 46  plate_carbon_plate           443 non-null    int64  \n",
+      " 47  heel_lab_mm                  443 non-null    float64\n",
+      " 48  heel_brand_mm                394 non-null    float64\n",
+      "dtypes: float64(7), int64(38), str(4)\n",
+      "memory usage: 169.7 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"Heel lab Heel brand\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f3bf442e",
+   "metadata": {},
+   "source": [
+    "# Forefoot lab Forefoot brand"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "id": "83b3bb25",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0    23.0 mm 26.0 mm\n",
+      "1    26.6 mm 24.5 mm\n",
+      "2    24.4 mm 22.5 mm\n",
+      "3    21.2 mm 21.0 mm\n",
+      "4    22.7 mm 24.0 mm\n",
+      "Name: Forefoot lab Forefoot brand, dtype: str\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['Forefoot lab Forefoot brand'].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "id": "da1a83a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(0)"
+      ]
+     },
+     "execution_count": 168,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mask_missing = (\n",
+    "    df[\"Forefoot lab Forefoot brand\"].isna() |\n",
+    "    (df[\"Forefoot lab Forefoot brand\"].astype(str).str.strip() == \"-\")\n",
+    ")\n",
+    "mask_missing.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "id": "6a2bf00a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Forefoot lab Forefoot brand  forefoot_lab_mm  forefoot_brand_mm\n",
+      "0             23.0 mm 26.0 mm             23.0               26.0\n",
+      "1             26.6 mm 24.5 mm             26.6               24.5\n",
+      "2             24.4 mm 22.5 mm             24.4               22.5\n",
+      "3             21.2 mm 21.0 mm             21.2               21.0\n",
+      "4             22.7 mm 24.0 mm             22.7               24.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "forefoot = df[\"Forefoot lab Forefoot brand\"].str.findall(r\"[\\d.]+\")\n",
+    "\n",
+    "df[[\"forefoot_lab_mm\", \"forefoot_brand_mm\"]] = (\n",
+    "    pd.DataFrame(forefoot.tolist(), index=df.index)\n",
+    ")\n",
+    "\n",
+    "for col in [\"forefoot_lab_mm\", \"forefoot_brand_mm\"]:\n",
+    "    df[col] = pd.to_numeric(df[col], errors=\"coerce\")\n",
+    "\n",
+    "print(df[[\"Forefoot lab Forefoot brand\", \"forefoot_lab_mm\", \"forefoot_brand_mm\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "id": "8eba2eae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 443 entries, 0 to 442\n",
+      "Data columns (total 50 columns):\n",
+      " #   Column               Non-Null Count  Dtype  \n",
+      "---  ------               --------------  -----  \n",
+      " 0   Brand                443 non-null    str    \n",
+      " 1   Name                 443 non-null    str    \n",
+      " 2   Lightweight          443 non-null    int64  \n",
+      " 3   Rocker               443 non-null    int64  \n",
+      " 4   Orthotic friendly    443 non-null    int64  \n",
+      " 5   Season               443 non-null    str    \n",
+      " 6   Removable insole     443 non-null    int64  \n",
+      " 7   pace_daily_running   443 non-null    int64  \n",
+      " 8   pace_tempo           443 non-null    int64  \n",
+      " 9   pace_competition     443 non-null    int64  \n",
+      " 10  arch_neutral         443 non-null    int64  \n",
+      " 11  arch_stability       443 non-null    int64  \n",
+      " 12  weight_lab_oz        443 non-null    float64\n",
+      " 13  weight_lab_g         443 non-null    int64  \n",
+      " 14  weight_brand_oz      436 non-null    float64\n",
+      " 15  weight_brand_g       436 non-null    float64\n",
+      " 16  drop_lab_mm          443 non-null    float64\n",
+      " 17  drop_brand_mm        427 non-null    float64\n",
+      " 18  strike_heel          443 non-null    int64  \n",
+      " 19  strike_mid           443 non-null    int64  \n",
+      " 20  strike_forefoot      443 non-null    int64  \n",
+      " 21  softness_soft        443 non-null    int64  \n",
+      " 22  softness_balanced    443 non-null    int64  \n",
+      " 23  softness_firm        443 non-null    int64  \n",
+      " 24  toebox_durability    443 non-null    int64  \n",
+      " 25  heel_durability      443 non-null    int64  \n",
+      " 26  outsole_durability   443 non-null    int64  \n",
+      " 27  breathability        443 non-null    int64  \n",
+      " 28  width_narrow         443 non-null    int64  \n",
+      " 29  width_medium         443 non-null    int64  \n",
+      " 30  width_wide           443 non-null    int64  \n",
+      " 31  toebox_narrow        443 non-null    int64  \n",
+      " 32  toebox_medium        443 non-null    int64  \n",
+      " 33  toebox_wide          443 non-null    int64  \n",
+      " 34  stiffness_flexible   443 non-null    int64  \n",
+      " 35  stiffness_moderate   443 non-null    int64  \n",
+      " 36  stiffness_stiff      443 non-null    int64  \n",
+      " 37  torsional_flexible   443 non-null    int64  \n",
+      " 38  torsional_moderate   443 non-null    int64  \n",
+      " 39  torsional_stiff      443 non-null    int64  \n",
+      " 40  heel_stiff_flexible  443 non-null    int64  \n",
+      " 41  heel_stiff_moderate  443 non-null    int64  \n",
+      " 42  heel_stiff_stiff     443 non-null    int64  \n",
+      " 43  plate_0              443 non-null    int64  \n",
+      " 44  plate_rock_plate     443 non-null    int64  \n",
+      " 45  plate_carbon_plate   443 non-null    int64  \n",
+      " 46  heel_lab_mm          443 non-null    float64\n",
+      " 47  heel_brand_mm        394 non-null    float64\n",
+      " 48  forefoot_lab_mm      443 non-null    float64\n",
+      " 49  forefoot_brand_mm    393 non-null    float64\n",
+      "dtypes: float64(9), int64(38), str(3)\n",
+      "memory usage: 173.2 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"Forefoot lab Forefoot brand\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3bc78a8",
+   "metadata": {},
+   "source": [
+    "# Season"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "id": "af808af0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Season\n",
+      "All seasons          260\n",
+      "SummerAll seasons    113\n",
+      "-                     58\n",
+      "Winter                12\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Season\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "id": "1c74b369",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Jumlah baris dengan '-' atau '0': 58\n",
+      "\n",
+      "--- Detail Baris (Season = '-' atau '0') ---\n",
+      "           Brand                         Name Season\n",
+      "0         Brooks                     Launch 9      -\n",
+      "13        Adidas        Adizero Adios Pro 2.0      -\n",
+      "25        Brooks            Adrenaline GTS 22      -\n",
+      "34          Nike  Air Zoom Pegasus 38 FlyEase      -\n",
+      "47       Saucony                         Axon      -\n",
+      "85          Nike               Downshifter 11      -\n",
+      "89        Adidas                    Duramo 10      -\n",
+      "97       Saucony              Endorphin Pro 3      -\n",
+      "101      Saucony            Endorphin Shift 2      -\n",
+      "115         Nike       Flex Experience Run 10      -\n",
+      "118         Nike                Flex Run 2021      -\n",
+      "119       Reebok           Floatride Energy 3      -\n",
+      "125         Nike                 Free Run 5.0      -\n",
+      "126      Saucony                    Freedom 4      -\n",
+      "127  New Balance          Fresh Foam 1080 v11      -\n",
+      "129  New Balance           Fresh Foam 860 v11      -\n",
+      "130  New Balance           Fresh Foam 860 v12      -\n",
+      "134  New Balance        Fresh Foam X 1080 v12      -\n",
+      "167        ASICS                Gel Contend 7      -\n",
+      "171        ASICS               Gel Cumulus 24      -\n",
+      "175        ASICS                 Gel Excite 8      -\n",
+      "176        ASICS                Gel Kayano 28      -\n",
+      "180        ASICS            Gel Kayano Lite 2      -\n",
+      "183        ASICS                Gel Nimbus 24      -\n",
+      "187        ASICS            Gel Nimbus Lite 3      -\n",
+      "188        ASICS                 Gel Pulse 11      -\n",
+      "201       Brooks                  Glycerin 19      -\n",
+      "213     Skechers           GOrun Razor Excess      -\n",
+      "214        ASICS                   GT 1000 10      -\n",
+      "215        ASICS                   GT 1000 11      -\n",
+      "219        ASICS                    GT 1000 9      -\n",
+      "220        ASICS                   GT 2000 10      -\n",
+      "225      Saucony                     Guide 14      -\n",
+      "226      Saucony                     Guide 15      -\n",
+      "252      Saucony                   Kinvara 12      -\n",
+      "253      Saucony                   Kinvara 13      -\n",
+      "259       Brooks                     Launch 8      -\n",
+      "262       Brooks                   Levitate 5      -\n",
+      "267         Hoka                       Mach 4      -\n",
+      "277     Skechers         Max Cushioning Elite      -\n",
+      "280        ASICS               Metaspeed Edge      -\n",
+      "296        ASICS                  Novablast 2      -\n",
+      "314        Altra                  Provision 6      -\n",
+      "319         Nike                      Quest 4      -\n",
+      "324       Jordan                  React Havoc      -\n",
+      "326         Nike                React Miler 3      -\n",
+      "327         Nike                 Renew Ride 2      -\n",
+      "332         Nike                 Revolution 6      -\n",
+      "336       Brooks                   Ricochet 3      -\n",
+      "338      Saucony                      Ride 15      -\n",
+      "343        Altra                     Rivera 2      -\n",
+      "367          APL                   Streamline      -\n",
+      "378       Adidas                   Supernova+      -\n",
+      "397       Adidas                Ultraboost 21      -\n",
+      "398       Adidas                Ultraboost 22      -\n",
+      "415       Mizuno               Wave Horizon 6      -\n",
+      "424       Mizuno                Wave Rider 25      -\n",
+      "437         Nike                   Zoom Fly 4      -\n",
+      "\n",
+      "Frekuensi spesifik:\n",
+      "Season\n",
+      "-    58\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check weird values\n",
+    "filter_condition = df['Season'].astype(str).isin(['-', '0'])\n",
+    "rows_to_check = df[filter_condition]\n",
+    "\n",
+    "print(f\"Jumlah baris dengan '-' atau '0': {len(rows_to_check)}\")\n",
+    "print(\"\\n--- Detail Baris (Season = '-' atau '0') ---\")\n",
+    "print(rows_to_check[['Brand', 'Name', 'Season']])\n",
+    "\n",
+    "\n",
+    "print(\"\\nFrekuensi spesifik:\")\n",
+    "print(df[df['Season'].astype(str).isin(['-', '0'])]['Season'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "id": "d92d1ad4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 443\n",
+      "NULL/Unknown Value (0 dan -): 58\n",
+      "\n",
+      "Sample Comparison (Multi-label):\n",
+      "               Season  season_summer  season_winter  season_all\n",
+      "1   summerall seasons              1              0           1\n",
+      "6   summerall seasons              1              0           1\n",
+      "8   summerall seasons              1              0           1\n",
+      "9   summerall seasons              1              0           1\n",
+      "10  summerall seasons              1              0           1\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['Season'] = df['Season'].astype(str).str.lower()\n",
+    "base_seasons = ['summer', 'winter', 'all seasons']\n",
+    "\n",
+    "for level in base_seasons:\n",
+    "    clean_name = level.replace(' seasons', '').replace(' ', '_')\n",
+    "    column_name = f\"season_{clean_name}\"\n",
+    "    df[column_name] = df['Season'].str.contains(level, na=False).astype(int)\n",
+    "\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "season_cols = [col for col in df.columns if col.startswith('season_')]\n",
+    "zero_vector_count = (df[season_cols].sum(axis=1) == 0).sum()\n",
+    "print(f\"NULL/Unknown Value (0 dan -): {zero_vector_count}\")\n",
+    "\n",
+    "print(\"\\nSample Comparison (Multi-label):\")\n",
+    "print(df[df[season_cols].sum(axis=1) > 1][['Season'] + season_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "id": "da655e5b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Season\n",
+      "all seasons          260\n",
+      "summerall seasons    113\n",
+      "-                     58\n",
+      "winter                12\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "season_summer sum: 113\n",
+      "season_winter sum: 12\n",
+      "season_all sum: 373\n",
+      "\n",
+      "              Season  season_summer  season_winter  season_all\n",
+      "0                  -              0              0           0\n",
+      "1  summerall seasons              1              0           1\n",
+      "2        all seasons              0              0           1\n",
+      "3        all seasons              0              0           1\n",
+      "4        all seasons              0              0           1\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Season\"].value_counts())\n",
+    "\n",
+    "print()\n",
+    "for col in season_cols:\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"Season\"] + season_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "id": "7097e6a5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 443 entries, 0 to 442\n",
+      "Data columns (total 52 columns):\n",
+      " #   Column               Non-Null Count  Dtype  \n",
+      "---  ------               --------------  -----  \n",
+      " 0   Brand                443 non-null    str    \n",
+      " 1   Name                 443 non-null    str    \n",
+      " 2   Lightweight          443 non-null    int64  \n",
+      " 3   Rocker               443 non-null    int64  \n",
+      " 4   Orthotic friendly    443 non-null    int64  \n",
+      " 5   Removable insole     443 non-null    int64  \n",
+      " 6   pace_daily_running   443 non-null    int64  \n",
+      " 7   pace_tempo           443 non-null    int64  \n",
+      " 8   pace_competition     443 non-null    int64  \n",
+      " 9   arch_neutral         443 non-null    int64  \n",
+      " 10  arch_stability       443 non-null    int64  \n",
+      " 11  weight_lab_oz        443 non-null    float64\n",
+      " 12  weight_lab_g         443 non-null    int64  \n",
+      " 13  weight_brand_oz      436 non-null    float64\n",
+      " 14  weight_brand_g       436 non-null    float64\n",
+      " 15  drop_lab_mm          443 non-null    float64\n",
+      " 16  drop_brand_mm        427 non-null    float64\n",
+      " 17  strike_heel          443 non-null    int64  \n",
+      " 18  strike_mid           443 non-null    int64  \n",
+      " 19  strike_forefoot      443 non-null    int64  \n",
+      " 20  softness_soft        443 non-null    int64  \n",
+      " 21  softness_balanced    443 non-null    int64  \n",
+      " 22  softness_firm        443 non-null    int64  \n",
+      " 23  toebox_durability    443 non-null    int64  \n",
+      " 24  heel_durability      443 non-null    int64  \n",
+      " 25  outsole_durability   443 non-null    int64  \n",
+      " 26  breathability        443 non-null    int64  \n",
+      " 27  width_narrow         443 non-null    int64  \n",
+      " 28  width_medium         443 non-null    int64  \n",
+      " 29  width_wide           443 non-null    int64  \n",
+      " 30  toebox_narrow        443 non-null    int64  \n",
+      " 31  toebox_medium        443 non-null    int64  \n",
+      " 32  toebox_wide          443 non-null    int64  \n",
+      " 33  stiffness_flexible   443 non-null    int64  \n",
+      " 34  stiffness_moderate   443 non-null    int64  \n",
+      " 35  stiffness_stiff      443 non-null    int64  \n",
+      " 36  torsional_flexible   443 non-null    int64  \n",
+      " 37  torsional_moderate   443 non-null    int64  \n",
+      " 38  torsional_stiff      443 non-null    int64  \n",
+      " 39  heel_stiff_flexible  443 non-null    int64  \n",
+      " 40  heel_stiff_moderate  443 non-null    int64  \n",
+      " 41  heel_stiff_stiff     443 non-null    int64  \n",
+      " 42  plate_0              443 non-null    int64  \n",
+      " 43  plate_rock_plate     443 non-null    int64  \n",
+      " 44  plate_carbon_plate   443 non-null    int64  \n",
+      " 45  heel_lab_mm          443 non-null    float64\n",
+      " 46  heel_brand_mm        394 non-null    float64\n",
+      " 47  forefoot_lab_mm      443 non-null    float64\n",
+      " 48  forefoot_brand_mm    393 non-null    float64\n",
+      " 49  season_summer        443 non-null    int64  \n",
+      " 50  season_winter        443 non-null    int64  \n",
+      " 51  season_all           443 non-null    int64  \n",
+      "dtypes: float64(9), int64(41), str(2)\n",
+      "memory usage: 180.1 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"Season\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "id": "202a9d4c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    Brand         Name  Removable insole  Orthotic friendly\n",
+      "0  Brooks     Launch 9                 1                  1\n",
+      "1  Brooks   Levitate 6                 1                  1\n",
+      "2  Adidas        4DFWD                 1                  1\n",
+      "3  Adidas      4DFWD 2                 1                  1\n",
+      "4  Adidas      4DFWD 3                 1                  1\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[[\"Brand\", \"Name\", \"Removable insole\", \"Orthotic friendly\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f95804af",
+   "metadata": {},
+   "source": [
+    "# Finishing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "id": "b64898ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 443 entries, 0 to 442\n",
+      "Data columns (total 49 columns):\n",
+      " #   Column               Non-Null Count  Dtype  \n",
+      "---  ------               --------------  -----  \n",
+      " 0   Brand                443 non-null    str    \n",
+      " 1   Name                 443 non-null    str    \n",
+      " 2   Lightweight          443 non-null    int64  \n",
+      " 3   Rocker               443 non-null    int64  \n",
+      " 4   Orthotic friendly    443 non-null    int64  \n",
+      " 5   Removable insole     443 non-null    int64  \n",
+      " 6   pace_daily_running   443 non-null    int64  \n",
+      " 7   pace_tempo           443 non-null    int64  \n",
+      " 8   pace_competition     443 non-null    int64  \n",
+      " 9   arch_neutral         443 non-null    int64  \n",
+      " 10  arch_stability       443 non-null    int64  \n",
+      " 11  weight_lab_oz        443 non-null    float64\n",
+      " 12  drop_lab_mm          443 non-null    float64\n",
+      " 13  drop_brand_mm        427 non-null    float64\n",
+      " 14  strike_heel          443 non-null    int64  \n",
+      " 15  strike_mid           443 non-null    int64  \n",
+      " 16  strike_forefoot      443 non-null    int64  \n",
+      " 17  softness_soft        443 non-null    int64  \n",
+      " 18  softness_balanced    443 non-null    int64  \n",
+      " 19  softness_firm        443 non-null    int64  \n",
+      " 20  toebox_durability    443 non-null    int64  \n",
+      " 21  heel_durability      443 non-null    int64  \n",
+      " 22  outsole_durability   443 non-null    int64  \n",
+      " 23  breathability        443 non-null    int64  \n",
+      " 24  width_narrow         443 non-null    int64  \n",
+      " 25  width_medium         443 non-null    int64  \n",
+      " 26  width_wide           443 non-null    int64  \n",
+      " 27  toebox_narrow        443 non-null    int64  \n",
+      " 28  toebox_medium        443 non-null    int64  \n",
+      " 29  toebox_wide          443 non-null    int64  \n",
+      " 30  stiffness_flexible   443 non-null    int64  \n",
+      " 31  stiffness_moderate   443 non-null    int64  \n",
+      " 32  stiffness_stiff      443 non-null    int64  \n",
+      " 33  torsional_flexible   443 non-null    int64  \n",
+      " 34  torsional_moderate   443 non-null    int64  \n",
+      " 35  torsional_stiff      443 non-null    int64  \n",
+      " 36  heel_stiff_flexible  443 non-null    int64  \n",
+      " 37  heel_stiff_moderate  443 non-null    int64  \n",
+      " 38  heel_stiff_stiff     443 non-null    int64  \n",
+      " 39  plate_0              443 non-null    int64  \n",
+      " 40  plate_rock_plate     443 non-null    int64  \n",
+      " 41  plate_carbon_plate   443 non-null    int64  \n",
+      " 42  heel_lab_mm          443 non-null    float64\n",
+      " 43  heel_brand_mm        394 non-null    float64\n",
+      " 44  forefoot_lab_mm      443 non-null    float64\n",
+      " 45  forefoot_brand_mm    393 non-null    float64\n",
+      " 46  season_summer        443 non-null    int64  \n",
+      " 47  season_winter        443 non-null    int64  \n",
+      " 48  season_all           443 non-null    int64  \n",
+      "dtypes: float64(7), int64(40), str(2)\n",
+      "memory usage: 169.7 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Weight cuma pakai yg lab_oz\n",
+    "df.drop(columns=['weight_brand_oz', 'weight_lab_g', 'weight_brand_g'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "id": "42ad805f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 443 entries, 0 to 442\n",
+      "Data columns (total 48 columns):\n",
+      " #   Column               Non-Null Count  Dtype  \n",
+      "---  ------               --------------  -----  \n",
+      " 0   Brand                443 non-null    str    \n",
+      " 1   Name                 443 non-null    str    \n",
+      " 2   Lightweight          443 non-null    int64  \n",
+      " 3   Rocker               443 non-null    int64  \n",
+      " 4   Orthotic friendly    443 non-null    int64  \n",
+      " 5   Removable insole     443 non-null    int64  \n",
+      " 6   pace_daily_running   443 non-null    int64  \n",
+      " 7   pace_tempo           443 non-null    int64  \n",
+      " 8   pace_competition     443 non-null    int64  \n",
+      " 9   arch_neutral         443 non-null    int64  \n",
+      " 10  arch_stability       443 non-null    int64  \n",
+      " 11  weight_lab_oz        443 non-null    float64\n",
+      " 12  drop_lab_mm          443 non-null    float64\n",
+      " 13  strike_heel          443 non-null    int64  \n",
+      " 14  strike_mid           443 non-null    int64  \n",
+      " 15  strike_forefoot      443 non-null    int64  \n",
+      " 16  softness_soft        443 non-null    int64  \n",
+      " 17  softness_balanced    443 non-null    int64  \n",
+      " 18  softness_firm        443 non-null    int64  \n",
+      " 19  toebox_durability    443 non-null    int64  \n",
+      " 20  heel_durability      443 non-null    int64  \n",
+      " 21  outsole_durability   443 non-null    int64  \n",
+      " 22  breathability        443 non-null    int64  \n",
+      " 23  width_narrow         443 non-null    int64  \n",
+      " 24  width_medium         443 non-null    int64  \n",
+      " 25  width_wide           443 non-null    int64  \n",
+      " 26  toebox_narrow        443 non-null    int64  \n",
+      " 27  toebox_medium        443 non-null    int64  \n",
+      " 28  toebox_wide          443 non-null    int64  \n",
+      " 29  stiffness_flexible   443 non-null    int64  \n",
+      " 30  stiffness_moderate   443 non-null    int64  \n",
+      " 31  stiffness_stiff      443 non-null    int64  \n",
+      " 32  torsional_flexible   443 non-null    int64  \n",
+      " 33  torsional_moderate   443 non-null    int64  \n",
+      " 34  torsional_stiff      443 non-null    int64  \n",
+      " 35  heel_stiff_flexible  443 non-null    int64  \n",
+      " 36  heel_stiff_moderate  443 non-null    int64  \n",
+      " 37  heel_stiff_stiff     443 non-null    int64  \n",
+      " 38  plate_0              443 non-null    int64  \n",
+      " 39  plate_rock_plate     443 non-null    int64  \n",
+      " 40  plate_carbon_plate   443 non-null    int64  \n",
+      " 41  heel_lab_mm          443 non-null    float64\n",
+      " 42  heel_brand_mm        394 non-null    float64\n",
+      " 43  forefoot_lab_mm      443 non-null    float64\n",
+      " 44  forefoot_brand_mm    393 non-null    float64\n",
+      " 45  season_summer        443 non-null    int64  \n",
+      " 46  season_winter        443 non-null    int64  \n",
+      " 47  season_all           443 non-null    int64  \n",
+      "dtypes: float64(6), int64(40), str(2)\n",
+      "memory usage: 166.3 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# drop cuma pakai yg lab_mm\n",
+    "df.drop(columns=['drop_brand_mm'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "id": "ecb61466",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 443 entries, 0 to 442\n",
+      "Data columns (total 47 columns):\n",
+      " #   Column               Non-Null Count  Dtype  \n",
+      "---  ------               --------------  -----  \n",
+      " 0   Brand                443 non-null    str    \n",
+      " 1   Name                 443 non-null    str    \n",
+      " 2   Lightweight          443 non-null    int64  \n",
+      " 3   Rocker               443 non-null    int64  \n",
+      " 4   Orthotic friendly    443 non-null    int64  \n",
+      " 5   Removable insole     443 non-null    int64  \n",
+      " 6   pace_daily_running   443 non-null    int64  \n",
+      " 7   pace_tempo           443 non-null    int64  \n",
+      " 8   pace_competition     443 non-null    int64  \n",
+      " 9   arch_neutral         443 non-null    int64  \n",
+      " 10  arch_stability       443 non-null    int64  \n",
+      " 11  weight_lab_oz        443 non-null    float64\n",
+      " 12  drop_lab_mm          443 non-null    float64\n",
+      " 13  strike_heel          443 non-null    int64  \n",
+      " 14  strike_mid           443 non-null    int64  \n",
+      " 15  strike_forefoot      443 non-null    int64  \n",
+      " 16  softness_soft        443 non-null    int64  \n",
+      " 17  softness_balanced    443 non-null    int64  \n",
+      " 18  softness_firm        443 non-null    int64  \n",
+      " 19  toebox_durability    443 non-null    int64  \n",
+      " 20  heel_durability      443 non-null    int64  \n",
+      " 21  outsole_durability   443 non-null    int64  \n",
+      " 22  breathability        443 non-null    int64  \n",
+      " 23  width_narrow         443 non-null    int64  \n",
+      " 24  width_medium         443 non-null    int64  \n",
+      " 25  width_wide           443 non-null    int64  \n",
+      " 26  toebox_narrow        443 non-null    int64  \n",
+      " 27  toebox_medium        443 non-null    int64  \n",
+      " 28  toebox_wide          443 non-null    int64  \n",
+      " 29  stiffness_flexible   443 non-null    int64  \n",
+      " 30  stiffness_moderate   443 non-null    int64  \n",
+      " 31  stiffness_stiff      443 non-null    int64  \n",
+      " 32  torsional_flexible   443 non-null    int64  \n",
+      " 33  torsional_moderate   443 non-null    int64  \n",
+      " 34  torsional_stiff      443 non-null    int64  \n",
+      " 35  heel_stiff_flexible  443 non-null    int64  \n",
+      " 36  heel_stiff_moderate  443 non-null    int64  \n",
+      " 37  heel_stiff_stiff     443 non-null    int64  \n",
+      " 38  plate_0              443 non-null    int64  \n",
+      " 39  plate_rock_plate     443 non-null    int64  \n",
+      " 40  plate_carbon_plate   443 non-null    int64  \n",
+      " 41  heel_lab_mm          443 non-null    float64\n",
+      " 42  forefoot_lab_mm      443 non-null    float64\n",
+      " 43  forefoot_brand_mm    393 non-null    float64\n",
+      " 44  season_summer        443 non-null    int64  \n",
+      " 45  season_winter        443 non-null    int64  \n",
+      " 46  season_all           443 non-null    int64  \n",
+      "dtypes: float64(5), int64(40), str(2)\n",
+      "memory usage: 162.8 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# heel pakai yang heel_lab_mm\n",
+    "df.drop(columns=['heel_brand_mm'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "id": "ff10d872",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 443 entries, 0 to 442\n",
+      "Data columns (total 46 columns):\n",
+      " #   Column               Non-Null Count  Dtype  \n",
+      "---  ------               --------------  -----  \n",
+      " 0   Brand                443 non-null    str    \n",
+      " 1   Name                 443 non-null    str    \n",
+      " 2   Lightweight          443 non-null    int64  \n",
+      " 3   Rocker               443 non-null    int64  \n",
+      " 4   Orthotic friendly    443 non-null    int64  \n",
+      " 5   Removable insole     443 non-null    int64  \n",
+      " 6   pace_daily_running   443 non-null    int64  \n",
+      " 7   pace_tempo           443 non-null    int64  \n",
+      " 8   pace_competition     443 non-null    int64  \n",
+      " 9   arch_neutral         443 non-null    int64  \n",
+      " 10  arch_stability       443 non-null    int64  \n",
+      " 11  weight_lab_oz        443 non-null    float64\n",
+      " 12  drop_lab_mm          443 non-null    float64\n",
+      " 13  strike_heel          443 non-null    int64  \n",
+      " 14  strike_mid           443 non-null    int64  \n",
+      " 15  strike_forefoot      443 non-null    int64  \n",
+      " 16  softness_soft        443 non-null    int64  \n",
+      " 17  softness_balanced    443 non-null    int64  \n",
+      " 18  softness_firm        443 non-null    int64  \n",
+      " 19  toebox_durability    443 non-null    int64  \n",
+      " 20  heel_durability      443 non-null    int64  \n",
+      " 21  outsole_durability   443 non-null    int64  \n",
+      " 22  breathability        443 non-null    int64  \n",
+      " 23  width_narrow         443 non-null    int64  \n",
+      " 24  width_medium         443 non-null    int64  \n",
+      " 25  width_wide           443 non-null    int64  \n",
+      " 26  toebox_narrow        443 non-null    int64  \n",
+      " 27  toebox_medium        443 non-null    int64  \n",
+      " 28  toebox_wide          443 non-null    int64  \n",
+      " 29  stiffness_flexible   443 non-null    int64  \n",
+      " 30  stiffness_moderate   443 non-null    int64  \n",
+      " 31  stiffness_stiff      443 non-null    int64  \n",
+      " 32  torsional_flexible   443 non-null    int64  \n",
+      " 33  torsional_moderate   443 non-null    int64  \n",
+      " 34  torsional_stiff      443 non-null    int64  \n",
+      " 35  heel_stiff_flexible  443 non-null    int64  \n",
+      " 36  heel_stiff_moderate  443 non-null    int64  \n",
+      " 37  heel_stiff_stiff     443 non-null    int64  \n",
+      " 38  plate_0              443 non-null    int64  \n",
+      " 39  plate_rock_plate     443 non-null    int64  \n",
+      " 40  plate_carbon_plate   443 non-null    int64  \n",
+      " 41  heel_lab_mm          443 non-null    float64\n",
+      " 42  forefoot_lab_mm      443 non-null    float64\n",
+      " 43  season_summer        443 non-null    int64  \n",
+      " 44  season_winter        443 non-null    int64  \n",
+      " 45  season_all           443 non-null    int64  \n",
+      "dtypes: float64(4), int64(40), str(2)\n",
+      "memory usage: 159.3 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Forefoot pakai yang forefoot_lab_mm\n",
+    "df.drop(columns=['forefoot_brand_mm'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "id": "1b4897cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\caxyl\\AppData\\Local\\Temp\\ipykernel_15940\\522114045.py:2: Pandas4Warning: For backward compatibility, 'str' dtypes are included by select_dtypes when 'object' dtype is specified. This behavior is deprecated and will be removed in a future version. Explicitly pass 'str' to `include` to select them, or to `exclude` to remove them and silence this warning.\n",
+      "See https://pandas.pydata.org/docs/user_guide/migration-3-strings.html#string-migration-select-dtypes for details on how to write code that works with pandas 2 and 3.\n",
+      "  for col in df.select_dtypes(include=['object']).columns:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Rocker</th>\n",
+       "      <th>Orthotic friendly</th>\n",
+       "      <th>Removable insole</th>\n",
+       "      <th>pace_daily_running</th>\n",
+       "      <th>pace_tempo</th>\n",
+       "      <th>pace_competition</th>\n",
+       "      <th>arch_neutral</th>\n",
+       "      <th>...</th>\n",
+       "      <th>heel_stiff_moderate</th>\n",
+       "      <th>heel_stiff_stiff</th>\n",
+       "      <th>plate_0</th>\n",
+       "      <th>plate_rock_plate</th>\n",
+       "      <th>plate_carbon_plate</th>\n",
+       "      <th>heel_lab_mm</th>\n",
+       "      <th>forefoot_lab_mm</th>\n",
+       "      <th>season_summer</th>\n",
+       "      <th>season_winter</th>\n",
+       "      <th>season_all</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>brooks</td>\n",
+       "      <td>launch 9</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>32.4</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>brooks</td>\n",
+       "      <td>levitate 6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>34.3</td>\n",
+       "      <td>26.6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>4dfwd</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>33.3</td>\n",
+       "      <td>24.4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>4dfwd 2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>31.8</td>\n",
+       "      <td>21.2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>4dfwd 3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>32.6</td>\n",
+       "      <td>22.7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 46 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand         Name  Lightweight  Rocker  Orthotic friendly  \\\n",
+       "0  brooks     launch 9            1       0                  1   \n",
+       "1  brooks   levitate 6            0       0                  1   \n",
+       "2  adidas        4dfwd            0       0                  1   \n",
+       "3  adidas      4dfwd 2            0       0                  1   \n",
+       "4  adidas      4dfwd 3            0       0                  1   \n",
+       "\n",
+       "   Removable insole  pace_daily_running  pace_tempo  pace_competition  \\\n",
+       "0                 1                   1           1                 0   \n",
+       "1                 1                   1           0                 0   \n",
+       "2                 1                   1           0                 0   \n",
+       "3                 1                   1           0                 0   \n",
+       "4                 1                   1           0                 0   \n",
+       "\n",
+       "   arch_neutral  ...  heel_stiff_moderate  heel_stiff_stiff  plate_0  \\\n",
+       "0             1  ...                    0                 0        1   \n",
+       "1             1  ...                    1                 0        1   \n",
+       "2             1  ...                    0                 0        1   \n",
+       "3             1  ...                    1                 0        1   \n",
+       "4             1  ...                    0                 0        1   \n",
+       "\n",
+       "   plate_rock_plate  plate_carbon_plate  heel_lab_mm  forefoot_lab_mm  \\\n",
+       "0                 0                   0         32.4             23.0   \n",
+       "1                 0                   0         34.3             26.6   \n",
+       "2                 0                   0         33.3             24.4   \n",
+       "3                 0                   0         31.8             21.2   \n",
+       "4                 0                   0         32.6             22.7   \n",
+       "\n",
+       "   season_summer  season_winter  season_all  \n",
+       "0              0              0           0  \n",
+       "1              1              0           1  \n",
+       "2              0              0           1  \n",
+       "3              0              0           1  \n",
+       "4              0              0           1  \n",
+       "\n",
+       "[5 rows x 46 columns]"
+      ]
+     },
+     "execution_count": 181,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Making all dataset lowercase\n",
+    "for col in df.select_dtypes(include=['object']).columns:\n",
+    "    df[col] = df[col].str.lower()\n",
+    "\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "id": "5315d530",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 443 entries, 0 to 442\n",
+      "Data columns (total 46 columns):\n",
+      " #   Column               Non-Null Count  Dtype  \n",
+      "---  ------               --------------  -----  \n",
+      " 0   Brand                443 non-null    str    \n",
+      " 1   Name                 443 non-null    str    \n",
+      " 2   Lightweight          443 non-null    int64  \n",
+      " 3   Rocker               443 non-null    int64  \n",
+      " 4   Orthotic friendly    443 non-null    int64  \n",
+      " 5   Removable insole     443 non-null    int64  \n",
+      " 6   pace_daily_running   443 non-null    int64  \n",
+      " 7   pace_tempo           443 non-null    int64  \n",
+      " 8   pace_competition     443 non-null    int64  \n",
+      " 9   arch_neutral         443 non-null    int64  \n",
+      " 10  arch_stability       443 non-null    int64  \n",
+      " 11  weight_lab_oz        443 non-null    float64\n",
+      " 12  drop_lab_mm          443 non-null    float64\n",
+      " 13  strike_heel          443 non-null    int64  \n",
+      " 14  strike_mid           443 non-null    int64  \n",
+      " 15  strike_forefoot      443 non-null    int64  \n",
+      " 16  softness_soft        443 non-null    int64  \n",
+      " 17  softness_balanced    443 non-null    int64  \n",
+      " 18  softness_firm        443 non-null    int64  \n",
+      " 19  toebox_durability    443 non-null    int64  \n",
+      " 20  heel_durability      443 non-null    int64  \n",
+      " 21  outsole_durability   443 non-null    int64  \n",
+      " 22  breathability        443 non-null    int64  \n",
+      " 23  width_narrow         443 non-null    int64  \n",
+      " 24  width_medium         443 non-null    int64  \n",
+      " 25  width_wide           443 non-null    int64  \n",
+      " 26  toebox_narrow        443 non-null    int64  \n",
+      " 27  toebox_medium        443 non-null    int64  \n",
+      " 28  toebox_wide          443 non-null    int64  \n",
+      " 29  stiffness_flexible   443 non-null    int64  \n",
+      " 30  stiffness_moderate   443 non-null    int64  \n",
+      " 31  stiffness_stiff      443 non-null    int64  \n",
+      " 32  torsional_flexible   443 non-null    int64  \n",
+      " 33  torsional_moderate   443 non-null    int64  \n",
+      " 34  torsional_stiff      443 non-null    int64  \n",
+      " 35  heel_stiff_flexible  443 non-null    int64  \n",
+      " 36  heel_stiff_moderate  443 non-null    int64  \n",
+      " 37  heel_stiff_stiff     443 non-null    int64  \n",
+      " 38  plate_0              443 non-null    int64  \n",
+      " 39  plate_rock_plate     443 non-null    int64  \n",
+      " 40  plate_carbon_plate   443 non-null    int64  \n",
+      " 41  heel_lab_mm          443 non-null    float64\n",
+      " 42  forefoot_lab_mm      443 non-null    float64\n",
+      " 43  season_summer        443 non-null    int64  \n",
+      " 44  season_winter        443 non-null    int64  \n",
+      " 45  season_all           443 non-null    int64  \n",
+      "dtypes: float64(4), int64(40), str(2)\n",
+      "memory usage: 159.3 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "id": "b5bb5f00",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.rename(columns={\n",
+    "    'Brand': 'brand',\n",
+    "    'Name': 'name',\n",
+    "    'plate_rock_plate': 'plate_rock',\n",
+    "    'plate_carbon_plate': 'plate_carbon'}, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "id": "a88f5ca3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    plate_0  plate_rock  plate_carbon\n",
+      "0         1           0             0\n",
+      "1         1           0             0\n",
+      "2         1           0             0\n",
+      "3         1           0             0\n",
+      "4         1           0             0\n",
+      "..      ...         ...           ...\n",
+      "95        0           0             1\n",
+      "96        0           0             1\n",
+      "97        0           0             1\n",
+      "98        0           0             1\n",
+      "99        0           0             1\n",
+      "\n",
+      "[100 rows x 3 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[[\"plate_0\", \"plate_rock\", \"plate_carbon\"]].head(100))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "id": "5561d3b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 443 entries, 0 to 442\n",
+      "Data columns (total 45 columns):\n",
+      " #   Column               Non-Null Count  Dtype  \n",
+      "---  ------               --------------  -----  \n",
+      " 0   brand                443 non-null    str    \n",
+      " 1   name                 443 non-null    str    \n",
+      " 2   Lightweight          443 non-null    int64  \n",
+      " 3   Rocker               443 non-null    int64  \n",
+      " 4   Orthotic friendly    443 non-null    int64  \n",
+      " 5   Removable insole     443 non-null    int64  \n",
+      " 6   pace_daily_running   443 non-null    int64  \n",
+      " 7   pace_tempo           443 non-null    int64  \n",
+      " 8   pace_competition     443 non-null    int64  \n",
+      " 9   arch_neutral         443 non-null    int64  \n",
+      " 10  arch_stability       443 non-null    int64  \n",
+      " 11  weight_lab_oz        443 non-null    float64\n",
+      " 12  drop_lab_mm          443 non-null    float64\n",
+      " 13  strike_heel          443 non-null    int64  \n",
+      " 14  strike_mid           443 non-null    int64  \n",
+      " 15  strike_forefoot      443 non-null    int64  \n",
+      " 16  softness_soft        443 non-null    int64  \n",
+      " 17  softness_balanced    443 non-null    int64  \n",
+      " 18  softness_firm        443 non-null    int64  \n",
+      " 19  toebox_durability    443 non-null    int64  \n",
+      " 20  heel_durability      443 non-null    int64  \n",
+      " 21  outsole_durability   443 non-null    int64  \n",
+      " 22  breathability        443 non-null    int64  \n",
+      " 23  width_narrow         443 non-null    int64  \n",
+      " 24  width_medium         443 non-null    int64  \n",
+      " 25  width_wide           443 non-null    int64  \n",
+      " 26  toebox_narrow        443 non-null    int64  \n",
+      " 27  toebox_medium        443 non-null    int64  \n",
+      " 28  toebox_wide          443 non-null    int64  \n",
+      " 29  stiffness_flexible   443 non-null    int64  \n",
+      " 30  stiffness_moderate   443 non-null    int64  \n",
+      " 31  stiffness_stiff      443 non-null    int64  \n",
+      " 32  torsional_flexible   443 non-null    int64  \n",
+      " 33  torsional_moderate   443 non-null    int64  \n",
+      " 34  torsional_stiff      443 non-null    int64  \n",
+      " 35  heel_stiff_flexible  443 non-null    int64  \n",
+      " 36  heel_stiff_moderate  443 non-null    int64  \n",
+      " 37  heel_stiff_stiff     443 non-null    int64  \n",
+      " 38  plate_rock           443 non-null    int64  \n",
+      " 39  plate_carbon         443 non-null    int64  \n",
+      " 40  heel_lab_mm          443 non-null    float64\n",
+      " 41  forefoot_lab_mm      443 non-null    float64\n",
+      " 42  season_summer        443 non-null    int64  \n",
+      " 43  season_winter        443 non-null    int64  \n",
+      " 44  season_all           443 non-null    int64  \n",
+      "dtypes: float64(4), int64(39), str(2)\n",
+      "memory usage: 155.9 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=['plate_0'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "id": "11eb637c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 443 entries, 0 to 442\n",
+      "Data columns (total 45 columns):\n",
+      " #   Column               Non-Null Count  Dtype  \n",
+      "---  ------               --------------  -----  \n",
+      " 0   brand                443 non-null    str    \n",
+      " 1   name                 443 non-null    str    \n",
+      " 2   lightweight          443 non-null    int64  \n",
+      " 3   rocker               443 non-null    int64  \n",
+      " 4   orthotic_friendly    443 non-null    int64  \n",
+      " 5   removable_insole     443 non-null    int64  \n",
+      " 6   pace_daily_running   443 non-null    int64  \n",
+      " 7   pace_tempo           443 non-null    int64  \n",
+      " 8   pace_competition     443 non-null    int64  \n",
+      " 9   arch_neutral         443 non-null    int64  \n",
+      " 10  arch_stability       443 non-null    int64  \n",
+      " 11  weight_lab_oz        443 non-null    float64\n",
+      " 12  drop_lab_mm          443 non-null    float64\n",
+      " 13  strike_heel          443 non-null    int64  \n",
+      " 14  strike_mid           443 non-null    int64  \n",
+      " 15  strike_forefoot      443 non-null    int64  \n",
+      " 16  softness_soft        443 non-null    int64  \n",
+      " 17  softness_balanced    443 non-null    int64  \n",
+      " 18  softness_firm        443 non-null    int64  \n",
+      " 19  toebox_durability    443 non-null    int64  \n",
+      " 20  heel_durability      443 non-null    int64  \n",
+      " 21  outsole_durability   443 non-null    int64  \n",
+      " 22  breathability        443 non-null    int64  \n",
+      " 23  width_narrow         443 non-null    int64  \n",
+      " 24  width_medium         443 non-null    int64  \n",
+      " 25  width_wide           443 non-null    int64  \n",
+      " 26  toebox_narrow        443 non-null    int64  \n",
+      " 27  toebox_medium        443 non-null    int64  \n",
+      " 28  toebox_wide          443 non-null    int64  \n",
+      " 29  stiffness_flexible   443 non-null    int64  \n",
+      " 30  stiffness_moderate   443 non-null    int64  \n",
+      " 31  stiffness_stiff      443 non-null    int64  \n",
+      " 32  torsional_flexible   443 non-null    int64  \n",
+      " 33  torsional_moderate   443 non-null    int64  \n",
+      " 34  torsional_stiff      443 non-null    int64  \n",
+      " 35  heel_stiff_flexible  443 non-null    int64  \n",
+      " 36  heel_stiff_moderate  443 non-null    int64  \n",
+      " 37  heel_stiff_stiff     443 non-null    int64  \n",
+      " 38  plate_rock           443 non-null    int64  \n",
+      " 39  plate_carbon         443 non-null    int64  \n",
+      " 40  heel_lab_mm          443 non-null    float64\n",
+      " 41  forefoot_lab_mm      443 non-null    float64\n",
+      " 42  season_summer        443 non-null    int64  \n",
+      " 43  season_winter        443 non-null    int64  \n",
+      " 44  season_all           443 non-null    int64  \n",
+      "dtypes: float64(4), int64(39), str(2)\n",
+      "memory usage: 155.9 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.rename(columns={\n",
+    "    'Lightweight': 'lightweight',\n",
+    "    'Removable insole': 'removable_insole',\n",
+    "    'Orthotic friendly': 'orthotic_friendly',\n",
+    "    'Rocker': 'rocker'}, inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 187,
+   "id": "decafa32",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>brand</th>\n",
+       "      <th>name</th>\n",
+       "      <th>lightweight</th>\n",
+       "      <th>rocker</th>\n",
+       "      <th>orthotic_friendly</th>\n",
+       "      <th>removable_insole</th>\n",
+       "      <th>pace_daily_running</th>\n",
+       "      <th>pace_tempo</th>\n",
+       "      <th>pace_competition</th>\n",
+       "      <th>arch_neutral</th>\n",
+       "      <th>...</th>\n",
+       "      <th>heel_stiff_flexible</th>\n",
+       "      <th>heel_stiff_moderate</th>\n",
+       "      <th>heel_stiff_stiff</th>\n",
+       "      <th>plate_rock</th>\n",
+       "      <th>plate_carbon</th>\n",
+       "      <th>heel_lab_mm</th>\n",
+       "      <th>forefoot_lab_mm</th>\n",
+       "      <th>season_summer</th>\n",
+       "      <th>season_winter</th>\n",
+       "      <th>season_all</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>brooks</td>\n",
+       "      <td>launch 9</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>32.4</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>brooks</td>\n",
+       "      <td>levitate 6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>34.3</td>\n",
+       "      <td>26.6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>4dfwd</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>33.3</td>\n",
+       "      <td>24.4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>4dfwd 2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>31.8</td>\n",
+       "      <td>21.2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>4dfwd 3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>32.6</td>\n",
+       "      <td>22.7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 45 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    brand         name  lightweight  rocker  orthotic_friendly  \\\n",
+       "0  brooks     launch 9            1       0                  1   \n",
+       "1  brooks   levitate 6            0       0                  1   \n",
+       "2  adidas        4dfwd            0       0                  1   \n",
+       "3  adidas      4dfwd 2            0       0                  1   \n",
+       "4  adidas      4dfwd 3            0       0                  1   \n",
+       "\n",
+       "   removable_insole  pace_daily_running  pace_tempo  pace_competition  \\\n",
+       "0                 1                   1           1                 0   \n",
+       "1                 1                   1           0                 0   \n",
+       "2                 1                   1           0                 0   \n",
+       "3                 1                   1           0                 0   \n",
+       "4                 1                   1           0                 0   \n",
+       "\n",
+       "   arch_neutral  ...  heel_stiff_flexible  heel_stiff_moderate  \\\n",
+       "0             1  ...                    1                    0   \n",
+       "1             1  ...                    0                    1   \n",
+       "2             1  ...                    1                    0   \n",
+       "3             1  ...                    0                    1   \n",
+       "4             1  ...                    1                    0   \n",
+       "\n",
+       "   heel_stiff_stiff  plate_rock  plate_carbon  heel_lab_mm  forefoot_lab_mm  \\\n",
+       "0                 0           0             0         32.4             23.0   \n",
+       "1                 0           0             0         34.3             26.6   \n",
+       "2                 0           0             0         33.3             24.4   \n",
+       "3                 0           0             0         31.8             21.2   \n",
+       "4                 0           0             0         32.6             22.7   \n",
+       "\n",
+       "   season_summer  season_winter  season_all  \n",
+       "0              0              0           0  \n",
+       "1              1              0           1  \n",
+       "2              0              0           1  \n",
+       "3              0              0           1  \n",
+       "4              0              0           1  \n",
+       "\n",
+       "[5 rows x 45 columns]"
+      ]
+     },
+     "execution_count": 187,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 189,
+   "id": "091429a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv('../../data/road_dataset.csv', index=False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/data-preparation-v2/pre-eda-trail-v2.ipynb b/notebooks/data-preparation-v2/pre-eda-trail-v2.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..ef7479dff167db1af3087492cd30cd5ab0ff1377
--- /dev/null
+++ b/notebooks/data-preparation-v2/pre-eda-trail-v2.ipynb
@@ -0,0 +1,8478 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f5cc0f6b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand-Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Trail terrain</th>\n",
+       "      <th>Shock absorption</th>\n",
+       "      <th>Energy return</th>\n",
+       "      <th>Traction</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Heel stack lab Heel stack brand</th>\n",
+       "      <th>Forefoot lab Forefoot brand</th>\n",
+       "      <th>Widths available</th>\n",
+       "      <th>For heavy runners</th>\n",
+       "      <th>Season</th>\n",
+       "      <th>Removable insole</th>\n",
+       "      <th>Orthotic friendly</th>\n",
+       "      <th>Waterproofing</th>\n",
+       "      <th>Ranking</th>\n",
+       "      <th>Popularity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Adidas Terrex Agravic Speed Ultra</td>\n",
+       "      <td>90 Great!</td>\n",
+       "      <td>$220</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>High</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.1 oz / 259g 9.5 oz / 270g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>30.6 mm 38.0 mm</td>\n",
+       "      <td>30.3 mm 30.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#76 Top 21%</td>\n",
+       "      <td>#177 Top 47%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Adidas Terrex Speed Ultra</td>\n",
+       "      <td>90 Great!</td>\n",
+       "      <td>3559500 Rp</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.1 oz / 258g 9 oz / 255g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>32.8 mm 26.0 mm</td>\n",
+       "      <td>24.6 mm 18.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#49 Top 13%</td>\n",
+       "      <td>#298 Bottom 21%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Altra Experience Wild</td>\n",
+       "      <td>88 Great!</td>\n",
+       "      <td>2966250 Rp</td>\n",
+       "      <td>Light Moderate</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.1 oz / 285g 9.6 oz / 273g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>34.5 mm 34.0 mm</td>\n",
+       "      <td>30.2 mm 30.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#263 Top 40%</td>\n",
+       "      <td>#326 Top 49%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Altra Experience Wild 2</td>\n",
+       "      <td>84 Good!</td>\n",
+       "      <td>2966250 Rp</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>High</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.4 oz / 266g 10.3 oz / 293g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>32.3 mm 32.0 mm</td>\n",
+       "      <td>26.2 mm 28.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#245 Bottom 35%</td>\n",
+       "      <td>#154 Top 41%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Altra Lone Peak 5.0</td>\n",
+       "      <td>91 Superb!</td>\n",
+       "      <td>$130</td>\n",
+       "      <td>Light Moderate</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 302g 10.6 oz / 301g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>24.5 mm 25.0 mm</td>\n",
+       "      <td>24.3 mm 25.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#68 Top 11%</td>\n",
+       "      <td>#55 Top 9%</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 36 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                          Brand-Name Audience score       Price  \\\n",
+       "0  Adidas Terrex Agravic Speed Ultra      90 Great!        $220   \n",
+       "1          Adidas Terrex Speed Ultra      90 Great!  3559500 Rp   \n",
+       "2              Altra Experience Wild      88 Great!  2966250 Rp   \n",
+       "3            Altra Experience Wild 2       84 Good!  2966250 Rp   \n",
+       "4                Altra Lone Peak 5.0     91 Superb!        $130   \n",
+       "\n",
+       "    Trail terrain Shock absorption Energy return Traction Arch support  \\\n",
+       "0           Light         Moderate          High        -      Neutral   \n",
+       "1           Light                -             -        -      Neutral   \n",
+       "2  Light Moderate         Moderate           Low        -      Neutral   \n",
+       "3           Light         Moderate           Low     High      Neutral   \n",
+       "4  Light Moderate                -             -        -      Neutral   \n",
+       "\n",
+       "         Weight lab Weight brand  Lightweight  ...  \\\n",
+       "0    9.1 oz / 259g 9.5 oz / 270g          0.0  ...   \n",
+       "1      9.1 oz / 258g 9 oz / 255g          0.0  ...   \n",
+       "2   10.1 oz / 285g 9.6 oz / 273g          0.0  ...   \n",
+       "3   9.4 oz / 266g 10.3 oz / 293g          0.0  ...   \n",
+       "4  10.7 oz / 302g 10.6 oz / 301g          0.0  ...   \n",
+       "\n",
+       "  Heel stack lab Heel stack brand Forefoot lab Forefoot brand  \\\n",
+       "0                 30.6 mm 38.0 mm             30.3 mm 30.0 mm   \n",
+       "1                 32.8 mm 26.0 mm             24.6 mm 18.0 mm   \n",
+       "2                 34.5 mm 34.0 mm             30.2 mm 30.0 mm   \n",
+       "3                 32.3 mm 32.0 mm             26.2 mm 28.0 mm   \n",
+       "4                 24.5 mm 25.0 mm             24.3 mm 25.0 mm   \n",
+       "\n",
+       "  Widths available For heavy runners       Season Removable insole  \\\n",
+       "0           Normal               0.0  All seasons                1   \n",
+       "1           Normal               0.0            -                1   \n",
+       "2           Normal               0.0  All seasons                1   \n",
+       "3           Normal               0.0  All seasons                1   \n",
+       "4           Normal               0.0            -                1   \n",
+       "\n",
+       "  Orthotic friendly Waterproofing          Ranking       Popularity  \n",
+       "0                 1             -      #76 Top 21%     #177 Top 47%  \n",
+       "1                 1             -      #49 Top 13%  #298 Bottom 21%  \n",
+       "2                 1             -     #263 Top 40%     #326 Top 49%  \n",
+       "3                 1             -  #245 Bottom 35%     #154 Top 41%  \n",
+       "4                 1             -      #68 Top 11%       #55 Top 9%  \n",
+       "\n",
+       "[5 rows x 36 columns]"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df_ori = pd.read_csv('../../data/SONIX utilities - Trail.csv')\n",
+    "df_ori.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ccddc8ad",
+   "metadata": {},
+   "source": [
+    "# Separate Brand-Name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "20d5e4a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand-Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Trail terrain</th>\n",
+       "      <th>Shock absorption</th>\n",
+       "      <th>Energy return</th>\n",
+       "      <th>Traction</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Heel stack lab Heel stack brand</th>\n",
+       "      <th>Forefoot lab Forefoot brand</th>\n",
+       "      <th>Widths available</th>\n",
+       "      <th>For heavy runners</th>\n",
+       "      <th>Season</th>\n",
+       "      <th>Removable insole</th>\n",
+       "      <th>Orthotic friendly</th>\n",
+       "      <th>Waterproofing</th>\n",
+       "      <th>Ranking</th>\n",
+       "      <th>Popularity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>adidas terrex agravic speed ultra</td>\n",
+       "      <td>90 Great!</td>\n",
+       "      <td>$220</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>High</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.1 oz / 259g 9.5 oz / 270g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>30.6 mm 38.0 mm</td>\n",
+       "      <td>30.3 mm 30.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#76 Top 21%</td>\n",
+       "      <td>#177 Top 47%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>adidas terrex speed ultra</td>\n",
+       "      <td>90 Great!</td>\n",
+       "      <td>3559500 Rp</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.1 oz / 258g 9 oz / 255g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>32.8 mm 26.0 mm</td>\n",
+       "      <td>24.6 mm 18.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#49 Top 13%</td>\n",
+       "      <td>#298 Bottom 21%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>altra experience wild</td>\n",
+       "      <td>88 Great!</td>\n",
+       "      <td>2966250 Rp</td>\n",
+       "      <td>Light Moderate</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.1 oz / 285g 9.6 oz / 273g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>34.5 mm 34.0 mm</td>\n",
+       "      <td>30.2 mm 30.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#263 Top 40%</td>\n",
+       "      <td>#326 Top 49%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>altra experience wild 2</td>\n",
+       "      <td>84 Good!</td>\n",
+       "      <td>2966250 Rp</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>High</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.4 oz / 266g 10.3 oz / 293g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>32.3 mm 32.0 mm</td>\n",
+       "      <td>26.2 mm 28.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#245 Bottom 35%</td>\n",
+       "      <td>#154 Top 41%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>altra lone peak 5.0</td>\n",
+       "      <td>91 Superb!</td>\n",
+       "      <td>$130</td>\n",
+       "      <td>Light Moderate</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 302g 10.6 oz / 301g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>24.5 mm 25.0 mm</td>\n",
+       "      <td>24.3 mm 25.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#68 Top 11%</td>\n",
+       "      <td>#55 Top 9%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>altra lone peak 6</td>\n",
+       "      <td>89 Great!</td>\n",
+       "      <td>$140</td>\n",
+       "      <td>Moderate Technical</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.8 oz / 278g 9.7 oz / 275g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.1 mm 25.0 mm</td>\n",
+       "      <td>24.5 mm 25.0 mm</td>\n",
+       "      <td>Normal Wide</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#147 Top 22%</td>\n",
+       "      <td>#341 Bottom 49%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>altra lone peak 7</td>\n",
+       "      <td>86 Good!</td>\n",
+       "      <td>3164000 Rp</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.4 oz / 294g 11 oz / 312g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.3 mm 25.0 mm</td>\n",
+       "      <td>23.1 mm 25.0 mm</td>\n",
+       "      <td>Normal Wide</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#403 Bottom 40%</td>\n",
+       "      <td>#273 Top 41%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>altra lone peak 8</td>\n",
+       "      <td>81 Good!</td>\n",
+       "      <td>$140</td>\n",
+       "      <td>Light Moderate</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.2 oz / 288g 10.7 oz / 303g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>22.7 mm 25.0 mm</td>\n",
+       "      <td>21.3 mm 25.0 mm</td>\n",
+       "      <td>Normal Wide</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#567 Bottom 15%</td>\n",
+       "      <td>#177 Top 27%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>altra lone peak 9</td>\n",
+       "      <td>91 Superb!</td>\n",
+       "      <td>$140</td>\n",
+       "      <td>Light Moderate</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.9 oz / 309g 10.4 oz / 295g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.3 mm 25.0 mm</td>\n",
+       "      <td>23.3 mm 25.0 mm</td>\n",
+       "      <td>Normal Wide</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#25 Top 7%</td>\n",
+       "      <td>#41 Top 11%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>altra mont blanc</td>\n",
+       "      <td>79 Good!</td>\n",
+       "      <td>$180</td>\n",
+       "      <td>Light Moderate</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.6 oz / 272g 9.9 oz / 280g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>33.8 mm 30.0 mm</td>\n",
+       "      <td>33.8 mm</td>\n",
+       "      <td>Normal Wide</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#332 Bottom 12%</td>\n",
+       "      <td>#241 Bottom 36%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>altra mont blanc carbon</td>\n",
+       "      <td>86 Good!</td>\n",
+       "      <td>4943750 Rp</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>8.9 oz / 251g 9.3 oz / 264g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>27.2 mm 29.0 mm</td>\n",
+       "      <td>26.9 mm 29.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#189 Top 50%</td>\n",
+       "      <td>#273 Bottom 27%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>altra olympus 275</td>\n",
+       "      <td>82 Good!</td>\n",
+       "      <td>3856130 Rp</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>High</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 303g 10.8 oz / 305g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>30.8 mm 33.0 mm</td>\n",
+       "      <td>30.5 mm 33.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#298 Bottom 21%</td>\n",
+       "      <td>#240 Bottom 36%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>altra olympus 5</td>\n",
+       "      <td>83 Good!</td>\n",
+       "      <td>3558800 Rp</td>\n",
+       "      <td>Light Moderate</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>11.5 oz / 325g 12.3 oz / 350g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>33.0 mm 33.0 mm</td>\n",
+       "      <td>31.0 mm 33.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#526 Bottom 21%</td>\n",
+       "      <td>#302 Top 45%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>altra olympus 6</td>\n",
+       "      <td>83 Good!</td>\n",
+       "      <td>$175</td>\n",
+       "      <td>Light Moderate</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.6 oz / 357g 12.5 oz / 354g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>32.2 mm 35.0 mm</td>\n",
+       "      <td>31.5 mm 35.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Summer All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#273 Bottom 27%</td>\n",
+       "      <td>#116 Top 31%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>altra outroad</td>\n",
+       "      <td>81 Good!</td>\n",
+       "      <td>2966250 Rp</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.1 oz / 287g 10.7 oz / 303g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.1 mm 27.0 mm</td>\n",
+       "      <td>25.0 mm 27.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#578 Bottom 14%</td>\n",
+       "      <td>#527 Bottom 21%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>altra outroad 2</td>\n",
+       "      <td>79 Good!</td>\n",
+       "      <td>2570750 Rp</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.3 oz / 291g 10.1 oz / 286g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>26.9 mm 27.5 mm</td>\n",
+       "      <td>25.5 mm 27.5 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#610 Bottom 9%</td>\n",
+       "      <td>#564 Bottom 16%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>altra outroad 3</td>\n",
+       "      <td>81 Good!</td>\n",
+       "      <td>2570750 Rp</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.2 oz / 261g 10.7 oz / 303g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.8 mm 27.0 mm</td>\n",
+       "      <td>23.2 mm 27.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#313 Bottom 17%</td>\n",
+       "      <td>#283 Bottom 25%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>altra superior 6</td>\n",
+       "      <td>78 Decent!</td>\n",
+       "      <td>$130</td>\n",
+       "      <td>Light Moderate</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.6 oz / 272g 9.1 oz / 258g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>22.1 mm 20.5 mm</td>\n",
+       "      <td>22.0 mm 20.5 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Summer All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#631 Bottom 6%</td>\n",
+       "      <td>#524 Bottom 22%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>altra superior 7</td>\n",
+       "      <td>82 Good!</td>\n",
+       "      <td>2373000 Rp</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>High</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>8.3 oz / 235g 9.3 oz / 263g</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>20.6 mm 21.0 mm</td>\n",
+       "      <td>20.0 mm 21.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#293 Bottom 22%</td>\n",
+       "      <td>#271 Bottom 28%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>altra timp 4</td>\n",
+       "      <td>78 Decent!</td>\n",
+       "      <td>3164000 Rp</td>\n",
+       "      <td>Light Moderate</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>11.1 oz / 316g 10.6 oz / 300g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>29.0 mm 30.0 mm</td>\n",
+       "      <td>28.9 mm 30.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#626 Bottom 6%</td>\n",
+       "      <td>#523 Bottom 22%</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>20 rows × 36 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                           Brand-Name Audience score       Price  \\\n",
+       "0   adidas terrex agravic speed ultra      90 Great!        $220   \n",
+       "1           adidas terrex speed ultra      90 Great!  3559500 Rp   \n",
+       "2               altra experience wild      88 Great!  2966250 Rp   \n",
+       "3             altra experience wild 2       84 Good!  2966250 Rp   \n",
+       "4                 altra lone peak 5.0     91 Superb!        $130   \n",
+       "5                   altra lone peak 6      89 Great!        $140   \n",
+       "6                   altra lone peak 7       86 Good!  3164000 Rp   \n",
+       "7                   altra lone peak 8       81 Good!        $140   \n",
+       "8                   altra lone peak 9     91 Superb!        $140   \n",
+       "9                    altra mont blanc       79 Good!        $180   \n",
+       "10            altra mont blanc carbon       86 Good!  4943750 Rp   \n",
+       "11                  altra olympus 275       82 Good!  3856130 Rp   \n",
+       "12                    altra olympus 5       83 Good!  3558800 Rp   \n",
+       "13                    altra olympus 6       83 Good!        $175   \n",
+       "14                      altra outroad       81 Good!  2966250 Rp   \n",
+       "15                    altra outroad 2       79 Good!  2570750 Rp   \n",
+       "16                    altra outroad 3       81 Good!  2570750 Rp   \n",
+       "17                   altra superior 6     78 Decent!        $130   \n",
+       "18                   altra superior 7       82 Good!  2373000 Rp   \n",
+       "19                       altra timp 4     78 Decent!  3164000 Rp   \n",
+       "\n",
+       "         Trail terrain Shock absorption Energy return Traction Arch support  \\\n",
+       "0                Light         Moderate          High        -      Neutral   \n",
+       "1                Light                -             -        -      Neutral   \n",
+       "2       Light Moderate         Moderate           Low        -      Neutral   \n",
+       "3                Light         Moderate           Low     High      Neutral   \n",
+       "4       Light Moderate                -             -        -      Neutral   \n",
+       "5   Moderate Technical                -             -        -      Neutral   \n",
+       "6             Moderate                -             -        -      Neutral   \n",
+       "7       Light Moderate                -             -        -      Neutral   \n",
+       "8       Light Moderate              Low      Moderate        -      Neutral   \n",
+       "9       Light Moderate                -             -        -      Neutral   \n",
+       "10            Moderate                -             -        -      Neutral   \n",
+       "11               Light         Moderate           Low     High      Neutral   \n",
+       "12      Light Moderate                -             -        -      Neutral   \n",
+       "13      Light Moderate         Moderate      Moderate        -      Neutral   \n",
+       "14               Light                -             -        -      Neutral   \n",
+       "15               Light                -             -        -      Neutral   \n",
+       "16               Light                -             -        -      Neutral   \n",
+       "17      Light Moderate                -             -        -      Neutral   \n",
+       "18               Light              Low           Low     High      Neutral   \n",
+       "19      Light Moderate                -             -        -      Neutral   \n",
+       "\n",
+       "          Weight lab Weight brand  Lightweight  ...  \\\n",
+       "0     9.1 oz / 259g 9.5 oz / 270g          0.0  ...   \n",
+       "1       9.1 oz / 258g 9 oz / 255g          0.0  ...   \n",
+       "2    10.1 oz / 285g 9.6 oz / 273g          0.0  ...   \n",
+       "3    9.4 oz / 266g 10.3 oz / 293g          0.0  ...   \n",
+       "4   10.7 oz / 302g 10.6 oz / 301g          0.0  ...   \n",
+       "5     9.8 oz / 278g 9.7 oz / 275g          0.0  ...   \n",
+       "6     10.4 oz / 294g 11 oz / 312g          0.0  ...   \n",
+       "7   10.2 oz / 288g 10.7 oz / 303g          0.0  ...   \n",
+       "8   10.9 oz / 309g 10.4 oz / 295g          0.0  ...   \n",
+       "9     9.6 oz / 272g 9.9 oz / 280g          0.0  ...   \n",
+       "10    8.9 oz / 251g 9.3 oz / 264g          0.0  ...   \n",
+       "11  10.7 oz / 303g 10.8 oz / 305g          0.0  ...   \n",
+       "12  11.5 oz / 325g 12.3 oz / 350g          0.0  ...   \n",
+       "13  12.6 oz / 357g 12.5 oz / 354g          0.0  ...   \n",
+       "14  10.1 oz / 287g 10.7 oz / 303g          0.0  ...   \n",
+       "15  10.3 oz / 291g 10.1 oz / 286g          0.0  ...   \n",
+       "16   9.2 oz / 261g 10.7 oz / 303g          0.0  ...   \n",
+       "17    9.6 oz / 272g 9.1 oz / 258g          0.0  ...   \n",
+       "18    8.3 oz / 235g 9.3 oz / 263g          1.0  ...   \n",
+       "19  11.1 oz / 316g 10.6 oz / 300g          0.0  ...   \n",
+       "\n",
+       "   Heel stack lab Heel stack brand Forefoot lab Forefoot brand  \\\n",
+       "0                  30.6 mm 38.0 mm             30.3 mm 30.0 mm   \n",
+       "1                  32.8 mm 26.0 mm             24.6 mm 18.0 mm   \n",
+       "2                  34.5 mm 34.0 mm             30.2 mm 30.0 mm   \n",
+       "3                  32.3 mm 32.0 mm             26.2 mm 28.0 mm   \n",
+       "4                  24.5 mm 25.0 mm             24.3 mm 25.0 mm   \n",
+       "5                  25.1 mm 25.0 mm             24.5 mm 25.0 mm   \n",
+       "6                  23.3 mm 25.0 mm             23.1 mm 25.0 mm   \n",
+       "7                  22.7 mm 25.0 mm             21.3 mm 25.0 mm   \n",
+       "8                  23.3 mm 25.0 mm             23.3 mm 25.0 mm   \n",
+       "9                  33.8 mm 30.0 mm                     33.8 mm   \n",
+       "10                 27.2 mm 29.0 mm             26.9 mm 29.0 mm   \n",
+       "11                 30.8 mm 33.0 mm             30.5 mm 33.0 mm   \n",
+       "12                 33.0 mm 33.0 mm             31.0 mm 33.0 mm   \n",
+       "13                 32.2 mm 35.0 mm             31.5 mm 35.0 mm   \n",
+       "14                 25.1 mm 27.0 mm             25.0 mm 27.0 mm   \n",
+       "15                 26.9 mm 27.5 mm             25.5 mm 27.5 mm   \n",
+       "16                 23.8 mm 27.0 mm             23.2 mm 27.0 mm   \n",
+       "17                 22.1 mm 20.5 mm             22.0 mm 20.5 mm   \n",
+       "18                 20.6 mm 21.0 mm             20.0 mm 21.0 mm   \n",
+       "19                 29.0 mm 30.0 mm             28.9 mm 30.0 mm   \n",
+       "\n",
+       "   Widths available For heavy runners              Season Removable insole  \\\n",
+       "0            Normal               0.0         All seasons                1   \n",
+       "1            Normal               0.0                   -                1   \n",
+       "2            Normal               0.0         All seasons                1   \n",
+       "3            Normal               0.0         All seasons                1   \n",
+       "4            Normal               0.0                   -                1   \n",
+       "5       Normal Wide               0.0                   -                0   \n",
+       "6       Normal Wide               0.0         All seasons                1   \n",
+       "7       Normal Wide               0.0         All seasons                1   \n",
+       "8       Normal Wide               0.0         All seasons                1   \n",
+       "9       Normal Wide               0.0                   -                0   \n",
+       "10           Normal               0.0         All seasons                1   \n",
+       "11           Normal               0.0         All seasons                1   \n",
+       "12           Normal               0.0         All seasons                1   \n",
+       "13           Normal               1.0  Summer All seasons                1   \n",
+       "14           Normal               0.0         All seasons                1   \n",
+       "15           Normal               0.0         All seasons                1   \n",
+       "16           Normal               0.0         All seasons                1   \n",
+       "17           Normal               0.0  Summer All seasons                1   \n",
+       "18           Normal               0.0         All seasons                1   \n",
+       "19           Normal               0.0         All seasons                1   \n",
+       "\n",
+       "   Orthotic friendly Waterproofing          Ranking       Popularity  \n",
+       "0                  1             -      #76 Top 21%     #177 Top 47%  \n",
+       "1                  1             -      #49 Top 13%  #298 Bottom 21%  \n",
+       "2                  1             -     #263 Top 40%     #326 Top 49%  \n",
+       "3                  1             -  #245 Bottom 35%     #154 Top 41%  \n",
+       "4                  1             -      #68 Top 11%       #55 Top 9%  \n",
+       "5                  0             -     #147 Top 22%  #341 Bottom 49%  \n",
+       "6                  1             -  #403 Bottom 40%     #273 Top 41%  \n",
+       "7                  1             -  #567 Bottom 15%     #177 Top 27%  \n",
+       "8                  1             -       #25 Top 7%      #41 Top 11%  \n",
+       "9                  0             -  #332 Bottom 12%  #241 Bottom 36%  \n",
+       "10                 1             -     #189 Top 50%  #273 Bottom 27%  \n",
+       "11                 1             -  #298 Bottom 21%  #240 Bottom 36%  \n",
+       "12                 1             -  #526 Bottom 21%     #302 Top 45%  \n",
+       "13                 1             -  #273 Bottom 27%     #116 Top 31%  \n",
+       "14                 1             -  #578 Bottom 14%  #527 Bottom 21%  \n",
+       "15                 1             -   #610 Bottom 9%  #564 Bottom 16%  \n",
+       "16                 1             -  #313 Bottom 17%  #283 Bottom 25%  \n",
+       "17                 1             -   #631 Bottom 6%  #524 Bottom 22%  \n",
+       "18                 1             -  #293 Bottom 22%  #271 Bottom 28%  \n",
+       "19                 1             -   #626 Bottom 6%  #523 Bottom 22%  \n",
+       "\n",
+       "[20 rows x 36 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Ubah jadi lowercase biar memudahkan searching dsb\n",
+    "df_ori['Brand-Name'] = df_ori['Brand-Name'].str.lower()\n",
+    "df_ori.head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0bebd201",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "''' \n",
+    "Running shoes for trail brand in our dataset include:\n",
+    "    Adidas\n",
+    "    Altra\n",
+    "    ASICS\n",
+    "    Brooks\n",
+    "    HOKA\n",
+    "    Icebug\n",
+    "    Inov8\n",
+    "    Kailas\n",
+    "    KEEN\n",
+    "    La Sportiva\n",
+    "    Merrell\n",
+    "    New Balance\n",
+    "    Nike\n",
+    "    NNormal\n",
+    "    On\n",
+    "    Salomon\n",
+    "    Saucony\n",
+    "    Topo\n",
+    "    Xero\n",
+    "    Scarpa\n",
+    "    The North Face\n",
+    "'''\n",
+    "\n",
+    "brands_list = [\n",
+    "    \"Adidas\", \"Altra\", \"ASICS\", \"Brooks\", \"HOKA\", \"Icebug\", \"Inov8\", \n",
+    "    \"Kailas\", \"KEEN\", \"La Sportiva\", \"Merrell\", \"New Balance\", \"Nike\", \n",
+    "    \"NNormal\", \"On\", \"Salomon\", \"Saucony\", \"Topo\", \"Xero\", \"Scarpa\", \n",
+    "    \"The North Face\"\n",
+    "]\n",
+    "brands = [b.lower() for b in brands_list]\n",
+    "brands.sort(key=len, reverse=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "3a304c3d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Trail terrain</th>\n",
+       "      <th>Shock absorption</th>\n",
+       "      <th>Energy return</th>\n",
+       "      <th>Traction</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Heel stack lab Heel stack brand</th>\n",
+       "      <th>Forefoot lab Forefoot brand</th>\n",
+       "      <th>Widths available</th>\n",
+       "      <th>For heavy runners</th>\n",
+       "      <th>Season</th>\n",
+       "      <th>Removable insole</th>\n",
+       "      <th>Orthotic friendly</th>\n",
+       "      <th>Waterproofing</th>\n",
+       "      <th>Ranking</th>\n",
+       "      <th>Popularity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>terrex agravic speed ultra</td>\n",
+       "      <td>90 Great!</td>\n",
+       "      <td>$220</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>High</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.1 oz / 259g 9.5 oz / 270g</td>\n",
+       "      <td>...</td>\n",
+       "      <td>30.6 mm 38.0 mm</td>\n",
+       "      <td>30.3 mm 30.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#76 Top 21%</td>\n",
+       "      <td>#177 Top 47%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>terrex speed ultra</td>\n",
+       "      <td>90 Great!</td>\n",
+       "      <td>3559500 Rp</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.1 oz / 258g 9 oz / 255g</td>\n",
+       "      <td>...</td>\n",
+       "      <td>32.8 mm 26.0 mm</td>\n",
+       "      <td>24.6 mm 18.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#49 Top 13%</td>\n",
+       "      <td>#298 Bottom 21%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>experience wild</td>\n",
+       "      <td>88 Great!</td>\n",
+       "      <td>2966250 Rp</td>\n",
+       "      <td>Light Moderate</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.1 oz / 285g 9.6 oz / 273g</td>\n",
+       "      <td>...</td>\n",
+       "      <td>34.5 mm 34.0 mm</td>\n",
+       "      <td>30.2 mm 30.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#263 Top 40%</td>\n",
+       "      <td>#326 Top 49%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>experience wild 2</td>\n",
+       "      <td>84 Good!</td>\n",
+       "      <td>2966250 Rp</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>High</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.4 oz / 266g 10.3 oz / 293g</td>\n",
+       "      <td>...</td>\n",
+       "      <td>32.3 mm 32.0 mm</td>\n",
+       "      <td>26.2 mm 28.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#245 Bottom 35%</td>\n",
+       "      <td>#154 Top 41%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>lone peak 5.0</td>\n",
+       "      <td>91 Superb!</td>\n",
+       "      <td>$130</td>\n",
+       "      <td>Light Moderate</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 302g 10.6 oz / 301g</td>\n",
+       "      <td>...</td>\n",
+       "      <td>24.5 mm 25.0 mm</td>\n",
+       "      <td>24.3 mm 25.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#68 Top 11%</td>\n",
+       "      <td>#55 Top 9%</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 37 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand                        Name Audience score       Price  \\\n",
+       "0  adidas  terrex agravic speed ultra      90 Great!        $220   \n",
+       "1  adidas          terrex speed ultra      90 Great!  3559500 Rp   \n",
+       "2   altra             experience wild      88 Great!  2966250 Rp   \n",
+       "3   altra           experience wild 2       84 Good!  2966250 Rp   \n",
+       "4   altra               lone peak 5.0     91 Superb!        $130   \n",
+       "\n",
+       "    Trail terrain Shock absorption Energy return Traction Arch support  \\\n",
+       "0           Light         Moderate          High        -      Neutral   \n",
+       "1           Light                -             -        -      Neutral   \n",
+       "2  Light Moderate         Moderate           Low        -      Neutral   \n",
+       "3           Light         Moderate           Low     High      Neutral   \n",
+       "4  Light Moderate                -             -        -      Neutral   \n",
+       "\n",
+       "         Weight lab Weight brand  ...  Heel stack lab Heel stack brand  \\\n",
+       "0    9.1 oz / 259g 9.5 oz / 270g  ...                  30.6 mm 38.0 mm   \n",
+       "1      9.1 oz / 258g 9 oz / 255g  ...                  32.8 mm 26.0 mm   \n",
+       "2   10.1 oz / 285g 9.6 oz / 273g  ...                  34.5 mm 34.0 mm   \n",
+       "3   9.4 oz / 266g 10.3 oz / 293g  ...                  32.3 mm 32.0 mm   \n",
+       "4  10.7 oz / 302g 10.6 oz / 301g  ...                  24.5 mm 25.0 mm   \n",
+       "\n",
+       "  Forefoot lab Forefoot brand Widths available For heavy runners       Season  \\\n",
+       "0             30.3 mm 30.0 mm           Normal               0.0  All seasons   \n",
+       "1             24.6 mm 18.0 mm           Normal               0.0            -   \n",
+       "2             30.2 mm 30.0 mm           Normal               0.0  All seasons   \n",
+       "3             26.2 mm 28.0 mm           Normal               0.0  All seasons   \n",
+       "4             24.3 mm 25.0 mm           Normal               0.0            -   \n",
+       "\n",
+       "  Removable insole Orthotic friendly Waterproofing          Ranking  \\\n",
+       "0                1                 1             -      #76 Top 21%   \n",
+       "1                1                 1             -      #49 Top 13%   \n",
+       "2                1                 1             -     #263 Top 40%   \n",
+       "3                1                 1             -  #245 Bottom 35%   \n",
+       "4                1                 1             -      #68 Top 11%   \n",
+       "\n",
+       "        Popularity  \n",
+       "0     #177 Top 47%  \n",
+       "1  #298 Bottom 21%  \n",
+       "2     #326 Top 49%  \n",
+       "3     #154 Top 41%  \n",
+       "4       #55 Top 9%  \n",
+       "\n",
+       "[5 rows x 37 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def split_brand_name(full_text):\n",
+    "    for brand in brands:\n",
+    "        if full_text.startswith(brand):\n",
+    "            # Sisa dari brand dijadiin name semua\n",
+    "            name = full_text[len(brand):].strip()\n",
+    "            return brand, name\n",
+    "    return \"Unknown\", full_text \n",
+    "\n",
+    "\n",
+    "df_ori[['Brand', 'Name']] = df_ori['Brand-Name'].apply(lambda x: pd.Series(split_brand_name(x)))\n",
+    "\n",
+    "# Atur urutan kolom agar Brand dan Name ada di depan\n",
+    "cols = ['Brand', 'Name'] + [c for c in df_ori.columns if c not in ['Brand', 'Name', 'Brand-Name']]\n",
+    "df_ori = df_ori[cols]\n",
+    "\n",
+    "df_ori.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d7d61ef1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# df_ori.head(40)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "209d339d",
+   "metadata": {},
+   "source": [
+    "# Remove Duplicates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "498021b3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "183\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>hoka</td>\n",
+       "      <td>mafate x</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66</th>\n",
+       "      <td>inov8</td>\n",
+       "      <td>trailfly</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>83</th>\n",
+       "      <td>la sportiva</td>\n",
+       "      <td>prodigio</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>84</th>\n",
+       "      <td>la sportiva</td>\n",
+       "      <td>prodigio</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>124</th>\n",
+       "      <td>nike</td>\n",
+       "      <td>terra kiger 9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>137</th>\n",
+       "      <td>on</td>\n",
+       "      <td>cloudsurfer trail 2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>141</th>\n",
+       "      <td>on</td>\n",
+       "      <td>cloudvista 2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>180</th>\n",
+       "      <td>topo</td>\n",
+       "      <td>ultraventure 4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Brand                 Name\n",
+       "52          hoka             mafate x\n",
+       "66         inov8             trailfly\n",
+       "83   la sportiva             prodigio\n",
+       "84   la sportiva             prodigio\n",
+       "124         nike        terra kiger 9\n",
+       "137           on  cloudsurfer trail 2\n",
+       "141           on         cloudvista 2\n",
+       "180         topo       ultraventure 4"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dup_mask = df_ori.duplicated(subset=[\"Brand\", \"Name\"], keep=\"first\")\n",
+    "print(len(dup_mask))\n",
+    "df_ori.loc[dup_mask, [\"Brand\", \"Name\"]].head(100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "0f107030",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Before: 183\n",
+      "After : 175\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Trail terrain</th>\n",
+       "      <th>Shock absorption</th>\n",
+       "      <th>Energy return</th>\n",
+       "      <th>Traction</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Heel stack lab Heel stack brand</th>\n",
+       "      <th>Forefoot lab Forefoot brand</th>\n",
+       "      <th>Widths available</th>\n",
+       "      <th>For heavy runners</th>\n",
+       "      <th>Season</th>\n",
+       "      <th>Removable insole</th>\n",
+       "      <th>Orthotic friendly</th>\n",
+       "      <th>Waterproofing</th>\n",
+       "      <th>Ranking</th>\n",
+       "      <th>Popularity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>terrex agravic speed ultra</td>\n",
+       "      <td>90 Great!</td>\n",
+       "      <td>$220</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>High</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.1 oz / 259g 9.5 oz / 270g</td>\n",
+       "      <td>...</td>\n",
+       "      <td>30.6 mm 38.0 mm</td>\n",
+       "      <td>30.3 mm 30.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#76 Top 21%</td>\n",
+       "      <td>#177 Top 47%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>terrex speed ultra</td>\n",
+       "      <td>90 Great!</td>\n",
+       "      <td>3559500 Rp</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.1 oz / 258g 9 oz / 255g</td>\n",
+       "      <td>...</td>\n",
+       "      <td>32.8 mm 26.0 mm</td>\n",
+       "      <td>24.6 mm 18.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#49 Top 13%</td>\n",
+       "      <td>#298 Bottom 21%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>experience wild</td>\n",
+       "      <td>88 Great!</td>\n",
+       "      <td>2966250 Rp</td>\n",
+       "      <td>Light Moderate</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.1 oz / 285g 9.6 oz / 273g</td>\n",
+       "      <td>...</td>\n",
+       "      <td>34.5 mm 34.0 mm</td>\n",
+       "      <td>30.2 mm 30.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#263 Top 40%</td>\n",
+       "      <td>#326 Top 49%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>experience wild 2</td>\n",
+       "      <td>84 Good!</td>\n",
+       "      <td>2966250 Rp</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>High</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.4 oz / 266g 10.3 oz / 293g</td>\n",
+       "      <td>...</td>\n",
+       "      <td>32.3 mm 32.0 mm</td>\n",
+       "      <td>26.2 mm 28.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#245 Bottom 35%</td>\n",
+       "      <td>#154 Top 41%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>lone peak 5.0</td>\n",
+       "      <td>91 Superb!</td>\n",
+       "      <td>$130</td>\n",
+       "      <td>Light Moderate</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 302g 10.6 oz / 301g</td>\n",
+       "      <td>...</td>\n",
+       "      <td>24.5 mm 25.0 mm</td>\n",
+       "      <td>24.3 mm 25.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#68 Top 11%</td>\n",
+       "      <td>#55 Top 9%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>lone peak 6</td>\n",
+       "      <td>89 Great!</td>\n",
+       "      <td>$140</td>\n",
+       "      <td>Moderate Technical</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.8 oz / 278g 9.7 oz / 275g</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.1 mm 25.0 mm</td>\n",
+       "      <td>24.5 mm 25.0 mm</td>\n",
+       "      <td>Normal Wide</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#147 Top 22%</td>\n",
+       "      <td>#341 Bottom 49%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>lone peak 7</td>\n",
+       "      <td>86 Good!</td>\n",
+       "      <td>3164000 Rp</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.4 oz / 294g 11 oz / 312g</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.3 mm 25.0 mm</td>\n",
+       "      <td>23.1 mm 25.0 mm</td>\n",
+       "      <td>Normal Wide</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#403 Bottom 40%</td>\n",
+       "      <td>#273 Top 41%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>lone peak 8</td>\n",
+       "      <td>81 Good!</td>\n",
+       "      <td>$140</td>\n",
+       "      <td>Light Moderate</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.2 oz / 288g 10.7 oz / 303g</td>\n",
+       "      <td>...</td>\n",
+       "      <td>22.7 mm 25.0 mm</td>\n",
+       "      <td>21.3 mm 25.0 mm</td>\n",
+       "      <td>Normal Wide</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#567 Bottom 15%</td>\n",
+       "      <td>#177 Top 27%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>lone peak 9</td>\n",
+       "      <td>91 Superb!</td>\n",
+       "      <td>$140</td>\n",
+       "      <td>Light Moderate</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.9 oz / 309g 10.4 oz / 295g</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.3 mm 25.0 mm</td>\n",
+       "      <td>23.3 mm 25.0 mm</td>\n",
+       "      <td>Normal Wide</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#25 Top 7%</td>\n",
+       "      <td>#41 Top 11%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>mont blanc</td>\n",
+       "      <td>79 Good!</td>\n",
+       "      <td>$180</td>\n",
+       "      <td>Light Moderate</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.6 oz / 272g 9.9 oz / 280g</td>\n",
+       "      <td>...</td>\n",
+       "      <td>33.8 mm 30.0 mm</td>\n",
+       "      <td>33.8 mm</td>\n",
+       "      <td>Normal Wide</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#332 Bottom 12%</td>\n",
+       "      <td>#241 Bottom 36%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>mont blanc carbon</td>\n",
+       "      <td>86 Good!</td>\n",
+       "      <td>4943750 Rp</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>8.9 oz / 251g 9.3 oz / 264g</td>\n",
+       "      <td>...</td>\n",
+       "      <td>27.2 mm 29.0 mm</td>\n",
+       "      <td>26.9 mm 29.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#189 Top 50%</td>\n",
+       "      <td>#273 Bottom 27%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>olympus 275</td>\n",
+       "      <td>82 Good!</td>\n",
+       "      <td>3856130 Rp</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>High</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 303g 10.8 oz / 305g</td>\n",
+       "      <td>...</td>\n",
+       "      <td>30.8 mm 33.0 mm</td>\n",
+       "      <td>30.5 mm 33.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#298 Bottom 21%</td>\n",
+       "      <td>#240 Bottom 36%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>olympus 5</td>\n",
+       "      <td>83 Good!</td>\n",
+       "      <td>3558800 Rp</td>\n",
+       "      <td>Light Moderate</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>11.5 oz / 325g 12.3 oz / 350g</td>\n",
+       "      <td>...</td>\n",
+       "      <td>33.0 mm 33.0 mm</td>\n",
+       "      <td>31.0 mm 33.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#526 Bottom 21%</td>\n",
+       "      <td>#302 Top 45%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>olympus 6</td>\n",
+       "      <td>83 Good!</td>\n",
+       "      <td>$175</td>\n",
+       "      <td>Light Moderate</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.6 oz / 357g 12.5 oz / 354g</td>\n",
+       "      <td>...</td>\n",
+       "      <td>32.2 mm 35.0 mm</td>\n",
+       "      <td>31.5 mm 35.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Summer All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#273 Bottom 27%</td>\n",
+       "      <td>#116 Top 31%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>outroad</td>\n",
+       "      <td>81 Good!</td>\n",
+       "      <td>2966250 Rp</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.1 oz / 287g 10.7 oz / 303g</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.1 mm 27.0 mm</td>\n",
+       "      <td>25.0 mm 27.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#578 Bottom 14%</td>\n",
+       "      <td>#527 Bottom 21%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>outroad 2</td>\n",
+       "      <td>79 Good!</td>\n",
+       "      <td>2570750 Rp</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.3 oz / 291g 10.1 oz / 286g</td>\n",
+       "      <td>...</td>\n",
+       "      <td>26.9 mm 27.5 mm</td>\n",
+       "      <td>25.5 mm 27.5 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#610 Bottom 9%</td>\n",
+       "      <td>#564 Bottom 16%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>outroad 3</td>\n",
+       "      <td>81 Good!</td>\n",
+       "      <td>2570750 Rp</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.2 oz / 261g 10.7 oz / 303g</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.8 mm 27.0 mm</td>\n",
+       "      <td>23.2 mm 27.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#313 Bottom 17%</td>\n",
+       "      <td>#283 Bottom 25%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>superior 6</td>\n",
+       "      <td>78 Decent!</td>\n",
+       "      <td>$130</td>\n",
+       "      <td>Light Moderate</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.6 oz / 272g 9.1 oz / 258g</td>\n",
+       "      <td>...</td>\n",
+       "      <td>22.1 mm 20.5 mm</td>\n",
+       "      <td>22.0 mm 20.5 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Summer All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#631 Bottom 6%</td>\n",
+       "      <td>#524 Bottom 22%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>superior 7</td>\n",
+       "      <td>82 Good!</td>\n",
+       "      <td>2373000 Rp</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>High</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>8.3 oz / 235g 9.3 oz / 263g</td>\n",
+       "      <td>...</td>\n",
+       "      <td>20.6 mm 21.0 mm</td>\n",
+       "      <td>20.0 mm 21.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#293 Bottom 22%</td>\n",
+       "      <td>#271 Bottom 28%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>timp 4</td>\n",
+       "      <td>78 Decent!</td>\n",
+       "      <td>3164000 Rp</td>\n",
+       "      <td>Light Moderate</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>11.1 oz / 316g 10.6 oz / 300g</td>\n",
+       "      <td>...</td>\n",
+       "      <td>29.0 mm 30.0 mm</td>\n",
+       "      <td>28.9 mm 30.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#626 Bottom 6%</td>\n",
+       "      <td>#523 Bottom 22%</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>20 rows × 37 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Brand                        Name Audience score       Price  \\\n",
+       "0   adidas  terrex agravic speed ultra      90 Great!        $220   \n",
+       "1   adidas          terrex speed ultra      90 Great!  3559500 Rp   \n",
+       "2    altra             experience wild      88 Great!  2966250 Rp   \n",
+       "3    altra           experience wild 2       84 Good!  2966250 Rp   \n",
+       "4    altra               lone peak 5.0     91 Superb!        $130   \n",
+       "5    altra                 lone peak 6      89 Great!        $140   \n",
+       "6    altra                 lone peak 7       86 Good!  3164000 Rp   \n",
+       "7    altra                 lone peak 8       81 Good!        $140   \n",
+       "8    altra                 lone peak 9     91 Superb!        $140   \n",
+       "9    altra                  mont blanc       79 Good!        $180   \n",
+       "10   altra           mont blanc carbon       86 Good!  4943750 Rp   \n",
+       "11   altra                 olympus 275       82 Good!  3856130 Rp   \n",
+       "12   altra                   olympus 5       83 Good!  3558800 Rp   \n",
+       "13   altra                   olympus 6       83 Good!        $175   \n",
+       "14   altra                     outroad       81 Good!  2966250 Rp   \n",
+       "15   altra                   outroad 2       79 Good!  2570750 Rp   \n",
+       "16   altra                   outroad 3       81 Good!  2570750 Rp   \n",
+       "17   altra                  superior 6     78 Decent!        $130   \n",
+       "18   altra                  superior 7       82 Good!  2373000 Rp   \n",
+       "19   altra                      timp 4     78 Decent!  3164000 Rp   \n",
+       "\n",
+       "         Trail terrain Shock absorption Energy return Traction Arch support  \\\n",
+       "0                Light         Moderate          High        -      Neutral   \n",
+       "1                Light                -             -        -      Neutral   \n",
+       "2       Light Moderate         Moderate           Low        -      Neutral   \n",
+       "3                Light         Moderate           Low     High      Neutral   \n",
+       "4       Light Moderate                -             -        -      Neutral   \n",
+       "5   Moderate Technical                -             -        -      Neutral   \n",
+       "6             Moderate                -             -        -      Neutral   \n",
+       "7       Light Moderate                -             -        -      Neutral   \n",
+       "8       Light Moderate              Low      Moderate        -      Neutral   \n",
+       "9       Light Moderate                -             -        -      Neutral   \n",
+       "10            Moderate                -             -        -      Neutral   \n",
+       "11               Light         Moderate           Low     High      Neutral   \n",
+       "12      Light Moderate                -             -        -      Neutral   \n",
+       "13      Light Moderate         Moderate      Moderate        -      Neutral   \n",
+       "14               Light                -             -        -      Neutral   \n",
+       "15               Light                -             -        -      Neutral   \n",
+       "16               Light                -             -        -      Neutral   \n",
+       "17      Light Moderate                -             -        -      Neutral   \n",
+       "18               Light              Low           Low     High      Neutral   \n",
+       "19      Light Moderate                -             -        -      Neutral   \n",
+       "\n",
+       "          Weight lab Weight brand  ...  Heel stack lab Heel stack brand  \\\n",
+       "0     9.1 oz / 259g 9.5 oz / 270g  ...                  30.6 mm 38.0 mm   \n",
+       "1       9.1 oz / 258g 9 oz / 255g  ...                  32.8 mm 26.0 mm   \n",
+       "2    10.1 oz / 285g 9.6 oz / 273g  ...                  34.5 mm 34.0 mm   \n",
+       "3    9.4 oz / 266g 10.3 oz / 293g  ...                  32.3 mm 32.0 mm   \n",
+       "4   10.7 oz / 302g 10.6 oz / 301g  ...                  24.5 mm 25.0 mm   \n",
+       "5     9.8 oz / 278g 9.7 oz / 275g  ...                  25.1 mm 25.0 mm   \n",
+       "6     10.4 oz / 294g 11 oz / 312g  ...                  23.3 mm 25.0 mm   \n",
+       "7   10.2 oz / 288g 10.7 oz / 303g  ...                  22.7 mm 25.0 mm   \n",
+       "8   10.9 oz / 309g 10.4 oz / 295g  ...                  23.3 mm 25.0 mm   \n",
+       "9     9.6 oz / 272g 9.9 oz / 280g  ...                  33.8 mm 30.0 mm   \n",
+       "10    8.9 oz / 251g 9.3 oz / 264g  ...                  27.2 mm 29.0 mm   \n",
+       "11  10.7 oz / 303g 10.8 oz / 305g  ...                  30.8 mm 33.0 mm   \n",
+       "12  11.5 oz / 325g 12.3 oz / 350g  ...                  33.0 mm 33.0 mm   \n",
+       "13  12.6 oz / 357g 12.5 oz / 354g  ...                  32.2 mm 35.0 mm   \n",
+       "14  10.1 oz / 287g 10.7 oz / 303g  ...                  25.1 mm 27.0 mm   \n",
+       "15  10.3 oz / 291g 10.1 oz / 286g  ...                  26.9 mm 27.5 mm   \n",
+       "16   9.2 oz / 261g 10.7 oz / 303g  ...                  23.8 mm 27.0 mm   \n",
+       "17    9.6 oz / 272g 9.1 oz / 258g  ...                  22.1 mm 20.5 mm   \n",
+       "18    8.3 oz / 235g 9.3 oz / 263g  ...                  20.6 mm 21.0 mm   \n",
+       "19  11.1 oz / 316g 10.6 oz / 300g  ...                  29.0 mm 30.0 mm   \n",
+       "\n",
+       "   Forefoot lab Forefoot brand Widths available For heavy runners  \\\n",
+       "0              30.3 mm 30.0 mm           Normal               0.0   \n",
+       "1              24.6 mm 18.0 mm           Normal               0.0   \n",
+       "2              30.2 mm 30.0 mm           Normal               0.0   \n",
+       "3              26.2 mm 28.0 mm           Normal               0.0   \n",
+       "4              24.3 mm 25.0 mm           Normal               0.0   \n",
+       "5              24.5 mm 25.0 mm      Normal Wide               0.0   \n",
+       "6              23.1 mm 25.0 mm      Normal Wide               0.0   \n",
+       "7              21.3 mm 25.0 mm      Normal Wide               0.0   \n",
+       "8              23.3 mm 25.0 mm      Normal Wide               0.0   \n",
+       "9                      33.8 mm      Normal Wide               0.0   \n",
+       "10             26.9 mm 29.0 mm           Normal               0.0   \n",
+       "11             30.5 mm 33.0 mm           Normal               0.0   \n",
+       "12             31.0 mm 33.0 mm           Normal               0.0   \n",
+       "13             31.5 mm 35.0 mm           Normal               1.0   \n",
+       "14             25.0 mm 27.0 mm           Normal               0.0   \n",
+       "15             25.5 mm 27.5 mm           Normal               0.0   \n",
+       "16             23.2 mm 27.0 mm           Normal               0.0   \n",
+       "17             22.0 mm 20.5 mm           Normal               0.0   \n",
+       "18             20.0 mm 21.0 mm           Normal               0.0   \n",
+       "19             28.9 mm 30.0 mm           Normal               0.0   \n",
+       "\n",
+       "                Season Removable insole Orthotic friendly Waterproofing  \\\n",
+       "0          All seasons                1                 1             -   \n",
+       "1                    -                1                 1             -   \n",
+       "2          All seasons                1                 1             -   \n",
+       "3          All seasons                1                 1             -   \n",
+       "4                    -                1                 1             -   \n",
+       "5                    -                0                 0             -   \n",
+       "6          All seasons                1                 1             -   \n",
+       "7          All seasons                1                 1             -   \n",
+       "8          All seasons                1                 1             -   \n",
+       "9                    -                0                 0             -   \n",
+       "10         All seasons                1                 1             -   \n",
+       "11         All seasons                1                 1             -   \n",
+       "12         All seasons                1                 1             -   \n",
+       "13  Summer All seasons                1                 1             -   \n",
+       "14         All seasons                1                 1             -   \n",
+       "15         All seasons                1                 1             -   \n",
+       "16         All seasons                1                 1             -   \n",
+       "17  Summer All seasons                1                 1             -   \n",
+       "18         All seasons                1                 1             -   \n",
+       "19         All seasons                1                 1             -   \n",
+       "\n",
+       "            Ranking       Popularity  \n",
+       "0       #76 Top 21%     #177 Top 47%  \n",
+       "1       #49 Top 13%  #298 Bottom 21%  \n",
+       "2      #263 Top 40%     #326 Top 49%  \n",
+       "3   #245 Bottom 35%     #154 Top 41%  \n",
+       "4       #68 Top 11%       #55 Top 9%  \n",
+       "5      #147 Top 22%  #341 Bottom 49%  \n",
+       "6   #403 Bottom 40%     #273 Top 41%  \n",
+       "7   #567 Bottom 15%     #177 Top 27%  \n",
+       "8        #25 Top 7%      #41 Top 11%  \n",
+       "9   #332 Bottom 12%  #241 Bottom 36%  \n",
+       "10     #189 Top 50%  #273 Bottom 27%  \n",
+       "11  #298 Bottom 21%  #240 Bottom 36%  \n",
+       "12  #526 Bottom 21%     #302 Top 45%  \n",
+       "13  #273 Bottom 27%     #116 Top 31%  \n",
+       "14  #578 Bottom 14%  #527 Bottom 21%  \n",
+       "15   #610 Bottom 9%  #564 Bottom 16%  \n",
+       "16  #313 Bottom 17%  #283 Bottom 25%  \n",
+       "17   #631 Bottom 6%  #524 Bottom 22%  \n",
+       "18  #293 Bottom 22%  #271 Bottom 28%  \n",
+       "19   #626 Bottom 6%  #523 Bottom 22%  \n",
+       "\n",
+       "[20 rows x 37 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# sebelum hapus\n",
+    "print(\"Before:\", len(df_ori))\n",
+    "\n",
+    "#hapus\n",
+    "df_ori = df_ori.drop_duplicates(subset=[\"Brand\", \"Name\"], keep=\"first\").reset_index(drop=True)\n",
+    "\n",
+    "print(\"After :\", len(df_ori))\n",
+    "df_ori.head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8f770d4b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 175 entries, 0 to 174\n",
+      "Data columns (total 37 columns):\n",
+      " #   Column                                  Non-Null Count  Dtype  \n",
+      "---  ------                                  --------------  -----  \n",
+      " 0   Brand                                   175 non-null    str    \n",
+      " 1   Name                                    175 non-null    str    \n",
+      " 2   Audience score                          174 non-null    str    \n",
+      " 3   Price                                   175 non-null    str    \n",
+      " 4   Trail terrain                           175 non-null    str    \n",
+      " 5   Shock absorption                        175 non-null    str    \n",
+      " 6   Energy return                           175 non-null    str    \n",
+      " 7   Traction                                170 non-null    str    \n",
+      " 8   Arch support                            175 non-null    str    \n",
+      " 9   Weight lab Weight brand                 175 non-null    str    \n",
+      " 10  Lightweight                             168 non-null    float64\n",
+      " 11  Drop lab Drop brand                     175 non-null    str    \n",
+      " 12  Strike pattern                          175 non-null    str    \n",
+      " 13  Size                                    175 non-null    str    \n",
+      " 14  Midsole softness                        175 non-null    str    \n",
+      " 15  Difference in midsole softness in cold  175 non-null    str    \n",
+      " 16  Plate                                   175 non-null    str    \n",
+      " 17  Toebox durability                       175 non-null    str    \n",
+      " 18  Heel padding durability                 175 non-null    str    \n",
+      " 19  Outsole durability                      175 non-null    str    \n",
+      " 20  Breathability                           175 non-null    str    \n",
+      " 21  Width / fit                             175 non-null    str    \n",
+      " 22  Toebox width                            175 non-null    str    \n",
+      " 23  Stiffness                               175 non-null    str    \n",
+      " 24  Torsional rigidity                      175 non-null    str    \n",
+      " 25  Heel counter stiffness                  175 non-null    str    \n",
+      " 26  Lug depth                               175 non-null    str    \n",
+      " 27  Heel stack lab Heel stack brand         175 non-null    str    \n",
+      " 28  Forefoot lab Forefoot brand             175 non-null    str    \n",
+      " 29  Widths available                        175 non-null    str    \n",
+      " 30  For heavy runners                       171 non-null    float64\n",
+      " 31  Season                                  175 non-null    str    \n",
+      " 32  Removable insole                        175 non-null    int64  \n",
+      " 33  Orthotic friendly                       175 non-null    int64  \n",
+      " 34  Waterproofing                           169 non-null    str    \n",
+      " 35  Ranking                                 175 non-null    str    \n",
+      " 36  Popularity                              175 non-null    str    \n",
+      "dtypes: float64(2), int64(2), str(33)\n",
+      "memory usage: 50.7 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_ori.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d7f529a0",
+   "metadata": {},
+   "source": [
+    "# Removing unused column\n",
+    "\n",
+    "ini df-nya full buat machine learning aja jadi aku hapus semua. Brand Name dihapus terakhir"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "86901d21",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Trail terrain</th>\n",
+       "      <th>Shock absorption</th>\n",
+       "      <th>Energy return</th>\n",
+       "      <th>Traction</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Heel stack lab Heel stack brand</th>\n",
+       "      <th>Forefoot lab Forefoot brand</th>\n",
+       "      <th>Widths available</th>\n",
+       "      <th>For heavy runners</th>\n",
+       "      <th>Season</th>\n",
+       "      <th>Removable insole</th>\n",
+       "      <th>Orthotic friendly</th>\n",
+       "      <th>Waterproofing</th>\n",
+       "      <th>Ranking</th>\n",
+       "      <th>Popularity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>terrex agravic speed ultra</td>\n",
+       "      <td>90 Great!</td>\n",
+       "      <td>$220</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>High</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.1 oz / 259g 9.5 oz / 270g</td>\n",
+       "      <td>...</td>\n",
+       "      <td>30.6 mm 38.0 mm</td>\n",
+       "      <td>30.3 mm 30.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#76 Top 21%</td>\n",
+       "      <td>#177 Top 47%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>terrex speed ultra</td>\n",
+       "      <td>90 Great!</td>\n",
+       "      <td>3559500 Rp</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.1 oz / 258g 9 oz / 255g</td>\n",
+       "      <td>...</td>\n",
+       "      <td>32.8 mm 26.0 mm</td>\n",
+       "      <td>24.6 mm 18.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#49 Top 13%</td>\n",
+       "      <td>#298 Bottom 21%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>experience wild</td>\n",
+       "      <td>88 Great!</td>\n",
+       "      <td>2966250 Rp</td>\n",
+       "      <td>Light Moderate</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.1 oz / 285g 9.6 oz / 273g</td>\n",
+       "      <td>...</td>\n",
+       "      <td>34.5 mm 34.0 mm</td>\n",
+       "      <td>30.2 mm 30.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#263 Top 40%</td>\n",
+       "      <td>#326 Top 49%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>experience wild 2</td>\n",
+       "      <td>84 Good!</td>\n",
+       "      <td>2966250 Rp</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>High</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.4 oz / 266g 10.3 oz / 293g</td>\n",
+       "      <td>...</td>\n",
+       "      <td>32.3 mm 32.0 mm</td>\n",
+       "      <td>26.2 mm 28.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#245 Bottom 35%</td>\n",
+       "      <td>#154 Top 41%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>lone peak 5.0</td>\n",
+       "      <td>91 Superb!</td>\n",
+       "      <td>$130</td>\n",
+       "      <td>Light Moderate</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 302g 10.6 oz / 301g</td>\n",
+       "      <td>...</td>\n",
+       "      <td>24.5 mm 25.0 mm</td>\n",
+       "      <td>24.3 mm 25.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#68 Top 11%</td>\n",
+       "      <td>#55 Top 9%</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 37 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand                        Name Audience score       Price  \\\n",
+       "0  adidas  terrex agravic speed ultra      90 Great!        $220   \n",
+       "1  adidas          terrex speed ultra      90 Great!  3559500 Rp   \n",
+       "2   altra             experience wild      88 Great!  2966250 Rp   \n",
+       "3   altra           experience wild 2       84 Good!  2966250 Rp   \n",
+       "4   altra               lone peak 5.0     91 Superb!        $130   \n",
+       "\n",
+       "    Trail terrain Shock absorption Energy return Traction Arch support  \\\n",
+       "0           Light         Moderate          High        -      Neutral   \n",
+       "1           Light                -             -        -      Neutral   \n",
+       "2  Light Moderate         Moderate           Low        -      Neutral   \n",
+       "3           Light         Moderate           Low     High      Neutral   \n",
+       "4  Light Moderate                -             -        -      Neutral   \n",
+       "\n",
+       "         Weight lab Weight brand  ...  Heel stack lab Heel stack brand  \\\n",
+       "0    9.1 oz / 259g 9.5 oz / 270g  ...                  30.6 mm 38.0 mm   \n",
+       "1      9.1 oz / 258g 9 oz / 255g  ...                  32.8 mm 26.0 mm   \n",
+       "2   10.1 oz / 285g 9.6 oz / 273g  ...                  34.5 mm 34.0 mm   \n",
+       "3   9.4 oz / 266g 10.3 oz / 293g  ...                  32.3 mm 32.0 mm   \n",
+       "4  10.7 oz / 302g 10.6 oz / 301g  ...                  24.5 mm 25.0 mm   \n",
+       "\n",
+       "  Forefoot lab Forefoot brand Widths available For heavy runners       Season  \\\n",
+       "0             30.3 mm 30.0 mm           Normal               0.0  All seasons   \n",
+       "1             24.6 mm 18.0 mm           Normal               0.0            -   \n",
+       "2             30.2 mm 30.0 mm           Normal               0.0  All seasons   \n",
+       "3             26.2 mm 28.0 mm           Normal               0.0  All seasons   \n",
+       "4             24.3 mm 25.0 mm           Normal               0.0            -   \n",
+       "\n",
+       "  Removable insole Orthotic friendly Waterproofing          Ranking  \\\n",
+       "0                1                 1             -      #76 Top 21%   \n",
+       "1                1                 1             -      #49 Top 13%   \n",
+       "2                1                 1             -     #263 Top 40%   \n",
+       "3                1                 1             -  #245 Bottom 35%   \n",
+       "4                1                 1             -      #68 Top 11%   \n",
+       "\n",
+       "        Popularity  \n",
+       "0     #177 Top 47%  \n",
+       "1  #298 Bottom 21%  \n",
+       "2     #326 Top 49%  \n",
+       "3     #154 Top 41%  \n",
+       "4       #55 Top 9%  \n",
+       "\n",
+       "[5 rows x 37 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = df_ori.copy()\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "8457eba5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 175 entries, 0 to 174\n",
+      "Data columns (total 37 columns):\n",
+      " #   Column                                  Non-Null Count  Dtype  \n",
+      "---  ------                                  --------------  -----  \n",
+      " 0   Brand                                   175 non-null    str    \n",
+      " 1   Name                                    175 non-null    str    \n",
+      " 2   Audience score                          174 non-null    str    \n",
+      " 3   Price                                   175 non-null    str    \n",
+      " 4   Trail terrain                           175 non-null    str    \n",
+      " 5   Shock absorption                        175 non-null    str    \n",
+      " 6   Energy return                           175 non-null    str    \n",
+      " 7   Traction                                170 non-null    str    \n",
+      " 8   Arch support                            175 non-null    str    \n",
+      " 9   Weight lab Weight brand                 175 non-null    str    \n",
+      " 10  Lightweight                             168 non-null    float64\n",
+      " 11  Drop lab Drop brand                     175 non-null    str    \n",
+      " 12  Strike pattern                          175 non-null    str    \n",
+      " 13  Size                                    175 non-null    str    \n",
+      " 14  Midsole softness                        175 non-null    str    \n",
+      " 15  Difference in midsole softness in cold  175 non-null    str    \n",
+      " 16  Plate                                   175 non-null    str    \n",
+      " 17  Toebox durability                       175 non-null    str    \n",
+      " 18  Heel padding durability                 175 non-null    str    \n",
+      " 19  Outsole durability                      175 non-null    str    \n",
+      " 20  Breathability                           175 non-null    str    \n",
+      " 21  Width / fit                             175 non-null    str    \n",
+      " 22  Toebox width                            175 non-null    str    \n",
+      " 23  Stiffness                               175 non-null    str    \n",
+      " 24  Torsional rigidity                      175 non-null    str    \n",
+      " 25  Heel counter stiffness                  175 non-null    str    \n",
+      " 26  Lug depth                               175 non-null    str    \n",
+      " 27  Heel stack lab Heel stack brand         175 non-null    str    \n",
+      " 28  Forefoot lab Forefoot brand             175 non-null    str    \n",
+      " 29  Widths available                        175 non-null    str    \n",
+      " 30  For heavy runners                       171 non-null    float64\n",
+      " 31  Season                                  175 non-null    str    \n",
+      " 32  Removable insole                        175 non-null    int64  \n",
+      " 33  Orthotic friendly                       175 non-null    int64  \n",
+      " 34  Waterproofing                           169 non-null    str    \n",
+      " 35  Ranking                                 175 non-null    str    \n",
+      " 36  Popularity                              175 non-null    str    \n",
+      "dtypes: float64(2), int64(2), str(33)\n",
+      "memory usage: 50.7 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "2ae0eae6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Trail terrain</th>\n",
+       "      <th>Shock absorption</th>\n",
+       "      <th>Energy return</th>\n",
+       "      <th>Traction</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Torsional rigidity</th>\n",
+       "      <th>Heel counter stiffness</th>\n",
+       "      <th>Lug depth</th>\n",
+       "      <th>Heel stack lab Heel stack brand</th>\n",
+       "      <th>Forefoot lab Forefoot brand</th>\n",
+       "      <th>For heavy runners</th>\n",
+       "      <th>Season</th>\n",
+       "      <th>Removable insole</th>\n",
+       "      <th>Orthotic friendly</th>\n",
+       "      <th>Waterproofing</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>terrex agravic speed ultra</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>High</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.1 oz / 259g 9.5 oz / 270g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.3 mm 8.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Stiff</td>\n",
+       "      <td>Flexible</td>\n",
+       "      <td>2.5 mm</td>\n",
+       "      <td>30.6 mm 38.0 mm</td>\n",
+       "      <td>30.3 mm 30.0 mm</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>terrex speed ultra</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.1 oz / 258g 9 oz / 255g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8.2 mm 8.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Flexible</td>\n",
+       "      <td>Flexible</td>\n",
+       "      <td>2.6 mm</td>\n",
+       "      <td>32.8 mm 26.0 mm</td>\n",
+       "      <td>24.6 mm 18.0 mm</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>experience wild</td>\n",
+       "      <td>Light Moderate</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.1 oz / 285g 9.6 oz / 273g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.3 mm 4.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Stiff</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>3.6 mm</td>\n",
+       "      <td>34.5 mm 34.0 mm</td>\n",
+       "      <td>30.2 mm 30.0 mm</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>experience wild 2</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>High</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.4 oz / 266g 10.3 oz / 293g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.1 mm 4.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Flexible</td>\n",
+       "      <td>3.5 mm</td>\n",
+       "      <td>32.3 mm 32.0 mm</td>\n",
+       "      <td>26.2 mm 28.0 mm</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>lone peak 5.0</td>\n",
+       "      <td>Light Moderate</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 302g 10.6 oz / 301g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.2 mm 0.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Flexible</td>\n",
+       "      <td>-</td>\n",
+       "      <td>3.7 mm</td>\n",
+       "      <td>24.5 mm 25.0 mm</td>\n",
+       "      <td>24.3 mm 25.0 mm</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 30 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand                        Name   Trail terrain Shock absorption  \\\n",
+       "0  adidas  terrex agravic speed ultra           Light         Moderate   \n",
+       "1  adidas          terrex speed ultra           Light                -   \n",
+       "2   altra             experience wild  Light Moderate         Moderate   \n",
+       "3   altra           experience wild 2           Light         Moderate   \n",
+       "4   altra               lone peak 5.0  Light Moderate                -   \n",
+       "\n",
+       "  Energy return Traction Arch support        Weight lab Weight brand  \\\n",
+       "0          High        -      Neutral    9.1 oz / 259g 9.5 oz / 270g   \n",
+       "1             -        -      Neutral      9.1 oz / 258g 9 oz / 255g   \n",
+       "2           Low        -      Neutral   10.1 oz / 285g 9.6 oz / 273g   \n",
+       "3           Low     High      Neutral   9.4 oz / 266g 10.3 oz / 293g   \n",
+       "4             -        -      Neutral  10.7 oz / 302g 10.6 oz / 301g   \n",
+       "\n",
+       "   Lightweight Drop lab Drop brand  ... Torsional rigidity  \\\n",
+       "0          0.0       0.3 mm 8.0 mm  ...              Stiff   \n",
+       "1          0.0       8.2 mm 8.0 mm  ...           Flexible   \n",
+       "2          0.0       4.3 mm 4.0 mm  ...              Stiff   \n",
+       "3          0.0       6.1 mm 4.0 mm  ...           Moderate   \n",
+       "4          0.0       0.2 mm 0.0 mm  ...           Flexible   \n",
+       "\n",
+       "  Heel counter stiffness Lug depth Heel stack lab Heel stack brand  \\\n",
+       "0               Flexible    2.5 mm                 30.6 mm 38.0 mm   \n",
+       "1               Flexible    2.6 mm                 32.8 mm 26.0 mm   \n",
+       "2               Moderate    3.6 mm                 34.5 mm 34.0 mm   \n",
+       "3               Flexible    3.5 mm                 32.3 mm 32.0 mm   \n",
+       "4                      -    3.7 mm                 24.5 mm 25.0 mm   \n",
+       "\n",
+       "  Forefoot lab Forefoot brand For heavy runners       Season Removable insole  \\\n",
+       "0             30.3 mm 30.0 mm               0.0  All seasons                1   \n",
+       "1             24.6 mm 18.0 mm               0.0            -                1   \n",
+       "2             30.2 mm 30.0 mm               0.0  All seasons                1   \n",
+       "3             26.2 mm 28.0 mm               0.0  All seasons                1   \n",
+       "4             24.3 mm 25.0 mm               0.0            -                1   \n",
+       "\n",
+       "  Orthotic friendly Waterproofing  \n",
+       "0                 1             -  \n",
+       "1                 1             -  \n",
+       "2                 1             -  \n",
+       "3                 1             -  \n",
+       "4                 1             -  \n",
+       "\n",
+       "[5 rows x 30 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.drop(columns=['Audience score', 'Price', 'Size', \n",
+    "                 'Difference in midsole softness in cold',\n",
+    "                 'Widths available', 'Ranking', 'Popularity'], inplace=True)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "8f2cacd8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 175 entries, 0 to 174\n",
+      "Data columns (total 30 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   Brand                            175 non-null    str    \n",
+      " 1   Name                             175 non-null    str    \n",
+      " 2   Trail terrain                    175 non-null    str    \n",
+      " 3   Shock absorption                 175 non-null    str    \n",
+      " 4   Energy return                    175 non-null    str    \n",
+      " 5   Traction                         170 non-null    str    \n",
+      " 6   Arch support                     175 non-null    str    \n",
+      " 7   Weight lab Weight brand          175 non-null    str    \n",
+      " 8   Lightweight                      168 non-null    float64\n",
+      " 9   Drop lab Drop brand              175 non-null    str    \n",
+      " 10  Strike pattern                   175 non-null    str    \n",
+      " 11  Midsole softness                 175 non-null    str    \n",
+      " 12  Plate                            175 non-null    str    \n",
+      " 13  Toebox durability                175 non-null    str    \n",
+      " 14  Heel padding durability          175 non-null    str    \n",
+      " 15  Outsole durability               175 non-null    str    \n",
+      " 16  Breathability                    175 non-null    str    \n",
+      " 17  Width / fit                      175 non-null    str    \n",
+      " 18  Toebox width                     175 non-null    str    \n",
+      " 19  Stiffness                        175 non-null    str    \n",
+      " 20  Torsional rigidity               175 non-null    str    \n",
+      " 21  Heel counter stiffness           175 non-null    str    \n",
+      " 22  Lug depth                        175 non-null    str    \n",
+      " 23  Heel stack lab Heel stack brand  175 non-null    str    \n",
+      " 24  Forefoot lab Forefoot brand      175 non-null    str    \n",
+      " 25  For heavy runners                171 non-null    float64\n",
+      " 26  Season                           175 non-null    str    \n",
+      " 27  Removable insole                 175 non-null    int64  \n",
+      " 28  Orthotic friendly                175 non-null    int64  \n",
+      " 29  Waterproofing                    169 non-null    str    \n",
+      "dtypes: float64(2), int64(2), str(26)\n",
+      "memory usage: 41.1 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5729690",
+   "metadata": {},
+   "source": [
+    "# Trail terrain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "351d019a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trail terrain\n",
+      "Light Moderate        54\n",
+      "Light                 48\n",
+      "Moderate Technical    21\n",
+      "Moderate              19\n",
+      "-                     17\n",
+      "Technical             11\n",
+      "LightModerate          3\n",
+      "ModerateTechnical      2\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['Trail terrain'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "1370c40c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trail terrain\n",
+      "Light Moderate        54\n",
+      "Light                 48\n",
+      "Moderate Technical    21\n",
+      "Moderate              19\n",
+      "Technical             11\n",
+      "LightModerate          3\n",
+      "ModerateTechnical      2\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = df[df['Trail terrain'] != \"-\"].reset_index(drop=True)\n",
+    "print(df['Trail terrain'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "d67f4175",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows after cleaning: 158\n",
+      "\n",
+      "Unique Values in original column (before drop):\n",
+      "<StringArray>\n",
+      "[             'light',     'light moderate', 'moderate technical',\n",
+      "           'moderate',  'moderatetechnical',          'technical',\n",
+      "      'lightmoderate']\n",
+      "Length: 7, dtype: str\n",
+      "\n",
+      "Sample Comparison (Multi-value Mapping):\n",
+      "        Trail terrain  terrain_light  terrain_moderate  terrain_technical\n",
+      "0               light              1                 0                  0\n",
+      "1               light              1                 0                  0\n",
+      "2      light moderate              1                 1                  0\n",
+      "3               light              1                 0                  0\n",
+      "4      light moderate              1                 1                  0\n",
+      "5  moderate technical              0                 1                  1\n",
+      "6            moderate              0                 1                  0\n",
+      "7      light moderate              1                 1                  0\n",
+      "8      light moderate              1                 1                  0\n",
+      "9      light moderate              1                 1                  0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Naming convention: all lowercase\n",
+    "df['Trail terrain'] = df['Trail terrain'].astype(str).str.lower()\n",
+    "base_terrains = ['light', 'moderate', 'technical']\n",
+    "\n",
+    "for terrain in base_terrains:\n",
+    "    column_name = f\"terrain_{terrain}\"\n",
+    "    df[column_name] = df['Trail terrain'].str.contains(terrain).astype(int)\n",
+    "\n",
+    "print(\"Rows after cleaning:\", len(df))\n",
+    "\n",
+    "print(\"\\nUnique Values in original column (before drop):\")\n",
+    "print(df[\"Trail terrain\"].unique())\n",
+    "\n",
+    "print(\"\\nSample Comparison (Multi-value Mapping):\")\n",
+    "check_cols = [\"Trail terrain\"] + [f\"terrain_{t}\" for t in base_terrains]\n",
+    "print(df[check_cols].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "66775876",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trail terrain\n",
+      "light moderate        54\n",
+      "light                 48\n",
+      "moderate technical    21\n",
+      "moderate              19\n",
+      "technical             11\n",
+      "lightmoderate          3\n",
+      "moderatetechnical      2\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Sum of each terrain type:\n",
+      "terrain_light sum: 105\n",
+      "terrain_moderate sum: 99\n",
+      "terrain_technical sum: 34\n",
+      "\n",
+      "    Trail terrain  terrain_light  terrain_moderate  terrain_technical\n",
+      "0           light              1                 0                  0\n",
+      "1           light              1                 0                  0\n",
+      "2  light moderate              1                 1                  0\n",
+      "3           light              1                 0                  0\n",
+      "4  light moderate              1                 1                  0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['Trail terrain'].value_counts())\n",
+    "\n",
+    "print(\"\\nSum of each terrain type:\")\n",
+    "for terrain in base_terrains:\n",
+    "    col = f\"terrain_{terrain}\"\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "print()\n",
+    "print(df[['Trail terrain', 'terrain_light', 'terrain_moderate', 'terrain_technical']].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "8b78d616",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 32 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   Brand                            158 non-null    str    \n",
+      " 1   Name                             158 non-null    str    \n",
+      " 2   Shock absorption                 158 non-null    str    \n",
+      " 3   Energy return                    158 non-null    str    \n",
+      " 4   Traction                         153 non-null    str    \n",
+      " 5   Arch support                     158 non-null    str    \n",
+      " 6   Weight lab Weight brand          158 non-null    str    \n",
+      " 7   Lightweight                      152 non-null    float64\n",
+      " 8   Drop lab Drop brand              158 non-null    str    \n",
+      " 9   Strike pattern                   158 non-null    str    \n",
+      " 10  Midsole softness                 158 non-null    str    \n",
+      " 11  Plate                            158 non-null    str    \n",
+      " 12  Toebox durability                158 non-null    str    \n",
+      " 13  Heel padding durability          158 non-null    str    \n",
+      " 14  Outsole durability               158 non-null    str    \n",
+      " 15  Breathability                    158 non-null    str    \n",
+      " 16  Width / fit                      158 non-null    str    \n",
+      " 17  Toebox width                     158 non-null    str    \n",
+      " 18  Stiffness                        158 non-null    str    \n",
+      " 19  Torsional rigidity               158 non-null    str    \n",
+      " 20  Heel counter stiffness           158 non-null    str    \n",
+      " 21  Lug depth                        158 non-null    str    \n",
+      " 22  Heel stack lab Heel stack brand  158 non-null    str    \n",
+      " 23  Forefoot lab Forefoot brand      158 non-null    str    \n",
+      " 24  For heavy runners                154 non-null    float64\n",
+      " 25  Season                           158 non-null    str    \n",
+      " 26  Removable insole                 158 non-null    int64  \n",
+      " 27  Orthotic friendly                158 non-null    int64  \n",
+      " 28  Waterproofing                    156 non-null    str    \n",
+      " 29  terrain_light                    158 non-null    int64  \n",
+      " 30  terrain_moderate                 158 non-null    int64  \n",
+      " 31  terrain_technical                158 non-null    int64  \n",
+      "dtypes: float64(2), int64(5), str(25)\n",
+      "memory usage: 39.6 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop('Trail terrain', axis=1, inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee89a11f",
+   "metadata": {},
+   "source": [
+    "# Shock absorption"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "abd73756",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shock absorption\n",
+      "-           81\n",
+      "Moderate    45\n",
+      "High        20\n",
+      "Low         12\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Checking null values first\n",
+    "print(df['Shock absorption'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "7b6b0356",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 158\n",
+      "Baris dengan semua OHE 0: 81\n",
+      "\n",
+      "Sample Comparison (Baris '-' akan jadi 0 semua):\n",
+      "  Shock absorption  shock_low  shock_moderate  shock_high\n",
+      "0         moderate          0               1           0\n",
+      "1                -          0               0           0\n",
+      "2         moderate          0               1           0\n",
+      "3         moderate          0               1           0\n",
+      "4                -          0               0           0\n",
+      "5                -          0               0           0\n",
+      "6                -          0               0           0\n",
+      "7                -          0               0           0\n",
+      "8              low          1               0           0\n",
+      "9                -          0               0           0\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['Shock absorption'] = df['Shock absorption'].astype(str).str.lower()\n",
+    "base_shocks = ['low', 'moderate', 'high']\n",
+    "\n",
+    "\n",
+    "for shock in base_shocks:\n",
+    "    column_name = f\"shock_{shock}\"\n",
+    "    df[column_name] = df['Shock absorption'].str.contains(shock, na=False).astype(int)\n",
+    "\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "\n",
+    "# Verify all 0s\n",
+    "all_zero = df[(df[f\"shock_{base_shocks[0]}\"] == 0) & \n",
+    "              (df[f\"shock_{base_shocks[1]}\"] == 0) & \n",
+    "              (df[f\"shock_{base_shocks[2]}\"] == 0)]\n",
+    "\n",
+    "print(f\"Baris dengan semua OHE 0: {len(all_zero)}\")\n",
+    "\n",
+    "\n",
+    "print(\"\\nSample Comparison (Baris '-' akan jadi 0 semua):\")\n",
+    "check_cols = [\"Shock absorption\"] + [f\"shock_{s}\" for s in base_shocks]\n",
+    "# Tampilkan beberapa baris pertama termasuk yang ada dash-nya jika ada\n",
+    "print(df[check_cols].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "3fadd289",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shock absorption\n",
+      "-           81\n",
+      "moderate    45\n",
+      "high        20\n",
+      "low         12\n",
+      "Name: count, dtype: int64\n",
+      "shock_low sum: 12\n",
+      "shock_moderate sum: 45\n",
+      "shock_high sum: 20\n",
+      "\n",
+      "  Shock absorption  shock_low  shock_moderate  shock_high\n",
+      "0         moderate          0               1           0\n",
+      "1                -          0               0           0\n",
+      "2         moderate          0               1           0\n",
+      "3         moderate          0               1           0\n",
+      "4                -          0               0           0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['Shock absorption'].value_counts())\n",
+    "\n",
+    "for shock in base_shocks:\n",
+    "    col = f\"shock_{shock}\"\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"Shock absorption\"] + [f\"shock_{s}\" for s in base_shocks]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "396242dd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 34 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   Brand                            158 non-null    str    \n",
+      " 1   Name                             158 non-null    str    \n",
+      " 2   Energy return                    158 non-null    str    \n",
+      " 3   Traction                         153 non-null    str    \n",
+      " 4   Arch support                     158 non-null    str    \n",
+      " 5   Weight lab Weight brand          158 non-null    str    \n",
+      " 6   Lightweight                      152 non-null    float64\n",
+      " 7   Drop lab Drop brand              158 non-null    str    \n",
+      " 8   Strike pattern                   158 non-null    str    \n",
+      " 9   Midsole softness                 158 non-null    str    \n",
+      " 10  Plate                            158 non-null    str    \n",
+      " 11  Toebox durability                158 non-null    str    \n",
+      " 12  Heel padding durability          158 non-null    str    \n",
+      " 13  Outsole durability               158 non-null    str    \n",
+      " 14  Breathability                    158 non-null    str    \n",
+      " 15  Width / fit                      158 non-null    str    \n",
+      " 16  Toebox width                     158 non-null    str    \n",
+      " 17  Stiffness                        158 non-null    str    \n",
+      " 18  Torsional rigidity               158 non-null    str    \n",
+      " 19  Heel counter stiffness           158 non-null    str    \n",
+      " 20  Lug depth                        158 non-null    str    \n",
+      " 21  Heel stack lab Heel stack brand  158 non-null    str    \n",
+      " 22  Forefoot lab Forefoot brand      158 non-null    str    \n",
+      " 23  For heavy runners                154 non-null    float64\n",
+      " 24  Season                           158 non-null    str    \n",
+      " 25  Removable insole                 158 non-null    int64  \n",
+      " 26  Orthotic friendly                158 non-null    int64  \n",
+      " 27  Waterproofing                    156 non-null    str    \n",
+      " 28  terrain_light                    158 non-null    int64  \n",
+      " 29  terrain_moderate                 158 non-null    int64  \n",
+      " 30  terrain_technical                158 non-null    int64  \n",
+      " 31  shock_low                        158 non-null    int64  \n",
+      " 32  shock_moderate                   158 non-null    int64  \n",
+      " 33  shock_high                       158 non-null    int64  \n",
+      "dtypes: float64(2), int64(8), str(24)\n",
+      "memory usage: 42.1 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop('Shock absorption', axis=1, inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0dfe6ab4",
+   "metadata": {},
+   "source": [
+    "# Energy return"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "e1583547",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Energy return\n",
+      "-           81\n",
+      "Low         36\n",
+      "Moderate    36\n",
+      "High         5\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Energy return\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "74393e16",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 158\n",
+      "Baris dengan semua OHE 0 (termasuk '-'): 81\n",
+      "\n",
+      "Sample Comparison:\n",
+      "  Energy return  energy_low  energy_moderate  energy_high\n",
+      "0          high           0                0            1\n",
+      "1             -           0                0            0\n",
+      "2           low           1                0            0\n",
+      "3           low           1                0            0\n",
+      "4             -           0                0            0\n",
+      "5             -           0                0            0\n",
+      "6             -           0                0            0\n",
+      "7             -           0                0            0\n",
+      "8      moderate           0                1            0\n",
+      "9             -           0                0            0\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['Energy return'] = df['Energy return'].astype(str).str.lower()\n",
+    "base_energy = ['low', 'moderate', 'high']\n",
+    "\n",
+    "\n",
+    "for level in base_energy:\n",
+    "    column_name = f\"energy_{level}\"\n",
+    "    df[column_name] = df['Energy return'].str.contains(level, na=False).astype(int)\n",
+    "\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "energy_cols = [f\"energy_{l}\" for l in base_energy]\n",
+    "zero_vector_count = (df[energy_cols].sum(axis=1) == 0).sum()\n",
+    "print(f\"Baris dengan semua OHE 0 (termasuk '-'): {zero_vector_count}\")\n",
+    "\n",
+    "print(\"\\nSample Comparison:\")\n",
+    "print(df[[\"Energy return\"] + energy_cols].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "d64613b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Energy return\n",
+      "-           81\n",
+      "low         36\n",
+      "moderate    36\n",
+      "high         5\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "energy_low sum: 36\n",
+      "energy_moderate sum: 36\n",
+      "energy_high sum: 5\n",
+      "\n",
+      "  Energy return  energy_low  energy_moderate  energy_high\n",
+      "0          high           0                0            1\n",
+      "1             -           0                0            0\n",
+      "2           low           1                0            0\n",
+      "3           low           1                0            0\n",
+      "4             -           0                0            0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Energy return\"].value_counts())\n",
+    "\n",
+    "print()\n",
+    "for level in base_energy:\n",
+    "    col = f\"energy_{level}\"\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"Energy return\"] + energy_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "8b60e596",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 36 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   Brand                            158 non-null    str    \n",
+      " 1   Name                             158 non-null    str    \n",
+      " 2   Traction                         153 non-null    str    \n",
+      " 3   Arch support                     158 non-null    str    \n",
+      " 4   Weight lab Weight brand          158 non-null    str    \n",
+      " 5   Lightweight                      152 non-null    float64\n",
+      " 6   Drop lab Drop brand              158 non-null    str    \n",
+      " 7   Strike pattern                   158 non-null    str    \n",
+      " 8   Midsole softness                 158 non-null    str    \n",
+      " 9   Plate                            158 non-null    str    \n",
+      " 10  Toebox durability                158 non-null    str    \n",
+      " 11  Heel padding durability          158 non-null    str    \n",
+      " 12  Outsole durability               158 non-null    str    \n",
+      " 13  Breathability                    158 non-null    str    \n",
+      " 14  Width / fit                      158 non-null    str    \n",
+      " 15  Toebox width                     158 non-null    str    \n",
+      " 16  Stiffness                        158 non-null    str    \n",
+      " 17  Torsional rigidity               158 non-null    str    \n",
+      " 18  Heel counter stiffness           158 non-null    str    \n",
+      " 19  Lug depth                        158 non-null    str    \n",
+      " 20  Heel stack lab Heel stack brand  158 non-null    str    \n",
+      " 21  Forefoot lab Forefoot brand      158 non-null    str    \n",
+      " 22  For heavy runners                154 non-null    float64\n",
+      " 23  Season                           158 non-null    str    \n",
+      " 24  Removable insole                 158 non-null    int64  \n",
+      " 25  Orthotic friendly                158 non-null    int64  \n",
+      " 26  Waterproofing                    156 non-null    str    \n",
+      " 27  terrain_light                    158 non-null    int64  \n",
+      " 28  terrain_moderate                 158 non-null    int64  \n",
+      " 29  terrain_technical                158 non-null    int64  \n",
+      " 30  shock_low                        158 non-null    int64  \n",
+      " 31  shock_moderate                   158 non-null    int64  \n",
+      " 32  shock_high                       158 non-null    int64  \n",
+      " 33  energy_low                       158 non-null    int64  \n",
+      " 34  energy_moderate                  158 non-null    int64  \n",
+      " 35  energy_high                      158 non-null    int64  \n",
+      "dtypes: float64(2), int64(11), str(23)\n",
+      "memory usage: 44.6 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop('Energy return', axis=1, inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "050f4527",
+   "metadata": {},
+   "source": [
+    "# Traction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "6a938fe9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Traction\n",
+      "-           127\n",
+      "High         25\n",
+      "Moderate      1\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Traction\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "7fd8d46f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 158\n",
+      "Baris dengan semua OHE 0 (termasuk '-'): 132\n",
+      "\n",
+      "Sample Comparison:\n",
+      "  Traction  traction_moderate  traction_high\n",
+      "0        -                  0              0\n",
+      "1        -                  0              0\n",
+      "2        -                  0              0\n",
+      "3     high                  0              1\n",
+      "4        -                  0              0\n",
+      "5        -                  0              0\n",
+      "6        -                  0              0\n",
+      "7        -                  0              0\n",
+      "8        -                  0              0\n",
+      "9        -                  0              0\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['Traction'] = df['Traction'].astype(str).str.lower()\n",
+    "base_traction = ['moderate', 'high']\n",
+    "\n",
+    "for level in base_traction:\n",
+    "    column_name = f\"traction_{level}\"\n",
+    "    df[column_name] = df['Traction'].str.contains(level, na=False).astype(int)\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "traction_cols = [f\"traction_{l}\" for l in base_traction]\n",
+    "zero_vector_count = (df[traction_cols].sum(axis=1) == 0).sum()\n",
+    "print(f\"Baris dengan semua OHE 0 (termasuk '-'): {zero_vector_count}\")\n",
+    "\n",
+    "print(\"\\nSample Comparison:\")\n",
+    "print(df[[\"Traction\"] + traction_cols].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "3a83c57d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Traction\n",
+      "-           127\n",
+      "high         25\n",
+      "moderate      1\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "traction_moderate sum: 1\n",
+      "traction_high sum: 25\n",
+      "\n",
+      "  Traction  traction_moderate  traction_high\n",
+      "0        -                  0              0\n",
+      "1        -                  0              0\n",
+      "2        -                  0              0\n",
+      "3     high                  0              1\n",
+      "4        -                  0              0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Traction\"].value_counts())\n",
+    "\n",
+    "print()\n",
+    "for level in base_traction:\n",
+    "    col = f\"traction_{level}\"\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"Traction\"] + traction_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "65ce998a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 37 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   Brand                            158 non-null    str    \n",
+      " 1   Name                             158 non-null    str    \n",
+      " 2   Arch support                     158 non-null    str    \n",
+      " 3   Weight lab Weight brand          158 non-null    str    \n",
+      " 4   Lightweight                      152 non-null    float64\n",
+      " 5   Drop lab Drop brand              158 non-null    str    \n",
+      " 6   Strike pattern                   158 non-null    str    \n",
+      " 7   Midsole softness                 158 non-null    str    \n",
+      " 8   Plate                            158 non-null    str    \n",
+      " 9   Toebox durability                158 non-null    str    \n",
+      " 10  Heel padding durability          158 non-null    str    \n",
+      " 11  Outsole durability               158 non-null    str    \n",
+      " 12  Breathability                    158 non-null    str    \n",
+      " 13  Width / fit                      158 non-null    str    \n",
+      " 14  Toebox width                     158 non-null    str    \n",
+      " 15  Stiffness                        158 non-null    str    \n",
+      " 16  Torsional rigidity               158 non-null    str    \n",
+      " 17  Heel counter stiffness           158 non-null    str    \n",
+      " 18  Lug depth                        158 non-null    str    \n",
+      " 19  Heel stack lab Heel stack brand  158 non-null    str    \n",
+      " 20  Forefoot lab Forefoot brand      158 non-null    str    \n",
+      " 21  For heavy runners                154 non-null    float64\n",
+      " 22  Season                           158 non-null    str    \n",
+      " 23  Removable insole                 158 non-null    int64  \n",
+      " 24  Orthotic friendly                158 non-null    int64  \n",
+      " 25  Waterproofing                    156 non-null    str    \n",
+      " 26  terrain_light                    158 non-null    int64  \n",
+      " 27  terrain_moderate                 158 non-null    int64  \n",
+      " 28  terrain_technical                158 non-null    int64  \n",
+      " 29  shock_low                        158 non-null    int64  \n",
+      " 30  shock_moderate                   158 non-null    int64  \n",
+      " 31  shock_high                       158 non-null    int64  \n",
+      " 32  energy_low                       158 non-null    int64  \n",
+      " 33  energy_moderate                  158 non-null    int64  \n",
+      " 34  energy_high                      158 non-null    int64  \n",
+      " 35  traction_moderate                158 non-null    int64  \n",
+      " 36  traction_high                    158 non-null    int64  \n",
+      "dtypes: float64(2), int64(13), str(22)\n",
+      "memory usage: 45.8 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=['Traction'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d283f13a",
+   "metadata": {},
+   "source": [
+    "# Arch support"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "1d055a43",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Arch support\n",
+      "Neutral      154\n",
+      "Stability      4\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Arch support\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "8ee15f86",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 158\n",
+      "NULL Value: 0\n",
+      "\n",
+      "Sample Comparison:\n",
+      "  Arch support  arch_neutral  arch_stability\n",
+      "0      neutral             1               0\n",
+      "1      neutral             1               0\n",
+      "2      neutral             1               0\n",
+      "3      neutral             1               0\n",
+      "4      neutral             1               0\n",
+      "5      neutral             1               0\n",
+      "6      neutral             1               0\n",
+      "7      neutral             1               0\n",
+      "8      neutral             1               0\n",
+      "9      neutral             1               0\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['Arch support'] = df['Arch support'].astype(str).str.lower()\n",
+    "base_arch = ['neutral', 'stability']\n",
+    "\n",
+    "\n",
+    "for level in base_arch:\n",
+    "    column_name = f\"arch_{level}\"\n",
+    "    df[column_name] = df['Arch support'].str.contains(level, na=False).astype(int)\n",
+    "\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "\n",
+    "arch_cols = [f\"arch_{l}\" for l in base_arch]\n",
+    "zero_vector_count = (df[arch_cols].sum(axis=1) == 0).sum()\n",
+    "print(f\"NULL Value: {zero_vector_count}\")\n",
+    "\n",
+    "print(\"\\nSample Comparison:\")\n",
+    "print(df[[\"Arch support\"] + arch_cols].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "bddc5889",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Arch support\n",
+      "neutral      154\n",
+      "stability      4\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "arch_neutral sum: 154\n",
+      "arch_stability sum: 4\n",
+      "\n",
+      "  Arch support  arch_neutral  arch_stability\n",
+      "0      neutral             1               0\n",
+      "1      neutral             1               0\n",
+      "2      neutral             1               0\n",
+      "3      neutral             1               0\n",
+      "4      neutral             1               0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Arch support\"].value_counts())\n",
+    "\n",
+    "print()\n",
+    "for level in base_arch:\n",
+    "    col = f\"arch_{level}\"\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"Arch support\"] + arch_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "d8b2e073",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 38 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   Brand                            158 non-null    str    \n",
+      " 1   Name                             158 non-null    str    \n",
+      " 2   Weight lab Weight brand          158 non-null    str    \n",
+      " 3   Lightweight                      152 non-null    float64\n",
+      " 4   Drop lab Drop brand              158 non-null    str    \n",
+      " 5   Strike pattern                   158 non-null    str    \n",
+      " 6   Midsole softness                 158 non-null    str    \n",
+      " 7   Plate                            158 non-null    str    \n",
+      " 8   Toebox durability                158 non-null    str    \n",
+      " 9   Heel padding durability          158 non-null    str    \n",
+      " 10  Outsole durability               158 non-null    str    \n",
+      " 11  Breathability                    158 non-null    str    \n",
+      " 12  Width / fit                      158 non-null    str    \n",
+      " 13  Toebox width                     158 non-null    str    \n",
+      " 14  Stiffness                        158 non-null    str    \n",
+      " 15  Torsional rigidity               158 non-null    str    \n",
+      " 16  Heel counter stiffness           158 non-null    str    \n",
+      " 17  Lug depth                        158 non-null    str    \n",
+      " 18  Heel stack lab Heel stack brand  158 non-null    str    \n",
+      " 19  Forefoot lab Forefoot brand      158 non-null    str    \n",
+      " 20  For heavy runners                154 non-null    float64\n",
+      " 21  Season                           158 non-null    str    \n",
+      " 22  Removable insole                 158 non-null    int64  \n",
+      " 23  Orthotic friendly                158 non-null    int64  \n",
+      " 24  Waterproofing                    156 non-null    str    \n",
+      " 25  terrain_light                    158 non-null    int64  \n",
+      " 26  terrain_moderate                 158 non-null    int64  \n",
+      " 27  terrain_technical                158 non-null    int64  \n",
+      " 28  shock_low                        158 non-null    int64  \n",
+      " 29  shock_moderate                   158 non-null    int64  \n",
+      " 30  shock_high                       158 non-null    int64  \n",
+      " 31  energy_low                       158 non-null    int64  \n",
+      " 32  energy_moderate                  158 non-null    int64  \n",
+      " 33  energy_high                      158 non-null    int64  \n",
+      " 34  traction_moderate                158 non-null    int64  \n",
+      " 35  traction_high                    158 non-null    int64  \n",
+      " 36  arch_neutral                     158 non-null    int64  \n",
+      " 37  arch_stability                   158 non-null    int64  \n",
+      "dtypes: float64(2), int64(15), str(21)\n",
+      "memory usage: 47.0 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=['Arch support'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eba72c42",
+   "metadata": {},
+   "source": [
+    "# Split Weight lab Weight brand"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "4027e6f8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(0)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mask_missing = (\n",
+    "    df[\"Weight lab Weight brand\"].isna() |\n",
+    "    (df[\"Weight lab Weight brand\"].astype(str).str.strip() == \"-\")\n",
+    ")\n",
+    "mask_missing.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "dd34c04a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         Weight lab Weight brand  weight_lab_oz  weight_lab_g  \\\n",
+      "0    9.1 oz / 259g 9.5 oz / 270g            9.1           259   \n",
+      "1      9.1 oz / 258g 9 oz / 255g            9.1           258   \n",
+      "2   10.1 oz / 285g 9.6 oz / 273g           10.1           285   \n",
+      "3   9.4 oz / 266g 10.3 oz / 293g            9.4           266   \n",
+      "4  10.7 oz / 302g 10.6 oz / 301g           10.7           302   \n",
+      "\n",
+      "   weight_brand_oz  weight_brand_g  \n",
+      "0              9.5           270.0  \n",
+      "1              9.0           255.0  \n",
+      "2              9.6           273.0  \n",
+      "3             10.3           293.0  \n",
+      "4             10.6           301.0  \n"
+     ]
+    }
+   ],
+   "source": [
+    "weight = df[\"Weight lab Weight brand\"].str.findall(r\"[\\d.]+\")\n",
+    "df[[\"weight_lab_oz\", \"weight_lab_g\", \"weight_brand_oz\", \"weight_brand_g\"]] = (\n",
+    "    pd.DataFrame(weight.tolist(), index=df.index)\n",
+    ")\n",
+    "\n",
+    "for col in [\"weight_lab_oz\", \"weight_lab_g\", \"weight_brand_oz\", \"weight_brand_g\"]:\n",
+    "    df[col] = pd.to_numeric(df[col], errors=\"coerce\")\n",
+    "\n",
+    "print(df[[\"Weight lab Weight brand\", \"weight_lab_oz\", \"weight_lab_g\", \"weight_brand_oz\", \"weight_brand_g\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "4ad81586",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 41 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   Brand                            158 non-null    str    \n",
+      " 1   Name                             158 non-null    str    \n",
+      " 2   Lightweight                      152 non-null    float64\n",
+      " 3   Drop lab Drop brand              158 non-null    str    \n",
+      " 4   Strike pattern                   158 non-null    str    \n",
+      " 5   Midsole softness                 158 non-null    str    \n",
+      " 6   Plate                            158 non-null    str    \n",
+      " 7   Toebox durability                158 non-null    str    \n",
+      " 8   Heel padding durability          158 non-null    str    \n",
+      " 9   Outsole durability               158 non-null    str    \n",
+      " 10  Breathability                    158 non-null    str    \n",
+      " 11  Width / fit                      158 non-null    str    \n",
+      " 12  Toebox width                     158 non-null    str    \n",
+      " 13  Stiffness                        158 non-null    str    \n",
+      " 14  Torsional rigidity               158 non-null    str    \n",
+      " 15  Heel counter stiffness           158 non-null    str    \n",
+      " 16  Lug depth                        158 non-null    str    \n",
+      " 17  Heel stack lab Heel stack brand  158 non-null    str    \n",
+      " 18  Forefoot lab Forefoot brand      158 non-null    str    \n",
+      " 19  For heavy runners                154 non-null    float64\n",
+      " 20  Season                           158 non-null    str    \n",
+      " 21  Removable insole                 158 non-null    int64  \n",
+      " 22  Orthotic friendly                158 non-null    int64  \n",
+      " 23  Waterproofing                    156 non-null    str    \n",
+      " 24  terrain_light                    158 non-null    int64  \n",
+      " 25  terrain_moderate                 158 non-null    int64  \n",
+      " 26  terrain_technical                158 non-null    int64  \n",
+      " 27  shock_low                        158 non-null    int64  \n",
+      " 28  shock_moderate                   158 non-null    int64  \n",
+      " 29  shock_high                       158 non-null    int64  \n",
+      " 30  energy_low                       158 non-null    int64  \n",
+      " 31  energy_moderate                  158 non-null    int64  \n",
+      " 32  energy_high                      158 non-null    int64  \n",
+      " 33  traction_moderate                158 non-null    int64  \n",
+      " 34  traction_high                    158 non-null    int64  \n",
+      " 35  arch_neutral                     158 non-null    int64  \n",
+      " 36  arch_stability                   158 non-null    int64  \n",
+      " 37  weight_lab_oz                    158 non-null    float64\n",
+      " 38  weight_lab_g                     158 non-null    int64  \n",
+      " 39  weight_brand_oz                  155 non-null    float64\n",
+      " 40  weight_brand_g                   155 non-null    float64\n",
+      "dtypes: float64(5), int64(16), str(20)\n",
+      "memory usage: 50.7 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"Weight lab Weight brand\",], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f8f3114",
+   "metadata": {},
+   "source": [
+    "# Split Drop lab Drop brand"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "d96d69ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(0)"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mask_missing = (\n",
+    "    df[\"Drop lab Drop brand\"].isna() |\n",
+    "    (df[\"Drop lab Drop brand\"].astype(str).str.strip() == \"-\")\n",
+    ")\n",
+    "mask_missing.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "33f141ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Drop lab Drop brand  drop_lab_mm  drop_brand_mm\n",
+      "0       0.3 mm 8.0 mm          0.3            8.0\n",
+      "1       8.2 mm 8.0 mm          8.2            8.0\n",
+      "2       4.3 mm 4.0 mm          4.3            4.0\n",
+      "3       6.1 mm 4.0 mm          6.1            4.0\n",
+      "4       0.2 mm 0.0 mm          0.2            0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "drop = df[\"Drop lab Drop brand\"].str.findall(r\"[\\d.]+\")\n",
+    "\n",
+    "df[[\"drop_lab_mm\", \"drop_brand_mm\"]] = (\n",
+    "    pd.DataFrame(drop.tolist(), index=df.index)\n",
+    ")\n",
+    "\n",
+    "for col in [\"drop_lab_mm\", \"drop_brand_mm\"]:\n",
+    "    df[col] = pd.to_numeric(df[col], errors=\"coerce\")\n",
+    "\n",
+    "print(df[[\"Drop lab Drop brand\", \"drop_lab_mm\", \"drop_brand_mm\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "1d1c4eac",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 42 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   Brand                            158 non-null    str    \n",
+      " 1   Name                             158 non-null    str    \n",
+      " 2   Lightweight                      152 non-null    float64\n",
+      " 3   Strike pattern                   158 non-null    str    \n",
+      " 4   Midsole softness                 158 non-null    str    \n",
+      " 5   Plate                            158 non-null    str    \n",
+      " 6   Toebox durability                158 non-null    str    \n",
+      " 7   Heel padding durability          158 non-null    str    \n",
+      " 8   Outsole durability               158 non-null    str    \n",
+      " 9   Breathability                    158 non-null    str    \n",
+      " 10  Width / fit                      158 non-null    str    \n",
+      " 11  Toebox width                     158 non-null    str    \n",
+      " 12  Stiffness                        158 non-null    str    \n",
+      " 13  Torsional rigidity               158 non-null    str    \n",
+      " 14  Heel counter stiffness           158 non-null    str    \n",
+      " 15  Lug depth                        158 non-null    str    \n",
+      " 16  Heel stack lab Heel stack brand  158 non-null    str    \n",
+      " 17  Forefoot lab Forefoot brand      158 non-null    str    \n",
+      " 18  For heavy runners                154 non-null    float64\n",
+      " 19  Season                           158 non-null    str    \n",
+      " 20  Removable insole                 158 non-null    int64  \n",
+      " 21  Orthotic friendly                158 non-null    int64  \n",
+      " 22  Waterproofing                    156 non-null    str    \n",
+      " 23  terrain_light                    158 non-null    int64  \n",
+      " 24  terrain_moderate                 158 non-null    int64  \n",
+      " 25  terrain_technical                158 non-null    int64  \n",
+      " 26  shock_low                        158 non-null    int64  \n",
+      " 27  shock_moderate                   158 non-null    int64  \n",
+      " 28  shock_high                       158 non-null    int64  \n",
+      " 29  energy_low                       158 non-null    int64  \n",
+      " 30  energy_moderate                  158 non-null    int64  \n",
+      " 31  energy_high                      158 non-null    int64  \n",
+      " 32  traction_moderate                158 non-null    int64  \n",
+      " 33  traction_high                    158 non-null    int64  \n",
+      " 34  arch_neutral                     158 non-null    int64  \n",
+      " 35  arch_stability                   158 non-null    int64  \n",
+      " 36  weight_lab_oz                    158 non-null    float64\n",
+      " 37  weight_lab_g                     158 non-null    int64  \n",
+      " 38  weight_brand_oz                  155 non-null    float64\n",
+      " 39  weight_brand_g                   155 non-null    float64\n",
+      " 40  drop_lab_mm                      158 non-null    float64\n",
+      " 41  drop_brand_mm                    152 non-null    float64\n",
+      "dtypes: float64(7), int64(16), str(19)\n",
+      "memory usage: 52.0 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"Drop lab Drop brand\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a60c8a59",
+   "metadata": {},
+   "source": [
+    "# Strike pattern"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "53e91a22",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Strike pattern\n",
+      "Mid/forefoot         83\n",
+      "Heel                 48\n",
+      "Heel Mid/forefoot    25\n",
+      "HeelMid/forefoot      2\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Strike pattern\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "8f141a55",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 158\n",
+      "\n",
+      "Unique Values in original column:\n",
+      "<StringArray>\n",
+      "['mid/forefoot', 'heel mid/forefoot', 'heel', 'heelmid/forefoot']\n",
+      "Length: 4, dtype: str\n",
+      "\n",
+      "Sample Comparison (Multi-label Mapping):\n",
+      "      Strike pattern  strike_heel  strike_mid  strike_forefoot\n",
+      "0       mid/forefoot            0           1                1\n",
+      "1  heel mid/forefoot            1           1                1\n",
+      "2       mid/forefoot            0           1                1\n",
+      "3       mid/forefoot            0           1                1\n",
+      "4       mid/forefoot            0           1                1\n",
+      "5       mid/forefoot            0           1                1\n",
+      "6       mid/forefoot            0           1                1\n",
+      "7       mid/forefoot            0           1                1\n",
+      "8       mid/forefoot            0           1                1\n",
+      "9       mid/forefoot            0           1                1\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['Strike pattern'] = df['Strike pattern'].astype(str).str.lower()\n",
+    "base_strikes = ['heel', 'mid', 'forefoot']\n",
+    "\n",
+    "for strike in base_strikes:\n",
+    "    column_name = f\"strike_{strike}\"\n",
+    "    df[column_name] = df['Strike pattern'].str.contains(strike, na=False).astype(int)\n",
+    "\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "print(\"\\nUnique Values in original column:\")\n",
+    "print(df[\"Strike pattern\"].unique())\n",
+    "\n",
+    "print(\"\\nSample Comparison (Multi-label Mapping):\")\n",
+    "strike_cols = [f\"strike_{s}\" for s in base_strikes]\n",
+    "print(df[[\"Strike pattern\"] + strike_cols].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "1b1de021",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Strike pattern\n",
+      "mid/forefoot         83\n",
+      "heel                 48\n",
+      "heel mid/forefoot    25\n",
+      "heelmid/forefoot      2\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "strike_heel sum: 75\n",
+      "strike_mid sum: 110\n",
+      "strike_forefoot sum: 110\n",
+      "\n",
+      "      Strike pattern  strike_heel  strike_mid  strike_forefoot\n",
+      "0       mid/forefoot            0           1                1\n",
+      "1  heel mid/forefoot            1           1                1\n",
+      "2       mid/forefoot            0           1                1\n",
+      "3       mid/forefoot            0           1                1\n",
+      "4       mid/forefoot            0           1                1\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Strike pattern\"].value_counts())\n",
+    "\n",
+    "print()\n",
+    "for strike in base_strikes:\n",
+    "    col = f\"strike_{strike}\"\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"Strike pattern\"] + strike_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "767fe00e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 44 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   Brand                            158 non-null    str    \n",
+      " 1   Name                             158 non-null    str    \n",
+      " 2   Lightweight                      152 non-null    float64\n",
+      " 3   Midsole softness                 158 non-null    str    \n",
+      " 4   Plate                            158 non-null    str    \n",
+      " 5   Toebox durability                158 non-null    str    \n",
+      " 6   Heel padding durability          158 non-null    str    \n",
+      " 7   Outsole durability               158 non-null    str    \n",
+      " 8   Breathability                    158 non-null    str    \n",
+      " 9   Width / fit                      158 non-null    str    \n",
+      " 10  Toebox width                     158 non-null    str    \n",
+      " 11  Stiffness                        158 non-null    str    \n",
+      " 12  Torsional rigidity               158 non-null    str    \n",
+      " 13  Heel counter stiffness           158 non-null    str    \n",
+      " 14  Lug depth                        158 non-null    str    \n",
+      " 15  Heel stack lab Heel stack brand  158 non-null    str    \n",
+      " 16  Forefoot lab Forefoot brand      158 non-null    str    \n",
+      " 17  For heavy runners                154 non-null    float64\n",
+      " 18  Season                           158 non-null    str    \n",
+      " 19  Removable insole                 158 non-null    int64  \n",
+      " 20  Orthotic friendly                158 non-null    int64  \n",
+      " 21  Waterproofing                    156 non-null    str    \n",
+      " 22  terrain_light                    158 non-null    int64  \n",
+      " 23  terrain_moderate                 158 non-null    int64  \n",
+      " 24  terrain_technical                158 non-null    int64  \n",
+      " 25  shock_low                        158 non-null    int64  \n",
+      " 26  shock_moderate                   158 non-null    int64  \n",
+      " 27  shock_high                       158 non-null    int64  \n",
+      " 28  energy_low                       158 non-null    int64  \n",
+      " 29  energy_moderate                  158 non-null    int64  \n",
+      " 30  energy_high                      158 non-null    int64  \n",
+      " 31  traction_moderate                158 non-null    int64  \n",
+      " 32  traction_high                    158 non-null    int64  \n",
+      " 33  arch_neutral                     158 non-null    int64  \n",
+      " 34  arch_stability                   158 non-null    int64  \n",
+      " 35  weight_lab_oz                    158 non-null    float64\n",
+      " 36  weight_lab_g                     158 non-null    int64  \n",
+      " 37  weight_brand_oz                  155 non-null    float64\n",
+      " 38  weight_brand_g                   155 non-null    float64\n",
+      " 39  drop_lab_mm                      158 non-null    float64\n",
+      " 40  drop_brand_mm                    152 non-null    float64\n",
+      " 41  strike_heel                      158 non-null    int64  \n",
+      " 42  strike_mid                       158 non-null    int64  \n",
+      " 43  strike_forefoot                  158 non-null    int64  \n",
+      "dtypes: float64(7), int64(19), str(18)\n",
+      "memory usage: 54.4 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=['Strike pattern'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9e9ece2",
+   "metadata": {},
+   "source": [
+    "# Midsole softness"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "b524c2a4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Midsole softness\n",
+      "Balanced    72\n",
+      "Soft        54\n",
+      "-           19\n",
+      "Firm        13\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Midsole softness\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "f61696ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 158\n",
+      "Baris dengan semua OHE 0 (termasuk '-'): 19\n",
+      "\n",
+      "Sample Comparison:\n",
+      "  Midsole softness  softness_soft  softness_balanced  softness_firm\n",
+      "0         balanced              0                  1              0\n",
+      "1                -              0                  0              0\n",
+      "2             soft              1                  0              0\n",
+      "3         balanced              0                  1              0\n",
+      "4                -              0                  0              0\n",
+      "5                -              0                  0              0\n",
+      "6         balanced              0                  1              0\n",
+      "7         balanced              0                  1              0\n",
+      "8         balanced              0                  1              0\n",
+      "9                -              0                  0              0\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['Midsole softness'] = df['Midsole softness'].astype(str).str.lower()\n",
+    "base_softness = ['soft', 'balanced', 'firm']\n",
+    "\n",
+    "\n",
+    "for level in base_softness:\n",
+    "    column_name = f\"softness_{level}\"\n",
+    "    df[column_name] = df['Midsole softness'].str.contains(level, na=False).astype(int)\n",
+    "\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "softness_cols = [f\"softness_{l}\" for l in base_softness]\n",
+    "zero_vector_count = (df[softness_cols].sum(axis=1) == 0).sum()\n",
+    "print(f\"Baris dengan semua OHE 0 (termasuk '-'): {zero_vector_count}\")\n",
+    "\n",
+    "print(\"\\nSample Comparison:\")\n",
+    "print(df[[\"Midsole softness\"] + softness_cols].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "bf43f93c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Midsole softness\n",
+      "balanced    72\n",
+      "soft        54\n",
+      "-           19\n",
+      "firm        13\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "softness_soft sum: 54\n",
+      "softness_balanced sum: 72\n",
+      "softness_firm sum: 13\n",
+      "\n",
+      "  Midsole softness  softness_soft  softness_balanced  softness_firm\n",
+      "0         balanced              0                  1              0\n",
+      "1                -              0                  0              0\n",
+      "2             soft              1                  0              0\n",
+      "3         balanced              0                  1              0\n",
+      "4                -              0                  0              0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Midsole softness\"].value_counts())\n",
+    "\n",
+    "print()\n",
+    "for level in base_softness:\n",
+    "    col = f\"softness_{level}\"\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"Midsole softness\"] + softness_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "c361b27d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 46 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   Brand                            158 non-null    str    \n",
+      " 1   Name                             158 non-null    str    \n",
+      " 2   Lightweight                      152 non-null    float64\n",
+      " 3   Plate                            158 non-null    str    \n",
+      " 4   Toebox durability                158 non-null    str    \n",
+      " 5   Heel padding durability          158 non-null    str    \n",
+      " 6   Outsole durability               158 non-null    str    \n",
+      " 7   Breathability                    158 non-null    str    \n",
+      " 8   Width / fit                      158 non-null    str    \n",
+      " 9   Toebox width                     158 non-null    str    \n",
+      " 10  Stiffness                        158 non-null    str    \n",
+      " 11  Torsional rigidity               158 non-null    str    \n",
+      " 12  Heel counter stiffness           158 non-null    str    \n",
+      " 13  Lug depth                        158 non-null    str    \n",
+      " 14  Heel stack lab Heel stack brand  158 non-null    str    \n",
+      " 15  Forefoot lab Forefoot brand      158 non-null    str    \n",
+      " 16  For heavy runners                154 non-null    float64\n",
+      " 17  Season                           158 non-null    str    \n",
+      " 18  Removable insole                 158 non-null    int64  \n",
+      " 19  Orthotic friendly                158 non-null    int64  \n",
+      " 20  Waterproofing                    156 non-null    str    \n",
+      " 21  terrain_light                    158 non-null    int64  \n",
+      " 22  terrain_moderate                 158 non-null    int64  \n",
+      " 23  terrain_technical                158 non-null    int64  \n",
+      " 24  shock_low                        158 non-null    int64  \n",
+      " 25  shock_moderate                   158 non-null    int64  \n",
+      " 26  shock_high                       158 non-null    int64  \n",
+      " 27  energy_low                       158 non-null    int64  \n",
+      " 28  energy_moderate                  158 non-null    int64  \n",
+      " 29  energy_high                      158 non-null    int64  \n",
+      " 30  traction_moderate                158 non-null    int64  \n",
+      " 31  traction_high                    158 non-null    int64  \n",
+      " 32  arch_neutral                     158 non-null    int64  \n",
+      " 33  arch_stability                   158 non-null    int64  \n",
+      " 34  weight_lab_oz                    158 non-null    float64\n",
+      " 35  weight_lab_g                     158 non-null    int64  \n",
+      " 36  weight_brand_oz                  155 non-null    float64\n",
+      " 37  weight_brand_g                   155 non-null    float64\n",
+      " 38  drop_lab_mm                      158 non-null    float64\n",
+      " 39  drop_brand_mm                    152 non-null    float64\n",
+      " 40  strike_heel                      158 non-null    int64  \n",
+      " 41  strike_mid                       158 non-null    int64  \n",
+      " 42  strike_forefoot                  158 non-null    int64  \n",
+      " 43  softness_soft                    158 non-null    int64  \n",
+      " 44  softness_balanced                158 non-null    int64  \n",
+      " 45  softness_firm                    158 non-null    int64  \n",
+      "dtypes: float64(7), int64(22), str(17)\n",
+      "memory usage: 56.9 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"Midsole softness\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00df0f14",
+   "metadata": {},
+   "source": [
+    "# Toebox durability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "6e926181",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Toebox durability\n",
+      "Good         39\n",
+      "Decent       39\n",
+      "-            36\n",
+      "Bad          17\n",
+      "Very bad     15\n",
+      "Very good    12\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['Toebox durability'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "8a18a9a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 158\n",
+      "\n",
+      "Unique Values mapping check:\n",
+      "'-' di-encode menjadi 0 (Total: 36)\n",
+      "'very bad' di-encode menjadi 1 (Total: 15)\n",
+      "'bad' di-encode menjadi 2 (Total: 17)\n",
+      "'decent' di-encode menjadi 3 (Total: 39)\n",
+      "'good' di-encode menjadi 4 (Total: 39)\n",
+      "'very good' di-encode menjadi 5 (Total: 12)\n",
+      "\n",
+      "Sample Data:\n",
+      "  Toebox durability  toebox_durability\n",
+      "0              good                  4\n",
+      "1                 -                  0\n",
+      "2            decent                  3\n",
+      "3            decent                  3\n",
+      "4                 -                  0\n",
+      "5                 -                  0\n",
+      "6                 -                  0\n",
+      "7              good                  4\n",
+      "8            decent                  3\n",
+      "9                 -                  0\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['Toebox durability'] = df['Toebox durability'].astype(str).str.lower()\n",
+    "\n",
+    "durability_map = {\n",
+    "    \"-\": 0,\n",
+    "    \"very bad\": 1,\n",
+    "    \"bad\": 2,\n",
+    "    \"decent\": 3,\n",
+    "    \"good\": 4,\n",
+    "    \"very good\": 5\n",
+    "}\n",
+    "\n",
+    "df['toebox_durability'] = df['Toebox durability'].map(durability_map)\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "print(\"\\nUnique Values mapping check:\")\n",
+    "for label, value in durability_map.items():\n",
+    "    count = (df['Toebox durability'] == label).sum()\n",
+    "    print(f\"'{label}' di-encode menjadi {value} (Total: {count})\")\n",
+    "\n",
+    "print(\"\\nSample Data:\")\n",
+    "print(df[[\"Toebox durability\", \"toebox_durability\"]].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "96538ff1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 46 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   Brand                            158 non-null    str    \n",
+      " 1   Name                             158 non-null    str    \n",
+      " 2   Lightweight                      152 non-null    float64\n",
+      " 3   Plate                            158 non-null    str    \n",
+      " 4   Heel padding durability          158 non-null    str    \n",
+      " 5   Outsole durability               158 non-null    str    \n",
+      " 6   Breathability                    158 non-null    str    \n",
+      " 7   Width / fit                      158 non-null    str    \n",
+      " 8   Toebox width                     158 non-null    str    \n",
+      " 9   Stiffness                        158 non-null    str    \n",
+      " 10  Torsional rigidity               158 non-null    str    \n",
+      " 11  Heel counter stiffness           158 non-null    str    \n",
+      " 12  Lug depth                        158 non-null    str    \n",
+      " 13  Heel stack lab Heel stack brand  158 non-null    str    \n",
+      " 14  Forefoot lab Forefoot brand      158 non-null    str    \n",
+      " 15  For heavy runners                154 non-null    float64\n",
+      " 16  Season                           158 non-null    str    \n",
+      " 17  Removable insole                 158 non-null    int64  \n",
+      " 18  Orthotic friendly                158 non-null    int64  \n",
+      " 19  Waterproofing                    156 non-null    str    \n",
+      " 20  terrain_light                    158 non-null    int64  \n",
+      " 21  terrain_moderate                 158 non-null    int64  \n",
+      " 22  terrain_technical                158 non-null    int64  \n",
+      " 23  shock_low                        158 non-null    int64  \n",
+      " 24  shock_moderate                   158 non-null    int64  \n",
+      " 25  shock_high                       158 non-null    int64  \n",
+      " 26  energy_low                       158 non-null    int64  \n",
+      " 27  energy_moderate                  158 non-null    int64  \n",
+      " 28  energy_high                      158 non-null    int64  \n",
+      " 29  traction_moderate                158 non-null    int64  \n",
+      " 30  traction_high                    158 non-null    int64  \n",
+      " 31  arch_neutral                     158 non-null    int64  \n",
+      " 32  arch_stability                   158 non-null    int64  \n",
+      " 33  weight_lab_oz                    158 non-null    float64\n",
+      " 34  weight_lab_g                     158 non-null    int64  \n",
+      " 35  weight_brand_oz                  155 non-null    float64\n",
+      " 36  weight_brand_g                   155 non-null    float64\n",
+      " 37  drop_lab_mm                      158 non-null    float64\n",
+      " 38  drop_brand_mm                    152 non-null    float64\n",
+      " 39  strike_heel                      158 non-null    int64  \n",
+      " 40  strike_mid                       158 non-null    int64  \n",
+      " 41  strike_forefoot                  158 non-null    int64  \n",
+      " 42  softness_soft                    158 non-null    int64  \n",
+      " 43  softness_balanced                158 non-null    int64  \n",
+      " 44  softness_firm                    158 non-null    int64  \n",
+      " 45  toebox_durability                158 non-null    int64  \n",
+      "dtypes: float64(7), int64(23), str(16)\n",
+      "memory usage: 56.9 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=['Toebox durability'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "554157d1",
+   "metadata": {},
+   "source": [
+    "# Heel padding durability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "0d78f5d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Heel padding durability\n",
+      "Decent    51\n",
+      "Good      50\n",
+      "-         38\n",
+      "Bad       19\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Heel padding durability\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "c5df3591",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Value Counts Kolom Asli ---\n",
+      "Heel padding durability\n",
+      "decent    51\n",
+      "good      50\n",
+      "-         38\n",
+      "bad       19\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "--- Sebaran Nilai Hasil Ordinal ---\n",
+      "Index 0: 38 baris\n",
+      "Index 1: 0 baris\n",
+      "Index 2: 19 baris\n",
+      "Index 3: 51 baris\n",
+      "Index 4: 50 baris\n",
+      "Index 5: 0 baris\n",
+      "\n",
+      "--- Perbandingan Data (Head) ---\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['Heel padding durability'] = df['Heel padding durability'].astype(str).str.lower()\n",
+    "\n",
+    "durability_scale_5 = {\n",
+    "    \"-\": 0,\n",
+    "    \"very bad\": 1,\n",
+    "    \"bad\": 2,\n",
+    "    \"decent\": 3,\n",
+    "    \"good\": 4,\n",
+    "    \"very good\": 5\n",
+    "}\n",
+    "\n",
+    "\n",
+    "df['heel_durability'] = df['Heel padding durability'].map(durability_scale_5)\n",
+    "\n",
+    "\n",
+    "print(\"--- Value Counts Kolom Asli ---\")\n",
+    "print(df[\"Heel padding durability\"].value_counts())\n",
+    "\n",
+    "print(\"\\n--- Sebaran Nilai Hasil Ordinal ---\")\n",
+    "counts = df[\"heel_durability\"].value_counts().sort_index()\n",
+    "for i in range(6):\n",
+    "    print(f\"Index {i}: {counts.get(i, 0)} baris\")\n",
+    "\n",
+    "print(\"\\n--- Perbandingan Data (Head) ---\")\n",
+    "# print(df[[\"Heel padding durability\", \"heel_durability\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "5126e7a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 46 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   Brand                            158 non-null    str    \n",
+      " 1   Name                             158 non-null    str    \n",
+      " 2   Lightweight                      152 non-null    float64\n",
+      " 3   Plate                            158 non-null    str    \n",
+      " 4   Outsole durability               158 non-null    str    \n",
+      " 5   Breathability                    158 non-null    str    \n",
+      " 6   Width / fit                      158 non-null    str    \n",
+      " 7   Toebox width                     158 non-null    str    \n",
+      " 8   Stiffness                        158 non-null    str    \n",
+      " 9   Torsional rigidity               158 non-null    str    \n",
+      " 10  Heel counter stiffness           158 non-null    str    \n",
+      " 11  Lug depth                        158 non-null    str    \n",
+      " 12  Heel stack lab Heel stack brand  158 non-null    str    \n",
+      " 13  Forefoot lab Forefoot brand      158 non-null    str    \n",
+      " 14  For heavy runners                154 non-null    float64\n",
+      " 15  Season                           158 non-null    str    \n",
+      " 16  Removable insole                 158 non-null    int64  \n",
+      " 17  Orthotic friendly                158 non-null    int64  \n",
+      " 18  Waterproofing                    156 non-null    str    \n",
+      " 19  terrain_light                    158 non-null    int64  \n",
+      " 20  terrain_moderate                 158 non-null    int64  \n",
+      " 21  terrain_technical                158 non-null    int64  \n",
+      " 22  shock_low                        158 non-null    int64  \n",
+      " 23  shock_moderate                   158 non-null    int64  \n",
+      " 24  shock_high                       158 non-null    int64  \n",
+      " 25  energy_low                       158 non-null    int64  \n",
+      " 26  energy_moderate                  158 non-null    int64  \n",
+      " 27  energy_high                      158 non-null    int64  \n",
+      " 28  traction_moderate                158 non-null    int64  \n",
+      " 29  traction_high                    158 non-null    int64  \n",
+      " 30  arch_neutral                     158 non-null    int64  \n",
+      " 31  arch_stability                   158 non-null    int64  \n",
+      " 32  weight_lab_oz                    158 non-null    float64\n",
+      " 33  weight_lab_g                     158 non-null    int64  \n",
+      " 34  weight_brand_oz                  155 non-null    float64\n",
+      " 35  weight_brand_g                   155 non-null    float64\n",
+      " 36  drop_lab_mm                      158 non-null    float64\n",
+      " 37  drop_brand_mm                    152 non-null    float64\n",
+      " 38  strike_heel                      158 non-null    int64  \n",
+      " 39  strike_mid                       158 non-null    int64  \n",
+      " 40  strike_forefoot                  158 non-null    int64  \n",
+      " 41  softness_soft                    158 non-null    int64  \n",
+      " 42  softness_balanced                158 non-null    int64  \n",
+      " 43  softness_firm                    158 non-null    int64  \n",
+      " 44  toebox_durability                158 non-null    int64  \n",
+      " 45  heel_durability                  158 non-null    int64  \n",
+      "dtypes: float64(7), int64(24), str(15)\n",
+      "memory usage: 56.9 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop('Heel padding durability', axis=1, inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "46a4046e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Plate</th>\n",
+       "      <th>Outsole durability</th>\n",
+       "      <th>Breathability</th>\n",
+       "      <th>Width / fit</th>\n",
+       "      <th>Toebox width</th>\n",
+       "      <th>Stiffness</th>\n",
+       "      <th>Torsional rigidity</th>\n",
+       "      <th>...</th>\n",
+       "      <th>drop_lab_mm</th>\n",
+       "      <th>drop_brand_mm</th>\n",
+       "      <th>strike_heel</th>\n",
+       "      <th>strike_mid</th>\n",
+       "      <th>strike_forefoot</th>\n",
+       "      <th>softness_soft</th>\n",
+       "      <th>softness_balanced</th>\n",
+       "      <th>softness_firm</th>\n",
+       "      <th>toebox_durability</th>\n",
+       "      <th>heel_durability</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>terrex agravic speed ultra</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Decent</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Medium</td>\n",
+       "      <td>Narrow</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Stiff</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>terrex speed ultra</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Narrow</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Stiff</td>\n",
+       "      <td>Flexible</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.2</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>experience wild</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Good</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Wide</td>\n",
+       "      <td>Wide</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Stiff</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.3</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>experience wild 2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Good</td>\n",
+       "      <td>Warm</td>\n",
+       "      <td>Wide</td>\n",
+       "      <td>Wide</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6.1</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>lone peak 5.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Rock plate</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Narrow</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Stiff</td>\n",
+       "      <td>Flexible</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 46 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand                        Name  Lightweight       Plate  \\\n",
+       "0  adidas  terrex agravic speed ultra          0.0           0   \n",
+       "1  adidas          terrex speed ultra          0.0           0   \n",
+       "2   altra             experience wild          0.0           0   \n",
+       "3   altra           experience wild 2          0.0           0   \n",
+       "4   altra               lone peak 5.0          0.0  Rock plate   \n",
+       "\n",
+       "  Outsole durability Breathability Width / fit Toebox width Stiffness  \\\n",
+       "0             Decent      Moderate      Medium       Narrow  Moderate   \n",
+       "1                  -             -      Narrow            -     Stiff   \n",
+       "2               Good      Moderate        Wide         Wide  Moderate   \n",
+       "3               Good          Warm        Wide         Wide  Moderate   \n",
+       "4                  -             -      Narrow            -     Stiff   \n",
+       "\n",
+       "  Torsional rigidity  ... drop_lab_mm drop_brand_mm strike_heel strike_mid  \\\n",
+       "0              Stiff  ...         0.3           8.0           0          1   \n",
+       "1           Flexible  ...         8.2           8.0           1          1   \n",
+       "2              Stiff  ...         4.3           4.0           0          1   \n",
+       "3           Moderate  ...         6.1           4.0           0          1   \n",
+       "4           Flexible  ...         0.2           0.0           0          1   \n",
+       "\n",
+       "   strike_forefoot softness_soft  softness_balanced  softness_firm  \\\n",
+       "0                1             0                  1              0   \n",
+       "1                1             0                  0              0   \n",
+       "2                1             1                  0              0   \n",
+       "3                1             0                  1              0   \n",
+       "4                1             0                  0              0   \n",
+       "\n",
+       "  toebox_durability  heel_durability  \n",
+       "0                 4                4  \n",
+       "1                 0                0  \n",
+       "2                 3                3  \n",
+       "3                 3                4  \n",
+       "4                 0                0  \n",
+       "\n",
+       "[5 rows x 46 columns]"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20b755dc",
+   "metadata": {},
+   "source": [
+    "# Outsole durability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "0647abf7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Outsole durability\n",
+      "Good      77\n",
+      "-         42\n",
+      "Decent    38\n",
+      "Bad        1\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Outsole durability\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "fbba911c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Value Counts Kolom Asli ---\n",
+      "Outsole durability\n",
+      "good      77\n",
+      "-         42\n",
+      "decent    38\n",
+      "bad        1\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "--- Sebaran Nilai Hasil Ordinal (Scale 1-5) ---\n",
+      "Index 0: 42 baris\n",
+      "Index 1: 0 baris\n",
+      "Index 2: 1 baris\n",
+      "Index 3: 38 baris\n",
+      "Index 4: 77 baris\n",
+      "Index 5: 0 baris\n",
+      "\n",
+      "--- Perbandingan Data (Head) ---\n",
+      "  Outsole durability  outsole_durability\n",
+      "0             decent                   3\n",
+      "1                  -                   0\n",
+      "2               good                   4\n",
+      "3               good                   4\n",
+      "4                  -                   0\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['Outsole durability'] = df['Outsole durability'].astype(str).str.lower()\n",
+    "\n",
+    "durability_scale_5 = {\n",
+    "    \"-\": 0,\n",
+    "    \"very bad\": 1,\n",
+    "    \"bad\": 2,\n",
+    "    \"decent\": 3,\n",
+    "    \"good\": 4,\n",
+    "    \"very good\": 5\n",
+    "}\n",
+    "\n",
+    "\n",
+    "df['outsole_durability'] = df['Outsole durability'].map(durability_scale_5)\n",
+    "print(\"--- Value Counts Kolom Asli ---\")\n",
+    "print(df[\"Outsole durability\"].value_counts())\n",
+    "\n",
+    "print(\"\\n--- Sebaran Nilai Hasil Ordinal (Scale 1-5) ---\")\n",
+    "counts = df[\"outsole_durability\"].value_counts().sort_index()\n",
+    "for i in range(6):\n",
+    "    print(f\"Index {i}: {counts.get(i, 0)} baris\")\n",
+    "\n",
+    "print(\"\\n--- Perbandingan Data (Head) ---\")\n",
+    "print(df[[\"Outsole durability\", \"outsole_durability\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "b9a91d05",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 46 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   Brand                            158 non-null    str    \n",
+      " 1   Name                             158 non-null    str    \n",
+      " 2   Lightweight                      152 non-null    float64\n",
+      " 3   Plate                            158 non-null    str    \n",
+      " 4   Breathability                    158 non-null    str    \n",
+      " 5   Width / fit                      158 non-null    str    \n",
+      " 6   Toebox width                     158 non-null    str    \n",
+      " 7   Stiffness                        158 non-null    str    \n",
+      " 8   Torsional rigidity               158 non-null    str    \n",
+      " 9   Heel counter stiffness           158 non-null    str    \n",
+      " 10  Lug depth                        158 non-null    str    \n",
+      " 11  Heel stack lab Heel stack brand  158 non-null    str    \n",
+      " 12  Forefoot lab Forefoot brand      158 non-null    str    \n",
+      " 13  For heavy runners                154 non-null    float64\n",
+      " 14  Season                           158 non-null    str    \n",
+      " 15  Removable insole                 158 non-null    int64  \n",
+      " 16  Orthotic friendly                158 non-null    int64  \n",
+      " 17  Waterproofing                    156 non-null    str    \n",
+      " 18  terrain_light                    158 non-null    int64  \n",
+      " 19  terrain_moderate                 158 non-null    int64  \n",
+      " 20  terrain_technical                158 non-null    int64  \n",
+      " 21  shock_low                        158 non-null    int64  \n",
+      " 22  shock_moderate                   158 non-null    int64  \n",
+      " 23  shock_high                       158 non-null    int64  \n",
+      " 24  energy_low                       158 non-null    int64  \n",
+      " 25  energy_moderate                  158 non-null    int64  \n",
+      " 26  energy_high                      158 non-null    int64  \n",
+      " 27  traction_moderate                158 non-null    int64  \n",
+      " 28  traction_high                    158 non-null    int64  \n",
+      " 29  arch_neutral                     158 non-null    int64  \n",
+      " 30  arch_stability                   158 non-null    int64  \n",
+      " 31  weight_lab_oz                    158 non-null    float64\n",
+      " 32  weight_lab_g                     158 non-null    int64  \n",
+      " 33  weight_brand_oz                  155 non-null    float64\n",
+      " 34  weight_brand_g                   155 non-null    float64\n",
+      " 35  drop_lab_mm                      158 non-null    float64\n",
+      " 36  drop_brand_mm                    152 non-null    float64\n",
+      " 37  strike_heel                      158 non-null    int64  \n",
+      " 38  strike_mid                       158 non-null    int64  \n",
+      " 39  strike_forefoot                  158 non-null    int64  \n",
+      " 40  softness_soft                    158 non-null    int64  \n",
+      " 41  softness_balanced                158 non-null    int64  \n",
+      " 42  softness_firm                    158 non-null    int64  \n",
+      " 43  toebox_durability                158 non-null    int64  \n",
+      " 44  heel_durability                  158 non-null    int64  \n",
+      " 45  outsole_durability               158 non-null    int64  \n",
+      "dtypes: float64(7), int64(25), str(14)\n",
+      "memory usage: 56.9 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop('Outsole durability', axis=1, inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6e10b84",
+   "metadata": {},
+   "source": [
+    "# Breathability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "c6a78523",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Breathability\n",
+      "Moderate      92\n",
+      "Warm          31\n",
+      "-             19\n",
+      "Breathable    16\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Breathability\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "35945fd4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Value Counts Kolom Asli ---\n",
+      "Breathability\n",
+      "moderate      92\n",
+      "warm          31\n",
+      "-             19\n",
+      "breathable    16\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "--- Sebaran Nilai Hasil Ordinal (Scale 1-5) ---\n",
+      "Index 0: 19 baris\n",
+      "Index 1: 0 baris\n",
+      "Index 2: 31 baris\n",
+      "Index 3: 92 baris\n",
+      "Index 4: 0 baris\n",
+      "Index 5: 16 baris\n",
+      "\n",
+      "--- Perbandingan Data (Head) ---\n",
+      "  Breathability  breathability\n",
+      "0      moderate              3\n",
+      "1             -              0\n",
+      "2      moderate              3\n",
+      "3          warm              2\n",
+      "4             -              0\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['Breathability'] = df['Breathability'].astype(str).str.lower()\n",
+    "\n",
+    "breathability_scale_5 = {\n",
+    "    \"-\": 0,\n",
+    "    \"suffocating\": 1,\n",
+    "    \"warm\": 2,\n",
+    "    \"moderate\": 3,\n",
+    "    \"good\": 4,\n",
+    "    \"breathable\": 5\n",
+    "}\n",
+    "\n",
+    "df['breathability'] = df['Breathability'].map(breathability_scale_5)\n",
+    "\n",
+    "print(\"--- Value Counts Kolom Asli ---\")\n",
+    "print(df[\"Breathability\"].value_counts())\n",
+    "\n",
+    "print(\"\\n--- Sebaran Nilai Hasil Ordinal (Scale 1-5) ---\")\n",
+    "counts = df[\"breathability\"].value_counts().sort_index()\n",
+    "for i in range(6):\n",
+    "    print(f\"Index {i}: {counts.get(i, 0)} baris\")\n",
+    "\n",
+    "print(\"\\n--- Perbandingan Data (Head) ---\")\n",
+    "print(df[[\"Breathability\", \"breathability\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "f9163564",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 46 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   Brand                            158 non-null    str    \n",
+      " 1   Name                             158 non-null    str    \n",
+      " 2   Lightweight                      152 non-null    float64\n",
+      " 3   Plate                            158 non-null    str    \n",
+      " 4   Width / fit                      158 non-null    str    \n",
+      " 5   Toebox width                     158 non-null    str    \n",
+      " 6   Stiffness                        158 non-null    str    \n",
+      " 7   Torsional rigidity               158 non-null    str    \n",
+      " 8   Heel counter stiffness           158 non-null    str    \n",
+      " 9   Lug depth                        158 non-null    str    \n",
+      " 10  Heel stack lab Heel stack brand  158 non-null    str    \n",
+      " 11  Forefoot lab Forefoot brand      158 non-null    str    \n",
+      " 12  For heavy runners                154 non-null    float64\n",
+      " 13  Season                           158 non-null    str    \n",
+      " 14  Removable insole                 158 non-null    int64  \n",
+      " 15  Orthotic friendly                158 non-null    int64  \n",
+      " 16  Waterproofing                    156 non-null    str    \n",
+      " 17  terrain_light                    158 non-null    int64  \n",
+      " 18  terrain_moderate                 158 non-null    int64  \n",
+      " 19  terrain_technical                158 non-null    int64  \n",
+      " 20  shock_low                        158 non-null    int64  \n",
+      " 21  shock_moderate                   158 non-null    int64  \n",
+      " 22  shock_high                       158 non-null    int64  \n",
+      " 23  energy_low                       158 non-null    int64  \n",
+      " 24  energy_moderate                  158 non-null    int64  \n",
+      " 25  energy_high                      158 non-null    int64  \n",
+      " 26  traction_moderate                158 non-null    int64  \n",
+      " 27  traction_high                    158 non-null    int64  \n",
+      " 28  arch_neutral                     158 non-null    int64  \n",
+      " 29  arch_stability                   158 non-null    int64  \n",
+      " 30  weight_lab_oz                    158 non-null    float64\n",
+      " 31  weight_lab_g                     158 non-null    int64  \n",
+      " 32  weight_brand_oz                  155 non-null    float64\n",
+      " 33  weight_brand_g                   155 non-null    float64\n",
+      " 34  drop_lab_mm                      158 non-null    float64\n",
+      " 35  drop_brand_mm                    152 non-null    float64\n",
+      " 36  strike_heel                      158 non-null    int64  \n",
+      " 37  strike_mid                       158 non-null    int64  \n",
+      " 38  strike_forefoot                  158 non-null    int64  \n",
+      " 39  softness_soft                    158 non-null    int64  \n",
+      " 40  softness_balanced                158 non-null    int64  \n",
+      " 41  softness_firm                    158 non-null    int64  \n",
+      " 42  toebox_durability                158 non-null    int64  \n",
+      " 43  heel_durability                  158 non-null    int64  \n",
+      " 44  outsole_durability               158 non-null    int64  \n",
+      " 45  breathability                    158 non-null    int64  \n",
+      "dtypes: float64(7), int64(26), str(13)\n",
+      "memory usage: 56.9 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop('Breathability', axis=1, inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "838283e8",
+   "metadata": {},
+   "source": [
+    "# Plate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "eb229705",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plate\n",
+      "0               112\n",
+      "Rock plate       35\n",
+      "Carbon plate     11\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Plate\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "a97958df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 158\n",
+      "NULL Value: 0\n",
+      "\n",
+      "Sample Comparison:\n",
+      "        Plate  plate_0  plate_rock_plate  plate_carbon_plate\n",
+      "0           0        1                 0                   0\n",
+      "1           0        1                 0                   0\n",
+      "2           0        1                 0                   0\n",
+      "3           0        1                 0                   0\n",
+      "4  rock plate        0                 1                   0\n",
+      "5  rock plate        0                 1                   0\n",
+      "6           0        1                 0                   0\n",
+      "7           0        1                 0                   0\n",
+      "8           0        1                 0                   0\n",
+      "9           0        1                 0                   0\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['Plate'] = df['Plate'].astype(str).str.lower()\n",
+    "base_plate = ['0', 'rock plate', 'carbon plate']\n",
+    "\n",
+    "for level in base_plate:\n",
+    "    column_name = f\"plate_{level.replace(' ', '_')}\"\n",
+    "    if level == '0':\n",
+    "        df[column_name] = (df['Plate'] == '0').astype(int)\n",
+    "    else:\n",
+    "        df[column_name] = df['Plate'].str.contains(level, na=False).astype(int)\n",
+    "\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "plate_cols = [f\"plate_{l.replace(' ', '_')}\" for l in base_plate]\n",
+    "zero_vector_count = (df[plate_cols].sum(axis=1) == 0).sum()\n",
+    "print(f\"NULL Value: {zero_vector_count}\")\n",
+    "\n",
+    "print(\"\\nSample Comparison:\")\n",
+    "print(df[[\"Plate\"] + plate_cols].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "ff871a52",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Plate\n",
+      "0               112\n",
+      "rock plate       35\n",
+      "carbon plate     11\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "plate_0 sum: 112\n",
+      "plate_rock_plate sum: 35\n",
+      "plate_carbon_plate sum: 11\n",
+      "\n",
+      "        Plate  plate_0  plate_rock_plate  plate_carbon_plate\n",
+      "0           0        1                 0                   0\n",
+      "1           0        1                 0                   0\n",
+      "2           0        1                 0                   0\n",
+      "3           0        1                 0                   0\n",
+      "4  rock plate        0                 1                   0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Plate\"].value_counts())\n",
+    "\n",
+    "print()\n",
+    "for level in base_plate:\n",
+    "    col = f\"plate_{level.replace(' ', '_')}\"\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"Plate\"] + plate_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "8173c5b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 48 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   Brand                            158 non-null    str    \n",
+      " 1   Name                             158 non-null    str    \n",
+      " 2   Lightweight                      152 non-null    float64\n",
+      " 3   Width / fit                      158 non-null    str    \n",
+      " 4   Toebox width                     158 non-null    str    \n",
+      " 5   Stiffness                        158 non-null    str    \n",
+      " 6   Torsional rigidity               158 non-null    str    \n",
+      " 7   Heel counter stiffness           158 non-null    str    \n",
+      " 8   Lug depth                        158 non-null    str    \n",
+      " 9   Heel stack lab Heel stack brand  158 non-null    str    \n",
+      " 10  Forefoot lab Forefoot brand      158 non-null    str    \n",
+      " 11  For heavy runners                154 non-null    float64\n",
+      " 12  Season                           158 non-null    str    \n",
+      " 13  Removable insole                 158 non-null    int64  \n",
+      " 14  Orthotic friendly                158 non-null    int64  \n",
+      " 15  Waterproofing                    156 non-null    str    \n",
+      " 16  terrain_light                    158 non-null    int64  \n",
+      " 17  terrain_moderate                 158 non-null    int64  \n",
+      " 18  terrain_technical                158 non-null    int64  \n",
+      " 19  shock_low                        158 non-null    int64  \n",
+      " 20  shock_moderate                   158 non-null    int64  \n",
+      " 21  shock_high                       158 non-null    int64  \n",
+      " 22  energy_low                       158 non-null    int64  \n",
+      " 23  energy_moderate                  158 non-null    int64  \n",
+      " 24  energy_high                      158 non-null    int64  \n",
+      " 25  traction_moderate                158 non-null    int64  \n",
+      " 26  traction_high                    158 non-null    int64  \n",
+      " 27  arch_neutral                     158 non-null    int64  \n",
+      " 28  arch_stability                   158 non-null    int64  \n",
+      " 29  weight_lab_oz                    158 non-null    float64\n",
+      " 30  weight_lab_g                     158 non-null    int64  \n",
+      " 31  weight_brand_oz                  155 non-null    float64\n",
+      " 32  weight_brand_g                   155 non-null    float64\n",
+      " 33  drop_lab_mm                      158 non-null    float64\n",
+      " 34  drop_brand_mm                    152 non-null    float64\n",
+      " 35  strike_heel                      158 non-null    int64  \n",
+      " 36  strike_mid                       158 non-null    int64  \n",
+      " 37  strike_forefoot                  158 non-null    int64  \n",
+      " 38  softness_soft                    158 non-null    int64  \n",
+      " 39  softness_balanced                158 non-null    int64  \n",
+      " 40  softness_firm                    158 non-null    int64  \n",
+      " 41  toebox_durability                158 non-null    int64  \n",
+      " 42  heel_durability                  158 non-null    int64  \n",
+      " 43  outsole_durability               158 non-null    int64  \n",
+      " 44  breathability                    158 non-null    int64  \n",
+      " 45  plate_0                          158 non-null    int64  \n",
+      " 46  plate_rock_plate                 158 non-null    int64  \n",
+      " 47  plate_carbon_plate               158 non-null    int64  \n",
+      "dtypes: float64(7), int64(29), str(12)\n",
+      "memory usage: 59.4 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"Plate\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "273e5d26",
+   "metadata": {},
+   "source": [
+    "# Width / fit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "229ae3b1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Width / fit\n",
+      "Medium    92\n",
+      "Narrow    47\n",
+      "Wide      19\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['Width / fit'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "9e4b2c22",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 158\n",
+      "NULL Value: 0\n",
+      "\n",
+      "Sample Comparison:\n",
+      "  Width / fit  width_narrow  width_medium  width_wide\n",
+      "0      medium             0             1           0\n",
+      "1      narrow             1             0           0\n",
+      "2        wide             0             0           1\n",
+      "3        wide             0             0           1\n",
+      "4      narrow             1             0           0\n",
+      "5        wide             0             0           1\n",
+      "6      narrow             1             0           0\n",
+      "7      medium             0             1           0\n",
+      "8        wide             0             0           1\n",
+      "9      medium             0             1           0\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['Width / fit'] = df['Width / fit'].astype(str).str.lower()\n",
+    "base_widths = ['narrow', 'medium', 'wide']\n",
+    "\n",
+    "for level in base_widths:\n",
+    "    column_name = f\"width_{level}\"\n",
+    "    df[column_name] = df['Width / fit'].str.contains(level, na=False).astype(int)\n",
+    "\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "width_cols = [f\"width_{l}\" for l in base_widths]\n",
+    "zero_vector_count = (df[width_cols].sum(axis=1) == 0).sum()\n",
+    "print(f\"NULL Value: {zero_vector_count}\")\n",
+    "\n",
+    "print(\"\\nSample Comparison:\")\n",
+    "print(df[[\"Width / fit\"] + width_cols].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "3d808900",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Width / fit\n",
+      "medium    92\n",
+      "narrow    47\n",
+      "wide      19\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "width_narrow sum: 47\n",
+      "width_medium sum: 92\n",
+      "width_wide sum: 19\n",
+      "\n",
+      "  Width / fit  width_narrow  width_medium  width_wide\n",
+      "0      medium             0             1           0\n",
+      "1      narrow             1             0           0\n",
+      "2        wide             0             0           1\n",
+      "3        wide             0             0           1\n",
+      "4      narrow             1             0           0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Width / fit\"].value_counts())\n",
+    "\n",
+    "print()\n",
+    "for level in base_widths:\n",
+    "    col = f\"width_{level}\"\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"Width / fit\"] + width_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "227f8218",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 50 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   Brand                            158 non-null    str    \n",
+      " 1   Name                             158 non-null    str    \n",
+      " 2   Lightweight                      152 non-null    float64\n",
+      " 3   Toebox width                     158 non-null    str    \n",
+      " 4   Stiffness                        158 non-null    str    \n",
+      " 5   Torsional rigidity               158 non-null    str    \n",
+      " 6   Heel counter stiffness           158 non-null    str    \n",
+      " 7   Lug depth                        158 non-null    str    \n",
+      " 8   Heel stack lab Heel stack brand  158 non-null    str    \n",
+      " 9   Forefoot lab Forefoot brand      158 non-null    str    \n",
+      " 10  For heavy runners                154 non-null    float64\n",
+      " 11  Season                           158 non-null    str    \n",
+      " 12  Removable insole                 158 non-null    int64  \n",
+      " 13  Orthotic friendly                158 non-null    int64  \n",
+      " 14  Waterproofing                    156 non-null    str    \n",
+      " 15  terrain_light                    158 non-null    int64  \n",
+      " 16  terrain_moderate                 158 non-null    int64  \n",
+      " 17  terrain_technical                158 non-null    int64  \n",
+      " 18  shock_low                        158 non-null    int64  \n",
+      " 19  shock_moderate                   158 non-null    int64  \n",
+      " 20  shock_high                       158 non-null    int64  \n",
+      " 21  energy_low                       158 non-null    int64  \n",
+      " 22  energy_moderate                  158 non-null    int64  \n",
+      " 23  energy_high                      158 non-null    int64  \n",
+      " 24  traction_moderate                158 non-null    int64  \n",
+      " 25  traction_high                    158 non-null    int64  \n",
+      " 26  arch_neutral                     158 non-null    int64  \n",
+      " 27  arch_stability                   158 non-null    int64  \n",
+      " 28  weight_lab_oz                    158 non-null    float64\n",
+      " 29  weight_lab_g                     158 non-null    int64  \n",
+      " 30  weight_brand_oz                  155 non-null    float64\n",
+      " 31  weight_brand_g                   155 non-null    float64\n",
+      " 32  drop_lab_mm                      158 non-null    float64\n",
+      " 33  drop_brand_mm                    152 non-null    float64\n",
+      " 34  strike_heel                      158 non-null    int64  \n",
+      " 35  strike_mid                       158 non-null    int64  \n",
+      " 36  strike_forefoot                  158 non-null    int64  \n",
+      " 37  softness_soft                    158 non-null    int64  \n",
+      " 38  softness_balanced                158 non-null    int64  \n",
+      " 39  softness_firm                    158 non-null    int64  \n",
+      " 40  toebox_durability                158 non-null    int64  \n",
+      " 41  heel_durability                  158 non-null    int64  \n",
+      " 42  outsole_durability               158 non-null    int64  \n",
+      " 43  breathability                    158 non-null    int64  \n",
+      " 44  plate_0                          158 non-null    int64  \n",
+      " 45  plate_rock_plate                 158 non-null    int64  \n",
+      " 46  plate_carbon_plate               158 non-null    int64  \n",
+      " 47  width_narrow                     158 non-null    int64  \n",
+      " 48  width_medium                     158 non-null    int64  \n",
+      " 49  width_wide                       158 non-null    int64  \n",
+      "dtypes: float64(7), int64(32), str(11)\n",
+      "memory usage: 61.8 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"Width / fit\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f75044e",
+   "metadata": {},
+   "source": [
+    "# Toebox width"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "5534fbde",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Toebox width\n",
+      "Medium    68\n",
+      "Wide      38\n",
+      "-         32\n",
+      "Narrow    20\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['Toebox width'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "8a4afd8d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 158\n",
+      "NULL Value: 32\n",
+      "\n",
+      "Sample Comparison:\n",
+      "  Toebox width  toebox_narrow  toebox_medium  toebox_wide\n",
+      "0       narrow              1              0            0\n",
+      "1            -              0              0            0\n",
+      "2         wide              0              0            1\n",
+      "3         wide              0              0            1\n",
+      "4            -              0              0            0\n",
+      "5            -              0              0            0\n",
+      "6            -              0              0            0\n",
+      "7         wide              0              0            1\n",
+      "8         wide              0              0            1\n",
+      "9            -              0              0            0\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['Toebox width'] = df['Toebox width'].astype(str).str.lower()\n",
+    "base_toebox = ['narrow', 'medium', 'wide']\n",
+    "\n",
+    "for level in base_toebox:\n",
+    "    column_name = f\"toebox_{level}\"\n",
+    "    df[column_name] = df['Toebox width'].str.contains(level, na=False).astype(int)\n",
+    "\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "toebox_cols = [f\"toebox_{l}\" for l in base_toebox]\n",
+    "zero_vector_count = (df[toebox_cols].sum(axis=1) == 0).sum()\n",
+    "print(f\"NULL Value: {zero_vector_count}\")\n",
+    "\n",
+    "print(\"\\nSample Comparison:\")\n",
+    "print(df[[\"Toebox width\"] + toebox_cols].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "73514a31",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Toebox width\n",
+      "medium    68\n",
+      "wide      38\n",
+      "-         32\n",
+      "narrow    20\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "toebox_narrow sum: 20\n",
+      "toebox_medium sum: 68\n",
+      "toebox_wide sum: 38\n",
+      "\n",
+      "  Toebox width  toebox_narrow  toebox_medium  toebox_wide\n",
+      "0       narrow              1              0            0\n",
+      "1            -              0              0            0\n",
+      "2         wide              0              0            1\n",
+      "3         wide              0              0            1\n",
+      "4            -              0              0            0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Toebox width\"].value_counts())\n",
+    "\n",
+    "print()\n",
+    "for level in base_toebox:\n",
+    "    col = f\"toebox_{level}\"\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"Toebox width\"] + toebox_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "54c66b79",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 52 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   Brand                            158 non-null    str    \n",
+      " 1   Name                             158 non-null    str    \n",
+      " 2   Lightweight                      152 non-null    float64\n",
+      " 3   Stiffness                        158 non-null    str    \n",
+      " 4   Torsional rigidity               158 non-null    str    \n",
+      " 5   Heel counter stiffness           158 non-null    str    \n",
+      " 6   Lug depth                        158 non-null    str    \n",
+      " 7   Heel stack lab Heel stack brand  158 non-null    str    \n",
+      " 8   Forefoot lab Forefoot brand      158 non-null    str    \n",
+      " 9   For heavy runners                154 non-null    float64\n",
+      " 10  Season                           158 non-null    str    \n",
+      " 11  Removable insole                 158 non-null    int64  \n",
+      " 12  Orthotic friendly                158 non-null    int64  \n",
+      " 13  Waterproofing                    156 non-null    str    \n",
+      " 14  terrain_light                    158 non-null    int64  \n",
+      " 15  terrain_moderate                 158 non-null    int64  \n",
+      " 16  terrain_technical                158 non-null    int64  \n",
+      " 17  shock_low                        158 non-null    int64  \n",
+      " 18  shock_moderate                   158 non-null    int64  \n",
+      " 19  shock_high                       158 non-null    int64  \n",
+      " 20  energy_low                       158 non-null    int64  \n",
+      " 21  energy_moderate                  158 non-null    int64  \n",
+      " 22  energy_high                      158 non-null    int64  \n",
+      " 23  traction_moderate                158 non-null    int64  \n",
+      " 24  traction_high                    158 non-null    int64  \n",
+      " 25  arch_neutral                     158 non-null    int64  \n",
+      " 26  arch_stability                   158 non-null    int64  \n",
+      " 27  weight_lab_oz                    158 non-null    float64\n",
+      " 28  weight_lab_g                     158 non-null    int64  \n",
+      " 29  weight_brand_oz                  155 non-null    float64\n",
+      " 30  weight_brand_g                   155 non-null    float64\n",
+      " 31  drop_lab_mm                      158 non-null    float64\n",
+      " 32  drop_brand_mm                    152 non-null    float64\n",
+      " 33  strike_heel                      158 non-null    int64  \n",
+      " 34  strike_mid                       158 non-null    int64  \n",
+      " 35  strike_forefoot                  158 non-null    int64  \n",
+      " 36  softness_soft                    158 non-null    int64  \n",
+      " 37  softness_balanced                158 non-null    int64  \n",
+      " 38  softness_firm                    158 non-null    int64  \n",
+      " 39  toebox_durability                158 non-null    int64  \n",
+      " 40  heel_durability                  158 non-null    int64  \n",
+      " 41  outsole_durability               158 non-null    int64  \n",
+      " 42  breathability                    158 non-null    int64  \n",
+      " 43  plate_0                          158 non-null    int64  \n",
+      " 44  plate_rock_plate                 158 non-null    int64  \n",
+      " 45  plate_carbon_plate               158 non-null    int64  \n",
+      " 46  width_narrow                     158 non-null    int64  \n",
+      " 47  width_medium                     158 non-null    int64  \n",
+      " 48  width_wide                       158 non-null    int64  \n",
+      " 49  toebox_narrow                    158 non-null    int64  \n",
+      " 50  toebox_medium                    158 non-null    int64  \n",
+      " 51  toebox_wide                      158 non-null    int64  \n",
+      "dtypes: float64(7), int64(35), str(10)\n",
+      "memory usage: 64.3 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"Toebox width\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a723b8bd",
+   "metadata": {},
+   "source": [
+    "# Stiffness"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "76d488f5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Stiffness\n",
+      "Stiff       99\n",
+      "Moderate    53\n",
+      "Flexible     6\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['Stiffness'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "4e35f6e8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 158\n",
+      "NULL Value: 0\n",
+      "\n",
+      "Sample Comparison:\n",
+      "  Stiffness  stiffness_flexible  stiffness_moderate  stiffness_stiff\n",
+      "0  moderate                   0                   1                0\n",
+      "1     stiff                   0                   0                1\n",
+      "2  moderate                   0                   1                0\n",
+      "3  moderate                   0                   1                0\n",
+      "4     stiff                   0                   0                1\n",
+      "5     stiff                   0                   0                1\n",
+      "6     stiff                   0                   0                1\n",
+      "7     stiff                   0                   0                1\n",
+      "8  moderate                   0                   1                0\n",
+      "9     stiff                   0                   0                1\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['Stiffness'] = df['Stiffness'].astype(str).str.lower()\n",
+    "base_stiffness = ['flexible', 'moderate', 'stiff']\n",
+    "\n",
+    "for level in base_stiffness:\n",
+    "    column_name = f\"stiffness_{level}\"\n",
+    "    df[column_name] = df['Stiffness'].str.contains(level, na=False).astype(int)\n",
+    "\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "stiffness_cols = [f\"stiffness_{l}\" for l in base_stiffness]\n",
+    "zero_vector_count = (df[stiffness_cols].sum(axis=1) == 0).sum()\n",
+    "print(f\"NULL Value: {zero_vector_count}\")\n",
+    "\n",
+    "print(\"\\nSample Comparison:\")\n",
+    "print(df[[\"Stiffness\"] + stiffness_cols].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "844410fe",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Stiffness\n",
+      "stiff       99\n",
+      "moderate    53\n",
+      "flexible     6\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "stiffness_flexible sum: 6\n",
+      "stiffness_moderate sum: 53\n",
+      "stiffness_stiff sum: 99\n",
+      "\n",
+      "  Stiffness  stiffness_flexible  stiffness_moderate  stiffness_stiff\n",
+      "0  moderate                   0                   1                0\n",
+      "1     stiff                   0                   0                1\n",
+      "2  moderate                   0                   1                0\n",
+      "3  moderate                   0                   1                0\n",
+      "4     stiff                   0                   0                1\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Stiffness\"].value_counts())\n",
+    "\n",
+    "print()\n",
+    "for level in base_stiffness:\n",
+    "    col = f\"stiffness_{level}\"\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"Stiffness\"] + stiffness_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "429e0c4e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 54 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   Brand                            158 non-null    str    \n",
+      " 1   Name                             158 non-null    str    \n",
+      " 2   Lightweight                      152 non-null    float64\n",
+      " 3   Torsional rigidity               158 non-null    str    \n",
+      " 4   Heel counter stiffness           158 non-null    str    \n",
+      " 5   Lug depth                        158 non-null    str    \n",
+      " 6   Heel stack lab Heel stack brand  158 non-null    str    \n",
+      " 7   Forefoot lab Forefoot brand      158 non-null    str    \n",
+      " 8   For heavy runners                154 non-null    float64\n",
+      " 9   Season                           158 non-null    str    \n",
+      " 10  Removable insole                 158 non-null    int64  \n",
+      " 11  Orthotic friendly                158 non-null    int64  \n",
+      " 12  Waterproofing                    156 non-null    str    \n",
+      " 13  terrain_light                    158 non-null    int64  \n",
+      " 14  terrain_moderate                 158 non-null    int64  \n",
+      " 15  terrain_technical                158 non-null    int64  \n",
+      " 16  shock_low                        158 non-null    int64  \n",
+      " 17  shock_moderate                   158 non-null    int64  \n",
+      " 18  shock_high                       158 non-null    int64  \n",
+      " 19  energy_low                       158 non-null    int64  \n",
+      " 20  energy_moderate                  158 non-null    int64  \n",
+      " 21  energy_high                      158 non-null    int64  \n",
+      " 22  traction_moderate                158 non-null    int64  \n",
+      " 23  traction_high                    158 non-null    int64  \n",
+      " 24  arch_neutral                     158 non-null    int64  \n",
+      " 25  arch_stability                   158 non-null    int64  \n",
+      " 26  weight_lab_oz                    158 non-null    float64\n",
+      " 27  weight_lab_g                     158 non-null    int64  \n",
+      " 28  weight_brand_oz                  155 non-null    float64\n",
+      " 29  weight_brand_g                   155 non-null    float64\n",
+      " 30  drop_lab_mm                      158 non-null    float64\n",
+      " 31  drop_brand_mm                    152 non-null    float64\n",
+      " 32  strike_heel                      158 non-null    int64  \n",
+      " 33  strike_mid                       158 non-null    int64  \n",
+      " 34  strike_forefoot                  158 non-null    int64  \n",
+      " 35  softness_soft                    158 non-null    int64  \n",
+      " 36  softness_balanced                158 non-null    int64  \n",
+      " 37  softness_firm                    158 non-null    int64  \n",
+      " 38  toebox_durability                158 non-null    int64  \n",
+      " 39  heel_durability                  158 non-null    int64  \n",
+      " 40  outsole_durability               158 non-null    int64  \n",
+      " 41  breathability                    158 non-null    int64  \n",
+      " 42  plate_0                          158 non-null    int64  \n",
+      " 43  plate_rock_plate                 158 non-null    int64  \n",
+      " 44  plate_carbon_plate               158 non-null    int64  \n",
+      " 45  width_narrow                     158 non-null    int64  \n",
+      " 46  width_medium                     158 non-null    int64  \n",
+      " 47  width_wide                       158 non-null    int64  \n",
+      " 48  toebox_narrow                    158 non-null    int64  \n",
+      " 49  toebox_medium                    158 non-null    int64  \n",
+      " 50  toebox_wide                      158 non-null    int64  \n",
+      " 51  stiffness_flexible               158 non-null    int64  \n",
+      " 52  stiffness_moderate               158 non-null    int64  \n",
+      " 53  stiffness_stiff                  158 non-null    int64  \n",
+      "dtypes: float64(7), int64(38), str(9)\n",
+      "memory usage: 66.8 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"Stiffness\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "347d1f39",
+   "metadata": {},
+   "source": [
+    "# Torsional rigidity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "898bbd3d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Torsional rigidity\n",
+      "Stiff       93\n",
+      "Moderate    37\n",
+      "Flexible    22\n",
+      "-            6\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['Torsional rigidity'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "94fbcfb9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 158\n",
+      "NULL Value: 6\n",
+      "\n",
+      "Sample Comparison:\n",
+      "  Torsional rigidity  torsional_flexible  torsional_moderate  torsional_stiff\n",
+      "0              stiff                   0                   0                1\n",
+      "1           flexible                   1                   0                0\n",
+      "2              stiff                   0                   0                1\n",
+      "3           moderate                   0                   1                0\n",
+      "4           flexible                   1                   0                0\n",
+      "5                  -                   0                   0                0\n",
+      "6           flexible                   1                   0                0\n",
+      "7           flexible                   1                   0                0\n",
+      "8           moderate                   0                   1                0\n",
+      "9                  -                   0                   0                0\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['Torsional rigidity'] = df['Torsional rigidity'].astype(str).str.lower()\n",
+    "base_torsional = ['flexible', 'moderate', 'stiff']\n",
+    "\n",
+    "for level in base_torsional:\n",
+    "    column_name = f\"torsional_{level}\"\n",
+    "    df[column_name] = df['Torsional rigidity'].str.contains(level, na=False).astype(int)\n",
+    "\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "torsional_cols = [f\"torsional_{l}\" for l in base_torsional]\n",
+    "zero_vector_count = (df[torsional_cols].sum(axis=1) == 0).sum()\n",
+    "print(f\"NULL Value: {zero_vector_count}\")\n",
+    "\n",
+    "print(\"\\nSample Comparison:\")\n",
+    "print(df[[\"Torsional rigidity\"] + torsional_cols].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "bdfc1ffd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Torsional rigidity\n",
+      "stiff       93\n",
+      "moderate    37\n",
+      "flexible    22\n",
+      "-            6\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "torsional_flexible sum: 22\n",
+      "torsional_moderate sum: 37\n",
+      "torsional_stiff sum: 93\n",
+      "\n",
+      "  Torsional rigidity  torsional_flexible  torsional_moderate  torsional_stiff\n",
+      "0              stiff                   0                   0                1\n",
+      "1           flexible                   1                   0                0\n",
+      "2              stiff                   0                   0                1\n",
+      "3           moderate                   0                   1                0\n",
+      "4           flexible                   1                   0                0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Torsional rigidity\"].value_counts())\n",
+    "\n",
+    "print()\n",
+    "for level in base_torsional:\n",
+    "    col = f\"torsional_{level}\"\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"Torsional rigidity\"] + torsional_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "5087be70",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 56 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   Brand                            158 non-null    str    \n",
+      " 1   Name                             158 non-null    str    \n",
+      " 2   Lightweight                      152 non-null    float64\n",
+      " 3   Heel counter stiffness           158 non-null    str    \n",
+      " 4   Lug depth                        158 non-null    str    \n",
+      " 5   Heel stack lab Heel stack brand  158 non-null    str    \n",
+      " 6   Forefoot lab Forefoot brand      158 non-null    str    \n",
+      " 7   For heavy runners                154 non-null    float64\n",
+      " 8   Season                           158 non-null    str    \n",
+      " 9   Removable insole                 158 non-null    int64  \n",
+      " 10  Orthotic friendly                158 non-null    int64  \n",
+      " 11  Waterproofing                    156 non-null    str    \n",
+      " 12  terrain_light                    158 non-null    int64  \n",
+      " 13  terrain_moderate                 158 non-null    int64  \n",
+      " 14  terrain_technical                158 non-null    int64  \n",
+      " 15  shock_low                        158 non-null    int64  \n",
+      " 16  shock_moderate                   158 non-null    int64  \n",
+      " 17  shock_high                       158 non-null    int64  \n",
+      " 18  energy_low                       158 non-null    int64  \n",
+      " 19  energy_moderate                  158 non-null    int64  \n",
+      " 20  energy_high                      158 non-null    int64  \n",
+      " 21  traction_moderate                158 non-null    int64  \n",
+      " 22  traction_high                    158 non-null    int64  \n",
+      " 23  arch_neutral                     158 non-null    int64  \n",
+      " 24  arch_stability                   158 non-null    int64  \n",
+      " 25  weight_lab_oz                    158 non-null    float64\n",
+      " 26  weight_lab_g                     158 non-null    int64  \n",
+      " 27  weight_brand_oz                  155 non-null    float64\n",
+      " 28  weight_brand_g                   155 non-null    float64\n",
+      " 29  drop_lab_mm                      158 non-null    float64\n",
+      " 30  drop_brand_mm                    152 non-null    float64\n",
+      " 31  strike_heel                      158 non-null    int64  \n",
+      " 32  strike_mid                       158 non-null    int64  \n",
+      " 33  strike_forefoot                  158 non-null    int64  \n",
+      " 34  softness_soft                    158 non-null    int64  \n",
+      " 35  softness_balanced                158 non-null    int64  \n",
+      " 36  softness_firm                    158 non-null    int64  \n",
+      " 37  toebox_durability                158 non-null    int64  \n",
+      " 38  heel_durability                  158 non-null    int64  \n",
+      " 39  outsole_durability               158 non-null    int64  \n",
+      " 40  breathability                    158 non-null    int64  \n",
+      " 41  plate_0                          158 non-null    int64  \n",
+      " 42  plate_rock_plate                 158 non-null    int64  \n",
+      " 43  plate_carbon_plate               158 non-null    int64  \n",
+      " 44  width_narrow                     158 non-null    int64  \n",
+      " 45  width_medium                     158 non-null    int64  \n",
+      " 46  width_wide                       158 non-null    int64  \n",
+      " 47  toebox_narrow                    158 non-null    int64  \n",
+      " 48  toebox_medium                    158 non-null    int64  \n",
+      " 49  toebox_wide                      158 non-null    int64  \n",
+      " 50  stiffness_flexible               158 non-null    int64  \n",
+      " 51  stiffness_moderate               158 non-null    int64  \n",
+      " 52  stiffness_stiff                  158 non-null    int64  \n",
+      " 53  torsional_flexible               158 non-null    int64  \n",
+      " 54  torsional_moderate               158 non-null    int64  \n",
+      " 55  torsional_stiff                  158 non-null    int64  \n",
+      "dtypes: float64(7), int64(41), str(8)\n",
+      "memory usage: 69.3 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"Torsional rigidity\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4fab1922",
+   "metadata": {},
+   "source": [
+    "# Heel counter stiffness"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "d17ba028",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Heel counter stiffness\n",
+      "Moderate    55\n",
+      "Stiff       49\n",
+      "Flexible    46\n",
+      "-            8\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['Heel counter stiffness'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "1ffb3c8c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 158\n",
+      "NULL Value: 8\n",
+      "\n",
+      "Sample Comparison:\n",
+      "  Heel counter stiffness  heel_stiff_flexible  heel_stiff_moderate  \\\n",
+      "0               flexible                    1                    0   \n",
+      "1               flexible                    1                    0   \n",
+      "2               moderate                    0                    1   \n",
+      "3               flexible                    1                    0   \n",
+      "4                      -                    0                    0   \n",
+      "5                      -                    0                    0   \n",
+      "6               flexible                    1                    0   \n",
+      "7               flexible                    1                    0   \n",
+      "8               flexible                    1                    0   \n",
+      "9                      -                    0                    0   \n",
+      "\n",
+      "   heel_stiff_stiff  \n",
+      "0                 0  \n",
+      "1                 0  \n",
+      "2                 0  \n",
+      "3                 0  \n",
+      "4                 0  \n",
+      "5                 0  \n",
+      "6                 0  \n",
+      "7                 0  \n",
+      "8                 0  \n",
+      "9                 0  \n"
+     ]
+    }
+   ],
+   "source": [
+    "df['Heel counter stiffness'] = df['Heel counter stiffness'].astype(str).str.lower()\n",
+    "base_heel_stiff = ['flexible', 'moderate', 'stiff']\n",
+    "\n",
+    "for level in base_heel_stiff:\n",
+    "    column_name = f\"heel_stiff_{level}\"\n",
+    "    df[column_name] = df['Heel counter stiffness'].str.contains(level, na=False).astype(int)\n",
+    "\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "heel_stiff_cols = [f\"heel_stiff_{l}\" for l in base_heel_stiff]\n",
+    "zero_vector_count = (df[heel_stiff_cols].sum(axis=1) == 0).sum()\n",
+    "print(f\"NULL Value: {zero_vector_count}\")\n",
+    "\n",
+    "print(\"\\nSample Comparison:\")\n",
+    "print(df[[\"Heel counter stiffness\"] + heel_stiff_cols].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "e9a07e6f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Heel counter stiffness\n",
+      "moderate    55\n",
+      "stiff       49\n",
+      "flexible    46\n",
+      "-            8\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "heel_stiff_flexible sum: 46\n",
+      "heel_stiff_moderate sum: 55\n",
+      "heel_stiff_stiff sum: 49\n",
+      "\n",
+      "  Heel counter stiffness  heel_stiff_flexible  heel_stiff_moderate  \\\n",
+      "0               flexible                    1                    0   \n",
+      "1               flexible                    1                    0   \n",
+      "2               moderate                    0                    1   \n",
+      "3               flexible                    1                    0   \n",
+      "4                      -                    0                    0   \n",
+      "\n",
+      "   heel_stiff_stiff  \n",
+      "0                 0  \n",
+      "1                 0  \n",
+      "2                 0  \n",
+      "3                 0  \n",
+      "4                 0  \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Heel counter stiffness\"].value_counts())\n",
+    "\n",
+    "print()\n",
+    "for level in base_heel_stiff:\n",
+    "    col = f\"heel_stiff_{level}\"\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"Heel counter stiffness\"] + heel_stiff_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "65d93f9a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 58 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   Brand                            158 non-null    str    \n",
+      " 1   Name                             158 non-null    str    \n",
+      " 2   Lightweight                      152 non-null    float64\n",
+      " 3   Lug depth                        158 non-null    str    \n",
+      " 4   Heel stack lab Heel stack brand  158 non-null    str    \n",
+      " 5   Forefoot lab Forefoot brand      158 non-null    str    \n",
+      " 6   For heavy runners                154 non-null    float64\n",
+      " 7   Season                           158 non-null    str    \n",
+      " 8   Removable insole                 158 non-null    int64  \n",
+      " 9   Orthotic friendly                158 non-null    int64  \n",
+      " 10  Waterproofing                    156 non-null    str    \n",
+      " 11  terrain_light                    158 non-null    int64  \n",
+      " 12  terrain_moderate                 158 non-null    int64  \n",
+      " 13  terrain_technical                158 non-null    int64  \n",
+      " 14  shock_low                        158 non-null    int64  \n",
+      " 15  shock_moderate                   158 non-null    int64  \n",
+      " 16  shock_high                       158 non-null    int64  \n",
+      " 17  energy_low                       158 non-null    int64  \n",
+      " 18  energy_moderate                  158 non-null    int64  \n",
+      " 19  energy_high                      158 non-null    int64  \n",
+      " 20  traction_moderate                158 non-null    int64  \n",
+      " 21  traction_high                    158 non-null    int64  \n",
+      " 22  arch_neutral                     158 non-null    int64  \n",
+      " 23  arch_stability                   158 non-null    int64  \n",
+      " 24  weight_lab_oz                    158 non-null    float64\n",
+      " 25  weight_lab_g                     158 non-null    int64  \n",
+      " 26  weight_brand_oz                  155 non-null    float64\n",
+      " 27  weight_brand_g                   155 non-null    float64\n",
+      " 28  drop_lab_mm                      158 non-null    float64\n",
+      " 29  drop_brand_mm                    152 non-null    float64\n",
+      " 30  strike_heel                      158 non-null    int64  \n",
+      " 31  strike_mid                       158 non-null    int64  \n",
+      " 32  strike_forefoot                  158 non-null    int64  \n",
+      " 33  softness_soft                    158 non-null    int64  \n",
+      " 34  softness_balanced                158 non-null    int64  \n",
+      " 35  softness_firm                    158 non-null    int64  \n",
+      " 36  toebox_durability                158 non-null    int64  \n",
+      " 37  heel_durability                  158 non-null    int64  \n",
+      " 38  outsole_durability               158 non-null    int64  \n",
+      " 39  breathability                    158 non-null    int64  \n",
+      " 40  plate_0                          158 non-null    int64  \n",
+      " 41  plate_rock_plate                 158 non-null    int64  \n",
+      " 42  plate_carbon_plate               158 non-null    int64  \n",
+      " 43  width_narrow                     158 non-null    int64  \n",
+      " 44  width_medium                     158 non-null    int64  \n",
+      " 45  width_wide                       158 non-null    int64  \n",
+      " 46  toebox_narrow                    158 non-null    int64  \n",
+      " 47  toebox_medium                    158 non-null    int64  \n",
+      " 48  toebox_wide                      158 non-null    int64  \n",
+      " 49  stiffness_flexible               158 non-null    int64  \n",
+      " 50  stiffness_moderate               158 non-null    int64  \n",
+      " 51  stiffness_stiff                  158 non-null    int64  \n",
+      " 52  torsional_flexible               158 non-null    int64  \n",
+      " 53  torsional_moderate               158 non-null    int64  \n",
+      " 54  torsional_stiff                  158 non-null    int64  \n",
+      " 55  heel_stiff_flexible              158 non-null    int64  \n",
+      " 56  heel_stiff_moderate              158 non-null    int64  \n",
+      " 57  heel_stiff_stiff                 158 non-null    int64  \n",
+      "dtypes: float64(7), int64(44), str(7)\n",
+      "memory usage: 71.7 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"Heel counter stiffness\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "62efa68c",
+   "metadata": {},
+   "source": [
+    "# Lug depth"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "3c47127b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0    2.5 mm\n",
+      "1    2.6 mm\n",
+      "2    3.6 mm\n",
+      "3    3.5 mm\n",
+      "4    3.7 mm\n",
+      "Name: Lug depth, dtype: str\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Lug depth\"].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "31d7c1e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Value Counts Kolom Asli  ---\n",
+      "Lug depth\n",
+      "3.5 mm    15\n",
+      "3.0 mm    14\n",
+      "4.0 mm    12\n",
+      "3.4 mm    11\n",
+      "2.5 mm     7\n",
+      "2.9 mm     7\n",
+      "3.2 mm     7\n",
+      "3.6 mm     6\n",
+      "3.7 mm     6\n",
+      "4.4 mm     6\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "  Lug depth  lug_depth\n",
+      "0    2.5 mm        2.5\n",
+      "1    2.6 mm        2.6\n",
+      "2    3.6 mm        3.6\n",
+      "3    3.5 mm        3.5\n",
+      "4    3.7 mm        3.7\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['lug_depth'] = df['Lug depth'].astype(str).str.replace(' mm', '', regex=False)\n",
+    "df['lug_depth'] = pd.to_numeric(df['lug_depth'].replace('-', '0'), errors='coerce').fillna(0)\n",
+    "\n",
+    "print(\"--- Value Counts Kolom Asli  ---\")\n",
+    "print(df[\"Lug depth\"].value_counts().head(10))\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"Lug depth\", \"lug_depth\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "e7b1ee60",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 58 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   Brand                            158 non-null    str    \n",
+      " 1   Name                             158 non-null    str    \n",
+      " 2   Lightweight                      152 non-null    float64\n",
+      " 3   Heel stack lab Heel stack brand  158 non-null    str    \n",
+      " 4   Forefoot lab Forefoot brand      158 non-null    str    \n",
+      " 5   For heavy runners                154 non-null    float64\n",
+      " 6   Season                           158 non-null    str    \n",
+      " 7   Removable insole                 158 non-null    int64  \n",
+      " 8   Orthotic friendly                158 non-null    int64  \n",
+      " 9   Waterproofing                    156 non-null    str    \n",
+      " 10  terrain_light                    158 non-null    int64  \n",
+      " 11  terrain_moderate                 158 non-null    int64  \n",
+      " 12  terrain_technical                158 non-null    int64  \n",
+      " 13  shock_low                        158 non-null    int64  \n",
+      " 14  shock_moderate                   158 non-null    int64  \n",
+      " 15  shock_high                       158 non-null    int64  \n",
+      " 16  energy_low                       158 non-null    int64  \n",
+      " 17  energy_moderate                  158 non-null    int64  \n",
+      " 18  energy_high                      158 non-null    int64  \n",
+      " 19  traction_moderate                158 non-null    int64  \n",
+      " 20  traction_high                    158 non-null    int64  \n",
+      " 21  arch_neutral                     158 non-null    int64  \n",
+      " 22  arch_stability                   158 non-null    int64  \n",
+      " 23  weight_lab_oz                    158 non-null    float64\n",
+      " 24  weight_lab_g                     158 non-null    int64  \n",
+      " 25  weight_brand_oz                  155 non-null    float64\n",
+      " 26  weight_brand_g                   155 non-null    float64\n",
+      " 27  drop_lab_mm                      158 non-null    float64\n",
+      " 28  drop_brand_mm                    152 non-null    float64\n",
+      " 29  strike_heel                      158 non-null    int64  \n",
+      " 30  strike_mid                       158 non-null    int64  \n",
+      " 31  strike_forefoot                  158 non-null    int64  \n",
+      " 32  softness_soft                    158 non-null    int64  \n",
+      " 33  softness_balanced                158 non-null    int64  \n",
+      " 34  softness_firm                    158 non-null    int64  \n",
+      " 35  toebox_durability                158 non-null    int64  \n",
+      " 36  heel_durability                  158 non-null    int64  \n",
+      " 37  outsole_durability               158 non-null    int64  \n",
+      " 38  breathability                    158 non-null    int64  \n",
+      " 39  plate_0                          158 non-null    int64  \n",
+      " 40  plate_rock_plate                 158 non-null    int64  \n",
+      " 41  plate_carbon_plate               158 non-null    int64  \n",
+      " 42  width_narrow                     158 non-null    int64  \n",
+      " 43  width_medium                     158 non-null    int64  \n",
+      " 44  width_wide                       158 non-null    int64  \n",
+      " 45  toebox_narrow                    158 non-null    int64  \n",
+      " 46  toebox_medium                    158 non-null    int64  \n",
+      " 47  toebox_wide                      158 non-null    int64  \n",
+      " 48  stiffness_flexible               158 non-null    int64  \n",
+      " 49  stiffness_moderate               158 non-null    int64  \n",
+      " 50  stiffness_stiff                  158 non-null    int64  \n",
+      " 51  torsional_flexible               158 non-null    int64  \n",
+      " 52  torsional_moderate               158 non-null    int64  \n",
+      " 53  torsional_stiff                  158 non-null    int64  \n",
+      " 54  heel_stiff_flexible              158 non-null    int64  \n",
+      " 55  heel_stiff_moderate              158 non-null    int64  \n",
+      " 56  heel_stiff_stiff                 158 non-null    int64  \n",
+      " 57  lug_depth                        158 non-null    float64\n",
+      "dtypes: float64(8), int64(44), str(6)\n",
+      "memory usage: 71.7 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"Lug depth\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "edb40b00",
+   "metadata": {},
+   "source": [
+    "# Heel stack lab Heel stack brand"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "6f8956df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0    30.6 mm 38.0 mm\n",
+      "1    32.8 mm 26.0 mm\n",
+      "2    34.5 mm 34.0 mm\n",
+      "3    32.3 mm 32.0 mm\n",
+      "4    24.5 mm 25.0 mm\n",
+      "Name: Heel stack lab Heel stack brand, dtype: str\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Heel stack lab Heel stack brand\"].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "9123ea7c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(0)"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mask_missing = (\n",
+    "    df[\"Heel stack lab Heel stack brand\"].isna() |\n",
+    "    (df[\"Heel stack lab Heel stack brand\"].astype(str).str.strip() == \"-\")\n",
+    ")\n",
+    "mask_missing.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "dc55349b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Heel stack lab Heel stack brand  heel_lab_mm  heel_brand_mm\n",
+      "0                 30.6 mm 38.0 mm         30.6           38.0\n",
+      "1                 32.8 mm 26.0 mm         32.8           26.0\n",
+      "2                 34.5 mm 34.0 mm         34.5           34.0\n",
+      "3                 32.3 mm 32.0 mm         32.3           32.0\n",
+      "4                 24.5 mm 25.0 mm         24.5           25.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "Heel = df[\"Heel stack lab Heel stack brand\"].str.findall(r\"[\\d.]+\")\n",
+    "\n",
+    "df[[\"heel_lab_mm\", \"heel_brand_mm\"]] = (\n",
+    "    pd.DataFrame(Heel.tolist(), index=df.index)\n",
+    ")\n",
+    "\n",
+    "for col in [\"heel_lab_mm\", \"heel_brand_mm\"]:\n",
+    "    df[col] = pd.to_numeric(df[col], errors=\"coerce\")\n",
+    "\n",
+    "print(df[[\"Heel stack lab Heel stack brand\", \"heel_lab_mm\", \"heel_brand_mm\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "2f899e58",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 59 columns):\n",
+      " #   Column                       Non-Null Count  Dtype  \n",
+      "---  ------                       --------------  -----  \n",
+      " 0   Brand                        158 non-null    str    \n",
+      " 1   Name                         158 non-null    str    \n",
+      " 2   Lightweight                  152 non-null    float64\n",
+      " 3   Forefoot lab Forefoot brand  158 non-null    str    \n",
+      " 4   For heavy runners            154 non-null    float64\n",
+      " 5   Season                       158 non-null    str    \n",
+      " 6   Removable insole             158 non-null    int64  \n",
+      " 7   Orthotic friendly            158 non-null    int64  \n",
+      " 8   Waterproofing                156 non-null    str    \n",
+      " 9   terrain_light                158 non-null    int64  \n",
+      " 10  terrain_moderate             158 non-null    int64  \n",
+      " 11  terrain_technical            158 non-null    int64  \n",
+      " 12  shock_low                    158 non-null    int64  \n",
+      " 13  shock_moderate               158 non-null    int64  \n",
+      " 14  shock_high                   158 non-null    int64  \n",
+      " 15  energy_low                   158 non-null    int64  \n",
+      " 16  energy_moderate              158 non-null    int64  \n",
+      " 17  energy_high                  158 non-null    int64  \n",
+      " 18  traction_moderate            158 non-null    int64  \n",
+      " 19  traction_high                158 non-null    int64  \n",
+      " 20  arch_neutral                 158 non-null    int64  \n",
+      " 21  arch_stability               158 non-null    int64  \n",
+      " 22  weight_lab_oz                158 non-null    float64\n",
+      " 23  weight_lab_g                 158 non-null    int64  \n",
+      " 24  weight_brand_oz              155 non-null    float64\n",
+      " 25  weight_brand_g               155 non-null    float64\n",
+      " 26  drop_lab_mm                  158 non-null    float64\n",
+      " 27  drop_brand_mm                152 non-null    float64\n",
+      " 28  strike_heel                  158 non-null    int64  \n",
+      " 29  strike_mid                   158 non-null    int64  \n",
+      " 30  strike_forefoot              158 non-null    int64  \n",
+      " 31  softness_soft                158 non-null    int64  \n",
+      " 32  softness_balanced            158 non-null    int64  \n",
+      " 33  softness_firm                158 non-null    int64  \n",
+      " 34  toebox_durability            158 non-null    int64  \n",
+      " 35  heel_durability              158 non-null    int64  \n",
+      " 36  outsole_durability           158 non-null    int64  \n",
+      " 37  breathability                158 non-null    int64  \n",
+      " 38  plate_0                      158 non-null    int64  \n",
+      " 39  plate_rock_plate             158 non-null    int64  \n",
+      " 40  plate_carbon_plate           158 non-null    int64  \n",
+      " 41  width_narrow                 158 non-null    int64  \n",
+      " 42  width_medium                 158 non-null    int64  \n",
+      " 43  width_wide                   158 non-null    int64  \n",
+      " 44  toebox_narrow                158 non-null    int64  \n",
+      " 45  toebox_medium                158 non-null    int64  \n",
+      " 46  toebox_wide                  158 non-null    int64  \n",
+      " 47  stiffness_flexible           158 non-null    int64  \n",
+      " 48  stiffness_moderate           158 non-null    int64  \n",
+      " 49  stiffness_stiff              158 non-null    int64  \n",
+      " 50  torsional_flexible           158 non-null    int64  \n",
+      " 51  torsional_moderate           158 non-null    int64  \n",
+      " 52  torsional_stiff              158 non-null    int64  \n",
+      " 53  heel_stiff_flexible          158 non-null    int64  \n",
+      " 54  heel_stiff_moderate          158 non-null    int64  \n",
+      " 55  heel_stiff_stiff             158 non-null    int64  \n",
+      " 56  lug_depth                    158 non-null    float64\n",
+      " 57  heel_lab_mm                  158 non-null    float64\n",
+      " 58  heel_brand_mm                145 non-null    float64\n",
+      "dtypes: float64(10), int64(44), str(5)\n",
+      "memory usage: 73.0 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"Heel stack lab Heel stack brand\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69155f32",
+   "metadata": {},
+   "source": [
+    "# Forefoot lab Forefoot brand"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "586618dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0    30.3 mm 30.0 mm\n",
+      "1    24.6 mm 18.0 mm\n",
+      "2    30.2 mm 30.0 mm\n",
+      "3    26.2 mm 28.0 mm\n",
+      "4    24.3 mm 25.0 mm\n",
+      "Name: Forefoot lab Forefoot brand, dtype: str\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['Forefoot lab Forefoot brand'].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "b1323541",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(0)"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mask_missing = (\n",
+    "    df[\"Forefoot lab Forefoot brand\"].isna() |\n",
+    "    (df[\"Forefoot lab Forefoot brand\"].astype(str).str.strip() == \"-\")\n",
+    ")\n",
+    "mask_missing.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "a29bbb63",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Forefoot lab Forefoot brand  forefoot_lab_mm  forefoot_brand_mm\n",
+      "0             30.3 mm 30.0 mm             30.3               30.0\n",
+      "1             24.6 mm 18.0 mm             24.6               18.0\n",
+      "2             30.2 mm 30.0 mm             30.2               30.0\n",
+      "3             26.2 mm 28.0 mm             26.2               28.0\n",
+      "4             24.3 mm 25.0 mm             24.3               25.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "forefoot = df[\"Forefoot lab Forefoot brand\"].str.findall(r\"[\\d.]+\")\n",
+    "\n",
+    "df[[\"forefoot_lab_mm\", \"forefoot_brand_mm\"]] = (\n",
+    "    pd.DataFrame(forefoot.tolist(), index=df.index)\n",
+    ")\n",
+    "\n",
+    "for col in [\"forefoot_lab_mm\", \"forefoot_brand_mm\"]:\n",
+    "    df[col] = pd.to_numeric(df[col], errors=\"coerce\")\n",
+    "\n",
+    "print(df[[\"Forefoot lab Forefoot brand\", \"forefoot_lab_mm\", \"forefoot_brand_mm\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "081b4449",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 60 columns):\n",
+      " #   Column               Non-Null Count  Dtype  \n",
+      "---  ------               --------------  -----  \n",
+      " 0   Brand                158 non-null    str    \n",
+      " 1   Name                 158 non-null    str    \n",
+      " 2   Lightweight          152 non-null    float64\n",
+      " 3   For heavy runners    154 non-null    float64\n",
+      " 4   Season               158 non-null    str    \n",
+      " 5   Removable insole     158 non-null    int64  \n",
+      " 6   Orthotic friendly    158 non-null    int64  \n",
+      " 7   Waterproofing        156 non-null    str    \n",
+      " 8   terrain_light        158 non-null    int64  \n",
+      " 9   terrain_moderate     158 non-null    int64  \n",
+      " 10  terrain_technical    158 non-null    int64  \n",
+      " 11  shock_low            158 non-null    int64  \n",
+      " 12  shock_moderate       158 non-null    int64  \n",
+      " 13  shock_high           158 non-null    int64  \n",
+      " 14  energy_low           158 non-null    int64  \n",
+      " 15  energy_moderate      158 non-null    int64  \n",
+      " 16  energy_high          158 non-null    int64  \n",
+      " 17  traction_moderate    158 non-null    int64  \n",
+      " 18  traction_high        158 non-null    int64  \n",
+      " 19  arch_neutral         158 non-null    int64  \n",
+      " 20  arch_stability       158 non-null    int64  \n",
+      " 21  weight_lab_oz        158 non-null    float64\n",
+      " 22  weight_lab_g         158 non-null    int64  \n",
+      " 23  weight_brand_oz      155 non-null    float64\n",
+      " 24  weight_brand_g       155 non-null    float64\n",
+      " 25  drop_lab_mm          158 non-null    float64\n",
+      " 26  drop_brand_mm        152 non-null    float64\n",
+      " 27  strike_heel          158 non-null    int64  \n",
+      " 28  strike_mid           158 non-null    int64  \n",
+      " 29  strike_forefoot      158 non-null    int64  \n",
+      " 30  softness_soft        158 non-null    int64  \n",
+      " 31  softness_balanced    158 non-null    int64  \n",
+      " 32  softness_firm        158 non-null    int64  \n",
+      " 33  toebox_durability    158 non-null    int64  \n",
+      " 34  heel_durability      158 non-null    int64  \n",
+      " 35  outsole_durability   158 non-null    int64  \n",
+      " 36  breathability        158 non-null    int64  \n",
+      " 37  plate_0              158 non-null    int64  \n",
+      " 38  plate_rock_plate     158 non-null    int64  \n",
+      " 39  plate_carbon_plate   158 non-null    int64  \n",
+      " 40  width_narrow         158 non-null    int64  \n",
+      " 41  width_medium         158 non-null    int64  \n",
+      " 42  width_wide           158 non-null    int64  \n",
+      " 43  toebox_narrow        158 non-null    int64  \n",
+      " 44  toebox_medium        158 non-null    int64  \n",
+      " 45  toebox_wide          158 non-null    int64  \n",
+      " 46  stiffness_flexible   158 non-null    int64  \n",
+      " 47  stiffness_moderate   158 non-null    int64  \n",
+      " 48  stiffness_stiff      158 non-null    int64  \n",
+      " 49  torsional_flexible   158 non-null    int64  \n",
+      " 50  torsional_moderate   158 non-null    int64  \n",
+      " 51  torsional_stiff      158 non-null    int64  \n",
+      " 52  heel_stiff_flexible  158 non-null    int64  \n",
+      " 53  heel_stiff_moderate  158 non-null    int64  \n",
+      " 54  heel_stiff_stiff     158 non-null    int64  \n",
+      " 55  lug_depth            158 non-null    float64\n",
+      " 56  heel_lab_mm          158 non-null    float64\n",
+      " 57  heel_brand_mm        145 non-null    float64\n",
+      " 58  forefoot_lab_mm      158 non-null    float64\n",
+      " 59  forefoot_brand_mm    143 non-null    float64\n",
+      "dtypes: float64(12), int64(44), str(4)\n",
+      "memory usage: 74.2 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"Forefoot lab Forefoot brand\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21494e05",
+   "metadata": {},
+   "source": [
+    "# Season"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "c3e07080",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Season\n",
+      "All seasons           107\n",
+      "-                      19\n",
+      "Summer All seasons     15\n",
+      "Winter                 15\n",
+      "0                       1\n",
+      "SummerAll seasons       1\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Season\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93dfd937",
+   "metadata": {},
+   "source": [
+    "Summer All seasons = sepatu yang dirancang secara spesifik untuk summer tapi diklaim bisa dipakai all season"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "fa7f5747",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Jumlah baris dengan '-' atau '0': 20\n",
+      "\n",
+      "--- Detail Baris (Season = '-' atau '0') ---\n",
+      "       Brand                    Name Season\n",
+      "1     adidas      terrex speed ultra      -\n",
+      "4      altra           lone peak 5.0      -\n",
+      "5      altra             lone peak 6      -\n",
+      "9      altra              mont blanc      -\n",
+      "36    brooks             cascadia 16      -\n",
+      "57      hoka                tecton x      -\n",
+      "61      hoka                   zinal      -\n",
+      "67     inov8              trailtalon      0\n",
+      "68    kailas        flythorn air 2.0      -\n",
+      "70    kailas            fuga elite 2      -\n",
+      "71    kailas               fuga ex 2      -\n",
+      "73    kailas             fuga ex boa      -\n",
+      "75    kailas              fuga pro 4      -\n",
+      "89   merrell                  nova 2      -\n",
+      "101     nike  air zoom terra kiger 6      -\n",
+      "104     nike         pegasus trail 4      -\n",
+      "124  salomon             sense pro 4      -\n",
+      "140  saucony         endorphin trail      -\n",
+      "141  saucony            peregrine 11      -\n",
+      "142  saucony            peregrine 12      -\n",
+      "\n",
+      "Frekuensi spesifik:\n",
+      "Season\n",
+      "-    19\n",
+      "0     1\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check weird values\n",
+    "filter_condition = df['Season'].astype(str).isin(['-', '0'])\n",
+    "rows_to_check = df[filter_condition]\n",
+    "\n",
+    "print(f\"Jumlah baris dengan '-' atau '0': {len(rows_to_check)}\")\n",
+    "print(\"\\n--- Detail Baris (Season = '-' atau '0') ---\")\n",
+    "print(rows_to_check[['Brand', 'Name', 'Season']])\n",
+    "\n",
+    "\n",
+    "print(\"\\nFrekuensi spesifik:\")\n",
+    "print(df[df['Season'].astype(str).isin(['-', '0'])]['Season'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "id": "40f37790",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 158\n",
+      "NULL/Unknown Value (0 dan -): 20\n",
+      "\n",
+      "Sample Comparison (Multi-label):\n",
+      "                Season  season_summer  season_winter  season_all\n",
+      "13  summer all seasons              1              0           1\n",
+      "17  summer all seasons              1              0           1\n",
+      "26  summer all seasons              1              0           1\n",
+      "28  summer all seasons              1              0           1\n",
+      "29  summer all seasons              1              0           1\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['Season'] = df['Season'].astype(str).str.lower()\n",
+    "base_seasons = ['summer', 'winter', 'all seasons']\n",
+    "\n",
+    "for level in base_seasons:\n",
+    "    clean_name = level.replace(' seasons', '').replace(' ', '_')\n",
+    "    column_name = f\"season_{clean_name}\"\n",
+    "    df[column_name] = df['Season'].str.contains(level, na=False).astype(int)\n",
+    "\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "season_cols = [col for col in df.columns if col.startswith('season_')]\n",
+    "zero_vector_count = (df[season_cols].sum(axis=1) == 0).sum()\n",
+    "print(f\"NULL/Unknown Value (0 dan -): {zero_vector_count}\")\n",
+    "\n",
+    "print(\"\\nSample Comparison (Multi-label):\")\n",
+    "print(df[df[season_cols].sum(axis=1) > 1][['Season'] + season_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "39a01a66",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Season\n",
+      "all seasons           107\n",
+      "-                      19\n",
+      "summer all seasons     15\n",
+      "winter                 15\n",
+      "0                       1\n",
+      "summerall seasons       1\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "season_summer sum: 16\n",
+      "season_winter sum: 15\n",
+      "season_all sum: 123\n",
+      "\n",
+      "        Season  season_summer  season_winter  season_all\n",
+      "0  all seasons              0              0           1\n",
+      "1            -              0              0           0\n",
+      "2  all seasons              0              0           1\n",
+      "3  all seasons              0              0           1\n",
+      "4            -              0              0           0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Season\"].value_counts())\n",
+    "\n",
+    "print()\n",
+    "for col in season_cols:\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"Season\"] + season_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "id": "d8eea361",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 62 columns):\n",
+      " #   Column               Non-Null Count  Dtype  \n",
+      "---  ------               --------------  -----  \n",
+      " 0   Brand                158 non-null    str    \n",
+      " 1   Name                 158 non-null    str    \n",
+      " 2   Lightweight          152 non-null    float64\n",
+      " 3   For heavy runners    154 non-null    float64\n",
+      " 4   Removable insole     158 non-null    int64  \n",
+      " 5   Orthotic friendly    158 non-null    int64  \n",
+      " 6   Waterproofing        156 non-null    str    \n",
+      " 7   terrain_light        158 non-null    int64  \n",
+      " 8   terrain_moderate     158 non-null    int64  \n",
+      " 9   terrain_technical    158 non-null    int64  \n",
+      " 10  shock_low            158 non-null    int64  \n",
+      " 11  shock_moderate       158 non-null    int64  \n",
+      " 12  shock_high           158 non-null    int64  \n",
+      " 13  energy_low           158 non-null    int64  \n",
+      " 14  energy_moderate      158 non-null    int64  \n",
+      " 15  energy_high          158 non-null    int64  \n",
+      " 16  traction_moderate    158 non-null    int64  \n",
+      " 17  traction_high        158 non-null    int64  \n",
+      " 18  arch_neutral         158 non-null    int64  \n",
+      " 19  arch_stability       158 non-null    int64  \n",
+      " 20  weight_lab_oz        158 non-null    float64\n",
+      " 21  weight_lab_g         158 non-null    int64  \n",
+      " 22  weight_brand_oz      155 non-null    float64\n",
+      " 23  weight_brand_g       155 non-null    float64\n",
+      " 24  drop_lab_mm          158 non-null    float64\n",
+      " 25  drop_brand_mm        152 non-null    float64\n",
+      " 26  strike_heel          158 non-null    int64  \n",
+      " 27  strike_mid           158 non-null    int64  \n",
+      " 28  strike_forefoot      158 non-null    int64  \n",
+      " 29  softness_soft        158 non-null    int64  \n",
+      " 30  softness_balanced    158 non-null    int64  \n",
+      " 31  softness_firm        158 non-null    int64  \n",
+      " 32  toebox_durability    158 non-null    int64  \n",
+      " 33  heel_durability      158 non-null    int64  \n",
+      " 34  outsole_durability   158 non-null    int64  \n",
+      " 35  breathability        158 non-null    int64  \n",
+      " 36  plate_0              158 non-null    int64  \n",
+      " 37  plate_rock_plate     158 non-null    int64  \n",
+      " 38  plate_carbon_plate   158 non-null    int64  \n",
+      " 39  width_narrow         158 non-null    int64  \n",
+      " 40  width_medium         158 non-null    int64  \n",
+      " 41  width_wide           158 non-null    int64  \n",
+      " 42  toebox_narrow        158 non-null    int64  \n",
+      " 43  toebox_medium        158 non-null    int64  \n",
+      " 44  toebox_wide          158 non-null    int64  \n",
+      " 45  stiffness_flexible   158 non-null    int64  \n",
+      " 46  stiffness_moderate   158 non-null    int64  \n",
+      " 47  stiffness_stiff      158 non-null    int64  \n",
+      " 48  torsional_flexible   158 non-null    int64  \n",
+      " 49  torsional_moderate   158 non-null    int64  \n",
+      " 50  torsional_stiff      158 non-null    int64  \n",
+      " 51  heel_stiff_flexible  158 non-null    int64  \n",
+      " 52  heel_stiff_moderate  158 non-null    int64  \n",
+      " 53  heel_stiff_stiff     158 non-null    int64  \n",
+      " 54  lug_depth            158 non-null    float64\n",
+      " 55  heel_lab_mm          158 non-null    float64\n",
+      " 56  heel_brand_mm        145 non-null    float64\n",
+      " 57  forefoot_lab_mm      158 non-null    float64\n",
+      " 58  forefoot_brand_mm    143 non-null    float64\n",
+      " 59  season_summer        158 non-null    int64  \n",
+      " 60  season_winter        158 non-null    int64  \n",
+      " 61  season_all           158 non-null    int64  \n",
+      "dtypes: float64(12), int64(47), str(3)\n",
+      "memory usage: 76.7 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"Season\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "acdd890f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    Brand                        Name  For heavy runners  Removable insole  \\\n",
+      "0  adidas  terrex agravic speed ultra                0.0                 1   \n",
+      "1  adidas          terrex speed ultra                0.0                 1   \n",
+      "2   altra             experience wild                0.0                 1   \n",
+      "3   altra           experience wild 2                0.0                 1   \n",
+      "4   altra               lone peak 5.0                0.0                 1   \n",
+      "\n",
+      "   Orthotic friendly  \n",
+      "0                  1  \n",
+      "1                  1  \n",
+      "2                  1  \n",
+      "3                  1  \n",
+      "4                  1  \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[[\"Brand\", \"Name\", \"For heavy runners\", \"Removable insole\", \"Orthotic friendly\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d9886d0",
+   "metadata": {},
+   "source": [
+    "# For heavy runners"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "170c08ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "For heavy runners\n",
+      "0.0    147\n",
+      "1.0      7\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"For heavy runners\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "9dc14af9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Value Counts Kolom Asli ---\n",
+      "For heavy runners\n",
+      "0.0    147\n",
+      "1.0      7\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "--- Sebaran Nilai Hasil ---\n",
+      "heavy_runners\n",
+      "0    151\n",
+      "1      7\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "--- Perbandingan Data ---\n",
+      "   For heavy runners  heavy_runners\n",
+      "0                0.0              0\n",
+      "1                0.0              0\n",
+      "2                0.0              0\n",
+      "3                0.0              0\n",
+      "4                0.0              0\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['heavy_runners'] = df['For heavy runners'].fillna(0).astype(int)\n",
+    "\n",
+    "print(\"--- Value Counts Kolom Asli ---\")\n",
+    "print(df[\"For heavy runners\"].value_counts())\n",
+    "\n",
+    "print(\"\\n--- Sebaran Nilai Hasil ---\")\n",
+    "print(df[\"heavy_runners\"].value_counts())\n",
+    "\n",
+    "print(\"\\n--- Perbandingan Data ---\")\n",
+    "print(df[[\"For heavy runners\", \"heavy_runners\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "effc6bb4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 62 columns):\n",
+      " #   Column               Non-Null Count  Dtype  \n",
+      "---  ------               --------------  -----  \n",
+      " 0   Brand                158 non-null    str    \n",
+      " 1   Name                 158 non-null    str    \n",
+      " 2   Lightweight          152 non-null    float64\n",
+      " 3   Removable insole     158 non-null    int64  \n",
+      " 4   Orthotic friendly    158 non-null    int64  \n",
+      " 5   Waterproofing        156 non-null    str    \n",
+      " 6   terrain_light        158 non-null    int64  \n",
+      " 7   terrain_moderate     158 non-null    int64  \n",
+      " 8   terrain_technical    158 non-null    int64  \n",
+      " 9   shock_low            158 non-null    int64  \n",
+      " 10  shock_moderate       158 non-null    int64  \n",
+      " 11  shock_high           158 non-null    int64  \n",
+      " 12  energy_low           158 non-null    int64  \n",
+      " 13  energy_moderate      158 non-null    int64  \n",
+      " 14  energy_high          158 non-null    int64  \n",
+      " 15  traction_moderate    158 non-null    int64  \n",
+      " 16  traction_high        158 non-null    int64  \n",
+      " 17  arch_neutral         158 non-null    int64  \n",
+      " 18  arch_stability       158 non-null    int64  \n",
+      " 19  weight_lab_oz        158 non-null    float64\n",
+      " 20  weight_lab_g         158 non-null    int64  \n",
+      " 21  weight_brand_oz      155 non-null    float64\n",
+      " 22  weight_brand_g       155 non-null    float64\n",
+      " 23  drop_lab_mm          158 non-null    float64\n",
+      " 24  drop_brand_mm        152 non-null    float64\n",
+      " 25  strike_heel          158 non-null    int64  \n",
+      " 26  strike_mid           158 non-null    int64  \n",
+      " 27  strike_forefoot      158 non-null    int64  \n",
+      " 28  softness_soft        158 non-null    int64  \n",
+      " 29  softness_balanced    158 non-null    int64  \n",
+      " 30  softness_firm        158 non-null    int64  \n",
+      " 31  toebox_durability    158 non-null    int64  \n",
+      " 32  heel_durability      158 non-null    int64  \n",
+      " 33  outsole_durability   158 non-null    int64  \n",
+      " 34  breathability        158 non-null    int64  \n",
+      " 35  plate_0              158 non-null    int64  \n",
+      " 36  plate_rock_plate     158 non-null    int64  \n",
+      " 37  plate_carbon_plate   158 non-null    int64  \n",
+      " 38  width_narrow         158 non-null    int64  \n",
+      " 39  width_medium         158 non-null    int64  \n",
+      " 40  width_wide           158 non-null    int64  \n",
+      " 41  toebox_narrow        158 non-null    int64  \n",
+      " 42  toebox_medium        158 non-null    int64  \n",
+      " 43  toebox_wide          158 non-null    int64  \n",
+      " 44  stiffness_flexible   158 non-null    int64  \n",
+      " 45  stiffness_moderate   158 non-null    int64  \n",
+      " 46  stiffness_stiff      158 non-null    int64  \n",
+      " 47  torsional_flexible   158 non-null    int64  \n",
+      " 48  torsional_moderate   158 non-null    int64  \n",
+      " 49  torsional_stiff      158 non-null    int64  \n",
+      " 50  heel_stiff_flexible  158 non-null    int64  \n",
+      " 51  heel_stiff_moderate  158 non-null    int64  \n",
+      " 52  heel_stiff_stiff     158 non-null    int64  \n",
+      " 53  lug_depth            158 non-null    float64\n",
+      " 54  heel_lab_mm          158 non-null    float64\n",
+      " 55  heel_brand_mm        145 non-null    float64\n",
+      " 56  forefoot_lab_mm      158 non-null    float64\n",
+      " 57  forefoot_brand_mm    143 non-null    float64\n",
+      " 58  season_summer        158 non-null    int64  \n",
+      " 59  season_winter        158 non-null    int64  \n",
+      " 60  season_all           158 non-null    int64  \n",
+      " 61  heavy_runners        158 non-null    int64  \n",
+      "dtypes: float64(11), int64(48), str(3)\n",
+      "memory usage: 76.7 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"For heavy runners\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00fb8fe8",
+   "metadata": {},
+   "source": [
+    "# Removable insole"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "68f9c5b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Removable insole\n",
+      "1    147\n",
+      "0     11\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Removable insole\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "4916d5ba",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   Removable insole  removable_insole\n",
+      "0                 1                 1\n",
+      "1                 1                 1\n",
+      "2                 1                 1\n",
+      "3                 1                 1\n",
+      "4                 1                 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "# rename Removable insole to removable_insole\n",
+    "df['removable_insole'] = df['Removable insole'].fillna(0).astype(int)\n",
+    "print(df[['Removable insole', 'removable_insole']].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "id": "9eb46039",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 62 columns):\n",
+      " #   Column               Non-Null Count  Dtype  \n",
+      "---  ------               --------------  -----  \n",
+      " 0   Brand                158 non-null    str    \n",
+      " 1   Name                 158 non-null    str    \n",
+      " 2   Lightweight          152 non-null    float64\n",
+      " 3   Orthotic friendly    158 non-null    int64  \n",
+      " 4   Waterproofing        156 non-null    str    \n",
+      " 5   terrain_light        158 non-null    int64  \n",
+      " 6   terrain_moderate     158 non-null    int64  \n",
+      " 7   terrain_technical    158 non-null    int64  \n",
+      " 8   shock_low            158 non-null    int64  \n",
+      " 9   shock_moderate       158 non-null    int64  \n",
+      " 10  shock_high           158 non-null    int64  \n",
+      " 11  energy_low           158 non-null    int64  \n",
+      " 12  energy_moderate      158 non-null    int64  \n",
+      " 13  energy_high          158 non-null    int64  \n",
+      " 14  traction_moderate    158 non-null    int64  \n",
+      " 15  traction_high        158 non-null    int64  \n",
+      " 16  arch_neutral         158 non-null    int64  \n",
+      " 17  arch_stability       158 non-null    int64  \n",
+      " 18  weight_lab_oz        158 non-null    float64\n",
+      " 19  weight_lab_g         158 non-null    int64  \n",
+      " 20  weight_brand_oz      155 non-null    float64\n",
+      " 21  weight_brand_g       155 non-null    float64\n",
+      " 22  drop_lab_mm          158 non-null    float64\n",
+      " 23  drop_brand_mm        152 non-null    float64\n",
+      " 24  strike_heel          158 non-null    int64  \n",
+      " 25  strike_mid           158 non-null    int64  \n",
+      " 26  strike_forefoot      158 non-null    int64  \n",
+      " 27  softness_soft        158 non-null    int64  \n",
+      " 28  softness_balanced    158 non-null    int64  \n",
+      " 29  softness_firm        158 non-null    int64  \n",
+      " 30  toebox_durability    158 non-null    int64  \n",
+      " 31  heel_durability      158 non-null    int64  \n",
+      " 32  outsole_durability   158 non-null    int64  \n",
+      " 33  breathability        158 non-null    int64  \n",
+      " 34  plate_0              158 non-null    int64  \n",
+      " 35  plate_rock_plate     158 non-null    int64  \n",
+      " 36  plate_carbon_plate   158 non-null    int64  \n",
+      " 37  width_narrow         158 non-null    int64  \n",
+      " 38  width_medium         158 non-null    int64  \n",
+      " 39  width_wide           158 non-null    int64  \n",
+      " 40  toebox_narrow        158 non-null    int64  \n",
+      " 41  toebox_medium        158 non-null    int64  \n",
+      " 42  toebox_wide          158 non-null    int64  \n",
+      " 43  stiffness_flexible   158 non-null    int64  \n",
+      " 44  stiffness_moderate   158 non-null    int64  \n",
+      " 45  stiffness_stiff      158 non-null    int64  \n",
+      " 46  torsional_flexible   158 non-null    int64  \n",
+      " 47  torsional_moderate   158 non-null    int64  \n",
+      " 48  torsional_stiff      158 non-null    int64  \n",
+      " 49  heel_stiff_flexible  158 non-null    int64  \n",
+      " 50  heel_stiff_moderate  158 non-null    int64  \n",
+      " 51  heel_stiff_stiff     158 non-null    int64  \n",
+      " 52  lug_depth            158 non-null    float64\n",
+      " 53  heel_lab_mm          158 non-null    float64\n",
+      " 54  heel_brand_mm        145 non-null    float64\n",
+      " 55  forefoot_lab_mm      158 non-null    float64\n",
+      " 56  forefoot_brand_mm    143 non-null    float64\n",
+      " 57  season_summer        158 non-null    int64  \n",
+      " 58  season_winter        158 non-null    int64  \n",
+      " 59  season_all           158 non-null    int64  \n",
+      " 60  heavy_runners        158 non-null    int64  \n",
+      " 61  removable_insole     158 non-null    int64  \n",
+      "dtypes: float64(11), int64(48), str(3)\n",
+      "memory usage: 76.7 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=['Removable insole'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f145cc43",
+   "metadata": {},
+   "source": [
+    "# Orthotic friendly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "id": "d52a6df6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Orthotic friendly\n",
+      "1    147\n",
+      "0     11\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Orthotic friendly\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "id": "8f548dc5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   Orthotic friendly  orthotic_friendly\n",
+      "0                  1                  1\n",
+      "1                  1                  1\n",
+      "2                  1                  1\n",
+      "3                  1                  1\n",
+      "4                  1                  1\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Rename Orthotic friendly to orthotic_friendly\n",
+    "df['orthotic_friendly'] = df['Orthotic friendly'].fillna(0).astype(int)\n",
+    "print(df[['Orthotic friendly', 'orthotic_friendly']].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "id": "059a1824",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 62 columns):\n",
+      " #   Column               Non-Null Count  Dtype  \n",
+      "---  ------               --------------  -----  \n",
+      " 0   Brand                158 non-null    str    \n",
+      " 1   Name                 158 non-null    str    \n",
+      " 2   Lightweight          152 non-null    float64\n",
+      " 3   Waterproofing        156 non-null    str    \n",
+      " 4   terrain_light        158 non-null    int64  \n",
+      " 5   terrain_moderate     158 non-null    int64  \n",
+      " 6   terrain_technical    158 non-null    int64  \n",
+      " 7   shock_low            158 non-null    int64  \n",
+      " 8   shock_moderate       158 non-null    int64  \n",
+      " 9   shock_high           158 non-null    int64  \n",
+      " 10  energy_low           158 non-null    int64  \n",
+      " 11  energy_moderate      158 non-null    int64  \n",
+      " 12  energy_high          158 non-null    int64  \n",
+      " 13  traction_moderate    158 non-null    int64  \n",
+      " 14  traction_high        158 non-null    int64  \n",
+      " 15  arch_neutral         158 non-null    int64  \n",
+      " 16  arch_stability       158 non-null    int64  \n",
+      " 17  weight_lab_oz        158 non-null    float64\n",
+      " 18  weight_lab_g         158 non-null    int64  \n",
+      " 19  weight_brand_oz      155 non-null    float64\n",
+      " 20  weight_brand_g       155 non-null    float64\n",
+      " 21  drop_lab_mm          158 non-null    float64\n",
+      " 22  drop_brand_mm        152 non-null    float64\n",
+      " 23  strike_heel          158 non-null    int64  \n",
+      " 24  strike_mid           158 non-null    int64  \n",
+      " 25  strike_forefoot      158 non-null    int64  \n",
+      " 26  softness_soft        158 non-null    int64  \n",
+      " 27  softness_balanced    158 non-null    int64  \n",
+      " 28  softness_firm        158 non-null    int64  \n",
+      " 29  toebox_durability    158 non-null    int64  \n",
+      " 30  heel_durability      158 non-null    int64  \n",
+      " 31  outsole_durability   158 non-null    int64  \n",
+      " 32  breathability        158 non-null    int64  \n",
+      " 33  plate_0              158 non-null    int64  \n",
+      " 34  plate_rock_plate     158 non-null    int64  \n",
+      " 35  plate_carbon_plate   158 non-null    int64  \n",
+      " 36  width_narrow         158 non-null    int64  \n",
+      " 37  width_medium         158 non-null    int64  \n",
+      " 38  width_wide           158 non-null    int64  \n",
+      " 39  toebox_narrow        158 non-null    int64  \n",
+      " 40  toebox_medium        158 non-null    int64  \n",
+      " 41  toebox_wide          158 non-null    int64  \n",
+      " 42  stiffness_flexible   158 non-null    int64  \n",
+      " 43  stiffness_moderate   158 non-null    int64  \n",
+      " 44  stiffness_stiff      158 non-null    int64  \n",
+      " 45  torsional_flexible   158 non-null    int64  \n",
+      " 46  torsional_moderate   158 non-null    int64  \n",
+      " 47  torsional_stiff      158 non-null    int64  \n",
+      " 48  heel_stiff_flexible  158 non-null    int64  \n",
+      " 49  heel_stiff_moderate  158 non-null    int64  \n",
+      " 50  heel_stiff_stiff     158 non-null    int64  \n",
+      " 51  lug_depth            158 non-null    float64\n",
+      " 52  heel_lab_mm          158 non-null    float64\n",
+      " 53  heel_brand_mm        145 non-null    float64\n",
+      " 54  forefoot_lab_mm      158 non-null    float64\n",
+      " 55  forefoot_brand_mm    143 non-null    float64\n",
+      " 56  season_summer        158 non-null    int64  \n",
+      " 57  season_winter        158 non-null    int64  \n",
+      " 58  season_all           158 non-null    int64  \n",
+      " 59  heavy_runners        158 non-null    int64  \n",
+      " 60  removable_insole     158 non-null    int64  \n",
+      " 61  orthotic_friendly    158 non-null    int64  \n",
+      "dtypes: float64(11), int64(48), str(3)\n",
+      "memory usage: 76.7 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=['Orthotic friendly'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "442b3a27",
+   "metadata": {},
+   "source": [
+    "# Waterproofing        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "id": "a056f12c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Waterproofing\n",
+      "-                             137\n",
+      "Waterproof                     12\n",
+      "Water repellent                 5\n",
+      "Waterproof Water repellent      1\n",
+      "0                               1\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['Waterproofing'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7494a16e",
+   "metadata": {},
+   "source": [
+    "Water repellent cuma nahan menolak air di permukaan tapi kalau terendam, kakinya tetap basah. kalau waterproof bener bener tahan air"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "id": "d52d4d09",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 158\n",
+      "\n",
+      "Sample Comparison:\n",
+      "  Waterproofing  not_waterproof  waterproof  water_repellent\n",
+      "0             -               1           0                0\n",
+      "1             -               1           0                0\n",
+      "2             -               1           0                0\n",
+      "3             -               1           0                0\n",
+      "4             -               1           0                0\n",
+      "5             -               1           0                0\n",
+      "6             -               1           0                0\n",
+      "7             -               1           0                0\n",
+      "8             -               1           0                0\n",
+      "9             -               1           0                0\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['Waterproofing'] = df['Waterproofing'].astype(str).str.lower()\n",
+    "base_water = ['not waterproof', 'waterproof', 'water repellent']\n",
+    "\n",
+    "def check_not_waterproof(val):\n",
+    "    '''Cek apakah val menunjukkan not waterproof.\n",
+    "        Asumsi: jika val adalah - atau 0'''\n",
+    "    if val in ['-', '0', 'nan', 'none']:\n",
+    "        return 1\n",
+    "    return 0\n",
+    "\n",
+    "for level in base_water:\n",
+    "    column_name = level.replace(' ', '_')\n",
+    "    \n",
+    "    if level == 'not waterproof':\n",
+    "        df[column_name] = df['Waterproofing'].apply(check_not_waterproof)\n",
+    "    else:\n",
+    "        df[column_name] = df['Waterproofing'].str.contains(level, na=False).astype(int)\n",
+    "        df.loc[df['Waterproofing'].isin(['-', '0']), column_name] = 0\n",
+    "\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "water_cols = [l.replace(' ', '_') for l in base_water]\n",
+    "\n",
+    "print(\"\\nSample Comparison:\")\n",
+    "print(df[[\"Waterproofing\"] + water_cols].head(10))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "id": "9d6e1eb8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Waterproofing\n",
+      "-                             137\n",
+      "waterproof                     12\n",
+      "water repellent                 5\n",
+      "waterproof water repellent      1\n",
+      "0                               1\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "--- Sum Per Kolom ---\n",
+      "not_waterproof sum: 138\n",
+      "waterproof sum: 13\n",
+      "water_repellent sum: 6\n",
+      "\n",
+      "Total Check (Harus >= 158): 157\n",
+      "\n",
+      "  Waterproofing  not_waterproof  waterproof  water_repellent\n",
+      "0             -               1           0                0\n",
+      "1             -               1           0                0\n",
+      "2             -               1           0                0\n",
+      "3             -               1           0                0\n",
+      "4             -               1           0                0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Waterproofing\"].value_counts())\n",
+    "\n",
+    "print(\"\\n--- Sum Per Kolom ---\")\n",
+    "for col in water_cols:\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "total_sum = df[water_cols].sum().sum()\n",
+    "print(f\"\\nTotal Check (Harus >= {len(df)}): {total_sum}\")\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"Waterproofing\"] + water_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "id": "1804482a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 64 columns):\n",
+      " #   Column               Non-Null Count  Dtype  \n",
+      "---  ------               --------------  -----  \n",
+      " 0   Brand                158 non-null    str    \n",
+      " 1   Name                 158 non-null    str    \n",
+      " 2   Lightweight          152 non-null    float64\n",
+      " 3   terrain_light        158 non-null    int64  \n",
+      " 4   terrain_moderate     158 non-null    int64  \n",
+      " 5   terrain_technical    158 non-null    int64  \n",
+      " 6   shock_low            158 non-null    int64  \n",
+      " 7   shock_moderate       158 non-null    int64  \n",
+      " 8   shock_high           158 non-null    int64  \n",
+      " 9   energy_low           158 non-null    int64  \n",
+      " 10  energy_moderate      158 non-null    int64  \n",
+      " 11  energy_high          158 non-null    int64  \n",
+      " 12  traction_moderate    158 non-null    int64  \n",
+      " 13  traction_high        158 non-null    int64  \n",
+      " 14  arch_neutral         158 non-null    int64  \n",
+      " 15  arch_stability       158 non-null    int64  \n",
+      " 16  weight_lab_oz        158 non-null    float64\n",
+      " 17  weight_lab_g         158 non-null    int64  \n",
+      " 18  weight_brand_oz      155 non-null    float64\n",
+      " 19  weight_brand_g       155 non-null    float64\n",
+      " 20  drop_lab_mm          158 non-null    float64\n",
+      " 21  drop_brand_mm        152 non-null    float64\n",
+      " 22  strike_heel          158 non-null    int64  \n",
+      " 23  strike_mid           158 non-null    int64  \n",
+      " 24  strike_forefoot      158 non-null    int64  \n",
+      " 25  softness_soft        158 non-null    int64  \n",
+      " 26  softness_balanced    158 non-null    int64  \n",
+      " 27  softness_firm        158 non-null    int64  \n",
+      " 28  toebox_durability    158 non-null    int64  \n",
+      " 29  heel_durability      158 non-null    int64  \n",
+      " 30  outsole_durability   158 non-null    int64  \n",
+      " 31  breathability        158 non-null    int64  \n",
+      " 32  plate_0              158 non-null    int64  \n",
+      " 33  plate_rock_plate     158 non-null    int64  \n",
+      " 34  plate_carbon_plate   158 non-null    int64  \n",
+      " 35  width_narrow         158 non-null    int64  \n",
+      " 36  width_medium         158 non-null    int64  \n",
+      " 37  width_wide           158 non-null    int64  \n",
+      " 38  toebox_narrow        158 non-null    int64  \n",
+      " 39  toebox_medium        158 non-null    int64  \n",
+      " 40  toebox_wide          158 non-null    int64  \n",
+      " 41  stiffness_flexible   158 non-null    int64  \n",
+      " 42  stiffness_moderate   158 non-null    int64  \n",
+      " 43  stiffness_stiff      158 non-null    int64  \n",
+      " 44  torsional_flexible   158 non-null    int64  \n",
+      " 45  torsional_moderate   158 non-null    int64  \n",
+      " 46  torsional_stiff      158 non-null    int64  \n",
+      " 47  heel_stiff_flexible  158 non-null    int64  \n",
+      " 48  heel_stiff_moderate  158 non-null    int64  \n",
+      " 49  heel_stiff_stiff     158 non-null    int64  \n",
+      " 50  lug_depth            158 non-null    float64\n",
+      " 51  heel_lab_mm          158 non-null    float64\n",
+      " 52  heel_brand_mm        145 non-null    float64\n",
+      " 53  forefoot_lab_mm      158 non-null    float64\n",
+      " 54  forefoot_brand_mm    143 non-null    float64\n",
+      " 55  season_summer        158 non-null    int64  \n",
+      " 56  season_winter        158 non-null    int64  \n",
+      " 57  season_all           158 non-null    int64  \n",
+      " 58  heavy_runners        158 non-null    int64  \n",
+      " 59  removable_insole     158 non-null    int64  \n",
+      " 60  orthotic_friendly    158 non-null    int64  \n",
+      " 61  not_waterproof       158 non-null    int64  \n",
+      " 62  waterproof           158 non-null    int64  \n",
+      " 63  water_repellent      158 non-null    int64  \n",
+      "dtypes: float64(11), int64(51), str(2)\n",
+      "memory usage: 79.1 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"Waterproofing\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a88a6c8",
+   "metadata": {},
+   "source": [
+    "# Finishing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "id": "26dc1dc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 64 columns):\n",
+      " #   Column               Non-Null Count  Dtype  \n",
+      "---  ------               --------------  -----  \n",
+      " 0   Brand                158 non-null    str    \n",
+      " 1   Name                 158 non-null    str    \n",
+      " 2   terrain_light        158 non-null    int64  \n",
+      " 3   terrain_moderate     158 non-null    int64  \n",
+      " 4   terrain_technical    158 non-null    int64  \n",
+      " 5   shock_low            158 non-null    int64  \n",
+      " 6   shock_moderate       158 non-null    int64  \n",
+      " 7   shock_high           158 non-null    int64  \n",
+      " 8   energy_low           158 non-null    int64  \n",
+      " 9   energy_moderate      158 non-null    int64  \n",
+      " 10  energy_high          158 non-null    int64  \n",
+      " 11  traction_moderate    158 non-null    int64  \n",
+      " 12  traction_high        158 non-null    int64  \n",
+      " 13  arch_neutral         158 non-null    int64  \n",
+      " 14  arch_stability       158 non-null    int64  \n",
+      " 15  weight_lab_oz        158 non-null    float64\n",
+      " 16  weight_lab_g         158 non-null    int64  \n",
+      " 17  weight_brand_oz      155 non-null    float64\n",
+      " 18  weight_brand_g       155 non-null    float64\n",
+      " 19  drop_lab_mm          158 non-null    float64\n",
+      " 20  drop_brand_mm        152 non-null    float64\n",
+      " 21  strike_heel          158 non-null    int64  \n",
+      " 22  strike_mid           158 non-null    int64  \n",
+      " 23  strike_forefoot      158 non-null    int64  \n",
+      " 24  softness_soft        158 non-null    int64  \n",
+      " 25  softness_balanced    158 non-null    int64  \n",
+      " 26  softness_firm        158 non-null    int64  \n",
+      " 27  toebox_durability    158 non-null    int64  \n",
+      " 28  heel_durability      158 non-null    int64  \n",
+      " 29  outsole_durability   158 non-null    int64  \n",
+      " 30  breathability        158 non-null    int64  \n",
+      " 31  plate_0              158 non-null    int64  \n",
+      " 32  plate_rock_plate     158 non-null    int64  \n",
+      " 33  plate_carbon_plate   158 non-null    int64  \n",
+      " 34  width_narrow         158 non-null    int64  \n",
+      " 35  width_medium         158 non-null    int64  \n",
+      " 36  width_wide           158 non-null    int64  \n",
+      " 37  toebox_narrow        158 non-null    int64  \n",
+      " 38  toebox_medium        158 non-null    int64  \n",
+      " 39  toebox_wide          158 non-null    int64  \n",
+      " 40  stiffness_flexible   158 non-null    int64  \n",
+      " 41  stiffness_moderate   158 non-null    int64  \n",
+      " 42  stiffness_stiff      158 non-null    int64  \n",
+      " 43  torsional_flexible   158 non-null    int64  \n",
+      " 44  torsional_moderate   158 non-null    int64  \n",
+      " 45  torsional_stiff      158 non-null    int64  \n",
+      " 46  heel_stiff_flexible  158 non-null    int64  \n",
+      " 47  heel_stiff_moderate  158 non-null    int64  \n",
+      " 48  heel_stiff_stiff     158 non-null    int64  \n",
+      " 49  lug_depth            158 non-null    float64\n",
+      " 50  heel_lab_mm          158 non-null    float64\n",
+      " 51  heel_brand_mm        145 non-null    float64\n",
+      " 52  forefoot_lab_mm      158 non-null    float64\n",
+      " 53  forefoot_brand_mm    143 non-null    float64\n",
+      " 54  season_summer        158 non-null    int64  \n",
+      " 55  season_winter        158 non-null    int64  \n",
+      " 56  season_all           158 non-null    int64  \n",
+      " 57  heavy_runners        158 non-null    int64  \n",
+      " 58  removable_insole     158 non-null    int64  \n",
+      " 59  orthotic_friendly    158 non-null    int64  \n",
+      " 60  not_waterproof       158 non-null    int64  \n",
+      " 61  waterproof           158 non-null    int64  \n",
+      " 62  water_repellent      158 non-null    int64  \n",
+      " 63  lightweight          158 non-null    int64  \n",
+      "dtypes: float64(10), int64(52), str(2)\n",
+      "memory usage: 79.1 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# change lightweight to int\n",
+    "df['lightweight'] = df['Lightweight'].fillna(0).astype(int)\n",
+    "df.drop(columns=['Lightweight'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "id": "9b96e4f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 61 columns):\n",
+      " #   Column               Non-Null Count  Dtype  \n",
+      "---  ------               --------------  -----  \n",
+      " 0   Brand                158 non-null    str    \n",
+      " 1   Name                 158 non-null    str    \n",
+      " 2   terrain_light        158 non-null    int64  \n",
+      " 3   terrain_moderate     158 non-null    int64  \n",
+      " 4   terrain_technical    158 non-null    int64  \n",
+      " 5   shock_low            158 non-null    int64  \n",
+      " 6   shock_moderate       158 non-null    int64  \n",
+      " 7   shock_high           158 non-null    int64  \n",
+      " 8   energy_low           158 non-null    int64  \n",
+      " 9   energy_moderate      158 non-null    int64  \n",
+      " 10  energy_high          158 non-null    int64  \n",
+      " 11  traction_moderate    158 non-null    int64  \n",
+      " 12  traction_high        158 non-null    int64  \n",
+      " 13  arch_neutral         158 non-null    int64  \n",
+      " 14  arch_stability       158 non-null    int64  \n",
+      " 15  weight_lab_oz        158 non-null    float64\n",
+      " 16  drop_lab_mm          158 non-null    float64\n",
+      " 17  drop_brand_mm        152 non-null    float64\n",
+      " 18  strike_heel          158 non-null    int64  \n",
+      " 19  strike_mid           158 non-null    int64  \n",
+      " 20  strike_forefoot      158 non-null    int64  \n",
+      " 21  softness_soft        158 non-null    int64  \n",
+      " 22  softness_balanced    158 non-null    int64  \n",
+      " 23  softness_firm        158 non-null    int64  \n",
+      " 24  toebox_durability    158 non-null    int64  \n",
+      " 25  heel_durability      158 non-null    int64  \n",
+      " 26  outsole_durability   158 non-null    int64  \n",
+      " 27  breathability        158 non-null    int64  \n",
+      " 28  plate_0              158 non-null    int64  \n",
+      " 29  plate_rock_plate     158 non-null    int64  \n",
+      " 30  plate_carbon_plate   158 non-null    int64  \n",
+      " 31  width_narrow         158 non-null    int64  \n",
+      " 32  width_medium         158 non-null    int64  \n",
+      " 33  width_wide           158 non-null    int64  \n",
+      " 34  toebox_narrow        158 non-null    int64  \n",
+      " 35  toebox_medium        158 non-null    int64  \n",
+      " 36  toebox_wide          158 non-null    int64  \n",
+      " 37  stiffness_flexible   158 non-null    int64  \n",
+      " 38  stiffness_moderate   158 non-null    int64  \n",
+      " 39  stiffness_stiff      158 non-null    int64  \n",
+      " 40  torsional_flexible   158 non-null    int64  \n",
+      " 41  torsional_moderate   158 non-null    int64  \n",
+      " 42  torsional_stiff      158 non-null    int64  \n",
+      " 43  heel_stiff_flexible  158 non-null    int64  \n",
+      " 44  heel_stiff_moderate  158 non-null    int64  \n",
+      " 45  heel_stiff_stiff     158 non-null    int64  \n",
+      " 46  lug_depth            158 non-null    float64\n",
+      " 47  heel_lab_mm          158 non-null    float64\n",
+      " 48  heel_brand_mm        145 non-null    float64\n",
+      " 49  forefoot_lab_mm      158 non-null    float64\n",
+      " 50  forefoot_brand_mm    143 non-null    float64\n",
+      " 51  season_summer        158 non-null    int64  \n",
+      " 52  season_winter        158 non-null    int64  \n",
+      " 53  season_all           158 non-null    int64  \n",
+      " 54  heavy_runners        158 non-null    int64  \n",
+      " 55  removable_insole     158 non-null    int64  \n",
+      " 56  orthotic_friendly    158 non-null    int64  \n",
+      " 57  not_waterproof       158 non-null    int64  \n",
+      " 58  waterproof           158 non-null    int64  \n",
+      " 59  water_repellent      158 non-null    int64  \n",
+      " 60  lightweight          158 non-null    int64  \n",
+      "dtypes: float64(8), int64(51), str(2)\n",
+      "memory usage: 75.4 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Weight cuma pakai yg lab_oz\n",
+    "df.drop(columns=['weight_brand_oz', 'weight_lab_g', 'weight_brand_g'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "id": "40a900de",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 60 columns):\n",
+      " #   Column               Non-Null Count  Dtype  \n",
+      "---  ------               --------------  -----  \n",
+      " 0   Brand                158 non-null    str    \n",
+      " 1   Name                 158 non-null    str    \n",
+      " 2   terrain_light        158 non-null    int64  \n",
+      " 3   terrain_moderate     158 non-null    int64  \n",
+      " 4   terrain_technical    158 non-null    int64  \n",
+      " 5   shock_low            158 non-null    int64  \n",
+      " 6   shock_moderate       158 non-null    int64  \n",
+      " 7   shock_high           158 non-null    int64  \n",
+      " 8   energy_low           158 non-null    int64  \n",
+      " 9   energy_moderate      158 non-null    int64  \n",
+      " 10  energy_high          158 non-null    int64  \n",
+      " 11  traction_moderate    158 non-null    int64  \n",
+      " 12  traction_high        158 non-null    int64  \n",
+      " 13  arch_neutral         158 non-null    int64  \n",
+      " 14  arch_stability       158 non-null    int64  \n",
+      " 15  weight_lab_oz        158 non-null    float64\n",
+      " 16  drop_lab_mm          158 non-null    float64\n",
+      " 17  strike_heel          158 non-null    int64  \n",
+      " 18  strike_mid           158 non-null    int64  \n",
+      " 19  strike_forefoot      158 non-null    int64  \n",
+      " 20  softness_soft        158 non-null    int64  \n",
+      " 21  softness_balanced    158 non-null    int64  \n",
+      " 22  softness_firm        158 non-null    int64  \n",
+      " 23  toebox_durability    158 non-null    int64  \n",
+      " 24  heel_durability      158 non-null    int64  \n",
+      " 25  outsole_durability   158 non-null    int64  \n",
+      " 26  breathability        158 non-null    int64  \n",
+      " 27  plate_0              158 non-null    int64  \n",
+      " 28  plate_rock_plate     158 non-null    int64  \n",
+      " 29  plate_carbon_plate   158 non-null    int64  \n",
+      " 30  width_narrow         158 non-null    int64  \n",
+      " 31  width_medium         158 non-null    int64  \n",
+      " 32  width_wide           158 non-null    int64  \n",
+      " 33  toebox_narrow        158 non-null    int64  \n",
+      " 34  toebox_medium        158 non-null    int64  \n",
+      " 35  toebox_wide          158 non-null    int64  \n",
+      " 36  stiffness_flexible   158 non-null    int64  \n",
+      " 37  stiffness_moderate   158 non-null    int64  \n",
+      " 38  stiffness_stiff      158 non-null    int64  \n",
+      " 39  torsional_flexible   158 non-null    int64  \n",
+      " 40  torsional_moderate   158 non-null    int64  \n",
+      " 41  torsional_stiff      158 non-null    int64  \n",
+      " 42  heel_stiff_flexible  158 non-null    int64  \n",
+      " 43  heel_stiff_moderate  158 non-null    int64  \n",
+      " 44  heel_stiff_stiff     158 non-null    int64  \n",
+      " 45  lug_depth            158 non-null    float64\n",
+      " 46  heel_lab_mm          158 non-null    float64\n",
+      " 47  heel_brand_mm        145 non-null    float64\n",
+      " 48  forefoot_lab_mm      158 non-null    float64\n",
+      " 49  forefoot_brand_mm    143 non-null    float64\n",
+      " 50  season_summer        158 non-null    int64  \n",
+      " 51  season_winter        158 non-null    int64  \n",
+      " 52  season_all           158 non-null    int64  \n",
+      " 53  heavy_runners        158 non-null    int64  \n",
+      " 54  removable_insole     158 non-null    int64  \n",
+      " 55  orthotic_friendly    158 non-null    int64  \n",
+      " 56  not_waterproof       158 non-null    int64  \n",
+      " 57  waterproof           158 non-null    int64  \n",
+      " 58  water_repellent      158 non-null    int64  \n",
+      " 59  lightweight          158 non-null    int64  \n",
+      "dtypes: float64(7), int64(51), str(2)\n",
+      "memory usage: 74.2 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# drop cuma pakai yg lab_mm\n",
+    "df.drop(columns=['drop_brand_mm'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "id": "84034462",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 59 columns):\n",
+      " #   Column               Non-Null Count  Dtype  \n",
+      "---  ------               --------------  -----  \n",
+      " 0   Brand                158 non-null    str    \n",
+      " 1   Name                 158 non-null    str    \n",
+      " 2   terrain_light        158 non-null    int64  \n",
+      " 3   terrain_moderate     158 non-null    int64  \n",
+      " 4   terrain_technical    158 non-null    int64  \n",
+      " 5   shock_low            158 non-null    int64  \n",
+      " 6   shock_moderate       158 non-null    int64  \n",
+      " 7   shock_high           158 non-null    int64  \n",
+      " 8   energy_low           158 non-null    int64  \n",
+      " 9   energy_moderate      158 non-null    int64  \n",
+      " 10  energy_high          158 non-null    int64  \n",
+      " 11  traction_moderate    158 non-null    int64  \n",
+      " 12  traction_high        158 non-null    int64  \n",
+      " 13  arch_neutral         158 non-null    int64  \n",
+      " 14  arch_stability       158 non-null    int64  \n",
+      " 15  weight_lab_oz        158 non-null    float64\n",
+      " 16  drop_lab_mm          158 non-null    float64\n",
+      " 17  strike_heel          158 non-null    int64  \n",
+      " 18  strike_mid           158 non-null    int64  \n",
+      " 19  strike_forefoot      158 non-null    int64  \n",
+      " 20  softness_soft        158 non-null    int64  \n",
+      " 21  softness_balanced    158 non-null    int64  \n",
+      " 22  softness_firm        158 non-null    int64  \n",
+      " 23  toebox_durability    158 non-null    int64  \n",
+      " 24  heel_durability      158 non-null    int64  \n",
+      " 25  outsole_durability   158 non-null    int64  \n",
+      " 26  breathability        158 non-null    int64  \n",
+      " 27  plate_0              158 non-null    int64  \n",
+      " 28  plate_rock_plate     158 non-null    int64  \n",
+      " 29  plate_carbon_plate   158 non-null    int64  \n",
+      " 30  width_narrow         158 non-null    int64  \n",
+      " 31  width_medium         158 non-null    int64  \n",
+      " 32  width_wide           158 non-null    int64  \n",
+      " 33  toebox_narrow        158 non-null    int64  \n",
+      " 34  toebox_medium        158 non-null    int64  \n",
+      " 35  toebox_wide          158 non-null    int64  \n",
+      " 36  stiffness_flexible   158 non-null    int64  \n",
+      " 37  stiffness_moderate   158 non-null    int64  \n",
+      " 38  stiffness_stiff      158 non-null    int64  \n",
+      " 39  torsional_flexible   158 non-null    int64  \n",
+      " 40  torsional_moderate   158 non-null    int64  \n",
+      " 41  torsional_stiff      158 non-null    int64  \n",
+      " 42  heel_stiff_flexible  158 non-null    int64  \n",
+      " 43  heel_stiff_moderate  158 non-null    int64  \n",
+      " 44  heel_stiff_stiff     158 non-null    int64  \n",
+      " 45  lug_depth            158 non-null    float64\n",
+      " 46  heel_lab_mm          158 non-null    float64\n",
+      " 47  forefoot_lab_mm      158 non-null    float64\n",
+      " 48  forefoot_brand_mm    143 non-null    float64\n",
+      " 49  season_summer        158 non-null    int64  \n",
+      " 50  season_winter        158 non-null    int64  \n",
+      " 51  season_all           158 non-null    int64  \n",
+      " 52  heavy_runners        158 non-null    int64  \n",
+      " 53  removable_insole     158 non-null    int64  \n",
+      " 54  orthotic_friendly    158 non-null    int64  \n",
+      " 55  not_waterproof       158 non-null    int64  \n",
+      " 56  waterproof           158 non-null    int64  \n",
+      " 57  water_repellent      158 non-null    int64  \n",
+      " 58  lightweight          158 non-null    int64  \n",
+      "dtypes: float64(6), int64(51), str(2)\n",
+      "memory usage: 73.0 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# heel pakai yang heel_lab_mm\n",
+    "df.drop(columns=['heel_brand_mm'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "id": "becce231",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 58 columns):\n",
+      " #   Column               Non-Null Count  Dtype  \n",
+      "---  ------               --------------  -----  \n",
+      " 0   Brand                158 non-null    str    \n",
+      " 1   Name                 158 non-null    str    \n",
+      " 2   terrain_light        158 non-null    int64  \n",
+      " 3   terrain_moderate     158 non-null    int64  \n",
+      " 4   terrain_technical    158 non-null    int64  \n",
+      " 5   shock_low            158 non-null    int64  \n",
+      " 6   shock_moderate       158 non-null    int64  \n",
+      " 7   shock_high           158 non-null    int64  \n",
+      " 8   energy_low           158 non-null    int64  \n",
+      " 9   energy_moderate      158 non-null    int64  \n",
+      " 10  energy_high          158 non-null    int64  \n",
+      " 11  traction_moderate    158 non-null    int64  \n",
+      " 12  traction_high        158 non-null    int64  \n",
+      " 13  arch_neutral         158 non-null    int64  \n",
+      " 14  arch_stability       158 non-null    int64  \n",
+      " 15  weight_lab_oz        158 non-null    float64\n",
+      " 16  drop_lab_mm          158 non-null    float64\n",
+      " 17  strike_heel          158 non-null    int64  \n",
+      " 18  strike_mid           158 non-null    int64  \n",
+      " 19  strike_forefoot      158 non-null    int64  \n",
+      " 20  softness_soft        158 non-null    int64  \n",
+      " 21  softness_balanced    158 non-null    int64  \n",
+      " 22  softness_firm        158 non-null    int64  \n",
+      " 23  toebox_durability    158 non-null    int64  \n",
+      " 24  heel_durability      158 non-null    int64  \n",
+      " 25  outsole_durability   158 non-null    int64  \n",
+      " 26  breathability        158 non-null    int64  \n",
+      " 27  plate_0              158 non-null    int64  \n",
+      " 28  plate_rock_plate     158 non-null    int64  \n",
+      " 29  plate_carbon_plate   158 non-null    int64  \n",
+      " 30  width_narrow         158 non-null    int64  \n",
+      " 31  width_medium         158 non-null    int64  \n",
+      " 32  width_wide           158 non-null    int64  \n",
+      " 33  toebox_narrow        158 non-null    int64  \n",
+      " 34  toebox_medium        158 non-null    int64  \n",
+      " 35  toebox_wide          158 non-null    int64  \n",
+      " 36  stiffness_flexible   158 non-null    int64  \n",
+      " 37  stiffness_moderate   158 non-null    int64  \n",
+      " 38  stiffness_stiff      158 non-null    int64  \n",
+      " 39  torsional_flexible   158 non-null    int64  \n",
+      " 40  torsional_moderate   158 non-null    int64  \n",
+      " 41  torsional_stiff      158 non-null    int64  \n",
+      " 42  heel_stiff_flexible  158 non-null    int64  \n",
+      " 43  heel_stiff_moderate  158 non-null    int64  \n",
+      " 44  heel_stiff_stiff     158 non-null    int64  \n",
+      " 45  lug_depth            158 non-null    float64\n",
+      " 46  heel_lab_mm          158 non-null    float64\n",
+      " 47  forefoot_lab_mm      158 non-null    float64\n",
+      " 48  season_summer        158 non-null    int64  \n",
+      " 49  season_winter        158 non-null    int64  \n",
+      " 50  season_all           158 non-null    int64  \n",
+      " 51  heavy_runners        158 non-null    int64  \n",
+      " 52  removable_insole     158 non-null    int64  \n",
+      " 53  orthotic_friendly    158 non-null    int64  \n",
+      " 54  not_waterproof       158 non-null    int64  \n",
+      " 55  waterproof           158 non-null    int64  \n",
+      " 56  water_repellent      158 non-null    int64  \n",
+      " 57  lightweight          158 non-null    int64  \n",
+      "dtypes: float64(5), int64(51), str(2)\n",
+      "memory usage: 71.7 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Forefoot pakai yang forefoot_lab_mm\n",
+    "df.drop(columns=['forefoot_brand_mm'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "id": "3dcfea5d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 57 columns):\n",
+      " #   Column               Non-Null Count  Dtype  \n",
+      "---  ------               --------------  -----  \n",
+      " 0   Brand                158 non-null    str    \n",
+      " 1   Name                 158 non-null    str    \n",
+      " 2   terrain_light        158 non-null    int64  \n",
+      " 3   terrain_moderate     158 non-null    int64  \n",
+      " 4   terrain_technical    158 non-null    int64  \n",
+      " 5   shock_low            158 non-null    int64  \n",
+      " 6   shock_moderate       158 non-null    int64  \n",
+      " 7   shock_high           158 non-null    int64  \n",
+      " 8   energy_low           158 non-null    int64  \n",
+      " 9   energy_moderate      158 non-null    int64  \n",
+      " 10  energy_high          158 non-null    int64  \n",
+      " 11  traction_moderate    158 non-null    int64  \n",
+      " 12  traction_high        158 non-null    int64  \n",
+      " 13  arch_neutral         158 non-null    int64  \n",
+      " 14  arch_stability       158 non-null    int64  \n",
+      " 15  weight_lab_oz        158 non-null    float64\n",
+      " 16  drop_lab_mm          158 non-null    float64\n",
+      " 17  strike_heel          158 non-null    int64  \n",
+      " 18  strike_mid           158 non-null    int64  \n",
+      " 19  strike_forefoot      158 non-null    int64  \n",
+      " 20  softness_soft        158 non-null    int64  \n",
+      " 21  softness_balanced    158 non-null    int64  \n",
+      " 22  softness_firm        158 non-null    int64  \n",
+      " 23  toebox_durability    158 non-null    int64  \n",
+      " 24  heel_durability      158 non-null    int64  \n",
+      " 25  outsole_durability   158 non-null    int64  \n",
+      " 26  breathability        158 non-null    int64  \n",
+      " 27  plate_0              158 non-null    int64  \n",
+      " 28  plate_rock_plate     158 non-null    int64  \n",
+      " 29  plate_carbon_plate   158 non-null    int64  \n",
+      " 30  width_narrow         158 non-null    int64  \n",
+      " 31  width_medium         158 non-null    int64  \n",
+      " 32  width_wide           158 non-null    int64  \n",
+      " 33  toebox_narrow        158 non-null    int64  \n",
+      " 34  toebox_medium        158 non-null    int64  \n",
+      " 35  toebox_wide          158 non-null    int64  \n",
+      " 36  stiffness_flexible   158 non-null    int64  \n",
+      " 37  stiffness_moderate   158 non-null    int64  \n",
+      " 38  stiffness_stiff      158 non-null    int64  \n",
+      " 39  torsional_flexible   158 non-null    int64  \n",
+      " 40  torsional_moderate   158 non-null    int64  \n",
+      " 41  torsional_stiff      158 non-null    int64  \n",
+      " 42  heel_stiff_flexible  158 non-null    int64  \n",
+      " 43  heel_stiff_moderate  158 non-null    int64  \n",
+      " 44  heel_stiff_stiff     158 non-null    int64  \n",
+      " 45  lug_depth            158 non-null    float64\n",
+      " 46  heel_lab_mm          158 non-null    float64\n",
+      " 47  forefoot_lab_mm      158 non-null    float64\n",
+      " 48  season_summer        158 non-null    int64  \n",
+      " 49  season_winter        158 non-null    int64  \n",
+      " 50  season_all           158 non-null    int64  \n",
+      " 51  heavy_runners        158 non-null    int64  \n",
+      " 52  removable_insole     158 non-null    int64  \n",
+      " 53  orthotic_friendly    158 non-null    int64  \n",
+      " 54  waterproof           158 non-null    int64  \n",
+      " 55  water_repellent      158 non-null    int64  \n",
+      " 56  lightweight          158 non-null    int64  \n",
+      "dtypes: float64(5), int64(50), str(2)\n",
+      "memory usage: 70.5 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=['not_waterproof'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "id": "f4fa327a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 57 columns):\n",
+      " #   Column               Non-Null Count  Dtype  \n",
+      "---  ------               --------------  -----  \n",
+      " 0   brand                158 non-null    str    \n",
+      " 1   name                 158 non-null    str    \n",
+      " 2   terrain_light        158 non-null    int64  \n",
+      " 3   terrain_moderate     158 non-null    int64  \n",
+      " 4   terrain_technical    158 non-null    int64  \n",
+      " 5   shock_low            158 non-null    int64  \n",
+      " 6   shock_moderate       158 non-null    int64  \n",
+      " 7   shock_high           158 non-null    int64  \n",
+      " 8   energy_low           158 non-null    int64  \n",
+      " 9   energy_moderate      158 non-null    int64  \n",
+      " 10  energy_high          158 non-null    int64  \n",
+      " 11  traction_moderate    158 non-null    int64  \n",
+      " 12  traction_high        158 non-null    int64  \n",
+      " 13  arch_neutral         158 non-null    int64  \n",
+      " 14  arch_stability       158 non-null    int64  \n",
+      " 15  weight_lab_oz        158 non-null    float64\n",
+      " 16  drop_lab_mm          158 non-null    float64\n",
+      " 17  strike_heel          158 non-null    int64  \n",
+      " 18  strike_mid           158 non-null    int64  \n",
+      " 19  strike_forefoot      158 non-null    int64  \n",
+      " 20  softness_soft        158 non-null    int64  \n",
+      " 21  softness_balanced    158 non-null    int64  \n",
+      " 22  softness_firm        158 non-null    int64  \n",
+      " 23  toebox_durability    158 non-null    int64  \n",
+      " 24  heel_durability      158 non-null    int64  \n",
+      " 25  outsole_durability   158 non-null    int64  \n",
+      " 26  breathability        158 non-null    int64  \n",
+      " 27  plate_0              158 non-null    int64  \n",
+      " 28  plate_rock_plate     158 non-null    int64  \n",
+      " 29  plate_carbon_plate   158 non-null    int64  \n",
+      " 30  width_narrow         158 non-null    int64  \n",
+      " 31  width_medium         158 non-null    int64  \n",
+      " 32  width_wide           158 non-null    int64  \n",
+      " 33  toebox_narrow        158 non-null    int64  \n",
+      " 34  toebox_medium        158 non-null    int64  \n",
+      " 35  toebox_wide          158 non-null    int64  \n",
+      " 36  stiffness_flexible   158 non-null    int64  \n",
+      " 37  stiffness_moderate   158 non-null    int64  \n",
+      " 38  stiffness_stiff      158 non-null    int64  \n",
+      " 39  torsional_flexible   158 non-null    int64  \n",
+      " 40  torsional_moderate   158 non-null    int64  \n",
+      " 41  torsional_stiff      158 non-null    int64  \n",
+      " 42  heel_stiff_flexible  158 non-null    int64  \n",
+      " 43  heel_stiff_moderate  158 non-null    int64  \n",
+      " 44  heel_stiff_stiff     158 non-null    int64  \n",
+      " 45  lug_depth            158 non-null    float64\n",
+      " 46  heel_lab_mm          158 non-null    float64\n",
+      " 47  forefoot_lab_mm      158 non-null    float64\n",
+      " 48  season_summer        158 non-null    int64  \n",
+      " 49  season_winter        158 non-null    int64  \n",
+      " 50  season_all           158 non-null    int64  \n",
+      " 51  heavy_runners        158 non-null    int64  \n",
+      " 52  removable_insole     158 non-null    int64  \n",
+      " 53  orthotic_friendly    158 non-null    int64  \n",
+      " 54  waterproof           158 non-null    int64  \n",
+      " 55  water_repellent      158 non-null    int64  \n",
+      " 56  lightweight          158 non-null    int64  \n",
+      "dtypes: float64(5), int64(50), str(2)\n",
+      "memory usage: 70.5 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.rename(columns={\n",
+    "    'Brand': 'brand',    \n",
+    "    'Name': 'name'}, inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d61c3f64",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "id": "5cc8f859",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv('../../data/trail_dataset.csv', index=False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/data-preparation-v3/final-road.ipynb b/notebooks/data-preparation-v3/final-road.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..dc24d6450a447735f79a4e9e52136d30cff84c3d
--- /dev/null
+++ b/notebooks/data-preparation-v3/final-road.ipynb
@@ -0,0 +1,7664 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b09a9f4d",
+   "metadata": {},
+   "source": [
+    "# Road shoes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "efbbb069",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    Brand         Name  Audience score Price                Pace Arch support  \\\n",
+      "0  Brooks     Launch 9   87\\r\\n Great!  $110  Daily runningTempo      Neutral   \n",
+      "1  Brooks   Levitate 6  90\\r\\n Superb!  $150       Daily running      Neutral   \n",
+      "2  Adidas        4DFWD  90\\r\\n Superb!  $200       Daily running      Neutral   \n",
+      "3  Adidas      4DFWD 2  90\\r\\n Superb!  $200       Daily running      Neutral   \n",
+      "4  Adidas      4DFWD 3   88\\r\\n Great!  $200       Daily running      Neutral   \n",
+      "\n",
+      "         Weight lab Weight brand  Lightweight Drop lab Drop brand  \\\n",
+      "0    7.9 oz / 225g 8.1 oz / 230g            1      9.4 mm 10.0 mm   \n",
+      "1  10.7 oz / 304g 10.9 oz / 309g            0       7.7 mm 8.0 mm   \n",
+      "2  11.9 oz / 336g 11.5 oz / 327g            0      8.9 mm 10.0 mm   \n",
+      "3  12.6 oz / 356g 12.4 oz / 352g            0     10.6 mm 11.0 mm   \n",
+      "4  12.3 oz / 348g 12.2 oz / 345g            0      9.9 mm 10.0 mm   \n",
+      "\n",
+      "     Strike pattern  ... Heel lab Heel brand Forefoot lab Forefoot brand  \\\n",
+      "0  HeelMid/forefoot  ...     32.4 mm 36.0 mm             23.0 mm 26.0 mm   \n",
+      "1      Mid/forefoot  ...     34.3 mm 32.5 mm             26.6 mm 24.5 mm   \n",
+      "2  HeelMid/forefoot  ...     33.3 mm 32.5 mm             24.4 mm 22.5 mm   \n",
+      "3              Heel  ...     31.8 mm 32.0 mm             21.2 mm 21.0 mm   \n",
+      "4  HeelMid/forefoot  ...     32.6 mm 34.0 mm             22.7 mm 24.0 mm   \n",
+      "\n",
+      "  Widths available Orthotic friendly             Season Removable insole  \\\n",
+      "0       NormalWide                 1                  -                1   \n",
+      "1           Normal                 1  SummerAll seasons                1   \n",
+      "2           Normal                 1        All seasons                1   \n",
+      "3           Normal                 1        All seasons                1   \n",
+      "4           Normal                 1        All seasons                1   \n",
+      "\n",
+      "        Ranking       Popularity Gender Terrain  \n",
+      "0  #301 Top 47%  #352 Bottom 45%    NaN     NaN  \n",
+      "1   #72 Top 20%  #255 Bottom 30%    NaN     NaN  \n",
+      "2  #104 Top 17%  #368 Bottom 42%    NaN     NaN  \n",
+      "3  #126 Top 20%  #541 Bottom 16%    NaN     NaN  \n",
+      "4  #116 Top 32%   #339 Bottom 7%    NaN     NaN  \n",
+      "\n",
+      "[5 rows x 33 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df_ori = pd.read_csv('../../data/SONIX utilities - Road.csv')\n",
+    "print(df_ori.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "48c076e5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1170\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1170 entries, 0 to 1169\n",
+      "Data columns (total 33 columns):\n",
+      " #   Column                       Non-Null Count  Dtype \n",
+      "---  ------                       --------------  ----- \n",
+      " 0   Brand                        1170 non-null   object\n",
+      " 1   Name                         1170 non-null   object\n",
+      " 2   Audience score               1164 non-null   object\n",
+      " 3   Price                        1170 non-null   object\n",
+      " 4   Pace                         1170 non-null   object\n",
+      " 5   Arch support                 1170 non-null   object\n",
+      " 6   Weight lab Weight brand      1170 non-null   object\n",
+      " 7   Lightweight                  1170 non-null   int64 \n",
+      " 8   Drop lab Drop brand          1170 non-null   object\n",
+      " 9   Strike pattern               1170 non-null   object\n",
+      " 10  Size                         1170 non-null   object\n",
+      " 11  Midsole softness             1170 non-null   object\n",
+      " 12  Toebox durability            1170 non-null   object\n",
+      " 13  Heel padding durability      1170 non-null   object\n",
+      " 14  Outsole durability           1170 non-null   object\n",
+      " 15  Breathability                1170 non-null   object\n",
+      " 16  Width / fit                  1170 non-null   object\n",
+      " 17  Toebox width                 1170 non-null   object\n",
+      " 18  Stiffness                    1170 non-null   object\n",
+      " 19  Torsional rigidity           1170 non-null   object\n",
+      " 20  Heel counter stiffness       1170 non-null   object\n",
+      " 21  Plate                        1170 non-null   object\n",
+      " 22  Rocker                       1170 non-null   int64 \n",
+      " 23  Heel lab Heel brand          1170 non-null   object\n",
+      " 24  Forefoot lab Forefoot brand  1170 non-null   object\n",
+      " 25  Widths available             1170 non-null   object\n",
+      " 26  Orthotic friendly            1170 non-null   int64 \n",
+      " 27  Season                       1170 non-null   object\n",
+      " 28  Removable insole             1170 non-null   int64 \n",
+      " 29  Ranking                      1170 non-null   object\n",
+      " 30  Popularity                   1170 non-null   object\n",
+      " 31  Gender                       10 non-null     object\n",
+      " 32  Terrain                      16 non-null     object\n",
+      "dtypes: int64(4), object(29)\n",
+      "memory usage: 301.8+ KB\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(len(df_ori))\n",
+    "print(df_ori.info())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "78885e1c",
+   "metadata": {},
+   "source": [
+    "# Pre-EDA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "60412a53",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1170 entries, 0 to 1169\n",
+      "Data columns (total 27 columns):\n",
+      " #   Column                       Non-Null Count  Dtype \n",
+      "---  ------                       --------------  ----- \n",
+      " 0   Brand                        1170 non-null   object\n",
+      " 1   Name                         1170 non-null   object\n",
+      " 2   Pace                         1170 non-null   object\n",
+      " 3   Arch support                 1170 non-null   object\n",
+      " 4   Weight lab Weight brand      1170 non-null   object\n",
+      " 5   Lightweight                  1170 non-null   int64 \n",
+      " 6   Drop lab Drop brand          1170 non-null   object\n",
+      " 7   Strike pattern               1170 non-null   object\n",
+      " 8   Size                         1170 non-null   object\n",
+      " 9   Midsole softness             1170 non-null   object\n",
+      " 10  Toebox durability            1170 non-null   object\n",
+      " 11  Heel padding durability      1170 non-null   object\n",
+      " 12  Outsole durability           1170 non-null   object\n",
+      " 13  Breathability                1170 non-null   object\n",
+      " 14  Width / fit                  1170 non-null   object\n",
+      " 15  Toebox width                 1170 non-null   object\n",
+      " 16  Stiffness                    1170 non-null   object\n",
+      " 17  Torsional rigidity           1170 non-null   object\n",
+      " 18  Heel counter stiffness       1170 non-null   object\n",
+      " 19  Plate                        1170 non-null   object\n",
+      " 20  Rocker                       1170 non-null   int64 \n",
+      " 21  Heel lab Heel brand          1170 non-null   object\n",
+      " 22  Forefoot lab Forefoot brand  1170 non-null   object\n",
+      " 23  Widths available             1170 non-null   object\n",
+      " 24  Orthotic friendly            1170 non-null   int64 \n",
+      " 25  Season                       1170 non-null   object\n",
+      " 26  Removable insole             1170 non-null   int64 \n",
+      "dtypes: int64(4), object(23)\n",
+      "memory usage: 246.9+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Remove unnecessary columns that we got from RunRepeat\n",
+    "df_ori.drop(columns=['Audience score', 'Price', 'Gender', 'Terrain', 'Ranking', 'Popularity'], inplace=True)\n",
+    "df_ori.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "8d6a771e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "       brand                    name                pace arch support  \\\n",
+      "0     brooks                launch 9  daily runningtempo      neutral   \n",
+      "1     brooks              levitate 6       daily running      neutral   \n",
+      "2     adidas                   4dfwd       daily running      neutral   \n",
+      "3     adidas                 4dfwd 2       daily running      neutral   \n",
+      "4     adidas                 4dfwd 3       daily running      neutral   \n",
+      "...      ...                     ...                 ...          ...   \n",
+      "1165    nike         zoomx streakfly               tempo      neutral   \n",
+      "1166    nike         zoomx streakfly               tempo      neutral   \n",
+      "1167    nike         zoomx streakfly               tempo      neutral   \n",
+      "1168    nike         zoomx streakfly               tempo      neutral   \n",
+      "1169    nike  zoomx vaporfly next% 2         competition      neutral   \n",
+      "\n",
+      "            weight lab weight brand  lightweight drop lab drop brand  \\\n",
+      "0       7.9 oz / 225g 8.1 oz / 230g            1      9.4 mm 10.0 mm   \n",
+      "1     10.7 oz / 304g 10.9 oz / 309g            0       7.7 mm 8.0 mm   \n",
+      "2     11.9 oz / 336g 11.5 oz / 327g            0      8.9 mm 10.0 mm   \n",
+      "3     12.6 oz / 356g 12.4 oz / 352g            0     10.6 mm 11.0 mm   \n",
+      "4     12.3 oz / 348g 12.2 oz / 345g            0      9.9 mm 10.0 mm   \n",
+      "...                             ...          ...                 ...   \n",
+      "1165        6 oz / 171g 6 oz / 171g            1       6.3 mm 6.0 mm   \n",
+      "1166        6 oz / 171g 6 oz / 171g            1       6.3 mm 6.0 mm   \n",
+      "1167        6 oz / 171g 6 oz / 171g            1       6.3 mm 6.0 mm   \n",
+      "1168        6 oz / 171g 6 oz / 171g            1       6.3 mm 6.0 mm   \n",
+      "1169    6.9 oz / 196g 6.9 oz / 196g            1       7.7 mm 7.7 mm   \n",
+      "\n",
+      "        strike pattern            size midsole softness  ...  \\\n",
+      "0     heelmid/forefoot    true to size         balanced  ...   \n",
+      "1         mid/forefoot    true to size             soft  ...   \n",
+      "2     heelmid/forefoot    true to size             firm  ...   \n",
+      "3                 heel  slightly small             firm  ...   \n",
+      "4     heelmid/forefoot    true to size             firm  ...   \n",
+      "...                ...             ...              ...  ...   \n",
+      "1165      mid/forefoot    true to size             soft  ...   \n",
+      "1166      mid/forefoot    true to size             soft  ...   \n",
+      "1167      mid/forefoot    true to size             soft  ...   \n",
+      "1168      mid/forefoot    true to size             soft  ...   \n",
+      "1169      mid/forefoot  slightly small             soft  ...   \n",
+      "\n",
+      "     torsional rigidity heel counter stiffness         plate rocker  \\\n",
+      "0                 stiff               flexible             0      0   \n",
+      "1              moderate               moderate             0      0   \n",
+      "2              flexible               flexible             0      0   \n",
+      "3              flexible               moderate             0      0   \n",
+      "4              flexible               flexible             0      0   \n",
+      "...                 ...                    ...           ...    ...   \n",
+      "1165           flexible               flexible             0      1   \n",
+      "1166           flexible               flexible             0      1   \n",
+      "1167           flexible               flexible             0      1   \n",
+      "1168           flexible               flexible             0      1   \n",
+      "1169              stiff               flexible  carbon plate      1   \n",
+      "\n",
+      "     heel lab heel brand forefoot lab forefoot brand widths available  \\\n",
+      "0        32.4 mm 36.0 mm             23.0 mm 26.0 mm       normalwide   \n",
+      "1        34.3 mm 32.5 mm             26.6 mm 24.5 mm           normal   \n",
+      "2        33.3 mm 32.5 mm             24.4 mm 22.5 mm           normal   \n",
+      "3        31.8 mm 32.0 mm             21.2 mm 21.0 mm           normal   \n",
+      "4        32.6 mm 34.0 mm             22.7 mm 24.0 mm           normal   \n",
+      "...                  ...                         ...              ...   \n",
+      "1165     31.7 mm 32.0 mm             25.4 mm 26.0 mm           normal   \n",
+      "1166     31.7 mm 32.0 mm             25.4 mm 26.0 mm           normal   \n",
+      "1167     31.7 mm 32.0 mm             25.4 mm 26.0 mm           normal   \n",
+      "1168     31.7 mm 32.0 mm             25.4 mm 26.0 mm           normal   \n",
+      "1169     38.6 mm 38.6 mm             30.9 mm 30.9 mm           normal   \n",
+      "\n",
+      "     orthotic friendly             season removable insole  \n",
+      "0                    1                  -                1  \n",
+      "1                    1  summerall seasons                1  \n",
+      "2                    1        all seasons                1  \n",
+      "3                    1        all seasons                1  \n",
+      "4                    1        all seasons                1  \n",
+      "...                ...                ...              ...  \n",
+      "1165                 1  summerall seasons                1  \n",
+      "1166                 1  summerall seasons                1  \n",
+      "1167                 1  summerall seasons                1  \n",
+      "1168                 1  summerall seasons                1  \n",
+      "1169                 0  summerall seasons                0  \n",
+      "\n",
+      "[1170 rows x 27 columns]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Naming convention: all to lowercase\n",
+    "df_ori.columns = df_ori.columns.str.strip().str.lower()\n",
+    "df_ori = df_ori.map(lambda x: x.strip().lower() if isinstance(x, str) else x)\n",
+    "\n",
+    "print(df_ori)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ebe6b8a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1170\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>brand</th>\n",
+       "      <th>name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>4dfwd 3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>adistar 3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>adistar 3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>adistar 3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>adistar 3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>163</th>\n",
+       "      <td>on</td>\n",
+       "      <td>cloudflyer 5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>164</th>\n",
+       "      <td>on</td>\n",
+       "      <td>cloudflyer 5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166</th>\n",
+       "      <td>on</td>\n",
+       "      <td>cloudgo</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>169</th>\n",
+       "      <td>on</td>\n",
+       "      <td>cloudmonster 2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>170</th>\n",
+       "      <td>on</td>\n",
+       "      <td>cloudmonster 2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      brand            name\n",
+       "5    adidas         4dfwd 3\n",
+       "11   adidas       adistar 3\n",
+       "12   adidas       adistar 3\n",
+       "13   adidas       adistar 3\n",
+       "14   adidas       adistar 3\n",
+       "..      ...             ...\n",
+       "163      on    cloudflyer 5\n",
+       "164      on    cloudflyer 5\n",
+       "166      on         cloudgo\n",
+       "169      on  cloudmonster 2\n",
+       "170      on  cloudmonster 2\n",
+       "\n",
+       "[100 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dup_mask = df_ori.duplicated(subset=[\"brand\", \"name\"], keep=\"first\")\n",
+    "print(len(dup_mask))\n",
+    "df_ori.loc[dup_mask, [\"brand\", \"name\"]].head(100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9809b458",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Before: 1170\n",
+      "After : 433\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>brand</th>\n",
+       "      <th>name</th>\n",
+       "      <th>pace</th>\n",
+       "      <th>arch support</th>\n",
+       "      <th>weight lab weight brand</th>\n",
+       "      <th>lightweight</th>\n",
+       "      <th>drop lab drop brand</th>\n",
+       "      <th>strike pattern</th>\n",
+       "      <th>size</th>\n",
+       "      <th>midsole softness</th>\n",
+       "      <th>...</th>\n",
+       "      <th>torsional rigidity</th>\n",
+       "      <th>heel counter stiffness</th>\n",
+       "      <th>plate</th>\n",
+       "      <th>rocker</th>\n",
+       "      <th>heel lab heel brand</th>\n",
+       "      <th>forefoot lab forefoot brand</th>\n",
+       "      <th>widths available</th>\n",
+       "      <th>orthotic friendly</th>\n",
+       "      <th>season</th>\n",
+       "      <th>removable insole</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>brooks</td>\n",
+       "      <td>launch 9</td>\n",
+       "      <td>daily runningtempo</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>7.9 oz / 225g 8.1 oz / 230g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.4 mm 10.0 mm</td>\n",
+       "      <td>heelmid/forefoot</td>\n",
+       "      <td>true to size</td>\n",
+       "      <td>balanced</td>\n",
+       "      <td>...</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>32.4 mm 36.0 mm</td>\n",
+       "      <td>23.0 mm 26.0 mm</td>\n",
+       "      <td>normalwide</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>brooks</td>\n",
+       "      <td>levitate 6</td>\n",
+       "      <td>daily running</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>10.7 oz / 304g 10.9 oz / 309g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.7 mm 8.0 mm</td>\n",
+       "      <td>mid/forefoot</td>\n",
+       "      <td>true to size</td>\n",
+       "      <td>soft</td>\n",
+       "      <td>...</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>34.3 mm 32.5 mm</td>\n",
+       "      <td>26.6 mm 24.5 mm</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>summerall seasons</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>4dfwd</td>\n",
+       "      <td>daily running</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>11.9 oz / 336g 11.5 oz / 327g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.9 mm 10.0 mm</td>\n",
+       "      <td>heelmid/forefoot</td>\n",
+       "      <td>true to size</td>\n",
+       "      <td>firm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>33.3 mm 32.5 mm</td>\n",
+       "      <td>24.4 mm 22.5 mm</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>all seasons</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>4dfwd 2</td>\n",
+       "      <td>daily running</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>12.6 oz / 356g 12.4 oz / 352g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.6 mm 11.0 mm</td>\n",
+       "      <td>heel</td>\n",
+       "      <td>slightly small</td>\n",
+       "      <td>firm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>31.8 mm 32.0 mm</td>\n",
+       "      <td>21.2 mm 21.0 mm</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>all seasons</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>4dfwd 3</td>\n",
+       "      <td>daily running</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>12.3 oz / 348g 12.2 oz / 345g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.9 mm 10.0 mm</td>\n",
+       "      <td>heelmid/forefoot</td>\n",
+       "      <td>true to size</td>\n",
+       "      <td>firm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>32.6 mm 34.0 mm</td>\n",
+       "      <td>22.7 mm 24.0 mm</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>all seasons</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 27 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    brand        name                pace arch support  \\\n",
+       "0  brooks    launch 9  daily runningtempo      neutral   \n",
+       "1  brooks  levitate 6       daily running      neutral   \n",
+       "2  adidas       4dfwd       daily running      neutral   \n",
+       "3  adidas     4dfwd 2       daily running      neutral   \n",
+       "4  adidas     4dfwd 3       daily running      neutral   \n",
+       "\n",
+       "         weight lab weight brand  lightweight drop lab drop brand  \\\n",
+       "0    7.9 oz / 225g 8.1 oz / 230g            1      9.4 mm 10.0 mm   \n",
+       "1  10.7 oz / 304g 10.9 oz / 309g            0       7.7 mm 8.0 mm   \n",
+       "2  11.9 oz / 336g 11.5 oz / 327g            0      8.9 mm 10.0 mm   \n",
+       "3  12.6 oz / 356g 12.4 oz / 352g            0     10.6 mm 11.0 mm   \n",
+       "4  12.3 oz / 348g 12.2 oz / 345g            0      9.9 mm 10.0 mm   \n",
+       "\n",
+       "     strike pattern            size midsole softness  ... torsional rigidity  \\\n",
+       "0  heelmid/forefoot    true to size         balanced  ...              stiff   \n",
+       "1      mid/forefoot    true to size             soft  ...           moderate   \n",
+       "2  heelmid/forefoot    true to size             firm  ...           flexible   \n",
+       "3              heel  slightly small             firm  ...           flexible   \n",
+       "4  heelmid/forefoot    true to size             firm  ...           flexible   \n",
+       "\n",
+       "  heel counter stiffness plate rocker heel lab heel brand  \\\n",
+       "0               flexible     0      0     32.4 mm 36.0 mm   \n",
+       "1               moderate     0      0     34.3 mm 32.5 mm   \n",
+       "2               flexible     0      0     33.3 mm 32.5 mm   \n",
+       "3               moderate     0      0     31.8 mm 32.0 mm   \n",
+       "4               flexible     0      0     32.6 mm 34.0 mm   \n",
+       "\n",
+       "  forefoot lab forefoot brand widths available orthotic friendly  \\\n",
+       "0             23.0 mm 26.0 mm       normalwide                 1   \n",
+       "1             26.6 mm 24.5 mm           normal                 1   \n",
+       "2             24.4 mm 22.5 mm           normal                 1   \n",
+       "3             21.2 mm 21.0 mm           normal                 1   \n",
+       "4             22.7 mm 24.0 mm           normal                 1   \n",
+       "\n",
+       "              season removable insole  \n",
+       "0                  -                1  \n",
+       "1  summerall seasons                1  \n",
+       "2        all seasons                1  \n",
+       "3        all seasons                1  \n",
+       "4        all seasons                1  \n",
+       "\n",
+       "[5 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# sebelum hapus\n",
+    "print(\"Before:\", len(df_ori))\n",
+    "\n",
+    "#hapus\n",
+    "df_ori = df_ori.drop_duplicates(subset=[\"brand\", \"name\"], keep=\"first\").reset_index(drop=True)\n",
+    "\n",
+    "print(\"After :\", len(df_ori))\n",
+    "df_ori.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "00aa7a9f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ditemukan 9 baris yang memiliki spesifikasi identik.\n",
+      "\n",
+      "           brand                   name                pace arch support  \\\n",
+      "40          nike             alphafly 3         competition      neutral   \n",
+      "285         nike        nike alphafly 3         competition      neutral   \n",
+      "119  new balance         foam arishi v4       daily running      neutral   \n",
+      "127  new balance   fresh foam arishi v4       daily running      neutral   \n",
+      "281       mizuno  mizuno wave horizon 7       daily running    stability   \n",
+      "406       mizuno         wave horizon 7       daily running    stability   \n",
+      "424       mizuno        wwave horizon 7       daily running    stability   \n",
+      "6         adidas     adidas adizero sl2  daily runningtempo      neutral   \n",
+      "23        adidas            adizero sl2  daily runningtempo      neutral   \n",
+      "\n",
+      "           weight lab weight brand  \n",
+      "40       7.1 oz / 201g 7 oz / 198g  \n",
+      "285      7.1 oz / 201g 7 oz / 198g  \n",
+      "119    8.5 oz / 242g 8.7 oz / 246g  \n",
+      "127    8.5 oz / 242g 8.7 oz / 246g  \n",
+      "281  11.6 oz / 329g 11.8 oz / 334g  \n",
+      "406  11.6 oz / 329g 11.8 oz / 334g  \n",
+      "424  11.6 oz / 329g 11.8 oz / 334g  \n",
+      "6      8.6 oz / 245g 8.4 oz / 238g  \n",
+      "23     8.6 oz / 245g 8.4 oz / 238g  \n"
+     ]
+    }
+   ],
+   "source": [
+    "# Searching for duplicate technical specifications\n",
+    "tech_columns = df_ori.columns[2:].tolist()\n",
+    "duplicates = df_ori[df_ori.duplicated(subset=tech_columns, keep=False)]\n",
+    "\n",
+    "duplicates_sorted = duplicates.sort_values(by=tech_columns[:3])\n",
+    "\n",
+    "print(f\"Ditemukan {len(duplicates_sorted)} baris yang memiliki spesifikasi identik.\\n\")\n",
+    "print(duplicates_sorted[['brand', 'name'] + tech_columns[:3]].head(30))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "6fdef3ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Before: 433\n",
+      "After : 428\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>brand</th>\n",
+       "      <th>name</th>\n",
+       "      <th>pace</th>\n",
+       "      <th>arch support</th>\n",
+       "      <th>weight lab weight brand</th>\n",
+       "      <th>lightweight</th>\n",
+       "      <th>drop lab drop brand</th>\n",
+       "      <th>strike pattern</th>\n",
+       "      <th>size</th>\n",
+       "      <th>midsole softness</th>\n",
+       "      <th>...</th>\n",
+       "      <th>torsional rigidity</th>\n",
+       "      <th>heel counter stiffness</th>\n",
+       "      <th>plate</th>\n",
+       "      <th>rocker</th>\n",
+       "      <th>heel lab heel brand</th>\n",
+       "      <th>forefoot lab forefoot brand</th>\n",
+       "      <th>widths available</th>\n",
+       "      <th>orthotic friendly</th>\n",
+       "      <th>season</th>\n",
+       "      <th>removable insole</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>brooks</td>\n",
+       "      <td>launch 9</td>\n",
+       "      <td>daily runningtempo</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>7.9 oz / 225g 8.1 oz / 230g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.4 mm 10.0 mm</td>\n",
+       "      <td>heelmid/forefoot</td>\n",
+       "      <td>true to size</td>\n",
+       "      <td>balanced</td>\n",
+       "      <td>...</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>32.4 mm 36.0 mm</td>\n",
+       "      <td>23.0 mm 26.0 mm</td>\n",
+       "      <td>normalwide</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>brooks</td>\n",
+       "      <td>levitate 6</td>\n",
+       "      <td>daily running</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>10.7 oz / 304g 10.9 oz / 309g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.7 mm 8.0 mm</td>\n",
+       "      <td>mid/forefoot</td>\n",
+       "      <td>true to size</td>\n",
+       "      <td>soft</td>\n",
+       "      <td>...</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>34.3 mm 32.5 mm</td>\n",
+       "      <td>26.6 mm 24.5 mm</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>summerall seasons</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>4dfwd</td>\n",
+       "      <td>daily running</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>11.9 oz / 336g 11.5 oz / 327g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.9 mm 10.0 mm</td>\n",
+       "      <td>heelmid/forefoot</td>\n",
+       "      <td>true to size</td>\n",
+       "      <td>firm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>33.3 mm 32.5 mm</td>\n",
+       "      <td>24.4 mm 22.5 mm</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>all seasons</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>4dfwd 2</td>\n",
+       "      <td>daily running</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>12.6 oz / 356g 12.4 oz / 352g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.6 mm 11.0 mm</td>\n",
+       "      <td>heel</td>\n",
+       "      <td>slightly small</td>\n",
+       "      <td>firm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>31.8 mm 32.0 mm</td>\n",
+       "      <td>21.2 mm 21.0 mm</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>all seasons</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>4dfwd 3</td>\n",
+       "      <td>daily running</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>12.3 oz / 348g 12.2 oz / 345g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.9 mm 10.0 mm</td>\n",
+       "      <td>heelmid/forefoot</td>\n",
+       "      <td>true to size</td>\n",
+       "      <td>firm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>32.6 mm 34.0 mm</td>\n",
+       "      <td>22.7 mm 24.0 mm</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>all seasons</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 27 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    brand        name                pace arch support  \\\n",
+       "0  brooks    launch 9  daily runningtempo      neutral   \n",
+       "1  brooks  levitate 6       daily running      neutral   \n",
+       "2  adidas       4dfwd       daily running      neutral   \n",
+       "3  adidas     4dfwd 2       daily running      neutral   \n",
+       "4  adidas     4dfwd 3       daily running      neutral   \n",
+       "\n",
+       "         weight lab weight brand  lightweight drop lab drop brand  \\\n",
+       "0    7.9 oz / 225g 8.1 oz / 230g            1      9.4 mm 10.0 mm   \n",
+       "1  10.7 oz / 304g 10.9 oz / 309g            0       7.7 mm 8.0 mm   \n",
+       "2  11.9 oz / 336g 11.5 oz / 327g            0      8.9 mm 10.0 mm   \n",
+       "3  12.6 oz / 356g 12.4 oz / 352g            0     10.6 mm 11.0 mm   \n",
+       "4  12.3 oz / 348g 12.2 oz / 345g            0      9.9 mm 10.0 mm   \n",
+       "\n",
+       "     strike pattern            size midsole softness  ... torsional rigidity  \\\n",
+       "0  heelmid/forefoot    true to size         balanced  ...              stiff   \n",
+       "1      mid/forefoot    true to size             soft  ...           moderate   \n",
+       "2  heelmid/forefoot    true to size             firm  ...           flexible   \n",
+       "3              heel  slightly small             firm  ...           flexible   \n",
+       "4  heelmid/forefoot    true to size             firm  ...           flexible   \n",
+       "\n",
+       "  heel counter stiffness plate rocker heel lab heel brand  \\\n",
+       "0               flexible     0      0     32.4 mm 36.0 mm   \n",
+       "1               moderate     0      0     34.3 mm 32.5 mm   \n",
+       "2               flexible     0      0     33.3 mm 32.5 mm   \n",
+       "3               moderate     0      0     31.8 mm 32.0 mm   \n",
+       "4               flexible     0      0     32.6 mm 34.0 mm   \n",
+       "\n",
+       "  forefoot lab forefoot brand widths available orthotic friendly  \\\n",
+       "0             23.0 mm 26.0 mm       normalwide                 1   \n",
+       "1             26.6 mm 24.5 mm           normal                 1   \n",
+       "2             24.4 mm 22.5 mm           normal                 1   \n",
+       "3             21.2 mm 21.0 mm           normal                 1   \n",
+       "4             22.7 mm 24.0 mm           normal                 1   \n",
+       "\n",
+       "              season removable insole  \n",
+       "0                  -                1  \n",
+       "1  summerall seasons                1  \n",
+       "2        all seasons                1  \n",
+       "3        all seasons                1  \n",
+       "4        all seasons                1  \n",
+       "\n",
+       "[5 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# sebelum hapus\n",
+    "print(\"Before:\", len(df_ori))\n",
+    "\n",
+    "#hapus\n",
+    "df_ori = df_ori.drop_duplicates(subset=tech_columns, keep='first').copy()\n",
+    "\n",
+    "print(\"After :\", len(df_ori))\n",
+    "df_ori.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "cf8752ab",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== HASIL VERIFIKASI ===\n",
+      "1. Total baris dengan 'brand_name' yang sama: 0\n",
+      "2. Total baris dengan spesifikasi yang sama: 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Last Verification of Duplicates\n",
+    "df_ori['brand_name'] = df_ori['brand'] + ' ' + df_ori['name']\n",
+    "dup_brand_name = df_ori[df_ori.duplicated(subset=['brand_name'], keep=False)]\n",
+    "dup_tech = df_ori[df_ori.duplicated(subset=tech_columns, keep=False)]\n",
+    "\n",
+    "print(f\"=== HASIL VERIFIKASI ===\")\n",
+    "print(f\"1. Total baris dengan 'brand_name' yang sama: {len(dup_brand_name)}\")\n",
+    "print(f\"2. Total baris dengan spesifikasi yang sama: {len(dup_tech)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "fc730313",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 27 columns):\n",
+      " #   Column                       Non-Null Count  Dtype \n",
+      "---  ------                       --------------  ----- \n",
+      " 0   brand                        428 non-null    object\n",
+      " 1   name                         428 non-null    object\n",
+      " 2   pace                         428 non-null    object\n",
+      " 3   arch support                 428 non-null    object\n",
+      " 4   weight lab weight brand      428 non-null    object\n",
+      " 5   lightweight                  428 non-null    int64 \n",
+      " 6   drop lab drop brand          428 non-null    object\n",
+      " 7   strike pattern               428 non-null    object\n",
+      " 8   size                         428 non-null    object\n",
+      " 9   midsole softness             428 non-null    object\n",
+      " 10  toebox durability            428 non-null    object\n",
+      " 11  heel padding durability      428 non-null    object\n",
+      " 12  outsole durability           428 non-null    object\n",
+      " 13  breathability                428 non-null    object\n",
+      " 14  width / fit                  428 non-null    object\n",
+      " 15  toebox width                 428 non-null    object\n",
+      " 16  stiffness                    428 non-null    object\n",
+      " 17  torsional rigidity           428 non-null    object\n",
+      " 18  heel counter stiffness       428 non-null    object\n",
+      " 19  plate                        428 non-null    object\n",
+      " 20  rocker                       428 non-null    int64 \n",
+      " 21  heel lab heel brand          428 non-null    object\n",
+      " 22  forefoot lab forefoot brand  428 non-null    object\n",
+      " 23  widths available             428 non-null    object\n",
+      " 24  orthotic friendly            428 non-null    int64 \n",
+      " 25  season                       428 non-null    object\n",
+      " 26  removable insole             428 non-null    int64 \n",
+      "dtypes: int64(4), object(23)\n",
+      "memory usage: 93.6+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_ori.drop(columns=['brand_name'], inplace=True)\n",
+    "df_ori.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "b2019371",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "428"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pre_eda_df = df_ori.copy()\n",
+    "len(pre_eda_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9baa75a0",
+   "metadata": {},
+   "source": [
+    "# EDA (Exploratory Data Analysis)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e468f573",
+   "metadata": {},
+   "source": [
+    "## Brand"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "ce835e97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pre_eda_df.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "caedf9a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "brand\n",
+      "asics           65\n",
+      "nike            54\n",
+      "adidas          53\n",
+      "brooks          51\n",
+      "saucony         39\n",
+      "new balance     37\n",
+      "altra           21\n",
+      "hoka            21\n",
+      "mizuno          19\n",
+      "on              16\n",
+      "under armour    10\n",
+      "salomon          7\n",
+      "skechers         6\n",
+      "reebok           6\n",
+      "puma             4\n",
+      "nobull           3\n",
+      "topo             3\n",
+      "diadora          3\n",
+      "allbirds         3\n",
+      "xero             3\n",
+      "inov8            1\n",
+      "jordan           1\n",
+      "apl              1\n",
+      "merrell          1\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['brand'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8f8b6950",
+   "metadata": {},
+   "source": [
+    "Terdapat kekhawatiran pada AI yang akan bias terhadap brand tertentu dikarenakan jumlah yang tidak seimbang. Untuk itu diperlukan pengecekan lanjutan pada hasil klasterisasi di akhir."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff64e16a",
+   "metadata": {},
+   "source": [
+    "## Running Purposes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "e1102b55",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pace\n",
+      "daily running         294\n",
+      "daily runningtempo     53\n",
+      "tempo                  31\n",
+      "competition            30\n",
+      "competitiontempo       20\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['pace'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2828887c",
+   "metadata": {},
+   "source": [
+    "Terdapat sepatu yang memiliki multi-value pace yang berantakan"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "32b00a7c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Jumlah Sepatu Per Kategori:\n",
+      "Daily: 347\n",
+      "Tempo: 104\n",
+      "Competition: 50\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib_venn import venn3 \n",
+    "\n",
+    "# encode base value\n",
+    "df['for_daily'] = df['pace'].str.contains('daily running').astype(int)\n",
+    "df['for_tempo'] = df['pace'].str.contains('tempo').astype(int)\n",
+    "df['for_competition'] = df['pace'].str.contains('competition').astype(int)\n",
+    "\n",
+    "\n",
+    "print(\"Jumlah Sepatu Per Kategori:\")\n",
+    "print(f\"Daily: {df['for_daily'].sum()}\")\n",
+    "print(f\"Tempo: {df['for_tempo'].sum()}\")\n",
+    "print(f\"Competition: {df['for_competition'].sum()}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "7ae0d328",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plotting intersection of each shoes\n",
+    "only_daily = len(df[(df['for_daily'] == 1) & (df['for_tempo'] == 0) & (df['for_competition'] == 0)])\n",
+    "only_tempo = len(df[(df['for_daily'] == 0) & (df['for_tempo'] == 1) & (df['for_competition'] == 0)])\n",
+    "only_comp  = len(df[(df['for_daily'] == 0) & (df['for_tempo'] == 0) & (df['for_competition'] == 1)])\n",
+    "\n",
+    "daily_tempo = len(df[(df['for_daily'] == 1) & (df['for_tempo'] == 1) & (df['for_competition'] == 0)])\n",
+    "daily_comp  = len(df[(df['for_daily'] == 1) & (df['for_tempo'] == 0) & (df['for_competition'] == 1)])\n",
+    "tempo_comp  = len(df[(df['for_daily'] == 0) & (df['for_tempo'] == 1) & (df['for_competition'] == 1)])\n",
+    "\n",
+    "all_three   = len(df[(df['for_daily'] == 1) & (df['for_tempo'] == 1) & (df['for_competition'] == 1)])\n",
+    "\n",
+    "plt.figure(figsize=(10, 8))\n",
+    "venn3(subsets = (only_daily, only_tempo, daily_tempo, only_comp, daily_comp, tempo_comp, all_three),\n",
+    "      set_labels = ('Daily Running', 'Tempo', 'Competition'),\n",
+    "      alpha = 0.5)\n",
+    "\n",
+    "plt.title(\"Running Purpose Intersection Analysis on Road Shoes\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ee8499f",
+   "metadata": {},
+   "source": [
+    "Kebanyakan sepatu yang ada adalah daily running, dimana daily running adalah sepatu yang dibuat untuk kebutuhan lari sehari-hari, tempo adalah sepatu untuk kebutuhan latihan serius sebelum pertandingan, dan competition adalah sepatu yang digunakan oleh pelari untuk berkompetisi. \n",
+    "\n",
+    "Feature ini merupakan feature penting yang memerlukan input user langsung \"Running Purpose\" untuk mengetahui kebutuhan lari user."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "1c33f9ef",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 30 columns):\n",
+      " #   Column                       Non-Null Count  Dtype \n",
+      "---  ------                       --------------  ----- \n",
+      " 0   brand                        428 non-null    object\n",
+      " 1   name                         428 non-null    object\n",
+      " 2   pace                         428 non-null    object\n",
+      " 3   arch support                 428 non-null    object\n",
+      " 4   weight lab weight brand      428 non-null    object\n",
+      " 5   lightweight                  428 non-null    int64 \n",
+      " 6   drop lab drop brand          428 non-null    object\n",
+      " 7   strike pattern               428 non-null    object\n",
+      " 8   size                         428 non-null    object\n",
+      " 9   midsole softness             428 non-null    object\n",
+      " 10  toebox durability            428 non-null    object\n",
+      " 11  heel padding durability      428 non-null    object\n",
+      " 12  outsole durability           428 non-null    object\n",
+      " 13  breathability                428 non-null    object\n",
+      " 14  width / fit                  428 non-null    object\n",
+      " 15  toebox width                 428 non-null    object\n",
+      " 16  stiffness                    428 non-null    object\n",
+      " 17  torsional rigidity           428 non-null    object\n",
+      " 18  heel counter stiffness       428 non-null    object\n",
+      " 19  plate                        428 non-null    object\n",
+      " 20  rocker                       428 non-null    int64 \n",
+      " 21  heel lab heel brand          428 non-null    object\n",
+      " 22  forefoot lab forefoot brand  428 non-null    object\n",
+      " 23  widths available             428 non-null    object\n",
+      " 24  orthotic friendly            428 non-null    int64 \n",
+      " 25  season                       428 non-null    object\n",
+      " 26  removable insole             428 non-null    int64 \n",
+      " 27  for_daily                    428 non-null    int64 \n",
+      " 28  for_tempo                    428 non-null    int64 \n",
+      " 29  for_competition              428 non-null    int64 \n",
+      "dtypes: int64(7), object(23)\n",
+      "memory usage: 103.7+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "427236c6",
+   "metadata": {},
+   "source": [
+    "## Arch support"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "414218c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "arch support\n",
+      "neutral           366\n",
+      "stability          61\n",
+      "motion control      1\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['arch support'].value_counts())"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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ZyziV4oZkyjKxgVIeRNfEX6ErKP2moVwfMbPHB3ibnzl62LC2jKojVzZ9cBqtgUY/L5MRBT+pDlLKiDf0S/vOBB8yVXJxV0Tri6MtO+/eMue2F2sZnE/AMihaoipT4Q9gkAo6w7xMLyIyJeaiLTclV+hpk+7ARHqF3UVlHcLndlJyEL6kBRW0qC7vGuQiBkhFc0YnOir7dJdw61uZJqpvK+edAWZThZtJUYCoSITOtBUBPgzkkUDaAOuqVOvu7Nk1hV1LZXiKcn2O6hYO+TempCErd2MrKEpK+3juR/pF9RLtiFL2TFkZZvbXuXwFUXJ0jJGNqHHaNU6BtEbrBwz4w5cnKXje7aGR6CxBbzynvd2kcxsJ8asL2eIqc2YMfZD76vo7NGiqQSlK66C5GVwHoBFpoNvzjQw78tt+1L8xCJ2W5VaaAldgtUZ2aYgGHt/qg9ZET4QrnQV5jlySaSvvaZUeAmLc3lFKQZfi9/BV6rkHFcjq55I+MeAFU1YlAToHBFW/gZoFfwAAEABJREFUQSo8ZQF2OPbFvJCTRb20QaCGEBrSU/Q8SawTu/Zt/mMv8StP4qOFgExzKHAlvAoH8o5WJhQDR5ftu3NTQCRsT7OQXJWkBtkq1QHVnvoDTd81WfVcq8olAVQpmtm9EmNrA1NTUNd98aRYaTRZTtGUiC7oe7djPdylwONV+ZVPegBBgeXuVqPU6m3V3Zlykj5iLJgN+WmjktSeSnAnHX9rQawt7NfTvVusdFnW1TkUgS7BABihPXJbV1EY2HAkIR7Ubx3eIBLMkM/sBkqkaQzTELpqcNZACxeSmka2j+a0pzg0GfLRctOBKIS+oh3HUGiRJFXoN9gqD32XNVrnjGI5eig3lu2lS493yr0ixqNZBVlWKa+kxDk9h7Q0ooCjaHS+D6NSlhTkZE3uhcNTLYOwJ0mHVvpEBRXTz+xQ1F122oJItLjUGygjXSoPLYJgg6mrs4FkPqtYPefMBPmhhTz0gJv8WosaDe64EXd1c7pfU8roa4uo6Vzlq+d85KQTJquqhU9nUG6mhJdej71WpVPEHUUxUES1opLEnimNTvmI2EDbSLetAZTe5PMhXfUhlNHYpWgoaITpsnzlvLk+7Ie2hol1TGpX/CBn87HDB4a4QmUiQlHRhlGeebqFxBLcqKPrDfYYTccpqZ3KvI7qSla597P0+BmedGbbUQJ8uO4xBKpZYmR4O2VFoaDhBWXFGhgm6nD5Q0KwNpXcpGwIkKyVluBeSMeGYnQZ7hngjgoNKk1/0f+pcUEeRBijvNXH7FX0bCoOKZ15Z9wnU81aOKbby2Qqo/VEsp+WoE10M523RORpyJdxdSspjp6zr1mDyTaIoX50X8N5TSU8q70FEg62q4Q03xVzTevBIVb/svDMiW5SmdCqZzbnFHyPFhUPtxRNyzUFozGS8m1Qo9aAU2PHEHqe9BPcWaXIzNvonUL2mjaX0j/Qo8sTljQAs4JIVnZFwMseBT8jdkAdPTfyHifU5aykk1zQ+Obmc2Q4VQCAqTNav4CvqlnojqrnXiX8Ts+GoQUEks8YdPdpOUs3fGcDoUs9UMPSOpWicBzrrp7blMsJNxkQ1oo6UQGrJzBAXlLqWcYXTcmweF5HimT1jOE5mxpMTX8wCOf/jnbioqv+HGbVXs+wmVQSHxShECq9da8UcLZ5GbI75VNTTZmAe0LovEAIiJzmzTqwndQzB5WZYtElR40SYcnd6L/+nUkdXRbNQBfSJobLFQClJmgg6XSTK1K8eXOUTgOOpxe19H8sy5Q5Vrm1Xe7Xoj4RxZIHE0TVu2UAGggXuoNKVUa2MpJm98CZRWAeVzTZKbtC+SDttgTPaK2TVQAIbwxx0wiMkHlagELGWFqZhlgCunHESFd2dQ301TYFPZFpfubsaTYUUxstibuZwEgRlKlB1hxlGgUf29XTPGUlteNfigLWwQdRlkIA0qtmQL1XDlOdSYRKmnIeUNwJn6QVSmHbM79J2ZDuewJEKa96g6gt8SrthVppWyVK+aa8sE2AVqtwpNAm3Kl84aL6kjm9ZQ2SEQVGOGNWswBrWzFUqIRBBq2CpY2QdjAhSeCciIwS2zT5EBZvpaK7K57MKhkPIU3zYQYgTF0OSVEx6OSCXaYYgJa7ouTkoYCKJAZ7Eg07Pe4UJCggSgGpqeUDjvb0h8B2YXjyWGkZUvToAUHybjb1CQZ4USzovKGcOVtNVBMD6skdSjPloKpvnZGNWC180HkZ53E/bacsntYFvZjuaavqSwg6W1eBVAZke5iWhoWeCotBaYW2ejy2GOhNEQlddqgjPplQ1J1Q0wgiREh359MpSujpVI4MFKUVulcRWNpFk292VaYWMIi2R7QuDN0yEfXsL+boa2zD1kCKztY074mydsLo6ayHpdDECVuj59hAiXe2skpM9jBoyjLNkp1RNXVod1fQSznV6ButheWw9AJLNETxaQ5W6aGsKKKihicsyRRM6f8kixbAQR6LWFErezW9dK7F1WhqmW1XtkzsdkZXKtnWcqGj9VRrHXOx49VMthnGkMDpgKHIIOWNuLOfjcaR1kU5DWh7ylt0O2jFTKYxXRfYiX46orLVJZCewziwZDytj9XJu+Lj5+2p/lMmwM+8FBctdzAzwTpu4gTcpQR1laNW1kz9BSDd4Q2vHC2IrKGGrx5ENETJqtb14iwqkQpQXw0QPrkRfQcGXTGZgHYzkQd76+hWuSQsO0ulACw6q6FFkEIjOi0f3qB32YhBZdNWXq5u5k39spbV8nNMtyDqbtfBIK19xHpue8hxUZUDsVs3dj1kYRz1k3A/iQzomuHAU4x2KwkJNQugzMlr1KAIFlTRgmyNP8adiKhSJA9eetBSUT/69EAHA76HHzVwfaeauviAjAsbJZrL7ME5TF2NQ01aWanT/NB3j8qhQF6KyrSkRxNGvQY0VMGcQcCQRNB8cOg7R7VHC+ODtqQWqqv2KeETJe6mHGg8g95wAGxQ71JoyEEqytFSwlAYzYKlUtYCCAXdIbEgvHNWjwb599AQ6zFOeUdLoWfaXjNlRiUi5VE+rNt5GglDE+ijQ8x1sVUR3vEgTzOt3Hkde+7BlHzATR0nphcZpbi6muY2TAwE1XFu9HZNp0+MWdKGNkoiPyM6tTq0F/7C9EGOVoCypDOCF2b660HgMM4IZSuzWorvmZy67sKj3WmiWOzTZ/MuCFyyILOkIJ7pLY60BzDSkbQ02IyhFia+8kkEOi1SARgwLFbd6z72yN4mx+KYgoZerNdR6gfDodb7qnIlhl0Uk2dbSa2lKRT7YZVlDEV0Gz/NwwNNdDZfx8cn82i84hHzZZDm/x4nGl1IKFnctA1rtLrBuG1q6xwPjDVKt0qgJwErH/X3VkOCegjT4ELngBoBkbPVbFVK+BKduoeOW1TpekMxI0l0607gmlxeGg9Jiamm5GOtMc0JT9N2VqPSqAwjRZNa4oSvdC9Q9zh12Lsrap++b2jLDQ6O5VNyoCJMlTH13FPbaKWxO/sZlGInDMoYwe6+2WVKMl2OzfTuuqqGE59Ue2SugTFoxjO5F+9RRy2FkZRyp3GYSWmr8MpE7/QIdZL+GJV7lm79S7kjxP4uqapgQTu6PGUgrZ44HMcBD7Ye0t8GlcYu7nhtfEExLZesHM6ghIcjyArI1Lfg2dfhrGVYyZWYpRO2zhg2/IINkB5nxWaFSNmoI89e/mQaLTbOQLhUBAxTQK6Zk0r0lmYyR2A02N5Y+u3kmL5qTJX15ogeOIOHkhsUMNmQQuDmuXwbglFKVVdRGq2O1uCnSusiphkBXQtdg8yXWS9vNWqVgTdVfO5Ql1cCJdYDHO89c09V9MmPsoEOjfC4qLv0gijhDK7CZJN1Kjepdjo4Z5v4KFkkFb4Y9O7Z+XIpvHv1IbUaYasEaAapVeigLaSSwwQylcPEaHkRKNcI6zZdstBI6+Hao8DUam4c3QWGFAHtvTQCJfDJrUoQuxTCXlXGB/62Lj8+77HUCFSJfrcHqJybwwhUWx6nNLgnDyuUVQq99Aq90tCDi37RJloDURJciu9CNkynSogVlE+TpNlKVp8nN7NjiqmmtX6lQ5fEW+bpSullS+7SNQ8koOiyykHlWH4WvHocZq6blhzd6FQlyEo6e1y5JqzttH2a72aaccJVFBF187zHuXFXxUCZwxt8QpN83ckuVIREb6V2/hKvWcpQVsBTdkXraJuD4p1QjAOcmVaqsKJyUrfCKJxQS3eTmnNfkF6959cZr5TDE3biapPsuWYPUsVBmNpqTYHRjmdBpmtWTk9fG1ZWiUKXnOoaFDipz2YaKA6yEEJvpgQq3Lm4Ll6vprl2IzKQolGDGiUr6GY/1TXQmuXAKA7TgdmlJD91rTHoMJ65QgpAaQGqM7BdQhmh3I1yT1MWNd0nrj5LACmOEEhgh2v7wMAzRL/kbEgtpAp6rGEARHXm3dhgesfMGM+6MviIZqZrJXMQ1tFIQk3B3JyDLWbQFdmngjL+9CGkd5KgerJDSi8lLs8+R4tCKN3SpWd3Py0pm6jhWg9zheoPp/vgOPZqhd2JPp2ZO+wUM2hnar9Z7TQOX8LRQOpfCzx7K1htQ2YVlF/lbqENQITEVtTpFiM8/QgJCqNtpk0frmg8VY5p8TQHVzTcgVMCSsUU6okpNLJFiRYYUDXoGNJFJI6SyjLsEU9lpD2CqwlCBqJToVjbkNSWqDopMO1lz4siS5lSiqKZ+iZV9EuSAel9exx86qmraXOaUmZUtZWmwGy1e7exWyVsRGA09ZGPywOUj7iunKOVizQvmrUcqJrhvowDw3oKAquty1kLqW48aMr6yqRO20jJdOXDUkMCa8BIykTXQDaPoj5s/rXWPaLtwKOv6JFhcTrEgkIPExWidUMOVULSFRMuKwJmimKvOq9s8egRXgqAgunyVyN1F5MGGwznpodht8iUbLrJcsR8kCCWaT8i2K32DZRsL1cT1ULbYiKx5LOd5tPPcpVUs73TuowaPtJF1VR/q921i12NaMGZxCC0h/i0VavR+YQYt/z6qvlyDisFBnmB6D9+ehZf1maqQNEmztKkrqoMcQJpp1r/ROIIXlhms1/UeKeJRmyiYWoK27kxP2KhlM1KVk6xr3SCfWjlC9N9tT4K76Z9nJFsyaqTFXFO5NbvjqhA7Zkxs104oxMMoyyXRz43H9FKeZvIANRrJTy/QD3Bna2lxGG6REtq8Zq1B7aNXTMkgvTsES9Cg10Z6Ydojc9JW+rbFym33N3RTOkJThKPOFms1Bq0U9wTBGwrV/LspvLb8SBvMVvaNUlyhBsGONLMYIuY1v3cU96px6BXpU65GU2nuzsNZzFGVH+UUAnmmLC4WgIqgYLolAENPSldsgGhaIuHYlG6L8bzwNLU1y9paNs8XGKB6do868bFPwPHdUfo6SouShBXS/WmNEZWpBxpyVysaYG+6373vUs8qBpce0t/1XhxiY+nlysJo4fbsFFthxV9Emd0jFRSR/Y4OQ4GMLzGJbLrUWZBitbPqAGVdEmWH473Yl6Gy+JZUf7pITmXnVCjy3ZZ8GdVeSMDAenRu6Zhgg5awgqLupP2VYUjZUGybWlYXGtdpepy2T2Q/ztNaxfzmm+jJks8uOzK2ZHTdgvzGya9cMTUoq6W8xDe6OeX3rvrGdXw7uUcgjFLwDCDWi8Nz8vmNJt8MKp91qC5poeI2lQgZs0hCbjyxi3iXOtE5pXtHZ2f1n4kYow5fDwzZezy8Z++vuLJECttpmpLx7C0B5onr4Olu39ZaMLGMFtyc8BkZ5VWt9BW8KRqu1tRdA6EmUAuEmCxz4yeWd1RiKrULKUJQyRHSyenkOVONxhowGxaie2ufpRauAJLh668yr40FmKHEkMo+QQJeXzVWj6v6SbMvOUOutILbUGU6hDvzpPVt/XfBhtp1UKYZVWQr/DaT+YY9Wzrd/cNfGDpaE8QYrTe1ncqt8zpMfv5eNJ2+KFJRTWsTZ41PKksgrQAABAASURBVDq7ctX46jl16klQoNKO6EFPq2vW1/KVl99DKJsTqglo/ZEvFhh5Z2kNaYxuZ5eWrNfCa9ImVOIfjhsrY+FWqvorSlCRnjW6fBCak82jdRtKYKCrhfhUdWiQB60X5qRL9nIUpzpLDeIxM2QsPYrPjnMBk9m5e98teZ0r9zwfExOmtjyPLmNMKPhqeOgGQFIWjENxSRx48aLRAPfH5dTGDCL1f3Bv9z09p5LZXbtltzMlIJHQ2h7JNxMILY7XkX95Lk/MM4aYtEmQAZy2m7OkBxR21D67P0/7v8JWwHX9WKcnV+meaXjPAX0O0Qk00lA+DXNqRTvtUjqEeKJMKtICiiViY+OcrgiFPht2sk90NJpJ48qpYCmm60DkZAPU0WQtxDXCZ+XDiQLdlUBO6LtRAujZesmxBYZZ95AOVeUQQzktxeTRklBP67x8uBmvslcSzHr1+WKMmUWrPfy+rniNWsf9Y/mz9vaEyfPkpvKd4nlkcBduEfYw8HgkDcN9ku40Fek5FdC5qRTidUqNcaymNjBLNzjd2WLoq+JLS62ZpNDikWLAPG2owtUkWotXrkcZ+beg1Ord4ROh52jjqqfnBl4frD2JlY98zQpgQ799nGFHoxf28uPrm/auCmHkHHaFthHC31SqvNsyWdKetjDaaPckLyW1nh5zRP4roN2Fdz1Tmip0qRTbeS6cq12FONzmU2Y1m0q6isTcNbTImDhDWssdvVUJrR3R68IYKbem7sFuD5ubQ6KQMlKvKo2WUp5WIUFAL2voUNZrTky/Bh5tMAKhPD8mOjxUdR6+SoCWwvAWw6V8Jygb0iye1JlxorTeslJSTEDGyyn2/Oh9wNTyzTVW2bx3Z7jXtQZp2JF3qMqKwjzfPlS4MIJqzdhKfj0f0A+7SIjbO659/tPnqjOTWQgjTaXXqDpa7yH+m0WthP4UUtVfE9KFhz7a5rph1Rv6+LK15uSohuZgYExbZqL9Esyjei1rXQMeTOBAON5APt+TYyc3VG/AUMezJ5Lbcq99eHbc+0YHhoo7R2edyDhZvfgMPz7CTDpdjaEzq+b40D6mAoK4Jg2Y1TpcIF6h1gbCbIw5KUI31ClNojdJ1oePxTEhHMpyURS4qG9jemcPutJA2QffT4bNKFvW7uVsr37I1Ezou+WgzvIFu9ouyelj3QPLcQEUIeJajxi0e7aASSwjfCKk11imwLoneqrkt8WRNqm978vjyqL+SFIIFYASc6QmXubLpBcYxHdos6Ur0Aw3MTQZzhyqvXnmP1xzPpg0tjTEMMiLNl2NbyilJcI/xj1czetWSMaczNfQmQLAJrUS9umuTgWhT0q+E816FM0yyPgCdUwqtf2pOD9dR3nkgK469OTBF3C1MlAopKlDXPl3Hy0fzjP8WGWfu++taBue5DYr8WJlVTT0Ekcqhue/WVER3NJLfn7s3rokDFAGkS5YLoXOAsEnP5oGd+CqcsChiGRMUS7bwyh0xkkQaFR1OXt5rQWfjWmliqZGs48itwc+u4xK1kGrX+6qqFMpx8oIS32gwnGcu7FD2E1p7Bg4Mkh6izL8D9sDe6DNmvPQjlnQi6PMklfRp80x3djW7VnY3EdNhzUds431og/BUz4pEPuO07ZX1+zmZ+Bz8SPkpcr6bN7JqtKVpibPv2/pLuNEZuu2wzNV9paQNNXoW3W4qcYI34raa1ibIk10sPvpVQrIUD6O6VnxqhamsKXDh+lGc1hYVxOYHCuGK6PJ6iLasYXQKnZk7HLk3z1TQZF2UiAdS9G59g7okyf6lHcB3f3wWlJ/PN8m3OAtVyFia5ZdZ77SVY3ua0wZQS1PqTCmy2uyqtDifGOitI2YU+GsqRvFFH1D52R9b/HNbKg0KPLnEetFC7czEMywNLY1vTBwa4Vsqsk9XWi+nsdt8JOhAPF2hG9gezS8xGClo935gn/10zgIsdt6xBJZBltzfF2cAwk7sJ0q2N6WbaegInGr16v89VdqoEiZNHPyy3z4aPW3fEj4GgJS9rSlN4XXLOmBq7ZUg9Wt5CjhlcZtCJQcdYVOpTMMCt+ydC9tkYKFuVorKKkRWbrruJrmnqzNKjhM9FuXVfOB/VCnyFjJ3NEtVYZ2Ad4zFvdCmJTkKLBIzam/bPvsMSELWqM2onFZX8TVKgtYSnljcq0z5yfnvfW3j6tz4mmeta1DeHBYn+6thrQ7aVSl9Dpp2sq1orIef3sBuTjscdhhCD7eEigbSswYL1Of4lRYoE0kKzbet2/OmkPKRzlanHJP3FbFXNYTsqDDiUkMj56qvhsV7A1oRyQzlgF2saUGJXDIDETitjhivHXfHgp7EQs14sNb/O39TBHH/Vyy1PtrYE2dZbXTTMSwHTkZegdadY27O0rdZUI9zx4VexF8in8oWPb5W2AeiorIqr47zzj+ijFkGwgxWDXqrBK7ZmctuvIxyjiGGkX7GXx7nQbqgrrQmZL7M/rG/TcuhMSWXdhHd5vuAEu4Vsci9QXtjKE9pZe13Ors4IG9U0+arrt8l3C86T/Tfl5y1pmYByttULGzhcZj2T3rWDcLZb7dwhTkQ0R+gAOvaM/WmqRnoFwE7cT8HJ6M7FD8s/qrc/VHk6Kf4cceUAIoYMPx6IUU5rRRa8indm+Jq3GVRooCGkp4OJNX1OhNZcHk/+mHn7A7vJUAk7b0YO6MT4+bCJT8JyNZlehjiIC+To0vM3efZoQhpRHLMKlH7+egP2LnEAmC1Go8+ChnjaZ1CITW4a9L8hDJKJ17GS9XRAUrPCYpLv8+6+2eNWdMS5EDp/YoaWw8jFIuNiTUh+tz8Z4e5N4VkUnIkwTHMhdRT2kElOsXZayrcYJ1szWOpJlSIBTj6b4TMvxmv2Wro6UStPQJrZoX7KtwRc4J+ARBhdy9ZGG+JD9rMultL3C0Tw4Owdf9gtZLVZlo9OCoZybTfQ/PSVoyE7tLOmGfWfCWgekC3yHivfthLyl2OKxNM5RFfIgDSMl999PB5JsGGOiguNf3CvNT6tzR3OUKfTYnrDVQXyo8zpJElQebaSux4tkftXVNqzHYrbYhWXTwBqqCOloCigirHnIEILQxa00CAW8M6SDjWbdM2r1fhiUSx9ymLBjKu1bXSBnkM+XBU+CpygebKCfEbnWKUvpwXNR2lx1eZcq9SJqIEeF3X8f+qfoP4iXAV/sS765I7KkjFY3x6X60oHhYeBgElVo5RxkUMWeNi+7y0ckuSOFwQVDNFZLmbsOswW26hVFvUbsUyHHmOMOkw5vKifVQMG1GpyAv6uTCZ4g4E1IZEp2L+bL27iJirB4XfJSKPL1RFB7KsUGtsitbrDQgD+qAtZCvOJa5S/KtuL3rSrx2+3k+0LHFThPVC7hUZssMtCD3gLk+ug1oUdb5lXTdIggHKcpUBZwGIxPq79rms/yCDV01U0AuSrJQWkLO6RR3VtMfPc8uR45OHi0uRz2DPeRP+VFOM/2A2lFs9Xe2X1zTprDe1nfEzlMa6zTMqvHOfne3zEHJPgEkHvPuGE+DqJaKGNatN8NADEIbtOGGdWmFxkobmqM45qIvt54LNoaxhSRWedjcw3Qvfl5BL4qwbnDnq23imqXrMKhvPceEu79G+jzpURZmn3kd2UWJvGivWtWuLZqWHR6Y2idbp9vVZNJBs1W0VJkoGtN3R9OOiM5oFr+6v/j39GsKfYnpBkgqKNOH9u7rL6cgqik1ncPYJkYj+ljV1Y+hhD2RKRtQXFf5P8X/0zlKXfFkpqVwNiRlAamtr2ijXcCIKkBboRYFw5VQKAH4+7sbDZZdFIuaqyfshmYTNtptLInEH9YGfZnpUN9n59Oe1AmU1agVPXXzEr6Dx5CKyBDVEcxiJTI7MwpJoDZSi6+JgR7oiRiCymgzrouiJWgNV2Qtpff0zXX0HXRytOzZXZ0yi03e09K8oZfBPdmFrpI29Z7jb5BViLDngqabILjdZmc6h8nVCHiawD1WGkI2XR3eIV7kzVtpfiI4zShZgZCapg6jb41pGw5fAoxsF6gRymjpZMGhAADD84kz8HX4umFlSe5V21UGN9RG+GQQAo/REMfDYLOqoX/JbHuQOc1o7KZ6th21e85lfLGcgiLyy5jChBLomjCHs4AufdKt3oU5ezRHuUJox2fV7R6czUkSxPC+BvS9jkWgUU29T/QLsbBL90mHebgKE7AWWK0CQG7dQokkTqthoZ7hOPDDEfSa4ierTp2II3nHvuqt55RSxq1wKKzjkWZO2+qdGCzErnf1GSgzGFeer3NfESMtznX20MI3znmJHXnAPOxtSDdtSb6+oF7kz88w74ZioeI8uAQqtf+Q0carZE0tBS3Kuj17ZKNT38ahC41bzihsnTRdPKxP2afXmS+rr5UvYKE5lEjtUdgbvDfUZitUQkMtzDNL1CS656jJmzs6Cbgqa12tLGVQp7nNi069Wj9QmvKqPTo8pOxuqAWNqvU8P9GMFPWXudmsFr+C24Pho82VUPf0qqcVRVXeqXYc6tY1xtJzthlS7+3xMQhxh/L7SML9kp6ql5ISUMzdWopCauTkg2GcVtYHy1YHkfKRBEa71yNSluPpaQKPsfi8suTsivE6tkBbpa0i/X3Fxz9ZPx2yAxjoAGhwh4NspaN7Ss4S4h5xT+QAZjaQYbVnhlT+meO2K7iYMh0Dz/XTFs0CijzsU32iMhrKUu5Skhau6SvUqlUPTReAJ++102JW/nNaz7fd75inmILiNZb3WBHMiOLBpIrpi7pF2q5V4mvUNCoAhdszkwm9OOYc7gOX74FDpaCE2oyOsDwohHgj1WhBNbBx8DCpE8v5Ai9A/a0aWak8MqWHW9w82Rmj1iuQETs+0dqQ6WHoRnXDUgz+haOcn/I65jmmCwtRhQraoySV5yl7epwNRZQDmxW+03dzgxnDVCkECdBTwA18GR90zFnwVs5Mqb5cBqBaKO82jf6b0yeP+hkR1wnvm/NgBDobpdQEu4o4w9PXtiU8yYEabJwUO5ShTvH2xM9OQT/WWqNtrDkkCGbreDhT2N1dSFM8stLl2jGsFr4msnU0AOezjq2F6QU/7X8BsY1wo2t6wO6n1tqpkDTXUXJAQkKP4aKXKYsOXaPO4WqOGAoDxzIsFdTqvtDjIW6yAJ3qSmMOiuIKPTQoo8A5tFZMue8pW27wogtteD5eNbUqOs5fI5K5DsrDQqnorzyzNwVJG9brlUISxgMrDtLN+38BaUyfFvO9AhquVBQECjPwMCQcwJX5FZoyTGgkk67Kx5KpHQt41911RoqTYZwAOzu3Tie6yFKl6FMqK1QpeEMstXks3IaPxqal3iUwNsxRREBKJHyqTgy+j58czWVmInAgZpcbAH10VsmRzEJWyb/iC7e6BhQsaFEjauqqhokiaZmZWm/kNXxieV0Z64S6ykCLdo7OobKs4gaDFGtw6NAVBQmKLo9prfxboaxy1GbfrRXFalt9LG5moJ1RTZZbAAAQAElEQVT6d9EFpYYqWxU0elR7tKFbfE1KJa5dAwhVjLkYpP8No6bVgE2V6cYsBaFrM2ukVVB4/CoFKSR7IOcZfiYxdGGMj6f0AZTqW+vBhb9jHdSh7tKRIhbzQyFK1j36HiJ+dJEFNJbhRY0eI5YLrE51WWCUDa1/oHpIlEG6HDQM3273YFCVp4tmrZiXuenadIUVbal+HXM4h4XX6Y1Mg0/VXk+lUyFaXtwX9sgCV61Z04+js1r/CijzFCZjhOgG//4rHNxhd/fRUOji8PQF7Ra3mZBaQy+zGjyuR+gdNxSuy0tvPiOSxNCStYqIx12WdWhQRt3pKWX5JF+W1hst56lsNCfhJgI6m4cWT9TZ1oQuG2zhy0o3GfOCjbDC6FYp50IUGqnPdP2yEHF2enycZ+JccjQ3cCOOdHjpyt22xXhDvlpWgAE3Ps85/JD5NzOu9Eu+4Q2Tqc9SYsemHrFcSZidyX2htIq4U+h72FMJO6GeX8WQZWvQJiACKTtXbxcVO/suIR2tTfmSCKTuNG4yUd7qwDc8SgTE6CCkUcnBoXQkUNAwHKAbnlRZoJe3Xaw14MvMwK6ualjySrOg5sfBaRmtf7yv0UrhM+SAu+fd182VJktJCW/3dcYXX2s6J8oK/byxnFzGRc5uq+w8TwGc4kaJrMFaYNsV/k+X7s2EpYiHkKZC6T3sLFO8MIAU1YqjVp9hd3zt+9W5dGVlyroutWuUeU2kb+kiWimzFhWl6DptJL9UaggNFlPkLruzqpoUgbLVaMHXuxCkOSU+4Vt2rtYaAi2sYyjp0rsmjRPahFmacfq0OUXYtk5Q9vtRmlaKLn0y4uW4rrYLOa1cSE3cAadbaIBnjoFPTWxKfr0Rt7Br6C5kJivsWc/yamq6sgmoRLEwFw2zy7HdYowCKrL0DDm03coxcZnPZ87IHp2qnWGTdhEubFXHKM5sGny022E+8q8HH8F+2G/jtqcJomhsJ7T2uqi47rZAtZZpUZnnRIT0NnJcFCkrXFsMBbBhCWGwV+Fq/5ski8MontH75QdPk/AQ/3GwlTViOpR/sjrlAzpXnsey9C/EFrmCrw9eh16AsdidUVs5xRm4p28Jyhhogt4SMnp2MIl4BVxBCk4/2Y22Y2hZ6fH81qandtWpUgoLLUV0t4cyxfQW9AOddeGAI01W5d+n2fKkcNJvH3uKwK50ifQw28XkcH/RJkCKlApxihpUHZ+exPUkLFwKQFOW9kLN4tg2gAHSmLNvBZbZJy2VijmxJsgHEU70fRBmoS2rorQQ7mz98X3ukhlkW27DplrjaPWDCZiNMS/tJa36HPuUhTW8RZm4M4gmTujMndfOqWbdChfQxaO3Mp5VZ2VJYeEQvDtPn83cQUS7gjBci9KOsg6FHfqRVrzkG3bxiaMh0yggqdgRJYYc0qtPp0OW6NXWNQB3SsVmU/qDHVy3X8EhyN1VhWfXGgBqzJwDgzCC+n710Rx9R5uYSulTDnECd+NTcc4u3ULede4BdapUHh5DmuyMhY/hBktwxjFJ3Xz4MNGFhLVoossN0cLMxVQm9qOiCS4sj6GoA4+Nj/XxjY71mdfnmpsUysX5NdfQ3bcNmAvlTKWpXiu/vOehnYcEgt6yNbVRe8fYEhdAttWjRiieUSE6BjSqXkVXm+A/zVcrICHSnUpE97m7TioawFP8iauZOQLUPI5aoyZEn7Xq1pzyccitFI6glrQ1Qqqnr55X+rNmnjOiV/MaY8xBHzRx7491FU1EKrGj6cIKrKB/QJkEDFDBOKl2oVpr27QsdrIbIpRSBpR6a+q0Fuqc6JmwcOn9aSOvPmrXTvH9yEIRmZ/JzchWs/G4WFe0Fo1pcY18efliLSgrSSrVtyYPkV5EVqOUAy7BbPIIiaqp+myx27rqBreWHs9mfFibI/GVvAJ2eAob8uy253Xb6z3WR25fhAgFGigOsUZX1XVYp4uWF3N2ZcWn91YgVkQSH/TODiHSyumFSqvjRCzeL9cjnJQKe4gdeJXf6GR0o09baE8WptOraxjQ4jFJXWS3YrVsBAHo1NFHlNKHJWJhBjr2yLNOXqmswLfzvpiv0prwmPsi/3EKdOU5Kdvw2ZhZlXFCpUgqRNW3Jo1zGFo1kMNEqfzLmULB3G0bp/xD9aUrfOTwowQKYBhgqczXxX2Nz3XFvGyQoilPZy/jGrNpTCiG4dRFT5qgJjCI1+S6hmkXC4vbeOZ0M2+Rc7N3j1N/S0hssCxv7kAF58GSZhuHkgKA8L3HnUMdLlumj7Ix1lZ7taHGZ8Nnv+bxVm1v9ci2sbUvZiEVeKIMCjuy627c9KbSQXkSKZU9ILaHlFepNrMX04QwKXIUzTXtn4FXkxzJOo/NJkSumfgRFXd62jU5V7VHjUqXfmTV2sVJqrKthH2rMY+MCc4eHrDc8I1xWpP0uxw26kItIS0eT4WuyQxt4thpQuCURY7Jdmhe99bntbiM8jvsY/OG+vm17dpztWlSzVlT+hezmnb1+DK1CZpTmRtRhgrgiNpC1xMaeyIZYuyIBsxj64xxlFIoaeLw9JaKvOfAIpymmGs39u48YCuUcrEUrn41Ik1dEeUoEvpte63Gi+rbzVO8W8WoAj3T0qFhK3NlXCoJuDCbRAb5QVbU/YGWjWrSJG+OOBMCIkqbol8JPbw+/XXf1749lcqJWBVrqz90N2/z+9kRQh3Nrdsty0Vy4ZzOu/t+ynDIGRX+Ac/fGAdDbUKnO4koRFTvI8brnKqn+bkgAdMwBqxriAYrWVeqRsWsXKnc0LX6WtkoH/BhNWaKXLNitzAxKWsZUZH67ObOMcSxkaK2NUr+M8REVgRCvsT1KGpyKZsNQgP1jaCZDZOfiadrSOgY1KpFivIw9v+YRcMLgv2Ad4wrB1NiQGxJDK1yH6XUVyvURuYz39oM1CutIxu7R9YzLICwKoRs+BkORZCGT9c74xbw2MZcBm01Re3osMbM0vMohJAr0GHGRQqzGQhtooGy0sVkHylkiL4lwyAFBxvirBqA4gKCTys5IWX5KeEn7IkU1PUQsgJKbKvDJIGF68JwaU/hnkQlWUVopsZDKTX3hgc6dEXgfZj+DdpgiSp30EViI2VYanfF9FgxKYuP86ZXQG3gqRyKrUjkrjodYX8RhYw3BUpwC5qsHyiwoT4kxPBoyS6On2045klTvCU6mln6bHpRJTFe26+gkDesgvXp/ERl0SkC8uDcuQ/EmLm6FjgVlnDlbShFYkbTwGpDD9RsZRUjYUeeUtM0HPHMF4ybQyViGSox5Z+tudY2UirDDEgDFYyUchjd6SNMWpWDVAAPJIAFIdFPyuNDmfqVfGqctm7OkDiVG9VLdlbv7lazULSPEWuR03oTdd6UkRY3bX/dN7xJlZ60axbireNm3qPDvPpVyWx7gKwhAXR634l7uyIkAFaPDUpdCUil1US4d9vTNyO9knBIXGxAyunT666viK98AEAWkM4YdLcBTIJudY2YM0Wz8VUYaBM3T6sEje0rpzeaVavF0FA16DQHJDozJwWmGRpL+Kg1bYx2ymOjPHvcm6pcEwbk6DTLOeROCHmKXVPRTpih/+bnQ92cVDxWn+amsLSYqsreRsWNbhq4zFiolCGX5ukwQNewS0at9bXdto9trTxDZaQ1wL0w6xHv2LmCrgwyonS1ZylN2GnRpPlUFIHYlDblh30YhSBkRlwgquugE6o0ISe7zHMuTBe1GtGuy63aEBgijAeWkqnauGd5FKtT3LVwA0+81Y/XF+01RaTsgomrsFpF3ZlbpfMD/PRzp91Z/KgdeobJjI3DlTVa68mbLXq9RDT6VBxF70WzWdMxQIPpxwenP9nyHkCbCBym+4D+DL/dNhVtNUVHliycm9X2TB+BoauHgm7GMTHxF6dpQAnMFslURyrzbaYVsaQ0t6ZS1+5h8+4rHy0AuQ1HcO+13te614NsNGa2f7AOrxS6xDh7QB0h1aySzlHPsojd8leQYT6C9DNGuXRCYSQJbYL6QORWuee1eo2ePmhTDHfhPtz3YwsKxEG03givMnXCc6gcWR+vHiHaW5mjrInzui0VKsqA0VLPXvjoZowssgPUkaYkLiRDBD7df5N6YFeWjhwu4SsrVUGmDcccSWt0tTDjY3i7opMdUJKaK6cJoXVNaHynW/S1++uUZz/QVvelDxHSWCfEdyCijuwTEruaS0NN/rQFjQ2rHlUdfTLVQgOCba4Ndrof71t2npHVrPoLGT7MDeWMNWPMcGb4CY9jqSyii51k9HPoRAnxAe/sfRJ9OIL/AfVnq2Fm6ubwpqNY/bzwuKIT5j1bgxe2P6XmpXAYxq3BQKAVcjXQQ3ens/yqHDgD/UwGBvBQcTTORXDJIrlgL0kfAhnZZLnnEImtUWU7/VagDszGiLpO2BYliQsmhTtJYSydRaVI425lXolzV7HJNa3wnFFyO4byxIaJDk91iVPi024t0QaPHvsZ0dYqHtGX/UAVptZP2Tg2O6xOZG2B8VkQwQp6t+7sKCq7c3hOtqflZuFoXBOZV+0v7TZOVz1EQXHieG4dzCJTZlD656B41QfoLER3i/QnLzR0d6HLIcEeM33SwdKHqnrLW+q75nRWZMqPQdoITrW5QBvTFXlCHwGkarxkD6aPeSpuTWLU6DKJDP2BIpBbN6wWpaRWiqko3ml6Pw3RpexUtabZRoYIYPqUyB4n3QYoQeYlRQzNL1pVZmMvnpfpntpai6+ECjyBm8MG3EPBJitGxhDLrYTk9LY5zpx7UNxWCZ4SbAaAKCq0amR17ypxK7TwIlXA2cJ4j9iqLG+QluBfvVSxHoWkzZyDTds2QzBKJoVV12RfE8xm64CmF8SIAgEVL6x1FbB0pCF0U48xuucxrYOmQJ4amAxqONngHGPQIaSPc9gZIwerCtmO9hf81WJcIRTK+8HnSeFkjYiV4RqcYhuy1lT0cqwGpKbs1iiI4Di9c6c4yjWkmJZmUqB4lYJruGgqptBrOCqkfsUX6ky1TSYaJii+OY94JOrCvVp+hpGT6SOGQ817fh18vfu1FdJIInj25x3fbwmyQ+qBzol+TVsTr6lhHvnoCwjjm+Mu15KhUTpyCBMkRmbO6hd7aT7X4RLW0byWvf5j6z/T3aFqarDryvOKe63Nr8YSIzW1fKGLFX2ffsctbUcjzT7uN/SSoI2iq00dqs+oHhvcwzEovhQ+lgCCnxynRIa0hV04lcjIHbHP1lbx3LijqtVQK83t+ZsCzUmYwQHZzVaG+6tassFiXZY+s7UXwRFIZQZCIZllx2FpaVJM7czFjxaw5mdX7lOhzLyHNaCxkxrBhPTRanrcmQoC02BDd/Uz1SPU4WXOBnkNV2QonUyXia+O/0T98+H/OebwQ/vM1rYf+9hWcd16cBwTjWGlPMwDi4dNIDCOT+0ftrQ4baz5j/V/KO1F1nnnfu/Qw8tUg7gPTxz4p2e0K4HryAzzWEWhNrKRwu/HQXAzcAAAEABJREFUoKdltxp6+3WPKaWdqx6l0Htyr3ojZbp2MuRaSLrbRpfUMYsR61PjTdxZcWdvsFoiwyaS1s45W6aejpXRfYie0BJSizfs3bx5xOJ62nsrAafnkaowEiYNE2yIiIzXyVepIRchXYbXyX3fG2gOG0o/hj4iEu/bfsprjXldX9eS9pW1ExVy1JjC5WF1GERIsno41HCzGPODr3deRs2kq/v5wdrQOghjv7wnaaWcLYik9uaRSN3v/+P1f9r/uV1MDwT7ILMaD5FtjKlusMm7eETALlSY9+vyF6fV2v6fezwGaKOk7/wYUwe9iil7UJhlktGwctlKleNwo47uAXJ5/sO62toyTVVW4uIz5z1e3XPAvy4bX44Xa1iqs9hKta1oUXBW1xelALMqi9vio5D3q467fuy0x0b9SAc2Olx3sK8zrnNRs1ecr37+kykFG+0HEK9Y1XNODtutJfuDfVdmBDIAdywS8LYlv/jqs86BlCMTT7ah4JZoMqS5WmDMxte+5vkSkZ1k5vOfH98K0cHXwtfaww7K0S9gPJR1om1Ne+kObu3ykziNu/vdoQ1uvPKsfF94v0SRsWA/3vu16z+3yHGXz+z426gpwSXyNDcYbb0pgu0MSow6wNuZL/A/VDppzvqPCev5Gpja1cKPmmBSGrvM5A+zxqzL+c/LhxrdVAlM5mXbWFAbUcs7d39OBxqje2RY35Ca4BKBr5rmsoCP9x3GbO/2kTXjRu0dvmsZYJ1Vh4CXT160gVJId+zToqtRCkj12MWAGln1bv+mn3Khncc7rzC3l72+xvgaPa16nJgn5cy6FauBnphdNiTw6hLxCew1hq//YH3F+Iq69tM3bpn20REZ2iJZtiGaHRqfLeG7HXrjKQnahnQfGAdDPiYNw8URRkEC6Ew7j5l3V/SxCLFQBwoEMap8I3bnbqhvZWd3AQbJIkRqdHcVo3DMQjLkK+ZU09WJ+e7rxpVmlhxHImt094M0G2ijrMgZe8Ac7picdq4+zwIoLxpeWfy0trSTkES7I1slfdY68+p99ZsWqWwM9rMcjZ/kvrtOW9ChSOMf7n9SSq1qHrw6LvdBR3rU3Lg+OffWZ+vKnFWgtZCBVXl3HGXaIBW+WXsXboUz5+r5dfilptYKHD7wsv6P3ebf6We7Fb8Ml4WyVWjKmMwfnHs0V2hv5XzuKqoBqL6/gCV4whHi6DgTQxdiTK2VG8JwRgWpnnLSEmybnVNJ8oy5a7TS+PnMW4hx7DrL06lhRET/fOp94nb0S2Rzt+aJ69dr9fRPRt6bFevV65XX3DObtyFU6NVZyBKovDiXT5OgFQnQEE00CeAjUvM/ll/jHitD4urBA81XbNSnIdp0tI/LfAVbmzoROzfXMW+5iJOUpaBAxtsZBVUQ3vSfGrdqBbfP7/GKsXr4I7/oc3eKGQU0RcZRx0zejY/zY+Muno/VBzj+tJRaScE4mnYGfix/eH/67G6Rijkxr2GX06m04Mv8NZRTe4jAyLXgbnQva/1boXrM/8T+ynbJmNXdCRTpk9dsDVTLi0+dLRdC6f23JLqrU+fyHNVR6yWK9zs/R9+rTAl7EYsjOWNZD8j8MsXL6PEJfhc/F46BpwsFDnJASt/0D4QDg5gYAxLUP+SyITCpGRSLn/7kTj99jbJZoaJ+b7VymehXl46VqT/rv7YpZuuLhKlDj51Ve/P97lMCDqavrTL/22MobhR5CjSSaBcp4qnaAqPxVeeSI8QZJqJrWFOLcCtVuS/EP5bXqnURqmKqOXia+CuoDqb31LIzVc61ldJPjP1Bx5YkrRQChxVW5cKh3+08ZuWiUemIYAdgx9ateps2lUBeXX5X/On+wTw+YV7u4Q4fqqdzDbxm+1Mnp2SlwFhSFNULxhsWoCTQblhO2jzQtrCmljKXHnBLKv26VEtl8Gi7y6uH0l+tTjruOcT++lrrWpYm64qz+mRGNGq425hNOzvj8W/IAfhD5HVs3IYwtChAhRJ/9fZT8RO5///hbCkjsxLicgaubradst1aE/2C0qAxaAUi9xpWps9GRdNppRUgGW6xoLBGt5exeKJSdFJfCLDC1HD6meqygpYsUj+7VLeJPKhHnyYx5iC9h7VTfupmBntrSEd3YHDYtDHDK3lkSEsXzjzbFFGRIR/ZDJp4Lz8x7k+V2LLggDLQtmowVz/W4nSUBgIER+EKW+3LqMYxp7mvrNfnhsgIB51mF/BP0DXNmRdscLrNNd2liSWeRDccAxhHOPZhHYcUYOIicE4r6LLScRqnoESmZ1kKpmgwEXilK9tdKGiVlM2TJ+tG3oxQdJCt0TQBetrOMPZAQknOfppYy1qyZJS2DIItUl9Z0p4VIY2BKquwLs/HmOJPc8jiJCWocWzoGnLqirc6SAhLhgFGPtRVIVM1/BXXyqvTlBvQMUc8a4nlGHO4kI1uFVaVrksnGtKlgPQTo4r1UMMSXtmD0oaDZ2TZrDko2CatVHYLd6MiK6vT+5G4wR+EezIHmrR260Ez80c8WvxZ5fJvuhimJtj74HNKS8EpDR+LXJTlekx3V6XpANQk9lTYu86BRKd9HzS2MZ9pdNpQgkV4twCdlg0HdLrmpUpQUAZaGtoTz5atwgtstgLvcEoL0LfdctdYyfFx3Bo81ZnOE6V4QJjrQUkDFEC9AybUHDVloEouV9rY1kIAuW8FaxHMBtsADRM3ij7Dojzb9f5JPwmcMLEavOHHRy+QoQQUUkdCaOXHlkiRObtpUrEmnAhRoo7sW23XJ0U1u4GoLq2vdw+Y23T7Mpuoqr63feQQthZeL/e5hC3F+yA4NDUf+kQKFJCJmzlyXKjLtbk3IyST9Cy3cN31XPXzyp+VKqTTilRZdwyIXJanj1QRDlUXvlrty5ekMRR5Q5aM6SzZ5Fx+LZugG5fPh1cIZMLLRgy1znXDIlvMl3UkMhFCaqjlrUONV3btdhzjoVimswfDmT1zXkcLlawrwG7Ex7YWz7u1wneAaImEwLOfaLfrH/WQdtYIReAGmXMqKkUWXoeaYY4nBKSZylNLFwCa1Jsmcu564GVS64CMBpWQgPjZ6NRgSQzej43Gg0dvnlzPgjS8n70M7DlPytJm6P3IyCGDMkM0He4Nl/C0/0skmXQY4MQwTCqb9OyB4jNnBEXsUD4khIHhauzkNty6Ou3M5z8Bc85Zz25VWXE3r9ZN1GxMw+DDvJR16nEGL1WdcSKNmzNpBCB+w0N/AG6SIv9cVtegnknZUOgJtdw77IR/jt+n7qxQAo61QX1DVhr62IOypxAwRmozNWWYnYPKNSZtwFjepuQ+XVusePvoJaTHcLox0keNYm/LZHG0L/pQA/QuvXMP1dEU8tuum+KtXT4VgqJTnckEwAR3V3RlM+tFuj5FqBEdtf86a9hrrDnMWMbjGdryyA59OoYXEHE6ttWt8AdgaaNtVA98FrVb1vlwpHVqbJUGCG2xOlXCBAmF3mUI6qXAezas4E139XS5NoyULMUHUeo+/SHe5qmANO5tT4araSnj6FJ8kCxLbkOgrNjFp//jUeizMod7abijpJKxHDYTHmU7n9cn8ghkE0WBAwsr0iPsvffJfaqy+wTi0FMwi97Nk8IGeA9TOIk+7OyBM4FFRdroLZZdZPg8cxxCL2taKROn75L6IK4sChUHGboOqmtdYui5ftg++RLgg8jc6JZw5aRHczYHYTohqo710+QFPp21BMkKcEjQHMPHNNOCahEW/cRp7Fk946goMDVMvznS0HaUy1H5KKpl3RtEK23uboPmArUJ+ZRnN1vR45Tel0SlcYDGQ+Bk6hkVAtIgp9tUim3CXjWny1SFWJQ9U2qfWayb9WmF2nmYWntBJzpbB3u6DRuesKyKG3mLYVEZ1un4S3ojVVV8FGwi8YpwTYY04sUWI9VPh2OHyGnFb1SBFZ8Aq7xCYJUh0tr+Or1TxsV6o4dEce6uO9mESF1z+HyGhkmwXaBcPD0aTQJWpsNbIo5AHGMON4Iw0KShOaLZGzwOTOGuKXnFsnDKvUm7DpaeyE0NvA0fj+zMLh00COEISFezeimpJIAqz2QekVjFhFYrHzzK9IioQIOedWVMDauUDbkAFVObk4M0SgUStc9aPed5/YVgLYcANds9Hn5UT6xoYDt+HHulnr7ZxWGcBTv5NMcQd1dhyFnUfnA3LTGgc6NNncvqdO2iUWxewy+Dhp/ZOQ1zQOIUaXC5HxR1j6ip6RXSQHU1KRUa8Ffcx2sPHBn1FmJUq7h247bYdrbgHHfloSd8bhEz7vajZJCJ/qB2tyjkHLZEiEZqq4A16QOQ2xZVpcint7jqQt/R9819zx3aXym2VhKQVdFPFjbKiqM1GdYByFmClIYGqHg40hzePmN4Esr8QL/KrpQfiEVLWd6urf1ubprkwgtDl0wtaNw59Et3KaMOarbQTs4JbXN0/7imfVHtIjPthhTn1LL8oe31IGvx0wjibiuwxCItJv9W5FNtY9JWNpXogzhjY3LIRQ0NbolPSF7TrjGmvwb/WbzsIV7oWDZqIdAtOtLI6WX8HNw7Ik9flS97LmvnnHexwyDv9OhZTyRX2EwSOMAN3Syz2lFCWeoGUSNu42nWZFwMmaeftndaxHXyq7CymJQZXJMv+sJcWFdfy9aQUl0pMEkSaMpltreYNecwH1QgvRgvO1PjMpmbfWTUFimI/Yl4s99KmIQqWrwmoImTa+7XjGnGsR4VWcMTlssFg19o0Y9X7hfOSIMiz/Q+l/Jvo+WiZ6rRi2v10poZXg+lIcUUBILYzi0RXN3SHSSKW51E3MmPLIxFF+iJcjOMi1CIpRRu61MtlgJ+TLAeshj7dLwbHzrLV/asMG96wyok5bs7aPB29RxLfWw0Umejz6fVjkPfr+xVpLTEUapMOR0v4EtF2bfXbV3XxuuTI4OvWP+0L+7CrijekL0yu5duWK7dXo9U7lLLbdzKj6wVqnC6Wxm6enyFX/uyz7Lbhlr7TPdn22KpsaKpTLpRUH1bNXPGGRLHuxjHexOlrn7G0aUnAyF4WkBO5B0qXkOMrJnKgYuosu4K313vDL3f/nDqeKRiy4b0031s7HFFO8kvp/LsrVeGokiyRiorU8On1YVjTyyVSir4aixz8+U2ByiPXJWruuE/7R/IOEoPD2y22hc9yU/z7ohTo83ggCkkKMX3hEIuNsHFdovCXfbBOK6csoPzjJe5FlVDs5VslbqKxDWDZU4qbkycxUcmKcOyoUIsXZ0x1v/TR1/gEhQJUDrvMdqbbjkJlYUrqKwX09VBuMXqObF3fCruFtcQpVX3Zta4xvKvKdqkoVL+Ah3emqlkfpLnBiNVjs/9EXW9SfM53CGGld7T445/9vOHJzuhsRaIxpEONLDgxnr+dq8m007YKGkN4w1PXzDpdIhr3Q8UCV7sZzA7zPa0D+UkLcaAD5ctuOLLcXJ+nLejmN27FKMfTA3kdvUKlROGGF7IRVAQLi3XiywAABAASURBVOYXW084v+pclZTol1LGT6ndvfDkvyHgV/vLNMcLHX6UhIqvxFfZHOKWYqWbdtzO4PG4WzeB2Ra07korXGPqL6UvdWa4TmQ2xGvOytlhuLVuPEJr142FEjXNbdgjBHezwTF7tlTNY56mkYNl8pimFMdX3SvPxEfDNP7aX6/4PyMvUGJSf/ibuhZLWKMFWDOrLMnywNp9HS5bNi6OhbYEOtVSLz5TFHP2fSNus/AR+apjtTu3RilfnCo7mb3CXQ5BhRMTEJDWT8F34VR2AYQS5cOv5Ov0CrXofKFey7QI0OuhFw0zuV51kN+Js10p6bvwo9tHOhYEy0ghFRGTZfronsIm7Bwkje1ZSFkcn+70R4NP3W/wANu5DTFCEkmo5eyRubKsDezDYNdqXEG1f29M2FQyIF6qGh/EJzsKapwW8Gh0pYdqC1pTvGJHZkDAZFUPLYtnQOqbKI+AkLWEQcB227vGwWW83IbcoXBXHwQRakwGAy4o6ENdwIFKTfHq1z4QrUC/pMQNEdESGp8YvNxjVnuPHLOWyUpTmZzwYVMpw+imAppZX+ce8+ZswEWZCsjOHG3DqPZmq1RnKjNzxWgf2t1euqqILtRr5LWokQQiY8/iPz3/Y18DL3BxLptTOWnvalEipkqJuPP6nBXaSWkreIyExgSrNe6pH8tIICf7unpcsOGDU6guxLhFkQiHXtVg+cA1SCWtbk0ONS66kVg6zC6sVXOlvWSGKv1jdUfW3bLVZYFUM8HkdCl4+rGp0ui1zQ8hloSpPWhpoHHq0Pi76xO8D3bif2oAoSOUEJpRH4FExrO+C6XmDkJQx4Su9dMYYnQOAGzt7PPLoUIYehQwZ8vbQWl+j9IKIxQQQ8/EbuYe/Z6h7qqkXGHXLh5Je9dADs9nGJjesTJnhFHob/pdrA1/UzW/EL42Lj4CacOn56dZsEkfY0EbsxclH+tERI/dc+fMM2tLJXChFh/HRf8zw6gTgEJhkTfxmR2X28upjjr2+Sub1Ml5yjfEe0NgIrwxuAYHDQr5bj+1Pj7vVZta/0t9QBjYT1+wR73wrNbjgMycBsnLlgQ/DsaJoXPpU+XlnsZCyxOrpL2WAzhh1Uyc1EV3DKlisaEBcp+Rnac6mfI97jOjRqW67Ro+r7EWl4eQXdlX+xfEFLpkSu6thW7NedlYRSd58b4yrHzL0CazVQW74iCyQk7N3OgGexBUq6c0aD767zpuDl3D7LYkHhm8TC3ctM6uNUywL7MvPs8feU7nDXElG0mqvsGyKoGCj5h6HkVdwi1hwFCXncHuRxtyZ8YxuUZJemJvuE2nr2ivIALzNApR5xROP/Ao4tOeTN4U7QERC12R78ZiuVHgj40bltmKtiq10ws3a1eFWcu9hQqBIGkg0MAxu4f2jm1DXeimKSFexzN5gDCZ1dhWFFKj0ms0G9PVGuzvNUXd63BFCK6Sp6yJTxqHwfFQENF1J3YgkcfybbY5MJxCLKrb2xyoejTSpVVPYU7WBXv1a5RuE2YVipBVigtlX7RsCmXErJxoL/aZ/V4WU1bpOiX7F2tKoVYfaEX6SGUCrUx+sV6IQRm0hnBpYRjRszh4uQ+YRIYOt41RRamTIYhg64oJixPTMZ/vp+PEUaYe0hWha68/kmrHQ1b6wD7igQS9iLT6CwPmTl8trbi3yJggHiwaR3pfkscyXLPd6iRO1bFze54Bin1KIqNs+XB7JuZQd+9UWq0gH8akFQ2aqeXYFL9yT1hbgD9pym57z2VzUS4KHvrePg0CBCtTBwBppjBlJ7u1Da1QyZ9ryzYIYFjhqv0wrNDN3pDhPMyQfFgS5svyS7iqGpamOyhVrlzN1KTdyE3dAgkku4ap5uQKIk6xL0UAFOKGuv89KpWLTuz23SNhPG4fQkc9YAqtRiXhxt+4R6uSdQqKw0DxNLhMv2MYRMK4h2vNCVRFRjOUEFLUMfS0BPvIoYtF1fjeKfxc5DQ1GQ6FzvxrO7uZe2zd4gXraH7pl3zOxydruA7AhsiS0NDPv8M781M76niA0hintEHqrcJ4GzdbNaxoxZ0l0Aoqjz8y7G5s4LhmV3r3YH01XidnJKrFA5oOPqUgzqkeoZ+1e2abcEgvw2JajatsmQ0SpimAZtmU4sKRyoZ00YHeGXe0FGhSj11GHYMD8SBgXmzTsI3K6vYdagYuP2bEMtNW3OGV/JR9zGsMe7kpxU8Kd0gv1qRMu5LiisZp/eLWcnhdVJssBdRFHuIgIupoklnOibEcUyom9NvMIKI6lRAqYDPaUG/hKIwHmmbeleIIjYWySstCdGhtSjSB0bDE40khKRT43TgaNdQ8Tiut7lDo1g7F5AzTpoVaR9NAtX3PKa+iEUYxlyJ8l0TPoEKokoS3uEXb0ZECAWFTMGNgDdiC6ls2bM41fcmbRnvQY6JXm7lDXedRU1iVxeNX59zKsoHTnQilbIQ6AhVnQDOVcTjFjhcXejxMH8mBV3MRq5KRmcoicrDH3aaF69Y2rNRAh9ijnv/iTT8+bGJeNKXdMQlrMUfoX9aK9AJrxvOxk+LXzny91yW7ZVE4RYgcEkOUH2M64GYSIqcXTV96MRaFmCyT1rnh95x1LRscls/M3Oi7sI9FTBxX8FC8rT0Phlkbi7RaEEDKrHTnU4Q6hkbXkfMySuvFPlm74ihPK5a6RBNZlbfyA1s+L3fSlfNqWtA3bRsehijEyzkux9fsr5evCXo33IERzYjsgrQxxrXGglmi3XuNdGa3n9K2cJUtkX3PO67oS6OUE2/xyNnD6YNQGl1BJiP15IOKWFdaJlUHLkFMwIYpNTmuFgXP7GJ0pQQxcg7psHqXFpNyG9McjV34aAHrNMbTHaJG9WTOSvnTRl5ui9rBf2Ycr5S+qlM603h4cxjheCwb2zqpmEBLmVi04WLTjIUCwwzg889EgEaj2QzgKcS1w3WDtcu3W47ueTiDDc8FaUEZeSaXQSdbjGZgt4UKdqFd9gfTrGUegge96FjTXADloltvPjivcj0LFaXFznwAU89uvy5bxsk2HFaIJdxZJ1pfBZlo854NA0ttrlsKXFlXyAor9cD1lTVgmh88UbJcMl79qbO5iwyyRMD0+cWxNHAWZtYEuhAHoVB6GVwx2ZaAJZUBe7xpSyeJjCBSeJ7xBaTlUTq23MhAg6dYR37QosaqnajGs3ZGfvrcVuJHGGb5SJlSWA819IbXExiUyB4V3mdWLKtLdJQ+ep2rtezN+QyrkhYvly7aU5VYBDqsj1kYm+b0gfLcjNih2lIQABssDVXCRhrVcaMjurOFMpk1M1y9VA8BFuYMV5F2AUZpzH0OGxNuVX2Qmp1mBLRVn0gbnsozx0xTfmkuAuWkS+PnJkJUxKkKzTI+rzR83NAGKsq2JdBubdkdjHJw+phmBnM8xEBeWxFkj8AopUl7GbFzlvnLv5bpzovNAGH2TDToA1Vsh62D1iqoB4rGpJy6lfxVyiUqS7nM1JOIu8e5tJDoami0VUiC2VZrYKlvQHapDMyZQ3QhvcMjR0KaXheXmPJZWA9vV2HubTvEQDdQkvALeCrG4KPW3IPHUpetU/DpD2hvJWhoBBrikKV9KidEARG6H7tWD9VKX5nWKrcqpdpOu4HmAvIMra5SatUwtMfZzGNPOklMz2uey1VDrGwFDh4nDAOSlDwiBFgTk8qQZqkc+6hhR89wCaF8SOn7NA8V58QkhzgtSk7sdvRjDC8qVF2U7nTiyQxb3UShysi0eS1JBoIEKNIqRJb+DLtPHK0sWegWIZKC9VJYZrTB1vSI2h/mPRCDuZJD69jtyrOi1gC4cntmiNquY0fhpFtOFfYKOauanbJWHAWihZHmFlMB4cPIseOVNstx2DG6ByVCrxbLpkpro4tJKlTBblQ3pS9408Jc7DTYhGLrlpQgN48U7wr9g2hy7b+fmi23YXL+IEKCiUc88SQCLRtyyxmmKlqlBMngAgSEXQZ8tUkX0yk90OovEIO62nHsRwpG13D28J6X5ifa0FfVAI0weRCfD6saB0joug53zNGQQWV/l8dzQq4UUfd2nuXSrubjSRzDUTIHOaTBklXtlSPhuXj+ARd8jTEmdNtnGmziRNO8fVdHK0clmsAofdwxtd8o1akYkAeZv/VNgtSuOx+PLiHMB+LyfHW8rB32ZGqivs4Y2km0dgil2zVTHJdJJlKr+JRzY/PpqikazARqzeHQ/DK5yTJy4NkO+F9I6Qj3jY5zMraCa3PJgd0Zccdn78K9eOsval5Hoi0KxvO3MWFXrLGRG6zGiBqpaI57cJ4a99Zdo1JzTdrT4jGX0exUJ1KBtllahau0NY0s7evUcjlYy5qOJEI4EJoRB6lJlU01nAyciuzjeV77rL0pOuyMmW45qpwhfHxeutOoCynUI71zVo0tpLoMOUjlFM5+oZeoY0RlqW2Oleq51/DhZk5dalXGLVTSUSpbJAaewCvAMLZ146AOEBIxC2pRzZaPRz7ustOsoOovxjpfo3zAhworroylqqrRT+yyKmZooXLpWJQeMeRLcCPYdeWh0qIe0fdkXALikBnJBE8o40qbfTpPQI1Nfatn2cjBHB08u7SHUMPM8RREmB94N6nAJYIq+ejG31cpGKK6lbEad4ru7iPKSqISA8qIAveu7rxninY8SDiOU4GjSyA+UffxHRZZtENvzUEZK8ETgkoooS1SlcZ8KB5Qae3pcTmui2tgeo5K37Q0gxBATPTJDL0qApk9wIHqHU8Xyk7r8Odd6YNexdIkfXiONhw1xMEWZbFQrz6j7tyfk3GXtwZY1Wvvf3a+KlyTsXVtpwVmwYZ4FxL1t5f7oCQLpWFMmBZSWzUdg8EKoAOabszhZ4wPteScyCCPCkPteVL59QFM5GSYALaJpFVcVTNgG3Z0ONtVSgJNiATSE8wwbnjAq1v+rHWPjEvaIsEiGuzn4+iajSmo4wV/tS5lP6EXrtSYaOzGmabm4+Y0FpTtzFWKx+m0AXsS3I9RNBVYBHdqmKIESd2I2Piif6E0stv2cwhjoLeR1xqvy0Vaw0jYpBCkIbQtzAuiUGme+Rk8rm8KSDzN8FFzajQUgaFYqO15W99OkductNdzwK17stbgmG6ap2UjgE+lNS/oYe6ser4GtJL0VbTofVQSH/T2CHvuvIxtaiYzc9ytfnJdx1/H7QmqWh1cjFdVGnN52UyJL2jlK8waQJZVEDIHugvdNIOPg87qTaQVO6FmeHDSo4bkGF4H0XiMiYDxfJDWhkOaomJUVulwtmtu5N2xmYkaAQFCIGFQzSyJRYnJpHzSJviQTf1qQ+OYjPgAVqamZHm2E3BrNevXsGuKloNKRFk0Ey3IgzERct3tmV5Rwu8BGQKs6ZG2Y2QjGtXWBf3yQvY8bSclUr9yKpelq7SR9lX8MhrEGE7g3lB7r1D0RvEo0woHlMUKtZ7ujVKjKIhOyjS1Ju3IOkenq/pBUZabaLRXeTVhw7xVAAAQAElEQVRUsRLTBrqFOg0z7Npz7K/sr1vPEni1ec8mJQlc6KczKDZuaga4SjtAswMWKTAVyD2hwcPKLDmiRzri8phakmmQqcU9+hPxETQ8PCobKYgpG1PcAx5dlTDxhO4qsfcUdIsZ1654XDFNq0AqR01NmcvHxeFliBbNNvgyW8pbuGWZb0rP02p5ubWAYSvmlDAs10gzeXkk16XZQBVDA6mGpUleGOGzR09PsU9F2WRaAu+eyS9SwvB/ovWIe41hfhmWBiD+FwH7SdxFX/ur43XX1+6XPGW2svoJfiKPOjWyC8d9v/ys5jpzJRdyskv7fi7v/6x6NLA/ue8tnSvn1JhtVzi1nqtE2keY36LdVQ31qPXsfXhC9NDI6SKNSpvtpZxmR/eeM6a2yvJPYAzS8UTYjXQtWyOSqS/I0T3EdXp+iL37VNbSEhg7sG+csLzY/7j7MLEzrpxffX3BR3rTrEpVdYlTAG3Gkiwf3nf5fp6/uHlOk9Oy6T7/0pm8Gu4UEzdRzdF/i1cmqrGTG9p7SNwa8CAHqsq7puPLl2Pk7tqh03HhnhoeKkVd7/S9c7+zPpOhzc5cYyw3jErTmCrRmgxem68zvlKL4ZmZEvkiL+IrfdbAcP1tY1gP1bNc9cmnPwYf7XnfFrvfKN33lfpWjsARbBFq74HtKJR1XMH5mTP1ec+0eb9wswQdegzfX6Aezr5GLykxbWu7UvfCZyI4wEtAPiIFY26OG/3AABSKyf5Vn+E/y36W3FUTCpxyfJt9zN7N++FLyhg2Hg/5AGo7zrMs/xk+FevKnkFqi1SreiIfDVV3QIdqvxOZPOer9rI9u6k+sdFqSrA15xwTa8AhSS9x6thr7DHKFLzxs9YWjxhMfyaxuZjaEk7FZu0MIJR6W7N9RMy8Fz9zaAw/fj68T4RHL4lsjKTuEaY26FK4GwztfbzL2HNSOHKNWHaG7V4FRcg5pL1X2ZUmq7yQJKr7h/d35zsj5b9LjeKdULDQncYZmf7oSqE/Y0EALBG3J8UzkY+WLqvr+Rsm/JIrM/eEFqN1OEPDGxq2PvWfnV+Yaf/s65XKJGvNGrOWoj9Wj4TO2fVJvzFBd2g/7uqFj08ouoleOo8170sfDVitiVlhVhrPMQja41NFH0URgmx6YzlexovmvFxtAkDzifLjfH/hR1m9FJJpNfN3YdNlbL2u9XqNpa3exfK6oTzUxwf95bUQQ6KH/OSx1r1p+wscxtuwTST2cHKNevLVh3WLTulBJ9ADLaWXUMAMPkVc+PhA7UOU3aJMyYyL2qv0eiTs6gGn42CfvPMElJmYLBKGtTAve8KX/Cvtjr6rP3ffp3MxrLTV2ceOdAKJRMvkKVTvsrvmTU+jYh+tjTnnYxN34XvbneMoGWVKT3yEn71Myf90vqEOnw3IX8ar8tX+Ty3mBZmXlgMeNFG5wZ25s7PEljNXlyTOc3udLqGAhuuDVnsrKKKCHSKqBH348ql/wpvq9Dtu9M8636+9/SF15vtLJWKqRMqfnIOnJDlYu92enxHp28f7q/fIUkdvyG+LSGHa3dkbuY3PZuwa5euYWt1jaCau5MyyXfg2fDc+7S2EnuX33Z9z795tYa4MWlunpvoG4vl3HiQfxnlWIR5go0pyBYbVYC1zbZtnc3x0tC7jeTVRwtjqFASUjlDwB308d+2+A585JA64RoK22vy0yTQrXGqP9t0RZ3dvXjWVGnE+O4bajs85F5XrgZzVI/0qrCxt13C83qs+jnObHYy2a+GarjnEkLIj89s/3+v8eL6rPqd2GKThoZ9Gnoqs6FPXnV+FSxZcPgvek/GK+PpgboEZJJ/4eGAB6yA/qA9kgr4XVSFDBBVM8SeI0s961fiHfQEjbNxz5Jo2nPTT/qm1x+xZc3xW3yPOrLPqjNh98i7Zn8FXmfY8AJGFE4DaxDBc4GsoK0mRxJpgY+1aJ71K5/kP6x/fL09T6sLvgzM2vu557TXTTYYsGqJuM0fyFfY1gXW27zf7Q99lfbdpt3sy3LS2nU7uR+qTE+rKU1Gzp4ma2UIQXXfVhnTr5/KcLd//oF9yRuh8dZrWTz+qkTWulw89Nn74UDbvGmJuWKPzWORUGPTrypcaEC3O685XwVqN9jJOmuXtnzgHn143LOtwPxVz0SUPHe60i7CN2lSYWlu4+mVD+npZuRx6OXIqvjjaZjL8uifE1eat3UBw+a3N3ufqD8VwkzfyRmiSCuErE0ebua1K8M/tW1x428qkeDjdAYDOcfFaXKBR8K1XinhJPdhn+TF0XOMz9Un9MaUJ3d0sLMIO79tvzhxjNK5sJ3F5X6Q2B2XcyLNDagl9yjKca8RyGKxa5rv2qZ22H6cTDRPVH+K9dkrsa/bq+M/2V385rhJ2W8KTHZ+soKW8nTHeOd6NklBHzHFBm4cXbMb0MBZ0gKiwqHHbCHd0i5rexTK/BhcD3NGd4bWX3lNDQ+F1ZEezR/c6taKcm7pX8VH+gl+AH/SnVeGaQqZ3joc561vhpvwaYQafGC9cr14XxYAAyLJHvTKsNSFWtVYH+1h/RMs23CZXvIfdUjIxUDIkf0jRhFFqAuVIud9Rb2gabjJn8xU+3blWiaIT+7yzf6g2BxtL5YnKOHUKbcAsl2tntjregfICHFr4401vrlYzFpbVsqYoN3WlQJUYb0nAFHK5rgDjib3noRdZaglx2Mp6g358nnmFO0BaY5BjuvC40a01Rc0iZVaLWzQABQ2iaVWm9PSRPRxlTAntdsTo3XG3KkyBjFlAdhaznGyRZUcnJ2RS/Hq7gX/hpxvdpfdZ7XX6maEzGuzzMbyJpC51TRVm7+pP1d3sFuOw0gTBUwDG0cdxPzWHA0+umpMGVYbhact20KIg+rH3xNItF4OkGa1p4u5m39mnUGb9pAEd+oJSDIVjY5v3AG24Xtr+3Ga3d7pAQjMGRWMRYJEHmtfZVMj4aHo8J5wcZblbxRhdzUrYGYyl0zI9UQMKCFooZpVno1HQuGP1efhplYwELNSYaeMMHLuDERo2CUe2R1xakG4btYjJYdQdMZ7UPTJxucxR+lh1EIKFkWYt8MvsGnSdyqzn/4FoDX4mSv8zdPur2pCjzNY0SUMt4OU1B5sqoBFmqvScogityxp0DDncIKb1i66RgzbMbHqvSo+2Mk3I1WlqQCcVAZqWPo6xQJlqiEns3U8DS5lNmNhRRvOTnaWsmL0mZ9vaY8W4ULOPRDFre6QJsQyp3dOrU90FAFWaNtlW7BhVozlA64rMnV6hlWlBH7IjYsc5mRo9ghPqPOqLUYKoOGsiRqhk5KN11aZ4JK9OxxH2xkRTjWuomBd6ur52lyuAINRzPuZB7x4KLFt5OfI/mC41QZcsS/mFrkCtckNzsF1Jj2ZwKBQjJzmSnoxGWul3lT4RZEtBwU6TY/PE6pJ1ooqAN66WkcPaVdlCTHVV3Y4sakU7s2FOXnDPh6TPZK6Jpd3rHOLxYcDaLvhF9ach+y35n2JovGI3t4U1+R/nyxR3pbhq+9sIDOiKyqjoLH+y2lS1KmipZAwON4O1WYFRUoBCQg/UhZpET32yWhBX0YYaQ71WNjXJknP7FWO2pAHKgWIrjWFPi/kkJ7RXOmF9qF2TVfxNT9Gwc56cEcIi6tV8mBKHtYlTga0WTyb/QDhr1GSpe4/CTFJuzi0V1V+CJeNRZ4bq+YmpF7HgBs9wyHF0H1RWeMdVZ5Xei9VVlj0Ll/BkdpyM7GSe1mALxrlOLW3ZMkSIbJrJhCYppaIB6zyzCx3qarm9n1WpH42vOcrnNv+wEmE4nqm8OYpKC41Wabk1lR7uj6LdayCJSGuNKgTUcTWZRamADhtstjB2CnuXAfIWx86bVSjvemWqMCEkrcA1bYiGofig9CJbVEFMCr2kE4L6nFa5dRCt7PqDAOJdX7uz6m5EGdsBpbtbfPa2rDoma5oic6ZwEgJOBdkSkzZWkoSrKZl5znXWDLKgA3iQ7FaQQZljS3lTXj1EJolK1tZ7iydx2KqURCefgYbpdfUZCcrE3VoVI+yD8ZESgxGwx70rV6oUH6B5lxXUZu4V0ghHmFqMy5FMqofYMonGzCBu2dWndePWJ1ubjbsqqnRp0DVELKiI7+QOhWri38k5bNGX98NaW3GkQjLIqXV2Sx2dIq+94YXej5PqYrVXU24rLFXQ7exn5ml+4Nu9fNCmtSYdSTLB1BQzR0zuq/bKmgkdjMzEcwgFzWw0dKgAeaNyGNYlax/yliFkAYjS3/b9U0xiWIyxp+UySkLcBzsiA5KxCMPujEK1oZHWikOr2Qtz5fTnpALMxge1O7bGdrKqsk5i10N3vUTUkw35AhcUJ/4y0D3q2f0qUqE0ikDxaKQ+pTFpVd3HcSaVVaWB8EBLfrqTbS2n6pFi8bTfzVCpPJH4qDRFaFFIPMpu1c6MhqzRwGn7sue//+5OiBmMpn0Gb/ebrWotC1TagVRPkC4fGtmKXumFOZuV8xS2tIIDhlag6A5tOHUB6kK0culIbaWt1KSbbO9jOGWaGOhy4GU1WlzXXfyAG4ROtNe0uEb6hUJnlJL2fFCPEBOAwWQ0OT++woAJn9AVNjDgEiWG60IMicqc5hAjjV3qtRUnOoraYW3nmSChLdFa3f4ce2RMde6IFFg/yPYILeLLlHGYFLWHqahUbeYcSt3IHnfOLecxVmn/bywoC0rZtNfw1VL0kyJH/K8TdENoy7HNMxh3xtZ9XHdN2KsF30Vsdh23j9mP2adks2gtGwPRCiFg3SKjbR6uwxHAyaObAVqYkT6HciFyVtlsolsFFj1Qs83bPa+ZWj3JyTNGw7patnGzwlVT4kFZ3PUsDBAhKYAuQm7YDenpoE+ZSr3AIps8lV0pqmfDHOy2hENELfeL4Zp2Eh1Sm7II6vNB6EAX1LiDldfp6wa37pVmtnxsnluofbCWVBS6NnEP3pqS0TiPKwdSYgKwdKR8z/TF1xhfXE5DW8djpbuTBcU7JcMuUzLSxYAduzavY6/ka0J7vTBmG4ojyyuooFQbRyyrmxSNoJXPMjfkX6CtGAq78Ya9OY6L+1fXF6DBiXmM2x9jpKxdrY6guV5m0BnQBt2xwIucQuZIOqUIpe2Z0UpR7eqz0fLypMbv9JTbLCLcerRZEC0RtF6imMdKi8Bn3jc/hXuKAOjFiq4I3sq69UGnBHI0B+nYhQw0QKETfCdUIrAaondSMsdQ4sS/JqjtzoVrjtXUwxaHgjDI70gb1mvEVfe1Yzx0Sv2xZ8Xw1bgqlIYhLwEG+TjUzGq57sBtFa57offQ7XdHG4rLxmXQnz+Wg3CWkdm+bWr0+fv5UN1M7kFiDa4vw1frNgPr9ETZdabvgfMwQQAcRj3OwkIttrk/XHYacz1cIMUNgEAVcla7FJQSPoRnAq8c63bfUsOgdO3s1UotBdBALRmc69jIKD9xJeZavhamY1G3NUclUPdRI2zRf9gbWUxNr4WYwnnOl4D6bgAAEABJREFU2Nsj/RRPMnTVfL+wX1U8HRBE6MVoUegj5yV9Ql/tLn70TO2+Tq2Aqangec6ZIJLjhn9OSdLdPudYBts7z13VZY9B1HXwtTk2q7ENR9ExgQiP73H2I5+Pcg0ryPosemqIqByl9SR5A5JqnE5VTbjYA7L7Xf0e7KvnqC89NHmBL9o0N1NlJVmDEEerffi4qKPNToxPoKVgVv3PcOXo/cWzqHo9Ca3hej9hucmHTNrd695r1yowveGHfBMf4EPeJgmMGVLpZDlzuPzSD/pwN7cYhgFKB0g01BQbrUk+Rozr+YRCy/T0Gt5qRwVs1Gb0KBtQQHXYgpS2VWZ5p74D0/D0lnXyOu31RHQofG1qhxH1ueuc0A9KWN8+jxSshKi4AWkUGB7jlXNqYRApdbo3WX+FNRijawgKAOecdc3wJx1V5TDDqlQzkmTuk/e9qxJ27FXz8oULJXX0p7WMH+mSm93OOam9hw8T8hZPvdCG0oiIPXa9znz1s7RnPRTcYmfm+Mpr1N+t0DASgzbg3v73zYSvI85pAage2lN8wJ/l+1o9RWe2yBVaCEO98/7Uid5OqEuXstN+oHHWndV7276/LK+rfeXgZ8aekUSmZb46rsN5+Gp8Wb8UZIshq15adqy1nZ3grEV4PQHLviZqGPrS5dFzUQkmxyysFo1WYxx/RS/GtJp0l0oEhSeYpX4jO0jmceCyMWXnxbwsRKQeBn/hWniZ6eHyiFzxvjwufwK3qLQdSwF42OwFfhHLC2ymz1jOL4wvrEH5pFQJUH3j/FV4NS7F1b3afQxYKr30smTfBnUKt3IgwaDU1EQPdfO/5VoIAY7Pl39eY7sauUOxCPYj6OTz/FaEYr5bRRncqcXVhuy01Mk6AEYO3sAdFaUEhGDrrAldyDt8QKKc40zbrNsroTQqRXnROObWYiyFQYLRtNYUYhQnJuFdiG0uMDbKuK3E2PC/shVrYfO+/DhVmAZfFEUz7HnKvZffC/uR2Wl1dPPEumOYGH18ZY+3GmYF4lbSkLc6idrpy2AjD+ITXUF2jVkcrUijcRzaTj49C0UQQihAKCG4GrOgIJ2f1r0H25zLsAA0j1ZnrV39iMkTAiTrzV36hDR52THuh2o4ODRHqbPeqD2o0oMyFnds1bCQ8r16axB91BwbS7E552q7Tg0NVQ1BbmzirD7+LB0lEwy5BJ46o8Ay7C5/27glMe1hVYjj2hxb9r4+9vrr4lxU93vZvVrN/N6tjiUi1Ofr7jolYdlDyLKewozOxOfkeVavtupRLeFlUKdQ3cXDFVL5dI1fpfI1azSFxqUJjCC3Clw4yy3dC3JTof4Q4N1agT1LDDl6SUKm360cfwqhs1RVDajNnGLl8qlSkixVo14ytZKEtbt4BmDh3vp4ylPP57crrXtUeDaiOhLqtNv9LNYD/TP6YwwB28vCUKTnus5cWZpygzdNmYc0v6wW2nshvup4hfxIDpV+w49arPlMvt7QmmWynr1bvW/2GMOnbkwpNzJ6ttqDFU1qurK8TncyWkSI7eJpz0M/ajtK1YA2TdptCRkMRGtrZgJqBKy8YBGs0Py+gf2o4vkpMBs4Oe/s7IC2iafVRMxdMYj8pAjoekH9lAvP+Xr1B4ijNNGZ16oxmqQSIekK5j2yVsSr+3L6suE2vec4L4u1w+7b9zFF040sZZFcHC8Mx4CqqPwjRod6VKXvEjCV2/3iPVOkfCx2edyzdKdyRx9EMp6YcxzVaXBZq69dlHjbyxnNKJYWJhMa0hxYjIthAtzR6G5ai4muEgsNS63/vACazkulxawSWpciI8/JgwqgjGWJ0UaneY+qdaBzGxB7Ji3WQ79DhLbJvCaHbHlyZJvcofUP6GQd9E4tLSYTQqi1Z7pKPQWY7SwigTqpV0n/c9gC1REGasx+Rlk+6pL/goIopxqqnho9C5su1+B5jfxaOfswDpQs65qA8BnmgDlFQxMkpZ8eTFqzzba6oYGAkRDwE4FdKAQhP6f16e/AYZxmqoRhQZ3NWVTRdDBopWXJ2C2NtTXKkDIg1fWB1gjxo4uzOb3JRkubGqrAQYOVDBAZ0a3UlQA6aQSpi95m9GecqM7peY0SN0/qZcgtzaq1QcNdFiBaOHzSXeeJEJLJXoo3b+mhgCRyHI5oISud498fmNpaeWN2r6YZpFpe5mtc+vr0oH0qS9jpMo8JjZM9rQbq352aNFrec2ZeYUujLIGEKEJOa9Go4R1uPTwUG7xaMYC7sZ/vWhent4md4rtw88Bul2xGqJOCx1vbP46epDbfX9UO1Oqe0bJbOUqgjXC4MD9XyUrW9j4uDmhVIw6fzKRD9ddDXaKeVn2plOmCfaWNIDtl9FC5MwBNaaIZPgKGKrZshtyDhFtJq5Nh7FOlEQ1rmdAwab7MlMJBej+WrQOdBb1IWvu06ctFqMxKvmoCVPVSho7GWdYcw16zllQMj4aW87PrDoZqshNq11lR1fpcdyGaRdM0apM2Jf/h9AlzHfIowq5UnuV9GE3AtHi5Loo8Ld8xs0dOQjkzi+Kp0aF1BKHg6rmhWsiXckqQrkm9KogukYmvhBYKBfh8b7SidDPV9DXm1NgaaiDBBJs881liRGMPjQu9sNPyb/GzHLkKQiPGKMFbId02/ah4VGx9JsITdXC/M3T/VKAp+ZwN0deMM5DDc410mVrFUS49ZWSdeko9Im9FyCkdea1RDeQhksy/FZREOUOnj3mILsdFTuHf6SVnigkufsXYcZxxDJKGIIobd8X+hAYmr5ZUobFbu/lHH2ZNsZBtUV5ppNk1l1+GKdh5Zh9LbR8CqciKFJ2GMohirLpWXpIP5YPQReERanZLn5AB1NXtGV1ne8Wi/MelVCnJe0LriJJBEyMmE22VqKe+ZAxQmiwAtuK6zYsZEoJqBvoxNrpadGQTmuwmACGC0sRqdZvO0tzpxNPD20iaicq0B7MMowBhbTCdOaXDMXMBV9kFfV28RvlKKATbZdXy/JfMVttKnvTj81HO0O/jfJJtlFVdhsVIjKAU7kN+NVferDUo5vN+pO32jum3e4zxcDq7NL9r2lmvo2ZArhaLYBrCSAxqTo0qZSGXlbY35DJooYKHWEdrjLkC3daPNWs4WyoeEqinjTZHtodMqjmcJzD0pMYMrmvaWmWe9URizyfwbuvETM5IRz/5cXLCDG0IOmxBSJW8qg0GjBymsfoUPaJEhDcAS74kTc8eGdU7OlKvCAuthLbmwuNeGK0V0lyFUW2lqdQPWv4wWycfjvVNmhlQYsIZNIUkw+MZyqa6zqS7ac7BOLie68VHOLQNu4Wg+9HI4pilGFtnUgHZU4FoBY02mkKZth/q6cFX2Oj/F/AolYprhXOLsp0qmC6fwFfk6JCB2s7mgjCKGRnvLoXTQlPLk4xtEDzgooaBlyZbzYctaAmqFK6qfbRAo6JHSD5hKEgp1CgH7QlCZ8STh7USBC2Fc1FPzIYXK9HQT1Ydsa3sL+W32/6+VskoWjbDqRo+h88SJTk9ncvC5FV60/xCyMQNWRMAR8hqm1SG1KVCtetoIUtFmZ0JbOGqZ6Ez62Ux5AHWNFPlmDiqrtY09yAEXEXqvjBGmkOWujVfH+xxyvirAUetImOIN9H6bIf/mVjTS2HfE2EWMmkl6EXoy7hr6K47YCylY1Ddq0drdY1qqzlylDSvw7uh5IfOQlMpsWm01ie1z9Sdx6u01eunMywvA87Nz4F6Dg9WwFtvIxSlrllAZRZT6sa2UX5xDUFYDc2Vo2pN1Dqg+ZbVClFiNOUTtR5WG9Olh/FUzybuRkKlow1iVZe6ypgqzWPrf+MUMyBxSf4UdoUubp9MKD2r6qpe3VINdE6E7F43uJWleKJd4n9am6YCMmGFhpYNhiJ13R/ZGaYUlsm50DaVoytU/NXDeyK9JeWCgAWgZYiqwL7Yl7TFAEtupstEG3SPmU0Fmce0t6tuczlVal0iyVjBEUtEQ+mVFrJmHi37o8tkSlOpO4L1lJSWzitEfnmpI9lQ2ZOEMsmWemRVv7sLwnq0wl7gkDdp3+1pQtWc8AV1amtjsdOynQYfPZU2ATNvG3CtQJGf4MdwW+QqHdAdFmvgeYkMXBC+LfFrsAufCD6ZefgYP9VZKfaG5zRp9TP80AvKrNpFNZIdMEmGN74K+jvI47cNZexhx9VQ6vkMPQMbqoPrxDqhUJTdVGwsNSq0Fr2rdfenZsQ18brqMhGouJYZOXZLfFHvxAd9iyFr1aWxXGWRIea6qRSlQ+2h+++NhJTjFV9QT2PykoJKUvGuaTG9evIMz2tgunRuBFyN5h1f3/znLKulgLq/TPdGrUSmOP47Ui4SrmLoNse88pp1Wc/qVbSCTjcU1lohAt5Z7515dJ4YV/slZ8rN4qaGBm37CCDDMuZJbN2bqG4pfJ2Ku2QUY5DLC3qsSeBRrrqxKuIr05kxEKoIgmwpTiXO2dekLrG6ttVn9mfgPGk3mNN99PS2aOy03nO9X1/7y/pCV+xz7pMF0AGHcvQBt2JzGqlJ+ak82eiWcgvyQRtcX+DXbg/wIfyYOtHUaaNby5iiqYLe1Wqa7U2gwLDSH/2PN6qVaT8QhOROFtX/1PraCIoinWfWLFw06QLGM01J1NSh0Vl3BkK58XYj29B0tzWHNgrEwB6UDDYMuEkFABV0uB6/CRh3rbuuhtu4cE0tXf0V9Q9tKUPs00nuxTMcnDNdjbib+qfh89XlLcwp+MNwLb/MB13Xs+3DGa3NwShyD+jPhx9HCpbq4sswGaZC3htR6kxFtFR7u8NGtyW8ZF2w9d6vzdcHU2DsYLyHfZ4OPkS7ZxJFPXF5OfU4fg1i2SP2VT3D5vbrTJcRHdv77XbX3GAt+LLFOXrpXfQOqEdOKO+3P3lw2KupRqfsow7thmK5oI7Pwe5OBBBe4YlJyKZDf5R6246Tes71ir0ajWk1Zrf+sPVVefUeEeo9DFrXQJKKdJedzfokfkolaf7PdX3NOao6z0kZFCUX23tYXX40r9WpOqUtSGB1LFv8LvtoS8wBa3pkpla/fcYuv+nbRnJ10qRsjUafWVr/JMRwSZEn7dRoYLZ6gfE4b+33OBb5cr+uZV9Ws3+Qn8xIqD6v6hfm5HziwMfr9gM2OajaLTlQDPexz2HJMhRCryhWijGYXVOHPEKOwx91lXVC4aQ4gQvQPtSz2JMo1UTXxD2v27S+SM/93O3uMyJfh1f4Cw7ngOnvCpfxmRAjpZebrue08ZH19pl/LdwqnDRRoOxuYyauO//Z91XR2Wd3n76Of8EmPDn3+Dr+T/aqyq5im1E+FIg+l2d45VSEKmfPow09xNAYxych+R6ern4y3j3tSOiQPhQs4i/g1xcuC6WNIS1IpFqO7oqqI7G1PcusSLXBMVlkHPTd/qRu8UxrBRtlWV2Qm9LYIQeMWnnYpYePUj3dxnBSqCTBbmxhGwV1FFj1wnl5rsCr1H0fP2JiH79bLjyjIRoAABAASURBVC3ot4xHT+m5saao7M7g3VB+cOi6QF/TXJuOfoxyxnqV/1O56qaiHcgL9+xykk9NfZV/5Zr1snP9tRjEDRwkeIqRigKQQBBX0QWN/zkYApalpvz5xP70ffiBeDthmuf1bCJG1/L7GlvaTFMb4naJKGk19YcAu199VKJeAFt98RDKa4u9Cit3DPTSQZtz0K1bI1tUDQXk21nDALd0K2luTGjVgsKy9gqpfCiGYApSO9pa8WqdYChTh+ODr20XzNTmvMEEugq42z5BtUbe4WcrOQTt1PjoicZ7IaxDIJIGEg7Z6EtDxBbstOpskV/Dx3A3M5zZsfjcsGkWNffj/fFHD40nREduHgVfvj7r6+gPjVKItTzf7qEOsK6co9apJ/0hkCmcrRTUPLS7kAZ0M7zpObuuO147O+pUVYvd9p7olS3GnmWBGq9WNi+0qfPtbV0Khhxmqtsvj0uisluseFACKC+tbuu2r9Orm1CgByi3nlK6jZ0j2Hilf/X4cvtSNrwE5/9j6V8UW+d1XgEQICk7XXve/0nnfI0lXgbuP6lXmtgSCYIgJbv74jFqYOYX8P6VEo/5V1TJG/rqvFoOP5fDfB5X0Ia4PkTwNLbt4kFXp+qICturQlKf0gY3/M0sv/TvyIx54HPXT1uUU+pwDJRbLPSyiPWDS/BcdJDjZsHgEN7l4rOAVGC7vU56fS98fQb7nl5l3JwNlCaZEZQurTmgxKdWU37qC/yyd257jvIS+Y6R6OQwpBQTAf8y71T5OeBTNXycE24yNLvtqyVPsl9gk2cy/elQ/uakvSkymrsrRTQ5LpVo67qJ4E7rBM3iukwDgAt1s2CzTUBhn+K/Op/uT+LzBL6f8/+ueZYmw1is7cCoSPKquspMHVPZ7E+PEwATyJ550NtNXQp3cBnM6f4ua5aZp4+WHDj0khwiA307fpbffltdfNs8TVfF+9RqX30RysaCOTT62lzHFPQVx1UVePKv7B39omRZH+CBqirzk0dQ5jbI2Srx+HpWY8reuf6r+DXMlXO//9EpBmfdWD4U9T4TAS5ONIL2uRTMDJ6SQAcLJNbe8aiF2OBK+G4+qESOt0tiwYvmLYpFPnpiw/fYMbYitDW4G95sUOlE6KPJNz3n2oixe/hRwzXR+px5GkdVZi40rmrMU/Ucz/aEHdkYGpQWG3T6GUF0ZQyGY6YhVczk71AYmtPvnK2hlmNFEznxNx4z0/udRb/80t4BwCk7E4NFuis3yUrLtu7FdjSr6oUn/eN/zp+p++Q6M3jNaeQaMizdHrIxVHyIEmoCl/NNMpRVaxiu8WixVGpQ4lPJTMqO6SJ0bvqxsxWhyYwgJLo7dUSdyAmM9Qjx/qJrBC404rAPKZQ784tJc2Ddxy4RgYQ7DRAy/SuzbXcJUwwVNnJztwbJMBD0Cq8LfiOWYS0Y5UXVujcyp7GVZ+MJ9HA6dKBQkh0PHUnthnwObadgj4W5mR3O07O35zvx7lkFpvVhK/gAXIrzHpkySdMZMe5FpBjOVKjURUAu3Ri9rvNZbesce2q2/MvlbDnWPjUouVEWGzjTG7r2hglxZCjvktFJvM6yX3E3LrPw18f8cQGygJ5+cwzOrJ7LIRq1yow4wDGv0W5l6TGLZ9ooVF4QfzPaNGvpGeply8atm12BXBqnLTh7r9PTWsWvsisifsx//I75ZFECsEM5a1FENuQoApL8pp12UyEbTGcIc9ceNtxk7dSjAuVUM1u/fMpOIssHmuOauR2/wWdBO7ZSAF1FFQxkzpSIVLwJFZ0GXD6f29aHvNtWQ+5Miol27zU0sMa3yv4wldO8Z1/U2nR+Q40DMIo28W9dq3FnXIg2gwgV9YI24thZ3sn3hI0uhpnLi3np4ouaTOexKL+bqB6khb6/WRJ/gHAAU1DLy8EX9jv+FKfasvUOQJo2j3c60DOcAfRvy5rWNco9RjW2h4drLkMAUgLzfa/slJK7oJ8pDVR2O8/KuTkfoV6mVCiuTl0hgRUd1wP5kTDPtl1eBlj+ac+MF12Ey+9D1VV7pduBP4kaQdFnSh1Pv07H6txv7gEQtI7u68z6DjbjiBbX2na1rlRP9xjH7sZVXB2GG1j5q8RwaCDdseQ9olfgNmqn/TnBQTnyrn3l17vZHn2L9ahx2bRxTc827LJTPmpZbQlTEELfJyqt3MZfiOQZ7pm2MW+zCf5RlGX76K7mzWkO+ygG1edbPF+pTgFS26hcdbyAAfVyUfyxNopOrQsupKKRc7LOVlhW/V6RMHyDD+th/l7+fK72a4boKk7ZkCqCufANk3EcUH3h/TSjL+hOHEk5C8JNBKvjtBoMs1JY15xoTCa2nklOBngp1Scs8b5UH3LWPkVvLokXZ+Qj9lwO3rCwmpW9SiwVrGrJGWe7ffnntL372rZyOIJ8Zo0h5l3oP6XFyX1dpkw8Vv+Z9kZPzPkc2HnF/x3dXmazwBZdA/cx9bYQNVNqG6X9ZVEnfRYrkEAf4DBGm7BR3LtbzfQUjih2aKXSNc5xKJM+uF9ERRFiebAfBei/rF/sZNII6F+zt89jaGmtPxEfc71sjTjo215sU2Qv7+sCVxztFEjaWb4p/DOA/klEKH0ECDSiXzDAAsxg0aqqshz1f+QxltzR3I7bMxLPtHy45Bd4p1diGgYpoN+d6egkp12ht7PHqz3NyaG/q4ec6JLeSZVHApm1O/XDVlmpURmXcLgpzpaxFk1pdl6tWJZ9x7bY1noQ6JfixP8fqtgUkF5VCp0ZQp6Os7qccuim4jTujpwrQs894jJZqp4SavBe2q9puVhGS3pykbYkLw3SJ7SM3fz9efv82KAEuoe5IpcdMJsvQerGq2g6c+25dcy4kc527TArq+Y1uDAUdRfswlpzpwxWHtlptze5tJlRtW/VlWw2rDi0diLMDA4D6mWAc7kcgAbjqOS2fACmIfvG76otGZ1R2bnSyYKlLUli50y2d93ulzoJKUg53XmNqG2ojg4boXxWWh8bqTqhDbJaJcYIMpI3uS6ut/8ZM+fk7CyhF4IQ1wWM/u1lj7sKHOHj7RANKlx6t2yjTxW2UFPDMQ/Pvnb6M0V814FE3L0IpUVNRFrzigVfBtPZm30N7jKbGOsn9pfdAmZYNubJmdiMX/qvdaONqsMhHO6jd808EKFgjV0usRgXhxSXldS1pH4n/i8DUS+F947YqnoOpvvZ/fwH3SMMQibLJl1t51ne4cT4tGV3vRc9+LNGfUcdA1bw5kUF31ADIdNwwkt2VI7diX7aHiiCHjU/1bA8CiosR7M+GaEYVMmF0tI4BzY0w2AVOErqoRiQ8YR6ljGuUM59jauh36D1SM7nZGu93mqL16gIYjfhdV15fzSSzdHPBb8trstcJDZYFZlAKTphDiI8cC2KROaF/bNx/wc97H1Vk9BQwN6X8hY1/c15npkziykDE5CXSehMv+fR6oqlvECvTsvN2vmexSCAFVQ6v3hZqX6JmTGgJQB1DB93SnE0kA6HIL0Qxs3uuVYr/wnbqe2A30ipfcRxYxe63t5QKOAs/P7YoyTxCaioHvmrP8+oNwkV7z2wCseHptT11Jk8U2mDi2PK33pv5dmwBXOzF0zM+J5+lGEhl6OakUFDmUMDDu23/Syre8HdJWOUYzuqKpun40hmiadVPUrudL0mRoMV2sEMZWbu8B9HiP0RR152AQtmuvqmXnOZFGZF3NaSx/KgJPu2X5+jWKpsYLCC6cHOdn3vGUWmHCrKyoq6r1nRFDbOKJ/9UXFjaD1MlfQbJvgAipHSzQMKfNhcNguCx3alQJ8cgsc7zb9QG9ycEyDjltQnRE4FH7dMVlo2Egp14+gab/CeUlMe9rFq4ZYfYQfDaEapSosy8mkelWP/3B2XrAtzv8mGKJ72hpOGuXxfd9FnlJ+t6WiRfZCtc2KJ3OUnl756v+lkVs3e/vQ9c7vL62LfpVGoln5VZKlQrS/P0DwBm/ghP/bQ1AjrEYTX21ms8I9FEIhSnzAsHzPoI1bODYSZY7wyMteT6vMdODFlWSw98e6jehjN5haXEFNd3rVS+8uZXJ13t1Yr66auYJQQLrPwgIMmttU+WlHiUVFkeN0+d6sfjQg882z7rbXbS9Ks7lNd00A6eo1Hrak788q+GgQ5wbgUFNScZlhFTBtqccymA6W77EjPQSqJeHH03eOaoEa5Fo1XJr6HOQPWGO29ljF60v+sekypx7zO1M5XBCzKo5c+vHHysf/b0PxftJLovO5WIrKnM20k/d1HP6Mzb3DiVGZfrJPE98qz5BfWvo6A6nHL3rO/wLethhSXQrbqaKRNFaT8yFlmsE+NZ4mpI0Z9lE1vfA71t45rqEz6iPNI/Dx5fYsF4OY4pHTbY2l8rjpBIdDqQiuiXbzaPZ+lxUOVsFNN935Uopdyddzmtrkwq//eY8JhYi9vaxtSZUlvaMQHSgMhauXOi2+30t1wxcp1D7TGcmkivPDTfcXGu+dqzXLQUa7GLZRXzqdpMkTUzFO59ylBVY+o6T2pMsF8C1p4UiyP2t0o+/pjzEVootJqf+oZt6PkyHQiHq4v42m9319hsjDOIlQMM1ZYJXqKhNwSR7lpe2DvmckL2x1Dld2CthXJ52E/9HpNGIA05dcOmPV+ieTCuLqjgmjF0PUM/uJy6kNC6alrbMmmw4LUhL59ExJEKfE2OqtaalOSWvQdny9q1+ksO/0mPPJtkHItZ7S979oOhYMHfchkvLwjGjpbk49t1yb6ZUSCPaKT84jDY15KIdOg32dhG8awYDf7Nr2rGGZBwZ1UfKVUH2L3S9RkWw0GMG+uEiu0kbAqnrqyLjsRPVT0A58p5FOpjpVVXZiN9x1iZ2RlJM/L62O1DFhewV397pMSaZERs0QG4qiDRdO7JQh151LmBA05aFAJ3GoSYwOrmLxx3EoA/SpfUEFt+OESQ5f+vHsr0VdRlc4LbSFR/aE6sAZtRgFIsTw9c9LzeOujvtiq+cf3uM1XXLwk8msS883+Vs7oWUwv5Vfm76NCobvIVXcDtZv+/uIdhOppUW4aClw8VxWHIrU3Jil24OlitTzOuu0KCmSc59/uf4QbNf3MUU4bNkXu9qdXNkC+1wOSsAFSggLSH3N+Vi/Ner/ncHSFf8Am0OFlnXytcbtUkzJ4YbxhEkAPhK83zxfar3RLsH02au62NRfrw4m3GIl6+2vUl1qSq4Ax0jnvhxmZvKR86UfNTYvZfS27TDWR18mQOVyGJeXNnRmn6kxNQXw4DqVxtYM8+WCeuTZ/Hr/3mm2dfbSaUs6uT98/uX4exhlDv0Hifc2kQgmsf8evjfvpex9X9TUhWXCW9VINrsCH+ElGSZLWhFQlLkNMqBlnPZC4iJuapbAUnRYfWFc/p5LGu/o+IgADpwCMo0X7NNs5RiExbHJgb2jX+D2SEsJkFEFwcVyUNXaVFphqIadkKhiOdE2G9KD1DdcTJuoBowgi5VFl/FfgRIdMAAAQAElEQVQVCTnIaG1ZPyHLbb3ZPepdIu0oHt0dKX09GAlfgB4pvWo9VIlQLWWOcH54fvo9mdkS5yM3j7EEz6HlThmLFb4Af9C/kXl/l30VJTiG9h4tJgYbsEo52FOPpd4nhSWmL3NT1KMK0NpJlpGtmaADIufCUp2FGVxSEBOd3VLBqVPlxL1gC614HNLkmaxsSU89odjqaq95KVndOweK3gwvl1qtYPqoYL1oSTv0dBFPK0YPMTUsdY6EFQY4wJYlZQ4lVRxij3piFzmKNK2/3jrbMCS5x2sCCHeIBH2fDXZrco0m6eJUiIunIVpLAdY6dU0unVdINbdmU4SsMk8drvTpEZDqa22/Wsyrv8jG2GOqWwPscqfZKAOKTnOhVw9w2rLusU8vhbzU1+Q25/VUFuWe0bn6qB/QSRc1skFrXfHak9q9vFsR8YKhFf3J9ewLuS6YyoYBHdZkijrnCv4D77RSb6p1GiLuRl0r/T722byH63LcMRZtFNhaflyf+nK74Xgxc+Tsam1b8O4h3hS5pvlyzVGGtrHD9qJmlo3av53Ns6cU9owr0lL7rod4aurovJBeUGEAmBzlIwLLzuX78mYI1kNLAk6ETIxcsI8GTjnGmRi1nfaEhBLIsNJB+DzX6G+4e2EMEj2OdYdmWbo1Tf58kiyjEStH3yCx4PhAS5S6oD5QJFbguewxdI6diXaHsiGjYhmX033c2ylVJt2gvdrlumxvp+k/XLKOYY/eMDDokBXzIhU5wHR+xzI1x6jWo1mWJcgj3qDb8Xvkt1rbZY7KoN3UMtSpXCULp3nbcE3ACMsstauZ4diilJjGnN3zJZ5mzTV209VfFxsXNnVnC5VC15zD3J7Jpntc7mtcnJWt0m3phzSxz10aumFf2MN4JjJdxYAEQNJymMe4L9++CugZu5p3t1OEJgi9ujFSgd6o7w7Ye54ecl02ZcB7cqz9JUTWTSmDyZTdRZ9yU+878dYDyFUfP9KPiBhH7zP5W9oSoMu6vMqeEQci0JkCWRwEdYx0d0F+mmhgwOW4Vi8d0dKQqscrgDge+sObbjTNXUN8rovaHF2eV2xbnSEzL0CNA+EyeJonFSZBylmZH7u2K1VXD+3AVREtiihSd+5Uh3gQG/JnOmen8cyptXH3aFWLap8BclqKmqtL++1u20TFHpPUSxW6WrVkuqGKT/haUOGaFQumWT1TwEsvBlX11kNvSiZqjyiwfM41ijnIaHKiqJ0JUHCJIRZ/0P9KW01o31uHaEWksSIJQ6CMDZqCNne6UV8aLrCCOp60Q0dYeDDCrpGTh2wpZIwzY6PSPoIwChoK2tjjHMSAQ2URoxBgbXOkBTHYHWVXhigx6kHcPlSY17yO+DbxmypFhQ/Zz7Qs8pxV3wuSEq1vm4vq/C1GFY6EuCBtNJGOtAZUab5yVqY22Z+tHdMjYYXwYBh7UaylZ8+UmZCUNgasRKsc4PqZRQ9fChzENrVDboaMQ2vaRIN9Jr+1N3+dTyjVhh39Zu517/766VVHG1ZedRziqWq0749vszGgeDJxKIVUA2W++dPjErfy0MZ04amcTD4ZWVe/NPqD6hZW5cBsAmJWNdkml2KZCWY7judYwUfR9PKSuwBEh5+ybopHY4O6oPwsvkr5JG2A5KTeUaltsr375urRQHmIK+Jef4befHtTpvbMAdu94a1oRMrZljJTzlb3or6oZ+WZ7oGZHJsVySyrdpZN60DLzG5P5f5FUAMeYJoQGwNQs0WQOdRU4w1N10p+ZNOJxTEvU/lLA22lW/X/wo+5FVdDhdLV2XMGDXnXaBFtTQFSC+ZNCO9cOXdTvrqkj/GKu/wSDuEU2oE4WmQQhBYrUy7mAdJoutEXN1FYY5ca02g9pOwDPianyFGMOLf1h+7UYADNqUjNgUM7ik1WRm9XO0qUVMwFGIYairMrn/TdV9OH81KS3ZKJnbxPX1uddmOquUy9HKGVomtZCdakZV7f1oYGs8dP43T3PDl66NSSKFb6KjMJQMYZwjKRY+NYbiaETbxQWtV4EbetZcv9rPVoVlHRcDQe2s5ZN3UmhV0RnrRdcjZGpX14kII6VP6cHXVWlveIlnY8Nl+TEnXFfRu3Qd2rZnVfykqJNoiTtyaohKgMtEQiHFRTjdIXLudtyqtiIPSTPRuC1rOGa9FeVuWxJlM19ai8L5YWCXkDqCHpphklkbl78MZc+iwOGLpOx4QRQThtyAJHXySoQQEjPeprk4/bCdJHUzSG5pAg2uvp3DYDA8KyF0o2Xaoy/WleCyZHeDXxA83UuP2WmthJfe6K6TWKVLbGUISktsqvTWU5Zoj/exWmbUAYY71K0IX3UDqbSogkUKVyRdk17w2KRwSk0SXCVARKWkJITuDRIwezTegEWD7t3WikeZfaHLN4EmdWmWqE1s2yyZDy+v8gmC33uCgX4VDsNa6S9Kew9QwLasFjgqwYR8vuGdOnv+eGaGozfo8q5QJ1a/r5OpKHnWojS3qWBxpe1XsOtg6PzattBUXUaPvgcByOLp+Mk/xOP+uMgKAgW9yee/bvnMc6AWw1GuX3mmWYwiT7Gat4F6x1KKg9U8aDPpj3RFgHB4XaVGfwZz5zlu91qyPpdlcSvR0/zXvmasgCHqEYsRBbN9R8/6eR1LBGwoQaoCrZsVab9ZvUNo5Sp0DHWO6wZVxrXBAU8yn/mj9x1bqJFuhcpmFCdPVI8wwD9Q209KkZ3atko0QMWFSMu6aEi6Qsi+8brnmQKecVFmoFmrBL7WO/EdDvXj+/WL9l2eTF64frB7ppmEAve++j1iyZs7A7ev3hmhUntJy9uBOgAhmIk2U5nukJHvfHL9gFtRN1LO0hRXIjxa1t5b7QIwnzBe/H1zahbi+1J9I8F/bFXgvXRZvWCqxcYSJGKlKbmv4rEyphGmfIFoXAKi6aAQwA8zpJlFI/hCnCwSn5v7sDo4FvMkA1h9Zp9H9+/gs8l6Uu3+hLQk5C60rfqPW0P5yHDVHB/vG5lV095gmhmQbAa+Ln4F/jY3TwBaB3pc4W8eICqBzKhD4k7kqbgl6MUc45jJroFjB1hMfjIaVGkaXZbYR+AdSU0xTdAzPNUbjXmg/tp/11qxa19kFq2RGnr8t1ma3J9dSbtiYFGRLBXzIFOTpHPEM71N6lXdLedoZ5kTfvUP5H7tMbqz364+PSFWufzgM7+Jy5tjiCb7O5gM8g2jFreENd4/VZtA0lgP0lnyVO2znONh8s2SlxdLWKrd8ufIH6jgZi/DNx67ti12HdmFTqDAGpjtyYsTGdIqeuaz6LZlRY+7961IgwMxyoWjU3w0uUJ9Xz0DOQEOU7R7yAgCwZxwKK0gOqys+dd3C5qR7mY6m/ecOSciaV84JcmzMceKskGnLffpGqFmdiZyvXkjj0zFTt3Pkv7Mdemkwe1ZpoWlRUJgKhe5pdVc15bV7LSKCO5HK/7LurNSbfSgNgRZdMiq06yYmNJXSIIrpNvA4uG1XzHXHdwbChqJqbYp4CTPnJQT+GHczInOoRyxj5pbiAfstTL57lJ+w4KiDVmdVLvRaAbGZ7l7Ne++M9cYjxQz8eZbrNizZID6NmccFkMNOU3Fng/4w/91B/JY8zCoG6iuK8MKgkKk+WCIlIGYY+QmXxxZQGBsvRrg5xGoqDVraGboLPoRYNYy7PK9IxikwNoXiYhayifXUBvg5/vlpJCQ0Sar6hMnJWY00sxgdzjUpgpqNf0NKX2XhpY1Y/J5fQWHYcrO1WIkjREfqQImJmuhswTSXMwSu4XJQc73OhlygxK8ictnpVA8WPLkCFsYda3+o7teEMD2NM+Z+mlMa6ZGKOwLQkGD4UTTSU9EvKh2C3OhWb046mDVTL1P5zlO5Jra3n8vmh+sjdUAyaC1+xfF0qf8dCqdsC3ehfzI6pdez62r0pbJLnZNl+1nup4TNmaOf4wh2+zKFEWdW0nkmfd90XLU1Crw3bcyttNA0NKAiG090HfMGJnf5M39OgFrA6VWeQNt+Fx4UpxVTH7o+204CMbvAIQ+U1GdZvX6QSN2ppyp24hniIahMgEfIONYybTaBWJKPEqkYfhTLyT80RjcPySld6YJzLO97f64FJjntOdvYw8u8/BUAsg6mRtFt5b84zPcoaTpu2MX60IfHLBTgr1YKfwW7t3+pcrNBpu2v+Zd3ARa6Zz1YDSWuh57IAYiaMDBuYev6RGwjSG0arB5lszGCqfURFC4+vHn/Kd0U2hj36rdgTp+Z0depCOXKseqS/GmZaHcap+/SqYrVDrgN56Vz2VW3MATTwpGGP72PnVwpVLqALhxAJ18IPMkQBXll3rwS3R8cNHdpG4Ycj20PAcLl/cIMa5QoAzKmTmd0lk3+hUWy2OO0808JrUK0uH9Ome1spDsO6Un8OXp5Y8jhnjf4o2J+FtRZgtUecG48BFB+Wx0YyMM+10lwUQK+7XuoXU/m6OT/Apckk20bc7Xka4qh5G+4LtsCGKdGdEyevJ6+dceSsC5oRbZ/2wIK9IccgrCnO5WuC61PaHi5xQ4GQAlPGUJNVm4/iW3yT8IbObqDxTpZG/NJqf8u6loA8nd+/JMJ5of6xfwhvL63vqSnyDRrxUVXmEX9VZod9eg+2XCwKlhSlpBjeAHOLchl4VivhTTAArSNcNvLqcbAal6ZB19AxJypn8gzhc2uUa7xB5X/ApA+ph9la+kpMNtn17rwq6J7ORPTIvl2cT9HS6hWQeYvfHsAifUrSsCzn8/EtGKYL36kvT1dpTVESDUFpLEJh7vPKCAd++Goi5rrt1mWFXCY0X+yaMYx7v6uCGcdo0GiKn2rIAcjxu5XDuKh39rLnus5ya8a22P+6P5v+/XwRirQhC3w/ahdw7fLxQdiKa3UoUogUOybRdL1396V8o2BP8n2gIxYhsm+va30/q25R8kO1KcZLuCk/k+N7rud9FyW0xhoLxOACCL0Kb9yZV+4b266T1zneM8f6a7qmNqPhGpnypui+Pr/X/t65JX6VFuZu3FpK36ldF2KVyesLNjBSsUfBCPGwQoyhH6CtBaS4jtbHvGyvq2k12pJ+jyn1GMgCuxfqAu1EFnJK3Qvdugj5WJnRgAI2k81stRocDbEmCVVvu3HFCnMv2m7ZFKZRCbbKhg9lc6zl8sw0uDBLk21Hb1Mh2ZQEJ6i4fhAX+g5JscMIQzs01zSlxCtmyJIIKJ7Nzq1e+B2ldqE1OpOlqGnaAff7HzR3CMxb/G84Jfyr+qb57S1aJFNhKF9DZWCNpo4SRrDnYrtkxyfrPJXqVz5jQ3tj3xqhHHTIDL5E0UpVBSvNXyuwVgnqefD7YHfVFKCaIQ/mV0VSp5OeSkiY4IkjEfKDuuZRij+qla0Rfx1hvPgi8vdGevAN/K4+kWMZgOunHAJUdaOWDZo47fl1sXvKVPHtE5Pcnb8QRbCYdR+lSjVccWSCTekYM24pyXGPYJqEMmdGsUsBbm4T4giGuG5lAQAAEABJREFUvlRrcnpKEwkFLbO7zojJn2X/QgOOzeYLpt48rhGjsk4z84x4fH39SlsKTpDV2E6PWmBNzW7+0pIhfuu6v3btjifP7zxPnMd3Mt3CK+w3sF8KGEJ4yVZPM79rdvi4D+vBtNMNS5KB66NqRK2FKLO5TcKHQZ3mODZUfiqtCyNqdbpN8amwErZDubMasgcCOTPBWiwDZ1/YH5hTEJYKqPBtPMqsn4J2Hee7Tn90eeHyE9jLpAZjhp/L1Gf1DC5mDBin8vgsJv3k53n/6yLuq3nv+qm5YD5tSISOhfzYWVHLMXAd10hSSzDfo73qg/OJo7CjwvclGhSWBZZPKKpfnM0vJD4xJBIhAhqr8en+ZKt8USmcZm3knf6z16XVfKL4f4Cfq9XU2iJ63WWLQsKnp3b7072rhkpAz1SqwX8h/mzGxpYmQO/FDd/hAzkae4bqi0q/nw78N/gd7F0S9AUtk16PliMldVv85xKR51seXEqiriG3fuRUpWG83FXtq62HqeA/s3x/Mm+UWfUFJaBHYa007Mpn96nZ4GYReXcTXmaHPNbAEFSWmz6MA3tQm3lWljgPlILr8NZTiisy1gnSzGTLHjXRDZ6RtCY8rlzxHX57cppRofVqHcVgylr7qTV9Fbw4qbhv+y4FMaxXAv/zhdcBGjMKLu2kGtyefqJK0y7jXMylX+aDF7jVZ4kkqqsaW52K0bfjpmr2kRWtSYkYQAm8om7hV/OHUtioUfh2/I5Yl78pzp4693uqbcGJVpK6TwM53t4W3RfPQi7oHDqHsMH94Eo2r75++iPNVGILPX5tPQFtcRQjFdTlft28ot1qVEv8Og/Grra7DdWlVhY9RpdhO4jutm501fBXLWTyt+qYrqpqYuj7Tdg9YmZgkS8rhYVzXV8d7oUYu6HWuJbUVUXly6rN4Mv5/keMVcbsBKvvnqV8J85z8nw7H/BhyEakqK3Fs6i/TbSSlAclao4MpvGQ6jXn4l6mM3tUgLnt/+H6NQkP6jS1QrwxlNHvs4+O41nX2X6e87XnUTdYzXHvK7CGdgr9AmuuhuRNopwTASw+8nUzL7S+ig9ArMynbT36Q5d0eLZtZ2m0OmJ/TPNMTUJG+BT/O/ZsFx0OGjjwlO8pN1KJrM9RwrGA4LjFQLR1TdsY/y/rihlHZRtZ7N0jZ3057tvuS+/jy1vtBCfnmfPf9Xzv7+M1sgi3nHXO1ScM+KjX1rPYM0yZ3MGm27nssemd9p3c0YzL4iJhCf/SHpqczNy6siNEiKjQvEVzvJJoBbWh8iyHJtrH4Vfizr56X3PuTJ5U8H2f/hTuVcvnL39rH/8+OJLX5v1wzdsIctAQZ0/7jnWWNzAATJ0dWpQq9HlByUtFs9AmUp8z+zF81RXtE5eWhpX5h0oQfBLZKasO0+NOSrq2nheNAlXb29/SbDZz8tf6P38l6N7WpSr7ytVbTDBrZQ2mYMr4XfH7uc4ElLvB6mGrq6DnDy4y+U0/yltzasJG5cBhE5DavcxJY3t3nemv+6G1xv+/sd9YHqKXNtoFITXfJPdn9C4QDSvEGdnFPKnblV9C3f5pUxZachWVoAz+O7lkstDtZ+7Dj12XX5QBZ99m6w67zOONocgv7Ssax6RYNzHtXUsb0E6KiZpOqM+XjcqUdJi62vLFIFxOcjpO34m7pcywiGisPbGzJpUBSUwR24K5eBhRQaXXFH9XOs5iBvaV5+pST2/jA1SOdbnLrHgGvJX+S2/nnmIkIk2veK0WTAJ62WagR2gCqTTdej7dajjzpT8rxhZHzRC2bbWttLXjLtdxUcGbP3FtD3nfqGMm1SWz1ahVhc+EsMDfGGqm0ZKo/vWYCIA2ezU8XQ/PY6VArWllrIEKap3yPuTBpPHcvpfBBJcIDVbfeHAdxhkmNLnYTZVtzulOt1nB2/6UOC/UBXrDFTkGgdGIzpQoTip7hnPwPJa14KZgy7UJgmdHY+25W1wfFjL1ZMS7W/GUCjPtgY8ThOy+6hVEt2NzDHthVo8i6jQJ7BBl1uMzHu0Go8y8Mt824uP9TtPVi2M3c/nxl5kyb7uKC6/YZhSiOF9HU1q5l9XotjJLssmyk/czn5R5V6Td4XDnGnTOqew618xFI+7yz+77aasBOA5j2tWiCO8rfdTYy8dabEAd1qh9x1xDAxt8p9SFHQoNLYC9FhyMDZX9JYdC25Ny7YpcWEQ/+8zZ+bv34eH738JKsVTaI6FLkyF4K59kbQV73EulA5jhBU3XpyYGTeZlc0FfHBv8YmVdltplC/FMcd6C44xGY6bV7fh93YNUWdKCg1P91TuYolCJN62gfsaquySSmt0jX9pALcDNnOE03ngzOdPZqLKmb8er5t7olvW5qBHqaQt1tTppXWiVVACBIdLQwIAScbUBhHVPKY3TnHQBgMSunIiVoU/ZlHpEla5U4rXgoY1O/QhAcoq6M7dvxc5BwVluM9HjbVGvTGWhA3YpkJPTewy2zK94az3Q1ldQiyds9PnEqPQPb2mtBjPDkl5tN0ooVZyTfff5sD5VaxuLeHms1FKJR+ns9O/2cziPutfsXI3bJyYtNT5NsvQleLRwqZScw3yuo3VWTVrte8Yql507mhGjAsO1uC6LD6mnX6OgY56X23FHvFVznBPyEkV99XQ/ds0dDPxZMBsFK4D2kCnSdVSjQdJhlj9z/vUJ24X9TMsY7cfmJi8zu1aH7lFdASpL4nBGhLjeEA/XARtiN99a0xpEb0ppbV7NnRo6yLIEy3zcpAOfwmA07j3S6u32M1fOSq1+upSJI6l5ygrdZt4nRQu2lFunCbhyBnezReryvG6UtPIYW4Y1WqQm2qi3wAnd7Fv5BepiE4UedLOrUZuSpTlNDnos4ZaMZ3CQeCttQhZtINCogkroNJNrTOoi9GoYcXnfzM/Ke81tc8+YNa8aT/qsN0WrfI5VRs71mB3rgUzBJJlDwXrt0GoMNauoQT7yoIB5U+vZ3X1lk9mf4aIp0hxFgNZFBhXj+EV7L+De5rlWLxcSaLCZuYd+3BQLH+ND/wJlQ46bDUYNA9AOUB+BNy6/4gqLlqKVltYzyu8n0hfSmG6bSHottzC7RPk9WAlJvpWNFKkxQNswyr0Enad9j385E3XdEPWcyxi+5lqQM6VNSrBUZvbLeKoZi5JOx77ZH4xYvYdGvWTTc7yLKtMuURtY14p46RnrBhvuGFdaRZy+reSr6Cq3hrVVa4FZwgtxm7VyLvCdE2qZ1htZQ1OF5MVhMk99S2Q87ttNZYf2VcpzeTYGBezpqixirPXe01CtT5k4nXq7f9I7vGa1RDAaN71wRCs7SybYRoRmKILeWTotHfqS0BjWPgf19K6oufKEyHqdCAA1ecR9K2gPeZxpcS4iZbYlOt2WNxQgvCFeGD0BGGOcpcE9rA5dc142Rom2OCDfpmqG26AfVydUO+Z9gwuaVhVr7puXk8DVjHP5+WDTD23GwMviQlzFdXiVoxWU1UHnzAtOJI0otlGZPSe+J/SBWW+Ae9epRwNbOx3+AItD+Jh1YLzGQXXVhTf+Ys/V9vZAMJWitZcfzXDZx03FuvBrb8/isVW4pg1H6odzXMpSvJMxx6HuUTKaZkfl1sDZvnd3btQvM+3M5PToSnIOKb/jcv16Ma+x1OiDV774o2bmAmyWNalMcSsrw1S27YKxEqPhBDR5Ct0JPyJesKDXC7X/fssviGBDk1p2dJf1ybAdmVRnPj7CNVnWaUwXXXONFmhFcubORJ8uIZ8ySINdyJ49ePmzlw1RpnQv8+471UwlGRkWJnFPCL1NeiSvaWcTo1kf2A3QMrnTT2qCXLQcUKOqF2vZyMXu2qql1NpMPPPS4eRi/HD5kDNj02//YONKu3vddPN8TVzWWiGX8yqgUQgNUOy8zZetsAW9RKQWoWN71A3XKeEfayzDxTftmzci/A43k5MegX52KAUQddji9ZZd3oHb1Hm2T3MA2Qh9qjrEdxSkzzbte789v9nVG5OojV04PYOJjc+5r/kx+9Bn+G3+NlLd7woKTwxRbqIREI+B+kyYYIYFlLgPr3+2fmLZTfsYJe/1wJXlEbNiV2lT/qZgUzI1LYN+8/JRDXo+owoqQQMz1/eZX6W+i9zLCzeE7KVFNCrvJUYxSpTOr3ZgzSs+lcK2UrjLU2Y0KmmADrwvoz7qTfUYHANNRZWmvUUda/lCUU3kvu1/YT+KfAGODe66T60yfm9870kzP1ylzpgz6VI4P2OShHhD5ap9+dfvPaExOBv5y2lb4iTAc6WOQFxQt1SWVLCnCcJdtX0g1tEBMLU3uDQRPhHf4NcoNBJIa61QyFJ/qAYRnbd2XVMYnFEsb2PyDtqtf1enZ9aIMycuK59SZZ6anmnIHSGZvLqRvLq/X9/nNmXxNr+9hLhl2t4IW3knNUlTpxoltBoA0znJrlSHXsquoimsjjvWZ40Q2O7WvnBTS5m13PaeyinlQPBjQSq7B1p5cVxUzD1Lu3wTEd5Lf6+17ecLrSfqD/HQn3HF1C3vD+xB6NJjdmLexXqWlbYWTvO2T/EGQw1f9/m8/VorKO6o2KGlmdEQje8itJNafuL4fS6bNRMHVnDjMio5Y3SdfA7yq4BMn81n3cnYD+qL3vHNz1N379v2T1SQmiiRKPqvY5NnHd5SyHY99rvV6tpC3AIGCUZlyE9h2Ru/+wM+7U+vHJMSxs+Rp+voeV5bkUUJ5br4uT4WP2m39sE78vHJN7ocgK4GwuuTnxvXD641jtZEQvXLPJASnsnqoxQH7Bal21g3pf5p1eRMWQEHfU5nVTNnaff2OJ628y4XEZJkixZNcOWx4D3GrPsZZe9GOHjgx5e5Ik1nm0HMnrEqcDefwn/WfzXGhjOh+FqSUKbVQIitetCpGZ9elTzMLWK89aT/85UBDfkQH8oFCHYBybW14yOOYS5HRNGreXRJHXHlMd/t8oWmxHLTlooGgYwLEOoShLqzVs8o9n65YvtwacIMoTBC82VzJERrgZfA2WSIeY+r1t0/MWsw2/Lr+WCQfU/98NgUSq98qk6JjdY+BZ/xy0wZ37oag5/ODx7nd9ZDsVityHrYPdBtvVe+3CigadWyPEOat+VwbSzcH1vw6+e2/33i9svzw3PNoGVFbzSu5R7am8c6fj1rpV1Qtbd5ue/Vio6oNbzmGtNf3sVIpIpyWKpg3FC996uZn9Wff/lZYEc2Ac77j7ahlGIPHzLvmbtnNYxwMph03ZHmubocShyLAXvT6eKRPnrANUsywWVSs9uP8e4uzH6T24gnoKkwBHt1ruF97E63bWiWTlELeuOQIKIq2i+7/BY3OgVv3pKucoNuKJ65tMZdX+LL/eU5NOO1sADLxVdN9+1+haxZ14WHNm+Z61MUweg3RkcaVccv9B5XwjASQAo6fqGIfqp/Zm6rD0p5/wyuPgtfRSb+UsBfMLlvVGA4erVc6uQMpIBQMLSrr/usVeQkZuinKn8AABAASURBVLrYgEF9TMtiauBBHKhcjGCI3JyGBpsXhLs+1e/kY8QVfodpbQ0hW/O592epLPVMpYhiZk/tpXBlMDaUZd8JPa18gAemQKtnWpZ0DOXosN6KGeK55/xMuaBj5Du4L283B0LpU116az/YwbJRHESGw1m01J0EX/JFAsbrYSfFAsTYT+b92/dvxen3FNxMmb1nfXCW7fiM5ATj9FU6IAOpjPvduLdOqA7PyJ6YEtiD/UXmO4cXXlVrYkk953v2zt2nsDmCrE2eCultI5XgGdvFnWpncy6O9LREvbyRmtJFh0nVG1dqtlGmF9EhjSBbZrQ7DWctO/wk1zjwAT/BFcM26zDSuGcEcKMGvTT0y3+Fy9nsB3wMB1OjV43pA56NzngFVIX6wtMveJByvRfpXFzuLiWggO/Yb13PuTuXjwU08jrwL+xpWXbhnWOtMYOimPAepteefXbifb7/4bb72OeRBQVU4nl8N5KaqUd/9rAmDgVl3io+0BL6nfqN/dwp6gJ61DrX7HU9fn/J70B/6dtqk1qufNKliqsYrSQstpPQGaW/0T1v3UTC59DOCpHmA7Ez31EVfLpivqLeXN1k/l1qx80Wod6z5nzOIyHbm4L5sWfFM/H8kv/l9ah5tXYXi+qqNJuc/j3nqe9cadHmQnZkOkof1zX39aYGpHhCcwaDhoI+2sAXYWZKxZJfQO3Z/qFuPJT6L33XFtkg5nIdQc5QxEqJEiVX8QGuu9ddZknJTwobOY04EZqlBnnF+VhKo1JUb3YVrHANLzlWU0OrPZayUknbXG1XhMofdpo6VwJsImbE6HRr5NgRNGwNj7VfmCIZ0e2GdoxB6dTkXahO2K/p3sd1xZetcE3AHLRuL/ZZOM6qF7pQvS4zezQLcpvQFrFnxBXe1ytlnuzv8FmsBQmTxgZh77+oK/ry4+85k0PtpzpyXo+CJv6xbCTHGx1GLgDsgfBaDZUX9CH2cPc1stmKRcNMfjSSeM/pVI71o97IJyTOOX72yEZAS0LTS02+l5Kvtcf7RQabUfLdsDCLabF5t9/w2+xm6yxlQbnJUlyBOzRF7vo9hZcfSZujMg23cJgMokZpMCLAUA63SSBeDTARo6OtkbOOhOIaNhVNV3ePT6xlRrNtlqAYkBjTRnlQEsdCgUy0sey9JmZiEueLVk4JRVHXHGULmjFGWhvKQIPP29o8W0pz8Op154rUJDMsYgEuR9HtRUtXFDLvIGDJlVh5Yo5I4lBn+9XZl1pAp4uyHl4W5dMsKPVh/DB+bN2jknRPt2NsQuxhNv3RNwWWGd+KB0jbsmSJ6KFXxQtVVKxx76w8tSdKj9Ie813ALp32LkF0knj9Y2Mi8wKu5fNxvg+AG6GM5IwBH6Xwclscn6PJwVRgHVlt3UIEm4bJikkZSPL4pNfYKKeODiLMKFN2qMlX49aKAoRoGRkZqE9d/Qf/zYSlyBEZmmMyPj1+njWPWZF91dzC907WfDZkQjmL5jUTQfNFDWvFzg0eVq5SrsLpbCIrZIR2xwqdwZgIobKk41EY7xgMTOHVcGSAORdS0beUoGqbdQWWC553e4JpPDaH+fyVD+BUyO+7OjJu66UEN/ASv+Z12IelvlHj4j+0RB3pgxBXJ7qgHyUWkP/RP+pTQQbeYHVS1/XJoNKJaR97D9Ih1O7HPOWp3GBhdrkzrP0aXwNJA9ofVUzSBGXayvwQZTVo8i0XC8H5IlMl13yHlJ2yXQV8bbbnkQsUoSkX1PaeiOIAGJkgjaCXLSEznQT/3iFTrnKyPHEO24bebuMIrhCxtaripHRax/WxJcM3RjM3o+VlqXiZ8qJdWTetxnoI5er10aAQUrnBDbyVqlzrspCZ0FrWkrbsLSXdYvWv4dFU1JoKZSrud6YIr5bRgQGiyEkpeJVE+LZvXUFBpjzTvdzFwTq99nhygv0xtTwfu7MvwMJh8g/db8VIYlqQ9Rnfds1o+5m45ro6otsKM9Y0EQhv+r4FIt1Kp1TSclZjkNRyiTrGGM3TXPricnLcOry0IkI21NfauuyvoWMoQzusNIaKurUenRmtUOixOT4HeLL04syLWlFrVrU/02nPXMPLCFBIz1avUcut0YnRqYlptwwgyrSLix5YSeJZnVoH1UuOiisCDIzT2tQgIZwqkwZGnGr0sMq+UHPxcyZPT/WM5VtmEMA0ceN0W27U6dhlnSqRnD7WRyjkpWSAylj3PKeqkiiz4epX95P1KkjhLZPy8BJCwEamaubNbpmA+cBa26foppptE6DGm5krq04jTJHkkiAh1X53fs/o2U3xBn9eEDNtQnfkiGM8xk1ZLXrCqYT0AHJ4lVqiKvRiy2Q3K2wWBKLJpmnQGHoaZ5iSUAeLCrkyJzP7pQmtYSzThL/vh/VYtxnpn2NLKe4QCW7v/9lCQD7avS10naNfcMiJl4rmLQQHKYvAuPDovfra0HKPvgwrsCx9hjNWMgyfoPJyWYTK5ppHtyu3TnFxLsq+ku9q5qLqUeHTDbSRoXFN0EIoCzPgOHBzbgp6pXfZCAdVHG2zfK4ik6dQoMtzba+tBfzdJ0Kk3UZ5qqv32+9NZthI9Zt2BCIqRAXSR1i0QcIGtqidLqa6oJ539Q+VQot2AqQwNa31G6piVOuzMYjoNlFm4XZZGZ9U+k+xGAkmlJiS7zW6/OcZqh0A6cjVGDPVT+kEp7tzUJtzlsKCWcNqvGdVr6wleU8WJLDn6KZtzlibB5sKoPaZeiOEF7SA6HQYPF5tG/XuImZCficOtbd88xHjlnxVRXGcsud0YV0Qy23WtKHq1SgLSHu/2cSqt9MBTvf3ss9lyq90UIYS5e5jrIsT7rdbMMlvYR+Qea/2RRDmJoiRChztDls2dgZp6GAYTcvK1gWOYS6YIL9PUNAfPI5TfWp6WrDctXW5QVMBGBXf+CKX0Qzvq/TE2ayqRC7ZIrV18ePL1RHS64Sl8hfit0rboVRHvXytWRpcXWKmoXxR9f4iB5ayeTXvKpukykAia3MR2mjZuUcxGCGBRPV1MgoDNFQYmEzu7DMPTfeNTXUQ3ST1TRlTIn93/3fw33Su0vOEq6AOXK8y2qo/52gtRsu60PXuVBf3cpvX/szIS44cXzOXEK/3zJvFHpFWYKbz4CqVv3gykpoJZfLcsz88ktykjDhv5z/nmmzUM89zJL55U4yb+Q95AwYc8pfq+GtkowP5Gd3RvIEpQ3uyZt5UPoGDN6b579Obu5QskBDEqLj2fOZcLxRitELEtIg/jYcLoFDMOun5jcHF5R3MVbof36rm4T7VR3Tzf1TxKnF+XtZepmZoWOgL5++RkqOC7nG/6ugH51d34Ur5mBPA2V2njmjV2nIBi3bDzQLuu+d7dODb9t+mFkGkaZIxTdbVkOQMx5Ao+frEFkTUsNSQltviBQSgAQNt+sYQK9dVcOjlM2vy3WpS/COZpTx6piQjtkg2oWS0l13aCaWCvHpgtoPfj/3eEh/c6E78nPw3eV+Ne8GugYI2vyaCYyzr5B70mDXaoaxtmzM2FRhv6EBJmw0a7xs/V69LI98uW055cMBhpuQAU4ZDPszyhA/NJZQ5nKOtkh0qmTJGDe8rt81uzmN8eh3YO3mJyuhxb2WqRVTdV8U9lBsR8JBSJcUUwTjOr8WBnnxce9tTwbjW+nH/B/x859pcHdY3fheEdmL6rvnk3LKydK/enbAH+CZyU196ciyHQ7x7Ccw0LI00ioJ2aw8Rt5qu689UVk3PaKBm3KgAq0Mdwcdud7WgNJ68uXU/JzhUqiSmD0iG/q2cOCoInhl2u84b54Ib9RkF27obxz3Q82d1P8GS57uhjeXg+Vo+NxQPXUDnO7ouamCEOBwrhd1mZDsfzBHoag14raMrRpqRj1EQO/bz0tj68tYrlJwkIEQYLPEW46aU7f3UPOOKG5kQs+8QDGvoY5dzkQ5M25zPPNd89aFnBrx83eu95Vo20fCktWhw2Nmfk/8E1ajm+/5PLtm7yXhoj3kG5MdsZIC8eWx2AUdeCCp00Wc7sbHOkKf8PKwNLy7Q0N378t9PSJ3C0fvRKs0WM7itf7QgWS73kFoS1kf5tMAyc5nHAD2WVhNDVRC9wBN8gm919umROKg64CCkkLfAlDE3RTKZLcZ2iv7kaAV/C52r1ucLO4BoHPnbVD/sAmLR6WvmrryyXSOg7c+/6U+I3GV1tfIpId97JB/Cxv1lvEWESL0Meu6uxcdlVkNtXj+iJ/Z5b1mv1eo3tITiFEKlEGiifQpi50w92M+c9xx8Rvyl2pzZF0ywaUHGKcvfWl/827xhJurN6wNcCRQNYgsh7ty2Rc2P5T9+FVQ8VJ3kO45DIjxhgurUii48A6ltjXKojTyW0O+TXz2h3lbnbrUNKI1P4r8Hv8/5TTyDwjyA6DSA1ZCgv8rZWAG9eqI41omn8EsokJo3k69SSaaLDQcwPLh7BKdgfUy6UsiZI3EloRRPsw7UNWM66zzad/+2Fi3UbRqR3T17tIBM78mSSzmCUzVdrM0/FQ3LMQseaThn8uv1e9fjladb57h4WXjfve/5dfwHU6tLKr1eRDLbus3GLyinbsq/4Y19/i5xMgo/bf8b/3f8HrIxwve2sKYYGpVwfrnTVEXjhPIH28MyuzD/O/Pv6VWowh5U+pwlb33HhIE41InKz8N4TNyv/SgAzRSMWG7WsNqcEatCvZSCwWKru7qAhL6YLhXm6Df77TrK975LwpCQFkSataTmsNBnYTwq1nDtS+f2Vmutu/Mq6k9M1/XDS5h/dq5saHnTWcF21MJxH0TiAj7Qytqy2AqrsikINzCNL7ErUoy7GT1obg3fp492KWOXrZs/F5biv8gI0M1Vqposse2YX0ot6n6f+77W0nxyiQz2yTx1akbMpVTiU+Lhgl/ECMDh+lbsDsq6qC1dlsQaZhAZ498RvP2fVdaq8l25v6kiVafEZqZv7Yly+p3IAQkqP7FnPXalof04tTX4b/nmJexxG0JCE7D2rstVnXVJVspDELdatDba09zZ8xS3EPUuVc9ovRsCBmniTa4rDKgBtE1iJB+FtkaMD/pp/r/sBwfXth9gOcb0b3VrNeLx09d31hfr11Ytzk3/cNE8faWHwS7cZMDUso7dZnGNr3lpB72NpJnRLlLZddCIpWRT55f5+xRGfAgKRuKqT+Mui/RWTxBiVvBIs5BORZM58c/OD/bCQ7QZFkgA+mcN5bfLajwZ/3cOsucfhL8jQMv4zPVxWxd9SVfEM0i1j56iXm/5/Czo6iU6UBhZVYeh6jm7Z4RWbtPrOWee/LSFLSyW2xgpK3VQxzM/e1/1rdkPXtNrgdrlGLLY58Jz29egbEEkax3G41EdlTfLguAbH0vvyTmLrVXlTrtz1P/eexWlu7OFzabQZ+wl8Mq6v8Xf6e+kJfBCHqm4KfLcfHEuq3sV9etzHb82rket4B00hXnKZKtgVP9tF5hW3RhpiFU2+vNg7QhIAAAQAElEQVTLEag1Rjg0Qwqv8/7WFMY7DnJL6KqazJr+1KxeZq4NZHXWqM0Zld3T1ZMzslecpppYlbWQcAY1NRzZglDV9GzjaBr3UmDR5X2gtq8ZqR2mqyJLOiEwZqRLcyHOrl9zpaM85SuBWk61CTFwWS9yzVp+r1eUcj4bfAxk2iuji7g4YYKUI7LL2ltdCBzXoJEiCodxZlm5NwZC3tNCcmbkLqhx+qaolAIw0EldnuHgtQucUenWjDY3ItkjTX/6YxPKlOyOKVnWLHajOAcjVRDucI5kInxpd/PfYLnkOEQbdigxho5W9ElFc1R5gp1oCf69eNxOqPvywM8wRyDB1ZIzwDaRILJnr/lGPz4CSiu+kkOrzpIWva6671Q7QEC9P2SS2xQhAFOUbxLHnaYXQOUwgpKhqsa9Vny1T+usrBIwEXKhF56YZzpnkPARnqLyd2VfYIRGrRIXAmTP+LR6ZV2p+WI/zHQ7dWtv+tmyBLpBUmAIjdbcFHrFqUjrjc8ajRGlp+WMJvPKl+b1bbVEjABUG4qvHq0gxx/SI6CIXsejiyoZ/eUjLSP2xVw9VqKAEu7uzD69wc2remUpngvzcV4e5JVi4bZ0DsgSqN/AAxnQAmkWi9cyjw4+draVdBCOCLe+/Ih/+7l53w2eU09qLWhzC7ViGpHUC29GHxl3xboSilnpm8rKETQRmYazmGZPrNKzH407qUXlqgnCx+LFV429u3o7jzGlMtHiPaoQ3S2YQ0I7dtpyrBN9Si/kmZw6Qh5FFVvws97owyXJUkS63l2jCKzDKWKhyQUIv8L3G5DptEq3WS4CRhYHYywBgx2boaZDAzvvTk42MiezqnOf1Oqub5xRQDY4Kgu35TCTMpXa6aIAuKWZnqy0wIrw+qxeV2ug67VMMMZJwnP8ufK7dhmGfpl/UGs2LN3pZZaq8X6qEu9JLjkBjlC1pewkUNDFs5kFvRZEo63xlfooByiKb5bkPa4B5j2eOll1uHNtA1iOvbqpEfooH+9J6jx6Jq1bwrqhUEx6FCzVQLheUYhUVTGC9lof2IgY6Fc7m5F5nZJYDUkAr2kTbkB2e47OSZKLyo8ZOIVOTBZmIIJmavS323PmSJWuhXA2BeeyvRwGqmGKY4fIzqvIst3z35yNp3sMWNvtEUykzVFeEDXTVdBtjQvTarqqduS2RxPyRi3SVvS19s3U6nZduUKIcR4/35nM6JId3+EFZy2voSWWUsBWWN9WBWU+lAc/jPYohYoR49fGJfdujBEj651W6gY5PL4UV+n8MqnlcokYwZEuF8okbgFvJb9MihmERhi6dp7XPJXxSF2bSPf+zPxr3t1e2zORg0eHmu/v2FAu2mID0vZK3Lk+9blLhTs+uRqXqKHbQAAAmw7oiplf5EWIA+RBH+OQigSEq6FMuNG0DDoGOlduioHvfkFJ0PJwqFdbzQLd5083ayT7oast+0DkHp9d7G3qeDX2UE5SKRSjmJkveqvWsq/yS0SSe+SuBdNIli6ndiC+jwy45tLMow1KrvZMYxZ00+7Y9c/0sS87l2eEZFrTivY2amnRRqp53txuLrXJsXt4QWLF0FS2SXt3PUUm/O1ZIsYt72oX6MfVx9jdaiya4xNWChkq12dqM7avXuFKxpGu8nQOYAA0aZTVQkA4prxSIc9jrPHN6wnfFzd5oHCpQfSw6x379pXZasnERCvRDA+/zKJ6Sv8GVPu7iqZ04MAaMUO8r5nuPjMn/rqouUGpY7ky9OLpPbYh1Y1MsR7OBhNoZMuprq2nuDfqKPntymhAn0hJBnTodiRSkwXcj9lUM9vlnjOYVqFE+90W7n752PSoMRVB0tmYeteDTeweTUa3gex72tveuNZj8TSER+aAGfdeK3grY/3t0STOhBC74sD7Kn26ZrHveqBb/SohxAq7Axfgr1cY6zbeDI76KnONCL1kSdgk1bYuQDfCiSLL2GoLP1aKfshTs7NKIFHO864mj8Y2nGxX36v6v7lEK8O9Q4WFk/MadacHbJVSQXu5Ebc5PIMUgzaNeVAbMMvg9vmWVV2o60XBEZL7pRJ4M1VtlYSNeS23GGF1ZYcMjHoIwOol45uH7wwI05S1kgM7ogBShbLVbQqtYwQzBy11t0kBPP2qjKCX2XuRxZhFLqkNOye39RiEmwNsszaFSAMwlfKv7pNeh02bJQmUWk0e8e7GumcvppN22RXkNHpnPorLPnJ6GbURx+W8YBdc9PmC3TSXB0k9BizMcEwqcxWJ5evcHUF0ryotxDfaD5ACBXjQl+F1Dck2ofGj7ENBoEdjRtTdk2vGU7QaLh7TI9RKuscVazlMzYI1k61C5zno74nfrb89TInAPXxmHYlZbJtPUIJeq0071vXN9ZzpvZkDJ/5txC/smyg2L/nTydXjPamhiJch9joILSy9PuU/R+pRFAv2UtCt4BpQJxuCiiE55+KEiocC481P0WQfsCZfNrqm9fzqy5Le6ObxHmxQnGiMiPoXMi+L3dPdUydrf7sPxOD68La5ahsJ7Yp+iOtIgAOZkE7jAiWT0UI2OWLFIJj6KJgmhEYYS4PWaBZ9js1Z/dXByQYEYSRA/3LtuA5/updSIIkY97KzqCCFCrlQi8jhrrXnJ6nUKf6pL/ax37meXk9xd7eaXB7RmiUwDqqJvd1iGIKSN5L28BrcTb5Dyu/83KP2sWLakW2VdsQ3Z072s7/P+eb+xtm64binIjOyqeYwN/AiAZ8V32vVLflIyTLRO+f0ALNKM8yOOuRnzeW4cELCeJd7U7J8+pLRbpSc4zHsqI6z1m9QaTxlJ6PyZv8s/kgQjdwqI0yL4NZ8CnSK0FGpSEvaUGoUreE1nKqVjzjTYxz3P8XoHVDB8GFK0VhHUtxrvp96LmSoz3jMrC+uL5cY86nL9y2EmDPWY0upv6CSBYE+C0/wGdNFqnTKeKSLjikKQPs+zCmoiM3fCdbDZuVoo7OaRn3TlEEWjzaEW/v3Z3CkzlASFRtHLYoxwCrZa0UHh76F8NZMa8/P0Vq4L52RE59hrhm1mUCDOfH4te0+c+dh7zPVRmkT01F2jdOi50JfF6m/QCypVGV8Luz3f5Ww00qGak+WYGCBqnedU+AYfbXwkV1kZmED3/anbNMP4+BSgx1iZDR7Duo7efpb81v2VBR0obUtObzS1lAYLjMjlRtVTnORV9s6blqpRNmiKtAlXL+HP4M30G/IGbzz019hvt6nbZzuyoTEsVCFPjss3XZm2oP1bb2e5d9LOsDsmV/mgzOdrLkSN2L5Ndctph8NqJ311PnuekSJL+j2+KNMoEV78SrcA/go91l2PROn2cO5AksUKVB/Hpf3a/AJuCmZUoHM+EVpp+nWl8hlA4JvMzI4pI27/+Ugn2/vngLh5BqVCrWukt/lWyy/3aNEVzzGr1RWeU53Tr8/OOBDlyd5NqVKNWKGy3nR4lBpi2dbqf2YUM+sLGY2uSlKZB4tWKN+53iI3SgB7HYMQQkIZ+WYup3+1KYFVEcy2Je12o1fGVbuLK7DJTH6fsXnvgwOviboo+KmL487QrjaSgEoe1KQyIx083/0q92wCnerhU2Et8eYByami8qmBGEnqJMY0WrBcCd1An2hwmvizZRZ3mZhn6HnHAUMG6e+Ddf46hW59HVaCJSR6FS12FsVyazCKZ9hdK8+MWO2jOpDq/XHYmkoiYJPrH3fIkJlLaFAhA646P9MlZqO06P9troaA2bAy/cIyRBTU41Dzj38QQgNcND15tQqI5+oHeoa1qD1XH19cK/Qk5fFk92dQBoTfaT19V0orc2fZyS0F1+XnVZtdL//vYiCbQVL4GY5B9VCI9PVGNX3sC/vm3QYdBlapEariPLbN9qF7+bWn1tvsSGFvvH4bbomqM30QSh2uZkZQCbaOxcew2lUTmdJsjCx+amOo6ZBaPNS1j278AwG5x7cagoPR5uO3BdHNfUjN+wFwLl9dPGX8yW2xxeralT2/rvzOWJvhUS2rnTK4jNzEvYV3Si58wJqrM3qyn3rD5YFMSjPZJPKwJlpC18Wq2hnD/ZI/gtGiBZhzWYPSSDwvrcPP6lKDXzVLlWOb39s0+jpiiqbd2lZpQwMdysrke3ZNvAIqEEHNZiSqdar9cTS8+8ePQrqz5RXZdfBzKiOqAytIdLxKl192F9a9xd9qAGwq2rhiMwhhfkitG6gvPtn5idaN1hdUCriL3QW3Iwf4vKUAlpY7lvnVFaGBqh/jTHHWrDosV18zk0dWv5hZ2r3mdw2LY8jd6sGhrNC260jxjGcVxT6qIRMppRuCe6x3+YjOAK/u5VC4PHebw1W/87/JU2x9xa1rC4fUarE+KTPi6/Bi/U/3z/2DYooacIfUOl2H1sgCcwZnPd3R6obZPVRXWupJ9r38OkpJMTM4WyVUhA0G0SJkRET0wJcDHFgTqXidV4FvWzq03Pb2Mhcs8sBq9dpjqzbDnsuahEbiyvt2iNVSZiqdq+zuM1SpQCtioQf6bmunfe0OzImfUpdy/GCSlTWZuUUT7KHgfHTrDKO7ujVhqOc7na53bx0eFnXOyAfUbrCPuYfkjKFVJGdYMG1m332/cX1O/4k51k8d9QtVDaKrQ10W6oeP/A3oRwXXryt11HK/AlgecNAqUXVK6y/J759jagBBmJN8VgamjDzN6heI+xsZ3nPve0+96fjEsuN7E7bub64j0UlsG3t+Ddtkt5MF3hqPWknZZ6MZdeHUqoS3qqLHfqCiy/kzqvPxbN8XJmE4Cq0GfTMsExTcMDEZMM3TDzoy6gIVw6PjVq6Ft/zfSB+LIMs10CIIAQhgxJUV9c8PicKC/LvvZD+hj80o0EOc9f3u1P59+/yQ+7mdPjxS5vlXk0CoH7poM3FupurzTmvuxlZMau58lx7R30FcFbvm49f37yfZ2n7iZNjz/DL/vopJVrex6cwrdoZhXVBvWGrazjaNu1bItxgHsYpgQKcoFeuSu9mFqrxXhpxlDRYhR03rWm+T/zu61vcNVVUWhXx3L2XbiOAsZjAsGbKZSwyVdq5UduZFxRkrSzTxgxVQFMiU4E03czcKCRmXJib+8IOFEUyrxZH7FbFImxWlHmR1GXFjARHUWecVHCjdjDetqS58Tf3qGADI4Zj7Ce/qx/Hl/NlqYs9jEfboH09Gb/OZ3kvaoOy7HFXt9mEzn3tEsN8BopO/2ittGn/uj9biKy1fi4o7GEMTFXA5JSd0UADvYfNk/jmevY1aj2WuhWLW+BXHXuDYc/KvlrQA7xLdQG+4UVx7Qkx/xRYWG1Xh2sRzvHUJNkPNern5Rv2vT47Po9fz3CjS9S1o5eZx926Utf53lmRMOEDJnR1VEm8oPewCEg6voffVq7Ua/Ex6XPQmPZSAtzHgrPYphB0YcLnirdZ0uZl6bSfJ54tZB/NkK/xZmB9ZnWzBsrd/WAd5Sx7GmUoRNfF9gFNbeEQWdpTng089OcqgR9x4+64aQ+rNwAAEABJREFUPue6k7dUo47oFRgAbfOEfc31YfmmPzuyhc5u+N1jkm+mTWtn9zlzp4buvGvHVOao/iua14FlJevEhs8aRg52c/c76el1hu+PI9xmLjW/Pq1XhkGtKuih1aiOMiCywsrjGT+Svf4lSXS0dnj17t9wpLPROml4zEoeSTogwQelhlV6HwPE0OyZL/jAHmtQCWgNVQOJq5S5gFfFd+Z33gAu+aUd4Ldad5P8liSpot6hmYrRLV2FUKrhJ/2J2J7fg3O8zWG0pnxmZ/fDqQBIwPtAtSaEpFvfVpddKxe/I82RhOqAB7UHXVqT9Q5c8B+7l3/gC2GaqXzeT66nXqrbapZ0dVOXmEper13xmvBjplJsSzXYPq5q3C7XoMjSLrO1jxBY6N8I7xpVxcXzr/HT0F8u1hHk16yF4Rp1mWXC4MSbxNOTW/EgOBbJaPd2WXbxW4rabW7l8x09Glv4Tv6meNI3Moafwl1cfJfma+Yn8WmJ+Iw69JUmiZqxmM/91H0YoivXEerGEUOAe173c9+96FUrT2/9OMjLekHUqxQTmCA+q7SasHO+NQpseCfUYu6R5FuiPMNSpvRpClYS5fNCni+f7mM7Je2e2SXFmiZRL+BtLTorRtxwNZU+bStyH1ZGq1BG7yZIa2LmlmUQEuQYBKKcvJWlr0+zRTXFy/g+dtTeIINYu6CelBwPxSFt36/jalS+QGH3mJhciREYItmFaitr1fCvhEmdcKrRYt4WkcDYiFofI2ExiH3qm/XFNJjDR3FEafTMcHP9P1zfwMSz4vc2dXhZsDa2y/QogF6jZKzeqvP1NH9n/hvd7hn8s/y+GEyk6FUSSgtUcgSjrVJto3h9ebdkcJw5ahiTo+LLEXKfjXfVfumacWlZhM7gHXAa/Yp3rV5x1otoiFEFrrEoX7L8fjGzf953PDSV6/kt/Dc8HNXBLQSJSC2cq0ZGAA/jKvvX13XdcYVyki1GQeg1rLaeS/m8R/019z6/AjrOBiqN2+0ErOjprLvs007o6lRzNpU1oYS0x9EEnQprOV1UKrnmkvCDGF3wOgVZM1waUbq4zY9z7C30JThlKLQurEW/Vq17j1oo4ruj9kL+GG7G9VbTD2LBBnmma3rydOK0P3CxPdNS6IezDIwRZj1y6rgkknSlhTy4npEslMrmvflJfAYardm6czlzGXBpwFsssOYH5gp5TC0bFrhId6s7jp7sUyoddbAjx4pydxvmcvnm+0sdT+tT2JiWkWvceQ2j5a6G/VpFjbrpHFIza3n7mnVBK4s8Ta55QsU738HXeS5ddzvLpbg58F/cv2pYyyJMlk1Wj+G4ZzNV/ZDRoX7gW/Vl0dKdJ/o3tELnYNBauHXfN25rzWVcrSxbupJTzTIpQe+ADVFUmNDCZjMhPZIEaLSmZKLlMqauyOuCX8OFsUPb0qEHzKh48QZdRGKazLQ8is9a47ojtRttr8fnMUXR6bXvyYXtLNq0nFLkiUDZke8FLXmNRkEDvMvQEb00aABw2kYy1vRhgo/Z9pbvGZARsWQA6qb9/lc6L7aPi/vbfS0j3WS2YTGMfb2cYIKy/lL0mp4ZPvvSbH5pyduW8tBuG0/ySP4u2czU5NaBqpBVGu2SdzP6HOaDs5ntCb16MGPm9i6VF0igRC7fTpimRyh13hEwYdAb7YBZmFYk5m8E2/3U6ryEUNOVEivRINugfmB+2F/WrlHZOFbQi8iqwpFJHcrcvRUzrAbK0/joI7yKVQJkNu6jvFL1b92U9Ge3/Y6COD6SbXkL7oFOBMoLMfmP+T7qMhURNcinIWpgPIv77ro24rR1SoRUPCrwnoGPYE/FHEXlkuwA3YA+Xk3ogga/eDRedG7LJ+qX9XU898zdZllTp7EFGWvGi9jWhZrZlPj6uOxgLvRlcwXcFScd0rE2C4o3Rv5sFMZ2U0Yi+bNh+0UiWRU1mBfNpaLbk2wT6xvvKrSH9XC2VUOf34UQbO/hYGgMw5JjpMrPYPpq3boE8bOB7yjJ8Yy2oazAGM3iciUuoV5C0aEjJ3b/6N2hEfKo6COPaTt4Bj1w0gAY21xbwAxTZUhzRQz0b7xzdf2rtD7T2XY1f+grAIF/z+RI1tqXffnTXCBzqJY3o2BqnYpdnATNbC3HWzL97dED/T9JUK66fOoerlKjsCjETOBV722mvgH4Y++SlyUZW+GQJUM3d1RZK0qshNH1wy7BAlPw7T1FLxIiwLEQrpsUBBkO2kh1QsRFu8q8RnMAnvaCJ/TiQNmAqXWJQ6f6lfyYq9XpClJK2S0yXBkKbx0mq0alXN5mFe6Dz6Grx5Ssy/sjKcTVcj1Gml5EEDB5wnG2B3SRBqLBMvZ7KYcNUK9xqv2pJFnHMy2/U0+1tHyGQj0jT+JwXRmRogrdOjRdpDKnT/RcfRH6C4yX/WmeEIhluKTQgTxLWiJlXRNUTda36+mR8lL5sRIiHzPAXXjnLVDoJdDrQkhXR6vlaFtY1+SK0gVlKoudeBPUwhHaXhXeepNYwB7VwvRunPE2cdOsLk3BpMinsHmkiZFijeXlfVnxSmi9dhnLiXITkXHXi0tFnFvyVD3HqK3HODpE/Scv/R3ip/g+QDIDFfX7l1zMGLI7d1ItWMaVN9PS0wELi+XmfI0o/L+YhBiPoOAa+8wd/LFZc8rOr8+7m2xKeX09rQdu9rYXJmdu4GqV1Nd7Kypc0lNATGuJ3WbtIwbHeUbBnGCbEpgxELdrIX6Iz/SqY3vX0SiUo685DvUfZfDlxw6u1PJjyx1iuAdV3T3V2oUKhS+YV6DevUiAJqflfIJPaxCM7stJtDwc2l46enuWPezNEU4GtY2xIHlN3XPonfTNC/BRw9gHeUaKYrvlz9U33pWJfE8F7LPf0ZM4WZWTLbF5S9zC3KYzZ/qozVuNrsyQxpa2fBwaBZ426SU9OhzuFOzJqUJ1TxzEUxLUGWFWMYTZKG5oS+020K2SNApo/zVEtMjlIvs2aW9URw2D+Jc4fNBmvdxCfEHLB/trakUCo8zIk59yeQYOsKP3Yln6Pteun0xd6mZqSFOqJ9TRLmuDAvWaUHTNVMjFkau2xjUdhBsngJiM05+Mq5bVNcMWI6onk9lm0Uvd5yZWwxtKYPWpOpxWu5JuQUCXQFeoI3pGLxK6ZAqtE71ttOZ+gymnbu8M2l+CZAUKdDWtIGVhy7PsKdQH0zKioxs91ZBqNXeoarx0qd9OGKZFl/cx3UdxFytLN8MsbGMujksxfeVgNHWJHKcZyXBcf3WrXI5XaKTEAilLQb6kjV4lyZ++e25zh3PuFA2x6L26XcqnclFlo3qcHtZRAnthLiNGpQcQvsalj1RjdEGm1lFLL0SH2B5h0QFrj7NcWymKCtZwYLSI5qd09pilKDjtow5Vtx+avJrNMrrUhdCUh+dh1YvVBBKjYoFIFnYTFNi4p0dfS9uj/GF/xm1uuSrJk+7yaBYL9/L7WsucktJ26yt40T9tvo2gKlh678CxqYHEprNpJuluAa3T7CISQAMK0mbwpywS6jfU7uxyWdMAJ8PJ6PIuDadJo2JVf49La0nYcJGXyaf5KNiWKQ0cyLNKuSbJiuk1DiW7vUB9vEI5lgjebQkEqGgjtNbpow8DdIvru3o19E25xdWiUhNjxKYByz1uOq2dtabNDH9IIX0mDToxYtYAp+sGzOgp72YuxAooFfsOLxcrmAepFBXhFhHLbjfOHHXbp3aJTP+6SXZ86t642xdjQeq8oNzNtJpZphg+d9VV0twkUeSwkuf40yidrrdjqlF7fv6l/8teWWpcDV+XQN5TUTxbpSI2TI14amYk2dHizKsYZ7yJFrohRpJW8G+bMcV1S9C6ACqh18xFKEDGXnx8tUZEwUeClJfhm38ku2+YYnE/h/vkdz96EtDPxNPq1lciBM9ZAWU9DaXPWgQyePRpRcR1+R2XcZksvX4rvIutUVompc+m2rLuDoRLxVLSSGqqSqbNcnldNB3xRiRBtECNYhr8Z1Z26yJCpsGL82FdaiIapu7Jt6ADa3An/WvzcGsmAFXhGevRNANDh3KByFmdBrUDSL/nRn+MYUZNUV+oy+vmOPW6YKrHwad1OCC2xZkmC6VIE8k0LOnRp8BJt6N3boPuBlu9MFrxY8Fcf2u7bUyGwZeVcp19a/QaWlMiUWImyTSv8SS2IpA39f2+yrVzUltRLud1R/8zlEolL8zV18JHhT8GQCB4JGl+bf+uva/Ke1yxEKypM6dW82f1dQ1FZwwUvCSo9Ti79FDyW10J2yMo5rDPhu/qb+dGiPx7f668rSNP9FnIz3U+Vy2XGGFm+ieS9b1vxw9xwwQYHKGDMgFrMKVqV9aC92fi2lxZwFP+LexTWs99ZCUji0WoyN+ooezaAz9tqHdhJI02o7sCtgMBmhEgNoW29SpMW9UtmL4/c+Lb0LowKi95MMMYxfJRZNSeA91lpj1ZXd2rOvyY1pnQHAgHpy5q4+WrXP7mxbNpO16lZWmU9WWKlUi0nh0+ohydGGU2x08poNWyNNAwCimH1i9Ddm9cxLoZ/4a3t63D68yUbci+94TBfJwKQCYI0+RuijmZc/TiuX2WRvZpPL5+L+1SlzRI1wR5XB3XXKFlasLFFjyhnsb9VelrH4UcVmXNKW3w7GMmPAxJamWbdHW17EMxujgZPMNRMkiE1YrH1sO7ueRKCRN95VOziaNeeTv97cZG8jNzV3upZrV3Oj5drv5JwVG6L9UlzVxVfRPLZEPlAShEq58ZNmRDjemgnqfyW3Oklb5Qa/X6kP/cbltmbCJl0TSpMND9Rp4+EKvvecyNdxOgKyj6vOeS2bPxcD9eD5CQyIPpI6IzrQ7RsuvnWm2iUddf9W2MJnSMXyvu0AEKQOh1rcV1X7x0MWZhooGxkpRqcQI6R9ayclWpmRw9M+nHrhOrAqoY261lIVj2U/kzx/rk2wVLdmwqTnVV64urZd95uTC14iRbBJpC71Hh1Fxlawz2ZlQ2c1bDOGatzla37c9Ktb56J14T8vbZuZSiWk/0vvMtemh3qGYEpcK6ODM0ShbLjR+MFqWsfvTjSeHR0XI/vrnqBJOQEkSeGdonl0AuxA0LG1yH14bv7nPeVrDbv1jFVz1r6u662eN2Zu2+1KmyoXWSdLvX/HjexFufGuszXK2i9PBaVtQCihL7DdKGmMHAhWZF3FcwbJbSYK2S6XVSxwi5BtYI/d1aLhuRNNhcd17X48qgjHnYZ5nDakw3lAdefvcV56Kkge4FUQNtsGS8twSQBj2WTHhiVRTWnpv83HOZKsaeZ/0nU+uVxzofQ/BFumJzbawU336Dou9VzNUv4qmXtQ5r9wEbwJ5REx054UhdV7niJqFjlPM799rnnM5Una+1PzwKoXN2/7HnvqJd2rMvmMvbfBl1XHPxuArFUebJBQikbO6qbN1GPy7EoRZtAax6WeOrcKELjuWY2kyDF2MRNoDnKPlxJFK/Kd1GQaoAABAASURBVM/+xPxaniomfs7oD0SiyDToLoS4dlPcgGbPbXV13ac+u2JLNEcbFihkajHINFAEDLwnejQlj/6OJ/xm+XfFrgkbA6ZUWKWHLbyUaozQS2vz9z+6O2MzkfWaHfowuiM27jO3yut7mM/Yw/Ua8hEvOubyuWT7ohga1UhfOFHkc3WqVhbdzKJMkRvGFvxS9wKCWE07ht2deBI8KgzGYFz6EnJdpPAXqK5kTr9IArl56hKx7k21rCImNLcgAtSjsSZRT8+jNb8gPoYvN9KMyaqsLaX0fqB3qI/8jQBKJsbDoO+nlPvJiyIfYnBMMpmP1cdezV/R7oFZaCuNFdwYg68UgkakryK7vp1nD+ptX2pp2V2DVXNnr9+exgRxaSE89S0Nu7rUYW6HG52qm3Qld0wVOVHQqjlAem/OtFRm8g9cA2EBTOa6rqe0vcXJOU+f3adnYHrdUaoCMDVxKMK0Kq807V01APLpMi6iFH4ZHnVd/9XVxe2mcrD/hf3vRgjXXM9ZKfQXfeGC+44S2rh6uRibGZ5h+mdM8nBlLhSDFyzlqN+uoqDGBioOogfZ50nlC6VTddu5DGYsx+nOGqsUeL+2h7CHXfdcV/0AP7Q1BvSGAjv0hLL98hp0jNm0T1M+CqnKR5qqRCAZxVWSUE39HhHVtuz6VyFmyKESoTVXg00m+ZJYyUof9VooJHHeHOB4HzwHI514XK6JHTOoGRXSVXqsrza6tewo12twc/5My8YLNDGHxrB1W7yxo/a0QI6vloWqGmBkL3FUmi0rGuhXwSvRj42YUYaJGKFdqj7ulwabhl4ETRdefL1XquPstN6OvwsSzhAlptZQh+n8arPuttPcMtFGrfE/t92Y9T3r7EAvgwV5wz6kcyjKAsoop+wkpY6a1iAz4rXUkmcv5oWilTR75NZck+OE7+VjNFAmvBllWlvuCQOaWd4Yt/Q4/n6H5bwX1CnMpCF9GR7F7HbWvAgqidKkgZndbj8Wd/GuiWT3eube8uV5Rb0bsmGMaNakQ2bAr9Gdh3yN8k59F1ciUJlq+Vwm2aCdcrCQSxFGTASoYZSR1geFmZJGqVJnwEmHpA/qR5Y45eKFRY1XdACnvXM1Is3K3mE2bhKGJobC765UQzYRoKtNH9F7CqpylltfisbLTIDmfPqI61W1ajSaQJu1MXVPpT/s9vs34aFhdNE7vH1BX1uDRIl7zmDv6EeHqWnNAaHSU87lAaBYlL5C53Df1Cqq9Gb6m3kRv6qvl/6qfgkcVKqAIfMVdn6oxczQN8XdF9aK64t62KLjDWxZ3y0LmDH5mh7mWfi9+BtIy55mc2qq1TvTeNZ18PlqFaBIK7BtxrNsVz9olUnJds3P+D/YB7j4Wlaf6u6RYuAJb33IMXWuHgejaQlFfrRpkEGHheG+876fT4hiXi1tiDblDtOO9qmFupjLYE7qHUX7hWzRaDIhqqctzYvBvqDY6bkqeaqekYwlCuQMhu0svwAVx2jroucD1uhNxRuzZK+g12s4cAldKvBWseh3vkmVJPXs5eInGBPz1t5aWDFtnTZpcFPBpOWeFP3a41eP7nwUdA4fX+nq97a8oeV7Elm2+r7mlTJwCOUz3d586I5HiO04ZPK4H/h5F2OVhsLcgeMKSAFUI1P98vFO/r1s5mpbsLuxaggoKT2s0UgefVWVs++RbkdPDmsEZTB1o5dGU/ioRKJKFUKCBjNo58UPcfdcxI/bv4s3dd6b1q7sGNVGtS2EuJdqTfOcCMBT0StLOqkmLClbTxfOmEytZUHTTyvpVKaeURlupQ4QsknJJlw7BeXEXVHvZiU0Oq3Oog7RtzjKLGED0EvtSfcmN9swGDd7eb+aN6SdbdmQhbyO2aEdWOm0u6hPQuDWMDoGFIAODoYo+BTR6TxhDJ9htl66ONRHdL0Col3Exb5irsVbsZnyiWqrr47unS3kdQ3dyJt5Tfo5ahDgaDXUTU+5yaSZ3Z+5LoTDYDMLHYq7RsbYgkUCI78TPXqM91aigbra5RjTVcmFF2fZufxEFC3fKQybpQClIbFfunPUfbayywBJPx6Ha/qyMS0emlOTAi06Ka4UjFqR1lWZvq7LVblCM5IYyn0gxyO42Upy0poG957Z0N7jDSAUpzKgPCyRvNy9tPpWTqkeNNSornEQ0s3QxgxCacJm0Ek4iGGPdIVSkG69VrlB2Nk2QGF0W1xTdLXdPU/3t/PRidSwWKZNoUBYv9NBkeUl+av8QNE8HO/+PGqCsM5GJWpaQIZOEPPKZF26rz8+R9NX8Pow/NQ8M9+xY3okcaHD266M+/j15dreivVjuQRtugdlPg6TBNBO2QUQMwKH92Jj9bqScc5qOUpEJeuZTqmqW2L1SUM6Uvnsw6OQeq7Gz3A5/eN2D5YILUx0rZxVuu22OJ5FbY/YShBPz2Geq/JWOavvIray0g5jlS6TCN439EBmDs6ZHBO/LylG1OBpZhCLSpBxbKXG9m1j4tyUadnFkdo1HLSG0Fg9ONLFi21ijY/bQMp5slUTqhShgzdDIWtcAsLjeHOQmK/1QbfcbppYUqBF9GBUI5P5wiIM3T1TUzFySnVaraynMaicaYGBSraMT9c2ySSlIYjQP4q+hq/PQ2VWwoKCcTTZCllEQtH2VOlmbvZYUtbkal5gOT3qzc+QUW7tqqryGJo3LLta8zq3QvXneJfZAR/2kS+1i34kH3GSyLSnRy90zTsNpUALTLB0utI6V7fXcAY9reDYuMbEfv2bvnPCet4ImsaCdUX3slY38zGUI61eI1erohWGV4T6BpxK1pps7pS28dhosA3YUbzKFt2kMt41Ic+odq+14Gihw4x+CVK30FPsxz7rW6++pLppTEtvy7f5m8aHTKxxdR93aHK3hHZ13HQbSc2nDMoOVU8+1/bPudfvzZL0aDF2ja+yq3Ws6BvnXRtI7/R8hK7wvmYGJ3GKWm1qrlGsFXIR+iPZv6xP64n4WRBslc/qUsv+IC4xw1EWTmGEZ8EACqOiGCDRWRu6Ad8lV1NUNMJvZpIOI0w9edkGBtLKDXv7xoJ/Zv3g+vgKuYraoYcSu6hCeGQI7mpa7gRwkDOFnpNU8bylqFO7K3Vq5G4Vq9n0Sa490YFa2PTvXAktguYCM8OXiIFGImi3hbZHFbld4lXS/7fs/2P8H1PZyc7ZWI/2IDT9wJkX0jGNJU9tHKZAZUelSETo+4nzvcVr+r3X5zEMcaHuGWWtf7G/cjZ9xcTa6mut6ERYOcpHPM4+Nrp/rR+lMcpNCn3VbQlJ1ePgOiPBm5nTvUl93bR9X7kW3aiBCmfjdG9LtWdR4sAyLgNVDd38znoQBbEiGtxkqyVFufAeKlNV22d7b683FW2DaLXRl9eQC34xx4aU+ITiFRZILoUld/pDgvotr+G143qoYpqR5yTL/v9YwKC739ABFT2tBhpFETOuGehS9d3P+Tn76lYy+7f9d8fzFcJ87vqu3taDdGw/z/k+WvNwKM58l+2NfDvZB/saLYqJl2yDbIsIxo3r9rWgE4qvQ1cA6ud452XHleXVYwLtOCgo8QirwC7d759EvpkPAXV/rvXo3W1rSVSO4PCZuyTtMJvXwcyjtoetL9YXebvf65h/yV+fGlovlLVaqM3YPJiNbpnvriNywrHG0BTdPWUj5Y5JYw+hqIdOg19qAkRcjMVwuoMjv+Inz1Sypg7OSFDd6t/9ODfvk7ELx7OUNQMlbhR0FCPjegW7rC77/fTvmgRjZmm8ja/4yM/0nOovevukZFbWIvzM4TzWv5Saai6uyy4Yxh+7znXV5X4pFWaXFyflUBXj3ZZlBUkUI7ASuVusa93XRJVrp3TU37U9kFa7y6xNibxpbosk9IqiC8sz/T3+PWw1KhR73AW9RWTOjOYLKaycQofLKFGOedkamCy5/3kR3cuvH7t+EGq2PhovaDUSgU0qYPWMx9fmZ7Aw88YIBuEscwzZ3YPRGTUDHxFlngzErIXb8EEt7qVInGfZ7+L34nZkefU8sx/fOY+SOcWEl5Eygz7ckrBilod3a4FNaQR+xh/i66OitVvETNo0aU66lER/M75lC4JxmYRiR+a0YED670viX2/hfC0OVGsSxySAMN7uamEuR4jmx/qDvHFUGi1NHWX8qHziUmsJlgNtsVfsT831mKbQsOkHd/PnNQeXKrMKXSY7U4QVrl9b2204BMqtIjp8BJ86IZZFP6344Swv2gDjRqfPXEerfZrv1oqS0uYUDCp+/W5NFaQe2d1e4nNwwNJMLGNABtpfwQyUyRzW8Sv9gtIXnHCY8gb19Z5jO4WBFF/32X6eqe+2s80rLkjd7deYcWbtmu85v6e0C3w9Nin79GB8JqKkOpPxy1oyHluNe4pM79KT+3tvyzmH3+Qp9Ybpp9VQgBbOmdJD22ezVJlOxoCpc9gYwAO8cO55rnqu07ofXy3N7ks9Qd3ALc3KXI1AOZfTHEnbJZ6qyez9qL7ZgIx1Ts4yu5ffYboJWxhBvVI+SYLTg05ILWozIp3jaFa19NUSoWNQu589v6d3Ad3OakPj72pCI2v8kLuRCkBN8mv3wTWQfY7md1apLTp9TZiqV9kbyLOE0nGPfQ7tO+8Jcu7omycKs6kqYqoP+Q9N2cS0Dg6Myq8ZNFxYKt5gye+s/299nlJDKNpDOzOFM950uEG/CX8hxPBqLPaKRswxbWHvZ6wpu1y0xtrzKho/fEV7XUc37naR9pqiftvQUlYVJbiUkNY1Ka7bhwozImEpS1f5B4Yb+cH3h9plkMfy4Tktbqv6OJ+Fh0xxP5z1mgZS6LuMBaVN5iTTVqbOwd7oFvi5P3GpDtBTGl1T0r/pMdYe3zVSQ4t8mVWXznkTPdkzsjAVIk/5rDzzzThlKvmf3rc/vBMhfq5n7FtTh14DjeMzeIRPuUlowcPrtCGN6NCQmpdCBZ2JU8hG6V0GdEgoznKXIIBNPvZSfyXkhU98v9ezRYdUNsiAhSky3GsuM597UTKulxMYYRY1dgShraC7uaXaqHOokIEDqyZGAk7MaROWow+Cd0JebwAUxC79Jb2PkDEPzvTWerYKt9Gujiu1J7gMDsI8fWnrsM172INgX+yPjfHAS/IyaGWMGc7imHEi+O7clzL9HBV/76nS+tCZtfeo/lUliU+15AglXRNZgePaDe3HVMOLh30mdUPYtbpN9cSyHqvJ8q3fBI3SqHdcRddXh1mDhHvzphs9Yeqax9Ca++L4L+c3kXUc7woS9IG/sBtQ0qiYkoA13SkaYlVokdZ3zPvjUHvHulQNYb2kSjE9wrXdxmlu7i8Od3roRI+mPdD9GcZzeQdtlDh6hSFAJPk4cxSvDkWXVankTNoks/gKjgNbbffRPmjgKLIAb0Qv+t1vkYiFJdjyKsMzgoN2gkYzlTZujltJfeUynznTiT59TiYlI0IvGT4jXfF7xfboiRmKpF6vqkZtHmsYPrxTt0MHI3ie4DTa7+4OAAAQAElEQVRZ7V2w2ReVvsY0epCj23d/jh4d+dY5+Z0uf8a/tBYbRlKbotcArIs6tSBr0VpEcpT2Xj5xDS0K/i01LT/DZLeXK25yMCr8+yQlNow23l81Mv5nHDO3dSFg2GH70jb4LlXVjOEo6AbFp9CtHm+bs1iaNo0aPCXV5KtEYtsUqdGhKe9Md9WpnqaAyl616vIu/MT4lZpMNeABHWOseROrfVDJDxSgmJU7jghStqcqUAZ0RbVIuSc+xwLdto8/8OfDRNS5dmnb4CUU5rQ11GeUm10e4fBoG3LT6qLWIv4krzorsyA5Cf1cyCs6Pv3eePabKNMUG2AgAjDfyo2j1Kr6BMrkeY5EmYpe+m1zPOS3ceQ7dNLGblsrXIjHhGb15ROtETmvzoyloOUBwj21kV90utCAaTjRZVPoUjijdjI/ytayc02urJ+Ne95kaF0cY2BWl++8ztzIEAeLb+oyMlHhOmhmQMzj+FJyCfn4W1tXU69cwDUwEtFD5UXdOGLeHthmrwKle7NBsH31LIzAFqyhKIbyJxm3iqfOtv34fti7OO2v2aGyiEInVGf7OaN9USvMlq3oDuZr2OHDqGXpPOLIKNLqJ3M9Z23t2nq0X+q3g6l1xyFP78xH5WMGivBr2Yq8/GEk14TNJSYmVbHj1jL0eClvYq+ms+sp9XtSwUCapR0BLvcy7ZEuK0RWN4vRXB0cw+B9OeXR2JQlGqAF4H8u+rmWz+JwqoEv7q8tGJ1Unnhb6Dplt5pnxAjc1LEm3O2zXIodf8/KgLwc8cfGFE+uOn9/RKan5psPL7hQDNUQIdyhbMwQx/rR9yNZ3w6DE9ET2Z4pwHZjPtNK58hwF/qsk5qPNmrwDYXjcAPZin3PTsuD/s7sriqcNkHPVuR1TC6bDc2VSjTrE1ieg91IeMWFO6AV8u5DMQk0WASEKv3O624E2mwHRT08bZWZNxcYpGOExMf8mB3JaCZALcY6dO0117S8VmrE9Jmq7k7MURMas4GLvhdNqaIuirUFrAMIREKZWC8AAFQuhWCETh9HaNMhCHDXuGmNGI7/X0/Bkoh4cB1iek9KVHOgJqB71zna1mwtOHvZs0wBmBvNT3hHzRyrNLy+rHOdGfl2L6X3hsiAKpDT3LDH/vSJltiUsZVTUjxqfyLXPX73RPUIc4e1E4ezZ14kJ1lpVBmlW66Yy80Y4xqdZIY95EM7QPJ8+eT6Ldfda1MxbZxiilkBMpJDWbK56A4fSRJHNlwkWpPW4qMjy5WdxpVaVGfNGHQRmm42jtKwGnsnWnV2Hq1IWsExurRLD1zFi74Q1PCA2KIimcM3n0pXB0aMiZsa5vi0hmqwjkEUmIxRfpaBhdfSmPYswp5qHF9o8VrclxoMGoUuZV/GRH+VyHJTTqfIMjthFTBa8JpU5ZRYlZj0frwz8Lfq7+Yjaemka4tw7zbdSc7z5kTy/Mj50LWD6mhluy9EaNWOVTGwI5DCP/gbIW4EX42qx9sstRm6gPtNKs3cAqGd0lJFLNKv6p9usahAmpfMSI9+mtmVqDdFPu50viG0YUJn8cD3ZfYTnxVaqlT8o6kwqULBdogcP2sYgBtnoQLjFJViV42UCt1ms3Uz8JRNu7Vd7aX728OjPJIvmzbj7wfXFxHsNZQqExwybLndcgeRnkqMFK2PM+KjtUqqkSLsi3m0DdvTeZceQIztZAqOgj6Q4m0MgqOjWtfi9G31L0ajse3U/c3oMjbFlZ/R+DFDaFIeiQiyhjP1GM9yXP22HcJoLZ34jCeu/R7W3b5PnBT8cZEtUqapoArSYSm8EBq/F69Vjmlz6mPro5iI4VIxcD1FCSbHoaZof20nbUrBSEBkLfaigAbT2RiJAX2PBrsZYDj2pExgj/pcNnZZDkuit+hQeGF+AQvTMwlrmRH9QOnRikJhtL0xQBbwtGe/CM1ssS/rhfahyO2D2k9+z2SNgAUfZ84g0YfVTMNe51xP2e7JfDdD3SqJuTAm9zX9WEuybopI0ps/ybNp21zdUc3eBPWy5FW4lahr8Clo1aU8kkOrt0awe+9vZo6eB5XezzrlaWk62IH1BrWzzy8qD9aRJAygfoDinHaFtsbNhMxVz+Efs6vAKTZEweDtBTgE3KCyMcnEXpKbbIrkN547xRBH1estZKP4quyUhDTQKfqcBToMNu/xeheE6TdRuhXnqSu///LxrJ0KbMaqr52+U1lMv85157pTjS98rmuuzyizg5kFLOX2thFJuhERDgwVJtVSglp03Wyk4ftToSVBV7Zk+g/4ufKznqhH8sm8bCKGgovyIgo4OV3ZSq8m1dSufDbOc6kfGZrcWaU096k3B+wKSKCP9tAV7aan8j/en+SlZUUWw9tmqvpg9uK54Wu06XCRPBBFdc/yvlruG2GPr54Qkpkh3/qBNKRmqZnKDkWlXeNLk1ytQ+tcsO6epXwVCtYM1VsQocrnd0wdd7e9T9wUJGaxw2hAQ0rNmRwH1jX8TK7Rgjr713PzffmA3XhfFgOf1sGXJiJBXcVlcA0tWkGLMudFoWhq1t85opfttQ4yW0A0CtSq8et725csrfIfFyuyCmR4uYsHMHJWhvJUhu+H2689InX9Xv54pKIwv7g4ksVn1G4+xKJDe9zy72HyD870DBQsNNi9Yak+AX8jvlaFwtAeIdOrpFjHeTVvUJPiMvKSTaibVNRQRLVrg7K7O8f50iJ70G5LNbTMVs317RAzhjfJetb+Qay3yMFEbdsbz9NVExT0dxs9ZuzP7FukVPVv8VuyAOXPlt7g6VafSt2RaRJ0c3YbLk23MWqVgbibiTPRRqEPeOBn9fv/5swZk7BIGWrWRsm5+gKPOS7X7ttcU8rmmESf3vLdN/K2c13nFgUWhbUxJ4vVs313fIn3f1LN+BD/92LwEBuw93sdnNRA6TKYnP3U/qVWF8Djwz/12ds01TFA6Vyr+AdUlkbdmaL4GYHVpfCxK6FVSxdFeidmdl35XP3Lc9Qq0vQk4nxc782wuhwKSTn41j79DB/gcX7Z29RV25Uo3RX6PR7hdjEu+u0Ik5TDbDIy36BrKDhN/W6nufI0+bSImDmhVUA7KKquREKvHE2BAR+2f775OQlMMTZsp+lyj7a0OpYnIpOYh3EQs8FEH095UQPtp7k358F80Y+37Fcu5sfS34FsIp1YrrzelJTcGQ5GTwwDNLIhAvOEbfrD+Vrut5L5pgewmT7VqYqW5f2hlltJ4tmRclhX0w95MMeQ2oYsIE7FVthzXVgwAs3pGHh5lZ8cpUeEUVf06//8tKavTmH7lF2AU8U8LDIVuJBDnqttz3wnf9Hf6cNSVg/wiB6Pvj0uLBUfh+ul6WujVWQ1ZJCO7cptbhmYX6icYWhXBd3dC5sNyFZH+TW8RipzmNP0ocV6VlWzJXvFUCw4ZIADqU5taTnVReZp1WyhFC8xnHJ1lSj6UJHM+0qkMlZgD0VNtsrsbPXn69/YD7DQFq1fKOJrfRS6OKjwvheu1dQfV7QwcZ4H9f9mfltgKfIvIymvddD/taKRc6/EPCNd6eIdlN3Byvn/nH5vZ4XrA9yYFfCL5q753MfUnXbd5O2xaNNz9BZ4lxQHx8qJgBqI3gqf7BtqBD3S7b9j93MryYcVC/9b9pOO3zj7PvmjPBHdI1G9sgBQxffJR8931fff5A8YdGOat+GlzpbbBe1a3kDdYtnnvq7177P+/cPSA2DmPjWV1HhtAMSfOClDr3eRnBR2z74OtP2Xhj/EuvH59Ed/OfGdlc/D7xPfvPUhn5xm4LrHYrqOBny9T2Rd6mn9VtWga2S3eqkcvjGP70FJ5QvLuAr2mOUlPIpQ7mf0W4eT4eG2uhQhTTxKI3VBiYlVy2BLdlimwSOGrMq6faCK/27sw13iz/jSoe7uPvXpvf6vO3BoJguBM5U7e7c/cetJagdJi2rpiE5lZEAHL7QeM1I44oeff/3vc9YSARt4EEAYFOip7FaFNMetcVVhWv0mPaibolcR1AytMvV0leDen/l86iKrlvT3OOaeddPu29ypyLofCG/YLLwWr8yFc3sLP1e/zsme+zvri/5y08/n2j8CKLhCFuAPtdWHctD7Zn9ggZVwwYvJSC0bbu/fLMzu5r/iGsKsFeF9IRwAFUhPViV7axIhy6R0c41GSGtrOqBZeMdiEwdsAnrf69v3bhcbMqMzSpuSdpWEZjdIG50p2FEDJekZsUN/OP9YC/on+mVkqWi0P1XuJQ2OTaJ1jDeuE5ahfBPp60hfnmDKnyz+nvXVDdZmjyVmxzk/z3z2XGdLNlB+9OgzjDdsVXt1NU/H76gHKt7nxqO7bVPF3D5Kysxdx3EqdkU96I3avpuP9E7rWm8VYXysVZpSxps8Fk2o7h337784P8gF6URsJOKL+EVoQV3KmWpCcrBOijHhugjraejf0PQzMIqxd6jIWlQr9lKIhdaYFvc5gtevzmAlJtqa0aZMdtc5R5diyHbfAV0E3x+21Sm2ghutELApYuay/eG56phUCzEEusabYKm6R5T34UjEfi2X0inA2gD4iri4rnQey1YsNsBMD7tZZfvM03/Ppmu6Ez1DQCDVGGiqjpTPJSlxz3ucEUAnXY0hXC8hUAe0ZW7mNJuYWH25moQ7zBQ6LBSk+SLDyDEeF2+tvIyjlAIzQActDwVtqjUmTL92Ui5V8riovBdEIszNRF2UXUNCwSdWu9PNx1aOKQdA69IQOhiF60hRU8fENXQFDY6HSDbXXjCrxAuA0cVoKE1yCSndrKETujJguoO31W0IfbPZYpFKUQfgXDTdxsRzYQezsbcOr/F+L5m5RpEAPLn0sObrClJ24DNrSsoLpUCJYOfd7zRWA0WXOwRx9aw6i/nCIUp89KACJReWhjSRbTBTdFSyyNYJtEMJPLT5uwJRTOprygBixIO7ovi+htD0qqnJya/3sS6UTYk5IU/x1B3WfjXExqX7U4bD1EpMBZ5RXOdeSrejZpS+40rN0kA32V+xI05gmOBG4MBOo/Z0ATSEzQxSyCGwy8ZtGpRosjnZaoEz/p41tmkGb8c19NZQaMhAJ63f0JzvuOlh1rzdUBPaHOrw2lUMxdIAXhXa9bZW3wn5L6152onbSV0xNKLrxysUtJEEOfARSBT5wFt5nfA0G1KY6e9mLTs01acFQlhM5usoxmSYX7zX3CgtpNF31FplPkOJRRy5IXx9+AG0ZwjOWBWne5SYbHRKF1GjnIUEUp0txfgfR0pWKyZQapMZuZ9qKyyT3xuxgHvkP5BAaWg56qq5H8ZDlVuPQGLAIN3+3mBj1qPxOi8xWVvUEL2UeVfTvJS9qWSmN9CKFTuz3seIgw1rcawcgw1iLpft2+xOqixfEMHya1ts13zhGe5v7e/sg2S3mrlRDidHABqaknVSdAhsiwGD4ut1P+aVlo/YmClRcbqrRae7+BgB0bnWBSVRlrJSO48Xs5vOTOOszJ/Z9/TVpbQbhTvsJQAAEABJREFUspGl1CivA9GzxpbzuukOoskZA3QQ0JmggdOWUDlqmtjQN7SSGCaQHnQqfCC6r1Joh0/N7lHBaXBQQ7N65zkvWxYYH6V3UwVz8b0bUw7XgukLvRNVYiKP1e78Zj67WxIZT4wiBlcr47nmFGTIxpTxvKvsYPanWxHNxJm120oSNmDydbrYLpbQyTqRUgkjiYM+mCLetFQJ+LS+25QW51hKpZCC8l0CUIma2GaqeRq96RM2rukM1wOkvDDv9wo/uB6VgCu2mVsBxJCwoiftPSCYLNGOfr/rWXZNj+Tf/z+W/kWxdR3WAUQBkpLTdeb+/4/esxtbfAzcM2maJrZEgiBIyc5+CKPBReybK3UdQeRxcvkO/5iWwXH0MDtGHsuo4Py4IramnVHz8tM2tw69marxMygALcQMQH/qQmprQR3XrJ8yVzYtGOEGZXaZhY9RT32c1cWUhE9lp7rLsyxDzBR7DEaEtXv5eqCtrVYAnfTWkbAxyrSu99SRzHzrV1rTnAZfV8es0d0SHccxrM4BoiCuVMhvXPYObQ68ffGtZLEGHzcsWphcQNG9PNW1Vnw+vpdCYrAW2zmHJvJlhnKxpi+Oe8/1nM+Tis6tbDK6TchsccNNC0aPgi1oej+WtW7DibY1SoM7fY32CfXC71c+Sstp3tkPqn0wM02FppeHSnewzJSJN54/Osr6kAAIuruIl39whiM/5S9DKJvjfaazqqvx2hZKDcJjyFBgDGBJCq2pM2h2GVr2KOG53r/WDA6gCzLTddaj++Lgg0kdlHki1TS22C/dUbhaokwBgxs8lq9FN2UGHJv6UPdyaWYj/Vm0drzKns6REt+3R1sGjHM51PxdTrpPPZXHEtERqp02zZ2N2fbqw9HBNpdVpWe38uIxWxSjXxyvrwtjGO0Tvotnsbka1nOqb6LNzbaBq0I3NUiKf0yxBzNCbawwRWHmGjm0Meq0PWOvCgRlO3bNumu1SBT1dmStr0r5k6kzyoX7ih1ryYiI1oYEKbGCBWA0T39eIoRa4HKs5Fwi0TGdLLoSo9QCSd6v9T2P4+5GHetS1fiPxSfarNJSYc5lcRmVZCswraVUOkWis8mXE7yDldWntSN/0KK57CQya3DAog+W5IyeZNU68FZ+uq3G5Vut7h0Q7+XJRRhpAXO9mvws8aYdBZJQiP7nEW+A7tqy+G6HoA5qRFtHFSS6XuYXYtket9HowQispuhvt6xoTTDrF6A/GU9CQAC4j6knvVLcZmttj21YdjyKvjBvIP3lST9ixQAdynvXM+dUPVipZU/MjCSWg/uc+7nvc8/J9YyuV8lGQCK4hkuynO9ZXWQ9PP+5CNvRvh6r23/bRSat9igwhWgz8WgN0QHYK4TrCl9utI0VyorZrljpaVfuDYm4OWV//maY4l+YaImlfoClDTovjulM35lP16k5z8xdc2MOX6eSLsSVN3jcynxmJD2dLfgTdu+5338dAbp4QsSi/ZD/OB9amEEsV/MVwoAzNjR5XDWfuUPgVsAWobFp4kdv/arYCFvBz7IIxXcWHrEvvgdWZg3ZagHpRg4LvIrb5d1Vt8dE+J1zYOA2D9uNn/udWTglxfPWBuX7YW4TKlnEubr+EZsYZJdq563HKGm+oCyjuisakqS6sCib3CcjFYwCUkLv1jxe4T/BHYMrcR2+SGONMuG4gdPod031tXtfd/hXaZcNGnwK0y31ItzpAa0gz7Jn7bKYY/MULLGaE8iLr8jXCjcXFwM758pc0jDNpseevmbZuXBvrSFzDx5j6WpAY8SYnXbNanizlYnG1+b3ivva52J9ju443J7fwO+s77u5tQkiMEMlNGvkdpGr4rp96W7ZMzsViIHhLdR+ZVkxz6fvH+r2hKM/OKGx11ezWSGeW+1ZFlVkWCLK09UmpqtHT07tdda/e/7nyUvJ9uK6FdW2jl5V7xeslTJWb7p0th72uaDGZXsl/b/jv2O/tFvehU4cTBHpXJjNA6UlH+khntxa/TRE1/mfllCVdqpclTL9gRIzCxam9Noh6t3wYGqeE/UsP0pSAwQuIqLJIwVWFYfMBYXOz6zdO87aqbqo1zI6UCuOfR6u3MFYi0J+8dlIm9uUehtNlSSPTN6PfZ8ohaOzjZnBdvzb+Amo5nGFKkXHXXPqZ9sP14U3a19UMhnN1dh6Viw4ZAFCqKRxdLvq309/nBuEdHVxLje3RVcxFzbOh0/oi8Oqr1oeYkZq64N5qKef0ER5+e37e+48xccgKocJvLvqk6ufH8/P1byq4jw4b+NU+9Yf7dmZGeYumYpAr7OfY2+Ch2rbvTsD41eJtVRuZhqt+jmGm5Qkx/jyr50vp7OI9MjepZ4mV3evXLs8rMBS5HpZtG3Lx6e75lQmK3ZdPzDp0aLNO3hTl2Ooe7zT2fpBL2CZCc/V56cfNTMKG87CbGCPEnm5u/lo9aIQfHsea9XaHZaiLk7522mzT/IpE5cUgQqS+Tut5SB/lu+9GZHR8Nz+mO6z8ffU/TzP/cx9rBSFingGgI3xEFVTrzyiaGfmS2mtFMt2+tq5V102DvOaKx/vwx5RIqdnuom9uPaYJSbRpPh9bMob/MIOfYJz6fQ6s9swP6XdlgNzIDFUjI6FLL7Kks+rLNQEy9frf9r7hp9XlmEwgPlK7pL8P+tsPo1SesxmTy1BQPZ6Sl3DHvcczQCtR33KMWhhRlmc9al1TUyzH7y4DZu1pclG9yjh6Bx7TsyJj9c/v7UGhNi6zzzPIAVzL2y3a/DvjdPGoFCZ//j8yNYYEb1XbkshqYK6j36lEGVR5f6luuV+cJ32lFcXfQoDITEepUGR6Ew1he+Izm3fz/7d14EPlH5jX00vEuKvBnoQqadYaoz29xgtinABScRA3wFuk+rHFThyRS7DOsDxw2iXmUcLEIWkz8wRD/8fE43SgPVpuybdfrFrFk2zMyxjJkvptGKP+qfyUPOStGakfQGcFzI5+gGsABoWRxhxI8p+bH9+bK1j/HIO3yHq6shXJyXdHpN9KU8TjTrdLOWd5M6Je9ip/tBQ0fMf8PP6FbwwW+1WmrCsw7vX/bT6UY16ztwTyiNNpTfeIMjYsXw719COdY4S1d2wlgZ9oZY9ouDSaHWAMxjfXAutClH6pyXrY/eYKG3/+nx3fu18BVaCVMFBTGUrGUHIH9zoULYA87X2WpfTxEDdLumN/GMknFMzYC9qyWrHiGnOE/3ly9FEkrXb9xN+xzn8PtXZq+3HY18xn1Y8/m6kno/43hz1VuNYEydUsFwCAFWRy+a516lPefQlWTgQClAh1fg9SkRNcTE8FtbmMg9AQUB8VHclKmNEvWx3Zt+jfDQ/Z3/OpZxU1Jd5V502wjb1yjOZkpy82w0tq24v5YIHOVeRkeI6Gp+XhcdmbMymtvQ4hcK01j0df8LacF6/T5vYXcP1gM89nePg8nCIhO0ap96wDHZxtpDQ2kkmW/Q9hXPsKA+jFI1pdrLmmtizgUuUSK/XcS2Rr/f6miIlOtZgxaxr7WuHQa1CrWbG4swO8N/gp7FsnWXfFd995Sf4Y9CFTRB3nK9pDVKN+ijCobRNYDlXvAr/8pw+0ozxchWr1/mc1N3LbfFAnVrhuz+yJFH6euja4WEEy9xGUtbrHm6Ff17b38zsGdradpktsEdTYozmGIPKC62HWHadcqOWQXO+p1Zr+EZ9xDLMyL9Keoo13TUYmHYg35jHQbkQGf6u9cc0IP2bKmCBongbnrGGs2OOS/E1CcwiXbELDCFHgMTDXw/dIFpeujZnKJSrJnI6hZ2JUf5kYIaaL+IfWs700/zWaB8xz+Hp5pSeg8pupVdZh6jo7Drzm3bXyhbzAPEAUreXvAh6Mg+esmq89SdjZYYIbrGXbclls1eGt09EhpSyZlaBN/pBtdrX3fw+VIzzOpV5K9gkcXpwK8DYipzj1lK6Y7yPmDENi4InBEoVAx/p1jSuyltH+/ydabbMGUjtV89+r16bjwrj3E9+1S5Td0jWJqMfV2fotIQw5izgCuzdn13XPktKcGrpwi16RIHCpjqYmzubvDvu+hxYR/Uacw3eBFWs+0iSm3lBKCUjDoygBjgoZ4pLnwPuJZjk+13O9dq00tBur0Za3foyO3DPyl+3sw2W6LvzzudImQu5+KwuFZishsxvGfSsQLmR8XoQi5q52MPKUa7rW/00vsMv5C8ibEVzq33m0NxXqOJKqEbdBJwJTqgN+nG07K55leeKCbBMZhqANenzu87j90He0lLrkvm0qsHoqtEF2tRM3nb+11O1US81IFtkwp6klfhCY3VvqOvT9HAC9aBuiVtXUci4bvv8mj8Kdg2CguINnwrRwTSWsjGmabQ7Vm9jDMRAd0/T4CYXXAmWQcRkIb9TnZi0U5A8pfZZ5VuRU3MU5pNSKAYwWVa0u89OUVKyWU8q5OfuWxxrqYxnLWKHUuCut77hikPxPvZ8IyXVbs+hqlVhjayCHBshOEZzd4tunDPdZVR1wGsBqvclMvC/Hb9Au6iDm8mRAiG0Qf3iqQIOZYbmmBjj/JhYKPo84K1pCBjMAD6DB2NmP75+Yjm0kLgNF4soeHW0dPM9lWIGJ/CopK0sFX7ubnsF3SlGfIfsQM3c2qZkXdgCvpLx2Mq9/Vrcqz3K3iGKmbwM20qNwzpcY+iLemB66ojbfrv95bni2CrEKJ5WskmhF2d6BeZwhHJ8Ztez+0zl6X7GYe/12kxkKkVHvUI2mUVFhXzkRBGY1Y6Ue0iCsjs6AK1Z7Uznm55uSueZImPD9ixPiUO2Z/sZfyoesnfDZVpF0NrTLnXCyT5ZmBLfHKcE85ineS90VO/36d5XcJuN+0HIyJBJGTYO0d1AmUOQQBMx2dbvh9DpGXW+8/BgVegrrs+3dqq1patQfRZN26QYyNTbU5bq/Pa+1zyBQxasFbG9atAol3Nw4HKR7kVrDLrFwpKa30JCQ0hmZMHwJeVLKQ4/xAHPCDnK7CWMR4S1FviLWMyxTrVyX8WriOK7rZJ+3Fxr3Z9BCbTnwAYuv4PMOSkAykEDgvxWgYsU0WFGuoPUGwfsaUuweqA6EgTOxbPZOue24P+Kuh4TUd2mhIw0o75SetuajLmtEqlgR4x2GkvCdHJBV7oS5YKJHhT6eB22QkUb07pQgqoTrbB6qIW27m/VE44XnGL1nOt2G6l5ebY/ilGpGTIxx6a1su2koN3YyuWYYRigEgh0MrvH06ys2A+f9LvnW/k9rTc3TFNZzh5YkgStjc0pSoA28J5XpFJrj/xFPfY9dj/aOBuBvU5cj/G4BMaZEZVjWilSwY1cTGoaTI5s9bZxE9vy1lPv80VrJgmtyXe6c/Shmg0UvQnVM+9fu28+hzUEJSTMS3i1SrHv0ki803xeRZiYt7YavNpquekcNbcXpWxTEYa88LUttODoXUMUkQ6E0Zbh8v7ZdU19EluNhp9WAOIU1S291sM+qGnYQ81FQ7FodacAABAASURBVDjGci2NHnv7SD06hJCgSnd4PG/cR61zhSLOMrX9g0fEKV9t1mtMehH7jRFJaUlPdtooK1QR9f6Faw07hrWxvVYe5SGQbee5oFbI/eP+gYK7H/8+1zP/2nXFuFVJ5ZMNcR8ySMHlbTZu1NNMwX/GLxI+R7STG6062MvMIIvZL9U50/YMnyYL0SaIbzZwMCk1raHfEDEzIy1K1VWeo8wNZK9b7av1AKlcq1RsARMsl0IN6jrXab9HOLBgLL2hu8X7sKhxVXZmjWDoNzAb2BBCl8WOv80617zQLE35AdBe0LseNMYalsHnYhmVuoVcPsvpgHnpvdv/N6jOTD2teUKxFrXeeGlEb9ec71gVZZcSqzjgwDrYV89uMy08R7cAaOg3i67QhUqFGaBokVEruGirOSLMjFSwha4aAqaQZB9p1LSpi728XRennzOWZ1rAaukoXoBJHNURzafivrv//2cezTQY/DPhhKDu8TVrQ/0BrkPyQYzp6tT10QMesyeVMGu8rs9gbvrAWwU+T8dROZQrwUKPVAI/nvHa4YgkQanWSZFuVCe5T91P5/F+O0af53vn7ZW7BPYqdW+RZ2WY6jry4rG0p/gYZHntCq/O7G+NrgAEpq18wy833WriB9rbNE+mfuecbBMJPazGt5l05VpTxbDNY/gveU5PJyblEAlMD47IbP3lg3X3ysfqbZWYGW1r9zZ3ryfyMT/uoxTuWnG//cCV/m6lIu+5H/UFHvaEjAFdOA32+cyzlSTOcWawRE7KK0y6N2nKoNubh/a07pev02yOVPkZk44ItHdDJnumu6YPqniMZ3f5edCneHfddaraDabMAqWx6I70OeuZfSaAtbkuLIhx8TFxOk6OWk19uj7oH86l9rCnps/Np/BVj6RZ7yX0RQ6taWXdlr0eKnVtUxvzNi+n8iod2CjIgrK3Ti7BxTT17PFp4G7Pxhxfh6tgjeSgUWfU6O87bsX2XK1LsVbiSq6FOz7Yn9ayGoRA6nUpIr1XGY9ZgreElij0W1aleJW64efL/ZDKIHbGZ7BEqiiox+Z2cfTHR49wU3PadC6mRfDMSdGo4a7wFEGivm9iBVdFcHt0bPflxnFojyvp5DRz/HmpAJ0wpc/roaW4wxB5ZBCxFEq/J61euJhy8vIYhRJTH+bmhLQKR/t6/Ofsn3tpvTcpQbAdsynppEEt4ZZbWedsw6t7rXrTPSaiu6nEFSbZhbsqX8nf5HHzNWtgWVNlA050rbyv51mv5mXRRGxlnz4SeMWhq1/lIM76pKojlBH5XAKV03lmsMa9GU+uecgGIcuQsnU9JGHY0mdAE+wUn4Hm1OhYgda+RwnR1tvvEnoy4TZqTiN99DyPMnXG888A5a2lqTWJut9f8J5P8R937Bhdw/jAlQSP2FSnMpGhGWLZEMYAd3Vklwrpjjl7lEqvY+cbz6MSA6cW+0dXIf/An34hQqQpM6cx5OwW6a51XcLahSjnC7lFSch5+Nuwlv09OC7MKtlM9S0osbbDlkOw0LqO2qTbHlvHdVDGWmLAJGdeJlLkpBEJ3cu9ng7A+HR9e5C8BgGTnRSvVc+7y2vHiXn0LeGPnGN8Cjme74Sc0eXfo6RV6TCLrs7bl+Gf+aU8mT9pJ1cry5e2e2PssWIUFwkyPcu/4r6qq4Sr2dWMtg9MfXqH//NQ8BAuH6XEqVCf0ncPq49BtKe3oorLjAPhOJ0YOvQcy+wsReC5tGp4KXHl+6j7EmBCYJbAlOFVpNdFSEMoQMilwG61WGeB+jH96qk/a8gOEtisq7Hakeo7/qgMRo9sEbWATxtFvjT/aON78/fBL/t75bmyrunARGmIaDmzxhw2bqD38WwMnCv8h/4pixoWe7zgeAEgqtEp5ipHEJ9lebkgxQX7NDYwKpZ9YMf4yCgGLPw9GlUuBWQbnUFTPcvpo4Pjqnk8SoXVoDQj3v0AvtGPi+JF8chZn7OuJ4hrPEYK01AkrXUFpHtZstSPmtmkJGD3s759lZJ1p7a2qYbzJtnV+o1Vq0yCWSuMRCvONz6ifB4JeBOmw1DuvfVtSgjA8GCPVL4O+Ewp8zM2sPqBuKtKHek5mqdWkaCeBm4oQym0xrYHqIkXc3E0qttSV9QvUZh7Ss+vV4sbSS/Dvy1W5sjMKnqpbdjzxVH1TG3axbWUUSXxf6L+x8+atu9rWToJF8dNkeGPSd/AqG9/wafhE386UBKfll8V2HSnIAAn51TqOT2LdNNDkvw4wlqpORqhrTh+TYNOnYa+XH8IRShujKZ3CnKeAz1rjBzqJcDN1jOYC+nzimC5KwyTaWkxVtruWn779dgaRf0nI0xJ6/Z8JleqFfXNPlFymF5n4xhFetoqSLSqtRw+bl/zR4XRimE190woXQABsMiCDSXYDWmpQ6Qrrc12L57mTclnz0Or8+EsdWP/N3j/LSrFfNS6GR7+2Yu2z/H7Fh9+RK9PakfFMFzE+tAXqFCk69cwjuFRDfte+Be8xAQfyE+puGLB1at0c0MioRcGqDBzE1aW40ueOSa7l8kutBRpm1kgATy/zMd6ROP17FdkLQ6BCZcBK4HYbh9fxfW0cezH/N9e115mu9LyUWl/Td1rd4dYR8msnizKiug6E4lXRzWtB6td1yL+krMECq7cTukLjzBpOqDIGgZ4kHz/jhtMC43l8bqdg2BBpXN33yd2mjY8NsiWxwaKb9IG5BMqFc5xlslK0bPIE35/Ph27cQGiadTjpvOZ8x2tQhZu6l2TObryzqF+Slvovu1xYiJmG/b8MZ/gOdpFkpb0RygGDcLUA0xfz40pCp/TrOfge+zLVTuwPT0O36oYxuJSwkTFYJQf3KKKFWhmK1kzOn6ynzrE78VHxKF36kLMPT6Pf762NCS6OW82747Tiqy3GuZKfLKlKGl4+PgkptUljkaiBuUadHRGhyWc5bv9+o62/1JlAUrGMm4TwppTd5Vo4Lous52wg8LKiFKK4lFrK/Bsw2r4Q37Dz/WvhY/lKxX3irOvunZtezBHsR6Oj2RxS4ePLoj4Qy3EMjEVQ9JfDPCxIMIhJEUbIPQkjAZQ0nvav0pDDVu9ugO2L8NSJggv4ug4uegv3dMirDHo5ikeuec4ykyT1dcME+4V62t8k+zRcXHM0pYWjnvZo6EH9lUMUoVSBMBX65Jtd1i5vdJpuGCUvws7CKueG6UnWBtl+HsIh2vuQF4Fov9a7TzNb7+aqq5To8GzEPuY3cNUAKOXAh7HI46O2839HHsnzrzkyPsB1cOP6sVTlciV43KnpejQ773z6l4VrGUG37kVoJ1Xemt0+TnsRo4cTPIZHPkr2eblhmWi9C6qtWmIjuLquI5fM5/dW/uKPbbBIF4pV/Y0IBe2yzzZYK5pReRS6iwSiuoUlUn5gWfG743fL57bLBGT6rq4EMbdtmnL7Xr/USXbK2I59SjRjF6sa4n2zCvnQ24EqQjNyzure6qtxjBUf4AubNINuCRj/bGwWev1MJoDket+JAwBa7Uy0wr4gIS1av+xr/EhBR1P6UqZ2Vrv7VCRHPjzZkD8j3j0ZOhHmrQeQCVhRX9Gr9DxuMaooxywHQyhttfRREM2rwpFuzzWC4+h8u3Td+I7TMUiApzvzxhui4zXy8xLdLvb9fEIDVPsOTZp/sV6Kvi4ZIyTp3/v+uZd83U+a40twxZPnWfOf9352tJNzuDwDctvs2cpr4/lbfz1+c/79y01x/2JZ5tmSGvCIzj70je1OcBN06DbKif7rTxpzeE0H1+1VuI0vuKgTYlgLq00xQe8FRdUcFiQ3B4HvMkeZUV7B7VvRSKuSJH/m7dQqy/wj8HpwmOesbDRXvcoWy88awA0bXjoIjCGnocDi9h7Ir7w49vW3ofX05+aBfcSBS6EXG07sASi8pZHLTXmCMfgyt6YUQd0lhnRbOAIHRzQ1cBopMLJg3rqLdtOzgOAQOk5wlY+95JYW1N1Qi2xZyO2r8u4ky7paZZAqfqSfZ6TfWvfOOuUnZLqtOThDnshmFjIwxpoGVOtvlhEXzFrMmGPIhNQQOv8Zdv1Xhztg2sYayzmhW6eimg7LGijkOq1IdROe59hvl13uX4gBs2Evlrth4/FMXHcoc+Fbs6IKFEZ8aj5gCYd855JHwVnV6bbcyK/e2p9k9lMZPnd87bEKS2tMzBqKmCd2c/RXIblh/2v+S+pi2WWeL6aLvVZrf3SwbPwhB0RbZS74DX1urorvmSxHL2sFidWc+M4DkwWLuIyeKgXQHaBBzLDx47gUcMd2+slixCQTV2eCqPSfYNf6dfGJ3bFh3AXq13BQ0i1OT0lEcjbVdSQsK0xZZpXqVaxD6+DrR0tj8J2BVbD5psZEaG9g3BgSAeVPnrrg+LkmNg1AxbU9JWJvcZDdJ5v30+K1jZQJg50cKjY1S38/X+IuFaH9DdgGEZR+hx6zVtFEr4YUbdSJkvqWZ+Mz9FCyPIWsDXms6evaQPrRI8Twgbzs4xbQNUmYvSwgpVCNa5FYZtZaVp4g8ROzFCwVHEMDtaDqJerG0r7qJrxKDvuuNxsWy2cmNvOqazvpJp2kulsZRsoQzrKE+eMrndT7wveSg1G1Jt5Lc8P3oFjXdbNp9e3LNUzz3P1l/3cZSftlbI1XOaFud1T8eBZda5x44fUkr6i9iqpple8fKRmFOMEjk0NMW9cq8x+A/8ZpTVQs7EnOWPgG38MqDeKKY/EryjMkxewiWW+dFYp94d26z0UYPJRC8YZ/45uqexUjB27ryXqgRr96p0JFdlVuPUcJFGbI9Y7tTbEmVVB+hn8zn1C25MEzEnBxoL2aoc5z9EOJ0UaV2wzTiPVAq5lnyV808wmJohLENBb89f0RtsZe7BaVH0itCSs8+zM3ekK+srzOedKpYisl5aYWSHq3fK6WpdNdC8U8mFno2+N47K4yIVU1c2UiM73TN7T/xeqaffSJViD6Z4ZUK9SFMcM6oC21N6FhgGGmnlfPs4zeToLADVG0QmKm8KFY0grxPcl6rn5W3jE3dsrFSvtUSKgdtiss+cbk1DhH/+9/XninLixxi0GVw9vKCTzCZWCsPq5eefo0uL5MG3ZOHrSngctIeeJ6rD0ErR5NSPkb+oy5cf+LzRZ9NfojA+ota5ttBqFHhN90JWpZYjqd1g2RONVidiBUvdisSZPaSBaVFu+ScnDeyxniq3zPha5WNagSPIyL4827+mWHhNaco4+6KyIRQeqzEYisR1Gyk6lN5wm2mRipmeqskePMOmJsUdUW489DWHhTMBc/GlIcbhdA40YZOIcBaeD4MhcT7hYVSPrGu+WIh0YtRd8CLvAj3PZ8rkoEPPHw1gd6xxrWtBXxHZ3C3mNrl1tk+xGCn6wXe+92ydtiL6gmvxaH8/SgupGC+AjvLtwQRAEdaEEDErNMmhPK7QKxmpQMwWZ1gDWYopOiLGWLNNXQeyqJn6Keu8sKc2ZG7NAggmR1GUzWKnyiqUYs930AAAQAElEQVTjUodzWnFjqgVVNsQKFme9qcQ9+AbuTyflftiQC2MbB2hC9jA5B9pcWQr77naMvAixSJC3AeHIfZf2MRgci8eUM2peKzTIfbEPs4BpmSP0jq8VTOEZFmlLZPTHm2Gz2QGJo990b3Ny00KkIK0YCdmcVh5e1hr15JSaoHpTp5YwhOY7IVizQR9eDSX9os4gwWeE04N0WjczlfIsAqESj5cB7a1Ky4soG+jEI6DKNauXXHcppIbpGoh7cUf4tdxjha0lRroVuqbZSEPm731kmdaklDFlSmwtO7Ee4kypmM+2J+xQ6Jl/5DpM2idtTFwvIKCAdK9ptYuHThoqWtLQkAp7fGrEdrqK9JvIwpBqu2VZdeOpdbDr5aJZCG0NxmWSC+RcrZFDqbvESNinY6uhHM0dpa6lUbVZe6/bW3PlNRzKi4+VofdB3D3aZEw26w0SJXrGj2TmjljsqLTWlmxWLDeVgCmZVhPTws5pyAujVBmv7oAU4VmmEz59Te8cvReF7SBECFjBYXLgai1JKiRDRR0VIB/ZBa/VL7mjWJRl8aKCEYOD54uWYKrmnrl90tywDBuiBeoPYsLxB2VmsI5dGc4QSihzkbXmLYvyViUGz5byq9FRdVVePUsZqXNr46/ioRbbnq5zlPICpEAiQbW7P8bkIigFgcYhQfUbGW0viUhPGQaGBmFwN3PXaA1sjY5ebtsF/iLXFJ6EuonGkjEtheSOtOh2EeYG03GaQ3bHgm1LespRb0vZe/2AH87qV4uLOarbNNE6WnRlW7LtQ4HkgjjXG80p64yZlZAj8ZvQvN4zu+lDiBsJr9Z6pV519DPd0yYTaQHBD9kEYGPCVMPRB8VM1dZm6EQJULtGhWFTxikAgQxWd57TpUBemhouE/HoVoTgKGLVykUENQHxSoXSbq+n8Jwn6/kT4DQlOkebdQMzEED6K7YJjC1iTUu0fXBSWtXyaVbtLxkgacIVOJjbuswMasSmld6hJJhNrNLXWCtbqVd4jPQtMK6P9fJl40bnCm79xUhCa8Ygp+qToa3F0Sc2kcYyjtINQgTUwQ1v1XhbD8bGLNXZ3Y6XIvJOMVQckjYXqEWEqhClJkyByR2xIe5nXq2lhr79yZ+02/2s3RqHV93+YJ8ddTkUV8mZrC13zb5OxdNWhRpmT+nF9demo9JqIGzEUH9uCLaudh4PRYJ3lp3sZ7RFescCLkGK2V4eGyta9G94vJExTtukhswa1RmOXgZoDn2gNIpbrjRrEQZr8rxs8BhKfGpqHtElJ7TFZVziXSeUVErGbdBjl0fyxbwz1q3ST/SjP04aJUBTC7BEnIkDps6QDVO62gK+FuOT6+fxpXPV1ewGORGwy7kdMVR+WPrpjDo2gE/vnkWGRtY43hGtaVX1VJ4Rr5IL02k2MYV6XhXPiJPx1qtSDkuzm/MWWjdEQ0vkdCETeJSth1Iii8RKKst4fBQu5NDfcZTP5FTAg3s3/IiUARV6RC5K+AoZSqdswNQD+SbJqGEBsJUQZPPQzur0TJR+WzvPh8yxfhv7okQ5I4HP8wpklve1zqIEo1Kq1/7/5UOD4CbvMKwxqwtl2SfnZM8MUTGjuKHmTIoXNbVJTbHl6Tpa7poHhVBKlx+dWmbh0eoi6J7SjOQAMNh2xiacU106Yu6hLZuJ8VfgoocEw2xZa0oVhZl6S2i2lWGy1SyRNLhjhdFCH3J6Xj7KTo2W69drycPAlN3UlPf7mDOsyLIaOQHFkw0whvZqT/jjSrAIqD71ApqQYJoz1nQzX2+c1aNirjdJmoCv1CrVDZUq5dOPMn6OitTTmNHtDSoPT6N8QDhgorb1fuSd0Ac0ZXCc5f34nMGULcMOUGdETLrnx3gnUC+3Pt+e783SCZstT3VCWHovMyUcjdGGa7b7+rdN1EsADxXwsKEHN8ZO953nbdgaLjQYQXQjjD5tSfIH8VP2r/hZ8J+V//hcSGlo2ql85yic3lbc0+LrqfealmcWzD6cD3JLMl0RFrv7enJlLx//ad3AdSLUaH+nnvkFb5r2hop5T/3NbN6t1aTECMGYcJv4PBZCu2v8ZAl+lrYej1l/zDalSXjlqidkEMWHcfM6UjJeG34ldpfPK92As6eyanK8R15/2BGAyNdyg/Ud0T51TtXz9W+t0+ST/nvzv4Pf5tcgqht+Wu0utLVbl7nTVN45OJnVL/X0Vbi+GXfNAJ9mHElj/fq699IlTnrrglQ1B0iUUP0QBY6J6ofnEReoKahZSRce7fouL2Ts8Kk4e/VSs5MaqTmYIKj27P3+J39Z43osu4yO6eqeu+1+ripg7lz3faHNZLzOPHIUzrXMf6DNCy+T6aZWxf3faF2iFvBLUllLklQrz8EQRjPOQvK8tGu10xu7V9w70uZMi6w7Tq8yAmBG+c+R1Ncr7HkceemwHI1pyrXLVkta8SlQKtv++voWvr+lxKMfj9tXbfAq7Ao+C4em6/7zdNfTk/lSuM1kzz2hR8D8LcxO+6KefVqXApt38zkxvVb7wvSwbY+Ee2n8PSpqooTbJXlcmI/r/uz9Dsy8n8zn6fvxL1gL9hl+Sgl5xr9fz//Y31HuCO2FTTFlKQdmQqV+Sbma0Z5DxA1NXixhSrJtmhdaam1Ft9tl3RRytGsysHx06LQ/z66zpi7az5jfgdsruw3YsNBA6qy1al6BOfPj99o3d3KRCIUB3GtUN6NF5nvym1OqiRnDyIBkA0Di/219sSx2735mZQTZcd/rK/j97uS+zRQxahlZSpudsIooCAncS1dX5lCQAUVzw0+tW6lz88vWP3fl+vyce2cjcU/eV82Hsa6oj+XqhFJfo+pqATJwt+2Obcd526QAXcsi8GpUUnD2fpbdW8rsgRjQXL13J33MexNLgCSi76efn+rPiwqvoCeAMGiqiSB9N/ETsyitpuEOzxjs8Q/iQ79obSGBq0ZDkMcdF4cn58mRTp4P7j33ylzKrmyuGS/RZ4LrZpDLgiS6G+pDG1+z/3p9O3rcHNeabTSf4gzbBXs59kbsV2zUNblbK9jCjSOa2PB7+7kmd0snQAT2ogfK1DM+pTssD30wsR//HLvaDFf1P2GcXBpmpa6+ZKiTcmyqcl2dqofhmXqiHjfB/F3zCHgX2/bYD+yacBPBRLvn2uNBHjWRonc7EnnTy0P19eP2CVsBrIE3qlWIuA+yDIimH+aNL5nxfrRv5LMq4eexwcxijzI/Eot+IQNz3+d+1OXEyDZ8yItUlceMXEsDqb0f68ikHGqJgKL4b+N7da4Hq8MhKiSP0hc/6xXQb/j7397kMvo18W8iLLAuXDH7Ovx5EIy5dm03vVPwHXxl54sTlpw7m98RDZIFYEh7PT9W5SA2qEXxM7L3gcUx3qjkg9FzcuKelaRG9kyfsm9dz5FkfX013IkFmM49c1S5VHBfVaZ9b57f6JLRT372Ed3iIn0b1g/2+w8W+/BNfYc1ptGYQo/0qEXVTEgDdkFxXVQw8La3ho5IjWuHO3UZ/rwzZk1ddXs+nVpHzn+c76qzn+PnW/3fg6c6NbJmntE7JVywXUno5K1b1kl7iweXEpsmJMR8eraoy8M5jKD94DireNXzI2+aeny0MxFSTKoAleZIkWIiImXk7zNsJsXNmUsd3tZ2uN1cX0aO2SFv4k96f7E4jGNU3s4x9I9qR6GaDo6LpEmpbqlmzsfmo93obIwqJYC/JJXzvwv3ZeN+ZXwstnswVu5La0drwVplhqttZ/gD0Q327t5PE0ddtX+yrs7gafVkPsFUJeGjBW9Bin/h5qxTYS+POd7lkPspTdHTTjIPMudZOLL2c9rPUL3Ol5w0Zzig5kzTjrG9MDVEbev1GUYl5phXLFvSaF3awj0aKPls00DlxybXd+I7f9YcDbRs6OjCJc4QeSJVW/e1T+ye6LvnqHpLN3gfdqqbdCs6MVnYX+Lrmf408HqYZWql9mN74f0XvpdvM6uu+5yuBz3IwKMSF8e2MVN47lAHIXLH1/hw5PGZcettytj3wtmEld3DZ07avfz5Bz0riGOOoiShuJb0Er1WacrnPtedCl14ZGvK4rf4e5/fVI6w4lr7crgAaD7eB+F4+8HAZtr9ifgGJO3Xb7QtWA9PaVzzU+oBtnpVxRmZUHKK7MAhHuK/7i+7FiegWf3F8/4vR2Rb+nm0XEaaKrT2KeV5WfyIseLOAQ6ue8nBAkiWsUa9EHCPWIvXfmI/vQUSYtBwyBv7VtPpxWIgJx7/iIneqr18alrY3NbH4mf7FQzSQeE3qSLtfHm3eDlKiPTVy7kOVrr4ZSFQZiPFfh3JIfxja+1/9m9ji6oGezw1oSPPtH4rtRWCK8Yd3FHWleLPllsEYFlTD2WLq+bKEkvmtHbcoNqvHcWi54GLALoEvDRt2iwtDlGdxTmLx5CieNyxQs8RlimvEa84VSqr1vghFcDRXgCmXtNqNk0uXB+qZmThGW2D1WMvsdJ+etob7i24HmMGGvWeDgyggjwLfVlvSbp6PbNurXPgztml2VPlfkLStOxiqxEmeWbeuqWk9HcMB6bntL1cDMW0sg2jiWH4peNmKnF9NZauqWjoKXbRCrwhHDl9V73H9QECibXGu96E9qBhRU8KdbazFEh0b+UoFbUGGHe55GMtxOd8HnpF4yMtv5N4ZLYh4UtXUPqNVLbE8hkmRIcoYdTs+22RTqYIrm+p29XrW4P6jA2ku8+ay2zTN3pTKOntoxprSMpLY4y2HbTGouq3VT9PyARFHNGSRIULqM9sUlb7KTzpNQsCYltR1SMNZkNMOuBO7KWQhMz7tioOwpfHhgTQQiEA5QYbffbVe6mtKArwgaIBAuxP1fpW/+IklCc3EihBuSr3OH3VUj9gRg+03Tnogt72yKH+VmtsFIOxzNyU2VJZPTRdOuc7uJQAZfbkUasb8+53rvI4VBZ5BELJ7Nmtz3wZE2ejoyZA6TxhvTbjXzMOIdVNViRMmcRqilMkKjulG0110CcUxVjSJSdiFjUZcgEWyXCTcaCjEOXruLrdDMaB/SbCUegWJc9Yp2QAJUaBIhXVlLHkwP+4SHgaz3TWoFo2XOpJvskt644B6ObXWrG8+HaCB5KOOJHHZenREa6v0d3RrNdQwWa7tIIBM3enT4/zFccyt8vCPcbdQNMYmA2klIB2J4QOsVVAmc3s3aU1j0PAGtr1fzAX/WK4bF8SgE4gjI6yN8c3cUyMeCvs3iPp0kiCi1wm3zFmSlyKHZB0QmeIKNiLZjQzKldpb6lYRQO8hJYMtnpHxN0Klba0qcOWYdE00aS5bxNl8fZLc76fAuHHlRGiDSD0kExaIQ9FaE3Ou827t25QHeafd1hTPqPau/40IoVAA7KWj25WSIjwMRPyMYxxbCkPNezTqj4ldJiSaiJbljyxyuyNZZoGKaD+Dwv0IJUf3VmRxEWaeSxr45PPaRXbCdQ19WFv9kpaXnprZa7mPTK7DtmtZwBOCoJUiwAAEABJREFUlRUhZmrxnqfPUTvFUMOf4OgHYGuEtYEODX+Sj8oMZ1AjVak0qjln5F1HKFfKZ+SsnD6jTpLPZLHkHzEjl5I51EJkV5bRLVXHLXfeRmVEqKxVXT1alfHXnPTZc2mcGHbpJgrR7mqIWEuUjiJyIqRLS4zurhQaEDZe5js8TB/nGXyhBmbfUBlVd4n2GquduM5o+zOKqSEe766p5JRRiROp0Q8qU31Su6mU0O0807Laf8wYxXUrt6R7MMK4TY/ojHOiJkCa6yGDujzbJu6qtTQceLsQajje1FqlSwd8Hu/bLakGtJsOX0b+DcEUWU49Ro+qw/MENHpAikLt09VELcfMKIHQ+EKFJmrW21CUsmIOqYTb9+gy1zV6qPEGN0kVrzegV6VuyFpEGlL257h9oSQo7anE2+QKTURxwa/QYyirI48yhAHAGe9cKIPwTnbPewi4nJt08Kzpa6C6yLYbarOExrTKQLPBVjo0RYMDDJq5iXIHNuGgjRzMGtEMF0lES4VXwxS6zjKkh/Vbq8bS3rGRutZFKCSekquWb7debW+gMqXQ/cBqO2kO0x+BZw3fIje2A0LEASGe2y7Mz5oPoROYzqlWuCGJ6BNQPnhvlDWUekAGFsqmvA8rRZlOKHBlloJFkx2f0KVYr9Dm5fmMrrBvk90eGxPdaOKhsiy2THIY8gB3SlUzLUMd05HlEtSX80uWUr+xlng3k+7kaHbM+iA+afGAepbZEDaNPtP6Y/0GIIbPzHTPlFGHc+QXgzY9Z0YtZkW9IuWAc6A8slpvexHb/I25LUvjdVDG0s5YdlRTS1qO4Mscd5vlatWjiJEtUyp0XBTKL1uP0qv/XxpGUNwKLAin4BSzDTrQD98ASISWMK7RAGFHZXUmxrSXdmVVa5Nv0tn0nv1K6NOI8WB4gGiDFgXSrMlS5Ejn4U53bXMarkTfUCyvAyHQs6zloRIota3tNTY5EP+2412eXwEQPWqcupxDbeASdb5HCEqw6tr32rnXc9mor0mptFuc9Mr2kcjelxzPRsLYkK2e5xERBVc6zEwrLy9ZfNQTNFzOUglE0UH9dJikr2EYtQulsMGk6RPauwBURO0P/ONmH9R6qr4zAmB5vQz5LNWZsmek2JLg3rAVeylgQj5uLQGZJ1V1IirUuXRQY0f8YXq6lcFxdG3OVqAOdHeJZuNcfK45J/vbTNEwIblgloHSiKIqhMZTrT0Hj/Tu79+yTB3XmZrntLxwmSDOiKtW8lU6LguhliWuyAmIt8+DD/izuPbgMjN4N/ymFk0VK1Ud/nDAYzWTqLY70CFZvHEdlypGJ2ZunSxSXX43+FK6MQaon5hWrs3Q881rfqiu4Xs5CfFhnT63diHw0bwgRvGzclntOlc98Zys6ulteL8Gmndd6tNoEw4V10A/7fOufhsSyhGLwX5Xxw2Pg7nfqwuNFyk/HjTC0JolAm8bbWTTcHb0tekBE7tvAm4ZbnXpumRrliCDfQ8AjjI1HzkzfWQDDZkZeJpnr3l+KOy1BdayxFGPV/1wPs6Nba3w2ieHXcbC1Kj2xq7qzdzBTaw6fhQisS32ODvlpzXJGroZy4v8gRuMfabfL6y0CNA1UgjfwyWSw+LDfVmEmV1QcHwOpTfIcS7WtcDwdmsO3lDeVyW68biCPShwuAbXBHuVFhOOsUzSMlWYkj/2zCfzR/hhS7xioJBqJORbm6wubbTyti43N+5Fj7J64jzUEA2T1WeveWViPwaJJCKdyo82yRF1mQAWLBGn1UIVhoITMXvTpZyeeVpXQe/JMUUpBqRUV1Vp54OnkVIWyFnQwmc20O8144+OrxGdS2JGdnofBanr+qA4rFZbrWMyBKDRNzOHz1zzbrXClZt/2MvpdqhzijtMHZRyBpjqjONmi7jILUwb38/kLngZ2fZ5XDUThqB/Bp+n437E1wNvvxK7v4bb8DVmcK6GwQlCDyVmqzE/2+biXusHpsaWR9cANQ44FMOKUS/TRvkJag4gpwYsF81g8TxQFx0U4rGlZaxmuqhwdvHftHZvy96sW7EbDQjsXogF+ojtZ2ZePLIuJedozbDq+JbuoI1uop2MqnU67nGt+KNx2tCaSE0XRF+N0CzVZ9hsj4/4LPjUArYK2IzRbrlGW8TEQI9RFNOc9EnK206IFBjL5gtdHaVOzlBYfMo9pZrTumg4eYACBwGTZQtNVAUtrUks7WkWYSi18HPues759tyMw1XvQh0qmYx69vlGnpgWuSSnoSF+G9NqlPS9LXR136a9KmewQKelWztGe6Z/Tm1LcGXZyUl7etVY9UAB3uyDLsCN0tnK8kcnKvv0nGRqmOz2FKy5zeRto1kHR+NeqtaYC5QMthQRMxSC61VbeaWN9UTdNr9UnV0RegpaHW1jpmpGdh5AHb5f2fpGXBnRBthgI98vJ+6Z9CZURFh3rZI0eM8+48DHWxUFPu83UY6SQzxVd51Ht+vWkXQ2GbQZPImTUGJHm5mHz2jBCdC84CeqltDMfL2P9gKhcaf60fcCL5yXrp4sTkzE+0+IWlWrH6aJET6wh26pza9uJe1whtQENsSO1Ezb8fOxy7l4LfzPR6/S1jnztJDBlK+yqbrvSjHyX+M8xSfnHh6o+j6lHvRDfs7MM9/mF5BOzzxfPHqTRpAAEpCK7k2tT9r0rK2Jz89KX6lEuzal+9/4ainfHfpo/7p0mcbhXCIDMbTnGlyQnEYW3Uslrel//6XJ10HaKd7Or1K0CFBKEbGa6dpstOqLoLXZAZUkSGAaZllVUHtQOpvla5RwXgGzVfAHM5PIXHk25HKuqG1gOGhjQ5R3fcCPRSxzZcKPGTSvui3vi//9+H8f3A614pb+Wu0HQUgyD0yJndtM5Cklol2vmdl34Yscy/H3oB/4d94wvWwftc1YsAVs6yt0lz9pPZvqWNgDDtqqPYXDDPZTPzu106i3ulzo0dP3tOAT8BfNCuyNgD45OwaRsG9CQEpkklzgp/CTvmstW3mtJ1aXdc7hmXXbalJ0Fr7PuiGhQ/4xT50b98GX0Sqpay60dG0v3rDHVo+RYrwjQIn8gzfRQXJHL9cpJhW4YdxEm/rDQEhtHM3mfHc/q/ooqH6eubOfU1l9U8e9r02aHUgeSjWeekZaaTc9ombIe8/ZNGeJwzAUFJE6SumsqM8xqGq1NjsgALoxOiYIgYo9/YP7RxhK8ld+ZY4xo6hCQ9tm0FZcadJu+Tw8B7KLeT71aNh0/3ef59udYp5+cRmVxavXhhTlrqklXi7VCCmGsIoU2UBwJJAGMQhgoT9/z5+5P/1rclPaU8zLJ2FJP7IGqXMixx/oFY+C1aEwk6mZPlNCmF81MVJRLmKtGWdqFAQApqBFhojq1WdPIppOUMN2V7Wa0tPe4ROCRXbq0MHL3dgo+xlptLI6OMdP7VYcFMTBUpQTu8xmwXSlOgMFgeX6BM6D+a95WkEcsICRHoovdSMVryUoAquUncmSNjnqHJLHm/h+C7HXUAxqJl4JCeGs6YAeBphzIlqeMJ4tetDeT6vB2be1jMeJVdpaVCAtkq6HaBqpBkOIof9DfIJwU7raNBBH/poqaqiF4pBTFLrXfrba0zKhUTlp/2Fui8trqEsaxTDDd9GqYNoyOXzE4tHZxkyaPfR0vQWq2HIySqUNiNEPSZ2UZ4h/b3Ej70i4TQS9YUYB6Df21GLepmWymjmdqBz0QA8vqI6WKyAxJDcwVqwTUT5nqquh51ukruAQxV0W8HK1rxmZmemxokz0S+En+VfODQHVc975hDS9YCJ9oNbWD5M9+8urNTPhqfWuhi+yfNHZ6AB2w8Yg4hSUgF5c0b5hP/QYOw/vxrz0n64bvMcyQ3LwXN6u5EmL+WXf7sOFXlb+iqArXhX41AbNTbokyg3LVQexJhyXDsuLU4YAOuAa4C2lH0cSjWqx7N4yKhgzA+/HHQyjKE1FHb2QagjafPrY06Fsigbh2PUWhJvDoqDVd2p6dbvEwZN1TqoidvkaG8gbG6vmsvE3uHUQOoxpaMt1cxRj6Utnf50yrYeHVGto2yHepDDg49CHS+eyqll8lSczgaIDYvfyCcBoitWiFWV2T1EQfJRTus4JsLliH2c5k6o0L4U22rZtDoPaS97op2oyNSPIa9autd4hq7SmKa9DPRZtgSbl97imjjtateqUsJZUO0CRrRRJeGQGngDJwDKzZBz6mIGYGTR2zc7UG01rUuGUKQAX5e/qlhbmyyUoyN5ZYj3nAST4UW9tGXnTWLtK21ldn63iangBCQ2eeQw37cAKqssewJ/wr3RYqctDdq2Brn0u6EGaQeJAaRbU+fxhJ2XIWiJ8UDLcsMethElP33SD22wrY8v+pOT9qEd+Z06hxpqQ1TXpml0yhkO0OGrrNw/evNpibKwTXepJfGwe2vMYb41K6a6MBeIRqR6jRgSt5+s3tA9whM+wOc3Sm0l7X/UeM23Zm/dlDzfzE2V/6n7TmKGzZyZZaS3rhDLBbuOAPnsNdRX4XoTNyJRe3hMNDRuFFTTp16mpnfPGSI/2KKfhbfBW3V1i4dt+j5Qk4fnQ+y2Tdxf3MpJwqg/6YqyzrDX3bDxy7C1qvbgOrSz1TGljRrBRx1rq6o0RWgrKFe4OCOgBChKAHL8pU66huNzgilpDYdF2tftjOOM1bEe54i6KxXgBOaRYX9v0RgTJrw30bPRwho/pOmmUrGzV1LS32snYn9IkqVyo5WWRIl1XJVsKNHbgGJ6p6jv3c7zTo2Z1Ig9GmaYJx/AMVAdQgzH0QARjRtkZ6O+cUfFZzN6+Ec5lWORas8LeYllbn7gU11P5ZGWXzGwq8x0JwSh3dkMEZRGN6arRmUSXlh19xqTNA9yaQVmHv8zmMI+x5JfL9RcPUdLzHH+ys0+1ZlnCkoTplz2Y/3s5w2dGT3WbCh3sq5XAdA7HUN19zpy0sh/GDkJnlIA2kZILosBl4x5TJ5ifemOnEWaYsNY7/R1jscvT9+x/FbvNFmgcDTgydEefVS2OXCxs/NGEgVSjACqtPTpUPIf5G8/5tOKH8nlV7YTnx0eEWAeKcu6qAeVDINOqrFp5IgdoWxMLcrRNi7GNqC00YPSFZbgU6vM9kw+korJpVmtFTUh8NVfmVQllMF9eOGWpuJQf/y7chsz2GqsR62dq5vF64otQihAwEwwJUjum9YyKxmmoyNpHMgtxleDMeNIT1qBLY+JGbd/NFk3H3gS38Izg+H4sjph2PAu92tfz2d8fu1l6oGRs2Fgpm+Bp4UUOS+/VzJxtTRu600nR5paMM/Ihtd2aZws+0Pcj6z+yXP2O4jDk3wi+ebWZ1jvTIZkymRntpNOrQvgLatr2EBI8272I960tutHewzFwHapCKXYdkjqffpETI4fiS8ubzPMdD8Pj8yUki9HtgPSx6N0vGUvriCqdUbSG5+ynOQkMHebONL+VsltwZ5dtuIePKwIX+sQ8lAB6OJoDk+MNY3QAABAASURBVEOTAcHqF/pQRcPKqQctjuT6O3kwulGCX9cuqct6lfJVotKc9CTVFgtyX4g8VuWccJ3cHHUTcTS9u6I6hhfsp98OL09N4Or5wB1LyDXa5kSWCJmlczlXdT3fqlMD2c3dGWoPx60nq59S8Hp7ERJ+afbMCwWqHWGo2ZVXY1GLFwknwlvXxD7FZoTIMdqIjrctAw/wtemg22uoRkQHLNAuky/ucHzOjvrXrq8TTM6moDTyooYZpb25+7mf+2SPyofl08bmet60uYY4pueueW6z47LPrbBHF95Z3rHnE759ZfPM0/vBlROJ7kiBtQmc623qEIFFvZsG5/h5PBUX4KEIFrFGyvEraU+j5uWwY0p0mJvY5ttntP+UpNqlJlQDjegistEFpVeBLRv6SLGV7CZfu5B68vA89X5ldI/9XVfuUXXm3rLzgpK3WUmWdfi4gZRELQ0aAGDg0tzYAP6eW4PVOgKNHbmZraksiIY67DdJ5Dmd7+X8PIMs5Q42AxYtXY3UzTSNS1nHz0hgRjngjAqfB0gq7bpt85731nEtzDHuZWzogBCZUMBKM7RafSVF7g319jKUzxCFVtpdQkhpgZf3JzowUiNrov52dy1OW9TeG9+ox05Ps3xzuztCb2Xtxaz6Ea6nOOqoNdCjDTkDQQBByrTyJqyZg+wLuUYkTi6e6GLFeSGPLARacbDgY2tFfBRbjM4kMBu6ZOKH5qYRp+apvodH+djKBZ1UoyHmdH9T/pSWwTnQLfZ5EkfdQEep6wtTi4+jD0IrKFpbZw1dXg4FgTQse6h2QCWKd/G3IyEPAHP8vOUSi1QjiUvVBF8jlT4K+uJLHbRxvKKutpVmpBYwOohCH/Hjey8BGShcHY4OtmXn/TYGLQaKafV+ISmk9cB1uTRFDIITVvTxhp3RdneOchc6A8hBD15dRfGtTZt5s91eqpS8LEXi6uixMgC0/h/WD1s3sIIwBp3UGGeRs6zDCuOleWMOsbKWm8ub3817RgMFRGFxxCJg3o3ugph4m+bTzCkFjXqbgMOJXWKQYWFXzEXtSr/M//r53+qv+VlBS05SKJMYU3LL0SQUP5THvruPDp2FnuFTo/bQmHQKECD1PaN2UG1MuJ5rfGkAoaXumB10jqbp6TiuQYUZ6LFIPd1enIv42Ln8NrEx8/J8p5L0FO6/59EUw2JGzTw4QBqwNqgbBetwVSiLMeKLBj0UAcCq6NYUTqtnP5zEDNP0hNrDZA67F45D8bei9pp9TBYxyMzWktaP4bwpEeE6Dfh38/5BXbq/oZpIsZHveoT6i027w73MfTGEbzfEh4mjSeo1oZ0mVStaPC1NdF1KToiM7mDulWvXjt60i+5wio9+aTxQd8jH5AXKqS8nHVREOVqeL/UL+ZNAqryn8MwcVu47fx5IdsuU19W9TXqgCMU0e9moHdLKvfbmWvAwuxzhNRKEerKekNYqW4TRzOL1LNdOoA2aKuJB1tSM+thz8VycTSqKBSOIxiTw6HXG3BbdGFDfwwLipGd6yVxJ0sd6FMIufIo/Zg5oHns2Zi1yQyOzVN8pJdQEZEpORtWQQmuqyelIruMsue4zTFLzabKl2W/ocLqmOuhDVLj6JBg+vpp7LKDTi2c6T4vOa9SV19Q+5+pm2aSjBtViG8VXlrd2Ng9L4jqn6uE87mpvR8NPj9KeaqdCnSl2ObGgBFhlKV9V/X+Bo0f9gj8zP1Tmeufh98H3zG/hxohlJOIWdRaS26UKVy40GaWTCXuwy1wBAjN2WiJY9QxKU3PeiAYU9AbSDC4YqxYfaiUrwyzCwBnctCfU28aR29JVT+T37EcLdlDdbEWF3bA58pq4f/O50WlyXJI5O9G1Dq7HPonAmFGqt9C6TY7cq7jeaN78eZcdUtVWJguESqL3zrgYQjSwfg1qz9RTNrYFSGy3yEwq4zV/AbTjLOpS/ews3o075zmAWDlLkrTVjAw7S/JltKkMRNppCOvu3j2rIPuKulePNJQlJ4CP7+LOXNlMsM2BreMGarJXufqwSDu+jJJSskvI1E2AlEL4N2ukjPMwE0KlgJMrJ6S0qFxPrnP+ktNdXflMpYvvgm4GLdJU9VINts+2l8U1Y5hJCsTiumIvOHIKajVokIRKVv0ECkZC7cKrX10BCN5IM/n484R8PIgsk6DTcAJp7uIf6+J4N1BGhSuorMsmzJyxqLIwE1dau2rVqFSgsQBiRnX0d8rmnu40KzdIXEMrZToml/d1zb6e5V8zbXYbu/tHOYBSY6WcV/sNpnN0azHsCZyl1AlPqUtLC2Mt/yOhQgjlIOR99MuJaUGn3vuQZWxbS8tN67sJO5P1whXf4CzoBSyRF/NEipWcTFq/qgl/cCVdDii6rue58iyKhVl2Ly+qFszSpluS4ao3UcOnXLFE/92mTVThHu3p7+c659Mqcf8ZLonv7QLzdKRiaLnIn/uOo5apgmW1KM7Ljhy5K7TBS+G65tomTNI8LqxlYQ5DBm6OnrWaV7o9SiCu4HtrF14+tRurwG5OYu4pNaj50trGWAa+etIOwjo77+fk91SPSXoFPClEjcmgWC+VXT8tnI9ZObng+mt39FnTZgF6H86ROl7TMYMjq6PO2spCJN/ljOJ2WLOpkEzdgdn7UQg9PrkmbaZlQvKd8zbYSnI080MIc9r9hIiUFcF4M+Wm4zJitxbocfoKsyUwKrqHUbho5izw1+LoPlWg2UJY7od+VPBDGzcSIOy1uay2RgHUQ2GufXiNeEewvRp3+63su4sEhWVKULhzNmpNoWr0agkoha1MFuywE/kQDauc+U373pYt+B7vP4awKevQQ0OGJaKWMRK8WR0TetBGeWcPucyvxY9rsE3GoIhHr93mZRu6tFHH1zSIzepW5h/mxNnmP/HZsz17BIQ9IyPuGdfYZkPDpO/qlB4edcWmgXLEMh5Q7GtQKIwcP3KLdtcYTaxyBb0SnsRR/b5KZlsfnJR0clTEyvlA48X6SSAP+5bK5iTzyI1p9rH7K3AAjR5l2uC9q4XHkFkm+ovqd9eaH2s7GMlzZuwe+4raKu0vmobh6/XwlM2LLUzqfSA81TIOpVx/NDP1sUFCGzqnrdnWws96OOpFjyPBAWEQgyd8eq3UPk5SQ3BkiGNZTOrDkBqxeXbPHNThHIN49laH53Zt/qyldLTmoKSXZzLfBD4i0L7sWyeszEHjQA3SOcsL7OzuKsxTnGdFhqtspKt6gC/2mXcFkDdNjTaZm+nhIQajQrdpNI/FY1oWGkgtODEwcTYygx4UO6fFSStjj3pIL81lk0kfPeEAJupZT123azPCoZJ3h3h6W8EFDRn7G3thPFBASS+QcKgT2dWyTvGI3FnrWZ4/51zfWt3BYybWajTsyPQwuxQvJl/eRIZsPCxlRUlBYK463l8Td/HE1E784LEXzrSzA1hQ9vo1OYrRjPpIvHFELe1fkBwItjpCoZtIiD5bx1bDfBgHUhhqCtMayxzcoyjMnWHucMqeQ0hKmX+GMjEBrokwCsG4iKBEqRVLgc6SKCaty1t0NjvZjwTjNdESCuwquvrgeU4/pXgL60zMgJmmWkxMPZpEFNNb7Ul5Zuu067MDEQyHG9RT+ATbnCOsbWzdKnqLo2Xk0FLwY9QGts/yko2GY9yEjfOxDCSnMTOaKpRPtUalIYOj0IsE3S1M7HLEgaSf34VjM5GjKUPVaNEUm231vfH9uN02kmFv5YkDMWUG4QNpMH+vVd5/00GHqv28Kqyb2u6dB3yUGfMX2O2jrcWY77bd5EwvtJN0QguBn4Gy0oK71PhOphAh6D+m7Q0VsavPRythTUlk+LdP211BgHbCzmfIY6f6Pxt97jC4wh33kZMFRe/8eC2/aTfwjIQ0y+YzZuU8OgShwAjQz7ErueoFRzVobWP/msV5+E0JvUpNEX9xWmuvOz/wC69H3RfWFhLqtOoMPVe6N+JNwyP6urVz84/+PIVv5VeGpiWATV/kHPbDbJS2QC5VoX/dHgZJ62bOQ9fudtZu15spKur8k/UX9f1DdMQseum1IfI7xZhTu5eWFABXgKPfPlfmyp6s+67nZt+manNbSlugfHLPkTT8tKXWMd01vNcSbZPiSuWDsFnSNo71Ay2gYiePoxZr/IVwjL3lMocPJa1xEjnI4lR3VF5fuLI2cbtME4fomZi+mnuCiq0D0E5WgErNIatbCcG8QpZ7bXHevbLsSgiT1KPCfhG/j923bKl6brU2uoOytGBxh5/F8eGga6Cq7VolMU2JA7PmtTw0AzwaFqEt6P4ZX2J75LmdcCp8Y0/LDKMFM8UZVavqD65Q5y2InsHD9XgMyK7dt4ou6HtsD4IF1cvw1kUar8f4uKaMWpkyM33q3NmiDHqoF4zD3UyBK3dH9rqVdnyHpx/6Iz279+LZM07HwKiS4bwQ1r3XbMrvZVzvNnwV5a6IcigA6nTgL/CYL+OeLaOan3OenkqCY3zRZXkJPWdJWmysKH0BZmTSDs1NtUZdIu2Rcv/sz8Jyudes0SiO4NECulUq7xCtpA8D5tiTn/NsSb6sX0/ORVu5lLhVveusvjclPLPcnr6s1vpaCNbctMJaHTvpgvwdS/NZbDUMfwM5qi/+LF3F6aAc2ClTahfkRSGu1j2gtcOUdvFN2BjSUJhXngYRrH2HlMMHnk1i7A3TSmPL1Mh1CGgVdJ0zJ5HtB7v4sTdIKL3fY09bK4b4Db2maiCIjbhSyNeqK+rTsi+RipOhCmTdIs2h/EbqTpx6kdKSWF1VR6ufxNPvkljWB/2MesypPvJpvFor2/SxY1HbcIUHFRDeBzu83dIDEW6yQc1KJlTL9owazzl2V97Ubr7aAa2NOd0KMmMq+gBihpMLby4V4kwDbNPOj//VHJTtcmlfA0/4eNj2FbQ5FNYu1QoGEKj5e9uENN46kMOv84Sq6GP90/3z+K74LN97lHw/SS3EeI5lTVX2a8Bp7pU+J0YFS6OwqWQL8bWreqE090WwkOzqslNrsAL0BuS/34ve3a90+B5w6AJ2iSKpNNhXzaoiv9D2ziZg0TBAamOMUfJ74bNMYar3f+K4P0+dPsRtUD7lx0hzU5fqOdPFsXG+rAqrDdswy0ygtM9hDpRsy7PrXt019cgh38KRR4g8oojEvLqlW8uWd9oMBtB83vBbGxwFac2r2sSXvKb0qlwoiE1yDDSND0sNiS8Alvfzyf4BPfCs/mbmPfZgKWGcpuRg1uJg376/y2/XEanyaIC1QHmpobZDMJeIVrTP9D18c69pi2WeuBK6vtJYgeYfp8FmHFsPUDkU/HK3GM1M9HgxHqgc+8ja8zuCpXGvCrRvvb6PdTVmQI7vtpW2n9Ayv2XXj4y2XnR6VbtGwiiBXhfko3PX9720/S/wVYk2eAdyo1xICqoUpKtS2pK7/U8xNIo8K+LueO6om6vnshfIPJiv202rJOtNLS8COMfPt+d0wH32nP2m7f2P1RnanHbeAAAQAElEQVTEmU2b6iTZY3SOJaZRcH1oZJfEk4OprCopoyFpimUj6aDRXC9cRLgcH9SDp3e1bg5+hsIrEGIpZxcuTqh5vxf33uyb/coMtbVon0eOk3l4Esf5XBBkiLeqeXPbyOIQ9LEZDPS5jgYYYRuq/B6r4NnQl7/NwqSc+Vzot74vt//xWAKwgHWbP5qnIXykrmLOJADSyyAuFii0yLebY3wUrVLcvksldz2mtrm+rULd0bPxuwU6xrcbbGrAETXQcoAtuEx4hteOxxTfC5/PXveH9eG+Xp4APK9zcgE6MAtf8MYcPTmjeI0H1pyr88dqXcH4Geh5FfZhzOqll6SJNvh7RAe5E/+3PRRgJstdvesbcwyImFjHzfBc7wbYjB/0led63vpa7pvxT+aVhXP6bn93jxfGO3Q7YHLQIgpFCNsRTFXtSLqPfY//dj+eUOrZPKMOub9gKjVjGEVUnX2oLEgEgElMrkKtrZ6r6m2crunu/MsNljfXM/Y7fsTK98bza/0mRB1bQBeVFljSRPcDexqZTFj5SqIlYjVJ/aGG9BqFCoMCmO+ex03oyb54oiZu98fXQ4H2u+1dzt+mSQxbT+DVybLZq5xQI/0pM3SPvGQV7MC7vUt3fyPfQI6ECwvoYeyg3u2RolM1yIp4sL7jXyJd4pPgsN635x1sFqYi24RLC8vOsjv8sPslyZTqWqjdhS/rOLCMy9yxpjUI4sCWVEvDeGU8XHVpxFm4bY5QQPmQbPJY9i44FBWhE2V9Tz+dKAngPnUL8KNymYYka2luHhK+gFXp4CIug9NnPlDRQ8xSJz1sh12SgiSg7YCzsGEfm8+Vn3UMBTNQ8/XtoanXChKkYN8TjP3oztzH8wrsaFChJ6S/FOoaarswjtGKx13+kV0oTV1EjfUjHgyLlKLX+pipc/a5//f5fu9zn6n3ouKNAQTA9RLqPXP7fAFFXLfApMexkD+N55QNFAKOulbr4MEbAKXH2+NLr3cI1KKmoFy7UapPY/s1s84LGXP1/mllEvj2qNq/5ONIQ7ZVs42aJyrVyEv7rgfdm2ebrlAqbe7d3+jjT7JHbSNj11ptMckXeQMq8a1FR7EbZMh11iDsgn0DB+umOs6KwAXZQGPgY8zoO6ZNaLVufajM7eCarSSl4XdaDQBtgAlqLnCbIPTIyHfbYxyS1fbFPIC/PvPP4Ljw7BzcLVWpa2ec8scwPDpouCEkVWxCKN+C5yNHYs0Y7gTE56fyxydsGB2onefTtWvqif7KxtEcHFNmpnNJN4ub24/zZj1zvNKzfbo0JppOh/hR4IQ+lSQSnJ1vWs748+P50x01avgfzKewOJpfa+ZM/6rXTLlm+FAJ/hjcoBFIBdUZoNFG0U4pAJLYpnc1UxAPZkcpWMs1UF3Dqif95DpNjQKpSQAmcNZu7iFM4a22LYcrz/5+PcWcrKktKR3WYNuaCMqqtU+mJPTK1Nxsb+DKibxNfPmoytzkxQILvTF77IMINyhQH2uO8ld7Ws69w3JZIwhCIeCZSQDW9DTea+r9d8tFDeXx0HLEtVkqraWwfq93Eit2i81oIKyueNaF+Nj6uLljIOMvSq1h0ZfZhwoe89Sdj3Q18KbRE3x7NJ0ctDrCo4x0U7s3CkxbkMoy62RV2ShORbeQm88PBcxo6+plNXgqW2WmAJ73n0c2WMCJFWbbsC2X39TKdGp+277QiuTCYUDs0R1lkWVqBD0CDxgLXdaz6HFdVCmGQxOuwdWUnHBPVbczf1ZHaAZSWmqQocwxCA7xluVwSs8/UoI0rZcQZmGSQXbX5NsGbTTVEJrvl821n/g5VOO9vJXaaFq5Q1wV2KbhAYQqotOewoMSAMcjfigVjiiJR4al9NpdkfNeft3Syh805bdeuo8Ot32bN+y2q9rzzP0MRq6wBsjuFC9HUoS81ulv9Tcrn/M8eefUQLtUtwZEX5fIc8ANinRN25zCEYC32xCsZqI0itP6WzX/UQugw4S7S4cMYww7mil2TnZXx2TUmcT3wX3iwdROiVXcKEsAURSSnnfuxDy7T6QgrUKkcjs6OzoticvWEfzpZ+pQm+1RvlzEz9qjCjMunNCmuN94lPdefvxzSne/oxTuJOMeDh3qiirrN/kzx3jzT4aSqgVfca7eb8ysIQH3Dm9ZnDWzu1e3193PXU/NEzM2I2ae/xtDdJbI7xrnbOUfD6n9A+thiUfdyIn8kKs8xpYi2G0bkI/Nis7VfdVciq+UDJqq1SvfUp9ALGyP6BCXub71OXKvoAgC8Ld3U4A9WoITFrLnzU96z0/jeuK6wQFNoxGmCJUCGziFxovvBJ6xp+ONAzlexjEzcEke0ePQMc5rvWe+0MUQvyMFmUORhNerOdPbNhnRcPlLyFSn9bP9tn3TzzTYCPSQaShHa9Wn7LteCJMiVlVMqWqkL1jrnW4+FAOF0VT7YegBLLR3wqppD+1sfWbXQgKggIWM63CcSx/lYsJn/g0vnQZaJMxIq5Wz6OoNdaw0dxQ5bbtdNhIFgpoObNBB/U5wwvbeYZfiQRprRcWadtyhp8EdwMlJxRtmK2Qw68raDBftOkvVDZSfGPFPjbsvU3Grfvn4hflAey308Hc01AQX8wW+a56L5HjhBUYDTWS9H4e9vT8uKB+rH/mEK8VPq4Fz5FOoYuR+mVYC+6BN7WfulLXRJGdb043SqFbrmmmdato7B0vHCUM3Hj1ve+4rhQVhvmDvWZrOp+PZXm7ALAzLi9e9/ajjf45vsYQBb2jVenPQowz4yL6YfAtHE31Mr8xmSvX660+7msls8uJx/A5+c8736aO7anUbn/CK8OVcGz8/87N4+aKrRsVVSyr2GvmWcvxA6V6AD0ey+RjDP162pKMv0FmTZ6oGTUyMGrikmujT6IHqL+3cfJ5R3bs+d0DCm5eeCr+5vnNVbZRhZCLXZPSNvA8O2DrKW7uvFBF4YYz7uTwDnKOxSz7SkD1q3aeOPiJJwYC5xrfrbzoVlcBSV72/iZNm5mst93BS04mDGRzqBWqq1an971lmtq/eezb1sNblrDx0EdU+46qVMnllc3FfpqWSb2LBt/LBdwzbwsRwrHYv01AIPNHGAVfOro4cS3rFboqg1/xE3qvviFw/FSvhkyO2PM1kV5hiid5xzTsw58S3QgQoWi63JY5nAKOoiwrB6ObTXlK0Uv7x3vY475lUkTBsPtejSrp9REa1sr5IFZdJGZd9u99Un0ye9uyVnOahon8ANfhD6GHNrLofKqLaXj9qUE+du4+gHdtviBFNV7IxLzaneBHnqQR5j7A0K73V0SJGBY49ZnJpqPCXs5afSlMyxZVT7o/JjWYUNV+L0BERb5awlSJvc9GXoCKYKSHp+qspYRgEYDCK83jOleOy1xjoyaaXmqMXKmOwpG34wEDQlA/eVd+88Tz7qZVqx5ongxuziHBgNVh6TMm9YxSh1fteRkzvRJWeMhVDjel6ug/sXSDphhnmTdzjeja71wMXk6MTABk2y8tGgPkqA9fBp8wbrQCq/ZAT3HGW98Zy+8xIDfUOtF+atjJ1SpzkVA4bFk4n1ozXoOSkJnQm1eGex58eGbbWC95fARmVxJDC8k606nifM81n7I5lbjZUWY5G1ihf8ztVlbJuPVPdcgO8s33wQlbfGp9ZWZa4Eaeveaih8GfP8zOPrrtY2kqwaRM+4orwHOvukT0W99CdK1QNbZIrB4kXmz+0W6PmTzZCMUWU6anshAIhqfS0KjMtiz0EiGdHGSFCk6n2CxWH/aBY32O/J55hcZz8NK+mAcNsPou5BHAZNv+OSQkjl5nXfT4HajtamQHRRLeyTqtCP8TNSAsZCiWUs2Dv2jrG9hZ2NWAlw4pXMo6LUJQUAzlRysijDw0fikQoduV7i6uFdnSIDoBjaApMz5LziEGDd+xzxVsMLH22M5bP8Mk4x3oEFbZQphN90I2aDpQiFFrJjdt9x16LtnN4fNJTqVQZnWD9BDQg1rJN0TSQRbygbZR1LjWzd2qAeNH7mE5oQNPLdEtXrpWg9OhP2CI6qwVgxfUvQl0RgaEfSGlMGZH5JmBDa0aH52ab4S1tU21swwaWjYUOjycU2KqJnfFTfJmKRgyVBfRQIhhHeT32qNE9FlMbZ6kVvlDk3JLXkaHDp/BMi4Q+Id5fO0g/LWXYyFaSOnHIX6zftPM05tFIIGuymFxnx/fy9wZLZn2fyjvP7+nDHlFE8q2UQqT602yldc+7FddRqWc3VASgHG+JkZ3AmTmoekmTjdV/JoCujNY3c4xkP12PUpuuaKbyya+8SmHdqycO42shhmtqNHGkASVIRWPKXgiXcuDqImrTWd0zMGkrJqGinbk5X5VyscrtHe2g+ZZ6YCLgffJNB8zRpouj9bQXDCPKWuTriQiMT8qGPRBRLDYdmvM3lYh5eQiAGE0l0W4dhgAXkpZyxrQp5dZkwqJUEdOGmiptWrJMYx16DCZfI8qcAmLH1Tt0QfRZpWv3qDHYrOiIh3Gb32AhiB9TM5CEYmzAbCT010xtdjGnz9SJnJ3mvR1L8Bwv4vDW9cQ6+DxcLeOKveZBlohfCMVZKjp2jZ5alKNq+TjJmamixoroc3D3KbsRjwxjAi3Tvmctj9jDuIVQLgZqH3wpdIgCYSCGhBsby8t+RtfeE9W723jmUltOFXc1lJYCKqjTAuUtmgg9WGEdoksKgDwA9g5rsxG1esZm2CIXwGFRi5PbOwozahre2aNFKr1UCVc8P8hLqtJgXJiryroU6zuDclDVNUpGiYhxpGWKDMXiSy7Cn22lFmIlAzaIFxYQbVe+CXjfqhL4glFTkAXA0U4NRUVjSl5oiKXAphdLKjM1z5YpGTtYJdNDHPDYrnhXgxrJ+TizqWx4z5q5FN9Uz8slA74cQ/VsJQHdeHKfs+UuaHQzctFjbbHVI0bq9PRALtfEMicNGjW0SirhMbXVhmQSVI/j7tIeIm7JcDRNFaPVuUeKWZ6rEpOA/jDXwt40SWzRAlA49ISaSdjmcto2OMq6oqiw8aJoeMNKNvRxxNQuXCNWZFlUtLX7rQKTOjkmkq5j0UuEz5gXd1EoxEGJJotT6mIXZ7MpJYwR27i9xfjioypDlGaOhKG/0zS8YI06wjKE0YwUlfOMbmvX//WhgcDMLLjaofcIsZIinN2GKfQD1aPu7vw1nHc6ULCBZlyDqzNKiuJbFdKfKwesWsl1kyKeLNNfq9k+IlfEebsV6XpmT568v3lUlmxHL9AIcxjNcuExFZZVY3XHoZX8OgMuiaqwFJ11z0AG29iEFPNeQ9BXrMvdZlWL6JeFjOmdVEPCIdJYBEhE8IreHHF+FRfgDq6gDOjVoynXLqJafqxBsWgnXcVe2gA4c9mjislLSFS0x5HSvay4GRhypWHakfRqOJ1KOgTAjytpZIIP8AiT1PhSQIIDH1OXZEV31Ip5V/drx2Vhw24CkPrtomuEqQ/O4evO9CWS9HV1UQAAEABJREFUVoJBo5qNoULZLl1zn5SUOKPZRck7MY9P8s0b23S8YEJhcgCo0XQKytCBNbH73RUAmPdnPJV6qU1exIlbbrGPUogqlBiRwQx7aZeymrr/9EH6wqy3iLWp4F0jp6+H4VSUZCXGpOy1qfciLgGSrzsN1R5D44CWUrbN9cF2wpUSk6XsB/0dLaXIPQIzTroZ1p7LJSbokbUqA0qRZCOfBulmTC/ZUOMsbzFPqiKl0vpDB1FNRdOqcaWn/+C0Hp7tk4u32w19UGJMYGmKvdV4T03qR3JxrdhXg14qXRo3zcesLPJlNVTlDUWp3Db9Tw1VdhCN8PHVE83Vy+VQdLWlpJyOx/C1uqPIWKNbEVcCt+IzYmhpIbPo6TfF1qNIhE2e9E5eMr2/C2UYUI+myaJufRaC23DV2Kkj9ZbPgdVQHBnABiebuuicdJE9E7DBfmydse7Rh7FKsTI5cpxBUQ9rojtLhYN2U/JkKHpWFkrbLFenQ/7EfFabmqnSweOlXclpf1Rhj83t3vQ2NBRZTU6MeGkAifeUj1YUfZ8u/T4DfZ06y13hzZRazmmxF29DK50Eb6r2pqrZCMBH9Aj3USC71eNjgoRgdmPaPbFE9yjGTlGhk1obdFT3DpD0o979zvAw67ByTVV3qZ82VyRqqC0P1X7SnhpdQd5gtsk0paVnIav43Gpjl8kIRcOSf/9wPhj2aOQ0Byzso35F2O4XYpHHbATKh+/QQiZbx4Ti9cA0eT0SToU3rsHVrYLUez/YX4syqRNygoLjNaWXZy7tbSTA8Zjl5RRRvvDnI7WxsnNd1DM0BczGUV/XDbZ+tC1/nLLjXh+0fA3wtt/3S6XUnsu5hxqBNfnh92cykOZNhUDOcGq1vU07lW2rN97wPdf6bk/DKIeNB/EoctH0HmBCe+CoktwEHdfNn9MfnogUcgRBszL/On85mZhcAXfo6OgEy6Zmhk11b5EiSbxBm/ha0VfgY31BVyYJqNTCwJgD/H7t+V6KxEXPxdpZ3jOQlavMHKJ8FeUCfcbSXWtPXOJ4HtrTMXk5PxrpUdf1u991dE2L/+ikAuvj/f3Ec+3v/vQa92fJjs4d0cVh1Hw8P5diq1ekSdwadL2Ae4+e6U/no3MtXMtmN/a9dl7BZXIzd/XdXbAdpjoMVYVKLA1t1kqx+APqWU/tBzYcAVNtIN9lh0qm9XTNG2A7LeznZ//za4+JimmbXix2kQ1bVLOXxVc2KsqU7F559TW6ozkzXqq3wvPgftZv/qRdtrG9PoSWKWy51+8lWq/78YNidIgYg7I2QA9x/KWOGIcmK+YQZGTbDWu25331WZ3r9JVuGqaxc4g2kgocdbK1S9FgeLW71rNqredNP7FK8qFDXOvmxxevrY4e40DS5ZdqFbq1+LyQLGY04jxZ38aN7hnqNmNubUGzj2VaTossN2XQ1BlbdPlhnNkooXeu8s/EgpnwL8yewdiRIb59qJ6heuaKcoPe+ZFIVH9XQ7aRPljd4+dQDeggB1Bt7K+qYRIl1vVUJyM5hSmWskbgfc6wZRwxpsJTt3qiey2x52VrKub4HM0r9M3WO8Fk27s+/1b+N9/v3LfdD3+B22aa649RESFlCooSCx1+HF+zwp/NSntq/SJu7IZPsAPi9D74gs/2m353VC4r/tizeEc/cyS0FfwBde3oN8/jj8dXU//wSORo/Tx3/Heu/0oLxAHga/nim6nEeeZJUfIs/11+rHKyRsjme6GD0f7BxHz7/Jd5D6tj3nw6CFqbn/B2c7bIwEneJ0q3SHQkIN8oVckdltswwAPpsgnz8Smpvqs5vDCG03U/T933/Ilkpy6H6+fBSkfZ0/vb1527pBO6oX3ucakxkrp1vUe6i+f+3HX1xZdHm4c6aVAPUaWZ3qyhpMwF0xMOVYhzoC7gAmjUj5MxtAGVm7nMbe91Xe4O797QrJa66/ldv99133hu+tf2kVoXINhXYcOtgcKrlbS7+plqNqc2dHszRUZ6aB1vHSSodr+SuuLazmXOXFVCU3vnWhMVeKDftb5tmc+xk4p49ywJq3jSslZDcPe2ayMojRn018Zxc27MYzxrnms6FN52/BCfkemvVgQmK9m10CIEwm7MjV7TryjSOOH0Bf41M7ENCASf0nCdiU2/WD9e280RskD9utJ1yp4UHihzIhTScOegyrxNMC7Ccw7qu/J3I9XgVGpuOmyMRmTj9EgiFl7K3Fj0WOZdT9nBKrrwmI5p+V2lhSje2QGtLaPbrhfcLR74ncgjPpKsaVKts2dH7ivNn5nsrpP4fa6qCyZgEZHKLa9m0IaYAB2xsa45P8h/PpfPMtB8TEbHDTAm5jvPd76P36IEn8XPpr6N0V4RnFwxuvSOWGWrhC5mfWixuaXEIvqZOWyB9Zcik6rbiyZSxIENgO517tWPvHXArK1kGeapertSF87Cq5queIK3nV/pUANs4/r4vsKcgM7nhV4so6I3GZ1wh0VNUFveL+NWo4zjUWQggSwqSxxXUodvAnwVV4lAr+fqlMSuYys3bCnDrnazpPProX+LfWC1P/jstfe2tWqZKEihycl6vlCvbxvFSKo2aSomH+JEF0PFELd7euC65qONwmd6uVBzyZokAxDT8xY7tSSrbR5RmaPDUurP8k/MeycP9LyiPmuUQvfK685IB66MlUsovoNM+oHfUhjHxL9ZUfwrHvXHNfuqtY5NZvbTTFBnVqk9jQKST/e41lKYoyWLAkQ0dIoEKVN3hfp83/0m1J5P4+dZ8fxYKS7rEV8uZQLPeMEs4rP9758MK6h/jT+3/aoNPd3VhmfhBg9LGQwLpvMUWAPU2Ot5g5fsjD0DpVbL3aw0G5txNeU+0+oWRRLKwvtUJDKRhX4MGewfzOqWFz+UGQXf+aQcdE5JuTfiWLipWd6nv4/Mktt9+1q8FHWV1jc8YeUbXApk3guG4uOspdQfyl4PDaPqltRAt0JMLeRmbOjr/CWpHfdno66TeOqo6612fXm9VluolUmFHgd18PQ8ZPso0G/PbyrMNunxd+agYQf7N6+nVrKfqNvqZcuxHB/v7Vze06jT5zvfjC+vkvCXF1E8wLfW813nux+ZodFndX3w3jVesQa7Hv8+eCx7kt7uRjpmn1n3sbuFY2qy+sl8pnVJVbtqt/Wz+myk+6O0DqpesjHGdjR7WOZlS6/tPKYSU8c8mX2ys6YnICQIWGI98BzgmMSxFJ51oeR9br6VVYaJgQOm0TwUppkq73lbcffMlCsvennbx7zSr9IotnpJLQGYauV8HkhNPAdZoXAmw8t9GDabfsFcoXilvVS36JpdMFA/ilNC88FqXOJJ2kxLpUZv3EavGfWsc/T94IX2Fb4XRFUJIMbN43JbbKAA0ALxmRXhHsaQi55RaK+clIoFEl0jskQNP8MPbEebzTI5E+dv6dYoLI10IqgYuClby0/Mba2qUwy23GKRRgcXjGG9rr4uOGnDXXbdy1K3JS05YNm0duv80pQTM1VkIc90Ak1v07xltrFEmgJgOFwOhGBwlx9lrClHeMUdSMkiyTPWyjzbVi9H2NLTDBgl5VRPzsLQOWEwgVOPkLLVf5/EACo28zLXW0qA+Eq/W3vMTgP6A6Xlbw0xUdwOXOBHzno0meA4a0zdoG6Uds0lv6NA63zu9AdV8XY0wcSZUfubZM2WnWT8UaDpo4cNYtzb2BTDB/VUdTkR1NIQQ1XgdAtGaWQRr0yg3FEspAgc5kyVnFTJh0DyAEq/UNoIGNV8BIKthhquR1B8wMwhfminQ51vvGfVW10taxjAKAw0GCqQEjN7CFnMqlN6wCtMW+LGs1jbZ3HWyeubJralBlHP8imOzL1PDTC9T7QgwhS0EE4lzsO3vwMOaDNDxSbGcBsfQm43x2gFuwcpRCZ2gi8cRdqopsKjGlhny9I7d+QIB31D5M0FpbCi03NQryjco0ABnTJWeAsw0vvZnQtCa8Muy2QdqIF7pWsfpRuCMSLO+70voUbPu5mrbFdo4F2TSsGVpsY8Z1KY0dS7Aorz1tgp6SKO2tYMRkzkKMNUOBS2mjTZwBQ74y7FMOODLTMK0mbMS/vCQgl8gg/wJb6Yo7yaRY8o8kPpitAkOUEb21XuiBrqKaZGpLVMTLuG0UA0cTyPn3vqSct2CS/AKJisUqwIaLcgZreGe9H6ZdVkQhitxjVPrTT8u+zWFaWhVSh6inWIGqQAiL+enbMAjLjx8e1xhQSzRl1GfVEG2+toudkJJ6E93b5s3M5LiaxON5W7l9WBvXSNjSCiYdUL5dagdjpRrd7Ooi7y6hHfA0dfuNW2OcO/dgNKUgNNhkGX5bZlE0oUZNlETtQrCxcuXxBdZZKkVJ8RkGOOYjQ8XnLtYXvChokuET/jrMb/gZtHYHVOApy1lZkRuYv4LOy1zN9DLbaeM63yHQ5NBfasSf+bti00HKOTjRlpc+bN8HB5+up2pjCtAgvVjTrMnrJ6XOwOYqiAOiD4z/IxX6S39YPXqVjYmMvhJrPTnP8DjnFAzLl+jUedyUA2pVBoFHvmjbJfUeSyMi0uy7u87+DjaPuzAFAeSkx3oqSflUctqS9rvmdkpnqLNm0bl1ZBqj4H2/RwSvt6QjP7Hj50uPkx+8/wzKgE7Ms4hIzIFu/RNiGlZiDXgusy5Br6qH96LldACslkg9fYAhGG5Rb6AWomqVcaYnMtc8Y8KyuyJOUqm3b1vjkQRpWbBgFLVFhKxMLLpPS33iuZs97860ra/3I2NsZX/9RferhGD3i6s5JZSp7Yak5RvFmPgTLQ5gjCCwqXMwaplJJIIm/WE3Vc/gmS73qqzBHjlmuE5GJ/dAaRGXfrbkzQFFXA2/2OuD98Fh7rw6opdE/9WdCgDV8OWveaUh930we0U1NhNuYMhlPfdplZD+sFB2i+AXI5UcOjmfsPGv3SzCaHfIPXJJqE6WXs1QapFMMoWhL51HyRia/1ubJ3LrTuDf/0yCd2jHqHG6xaQzNP427pcuaNQHZ2K6++w80x0OHuNiVu8v07eyxeeinggLdZ0WhmcH0sV6AQ51Nsghfj/Xcn3ojNxqM8ZtzKrN3Q7afltpVomBmXiYDVJQwaXaAA6LCL98VAs1t4qyjzeg+WxlPtSwLL7vkDhgprynkjRrc2aCAMGTictBkUhtbBF3xOJyqtbc2K1ryxpOcAU3ukdgUhEzPTMyxDke/TCq2nuCN1Di3vU63DXqAWwuM1ehJyH17m6SaxL2WEpTHTGoUOc1uOTYSRiyRcMu3Omkp0u8NcbvEXtL28zl9upNcXq523Z5g0MzWRvQzh2CaQQwOpOP3Qjoxc9poSAyPr72+yk/WwxhQxPRm36QrdqmW7RdiMYWVrDdqk7tsb9FAaOrPR/63zH57froPujRPTbB1X1MWpn3p+7lu9CZ1Vh+dcXj8e/2OiG6+Vb2qduXN3K6ZyziCe2VNNSdjvXnNj3/XJlP+A8lcnn+/zTUjH8MgAABAASURBVJR2Kng9AV/XjmOjLj4292gvXKOLCBVz95T2wNkz9b/W/9v9X/cXofzjKrN7zcM+z9SXnVOVdap/aclN2DKqv1M3CrbddeXvhVtIESHC+uA00wd7NHJz79HlBZfEQx5v7fmI2/Ph85utLUoHxtVf9yZjn/OZR/dA7H/0ARLodZ7riO7l7uey51r97wf/forIzhM8S/mxG/x6tBOirWD60Yfia11I/y1SDcqZaTw27vgEbBNqap+Ca/407KbrXK84sb/z8+QHs661/ueK9WNYGDn8x/xw9H71rDMUjS+hWaBifyQdLSI5XV1VL5WJGTO1GYoSc0/s5sZsH7vGQmdDpv6WEkBNuK2f7DnqIHZ5r09NTI/kofi2knx36O51H9IXuaD6U7Keb54saS2NQ4AO7NXbQPFBIgzhyiC/O29/JMyBZN7N/K75Bh86Yd7QY5hVT9+plfD6zvXbu/qifQaUOL7zPPOMfWNK9xIjVSA4DqpmP6alLJVnMLWboQHK5fw/0/8/1sZBTn/NshZ1wfctQsZ/k9m6znts5Titdoygr2d0A6PtkKWi9Ns0X1S37daKEbY+WCEXmrUfxFhYfMI/F5eHieDl/Km90wBJ2NpX9uT3tO7+n7x4+/UfdcGMyZYfrLs/+VyQuCgaFcfowRGxFI9uMuO8f/iK0a/on+G/E/uJ5KqINpHQbV62kT9QTGdkW+hHAqvxkRnOHfGfPKUEQHWAC3a1lvq7Km9qI6IAlNml0Uq+abYICDFDNxqV9dmDy87yr6hu+47fsfIHvETgI5Sgu/0btfx2KNS6/8f6/4H9HH4OSKIL2eLjQv/juaDaH8EfxQr5cFEGh65rnvj288v/0pXw5wPZhOnUANDvoEyQP7/msmINSel58yUDx0wjfcLTbdhuwtg/yI9p7MPvU9ITbxjbLsaiOJzVbbLcok4g5WTwfnVWJkctFftoON+sDM7E0yulvgd2jtq34w6Ilqel4Hu9dvcl6enLIzu2yy5gr8fW+98n3LxtfqMe+4rFiO8VJ+xW3GhFMvhA4EfQa+Ygihd0y0GSbijJTvJjDLLoR2WTugJC9bTWJxl9e07AlazlgCsAe+q9GsXQVIS35W3nVvMuoAZpcw9/h0fCdosFX9aXP/v62s8X/0r83QS/Ff/BWthOfL72uRdH1enovO77qmZAPNf/FPebsn/pS4Olpv5t+bwfv83TkJn2e9a31sE0JIBFfMgfC+PV/fOVwdwjCi7wp+D33FVqQrM1Q7qjIhvP78u+xJvyTNKcdg04J/F7+2ltff6fvT9rGyKrv/48/jQkFKiTSlVK7DhazIDe5iDeT+k4RJZLVrzP5N3I4QP/BX5zfr9995zA7bwjb7/Lb/ooON2gkKEBHj/3uifOmvl32+fLT/EiVMcffxzphTXvEYuXBb43ua3Mxtws2BLInqOQij/ovY6OJKCnKvze8/3k/e9JNZpo9S4sYbZHaxf8K6y18vxo9ffnw1+PxgqheETek099a56F3OzV4gb9y/s796NcAtRRwKrq+/R5pBpEwSVIz7lq9sPRbm50j9wdFpC84wfxE7XiO340+FCke1iTEtmCYrWk5MvBmAbou4ObqgxruRopsvpZCqfLnblBreSr9zR7DCeoBHjBBsCoE7gJ0OfEP9q1qdWAd808h/8dPghu7quXPdPd2TNK8sk57SibMrTbcxF7Li81AHykNOlvVisFwkvN6Jo7+RumqP3niZ8vFWLPum97ZVGTPqr8R70IczWlAt7k8/Tz/X1OV2IU0DmXzfoxrSHH60if6hpPKcm6qlZufHrifj7Pc6EvlvER+zs7OO8INg7uL77J7+xvxn+5tTD1Sm6YBm0q/WKmj6MCXKMjH87F2cMg3N2gbxk5D7u3m0n28/RJq7PPvc7j/8H+l3Vzztl4pNN/nB94jAPPg/tW3f5yfkd8qoBHWJAEYHNs1Eacg6z5fUoCHN8L17/yFwBkpvf6/vT9P9/aX/Brdl/riPkeIXKOEadxuz2rgbPwrMv5IULJs7nxfPEt/zXTOWtulcGsv4IZqPgQp2eKlibm/FvWsrMf7DfItHI/6/qK29ZiLKVLhoZ7R+7okAj0XA8/j3CQaf6syB/Hhb2GZo9r4dM6tjDlVkonf5j/ztfzLtSXXhazjbrIMkWoFOvPevojwhN+htUdADkHJe9y1DhVlVl17Jz1HJFbQEIyJEN544pZn4lP9Zqv4R47FQ1/VBK7+XMuv9d9TBYhOfPKf5jPoSXNA+vCdbXehOvznt5z/HrqSsM4XXiSeuFPxP/srZ0BbcFcP3DV2/fDr77j2S/rxyWfZ+pQM+ENe6qfytsVIiGR+5vNIlR5r/LsDVY+bBncZXWxVW8L5TMgTFspX7RIhPpMymYFD5Q6I9v2w6sREHFHNPU7YITbfIy5vLieskHRH+9imqW4sZRkil1A9u5781jMeKdldeaR7qvRQtHyVa9F6MptN6j2lBS4t8tMRWHdolQtaYRCB+lBWzPBaVa5QsFEANsaW4B6xM77skjJJ9CcobL/tXrsnqf9hPWWpo6onKwZC0NQcKoGAFXTHgix2Y2cLg5Zm4okyafn7mo79CbkzYomNKIjytahLIGEgYNJ8vYNBt93Y0d4CpDhLhixVoU4ix5Jwk5gFAimh8eZNoXRMALi9DxiL6HL9df8TgWslKGn0TYjFtb4y7S32QMKelN/DwT2r8lTgxiSD2VVIBvi/UL7tLKLu9czMeX2hD+OhhkMLixzrHM0U7MMRVf4z7L2gjCmQwyG0cVUbuZ7F05xKTPmFFnzmODIKwR3fUcLFRKQACNMDoRLdhQE3ofCff9Ms7PO9BkNXsC+2sWxQh27jCHbQ5vg3mW72lO1bUdU9UDVKoxja0f4G4iALKQhU3qAbCKpvsIMcqRpZwPzyvOc0SZGaihRwLfTOenOJYe2J0zjmO3iJqK26IbSppNcoDcU8Tsi9NhbTrrUPcSdaHtG/ZXQ1EUDYTZrqEa/R1z39NPCrgLWSjTQveQQBSaKrSEQsW0Ch0yxp+lwmS5FcIJS0zXwGXAA83ZPRqpc0MyScKEkKtf2JmnmlPQJk2RaGIbWrYl6acwh0qeIUlkMuJI7EXrHpqeopL2DyuTpfdqIgd6Fa2JlIHfXRi00IDc2GjitWE4pqbASW9mCdVJUS12Wi0kRoMS0aQBUFtYsqDetkaMia8TOHa91sSHuONXdMwt2gVGOXhzypUbmdvuHP8afZBwHnYH3dBosLM6+nr3auGc+w2hlzhRgtHnFHDyJHAKOcX1kqtFjzIp5j3AfGVWRqjOXuXGtkdREX7LU/EAFqx1caYiYKGXc5mPYAwqGiDFEwBaU03vFLIHChnsvlOToHHleptyuauUHZr1NBmzRKQMNvSQ8JUV4W+QWPoHtzdiEWp0pZI2lj3KUiul0VU61l8uh+yw+i/dYa392KzTJgPL7mjvgd/I+fT+o0SVNmEL09ROm68r92yEWOq2+VofS+SgZrez1qDyGwuhYBgUWTiN32upY7vpgbezAvDhLQmyyTuWpvsGHdlMpZBtikasnmOpObYtuQVgPa2rEweB9FFCmCLuot1zYMRebw5QyRzfZou7dz9ZKgHvwHGRp1V8jH6BQYtg9ac0xviYBvQOd5Htg0Hj/SsymUbNH6ZrAwZTYK9lUPAGfZbfQPF6/0QP9HPRdrRGoANbIpo6mFsN6UQyWXI1ly5hLi65HGgrZVlDqQj10ngwUQW/fGXs8jNQyGTf9OOtNj8pWVkZQI8W3uoBNvt3xiXg2j72+l7onnCPLvXxCQ6/iJfuvPzk+Zg+0s/bktGdLy4pxctRf/UnejeQYuLhiLrkWiQIR6/GVOqUC8rgv3nplpNa3vnKElaIHfF04RugpcZRb01yBDQkzim+oVZUy3KOQ5cUldUprig8aHTXe4kxlOyMVFACRCj8zCZEvcG2uOGXYyjTY2CLIxQXRx7UZsE5HvlY40qSU8HKibmdfs3f5mMVYymau1j5rYEHfbmaDyZ5pqqmOVtiBR77WZwTOm8qS6Gi/W0S2dH0rlxNQxNMxCWn3d6qT2tahQExPCzhH2abejl5m8k/zYkRz36VI2nxbs0HHIXGtyfAbf5vB7hMpy0eujlbEUEd8sXqNPcNsWe2RMqhPx1VeU/qkSPtgRH3Jjr8Xt3O1mPOlJYykNfRLDc5iJVHVJe3qOZMl1e4JjwuXIxI4Pfk0v717fKDAkpkc7RpWAZo/OuallljLtSF7oEcZe5939nlSNzcFTSNNJuReAAYm1w8qIfcnu+7Xe0DK03Qal/FjE11eJAhMDm+JPw3otXt5wcSOnoBjRbndNq8Wdveu1cqZU5/VB+TnaRx0E+oLsJZRwZe/pQXDsACSo7OQ8gK2IBE5JbhWgN2FMRtXL2g1GuVAwfALb0BXNlp21D7oMbaHy9L99tGdxcfRXCiTLJSxsXrW9/iNtx7VQ0bsmMLKtMcgabYrjloKUu1hWIV8kAfndIMDJ0KRR9OkOpD9sZZf+ECU7I0IhuPavX3WUPduLluuISpM86JiJGCYNwLWOChXZLVVE1KSH38nuq9edgyVxVPeeIVtOd5tk4pPFsFpa/iZRRc40EB51to3Wrbict/WC6Onm3m4Nj6OMGn20TKwohVPeNoiHV5YBzEjAWGkkhFRYVB4NgwdmQJLwQ5W8UqsV1TKz2QrvYLAbW37mc+TPwefE8iYnFEET68X+oxGDqL8ep/bJgCCrUNm9QrkzvW0VsQ1s1b42jQj2vwsf6Dr4l1D5PhJHkw71TSgaxfjFfmx/PTsMjxWpeX0MEbzp+aop5U++Wf2ys+oOlWZ/qZnjs0DFaHYTo5Q6j3x5Dy6mfAFXnlTa3N65ZoiS1QAyiSBXqwLrXcKJT1OXCPSU+rXwZaSRhISJMKX8gAO+LTf04c5PM3WTCxO8GWVmdB1yvMVimoNLtgBEw0tt3XSzjfOiW9f37iOhRXtO/PL/kohVPwdxeabNcG6SKkWd1NL01EslZTpnudhHsfRpDVU4Hgf0mWSNU7YBhyjcDGuCmsNgmKfwN6tbZiBqiWF+Nfyfa6L5uzxhgmAKz9yloUahuP6IC6OTQJ/kQ1eLw5sOVBw34PfB8/IrhupdhAW7mub3jMM6jZrfU0LZMzuax8VoQ1mtHqvjrgW9PVVjA5hOCB66ihaueTRJ1egXIN/g91Z0w/6GRe/MuIzjqbLPW0I8MBzfNocXMEImx3whXjRjfdBSf+sMDeXaKOLqlko5lbas/rFMiPKsDA7ZdCigkZo+bVHbNDlysQ/xYoWLvk1rovqKbqTrk+PxePe1uOp1KoDyBx3KZATEoZOyIJgi5MK6MbN06O01p6CV5kd9+KGuczA1G46V+WVpantk8Rp3I90GelmolJNtBOj4E6N6BS21VjTWi/9cTwLvSFZV6PfMqO1eltIj7fbQ5aC6c6kziIBWfODdy146facEXPWgBt6AAAQAElEQVTTwFw8MRqYat2KZ7jVx60EE4d1R5WuPy5QVoU0uXsF1mne4LNhK9d1oP5v6igfn8WhxXgYx1jXuDPedxh5M+WuNnK/OXmpX5cuFHU76ViXQn1Sj7t4P4GHl+DhWD725E5B/X9J+/eg+7ouOwgaY8611j7P24ka8B7FgHcQUEut8laWlprygiAXUTAhIEkRpIJACAkEuQgJuUgu5tpJDAomAlWKVVpRSJSgZWki5EYgSkJCku50Ov1199fd3/ucvda8OPavQQ2J/uP57ed5ztl7rTnHHHPMudbe5+2vNUNW2MqZd3rufGJs5TkUMdGreTXntnn7zLV4+TQY9KLDL/U1emK8xzycVmMqP6SZflbNl4I7ypvyMOqaHATh+GH4j6StAVZb9R4VqzDp5ARJFW33ojyOCUN0ZGdTbzqgOUrZ6MMqSkVniG8F7KjBSSwMqZM7UlPseTlNRtuyxt6unYKpNd89E+iUDTS8RPAaafJ/Fa6kpxttPmmpkvgwymaYbxONgNBLk+LDB9iI7efYpvXLOQVc7BzqgSdq38jQBWlPI208cz2JDW61XD0LhjSmqlWah7nL66hH3o4q9jGGZxgCVuVWSo0A987op9vLN/XSow2/XAw6zvTPuW6urKtSMhe+btqhbdkww3AZJkKHt6FGm3jrsegLqd2G9uh2BIudptl6JwmYZUP6OCKtgFI0zrLYdUdmKSRMYFYLd9bJ1tIWoQE5I/TlkcXAkevpsKm4+kCrvvioMTavO/XcmDL6qOQivJrpVbaJPaqYFNcSuYQ2TCkpkeStumS+0TdmY86m1O0H2MzjWSN5ba4NPU6+gpayScnfvtJ0eM5OPRHQN0IOSWt31+y6Apo32OaHglkdMTZWtrKjsgP6OZztAlxAaYnyLypL8T9Kvx9KYRIIFJKJJ4NmH5dF2WY8dndacHT5kOUTPTo+LD6u9OfJcR3DO/RcVXI3WR9Ew87xe1s1bGIR3u4PYcqUYEBBIChcgFODCHBgaUijo1VHT6Nnx2IuqZBoCI6hrIuQ3WGb47TKttJ2KY8xkMvgVqz95O/FdouhyrcSKLm5kWlNc3v0TWNgKLtQzVZ7JpW9jEQJzJxGjgCiVDYdp8SW2t9pXfa0ZWKVYddtSlu/u95qsV1ygUfwW4Hw5JfVEBmNgKWQe3OGuuaH4eVjCIq8x+C5Wnf6gS8pk5SgHQTu8HebtjxaAt170F4Q7LKlZAjfw2VsRtIszUpdqSGpcjinZYvJahFKxSRaonkK2sdt1OfUk4Wtr2FTtE8DEoh2gAEelkaVEbUaszi91xqXyQ095TYb7/LdWlhEeeqOE1frU6r0APSjBJfb3uQRLMPU5ePaAUWRLCA70KcrhbMh+Gp3lWPtMZMD1jZSJHYf4pBpF3jZ8UoVcbR3CnWj29hT1eNvaLzAQf6n25yLY5HCrrD8GI9DSQRNdLEVh2klVG3xpYWjpznKlH97dilVRbXhUwyp8zH5IBZOsJTP2pEa+pwPqzMJNixwSUhoEJg86wq3BgakxE5mqoImRKX1GnsoGGYZwgAlTwZP7bNuNcSu3kcJsaaBxBhYC6+Bi/KT5efMPoMxvL8kkAk7MJYT3q3Gqw3GK+pqzETKcMdWNirUBC48Y4xp1kkc1B5x5g7PIw5DPl0hySlHURhq6LdK2dxhOuXtZpRqSW27utJJrY1WGaoWxx62zQPDv8CeHAaBgzl7LilmDqqwfew2VFPKDC+d9Mvb2KvMJT1enejdGXXQRzKdUNuSoqpX+quvV0PI+X6LrtI4dD32CqxSMB7H28wXFI8P41T0Cz1KjYd0UO2TLnmFdQvjsOFaGZu5+9yqH4VJKYjUe6lx1K2158rWl9kriW5JplgN5lDYvUfeU/dgx3k4UruYd1d0S8WGYSYin3yHP3wo0kpHT7Cp3WBJedyj26ITyFl9FV242IUvp1bTWUqD5itxyNKmbRMbHRmRb91HVb7cL1xoqYptHcqZdur5oZmAg+ZKRZC7I1BaLauGPcOFpjgeEcfy7f6mVKXepbSJBn6UzwCBmq63kPBv84S1rCRVztKDeipu1puxpUpgGm0Ao2t4YfChochSuZjSWmUqoNzKYydLQvNZWmuHHdOeUzvP0VKBJced496jwiGsas5S2MzWMaIEEs2G2+Nu0Fc7RJopelH5xNp52k5fiav7Vc8WwuR74V6WdrWv4pXGwZrylq/Z62OYTrdJ2Kf7vO7bY0dUHXsd1+0vGUmUQrTLnWDok5NekjBl31k+n8sU82HY07bEVsFU0hRZK3VPHlR+R8XK2e5o1bYx3XZ35iNYdZEJdak2kZR4rnEaLwvdYefa59lMVsXsM3H4JQXihdUFnNImJoflumyqKTbjuACtV7+u+PBcCLlr5YS1WB9LXYowAOUmA55lXT0FO24eZUuN5B4UXeqkVm0JZ5nkMQ6756b2J9rZTU069G3CsmBrzqGubI6TuGMCw2ePa5vrjlp3H7HShEw5oAKVg0c30VarMLSK0CGSywCTHyHTqKvto3vdPW54mS6KPg6zdh5HusoTRszgjKJHvCLEp4GD4hMRdt+txLZVaeWcpBmgvjGmNUWfklCRjdBw8sP4lftrkN4+9otn5vliopPCce3AznP881xxFkRvOywNUchmwgTPv2JduCm7R2Kxl9dlRbezfL+Qiz56+Hv4p9tb0dAk+iHbse1LTKDWgw+cryzV2dxRjbufx/aPi0zEztiIRwOUT3eXY3nQUELBRuZuHOAYQzU9ZonPEvTuWBrBL/xoRitrWErCUzj1PrHfIUG060KOCzH0LHBsH83h0OAxzB96pyu4DbVKAp7Gt8Wp09GsQgU6da0MymVxJT2UdRfccfF5xISY6ov7xPOvnmZ1et9dd+OL2WWl9lKyug54Lkh5kzE0LaZKrDwxW/wDSER2kOhJceeva10fhtWH0uMB3hxp12XybYNlrBQyYVL32RafjLdHjmiXxfH8Nx8mqmUZs3zBlD+LRnTq1xwYLzc9w7ikFkjoS/SYwkWcRpXGPMRYnRX1ETUDlSpNLd+78+C0Hr/Yp8fhXZnxxPM6LAhvheUUdEAwT1p0le8acSQgm1mu1B4N7hUPI6ucCoeUj6Od6c7aOZRlbvM3xid08yH6QaXUWihpZ/ARoik1OdAKBxgtj6xhPmqM094y3PRSkYenOk92Vz/wMx9uRGJLruXdjH5Cq/uNDFVYTWiKThPlIClHR5cgb6XqUpIkWFPNg90C/l79ntjUJ2Pzh6/TJJF+ptSujLJWC5PCp9uQUahZ3rS3TsvEkxwVOWoU57PXHBrTg63gHBNlXj0IBwi9MnlC+bwxbzVaWGuF3XFXvDvuzAyNdJvUo+gX07tKnT2N4cxEbLetFURLgL8wV1MwYlRMKfTK9eIaw4Qz27qlSoW9y+LtOD7SJ67Rbl1dT1Hmm6noS4nJobxTID1g2egNHmNBqoJ4Vxi0BE4zI0sG2LCyzIi8O7OJQhdPdOjc9tqjwxUrlOInz85OZKOIxw9hZj7HGDDXPTLG08wFzr0pWRBqTwxCW5K0Xpfru+xx9FRtCZiWDDVgnzQKHXMOzkn9xgD8gAd4RK+6rjBA1xxLuDOqoqU+N9NkgyYM4+Xz+TZ4LIpeMHqXHxugDoOE2/XeeZ8u01zQyhVEs8G2HmrB4UblxDGqPI/IaHM+Pv3cVmeyp0nlmooSvuFvn1+z49EP+4mp2OkKx/TTu+NtvJ3V3pXQuVk2WrBjXMWFdmG5ql5VRgNcR8N0DMIdDST0WddoE/Rp+JJDEsP9srFsOgdd0w1S6+TbPO3x5YlJOmTDwwzTYA4hrD7Ytx8RLEVW0BJSR4EBbns0yQZP2PvG+/PE+9374DxT6xlTpoHHs637GZmGko3ceLdvly1K5K8Wm4IAK6ozepuSqxVjIGeb9cv7NULbjTVve22s7NGtxJ0j6satBTy4b91p76qtXVVIRKUOsTpQqZOfzU1ZY/qj0uxVx3o3qnE1ZM0KgE8hYY5RcGsXWaASwqqO0TmjkYoOR0qg1ozrDe0yfYRdpz9OTwtT6fkYvkT0sHrZ8VHHBdwaMUcsCoNYrMlQgGSa+JnBauVtOkwKa3VfETW9Tflysigxn1NIWkNUKhmhWtylrnXsqrHw8YhSzxVBbT1Wpz/DgeEtPVJxl6z2QA/lsXqfd2U1x1gcE3Na2fhhcvkMGZBvwNCtgwQ5ury2IV2cqbfd1l9jblyVFJBCOG1x0ZhWPkjX1LBQXUNdIzrVwNYu3zJquiiLF2qi0WRoKyI0zg/r6VWF3ONs14JTGjies2GQrxwCIB6IpYaOlrzWTr+bKd0qk5zUlHgCPLS3AkanGDbDHPeHtLdSvf44MrThwm15FPx1NIAByodJYKvPlIZalYHW8kpxjjtGxEUMdbdCUO7JWh0Losh7ApBsrgFJS8tpK3UdjBgloC6/x6SHsZ33C2dApi378rIV59K4kMMR7adRjQQTSIOeN98LYkCGruw5MV+0wbQK3XMesazRlmFdSrOXm9K3KkeHwpBOe21MGX/yB3e4ZvtQz6unM7UMHe+oOsrUKVlaqhvTHqGLBo2TcFXvw/JFLrAk29jVt1kOfXZSm89zecJ4qzqD79O1SyVTmzJ0gII82wwZYon4e1hw8NhKyAahaKuTPGLxkR3aqUlZU7aysyNa6mBLC7N6iHahntHD/IVxtXTkRrQ1HgbcwphfBNBNmTJgOH2xJ8N7TyatUm8Fr7u6W+haltMZZnKEllQW5UsgbFcpNcNymUCqn6ejBbG0wl1+zXXRlfzxpKB9KGBfsFGQNWid4JUQ/OMeqvhRpB6o3QMpBzmevqOeuKj4oyOBS2i9gSxEKFBjaz6UkxR8y92KUE8COQVTWcGOFPtdydBQ70GsaI07WVmGM+zWuQlftS6YSbIYl9ujwiuftUIlNKat6YLeM/fcxyKlrVgss+p5p3IqlkhV6zW3+Wfj3XVzJ+UGJdKLCIjOaMvybuNV2jC2Z5Ns6VkdcTwwCz3bVg3mwDbonLasV/cwxLDjo75QoytVT3iFu/K+c2s39RY5GFDRF5b1Gq0YhOKdeWdnyKJud5YFF2gA2qzLKP6bkC/VRT/ZXG/45+n33nGqj5utpuhvKU2PxRYwJ+zFsVo9YCm7XSty7LQEq3GkpJFpSEmiSmG3HcixvECvMulhkuYzP/w9GaOzT52sd1j2oqHYN/xWxoqarJ85P33cimKID6Ha09rLOi1QB9VOKg40A8lTo2p2m9Gdc5qifk5phEXPxlMejta/kG6FqlGJws5OzU/Fo0ylr3A/wx8RZVnm7GcLMrd4j7tVOThaneer7bI5eS0sbxK6lggivMocYdiDb6eqBfDHn9jVzQRO6eJoB62ENnOkpsG7T2ZXDrS8e1FDcpaCSKKb1hjFUqi33cejfaul+IQntZm8gi5V3Pukmg+rGWhVT9dr4qtrfMyhN1yZ62nxW43/FT777gAAEABJREFUyjkx3ZbYQUzbi3lVj7vU8QtHHjAOqMrDKZWlkLtGdVxZugW7WmlQx45jkV+GPYZSmVR9oqL1KiK7dVVUM1WVPWdcI+cTiST9NDZ2Dc9paQXWMFyzXfGMBgbDUUmEWbkK2L3tAqz6dJeAfKXZ88TcutuMUnUPderpXQ6VVFhjnV6dl72nrJSMdrGEu+RO4W2r3BH7PrFTCVAAAOVqEYYBzt2UHVobkMXOZY8I3HyY28JcaSyZU7Q2y17Aa5W/oi+dbT1I81OmhIpNl9OAhXk1KQbsrqGr1e0MR0LvGjKGA+V1AnTKMEqr2p4a0FDouz3NiOfVokdkKDLhgQNdHa3uUhNn2dv9DftsmW+oTKh8VMhQpMt5qs3MmZjsuWyNdXFe6FedVzaVYgxtM7bLlGMsF7Fvi7eLulaU8xi6n4EMY19LYzpG3EvPjmq4MISfVsqveV2XPzd2j2/2UVw2MaxXchUnVKRjXDYXh9DIXpWdRBSq0Sp+xdBMgCP8OmsEpY0AGyKZt1O6zpGchFJu55mYNIHfK3AlLnItXANqa4MDfKW0xIL2CR6AaFMwYchh5V41NxSpKGXAtj0n4VQzMxsjp+Z04Zi9VTze3vRoxs16fJeAlRY5E8qmOF7kNJAt9dbtAx+TXxGjhMACq9Jn9eRos6eTMFpXJm1hdl/Hr5oX7BKAnlHisChFQoDRARUkD2VtOXnVmfc7j3QdnYrqGbD1WduTLm2dUM1sV5IBx8TDiSe6vcaVMpOlbesCTOErw7nVKeaQb+DZNTlznHuGmvQ9dN86Wg+M7ar+4HlNncp21cvodHXE9Yo5dGcb3KGwmqbTb+INEahwudw+xnOueG/0Jo8lM7HlXhBoDZRF6ashjD4f415XuSrsHRt5hnUvP9e85zymfrMNu8F6sqrrxvOICiMlpN1K5sRzg06JXY7qLTYd5ZqSwHC8Ln68pMWX34/qh0hSB2jVcdFk+YblGvbynHYoHgcBdlc4YiiFA01JXaeyEKlfB/aJuuOcsxmVdA08z/ZCSoE8t34VWM2CXvpN9PD+cL4Orq99vJ17jJhdSl4+OaCygU/rt8l8LSWJKv+oZ+bCWhhAHyKIQQfFDvW+ghlyW0qo++jBGAgvlZyod43v+S5X51eqOFuN2R/mHN9qxJu+Y0pGtOOX+XzN8XKnDfWaLeilZFwfr8tXx3wfvK3yagwwzLba0EoTQdPebhnwuJbK1pjWqaGj2qL7VEYE9oae1yUdOns6TyAKCcRqKKRKBtgcZr5e9TFgyGBlRX/Jpa9T15HBErUePfo2vHfVJ9fnWE3RtsROsww1DcPFO3oOoZrZ3kZTHta8lXXIF+Zj8X51XoZlSrcHLUxj1Wpv1ZN/EdaddZIE0El9F3mHsdWH+gNYQHemeTjbT3GbQqjV6mGnz1GY7W7DMMp9r1lTze1p/59Qiqcp26kWSrXXdkBRoymBo++Or7/u/XXhKM9aMEJE0EBvMhG7dtXRqGCPlb4KQyFqBJQViViySMXVU9BQKsjTsi3O2Y+lbIZB7XZfpkZc06eou4sh+9ZtSmYvyqjMb766nehEhzHWPLRmOmIpmn34ue1z+zs8VYABO9JWOCyLVYrL0mYQOXjTD0znWp1JmfSU5fLiSlqEVyKlsYG97EzLASg3rHaPtmrUMMwlYKTEIP41n4u8KCXJ7ua7eayNdSG/LUtVUeud/iaPA055jtnSBAgUGFRGlclCvud9e0UyiiKsmJWlCLpq8r38dnic6655tyZzgtPB1S3Bi497SO1va/XUWgPuikUeyDG0hfuYTb4r7667x+mP6NkhDI0eVGseMNXYlbYG8uWxXr2uM3jsYV6OJuSfvIPvwK1SH8zV0Rl35tc4T4xQbY2THURfFDYOgIN46lWadsUMpc3e8DdW1WDJtC77lALLd/gpdhMO88IMzE41CvF6gvEeeRvTJATWeFQFSapBg09C+ekBhYw96z1zV+eRjS6ISR0DtUBxASJwtmlnjm1jcyEbp5B4XmTBdWfWRXZ5gbQ5x3Lzke43hdvgtMYr8uO8vWPAXjnWDZayeB8L81JzGB8wcysKiHnwq3MWtMzE0UL8NIb35OdMnaG8aI5IH1JGN4pXU7e3Q1Epa7Uj71N3noNzV++2pkoSyiWuBvvk2VoCz+22udLECUbLyphtTHu8FI2yrR8TtUSaidNbRKoemkqI0h67RU169wv4quH6HFZmHOhho2zluErdZs8+4lyNbzbQ6uuoRS+7Yk3onUbTEpT0RDHrrNxEtZFriFKp2KzaD5eOcq+XWAWMFCYNRHfDUorXXYePoN+QhCekvzN3SUpDHNZyNzBZUdUFmtMnxkCnuBIPtZTbN/IzcaIfErdnTNMaPSutCpve42Via2GPfaADmSrZQcoclQhZlpnWQ6d5JGYtMjqgdXWqn7c/oKsEovtWdRx6ztd5aZdDnhrvnnd6hG92vDJXDeflwydrRmT23fYJ7u6zKz7ZZwnOvI4P0WfhzMmWXGcF96m6vfd0Xb7xtOKQKKo94Wm4nTeZYkPs35pAXe1rcl5V2mabrqgovUXuMExrV114wGiLPfzgpMJ0GRn+afMOP+/xfvsWVUdL3DLdqmyLKElnDgwv9k18FkMAmoNj5hol1WkIGt1IUYGnCWxl5bkoQyRAXczku8Y5Epd2BbaWffWyr9qVSaOZq+qeXLR0Mr2mdD16Ij9aOGymtP5YagWGNmt2ND7PeWeC0oPLCIwYFBMRnXfvgOoltUZewo9wxVK+lWGHDbiV57ZUwlrk8HQncLIjm+nz6GHyKG85H491SzZebnq8pn5wfcKe/+EkQyGjT8hEFwvYbZ9mAYPs5U3uSbv6NfuliEbTJWZMjdScVMvVxjbC3vp0s+5h+1r1MTjT8AkJp930mphFLwgQON94bb7KBpVehX84kJqKgeekjavXlKByoj2NPayty+QVgJHjYn7wnrlRsQUeUtJGFoPPuQ1Ee7VI0/5Nb5pIDsi/Tqffm2dD3SrfJ1VNGThqB1yVy26gdqvqkshBXGURdrfdytwcc4zRbS4mrsWp954Py1NPTbcc5BAlXS2cfl3zq4mvVC3YoHpX3fm2E6uajYM80HIDGmTqw3smqnX+KSC89a6h1rUefqr83P4OxEmdjIw7BbOFG6TMtYnJVD3OdYtATnJd45pz+hf6oktHtkZzmLtB+VhtF325aUhXF8ShGk4iUu81tnpAGYiyePInWRwwzTGovw/akqExVrm3ktyzMNBmEmSgj1SEJhtWxH1xv1Bq3hCVjqB8ZqsTAqAiJ2VCeRrkeJwUeKD5MS2uUpMyFUWdUbBBe7lPKp/hJpWRdCiqyaGCEboWi++8b9xHUVmrSW3ODdtiy8LUnVijrfVRlWlsGVUUE4dqwwkIpWCbbrdDiQDk6AyXEC0xEt60wDq4kuv5pFXAopQdxcunP4sMcaooAjKapQ9ogw9ZFkeSiL0mX8RKWnu7XEB1WKlfRtozkJrSyaoUkZrow8e08TJt8TSD4KjhpWHRHfiSHVl2TzOIVqMub3VNepQ1R49X+irTDM3XkRjxdLfOs1feHyhPMLoSiX5bxtyp4BjF3DpM8ihRI6KL7Jaj9UA1dCtfJ87ZHYcsXQkgCpLKzHbxmIhECUqah6Q3uSYG4cBsHbVGz0G6d7ksTMHXfDwvGiRDtVYqKhmzo3SqvrUk4ACtfwCh8IXJa8wYVuhquS9jQnOo9wqAoY4qKbxZCqnSmYtpceqciNQiFZflQj2SOeiWFQ8KpTmh9ZE+FLyqEcX5eOXu+QlpTUDSnzuyXYSKNzFhCpNStXODt1BTOR857e2+lXwCD1DHGf2oySEqd0Lm2NtU5xKPZyE6MjcQHCWjNC8tMlxBGRAw69HOZzc3YMBMWOKxQy8q/EZsj5gh7RcbaHRbJQVMziQYR47Kp4pd/XnMu9Y75xGZa/WlwoMmNQtsB7K5XcRAf4fthZAx8Wsh6wWvEK7c6KfatcA9CWE3E40qayjSpwbrh7EATokAJAWfwibdd59mmj4AOkUUq3TD+SKVCBS6vBWVVgH1VxMVsq506LB8RtNT8MdIOWw5KkNePJdxjTGM+mi7LPOH83b3dfhK0SWnaIGkenW83xG7EMWjIMWat0QqTk2oLbXyD8cwt1h8T3+PUQ4zGybSpGjKSld4YRUkTbwR99fvz7c0V6azCd91ghtib7CcgKEWDyUAPmCqmY1MWIb1kYY7RYpf1pOQRk0IbHovo0HXMoSW0oxE3UEjTaDJASjFQAHJLmszfVC6qJTaKTD5iASPOstnzssfroDK6EjcOGUgHz5lWN0tWrl6+FGFj65LSvZHsUTNNqPLe/k4c72va7v0qYagIKtaPtlh1FvLnuUTc3BMzYEVRvgIsYjhFKrWBrtveya0WTVv9nue4+pZ5rEagmvn8hgGG0oJtUZYvwt39T4Pfw0ilp0Xz0fDuoAvTWtnH5+3657pinpWv1V+1fI2+jGLCSrsjityVcl+mucc2u5iUPfYx+uu/DryM1u5LtupWmniTObiMC7zxRos85Bw2x0coH6bew0TczlptAnJc+lC8hR1MzYIPyIL3ZQszTW6OapFQmc3ACq/JAG0IZ0J1WmjuhXh48K6mbHivPK8oIJN5ecLI+FEU9KzVAdwKA8YUdhZym6VRXqFERqmCZWybHRqMeHU7ijMUO7qdrrQ0lxXoEo5m8MB3bCwDyw0wBzGoqzk7nPsaMZo+BNEHLmrFhTiCy+lQsLoVLgJKNdHF1xnZFLgQdLRntkntejL5wai56mP/WVpfYwTV7vlOK78auMlJyEmSWuMwqTowiwFWSayDI2yE76ry1vDzFRSXy27nDnyJu4ed/nWuGk9lBokRI+XXXiyq+gUn0Lzflz07DKdq9A3k6ehZPlQjrvETQ8Pn4GRD2+j9uhjs1XFIniUWVs7Q5l2sRaSDvQrAM1uOAiv8tMqO0tL2cFs0A3CNYELUl2pSTiEZh7O4x7Lbj0NFrs0Dmtdlt7d1PgSA3P4FMCSDyVEFBDFSkWpeNJkXKDMS6TpnuJF3asohWg7PVXdoWBZj5QyvNI0CYAS/SyZVi4DDQahuiDNlIjTdVoD3cecvqBTJgrxFGZEle52RNxRbqnQi8wuvWQFEDduPUinpXcYWuwIjYHyy2PjuBXRAnLwTAMG3W3CWe7tq6dYLqWGcEDnjUoB8EEtydM5+exNvKnd3+jHk/qUCecowuTRGY7dUnC07BzrlGFcs+zKGL0LOE0RFHSpRccZTL+ELoSrCim9gY85MS6sB1ALb82SqHV0O1tGpaVwPGFa9Lj5hKZ5rcCKXQ7D9L5mPvguKhqzdNONexpaILXHbQkrFZ1PM3OacSgNruQAoOTkajudj730LOckZ5u1dP4lip12MiLv1Bl4tXlstYyh9ECI2BrfK9sTEhiVBR6ljaQDKMU+JlyFZaNi9dYesdPqsFC0VgmMAbcmWlPsWIsAABAASURBVBN0wtWci06Q3RmikjUyLvV/e9NTk+TYvqwOLdYUqltN5cVaYbrCzAcA5Hr1drnqjed+XBq91C21+H6xolDltjq3b5huZIiEsoNw9CwMxbEtE7qRzM4UGmFWwpeaRrSo3QbpzFjaKA4pYAUsxLO5QtJBg4q0PM7TYXdAFSxSCJu5PK9RwzWlAQne1P17JDu8zJ8XrAMJvEntJ1Vg6IbH9rzHCQhecwoqe8jrXqb1kGjLbSN1h9gzn6dJfLP3lf0q+ekbEXWQx7vGbMnBRbwynxytByJzUgkxq0YlSvILZ9O8W5uFTp0UWEdprK3JawqqUnXYj0bglrh2vKDeo+xVKDGoixwmHRg9ODaYZm2QE0Wd2sK6WY6HCLvbdmiflmDJ6wtSF1don/4SnturrIZ49VPXo07xIDESK/japod4TuzZSVQKrSy9a2cHmmoh3RwFao2/B461SktH53OVV2GmQ+NMPI20pbiHhZTkFFaLKrSyrt+thQ60do13ZDGzjw6Zso1194og7pqHU5GPoTagjCnukEUrQ4njL8tUFw5wdxaCncZWfEPXUSm8gdqyLlasTnA3lQqq5bR6IeyLXL1IdPvpsRuJ8lnXV/jqpccOuhvXqoI2aIHEVGGJe8njNUtt73KYPzNCMISDka0KsS0aZ/l1nj310wQkOWkg0GFo5UaRpUUzqIodG08SHhM06jD9vvQXSDb6XrGv+2kSCkiZZXLWowUXSQFt/OzFvjznTM7MhXDPdjki4MpLA4WqiRxo+rSxVpPV4j1Y4ar0eJ28Kun1SFpqV1XjLuwIi5g81fFOvIPReiXZqla482kPt/FezBdt1WWlpu/liXXU2e3iGYqThwk1BDF0nf7YpUSnmcCCD1hZhfEBLB0I/335UYklfEP948xTM2hYVIpntsXpuLnPlZzmtgTovHRv0Z+Gd0Hdh4CUOvOOunemJPiEi3N67/7UyRA1PXqY0lG1UcdVSa+Z11JWUpeVFoO/xpRbz8Ge1LI2tWW4WmCs1Gka0nbxmO3lGkA2E5odmfmU9maGxN/XHa9bnbwES1wEuKElr+KEMHWDUDphBtHwcAEM5URK15bqGZtWalhYiY+yV2BkQ99SzjssbzvF2yrkeN84t2AZhvslm2zYjUeUy6/ZQ21MYr8dag9jvjnvw7jV2ER5Vfa+87wV1fQxnZJTBeOIcuRze5MkHqb4w2XJOduvzSEc0SwjJszpNJo05xnE3dTvGjwfXsNZPmLOgOvsnhHXeZ5vex4eddkoppvEXC6LaS2LU9ue5mzzkHGzCQ7IiSO8haTgSBNh1ejRxmZXmi6hKW7hQkOzpB1H+YGaRtY6fJVbX11XOvNKeLfJqIm13tSDSSj+p3AvhKFl6ItbFV68+nzYmfM2PxxHMAGvPfF2f+MK6UGu20oHuhW2F5egnh6fqHeG6EZ7tXUjw+qoHbwORY1qrbQBzGUwm/Xl1K6SJrFHh2UfpaPbE+vAb9TNXZ40asrShWIDmTxZUZAn0VtnlG7ZSjJfV3J0EzrM4mPoJIw8sKALUZbixxY0tqp6qZN0R1dBJIWaIq/majC7NhnkY+tk31VHvbSkjKmFFjiojUxRUF546rdhkofc4Oku7w7FnrSifD9IZdLaV007oJRV5Sy11PksNIGUTLLlWiG6esNNEWBlw8ymRGUFNHQvfmLGmQgf7uua67V8ensGLJ6l5imxsiwhB6q7v6Bk9fMmAkCbrs3WfWdOS4WE2WR3tXzICclxma3m7NZ4sN1isKZDeGgX4gU93o2RZhK1VpsRZ9yYe1iF9bNkwBjC8iJWY1ISpTBlog8QDeXVYyC9k2VRI9KqNSb1t8ytF1uzB8ACQ2BslQ0fdnnPAREjyB3ZGXlqKzQBH67uNjG9XO3EIdf0rFE2hagdT5zRfGrOaf3lpb6TPVkvpWdShk4aAh6JnUxFlDJmadbTYChm9d3n3XJvTJhkrFOo2SA7kNmNpj6QsBm8Dm1Te3FNBhZsqHdgkR9PRAMAI/zkiBasuZZ/+HPZ2wqdtiWrAa0ZD/TTSMs29cejHLib+eOVWwLWkhXLRG3LPZTurnr8noo+N+7A26lnATZa2XILdeVeKGfUSL1qw5MvcFGzse1JXkJIFubVNqmGb8O8P6xeKD2eVkKsbBy+znQbEBoiMD8x00xEiI0jqpXjTLXrwUowZJ8Kt00kyfdhPUvvvNRQE8i+C7eN4nJebtfoZZjQtN0UIdufj+agQntvCUvWJnro3cl6HAKCqWHDafrx8fyb05/jwhh2vA5QIfoWa0E9GA30o4wUHt4pXqbM2Jwkq4gE22RZeVEiAoplsV8LrkVsNemNUWGVpmxCZrql0szCG4xmlUaoK3uFosMxHPTh7qECeiqiWoKC9VAidIFZ1kGPs+JNPbMG9pj1khrTCGIZxzQQFRXvkqOsgRBAya3U8KrhB1fYLLasd9Nr2jbcyrh0LsBP6LIGyndKMapfb+0UlMAhJ0442osJbc0ZolerVJIwvZSEmeBmJ7zUUXS5lGHf6Xgb5WnP6kFvcTUbFA4vmtr3sUyUUeAuF6rbkPYYGy0ZhzjuqoqnUysZTVTLEfeQRIRJ/QgTgLiM0Q1TzGS1GhJvMeiUXiXUcGS1rBhaQKJaW4Q8rl7zHCO1cdL8qtwCdB2fqW3/7KnVUfnOEVnVOvxw5pyk0Cfv6g0RQ3qN0cPMuTCuM/x2bIFNwgfnEBA3+hxNQi+lVTAV6aFyVS2m2kQLLGjbWaRbT0/X+BitlTN0pvA6+yNDd1ZEynOkfzZuogATNxB92N7tAdWQwrTz9bxvy7tZqXYEDzGEHAhjMFAhcU21h5PUFbE2caMTs3ta2Aho6ulz6oQ4MPmRdHVFLuF7+Bn0Rd0LktmnMjROhFmkPYHFq47qsj33gBrhI58Nrd87eDASaIM7xoV1UQyZcmrWVIRVZazLOeawCR9SKQtULUoV3UXLdkiHD2GaILPZPMwcIeE1WhfF3KC9sl7RM+JxqaCklglJ0qz0VwMNcGzBxPOd643Kjq5U4sEULwPqF9lGCgq9CUJTzR1uykb3A0UBIBoVQgnjYOtLTVOx1lXluHO8Y4TNGg5a00vMELDqJQNR2epfVZWEiK+Oqpt90CCcRqKo7oGzmArF1CAUlbx5YZYNw7R8zbiUk0FNksKgbfHu7lJ90QodhPBDL8K7Qk6rWuca1iYsXmfKKUuEHDW30YAF1F+2aZlQeG14erwrkJ6CooatBHR3mobKWibYIkdwyB1Qix/hbtSAAhVossJK4i6CIWb2a+vWBy/STVBRR1fSzDHUYyn+N6QuYbxevoYts0nVOFddM4zBTqSwm776j7vspCbR0q6or1qqF6tf4qRoCwHBJCUsNOSphpJxepwCullYbhIehhfF0shi3lCuZ1G+uB0iUglSLM/o8sU5IcmiKyo6j3Wyn9FuShgHpnF6gCcREd2HxmyrYxZz3tNCCcCDRJgOT9Q7O8Q+kOjdurtpJYCbs9Ta4Ok4r9JvFlVKyufB2rVSiXUrjh3jvH0FP+jjmhJpV/P4uJcnqmpnygVaGutMzxwllRH6VcrlAAwm4ZkscgT4BU1jNFwgMaijrQtC5TcsNTqgavNnv/IIRaaaK1qCpZg197lAg9B2esDOQMmYuuhoMpBNXffhml2dQoye1cKiTksTgkrfNtKHNadSmSUIQqiJla1P7sAEHV7Oe47NzBMZqS6PPTyml7LxYq1Knq7TUa2x6YYBt/YlMUSx071sKMAuSih8BpSqTsgiUE93UOptuQKypBZ/qxY1HcSGhTmdY5CXxTUKDpRioZk76TKpAB/4V8JKF5NUcDpqwIa0USA0plIZSWlsDg4ezxKjYPUXDZH+IIcrlXnXO1pE+NQzwaNu44TLTCIf5pSqDARoupFWFaVAe/UcUKk91Rh1bxfJst1pkDgiu6Mt4SF/2SHtEDmt5xyudmRQvLXalrkr2kkbfimAYqdQilXMYZwGo45SJ5JCZNdOuB5sRXSfnklvFkz1Wovngsri2HxMdGZnAf0ENCg2tCMUP0LYdPYcxdmir4302T5hTXFH3C14lrAoV2EyQGljNEAdbhi6fVYPZj+Vq1WMd44Tq8tMjnyTQtAF+6QqsCSngQeKeIjuJnT9YRjAGHwEfo02s0yVerfGPj+D5g43Rab4MGS+e3QL/APETIzIKiHoY5o7yEIfahk4WlINREL64cKyGhmsOl5hJ7mB9+D9gf1BsUJgBG27jlbjfuwOmLtLKaIM4maSq3se0Y6Xrs2ZnNl+KhsxLT8s19U2Zat7Vcx6OBkCaG6GborvI2kgb96BLbAWqbe59z5bGwclrR+IJaTH9p4RXtUQmUAbii2KBxqFLCo47DQhINIUr1IK+TegqLx57BWfE2dYGS3bds6oyV7XnvN92Z6mKGCTy6bUqFHh2tdYAEYuPuPFRnhi3bKkw+32/sw474JEtaOqouzB+DDhJUTUyfaa3hfbs7LqZGXSTFJLH4LZVjpgI2w93gqQOlCFdsr3qJdz9IuY4nAAE0qItUyYwah0Ozigz6K2LDUL5YVRBruIlTq/Qw+37Ij3lIOku63pwwaq8528zWsIyLv8a/hxSGw9ksNwrZ7rCWtATQiQJ8D3VnCNoYTP/VXDxI3Il89sCHulRB4HUYoPiBr30+KVNzTlVAZzY+++j2l7oDNerY74MWkCb7rXybljfmvPd6tK3eEoisIQU9d9cb80oYaZC2uMhG/O7UNr4E7F2xA32TgpX2fMvF75eslHT3Y79lWfr/N55bFUibBo3W0l7ugbDJ0t1cPcc5wn4XWnS/fSlC7OUueaZmIoJu/BVPZW6vFuCucTY+icBlykbpH14NLwrA9fm6SdouHVvaTa+0S+1T36MLewo2uXMNuJDD+p8lpK79Pmo0sAJ/LbRnw1YRLKrvs+761nll+Tb7eWpHwBz3/qOTpGfNK/5ixYdEcgFKipxEWLJ7RxuVuyHVZCudDT0lC9R+tL4FxzJda3zN8vYKBBkcMsz3Z+0UG4JpDemssuiIO46nbZxNj+Ede31WsO44v+wlg2uMaefped516me7rr+cVw0efqQIUY+fb3eVJd44BvZFgo2AYKcjv0q6BIcNTqz7SjCxv4XBbfRv82vAwzngzabkoxCn0FuFX11zmi/RqHqsMjqruRRM0mhd+fnErR07pGpyQ1YkxMh9oM7u4NT3vxGg9fUDIqtiLxRIcSVgfaMrh4Bx0fleuUJaJ9P/Llo65s7TXzACGmF3xxDQwa2AEZqLevHq/F6yojRPWtOkgTtgsQBed87PdXjDWaF5Tx2X6F+e6+mzsdZ3Uz2EdwEjCqe6o5dnSdmfUwcXecLY/y0IqulLs+5p92fcbaYV02ql2lND7r+qT2w9YnyzrUDj5mvLBxf6vvd31d/Bx2mFmtlX/0XByTMlHZXSodZXzWNI2YT8p6DOM1ckIDFNboILLpPZSH5onLJ291AAAQAElEQVTuKPQP/2uRhh5Ju3WuCDVpvqy1mFMB192lf3ZujxuiB2pJ9MoZZ8CLpi8yRUjZVdr/JsZDz3vFWSf4xUeREI+17lxfp9IoYK8yncMgltSBbSFOcvgY81LTNM45rmtMReWKbZggAldr+LA52i60sQXhWHVD/Ky7vYudOvw8CWhQo/rghCp4v0njj3jx46o5s+W/wXTvy23MVdfYUA03HrLAi3QayliYaLjDh0/6DI043rmouqRRyax+qCgUUWJO9ucKjFtMKXpGU/+A5Oh6FafbHGuO8UQ4p18OVpwtmKHspHKtzEv73cvk0bEeFo7Moau+/GSQNNMBpR7qqDotMLMwU3+ZA2eyhHKeVrogB1251Sb5Ub4ciXrviHdBWS6VmEPIP547EW1bQmxWeZM9mvP2cZ4lQlD10Q5qy4s1KNJ83+NuO06wOgtv1/6ogUCWGu6JvuHRH+CAlWBoFz0b46p4oEExtSl3ml03+FncCWRPEeeGy+PjOvNVAnLw1U7dzRDVXVoMXoNfmb3EPeSvQQ6P4bfhVDB0AMqUqcteJ7WGOGkLQ0gIkcdWQa99+lYJfKufilpuH+wB2e8nnFJleimCeKQ2etCMxBCEPMXkPMYyFWSyH/Yf+7Mu8Dru6tklCX0Ql/wBOGvcPnZaVRuSBgOzTctRw42wCZCP3azxzGFPxqUPO3n4fHIfTtEzy63VQj98D3+rU8g81IiQKp7ZahRpeFMJOuUcL2iZHgzTvjblxMwxVadRvYWGsZjq4S8F2xzdugbUzrz1XW02pqACIkDV2krI+rbiy/LiVst6DdVTFOsm3w71xZL2lVYozAYQK/c6+3qn30NWFOg0V8CSBbVQfay8LrUC1MGd+IwWqMFWMqaVo2a4/najmMdEnSDpEOd2FOpRzfUQdYObeKenJItJrVSf1RK6ou7Hb88yK0hh4FETlSxCfHc7RarK3PQJzBhd6jVTnKMpdT7lF6oGiW60mwSibEJbWM1R7u9Sm+xPyL2qRD1EoOTfFuvF1iZz3NyffVLLyTbsmVoxy4CD3kLmVa54TptOJsVbtznhnZmhWX733LRPIJjZQjQCeuAw63xRvXEMcSRuACR4d97obFVgyBSowGyVXWeMhCk9uqqBOnqTYQ5/eb9Wj4/gOGapQnEVrcTrH3a9MP2AkqVqSrjkBnOa0gi4NBJaFu/ju4ignVRE8NFTJZOOG9hgOulgP1UvlGXnVGSrmhgawAwTP59x33kndw3V2KmMaIRJ/x2yfTjzcr5knavVuBZvV7e/73PUktbToIHRCpJqPSPs1U82OrezbFDPQW8rfdBp1CM5bNfGRa3nuvlxtNCqwqU1QNGeUW/foUQ9h6KO4IkL9SG9jVTDaui3Jf0obmJ3n4o493WeBXIwjSkAZmL+g/v1yOQOL2tQ4npmV7tTxTwzmacr3+7nGn256n8v5nRVy8s4FmiQ5l+nWSU+Pkt9Np//b76xK95l4tjgEu1xJqqVnpKsgsgUoR8br23aZhdB/YTpahuVBSIGS0JhiqUlTkuCDIwnPvm1tpk+ZbrMhbjHxLX6OTNJv2xc6kg52CYzVmkWWoFiICe4ZCEpAbG9OUNj2AL2pEpCKc/ytnQTufesPeKN864mapm8TVnV8FQK0hgcKgcttMlVPSQu8f5c4zCjt8JRHjrv0yfQgmf6BTatdBnGTEM5tU6VznpScdgEkvVwmtmZo80xDYppU2zuVrlpVk/jcE79arNGNMX8jXWb9KaPh9Kq7OPamAK6R9cs8gB3KIN+aQ/gLvvP4hFlQtOtamdsiw15D6KgiLqtCVQyagb0u/OgywHblJssHIHWUGSP40Pvvpy6oySRUWkJTbzhspnVcZjpXTNrndTwEcrZBB9WRtqU7mrIvZvwtl5QhWRYFujWyqQugkVvxUVmeIea3AWfsIYdw/2qmg2nTf2YBnNzSE+q9+7b+vYTFhz70RLNy3nTt5mUzfoSsG3VkYKzjXHjihoN9/n8s5KngS+LCVgFHVEEHku4qi5x0VLUYxBeKgMWmWOWrIQNZaFhZnPocDpSa7mJCM4eVhfSSeiDDc6xxnhxrLwmXnPNa5mJLibtaJQdMeVVrc+dw/aqGGGWetb67E+j0XUaWyNKYsQpr3v08RPqiwLdBDCMGIOqezPy+YEi0yGw3m7utozmaLhv+psIc3UITNMOuZd0gQKqkNWhtvmlIwJPFg+0VbxyiTPKutyVRVmDkElxQLzEgSqNAuux1fQMqRRjZc8bdrPa01elWGdmd52TXUWRrUGwJMsAb0kIi9QItsKq5ULZ1ibAIkkA3L1sHCqvDIHhM48yVEPyjJDCj5cEkn6UPEZn6abkvJE7OwLdmMDH7K9WXeJeLVcJ8FC3UkjZlU3jXGNc7DnC/ZBKvatCnFNs8uEbXVRmYgCjhiLofexO37xoJqzQ2SBDNbNna5vU03gZBumjtcDb8BL1nPUEbm495nvxa8MJ8VWXpY8si9M6jlgyljhOxLHWwngHYreyJveOhqGZgaw+qdFymXpUJVUijdA4YV5NZZIuvinWQbIV0jB3RZ2RmSctQrEvN5g3RbgOsjAzvMQuXazsyfulIuRRj5IDDZ4GXmluk9PEXQGbpW7cHaIVAkEFwIYOYBysN/B1YcOjv7KaV2HxpOfhKHhCOW61nPbu5wRyDPo0m8lx9AzJV+p3+71xH4UovLMo81rcQm1Ma2JAOoDgt7LSZfTBReNjXp4rZ53LzstrUiPNTWUshhSQQfPqkURlJM4ZdaadNW8tyW0Pgaaip7V1r+cJ0V29Nd1soD7qvvjJddoL7M6Iu/JAZdVKcRZG4qMgt+APizPTo5iChTQhkR2jTLkF/KgsSmSo837xHeiUXk9hN6O8tdpjDFB3DVNjpXa+t353VikyjZlpkzRpcBSsNXK+bLozRu3BY2hAMrGoAaMUOjxbEmQEFW4sa+dsejpg8LLrTP2WLbuVErMcFBtgQpipEslqOX/CUSe8bA29XNcNoiJVjplj73mitPcMpW5IkVWzUlqBT1w2NVMt3LpNg1Nu5Vwzke0esJJ72pOzibasqFanzAJEThwihsc0uFFyziGa86EMWdlVI6H+eFGGBDbQ2/AAeWw1m+R0e1q3+DCG9RlRHuY9G7N80KkEe9LOlNr4kOjsNR6WNAE3cYOBiXDcxZNW4VayvVF3xKkwviG7gFMau2ZMz2Falk8RIWyQG2k0rlYErXjb0ZRj02XehxWSpa82CyqGDBrqIUujlIRkbml9NYhn4ZkjJ3kptAuaJWswgw1cjoEyRpf4sAL0xliwhsR6WWoYa6G9lIVx5nhPi7aq3kB2s8hjcexI8K0i30aBIFLACq2XedATDLM3pW1r2pfMMsv0bjQbCHI7tYdSclzBIKrDD4emKXIWnjV+w7oNp3BoId58lcoGC9ag3rY++TAj9CqwlbClX1s2dY78MnB7lpDUwY7sPCZCw5DW6v+g4BZUxKfUdkMLNZj2lJ7iQVdTqUy0gMjxdCkDloSE5tXXoZTiimx2zdREIjmBB60Vu1vsZQU6JGF7BpdIdRYMVC4YD4c0QDpouOXMvkoJHD5cAULXzJ9I1qmxE6VkbPsiOavbeHdnpN1H172sz6r9yjPjRMVJR718z7q9jhdn+gU69CpAvt3LR1mZhyFmwjZHP6u/I4CygDcHbaBduX/3PLDPtlA0GBlTezc/4LkVp1yapw4qYEZ5SjedLXXIWFCto9FlqLZMxuY50M6P5jVgzNHb87nt75vMNTm0zLpm1BI1pnjOXXdK6uhnj0kFYpGrMFxxVfS5dYQiL2l2ZH/0/rDb/U7VoUgdc7qEMqP9cGDRX6bfZdQBXoKxGBfiIjiJMbovvB0CKHGp1U76lb5QYrPWfghhIz1rvKt3KttJhCbqBq1lF4SB2SKirXtVrShGiwUNPANqALgGl3NpaCnJ2ez2dpnhFi/UWIkJDS/v/SxtRtZV6ew2WSX7Tnl/PgLqByta9I8sQ1MJKseYcfl5kAsKdO5pqAHAODSDeep0hcB2XTs/7ubGXTw9O65SE7Nco5fVQk9LhXgZLxHRsiLY1moyYpCy2Uq0QqAfTV7n8E67229b2sT0gl80ZzVwD74NX6O/buU+LfD0nYTqilfalN9K7ZeqyoR0lAtAkWcwHAkmvM2B0cKQqTOq7Rg7uvtcjW/bQ08INEDOkLT0EaDuiD51XYsRfJB6VjAUhh8sxavsaHLr3S57QxuALlEEaDM43i2AdIetzRUwIeHEq2tG1M7sR/5ZEHO6GzUxCjf9m2PatXJ+AFplCGhxfhONGCyoolxbhytxaYXIRwKwtosaXFKJi1W3Vstzm/48mZiKptOFIIbBVI9toqDG6VdyaYz7nJzWFEUi3V41XmWS/PCqgZTpSQ4Nw6PFjm0N0grrVlyM7NR6Xy00JTTN2ozA09OEEHD6WHO5kQD1z0y/9MG758DLccH0jeDqNYsWyOwbkUJLZdYlS3i0yLoNG9zknTwnsnaP5gvKTE23YXIyJW8hasrdY33iosKjlLhcYtjsAzNTIZFQB7Bw5mhwA2/5Hu8eiQZIR3+M/dV8T91rMqVXK3l80mzpGtEqa8dEjS4vIUSaly8pqMjj6gAaMVuqPEqfEqEqDUSYCIpTeQcPILISoXb83thH9K7h05f7JUNqfidWSju4AiYB0/kadkHhmRA0g4hWSHqg4h9cU7SN0mmowDh6LEE04VdKWMZ42f4K95WCDfUbAdImOu1JS3eK380pDscJaZ0q0edSFzot01QCo3IYYvBGfan+h45cXcNaBqWTBJADJTVeo90p1dxDyui31P+Z4855hLuyu4oSD5Rv4QRTOiulOki2WZVCzrRIrT3EOUhdCcXlXitTBeVszCrnMUsazK/OC3vYNpc7LTo92MpLe5LizKulIMvT2R7m7cNN5aCvSV8aGo7bxl191CrQBp8Y06eotdFqV9v0sGFpsSmMcmC0sWX6zFG6ex1aLp9b/Wf5cDH1al2Pu0oaY2PVGHRvSg15wIx+kOxGiql1kS+m226myEZiphbv20+iRnOZLdqEyfKcKXHinPOWmZkmpFRE0uyMJSKwrElUV4qG1E3DyBzWcPE9Zo55+mIC8VSGM8YIk0J3bHV6iFfMd/SW61JOK5i3bLHaofD8mVqlGZZHB5FGuCmblX7fve/6DEhTQRI9oCsaU2F1zlbYpXRQXEQj7MBjAkpspBrRemIddIoB6UWl8fjuIDhsGkdRU3ijd9fN2qP1+BPWXvAsgbuoak2o23Z2NUqYVTr6DXZnnnNKMckra5T4UNM0F1LXvrJDM04Ald2tflNZhS6sKJcQdYEzOZtuD0jlxtIrvMVt1fOxzfDIEy1Zj5Z6JklYFithpUUyhh2pE4p7CS1iZHiIZ3aZ5nPBXsVVFKvynl0lRNG5tTy0QZmAmflyd1Bxs7eocbOBa7a+xZ6TtihV0UUd9BITKQSj11mFCwAAEABJREFUb0bGVrCiBgM9ZYFWg+kKPvLkfZ7OVK0I5u6ZTVkZK665J6vCoofohtBRuWyjqRnLDwH5feV8bWUjSV3iEES9sVrGIUOgMQ0b/Vnn8KQpm4AxVvfVc0Iy73ntsWqyl7zTevoT9F3YqpCG3GKWxFU2b1oARLgEGZVW76tTzLQ8Tojih2xlvyq7qoFncLu1iFZW6hR0shCEutCb3u5mMKkWqcGWNrY9m1yEhJaNMARa16pbpWinZr/89ZH6hnwW/ZieiRkyqzOMJfWGrTbCGn56Bq6SKeE+IQquxCUKjGJItMnD6Uwew3vphu/WJ/F1m9pJhqI8Ocl1OdfYbqeVEYUQzdOdmcbja0Obls7aJjL4+J6Y0KriRsfDpnvjZbwca05zJ6iN/7wwLm8JtzxlaVF6thhID1ZS5iNH1dW41NXmBROVTaY2cbfSJ/LAV0vC2mLxiWghBhMFMf02bN2JrMjpZwytkqH1WSy8rY52g3yTByKhhtMkPjXd8YnxrhE9M1+Iyaal3MKd0+kTQ4AVYg8qCALd0FOGjWr4Bf/I9kKWdl76sucFBWc4onnTJMBKtaW9+YhDMyGsfewpgtjvE1tTdZp0hZAFNFKsXOYfpqXWBBbN0RjYJugaxQYGcZmOUoU2IUWKuhINZU0nhnilK4xjVGGrCo4IeqAgMCSbbRVVlZbvITxeKm+zbO5cJ4SyPHuWj9YSLLnS6F7OMKWsed1zHbcuyTpL04QRGiNs7ALKdTTVkliNIntcvJZ95fyA7nUemT77TyviGd8Qh5aBOvBQrNdWJrfFAdNG2chBmDl8pNwivOKK5FOYDBFnm1/dXzY1ikzre+/qEqrigF3pJg+GMLHpbEGnSFW21AMs8BQvxF2J23SIHbFdC73MOGa7y1VLRAeq1aG8RkfbLRFRlzQt+iR3e/Uoziq9MZ0WFWiwpmH1HHvJtvWepuf0GPeccWm8IlK24JNSvViDGNdGb/bzQheGw5bny8+cldNjzKKKT2ppMlvxuDSzZpvLH5CUd3PxSshYCWUeP2/xOiJH1OyYrGGmITrxJARTwTpUgIkKxEM90g6ejVtFe2LIFbwbd+irbREsjXAYW+4KOKWw5+NQbyuDwop+UKQu2k6cxiEKfMw1RI9JN09L07aEvDHimH2W3yWvllZuOYXJRA4mwluFnhhwpxrV8PbRcHmCVNCgBCoWa64UabrxspsdqNwVuyPFjneZ7LLkfe/+uvn19LNcc0WMajeq2mBjzCEX1miZUDI1obuLyp/HWm7D07CHykiOHWa04/wc2ETrAyXIvMSKTCgLoIzQ6KAnxlvE0ptT7xvozs6NTLAx3W1M2QhNqfhq1HU1M/OUpV90hU9uoBQ/vA1nCrX0AC/wwHev3VeWsozR6cmWNFU+LvrBMgmZp5IRGjtzDSyYBali6X56gQwqvkBHEDqUb2RJ5Ypyyu/gmb1lXHl8DkNLyUbtQFIo/JjnGNWjt4BlVuXJOkpHJ+cw9QTheldlBvJ25SUhRwNctHHMDvNQg5WAFkq19JZCataz/e9C2IzpvVhSJgPqJzTo43WGqT/aQnvFSPNwQMMY3rAkIG/PtspEYVekEuuE9NxGtlikNKoBcWaG1gqFUjlPW0U/JpRDjRQr6W2DY3jVPFi1Puyrl61Rrhb5see6lYwq5pdUAHL13ghZOn0NaEdlQ2c7tlKPssFjAoeUYkX9GO2re945TgKqnm15IzUoS7sP0rXW1RyYhgulxdQUEitMu1hFRqWe4ri13Lyxg4LNBX4MfLwgtHT2SF+3qkAF4zGc6nvCNqWIi+U8jxEvk8JzqTFsBesqeoptMQnBhq5Vizm4aEZNYW4s1rMcnxotq9cwWiXiWTK2W7dJbR1kpjMGn1otU3b9kF3dWX00CjUcl/cSRVH6xKecqp+PjR6FWTSHLR/DRWH6SVOz0Txvt2N1YNjrijWaEMzR0+drzMtMLfiDstDPuqwNauw8+7GtqmSrRKWrkmREP+SNKMAalNWyLJ3LIdaQzM7wMmtdVrOa2kJ5ByzoQNMSIKCLZtCqxCttpiKFtic378owyLAVqNeyHgpCt7cjlJTyKfpc5VAhcjSSVFSfzS16z2SwsgWHMi7S4ri6RUGen8OKrot1tBU4yBJKDlR39A51cx6JBe1yLLec4XNDyjQiG6mrhUpkMeAKAmm9l+oJLROdIrVhtLK1oUQxq6pl4rQs+BW22jitLtEDJmyTaexBdwyTH9ep+WkjB8bAVBwxVnIWvIDngSMVYHopfJzIOCe549pn7T1DOyebuuMdowwhuuGaPTaVf0hWcpqCYt6Dz99KAxxqRcCyng8QqUPESqTi2XU9ANXuCDHcBiK9VbkZM9T4wEZm3xIFILRyQKs5ew2JESbcOs9Unh6jMDf5sHJIJccyRz98IybKrcUF0VWy+cyVcfH/bHPmUQZ0vph1VQyl2TWD4GHlEKCOVsSPLpIlDGYwQHhllzYdPqNsp9/GWIHRAHzDb6rJKg3ZEsvINBk0NLqxp+1X5xW9zjOWiZKfDrFjYjcRVXfWjmK1G3yY+STnoSVASkfyxFV1dcqklCAHEHZ2KwhZU0QNQtLsDhleUCdoFB4h92PEW9cVeY+i04S/+M6hptFaD2bZMiqf3RDsspRv1zjXPe4qABH+xF2IRj+j4I4eCqAVPubdytNjYYHzSUzZc83ceox72HmGljuGwRyGp+mUnD22WoGAUJ4IGmCJcbDUZXHL7+22y/YZEaNEhQ8ZahGw6WEEwywd0EXrHK3iZMSsnIANeA/fg+For2ZVdYQqUPSU6LNqQR1y7MJqsZTKbuKZyuXPPlup5HFKbhiEdIAWJWYwRTVbJ/nEZP+ykkuxp/GxoWFKtxgrudJyJdGNEoyBnlDqs52crsP12wJPT5mnr4P5OGkB7nJocXpP/9RGAUgqmYtSKOUlRoc9JMyEZ0IaV0spyxYdg7fYFzGpPlF2Y6QzqeLLd2EL3SHu6vfpz7TPHp/QIFLkmhLcmhXqndT0HPImOCFqvXtS+KaWUKQyZzaok3oX08+16qXqXdPNTC0v75L8P/LZxxmile1pz33iwlvwprVJK7odDD2a6hAqa6j4bZKjiioNLTwI96SSxiymrADDAc9+otbvzljRL/jyKXhlfUYe7Sh4adjTT66YynZE7tvu9HS0flHP3Xt1KbVO+uWtRzSmUlPK9aDiflVIHYWVMTJmUyy8uT/H2eygkmap1Frd6NRVVHkopGYa0pUbl5qrrDA5tHoP294QQ0PKUQlwGi650NsC6HA5UZ346+CVNTV7RI5MoeccNo0evN5jKHut/Ni7EaWUizeiL2LOEl6wWkatRUx6u7hUl9FxCgHvXganZgRqn94nOrfVsS7sTH3TUhAcyjXrTfukIsXFBwEoTB/CBFhfeX7EiY+TXvoIZZPEExG7vI9a4NAyyGfTqouNbEmwM6J5K8R5nTVy9jRNCnZTNpWOrzZfm1Nmo1uX+gLpI9zeUGfQfVAlaO02FscK5sFJaiVJZkdqGD8z33UCjT6M944TCSRIMVOZ3Xof7nSjGX3pd6/xTDhhipqhLmBrTdNN91daJFMTmIIyzTmZo9+EKkhwbMO0M5XZqlNPhTYmw2ujA7U7T4qh6biMkjYy9bkVACRa8wvS8xitTRUpEXiagz1RrywTXSeBsASTAA+6+O6+o7UohE7YSYvuLo0oZnGbv4eU1wtKOARPf9xcHhZpNGhNvoceOtqn0uQ4Ai9hh/JmpFDHA53lGt3uMfl2fPH+UKq0tFQeI6UuzlxDLhjEs58CC60Rjp6cQ7eBvkYvBKMYcXbcgR0KelobyrTB3SEOSY2pd+t74d1nj3euGyI6Kd33wa78jP6s5f3xqsvCDhijzTsn2iQDQnq6tYQ85trom/NbTulmOwE3oRY4L64nHiR229dp75h3Pc/GjDCe9N0DoovqZKwtfq+mnsnN0VgVa2CIRyfLTEu9pH4utimlkE7nmZTqmsq/a0LauFyK6AyX7TbrCUyR1ALUqZK8zC5cq90OW8lAlAq7USKSlKMrat7KqmMPqDndX1W8vMtxAMmZFaM+pb/TWcPpl/nVtFIIJaIb/ejw8emWrxEflMAp4VOZGHVEcl/o5V4f62iZGge6UcoHbYvewaOMqrFApgqtvoc3dXIYL9MAsT42vHzBLqQp2znArzC/MnzFAtRsnpJIQjaUOS1TasgW6Te8Z4xXzCsV8i1t2/ZVPmgcE1xldpqSlNWLitpi8zPy7kgrn9f4yvFxOJIlQfM6MfEeLsrM7fLhxmm4RiiFMhOp1ldR4BcsiruoQBxAZUafljZMjqzovcBrel14X/Xpah1Gu/z6ii9BGywig7gBUQRjy9l6DVN+lp05GxchvZZsdx6NpmoBpRq7eyReJf0mB/kaXLSuR4KnVvdr9OWlnjFSMlEySuKIRB/vY7K1gzuvaAHhEwwVAVqYrWtZutWwMnRExjlbWbPEYl2jtGbOc81SygfdnompqJljlGkW7Ea/JQJxpdmFBgsa5yxCXdfukKh22qknGDvKIqcPu142lngYGdfpa2tStwCNHt7DUlwOavAkqa2QtNcCdD5GvMpHGn1s8o6499k6Mu5A3J4xJS+7SMuhz7ERJbbSIKmlfPlJzWOWgib0UPnb2B/sxaZ2MgspEWgtdQqIqvjlduljT0WkqPrjnNd95mcrdIucDLeNxgmIOdWiEebU0FXtx/P+4NYDJvf+4XZfXmPVkI4269134pC4OBTYtVu3BGrpQwrLjgPskkqnvY1HPuquh+8CNV869QvqNtQdWIdZthctOrJTLW+sQRtQJsIar+j1BJ0xtFaVFiiWc38c7b76IWn3TB9QUV8HL3Jqkp4/QU9ott2H73p2gdFlEVoGoej4OubZjMgsZquGfJCjKBJ65F73vlB+OT4Gp7uBGlnKiFePblidR3n6XW0KaqzNLqm2aiS0eoyWUqepvHWVjeE+VWkvtO3ZXJ8XbkHmcfLi+Cj/2CJ/PAHPNaYPdRjLHHeO3b0pRk9KGVUlMFjHUTLQgQiFILvneKh4NjRtBri9z3TBmxrZBRwlw5GSoYUzht2wfYBTfMQmNeggAuedJ/bhvrHvPgef4Kd2PK/L+MH71dp/4brnx6eYlGwG5gsyPjpVEmObAz6s3bKsYySvuHwbolv+3q2wO6vki9CrWfAzGFedGe2TKpot1lHzSdpNVaNK12uphnle3JcwQPsaQEVgJGg0Bz0njiPpmJdqf8DYysd618dtV3i3Z00L+VB6piENYUP0oLKJs/pToegDbuANZKO7mm1JKrR3z7v9MfsM0yCmEVfbK67Xvb5K/2iuYyyD7OrmzobeeSjzB6WqKTfOLwcNRCMTjxTPrniv/fXrfNqOe5uKqDALgpwCPowcxHC4CalgzQbF6daPdFmNPDu7b/Au1K7W9YYGP2pSjFG1SXNbwwfP5FFHHD3RIzzqerd0jPZ2aGYAABAASURBVE7Nb387+6vQI4JRNrYWhMvMyvq2zPG1R7wC1x7rveqNvjkaC0Ys6+kK804Gr9a+TyA1r5e7TS8gHViNaYkRu1KYd0k8c8R0DOtghEU7+mJfAGHFUXP5cJiovUbihfcYnxzqLVkbha4R4RlZzFYbWCtsCkhID8OSwj0FaYEXHlQAUz8jjK1bont0PI0jDM2G2JRAKW/OOf01x7o61zkmnWDefKluWpVxs2J+LeYwUD56DtO/y0Szugzm+7b3loA3xqfb7borudn7qvzQuvKZkpA2qKcQxJ4WwMmnNNPG+sBXV354OeqAobyv6g8rjqguxTydPh+0WTngL7+ueQ1cyA/Us+heIuxSEYl2Z8w+3jtV9stu8/K5aEvlaJuqEs/NEzz0mK6STVGGzxPxLMbqK7Owbl9fV713JE5fCWOL7ZNqRiPWxHxpF4avlYoUQKIH2ybrqr3Ott15I0lfZnNMG22pyJQH8gWO4TF6z3OuSH1eJMFWz13BsSfDuqywoi2M94xYO0a0b3FrAnUzS/1yb1PjAqBxKWF/ydo2YnKNS/2IW6dQrVOXu80LNjemvAylelpfuCfeysZSgXAmB62fPgdu8uuG7gkUfj1ka3gvbG3gZrd1KMrWG6tpGMoQOF1/L0gW5vra/nV83rfjNmsOUdBKIFBQkS82kbA7lSfLja5qSJPNCqCqeM4r40KLbgpoLuV1da1ZZnF/vn/gm1//wB/+xjf/H9/93b/3O7/7O//Y93/fN7/udH71sa/Xe6g6j+zQWRfOJT0cL326GrPd0SqE1h3JSa9cRlIn8hahXa3OdrMkJ/0w8LBN9Rw2iou59sY7ng1x04d8pmVVZ6NBQvcYV+7pZQPdtbd+DOnPh2H9Jfo1MRZkjKKs2yrSAx7UKLNtvmkn9+G71knDLry779l68KzNwICYKWo0VxjIJEM6LJkAw1uEUef3NG0SlTbOGK5B4HD4CLN38IafsWLMnNc0G1Stnug4iXfgk7gHbvODlyiXbEbl2ChtZczWqOuCmVoW18HompD90/WeR22zrpR1VJ5Mtb/obNZYPYxGCnoKLiZT4jinzhtdiv3q45CECwbRGYa0SoU9x+Z861qfPoW7q5QSDPSMlPbVBL/YnSKua8hXb8Q78luf++vzQ3d9z+f55ufn9/3QN7/3G9/7R7/7m9/1Pd//3d/8oXamIhiz/Rp4DYnNmuM97E0KNs6mdllqftVLzygkictqjeQsjU2AkR1VdZ+69wmtRxtWmOiroC3shV4KRAPhBk6s6RdZWZlvtnYEoM0eL8w1JtXfMAavBTNz6aNN7x6tmHs14ki4EFfL9+U5BIgOUwyQmy+zZk1VSWGQ1MjOZNz5vtXfdHLCnZqQ6ETJkBed5qASJrA2zhm3FGRL6UpFH5GqExXqUdfq5QBOnLfEeb/rrvqB+/P7v/UDf+wbP/BHvvHN7/6eH/iub/zA3tFqozNNewREg5a+7rHk0eM97cyFcUmJxijTTXP5ybwzc/f4WhGeY9GWI4uR1ZWwXJ6yg0RHx1H6bzVLEWpjVtnnja/v8/XXX3/+4A98/X3f/63v/cbnd333D373d/+gEv2d3/i+E3F1XbvWjXl85rOoVLMgiYViuoBXwrdS6lQudvNGHKZGIJXWSLfyVeYByRroDj/72jEKIpoPsh2ZeR5FQPQaObzHUEfFVf30yQnTWIUUj9/15Na2Z/CWP425Dj6ylUBvMgdvh1QSWhD08QGAh6qhCo2oOoEdLXCn5sE6ZvvC8ch9813aTAwhTpvbLJ0SbFvbPDZTkXaFYmiVAJ6uNIWPYw5Tp666Dzc8l6igIB+Nx8wuPFUFj/FRdm2JxcY9zcChJAtyyySooBEXD9Wvr6qOjGglEQ1IdCYuhECsmSgl8dzSSPzmHEY0u/pIZZtKQMkkDk7yU/kT2Zte0vDVj3fFNU3L7139ueO++4fu/uadP/D5g9/81vd91ze/8R3f+L4/9o3v++5vfPMHf+gTqa/K3h+9v+oYreD1mCq+ZsmVb1x3X3kGQzG/hqu5PViqqYFgwTa7PTDD1BxMk7aCAVZjZi6Eo3ERS+233t6KYkz0pRDOu1HmcIceBmDrpvj02d7vwfdQD5CYxcvsWjje2+KNc1Og/JYTKaxL6StXOcqJhj6Mq4kYil2jOSje8FxzgUCpVqaD4rJoeYnvY9wjw8K9XZX4er3nFXbx8nnZNKCqnyXtlsB6NFyCWC0n1a29Kj1cfZ5V5XG0TBY8uE4RCbb8i9O7Y9N0ZZIXuzvPGZ/vK7dNcDldMpEGNRTBGfOq+aILrdmNvrt3l1FiQZwhhhKh/cnozWTlqjMjpB4oypX5ccfstAEbapaCUXgoMOtlWgI4W8GKI3SVpSZ9Gffge+qlR3JYLK8x0sWWGzRv4qnM1pQsRI5sL1avrJmJPB3VbhwuduVuAGuaXd3jdO1UYzqjyz173sd25mG/u6OlljaY1UROlHVAJ9P5SGhsPALDgEiaoaV4dFsKRhfyWLan2VjtbR4+ygSVc/fU1z9pxEibqVYjqH06G0koYoU2GaisFIUu6wDFFMzp1IGhd3Odcb1tSeL3ObHvPu/V91V1yVLzDL+1SyCov90fG6/y2a+nWXjzwlCy06KsiWnlqCaPMAdLKXEV8Nhc9+JZVX2U4fvw2bxGmlQJdUscVs+WLOglGncA2QKgc8XeDm3VKLw1vGjFou/iuc02qPhGKqWtCkK7TjiPi7c1+xo9V0qs1BAvMcpy9qSyG2er2XmIK5utPZJbOeBlI31iDG/DuU49G3WfHNS16u4iZKHpUKVXpsKdEBEoD6s9S0Ss6a7VyayzhoKTXUWaeIdOYI58jZxXg7P6qy6JiN0hftJVhrvrZOzwzSuolccKTOI0KoGKueLynIzuXRWtRIPGZf4yLIGBPtBHGRQO84O5bEo5nMKuStqyr0gkBDW9KzBC/qXLrk7NcRkbowixbV6knBI10leq4c1MVAqknXod0STesA1nda1sca/BrnmKroOZflRGTw2aFTVS7bbkwiktmTcbqLayC6QpT95NEDYD0haryVJcMZUu04A5zxiNC2XUEVKPdo9HwRkg/xhtbMhMP6/y3lfniwv+oRbZGpVpQh5gt4R5MSfC1JsE0EEdso2Cefg4xcqCTOuHJbv6WxwQI8fjtoL6yrvW1s7ht/3+7/j2//Vv/O/9sl//43/2t/+F//1f9Of9nT//L/q7f9Ff8DN/0X/1p/2Cv/Rn/qJf8Y/949bejyiGTff56P/JtxBPqdAJB9ZI7UoXH91f8YjgCc2zPRWuPGuUNVAV+VlxEwrJGyOTHdbhHbocxu1SqbbREpqp7aEH2z2f8efy0vrf2blbXahN5+eNsd1b9FurVEiregzmE7+qAtJ6oQ81JbfW6S4pIa3aIuY5elA6x6CEUiByjnMNTrsMH8BsabwD0zgmy1HK7UN6c8oReMMivc/kflJOKhLOs650a8Wu7lAoDUJlqUO9e6Y2EcppjouupAsJGsgLzx039Y44Yx17pWy96UdNhl1jz7E/OrWuxM6jFESJTlUYTfPRA2QPNfSwu6jGGHkkJihhjESJ3DYq9ePYCpsF62c2VEq71Uwzu0lrwcnD1G6u6MCArEQwFWSOb/9f/VN//S/8n/11v/DX/LW/+Nt/4s//JT/x5/ziv+xn/II//2/5Bf/Vn/Hz//y//Rf8pL/v12RONYIEh9PdzHuOptx3n+A5AokB8TsISs7JlAe5rQfl0cfCoMBWtW5SIiOQwBbDc/aaQzu/Mcp45Gd0qnTarDmyLTNwbw9ZgVamkOOGIiB0fzMbrnL0KlFSfdhxdUyctr2tkoAsFE/2HU+PSnrCVf9uNb3WcMmEw0WyJp9An7QTK4OMaCZEry6mvCCN5U7dOvtk0SNeFS+lsdnE40yWR0K2bfS/8J3f+ZN/wT/w1/28X/PX/pxf8Vf/vF/1l/2sX/6X/j2/5C/+237hX/LTf/F/42f+j/7iv+MX/19/z+/f4shEZNuBbe+zeMRgFfM4jmnZ4lEcOOg2QTXZdrLAZHeHxYE084Pn/Y0f/MHv/cGvv/nNH/j6+3/gmz/wze/+gT/2vV9/KzOpVWq3VWHUb/t9v/+v+4X/wE/5ub/mv/v3/fKf+HN/yV/2c3/hX/z3/sL/2s/8Rf+Vv/3v//P+7p/z5/0dP+d3/95/yU/OCuBI6t6KZZgrN4MbX3/r6x/61uf3f/Pr7//G1z/4jftb3/etH/zmD5a4jcqufgAByvKXvLay1AI6An4/7AiuTkFKRHVHK83yE6XW/JwzDzUHafPpMponZjh6FHj4cGvKUi1wHfgtFiza9VY1q2HFLg1SPVRZdrjycp7rHcq6dFDF6tFNRZZoORXfiSqgoPRr2wADmk8mQZNZV2s41sHd2JBsxIU7tBLOMgHEzhFpXUUlA1mRpRjhXaYVUX1J1sz0OceGBRMs7ZteT4ul05zyYq2zLA4pKyAe40HhbUiPdCF2YMpNmwbBn2ysdoOCEXT5fHedNjNfblYFfTq3VcBJji6VTlcoMBv+g1/vn/7Lf/1/7+//tf/dn/0rf9Lf++0/7mf98r/8Z/2yv+Rn/v1/4d/yc/+Cn/b3/fk/7ef8ut/wT2WAne5Bq0cFEsJgXXXPLlP3bJe1PoZ2UE7KKzWhEG1yqp7l+jLWbxWiK8duCdaxCgVmqedhplippv9CzCrZMEWJALNN4oO8IJTEQIeh3DDUB13TUWyKORtGtCgHqmfUR5VOSx0QnqSUIFYhhmWcVY3CY0i2NB30fkJDt4vFAc2BIKpfFIS+LtoqtZmpJJjO1DNqmikZhiiLEtgT6jBza90WFDGjMvisvg06mnqBTAWQwUhFhhaGhAeUnwwruLZgNJugi0BEM9IEZBFqS6Z9AkcM5BVUwhfpFq09JzRShFlndp/ys9d5awWCeU1K4Ha3BDdo/QrO98J5MfTtgoACZqAF7FYcwfLc1/1e517Kb707tcwUrGn1vJiUnTkolDR9kuPiMb4vf68Bw6y+8gE44ZdTglNsO0MUjUemhPA+B3xCtYAKHvDMSre21dq/+wq+do8o74YS7WV2LNOSBOQF2dzEqcyoCGwyB9tGY0JRC+bd1NK4h7JaXNLXM41FHuce1QZQpuXQReCXQ4sN7uCWJ8poJ6VEx5noSRuKfHUNgbiynU4sJfAxJPEyj4BE6lngc7zJmqUlSftB+an01l04TDKu/D/9nt//j/+Of/6f+Kf/ud/4W/7Z3/xbfvs/9U//9t/8O//Z3/w7fvc/+Tt+5//ht//O3/jbf9c/8dt+z2/8rb/7H/8tv+M3/tbf8Zt+6+/8J3/77/jf/7Z/5jf9M7/tN/3Tv+M3/bbf9U/807/tH/8tv+1/91t+1//ut/yef/EPf4/Sc8hodiX0L0fugQ3xIS5jqTRZhd7Qcp+KPiYVUpoWuNtTt/zvgXhBYG0qpSN6JKQtYBff7VEAEjTRAAAQAElEQVTmzmtSJBBuolgShTXxcDrpPkY/q6crAXrrL5uXDdVm3ZCOECIRMjW/lR4Eh/eYyYFsr2fB0huosz4BCKmSWH5tnxtsXVfKFE9G3uqx3n7xNdzngF277VOPMOqd/W6IgVmhBoCszCommMqDltHWVqV5jt+Y7TB3cnXA9xf/UHafL29oo3ocie084rC++LQfYTZ8SF/QO3RX6+WMhbTYuI/q0ORd6X3rGtzGlMnqqScn2sdFt0IZpZBI3WpCaM/p23iuR+xV82CoNPcCyJn26jHG5FMHH7snPJanGwp2V72zFDk4aN6GTuAWbKzAYrk/MoeKeN/aMrWGnReeOKrZzavtSnOVLfVL3y/5gZTT5VWjavk9536uC2C9qZqwd+IgGzf7xB3xefCuKW8m46PzyhhMijMz6x772DlOPN83zRi4V+dEibDw1dTOexCP0zi939Fx/rnf+y/9zb/kf/LX/MJv/2W/4Z/6v/ye3/eHv/HNb+3QkB8+svobP/T+w9/7/XbCpe2y5VPCO5WdPRTiswaYsudWY9P1XLemUxCIxk7rwgCFDmYDkp/zVsZYfQUchM1yRfRE6e6S+8fV09C05uTQKWIyMQ6UH8egO2aRYPkj8aDExii8d+73iQMetz1FTbeyN7zM8OUV0Yh2e/7lcORce65DvTBl2IjN/NoRAz7kli4gdToDEkbNkUCfxg2GUjIkKmSVvbUMKSpHXa3b20aygQJrS26asszHFHMWVjmVbNaiYjFywka72KxyuYJ2v/tmBOEOOaQEIkBTtxaHo9okqebgdM4OjzuPHnnmbibtnnxLFExEs1QynZSW1IWgAVrWuxZoZ2Ev9X1XeJ1yPltoI3HMFOggrRup2Ga2AJqMRcbeFTXmH/iuP/Z/+xf+wO/8A9/xz/3BP/IHvvt7/+D3fP833+cd+cPHju5Pzw0LoaQxgEMEj4eWuMcv5zivta9RDshP9YFeSd0c1WkFqcg5lhFOjOSz22vY7NaTQoVOcGa94iwcT1lglt9CCkXlppTsqo7GLlVoSugtIiLJgxltp+rEvk/rO2uJFGkyQF3vGJV605mVISy19VH4GpJ/Y05wih1ylDkgcU0VGHPHiYSy7tM4WP0FQ0sFw2DmoVs1B5ly3orjWr2WLSl8wHCa+c33/X/953+fiP3df/CP/PN/6Lv+pe/5vu/8vh8UsXfUnc8hAgJ6wOiqaT59aRqVNcST5ia7u6pz59lKZOgibc5rqaZcHLrQCk5QOfxf/ebf+lf8/F/743/+r/7xP+9X/OU/75f8uJ/3S3/Cz/v2X/KP/UajG+qJtdSL7Zs/9P6t//ff/9t+3x9Uon/vH/ljag7f/YNff31SeN6ROkJdWH6mgaLt64NbMHQzNzD/xT/wR/87P+9X/+U/51f95T/vV//4/+Gv/qt+zi/7CX/fL/3Jv+TX6ObFuobSbuYGb2YyFEOBKgiMHsAj9n6II3SWHM4xBL4rqndDesrZWKnlEBJN3pRE5/RFzpKAOJWH19S8hX7F7fskKuXW2kmDVsBSu7zeN59Lp6iwzWCmKgKG+3Sn0bvZsC39dC7eL3VpC0WJilFbm2e1+A8M4iP5VVyrRMar+LQ0zQMuqeLd8RZBbc+/fK98Iz5L+qoHv8ntFz/hfTTPD1W6yERqe3hqpV9YT2yz6CqPI1YsB49XWQw7X/n5ymIx8Sig4EHdnOXngFTQ4lhuNMcLSMWzazWGcH4QYkRnq0vXZ2HcihUIIIm2PPnP/D/+RWnyn/2XvvOf+6LJ7/i+H5AmlXppQEeT/VDfcrR9BFdqwTly0pC5FzFR41EpuuXoWG1Vh0MVp48iZEI8w0EPMB1mFdZHNZ3FPsPeLlBMkD6+UBWwwNCl1sr99n5n3hFArzXmUh+fMFcv1fQWtRiCOPcjlgUOmHX3IvRhekN7mQ81Dw7ni9Jei3ZNtlHW6Xdpw+T05mhdhD5ph6S9mRacUJYoVDTzBQxGS8hm7+VndpnW4vbtU30uxgyaJgS2aVWu22N31hOiiG/YYwiEqVOXxqXCQSst23RjsVvezY3T5YavaboVGdmuxU66v4wv8ynydMsfChED6RWeQe4xjrxL1+lMhVFE98qWy7ZL8imTX/RsstV6x013+AQNUOYafrCixl2ReDdTA5NWeie8TQdcCS/9Gq0ynVaa2ECbjipWVXfJ6HAT+xqnzc+KZU+3NhFucB/yJwAl88gGWypva6scmdkCTYFxm1iD1o58gYvqxjHFWEDBhvZRY/a0lqXIjGYks6s8FZ+1UfSnC5rmXJkrCN23xdC5GohuVd2sh0ILU48qZ3yglii8CfUQBhTNndhCqgE0K0MJcJnkK8fFBr6MPeUFMx0tpM/18g4iqQFWw9OGIG4o1iidzrb81ue3/t5f97/8G3/FP/I3/tJ/9G/6Zf/w3/wrf93f8qt//d/87f/I3/Sr/tGf9u2//qf/in/ob/32f/Bv+TX/4E/7Nb/up/+KX/czfuk/+DN+6f/0Z/zSf+in/7J/+Kf+yn/kp/3qf+Rv+x//z3/G//gf/hu//R+WhZ/6y//n/4f/y28z0UmEAlHeBawYLSHQRw4vE7smGSm0aoWRyqXkY2yG4R4Wgkkedo+jdFMZEVNIiKojBbdIwzK7fA0s9gIm1lQ+oVl1RtYqnXLlakHpslI5uRqmV1XWLgSuLkoHKig7jlws84ZZV7acHK3X6B+u/uzc+ex2as6avccpdQuLqTml2HBoh+vIpfCK26w4ypZXU/apP6kYTTmraABBD7iWzAroquvTK4WI5oYhAJ2tcFHK5PJeHRahqYUG1VscepepnA5OF3dSCESwJkTzhtAh0DXD7LQ0mV80MJoWeFjsqG7NN8bg0aZmPJywc4a6k/rHtFsXrMq6NWfcsJAwMZwCZEqV2qXKJR/GhpUhS4cXJsRrVGY9GDsG3pPHGqdNEffBSiF2b10/pV1GnMcDmatybdUeJmEERQ2EfPApFSi+IU1oxQqWfreuPW5UIlkBxc4EoaM0Ni02/XAYhxuLPO0hnM2ZY8XrFWtsX1mTdz9LnHatCpKiGIiwf+w3//af9PP/p//H3/Uv3Nod43n9az/Wf+Tf9af9af/6H/V8+Fd+6kT3G+quXfLSGNkDkkLAj/zCIZ1sR1rBmk5JsRLVGg+hM0BnehZmpbH9KV6vskqiQaKqETKKRTHj3u6EuJEMRyru1tiex680p9nkXC3D0FP/YAhG4Y7Shv0oKUoDE4CUCWKoOTFTK5tbKJVm3i2B6W/ZqIV+JWcKxCpXpyU9YWWchoslQktGVRqmDKdXy3YJLaBlPPWu5coEsEvgx3RzndiANt9bgR1N6C8M0JZxCUK0nxzY04LsdqZW9EVTn92276c2lXzVK2xANOj7GX0faSp4QX/49TVszGeuZYm4SJyS9LpTIOVY4bhj2lR0UXr85EFUUyXju8cGT/Gkd+o5gA6HAdIttED5NPcRtPfwt9tWGnDMFGFu18G/9cf9Jf/Dn/jf+nf+m//1+P/xajzwRDw60XpqkOHI6aD2LZK7zgmBQDn2QGt4mguD4zjzkcvzMyw13lZrCS2vRAaklW5CDalIIegW8B1SpnOYWDEzvSOMoMISDs15mt1RXqEOmG1340D2Wi4BpkktVYi06HEgur58aiS1d4DqV+mnXLEACR5u4VfPj+FDPh5HhkTXQC6jznaayEp10oA6G62Wc5akhgmxq9y6DwXoHZDC/r3/rh/za3/GT/4v/If/TCPl4k92mGg4aUeY5NNT2NU2hF2DFZazncdSrUKqvQoXTPsNH1jiKz3Sk2ir+UNfv7/jG9+v4w99z/f98PEd3/jm9//Q18MHVQPMYjXrP/ln/Qf+gZ/61/wX/0P/frf/b5AIei6/Fw8lxDxQ3OQz3r7r+35Axv/w4+ibeqPjO77x/aLQrUGVRKnORYtcidNWwWsWAcKG6r9soKiReBLpXpqFY9BDm7uQQR5KKIp2R+YXURhq9EMSIMTppu8o23ZJH1oQo0B5UQ7qqrw2ZpKtn2hx1kxFvA3dZj2HqcbmcPkdDJevRvkseygsBCxqhAroOKqDUcxyaqLmDDRQOlqKVsfohqRaw6gBVuxsyIu0Um5VtLCVw3pU+T7jSbEU2Kh8XhWp0NkQzFgpWTULmgZ3UTOhy6Ii2jOfKwaFz3Q7rVH6QEgANYWhvMNlR3AbqoKelJZJbwHAVLtKmXaWKQCr/FN+5Mev/hv+qp/0Y/9TH2viT/7qHOyF436nf7ZVjxXmigFH7rbzk9fJiRvKwa4SZ8qsfLjeVk5NaLczobu+HF3SwZzN6S39qqVmmaBWqs2LJGE9h7nRPVj+FE7EUd9zM/pFKEwHSAXtkA01EP8s08p4ep6adSZyGjEshgzT0p8AiHYNyvTO9p0eDxXVo2ElZi2iERiC2H68Qvmu0F9jtOUzBGWR6y7pQhsUhnUP9WDzZVr7clRDJgyhw8uGjjYHhDaNRZeIHendiiX1rjuK2c0qlRXjmNL6Anw2Fk7bPozdHu0Y4FXyqCUFT47ZKCZk1tooi4aX14fwK5Mmg65SLbv9276+vvp8iav9Ksxb991O2QMakf0uPfOt9jYAm1ELvSjqV9XVpcgkoJbJZeO5W3IIhJjqQIcTsyhQPfVt8mkfph1guxqUDnaNPGNqVec0IViaV/3EP96g4BnqEowHipLyvGtnLd+vzFfjw8ccr/ueX2d/Wir+xR4zxrWH+mBUh1gALW3cxNuOzriAtT1P/dboa5jzSdS78Ulu5XVfWbo1mZ08Z73x8cYM71E1lSSOViyijyWwWaQM75YC6y2rzW7xcfrUCe69IrV0gYD7XNcHvvJ6kater1z25O2+7cau1xlrf7zbP/Wo48f+x/7sH/2jfqT4/v//kKoXtsPgA8qS77ZMV2RPcV6CwsI4e7w3t3nNFTZTI3uxlDhoS/MB9wIi9VxvzxvX6dcOPeXJHl/D30UdYmNATJ2KShKv1V/xfFuea99eQT/GPWqPkKnelbrbUBVD5ci3esRr5VPxVokI/SbYeapU6PeqGD16Dl7pvPu89RMi9pq08WRAlTTH/Db2lN06N54b6s/CJ+5QnagA6xr3GNozbLNyL58tRgiwMU+MUM5EydjzusnPxluqS4zg6zSjM6qeDiALaPObJjkn9B3pVadxNvG5Opf0HBfRUuCn2ZsdH4mvkqOfYsxRtM3efbJL6gkwh1poqvRRg9ttY1UZjGuSBi2qY3O+R9/Xfa/YwLnXvdeWKqmXhshU9Cm73dTd6Jpm3Xx87OitKfcZ7zPfNfT+AHKlRH+bvpgxH5eayvUJvxUGN8eBodp24RSk6Am8ssdtJY9Ce/rJSgUSxlZktkqPBwye4VFus+fLcPkmVQeJWzZ79Vmzn3pTe0Mvr4EOHUTB2as4k3B5PDvOvhNvn3s5fsNv+Wf+3l/3v/nWe+Nfef2b/jXf9uv+jp/8y37GX/U/+Vv/2YWpWAAAEABJREFUmj/73/Gj/5XToFksHbv868CJtuJFnzzs0y0/KPPkvOHbOuk7v9r9sWlN8Uh0Vj88nOLpeahHkKp8PZrditfTpzLTtbm1UQgOKEycpSlxv+Pz3J/2zpVmMLEcJMec/vIz5bxWYmyHaMliNTfGe4wYY9u8baqEgDjznAvudlV5ZG8eLWMteKft6yP7J7GhLqJ6KGoFa6G20iPOw9otTjNQIQCzOemopKxWONBj4Fpz2rwkr7yQI4DbusZ68WPmK6JvdHGOvnSb15GHFvBbvATzwAgXC5SzauGmFjzLauXvq4OZLbTwAlniR7dD/ELugCDbR/NqSeoOP3t1vdznWuM1/WU12pCL8RpSihzJqPXb6rPiXQrCF8dqCtWgCYK1o9fExwfmV6f8PlBp4SIvg5sq+kf+iB/xY/8T/+G/66/8i+QAf+KLGB92vcChSjkHrhK5RRxriCY5gYjzzzDdWhLv14nXgWG5LX/BvgJWSXGMwJ6dWqoo6pDJO7gzpZnTvDELk0+jqWBXC0iOdezbHK8L1/jKxrdxfcXXGKOHkncjjmuUsy7L5by8PyaumWMeH93Wwes8LVrf9byIlcOCJc+AfN0q3ji1o271UB+Hq1OM4SvHV8unzzD7tHPPko2FWrlx3rG/zn1XSAwjP2ZflhIw6CCVGMSFHEcRef1Z/54/4+/5q3/cn/Wn/VuF8k881BQoOO8oseB3+538uu3dKsIyT//o/sj+KKri9TFrqheihd36GmdC3Y2vvFb/iOv6E+0/Z3zs6Xv4owFrf11/5r/3x/zMv/ov/Hf/W/4Nz9U/8YfjUymEaFrk8hiddQOfLmSRlX/ijHQq4lqhScVsS8NxhDY7sSLsqQyNqLbN+/Dr3buJ9ApPWL5wLu6BI0fn1DvjzbrZz170oI4X/AyT3d7oT8f7g/eSgktzVrc/7N2tL9OO2olTEpIS3AAeLWu7G+1GSlhmXHSDYbeFktYBhMjFLEHSzl8sVacgxDl5+lklch2K+5aj4/21nc/XxhQC9YlFfPgTwaiYwBrTaFWncQ/mJWth0UHsCQ0g5TrRqj4JSGvhIW7wLkqEGIqSfk1XrvrdUUhrDBCja7Kdj21WTdSrrd7X/bn2oV1lrzf767jvvo3xUfsrvpe/UYXyEetq9bEu4oxZf9a/80f/lL/ox/43/jP/UfxJX2wb2UOJ5JNtNRf0bH+FXZ+tHG0w01I1oZVhAznEL7xdxu8bnzrS32PkYnl3A71GXZfN18B46treGG/y9l0sAyc0TOh0cXbZTpQGwpSpYtx9NE693CgCxvH1rlG7+y2eKzT4jM5XTk8PHHp9NGeLvYw8R3lWfZo6skXMrJGCwGxZKInzlB+ulGXIQgvQvTM+73p/8lOt23NOsX2WUmC1Xm3fVvyotG19Ky+jlVICdpm9Fj6e/+atGV/+r3tEViLh9zU+19RdX7yhbclx29O2xyfjzfEJqwBOCdSM6qo7cKNTlPq+Iv0T2EClStn4EHos9wyT0n2Yc5hYdOMsm2/Y3b2NGyOSTxWOsusuRN6o23eP3S43rL3QI80+X+O+rrNmLpkYnYUUSwrM6F4cfaj6QysvsB7S21A5eLvQyFIL3R6AOwn9lDEHFVPUPgixuLuAmuxL16GhZkpvQ6WxD1XaxuFjck6WIVDVbd4yN4bRByiJhKhxYJCzW1nz8IQJaARSxOntKnq6sPY+pQKWZeQUZIct2mVDBgrc/bgge1GX4cPN5Xp2zTx2Z5yjhmLaGGUF9SGzNANNyLuZ6mlhLR8mNCaC56aH9Rc6jVMH7KLchRmu+d/5i/+zv+Zv/2v/nD/9/70Dwx//+nN+zI/+c//0f/t/UMeP+bf/uT/m3/Zn/5h/25/xb/k3/qk/8tv4xw97PpXUkKrQJSQ+1oLL+YzyFDyq1UHEWbvDVQuNA7SVPaQwY1Yu2Ev1XG27T94Rz/8imW+l+8nBtGk2D/0G6wCpSfmWKCWLQdCU5tFu5RLkDjubFb2jdmRDeX9f+Sb03vZ13RK6EmodVj/0fv/BP/Ld3/ld3/dHv+sb3/WNb37H933vd3zjG4QNrDYWguogYR7T8ApcPRbmMoOkW6nmRXWagRjqcCM/lol/1kJpBCBoChiv7ivbvGUSXK0ljBQckdZg2xRdZmsqgpT8Eiq2XUjZ6FAkhoBFa36hKxDlqW3AsjbJT4SF3zHvmqftRKPbMceZfY9QZkQOl/fwhhtKzaWulEBWzZWrexV+WIdzmKM1Z20uKFkshdl3VV4JoXSOwxFsQHfxt/abdtepfvtJP1Cr02RJnf7Ejp4wVU9c49ilMywV7ZOyoUjFYKOrVbyORO4TmeLxXGxX9k6iNuyYk+EreRFuMqxj8JH36XHCqwxNJwolM+0wjaIXUARA0WZZllDD4iGzPXPcMc6pYGlVGfb7vusP/aL/5f92hwbh//X6K/7z/6k/7U/9N06bf8q/5kf+1L/kv/z/Ok+3sCeENrCzFcZichYEDTayFY9Gi+9LA9Gj6ulN4DLMbEuNRiijqvl5lIINhJqVhKpQKNgN4CjVUdoV+Cl2tBYJ63aWMT2V3biQkh4qapTe6YKpuodP0thy+Sobc8zL/Aq/ijrFB+4+tmOcHI8fAyZr2E6euzJZ6E45FiiNHiW+ut5x3jv2OSrpRTgjhA/CWj4NfoGXlfnB3HuWSgseUAWrXpNdHpxnqVBKhWz5Hd/3x77zG9/zHd/43j/4x77xh77vu//I93/v/ZQnnqR1E2qDyNMPPtJHLcboQOOkFahxVPL6fHkjrsJxJnJx+rpwsUzwuk7jidqX9GxTm0I82egpdszdjA638+zcrx5XwyvANGvOObSl9yzds3VY0nJ6rNGm1u2rzV5my1mdIZjkn/Gj/83/1j/1j/vmTdi+HG2+OTaUbVSHoJEQUPW7xMQZDBvkdHqLeqKL2pxxG9qLQ3gEe3ZS7I4WgtvG7V5TncgFaVrRKCe+y4MGYaX0dMMBOkgKZORD4CHSrcdET2z23bqmKNjOMZ6ut7Z//a0f+I7v/o4/+gPf/V3f940/8r3f80e/93u+//t/iKora467/U7TTU4D3SfijtiVhcfKzrrrCRSdlmGdnSWp6Hwq+HxCIszg2RQerfvaTejsF2NtkSqj0Ze3Vg+8y2r8Z/7cPxN/shdlr3frIU6f6t7Z787gUQJRpvri40JUJh6p3+0hCIFKS/RDinn6CJvxue8/mYfHTCnjvpjL9ovJSqig/px/57/9Tzo+rJvPleHlo4a3i7Aqes0X3Py59sf/lPXxzmE9BstMSR49r+R4h+3v+aEf+CPf/Y0//Ie+5w/+4T/2L37nH/293/lHv+t7v2f3p1g1lfaadE0mQinHcND8iceGyCWD9sU35kOKylo5AodNl7Sf/24Z5/B9a8l1mC9oJ4RBqmXbUJjAMgEDTF1/vSGaQenu9lYMLGTN1JIIK+dZz9FKSp4R7xGtaySaWZWUFLfGtYv6HjSj6VpR6fo4eFW5qj2JooGyHqVK70a7pep6xpnVXuZ0DWjTh4BG1rRj7O6R+ICN2b36+LjLoqm11kZgW78v7Bfiqzgrb+TdrdbQlMGTMyCjDdyI3fLCAQqHTeidrAhGQxWUp3jE07r+M3/Ov2eY4U/2cjSCFmCFmXpLkG34Mnhr5xlPeJMxvBwUi+xoe1qTLjRbqUWEaZ3Ni8pIsfbue0cJuPSO6kCnqSvILNUoUjcasWrP+Z6vXpcNXyPd9kN8DZmSl3yyFc+igGbUKExAEeoMn2S40568boX4cKbQlOfDpvX0HsLPVma3iKgGIV7CoTdOvNyGGeYjHgUfR4XYYs2nmcvwABx0KuKduLO7YBA5vGfF2OSz6pONpwAmOEdytBt0LnAid6u67fTseBku+GzJ3dLlzjftuCalr/f0gCUgYTUohtpKZXsMt3mGdyl0hpu9oOO5z3Nvrhry18kdev5z7RwJAhh1VoWa2vvE1yJOtTFVqzFRbgRVNKNS0fWaGOpdPIrugM/0AkNcUpWTOk8IEEWCy66FPpYExwxKxXjJmPVFrepPWw7cQWn0QWyw7iFuanTpifSHDCkQZFVklIK01pBsJIAuz7pCJBk4iNWhb2ogWPJIGpWJFE1LF9NRmqGju4UZUV+cWmxB0znZbkcv4zAHhhiSADV+sYbCaW9dN5v0y3qVkqpkmjK19lO45RD1ZW0TqhwzQRxpo4x6GUikpHkqVVxKnb5IurhmvDwXZz577gnav+5HffWX/uf+YwruT3r84r/hJ3373/xTfvXf9FN+zU/9Kb/qp/6Ub/8Zf/0/9Hf9Db/2b/tr/2v/8f/gv2p8Q+4UaI7GYl9WX6nbCkZ1Z5eElk242UveUYQkvh3d8FLRqXCmNHjgh1W9pQ7L09GttA4fXMtek84SdaWTbU6ryh2VaavXR1xrT0+aoBxYaqL8aKzorNW8Kl+x5Vg2n+w4DGlxfsvv+t3/7Z/9K3/Cz/2VP/7n/ZKf8HN+0Y//u3/pX//3/wM/+PlDY0ALkjkqUo3BQ2o2wFQTqX3BpQtAKEe1Rg2DswdyqDKmcT6fBZfDTWvOEC539OV5DRu6O7CV/eL5IF5tZgvX1WO1uSYBLTIRyS5D25DLJ47WB5i67wHSqxc4oSLSvthqFD/Ur3ZCK08xBMeFJh5M1i6IrmKggIKFLN82a45huc59nVzpE4J9kQJKqDfFXKR1dQTeoooaAJ8FE4VAEmlCmKLnLiGeEAmU+BWOyCIBWNTo/kK1km1Zap5T3+q1ms0K8dpnjjJX3wql8ij01eMSMUpbmuIQ8knIp0qwJBwZxTRKZGJpY+dJiaKMwOBxv6FlB3DQHglAolTAftz0nOx8IGeH2tr+svJhYNiTC/763/R//r4f+vox/v/x85/8s/9Mq6kCs8k/99//Z/yk/9J/+if+2P/UT/yx/8n/3H/w3ydvRhtQxfkAzKj0h5tCxThnqSsySHAIitNnm+sdZV1lWWgE5rGPSGVwpqgQBQM2vUVqh9gCWGALvcXxHbbLlRCjyACYXYe9PQ8lUeSZp2ZPs1dfg+tKv/Ja9nH5NX0QBTl1CkRahfnuVWWyDlnUeXiEndDznQ7vNoxq153C3akHtLsqdtbdFQM1R2ujG/M0Y1optJYR+IS9MtdpLfkM+AZ2V1baqWsPP5aFKPzBP/R9P+ln/6r/1v/gf/SX/V2/+Mf9Xb/kx//dv+LH/w9+5Xd81zeDs/hoR9pARNqWAx8pVsSc6qF09/pgaL0Hjq52V5/nrUWZ6ILg2F52hIsS0Ji6R2joFUCw4SWdrFJiIFbUMmwBL9qcyhL4YH43c3Jc1LkKxGGLEMar79W3ahw2oHWANp7zuojs/ni9fsRXWkLk6o8/5F2rX58nBHGXeVV9UH3dnxamOGXlhfGim1gUSUzFd3e/UXvEU0HTYjiUqbBRwt2uYR7DfJo2nGOQy3N4//D1AV6d4tKeVDrVSs6+7/N5x4qNZB8AABAASURBVHurnhslO8tByEW10D8W2C6OQOav/83/p//mz/2lP+5n/Yq/4u/8lT/u7/hlP+7v/KU/69f+L55cCpods8BMLSTpsu0VjESn81grQScqI0unuXVuUBd1pjoMkIdpPcwkyNoZVTfUxrOOaKpk7JnnZWNJSVqoQgD/Tf/af80fT+i//ElRKFHKsZEKKEPZZemfKQSneBPlIT9dlnYlLaFEIZAl+kbapLqdTjb4L9v8V/1RQ3KKb5rWEAVYsmEK89/4p/wp/6qRP/yxjINSRg0GRpjVFA5ANGXeWfL1wwP/378p9ARJa46gC6ZrWieqgH/wN/zmv/Ln/NKf8LN/4V/x9/zCv/Lv+eV/1c/5Vb/gH/0NG5GNiaFnKJgz2iOUCR82fDh7EfpbdkXrokFqavtX2H+0YbrBXRS97NsiHZxu7qW5Rvm1NudYa8zLTMLiuBuqMrlNZSTsQTmePYqMG9gCL+0c2jaqETqg3dtI2fMyGUyLsCceABqog04jqc8ipxdCbSW6s4aBMARV65XqQauvWdpRWBaeRLVWMxsNa69egdUcTeqQtUaXI8ds7Qpohew6AtCam2xAxkPvTw2tpBpqdQainO4XY+RWA2QPB5dMgLoIbFlldaND7YwF/qgf+SPNDH/Ci1DOh+dg0pHLaoysaXpno6y233sV5kB/dF5pFmwZtIS3L36J5njFEsJ8yEypJk+cs3McrNRg1kXdH04ej049kEi5mhFX9xjOeY2a1+2+GeRtI+B+/HpzHlFClvy8zLSjGitlsqQJuvvkMFbZrglMN8yh5TmmqtWHclKWp4+SghJaOd3Ent0qIzkuWYBLgfV8xcBbsbfN7At9kWqyjiZOWVTDak1+gF/F1Eb2dJ7ROYVXQ3opJ3o7i+poNqOGhEclyYoTpYC4ZHFgUb43bGM0KOUKsM/Up2YXUAFu8sv6ZSenoXWzOqvHMZvUS5BMMwt2eiWFcqRpgrY3NQEdPSyebBYPVnAKkx5RzcFZ44o5kzQmLHS/G0PhZcv1YUk0LQiIY7ErI/qp6WaYHTATwBNZry6HFTyEO0c/BeNoEVHPQi6H0xtEVWewUuRTl1Hqbu/qd3Ef6ivqc06WzGrwq8NPcsujx7a9PTFMWrtmvGbNBXMzTONU9cwjx3SqjqNlzLENiqiz4+6K0hVraOFJLenmYcvN3caIYXoULu8NZV8n4CvHAq+B5W1ebhhNg+u0FCFOQmvh3bwb0fKieERXlQYlvWBtLdpDcnDoRbSScC3an/5v+lP1+U96ODFb1J3qSDQca/FP/7f8qP/+T/iL/ov/oX8//j9efGLRjzLwxpe61q9HEBjZtfs+eStuiX/oRwQZG/Cqyc1L4MYJKA1J28N6zHq5HosczJPAURQ099H98G7F6VMVQZ2YnXOE+219TIhEzlo9HO4Yo67Zy82Ug5KdGu9jd4yg1LG0C8j9nd/9jR+64/s/729+7m9861PHD95aae/2bWI3MdJJ60m3cGQ23sTbmUpV03XGUz56DIlUUoHkSHJOXKte83mSI/eMFbr/elSd0DZMBeygt5kooIfxbTgqiAHdNNzsI3JGLh0oUlfCTTxU9mg9VqhZpTfmjo8VcwkilPAGVNhz3lDhECQucBXQLHEgfvS7u6uY5u1s17TIlGhk2qpMs9zYRBbK0EI2LHVGGmKDsqEPphoYOE+x9zyh56IINgehEpbWmtmQGN+V54aKpY56cW9J8clIrqA9C8hjOagoOLsoj21QCJxjLP0bV1+jZnMcg1qkSscM6vImgFAjIpL9lEn3KH3w6DoNbZBaNQiX7PpEsyfd4P4oqpqedpVN87U47Bs/8IP/5O/6v+NPeP3r/rU/KhVwi0aFhZ/yF/6X/7q/4L/0k//r/6X/gm7UWeT6f3L2H4C2FUUaMFqhu9c+95KRYCSoo5h1dEYdndEZcxyzY1ZU1Bl1zDmPOSHBQEYwooIBQRBREDCBWclIzpl7z17dXVXvW+fC9YbD//731um99grV1RW+qq7uTUgxaM/wZYEYwp7EAQZYlLRzqbTQKU1Wscg91Jg6CYxiFFBTkoRAteJMgFrmXrIpu5s3uE+ZhZVwcmaHvmJKBkzBwQrreo9ew91EIsEvxo5RFcN10BWjQlhMINQSngmyLCGGiSg5GKRiKTt6anBmGsIxRUMq11mkmZUhiqdkgxG8xJ3IQQXvCkxPhJGkQ/oh0SA0pVGi8KCGLyURT+TiwUTE1sWMadIB/nU894irrr/pqhtXI9bQrrsl9BDpYWWK/6pqlChgFR1aKZUpwKr3hIkSYMJ+F9ZDOUeUwJjmFCbkpVMeLSqydw3iRKqaFeoTwonB3iIIPJHUGIYlyBdO3IUQ0cj6U0RNMUpOZImrBjArVofei4sGob/XJq2CogZSVomeRBMLhGrRLQHItMyhI8tIUEpMxQLkysKQudKUrjkI5hLrBqyWIEQzE48RZo06MkFXcWibPMLVAZqcAGh43LpH7+yknbSm0jFLiDB1JRdgRptw5cDixAAZ8pC+NGLAJQh9ZAcCekGQ1VWDjaqTa5x/1ZQV4R206xfrdYvjqrFxT+YB9RBJCV/TACl4cCmYao3DyLtALSHOFBqkIQKjOHxQiAfDGExJGK4BGqdMPHZHWPeA6gYnwx+TRpNTMgulgNp9u81X0rJHcDYuISBNnLOkHJRJckozUglsDSI1KRnIYLMEPymzdtWGlrJDD7xUJ12YLSw7ggUJkUg3gR/CUqQkQxo2vRV6hnrUE7hSBLMk5nD2SDAE6bJDMFFmGYhL69ygvXflxrnBSlzOv+Kaa1bNMSVhklrTVtUWkJ3yZK6gyjYiW0QRgWGhrArzADmLBZuLwSGJukDLWfjglJpzd2drhOBJRJxEtJMQSWEuHIT5wHpQJKHEztKInRgvyM0IEWCiqWiWkvC4e5hYT0i7Jp1K52QlOAJOZQAjKQdLOCxiJDUxwRBg70ShNGkpzEnADnqhfxBPskgP7ZbIxSWZcDAYYKEAtTVC8MViltxF0B8/dhUfaSrWQJME9haBjYwJ4zApk6hRGoF3GgBIKg5dfdIMubkAH6pOiBAksnCCIjIPqc4wVWtwjIIBgw3ZaAYAYVBa5giKPr1SZSGlELcp+lF4UQoVnFDQjgkentKluzmmSa0kBnLmgTyrcVgYJTjFo5MbBasmHQKiU4YkGUYl9clRxnCdME2Ya0qUHJHXWZYkhxutO7BUOjTMzAjDwgE0RGJWYnaawG2dCdJxVtAg00hEVgGmiHOlYlyEMmMx0TVAyg7u0NQUM3tUIMTSxKIH/JJcExVllHUq3AlOEWtCcKAnFohfVCZbeMoEUhVNrEgkGIEhnzRwjdZhGYQqgWgYIIaSDgQSh6Qewq2wZwrpKToslZsOTaUqFnidtEMxD265tzKHASYgJYJ5WSfR2cDBIsDMdQILBfDpnoVnhVaKr8iBCg7GZAEoBorMOVRrZoMUkJ4pd1agGdtOqBU7YSHpMA0JQTAOMkf+CQQHNo+tK2FEqoQEWoVHKs3KhJRwBAAs0zCztZEd5gqiiQzYUVWCpUgCII5OAoBObzkcAGGfHmcnrj6NA4t1B0lmZkKLoKDekVXAlGDjoUSeMm8UEhARZffUqhCEDHRwlgaNTckR2JmI8caxrjCMYUEtgNae2GdEC8qaQQczY5hGADHNqSxig3QYFPmniKE80owxsRdfCFPp6ODXAeyp4bqTQUKCxSgxscBoBLqJ1QRkd4xIPYIFPsLudh7oVo7EvcgITDAZMysD3TV8hBz//fTHLQByazsyx6BwYwMEyKgxN/U6i1ACtKnDlEKUKOAXGtgWyFMkskFpyKo5zA1rw9WMoFjhjmljgReAPEaRAgXHGose5D3HyIDlQJI4MScX6QGFwqFRb+QhllJPuXdEbHLNCA18BiftDipCQk2wugWbJ5FLbthwHz0Ibu9OHTpkgBPhwOQKuzlUgaQQAx6DfVmtMbIYj7nUPKuRrQnSCoGetZOOgUDFSI0JhjHu0Q0iAmdunSKIhOEGok5ujFXOGshRU57DOMWRXeAlvGYUTdEl3BHyqKbGZLUYDcANpyhQlJaYJdYkpAFLVDFYH+6Hbyx8VAcmg0mIhaI4cqgTATReCWEWY+fugYgMweOxeZ3DXp4J2UOUCMEyiUkBN7Ik6Vn7VKkB2JJ7LrYQMQNvpqreEJQEhaFnR77vebFTs0lXtZDO1CmAAErhxPBsKAuzCjthqAItEmChhTHn9RRQIVEPM4f9GiFoiQAlQVIgpggDS+mK3mIW4S2oJu0M+VS6KJZ2IEWEVdhNMSRRJpqlyBxMZ19wyZXX3wQN121bLhQVHbnPpQeEgprAfucWYuRKjahj2nYjMjh+4iccBCmbwahMGb2iMyNTGJ4w4bqS9UwmsGWKNoQj9lRgYaU8c4LHYPKIDi5iqlyYlZnAOCisoKc4zarM5plHotq9CzurcFJKwkQQxsONjbQnosSwK0FGlL0ju1GQC1mRPmjNXPFIURKhePHOapJzQrkiMAx5ahRdAjghCDFk/DiD3fshNFcFKSSjSNpFwxSisFXQQ5hAGkyZslGB70mIBfZidcI6IlfcXX/TTd0Dwq7bIHJMolNYD3eQpSyYwKAgkYTNkNthQk7QY1EReZA3p6rSmCiUgntwdbPaUGAgSchMeBbIKI7HsGiF7k7aCQlaAzdhDjYVinrM3cYW0DcbFIuAYcibd56MBfiwhs6bO2iwUKmNWljT8MwE41Dvdax4HbTMwcSFWrY+wIcOzIMZNOyO7UltlIOJIGLUxn1KGSFkzDBB79zdQMYQKFwwYp/DoZ6oFp/TfO7zjmExF9ZgYxXK5BA5YF2fXO/oDJHhh0xpABwoedc2hR5jVDVkD5bUYdVE4T6F7Wriq26stNERDAoBTSLUTDlXic4uQkWiMDKKFbahdOzZiUAahphemFKSlDL+8B0OtEhQgIe6qJNSxDRU4kAmaInnGo1rCPILT3nNEk/vN/5M+gUQkMSZs6fCM9YS2J5SkQnmNeD1zD7TmKllDnDSCGGUchh38kL2UIvyz/e4+9uf8/g3Pevxb37WY97xnMe+/bmPe8tznvCkf3lgClLkMqptqBXr40xRImVlhNvGAtFkfYlGvXtPQGwYOiSDGKZ32Oa2b3ra497y9Ee97dmPfedzH/+OZz3+HU9//Oue9KiVeZZDcwvuUMMCTiaACx4frNE1N843Hodphpzd5oTypkVHviO4xaV38k7KyBxGhCREeIIEKwTMkKjJtKFUvXfURL25UZNiOc0pWlAErEaKbtwNPKNTVJLRwoykU7bIWmNWDZlKVXlBJJlz7WQO2fsoU+Zj59JVMEcZuXsoa8ksSz5ERooFaglSUm/I/QOhGAjEECY6WhFUDBgDDsizpjRL0+MJlUFCLlbFOgPObsSgdCd8B3MkACnXzmEd6ufknIPElDBDceKBYuaCyGYPGEbZB8YGOmnNdmJzAAAQAElEQVQTQfYIdcebmE7gtRT/UMmiTnYgcSanaZicKIk5ZsuiSsseERwYWklUAFCHSMI9uSc4Bo+E8N6pWyJXJkYotppG19qJqWfFhCcUCjYyeaCFsCzkYTbMVDYhnvmsyAJl4J1GCjLOvCbPR6Y+NEvTPo2ZR7CEh8kI9qbQm6hqVFhdYSTXJXUJGsJs+KIe6MKCQBURmnynEYncSBpBgcweAiuSwpCCGSOCMADQZMAJgYu3FqjUqKN3clJSJ+gw9ljttojh4QKdMeYG5WB0hg2LpdwH1zIZjCK7IxTcYSBkZ6IIhf6aeIphodlkU+oyb4yxwDs1yzHOrKLyl1FaK3MrFeYjFzLIbpxNUsMgqRCcYIhIJbhYahvHeavVo8tkFGpezDKH8NAYe72YYRnoB4UHwTEoujVVoXkgCdceTkAlkXIP4BKWyhJ4EkAmqcXQW6kjqsIB3pJBRcIoGnmQK1FCZPiMA+AnQvz07og2IxYKRnURmAmwC9Fh0TZ2PNCA5YMXOg2VFpRSCS1BwBNpREFwpuKFLDmzzDwPeMbJsCbyTKaMIbFizYG7Tu4wXsekhyEWNVkKKk4LXZWY4IsZl4VZSYhAZYXBLdgk2KPUntFVai29zsIHpcIM2TBqhzRGU+VVpcAV1JOQCNIB5uwYieYijFkoBiIlEY80b3mRuAnDcMV98MaTl1x4ZB4d8wDDXD36RKJ0K4cDMBQikkUFVCpEiYIQBrffdtvH/9N91/aL4EaDwYTABaXWddHTCP8Jo3/OJeUFVdwwwVlwFcgV+RHhIvBUoaY0hnduzI3yaAsthmAZKBJ6EDKnNTNgSahKH6mO0Tl6xtQNJ1Pr0kbpjcNNZSQaewcOiec8NC3jLM11qGCJRorFT6foSphfL7nyqrVarL1oiHNzoQAEDCM6zReprlakhqH2hRqzjpzaGfIEWywpJsSZJDEJV6XVRKs72ehpsQ9mHOqaXT1JFV4kQKRirYtQ8nDwIY5CvVhL826LySCmJmKgNpihJyMsXI0VSUkIkYBuiSJn0pVQsYYjkxS8l0y84Iw1A8ga08gUyCbdOsYL6g5EDiRFCeZNPaQFUYaOTm4waXJhwvjcjcQ5KWdhdiJ05KhS58mQ9SIxYQDqUI1qVp8NnJYKBvCrDh8FU6OoU1fwU8K01MWZBm0LUbMFdWaynrtn8FLmQU2lG7FhuBEmq9C4CtjIwIqmwFZwg5w+xxAe4pQLlEFfjiKTVqIh0AhtGiHUBQQpM5J2NKe5J2R+fMF+YcygI6K/nn8hzhs0ZqJAJDf36BOdECUkMQiJuBQK8tapQSBkLFiGOol17hAPbkVkOY9dK4wQ3UFHlcgE7k+LMVRxJBBKYwiyU42AMWCx0IjkAEMEGe6hG3ECzzQSvJsgInK/sCTKGokmBbizGAUuFbSuLEJsgRzdIDhDdDDEcCsoZta4L0ZUojEBdmOzVi1GDCbQ1gk6wUWQpXV2RNEc+IGzaJzqPLicOQTFQ8mKPd2eYgIYZoMG0qpsNOU/phDhBIGaJW8zrjP1IuTFxuKswJNefNkVUGWDNg0erWqtPBlsSZowgn5QLDOJQLGZ9MyevJo1Z+NMWWXJVdQpOReJpAA95nCD03jC35gQWo1yC+pWGTzJYZ/W29itwwOReyhGYocpB8hsNTuWqE26RMmWaRpt0QuQDsSlkiNy65kCU9NAVCgUY6sFYUza8GBqC2k+lFHVRJmhC/zcjecd0y1h4CltM/xpMjne8YoIDqEIdqeAT4wmh1KYdOpz6EHNXQL5CVgIp7n10WpnI6rJEadNDGo1afOoDbiElIlV4SKEV6/JaEYExdTZF8UXuVevAQWUsHN/1XXX00aHFfYYuC/ofCZjEnMsLiQzARmJjQPRwcLgAVZWm7YoLsMEBxFPqSWFMtojsSQtRcuMUg4AVlRA1alEJAlPiAnnLqkjTwkvjr6RLNODgDKcCAEPQDAHmOSimPw7N68VuHZjml4Qk3ksOlUnT2FD2IwMyKGcmqS532OH2z/38Q975mMf/KxHP+y/HvWIFzz23174hIc+8oG7DIA4fuVwCobu5FyN28gGAE0SbPRhhwMcAQwowRG9z4wLKUxDW6zc4lmP+NfnP+bhL3z8Q573+Ac//wkPe/6jH/nsf334yjRIIzZEs4JSskw4J/imjTfedN2NN240CFxMk/t7a80gGWyfwjtFQEoiYZm6dGIo3MV68pa5qtTgHvCOwcTaAR3O3rTKtAFg2rkFNw9gPbFP5UQmJI/oKBHmPHTK0ZGfaAnBzrkGMjP5xDCilejZ0Z9qC8wJQEHRpglEIJCsPANMKIsrxKTozE0Z0RCRqiMNYtTOUZnDPI+RTVhLDCVSXqPRLPssU4J4jtlFQ0AQKTVWIL2Kt0wuCnbJNXFobio9iRbCJJmYRSJK1BmNoKWRDHMQqWdSmfYbvAEhkpOuzJyVMHw38q6ly0LIjCJBr8bdEnlJXTAEE092Xv+DUbijI6RjzSFMSjhB49RsFjnrICRiLkSpEBUncGSaB3mQBDGzsuBhgLtpMh6SpsK4oznTWBAeLqCdSzTNTgvF80rWGTGm86jAnnSbjTFbTMUhJgXm/xa9uo+dRkcCSZSyJFgwhDyzJEdyNGSQatUTcWFTULaKQqvDTpGMMGQRHoQTs7oMAfeITlHNME1hH1KbBaBlPTWH2fKkN/A5QoAwZjR4vRNsT4BF7x1AcffOSFstyMkiGkUTDqg75QhKcC76wjBORBqW3Qc8K2YslYAb7cqwlMJw4NRNGCDjFEpQwWHKEPidqRBJMHzXI7uwq4tF67RYfQSGc6jAR1J76VaC4cMICGdWzUZECOiVtBPPu8NKVnGCKgMjuoIdwObkJgYlFFQYghJhKC/OxXNJnCGUhjJnGFBDFHgPJlJzNTgiICCUXGrM0M90ksLdw5zwVsiZmii8blI8FoQU4wp2mbKDO3zKLGKs0oURGObJiUODFV8kY+Slp07NYuyOpQM3SAQQIh3GYMQszrAFAL7CGDYrygAcE5mQZUsIKVDVqGEVWw0aDqvQxCMlytxzG9XmEROc0cuDoxNBzZAWeWRhiayOmM40SYXzzG0YXWpQeEhVtsxgGM69chjjqTEhQwanJeMsc+rEi7m02SxmCvZuXk2tScCGRI958AO23XyTNW3FkCkAicRWHKnAFWHuUb1F6al07F3DtEAwWQseA64HF08GtYNgxkgBz+LCi9tC9GQtW0eJk2bkBXYK4u5JLCmmHBSrowNDLVPLDLdxzChmhpATk6gQgynYrXTPI4lLiHKCezkhEEy8K9wuY6XfnXMBbXTwZCISIsBwPiB6uxl14wq7gVWGtJG6p64phkKSyKaCjKcrVwE8SIKIkhkWEAovE+pMdfiOfRae3WAsBkkP8LXGvRdD3IsA8wwds3ph8BXhDEbhjTA4UqoIMVQTFzYSN4pOUNS1t4JQbeShYVlN1eB7C+0BnMpEG16dm+WAtJYlOAJWx7jEJcCWYKcxegvvoiJ5iFyYNTEpkRu7I1h06YIiikUKIS0+K+MgPTuyRIzS5jCNkuZIysoCLTKuU+PMjqCA5YJHogZXgbMk5oT8j+m/C2xs1qqN41jb3GkRxEkjWQyWVTOlHOTQMWw6cSawnnEfjBh665BS1swEA6N2A2Ugu6IjdQnsgQC7cJ9SUp4YdPKOcItzLrmaNjo2W7GCYeVgXYJTUBB+w5xJyQLlcOceTq2l1pQWGWNBhAhmh1laTrXPUHKbRYh5rpEcwCjkSiaYG3XERaLOPrfmvabJKT2HUyLkigRUE/p6j2iBzpy9TNozSCQc2NCkWSiDStwJ0bckrlH0CppCIksCdYfDGA5mITIyQ07IhnznyTyCqsP1oRQDAazRbZoPGxPG8K7u3MHbOmJ5vsgo0qgHk3GulmqTVqP3RtwFhp4pysxsNDgoYJ4aXDHXFEaOQaXEloSJsrXfnfk32vjAIBglTWI4MTfqLk5DjyGCSKYU6mwhkIiIxfAhHlIodq10dXhLYx/C80CkvUvrlqENdZExDS1nTkBoZ7CAvO6o3xFVvZcmZSylLeV6CvbKrfG8YfVSYDOse1Tm5mY9RUrDCsJOk2IurYkrKwmRqKGUmC1ogkNomcPVHagUGII8AQNs1Ix9zI7KoxHMz7OmBbpBNNbqGsySCUE8YZW5iYAe9ksc5jyH30RNB1XNmQoupSYfmZxhY59+MeOavRWKxBA8SiSlgVhh2rAggA7au0jDr/UFsUy9T5MM5qWrr7v+b5cvs30D06XEmRM7hRsLLGsSpEHQJzlnh8gk0oJG45YrlTmnJmSCEZ1gKUKiJjAIJmbAJWXOKjxpl5yTJp+Jz9iQzpnEFG7BXvJAyx4ckowkd4kRQ4Y4ZYJ/nWq3sY4xSWLBQEprghK6NzbXymkuqTGHykTOhPrcOnXVUA+uwqNKRJImCucggFJEYRJobdGxBCKYgJY7mKG/KxH1iN47eqi4SlDgWVYsckmUAGjpVBqeuVGHPYkp4CxBOKfgwTuN9fxLr7jk6utAs0GTDptCu8ocuSs2phDwgaE8lCKBGgHoQU7ArHs2T0vpLiwkRCTzkH0Bw4T0jgI6sQ0JpiPy7tSR7ZHzGRg0baZuGpVpDmIAlalIAC4Md3IP4u7sHMyYuCRTiRjm8D0EUU7KSghBM2gfHF3MUjeGFCQUEYT0Eh0ZrgZcEAp2TC15T4Z3BPNUmt5DnSiqmeAhWM9Fq6gn0SEyYGMRkK6JAFSZAqxdECQOoZRYBfgKktqTQ5WOEcLHMBgdxoiUXR1vC7chkwBRrN0kGkMcEWOtCUhH8dMdlS0eJOibFJxh6I2aBAzJRBwtpCHsSAVhC+ruyLzSM3nRwPwOeJMyA2E0EmKROkWCecmzs0BgcIJ0nAWSOuwPmBIyw6JJa929YxCaCHEi2JvI8EQJSlEmKsSqRVBCDQ5ppJI0GCgIBzRDIxI0JhI37kY14AQjRm+EsqlhxwgGK3BHTLbEGwYH0lDllAZJCw0BmpJJYc/JsoTAahw8NM0jEfeWvQmxUlaCGcwIgWbdjaKRzanPuTfFrQdO8HoPSlXUimIsdriDUsPDFtxHCNo4rBBqghKWCBjS6Z7Uh/AF4gVMNRFsjpP7ALGKKAQW6I3gb0yeYKtpKsgJ6SMUGUS9w9GkZE5Wg2vNMgIPpQGIRJQ7LcwdtaRCwEpkxB7iTMYxl4bfqTwlS6lGWB8ZveBAZiGGH5QDdCiEc0U6TGXMWpXmvZuNyRczjQJ/gwoBUmGxCGjFrhIqwpJFZ7A1hmveezPqLiCYwNLVMcMl+KsLvA2hXCwS3CcKEzQaw0bgECE0qVCMKLgDBkRdrOvS/k8SzSKZjR2PlebCi6Hz4DluJ3FIOsPDCnFcMkWC+LmaInTZWTtzIwgCI3NmhxCVplGrIMOCD1WiIKjBAwXKMqnEjdmn9GSyJGvPAsE5RqHmiWDOmgJwjVAmZ0QHVU+dHYL6vAAAEABJREFUcEfLHyA0TVXYxMkr+Whjh4bonZQesMvO+7/rNfu94xX7veNlT33kgwoTG0EOddcw3KFbxniYPTtBBG+GlVy1gLc5BASOx+xz8sqlU1GWIcdsRfDAnJy4Mi0mnsPslI0GEknCJfuQlnIHU3IVysqJhSOhkUEbZibiWc4K6wW5OXvFUinPGJw905RTlZCoT/vr2TfOK210ZJIMKwayUjhXL7FE4pB8Dt2mactDRHvGTJzNNDqHKZHTGsfA3jaARQlPapKYSMKcAn9GlEMHSip4ShigGY9BzpwHmmUpwurh6JCJUyhBO+qCzASSniMnLgGSKT93GpkNPmQGf9RToy8F/mQM8MfAuXNG4E3qIhD7VCC1CCeM0DPXHIhcEmKG9OGjuyEvjKKsKQchFwgSHI2E+dkd4lMEgcGcwrLKMCjG4IjmoPEKCnM4TrtMB3NByukoPhZYJ7VgfDhLSGlknwsTIxWpVuJG4dw5zTnMMEDtAWYqcHXjcLPoxAiJlGgyVRN3JsRr7iEdCGo1N8koN3uhMRvWl6PIGNpIekBmmz4IjBh4ggyBWWWvhNr4iquvoY2OUmBpZlY15RGmc0NUJssc2Vg6s2hosiS1eM/kiDaF7mJUhBSzGcQDV5xVVFVETdjVoUOEQFVwlZEFoSIkqTs1SGORmEvWLPAMdxzQWyMJYp04VZO6GLWGpeCBpQTKeFNiF4Rbd2jTzDs7gJOmfO0WMqkPO5QKLHLiPgjcTkMfZpJnaXJgsHrWKKKJlIVicr0Q9lVVGSjosGPrPcbG4zSbNQIGqLKOcEZEoTqIzRINk+BQlCK8ZespCJMUa6XUeboB5WVXXnHWpZfTRgecPAUWRUkgZcLPEHUhDGHgjqzhzTDRe6cKy5SUFoqmTHDHnFMzTFPawo0gUGdReIenqXBMNmZH4uuwIKeSCLETCbjxIIMMDNNBYRESDRZnPK/mcyQrvFdKPPlrihaiQKQlcmLL5PA1xuUeqTojZMhEmJiWOYKpDy1msIJFTzRXqsqm7FpIxZGHfVSHWMQs4O6hXYJSL0h2Ssh6TmSaAyZEzssVGkIRS8mSYFxhnQkPyalMTYY+cC2t5Qihoj7kJggwhLyl6AXgIO+Icwcj16SeU0uliTpezn/9+792j4314AoulDRE8BLDdiEnC6pT2s/zKGOgPFFnzZx0ehlG1BjCV5lUbhKIEHPtjRctapASaFmhNiZCAnPSErKAe05EQiyZRBnjbdyEA5HGSHUMZ1Dv1p1cMSAxScoSxaM0uCu6iEPsFlyjG1dLDZhA0q2EriXGzMaSjDAPkaOqQELGHTsTTNQouQsRBZIIFGAYZ3mRApRROnPL3XVOXBGD1IMpoAyyj0sifPFAlPCkOTJuQI6u4dSaWYUKQQyo9Tj5z2f15RyRpCucWAISaYN+wsKubEJkBDwxfJDYMpMIg4rDoabEnBCwBaJDXZBRSzSFaOacRQW9BbkCxpt4cXCz3A1rXs6tc3WYKIKM+xjRMYwXrUOpOsxjqJoCFi8Kf5lSZQpPKVSZnMcaI1IbOcGKi53mDjCmRtrNo08UQR7S8iSIEmuzaIYkh2ajS3OOCJiQmLtKUzAPmp4yDnHX1nNrGsQCIa0HsE2OiS2mMQ2dg8Olm3RIwIwMQkMNbTAgSZEyKEtyhs5EsHinMFARxso8Z4YM1QO6KN6yWyZXWvZwYRoYHhb2cDhXmginLqmKoXCsBpfjFTtxN3LMzG2W2pA7xh4725y8S8/cCju8QbmHt5jPe2vVW0QLG2k0b8l86FWodu5QECZxCqToIIITS7cSrkJcVBNlicIZ08qQaPAQMIBCTTyGHoCG93CfxsvoPimmxClgTUaEJqbkE1gJlAqbdWUvHAqjMhOOpRNFFBXJmabCAULgBcFOPnNecJp1zsg3PAaCQmoSR8YToxKaQyAJnFwV8CIz0jGGMWatDjayhvEwjgPM4x6Mw0sxncHBAvRYT2RJBHqm6Yu612g1R1PhNkgboIsHdUSXRThxkBCJpgn2WQnaMFFQJs/ciZ26Shc3cUjHCA0XggGChQiysg8iOekUsqOTddIqQ4SQEHNT3JVOk9YGoInjWcBH7FOJAM1pzBjNqbbJeWFFCbJoBMEnHZIE2KCpZE5paiJB2peGQjnv88nRMrahQWCVVKowDAHFITZ7Yc2OIdjczGlEXrYpDYKtUqcE+wKbwkaZtAiKTvjXrCG/l9FS9+547xw2mSXIA2aOyd3MxFldM3mRqjo3lJPqnpiFJnIjtRAnUadUIxkgYjA9aUiCfTBBw1xh05/jmJQt6MwcCJgsJuZBDgkNoabE6kkxZzWWtoSqCVMbf5gnCXgCz8hQEVESOFQhCMVC5ttvt9X22251u2223nzFAgVcPMGdOJLSAqWF0By8qi7+5Le/+/TXj3jDZ774X+/5xJPf/ZHHvuejz/ng5974ua/t/90Tzzzvou69C2EynTgOaiqUBUw8jMA0ekyZPAg6B8qEpD2nrhddcvkPf/7rj339B6/d46AnvvWTj3vzZ5/wlt2f9c7PvX6PfXf//pG/OP1MEUeuZqmB/NxHiTEJQI84MWZCZr5pPt/3qBM6XEnrHavm9asn/vpLPzppvx+cctARJx76vRO/8sOfHvrj435/zjkhccb5l+57xAlf+v6J+xxzwj7HHbfvMcfud+QJ+3zv5wceecp+PzjumJNPjUDOwUToGZw5Hf+Hv+535E/2+d6P9z3ypwf+EDQn7/e9k/c9+sR9j/vx/scdd9Jf/mqRrWvAhXCm8LXXLh70/ZM//83jPve1oz7+1W+//5BvffDQ77/1C99415e+/vb9DnnjPoecdf4lCSAi2MWuW1UPP+m3b9n3G//5gU89+Z0fe9p7PvPqTx6wxze+t+rG1anbTCIrKSF+nEWgJAfASxdded3XfnTyBw887GUf/tzT3vnRZ7zjo09/68d2+8CXPrrfd7/9s99cfuONoA5lJ0YXuB0FNbUga0bugA5JDzMaXUkEk+Fw7kVXf+WYkz5w6GG7fuTzT3/r7s98x2ee8vZPPO7tn3j+B/d83ScP+NC+3/rid378k1/+7sILLnEyygQYp7AcSFVOcCvjNI0leEIkHDlScsoNEKUgBDmhHoo+Cd+TCsviTauOP/XUz33niFd9bO+nve0zT3rLZ570jk89832feO1nv/iZw75/1K9+e+1Nq9iSAuDdAfzwCHg9QiwSODqQlR3uFLrm2uvOvPDSSdP1P5defd2hR5980I9+ut+xx+53zLH7HnlcD2AJeXpKYOTCPhDyUypIXqoOq+BjoRdccel+R/3o8z84+lNHHPXx7xz5ga996wOHfvV9+3z5vXse8q7PHfjB/Q+56upr4IgINdNVq9sPTvnth7/8nWe+d/fHvvUzT3rnZ9/02S9/9aif37hqDkUF2ocQ5Y4BEmynZEwRQEvzoNRlBjOFiYRpVL3phnbib/+412Hf+99Pf/EZ7/jU4976qce+/VNPffsn3/jZ/fY98kdnnHe+BmfEJLh0kZ5TSBbPAZZ81jmXH/r9kw888qT9fvyzL/34+H1//PN9fnzigT/52ZeP/elXfnDCkT/9rWNEgjOca2DD46Q//vHgY4/78nE/P/Cok/f/3s8P/sHxB/3gJwccefwBR/7kSz889qQ/nU4KLZkI1eJCxEzGmc61z8eDjz7hqhtW0UbHEb/8wwFH/eKgH5x80JEnf+mYn+3zk+P2P+bHP/3Vb7nWcXE89NhffPEbx+z91aP2+MaRH/nKEf936GFv+/zB7/78V9742a+8afevH/2LPxiWSizd8+I8TvrTOR858Duv+L9PP/VdH3vcez723I999l2HHHrRNVcqC1NiRx2QJEREGVZONMZ42tlnHnTMT9518Nde/tn9nvS+Pf7z/Xs+5Z27v+gDn33fPl899IfHX3T5lQwXzLRyVAIOvAt1KAdvsDvDoNwZyJKN1JoeaIQ6ixEbhbtJ6yqhqI+U2YPMWjUyBJQqCwmZmk8D4Ua1CUcodU0eAxMr/oSZEzFeTDFJpMKkiSVFT37pddccccqvP/Od7//37l945vs/+aT3fuyJ7/7o49/+kWe997Mv//i+79/3m/secfTRp5z2p3POv3HVohrNRhkqcIHB+cLLr/zqT06hjY7Lr7vxkB/97Ms/PO6Ao5D0jvvSMT/90g9/vu+RPz3/kis0CPPEpJib98BPKANwxexMZ1928bdPOuljXz7sTZ/60tPf8ZEnv+3jT37zJ/7rXZ984+e+9IXDvv+bP53ejcRSGXVpLiQjcmbjaBzuLtYkJsRvJM7Sg5jOSGxo81Wrf3jiL9+7z5ef+u6PP+k9H3v2hz77hs8d+NUf/vzy667BJEmhuaelWiIQ9WTKjbj1k3535l6H//Rzhx3zuUN+sPtB3//Aft9558GHvXG/g9/8xf3f9rmDP3nwD+YWDQIaQyx1ygBBaHYMuLyjY0qw8F1aSjCBTdbLrr507x8cvce3v7/HNw//1CHf/tD+33zfvt98y56HvnmPr7zhc4e868AvI11ENASVF+z5RyfuJI307EuvOPykUycN1/9ces31B/zoxIOOOungH5588FEn7XfsCfv95KR9f/TzS6+65sifnYRMtc/RP9nv+yfv+/2TDzrq5EOO+clXjj3u0KOPO/iHxx80ue/4Q77/s/MvuZqmLKjhTBE/++3vDjrq+AN+8JODf/CT/Y88fh949pjj9v3hcQd992eHHHHin8+50CPgVsIXgZ4gYkT3qT6RYAkCgtJIqWHWLpaRlLIjJeJDVX30M86+8JvHn/SJr33z1Z/cHRPNf77js4isF3zwc2/Zc/8vfffYP551oXcKs+Cq2VQwBgMHhkiRuH5x8dCjfr73Ycd89uvf/8Qhh3/soCM+vP9h7/z8IW/84qFv+MIhb9x7/9P+9FeUd2yE4HLrp/71Lx85+Nv/9Z49HvfOTz323R/b7TOf3/0b3z/roouaew2dc5nrzGXGSZOaTN3EMGQRrByZQUSODbNuUG6+OP7sD3/+zDeOePMe+z7jPZ95ynv2eMJ7d3/Ohz/z5i/s//nDjzvlj2dFLOFvfe/gzoWg/nRRhaAalElWpJUIadErYTXhgKE7gwNslYSzKjHjBVoYGeqcTJbhHVA2N7MO6aS5tFCYioMKeyFi8cqGrNQYW3wFe2ZNu3BLvac5ERThAsQqtpBYB9GsJN6tdZTtgDZ1Ds8thDggxpRACIcTNaTJFMpRiJVZQnh6SmExtU7hFMyGuaNDBgIqqLE2bAXp1CXY8dMYEYuruHRwDCVCzsKzHJHBx+FoCVcScXKnSpjycyD1wQbaGLWbIhIsHKYy0GjLYpKDkO0ic83UdRKLOZgQ5ym75k6448CLzDYNK4Zp38G2RgTGZBIKFmb82jTjtqCVJOArYk0iSQgHh6XwEE5cVvS0osqsFoshsNu9SZYVeUFztuyo7s0XNRZFjIYUeSWN/zwAABAASURBVGX4gnmZe1psujjHj/wY0iBbkHTm2oJGUte0pjs5+lAMEChgiLnyqNyUVbgwrEUMa3R0asSsOTGTWxNaPWiHmYUwrjARA4vByVFaMa5FsW2lYEImVIM6CFhxIu5ElQVwmbQJjSCnaJXHLpWkzqiVqOqNiKRQThCiVwf+2CDRjAmGj0FkICDLOBlgyOoEsUZ3x2uZ9jdzkBpxJVlkmkuP4ilRMUoV6hN8Oiebx4T3VKVgeefKzkO3YbVon3ZxaKHnQUqU1FPpTGaQadmWjHP3gWpiR2CoSNGExtC5B3fJIRLanOZOlQjMepI+MK1Q2YSvb9fv8d3vPeptH3r9vl899PhTTj79b+deftWl11x36TXX/umiK4753Rl7Hn7cCz647/985IA/nX7mpLHSSDIPabCoCXuCG4UKBcxrMcbi9fW3fzr7U4cd8Z/v/9izP7jH+798+Fd/+qvj/3jW+Vdcc+nV11169fVnXnLVcX84c7+jT3jV5w997gf2+uOZZ4ZXMOss1ayNleZdul173TXHnHLaqz978K+W+0dQrl+1uN9RP9vziGN3//bRex1+7N5TO2bvI4459cxzFyKdcc4leDi1I47d64hj9jzimN2/c/Qe3zn204cdBfpvH3+KAla1j6O3xRRdf/yr3+/xnR+h7Xn4sWh7Hf5j9N3ziGP3XOr4k9/+0QXFouCH+BQBTF15+fX7fv/4/Y/++UHHnPSVn/zisBNPPeznp/34d2f8+PdTO/b3p4tQWF286aaDvn/ck9/76fd+5bs/PPXP51x6BQx72TXX//qs84/85e/beNMgkUyZgoYRqahDfaPfn3f+67/w5Se+Z/cPf/0Hh534m9POvfjiq6697Orr0H5/3iWHn/y7//v6kU/9wB6fOuwHV15zrUdjg3dDfJSojlQEflO6DYkAmMa2+ocnnfrc9+/9tPd+/qPfPOqwn//m1PMuufiq6y679noIc8nV1/75oitOOeN8sP38d497wxe+8az37/nXM89WBSMwZgmRTskiUwzCC1QWnAd3RRQsDFnzgBw6hwDEzETCwsZx3vmXfPCL33rSmz/9jgO/9eXjTvrFmef/7fLpf4CDEc++7Kqf/eWcA4458a0HfuMx7/i/D375K+dddmVQuLrljvESS0Lls0po9ax5uXK86aQ//P6Nn9//xrFtjH88/OL3fgIXo8FZex1+TDNYhAyxoF2L8ZTRRAnzHg9SNaR5wjAXXH4FVjX7/+iEg4875dDjf3nYz0/9zsm/Peo3fzrmtL/8+Henn3LGuaRMrvPF9s0fHv+sd33yw4d+94hTfnfWpQiN64Hkn/7x7M8c9qNnvOdz6EKSS+SClO1RIxq8mTil0MQhXJEXogdED7n6uhu/9N0fPvFtH8aa9ivHnXLSX887+1IwvA5mOe/yq4/7wxl7fe/Hz/v051/y2S/+4i/nrTJabQyGRIbQQsLAT09nnHPRHt/+ETAMPEPfzwHVhx+z++FHQ32EwOEn/lpsxiKqigSdGh936u++8N1j956i4Ng9v3Ps7ocdvfu3jkL77LeP2vPwY3762z9mJghLpJAwG483rDr1j2e8fo9vHHbiaRB543bIj0/Z64hjP/ftYz/7rR/tuRRZOP/g5NPcfHWzw35yyv4/POHQ4075ypJJv3nib44+9c8/+NUfj/3dGWg3rq4hpSY98fQznvfRvf9nr68cccpv/3zR5ZdN2ea6P198+Q9P/eNV116/pO/YfSQCHIYbV/cT//CnNx/45Ue85UMv/eyBn/7O0Uf84ne/OvvCS6+5/vJrb0DfP5x/2eEn//az3znm2R/+/EcO+c7147yRdVsqaIxyJCjIhM3t5KRIVhS03MHOeZ54MXOXyB0/dLhokSjZDb8Z5XBKsihWB7chElEitl7Mk03sekqLqKtwWS1bX4kMm1xxm41SA+yog5KLGf3692f+z6cOfPzbd3/vl79/6PG/+OVZF5x/xdWXLhnh0muuPeOSq359zsVH/vpP+x350/d++fCXfuaAo078Ra5z7kASn3/V9d844dev3P2QS66BocB+vXbBldd8/rs//ty3j94d7TtH73nEsXsePrXzr7g0z1wHqjNenalyjKvG3/zpnA8fcvjj3/1/z/zYHu/7yhFf++mvfvansy+84hrkGbQzL7nyhD+ffeDRJ7z60we89P8+f/o5FwGGYl6JFl3mnceGPB2LvbY2t/mie19PlFtuPGikho2ww39ywjPe/ekPffmIH/zqD+deftVl11z3tyuu+eWZFyDZPvd9X/js4cesqpVMEZ/E4YjVYQa0UF845fdnIcb3P+pE4Oprx//qu7/4ww9+/Sfk2B///nSE6q/P+Jugl2Mm6tL6jHRBh5UxDHNkJrlFijXfN595JF0kaUmlJNVc6NKrr9znB8dN2eDHa/L5b77989OO+s2fjz71L8f+7nQE5jU3XKtIsNS4UFpAXSB/u+Syrx9zwv/ueQgWhzfzXefr3MuuQpjsdfixUzvi2D2neWSaXy647PKDf/SzvY44Zo/DcXvMnksEex+Bix/t+Z0fTcSHYy47Zo9vH33R5dfNeZgrlKPC9Ycn/3r3bx/12W9Nwbvn4cfstcTzc4ej1zEI/N+efr6GF6EhU1JhVmN1DzZLI5VK2RxWFaWA8tGUULjQvNWT/vTHD37lW497y8df+NH9P/HNo7/189+cdt403Vx+7fWXXXP96Rdf8dM/nvnFHxz/gk/u+9rdDz7/kqu4OmJLeEw0lSmJh0Fm42L9yo9P2v/oEw/40YmH/uQX3zrx1O//4g8//t0Zx/5+qf3u9Dmi2cl7nPCnM5/zkd1f9bkDQfPXi6+87JrrLr3m2pPO+NsBR5/4gg/s/flvHXmT9Z7FUd8H05iLlYI0Krxk2iC1XmJuuS7ma65Yvedhxz3pHZ98w96HHHLszyEn0vJl11x/6TXXnYUp5k9n73fUCe8++HtjWw6WTtGdW3jNKKtzp+ywSUO+0SjqC+rFWu7IQIuu1Qf3BWkrpA1sEjw6rwosqKKTdAQ386jd2LwMgYzBNIu+IkbsERfhTNPvaoOR1fAxiiEFDFaH0fC7JhH2ruqYai8tFpwHZnGi0Wgk7iw9OIK0szZJDG6S1DO5BB43pyYV3tWhEfxrxMySEqNrePPaaYywoBnFyrBZHXWc4pYUuo4qcxpW4xqD5MBkp7XxfBF5PGaZFmYxGwzCCCNreZ6zrw6r3jlakYrmmWMgnhEzEmRy1AAVtdSIjKmcW5J5ggOtJVTP5KG5BRoFNYLQSMkLJQ2FpUDD1Jo4rNo7RTiFO6NLjijiJl6TN4R2MpSbZIwOylBLakvWixciWJXHQhWmCeDcg5xA2Y08ujIcyDOT0gO/RFBveCrMgprGIJO6OOWu2oJgQyJwF+rCQUVjKDSDkpkkgWzoNITkcAkSzCyexQiDBQCqzElm2VPuoCAV0myYEmAH7R0/5xsxERc25WaB3968m1KkwpqE0NkzTYeD9dII00o9YzDFkrXWPnYzh+INuoEE4+Kms3YpSBWjAsdMiBuT7tQbQGEqzJjidWLpKi1BKSFjPFBmaEpFbKCOtgBYddgUv/woHnkJSxREaiHslGqWNqOavAcxeFEOFiaDt4hVS9GCkWTqQsseSjQY2MEKEpxSmuUkN6y68Yprr7vy2uvmDb/eg7VFeLDjRAGDjcaEEupHv/rd8z7y+f2PPQkFX0SAfxLefqvNd9x+m9tstgkzHkzN3E896/zXfOagA797bB2bW4/owQ3TNJRgG6SV3PXSS6/86jE/e+Un93z5p7+E8vRvV16LjkNOt9t6i9tutfmmC7OJ1zofj/jzBZe+4/PfOP3si0RYspoyKgwon9xe8r7PvG2fb/zhbxet0+P/+6UKHCMPuts/vOARD14xAMPLd1FnQMGYO5NYPPVh//yIe90FsxYtd8Ay5lCUJi90ay222WLLXR/3iLvc9jbLkU/PNlm54sIrr3vPl76x75EnXLca+WJ6uO4nqeqEKm6snTiUOPmqcXHP7/zwVXsd/NM/nz3ZYanDipJ22O42O263zSazm9WBn1aP9Zs/+9VuH9/nt38+A5gMbyyBJYUC9AgVjZIIee2Gaxff/6VvvueAb/7pbxeb+xI/Uma448633Xanycsr13oZb5c09YCDySpFo+SQzJUM7Ckl0lCJNAFUpEODGfQIRg3VkMEyca7UvnL8T1/84b2/94s/rK7ACYQlYd52i013vu22d9hmq6yCgdAwFnQ88ld/eMVn9/3az35ZY+zSEUmkA8X0z8gQy2K96bWf+sKbPn/o2ZdciS7/bxpHIqgI7jRNJMSeqAFOqFIzaeYkopR55523e/Fj/uX2W2++LM+S8yabrLj86qvfv/ehqMivvnFVTHpsSHvljavfd+ARJ/7uryKhKRIbU3Oe5kZDzswC07SQ+Zh75RN+86eXfHKvvY86/sZ59SV2iljbcvOdbrvttptvChOt4W4evz/3wtftcei+hx83h2sTkgJ4ITH1zOMDd7nrC/79IRuH0pq+A7Ia8uBMQRrqqvHURzz00fffpSRdQ7DBmQmWwmQwg6W8Grf27gO+DPid9JezlwTcgPz/6RbmSbPZ8x/1sAffbSeVm128QYctV27a5u2L3znytXsdfPZlV68xwro0GHRx7L06tVbr/HcXXPThbx7x7Pd/6vV7H3rUr/9447yhyxYrF+5wmy2322LTjZUy98NP+d3b9jxkvOmm7JgCh+wzxoIdSMDZsT2mQxCvO+Q6105iQj2ZaRW2WdOhK7l4hw+4MBILw2XBSH0Q0QN9OQUNTtmJnI1oUb1igAgmo9RMAXGfki45YQF002J85uvHvGaPQ37+p7PwJsCBiJmQbHe67TY7bn+brTddqbinmw8Q+DQOpgTx5Oddc9lzPvI57FNcfM0yq5Sb+yz35TahUszC+aLLrv7yscc//5N77rr7wd/4+WmXXn8T7DYrefstN4NhN5kNGzDwiN+de9EbvvTV0y+8WKwxjyF1muthGhlZrUs0hpS+QcebbyMuv/LaN+x14Ae++oOrb1g+jm6ajwcec+I7DvrqjWNtmCFDGU2Zhkykj7zPfZ7x0AfcGuYxCnPKQspOgugjXvqLSaTA240bZIVLhBBTcF+pIdttt8XLn/CvO2yz5cbEeKLMK2bYpUG9pGE5ef7b+Re98AO7f/IbR15+/U0g+H/fJOi///NR993xVv/3zWB1x9ts+d9PefRd7nTHCOpBhiki9Wc98p8fco87QxIQrNvw5D/ue7cH3e0uRDnnNIgWCoA9EjEjJzM1ErhQ8MDVogQDBedcdNUXv3XcC9/7uf/Z6+BvnvCry69fZR4rZ+X2W2+BtmKA2dcdhADCn//l3Hd88RuXX3kjNwoUAI5cx4k5uW+7yexlj33YfXa47Xp91rnZbMWKVYu2xxE/ft0XvnrWJVdAnHVe3ny52Pq+R5946A+PRbBCMuECAAAQAElEQVShnhFo7giixJEjeBp00bHCJmxoCf3mzDNf/Ol99v/Rz69fPZ8y/RIPpNDtttgM6fSO22xVljLe2ldL79c9gTMhT0uXFEmDYBS8xjiY5oUkScJkl/HKlTpTJfXgCKIgJo7kNKBwq2kSByQ2MA2iSlllNkNn4UwsQBnbKAEOTDx1hjfRMC2a43kTdsF0RKl1rmZhsG1HkHYMlTllzsmU0R0mY3IkIUoQj8mcxg50QBRlIWtcR16qwFWCh8DELcwwovbwSehIYlliihOhIJ8WaYEQQbWpoU6C+IFieNW6uqF/iEioVs2rElWYslUiR1YVFQKz1HJxKtyHtKh5LkEEM2DZYBC+w6JMgfUEmMNcxjJhprsHioIpSklgfB2JRg9CwV0DOiG6xDBtQZyGQUWkhdVwmGUkHh3bxC7UnJpBrd5LeIJBWIyYpWWap2Y+Jxt7H+fU5lGrBA9JF4pohge9t2Y2/TSA2bTIPPN8YE9Tka+NYFHwz0olgsmczODRXJQXyAQwEBOFF2RgAhWzMY8UHXbiWeYVyXWaHcJzyELwCuozmg+9Retjm3Z3gjyRqfdqdd5bHamDLxXJhSGHInYJ1md2TYyhSGAnkxjNkHMJtB1i5AALBLkLT0sSMzZKoQknUkIyHt1q7x2aggGklyDqlaxrpplmptKVLDvlJjqmUnWhk4BjhFEnwDtV8nnyGKIMreSmQhCKvEfrNqrHLCRDf4ru3dErOEuSxAhaWv4QaAfNfVr5G+FXH71x1ar//eJBL/j03rvufsA5l1xWrZpXIS9uMFppPQOM1r5z/C/evf9hF1993Vq+my4Mb37mEw996+u+8q7XH/zuVz//Px4MtK99O+/2he//5KDvHsOLi0Ot2bFW9kXpZhFzOvrnp73iM/vt+d0fn3HxFTDLml4Pv9dd937di7/8rt0OfM9ue73pJU/4p/sw85pXa8+XXnfTF751rC32GWUgilERiJPG3Xe4/b13vAPa1puuXEu89iIn3eVOt8Pbe+90h/vsfMf73mWH++58J9xuu8VmlGnn7bd52zOf8bZnPmkt/QYXbCoKy2qeURriwXfd4ROveN7TH/qADchuuY1wMycPrjHBbpNNNt31CY/Z5y2vuvNtt7mFZr1vzLjv3PfrJ/75HNhmvRe33EQEae5pqExtyhiyOO8fOei7X/3JLxYnSN5Mt8sdt//s/7zw6+993WHv/t8vvWHX++x0h5tfLH1dePV1b/vCN1FAQLQJZzPOWVBoDGYpfBzbuw44DPuO3WOJfDrddotN/2/XZ339fa/99oded9j7XnfwW1/9wkf9i8h6TjFoi9QktbPX4IhCQbGWRpQQIMSdpoe8oLFAyEW9p1Wj7fm9H2KDf9U6KhSV1z/9UYe++xXf+NCrv/H+V33m1c++7ZabTaLc8rlm1fwTh/3oi9/5SQszoZpyLCivCF5YzFLvcttt4dZ773j7zVcs3NLj799IIxMAgIGd7nifqd2BeVoyJGNz6QG0OVYp2ppjdeAKRfAIttpuu9u85nlP/exrX4Ia8e/sbrnCbs9fLzz/TXsf/LM/n3PLs+W/V48VO5pXLd4AC5C4UAgZEnSnFISV+eA+684/POk379r/6+dfcc1aLigL/vepj4UjDvvgaw9+zytf/JiHJiSDW16P3fFj3aFHHU/ZnZW8IGOlbDveYYu3vOhp73z+k28h3PC7ZSSt7Mwtd5uN97vHTh969Que8bB/3JBu6T7gXIecQd291WZ1h9tuO4XSXXZAzbpEsuHpH26/3ZI77nCfne84tSXL77jdbThRmclT/+2hn3nNKx79gHtt2G3pvqTyqUOPOPBHJy7dLXcCDIUdmbanDx5wxK6f2febJ/7qqhtXryEdcnrlEx9xwNtfduh7X3ng217+4V2feZflou/XZ/7tuz/9VUmURYRQSliPsaHSoUDxkIImANCyB1DuJJWSBUCOWd2I3Gtox8xFKXkqzjAwvOwICyJc6MQuOyEFBzeHu0rxtGCklW0kBKH7qNMEZKvb579+JH4WmHfwvVmAFSW96ZmP++p7XvOt97/ma+/a7aA3v+yNT3/cytlw8+ulLwhjQx5nSTYZ7r3zneCge+14+5VDWXq53gkPEQ43O2gpHECMuNhiYYVj8lr07x77q5d/bJ89Dz/mrEuvWtvz3+93991f/6ID3/PfB77ntV9484sf94/3WPtq7cWFV1+/5/eOxfpR3WAbRaopPkPoIxkkMQ2XWEu87sWZ51/8v5875KS/nrfuw2Wvj//TGXseeWR1kT4bWGZiuPIc97/rXd7/ov/62Mufg2DfuGNrvTOxRk6gD6SQaeIUG1dgydM3pscT57HnasWDyU17y9tuuc3/PO3Rn3/jS++8/W1AsEHziM7sMkNE9XmhOa+Q4Z5L09M9d7jdQskb0ON2k1lBdKxxxLrnLRaGJ97/Xp975fPus+PtQLZxg+s/+vLnveAx/7b1JgsMzVonbyH0z/e784df+awH3nWHDbo842EP+OBL/usf7rADcQnBwoGVI6VGxSglJlT5ZmQeXasMc5Wa9//eSS/68H77HHniBVf9fbn71Ifc/4A3vOIb73rdN971+i++/uX/es+7bjAQboGZL37vWOJkrhVMIwi4dtukpBf/x0P2/d+X32uHZRZgSRAq/O6Dvnngcad0RxdwutX2lZ+cfMEFl5RJ45F4TkzkTD0c9WdzGiVV/uUf/vSWfb+yrvBgt8WK2Vue9YQD3/4/33j3G7/xjtd/+uXPv+ttt8Xz5RtzqBCKqmhBDpR4iPVkxFa6FxTnVpLkIixgwNyltryIxQkICHUjCQRD01DtSRxz3ILwSvYVk/GFFhLNUidumHaqzTlaJsksWXqhjp1v7J2ZSx3IV1DKkaMDsd5bN6GOZMOzoCGTDt418ANolzAU7RykQpApRjZXS5pzDILfA8Qbck2AAZNqUnyIppIfzjIHcQoupEXTjDh1hIDJSLlKaaJGwiFSWEpEVG8jLL5oYvMhdcy6mdiR4KQIsWSjopRnEJnmQQ0CWmFbEJyDwqYa2ZBzmQA+lc5exSw6d9Ie0CBgROeO2b71RtZFnRKzsCszit9CLkJO1CQEFTK2GXsKm/IuGwCEHm6sAYk9mMMVLms+gESM2cxxGLG7BFPkxsOYFAsEmg4Ohh2wZhEMEU6CrGGJSLuAq3cgmkigJ7nR2NJopUF37j50KiEDZeGkwqJEQRiNOEg4oCSIjLD40goWjgQzT9CasGBliS5UXRoKyN49uqEDiXcnmEZdswj4Rk9ObJOzIpGRucGmoWrgkCgQ0hn5RgtTVpgHI0ljoNeZoV04Orijz2RCVRahYFhlaTlh7CShMIoLjGfTUG3SlzmQwwe4ci5cC6sQqnt298DAgCs8BP4sBnZubNBDuEuoT+albh2yBpHlCKVbOYI6qfWknUHdnO3cyy7FDvol19xw2bU3dMdTTsYFrTEyWHIdiH/2mzM+9Y0frVsTg/2rn/Ko5zzq4dtuscmKgW6/7aZvfO5j/+We2KrBm5tbNz/kuJN+d8bforl3gJJgxmoRQWdccuU1Ny3eTLf09YC73Onduz733ne7yxYrN9tm5Zb33HnHt7/sPx+8y85LL9c7/easC848/xLhyQ4lJ1lILvkjr37pPm/c7YD/femj7nf39aiXblBffvwVz/3S63fd/4277ve2l33xra/Y98277fumVzzhX+6LEo0yk9Aj7n+/7TZbSRsca25ZFO5PiZVhWh1kocgTH3jPhQzMrqFY5xwELLpzRxywI5UEQWfeZost/vNfli8B37f/N/9w3sUrh/KI+9/zGf/6oB233WoddtNlUhXkkyDks2RhzXf/2o9+fNpfp3e3fLbcZMX7X/bs+9/9zklTKsM97rzz+172NDy85f30fd2qxfcf9O0b54twSADuLsBBYPVT48Cjj//FGevVBznpm577xKc8/J+23mILzWmYpTtsv/V/P/tJL33cv028bvkIKRoig8SdvLObLKlNMc0eLHPKk/7NAD1ikUSIM+P4xnEnHnLcyQDJLZym71c+4d9e9IR/3+I2W0fWFSvLIx94t4+84hmwzPTulk/3OPDYU358yh+JkhEqAR7FW/ci+YMv+s993/SqL77pVfe78x1vIf/7951us8Ue/7PrPq9/9f6ve9V+b3jll97ycslwFmRxiBzMCKtAwjNoEnXe2nyMcZzyeUf14Xfeftt/u/cyU/LFV137ps/jZ5wrtt1i01c96d/2ffNL9nz9C57ykHsLT+ng78MvXZ1+8eW/+evZFQHRRS2K++CWiDPrDKvgFKedfsZHv/bdVfO6RH7zadfH/NuLH/vI22y5WSllu9ts86qnPubf77vLze+WvjzikB+dcNqfzvSwKZuYck9Krtz+/YH33G45YN+ExS5o60QFKxCbSk8lPerB918e2ETWLLySVqSgINntWU/54ltftc8bXrXrY/91SYoNT5/Y7TkHvPXlB7zxJQe84UUHvOVF+7/pxfu9/qWvfeqjc9aiVMgWND/nXx+SVWmj40s/OPbwU06DBR+6y52f+/AH3n/nO8pG9kxJGDOB5NPOvaiar+UBypc/7l93+89H3fmO22656Yrtt9jy3+5zz/e/+OnIVWtp1lyYxxEn/+4GRAT5tJWFPBWdCBUJNWPk8MmYa0jXOTNFsVaszwKbggHjoxSoWOqmKWmaW5tzrVNXIVUmKfBHU+oMnjA0sQhrcPKU8RZzQIH0ZkgUEsgwIvTT3//hsBN/BfFoneN5//HgFz3xX2677aZpkLKQb7v1ds9+5CPe+dynDjmtpYIVnFmItrvN1p9+0yv2efPL93n9S+52h+3WEqy9wMM937DrF9+665fe/LLPv/Ule7/9Zfu87RUHvGm3B+60I/A+Rvzl4iuuWzVfS4+Lh+6y0/tf8OQH7oxI2mKLzTe9513u+O6XP3XZWDvpL+fgd7Ch6+BpYE5MS6YoSZIElKRljwOOOemsS67cbFae/8iH7P3aF+3zhpe+8emPvs1mmyxLfPhJp/71vPOSRyLGvEXwWOq1UBX+p13uduftl1lFiHCv1QWx58Wdq0WN0cMYWXvZQchztOJGmIebcEuimWeIldtvuc3D7/0PG/dhHAJ20xvMdIDQjttv+6XXv3j/t77482964Z1vu4xU2FjZ/y0v2u8tL/3Cm3fd660v3/NNL/38W16631tecp87bg8OW222yUv+46FJEAoTz3U/Q8l3vP1tCFNrH6PPpXcybCVHM18os/94wD3XJd7lDtu96smPXjHLOZtmZDwmJw5TchEsSbQX5txVK9bPioEBR+ffn3fJ9Yvjunwe88B7veVFT737jnfacmHTLTfb9P677Pz+XZ97tztsvy7NmuujT/3zpTdd2+CbyD1yZYEFx9BOaeXCJi/494euIVv3jGHf8KVDT/zzWSuGguXQ7q95/t5veMkrn/CIDaaANV1Wje2oCsTBRgAAEABJREFUX5zGKSg5ixN+14fmcHDUTjXITr/gwo987fAb18+lSeXdL3jK0x/5kG223CyXtPlsxSPuea8Pv+hZGyeHNaNQIJpgGeJZM9RnhXuecSvakyDMSvQE/FAocSI8CYmmQKV0FWzsoAIVpuw0gE+oUhS2lGB/QKONhIaJUp2zumYJkm6K2jBSBOcEagEb+NXNyIOnP+rweXNqrEGJMaGSCjo0JLFuPrUIis4dbe7Re2JWIWYT62IQKBwVIwP1mRlbh8qdxRwoUxIzLEvYiCmRE2NQQpVJpces1YU+ZqvilDWBNqr1Wru1cO+dG2cXlcSiQBYbCzxDyk74IbWaGxEzWqE884GbeHjVZqUTxmgYrAf1gOnGWWozZiYEnyJGuTYkWIaiPBk5cZdMEBC2jzG4E8PwMLEyF9bMzGTOKI6BaqzOtOSeUq9OtanVTM5C06qGw8EvC5eJ2xpBGRFPAjGz4qcLHRhMpTHjh5uYPAcOIR2LoQxbLYEP7LR3bc6YOhpBkZpKmzxNDhMQ3AHV3TmCKTwCtpj3NmqLGUWRFqn1ofvMc+ZBWbUYfGdk4aJEJYWoOClXhVWDiIIZa1YsiyhCmkbHn3c8lMwpUypSyqA6hBSCFsTT+kstuaVGhHRGldoofdK1gHiWNEG26BYdcVpZe7DB/xiIWJg8sNbkamrEHYbNRmJYxk7YxbjGUM7JIQ5RgsgkEBl4CG0BLDpYJJLkzoH6p3WHcgJFlm1ObsHT6IFTdx+/f9Jvx24gVtVNFlYmKtly6krGFICbXHPj/FPfOGq+zrY3iO9+x9v+58P+OQlpqkUiOeUkuz7hX/Bq3bZ6bF87/uSxkSFvdHPzTlGZIOu6ZENOL33yv2+2+abmQm2a1mckm8yGlz7h4euSrbn2iBN+/xcoQpCPk8iMBfPfkHQYFhYSomcN3TpnZiqJS6FhIB0SUvVMyoDZXslpcgc8J0m23XLzdTr9/dLVSdgdnmUHr6S8ILfZerPZUP5OdMsVwjCR4i5CU2gWdIjuYT22Xq5eBOUf/nbxnbfb6ktvf8WH//v5b37BUw5406sedJc74fnaNrbmAYRSYUJWPO4Xvzv85N+tfbvm4pkP/6cdbnd7CBkBDxtF2/m2Wz/6AeuVs6A885IrfvSbP3Znh0dQO1mi0OtuvOmwE36Nt+u2zVbM7gcxwNHcm1vvBu1VX/CkR0HatZTKPHhKMBBwSoGB5xHziT0iLRqwHBzNpTUaYUHWSIX5ossv3u/I44CItXxwgfLn2f/20NalI3g8GU3p5353v8uTHnJ/vF23mcfe3zr2xutW0RRwbmDfsaOBRJtmM12YDbks4xpVLVlSKjmXYWEYVkD3TgQAAAZg724alsOLGfXeax29tzBjyF8rW91qud/r5q3fuDg+YKfb7/vG3V71tH9/0L12fNj97/LeVzznaf/yADDduJ12+nnY+rSeuZaM7MmRBKmQEsfijdd/+hvf32BHYMftbvPMRz6UlLtDQlLXFcOKlz9hveUiRoEYBx913OpxVYtuPXljavBGz3l5YJt71ICJqauAO1HyzmZbbbbpssCOIIseCF/tKcOWWiQnTshvSRUCLNd4YTZbUfIs50FTyUWHyQFCxOKEvEix9coVIkwbHX+58NJZkk/u9pzdX/Xctz/3yXu9+ZXPe8Q/rUvFxCVJUmaIT7zuqzveZsuXPP6Rs6LCWQhUC0rDnW9/p0ffb5nt/wuuuOqsiy7qPm/eGkIHc0aQEDXWEbMf0zJHkM57xj57F6RkD6+M5N4xl4tU4KS1PvZeydkAeGHk9mJCsCBFn3wtQikwmyrFNEAIj4lNQcIwRrfxsJ+f0sxo/eMR97o7W/foTqRcRAci+fd/vP/D7/H3HaKgcLNc+8xjlpVTSZpVoND6vIhUpKzMeWHQhZQGLYPmwjNkXsX05MYE7WmdA78G7PbUR2w1bUzRgsVURXCelRUve+wyWdqQpf94BscSwqFpwE6ElDjto1kmX0YeDAUM32mbrfZ982ve8LynPfQf7/XAe93thY9/9Jfe/IptNl9mrQLibx9/UoYFHbKqETexrgBohNDWm28Khhs0oJaQdJyYmaChU3f3Ppl9A8q/3yZmye4Sk0k6KoY8+RNdZNsttvg72S1XECWJKUMmdoqm5Eo5DyukrChJZBnF8XBhxYoF2FcJW4QDEKtwRiZioujU7/0PO++03Ja/ma1etQqhFDCthCRW/DWhrhQyDIAHrTmS8Asf+dBtNtmSIcJgkhtzj25LKYKd1FTGgZD/ghzCC5kxCkCyNf1vOW+2Yvby/3zUbLbAU4gQTp1pky03e9YjHkwbHd3jZ3/4s0kwQzIU7rGImNKh5aEPcruN9uPAwCKuvnHVdpuv/MLrXvCu5z3pIfe5xz/e4x4vefLj//fpj826jOl+cfpZNTlydicY27gvkldKHWaf2/wLPzz68utuBNt127/fb5eH/+M9oGthSxKkxFLveodtHn2/DefKtb0YNbC6I6cOPYYIFTwRg62FRUPEgsjBiqNEXcBLYmZSsZmihCT30iEUMSUinPtErYFEPW917HU0dsMcOmgqWNA4Ybp37ZR9GhjmI+7CI5JORKMquWcBvpK5wiaJSXx60S2QcyavTQOg+OXWvbbOBPqOMh1JCrlDBQoEZVTVVlmMODCE9RQmKgLvE4xJKGk9IEgXZsiWoVVQGKbDcCPI65Q5E+TwHmQuYpRHH0bJriSoTqm34EDkWDDV7mN1iJMb44eZ5LmnmRWFKQfvsGBjlNtKYeqdhU0UUSRBuQcLkeIqYlqUSjAH+gmm74FC1QKT/UAiC73PGlZT4DSpkFgkK4sIA/UcnatJIwuYVtyYsaCMpMI5pIycmqAhdrgmdposHBg1OTMLiRIlJc/WtPfswppZlEkRSIMzFJJAzSKwSDgHSRdpbKM3TCydOmMgHYBMa52a40dr9mlEKmFpgDHRhYWJJ3FzZ3J3PAJEFxLGY/GslPFnbN5gJ8JbhvOIMI60xC0Frtk5MWMdPZOuQuiAFjlEpsassBjogphYjTtxZfaBSPBnztZK9Fxc8zwSGoNZosxsaJh7wiT6wKqc3XIbmUbKmldK4V7qOFhXbuyGddEs6QyZhyZYdojMIjAhwX4m85YXPXW6lWPsvGoMW9X6jeP86lU/PvkPR/3yD2tpVYOSMqsIs4gABgt+8HE/w+8ta2nWXDzhQfcZRHptLlM1bj79RxF23OlOm87yGoK151P+eu5ZF1/ep/iGJcQnX8L1vpYAF9j9ut8uO4mYqGvGec5UMbPfd8edN2YI+gsuvw5YgGmZQpmSkwSB6YhoYrxfvglDO1JimpxOUFEN2xjhNDdsIWCC42U6zrFfINXFYFODnyYSZZY8iIpMd+t/MMqM+6CEWV94IE+OcHck0MXNZoDD+tRLd5uvmH3yf/7r7jttN0hfIbHtJpu95gmPHZIuvZxOA4pNjuDRs109v2Hfo08YAfbpzc2fIaenPPRBg8hMy1BUc0s0/bOB/3jXnaDmzURLX1Dh2z//zSpD3T1RgK7N/C+XXnzD6vV+4AJtRCzi59ZWaZxrr0ysrJlpi5Jf8uh/w281axrU0+7Kg/BMuDhjVYdsiJRQQTwwIZchN1SLpaxFtMgyxpe+cwz2wzDKuu1RD7jnZqguWyuj5bkyIOCzkPKEh9x/XWus6XLFDTd957iTtGGfi7Or5oU005jiqyU173DXGsK/nxfnYxngmlEQ2dyFXCg8ZHRtrmSMaYR5oseFsuciUQqVgVULuTpvvmLl9HqjDwD8wZe/8HZbbye8kLlkGrIMuz75P2QNu/Xpz7v0SuBG8aFUKY00jKQ9CH9fP/bk8zb6/1089p/uvWKL2Wr3+eroq0UrAXe73Pn2m8/K+ozp1LMu+OPZ56+yvsrni9FHoUowu89K3oASt5BthkkdvkFGHYeMrN04N2xjkcokHGjWa8zBQ+NijjwOWOMnhRGzXTFSWrLaetTTTTh5KHpxJMKFBiYXIUUhDTCMGn2ghc1WMC/THeK954VP+dd/uifcUF0Xorz2aU/ecsXfIygi5qvrkgVhuWm4tZ+n/st9seUS0INycDLG1bSf8YC77gi2tP6BGfSyy64vngoVxXTboaEoCUFkBPNyqgVR9TQG9pQwaUBHJyNCsEeSTtJEACUkshBYXwB9lBEpYpYIL0bXOQoToplzalSrj7FoQ6OBQpADZ2LXLV5/9sWX00YHgr72aBZCnFTz4Dzpl57/+H9HKb8mHgcUFbUiNac+lqCsost5fw1vCUwnKROMM3mokS8ms+JaYsUgmtbzy73utP19dr69J0HWhOoc4zSd9vIPO+2wcshrGK57PveKq1qyJgSxHdm5ubSq3Vkysa5LufZ6s4Xh4698ITKh5rHxWCPQ8W53vP1rn/ZYXk+Wm3v8+uwLrrj+Bqphq3S+WKxzIJOnxa5tyPlmonW+kAGywxwwzMxiMAIN9NFSS3JZh/Dvl5liofPQBVUSkkSwQRLOLKnP4NC/E958ZRG9jWlYLAur86yGtEWP1Q4UJJtvGLBr+gwZAaXBrMqkQsiyKYVohCB8XFdssulWD7vHMr/lLo71ksuvXlHybOBhSAlZMqXklJ2GRBdefgXdctzt9ts96v73aTGJEY7E7IAwefNGqKM6mZCjV6ulWqqZbIX50COI4xYWS98PvvuOO22/eYFX3YiMYsox3myXO91xlnWJZL3T+ZdcE91nuQ1lCjCabMw06RW5LEOPzjnpx3d73j/+w52GzCh+QmGWeOwD77fDtlvj7QbtyhtWXXf9deBomebZKlgKhZY0G35z7vnH//GsDehx+8JHP1xblsiKIBWaq4/Je4773XUH4WVAhkdwTZi4RYkoDInC2Z06BQtlJSlepWInLpHPjOFlIMUExSsAJiLRCWmWPKJ2b3MPWD9IGVCad5oD6RFzMuM+dUmkQTIGzRlJvEvP4jnSzBWMgtyZXQSbFJLn7HOS1SgoTWgx8D6J9yJtRXbhKc+ONhv7UH1WiRt1xJN4oYxl85ARXWPM51FF66wg/zsruQB/rkOlvOit8hhKmkQz1RnP4UZYzQV8CIjJvjDIUHhYkLSQcg62RoviFXOlJ4Vtq3fiSuomThWNIASmjEw8UFqghRkPHDZSbQHFZEqTYQsVew4BswlQFkI+uKKsERV2ykGphAwEKzFp5lQSpcwsqTWsUVNtabQ8eu6IocJUAFRpluBdTT1pS9ScupN5DqgMJ1qKnsYec+7zAhjB11MirCaNrEOHNXSBKkxqwE8C1gLPhlpXLN9Ye0rjAsese7KQyjJXqlq7jr2NCILkLKB26q1j3z6DSHpLMPEaYwqT9NCREIeS1RMRHkExj8ExkUIIwZMsDEXUJ7k1AncwQ4czlIlFnSAxZILNhIxhv+SmMkI+ZuVIJElSJEFPIpiTucfQOuYtxyGNYR6hFBKqHR2IONghokYbGIHTmCfTQZVikWcIqjYAABAASURBVCpRU3wLc6aeunHrEbCJEZOSwDvJUwuuQpQZqCUUMIFuqCCQ9uhWjjd87qDXf3bf1+2x72v3OOBln9j/7ft++6b5uIY2KMwqSyf4VoSK2sBXLd7ww3VWMmsoS9J77Xj7cIOtOxRjXerGQ9F/uMNt19CsPVfzk/98RlZNaZCkyMk5s+ja99PFfXa+04xn2iWxQdNxgnWQ20B859tuN1Gs/7nhplXUExmwj1zZ2UOImQg3EeuT3nIXPJAOSmglE6m6KpVecivq8EnNJYaMN7TBgSUBW4PTExNgEB41zCmSJhGmZQ+tmhqiuQeFT4ATht9sWVo8fOg97rzjHbc1dzAOV6hyn513esqD7v+Qu+384F3u/OBddr4n6gNNnWRM7ZQzzjz7sr//I+PojrbdZit32m7bHIGYC8gFyVjF9O53vH2CnqBYp513+dV/u+pKGzDJxKJyTXL56tW12zok0+V1Ny3+4k9nN46O4kJc2SGZkkeM//Gg+37lba/92jte+7V3/vcuO9+BxASJnCVEJ/NPznBxytEH6cjswozAclgCBnM698LLjv/96dMY63/u+w872TSQFcWUILkmHuFl3vn229/2NluuT0sRdPSv/rA4ra9cE6ecdBgYoCVx21CXNX0XZoM4E+CivVNnZoVLo0z1OSGPkGqkFKrAhg9ZU8mSkyhwo4lJYFxa/njmwx982822hn4AhkfinmCz7bbadKdtNxQb/a+54SYWTlkoM1JpC3HyTu3q66///im/h160zpFU7nOXO4k4TKuhxYk6dO8mcY8db78O4XTZzH/+h9OFimf2Iq5iBORNPpler//ZdMXC5NOl8QLYA2cLjViRGPBZn/aWO+WI1LpPiZdNpyCcmE+fW0jW/bYJLQ7fu2czcScmUhKM2T2MumvTQdbtsvZ6p+22/tf73xUJNQb2BF/xJsPwiif8x0N22fmfd9nxn+6OtsNmK1eCSybmtd2WLh50j50bBbJjD4zZnQ1uhx//4Y7bw55LJOudbrxplcIhRShpAz4M+SlmAkcS34pujQmUozPOTOA9zZTJBN5JJIPmMgEoBashFIJZ1BIG8aDejYOFoHd292gj2dyhgmQaEhjXVeP8mlUbbhwQ0VG/+b3HxJMCk69wTKigRHe/y50Offerv/7eV33t3a96woPuz7AtMo+ROBVhVUXfZVtqoZ1yZGWFOBC+GVXgi0kDIq2n/H3vfKcUqF6GxaSVerTq8+DQlZssLFtEXnv9DY18wgDObCAlM2bHLBxLo9FGx+P+8d673H5bpVGio6jDfOaNaIyH3ftu222x2UbkdPUNN/353PNBSxbSCXCXSWyfNN+YmlAWDSln0YxMZQmoUmFKQkGMz3I9JgCI+eRsS4TJ1SIanMgiJAynbdhJmXPGr1kM92QkxikEIlgd80XohtRL95hirKuHckkyyzwkZw14QDLngWaZZvrIf7r/JrNhifzvp9rtlNPPkVxKWiiUKWAAFlLsGEXvvz7j5v8HK5R8/r8/dFhYaUyTmi6CiHIid2jWg2HtTFhFTdNcjTxK8ZSkMJw10f99QHrAXXecKfG09uxNI8ilm7a+9aabbLucg4CB0gVFmXIv7AsJRiSqwA51hzTrsL7l8l/vdbf73/WuHqmbBJI0fMOy2aYL917uX9QZW7vhxlW9khF1FcsSXATwivjuz39zC8u/f9/xNlvsvP02mUMIuUyggClK1KDMd7mV5AAxG8to2Y05WCkyA8yO8Oqm7jyFy2T4cBdUfESRoKxbNoRm1hiI3XW0MnaxFmTNzIiNU9ch8pIvAr2AKnfQskhQhgEIeSLYKLlmhhNThEfURE2FgpI5j9abodbtY8CXw5TEUk4J3IQlScqBRpk6O4ziOcPFNXoQ9NDEiatbWGRw41BypGUnQDulSZJO4RzEjQKXzE6qnmZEhWDAwCiaJ/RlSQXDsfjANlUhVjzyBGCu7CO0khiUMIUJCbzeIX4ArIZRRNgSYAFunYMkKScCJ6w6cwcLCtLFyNULB7Kes5MQw8JCphSqzCyQBHYVF3bYYcoccxhKutNcYm7YiY5uyp6LaB7MMxAD0aRDGGJBTqVcRVA+27RARF/vELxYn9VOsHC38K5hilWdjSlagjU4GUCAZVGrsGEwKjSt3OAVoSoEUQMHBCaf8IHM3smDHWIGMTGam9fRYx4ydjYU1AHfdiilfRjmmqp3irmi8AMYHOHGnEQ4ZadskTDfBHxEDH7GMCQkxBAYlfCcmPDF0Vl6FO9JTGDbJKwz9kTViIxKsCJAutWOBVWD30iJu/ZaukNTBV23cZFRBRcr2QuSIXWDpeEJ8B1Lh5c70aSYk5MZm0ujFJQ5CsGLsIZGSDA1xhQrISIUPHkOYy/Tfnvuhb8+829ovz3nwnPWr3fdfHGcM3dWgwQV5qt2yh/PxqbFBow2W7mww/ZbF51SPgcbXqsjejG1b7f1lrjboJ16NjImD1xmMVPJCIjn/vvDDnz7K/Z9x8sPetfLD333K3d97H+UJshoyQjRh1l7HgnJgKItDFB0A360anHuNbp7dR+jd+5OAbQWVmbekHrpPhKLFu5ZGiM8oGZoOJMbhVNJOivDEuEyJ66qQQtKC0TSvHcA04liGVI8ZQr8dsa1WhsdbuMiGVOJ5kHLMrqAyT3vvEN3DqYuiBU1pqHo+17wnC++4dW7v3G33d/yyv97zYtWbrJVDUWKO+bXfzbfcOh77Xwn6O29dgzbqSHyYO+Y3XbrbVQFQ6zbVtf213Mv0STEHIibLkUyuq9Lg2uPOOion53217MbcKvQKjjMqXmKtIK2vc3m22xxm2233i4trAzGvNoykgLyYKlL/3CmZleOPoGXcpKihbQEgY/QD0/7Pfhv0DadldvddjubzXxF8QHzOhGTIIt6DJss7LhcxX/FdTeed+mllepIk0UE2Z2G2nRsdGvHWIe5y0gBI0cIYCsIHyJGDGnAwJRGymgQlcI9rGrz0pViAIBYlTY6Zjnd6453gGuFvHFtvXszXCZu2265xUbkdP2NN7k7wfgiLhi6Cbfk9Zd/POuiq6/fgH7FUO5yu20z2SzZkJiZiKNSm5Pfbttl/nn3P/ztYrDOw0LOIM8gD+t0K4ekgj+eRU/ATcNk11k5FWH027APMDeZSszFq3rFhEQwCzc1Z9+QeukeiGZfDK/uVh0ljQARAjMzcEcI9cEAG2L8LdGve9r5tttsunIT4ZBENCMrFEEv+o9H7vvm3fZ520u/8JYXf/Z/X3inHbZvmDYH3uu1Lzr0La888K27HfCO3Q569yvugYW9W8PRK/V5okWhChFnaYGZ1x1lzfVNq+csRAP5QmDjosIIhIRR1RpB7TVE65zBY1YMZSjyvNdEoahHMLcz5qPESIVcOCcqWbxISyn1oYwDIxrYrLSGh1yMMRFTD4Fg4l6irtCu7Kg+u2uSZeT8/i9/e+TJp+JtQEJ0g00tAMlZ0tttu/XtttvqtttuselsCNeGqT0KYSLuHg7SdaRf5zLZyOzOHAaZUqo9w06drLN13/XRD/vKO155yLteffC7X3PIu1/9/H9/KE37FuHd3TwacoEbh4rOCuC/Dt+lyxuxiYDpsAMvjdw6HJ+ZEwM1vJx2RPSgXXZOAr4hFb+UlsEEncJomy222OWOy/xbEB5x5iWXdqlBiwPVBbIBzgBQ+nJuwwBooBDC61QozRhnKoGi0vBquQbbtYC2pkbwD8E4Eb1TB+gxcyzXpbv2+cwqagMV56Q8TC0Eqi1Hj2cN03sgCQn8zkowj2CeT0oFBgth2/n2t9lhu2V+Ujjxj2fctBpzYaJRbU61xzyXrvn8y6/58/kX09Jx5+22/rcH3XcsDMunxAruplET1iQ1J4A5Q5Ew41FLLamLCXmSEED6jc947CHv2O3Qd+164Dt33e/tL3/sQx8YNGuaxsxVtDG6CrLAisIlJdrouGk1oq9QzWROsZhpLEgBQR44lkE4GNz/rncy9XmkHjNqSVA9Cvect7/NMuojxK/FQsWAYMpCokIEyeLGG1f98vSzwW2Ddpfbb7+ikBaH4NPc3ClRT8op0qZlBfMyInnQCLBz6mnBeCBSJvSC0tqR7hejLVoLGUsai7bpZQh18ZYisrMQsTirBSOqxCMxMkdnQJA55VjIUopGya3o9K9qzJrPumWWKMkHStkQWgoFR6YGVjCFtIGUpgjPzfNIWmFQw2AxsEgauq/ofUCZRYOlgRjyOsnIxXRi1B0r40rBg+cFLzMv6Dtn/ELshnK5IqRhGkJAOIyTqXNQwAhKdYbfJZy6MrQ1Fm5MrsyFWD3YXZOmWZ4WykqoBdQ0HEUyDJWEhoWUkX8Tk2WaU60dE3Xtg/eZsqh2TmaRtM8yZUjdmVsM5LOpwoweHM7ccyNpRIjAGIPmHI1hfh+942dkzSNjrUAkFtI9yLtEh/TubuGeCCwgUhIZlMoQkongECFYa1ID8w3EE/IUrkFkuMsUyQm6OhKBhYnLtCRBvFANsclOnidKJiYHzCuBJPA7gimRMCVBMCletslrintioWlctZbIgqwqtUxNBdihSZgluSmI3RVyO4flFtoI6pAbYwHjvbPDGl1ZGOsLTVEM6osaLnwwKgZJYslwQRLClQn53TsLdIJA6CIiRALxDRFJwR5EeIO7ztSKTDaLZtWMnDShMclkY5NOqTt3J28RI3ogwwUTPM7RMAUJhDNmWCGQxr3TJA5FyBTVgAGmrUz/fx1B4tNIHvBy7xj8V78/HTllA2YrhmHLLVbIwCmJOmfHIgt1S+ceW64cNiDG7ekXXtJbp0ZkTMRVadstN7vnjjvc60473WeHHe63ww6323IrxQsHvlRdGMP3mBMhFRo8TxseHQ8ZdpuawXUJFmBNiET03ZB4zb0oA4twOiEYRjMEivliao1HakE9i+aSl0m16N6xw8QqzEJcXKgBJtFhqMDLDRvjkOQqnaDwIgu8wgnzoSQSaLkhPe63WLkQa6JEmdQIMMiVpnRFiCsNzkQFl+Q33bj41/Nunn5oneMO225tbazWw3p0LGeiuYTwyjSsWE6pv5578eApO6xN7HyX2223cljGcRdfc/0b9v7aJw7+4TXXLTLQzE25w/Ch1CWczacgE47MBtN11pG5SnTpgdewqzfcKRTnwqLG6uj7+7PX+7f2aenYfOWKTRdWRCRCkhAGpGiSr3I43u98u2XKlFVj/etFF/XutXWM1cmDYkkk9FimLc5HwMkRNaTiAg+xwVUE82q05CZhBBATpEdWm5Qwr+GLHhaapkl9uckYsFm5cuAc4KWVHG0SmVh4xcJsYzmW3DONA64ECLHnCCE69fSzId4G9CXnrbbYLMKDTBIashiw59B1i82XYX7upVeYr86KwQGblGJSbgOea28jC6IYqIwQAKa7VE2NOSDVWqJbLphCyBiuAfeUhEsgGSKDUg1eUvgWyrXfAkaO/OEAY8D1DB4k1LNDLEkQ7AHDAAAQAElEQVSCkYlj+b6bL8xEgMnEEcFmMu11eTeaR6pDjpxYY2Ytt652zx3ueK+ddt5l55122XGHe++8Q9GkGNda7+E+ZWIlBG0Lt7WyrXvRzfDKuntwMA5hI0bnbhh8Xcq117nYrLQSLAbXqSlMEyGCNNQ4j0aoSuAjnsKWpJOMFJWgiGaC5kEaTtGYEA+kSbyoY5Kw5ljkrphtcoett1g71tqLxdo/8tUfvGuvr5x74cUhFJSmj7tYQLMaGMFNwjkhFA26eFBHTKxlsOGFc1jyUaXCiKPkztlJKQUuqdxuq63ue+c73ueuO9zrLjvdY6edt9p0CwxXqJcIhGaoQmglGoxVYIQNmeNemBQSWZcO45IDPBrKSCNByx1DVmhhPdG4IFYyi6CMSFxZdtnpTsv1oHMuuXQs6OHEHeyJgSjRXpYlRgYImAqNnOC5HATB2cSNGVfLdOrBsI2QYeesQFNkJRGMNBLcxRt3sIix4cThZEFIZiq0hBMYDzPCxj2mJ8AyhhfkfshFU7YvHNAdWIQB1UylPPYf7808Ea/7+dtlV51x3kUxj17JfHrjzKOUr/3slx3enx7QU/7lgWkh9eiGtE0d8pBz9GyULAkO7YwgJsRZplx0IaTMSUZPXfD7w312uNP97nTH+93xDvffacfNN93UOBFloTWiKEnCRJInV8nSaBueTDXQZZItMARRoKsQK8eGpEv322y+IDImgATGc4jo3vEXC8vNUEguFQFDkjkVniTHDx9IL+eef/G1q8clfuudbrf1prkoacA41cga5R6DIx3FilliXqPUel0mMZ0grqgKFIHfKRzRxwhesBgZXGB6po6iU9hQPkIzBdot3DESd+Q/Eg/tJC1zzJyKiWCO8xlpFkyOlBulymzqmAIzU2GkiYS5iTM81hc8FmpgDZKYRDQEVWBEijRwLlPWCc7kioFjwuVUr0sQY7Q2Y+ReK9wgryhUcFy4sxP6cZrmrQBt0KRWdW7OhpvJFEI0fTFUY8fMMGUT6oat9inkKNiNvIejoOq1A1rGObLKEJw4eOaTeIkL9YF7VkpaWGZMEt28mnVDAebB4li8japjCMyH0FlCiuu0R8jJh4C9XKKRtmCzFmOl0cI8rM8ZS5QxvLGMIj0NnooXsRyjUocUSpQYxidoBe5emRoVUYXqVGBuWA00zKoyU1lQmQml0rWMSZBIIomIQkqHCJRIxSc/mWm4clfEkQgzhsQD78KtpPmCtIUmC4t51heSDpqpZW8gIGIU6AuFeUaxIAE0OtaW2JTBnIUtQzg6NbKeOoY2FjGVkTlCCM3JJ7W7UavkY/C0BacR2bISmmGcRMaQK4WhgkGkm1JbiDqLICer3kanaNBYocuCUCHT3oWEstiK1lcshlTpsFJ2mnQvCHjm0cuch5HKiOwPpMERKWrBKOZBYE5EQZwA76AJTx3TEVXiUCI2aj1Gx/LdAQtdqFnmad6CbuXY9/Uv+fJbXnnIW1+53+te8qEX/OcD73xH5ptJGbAFg4qZn8kB3R69/+WSK29+vc7XZoOec/EV51x65TlXXHHepZedd/4l5553+dkXXHnmRVcDeusQ3nx547xdecONHUCu1GAkN0fUWIhlHgfCwgUqKnWB0pRVZ1DXejRxKyXBSjfzWe8LU2We1AxmGKesQHxM5qC4RZ/1qKlMtN4Z8F70jl8edISxc69q1cKNoq/fYZ27nrSqABiQWJiHnrVl6zDSxHQdwrWXUxmnQL5Y4sbsHMGMWWp5+u22QlEiFIjgPEzI75G6oWnTVIWatFHHxVkbb7jupks3+rcDMWryftZll5132RV/u/SKv1106d/Ov+iciy4869ILz7ny8oUkINigXX79KpozdlKUjTPd/nbb33Ojf45oTZfVYz3shN887h2f/cAh3z3zwks75CIWJHac2CAbwpQrM9AoyQ1xRMCnW3T2HtQ7T2nc2CmczKStssUz1/8pb81AC0PZBJPNlGEpdRKDrXpwwxCwxG02W+Yf/EDHK6++Vkx4lD637rBwK9QQoni1ccO+b0ptprRAOXcRkJur+0KrC91zD4w5Bo1Mo6XqM8M0IeJqLde5grv0hj4bMjazRWptME+RTNUEvOuUv4Z0K+iFS6KRd4rGDnqW7nLmpVdvyJpoi1m+4PJrT//b5Wece9np51945iUXn3fVlRdcfNX5519u6//XeNb0XTW2K6+9hmO1cPOg7tmirHm1zFmrUydnMZT+A3FykR40/W1EzUFQDScAVWUhx4D0LOTajSI2Ip8eTADp6pIm2hnrwIxpmoQpBpLCRRDrtHzATj9GhUQoZgUI5J279Uo2JZCuEkUwCTI4dfM+Nq8uhOQaiT3xkpSFWDFd+cJEKZy4l7T8WIT4pLCRaLUmTDPE0xMP2AUXkyZEG5yZMjMpWfbpQPIAOVHyaX9Susc4r30+CpDUzQ3g6WyWnBLLwAkX3szMWSNnwfzNztQ4GuSQzVaufPh978W0zFG7/eR3f33W+/Z4ze77nnbeuWMfGTE2RqsMXxrskZmYAw1eiRa4VF6G0dKjBkGAcA9gxSKCCRNTKoPiF5KUISz6I++18G6OoHRlPJ7yVCpaZimJgg9ex/IASEqSeXJjUGrACVrXaErLxBE4uXMNmZM2zEaMvqQDsBIuftvtt6HljkuuXwW3W0alLcCARSIuImk5WlqYDdQWzaozpEFmcvTlKkNDMly2B4XHkg276oiIFSdSQQ3hKsTLdFHmFUWGYdTcgrsDEShRnaITAm2ZDkTMlBNnIuohzbCnXihUTdkD08zYeB5K8vD77TJslE/Gbif87o+jw//oPzV2Wr1q8Yen/Ha6Idp2803+8yEPFjfmOdkccx9GwYis07g84cQwjnbRmpVnqjllPB2jNzLjJtG4YyJyJE5xdhy56dBlFpaCWPEnDCtFrBlxvTNTz1ETNeXqtGjR2VIeV+Qxka1HecvN4jhP5Dl1VH6ZFjUq1eZjkzRZ6Baqv38L81QlpJn4oF0G90x8zoXL/Cte6HPX225Lom6ldmlIcx7UleGacBa8X6ZxEKrZwl6IEhOTO4ehEks9yTwxypdA5Q4/KxN00pq45U6T2ebevDVuxMaYFVI1BD4p+vCcaVF6K51KsLA5dTNiIxGTHDAqhrPQKtxnJrIag0nmNmRH0gxNASrSzElQNJQCxItMYLXuUE0bJHBI21vyxSGaaCMgsoTMSslJmSFrD4gceN6LdyWXHD13hwpkClsEUVXtM8ZqqXAv1dMoBqGIRpd5TfMqc/yg1Gqd9DSnxqPHIuCmYA0WQ2jyJBDEiPCImZUScXFhY/IQwiB4BWlwqalbmnce2Ts5/mIEALJHgmpEnXhM3AXRq83TPHKPRB0vVFsWj8bcs0qhojzryi2HYXSYSTARwjEkU5Jw4BV+5YAUndga29zdnDWGRAXOZTIpraBxJCcNQ7yrQvpgwH/aGhd2PKCUWIt2SSNPYrsS/pIkERamQTUr7JC6wMKVZBpOCUbJQsQF3NU4mwnoA2JFpx7USDEUKEi8JbWMgPAyYZBRYogFaffkjQiX1BNgoqNgpatdpGuG0h0yg0FEYhdFuqORhvDMPawBHYRLCoomPJfoYkxMiM7OFcwH0dJUAVhoTkgMCxQJk9NI3IVjqFG6Jcf0jF4wZSMxR9wqR2byJJV5xEAWEcTFeGaRLHqIQ7VgMXLCLd3Kce+ddr7vXe56j7v+wz/d+x5P+9cHf2a3F937TrdbQxsec8ydrNOYJhJpcV7n83HN23XPf7rwimd94AtPffdnnvKuTz7lPZ/6zw987mkf2PsZ7//8sz+w19dPOHVdyrXX169eBaC6Qx0o0jOqCbESkYIp2Nhq6rX0PlBTvnE+Xnb19WdfdPFvzjjjmhtvWstk3YvOI/UOBGfigaSQsbX/B8XhMgwEd5l7kDM7qzBrwGSsFm7R6uS+dQe5+TrELBCKo2PGiFHIBN106n8zxbpfQfB44rQgJWt2BuiaeaVwoliXcO01ZIMoCOGJbccdgYW45Y4J0TisNiTqiCqXX/H3/48NrXN8/qifP+ODez3rw59/JtqHPv+sD+75rA/s8bQP7PGU937qouuWMeBNq1evqovGHd4WbSLy+mc+ccVQ1mG53uVibd884TfP/uA+r/7UV371+/PraglkmyKUzYBHbsShnHWavNSCG5TQ8BRGzXrjhlI/ohuQev2qG1o32ujIOJSTj+pdeihomhkIjcBxy01mG/WYHlx45VWUNNBE4FXtVBhhhUzI0+v1P7UDXKJBSsRMsDRGcpq79+jhLMGEP2UVKrjJnAZNOeGBQyGpRE4bH6qasABiGyFoVh4EKQDpcESGEd6YHk+E3aU36oG4dbXGN62uN66++f8BAoK17ezLr3nGuz/z7Pd/4dkf/MIzP7T30z60+1Pe/alnvnev57z/8wf9+Fdryda9uPraG3huU7rvwcYcad23a68RCG4QATYg5JacGNuiDJG8raVZ/4KRBQHoxCEkxOKJIrsIhpD1KW+5Q6rGr5G5uCbhBLNSis6GOQhogGmsax8jKG7p8Pdvj8Ar6VzGpKNijpeYrG9MljqAJ0zI2dGRBmr3ireFZEacREkzq0Kjol7b/IobFv921XW/veDiU88+zzHa3we55YoZCoUbO4WxEZMGJYLXl5GMcED2wWzmglmYXNENTyYECvVMgP+csCNt3XCIogJ0njJBchgAYSOJWIUlhyw46g7YntqMe0qkCc4o8qxHPfTOy/2PXzA2Gnx3wh/PfOkn99n1E188+pe/WT1fFSJESjC468AyBDMRSsPu3KAO+izXlCn3hrkwOPzmyEiC3C/kyK1pyTEwe+8wLyX26f+lnDxH837D6sVLrrjqL+dfcNJf/3r1DRv+t5XWjJaEBRO1ZA0FR+spnMEbSX8NwQZniAFEebFI3ZD4ggz0HZNi23ZlzgodN+hBdYQsxpw7GopbV5XgbBvSLd3DGyM74p29OyHoyZG5XKILTLpEsuEpgF3jBBdLJ8KazIkiSUsCATckxj0Aw0EKK6ZAiES4QQkM5kqxjPzoklG2IAbCNDoj5TW3StY8WudWKQywKKnf4fZb3vNO24N+g3by6edc326CBiK8wDJjPvaXp63CBvcS3RP+6b4LK1ZyEJsncg4NIxduhTH7UjRjrxlhkzlm0bFBEyPXqi1QQMGUSbukRrOl3oRwTb1xJYlodbz+pmsuufyyP5x78S/P+Nuq5UoFiMDUkDjnMVQpDfEikUpjhLA4EYFgg1Ykw+mg8BXEK5xLh9XYE84bUK65zSWnArBKQw6osIEwpQuuXx6Qw2xFixwB/xDqR2dYRbqrA/xTxKxhud6ZNfIAiGCl7sgm1FkM5RglIkXGQVCwA7caJtam8tEiV5ZOBox5dYflBCEDw6NICXdggSLgBXh7Mr9Vbt1aar2Mxp05eJIronuQw2mEfrhsEDQ3GqafHeA7ioFo1ilBoI6pKkmR3H6RYwAAEABJREFUGCK0q9bELQGnoR6KwVIOmYKHiaGx5mCddGSMhlEsek+jYfZClTvRu/XwIAIT58GQIxRcm0vN0WE98kLQqIc1qt2rBXWXCbDNeouKp8bAVSQLIXCkHCQRTjWoUcBDQ+RCqiRiFt4dgwkRHhVmIjcJEhVNoTwq9OcQi1JpBkW4cFFWEBD0K86ZJVggPFvmWrznrtJhE6bsxCKuWkQHIc6CpUhLXMmnCGmk5gj/gK0rLA61DdEXsZQ/mFKE1F6xEQkqEp4iUZmFKAQZpxglFlHkdQbKval4WG5W5lQ8ZdQCpEwMPxrVpjXKNCJ5TJLa4ASo5LDgCIerIvJIVAv7AsuCcendA6VEKAbx7B0WSFBiYJmRKwzKDp93BDaRdwJEOpjiNzTAUntX+AyCR+qcsBhzrVbCBqmgjj4hg7kFBQEjYdB5uiKYyyM7ct70j/o1gwUVzpiUDsaw1JRdkP+mRoW5oDsqSR6CFiJKZQoFBMdsQHWIqVARyZOtE0bjHDRzLhiM6VYOINfVJsoML/BWm6947jr/YcHJozA/1EZC4bR63uYA3a2w+v/pcXWLJaHgRCQVTZyLJCVS6klWu5978WXHnHzaZw45YrdP7vuSj3zhRf+314s+tvf/7H3QXy68bNmBMK+06jSXYdpjyNEjohrPg3xjehVJWZVEDTYVZ1fm6fc9LllWpDyjAjHgvY27Tk8YznSjbgSfIbalMTlBH57ebvCB9T3wLlRTSKlaGntFQoKAtoxs6N47FiRAM1uV2opXnQYaJbXpn1BlGlrMKi0sRrn6+mVqWXD4/7WtWpzfFL0nJ0S4GTe/yx3v8NZnPX6h5P8HVlhv/eL0816715fff8AhF19yWXQ1GfrMCbkzzVl6QhwvpBgkJS6sicNRcAhSU3CnwCw/jxtuxI6mbTyKwkkoL0qNjGxWkeYDSPXcQhtHHoSXs/Z1o3lRKYATz0KzCWmiIUGCjYcYsEHSgW9x6h3G1qjFqjgGmyuK4YEkZ9ZMA2rcnG2wSBXzdyKCI7i4M8XGbN2Bwy7ziE4VsVOctGIEQgrYmHrpSWg0NRMTjSKm1hcXF1fPbxWBS53+355uWDUCP9RJKuUexZbveNPiHFaglhShmYCvMZU+RMvdl++Ap246Iv2M5N6FHDsNA1lJtKxvkP1EIiPUSGyyHMf011I0sSBHpWPNkWzBeOM2tm4e0rlYGkxzeCFkPNHsXroDT24OL7qGCBdK3LPHBN/gVXM79cyLv3LcKe/a72sv//SXXvyhPZ/9vj1e+LF93nHQd8B147EgP6tz6rGEsUk1xjPkZHwtQ45HEK6SjFltgbW4as9kuffUG+bKhQKrBCO+MiwgmK98gJTBRtExl0XAKilLTsHh3sMYwxvNRKeiS3PbbvvN3/Hip2A7HGPdWvOIP11w6bsPPuI1ex1w5gXnC0OEIl0wSmFKMvEdLVq7NQaU4MbWc62SzBeIVyplZrcI5NKRcs3JsBpbyTJTFopzL7niByf99qMHHrHrx/d78Yf3et4HP/v8j+75ui8e+rd1/uek6w3mzkHEiRGdMQTwVRMsQC603CFK2NFMjJlwLjy6I4zCa+hoW5XZlMJpwyMiYOYiyspJREkIuYOXDyX36Ik9XLtkh72IJRwj0TSBbMh66Z5NzckiPAjdDXUVXGx9Zh0jLZGsd4oIkERkNCaSCHU4GVYII16P9JabigmgE6YUSgRqpLu5CfaqbUTSNALAZiGpEftjH3rfWzr9/fusiy+/8OqrEpzFBvwz2Q9OvnkLY8uVsyf+2wMNy/ecga2BJTMji6GUqBgrd5Yq8GsSlG+RmVCsLaIa72PixQWZz6gtsM/WdAlCwM3reedf/L1f//oDh3zzZZ854IUf2edZ79/rvz76xdd/4auXXLPhv1+3JGJkh+FoCvNApibsHcAWMIVNnloiWf/UjMf5bOxlJNgeEgN7WlhK0vUJb74TEWbuTtUCNiJWS3T9Tctsz6FDQqBC/9zzQh8WHBMH9F/kVBUgWt47zMEwE6e58bwRCiIAOBlSmwTG0kBC61lsQpJLwLSjkAkIurDThPoyZdmOqE7FmdTnxcdZt9SyVpUKhPVEGF1hFgrcGkBfwxorsolpJ2tSkVscJgghIJY4kTAxUhYmnjEIXZU0I4Ek9ZRDhMBOod443WHtXzwygUMjLEUnfxCJBTioW4qqDrkhkQscZkG9kyThzC1DKQvzhEBsLJ5JMRSQoyNgyUhxXMgHGtVCmpopjIHLatSbRENCSkSZXbkLdXL3RskFeZOzdWa3gS3PLJXAZ8zDvEQUyVLEhugJAo0kc+JuTORFelloQ+kFVGATQuwsXQahwgx/EXEg5+BacElBjIpEEllmGAZKIf4pgOcpTyBVBLtog3jMQBHWWtE7m2kPmXOH+WH/GGEK6MkKFj45YcocoRiB2DQFC4NnEEzaqLWYG7lTBHjF2KKjagwHbFhisgEZoxgu4QTRM6My9eQqcGHK06MuVhO3EDZPMnF3kaoOE4SGUsCmAisZMg1N7qHuwj2rKZvCIGQW1bgRTfSJ8Y0njOong8QxPo0JbJi5q44FL5ijUAxd8MNV9wQEBJESkw3mxcQalk6l8dCmObl0RWmKaTkJABQSGJQhYYcdLEGZWQAYIcyUEiRFBQUTDoqFWE8MGKXJdrTsEUmdWWGcgMKdNe67845bb7oCxKqyyUJO1JVDEiXIHd3D8WqDttN2t3n0P97r5vbAez3+n+578/U/3utxD7oP2mMeeG88eeyD7rO25VkxBi+k+whXAv4xTy/IDb76Oyed/N+f/dIrP77P+/b/9ld/8stTzzjv3Muuum5x9ICRNhj577fRJz4OuyIORmouc/Ex9+C/02xwBUhlRYEwcBQO1YoVQJ+FZyisMgFggw5Lt5sszLKhWCaZAjYHwMCQzNmClhMwiDvhpQfiUShSTE2xLvbeAZolpuufxJlGkjGkkQWyobY5B+LGc7CyakqQXYKQrHz9rjff/eNddoDB1zaYHdc4o8Ed67Y1z//lXneblYJMQCTe0Nx6f/Q/3/c9L3jydptvcjPTW/madzv2t395494H/vmsc4FMouxCxNC3dTKWJAkJJ6UuObJocvzaUNRyMlKD2eoUu7Tc0ROSEgUbKAjEkhs2ElM0LD1mQ2IMs2G31gNRPbDPvKu3yfCJqsaGdEv3Y60okRfF5hJGqHA7EwWiTDHvZGZcJe7FTae4S815EXaxBnZFEmtiRoclVuuemDkHlS6lZSVy/FknZBm3dcnWva7MlZFrYCIaxIYEgQGXZZx7x222hMvWNHgTF2vOuFjTcLu2rXmy1aab9WAnCXIlz97XHXrda48FMoW1DbiD4UFvJJAi1qVaex3BFRj1blRjQjmCDcor4bSWaN2LQFpNqLuq9Kq1U3ggaWogu0QTaiQxMvV1u6y9VhFhZ2GG5TMlCVXKA2sRDrWuHfzMhFw0cwbMxMXOvuSSvb/9g+e+/3Ov/OR+H/3aD3/wmz/9+W8XX3DVdavbrfpiGjHc2b1Q8HSnTkHI0mq8dD89W+8DLUaauzT4UFkKUYmeACtvhPIhVCRhwZFKSRmoCbBhVcfEbNmRrCpNG60kYska9daM3TJjk7vREJ5x52b3/Ye7fO61L73z9luvN/ZGN8jRp5170Wt3P+D4U07NpmEM53NqSSpmA6WeyDbqdPMDkE5XHoM7ZgbUKJa6cxfC1klgFmN21n7jqlXfPO7n+PXmZR/5wrv3+9bXfnrqqWf+7axLrrx+scbyUJm44mO1d/xyGz2EVFido2ufq92KRAybsAiFBHoAiAjYOmJfMDD30bJHd+PG0jkDIRnmR9fkwMVy1EhSSHfRBSqqUWJC5Ll0m/CP4ZbpE8gVQZ2m5YeHOEGFZigAKlCwDL0ws/JIQLyTJqWsjvTSGi8aei/TY+nRFAt5oh2kDQEwOAl5psjCBRw9YDR+2P3vMaQN0yAAcOpfzgEIgRlcn3bWuWdccsUSU3rwPe56+9ttFVLBTGnGBAN1QTEJW1EktgUOdORJQW4WiKnUgR0lRzxQlxbSkkZOdM0N1x/ykxNf8skDX/SZA9558DcPO/m3vz/ngvMuv/oGFO9rBlv+zMIwAk1BK31GDlAJiSEt0YaKrGEwpAwXEqpFt8kIpJJ8yM685v2GZ4DLepCbSqNcx+xz5m7LI0yClaBcOAcYsVBXduAAEe+OJxu3gOvJA5Gg3IUalqo+4dnR1xkGgpddIR+ijUEFSPnAIUm5aCqsQow/JUfVkYTx3pQ6QRRmEqXEWMxNYilPWQ5yBTdSl6kSZQ5zzCYoPiKRu2JFQVWoEjUSIwCf5+RzskpRg0CQcpuh0PQanbwLxsnRGUx6cOBgpwhAvptMalEQs4YU08QeydBj6JZhKRVo0oQN4aEVuAAUSqQESTBHtoF9IVGeKSnEl+w5kXCGhl25KhOBAcPQgRG7eFPIAXWczEJ7H6JrCiqoEwYVXHGTaNxdMKnDTCSEGaTmPk8xaoRb2Cgxz9yYjYsH9tfgdI8wCWfJCBWbYsYFQhBmCpjQ2Q2KekQQOAPvklhwQAsqGKzjFZMmYnWMxz0U/Nus16E2DCU1I1dks2yN3TkccjF1zx3M3KNDPSFwVcK4QgYWUBEl1ejealST1rWzRHKftZ6QPRpxdFILhRFyy6VilZXg9Zz7wC1TDyIMEQwDYinsQTqSVqW59jHCTfKoszFnYyLcjZHMB4IrwNlgHkFW9Mna1eG+BD5JulJjmngWImgBw/DQZytslkIxkGpklhyUO3lVp5xJSkzXo+lcqKfapBLVLPMhjRjdmUeWDuRyUPdu3tmEx8JY2BFzBoF3DQNj9UIo5hqJRQqYkenWjtrDA+ORTzwnYG+/7ZavfsqjX/u0x+z6xEduNiwMxkBXJI9kEHBZPg+5+50/+eIXfvjFz/3gK5/2sdc846O7Pu1TL37Op1/6zE//99M/8Zqnf/TVz/nAS573wZe+4BOveO4nX/NMtI/u9l873+72MLxTVOZF89pl3uxbPz3pae/59P8desSpZ51/7apF4AXDzUr+13vd9ZVPevinXvm0/d784vvtfAc83LgJTfCmlMII6bWRNsE0LbTcYe5mRkycaKppuLTg3rv0ygHIuLu6wXfLdFYRsNaePXJ4Jh5iCdOt+RqBN+5jJNCUvIaPRC0JKWxKEbwx7fSkjh1SpE4CEAmiyVuQEcFRjvcMuERJpFCAYEU82rD971Mf9emXPf+jL33BB3Z94Ydf+qJPvPAFn3rRCz/+khd8fLdnffw1z/jEfz/nI695/ode84IP7/b8T7/8eZ944XPe8rSnbZFWZGRo4BMsw8m6sDzyn+554Lte8fSHPSAn3XCM9e8vuPKad+731YsuvpQoOZrhB9+Y/l0Bh/KICOEmMuWhwim5aksaQwkdzDe2wsS6t97gJwhBQuDB0gtzVk0qSH6mQTHRrf/ZJOvQOC8Gj42oB8KHvIoAABAASURBVLzEBkR4w+36pEQlZRka5TBNZAgwVGXElkRyZilQwxTijYRlo1cCH2tRMQF0girMK4Ig2oZcyR3haewpuxaTKW7MMSkVWUbgNb07qbtwV60hnVlFINKad+uf//Eud/roq5/+gVc968Mvf8EnXvaCT+76og/s+twPvOaZ//fqZ3z8Fc//zK4v/NSuz/vUbs/8xKuf9qFXPftDu73oYy954X12vnMnQZoJ8YAbqK7P8u93ERKQEUoxbDddhmgL8uny72Rrr5AWTGICSnDpoc5AbIK3lnMNegHFRvBERydiTLeGaNWekuWEGsmnsOCBQblxS3ALUzihM+aqPgtMPCQirlST1dwsk1hKnoWY8hU33vT+rxz2Xx/fa58f/vS8S6/EDzLgiey8/RabPumf7/fWZz5hz9e88DO7PbekhOcbtHDkBzhXCGBTTog8wKMvyBTvtMwR4ViTYDGAKSWwgEKCh6hLZpt+ZlroPus0U0XUApJT7mBiqOtgHRENwxEbeWOuHt0DmTu7pcCwhhqhEp776rjL9nfa98277fbER2y2YraMGOs8uuam1f/35cNPOu0P8AkWPFaciqMEKKWXZOsQrnfZpbhO5uMaOncPaxTwayZCg8xj9a/96OQnv+ezH/76kaedff71qxfX9F8xlEfc+x92fdy/ffSlz/nS615yjztu+F+lX0Pm5s1tjNa0IkkALTBtD7YA7NaQbHBmx86dizmDxJMxdolKNM0rZ5spRN2AnCiJAgWObBMiEsguvSVHSbwRJR6UlDVCQGQOlEPXYORbi9JZ8X6ZBgFYqBnPNSFeNRNruMXozLIMlqDavC9aVHcT0tyLGP56HVCh9GUGWHrE1MiYIhMMD41yCOJCEscQLYWnsEFpxZabbv3gXZb5H6qc8NvTUcIRMWrWw0745RrwL5T8nP94SFEhQtQGd55UxkfGxB2rlkLTO6VQpIlKYw+QENEQQ7HEHIQUSK3X+cFH/uiZH/zUZw7/0Z8vuPSGW/7VuM1XzP7jfrv8z1Metcern/OF177wTttshb4bN1aalb4CI3rX6TVy7yC9wO7T3Uafsc8L22Atjy6rU9QSKSiNfCtJxhphmkXizlK1ICs00iDmjRhPDxrYpugslaQhAJccIu5ilX3pZqLa8BNsRK6QPgWunZolzJfWLGDcIpoZGYBTMAscnuc5j5oMKb0k5sSEV4g0QAnZIFQYeaDDDBp4GYkxOeKHxpCeZWIRlB0wCMWXdujinDUV4UxNISgyamsBcySjmVEyZpeANo16DQjlQU7ezbpBtwxkAbBWDfsjalQomPAMZjAm1NqkLDwknikXFwjsRXk2BRbGC8MWnFbljkmq9KGMRbCecA4Rgm+zcqLCJEAql5xT5pQ6ho0AslKiQItutbcxKrIgM+JPDCbVpgpkozJQNubGeE9jBDwaiC4iiqa95tFmyB6481yR2BwviEBOdeicOgucUmrMIDoTCwxhTJkjswt3oz6PGCt1VGy9kXUOWIdwMGNC92jdsCAxmHJg10yl0IIARFBCg1QlzXyWwc6ICL0D4xnxSLLIhIRoLbgDpAEzZGeiQuArkdx03srccxc4kOFdR4y1MBQ6uHBC3JF2EggkQUxB7jAF+o2RFpOOqpYEdgm8EoUsxBi8NUG4o4xhUCMZCSzUfRYJojaXxZbnY+pR4BMy6Z1bJzQ4ogwuyDHiwj0HfjHtmUQALJ3VKLUPFoWkUMqYhVi7agQzViJzlxZsLhIiFTab0ETARYoZhTbz2uA5VpEEjIljUjN4r3NzqkqjcleMwzBKqq5zSjXYYdDlm5LjbQ0aJVUdurFwPO0RD3rxkx/1wsf/xzabbxUy4b6GjeycU5qic0NWV99wYxexDJkYdhMPgY1ZDTjnQZqW8AxvdEgScEIxL5UFNi7kGFPt2muuf+cXv/q+g4+44robPGLNACXpsx7+wB989H/3evMLX/esRz/2Xx5wr7vvtNWmm6x5u8HZMOVkZu1dA7AsjZAhaCxj7RtQrrlthGxWAdMQksyUuuaIkoLUGnXDnKVrKDc4z2v1DiOBEkaLViKGJEMuQxGovQE1ESySpAIHxMYNE3RPAecHYp9u5ZgNytQ007DAw0LwAlEJSk6AojHMY2qAfPDiJgtlWR7XrrpJs8ySrigq8ECBdzQU8iUVYQ58cJJO3AnKKObFGm1kxJi6D8EFYeSSo9xhi60/+OKnfP0duz3xn+6z2QqIsuyA08PLrrvho187vFutPY9tFr2kFmneeQwXRtwEAtQsjwJL8KSHcEnDCthNpv7rfxCKAJBSDp05YkG6I+VAaJZSpa12vxkm63XbdotNgwlvOjnSJzPe5hTL8MeLea1ZArDNopoyqSK4vKFKsRm1xJVgZwnHUoMrI9I6UEE08YxGZjEXmW5o/UNVZYbAhteI1RNzkZRL4pkXOHV94lvuIGzXwGLZG75jBsNkZPVbXq/9vvr6G8WYRyLpMvS00PKsCYyZVQeShKDq3rnXomMaqhNSHZkwrBUO6JCH4uFafutfAGLZuRisoeDOsDRREmFen+7mO4tsqoz3qVJuXbrhDUOI5ekxMBDXhGpKgVgNtk7oZlC5owuitrgreGzc5vM5dWkOpPbONuU6IarEIykUQwItFJOVNYWf9bfzXvrRvQ8/5bTFsQEMa7htv+Vm73zuE779/td+5CVPe+FjHvqv993lfjvvBNnXvF3vDAhFsBfB3Jbdva2Z0nNTcci5Hi1umGAzFjPCaHOKebGePLSJ1ETz4GlatOjUA/PzYvgYYqTSI3fMBVSmjE1CEJVhI2epIKhZaxoMPi2SiieJ4KibDLNdn/zv33j/63d93L/eZrPlkyFEQrtpPn7qO0deu3h9ZBrBnJkycQrSyUsg2LiZypgL6iPDvNBI5pKDNZxE3WdXXtHfvNc3P/y1o6++YZVHrOlecnrhox7yw4+8/rOved4rn/SoR/7jPR9wlx0332T6TX4NwbrnYAAme1aYp6ce4kTgTrcwW5d2usYo5vhb6LFQCaUeM8NuQkluGEczdKcNjpSR4pQ7iwm6TvMl/HKzsBvQEgsSHQ0MGawj/1mvBmonzOGJN6ReukfGUBayEsI2c0IMlmhD1Mzmy8ijzAOmlUBgCi9mxs6cYziPSfPlh8A4TKNTZbhtrtEBUkvJsViYWyC9QtYEtsErqv7HfXYB/Qbt93+7+MIrrlbN5199+cl/PmPN2/vudPv73eVOBEGIqBvZiOKoMfSF+TvAxxVAKWQpAGeHU8JiDApmGpAmEXtMl1521Ws+ud/nvvdjoAvdwAlt5ay84kmP/NZH3vrpV7zkVY95zMPvff/73PnOK2cDXm3UoDe0aooap6p1RMpAVVOfongj4unBbKGgUspTvVO6KzrPxatEzml6vdFHmdSCe0O09QafGCdaGAotd6xaxJ5WRLBBz27oxVN26dJiisTlunAw+eQKlfksWcmetAvPXSxS4jQkDeWm1AArDgng1gQriV4rW9OOGBg0JMg8GnOXxM5iE2LxFYYt6s7dM8qE8Aw3MKN8p9wIVpIITsqFVGgaSLp4l2p5jDRKmouOZbBhCMkI4TnbYq7zXC0Z6psFp2IGyaj0GLpLdTKCQhKwGqGDdqQhwrH0QEgVTRMPqTObjdoXS4wSBu3SnLVxdHbnKWkDoEpdNUgkMiM+WIiYRDwHBIBJgoUw/zTC71DNJ7uzC5SWbCJTUPGYvWMK7h0xYqTzyPPEHa+zQxYSM/AUiSKxIsoMgsVAUIehEU0HSp2FNM6kDixu3CGdQDqM7zoJyoTxmBqFQddwJshBzpA6IAbCO7lIy9FT9+ScWQdJwsmYK1QsgXvN6lE8lMwwALDcpgGSEQcHsQuM65NXNRFPwi4w50EoYTDLZAvCA7uGE4hcFb26eTVtkJVEbJKQOniZU4WwLaktrW2FPEH2wAhw8iwwTRP3CGYSDaGIHmKsRMQUrD1S79IDNGqhChGRPnwED9CycJSgwSJVcQyPJtSTAadNehVMtObFZWZ5MAZzdyeDtbXULCTo7dk8NUzhrXAb1CED1EwNFbhnDJCKqIg3xRoC+YRhMu2UoFiIe+pR0N8Jz8jo1o5ECLNm2R0SwymgnNRoZHPu5J06icOKAR6SC2Yl3ZjVpVdf26VGbgAbDCGWiIaWMbXMxsmqPmhXcmBsvpQXYaBM8BksyazsYR//2vd+9Ju/rMs5J3390x777hc+/bZbbqGRI0qNwUjXpVn3Go4KQWU5GkJWSZmGyrOeZhlDrUt487VhWvI+7wgXT8XSQDA5/Nt7aiYOdEGJm2nX+8LulDGbECWOWYTCOtCb4Q4BWNajnW444BMTwIddiYEhdorpDQlQvHSxwWmxL/KsMWoUCZ3GUU0J4KWohDAmqkHNnalvucnKDfquub342us8B2XGCDBeH1pb6FwC90alE96RBgLFlgQyYiTNcODdu0hHrZcZwanwlBP3lnbcdvsPv+iZX3nby3d9zEO232LTNaNsfP7lGeede94FPG9kiakoZaDfrY8caJUB5wntswmcLCzEtNlmWE9Byw2ZjVg0WCRSCDD9p17gk+hIHwvdc4/VFav4NVZcr+P2t9myCRZ50QqZshEArhP61qO6+WZhNgwkS8UGwTJNi3eGtAH/qLkgW9p06aFVEIPCs5LzQkIut0CupXC3m3mt/4X8XQdvGWEVpKKaWRIpr0/197tMbeaT4J2ag7fxTMuK5Wb6y66+DnwpBAIyV+KWgEHn3HWyJjISeYXoKH5DJZANQOO40ukhgTfD6H8feb0rZqJskUyIUk9UyTkk07I9Ak+9kC1AGE69p7EzUioswjQxoo0PD4QI5MTUgmIoKydiZBgb2RFFzOiIpMUbd8STFQU7NWte9XD3iGbRDf4imvhWIDk8hZXzLrr8zXt99dL1/xH52261+Wde85xnPuKBCysSwhawkgj0BOdlmnDKGVOSEEOwHiBt7JYs1kiwcZcUCk2wTTUGGbH1BfcVLhpsHI5Zgmqn1iBzCwGaIrpMWbt3FBSJYQlimswpYDTJlYBzr8xzSZaoFMlpiUgB6Z633nzL/3nq47/x9le/4WmPuvP2W8OsG4uEJxdccc0v/vJnlobrJsKgY8iZcLt88xCZPB6T3pL6MA1O0olvHPsHD/3W8X+4uepd070kfduzn/DWFzxpq9ts7plU+6yYZhh2zfsNz5FSklki5M1cKZrCGIA9EVxPyxzMQeL4w1en3EOZZUackJmWyRkTh+0335QzVF3yXEWYjmKYRKdXy35SyYhOTjH9H8y8O4N7YlXiZclJDFOq4LV4pOgcPmmrCcpz1o37WAS8DtBLF8AgAujoDCtzEsp0K4cR6KITRZA0S+ESAdBXjtGBfAOfKWgsHny3XZb9N5eO+/2fItMJv/3T9avnGCSrPPORD0wiYkmBuU4hjjlizDzS9E8ZG5508l76tJWshEpQANyopQPIkbjnNQoTAAAQAElEQVREvvHamz6w3xG/OesCWudYKPmDL33GK575hM222GSaQ6l1B+tlTLGmU+CY/Jq953lPoyGwgnyp+l1Dsf7Z3IlUZCZaAOAerRuypMatxeKEERgO4cTeOCwR86Yrl185X4LSheDALmbJUaVgrdbRM0IollchglKjZFYCIUwpVIJBjZkGHlVcsUc0eImio0lzxA9WMdCTGtI15ZCgqNi+kDnKN4eANC061BB25CaQmRuToWggWIvckxEqKzdy9IR9lowWjoGdGOam1JVHxoSgI6UxaeOEtyRs4gb4FAYCslBmjBGoWlwm+y1xY1LhFDpV6AamEJshfxBEKAQzcFdqxDyiqIpcmSKBRbcw687VckdYwAzC6E0tsjOJ4NSNqHNYNkoEkzoJzBdkeB6RzLOJEIG1YPgIBIiDqffeq2v1mfHMJbP6QL2EpdR5oces+VRahypnktRjmvaIp6g0AgmHGucuY1fcawKdI/9GFYmcuaC4hCFkSvAJ2BoTzRdaXTG30jXrrAwLjIKQESiMvbNkLAzEKlnCwM0KeWHLbDFpQ/BJgD3srzB+Jik5NBuXHgO6CN6TC3FxFSvc8pT/29ixLvGUpcxSGaSIZerkLMKJ4B9r3Lt21qrReR4osBkWBNcyUFbTalPY1sjRh9J5EM8a2XIK/HSfKQXryN41RIIpYGQKB4IMnYfiuUziIzWNPUyEFBJSA65Gx84ydM4EnVurvbbeiAwuDvbR8yLBv5wTFw3MyQqfkjoXeIcLM/zOwTCuzAyUMhAvKK0o2M+HNN5yTJGTuQpV9yBmEmHiCIP/6VaOpQTbZoKtnoY4EdYsGINyiwSzGSHioO8wyoomt5ktbLVymcr4ihtXz+v1qEmVSHxgLpIZ5iaKYOul9wV1GFOlwXiSvCRJlIi0cRrLz0750zGnrbdKgbAPvMuOz334vyRo2nJbVLTeiQkj0LKHBEbjaelJ3ZAVYGaFfyqe3gq9sRkHCIwmBKk3HUevFhA6RYgimJbtSjSQDwSoZwGVUiVv1qN5QIgNuwQxTAAskAdnSrMUqpUgKk2w2ZB8ui8rsH5loo4pDvMS1exW3MW4j1KdQ0NmQZlomy02SwLKqde6n7Mvvmru3JxqhIPPlGZGEbSIgHXEYEsganDga2KkYAOPhSYMRe3/Q9h/AFx2VeXj8Cp773PfSWbSG6GFlgIkoSstFBsgTTohhRYSQhKkg4CoP6WHEFQCSgfpoKIiCipNEEG6gPQaIIT0ee/Ze5XvORNKMjP8vzvnvfeUvVd51rPW2ufcd2ZWPDcyTuPo6MatKE+a9bCDDjzrQXc/7ymP+p1b3uSq6q66/19f/EbNEJQb4dGKL9UDjmKFFk5ihQEduKlGHJiX+2zb2LKCK9i/2nbF5nqzGyehZrkH8wJ1Mxcg19aX9d385UhhvvZBB7EhoJ05FpdYM4D/biD6mbJeaU0oYkhE1sKNpEgUXh6LW1pn6YyvgDQkRKNWbXXH9Q5yMcpVaT+Tc5UPdx9mkkYUHjJSB+YKR+hVRl1ttzmCwMo5gVYEWo66or333KBdXhdcsb5ovYnkSWI8ubfN1tcrHavWG3dxlyEla6FGvkG2UtWCt1ZWMhVClxKmkF2k/uyEKHXWgSRGxeprGaHGhUUQsJ8N+eUHMBVE0OBYhclsGQbkwSyUNVz45chf7K2vmFe9ruacBjWDE1Fq4nGOt3luw2sCThTtX4y/6g6DAZVw9wDnYE+4Rio3kxJMiqUA9ZE9+9pf8tZ/+f6FV/uLvBh/8m/e7ibXObTUVtoWkclp6UDAkHb3YsIVhngNLiFcajAKKUi1JsrdzGAWbRQF37jYRFiulWIURKbFc+JYEa88K4BRzQ2RmgiUjYTBYoyBncghmilW7C0hDLUCZ8bwsd3JiIULbl61IoqT5CRrLeuD9t3jpHvc7uVPOfmRv3O7WnbPro9+8WvhXjw0NWiDArRA6d2NEwPhm2NZgXG2FasKlJIjm9HT9B8+8vF//9yXd5p2uxvf8J53vDWzZJREtkctMTG3nYb94tAcDytyWktZ10RZYwIUTeNXMTJZmdD3DTQhEB4QZQSQFfrJFZd3B0K00+taB+7vLKEkslyJJHzu3mGizfVMVrqgCwmXwlWqcuUitJS7Zf4uP2EqkRuUWwLfWFbunIMZS0PUmytVXn2KMm+AcoSgcqCPgncIVChiqLJ7oC7fvpm0gdJsDaXYGF+MZqShF3IhIVazWM99e8zrMvY9YONmy/ckV9dK9IFPf+Hiyy7/5//6zJUXDjvkgNvf/IgkLaati4AMtXhbkU4pNYZmzwXXBvGcycwEDGij5YSL86ggUHnHv37iU1e/S4Hwu93q6NsffZPMHXlRjCcreLYvhku721Anlt8qMSWrCF9Pn4PmzEBsdzeeNreP6EpMCIxKoE7IOnUuBrrubsKshOVgFpiCelipwzs6YNtu1i2Y/c0LLiZL6oQ+G5YiPKnVGlyCJDFg1y1hiouiGGTxLLBlBPiFVaJM3jn6yFyTzMwmEQibz8Jei9SihUUJDTy9h0WMho2DEncQTSgbGMJlxRX0TXJ2FMZoGSnGNAoZxuBgnWOmTceKkTpiVSRrI8I1CqwpAHD0tBjBUReZAl7rDuZkMZjqXK1P2QsnrCG4Q5V4IvjMBsOTO1Ta8Nl8TdGNlhBRUuNWonlM3ac1uExlcO+rbjIMUlJKUsEOq1EwrwsZDOpwULKpTIyKMljWrF6R2AyXcQ1DK5EmkEvo6gvuIpKpmUxCUmQK1bUTkjWdkzI9xhweTmzB66B1YCd1KKhF3rGSG9NmSCeQShhCYFUSEOUBIwW+TsVRMIWgREfCDFkwZ48U88LC2pCAbopLcB3zGTMqI/0yAFOaDq82VEZhUmVCFuVqJB7CVphfGVJcycVisXQhwgjEMV0CkYEPBBmqspghyQx/WaNpTsqqQCRgfmf1IhAtbDCVgok1iTPhmQNml1SBdkAvFEq4b8GDoqVDAEBLN+ivJCqC5WDEjnlFwAaIWeZBoIunOlUXUI1RVD2COFuNWky049CMGWATzDQRQ+AAYY0C02KVwIEY2Vklmwt3YS8ULVwzkKWzI7mEmSaOyh0URVJkhHDgJCdhsqs7lriFftVryZ2kEgxHgVUEpzPk0IIx+JBOCTlL7XZWlhtfeze/fPzTSy/72vd+OAnklLQWJF4JmAlRESdFeMSwSFoe0uw4LYGQcvWahOb9jg9+inZ53f6mR0xYtiysRL5yOIQSxu8y8GcninMk4pdCjM0JKISQIZ4/G3GVj+UesVsRmAacFuFgACMKHAhDpbGK3sJqKVeZ9LPdiy69jHhINUQ6wX0cEHVC7QAjceJnw37xgVMj1LvSEKaSpdKSodjnX4zZaSdDGBniwWG0ZCAIGQkyRe0417NBP6kW3mevLYfut/dO03H48f/9qg9Mdk/LK2M4KIaldaaEYk4CWMB0VBm4h2yVKu9YBTKJDCheinAkEWgqJcoqWYUIxk/XuMY1nvbIhz78t25Pu3v99LLtABYU50rGvPgBF/Boa23IdCXICq+RkJ1ImygqR+3uP5m+4LIrfnzxZeHO6WBVdTAfzlOKe6OLLrtsV+XbtqwOP+RADrgK4rMSCWUwZQEnZNfxWKbkLAvViVStFW9TlQqWFQrMhH3sC6lYJLlalDHIBhAxBAOGCIbtKlZVVw3xTUEVtUQYO/GapTuS3nYdv5zJRqxFWJVEM9EGa9zkurvJtcu3rz//jW+IOjiyzjrjGUoK5kIfAuuJVK7MLCgRRINFKy2Mm4KKieJEssqvInYGEJYYCJsv1PFAJe7rMI/FyKv/MBOEMxGhf46Jk0tmcVfz3cKCgelLWVGGNIAIbSSBupxaEw3QmnEZIs7EGLzTtr3PpAwdEpViUp+mRI13KmhtXSKgl71/8wfnf+pr395pbi16p5scLkkiqhABlFLDhQDdTkN3HCayjqKjEaQVHaq26FTOWmgBesegq78NZRVGSpJ4oBdLFxkXXvzT//nq1//ry1/6+Je+8F//+5VP/d+3/+f/vm5hKgbUhjrKWwkgNmef2UwpSym8KlElRJIlwLlAJ6VMIiFpjKII2EmhPi0Is/Htymm/91t/dMJ9cGrX7dJLN6NnWatuqg026GTI2nUgbZ/xVJsIsDCLqAgvg4KYVFLe9cH/pl1ed7rZjaepJUkATyxSEpDCpthl4M9ORCT7SDMyQ/gThjBQE7x+NuLqH/PcqQ+lUagXGhoBB8icwn6MZ+G70FKFb3idQ13YsVAAmgnDqksBXHDp6rKXI3yn2pO7aoqyinBhwmhqmAMa0m5fhZrERKkcqHFIJkZYcqKA87udwIMJ9xxSMKtPjofqJUvLInUHwrvMMUeBXCxBrIiSOJlTiZWo4UgYU1kSFYmmgdXFb99qN4+NvvqDC97zkY994+f/RdUDj7vlnq0KwE7SSNQYwpqGG8FwykiU/JmwilAnNc5l8dKYV0BEmBJaB26N3vPxn932XNXkOx5zkyTAvZCASGBoqYO1X3XML/eTaOZcGlpQjiKDxQelw0mSXw67yt5GW40gQJEwREhEWmgNQXW+yqhf7oIUm8p9Q3JFooRxyLbrHHzgL0dcZe8b3/2BrzuDUMgyqvAbFwslMV024/YpcbjTlpQjOWLD8dUTg13UmY2EjZcbLs9BALFYwjfsENjInDIZ6jCMT+dY8pYZ8WQN5mAhUioMnyAjRbRBJ+JkEB8anZHBNqhHCXAh8RbihCy6cm4VUhdyNWvuLbGBcR2nMhncr0kc0Sm70BBZa0FB4NHqetIAAyR5kSloD5mODQHKzHCGr4F8DacMIRb8QBLugTBeSTihjDGFLQRucGUtOM2Et6CKhXNIpvBQ1HYUeU0jSjGRKCzihSKSEN6lHKxJu7as6E6qImTkJIHlLYGCsJDCc8CmYIe/aAJtRF0nm1CUZM0qDhBdY+A5uw4VEkrC8IDpDPgpyIkCBsEGUDyIOJxl4LNQ1iA0aQIf56Su3lUcJ8FoJ04NnQxhVPXw6MRzFLPCA0kkBV9zTLzoywwA4ggGVSOidM1RgA9CMsR6Rphmn8hWvCwEOSk8I5OYtHmpVsBctanN3DoTaRZmJIoCRfYlr5Z7NLCMBsdcuKNguXA3bNQlF+lgQkEbJxtuvZeMVUAOcyI98QiqBtdEsKhMjJYGPHMwTjYhYpCthE1O1dtkOkkK9ERxK2XwFLTC7KiZbc7SmSVi5dwiNZ0IUGKhZltGnwJVG6Vs7ut1z7XVQRuO7mUZuOk0MJ6qsrDXMEl1n5AyMIB2+0pnmhtwS6tBPLNfETxnM5REXl5UNCfxSUzxkKDe9PAbqPBOorrHB5Zf3MKFYpDHNJgzE3Spo2gUWkoRsnP++re+zWbsHaqoranYTy+/5Os/umAngTg8+MC9US8Te4VpokV0cnH/8UVXe1aK61duQgg4e9ay/NqihJEHiTD+XDngqu9XbK4vvmxo7g4UpAAAEABJREFUCgY4J2gZwrXKCs8sV7k8ixlZZ9rYWF111pX7P7l8PW+udWHqGph3ibk6su1HF+A79vnKMVd9TxxIiivZlkRCpRJpyalwZWJc3HXbfulMm5IdCDohFFMnBa4i0Wg0FERx4mRW2WOfPY+4zqG7SvjRJZd/8f++UsBqwXJI22aTde3rHL3L3JHdZU7pednl45vfvyiowDqfCDcAooWd/uVDn3znP//bO9//0Y9/+sulC6cRKAnuqtASWSRYefBd7nzYQfvtqlpUeGLZoEapgwhJWFCwvHiWzQI/hNxhhM5BRhSgxq2OusGucjLpG9/6roxRNTdKTuGCU0gaFiH99g93w5nDr3XIdQ49MCk82DDcK4EV6rmUMd1VBZYpOAl5XJjVCg0hYhLxrI4ajNm5hE9Tq5elnNno3jfZ0QlHiK/T4SHt9DJzw3mO8KCRbEgF2MEgx3DfafDPDgtWP1O2FbfJaxkgCNHNj7huVVj0syFXfljEf3zqC5QRRbD4MdRr3IaVoBYomIgeM4kNX2/+3/993fF0L2yHBcZh4gEOUZVfRezN0dHUETWIJ9hPGqk/uODi7WvYTju/mFhQQZOBOCPGq6XuJNNQxvvOo5fjH150YTbvG6g3vFaejfDIrqylYU3BqHYAqod7Aq1l+NV+PDxlZHefha0AGajjZaxTQ7rCt+Q6vvLdb1/287/d+4v5qnKN/bcRQy4aY4J2mSxJF/700oj4xbBf7CQxnmWYwcDtRLNQJwmfqmmN/MWoX+7g3EJzzomN2cLDuhHbBZde/KhzXnPKua8/9S9f/5iXv+GR5772Ka94609+8tPJvA4iIFcY1NIwBfIdhQQ3rFVj8pGokURFSkOgP/zFz7/pX//tzf/0gb/74EezhlftuVrblvDlPzzWjrKw+q1fu9Xtjrz+L236+R5iRGCGlRwU8wiaU2Hkzy9f5fMnF13i5E5lhNISg8igTIzgC3960bd39y8OH7z/fij0GGFMweRksbi+/ae/4r+tIHMjN7asnQVLJ01vgXWZMoTsuq3X60SF0KBp4PuLFY+NTmWNchjf/N4Pdh2/15aNw697cNY0JHOwkSQrqfi0uLHr+IwcMIMRuLpBq5J1IGzOKJwOJHadQOA8UavWyrrpPFFUpinrNLRi/WC0y8sjrtjsaBeK5IPwSAsnIa0of8wg5S5TfvTTi4nR95Nnos2qcytehQRh5BXpxmgrahv4UyfhYnTz6177mvvt/Liqm5397n+NHfE7ZJ9t97rjzYkF+rG4IJ7TnVGaFu5jREgxrsYoEBTLBZrV1xWdwqylNC7IsB+c/8MfXHTpLsbSIQfsT8zgVOkiVlNQCng95su2I3d2HZ7F1m1eK3LLUcmZ0Bi5DcXyR2l3r8vXmy4M4kZPmNinRlsareiXNebqsxikJcKbSmyo44HvKvOG17wG89XH7Tj69k8u/u6PLmBhkNBYN0kIqyZnD/3S987/VeXambvueHxAKTXKNDJ8DJqjuE1MKwYnwClWRmEviB4HY41twHv4kl+qujySUCXSyBZLtImMwZOR4pLUZpGuyD9G5xvBIwmnDUgnYZUY5JmMc/gGtuArEOLu0wA9oBwmWFViRsqzjcjec2kF3hNkbybsCFOKeuWoRKADBcZSUIEyrNZ4CDCZlIoLBBFjkHK0QbImWpcIiazKYCUNyi6YDVuyJPJMMJhIQnTm2vGuBevZkYRB5LA1Xclxna0KGyy3nLt61MYNL2pC4upWYyh4msbAenLEhj3LMCzRVjSVmDIKM3EhadSmhcI0cdRKUgqLSi1Dc3YH9NBFkJq0mTZieHb0cusjk0IlZBgapfXCgBptRtwYxSEF17HDaZROHFKIlI0xKVMW5QiVxuJ1xIb7hNW5z9JNPDEawCUrqWKscwxhZ+pB8KpynaLETAjOGspLZZQE6PTwoKSiuZJokiUYCpNYlp2EJcEQ1yuoZq64kBAYgWkewqlsQpaUAhjx7ZxGBtxMVcCktapIZS5MVVPhVdCcNDshIS3B5NWglbkkDSdHKHd4GR3PvbvoKNUFJy3ZKCPGCPeiVIos7lriYlGvBJQaI1IwG6KUnWuwSiLi2gihswkWkiTDzlDlxSJ3o1/xWo8+kteMVRhcHV48GmGplMqkrm00hSgkmVgtuSG/duwNDthrK+3yes9/ff5TX/p2iIMyOjEC6WP4sBwQg4VraKxf/a5/Pf3Fr3rvf/7P6DSMB9NYjSto938tOtJTRg+bowcWtSuL2r/6g+9/9Qc/2kX5coIjGwJRqiIQBQQZRJ2E997dX77fHP6N8y8cuUqrEsSSxIhsYpWGT68yRCnb3htbFtFX/0mir37jfDyT4BHhiJOBB2Hrt3/wY9v7uPrYK49ShLiJFGYWlNAg3KuZw8GEsCvHXO19s4PL4s7W2bJgDrBXELamFlZOwAuhQ1rS9Bu3OlYFQ64mwTxe9ncfuHy9loUc0lxLQj9CCWYiT7H5D3584ZkvfcNZ57zuu+f/SBwAwBgAUQD6q//xIy9627+++B3vO/td77t8czsFB1LbjHgo77ijMT5oj637bd3ralp3HOy759ZEsQR7PVouH8LEUpImSzZbssoJ6cbMjhPI37ve5uiplh2zr/b2n1/6+lKNl/k6UPOyLnctJJdesv1/v7szDUT49+5wy6VCaWOET9SRNVbEkgC1+9VE7zhYfsWiUqK6CnzHKexpIijgAT4yg92rcR2cHqndIaxkUOIwHATOK+dh6lU2D+/dImkkKILHYcG5hKyKx+7MWKYyETOpWtG5YGK1bEcfdtg199+Xdnm9/zNf+fgXvum58KkRrWAQuKhOCpipsceY3/DeD57+klf9wwc/nqaB4h7qKJGRyeK1bttjYxepBFe+/u3vNHFeopRzCDMCGO/68O6JncA1IJEwcCmNxEYgZPWse++9967yceaL3/mJLclWQ4tW1igShfHws0uhkLQZNQNLfAzdZds+zwzknVHzQKaiIZqG2Hh1K0GV6opaW9vYZSrBN7NMo9i0gYaEylBQmOwDn/vcsN0Qg5x0FCgyV0skFyOPmFATwImkXV+ZZDBMYUfziFG6r3o0FB+PjGXS8o79S67Y/P75P+Y5q5XJmyQegVZEHhRZhkGIoRan9UQN8LkU0jbx3/3Xp577zvf+6Tve+wdvePd3fng+02KPhCqJiIuaVOzpYYceQru8tu65havgdiCx7pFRNLXxXlu37jKQfrrZf3jJRV7SMmw4uDozHtERmc3rKxI+7jJn3tw0n4OceblmC+f9S989/5s/+slyvMsPFm+hBERThCZJ4kEx4DnyY5fBOPHtH/xIkxLS4aiyFpHCJLx93vyfXb43I6KbXu9a1z5oH8VKg8IimURVaIdHGUG7vC689FIYg0pROmFZBYOSCDUnsxvosst4nAjG6onIlJCxTCz4WYqrs8TuVODk9o5kMvIh5hLwJ5xRGkiLbt3dXzL8yRXzj664KArVSHVKw02mEmdIGAxNQywSXuUkgdCWfbbte6sbXpd2eQV07Th5n9vdfGp7EEQs/bhTCZZkH633AndYRHQJR4PgME/M43TFYEMJXFjK0dbrQbt7RQTJkNHZlxXWIJion/nqd3+4u7saCGAJyuRgQz3niagpXsyKa7vbNvs6aA6g544H5iAwA5QCZb674USREC1JmogNqbgU23+fPXf7vX0Svfujn8I4VqbCPMlcCQ/HNi+nv/3gf0fg+s5KYLkQ2gb0EFM2pgkAYumTZMKpUoiEskhiELDDI0khGkt5JE9EH/AjEhimBSqzmSucwVJZoc4pOkYhLj4xp6djbAGNUd0KanNCRZOiRZjUWAfrOtMMglWqRiGr6RX5hZUfJFRPCvz0LGuWeSlqnKzK0oiRikk93WJgxYnVi0HlimkSFoiS0VqAGuHL6osoJblRrVnqetUGitPiPtwEnRh0CRqMrMFDliCUgmCi4gwRjq6jQYxNHTabUJIKVdbldi2DAaQq10akxKnsLJko7ArjOIaQC8Mxg93EBYUU5ylJEoyYyBqtIxPau3oIgjalb0m4OGOaBcp+rAnhMTiBQ6zJ0WWGZ4+ZZJYKhm0GnvPPDORaS50gu2gIciB4nhGcxLxYLE1WYW0iTUExYOJMGcbeOWdd2rdJBM+F8FUGF5qoVBYBVxiuLauNOqSMwok/WAf3voNJ6AI1SGDJOsETJaDACIeLDZoRX1dkexJHZgI/0WA11aHWI2PAUs2u6TB9gn1MwrlskrDPKAcoU1mZNGA0DJIgOGUWEG4cnaBGAgg04eI+zIFTRJEgYgvCOFhYoJ0DrIzUYWh7ZpLwbiJX7ShZvXBUmqbeprmsWKaJphXVimBBjkidykrRt4nMHJ14WlQSI+BwAwGi3b42h82Ie5kpNhXrYjaZsqyibnHdYlpHiYGLdc3VeWLab8/VfW9/i11FXb45/9Gr3/35r34Ti+JWqAIcp59DYN/98Y//5NV/++p/+uBl6/78t/3Thz/71egrDwVtEERm3lXgp7/8LafoNsI6opNiF29e9tK3/8tsgG3n4ZdefkWghhNVYjhM2qWuYUkWv8Eh+8ru5L/vvz+DJzU0cAuQk/Z5Xr/1Xz9y8U8vRWEwVqvFCh917YN31rTj+C0f/G88CQbdapbJuWzaW//lw//66Z1/gXvHWLrksstJ2RvbFIQosxv1mcwytq/XV47Z6b1z5MSddIwy1o18A4hKSa2jtVGKoRIHl8jCnW9xgxsesbt/DPRz3/jeC1/395deMrOoC2nNusGlMSeP6P/xhS+ccd4bPvnVb3/ngp/+wcvfdNGFP5FIApdyx2difzHqBxde/MHPf3lEHV18hNucuS5kK1yM8NxNLG595PXdMgckuAqA5Km2AlYximo6yLiQXEFmTlIwPnKPbRsPOu7WELnT9t//980f4uFiULDMWnoTSAziT335m+df/a9KY+KNrnHgb9zqZtBaqBRtrJLENBQ9Lq0P5BQGXX1b9w6mWjpYlt4yarigfLiEc1ooVwHgIgAMjXiKxLOcJlUKZxS2JlcgkleXiSMAach6yBUUrBAEc2RLa7m5WzMum80x0BO3GEkjyDPFHahtveevHcvMkHnVbfvcX/Smv//fL3+1rXOaST0obSQmDqb8wQUXPv8t73n5P/7H5fM4593//K//84XZV8NBvka83Egk6VHXvsZVBf5i/y0f/FjwmtSssFNxG2/7wH+8b5e/P3bl+Isuvaz7PLh7jSiJqA4rPQvu/Q84cJ89p3rlsKu+/8dnvnTRRWvp3BwNnoXj3z/zxa+c/32nAMvBa5v1sisGKvFVZ125j/ZNgkIrUqLoEBrGtqmyyW2ONqiFVOKp1Xbl+Ku+d/PPfP27EQmg07qQCfXPf+Pr7/rop/Kq436+f8ml2zdytaJJfGP0KeCSiSSFu7n/fNQvPzPBIQ9WoiwcJShA7ZBpteWXg3bsIbQXXnIFJYI6lZh4VHkZclIAABAASURBVLdiKtF4lDowK4kEIYpYwsBhXJNYZMfs5e1v3v8xZZ6YV4W4RLYO77VBAPmwZcTVf259o8OSMqZZNqyuUkWTp8MO2e/qo3529J7/xKKtM4IZTpw/3bz0XR/+6CUzYP6lAT8buuPjM1/7podHDo6ODe7/9Ip+7js/0D12XL/a28WXXY4qRFWCC3NjrNKqkfaknnD3amN/dvDF5XnExLQKB2XgsHJRPAb57De/+9Xzd74XqqoP/407TilKid4jQYBu0h1Fl2L7PP9M6FU+BuyMkp1ybdmRdbwsATi8xGzjKgN/ubuGnGFTp42ORy8iVjxq9xW2Kzb7L8f9fC8IbLOsuPOZ07MQiapDlZOwXOeg3TyJwNS//+h/pwZ6GezB4Y8vuuRdH/r49r493GKQzbI5aNMIqqkpN77jzW5Sdfcx2n/rlrsf92u0kBMVNxEth1AmxFcdjwpzI8sEEdxIhTUlNZMRKexTFASHgERoK0vVp11en/6/rwTNpKZISWRY0g9+vP2V7/mQRe4ylsABZhIhK+yVR0pEUQbJc/t6k3b3Mg8hJ0Ij2yyyWWnN2Cdbr+ddhw/PGdEJmkzYG8eKkStwR/m4Y4/adTzOvPuj//O5r31TPADk1ERbXWf89Xv/5RNf/Tau7rpdfNkVzalQAsJw5xC2plYIx3UIlkwUlajm8sWeEi7MhQcluN6SaiUWIg5soljkswKnHEN8AI+C2iFzxFBIS8JaAcWn5GqiWkzT2QjZQ7rSjVr3pII7CMadPvBhKY66yISl3IwMRu8iKFMWCU0KfPODFV1uWSe4K6hYjWIiZ88xUx/RacwtbFUJha9VNCPOWckJBnrx0QJeMVbPWJrwFo7JjDNTq1JbRSvJJtDtHRTwQT40QgdDPpOj+wEwQqpMc1YUPdyeIa03UtCrGcubSRMPmRJLTXMyTZ/Yq0C34k0GLnBb89RLdeUg79Rd5yJjYivRw1CYeekDnpGW6IYmXXSUErzYkN17H5Y90WCrmwT6PBFLiMAn7DARCcAlwilEZzlixcp/JFJmwFdlJmhOuJahhEJGPEXWQMJIplpqJCQqhinviDJF4QEzvM1UDCeLTFobFxRB4c7kklysFVfpWedc4RCUAucjuBsPc0NbjVhFlpJapDJjmgtRYdaC6tsDdd8CFlIJSyJhQhOcIsSM4ZJgZOWUdMsYSUbkaWHdfY7iAHouzUkSbnORJtmIuQt1TGRpVLdEaUYyO894V5orLAfxBbGCRgN2RAGxMHzM6QOifBHIQIRT3ZothMAgg51SiEqSUMEk3GjX2W3YTD+5+l8tpau8Lr58u6ji3rEJCnZuuK58uaNkZkI7IU2EqKcT6aCCfSrH3+24A3f37z5964KLznjpm577mnd+7DNf+vGPLrz8iit+dOFPPv6FL/3J3/zdSc979Xs+/rkr1R60bc9r7rt/DWkZkrnPPlsP2Hs3/8jmez726X/52P+AcCqlhXz9Wz/8w/Pe8Z9f/saVQnZ6vwCP/ZFplAqAiZJdlBExZz7qBtdpBbjsNIM+/r9ff9nb//Zr3/setvd89FOPO+ev/vRt73n+O98bFjW8lV5XdvSNrlWRKDtPpX//3Fee+/Z/+Nb3f3jxRZd87svf+P3z3vwX//DB2WyXgcuJ8y+5IhwFB7TkwRSUDr9NNfWHF/x0GbHLz48uulSKFUEcqbhxYJYmwyUhPLoaCraA4RWnnLZMGw/7jTtUVbr6KzL/4RNfePSf/eXr//Hfvvj97/xk8/KfXLL9G+df8Hf/9emzXvGmp736rV/d8b+ACfMR1zxo6x4t2aAnOHArswPFRZxnvva9Hzz/+z+RQBKKuyYpi7DS+Rf/9PsXXrgMusrPsde/1rWvfVCUkepOOZM6gVyq8CIH5IPAZdZqiI+zOfWQzuJy8j3vcJ0Dd+7ZP7ls+5s+8DEkO0lKc62LXZes7TXv+/DA3Kvo3baxeuaJ96wNTncKPMlwwVjgDEPSmfySK664yvCf7V6yHpvzmiLMZAT5QpkQDzalmKpMGxV1IZgoBIkJv2kqMZWUWmTJ4fzBT3ZGAKKHx4WXbKfBPJQxKaQ4i0XG7s3A+B/+BN/vzQ7AkG+URDtMYb737W9z7QP2oV1e3/3pJU96+Rv+7E1v+fiXvvCtC87/4cWXffv8Cz/22S88/01/e8Lzz3vnf346EkLogK1brrH/PsbdqKcQoSsQjBlH3+DQujti/9tnv/Knb3jPN773o59edvnnv/G1J/3Va//in97/q4j9g0su79y97egikipMksSW0g/ca69r7L8f7fL6/oUX/+lr3vG5r3z9m9/+/oc+9fknv/L1T3/9O/74Le+8tF/BJOpaUi/86WUeQbu8fnzJFRZOhcHBURzsIRz6SBqIXoJDgot5/WsdMtWd893cz37XP3/1hz+KkogbdXrvRz73B69+xwWX7YYVRATXkgPBq2mEh43rYXOP7dYv7+vd3ZpC/mXryxK3S+pJWdlR3Eho2t3d2uZ6llp5SpvSwSty9igisB68G0pZhCfJKaNyj5h7gqD089fffewzn/ji/y1QT57TMBXngmiPYZ/++rd+Pupnn4fut/dtjjgSpUeTWZlwa8jaiW96g908gCeit/z7x//mHz/8gx9c8NXvnv+mf//Px77o5c9989+/7T/+a9t+++23dQ8M2Gl754c/+cFP/a/3ZB9E/Svf+d4zX/22//nGd3caduXhjy7dLlGCOSXEvXbXINQtUBGmXTlmp/dPf/07n/oqan46A6RqYxpeL9pcv+I9H9ypAmDi3W51k2OPOIwZnUWnxNp3AYlNFJGM+PFPL8aYnTY8I9i8Yk1OTEmwRrISlphMQy+7fPtOg688vPjyy6kYqwmymg0tX9fCnWT4Dy7YTTXwSBRezqJCpCkFpW9qxg3rabKb32j3gXjT+z/6lvd96Js/+uGXv/edN7z//aee8/I/ftPf/d2HPxUQBwxBMs+AQYUg2FSPPfyG+2/bTRvFkDvc9EYH7LMleTgyc669aw/pk9pGG1ODEBnGMZjQv0SpAIFJHWU+BMAI7jm7dXSwa+1/4Lbd/S70a977wc996euEdVEld//0F776jL94/Ze/t/M33rAE2w8v3W4sXEudqJZ1c4Cfxmktv3/hzneeGI/thxdfIVqy1dHEJYmCPXNNP714N39NEXXvp5ddgvQSIcZgOAQWePFefvs2t9rtX6m/fB5PfeVb//FDH/nh+T+64IKLPvjJLzzjvNe/8d8+ag5soH/n7YeXXiYSkJ8lIj070QhCNGokCg87o7FZDI9AT8aWKbOV2WQg3zVRgIRIiDPLICyrCHo0ojjVXspaS4iqw2af3FdqOjnhYqY56q3NWHRncKLKTcLFkz3YoYcjcoxwiwgxrNaWT1TnEeiLNQTjEoZiTdWpb3Iga0lYKuXCZxeNUnJCCmDtD30SjqaFZdUYPDJizNzHYqvU4NqpDpTpHHjgrsLgdS7uguHMrLkYLFjlm0KKg65Ldxgqm8IGCDIqRA2akwxIVJaag2gO7ozSwLT401JbSEkI5CDGQkKFxJNnBdK52ItrLgSyUixupFAqj8gAaw0rbZNEvaEENOwG2gyIh7XKUpYWLggjTKamBbd+jNsgkkjIWOoSOSM1qid0U1VWaCo0OGE1RC2SE96vhCZmWEYYaDR1bC2tdqtgNnlwdsaTtealMW6GVbkURF8yavLK65RFiSHRA2KgBpLHyJnT1CmDFhfgGTkcBSAu6qNlXwluMYZmgIfLc74SXDiKWKYjXtqjGRJGGB6K4CQRfIkkvJwxAl2DWAtPJVe1rxrmoi3RKkgNAVnYJbT8YeFSo1YzUD2N3AoeT3rCjcaEaCUNZ58zzRK3QmEgaunUjCS5MxouzYi7cxIcWuMyCSzHEWwBTccYbp0/9Jmv4ni32wc//UXQbnmWIlIYCFSdcRPIFLJww0u6OotDPraAd7yxZY/nn/LQffbc+WEh5F90xeab/+O/Tzv3tff+45fc8w/PfvBzzzvzFX/z1v/4xAWXXn6lTUdf59CXnnryjQ6+BjQJM6ZE6t1vfSx2dtou25z/8A1//4Bnn/P4l776Ic95ycOf+8qPfOFrVfWpD7z7Da5xwE6DPfJTX/pqWLIxL9FGqwdxq2U7YP/973j0jXYaj8Ph/vYPfeIRLz7vhOe/6pmv+dtPfPU7EfmBz/7v1773A8khZMioax+yz5G7e/Ycme/4yCcf+oJX3vcPz37My17zkS9+NTNP/M3b3ufXj4HknbYfXnjRt757PiPViSM5kQU+iU15OX30f7+x0+ArDz/82S95n6tGoWXz8IG52PWKDPaIMF9iDzsQFMo7HnPMg4671Q44rxTws3dc//oPf3LOu/7pkS96+b3+4MX3/INzH/wnf/XsN/z9f/7v1/DYCYOY+YS73OYZJ91vY4ViCBoitzvyEcTE1Su3b/3owie84nX/85X/swANJkPVM/nxxRc/9y3v/PHFV2sVG1Ufc9/fIiUqyUpJklnSCkoV8ImcKWZBOXMpFMIzpYPEMmTyuv+2ff/k0ffflVRv/OAnX/u+D/dhLdaTz/2K7S96/bs+c/WV0Krokx742ze+4aGDLAA0D1jOS9nkVCONb3//gi98+3yc3GnzyPd94rOJGlAj0Ea4q6LuoZBNKpM2eAJ8F+ZKIale1Wt2CiYuSJYrLrn8M1/7/k4yrzz80Be+nMILkskcnEHu/J0fXbZbMzAFEe8SoYhx1iRmF+UosmVjj2c/7L574DtKDLr6dsn29Tv+81Onn/eGh/zZKx7wrD8//jnnnf7SN7/+Xz+KoGQuQ4849KCXnHbikYcdItxlMdsREUou6dc98FcT+0OfOv65r77fs/789Je9/iM7iH3Cb97uPr++mwz90U8v/s6PLkRkOZfnX41D1FJ6eF8V+Z1b7SYXQMgPfPZLp7z0VQ953l+c9fI3vv+zXwEPP/+t733sC1/uO0jTKD7ypf8zj8WBq/985Tvf/84FP3EEmBfWGGumlADJhqB15WieeBB05LUOPuLQA68+dTn6wnfOP/GFrz7hT/7y9LNfe/envOjZr3339y68+LCD9//j4++5XL76z9d+8IOfbL+EGNJhF0gV7k6RX/z6d77zo90spy7fXH/k819kMSdaTJdoMlrpbctydHXZZJnRGLcyo9lAw09jELYzSjopgVxEVjhr1eVQcedHdGVEdwhCYXzyeW/+uw9+7PLN7Wvr86DR5bLLN1/y1nd/+Xs/3DHkZ29F5OS73uGAvfYhE/IVQRyrBVvPIw+91o2uedDPxl3l4/J1f8k73v+Q//dXJ7zotS94x3u/9sOfeMTffui/Lts+3+02uwkoSPiMV73r3k87+4yzXw9sTznntf/1lW9umdrTH/K7u/5/fxb5P5//eroYBxbc3FOBbmhBnihfxYpf7nbzP3rDOz/zdRRYLPxoHvmTy9YvfO3ff2qX3/s68ppikMcJAAAQAElEQVQHnfHQ3/VKrpysEPozkU6c9K3v/uibF1z0S7lX2fvgpz6XgIfJKgMnXEGur9fjw7v8E2e4hO0Dn/rCGjGcfMjAytNwivB1Xt9+xcWf3MWqHRfpo5/9Up25iEqR4CpwOZHpo8Z8qyOvf72Dd25nmHXxFZsvevs/PfR5f/Hws88752/f940f/9Q83vZvH99+eRcCXiw0JnJhggGevGW1cdwxR2LiTtueq3a/O95qI0xGzxFpbdmCkmiIjlKDNN0zjNJxFnapYL2DXeqiWJJ1SsuRMvaY5DePPWIn+Ti84NIrzvrzt9z36eee/pLXPOQPX3LWS1/7xW//YN+te/zh8ffAOwZcdbvkis0vfv/HgYfKSqgYDWxA26YcGv/22av9y9f089dHPv+VpJCKXJ+cFDcFPuLyy7b/55e+/vMhV/v82Be/gmfMKCekRLIkYDJF8CF77/eAO/46X23szw5+dPFlf/bWf3zgn51772ed/cS/fMsHv/C1iHzsvX7juJve8GcjrvLxte+ff8EVP5VCVXDDpQqooITR0RLhwEDOBLfJeUZh4sTaDfiV4brUELZlMGUdWZw8pC+fAtArIRRLFWCQuCbCa1UTbGFGXRnroA4cIH9ZiRoQIYNutFryyikSyR6c2ETRdz27B7bIJEKjp9yxk5zJkeEBQlGwEki5EiqchSkhhTAeQpY+hKNwGYzvA2ROzEA1oEWDw74iLiLO4kTLnAVuIhGeCOBMRCuXaSiZGIkvQpMzUhJPAgRqI929I8NRj6IngNElvMwdGxESJOG/MKyCnxNj4REFVB2ZAXO5sjCk4VwIJWWSEPESA2Fyos4yDDbjHAu0LnKYlcH5OgUeyJfKunQQDYI9uiq4WzEqjqo0OTf4Qgnrc1TxlcAnwAxKMWMZM9aCS8SkjYHCAtzIWEuuNUmsRC/munbBCpxGSsAuFgRLeuWZYZZmbzKqRoFNWUdv20dbWx2O76qaGw+BHMFN0PK3kNIjR48eY6jHFDhtQl3YVLMoYcHbpFTCqjeH5xi9R1KjuuKqBGATN1jBZFx3EK5ieaSVpcFpJqCTCL+tajRLmimNmEULg5LpRCnK4KdwCA0RL+Sag9QKVDZBsnWziJAhjChnMIGUXtSyxACfOTGGSiGSmb3HnI76CZ8oPdcjvvzd889++3ve8qFP0K94veIf/v0v3vaeL379Rx1YTauRikIwgpc/iYZiEhIINSU8kwKXjIfd4rrXe+EjH3KNfffi3Ykd7pev58s219vnPjyuHFJV73f7W77iiY8+7Br7cl0Wnp0E0eEx3f8ux934Orv5XRTIwaPij3zpG1/6/o83+7jugfu+7LEn3PUWR/vPZV4p+cr3573lvW98/0e/jZVTgKeForI3zsqkT3rwPfFomXb3glhsEYmLh+6714tPO/F61zoMRxbZEbrV6jH3ucseU8XVnTZMwMTLN+duvvceG09+wN0ed7/f6cN2GobDCy/b/sS/fMM/fvjjWNcqCZDV5K9/+zvPed1bPvkrvlz+3++c//Tz3vbF73wfmDO84XRgYRTgQE0tyP5BPuZhfepZvKo86QG/95i73WlVwQTo3HlbD0M4YC1sjoTtywA8XnrOSb/35Ifcb+IWliimMgyqCOm5XP/lz9fPv+BRf/H6B//p2U/7q9c9/RVvOuWFr7j7s573oav3ib33WD3zYfc95vo3WPKFYYYKcXOqnZByxuLKPQzVgpdXVkm8gpC8IiTYufEND3vBYx4IUv1SMaGSx4ve+b77Pf3cJ7/s7Y8/9433evoL8UT55x4Q6HfwPtv+5BEP/O073HbwNIg70UA9qYrIR8tLYvMfPvG5J573No+f8fCqwrF/zjv++dX/9IHv/uRHRazkYElB6laORrPQPPvYjJyhKCfunB05ZsDe8xNf/PozznvHdy74KYTsuv3Lp77wqn94/w8vupQzndebPv/X/371Ka94668y47y//8Cb/vFDF150KQ1C62IazFbBu1KOut71/+LMh19r/70Z3u6iaXggyxBZvGP/yutV9XduceO/eMKp17nmtbS0VkoJCiM3VBBOLlvqxqn3+P9P7H322Hjag3/3zAfdq9vuif3kl735vR/89KUXXCHbXedRoIOWkEXY797uFkfvLqlh4WwGHtqOLN5v65anH3+v425zC8Tuexdf/Jf/+C9v+eB/Ycyu20VXbD7lL17/H5/+4uXbPbOGL2W+kW5IrpwmyzYzz1m1nXLPu+65mnaSkESA6EvfvwD15PyLL8uku93iqFc96VEH77fz93iY+L0LL3nKK/7m3z/3hUttZE3U8Etne/+nP/fMv3nnejgG7LR55Nlv/8A7P/Cpn16yNm6jtWwKE3WHjzsN3li1XAgFrmWB7GCOmt4ilSmLzdK7hqEzT0pTZWysSld5/eTSy//g1e+679PPfsq5b37OeW9+6rmv+d2nPO/1/3G1Ij/V8tjfvdN973z7XPIZ3UAicWMkfc3Uc4+NPR5z99/cfWXL3D6PdR+ZqFZ87PUOPefMk/bbe8tD73Y7VOCrWPGz3W7+/Z9e+tEvfeP/zv/JeowbXePAv37So+50q2NjdxmHu46//cCHLrzgEgsaVMmbGjqtM+0G1SsVfO/Ci09+8Wse8dyXP/OVr3/aK/DQ6kV//4nPw7Yrr+JdhG9z+GEvfcLJ+22bWChZnDiRyBMT2xXjsn/+r0889bw3X3UKZv1i+/N3v/dt//bhCzc3UZsGEcz+/De+8YzXvfkrP/jxL8ZcdeffP/e157/hH7/2vQssde34NoW8xqe+9vVnvfodX/3+7qd86Atf+eM3vg1TBinailNHkosEO++z5+qM+951o6FgXlXJsh+JQPT1WFIPPt7yhtd50Vkn7bl16xS5xcYWjxrByOhBPKKJ3e1WN16mXf0Hs25y3UM0RKHbspZs1SvQHoNHx/QkTUhnQjUeJBnEUYgVGBqVkEwdDnpW5sYn3/3O19h329U1LEfzsG/9+CJk1jd//NNudvPrX+sVTzr55je+XkYsl6/yg8R/9qve9ncf/u8LL97OUliwjMrvfO+7Z7/27f/22S9fZeAvd8+/6NJnnfemb3z7+2oifcWjfO27P37m69/51R/s/kubf/vMl/7fa9/+1e/8MIlTjSqItiSlKh//23e5921vLsy/lP7zPYu4Yt0vX88eccC2PZ978v1P/Z3f2tzln+XA8O9deMnpL33t+z7xP5tXrCcmkSQJAu0ChE6sbymQ+1qsZE4maICVA6nNxTlH7BibWOMlz4gBp00xADTzxFyDp9gRH3XMx+bBPnhtMlRItBStLCJKKCHQmuyFqTKxuAutQ6TQVLzQyMCXFl0Tj42sFYhKCZZeK2qkUHHSYA4iEqJJZKP4FveyPWiNkGcVUhEWwg5cRHPlpQHOFD2ZBq8GjC0h+DJGnAKnuV5BbQ2vdNBGcCVNLlEgwZ3xxQyvNayYqE3USmyI1lGsx/btPrbDWOEqMJ7GkM1ebVRKJWIEMSSH7rilxeq/RiyP+oO4E6rzSE6WgAloy0ZkmR7gdw2eBNbOBEqjGDfZYHx5JCUYfkviTZ0lgUl0HpkhgRIh2IEF3GGykXcAxFh8Gy9QMaoWTqpYYZPBjqJtgphSH9WHYnBNksQnJUoJRZIz4qPEACkNi8rokErUXRh3KUMxELaE+tILFqMjCwlrEaEm2YRTp7GSLgQHR7hxesFZ5mRiZ1EmNJQGpBTuJGfyoEC/VweTlLQkwyKocE0mGKU7QFPVWuqUQAXGQjIh54EdpRMlhQSBJBE+s2+vMU8J4KkQ6o6tPIpnSVe2Vk3K4DIAILQpFYY+SVMPoFxClKTR1BZOJWy1EsZukUFspTtdsrl++qve/uYPfZJ+9aub//W/fOTBf/KyL33rfGOdVfC9WlenRBwQ9Aw4JynFQM1oAyjJcI649VHX/8snnnSv2x679xY8q/vVCohq0Vvc6LrPPeXBzzrpQdu2bFCLsfI157DGgDxkj7p63qMf+mtHXE+Zdytoz1W7522OfslpJxx7g+sDuHXvuw7DQ5EXvP2fnvW6twPmDAbSwlAVAPPQA/d+wSkPPezA3fwuypVytm1M977dsa98xqk3u+HhiOE6y0y8NppHvfURN3nKA+6x9x67+cvHmAvC/NoRh51z5gn3vsvtqE4X/4rfFvj2BRc9+/Xv/Jv3fUitF6CXdubLX/dPn/oCJOx2i8x//fSXTnr+K7+PryykaYoiHGEuQQpiRFOwFB1sZh219g1KxOex97n7ix7z4GOud008Gtit2F+c3GuPjfvc/hYve/Kj73HHW4T7DtpUDgazsgdlHLz3tqnoL8ZjJ5O+/uOffuAzX/rnT3zuY1/+xuYAmXF62fZYtTvc5IbnPP4Rd/21W1YBezlRYVyRLEtKS2pS4l5Ip7pCWjAVsJWlYIezgMq6SPHgnre+wfVf/eRHHH/nW2P9upz8+c93fnLR+z/9v//+2S9fcOnlPz9H++658dA73eZVZz36LkcfTV1ijVpRA9oUVVlAgSD/8Oe+8rRXv+vbu/sHi66Ug+/9X/Z3//acV787DCcakQgyq0UKmVNHJqUkgZeqJOw0OkfYvL78kS9+7Ue/9E3M2e2Ge4ZX/NOHX/LWfwrcHdD45o9+8JS/fsv/fX/3nRUSYMZL3/X+N/7jf9KohuYySCk2OCcVmuqRN77OeU95xP1uf0u4jMH/H1tROfb613rOI3/vmac+aM+994jGXLRQKwS3nNNIkkgjV7c+6iZPesDd/z+I/etHXO8vznr4fX7zjlLkkst3/ytSIPYfve7vX/+vHyOD7ECxYFpprlRy333an536ECxZfpW1reidjr7ReU95zH3vegcGyJ4vfee/nPfPH8FS5ldN+fL3fvT4v3ztRz7zWSUuCdeEqwqXgtAE4/YFOYu6d4tjDv/jR93voL138/fFr5R86H57Pf7+v/XMR/7evntuvWL7fOXJnd7/+6vfOuPlb3rfJz9fFXd6G7hBetKr37Lr34z6xaxLt2/+0Rv+4a3//LH0yXiaS8NC1q6IXwy4ckeY9tm6Bbaq6TTLNLRkDWno+MRSLGVYGpYfLERYf0y8vB+0z1673lScf/HlH/zCV//lf774wc//30VX8UKFb3KdQ170mAc++j6/DWCFecm15uAyo+BS3yG6HHfro59x/L3237abX+i60tQDtu3xyHvc8dwnnXj9Q/Ze2bzX1ul5j3vYzW94HQi8csBO76ii973dzc974qOPucG12fo8xk4DcPiDn17ygre99wVv/geiEiRBTO7pgaKDq7/YDtln27HXu+bWjenKM5H52W/94H2f/MJOFQBXD95765n3uetLH3/SoXtvbbPU5VED2OCJflwCLPnPr3ztKa9++/9HBbhsPV70jve+7n3/AazQmC+9+IqHv/AV//653S+aoRHb2z/0qbPOfo32ioLgPDZ5PvnsV//HF76GS7vdXvn1cgAAEABJREFULPLdH/v0U1/xlrW5xxgx9+KuS8XlQb9x85s8+f5323t3v6FwpbTFx3vf5WVPeOShBx+84IZShalCYTnWnmuLkZF+4xtd88CrR3Oq5YF3vY22kiHDSiZr66VFqdyIy7A6D/EwrQ4GUkUWxxIJTqrBlTRLGaKIkpBW3OJc89D9nv+YBx157UOYrzRt5/d999xy0m/e9s/PPOn6hx56RY8rH0bsNOjrP7jgma96+8ve/QHPNtqUk/zh6975lt39FwW/mPjPn/7yk/7yTduvuKKCwaZ/8Jp3/fvn/u8XV3faQe1910f/5w9e+Td4xjuYrGIRNVeN2mjaqGc98D4PvvOvb2l1p1lXHlbh3zj2yJecfsJdbnXLCP5Vde9L3/vRE8978z989JPMsUClmWDzQO9JYuFUwFOTATatWw7ByopUU6QRlqa+cJ8I6WFleBnoNRRKXilWZDUdUq3oLDpi8lF9ZgGvQ0sprVSRDZeWMDiJ07DU4QAXJNbsWF0Gwszm6oMysBBULxUrLa6Yv3yWqEIQUgQLS0LxM1qiiz7Bi9kRYSEcLIGlXzIVwuiiXIvm1KKUJFLBGyXzwA0MmkyXOmdKjNoHgu6APTBPMSSxlETjcVhlwmNyfNECpkEEiaqgNDE8gFibKpVFZC73HzkPGqgM7GM5hFO2qCRhnRJYoWZ0i5kIelRVHOaGJap/JC+Cm3ITYhISjCeYz21ShhOxA17uoETLkqGwzd18rHOsuY9c03L7RL4uEKtOwNFGBGqKKcNGaslC5MuDzbnTGD1H56gsK19SCYN2kAIA1l5WlBPyq3SK7rAvmQgjBLZB8kKcCOpU2VfNJjU8S6iaqPv4Nl7TjTw0oHJiXjEXSRFKzhBNsCA1A4cEPwEJDSjoWR3jOKsN7dmtdJlShYhQOQI0S0msbjiCOdmWQrB4vJjHPpZfIgM3EcZuHbgwIpexGMIiXNOKbVLOJJgYKgF0QnlsqG3UMUEgV/SzXtSC3IE+T0orpUJMwTK4+hIbr6034ALLmrRyq6Ouf+ebHXnXY298l5vf9E63uMldbnGTu97iqLve4sg73/xInL/LMUfe9Zgj8X6XY47YWqXg9mHluTIWwJ/UNaL2lrGCyK4yZ5jhPgm844Cz1z7kGs959ANe9/TTnnq/u9/5mCOOuOZBB++1B1bJ2PZa1cMO3PdmN7jOyXe5/bmPPflFpz/yuJsdq6AaEyYihTJd3EswTkbmIfseePapJzzvUQ+4y7FHXueAfbZOZUvV/ffcOPq6h57y23d4+VknPfNh977G/vs3bhvTdLMbXudOxx5xh5secdzNjrzTzY742XbsEXc65qhjrncdHxRIFCZ0KaQ60Zw5jrnRjV7++NOefL+73/JG173Wfnvt2coerRywdcux17vWI37z9q98ysOf/qj77bfv3hpZHSwoZHhys8Gdge3vHXe7v37yKfc/7lY3uc419ttjBe82qh5+6EG/fcujX/DY45931olH3uiwqVXkPso0jAcOhx964FHXPuSYw64J+be60WG/dtQN7nD04dc+eB/2wTZ49NseeYPjbn7UccceBRfufMyRV253OuZI+IW4YINTt7vpEaWCv1wItcUIC3kf2ZPAi8KMaG8wMwkTEZAlTb7jsUe8/KyHvfzMEx/527e71eGHXe+g/fbdMsFgbNg5/NCDfv3GN3zi/e/2yief9sQT7n+dQ/dHJZg518reRCoJc0QWKc8/7f6ve+aj/98j7nvq3e5412OPutn1r32DQw4AXAgrRGHbe6Pd6NADf/3GNzjzPr/xyic8/MVnnXjTG16zVit1pHRL62nGltpJZwSiMisVqVVXhXEgnIh8XWWbcNdlgHwE7q3F8pA993r8g+/22mec8kcn3fe3b3nTG1/7kAO3btmzlUkFrDho2x6HX/Og37jZTZ79sPu8+mmnP/HB97/2/vvVtOqozNDMvEmyFh0sgyTzoH223vHohSrA9ribHXWnY38O9dFH3PGYI688c6djjrrZDa+HiOeoTMhnp5gJTjiSq8m04W0VPHFUHiIgBMHkvPOxoB+2o4479sZ3OubGdzr2qCujdoejwUMk143vfLMbH32D60qpyrTHRv31o254HMYcsxhw3LFHYv+4ZSIOj7rzMTe+8zFH3emYo66x3/6W1ajJaG0ImzfxjZrEesCBBzz9hPu/9gmnPuX+d7vDTQ4/8loHH7zXnggENgTlugfsc+z1r33iXW77sjNPesmTTrzrbW+6UdFYCN50IjxdEKVSAbkttQNvwSH5u3e45Sue/MhdiX33W9/0pWec8NLHPeKG1ztsZA7qe++5cZ3998F2+KG7Ifb1Dj3EGevyZtwIDpe60oKycej++7z0rFP+3yMeeKejD0c1AGdg7b5bJqTGfW5783Mf9/Dnnf6IG1zzEMxc+dK+bnKtQ+50zJF3uOkR2BC1Ox9z5F0WZI68y7FH4vyODRE86qB995rCigSKtDMliKvFKm1yH9FB6SS6wy1v8ro/OP3x9/ttUPdKoIASEv82R1z/yQ+8xyue/rgH3u0uZbUVIzeq7JD8MxVQuuNwCQd2rrXXPtOc0+Br7b//nXbE6M47IohYY+dK8zDsdje5Ed7vdMxRhx10MFr20pSS8Qjhos3tdPXX3nvucYOD9ydOtDqmVCSzMq+YJkZ8yiA25IawFidkAzoEqfMZv/tbr3viaS9+zPGPv89v3ONWN77FDa97+KEHwi84BUixYQfJjruIk3/jduc87vi/fOojbn/Lm4RJIWqCh1k7uEum6m0aq9WYatYi9znu11/31NNP+a073uS6h6KybVTdNtVr7LP1Vje67u/f+zf/+mmnP+r+d6+rjYEu5RlO17nmNV74uJP/30n3u8NNbgQw97xqFf2NO5x31ilPecgD997YkuuhEbe84XXvfMyRyDhsiOCdb/bLQn30Ydes5K32ukJxCLKy98bWX7/xDY+45kEwA2z5y6c85mVPfdTLUZaPv8edbnbEEdc8+Jr7boOP8BQbHD/yWgf/1i1u/JyTf+91z3jUo+51hz23SDqRqyBDIwtQzeDBlGX/vbbdaUnVHRl3M4R1IRjYhe1OxyB5jzru5kfd8WZHHn7tQzmoDmpcbnfTI4/DyJshN3+23fHowxdO3vSIOx9z5ZmjbnPUEZVokkDxkaQ7HbucP24pNYejpN/hpoff8aaH3+noI447+vDjUORvftSdbnbUbW96eHCBFglGudousq4ANdn0AXe646ufduojf+cOCMQhey95vdeqHrrvtlsfftiT7/+br3/6Ix9+t9tPWsvMbDQ0Z6GZQQ/0AzwxwnKALVhUHnGP425zxPWKMO14Xf+QA2525A3WEWviwTVAB1wRkbKiumKuKPcRPoiMKmXTYKYFR3TkTCb2oq6By4vBOC5Uj77edV/++BOffcI9b3GD6yAoW6ouZXmvPWDqmfe6y2ue+ugnPeQe27ZW4lSutznqhlfmBTJlx3YUgMJ2x6OPuP4hB5mTCaO33eLIw+6M9n0schABWjaAdoebHnH7mx6OzAKYdz7mqF8/6ka1FQd+RW55o+vtCMQRd15itIzHIdYwOw4hZNmOREDRg3KggjmJZFHhorRl0jPv8zt/fdYpD7/r7dDQwSVwfo+pglH3ue0xf37W8c8/5QFHHnYNb94n23+vPcHJ6x2032EH7osBxxx2zZtd/1porLe98Q3vcMwRB+6/NwpIUqagDhnWfb6UVhwn8RKAYmBUEBDG0gpc2ZCykbLqOIsGFSQUKAE8mgAsGuTrpXEWsbLKmLhP2BMS5dKirkZpzKHSWS3Zgwh+uVYXddI5M3AiLW2Yede0LRrTRNOU3AIrX5ugButsxw0F0WAY293mnL0FAyBqQ8pcpTfDfbSxOAmoFcuyemq+LKBFK9cqnEyj5JhisWIumCK4pYqV0UTMQiNyoGBo74pa0JeVNg/ieZnmpJ198OxuxQUGVoSlcEaQWaYpG4mFjE54Ir08t4kwnmEyKv1UlyAi2yVDFgbD8CRh0soO2wuDxnWCkZOk6MTSUoQiMl2VSsH0ZHZswsKhQC2wxLfAI4REW7GYLGhIGpAXOFxooV0muRDuaxgLeSUTdsYpFsqgMGKQ2THLy0ho8xK2SlupFQRL1UkpIYMWB1HoOVtEmUmDgBagxpoOyTaUupJrQh75Omzuw6MTUxWZiiIDKm6uZBSCNeFpxgNrlSDyZATLnCOZRAnhEgn2jvu9nmLaXNEymRyW8HIh8ZQDGCNSFEzJ6WxOzkSNVDkIw4kkVbKIK7vA2pqjpouhxuaKfAHDUpgFIGvNbOlVsWEecAlccoG/AgXw35PcC4dUzdqsVLCcte2557YnnXCv5z/mYS967AkvetyJLzzjpBefccJLzjz+hWce/9zTH/y8Ux78wkc/6OxHPfSFj3roCx714OsesLeyrWSohBhlR6iaibpwKJMmEcFWspaktnICY1hLyPUP3O9hd77dix9x/F+cedKrn3XqW19w1jtf8IR3/+kT3vSUU//q9Ef9/v3ufasjjtzaVgoghRLYIW8QJ9FJpQqQIKcEPKute/zW7W75wsc97DXPPPUdz3ncu5/9uLc//fRXnPWo0+5/txvf6FqlqmL0SvbZZ68/O+0hLzrjQS8+80Fnn/XQc848/qVnPOjs0x/84tNPfPHpD3/sve4WSSPChKIQ4ATCnaDCDti218OOu9N5p57yxqed+Y7/d9Y7/uSMtzzz9Fec8ejf/717HHXta1dwOLLoqCUm0inK5Hjnyh0r6BscdsDTT77XXz7tkW/908e/5wVPfc9zn/bqx5/2xycff7ubH7vHxp4rLijM1ekZD3vgG55x6l8/+ZF/9aRH/fkZDz/3cSf/1RNOfMWTj3/5Ex5y7uMfdI87HD0CD7AoyZ510r3OPu1hLz7rhOef9dDnn/6As095wLmnPOTcxx5/7lkPO/esh7z0rAe/6MwH/+kZD9x76zYKWiJSlneKxD2Yz81pgytadlXmhDiVbIQaBK5uaLvl9Q97/APvfc4THn3eM0574x+e/s4/POOtf3jmG/7o8a9+2qNf9rgTHnjnO157/wMaZeUsAV4jzRJAwQnmKl6RBnuvNo647sF3u/3Rp9znrmc/6iGvPPPRr33K4972h2e9+zln/cNzf//vXvD773ju41/7zFPOe8IjHnPf3z728BtsqXXyaAnyIG8zcmAj9iiEs6FOHKgRoprIAE10156aKBOWHmnkGEGsoBhrQagPO2jf37vjbf700Se88smPRaT+7nmP/8cXPOmdf/b4N/3xGX/1lMc+9zEn3fsOt7/OAQfXxKSURq3apEMZPFI20U4tsPGtb3Dtc0574Ise++DnnX7S804/+QVnnPDi0x549ikPfOFpx7/gzBOf/7gTzn7sw/D87PH3vccCXwRbsieNlOFtxAbTVAqJDArvQl6UKo6rluc/9gEvOPPBLzrrYS856+EwjjwAABAASURBVKQXnX7y2Y878SVnHf+iMx/ygrMe/PzHPvTsx5xw7qknPepudy1aa11d+6CDnnfqQ88+86QXP/b4l5zy0BedduJzTzvp+Y8+8cWnnnju6Sdi5LmPPvmlp5z8gLscFySQ31jIBLcIlIm8rkzYyRzXPmjfh975dn/+uJNf+5TT3/Csx73jj8782z/GdtYbnv3ov3rqw5/y4Hve9vAb7FEaamMJLKE683r4mNECQwlCOMPNgjwT5axwHn7Ng59+0v3Oe9Ipb//DM97zZ0/8h+c/7XXPfPTzTrv/HW52Q1lVQ30BW4medPL93/isM97wB2e98mmnvfIZjz7vKSef97iH/+Vpj3jZYx/5wrMecbc73noubFw4pDBVYk7JoOg21XL329/87LNOfP2zz/ibP3rS2//oyW999uNf/fuPfOYJv3fsETfEYB1WIhtTITrhrnd4wSkPeeFZD3nx7z/0JY89/pxHnnjOI08CLC897WEvPeVhZz9qB85nnnDbo64j7okNOuCUcFYNxUZEwUs9Vk2+xgF7P+qex/3VUx/5huc84W1/+tS3POcpb3z64//89Ec99C7H4QuKJGHwTfT2Rx/+ssc86KWnPfjsx5x4zinHv/RRD11K4ik7QnPK8bc74jBwOSxudeThLz7j4S865eSzH3XCSx79kHNOfci5jz3x3MecdM4pJ77olAe98NQHv/AxD33pY0+++61uJRZ1djVyya+dv/PfjDr6utc46IADKYnSsyRPTqtBil0q6DruQTi1YYGl/rLogBh0mT035HrX2v/Ot7jxw+913HMfe/8/f9Lxf/H0R73+Dx//t3/21H94wdMQtXf/8VPe+KQz/uqMRz7+937nVkfdsLY2YybaI5O2YJkJPiApPcGsIsjT2SlAg+secuAZ9/ydV5xx8uufdebbnvuEdz7viW999mnnnf6wR/7OHQ87YH8O0ayUSkQqjDDts2XL3W99s7Mf+9DXPPux73zek/7uz578jmf9/l897tG/f++73+RaBzfkD9gVfODeez3vlPu/8NQHvODUpTKfc9YJ55754HPPfNBLznzQix/3oEfc7fatem2DlzsDY4nrHXLoOWc97NV/8Jh3/PETnvvIhx68114qeoPDrnnv37rN8x73oL962smvefqpb37W77/7j57493/2xDf+yZkvf8bpf/zYE+9x21sfuOe26I5mzOisxESo+zBCEpgCY3y5dKPrvfisE88+88FnP/6haC7PPeVhLzz1+LNPe8jZjz3hBaec9KLHgFQnvuiME3/zdjdDW4s6tu5Zn3fqiX+G9HzsQ8553APOOe0B5z7mQec86oQXP/phL37MiS867WEvQdxPO/FZD7sfN5eGLiyrKC879cRzHv3QFz/6QS869YTnP/bks8942EtPe9A5j3nQ2aceD6UvOuP4s888/rEPumeNCVujoimBSsM5i6yxYnG+5qGHnPGge77qSae9/mlPeNtznvTWPz3zjc859WVPOulhv3vcIQfvZ1Ii4BrShGkp9FOKsAoaME9J2Aqyrz3st29/91vfBGFFvMDvB9zptiTFAisoRx2WQgziUUFLc8RTBXki5ATWxVIRWCQV7WDYGIkCbSh3kKs1IJxwQUyKV3Sl373zLc998gmvfcap73zOk97xnKe+5Y+e+BdPeOSj7v1b1z/oUDEFvZntetc88LmPOeEljznp3Eef9NJHnfTSR5/w0tOOf8mZD33x41AAH/yAO9yCDbmPwiRPfMC9XnrGyeecdvILTzn5eaec9NxTT37eace/8DEPAp5nn3LCOaecjCx75vH3WW2dZiZmecr9737OYx547hkPOveMh557GiSf9JJTgDAI9mBk5ctOwZnjn33SPZkMWcROhEVLoN2k+rpSF/ajrn3Nx9//Pq982mNf+8dPeNOfPvld/+/xr3ryyX/8qHvf5pgb6Eq4gPcjav/jMx/6ymc88jXPfsxrn33aXz7+US993El/fvoJf37mCWc/4aQXnvXw29/8xggipxAxSQRZGEWEyzxQ1ySNKYmBbGZYmokvmaTkIWaN5o1mU4lCSDIWN7CYEAfVUTgR2uSWVIroUv+ZAsEDyfGeTCRJyythKy9tkAjRo1K54RLuZa2SNDQrCEIuGIkhuCi4RJSwtQQESmBd6+GOYPOaeM70MqQFo6YaJe52hsyhgxcXJQiXUZlwmMKVk6lEX8pWslHFROZWtBCxj/R14osCuIxxlWWDYSYTMPBixGZQ2tOYtLRWGllxs5GUUgpN0yI6C+zjJFXAHIRpeAJUI1vKol4nPChwMbA1BhdnBQJKC1tlaUKFhQuXCltdAndvObySox5SWYo09ULAkVIAbZGQCUcCxayrHVthqgIhlaEok7jDfkQgUwaQonWR3kroVKxyz0gL6hwjZ1pad2BqEiVm9mRinlg2sqgleyAeqB/U2Bon/oRhnDMHtsT9AsTNMYMTmShnbMxUlKdKOqVsWJli4ZuRZUSVWRKKIzhpilLmCYDAtyq0wDLzopCiCCPsbBROIMR6ZMdtoLjUIPhKydpVg1VZCzYFfWgjeQt6FMmKVJioMik3q9MAlhwBB2EbKxGHAI9qBX5zJXiazA63gEiAZLBuR2mgalnGMomoEF5MXrVviNcBjgo1zF0kc2RNxt0PcTJx4dp8S4kNgTZN3+JZrUYWPGsnJWGhrJ03kiaJqSUhi5gxMTEfdjhAYasae+857b331gP32e+AfQ7Yd8/9t057tVodqqasCxTDcBMOxwbVUSZu0iAPchg6onEoM2dV2W/LxkH7bjv4wL323XdL24plM4Wu8GBbVyiSYWKchgf3G0YTQIEwxHouxaUI8QTDe+bsiD5JSB3UsLGUmiGMwiD7bWy7xl77HrzP1n22bdmoTCnkpXRWJGLLaGhx3IKw8m81eUoXClLlunVjj3323ufAffc5eL999t5jjw2WLZkbTCU5NhM3p9OW1R5bN6aN1uryzc/W1bSx2qOVWovglZCqkyFRKuITNGbyrotsgXiqRaeKYcRErAp0ZEPBrJLIXqLltCQzAUoNYEBAq8iSzK0XXk88lIdMjlYTTHO0qe6111777r3v/nvvfdA+2/bba+uWPbeqAvRQmifuU3CB74Mlghk00sRzF8a3dKAgahIDgygsGq3IxrRl7722HrD/Xgfvs+2AbXtv22evumWFioeEDdg3LxoZolwqFQ1dnBBWhfWoc+lKiW1hbYzAiiyNlHsvYy02E0VUHlOD0VRJQG/koTknr1bTPlv3PHivbYfsvfWQfbceuPfGXtuaTrRkAqNcrAmaSuXCYCN0S8FNmyTgwglGQWuFp8alCi+THBcUWRarxsKoeM0LFGrxLGvPTqiV8MUnSWlpAt9AcZQD72kD9gRV41XPVRILUWUWZqfhqFRkKgSiRKLmzon4dsswOC6l1EWPRG1jmkrqHsOmnGsYw060xg2miUFQYWpMhYiYR5Y0ZVNktoZF8bEleMNRTLYU2W/LHgfvve1AbNv23HfLnqvSSCgp2ZLmyPWceJriRuGIS/IEiD0gE04isoSBKAIkS2nYY9rYb+u+B2/d7yAk8JY9Ica6ua0FDrMUqvts2WOvPbdu29hj72mPbW3Lnm3amKSWJQiVXaQnIpiROauhYiaZ+ICWdLFB5Fq3bdnzWnttu9YBex6879Y9t25IUy0DTiHUycg/8kpRhRs1LU0mlWaiXYiJyIiBKZlLJi8/ZJlz0pxoC0BDdthSoylPhVRdCM9ZEM8xivLe27YcvP++Bx6w9777rlY11G216avNbJalFWobqVNyKc1KIaYqhEledJbqUIAyCI5GMdTPxpmUI0sfzV2JGVHjVlQ1hV2ZIUEIQSy4nPFfX/wmXeW1avWUe/+Wak0TxGKtiW3wAOtk3clHEoopdbhrQd0ljHMm7dxCJspqIe7MtdY9dY99p70O2LLXIXvvc/D+e+9z4B7TPty2pDIVK8A90phBBeRaH5SbFpuD5jUPQ5fl5bungYoVwYOrw7D99tx68F57H7zPlr22bdBKenUro5i1NU+mRUolrmbgID6mmgftuTp025ZDtm7st+e0AmaUoE1tKav0pr2WWcVEnUA5xjvxAkvAJmWfMlE0syWrM4EkwrbKsrWu9t9rj6mROhSZeHAIyVS3btl2wLb9DtrroAP2OnCvrdu2TtNGkhiDZwGJ+KSk5BWF0kjpJGuiJRxKKKlI0opA0Wg5Cw0JnGwrLRs1p2obRBuBOgVrR9fuFeVolMFqKsTaVIpW4aYKaqJaBBcWoilQb51FsmAkBaRyEqkGODaxVK5KBV6uDJW2Fxo1Qo3FkTc6MR41EQcF3FByyuw+3KTSPlvboQds3X8/RGJvrhuDpp4r6PUGAiRgbFmnkAk2K+tKSqNVi1XhynrpJes3/Mt/gqNEdN2D9rvTbW4emBAigcoeop4JLbHwDDwKY3ZU5eb45CEEK11BmowgGSK9RFStpUzwkwLJAAGwOUvA0aLbtq0O2HfLPnttQ9vL1kIQBFuKH+LGJIK1zCjpTMzFeOo0mShpkanpVKLmaN3qmmQW7sIIpQBOCQ3lgVxD7FoT3UjZ4tRm8Deik4WQKIlQkSBevPG5Ta6rpMqpxIq4elUq2KPmhGKeAMUGDS+cteQohG01TQfusfWae+516LZ9tm7bs2tds3SRCNbBErzXHqttW9p+e2zZt23ba2Pbnm3L1BpygbmyKBOhhtFc2SRqSIF65gSAMZDPZXNsMcPXGhv4fgT6gswHyaDqvdKa04i9lCj4ImLJySBEJ7NTBBGpY9xifAt0yixLpBzQkK0YVHCiSEJ+jyGOhE/QeAugmlotGy031NDEE4OYB8E+DirYEIsoyS1pSsAjXKqvgL1thm+OQAK4N0PF0doFbU5EnHmm7EhGMhrdFyfYqFJOsEWsSiceMImYBD8kDPk4DqYspBsMunINFa4uZQCF2C4xl0w1yVl9Ji/kQr788JRlD0W7LEuAK5VK3JgmtZpG6C4dMweCywVQuyVMglFILV15WTmXcKKOTVIoEEuFRYKDTBvssy4kQJnV2SghjzrXpFqlCkRyIZ6KTIUhPSODMDsVgixwO0drsmBQBFxZ/OuMvCVoFGIpTtWSF9DFMhnTUwLsY9CFVyyTFpEpGaV8SkRVU8BWgZkoq0TrXruXTtJhI7uoF8DGyjzNWQcrBSGbOIUXgYr7YqMIjMsJ2GUwjBYS6CTDSBwvQKmUhE1EiCTDrGHuudx+6hTAUEOYCpEqKoorOUVSCqsIrIMXTExaiAVnpYv2Mrk0IoUhuN10D153NVcDToRByQI5qcKs6Ql9PfCQDManJK7EoiBAdFiJkgdsdxhAmJeMeqEL2+AAES0GVmH8gU1YmlPhIEpfNlx2lcHkDB7ZRAQjM9WpQA/XrggIRhqH92U5htvTStJ4KooEYFd2TsDSaF3JeJFsGkMdibDcxeEgS2A2KMsWoFDloqRt8xKjAAAQAElEQVQwlFzMyID6YqKsRFWRnOKsLEoCaz16dCcnCiETD3UpJswULACHAHChokFCzKmZBcYLXG3BS7R7RrJX5VaEBAJyhDsbI4ZgyCw5kiIRCKLJUsIyYT3PWoyVPJGVDMrD8sHcOeacl0zpTCgYVGAKTC1VlFWIhJGFpop777kSMVhapsWvgELHt3wpJCNXJs1JjKRnmgEHmRwichFASibAhkVTGH7DLY8wgjVNqCg4msthZzFeDSqRTCDIGEtMsR8oT8nGFCqSqNSgJcEvHFZVzeKR7iIhhRhAKWWIM8IkprjgJMSirAxV8NwsLGKHBtx2YKpFEME4ApNwL1Qo4CxGM+xlj3BCwSzm2JREGCZkoaxkUIqqFxirkY2jCvbDI815WWdbzzI7LwoihVLZMdYpE+FFCmqSpCOQwiSkTMSZCXM8cGFR0hLzzNgXNDxbKKbBnUQ1YthCzEwpMYr3QKWBQBUV4UjA58AtuTvnQNZCt6FpsMpozSFomYr6FGTADFSrmcIA2iESNiKUfQ4bNGATMexMsEJDgSstqWtEJEZlzQXRQ4HFiUilgHoIgsMwYJD4MgcQs8CtYGaF/8txjkUNM/SHEsMhSKRInMVlGA/NBsUck4zCJJgJwJdALY7gkGUHUB6yLF07BSRaaMRkMcGWxRQlZ2CaxjBWPCF/UISmsk80lNdB2HpyT8TOhaBAVAVUVAcelggRmCqKKyJqhRxhgNnO3IUwhJC3hStpSQUNsFQh5R1eohcKdCBGgAzS0ivDApvDB4gRCTNkUt4gXiWjComnG809kQG5hDREhqsh+EQUKcC+CAklJc9RBnOqU3UUBpMCeFkIVbKjQU7kG57VWI0xlcQxWMLUvfhCupSaopCrno0WbEHhGFdceOn7P/l5nL5yU+FT7nbHo69/nQLwOGfm4AJRiBOs9IAJLsUqaMADHiHGRLn44Z7JqgIJQRwQxwx1GoFin2sQayR10sCqEMW8NEZOo/NKSa4p8JzItWIz2XBbxbwK11xERQLaugCUQdIN0UMuGbeZigcDHcGP18gaCA+JEKdQKrkFjUzmpfyoEimpoNZG5YFNgFJ11mAlhqQwLmta6m8yhCTDtxQ2GJ3dPMwAJycxXsqqEJMrQk2sCyS2omxcyg4TABhhukiiiEAzZjAtb54cSQtLh2V2X/aIDdgElhfAikdyRjAFTKjJxQRA9yRjIWXEPUmCWmJR1OG4M1J88lbAKm1WCi4T5rhCkHmioRk5jA4HgYlhRlWwGl4JxcS0UiYM4JkALPkIgfYMZsQFDN5wacng4dKLkxdx4lG6CPzQDEHkCa4EwczgOWnGWVihzMJMyw92qFDiPFn8/Yc/8bUf/uxfpfu9O95qtSqCMVqalrL4Y9GBtC3sw5KLR0I9PHcEguGHZYBwTIxZTbgQwdTOssCF3Rwpw0p3HZHBMKIwkt8lM3YMxacuWRYctFgnMHUxHUswMcOpVCYFg4CDQ2AgLsZ4oONmNjIcl6iQVlMEV6UpbsmZK8gzBiWgFJQahRk1dBpSAuCWEc1TybKQlWDqlXvVYOQZSTK25JgpOyLmJCQyVdRCkDo4ZJAOXoqbkQelc6DQmEpvzVqjWlBUO7NTgSGTlJoIC8UCVaQ4rEpalEgVhdVFBsAkSMoWiYohhAuVWgPIgM0iXQEwExEniiCx8yDCRoAGymyJBJ7yQChhic6QHs5iqEAyQufBNmMznmfpm2k9EtikmAhQYKJColyJALn0QXNqx1VndqIUoSKxsBTCpdRWFFjtCFIuOb4yZhPyXLxSaQULGdYd12G3CcMsJuaUjMLGajF5bImoSUTQYEUMiQIfSViVanGdjFBNnHVos6Io7FkgVEtX5EWaZYJ56j7p2ChemLi0LKvgAoA9kn0RU33RyrIYN0wMSDkwFCUVZvglqlRoCUAkjSCJdRs+eSlaZEUEZOdIC+DgGkNmS0AT1IQWrb2oR5VR6+IqwazRi60n21x142BEntPJ2HmKJqC+yTCz3ilTQicW5LyoSGWgQNA08NhjFTlVQmn0QtZIV9lUIEC9AU0lb9DqAcNc1mDqYkTqHjrtoRtNNrioqaOkwG0PRn7h2QWikyCfcAksKKMUo2ZEpvjsWkEEYoJMkyI8TV03Nrl2AMDpYUDASQmwqTJzBmHVqTiFwUw0PMPB4BBHfnDOCHQjKmHJnaYhbRQQgJ3Iic174n5vDBGTiQBmsAAqRLEQcyakDbWsvgSJUrOvGKvsyBzrnGft68JDKuJHk6eOkWbUQ90r2UQDt3WUuHcJJzxqch3hc3LS4sGUAWax4YXLqN3iRjQTes00uHgOsbUPGwGTWsm2QW1i1tZzNc9TeJ2YpiDt1C06jSQozy7kalzXBYbMw9dks3KWJNAcO0zgEERyIMW1Ok/F6rSWsgn0mAACSTURBsQsmoTIYChmbuSYUL864tZiIXkdxIYtQU5AJcFcmfBoo7nJTEzCTbQEI0M6uTk0wJCAoz5yrIU2C80SFoko2Ex4GMJADwZn9yvxlbVjfRCxpDEwwRNaRIhazdVUW92YpKFWYh1hMJwDxQH86UWjUFXZoLqBASW1jsYuEGOUTmlJwy1sgDkwWSojXw1Bo8oI/kS0ETEhVDPcJ0lwZhqjbm4GbludAEqCvlXUXeYBNhfERRSrERphTiYNwUopyNm1DFJWUTCFPFk6tXUiuUpIcQbXSeamOdG0sgK0UJi0wGEwJw36GMU/DWk8wKFAYS+NiDzUqAxGrCAzlUnBgNmb9Roo4NpIkdVVgxvLtFjAOTRN4OuilFC6GKtRnLTsEbPQXCklKYkdPgzcHg5j10pgj7ulD06GSpxNZBpsypkSOTaQBs7maHUdi/mlioMzGYr8RNQqEXFaSZ5SZJQEh0aLUCEWUgqh8MYoTIohO2pEY51guGqf2BQWFS4VsQnmTLVYBVohy4YGnkwn6OK927CF3ESUTFEZwYTaIF5jA0HThXqROWiz82zqWVM4RQeVTYLj0dRRUJnBD4IlpbRWpLnyXHUIkWd2VtcmtU0rnvagWgt8EDDEQKLC8KbW0AmoaqT6IHNxYAozRhBCZEbZRUwqS2NAG+wcnuiw3cmDLNJjRDiINkrpE8EdpRSybCMLDZUswsv01UpXG1ymTmWdYp0kSGsiO9mYvAQkYuZiPLLPYSpJySUXCFoywOBSUzcEzctWCHQpsDw65nWMAJwciPRm8ExEklKIEE1NYRDJTMrIlS2lDifcAxXQZ6XtrWxXNoyEhDFnnzMyWJx5Fp6n9EamZRYUngloMZ5pZCZQ4CEqqiolYsNgaRaFZpBIUwuJcCTETMFtgfMN7/vQjy65HAOu3O7x6zc76XfvwgNFpXvNKAxzW7JSMlloJlgxpQhMG1arVySh0qB5cDdBTpVcVd5gLpiAZkFiwUtQEBD0cMRrUVSkNNmobcVNoULgWKm5ElnppHVCqNNNJKbKoDhmMClLhSPpQShAYk1RkXoLR4csCykl59A5l4x2rZHTulecGVyoTtaarYpN4mUZYEtoRiZlFpGpiW4IKcnI5rTiXOmorReG6WJsIbPQIE9soAAnqFdgea1lkqzNBM9dpplXgyfjKgp2s2wwTUoiSrQSmignTsASyFWqFrUPwTdUY40qGm595GYnGyGhnDWkdk1nT43BsZ1oOxOmTlmn3GC4H9wqlPFa2mbomsKKIbWqWw1jmVMx22RtBNExOy40YSQkF8SMFq6AW0xCzE0IoSUzcCyyzEOiIyYqzAi1YpGy4sX8SURLw1i0JKAXzmGOFb6uRwPWa3jk3EH+3pnmQr2ZcdCQHBqDs/sPf3rRK/75Q7TjdY19tt7nuGNVZ1bUqtVUJgLMETBZAnXEpZI1SiGSFFZ8EkUAo9mKy4Qn2hsiUyAzxoCXzJEqQ5pRixTXsFVmXZxorGguNPXQ8ACwNUWzkBb0rCgQi6W1JTn0jSbGGT48eiLsSQJp5jw7mWPpyN4kV0xTIebK2QjDKQB6LHtJ5DR5bmRMEpiryRiCFPJkSAzK4lQD7DPXPmOPozKqujm6akmqXEvRJiKZbjR3QXdmyhAfdUSbZWOuMhB+NcaSvpEh5mTQ2waixRNLo2IDD7/Tl/7i4KUG85TY8DmqoCxSYcHAgKUbIxFcaFVmGa4MOSbVqPZYzZieSI4MIitsVVKcUK26BFqea1rASh29+UBBo5ls09eG/ubDCC4k+qNTJrGH9ZhdZ1rWxoPJskvONeciPctwJV+k62DtokzIJuXC3LhM2SaUl8oyJdHMHUHDekhFVqTo/dqrexkrHGcTmE65rGw66yjNpUIap4+wNbmVVODcQtA9hCgz5swBnlplKpVkNeoGx1RSlAbnrGOIC65Fgp6UnK6cail9IHEpI5TAPFrVUdvsnH3IPHRElbaqTVdKG+wIVtFAhg9du2ySTEHCGTQo4M9gSq+LXiqNo8C0HcZp2HLIJInhRBSILLDW4JSEloQfJFAF7tRJY4UaKbCdRYk5lYOJBG+WbaZprTKruMNtkwR3MYgKgcBMrCHKKdTZZ7ZBZMGO+Im61DkZ3PXMdBfvLefC3SY3PKgRtlT0UvgC3ppkaMuo6AWGZJZadKpahRaFs5ZQFUYOknRiIw2fKJC7VbWpLrefjQJnE9GOJTi5tNlGwlEWk5C5OSnVjHTqzoMYEUohJkJ0bJ1u0C2cykG6GIdpsFYN3izlz5M5uCy6qjBemYUGwgTLHD2gVgRDlSmK97KwHzjPFA4FGAnMGXUaOEUMXG6iJFhJsOFKusCQLGxSBmUMAvGjd7JhFpaUxExSU/BOIoBqIcNiJo8gxCYjgj04Yb9NaY3WLdGLWFH8XaeUooxooaQsY3CYVCAYQTPyFAhnnUTwDHOaE01HyAFeJhI2UtAbY1Jk1lRiauinAe+NJNWEk5IdVpNlune4DI6CpDpMlg8YP/UqY+VREL0IaQO9SRuVkkVUSS0mMoBIGx4NhG2UKpCZCD/ADxgDswW6KgEmrFxSK7WqZTlHSeRgKGzmEoheUsmh0Yu5wDcETgOLZVMNromkKhSAC3FImJ7cOREyBLjkwodBlMBbhaWSt3Tc+LBFDgK8JMVEAvMQ3BBn6tDunFZrVhSKaA0qCIJYqRGpc5ikK7tXj+qFgpPS4VTCTarEBcOZCO+KU44oVcJcJl6qIgVCodRQT4c3tC2q8Hsp5wUWFlOZM9dCQ8k5HepIDK3DJdBraA6HOiHT7Dlm7w6TkUcUg2SWMmQxJzUSUhXhgHvCocSiogwjhKQkNQnmNFfvDPk4GUW9apQ0imRlaUxoUjKMw0pyRWgRgFBdmACqypqkS2QMGUDfmASIIFxaYllvQWuSUlYxhVFLjWHWwjnFrNDiKaEwiIQLc8HyfEAAYS2UAUMqR0vB3JRCokKV4UgLm8zxvqBcJykTl2XXYAsJETGlQGlLtKQgmEqLcgkCtDxXeNOBHIUiDkbMTMW5BAAvMoq4juTgQFwhGfFptgAAEABJREFUViRaIVQDrQv1gAs5CZNKCBY5JVNYJaHTjMw0nF01AR1rA1hKTOGUkURGtKytq1PrXd2UpBapylKMiplE1ly+fSnBulbYyoMxldhGdas5WDyUljgnz6Cis4UaiNBwLoUyKRY+QOGicUkEnMjlXCyXlRZj5mDL0PRFFDlxLm2vELHTcO44D5mOFCwZlbMyyWJ/goEQgHhTUslsuASsM5EKMkS8kDKtkioxA0/wHoZgJqeWURiLKPd1plVHIAsims2TsTTqERZBQRkaXjwVI7rEOz76yTf+xycS2BEQ5wcdd6vHH39vKguSIQIyVMq6rB6EgaBybSUa5pLZjkpHAh5rFOGmWhFSmM8WMrMboAwo1MbcyCYyrjFWDBJmcqJozDzWWGoQGanBsjq4DWrGACZzmDqF6QhsmZq8SlmRLL51WCYQt2PNFUbsKIM5iVYrgvnhzImuJ1Ny81KMmQlp5dLgJwupRl2q6qDJeIX3zijaNDCLiUgiW1eG0ZjHliA2HIVMoVl1qDhTxDI4F+jSw4ePdKPeeR7cSVIXQ9FSUOCNCQmmhTBLgH1iV1gKbHAVIa+0rtIxA8t1pBvVQpMgd8lQQ4EOVllRJatTuqDHGUJdjSbKSgJzPakHOVhNXNhrNa1ZZLnG6WopYyGIpiyo+DJaBxUw3iYfNb0JYREoGvAXIkiLCkMAV0L8YGmwDTzr7+zWjIsXjja8JiLRcRk9QiS4wh1TpvqT7Zv/+unP/Mv//M9nvva1AFSgRZZBy+8svegd/3jR5dtpx+uUe9x5a0UgfJoDvY4AK+wtZFXNKw3NKMa8hsGVomUVQj3XRSIQ0DQ4TyyxYKPZYHIKZ2HGLoqDyqAAMKHFFGZPMTRmAAU9wXiBBsqYgma/iBaEDWgNoBwpmCWl1JXgOcakuNXM1RR4voeSklxGAEEewkrZHDIpUwF4koENOzbirmywhrgmYVPqlsOcoBJhT9AbA3FAtWXBAIZV0CnTRKVQsjpRFsKn+qhr5IaINrDYUnIIRSgZY/EzXCIagewsM5ElZLuQC9b9NVgEshKSktNVvGAWD1ZKdAEGOLw9yMBU1AmQmcPJscCLWXjAjipKqtZorLJX6kwRmUyE+EgGxSAmYWVYy4Z3JZmCphEFw8CLQspSa5SC+5FNzk5sElk6c8AGkgbaYoMYeM1JQngJgX7LCLTkHMGlt9pRgLJEiAcS0L3EUB9kAS5ALIy2YMdVHkHwIjkiKVJxd+dWEIA6r+t6rUjUnFXX3NbSukavYyjPoi4IDVVH0RLwAlaxLCEwEfRy8R2BlLASXaiTGtISWChVBhESq0SU1wAk4eqijDVbERbQDwv8WDvu3vAsigbhEWzzXrASkYlKCc1Y/MhgjVKYZfGfdVVKAXRKlFaADkI+dsQABSdMg6fIiTHbhRDhoqiDrFKYlHrtBq2VueALkg0uiMVwnb2gBRvrkExPiQjUD8U5UH9oOiKQSRluHugew7BkoCAY1DSW39fTwGGa55zeEQBNTPaBZSRJTxlZIuE93KMhZZmYPVr4tKI6YS1fIIoinAeshnXkvOSNLcTNFGBWpUyN8CRGcqbZzCJFwQFOjIRo6gim4IBVGGAR9AWlE3aIVxRAg8IpTHgoEmkljpUmCD8yrC8Oi5GHuwUNWKiCJC+QGEthX9YJpCRTapXGDdVEcnBiRjGCOCbWgovEBVyw3uZQwx8pMmoOhLuMlaYwJZWeS9lapiSRZwboYZaLWq5BnJzZlGsrrCgmkp0oBhOgdFJDyI04RJnBSGBXGYbBSZ6YWiGBt4CAJ41VcRK45AMPN9dDZ1BCtFIKZeZS/IzJ2LxgDdyhJ4A8TyQbydWhgVyGo4FlwDANdHl243kUrKg6AszZ2eYwfOHglhI0odEE4rjclIdrkWnSUqiS1JgYVE30isIzuKsEBUWA3mS0JWMSxkVbFZqqCIs4a6eCc5HMIxl0QLQ8HZsFRcJCZ7GMVMuazqhvZQ6ioGrEncNI2FFnCg9BYBY3mBkPlXQRF7N7CMSIUXHUCcQgTAUzTCcTRZMLc46EPaVIyVqGyowqxIVhOrEDX+KEUCrMcMkJKYtqg4RRMfae4aGcxDCEHInLRMRD27qwEZR7YIgTcI4s7JVzI2RFbUNKbaQIaFHlEpgEulAkJstcgA1F9zpTBcW8uCk5ks1pPRJlZWaAUmJIdpfkTGETeBJclw6pYCID5eVpCYgNnBBxAizKxFWHpu34U0JUiCpFoeCSVJkmBi8LcpnmJZoiqrh305KmiZ5BGYVhUMjMgbVfVjhlU0qjolThfYrTFC6ZsEKdNQkI4w1wzKCPLIxXx16Y0aAFqBBPlFPcB68S93EBSBOrkTaRTiRYg0SvY67zgNvIACqVmyqCQjqyOJqLAdAqVTWdo2damBNeElKtgEVgEkMbYHSLHCm0I7DEapVx3+5C7onLjCi7eJ/EN4JQ3RaDIQsWU6j02tbUBsOziEgk/NR1w9vkwJ6KMK8ymidUOjMLjCzLtESprGy1lqZbcB4AmTiQsyaO4qcamsJoumtBFUS1GpEzixHiwsCUkSXDi9MqpAxQAo2LvMCSnsh2dJp12lDUkkVujHALOKk5i/X0dPilnKgjU8lpYkSVgTOhCHrESJT+sNRwxWktUBKkmQRcuAl6JJxz9ORGxCmm2if2GoRXCFMhSoAvC8Yj02kmWYsMTE9iDkKMibtoF06ku800RvUswUJgH2ZYEnD+9gUXdRCDqBV9zG8fd8Z97onvT/H8vXNNaUWoRTYjsipUlWvJQqndy8wE2QHqRihR0aLgg4bQyAAcPbsHGNQhQLhSVqjVoOIodovuSEDePWdfADPKcLJQlPs5GbhKkHZiYx9sI9M8OYiJNYtEJS5clJRhoZcxl+ylOpCsYkUHywgAV0kmxUiKQbHm7MqjCNfg5trA+mXHihuFmyWQgNqauTJjWMOetLzwDrjbxLzRcjUZSwYlbMKMhGHwoM851jSP7JSOP9yzrKn2yJ4QT1mYgBMxBQOihelEWZmXX3vKKq7CVQogB1taleXIK7tmxjJMpQhOSlqMdYztM+eapEejwGkjdsYLhMsJZ8iXRJQoRIWwdBGkzRSE/gH9SyWdRUwqlaSl0pspDdjQBK1FJBdkkgsEElMynO3hV4x+uc3Do8w59Sa5AtyIB6RUqqSS4sJcWVmnT37lq09/1d/8wSve8qev+ttLLt/usC+RU/G6937g/Z/+Eu143ex6h97xZje2TjJL6Qa83QdlqAovd0117ig4SLzmLEOQfsFMNRwsBmIJagMF3PIMTIhaTEsnRdhrwh/XMhrZNCBknTmyeCcakdE9ImE4ikBljAxIzpxKLVoEF8xkDOKQWmQ1aSuqXDaYN2BWqapNMSh79DnMIMmoLmSjzJpRkwQmdHXLCLAETwNg6OQ0RSqFJfWsRsJAi4wArk4ExYK3UBWiRlk90sNnXrsg0wJSyQSLukgM4EqkRB46qnb2uee6s1FNgfug1ci0SrYSwIRo0o75mZ4eYGtmNBkrAqubSZuVAUwCefcEgc0gmqhbDItwL5kAXAmgeq/Wm/UKcCRLSoEpwahqgSNqSlKdStRKU6FaVRqT4uHClIq6MqF+OAQxEbVgifTFCWKGyyvyZoEqIThXImtQ42zk6oNBkrEm4BHek2aKWd0LN4YC8NPIM2ZCYGE7sQsNBDUoUilUrAAlxqyRjsLrscpsHnUYIx6MUc45JDrr4JZAEbMW0GAEw1gwOuvkbaNXNVyCwDqzrgMaM4dp2KrEYsoquWYykMNpIqbWeNUYySJpZN199LDRBWTRBE8Kojakw39RWbBRaBeFFVEcnlAycwVu2iSKjOT0AkzME0EbGlFNcMUdX+BNM9c1sArHOnwO7aPMg6IbXKvKO9xoKSVUoxVuxNUYaY5eCIiHDaNFKGVXmoXmpDloOEhAOUy7tphgJqKuRLCeHKpTMokoKGDcYBqWI3FIBOIgyrDfqs8bSA32QTYyMHaDcxXBmzE2+3r0jnk15xZrcnRQsAP1Ngj6c86A002oFqpgWBXEFjRFiIFQ8Uy2IWmMcQqOZioTZvsE94cgJ5qg61thj8qhhTDCkqGdwhEM7wa8dkTGZ6UuzEE1qACwRlKWjUjnXG1GXX4zrZJN8E4zm3FdlxIiQiqInAlKMpryQH7q1KQIMc2ccyFrU1ltFF21qDoKExjXZxojAhmXPYIMWqTAhiDxZWsJIlCzFLCeWAjMWCqcRhKsZIqI0cfsQewr1DtJEKZTWVcZnImcGGubFzXz5OsKywTBjSGYmJiNMbmEGEsEfFIhLhTsIwewKUZlyX7uRD4q9wKTZyJ4z+m0OUPyCAA/t+GtMyctQKsiCZCDQDGzUBKNnrmJdjgAGUlqQ3VwWDiINpH0CE2XZk0tM93IN4O7eXSDaaSIpymvWTZNI3ziMZW1cgQObTEknIaXgblLBkEgwG2miQQvYpUZrEmmoPR0T9SSDh4GhSd7qgiBMQpIKLokZS1j1dYbFb2Xq2YrXXMdfS5jrAavnJZH3tAYUhIJX3yHYIhiFrBRnKt5DRecNyCXzESVczJgjHhbj/ReyYG6L0CzEsoG3hdDOF2yi1iwrEfZnHVYBbDsDZ0vAQUGUzbyRjJlqkei/FEKPJWV4IkE7BLTGGojHEBm0mJrq1wWT9WLILc3YR5booxldyseWyw3nCUEcV/HaowWBEawUXgQXpoJ13kMzWip1VUcvrGuhKb06ibEKSxQMERRVEms1E6tZ4XvvFhK4mgSPBnEdkZyQB22GDI2ZXO7L7Qa6lRVFQ24EjGGrYmsaw5SQ8ab09xpzOyBkek+j3nYTLOrKZCgKJY5ivlqLMVeJahaSvocsR0ZR0wM0LE23YDIARWkeAeUYlQ80XngSqgauNNQ8wbzbBFIw5kJTnciVDmDRIWBTMtopuRkQRkIRaZmYQxFLB1UhVkmdeRkURBf6CcQEkueSMwSwuKuavAErAzqpEhOgQDEyhOdukK2khV2UFY8qqMLECE80EwJpF0YcypnuAQ3p8kEcyuKMRtMFVwHAKnhjBMrtxUHq3bZgEkwWmqpFdXOCW10Bm9wTiEuIxHlEnXVBTKrCOEsp45aulacp7pyQbEALsYtpIS64QuIiDCYYSuaJx4r4wLsRneC7W2qq8qIS40gSiIwkDokiuZC5An0Au27+hBDMuUZ973LPX/tmGvss/XFpz3ksfe9y7Ym00C0q4SGCpFy7ABU2KRlCpnHEKcpSs3KwJ7VVCQjaXQdjuoXhRKnMNdqsoJmSEnFOTYNrBl9ePWhPNiDe9ZEnzZFigBCDuXgoiEiygUySJwJRkRgAnOpMlWtKsxBTApGBKwAD4cmeESo9gldgkzZZLbCVJNkDFuHrzmMyTsPdENwj6ilTM7VokRweAbNgdth4WDQqoHTwugqgwn6sFiQKoQOTEjgki6Sxj6QgMCCBHgLE+liOGVwkjLjCD+0vM4zW/YAABAASURBVHBUMFYMVNi+1rFZaIBo8K9TGvZoyoCpsAOx5BUVYQ1Fd1wNKkGcAvsTSQdYI0EE2Ee0kAMJoHPAggyOIIcnRmSCKarJIC5nJouTBu8QxDq4mGA1sSAO9ba0r8LJ+JEQqJvD1t3W6wRiac5Bql0ZAAgxlmYblVYTT8BCCPTr1L2Cu74gkxSZ2L7+wwvP+Zv3/u83vvPJL33pz1715vP+/t8M9hHttWV1+v3vvse+e3vRTCCXDm6ThSzYbuHFACexBKJNE9qgxlPWnOiZvlGzQCdmGmGF5OyeKH02JGdCFXPULmbXlhAIZhhCXEewG6XbZJuVsSDItBjddmhlBXrqWaA1xJVHSicCkBkGVvCasOyrsVacrmgl2pPmpGAYT0TMnaLDENw7Fjwdm8YqUjyZerKxLqOYWVlSBEXRUkgETNbKwoRIMbQrDKR5zX3T+zwPzzXqkzqrLZMIFceSlPpUNlslhYumNnhgfiAJscToM81cUmutIpMShhUiBjg5Y64FOcwRqVEL8pDYYUdygbERiSNNBvspCpsuFqUlG5Yay0qAAEVAmo6pIXDEcF4ZiLkJ9UJzU58KQyHcRYFm4taoNCkqoNVEgRUE+0a6ZufowRmqRoq9ZVGcYoteZjRDM/EQ4KsjSyd1Gmxz63Nz3Hhp0akqdKkwIld0LekJcE2KS6pmKaxYmg7UyVqdVgoaIPF4Vu1tIm7ZNdYUDv21c521eqnGHO68Nl0HByN2IzJMhUolUNNWMZrBfFim3mndQYIEhlFpkQSA3IA2x1BG8cdbCQYaa6VAI6hcS5lKZa6pJZpaQ2QTOiMNVXtl2oRYiSVAPhoE4QgPPAphS3LnDI5sEQWRykZZEmcY8KOQDiKk7RxoUGIB1xQ8UyViSqgEckRZGPfwAV9XrvAqCxbCqHqEiDmBBLFUr7AQgxqB/ZFQFQxcmWEXB02SkwTshATjGg6M4Y9pZs2EPqhLWh5sqQS+L5QaxS3HPMYIvJx4CC++pEeCQQlZLWlPH6vspGliI9xgBAlhNq8A2TSp1mQxMqcQeOIW3ge7KyuxDsIzBnPyOblHNRXhlB3+JCml1KSWGKqQGkwRkoQxMJISlYGhlzFwwZiA9kjtBBg4cxGjbgmrghqBC0RG6ZzYLYUqUw0tA50TgiEV9okC8KgzSwpepSAR2BF0FD0JzS4oP26DPNjMh+NecbbaY3KvYqIQDWOFXNyJOKZE6yca0oM65ToJ0yJtkVcYIwwxh+BEygmOIzMtMj20Z3OulXii1NAAfYILKkqidmLRFyBpRrgZe1AwZgMjYpJI0UyUUuaMykGMoRyWbo6iIqbkDTcx7K5IibQeGRAB5gESYoBMTmlpA5MgtwgVds6RnokrKQypmkzuYO4wg7eZQA0WmrSR1ZdHkRQO0gBN36HdB3OX0pcBiLw6IdYsLI1FmdizOLdg1eKKKoJQY4B7GOTAHJjAXHJhFEdiOMAKEV4J1jpChBZA4Tp5NpTONSZpABYlSoJPCSyXoOOYMxmvxORWWVHtmIMTZjpil9gvVmQwZmL5bIWEmJd6JLZIlEIqVDyBIOVmovyEmRX3iXyVgRKxrHiAui1DhIzJoc8WQwhuKXFR3ailEkIlCxcbYT46A/BcwpUlqYQsDsDWcAnj6BQQ4RaEBkLCghE1iEIwYHhxaa5M4WyDDedpaWYdSWaIHCBguEcLp6EYRUAoJfEJfgUlJmp25hmeGcDIgPGsQUxJyqIYra4lSjI7sYUlTDJDuaVRNbWwsNTAMgtzln0INoxNymVzJpc0Qqaz27BAK/dguGpQ7IT7dEGGODjjylSIyEmMZM5CsWJSZU4MjykAWUBJIgjVCBZhdKVFJw5SPX2QGfQihphDhAyhZHJdNtYEG3CYxAyTs9Qsagy4osM0DxCmJmINIDBGKDXFgVawmICDEAefGCIFopyrscC5zJ66JlBwYgZmoXCPvbDFMh1yqiOvYSPsSkxVgc0O8zhRRPFMgGvSwjYWnZoi8xh0TGEutXCbEm2ZgILUZMgWH9nnGAj5MkSrFA5NFYcKqs5lhBBlhCDDe2H4TxyZV27izLgeEdwt1pSmTMIpQeqiWSBUJcuqVp0qrcC0IahmCdyL+pTUMELwUyAVylRdISxkqu1pD73PG5/5uDvf8kiYwhQldUoMhfVEpmBsMBkQYYJ9FKB1JsNN3BShXiPMiePFTncPjNQBp4tiddlqiqbH6POgtQFSBk0VhWJKPAHkYlPrsI/VCT2lDNwFlORm1AANSkwR0VhxTuyU4AqXXlvUQpjAsIkIhGeFU7CNKNJDLWqkJIUSguCZ8MLYg3HVoZtRA4e75SA1LkG4KVuxr9gaJxN5RviyU0IyBYoNuImLmqiTCoCx5A4diGASppCQlMbTxILoy/IlOpEGl5TGbVIYaAN0ZTQUHsJB7th1a5EwAGMh17M6vqIb1C0toBYx16zSCaSkTtI5O3lIKlEh2MRJyZk4Sl20hStGiJEOK72DUpGEo8yiBinwTRJjorJIBh46yCyybggYd5dhNChJIZAwDfiER5qjnSCqEtNKuHJKceCNTbMK7gadBWEA+BC5Y/YUJKMo7zi7vL3n45896XnnPfYlr37Pxz9jMIlIRU7+zdvf6vAbLKNWFYU4hLsshoQnIgVyCuyrMNt1xAr3VMgIjhkQwkAOdVR7T4oh5hjB7gibZQQ7hxNqyo5CO6lsKJUCKsyTUIPCSkAwNIxJ05XhNBKTwygI8UhuBHxJghJR6936jB3CdfIgxN2NxL1JNJCWcSIh3SVcrHMkw/dKVaOqr9yLuSP3yXbAhlaiBSBGEGdWJVgkTE7kSWmkuJC++BLpzEvpoBTnEg0CyRgemowAc7QkwYjCwC5YLH2Oec516GAOUHWpuexVUp3TM5PhUlAsqQASeo0BI0g0NaNGSGBFqbirIHigDEbLlInyqgGtEQR9zhsBrUwCg5th9ZLK7EVmAhJqIshIIubZ1UzBnSq+IdZ4CNS5t1E21lxn0llxjolUQ2WJHGhvDkPJgn1k9HQoJZLFCCqgYutWKQonCp4q7GEhqskTeAgUWFynkKYpK6GmMAOOM7EwKgk4UJ0lqDavbdRqXIdv9PU0vJkW4+aBhYGyw1XncJkJnrtRdua1cKC3AnXDw2XISQbAxbukIRK46KkYNwA0EYkyLFQn0FjCNb0STTRpwTKoEBOJY1usVskZoeuGGuEAwFiI4WRlAGi+2WPT05I4U4eLBzepq1KpkJcITS5W2qau1gInpAgiV1PEm+Sq0LRSnCGuiBZynOBcuCV2g8km7Sse+L4QZbaJFSXEE5orzTXQOVptG7wxjZbjSoqMMvcaWQrLVKioR/GxQdSKUKkuK2+VKmWMjE2qXRtXXq4lOxUABFBn8U3pVqi1jS2racKXAYr1Zk0lbiSte5vnmh7aXAonVzehhdYiDO9tAvVWNtqMtho9kDjCXDjEbMx+xZp753QVpVFgBdZK7KOhoksjBjdqY2riFZNaLXX5KfCoMEBmCiGgatKdEBKYAcsJLkzBAsGbpENosRVVeh25yWpcA+GWJLyEvZS16OwUIYVWyhP7BkchI2RJDk7WUGEi9qSRjIklrNhmMfQAjbWMNQ+sCGmU4oqn+DqT9zTLxL2TCblAGELPxKUJi4hn6TT1bJhTlw/VjSZt4grPYpWGOFcMJ8mSpM6UC6EKWwmKyG6EPr3GOyIF6ICQe4kUmUJX1AuPMmWwEjwtlWqFlERyiTK4DeqV5Zc6eXlSMsdSh7aTbG+FptYq6zSQbqS25HbxGfwq06CJexELjV4NLgGYJQJiUsrEYB7YOHArPzfcwGMEZaxTO0oXiJSdCg0Ul4lZqTCySrow1v9E0YYqeDprGgdjA7WUqSSwRq1eC27MV/BfZC68RlhGaI9i1IDM1KkOonRi58SbEpLeKWeV0dQmJBYDaKidaKGUsKSUTLQYllq4NcwW7sBIEZ3iBPxSNqhNukVkZVP1iZldQM0yoiaKjEMlKKc+JpxlgxFOE/EO9Eg2RmzMS7KO6mtxD3PrxbwQEQzaoo6KRoY/GQx2iQoiHSUJ7XMjhQmw8lxoeT5adFDzAdcX93LigMU1iDOSgEALFhI8azFVZlcy0MUITOHMoByRaIjFkzWyUCpTAegRbXYUdzRzHkIpmZSZ4cOwXscd5lppLZhNGcIgUVtR2aK00QwWNm8VSQuCpjrJTDTISde1YUOFTqoqbaNwnZxgLwCcSWfwsxZqzMqwLJKdOJisWjRnpSzsqFNMRFoY81bRp5gpzZUH4BPRFtTEG2O15YB0TgmMhowcFN6NhwmHrJI2hCeRmqzogNyDu08RU0qJClW+pEwjXVawkZ5zEAObBZ8c3DohNWUWiuoMvaRwNXkzbTMhyxXKN50HwhatrmnqIUDODS5RxaqZciKUhCl8Wrt0dhHHLVd1556cqpAqI+rcEQ9lTvS52gw1b4LFCiqU4UgV2qC6/CqwqGsxVY/MdBJHlXJmE04mLlxrKSIshRLIgUERJRxkcApCfDItY136uq5N1mXM03YUzmDzqoGKSyKE9UWKwdVCZQvVLS5o5knrzLlyTJwbnKWyV4nGhLuXlfeCJB9csgg3sSKbSnVb3W//rURAtczSEFPZCGkIkOVIQuyJBIlkczq6Dw5cFLSUiWqbtayVjVIkavXSjJeqmpKQNNXRZMB37xkmnsWwQkAFokhxryBrpsJAQh90SS6eG7M1nynIEbRQlpCCDmVJNizYCSu+8Hlwt+UA03En46ijFSSr2qPOGV4cFS6hL9gigrPAVi7AQGhNiC9owonQEgRqZoNuEqDGmZHpifRFuRirhYxkWYBtIDDYfObonIMYmxMTppRCDETLtIIMgsxwKkwtaHJgB2/Ws22uh4GhrIh41kIkjBK1GrRiLbRUDh2hRsyUvFhhQqYQRjRH9BEe5smjN96cFI9EZZJalUnCGR+KIMsgWdOq04YDxg7IIStYsXEIr6l0Ka7QHZHR05lcdWBJzTgxCHRCtSpQT8WkggZFsNypgaBtFG2lEXCe2JU5EBLrsZ7TKMBCnWSj6iq59qbOSb/6de/bHP2wu/y6jrmZaaZPbd7ScksNLeshNjIluMGroT7LWAfPoUEIg67mMq1JfbAT2yp9wxwjEbxR6zxVL6UIbA5V4xJUmqyqVs4q1LTWIlkymCgFEosIC0HWHDaGqE3TKAW0o3SC6t6zU7cyF54bxmGq0MxlZFGSUkgaRYtOYRQukS2iwW8WU+XikqE9fYRlT7QMbUI85eaKNgv4imWJUA8aGLN2smSF4JwKNtmy0qnIRCTuQUy1rqRtCYSAAp1+ooCsRPWSLSLVTKynQYRnk9DE/e4Y5uFJ6UJZGRnfRDklyYJmSwprtF15u9dNL+iqPNUVyZ5aV6tJMJRZWYk1vI5onhsSGyWqBpS5IzJdIqIKQE+WTOYkSOeREc7cK3mDds6uAIgwQCkFkavpt16CAAAQAElEQVSeJVUdArewTkpTocKo8R38duBRY7SYSyDUa/CZ68y1E+K5pBuRsw8UhJw9B2gkhsWioARmbSo0BSlcdu6ao3Uce1MQPzVW0JmmDAgZH4HohyCZQQkmgpZpVo0aNCVGR6oRRyaNHE7wJ5WEKC3UEqFBPHML8qcMaUlwzphdimvLAsBRaSzXawapZ+F1sy42GPyObgDOVYe2zpjLnOqMfA+QPMRHgnhQk5RZLMhiuPZolgL9hbXwpLAgSiaziCg1ziIuzExCVcuKW/EKczzJIQN9G7AF9kpa4UyC7Io3GCuFuSk3KpQTZ02vw5f3QqIcUmMp7gg7UGCyIt0hpCpJZUQlKU0IeiJYhFRSiIQ4CbGGLMvJFqIuCTM5VVTy8FCWVkrD2g3JxMFLPRVKYkF/cDONqMzgSUo1WMGUGEUm7tVdaaCupwQRpoB+UMpMhBMMtVKywiHGK4PcmCC+ExuMaiQtuDgzqTJXipbeJFQpmMGHxCBOvA/yzpud5wQLMBDgxISgwVdhzGePak4IjwohuQBrocACeCKcdRPY6JLRRsroRPiIIMSVIipnFRSgIimctbJMBWSKlSRyiJgQ26hDpl51Xmo7cVD04iEJciZGCTgs6ewO8zJLkApzy5y6kydxlWniiq928cREGCfNGKFK17DFWciwlXnrUSGT2KFZSbWsYsWGZYf76GxZUoibq/iUAeAEputUSp3KSuvkRUySM8Gx6gTFM9Nyw8DJRGCh2hYyLJaCtEsiBZKCaYkRhZBZdmdHDynKJUsrbSVcmdnCbMkFzGNSQK0xkCg2EZqoUopDvmYsVE1IUk8dHCM6QsJBGRY5yKx0aiYKpZ7hmZGQroVqTaRtSMBBJS4YYAgTEQFmaB2M9QSCxRLEVXOiaEGVMCllxxCCIJyaZoIOSszEBhwTg5g4UgMoQzwlS1JJ0CxcAut4XGUwSMasHh4ZEYAmKokoY7CDF1woITQHyciC3QwqxtMQ6VjBTUkShWyaQ2biHUa0mdUgkVNY8WJYQI55aWkIKFkSbEHecLpQAgZREjVuawbVKd1r5CTJSohrCBNpcnUpM9jKhBMMpxbVmoAN4FDANg5jdXjsLAE/kOIFAUZjSs9kIhbCMQp772pdwzmDg1zSVaUp6sEEmzkkFnmEtwQw4omOoUFMvGTMSqM1J/SqQqsiBWVYa+Fg7akZQrKMq1XLIow4Ub7NQQxvwRtR1CgccnuycWGmTPGoszXka1gkqnQ4xjAjtcgJirMlAcjlhKcwBRwQN2EjNsghDuwrB0QZ40JYMSuOiCsKjhLCUGQU9UqIzmDvCxYqwEQSs4Hm8gFcMz0pnYup9ESTGx4joXyJCpEWzaqMSWJB4FEUS5QWeOTKiU2ICNcDFJTICO4Z68UmYV5SjDmXLZKHMtaTXQSOuxncmgrqEAYFI6qCd1CEIAJJFon+g/qjLFjt6oIHWMmQDmbBfQJZTJlqamN04lKkOussdZPKTNKZPDgi5j5vmgM4grnVRQIRG0Z4yBTdE9YSJGLZK3PWGfJkAuTutnYsldi5umqC+wqRCzgeS5xEUzMFwfY0Di5UgBZR4tbeGSMQSFAyaktuaJUVbi2uMSpRLQuDhMkzZokuuK4sACgGkOeYIguYEDP7mqkb+4BgjEbACwSIUKOQhFdDYrtap9HDLDFhVg5a6mNqDd0QaUWlMDiQaY0BgMNYCOKoEAN4M5F6zK41lxIly3xY78wpJBk1BVZnMhgQxIzrYQm3ieEhCUBmOJxJCCmIhALNmEhKAUOpsqeAEgEfxFI7hVFwugohw8kjjRXamVdKsIl1meH4KPAxqHOaUJdwMcxbTBCFLuORtE6fwzOjMCsCVWpyJYDtnQNZApOIYBlzFk4ljMR6x0m4cJkIC/DFORaWkoiD03BZo+wTNGCIKGkJTPXKCAOnFU4pVlY+SRYRrQrJ4gygOGOwwZyBT+9KASGamZJZg2oHqrSbF9Lg4b91uyc98O5N4aRTH6UbwVcSYiaW4IWnxlqk4dZoIle1UMcFHQV1RjSRXfPSdxohZbWE7ACES2EwlVIWfk4R2oPHKDawXxgnhQOfCB7ATGYWkhLakkoGDWwIkMQyKlSWWVOIJGWhaBx4ILmhhtsMCAlGH99Q3E9UJaVBMpyhWSovUSlUgUGmwnq2SsZBkLvEJhEdBYSRYHqYR8AekuxqnRFdEeZCUqqqSoXIcBBuMPwvlFVClaksIxganZircpsAZ4EGQWlQacbshNqWMSTX4qBFlZxYwG+FnUpJwl5yiWxj02IEGCoPoh5BTrjIveRmk44JShNHI3hZwIoU4mLSBhXTKiyQlohkrvtyn4nC4fCOFk8rOztlpMNFasF1aJiYQwUw56SiRmWpwlwZW0iOFDItHW6QOmlSI14lTYSQZXjsUGBGqGwRg8mI0qx1lCNbnMssDlNDukkfzbv4gBEgUlQUWDjeYA9nFHI2SGSLMNQRqkwS1QNPVHUNDHGgQHWQYbVEEXBnSlMP9qBQoSIiTIKocCKJYaxJhgRyrdYo7OpJHk42AJculqfj2EcnGtCAioNlv06NW8uCsGo2comZrC+mgXtZU8RgFYLEyC8G/MwYpLMWY+BDVJi2MBcABROZgihN2IrA6BwRm/CAQ5x13ahvZEwZJRU+MGtMvN4o61qN4I0LRMM6Z+EdMFIu0fTCSULG5CWo2pAEvE6qhUWSImnsuIxd4iBiHlo7I7fXbptqhgVCo6YoXEzckN4TVSU2TExlx70UotHEV2EanbhTBFZejQoxgbwBhqI2IPBONFg71QEbqzQVIM3kJMSMCrOauOHpzyQkzICMkjO4zJFBMwTjSIIphBAiY3aLWBOvEfzQ3KFahlSrHJNzTWfvQBHUZ8UT4EqhiJ9bqgbKD0XKzDywu0Vo5aNll2nERlgN44jwsMiFgzjUubJzBmXKRLqRpWkT3mBZCTq7igpNlXCOppbwgWEqK5KACtgCncmNQKeYZpmMOdASRhq8gbsoRlnJU0B5TmNKSpjWpW/WecbYpcyxBSIzbca0zoJAjlUw2FpAvbpF2kZb7hOY5xFotT7IWcZUs1Q2rZ021rZaj0QUJmXcLitWIDyYxKRGq1p8Yq8YuHKeKBSRJEDPnJUYD+Y2jAoOGi/5xuqAaAQHKXMh2JCrCCVHrDqgG5weLL35WDkpMREQKk2oVkrS3qe+1pgzBzGIxIxsM+rh4LtMIbUzr0v31hEbrp6J5oTm09xXxCjYTCIuJVkhvXDXXEsf6mJ1GrUY0hGXvZZYlaRKyByYTxFm9DMeRGoQSOLrTJSKxJNW0xgSw8gAe1ByCHyMjC6x5lwnRUghEiAXZIVdMoI4cmqIfGEoz2meYU03mz0sZaiOBux4C9uWjVxtqaxbuK+yF1qwEiMUWFie5ms3OB5VWEg8w5Y+4l7Ny3AsyCU7keUUJMq5kWPDewkMzHn42rIzESde0Xlay2osCLGoklZKQWkpklbKPMnIGOD3YGOhGl7TFKZoCkWlXtMlETdtIVNQBQzFesNSdT2ydxpzotalEq0EG0uToOaMilk788xj0ABa7ItJRCRAkx2l0VoxabxkSQ1ZvkSbo65NFyagnk4TwdxkwvjFEYCwxJ0JDjuNmbzDRZKkGAwlMZStUUhop1yTz/gUoFQlRYozd6bO6ZHsM+coYZowPBJhtkwoqoAL4beZxiiJpVRMCxHhPhNagqLwtggO6x5gBUE86loNIMeJDC+gWGMWGA0q1MjJhaEiWbIrgQ5emBujV6hiUIEQS+8K44V0x9hRBbxlLjWBY7L3wDPLtKEwSEKAX0oftI4cFMEEy2GkOT5JsWJHDgtOb6fcDtcGcMPIIHelMXGqIkSKYE2Z4uq5QuggwImEeDXpaoPrBtFkCZ8IuFkhR24njbXN23PAeethoeSNBr4O0AAXYznVTdxInYtlWrcwIiauUdkDLLZE3jA4yBqxhCJh6cLcalJMOSO5w2cBFkvRhAWLqxy9dqsjEG3iRaBTtWDDwq+ObBm11da2MG2xqHNkEBCHjwiIYA9deNQYbTbpkRawz8FAr5SVmoqARixjUpsYPrkGomVJAtlumgAxkH2BhbgoDEBoSnACEWJ8q5csylLTRTaZxw6Xi0itVFVE1BJpacOTYJiYFi+VBP6lkCZtpK+4EzlCvFnFUI5G0XUtnWraVEZZEaQzEWVyOOUcujycjjCA7D73QAmFRRtaJi7KgeLVszLxls5bNqkB03QKY/DRi5NucnSs3MRZBymRACatjUoJLYtZzSoay8S9sBMTvPVEGZNwLFBXA4iaaifUohZLHpIyqwq024rmmiFSiRuTEBWjaU6kQ+NsygV/qAQlq5H0LG7ITBk0Oa0oC1MCVqHBnkso/39s/V3Ifl+3HgZd1xhzznU/v3eLgh8VaxWhgoIFT/TIQ48EQdAmeNCKWFO0VcQW6YEVJW0sqVobYxqFEAkBFTypB1U8tJTmSLCtSZOGWJMm2UnzuZP3/6w15/jwWr/dI/F+1u9+7nutOce4xjWuMeZc69n//RZciA396TPjYBfftrsO/iP//n/f3/Hv/o0f1/qsuYZ/rfnv+dXXf/4//Z/4/f+9/+Z/6+/9L34+k4R+XqlJLbv8UV/qr2xXzRsFgO3OMRbViYa55RCOufVM5BlvrAMvWjqGgLVkMgNfp4fYVNSlIXZu7odbq3SMp+2BGtPuLtJlmny5aw02giwbm+vBVERKJntY64J61BSvEjTCPOqr4+d/H0NFTImS04RBA1/R/EwTbPgLakUtwFuccpiZklRqzN2HIzijZ59iPM7kzFqlxrbFZb7yTUMBgT5hG/MoU9SJ3Toth3JRjtYABE3a4LiMa/S6Wp9q2XMN2ZBpJpqM2eUKCxxOm7NtobVDYivzPrjcP+m28877l/re2KNj4RWXuJGL5ViePZ6YkWLefZpdBodLKr/OOoHYpu6vHj0OfsX4sc5cCYXR89gUL02EPzm+zW73W7uWveKGGrsERQG7oHEaBUxCKnU5d2IW1UlsRI39+itU2a4+cjlLuA1hdfo0nmPnXuee96G+Kvqxg3VvIXf1o/GROXkzSGuiMwhtczQim50O1Wag4/2cef3S9utJrV/lb9Ko1Wyfccdn93WkGbUp3z2elHdH1LRUomNZGavAmLbHfPp6bBwNHkbqQFkb5O04HrrR2gmHsUU7G+9VCaUVk07WZT3cAFmQqngUf6dGNRSFBVjZVr3QnML3fPfzffKoVZZbF9AYjUnz4S/ApBW8hTqheS1jmZWqXzyB/UAvKvnvKWSom3ib+oJloYqM95Br1wKvXqUlhGrIJjfznamWWcdkVPVixysk7ylKzc+c58eQktROBqv30/eBjj4ErTWixt6XhfQGEmm4tZlWbtRR9zmZ4RQ7BiO8tF4t49U1W63kBMXcjOktVTMFNlGpkxygRDQO+o2yd2RnCqKyMBQo2dFomEHmGWplYeGcTqXMKaubBgAAEABJREFU6eoapo1C3toZJ/Ot0SZrmIgddSwSu8WB6GUAoQEBFghYQy5kXBGImfHe/4gNYXEkHFK8a9XBUEI+HVMnNUV617MQveN9WZLNtJZHlYJ7sNPCrGwa6AKeSs44ydjpJy7XjlbqeomTAKoKmei4CipJJ+GSKkpal7R7CCbJIYmYeRljnppppHM0V3KMsLEpJ15p6joeqclyoXfAzS5ouhNTg9kuTDN6PVDbLlZ5iZQs9pu5ZRJ6Zc2N6zRL0I5VjADx8rehdRkRJU+T5QM19VkLki+YNmA1kfButYZj/Vp+UNEtr6OHlYd7LnCCDjk4fFRMLF0bpieq8GpGorIlnXdtA6TUHDrtpRhCQNMp3r19lohZgPVxqQBtLQPpKT31Ww+jw+JwPqVHNazXV79BQVezO1IS2aU6GkkZLZMbhTpW84s5rdtGusmUv4A5zE2ZLRfRmSnqDK2Ofqn8P74n8yJXm94FBdUpby/YKDCbu09WiUUtKIfU/CilbdCG0ZDA0buZLqehpypFjTVhLTZ3zPP4k7Ul9G6PklYtklb2MoFPKwvKyjwtqbV2wQiN5WxOk47TeIDWq9ABkWxec9IHSIfIhLFb7bwPMzTklClKljxxlA0No6q8vJIiJemnePS5CDaymFUVJeNgwqI9wzW9nVLBqYKYb2gymgoQHDIqnpUpRy+UBkrmpMoCRUsCbu50k3vvWjKrUXIR74Xr3XsTP1sNhiLs+ZIJUyAKtrTD0lxhY2lPeoMiv72G66JwdjS3c3/suSik7fr2eqAV6miNwtaCtV3nnFCOHB42njHz0searlwFI5CtijnGnIxBnaoIbkUghmkGYwl+Ik/hpENWSmpFwztNtjTkxfcqonXGUhmCNFE1CmiyHek8wnP61jD5C8NGPo2XaXRXvg2FBHQMf5aFXBODRYZRU57T8cgBrG2OMWdOPER154DojevsIegFho9jbqPHcKPrRXXFuiK8f2Gkquba/hWfiYuYRJsFEQ0EGYYcXUYUoN/edCuzShaaCsjbqAvZEof4aw2s8ipr0Z1pZ/dLavkUTE1vzSmyB6VmetO6Xj+yCDgV6Rg5r3NduZyuS+ywasjpWBgDKrNhvvxauAbMO3XwJxqBTsSp7IKnyU05ABqMToxOy93Kh0LVjZKrKpTLzJMn5ILqnNBGwMsNBm3nltFhY9j4Ai7ALOLV3WxtKluxt4y6UBgrrKqtXzuV2rKPVlWd8oC4KoisyfFFW5XFTI1mA9JQb+BGZ6RnD3cbPoQ5yXRdTl0u/jw9l6abiUtdfDSrcXLmWTxjhboW3NgCadgTuYjLbJGT7bJYgIQbKuGo7G46xsRPZ28anBJ9e7syzAQUjJlkvkOtaAY8rIMp/rpVGDjvF74xwF4VKBgRyuq2OpbPf+rv+rv+hf/p//Cf/yf+kf/jP/4P/eF/7B/4o//4P/gv/J5/9J/5Xf/1/8x//O920lz8mhngyHcjaOiaT2uX9XXGBQnkJwg0CJJmU0HUOwoj7AN7/8MWV5YP1U6rCDGtQ13hKE0itH8ahwkjuyFFxhHqeB15fRRmV+bZzL3saCnCAHooN/lGNGJYkxMqmJW1dl6xr9xm+tff7vLUPwlVzJTa4DTlTw4FOptoWgJVLOWrkH0idz6RWTXQi6H7Q2qEsYyhCQCllhTR2vroCsw5al29BuB1ukO0R0KjZF75VKieqYbPV260Eq09tPy82JV1Pekwju6JYzxgJ6AUtxNmFJ2m6M+zTnjD8XpRXquzhRsab0g1LztKiGO2+nRL5e/MQfk9QJR2qqsapfmWztJar5Fubes1SjeDu+wGqmAK6nLVAr6Apeknnqd+Ca2tcHLZRTNJuy3heMGPfBdP2qFveGM618SYouxhd6fEoduPcBFnEEsngQMP4YdBgFla5cinPDe1GZon2RWIAJQc84k5mh7vCj1iz1Yz6E+2NNQcVctsOmB1akdFi46payjedT00RZbatFS6zFzEqPBSIqsEY47QUrNAb5+tFBs55zCOBlrdqiwYxrwgikWnzZAdsQaK7DRZ6VRae62Ur0awglmt2RpA7WR2xvvfqJ9in6Z+7YJOd8ccvIZsjdI+YoJr27wd1XpVW4ApjsRG76NbeTydIkYpWKs0Ldpk0AAvkMwhvGoFKUs6YAXE8Q71n1yjbUk1LC28p9RqCvTkOG6y1ZqqE+Io1X+NkdHPznrKnkqsZ8396RyFkisfF+cnbOzs8byLGqoyJ6XB9aWY1N7gZBNwcOAIs3KYsIRIUG7PrnNnRgEgaIOLGAd4d0dWB52tf2ybZZ/Drxt6tDMt6dEIAPTX3Zx5zduuu8bJhPSR520gg4cjoLQ2bIPvMu5QsSkLsV/RGdygmlQnvkpjOITNCB/lDrbWgpacGL2QxNuNpPWiGLBT69EBlyh7aF0ToTzCb0OmKPsbRTo/w75cAgZQWWL16To+ellpGeKTeg4wjnmrb5YhDC21X1fMddg9tmkT4E3NT7zvCj02TgwZncsu03Dv1Lxw3HLf6+XdXgGG8CMnMG1ijJ7Zb+cO79ZiqilplV9i9MCi1Fd+24N094ptQ8mRUemo0ytW5stQIY9UDXPML6yP8tDQgOSVKn2j5UFXWfGNEQsFFClAWT00fAxT+D5KM/AJnwIUJgxlPl0h9bRj9TLVyILMdyuWx/qtfqapfWRxD2ifaHMqOH8S51geAfUXG83SlHgl56r64fZj9nWVXU/5Kd8cofR3Q1BzVx1rDqzJ6W4UXImh2iyc2e3Fq8fAaL1YmPqON7SWUGB6DRsaoC3p+xBp3jXvpnoDqpKpyTPz2jr2qHSDkkKJK09WIssIo3t/7Hx6WzVgsFm1cvvZ1gnkQNBBt7LeeErxbvOXXQ3P0Xm1KGe8LSvTNCtMHWNTLpqAOXLxWR5gfWd9Ix5mUWr3C32JA1TYeObXsaU2WCqiQYKqS69dOBJ8K1DdPInC8GiHm3DaFLRTrBICFybLtgxEVtU+UGW6AoRe2TqlBrBDy4FyCZrUiFHDQwC9Yljpr2OSMUqy9KfXmZ9zrVxuQ0E4ad5zbtP0ZBfaaozns+4fHz0CaJCETrLATH0kzWBCZzhj7nnJYI5ZEw2RUxsowEFZ71Fv8JcNJ7qhKqjdiuFOO/UO+tJ7QbVTs+GcUNAb46bpOIF+urX47ZE6uotoN8lLKEhJKVvN74k6DcOY7lwjpoSK3i2X+gcYQe9C8o1Cn82c8qeM9g176mvHlTlZw3J4QXQ7bhfGyqNHkupXkV7lyVly76hubWVPMbIzd8VRy5qBT9kXRPlZiGn0yc/S9iBaIxrZpA64r4XPZVob0xhDwvgAF2/TetdQbXy4Fg2vea3hBVUJHDRy1KuGApMrr8uW+nJ1infLaZxtK2ClAdGqdPY2POYJoLbVd8bOnTg2QTmdTRWi4l7IWejioynetdiqYJ+iY1ZrTFceMR1hpe/SOPFxvzgvce5z6PdYYBu29814MuNUPPXWJqtGBt9f3WYwE9d6+CNGKp1UhgqILom8GzKjfwM2jyGpE0VXTB3tCnAN7aveOrFDoM3DR0Dz67rzUz1hyXGMcjQctBYjTateLTsje2iT1RhJl/Ygs7v6GCBzpcphoM6KHOUcnzF+Ne2H61ETGNGt6aRs0zloM2mno84ZcVs/WoxyjLRRb2qpzgpTSXKGz6BauMgVD62wwt5MRPUJbnjoeY7pqd0FVW8rFV1AsV6IXvai72k1GTLE0/U2g9FlcfrUSwCocUTWeuhbjL2LA7NRZRbX4n/oP/Dv/bv/zr/zP/kf+4/+h/+Df8fnUrkddXBT5zTmRUhAH0Lt4rJaKPnoVkZd7aJZiB3aagDl2a7sPYva8kRNa5d+2IdxcA7VZlWR5TIxs6Z616CeXZzPio/ayrVxqQUGhGwPuXVzkJUnd9pR1zrX2GMUsmz3iIZ5LEBAxAax5xDPp5RYr+6uSBE49S7vLy8yp8rAALrkHVGSlDpswyX4cJQ6pihLFqpeY9+SCOleUjxp4ES7qvMMQ/XckAM0uXy4LbZGlFPK7FD2n9dplKFz1PbYLa6iU9YhDNszr6Mg0Wo0n5AUXaeDR8MVRbOLgLWkkPmkumQlz0B96F/k1GiyPyyV7Kq2zX7znQqJk3C0InnIR0Wwux5vLhtcn7iunkqrW7lJY4ejMLzL6u64U256TPtqVZR/oS8LmNb1DBN4BXmZvHfr1mmeFCfAid7ZJ3Hk17SQ+DJTLzPhkF2mzaavda5r+0CbIk3vMyuWFZUTV751YWbak8inGQnsNOkBBt0aTb1UXtWIEGwtCOO2StTMxytMg9RCQNVPqLPYVXMoF6eg3LYa1pJsjihrc6MVukb0euAPKNzeNvT04F74/vL9GRx2dX/qrHpGyZVyYWU8k2d1+57zGStNLWnQ2InWznFL7j+pLNZu1SDxYO5SsNX77n1UCxtWpDnYTsyktSRvYMIE2Tp+ElttmRZFwJVOz1TdFqzWqrHCxiltSyRO0Vxp+1i0jPpVvuLy1jAbmsInNHeSY2B4gSiHhoaoSUh5TnyQP3KrYqulQI9DhABXn0Brx5kUWEXppMW0xyk0VmJyiOegS61eNjHNR7sWF3943X7tUYVUS5JGf7I1IMSOjdiG494Nz55HLFnnlZw9J2QYmDgXH8vHjsRQ6jHpdUYcE93Tfy7M3Zmok6ZEluMd16VanlBSAFNFt37lkK2oz4a2jtaKQlSl25FGtCNil4Wtnj8MPooW7rFMlS+D52hLkc/T37tSpG0IN/bEJiOsJeMzKrxC0OaJS5l4MvPdbygnOZXgwb46PfqdGoZgp+qKp7BPdxNY8/N1rXGZhu1xemxhuAY/w6e0quzU7f2kNIS9TYlJItsBwpgFPJBdKUlJarLYsGRH6+D2fkx38F1CvdtyK0j14T35XKyh+lFeqAle9JbNErFBdFU/Tc3qAV0hOAssY41B+zjdxxleXoY2GkaT/QLa1TdQWuZGmuYO5KqwqlN8tCWTqmUtNBa6dDmHz8TYpmyIkhLKXKm2s0/lSVZ4h1ZoOzNyHRu5zBkLR+hh1iaw1d1oMBR8CtY0sxxUnD3Nl3FNqCw0vISkssPZE6kpqCtskfBS8kWqpejSdjKqSkRIs5nKXMk47tcFmioYdNIOPYFu776iLENpDfG0W7udQrUbnAOuAEaN6eppNmAo32k7kbrcw8JGEDE7vyq/wMvFMGWAp6/znrwc2laVRMGiYs1R4hVTkWDTNzwARCttWVWSHkfrZMuwR6JverkRmpyn6o6mYc0amrX5FL6tjlcbhtNXjiv8gl1LlXmo7KlF6w/UO7mDIiSO0jbHy67mSPiqGGmTHJPSror72PPUuZtHS/hrx80UOiUja/rLjbj2B9eDWUKDny+6zeEDQ5mP4iHDZvh43J6GbXUYzjBFOOpQe9HvOGrCYbsIDWDSotT4JRYUhOl0neqkk9N90MPnPcY9cTzNQkJYp+Zd73YwVCarD2wAABAASURBVBEXecEd06A+4d4/38sgwYi3aCR4piREh45J5VW/2aVL2SekIVWO9v0US5KJgECTlCphrixBDZOUV0wGI5WhEPvnY/Eu1ravceYqDiqNLsXQvATVWoXShK3hy22xrdHOUC39hFfGdOxZMTZwz76Xx2BFpQoqCtYmHU4O6yG2HjGq6dRLYg4JVeWzOr0f5IOt+zTI55uoCGy8QK3sMtWVN8buIeEVE46GLpe6pWjouSVriUuh7oDYb40Y3irGYWiKypv2WC5I9wFUgdEt8WoxYncsO86EncMtq3w4njG+zdVJsKsz0/Jo9zDDVQYUfwkhCaop9hant6jVNeSQtd6LcfGMPAzyiLshekVXPYhHHkSY1iaYCWlWn0KUuJaCGt5KhHmYMNZ56/zpkZiDY9Evisx2xVQNCDBUIedGPbLRJDgCLt0Y63J8dWvtw9NHXqAQU8YlCR2gMviWuROYVJPr43Zrnp3GpliqZoBsetKNvAqfTe3MkJWp9gBzXoMf6wvq29PFo4DsI3ITft7DitCaN3fNOoYbvAs7dZrT6kI6S/nuvptadaQGF92KeFm6sj5PWdM2LJodyhKuoLdXD7kT4POG7dKZuhlUYqzsNojdgUZ2yVl9sSYKlDALgkc5yfAjv6xnZCJaBER7GmnU7IYl27IHMd/410tUwfqM1hVg8OcLVXljP9zbnsBBh509+xn2QKadZ6gDeLqXl+7zniv3JTQqzHRLzMIM4pgHlKWuOkSpmkINyZiA99D+Ubm1Bogw7VQyBJ9NoHuEmfrWnraHh6pB26TG1RBuyLgsFyAKVawM6EA26rR/qxkl7OjunpfagEqdY7jPcRk8U2AorfgsXiUZ9OUi83xej2FvZStcIGAX+mIsr+HWHFnWsb1+7RQRDWXbNK6ZaiVmk6+xl0zF4szUpoQSSOp6BbJ4gK2jrQibOedv5/p4t2OYCyNNKf5AAafJVM+kobrinawUNSGCGLTqFtErqIeWH5Nr5Gwt20lGr5Offebz5K5HN5T1snS6RKzQ2G5GcJRds5Za3MVtkQxM75+9M6lkCvNBRlebXnP0nNF6PrCyPmN+ue6GfVQrv2yWc89xrhnuKqmwq/wqfoXep4dApcV+OSwySQDS3CVl67O6ib4XWlSou8SVPW7WkT89EVZVIJTpWePDz8ihkjrBm9r6zLRhyhJEW1lutvoM3AzU5E+pbT0XVVZplUASegchUgfKHWY9LQ0o8YEJsBH0GKvFjOqhIWqgdanQWN1dFR579VH5tbFffckXmvta58cP//GrH+NaYeJG7NQbauOFtuVpWNscga/7fO1zoYaEMHBhjZIQ5yyyBAauCAQ6auuEakTIFBdtVq/KVdu8aqFNEegjNeQ0u61HD2CUQ9NNvR9HpReJffwOcWSvvLoGjuNh3462SnuZK2XCF5pU2KQetvRy+HQdy8L6QAczxYkF0/gs7GVF5d4/N6T5OoPP512EFcJKjhiaudtu8x4T6voU1/F5cmhbEpK3PeDpjDqWNUJpaTKLOtP9VBdofOMkGRdy8NCruglDumLkMdZqBdWqJKMPtftn4zlQ1TVsbbtujscs58jhpFuid+P+3nmeQqir1OdIo8+077W0XKaVvzZZmkkTkpMVLZalrprZaGwhPN0HinZOzimSmE93AGhKQcMxPNY4PyxWnKpUjjHHNBvV85iFAhrcaedodbi2DQk5o5VTRZkpPWKVRPJm1lpLBdQcQCFJ7MJzVOenmJCAR1PANkvrdxrUKEYqkUorZsO3YAHU+ekqJXgIOZZLSIL78jIwhgGfxKegExgERokeuBLU16E3SKRdx64R6+uZX+kDLQjW+ZYdqrvjlQs13eSZLRkyz8AZVuY6NL5DwXGZfZlNeetZfpmtBdFW4FExJARX4F7yrZ+Zz5RwxZGLIUuRUoNn4VzWY4HzwLd3rWfO/EE5d0/+ZKUsnbaKDgnkpGeZZYnEitrvJiDqVcwhH+BEdfZ7/v3SRPSI1CKVpT2FNYMjzTulcbY6pEjuy5ZzLvgKeLXhxWcIaLaxAGo826XCdTAfWjhjWEzVtXrVZbEgXyaEKQpZDWtA75s8BC0HE0qsO5RTm+l9WmhZWywshmC2Fxenm4buU9EKPadi8XwuU3cAahqXGg9dvmYpt0dtVjUhhQISUZVHsUm43pSJUgxQtvjlnLTX5nExZUKmhxyyamZDdnt2LwLGl4QaaHOD5rQjlM5BiKXhGlB8z/QbX5qVWadKpOWNONNUX3m3nrXVbfGYHpzF2wK9MFpebFC/O1tYrFVTvOp4ZD88PcWsjbh4SzOI3dUs55k4o2C5Rs7V62usD9WxL9uwLR8Pb/O9WLPEw8D5Qv/o8TVsfCQgJKqRJvNL4lo61TIbVR3txdk0UZakWXAElas9tWDNB3YGY3FLHcrG8VLu2oNUgoBw28OTBnLUGFDtncmzWMuhWPsamAbOAXEKWDdb9uW/YTDzUdNVlD7XlMhFTKkIH+/4Ef2rUvE+18nZ4wN3QJmWcrZzmxjr3Tpwjvf2EfjR6qlvrNrKPl27j0KUFF45Zocffm6TnMncXhuachWHWZu6EMVTp4Jq674Q+jsaLpbapvq98jo63eA69bFYdUzW0XtYumtaQymKmmz3NsXcSl9r/6ZuG624dYHA24M84IwaT/lz7N54dt/ZtwTUgrb2fp2eolRlaYtjuuhzGbSOwaSCbDAFezpHtW4/ztZlEHOM65o2ucfekHNYTJZDbeNdQrI6o+t0nkyBHqxh0EQUNRLfU2o8L7w6CzdQVa9X+SPeuK8sw0jM08S7xtTIxz2WiYtQgXOhh9vRiUn56J1otHfPXqPXLH972NFu7akSM3Ih/rWxWGkTbdb1kt1hfVhtAW5HdrcWvn0nv/UJasWZUzN8+ppjKI/UEBiSjAaD8/gIdCOKMqL1MR0xRrrmVeJUn5n1VT6VGSteOVxsaYrkkac3tZ6Xfo1H910cOyW4OHyyJdMkQXfSjFgJJabK+Sq1V7ZvvOv1QK4SLdY6OZyj6DqfofjLmPBdc4fHnablvPdFToF8Whe404sEyJpqxC9fkvWj9DVBmEujZ/ljtgmDzVqrxif4Y+cKFVK9r4ZSkA1A5JqRObHXiHk1J0ZPrx+WFxvGMzzkTVMc5W/msjx6ZlhsaFmaBvUVuwyft8OIeXHgxZFbOW6jjMgRq5Lv1ouGNsuhC0IgVuuJcT+rt84Urk4fG+v0VWVKwopeYUNbkPGZb2I5XwqsIWBWLmQtDYer/TVpXehDWcNNtpeN/ly12KxWBC27Y6mHJEN7QNRZT2t/pet8JXY3uhvHxnZPmYN3z1JCaUoRcDSFJQ8IjASejlMb1Ys+RV+pHI9OS6WJCk3PtWhfVTPfuR0yL4FN+WGc1sw2tMM5Rb0dt7v5JDJlbXzNsaxl8357g1WkEjU+kmnvC49bGIC0UnnG8EfCjPl+kbbF8ZB/5v1iDHUQehVSuH2dax5aZrC28/vio0y7QEwzlSyhRnqXnkjhXOWOCVwH45w8Cv6YqB+4jLPd5EusR2RlgCkfYuCAmldjgtNcrUNdOjzDTpe6HaRS10TSKTvqsfUxDPOgEEbjm62uSn/GdfxqzOHu00FTIgmIMF6FJY/de+KZJfd+tytzTWOBgMZuwy0fi5ptsNbUQD/uydFl1B60NL4p5Lm1uOZCoymo1f5+EKj2TG8Bxx6sSZvQVqnbZUgbZj116khd3y0xoONCDsLc+JoWSquHe9eubOPHrsEl02W1l4WtKrnPTu3aJKyM9UiYkilIb7pYxdh8mzv7NoaZhK6zPntcHINjlmSkUmjchnBu4yEb7rQhsurqPeIxPJ0dwUhAufPhL9CWbTP5cf1DEZIfkqvdKRoFld3yuYpFE93n6hbjIipKFZN6+3mgMJoc5CVB2+VzLRlmOVGfea/5OFOHIhAFarcq7uH3tNuvaEVoG5Gz5oU5UKiDbCn8jelcPB/vH9RM01ABGKUKeNd8Snsarkqf7t1T66aaagv2appiGMYpJPBRrr7QZiBoMfCtmvJFDKhZmd6Xyfd4S88dpVLVyCxrabp70KfSN1sMPkrqzXo261inpVQ1DdeoYWei3MYSDZAfCU88oQ8VTEtMaR1o1E+HpHF0exEwDS+hG04OglKsJtOlxsaQuVq4B2/nL6a2R/HuuXWAB+pSk0JodCuyytE/I4bMDSWFA5SLlhfJxtlWRwregVNXqrP7ejBDFEHdpqPG7nWX2rQSEZB8j5lmOzHlI4fpt5kwxcxTLxirlwMzNa85l8AofHc9lbg8p/Sx2+/jEa+ZWC0EUqOfxEE/YJRFqYed+6pNBD9sKaE7jYr7EYdyg4KXaFR6aO0uAiX2NHt56cxKvvxmIXIcXCGYbTM4i9Z0JeltOhOoYudrTKu2WGlz6Xsb3PJD3ZufEhOy6NvmxtBwlAhI9Un/zGvBvFT30aL6srOqRzoPeYO7saECH8jV4SH2fVKigAMQuJ5+pu/SWkC2TKFbxr7T77SIiSMFSww+ADuB56lQAVu5JZlAyEofU5tWDgZpqB1+1/Xrc33XPDZs+hoYaD+NYxNDiScg0T6MvpKXY8y2IUKoO3iKRfoyN18NSqXe5TJNe0UkS91HT5WejOIBqZHiqCpCQB7krkqB7OCIMiRfKTLkICb7jbzQQlSEqMxjz7fvzZNWMELj7JP8KvsBu/S9Kd60D0icUtWRPeZY9pWmNWkdjMO1xzocozAa9Gjc1U9BzLZ6Wdzc55w6p6V0ceygybG0j7PBUIIM28c273Zr916VK7QIO+xzsG4b6tXjEjEX8NWQkCTCzNP9DX4X6tDo7vCpkI8yNAbmKtNGwaoU2QAMSoTFMiyFFefge4/vG09VUSkVo2bfE7EUR5XeUlhXtiwMSAy+e9wYj1mCBbE7ImcD3oJkiz7lodKzZmEeJ9lyS4diMwu9u9Fg7XYWtFFvDuvVct2VhRY/MjgIIRTVvC70sE2TehKuLO9Q2JWAJBVP95ODZ81YV8muIoFQQ21gDhumRj5Fbm9WWPUom8UXgvYcVAvzpBAJPUY1sxtpM6fMARdVIrJhbp29tXqFwjYgAkplmcNnK5QwMXuM72Vya4AET8smxID3WGUGxnatgoI+Bj7zWVYUi35g6d2f6g8EvYhGVH0jTh0EVHDd49SomN7LIHZIyGViWz0L4jU5rCbvZaraRFZmx66OwVyssYJrl1fIZWKg5siPY7EXzxhaGTfY0E9UPiejGgJdbTtFHYoquIz3Y5RHfejzMo4W2p/MFTr0eVpP08TG1CWOTHa2UWtUQ2cC/tCTysGZpXvDR5gm4sN7KkmSiOIqNX61sNx8cU5bw4cNoS0pd1zg1WFUdLphlVepp3rGWR0TNdBl2Ot6fBya6H6oM/IO9HSzNWBdvS3D0K2oFYXr+jGFCYzky+vG08oDfGOeXFt7DrpWAzj0otIUzrDWy2KPjJnux/0uC50WnUdeatz4AAAQAElEQVTrRtPwth2mjTbKSwuuTOgO9GobGM7Bnlp9+g71BIXcAmX1fgCquXECTyKekvIFcZW0rseQC5qq79qA8nuq8L8gg0bQEp/AxBikM0Z/99lZlLD7M8+XdFKFRqsr12TSD3kkf3+CT9g+CllpFUfPPHrf7EQXzOhezvuC1sOt2g6u29cNU3mIbM9sVo6QMlPbCvHiqNInMzft6iSJNWAtDgHqhX5ZiWapCmqU+XZuteR7xmNRQf/mTCyaKW0yr7Fg/IxrM4M75Ri1++5+FMRsCVyWlXwwITLMtlEalx7kEwq9ntazFCDDbdOqTWdlRrh2X5su+z05BlZgHUyptjTX+LIe4JMdQWtzjGPj28dNHB6dFgVIN+hAsc9oeZeHDNcTiiydJPwcUcbNUe4i1RBH4TUs20up8cAV7aFJals1LMbVc7mPWcat4rm6JiJnncmD0XBlR4jrjUXlgWwKaXSfxpGAgvugdEpgAJc8x9jTeqQ+sh1cbQSixMsURrLZB0ws5BqnGBu1CVUOGqjW6JYqZHZXaYW0KKFnlbHdhMhKuuk+5B6tabLvI30lDCJYDJeNHLJoxUbKvJ+jo1EKPe2xPi5mGYwoyTi0+AvFmb0nBB/O0bUKrpBEn4gYZZ7uSWtAh9alNXt1WVbdBan8SCmnXJUdc+GLmG2acq6lIya6DuMBAka3OdWhHm0iP3y11bRUW0v1Q7Dw77waHtPURdT08Lg/yinSUcKQImqgWamfLrLFDhyyPtyvNednzK8pxS2R1lpuL8whQpTZV4spQOCbgyV1jvBRl41pg2aSpUB4SgJSI/kmxWGrx2pfh9fpz0k70fmkh4jTSjMGVeCqHWY3aORAedeoMdonMR1UYNYK5NMxEDabaijsHsRlWLN8aNRFTHrTjmhBe6hG2im2Ge07rkhKG8Y9+2injtM/W/1gga2ZdjWmweSuIF6VZ2FqWJqGtLX00RB5MfSRbaK0SGEaFxfzpbiUKsVx2gp0gaOKQ/MbQjaIkqSItyvTg8Nsmg05POitgoMJiQBo5BSYKtFSAtOnLFWpyVRpCzaOzeQyV92Yv9aTwUi7W9YrIFyLj+kmCTkoYodx6pey437MYju+x7wnJWvhWLpNFeK2d9jH/Oussa/MGfGzeWqyuxnV/UbVjCMuzTcGQemeOOVRFv3i6xJZ3qFmWOpQqSZHj3ce8G4NJIV3JYtmGbdhQ80q8w30JfvYeKgHqhdqzrYJ2mWvd5CFVISD2rcpAaN9bVM6euJc9tAf2mOMMY5f51x1hpVfZpdzQJhTPsXxhMtwSZ09XI4jgxqLcJUaT4/ALCltGq5ZmGlL+5MyViusCmVylM+iCXQ03jOHfrAAPQdclP4qU6zwsMvlstX6Wh9FAcQmJC/tcNS+Xf0rCkLRwBhYjtf95jiUoZ7ds+Kr1GcEdqw5RIbzIr+Mw2VSBszeSKeB9OaFukSCV5jyEfrF8wP5K7QDgrvd9qIe0hyvoxPK20+pU1J8j7AsMaHSkGzQCntacER4tGULOzK6syrBVIdwH++8yu4OKmZTA2JD5T1M79IN4NlLxzFLtgEUGIlrxLHIUaX8HOlNTORBnurOIAowjcxS9259E0nWymIYwwmSoievKE878EdMKLkKlMN81KsuRdgl/nE8c2Su815WKxY80hII+HGPOeTLJvtiKQ32XhyjrSY2UyJB5hQ1KcMYSFuhvmY8lDDbmxMS7NeZV7gmQ6lvJPGGQXRSkgBK4TcBcdAUKWp5UwFhVHrxJeaFoa/uOQmxRPC4pb/JpgJXMPqo8Rft1UrUkCUOutXr1JjOslnQ+VU+Y3oaTldWNdsHp/lQB9UeUfksYXGdNYGmCK5EdcFAGcCld3i/qqTa99tHDZoiVKZ/0CiweqR9mnqI+V7MVwnZylQjjvoXN9CkKZW01u+f0wypb+pQh7JYQF7gcKOg98GhiDHhMAcTDCIF7PRrXgpNo1gt5beilZ+q03hYe1Rc3uuCwqTCPNaB2F2h5T1aK4B2n9cVVImEnT107QWJ4dTuxpwUgS6fdfJkCF4qTQM9UcoEZ1C3B2uX3bJrp1bwKgWgejoni8LZSHRUdRt9DskR70tG1VsGbNLmdCdF2Gr7Qn2lKgT95lq1B3a39HE9kOhsnpB8oe0ot3fRWwNKCW6oEqN6vzOV1Olj2gWsduqACfany5hJbDlzMZo+b44wE1THHgx8snUCZQajXkYfNmoqt/LQqKouxBlPXg9nKhiNUiCWQz0zKkP8Z2qxFZ+fyHmaSlkb+v2pKlRC47KDVqJ36BuUFEXpBTOWxposHyH13oxQI0g1l0WsZdoy9KVdjVbLTtqRLFubnVVnZIFSI1vaqCSiXrYM6aOodcSv/UboI3p1U+4IIWMrjGrb9f5fEzaGt3kMWRBS2jN66w9d17uWlMx3s2pWetQ67zsUSifqIA/FghcVBjyLBQWzRl4dswsa4/0MobmSg69bvO8f9NLQFkfh6OkcY4j5iWZOSnFKBDS/zXqqg6Izmg/eVTLNy1CanNKExcy92lZOKwvR/BzBoLtdFuPFuasDrbKHMvATgxfkakBBZMtQpXVcnV/IH14u7uk/XRRVRjnaJ+f4QOQVq6l/6q5VjprMZZhND+/zAWcPaznIUAA2bGqq7EP4s5lBgbFSduaEA028vKUjYKlaeO85Xu0nZAQNxSq3Okz74j2qLvPLLlKb94JvFfZUc9+VJUkglE3B8lcEPfRsyzZtyzHr8DyRsS25BBLW3ODd44kZp9p2+ukkxWdfbSQxnLnmJhOg6d7GnvDdnm5unCwF7dy2OJZydUSnpNc56gBFuKsVpdWh7hDt/X+UX9m5R3f2PFTHkpGHlmqB8Onlg+PjY9JaBhwwmo0vjl+Zf/VauHwMQ9V+9v4ObHq2V4ioI397KmNKTyFSnxorscK8q/D0etQhIJw5Byid1vAbX7coQKa6r6Rye4aF0D9Uyb03QVe6hSmEfVzbCHxMIM1qdboK/JSmRimEz+HYrLQgSkinaAnkc6qaJC72GscZeCqq6yqMAslYV1zXnjNh7BbX95sbkfmgbuSWaET3WC6KQABsTMIG4OiRjcdwD2xnj2VzzjWW8wopsxV9i0gMh57qmZrC2vDuZgSLxGAuPm6Bgk6MAIXXhtMdQhSYG9CWde9zdkQT0JA0uQZnTFQ3ZFAa7qgI/Zk+D4SJmpdZihjxSuiMU0aLwRtit0SgNnxwN5urKbco1q74JY4eKoQk0Cx0ePYE3pCtIPfdCLB8QmtXD3/0/QR2kCkKPTHu8F0IhhEWA3oMkVaj7hE3lM3qhb6ID31BZH0o8wCs5bKpOpAHyUs9b4f1MXswnxpPtVL+lLp0a53M8+A5o2ta/gxLurlFp+5ZL6OOD6TAGDs0RpyWaaE3yZGhCjCvkZhFgPLf1wi16/cEszolW4Ubjew8rb8Q7J5lBnVPtStXexBDmtFTmb0WxurpOW37aComLcCkWwOI8DqeSYDdU3iL4jKor7gCl6paqITGgTH4GWqH7Sr9KLYq9qw4K8MhAOwZvr5tHl5ll63Fi2/XtzwMQQYNnEhX34YIZdsEvVVIo0hr2WwBUw0O2yrm3Ics/1QM3f1rozFzJa4n5fXyWPOZ/sAD4sp/JugATnzNvpb5WMiP6/ZOCYq0LHfzZb0M5ooWq6EKojQ9l9kFTBhVfoZYldfj9ngeZZLPowX/LU80gUGp/EV7ErmNqVmmfAS2EjQCM8zoihVmnVcdCYR7WQ0yNvd3540FTgM0oFv9nwUifGCs5euCj5JirVaO6/gsFkZiSvYyts7b1DgasiCVxfCGvpRPeX4PfXPqg/heKEc/TRXQTnWvmlTCo7gbWWFKSemM6olITWRMPKvOVT0K2IHe52rdKz0+YxiWUA8era5vL4c2vMp3TAljPnl1XGb10civ9E/3yKBSPkKUltqM9VCoEm9oi4hOasgBDr4rbz0EQTDbDxbPtbYebNgYaLPQ3iS+cv87/1Nyqczr/m6PedfsZ3B7NxoBwGA/uaF+Y+mPNJfqpvGI/mUQrKlOPazVXBVgiL2xWAvPZSFVXICmqxV27IOzUbf1d/Z+IDsGJPXMkg8B1UzH3G1nsJzo7mr1dl3Rr3rqfc3G9XZ/VqO2c9sMzQjuqrZt/j45W45r2ljTrzE+Zj/6XLHRpWzcX3z0F++hNXDImPVJ249xh9K0eFZhbvcczbnNlKlSx+t48okTvQ2qaYCjbFTXfvVX3yjFc5LnWNrCvEyibViRBBf7C1Snmm+SF/Qt3m+UNHNdR+uT7nKmuG4lr1qvgD1rPWM8x57nrZrnaDNhT1vBsq2DSTmbAK04ktYJhmgpOnoxLm5xNTqV8yt6fNO/h2+MVDN4RJVm1RztX8AP5NeS/DTLzpzt5sCIshatIVToA8o1rHuEqPZZ6uwaOc/MlmOh1thUAvIaOcceQ2y1DOVEWTXbl5lfAuemJogqnmLQbGCB3brnWTUXLs4Lmmh1ZtwTZwhGs6AQoWiv87l6DkfohmkcdouM0LMHKC0WM0t7AKgmoVdjmPmH9kUfEic/XV99XsFmQwJUpBda24UL4+LSSnXVLNjt3IoWCvw0X9dVgSxlFGZ0X3MOo16W4bmJMJgn54GnEtS/TDxX1RXwKKHX+RPjDj4pOmtDtsfpmRxVHrqt1TRLfSzvd1FtxjFBhNhByHmjCSyfLk5dIw9Q0L+2Y4emhmRTYQrXal4Rn7wXghy51r4YQ2SBwdYcoL27KCwIa4Uy3ZTU8tA2Q393iKfz5Oz44pnj4Yoeo9SNk79txup5gR5HfBpfPWes1lbmZHXAQAN4AK3OPdWqMOmTVFNzKQcc+lrtpeuhHjhId6BgCTsDmwqWV+Z1bnCX/rQ3dZ9wAaMsz4xiteGYd+mv1X7RxvUuE7dQmRNm5YJ+2h7+dOIDVNaoGRSXZSiphTjV58hYLdiHXBx2XTa/ui+NSyXfjxlrzkd4MISgl52Lz+yb99ORdNAgn6TdmL+0baVVYbXMxcfLbVZ9PanlcJ01SxpejmEi8xQfyUDtvPm+xhyDZtXKxKfwVVquXhlsK4uB9EotkxDLnckUHoObFCZCkIkO5mG+UlVt2afVlxfrg6NtNSo1UYoSGaJMmaB6zp15m+ZBjq/m5BCQd0UGqG/6OlvxgJqs6eqFUV762uxohHQq7A6bbV6mD4ugJwi9RjvhJRGIwpKIDKxjJUueNaHIjerafS1oJLRYx9Twd4/Gnch2SDx0pzlJE75pF2HN1xkM7SaKKFRJh/t097aC1utX3YDA7jKxo64z3qW4UgH1MEyTdQ3N03tHnpLrK6nHeszuLrjLASgTnV3ad7V65gUMH5yrL3/zo8XgBLs1uOUtu0WGDuLCmZkd2Fg1JkAmud9BVPqtJIIxYOPtm2p2e0QOa2dLcvK3T+4oLWFaqdQSu6z1wsF40h6tbJvz6bHZOQvqZB/kspijVptsZoxzW93I8Gi1pXhTvgAAEABJREFU77n1XqNaB1vZD+TPoKNVbc7U0f76EHWoCz3JaKiLxfO+ZZzawb1bS163AkIRQMvaqAqeaP1kPd0H+thn9767HnY1vTGyoKqRcBoJaYOXikJwl3hQhktXO4ORlVBumKW1WqHXsp4w71U0/iSyEXwhQBliJloGiwictHqdKcJdKHmuZJFKqEBnnuzAOpig/zwIC7dUlXip4CNkq959udWYVsYDyXHsQwWzt7BZg90yLXpK5TApgKXUoV/YdtoLDh9u02Pgubo/KY24CC8PmHZbr4lEpUCxzZXOrcZznE9xP/3sel6kBRzr052qthTomaNiiBpjE6S50RXJ0D97kZUBL0lTMbGY3OGZ6tU/Ej+qLsMwmBG2Wh9FTQiGqZgg72cUrnbXbciYwlyYb3QYr8d8eBJRXdCBBsmffgsS990eiZ/50PXm+xFNygnXAmbV27AWauq0MS1kb1e0CoLKWb+bqROITg18DTtVq+VMCZMtq6cp0uW4NX4OXuCVYrnnhDugwDDkijbtJy0ECPL1EGbqRDbc3fw9iYYbBs2aEto5R5G5AJOvh26DvQwmI4DE6vY6OKfflFxILUyjBsX4idRP9D6eqjBIjpVETrYMVmN3HWQj2UIw1ppr0WcZ27ut5BBMFzfEcGVndekbvJonaj9VXZxN9eifsWsMATDo6RLY6p4JhMk1JRalAigNQI9W8xinRzFbgkA39ArPM/cz3iO0Zxkjex6twQfW/Jr+mT4n4Yg5MZcvswV6lEWrgoTEaGV2esb5ZH0QxIsh7YFvW0eDs/v5ttjvH5+YpZ/YvbNPjdTsJhorcXm7EmcsgYyxy3fZO0brvlLN1dUQpV1gtrw63GBj0J1++XQzb3hIhDV3jXKjhE6zIDM1WUG3GWb3ilDH0qr1pkDj2A02u0xZVKoIkvCkJStMrts0JJsneUrZKqjnZLTeJUkEVMCvezrJAsoGuNDGKOhOjRmjVcFPszCas4bXZLgdV+dfOBxyrwlvQAs1EKXtjyntjOAJ32nKaaKL3WoiOrrZeLnIYlYnm/augAJb/m47s04KME6r76FbdNkFLAZH5WwtGWkePRrCrPndkH01sOAk5uzpIJTv0VpqutCdgKJGmD20QwS0rOmY79ZoMGUdmvXS6ia2XXnWxMzezRJ5UthjSCrxtDWUJQ4heG23ooFkKiqVkHa0m3KWhqrSklJbhLrpJXQ6eailUKvTk0PL4t2KVezAybXGnCoeg2pQOVFKDT1YUvCnef2MWSfLY/v38W+zmk26vRVm8u7IgRSVGtXihcICLj82Q0smZsEVDQAHiArrrTr8lF3tPrxXqUqMbWgkTmWIqw4ABpABVqKVeOY0yLHeBjB+EtzomoEX55ESzXOOXgPDKbfSnXDweJcrOPM36kre+brnGrx6XLHmGfOdHlTa2CJzgMJTzGRqaxmu76ZK9qzxc9VWnk6ydZoc0FaCfPCiLlTjXYiGAmgXyoMqFkQOGW27J+rDujQblrpEinEv069epkSUyblZ6xBj0hFPQ8Tpi1MLW6oNR5wTUU8gSidlWpZqgh9f2iPDpYOTKINJttrZCLdqwbN/akI+3WPYsaHq6mlmylKrC7iYCCC0vTjJaqVxaA+rrzOP/mx2pbNn0cNoxAr3dEq0b6hNMZI0bS821dsEDqWgvw92IrppsWZ+RmlqdyUem49/tl0FWCvFusI0C9H9ulCiLXPsdLXJTf3NYZYNX+az3IovO6lP7PYodOQs7SnLugrVSyCnFA1jTdZimG1QWF8sgutQ08pW4YssJ8WgFxwAG4N5EZcpISl8Xbiqpg62CHM3agQYOsor6cmZHM0BOkxvARRpr0GXsktrZwiK23Z857nr7tadIjzGT1btWa0MDUmp7+hv476qf4X6DfaPmXPA/DX9g/jh/aGc6NTg1TmiPM8bgS3H+jG41uG6wS0mHNpscyk+WFWKKLSRUEyplXpDC0mUQTJLbxsieGmKnzG2GALQ73ifw78G30p0YCJT+Bu7G47r4lyQ0TzV39GnM+RLJR2Be9sT7IH+Yi4mO8Hmml8/rvUZUKZkEWYoPYtRT+XH4utDd6+q3t8e38ruSd0rMJqlVvFLxJNlVIpSCorZ2wNZc/s8H6sv4iNzzUk3W3rDgBS/7Mxm5kROpVK+7Us9iyZUwRGa1QwcLfyYD3g/EL8D9HFyHuG8lOZN6EkkhxeXBBw+e770N/2oSreQm25lJZ8hye0s7VAqGyf43FrcdQuQ38wH59RWebzLcZ8L3z/4y1KIyd0Kh2Q2D6vq6NSNO1vHeTfc0J2YqztJ9KEa4TOxP8jRnceex57sqM19TMIIqMCwiwWjYNNj+0mP3tGPeLXmbL+2zaezqO0bXteRete8TkEfO3yUvxsRj5z35uMeX2N/2XONjS7EhK+cHjbgwycwug47u7wxTVLpWomZg2vVhbaMcx4x1K2bcbvAy+FInKOwTVq8muyoLtX1xfiwHbPNwsRoNLu7oiO6ursoUILjt0LUI+RjTr8MwwgzQFlqxGk9xQkYbNGGgdYckNU21ew0vjB2fXJPfBPqcBpx8T3Noqjr7+YTXzu+CuPlq6Dl9dCUrgzg4Tr4OnlV6lRmvyqYK1WZH9lMO45Xf/QK6+6yNypAoTp7VS/khXOJB2CTx7ULKvUJg5cGvSvT5mh8Tc4vwwUTvwaF1L7PiPQ+NOm33Gg+wldcZhd/Nftrbs8Hz30q9Ggqpm1h4MZ8fipgX36GZFnfz9z3rzyu9SKz1vtS2eZnnOVtNRDOPaCnmmmjbcLZEsNnvWfKKla1tXjFaiMBFPO2822VFKUYCjDRj2ELP3OivthrYvxoW5u6HJry8lJjQO2vhaKqOq189RoygSpXpN+Xndlm6vQ5ra5h2qH7kPkDVWdPvByhIRxi53Pqx2PjmdFzt30jfkHcvLG2eymThruxz+J9EcPec4GgmorJC2SGciVRg1bsjX68UmTs73VC/flD/+rxqZ7dY0SP56kTO36u3N3NbqBzrFbL07fNPONoF3naTv0q+YO+bHqPTliPBY0bVuPQD+xFQl0zhkX0yUz12g6fvuYyE/PHWgXIF/5M+9XN3wheudiXChGPWcoKOACpRCzdvU8AOahCKXR1b8LiZWgMjn8X+SPLa/f8rvFLKw+HEkGLDBkp61ohhUK+3bQgTEkSCDCMw+yz6B/El2p3dDtH++cozxplyg+/2r0d7yqATz5XfZvf8meuRiAKDvjdtimQWcF8xrlZp7qMWMOveX3IMdlfTBuONevL7UfqCSk/bWiqoyrDPQNlFWKgF+/Jw+PW8xq9RHCPKz+rlloYfVq8f21aabKZZQ29TufT8SiLbxK16Xv4bKqTduVV9SvgB/X4V4Dj8TzzdXyRS6O7q4/mzkxvIwwgjcRwXMv0sBZXtJIk7YaWtTlyDdKVh1dmHNtXuIEAtewr0TyTN/K764ZEW1XKW/Nuu89I8SeVwKPV2+dddicKll9KRB8/WdH05qfx1bia67HZtsALSrpOa9sjeEIAOAIZjJbd0cLTeH8U34/0H/x86gfPV9y/Os9vOOaH8dXfQkdLkJQWYuf9zedWCzKWkEkYZXbAA6T1XedR2XEfnGcwfjiuGn3kzcfH1m/QvlCzxW599I7H7YY1jC2xd1WpxASqO6ADc+BH42uDJzce8TLyfKJmaZBUxO9lvx79pJf+NvHr4rdIO/SQNRAob+hoKP3AQHWnXmvj+l48+qPVoGdBfrl/Gf1r5aDQjVmaqlyPetslRVZ4Sz9ZTPlOpW+Efe7l8ZXzV+lrtLYyVEZDDwvsJlNM9zY8NgKmeuEVNbKxSa2eypOLfwTRBWVs6esQZiC7z7D9cXVrzqGSyrZsReWhJFocwXt3rDmVlhjft30nJaHsvFpZewaew3z3DAXvXjI7mWos5l3eu/LJvIMVwzclL3a3ieaq7lCoT8W5T/4SefpZ/OXC9+jIZrZKaa68hn/5x/hV+VVn4hjVRTuJhGLFtZ2/AGqH5Fw+epjcnTqdPUE3Bz+nf/bDwVS40CZNNnA88vruEaJpywLYQ4WQhwyT+oVU4a96y5IrYE9GRxf7MW4JCSJdISDAGF2r03W2KNp+OXYnotsAFU4ARbZ5CoJGZujJCQOUbXeOV3Hf4IYrfyomE2/Vd+Szn+fJfFBbqSUvSqf94+TspEnHB9g9Nn6167NrnvaUVRtvKoQ5U38F+blEmFJTjRMKQ4I5XnFFrsSn+lPP1cc7qsM6RlezlcUYJtjVrWyIB3vYu6ui5lPXLpuUI45UZ1BHDnPNTs2s7BMqIFiap+qO1m991FuBaKOIuruVwzcoYxC7qjJV69Yf1A+Kra5AvGebZaNoGNR4FKFCb+8pcrUZWCwoOBcYKPtpM8p3ROa3WS7UJ/tF7wf+tERfxrcOXOY+7ye8rz6jtsljIyRhvJiGas2nNHOv3jOhSG1X75TkrQ+7KjK2f6dsi6OO6KdKzbzGa3MeMKMaJQ58yKHPaotC7vaUPlsYdO0dIquxIb/WXPAp6RrSXr3RYhD+ZkOsrrpH31WFWhZzbJ6M7+4DqQyeJh03JDA/54Ubs6WbRucTlWVlMi+lC0OroPLz7j/UT1CVUFWYSi+72TW6R8HDEG4wcIZdx6+0FTZr2HE/0Timjuc4RLBvKIsE2QrbI0ydNiXgbliIQoX0Am0kWsMflYtFU+ib0G/JYame0IN1NVzmVElaPat6Vjra6GXT1liLn3mU4YZ+RmsbtCS8p+29y1mUB9SjFHj6iI+dT++B9uZQf/J5hsOMin12fza1ahbGtvEYjudjp2zP0R+3d1eienp3aF2uGTaqTAot5ddEEs0dypIBhgYDnubhHrCtznfNXk79FH+WhBGjQ+QwyHSFrnFp723HWwjv+halmpSZlMjna1P26Q1pa1l/EVPO5sAYsNW1MtaDFX0ZBw1efp1XcyXhtGOrM2qCiM8OqMJorl+qwwSUeoGmhMgUNDQVnkYZZQsE0kf67GWqqDCkToHGGq7MAcpkt1nDCrKVVU1h9m7TsB6kmVNYl/qKAkqQ7GE5eaM2ozRR09ls5RI9Zvq1x4zh0SYzDMxjE4N4CU7YC9ho082tbcD5MmEGT1rAwxyLpb9TmxCXhg5ScwlqkE028PoKvL7fz/rHVx5qs4t0N7pqwKq1eM1DTY5l5xrJq3sc+GNdReQYOWY3nsg7aie0RtXLCoomNEdjiIJ+Aa/bMWrpaPDnJXFBQFTBOpGFiKw0l1XOa43LhzdJYJaW5mOr6nLR8435C/hQRksjpG7ZV/VWdwiYEH+mSb/jba7Vjpqfjc8O3Q2p5QuSq5vGL6bmdxKnoaJpne5sYw0XiDPYXphJvdpwNPgwjx/pVeSMbOVbeAFRcnymz5KoVYY9TwE11KKd3g6ldwRG1pD9zEI3kRoPQW9XKo4+DBWKUtQvlj6U+UaU9DQIU6Otgwbe/KXloYqlQyKR4iCe0F2HjxDUWXcAABAASURBVPCVQ/iFBfIrR8PaNRHT1AM5xWaG54n8GZ55U/9aKfcBrlL5PeF3m9pXiRMT2NaekNZ22reS1eXjUOGaWbmd6q21zxRHg9UIQB+MmFA2ZbrgXWRCAel6q+uoDT2g2G+15yhhr2KXQS9Ls9ROe/owaVHQRtrsL/eL7mHYloSqD0PcR6tfzxQeX1Ax6E/sWCWxTI5Zvg6WlqgSe0XQy7hnnFlniGrLYDaS/bR2Y3XIbapuNmloVomC9NYYAk5OQRJIKll0kbEbx3mMbwNWDvTRy+lv3hMllzCYjTE/SjDaEi/W7sH87dxC1q9MKwRkZZ6Y9T0RnRZ7SdDRoNIbtDQm+ZSAC1+Yl/xcBjY12jin8cvqi3XhNk8xmmAV2k+NSJEGyW3FqwQr6/Lcs54Ve2Zaw0WQbEF9R3ToqjCGM4HMZNmsdXHQXB3qTL5TCtRtQY9cotu9DEUG7RHWKe2wmlUaJVby1fdAw6pGug5PUAM6t2nhbvOcbFPzyGpUQg+7etMPxzZ71Hc5yeEG67Dcs1NRqg7E9NQ6RzmqIlL0yktYlNaqozec6khRJzvlReXOtcHIoVJ6hagFdXoJKqDtXubd/D4zBAfGVkyy6aXUaI0U9rcVVJ2zKx7TNDrcmhqpJni6UC+XTRmT285E10BeJuIa7NaQPBHy/kbbowGi0c1e4Kg3t1fNBQOJ8lGc4UPbNNViz5NX9lKIdBplUEejduazD/QA66Bki3JaLDbfN0OaAFkf9wMc2ykxRW51k5gWUwnp9r2w1wAvazex2WUIy5ARJTDIk5VVutNq9zE4h7J59jzhop6YwaGP7elQg3Jvmg+fhpmwG9R5Xz2u5rBqK2kgzDGN00YAKfY0E+UsK6kQEKE6qO6FllWt/F5Q5O4x+8yu2S3qDlJhQ+wpfGQoQsuktdOGvFEVImkcCSIPTgyEAYSqFnzF3XPK7HtKPu0MCyNSyYny7qlANN7b7JiHs12kV1egTjMFAXSDXc4Pbfj7z/Ve7uWjFPI1VUcHHYZI5QBWnS9G1XSJ4uoWGio5FUwxcQzBF1CXkIiI0FY7lO40FpxQk1MGVqY/El4UmWbbjKvngJtSZOuyRRg6C09gQ7BxRV2Jq/tCDU3SSbxjujP7ZFcDaNOU/dR91OkeNYBrKSjT/B33c44qKtziwxoa3NSoWx/ISb+Mcqm0iK8eo+citVS9XCiA8DxUaA+RbOseURZIVL2z0PV+anhBRM+O0aUrT8U3nkQs789a9u4ooCh85uIeWrZ0oyaJxqmMGTlO+xP9xD7P9z73w2df2WYVhgcvF3JplssgzCKAFmPeY20bKOP7ksKxAyIl6MVB06tNk2t7iEZaedXax5/dLS1EWLGbT+EX1YrQKMGA+KQRkg0qTiBNvNjnYJ3T+M7rrqnIvXeVGGc6ofFAlp2UTvzI5oGk7Wdee8wNHs2AqtPfYly1rrrmXmvPFf4pmltd6/nM+5IeZH400ufTn62ZgVaBZxMADa9KJ67RX55fHnpMQjlzHmIr9GGYgGoADbMzEcYEu93SLBf0eYJrTI7r2BXwIDkwPn7Vmns0KQ5DGcOQ0gw5YnrLr7rbZfkD/KL57JwtLYtn0trZoiEmdZNzJstQnYVE03uaLhK4Qvd8JogQX+knejPSHqcEppxNW2azCj8TdLQ9tAPPvohhP2OS2HXj6JqPloMdEYnCgA1Wq3dKONEyinPqRt9LHMiINSFIxmxX3U7ggm4bbEwbX6AiMWUsc6DfH3ZdByMFleF4qHVKLW3pC09ZbsopzeAU2ohyfSnjcYtpsg1L6GUOX8gPzsWjQtC1qUxG7dbTFUUi6lV+uys1e0KsorKfDSShU7LOVmuwBb4+RtvYizk4zC7rifB+TAhFR3RtxKYy77rkbVU67YQZiVHw7cipTZ1Wz0CXiQTia4xrTnOjfgCv1gZ5JK6ExZAHoMfKsQoza4AOlvVBnL6rH1Z7QwvD2C1ZDL/HrDlsJeRr7bxkkeRorOAoM/mdaGGkIhiwq0w7RYvojbxZCSibcaDHTKdO41gDTXnqKrwAJgVCeVpGAwLUAGvBKmboO7YzLjPFxe7sRCuai/vrDajT/NjUIisaupsHttEGbczygh6b2cRy6hnuj1mf9czxwItvzyiqYnIAFKSuAx3Pli7ECMMoIND7NNVn/Rj9iVx6ErI935lIwUtldxiX+UU5IkOX1D2KGxbyf5msl/mZb3lGYVvv0RqFlKuduVMSERxxCUtVSVVFsXzaGvPNt9g6nU+qSESmuzDZT3FFnV19KLkiMjMesrUWajUytOBvjrAB6j51SVoUrQbHZL2w+NOdEqeir7J8iR4XORewspWpNyNuUb43dBuUEdFZKNng3INBpXRDVdDhMo7i+0tpSnXVtMIbVStChf06WmOrO2k/tc2ygXzlcdqtxyi3Yjc3qd+V2XJZd4to5y7Ksbtyjzcx1UXUoM2+XmbzNKM/0J4ObxMKZ+qPeOYFOemgq7+INUodMpZ3xi8vX2hzH6jJvSzWLJW5LBvqivAnsSvZEpjb3ZATRWltjNEvQQACq/yr5w8lZrJcvupQWUEUIrLrDJ4PW1WD1vARFMEfcNqQdsggjqPMOjTSrVVamC0FVszaLltT0bx5W0wnxGrA5ARj4/rOeXSLtZvC9tX8Ml/TShaHl0+MAQy0t/IhCUG5fKpv9DN6DxyztOU+za299jpn7RjKdB+F0NVglJb12qkugQOI086wXXoidIUPLp+XzWVmjnyZPlawHibfFxW5toI+uT7UoGhsKH2lyiCa2V1Vwo6ePJPh1IiWI1FQZF82LgxrT5xtz2NHGDZW4Arlf4xBX2/dN49qL7wD3G0vzxygnIAmTW/6Rj3UxuyRKDspDt9SRY58Wx1+5nR378Rt3ou2xGQF1Gr3sQxIoEJiYLdlGmIivqqurO7Y3FtzjyLi61fyqPIIxYhh+6ONlEN5uI89Sm0s/kx8axy6KIYj9OBU6tL64IDOICPZuaTzy+b1M53eYBZFZBp7Ap4CA28y30klJ0Yr80LriETFyAhUOXXFBa6a0PlkKZ+aJxuU6oZYTr1a5YhJh9jzM0ZM1xnncHcs6gIku7kf3dXSTtg+1ZmueSRGnlVb4m2GmoUXdZS1rhtSiOcwDRPflVqzp3NymA3az9NeWqC0TfUBnashAJW2re8Lzxef2Wkt1TQAzlHiUacucng7g600g5z6J5qf1TUoIbjCRJYiXAnV4MK6cM2x8CWDkVF4LuRHNlr2rdGycS0ub0nz2mGP4Ygp044694Tisp46csxYs9aaY8xBUtB25229FczlGCx1h+ltTq6BebH1BOEyGNhvt3v6bX1KUoedLN01ASnPZmWyBgS50YfV+kqI2FBNZCFjZoxKopPtNbwWa6RKsFglJegTzeDWHyrYXpY2U1vD96H1VXW1t10P523X7Z+H11GeTEV1wEMXJGRV5TNVGPwGkqmlpxPcat0Xc6GbEBjLGkVFLPYRWRvVCGpouQentIrdJXaANszJsTC0RrvVmdjiA6+WjW2Uja7EfgA960u5Zb4JWXAz2gq+PQoNC/L0MGAh3oRDIhx+xLhCY6FI+XcbTmbXS9UpyybbRjuVIUiq6dqv60+11kNx7xaLDhuc7HdbIyzeWGp05p6Ox/bJJ0oJy0arx+sPbBcNfkHLXNII7YrOfOPN+4y7/b2Fvno7d42XjEGbGmXiUjWE6BGK3ybmhLI6do6oOXp4syC+obbdE1xCDpSoSfQN3HRVapm3oCQd4gImEk3svKEqanXHrVYkS1LicPuQl/e4ys2UJbR+5fSa+t2LfYFT4Y9pytAyuzgnLg+vPWLPTAOa0It6t5jj1YMGHajIBLkmDmOLOG2W6lD6EWRhhPYmE/LvQtuQHfcx1RjGtGnlyCHfdIdbUQPkqggwndo611dybW/B6AE4ZHJW+gnkZoU1m9YXYzHEmvoFy4xDEUoqLhz9zFIy2FZnPDUOcPTRSrYgrnbkHRapDo69674R5EvKkhH5ut7lCkrEUL5kBHU8WveYP5JTQbFLQhMUna/HqZoqGoxUh1i15rk8pSx0cYsZfe62rkfTorqZ+iJ2QBPMWqhZb8WzDzv8DW/UK7nHrhTVBiRabTLUosa5TNUdqyTM3X1TOrodObNw+tQRVy+mDjulzhhEN3DAO/PJPCpe36mqaq17VpRafJLmBpUQ8HSfUNMUuy/sYTHy8b29mg4sau4TsVPfbcWx5xvnznqQce2a8tR6CTLQrgQ/4DMqmEpF2Y6WXDxVnU2gxCa1zUt6qE85juf22DzbEa7rRFkVUxIhjEjaBm7n/aafYPp1XNRz4tWW0czUDtz1CWpyAKo6kDmftHOGoFZm4qm+Lbf93FQQgpOIE5HdaqN2bR/JC6BF68DJOHeo5FUC6v3puEffolfZ15djD/rxk73Rp0e5c9VYiiIsD9RVPJerd51etYc/vLbZmfVG28xaxetxr2XtqLB6ZpfLDMwELx9Uq+LiWJz2Gh/jNWLMhCvFhFMSBKpSw6TUnnOvr3NdkpEFhTVuCl+m3i0e2M1LvcWuugTTW0o5HeWP4hd9hNVkTqAlgnIOEduGwnlDdHvgea6O1VRorDXyvQ29Quhg/gYy2GVQMpsGXCOFyZXNPPXKMcFFfFGB9HTa+03WWOzN/XQp1wdd1Yr6OUNx82VEvaH5tO1medrUPdFDCaOD3WKBajdceMt4Fl/tVoHEGDS0JVIcFZRp2Jx2LV0RE+J0+OW2VJvh51B5UgG4vKgqjqgdEBi+0ukIVBu5hq2aI53qE29q7NTKojzuqrsiLNPVdk7dG0+OPL2yv7IWROnyecWY4QvyizUEv8ngVZh+fXQZtA4ofx4+2vmTHpUSkwdKom1jc5liSyDM062sklu5pGlqqezaS32SWfbWQcsIh7ZnYLZHDqmG/aB/cWg/0BMrbG34zg7555rwy0jXfYTs1tsNHFwxL+llv2SmjeIlB7Ptq0zMI0btqc7Ghg7ISWaoDHNEv7mYjikjiVWgclcFlD4MYnYSTyig9UkhaOrGSDbf8eAnbHQDLZtWMk5hU70MCccSDjdYh+ezkJ+SwOgOGMozrSmnRyF2NQ+n4ozuYqf7M2b5hZ4D5vJQDWaMLjebPrytnnHSwSkjlt4FsFoFbU8JfdOBxXIxtpyf4WOMcNdIm71kYDIH6iOLcVVeoJsAndGPdRqagoViXNe+lqpXZ1bntB6jXtDW9UvjCC8G2zXcQh3H5Mnm0gl049GIqHJcw+hTyXkGUp67PlmjWnhOI9U991yhueON2YVF43Ai996294zXs9MggOllVGuKLuUkbByivTmAhqTXj/g8mm9OmuKytfyaGDM5S6G0r9lCI3C1Q6Uyma4utkpZtln8tE1SKQn/jpdTULYVj1Tkndes4SUHs3NVad2T9osoFaHApfQQwmpEWnafrI6mTvV6gVYbawzQBVeJf8bnXuPhy9bTKsvCcBdMAAAQAElEQVSPx/DTiiMORY9yOYaJwUY0NlnFcTCVh11eJ4VgCKGCpXCbzAef4t1ievW1aq2A47zhXwIcbc+pPGwhkG4gaaBng6Ph2fbt4xvjyXqYx6rz4rMkuTzArUmRCn3ME+NdJNMXTfEU2KTZZbZgApoqafP8GMarhn36+whu4vR8U05uKbnL+Uz/HrYvIYiBB1AJuUpn6gn0s9hXD4oa/oz0ZidU7ZqsD9gYSkabKJt787mjQ90oTeGqZny2sw2wVCQv5xhZJtfjKcU4EHPA11Vqd71K4sZUwxT9fQICoyTK+3RhFYmjD2ZitlkOMblbhKjBlFLgsiSpG5TF0bRGn1PY+SIOpc+QFwrdVX0yDiLRKJfy59X2Y9c6zXK0y6oJTJcxNV8rbjGBemcDsv6jfbX1ZL7iqamCT2V8R28NDgi7WFOh4f1zsgHmpUxREvGmqngOWx8b091twARX0gJASRPSC7tWnVmluTRvt7DSjtsKtqyv0cNB4VGM+6lTDBMulxSGPLjQOPBZfV2U9EvzKizSwr3epPpxf9pTfDjTRKwylJ6bFdUA1hL2vM6ZqQaAi3Xl4U4tbWYCTdcvz8ntVLgotuQTktHsoY+VThEjhpvRWpz9Dt/Z0n75yU/HF/sH66sx9CMHHJ3D92dtcwFQFoaKrelsWez3S+jKaBun/XCVGd4iPaKtrDVsll9upvu1VW1Cy9jvkDwVRMBbFQIjgSxoiD6n8zjLZfpa+Bp1IWdtq5oUpzNjbrqamnTBOqbRYw9CsccRP1/RX4FPGCSGnNoSbgVZGEo0WmdpQSuoUCfmKH/pkvNkqL4fjKNN1D1wgBLz3C1sao47tdmMvfZjElWpvEX5ojQk2/E24zFolBq0MkyoAZJCV9LT9Deu7rCfiUAc2etMlFpbWs2Za9byPcVJRumCnJv+caaILi8lChRNk61A3jeFX1KKJebpccy0eMEfi7Jvhego5bp41ExF38hQzqlWkaqgoCdz8edjcuXJ2aYI4qvwFbYSTCJhGXiDztjcN7XVuNN3sRPeJSHYF4a51CFkGg834yC9yZhZdupnNMjRgTwZcdKrVvQ42aHvCbG+g0eX31aAKB5De7Tvtscl2jJTp/9O7rRWTgSuukEtA1zdViZA0ISAVRU7PNUNXMR6T6XCW06Z3q1YE33QHfQDVewZqY29Git/DPvQV3IBk8VRPe0Nptyj0LIeAWWog4o+DWHR2XgrTpATLSo+Tv3Vz0b3zGYgTuMIF6zVWK7CEKlIjiNhn8+RoITVilNrUmAZ5ySoOqI23EIKNPWSrWKF27lsX3hGP4PbvOxq87N0Jo+JHvPgSFBZ+SliFPVEZKqLECosmF2wrxqfdIUh4y6nNd0sYY+YakdM+WAumCw2N3ubIu96FarGPkbSw9K0PWvtkjattRKY1p2KDoYxFC1wHfXy6W9A1eKqGkTZqHL9ok9T2/VZeJNxoEojQFIJjWipvtNMqdIu4DF7UN3RT/CEbK5la83B2RLbQWV1nqoTEAhXR505LFnOpumXJ1CJ1iYhXwnCq7U6MVFndU1wccw2DW9YtqGLBSVx5hk8IoP0Sj8lTMNseI0+kzndOUcZTKFLrEuQYRjCC5yCIkMdi40TT/STnYqrXJkWeDvBox3IQ9NAgWx0oHVG+Tod5xwPNSWs2ZxGOTP3doWn3jyiHCFcbFWN8M/rWtcHH4sP0/EGkS4wtFQsq/K63+ii+hSfsqyLekbUeh+z3gjaLC886zy9IyiWflZO1Nu2xIm1fiisVcyybjkR4GYb3695iO3rts+hHGN96jOPIGqgru/VW21ywUe/Jfj0usv7gUKZOGQr9+q0sX90fZVo94AHFSWKJc7bG6NambFQFgNERfetdJsuyQBe2mk+J8RTX6EFbS5bNo3Wgsc+VrW6J2BvOiq0d/CcaPUZFEw/uiZWVsK7m6l/COce2b5ZMXdr0X62P8ezOIC1YCOh9BVDfSWW9/xy/7p6XHevrYrvJdrUFnIHoL3+I5rUhZIcUpFcjhIuTDhajcdDQByuv2aJtOE9TFIbvqGtF8pEWIaJjBcy2Ab4q8ha1zT/MlG//Sv4IdytoTCsUhNmpaxxsrVPW3PRvRShKqSMdZnkRqVPGJRuoaFwIIF3DdD8475de4upcWZOsqDAEqHQZYflzNeiqLMiBe3NEkt7DeXDhDHqaoyBMmyz0wLVNEE1+zHm5R/n15ugGiza24uz7UEnkGZJD5sx5rP8cQen1+BP8iF6gw3Err5ryDRALx1uMArMC1fhsQZkTglD47Pw9YlraqJtFFpS0+hWv8KbWKcZdFadvKeho1rD5FH2uyjfEjSKMHCljbA88w67D5LAlbh2fiogeHnCojVeo5t+ppwJSOClrusDvDBe7lrmMJqrOJ7A2b2Lu73g8EEfBpZBjASklwrex/YJNQ5Ul+rhqpR0bMzuYeopT0kEDo5qqmGWhomSUVACRAOGHqJo2rte19N9dr5H4ilsMqxK8WQvMTbf3oe6dAKqk1kNReoQ1uolF26t4rF0NQU1uTyCJPIG1+I1X7fPXo+6d2j/0leYqQSNvGhqGRamHZz/QupBiPo7n+Jd86Fuxae2wiq7tcU1tbBaAiew89WHaSare1GtjdSehvN7Yc9m318lH5ACzGqwP+2rdGdmjSwcxdhlBdsaKzq0bPHB1z2v7EkZ7+jYluen0iniodUhGLIYWHvPVA9TWl3SGn2Q59zn/s59G0L9SVo48YVUjldzQq0r7437Uc/q8sIHWjKtzcnBQhxE5ygt+/npOXC52dRIVCUThBOzj2V3XbQFk1xG4lN6qpFAt++a4VMFn6tioKlZOVnDqTyF4j3JkyBzzMfYPDafmsBcCKW1pS1Ys2FZ+tlx8v2PpIpF9Bppn3PWs5U0wZ80vthew0nsHjvWSaV3tvSqpdIaa/SaMX07j/72PH4bNA5US4l3l79pB0xKcTqWvby8EE+dB7IveG/7mPa4VUOvVFWCrZeo7mAHcuMJ3IAeEO+vEvP1hRipOrpbq3GoUPkFqRFwlIkvM8l0n8ouG/CFsdznKp9Pz7vqrp2SCQLIhlLMI+2x9NK5KMi55LnSzVqizgsc7q1szgqVhbKE9u/uX3huCvklDhsWjOJjOAzH99VxFfzAdGq70PWF+vn4baSqeXJ1//iu6xeOUCnVbRWevRKT8SY5DfVOG5W6rxlijWf7SX+MCfOeetlcmVqdrpufB/7AmuSbhD1iLwjgd+W3Ys6zUIqFzU2mz9G2lONTF7n0z+00dURbbOOReyorZikfYxYsdB31eGbtSn2yVMFsj9u1LRKxe9w175BMXVH0l+EL7RjAl9s1SCdaVUjQiKXC6BcLzagkwUz+JMkcrwb28TsUKYz+A0JoE31pKt9h/crZpXCT/Pjc/X063bQqepcE8r6XfR6/Ys6eo4ZMceeMmMzGPnys1VOU3/FZY32MxgbuSrn1EOrjdmwRmJYfvrmX5O0Mr/6cnDgttse781z98V7wAs46/QkMlY52Fnef86qVWQzywQiT7T69NyRYSRTr+Iy1aowCA2qeNt0nVB465a55bB8CJCuuELIvUAlppXXsXODwy6Zqd3EaXhiONAXIM455aac97frwx5f5l8bMNgvDdmXgtCr/cz9fu2Sy0AGw2epsLQ04/aq1JJ4znifrOby59uV5MVtRWDwyQ7hxCHK5VVpsFsDBKWZ1pSqB+qB/NK6it1DifRXfpkX7oH4cijrohMOdbGiSKuFuSDN+OFN58I5CFXrAphIe9Cw9OeE4sIyZ9ame530f1nQoVDU4bodsITNCMRz980r1r/FuwzdGwV+rkNowmp+eKsuCWPDq5a0UJfuFYqfsZxKUt8XrMv98SjID2zYkD3LEZWd2dDL3QJLEG3T3OjXvg+fUHfF9Kp6W4qbKrioUZ9lVIsNn6yZ7h64hHZIYGq3h3UcDVn+0c1rfnJHSnJV8OLfgnrLsFbA4ue94jijpWALSzSA2Z/Z7q8wxVZGTtU6sQ+vVKkhyXPvzo+YFM3S2JC5jvaPufB8jyApzzvqaNkVZN8ZTH2lQASp1tOk2SDgr13O0p1GVYCNPV1Qz6cG3lKSAVqMppjKZlXXIGiwiUYqSO3TfO4C2TM1JtVMumICJ7SwPH5gfPZPzhlKrcZBqJp1ygdK6m5C5RQ6Uj4ZZwKr4pqVCDmWbOsMM3031Yxn/8aN/jPiiNL2FYyeeRujn5b+7vVRR35/Set5T/HA/CAEYfJdGEN7D96q000g5bTczGLAN313ZCTzoByhARu0MhDBZheN1sIu1te2FpRW4lfYuxC5lP9EBpBjBowsJWsOhXjB0FgE/6iYS2eMhXBX32yK6AuNwFDu9X6A0nurvXfepzGQFqwqdyoFQod9hs/tXfbwaxtQXRDMapaZQE4t6PinfCm5Cb6553fDl/jV80KMr6+k8Q0RxHtaD3MeqP2Yfu5aymb3l/igkWcKQjdkUrUKTVUXRNvQWblkjwk9E5jFyun1mrCk53/A0uoOUfPRHhw/6/atMh7jp0/0oqAEopJlYIon1sO+hnZ50d5QLOjRfCdHD8nP30cweBWshe0pUKU8YdNQlVdbbUWrUYbcSUtoKXfKOvDrWyfmUb6wcl/ll9bm+r7HVT1MwMCCk5nbpwBhNGKB4AAf0dYLASaq/PPh8Y33nyufqZ3LTnhvVzjXsa9kylzVkIAJZs90PxwFvIJRnyFLUyCCyc+OXxNZCZmvtz3o+KZXQDwbDxpNu7R+s5VMNCPM5OFvOMHFNTpMKvIRTHh1GSdrKqjJjt16x+D1XD9Myv9bkZTGnbD9gmHDoABPKBnGWp9agH4Nk313fmd2YsAllICRIfQVb+K3yClVaXORo8vg4NhKkrhe0v5yHLAuMR32rtSLhqmOW50IJ+fnyd3czUyJy+Gq7WDbQc8FdVqDMUpYG8PadI3il6qIKBcd9cxy1rXz72IoWwmuGj5qEmafgSANUA4ONCa6Dr1QBgj9beXn+dk61n0lCmGGYTljBw+eZev/pV8NLl817XKU7eokBKvRXX1k4JQxyBDGFo1LGkDUnfzbb0fBduE2Cr3wDSnR1odDptRira4Y6hRVncXS7phMtL0oO2zvVPhwNFVJ7ObXax+iiAZaM9tvHWXlmKt1VlIGP47IeHpKBJ61LATSqFGOYlYoR5m72HpimOQ1WCFRBRoeSaNA2Id50Y5LL4Iqs5bjRjyvU6Mp+R88Yw6gmtKJWSBUCz4HxEZa6Ox8tJ6b816xS1nxLAwCREpuv5mQt5BSpai2uVFupUjZG9Mg2+XgRDDNQIhnFinN6C0e9jQNt7xJpovewUr1N78XTFmVNEb6lOUZ2FgDSCGqyAAiIsqHzAxzeAl0f1lSKlLZWZ3CNW3auEVpR01fyyp4Zrg4oU5pmQHUnlIjeksKpn5KQt0q23Pc8xzWj3/FhvO2oV9L3Zd9DNRYaM9ym4ls0p/KG+0YWUAAAEABJREFUklWkoYaXUtF8EUFUkDl2Df0V5Z6AYYIT70vWW9c1MVRCNmikK780fZgwilZmsRqWLWaRIRrfaT57enk3g10aB7w+bSSvg6vbXR0o38IQhcdPzd0rNNVtjDma/Cm8GtaTFOXKZFdRv2SNGkijjxePmcxVuua0PcbH6iYKBD0pZB2yP4Le4uRyqun51ZLXgCy3M+fIxXJCr25E9wHeCViaZJxlGlpkm8lks6oj6gQ64c41ZK5cen6Dzc6UtZWfUctqVBPmc7nqx6tcKrqhJLhxts/n4j0rwMjZ5YCC/EkPbLT6yZwUCnYx3yu2bR3SGYOpKjjGVCqkJtsYOthvVNyn75ToAyhC4x0K8KdFZV5tbjyQRe21EjqhdfMuboVdWiRTkHVAPMvpO13loHEm9Zf/nEP5ZnirlYSrJzCjcdK6AE3P9oBtl5SWU1FFY0Ny6vnUOhIGCsRsH2No990/QqdT/MinGDqtjyPTKjyfgTBPkfHygwTqNXl5mlygV4JaKgWcxYut4YjDp8fmZ4/Vb47gC9113oPAT/GU7eATmdiKwQW+VLnQJr2/DVpYeg6fKiMzg6sEZD0/laP6zZMxS9GSx6HktVWzWlbU0uYp014D4mIXoilre1x5LTdbrchRpfZapyS37IdaWkMUFexFZ2+Z6KmTCGwyzLQlKGISg7KcfVriAzqM23G804uf9JXuCs6Mk+YcgOpQxUi4rsS0cAtT7nt2aKnQRwi2gCuaruxupEIy1htOzEoJh07ThROWDTLRCgrVo9RA9WW3ZiYlQyVjZFm095g9r9JEXRuqHbjRUq6TZ/tTXq3wRjXMz5iP6lDWVCdD3shup7t/TJGE4Ej2N+Yue6JOlKGm5fAjm8Uk9GpUtDZ6fYodPRQhnqkkKMRhn2VzUBhQ1zlzp2WAp5ma11XAe9FkiU1IRvBOqswfel6Oy1JWS6ErkjYBRDpisi5ZZIrK9HFcWbaaLTa6A7IfVB9eRsXfVFuHSFbk1zSffJ3KmFpOoQWVypnP1322cJBml8EhnzAvkX621oXWWnosCHmCt8yh0A/Ex1U+uuQDwouROgfBQLyaaSDY5afx0yVAwRoQn0owQxlox23iRQYR8E1XQvXdizVMEpIQDa9LE9g20SJv1U/sW/XTpYysmdNQyiIgRzyu/sQHygpK68PwprHKU3bor0zLGKPko8KjqqsQaaeq3nkFm+TVClKLg+DPCvJY1WsJNlp9QI67xaJIgqbJv5LaMIhx4ZgwI+DRTJjSrtlyE9k37ZApJibhOJnfIrnVdLRzZA+RX8PoGgDFRG+doRMoy2MdgldYHMsne+FcYpJ8/RElBky8sRzViGKgKPVbcVA0qTNZGFI/qMg4O7QPbUrA7exsbc5PNAgOvXeroHqP2Ffkirqohw/aEVTZuJeegTFKRT5N0V6IVQ/no/0/ZkHzxdRUIpneciGbLnFNFKx7Vlpnodt+omaomNUlsAMnSqVF5aSPldzndXoVZ7yMiGOpThU52aab3K1OQdVmuS/3qXih1nWm57AuUpNO80RLsyLROQaHlzdnga1YqxDRukdTagTbKTqVtYbst6VWBPNKarnvM6svcJl9bC5r7xDnLcldBZetUdB7abvNixCnrFcDUh875a5h9cL3NlRmR7Mgel7YfVksqsfrDNoL1srDtvndM8AjwkgBGajFmoAyS/WhqnA/oscu2hxfPn4QGtFVkZ0dstVDaJQQY7LTNGFP5oA8WJkbHMKtayWEwBW9BE0UAG8pZUk1RBPS/7V6XUafB6rINivKkFijGcBa6oZRWnzFTnvrormF2kCZLFBDWMXirDliQN0hgtus38wA7glxTEePpAdNfrTE7Qr92G6KhTHpl2E4jOIpvEp6WMvGvCDxpbHS7Uhuikgr4xEDY80517U4/Rg3BMqLLYy1II7NyimWh/ZlpdiNXQ7dncjgIpe+iCFmMoBaga87pwSbLExoprSNI2m9ObQ01Ch6Kd8Uwz2t5mwfpkh/5gs8JVddKrSGKyK2u245jxZlTA8zGqf7WAvz3XAYqTgU8NwHzBoIW9XLQs4cmA1FAnTIdjtKVsNN0YaST6BU+6GaK+dxineVmIIUh05eq76ufGdJgfJs8G5D7+6HiRIlg3oOHWOH7yjEM0tJJQZsPXNKU89XnM+O9TzjyBRtos0KYmJs2G5Es8GmirahHcoA0Hnz3P4cZcVb0VNbEj03+FHTOTRYMhZNGdl6shPRyG715XP82Su0F49udFkCDyoUf+tO42EnrczLpnhfhIHlSoJYGorOWaOVIoU1ERdEmRitDZyUAyjmNuSq89W5wKEB7WxzqXdIAYM2fE1+ptTbirJnwvWzHAJxDqMLijmaMcbWE7y15RjKoeCO9AlQIdPEiE6CqlcBkC6seDlkCITq5J7KgkXM1qFhSHaObjnSZOiMacf+FfWD5+qcWgnrg7HOMpUk1/GVfjntvWVivSEsu6ZdHBNmDaAKsZkPoYYqPENk5NFPRxpCivSXYeH3gQv4KqVqgvaMfuzoXqB282DCFl1R2au3c55z7qqgYwxTlYtHEyjnHAr5KJP91oBlaxOAsh7VViKuUkam0Sl0kARPf2ffPs4avYZ/2ixlaqSNsNq2C7sRaI0tiYSpetHnHfFsvU6o5JJ8o0WNkPs3PbRYs7+MX5ZfkHQfPNEp9LKBfnMYsc6RpllhWaJBFcGzM59UKRyRR6Xg9S2R4kBQ6um9pWgrd3NABUfumnvrqDtPyE6ubmUDDWNap5DtM+6b+5txd+8SiRInJKDSIjm6ZtRUi0hcmmEBl8j3xDGRxNkcgHI+O987/oe238O7e9herhuEAyYbph7jLt5OVhxEMRCJDa2F0fwmnjz1HEYyi3VEBjlGT9Jd2TOZyOeX77/9t//m3/wrf/m3/q2/8Nf+zL/11/6f/8af/WP/6p/8l/6Vf/1f/H/9G//SH/9Tv/XLLxVIhUZUaylWfja6q2bWlZiqR0zCo/MWmzAFVIY25AQG1sS6oM0utBzM7BVxxdtmfKZ9Cp+f407yHEu1KtVfjs+ZK4ZJG0abNbBXv5rOWXv28SJlPSYKELOmET3swXrq+u6xWcpktacvLctj2aJ5XKzR5hhu5qxKPgfh6FHm7QCb2ePdrWAqKQEEycJoaOFsH61RqX9nOXld6Vc5jXDTsYeXKuJjtlqXlSPchShkeaaH0jiyV7q1JcRWRXarF0eY8ilHAjZ8cDknDFLJEcI+WbIiDA5BprL/9E+bYvdDfl73fOMSY5uMjfPEOQrtDCVIV0hzo6PnW5RPV3e7menMCPecLoLcALDQ4EN+Fx7BrjEwVcsMRvgGQ72ED8aBhrcImaXeovRFOmikd6vq42N1IafOSG5tcjyh885xYU2BzgstExo+qqskrrGxwhpXmP4grlY3PFzFQjSFcA7COrOeaulkdwpQ4wyoUE7vJ1TI0TSsD35c5zO3jGF7fy8r/BRmkuIAn2hp0wFFJ28Wi7ZqzfpAT5UulNCLm/7bz/PXv3/5S3/9t/7iX/obf+7P/Pk//af+9B//k3/ij/2r/9q//K/88X/5X/sTf+L/82fbBZdjrM/4sXwSRd9U+j8wC5YQSjTDnOZp3eV5RhzrUjDvFTbFX+zFb7gIj2gFKcpNO9RZoJ6X2G6eUjGXrra0I75iZz5R2QRpbQsm5+V3UJk7VNIf57EoZLbagP55veu9v1TbhocNn/OaU+sHF3o1JnpWT3HIlTa2mbpNw/XLDwELY8gPKJFUS13CV5n9DMQ0TKe1q8hHZc2KOXaPp/2RaP0Xzm+uTWxqRoKG0fQoj1CkE0ruOHuGdjbvRbkNF1r5W8jJoqoqrsorYoW+Qfkr+AFDTUBDUEr0GoFRYXr4W3drOQnRhju5NW52DLXK89TZ8ZQd0dAV0rYutgsx7uF7SYcjAmqkfWv9QHT32v6Jz8qJNgF7tWMDnC3y8elcfVBVCkP+D+2UFC9hDWmtJ6Ms9uhdFXIv0l7MNHIaZablrUivYcH+7qPNT0zJkcvMh7cbS7uBwS50yEqe12aDAjQpZVfA9Uu0KD+kgV4iYGsNaKgGOPip+sKZngMx0Fioq5XrvqJmqrs+Oku4on3atwvYMLQuQYwruoKskq5MD8U2qyv7OfV9qJ201oWLatWb3Idnd3W2dw3F6qNsWFmf2Xsilk34aGE0NpoVU/ndIx5lSHqBSW8JNXWmnBIiti2Ao0PXQMIEwcXoUR3oCklL8C0XVHe+LNWooARiLSdJD64QjFUaH2FoW90OMzdYGyiITqNZ1aw9LehCJ5UNpUN1oqZrrqy7hzEkTpZ5EQFxU+/MoZXBr+ED7GJFROdhwZveUIpeLGyO18OZKLPO0WFtAAvFF6W1DADyXTJyWhuOceqy0v5Xo/gEHuK8PXIkCS8baQqoRussqfy4eaLuOkf4NcYEdyAcDnyK68z5jFHqXG5qBRizaJJJj5/eqZqpJ3FvP63z2rD0j+Yq5RQNEcMB2puZsjyd2rtv2BEIJLCBKkEtiEtPzu0roNY+aGS2lr7O4jBMC9aJENCw0ws95aM0aDCtgGxZEgTUQL3doF5OPPesdCU/zVePT3MZzNhdgRO9U2WVcqZzugczORHI7lOMlxLlfEybw3wIBRvdYjS1ZGiLUaY8KTP86bs7iaCT9ITtyKqkJmQr0AZzmFY8DpySc52Vk2NZI0Gd9Qsquyufpe2jGoGWcnh4h1eLpiP7vZXPZXF52uh30lj7su/V/+a//Zt/9i//pT//l/7Sn//Nv/hv/sXf/DN/8S//6d/8K39DBWf+Rl9stYeUvUAGutDWGKmSLutW/G0eQ8GxxB4UsCKGaW53i+YYLIciHEf50UxlDE4Xm7NbcUv697QNL7F5YEdjalQ6EiUTJT8cgtymXLExacMNn7W/PBZ6pnaKxyqUEDXgXSWG1BsClC9zkSNmU3nBW+BZ8KQuuQsHHNIsOGYszylin87ufBGjvZo/s3H6jT7Fogqan9ZCpOKrRI7rzFcchRkczzoxNOoAJsj6tFujjPIzBoYuz/PtRzsoy/XRQbsaYpKGmgouLLdWMteN68Fuy5x9Vm9DROM9q0AFi3o5VCrnwZYTUTnmuOYrOElWxNSs8K6Xu7naRxnzrdBWw0iIXWuzMnTLjH7Rm2w1KapF2m6CWjHO1DeFIDPLebGYXVtgPEpX/In3Ayx9HCMhao+EEhC0LtmOqBM8ZaHj2MZ4bN5zhBesFGAMPPM9s0eVshFnbHi6ueo3HKU5tZWVtMk53Qa7FddREGXK2mepKe08z8mqGjAVnL1YYJ3Mx4RHmZyC1EyObBWOvCNn+cD8v/8//uR/6X/0+37H7/5f/f2/+w/8N/6J/+3v+r3/m//27/9D/9Af/MP/8B/4I/+d//Uf/Yf/wB/943/xNwEJlMwySxupZh3mNwTNbvBJBYjMFH7g3WQsjCkBEG1sfYSaKf3gDa2obcyMMgVgrfSgN+QAABAASURBVDXrzKC/RbQyLQKh6KwH7UpM5Rpn2NuQFUgyvl1UskPU5dDVtq61W/c1UxuFNHU6i1Svqz07RPTwdK9pPkSoM72DCeKlkS+MljOXckSsNVgKFioz0IAkqpqtk9v8mIVci9fhtKHS08Ih9MMmODut92hMqW0uuhxVJQoVjGDRW+7HEWHG7aWLvg+f00cwqpbIYMEbVuYJs0OX19BnHDkVgf3aO5mdG+eGHktHRdlRLlOrx6z0/gt/9W/+yT/7F/7Un/0L/++/8Ff/zF/4t//1P/fn/uxf/M3f+uu/1p7H1aRKWC0prnfLooLUGUynT3vVwd3YmTgtdgpK3QuJ0bX1L8U7xVAr0RoQ1mFWYimrVdbVqkpdVmHt4c+6eg01Oo9CEw53Xm1Tov8e+T3O/fWUUmda+mxtm7o26Qs1RvMa+/rsZUdVIA4W1mWg0m/93qcB5BzQXeFXDA2K7rv7SOeDL+rLJNWBFI1MaiaUMCEZEHimv5y2ecCiKX5rlc+SCgZ8ua80+z7/2B/83/+9v+cP/M7f+8/9zn/69/99/4s/+A/8z//wP/g/+yP/3X/mj/z3/5d/+B/9/f+7f+7//H9Zq9yNpN7nlChYriyW+NhREcZeqz9f+HFxTRrN6QOczZUaOdrGdkUIVmuD5jxkLxsf2nSjoJ+jTsH2To/s5tPqO9gn4kk9W0olCUqeDnS/+chpOaVazCc1uh+cZA2HWcOzlKnqrRQHK8lhtqZgFFDvCSgR6WO4TSIO81DMaYfrfFOfkdEvEEzYPKOrEwW3GJesqQp0tFVaqdcCHDVHKRtO4WrfZWHWwykHjmnlGuTU7kQygEyhlJe6PCQElje9xBeq+nRI+OgYqY2EfLeziEKWfvFCX5Dxw9+OOas76Tmp5cB0RSaK/RNxQJeMYzov2x/ujzSkSrISD620VVSK45L1n8Wg2Eo7DRsgRTQ9bMSLSxCGYSTexiaZK/DNl98IsTAA0LqU3nTE4EnsqMjdBkprjjGSnXXUaaqnQXBL+S0tbq/zyp9Q24b3mKcvdTBu3QaEKNlUDxq9vY9DzkHoV7QYOeOJ8cthBUbxq/kFTiONpTEaSxzzdANcjG+Mm/7Net41IkDhy1GYAdHnx9WFuxRIttoyqtF6wcpa4L2yFLjSy5Hqk1anU2s4Wh37hu2wUBwanDtt5zxKv/ll6yO1v2mU2saaw/2ADzpwM7/X8z3jEX+YwEKJeSA7IjuFz2dixHtGX6moFIvigzTbeqq587v7+9r7K89g0QrWeP+RUN2EZlnoMzG6Bmo5jKPHiFW4XkYK+kPkU9HYckOfJomV5+Z+kI121ZqJe3RZYFRPmeEB1PPSqEDPj2dfynaKR1NoeXueKRu9vvd1l93sVgbQGtCKMZXNbhvPNR6VoMcQEfVcgth09sfPGLsZeiCaxzqk7nxrT5GZMG2cwkPA9BO2T0twwLyui78a51MN/RGg2NBJ5BdjSojDy7O57T0k1ZQ8Qj5Yv1RGH1r6ABeVisZGh0HoLiovbbKWr9tfmA9PQBnUKWbLpvQ0Kz6Vn4y1Exrwvb5j3N7ljZajmcG8E3qKvefBvNuOrg640z8GtxKjB5X0Mh6P8kP0LMmo4a3SUgY8OBSMUolZVS3cynFcVV9UmcOFVrs4h148zQj0MzrVOFhdmeowFtPOr1j604Wed9kXS0tYzZFfV30+dc1YU2oiKfFZuJ8v1Ff29YxVklievJ/nu77vqnvX/uZWO2gMYjQo1Cr0o9aLARvG66vmrzSxtXzhqVHlb839auM3an/VHtG2/we//w/9jt/9z/5X/ie/7+/9H7/Hf/l3/77f8Xv+2T/2x/9EVD8p260V65OfcZac1HX2j13abZmuv6pUuIPp/hTi/Q/E5vr+iBS3B3XbXTN6qOLsiEeRhHXBfcKpiRK4Q0QiWVu1XkChO7uqk122LR6T5dtyqxlhe4ecMqfkBwviwal6vDVoi5F6AhU2Wg1bJIBS4mp+0RfoAWYRKb82xAtAS/Emxs4wDX5PeGr5pDVkab94DrWLQpHQc6olDMhWYejk/kY+w8JHz+WcBitofucHtjpXSy4b3FlPefjanG2GHthmu7tRVr+M71/WXYYfxonZmCAK1XWE3txck/AG3DpfCsRUHLrzoCJUeXTWz4YArOwp4hCyC855rfEF/iifZhJiauF+zGoOTD8K5ot9ua1rreuaa7jI0OGFS4/5U82glmKS9t4HjdYwCnmuHbPUIdE+ydFOBeGWi/aBOZQ9IOHAZfMK13ilsTvTRM/q/cG5cBSlQbtBpKZDtb8cJoRnxrazt9U9kdegm87nfM7XxiwNnCovHrTCZEpdJKv5MtbPT3Kvgd+gdohf4JUqjtmCW4IKOh/q/gL75eyRwuTcafO3fv39V37rb/+1v/Xrv/Hr77/1/dwnolp5Tr3rcMC50MtkArnGmeNYtOhCdkY+Z+uPR2eql6lNK2FTjTSuNrVYbFcqUYmWuY73d3unqz/t1iLjxpKM5gLFCoAickhKXWiguwSGuth0WajUL19PjiczrOuTuFr9rqHOqlDZHOojysLsPbOEsDpwMIIj0O+X3m9LKoNBQX24vL/KbPu7/IM5SzrURZDC0K1KUwd71wQ1gnDLNgK0pFLBXRrXrunzLt80kaBksR6PZ3qYJ62m12eUskOpVMgJ41fWVz0/xq2VEVQAfePclXm6N4SaGLQL6wpNb3EXjVRlHA/oa+w69z7PPnVyZk4K2B/4P/3f/mv/1B/++3/vH/qd/+Tv/52/5/f9ff/0H/iv/pO/7//wf/0XveepdfgJrJ/A1XwimGfgGAo9wz8x/aXm7nqMW03ZvKXWWo0Vo8IS1h+jA932Phq6Z7xiYHU3kSKmmUDocsN1qitxwh/ZV6bgCuFRAs632nhUbjFZdsVncrryLtFeGLP5dB+RglbaOG54gWLjQ6ENcmOR19DIiX1B4DhKfwfRAexj9/ncEPN7ob7M5gBdkjrfnU9jc0ZeeY9Si1LEfbozq3/W4zxmz8xcaVX91//2r//q3/q1qkOl8cs+9/ntcZ2tV03BBxRw1QNuuZkAQnnJJ3zbekc4PETkog93GxgDE5JK8CmRb4StLVnBADN3vbVBwTdF+U/OW4MwGd55jnyzebUC0kUWMCA/otwKF7iouQiylS3ZEIeyRsOYgRGtknodV5x4QvvAPM5UoitCfIi0ARitekkxmeX9XHbmVFzKOc5hxI+oz4NxrDWUNugTLCmgNVHTRe9WVcWXP1/jXObUeQINEGPyy+bVgz1iqLGNvopqF6xV1WOXHaBYGvCh8hfO0zxvZ3q2cn8daUlavONr54qyJFXPWJA0O7nDNubNidQCMNZn2Q9ypkf6edtmu6uyZo7VPhzKjjsuQsuIR8xf7s+thfc582gjEwSjRgLwHran1du/CLkskC2aoQzd1/y+1i/Ok1WZXm0iEU+db97HEqt81jAQDkUFJaoQ+zydt6k7v+ysHJ+co3sqLeeuuqNOvVJUZgsW3ppNxW+jZ7ViVSWh29AXZDJN+47quBnfrKSvsT4qFArPjF4JbVVZBxnQtugDWivhrXDQZ8RjuEV+tR4AXTe8ADS71RlQ0aiyDdyFvfM8FKP4Gv0147KbCtKMfk36p7hayhxI6yPea542mjix1Rwo2UpDDE+bhrHMxxg2XOxuO2khosrt0DNUxg4/MNl4tAA+IqA+lpeI98zZ4Yq1ugeTiMo6B5ZOdTba9qv5mVM8rIJHtuRUQspyBilPG/5gZb6OrKzLA6qhDvQ5KlR/6Kff1Q0YWTp5mHBKO6xBiVVaLu83WGQquiaPz6N46CUiqng2Q8SnZ8xXTIhktne7wkBeWhI3sxhWEM8V1qXfXZKcugbFHsyUZal3zRgW3lgh+13Ero6wrAw7MXYqjxkj5ZpzTPepuULYRlCqRKrrBZiWwAPcbUEbQQ/zxCwOtxgvut6Gylc7M1nPjLiyrAmwA6GDUFmc0empkEI4js12V/avbG0Uoncrfy8EzelKyH5JfEGkDA0NVTJXH5GVhhxpPIM9bbhL6Jb99lzE6Lq2rb0sF/syfFmQFd3qG13GEkPik81CWbUFIFaa7zDV3AURpjxTU05g367dfvYDkdik+9Cbmbm/vxUji1Ych+OhEgp1Yo25tM/Kz9LGiKPktUoOR8FT6JU4yX1YFrWO9d3Y3S1IVMx9JrddycUhQqzpGR2nn+JDU5xI5SS7Qj/S6przRP7/HIWGz+ZsMzoh6/rD6FjPYlgUTkPy6qCD9irnegkrotCJt2CqJa/BUJLMta6+356cdy1A9cnOEfkS+JpAkcnW5EZ79kxIFJR96IQ+qpkoerYIgyk+VuGdEbW7tvWRj5dBNDXIsekn5yODBHXAhqlbv7JRitmhCFrua5USva/MDqZ5aaMA2JOv7srrLXkQhzDx4SZbJaxhPO+qobOvP+DVvXLDcHRzkMxxksLryrTZi7YEbso4ocs9EJPn2lrzozJOedjgF+0HxtqgTtJKFTJoBoDec9blfKdbm6Vc6a1aKTa8P/qUOGEZYkzDRo9FX1bUdzCb0WH29jzz9hGQIwuG0iAysV8cpys1vAmZg3rmV9nY3u27W/f797EUHUce1U+N+gn6dmKGkmAdCCqVk/ll+LytwUP7ZfK7Pdx7MVeWbdhj0rZrgxBUhIPwHga2ZSvZnSXA8Bg8X8yPu8Hsla4o9ozV+VauUCrUeUpBgHgzBM+0G3nbjllm7rhmfxbVqGBAopMMlTVUGPHkf/bv+Xv++X/qH/ld/4X/3DUc//9eRAkcBjEuWxfVwJplsJlrxRpNfdbE9tnX4JKmWyTCiyvFRifE/gBW23Wa6voozMyxW03UiGFmHA2S6gC8zCQVBSqj/1/C/gPQkqO4HodPVXXP3LfK5JxsMMmYZIMJNpjonw02xtjkDDYmJ5GMiUYkk0ESCETO0RgTDJiMyUEkkSRAgHLY8N6d6a6q78wuEtrA95/X7947M9XVVadOVXfPfVqBeWGeA1CX2xIi1K80RuD0ZVL6AkWW3mtrg+c2xUqkyiowJjKTvs4CalfKUdJ6s5yJAdRrYggpMBHRpLg3RGPNAXG0oWntYhPSB6fFps7cc43QdMte08egDuEoTbULfSE00RMz3zlo6al0mWGcjcGuZBbzShD0oAxFKzmmlrpSeiWR4hGOiAbpTg9TO8y1QGiiq0GkaozqIqmImh1LD3Yjh9VMmDDjULs7256ytucDpdIRNBTdKMsPrk4nvPZkfefmuMfCI2qAWZigZCgvpbtoL5rCybSPDbVx8AqgE11tYgouNQGBZjKKUSSMXnhPnz1aWgcNdF+Q8NhtMGdUT7CLVIc20EGTxaPY7ftUscShR3QgRtYb5bsEk8zJDWZ2KofXiYpBTDtHSY6tgyuNANKT+4ZYawS0MIwhoyzjeeY802MAJQilJsJL5spbWQd3TVNaM+msUKwvWo592H1f/ah7qfrsAAAQAElEQVT7XOsKl2aH/VsCvrgZwpCTPrQJS9kyWywtKqpw7T16R3cRNVUVs1wimcIwt8ZyTdaaBqFwWiSFH2lUQ5+zOQMFT5t4F6qRxd0YRrK1KAarIxWKgf1pHsf2AWJZLFgtixiHDMZFfJA+mqNIV3ET9iiphJ3CPWKmG3Mu8TTeK8k5Gdlp4QybnSXFAIEKL80ZjTyFxCKfCCw5ybtZxItkAaRDPUVTjOcWJfpA1uVqSj5hoDxoUtKSjCV4RcIIk/aKLMFVa11imNmXLEMucDDcKvwpIeKr5LMwqzZk4QwmfYZDAmjSm87JtYR1ZUyNBoeaowaqcEgT51xqqfSPpild2G2qmqIW6CAyFP4ihkzL1DA6zGrRtZtRhwkKne+SyY8mogCbaESZo3IkqjQxdQ6hTYUZxWcfJIBn78qkgKgMlQsvVj5rHIGYM9WjsaZISGi6BoGQgTZE4dQYW65cu3SIz108zAUo1KJWi9CrljkJZjKFqSAu2pPopyREwFWeF7q2SC7CkiGtyzTbOihFS8EhKVeyFldinTW9BpPWS3ZJrmUBBi9MRXOh2RigBQ6bIsNbjcZ6O2ix1Ay4NB+6DyBcpGZFHyL5AQRXjJbASpU6MCdnZLaWc/NgrKUbkj+Sg8bYyRiqT8KblezFDDDKo8pBjg3kysIKPQzjhypVhuSipMFblAxGGyD3BoMNJOTgfZCZU1UdRKt3JVqtQFSHpFdNqF46ELOGF5qVoCUh2oUTAFVlBcbZh3UDxRYLeV3IjGkUklEG9ikcxNuALkwTczdwE98FoYIVbIyibn2poBtzjqGqcCkNhQFuU8ic4PBRA1ozNGUxywheN0DJV6nLe1Jlp9G1DFWJxNDAEl7TAZ88thoCwmexRVrzzowI1Fq0GNy5XI7Gclg6FxAcsO8eM8NmeEdPxsarzSVnJAOUavwpHEyLV0lrsmx+srlx0BAQpYjw2aN3SJSSq1VbWbfkTXOwHCNZvjKCnS1IFlR2UyVPd3slyY/JSNLEyqPIaK2aFxEsKpOEZWSZFQU2aCoSolCFsRGnSnw5WIqAC7VZM3IxC+xNI8RMWU+Th4uDMRbKF8HGEHVFHmufNGaaF05um5HhMOInoGJhVDNDA5TM0pMq6I6gDcbtp6VWCZVMIpTqGUzFbgMGWy3foNtGGTcwjtDwJMnXPcPZbx5GtwKa48Jc7Uy3mUT0bUL2cEiOQtcnwAP0potNtrnFc+x7JCTNYkQS7L74L+IxJMjhxeQ9pkEyQsEJj6MOBIRgkUQzrY6BUeMNHbuogE5MymoiM4K29WhLtWcCl2LJlXk1GkYjLMVcSmD0LDCBqhbjYqaIGnWqsXi0mjMRW4Nlq9fmY2dehhYxYRQZKI4RU6eX6dI6mmemVBVSFXQjG7hD606gwPEkOSqcN0oUzeIhkT1yNl1XmdWgMItxdFSKZ1oPjVG0qElNGIDs3TbDNg0+LCk9OLUzBK5j6EpYo6KkVwSXUDqsUZEyYFZMJZtkoIUjQzLLTBbQdHFBFKoWZ/kDuZ1FXGWGSLI+zOIt2UeqcPVshamdvInQXLQbEhTlpBUu2lvhqsUjMwEB1S7qm0WX6Mzl7q03J04LzyKcUkwImAQsgsYSQyZ986BAslCwI9RlbDL0VuEldSlavMtxhcZaNm2eMz9X0QGiadrB4jYS0alKH1AqNjRHbVKm5LSVHExqLjFAZmqDNnWhYmgW+kmnnIVCiBMXhjaEsaa5ZC8hhlpRhZLB2M+dtYNBiSQ3kqv8tJKMs4u51+wriSJgknjrng0XOeTwf7rTX1zripfCgQ4NMogFjwERVbFIjUhBFsigdTQ+rrOV8HGNgdYPzbAumExCmbSF/CysGrR15Phh6gEnMr1UqALqaYAFkFDkIEwAE4DDgDZn0SXwjZ2QoqlKJjHISQYRLqekoGofncmQywyrbhQj4EknITIb2ERyFM6fzrj6IFE4NInms3bWVSlQkiIy5woSM4CKHJ1hEPfSUhalQ0btXG9MKOss3S1JSTEav0AiJqzyzQj6urcuXoqUwhWIu2ck4CEeqCLb1FZ0RBSFnQdglD5W0Ozki4hhuSFSZ7E1ZFaa0MNogqrWEPqCMUw7uTdSlKBhVTCYlFpkPc3Y/0gw69G7xVyiW6Z0k0bgVD1IK+aBsyhIWjVbWREGAK3DuzIzAXNI7FZrAenSE6wSwjCIDHWUYVsbB9dUsO7lOOewnnPdg/6rgI1Jjjm4WdFGHMgfs6GKlgB6YbmBpzQm9RTZmUlrxZy6Dt43EJCh+zCt4c2H6uNGVK4bkvI9s7t4sx6F2M8aa8koBDEcBIIlxjLGmWMCyR/pIM2qSiUjFMzOPtpUjJ1pACvx2DF2tT5IG4lGueFVf++f/+oW1RT7HfRiCe48a0KWsCmhYYUEy+CQY4kavU1ty9ezTNAmID4p1kTnoMOMQiJdknUrQZsKFSk6CEHPNqM3hAcyInxOIYtE+kbNOqiOYMpV6Ap1JVJIX6pxHTq/yR1XuTJZUS6HCEugWOoSBY0sYlWNP1rFilqK5GIOyNcqRZYAwoHGlhmeZU6CMqTobqoDGiLko3NtJkCYMPCRHXRwUk50SZuBpLNqHD0DOSNCI2ojuItu+oEUd+1AJwiANAoX5SDLtbkgDbwH0nmd2rNocGPC6Nco2sj30cSE2QaCRBYFhCHvoQ3DXIe20rlIg3RXJ6KRomF0O80yjDTYDfKcSQoJWOdlUYxiGAhwAVaag/YqjfAFsYbVHsWbEBPJxWUQOxoploQ5C11ppgvOVlMq077NjklrJ+WQjtY0fTCUyl5MrR40GqoMgwCL/dpnMA0EM6pD6U1mHaKsuhR4SNATgkFZQUmew1nRqdN1QS2JBTp/qMtKLH+pMlIWTCjPREUWD9B+eA/2X0oBPJnNOtk4D5p11spsmEYm/lp9LtNEmwSTiBdhDRWDuGEuFuQPTJPOEaIIRtwya+u6WEmHSkTpXrsXoJqMlSmhM+FGKmkmJq6+HN373Fvrvc3R5j3sCSdd0EmzHBqYlxB2M9fSVVOyIGTBx4N1Vgelm+FBT2Q27YNJLaPVDWE2qMzweU6ssRAiU1RJJ6lp0krvOgOh4QQGQEIzS0ASEmp9lOA+hAHVEDgtgtN4luHSJZqGF5SCGkHpCCU9oHPRWTJ5mvMIPmukQU4Z0QS1hBAdDs6nJyKKQHgX53ySDKK6MjJam4/sTNNVxAaM1ousBZsz5plGMltlSKPhXRA0bwk0bc5MpElYJJp0soSMLhLZ54bewWGIlWagZ3Myw2aDM3nh6vwC0emkU4pG9SzOIMGq0CQRWr+EGwp+DlC59HTxbm3WnpKAFNNRlNPQSBeRjGyPhR2qVllrKKOZijQAIbn4bDCBAhwqBwNLW/XISE/jCq61yJlmJ8JSigPiUrtQAQPNNepUsmv2FE8OmcQT4clendE2nSWEPTSrwBg/aQSrG8XYaHGG+2KGCEhTIy4xIWYSM3k9SLPdLbU6iUi6TBVNsYzF4ZwuJm1l9BUqyQQVlSJWtbJxmFqzMOqU2VrPX/r+iV8/8SffPvFH3/zO977x7e9+67snTtNWaHTRUB5030yD3hOBGbyeEDZsWzG82PdIBggjZNlo+tx0zWQrIfx2nkuXpXXhnFxcNGWJcCQr4iDTCjO/iN4IFDMxC6MKyYKZu9SU3iHhKk0zOLhmEaIt5JDUoJNIws/cFlZ66aq0eCwMWzCMKcuVQArbEpQOdSO+EHE1L6UPMq+wZFWBAz34pugu0qQk1YuIcphIZTcL05RgzJNhEOEYLuyXlKrLyAbWBo4HIc1HZfHl8wSu4mqToYt5LdTggkxkKFi5oDAPdRGyoJvSgy46pXaxmc106jWnoTQdmlv0TGGLQbJGyhqMDsQt2YPVSY0ISC/c8U5BS6Hig2Ux1gXmEfO6h3OoEcroSuGMFDD6WE2sMMIxiTcaB+2os2oULaoD1FKA3Wa7WBSociMz176YQyNUhIQlKprJwjdBJqABtHdC/vT00z/33RO/+cMffumE737thO9/87s/PvEnp8AjKSJNJZiiLOHCQcFxRG2QUjUrOJLR3uVdB9ViaiYqaXSjZXQNGiBddMaSS/AiWEKrYBgCrH9NWFatWfEqsoJq2hzCGiF0vIgu+YClsLF6eM7hU3YgNGTwpE7vzBZJVYEaIJHLmDwtSYuGa13p8jjQoS7qSQAEszGDYzLvBgaqNizw0RDZvU7MglRhyBuh48AaVei5QLMrsYxkrZmLeElTqxgTNiObTE2D/kYFrRKwznSXmTZb9Vpdl7LDYpVBuwWeMQeFQMssomaY0D5Bai79g6BoxBi90OWlJVySQEBgEGFglvgHuFDodI2h9yQ2CYiRHEBg91vySnSBG1KWeqfiMXSqbkO2Aqo07VKlDTozxKLaFZ06qQ1QgHp4ofTOUDFvQwQmoqpSJFUYCuaXWTqlIIRzwXCovF+X+2mAOTVSg6Ap1ws5VfcaplohywhRFNWIMU0E0g5c00DYJGIZsKZYsHXNQCb5IawYkeCnERircD4QISSIqiHaEa6uTPnM3ROSqzYY/YzZ4VBRG432kIKACo9k0RH29Sxrr5NgLr5VY6pCnHwIZgPUTEGXu2KOks3MtaqA1hD3PkQOIdI1syBZG9UhjXdNoFi6AjIZeJdL1g0VI4CZ2bSvS04rm0ebDcEisM4+ZU4pTbUM3F4zpgHKM2amTSxQZTkXAJIQAW0TqwnrrAl/cLnLbowD9j9SxIV2eSa98D0CklTDppHZ0ptPzV09mdzSTZZMjuRujSAIFIN7cWgaoxFOqOF8hQcHtkQJMHayUFIhLGIFo5A0Gi5zaBdNKuGQu1vyVFVFbLGCIlNaqyIDikrR3S6jQIXRwiKjocAQREXUdo9Ca1tr6rPKzF65iFG3SwmppjYsuJTs5nPNXtmJYZHu2kOCZZ2VpjKjuJgodavoJOkWKdAZ0iFU6QOIVlbXkowuBZKVqgCmlPOO5gtcCSQfIDUWRzB3wLJAiEpKyZToPSx7UW66iWYyCEqwkt5UWJHFLA92FVGwlIo0KcyyqrxFWDGBRkjnVMEKa4x47D4Wg0zbMM5l6DALUPNgIeQgrZo1ZiXlJROR2ReUs4iqGa2FN+u+msrgpqEB86yZo3KRDtqRwUdmi/NiXNyy8yyeIB9rHUyGoVc0nXs2c68EnmorSg3Czi/VCXVJlEjs/kmLSGLH2Y7jd9EtF4IdAmFXgqWz0jLUSO2ECB4SoEdSHEPR5duFEigTMVj6OaDCxbGPvtitkqHBwhw5SzZ1D46rAhhmwDMleEZdSh88mksHnbZCJAiRuQMdiBbdvYF8is77bZpa7v7nTmoqYeqMpjdpk82bOU1o3RumHCcZ17Ix5WrLS+8i2H1oz2VEy6zcdvR1RhMILaGl5E6aV2mjTLWklMrfYEDJHPSZjYWZFtFN3h4QSic6rR4XpaGQhUHiupGoPKPiHDOKNVO2arFLhQAAEABJREFUGJAiIMMCzGeOmTyCOkJ0rtZGbVJ71pZjE+qGraOuW51Y+yalT5qtrtZWp9S1hFOvxfIXr0KLSM11xbpwmpsiSJ3atc4iSVuAYi6lhTUyI2xWNt5AaVRblvWRZjVUMh+FpdXZWWYydiVVtTKAjdvAdIiLNILHspEkCmCU6sIwyAhfccWQ61nXbu7izmTuXpKcER1CC0oOIispEpaz2pJC4JGG3OgT86U4bbLifEoyCFd20qgkOrKsbFVKRaU4bQgldRLZAz2RkWQIi2QEhzAuoC2kZzi6oDfHutsWrCHpQIrMtA9Eg/CEZIp1tSwwZaKbUrMA1LxGTlyXZCBpoQ81ZBVRMZHLSYgDGmmSwu+Aihp86PS6Bx2fuk/hwXDTNSqk7yIGZQDnLgxQD/BuB49Ad54SKM4XYg5wPYKlxKQmRDXq2ImJnHLKr498zTse/PI3PejFb3zYS97w8Je+4Z9e9KZfnn52UAlSBy0jBnVkTENuroRpB4WO4POZzWm9SO39K1TPztIlPTo7dsiazpUZtRdbr2zaIEXGZAalsKi2tfU2hPPJk3YzL0NWTUkVWt8L4VZtVuZgaYGISR2jDt0owTBxKPq2hAA9M1gEOixQTLUiFU7KkGNIlijFjJgFggF0Sml4sC5xKbdW7WKuwpYyCJ8GtgEppimaqsnYUVHNNiJWIRrCuxo29mHk+sCA7Mxoq6IbRcbiaY2Fs6+KFtUAJuhWlHUOXVaz1WbK8DTqDVUI5UVoDpWAkFkRs+VexpoWD9FJBMMkzJOgr3xIMjrllQSTFdKYFM7FhGfPVacRRQeIdY3QGcq9cxathtFC2Jc5EiELnuIAQrJDETo0dksruVifLNGWCdI0SlU6JTkk5RBJ0yeTblJMS3B3wYmiQpWbDBPaxU4SwGxsQWyWfww/U/DWT37hsce+5REvP/6Rr3rDP73yDf/0ircc/V+f6uxRGLHOgY1QaUtbm01FXDGq89HcCKbBPPRWOyysoAgSas6i5mOPwvXbVvWZw/m0DX2BRHSuMg+0tvWtdZ8nw1zYn0NxQKgnCQMVToQsomGEYRLtq5obWJV5oBmaU66mZPnjfNEaI1YSpYcmTeyy0loqE4HGeLnI4YcRyv1bR4Hn4FPxliw87hzZnIpqLEZkoHvMfaneEbSM0YhcAs1YW0A0YR1ooW09ZhuEhDEyJisw9MyerasvCgUGILtn7xo+BsaQZahZenjX3O1nztk9IlRatbVpk0gypUxms6aTMuhik8mE3oXp04U50zybSCxxJjHFAxkcy7oXPmxbe3dxg4w0jJeRLcKTitDrnHXL6tbyP/TptDClTlLhpHyZi8xG0GslRYMZKpOCliVzguGFbGPIorQsk0tvdCaLCGsD4WIUmMhER4gvvAE9QBs4BJFRC4JuY7KEggmWae7imF1ZSJIqpEB4pKhXaUVY6TnNYCe//SeKe7cEl0BIWZQiVwqYdLV1KiOjpoUzUOV6f2DA0DkUVVu1qqMIKceprmeJYjl6JxWUhgzh5nO22RutgRMRJVchScKIAaPqCBsiymKzqZfdmrVgKJwgnIHG0i02shd4FI0NVutBKBFFisCSQcveLemnCTYKzObMQEDKKrZVpytmxQatEA0gGpund6y88Hu+spjUtThRMnNVGb1sTNjYypE5ELQdjTSUMen74CJLBAZIFbqRYHgSh247pJhhv0OhA1aBOmtfo61FOkPlillkMhApaIiUSm1q/NSDTksmOhJRSgyVCT5JRrp5K52riGYSuyM7iAwYSIDKaEELKlbM7AEq0qJsCWbElKALKbwILvkWTiKNwc6mue45d2uQHFpZUah6G3KGAGL8pRE9EmY2LEvlNGGGz20OzmjO2bJbRXLhyYqjc9qMQWybDRtptXd4l4RCJRXMOvpUG2x25idLgNZpLK1EDwlTAmAuG00OalKZ+LHhOQavwmhdqgTIFJjTGVdqTBFBUKenBoQHo94Sk2Ae2lTWzbeEE5RKoQUhaKXmWFgLnRNcTTCq4lJCNhRFU7Q715Qpi+/KMOcACuegrUKYWb1Fc74sUuGSrYCWkQVL/xpIjznDAQgUYAwnyJTRSOoI62odysyc+FpteY6+QoyKMkCKiMGqZZYpMAcWzbWPSFqCtCKoJUwWuJhWNDp1o9VtUscqg86VpFQppoqMIGdBUJEQmCSX1atk3zFjaKZdQT+be3D51JFZE3SfHq1B97J0rcS6DBtWNgB4XwpcCTpisaRQ67JOdHo+5KJOwlfZBw0RzTSQqMFRcl3Dxbmq2VizUBEPc7McpDLrhozEEsopba3o7k6vWYKdCUPbew9em/iLroIVbFuUKoXIxmjLOPCiOSz6wIiRV2tiTqhpAlN6dOiaPgsmR2fxnub0pqoiZKwmIeKTai4oEEggZFFiFJgFGZZNlDcW9EtUE6NVbcO3WIhULaUGgUEWgPim9tA2Y06dC73ZXckh7Lat27hOnebe5i6ZpLv1khmdyTnQvqj8SNB9ooT32eeJeDMCjLCAnJeUAbZNUYn1ytIgPcOJFcDSzxI119mxhZg502yV0mrVWpbvJswTPTgsq2TNGNNlSNVB58oMd0QODBo1cpwhYWGCBYVQ5i090twoxDpQJJbRK5AEjo1yS9IbB2KHbFBXJRKaXXrr3lpdY2PmcCmhyizSMa2GRuYa/AZknX2X9makUw4qdLFXNKQKI0xebetl2/L3TxsZdW7ZGbw1U12kq3AwMQ6JngMDVmaxJYCGIMCYoVOnHWAaU5enTMH1zSqyGspgPrKDuGpUsmlIMRd0CttuGrBbCgRR0MR6aHUdzYeRlmOaxOdu6KVOzCRUsA5xHT8IKSPitCx4l4bOSyh7JCc0buCK8j1WDdtm20iigsVSd/rl2ZyZIMVt5b5953k7p3nuHMWbBxs/9zkJq4kyDGprl4nXO8CcXV5UB681x2pWTfdpIsIiz83YXGjrYN1kztm3kgAuHC5zgkQAscytiPUMj4i+la1xnhigjEUM6IpGsUgyJjbSR0sPzLHAJCb0ZskAwEVcCL5oK+lqrZStUtdaJhWixRmUFmMyPjp3Yf4iRvFakhkpXrKteoyTDpNpG4dpY1hv02W91hKzqkqv6qaLCiVTeEEQnanV3EVQquZQvCpqKgPSaWGiZYhwnlmPlQFNiAgsosAhHfRwMC1UZ5wppjasI4M6VlyTkUWzzl47PVSzSHJgWRBMADeITcvMgrDeJt1IkmFbW/iVolkKe2TmMpTQ3AoYtAxigw6DVSOmI3oNj6a9ryDbdBxoSpO29tZDRVleBM2T5lC9GaqKGKQApYdNIesZTAz3JWDOKbRntMWhlKIohQUzV4HaFtgrqVKSWMtGkVoWlIYhfnXueT1y7t5oiAc/8LStSlcSTdJ9y31XJMdCUgGyT5BJK1tqyBKZRvSnubVsXdyNJqpU4d2o3avLGLpKQtJ7d5Za41ebRHdJyXmd62YtrSPnSO8oBl11VUdS3zrdCSE99rohZZvGIMwr5FzKZDXTBgcpqrIEJiFLOFVpYevAxmqFAx2hCTSwXi7KRaJmX7FgD52VPFmretKPgnlQRj9oepguVEYEoUbu5vekObFvq8s84aRg83kt0atYEe4eKEY10Dm1CakVwsAYVYcYQ6tShijDWsaJsZI6hEmSpcFweyx5Bsr1Ys0oXoqrzIrIwuZY9Yoo3c2bSK81hqIwa5XOIDN9twrNXBDIwq4x89o6xqaDlBi4qDCikBQuiRFllNwI23AtqUXIFzUQXLACMIa1YBA18n9kySwqY0UlKktNkanl1uyNGyGGzSAGk7AI1ty+hs/pwcgGMUrrzhhpVslaYQM4i3qdYWtIm0vMG8VXA6rCWVayKzdkcfDGqpru01SBYcnGsJhLZlXdKKgSxYEQWJUVx8mWMSFmrb2S8maq5oouHkKKp4pkRwQgYmUsK7pZskd2xnJT+hamyC3IpNYlqpEoXuC6gFFVBvavUMWglUnFgWlOH8Q5GLmcVBqicBFPo2GZsXg2pfYcIBVMqaxTW8hIAqgtzzg0wMiuvay5DGcxXAVpD0OSBBa00oYVr+QQ85BbausEQ6mx6CIf2hrYQrpNLE4MJW3vfQFh+XfJJMmiLLUUEcF+RyopI7JNrEJccrPOm9umibQYMNDBRFWsBh2rDVVIJKFX0kOSOt2GWJdgQeozwHEakIttHMlyGESXEXmyoboSGVMsJSkpTJy2pQhKLV4m2JPNNWFUENI8o68RnoIORHdMrn2WpGt9dvRQWexR+g0RNmFU6J/AeBQzmDpnihDhmMsHTnBcQS0GqnaTVHBcz+5u4UPo2IflulkKM1dmDixdsqZyVcklidZESQjtAZrBLavkQAkBsPt37tFc+JGuVxb7bDqk1iGHDZGD1DmDSgKdtkrhPtQ4Tc8ldIBVpf0hEOUsFZyok5XGC0m1jJgRU8c8RZ872Sucb0EAjPIOnSQZgB5VnfgRqGktLLxUXGiIENZNYJPYybrkbLUXY29Ss0/Z55ijde+9a2MFKGLcE6KlsBAyFiDAMskw+zaiBI0QhsHJFUhVAkIk1sGVQ/N15xOXXrNUWQm3v0kDyFJz4RwqgYz0OWLOPtU+my6aU1h9YizORRoIVDENXTUjOGOgzF3mvkSqhpeeoBIWO0HXjCJtlDYgNOlKmXScq+gQtTbDxADbMKQNmRUI1tXdZ9Aqoqi6jGniQKbyiuqgMnhaUBc4FjCXPo3ZDxIweUhn0iMGpFIzIGTouqEjpWjRUaUiB4iYqlJIqYaN6pW/EMqlI3uDbPXKlAlpGKcYPctuE5yRo5BgoHIRanaPKQg3spQsDHUtPuTaSCqqA4FB0/Cx2UYbhtCQ7AJ6pHTDOSRcAb6Ig+k0R84SoYSOYcokK7SE1WVC8d5ySrQkwjRMcxpiLsJT6zJ2qa0POVdSJKJndqqFqMAA2uy9RKcXKrnQtaiaahGU4uwZ2rr3aOmtJriMXElfwVkQnTMomgx9FNVmOQckzCzUEIgujeeCkEChh2BFkRh7jkv+e6rqWIYRZtmh3RFQxqYKpxreFBGTYBEwwLryY8JCPdA9wqmUDUkH0jS5Bx+KQgwpXIIJbZklXQSMbg6WyLaI0i4XR4M0+l+6cRFh3gTJag31FIK4KLUqNgo4H9uiXpyLSF2AGjMEPIrKUDuXAx0s6JOoE4Vqhe85iTvBGiE1F/ryiYF59jl7o/1RJYoGRDwLwKwRy0RGpAiszlVbyWDeVdgKJKjyNiOgHiYdpad5aKJzULOoAwOpi8IWFl2NcDbXmN1ba/BZJERM9Ixzt3vQOVz4oEG5zuoxEpcIn8lJZkDJFAWY+aObQp/5gLu96uH3ftUj2e7z0kfe+0WPvNuLHnG361zjilKcoe8km1X00VtpBesBzGUGzQVB34QTgfd06RZpXXSGrUUm5pylhZfukokmpcEkTVXTnJdNY5AsdICxhxGHRQ5O+mEE6t9bUikAABAASURBVNCtzEiu7bZqtupSUigRjBGt9mpQeGZno2vwXiJGR+mWXPi68FQwwZoz9m6YMzqSncQCDLNECJfYDY3Apac7SaSiHEZ7JHOS+aQwjqOMIH0giFYSQ8jGMk5wIHpAy60EreYXkinJ0A5N1QlFnWR5/qxMmqSPgl76vOrTkIRPQzSq0h61ocAgHKUscaEdvaCNyoxVgQgwqIx1UZO5kARkQGc5KZACBLMYc0QgjE1C0kGUhPOjWIJ6qbYUaqEV0SPmoMtdEVRN6UBHtNr7KnJMlAxrfZhznMN6TwKcHAzC2BGNFn72zgn7HZ5lytqidqmutgehTg2e3iN7E+3G+VDoUS69s0dvGb6gEVpQNJXjZCApQkhGoAbPek/0rAFSJ0Hxzm2hl3VIi57MgZiUsCAzeEbpmXFYijVHCWEvQdKObvStlGIFYdE11XTIHBswefMICdM0wYGOpG4qSAmVrCwAA2htsGg52Rw9+hzZzRjVnppOgwyLzZnCBAiEOkrjBC9VupS1Mh+Eo2aT7gSsBiuZBvGI7q2jh0DNRk0LV9o4q4HWpyx5FkI/UIa0kpkOkrcxV1O1CHMqlhfGOwcWC9oCLbbwp5s0GheCxgqlJWA0WsJMVAEJjawixk/CG4gU9cK4FUg1oZhIASDZTOYydGVeSpT0QqdqypDL/V6lVaO5JkUorHQfGpU+DmZWes953Z2t9exMcuamR1BSANKgIko4AZMgyKkLKN6UMdOC0Xxb2rh4DvEEHNKLddnN527KZYTZ39/ixi97yN1f/vB7vuyR93zxI+/1kkfc98WPvO9f//mNTKOUhYqpnnSlKJSpoWZJdjlIL2RDdOoVyWoQvmXpoSRZK8F+u2PbEbQYGEwrIaP/iYl+efQe4RlgJXMpbrVVnccyV6oTG8EFNxOVhgu9ZX1SNMpWzyIhDKcl7xmgIiphQv8FyzUPXouV+EZv6iFzJv3XZLQ5GtLZ39jBi3BDYkOYzcISiKWfawVRqxKGLs51RldwDlzc7pYdTF2LXGILHimYEd5dZ9+YY9WL6YoWCe/t15L5pColdJgYXUhkTzStYUxjLpA4iImuOAjotCNcPCtykEjz9MBUZSrkUKqhFGHDkHSXYc1QWixoA6E4f2wyKkQCAuWBaMaQsaGnOFHh5NgmNNIcWlQFiMwUhrVpNslAUtQSVYJ2cuHhc7r3hKeI2Koy10bX1Ry1s6v0TO+cAZvNs2RPdpVMBSMcZAf4aeA4WWhTrxHbNEpIdFZQTYgwOZSOsEeyHzMfa1dqBm3SCEtRqDKkXWlssksRHbQP0lbmZVVs4BBAFhYeUcI0WgxQC1XCbDS3FhVBR3bmRGY0pFvIEFmJIemBrgga37tvhXaKjyJD+tB7aQEaRl1VhCkIdV+mf5ltSCniIDAa1Mq0HFqpPgiMFjGKNdxIeEkC41gsp7+tgqsFCCiz8CoWAEN6z4WrdbYyezYnFbESLSnuMjeJKXUOLr1XaWrWFD2TSiLLhDpBJgW3Za24FKioiKgKClwzQq0Vcy2ALrGhgbVW9cw2OTUvRKN9tCiLgpDDg9a4Cde5xCYdpWdJLHm/oLlgTe9LUalc/AxdBtpqCZVMlja1gqpZIOCIMIiwc5JH0Rotr3w45gMNNahWBatMGjg4geyMgG84M1WqllKB2gS9U4dA1CuSrniyOkhLBJGaBtsqhCe7pzfGV6ZB5qFEHQtY4RXkCL1vVdoGolRBlWSA1YOFbxhlqM4xllgWgBhGL1PXGREyQ2eViWEbdR5kHek9sVhZ0AdSJ2aRWLqBuHfUWSw9aQ56ZvYCr5yLvcEdmXRVtIukq7hkJE1bKe1RJfIc22ovZuHWG9YdjTkl3B9znZulDsNqrDKoUAGSvflLw6tNqhPGGWWNea1bc7bOYbQ0tVTWg5xgLYsI15Dc1NA8i7XSrnkxUUJ1DF1AiHARV2bQSmQ0MDMa9349ITxhttYFMo3sHZFWlvzMoKuA0DIpg6s16JSYwnvLxa6J2SQoGqXkqKyaXuZgh5QiOQBkjndM8Al9LfyCCq6ZS5iK6CC2AAKZMiaLVnlWVpIb2gcGlwq9JgaupEMXWEpG5TKL6emINfpWQ+PaspUSKOHoES3Smag+mNBBCKHs0VryhgZTGjNK42VSTVPFLEdaUS21JH3Z0tU0jF0AEUFjSyqEumox8PmcFe0qiwZ6SCWe2RZmeqCF+VJqodlgcy++VnEpvzrzHOx3ZII26Txpc2vEagTG4mONKj2DjQ6VfvWrXO4GV7/ajf7gWjf6w2ve8DrX+OPr/MEfX+eql7r4QSvrg9DGdOmdIROLUglSz5T0AlGpzbSnRFaLDcsNqUOOHsv6CAEFjU9Xhzg/shXoUHQsKICEJEyyKExtQB0cykKWXS14nd2oIyUbh8ekwqxvqFNYN/Vx5gS65so/yUDRXjrGxsmtK+ulzEPo2MeNYGbMBDgQPqP1xeegHfwtgkJHfPK5bbWcCDDHkY7U2tqqsxbCvEqMEhagwAK+KsgO4xnJNjdLryiWFVm6RStzt0yFFNBFp23oqUGkoGTyMIOqNKyyvMyqEbsB4D21xCB9WLqJsjB5hEjQSFqfsmTLHNJmCYeImLrpJGAJcGskcDCV1HgBSr1ZUhf4eo+Z3xuFJ6JCRshgu9eLhsJQadlQfliShX5LF+LRFd04SsRWtC2ZJltHn5cWiQKxnZvTuTt2YL9DJhDdSRRWhjKaaZr05Zpkr0FcZqD3LO6rEixDIjUTqT3onsacTBuhq4rZdK6YLPuQfaSriGyarL+wUlSrQVVFIJFgxWZFnQJeYq78OIvH0NImZE+ESq+MTbo6BsHAxB4CcGRoQgh8WLYmrVlAsDTsf1QvEmPmYKJFXcQBGj53dm0912FdlokdHYVBZq1xhESUSGGQsi7dJNM4cElfRRsdSnaK8T62wGlgzhrIjmmWmfXPvS5YKhB9OaK7BtQhDZZEwDGsozSAQ0CCjXyRMiBGzCprLK+9QjPHlrWpLGllQieXPkKHu82Ra+tZ04ZSuP7wOs/a2QcQEVRlgSVQjtKS+/ONLKtU0rQyTSPRjXEqU7e2hm7l0KNABXwfe+NDnAok0jFDOkTDzAdNDhAzAwAPbd3nqU1zzmlZrJquVIdBWGqYOUxBOhvQIJUyulLKxRjIAqo2aaoT4O4hnoOCj3Z0FP29y13qj69+lRtf/cp/eu2r3eQ617zRNa/5x9e85pUvc2n2YqkfmlZXy9B08zQs46KG52INIrRATeDQlpKsntGXZTIf+kzSQqYyzoXr7kGca5nQJS6AIAWASS8yF6xrmQvLvPGy8KaUAFIlNFLmzB4JV/REj4B3DuQqTKCAmKIWsSFtUNsYZaP6KL165Fb6OghEWKCIZEa0jjXDUzQ55xatydyAVGkDmmRIuPTGrjNA1RVZ6VVleFqruSiRWpY3N7QiqSiMtWUq08YAaDNpgywiwtMDNU8aXiZTLwVSMFdjfLt3ZBfx5MAR0hfXk/ZwXVGpNKhOESWZmGVlY6nbfNjo49gGzpADCdymplvTuDXJ7C0Y6epQYslCz2cMtJ7pAKT0xBzhaJbrVV9La52nVMySx1JTihZyn3O2DCC2bGJEhqYYRENn6NrBrAtnbvdlBEhIzBbESElg8o+v0qXSmxZIEGVOnxVRmacrK0YvfIx1YTGZTedac/lrnqluuC2BgbjJepXTar0e17M2jpU9nYa6pKswQTlXwUBFIIyAJC0JEULUtAEI1+aikmPxIoE2xMQvakYxA9EQb3QCa9d1YkpQb5Nk3Dt3LFlUVxUrZaHirQzAIEYvBbNJawWbw7CWMaKWZHakJnJBndZZluKDucExQxtjDAEYa6mjWkVqCEfoozbrkzstCaofpCoKM7srDdLFpnSYYFC3ZJQ6vZZqpQhd1sGsVIGB9buJIhPZI2fnoEJGuXUaNooMTGAsobBw85Z9kZkEnQkBuFuT0owDa6mF4Q733iJms+AAS2ZljVnnLZvoN+XGVuqsQLKaEI0+RG4E0QHMfKi9FhJDiBc4bCij0AFIhVZT/pCWSFLDM0lGD8KbKYkxlmbgxKBbrWxNdb1VF/xCKgWQJs4R6Cvx7KXPpUVtBh+61l5Iu5jAtUZMVYkhocmykK9nbfBk58pSSQSNDBRygl0isyurwWB1oGscjdZAVKOaj8p4LCOTVzAePoDj9ghO36Xr4EvEPIFYdrpMEQHM2SmKNrNeK4PshZdYVjJdPCojb+pWe9W0DHP2RlZg+WvxCBVARWmo1kHGIVZVBqhGCLJTvAcikPxFpmYRMRUBkiRNVfBetyBkgVXL3UUAjh7JO4XwiyWoIXO3aBD+xQKBCYUD3YMrj+hQT5tpc4RKr0aKRYpEFXA0V6E9SFEoxEVT4EKB4F0DsU3ZjQkFmQ5JXzVDwFLMF+m7a1AXiHqpOVTlj4QQ66RCMGzaO+beu3uqgyXL5+U2oGqcfIuAi+fiUWaRXkSFdO3CEkrvaExCUjJG7ysJBqDARIR2DeKlBxDk6JzaoQHaI3MaM7/3EsFyCY6TYryZ6hSfApMrbWmxPGIjdkRysVJalpnuYSQEHBDZnRpV0xASaRwcxnspQb4TMQspDgFjp0sdaylLIhJuLCLB0TrZu4Z/7Sc/px37NKVRHECWkNIjMRpio5GLhs6EKJ36aOg8KbO4uTsSxN6qy+AclFQrJhA6TcvHsFIhGtkQvSZolyenX1nqFLzwBKiKUniLVcimNIfSfgIOCxSFEH8bUqw5vIOHagiggeLCgl6XAV0xD9ySzCmt01WqoRcSkZQzqEIAieRASvWMVRUoaD6Bc3OvMwexBXWGGT2F8shEEL8MUwzKxfQoYsGLARZCndUJpomyiXUos9W813kqbdI2S2Np6HS7iTiLEmHAbLRaO6dE4mGMYlfn1BY1leGsMySo3FVmgsphaQPtXWrOMDvVCjgdT5ptzKg9tPEheK6ysYrmUnuT4zEs3sANxzzNaFGblKRDxC/SOWd6qsrA57qqcP7CGBPLZeM8i89gbPm4MGY6ClHRQsIxF9lKVI0hYnApwThwamGN7KB6J/KBDBGXMZbXcNAX+/lp20895wAbFQtYCNlrbkPYRpGB0MCxEKo0DHOX2SOYBcTWtAippaCpFSqZgR5G83vVHOBCe53jSlEZkFRdcjCuklnmxhKDcDWO1RJHILL3Oq9HsMbaIFWyhoAYcgk+M0NLKBs/7AZHGH5GsBNWOKOk1i2CgY5g/FNwoKMARVILFHRIUBJjxBhdmwvLgxdQF5KO1kalVcNgCeaEErcsGTXpCCqweEcH2ZjstQitEgKQZGIuYxN25liJZtqFAwmQTTO0R5CBWT2GFtId6SZupqSxJ52oDJjKlst6zuiglkETbOKRDohCjK0HS5chNG3hzsIJWGFTgzpVz8wTqNpim4ZIB9BT11k6vSrGHpoZTvpFbwiGK9ES1KYEyhVNerCghLigS3o0bz2+0loGAAAQAElEQVQbpO/WOQhD6CsAbsne4eidVgptNkABjorMdEKSBiFIdDej81qKOdcOo/XKSkU/kN0lu5kzfFzVcKSQSNqtZa4MCCIgTZTfRIjMVaJYGpu6SlcJtZJGTAr1eJgzTGpJW9C4gyJtaSaJZSIhSSfFVIeQOrHG+9wRUDXohhQrDCgXC1yFKGgBxSupCEhaQhwZ0j1Y4N0l3NwRwX3t5MlHXQsC2ZFOJNkB7KJJOGqgsFr23iN60OioNUpBJHogROgDzbdGMZEUXkNCXCR3W4FMuPM0DKnGqzIlGGXyJGiwMA7sleyuqxiKbxOWhoSVLquuKw2BkzqgIPY5MoWWe2eExYtizLLBwDSJuc0xuTDoyOAIKk4zkECYgk2g0VXdqmgZLBbC5xRL6nNV6UGTY3khzKykzYSMhcxAryKrrNZtWXeQeEGoGd8Z4uIoM7IFIdcKFXDE9FCdyQaWjSRwwlBaF0wUhKsQBLAuQhsWUNE1aDhTl1GFEwXMCh8C5A67sHikdFTvhaiyC8UGOj8VaaqZtNClt6TbIuZSG2qkcSSdl5grOpnOYAR6ovO1I0KcVFmgKsiKroiu4iId7CXcyTNi0oJK6Srt466dp4WfGB6AtDFvRse79LRWZSpUlAQYEjDNUbEysVpQlZY3zHMsW1jeXc22kYSAVvSeklmLGaOZ2RmH1MToOvJZYXA8LhyMEDsCECpcWTU1US2pNtPmyOK51OPa+RasSylGjIMaBcWi2vJRwzKsqbKISaVhqmNBYSVCWofMSirQQonM3D0oQ+aiImpKbBpzgY846DG9ABmaaeFBB9yJ44IywTNhxCUVMLEKr8rN0SwGJ+4tes6mbUDQ+AzG0xq1SHHVJr4gR34wCkqbKMSYJJVn5NJE0ky1GtQ8tHdedhPANEbkBljtWfazJsngiXDNUBU+Ki8qEqB8phDMTLfJq5cpdSasAhWIbHXviV5HBgTiVTY3xla2IQe15qt5GnPZeyxmBomBpu61hwU7Q4oI3wMa4RYLcjkWY7h0kk6GFsOGyjhZ8pmkBxkMq+hFWlFPIt6D+BCEtCLKConaUXNxcMGXHm/FqtvgHEGK6mAr7o2sQMxNs0LSdFImUxHi4QNxbBy0WlUK9FlzHixHqRVapdQ+eNgEbmr7zNhPgUSvwsd6QbB6ML0ICvm0ETkms1GriiG1ufVUGjd39SmE2TJ2KblY1xVdGukEZTlyA8j2gyarTvgjMzWyJK1fRa0pvDJr7xZzESQDyDDmiBDAwQizrHXyC/Ru1KHIinvhImmYIZPrFBqAQDQH6VLmxsmfjw/TMnVCTtIk55pRElNrmznvtD5Vehvzuk87su9w3+zOqsa2DqwzW3Zahbn5jq319p3ntR07bXLmmDnziBbohtrI+TTJXyLXu2oX3Zx8++aOHbu2dmzOuybWikFG05XChIbMZF1DLh5xfdli6GId0X12Pq/bsOkgyW09bY2Yems5z329ud61fXPa0Ta3t81d09Z6DgcN1E1ljDRWMqyK6kab+biolnXWnkCJHNwLcPKvT/vhKadivyMJ9CiMQQwL62KYcrVG6WaqUrPVviW5JQSu8/Bd677V5ma7BLsQWyluSCuZJdIkaklu5KqE8rzQvi4JdfIWAkZ8XtON83aut6+3ztti27ljc9dWX6cIXDn0xjzXPgs6MpDa+PXDLM6akSwBPWPOcCqXxNr7Zt+Mtr1MO4atrWFny12W64pWo5Pr1WXsg24WPlzc4bEDdMI1tmrOQjoDIlKQieUQMaYQRAZWKRV6jmTZrU4g6EyBlKw6KKtnSrKTSqx0HmSWhVOMUgsLcpbrmzn7zK9geg9XC5HClJh27OKxfcfW9u1bO7f3za2tqW9OsSlYD/BUm3K1O5uLrWAHo2zrWif1qB28PzWuKnXqmNy3x9Z5BI+82tV27exbW+uJswRK0OB0Eafx4dq6dh+CT+9anfhgpSiKedrcZSuzE77Y1eedm9PWTIMn8eX/Shnc5KxDZtpjZRbd4nBb/fRd01nz1g7f6bJFloqxlgUQEMUgupKyIXXDbKPQ38jWPD7y5W9FJvY7ZPQ6gMWD+ZDzrO41Y5So4qRpU2z1nBrmeQo+IGpTm5wurzE7tsbSreZUclMH15FdYtrcOm/HOWdu7tqx5W3q0lE5hdUqMhD4uUZjhFnybKWyGtmdueXr7tMsO7emc3Zt7tjsuybzjIW+FVK8ZFeu9JUQwcOzu3E7O421jzUE0WVu4ozKfr4BMrocPJehqwvmEVbAKXUoJI4YbGVlKMXUoIRORKppharD56XNPSb0XcU3Ldatza1PzSem/6zig2FUHaVo2jTJtJXTebG5Oa+nra22npA5UiFnkZhlgjJQkzAQmxmb0JbGbCpjlo2e1Xs4b7WeW922cuy0iPRhocuAR0YK3HWmMWtRLxUbOay8lDAJ3ibDOonr87ptkelb2bay7+ptV0xbWM8kQIenOQcrwzr7eX3n6VvbzyFVvXtjCE1aEmCOwtXDBG/oqaEYPGS9OU/r3rsjyWXhgJ7e6HNqc596a2127z1mX8+yRbqs1+vtO3acu329uSkxWbahUXapPesimzZMMAXGjKp0oMPnPm9O6/XWNLcWmR6V79N63opdk++a2zpZtxhrgSyUsEQNLyRD782zh2E9+E5t6946j9hy5wcqEoUqKsAM6bti85y247z1jq2d03rtMVuGGcBAliqSJlwkBgCZob2x6ll6ESUvUrokWyIzYzP6jr5js521a+eZO7Zv37metrrPLdoUvZO6k7VNm9cEcT3kpC18XXqMMg5lQ0kwOKJlFQzaLTs7srunO9V3WNdt0G1iG1rGUUthxHWYZNiM3JqmzfM2d5y967yzd+zYPG+atnY135xprSijMyqSChHRtTRTpm/6nAfKezqylPSJD4OHyJrKnJdc93n7ebvO3H72udO06S4FVRsbhJbWiQ8zFYxDiLTqYm0ICNAzt2bZ3MS0iVinpkpyLzFR264tnLfDz92xee7mzp3rHY1IRFv1ZDyyS4le4WopFcKEjzF7ITqVMfCW3alHUrr5zElvJeuhNpTecprXW229jvWutt5ab83rdZsm8sFpi3hFGxzQmjbkUmYDZc0MzJwYi44MyUhvPdFCHIr07N13zeTt2TvOPmPXedtjc9G6c6vvmnQ9kQvjgqIgRhAVYRdOWRmItB4D5g2QHoDBC2ZPhj23lKNMtc21E5G6FF8W2CmlsYusgVgEuvhUMG3kfBAtSZEiMSxgCJrJku7OUQtszEHNFvUE2XVNeifRL+oDokSz1pxel5QxvfamLROABEgNtqpd0HvGGrHFLCawUs1KZDin8JQhK9nWRp0Fc7eYBzdGuSrGEWOhJMruuWElqmBGRMOc2qFwkcnqpPzaM11morBcVEdZG7aqswJ2uCPAfJ29twnBMbEEyIokbAa4Tg1zoUEC1lqE64DcVny1mqvOyAUyOlRiZbzA/BLWysYsSiaTK0V0LtlrS/rpPRlVCUVaNGHCmS9L0CouMcM7PdAylWESJmdRrwWqBZMlDZ5WLCloRYQIGOqAOoqWgTWXayugCEyIvoZXY6GHCgAh2GOQzBi2KYgYAcYA0IbllyPUEhLmc8EsZElYzglmQQ0u/IXa0hdgUgyhvcuctLqwi8AslSfpGcrciRSqtSUi0WsGr0bSgqYBZKXXEjBXI/eSt3227mPGEDmkMCuWwDUupSpk6FkYCrG6eMURGP5Ox/owBuffCb5eKpY36VE9LCTIj8ZpvehQerUORi2RjUOz6PTCmq9R1Ex5iJlJybRM7bUG5y1hoqKJqSw/scQEmlIlB+krSimqFONZ4/PfMQtyTBrurDySTRanBJmmEFhidOryrOGKOYw2GmDRo+k0WyOW6cu8rkl5YlLpNSEu2WtHnZXxJILeHQ6qdV2Qb8hAJDqyq5v1Uh1f/MYPn/Lqdz3p2Lc+6lWvf8SLj3vI8159v+e+5q5HHXO35x5z9+e9/JFHH7djxy5xHwjFLv/qD05+yhvf9w/PfeVNjnzOTR//3D973HPu85xXvuSdH/zlGWdlBE0RAXXWiZHKc87d/q6PfPbxL3v93z7l32915DNv9dh/+5vHH/XIF7/+HR/77OYWlw9GQCrqGANtLnOQ5+ZIRSwWCkg4MojAtmnHubu+fOIPn//O//ynFx9/xyNf8OePesYtHveMmz7+6bd+yvPu9dxjn/jyN7/9gx/78Sk/n3Na+hNdkL+h4qRagZMgxJRtKFjv2v7y93yQcxz2O175nk885dXvfwLbsR940jHvfcax733aMe96w0c/vYWtXW3zJe9535GvOf5Rx73xwa98/T+/4g33e9Fr//m5x97/aS+611EvvtcLj37HZ77Uk6vnBSpxG70s37n1NmasJG2VMQYDpj1OOeOsd33uS4953Tvu8cKjb/3k59/00f/+54/+97949LP+8WnP/6cXHfO8N7/3E1//zlnn7QoxxmuO2Rl1bcyAIGnITMzkwa9+8eunvOpdj3nJmx71itc99GXHPPIlxz7w6cc84OnH3u/px9z36Uff/5mv/unPT1sQTDXPH//8Vy9754ce8h/H/NXjj7rVI55560c87Z5PecFTj37nl7/zY2ZAWgpLc5uoVkVqlWVKR/IIcohxlXB0Tl98q6YH1WGjYHDZbUhxgxRN0xSy3nMkaxHK2qAFosrrts448denv/ET//vI497wD895+W2PfPatH/eM2z/+32/z2Gfd4cijHvCsox//wje94PX/9b//992TTzkbTJxUYC6yNci6JGervoF5KLNY7uhb3/rZT175wQ8+8pjj7vrk59/uYU+/zcOefuvHPOOvnvycuz/rZY96yRtf8c6PfOvEk735xoiNoWOYPF0tTRLi5D21nHXudNTx73n8y49//CuPf8hL3/CgF7z+gS947QOe94r7veCl933BK+7z3Fd+5psnUF4kU+NXp5/5lv/+9MNfdOzfPPU5t3n0s2/zsOfc40kve/SL3/Sx//vGvDnXzMF81KhZFcoUcEWkS3NuCz/79e/+1/99cz+i4ce/OuNpr3n7vx73liNf/+bHv+Etjz/+HY8/5t1POPp9P/rZWRp2wo9Oeupr3/zU49/+hGPf+uj/eNPDj3rdg496zT2e/fL7PO/Yez79lQ973mvPOPsc056Fk/r0tRN+8OzXvvfuT33pXx35vL983HNv/6jn/90TX/ait/03mEoJZi3n8Q60zsZS23rb+v5Jvz7+A5960kvedu8nv/z2j3nWLR739D97/NNu8eSn/cO/P/9xrzz+6Hd84Jvf/96OXTtl6Wa9WRNltdaSxbow1CxUBLSYaTEp+3u3XFFPJOHOxumgRc8eauC6lE+QhmKDatVUEiWzuowdlhKnn3XGs9/wzqcc/fbHv/RND/+P4x763KP/5bnH3f1px977acc+8GlHP/BZr/jWD3/UZ3W3nnnir37xovd++FrLogAAEABJREFU+D7PftUdH/eC2zzyBTd/1PP+8gkvfPQr3nLO9p0wedfnv/jEN737SW989xNf//YnHf/mpx//1qe94c1PfcObn/ymdxx5/Ps++uXvjUAlJTTCymazl7/zo08/5m3/euzbnnT8O5725nc9801ve9br3vKsY9/y9Fe/9cmvfvcJ3z/J196ZDyq0Gj12nrfz2W9+/6Ne+ZZHverNj3nFmx7zwtc+9Dmvus+zX3T//3jZ/Z//ogcd9ZKPfeVrTRp85rx45pnbP/DJrzzyRW/66yP/49aPe/7NH/G8Oz75pf/8itf91xe+tH3nzpSeNYPWSAmufFARS/vol7/+1Fe9/l9f8bpHvfK1Dzn6DQ9++Rvu8x/H3eM5L73fUa+693OO+dfXvWtutF54TDl948cn/tur337vf335bR/53Fs8/Dm3fOSz7n3Uq455z4d/deoZnlwZzkumezIcGZZJanduXt/wsf99wmve+vhj3/zYo9/4sJe9/sEvO+beL3z5vZ/3sgc885UPfNoxL37Df7l31occsB5krfHr7Wc/7S3vfsQr3/iIl7zxYS94w8Oe+7qH/Ptx93v6y+/ztBfd/99fdvdnv+zzP/ihKCQ4pp9y+plv+cTnH/yi1//FY4662cOfeZNHPfPvnv2yI1/z9s98/YTcOddglBKsbBoZECYoEFiKyqSYSe6mGcLyc9pZu/7zi19/+vHvevB/HHf7p7zwFo8/6i+f+Py/fMLzb//45979Ocf8yyvf+vjXvueJr3nHk49529OOec8zXv3upx3/tie+/q2PP/5dTzr+XZ/95ndkXIqTAqoiqgnps3mQepHFOY6ZKtTTthSbYN4IRBF55ulnf/hzX33Oce9+2FGvvcsTXnKrxzz/Fo896maPe9afPvbZt3/qC+727y9+6ItfdeRr3vSa//zvT339G1u+kzrEozTO7DFgKjbTqyUX9v2lGVWrWI1d650f+fzXHveKd93pia+45ZHPp193ffJzH/LsV7zhfR8/+8yddRaZWclay2nO8I70UoQVuppIpDmMplbowLG7/Phnp731w5/6t2Pf/sCjXnmHI59xy0c87eYPe9YtH/bcv3vCSx/6wje+6O0f/NKJP9rkPqQawOxkQ+qqjSu3QbBR2oieLXvnG20Og3MIBqX38J//8oy3/NcnnvLKt9zn315x20c8/88fedTNHvnc2z7qeff6txc/9RVv+OD/fvXU08/rMYdxylHVwhE0s0iWAquZkiqx1ElTiJ1x3o7/+do3n/m2dz3o5Uf/9ZOf/+cPf+7tjnz+/3viC271iGfc/lHP/dsnvvyBz3ndE49+x3Hv++jHPv/Fdd/aqLUUelmS1IqWBHpULzKbzVr5AGKGRjM6Bg45RGh3NC30T2VlWkMZYUUbjLs+CrtbY2UL2hcsZsvylnZp9AVvzx7SO7UpEY8wCS5MtUhVvtOhIl052Zd5LpFasKy6OSKlR6R1i3n0NnBZ7K3O1XwDzVg9CGzrW0nyMXI9xmUr1Us6V9GcUIPjhYrPGjNH0lVa9YWamqZNmSZ0ttbZEBWsGFCPWGd07+HRAjRahcZUR8kuo8dotLm0MrZe4JGWXNyuBw0dhErZoUVqVG0bZV7ZZLTNGktWMAcypXv06DMiNGVF5hURTTjnFM1UMk9B62qr6CtfcqmFTt67IHnHoLCgRFJaEIkWMS21gcsLsiA5ZRZBBmZvPRodaQ5XATljOjB8xYURhsz0X6XwB8rUpQGNM6AJoojTJoc4RhOtqjQ0szL7WvVYJfeTTG4SWqRL3VKZwHkxEymEdzYlol170FWh+X35zLIEd0XUpOvJMXXIDsyZEbBkzNULN1Rzj65cu3rwkhCQwaFs7JKMXU+ZZ2mNxKoSjHO1iRyuXkpW4pFtGSbJQE0CQLwJRhW6mbLgQlVlzjLDuy1uSvBqpFBK5hDn0kZDIYqivQpBZ4uhyFDMqoChZmi8EXimwAAIvVw6M1U42xSJghxEB9iALDSXN4toKdLLOMsqQDuWbqJNzA0pktAuaNpCmnBnhDQAkZjdw5FOaKeInvA0QxlNNSNjToaYouQ47Sgdo1dGlDLNS6OPASTQhWTuSffWan2UUpNLrHLKqWf977d+8Klvn/j57/3kiz/46Vd//PNvnfzLH/76zD3trJ1bRVS7/eRnpz3luHf8y8vf+J9f+tZPTzubhrFNPU742a9f+9HPPviFx336Gz9M4TAh6TG1j3/xW//y3Ne98B0f/dx3f3TuFqlBcfDDl088+fnv+u/HvOS1p51+noAr3o1BbEOxITou8Snp2r0EK4PRibp9au/+9Jce8NJXPfhlx735U1/60g9PPnX7Zicoiz7snPuPfn0mh3jZB/7n3s971aNe/cav/fTHkZlkE5Lhy8IgkkJKXT/4xalv+PDnHvQfr/ncd3+8u/e+L5/+9okf+fK32f77S9/80O7Gz1/+/k8nb5OvP/ft7+8B6qs/+hlROuFnv9oD1Im/OuN7vzj1jO3nRbp3eDPl3sRrNkEPpo+KKWMz+I9/ecqz3vieuz/32Ge9478/+c3vs+OOqe8xwjNP27FFPe/5/Ncf9aq33+NZrz72vZ886+yd4T1jLbsj7qU0yDLGHDvO2/rfb3yPUWP78g9Ppj174rXn9aennTWuNiDDWbt2vugDH7zP849+7Uc//bWf/GL7euZwHOvkM8/976989yEveeuL3vuJ7dmXVOtz8TYaBoPKEsgGLK4o3Mi/iRyTZqUPClJatGdxwIOsFI6UagQcgIL1QjfEVrkacrT8/smnPP34997zea9+/ns/+qlv/eAnp529a/6N1xTfvm7f+8Vpn//ej9/1qa885ph33fXpLz3j1DOHJX9cwqWHNigJTBuKf+qEbz7+Fa/55/849nUf+yy7/PLcnbPzHtVgqzmd+ux3fvjqD33mvkcd97AXvvGzXzuxo2sJ5URcUZfnVt0QtHBzPX/22z/432//4FMnnPjF7+/LeWI4roqtOPzWu/7nM/901LEveeeH/u/Ek87YwWXMMtYvz9lBCj3hde9+4mvfuv3c81boq4jVDO7PybtE29y182Nf/u5Rb/rgU497347zyb/0PP/3tHO3X0AwcuyCds6ONXE9+Venf/xr32FkP/+9n3x1N9m++dNTaNV3fv5rvv763J2sQoL48cm/PvKlxz3iJa9/72e/+rMzz113xgPN41fn7vzuz34ZHg3RSjRl7cqgWX3+wvd+8Mij38INzyvf/6nPnPDDn5913p5etGvqccrZ2+nXGz76hYe84PX/fNRxH/3yt/js1NnTqUKFU0oJYTEbgQ2UUWs1M8abvfdrSc5a79YTHd57l9ktUMCaTHoJgyqsuoKeOmVlI9F3rXd+9oQTP/XtEz/znR/uics+xB4q51ucfsa5L3zLB+511NHHf+zzFyRRAmfsXH//F6fy4W3I8K2Tfv6R3bm855UZ/bGvnPCxr56w5/RHPz8VMM0UCyXbqnzuhB/uH5Hlyhe/xS6/OvPsZFnuxEL4zGAK29n6F7/zw09+43u0do+p3zppKZt7YsQwlaoYc+1b7/nfz97/qFc+/Q3vZzTP21xj9/Hr83Z+8fs/+dc3vucJx73tV2efBzPhEigMMA8leKn5k1/+eqHot09kxy+deBKhYM2hv9/7xanUv2NqXWv08r0f//LIV73jwS97w8e+/t2fn7V9sRHYbJ1ir/3w5xnHT339u81mzlUumSHLnO9qDmP1/snJtH+3/pOpn41DfO+U06if7eenn93gPniv6BJd2s715qe/RZd/8OkTfrjH62+f/CtKsu1xvG5UVNk1T2/+yCfv99xXHvWuD7Ni79pdduj3L87e/ukTfvik177j6a9/2+a5u5A9bZbapNDjESiBCOXEDE9xtzPPOOfF7/7w3V/wsqe+8T0f/NI3v3nSKWdvztSzp627Ew0W/49+5dsf2l2uGWUG68Lt+yf/gpgKOHUuzQr4ORr52IMlZZXCMMmgnKwlA837ukf79elnvOAt77v7c17+lOPe+b7Pf+0bPz3lzF3TnkH3vJ6zNTN3WFHJqJd+4JNPfPW7fnbaqVx5gKk2a8wMZB+ME8Ae8b1eVbCqMqp++Zvfe+gLXnPkq9/1P1//3inn/OavQ8/YsUkkX/WfH3/4S4//7k9+3Yl7dEgogr9CjNyMo9CjRWsxE1P52k9OftJr3nrvo455+fs/+ZkTfnjymeeyHi73gdmDdfLz3/vx8R//4oNe+ob7PPe17/3MV3dyXYjooevUuSTTedgQYtNnpikadIYmqnCd1uSnJ//6Wce+/b7/fvRL3/9xTpE/Pf2czeZ7lHP+Yj3/+Ne/94w3/Of9XvDa4//ns+e1HVzPlIwhUkPpq5XBioJlN6yY7tjc+ZoPffweR73q0ce85Z2f/Qopd/qOLU7ZexTy9bx1IxrfPOmU//nqd4794Keedvz7Tv7FKVZtWa2YuWARloQ6xshxEoNh1dqKM5glVsb5B6rJGLgFJImPmkmRJUAGXUqIOGfmGCetW0WbUquvpA9wmYN4R890UhC1q+1OehTXEXXFDRMXbxstx62uU083s0FlkNQA0ytTKkeQ0rJ0p4EFaamZ1ak7lyepZF82V8cIWWmOZSo2MwFcGIkiM7SxMpqDp9QZEinpEk7rGowlPSwGdBqe6qk9luZiSjtYdnSgpgaSJJaVMSf0UgOFVkBFdAzbFuRQcpxFW+02pMOmlC5IAhZcCWC3Ly1ZN+asU4xBRpTUkZH3jpy5syLKAOsJV/8oa/Y1r8jCa0jXBMDYgIhLCugSQgB4Bg84wekrZKXe5FlmD/TFpkKweF2E0k6i+qzhyCYWUrqAn91psXiRGHh5U2MCiJKgiFlqcRPpqM7pnlQWpg5hNfpkQVh8UdFNQm1GgKNSwAHCnJEcofPTYinnT6pNl+ROSbAshCxdeW3IKNJ6cgtA+mhXnpfkYh80GgkotKQqJFznEE8JSR+CQEoNWzgpoo0MjRCfpXvOdAyZQgfYz01SCSa/2OhjRKE9yl8imdZ7Tp0cjWbJ0u5EpRPG5XYi3DKoW6RI3y0arckEm02ipBSYgB5RO2iicQxeCZVW0YiR8EYgGQWOWeYYaFrPCBLWAA0QWa7LYio+lRZllmQcOyWlubhPmWsgSmRNLVLrIFLKFFhM5s3NFFdBFRkRGjDXIWVYPoDeMwnEGnTutcuqMN9MzWDjX9z4+k+519/83qUvTjf3b9s2NnSl//nFbz74Jcd/4pvfm7vvL8MrPzvjnKe97n0/OuX0qHLWesdz3/nup73xPSed/pv9DAX2aV/+0c+fcdw7Jq4zXWYBK4Suio4DbGiurDgg0RVn7TjvyUe/6zlv/9D3qTlzj5KNoV7/969w6+tf+wZXveJguuciX2kba/RDX/6GYz744dDMamCOmjr5ULjnH171/k+88N0fO/FAf/SF//+H28GrQ592/3+8+y1vNHKpdAkuu8kAABAASURBVCBhWRgEYcJDVJb3lsPsJYK5y72zfOgzX3vAC45/7xe+uX1rvkDBFS52xM3/8A/+/I+ucZkjDrngIj+ccuY5R//nJ4982Rt//ovThcxR18iYMU8xzUi337/0FZ5333+4yTWuQuH9m4oefPBhJ/78tMcf+8Y3fuJzO9bM4/2lwDnsrZ/44ls++qmpBOlarFddKI4ontpg3SxG0k0QQhOKF/TSOicz2gTeFIlojtZIRGtLUoKVB4QAWknZzaPf998Pf8nrP/y1723OHbuPanrd37v8La93zT+80mV3X9jrhevs3ubqPgTTrILkSA2Tc7fmZ7/+A0889u1f+/EvevyGCaZ6nStd9jY3vPZNr33VbQPT6reqvvrDkx937Duf/5b/3rU1Vyglla/O3YcM8IscMf7rvf/mb2/8R6ry2z4X+nTRww87Z9eOp7/unf/xzo+ees72C9357UeP/OS3fvDst7w/4AtA2TS6RefMdOrpZz7zje9/7+e/8buQ/62WvT/NzqqMP7netZ55/7scEB+KD7XYsPrEV0985IvfdGE0eOuCJmSgWljK4GVIq2ABedX7P/roo9/xue+f1DwukLzWFS7NWNzs2lc7jN9AXXAV+NGvTn/KG9/79De/85wd56E0VSS0pxH7RBhCNaR2BudCnS70MZmY4oqpYIZkUjC0AY6MbOid7FvKXFtSdemnTeVSlzriuQ/4u9td/+rLhQP9HrZt20m//tXjjn79mz/xhV0T1e0r1LvPTcneB9/p/z3kr29x8AH/36ygL+hB5rIGuxTfdlB9xoPucocb/dG+6s4/FyQRkDSNkswLsYMOOvjf7v23d/mz61ez86X2er/YEYdvTe3f3/KB57zrv3551rl73Tv/xDM/+90fP+et/8WnbtpVO7hItATncN2Y73KbP37iXW9/mYsc+P9Uc+jBB2vNj37hq4972Zs/990fecT5Wvd6P+28nf/2uvf/5NTTbBROG+Q74yYNylV04mF3vM3D7nDLIw7etlef80/m1jileknBXHyLz5AufbGLPPsBd7njja5zvsi+7xc9/IhTz9v1jOPe84r3f+IMfrW17/3lvEd+9Ovfef673r87jQsgYoXZGaikCYNDILiy/ekvfvkvL3nj6z/2ObqA8w8VueHVrnS7P7nuH1/1Sqbk2Pk3fse7Qhk1T4km2dPg1dYW6+Riy1MEJDHIalBVgmfp3/npT//pRa990ye/eOaF7B+L3ugav3ebP/7DP7/O1a94scMPWDXEBMaUKC3DW8CpEPsfHLRN0yvf8d+PedXbTjj5V/sL7Lnyw1+e/tQ3vuvU7TuIDDlWqqi5CtOFKdQ6+dthTfu6vfYjn3zMsW/95Ak/Yj3f05evV7r4ETT1pte+2hEHLf9lE6/saSeecuoz3/yfT3vDe05n/iifgiMxEfBqwVWeU0hWKKy+AyflQHzgk1946AuP/+8vfee88/fYgylLE4vGda58uaK/ReLXZ5/3yg9+8qnHvm3ace647nU2JXUWi9WjehvC9eRfnfHQl73+NR/+NIU51J5Ghdf7vcvf+gbX/rPrXP33LnlRJUB7bpz/KpHozd1bonGZp+eLZK+I2qXSg1mJdw/i00ZeLMaqw9PeOzrMS+8l3JaJbllIOOc7oQhnuo6Jc2t26Y0T9hiVdEwtPbK3noyhKIQouUqqKVQ6dCL2LIumvAnRgDhEWCZbpVGhJZSSBi3FJJG829I6LNyWNVR4imYpOQg0FWGgVI0oyT0InVjMST5lpnySvbRm6tpCnInekyvbuWtPKZJVMGipttKyEqM6R/LgOrrSeNdsRbyo1Sxj06FhcFFNLRNn+xFaQ9O5xgTRpYEcP1275lRyUul1dBvTEMm9zxyLA+phDdYB9KytFwdKhkm4qivVDtLNHc4YyKqj9jRzPkCXgeNZ59iFsn2RllRAaZ5KrZypktJumDWhjcZnn0F8BkVlqbCkSjrI9Z0BFbSCZ/BZ3QUSfDC6kVEmeJMYPIdktGT5xi0GFeVXRwDv9CFIAOZ/d51YCTv7Qs2yMsdKgB5JBiKIN8JLz9K4CGcFyQ7kYkSohdRIE3SaYqJuGTI3YTEnB8AtSVhKWO2lzsUm00kJCc2LTh4KIJAKo6cefSaDDDKqDaHKWHCYVZdxFm2C9YitVc5DapnVZnIlANUcyjxG5pruK3hkH+fGfaX0hCf3Mo1zLcdRmKQhKoQ6hgTS6R4IYSnEl5bTDc86i64luyAglKJMnfq4BiaMqGOaB9m3VDh4UMUkkeGyxgptW+mjIIPMEAZmHOZx5YvyDnSxLoTV3FmLS4LQSAiDgRAR06i1FwuDsm+ABCitWBtgEoNsDXqRi1/8jre6+XMfdu9xIQL2OQ5elTd9+DPPePN/nrNzUwSy+9hHZs/pWTu5Kv30qeduPeaYN77v/77efRl/Ed9ze7/Xr/3455/7+gnh84z1WmRd1Gt1IxB0lQlQTj37vMe+9C2f+w7n4AWxPQoue8QhL3/EfV/xuAe84GF3ec3j73vcYx9whYsdvufWntcd6/noD336xW//QNBBLc4FksALyaY3uMZV/+pPrsPG1e0e4X1eb36t3//r3QKUuaDd4PevqC4rrP7kqld93N3+7oG3v7nIPv2W02IEXxfeVQc3oUOfjKlf51Zmt3d9/MvPftOHiOEiuvtXRe55iz95zeMe9oIH3e95D7zPW5/06Hv8xZ8W1d03f/PynZ/96jEve+NJvz6D59J7bS4dkUwb21a33eaPrv/sB97rGpe/DO/u0zYG+8YJP3rki1/LZ37ETnYf+8jsOSXt3v7JL//8jLOD9Y6DcwBAeBKsckLSpKnoqLkxoJoYEnPPdYBZqStRdgkeszO1PNGHDPAa54+5zc95/fte85HP7rjQVwqHH3zQix9212OecJ8XPvwuRz/mPg/6y5tjvyMCEWJkJ5+Y2Zij7Jz7M457/3s++/XZ4wLxbbW8/OH3eM2T/vl5/3LPlz/6Ae98+iOucflLXXCXH7bm/o5Pf+1Zr3mv92TOeQqiWOdTjzxoo/zp9X7/Kff/h9tc71qU3Kdx4jzt7LP/+fnH/fdXv+uZu8GTA8V86ffxb/7gsyf8tJX0MqduaXCmwhGHHf7/bnRdUuj2N7j2Ydv2WigsfYCLHXLQHW58XZKNr2z8sKdd7LCDmKIXOWzj//3pDf7jQfe4+GEH75G/8OtQ61e/+9OnvvZ9Z11oIXVhAX4uxWYzEr+iDYO7+Qvf+b43/s/nt+bGu3vaqtjT7/U3rzryIS940N1f/vC7vedZD7/x1a+859ae17n7h77y7ae++m1b0+ZSOnu0ziKju5e5Ce9ztia+R3jfV8KmrsyFKlJHEpusyPDo0bPPvm7enGSS/A2wfFOUjXrT61/12Q/++z/5g70swfnHVp8ee+zrv/OzX55/Yd93M5ZkheCyl77oA//u1vf4ixvtK7H7PJETK4/PtCaStbBd48qXePK9/vYm1/i93ff3fZGqsjFIGSULUtT4cbjBta525L3/7q9vfIDtTTU997zzHvHC13/wyyd4pOw59tX6m/PPfvfHn/z6d5l5tWNjgnZiGmF5+Utf9B9uf+N//6d/+I3c3m+HHzS++n3/9ey3/iefwYtAdh97i/zmbNd6eum7Pm4yMIHVuPWfLAXJBK9Xvfxl7/83t3vqve5Eg38jfaG3bavlW8VwkinHKcfZDi7bbv6H137Wfe9x82td9UKCv/nIMP/61DMf8rzjP/GtH8b/V+J86CsnfOV7JwmXw31EENRIqGUts6zW/Ywzznj8sW/+0a9Oz9/oXt42qr3oofd85WP/6VkPuMdLHvGgVzz8vtuqLTcu9Hv4ttWj7nSbe//ln9/xT693k2tc5YiNbbkU4ooQ9Ey4IgfNCtGQDEMob0kshwKnnXnWE457509OPZOiF2i93lUu+/ZnPPqlj33Qs/7lbs95+N1e/5RHPupOt5MLbu/5EBpZvbBx0sEczA/Fha3fIwZs37XrX49723Ef+ex67vKb4/x7e7//8Fen/+cXvky2FZRqVgYGjYqn6NOSeUygdX/Juz78qv/61HkXqrGMwpF3vu3rj/ynFz3071/56Lu+51kP+7M/3CtYmfnxb3z/Kce+5bytZU6HpPIBeaydVisKWNdrKZqWb/3oJ1/4zr2mrcMP2nj2g+9yzJP+5bkPus8rH/mQZ9znztvGAecfpPrnv/vjJx/zrolr/66yuB/gT+dkjHO273zky47/3s9/TW6c3wNXu/TF3vyUhx73xIcc9bC7Pe9f7vraxz/kqXf/m0NXv9VJSY+YW+vznPSetTwRIeKqUdCEa7LiYDhC+Eykt97Ro4RwtsqIbD0n8VmDS/bUAlTnOngy6bViFDGuyXuuc6kEJEPVYbDlPxKeLZssC/NQ61y5hnpSCES9RwRHrSnGBRYkwxn64NYA1rhlsS7aqoVVMjpYA20GJpEAR5YhRDsIr7iVTieoIHleucwIRzPe4umsc0YfBVY0iZh7avdx7ta7MlYtE7YYW6sqw1UqzBLRrPlANEZwJuqNA4fNXpEDfOxtWLe61YxI0dRhgrlAmXoEd/n7mxjA5SEVSFAoGIdxKJX7oa7sghaLJfTAGNSAJzoN5k43szblwkTEOVCMumRWZodwxeISNFayKCqU7hjX6bVnKqBIFK1FV7UMNqRo0z4TeFl3nWHL4lW1kJBVdYi6WhdaTQvGAuaxCoKh5LAA1TUZZrrjCMKzzkyLRDI84B4yvSrxnnrZStmqMhcX7QXJXSVQBCZQEZb6WgM6R11z8RaMu4ALZh+oRNZtnKaavQw6DEOVUTrn1zqjUE/OlXvLNudWk/UkbRZpNnTjCoBGomSaYAFjDj5g6OE2iIyWhcaHSY6gPjGKAUJaqczDMG1DDlz4uLasYYV9au1VFm4UVRHQz8gAY7zNgq9wZBN0gJfTkKsiq5pVYBDWPZh3pkRgnlS8siEsmjL00YqQnzDJjcUebHAAqRkHZRm6kPU5tfCpeB8ZOSAgU1h6Edoyitkw+Dg2W3FP7IttWDgdg9o2Xa1sFErOiLl3D5eEoIlsSVlbcUgNZcSFUUrUHppkEOiaMKm9J+BXuswRN/qDK2G/41Pf+uFL3/e/7vGXN7jGax5/n0++5Invf+Yjb3v9a6rIbtm9XvgV9n2POvrrPz3liG0bD/2rv/jQ0x/7vy/415c/9J5XvfTF9pLbfdI83veFryBZEOCeExnfBfRfOhTbp/UzX/feb52014pkrPbs+//9Na96BeUu1fkd73CDq1z+6fe804VrJXVH5ps++aUPfubL06w+mc1Y0rut73H7Wzz9Qfd81v3u9Y9/dmOK7d8ee6fbPu9+dz7qQf/4rH+++1EPudtz//nv//1Bd7rnrf9UadBSxzzhN73O1Tln7N9319yyVN1NBqYHjDUpS4o4Pv2Vb7/onR+ZGonz2363+qM/ePzd/v6SBx1KqjFXM+F4AAAQAElEQVRahxx6yKPvfMc//8N9nyX/6twdz3jdB3ZsraGMYaxKKUPpG8kd9rrg8I2D//KGf/hbped/Onvn1mOPfctp526/2uUu+cz7/83HX3zkR5535L/e9Q4HXDRz+/Slr5/ISjInZmGSZBbhYcxLZUjIExVTGUwq1FCiDb5OCYeh8hJCIkmIAaXQ62loW7K59Yq3/ff7/u/bjMX5RmEo5al3/6ubXfuqQ62pw3jIofe6wy2vtvfugsJR4MZsCSnJ5OM3bC97+0c//vXvMx15d0/jaM+479/e5HpXXdVqSePk8pc45Kj73fmih3Chv0dkec3E/3zjB6//wCcke0SbMmctorUw9SXHEf94iwMsZLkdevSr3v6DU0675OGHPPkut/voUY/5zIv+9eUPueflLrrXlngZAKCDH/rsN9dSGI4J0Vv0tV/qIof+2wP/+qgH//3T73nnK17yonskL/z6B5e/1HMecOfnPfjuz33AnY+699899153f+6973XU/e/5B5e+zGr2VZ9rzpe8xMF/du29Vhh7NJx86hlPfs07d63na13hMg//29s85s63u+blLikie+7ued1c05bkwyKWW2Q/9v0feffnv0ZT99zlK8Uf+Xe3/qtb3XBcWcBYaC5y6EWe80/33Of5fQJfPPHkV77jfxChypKSqcqPvVM3d+A1KEF1+zVejyTWGIANTtkbJQedFQ5hTiy/y0wSMgvFFBRbwk4Le5eaG3f60+vtp3K58LCXH88vG2nkg//6Fk+51x3//A+vyvK43Dj/t3VHTmVYD7Ietf/F9a920FjPv/nb93N27sqi4mYsoV6EfFCsNuTW17vGb4Uu/ElE1Uj+gKYHGmehKHMd+niXmx+AQixuT3jNe7990i8veuhBT/rH//eRo4787IueeuzD73PFS1zkwlr3fKbXH/j8/2mmJZJhTHDS6JtDTOIeV73SJa9zpQM8jHjnp7/6+o/9HzXc9obXPv5xD/74C//t3f/2qDv+yXVNqYKX92on/PSUX5xyOjwYRzZrnFskVxUr4hd8NH6pIw7dq8Puk02WHZfepHVzL+DsuFZMDe53udmf7BbZ62Xq8dBXvOWk08667EUPf8Ld/t9Hnvf4Tzzvyf9xv7seUPnc/SNf+IZzshJAunDyEw6goysXgy999wd/cca+/4L8vW5/8xvf8GpM+G3RDy3lxte86j/d8dbY+zh3c33G9p0Pv8vtnv5Pdzn6cf9879vdqgQslvjBtHcubUeTsRQEm5qJ1mAoZ7QmKUe980O/3vsb1EO3rZ50j7+/zCUuJYWM0XGwixy2cf+/vtWj7nxbJtEFg3tXqmFFJN1Gct3Hho28sMT5oif+4tSPfvW7Yy13u+WfvvXJD/nEi574pic+6E9/xw75I1/6xrS1qdMafKAXCi4z0vlTOmrImz/xv2/91Jc98nzdEMEDb3ezu97yxodsjJ4jy+wlLnrYCx/6D5e/2BEXyPADO3ztJ6e84q0fkB41KuFvMwvknDlpYaZT+fx/X/3mS979Ua4nKL+nqcpD73irW1/vjwbjfkzrqtzuhte66y3+hAHcI7Dn9Qvf+8kHvvgVohnRa2tDZq2aBa9453+evPcfWWwM9ci7/fW1rniZlQ4ryDatR6wOvfOf/vET/uH/qfxWq0dMQfKKZSpTKy24uFUzFS5emqNzbjBwGccNCvcELPaYwcpiBkZkuaji2qFzaqihglDOxNKKlGFIs5ay1mEutSvBkNmbp4d6WE+N5FszX5c+JW/QLwQZO0uftbnNVjtKV+f0SEeNXZy3wztmF66cSxjXoDZABzGOaN2Ca5PQbuk6hWxKbFlyAmY1lK68zy5DVpPKmAODwFKCR7qRpjwpwekYKOAdZPec1y03ozVmZ0kSntB0l0y66nxUDWH2Z6TQ1Y6lQ26prncnQZERw7bO5zAsc0ov6AEADSlkVJEcEgqqCk7wBctf/Y4ILvXV1Pk0Kni/mILGqujiFYhDuuYkbV2mbn1y25pL72KJQvrRhPXQNgefB3AMc6GBLnPQvmS8kS1ZqWgMqFbBIwBnq6znozkpZQgL7gAUUlKMKpNK+srXlUWL5qIzBtpmlUlqoIqmmSsIfNCW6l1SJUbRUWi+dEaaoyYluL/Iml3RWyaNU0WxxBBhPVS6DYJKNMae3HVRYzQh7wQ6+Fi6ReOZo+ZgvpJeaWJw7NFgCOQcjIss+Aa78ZpZVS0ii4udpnv0mUPPLE2i6yprie50X2DiA21WF557KMyYYGZCxZaOoig1kZE9EFmUPmIu6cwekC9ZMoPD00JpmmsOqZnKjm3BRYJ9gQS9rUjLKAwNfzWDYHhIV01TVLeBqZWlQ5stMpAVEdJAnTMnm9fVJ02aJFSHINUGhWp2ySmJV09ZRtSI4r2AIMEWLkBIpKEwuK3MTmp46NzhQbtr94N+x59J0NN/+ctb/PsD73rDa/z+EYccdqXLX/o5//QPfMiEAx2/Ouu8y1/8iJc/5B7/fPtbXuFiF7/YYdtuft1rPPkef7sa6v7ivzz73J27dm70qHO3xsoSlnwMMJtsffT/vvLF7/94ny43/oMr/+HvXZEJFxGZtD5D/IbXuMIt/ugP9pHsHsf/9/+ec945YCDnglmthcVUiR9r2ILbPj2W00wLX5kPnCy1E0NjivZafUTXdF9nmw4/6CAVXaT3/t1YrWDKiCT96KYNQ3C56dt3bj/mgx+98JPsPf0eeIdbmajQvpwr1lbbuK08+K9vs21k4u4R+c3rd3/26/d/5usMc5paGQa+8jkBn3AW1s48mMvt3wju9UbK3/IPr/aaIx9wpz+7PmfWix968F3+/GaP+pvbKsfcS3A5+dqJJ/vu2txSlnRXT+3JaTYJX1RZWlh6EahU6PKnpZ7kMkTroMY9jImWwglJmCEN//utE9/+ma9RAhc6rnuVy/3pta7pk5WtsprMwlbbDrntDfbdaBEVju46C2bJ+PJ3fvT+z33tQmqWj39wuUve/AbXYpS5LbDZZQpOIZe91EXv8KfXXW7v/fum//n8KaecBp+RW56RnQivhGHKNm7Y3rK/PeOjvtc88j53vcVNLnnYxQ9bHXrz617nGfe508F8xPRbkd98+uZPfrbsgWQUxjwwN9+xnrZ6dmTB79SvLNkpEeoZUaasLU0EVXrRORE9LLZtjL8Z40Jv67mx/ePNrv/axzzkvre75b1ue9NXP/p+17jsJS4kgoPGcWxutCDw/ZNOPf6jn73wXX6+3EWP+Kub3GB0KIsZpOdSyA479OAH/PUteXef9r7Pfe37PzuFE3wZxAfMRRstZ7lhZrGU7yO9+zRDwocA2cpq3Fj6Z5hDWEar1SGqdbNegqkFMTjLlLKjlEiNkINWGzzbv521Y/OGv3f51z32/g++463+9lY3fd4j7/83f7rXFxpDLasatlRgOp+HHrQx1rq/Hs6qHG4sUknypgiaWkPrRS964L+zQqqlILDMRugZjWWT72iEerW//j1Xrnixw45/3D/f7da3uMzFLnL4xkE3vda1j3rQ3Q49UEy/94tfb/kuFmtO260w+sKZWOZBmlaRjd+R42Oxx9z5Nv/+kH+43rVYmQ+5wsUvc+Td7nKza/7+ntEv/Lo5zd866eTQGeqgJwYhswqgKRqHrMoBh6i1COm5eKuTDIxiLpUzVWNj43d6fZ0rXPp1j3vQPW9zs4tf9JBDDt/48z+5/lPv/nfbhgNE4Ws//tlOPlnU7uhMS40sIlr1J6ed/r/fPvHC9vPzUOwvbnhty91Ukkl1Id9tbnCNsdIN3v9t+8Dnv/arX5/KR4ag1ZZQdANnQa8MmrZu4YMz8kVNsIyoogoTP/W00/7vBz/9raLdny56yEEXPfQwGqi9WTRS1sy1tDvf8np/cJnf/r00dRiWYHHFUMF90BCQ3QoO8HLRg7c9/0H/8Ji/v9PvXf7yhxyy7ZpXu/IzH3y3K178AJvYk88498yzz2uEZ/LkExRXwBQ0WE8647TXfuRT+2i/5GGH3OkWNxhHqxglhtYHJtTGqjzgr/5sH0mefvjL3/7uiT8ZlniWCC5OohKrkr3kr885+4Xv+jBlLtyucsmL/80Nb2CNUGHBQbgbwB1u/EdMuguLReZbP/V/Z2xuzxRbApUVbcf2Mz57wr4xPWg1Xv6SF3OW+dnFzWrRDeGnW93gWhdeYCSnBGGYuUVNBWE1WFUL2My6FYYQKaXZ2LSA+AOIjG611xEr60wjc7HJtAkJJNAAS55HcBeFMaoF8yDDWlFn9qVz9pPgWGJYhLWpNRUn/V1oYOmq3VrUBuEkz7sQK6rVZICU2bgwbjNnAi5SwLEYizoqGVx0Y1DujMDtYE/1VrkSdVUOxYKfna4M6lZ2j11hJJFyUEpKVRYhmZlHw8Aah5Wk9GUxu465xdr7GjF5FOfimOqMKgIoSC4OB3TtXaOD6VyAJYsbHRMLDlENq1qIPUSkD+NcVq0YTEUADk/xWPAS9k90olXNK3xMCODFOpMoe8FcbfGA0QhFq+hqwSWVaCdbonsLsPoAJYPopkt4iV64JWEkIt2BCPUuHC5pJZAOcTVtbW7ch22lTCMHBsyBnqAeG3OsMYouHZLjNSm5MikMUWYLjtLH8MEmW7EFHU0dJmFp7cpOHAsSFUGruuSmtK0IaheDCM1JWpJpohVlFcaKKOzGYGTPnDWDlONafZj6MOXK61jURikmFVkh/IFkKrXQHdBfsU5dSoutcqC5Ze/hJjKQO1WYDBGJttRE3ltzxgFoick0sOwJTaPVBgd969GdXXVlaoJE9OAry+gydZBMFdBOZPvc2joZPzBUNVDS05JBTMme3lMQpgxcIgMMHIjZhHlznqZ18smfNuHDgnmyjhHCzRdBjlWZxzppUpk2hDKR586NfOvCXRDjYzNzipBVW0wqfS1TQ6c7BaX0InMRZLG5GBPTCVevESWSFo6KgRBVo3uM8hLB5tqYvnbQ6sBzz22ue6373/ZWA1Rmjh/R2jjiDjc9wIoQwFDs0Xe53dV//4opImUmRyVx3Sv//vWvcgXe3aeded6Oc3ZsZsqQMWoWjTSGzreft/1tH/+sM1x7d7jDTW5YyriquoEoOjMGRJyEuN0fX8OU0dtL+qTTz/7E174e1idYy6GjBMRrxhCpsZfo+SedGlPTgSC9I/kTg9ogpUYVxpjLmlEMcn6HC73z2jKAkBUZk2QzsFxo+8g3vn7iL0+/kODy8WqXufhVrnBFhrQzvOLFopC9kte48mV5a5G40G9kvuczXz9jO1m78gZpNCQH+KCTynTQ71gxXOVSF3/8Pf/+iEOOEMakkVauOf/lja936Gq8kO7ffPz12eetJ4kYw5lzIHUbCd8VURRMgW7M+PA5ck5Si66ZpRVklVg+0SLh0n+ItNThXM83fPzzUyMjf6N/z9tf/fEfVrUSg/SCHnVOLpSvd9UrDcX2COx5tXSBa0R25NqPft/HOEHvlHAHiQAAEABJREFUuXXB622vf+2h1sgIKCDqRXIltnG7G1//4NWAvY8dW/NbPvEFczLFazQCiElaZEtsHAgN9qaSZ9/nzle8+EXn4ByAdbBHXOsPrnSNKxzg2faOrencc84ZUQYto0Ik+wwniPRjP1pS+Z6W0kNiFia7tSH62NxaE3AC8UwPNjlotbFHeJ/X617lso+80+0PPkgruTmUIy5yyP1uf7N9ZExUhtpqOfr9n+Cj7n3u3uyaVzuiHqRz1mRIpGgyjkXkptf5g4scvO+gxP/4//psaFl80+wsJYohZdW9+IFTCaGxUFXnpWzkhOxkVNJjCEcETChCLhg/DNJIvmTRJNdY5wc/ZD8b9th/6Mb4rPv/3SUvctjymCtjqMmn7Idt26twEX+w3nY4cSxVDxSCsZSaWSylNMmGRroVglGs7hlon1dNcoxxlYVxEmlC/5p4ajto275829N32zg88753vuzFDl/HvNa5FaaUX+Pyl7/u711xj8CFXzenftr2s7DqOcypXOi40ia49aIxHPQ7WPp3N7v+nW99k9EqXdaSrEzbhvpXf3I9Fbmwcn6OzJ+dflqWaAM43/kIYXGWybPt7mvF9spBdmEbh2pDWm0pTDRkEQwiA2SwQw/ZRoH9Gx/kP+sBd7/0JS6OZGq687nl2K99jate8VIH+FL93F2bZ+88h2VixuCgA0KCkYuf/u6JpNw+yvk12iUPPbQ2pRVci4U7J8OLbjvk6pe75D6SO9fzV77/E2nBxRl0XnLeyMDe0llUCAWz031ZaJK9PCXWUbUU+c5PfraPKp5OjYXMVVKmSbeazI7oiYWi/3DLG13tspe4xuUvxS+uh41RTChP7rEmsfaHzMhcruz9W5TfS/zFn1/zqqPkEGDCz5McvHHoTa5zjb0Ff3N2yhmESHcvQyffvdaAVTd53cc+vX09/0bo/LcbXf0ql7zo4WS2Vjq0UH+3TXLT61314ocedL7Ub94J8uv+65MSjeRWK0VHW6Kg5MS7Pv2lU/b7D6tufd1rbhsHIC3CZK4aVu1yl7n0ZQ7f6z+wpHZOvl/5wUlSBgTLeUefT/rFL3etJ966cOvuW1MnSOEyQdeis2VbQTf03re7GYFl+/1LX+ygkaNKAd1Ro2O1GOcen7K1AlRZzkrEKnJDrLKkQThfR1lK/oyhSyYfrXSIQzkY4FEQNaQ3poB0y9iIGJFcqypTrrSqallBNFzRUp14N6V1Yy8bvYygwGhmOc5ia2CmXgM5BJoJGgWV2B19ZiVBtU5ymNgAsRDh8CiuxuiFQE2qyuhWQjULJZebhezpMk/dG9cSVKaS46DDYIt0gWj3nLl+n5dvkUOGUEsNzbl6Q2RDYZBUO7+cH6a5tl4kdaU6MmqzCEukQqqIVnQFiWhgXZiMy1IuYR3DrEPnxnymrjmCq8ze4Iio3UsLD19D1gJS3LhkcvB2zMLl+tA4oTVB8Hce6D6NBbpgMvMkFtIlWHL7IKgAMjkb85m/FFV63wdlI5BiSAax0tWpteZ0IbhUbaa0v9MSJm+Ipi2lg2YAkku3UjDSy8JFnIMVNKtynBCGpEShOkUKUlEEpLxh8b4TqlQPhRs8Mxf0CkXSUpRddQ5h1ZA0k0UqFiGIGfjNSkGkdMaNc8qK4s0DHeatekhMS8mggbkcqsiB3QbQ1pQkd2aOLUkcaBaZI0LuuQWv1IiBkRTKGgfuKc4hkYMzvByzJ0J4oIDOJ5Whi4sE7SgqksKSJ82JsPeF+9EJCccLKZqlsLMD3ClFsD9NE4dwImxus7PW5dxaeldSqgfjqa7CkbQL6WE9JLst9QIJpEe6Z1vWdj01nWMXjWpepZf07D2dkecItHgAUxUp3Wwewft0Fx3qAvYN824hJlWT8UlZJCgsjoHj40DHXf/iJqxhvCMenc/d5x5zXO2Kl650i1f3bhc/7OAbXe+azmUA9RmRFHfL1Ktd/gArvF1TO3tzDqiBXEuIz2BQ8/Pf/eEvzjx3b8XL2bWuemUdylCHQaVGEgeYicr1r37lYe/FLqVJ4k984wdTrqN6p6laRAv7kfp0mwIHaBKuyVwk2uzuBAxgdEUEoqalFC2WB+gIbK0nAYJUz2RVCVhAN6et937uy/vLX+MqVwK9DbghR7GVUj9kMZBfW2G/46TTzv7OSb+eRKIDM8eIglg8rp3X9hNfLtzuT657xMWOcH50em0qUPGDVsrSj/2OzfXcZ7EYNFVD0IXLJMlKyoD9oBFkmZCtZLtnBHnlYmgmayX3QEnuBrVTEvadn5z0rZNO2W8QXO9qVyzkIWMBpWbFkmvXvsoVn37vOz3rPnd6zn3//pl3v8Mz7/5XR2w7yBiDLtn02z/62Xd/cer+qq51lcuBObFMNgP9g0G1qtmVL3PJS17k8H3kE/jf7/xo+xYnwcKo0JOcw2dlXu0jecHpn17j9//gsldI026MJlO2M3OGcfjDK1/uApkLPrjHenPnoF6GLCvZXSY5UCGpvV4gte8HTw8NsFaWpTonsmejUTM3f3VZj/VGVGXfbrvP//rG1z/k0BVqGEmt1aX8+Q2uc+/b3PQ+t73ZfW5703vd9iZ/dv1rZk0Mw0m/PPOrPzzA8usav3f5iORWsCYnqyi1lzpzGrn44Yde5TJ7/ac+uwfE57930llnTx7ED6ZZVLlRIQktie4ekb1eE96y946ZLUD6GOaBKLaQBnolFX0jySPSEg7ywcFiW41gVLdB91J3/snNrn21y1/iEhQpmeZzjelKlzj8IX91y3vf8qb3+4ub3ffWN7njTf+I5YFQCjcgDB45fH7fC7+PdSjJQQmAs56SjhJqLitj2bqw4G8+0xpZsmLirE/EgNKqtMFjcGEQfyO119sNrnqFa1/tsi5MmuySLEGRXj2ve+XL7iW3+yQzd25tKZpGV5Z6iygOZVXQ1KKk1G6xfV7+8dY3qUWNAYElfRjDhukal7/Yodv22rnt6XXerk0UcS0BBrFhSYAmCDhdUFmyco/gb1+3uL4pNpS+cKMGiYbSFl5VaC2/lbvQp1tc5xqXu+wlnMZQo6AoQnoZ6wETp7mv57WopBgN6wj33lv+8JenXUjlbz4efvBBGzYWN1nog0xBllE2Dj/00N9IXOjtez87PSUzMtFEPCTJ9s5QYAILKHpGiksiuvgECSkqdubO3/yzfhfShFPP2fGVH5yoA4cVqtMQshm56Lrdza97zBPuf8yRD3rJ4x9w2ctesiN74yDeDSwXJLhcWNH5n69wiYvd5nrXVCNAwV+QootO/NGBnuWx02nn7Iiy8t06F6MJV6k/P+PMT3/r+7y7T7vm710x1FKUWrGsXV0EIoVPHw74kOULJ5506rlnuwa0Cgakdcc55+34n699j85eWDkT849YdUfRAaK+VPKSUqvW8epX3JfSBP6L3zkRxrHVM2f3M3auu8eFFfLzeZtbn/z6d6iuVW0qPaUBoRnVbnKDq7/2yH9+3aMf8qbHPvTal79CURB3MExFFwMk3dNbQkVYY0XEzbrUiahzJhroj4JdBOB7FqoMerno4C3PsYOlS4RORqpYMasStEJ00uJDRTUDjDGFpKCbdBqoJYopm4nSe4G65Fx2syqQFI0S3BmjQsQUYmBNWWKX4ESChYqcdrKE1OQl8JZzrhhUi5rREM1ckj8W87J79zmmVtewphJiHLFABIUNyxIO1LkMDKhaFEmZI6do4ZAoBYt0kLHS6XsNdpEs1kWCGjIHhMJ7unPT1Pg5BZns6gywWaJIZzC771nHFY7iNWlLB1hB0mlnoNI3iZSQ4DmdT5qYdIQL6hzSqsJKDuLJDaMhLU19qDOnnYVzot1oqZWKQWIBeh54qdKnIsGn2DqmGMSBLWP6EnIvW1Ph2nGKOaWlgj90lRBgNedqS4ZmRjeIrRFc9k66ygnV0FvpEze1NWqRocqw9Jt5WZoW6GjC5RjxZWxjjFx4oJI9uESa+JZdEsz+9aRrtwk2U7BK8odOQ5uXzWljntSd60cffC6cAydv0WkPDZEE3BCFoVPkBmSIajOd4henvVPVyjiqDMZtZRlGscGkjhTLIr6bsRCVMvK+RR1n4ZMk8DDex5DLqESYE3QdnYpBo3Uj5SCI0Qc+GehMHa2yqlgJti2pQZFoVEG9jHhjhAvnZqSR56OmFlfCJRgdY5Myi2Rw7m6FBBCEJSM/JBhnDErLi9iq16GbzQRrUk5pIrr8E3chdVZZFGjIoisgAqiEoGXyRivC2anz8dAE5ReyEzywLtHUjWK08kDt4odsu+RlLjJtC85PSaLBSXo0OXy17ZCNYf8ev3fpixNXrS7FUkqPoTF2gsMPPXh/YV5ZN9ccBBZdp7VyGeNF/ucbJzI5sPdxuSMOOeywAgkw2XwUrzVGxQqycfAhh135Env9De6erj885dSzz9vFhFhVPkZEFdKSyVCVdNkjsfdrqd22bU7jehO65hBRh4yVzzY1I7qEYDC+7N3pN2fjWLc0OotPCRql2xS1/Pi0s370q+U/hcfex7WueEkFxpLDKig2y/K3fYCw93WucnnsdxCNL3//xzCgAOlC5gdXAPCxsvt+4jDVa1/xcmgIZpUAla5bDrxgFzv8AH/cct6uXbFuNRgGYUWzqfD739o5Rqwjd7luKUOJaC6+Zk1bA3Nma61PbZ6i9yTLO/lBdnj8z5e/6RH7WHXwUK502YvoNvRtMR0k8zaNlUbxupLb/tkN73jLm9zxZjf8u5vd8I43udHGxhHIlSaXgeUj3/qeR+6j6oiDVpe7+CGCzKxdrJWYVx4b3aofvKFXv8wBnt2euX3nD08/M2UE00JmIAof3HUmKQHF/scfXeWy5ZDRNgZbKBY5zIOxssjFD4Qe11vn7jgvMHdtqRyjbuRGHTakFD7/2V/5nitzrkQwWF+JF6R5SucUMTN7dahFV+DlPaJ7v1bVK1zu0nMRCAI5ObjE1dz22Lve8bF3u8Oj7vrXj/jHv/zHv7xZYwICX1p2aNPeCrBtqFe8zBFZgwYwqJothcXAkw9kVK55+X0fUbP71ty+9v2T2oSSsqG2qqkDREEbcKCjY4avhV9nrhVrHdFGm00cLJKzgw8vSoUlZJIWmKxx9mgWLkBFmM/tQFrBbTajXhDbMjd6LqUt8s5/dqNH/90dHnOnv3nEXe74gDvfhoUpQwWFGLLYH1APL7raOnTqFlasLt5sACb78paSuxtniLWBe92oXmqMKgNG9CGVObhbYp+Xa13xUjaoKZ8qDzU2xKUF23zINo6zjyxLcZx9xmaddLVO68JyPY/DeltpowYkI/btALAeXvISBxcDWDVacjnh4r3GIYczvGV/+TPOOU9zVUnkSW0KjqHMg83oDFQj8w4wxMY49klIzDLkMMyqa+QMLoJaj+b7D8Er173S5WpmDTWrReg7BknOe4dt28a7+7Rpnrd2bTENKJqlt2htbn3Os8/bsY8kT6lR6CnLwcy4izJYamWbbRwI/56qgC8AABAASURBVPV67SyvJgIrylCJhILpxeBxza8iZZiGsku4ZWnmU5lDmx02rrDfEZkvfueHP/Hlb7ZhjI1tQhlDAp6gi4ccPB62IduqpLY51rNPW31rK1uWqJoHzI6LHLrt4CM25gFbo86jKTfHI4bqlzhs3+8l9thy2o6tpA+rMqyUxYV53VS/+oOTz921tUfggtex2FUvd4mOwubmTCMXb1HhNnq59uUO8ACCcP7f9346cxXV0XY3BL79o5N+fsa+/2JnLeWqV75C1uqcP8eQApX0DGq45MUOUHJ/8LNf7trcnAeuNcpsOq6qiVxg6p4PDMjrP/q5D3zhC5M2RnS5nQIxiIbW4eCD7IiDNo7YsIEJEJOYp0QiIp1W0i8Mk+imyRaLpRbMK7owZ7aQ5FPhaV7FuqJXl3FOM8mCXkbn9FybSDIXxs1VbauUIavBqJs1hUuc5F06mDUWchjhpFUqKoAIqnrJJgtmtpZBpEqwpK3RNxPOdFXpBb4C11UDl900nvN2l74rpql18W7ZG5Paa05V1mF9NsYoUpnFrfMn4KLJUpDpFiLTYLNyPY/VViqLZAJzKbJabRs3DpJx7MQlO1x7U5YCtHDnsCbzWDZZeasgaRLQoYiCFG7dNOaQadJp4l4oOjJK0lApjZMqhqaSJkmfeZH4yLChdahjYhQCpkMWIiKD8h2zlK1cTfxeSMeUAZSHlLpcRq45VazqRgx1HVg7UGotRNtnnaM4WKWVIqpoZvNI9DKr6zI4odmILfVSUIuOVIkFBp1E3TSNC/M+Tlln5uRiKTzRQIYEtPNmqRCOpI7IAFyCHUXYMUoICKajLzUN0iHcyG1LWWmU0dNaR/BNFn+xGEONQiXSs7agSjCQhDylQZwb12JZJQugi2nULgIUqzwFPxoI/jJyhVePXJhCI9hDQzWgrjILNwxL7CITyUEHhGkklx9EL+t6yqm3QIRBipB8JQWkDatyBi8Z50+RVsgbgaqOVsaqhaKlq/b06FsZPpQsQhXSBezGzBo8B4mRBhXsviMCsyIyQCoKCKZUWtJ79MZ7KDovgGnQlHnApCSQ2uLikGCNSMuuvQvNJYwpdLKiFnBI58zTuQe1ksoAVSpXGPvWhLq0ZhP70TGCEE734NViA6jrQWcI9j9WwwBL12iZPQ1GCyrfRpCzdX/5gzZGSXd4l2xprtINveZFD+Uw+4sju6uKDE2sF6gty1P84OQDPI+/6OGHktCC5kkG9YSkSAbIS9p+hUv+9s+FLxjmvM31Kb88nRabpQqlkZGIlLxAZK8PIqBQ0N8inpCAMKNDa7fS4Q1TZNurx29P5nlWZolM4VmAQQkQvv3TnyUOcFzy0ENLc2aQLAS1hjIlR+R485UudZEDdABO/NkpMU/CIGREtzbrOoaOKsbR9u1BR4ZaRDM1OUFDLK32rCwLIwO6rzjApPC55CwSnAQQQVjdt7/k7e9/yqve9ORXvumpr3rLU1/zpn99/Zuf8Pp3HPmGdz7p9W95wnFve/wx73nsse/74JdPCBftDCBKz75j64Sf/Hz/Ea5xxcugFFP+EKA51Ino3GNe91g37nvAFLLYGoIVHtW0GFRO+MnJ+6s6eGOjHrTqS5J2AUv/uufsop7agctf6gBMoNB3fv5zzgrMHhlTOTcVta7qsr9+XtnYGDsTeLF4sMoflJlDxVCMd/dtuRuwjAhpvcSSVMLZ0ZicOLB+alAUAsEkZPXQZtroz6BFCoxtDB3kwJ2HouOwGB6wnjGHEzrGbXEn1FyKQ1vElPOMb5y479/cA1gN9SKHbIgKh0qQ5tEiOxYvrOMKl7wE9jsi8js/PTl75GzsAOmtrBt9pZL9hJcLlmr0RQ1Fs8RcmEqqIrrA2oS8tBKlTiKTZ89loeJLvwbtGJCynOz3e9i2lUu4RVYlSSAiKpB0yCRoJqGcMmy20mzsSrYtY2K/Y9ea2NSQYmIWis5qFZyA9hP8zYUkOAQV4sssv4RmxW6SvN5YjX4jtdcbV/ltCorUwNjD0rPMreCggw7aS+6Ck8RivRYzxr/SM5FcxoxOZl0gdcGHbRurEqBWJ/sbfB3h1jhT1sFULxC74ENSv4hJcokS0Dlt9oEfgAxERl4gecEHd+8dHcwsI5cDYHHLCcJso7oL5C70gamp6WCnDIGpWPWi3Q7ZdoCNCpaDjmZKengIYyeMb+YBjElS0NMzZ4/WHQbZJhhMDuSsGWdqzowltSJREMt0q2MpVU1tTJg6arjGlutWaOuccG94zSsvFu33e9aOXUce87anvOIN3zv5lF2eTZZyDRpJk7AQj6RWuEmIZqZQbYdGLftp2n1Bpak2ZaZy8mMlbFqYHtPBB3rwxw4RbpaDQVXpThdaLt/44Um8tU+rpRx2yPJA0MicBInRWFmnbJw3ml7+QFMkNXzzx7/wUliQRTojbD2+csLyb6vw1oVbNTv88MO2IMxQIehCWah7hV/skANM7r8485yztu/KBGql2b9/uUtvW40XVrjn8/at9b+96f2PeP6x3/nBiX3erMQQGl2zUTHYuwtfUxRSpRdWC8wtsy9lZaAJvThZsTwkighQ2CmpsBSSVZtz/aXcbQRLiqpVlcK0MvFKmQVKSWfs+b2er8HKUzKVw2Qj1klJ2k9QuOeDjrAKXmJSuHTVGESHWPgtXOJlibBAADTVQixNloVriMDAk1Z4swfmQOOgAk0oQUwhoVjBwakgeu8tSG4PJU9rtWJ0W4Q6hSGKNScY+sqNRXNmDPuKQenbKrlobpAI8r0mSginFwWWYeqslevMonxBcSOqoRn8dpTgsQu7GRSiIsIeYXyxRo+YHm33leq+CjeDihYQPlkoP7gNs8hk5DEyTENLpBLCAFysq81A6ymTKlSJn7FsKm8FV95FZiXqbQKTBuKZEBilOA4BEYQoR3eHOnOD2/TB1Zrp5GPkRmKDtZLuGkebLZvBFR3UMynWZMtYUotQLTiCdJdGhiSdGADtaSROssB5BzUIQmMCWBgDxKGP0cc5FrBUpECU6UUGlyqEN2XMqB4ydVn3aEooeqlEvxjtwrblMaUGl9dWZVkqD0UMuyHVVI7FmcHpsjqGdQxTDKFG9KBzi2n25hmpNElCgEiJWdMjMYMT++Td6SwVKpy3PbN1YVKgkABduCchEOxYoCsZjESk0QmkpFmOlckl2pW1S3tRmJmOOa/6WqznIGk1s2ZDepp4FR8hQ+rQk7hJLF20SB91qhuzbJuleDArWyI5CroXp70ttGVtOXQoTY/doCcgHqCDEhgD/G6L/KUBws1T5b3dLZIgjZKjMKwhJqJMKbFWrNEN7Hd0JzopEQHCVUGqlwKl27qf7HJhLJVrepKlC7wgi+swM5nZZbm93y/HB+fBMgvBUxykdfO8+bTtu/YTxGEH8bkV/Y2ec2O+Do5Cmyf0pj0uctih+3fhlVPPPBdRCHGokfG9Zfe2u57w5r5trBuEg5gAlqxXIOwJMyYPs088e/fMfXvtOV/VUeZI9+oYmlkKNE/c/S8LY7/jYtsOVu/h3gltGnV6ShOSt1/88GWm2a8Hzjr3PHA77T3Ew2pnrnJnEbK/5J4rXQjpTDtUBInoZCAnDLda9gjs8+rKktR3exgu3EW1tfQvnHDi/3z1hI9/7Tsf++oJn/jadw7YfvLL0zTDupNuZPQ555x90mln7aOcp5ylgimAoSCGnNFnX4dPAgaDtjY+aVF+UQNap64KMZna1i/O3s6++7SBLihX44BFRgPjOWmmNtiUepHDD/CVETX86vQzfFg3rhmXJNQYM42XD9ym7g3eAUGtwSdWK+01GuEWHOhIRYai1x6s8BGY0IP0HNjjQPK8ZoJk0NfiW5as2IyRpIoBRZzjsshx/AOwbTUOB20UpC//cgbHi7laG4zCTUiN4GOOqDS19Z27tn669z+zw3HZWHEPY8lVzEWiagbAMhcGGTXlooce+MnuL884Q2hZT3efhTnIp+29KfXtbnu/WBlXw8Zqo662pVbMXmbfiDr6uJqqrVVczLoVkj6W4TWzJL2WSMxCK2xvfb85O+LwQ0K4VNZZS9ggrP66LFZT4ZqpS8mpkkRxBrYEWx6R+ZvOF3rjg3syTAurFWssYvap86vBFn4hob0+SmL02DZz5SOqlbB5XYBb+LuX4PknW1vkAAPC4XuimTblDLAqNnKaPF/owu/pkEDhhGijYZAYpY3Z94yC/Y7WuzJ9tpRbLsaEwzDjl3CWbcr82U8+QXMn1DU2mq/KNKymYYhajBlH4PeT54VO8IYkUJ1etyFigwwh0ALo0osi+zbP4ISCcC63eoRnJvNC1Ia6r+ie8ySwoGmiA5VnLbGKww70H8BMrXnAoS0siFRhtrBjko17NF349YhDD+JkLd2IqQttarX4hpE0yxMkiEJEXGQyykiaAILgQ6IbXe1K+B3HJ7/5/QcedfTjX/6GT3zpe9u3WnSJHAIjbJtrUUFVGepYh210F0ttGkGt2O8IEW7xSVDxks0wa+Si63dAipKmvdAJmEdloOdpPum0fb/x4DCqskHuEB2kpAgHmhEzn9L0deohh/6uwnhmKbChlzKP2W1e//RABXyjyMe+/M1PfontGx/9v+/+1+dO+NDnTvjoF7/5sS9946Rf/pqj79MYrTPOOCd7aDBZ5HIXu+Sf/eHV9pG54PSrPzr5wc9/7WNe/PpPfOGrO7bv0GB9zxqwBByBNMuhhqxmt8n5WMWVVaWMUjvARhbIOurMlU9dybghhWFhSJIJqF3QhNCt0Lli41qyE2mhZlB3c5k8W/eYyZZ1LfOqdK6QBgGB5zBglCQKSOMs7OB9zuhajJTWakioeLXGZKJJVly0LVNh8s5iOQdRvouqARaaIRGqVcoqdENzMUnVIZnhGV1oWTWHelbRjapaQgb3IV2NOb/VfM1iNce8FX2evU9gpxpZtallrVpXxYYC5QIkiA+rhHbJ2mWj1wqV9JRuvKJlyDqGriBkAEQ70hkT2juKmwUJxPKZmCsmkQbtgm7hhV+AeyvWpBKMQGMi5Iq+0MNFwzqzhUsLrmzHzMrPaOBKd1QFBw90mAyqiezuSyNMHLmIDlI1ijgs5ypM3pRQk854dfCtNzjzF6YiAppTZpNGTSFoJVOlLGReHkYo7y9NPI3PjBqnCG4XfYCoLCGOKFCDVnAVnsXRVRqHXs5G7xZAagMiaDMMIqYLB4xkyDBxDY9u6USrGJU165O2pumcHKYKfnex7mSYg2OurIvMASR1SFJlaKSxANBsBgj0kD4IXNW1mNEuxkBLh87ZKV91oYQ4cgFhXXKNoTWLBsk5cwqse5+S8QXDLAJR3hGNIG4kHXIwDJYK9J7eqEcNBtRUCXQX8ovCFlFlKkqWByqgSoZJguwVq7DCK5IJEVFVUYAIa09qAuAS4Y3LIE3hTVXjzDHwGR0TrMO6mZNtUbJZdmS4i3dIT9ByyUa4lyFlRIpbx+ClEs4iWZy+JIf4bE5qAAAQAElEQVQ4QBOBACJpbBpKsTCvVjfqOFTsd1DYmtJoCTcko15lmXwveqC/JGZv6iPP6EVrQHbp4HMYXt+/MXIzcnbvdIkGKzqxY4YXiJVDtx34id3pZ5+LVAR3fIwZxKU5nKPuPwCvpGpbjX2wcDBo1rrNsEnrpNbFomhRMC4U3bdNE5eJkBBb3E12d7Szz9u+r9zucz70BALNbe3orFCMOq2S7HawbewW2fflnO07Z9q9qFdUVohUoUWCAx4JTR/hRUIdmFI7iNNYSJ0Dd5npZErv3TmzVcwr4/7ygLr3uZg810mYNLLmDvrU887hhf3bQasV6U4agxatzSdDWFUrNmjVzhGZDjmGD0jrQEY/b9e5nRTeT9dQirIYrBlLAl7hg/CrjobCHMtYbXB9sF8f4JwdO1SSqDXuV0CSOoxJ3Q8gCpjAMlOWRwQl1FzTIVxyDwMOdGSaZ3UxEemldW2wtWbIfCDp3ddIo9JAnT3LzDnW6bGXKWqDI9rgnVtx2S2694t7JKduYXEIBMsjmIhKp6twSshhwhAu4cCOXZub6/XevZcz5VEKilsN8tmCoSHylfZnkUMPOjADz9uxY1Va5XzlTZFVF0ZpHshEMEmUXDPhfQvTSOadTkRJISwbRZwFhYwckCVDQ4rDQGUifBWu+HCgY3NuEVW8ei89BK2wJhnEWBdL0z5XDy7lTIIJCHgybAfSQx9risDCCsxFJmjrli4LnXGAQ0rI0JZX4k61mRSl9VQiBxAHtm3bQClSRdnoaRGRFAUr+wHlhTYnA4olVXuv61a4ugytxLGU/bsQSKQKVPhiqkUH01FlkOXAfgfpPDUPFgRLXSYJzrSc9yBUkrqf+HJhrNVAQJJlk2sB7bSxioyJCj1wFz69W5zotm46rb2x2qJz90HbFo37/wYv9SIsAKYLoRbjrnyZA3ynd+6OnVvzuoPzJE3gMq70xu1yP+9A/zehP7ziZVWLGFhIGCnaKuLAbO6YFVGS9hfoEDKKV0khC02m+vC/+38HrwZ2O2Cbu3/m2yc+8Zi3PfBZr/7EF74VfTblGCVy7F46qEesLvk4yOLMAZWAg7VlIVnCK3KBslX0EVoOIL/7UhWVMF9X3WLlan29Y2tra/edvV5MdVWWJ989jVQrxoWJM4KRTBc/6Hfk9Tk7dmqyjKQZVKV77tx1gAeFp+/YevKr3/bEo9/8hKPf8vij3/rE4977xNe994mvfe8TXvPO93zhm3vZcf4J9Ui25Mqe6AQedofbHXag/3pqj7hHfOkHP33Sa95932ce84FPfG5u240bJ+nKmEl0DehsMostxcdEISzMsOzmDZkJh821TqM1LtmzBEHdqjIxOBVmamDcgWCKuzTJSTvPlKUjTUUSTHAu5q0LhJQAR6RWda6RukRmR3bxzuUbC8ayay8oQnQRjg5qKNSVVGkyq83QNruvW7Y5kjpJcK06llhlDskyy8oT0AjWMa77udRISWH6jHVQqwD5KlRccnmqzMdrFqCf3XKSdKQLQrOp8iJJ7cyjMFqeEH6EAhBtrhN52RBtNnQDR3P0FulqosUkinrRIiLJLO8MwpQ5dfa07lUwWpp5zirrUZplYjZiSl2RBDDcutUE6W5VYpkccpbel5mtqYYUs2IAUdUm7NJMZhFOBJMM8zj48iUEgawUtYAkoKke2pJuMDf6hvgozFP6m546h3JJICESA2HNaMJvAhCrBvZnHVNRw6BSOass6sQcq0lXM7j1GHMeci7upYl0pRrQujpAx5bFMyomU0Yz5gFtlV4SmdKB8IgZLG0pSE23Jci+DJZD8TokURK05DJ2bui9Qdg8MpYS6JBIi269UZlyUDFq7tKpCwrqT43Gh0JDEJGxWq0jRq7PubLi5ibDW4k+GoZSFJZk5FbwImzO0WNwh4rTLlefSf0RaaClAdqUc/RAhAaKaBkshSsOXWe6IkVzcQYTmKqZtAMFDvMlv6yzKIB+h+SayJgGqTlklJ4Ic4rSfwqtudmBWsbIcWbEXBAqpuNKuDlSN+ZbNHgia+F1cdVZZDbah8gGvkv0xtgLbddqKI4Msl2nmr4Epy0ynoU2Y7+jO7V03qIRQ+2VEKHPkp08F9lPfLnA4FaaAyiYnVx7yhi01pZ7+/3SmF6keeUygWTImH7X4l5LaYneA04tzCvMiXUgS8qgB/2O55Rnba1FwhmAJW78Jeq0jhoO3KSJbdm22UpGL/NUG8ORnINWM8lciha6daCupZTONZJvQFVqpHQOes72HQeShQ1VLGmY9L6aWmXUKZeWLK593GCUeLp32z61TYZQKwRg4NE5yVZ+3Ftszxkjsyqcp1IyEJnORAEZTo6OteyR2ec1tDZhyUmwT0HYKBj3kTngaSJgEcV7cjffztyx84BiTL3sLYNJowQqshLKutJxMK0iNIqMysKkB9k6eZ/ajh27uvv+2kSlJKyLTBUxwCqvVPA7pj70fpCa0v/9up29Sc6acD6aazRhZhhVWOwnuFyo1AnRZOWjQC6XAFXd9jtolssSvNAsq6ElF95bJOjvnq4HeHVnvKWYiTBlY/aeHvb/I+1P4C27ivpx9FtVa+19bmciJIR5nmQUEAQUMnTmzggJszIl6SRkABkUUdC/KIoo/lBG/SmTiIogoIgyiYggggoo8zwThszpe/Zeq6red3cgpLsv773Pe7v3PfecvWvVqvrWt2rV2qc78eSkKDlbziopWwykGUUHQAVRhMgVMUtTUX4CJLpgrumm1Nl6xz6HkHkcAhjTOURYqWQQMYbSzepqKNS9z6jN7kVU0Q1ZUkx0FVLxA3D2EufV3cBlSoRARCUymtPHkqjK+8str5ANw7biowQ5wDxN3voxSgHCRadJEmvSJ+mEiR0etFR2JzOrHCaAHgcnbJqTxbyXYdd+NBUBZ5IuiNowBgNuRVcb47UCe79KUpxWKxjWmSSehLtYU5clzntLL583p2bDoNWk8BxUBkCRUlWW2/v+SIoEPDB3mbjkES5htwIDZIshVozXRdMspEBGDINXjVFVt5LPpSSx3iB7inuZd43LmgbphlBsdWyup2WzBRRxyywOgWS1PgzBzNhqyK7NKVx6anDJdHGWbI4tsap1K3GgiwQdT0uyMdWTU9x/z/9B4bUDv3X5VZdcdbmyOx2gWpoXUvuqa9af/+be/03Fg7at7n2nO1IodwNhooKB4UsWn8avcVvQCxUxVo5cdvWjTAQxqke5421u+ys/d8Z+P36vQmN65Be/9d1ff81bfuH3X/mNr39zdCKj6eRDBpoISc2e4cdgCogIa3BluLEcklUW6tOaYTBdLu35Y7kE2VEjlCau2Pdfs941bUVsEbKCliQ9RHINGwbUQXRMG3zbhtSt9H9/16wZTOQk/Kiz5zWb6z1N+P/x0+Y8KwLptAeeNzv44Oef9Ygb7Lf1c5Br5/DMr3/vst/967ef/bt/8okvfkWE3QFgtgCKpa9itViVIPWDLA5JacWSfJBSmSlGx5fVvjtiLRxAqZmPtcZKmUC49whW5XVOKfwa3otVJTBlm+oKSY6mkykc6I3DOTqipNMMusC1KIJ3szCEraPxvWWCf1pahyYWMi+fjRcaDemNRVi5X0AaWLTVaLobNXkSEbATk4Q1R/dUK8MwlDrQOyDZvcVE8LqC3biwmZIsXY1JTuJaKcbojpKDh/SARI7Cx88i9MA9tEd2os8ecK3ae/I5kU7wznkXaabw7oqEhV1K8jv6sg9oqd6lsTFupl0Gh1K/lV5qZxFnmCRQBC7ZLHPVbZyHpVEHFEJbPLuwpG44NiJHDSsuxQOOlGzJu+gpPZXPRn1jkHEsUkqmtjkJTERSzpW/lSOos4hiAlzAGRJI6KTscTMKLamhNlchMHNyWsqLIEp2OL9zahPaUltUzazAlB8xdZ3WxeeOzkcP0aJzmUxHXSZAi5mfEcrpLSf1NT2ahd1n6RNk7ppMGOJjJctoKruHzVk2c2zOLBWR0G7SBumDoRRiloCHSJehs0sQFbMUhoxZPJm2WnIIq60UH4oq1wotTTGJ99bY5dYpzWNxhHlaM4NDy+y1sb1WBdlNuAtr2lBICOkGYpjee9uc+xRk0TIkTIJP9kgTTUxeZ/aQTdqU3D952CCyYbkykFsQA1RUScO02Y1ABrdgztC5FBJAaJC2BPnFVIE0dVS+64mEgDbJihsGo7/NnAyeEJTtBaw13XKt6TRKkhapLOZmwrXHUk8DUjNKB1K7gO7CwVhEGqQMRBx7HWOtqzB+oyChycBsm9XmnuG0OPeSXT7SymadiAgnTSeeIMEccE66COz1k4TMso59FGfgID1Ad/aS+uFHgapwMYa0cHSXBmNNZVruVwxbHUFTQZynjphyyRCYUstWsrhmXu/K6Eiy1Fg3zLKUTSm7IDOHS+8yh9mWY6elKKvRJE9yC0lLWQu2wgjgFMEOpia5rtJG3bU8OEAyW3lr/2Gh9l6zBI2HT9rnwNS0t0piaOwldd1HEZin9nTGAIOEoXVMtF4UWx1DJp+ebAy6WslgBLmNRHcryb2uCSRtCAyNzx/6KrG1fiTIm11rmWh07TrOUh2WQqAXnmyazmPBtsTAAjLzaWy0AEftNR0/iujGBsZBjIRlJklHWcswozQCt5+piVBsrzMTmgRiqCGD9zG8yBKEvcSu/bieZhGTJuSyCPnM0GdT76TdtRJ7vjKbKtzAUu5Vx6IbnZXHLX+MPEd3gI+XZNQyetHJuElkpSCNaT+09EI8kFs4QufCxcJGGQctYgh0z+wzsFkwjegFfQREJLDVIYIqStVcd2ePNcDCXpQcCQfUtDJH9hl49RStrVqMcy+tD7QeBtqxj+ByoTfaRGWTgLSQFTAGuJW3eZa+Lm2u88xaKQshV2BATKA+aDNZQzZ7W2OrowrXZ6kGpZ3dWwZLpShUpSIDOuc4xUaXGqy7KeK5lRrwKtcj5FSxSzUg3H9YhOhyZ8sRgPekYTrNQ291WQcjwxpzbGv5bQM7H03RpPbF3sGwwlQyZMsBaRlD9Opd22KFgEN7cLGJdCK59yDuIlwGSLfc1L5LfR2y7rm57puRubc0ICImK/QB3ZASmj26u4CxrgrBvse21Qgiu4xkH9BpDySjZFdMXLT2HQCMXJxkKeoVwuQuAXVJat9KPxXQkGhlnsrcHK3R+2Eu97jlHY+4x97/ayz69Hf//l9Ul8DUJVOI2L9+9OPfv2rvx//H3vtuN9j/wAhOaUb30qJrm4lOdPO+ap2tk+QADD118pjZeWBzA+uNwBBHPODef3DRY+90sxtxPC38cWeP+PDnv/a45738Qx//xCjBVUmIpnfxzd77esrNyTNp7D4KyLfVLBZ9Huap9okRn/mkl/guObmPOOAdaCLX7jeEDGLR20ozDS7K7GDpocOb5M5s6itlpWD7sa3EyFjvo59GEoqYwV5k09ArL+wjBNzp5jc+/5Sjzz91OS84/bjzTz3mvFOPmHpgmAAAEABJREFUPnvHkefx4inHnM9z95XzTt1+7Xn+qdtvd9PDIJGlSB1Igsy4951u+7KnnHWv291ChcZuMcu1lwjcJ7/+nSe+4FVv/9ePc8EoaspCJ7ReeBQt1dOmVBJGqD6rqmJErsCd0ZRszXqPsXltPnQvOUvuAlhS2LlIb+wZspn7IC4SZLOLSlPbRTbQgOxRJi9EHAN6aXyrxoLq1FimLjF75zLNLnKtcFfxygpCui1rdKgvK7WkomQ1ZzU2B9Igi/1Fonhn1Zp9XpfOB/fQghw8mCoMQxEIijSbppjmdU6TTiRtDqJWx9youa3qtjoObMxGTiG9aNaattI6oPAPPZ5Qpyxc7kZrg0FoPbBp6argH0XW3UUAU0rr1jdV1rpbD2yEihWu421ovWzS215YuDSGyNKMjsRSdVYAz41QTY1e5k3zGbLGMGVlzecDV6mQhLQMD/b3k5RJhIujWiJZuadNmxqdLsReiF+HdsmGCLdFDsFo8GkEz9BsKl0BaKWhRcjwBNcrmGosf3+OtwgyaHLj5eBMkeKh0USQPE1iWTZGREXwc0JnEX7d0ebe4L0aQ2Roynz0RiEZhVPVPihLZmaRuRgVaUdpDK3SvYHTJ63KGT6FcD/KpqYLMnNI2SZlZVYYnHnEPKAVWizK+IZIN51LWXty5abtasb4xnI3kt8Bag8ERFJKI/KuKSqZTCZzVOTAkw4NzmoAyjWzxCrIV8pFRPqc2RzZu9C6xWgHQltWlyoU0i7amkzOeaKjQ3QwHQ0LA5OTsRaOLapTF6EUzpKeCaQmTFNLBB8VZXiGBU9WTofTQk0vJXTUKDohmXWzFQwiRaJkK96lc4XqLj0HZxFnFhrJ0xj51BBuxqvT1jnBX5C+gDGroFrVQPFMmoF9D0vIEnHNSP7GAEuRHvy0r3ACM9BSl2CKkCxQNpzzRDbsK737ShVSKI3JZgm2a7b76j4v0bsRzKqgzwZZbBrgY9Dv3rv3fUYsFw5cFZF0iUaMNEn2qgzDcmuLH3GUqdM38QGxkTrwgb2vPFczg+zpnmIqkH3HLv+2lSRUEBeaEmBwthC7dmBvfRabB+6fd7scaUHVmEdZF2HErhW7/ut+g1Vxi55IBiJBCnpnUlxf6IfvM/OaeW6RbCfmkBA4DeNC2jB3WvdDuev9HiNHi2LLqiIkcrrlfMsbH3r7m92Y5x1ufpM73eImd7j58p4fr38edvBBwRW/jEkFWQq29tp7y9xGRtNkq1755MGcFX+aPGbP6Jyu2FqtkQ+joCjrS17PwB+9DXcICYMEPWumTdBz+VC9sIpI8s6PxH/w7oChkAdwSXVykmaKiNbyg9t7/toYh4zMLjEnPMAas+ph3cP3FPzhJ2ZGbxoNAYkiUbuXeXn7Q4F9fku0ZEppSsmxyDDyUdqYAxNAfC2ylqEFPdlnHCKy+cQJCRKWso9wjSZkENmRk9rmWPtYPWj1vsN5hRpAP2TZLLZkKaK2WSPMRSJ75/0tkD9gLE2kw3rWeba5IzKALSQ5BXFgToZH6eA05lAsKFI+EeKzta490y2SOqVxWRWpuuSbJcNKzYuavX4mTlwBrqZGLdrBKgwGBmEKranUxpGR4G1JUmnr+E5Tc27c+9oW+8WjONe3FHeO3mvO5aNnNouuFOwp5BqTkEWwSfpye6ufTHqfoDRtyeUQl90d8lbSgJil2VxkZokTBjbSYhZhq5KxhVUbq5F7/r6bIkSMs7XAzCRzOo99DwXGlBKDY9s8jL2C7jR1PsXLWlVt3yEQokhMl8AJ+mIGM4ApEY6tJwGfz4ChTtRgJ2MlFV1iGbWFC5wxmV5ZvNfozBqm5rJ27SfyC6cfc+MD96PA9c83ve8j7/nI/1y9uemMU/RPfv6L/+dN77y+AN/f7iaH/vyO7bQjOpjoIqwj6Yz2jHSJoqF0KsgQZf5NqLMt9YNMRAa8ahttfe+73PIVz9p5/inbb3yDrf/pIye69rxi1/oX/+QvP/eNr1n2oaU1yCRgPnV2Ae1amX1eSYYuXYWtywz33sS75o+DNBAs23xNhmhUFBNlMLHvQb2ZfYlkd5+jcS/eGSoRMGeM1HbCvc+wA8eCpto0GW5lFds6tLc89Abnn7T93JOPOuv0Yx5/2tHnPfTonacevvPkI8/ZcfSFJx1/4YnHX3DisWedcvhjT3nw40/5mcef8rNPPPlnb3ubQ/uQyh66KE0W5Apyl1ve9OVPP/fpD9txi0O3+A91Xt+6a6b5/3ndmz/0qc+S3lW4nnL0NudjCBgLhCbUTYVNE++nB0915gzvJUqwcTI+os655FrYkRDAhcHBQ5wymiZpmcxSRsAzGnla04dkykrGEGxJxvRVc5uCsM4obakQybpVZmCWHugI1U6eE2SQVE0wL6sL/dBq3IMsfgtvcK0OBlmMA3h2ahHW05ptqFMdvNYQcYDgu7obzcGydPsSGdk9UReXVQopUNWZLYV0zRCOKZZGf+CQ7FKbla5DJnEKNUEpyeXZB0nNojlmWjjhig5fS5/pBXOAnKITpjDhH+1FJrDTlODVEspCrswdLzRuDp2tzEVcE0kMPIJlIQO0BqkU1L68BVxyaY6NtCTDoFlpqDkXyDb4NGQokqGhWBQQeSrC7sWiIlS60qUATVt8YS0fpA5lJcIZnEYUc1VJglrBp0cMSbaJHiV1CF1dFdSKaZD10GdaQk1lRClaYZUtRAoPwAYMBdatz9ZTavI+J9lPGEPjIbkgPCSqQE2y0gCKiNF9siGn3XBKZBLVdHTGgUpl4HdKyi4HfdZkyJK2jM4a3TsoRsLo3LgCGvURL1rYklk7NUTPAlZQjKgKIsNZjTDQWS75MoTW4A1eAbh4pTVQY5Q2VVqD9dSn6C7i5AFUUwah8fwoc0SakxI0FrvtGFzGbgODZ7nEoTFbsiUta64QMxlrYz+RikkAUYF0kamUJoXzhmbZkMF4GQJmPEhRSHEIQ0sngVqGsW4ohtl9M31m5o5tzKnwmTsdDrBMNV9wCxUS1S17cg4XaoSK0ApVJe6R6Z2TYq9jaq1pmWoNEe85B7pqqTaamupewrs/SpBTc/Veexog0Ijqw1Cw1UGalkg6DRiqSan7b9v6X5N393Hh1ZhaiJUZViarUOsC8bX7Vupxk4MPSixGCI2tKUMr2gy8hn0PFfLVKxurccLQaVudrGxCNhVtMGdFHy1134HLFV6uzFFwIp6QLljvv23rr7m98y4aZFZhmBd6kFc9nEoGmXosCvf8ueEB+x8gwwplLMpvWnVsbXke17YQBUTILX5tgOwSM6iQcTONshAHWx4Zkq3AMyM9jfvM0Ybnn/OwV/3yOX/67Ite+ZyL/vzXzv+LXz37tb909muesfP1z9j5l798zuufffbrn3P2o4+7P5DOOSusxAH7rbbUf83muqqNMVhj3hTI0FPWs89Tb0wdUhXJuslStZtew7iRBxzIEURkb33Bg1Z2RpEVgcQ0RJ16XWeZpU65OzX2HkQmHFgiKNqZzSUnSCbB2RI/BJjlrBdz82Q5J6eHkmIC8MS+BzW5e3hCZDkXODz5vGDYwv5rhw/SmPj0O9l62DiWcYBlghGdHRGQpSzEtcJ7vQbStfNil+SJbuamFqhr6JziNTEAG7UOpVBsrzMi5u7O5Ur5lCJNmtD02G16do/mkXsN4ceb3PDAHGZVGkZ8KFfQa7KU8N4+p5HYbvOmxNrQSBBAurAecOko/AUiBtbfIjPQeX+Z0LgEGLQibEkh7HuMK9aAgLYYkaNgqCwJPS19lNgwGUrh3Z5c5ucoCSm6rxJe6b0PHlx7G1i5h0bAkontxbaWT8mZkxSuuRh7qbtb++W/K23JW1S477mrTWkhSMBbm9frKeamxiZiX9lrr9CcjUBlF5UatAYjUOnPFhHkAH6j0olDNTEpfAGIg8egsbU8DbEOESRhZwktpXClq9nFZycjtmDarjWTyW12lgPszpfuwnTQTjC2kAcwrbvQgBR6riqqpWf2xj+dd7c6iQ8ZKONiGZ9eLEETmW53ixv9xuMfeqtDb3D9IbTzOa/82wt+/9W//sd//vQX/9+L/+hV37tyj79oyl3Kcx5/6s1vdIBq04V2nqBrPckHEKehSmXsZHm44ei8Ds2xwpi0GU4q6GYfulfkwRv7nX/yCa952pMuOPnom95w63+Gfq1t3Ku88A3/uO4OB5qhj0aFBUs+yrUie7ymo2+azKUkkYKZppZMS0ZoD8EffnANcmma0IhhgayGOoy1/vD2j35HMis9+RqyDmthEcrsShKlj+tWehDtH8lf++4mB98ATrWDVJXazHxFSK69d73X715+5a4x5lpcSTeBOAqw0hgQtF2XaTNEVIQumbqKW+HOz6QvWc4GVZT6aNN+w+qR24/8k6fufMpDj7v9TW/Eiz/uvHo9/94b396zCVcmgBsFFrilorDvMhUuhGXIVJ88N9fBBqZABrNihYHF6Lrh2HAfspuntiq7iZtVyipsG2JDyJNp7slaT396HaZR+ipB/QNMISJAkj8RLdwlmT+srIOE0u8hXJOJIeawBgSWyi1QMSE6LE4bjc2ZsJJPrdPAmRp8tGAHo7FKPquPNLAzy0HESICYEI16CJVI6JJLtdCR0B7MuKQ5amxoZ9DZuWtAlo6wNPCRTdBQDpcMABLQ5jrTogqUWlu1SemLG9wyuHFfCzpb/qyzl56CWXRTdB6iDw2FjJRCTRRaSsAkxt4pkdK6N58VXkVFS9Igk0mkh2BWC10mkSQeAKpqoQjj4hFN5s6dQIXxIpc9YZRoc0ZKKmSAWM6JhbtC32lbFDjBEoPyTfcCwMSLJGhaAyet2QZxRmMEqwh10bruGWZWSIQwuDpTvaPHgkzALOuQZYBWoyFmUiUN4MKZKlATEZfkR3Ci5Yqo6qjJBlBo0Jgx5kKRIhDjgp1zoIEWUQnPdImusmjopUyiLENJotSm2sSb9a7YXYa5kEM8zLu4R3dSDAQumgaZ1UWyFw4ZmwyhrNs8F5OqYCVakRVg7pUmQgcbuJY2U9fiXryb09hag4KLU2nmbMvFOyJVitFvMOGzLFzWQlwCSoskAs3RaTUaPS9aBmW94aBerRuYcLroQC4AuFBLFuPpRZxaOVtJoQjQkhTDshkbkmxSYUxNaL3RIenpbEQmck5UjPc0k0xkKlmgAgVSwIIjUboOfRB6m4KtjrHWLDWqeI0e8KaIQVG0MCDY4hCYseBpNukeuUQPfL2mTdjqyAgmcNKfVNhi2MEH7LE4XTfoimt2ZYc0kxhEKi1guHaHw5kaVxHZ60Sv9+ZmN+I3+BVeMnQBQ5Gd8JDk1xP64VuaS1WdsjVdEOSKY0n4lJL02SwMnVJ06Idjfvh7zYXEhE6oQhmBee7rfvD+ez8UvFb8ymuuKtKUaSPSkg9qxt4LfNEu6Ztb+XLoQQduDNyTluK1SlU+LqBbob0xQ67V+qPXzNyced1oSrDKRPPeEWFbB9t4lnQAABAASURBVHkZuHjlAnckY0zaS7itzA6s5YCh7FeHjTJsjHXbfnU1sl0ct62GbRs8V0MpQAickwK+bTUu6vb5ueyqq6tkFSHzSCb0IiGmXbUbIMyQjrmpR4XwE0TzoP33YzZhn2PXNM+bHCCGUIk0ZlpxZldHJjZnRmiLAN380BuoNLOWxVPUIfNcc5l8nwmAXdMUiF7QKxau9AQlswI/BsE0wuVuQRvgzHorSWDUtrAEuw9WRkni3FhGXREsosFXacigY6wbJXOr2SKYh93SRWYRvoKvKqxHLZfum19VNHJLNbdt1HEcds+2x0vr/cqruDMGq/swoDAwIl2UaBaLaV4TwT0G7P5w00MPrmhFsuZSQYxFQ4bUsvvm3i/Us2RasJUWEaTNvfaJ23bWMSuxrF00u2e27vwzpTdxj6SsUbNxFdlb5fLZgQ6usT5LR0FV5fzOqi6ZasKkYOm0cCLnoUxH02XYPj+calCzQurVZHIT5+XkwsBf+0gDKSzKQBHjpJIjU6NbddPgjS3keYnfeBgrjiPWPq/nPvfOJQLMyq0p4RDyMviLqARXbgjqMt+4tUnUX4UyShmpUHImijB9onD2Lc5MD3DtC02TNiKqguxm7yBkVG5h1cY4+mKTkGse0fhbVUPGIN0DWx0ckrKk9GTejbO5igoUP+aIBLUO2qxGGGZRLuZdMUfe5863/6OLHnebQw+6/tCp+/9++Zvv+Ohn/+1TX+AO4bpbY9FTH3CvP7jw8Xe/7a3rwq9GOgNCK7m2zMaSU2QcNMc6D6u12uxspUSTNagaF85lg2jdQX+bsKAobRa56SE3POv4Y1719POf8bAdt7nRwfgxx39//qtf+Oa3XckTS1MfVEaWJdlSPKHBjiFhhHIVMdawSpC4cGwpD0LUU8kK4tIgnX6stm31j6kics3VFEvQFAUyiI7Qpd4la1rrzQP7HDc95ODkZcrSbEWtZcvna9++7Ap3hisLAuQS8zVLauU8oVPKTMBUiqkW1RV0cMZkKCIGHs5eZXnWailF6SwDcpMb7P+4Ex786mc96bef+LC73eLGFNry/PTXv/0/n/tKLst2Zz8YBNUS1kEW1owi5EwQGZfirhpaOYWImGiBKQzJBobfKAyrYHtaNUkPsIil5m4pMbhqG7WtNBUiXiyZWuossIucKA0jkzKYKKMwaURZ2wfJIsoKz1xNAtNBYFSECFSY0E7tZnOtnupTYu7Rl8IcoqxWY0I5s6REC6FIiFBoyqQc8y3TKJGLoqJVWaoBtwbr0iNn8TlYULyHLB1REUA4vU9wLuEhWEaz51toAwihKCnZNWY+DjHep+IOCgyexcSMwwWEJsoMaRr8rCrEg4a7zBleIgqBSnP+do0wWt2gbkYaUE13Ms0ApLhny+gErbIM86kSxKPOM4sCwqWalcroFDoqc9fNlGUTVIDdZxqvp2gU7aLUrEzQ1Mg29d4nwWyITM0F9gnJhgUKExTVNJuztKCPmiM9z/RcpCs0QyZEk0hhxeSuBrWoqRI8z8xGI62iDqC2qXFr2QACsVRHesWiYN1T+FRr7sZb9B1JBanEtIRU0WoiqBNYXrS1TGoSGYcmw5w6IXPgpdTmg/sYwQ2WGGcAp8BmxqTopfpQZrO1yNrZ3hnmgmZIFYArdaWLA9TCdLKh6RhVZBU+elulVxGaMKiM6kzUpKexKm2jzIODoepee3CvCFBmpbqSICx0PsE/nQh0xVpyThpKE7oJiqSU5bZk0FqeQcQ3JQnAzLpZYboeMVdys9BEWTWsZg4TLPlP5S0IRksJoToQKisg+cB671FdRSp0VcpY0mRWnzUZR1tsNDdGwwdaTfw4KR9TJJHAXsfUWjqJlqogb8Aa3E2BlC2EOZZXh9LLEIKuE+u+iGgPWZXKu/ueGWi9tyREgAgShx1w4LahYJ/je5dfOe3y3ExjOIPPUcb0RPSkCtjlV129zwisit3qRoeJF200gpCKNG09e8S+wrxy6dW75pSeMkPnGGa3RosKypClzmK9I+a29diNYZQOS4aha+/OqtTBNYBq9z2/c/nlpjkEhUGCuxQXG4KJNe+68tJ95Xnldjc7bBhINjihbTWd7pOeunjP23ueIrKx4jfuVUpKbTn0LtmdlzH+mEBM4NLdgNDiFexjHDFKrCxsnHOYiYhJVisqq5xHbuIHwUowCKnRJuHDV09HudmNb7hRbU9zlk//+9VvT36NcBx534VPjsxjY8gVvzSsZeUb1veTtrEUN+rhE5ue+x+w3w22jcvgPX+u3tzctXaGhK5oFbFkCihcg1mIK7diAq2+zc1uBKOBMWoRJW4UV+Seqn/4adwNtdARfpNC2ch5spiYriT4D4Wu91tTDYSramdB8WIxQGVZ0Ldmy3qaG+s7aBEXnGmSvhltzT4imKBYltSayeyFYJ+jFNtf68iJsitCtWvtGFhVAkSBVhDLFSbLYdtw6AHb9lGA9dwuvfxqm1pZT5qpagSTJSLUBX71Net9h4jgTjc9rDD1yAug5lLmZUCq7iu8XBE37RD3mvO2nLbFNKKzVCIhkaZRCqHReZZNl82oU7M25bJoAyxnuYXjAHZtEji2RelYfB8dY6bR/RpeshdBIeO5GtQwzQLu6VW3UBWZyGIwBqlI53olVoGSTC5Os8+pMJNla7N7OQzRkNBsi7yK7iO+XNCUGlqaRgdysSFZsCLDY7m9z08HnRIuvYzholILpCwpV7d+xEMFtZt1zsO39FIHF9kdHX7e96QJfOzIJSAQlm4M3QI0F7rUPu8rzyu71mshTmpdZM7otGiAMZeoizcose+Z6UN0PsUeu2uHiGnhKZB9ZXklU3f7vHZbdw2XJK+dO8EsV+zafNHf/tNXv38lxVTkPre/1T1udZP9VuPGOGwMlee2cWCze/S97nzxQ4/9q+f+wjPPeiQfS1U1xiNl9x+WhBQvlnVgtLsosZdglqAgl19kz8phGDI3StaSYKzC4GPA+sAK1bTKjQ446OeOfNBrfmXnz22/vynVU2qPk49OPvXl74hKG3NzhTaEqhlYIbfwmmJlVN1IbHOWiS6WSZ2h2hJbHxomGK2bNMCxGuqNDtri7x3MrX37sstgrVgvqmK7C52YFqi1XVv9t7w4321vchjrcTOue9Dkk41tNz74QF7f67xic77iO5ePHhYBZ8/NZBWBWO8yU4FDiHEWkRGprQwxrLyMGCxqrCtbn5B0VqcK0SQDuQ6k2H4H7H/yg3/6T37pvPNOPoJE2WvSaz9++HNfC87Wu7uLdpV5wASsU1pblmtwnVsJmwaTXBKbViXnwLUloUt1LmValGIqVURFOoxnEhsKD9AaKI2tBdiJpAtc6SOy0T3hw+vBVDlqSNQQpkCUoUsxUWMidW1MuwhogJEfUjgYEkAY6GmISFvmEwCWjGDtouw9o8LNwgtXQ5mT82Ys48RTQ0CK0huEGiNZcig+eteezXIuvdsscBK6SZmkNjDhxuaMTvJRnA6wQWizwlX4RI7mMbVs5mStsJyhaAyFzweaDXOpUxkmZReS3IT0MXxQ2klLMxuYl1k6dKngqklvWPc22jysO7h6Zbh2LPUQsrgnqYLiUeepTo41ZGlXNuZgkgE9zbmhcUlDVrDqNWfRk05K0Vtq0FALsR4lGOfGZWNUirRY9mZcOHvvGT2FF9IjiVNFFrqJRKqDvCpF6KO4oHvh9CplyYfqDASFOq/TqtROlGm49MzZnWbmNvGxENhruE9Zz9xtWSQkQ9Pde2sRAaRUz/TZgwgwcWKMpSniQkIKeM0wbzKve3JRIw7J/CviKmld0Qo6TVz4xOuq4AQ9MoJc1GpWjD8iJVnBkvekS5kxLP1VD40swuFzyU2qojvkPvUay1RmsbAKHY3AZgmYd9NJSvSSXIfIsB6VPmcL7UKoyRFnQiBFg6QVAzKiq1O6Fc2aWmeGYrkKwGDK2A6YC6YEHxiiqxL8Mi+WeFKrKUAPqXAuOg1KhnXa4kgqlfVg07ZBhyzeS1PxAjEraYaqsCJW6QK9B1RkgSZoaCzRMxrgtLvz9xbnWCujUNfgKkilRLh08tIRDNgW8ountN4WptdQiZJLLzHu2mxbSi8kIBdqawNrj1GorvATt7zpvsLfuOyq7199TSCSkycZ7l3ZOuZUClK2/G+F3eqwQw7adoMWhaVS6hQW1L+Qsvd99fMKyQiIqGFa6VwRoXWyjc4khgTLVtfZ+QZbHL17hhuaZOcfSaI13O9Ot9tCFPjMNy5FXxnDxKLTaD60QIkX/PLLrthyyL1vf0uBk3RuPmcP1saZJm0pu1wUiapeVFGM71kWnMQFw5HY6hBzYelYVZKWnOAvqeZDdZMorYc3F1ClVDXramsra2ikgvj2HNNHuJnut9+22930sH1niMyPf+7LCWpxyTTP2shmS2V0BjJ0lTpCKi9I0ETPUWTjJ29/631VXXbN+pLLrxItqRYqKd3EWYSsM3nw/a3+k9AH7bftVje7GTe5EcVy3MC4IYWYu3GqfWfAPLVxwug5GK1kbFlKiD1o+RbSgEgzcy5npmpJgnbv7lP6TMZh3+OazbXoEEwnB2bvzZ28Fr5MVdZVJrPWPTNl37G8olLhyO4SXTMYBxC0CCWaRFGHLMmFEKL3vN0tKb/XuWtu37r8iiLOcPgckeRISqGPc0dsCWC1cs9b3Vp81YFGrnI13mhSZiD3Un7tx+xhlraRtqIVJGCBL01KLunHtY+OmaRKSBEtwlu2lGaZw2e2zUT8Wj17ve7Hveks3gZBNVFVFFKAhVWWqukCTRndRinCsqAiguUP9j6+9f0rUkpGau8qLiZZyXO5fHPvf5l97UjaOQRX/kGESSIMTIP0hYEVUq6V2et15vNDguUhNKqUYjBB60nQsdWRS0V1Q5gCNblIUErBZgZaK9/vexYSqAlmLmlmMQyd3EPq1hEBsSjQgQsSIiRRhS0YNrSY6r66lyvbVowLceqLMbWYYfGiFnD3bluPmdssiwEBoQaTwTiJDsJ48/O+pxSJgYxiJ4cABCKJTLvq6vasP/mr93z006wbAE65/73++OLz/+yXnvqOFz7rH37nqW993tPf/Nu/+LYXPvPv/88zfufCRz7qhKNufNiNrWqpSbtkacZqp9EhGuDkuyshtIHLXmP0hlkGR8FsuWmxC3AHA6uj6KAgG53jMIHkcBn7UKwUveENVk999PHnnXSEitCkvU4+O6kMNIzBAIPYxSZD7iW1fORoElQKLbXs8N7SO30Gx15PfhHd/aOSxsesNF8YavDRQjP5ydvfavfNPV6m7l/69qVJIKurxOiMMc8kxjbjsq3+n1Qcf+873oY1wMkJ92HGtrR7bVV159Y/8PHPlHAW/FxClciwOctapRsBzARZaHBPzIkGpFG9ZqsylcqT5crQxWOeWL4il3WEFYQ9z7bVcM5Djn7Wo05eFiwO2vO84spNJ60YCg2m6ZAukZhMZiXclQFTKZojkNsIAAAQAElEQVRSomsjmo6AMKhd+lyihUbbHbMRsVKvJYlO13DzYFLqYKqst6pry7lMaeytZxqqSDU4SmBUGUoNllVSCrwqWrF03sWF4ckFuI1Jx9mkW3Nk0vsA9z5KUxIGLRyxWrY5Km4ERisvR6omWCI3YEW0QKwF63pCxIROhdLVSAe6LH01l5W564wya+FgT+0s2z3Uw3qYZqlSB81BOxRlqBtWNhylhWfOkp3zKctJHWBmgipQpU8B7bmal4fh6oJUM2VFDmuLPYkIzVzAILRVlCcRTZQUDaV3nNmWbiBD6ENo7TaESvITEAliwhOJZVi4e4/evXvLCKoMfmxtPbVGKmGAVpRQuPEe4IwqzfAA3yNnVq+GDP6RCUPP6k5ScR2b03rSbAYCtQUTPkMlGPdRYCKOOnN3xs+sN32WNkd4F7SU5pKpqqDJi3LCAh2cfOO6xV6wZ2T3dOGWYFmgEbM3rlXORz8QHhqZU5eexXW1nKnO3nxNRvUyJ/empVv2odlqXVdrIcwCkLYpsaQLQ8bdjmy0HGc6hSJk6lw25tzmvWbS+5lQKjHVzq8onIj0pL0KNpHd6CAKoHDIpLouuh61V7ihDVjC0pirkh30JCyaIOmrDxJKYB1kTHVaERjCV5KDrCzH7tL4VQZ3ZE4nJU3cspXmJcHZDBhcZR57Kz1pm4RILLebk5PgXoDKq4ok4cPsCyt68QiSkSEZlCq1DUzlbNlDkFXDqNoz0xM8q9tAa3K1uNOSrHHe3+vszovr7F1mqakyuJPRxOkazK3vJcyPrUeDJMSElXiAaKYirDEWvL3PKSo6YqgUI1cwR+pKHnDXrfv7z3716wlSbwI2A3wa3NugWZix8oVvfHsf3fjZu//Etm0bDEqaL6sA7UIylNs2VvsK88q3v3vpBjC2qs4KGAMa+72Sa4kujk7kLGAEhLJ7n5vzsoKUnDlDyGBlWG2r97jTLQ/Znyr3Fv70l74OEoUcCjPO5cSIP5wAn/3aFo7w+dl973AbtElsjZHpMGknN6Zx7iTA3tqByLj66isNTZ0JoTDTIqhK6V0z6b7vCGg1Hcy1dGx4rjwHkrwNWA+y1tisba0+h6SbJGlHDemI2P0CgbCyVHrSrda73fpmvL3v+dYPfZSmRW5Sn2IWpt+8zBUkSYGU/N4138+6mePEhbzWmpAH7fMf/7lW7ae/9JWcMecQWSWsNJo+MrTr8M9txYR73+HWhxxyWJ+G9dpihqaamrBK27X69n6d17M6i4uX1hAQsRGoQGO52Ft292d1DLOMrlUyJFjCwjvDAGaB7pbY4+Vbl15O8BTShb+ttnHMOhBZGse9jQdztnsH595j3PJhmttmb4ziPGdvmbEIsYYmy+2aJjKVKBZqpDx++o63q7aFk5/72ldl5FBMkfyOkFYgW4uYXD77tUs4fq/zrre6ya0Pu0myrAyiKyxNEG3n/mqmkXvJLh9ZJgLCwFpGnbGayn6zribolIvZM7yZs2Cp2Qo2qAkBNhXGNNdB17fOsiuvuso8hKWRiQOkJcsRS1rAIwGXYcbQucjJUnxJjqJmuhi05893+N3p3LwhWOTDOFR4KP7nC1/eU/AHn0hz6RCnqtp97FFDOTtSSVxe/IHY9X+pMFkYJU9RdgwYbeGGscoFtjwYx2hsKpXfyBdJ8ZY9IZI1et93xJVXX0MZyQ5KuWBJTBSN0BaZ+8rLsgRgQIpY+NBbjTCoLraJYKujcfWGkkaF5UyGuniyxCWYmxy41RBTYcSRZVkdGQdTV8YI2HoGSIleWQtKYBzSSi6Lh2f84Zve+h+f/UEsVrWc8TP3H6VYtm1DHHrweNih2w4+5EA+E+ECvmzP2LmHKPPA+hKXrCBoCYeIkyMzK/nSc7RE7471VFunMi48OgaSlXhSuXpTXvDat/3Gq974/7z2L970gQ96AkgmJySE05igl8x62uE/c6MbHMB7e52lcJJVnZUo1YDO2WdPathLbvfH3QHKJWR8DNK8zD7MuRQ034IbaYgqjFWTmJRrErrFve9ya8KyW9keL5/+0tfo5STUHVyoV+scl8RAC/nM17f43zLe6WaH3faWN2UltESZyJ1Jer/3HW+938hqt4fmBN71X5+YvNMUc9TRx5rWTYKZWyOrizhibv2t//LRy9e7ptEnA4kbjrm3l771bb/5yjf81h//zeve9r6Jm/jAlFQTQXLPzMQytNVJD7jv7bf6X+ioqoWSHmPamArXPpFglStI8Wo00wKD5xDOchiRhF34wg+I1AQ/hDfPNTusKNRQalNusijf04JdDVbBM9UlQqmAcVBONVYHezBllsEFUaKTCCELE+C2aFeokXBh1lGbJBuFQEuqnlttvnLyVmWVwZPVAkwcQRVychKJRUOKJI0aMGqO2TRnkT4MttqoZTCQ9ehrxFq4ntO6bvAKXUkttYTRz5bunTNKqti43OqLEooLDGpdMEdPLLnBPsDAQz0HsB/lpKi+lMukc2I9KaEDEzPppXAitlhiy9JO/wXJsdKKzCtdAi8YXWovCpocIT0TKrKiHVk2+HValoRMmfMgs2mj51FsjjJHzvBJc5I6qc62YNZymrRH6YNlFRTOprPrpE27l6xD1TIyCsscKtqtejFnaUkHOpGFKJeoFPSwebY+Wc5pLZT49IyUntqatVmcszcSJMKJnEFMakoNGUCdWUyKCvWLzopWMFclBhBDkCLdk91vZBpSOKcOYcUHJIU0q+rIda2YFWFIOjFxJ+BA9BJ0lTnUxR1plE3lIPGivVoD5iZtNl+kChzCsxWJIYWBSWRE5Iw+a2/Fe80GCcAtU+AiXbJZsPwtvhjHMJaMNJlbKJpJ8zOXlOCIBjQ3n0ZQGz0CTWKqcRRDaBaqVIx5VkYFMRiqdUTrXVpA2XSNm7VOJpFqYcKDcFAHNwF9YQIJPpeSpTiQnJnFYA53vs2oEIWIGoomPQgGo3UmaUR0CG2UEAPMoPSLprqm1iw1O6cgC/c8r9nc3IXWjAMhhiCcJGkYZT0I0J7SwNWba+VStSgVkjKh3gSJ5n1v0d2fVXSFlfRR5yydYUzzeuJP/9SB27bYS3z4c19K6tIo4nRl8Z42ZH7j29/55uV7/9WvAzbG437qnqV2Yxg8dRqI6RAYDYcctPU/HfnEV74jrUaWVER1jMKD4Q2WCVYlkCk0Ovmz77lr15om6QJuUUoag+A3OGDbcfe7J/Y5PvG1b1169WXJACjjxITvJbv2FNgHP/OD5RnXO372J2532EEHOtJ18V9QCqzCRhOV68n98C2pQEJIhqDXIMnDACZVI83m9kOpPX5nDg3Fd+PqDIKIAwu6XjxKBpWhe4+52xS1LQgp7Wf1Dzo9dKszpXK2OQ+/x93KVma9+2Of/t+vf7PTAwvNAgixFX4Z3Xui//1HPrzzhX/8wU9+vkmKuqlHxgPufpeDtmLCBz/7xTnmdPGuMZdwDZFQufSqy76wz/9XwVRPfeB9h6DW0I5wJmR2zi4ctXU0uR57wZJSoRpDUdU6S2HB7djqcJZHpsdya2GoBxPTYGUcxgO3+ndKV6zbN7/1XU1bipkNI2zwWPrfXlgSc1qc4l1m7qJyzx+PWEeb1GYM0bnkjTKvJAwChqyxSswzOnmshvpTP3G7W271d+s/8rmvrDdp6hg6RCl8lBHEfcauK/2TX96ioTn9gT/FNY8Pe8rQB+lKDKcyT+bOOfe0b/cnTwSDHAvaZJIIq/yyUAl4fehZumN5XWiAgmZCDqgnI0JqJOm2W83eL91Z/Lo5rHf45KRjUI9EItHQeUbnu0zuEtN926rsv6p7awH4rdG3v3+ZYmBcoboY5fN3Lrnkb/7lP/YV5pUAFS58WEAq4gKibZJ0pvuMrY7vX3kVQ+GRtA2OoMljyVX1iK3EeY25relIAi18T5Yy32LRznzm/T3PufdeIxYsPag+k6sF6tRzzi2nSKC7EBfGBuRncsnhD6QpHdlT+bWfLrvq6lwagZpZCn3O6NFmd6oB/ccWx+VXXZNdpIOBpEXNGST6ZM05/RbytJVb3XDGwAaNQWaIf/Rzn/2H//jYddLMkQMP3Oali3WTZWIV1hdRLzov30uP2VeYTRrBa2K8vNDK0CVbSutkSIq4WIouTPeuLWqnXNcSYppuWEf+/Yc/9Tcf+Pjf/PtHX/mu912x63LOUURMLEwAlTbmPOx/4EFM6utsu/aNitxw//0jIRk0hqkYxjV9qR/XClz/NcIZI9K/Z6dLoqVYFEaw+/XFfvQ+pID4SJrEgN1Fy+9021ts2dP/9+e/ds0uTx20MP3n7HNsem9ylU8f+/LXf6Tzh+8e8uCfKkMxNsRWpCLVW/itbnzTn7jVzX8o8qPfH/nC1970L//JZJOkQWoiJWW5bQVjIY67rp6e/5q3P/e1b335m98xt7VKt2SuxgR/x8c/+cYPfexvP/A/r33Hh7hYT4yEYUGCxvWeHu6ybVwduP+2ReGePzc+aD/poCakIgxNk6+ExODCZSAmwSzWurGIyaziJpQ0FTNhCFlXZHKPaCVnkTBVUykaCu+JHlW76syTlxFgt2KdDdicZAZKy7rMjS7qC/pgnNZQt0zjrT4Es1BkRvYM9nuhZDuBF+8caAq1gDSkp7PUmKhAI5AARNilBZwDAX5SRmGQuvS9IZaoPa2FSHAjJgw+uTIMOYwxlhjYthnHJFVlUiOXAyOM3T0EnGpOaYEejGlEchIVNSjCWbeiS7hIF2XT6IO6qkMIh7EHUo5jx9akBntuGUcwxJzB0K0771oTyQEy7HbCMwWs2QE6CZ+HnEabBzRjdKYW9J0VJMeNYJ8JVdHBbFwigGiKXq/950OZREaQ2mEz7cRCrkCGLm6XOpRai6FqSEXsp7ENOaYJq0IzkIsbQM2k8x10m9FxLR0DibGUhqB5rtEkukdMpikjp8weyxjFIFF7VO8roceVBhKiSBrgyK4lhqGqmTdMU/ZMzogKERtdSq9Vi/FlEUl+FzEsLlYZapjNxaNEL8SI1oquUabZ+lpskupCJZqGVuABmYNRI0/BtMjZg1NVZ4DMRBMkGDyr5CBthbWBtIsUVfGafPjXIJ2pUEEZEZVQokn6yoTSXBjUDDeEReScybRi2eESFoHIYPzUCkoFcU5IhNrs1lvJVm3iTqplrnOchiHGKNWtzjHMnQ+2OURgjDT74DYK8SJAnEpdxi7aW+rMyLpNXbovXN4gz6GcxEvAuoDkW4r1Qt+eyvwwoRc0sUXMQvuK9GEAH3tgn4NAbSqm0achyQcwraTMiV3eZyrdR16A4iYhjC0h7A1BC0B530d2udAmt1YZI9JslDZI0sWbH3zj4+5zj+X2nj/v+a//vWrahRomZBWY+GiMS3vbv//XvsaccL973u12N69lWcCKY5i1NtQEd2W3uPFBN9iq/X33f3/q69+/imY7nV3pZuqfvO39//PZb4Askw3BqvTSr1ln5p52LZ82W5DniUJhAmjEhgAAEABJREFUTdrG5A1VecSR9z9on//FVY98ywc/BKX1pCmj5gJPxyXfv+L9//v5Rd0PfpZfHP7z2382xDYxThgTtUhVKVJMh3LNer0I7fnD+Xc1IFgku6kXJBGDTCKNT7P2lF0+XTW39RSMZyxEbtB0FdpD3g1NN+Y6tsJrwBy9xZx1ytU6xik0OJXMViemaixeV/F73+5299jqS5UrN6ffeNWbv/Ldy5KJIMaJHUyCPl+9+fzXvfE5r37TFy659P951Vu/9f0rYoG4d2+l1Mcedzgl9zo/8pkvfvm7l0iEdYhLivQFmPbhT37225ctf6n9+vL3veOtj7zb3VbhK3iVWSRD0QkPcNWu6fqS173f5d5Ga8WiD+KmKqieg2+ut5BvEd+fs8sYWRfVnIDRSbPUVdGfuPlh16m9/pvXv/sD4cWy8EfK/MVvffOFf/P2qzejkaq9JgrXq/U0X3/Ite9JHprH4p1lpVixgmBdQP+1tAGENHsrc1TWbliuxsed+KBrB17/9TNf+/b/fP7rZGy1kaXJpfap5Kyf/PxXPv+t715fku/vfIsbH3Xf+61NuEpZdnMou4FeO4Z1Cwrse262GWLByjkr4xNjy5GeOapqLajkpjhJKYxEGCmVE9wnysMgcjXTfF+lwGZ05RMMa9Kbzt0RnaM5Qc+MKTHN2livZkWmB1PQ7C632uLvkV529a6//sCHmBVmVdK4Gnzn29971stf/92rdm01Lb53xZUtY+0xSw/tqOyEhb8z/Co+ydlqzDVTX2eQLrRCnQXRElV0mPkd3T7ykXnFLnpUSM/OmbKGF+lAj0jmx3qfEVg3n5X0zFalKaPSs85QYtg8Yl/57++a0rv3vqxPcFEmwpx9QhDHadrKqs3uaVRdWDkWOEHTsnO52cyrfwxQ18zuEC4vGAMy05qYwwPXXLMFsHOPKzY7eqKb0Gbpqk3Q3/mfH+uR17lwzTT/9fv+7fL5iigeLGnYiBikizRwBMWUX2QYyzbJQKTNNZmt3LORpnPGZkh37WYxIEYJUY2Ff5HC0kESj+JWkFy6qGv3+aVLvvehj36iZIDc2P1dAcjVFAn1zcndd0v96GX/jdVd73DLSYUbDpinIYdoMudWK0X3cHrubU6yWbICFqLzrnkLiDjHrqkVyEql8ATfz1TPruVxO44wVQpc//zKJd/72Ce+WvtYRdScrq8Trcnnv/rNT3517wcQt7/Jocc/4H5pJY2wWBT1qi0hGM48/IFcXq6vme+7x4v+9h1v/sAHEx2pmQYm2oCZDeIgn//6N57+4te/8f3/xZ7hje/7z7979weHIHye6l5/hNh3r7iK6zhTIYn+IpC1JEPiiKnzMQtJz6n2OH/mbncMpgKZobwuAMtxeoUPvo7NKec1pHmJNkivGjwLRWQIrFzYLcCNUIc2aPYBbSF1hdRl2oikW+LY1rDhNkRhZQUJ4tLpJEXExFJ71qZD08KKBUVIcjXNyOIzezWZRzRL2i6ayl5j6HCTNeGkWRJObWskezRPUlSn5BLKHQggDtqFOZozuyK0YihW6agz81u2hBMfQQWKahn4IyquDAHHlmRCGCwZNChy6E24jmd2AaLHuvWppTsNNhKIWqJIA2Ya5EEHeTaUtdTNiqXFlBS4MWn7OghMBPVX3Y2BWEc0hLO4iiEVkUIrlwuhkZHgr15zrmRctJS5dX6vSIglqYLQGQNV0EfIoLWWIhZIFkgRvq8mg4mMyApaLA6Kp1qIDjGMy8NsTkkA16qpIUFlwPI/YIMJzNylTZVxQZ9JF9Qu1QuDMSaou1pZwYYuuob13HCtqlUEaehFiKA20S4htBYMW8me1Mg+vDaYo0CrBZ8JDiFGzGsyy5dWkfFAMaYP2TEoVkgVoXWhAk4wZlHCVdTFpJcyV9015CyMIPOiUBigkwZFRQ4Z3NPIQurWSwuLLAmjnZLJuKQ31CANVim8QdC7Sjediza+KroADmWQl1AR/55tku7d2I9ATDkXNhsaF/RNi3XJBq0qYwFX5szenfMUmSp9yCFy7DRPPdQ73bIhuYmRWukMw5TRqVt6qvpcYz2UKAWmSupXnYbebVLZ1KUFV2wgCinUukeKpQ2IIVJJHNEwMcCQtYRqZiKBJknZ8J6xvHgY4iv7NCgA1j2uvmqmBmZGBobZC5/X1n7Z+qqrtmravvit70rLymVD3cklFwSJIN/+7mXUtu/56S9/nWqTlbhS+2Jnq/CVnnXSsbe60Q33kr/kyqtf/U/vmaGJqj4MbcN8+NI3vv2G9314L8l73e4W55950swIgoGTLCFDT24phzkhh97wBre+2Y32GsKP37/qmotf/Kf/8IEP/vt/f+zP//69j/iV33vRm9/19D/7Gz4BUhFxoi1f+9Z3iByF9zq/e801E4K4tpybt+ZKmmXUO97qlmedcDgDt5f8a9/9oc99+ztOLgnaElHdFH/5W99LG64vWc1+8cwdt7nFrfryFEUiSR6R4n3V+0Zr1r/4zS3+ok4mvvG9K2fuyVLgYOIAWgJj+ncv3+LfwFD+W5d+F7VFaTQpwCI8jbL8ta+KxgwbSt8YGx/09JK81xPsK5LKIbBMY1FqCbAcsJIedNB+F515yv6rAfscn/z6JT//3D9+/p//3Qc++4mPf/WL//qJ/3nBm/7hIb/xotf9y0fm7hTftZ6+9I1LVQpIWR1S6pkP/pl77fMPLa6e2sve+M7NPrNGMGUwMjbtsksvfeU//EskDaGmH5yH7Lfx3MefbtZEJhIAK5dhNmmKrpFf3go9jvz29662DE6vNCrSU5Im2fjdrf4BDOW/+e3vhYAcgZgEy1mV5LDJ23yfO99WVSiz1/nX//Ifz33VX//rRz/9Tx/66DP/9K8e8byXvfLdH/zjd7y7Zc6izhK2mr5yyd7/120qaR7fuZSbMTOBizTBpFgSP7QgtaIoiTpm19bFvR//wJ980F3vwIHXP9etv+If3vn9vlktK7BUQ2F9n/7wzf/kDO31RG904H7POeuh9cAhUGI99s0C4qfKospidOnll2Or47IrLh+sjDIOsB66JuggnENRqealiK3ERnA3ZqxJS0nCFMHFDeRg5pe/cclWWvG1715toqUUBZeJ0XQwrYUMnUNahPhSPRQisNqVHq2H+/7EHZWfcf1jme/1//yBX3/t6z/8mc/8x/985o9e//ePfu5LPvalb2wMtRrL5R7C/PD1719RQjMsXE2cC7H2IPi92he3ihGHXHLFVV3gtXA7OQ7Cp3RD5xqR371siwRc5L/33d1LT00Mk4QXJ1CDu+6av/ad71Fgr3NX86uvXEcf+7yac+AD4E3zkLxmFzvbvpcwP16xa/Py9SahcaTDQwENWV7ziqt3Xbrnf+eX8jyv3JynqzfLjGVB4qqdxK2UNEl88ccE6BvfvwxFhOuxzupLipXskPk7l19OhXudTNRLLrmsxFg7vCOYa2kFdsmleywWmfkX7/nQcU/73cf82sue9PxXn/u7f/ykF7zsyS982cV/+NJzX/Lyc1/yp+e96LVP/j9/+euvePOfv/m9n/38V3ddfVXk7mLsTKZJkGbMCGGzFjZotbHEULqVbGCOWG+ePpN8dO9aC1lC/uhv3/P5L3yLK/zk0iAENisG8//8zKf3LaEn3v+eBx96Q196GGJqhihoV199eadX12q83utV1+xaX9O0mZJAXPx65Oy9x/cu3QIijvvyN74pElb4AHWq01TWjsbL9ej73P3Ye9+F765/Tt1f9fZ3XXXZldKZSBFsA8ih8D/667d7xPUlWZ+f9cjT91vtF05Rn2hEZpGqUQRy9H1+8oF3uf315a99z8L7nNe99dHPe/Fr3/av//G/n/+fr335g5/91Bvf9b4LXvDHZ/76iz/w6S8xWJS82y1vfMxP/6S4pwfD7xBevPZk0F/zjvd//HNfEK2iqzqOtVgZfKjtc1/72pe+ufdTkmPufdeb3fiw2UbGzA0T0NJmqbNiDY90RY6CKkv0nNVmaXkiraX2ZHsF90DMoxRLZRwjo0dyW8uWu0NSUq0XdSiUtvmquhXpisjUNetwmjZUavQUUU4Uo4RyZMucSLSMkNl0ZiW1FINWFbBDLhRlYQVR51YhEZq9cIeAeSnL1rvMvXumjCEDrW3kYZ8km9RkJRfpkhmCqNZR5l7YiEujY+hzdPZ63qhUmC/VlJOSfvOAPoAYFLTRW40Gyk2hUeq4snHwKmvLucDJvgjv6w6ekRDOhFEIsxOgyS3C0JUGzi05IZK9DatsepZOV4dmsi46q5ramGZRNU3MSqH9JJLrnJgJb2aWTHbvq8xawBraWRki0kWHUcaSpWVMCRouUkVHZ5MxIEWpbVipbah5RVOagFwsH2Uy5epGT5IWKkcSrMmD3YssAmDGiIXWWawtQagWAxdm0MiV0dqEsQQ6lzStUUov0aWhR/ZwziHU6rHbV7rbx9lXm1HWkcFgQ62sCOiYOhKovrvAKdaChRZI/mGovCNJPOm0QAnMgk+txW2cbNWxChkBoeka5FVwcIJ+oNBtoY4g3hPh2uAswXES3gM9xHfbu9FUgwNalbXJJgh1dg2tCclEqnalNM1l0GYSn9LKkYUrJm9lH9JHiUJkrQ8EmtOGCDGc5hYznWpEyFKdualjh8yBmbw0E9WKkJTghCxFHqSJNOfqHZxcRAuZkJnae/XZvJVELVbNVOlxMiG9p+fyGzqJ9IJSOHsACqJrKqZu6UzLDFmApLDTL3z0c1/74nf3WB4IwrXn+z/ycSopUE0luQyuun7Pf/4XLbtW4Pqv37tq18c+/zlRV7JPgkHXzGnXrn/+709cX+y69+/9xGcvufzyWK2zEmELEddOYt/kJjf8jSc87LA9/zYwDX3de/7jj9/47q9966pdV+fmNeuPfu5zv/5nb7jiGgbqOpW41aEH/erjH75tYyNmn2aqo/MIPsoc5ynobQ51PPPB9y9Glvxo1LXvPveN7/zGq970Cy9+3R/+zT9+aTcgV+7a/NK3v7XkbbSp9bf956c8COi14j96/cSXv/a/X/1Kq96XDIoWyvhIBwSPOuHwhzzoPiKC6x3fufKaZ77iDR/+1Bcuv2rNfuNrl172oje+460f/NFfeKBsUXnCsQ885QH31Y3Cpk/L7g1CBsRdfR3x9e9f+m+f3PsbGA7k+c6P/O+V18y9jTGPzscAWVkCvvrN7/z3F7/Ou/ue7/jQf/W+KXDSQpO1xot11SbawGgy9guZFGXoptPY+phJcin3f5IxlWSdTBXhD6r/9N1v/+SHHDMWwz7HVZvTX7z3Q+f+0ase83svu/jlr/vzd3/gG5f+oHW7xQ0P+u2dj3ngPe6WWRVWTEqVG2zs9xs/f+ad9vle4p0f+/QLX/fWr19yydp3rTev+cwXv/7Lr3jDV75z6fUnPOzA/X7r3IcfdpMDs7Ze3Ad4QQrB8+Lh0/qfPrQH4NeN/fdPff5737nCfLmQkvw9YXz9THkAABAASURBVPjWZVe/52OfXi7t8/P+j33q8isuC7hQvzKHE2jCQtHi7re+1b23+pev3eON//af57/ktU/7v2/4+w99fN05CT791a+v+V0EbYz+ua9+83+/unW//vZ//+g8r5WTJMLEWQi0o0XpuoKOysc6vARI8tdG6K8/5syf3Gez9++f/cpzX/n6z3/9m5tXbm6uN7/23W8/+1V//Zk9G1B+8fj0R554h9ve1CyKCEJ72DokDFrlku9+958/+klsdbz9Qx+9Zr3JCBqtFC4g0qJmpwbnjwoGwajgGizFk0glQRYWjG09+pXr9/z3Z7DVwem+c9k1kjBhwSyGMqSOS7klWyWQi2nmqzJvGPEs3u0Bd7nrDQ/c4q96zh5v+sBHH/+7f3LW7//fV7/z/Vevp1sectALzn3MnfdhGg350Cc+dflVVzAsEtWY2zEHn7Wp7Ep/+3/8LwX2PT/+xa9++TvflcHoZBlNBRx4zeWXv+s/P4Gtjn/9+Kcvv/LyjSpi6OmeKUbE5UOf+NK39vl7rdcq+Id//S9wLeOjsm49EIQx8M//+em96uG1wt+57Mr/+NQX1Yybw+VKJpQpPXrW937085fvWi8X9/y54ppd7/7Qx7KzinmouNbkWXXq7e3/+fE9ZX/w6V8+/qkrLr+KtWHwztAMWUrB97///ff/z+d+ILHnr/d99BPzNdeshNql+ZDBhkZXddhTavnEeH36m9/9wKe++G+f3OL8549/5q0f/O8Xv+Wdj/+dl+z8nT955799LNckaRb1wTCmWRNZozckBCXUui18YWMS0V3Dldfxo+Pbl1355Je8/q3v+9C3v/n99dXrzbldvr76Xz7x8Rf+9d/Pu1P1OtFbHXrQE3YcV0xGg9GRKMJ2pve3/ut/X705XSd23Zuvfuf7H/rk59VLDT584tRUl3zC+bZ//+h1Mtd/8z9f/sanvvo1skfdzTVZkr0OVovKcx5z6v3ueJvrC/P9f3/pG89+5V988svfuopZ7e2b3/vuc1/5uo9/+Ru8dd15wGr4pYeffO873LFmKsPbHM3FU5NeyFhzHOU3zj7zDjc95Loh13/D502/94Z/OvuFf/qo573k3N//s+e97u/+7ROfA34gctRP3vl3Lvq5Qw49kHW2E+RgXssP7u3+denVu375FX/xpne89/uXfX9er9d9feXmVR/61Gf/n1f9DbfTu0V+8HKzGxzwpDOP7ePKRxVbLrpEZ0uhGR3RRZOPyevKtFpobRgiKsPZk32iUyCnFjFBfIkNVgjtXXpLn7XP4iRFpkjLwj6U5IDzYyRBTmnB+VhEZqAH17g0Ty4gJmmRAyeZeV09q4N1F31w4YZpFF0s5evQlDWIjlOdlFQpBSqOjIyFgcxXh9AFFB3MSoc2h7uwJ2OXBzOpVYeSha1szbnELvBhGzcN3jr94vcVJVOxm3S0rFckn++pCM+U0nIBAxjAddqqpSzmJJQrq9TSB2sWM5PZWijXRomNoY3FadccSrEisG46s6xkWA+aoc0ya6okDUcXpGoFxhCFQcaO/SDbBhnYFlhbAgXCoUMYgUokOEASAe9BR6OqjnUowp46xX1EH9Gsg/Gia6HZVbyy4VCdB/EVbGSweSFhU5RrUvvoMsxgDq+TgY5uliOrrQtLY9+UtvbOti18DUyJDjo0Wl9BaiJ5xpgLbBLaXDZ7n72tm/vEuQ2WGCSKYlZMCGeabnrM8KA8whBcYkpfKzvLjCR7eraZxszOfRpPieySk2RTCQFEfdljc2c0wLYh9+t9Jeta11rWiTaZrEd1JSKqChGCWiFWxKt2UWqwRQmCwoZW4HUBVfgVVltXX9eyWYe11rWUTsnSqvk46DDIOPYNnUeYlg3d2NC6YRiBAZxodBjhW/feWxCUnuGzgBW0hajoaFYA5Z0lygPScmYAu4KDy2wMAJ+XjTYzRyYNPvShw5AZ0sy5y5GVOKsi0bQAG9G6gbLRgnvkdfRUV3VIwD090kk20K5caQw8ExpAtKt2bf7JW/7lmX/8xtYdWx1//q4P/f5r3vqtb10uJP84fvX7V770r9751+/7r61ksWuan/l//+aVf//+eZrKMIW093/qE7/8x6/5yne/v6X8Z79xycUv+79vfd9/ujua6KRDhyS5gLvc4Q7PP+sxtz5sj+9Vmvuf/sO/cDV63O+/9LG//5Inv+w1n/3mj57BmMoRd7vDSy54/B1ufBi/cljFQmObuWjW3tWbWVfStU5y4n3uc/Q97yxb2nS9i3e++Y2f/6TH3O8ed8mcPvzZz/3Kn77+Hz+ydV+ya2q/9Iq/4HOmXTOpklB4MtwNPq1KPOVRJzzm6PsPptfTjc9+8ztPfvHrznn+y8/77Zed+1t/8ufv/ODU+3UCfJL9jIedctbpO2QUNkeDNEWKdEhIL9KGt733v3/pRa/98iU/Btivf/s5L3/tv37i02s3pPZ5fvN7P/wLL33D1Pp1U1z/zT9++H+e87I3/M9nv1JTai82m7KSJObAzDYoa/Qx2obyaxWEWeNTCDHxlClUZ6nJNMxG+XWb1nOb/WFHHfnUhx7P53bXn+XHvTfVHT9195c9dedP3+UuLALO5IOPMg2lp+XtbnzoC5545r2X/+7ZHgr+9t/+86znv+yJz3v5E377FWe/4M8+8vmvXXdbBPe+3S3+4ILH3f9ud0spM5luTHsVPiCZZG74t49/5hkvfs2/f+qL1w25/purNqen/tHr3vyhj16R2UsF5F/+46PPfOlrv/TtLZ5tA/jfL3/z6X/45+/4wH95tLB061LcNAvkgGG/Jz/02Bvu89f/OOr651Ds4Yff9zfPe+TGamjNX/P2f33Gy//68q3+wgxHfeATn332S1/zvv/6GEstWcHGQge4xNJu9YrQUEjFOGBVycS44UGr5z7ujAfd7Q5KXDj+h+d7Pvqpxz/vZY/73ZecTRhf8Cfv3fOvHd755of99rmPOeKB90kYM2U1zGPZzFxzmb9y6n/97n97xite+40fbjJ/qPIHvz/y+a8+7cWvfud//jdK07I2JnbMyUU2sEDjmlOypDM7IEthalgsW5m8+6Ofuvglr/vEV775A0V7/vrO5Vdd8H/+79++9wO7dl3JyMic6lx3VcfBh1HYwM19lZsDJxHLLIE8aP/VU04/eq/U21MrTOWBP3G7F174xLvf8Va7tvqi+Kvfv2Lni17x5//4jksv5bzqBYT5I5/+zLNf/Or3fmzrPdXV6/nX//gv3vbeD2yup3mx1f/1fz/+S3/8+s98fYv/Wgbt+dy3vnPxK171tv/4sPgmI0k4vnfl5h/+7Tt/7XVv4d0tzz99+3tf+ua3fOe7362tS++XXHLpH/7VO/70H96/pTBry2/++Zv/6G/edsWVl1VNFpJs9Qvfuuw3/+xNL33re7cc4hEv+Ju3veDNf/eVKy6fueIpIZX3fvzjT3nF//3oF3+Ua9cfe8Wu9dNf9Kfv/ODHg9+8RYUN7/rIJ5/50jd88/tbf13wn5/7ylP/+NXv+ehHB0Ex9II26s32rPnX1///8X2P/BRL3yvf/Pp3fUBUUQcx5i8yGmKWaQp2LN2kmcx8XD5vSFvVUmwwNZDl+NHxrUuv+K3X/93Zv/fic57/kic876WP/a2XPOVlrycTfiQB3OUWN3n+OY899ID9RpkHTNZZEOXjn/v2C173j698xwdjWdqvL768p4W/8Rdvednfv/2Sy79H4tmgn/7Gd57z6je+9+NbE+nKzfkXXvz6v3rnh67edIEyhauxxVCzeuBBB/3mWY8++l53NdIFPzre98nPP+GFr3z881/1hN/6syc+70/+ac+98e1ufMjvnP3ok3/mAVW1hqy6rbyOMhYzlC5ssNkAdzlovwP/4Ck//8CfuC2N/JHq/7fvto317BMe9Nyzz7zZDfdnX0k8ud4ikp39XuMuveqa33/DPzz+N//occ97+eN++5U//1t/dv4f/vmnv7lHdb3tjQ/57fMfdeub31QUookAOrz3sFm4qIPhq2orLbvjq+l0ZuhctxCRnHhiQAab2eFFlbVo79rnyuKMNHGVXoRr6sxkYxPK2M2tdC+RGh5g06Q+j31dY43a5v25jscu5RInIdGlTYnZSZlVNW40+sxnd5lo0BnQ7MPsOZc5BkhBVQqNfag+FHVdrW2Yho0crZpXTAWbXCswDF7HKYd5Lt6Jl4rVIqUgaslByM8yLzNw29RA72GKYskNfnKyEPCiZowRG93H3VMPAzZWuVHDGmsvu+/mq+R+jd9BJRv0IXRsWuc6uo0ShhCHYhQ+jlT6KUMMK2xUPiVdbqyt9mEVKFPLXe6bKlOVbuGIYIxAPIF1sD83NtIhc0i0Os6rsszKvQu6J1uH2kcVeGnO0clCPYqZjbNxJBeFLA0raSlTA7tZdqXaS84VvYL0t+A7nZfFzk14uYn3dMpHVAVPDRPuqHpmaAudoFMypJHBZiY9kYQuKWjiIm6pNUoJQTqkgbRVBftpuq2DUlAUagSmKWeiFOMrkq7qND7oPQkHxHIml15385n3QULwGgkVwqyPtO6lx+iMJrVBw4mRdRrkAqfWRG+S3TStFFHlnRBpSjQMUFoGEQEWGzxoOpIywjlhkKopfG8u2heJsNqLxfI5TckWNXd4o5NkPzTUSK80GgRyPlgiaX6WRKEiyVREN23MoEGEdITQBhRwqIRDkisCJx0kCjgp99RjYBC+ZUYVD0s39WJdSxNdh08Zk6kwCzgy4d2dM2XhagTCgEy6loyEGzqyA92oQFwRX7/key9563u/ufxlEmx57JrbX/zzh//t459VFdr5L//zmVe/69/X7PWw9fGty656yZv/+XtXXF3oiMbr3vN+1s3dkdxa/hNfu+QP3vCu9a6O1plxWFtOGi0j8x53uuWf/eLZTzn9mJvu+dXKd664+lNf/eYXvvXd68wQ4J63vunvPO6M33nCo2596E1KKnPcKuMgqnDobh5LLGwgz7Gh5f957CNPf+C9trYJ2H+sZ53w4Ff84s4H3JNfryuNecP7P/LOj366MdQ/Zswll1/18rf88ze/c2kZAsVde2QQf2my/2r1lEed9PKnPfGoe9ypEMYfapha/8K3v0dfvn35VT+8hgNXw88ddf+XP2XnKUf8bJbSrCPY6rgkiZKkhCmK1t//63/8xFe3bnoAROb7PvH517zjvUGyZL/yyste9pZ3f+mSPVYCil13cif97o9++o3v/jBnIV4cJB1ICdFQCeRMDCECLxKDQMGrKUgNKS7qjIAk5Vm1qGvK7PLwo4566cVPPPLud7y+y9jnuM2NDv7Nxz7k15/wqNve5DCk83uw7uSLBtXPnlwbIu5w08NeetGjf+3nTr79jfd4yHfZ1bs+/dVvcrvLJ+LXKabC5zz61Jf8whN/8o631VRxVpvBolYfpCtLQQ/5s7f/679+4guRed2ovd588uvfedHfvuOqeZcrbYo/+/t3/edwr3TMAAAQAElEQVTnvrKXzHUfqee/v/C1P/7790y75miNXrDO2CBaRdXvfefb/M65D9/yH7Vfq+Fut7zJC8971C/93MmH3mBlQ7tqvX7RW971pT2/HbpW8tpXj3z/Jz73/L/4Wyu9liiaFkVQgkVELMDQ5VJGSohhd7nIm974Br/9pEf85hNP51zXKrn29crN9We/fgm758uv97XkzQ8+8BdOP+bFF597nzvcWfjYhk+WhL3wLEPo4CxvV1yz+fy/essnv7r1duJazR/81Bf/z1//nVuYcQERU6iKmBCRRPKxSGszA70IF7NxKGPVVf2jv3vPh6+34VzuXu8ngc98/ZLn/+Xfff17ZPKyEpFxMXZWR6u1Bg0USw5IkJrWRUIKdhxx36c9/MfumbmHfNpDT3jBzsfd8eY3s8grrr6G4/c9v/Ct7774Le/83Fe+FkG9Q6L85Tv/7b0f/6xH7Ct87ZUvXHLpH/3NP1592dUslhMp90//+u+f/tK1t/Z9JYX42PsVb31Xu2azTjlM8onPf/tP/ukDl/6Yfw1CDZut/9k73//+T/xvhZeG//n01177rn/ftdW/a6Iwz8uu2fy///i+T33py0xhA/cF9t7/+N83/Ot/XbOeeHfLk/R47bs/8KFPfwZ1iZ4K/uTv3vOBT39xS+FrL370y9/8o799x5W7ulhtEa/4u3/+2I/5Fpfynvkfn/vy//nbt2FwVOTiiRz3wPuORXn3/+eT3/a/4u3v+/CnvgxUh+ZS0fpSu5LBshY1egENzM48Vc6lpsX4KGRFVK43K4PCtebTX/vWJ7/89S9++3ss6NfdvPkhB51/8vYXXXTO7W95S40mnVxunvw+NPndCFfMNaN+nfSeb/gt/cvf/i8f+exnFvKEvv2DH33Xf32yRe4p9aNPX/vuZc//y7d/47IrRFQUsA7pKWBNPuigA3/tCY94/lkPv9dt9vjn76yHn/3GkteskNcpuskN9r/olKNf+pTz7n+vuwmLeGWSLOlYC4gDVFKYMJKMClnd46aHHvK7T3r0Mx9x8h1ucohcp2WrN0OxE3/6ni976rlnPWTHahg1lrwUvroGmxWp97jtLbiuXX8ovf3eVbs+8/Vvf/Ir3/jSJd9vztD84P6NDtzv8cf+zEue/sS73/l2ACtHKHuceZ3rDhfQyML8k2pkJTzBsdPS8qBGDpkFXLTG7AN8RAxFpNQ2YM2qAwmOoduLDL1NWdjBDyWgXiLoPMX4WnqOLHXBflbJSgsvKSqEiB0AAVrWQRvSC7KksT/k+hw6BRd+WpwVLi36pJMPkEGl1m5DE0JeWKG4phDtomLAMqeiqXZ+YkfZRZZdUGRDOBwJkZAheaZGkNAVOijXs5DiKfS+RQRtygT1laQrGoCkmqkV2qzk5bQWcl6dcabWhUbVuXSM2mmHKi8gETCoyQBjNa0iJgQTaQhRUfpvCotA17kLfeP8nMkIgriBwcg5W6O9Vip9VlMhwl3daRHfUp3wxxSKbk6IeEeD33CYD8nvuSK7wul8z86g8huOPiuvAJEQWijMtVZaq2hLssq4hq01o0quVoTPLVxpsQCL/FjIgFSiG+Mmt6Iti4NyIrWtsOY5Ci0v5mOxjcLvKsdS6zDKqhBw9nFRyKERY5ECbrlGgw0GWWYRVwNZAQZvneLUPDbhjky0czsXdUlSpRWexjgyRi6YRNaWPcAvE4J2Nj5NXxd2OdVznJdwJjRk9waATi9YEw1BhwTEgURgOdIlUlNElW4rH0sudiEj0WmMKROPsgI+LW+k1LAJmxzRezbycsbUpGGYzZpQO1+Ig4oYatUyMG2sAWGzopnARBSMBIweRzFmVKvJjqOUVKbPKDmoEiU6bGDmOekjRjSGhtK89BBuTVlcNHMAc1UkBQpXoZMSqNBaUYsqPaR3PRB9rtFXnFcPPWD/xx9/+Fknbj97x1FPOIHnkU844fCdO5bXJ5x4xFknH3X2KUedc8rRd73NzckSkuNet7nFuTuO3i18+NmnHHHuSUece8r2naccTZlzTz3q3FOPPPvkI39ux5Eb2w5WGVdaT7nfT+2k/IlH7dxx5BNPOJzKeV73nnp27tj+2GMeVCHwBLKnSSeJyN2uQx5wyEE/f8L2t/za01/79J1P2HHk9p+8851ucshNDtrv0AO23eaQA+9z25uffP+f/KVHnvyW3/yF1/zKecff9+7bxg1QQxFUxTjkYFyVeAGVOKabTWJrK+uaw7b9nvn4R/3Fs8/7+WMfeO/b3uzWhxx42IH73eHGBx93n7v+0pknvPW5T3nKw3fc6AYHGUSVQRqOudddz6OzPHcccQ59OfGIJ5581Fknb3/8jiPo0eOOfzB9OfuU7Tc/7BC6Ae7jLVIKfCVMgRiqjT95p9v/zgWPe9NvPePZP3fGQx9833vf9ubXTsp5b3/YDY66550edfQDXnLRo//x+b/4jIeedqvDbiYqqUTFW+Y8m89VuhnXWnKn9MftePDZJx91zklHnbNj+86Tjtp58hHnnMLA0bbt5+44mufOE7ef9jP3W1Wq6XzW9ZijH3DOSUeec/KRZ5/E4B5Bm3nu3HEkz3NO2s7wnbPjqAfc4w5Nl31ihHsXTSk164bL2EnYZuuo83I1tvlce4PkPNq0QNSq+qhYVawGcAURT3CXcddb3/4F5z/xTb/2tKc+5PiT7neP3S4fcNiB225z6EHb73lnTv3qZ5z917/ypJPve69tViRhHubOFJ7C1ssURoqLBhT77Tee/qD7/NWzz/vLXzn//FOPPf4+d7vLzW908xvsTybc4uAD7nqLw3Yr3P7qXzz/Nc9++smH339jY6MH0xbS1NpgfdTJhO4tpnXShrPzPPekBQG+ofsLCERyNyY7T9r+6GMetN9GQbIMrk/72ftR5vrn2Tu2P+64wx9//OFMgZ0nHXn2jqNOvN+9tAuaDMxjco52W4R1+nOfu9721c960nMfd+bxP3X3Oxx2MBG49SEHEI2HHX7/l1101mt+5clH/tS9VsNQCUGN1YHD2Sdv33nK9p2LMUcxN3/+2Ac97nhG9mhatfPk7efwPOmYRx79oDK4lAksb1ORZUU0TutKzgQLfnpGVD4yk9F0NWxs23/7z97rT5519t8858KnPezEk376Hve81U1uecMDDj1g281usP9dbnajI+5+h3N2HPHaZ+78q998xiOOP/YG+x0whmxMUuZA+AxZAyKm0P1X49knbT/7pCPPOukoOk5Yzj7xiLNOXEDgxXN4/ZTtTI0zjzk8pAQXLNRBqwyDlsIKbNJduZzlDJakUmQ1ai21NKtnHPXAs07Z/oQfFoqzTzyKCJ+1uyIRDTrOWR51zM8eeMCBS41U9pyzW6fHNX0QlSxMe5HKwJvOZehZSujwiOMf8De/ccEFpx195D3udCu6vP+2297o4BPve/dnPfa0N/z2Lzz6uJ85aGNVE1XrvW5/qwff9fY/fcdbHX63O7AUnPLT93zIz9zn4Ufc/zHH/OxZJx5+28MOlq7eaoYe+9P3OPvUJY/OOnX7WaccxfdPPPHBZ5945OOO4+tRxITEeOyxDz5wXDFAHXbiA37qnB0/INu5uzP0nB1HnX3KEeecfMTOk45kxInYSQ/+KbU6eJFZb3XDQ84++aizTtl+zolLRu/ccTShps7lZBbz3LHcveOtb869ZGq57U1vQXAYiJ07OMtRfL9Ad9IR55y6VOmzTzxqOXccdejBB4hCirK2/OTtbn3OiUc+4fjDeVKeFePc3YbtPOmoZepTtz/hRKo68l63vfXguUKOiIccfu9zTjnynJO3n33qUXSZyneecuS5O47YuePIc3Zs37lj+1knHnX6kT8d+5d5AApO+9mfYjTPosJTjz73lKPPPXn7uScfufOk5VzsOZErzhFnHPmATbQQug0Dbnurm//So07lg1z8/3Hsmtrfvu8/vJNgERKACmyUVa3SK2YFr4DrTLE1ywNk/zL+zjlP+Kffffbrn/mk33viw594whGnPOAnH3TX23O54UJz2IH78bzDYQezyDzm6J956VN+/tW//pTHnXHiwYcdpMUDbSnRAL8LIzV+5l53Oefko5gFZzO+O44+ezm3n01kTiZPlvMJJx3++BMOv83NbtZ6+Dof+BN3PnfH0Tt3HHP2SUeddSoBOZyInbPjyJ3LxaPP2XEUz8efeAS/FxVrIsmsdnRWV+5J+Tisjrn9p+76yqftfNOvXsgvsY+9z13vcaub3OqGBx56wLabHrT/nW966IPvdvudJ2//82ee89bfeupjTz764BsdBJ1d5lli3kBb9vmzKL2QObStLVpCG8hCqwfvd9Ajj3jwa3/5qa96+nnn7jjquHvfhUWDUPAkLPe7wy2J0q8+7pS//+2n/dYTHvMTt7m1JlNj7GIBEHVrC9BM+l//uUe+5/nP+ptfOf/3zjqT7pz2wHsffrc7/MTNDr31IQfe5KBlNb/bLW981D3u9IjtD/iDJz3mdb/6tAtPO+nmBxwApABscaw5okd2hIIcobkmWdKyobfJ5znBTtYciiJgJSCPwMNFQ1WMf3LMKJm8XVRrYNWM54gqLGkD2JzLGI4eoGIZU1eJjZUMG0WqZkpgCBT6RToFxNJsMWjq0jHSmGH2VbalyRIVFAiv99abejeE0BhNKWEqxaU6bLalUWZeiSKj9qn0zaJzEkHknDHTtZ59EvQUcxldNoKKC40dCzcZTdj2EiJygjNCpNDNGNjZRfCbj8UAmziPC5zhjLF16y345eq62cz+T0agToZZM1ITMiPdg4aLD5EE3pNTLHWacPPKKpLYFjoHV5kFmyZhWW2q6ALLubZeOx3SUutgdeycnxsXxs4ALRlD68Ma0sussqm9x1rnrh1I67rR6gZLtrRUnxCTtLbcFbJynsq89NuUnKymrUz2M2ICWdZ08dHMpAqKSqkofOM6poih69JVZi7Tq1rZLQWYAyGICOcfAUyrDaMVSkiFwHPKziBkgDyuXoYIqpYNs7GoqTFqPEVEi8QY89BDe4qmKsSSDqWSEG2lPmirQkOIbQ/37qx3MUsLom6KDWU02EM1HboODk5vrqVrYexthrpIGoJoKyJLI5Kqwpk7QU9O1iUYPGbuZuas8OWHhIDUzDL35DY9JVEXWUOoZioSESmRnJR8KeIKWipFB1ihOTP3b1IbzLkpXOtqDrZjWKoEiI43GkeVPcdoRZ0GJpV2TtlUOmrQfEsgenKvg76iQRKGLuKoqvw2kvs+EWbvkt+llA0iy6TmhKjeiX808SgoZje+wSHnnXz82aeecPapO849/cRzTzvmolO2X3jykRc/7OgLHnH0BQ875uIzT3jymSfe+8638+CMuPdtb3PRqSdceNqJT3rIsefuOOL8U4+46CFHU+aiM47nefEZx194xgk7Tz1uvw1GLa3K6Q++38WnHX/RSUc/6cSjnnTaMeefceyFp28/f8eDLz7tqIvPOOrC0464+PRjyEeBbQAAEABJREFUzj7lmLHuR6Y0+i+ZgKYS4CpZGYzEqsq973iLpzzsmN/+hce95vlPfdsfPvO9L37W3//us17zrAt/59xH//yxh9/upjcxsCSR69ZHMDQh2ehyGJf8pb51RaqopC4yfu1l1dvc+pYXPOzYVz39nL997jP+/oXP/svn/fILzn7sY7YfcfAhhzS1OXmEasRQT7j/fS48/fiLTt1+3inbzzn9yPPOOPbch5x4zkNOOO8Rx577sKMueNhRTzrzqCc95OiDDz6wMy7dbZlfvWav4YKEMIpqdrPDDjnl6Ps/7bFnvvRXn/KG33/OP/zhr/N84+8+8w+e+nPPePTx97/rHTfKtkTBMoLOi4rRnwgq0uzKEMPFu7OHOOe0o8897dgnnXb8BQ899oIzt190xvbzTj/63JO44B1z/o4TLjrxxIc+8P4Mwaw57rd69IkPuuCMIy986Pbzztx+3iOPftIjt1985pEXnH74BWce/aSHnfCkM0646IzjTnrwvbtEs2DdSUlk1oClFwZCs1ioBQ3ru6vHJJ1lKVedJR6kpBdNk5FZXG2lMnQvrWEm5Le+0Y1+/uijfuOxZ7zi6Wf/5fOf9nd/9Mw3/N4v/f4vPO6ihx5/7zvetq5qsHSXDoWytlU633MK7/C0kAKqGESqaRnH1QF3vv3tHnf68b9x/uNe9+ynv+X5v/K2F/3qG174y3/2/zz5t897wjk7Trz7rW+3/zazom42Kzrp3mcSVwLpQNcitYqe8cB7X3zSUReddMyTTj3+wtOPI+cvOu2o8884/kmnH3/hjqMv2rH9yQ855gknH7H/yKLcTfNR23/qolOOvOikIy885YgnnXb4RSdvv3jHsReffvIFZ+644GHHXfzwYy586DE/d+yDQxd8aKvmhs4rluPurEbpYQfud8COB9zveWc/+vW/+ZS3veAZb3ruL/3p0y76xYef8dN3+YlqljQNdSaUUg7Yb3XemSdecMaJF51x7AWnHcXt8QWnnnj+ycefd9IxF5127MWnHHPRKcedd8oJTzjx6GAJcF/KRYATMzRSehm9DqGeOUk2ZSRSja6zX9NWBhtuf7tb/txJ23/zgse8+rlPessLn/6u3//Vt/3Wr/3Vs5/xB095/M4zjrnL7W6zstUYHCiGIOHGSHGyQXoWj40hcOBgjz/18LMecsRZpxyz8+Qd559y3AWnbT//tOPOP+3Yix5y7HkPPX7naSfsfOiOR554JCflypKSIdnFXQKZJaO6E6rw4n3IJtpdp44eDz/28HMeumPnw4/f+ZCjn3TqceefcuyTHnrMzlO3n7dj+0UnH//k0068+IzjLzrzhIMOu+G0KjksXNF0i5kVVIQVdgUbWD+AUBDUEizyKX3Om97gwHNPP+ZFTz3nH37n2f/8O89586897blnPfIhR9z3wP2Xx3nCWlNk//22Pf/JZz//KU94ya+c85JnPPaFFzz6eY9/5K8+6hHPfMRDfvnhxz/lYUfd9hYHoTCfUmc5+b4/dd7pJzD65526fecZ28956PYLTz/qwpOOuPiUk3aesuOcU4+98KSjdx77oAMqn7ewfc1Tj/6ZnWcet/P0I847/ciLTj3qwlOOuYA15Iyjz3vYMRc/7NgLzzx+5xknPObk7bbtADF2A8PtbnoofT//VBLg6HNPPObck4+94Az6ftxFZxx90UO2X/jQo859+NE7Tzv+bne4fVOWBLvDrW71pIfsePKZx1380GMuPvXY83Ycd96plDzyyQ85kvIXn0L+7Dj31GPufKubMyghRD3vecdbX3jyMfzK+hdow5lHX8QpTjjh/BOPP++0E84984RzTt9x/hk7Ljr5GDa7xiZh7TnHQ46637lnHHXew44+76EsPkdfcNrxFz7kuJ2nH/mkU4548mnHXXjajrMfevyjdjxo3AbnAht55uEPOPeU7WexLj3kuAsI1xknnH/msec97Kjzzth+waknnH/yiReeeuwjjnuAinBFYqGtzFKfNjbqfhsr/PAQwVgo8sPP/9/9/uTXvnX1rmvIjjlqq4WoqomppPRWfCo6DXU2VjiNRGRaiXG1uv2tb33sfe974Wkn/OrZD3/hLz7hDc//hb/7vae95wW//I7f/rU3/Povv/C8x/zimcf89N3vsP/+GwIyrCemKOpaRFdWRlX72bve5cmn7riYYJ501M7Tjjzv5GPOZyxOOXHnQ3bsPOPEcx563HkPO/5JDzvmzre8Mas8lTz47j9BMlx86nFPesiOc07bcd7DTrjwNJagoy8++YSLTzvhyQ898eKHnnDBQ4+7waH79cJNRGAWm0UdwoJZhMW2rpT+3eHGN/q5ox74vCc85pW//NS/e96z3vv8X33Hb/7yX/7KU1904dkXnXrMve5w223jRqklNd09uru2VttccxKJVPGUdSib2cxShA51xKTSWNTH8SfvcHsy4fd2PvK1z3nSO//g2f/8u7/2tt981p8+dedvPPGhpx55v4MPOzBWrhYaMGjKOEvJ0JIyZBjSgpzeuNXNb/Kg+9/j7Ice86zHPfx3Lzj7tb/yjL/6zWe96fee+Xd/+Kuvee5TX/S0Jz7zcac88F53O2j//Y0hFjNahRCWDAVMSq1cXowRTHTyZMlxaIT0KLRZzUuZlE2lRRVhG6AQgWv2WE5RqUVVLbvmrOo0sbAbAP3srE46o7hJcgxnR0t1NwSvVIvBepGpWFsqbImqSQPmkAkChdZQi04cwVlcZVZglLIyMVkuFwjpXGl2tsbKVAWAzAGf2YuutKushf1G6YxxJG9fq6/RnUSQaLEU+LTiSpOFV3L3FVTVcSxmJiiBQeijaJmlrqV4mieaoBNJhTj4LcW6TRMad9aRNXPbnBu9J7hZWHNfQFakJ2dMdMfEkRylSsURBSnBsoqild9J1C51DskGIhGQ0CjVx9oIkpYQpYOSkEylpd4yOpY+3oUzSAZyMYxrAo2JTK/oK4UWTRFIERkkK7TyCrNTey8zp2ppSS+tajXwj2NIYgyVFETpWZoU1EGGEUR/laV2G+lRkeWhKhcNQsURgIPYMHyzYM6l2QhxEUoieLPzknvrRJmW8lJOWUiQSNEkCKHEwgUhVaOMgaHTVd51UAWNjBrJGThHmIUNYbVV24TMKe5KoqrTFTFggBaOjyVeVa0oSpLXUnoY5+JlPifSqNZX41QqvxkJMM6qIhpSHCU9Y5p6oztpuoBB1gIwKZy4MDQaU8HmRpm5fg9jUcLDUiDhxedV4ZnKarhazVUhKAy1B9HgmyFYPucSpO08S19nNA8neov3xbrQxAmkCy/GnCyHsp7GWA/og2ZWuPUsgcp0UOavUG1EpmgpZZQoOcGmVpClwIs2kV4kB+e6SVNESpplVS1WSyVQA+rYmI+WxbAxYBjFBhAqT09yeWoyNWPzk2MWlYEbgs79s8zhnszNECLDyVoM01rmaY6ZNWIJwMrnKmsZW7KYjUGW1wGjEkYgPBskgslAq1fFViDRUpPqJL1Gq2A6cH6e/KZwaToZF+mO5kRHuB3zzjTwGHoZ+4bmGA3sTPqM9KZlsmGtrOwymyxTpUSaC7kgiUxFEBariY2OsZM4llp2FwhtmZin7PQ7J9Oexq+wZrEuY3DpnGU1ocxKLAmjSnquW3qQPN1KQyttc5gmWzdlF+apSp85b82p6poLpSEoTEPn3rtHb/BeIywAU1RNWlb5W8bCCwkLQRtjHp2TzikzS4BNNdYWDmMq90CrDaueldPBKa7alFGrzUaPkZfUlxqiAIr6YE2HlsLp576mNcnrhhzgBZKClugKKJOfT5gKIXACGKk9S3dlLTOShnQEwWS6WOYgMNHiYpPnZtisLGsblmVb6EY18i8GwtnW0iZN6FBiFPqdJbWq1cokU99MtFD0sfqqOsHH0MqwtiWSmSlKnjMG5BFRaqJuEhUxaBu1k9kpwthCI0Dw1z3n5Fzg8sEnPSI1YRAZipiaoGYaQF/Ddhc6Sa4I0g3CFYBWyQCh/yu0QWZW4io5RozIyhRkjdJm7lwtVZMX+eALEt2yD+CrW9EyFl2NPgwslzSyDKiWNBNFZmhr4mtIy6LQ3Y+uIcCicwgbw6xD5o5pbr0x2bqTZJNjntGnbJF8lDsLGuhsU2mlKA9qMOJECLIyXbr77o98gsApZc5FqSKzdK5YvXDmwgCEmC6STjAxNvArFeTQVtmGyCGYU0wYErh3miu0JjkcWmQUesv5imSxWVjVyRFmg3effW6+7vO0bj6lSFiBVCSD012W5ssnYZGY1yuwSDEQXH02QkdqSkollY29m/Dhjiz1Kg09l5LobpLQGXCGSkDugcOTL8sVUjdIlrUswFBRsD3pQ5s36E2lx6P3VQsj2LSa4dIME62DYwEzo4n00fpQe9aAwk2VEZFNeNNZbC5Dl1WPYWZuqlTVSmsYmamnz+IiqWBy+KCugmBhsdqSN1zGTDPPsRPVICA5aqpYmMWGYZvISgxcBnoY68/sQzhvkYgx0DiLhe1FoBAw9jpjSBtECwsGkrXWmgSWW7NiAZUToiwPIQxEItFYTTtoktaR9LRiWMlcclJMJh5CxUoHqFABd28+z+GeHQQ/pEudCzU3L+H0iH5mFtqTFj6kG3osl0sGxOdom22OqeUaYLiaJJTIUF41yjBrmRObs1+1+fK/fvuv/ekbv3fl1dh9FLOdp27/w6f8/AsvftQfPfnRL+E28sInvPj8J/zBeU/4/XMf++uPO+Oxxzzwpjc8aLfsHi+b87xerxk3pHRayryiI71lrANttuhEAgs1ItIVXhUbEiPaGCqxredGr0b01umNLE+ufROjUDFAhq4yQTa5CEUWKYWtSFm5cBHRmVkZs3ck18hWrJnpIFh5Crkunllgw0jcy1BXQ27AxxnSa0PpImFJZWPJEaxWKAphJi2g7ZIF03DBXClZjbm6KE5ZoRQGoQEJju9GcZ2EQmS8YtLeLWBCrGkDWuYyEZSApXKEsGxMWbxXcbVUGTSto005rwVtoCWBjcgNaaXMRqw6hUJW4RtGLwMizcp6LAw9yI3IkXchbjINMkGymfq4wqhWel15GdPVGQvMgU5LNLF8FqQm07CE0T+tEsJObRQfhxjNVmHaBJ3NHhYODVQ3im4kSzi65iw+ISahwVL2Q92WwgA4K4eGMHvpn/lsnb6ldnAqhpqspgABqmqwQu5HQ2xm3+WsrpFgiHsfyWcEisMcaGw+Yq5NKjFj9geQXiSLhaO5h0LZVxmhG7rXmR1hs5wC0bin8w06lA6fm+em6LShXmkhGrObZGUYTZyP8NTYMGiLiM4ZupgX7aIeSmAQBh1SjditIoc5jC0XP43JOYKL2hJLYts30tkTChsB0CU374NMws2MNdWW6J0O076oRIkO0zLXFgyqqkuh20RqCFSGh+s0KtjwElhJ9wR3OdFB86vmiGS15CyEA5iLdiICSPQkKl7BkLJsSaveNpxFqYB2VEwDRDmaKbcAABAASURBVOm8VwmtWYqWYRhWYx2rehnETDQFnWdlOBBprbF37CZttB+URaUrbZHobomQTJOx2mDFJEO7S7QMIkBjJot1MTeFBXUAwdLQNoH1jHm9yGZNM9o521q8WZ+FDqXNLX2e+typPXOA6iC9IpXSnexBXaYK6851pELooIFDNUSj1Bh4Fq8Wo8ootqE2QlUYStaFGEK4+A0NOtMkIWZkp4pQAilwQ6ir0jZYpiJ5x4ygZmGCu0BMSs3BvEhHzBwd5jnOrt6V+UHMGoRfQ7lQX3BaSVOtRjd0MTTQOyYs6SDMeRRo0sNBY8hWe+cSV6xqqbWE6oQ6L1POmXPOEZGZDho/ZBrgltMq1gsk8ChNx6asSiVCGQdnPPlpSbtBZ9HWPKbGKC5YSdEsdVajrSESohDyj8ANwsVPVUpPncHX6llb97m1NiWZlpFde5N1KKtLuFTOSquAFCRfKQQnAIuJBqUZ4qSIGR2DSiJZTTnzOEymfRkCEYeTcn2dLbPXMo2ruYyTSIsW3t1rYLBudU5LTjYFoc5eQAy6MJCeHRZZ1akr0IMBGrrwUsyZIaiKga9Jh2fDPEbUCcOUqz7XzGIlyJSaCxa9p3fpa2PrKKEIzUTLoFokNx4kIXlHSrDdGBLmabPINCTzq0F6kDwmUlMthWhkOMKtc7zCKZCMAmFlUzJKK/Pads21tZIQLamr1JqiVqQMMUgbOonGNXLhFTOVJ63sEmJSTAdwui6EFYOzTM3mrlCwnynVhUypUBMDC0PNsJaldc6mOTFK2kQ7NrprzLRR0kRiN5CIkssnlpckDaRMUE8oaxrnzlRZV1ubhPgSMaHvDCO6Rssmncq5MDVS38aNwSotEHpUV1L4jIO5mSIzGLR0yc6ADDpzASTYjVAnHRL6H2BPm21W5kxZ5+Bh7mC4O5nSW+2ukmKu6GCUPaSkjSks2OjeGbIUiNVSxkErUj0lRXlJUExVwzQJyUIrBkC8MAnEaEXNuSbDFqQf0mU3iN0WgKzAahQRLma1wzpImLl5S4dhHG1bWWzQ1kubNGcwaZgWkdLdvFuEEkdoJskVQJigqpjwPaEEZu1N50laF9C1okRRx0oHYC7aGAg1qTaUXoOzl864K9hJeIFXpRl9AYquGejQkHTaibMXE6X5tfBKJu3FPKSTrkXChiTvFlBEUHpwWUUDSFhzkmlQDtrWovYpdO51YkIgMCrYZdUa9KJZTdIsoM2zRyTAz21RJoSxZ7Bg9OQoh3QtSmHyKBWMHz2pHsWlMCZtRNDFVAmhkjTW9CHonMAZ1EAosobreilKc2duca0wqMJagpNl8uHGgsWgHkPMK41huU+2lzlthnVTL9IL3aoku3SjtQ5wexHrLJOPcxLrvkgOaRWC3fYYMSyAuHMF6Mn1otEYiVKmoc4rxzjXAsuas2UwsrAq7CRYfiQgjf4nJ3a6yF2fSWoRRejUbZoMjQB4ySRUs6CLQ7VqqZUrdimhNN0bOjlgDBLplSUZZKXnxJtqklOkR+YkWFbJkhpDzmyEB2Vka0tbdApKgQwQTV5eoBFSEBXk57COcZIyqeYoGGl1RLPSi0FQDUqusAnqIx0hn0KlhwhEab7mWlJCxs7aIyZmXMtMBxORdCz1PV2ylwjOhJ6tg/kjkbWYSgb/vOnf/uPP3/2B2QM/PM540H2eeOoxP3nnOzzwnnf+mXvf7WfuddefvfddD7/7Xbff7ScefPe7HHf/e5/7iB1/+LTHHbbnv2Pk6OA00zT4VEF3Jrd502KK2N0ZpyU3GyjNNZzCDhFVExV+tgnSWNyU1acX91UH66l4UZfBtcpc6hqrTuKHAeLBqi7dhWujT8pNAuZk+CzVzNhlLWFGcYzuioRIpibXEitu2mSJratz0mERQJh1LdnJyPDeJpm7sMgEGhYhwZS907sGrvJUtcZiEcqGSsVQsxSopSkNDr5J7RKTZtdkuPjIQYzvxdOk1TrXYULOLXsKmVtKUkOKkgW7XNss1E0b2RGIa1edaXHJKJROOJ3hOMtSwblDbRJBabC1RCiqyUYIt2jWYVJNJQaaInMrC60i1SS0NrVYcEka4CqpvGhdpDmCS7aLWCTxQ89WvA+T8xEh5gX1WABeOmmfZuIEBJCe/OmQdaG5ptAN7RuVD3nXKh0Igrd0pRNiknCyunDPLCa5grNCMmVd1Xljc/L12ltr1AeWTQBsMiw6/SbASeanCz+lZkFX36V9XZMjS2btsdxX4ubhE/90ZDe4epPsglCeLWUGkGX0ukJVTd9wkkuGWKKIsTNblsB719YztKGGagEGfph9USZdJNJLW9YP8DJprCkaoiyEMoaUyXQerDBQUkZJUtmkm6ACVNXVpqHGaAQrE07brMAGSnSb57q5XrWpaIeg8XbS925jS4swRpbKW5XUxaShNZqEbJohsBThCb7SR3LAB7SRirCI0zj2PKV0c/c5neXUFT10MwZgZPGjQUREq9OWKrNFRqvhddHtgRaEJMXVAENHzGD9dDTBWjojI0iDqBBVkBoSgd5zItu9WB+rMztlMJUiyaM55hltCpnBpJVaoiAY4jZVbwYeZe6xbixdHmlig+iYMEMpUaVr8SwJ0KbllYbSLih2v4YZS10xDLJs7ivYLFC6F3BAJajWk0uUTqk9rcdyWTKWiDtGoUUBOoEE3Q9IIIhrhVTTMow6rIJfGW2IrFTpcvc+sTWRNlBiHmLNSHJkIhrCqRySKt0QRbIYdAVUZMK5inYNMeGJQaMa1OCFwuZFF56M9FxKCBbnCuWMA0JlLhCDiEAsJYJLkkkUbTI0ckXQGU9mC+vr5GhrQ+doFYMLcU6R4PxoEJI2S0nTvlyThAWUFoOTCsI8c61gvZmh67Q1pwpmYhBN5azh7J05C0ozTWXVRhUswHqIkDaMH+1EMeLqiyJORIdEhKlDVhOlTAtnAhYQqBQIE2BOMiic5VMlTEID0cGsmLTOxserK2mr2qx2WmFImrMYTvNFRaqEIAI0MGfVRuQ14W69rQIbMfARTg21LAJLSHJWZlIfkMUkK/qQqR3ahRUZMcx95SRehmmKonPVI3ECFtVs1aU6syKTc4C5E+oiruKgZlNYUQ6CJpKu0r9iTZTLDXflvaEzhrtvOlkyD9kHKSYjahFyanTwAUgROqSdcIMBdX6oEKotkaWhRqllGSN9iCBWWAuS83Faq2JikBpQ0LCxyWoWI8YBgEam0wQXjssUSUhAeF0qTCVocJSMEgJe5iemDDVnIAAKi2ZFVXBHMDqfKJu5gnoWIyKb0L+5IzsrwLCycTDS3xKcAHSph+Y0aFP1kinElGgIqegaLfrcOdxDGSo1oTVGtyAIQ1O6YMGoTcVnDY5pgC/oR0iKSZHKaaIH2uz00WmwCmq1kbdUDLQ86mJLotJkHxukYXFMk0nhpLLTsqS+ri0YT+ulzhh897TIQakONCzLYlO34GSO2ZFFlvCQiMwgTOZzmRrLHVoF1XHrxAroTWQhuoST2uLBAC8llOZHLiTrPFrz6B1JhjBPB1toyLpTIQOjnoPpoJyM1DUU2JAbocOcJjDhICptDKnkUkZ4MVMdCpilVb5rJsE+l98kcHJ4SoeRUxQQIhiL++QY04EoNPM29LCELxmboZi7ZLPUgjpIHU0GcdEOpW2CAS62GB+QNC6LkgOgIoBBSFhCh15kswwNLPA1lP0fhQyDLOHDEpaCJBMVgC5KavGxNgORmwLrJLc6Bs7AUEe6lrBKZ1N7zzYnWxCOHAW1QCtAbRBhXGdFhyQUTEuGwKTJbjNESMXgIM/iPnCbOcHYT1tt1Zi4EYLkbRU+uo4lnsHPuhZtlEsn/5Ugg/tXHajekg1XFLrMllEsOWlPAeV6BSqYQopZSscqpQrcc24xzcigx8xM79FmQEIVQy2rarWmoYHr41pkrWUC2yCjM6VIGSwpSApnQ3qADBSowSSStgmNRekyzFZmlZDlplWTUdOoAosBHEo4ioaaSwavEzeJJpg5YOKKPapalhD6u/A6jEJ0lmIjRIX0VXeI7x4KulWrkqrJlChmQg2VdkrtVttY2pDw2VrXpJ2g/aJXrtevfs8HmdK43vGEUw43CHKU5NcVdQbNRLLKYxoxrxJE5jY3PWT7fe5yvUHLW1MrKrvj7DTSsifgIggzx+BZPVk8a4CVSPnKi8hB3BgGCSCSoKR0sPAoiHMRs4pgS0G9AgMWinERLuji3j178J12paTUxEpilGQegHbAlhWTaauAdGmxe0EI+iKZQ2MbuKQn0ytDAwIyKufZ53VGoy3WiThzRL1GU1YcF6eeFO/JRBCUUoahcOUTiai119WkQ0ukIzgLwPzZLbiwUwYElOQeu4pTKMEpc9g0nhYChnMIHSPHbMp6li6UbKW2Ral2QXeLBukcBy/CBCYbDChd6U9HcWQxmGQiJClWApaKPtIBgZsodEzjWNeelPMk7gokAVCWEZbMORgN7SZEuKN1PkjEkORTMUUCxCoYXaUhUnuOEQrAM+ntzCrKVoAZyzZXxJ2MwzJ3U3GgAy0sSVCrWjVXSEsR+j5IDlSjiFQPVzitB1SYsFld1KUUK+NiCPccnqEaYhkJby7MMxkyLDqDm/SLCJMaylJiyqxIid6DnglkTGy05L6L/cZK+6CpiZ6lJ2frg7vmMp9a14CEASkK4U2RKOEBVg8kcUhu/iaZZ+kJQyxrljcJpiwJyBQtHDRaXclqMGOhXqEPLXgWTqWlWw1bSS1p0hQwUS0iGhIBuC07q+TbxSlPYQJKH+oE7vU5m4B/krJNCXB0DlKxkOymXC4iaaIKKlxA7bMNDmovBJ+ZrUrXXB0essSbmGMJT2QCrsUlRDsxMHDJhQYtq1XEJBEhHjVjdGjW7mUzyxzmXnRXzYkrQuvzxEyS3iU50QJiciCEREnlNQUYcStmyoamrDrh4TVdkC45FD6IJpCRlkJrEiVMomTfwFyx7jr5qmEA74Zn8pcKiV9ABrScozFCGZASspHKTURdUHCaYIvPMgvvgggUYbnt2aOvsQZ6Da8IEc/SociCzlIlwcPnTJbCjgRvC3ETWCEg5K0BBaltFveBEw5qDH+Fr3I2RiJqtMJt5zxqHzIJect0eFjLwodmuxv81kGOti78jmmzYB7SdUkpoWaGi+aIo0xZsaSfqSmpo+MYw8AikQPjqzMd7zm4rsDd0xCWaU05YQfIy97rHKxK4rQftGFmpkqBSHjndhEqqkk4eQuSKHybPELm5IiOJsI1OABeRMA0dSi5Gtu2sRsnA2GD0u9oG12H0EidoVNil/ddiUlXzZgGKnRfPReCpBI+YY2d9+OzrSkX2jvr+DbNESqlaiXEwubO+JRDYjGQKcMlLdvKGVTH4Mxq0RQh302yBk030kuKhIWDvsKTJU/DLV0IQG1BMG2Cpg3FxpUMQ5ixz10zySsIe4mgp5Ng09DNUXgfQoRCHNar9VVRY7qn9QTJmra8RmnZ1qXSF5F3AAAQAElEQVSzOsiQRjcVrFHrFh7Jui0bwl5pEIJA+wu5ZzkrqCIQka02PhsCOAmHS1FmbY1QmYmuw2lTz6WIuCsysaRt5+RaYSXBa9Q8DGJa5153Ne3LGmCFphYne1DoQ3TpjMqcvFSCnkmVzq6mSko6nUldm0xV1yXXEtQqUiQEAclUoIpo6exwYLSoZo5jr2PXldM52VbKoIY0jgw3Zpl2BhYMRbjQbrojSGlzTg0znU0wRt7IMi4KA2CwHsOM2oW8EovUSDA7RGysw8YwjMplKWsDXRiBbYrRVGhJoSwcMdNUkcGEDgsWA9aLb7L4INHhXZqjWK0yig85qzQYKemZEyMF4ajixSYOBp23FOnauyaYfkUhAlk7z2xNkh9TFRBgCVlRKRaDJvHsgU3EusydYAsKirSqboU4phLARQhdZDb0VZPlm8NZ3DEHOSiSohow+joxqgSHszQk0zR6KT6OvjFiUN5QBK2iT1YGGa2asFCkZWgmxFOmKA4eQeLT3FltUu2yO+skvXSz2aQV77V5xdRZb5n+BTJA6ZMOKRJKHZMQsqDmEmppKIAI2yHOH0KIGhgMXkIYEWGMSXiwKFbr0JAqVnWwYuTqoKX0QSahz0V6raElHaCByT7BaA+hob+15dhRhWEjizihJWc0ESp0N/RiWgyrmnWcVZtLsHStu0zON83LHAsnRRLSh+QTpS5LF9lab7NNfaSIpPCuiOliOQMOmQfzgW6WFOZRKUZuo3m6u4orXOe1TNOYslGq1FVmiQaEzJCQgkKDYYtpDEdWtJo9M5skqzPM6wAdqw9jE5vFJ3gXOJFIm4FGEnQINAomRWRap68tuGDwIletVtFtyS6Oi1ysXACRlAojD6CMjkhRFBXmF40rAUASdc7ClS01eY+e8CJtdR2I3pwRPqOTujNjiYF3AOfj6jWARA1Zkp9ZEx7skNswNeUAxkmIYYyxKeJC6jUY53Gazu1E6ZxnQjp5Tz1mMhA0IUaJoWHlVpfLix9pglRk/fIll33/ih/8jS/e5Hmj/cabH3LgAoSLxRIFb+ls3YkswkqOVTZg2nO/bSvKX/885KADDjzoBlMO3avCCI45xEvUWkxNHIooJlUXvJxBmJQVPdpALcrAOEoLaYmuNg3D5jbtXLGqBOtRDI3LlxcivEIsuxEU7QVNLegnVxkdWdqc++t150KRWGedQwlUjFyQiUwy5M3nzZzcHS5MytJpitLPptKwnGEhY89V6FhKHZhLVYZK7Kw4F44ytCE367w59FlLEIjBSbsmmh22YEZWxQy3YLfVrU9D71VIEtFSYAxwIKU2q2uUXZrOK0DTRAmtqQLJYHDRS3YGT9SwXFwsdpWo8Jqiyq7JNjHOokEyy6akaybVcGriRw2S/E1kRapgKKkjg1DBiHh65wlnsghUA4LlQwt4dLSpxFoKcza7oKRsS6xQgLEXWzqdhtYrtIxFxrqcKAxfcrBF0n5nmYO0LNPuhdBF6VcyjrV4sa6gzyopjEFsDnNbrbHSQcaxb6tq4+i2ggwS4yAwghVYGYOxShtdqlNfNBMnMj2lR529dleIiEJd6DvzmCSzUUutUg1cZEOCRudoJka1DTol1vR4cpbjbBAGoSlmMc8xQKKVLMrfI7z0ZNQcyCgRVdLAWAYCrP+Z3mRmbV+br9GnMvu4yTXcBs5bFSNDzykZ5RnTxAGiNEEQki7IVZYa4hGuDou6kmJGgyua0DsBIpKhYU0KdM/IDuuoLbWx1euT0GZEFg9yQVkrnJ8yJUyctsFD5mBpXBXf6G5zSi/mG3StiGuZZehZ0CkNpse6bap5K+GyWLdEdF4oCcCqFSwx1C4x0xZjGECUhFsjFQxAaYgWPnkQYEIkJFwFDUNN+kqVOjNGEUL9CpQBNprU0sfo0jR86Domq8RAj3oUVZFScqWkvetM3dw7L1sCamF5ssI05KmmOiiGIDRBd2keOR4Q4uC95Dx6q1NwtWIRgItDnMkitCzVc/ngRUxtEDBPRTwRImmDSxGBIkq2kgw8b2mPHtH4jtNLK9G1zQqvNceShGEUmKoadCWEoEgZ6CMKVXbpzpCgM0CJLhLqKr36jHn2nDqmjAW+5qWHLST1iKCJqQ0yWU4FrFJFc5AUC5arOszGm0BGnbOuB5u1igw1VroeZJPzNO0orMWijfzmwEywsLIAjTUGg0CUZwqhQZBBIYhEhCy0EnUmIuNS2AqSVx06mfUCk1RwcGKVi/46V2mGJGAZSKcSgDoIBQb1yokE9L2us05SnZBghG30UpvpPPZYnscH1CU6DTUVraWUak1lnQS+JMtwDhq6msNaUMdAlWAOFQKZEmrdxmUYpLpLdiBTRapl1T6kFKhm8CRR0ISGpxYU64PTgiWcIujCW8ZejfEJWSznl2eroY1DN7pMn+kLAFdaG7VnWReslaUcEFEV01LERKUJZohP6OR3cnDlUlK9UkTMIZ3qXdEZiyIdTNAyY/DVKoYCYei7Za+RmoxOSCYEUSxLQCaNSb2XSEERqAQ97k5V1ZxQgcGYV4JtRWnKDMypbN+YG4gIULLBQovbMt5WqhvgBdeAGaqmRbfZtZuIEciZGVoFWTIynZ45I5gG8Ewaoj1rNwZA28CRrfOawA1ePWkg1dRkdJFMgiRFwnNhG197dpEuAYC/bejCtmUkElRes1gKFEkJMbWqnFB7l5yFzpcQK4aaMvRaEsqTQeeELD4S3nvQT0AVlAtj4HPt3pzTJ0IjJBMAfxS7o8QQVMyrJaI9eDk4OUZLI88sISk16DpE2SBFY8I4R3NqagHJqfS2VlQzJtw8Z8fkCHYaObDAj1qL0qlRZDV4HTtUQOti4a5xqAgB9kar58iFhIPaoKOx++vMyA40TkRwmoxMX+oKowcBoSZVMkolC3Nkqqs5hzYb+cdnONKScM+aa4lIY9wtQKQSCBGSxaVHWefGpq66LOAlK3qX7iaogwylKT0SWxfljdYRpIbroLEx5AYBDyXHcukYEZmgG8jcba8i2YYIwYZxEoAYQBMWpuR/RlhMhdJCWxKswHMzpj2j0FfBtJwCM8twlkrY+TP0GNdeZyNiqUVEFEVlv6obUoSqPT1atzmYnjVVycZGBEoD8aWnrbSsCdEeQ2s8SaUKDMLkbMpH62XOZDlu5Jiydg3DQoGShj56q+4FiixtRmuemaKlDJUvKaWrzUZFnWOryEh7qvm4pAYixTukMa8A2KI+FiOsM3uCZZ/LgHMlmLW6mnMOC2a/oQjhSrdYDrJj9t5d5xxnoSm857QtjTUUzM7GVcqSpWSwGAAzyCpkmIYahVO3bWtfORwsT+nIruGGnsJh0h29z713GsqmQ5AbVVbKhnyW3rmOUo7miCE9+5wRMQ+YB5usTipcGhoxpnfpIl1r0MAk2E1kyVqqjGThkyhD6gimZKRlJ+Oqi2lRJo6JwxtjLutI/qG2684rpzaz0QdogUIGZwShAWLpBVktR0UFpH7lGz/6H2oBEOD4+9yljkOiZK5AuRiGpgNFSzUOVHRkMwkBXzSXdT6nyDlJFcaJJ3hHcwmNpAWAlphUZy1dyozsEpBIRWpNtnemrN8DvCIlRUJiislzohAy3HMNmUTSUzkHT4hmFtCK5FxBU61wzipGnbBg9SnLBVUtqCuvo0sV1FHGSgYOIcVZ7Co4w5xsS2kSIURPnxShgKrzTW3CjsEmGksaLjtsSKb4XGOyXP6PGGpdJZaLyPRA8yH5CDEqhyhalRyESWkSQ8aYUTp9SGFAbeG8ZgC0v+XgSRbRIDBkiYgQjUILVdPBKWCaUEtTgQ6N7pOb1M1PQhHVsJpmMzTAIxa6SvogfWVRSxGaSkGSxq7Vx+LuUCQX7nEAiFkywXspUx0npm3AZe5jazSCp0hn9kgp3DSUBU1zIZ/ShTm2hk10Mwl3DoPbNmhlRVhWyEGckRLtok7z6STRIsJVxMSQymCQoQntie7pSc4IyQciU4T9z1CTGIOhJpi5TXIUUdTAGCDMrtn5p9FaoJVcsBfdrcWAit2Tai6uSvRsvbNjNOaghVgiu3W3SNs99ZwEHsikRp+zsx1UhcEySrC4R1ibsM62RgbNCH6eY47uklIYBMjs3tVFWx18HL0MDqoPCy8lQRRYRlyJWwatkqCinCnO5YZ5AAGnR9AzsJSqDqnW0TJnRFebtHrAPTqdMQIjlowbJBOc1VqTzd539Tal1tqXZI+yakNli+GxGX0GpyxiJcUcZUI2xmQQ1NKG1ax8EjgkW96BHalUhWmkOmSymIZsmoFlLvSUOblse3CZY42sLSvaYnwH5t0ODqKF0KkPiFHY5Ctv0zuOmOYMp5ykh2IeSzcGlM43AwlHVbTBh5ikTRHTJlhsm0wufKzmXTy4Zg2thyx1tavIAluYxZgoMSg2yKlYpsvFnCqMQ+pY6GWUmsKDYeASNEdGj3T4LH2t2YWcI2oSrGhlYv9gg1SumzGyvGsudJapYtPKWpjNJVWTpvM0mo/kAZK6sz2rDFaj7wZejpin3DXL1Ic+D760B0SLMzpwraHhIi4FqFJKGQFhkJ0+kk/BD45hM8d1clWqxcqqkhzqinX2OaaUXio2qrCfLZkSIdEkZwFD3J2ppV01rKSNKQM/DJEb6PxmRMK9yTwZupUmw+yFoIGU4WqITjQ8GAp+sVQLBhKjoFia0PKEJDNLjCEIITlqkmhZNmtZDzrTNeI7r8M9BZSM5NqubTJfi/blSi9lc6NOwzALx27TqZae2TtfAqSqpEf2BCzHVaxWTKtJFLK4UbfpUkalS7Z0OglXVi3t1G3wcQ51gZPCNrptpNZkLEbG2G3JG3J2bFFbyMJhoNCxgE4xRo6i2zCW2AAdVUqRduIYRKuLMmdmBb927oseU86RvZZp3GjbNnwcS91dNjCA5EdxNMdEfhSrVYR1tkYUhNXGWm7WR/CrXJeWS8xyVFGVZcgsQZKyPhmCXvNqERiy78bECoxsWNYVT50TQR8kKmIwVptm1qq0QXLQNOaxhVkWYyQEkyqDHEQIMWtbSJLBNLN5Wnk3ZgWwkIrYwLuw0oDckaFp8aGw1lmQ90u6gmWe843Ri89QDwKjSsW7DU4iLgmkKD2oTEKpC/iAsy2c+rwUT6B125ytzaF9GnOuYK/bi/WhzmoTvY2ec1B32obXDclVLNQdM9kW8gud2eceE5VyurJ4CzXiMPY2TLMsjojMhZkrbcFdld50yBSyhrWsgtG48PCUAgVfUjATkHRn6yEmApWZbNTJyjyqD1ZtHH0cZh06isRgbWOcVzUGJTKB1s1j7EW7QSKHta4m5h5JRd8awIXCwyFpUkpIaW6YRZi0AhZfVvWe3rtHhwElJu2ePbMFeiTFCDRxhRZWcykqFX3AuhivS/Taua3hSB0EQ4XZlGBCttScDY0Rliyew8z+mAVpl+gcxWyRpfBadabPwuxfc8WxtbILay1m5KTJDHTAMztsZldO0UYXrYOEqn/2owAAEABJREFU1uIyelTCZqT4HDInCwrNUISgS3ZrS4kRCBTEvil6zomJyCl9rSBZEsGOMCq80BkJNTGzyvxLpmzSksYRYFqFEz21srAiqdNSOBmtj5B5lrVjVueL9+bzMpcFbVDPnjaZuSW0qblgSYWcCZ6D0IzIFXQMPtQgpZAIckIURcvA1YwLNiraWPuKJUHSp2id84RUt5FpN2WsW+djK6jWahsSwxw6SYsxo2RXB9fUmBmRtdSJNdarZi0yFl9VGkrH0HrENOvcShdxYHbM65zm2nMEq3HAooHOGFyZHxOxbyrM58a8pbg2jx60ovHCLJjTTNhWpOXMCZyzg3EXsZJGFFXGLKMUIq0KOqnijLRDMwvAeiWlQzLA6BcHcR7MlLes9YUqJX3wufRYIjuSaSQ5g5vAzQ+5wf4bG7jeMfV4279/vImkhpVWbF10gkSOta22rcs4A03bv3/qcx/4xOeuNw43P/QGpz3wXhV90FYJpQSQ/DOkrCAQcxRxNcLFMjO34MUcQA98jGDR3MjcxtGIQcqgZVQfwnX2IFzokCnrzIeifdXmqpNao1XmXlr2lu5R6lTHNcaZhTurkxnp5rEK2FR7DI2LyTCUWmupWgwrcHoQpSr8VMLL3DMkyAVWk3UpM4MIyaQfqlIkVfi2B2h6+X9x8Sct13Xf1xg0xpxzrX2u5y+vpk6MBRoTFYNpiAFRsGgIFpCWQmxYxGAiYpFYBgRbVgiKWGAsSLAgWGHHCissUMEvIHbED2AjNt7nPnuvWTj2/YpEz72v6zpn77VmMcaYc629z+/36ALnGQm6l6cwudPu+un546ofLYvsq3ENllawDXfVWQ+qjM8UKhXRZby2L0ew7RUEfunmFz/eHxejZAnDegVSnnl5qcu7wwdRZrmYhmF7n8/gIk16/OV2h3wXPo+v+x1QmId2Wvxbp40QD+fV5sr2k/aDXPvYckK7yzBX433CDz3N32q37qq6D+7GgaPWuvXbDqYsMakuplgFeEF8rXvwdbVDBSe8DFoW8EfHz0jEtkhCR8258/SZNWvXpp5tAHegYZBUz/6eJZeVgzORpbLT93q6FG0bSYEdVbF99tafb8Utk5n31KNGKB8yhXzsPMyksOfQZD1HKKy5ry6W4FMwY3RxKqZnznS9M4kxZdYKoLUMHCU82YqYT2pVnZGjvA5/jhYbf/ldRR8TPlOoA7RM6DdkZY6AenZSHLK0wVscqD+pf732UZBM+DN94YvzZz73ZDmlOoVVtFH3vBqrh6OWoI7UTrH/LLTZgAaGQXRF5N792dgicnRNQbT6qSx3iKdD3G6p2ULFTlIYeLYoJtIkFPjMtmEZ1I/o9AWsVEZ2l93AsMPLveKNz8yhPRXVJqVKNczR48KlYllmiJmlHqeyyuE9dreN0oJiknWbhpDKbo3JVFeJI2YjFYajw9R1uZteRKreDOTZBmMn+nb+Ikp9CooW2hBpdyY9tSTCx9Zgs4BO4pDyr9ljrkB/S4NiJ72HRL+JllJaTVojADoYusC7maoKZdmzR0LOsGyflF9Wdp3BOcTjeBNzGt4cMVoM/Yur3rqUrp5Rgo9RQ4EJ1OoKtcCCLrb7LGtpwtktLTqv5vp6fD+efwiymVIcpkX+dJzH+pv1uka6IECzimLnDIX6stl8vM9U67oe86eEOUGZRq9Vb5CzDFfowoNJT1gaU3m5vKE09sO89L56Rp4y7RERxjKpTVXSPDPiz+ZBJ3SSgJjoIu8r7o+X8hn4nfE8rksOI6+affQmODoXAntmDDVUKv4N3j7Ha3ZSB4hifa2P3tTus0eA/XbxLo2AWNYR1eur/tGuRtWd7AQ4QFonlbbyjIcuxwV26wqF08cUjqLtnKfsGWuBYmqD0phTFhLqOzFu+jv2YOo16pTZh+fBSaQ4nAlILZu9JmH3iFgrYlDVQAHkuuBaBQLmSPSBLrTybvWUUcuQWBUSPUija8mU7q+GtZSrIfpDlnc7ikxawSUcfTAKPhh07tEo7TMqfN5xM/W6FgtXz36EiqJfByonErGwFrnenKZLPuqwNUHmuGeWPtaDTsMYoRmvvpeiA7OEpqoKZ1kHayBxzztXn+7gL4liavpmpSIdQDU1F/sSvZZtdiN0m59VXclpOXCO+4NV4mC2M4ghR471e/hWFSPGtjFwJutWrStFq9HRXTlKV+ObQnfSFNmaMCi81wxo9oIjrNtRdjO+rmVM0uCtKMvm92UkJpfqSOPxfhigwY6R4X7xUKKGMasqLRYvBtxu16qFCfGSW5a6kCrA32EL7F5Re/BpWCl8yqTbmA9IoQNOmT1+pW2ZLkwr+3Bbe1Fmd9FT1VkaLN9MrUPPwdOw7k9NJNjDQZc45GQIIpynkjlLXaNnyGNRQbUx/XW2wYmYvXCtEdwyLLT43g9txSGAnnpNTS2N4wy0tp5bODed6qMmNS+YzQxlzo0uAzDgIle0irg/qS4iV+JD+4kaomgP37rKg3wAyaYKrQilgmV0HyWrYhQuM6HVqh8b9TFpCJfn9dLYLLkJcE3vkRXlur79+zY5C5UQVBPsxVGAuvmwy7BsIKDOzkcKByAlHNlupS5b3hYxzSw+zWdQgze1cR7UM31K3GOsejJnGnovKwfE8sttoTH5ahqUJwgK6rrGHeBbujQa6iYFA3JajLHdvspMgyh2OqDSs+u410suR4uwCxbVeXoN6QApzJeSEwHGhh9bJxykJq1jqPUWqym9YUFBy7p4O89zsl8jCaiJKT4MmPQ2r3hV8LZQjZcqtQRZTFjsce07dh4rpdEvQ705Dju9BuaY4BA+tsydboZgeVfc8ylbjR48x8/h+44C1fFs6rnRqxQfZCJfxsPgpoiI0slmnR7FnTHnb/qr/+Jv/2f8jfj/ff1n/wf/y3/sf/6/f/78BUK3z3UlPkeANUxd5v/5j//53/mf/h//Y//1/8Gfj/L9/8z8hP/H/63/hr/6n/KXOhIK0L+TZ+qYj5KGGrPIUFLdcR/ez4yGjW2aXVRjbNHHmTndkgNIdwXtWu7tkIdWotxQhiSbHOiNDmZz1LPuki+UevQsO9vFOQTp+KZRwwMpRWy8lLuDZg0tkyvH81hLd7+tskeWBwWqeqpGtOqMybkCeFh3TxoYcN0bqC4WBJAsyV/Jcdm5ydL1WSRVAFiu4+GcHv3zaUZzdV5zb7+vrYHwx+wxHu/RAuVwlGX5nKii6u6wc+gT8rsg/ZDKX91oK+WGjVzpKsuRaaL9YbV3a6/BI+n7VHTaOUhlBr7/rI6saq4+VEuc7RQRvnvPREt35aYLyGdS+IxIeHrVb3zjfOxo0CSqFKLCbcgQqD9Cbc04u9/L6gkIc6PEa1YTxTWyLOfddY7Nd80v3SP21+Ze/bC1sb2PIvp9aBye6QetK2fOMAedyj+J85bE2hm7Y6VVsqrZbSoG09i662R1v1HBVVjaV3TVHEzKlXq8Su29Wfwx9575Dm7i1+yCbg1+A1GF+UU7ZrKy6lEaC0CxJcwUYvgYPXqu7uVcYctnhwDSkp2jWFvyqm+24uXC3ljWCp3smJECO2Wqcr/wb7WNqaqWnPnQUYiDVTbuadFGIF4se0PHUA7GcnZODAzv8aINMwzfA27ObZAM2CNoxtaYFiZAy5rc9Bg4GP3SyIhYa/9GstGmAEuCVca7xKjcadJtuC/0j731ixobrWWlMXGIh6NS60+WbnfsdeSS8KSbbqfPH0+pmXJ07VrwC2oraIoQlAqrw7CT7z7oKQh0la+LcJYjPr2v3nu0wfI1wr+Js/hcvAVLgyoxtaT78edcp9Vgt+l2TdrgpU3Ac135ww4lAKlITg3tgFJMlYfeiEQTROKEn1QRDqAiVOF3usjgXUyl99We+NlV4SBT+4VciYVeGEJmnGfzEXSqnLugZTSb5xltbfOJbj0X2gMm8mb+kg84sRcuUxo8H78vii4SbC0X8UP7iXPZn2v0xVQtlzc2eJ/1nK2VHGWtXJBZT7dkVt5YqYYxUCVe1OTft26mZE278jeUKcM2/gzXkS1O8UA1mM+KWVeE+YKWUbqwH1ZPVaXSeux1Hnd+fmkd2u6f3J9b9xX9YekB4SeEhGNCiJQmsmrEzJ+KaFadgURSu+yTXImRzcmaoWLt/tT53BXqh30Zt2C9plce89uvYhRCjy0w4rV9PfZzoFrFoNAPJRhK3avMSmfkabAPTEqfeXpOTikaVEErI05bBodgmeudKgsrKFQMPpJ923mY9xQ4slF6KzpfAPXMJH+x1NxU97NVQvD0fSs8eTh5t/YEjz29bouzdFfgF/GB441316yXtKNwMD0vBcrkU8W38psa5SpcXZNbQ3G68tT92LfVNBwdOAsUzyzns5iG8lGL6mN4jDPWPZnZGO/jeeOcWyXla948BdCkqx2YRNxCSEke6aD5PIak8yXtm3PLEF80SpjZmOWFGoGmu2EzEbr7dbxgmzvmgvnsPp+TlgLtdxQzshipQLqVIaqRjx5D+fO03bW+4+O+gQ/Pjprt8E0J+QF69AMmRqXX9PIYN+plqhdb6GVPrHv29Ip2QOg9DWGGC9hXrx/Yh2OVb5/VXwxGmx8lMpfNXmPy52+rOi3mSpzXRpoVKC6UqMoRBlQha56pNkNQ+i013vd9wOV35ov5ZZ37xPQf6euNQhzUKGyx6Z2/E1IE0HjJe2R2iBlhmgDIwe5Yrb3gJgOmA3y1/eIwG/XBuYTon/V8O9V/fNQ6xq6e0OlOVgIlGiyIM0xBqAujdcd+9roYVitHdbNn5Bt69fjpqAxU2DZopkJq9RNpDrUCq8CjLpClRVzJ+lJ0Dri9QRoUd7+wFj7tP7VDy99Dom0/vNI/fV1cQUprsHqrazi3bR1f4BeZypQWwoIJHajV7SlFtioXHFs0g0+18u48o1gknJiQ8/cktb2Zc/iccB0di7oUfNNZPB98f+pR6nz7/aINqSwTeaStc/ElsdFf1IG8wPTSPk89cvt0KKYFo0WuSn1oTW9lCNLUBpfv0KC2A8kd0m0xpeDnNwV5+r6zxBfkDHOqFL8/hBSp7h/bnEaJfrwj/Cf25csGmewK0OW5NiWkmnVA/Qv3MaIoM6uBRuv3tCA2178d/AlE6EJNJ6bpChV/5ZXGEwvt1k26M1x1/2dpr78hEWy3z7IrfH3oklarOp58eoYK1GKZByE+8nfGkbV7NiZQ0JZNK96UUZxf7leVq7S80lGEYTbx4asCSp89KaLomCXPHfJGmeDt9nW7nXpOjP/wv/Hv+mf/DX8t/gmvf/wv//mf/x/9L/61/5H/1N//n/qH/xP/yP/kv/Df+1//w/+j/9U//D/8n/3n/1v/w3//f+a//G/8h/4z/9n/3v/8//vfMibwT/tr/6r/0r/v7/3n/XP/tu9aX+OX851+dO+dich2nOIUyIKJuImhq3gsW2UuTIjd8NbIXze/ya/hcUvYvahOwKX253+B/ZOf6wmhsk+rEV5H2+v2XI8AABAASURBVAWRosiZ2TWqe+4x2V3P89MdsNzQ+p4iSaPaWpqRkvtpoT438B2c7Ee95x7PUDAhiU7cru9m4ttULWCUI18RaNObRSvX10px2v2FW59ztcWEDdBV9/zKuNeVmzTMSCBnavw7UbZbIXqAapZA9ny7p495qaVuB8by688XUvS0CFutxOaMxjJtJgZWzZbJlsAS0j2+eKUKmdVNpR9qzvv9AkDU8ieuVHyjaOCzOFGNEiZdaOvjeKSUofJ8tmRhz+Dwtajd4vPb0bh3OI1RUASUxIbX2PS+Z30VEmgjFT6ECs22qPu163nvWfUWS1GrXHADNSxDmwHTVfakVv3z4Bn7Ur9/d8zWhNX+SaxH6RxANWHsMTXLrGxNmEMcMGdkU9rHFbOX9VpJxViyP1htP/fa2iAeMavQcs3jqB6gOY+Kwzzc9Et8lshUfmoEOhjacQk3q7Hq0pbPFvlpuypW6arts9fZ0oOpxNexNbQfbB0f7BixmYNv8R50kbM89qjnGeXdZnZzpZpTV+VjdcLo5moeKuzWHoBItag2lAwQgd5DdQNjO2cDm21T7IyGqgvN0Wjld2yyX5zmpdUNwh3zBPVuDVxsIZYC0vBO8sTKvepnQbcf1xhME7P5lOc4Ad+H6wCNUSxQ/BxVTPNAXV2s56tVTFPZ2qNd1L2iPCQJIcdPUg9XokyZN9HRYyUvPsoWF8utjR2YS2YblcxRuVoXqlSySLp6HrXfM9UVaSWi2mzaQdHtmizMxMEyWUu/jsb63m473LUUvCiobM6vfrQOAdREQn3Gn1mnITGJJj/jMq2PzW6Zn8lH0aTNKP2Zp/uuHvNQ6Dty+e+SAjEsvC9lMLm69HFyeFzw6j3U4VpnjRVejp6SbTcJk861sZesZpVq4XtVXxBZ8upIjxY6zqI+r2iuKjmIt2YQQr9FDBVu5RyFq66iIn7HNbS2hXxBCKLJahpp71voj3DXdkCLDL83719RZWNTcgXvMiuPimhhF8U45gfGaVqauo2AXUusWg8xurAMPgsyMWqQfASHHdvaSQ5082gpOnve2zeqFwid0w0OA+6an0LFkFsOhLXCbL5h64qZSzWeER3q0loPKe7lFCx5A51cZFyuxUJddjBvSPKvAqipGS0Rk8bUNKGlQASBlfoplZkp7caU6jyluUVzmuD3fHiXf8Gjje5k34/kfmBnrRPeClFtEegCvj7n4rNx+ztGanCYSQhPZFKv3zdyT6jD4l6TkWArAw+6CzZ01jl1o8oL++44xTbY0nWz1vXMFIAkHEbDiALCTCfMTjCdyR6UCVJXbjdxZsgBRjujU2g4EWQn7i8zg7OWr7WdVz2ffLY8dOTE8J3krUdquNDe6JoeFEwa4hnLAVwWtON316++mAa9aKN+8lmPuSptUq6f5itTRaoht2nRYvdqLM7g6GK6wXZh9dFTqym0sgOZeLsfj+Ewb6ryoNA+7T/tamDosOmlPzLkkfunwT59ztywNnc3FxpSVKNbg8IRBgcFnjlgaJXIVsiK3fQjh3hXGevZ7G1Qo3SUN6zMSqhXIWtG3IDWFhjZETDCd55RoDKGYWOyM6cRkeGSta2jJ3xbThsYHYXqPP2r8R1quoQjfszYzluJdcajNGQ+RjGjaakW53gT88IwZ6UINZKQxWw/r1Shl4HNF0AbGBF4jxH3bZWamcxjLc13a5Niz5pmeouQADVhMFp7YSpPwx6s0QB7AqKhSswauImLVKdR4vno5Kk48OGIHYHjYnl+6zFkUhVjSHTJY6F03VZ6tHFMsf4GpHU7yTumPcEkWr/bcuzQ09Z5dVennvOo07VmLW2q/VJCk8U+RUwDD148Gxd8K5uXC5k3S8Npq7MsfZG2BBSU8tQwNaoh3iUzgrsZ5RB2jOXm1wh6ljSvkFeRj6mAYAYC7ZbXmsv1njz2+2gOm0oUksuTfZ7nCKNKTr3sfbvuFvYixbwNo6FzlqIe7lEN0Z7E+R5OquUtTCUqbRD0S25p97LvRq4Cjs3D05BHyDEUmdnSTQ8N8jAqe9fbpg8olBbbxbT3GIwALd+i8IaNc1QlC63sjO6+xDHfFO5ScZLvCE8y34nl05ZidBwTA8t+42Sl2zs1IrZZyMmU9D49JCjjI4S6FRBtAEzbmGnBFLhDOuGQ41GInL/5r/7r/2v/3r/vX/Mv+tv/0j/h/xw/M/+vP7//5//b/+O//7/7v/yj/7P/w3/jf/y/+0f+J//bf+x/83/+P/1f/+//+J9fXQVA4G/8q/7Sv+lf+S/+b/yH/v5/wd/2tyRQFVOs7hfOFqrsQF6ZKwfPzINFOPUStzLySLIxQ4j9aU3tuZI7na2whzM6p+vE24pcc4FrcqeoulVFECTjjiUQXMlYTqD/YG0/bofs7mpx/Zl2oKAH9dpnjN7NmcrueVFPQXzOknZCLqzNBX+U9pHvKsN6OienGzk6u5BUZBijvDEMgLnZMi4f+lHCvYuTMW97l60eu8fTduIdvmZIkYaePPpXRZQUs7SpAHW2s8Ug4RpkLFcFrTpUumCx057TyR6FKyyfUenhURmHz8VZish3x2o1kHJUQyIKywgdAovbdPMHKxVHRSc6Zwo3oswwc/X8VH8gqmRKClLQiwZqo7LPQGOZ7+nkakAzOP3iAWv3V4867z571ESDHeg3dk17h0knM7LtdN8aDZhCX1cp22jtMWSC0D9DcGgK05KRHqM4FAXlzZHBZ9ZRu/w2Gk68DMTWBClBZ5T9+Npc1hQ6/B3pG4o4lx6tfptRXVRHN1ud0FnhuV6M551ywhHbECPCMGoNoQXdUcJA6oTBTEXUfsaGgg84QFJpxKj6Clqdu17XumxtkCvsGoXhrl45m3Rq2Le1rlazqMcMl1uMyZQSa6uZ16aiZrtpWzFcU7tbDVXkbfZaA222qHElmT5Vd+EWTDbomhmvWciP9Q6M5lg1xqhHLRf5O1JJYn4LRsMz09xhVuAZxdQXEEDv+v7R2ouxub+9/nLyV+cXWlhQGNiD/aiA1bWgCTXxmB9fuFyiG/dnx1G7RbVUjzq764f42A6/ls1n8tMTJ/DddS/oy4ZbXa2z+HZfxQwhDgjKUvAwLSQb/GCWt+sRySfX5b4+tn5IF70ksE2ZCivd4+kby/u5/zypxASlGd4N2RnXaGNoMCXOkhZ6UFms7ye/cZ6u/LrlJVPCkuv2/cQ16+K6OnbPUlSekj8A+Z23U2QHWv3CH9+zflZd8V2uTewTp+JhnEVFueASQQi+XnvCQ5LOb/I+MQyZe5TFtJImzMd3xkZtf4xqtD56TvjEiGh4Q9Id5jOn+pA9bqVozx/5vVor+EgUQuRC/IArk11vzOuBPbK0qveZ60s9A6X666/RXTamZXYt+7j/zN6lp94LJLf0w0sx9hIlZ+SuA+fCRCMem1vKew5OmtL4mdEcDVR5VIdWO12+1dhiImIvqmvZb3oR2dcX9p2qfFb2z8krtZmkgHJHkCZjYu27+Wvh9nbkz8a1bE1U7QSKmEMKpBzMK+qmWJo/gB8rD0lc9TVzndCjXl4813P3r7x1fHVzkvWD+WFdfILHX3RsOD0HdRTndr+uuT61Lmnz9K08VX5SB4TGImKtWGKz6DeQ1RDDitwS11FbRM+kBLKVkNrPp+49X186juexX0nh9mRov4WIxcvtamr2iCSoymbfYbKwrnFd2aPS+m7/mt9jKOAoXMKHVtt722MmKKDKpIL5Yp7pEkaqzbV8LdsCBumiT92FMdvtA/UWdV1J6yCPKC3Gvf39HxiPHE3JpsmRd1KLku5IrGqTl2CInUE1hlau1ZXdb5VANKih9AuTHCxup+V0aqt+DV032DiPmD/hh58+S2oeIS9T3XPYAqikEUvm8buvp3fnxFNe4xa2tgC4q44QFhJCoIY57SEko9U99W4Xfp76ox7nobV6lhSLrYL0i8rqiNbHFlbsgephnnX6OrjIWNn+dOuXIPsZs7ZRmfTkr3V/r5FqaDo7faciM1fK6m5HT2viOB7PxK3gmpX41ugEVtuq9OyRrWvgmMxTtzIkb0fb+8OpsLN27Z/lf1yC7oLuZ1Li6mOtZG76UytzrUNpQqvTYyDsj+LnPnbfp/mr/Nt86nQ+uCWDrJjZo6Dz1Pc8KSS19OEzWM7xGQ5MAIbt69jWU8LnrmdU3ajxNtR6Ds9zujNnamLO7j8vPLQ62rvs+8Qtn2qM/LK+6GQEtgMkJBbM9IO5qwov89f6/vD5qfxB/zAXelqSpR3RnpX7pAzWbacENewidrSaw9EuRz14FESnBGcQtnjx7jP4knmV/Xzn880tux/7rGsJe8PxlouA7VJRXUGp1xROjoX7Z1MPknqUARKLoVduKHthx67nKPMehvyZpmiXhlE8wx+eP5SKMMIU4s+zviowc/ZSAF/kn35/USXw/iyVffkMWskOv7gehhjCud1+wX9983wliFgq56dP2xPWkcS9dFTtgYHwfhItjMdeemTNxzbMi4oeNF+2zVTaHRQQ7SnXA844hgMJDtWT2UdxmWJd+bnKTSng0VfptU7OSZwhBi4SYdBkuJfj1lfjfBoHUPNdnldgXZFS7+8zOtnCNhJLGLlqonNSYJ/BoYyFcRk3bTNc7QR7as1f85f+Kf/pf/u/+R/9B//+f+Dv+lf/y//2v/Vv+Zv+ur/uL/3FJ6Qi/P+9SPy1/6Q//ta/+W/4V/0d//x/6O/+1/8j/9C//T/wd//r/rq/6gKSg/WoGtYWAUYzGzOdTdV93OC3xaZ5/4hxA+Z8R3V8Kgs90y0IFVb4Z1GxnYlfvR4xxmoKcgUywXPxXjq9f83+ct+Kn5+9/vjZ12LXUeUhX630YN6iqWcSpMOQpTNgYwWXO9uP2l4BNr7SopTv5+AneaVr1PFPKRm8Vr+pRv9+BFiKFvlOyw/q47iCn+GPWgHrEplL3rSIuH/ruoVAHeMs544wrLw/3/PT495lqawd84MjOS846TnywKbPrJGcDaociwYPTts9+KW3bRvicYQyUnJJ9sjmjc+Dy8Ouz3x2Xvn15zupnpJ8LEaRGd19rdg/ruwcx6ZlhVdh3Zt9SdESVKp9tzniYnwmHJ6wl48CDqiOWpMTeBaPoAbhKlL3gUIHLw51GA3iusBKTma3lNhOXxa2F6+wtbkvuyL+OOvzXf5s1razTKh1sAJqox7n2k9cbeHmmuezBdfylqfnM78+/G7UAOVUloMZ+PhVKg2HfC9RhVJSulpR83P8c7an8zTux76zH//j3n/c9seBNWRroBTebcSkVidcrL06pvwpnEJhzSwcImtPXvNwnqe/LWOfb2tvCDtqfynxcQ1kUqvh115FnrC6MIvmRvdev/8vRasUfkPLs+8b2p5JAk+cx7/lN+UevukhKnj/DoGzYtbV1N0PbyBF8z1akZ6+1FyXYFBN9K/aiSBe4WiRsl/dZTfjYDe5oNiPYPdB36y73xDR4ISpEd5EkQ5GW6g0TCheAAAQAElEQVRdCALrxJyDJ/0ZP249o8/9VJ/FWpKr1/FUIU0MFOCSzo42qPZt6eZ41S56kjqKMdp+qzkc1BPZwnxmnqonuwsoZJ2n5uEblU7AatksJZSDk63afgugvRXfo43Lrlxn1gG6pTvxMveZZ4LrCg/BnlMPzi+2xDGmGF8wxqXqnhv99bytOiqi1z6+n7Q5zhL+siHPgxkj+L4cBeqORkv1Xd1I2MwOjEPgjLUypO5BFyOnE6dlSIRIrD011bIFIFaoEH4Ee3phztX4sBfO+Bfr8ZW7mykBfGwMklQTcGVDvRnwt6UW4jtFnWntP6pAmjGsTTMhRVETs/qeKrZZ6mshHdT+y7Jdw2BQwVP0qCW0Z84o4IFJT+udjYdUTAUCmOiMqUE27+GdJgHpkhn5ZrKZVGlW86uh3OEruJF+VxwfQTQmMzreUPFWCseyTdNCSwofuX9KqBnMuVDex1K9G62M27MilYZbTZ8n7+eIvzJCSRdd7Yk2e+Nyp+prGIoZU9334DaU5Gq6koK8XvVaR8/L4IItjqiomXafvaiv6GLR9BhHRxRb0T01duaC2meJlei9MlZROOZCrxlPax1vlQzwRt2aAQ1Ja20YJRhPD5gJKnRKQYIAJnxSkvShW5hf40safAAJO1vE3KdUnhnKiG/CGG8p7rbokJj2sh/PNRllu+kAuurwVDywXg3xmGeqR8HEzJLkA6IHWtPdY2K161LlnBzFJQa2ni+H0xwBh8gb67YcDoJms/BoXZDQDSO6LDR4u11EUAHg0B7xHo2NQmRZ11As9hNrXDYGJjL6lP6syXeY+vmh3/CnwURU2zmsY/W4mplZWTwmSwjV8iJjCpg5MqVN+FyGpXA1rM80YCBpY0oWhFW6mFQ4ihmCJZqUtRAxbA1nQcU035nbOL4Ry8zdjPpodo+dl6gljNtUImQNlJYNRnVEnhbIKUvZapTZLdruoLhqhs2GwMA76UDBteEBu8OUh7F8TsyzkcLNGa7zy7m0Zl85rkY2qclAUx70Z1aXW0J0auEJgCDR1oKxxlp/n7a742nXzAENUj6taGmZ8cx1IxryfHvdumPi07t94YOwvoQcwRHAGm9f+RJcpMvQ2HnwPHMq+5zo+o07lA1O9xC2EAF7GRDaC0lVrk232ofLRWCTa3y1h/z4OLmVS+fqNL3c4cZlvXrE2hkeJRfvcJN8RlZv5C/OTR7gpJT0K/GFt+0FFyQbwrZMBkNFvCSLOLVwzB+/EtLRCCqDQ0c74bu1nJDNA//Sf2HlUpAEBniK+bBab1OV9OB1diaaQY1g6a1dFuYhU2xs62sx1AzDXmg804bVr6W7n8I351tTGCEFc8FWyJ6RNxth1+yUjlPmDWoMYwCdwgo0lKiBZtdDATQ4JX1yOrwEPTRWhkR8NeYYHwi/8UYUmHofAwddDkpJveuu5siujuFqsgmZ82f4Rfdbl+WrYxJ1CgOz+QSk78iD/L4IoTK9+jP4MV0plCj3kWvp8c1X5QK9RAo/2mkcVlrJ0TuEDYVnfMP62/7mv+Hf9q/+V/zn/h1/z3/r3//v+u/8Q//uf+w/9u/5b/5H/77/yn/g3/Zf+Af/Lf/Ff/Dv+S//A3/Pf/Uf+Hv/u/+Rf+d/+z/49/6j/76/9z/59/3df9e/8l/y1/81f737WzY7cC2sBb5c7M917T/Cpbfqv9KQrYMdaNRMT03Tmv50dpb0GbfNWQ/2AR/y29Cl8hNXSkSKUjLAr9+gYhl2zBUTIl2ASLNm5uaUr9P3g3kNCeZQh4zlcQ13UaoR8hx22lMqSYdsnb0FMUctyXpIEV4zLUwTCxbSEt0arfUOxnBxUGXzGBN+mmd09dXEQHNl8yICe2hgdUttT1vFwnIQbJp+e7dDSwnWhs97lkOdh3oqr7reeyJ1xCJpYYzQVYX1qMceQ3nk+7zJGl6zEnrk8/lCchGUg+p1GAee3dqheWqm0ou6h2rud+OY8nzd0RNXv8oB0JhBOsYord11nlcmFLcIsbKUaY29lxP2dJ8+NadUCe9UkHBNLwlMWM1QgJTXY3n798Svs+5aBwa5EQwqwSm1BmiIqy13qyZaPqVniQN8UL9GPY8OiC31s8nfg0a2Se/YRw1zExEN5Dw5+RTOUf9y4a9xfWRVi1UcQUYeTINUCBR2JlCpFVw4cZQqky7gSh9Wnj11YXwGsjPscj5leWI6mivdHvYXJUoA4XU0UvBoP/vtOtDbo/NyaQUb8Perxyo9n5nMeu0CHeb6VmH5dr2E4RvGs7225Gvb8oPngzs6MT3OxHrM3v+xQM0juZiqgFoCkA05tJgFrAprc9LZhHRdtZF7js0X+g3JD6TCarDSVBJaM72g3deesJMz8jXmbUhBADogqAmZYz98ki9G8ym+N0k9EsK5Bzeih8gT+V2pe4baKTF6lHtZw5OmlcXHd5kVOoWFYuCqiW47rX5VUCiRCIx7Q5pRCIdzjPfFR8aozE9op3nuyk5dWnOWJj+87/n+4vP1OlToB90Sat3CWx0q/tBDjY9BxOmURGU9ildKLIuvjmUpz0yiFZ/q/Yq1PTjT8mN5rfmxuqgHW8+8oZm/v6Xl5+g530uSrJNQ84AWpzRB3uOhz4wNrpxOIIXGzNQI6++Now3oCYN/fpb//HHv+PZjda+Vrq8jfkZgrGyTuROe8Rk6tBW5ij/dLup1zNC8GYilXxLaidL2pndITfOjcApWfVRm+oWmt69iFCItEnrK8fm6Cwws+mWM1bLyTB/JO2FAzFi/rJ0WJ28pmXKlTPD2VhvXRhJf+OP+Jqwd46VCklpEems4Kb1brMNLm7RabEoCKn1Zc4ogs3DVxLkUs2zMzuLd+eAUlN4QhxKaIvLR/uSB9CtJj/T3ZJ+uA93Zl26wBx6p5dNc/za3zYVeyDUdXsNR7c6DdVu3pXMsyHj/YxAYO62a0vSrudpmtDo7V+y13HbDQHW7stZq1Zyl/NeUSPm5fX19dYPPmKyPNiWxZnfJYHVURfX+jrfJYzW/yZPiogNv6XqPgnGRfF/r963P71BUFbMUBloDoOAH01OdNUws+IYoBxQXdKUWcZm+87s8KePgx5SeJp3uMyUZrPiuue0MbtQXuOmPAl8ejve5j+JvgW6ar2cmiBpfgNMmFuzHZB+uiN1nF9bYQrhEKWVXPRlP/kzqLjXM1NwuLJahpZrjuH9UyH3UZ1raCpxtDy3TcG+fD7g50KVpzGDYpgR7rQy9bUd9+r56rofrlvbMSQRrWA0YVGZrqYVgIDxa1AYVqw2onpCorpgSjTptCtoWx+rVTs4cpWpXhLYNtXCA7OixtmyesiqaW6wVymh8hsIuqXgVQ1MqIESWj0QmNFRiWqFbYPKxOewb0nuKOu8Tdi9PN8IMQFA991xIVQfqcuquGBe4mxRuqHnfOEYTYCxQGU/ae7GI2ehLknWCzt4a7ziLNNmGgBkGP5ZXHKllfClTbTIEsTf4EK2RtL7m7LfetLzgts4+yKyuNLmai9AjTos/Bp9RJOxg6xnMB2vbRniqLiWxTKpnNMewF4SWsdmwMVei9kboA+ZiiWyukrBrL+fSM8Q/jl/9WXOt1vLi3rvPz7kvDXJSxniNfWDeQqOfwrRxsIAt6yGtRmXMmXf/9a2vWgbe8iJ2nM971A5NYGnqMZQ+kCN8VVQQfTjmyYAFqJj5ngaaLJ/xlIp6NC9FAm4wsaYXJfB3UPU6uSY9hi5hR86eERER7mFc6Jhn2a2955YPEjRIVSCb0AO14zlXt/tjhljOt+fonAm8ZXIjIRWSeNjPPmaPsuMQt7vqFIdLNc7OGam68OVkQxOsQ3F4p4qrGlmWwmkCblQ1UbV5dB/H0gSVHSMktB4q2SjzMe0BlCcwOtdrRvNP8yw/EQ/54Hn8FOGuZ27XhtVjT7q6cp7sTnqbpm0ccFKXS1Hm1FQLZBfc7gXdCkjIlJRy8P6DOlaPDZr1UN+klT78k/7i52/6a/6av+Vv/Ov+hf+sf+rf+bf/M//Of+E/6+/8O/45/7K/45/3L/07/tZ/wd/6T/+n/fV/4x9//JPpH/py8SCAoUVAIplRbVy0oNHHTIT6af8Of5mdzVpUlX6leZi9iro0BAK0GxnsnaY+NdqTPe3nuA3VmvwzPXN/+SItJjdmxQjupY42HX10WSQocKxqzyqd2o0/nNfa18d9JePMHpVgaQkRWqV8MW7STHiFj3kPupui7tS0tNqjU3CLHW4ucaig6uPnWke+bvvNvihnv08P5mCaRpqB4uAsk1bLUVqhalZXyD5G32soszMzpRIed3EuvRkPoZPQ/GW2xphSrarPfbBzQrENe3T6utVDjYDYY85CbCwvwZ1xvgHd5ul+/Kkp5V0vFU6CTq1JjzYpqYAz0aDC622kTJFs9XKl2QeuYJ7prvRq69GA7JX5x3NHvlo2tg5l09PmGFOQhvEpTaDwG0B2TQ6OyaCUxdtHiDwarYsNKILqPuoUhXfI0cPYugunbRztfXJOWrfJxZF2XqBH7E5DXkcxKr/uZRXW1HjaaeJOvzOKbxidlYNcksNqsSbR20CY69BQTlBq6v0REe2Yd8VNicXVV87EjNcZfs8oGM+HOIq6w2LpV6JqnrIudA9mrJXgSIT7gSVGHMaoDxDOE2LO2W56Stoy1P70yLB2SLSPzQ9UQmFQIghJyF05DWjqgTr3pkMhkol+gGN4wpLK6sPHBObImgbFsqVuwq1TLYj0xsw8x6qcT6xnS8JGl7cxTBefjpOrExQ05Cv3CGrE+FTcjF+wP6WFfNa3tbqxIXKxyQW4tUUO2wTEXMhdXz+PvMMRMVTQlhXlMQFwzdojwYKQBIg/OX9WP0/1FM0Z3q9JpoouFi18m2txXA6bjk75/Hjon2Y8T7IrZPAqj/eq1d3znLlls87JQplm2iqKTci6saBJ7CemHORQGFO0RNOtETKI/eG17AKvhI5NbQGezV/7dNzOLyEunjltBxJVo2Vob4W1XSsoP9hOq1W6m6qmUNx44eqKAeVQfOTbQbpHVqbOF/UMJNfpeWmJynXrvGVSGF8GdVQDbcI64OaX0DGvwnnLyGZMqe6YD0epoFWUbdqJt4SmpGNa6akFq9NLPvmpRzcBSHtS/L/ujFtO0lJx2LGQVGrZq4L2Ih/UL+QzKq99l4KTcthQOULJL7OA+fs/9Ihue9TYBS0YVIvREZYL94e/An/q8vkhNk3qV0njvmX4GGtdw2sQdKcbT/s8LqOh5Aww+UPVCLWq6Mek2m50na56yk+scYXBRL2B0bjM98JSreBoKu/HdRxMbcAvCKsn505/eJ390/yjaWfmkdmhw36Mf3C0VjEk/IHQEYWOcjzberntCFvOcn4Xco/CwwPv8Tb+Vn0ZVbQ59eA+8a1dDtrj/FJjjBY9qizA6orZy8YuCIXGEoh9+mR/RRPXE/guPuL+p8LUvCyMgYf84lPYtLWAn1PrVIswb8l2li7MP6FdDwAAEABJREFU/hS36FPhGUclO8Dg7cu/Ym5z73WdtZs+aHWZyV+VmT1S0trXT6wLxOSDkWlIEWAFGOYh1V+w/TAkYu2ezitHou0F/VDeiI/RUwWlldhgbnjBqrNH8G3+RK+ZKenddNksRtGqX/lnsCX/7fDttfaz/aGRNOvl87FYvSicTCjMwKs5yF7PzF+O+cvuv3I9tV5KaGDTvunv5rVZC6P7omVORGmLSOjmih9bRhFo1c40P5oV6A+kg8LqVuXY0/MLJ6dT3sr8+ejem8pBbAwsl6dUo6UEhKuEQ06WUeeuYDhhlC2FircEnqe7tQp01INCT9yMeyC09Yv7QAfuydYvTrooK5/caGExEpA12v25Puf6oDinPRM1fa+5d50fm20au5nbk0vOC368y55BqmbQ8BZCA06rvBYKo+S9ltsCPXumxmIYTIrCuNo3t8210pcyCgxHuyVRsvyyvWrJ6uP406AtsJQ/I8haFLV1sQAsSgHkVqXfxiHAM3FmYcIPuwTFOmg7FSXzPf5t/hJ8cuqpfvcj+A+zeUia1o39waUc5+D5paq3IVvdZuGUI82LK+dS+oYUN7Xsu/aNPQn7Nh7W7X00fJjAnfzefB5qzYFLl1Orb6ZWRfWSnDUMD9FrgDV7LGM96mUesJnQ7cMvqaFNLC2Chiw8lWhtdrnnirwU0thqX4lNmVJLgXzZ2NRu8URqOVU35Zm6MXdL5y+01WfQL+4FFtW4zpOP89cK3ULNy6C9VCkQjSPx++Xqtl8NhpRa4fLpGJ9+QfvynFZTHKWF9XFuL8CKH8TloCBhCk9/Jt/VQtx4KXVxRZGp9z+P/aTQpDM27PK+dofMj91QXZYiWUZLnu88XyEWhiuaL8JzSlHTQgu7+017mhBmTNj3cXUMP1uI2R38UqNF04eP2cBFOOcd7MRltgGbrpQP5Bm9ye7nLVEndYXn6fv0AWZiFLAsONqqqSBkD2txRaxw7UWcGEXoeJW5CzvhAC6dHKKsij6GIlsi63nmpPbLu5NqLXeefL5Q+6wOOiT+ZpUkEYoSmjYFLeVZlQd1RqNqsVa2yzJ75F+GWwmeqqFo00c1ukcZtMyNMDOlnWseQOQnFd7p6RtzU2e6FYvSL1F33eP3aKtWx1Rp5k0x10eOjgqw8RwTvSXNz7H74fulZM7RFlg5YMK4of0cLhx1A7XP9Ck31VIufD9649DFvn6r6u08l1Kotw9/5c3GDdo6dqOxcV3+mWVvDwgBglDsY6W/ehNkqF/9lJr182N/7nlYgMB238tXHMw3+9f0sxRZD1NtA2mTBqZHuVpf/IRpm/02iZTFXdri2NhKPfadK3L5WX1iKqY3nCqiJqSThVoLcIeHJLCbH2nLP1GXPSvSXhIHBtI1nmG93YNt59i3+os/h8r58FOaZesD25TilKCrdkmjmy03BiLaZW/jvqwc082pNcIbCsLXaqxvUV/bJTnCPNn3c/zR26IdaI6CFTUCaDjYA+1OqKXSzIll6bIcnoC4vh6s8fB94XPV3o/F8B0oLY9GVMQxT6HwGo5Jq4Q6zgz2F5fujc4v6e8Lu33dF+9Lasmpr3eCzo9buAFanKHFqtBU2ev3MZiP/baVuJ7Qoki4ArZiTCzG8g7NEwQDGavBqEHXPXmycStOXwJMSpuzS21ygO4+Kp97Hy6YyYVTL3DPiHRzi7Vl3O20erD3WZGxj7BoVYaDMAXQhw1FgEUVy7NUs13gw2xraJS7I2YIRRyw1eYQgR/zGKULYIyIMAkGMjqLFqBGwWjY7OAJO+F5Mbfygk1syoYLNs3ANCYdWHQnNNPiCUiV0ui8+j8+vdu0CjJdg+t1GsEdFS4QjhLSucfmLq2nlgBk2zdjW5h7Xyb1tOjMWS05qZvwFAnleKiCdFXwrCnOCz6nBnUEMmTrsTOiBxPVlmDCZ6zaqzBAK11FxfHB0uVC6yox7rMdpMNRAm5NmJIfvD03Z1fvaSuV2rJFqBgkSa39t5I4gbYZ9Q1PTcjzTkpnOZumwXw9i9qjS5SNl0xdQvW8ScElM++r6kKFsprJboEwoi16QcnyOr3ugtKAjBZMiXWc7srMOjl9jEWA4IYKhZNXzU9Alc3laAExME1WaOSYGTV2NOGhp+uz0xeWUcmIZ+iy5kn3NVIl7sqnz1PqZ8JTV80fqt6UN4572XrgCUt5FiPlBi64XhNsKMIHrSqsbjxqAlSB2xUraHK4uzYQ5kUBsUmRMlVTfVpotGbVjJJQhuwVz8fnh0svC50qCt+H1nZxNjuguCn9eE0+lTMlJ8AoZSHsxh/39Xo2jE6Dhd0qAbpjJJ6ZLJwS2K0slO3hKxggF6T4lh257RGg0pIzhcbYVzeNdzHvgKKdktOmCYK1iKgRPrI7OODp354NsCFGr1QhRxnMxIaOZ5Aa1rJZ3sp2NrYpxlgIxUwpzCa0h/GV5l1WJ7I41MVZhU/2sjKrEUbU21fDL7BK8TfYIMiRx8FSAM3DNzQNYRkl+WJX9vl2Hku59wUHkMDpSb5H41Wxj1zoUgA+TZ16UX3/trwXPNydm5Bk+S1tFXFqDvnASz04lQyNdNqqUQPRWydDgaV9Ota4xahhmoXNyzEqcOjJenaeJacKTIkr+KKWHDyY9LtWUlCs6ujio9upmspZpySzy1pJ3WpMMbW6CZR7q5qP3o25jInuPY/zIUdEwQzDt6/cER1uhB2W2l9d0w4xPqxrM9RARJO7LVPciOmZvme+8CNHUhYyFCLkbiC5/J6qlP3yfe0lqk3Yj2IeadfeMTJRbNjvyaPLMUKoBeB7Y8BKDHRGR1O4P6/SpsBkHFI11C0zgnwgqEJ0wD2DJ/pMNjTUJHd1Y/8A3sZREW3rn65dD+Pu1Wk4EHJ0YGGMPuaF6Pap8Gfve7nQHwWDfoHM7lIYLYu8jB/2dZJdTWqqwtjEknSCFGoVYmqO0lOiNvSWKqdVHdbC157xoyyyeWbfrucHNpDvAam0DBbli6QpnWm9jC9Tl5YhcwHgY4qiHOka31HQAPUOuE6UPjrEmSIJ+ii/Gsd7QAoIcA8C0gBqrFoUQo4MyXlQqhcgwXoLhWhFpl/vx9GU/q3FAWxye/+Mr/6whIaa53osJsyKXsOGuv+CXFVYm7vGymFKC6AZ3UloFOG6G4AZhMkpnPG2NW6QNbHVnwFhDamFNrNc2Czr6CRKc9Jwx0q5gy4DHI3nbySmBEyx6hXGKDVVztxvcp2Tp5Qt0DQIJx0i7qgoTa7XxIXYExvhQlKWpZvpDca98Wvzlml9KOjeCwpFEc9Cf9D63Y3fa6XEXqqa052ZJ58jdZtRz0KWCMmY91tLq6w+GiomK0alO4qI76q5ISCVZIqHh37A05wSoCuEW8O7eCT9e1Q7DWvqBShc64JV2SvUM91UjF/UaQhiPqJeM0qlD9pneMG8CAE1peAxpU+Kw7pdqbTOMpVHPYoB01Bv3KPHIA/xQAAIdgBjYyF91VzdyiXq6NCU8cd41hkt6qsxe55PzU6z9jIlY1hta4QhTANWORXjKCCM5OpSnCxZwvQgrG2kIa29e1mwPIs5k1Rc4Ch9t16yEilZ0M3lYEF2qtApc7vpPQf+iFxKpKNaYS3ei0qcI8eNQ3zNT2i2fkwYLbrHtggbjaFe+5qINLrNBXzat/v1+2Oi8tVe6017jg+de3Etlck6y9ts8MYb5m5KF73G9sAnW9Ro/IsoXyLlCbRpih12W/Z6qC4lHaYGD/UK/WQ4tLyoZqHsxj5YF2R91Ji5aA4zlW65N2mNKPWDvUZPlSI4gqu2cNgtO2JSYZkBxpYQ+tFdEZ5G1fCF/Na7rMlZRx0Rn0OWTJzRMv4Z0WDK0OdwEiNfF1dAeTbkmWU4MgylK62rHqDVcB1hqIWWhiEaTBshN+iiMmI3M03O28gY7pKIsI9C0CEL6NZyN76J68rYZVqI4iI/LZEvW9fj72rKcSovpQyMdv1SRYaK5dYW/NnKRzWzzn1l2YP3peGyNECC5SUAN21qPRk5rhHmwvKpddI05q2KmkXgE719sJnvAduI2Nv3R0+A13bsfrac0Z5rnSvmN4KiFmNcLhI28JE01XxeLvC26V99vs+pHo2e03xux+2WXFiyWL1Prnp+mO7zmDyUKtJ2ORUdUTbfxK97vqrpAdcL9nwbugf+utY6CUFLaGwht/4o+0OiXgg9o911u0MZnzuyvc3MF5TUeFf3eTsaPJpWqqFUs6mnOzlUKsHLxjw78lBTKc3FYpD2Uo33l2N8WVxSbS1gvX9FJiTutpXjBQh3VyucPpCGrN7pZunzqkUudUSXnpd8YmKaB0wYzBENbSWMlCxGLQGDeUYrSGaolekJ6LsTCTuXYFyUZPJSlxbEX0jo6GnlVC+7N3H3u6OQezdcwE9xJyi8Bfa6bFFF/hOUbi4/P1ddHgufyeslwOFUDcjiDPnAv3SRcp+62wpXjhVFV4pMFam5UqcfsCmILEMNbssCNX1URP45E1+F1sCbVvWvrCOFKM/WUoM59rR9jbXa3k5lphKa1Xnm+6v6PJYPp4VU08Hrmh1GtwkJ52rokeBWKav8D1XVxrHds4+elNQUZ5xTVn0NrjvFALAeXL/egBIKEZ9GDJTWo3SWkPFt29tdw/9s/kKXtqPid34eRO7BVRt9td7mih4R2bgnT2dBtw6xGW5W1rdIx9f5y1bX9uNqJXbK82xlnVCjOufM/US1iNUUXd35Da+1R5CK2Ps79dAJkzTWCMaVts7bGA+zot2434wbr1hqfj/Svem/Zv1KPjWZw0dL0m74bf4nkL81w+mg5AC3N85n1nDDF9dm9MTzrPpGf+fpfhbkJYSiQ5qtnufp+9sSR8mEhxOYnsrJtha6fdA5vZifeD67pDKOs6LVLpUN5zLTEwztMp4ZXYnFWADVMeKonj+J63Y+aw7PyawkBDwMXqrC58efj3WUHiqdz6M3jFqmy+SYCmcVtaq2JMZ74hvMXWn9ML7qSi5H9SLhN/Arnl+fVpMRIZ/pHQKbw2tkYZUsjKj0U1utRDXukoBJhYEd/f4PrcriF9bdO+dn4foD6w8zauXec5uihk8b5K69WzR2jTLq5zv5Pfqxdwc4GhlmQRXTOohnztMpqs4SjAWqZpYLIkWHZ52v11d2BPjUPZWNGpXh7XNPZs9Y5rpzPWTxDHTNV6x9qZdlTKvnrGMLQPfUSBQoJ3XictvbzLRM2CUt55a/GHH0EIeC8JawnK0eyBUlZZverr1M9iTNlpW52D+8r/iuV60V06u5GeEe5AV+hgYU52ErPEdHIm/cj47MuHGps+SMQtSPstN7FZf9mC9Iby1wJq16P64Dz8yvZj5hGarQxRPQZYa+HO2PcWn37NrifTY/H997O9cUOl8HRt99XVDG+jP0Js+HzyfuzzoXhrVy1u3MK0ubHaF/HkV0sLN2tSNIhympfgl4Hkgu52GdlyCV2BosK3Ng0S303fgS15J3pSUj3T8ki2wAABAASURBVEadSEwMD5R2Y1LAJPYoduPGNjU8W++/D9ePrc8Tl65nsoqAyYWqr+3MetwUQXd6TXAnf3KumY1RTf7MuWo402Lghn3Jr3VJOvaVy5bmsBmzf3Jv+II4E1XqcUe+rK5Nobe99zrb72vO7voBPsQnCE5Se44zlP3m1LsJGxp6cdSEV5dEd8QfYApILq6miwhViC3a737CbhcNgRpTmyi32e4RklnMDJHDp/wRfUHfuDjSlMCCM/0dYOhhieIz2QTC3yojhxr109x6Z+IwU+tQRo94/Llz69Zd+WgLNFpqe6HMlEiWuukDvmCNns7vOhaYy7UY+NXmRg2/xaQpoWUeWE6lM1omJO8p799yeNCl8J7h3cgWYzs91KO10Hf0sZSXM1ofcLILGP0TUeBhHFlJJfYCVoODTH/91j6z37vAsAKzpb/7bSC31W3I2XNW6nanvaiuGQ/s/m3XjBPrUKYGYb79FRiuan96lHI+nr9cParyxbI8y6fUoNTyrm9+7vnjkNXo1i+W83cTQoJNhaw3NnB5sRMyc9KmAzBiLtSa3IlQVAzBaQk7buoWWsnVVkxN55FpjBN0aBuUaxFbxnhc7gZ4tEaULo/NY/U0zoRTyldffDJT17+2nu25CxxTrb5+KegNJMP9la42rBKh5Jq1ntw5soDpWadWnmjhPhwFtYB3HoYEAcW8T686jgzOWi1A7ZN+Edfb3+6c1kuRSo9pT7qZefTGTRUdbuO5ZA0+FBa36nWQ78NhaWQ+/eoV8OP7sZWOdqh39RKOt8038vyOFCoxmSj2M4qrdz9LB4pwGNXb9nVf1y9DQxX0RnSIlFpNyKo6XH/SOEP0eL82+lXcSUylhiJOh65iNQWC/niQhOprxPfAWUDKPMxmYcwMG/AwJftORBqSr1ymugrPiPo9b/Uw2i05siYBN6qR1XxUNDePmoA09CE3VzCCelkX67HJQUtreWDStcwbsbBMqieUiNnZ7DC2QZlTZkvZqw57GgYF1j0YwFg2x04GmroAaQwdEpaibfmpt7jymZ4xowhemlAQwcMaaIoj26RrG6eZh9taiCjH8E3eOLVCRA6WQoR0s/tedmzRTeZV1exSSXoZBKXCWjI1xG9X7dPRbTMNFIAxKeOV0ZmuaDmSJ0Jlvnwv7rDLuawCD5P+rOhlTm6OPKrkxajRZKd7QeEppf0CEEljQx1Dv1GIYox7q/wuwAbWjuQ56lmjLijBzadqd7vjNe8kh5KSwd1Xx3ytk4Ky8tfU4z68JECBLD6+loedsBpr00TY8fV9a2kQotMWKBQjyoJgHFXmg8pB52ruI6B3zNKBZrXptwZeruHqB2n8bvxyZFBqefFTpydaGTaR8FyBj1NhT/uRiaHUo6QBQTvFd70ZA3qxQyFLeaJiefmPch4bao1+V0eTDZF7GaWSEW85Vh39Thj1tyz57eBARTYOGjgXa/UdnVbZjXN7p2sy5RDzzBwFiSOYxIxD/37gW0qcOoUcFyNG9YcRo+2htfm8sV0F6Q0tU+qPIlIkuPfeo1BdrtGcmpHWvPkBw6hwWC5iuvvNGwc41JrBhxgWeDSRvrD3CaZZW7cn7EBx4kk7Z9VbdBNU5giZnGo9HKjCWJESa38abMhJdt5q3SVn0nOw7HUCEzRFS0TXHiXWzZ5d80lGvf4em6F1rXosS2I4bYU9sFcX0Y/VQcNe8YoWKNWm0JrsKmsygeQcw2MKPqDIHvojvbqrIL2GNZnTg0JxxvRPrksKiDSX7S70A/mTdqDAC5Mg4cPXdQtP48CPmrltnY6Vi2rgysG8wYd4No+2qVuCeYejFYCJOveCfwUL+iD7OTjaHNjYNfxjVHZjXMPogJKEdznSWnu484GKnjzGMSyWVrE1d3hqgQUov2d1+inmqdFmdY4QhhNjMbNQjefuvlMJFnR2aKOOMWDSCgRM8UpkdIGp0+4r/NUP1C3WmJLDaS91ClBwyq7UrmpVM6rBUJEJSCGKjHjDFTy+aftAgaFKECp2oUppQeTNfGcOR5qawNofD9LayZi1kltpzeTk0/VkVdb8jvyYPfCpjdw5Oq4bXkLCaG5B19803obHohWCyNCGEmI9oNX/Qd5TzzyF1/VP1nUSp57Hn+/F/PgLBt7fmBZgPNioGMVDOMv92aH19/nR+ynYCOEYaC/mN7pPMpUXAO80HDoFpik0QumSbS8x7ID+Up/hwz0wgxrSEyGii0CiD7M50+ZFPw19UAgets0FblhQJSjUB+RAub9/QAz06xBft1umBLDhAlz/YGIrMac63/zkB8dbXqC31u7S4V4TNvpQzWOTPmQ6sqzKXjsxXE0fXQOelqJl6xFbxglJRyxiYYLal6hRK/wiejA1ftIfzN2a16LhHnYPq1FI6P7s4+0SrejSleMshWTLOmg+YW2h7KxlfK7fzDA1fUYC7MhCaZo19qxI9xQq9k5poLAqLqh2JbUW66LC/XIlfQyPwHNB58qXT5hmjgZQaYiCOTN5pkrmn3gDcf22KHX7V7ab2zW8YdpjqY0oW38RQC3CVDNCJbQ1N+kcxmaNPaYC9BZxZiGVaHkbbRLEq70lmjZw6H5QlpeD9Hozfu2BDRwFrBhAjdOjN4ritYX+xlxdVsCIh1lVLnBQwZ7BGZekS5b1YbJHIsec0IExRWQ2hKIK2GcC5YMc3r7KhUXxqaXHSieRpapoy/JTM5mjupJ5AtTcZU5NUJ6d0KEw3ZfbD7kEGNaJSxs6CeQ3QjTNQ5Jn+uDukS4egciCKsLG8HsABQzXBd/Dq1mtuuORR5Q0I0VOI29a68bDdLnPiDmVsvE2Kg9lbNkY7/JsZhvNbDNi1hoHkMCDJ/s7ddDtCX/aTkBdmuwBTivTlNAsYRmlyAfWUBsOnDQN8JllrSeAl0ppp6m3u6M206hqcxaZx0otI1uWpvuLbJZBXQ3qT30OrCyUp04BHM6DePo6Z1ex0NAhxhQ01QdgSQ7mt3ks0+exM9ux391j1PI0a/O2pchsMTRGkUw8teVbrg1ivoVK/zZjSnbGB2/JvcWb1TrFIsrQ8oTJ8TtNctC5bR0chXEvFlvWAmOvNLT/v8vusUp1uOvKy03FmMnzWIsXChl/1zCRfqsfJzCDBm/rXvLKgimOEdu8N4+AvZSQy3/KZsWGX4BUp1GawoqB6W5cCtMbqI/dnvf6dmjrhhXugiKQK9PP22pFw9Pzco5iI3qsQcVRqJw3GJuT+9z+PtXTCRRgHDcsahUYNkEeRxIDgWux4PqubLHhzyz9ViqXHMpgYcolFeREcpMGwmdMdhJ1z/PYDT6vuaGaiYoZo4uAzoYQGDNNYHXfaG2Jj1nSCixCuCxFog8GW1NaxMgBKUVPu/l+81eYIpDf2Xdc2mZ2DNWjayjxHVX/kCKhMrNTQYFvtjBvYXIguB4whfSjiVTPxeUMUjWxvvOT/Gm4VjeBEJu2aHpBMYbNZRa98PzghKP9RRPNlWMJHNmlpwEvmrDNFXSUSrf6BfcQX/JWpOJGMcOBhdmw5b5jyS0mxlAKVhJj/X7DAtSusntkhp3IFMa6+Cjz0NlWPChXu4kdzuXnoszszpDKxx9+0jzCfz527SV6fwZXC6nJ92GjtVYw6d6niZqRcGa+zTNsoPWjMm5zpbNG1CxaqEpzdUkYz1wHO8OP9SnU08ieF/9ugbaNl3HZCt+XY+12H4wh1Xncz0gXg/ON/BXTm2sjACEm76NsPEyvYOjvCFtFXRAIargpaJ2S5NTbf+p7Ks89KojJ6RwkrH0RMbUAM6e5TVsNpQslN6StNnVMAgzTdw3XdS+X+/fpkCjmggdceDLXqKceOuHvdMq6MoHWp5QVrunI8dPR0LT+oDdI7oo/HqXlRrnUStuK8cmZh4rECAIk3qp4Gb7X5PKFz+dcJkLQFl+TdOooqZxsSwakztjObXjdPZLE7+L72pzJ3wtBsrtFpgOhRJeNez/Z+QB3jNqyrxJ+YUcPm/coJx0a6aVdgtljbL6pauxwfr+q2L+sH6J1dbNlnDnqeT6zfreUM/Nl3cwv5rHK/Tw/31/7e2wCWqDpSlUHAUNZ1npmNxaFzxoGX47ob22r3+pmS8+ppuQQy3AN93Ot23bq+e8v58n2/P7k+eOBYJLrzhqPWRsse37F/aVyxvX1j6YNVtNgDpWeBNyplZOPxq4ZL8aRluvKG/dzsh7vl/NGJepYNUeQNFA4xyfdUbNOKzReU5/0uM0S09n+nE8fPTIPLf/m8wp/760E1ZSzW2mr3q0lLuUee0VsvF1AhNdVvI57LcAHhqa3wy9G0AzZU60fxIlIacj3ewUmwruhrUB+VQ8op12UNImM+YbdsKdDq8eb0CCFv+cwtYg8M8+b5zmrTtRjcnDsOfsWh9QdsxYFw8gi9bKhfD1Tz9zAvfEV1+eZ02yutX29qegczs2DSP80/4K8VGYJu2fO03ZmQZSDexhN6J8jiEi6RvEsO+stu/LSg8m7nsMnuyutDiXKhe4+3+k71q/5+Z7PrydOTZ6qu/3GlfxJ2kCvho9yni6g0eNjV8casxldpnO5GfRpUr2wRyG9t2NrLqeI8yB/WwBrrGEvA11retfl8xeFz3M8R1dFghVWGep6uBJigS4q1w3eKCUyv56+8+4sSw3zGpes9mktvHxs2mrkpJs1rrnLeAmbuXL4RR+lIMXO9eRqSaRll/P1c0TISZYX5Hcqbzu//CnxhaHKVuKPFUYDBo/xGzwMcJFrsJ+M75d5Y4QbwgQa0EH8Bkcm15+/F8H5o30zqLqNB/jKP3TVDqempzGU6DwdY0UdTkqMZcoPN39vozDdwxTaxVKigkKYGmLmcyO+io+avih45Qfh3a7NQ5y88GwrrTBLw0OhTPi+erscd4lQvaHR3S5bGqhsMKJaeaIufLfIuXb/fHJvQF7QEyO1iaBOYdPBjPP409GUNrBB186h/7jn52uR24ZvPOwXVE5TJlxYiuPXedV8j4Jl+9ICuOiyyk1qoq0FhJ4V3Ovb+1fs4zaKfBRKD1Sd+fbZVR2/xTqW2lTCjfyMfe4thST4aLxAoWx7vfKR1Nanlh2bU3h6VVyzt31sNhHGERYOiBDIWl9QCXg8Sw8p/V6La13uThBzNs4e7vT9VRFVoBSaRMEQtzNzjmVyRsQdiMPDu01t5Ih4mMIrj4H8UkgtCqyqyrwFSpl215p/jvyMV0YVS6h5CTaJAuYqSzJH0plPai01e3fi9mxgI1zwud2Lwic0Gmj2GRFfg2MK1dvsBJ4o1QbwYWvwggpMPfpeeHXfLr9vyCOwXOmBDbWN9UCudTHyqcl3vbQxqALdR9jajM/aDNKgqTpiXqgXyChbaa7AJG8fBC6M6oq/Hh7FEnJRokYXIHolA1VztRVCMnhENcbS7GsK/ij+ko/Bqlx5WA9Vykpl21vjAyFg04PbPKHoaA0ICB0cDbVjim10Tmeh1yBo24RnkaUxfNbkxfy0+rFBJitEAAAQAElEQVQZXLxtTxEwmGfsWyq43UNOWmdCq4NGvRacysaKAqDvylQgInLWzDJz/VJxsF+QFuCcV/sUIZ09+Xg/C2XDcCp6+6S9zcxBS5tnMR01oMBWJtKkSx51imwPCXXDjZ/j16Fnp+XR4/m/Ai25orgai/SLkgwM7JLbxmMYx0X+GLQxeqHRVTgisZ4ya9VpXi0dJZItlSrmGuroMaDxlpJjCSnJV/EsTLQIKauyIWop/kxHERhydJIF67YppNYgKS/bKkeYMRDutbyiwx7HtztbXlpqGpyUbHFARaAyEp9tgV4ybl2sN25LtgI3J0OceA20vm7pCshBNTBj04ajT/rcVZhDElww04DH6tcnz6dbJqfx5Dn95OSbgy/ZFZbWwlxkXuSHpmVxOUTs9G9hKME1Hhne/oZugm6QwMiFIpMzAASsXii6opoDJYVd4517KjDOVtToJ1ASnrpKP4XvjKKyFg4yMaOVToRF0mSfmkIpZ5ua1cgvKxSxb4QlnuobL3WxcC0nTcIvNABH+KvLLRxmVgkkN7guFUqNLcGEOLuR6lC2evtcdNpS2MUmkz7iQx9J19tFLPSWdRMqzgP9dSotK60mw3w/qxH9mv4T56seU2/l/RC7Oe1lqvx5haX4JsZdfFd7n1WPoo1YbkskRY712EzpaNjD6/aliKfPEX1imzJBqUSuWzkBDoVCQgb6x1I3f+tNHTQdGiJTb9Y2ykx11UMJ5hWKtJI1b/lDvNlc5jrCHaQORQ6rwQtzmnLoXE+ug5CJxls7p+ek1g1oiyZNp3BI4zOOdKOb+7b5mRR61XYqHkZqusyaula/Vte0LvXWrltBvbnHKNKhkfKhHbyat9/nV/evijs/k4FjPGxhHBfUmAwQQKVu0CYUZ9ClisBwzdiDgfrPmnYxhTGDEzoVfq6Aej5b6ShViTsFD0XPgPNSkV59HU1BAg+1IZ5UvMlTrVnvKIWwOmLCfJUOTZOWp+t5sqYqEutuwQi46Z+/Sj4q4pG/KpmAyNl+iNP5SNqlhCgAPIglGMagNjPsyZlnWu90xXw6ZgKglqHEPMZU0+JDZdE0WXkvDkWen2Y+Rw0AQMkh4cr9vQMrQVTPwYMpDk2wjp+JaciCu3M51FCmH6mFfTbVqghFNWJwx/2xW/sQnYFaaxWfw0pp2R/U09kiX7rQHZY2IH11i7K1GAY5Q/fMKSuVgxBVbGKdoz56zmjzoCOntRC8BtG64nIio+wH0lkU45k4otrfQK9VyzXMkYt3oKQ0jih1Ty3FHFA4bz6KeKGVnYp0Vs5LE2Z5aeAI7l5AGMODsyvleFCEftY99n1a7DYTXjrf05XnqafmaT7FTlohHui3Jpl+4o2lwiS6I6Pgzpc0sdWu0dr8if7Xoly19bP6Dn7XPCFedM5CoZXbyLKYHmqSDKTNOJUH6aD6nfwoQ60hfeULKkZICTMpeE6dmhItrayszE/YTYjY6sns6tZE91o+W23p41o5QVCkoLN1zIyA/zpF48glbBSBtZv8zafNluvDENazJ30S1AEqaf0AfswO36Qahl4cBeN4y1bJZKBDDuE631zwMMh33qM8pDRPjQV1vXurwuY8KiZqjOEeJVjTA4eFtfTp+5jLCWTPUZaZVWfmdj4aEcb1oh2ntm56lNr02yikYNUnZ138rdUx1Ol7RquqUC940+D6x4ALiB5hNBKC4BD/M1l4KjJHqLN7povzmrbdpUgofBxpflMlazonLV+iMBoua0cVuNDio7zBJkseh16Io2gGIMazTLU9zT2Md5SQyGancyKaUUbNMxsLL7daPrQsKiIKguv4aoc2OOknrdpkWGQl5UURLakEnuAX/aA1dMQXvV3x3BJ76+RYN0Wgmm2UXCqGd692ZlUZcjv2FfzZ87GXXAcw2ZWnsjpLDSvGTGpipPvxrcJRDIKXCkxTWuHWjKmrRHKxwzO8DFDMp6dKlTNWWO9h62wgoIGGEXjy12KwTc1Ob3pAq1K0fMzSqW4nqbwDi7wpIzYj1Ob1xJGt7P3VrZ28ZE82CoQCNrdela5vt3OEhBIHQCUzxl42bnC6WSiWl0qTCvpp5KGd8SqiNcFJrR/N1xfkuq06skO/Sc2HgjFNSTqhH4VXUr0fU8UvjlMBnVTD4atRyNCrr5GdxzOj27M5lBldEk5MClLs4lVQmInVpMQldTpbMViNA2hZXpqIBgY+ijO5x8RpOMZGQ3XwtKU+KmlX6pqxQZrBdyt5Woza80RMtK9fy7INaVmrXOdYgZTgFV+r31lLHeJ0dL90XvmXV+JMTqRpXxUG2wp3OKrown3ye7r0vLQdcL3Mg6rc8PWWlIaSA0n+SbuP1yFGo4j4jO4nqEdcdjlMnPBB3Uo1Rw2gMeJXEdyTp8RWCYXArGnQON5tmTXZS2UxvmgrsAUTHb4Ae/1OC8jBnv6kdBDuM2JPBp9+sXYUcaBzHXnM0tiElZSGbMEEqkY93bRLQxAy3bZyPEXivuHP099znsY4uCFXTFopCMTyvKy2lYtDz7Q42GCsrs8rbehPvSd6R9nqYzJrnOP9Rf1qRdR6dKnuAlkItBGpiivx0RCmbCnePYOP+osikomcecsRqFznXry3P0tkFVHTrNYVRLVcoQ3NBL8bqlspYSbnaK2CMMaSpEa3bnfMfVFJGNyVlEc8tG/aOavgFs1duHyuBTWbUHOQYYOtkQk1KN0fPJ2FG/iCqaXvsfWQDbz+h0+3uNQKMhLmsXfF+bPneRt3IhXzdeOjkGcYkP5gwTR/ZvV4NFiYG+/y2Dbiyob9eN7+Zmtha5skQR/CpQFu4mrup68nIwcJMa5Yeu7pPmVP8mTZnIUyM5J4YZuJHmafFqCgcwGBQXRvSxFBa5mKB1rSYoaaqRslLKnxlQxqP73vg0xO6STC6lXVvfCsKs0GYVGuooUl8UTmymZbVYyit/507lOK+TR4YAoQq4HnnNYOY2BOfuR7vMcBg16mbV+IFfQT9XBS4WDdGSV4Kzt6PpV/SJHrq6doY33GRMCdlUfzNy59kRLHoaVu6UThlDhUxOHm1wqhfEaLedT0C2Wp2faN1uw0wSWdRd/ht2tfsCStnzt+vv4541PBeaMWtRPV9hjvwO1IdhE31TrW8/VJ7SdORNG7gPTA2mWXkJunlUtMOpuY/s1QR8A+jk8NlXeqh3kz0JwWLEL+hainR2kOHhW7VumCqINciLcKscAyPWC7b+l0BtrClD2v6t1k6Oupx1GPPbAZSoyRMvf+KN5fS/ddioHCyH0b3hp9CO0u71E20A7wt8GuOcy80n5qX0MO8TJHUcz1+GXqHH2h2C+62B0/vS41DjhjSjd7+xw71d8ZCbh6xfBim7CHPc5DNFwqOr1aZYIWUGzh8XncHqrolGQj0/JNUe/IY/GAd57nee7MsoYFwAGti4DZYB96G/UxG60yaF8wFdiaqZmvkvTphflw/qDpC4wQed9Tf3bfFPRt317fWQeS62tTa1wvGxmPNb5b5+nl0tulbcMjhPWJykNvv9anXbX+3DgpgCa3HbOmosLni8+zPvP5cC9pFziY2zCMDf0M9zOiWxl/2a3V02DuA5+z6151fOS46/2vMN8VCeVtJb8DPeqrn7Br7+V6+E0TMG7Qd7Oc6nxLlOMgXiKh0GqAWazNYzjWTYgDlZmnCBm5jQ+hJcP30ypz6SuRd6mSNN1qWJh5k3tYenUlXjcqmLRq+iqtqvzg9YlGZT0S9lM40MIt+V0lManr6dZGOHRinqlfMzeg2AB0ox9OL58VlEW5tET/dq2/T09q2HYt2sp7UUErlslEPYazmH7TX0W0UDQYhy61mGGZCSijBWERtj4em2Hj2VG13k6mdLzsUwxoEMZLEA26HbmtdJMZfswf6+ZBjxEui/oIZM6v25+0Ny/lkWNpKr5uKL9jUuDi+LtPu9kZNEnAOeRYZOzhBdv9uh7hjPHgWivWJ6Dt81a/fdoaGjCrivlI3W1BXldf79O+NwRtieJ7LLt7ZIayVFOFAhQQy/xAmRCJU5Nd1dWH9eUUFAre15QA16TzlVpJfZIta4txN0zMufCsRp+Q+ltfr0yprv6Y2nh8vsaHcZuVTZOSAoxY3tcPhTpNb7efvR+sfDEcs1S5JObBZABrs/d83d6Yzrp+bT0eDo2ZoQ/Yhe5X8VGxTvD2xEncfOWx5AtCqu6aU52ZyMacsa8qSEerAfiDKj7PejK6RBN8U4gHXAL+gbDVfM9cJ/kgj35x9I9vjh+Ffby1a5FOjVI+XLuw82i/KlEt56vMcNB3id3R1st/e4HzBaOx5vxI6Cw4YOgVovITc9nZlsvUMMHxGQNpZojrXOvYpFXYs1fR+cDrCKUTePSpcR5+D/OImvGeFfhshs+eRw5nZRuUBUI4It45NsLTjTvCzPRemY+8ciCVBdrZCpFDQpSvhPLWUuoao9jMi7KAGVl+jDnD0lAoTxia02hMUXrkJGdQF1PxamxX1mmMOemiPZiLcwGimuJrEDGQkO93qtFkmHDMNi6a3gwUBqHz9s6kMAn1SOWHt/QNMhuCs8719BtItFnZFGyg8ILqRbcMUTebguh+9+E5LLNe/ZoJJVY9NdC266RJDp2w7o1x2aimRDvvMI3URKSSJG1s03wzLgXR+lyYHHw7+1WkASFsSyRDKUEXM09mdTcrUWMpxZR7vuViys8NOZ5NLdZTmimjzUw+aWkNOjqsRmqdrMmoW9sYWa4RbSYeDSCdcGrUyD3fLOpupOyHWqSjVmoXpONM3/2cqmmxF09ZntJHyWGoct8jLO+jhsZZYw5Xd/u6P9gJb4M6VcmTMpwZHMU1KIKbrhqlYMxjqny5NSAGi290lNhH+OvHWnm+kGeLSg9bYaqqwBhalmzINj7E8cjts7pRUMCT3l97fmn34F/YCQImia0uVop/HQDa4+mVaSLYGsvGXQCRQ9lJMC/za+SzO6SRleo37ggEGemoD2xhps+LeBKao4/WwgyWtHaaGjmMg0NTnG3uPpvjb+iDrLbMmXw3vCyIWQWZes39O9jfNgGD8C3KuutdlDgYtX1v5cHX3RzZ8apto3WEeaE1siAg8rancJtWV7XEkktxfrtKwRQy50vVG9P4jbmj1bLoo0ua7/pDNt1aGbc9majxZ9ZXcWM0MFBRuY4KqF/88R18Q6jKD6CozQY8pfDAgSrUO6iaMel2Hk4lXj6sx1qXz7JnW+veJY232ruJOZoxWMOeakz3+CGOaEKuSbNzJr9HONoeFz2KP6taD+OOnj7TZnr0CiGnZWvV85njbrhWhUQ7glY+iv4U766nXvjlohct3NSa6rXBGcwEtUlx09+jjN7sDLjsLSFFmIqri8fn+VStFgTetsr3WAhPTJefZ/WtEdLOa9ERO0I/UL17DAUQ4MUZNg+mRuBSwZBQ1vU4IKqYg1Oj36MwCj5YaG1nLrEyyWlEuAtMc4vglpyvwufUrokWl6FgFMmRpSF+pgAAEABJREFURlqYWlEjpSLNTYmLwbh4rVgi7THIE1PRQfLXQdIAm37FNU9X59PfX9qI/I7Jmp/UDQMIsfyMPUvQ2SgMVxNhN2RScH5oH+yFN6rMkCSBJtNiWjuc+Bl4HMfRw9+4u09LzFMroe+b97xg5IrnGnXNw5V0n/ZqSgfNnklgiHbJpWClTxBSSszs42fFV6pblhfva74bRXhTK66eGjzgI1A9KZ/WtYasvg++jz+5+rE5LXpmZNVxoCytFswft7oMYQDk8mujtizaRPCLp7XP8IFwa63ad9pzolSY57JWCcyp/I6oH4PQb5mHW36u5/OTezWk06efA0heK1ynIlBaNMxkpZzqlZd8thw1JP+ceioVrWC/ll9KZTeEnju5TfuPCTCli1LRBpYUQ/NZWpOhavjV0EVCIjVppx5FkPOMshuwaAnPN5wstSLWO3hUurJhFgj0xlme4/60nX69rYu2loVY3Knbyiu43Hy3XzIo+UOidpQwF4U0c4cV/Tb/uhVEBl0KoGGDa3QPpJmzsnbnem+4bkSNEwj9GGw8Hrtu2qnOpzPxrfpOPrhnklmYQj5ZD97F9bhW/kFP5FzZ1+klFV8q6LDkpWRqFBmEFtYuWhNCQ6QFsQiqxk/eKdzbFaXtJ/To4arcow2zgvDm4trrQ2UerdXhghuWKVupft+mDbXwb2nlWzJ1Wp+sBfnwKU/xmsWnPRnANlXBK55TRxqxaqQommniCLJYt+OYBAFxRWzaXrbZ7/oIc3clqHG6iHTU9lzIsP7dl96phLvRA1SpXGPC3wCjQcpYepdEC4TBntZcgyAiKnbulerz6tSCOv2MxNDVZ54Hv1iPVgGN6zUlB7X8XpbuDXi3WJTt2Fhad6/SqYd9z1sgI6PF68Z+uXq3SF8wocJwdR08OdnlGM03Gc5WQNVloDTxxq2zegdXBkYygrGv2j9JeR1LmPQiSoFCVstAj/hZl82aX0OFodo2P9f0ZxBeE9XR1HVDCI3KrKpRTBhaKxgOqhTE3TIODca0jOev0znQSK1FT1n3Bggoz0a1n1bNGqHo83fthkPkUfx2/14LoBfLTI+8tAqlya6Su2hynG1TjHbXXJPD8khZEHdkS9EXej8TD69jS+XAp6lh15wPcjHoq2NNxOAy7KXPNJ1FXT3eOLG+n0t9z61DwpvzzPyyLhP6FsLid5cHNNE5O99V0m3bq0HftE8H1XINt7TdtWp4+qTK9FEtvbM8Wgu5beMKIBqmg6rQFkDTmo2BNFXAO2MPbfXEDRNm7Yb3dY+Co62lhrP9oaWAkmvuMgfpyZ+mUpQrxSnEVlmM0w0K0BKTWVZqjkqU1a2P3cOmfAni2dYfPs4znDGTqjBXlPsBHhgfTjYiBShjIS8e0+ygKWIk57REAySqeNpTB3B4SjKAnYp5TFxdavbfVc/WIp5FQR2n1kNyeVxiqWVcMJpWFywzF3VxwGpJBmwPXPBtce1YugpMVf7K8zw8Kc+tCLKn1cGe03oDeL0A2Wm2zBQ0BC3YgJ65uR7sFlbZMflKXWfbdRJvQA7DiKFpt2dr2W7ZW+me5BDMEX8g3t+pbtGrH92bNWeUE66eq6aHj3FcP8pBQahJjA+8u0qRl+Yr0c9hcHBNLc2nWpyQEOw3RIPJ4TFVUmOVtOT6USG0Ro6Nvc+CONzHdplwIkSixOFBNxtVjT734DEt32dGcdtAjxp/HC5PJj8dmIAE5NPIiQe66WDmtAJ00rRdV0FU6GMSupy9FUCXcMjoFI1trv2ActcsaEEIcVscE+I118Fq91i+lthTAUk+pf3fhVi2tatUj/IuF4Zzrz7CZaBotTZ0AgdWXAojJmw4MYL7lessZCT4WCXPtP7ZIIav+scp6fvdQpFNe8wThnaDm4CRQXJAZlju1dcmFxAF6ZZH0Qdjz6Xeg0fjzng1qiTt3FWf8+xv8ltzZqLWT8XHALZ6icopaWNsO5g/dc6H0dvxwWwRI6R1cuHZfmxNr0GE98ef3bcWCH+3TJleo76hy+KWhBXEybytWEEYQUQrGU0+0M7gnLyRKqGOMa9g7/ENu8yWo6lFBzlAtwnsqTd9fdTnmWYhnvjjuy4Vfb+7s8eEH2IqNNd5fB21urNLGaritzl/7HzqMX0yn+Cs6niST6LrsMr1+Rgf9N2W89Hw68RVaAgnZTODZdDy7faGU75MElnBy+RW8462OJ0qDKeZ2N/+2CtPLTw1EhhrvSP7lS/HMTtkIS5bVwsy2ZcvSoB5DL/kFf19z69Fc1P30I1gbeMKiD3VR8kbRka32t4GDcrDSgyMUzFWsPZwxzJzJ70rqkKOp6Ht8FOZaogS2tZFBbSstylUYVyGcdOnn+pPNh21sv0RQjEJDTLA2Bj8DlxTGnhEnKvjj8FchWhxFJ630RYWaRG9LhWwtfuhpaFZzSl7HnteF3o79RG2Dq5VP+t8oi1RXXf2n00pVnAl7HDpDfvEuUXtJdNW0gEThHSTdXBa9JqgiXprkdblbGX2xHrorUDJ1nBMs836Ku6vdSPXYD/hubuu0a2tUS9deMPmb5QkTzji0pXIcSWvxirHpFESayGtsLNUdmWe4UVXsrINQehPx68K1WZ9maqKl7HVvQ68Bb7btREGP3u0EcIaU8GrnO05z7e+96mptctWmyEW6AQ5NCgggHhz0hR/V81t2rwwju30xWN+43P3T98f3JuPMZ/33MWzfIZy81PqWWD7y4gEEOMxU9lPz3ciqfSiy5BSifqFSwoc+RSq+yCmDTMj8L2XpaMERh+9vZxrxeLb201c89q2N8TzD2Ssg2VdbnLRqWDpQ3cIiaOSvepRi7CfiLWDP6xdCjciVeE7BDQ6rML1TgcXEMpi1HTi9LpzPfny29QFltXpTPWwIlLn+eqrxlUqLo0W3CTLP2d9FYIpEgns8+0lYSn1udoDFuHhuqbsmXIG9SlUVp6qUmvyq7VmCgXBIZd+6GdsvnGVsHDxXa3ROY2m4qJWB7zLiQIZWk915+RT9ZT9ajvFmqp8z7VUjuL14Oc+fzxn9xngKZzEo4sGW4wP/QNh0SYfgwmG3IbP+jy2ErBh2KwXqkYezhNT+rLkMxK2wCuc2YMd5sulxYFye0qNn9iyUj82n3YqOLIcFk1MTRduRPtF24W3Ctut3BqNSqVmYz7hrbx7yFwfwaLn8/mH8y9Cr2W2L17qH9HmfYyPIPk9XJvQz5/iIn0099QAOlQDZmo1rTooxMjqAkyZe357bk47Sur1thAVebZOImdyKqnKUe+CKBMuClXZsBmNSHQylVY0wyk8ciFD9ZqQAoTMtPWxEau9vK46nkoTHQNBskrl73os8UVM2PXBtdxAo2xCluZ2PIGzM+Pdu9geWHfVffAc/urWXxBm5gt79U/Y5rIO5TgAAnWxg6S70PgOboNMhHG5qBM+yW6mH4nOMJtwjZ6avtGPtM6b9h3XI8DWSWHFGdS8vxb6gqRjbsu5TCaA8nOr+1mZ4XJqY0G8e86U744U2zbL5gf8BD9XrO3uxp55ctJmNlymdDMr/Vl236qEI5IeRrVpl3wmG73afpo/wFay0RlUW2uYYiv0ywufUM6KdKqjwKZim9WgEOjo9gIac8CBKe2wl9R29Rq2L+H23Xguq+Uq+zBfrjfsmPKBXt0yXQrHAV+c6MHjo846GHtg+fIwHCaiodnC4+x1z7obBzi0TsU8atr49CwmMVBOvDZoM8JltDjosGmTJcqvzipNKwspq8TBoM5UdvKkQcirOTsWTUaEp+MlXOcVjmMMYI/weN8O7Yy6Sa9SYrAxIfFzzUeTpJ+G+kfJqhCe1ryWm050a6hsSTArwmf5EQOQynTgdGk5Uip7zRV0RdsDvr4I5d2So11nVh9zdZoImJdmlylEXhSdCRYLK0e0PDO/Dr5HhoxGYfVE3+yzXJvXxrYJFc9ecvZx/6hvOV+H01fDCjlrwm2BMTboQn3tSTHkcvAMqw1tJiaE1baWdtiZfB7cZTQsAatGodnKS8T09KFSgtaYXVxvJo3h6xanmImXtc6Q6RT9P+ftyjgzeVKZSCZtRdxhGWyficHusj71ze8vHsnvSN6CLdOzX2dyMCfqu0+y9TrNfucFYWyjylFLJqiA1ZWxyvTC8lk2gr5xvxMgVHeeq9SfWBAkplO2Sl3s91RXv/Y1DECQK+YuJUKsMFMVuG1yc1/AHxAZvltaIQlHRx1mix1/xLzgQH76/plHd16h4HL7uA2o2HR1E2HVzBn1Q1+5V60FZ79Rx0gYQses3GTOTdOPVvdQALhCHSidKOSZb/Wvkbz2iMjEOljN0L91jQdN+lLNp1k7JIGmJcxMCq0l+ozZrJrTU+LMG3rTPUpqj+0y7XuXhgwVO23R9Thqi33rpsYG9AUrTQVSukXOyrExOQ1YnMNzIGNafnxoeNcHmmmenduesQ5fgraEpH3bU4bmqto5C9AxjoQEiVQtpHjLNeVqua7I3Juuy1MqrgeY4qibIktd1XresAvWVAkbzXk5JDYoRfCJSQOo9uSrbetQhOoxlTyKwS450jPSSBOIsczDCRjmtVZmN3nvefc+vas+76HmDt+tkis4erFotYglRy3s06h9xlMb5YYSMipAF2LVDX3Zf+e5H6rAduJCmR+yeXzdvgqL1LFpQb0gbmO5c+Go6qZGuYvidrYbaWzD67QV8Hu1BpLGGWWH5mieL5pG05JLnzdMTjdGvvgooBpVre5+4i9MEI29uArldxHsIdLssTjwae+GXmKWBAGOfgpQCCU1ruOu0sfcrpWC3W2TxEs/Fa9mKNyLfgE/HT/E1TontQoz9FN1Umv/Xci3XOSrSgI2tflomRFSoKfzsKZK69G2umK240Nbfe0ObQgumz/Mf+qPz6NNMud5sgR6PGed5+rZx+wLVT3yVYjBFOEIVzmcwrigXzM6S6fcSiu8R4H4NlNVqIHDHVxWxhumRi8ZvA1DJ1BdeZQ31DIAtdqfVdfbCI++DHgw6mwClpSV+YwvZ3VqczAHcrmRkQ+/gkCYKD717saQxZ3Pwi9A2SBPnLpoKzw2aWCBx0a5uafLEoO9TBbHC7v58WWinD35Ho/l9+pnv8qIsQU3qnFd6JAQD5TvY2xA67y2tPNSmcWxQiTW66DYsqR8reQs/fd5jpxqgTOxqaxemvSjYiSlQFjDp80kjBqULFbjVAJJyx3aL33WdfmlYDkvmdoprZ/jfwz20GZ5/3jLXp0pUYZ5k8WriF8UhO611k3PN1IrqIrZ0M1zffG98RwbgwoL7gqaNGph/2V+wrED0sunW5Xdiqg7nwP1r/seRdrSYsbUOr1hDu6QoQlQClCRH69a83q7NRlUfVkbDlpMvs7Ccqigc6YiqAh0k/JBfd6154z3/YPv0m1Mz6O1ooIjzDRZSIv6x+/BL6POY03HfXinyHxcbtTXFNVhiLVHtDyWR8VKBfc7VvMRSD39JO6ji6ZAFeIICHDRLaoweFUAABAASURBVH7mohJAtAaHb/MFoSK00pZSkgGhaYAp7c/CFXAzjmB/au7H693fwofeJmpGMo9hTOTsURfbC0YtPbFkfedaj+Hb+FVTUOsmjnciNU82W4tK5vlz7u/oC5KCBp/diAQGYygnjYphtVv15KPCa4qIBRCWzhTIcm/W81MjYjkzJG0tqs/vVqLKRxiIkCPaEcTec0XviZg1UqB2qjLDgiddnjE52sSIIFTgnpeV82DutslQT0J0bgOvcfXD7Piz9p9tD4UzGFSc7/PxerxnGYV6KuJJVJF4VMoDvQJ6Z1XdzHLx3F2A7geFFlIYKNOXr4pJ0/n40k5Pnhm5UufQub799FI5HAOsO2RMBJlNlPM43uY8YVQAcIX9dPwq/vl96vuVCIcjVWnXqd2/MG60lmVukzSoNA/mHvviXcWyqWLpUw7xoYkmkgwEO3m+nCNLWLaGqFFYzGGVz1HQUpdyy6kzWW+6BXJGc+Xim51drTaB3311w7ZFiLUzv08eStF3v3itlYwWXwvqFHtP++iC7OIX7fYrYxdXHL/u0WqA+rYOlOoZKkh7UZ+2fOzUSlyJffo645msfmOy4m77wC6GwYarl46gA1X9Ft+ZHh/JZ3Tu9/Gmre4MzDvJ/Z0XPTYCoGzSOinaTCtvKdcuvbprrLnhRvISrBqTnr27w2dMECwHTYlPGN02xy3NkyEAR95k+LG63Ysflu5iaKkJK2z7i/DMwKZDq3NAIlgt67Na8Mvs7yNFvN3jWugUZD8lMjTLnR8bP92tik2TmuxpheXLBXCQ2jCaT+iP//aRsyRuWC+5Mv0de0OcmkppR8+6Iu84t09KO6q3kg9ZoD2oUwK2CAkrJGxNUSn2TBYyIdxPMycOV4XhFVlzUpLmOVDFskeTYZDoj6kjLc2sm/WojGbUQSeNj2k6e9tZ09pVjlTLmuGiKS2lBBs5RjWEQ7cSKIzqovXb1OMQRRTnNpWp0J3ptgMjgAF11CAPv20JmBBY5/F5TKZSuL9sj6ksG9oTFsCGq7skulRJOlFQU353UP76Mcw1aq+X/kL+l0lq2po6h3LxsJWgYCLOh7k83Y6p16hFnJeZAA0cxRGpnGMUx3Qkr1oXtSPYMyZsa5yQvn4mP9OLmuaTe2SkV5mX98Qz3kSOyu2UkMWhFZTVNeCevjoXshtP4eSu+eFsCjBZGwq+s1xZttbxkzWsNGsHfkNOwB2+x9d7g0TYcI1rAqRcrof77pCULLVVmEtuuk+KJ5sSLJdTwGFV430pMGH0krr2jEiQ/qZl8wrzy5XXEwOhD+H1jHWZkNCp60nruy1LKNn4uNl+ObnAz6QSJAs9NdlzV6fVjMaOP12H/YrSDdq2qdkQQOk6+g3GTDE2pHecsSRhlNel/MMG/ub4MA7k2ceFDICuZmJaaCmF9Z3paNVvlJSgQ1bkc85FWVsFBUfpWT/q+E0RbjzmqUzKu/3YLl+D1t4i76nMU+q7PW7qI9qYnBlYx4a/21cux2IpfCJmQlpyjEN+NK4VFuJo4ZRjfR6VDJXhAAKIJafKRkfG3pd/Llp0Aa0LHW8WXouphCDotHc5T1TFIl1Lkiz1sOCP+xNRjFdFztA5LaxEjcC/Bp7hB07SVo4ejMcxFs/LDkpSsFuUlXTDFSNeFL6SmFnyM5BlO87jjQAa7CkV8T63vmtkJWYsJx76cTRhCJ83gDEBOirPCpTbhCmI7boM0+8NaJyIdiHwmPWbjDQyx2X0HhzBwBB9KJbhd29my0cLQIVBrGCA3psg1QFbejJAGxwTCkQFFcTMePWbeiGcEcF4wRPOdCimGcopS9UYeGiqPKGO+U2uL1vSoGKaZ55bFbwtl7qSTMCmusTqCEZJHdGtUwrRMM8zd/KkzVgIRlh4+LpsL+UviXEaaZayF2hrj97b3L3e3OuM1IcsIRNb1L0LlIkTUIF3NY4O9KF6N6ZHkq0Rk0rgKHWgMdAFJNStU3/EAuYCVFaHfXRRjepb59d81bHTp5RXeLl2CWqpjhTTXUDnTDZP+hF0tvbea+3Y27m2UeW5mM5xwo3iPtivg+7JhXv5vbYYnhlYg9VmX9jhqJqq501E7x2HPNOHKDrdIzwoPYHA2MjkTf5pftNgdAM4Wdr+ZHq/nVaWG4BDeF896rFOIyk7a8J7j0rVQF1uhKKAeQy9YMqSGNcJGXJ56Scwvy+SX/kcld6CbgZUSpXC/VSlONLE1kwVQk9XYCgDvasj3W5nGlPX1dVH0yzbeHyVollMKP5u0VMia8wQOlTQ2g1B/CRkGkp+RARI3X67C+gyKGJ3tzcVHzflxA6OvFdZlYRI4M2La9pnhIukWr+raXvvy3yMwvO78byLGsJn19vVrlHhHVmDRsWaauYzg+6AvT46J0vVUOUpiZWi6+ruVMhl3X5mfutNEFYMaNQLwtlVN2jU8FlsNbJVwdoDB4lR9Hj/9PyWqiuxMU/E43Ltf2WALo4weYGtgHQRwGJsdx+IYNQCzG7DYwP3KI972yNHGp1KQpeOsEdx9VgPhzjRBwpavwpqAHNUmdBandOPyS5ad8Mw5VN+oGXrTZ3m8t1r6NjENUp/Rp4UE+sluwhGmr/zTN2tD+s5lU9ldU2XT7G82nK00aQecUgu9kKz0pTF5Ey3ArApgdNI8phUIF7ynKpHdx5zFuya7Xo2R71SZFLSVvJKUZMf5MPnwXP3eW72d9kJR5nTfsM1bzSneUT4Cuc4jnSTH8xnhFAndW/uv2bdNUfhU49g+mwDTH1iqnrmDKp1ZEkKU3wnOt5XQQVQT5xcTcny3U9jBX1vaSrJpBKrqXQo+DNXjcGEXhNNDmyaae6iVvSPTppGrDnxHN4PzmMtMIPURm3JnmwZnpf4hYn3vgghNTU4h52dtnVL9Jjg4h6zeVeIM8xGKQ+agyojz7Rn3u901NCP2MP4qFLI9Lv6q/bZ/R3/Be82o1/wncvkOWUay3VHbaRQ0I8PtJwHxCSA96zO2FjNJ3M9v+qcZ7y1l6KWAtVk/cz5pO798xMZV6oNpQi4MSn8TEypehZE5JPdlZil0XXc7thzbbK9n+CX+0EM9K+nuwqJd1ZiJ0KngOE09OM9O493D9St/YBY8IsLFg9kCfpdLLGlIce95SmCGfb98Kxy/XqqntGddOEM2bZmQsi+wBIiKWT65/E/bi2u9tszpnpssPt85jGIdbh8XpwPS1+VmOP3awrxxdaRcB8BFRbX+fn5bq0ywYkPcOFgvjmdq2sfXgUZ2hfsg5YZxZIuRA5Oz5+n//L9dB3+jnVU37rhemAZyI2UXO5W5Z62Yz1+ExXsBRF+ep4zp15nd/FJgfyKVHD2QX8nv8Khf+Vz8LXqfX92/RT2+91+Np8OGoR4XX0+Ej9HcLc8qH5e/Rd5DLonO1X3Uzfsu+MOBa9QxA3enzp4HqtnU411dGZcu7w1rQJh3ZVfpGQDlWCnf9o/+HD2k+tRN2wE4IM4wNdwSzFT85Sq6HcqzT38YRketY+n824cgTJT3TNj0vTVNPbEkEOwiF4v7bt8fTe+H1me++W/udIujLHU4LEVSphU40+EDobb9qadRjlaSykfVfj6s67HHet1xxcyTujTbGV03f3H0ztZ0kYLW9mmxvr8kJfN1ix1R+lJM0XiPlA+oWa2zzfUtqmXs9dkCMX55XFfF7fb0oXJM+dBt3ssU4ccUw9XFYHpjZW4DL//C4h876cEJsBWx8LkO7VQ0k8nprXX8MMHdkOfoP3OHvUL8m7TmGNtUVJ6KfkmhYQWalgZS6C4qZHarnW9DYsoqgAQSEOXsO118poHkKvcVQsMMSiaFMer4JrMvp987qn0GAsPgRJRm3lB1vHl0deTf0bDQ3eDNOSyOziAmRwOlVyBNhVHXyrQsNoVtCVQJt2vjd7zNfvOKuWwuq+5AzXWLbIk/lqb6y+8L9ZiF+sbz7OOGg6bljEFTE2fHvbYjGSmRgJdPMv7wxD1pAvDxqhYv8lUz9JNOWTQ1Ye3x76uWA5omJ6b/yzGNhk8iXbwYoB+rzobE2pIYadMq6P2NdhGSTQIiKwl0DpTzjbtR4rtuAo2OfOr8UshWu6d1wUp5rgpyiLa3pIrkMbxddZKvDYB4cwyJjgNfIffVmk84Jf8mviTZWayoQiXxbJ3zjn3fe6TpQ+0wT5cdnH/Ra0/yncLYE1SlziYFpNfnxuhcdJ0/3J85eux/cRHJpSYBbHlIOJQI/uwBQ6GY85r2eKGGl7ZEbBqyKk5KrnRPcgYNFTpl4LJUdOBBNQLA11xYpr9VfeDfTl6sdOY7n1GpYCjT1I3uj21aCtYZXtO61+Kdm8EauXD79FZBX7M2yRXm5jcVtrfiNiQDCYWPUSno7cay8/pq2/3krYOoeAS1lIK3qiMt+OJPFfe1wv0U0qrlvnlyz+vskp+EN72Bq7q/SvrXOtVEmEbxnqC0H5r1rS1TuVgBgCFj6evZ3D3c/prcfQcFmZPrSma6nHmGl9mAnk2JgAh3jJUFdlRr1qXz+VNtcDKSSicoNkNqoEe6M31le+0Tb79rRfPPs/Pr9lfk/8R+tTKZY8rLC282G97R7EArLAlxx/CSxbJO7rXjErcfZSCMhm0phKUa17Oq2Y/FafrMB/j8Z/eH7uMOxEHVHHmyAEEEm7gABj4aT4itI4UnZAqy03mayxhqelOAZv9ALcpLyYH4+8hdWFMPGyEzXVy97F4uJ7f2mhA5Ghxna4iFS4sR7QvDQ6GIFzeUZz2M6vss3dI7puPGukFj5QM5u6+eU6ckRgbUsMzMPQWTgahoZ9qbbAu9iLQOK3XjX6onFBfr5vVPfpEVOyhmPaE7M5drnUjqx5oBWkB9cEJDnp3edNqecWCKWiLMnAdrbX+4d7isCVfN2iLJOWMPnQ6nqUekTdGbVwLe4EfcLeyL+0e71FHktACTlhNPAhaI0p/3dbybXv73lc4DWrduYwWC0GFA00x5W34ztEaoVbyJJ/zZmVYk6ynu8emXhF4mZfHcRbQRL4mJpFnRtkBF7kpTMr61wiRagCkuYVvj58qKQr2wM84EpG1zx3nl/VvlV0tRHr1+az+BC6UHGVDASjMBbhD1tjuhJmFhUdMaCNl2qb6zBa6JuD9YegT63L+wdBWwo7Ps+7bnweTNrMaHyHV5BjLS3cUj1z1xPS7scVT0eofOcvvzyq5HM1UQUhJV4Ny5z1ohdLk6ZnHcAePOQBCJ37/CDvVvK96pSIcB89hJYAhH6oTFyRbwtfgGo1SBFG2UpUg2aDFKMpxOMIDRmI6yTYH4w1BJKwjcSmsVTQI55w71U6qpwgynLbkAt3NqZYJffyEuZn/NtSIHD0LihlHjRIKCSbGTLlagQ3Q2/DOFyMTXbuE5AOcgu6XMpUSOCPj0BoOO8OiPtbSVCzMh/2xcukpddWt3xQphA6shX5p+GDcoMPMGq623jYkoi3SuvLwqDJAtdXPsm1vH9D5TX5csUNGznnrA63hAAAQAElEQVQ3dpab6fSB6UZFscjHdOeoQ/UA5sR2GQYfr4yaNercQzORXtuP401BRp+N1L9SjIDDNrHDoOr2hi7w0FqpR7fkEvvxjbfxupO/r7Qn/ejrOGGUZrkMCsrSg+1+4JmsKdiMMH6Mx4QSSkA2gT6jAd1mtqmli1oxxvEEc0GT7fUuItrr5abnDbRL82wOMDNEeC8OJA9OUSO0e7jz0SZDW4QZKH4g5TnfLMjFWAfrlkGMkhD8o9YTLRGpdD0pKkVw1V1aBQbUEpeerQKc6Z7K7lQ9Y3M+mAsySYNJXepo3+ZTVFMTn+WK1dOCToEKMNvv0dK6ROU5bEnSXoz9MKBxFpC3umz6msN8xOqgZRyitlGMe1sFJ0xGVT7JfjgvsioGR6DXDKuAKm8NG7r6UGquMqbDIx3qZc1n8FVIHAMxRFO5AoejllkvpMIm3rElixbtFBEY5YFqtlXtbkFOQuqhXi7zZPClQz5uE2gfQrLod4u2ez7W+qRxDgUDtgJzq8VWmTNleN7Gq4joFAzsHLaOdvTSwTEbXRHkDZbxBNtcUZ2u7lL2M5bwotqbYY0hkTy9vr7+ctijukjLJ/oBkB4zgdRhrx1zIrzsylmKTXHAqFfDOmge4IKN7yd3SURyBH0GqeKZ6eqqyqxUIFnI9/NkKQFNddAojt1gGL1K6QkijLU7qPOjnyvUBSiMJoWxQ+p9oLjRmqTkr0N+2QcmayQHXfKhxqSLKrb7dxgjo8J/FqEcqb0CczCn/aRKwd4kehPreBTYhK4qj3h7RqK7NXP3sjYW35bxsAfZnT2vzDVtBRbU7bZZMNb2UEiI4UZt67K5DY/sBFDQxqskWXN3I3UCpTUzbnq6fD+F83TdQpDK97x2KKbDKWSS0ghIaSq11yAjzR7ZfLsCW5zt6T31QV0smsgbELSBXob2vhX89DydzyRqPgkdu08JvyaGAYGWrVVGramFOw2qKTgmgutS1ZHZzJlk9XC4qZQnp2UDqVa4XK4xJmRjFIEE2W0C4GNc4bhCAZ7Fh9MlRG10ccHsWB+r1me21VlZq81UxV3ZJbG/PsERWzIMlmb/v1n6F8XWeZ1XFAVISk7Hf97/TdfX2OLlwJ079UgTWyJBEKRkd15qUtGUXt09k5h+wf5YR4BhbRzbNnLqBhtHKwbIbQH9njBSkVsXagpTZJuv2JvLQfEGlxTNoS9pPCo1Jww6GUHTII/v8EnMM1DDyiEwOqYr7ue60/EelIseL131sUU6NcqjP/uVIail4Fvvbra62AjSfLldLj9LLuew+50zgjrfwe2oZW/D1AWUT73IxqzdEZv9NpulgGfwinZ6DM3WtypUt/Q5PuRYQR4FTYNxmU41rAQW6hxyWtAwc7PwWVTyysp4nDdZgOZKbxd7u3s4TDqaUcCL475OxDFWcPYAZ9nR87PYqRWkoInEiCFtnqRB9ESV98vAHyrzAgH7AMrqZRUre1IxDNA6EqPX03aOhkJXzkw2phnZn6mN73K4G0z5lMpSTv6mEjBhtTnyaUm00c0v14uiRXZgN0w2c4xgjCjTFkyPHyDqddCdhncsaTbjPRwwFvfPrEtbP6ykl5Uit0erDpnsqqe1uaphmvHShDFNxav0MzZwyCzY5sC21N6VcRpa3P2oNFpXOJYaocyiRGOhfx+/D9VHsnKd25+bNQu+eMniocsJyTXjbyCjr2frLzNLKfyfQRgwOa0gjBq/qK7/KgUDNK7ERoQF3UYAh+EufWI5ilFqie4yLlhmRSuIDNBhAQ13jPX7DTImHaqFM9sEuF5Nxaf6B7nWwJZBO/n5cIL40ypbUKhLgM4MKTym2Vk4o0XsY/hZoR7ll83FYS+W0ahKiI3Rsm59Dw4wo7YwWgBjzrbcXtdHUTh9Jahsl7wLrKD2xXYACbR+zasiqZvvF48dn0WDIj29W1DJ0VSkU3KnLbRhNmcDLxRdLq/0+QuX70YnFAhshp3Ey2k7uxwPmVBiPNp263n6vVTzlUrG41qzVr9Xr0aY9nrP0+dgDgXUD69c1/fSI2aBMsjDGmqTR2nMWCx1ktLZEbHnt88j0IztaztqxXPt27ZstSW0YbsmI1pxDnr47c679+2f3p9YSwL4K/A4YIb3T/Afe8+5+vyE+cfLeVb3lXrCJcXfyaoZO7Hn357/w3y0lD88ZfdcmYZzsx7h1Npy8aPbAd/InXeo9L68T3whZqSW+CbOK9eGsT+eYdXT39GSdqNahDJgG/zQwgyXHUeWTYWyekUuPXamFfZYxBYkbb7hRLyHzjUXCLe6PDc0uXSn+QXPBOhwDSng/fIv8YFtA1eKNfIWfXXNQwxokgRX+Q+5o4fA68UdFlhdln91e7ud7b10sYISycNVGX6GPLm/z/UVrcJevLrXnMMqJpCwwQ/t//fgU/o8wOP9/eRXf4hxVClo5izztYc/zTBile71ZjDZeMQbCbdR1U/E7LCLcYXOjB7RlDZV5rRrpe7HNq6Yn01Fa2bLZ9t4K2T0L+xWxyrsN5nQXx43xKHBMTHd8C9/fu3fHbspejIeQ66iqHDFzQ49AtxYRs9Rx1xP6lGZPyWaZcTr4u+eX6sy0oJ6aN9qIdczqnds0f0AN1gdpBvcqXaA2RytIauy5zmccW/atGvx0DdjYaQXxLg9WmkkeJy2foIyJu9Ta7BelDYAnvFfwQMx+Bh/LFbEhv0QC9HmB9agYxT2iTz4Tft9rqnQ5R31IXanopNcWKuPzT04x04hu55jqSeCckSlKFFnUlAdZmPssaBM3B/cupnaT5meVZ6b80tI+TMZ873qq/dpJjfsH+ZS4eGZeKiN32fArM63Vtzv9dcuRBgYhMK41EPJtuj1uf1f8po03jXfhOYocJ2BRkE7SPWQ6kW/7NJ4wrmUwG5dO+9KN4UeNbV5TBfNzReoRzWiR31rzLbvgM/TClO5YOrKOGB666t0yf+z/duhRW1UhxLdErlAJf2Xk5GIw2i9g0pwuP1su5Tlmv4NfC+cj9IkEUqGXlwzsl4Q0bXT9sRaAiaYSQGfGRsuSDhyHjRpkrMOPCFcgblsrc/H1yb6Zd0f9+NKcLYiwBigNb2VjgdFKVw3A7cYQUFJEBt11fnY2avdXbTc5C3CqsXBCJ3MsnbktW3F5bh2rctUde7hE91RtDeI3ngiH/yeuXOqAr3cSExjJL78fPn5wp5+uo66BewHvvWBd9qdHJQjTSdQA7HXmHGZtp7dRbnT3z6Kz93KJvMb9dtUa5r5ybO+X0mgR6I811V7UZBzB6wutG4lBelI2qgWqGUWpIPgy3PN6TmpQtSk77/P7WgcSKmqPqyv+RixY4RX5D9tACFTuE+LSW5fH1qcwS/81zxdA4aJ+Y7i98X1WXEtvguJZc5Tfs+y3D+5rnYJAT/Tn7kDT/Ecqnf6fNw1i2akuJA/9c9Jorzap81gO/vzqG5rnLpyH3vSlX9lzEsDzurz4krxyjTFNW9c3+wzahtQzkPmTUtTbJWAgcZc6xGDK5y2ege2x4fXp0ydXY0lpAV3eReLIemtMjtY/Sw8IuYMRWb1M4qH4zbS6ZuV60PaPt+4v/jvmd/Ct/H34oFlEObUEOhNHyR4yNT/+Eg76tMjmUChDnWTuUVB97CV9O37wrXKF8CGsqN/97bHqlkYpYkdV+x/1/XDS6suvpwDckRKh30vPCsPnlPnnFRbUnEafHLxGGpwxh+1W85MopbVTxwbCE5JYHI82x8tlFTFrUFQBFMWDB9AA9UWbuivK/8eLaCDtc+OnjGhffxdo08LkSGv4VULkkNviJX78vSFWd490D5rvt/re/N0J7oMJdosXHFbCmTyHj97Q5n1/Bf30jcTp/GA/w9TWRIluHNF2ZDNNjsex4WbI1pCiI2ISKCk8Wf6YBf2gv3r+nwrGnQPbuV0Tam1zi/U5ES0L8mdK3rVkcyf9K/tZzSqTLKLLFHLMCyDWyo5Dau2OqQO53zQ2iDsihxlsHOSXVAqRkyBx5iIqnie+CpVGIAXTK31WhE/tGsGnPbj181/2sI8nXnKequjuC60wkZtZgwPoGJXoKbXEpe48oROc8asLSpQEsIIhMw+6/ONz9FJQU+SwbXp/9p+0patFVs/Hudcd169Yu3QLvWfx2LDlHnajyxDEDgm54Xfm7/n/ObzTdF1Ix6v2nfyeVOeFAv2lHQpBQ4s6HHBfxoblOItjMrdrBxCgfhMGCjdub6ikSmVuKJAUcFGa1YEfxj/bP8sR8z9jsSqYvVgSshmgZz3GzFuQLhptfFP89/p1bfNc6Z0s4sgfQK2ZGWkTGkIBBZ6qo9MwMjP9M+cD3PU0zGgRmCkx3V/rztNjZ08Es7n6p8La1FsKQ+uqL/A9xrtNxRy25C5cce623M6kP9nvTbqB1/Ho/U0cVJqUbAFPmYPrMot8A1LWB5mtuGs99kAaGN5VmMzghRrGgTZFWojpJLjCjO1dcvkEOLFFt1Ey7whF3CK9aZrhgSc/R6AtVVZtqPNpMCxlnSGgLSlpJmKwUTStByFxpzFCqebmdjjky39J74SA/BMvq9+oGMGDfWASsGZ/3OuIGxyIpsNUau3cq9t8LB2T9hR7Q9JheqmX7BML4UKhS0M2kcE4jMmkdjoqmftx1zQU5pmaTtaWPpuT0uf2bjHDy+T+9GMIcTQLt/p0YCLsf6dmikvt9p1+2l+WRUFAwT5GJ60b+4qb2tetxaciB2+QOnn1ZjsakKZeD4jtgbjPM5n+KTXeAmykiJCOTYaiyUEpNZU7/JGnFBoNjHakLQVKAb5QJMNBXTpl9sjhQQfCtpaY8RVcH29W+QRCZO+WklK+s2QjV39b7jFIGtEi8YpBSKWiv0AxQF+nB/Wxgi0Sicfmxk50T8x/44QWaNETLD5BtAHFD2eXOW7QUllP6X6KRLv1I3+GALOP+pk272d1ZwSAblG0Bv7cNdl6hlhbwD9UNbEvVIyL6TlsMuU/YNpnsYzKDGkz2btF9xacF8ZlXmOT3YNHqGyCyK07KDkFuZjkWZpA7IFokxo0zVAPC7acu0ZVOS22yPcV1Me2WMAKKtnjO3OeO11kuVDiWmd8WSlH/htTKVt69QhH+8n5ru9ZYMYkUdreLej3cqu0r1euPhInRy00jc5fBqVMbPbPFUhIzSzZcBm1IHZgL4UqD6X9cYxfrIxx/hA3pURWokbySxMyyMdtMz9HN0U5cwz0J56jFzipgeT/ZqlU+21jOMAIUfqR3kfBbVsQrfIV+Oi72ub72kfEkic1AgDm97Y3aqYVX3VLKcF4ZaurAJeTS30yudYtCnkmvf2Tp4OejjKmk7L96K52l5YhAEcA18tawcPz5dBQBQG9WMlPlozfeFyOvEO1pwhdOkMj5ki8ohlmo1J9GNVKAJhKs93sBgY9AuLhZHcylIGVJ1ScseUxDPAyFwT+RIVPu0rdX2wslelLKQwGqumblZzSOinKJgDH3NBdrnlwASVNNsamVbFYgAAEABJREFUM6hWGuqZeZSARSFWCoERuD2ST07r6vOoawEa4rLYeXLOBO4d7y3+yb5nesT3ERyyxsodAZOzRh9PiFkso9uC76+FdFveadLrSUKR/llvxiTnCz4m4ZKH0E1bm8AbBd9zgqNrRYOpyGIB3krtSCWzCMUKux5eZ/ZjrH4VrTZ+YccRIxLILGvrqupUMHJYjz0PnhHUpol4mrAvx0f5U/N1A/XN3M14RS2lSQkT7RP6yDyTN5HGN+pd8xZHdzUOkD3V0hkca9qLbNPbE33YrdBf/N6toJR4wS83tfOWWDXjUkQARIYVdsfSjO4s/SMqJiFWCHOZJzGyTuV3Fmb1rBpxrM7Wn5k9Tc1+qPKBiUR9r1NTZVSGJL9+sjJJOKVpAobmNCwrWo4URc9goPFRXHwBU2F3S5cMa4+htRRy6/b0SL+lk2/D8XG65eIJDWabFN8135ZkhYo2/5+cjVNCSB5jCke4NgFqTaaIj93t4gwPZQeP8AnLk+qpHKpES+fNSuBhW5VdQIl/IRzds0KOHua9vmV43pZYhPI4VExn6lnTC2bK03wqrxGLb2U9Hd+KO9V0zmk9F1I2Vhf6blV0jj1md6gXK4gZLUuSkqVD8iRtmtYMSp/wE/MNnGvNWw09Uccg5pHllYK8DY4wsm0mACnaYP1C2tPRJvWxw6Es6V+PJVilY4n5cjvWXzt33U/VjHtss6DCQIqLSFiZTFO0HHpKkE2gzRuY6e6St4GLxfWYDmhUCPgSpa4sSun9hcjfB9fDeIAO0N5DcCfYf6BhI+0zy2eMYBj0bJIo5kFOI1IbAe5FWxtwRRwq2FRerIbtbrFiefxz/Oz6bPFIjHgjLqU9pt8b1WrBtjAaEXg9vURvfUg/bWrFPc7SVTZgG2pBJrzOoVu7foeoW4MgHPPaMFlbNiGlNqp5B+9rHxdoVV72M6O1QwsmmDbPHxJoAly0CqOynIpVtMv8e2FpGxR2uA6VIEBVwuZUz5DmelEvYazdaZkGwdgs55nJmZpqJAEfWzo4QPe8yrld9LfvWteh1j89+TagVE1YcN36RMFOjp5IIntPkk/hNks9Pf70XDxq5Q2drMx5HruP2rhNd7IUSTMkIxDwHi/qfVDjRz5V/+EQW2gbba/1gAOSqb2BpVEMxcZeAa1xjykCDm0AGZAGGgdaSWTM5g1qebofkTj0aUxP2+VzxWgfIFtfigu8r1VQ+8z93n0tAQOFdWQlSB3CxtzsoOyqzSpwZUYM4+XVWI6ynipoBZhsEuN4U++DNYji67xH05loDUzETbvd76UketJLSn180rpW18ZRjGPNbj6Ne+pMnjmHIv/N326oLgSgzA5+bgjBIAtdTClyOdfEdeynxGFQbyY488abRGvytbFc57XPgUqGsuBV09aCo+alh0EfrwtFOfz6EeXf8MdWkYplFNTJzDry6w4GBiwYsPRGV+XMNcAz3+oHKINyNajupxJ5bOoiVmjOU+ykltTrWXN8qBJcm+szZOa583xlSCN/btvvVmFFLhOZ0NbtPtfBp3gpdybXmyKzWvk1GydHffFTGrE+qQdEwzxZgwZVQ6VE1SwZOH5173U2vz7CO+V27xZgn+HMgaLwr4XmThcKLGdBaRJFyQb0T+zsA9MY8XIrSuRAUZi3Btsr0teipvoLt2GjojsQnPcfrT82izOG4/WdHpk9cT2+DOV94mRPfx2PaY7hvV7KGHcta+FV2Q4yqU1yGyTcz0jfZrT6YV5VnprId2e+Q6CaQYqp6rzr+e26Fev0QSlqIxwjKSxWjG/s/dC+NTmKXP3elAjoY43edy+tsPiX+MlZU28EwZZ1iLBHzrnlMW1u2DMfegRf8u1pb7XIoSB7cC1ArqdqOoHh+037D7R0sqjbrtqRQcSYSbJolbU9uhX8tBINMYHVd8yXelCr/C3VL4g3FU38Yk51H51oUG1pfjD/fkyeBzrvc++BybSz7cEjSnJsj11979TfYrLgqU3QTT5DQoI12CYDgosZqte2OGIRqZNKVNBiIfbEmpixTFcDnZo5zXrGu9Y8HkpIWpzwcYejqQJsgTdnASeFXaKTS8yGlnnt787S4Nc+6ex5VdMKFvpn5AzUOI5+G2FROmOAG/VV0KgvdMBgPptYNO328CTnjjhYGKUp9MhtqAWa/7OukxQ6B0MZ5mmcZiqN6uFK8qIY5Dfv53xLmyoIyyARijpfxoLUE0mvxLcqCbt4xeUt5cT4cD9y5urcEr0KwgyK7H+kf8H/XoKDpUWqLoirB97prR1VP3ec9sF40xtkMr5vT1zxpjASimBEishDfab1d+r1IHqXX4/b16o6lZd15NJHZr/WN1iQqOeDsxVrPaUuMWmjRqbu/MBuc3lbrxhHH0pq8UWzUFUL3uiKYInldE+ksB7ywXVemMMeef/X8W/ZVjVACJ2Km7rUC6kvxegJt4G2TPyaa/PH9fnw56McqdWokbGh5jE+wf5Yrj38mI65Woj7e5RZq4S+4WQ/PeWkzZvzRjkm0MtsL6UDwaEub41YdgKPa/iA0DFCCPEEoGESsBY/dQEBr0EWEt5vjqAArBFf4pZbtPBpuqbp3Qaeutod4iGNCCxnSEZiya7Zq5w9Esn9dB6v2+shzrmQYpTzukwXcoklu2/L27pdGMrUwaWOqsqRPDAupzKmisshKEfmVTNdnc2CJUPPcRgTRiifGlVDjuk2+XN8t06jj9sRzllewUCsZ/EMW0Vv83zw/LP6t7hd5dkqaI50EEjTWpj6q9qTGl0KsrmaaFJmF57NhGUZBwCh94cn1fJdKsOon0zzvSKnvgk3qjwez8e0XJA+NsphT/fMwHQGqFVlT6KT4tmseuX9eX6v83h1J85j2RJPn2Bvrw0xrtmoxvPwOS32yjFm1vAsoNt7opvTgI14TfAYzyoBDJj7Nb6h5cDsMqikWYYhiiMcB47afm/eL20WSRW+OFYvcptQyVwKxrOtyLaBVbFL0yZGWDCc0oGUbsRrY5pv1I9mwLbP/7EuJeSMQrifeaaOaYojd+c6LRgHcq+WCzi39UUZHh+GFCg3ZhJqk5QzpfeloxtnSVS8A2hTOyciYR2m7m+L4ETV5x5vV9i66lCiVg3UJOY8rktnMIJCBT29xHZo2/+NPmtMhmxhPsyPrF1xXzEkoZM69j6GR5YOxNT2vryCGuD4Q2uU49EmRHxhFZZBjRUfIqp5ADUzeew3dV1gY0Wt9ZiuURhlG98b34REvkYLUrkVKusX9fi8u4yYH/qmW7uCjDYzFu1mjBSi5nXF/sfYRRzl5IEpxw9ZhvE5U0rn2vZZ2EINTGHujIGHlBT13fOVQu3RolUyoVlgyENRQlTwz6AN3Ig9ruUZNywnaM2453paH6ZP111zbpuzJFYq3EM+NvfCvfvZaAJ1zvM9JzEtkQFQPWTKwYT3MoLXodK5KzntgMR8LfjF3kz7nywcIAfCEe9/DT+8FkXsIP4byas1lKS/TIjWyXW+V93x6fi3/LPdzeDMZW8eRqVuIRd+sNJVOZDWwYaXQlQ3EWEnp1L8ZNy2Ht/HIcwUjgjs4DLAs+IMj4sAPNck2to+jMuUh5x7oFZX9SyRd7a83eznz9dEqcDFTzfUIabLKz6zwxycEm00SfZm1hB52VkcJY/4E0HPO82GbEeaPWYTYwrIqh/lpbugCKXzNXRR4zCZrezs7gFpswXhfuzOxtiG7aWUHEqNPhLlzbnxgLfxCcugiUQY6O0LvBSqy9s+tyAwKIKBrqmZnFGWqZsO3B9oKkLXufBXGdYd034qnokbihX+YP86HyJBwZuyfAZ3ainVd0XNkjObFnsTAV9GdzIE5xjuxWMS1UAqHZ1Ez4gjS/cb80B2rcGKfgzKWR35SfM7/JipUZ5lp3TJXVNU6hLnW/Y21rVTulBxPkVV6NlE/I8GzBaVYyNaoLOAEd6wGxgXtJYXilyta2KhL1D38M6aKJHp4+Tl9CAMTdMJr2BuQLON429K2iBzpAbQjBnxKHv6qqNP1VMv4cprAhVOUPV1T5YNPGDRzpHJi3yZERmPW5r6QQhqLZvL3GyRjlx166w4Rx07Xz9f2sOrIQPCkd4WkBN9ZsuMs9jG2tOsEs37UPeCu/2Ny3z5vH81UWKGzObRU7inur7T95zsO/P0tCKqECnFar870i6siE1zI1U/3vOaf0ap8NixxNs2uAbAbVZjPdxPrLMcOv8tf/9XBkxfqFAF8pEmlf9csn3758wnSxy83s4sJYur+fPY/40t5XfnWc9bvvYWkFmYXuAcokz8h7WWuY1UvkICye6ekT5JZYBqST0RkOUR65wBpDA/jycc9LiWuyQ2UIUPWK9Mq59yMSBKPae/qDv/WifRhX7ese3ARfVHKmEVG9vUE+hNlfq1aFwN9aX9FaGUZY2zpg8wUhj21PYHemSqeoQRToAYNuQT1Q3oHJ0ypXx8X2BMBfJXvTSTWPBjL+fx2FsXaZVgAmVI15lOh0YtRtCBtzQL00ZxAQoIxGOPpb+yQbe1GRh8LP5blia2qahMq9Lu+lSvFpTUyG6bkhoAtZiBDSGm/iDHFWsvk1orahqmFvZuy44ANPLweeL0Re4QTc4pmxOdROuYPlZKMG2gyalo1bHGoKhGQA/PwXlHOg0UD0JIEouSOD6uHiqvdyljU82C2vFOmmTBvjkPUabec6TkKalhlpwc6vGBo8TMY2hj4A2KcEgg21bYq1IZhG4wy4sYpisa9T0FACGgETCA748cikuIHlrW8jSXx3wePZQ5FCZNgHhdo5qetZraO1Ug1+tHEHrqW3hu3DefZA4G7C69ZgRL0ygRJHnPpCAItDxPew5LQ1gCYE4PkZ3qDNGGIQBygs1hCsjI39Yg5WJz79nWnfnc/T319IjtPMhGj5JgGx7YzfWQBS2e9WewxciYSihbGtSaofqcGtzlj9yIw6GBjVX4PLqbVb3ecTXlDZFaY9OFyohgLA9S7irvgaYD8HX8Ur9U6oudPXwUFhbKkTNdT3RtSOULRs3Gu7ZJn7tUqHyVZQRNnECu++OunMrS9Nd46IP0kRJs9b6wtKC84USSY28n1ge56si50L4LgisedB9wtMFy2b7egaagj5zYhFkYuzE50HBXDGX75nuP/oyDvoba/C85+R9zzgx9Qp9n7lIVCJWqgIn93Zc2yN+FWeMLl6ZDgeMs6628WnI97k/wxByqWWVad1SrpAlTd4qd2gALy8hL7n2kSZRyPd4IR0gErpGCJj5lvA67FrDo7tdoCdEqDZxGveKGHHzzyVG1BnCxtXY3OtUej5CHKeu9fcyhKWdwUtGxVRPNpwTZfK19XW7Oafo98bTPCyDEph7Kkx1IEfymoCEqWfIwmdCr0WWl+jtj5ah98vraJ3fMtlF2cBVXlubDpIctyHpwYC0pCPGWLa8D3DNaA5HFGWL0PlkCUvTMzvoWBHc6WJt099GDhuAqfuRclt/DYGyzx0ptqWMAABAASURBVOwYWlFOmSKlpc3DetDzuJQm+wSIpKkhFiDFrcUlTIUqHPDWpBpR+/zeYnMUm1XEb2gLQKETubyCn50ftjsJRpn3IjYM3NlLD/mTkUpf0NUmzBrVVZbivzBlM+3xmFqQu83yx+zrc+MZfjUTa+DGATAYHYrJSCL05T3nKO5btyFQ7lL2t2Gyn6efKvFIdXcXneienPlG37vzomQyuqxJA5kbWg+ECCgPbiejM/oOb9/jpD2BxyeFo2mlaKE5ciboSno9umfBTJFfs1/H11TT5xGZbMZI2uRpPMxvZK0+WnojVSwuDADxQnDVVVANAk77+1DRvUqP5j+XG3Fl/Tyz4dDGKnZJcq/L4caJrpPnK3plq5yPypCiTgECtjzWci0gBKU1fmduTnkZMwYRvpcU4J6SCQ+m1hN+tB7tjKuoO7GNxaPl18i2Tnsy6lxea1W8UQypZE0QQEKFJOJCObUPbcG9QgIJd7z/cW2toq1xVtida+7J+3SX2RgL+R2JNZUSnaun+8YrSBoWZlVmnedAKXvNMg45VNOGNySSY5Ocg1HCatHlsteo18I2KlRXS/sIHzFi5qO0f4+WGEmkZ7rwpM+Rqi/QbCn2GUfI0v2Z0gLgagZi8VLpHKKdQTqvnmtYM4et0zk9aEDUmu2VWlwlgiWYMz46Dxi0Jk7oM0H1Ot7TRzLt6rYW+AdKh6tHno13QyCTHK5Znj98zB/sM7JSouRSJcnxNd9Pfz+ulenAq1DFo/6sP/C46vAanTRCKQN26RuOy0go5qOqooEEjKVkXT6baisS+kA02clV7S8Vq2GTwIzZs7VcmSh6ZxIyAbe2aNNsyhkJn6YNAm0KQoynKVp+S60GmgqKt1On9Nss5JX65MXN2TOemMeqrpZLjpX7MUvYqVGlT42mXFSEspWhFusnrELV2xPHIuPFAAIOMgRPlzDRY0kBGN+tRPT7WcUCahkJk9eqfOY8q7SaaPf7HTwo1dqBRChQZIYVBSPRQxreV/NUlJwWvGsoSxu83rC059mQHGFqgyWYq/Y1/MG7OeqWACgzy+Gu3wXr8ioB9Vo2Li4VeCN5gNrRn3W2PW5aGeaVDKcVVLnp1CWqjIJxk12yM2t6mbJw1Vl5qPwJ3IiEVr1puyX9byuPFvEQXgWjTD3jT+Looqx7iQNT014YqKfNI9+L8kfYDEr1CiWrpyYPpsASatK5Fi5XtVjk+pyP1T/wZ8FMQ+XhenId8fID28BlkLSF0MiIMI1KynYP5pUBIMsjhyraPNVFyDTZU0erDDNjmg2Ocj5vmajW/QIkIktTS+m75mSlIC+eS4eGNWVSoX6HXxXMnn0tiUumclwBb0fwQfZJDCesdxmbaLARIGntoscL1mOTkGSE8SxNGn01tHmrPoYOkazc68m7A2HBy+YjW4rgtFTDll3/G4hBN1V/nN6YjWZn51AFXFJ6F/I4Wl3GTEGzNCGZGm4097Bl5IJuVeXCl388ts2C1oFnWC3sanbSlbIYTMxzqo4aUivAvJBrZL8wBuzFvXYMd+PSGcf8NFYPZsrbtoKh6kIho8TmGccJQcWwpYG1yD3HHjmGalplol8FKz0OGa5Sh+rJwrl5Spl2YLldxm3mEXDT2/pXK2D6kc/qLr006dSc4p11P6reVINAjw0WaOKz52kMfMN+BlepuzjyqlIgC2bHeRtAMGlloOsz0JbjX+7HxNpe0kXFSnpaZfdMK82KCKdHW+c3z42VYENXk0QMQ+ZnJAQ5HDUGTLPVUXUUbd4nJ053OGmCqMDk3HbPfnPe7ga+NCw6C1L5lAKhrYHVg3qURq/XJryVav29bZWIFgYcrpPe2dWYQusH6YSB80bnksReUH/SpUSXA0aHe/kqC0LkccJVmfy/jpCz8UOfMHlHvcX7aSx75ymqZ1abCdoyUhuJa/BJLgzl2nriKGJvEGaOPerGzBmsXrtXGEVfam1CkQSWYcW8PRCaAgP8VbT3mDK/pq55OBKUwodl4F0jVUubtn3zfRWj+QGvUSkPlI6+Ol13eD2tDI7N+4Je8/6rPu8tvKWvXPuAoq40cEJBOF7MnByd7jpTejJ+I7Qe6KpiDBPaCkGZWqJajE6TpWDNJ+FPUlsbOX3PGMx9xV6UUjOfI0cY56jC1pgiX7gWXRuu7eLVBuuuEGtQaAYuErCa+LbW1fm2qTF/7Xz7qS6lfE396/zJ2p27TpSaTNqD65frBp/BI3tpBlzao0Y/0CYuEi7jaZ8Mt8A2rRxa7njaSiRh0JQ+fp5Y9+Rz58m7kWBjNS9a+IK7zuDWYJ4rnuXqCSMA0yNkU62WyrY3Xm928ljLD3DEbwOlIp2dSpui3D5qmL0qW8FlJr/p943Ke6Acq9Lk/ZrcYh2iTt5hPqN7sy3oo3DXI+EaoRC6lnVIDLaGUHpLJfydETTNWd2e7cNBtF1kGATo4AVW6gnQJHUlcT/1OuGUDhvNkAt3mrSkGQ/VWpcAYn437qi2x+ux5/BpkUXlsfDSeuo1kdtrLZfok8aip9DsNicwM1CbSjXDtTc1MtavWWlgWKyXxqr8r59iGkaofRxu5ZnMpx490cnHeCJOQEMOuxDRO7Ck0TtL256V3AKl/GfgLDwxQ/Nt8QNujgkIBjG2Wt2ijApYnVEeMUJpaa69Z1JhDn4Ht8L4eXz9vpVt+I3PN6KcYhrFSemgyo/HiLYaPC1J3IGqwFlLe4MGZTrMP2PrDnsZgC0zLBcN/DiXGKJVcD5HYNx24vMlj5R22v/ffP4f//2nexp1MifSu9aUuY3W3s0KjAXUMczDTsR/8ANospyAKGW/NOm++EbU83559lEWq8Yrdq8rDaKC21rUAPYAB0x4p+Ewbuyjw+yGqux5qHf7wm53rULPj93UKdHyWJQtDnQrqZuiVfVJ/GT+nN+lfhP93Tyc6Vx3+rkxSY7S89Nipem/E/fYkOByC17GHTTHCvdxtUxoRpuZ09eEujp+Ob/Nb/l3eF+jmw8lH+Kq53nwfOdkZ/F5W9NnzVoThGFRovAIl0iGGm4OqHJRUFHRi7pjutSd/jvzX6vI2gpK6pb6FeZy7lrX2If8R16vQ3ZLt7cJDM/LEiG4S4gQtL8aG7uB351PfI9/i/dj35u6k3rcKsg0PkT1sWFUWKmL2AHuGeWrz3Spaa4esfrvuf+NJJlz+ayLtgMhyd3+ru2NMP4Ef3x+aAv6W3FIDLJtXUBBbpRaW6lV5Sw+ni0g/E5/83zrq52Cu22XHVDhLog4BcpYUOf9YjrKFc9JO3pAMIs2gj+KYGqzPdp20cgD7V/rzKhx+IQcnTaIsh3z75prTbiHXVfv3UBnd0/PAE/U1/I7k/pS3al9bM6uvtgBS0VQXm9vgHhhPVNPl7e0IiwbVGIr6l51W5VNGK7NFTrvaGKmjMdUe1d+DB8qWRUxA9GhScFHa2RZP7QvtnKrr4LVKGVszGQk6jiqZn4lwB79tHKNnplzcLpaJRkFoxCUTNv4B61jQxfz9/n9Pf/lc9uhqg6jG6OzxK7oX9siQA5odu394YRajU0vGKHzIRAj5R7WzVZIdKNNcc6U1ix+6YdvyzPMUsY0R6Wa01A2AkU+WX1aaef38Ia4bssMnVYTNFMKreDzhvqfSuBAV38mL/TrtGQ7ZNtDKpEI0vBb/d/BU0C4Cc45832e//57/t/zHAw+vf9vuJXkfDAnFtdy2FXjvyXA8/Zh24nrqVWTX7u/KoSNufy4HYoZbpuXgFbsOlqV19AJM+03FJWEck//0S0MpHi6ug33o1VFits/EBszdfI8uC1ybUhSJkJ6Yioaq2KVuY50a9+kRyb72eWf0XMuKvB7+38qgzSxEcMf6pg1wgOzHesK41C5x9zhx2ngGgtwR69l8T6XfE9Qq8J98YSzfP0Huw/Ol/nfen7x/PftOyWPBSrc43Wq/PSVY+JlM90azmnc5Y+6LPQNWEUFsvHA77SRC7/WtovaXsDe9Z33jntfd6jfLR9b1MRGVH2698xn195n73vJ3VSNTTlUOfw++d99/1e31AqRQ3kSdDk2ozQ7guqW0UnhSX0Tr+p/XzzP6PFiGrOtVZQdJGDfxn85XzGssRHr4xYbAnBdpZq9wg2Ul/JEpz9l55Qk7I4dcO+yuj1PqPdgSZODIfCx/ngGkzMqdv5W1LP5UNVtOMaCovZN+VuJ+qWe7p0eZad/fD7n+Pdb/z3Pt/KYx8/e/79LOxd/fGdbPZeCeeLMPD7pUviZe5Q6PYlpWlzYVoKqyOounFJ/GUnk3bwYMG6kRLYTrA6xlsjGt+fL85jONgdQtXIe2FE3GvoF/LD9Tcg+/ZMdTR706U7WMpVnfroWaUsi27YmorYOn5Xtd7PFDQVCEJgRrdvcvcc/KTb2unZ8ynhT49bU9dz/vvfP3OsF5w+1p9nWwyppwdSNOgQGmZ5HR49e7dV/FLEGeY59vzv/U8bGVZtEA7AmFW51nZzz8P4NtcJU5nw+LdHZfAv3Eac3+tc4iz+LmyMgRZtPrAU3WYsGcwaWvFIMXUviwb4g8RBStBCuGiuP8qvXNk1T9udJPGkotwSK3bMOrjfWQwz2pDqRP80eTJlwlwQ8EuCY0+iX93XdWzPumdtPQb/VgAWRCc7wJahFx0xnM90msnYdG4NfFXuWScELDeJZcT4/Jfxhmi3KA/0oqYLy3fiFxAsEzMT94Nfm5nR3Zd39fP3+Rr1Q7+mjWJpXUnw1wGhnSkRz65I9WF9Ozleo4U1XsvEM7sfu35++f7yv1TQ9tLZqTk0/befZXSs3fz/ztedNuyD9ovI9+oF+txUNFt4idTQk7XlYjRP6xv3d11k9pMMFaya7v93yncYKS5qkp3YzOscntTjY9HR3itfE08ipM/OMJ/yB11gXMqM+i5+wTyuoc05jZqGMZyzbWoav96aOPl7tt+Wv/6Yrgh5MS4eds1qa+SjwHPkBPLRGaUaOusiTdQ/aDH7RluuyPxV3N+t2tHY9V4f6+NoeCJ6LX+96GXkgGMQijDPTQyaj1qXvBNYZNl+szl+LYzD06pv5TWtIR8eYAcSEK6rRq4pTPtSpqRiiXpkezCMoBDXPVHqTViNrZtRJDfE7BQrjeDVOpYvOuchLjjoqkOuAamxtj8IFoKYTcFqUO/RhRgtL1yTGCJ9xeZniKAiF4tmhbow0wcT7EmgNpNoZsHS16W02pLRCS5MgcbSeLdAVuqu8FKiGgHQzKnKdX77dg2MQAIl6oAj6VD44JtQoz5RCKJF0AaUU7PdSQZpI1VGsTPbdOE28DCxpROLIMhzqoYT3w85Sutvv4aPQyA1z7Nb4tsi57lFnEGcmZBQGRsTs1bqnlVkBE0nXCCzp4DQKIqeL83DuUFJODdA+ulnV+8j2HixA1aoP0RNNEwk9y0Y9QmKakYI85w93m/fE9HrD6UGVseBFij1qBnbY1qibnnRBapqW4Ad6DefMKRWRgh21ODNxL7Em2a1wysLHAAAQAElEQVQWOCFVqwNqGtvRazqUSfloqa18ZMyPQ5p/wKM5/4tQQbFnV+4qjoC2DIZTq7xB729pSEDFl/6TXmOkunisTSl7hB20JhTR5OAZf2anOLepwZwY9Y60bBQbkXrctTm6kVw0Om1haAAxFu0rBW4w+fA7diZanQyqvSylw3HtcUuYyJOS8dQ8R37GyQDDzS8VOQnn7NG5pQtmJrCbaihRiK/5Y6Kz1fs8FZyPI1e3v4Cr66ipdAElLO0P+fJKAC1lvl0+SCVeB6crdUBBKJmtNI3mkVPNlD8jnbbkVvDFUU6lpPol7+WPFjZJCWi5mppW8Az6hgIYqVB2WgxYq13S8ELQ2XHWqyVMCWHCxJGNWFQyGuKxkim6Rl9k+v01OjtjAI1zrVkf6LCPfIkuxO4OtPOMbD3R764At7bblSUyTMCewe9psT0lzHPhXHYHSkF1YwZ6zRSqWY2aJJqsERTP9OlX0ktOonN7KUGly1DBT2mhD28aOiZl64wM+LzaggVCqfXJUHMqRtoajRt0NURZH7l440NI2b8lCU7NcBz0IeZqXaFqCt12cuUTqe4zVd51TW9Li+QFEX8tXI4wzRUcoGyDwQYL4qazXtsCwx5wFXd7lICPvo6vY/s0sm7Yox40ly6uZujSmGsC/MDGdcLS7dBL/svjMWtXFIZ5KTO0TXOs4EnIWYIHeyzcF+nzksccZhOIbVxm6mfa5+2ZMHOEY+Ne4ue1aT0vZm13np6sOm8/wbtNZnM0kjaEPol6YpyCU8AZPs3SIQOeRdy5n3ONoGdKKUTT4WtFLNtTfqogn/0spku9WiLM0InuGQxJN4sW2XZhLWzSSLHThqJQ1ghC96tkNzeBej8rBh0aKBvQtcY8YA2nVsM4GvSMiusoHhNBHggNylTeMOcNmpgIWx/qnU4soyHgfsgMiaaZKjUMeF87/Z/hwwrBkk2dHG/tAzNeXzPyqbheCKdY6a32KlaEQ98wb6R0jo/IBE3aEYjDAMJNM3vk9mndjKwKe3lwkuA7E269V6lnbXH/QB4P3l43KjExThtXJUARWTdl6qWFjnkFhidMHzSGIhUH/WWnPV3aYxFnz7OytGLYk3NGkRV9iBDUepS+hB3NbKdq8/FHnBrg+c6qghN7x35rlkm9NkUUP8BGqzzTWpCPCaH+PLOtTLx602cBS4bFaQbew0y9pvbNuEMlniUcjKmr9GlIKFcwqFiRUoeo64jclhrTkDxnKJjja0LJ1KRvTMrHhsegUerP3dVTY9PugL17d9cKiUldni4obtgqjwPL44M1vkHvbt7HT6IAuMa0Nla8aLLT6DIVfFEpfRNBTZsPFGSrXSjfslxCdCIV3DFMR8Hu7FONe3RnINlZWyiPzZGd+byRePjmkjvLoqIUn7nfqJTcoSQqH8bF99oZmZ3V/ME4BF8DoDEDI4K+JC1/qT7b5MHf1hlAhJk8uJv2jdmvjZY+UjRh7KHJcTmG0mPNHHgNT82ICSYETOa10dm03eb1Xs5RX3m8Xhq8OYIBPBQYzhjgJE0XAraorautl9RJQ+uKh8A0pqK0zvaY0b1NTd3vPzla1W51nlot45RJZXVKH2Ko2C4oXtLtrzso7yfPUzlovNmZNSU1Rqar95WKAoLoImlGJTK6Y50Zyt436tawNb6wSqNeKLq5imtbCCwmVdV0Ry+kdLIe0xZa1oxGug0kNYjKNoxRRNS0bIMLtH7zJGbZnCEJt1bcDZwS1oH0R+Axra9WL2GMnhisxjXYzNAIzGj0cMzpLjwJtOJ8HcHga2CVGN0uJrqbpixneZ/gyGeO5ehk6D29Ye1TjrJNv8yckLIKiKYWR+l25auBGcjn5akOI9Aqc/U+czN1g89QqZFkc+KlpgyDJGi15qwqpfo5cT/azX/v+eZbA2PAbh0ijmVdSsKglWrqCeqyXHwU6q1zeHWxIp2HTzJtsAByslnt4FJAZQ8kOgARtNliS5jNZ10Tqq4Lsdy0NgtTr85Iw7msdCMwLkp2vqUm/Fh1dj7e2vncZV/YCSo3Qy+KD723Hgm8W8ml6XLPzu48JJd8OGEjckDoZWwgG1lvhK+mOnWThw7QoIs/4xcVQj3rlsdzLpQIuNgXZhlAfxBP05quTFNkA44wWvgJv6eytPhmTg9nh0UgQh9Cowqmamkl5wZ0/GKehvC5cM0h9K1ysWXPqddA8LFGTHkY9Etkt8Fpix7K7Gjl+E5+0fe0HrYhGit1VWaGQC4eKjItHloZ291nu7pQLQRwsdfqHdwjm5C7FlM0KdkpVtpYFVmeTTS8ta6YzBY5YzxrZVztS8SQOlmS9TKugR/OPXm0ZoIqm2gSjlipFVWuFb0IVvCkyqJyQ4jkdz0qmuW+zNWKhETZanyxHqryIpXeV2rAhM9n5sqW6ReAmFBu16TrU6mszeSRsJ7SuaKgWI11W1tgxbiyY7wAUv23imA5VTDGWQ53nXDM1kH7XxBwW5pmaNYZJRqj8WlrxgFodNhcppdm6VtJBjAHr6I38ECLfz7fU+f0yVYDLC/thVWR+bl7k3KMl8xR0374PtZ8hJjXyEyRyWrRELJHo0h4V9htFGdbrjHXcTv/SnHJFMItcLmq5nkjN7qA2ejLfz5fO4+nhm33uCjmzdpIilLKdxprJjkFAjqPVgxNy+ivP1qTB8XTrp55up6RCKF5hdRIEfSMqm+t265DUyZLmsAdM9dAjYGQMlXwHUvfHaF6cSe3YcNeIiwaHFAMmhoqlJn38wVTlZDZ6AJ7ZAcE/v5tr+vK628Mu/1pf3MLeXT9zepCXqf4jPC+vZk35rfmkRHQaPauyo439uSTnmU38ViXHJDq0Igpr4n2aOHcswILs1VX2kCFjMZ39lc2rtvjNj54Sm3pQBhMveCmSHuVkYZ2CSPWWUxvdkXnYqlr/C9RVSPOBYtuqrimysxHYrMZey/mCJauyGwuK87UUdEKKDkISJ5gDuS8RWTWSviiaVt9kSLwLg13/o3DH4FoGFPxfU0NLVSnBZ3DaGRa1gzvWakURBOCfXqqBtV1cHo/cdXluaXgSSE6w9Qa9bSMhGzjbTQKARClD9/3sfccSQtJEHiaz9l9uz2001rYV82nybBciml3xkhcy3Gt8Hi5kSdRoU8NcaZ4v+CtdMmTTdqjErs/9VxB31HSTx2eO6zDPSQMnwGchg4+8zypeFCYE3iCWugkYIGxXeYNawG1mFepm5o3YNkn4yNtQtBaM3L3+Zx7fZO3poZDEVfZW/pTUJ214np75YESN6O2mP6igAXGR1lRfp9l+WOIPaY/MWh/LImdB31nnLfl2gbX8lhYricus8bUH9Oi9K0NNRw6d9i1/JqWKgZoIYI9YC6vnz3XNZSyR11p8LV+tmoP0Nyz6478dqa4KVyAKlSzZ46IVYGIov6LwkYr+kQ1kMaixg8MBEdf9yinx4eEime9/wGO8GnUY+c2JDWDj6leXshZzNRD4qVyeadoHWzH456jWw8P3U4pgQI4Y9lKmVFRQ637dS01VNPKPskrzSEQiKh9zVpmOjH+dntF7IRUp7NcSPezd21rn2lWRmH7EnVrwQR8eEYiB2q8Ja2yHDzD79g3tSIc79zoy2CDOuwH7/B5pguthWoPgVXOdJ1RaopRXCPuofird78cstwK4bXiJM4JnQUlOPvc3N83Eq7oyyckWfgck8JPx6Nm+p5aXopip+3UX3R8+4DaDYczttO8iIddURVHKeDIBpWmx+xxhU42qREpGBL4spalDu+JozK/6/7qRyv8Lrw3nZRNc1+23UL5qGp1BeAqhmzebdMf4kqYGMwYxAgPD4gQ/3I/kzZ/KtgTP/x8+hO5qHQuf5YERmtq+7g99ptPE5NwlAytbtfXD3qPWnkpHDNUt+pYUTopL4NlYYv9L23VZl9t3obsOidTPU9aW9LV3ZXpfq/1qOjGrd/A4zI9YaGveX0qpoy2VYulgooJau3lz9d3XzNxxN6SU33dK7d8n9hp5gY6Dv2M67RfuWJ0jqnozsVUdra7lZVoniklLjCYYj6mr6ZgFOeV2dqCQKZQqE71nNmkqCGLWn0rT1Zl4RlkmUwB3ctpEWL9rueMauuWYMzIk5OJ0xpb0zfny9HMyuHMjt7LYEc6WBLGbXnsiP+QdP2qK/AGKw1AsVgtF2tClF73St3z8ZKY4XxPUwzIRhll2rtDNxI9gxkCCneVOfwRS65zEIoc3N037nTVU/mB12TM8RnNU2uYffKqs6agKEFOO9PEXJhIwxNeC4zM0c9z4Xx0brJmKltWFYh5tEXiKlxCNQKo73R6iDYxknMDj2EoiqElyvMJrVU9b9QLSnzufIveZ5xibDtUsxNdu/uq1KOstSiTQokj36EvYSqOtFb2S9+H/rg35nSnKb6CoindAymXDX0JSxleUKTWM1pfHbfP10VREWIhobGlwVpYXjdoB6MRrffyThyz49BHLoarhPlD/Q1wLeVSeMnqVhngyT76zTnoLDYIOeQMqloJpBI/u/OC/hgylztiSxi/YkeDC5bGXKJjpPKe1rrjNNIt3LBUTKvhGBBAr4Hpg2rJm2VSXUBrc8idt76qeD+FUM4NvckNGgzv0exnPQcow0QgVstM9DvaPX0fqAqBSU5qgkFrIXYPFemDehRlZdZJO7fzMIR/Zb5kYaTGL3nAni2NlU+u7It728cRuKu/mQ/yljrHaqBIu4WGVvQx82VqBO5qLl3Y4EXBsrwpl9UPcVic2jP2wjOz8S6BaBkbBOuC9KDw6kVQnZPFqojjPqu5Wwk0wZOVP8dc0ElGwUSTOIGNbpeLolOkET4KpN/qSrsfzvfy2gtvFpWqUyg5MEsT40pQLtwbjxaM+I/rbSlWRC6+1WVOa3KogX/Gq9TjhGlJ34Fqm/eyoISohWK8yqyljbxUXxtuNrPu5tRohberuJOsfdqeSSpCl2iWv8vFuL1+ii4jep834Dr2QNp+ep72O0Pl3UxFLV1tCJy3BYCQtRwn3MXNaICKfnaDIyX+/Wtd2WUXYKxZkMPAtXWvAu6Ya3G5UygcRGkXcM2E8r4aPnR40OBviXRXAUmNsYFADOqdABteJmPtOvEKbx6ARl+Xf669KWL3+VqWI13yQJ2WQoft688Bzcr8Vn+9N7Vs059lhzbtHJMpTIE6YBzHmEBtc3crH+W7G94RvsuvA5+RNJ5iHzUVvM8tvFLfPGfDlxZAmkHXSxQVMY03ahl58K4yTfTLFKifl8xAmw5FvqzWLuxJ5yOYAt6yjCn02Ayz1bwUGBui8FOtQCYA+Wv8aqUTqL7t1W8PdD65jmKs1Er48bMv7fVmRYTii0kpoPMdN7Reh+tXtE9NfZlnGoVoj/J4iBmQcBPY0mf9arcxgRPVqcE0y43c3VcqBsnzTZ4u9EuGqS9oU6OVIJ7j2gbek7kef9VrJky4bLSB/4Bx4A3VMlUEM1YN/YxS1FkvMd8dSmV6KUKPdW1fPKFLTAAAEABJREFUdJgJkKq8SYUw2fBc+7lc63FNqkkju6ZOKKtMCBmEmStGGbd6e+FmryiolOaGHK+p3Xf0A8Vp1kqCF6WiesT5KwAFxzZPkUOG1KOTB6ONglxWE1yA0KWNmtoXeCh/C5/QBXbz1er39vwan5s4GZAHzuW2XOvgu0HlmGCZH/jk8J6tE8sZb9QGk3BBQXSmW41RVcZYpI/OdkdLL6FEjjpPvy+QhgrLMGE8SB0lN6Qf4man2UDzLaqQOT2GcYxoG9Eryac+AkmpDe0OwgG5NghlOXrQx+sW3dZS0zNazHUaphuy055uHXu25pWxwZF8+qIMoeEt0mhUMjd0vdDDo5xoHODDZQ7zG5e2GxoUBAlrLcuzRteECEvyvcZ+AEP2CDEuqnF4iORQQkJ0v3X62juL8ounFeszivqocX3s+pggroLnvKo5xWfmpSu0Rz+idjcjwfeyqlSwg6ZGan+ARsCkIogBfVLC/ldPe82PQyVXY0VvN6f4yzcRjwZHtgK7jgeC1/Bf2W5VrYipEs5SQC85vnRdqSFGifB5XDuio2xKJzlqHO8bZzBv9KKwxg+8R8M1bXgZPl3o6pISn1qtNcR2wmVeDo3SmUPc/s/RWOdUT9WMmBNbQC/ry8xzT10jri+WU3sFEUQ3ho8pw/Z5qEP9vqqflFMG1i4JYkaqm5XNR0jj0LQ/azULpMyzwnPHcY5wUMAGTl8QZZF0VCsXXqZyenKqDQjYSvJM/T7nyMqIYaOTui8IO7apTcn6gekBlHweTTaYuSzLrlRmEEFk6+I8SLBVjW+FJ/tZc2vhMRs3eja+h7cJpynJVzOUn5HklFe4K37gdFcnmVpOx06ySsSRFw1yOCf7aTRJBSmlPg4seJiPa/J5Tp0q5Qc/ba5RcA2G0a5GJJiW3GnX4dQUJjlKOzg2tdEbaTJvEGSYXsSfBjQuK6u/luJgOvisyE2/8rrqMk3UbQCuCR6XUm7yaPuDPROyn9Re8Hu8xiIYNnKaKcKmWMNjsjh0zZ6Acubxz/uHJ86pr99pNxoq7JH5z/x6P69wIKZU13OJn9Dq/0Ktbsv+1CwMoqB4HuX9L8AHOOUz+tJEwZMLF/DzIL7A39G/83xRPR4nQv37YU+keoaycvpdDqcTbLf5cb+k6G3FEA6QCk7r5rsamoEvgcqO+nCvEX5w2ZuorjlqjaYnaOM/5v+If+zP9SuHMYMigL/cnEfbMBmC5iuuZ/dcLtIdwe5GJp+a9/9xzvkM68iNDPTBnfM+i+VACcdBff8uh3Ht5U5DUUo8vb+IXK3t2Q7bLr40G+MU2LR158p8i+hiykdnPslxRXyUt+/EPRcqTAvW6RSWM1mvOz+/129ev76fHe1USIDBJQd0T4+MtGN6eX6uVDLu8fusPhdhw9a3Mq0xbc3p90mYv46amAGGbIs2pgKv1AD1/5fcqwfEs3QZk8DRBImdHFvwy/Dx8Q2T1T5d93nu+f21O+th1eKEHZ8cVvvomBc1bfYZu9EPFH9RjJ7T3+YDaaVGkl81r+xEePfUbX2WqHfBomCMAMfy9THBOHie6noAKZ9NURu9N3xV87APs3gg1xXT1+QlyUX2PjXfmnO8n5/1+7OOFq3VQLN1to7aw2BZaMurvsULFlqJ8nxv++/Z97Pru/uXGvb/Ju/Jh6cMvRBul9snsA0/i+vK2Ld6NawvIMQBWXxF1EMAVtww1zMk/pvzUaAbbRhDj9CwZh6OAnx6SlLr/Ax+XkYMKOwb1y+tx0b3uqrJ6jNqdZquuoJRptcaTgPZSNmzGhPLeJcAl2soy2phb8NEC9rj50Y97NR6bj/EJTSad/l8tl1hNsamYW//fLg+SHdFP3dk7qPLpLOuutc8qsExiD+qUw+R4DEHF9GTWd86bU/HnZgjteeMi7hNoX+yfut5vEBAAiqrmVujeY+sEAZElU70n3Q46ehwaCu2LhMI5OW/nzifIZtGvTfOIFW9GrB0gu4EXr0ToFAZksgtwowwdjvaZnDLl1LVF8vtqU/2B7iEQJMUkW+EGFpXaWOTAOTeVKLtVvJgf3fXiqTszPXMlRIUlagUDVpaoL7nX5/jGDdby7aqC7DRx+saMdw8BWlZMybNE7vz4hN8XtTJ+F3791oPZaKhrtei5clOdlq9MYzXE6qpuXq0i2KTGsd6w/jZ6/Oz4lKawERPz+J7AFOOFJoxLQXeyiRFDUUNUUaftSEtwHdSxX21EwYqlkwxRwNkYKVCb3RjEpUOrMVlumolAkwmhfd+KpWgLs1J/6nL+1K66gtV2ZQmAwOh7vTsVVQSNkbnT3vPp6FDtnLjqNmubZ/ZNq4KOPXM+QqWzQESArks7Ora9aySTF/LKDWbjd5FL0nRLaCXSJbOcHLUA+p8+xymUQQ5RyPIQTwYPRLU6txtG7ZAa1VdqHNODmsaT/LbdUb0yhu75iWj++714Gpl4nz4fE5ft8cxuo4cqKZlXB80IXJdLemOWvn/1zDKWeKloaBO8BYaUxs8K0rnzmOpLvqA77dRhneAfEkUlSq4nBG2HCtBmDXgkki/mNPP1CHC35dgFL9SqoKDKTQFhlFpdAugQlOsiTqmZvCdt7rnPt5ieupCv9EdrJ5Q5Epwf9vvPylCMhe1fqfn1+PXPznLSmLSn6pqdyvVO0sGrPmdn9/5fGc3VBYXTHuKqnWfn+fruN36TQQfdIZyOJ+BK1iwh9OUUt4CMQo5NIp5aPXmSoMIIGuqOm7uB2/+1AD/8YWjsormaoPbq0tpF2M9BnAG7ZNrUn/b6Vo9URjC1qiTWBbrOBjACyZ8mUu4/rV5oDxcxissLuKDo5kuzMpQkWMEppInkfOqYdk40S05qpXggXct1drcwpDPyrz0YaBNWHbMvaG4rUQK/Uk8jekkHiGyznbURnGmmLJp9hXzptIXkVu1PaURLrvf53rzUy31ChJj3M0ienyOrdvEjA+NbflcfeRS9GhkRFrcQKKJs1RuLTrx3pJJ6TwLDLGClWCeyZyWGiGQ+jaJ8wqKQo2RFM/011sNrZrTwbwC/y5uo8vXGJJ8/Qzu5hETYuocaD1zm88/XirMYT9PutNps9gi7DNidahcagrL7Hww1+Oqnf/Q39X48IdcY5SEsXPspMT6vIue8cx+BmkpmucV5OBMKUuCPWMHUtoImM8A7EZjoAGj1PVmvZtQzjPE4/GMl+Tid2hL6QVotEMUCfEyvP9FsUmbNjEgY28rIWk0yWOswXS2pCZX6tpRa0/Ib4rVnAUEYQrdORfbvOgwj2GIsTutZHZkVNOTfAb1oLWrE6Z8FLTSYz04rfRIC8KM9yUfbfnqqBIpUQCcXkwzncAcj5v+dJ/8iv7OyuwWV7tNG6V/2tIxWJu9ZuK9KXJFOEs7g2eK6Co8hVIEPcb2gNMJFx6Jph/qdMIUv7ev8fAe3jO3ELaboMy8wL5UyAaQ0qfkLYEIydFFH2iFW3Rda/Tp2+Ybc8J6vLUElvNZdpZK9MW5sZ0GuVEy0Np50JixnonvE0/F4L2Pufwv2U4VOXUvJ357bMQvmntsOU0krP6oryAXjl3VF554A+7f9wT7MTtCrZQcWNUIPHBpMdgnV90xys25+4GdcJPWY7bDF/qzanGEOaQAQFsIBfu80QueIqbdHQfeZnyr+o2SnLf4F8YSduStxwQcJp6/3gewxiqLnmjVpUIEyqSVk3zRiV56jt23fV9ROZWEEvShzhu1O8z+yrQ08U6oGRmtyc67+7cyJzlyzGp/Do6WaagTK5odVXE/JrGmqWL6kexykNBrmEeUXKf3Y0wjGU4DmkjJwM1szF68qiI8FreC0IXQ5fVdn9xSgJEWbEN1P3mynpw59tLDZdrPQg7iMTt0CWhKCKZzTikAQi5npMy2Hpqa05qAQGifqVTAjLM9Efd5Eeqau/SsRR0QUszwdDeU7DaC+jgmRG7OiaP+QA2TZ43LuGe1QZrxDYdpTKKS1Qd51hyTBaEouEpI8JzaI4AEZilE06eBNTkYluyrIcYP9gcr6GalflkLtaqvs9ZZLIEdZ+PtK1BRIfWDksXXrtGKyl4KOUnVM6vn1TuPs8xEesltHn+e+C8/3/okfrjcAjIg0uyZPEowRaiEJ1Dw7HnNumlnko2ZQjeLG7xomtReurFPlZ3TnWIk/T5xGljQbtM6IYf32a14hIuOcZT1g9MSuihpzK3x315P2xxFoG/neYdodhfO6XpeOc5ME2OGJks1FxN79FhbBSYT0m+Vwv726DitoFkOeLQr9fxW6dFCtncsvxgXjKIoAh/0VeUnTdDiuGbiCV2bqxF6mKK+PcvENmpEvowCUK28EUy1sl6dqon/MA81Zk1vYR2mKolWy7DGytfhz+BDWFivAktnz9RdVRpa5rjcNpkmUihC+oRJrd11Ix+bWhyXNBNMmNiI/uNxuqz0qURYzWtgpe3hblvgzHSUGPY4257K59z863omMPR5E0TFoaGgm0izI7fpmJCTtobDKb0vyIYNQp00VVKXTrhC896mk2FyDlHQVUXVIpSrtoB2K1ofjIVpCCi05V6lWIbt1dE1kyKhD6zsguv5wDWzJs0fjZGU9gNLN3JZvUKCUqo9bdaAeMnA8JzphL6NdkHbJM1uHPApdhMamOSrF0wpuLfiAPcicle6ms7zGhCmsPAy/lUsJMmCHD2TgmkjqkRpixL5fQPNOc98n7mfOM+2smDtnft6fn7e9SROrUexU7icO1I3k0LTec1xVWoLTfm0HNJtltrXNXxJHrv6PTZ8c5a3RjM3j4F8rTgxmOwUtBqW8zG7OffYVESZF3WntMRipcoO33CNyAZVNXeiCkoYZHckU1wy8c5PK/hsyIzb8/H7Y1K1OxhCLk+3h5yN/CvP1TKCMNj7b7stUrCUUZXu6enmpDHbqkjFqUTKzrQar0voUF56puI5q7Qj6S3f0WEJJaSneqaUuF/w0ESv+aBhwqFJmJhptd9kjwnedg+hmXhl5ZXXn2PpaPYb1YKpMrwX5UrSuK1vnyIwmFKJD5orubN9HuCZOZyxNVwjYg7lV+mxpSSYzCovHzt7iqhGtguMjlN+oEYJm6E511b9VQBC/oC/M1/k4Ok3LzonOAr53nOuEg9KaZcK8HIsCELdybTB54WvDrN0e1zTI51cVC+kQu/T+X3wWziVpXojzLVfaM3DEHoNIC3nzMSQpUkz/qDPSBTHUGhFYGXeZm2tNtBtkbMTJhujXCNijfpaYCwHVVZqKc+xVnqOe41Rw2ZE24XRTYN0AcqXhYxkVzWrp1uT9dbCYuHk0qckkzfm0UkFqy62fPwte9URhb1QX9Q91h0xdpGYKkVUdVE99DgFekLZaT5nzhsnttIhs/uMo+gFrXee5e8Mv3BZ7zo/V1zrs2WWefx7ez1cmjEFaqel8Lw84EIEOy2R+Ci0VYhyA6GYqkV+d84p3mbnE+5vW3eKp2/jnXCZG5XUqZnDnqWpFj6crO7ukUwAABAASURBVLkLkkc6jAzhhMon22t2LxsvC7UIM61PT+QD73qVDBPjNVASyoF3T+TR9RFJKcVNcqQ8e0FUi+GxdVRB8vUgm63SV3JCSZoN7B4TqzPVejHbptfRu7jkrAWXMiEiz437madOamfkt/k9nXfVkTcKG+321v3o3fm1B7u031s0npknu2uM+qZmNTVaRfVU+3Td6/le53jOgGoXAG1iQ1Su2aAWssrII6/Vg1auITVr2KFan6pN6iR05SRkR1bEN11qibdIV54f5EWlxQeG91WGG/H87W9iwpOaD3UrgMP1ut9iCVkhTdV7EkYNkPs5yRLxpx38iBtA5XSGz7wkJ3V1Uo0wwcxVI5o1dbCJ5eYb0NPc6e8T9zc6A3TGIrdhcVmsCFwl+ps4jgamR5GehiIuGwAE/MBvrA6GGC3XjPojRoMdKib7OHyJhs0KpNfY951iYhP7Fjqrvmr2DLAkQd03PHo+tHNWj/cgR2xw1n7xSU1CAoPv8SjJAbOJHyoi1RCc4w7GTJ9z/4oUiZQzqqrKhjJLN/XVvPpZk06IqgeebzDKXhVmFJzWyRi9/BgTx/BETd9xnvj23IYO85BAFCz6oCTCB3VcLemUlDWSkpUd+jEWAdkl0uQJY4+vXh/fH/t8+rOxwkrlNAZJ0zkxHXhcd21b7QaLocdJy5pTaiqFnxWffV1rX2Or3camDYN5e6UKzvOx89Abfxs8Icyq06xYHQGzC1gvJgEb60Q1lOgI+YlVptOwpD2NkzgFlm2EU8sk3/4KistOf0o2R33kAuMpnJHmHqiV+ryZ2E59UL+ZgdLwNnXHfPhc1+16lOuA7uKeYfXiXyr68dRDrbLGnFcxUCFd4Zcrv2uOd67T/tg8zrMwTh7g69S9QSF7ilKrgougRQA2cm4Y0zh/xu9eDePkByVaTTrnPqPsTKUfhUZuZeaf9uFtUrsK5NdUcVte7NuUArQveBZaZSTUmZyOXUsiNpNen1E6p1GAso8OStX3PPeZ+Y7d4nbQNrQBR7sG84FlqyR3PatE4DtUnVku/AQ7B9/BsM3OoiJ/3LoMD+w/mhbgViHCfLuJctMMixqldaDAT/EZnD7VgjbcXItLxF3TnpnPLXkQZrakCuoTLcAYNcRWgMxCHx0OCVpytmxrWXaMo2hy3hxLtV8YwPdzOe7hkyKu7Rk/jJQYANXht+bulkZbRLVTMR3I4rCOHY216dau5waPlnxhWXJEmUW6p6/hh+JjVK9PnamDEZNE6+Qpyyeqdo2icW8BK1FBTFDdYi5g870WbNjD/cTn5hLHw9S+RmrbHV6rAPFD9aLXWQBunLBEZIrOPPXMqegVsWkKZQq32b2v3AGjnk1/QIlE4DWsFJWr4YxuG+ZAL5rRnUUmWGU99LadtTIN9qZAgXVO27zcsstblkaqEV98WVne742Rj2nHgTiFTKbeZ3KRC8KlApTvNy9E9ssIYQt1ldaGDIBQyyHeDZ50JtGaAnIBWDCFXF6HT0Gatn5ZsAEbSOvb6taHGqlVuj1KXELaWz6Mke1SgGqWy9vXo+X4QDcCcaSya7Aknpb7MZ8grtYZI+z9Mei93dqDuqYvjOC+uH6wlodwDDV4QTfXYe/HWUogxITkq0RSmwrPLiUJD6eu05+nxESZhBypHeYo//iaLqOmGlVlLTPK+1DpENHsbuTIU1hduHdxT8irnPyFWL3OrIJUebW0qViUHLEXGjFe37Dkx7mF0YLLa3WVRNBmCtwx3inBqlvZXjNLOcfkVM+QTehXiJmeT/uW+lOkWK+5F2+alhwkArp3P0uwghR1gOZ5af4qQqZwpD1VzQ9sz4p0FsxK9RH6oHz0amNFS2g2h7h9jkHL5Fkl2T0zU1ztW8TL7CjVt6k+13zMwyynU0y3OubQWkqGslHU48lJv1Wiz8Fd+/fs+97nWdM0c0SMA8ZBv4vH09YnFD5aeroHqZnZOltXm7Iu5eRT95Ovxj0nUl2DNyOXtGHosm/sW5uOd+mpEZs24xTZQH7xKJLKC8+H5SaqvJuOXsglt1JAzRfxiJ20eEIhwkc5Z0gGaFXVuVj7Pd9MnwR7AHlqGGLN9TnXPnp4+/Fn7bSVib6Hd3EaOjDTIvVzbI2HS87tq4Cp4bfroWJ4gMer19iGjASVjELcpYSzjzzN4l62Q4IMkTGo8JYQANBXrI/ZstHDMM9RmKXoLjx7bn8Kd+dhJqeUqAmn6q9oMz7vdFEyVWIlCIis+ce41oaxrdUIzszTrGIlph/XdirnUmuNq67VgRbtM7BE1FinQfEJxbLH7Kmo3lIh3v9SIKBNFUeOltl2/PRcbzqEY/Vavm0FSMVcuj9KtWNtQywkGle+JCkNzLiORpkFlyNaIjzFzmhBiPU1uycf3FglF23rlN09R2W3FDPGhlPrPpZaBDDtpbmSvfFxlHp4N6pHP8XUHcNJ5BgRy96yNqcs7NmrfkQ+NicG6FGrSTZ2qdCM1W2qLU9e9zYVW6FbNAHgWSphiPMXi0+7OCitYtxtfBkRCNAgq2eynpqGFq/v3lptuCANk4Q7P9z/ENEXLdYiPwqvT+XJG/5ofWEwBUZwTMQGe/MECt7OUp24jWLLO0Sgtc8fi4hUjbxVsyhJbxdUZcV5WMIUSrh7LnwpqdKCoYkjRDZwQvwkxbmlGsuvWeo0aiYHPaJglAAKftnkPvCvmLfuuqFEzjTr6D7ICna8RYOPNm7r6rU0+EjOj6guCiSlzEkq05HS2Xo3H0rGbrBKO9TjVSPVq9dem1s5TMPfdk6oTV8BDUcbuqBixHexFbPmPz3VjLEPsVQ6YlXgMWwqF1nqRVe9OmT30td2xIakmlSH6tJHuoAIvJyQu8a12xXBGT3Ut8yQDlm+elYXrWorWS4fisnliA3LfrpviHq2xgdT6VMT8Gf4YJSyIVoRPGM13uYhZ738BB+fIxLPVfcW0ZGgZcUp7Yz9LqtjLOrlS+lQIbM1O5YKa3wGiZK3uWzUD0ql5pUz01PST8Ts7SrDgaGMgiA5FyUzI2Ecww0l3YZXq+mZ4uQopAllm1XdNTrQ7zzr8wbWRsTficAdXiFLspnT/Yw/2I1QmJV+jldtpy9TUn977hrMUKxodWlAyiGmzjmlS0e1ebcVI7a9pSIhZPx3dr6ZM92i6HZosZWvLjy3p2R5LNJFVlTT06OWOJkHTDjMqCDdygD9S46kMUY0lajPnIvvkiKUFBejGUI30E97Qh1Awnnop8wO1smlgGWY8NwhKWA1V4aUzQobuSbVtc6j2nPny5OeC6RxZBI9OrocRRFO96ErL26K4WgfgOXcDidMHb0AkibYouc8lY/Iv3auK0eOzE7E7ZakRlZjGp3EqNJFIGUZgynzXoYfWLRJ4AH9alvUtR4lbBIN3tu1m0LAEEbXTJ3VWI0ah9IIq+Vme+EjVC/Jxkqb28SHGOU1ao1wJZhSPN9kpOHdvwIWs+xxgZG9HuSocCEmaOYBvAUOgEWrmJZfc9Zir93hIEi8DNIwsgIm5UABV4nRUpWxi9WrX/ThbwiDyEQ9XVp9atuscAYO+UgqCwYNMLbcUtlQ0dkp5N93t6AH5S3OxMNQNBqOWkxAMbDwzpE9DeIoYuEb8DG004nrjCV6Rm1Ergb7Lx4CWsdP23FKB01qEpACL88KpAaPKs80j7r6lK4pG+pxUZzRNkutJl3BNVF413uIoD3qCDHhuayWEWvKRdKwpzO11FTneL+1uWmrucs2aNTLFFF2H0PT2lg+Mm5G1+XA+Avm2KRDYeAyhhJKGXlZHLHJ7FajOw1T+EOEIRYYw6vm/R/IbS8P2T7sSr1JD0MFBkdSDbQgyfspEzOWb/lffq3tcigHFJfoViw9U8bWrLfr9HSKPZCNyFkFC01xVda2ciZaC23hmI8b2pB+mk/pvOgLrKgfzw9XxGV9IWXpUOGPQy1MSCt7CtWin81Sj3u/ygBlCxkFPtMg3Lnoevq3e0xgUWXPm4QFX8FQCimr3PAgauELPCnmoKIR6kW6DtjyHY14ZlSeLhere9XffoSp7ERpZyxLwEA6ZNsUCdAe/ys/EAl76eUow28uHENK+mNjAoLGMvysCo0AOu1oAcJKjRWJ/tBu+BN2YtKQHBmCmymOi8oJ9gh62dM8XQ1NaDNrKgsAxlwT2OndTunAfJl7juVRbGNQT6JLBM/F46rTzKwpN5hpenNkqhEz1i/XI00ydBJKZQW4QQq3z1dxJW+hkd8wLiBoRho0vOdlKIGSHR0Snx7PZFLw9MR7ftAfdSsR2JIZFIEtLbr3Y+8Du5lNOF3HcpvtX7df4nb5OJT7w3NC3GAAFEddu/XR6OBqj7nMDFQ09vLYZqK/sQsmlRXrdoW/294qg3JmTB3UMMCriG7LidTKZtMj+ZlIHRVjyyORG4/PFKKF0lqtFeoJ2XG3n5ZurdKes14lqlha0xxNYTo+RxZTItMKstCbs0SqueTWxzmqXiZ7uqmKeZ8dnRpwBlaU1wjq9gqrIXPS5Im+iUIWnsG8gGRP7iBq3k+v2+iyOcMcE1xvdueNmpk4tm7jwWmxgPo72TK9wsw3ItBAKsSDeSbKQmdt0VgqwuZ39oMAzITdR8OtVb3aVaRTN7hHC8JUPehyrdZSUlN60Q3zaFljIB1lhLub0bt1WEKHbEoFHiriN5zhuL53WDskTQWy8159ghRB4JQOx1HHUDAi74xUjHZrYzM0mbYjtrtvKgRF6bAtPNjAjh/T36SkHyN0+7CWcbFWPBfq1ZtQDsQvqPLr84X25ZB1OBhZ6hYmPu3aeiZtS09PP6CQ9lPaSry3XsvTV9KaNaYS4sLlpvj4WapK43zwJnYSmcDTnojp17xh7xbLyvDxSnbmZHqbzq03/FRpnOW+GT6QfKaUXUJLwc+V2+UTVJGdpzF0XjE/jX9y8IYlTbkv/Ywi9gsIU8JOWWess9YXSw75SDiAgTAF5kprT1baWw6n7uco8UCRkAmveapSOPjd0/uh/cK+GfdDHG3x78u1kX24OtRCfHqd1mb8ShWDjUJ4xX8H7zfjO3jpmKTuPHP0Z8CDoClbVAP1c83XqrpEmVa+S4Loqkp1rfzn/bGX9XysHoEDu+yUthjzXPzdPGFSXNkgctSa4pyUXi87l910hQ0H3HzHXrMN1lSq3bXqp0tA0enZWpYEKcCJaBPlSt097x77ESmhOab6WaiLeY3Nw0nlbojRX/qvtxJeXjLsbDuX9xYamXU53Ek9MFx2fB2+fSxRp+bc0zWzpPBiHzGe+jqj4vCpWooywduEbqf+qPTsbpvqt6KL8Mtjh0Pje/QHnOfO38q2VkFICsf6YR4oGZQkemMcSR2EhbTn/RrXflML/rPyXI/0RrYp3zgrpMge4KCfuY+2hbIU7J/oH4hro0TxdraRcuqeU/6oCnaVcpNqv2LCO/vG8/BVVx5LNbzZ2J8VcbW+PzP9JocuHuVLPFCSPPCnJJ513aE/MBntv8eQAAAQAElEQVRXbYO0sb+wG/jVUAqENESOIbW4Xj2qwIsSi+cEwB7RKcnswYWxEacQiGoBtce64ujuyC1lTUSd9G6X92Ipe76/P+v72c+ONN4zT2bdTfV5rYARZ4lpQAJwvZk3r3wLY73/vx0RHpCiMQUfbutP5LWwHBSnmNitP53tPSOLacndcZ3Yh64pUwYlTJlu0fPnpKgbyLdb7Ho+0E5NAZohtsI/lxhALbe1r/CL2BRlFOq+q+p5vGZhbMmqpaiHAfDo6//w/neBMnffC2XEAo1L/dMjMWokNeOj8ADRYqNAel+j3pYGKUot2L33cN0Xv/toZXowuiYzRk63CrkleFVhMLZdsTZ+Fn889F3yrLe+YIvuu4LJ5nkkp8mS/zdbvr6bjRW1Pt5XSDVKxv3USWav7IttC9+NR3+yuHjZFiA/eNulEqZMDGqoIh6nme3ZV19TClGjdtaVN1PQK2nn+px/1EaHF91oB3iSpZlDWjl6Eaye6ds69bktSiSsQrRmxD24zzBTZNbsb15H7eIBEp6Q061KKUImXgpdutiQQOUlOeU9PJiEXhP+xLrDoK2n5+IzVvplhFdRq1g+XepzRwoab7LNEtQmZXzkTNCZ/YFtD/fRSzMDvjnLtDQenbOGZu7pS0ncGZKrR0VIRvFz4lItKq/5GCowa1STe3MF1+qI8mo/RrnspPzLDPflc0X+OC4zJyEfLVzjzrX1iGUrKJFBxRn99boVGR/4s8WhJ9UaWxyXebt6ikOmDQFGugb5Q41+Cl/iGzxDfT4pSvH+O1OFQ2QMlWmrfQ7u9z/QCJoQceF88qxfxLNWCxRaQ/Zzxxza0HUkOUZsSKlm+gOIOfQqIBuP27PsiV3YhAF8wY8+lKMM3YM+OhsqmeTPzN9/Pv/VMIp1lJsZJftAofdR9uXTTQ+56Qkc7Jv7RjW/puZUY2kxcGKH1hG07enL0sU7sqcbUg6wXOlhRCjln3viO/aLJ/1r5/a3BsCy9SxpSTZO57nbH+zHJDBQOiQh/9OaduZYtxqPIa3Tsz+JT9EUW8ux0r9z+bOFl+esqW00g+FIt349P36vSZsCdXIM+o3irvWT63N41WzvHRYqym57U0aQPLfsWZUY6jUSkV/Kgn8m/tGl4/YEc80gpbtss2IgR4WzgGViwRJ4UKkmwpKqSHUUe57O07tTGflQQvy3Pz+xA8GWKEIUZUwr8PcTxrb82fwtUGajXC++v0fV85lxw4xybQNLt2dlhbYcpzlD6WrgVtz3KBaRZmyph2xzTMSz16P3QAWaJTtiqoftItRuRG/xYkE69GoQPdrto7/wX7jq5tjoCptLU3ES356nZyCJihSzDqj39+rSRCkJbSQm+lh/y/J4njgc+MSVoYVXUD0a13BptYo5K59r6kOJ7bSK6BH6uAZTWTXKmWTzs88W+UwpeSiaArJ+n+7/vjuf3U+MtO7Qbu8sHHpaYNvaDFMIBvRRic79IBvqIfUvns2nR6WrRSiDomVMQr9rvu1V26lHaz7GRGVrk9KlqbygtKx79v/j+jVIRf7ubfDD+Qnb8E6Owp+AIgHXO55iUNOuXKsvhMlfUty1M1UVi7G84Ef5nuk6U6WP1qJ6luTrWDY6YDACZBAbYl9/f0kIIQbN1fCkTic/ZR+REVu5CSti+sH32H9lt/EO3kI+bzxreO0JrWn8khlVq96F+jK9MFLyD3jV6tnpSiNnY0apmJq+BfHn8DqIUuJ7eKdSw2cWeUXo7cmffLbq0tE6qVFgA5NCVJKGtjqIMPjiLGtw0tZ37S+9R0D6A60NrWZV90w1eT7Iix3az4SKsn1G5iFeTBFlW4nyob0gIeHQ+F5cz/k854oTH7ePiLlSrNkdOy/VFh50VfGbNemj1nIW+61ACFSrbnT9rRC1oL18ffyvroV5dtfivXAbCnoRz7YOSkJiAAopWhxNKLVjNauenc9Sz1tQJmlouwu/SsyaE/xi3S3MPQpNcRTR68y+V/gHdtW5KpfagAogq98VtCKN5d1P9z3d5uYvq5yhD9DQy4gfFyqzNByrnmdZ/zjC00INBVLBTtMJcI2ZTQqlaoMrfT+Xt+3u67T3GIay2XzM7ksdd0M5cYXd/Rvnjs40U5YAtbJP05Hwe64y49Iq82jrAQm98R1RS11eSmfWPJ1PQl26ctzvi/9d/bUjvNaMkbZ8uS+TEv/KYWH25Iz+nZrb/Ol4xm9AEn0kdOGkLldybrDacAIqD15cMK08bDnCClv6gcPFWuEi43Isw7QfsTvjeX6++ZPzEyobyu9ECZUfiQVEO561H7vC8eO10WIlDbhyr/r42DDJb1Dh7pwV9H+Rn38ndqNMbAL1xHNW2s/Zn/ZJSJagxQWq2OJ2PkTfxCnoqmzip+z93w0oqzS5BLzfXdHha1A3Rljt22ErsR4yXY1jXCn0mQtSM7mfM4/upZ4bTdTmfeVzycJsUOB65Ku48hO1cMAjHkmagtZGx+sfRjvkaBAzBNxsw9aRoGVsSfzJVAoObHoV1m0hLDRcYkGUI6VczYUyTE29gKWczgCDOa3P3j391Jw6mV8g30tkkNc0qzEsF5r9jehQ3s0oK93rHCF4RjLKOJJyWkD/zvq0mb9y9++c75O3dq384PrQlTJfNaK77pfpYj/O3BbUTQxN2fsEPw2UP1zpkopbcf7ChyrvfdT/PXNyBPdp2TJgDVxc+pnrmXWPNvRPPqcf2Ll4wjK9vyvr52vrVxmyvLp/UrxZ4eX9PJG1H5fLT/VV5SJCfo/LehK9mg5FjmRXq5rUUEpMc8+oc9jTBo7GqOQ7Ugrt7Hk6v/hqKwd3azE3xMhMaxS2RyBQ3f11bbC32pJPK2lZSaTbQxfBo3Q4FiZ0sUstPtFgrn3Hv4xlW893egPh8ICFlePRNiBKjK3RaXmu9oJy3ZWogxK6ow4oIA/x7RmFF0Yqnk9T1l5SBVlIHuDBVbZGS9YX54tMMcs4Rm7q7sG19KagncHhuhH6UC1t2PoalYnsQ9ziQJQZLeyydY2L/ReLECggyX4jYha1aB5Li/I1XITzuH3JA7CpOP7O4sJzrduvev15zhTF7XgNu1GDtNJJkdXe5iXDV0ImjXLdZKFbukGrVqzBHBHItcNg2v/goWmUvm0NiJHOa76e//ncJdUwUBfObi3/A4atlhZEvrKtfcm7CNgg2m0+iXhG7qAh4D9yC8dFMwTLpgRkcoG7YC/fave3QS9pfyS378Tvtt/P3KvAWaO6nLvrt+YMAbt8PjtxncftWZFrxGoKknj+xHijzFQKvf7MnjNxz37UJtmffYc/M1K2sQ3lz4P8Fp40JSxWrX+jiUdIv6qK2/1Xx8qvKMvGd5igGlUMV0cYuSq3Yq4RgXkRnzE/UYLqwpq6PjOt7c4jZdeyIiECWtX52CQLc1rhdyudsgeHRvjo3Wf+BS9zpP1W/Ub/xxEpsqFBbm2yzEmP8xEaQ/Z8045oEzLITfuxKx0KmAKuU3yToO8SjsbdbrNKzepn26U9r/D9Et8Zzf30+37sII4DhKWiH299p6laGs1qiH0Aqgsgpw6bQf+EUA8HVnbBl+Qea28z9VvlEG16n7DektIPvqE05TSHGqsUd6i4XK1nWmz7Y/wuHqC1gXl+Mv+5rUUS0n9wuV9zeW0ROyfmOI+Ec7XQDjCgue811+rL3qVmU4CFWlERq9fnXn6LEqGS1UvnwI/bNe2omBEG9DpzHTNBfwNz4BrswfIJ4qJb82Wg9Prm/Hfst11Z/t0z+41zu1GEpDKe2GnrXbS9DxX3l/HFT1Lq0epB/rv5c8b61XajINveUoeINC66nJmcIeHN/b6Peg1V+0XdmCQ5VBvpye4ZzfxVgLZGX85XJ9rXiv0p/6fOMnvcucYu9QELXWPTUtk40yXIRVMatLo9Sng1MMPIRVGnxzBGWcUrTJRTrfKhJ5YSvKt/IGq4QLjN3pqngE8zF+YCVOMLYBnBMTsDJVm9pHQSdZ375yltHAGdk1tM/8+cakXpWMK1BM1I5SWeisdYuiFaq4P9ZsZ0zan4Gp7tDbbwKp9eUG7JZ9TgvITuGvN32oauDDGKcgbQGpAa5N1/0edhHStBNnlVGDEZ3tvlr5SyhJLmt3mOvTI8L67TDihDEOLSAKD5V0MDJVmw9D2fuDu+0vklNGTVKMXqaCRg4Ex3Vh69jyjngIAtclHGoNeUlhJeRt+giOFofBGaG6hdExkgw6xACKz4Nqe9J98kdOqKeWGljEJSkDRqWW0TQRqvnBGaPvMzbqXb3GvvvQxjULt4qnISQJml+ZjFyDfU9dV0JY65bESU/3sVhWkmBcWbobzPU6jyILf655VKfOh76Wz25EwrbZLI3K1tEYrb6YbxzJELE4JxmMNZ0f2maMYF/LY4VPQbfc2YDLVQ6hBvhKBTLFo0FXbU8gpvSXgoYzRn0BWHMxWLvW4C6X4Ghw1tRK/Ht0gW6JUqbBZMLlNrU6xWZ7YFsZWIxJpxB6KHSc03MAA62qaorLqa7a4IcQdpBWyFhZcCDVWdNduuXrquk11oPTAojqJSSvXrGe0+0AGh0ExdoeKAN6yc3GOrrVMkEAKCQK9pqeHAUsNMEMVzWr0ZM2s3Ja3CBpJEbRmWtqgJK3C50y3F4BsVwx3La+22C7BhCTzBNXosQ42rmhSieXyOq0toMZ4QOWrU2sB4IYAXuIiCYlOZ9e1d1o0pUxWDJIBubSN6AB0JDUsx6k6oEkSl8KR5a6iyq0FJT+ivMLq1hqng7ZYTI/ax+Ab0p8T85KyneB6oeFmi2FdJPK1hcMFaU6vS6OTbJwSC087ykCsloG2aKLCf6dMqA20hbRfQR3ZrzOzf4v+92N6mqvFzNeIBU/qy8cSUo2jVweN9FKYKh1+ur6iUxLdRIYSlS9tk8Tqze36GFxZ4ldkESGVyI2Oavp69DzkjasdoaGOJG6dGjmVbjkjy1R2n6px7+jGURgpND7xT13yEGSVvvQA42w358XvbPaZoGxiBJLJrJNPKzszCwbtqPynD+SR7UMD/BoYgmgkT4UDTWyZdK5os4OlJK/ez4yyH7a4rUze31g22X8V4Rp5kruR69HtEwnxgoQBP4645QkIodGyMofFye/c6HZMioAI6Zfy65reNCtU4/XQpJDsBwZ8BywhanNAB+JiVsO7snaWbrajCxFBh0FhOUtcfuq4eLEFluIuyY9K/Bi6loUTFdP/JdEQbbs5j5MzqgUKK9FXLz4g1aj9ni/KQI1DzOO5PpG4hOVYHdRSpQoxya5umcqrqexRHsRQ6MChm8TWn7nhcZmpq3vQUobpw5zZR2AVd84IJBgBas7+8Hx76GN3EnE7OvD6rXdGb4tKvVODOslUayalGpdKVy2+tagRHzd1bgHJWjvocRmfRYk64NVS+X68zCmIl1rEFLluEUiVE7XOcb+X4gX+b37Sb9fD58uQLnVr11YT8iAeSZ610wMU+lG4dQ6eSixv8+gAAEABJREFUKSxyoNvotwsIoc66gYDQzFlvU6kBuYzaD5qS4H1+pvZIYG+aimwinflOKxWimMjORH7HntEs9fZRS8rJbEsJyKOxpdBxT/Ox0A+XEnfYYwW8nZFKlXuvefM6DTYw6DR7qGAjOXRwpkoPl0cZhQNa37w01Muoy1rTheBZzBgYRqyEYxIoTS1Hm0IF8aZEKBywLDvpBfdl0Irmo5dwvcXRo7lqKRqlOIbVb/rfTColvUjDApwtDCnQAJp4pnUc4wl/1sj+rFMqPlf3IiQTxugzMMP3lGZPS6VAIXtyJlsVX393VmZyQeeskMaUHI0y5ni9NfOmuC6rcA1YTyylXrH0eMOnXS4U82zONdgjChC2PCi+LKGNxZJVZcNdKoGDozcFMZa0jJgJpL35Fawyq0XZgbyjnpp3+XgdjE/qij6WZ6sHmJgYfW03s+2l3AqMK+D5H1EA5YvQONIszN0EOZfugXH7GmWyN7ToL50XaeeNZplo11PGtbbxQ7CAdIHsGeUn0a0QjAaGNUzxm6ZzmpNEBSWnlJgSp1RBVX5m5dIDrC1mEvOt/p3RB1JMKNCWzcK0jxroSGJJ1gTdzFy8v74GAEcjlvzCghPaDc3v1K1U1ogTVcZlrSGdd/eRjq0goBfwr201MERrZxKEjGPe2F5Hom7WuODMtv7YhE0SN5D04Rh6/Qn7AJWD94YA6F32KXjrxOPV3hjVE2Azq3spGJvW6q4vr2naeOi1FFm+KtdUvsYVF2XOuqlDjizrzNt2pgj1ZK0Y8FyustOmyTbNXrqVGX9kZBgYZepq3Qjpie42s1jK9+Jsf5W5T+E+zOPWEW3SHP+wzvTpp/GQ6YQPdV+etxpLvnTZKuwHuMPvzdvmwTChhuG38bG3lSCxk24E46Tpge30GRTYdFeTmwtPvAFVn8bdPC3n3dP4+9ii+nvVf5+jWwln+Tq87va7+AxE/oByOzyPjkTN0tlKFXKzl0ucRcF9ByMAh2ZNoo4OOalWAq7psPLt+9/Pv/i5xCBGqWySthd8ATSKoAM9ydHMOvfv3L9+P1fVRn/wvfD+GWV/5vMxSkQwlNtwGYUKKQQPLIWuT2hZ01ZiPcBXZXDX6MFe7+AP4wNuo3mh07q2ldpVsatrxGvK3LDbGsv10MJXuT/u5+1ABnNxd9TZC3pNtsSqbUJqbyY56WQJgt3wQ5+m8AEcKBdjTJZNck7OfCHHTiiUcA+/DFHUCemDugQMjStsLXL3vZ/Hnj6NA9RnRP1EDGBTnm73z3yvfjCV3geD178aU5geny2tl3bds7XZ8W0B87HhHqcjd+dFLotNN8VT6GmxUYWTuHOemakRYRquEqAEb4lWsEzFrknLfX3GFuBvpAvjOXOzbi1sqJ2vuHugPzPekOLv4bTzOA5KbOax6gBIQYDCspp65u5P9lIdRs5ix8X+x7owS9zoTyzpHGoWYNDGQw0qDq57+XPhdi/AW8+MvvBv8smWTqWDIjHD9Ci5jFqhm4Xbs/iADRSbq4zYN+JbvHvKtIu7F3MTq2VUOyd7kl0KgBNlaxaabMjrVVzlSmAPRCTyhpQVtS8at6genXcFPNjJuMEzpGakp5iOx/azr+fawvEGOI0swat2DW+qrbi8+Yu3ZCsPTkVNuLDF95geWVPwyRibU6cSHFvmG+sNcFhFKgnP+2HC8KHp5t9H2cNlrX7WVDDiVY0WkAKY4fbj186FE9l2WGffE48GwRy8Zhxo3yO/KGOb22BavEtODsnMf1Twyi+e7ufmuR3lY9SyKPKfUqjQi+Jt5v2vJCXy9/jvREZDNVm6pF2Z/rweXU64C7zNfn0b2u7Gr9jqu60U6pn3Sw/ER4OoyPLHlB5pSZvty/tq83bpe0TLoT108W5wEwVFFZ0Xh6vXghl01fEs6JzXuTBmktDiNv9QpUGIa2W97vuZX9iXa+BvZVBCWe0hUZpMVjNbKRxBhFrDVB9kMgsDvWZaBTA1CPcwB0gD4ph/0VolCs1oZZRxNuYHuKZQeT+4qw7O3/HmYZP7XYmu0eoBPoxqR8FIOEe0dV44H5xN2DIKI7WpEyENYVqQ9h/RhPc1w6HDAVLJe4I3I/kxC9OayyfrLqVyVBIVq3r38UpV/Buple3RxvUDXKc7NbKrMc68uq4zNun2cB14yXnz1FSRTRC5+9lIj+Z6sE9zbuC/R3+p+ypyzhgU1ES3rtsR1EezNDtWXp8K/aVfN88CP2LUNFglt+5aJ61Fu07EWIO39Gkj6OPi57b3z88zG3PNsXl4chItfJbkwJijDoI1CLahMLqHRoeiHLdjeVa3kEnDVhMHbJgTPkyxo/Fo/L3sAQ9kghq1odRfxg/EOIZRXGUXtTCRBnTP4CWGujij/KMGIxYrP9Cd/Bf+rfmOPmO2Er7kRD7hHW1X4fMo0WR9Huhvh1o4Gy+ikz5Z07esOfABNuwj3krRitYvIbudas9YAaxGPHYhrs21OkYh15Iw3lDSeZYAaq7snZfp5JSSGBb44fOpxzOn3WytUZuCKzqtb6GaWopPy9J94f5npZNHsJ76fvPb9V2TobQ7FP1+42apG/yxNfUuXi0FflzVWxTFpQTyEzuwSjwv0/dY6uvJRsEeTp/T96nTOSZyGz0w54Zfp11LojDbpghpWz0XxoK9TDhAeiOkm8Ey6deOMz/tnutN8ygUM8n/rTAMqydVA9oEVgncjBVWAoNapcvMHlmApsEH/DsaMtVnRdun8a6QZ6JUPSl5uqDCoF+WjnqjeMAynZCBPwuyDjs2J07xBg/93XmRmnA0hzg6qlO/qr0nFCAkQHt+Rn/4yIY/s7sl0NkP7F6s7eBM68nevVQrmcg0aXN2acFuo/q8IgRUE61Zehp8+QTNJqCJo7FGDYOWgGkRICHPk5Yd2etwP2bPfsa/coj1fdbzVLFmk/uz6iPtEVKid9rJdyEQfhkHaD1TfbSo+JvlsiK7VCz5TfUf4XIoX+az0IupXO7Vl4sY1LsNeY4GYy7OKg+xKbgx2mIc223XcMmg+5dx1y5VvYYvMSPrlLRRoqLQ6fejJXOgfgopwpGEvnlMsKC7G3VCSUObrP506Y91yitSfbxkW+HjBHpc+UTB3o2ZVgud2Y2oYbPVkHPzrKKVQTDRD/rMqFO8wVn7UrBeRFlnZF7zxkNmVQqBcFlph7GUjcGUMpGsdD7A8c5V7Y+6W81J9e+SNuE9vGduWO7dP4FNwNDBlkpHpp0JzujomYYaS7ZiTSGkuzK0xhbGprrzvPkrDYyEAM7UsMfxZ9NIDR0VeDaV0plkyoW0c4GKi9WEecBMqG+aNJNl8irOMLqYtIQe9NkopoROC9egE1bYsHBC2woPx9aZea2/peXUukWcxhR93GkQssrsOZjbxBGzY3JPiYIxHjVUQFf5dPS4HRnD6I39hD0mZa8WpjOdmcS04nSEgcZcrIsdA+2yXFG6Qn8MysORLmvUvnwpSmHG7JnPPfvIuGYv97BeLdlNNmR7xn2cr6NJpbBPIM0ARhlltuDV0b3fD24ZNopP15EQhmV4CVADdr7tg2K+lX5jUPqF0fD3qRpa/k9pGlszW2HD81DTWOwG7vHnqIv4AXoNqOuMVtJQnEGL17dnWcOeZcc5gJfv2YtXUL66psTaM5JHNXsEL32nX7ZtXX+BHfZ9da7xUWJbrfrqoPZJEcJl3gZQpq2IBgsz6iB4v3RX1YPsKuuBPAlyo71AISLFzmKFpORqN/MI4jvu5jlTqVRmoGRQshrtWeoCnWbjbOObMxahppaIRrxKQvVIqdmWRjEfRzcC4Xl1h4CN5sOsKTDM9qOkrcHV7u0cTrMM5ZMLrXs5OdPYHrbGMODglmKUunvw51XYUXI6ipiDN59VWqN82fXjSwaQ1TICUxSS0gzkDSZ3u856cv1yHSxAgF/IfaxKO0CFAsDHfcYagykU1YKfh1nUSxmwsF4hmZVYpI3OGs2BNUbh8TN2n5N5ziSml/HH4vK1YjmDDKfWHfcmASlA0lbGHmPJzLjYxReutnSUEYEQBbaSVtnegoOW5kboIYmZqYzxFCq1BmsNOBZHi893unLaaiy5WjhtZvmYXI6PbFKBluKcEhLP3endGaP1QcwKWnDUT4K+4W4M1KaoFNc1MCP0NdhhYzahATon+4qKoJQhh95Uwj01b35rqtNyVoMY93QkZNAnFl3vJrg1mTV91hwtSQZvbMU+zgm+QfvQMdYaOPm1fPak+EBACG0t6KmBlqUZ5U84ACOiy7Na9MGhZcvQL2WqV4MpHeqJAi7Zi/F3dRjhDTMLck1Flo/ewSxN+9OUH9W1jMGkGZaU/gKA2sK8Qem3WQsG0TatUvGBzZDAEMI7pt/QTOvBSdZDpkXT2nliHsObBTPKkEeb90RBE9A9g79v10h06jKqKbRbxdTr5K2BrUs5fg6lDYWVeNhntdiX/+k3RMEthzQzIk/Q+nUJi+GHo66jJNKZA8UIlYhWUYU8ra9zWOmyAo3WbICCOHwpE8XKmG4PtuXH1XVaM449pQA1o0ZSzbHTrNt4Svv+mNnEHq4UG52HRxzcSuU2fhC6l1h8X+MXfBnaRFhDdIF8AZadZ8neyAvzLKTWPk12jfk+kzrOnO+Tz+nqURxYM4plXtyyGj+gyp6EjEN0B9qVOhkf9QJThAERVcqqESm1YRG2eLbIUbOrjjo6lJuehmYo6S+4MYxxDIevuvtOy3qbtwgtEwH9jtX9yFZRh8p3VSmbqq+jDWdT2r2e+eDHbXsYvEqqOfJsfLpSK2V3JzshZdSgG0y33NHLhuiC+Mx55UWVywhIjUVvbwsA5SxYiZWRjsdrWIOaUbIKLavwGbY+2gM/DtqZSCrCcIKYP1wyxXHriNKld0Ytx0dDbB35oJDVPKjHm0aHvMGJKKx3cSVLxqwmTr7DQ5isXnTODLxhZWRFS8Z99VxHO/UZtSQ8wAGnTLA5ZvLUz/TD+2iXx29rFFitywNNyCq1I30V9iXV4JAyTM1VoCJT13lKjXexlpWJUeEqUISgGeXrcUtAdHWUgGchxRkOXntaWvWcKKmx5jbbH1U1Mjunn4ppK3k51XXwIlUeQbYOxXRMa4AoVgK6MXpacfY0K7tk29McZjS9SBerbPmfaaBnMOPOt3nRLxf7n/EluMNpKvcxHpo7JvA38BgT7EHbKCxES5eHaciVJa5l9TAeU+l5vdu/qcppMZ6qBOHyagd9GV0HCO3R5AlTNglYW5TkNa8hFzo64DyIR1xmvxkdcdElh30do6E1g8Bs9MVS6rsmH+t2avaycULOZaVRohLCq++zUCI7t1rIJMSGgVLj9nNNh2qwjK3TSrM3ZN27PMfKvF2p0vmB5rgkJMp1wW+zX6OeY+c8PSMbjTz6jOJMKTvsFz0mB4krUzYnq+7Efa/7/uSt0KbfmtcN4c+jJa2DrUBqUIq31OD+Ya6ndceEI8aENfEmhdCQ7ukBhrRtvpXaQAZediMAABAASURBVJvfzor6fPTsuKFXy1zePo+KsUqW0S0b4Vif2Tu3hovX3bZm2+g5ijaeLkbYiIN4Zt1rPZfdzBupqH+v9ykdWRtm6aokbQPWbjqHQjNcR5w/vm7uo33suqYc355fKajVzzbNh9lah2RfC5kB6vhSPEQIZhERNS74oJL3l82zcORlXcEt1e1fXnd/nm/Uw04vHUpA9t1zc258Z37R/21/VrQPWv1FCZR1a74PypSHHpOTNWVIh95bdff1wjpX5JbYpcwVdf3M1u7Q5uiGJZUVPUUyYaUqh2eEHWecXB/7vIqvROkyekTLynSVONS+UncI3HRR1NBc3M16kD3Ho3iZiAkVzcF1IKIgJ0r28JTfVffR27Nk9XVmD6XAKD30+bBX0o7PsbtCgox5bB4h01oS0zSOHAJFJnYrH2LtwTSODlVKdaqYKEnlmvZzNHxWCcisHNMxqMGpM1FSXEd8wx4tp2Y/E3oyZJgUy5GzSg2DgFyG9Qx/bf3upf2k231V7Zyre3V12TEroxukN2WtU5iaeBxHN4COYHh//NUmxCdGnCjkXvuJpf78dNf51nOrnHoe1uF8hVP6qm2z4034vTydKkSJVQF0iw4lXq2aXlyYyz0QU+uJUFcQ4VS0o4tXr1XLnrCvmvtaLfDMabPUBf2pwcSl8NNmrA1Ywwu+1+jG42erznwKJ0udQB8wVUgDjKCraH8aPznRcKHZ6xd8IG6OySvehlG84RlXhw8kOOWKwq7akAX+OSe0AdHEY8hxe/x6Zs07un1GpSElvLxMLDULEo5HIU64f3bQlGDJYGxwKYVyWK7V5bvx3X1rL+DQvEVQRbluVz44FzPmeAqbnCJt1P1DUq9RdPj/s/Qv2q3jPJAwWgWAlLO/ef8nPdOxRFxOKfM72oktkUChUAApudfqbcI0EIgzusH/nufY/23/lpi4vTOaHCOgkCqex+/sU/dXiaT1e9OrcDs0fyL9Iq8Nquo2De7kZ9vnX+0PXdrgQA8lvm1Tvef52SdWaZ25x75D3Q3oGZQFqUwrwv5BhVPSaPtxPXLza4eNe0Z0EISY+9U94a0VfIDjbGo9QAKPPo/yM8q0vfbo3M0riZv93+S3LLVN1WcC2gxPI++6Mw/sQI+m/5n/LLknJdS7O3usQOiXnpLpSWK9/pW1mU0E3Gc73f90DLFMWRp9w3nZ2hQpaGU3f+v71G2V2v+HhsNjLB6JQkJ+dVnQS8o00ieX3Z+4P3Pb883z+6hLLEygHcfixC43xXC6rcvnu+vZuuE9J+pelSEFS2EMRGKfsrL2XSrxP7EdNMau9H/t/3xcsFiZlN9VS/CwkM4H6sSm64u+gXUOW11bKh6Cvaa3sqinQPdMCrRz6C09dLy9dAzKwHvMyDRXxZ71aVvqnaXl8/7ssy90XNhaqyRQZbBH6BSvpsB09iqq8+KMHNjqUQxQhIr7hD6BRmtxPCmBPnmye+S3P6jVuSA//FxUEefP+f48tTK6rsb2WRtboT3XPvbBcVG6YDEuliwexqMYr9PsTBO36mG11F+zlwrbVJBu3XwfGvye73OeG3hW6OsfcOHOUllp3xriZAyQ/N+SHK3Uw1qUcxg4s5if/V2fx34mteCrFvJiX2YfX3v2fqJuP09l9hT0LweyyDAF2X6sT0yipwuY9hZAqeW55tBSozafcDEZs9bgvdlcJt9PV2bDWZe9z9bfJfahFhn6Cxc89G/HPdKZtpMJ1lSumuu1MYybl0AfFVizqjBfMdAQPMJMw16mUXjSvvMHIK69fiw+A+KcVZLzjEcjkDa1nhx9GyuFe3xMKOJG/NIfNezNVh9NzJ1x64v3IT83P+17UxsjBdyzxlatUMymupxrsV0Rag9Q3uPJeaqVsJJQ8PZcqU26vTAitjQmr5w4QN+O/zx//TlzD7NediFula/WpO+sW0KUw0M10Kem7ZWiTb0fT5XcFru1nkpwPR2Fq4IiPu7qp5XLoVgQT2PA0IqsrkRK1TH76k2zlsVTkw/8t/kkusqQV1F6ef9jmmkam8JWfB7P2VLmT8KPCvb3xu/prns9z8+pqw7szOqxrvZbCXiM+bCVuHuWIIrNpfYVDh/n7etgjZANKdfkQ212hZssxNh19fok2c2BDWPiwufySxd7PSWW7ABlx3s+Ex+YL8yGYhZH3+f7ZAEabT+Fqyf6UWKmGmJcnn/MLl/ET9tHc+Zqv/DP1Nu2vJUy43f5N0eEZiroXY/KDJSTNdc2WyYHT0OZIwtUazsfltztG7w7n9bt/SMuRgGMGqWc8ZoIEfCQv/z87s/988EHKxRHds8tXWl4FRQ8F9dSX7wxgFTfC6RZXaz3ndMCH6FNeJmYasOyXjPe3r2f8iQg/Qg0plL95nRJpnxooHzcWVUj8/7mrjjiiRuxjC5XZXLXrgbGY3ZoklBLERZla85WW+paXTFDWUmUFqURFtim9ptcwx8oZtpAYqqx1+RyXwrAAJyeFJYtP4KTkz0D16i4ub8IBSDTNF2H3Jt8FcNsO5p92u5lypmv8qtNvv4F6KFP4hQCEQKGajQqLXslvRUhTY7egzyOMzyD0852vBQgW6+32m7rM60tmd8HwufiNnyMaMEn9BPWOxRXvx8Y7mvXtTUkYWkL2GhTZBB74Bs1pl49yoXLvRdIDK1FBuxRDuXFvAJSE+NYFUveRvNSCfsqsWZYNUyUzgbflLmbNbKZrs+EPlLu9K9FZEeVP6ZOBtHupXq29+ZwoiBMrb+jz/bPuUJoNEo5U9Fkn0o1UcAMLOuKo7oCRZWD0+zyznlKEnvF0QlpTB8mT9Vv4pGGW7o1/0K7HbdxS/cEe7z6OtQ39y6JS0oLAlaInteBRS5XBx7ROdgFK8X+BMppb57NzsxJyOeMJU1xeLlD5hRBltdslgQKhXIrC3mowuhmjJnRrEwhzKDYybvxUG9Z0wX5y9UV1T2jdiP7AxOwMn9gmhSDBUERidSgPt2i4JreqKUyW6XrZ9kYo9Nc3utW43yGT6OeMt1bnhE+UuB15T0vMSaYHKDLgKVofTogAD3sEd2ZOJAQnVJOMg4Bb3P97Rb5MnW8ipN7ejV0A2OdmDRrL6Pg6beG5re/ie/xb+082uC4KXBKfdp5h0fr48rCt7RmZa15K01bKBeoDbGQLW5qrI2ziutRUh/grulJNyVb/uy7xKwfeTeAY1DQsnBhb2y+4Xa2BOoNH82BYDhls97a/GXd4AM5XVfYh3it1NTT/fSUVJA8OO/m3eDErtYq9jmin2aQR6rGRKr5E17hKlSaQHeI/rJWIPbGjXmnCxltmytAVZROJaVX71YPIRwMroy5dQ/DknEz4jIuQ1MTJJhvUJsQMmCrbEp1mlgQGBtad6uonn6Oora2tembQRqG0hBSPUMHOxwQFTCcerXxZhQ+RA5kxVhqMdc2ZUVBn9XfjS/8PG63r4PWMNE7wzTDooXrz/SVyoiixhk+p77TBXPosq+9rmuZbTvhupX6Fue44qzKMdnDYAaFN1j4Hv+UQ113mb4oTCekHqOjkdXzkKf3TCA2dqg0pGROefWSz2PzrJrVksZguiXvmZp5xspQVuWiz3CH62gLp1Tpa2JzX+M/Q4mbgvOCs3nniEKjXla+Mxwtb4/hLE6oChlFkwKWy5LZM/XgFNoPvBCA5yjfL6K91FsB03su3fNgBmwtIWNKHWjKDPbD62val5i2GXy39gbuwWcsBj33aRFBH3oDONSOlklXIJ8V2jzbxnTg3RLLfAJ3Q7l7JjEVcNMxcNJhNZLNeTJPlYIGwmapg4go0da53Xa8cxJMf5WtGdX3vMbaWg2je6d2E6quBt5h1a3wDyqFFoq9dncoZgtR8n9HbdZmHNQvk/iq3lUBnrFbFIzbG2kHy9gYvnZySgjPned5znkOMl/hBtqmYQ1vheMuUmCigrJfIl8Cenn+mhr1C86NtjgiqJv9xjT3et4Nk3I+MnpaDYhYvt0vdljTjAwJpD8Gh3qimqnuxB4pBWsUas883Tm3z3/Lv4aqnvuxp53p1w1tdX+EaEEZpE5WGOgjV2K9Wq4DDBj22M/xkEiBcuVZfGqY0mzR7h2V655I2MA1QSDXcnNdNeVyKH3XaNHl/xzu9oYflorC5tw8t1X3aG2TkkwLmFreGapqxIh1W2VR081WGBd50eRFzkiM7EGuXZh0aLZMfVu9fHW+G9F1IRZ8Fa/E6ll8fH7tPn5kFQ30so7On1LF6BB3kzqrTl9GIOL4Lqqlpp9cj5K8SotEmj3OAe049Znvaw1/8j1jwtwHfGgaE8xLY0oBsUk0tU1b0+btGJO3FOfpoy7iEqVMPGZKyBi7M/N7IvvT+PF9mfpRf0rrNqyp6e1TejeHcxvu2VqD2doVjPiNO+M3eaMiakFlMV7yCgaVfaaw2csyRsx1onJGVyAL6sB5lzT0low++JlrxqDp5QaJhSALECmNs2s2zJX5xaGLf61Wp/ENdciB3p1DfGllrGhl3FCLpvoQAz3PsX60nZWwgY+1WxEdQXfSZvKglbz07l10kHT1+pieamlo5dgwoXGnhcAMsGnJb1CaqDzdGotbxsARtbKgbtt2NS2HpcH2mG6OA2UOmYWM2UOhVXI4ijWfma8aNakWMNLQOKT6gHsu5aCTVTD1Twh2Y874eQNmbFzOUN0wHYdO7FUf1+DX2Jz/TMoEuHS/5Ba1YnwTyzxseV544qjJjCdeH9pffW0eWGqhVjujbpU2SACjowc1QbqbniX+FDwbT/GLSV2iGHozOS8g1dtXa+m+VsRnYDMSJ0AoN6ouAG3TPrPGPqw96Q/tqMqNS1fBGWsQ4wNBD19C8m/mSos7WDbKhskM68Z9tM5cD4VpMRlVZFNop8vQ4xyTpB+M8lXQ5IasKx1Tk+1ZljPDXtIGzBJqSv2eMaGxNXTlEt0QoJcOcULX2YAuCI1StRcu4zb6wsssrGp3LTPaNR7dQjvCtanL442El7DSDvD2jtNQg215ma7InDovSYu+7a0O6PTMwyG1Umr3sh2f3dfSqTNp+O8H32s0bHG24AEDQwx21sIs0iaYOnGVah6msVQ4DTR6+vCoYl6cC+ZNvoQ5c4EGjUPLdxYzO0+pkvNBNrU7/K7oDbXHkBxtt26933IAFWPzIk11JSqcE1O7cB2B8plQIuYjWBAjvXquY3vM2SIHgPIjvdcYbFu8KFppKlOpZOOedxcCKPaxxIzf43pwtWd+4lwrt0zk0s7uhrJes9pcNGv5KxCs+lvS2GPGt6sT0/qngDF2uo8/Ew+kDq82DZ/OoQ6NcuLyuJay4JS5LOYRvYM3g+hUgtch1SUe8BClc7LEsjcqKVwcj5dG29GicNmjJMl7Z0PGnhN9Lzx7SrcY4owHeCiraXnPfaSWaVi/hEBcuIJ8Rg3pTSZkd04hy7v0dO+aXBz/mfqH56N5o1QU1rfmt/JGJnvQ3ohUtoPLKd3L28jsV2W1oQUjtWPJgnk+AAAQAElEQVQjGjPWXMa98loHq0UIUBpzjUkxM0sZczfJFH4GpTrj00wpcmTNcaMEzzJ9DkTJx/gRo/iX9vmSeoyQeBLfg/ueTEEWc3wdnUAug8fmvuhLmdzln0cHfu5ZFRz99PvLAPehI9zDdSk0G/LXWsEVoIG8FtaPS4ZqFvNcfFgs7ZbaJbQKTDl0TFNazJOnczAOUCK1C7j04G0Lmag2lE6SLYqmjSGYklbjmg5PXZjCSfEKvYgx/ZGHrqxiy1s5R72REmUgtqmDLtaVtZ9S8UzghD/0w2p8x0+H8RNzrV67fGVrFL0Q7NeUj8jHOKQ4wtz9/Zb2k+YzQHo/MY9RuLvmxd3aOnQlmGZnqHc16Z6inO96vabmPE/dg4wLe+1YH1pA1jVHtLcV4nYdLB+P9o3icHThi9b24VvSjwt4VFt3yr0ZCWEK/XY3RGgr+vzwLWKHiLRIx23qgGmshdmUc8jti5vczr0OW2VCgzlpMW8nUgaJBmaG9t1+Bx84y7VT3wkNQkC7UiGZyHb1C6tF2ecHRl2cQAGnXLKIcSyqOAQCOSqPfigEJTpqqkEoEIRpZcv6nONobeeefyabsnYVLXnOqBG1D7dgLsgJaFakPeDd9iR18QJ9gZfR6GzBiBgKm3i5cHvreeRtnUC3Yeiy4NlL5ayCG/mYUk846u6pfwp6y7kQGuAYG+kvOcVeNns1Vx9KB2JqwktfeuBD17f1gR2DKIHLYTMoC+oaeZ/zzVKnUe4sBH+8Wwp0VTVSkNjKk0tNxJFD5VAszTnUVU6YXZyLKVkbmj1+cr4t1PqEShMDtsIvd0HOMcUh6HRz0IBSWPIyh0pqnOTbFg9EF9ybhSk7J8RAqQCkloQAajL4zne4SQGQsxo702feVnpmsZzH/JjufMnuVXWN4lS2R3wPRvaEgJx5sycJEVqb0zlOx1hXphQiKMFYBuvgs3mvuQdvgN+GltiRZBIsYyl+0a8YeoTkzDxQ118juP76WGmeeF0bhR8uz0XX6GIey+KbUWsPzFZcbLwADqIlPlWSlpKHzLL3ul3wa4A6eOOz4+xAh7x8IJG5rd3uRK+TlkqsGRd1jKtofMpGJ51jAzIyftp+aGFQ/NL20CiMbQoR3q6viKRgVVHK7eJyiBWl6g5PWyLwsrqYV4vpjjcb2qP7VxsGMxeHci9p9Wfqx/Cz+NkMEQ2AIzNHrR9XCwp6m8BZQj2D0pCNW0rnkgsBV5oIvebv3+gjCJo1vXB9h9/Mp/KF3ZJGTd07739zrjx8BWgUXN+YNT3v1DEbUw6BQZcPNveHFx7XTulBP+N5QgZbKDoHJ3hCLk6fg1edxvNDXKK2N7DoZMz22SYWXQGKLr+I5aOckIJuJTsYtwlCsZT8qLPYTZzVvWccRl3+Gw3kxJmdKnIoz2558f6ZZ5/nLZqH1n3ZXD6B1/xYj+RXQ9lGN0whQNO/o5VZgliHpuihPhqPuT6lKUfShNmz7LxOSEKCiMBn8QqshbjM3L3DGMAqaV2FNezaBo8oWKU4yllG833g9+AWc0mFWV1QBVax4aRTebB/ZXp05OZcyyVs2F9WIaHPc8778EdZyPl7QTKY58m86z7TtuBuCB9tJy7ZUBIHABekqI6cXYJyRBgxYCBcBMq2rK+ZgJYtHXSF/5idsjqcFD48gMbF0qxCDGaaPMQNnHv3+cwgWy+px8IVT2udqVOgQO3t/5b/IFYZKalN1qjbjxgQYllO9XekvJDQ6ufMOSi1/BRLHAZmAxwomaIaK+kKpJe1oh5a2jyKypwWWlv8cywez7Eji4aWRZsyLKuoh3UGEDhTeJC44UcoWkbNlSDY8I31cHIaPZYRZ+9SuUB0Rg/6yELKbiRkRWdEio9UM4YXKlKz/RzXfvEMZV/q0faGpvH4m1R95VzDoE5WTKo7r97LfF29I5eN0SujBKIFzdbI/W4BHAT1t3X60FTfRIMtP8vW9Wdx1IpVuEtds5p8YE9e592c0RMiZnzeTwCKLLOJUX7MLVaYYi2bZ7p7KLhf5ntf5QNh8vBFWCtmVFsqZKeDO2s9RLPnagH0au9SgA5EGdVTxrnW2q4OYH1mHkPGDFQIib5zHq041r2tRLou5RpF7QGao+MNOhTSYLet2pxAmUl1u/nTb182Bdmz6ZfRDb28Laj9vZiPlAmGJnVMrzEfG+TUjVtzX0pM4rMHDqM5XG+nut50EvO6B/Xjk5F69HAHvo40wBhL1OF1ZCJtwITdDZGYTNlgp/esMoic0VuqFOyuSLdZZ5k0m/aM5cgV4A6p2QIIIwNaJCE2vAuoMQB8nQSOVhsWqtjwaWGzSojektdGQ/aUXxDU3qobJQWqgiWKHKu4zpZPnKfqOXnrSgxdOHXjdq6ZBdXDarp6l4yFRNAlF1ONlJFo0avDlMh82L3F/sfLeQyPtWogc0OZh+7Z12KYK12SC85hHRpGclk8qCN4rxsnlIOEn3I8qjQSZsv1Grrm5C8ftRCbcHiEXfFOq+EQ7u1+bD2IQhMJnTRSfrqYuuXX2IqN97plbxxf2Z4dDXE00Qqa4baWfMreDJXO9Z2df8yrKHkXxS8IwEwOAW+UmXItJmumnEWXf5NMyZU28qFQxZXYwRFVgqbyA3qqtNeSQTFMR+xeccxvVKoPqZJg3NsUbXRqwSEU1LT1i0cTxknq11hYqMYwW1ensvOsMz+jlVx6P/qQ7AIpg/DqyLKukURdfXB0QainWdM5ehlq4X1p2KyxBf8ojwdZ8825C1UaE9bK7FpbLbgNyryE+PKrte+NczJ1oDu8Te3DtQa82Va4Zj6hPsDcPMbU4VawFJeKyZQ2Hy5hhs2gB9WWSTu8bn60Je86qDetf5iLNnbV+5+TKIUCo2PKe4wYGhnUb/XXy1vbw1jDbR2EuRnD5yI4p1LVcBSaT9mkqtWqG4fe2LKjDnQV5LFwdxX0REOjPOkviZDIbSP3pMEpcuoVvQtELWZ4agNqwhfLLIwuFBfwEaR/vX7KXIFaP6qjK1a0+pXL2RTHUIEO4G/e38nzPI8aF3OxHD0zz2hL5qPSOW6aEoIwmtR7oHzOGKE4XchHdDZDSqd7Ecl6iDqxv/7zxU/LOWaAInMI6JjD0U182+XcM/FgP/35PruGdLrYlMcQ0ioKEDRd/OLN5anVYrTl9xSOUlfzrjSnK8WgTBPDGMQc4JudR21iojVbeKOfmafnESbxJm8VfSsPT3f22NRCGrIxioZJGhTquiYm/U726dcMpPP9wHSC0UNxVRlnQNOqk1d51NJkkSajGKlOnpkQSeOtUUZTnMqULmkBqfsPlEbiZZd0YkNLkQDYmHW/N7NneKSoz/SFudDLW/Uw6llcgzfIopU798K1sa4ia2oUEFWnNSOEAiTB+AJjHazEqoXamZG2xgPm6a4ETVLu2JsdJM0HuzusRrnx9fV1Mw6CWtMdRGeiSrNQ5kIxy+xqX73aImO4ju/bN872b8Shzf/nKExtjk1P8yd4W6idoI2iwVUuBRFvXVEiLaS0dDZFO6LhjzTn6gfabUpj2QLokEUDYuyCmrUtn1i3AiaPcv9y0lAK4Ht5OIB5plN53V4G+VNRPqgzXT0yWZxSiIRrWnWkWGd/cH7yxHOsRrNW2Dbl1Dz9/xUdp/tdui85ko9Au6GlsFCddL4mw7Btf9wuKlQQdIRTW7HLeuOdHQVXoMpf36lZ3cRQ/cBREAXBYuQ7hninU05GpTtaxvYJO8Gbc9hnlR7Lv1Vnk4BWlfIZmQ/o76DLa1btv5zSFAf2l/FdqEUuiqQjEQ5sTCb5O/fN+o2uACWiSS0WP1mfegJj4qlz9deklE/HPqY9igNrr2tfFy30FChVeOo4367qp8S34148l0ICcOp5fn+nHyOMREOO1Gf3JZQtW3EkxzEbkVKt602QKottipNOpWCNqlQ8iyNt8Vyh8zSfeRW0z3gqyec2eRx1VfPlth2RjSfFCSE/a8YxjUX7BENc9czbiqSf8L0sVq6YDz3yslJZ5/DlSmCsy84jhK8NLBFQkA77yToauKgb3zZdP6rUEFjpD/OgHwg9nApU0l5p8kzjGDCbtabZPdAnQ35mrhz/cs5b6CMFhV96ntA7A7lQQwHTvgAPT9uj2+sFBSIQfZv0gQQBGmwe9kNZnj5+cg5mIAaN/coIcjpMoCxhirIxC3aZXg4R1RqnKcrWbdRGpGvk1I9FKwZXGGviSv57bKG1V51tXNvWGt816+lQhqpmSv5E8xDzgu83Z5yNXnwo0+JFAcO+wyc/1VGAvez0x57lXzDlfKqnQag43RVhh5fzpHq6SaqlzqJwtGO341DHt3++ro9zv6S996OZimh1rzTCjRcURyBYxqEXHaRJOMs109b1CkmTu7W+5EMlUnnMvjKXFE+DuVYOJew60qlHjT66FbrjXeUes6O8usompa7ly4XG7KH/9/a8xXVdHh/2QnqM4ChdLDFfFoDWakomXm7661lUjqBfBPCqapQmj2vhB/WD7zJJoDvRQ8nbFagfV+sobfvEt0ds/4Tv2ZSmviZWIW6RiVNd4rdWjQmO1Hz94OP4tIK9peTiI0naCMuVO46WFOMfkbgcJIEOjHQBrS+YfPvEjSilUmAxwt0152TLD9lh91abUBOt58xzWAyYQxuH1RAOVj0j6pH0lnUbRNtYkcfY3qYvUk1dRQ3QOK9zeXkGOjR+tH/4nGc/Yzd0Y3FkZIFGHeLS81BE2TQE6z54v0VSRUftpbW5jXhNUhUs0DDqIUDbJhhDtSB0jpCB7h/bPzNbBrOsZpPhxEJO5RkU1ow7Qy+dldV+4RmlcRJ4A+llrcAsYXAdCkSSQIWN27Mkmf0WHgSjcKv2H1qZ9i/7pr4J2WH2j6YvqlW6sqmLVGD/jz5RGN/+3BWqRLMT8C0/dz16rvRF3YMjfzyXDusdBK1IN3WDWVTmagbA9IiKye+02pO3fzKWzD3XSDJi0tUiMGMaPcnSMwM9ISpHmg+Cs00B0Dr4MqxWEbHcqZNDR3EKNQZKHRJAXUNdq5mbqSc1B/Wd+p1Rfu8WmFbXULV4wUceXLRmIRWLFtQ9FgeyV9L23PngfNG/5LOodXpdqvTl5XFmTxs5U7d3aQb0O83Gde51IfWQGSvJo1NIn+fHdLTSI9RHOoJiRnhegb3dzKs1DaN3nssP/cAgSWBA0/lAXbh/+Kjw+7LaLgo+o+5SvDsV7Ch80sLWwgqufa1wOJuimYSiLus6Veep0j2/Mg5QLhkTBt1IiBeygrdb9fABRV8qA71K+0X6qFP/PO4nJOdBYbE/PhEd7JeHKuoVPr4wu+/FG3ZLH68nmK7pvTF5DdwI/5AXrn8eFizMI7SutKto+HSdhFYDNNlWwxw0EjxzsT57YkM2MZr5WibMvODfsSYk7XBzdylRacHRxqJLVmbcu/1+rJ8aCYOr56fzUwiIiAVzW8f8XIr6eY0x4vlcZ+uB/ociIU3xQ7EOQxz4QcGG6wAAEABJREFUNdw6z1y7sFnKuMST/dymp54n454WgVgT77Be0333k/544EMshf0+mdW302LAnCIUD0uKlMhdO7CqkhywztgNlIdjf2xtRWxdfm7igAO2V1lhgIZ5KQrrlJCm5/gcTFPWLcYJG5yuYzI6RmXN9KclS9sYcf7Uuau+dr7a0rmPoPpn1hpbrytgak66uPBM+aURVHLRXNC7qYXb9MzAcWw65l33xMHu2FqllEhnEz1Q1F+bx2d2vskV3+/WZrUpuLtVSy5AkEdMmnVfeLak0EIJ5ziIYYIaRVeB5jfquMde//as/SW/yr+iRZTtE94BkHwoWtieEgGXMhcy8pSkprxHnK0YaqKmfWqv0h122PufMBVxGS/LhXQZkH8b9bU0vxU23LgDH5w1d+7zKOWyjwuxMmLM0NTLwowRSZ6Xhx42va9dV8ye0UYxRKrHWT9HMjOHYmzxMGNHuoEFqcOpD+V9Q0VTAUmsbM70M3nTxJG8k6nWyPnSRE7Kl9Ws0iourSFd8v7Xdk1BMezK3RZgrN6f+yf+u/DdkybVQDgs5IDoi6NB8/c6N6Gugbah8uEnV98Lr6cxqFKgZ2TcsunL1PuuGd6F8wBar46tr61junO52Um0+2yHgQ/4RX+lE9RQVZzdhzMiThpZgCiIfQdv5bNGgetajXdbo7jSol0jm6636J/gz2iS0to3v48k3P2KJ4YBeRTDOvlbWgpXm66MkagtvudlMzdzW4eJeMWDQSWe/1DPKEO+3+hiIFZuxSYhITRmxkEDW01x8B/6Rg3HqLNjnkOFhn7Cfn2lG7Qr0pfDmacMm/5z46OlPFnZSMwuU8Nx+NAGlK0euoOfGbUVLz9p38MDAo7YFsFLY5/oh/MU+57XfRi4YFSKKMZQQHbMd42YR9LUxsVdIbo3joKeBGqc43ts08JMNpCqZbINlm135tPNcF4mVnQN8mFY5j+mfajqSddIJdIxaJtyBTZMZxIYyzTx9wCv4YMe7S9NDKqatry8ItD+QjD4b6j9hf1G3V4TwIX5uFyp1uIGHyLlSX7MG5ETqWSd8/2/9zdbIj0fVy2r3NHqBmxFF89a6cHxkDYnTIX6nuE3b6gp99xGbRBEy6TPLTjqiHhWPR/dz6QDlJcS7CYHEVjXMf/GPAu1bJQrSCPyhVYG96EV1Kgqq3NsSH9/IZ33Zi3JoMQWrd8VdE/WpOB/D7IkcbySVNCw9Hpb7gTLWBtB2yVJSC5/npSukdZOl3Qwy+xy07ntDCLAgD56hIFM6HBlGVxQFC3uzqqkVJSLSikadka/R3Df/QyUIKsCikaAklFWY2fvGoesNLW69TtkoKTsmdDguFuP53xr91yGISggu7W990ybzHdB8dOmyQOAx6FzsNFP/0VshThYT4Hfpk7BWxucNf8W/7fs/4Da+m4zNeEF0ZSiLseTm7h8FmhztepMIRXzQd0JfTVTreHxyl4JG/3tsUPPGeUXym8rLlDvpkcNyB8qS9N/+R1FQrLhQ45pot1UPmdCkQhrrjV799agYXK9yrvIH6WB58n6L/FtlFrztNXy59NY6gD39cV8r4Of4WddV0hUfVrPHmaeGU1I67MlyBwJB9TJGygfpQONZhufra6NgVKgAtdVq9RujtAUYysxjsfwHdSB/xYFZIlfBLYp1TxFbboej4HCk0JTriVoh/mbIX+GD6gC8E1BbKoYp1Wi/zCXPHi6j6F7YrR69mKaMAnG/LG553EZGBM/YeVrRvXvu10dksqOkhHQjYrJDNunte59Z74jkhzNORl3xTErI1qR8P0Tz1i2wmxn0yZNDAMDFPVOWYMHTWer7u/JdwBDU6nSFwsNnUFbL8zSgqRdR+qvPTuOYpBnB29CFa+rBjq5g2u1nEypip6YIV9Hkgqa/LJ/qXDHQz2MMfR8FR0noEkUiIbIl7GN3vNU95uZAg46dclnBS9T1aspvaw1+xHLG7Hkq5V5xWY5YUtwSqHer9IduUzLTFxuy+W57GlKCXhxTdAxLrYOKd7DNEJICJHgxIJdYxvF9S1WD4R7neD8tG0ClgePvnu4TzeMZoBLxIqPBn1qQ4GjP28ylrd6V5xW3kU066iwx7fpeZuqQmOHTUrIYoVUZCFrWn7vvKt+QxVw3isZ0SCz/cDAXSsA4sYcFCbZOYept5jiXX8PUMdGH+Zc+zZ/eMZveq6Px//oroHvVOWtjK2xcED1sE/LkLjeoxukAvA4vk4MBc21uOHl4udoO3Xx+3P6UnuEMcT799P1c+bztfU1bxFzWqv4djppxSgsDilmzXPHLHNryxL0U1HibNb+rrfe1PQaOVb1Fl6SE4ZL47ksVtDC3BkLuBJL7UBUWPQOhL/l7G0GV2zmD1Zr22DbTDhUdqvKfh/lvF3kGeEavdgIiNpc0L5dN8zuew1F306X17mQLk61NDZHlq6Weqpwc9LF0+Sa48RrOK2m1BhzdzmVxaRSIBcdlns/2ADFhWm0sxaSmMGrOOgW+8Pv1V/Lbw2ft49vpXhVXxxFYQf+C30F9UF98CWOHBRly/G2mhz1HQiscisnAi1n3THzAa/FFTXd+qdQ0zthBWTNKZsRo6R1q2WbGs4ZloxiZjjjE0LAsejc+F72KyQaWpWvwNtWX9HLmph325b+JeVF4VJ1wBpkM4y+TLIvfVK/HFi1toNHeam5ulc2h38ir8z6dv/Sfqml9x/ymtLWx85ogVIDcKX5gn+SXq9biM64eu/WCaI8S+FNU6zIkSV4tD+/9TvGN3xBzk5nKk1LklRj2yzYYF5Gy9eYh4XhgoogTCwVqt616R4/CRyum/Z16O4PJDQ1+2Q/Ncpb3Mk+kNJ17aFiS1byXsyAOZ1ATSohYMulnEsI+TzP97/7VvA5QjKrO+xxr0XXY6//cXY89bl/ibuX4Kd8yczMpFfGQzY1ZYxmgjuPzbP6sXbkv/7+m/Oj+grURl51lujLKQhH63fj1KjNjV5mksFpFBuK/7N7G6jCpYR2lUVum4scj6Ms4Wf6w3K0fhRcjMoHevNSonQiH+9+JX+oEdKnAxpEumlkbHdbhg9tSytjhyy9wVv2Up6e6j3iP3sen3JaYHECzQEJ0+s9P2lvNDiPthKdBUoEcouZT9WqhOmdCXebthTiy4pTgMaUOLQCb/gX63H62O72noSB7gpGPP3Z0I7YosCe6sp5Ujee6qSkGZaNv3mbXC2CbcONfpmZyBqeRjErXuUJ/j64upaL45a1nHysxss9KeZfEAu2XioMYwLQhcFoQ/p4JkoPdj4XfKWJMIUH3Qr/lHmDpBt3W5D2tsBBlIyKAshIQxompLOCV0fLbJNHrTvUrZaeuW7Rmcw5U8drYuBw5lJoTCOMDLqsl7cGlAEoFXbFSl/JKHQ18vjpjy70INOe7yefSx9tTljGajqOTS7gYnyaH3+4jy7OXFEqWIjt8mnUo6w220TKBfo4hziq+NyOKx7wTFc9Mzlr2iP7qrdVHvpxdYKuR/w/jtmLmmPt1atqFZBxXkvyolg9uBfCE/4AZX0WdDPTWz0G9lilFk6bxlWzYW7eJqk8gyOeRjpwFyEUWl8TO+yzJ5QLNOdIuhsjgmZEmXqBeAIJIxznUoFm22kmNVwsYmbYbTbvISK+p+9COeS1dJW98DIIEBy0OkNn9SCNbqU4eFW7JCM2pmYOvKAO5x8oziXyuXKUqSncYAbrvaoIZedM5uRMcQbYY1e/cceIug5BMA6d/mroaCUxKHaF/cqUj3DaYWRwNE7YBAzZImogiFkqbYVgxRiNOK0hrcVkvmfutFR9wkVOs1tDleKrUsLfhqXzPcwzT7cGlkh3bmg5Z9xh9zW5q2NIC+PSG7cWQzRpp7xl/qC1X5uxeSlBWWCb4C7MPpzHJKnbShAsaMZq7xye41NLgY5FD/qhOqOGoRXLTBBabFxrZtI6vctK8YEusShjebofnRQ+vPhQSvEk2NBatEMiSM1Qmn59jgoWCZfbSvF5kiWeE+PDOM2caWCFr+0WMvgyZTl2D5+282zm5919CZ0fyXveV4lfbYqy/D7/6vyz+fmx+D8rf9ZZP73CugYnFUmsCFAv+BKVkHQSSg07MFZoVZV7Lsuuvql2EjbhutA8ZtjuF2AyaHMcFQ/3O2wtW+HIyaN/raT0mrke2YI4c7ZB1LgwYsBJSrkUK7k/pTQBC+5Nzuj4GH4MTkyPbq9u8mzJ8lpzac1aUxuFOLMGbmY06MVxr71yh0jrGi2X2sNZ5PJ2P+b3vPYgfZ3Cbd2LYh2g8tYUQ4O2xmqPF0g/TIWWdg1heKwKj/e5RsezVTP46+dv0mQwa6q7MGd2lqH6FSKdi3lBN1oNBGLRPSriDhTtGNoxClseWOG1vF1gqp7nfk53M1uZFlVVkzMPMDX8TfxKNK4VN0LrRSsjLrj3gbAgjWXTInCI2hxlW32wr898gn4DM7MptE/xuR3pXuZoLXX3JzVDWsFGBx/x83D+7+AxP3S2kDueFiTz5uWCnRBbz1lZn4EaKmWeknnkQwlXxKRiDFftykmLHCHg1HpFJaZOeVUAhED3OJWDNFOPeylFr3C7LKMP63hNyMvET4c7uLq2p3b8/qp5Uy7F0MwabqwNF/Nf2Z9n677Hf5eVyY/azeOTgHYl+GA+ZAAa25KFwy8g7EQM+GaySVuBz+5NrjGWA9ZwvPuQn+2xlnKZ3o2Uib5QK9tEDZAhmDR32h7fcMJTDbg/VgvD6plqHOTo/oF5efuy2bracq692fqaWBsIr+HsEUdR7hnZEjYLQLdodw4tWpU74f32ChjF2qPZQu2yaXQf8yw7h+qZaRC+x6BFryhDYbIu4YzMrrYQKfUc1B0U0wimzzEJXaXUgx6+6S79sqK1u5mNaVZinqUdQYzwFGrqEGNSIDD9VN18Tjzp8rpkKO2UFlPoM8dr9t0ms9GwqTEtXn1eUHRVkinBYL6U2OGoiGG9+bZz1ztcA91NZEipKqOY/pHENH1IubNsu8uztcmjoxQUKnsK4byUZNP7yRvzNbu1AHZ0sdqe9mlpZQ9YhTmN0t/6Mh+tJzjQA9+f39p38hb1FtUQzNCgmTQfLuHjO10K+pR9eoJyO04JSFUNtCbVvLPwEtnzWk6Lce+IdIGbes7I6xwx0K0sKPG9NNXI7eVaowdNL7EgvkY2uu6pu3jGz7zo64zm15OVGIJz26jb3sZWQ18dS++UqqmUs5RbCS2j2S1eUchSeXspqzOwUadTCuyiWQ5z8E0o3e2AeUgEjaxOaKxPa1mOX/qzfDY2+Xlyf58l7qoNb5ZGVbLKvCcKFLUzjpZE9e8GfmO+3lLmU/gFpEnOXOXeNkCy3gNz6PmS7lpXtjgUjxSJ6y09kxuHSUEKZEarxXV/5nd/ez3cDDVs29l8gHIaerF2a5HA0J7ByckcpVj4lNwyIQBbh8512vSCWRlS5w5XSrWy4jhkDroVafliXOaC8pQ9+e6docYVPKByr2Kwe5UAABAASURBVPNSwZhjNnvvXF7U7El7+Ze3lZeWesI6dvMHE1DW+nzzPOLmjPHFOwhBR2zQu0Z6fWaKqnddh1PXZdMKLBmdsh4zvVslQlvN2wyGv8zYgl1wfS7gi0nFmpKtUrX6NSWS05Eug685P009VzA9x0r/Sds9CsLTbZzvL5SYXTGhjob77lsq6aB9YFrpodpkH2cKuRD4LHe1FbzCD0f8cP0wVpgFJ7xExbBfrXVzjnuqeVOsmMiZUgnqAxHEhipwLbhCGjpLs/016e3MOOd6c+ZKEQ2QZq/DDx7XE+k5p7W1nRLWm0gATWiYW8XLoXXHgcMj+JdQh4AlUVglz+hoNd7EAj5mrj28aY1e6gp+RnH7a5MyORRUCZvoBtJYw1Oq5zv9lhgcJoQBAR+DPHTyHakuypZW7DYUE/Ug80Hd9jz25j7NaiQOQWGZ/HEoxMXBu2u5A4WcKpb8lsmY4sM2+DLRRwJv0AOD+BpT/eW/fD64IYCw6l33fiZS5b9M++FzGfdovQ5NyXjK2+w1JRJCgv7Oiy6tA+pCkjwCStrqdWmgKkZi0wkJXKJ7RhUoCRoFQN7/IpEcNSAXn4230SVPe3ErCSbWCSo4mw4KMof+TJ/6pgabtUsRkTrzHl7v/s9Vm+v4T/llTpvT1ffIiKzZgs6phq4ZE4Mdy9y8mxzlVZd3SzbK+9TkzczwWWvUWUZpJPLdwNA0/l6417soN9AzOcp+u0jQJnKbVjSaXXR92xKQ99Ywgj7wbB/6OOFTntJcnVWpPYRR+YTR5iCfqaNMDpTIs1geeLE7F8YzBfA40ndTBKwH8+0nq0dCme6unNEk9DqpAZRlvh0Jxa5plfgDZk3160E+l0SrGoAUJo+PalvUP1/LQ0XIGBgGUfNTE25z2X3FUQLcKMK4y8J8mV2ua1Dn0Y4gYRKs06R2CE/JoVld0pRDxfqp1nZOm03a1QC0otV311kHu95pcxk2pcBiCDZTBYKVXEUTeRV3mqjit/Zvxy0LsmKQNf+Mcqt3xf0s167ycW1fzkG9MQqWAattn7XOlvdFwPnd+F52FGtLogOdlX9hxprxMapGzqdzTUaPtxYp1TGJourhETxnL+AygU5CfsQxcmh9ogo9L8uotqnxg6tgg2x7StKXmdEVXYt8dAqPtfrXSXZtxxUZTINRcJJaMXK+nINEPQDBC9jWjOndE4etUQ6GS5SKpXOee1psgsw597vsH820DvQq87rM1MgvhcopnSQw3ayy+8v7i8qwWbEuXiuv/ZgGiEnbxm2+Ke2neoM81TzjFXClU7IXyBkDHaK1akH7jx7dhVxwbax4Zm5xY3SDM1vrLlrDOWJaMep6BTWpIMP5sEbMWbTzsWmKaoe808oe8ASP63zwpn1HD5CQNvIB3G7HfeDWIDjOurq1U1S5840OwqmkvPkJArK0/FbmR8MxZDqxIRNbZ3p6UPphYSV4SD1Je7Y9Yaq2niefp09OyX+u8zibF83k96iUjOtQD8JkWLMbM3r+4ccjF+CyoKVxkpSEzH1BldDoLKvcGC1XupBDGBEzwpG1prcIer4n84anLeX+Y31B/pfVh/GBuzxGqdJGaT4K0gHDIt+MUy4L/sCbTkvaF2ozIYmEITA4jZQ7j1G5zdVnt3b0uuCUuwtYYoY40qDhLdbdvaH+pVCEE9lTXZwypcFgRNjQW5bR7ywoFoPOMaMeFY9PqzCLlFjNXlikQ2mgvIIllaokddAA6WsBBHMs4YqJA700t/bYtgiZyKJaQrNmd25Lx8xrYEapxAPpyp3qaUtDBvfEZHxyfsaWkzKxqEyFNIU3ln5OPj1Yl++Qt5KG3mO/MBdG5Uu/hz038vHDV5Q9y+6l9qUasBx/ln0V6xTrWXgU3UAh6a9NYc6gGyxK+i6wPSU79+LZwG4RSbJhdyM1GnjwFhKG1uIDs12HaKyGUtzL31menKm/slzJPbmkXknVlWBtVd3pWH4Hf9m3I5Upd2Ck/un2fHY9F3Ht2KtXSNmF5nNeZfeIe+eQIn7qYnp3vQiFlFxum7rsPQrsQL0DEjzcZnGMDm6MGFA+vgtCZihpXeJVnKN/BJ3qjVK+WNp6a/rEkHqxq1f+6YmjCvFVoUg1zAATiDapMQeitQQQ4gRP4UmewimBiAVXrO2jhaEmi5noI0DFVXVV7RpXbh2IJiowZq5MykKL0ylhxLRjLsdHklEGlbWXPEOHENbYAF5nzQH7LUcCTsiLKADRqng9BnncE4Y5VLuEyJLTbeFSQ8uqWmG+zmg0H+vSuV2MdD869JCEouWEpcW4cR+/JPx3hbYhRiXZYmDoAx8zg5KXxmmgOvPVd0lQgzYHDe+LbTYwiMiS4ZXv+XZrdUnZGAU0yL+9BonAgspaU0MBUhe7ul7UKuxSslo2B9NMn9t1JQ31Fs97Xh9RxIzpUCjcSlHLN0pzRkAvjGMSf8gnHEvqcuUtdQ8wsqHBhjyej2cSne+fEX7C0bDuN941HfZO324qEavik8gyhYXRIHUDHGM5QHNKqmj2kV+JBbYgUnWmYEkbMypL2iM3eNjSiIhoepWEsimFmVIoONNSoSZfzYUktXylhRgWUYaUmzC9aHpHEgYhoAmF2hoZI5sUPJsk1KQHkHFFdrxS2K34zniTsNvWmAIeVCkFKdWbAkjLQWqeIMFDeGbE+0kNw5dK08hxTBtapOufdJaN6sM7d6okXvEZuOThiJVC5KzXNDupDlkQDzlezZYjN9eLZuUsKNJyDfCXf4NbCfNjWta9G9YS50T3fmRVLIKK10RxDMUZ5JT0VtJkQMNbORppQsCdvsY2ck87gYsvD8gR+lOd5z5HMsl6wxewgUa5CxHFpqLs45OidoTkwiy5iTSSYAnFCMZaCH00VUP/JRcIKd3gLdkIhzIkUi/W7ol0b9IgCzCaO906enbO58a+3TNcP0FjdyNNY93xWmPmOVOlay6NmgWGr36KnBhwWJrhU7tem7gHh8LIbZIPYKglb3CO1KQZst0hkWn3oGw6EVOOA5Z0IjLWmWjh7XSWuXgB0JB6NBz9ntA/MR8kgVEus0cvaHJLVDOpGqIsAjbF06copBUoIgcaSMwr+JWIm8hmjp3208r8y5PHEF0zClACXtOQkLBG3LpkmykvOgd4vXHtVcvL3pR+MUcxmb73vrzjSqxO8Gg54cW5Bj4DdRfPcgESMSp/V0r8vSTIPrSj8mJPi4RmE+YM82tpV1EWZzgpdLrwF7h6dE7PtF5ZbJlXIBLPZ0at2dtNcogxOZQWcKgyUtRCTALwrpC5etmctAabbPcx4YaCLUlPsKixaGeJex8Hh1Zh9Zp3X8Rmx8uR+EKOEuboYC/Vi0kn6NGcw3nXkJfil1T5npTKX8WRM6skAAXYmMHYvAY0eYTunU66RDU+GS/dPc1pGa5ucZAtbs54toQqag1UIDXMQisu6HnXWlwLVGdJIOVJuS4luwsS06g8PLYIU2Llg9t9XRHLg9vaTF77QEmG7HHIsUI8CmwgAwHT1sz+0a9Rd9c12dUdkSCY9TLtFhAqZKLIo9nD9YKxlAUq7/fuh3dNCpuN4B2bsqZrET1XPdE66e0LYsxYpkloGJvKUitFLyhC1IW12nqoB40xRy2oRFUrSgVwY8BS9jS0hwi6d1DbH32hlbuPIpUWSrzVVLbeJzBFn9AlzBlpMIXLwzfjGuGRycOu1TIXg50IwaKRI3bvOU/eehJZT6ugDxqcBVxNE9wZ6eyJ0/GYy13VaK1QLTBdMWNmTs3TfmLn3h2k94L2it/BadZMIg8OoBuX8oYcqKaz38hnTkRuim5TVl7sznZSjI1D23Ot9QElCSjhwTjVgHLQil94MZwyWY+/Paej3sWHGiYFlM1YAGuodUgbcN0a+4QxLPjGCMbD657Q6pYL2m9aVLOkSogiDAU3xa8KaNl4mE6L486ikquDenXP0Tc4hwli3AW09I6g0Y3BcsmmtI0VGlda6KZT6gmNGvkatOgYfcDD+Jr3XDaSpQE6+SICZno01kHYLlOPtFffi2rpwbZOnCTEjvz66MnaE5J9RxQ2MpgKwrI609R0sNgqViKVL7P5wzrawa9Ib7KJttJfUxqUk1HW48Ba6jCfD9rhQxMcoIFyG8O815/0kyidNl03SkA9Cviv7SFIInpMHaIU0swh7tEoOKA1xJyjP25QUMd7HAZgYA5yTISKHuzlb7kBpiSPNL6O2XErkzTk3TVhnCCbc15cCll3Kb4vV8MR+KQ/ZbhbifN03uQNVi0i4sVT9DKjkKiKXkuCUd1KtzJmWEuXoubCWTLBSWPSdIeQnaoK5cdaTWrbtZp4FEPZNRPZ882+jxhQCFgYyuBIXeGswPE8euzKWoAdy68/h1O82q/afitFV3T48/H8B/vpz4WPjwir7uqZbJzqPOep5+BpGfG2RV56fomv8Sx9hBGiYPPeFLaY3gWVwjQfrzvuO/7v4f+vz3d+u3MVNt5aWgJ9Xf1PD8EXHqNW12+7RkPdUPCz50mIJpvnB88OuJfbE56k8HlrpYBSVK3pUdxBTfuCd3JVf5Afe9ZKR0dLR3UWbucjcq4K1xPshV6CI7YBpO69755paQrseDPGf8ZL8UFu1Cbuyewgt+0dL8nCO6ZsNxHMmW/NMzMNryi8DYOzd+6fh/2c8/0+9ZwWl8+6zyfhcCPox+oblZ4GWMKeUbNrTl6qkR/sSIsHkc/u29QGIRQjTT3aMv+H/Fo9ezyW29LZbPQAfNLyG8rSNctQQyVj+KCKaWrha7QW/d7z+zvntklnYtVjdaPhudYDv3OOWvo8mt502Z3n6e/3yedr89345Tx6PqWOAL5KG+OX9nT7fKN/cZIi6G6Iw+241sSGmbn7tfmz8CO5/LTwS9kortPa2eq68onLbh+hOeqJuZzbfc9asFDvnlacB9+v6fj9rvu5HlxFFvQaTOlbnX0DeeHds7lgTX6nj1JLMcWX7YEdegZyJs/kU6k/cyyOHOm53pXh5yIDq5f3fuw6ushWsvKD+ydqb7Rh+iFu1bDKUN0dXcG3dYi579SBmJmFWufMN/k0JBRpdoeM/1RfTzNFprATYieAF2CYzYon1s31TPV8YU8Qe/bqy1rDZvTMdJtksjavxaX08gv+Ns/v5pc8UEHaiZlp2VCorY4TN+bpR0DMIvbnY5KbQ3HJNScWwyMgJde3qppecpdDti/X1YX3qt59YrttDoX28vUxD1CK9W+HtFwzI369GWv52kokU5iljDxe8ORufDCKx9+Z1tzHTEtI6yk9zpzAMbyqBB41ptLKkGr/jop4TD1yRika+h2sTQsPX5pAjdQeePO8LiyKO8d62tQJO9s8P+v8L56ryisoCEAwg1pM7qePQl77+vxv9r+7OJWznvJJQrnlJo0+Oq9wpHqqOFu0Fs3sH+3/NH+0xMH6qb/zUbpxS2lTeA0X5iftM2tR7S0GF59VB+qjHrb1c+gPN8bcbL2l/lQaAAAQAElEQVTSdlVEUxm8n9/vfI+9pfXiBUlk4X4eSXjmvKRTZ6GqjTPSY9oRn+TXsqKisZXVee7ArNnsj9rAUdMFLbYZ0D16X4PzRizYWDSj8Hnra4w2Wp793DanZ2qo8SOll8b/fq7fiSeJB8vID9pZ7lzUrGUVTv8svy5cEmao30ucmGfm/1bcs43rZxQ+kKNE5JvNEVQXLH8qDijY8a8+V+5dgVLDwmPnxOl2uzfe/2FPp4oGNUq5FUwXNPSwMmjLN9UJlUEJZtMtvZ/rPtd3drrj5aVjlOM2/c7w/rjgqj5npmpSDE0ij03TBjHaCgC95tmpdpsulAKO1Ko0Emo6ht3twwt78cdHSjwtkA1R93IY4Id7MxZnxJ7VeJARtnb4D/dP8kpIWArcbklqnukv8F/N7/A2ewyaye84mteDzxmVmT3oO8RcM9zCeFFGavW3/L5/Hn19qYXo9+C3xP9K6+NP6lP/9hxY0ossNQT/1JaF8xL25tal0KuxFNVOuxJ4/ReIN1Io04tS8VHbuz/136fun5OoKa03MqfU/qIVpZpatImeSUKblRYveLKr7ZWbUt69yi74Eo1+n7zPI1efws8XG461jSIUkeaEuYT43U/++/bP76zf1c8+kJzrK8ZSjwX+Hf0GntV94Zaz33zp4zpLDSw6OYm3gzK9RgislcBZVdo6eGo/f9FDiZ99S3tf4jujZTlZZr4WbUEeZrf/1PsxaS0V3Hgz9qBsNFNEP54zFtgfC9FpKinUMd4bUgYlntIDfd0mzgd/L5ub8f73MKDijF1U+xx3RW2cq+ticU0tFMcMvoLbsHsJi/otB2L5AU/bINY1++dgnelnTS4CrnWHOYWRw4CrXdHJkLEeoRIv4YBVTx1wluHTvu5rfvfpT0rGXOYzo67yPM9ht1sT3rTvyMazodb+SCcqTxSkdpatZTJzuBNDsYc00giYcahla5+aUamrpTixQD9UuX0rfmM/YQDEie1lpoc1bGT276lfw6iu1l+foEKs7UrQD/GPswe7dmDJ4mvgjW+gd86KmK2k4EdvVfe56/vp/h/2/6n1k6NcISoX2tW2jvYLkHxNsCEra+Za9D3Fg+Fo29rqRK3t6LNkEGN/SQpTSzq6zlnAho0qyfavXYUdNK6adR4/90B0tyoIYu4spgkm6T6LjF5xtn8jHriKSE6Zu/m5Q0Emic9d1y1OgCJrwcO2QTHGpKtixWy3UbsLc0X0hKidphAkVXP+3BD3lk7bXB+a92AymWUsBAYGVQiiXKNEgzaIdyvfveZ8eC/7rnU2RX96nNzzuICYdhdjCTbbyQ+xI+Oqteb9Eunf2A+gC26C0ldR1QhpL3uSihRUrPWwnphyGHUwZviIMARx6RRwhMKvN2STLuZ55mlUhqoWTzcrF8ZgJjcX3JxSmPxaGKcf5HeUAnkcDfGWwRr1yuEUmmL5CQp/YbRPLHNjfub54EFH8qchsp0khtWssgKmRaEOvXM5GWWhc6SsYyvld4ujE8/4V1c5iRJNyoaZL9s/oQ0YfaOCqalVqAFga9VmOhWg9h+Zz9Q9fRtTHmZAwOCjLrfrRH5xij0zDRYFX82vNH9KuQha69CEN3TlaB6faqsJwIscWEPpk6VKNlpnRvoZ6hrfzVqNFRZK3yb/2GxwQcNOv2qWYBrUWDcVCHirZ+hgtwCKI7GRXQrO5M2NPljTLlmmTdv4C8xLuRWMYRqP4Vs8D84zZZLnLQOKmzHupMQ/gOYT4qE1ZTAj38Me5LD06R0gzHZoZfZqSG7wkpNrwI4cSdQf6Xymvw9+W4K7YvQ9L/1UaieSmEd2+h3sMRqg3UIpBH2XF2ifm+Lrhqf/xf9WIpiNaXfsJUIsp1X1j6C2xxl91lCjXkIH4TfCBBytFbzWzArEei+73CsWgVFrV1W0mjScyt0Twz3YjvHr6JmA5reCtvRRNXxnhOHFLzLF177edQmgLlIbDLZbr+0dO91vnIfiuIYvGmFBom6d8nEz10KEF7h00q5Ti7Hw4+1P+SO1tIPpoiKPi9hYpX5kHvTllxutUjsXk0Tc2y5gxdQnS62cA41bYmBajM+UCsaJgyk2mphALmttt0yjlIwbVS3mRYb8rhGAZ1KN7rFWaIX3ZWoBTZOFgWhRDRW8hGR6ZqCrphdMK/itaX3UBjcZin+mRqvdt/AkvlnfM/fI0hX5Md1MnvAyVKNbYY+dmW+fRlpT4ZtIRqZi9gbKZNR9me3GjNd4W3BCwxzzNqUJje6kNRwIq2s0UdWM76nnqI5W5+7ZmrXL/h2+vBUxGv6S2ZrqdFNFwl6k73y1m4T1YHJmSEmfYR5bLce148FIkI8x4TZ7Vf+10BQNMbaKyrdY/ponWNnsm3yo7egGQzCrLctPMx+5kHW0EsXOlr9n0F+Nb2OvLdtd+ukiR0jQnhVfj2ez95t/VzAz6nB3dwL8O8ACU76ovcSfBDVb8enikX6WT7wsKhGVlsV03FqfAoNRF+BE1r5VOM6h/sS697xbMFey9bZ9ms52f2bnsdau7lSr8+QSbGqUgsm8MV/YTU+T4Vd8WaosQe2SRuVW6RoxXYd9huz4ObESPZKXmdued5UVL2MGl8wWJIYeBQhvatfMDq1sD/bjSyHL5jFr2Iyr1WVLZGyD4F7hpDciAVVHC0/lc/T6dn+DvWlUAoWs0aJ9zNKil+s0W3stO+QDwcvXfFnV3NV3pfYkd9pTGDk2I1fA+epu1ajfvGlRSmoUGRrVXuLm6ZlHMGEr+uNniYryfFYfrdoOGMzHo0XMqTlJTe8xHdNr83/LnIryhbke91n6Rx7i8Ix+9XiOTH1GsjWRMATh3j6KkF2YavpcXuJVJ9dgIPIUI27fZ11jtnq065MfyhRdRUop9QiUS0nLmlOVMkWOAdHSRF34uj8IDRtv+baBJww+SqPIobumzJxhtYgYIVOApZDehy8xsQquiJEC2cQ7QKp44U1bgKoQJbJZhtFAP8DBjGIJvJdh3f7GyMKe9krnzBgY9TrH1EuHsDnbrT4qcvEtFf83pjb2GsY6jDu6rxYYji2AhF0cBd42bTCrq9NEXGvgm3nbXqrdTypaTEMldiCnxKIIHMyUaBwA1cy0OjZlgLs6EUEMtV9LtXUNVQZ1VrzYOEREiecB27xoYxMqr8hZ/WIBfRa1qm3Py3JjYCzRoDrIMc2AThUkSuflUB8xoZnSL4MtftTl8AIQxy9IAUBKe5a9UgsOPvaIeadbuKCVPijMedFTvCu4VkLV2m/uR1DZbsPAmJRpcNKBa5TkC3A30ArQnYpP6cylgW8ea+qsrM+Y22r7gBup9zZrpTFrOkMzMj/PuXLgrXsZCUCZUJwtWEJHN8bqFWf12+fUdmDmtHWcShdICBtWcj0VrTwIp1kMrJWaVmrYYtaM27kNwoKySqJz4TeQ1cLrDbC15NxV/ciVzla+fTRtP0bBHYFrin5BU+JeRgr9Ts4nzq3ctrtU+Czvsa7A2wu5cvyU4Izplw8sJXQRq9GzXVMYIpyZI6ADHg1rMF0P2DbFs73S81FZtmkkBARzC/zSJPzc9VOjuwxpU9ffBeKz53pbqwD/hukMMZIXkz1xfNXeQ4cKLpU+vi8L4w/tIp1vkSW8RomyJ3Ave66f1H00Y5v2AfJ9Ow+I3joaf7BkG0pOpWd5AD+e//hcpyxHsR++uBvSe7hvSGsa9ioLHTXXDNbB9cw7XCxvUKK5OnUXEg76LrfTKG3wBVF6oY/ZposeaAc0PlsAGniR9JpnzRej5QC4/Tn2bR5f1JweMTeqT6WVQtLG3gkOWq38Yq/W2t60WHuvBaeCyFAvSTM5GL4xO8Qm+XB6Sl5NVXjOVJXCnfcsE+rgxBS/OVoyzltzojrifKKuCZxPH4U/ssqCpHYlVo82AznMMQ71R8mIZ9Y3/ffYXTDahscQxZlSCIFYUEuxl2U8jrxabNKfUa7LJFeXxOXDS0Z3mx+rJ+YOvflQmVT38OVci1j27sZJjWywzCeC708Z5BQYVC0f31PBJMWimA24ay6I84LXKFfNGRDTK4VIXdckAF3V/atiVKpq6oEWpJFJNYTd9+o7hNPQYq9wz4gKJPgcKC+C8ydM4Hf6a510Q6wTUbboLmaoZO4OmwtnZUMy0JKsYMfTrQ0fZ8Ao+45SH+5mKwzU0mYkEd3LZCrIS2+r0UTHPMh7StfLdy3lEP05n8/9D1/dKBueQRkn1sw+f6oGLqEDT1vD5NO9Bzgs9bO5fRKN7DhjAC9ALYraNYVr4xJqPHZr9yaz/Zz63o/deUnNKOjVsHMGp/eZz5moHsUxnAahw0wJlDPwQWQvpMbA6WOXIpC61/Xpn/V8vDBWgxnFeuitRE5H3ledKDUkvDZpJt6o6hinTJcMT3ffHMkQfy+ls5/U/bo64smwjl2O8mw+VvU5tfNMVj+sjBwbaBAb9jU+q9P6VD0Ppl2YTP9auNoKMbii1gUj0iajFY6xOU/1PTzLsP0H9nNkClU4U62kvccZdk185+fp6Jy4n+ub8QXTgeXGTVworftLmNwHPs3o/lhdMWaTXVUNWYGbXSzwrvn99vkeu+/II60JMA3c2FE/Uxe1UB2bCs5yjxXXRhit6A/nd+rJcyqnZgYsuXHSI7HPq2KFoKxZdSCDp3i+lkcPxa57reo29F68tuo2ZjiH0tmm7mvmcnsf3WolH7XNR/z3sNtNEi30vfNcUJEI4XosplwRnR5RcuheJgS6QIXzY/bRKSWhRWn6wNQFGtQ71cj7od50hc67wYmd/tO2LSjLjRFydeDTdqwrzom7ed6quG2SZ6BjGp5b/aGDffHtHJcRaIVQgPha4qC82/qx+fV5vIppagGankQis7LlYdpASpFyuA4/7T+qMOuyahv9cCA23i6noovhMJNPURt1KPNudFy8L/7neIY106z2Z7Q8EZhBFZQ1cDwYhAiTONfsQFyO95nfkLfFsXVYp++nqge2Oj4jthI4nQf1jDzAGvoC8gqsmAD/H8SD++6n80Y+c/7r/K28R0+G7oL63rMiXXnvEhSCwGpfdzB9qqnPPLXy+AC28wqyjY9LXk7IB6i4FA0M3Jbqoi/kysWwdfXn33wurt4x7hpv2gBjDSMD6nDk052ZM2f4aObm+QCq23Kpyv7WS345vyb2sJLrRn/xHX8QGieiEWORvo/stj4woiiZivJc88Du3q2TcYVdgvD2jDddzoEy8ihtjl5r4mo1Em/o1ZCUIRdxwdjFeuykaTP0+5nfa54P5lMR47bI5ctDOvmrRkxfnMvixcc2HJuCwhRT7qrhETgpUqccMNN0uOeuL7/naH2ez8N/Bz+gVEFrDcTpqmkBGvuMkw5T1la3T5pB/s2Hwuzb/Z/5JogHnrUwFuy4vpQoxtfjrDGTmLUf84/Px5+tSw4IqDiRI1ldU5bsG/gS701cJ+vwzjmtcUof6Q2T9iYlxGx5+kdcAys/7TfslNIkUM2ccDSkvgAAEABJREFUdeTR3ouJTAwxSl6HZLKwkatSNvslKSHacnYjCI7vUUrhNQMFg96zdJho+ID/Si2WY/trniBKlEDN7CMh60Qe71E+fM1Z+A/Xd37O/MzIMopdTN9nu5KkQlLWeaE/sIAhw1O0esEfzW9bAhDGeMnrRhI6QSAamCgRq6i9crGkj53G429V2x3z6/fhd0ZHZdrzRLWmGzjeujnJC3nZvVeaCDx2V7elW0vN/dgtT/IBMBVie2MD/+g/dPdpdAIP+YSuLNC5DEGBlPaYtaQF9ZAntAN6pgYPiJPrPJd0+9Opm7pFMdQ2SR7j42xp04CYchS8W0wDSPjNpQxbz57a6+y4d39Xf8VJjhK1xsxG49wOcia93eZn9b+VEWrlbYWLufc9V43jlZ7NWLvBETbCLlaJaSttIE4IbDHMLTZsK0A3/TIGuag5Aw6WKUemhI3cpk7BrcksnrEz+6hH1EYrJNMQHEwRZUKjitky5U2D0Ih2VVhAYQPfQorcsuAMeqTSXmGhAirragImZP6iwWZLe2Ildq03mhxm8pwZ/RkZUArZCLbi5OrlvJyuzFH93/5WfkrZXjaVyuFqbkLXA42ZFiXi6dAmW5l8VmaIpz+yTkXdujCs1FjKkcZhNXZil3nHoUvtwFAghsriGRyPnmitqN85h9L9V8v3xGg0a7Mog1avXLbRzU3BKQCgG30n6igg93HQicBYDWlYPuYC79IEk3nX0xLfHTOUnzOloAiArdDU6EIgj/E97aP+2uYlkFAoqhS4EOtiKKdqie9cAZBmNRvGWLN0B/hT9z43tWsR0DCPgRvaoTAwK5oLsegcQx0ftbgsZV3WzY73oyaXg7v95FUKv2XYNhDdJGxxhUL085zvc2elNQyAy/I8QFpRZNsxSGstsFEjMS3yyvHnqSwtsuWkDafBevy58ZvzkCNkq9hatueIFwIiYkD50L2V8TPvyhZIx9sWeRe+ra5KxSd7lKsU9NoPrrZ9mUV15SMJmtEXVHRnizQHw2Fus63XjD0GqUd3FWNhFZMuSQCgxlhAjMkAq01p03Gsf6PVKspAvVrxThHjTlvei7IBg8MdA3QVn8I9zzAX88JsDFst4gETmmigjNhknzuPNi3ThAyUT9pJKE9dOZ36N+32QMb6nRo+KqjqOUfOPts/K7ZTTYsSm3mKq3QKKHAEQgwAJqAkgJ5WOqZNYFJKXAFfXaEEHCEVZ8pEc0owXQ5O9Hkb/jtfnmnCaWnaJ25by3aMXE3PqVFKv7Av/Bt8rppdkB3YGVfhqOSeNc+2h5HYIkvKEvNKGeFGhwTsZn6F+oS5sV8YEQ/XMd6EQp6CPdjdl+b7G0zXu+VT/QbNNUmMsg46JRQp25QZZ8Ba3USsJLzL5LB0N+/8Zn+/PEfCkdWJjh71kFVajJcBo5e6RsJgpnO2n/7zld6aswgVotnFHfJOqtTP1zJF3iTwzLQOvXFUUI0C7ytpxz0ZlgovvEfX3DTdSzvLZVgesmiGactmH70zcMupisiO+Rfz7fPlVxtFozq07m7cndLzm/kzPMaiio7QjkJ2pEGKCbF+kT7mtbwuH+3uWhthZXYuIoAoYNacjZvD4sLEO//2PqulhmnXuonvlm5U5j1AV3djEHShH3hKuM83pi4wJnhr1/yqFC5Ib25jUpc2YqnhuAjDjAy8IbiPVLFLym0TLiejl5vPZl0sM76oqWFJjqKB3idb33pmJF2Km8e8Yo+r1Z7v3ecxwFxyVYpHzXm+PgVA4NEg1BdUFDX9jmP7GpHl5qZgMx0P/ZBOaB2LFSTkGgEdNmM5JmtJpMbp5liOZZwjA8si+OZzrxafNdmiC3N8moMEb1NQCr/Kstg5lKF7zx2l/CNqxexAeGK97yMWzZqn42nrfsHs4AqIfbLxl/Lq0fThEhCzsoBYEmwp8+n+z5/buvBKb4ADJA2Sj5lGdqCorA4fW09YOVLLUl3Rcal2eh3o+wI/BshfYR7n17z4Qu9BgbWqvU35L+BWN0VG9OXQz1EgdODd5QQhto7NCZtwyoYigEB1P43fg1t97Byc6rufuzNrvLB7XEuOneINZaxtaXlPs/HR522zlCMoL8JEM6z3Eh+s86poX0OJiRLIlH68JfrHO9+0iAyUaoDfroSizO3vgmrWEC2F77GT6kXSUAp/lPm5mNBk3Wu4Egp+y/7SY0wVWFV3fwca+aJnTZzGVMObGrIo/Rvg1DKu+7/rW+ub86uVe3qSeaNSYxScy0C7pKIiQz/D34YSCDzqrflBimTp18TkMQpvPOeTz+oaBXjWEbDqw/qKdLdXP09BN8yyUrR3lVSlOe+ZnHlMMQ+VUFxPXN8djzJHDRR0GIVXaxskg5Yrz9kjjrz6qhZrTqV0SyneileHTA7MxEOPtXCfeoOAlkZVyxXhFjXduK0fTtXo0nyjnzXPziNMkAEiDEJlYuz9m9lVT4wKPZ0TbywP6uknIWOtMVoGBmpiI1WOIlL9dAVuxzM2cFHPt1bpdogxaAKYJjAHdXfdwxuhtejpOvUCeZRFTRzrN6IX15bQul16VWzTlVOqh6FesHeAvLOEfRZry55XIoYO2na8/sP8dW9JlVvYgjhEc159HeGNnshQW3CZjRlPn7RsgE7qhI3ixDR1GMwm2G1ybAWfEqd5lFX1jI/oVr5rsb3JalOHtxv2FO/bjr9nVRqfwqW0OvXw8HE1A3l+/lKiBQFDG9utZUhghS31jG9PuRi8XvTATOUW9XhfPchWMHZ6pRJQQGIedhLp9vV9r9UrIkyLgwYL8iT2cYcpNL2XZkSrAGNUwH5EpmkpkSrzIE/Muyiv+VChIzi2GEbvsil0njqdVWUS15tqgPMx/DMtGZvKEzCmWSbVBNUnBEUB2Z19kjJqrQohSx8eLJFhOKu0YGZCOp+X+Tm2igFriXFJj3OKM2LFY2wn4h5P9qxH4omG8miKPzUqoVzgFc/oBTXIIbvEfBja4oHfbVVms8OXohVyXFokiB6c9/AyILg2Y+mvYAIY/VNQ4mLR9Wly+kQMzBN+lCiIvdTFoQtc2x/INbKhbZtJD2hQVySxBvj3AlVSh6z2gUoJaYDJCSmEY3faXdPTNMHQXcZ2XYihMdh7uN+0esm/DrTbMU3HVfAkR16nPTvOqDfYtGmXsgoLoBOLYqF7jrLa2ThqAFeQESlmRWA3xL/pj97AzA2qMVsxG8+njpdmG4FdvYSTqoJ54jxMQIz/M+logFL55YdYrcEq116LdnFEXqOkhn7eQdl1mmfsBM/VtWYIqTtbiQ+X764+o42nSltrIOONY8TvXOvhrjT1PG/MO6sb1MZqj7ooeevsOESKrO72VVecPY/Vmeec9Geuox2baW5Rak2NdbweHiomiUgAgoIRxuq5K/USOnmROLUgafX2hJxD9RSDF0gqgEqpv7XYHrdeloOnmSe6dBGaoEkHWpBSvfJNjHRF6qk9iO1rW4TPhFpLae7JOZrUHT18fMraiIBUZNOSh+lDeyd0OyUQo1wd4eKbXUVnU/Rj761IGR6f/LjttcOVkG0FgRqZu6Clfq96t080jDV0MRL+wn7Do07KQU1Qr56xLJvDVQyjBTK0gRGQlrqXUlYf9OVYUvF2m47noR1oU3QdGTcH6N27EGI0lCU/zMFRI2P71oNiXSEH7NKrC/1Ogag+bN1JpVF5biVVAl0S+9VprZIX7FSFcWFbLFd7NZWPS26VGraFV5efNbePBr4hKp3yBDwTxU9fP/3Pz78aU6TNKQCCQg1TzBJbix7p3pQZPuHqYtPaAtpqKn4AxEes5OZzzbgWHNtpKzNGCT1UGVR639sf6DlAZMuH4lN2xdZSI7HPzA9G5mQ/l1Y5xwukHu+BeS6rDeBg7oHoKHWRpTRhXsUpDSMBYDREDTVOeXebNWE4ws1HvQAKxe2Ea3JGcx/zA58xvP+4FUNR9cQn15Ni8SqLdOYaRsPq1RfWtFlR54T4BxWdM6otqneY7YZXuRK3wpa5l5FSe0/kYKxn14g12RhEVEc3WbShYCR6Wm9sM6x7TlZKI8rGNshQogZJ19bSDDotU7Unr241kBQM6LwIhRtAraozZ64efVG5Q0mjjznN1D8iYGr4AtUQAI6tiU/NJZSQE5QhjJfGypjHY55m8HBOeNsuv2pdvTxDesiSZEvsI5UUnpvd7nDX5OXHP2n/G6pV8o0xBYtHfkUCZweWDyQ4xTd9zoziHBJQkqnz7KJ2W6UnT3XO8Tyb4yYfrOEolMUdsUwI1esegZ9W+a2v8XFLlcDomvrNqlhJ7Wj8cDet5bbJabdkJ7ohJFgPFAimR9SGJoi5zXZ1tASZ7rOFr6ZK2afBFuYzR4Xn2abTXdnPF/cXc/fJzlJDbhXJXAG3IeSWK/H3CF9SKK9H2Uhfs3/cxYhNz0F1oMW6ELYkpZtN7YglRj1PYhpJmHWGFq1UMJgQPrALCbj5y6CPRdmb7SjY2xt6oODQKI0MZBgkCwBWWKeXkGQOS2xkdd7V/eUUqSFteVxTxcFsTYUS+faWJkqSVbA8ZEHtjWKHsnMvO2pNK4BZSOMDdhqSKP3MjOMF7iOUjpKroiUNA/2BsqKIF/TAoXi6D+3xfbC6BwpVNDVELgRI7Cf5nXk0eaA0DfhMFDFddo7NUI0nxFNOKVUjGzlzjMcEbuDtpgQFFedd8c1VpcxSzae7oxVeiomWYg5kwo3bVihRySro8j3zbUjIz/Bu+22x3hjdYPUeIxyKGcPJNXo+9u39PD9IPf+2BFDNo4KahsbA0K6TCtAaVPGftW9ep7fVVqu92n9oH4OHRvJo1PXFupnNb78oRyjh2pk+pCJIQRO9WNYXCkpQQQLnT/cPTnDQNU9p2ZZqXxyCoKPD0ldrA2lYQNTo5FzLlrnLObGD4sKgJBMmQEUJipY6FJXX/B0YoJ0tInIiew1oNbo9xkm0PukzJticKcOzNBbgVOdBPabTTsqpwRxwFFm9KomnkS0GCK1KuIDATEJntEh9wuK6Jamcfm7oHnoaRsZrvAvPtPy3zt2I//h5lCv2UFKdTUSntXm7O6W9+ZtWo3VYYhOzUpaQGNbCckAoFcxMARt9TS6X5aqp50hnMmaH02UU/BG3JXkWS/G06Tzy8Nw7b7ZyeesmCsiCrg9N5B3gO7jbGnopZVU14kErwprppuiYkCplkwf2iAnlFn5wHfPeWgzfVVnsimqXGcqAYhbQ+iE+qbYtXUA2ofk0h82LjhSOPqoRm2WiotUx4t783feh5KWBzu2jlFglpoSjbG45oNDUunv1BEmlaLRns7l8fE0TWjuUYnQQG/beke7B7tIEEwHL6lq5Ii9TkBR76l7D8W4AZNgO+H63tjLm5Adr91g/3ndU8ovnXs+x0ZLxsfgMVt0Vv7Ofvc7H1TV35uecVznb6CL4bBVy29hWOQDfzyjYka9DO7r0OCtI+jUfPwvFDsU3vLa5mafFb+yvrxs+hkX+DBO0fg4AABAASURBVHdyfTk9fXVsXNde+6ftp7kKs3H/1P3JW4beyKr9zPoOfzFqL+Ogc5kL/wVEAhqZ6i7B8LW6VTfOXJ9ZK4I2pnL6eP8InwrOC/L/v+TGmrqyrjk/0RfkeAZP9YGQmT2b9xq1MgDka0VijkNdPTHK61qI1VhoImE5V0ojqio4M7ycZ+Msuw3fEtN3TZ7mqajw15vJcPmpD5+f64tP3RHpl2OuylVTPVnAMX+Wd/hFhJsDnfMk8jGcQC6o5peqld/Z/VxhsX/s+ijNaoyDOqmoUg04d//uoweuyuCGobt/663iVxx2LF7KNiNu7wdabx7UM6NlsM49KTwYYHi4qrxu09W+iXOh1ZZNXc6Exfw4X3CghuP/vYp8emWtMuMu3/erqeVu1JSxeTZ+d7YSGvIxfe7mcMWOn+DV8BsKMJKey75qyWLewrh8rsHWQehH+lbFk6MPYDtEkRqGnpeeb+Esns1E402DL4jVC/+MSxPUF1lf79wjWerkBc7w7rm7jwfelr9X7/UbuK++9yNSYfzL053+KJs+s1q6095u4TIsKT9lJ2w+V6k1TPs5n6oLxMWcqQd8KCNyNciufrN7P8j/5ve3v4+qittpbOHOibaFbQqZIiq9H3EfN+0200IOAZ01uedQS5/IvIi1yFC+8X3mUVtjjcksZqb1KntsH6rZmD0eT6xapr1Tsyrbb63kD75H4GbFRPTSVuh5bE7sxy5xU6gn7+996ytS3JJ4XvNY35gSDvnUnv4757+ue7xn7bHP1P9alradMENQU1RZ7yz3r/sjBE0VlZWoggD3wlkYx7DFxhvSH+xHJ87ZqVVqIEJz9+9+0m/ar18PPhL/M4HCeoxlgAqUVOHY052thea7Kqnt6vdG3TApRnYA1Yoatn/O9XnWesIeuDIgQuHujZWjVNqkdH225bpGVG/6ElzqZTbKknbBI5oBRYlwKpdok4r3r/GIJq9nvUtw1WhGxKJG9ogTF0T0KOtOVUxcgBE6AwVYx5UTz4SwXNGfhY+HKniD2ygl0PpqLXtYijueiV/4A9Mz5RazF5bbKneAfqjCOKfq3SF8y9WDBpBafjk38/AojFIXrmqr8a4fumEj9YBIW4gx52WzTAKZvRjLtpvwRisr1kkc7FFf/PbnCBYX3ATQeCDjUB9Vxezg6uao1X7ufz/5s3j1RI2VWe64Pb5YJWU8/b8vP2c4eHweeN1uxRDLAQNQlHTjK1NShRD6N3/q+Ydatsa9HPOuLbfrs2m1wLpKIdSOHFCcY7ZYGcDaVnocR3XLtG16jDPZEvY8FSdVzNvwSpmUiFOo/01sp5Izu8ZTO4EGnCLqTZM41zWoIc0WVSt8h71Lg+dHnhORlXNu/9YumGFiXiRBva9j88zcMjv+KIHWag9argoJn+XhLt8BcZ9EDvpWMR/ZYz4+7twECWjuGl5HTd050Yzx5aCLhd1njXwH6xPPm4hPtd8+udMlLeyNT9zvOjgElsVnfa65IhflZBYkAHu73NfnnQbA2Q5MTx2vdCqEB5QTlbD2QIdIG5btoVOMJ6R2lQIbcy//bn6DRTcbKH2QAEobnZUzB3iUoJyrzzV1KYf4fu3/Zj8mu2ejxH0xEyca+xhTbcNRYXBb3Z/7hK42GpCw9IDmY72sMKljnVb8n+GFz6fe0xrU6TiKp/fpH/Xc0uiVTSsXZS+8LNaAA2/4oIAb3cRgjBGgyVkktfrDxLtiK6gi2Glvfdh3+N9pfb3F8E9cKy66T1uLuMOXXsphFzxhd2T9tAy1WldZGkbdVilj/8f63ZZrcXyGT6lxjV7Q0MJWXxeFT1Ye1cwomJ9HruQrC8+5p9Kae55P3yuVI84x4eYw/er1aQUjW/U89S38+tzxlH+FMBToGl8DpSraFDGhX2uyT92Y54L9m/WTl5XsTVlL+1Jsd1NvcaOfQY/WFtgMgBaZ9mnzGpsumVpb01NZjn6a94vWlYhzcFIy3m1iIjS5ZIJGH7MKys50B3rF7E3Ss1WtclCQG3nV+HafBUzjHDEHZbTV2HgaKVheUI9aTqeNrTGldSbPQT3ONuMpnKdRpbwd9UGij8LhjJub6h7KeVkV/JmVIPiMPTalEGgeSz2qncIsiAGA6qmt5MJVC266QnVGRLaf51VEqWasV5nfhhT53jFnWeJqXiAR0rLhurjVVWg1zIHrUIHMYY9ou1Xx2Mk9GhQTk6sf1qmsToziMMKgylKYneyv+fO2hB6lIXPIXjRrwQy7qJ64FHHuvk0+OMbW1agOjc0eyKnizyDX2tsUOczGvZcLsancnkIWCC7MZ/Yab6CYo5IZEWOu39bjJ20SUhrNsY0O1UNMe3PN+si2my/2FgwbBGapSViXFst9Az1f7XHkd6+wCMUyXUoi9Vsfyir1PcU6wy/jV/kC3jX0e052G+EIaw9IEKOAtK+Zz8eWOmzveRwa4wxkdCqAIspF5VAXqlFQ4AyF/tBvrKm1j5yY0CoGpWvEXZTPwdN1AKzFWGjWfeqpI+7dxAUlO8gsZnByvHoNAqbrUC37S1+qwFWNdXuX9gt7xlrYGEJwXabqpSHph4tmsZtLqPt5MA+VFG/JCwYnLjIiDJcQP+rz70bK4VDKQArHxMi3fu2JZf1DrXlq9tp+PQmt4y0GMGtB9MWMphgo2/qcttrWeCjp1SfvUX9QUaX3UeTyolR3p2XNYAL1Xn+AMdkIY2OSJKQlWZzV7xsfGsfZZIseRi/Mu+U7oXXfRhdUl6iSt2Tfrux1Rtaa9xrE8uJoybkyV6bVjJyDTi7jtfUbfOBJ2XXkQX3NjnR9OSgwI4BvrbcuzVmU35mtBrC/824fZAe25IO4Z74JTRDFobSEMKzpPqVupOjc5NadpNrSX//l/NGifCpPPLqkQl6tYZRJ39WWbb/mvzSFJnBKQ7cMYP2Ity4HKLa42KqVma7UT2lkso64VnB2c2f8b9bm7lhHvXw7aM4RR4/1jKhV2iSi6dUHuB+eY52wGxrVjtrS2GS17k1PebcbzIglLVc0FgCd/Tz2U5/Lr7VkPvu55xw5yMnREhJpMSs0c3H2mokC1W/+s3rmGeHum/O8tTu5vcZUM1Yl2U2rj/DbkqGSTiPd3T7mn/rs2gtm1jsq9ulLIVTciX5rdagk1JsHT3xareTNaefwt9ffft28Cj04yHQc86Y1ciY9M7SR26O7odt3mh6JuJhA14yoCauBnVkPVhllg8ft0RCUqr8fE25VNwVCUVRvSfN0tLm2lRdtl7zNmU5TNr243wJYgz0COpKT7IMwuSv5LCB7Sj+Y10vcj0+PdTOnlbPy5AyaUz5H2fRWuDSZsDEXaGorl6mE3G0ppk67ZnMIBSwazBDO1aNOyySwG25Qb+g9CMm0QlElWAB8KACIEg1ASw7P25DXcI2ZgIz2kl14qs8kBfHFdm/IjsHiQNUK5SlGsfs/8sMOHpNHczCazM5TLzixtXXBcL+VSKnurafxKVK1wMiwZ2f+POUtP6w1Haf7iDOaaUHFpugUoC5rWka3H+7jlpyrWgIJQiZHySy5T+0RWtWkWEXD0PQmmB+r7WPbRsMDsUeKp+iQDMYkxwWs1q9P47rh7TPxaEEoUUy9PChEGM6BFOSPQkBX15xmK2kgZ9eLBK5Q1QyetgJVQHSjwuiEymR6DUI9+TP8mQ5rC5h+dNFydouUMnvjGWUIoEU8tg6WLrFVZ25lHDuOr+MoEKwNhoZDopA4KSW3KlRyF+Ox9IorRChMvwMaxizRZtjOd7JTGbGxRSiQZpTppo/3VFpRxuVXC5FSLf1Qpauyfy+IlXX1m4nVsB2IS3/UgWYlIseaC7g8QtlPqG/gSJS4srwG02SahGajrfMsSrbk66+kJg8ug96MO2TuPDEFgdcxsGwWfOgFPVZcXnZ17zlWqXJ5r8mS7QrLUHQQVj9+3esz18d197jszZxqSqWFVLUFe2MMNApaScjDpsOMvtL92Ai2LgKmBKAtRqI5b0fyUYUjGt4j95KzLB0Iht42ZNOcm1px13gko/CSDFF6Qr8dvqAHcT99dhVnJoKhjhnybrZ82WKvdEsje/xQswYNFZxVld9nZ9thHXueurOUuzlOjXk3mk+/RVzUZ/Kmf9uzIE/mxOhswIIr3F2BVU2xa+ONTww6Ru9N4wVWZeRhm4h3YncpYnQrcjbRPrUj1zW4bD4uuEme0SI1+XTRbgpmTqu+FNMPqTBn+H3i90beGMXR6cy3Oi7BAn0xtgNXH9e9DlRMqyQjjfWn5kz1tHTBtLltvhQ/W6zXKHaR8Spx2/KwMkWWZsqhjYWFm6GHeUy2OUazNiqbo2xiJsHi0DAL6iKwZe2uE9RYxep3vY8OHxBjwxjuETPVrGxoNkzr0WP2FKY81g8+P7k/aQ6pi+CArUNQB90qG4nDsLgiNtaMyfZjM9APm6auIWeU1KBM8hZANKetD0+LFxWjMONomxRNPpJCpoYo2mv7uqhqCeUSYkyRRDe0qN3dp1j1p3izFt8rDCYGss4pcdn6nSMplTBrDtDCpF8JPHrbWsPK2nLWCzKk1d58uWOzldLSUuB6rmH3xed/NDXwz6l933EOGghjhDMoBvWlxJdzJPQbO8dssA2vDBbhMq1d1ixlhU4Oj85RYyhQ3iqqMgMM7UF1fnsD9uJbZVzkVbYlk3v6qZSkxN/FCS/83PPpJL4Pnv/Qv1CVx5UeIJFcj20pE5boisrV2TXfrIfakFQaj4kACop6fLzd3+HSClaq7DASuKRQmdn58m2tmSf0lAoH0CI4H+VmKnNOLSBU+c52S2ol8NPM52+fBurqB8+q27KfmbsocQ9dgrvDcoCcrq5AxzglJuzCTkohGEI/ohLjr9u8ZvRV8Fo9Kx7/JBao+s9IMB3T7aeu0z+dm19qf+fP2CuAadCc9AFvQF0HFF1Ko28h9+zh0UN2PcJ65FYNGxdgQqcZDnoYlqs7P3G1X1SiKd1EubCVUOjdu+Y+UDqBVfXTbTCayadyJrD7lQForURv/slC4Kp0B3cGhX1TR2QMdK/5t5WZqJaHEJTmW19jDSignPXMz39n3xXnbbC3NkvuXMth2h3VPaNcaR9yuzpNdOtQg7zIHwjHzGR15VRl84DVHid+kldjZXndSucKXGszjKFfvpx0zVPHEjEYA4JGMRN6JvN78ejtwSftKiwNnVXUyEZCKbpaCXzDaWVq+uqj9fBTow4gMwPMwJVnpEJ9MO1NxXqsqg4fXlgGpuNxYC9+Lmz3qPBxAWvnoxRdH7uo/RPsUHWMuXytuGiBl7syJUiWO0oeI2el7ltvM62xmTiJbEzl5NEtWJeloBvKjghcnOVQYLRabJ8p2tlWbkNJWPiCqlCH1ARZijPshvrtzy+uZy3/LDe2WtCrlJEp91h9RftubhiDCu0ypZuYEKHe77JP9QsV1iiodIrdpr0PU/uCLfxsfD7NKx/WGTuP7q9s/j5Up6gQoouQVPpN1NYBAAAQAElEQVTbT96Dh+tQXiBHLaLfGpa33dgt0wl7WbaCFwn3pdveE/Wog7PWou0FXjbXgvqOG6if1eSgOB1fSkHPV+1HnybJL9RXbECRRB+sxoLiVRq6G4UGtECIRUmsbYbmK1zO+2Q+t542nu/p8/X+LuUjDcdkZGvgKDeBBE2ODrIqlRq1XuwxQlEMmlAKy16lTwtB661F53qBlojtWi4RzUxreFub9Xj3HnFf5rKgS4IpJ4oDGFkY/ZolEj7gnu1S3ifsQ3XoXrdy5LDGOrVVI9dEEC9HUJXPgk37Q1RDBkc8aGN0vi3l2W/6t/qx84YRUu827II9hlyEA3SoEdsuSrwS2xA5PAOMXi2bHD7wkkBUxe/iwZ5WDvuBnbpacQneTpi+8vmCWSufrRR3Yc7clbLN68q9H/N8xlWFvX/mZ2vnVUpaTRrTRhkePXpXKe6C68nxd9+SFQCWjuF5LLs0Dvb4ss0VtDL/wv6758mcPjbl8AYfnvSiM5yms223LFBOMrUk1aN15z1aC/0Z6Qpq6GlVpkJuOCGx/9Rc9/HnTvvvfL6zB9owcCunLLEWO3c86ok2G7XizUNmHLiCR52R4uZph+3ro5/944LjY0qrzP0kruFSn/2GSKu+KKxbf9XmLNUVMYMElP6o9q9X7mPqIzpZfBG3Twd6pO98so8S6DMx6hmCoPDXNb7ZxmeY+uNXrOuVkBL4TU9z+XgJm9WaB2WeaKebOuLU+D2XNMIxI1V3V5KHoNVe91pfker1Lpi7hMOEHwY4LC43idXufb4r/3PlbNPWh/aBLY78Ctf1NiWN3+VaO/xpU4Z0FEzqep5/6E16Xvi6kjSn37pEYhVAnKsyCEoIML/pImDIhspuM5ffbgcUV5BuKQloTBquXf+L/AEMKFK2Yoqp/HOPSnGHm21A3XSPbQ1gJYeYi/lhx179CRVR0tzcRTRp2EqUo8BGBY86oe659L6gzdBMIY89NU+krY4f0cHuqSfrscHrM7wWa+fsZphsmlMdRq4HMqxcQzcS+gbl88TP8SWnCEjz5pdxL5Nsnwu9ZQ27gTN3K9UzZICXeR3X57c9dHsnGn2jnk7Bi+MX/FrYn3aDXOqXN72cvWac4CIujknkKlOSgmVWyp5ZkzPy4EJaNT0vOvei+IAQoNg6f0puma1XssaUoBtMWSJML4wbllGLAsNgkJJ1DEYk2cglDXTr2GahxgE0HYqmkA35hRlVAz4o7RBdQ6xrfNp8dIllenvINppC6T8n1n8QDNG+RK2Oak69LMzLZXQT06BS9c4QoB7xRtX7AVIw3tFNKoB5w35jfkF7TST2K86nrEQMC5awgYiFVItR+DJKjPPxqTS7A/JvuhSYTdACOmhFb0K0QhCyoEbTZRyjXoOVWOVOdjS8e/Sqfm1Bk2Z0PRT8TPPPTGO4YFueZRXI6ufU91RCSE0BFE9DCeXUhQx0td29pKQRWQnc0aVtGMWdrRH9Peu0UqDruSeFuf5kCsJaLl7nnIXZ6P2MHsREFWWYdZZsHXeZbQFo3TMVMJrV+m0Kckfv60TUyJZRHBopvlXPyyBtVDKnR5DRmIE315iE9vdRrOxpSVVR6IBOttRYd42y0yamlWPTvJK6ljFcHD4liS4LPVTZ0EvwEuxhscqeFGo2umXdASqfg7v5BQ6okYkpjl4A5MEwoieG1gwqBGC9f61tH+wHqzrwrkmvpZHVq7HQetcy9Iyn1LDKrEztwqEMP48fBZJVpawboJJdKOmepj9QIMRmykC2VzlaSORcnYcrpFrUUkpMAJHyJAMC0a9GYSWXY0WTyLjGvQ2DBk5DGCk7SiAhGY7a42W8ILka8V4upFKi4byhNcXzz9oNdBS0hI8Wy4N4PCQUjOCJybQ87BSbSqHFceRChh23MZ9gWf+/F6BUOMZ1/iVTFdKrj3WNXuKdleq+tE5n0udFqTDLxPHIXq0w/zjV3hYnfPiWSWKAnDdErR3AXqat9svHyB6kS2HCh7zcDcYmU7qZPwhAa2a1sjGCNbTk5B9eUJBcYpnhy6Ef58gC5MnaleDSbw2d1EgaIFAT3e8OXTcQXIZZCtpcdQ55gsonR3OpCQ4wDceQciqmzBGtt+JiGs0NhguEKTwNbSl1qkl3XCGJIEhQAUGvM8iqqPMRDCdUd5Hq9iboGudGKmvmYqOpjDzoYrWkssw++LsdHAiaYy3z0DbAAdujtXY8u1L7FrSu7bVNuekgzCDzagL8+jzTVaOd2PcNQ1e31RpBAmlj2lqYiuz1OOfvfgzWy6YbSn/LjrJqsmwASZHhoJgMBOdnak85v2TjpQND/TRRmBGBnh7GtRDLLMLEWbAC2gq4LICjsSrG8eprpLqBzFQPGgRG/3T8vRFroy5bUDHJdo+u/v0yGXeXqvtFLMIf1IN8rNMdHka5mWmvEYFQj/V4nYyeHKmuJLQAfAS4Mks312+reTm0hVfSRnQzR5NCYb7GeqaIx6WStxY0zk2XFEBgOwlWicGGfK6BOfAedEo5F3hx9hs+YtyPCkZAuamnJrmolnY5LkO0UEOvNwQXvp445mnRZuVV7KN29bqSDzWAe6Dt+CxagBRrmt+To6KVoKxlQpn6enxtJfUJoGkky6Lcy1BNmXLj6mVn1aMzQqFg9dtzvIfZPGUSS9zXu87erl5xLDJC1QUYUxw1ORR1sBEzfMYemVCBi+gYBgQPmS3Kn78RGi002/X3LuTNTvPBJ+ajtiYiJYnTnWRbC2nIMNAdZ9a89wKbpFvHHD6lxeiGNUfq71JSJwsqsuFkZKovTYU7zUq2fJJVjdKznLOyLVRnSygbb24ptA7Sm1603+GT84hqGRQV3+P5ONN3cF0isgHVMC3ho/cFdhjMRDm7hOMMDzg19u0Q1Bq0DSjKXBJJ06XOFpFWWDbqr5o/BQV23u3CcCk0zNOQrRyfFkOUkSF7Atx+2QRlauLQ5TUe7BEGkZa4i4flOHF6WsFjFPVAXzldB/bYPGuOULu5p2ncu7NCjFVdWrBVgQpyoAyJIbajYl7uRWjAV7+iloYUDUnoZYrnoZqSpkA+i6nWdHQljbfbNKzLLZP5SBWvhwIUVxnfDYvJKGljVnwX58GG8jccqarQDeiaEi5RzaRBJ0SqDFN0KsMoiq3BzKKUOoq6T05Rn6K1YSCfrkaV6Vmr8AitIFHm3w+tMHQoj6NCLpOQuv+4DUW8C1ol0PLoR7ElBGwrawoQNuNdi+VSqHEK/lRUGwaKc5QWrvBtRg2gS32hSMEhNUDdEXYq+gmOOcmFXpPe+rDMfJxZ08lJZ7tZDnrkmRsm/cDq6PKL2bSwtT/C86Zm0T/wa/ojPG0Syrd5izjA8eX8kuezO0Kbi/Xtffc+vntfFqrUl5zG5loh4A1tOL5jSSlQ7iXganF3iAIGClR3S3wCakTuoznxY7yMLtoguhpHoyKX1a6F3F0/ZJAisCnDb/UdCL09FvWx0QP03TY2akXf5VPhFX8TQDe5OManZBRjODG3OBx/VNWgqmH9TAhJrecsPfJI5FghKdfgMjfxDEki+3Q+lqfnDIrgMJqh9EqDXqv7842tRV5ZfYhCWF9r9HoS5+W+5+ViJhbmB+ffdLA3fS7mv+98VJxlgHMC0BoSpgWSpvCdZGTXwaNn9oMP8FP81F6jcZfBWiJxZB/U7c+jXaLElioScznsx59zffU0xiPNytiGlqAGamPfkyo3ZFmVoDY6JCgSX8yv6mBunlscnoe6Bqhmy25NNYLpnXp7q704WUElV4lm5nyffE69fgYaWNBSledGvUMu0/bFLaOez/3dv999jg0mAuF0I7kcy2nuWlnOhFCrXn0NdArX1hXSrM0lcJWTnF/HrnsgF6lfdjdLUc292CLdTZKH5UyfED3jMddOk8U1+cFcQADjb+4Rs40Brca5UW7KtVJ8Tnz/8/uGja9l68ddK22FTAIPkMwQEHKbKy3olm4kKHNyyTa9+GrRkXipqhA/jWqV7V3rzJJl6K/8zRBKobwui3+oa1LlBk2vOQ/u6jQ+n8iQ5U2EZ84tBMJxoG4xKPWNeosksK/ydWzRtmuZ9mgTEhnDVPa8T9EGJ20ewVF873L2oRZft6VulZltX9ht/SgoGnorW6M32I2f5i7kjW+P0pt0V27UM+e35rQ60SPgmK5k1TuZ5cVpP+5fumDfwEP1Jik0OhbHdGO2vpiHJSYIrPcYExDZJ1xcRTezTh81vubyCRuyzGotSUjMe1nBz0ROgDQSJGHQIQpOq/KvpkE3jeL0dDXhZq5Smbl/FdR0w3wsaKaCXwQhSeAEHlsPXamoVGiG/nC2VhQJv85X/T0kVxhoZk7/HP951I13Y4uMayZuztfqO0c8jWoLX+Kr7JkJgjG8O5620y9/0K9BQg3tVjHoIwf66QKPX/ey9nR/lj80ITFf17X3Rnjufe8lIQ/HacsjdmgNPG4NYgY52a0uONFkmOqTPHZEf+thzvfzc+9/GRt801bpB/zOPNBRWIyFvXP9JD+9nEHFDGN6pt1PP+eU0tQ9Y8Au/ivbMLeh9+h05UgQowh78uBRRh9srXnK8cfrE1iYPR20icgLz9WjpyablML9M9jUEP1u9bwl9xiUAhAltIqCKHck/Jv2yKXCfWbUdVxh1pm754zgKDn2Qnkef/Lqlwk624qGNe5wNYf58dkGWGvB0vEmQdPr8SonApCC3zQyrYDsoSRDEIpQfxbmB2pmoYy95HO4zI3sh2ry3btcZS/N1e5WQ4DucdMwPqbpp0eEziN2O8ClGytfoONlsttosWxZGU7zceY2LVHsiYpQgbZINRtKgZ9R+xK+ecZG4UkT4q2EI8+u3m3eL2hZzdmltUFZRq9bSRQf8mkctpemGxAIH8qmPUtfpn6Se9wQolbpbfQsiIWE1ijejryoM+mV0ofEdBxtEGfK1mpXCBxQL6dvfD71s7VhaOB50P+Nf02Y1lxnltJQrJ5SOYxkm6Y3Ih/iduLTW9jYNkmZlD63cY2RzevOT32VMUdoOdhZPuoMOZcc6cABDufwtH3x8/VPXzs+vmOAp+3RHyVIR5jT7IClNiTLnT6Po8LFlZnulHSIs4t23TuOaYxQxGbwUtZTeAec4AEevUlRMOJkOWyAv8sIKy0cVs2hxMhVGtnFLwT9ozRzJX6yd0veBZx5j/YY+zSkpkF/gUTjJCThIhAJ/525JYe4IB44mXnQBapclDkBsvOCUZahiAxmrSdteFcPQ25a2Ioy5rRYHylQrWjbI18Sr/6oc30P7kJ5nYBM54DyvokV+suuEWB69r/n+fk+P0/rfb0BnJFohPqV6qNoUyc7ORUsv9iXl7tPuJlxEEUeDXv+/ANqMiyscdLF2/Cbat262aOEv3s+rXwrnmz1qtJMNTlVRDz4ufFJcTAzqLLKEF2y0670yW5aKp7hYVuX33rXypYHycDZ1hbWbU8hpYYUuhk+41mE/vVX7efbU+OTa3Tjc9HUSAAAEABJREFU9Zj4syAMNXPO872/3+e/m7/lX0k9VS33me/pJ8/TT4u20/Vlvps40d89mFSvUq7U/Z0nmB/wmrdRX3DvlzglYdTwBU4L2TXYNtKiaWfpu2rm3C15zEwbVVrz3Ow02IfXTy9xbszG1+aOPuOnXJtfhfiU96MwBaDJbkpF1aLd3syGWlx9Df+Net6CTvFNvYX3XHdfqZYwvBK4UQnNpTJKzqja5NBHpnT2nPNUSn3ZGGXR4MSUPqQQM2XYPe3Pedloc7l3c2kXdz+RyRqAmtjGcSxCvGwoqre6lFNxL2UhfKmqHUDPFOSC05zHs1fDITFx9AugDC2DUWCGxHtMC9FA0nDSDboKjfGp0Qv6PE4paQxl+YVSUlB2ILZc7xK6T3CMa2TliNb8Oh+3Y1RP0C95sXQeqRK+KpaaRpJVBg/3kIHaVJAxuE7vVGOgwMTSNoBmxVEn/lo+CkMBVre4wzGUC7hofNSj0lV6nq+XUW3pkF8FWc5pUEdSrsKN7uUYU1DHbCb4Hj0ji0nALMO+g3qUzwMRZGy6MqTnj42sOem3lt8neryva/anQgUlP41qUwK7fGF8nbwmgzo5mQXdX6CUptN8MupOoESsWlPuk3bKUqcwctq31hzKvcLvqj6pOOZ92XP76AGI5rLn/8/S2yi2rus4owBJ2en63v9RZzeW+HPhnpvltoktkSAIUrJzZs8qYBeetnyXTQVji7ycvK1vCSCQgeMQpU6VrGcvxTCzMVlVTCHo9P4u6dm66AecN7TH8ngd1+8x2kIvnOXlSiz0KeqUihvP4Ay0B8/M16wGwMdcrA0c56buPOcCrihlWzxN7u7c6s5SfVyvCFzJONqTrgQhH9cOrbBu+gQc1Gm9vCM6EI1ruESUEnkZ/9bOKfAJNonTUGjT7Yc+IkOKliKeUA4wo5QtGZGKFOCCGlo5hY8xsHqDs+lpVHlWzIQ0Ifoy1g77XlYKTY0wf2RKw1P9yaX6k8om2OBQqNtz7F1UfGoZV1gu4bmUD/XQktfveAJP185ULrQh4X9eD1Npn17D9781CFOo3ubbyavs+uX1dFStKNkzZd9r08UsRmCHiTnB/c/7ApnMLSkpl7mKV69I4hRrTAlprTgKuJVwRhvHSPfLIhAi0jxFIEcZGImtp97M5e4q8Tho6NBlHcoRasYGS91Q9eBKo+zFmTvV/2tPfyWGd+goyZF46TY8zm2+BcTCsD4wl20CsJaq9CMrBIvoltv2yqTI3tZhyotBV4D0VrKQOamVrmw60vjcs3+mP/ZuL1R8aCia4arQVnNSjXJ45tojccS4Ke6RsmyUxPC50QopRjEtuOgghtYj7IAYknA0OiiwlrAUXRCHcGrewd8GgCMxnfIzUWNllNy2WQfJa/Ku1tJUR1GniQ5PUDL01O+b+eFz9YOz+9RAPkbTtbTk1nMb+b2a1wY3dfkLnvYBXYe8Zn5L2epWHRytZubBcITZ5bPWQC/2yJV3OI3WykoRST4LO6aUVORbDS7XnL76LNvuI0OKrv9r2+YNTry/fuv8vv9V8/GDq2a84LvxMNsfZU4dYGzl9WkugGC6V7v8ICFqO/U61XXctTX52yGKPb3n4/bQ1Ajwf6e/G2LD3WTmcltGvlSOHiIkFFgoC015mGX57huMHHs/D8h08TVSJdramavb+wAKUePumcUhG6+BMLjrbXePlModTCkxR+JpTEVmqMlbLvYIeGFaklCWrU/nKLFt0OK1rXoOR7YgW5pe4MjpGdGOGV1qzRaOmWLhmqb+lgbZSHDRdK5WX9QX1dHaEaqI5h37PPYck68GHNBWySH4Aa4gos5MzrSS8W6y6BZ2fI4J3mgPVRD371Tis/Jjb7teTc5dWA0zTd3JLLQp74gL10qzp9GV1vUB3nZjsliYxkBByGp6jwzEhiFfmV5pIXbKstjCoR0A14z4BaBMfJEHnbSxf8Q/4HpZCME7r0zoOoFV2Nm/W8uP1p0a9lhURAccuUZ9b9zlUpuJZ6VAwfwq/+z03M5a3lJO+OJ14dZvzhTqINN+6+c561vcNS3WMFLoSFzmI3JbnNhyH93lR292Vp8H+Z3T1dKRuMruLadpTm1Zi3ifHn4jzuWVzC1nDSrpfWdexSnfJXa7Tw2K7KV2UwKqYpxlFdX1qN7lTW5VPuC35oveaIzIPMidk+kGXzG81WqGeM1A7Eg4xnQ9YWNiRnFIEDq2sJeMGvaFExAkr7p69OTwAgNYRR9XZk7O3k9Wgmmr4s77p4KvrezWs7U5qCphsG/rJmB2H/gmG6CGhWPRXOr9f6dj51JTYGxbKQcS2LWGfwE/8npNwFiYJ5ktPPd7Uafk/al1dNu1r5Ne6NT6oqlCQpNhPBbft1bgbPeWTYKolk5Pb2vVXwpOauoowgROP6Wc+Hbxg1fgbg6y21MLyvfPdOCFCUjyf03InLzC3tPUWOnLKOJtHKIyr8HHYsEtfdTUQY0CHOPdnC6OMj1FAQQHELvZSSqDQ1P1DEbZSKEoF8mc8+lzj5L2/Hb9oqYychQlUJCb+/AnoUedizCT53nfQC97Yo4WFPlR7qe+pfuS1yYw7ie47Xz7u7va8JrC471pUhS22ZEuBCaPcjvoF57C2UpDLVJV5+J/GYK2/FoW7qEPUAov5oXH5H2oXMRLBvLp3KCCbnAvcVExfWnBnnVoB9PPKPkLdkGj9DM2o/Lnc+QfhOTBH0DPEjwOovsdBpBUAtjGGj6ocw0XQ+NR1UcsXIrrqS4RhPGBnSVFutPU82VKlzsNseITrphAmrsvuEsGImLGtmQdKAXdsEe69Vfba3ryyT514Sw+9AO6Z6wNsVrX5EcX8FUDY/DKivwaHirlTKBr+hXlt1U5kJuP8UNbnKkztbk3n5T8qqQTVKAv0+4E19zq11W+rerz+L1tAc6ipdrkPO9fx7rsjSg+7j9uywhwPCa8LScSS1y5c0WpW7yqGIlrYyU0VDV4gkdS3IYkOjWrTH94DjsBc7uCa8R6PiU43TfsLkO79lbfkZQgi7KmZZiGmNQFblaZUvP4pHqTGuoJ63W1aXlbaBKVlXnU5XopcziFLEH4O8Yrr2/fusvW0lEjnvtnZFyg5jQYfvuSqfg+Z+//sk7W7FPfc56WqfamZMeumUMY+6LkBSEk0qbcAhYSFEaMgQ2tnN8Y9akxAtD65o0eTEKsx1+XyX47JUn3YbwVWuK1tfuR0QZ6Ta+HvecX/WU1xsbNl3hkHz7bvmpF/rce33Vdc93QVdO4nvEzURlRsSrUBkViOJ0c4Jnao3URfOskjVud+ZG6Rgl7XG1tOtbEZW+12kUFKMFh9wiYZpGgI25e/xgyy9fHr9XGt8/3+h7teLymjzM/xE26XHnOSrpooFu7q93zHr9kAn3VsekCBKFpvRyXUqx83gkrYBMn2dur1ijO20fbz8nd2Q6zmHGtGEqIrLuYqJBMcYGf4UdmxcilvQaHgz6GQ9uA2mXO5CB7jrqtjPcH6SjxIv54GS+CBry/gsrVqH8cKmWatVtru+6tH1jDZqZbl73vH1zuVMqZsuR8OVoZMjVTz0h53bJhQpPEdm/7MaiH0EyM6AcC+bC0WD24vms2U14TC/PzttLetg9/ndsVO17vPbukfX1eM9fB9RiP7hF/klGYwi5tClSfJdeKsreE/7L7h3ukYY2SUm3PlfMuUNeaSzz1g/4FHxmVGEazR4oQdiT0yd0u50fMjxOKeubBbOI4cwFKugxvKwntDX2ARfuRwIJZyDMjhiNE5E7bZcrEKG//Vv98cF3j75nM7le2oCjQwXEJuHFvtdGblMkGimNAGMbUrLcWChtT+5rmAd4p6Na4tGvqxvn4hIX1P9NfDYKgpCm87Dyd387x8TCnZs98z3wz9wgx/DUzs+m7pfeqScABM3FAFrzaRA+lPatR1AhdpdfH69ZHeflbsLSFmw8ppjhyJHcdfhpHzcdXXetcnkoKt80RFazLexERLIfO1GKtC37L/AsTSFYxEbmixcVOCDMX4w5ap33PnUWlHDvlajWYcijIUN1Mqtqm+Fak4m+JS1zfAadCk8hLV1gmaUwrq7s6B/uabY/cjuzqR47IQ8tRCrrXowfiv7b/aBofRlwWr2qc2rn01SmNivX9PTutcPFafjm8RVSNn/ORifN8at8FNhL99b393S5s7bu1rSqbwzkOU1bXijCY5KMDugdZloaW72PzWGlwESKsFcScbedZ9bgsL5tlxFTtnK390VYjRXDewWjyvJXxwbJa840qT6il1HOh7hheLNdNjcSuPBjEm5tuPIqjuGKTu2UFMMw8kND0a89TOK9XU0Zst7yN0wPmYrnt1E+mdGKUUXWDQF7L1pIZ0tzDTdtx2dvJPWuMmkvfmils+F743iO8dc3Yi8NwB+IaLKqwtjIngFNCJ9cYqctQ0J5izmxY2gUu1vV5+K9UPNvxuKk9ZYZUcE5vEQa0bnP8NDe6RCYcrTS0+TP47SnZOuPYIVjsa9QjyhX5l/0Au7C14O8bqdyUyG6sKbfWXgbOq3lphgKU3Tvpj472OSv2zdS0nx59S8Rmu0EFsKLCbPHzL+/PsZWQ6Q02XPWz5RJLfgbQiZIuZtDKTI+Whmzb5v2uc0qktYsxXM54U/nZ70wDi6x+17llblqNOk7pfkNhW+jXI6Xm09NhajNHDBdeostsKJxf6w2OrN6Lt1K3moL8En6PFsMlFzbT4m5wwlpZvzm3qFOstB/zRQo/XhDxNhEiBvdEROPuupsXIuBDP2u+N/bK5AFG6cdlDtIolpwIYRlLEUK4DUMlnAU6/LPmjlIuSluxGreCBkhQKdMjYc6cRk+QYeZOikyhesE51KhsQQ4qLXvyVJYgtFCURrhsZY0+wntWjyvUQPmLZMgOm8uhGyfYo7W3JomWwUviaon5NljP0WbkMTyYfK93pMX+6e91duyxArqNstqEgqKHsPJyuxjOtXhpxrSWiQMeQpmalNgEz9o5OvGK+2xW0iWBDT/TM0Llfsw1k5nznL+dlpThKvGZJst0zFSP7tKO9N/HZCe3y3K0OxCAEaVoYahgqw5lesaRtN1estq/01tIbvqHtgipTlMnS2ukHBX6YZ83PTe0VUoTTwYPX+Sa9p1qDDJYNPUDXAesGW0g/PdoIXa5M7Ge2ThiWNMcTpuK1mTtLssEV2LgViaxhzMER/RjdPNhzWy2jexLqcrR6yy7qmcOq0mYQQ16hXwd9e72UPwX5+q2AzTk4vpIb6r/5uheCEb3MQU+KIOKMEDrZXXF1+4zNga7zX/Cb6WzFjaRivl23lRmmMVzfI6wYozHVKkuODMA3aQFjbuXc3lNaM+l+nSdlY9oehsQT/i+FtfF+5OBxDl9FNckquz8cr6jPsZJ+gztRU1eM9ZAGy2GK0lNMU1QKSKNrRhVFK2zjnK2AVeNb7MTXVqTLkP4uJAO0e87tdqxdLyUSydm8oFec2IehxgrUfeNhNQAABAASURBVBqMH8bl5h+7rD2Lz8tClBIxFJFDGvpNGyTpp3BGgBRaC/AbWdITXtMYzXlG215R06oGQPG1iIRlmwSL5sD7fzpW9NNoTWyQiLC3vG6KQJyUkOfrPBAfrus1KJp2N7o+mCpV0DNRcxFqBzV7zhbZElEbx60d4+KTdNrChMY1O8kH7/Kjt96wgKpk+SzNoasvhQOmvPnTsg7N4tSVgueCQ0GewHihz8pe5LqM1wjA7tZ6syd62+wWxwolrhN+HN+riulnPJWnNwiPDqRgdPdI6KDu+5cGdHGqh+pQ2TkyxW5Cp/0x/7o0xsRYwWjmHI4Wo+pO1NFBHI+JWIarwBNV1g2xLBdK5yyju5mpR71MD2rk5Bx7Drd651YLuMtMJIYdsxQ8XOQ1vMrU8r41VUL6UNF9iMs9Kvhtnu0t0SBoaj42H/yplQPzV2/WoyQpNBPfS0KtRD8ncdhHApo+I8OHuivRuWnx3SQdjoJG/slry32/AR3ynBFJyAZMjCjgNgglZC4txpY7w+BTXidORUmx03fi2riloexFEWkkZMKpctLoURbAV8KyciZ6iJw5rsc0ybtFn4y7hEPMleOC2QMkJGzS1Wror2uaoPAQpiCcPkrOuNWwCy92/x175MnarWOaTz/Vh17aj4zPY4OFV4AaRBr6FgbNh7XaHKPhOWt0hSbjvPcLA6Owhc9KTT3SrMLIko5MG8jjjlsjgBGZCkwDHrN0BPUcytYEZ5nFWrGusIiJdZTvunbelZeVh4lcs5tvrLeNYyxh2XI9ciWke1TIKS2NrYLSKB5OK8ndAuO0aY1W9M94QX0M7rqleVZst+7kPjLpZciu7H57s8pGzXsiqXV7zmqQym8P4G02ZpCnhBWmkG+3ePm42q/0lXr095n2YhWzxs/YUcpy/O3dwZeLo6Y9M6Ge2k4dIMWAmcB2TebI6aZtUAmXVsUvkg+YIQhaCUfNQ96n1CzEJCYrz+5nNNNsnJrZEkAOu41vIl29KPqyfZm+RcmFIl8MDc+W8gTVPK/mR5Es2qfmzlfRNluiXjU2xAA1inn7aizKhtQ0Kk0Aa0TcA3zBL6/NOy16sW/UpTMmOiivfmwNXhodGrEpghzki5kwrath8rUnv35qnXX5JSVMzFhrUEPBX8kY0+heyrxy4RUOe414zlXjGDfwZYKvWLK4dUALDEQzZnzEuXPMlP+eErZ1jd1D0W11l93vBkUfWhOdFZxoF2dNAmwA9BXu9Cq1uJEoRgb1AAGvDxUvbqPaP1ni2srUfUrsFXxcz0dWAol6K81tLrb6gEbZmBnbu6enUn4vQuo1gnq1qVr8L9j00p1juqQ8b4ctacbxdwmKX1rvd+CoEARTZv8MCEHmzEAhDMZYYku82zhKfi5X2ghA7nfrUcQ+szHQxoN9WzoHYRopIprWcO1v8MJ9o8Rboge9PXM1DBwO8I59wxp9bXyLD7cXQlXlmtQKt+rcvdfJeJy50tTU1Eu7jC1hAgUK4z0GXMMf40ftqEwDktKUKgvd1Wx6STF+u39gkQvno53kLKXKnFLE0n7tU3lleSlGjBGzLC8rpRdtM+aQmfbcyOxGD01C+8m6gRDiUCpP96+WOEptQ3RUOc00jvGUGksxNrzrsr08FxtlU9aD8ifFDeVl6Rco3IoPnFxVtzqh9l4wSWLC4Bbk6rp3/ui81pk8IStgK2ODGHWKh5ZNDdbKTLOa6EPW2OXzsY4Z6KpI+YF41IeD7p5I++m4EFJGAWqIZ1/ACtmA5ljjMt4XroXQIN0C8YMWytE91J6dcxovGEgxhLXp75+wCnKo3KsHrgO9pJLFMtS4HY9HaUv2rumsFp9WfU/+sH5mXISSG6g8eLaujlKgtCzSKBcAB2ZQH5DTUSIPpoumchIey7KiV/jcQn6TV0OMSMoP1FaeXrvW1p4y5eNwmsIlk7ILWI7tsSqaYpUcfcjOTH3RuDXuAt/+MApGdSRkkfGWOlVpVEkr1Sy+Syckrp7B5OAoCV6aRIWnxPqbIzuDx/tcWqO+jm1Mv2ZdMHP6Ze7QqYp84gwRvbxh4IvTRk3XwpYp6SP5CpVGTA1SkWm/BRzD8JXgFcd8RhpNj6aNAbIVXkYS66rP3aJLNYQuz3INUYn1pWQxiXIfrnEiaMFlvtopixzN+1Mp1ISlkNsrxMjqK7AMgvnSi6ISMKeVJpvjaNFSCZF1TS+Tcsxl2mGh1C5C3/kHJQ2vopLmfMyepfXL6hYWA40yrnp1RdNRDXRwPuDF8HC7L96mDYRK9l71ibMUiRZm12b01FHfpk5BmTBe92X/Yn/sa50jWy125/8dv76B7PHTP9NaOGd0A338eZPtysToYokgrfXZ9jJ3krL93stMLUVaV62fucOdRg78QT/smuuej67ZdNo+vrW294fCXEqb6EHSVRUEqYRrwsnu7LB0T6zXo6c62Zt1oiFV8lil5qFdU1R/on1cIkAaS0xHWTyJLTt5cM48UzlmT9zb9Jjqpn/81h0bA+fCc5dmDSATdYW65Pj0lOpr1mkWJGwd7JiizPpJ87KPQucGM0fnqdKeT9dKReJcy9ViMFYcfWGlbZYpQLRxxg7jcKlg1Hurd0+KTJVdTx/oscfnlHYnJktJlZRy/LnNlyAOpyHaqis1DQs0SdJFTeb5omW0Ziq4w6SUxYkaeSaoaWADOZlxKhSF5KEpQkgeYfJoyjr+hqZoHpM3Mlccf8vBLSASJh/ddOypbVMmlS4jb46TwP3Ez1m3LGEU2pk3tRfbsMH/WpnR0vSD51+eNeNNVnWeqbbjoxu93/scZldzDx/vDqDV7C11+68hhabLXNq04BZ9dxyYEuhRa9Vb10xRRR9cM3dD3dJrQnkDhlni1prKTb1LYt6TH6iRHF3byLRdV2LRF4OjoDBBuhtXwEcEuy6XRpB+AUqNzorKKWFammc0W2OXSB+eg36qOwVR0ZwbGZWVJ48opHq4XOgAHKba/oHFyKDVeLUR8rTPvc/qo/czq+Z+4FImQOjmUtmJVwjsmfrj1Nsj42qJ/bIVAymvxZjJhRnpeGMaU9KGN3i7LffQ7pgrqVb8qry0PxZakQr7cfy7eMkM1LL6TVgDZfK/rENLtBabc6Lrbk7azt71TCc5cgjlaC+xM5qlH6paXTopX4VBJVLFNUZbsrmtH7ROmgL8nLmeikREefSr6daocbS9QYAIjLMGHHjXVaoi41kzykUZJFVMLqq8ed9YITdiz5XvhpOUG4N+KxonPsDqtrE0F/wKfTjK5Bl7xr6KDkqPrNwThij6NnSjtcyfnNPT7HEM26xp2cDhNfFBLPINwOm+wDA5ndHkF26noQq2VY5etnIt82uNywVRHLGby0cBjN1d1/N4CiUWLMJ40YxDXR8gWw8jOicEhrLMfquUNvBsK6CjjloR5mreKW8CDfI99Fh6ARYuaX4Zu02NO/UVV1whcWM5Mda5Wjw4/aeXdvs+mIaivtT2231GFvflaVePYEAXS9UuNDgzCQgkSwK4OB/OBThJowXs5lJaZaB9+zqa1k+f5rnW0bORwI25NMLlUFAmNN9aVXzVjJw+P6vMt4jv6YG7v4/yQVjO2hGj+davEw9FICulgU5cIb7bKC7D/TITpTWTRK83Egvexpsv23ojQugcru6l0oOYh4a33rDpqMAOrVY240a7EIJATMhcHTzZomIcpqFAgWpDL417eOhlodcC7GR/Wx3R9TBFzN5sM5U4bFFBtKyCoGotRe/gvUxMVX61I5wT+F73ic9L+tBcagnW2AioklhlPSN13z2fUGi1+hVWtTrwepZt2Gk9OosKOUGArjiG4jhjHbMsptzhmflqL7emL40x/TQ/R0NCJTGQ/xQP96Jfwqjh9q14jr89EhkUSwAh4F1H6GBRa52rT3wPvlL3OA/8KT6VfwYbja3+sSl97GV5oSM5hdzoVDqgafYWvKddf42zonCJlV5Q4CdNlM0a1UJBzgdd3I+fXmn3ePiyjzIRF02LfKR8mflyESE0XVrcejuNEsKs2h9LKophqkxuw8dNMkvaF+u37hxNjPDb3ejg3TJrprNvL0WRMwJmDQ2q28ooVpzk33hASwO2nWNP2amb5ddp66MlUoe2+6QylSE3YABX9To1M+Ryu26a/tVCrmoHHPQr434YUoLdX/NN6jj3pdLUOhIXYJg5pW66vqM7v8mzZ8sXXKgilOwpZnKACYf2LbfrkcMn5qdaXVwXSJgwNFhmcnW0wHmvss8uP3vrJ3qo5q+Sf4gHdtT+whnp6wRFdOzdqKOl6Fqm4CaYFwuE0y1Ayj/mdUA21fjzy1YByTNAranJ7nGMCukFY2Pq3nmypl+9/NZ8BeLN2OVQF1x/y4AsnvQcuQKkOQlrHSCv7p8qn9qNPFzlN2hTc4pfw449s63SUVosICIvSBanUgr+nbMtQQ/7LFNRMCBeYOWmgKVap87/rFghmi/yEu6WX6F8tMTi62YaU1yHyhpq0e+5XVpWCr1UXhBUiIfBJWO8B14jVE1KB4FluMwgrDxfk7jFwrTyWba+cX+DDV4j9Dkt3DX9ftRy9jF8FixE3e/UMQXqqZwNtGBqbWK+QJZfdn/sXk7RwwuI1VQqq7WK7f7Ue44mWhwjcovoKvR/3r/meQUu9bmbE41RG2jupUAjjbssy1l0pbTcj09fLTdBc1zBpW+Sfva4usnfBZpJN7foNE7XOWefwiHM4DYzzORO/CaeZ04fj+cKZdnEwKfx6Vm71CSgSy0NAVgVd/7cI4Vxu3cYL74lc7GdG5gY8wkepWT8GctDSA8+KM5x2ZiXkkr0b/Loj0wngoTrx6xpNBF4uX6345Sd8ubwlcrcuqhxyOZXCucVCB/h0OKhUD/hzntUSrD8o7ijc2GUQLZBmWsMWxAnepmqr2rW4HZEyPcQogbvLCs9BD0Z9SV+e57W6IBQ0Q0SOd9MpBAHVGTFU9rMzdH8Lv766DnYs87goCTjHXHGT36RKpbnhQyp0wsxEH5c3q++4rTl6hPW5t13vVFijO5r8TYhPyYHpXWINRgQS1VckyOmmGWyRy7a+/yBIGl4y+HVYvwt8Lw87AfzmbxmVBIUAGOZrMWMOGy/Cj19ZBZWfW0wAdVXNPRg9Gp5zbl/y75npp9Q/fNrJqDiTiG8ZzXB39jsfUMG5BhBfcJI+ma9RE1PGmFYc0J1NWOiU3lA0zaklEyCHkuzGQBLOEDUZ839yVpZYtE1H2oNVfwPytxmtbeSHGfugm7GLxYp7r/At6u3PuDzYIEtic+GpZtWE9FQokx5KZ+vjWI76gwKgs4yZg6/rcdeHIEB1CTmGSYBmOihQH5urBjD9vkP/AqxmZ4F3RamfoXVKh4OV4uBjny1zYmWLT+IhK4lmTbdnXo9KeGo0aJO5/9laUXiMXvAQ9eY6WzWzJt8Hxp9pCevB/MdjpSPKWVUo7/3dT6XMMqdf2dtIAl2Xnk+3YvmaigSkT/5bXp0AAAQAElEQVSI8lBVqbdZ7Tp5Rlz9tAitJWl0K7AzECkwUxKdc1n8hK/s2HXl2AG/M78zZwbGd5jAcb4cuK9Qj/qhGjkvtlrsZZLP9lSrELCnIO6WlHrxun31zW+sB6o4uzFRUE+Zs7yE0izgtxCP8Po8PGINKRVgsciswRlJCpJQ4Cy0B2WnPnW0cYgpE737YZ7VZVgVn80lZcxpdIvFaZMEVkNry0WGaWCc9NLqpC/oUtqVagkY+YnPtX70wOgpx67b97p22POZJ442j+dG/kPfOLTcY/8d25lW/73kdaTK/0rxvJghv6UQqtb0D2fZqACyTN11f1q3keKfcIBqme8u4Q34WP/68+D0G3ZYmkLojk6bzV3zQC+TsB2++fk/aGN0lXy9+MEpq+ZIJ9pPS2U2dY0tLBR35xYXUFXWhXEMCQOcaq59Wta/1LZGDaqId6QyD9lqO/Baq99Mr8JVdJh+uBC3jd8D9aWljfIz1V5cPc5yW4aP923pdhhoipULdZNXL5uA6q2HCZB2Jz/NRQ/lwVxzl4NuX8avIHJIhKnL+ZXmaQl+fR52lYIbL/nsQc70nsye84pHbZkH4c917/hpjxYIAP0eBOqdkVNjAzOFRTRRcAklzMq40Y0HxAdr5c2imaQC84mSLv5jJjoc99JdQimakLznI44GUNOaIsWQ9WfVHcNYLVnx/h1vGjA4gGKQM29XFDCh6XsqTGlo2B7bfZ/5PH49Ym029hEBLniJNSphmHe9Bp1vR7t2+aOwUsO+y1LZC7iPsUoHpl1cnbRMsmMd5fGzzTR8UBz2QqpM0DMw87gMLljruKv3N0o06wxDTctWjkmzaj+Wbda8Mj+pCBbHTmOGCI4qj0mba2LZpVM2dMVLS5vHchqi4upAX5pouqRTYjDTnesSxxSZXd66C2h0wWjxcf4bsQC9JL8jcz/Fa8xlRCkm0e4VJvNK9FS/Vkckzp59sBUNOshlHVDhPWm5VUjdbOHVzFxKwbfxVJTGqPMZ3bDQd6dEbSyuXJaaTztut7LN54Pnw47gWi7yOYw21MKmpxphzVHV7bJSmYETOffhAoyAftXbJk2ORIc6J58W2D7TRdABvhFh4PB7XDoI2xbZd4Ezc1qV0HKXQqaruohDTEMpbjdtxeYidIRO0ltv+1Ibk5epmZYRSFb3ad3DUYmEQUGPl3ZhR0p6AHGFq6jZHerJ3zuPJyWIbuU7KDK23LNHJsWocB7/nNvzrdht/r39+YgR9Sr2UeGqGmDuVqEZo4TgBTJvSOgxvobzbrVhiICdVfuMAAkk77qibM5o4Wt4hR+PbVe6izDz4d/LyEAhu3ft7Cx5CcJDuPVx5MbNOFY9jyJa2ZZl+l009a++srzVlXmuy/gveN3k5aB1ddeBlVLqPSvDy3E4yaYEwh3Sum3aNn30XBdmQUGfPLV3dC6vpZjRmGLpD7+LauQIkQ8f6JRtaC+hlI2mWpoXY8agIuoeakjVtHYSST9hx5huOg75HfGDGdXxUWbWu+t5k1tlKAvV/sW9NP4tcv25jji9lK047RnsRSGTOsQPWpO24htWp85bwC8JbHp8yufotCrYisw1efUYQH3qNxnal95dYl45awvjZbjI7reVlCMka2lTEWuSk1GjfjwH1A8SAOnupuduP7YuCxmHfAqJLtaghVTe5B4W+xPPipbsz9KTttnT2VPz2jXL0HJGI6Bp5Cy3tRgrxfi8a87tvaIYby5FIQknQyMBRaXosRHdbzqdYUMye6UYKwi4HIOy/vY4zjT5KDVie2wmBhdgTuVJ08CkpxlJW6AouCW41KxDL7uOr9TIaXTBNKbgG3NMl9O0cqzQn23r6wtr+U21rLRiPyv3qqLPOAXXTPTbOyJ+8opefSTfXZMNDszHb/NlBGlt1trO34SWZmhXuaatYHnJyqpBZK+ZH19Lm9A1y/YV6Q5tcRThggWvUT9UG+yMGlkVx/7TXBQcfT4a7T+BS84U2+nz5tKZMiJluBnEhggHG25Q9dgF3pgohYM1XDZmB6gewAIRUDkZdSVV0UTx3nPt9iYcHvVj1CAyujy3KgOGFEOKf3Pg2sON9VRlV2Zt5A6dxlvfu98Vrlv+yKTEI6/l4kKldjSC4+amAOeq9FM27e/pXVrEudO7uFCfKLfOYh6f1Jp2u7iCFdHCEJ3XqE/AxgYsoB0VKXMDkPJwKewH/JpXOEPnalSy67RmKNSxfhHbdM18G78PH626rztTj7yfus8gz1NHD5kSPJhqlGok37ReYgdVFv0665HWDYTZOJQvXuL+ajrWS05LvkdlkzEJ2xNqbdn2hjJY3UrhhFuYg+o+YraqVfbX960wZFYdpdC2TZEFDq2dfaEC2b4rnnG5U1AbS8t2s+WoLWvxuMotYIHxKueY8aJ5FpW+3kit+5PNYkDysDZv/YWFwovCdebO1uOfRZNvJXnY1KspVCV0UOgveifNQmHX6Gz3NvuCD6zb6lDHSRzMTp6z8lxGldTcpqXq4CUx3U/wqTnPuJQJiaFVE3cOnq5T1QA5q9P6TRH84vVxBVUJJMIhczd39DEIaGsW5us4Qju0gYqCJns1G/aEfZUW20a1EeFEQtyliYsgHKiu3S3EAAZixWrh3L35zJTXXm8aTnShW23la7pFUv/p4WNzRnNk5rrmjnetXIMYM+HwbqINz+oTJWQs8aZMl+Xl5570N5Xm5n5Ds4ZDwEDklq8X6Zf1GKDEINSYwIbJ5QbHbEx45o1UKvPI10kva1+vlXT03Sp0MeV/5HHlIuTKD6CVckxs2vW0lyDplCM9D89G7YF0yQP/dTsXmh6z7jIVjmehmme8EGrL7FwvEqA4bcEXGqd6suflh3v4bv1qKPRXprM0WWLg7XY5QzFTYY6kOTI3821susY7FIdwwZYyKyJW+moPb6qPrFtDBnqthGxDxJW6LUxTNH0RQfrm+sb6tatuL5eQSkQSftkKY7AWZXWXny+fRJVP2luMunrFol2gcTANzLRXewpnYdRm3PJ6cp5WO22fvpGOwqsAuIolyeooTWmvydSuBAdThks7eszd09kber3mCALX2A0tAqH+9viUor3hUUASHYylWMLPD/NiQU1+nbRjo+rSZSh+lMUx6wGFeOAYb7zU1er0RwkGalRyh62+5nfFz7ntm+dIYjDTFGs4K6xqTfECRGgaH0oQ7wj9HOr52xowZq4ZvQu5j8T1fWzr2cpOdmFhX3fGB+EU+dXiR2QqosqD81Cl5K9VDLvZu2w/kTtsRg9N++I21En71tWm+Da1iSyIPDXJe7adrGpdcvDT9oIgFTsngTTF0befFQ+p5zeIuvLcz7E6cxPLId4L85dEILDH9HR/txDUtWDEGR1aUWr6QGLT9khd/Pzm8+19Yvpmf7rUT1WLMReXzYIUYJC75hB6GUjoV5S1bm7VfebpKksVgh44TVvlw/4KXpzouVCERLmPWHwqtort0FJGDLojRFBenjU7FEq31q++Rwm1IG7O5R2WYBM+E0pj1TDTWGN2xC/j4voE3FWRY7xpomMC5Thmwq3gIQtK/uXjNX+QLjb4/ltQg9ADioSbDC34mvOJ34hdFJ9f02Orb802e5ZtytSMcj+cSnQCDUf8v7o+41dRa2p1/0+goCSbtpubnQ5YW7e4e/lV6EIlNJjJwjQGHg5eQ8GGl4bz2L3vT64fV4seLuqwS9x0sQTrTIzfLfHbj+Zj8Ggv9nR/t1WKFla8eAP8qVlv6QKome+gkMMn1/dZv7ue83SmldPXWa4KRx7f6T0gC+to8ugt5q5z7TO/2oKIF7QvLoW+zvjgBf4ppeOS+iIlqr65Zu7vxDernueo2zQfMDU4O77dKmRV7pyEUoy5gtrr3cn4Lj5BEN6QZHnWyltOtAj0NEVcPTW/gCrZ1u23oKBCoYLi44GnzfixebySJeXksufHzsf7nrelY5ecqj3dsitulIKbL8euE585lzeXP7aO0kJvaO1f3e5Haj/IjS/7cS08w0NsxUwVKdGRx7rggw9wqUjuM58aGwj3fphfDZ9uZQ0vXEu/H/ov7Rs6pImJp6O0kxJZK0fS6VobVlQvHc5c2dd5RadSuHwHsyV9xbZao9AtEsZDNRt8CWnc89aqrTihZGtn5GZLo/hwPSQu2M23Nue6Jz5jSgP63vY5fo2au3I0o4zAlJeSh9m0xBpVkEWERGwAsmtyrBZrqfpP6anIt++cS/V5X/O55zYsSTFHA/VD5Sgfy4NWfP3yNu9SXYJ2fdfn4cpkHaJpIse4L24H2E5z3hX3EUQ3t1st0jO0t5rjBkmDWm7De6byiHOVuSkxHLaWWItf8Au1SDSMqbpoPsM91n173pOxW+/ba0wOl/c15ahgL5mYMjvL+gosJmqL+p545n6wJDBs4/ea57bDqAfxQFeiIQ6zmQ2McF7e8OfYb9tmtN0Y7dLp9Y4DkJT9K67b1lKvlKbFFMxbc20gKyU7o/bnbT40CfUq3E1OEBZWsZJXqzOPE0qanUKNEq/rEoXGtncOoeM9Cw1VZr1dy6LgDSRTr2XucxHepozsow1EiSB8mjfdYvny6+J9KRWwqWfmCXVt0T/iD38hKYmTxSpB4R24fbjAK9blyxgOxcsWW+vKa+nDQuuLqc/Gtcty7+kcfwPPw/2FTkCYnDacM3VEKwyj6r5orp97Jv7OdiPLnlq/popi9ddMtyMIt48maFH/7p1I2Eg1mfju0VpZj/DOkH3hCI23IX2kiU+vC9Y20m+/vXffIYMphbe0n+2sdZVSMhLHWJ91Hv9unNNjcDO7sNR7P/V+yXWRLoQHmXwn8DRaajFlxp/0pPGauORZ15XMjthqac+9Us0Ak9mZWZhZYjCJvfK5+nHmXtf3/jzmu/OUxnSBODf3ki1z4GPp2PKp8lFA8+T8dn/R6W7L7gtSIXlXqy0BBiV1SjhLBCrMLXX0rK07BUgqtqha/Y0uQxuJ1VQztDMP8H9uqlGl7TbJ2owqT1mC6Z/2SxVP42tdKu1p20/1/oq+K/yWzGijJC9aaCdRdnLO03M6GkZA5lwXL764RrV5c+6xOKbUVp2ZWWOfWvebvhXaoOT6b/o3SgmTAr1qvv/lfmY3kqZ08l2n9lvdvqeHdl2+gjMyJ8YJ0Jv2/jm4p5yFNXt0ZJfKrUlZOtAeo/P5r87v5LNP7YKOPuS2yU/l55RXTSGhX9Oo4nuqZ7DcGNPKptZN67m7LmynaI/zrgAoG8xCrskb+YP+LMyN/bETPgJZy1/lAV7hO65ujw3WAI2FlxF3tBpwqmgs30vcclluda3RPdWldQT7Ji5VfTFHKi20W3+4X68O6t3POdpqoIS7Jenn7FbpNZzyo4NEUFV669QIk7Kqld/5VQQYXfRzZXaOMLTIDH2+nxFFIaz0nd2Pevh/+Bz8XGM/yAtbahuOG4Qk8LCfYXnM/albpXgN75po5Ew6PVRyi7jb1jEVs0P+oHZRgKL3+6ZxOwAAEABJREFUtp7I8VR2ANVT3kUbXRTCK/EpOZK+JT/WKcvt8ohMkw8MSj8kXXPqZCp1i2XuZ+5T701QiyGUGK5acwLK4ATGWPCu1U/MYybhVMVs+Lf9hd5TPa3qCsmqdSlNtwB5sv3AARFYBERfPaytd6OKRATcS9bP39yrlcQKaijfKcpDw3RZiXm7caObWXVmNwRqdXxgzlkjaHWRl90u3uqCBKeClydNsNI2KMFR2CYTz5nJCTvuo7One86EgurgXKd9U+XRx+tEn8/0KuCY0r9rHuWglnEhLmEd9gKWveaBJttdR9oM9LLeUaUkdr40Go1hbVYaOOjK7DRCAmmB63dSEXW9b14KzBA3wtRErwdLzQ8xCtnC3fyCGYhw/SWamDDZd8CnvZXthB9t0OAdhTiLWjUQrRYHjR6oWuwZq6K3BaFZZxVZQiSIhAeXACyMSAaEtl9NuERzK31aTNzL4RKDdGpDH5kxASqBviC/g92Zu3BKC4T1iHUhv2TC3YxCk+zptkaVacvzkHkZ7rGl2mnBImaQLdEqWa7IhUuDCqlirtYVgchYdWvxcY31MRF1kRf7Ujh4hSRrF8JAgkIITmdBfbEDvtqXOKoM2/5pqIMZmt3aOkiy3eqKJyQMy4qT4nXAik7VDU8fSMiH154oe+3Ta8W4M322Vg/q7BWjdEIMzYwSbqxb+JxmUR55Ua0IlrBqyA9ys/Ztc5nJMyRcpcgGmG6ko2/YNW5EontSfyI5dVvfgXDrVohTgy+oQGumV+OypALwBsP7cmEGCjl+RrHw6b8aZ0gUb8yBWnW4H+aJUUVPmEicsoQrXFVBROnQpRZwRPGStZHURyMJyHFL7S0We8hS56etwZqJrKlqjE0zC5JKNadfSDxgCr+KXidahugZ1F5py3nZa4sQtxwNf1WUMq4znAAEB9cbg/yjzYaAftsqu3rdlIrGK1X+NVPCcTeuGucYFZjm0iXL1losnjwa9kAEdVNQEqu4mmHLuTDLEKQcHOM3WkkfpAk8WkzhcX+Dh9JWi+1GYa7so67SrZm+HM5cXZ5lp3zGoTHI/mNjDt7Od4QVL+s9I+RW847Rr6R8VtKqpe82TW/ZEEddQqKMYxSS2gblWjZpsGXm9lJrSIrqq/r24oUKT9NcFQHQANJwKJVP1+4tTkq589EpZPFw0mqifMF9YC202fChrMgysaFe49nWFiOOqWmq3zPdEh/K2VDwq9O6CE1UYN4M0TVaFi+1T5nzhDSIkWm1EipBgFrRdTM+H/eVzTN2yHJNMM4ItI27SNF7ajRnQUxAgRvSoyI85uNnWaFLcjS+ZSHoqWbMGMMIlCydEVmIMlfk8E5XGmQkhH9UvfTyeG57FisKpkAKkN8uP3q8VZ4D+oT5aq1VGazr6vfmwsw1JT/7rNQg6xcwbUgKpJcpFLJDBK151HkEX5cUuy0qojQUnKUe+FlzaaLZ+NW2mhwOzCqukcWR7F5S3C8Ph93Q3STW/MllqM4hbPLZhz1lkKCPoVUDQZFmWkl0N/4z1PYDhWnqtwWXKfwD3SlUu+bb5ipo5XPykxHvrpwrFELCMnHKBqvHp+QoRz2mx4oaDkdRgohDTTOt4GWyOW5hdg/C2vzM0nak3GgwjJhKeKUJ8JqI8x4YFZoqS7eeZAJgxiv1Hm9YwRpdKaoBcCCX0g5E54Qm3vMKHTnSar9MaPpLZOpG6Q2sgqMDI96xarxkLpHtgJ59KwstM+6XJrF7VLzV1fLLYcsBPZp6L/VDKBmusup1XlwYaat6cpQITUM3HfShH6CSXeH2uhHFLHRhamksu23ynVz35OUQaXKmp4BlxFrGYPUwbcnWAM0zflZM+HR3SuXb0QF3vO6KmgfBnMakM2/vv8UdlClbxPjkTDVz42/fq6TIwJuPUbTosc7AXzW12R7LE1lEAzaInhjBVw74plLRiCsJSZlxw2L7TAeP0uhUfBpc5ZoFuCkIQ6N1B5OVKrG8zrHTvcEjc3FRMXPQbUizUjQDexhpXpfV5aN+kPdO/1Jwzdt0yoAV7ssZ78SYXBsrlQFQycEUh6M9NxbHRq8cKDU9VTXc8M2rcI+FiHwHBTIocyvfa9XskQC0GsEJ2W3LBMRklxQehGIr9CZKlSTFzh41XygXskiHBkwIZkHKFTFuKpUZcFvs4HNhi7LwjCWNjhizxmiW8lou6x7H/0xrvpdstQmUfPRDbvVGF2DjSKZq35VMsICeKUyu6UXpKqemVfh2dqg3TYYZZqQJOVN6msRrO5gKFGE7YtMfpSi7p7tljmUxIarlYIYKEqaOzmEfqzK2sOV49Zrjo4nVu3T5Jj5mIQ4Z/RIx0JoUmeJnwA2AZJjfppuzE+w55DOzN77CoLqi7CggcpZDihuYHsGk8il278XP7U7HMcm/W8HZoom/hXO9i4ARH7fPuqLoTRuxjeqsrBmErt6wa3AxLugpncEswx8Bbtks6r7jzYqBB6bTtZc9l2V/lEH8kmn0KOlXHe6V4bjIvwtqp3YuJmMMWiMdwPiTPPocCvqaYU2P0jV2VBqlFmjfjw61SG8oUz6j/M9oNdDG8bPkCiibdpp76n20biR4AeF1/d2+fqhUMPJeYTpj/otU1YWde/rHIC7IJfPtUi+mL/ZP8+exT+p2Yuy69/35emhtgV7k1+brT2JjSo824hk7o170FPR9WH112pRJUffOR9ps086wAboxFt4vu6yt55K+it09GsyLCOjHSoT+CLaPBQEHliTGvma8RZA4wAO1sPoSX7DNYVzXe0Szk1N8nWlqhUSE/Ps4Vond/a6XYl9CmgFpYRzmnD16gLXnHHwbD/H6Sp9kvQb/Nyssgsqh7YXn4hN8LvQP/MeXL5PfHVWBy/BzoF2CT1POls/HELgz48jwatyYH65/4R90qJkfSeFMpC1MaL19/1e/sqfgZWe5xiw7P8aPLKm2qBdIA0SQCS1Xql2AuKaV29C7joP7yWt4LefdWOWKdrGv7pi5dEVML/8yfoe/qHKdHZk47FplppF2qRjHxofGwvryLgErCWv/4PE+uWd/rY/LphoZQ0TrbE/2tIpBDpdpNobiQhkL2TXtGL+DhAJ5z2vozn4gv8RaNHvzVT2zUkUNrKlA0y1d96jXBnqd6z7/3C5eiDvf9d5nB75uv4PvOc/zoH4/2HcX2rKiS6KZz3/+77/75/ffWwc1DwRNOjp+5YjFDE5Dwhzp3ukZXheVP52/ylwbu2NTLhmzw33xNn5KpiunnsZJVFrnBUUJswHAk/6t+WXvN11xsFrSkghLnVPl9Dv+O/dXPulAvHV++aE9pb1m9Fj+4Hw6BefTcRHl9az5SoQ2OSJ7Dwozgyj/wP+FSYiGQQ932X743fXt/RYzazL3Ed8XqKr0FjCUli5wHHMnvEEYKdovK9pd22TBqwZ+YMmNEAIhDlYMpnV67fiUAbbB/YIErnZ1QC+z1AXhm43OhWd9yw7nMdVFWaiqfj9nX/uNQkM7yGUIleeoME21O0VdbCa0fguJoRqzO3OyqKxNNSnVWYRPQPXYf514dC+pZ1FcgbkgMVzfK/aVJyQ79tz0m9KRprac8hMMv7/5893Xc7RjoNjNtOk1cjCo06Kwr3n7nDwLpb1ybqMHGEKoZevbfajTw0o9t1L1R4vYd7PpLcxPC/C19s+lG5IMKYfnWXvfSlLOqDzGmFdt51eUFqqfVpaUaB9c8nPb/Rk96Br2A9ci9BujuBh1O0S75WUe/In5oC5sXmm3MkwjpqZEZ3EfSpt7xCEIEXn8LaQxOOgP4jdcC9isMLtw3L+2MlbcWlUklGzZsvD7hz+XRXhfyKj0Hh+ueVfNrlGevOoaus6pIbkgQCrDbnZGjOqekGZ/1vyswBV9mQ7aB/g0r3bMxXfjbz6a6TGE1gI97BGtMUb6ZGiJm0FfVT/AMkiOX+d/Ho+prf5rWxZcEZJWTxx6qiFLZJ1QvK4We8GMh3aKSp5tKFrllW85NEa9D9hAqRROR7dXGlRWSXfCpw39Vn1zekBYtN+pVqz1QipnaBzaZ3M1jZ5mRwOhoapFWuSlY2UoFzpGkb0RFSBr4z6EUneUP+LaCpqloWLJF3LN16sIyBoEpAllQk3mTD5KAuClrwLOpeXz+pW2d1XS2y96aM7bFEbuDkpJwxwSHroSUOw1Bl2Rra2xWEDIoFnonxt1S9BfCYoOV2RZs8+IqxGKCeK+cMe4qkJflu1fFa71BEiu8dtCjmpWdVBOF2wVI82Jv59QLuf0mdpztu1eB2u4MCwFIn4ILKUSegmuBGq3EqEzpWB8xseXX/ct7fr1WdflviSljdj8gDdjSXe1aluOrMdWvMNmN1EL7VZ29RW4GzZ587nXf65GQh6CMRagrujqC08ONYcaV8toovGmyvDxPJgHksdww3/1eyYmw5JRCKUe0vRgFHLnmrmmgjXqlRxcHIekyTJbrfXJu9a3I+fCBIcG4bbX/IFVuP/E9c9DJTU/ai+n8Tv1nNrCcJaguQYDmtjLenWj89SkvBhodQSY38oH3U4SN3BhOW/wLoqpAn/DvhfLVXLDaig3epohRJhBD4A2NFnk8H2Vws757tTShPiF/ebsZ6h3lS7lzYveXFo99ylJfYk8fHZ656DEXOLqWQke85phQn/oTX0/eS9I1qia6m4pqFRZbfowq21teopEZc1hFDjHu5pelu8njL+hbGGd9/JwNMYHTIu5Fj/EbWf5mWun7mTkgsyezBlr2ABlWkwgZZgVm9jLEZJ2XM2rxsZaRNpNX2bNObZTxFMIlrIdPvfYJaJj5fIN64f6E0M3Kh4C013VB96hsd46Y1IAliFofbm0A9cE0CiC3gSg3PWjtVcsl4K1XFaOE8iwke0OeDDMflSCQ/Yoga6QWNAiP+p1j4JN02SiwSqb8abBRo2aix4doxc1trQSsIhcgc9nbm/XVmm0bqT73OYh18+n2+XVZSMbYmJvq1mimRMq+r3rnM5kZ6tobIuzXlIdMzhgpJoI7DLwMkXRx/kdfLOzenK667F8Lp1XOqKhbrlv1RwAhTcN1EIqlt6tPblyWaJQAReyMPrp6ujyKrUowsllqn0tvwAeCbIVPl4atjGdba9hMQOLB3FAqIpn6y9pZhGLqkmGIUh/Z05BRJUxJQ5FR1uv2sqc+cYrQCLDpHhvKA/zlckSOyNX1UzNHgbXPVrJ1MJySsvYwNwZVqTFvuLcntHSj2ZNme601VgUWhB/KXdMgO5i1Wpp5D3CasI/XTPg2OVzoY0H70t23Xwp5cqFjeDSlfaKM711mFrI5MK4bAACnAf8Qguf6wPKZQtCNIrQr4K986WLl0IMjmraAVaglWgT5hKCO+ixhutkfvN5tMeIsVs9XuLlzKrnrmMntf9mCXliigoAKLCJ13yaDcJowNvx1Ed/JTCzvpZqEzHqfr8226C+OKVB3T1mMAfFiYEBM2MHn+Vbmqbquz7J1av9s81VGqv7mlBpgGwAABAASURBVJ6a0quLq3CVlKRN6nFIagrftSso9mAzSubfw4aF+b14fix/vG4kfKcO68L70gJwR10DPKzHKZ6bMrM921tRvWEK66I+JKXBsnXkhkLMoAE+Gp+Dp3GOUM6ZSSK9lUVzOm0GAiYK0MoD1GdHbwa2qUEYcogxUEQr1pslq2flvnP8TCdPdTHpvZbHWmZC+UQJo3S3hHWa8jKUJ+EY/5pL5dG5dvM4KiQ37no7GRQuyeV0J7gK15GlV0wn0W3TCrlhIjxTX+HkjFfx5N79PxMCCxAFarhEq2oEfSCBcQRJ1dwz3zfVlAtMddY5WVMQ9igNHsw0OAl/xn71Zh6ZNA1Rj1MZr0EoqGx8S9uJT82l+ARN+frBuJ2+vvXZEz28Um1i7EA5RiXPE3m0+jNS9To2PopUC4OjxQO+r793NCGips956gxnuXJmyMnNfcS+HbOj+veXNsWM940N9LFacU1WFs4omeyk9Gd6vpGkZdtJkdnmo3hhIgvJ2fH+ri71ZHk3tFLf8Jwr6QOboY3OvRVwwiS1DqMqhdVdAypehe/uF8LhtsKv5X3Fw5W5qikD9NRAvEDn/dUKqlMGWmo9ZU+tnVee+U4d4hn0m5wBrSjil5eFyYlROhKhk219GQTE67Vu4iub5wVocjE86V+lU61G0rU2cin9bBXDS7oM4kPTfVeUD13LzbrNVPn2KragSoAX+tgjYwYFiz1zppRB46iCFR0MTReEGUvtINqmCqXBDSPshjYYDccBM5GT4wdXggOhHxliiGf9nVH2Crpkmkf7afk3fRSWmJ7DOc7mQpmEyYyLH1f/fJ8pVCnKz+CD0RHeDAwFzr0zXJ0XDRF7PzBp8VkQORJtt55q2QKvYRRMg2a6DMcFtqzTsywNvSYMPrNOc1hGMdnYValKVfUAHUyl3i6neQKFGW8P7W7BHiQHhLynoxV5z0IGjmGwDHENTGsiX37X473tbDyZNSks6gmnu3zcNU+GWa9MZNaUV/2YPNF6+uSp0xjl+15m96g/l+uMpgv5i8XGTOIienVF0D8R12VrUThmrrGAtyJ8C9qa3ZPZ44gQCUqC1qNqRbOvPq8kR/5Y9DQ3WjRQKXbALYuXoFV0W1LeyoOk5diBbUf6ouZ0aZroExNr+GlFWoVqV/rardDF0go7t2ZTkgsl9Y9l9Lktb8+lcjVT69CtyJF9ZV79kyirorJUag05Wpe9YT0U3o4AHcTfy0qLnXEAhXmJdn4b/1XvRubIwk4lViKYadpj9jW2Yn14PbyP3QIKHyXjT6he7m0yNwPFeqigUUcJeB3KUjQvqgWtFH0KyZfj8lSfIczz6LHNVbNAgwA1XQT064O0tqt61UDYh3LiTiXxlpS2UXdmSWVMcfdVo25tWnshBkFywmp1sqe4iIXXBxkUV9zgDjy67rLyZbXtyWO6QagKG4Yrw8a4LuiWTHKHQHSaPW67WalEKxNDpvmjOFDLnyu+Dt0zPhft32XXjb6hr3G+FIMzhLbPuXwuxQqA+nc3/h1eO7QfUGuMB2svnivGApLDjG3M+79hXM/4g07/Rmj3Ah+b7dRZwPQPTmWFZuNR4dtsz4hzHVBq5qDLhEE9YLEu5a306fi7DEo0MEWYkAIy8+iN1vQHd2IpWdFYBldYl62AbnPiM/1ZO/wZg7CQZ/W+e+xE7U+ee3dsYAtMf6jWBZ4bFuvD699cbpLmHEij1nAZOrhQOmI4pWM6taA6oEn7s979oX9Lz9V247yzgVHTGnNysWOSsqeIZU+a+RyLDJbPQDxM9TSPsndNh9Lxne/TT3YOmu1SQL4SLKjy2artkE1QbOoXCZCWy7SEFdS1ns9WX4m2OBK0BXFFcSldcgbFBqVHKFHAGTz9P1/MmXNVXxj31PIpv/usJ+1R1m+OLsQosin0mdLUPXEkMEcvKNbbqcdl9yiMpeeBZ5/OkkMs8Ibu/cyaF+2mFp3Jtz7G+y9DbFK8C6jFHuvUCKyov/9WcKl+RHZLLvvL879qmban8J2pwpIovxc63qSRA9NpZDHH7AOotaMxIGjowZyJ5/k8FU3YPbF09eKzZvc86YU17/8P5UFZ7eiyGN4txv+Mgx9FA9e/DJnFFDLxoHodIPXBKoEaE8HzO7rt3F0ZPW+c2tsp9NxlGytfThqs8SnTgiZeBH4rXT/gT601dMDA1eveK96O0DVHBk+F84oVnzX/rpRAjZimQrQAroZl93QKJMMR1q83qApHA4YarMojH/jDlXFrxqypq7eu53KJ+YRSKIeTo+EreHn4LDym2Hh6po/NIQeMdI7x9Z/5isT2JtJNVcCgVhodVHn+IBc7rR8/x5qlgiBbRPLsdUpAgpDM6zrfVWkFsTlvvRjFh6BDec/NfnRfr81H7MCOVr3IDKE3T5kggwQnPD3OWn0FwqZ2n99s7Wpb65EWWawUV4BchkNKlWTVH3sNfEPdijWJenLqiCDcF3gBr3xkX27XyVvIk6pj7J+qexseV1yzlt+2eGK+Pd8jO5M3nw9/17V1g22iR+T1AQWCw+ncWTunWzDS5mtdwPgbi4vmDOopkwbUPhqMabyXqDRjTzW29/Ox7dq3ZOtzAqoMIy7KtT7Zt9cuV528ByuQfu+6n1pHQjQMinXe4xl+lxB3Rb6ent3SXm9fZaZhrqbRiq/Hjyw2Yv404ZwwdxP7HNW4Wyo5CaoIFIzYcwmqeGpqN57jb5AJ/7oVQlIV+m51gm3z5fl2fvfTmXhL67h/bR7bZSmPFlVkKV9ztePoq7Q7ixAgUaiIip3zajG8TbVxWq2D0pUh46SdsW6X3n9K+qzw6avhhbdvxLy9b2knePp7+N2fjX9fxnFrNZQAP+THz4cq27ZDtQJlZO6Y9VNzVVriEh1ckHPlIYrWPk61kArbXF8BSORGdSonl0beo6tqLfY1bscOvYeCPzl58Kpt36eZjR6bJL7gt7GBkVQwyt48lduyWe/EARbH0J551SzatA5Nz+zz9KsoUCKZtz7xpTRZyjlHiIRFnUXFZOM84h5+Jp7BTuQWotGbMxoLH4YCHS4blUfRu1ztxeRNGHTkXE+v3kaR9SIKmZSZyM2umc4cdIspfYu2pEUyKbI9IBoTU9pjl9r4tVW67qUUyedEiQk7tkaNRqchDdE5y0QKR+1sm5ZeVz+FXorGhW6b5/D1+YpR8Bo8uBOrFw787LuP0QdL4ZgLcBXE6NhWwZTvqe3ZTtp1m18DtlUpxhAjdRAb1xmvllO0LKmPLl/u1+JnITxiPp5LMvY6Yfmz8BNa9/Ygv/Bv3zPqXX57+7LidXgVSIgstZSemXLqdvsAnTZpDZbn6F1V9CyfYHMEgMxY+9Lo/ubUr+MbAhXO2xSyYyI5WwECGHDEjYli6+jwvDkK0LqrbSv2kogKy3sZA3Scy773/Xzusuv0nEy07uf2p59/reo+aoV5wS7789eo7E4gTTWalU/uk+3HI4Wl6zz1VYdomgiO4TXzYesbvo9Z1CLYEmmr8dWYZqyZEEbaj/ODWaeobULOTE29EI1WxqOgFUMmRXJLQFdpjbg8g7KjvHNoyTyaza+HYgQMYX2tuli2iYfoFhsXhpUj3Fqen+HRHlNb9uFABRaV11F0061qvdpc7pIYCNOc91yF92IvTBh89VwvKEj0RU/zAyZbJaD4A5doftOlfBAsWgG/tIQkKqOnc3dv2A49xZUsyzBV4sXdSCUpeGmrm/HvAb58HpcQthVX+LtZjJi4wrgUpczPaL5KLy7HWmby4i34Ma9D7p5UPUxPK4Mxe+bpHhpHqrguxI956MaxVo2r5kWcdJGadKxm9LOk3srCKMjqsbFF1TKnBcFV5/uU9sTnS+yXqdUqBlIY0Luz8kw9gzMykLMO1H90N73CP27CAQm5rL7r7Zrd2d2CMrpib+JQ+tepheBTuebxeXGA42vi064QrrrX9tl1NFXmEqHW9ZBd5Ej1rzH0yBCllJT0yUaN8jC/JqVM4xgy2ih4+j2uQiSno/Zqjdk68+pZ+D3bZjsqiGbpIEapGxrUvSL1FZPbDF4P0hvU6EYX3Xh5L89rbd46Hlt7Easxher3j6s3LHSYmHEojhptbP4r7R10eoUcjD0yGhxtgFYnU+tSOdLUQn0gHfJvrdMGaOWydpiN+3gYTIvhAtgldyK1UwkuMcNeNnZ3akFcmXeNFZyx8Ln4/k+JPqf9KWVp7/t7IqGIQL8m3MP1XO9yk8L6zUEazXBZO3tiNHJaCaniPOOZ1xwfijPRXo1uDu4FN2Ecc4IhuCIJz1T5oSSPidN2phvZONLUGXXf0sY+DZJiu7UFqBK2hoxCUlWkoczt14gLlCJ8jVHczU1eNtOZZ2v3iB7SLvP74lrQsCP9zvyyzycnWozH1yjo8wY/I7duIzidx+x5a1xBlU53d70ppXVc1ETLsnwQv2udWxg3r92mTlo75QWjsJDK8NV91VzjV671MLZ6bYlFtPe4Lk7JHC7JqQRjqRfAQ93jqkaNqhHp2Eqhv00qykMbvXFJXzLvx5+xzZEpsLUcRphAklA20OoetoKgPlYgLx81PoJyB5iLQb0bBCamroaXlnOsVa75MzWTAuoSGl6bUoRfIyXYi6B6n3N2Z7lGrC47A/2bmY39ndYbwjgG+Re1NFIlrAvmiBhiBAUWL8hogx5xSDsKJxSM942z+Fy1o0rqN336xsh2m+YKoqpg3LOjv5Z7cqac7miKn1VzK7QxuZ9JVqroRs6jRcI9HUioXCwBSeNh/wcZn3ZYLB0cH3UohSqYBIJm4ml/nv4cflJZVvuBxuvKwoSXKyiJ9XE7qyU5MG1eBV0yO2OKTthr6n9tpjL2RHLQbQdXzVW8dvDcVSFQQmbVPyr7s1DX+duAZbB+hnq+G4NBFbQEpDzZX58Z5hCJ62Tobupz6iZsEesa13ixLh34uJnyvFJBnZ6duaVoLabagY1u9DqbskOF3Rg5AR1+UaVsN0vPclYPWpzGF5F6j2KTtfjm2XzRbyj7fZCSgCvjvGp90q/WiOEkCqYR7kU/i9KRRmZinzqQ57iNF92Ma811q59PP/ye2n20K09vsELGy3EWnmAHlOEuTgP6xHapy3ktLDs2m52jSc4yVveMhpZ6pRRMH30s1Jk+qgfMAeCMCx4w0I6G0BkUIiiALReEJpkKlXpadgyHEgUhKlQOao6O3VSV2E7rFxRdvmBVPGd2v3ZI6Frt08+DnjF3rugL85E+9gjzGXtmJR2gUZkVZUqQu3Z7WkMfE3OCl9KsVIBwpdV4Gkd7wuqvvOXdI1WaIs7uzrIWeW/3/hR0LINdRr/AmF5zOElMv1Q2IaAhD0ddYR/u8WI0lvBA1k5pjyFiwSSzyN2iQKHUeEuxuRZUHW8erVwpUAiMIMXrGxsjzQCXPD6jxG4rgZRDS55hysdUPP354vNwTjxl2kjlYAR5uRbCqOHerb1LlkgKj7tUMOMKwi1dHoRW1q/64nxmZ45PAAAQAElEQVR39sA0EI5jntQieb/6lxzER4v+LBOWAUstmxu2RQfRoMh4aTmtsyshhRCmQXM0VDiyiCrtX/VVh5Zb6/Z6xSEes30fz7npn2XLZwJ12QlBWBrn5BV++WUCL5cO7c7fQbRZMKO3QxRKJXrvpppeDRQHlKlcojvBFIFKqInke9TRRXXUQkrYpGq7ymbIglX9iURKzDWtFSvq2/vJowY0N+z2ta4lss5pZMMgtRJt8omqKY3DDJSwfLJ3shS9LrdkTNUkYVP+53CZLy6Rfrq+7Ga5SUN27bV+6U8vUS/ICpKKA+2jIJaKqmNo6qAvgq/310+yk1PQIHDVxflBLyiooW+OSkmywaB53HPdvaLALsxTeHLy6UntYAvfelM7gFPkakugkAzq4SoDLOJasW73y1oWUFMyatWQLXUa5zg92pSCkv3ZvTUXy22FxT32sTCL1vU5q75iDgvgmORzfRFTbbvsra3niIiQf6qfYUe3paEJc1oo3Wvmekb6n322YpnQeTNz+OogNFbJVywyK4+/0FdrWW9+zGrspBLFMseofrlAUudVZOp7lVAjYQ6mGzMqWt6WK7bQwxucvOrcWqsGAmNhcL4XlBogYDTi6lIaRmqqe44gDeRQEORB+TIksGuyBXXE8NiMrm70e/aYbDoM4ksG2+Z40eamBQUMdaDHfXMgmFtvczKj9ThdDavZKpnkPjW1r0ofkfxm7SrcgGTswmi2zM0XFHwPa6wgJ5LP1BIv9XnfKtG9WSVuossrWWQ6JsT09Tl6pshLD0AaWfXNIzlNzqpU+0SkugLWpPq/fvU4jNQ6PKvhp1CHdbpbcTya1i0tIKGXoXBAdfaiN2+TJfO+ZjwDfXWvmnuX90wAMQZaS4gYiMsdtVe9BqSFNbgMdmVfilIryjVY1YoXQ80bsCVdX2YMazHyelP80mkhKYCM42vHjMJHm+orvb6op/UTkT+fc73/x4jyBgzuhZ9l6hsXQI6ERkUMea/Dl4zn271nkqiOfm48an0dkGic8L8ZFJwbfvXYlE6hI6Wnl1lzc4/LrjDdLt5toRnb7FkhrZnhpQRGuCm4mplTqIJUYllG0rR9u78TT8a0j11w0mvE52Iiig5g1HAChsHJzp1dj+EZfLUBLXRzlFJRKItm+oBOoF52lBGQ0IgkDu0spyzZOCUI4RtZL85G5px5P11EeJDrpO2eqZTibHu1tnlWbMwenDdlNqH7QNlrRZZz5DUh7bgGyBSyeRoNvjTF4I3Aa0SUv7rhnjo5LemdF67mCj4v84tOLUTKxvEoVbG4d7zBXOAyaphZc5Ub6WE/oW+aSELFi2pmoW3wqbh2KOhBNHDAM5M932yNmFbe1h3uMdTE6umZhKVWStmDN01XAINMN9zgoqutpAPlxFxXDa12LzRgnldTR0NsMPqjWexpJXY012yKo+w9pQtQwq17VFoZHJc4MD2yqIvj0JhRoFD8Y12mqnDFBw6lqh/Pjz8BpbTgiaCvi+FYkLcGy9qgPVVdqNXSaxEIpSrARqQad/rz9FYleHnIzys0KFZQ0NuYhMiYMRk3CmCkOs80JJTK2dNpnDBErNaqfN2zQmWrqcqKKlv7/WtK5kpz0jBUUkZ7lAWaX8Kja+JBaDmJEgXG+nRfaupf83SQopwviZTOuwsHzMW5XYyAsOEaaNgo4nFdbUu6Ivd7FmmlVmLepGNMjdUOBj0tac7aXOl08hYnU67uQNWOdE/48kuLpbQhwoi14ufiTQe0LmFUw6gnqYpuZMc+vtN6aPayaSDAEW06liNcgNdcluKsFQwVxHyw/6H+FddRCE0TRM54pXeNGS6+4VnBOn3Sq5VMUW6X9eeT9i6/JyPTTmulXmlLSliQZT2x3c7DAeBVKuI3jr6B5b6k+WAFocPCRpf97ClFwvxhfjhLI6XbV0zTIy0oQ1TuIylYRl5Gv1uIDrsN3SyIGIcMX/u6tlFZbV3QXc1NKLM/SDnDGCYGMivDSkipNkLm0cfnmCDXot5qTT36MPMS5qXeOXmqM19PLcFU9clz3nPg9pUWugGb1aLzDfe07lVwKFzGdkC/zfe6nuUqDYFgTN6x12d44Z011VDXQtq8pL/MwXuRpj8ajTUTnatmDZa6kAIJp1J7nHmb/EOYW2oWIWWllUVK6EZtq4wsw0E/3buy0EH7zOgJpOFh7HvxX/hNEnuU1TVppVCt6MNgw7N4mlOBfTPda8KLuj/zh5XrGeoffEDSvM0TlpiWW9vmW2QTDHhovSFkfCrRUI/0sYJ8SDMGsl9DDWXLRpyXIGtVQEdCpeZPxd7r1KqmjcYXbGqZoAKXqvjncG3zL+2ZN0jP5RtzylJ8QXtZmlM/g0WAb2aatyugBL7TpxQg9GvmYOj6ukR1bzaOds+4D0LI3+BibDndZno0w8rtlW5TEw9BucAoX74KH4GFQHeBx1CrcR9fqWJwLEgJ7g6LdDsXMqDxysPrCUnktF7o/OON70vcRR/ub58HIz8IIaEG6hM1H5rMrKrJ6kS9GEfnZWik5pdiDIFqK411EI4yHGMz2ij+ydLFn4rPrBDVPimnyXNssEiP5nXobT2iQ52Iy3IpSmH1VthqdOac0QcfQOZjnivTskeoouhDoyRtYwq85TVbmA+V5mxdoKfp0FBhbeREzyI4rDStJkhaWSBiXAuOgC7Ahi+3HMWEHo1EmxSpKw73Jgt9TPL/4L4Ry66QgnDZqBPJzPy4vPhC/E1T7viFbc5BYY73WQPKOsWzVHNmtpe0yOsGvKHXTJfNlqxJl1Q0NBUkxClYXdSb0bgDVI5e8nVbX/KQpbBmRBjAnvc4NdlZo0zamZ9nlhRnMbFgjrR4BwjGb2F31SrhDymUwIAp5HZkFUPCCDczd7z4Wv6FWIkHR+FMyWFNdu3RMZrBoffcU2GNaHl2/4T9Iz6Myz8TP3Xd4waXM+yc71O5Mf87pTTkYJfqK79dX+CMVStFXIs/NnpcMa24MXjzyj69rH6WlKdnULb4sflp3OOukM0rZoKuf0WISmUYdQTfKMrzmlwcQ7fUX5Ia5et0Z+0+BxkuvnouTJRw9ZTuJ/nQvsE0G1soFxNIQOWcVaMMnXeybi80R2ufnMkDg1QLNTpoKsO64qz7CVOfaiEDoJwPXHIZGXOkoKMj2xtOs4AypD4br7OerTWW/k0+p58R05pqt4dDe4Y7RX4raaM56kQhDtb0u3Z1W6JmgNLhdWyX/bbvcf2bNbQkjmde+4TCz0tx4s11nbX7jbPVKHfzyHpb55oJ04sg4e1+1nV+1nNHfkzHuXGkKQNCobstUzwOXICRFjrd5EnoNvw7q9eN5ZKivcwaZPytDTNEHc6oqWgDUYNBPxe2fIgWZimHMX0JUO71TXsIbdGvD/wC5KFB/QGKTPrIebuGYMx1RXGLqbk0pAsqki7pBwaIU5sZuV7Si9aYDzJQQcUP8cFytsOupotUOcBICX2mW2iVYn8cOd9uUa/ARq/Ueo0bbrRqvMb9NdXQddzH3kQspaY27Giz5RevVaFi/vSLFgPqTb691QTPOscGxgjcUbaq+CZI9txcJNtqN/uBXcNAE23FtT/X+bEOgq44Il1GVA+7beiICdJzpPlpCGil9bkSq/gptZQG5Wi6ka06/sqpijlY0ne829SgZKciZTpzUjo6b8++weVjqg9vWrafM7lV0+JiZMGG7xCV14izLedoobYq4R4avIw1LXsrtaXMa9ksUaMrw3XuTi3sBnFqG9cWTCUdKnrYOGEYaP+x9ty6LGcrTHkGRvU20TJhPhrGookCt+WGdkVxFsJrQTUvAP2mWeZV/k/w95rnblhzI44gBcJkehg7fW9qkhGE92hvbaNa+Ld4e+vW3C+jr1eNI9auOY4H6+n7y0/6K63SzOQrRUBElRANUWTrH8aSfEaLwGp6XRAvgvKz85+0ATsej6tAjl162+aULHht6nFxjBgPuIH7wnNL6OkX123uHKgyEMpLymtqiEPZoMp5OJT/4jhpOv3yMhrGF9zlklVn9wbSYMY1uI6kb1hswb8IuMQUU8ZqLRmM8ZuuL2FvARwCGgZy4ND7oC3S1ER7TTlK8YvMV5KnZ46kJNKaZoBOWs7gJL456ueawss6TLsXUExun4d9GIzFhVbXtjMo1MzuTGybuqgEMXyUppu4sBZcUxZdn4qfMbfL27KttKVT9ApY7xJVOn235hokVlZdM1ePwQAauKz5M9pFtAfEc9ISpr4xZ6zGZ/ReOPKJk++GNV0Pklbdb2IC+57HeyIv32ZiWZu8nmvUBX39bVFYjZ6jY0AOlAhXJ5r86h10QqhD/fwpqOfVAVMse7wvKqk5mtOaxtVtamCqKDTX8B6GwWELpo2rARopkNYhcB/zG1Co6C4W9GvouKhekEq1msE0bEAAPSpMFaw2CC5rpExvMLO1QV8bF8joumq0TRLGU89USvNS7ARyscSvOnNM2zWUeNz5dqTn5H77UpqMuYZZyVlZgfo64V1HtilZ/d1UQSsFU2eysBWhrZt6JP7WEZ6FZjvnMrgxnSKmUcRcWM4Funwc7A2xTalNQpIA+XKXeL3leEshpbi8fmN+AYXROqfrBdsk+t3G/Ay0j2GAxtbBbnWkIO1SxcJrukWEVlUNua9RrPY3GtDwfXuJjYS3iB5lZWo1vUG2vIwfmt6jEWnvlel5k78Pm+A14htOjkmn2q1+qV2CbZOFNrGfyaOMrk0/gPhUBkGMD2AyyaTV8dk2j+2aB5Nx+v3+TIRd562BkP3bfbXfef3D9TETlDZTAk/xVLdTDz7bDdWGdJyuVKmqLiG8AuN4c2GmJWvFRJhaCpnHWkwPbAX9Zvn8lv3uVxrWvJyaYKBTLWy7P5Dl7EnyT8/TMsHO7h6eYYqHnKVhYhXvo7mLvrQCXNaR4J6uk/Nk7S4Hl0sOFwfeFXn4pD1tysaEI+6Of3MF16WE2TrxZr1dYDdeteRwpkNQCDgUeYHWdc/5eH3oAQHHzJw956kpOm53eZ3lo0XCdlMJJUbZ37wmlliIyxAXZcr6OI9NeZMTrKsbJd7E4e2QzmeypGFUIV9nEOX8pr5avFlrzUVcyRA2SAaVUIchBPFdS+xLfUP7bU/3ujkEbHCVxgdKw8prv3KDkmGtVaUwbPNZd66lOytq/Btgjq6cg9wU5BFUC6MZBoXKgZJzpgReVor6elO82lKFzJWK6oRyiiPhB5WM8FpeqvAo+ZNUYU4Xc+MLrg2s0kMpJgTJvS4TWVSjaDsrknb0pm1osFVQZAYq/qlSiiE4crLM/9EWYdQG4c26FITSRlCHhqBgJ2LHpQ0eLOTaXOgsAd0oyjaHaNZ4dpQo1Ri1AlPBMRudk1LZgU1YvLjR0vWn7W5eQpKJYWefRrJqZGvaCyM480NcpI9yb8nYC41j9ovP774rich1f9enyiUilbewVpnE5Gx2Q2HJPPSjz9YCvZP91wMM4HL1J1eK2lh4YlfoIwAAEABJREFUX/a4b1i29misTW2SdCEAH+/2/bX5jkgRwESrXyiL67O4roSf90qPvNmYHOqNpq2Kq8NICkdBTeEo3tlee027VL1D9xFHJpX+aZYG0uGrzYbudMKg8b/hx7UJ0xyxWBM5VCHPI9cWuOBX36s+gIN/L6nnWOc++S18jaACNUyjW9P7f5AB2Mtv18V63+Fw2lriMpqJ0VKk3eZpo7BnpNQTiRdLmiIWZQPJwmPh4/nP+/JhcAKKpzljMqccHcU+Dryko4HibdLFaGAZnrcO/o0iIQev4jHul3HJAiarX1V2TnH6KvtpeBFfUWnaRiYIHYRxJMN0gR2RpQJpaMAUQNdOREz0dJxZRwyJDOfx6zvrW5Jx9ep5H1dcjE+4RRx3yQ5hsgzFK6FatxOulqQ/1MYl4+T1PfaklvruIMQVe09lqjnNgn2oUD7V1x6Dj7yPzIiFpFJio1RF8Ed8uBSIrX55UONtilJXObL5wIrhYXFDqdZ+sERVj43iEDMNTYlKyRhPQPd9DWJ0vC4lgaCckBRbX6vjPYAWvaW+A1PfG62RVCXaQ/5NXaSHn8/9/VA1buL2sUgSkEhSuLtS7xGQATlycTvOWQaBtDWg41yz72711feuM7ltbfwkP27AVHV2SSssXlu7M3sJNAxwgN1MEYhlJiwvLCt32ecY26v9MLJVMa7oYFfHT2kjZD4+0CDA02wjNjRUPii6oiqq1wlPITWaYD7At5+9H8GZQUP25yqYZF+GQ8gcHB1T1hhQkygnulRvOxJDOjfCR3yIu2dtM9ULlnupegPPnbj35W1oQhhmHU2lJCnuGbNiDT8cw+ywvKOvj9FiJLnkXXWpUqabVuTGfPMIshZX7/3ivWP+/fPr32VrJa+HyqCmbscGWzIQS7QpOyeORLfLfs91xsP6s/aNbUCrJMdmGp1aTusxyVjRKzoOu6VcTxFxq6gaZV6h7IDJ2no0/6bjtAz6Yiw3jgbtbm2tdk+X59ybfi7G5T/l93NZ6uZ6pKdx2NVQoJ50N9wybVWsfl0Pd6qoRq5s6BwlWQdc7xVc9wsaJwZLH/1Scw45OH6PWb/L4n/DX07N6KePtiQHDTwXn3UD98K6bYL9A+VQaAgx0dRqIGpWUzQQS45ZmilgQ7WJm+2iF52YNpKCrhsbfejdWgXFouifZebuwkrK9CwWeNp2dnfRoMOE9Gpbaavm0oDRM7N6N2Wx5opx5cVEl9cCLDklapSyC/WvOh7wS6ht97aS1ddPBxBOtVtcwjn9RnRJQLLwifJQBe3Gt1oy74uzot1gHT46jCa61Gma1Wv3u0+cg8mZozOd3qfnFNl2+dw1S3JpXVdVvyzXpJlKhjaqA67Ja76fea7RxsdYa85lf/sW9EF9IRzq/J+fuPVPdr89X8N3ZufuSkNTTQGPqjeuuYZXOwcqmv9dyER8eZ+JA46EIM9GjWlNVdfft+WP8SK897tMOebH66JiI9o1hWaus5olBtxFASEMB0nsxYZpF+X9fFgfnkE1CjiT/e5mxlVikdvP1ySTEYhV0GHvsBnIsXI9NSrDp6WO04k6fmolWGrMUbR0e6JzlZx9KtXWs+p5OdfExDfXyUDdSxkjwnoFPbw/7nbId8+gvBvjgt/UoyDBrzTX2BW2HPNmsVtkeknUR33Z0BhVs8cH8Sn+bBWr8lQQ9lvA3+cKXiEZSvzbLdd61S4up6XGyRc/5va67UEm9jRst4pNsihRR8VXay8D+0Jrz3JxCKii7+J6DLP2+1crYZZaF27zsFaOOd7lJ7khdq65l0nEwTZXjdcKu8k14yMceNQ33dvMfPSXlmmpjDyAioDdBq18OaQgGTTIHuVLGXvS3mnLF9ZV7jlQJLrQukVU2jwhvGfWO0rvgOo1JW4k18KVCOFQ3a0JmM0aSIs9LAWtLiCPyWnleaHDzP4yp7d8FBg1yIdWd/QycvA8s+c+dudHsQKCUILIW/6HTZRnKYeAE5ctdydbqCDxLI7FKVQla8Su1HlZijVnR4yrFWh5gEp8JvDEfGPnS3GNI0FvLM94wUOfm0ZFGg52eF+X/UQsgThg6KDagCa8nqxmmtM278TXAySZZymJzQzBrJvjFJsgzMa1YQOX/gEj2hfC1S8N5y59x3Y8WnkaWco2Lf4aEvuM9lnVmX6KqZo1DSp7DZC/Ev3RSosLRp3q8UfqUPYv0DR8bxm54IBmcJnd69IrFskBJqdL/7ZUoPliqFSkwvSqx+YYuZaMi9R7+BlZ8JFaDUxcT/xDr1Xlj1au/fX95dnPnO/6PmtLNAEs8JJzeA26Z4RPuRKq1a0sVWReh3iw22STFoYRB/BeF7XZobLP03Omcl4LDbL06Ys6VbuUHEFk4GrcO1yoMaL7EtMqrlsAUg1Hee5RB7KH8ma/xS/6N/A1Fkcu+yz2R1UWt2mnJtgMwRFq9uGz7RluliLBjbrmiTxxhJoSWG0VUU0/5O9oG9ONUhxTf3+uMcrk3N9jKXFzI7Rn2ZSAoDRE992l9B+L1vIAixnCz3xy7rY1nGR/V39v1grMmwCF0ckcy1Yiz5ukJXkS7b0//F4KVUQdf577N+13S0p/zM5y5RiCpqXfGvr4koCGjQJ4oCihXytKm4Zwq7Hf5H+bv4VHendZwH2V2bFpjGuNGf5L/5l1+SI48z8bh+oNPk5FCTR7rt/yHBLH8V/Mf93n2600Zj/VT9e7WxjpBkao1JeZKxG1LN3IdDxLm7k7VQsKOwcleVMOlKnqfCQW38bH1fCw53zr/LZ9ucpNsVzjfqBdRx72Dq3z6Ol7vu9/U+D+hp2F5/IMLfb3pA9kWr+vVlJsFIEd+JtQA2ZPia4jnG4XIMGrfHtPpyRbgw7nfdlN1Qm9y5RHTcPYVFivW4a0rPZxU7CAxigvjWmDynYWxV0Oq+S5zJWsVGxg5kSXbkoXG41M23XtEbYs33lVXlBU6pHtJ9HZjnrnk3gNG2bJHSF3xdEapv4ZRb6puVbd/+y+1vKbCoo3tGPE5ea3q6udxS+5pXChmIkYBPXTI2nK5CjEwRzBZhIZWl1UvMl9kA/QC3abvbYL16OynMvmNt4DqxQa3difI3EdZdw6pcA753NwlcB2Y6bHU5utEfWCXE2V4LTR2dFHnmanQ4DeuB7cv3U3KC3fq53KOR7wQcL2zVKXDzN026b9Bje8aHgDy9uUA+CmLcJghGKT9CRUX7xMc855cneO6W5Dy8skPMjQAujTcvv4FBRXod6gXDtY7I7UDm1T7QOjQS3TT9hxNmmpdJbVG6p1XTXhDuX7knfga/G77loXcftcSm0pG28l+Fiq6C6dXeNR45PXYA0ETsB5jcpUYjXkPWIzC9k6qkoP+erhm54LzaPPkwlAWXWO2dP+dfx6pz1AMiEaPKUkmtTstof/bTxPqZbxGEosqJQ1IIwxysa5kauHbfaL9bQS6m3AVeAbKx0MnTDjGvu03aXZrZ7H6wV81gYPMfqnozLOXhhzTSKFqHQRo47hl0FngGArgJ7qeUrPuUx1YLIfVELlT2AGp0yJwkCY8SzSba3GOsfPBk5DqShdLeTBTokb5h5xx3W1KyGQ45mEwnhZr2mOEqhl6XL+mOmp34+dtX47niLmlfzH5/aS1moh/2Yp8fDmKlzHLFVMnnGnL9VP6zuUndpTF0TndY/fbE10IMW96a7qg/pYfTDLEGG2zC8Tos6eGbvivkM5acv9yqEMvKqXslyVLwHlkuPUDIaOBZUkPftNf4wi6jrT2lWc68iIbA7Eg3LjNrHKr6bsgzZpsgHVpgFEDyW2lvqf6Uc2UuowzNV1a23VGoAcG9O28FIjddtmGrlR6XMi0iLBklifskPCh6iRclFYaTIFLTUqGMHLfvdND/Y2NUYqEgMVr4YCELDt1HZrrhui/2bumnOivoatYhozSUzufSseWzCZAAeWvvbcx5RxTqAuQXgzFgW3oLYcLiCiujtLS0DvUjVYlnFBLdSvcNPsl72fwi2VwZrON5w3TVRhzLFRn1SogoptlTwp8jWKBkjPQySgLpwfx7tEk9ZG6NVKnRrfOdd0+N28dKHYY62rSgHHMbpjwH2gQrwUx2pNn6muxPsks8unlUiqEsOGUozFBMfszGybWd0LraXHHAQp052Ze8vE6MN74gE3qzkTF++b6zMSevIcnsKAilnAnkEVs6ElqGpwhCVM19g96s/NljVLLRItCC+PhG0IR25N0hSVJWaIEZGznHd/1tf5yHXbNXdUMCE1Cu2rLypVeIdjzC180SW5C+ezcWniEbjNO4V7hUhQ5jxFmbs8h9FBm4Yaz5Ofx+y5W4eckJfV5eUBVcFgVcL385lvqGV07QfzXSv9Y3ZdtL/dpv3YhOAfZdQwUFYsarE/o70fohpZirJVja6GqJ0jGnxfLmaAadLUJMehFBL9B1Dm9CD+ue2xR5kwJql+xP1CH4ymeNG2hBGMpUO3rfGohmfZ+DUi8tK9z5pY737IMH2gdqi0jBYp+5NkhJsaTAQQzWq1KaVKCxblomlNlciaQp0Hve2PG5+cyTMqDgWnaYLdQMstlXVNgmTLT3MpLJjE0d1zhqWtxgq6LKfrU+sUOpnJ00o3fVxKDXyoxLdXrcEaI2JGPWje8ni5bHRJhqyj4lwt/FfAXx5HA4sct26rFm3dhBKpjpmy+LIgQo9K4Dqzzlz0ZbESodVlp4Y29OKbiC5jzZg62Lz/nVeKxQUYbULw+fYhmPNaE/5QBaPs4WJdIwf0luLl5XrqvdNQhHTpbbPH5PL2y+lUJu5kVDqeiSO/iunu/pl2lRNSZMf0jRJmz+Z//eT8Yn6tpJIZVFm+qHoRYt22o5Vi1j19M8vw8AYWmySmTdn1gRsWZnW6HomW9jJFoA5LHgvqT5BHTAraEgmMY2qUnCkwe057jj7JSN810o/QvtNluTxazlf1mzWblKS8hju6dU90cXyBBghS6L2HcqDrqvknFE4Phu5clxkicSdWAzPdY6iPn/s6UmPppZ6WYlRInBnXSAxY53F1H4CMXq/eNPUwd6O0HdhEuoI16Lcg2QAvHeYSDR5MYhozJNVARG4/rAemc2IIau/C7aCZluqfMz/fikdXpicT2QraRh2PkUT1sMZL5ZdPMFWepDjec4S79PGiKsWoAAUjhmg/4hOvQwhxxff/PzOIhAi7JixZKi0/eIvgOEqU6gnxskBHbaohP90Jwt3WgmLrZgv1cl/KdTPHj9hRdr1S7Mxssp0jL0vVLhI3Z/hSfTvuCuhJh4B3K8YCm8zoHZkjbnuPJGwNvLpnSmCDW0GLghLjiTcZwicwbN3urIOYETatT8I3gVJcxipYwWVHzgv74BS7NVOHxPu2gUnD2yEtS1JsgUn1NWT18yYBDYCUCfStY4RTxkcJlY0lIDGkWykCES4BSKW37b9OkL1G1+SJPIM/41xJ91FpN9LOWTg+YgGz9bG8B2WzF8SfJnrxbxWY6mzteTlDm+kAABAASURBVB4TH3JIRbuudhW6nYbE+7VWHJI1H4h3Wtfd5fNUV77FZjZvKTiWNXyOe0njtGnzQhT0DlfDiaT90hNs9ZFn+KTewTHmPQTA4XtgoCMUmHBQ4+WosVQqFh2e7nnF8dWjfYhaqRL/bvshYYzMy0iqhteo4fxIXOp02fnteTq2m625qYensww0OWWP2jHflNU6teRtBkTBjsUxHKU8hLDg+aYrRIRSQEnZtVPANqQphJhjfBwH1tofHuyiNiSthxK/GUdhun66u3qAMMQIul1tetrxKYsRXUuza0x3+Wf0NcvYILqYmy3r9EWLkkNpyVubPDg5YGGVO4M6NHz2W0zXwCE6AEV94fqcFQdQdFYYoAnURL8V4ZYiTPjG76EdZ5GNBZr+DjZz2EaN9iMi0Dndp/xQnFos2N1z99E5Jq0iWhsWm6KkYG3GCqpnp6kLdaZ6jlmPM5cluwH1JpHTnHKS6GZPoQrS9PCw3tzw6IIlrFfEfcVih+1lz5tOUAhL6KU9xivM4OXapLnfko7lAsYXVniE18xA9Pga2JtEoDEtUCBUSrDyKZGQg2MGhxtHSbM3KVQsZVpDqvg/rohYbvYSNqZF4acFbdo9Y8kNxAXEGhVfJ9/bvMJuSwV9qgspamrQg0FTUy6kU3F3b9EgV6fXqbtEJLkcXJxbqV3eC+V9ZBoqQ5yeLGLMaixHGLAW7xtGJGZ0ahOtNDJE/16eYdOKA7L1E1KCWvbqfZ2OpCFCGXZ3A6WokdlIu3f85H1xrWUC0MFGMN1PWT6Db+s2R6jrqFdgehrQQt63wc1l1fKsPFToNIpest1yCVIMLg1vaq4EFTVrBF4DxqcFPaGogEY3pNU2R5vitnc3iwT30DDK91VYem+CbjMsoJwa7/Ex3ErRy+7oS9UOyLg2FLuqT3L3lQ7+jIe2y/AxwJPoU3ib/mM8YiuEXFbQi+0CqeJ+NnRs5XWBb+dIKOFWqnA6QYFUPjENg366iVKVNMjSxaxpfV43P1dcbUJSO6uqc5hjIN7EBsvYYmCoos6ec5DHqlXclC0P43XAzWzJmzVyA+UepazHddxOZ56aqZBFJUBokMSwHHoWm7KiLeEJwJSA8LmMQQn6dt2e5sq8hve8ssAIizMjTix1qjxQE3wNikMYy0+rgtX0p2p6pt7Y31l+9X0hTOwpP6O1IQVHyTCApDECwGGWbYkczSnncBGXjcYRipAnrVLhiaPDVyQqVOROZTKPpw3QQJrIunbf1cqobmTHlbBEJ0bR9c/K+37Ctzxr/d9Ua14RjLsmTk43TWjkfurOWmckVUX4Y/x/S5UQ8VmXv4lGq3L8b60us4aCoA291IRqeS3SqmtGS2yN/COn9hvoTtehh+LaKwy2opO7T4ros94A3OwtuR/WT+ZKkF1x6qqmbDSGeL29DrGy71ZvPAxUf0q7U6prInRvebWHEEGbiHyrqRb6HtgmlLyiIrvMGFYu6gbZMxxxCA3SjF2Hj542OOntKF1rYPSpxNUVtVYv06I7IvHD+BnlebHIlJ29ZhvR3ifqaDqojqGGOCUflCUppCQti2viHkYRah/afygpM1PSeo1Or1WSZEpvL0aRvGxu4Cq6UhVChAfUhqjUR6zsBUvjml45nsszTIots4EXCMGv1NMut7D46I76Sz2aidzuD27kD/eldXCEfgE0AErl4PfYlk73VL07swNR5udp7LGk3FwtxoEYaJ5GPVuFPQkF8IFdAd2D+zUvbrdZco+1gW+iznFt61K/B39xjPIzd5zrOnaVwB+unmCathCrGapTUhfXWVRR9bSvuYIEfI/vtkpCf7JQKgJOLeY1iH3Ff0slINRnFRfMr6t+PrPe2z7KkJK9dOam+dvzDLNaVuRDFv2lOwHKy3MuHfraKm3rhNlBF7N5zIuXyJYEL54PnoW8wm6Xzsdrd/ZJHLLWmhXu4iz1qdkJ/RK5VANTAOgGx9R44nvxWVPiXZXaNrha97pflkqprXnVZefTXzt6vENWxAq7AitgxGlqbzIjFTTRgN52lzqkeO/+JPzIEfKTWhEs1r1/7LlORmq4icOn6mnXOFNBQG+sQDpq1eM9BGmr4m75LlbNaE2SHKZFlrSwM7ehLqEpn+HX5wldnHX61poiMIG6cRtvg1mN7RFJ79HS25Oub0j399q/llv2H0oQ7Buq7JTDl3n5pKIFRRTScZzl7TxL/mIT/WYpQ48fHn4KF1SQGltNa2pdvroETomP9gt6UABqLb1QIXOcAO4ZH7zhOiLalKKekbj0luMBd4Vp9aBSaz7iMrtwep5hhsFkXxXGdowOImu6rGVVyA0Ko15TUvmQSe5GJ6+vfb5999sm3j7zBdW0uttMY4AESnjpP477qpZaYn1tPTPfg1MOWJSiuBSR1rHNUHLlGEMqnHIbdQm9H7ymrkTsdeePy1TFaD9wfsJVKCupERO0Wev7Y9871aOhMNTt6q6RBHyEBntR+YaDCgumRVyyb2BeGqQOKq95WJKA9qS5vHB1i1OOwSapRwmscKzlH9Ga5EMk0bLA/MT+uC6zqW63OgPNfiuECfk37Zvv5P2KADNHmMxIk9ve27Tipo/9VPyQl9PEgLwOsXruA8pSVuFt2pPTZ/Sh8z0PL783fw5/2Jd3XNk2Q44MmKdbOgG7v2qNwybk2KOd6mxaPdwWcE2HUHajIVVGjpvRnesDOAFv6DWg2qp+hGpVLSZX4zO861ZCfQjmnn5SsS+3IGBVU40hFXfTkr7DVKY92am6F4mzHRmjIUKtyGba7cS7wXmZgqQuW9n43fF91pNV3MMtq4YLceNeHk5at2vGjKr5bgNNXmPevnBN2sC2dAxq+xP1uU2zMG9vU/+4TAzO7WA3DtER4St4Y5YmmokoR+c+W/ri+DWS115vGdDiunitgj+DEcvRmni5TKr9ilXh9ZprzXzsLMtByY305piwDGlLbTbOsySHJ/Hw2m8TiHh6bZEzRUCHJPOxDswbygPPCLv8xer3Yiy71rpirXZKN11l1RyZ15LzhxhX+np8lcgR5TNTWeyO17yEo7Sg9dd5hYTOy+EX5Ii749h9rmjRSqcmDKehQDRh2PAZqUl8vSmGKsG7RGZC20YmOaD4/ThdhRmNOf58bT+iDFiEZe08Z1pFBwNBnVtQqdvdggwRVjiuJhrwhVi18AV+q0/lMJXgnkdWIL6s4a2t4WDQ5hX+KilPZK4WEF3nNqsIXkZDIr6w71R1SVyqqs/RPGChgX5EEtsLd/nijSUXL3PpODOtoQ11kwmk2bQTSs1afut95rsLKLcJa77mc55Sgz2aCJT2DhcPxJPSrLukXGHmMTrMWgPAsuDSrteXiHxSKswWuZxuJbBaA+BWN/uekHgoHtXmsoROv3aw/3Fu5R2VyhefsW9ha6uQMYqGbHXO51tbJZyAwkNZYXUHvx1b9VYFiOcZuRq6+6Xjalu/4K90vJbhcrhDE6NsqBX5fHnE6ilUvaxYt8im81mh9sGM13U/u3O3aJ5CA67kj7GW6MNQLtfmZ0fUPVwgJrL8fNFpwD1qYlyttjWwZiSQeVqF5EU5EKA5oxzrwjgtZBPTUw/nCO/fGUP9mRmYNhB2bYgT6NPISW7PHThmLZXrnSyBe6EDvBrBttjw14sMC6F/6GsBV+eqtDa+ypCmMS6jTrjDOeYTgkjYvBehX4EGhabKQNOv7j4bz0Gqn1abyBNxVg6fufyEzJCgtVbIiaY8OGTD6vgudXUN0GcTQ31hX0opn8fs8IpUTXOx46JJCyJtjiX4KGMtVbZXWdNoSrmipXguXMy3BsYwcoYavXpAMnLZCRRNtYP3aX4JNTujBXgUZsDDaC+YGaUpj6mRzi9HstzN97lNTr5cQmlqnGbCoVS+895AYQI8Kjc4zdaMQwBw4AXAVBFI+jHhMTcdasDv2ZRwsBJxGoURt1g2iz7hfbs2XQt9oYdSXglyO1os90WucQr+TE2n3OlHIsSslxQ9xpE0EKjoGYFIWpqdMY32dfwnpZONyua8xFygEU2OjoE4rrZD6jIH4Qvme8Xzub73OvEBLwwa6loousFMIDnbe950KRMG9RjqqhDWS4eUAlcn/xBq/wFNGDnAN5BvQ7w/c0ct7lDKZ2Q8YQmMAl2vrKobGzwUFeKF/QmsNwerlFm7GDR9C/puiqTjjgJFv2DIyJvI8YfrMRVknGCa1t4+tW1EOeMtEYdYuIhPi5RhYR+vWiPe36Ss7LubI3om7aVsGS9rF7lioELFpmg5mk0JX+gF7QPcpGPuqks4Duov7+Jj6pySKSqKxZGdsfOwFLtBAZnR9MZJUES8GhnJakZjnUuHyaq6QWoNfnGMxh10QcZf3GS2jn776DAd6qFQa0BC3dMKRivGo+hwAZcPL9ANskMJr1oLoTQUrdVLUNxV3dE5mdWqNhha4qzoZzybBr5A0dkvyHYUZo+qZyeysjuZicYDnKGUTcxC+5wejerE+/IxGxki9M8DdqFtStHwba9l0qA/UCBWM5DoNGCh/BRK2+mTVi0szssmCBfBcOUWXhJ+e3eMsIsuOq5LY0agsqfRTr7ysTpES5hLhgxjIkpvL2+XyzaUdZIFH7tUkefD47OVziM7qdXhnyOggcqcy6EOhYykadoMB940/dNRH5ZKP26uaPdNqLlDg3YrpklkUx/v9jXmVJr00aF2QQ+u2/kZ6AmGQUXSp23jLlw0/LtLG0J+opxG2Os30lbqA2S8Q3fHfQxdLhlbmaUCFGCUtKmtwR6qExloBR3NoXXwWbZNmEAUPXP1kf6FLMZ0WHb1e6uiNUHq9pvQbYyz1VQATcIS032RSyrQ2UzvGkLLmVbj+jjWGxslSL/Nw2lANOzAsnjIvFhh25E+TTTYG5MG+EC/ZawNTQF05caevp6ZkbszXnqUKzQDDZBGUPIeCnActEGw1EnBVD1Pmykvo7EFBH7crjEXZy9l/jbzGRoZbguwtO7afp576w4YhuW8OCGJdbd+F3sscZWvmahRtWThMRGJGnGqnxaJs2gBoVlFDOX89UkO9KPI8A6VL61R9uBVk5ktrguXcZEUwzVV3VMURZS10OVeMWEwAIf9tG1aiQ8lB2NF03B+h+/X+lOJPnVGZYvRshzKss7MuzsX8y8Nuw7OUTXexvtSroh1JEDShdyVEsFjK7STkXBx5PCI5XGPrTE7Ev/tUHPhAAkIc2p8ww0u7FYSGHFCGMYIkT3a3cN96MibvOhapyx0xbCmHX+vi/jIB3VBk8aJ0HmZNL2lt3rzOFonFueekUGAM0FcTjO0Af4mBKJRkhpAC21XWFmA6u44mILOaDIoHKbZHDEpCcPGHEYloE6ymy0+8KUILp2EQUvFDFNKEG55oPJqGGc6zcXSFfZPFWwCtBREzvvSKF+tchjuod44LNxf+sdf1QI6CaO1jGvRHDRRXBomNiSHPfO31sz0oKAzMuGv58zQyoej3ECmtZ6kyG5bg0NbAAAQAElEQVQhgupaNw56BMtQT6qcxl9i2Smn+1QyjSpNhqjI0ElZdpbKyfhqp3aPwEufCyE0i65/HYCD9mZRIZtRnk+pl03PAC3a0Xqs92SmCG+NFqax5Ohw8TVDGzW9tmi+x4F6C/TynpcFdkfnUr6s2lqR/XWPmNE8DGaGA9Qg3/fmRbERPvJVUqCpT04amvrtTTO7TJir/+YCikwMUEP2bb3UT949+JlOTalXEi4SR97FWSvA8YZBraFX5tUUtW5KxYbI53GDKZdMDSW2upYU5VTVXJ/xa8AUF50QMTgDtYkkFIli4FgrR8PteGg7/DhadHkhpF75ZQzZxIgioI01um6DcKWKCk1JsZb8SS67gNBiXt/GM9HDKNnD/Pr8fpYu6i5F8dCho61qJA5hV7hICaW10N9JanX7/2j6FwXXdV1HAARIyk6t0///p7MrlvgYuO5MtlcqsSU+AJCSnXO701NeV12WVyXOPs9/T/72mwTAyjhHXV7CPu/XCezQTdc/w/+7+/Pj/S62rfINs/a1VQmuRedS2Gv6B2Z2P2IfFfmNo2ZMnqAWioETYVJQBe3i7a0IeAABsqTI+/TPnutJPDnvr/sf2z/rCW4ooyOomW/ZONxmjQ6VHcHVyfOcc/DdkphcVBxYOjATFd63xrPc6qaq0IXSxPSnebcWFNqMYW7w6vKX7K2izXqpX5OBwihExTlkGTWme/hNq9T1GymvEorQPcbkMGvtvrdg34JiRJ8DavMqRsfHcZvCRkOiz5j2C/vmd9ke0dAg2987k+9jqUctea9HHqw2StgAtaDy/Sn+tMSRbV3VeLz0EwCvlxKtMVn+2+s3V36paOLUVJ/Vs8hbVBxk1RfnkUi89WwF/7L+VV+GdZkpSlcmirg4X1e/e3HgF3zGih8Xg8tl7J0/jbpm/h180kyIJTwZb1L/lFqgfT3hOwZWXuMifTb0oGztkTjK+rHRJMd9QcvR4gs1elZqCsq0jKmmw9fH4+fY/f/h9VV1aLzZFaQO77nOJg6khyQUG4zQqPQ5tKmLW8/nzMlZ2KsO+9U9N2yDe3pLSnckV1Il/ulZ9WCX7JV2Qevy8P+F/7scn6U9zXfb85zWNm6OVCebkpGWnRQ7JYHKTY40ZUxFwrjW+qxa8so48z+z++K4/Y79f4ItqwsjtQZTcF+t/QLsOu2nem7ig7YBtddNxW7NjPWNn20fmt+BWK+w0UCtrg/mh+snPpe7INcGjb6OpBRS7uST7DTIisnDF4Y1/EFdeDzTdlOd5sH14Cdxg8LiPxHmkDqvFW+f0vzzXc+zql18XXh/uoqRcpZbQk+0Z3P8hn+4zCPvaksxOVvaE/S62pK/aeS966dxsZZ//S7FvOayx2v3A3vWtdeUmt9Tqs9wWMSMfmS99wmpx64ydRY75ccipV8hKOYwmn1sBMgIllYJNCZHtbChx466a5kArsF1eP3eerb0TpRs7qnYIzC8rrDAErY7a+uhhK7cYfc1dBltA1RSihh9YQe3eQovHQfzbnvhi3Kypny2o95bFYntaGEMmps7KPM/BVOMUHjp2f9L+6GtRXCmMdTFi+fyr93PoLL47fjyc+YWE3NSSCGdfRXu84Kz3ps1hHaGajFe/m/bv991q4ee+S22/GmnoHeVg7y8B5SOfn4b3Zji15ADMU8gXJ7z1tYI7+9UKXZXPp/vM8+pSlpdq5aWxjVSyPpi/eeWwseCsl4xfVrCiycj3b4mcG1mOAk8QLPgz1rDe8CakSrvL70Bh/rouEq499SjIi/V1BXjH962P3VuzZIm/0VJQNPGMb2aUWPY7fvpZ6e2L1/0Lu8Da2nNL16XmV9lKqLbx18g8XTnt3q3gOEiAYzGe/WqvJ62r1mZmkiYWo4a9nO43fPKsS+RQI8cqCqiaH25vIxSOCZ2x9VsEt3CTCxuV//NVvroq2Z9R5CPFDDVG/ylJUeVE0HMLXXvL3KnyK8RYurm17QPQS7YJ32VmbXFaPd91xUdBaZVAVNXqyPPbfGJWLfxQ1+qI2q11MUTkf+oy46wfeFrmDmQqli8e/4dhShM0F4yWytrbXWKwrf7P56UB9ieUOBKiPJZD1uc7c78zjy2hOQMR4anWVBwNq7ADbe01QnViy7Q53K82IvAWQasK+M+iAb2nO/sg2qSSh2ENFLkfqezVMkrzRu0iYAJgwVle4997Pn4jjfyVoaNcQNjbKVDq4FajJcFEJnMzKnvKCRIMa9IN9fYP1fzC2csmhlf7y0utMf5veuJfppPRb6hhqulcgKzMuy5kKtW1D+fC+AeKiFFwA+064FgzD51afbuEsLKwRP+nag9EJbYFGJXw8awFuKD/pFRt3PzEACv1n7wH9a/irul07/QkwsmfS8lZmYh2rOu/UAaEwSFq/Pq43w28mRz5ufyz1ohYLVyHizjtbAkK2sZ8oCGY1/cVsnqafy9htYT7ztISTTPzHm4C7RybM+vlx7w/sLSzH2WH8fpt1Fe2xbu/gOqZC25+rqHIYHjqriG6oGSy0CAz3X7pVS2nW8+z7aBv0NCsTSqGt1Ngb5k0T0nugxdsG6b7OD5oLWC+Hfjyelou7guk0hwGVwp+/K9kF4zGlCncADBzJHlbi1Y97RDJknc76FkRJWAH/NutrqMucC/CI8aw5TWqH7seZctaJJ9VirMnN+H50DcHvApe1kghpDzp3bmrslqP+MdmP9xfnbfieWyLMOvt8NOtnNEnOp72cuZYPOeLhWEJfohmrQNU20KCafdJFtDpK7nup/7pcS4KKtAEZqSCkTItxvWIlVBXAWqjnViIuyT9+f/c8vBBnJ1rd59Tqdlfb51neLJFCccYF4YvwadtDedXnM81QrbUdBKgYbq7508eNpO2hRHAm/WnkZ1K9zpGXRxb5cGN+qQI4WQZpQcephKwTwcNmf2b1carnDtIR3DJf6t19XLzBqanGqF86m+JwECoUMXStNxshto87TVsL/rYGzyGWo3lTXV2Ii0qxGGDj6XRH0Vb/O43rKYY7X5BY9pyrSxS3ns9CxZTLATvme12cX+YHST4m3gUd34eUxdFX9sUKdtLNqv0XNU/YAzc863dPWm+kFpPtEvRD6qAZpkYz2hKv5c/WP5mVkQUhqSnE2J/cqlSqMqjqZs3+H1rtLbtYgE5W7oGq9Ue9vUC5AHRdpEudcy3HBrIpk99YeCXbDQUJ1KHKjyCWTM/pnvXX3vsQedqo+Lo9vr+aDdu1xYoFS4BymGe3wEqwsMzTeuay35Jk2KdKcNhM7LRgpK6MufQOR93RSwgJqRpEPZervlDADlIsSkDyhoOybAqisx09e0na5HOp6CGGeU+tDI1Nu/L0wMVhENaS/5rbeq5GMqXx/9FqIp5II+XVAfcIqP05PRtkZ83DX3a1qRvJgZRrO0lDQ7Kh27ooRYF/AHRoEbnk1VAcuY0cdHXQeUZmDplfaCrDwI/z/AnAoVpSrTBHM9LC6t+H8jjOfyVIuwAr7AHji0WMLtetWxuXZ5KSh7824VQR4oVJCEgtIUDmDz9oOr+0pJiTWfXVdNiJVwgOhmFbrmjD9zbVzTV8AMSLEL3QoI/DEnF8fIsFgMV1Ht/jaK3pe7tMuFWSILingO0YuQ8iwecz00yBBooyKb36UFCmPjp+2BtVrl/YYUnobiO1+OzD4w6XbcnttHK44KKi8K1B5FTfrlV6i3qESzrR3LXeuEu2RKygQuwNomCdDcaUPqhuWYsRE5lqV3351HAFApS4siVMgt1g35ehS3Ibt6J5/teaJfHABQRmFUqhrD2YMaSdVnnIoGC8NouBKqzHq2jIh6DwtFook+y3MpPEl7uCm0kZSoLXE9Zx0574W+Slyc0fRGgxOE+AF5wG0c3dJepfMDn4Q6IrWBsGIwlmIpShv7iTw32g0DHaOt1JUTJQDVLXze4JELEv7rs3V+ICze/EzKmX6GR7Uu5+LIKuN3sySERpa+3mdfuTVc8DRfF3I1iHmfvl+zXKcE9f1oM0F/M7AxUzLD0TpeGzvFwiTFl9IYJHgCDFvNUFVBL45a62IvpjrBdpWGerTt0ye1EKPViq5lt3wTh+sZq/fvFuQKZk8cuWN6C1anfGPEWrMVOSSJJ/3k5YyPcXXjWO6V5Y0hUnOveWvKqmc6TUex0gVE+7FVDOFrXbeOmUtxGLU41rRNw09ZaqIPLw1N2dcV9Jk6MmNYXdF5JUy3rG3WipvWASBRynKq1pG2fIW586Upq3K6WiE2LraWtAizgZ0jmtX33XTuDjjfs8kpa1ATBI7s9iFexfoEE3NGAZmXezjXwFuXl2/zZ5AlrMqzeSTJr9nxi2uFQwzbFHorGpX27vimZVucsBdnjmpHP5Kli09vgwy71WAXHsUvVaknzaRCmKvjSrPqkfZse8+LV3kV966zNypjBmNd5LG7R93HXrhnpIFwc8hcjn0hzuVS3A00GgO+/3FpDKGPxUY0BEv5gY3rn/qy5ovMQMGExdCKf0LtOlWtojrFhMyN0sap7tOeaQJHFmRYQEwfaBJRhuQ7lr3XKT/0Mu1pVg/UhiNpppCHXe9ADR+ODrCAxPQ1WgC4jE5lbcl4aN+AbrPfMmitiKkuO8oF1eJ2FiQyi2xWwliCc6QvymPNC9w1eZGuLqFymtaPWoBYPh5jo+JD5wiPuvwtKK/pVNZZ1iSjbX3HZ5OSj694NWemmKWfI+x4hVHa4aiEsWysc6qE8zaTjwr20tGJHmGAFr46KVYuqGnM5bVUU9RFKGdShqi23FqhB1FY+U6rLs13UMZetcisuj/G5U+Z8ySfHlWY3rKfR+xp/ChUgWmw6ZBnOq7xq6/otZwU8yhA6QydtNX5gRpDmnXrKu+xVWaFoTKow6i4cF/LfY1CG22cFkvaHzAr9sxuIfBWP1VopAKvr+NYjN32bpek6BuzlPd1rvtcVhqdIxcQ4j6CeuTpr0e4jawcrZrCcJ7L05T2EGkQdL5+/L2FiC31tlV7t/XsxBlqFGgir1137FmkKFphccMa+JPsTDZOG2CXDuOCCgML9SKgzPxV2iJu76WbkfMFlGkM3z1E65b7vABug0opZyprTIKzc1MmXkJ++6sdJhfXAgIN0zMwcJa4aPVKxdpTDt3t7cCI7W97pXFTJYGesBNRiM7gcSY9cf2aP9bl3ZjTurOy7309IV6VCny81dPVbcEBIE2XWopGC5CMLtNZvrjrYzWakgD0/WRpCfSKW1A4dKXF+fDmaJ+/Vsk/xgApXZeV48vL2Jlz+rdfJbS/DjkgKG/rmrV0LgdSkRwlMbHmvi+7Xh2gjAdXjwd5qaT0daFjVChd6jMvjzNSV46SqpK86oyle4odToz4lNGGOkCO715b+Q/Gyt7HTFyGO4T/IKYhymVjW+8YvZeCFC/4AJ/BPb2QmnC5CAqnOyLUZdLVv/1UQEY0pZVNynETmZReWWUDmLJQSwCV1wM7rzWnnPO60m0rIS5deQLX5gAAEABJREFUVZMC32hk6MUZ0VhKE1bvR5BvUCovPoOUsPOnH9eMpec4l9lSAQctu7+pUMJgnJcZnYyX6Jq/YHwZzVgCgz/t6nJSe6KHNh9XpkefiwGN+wFWS984o5DgPGuKrwol8BQp14hJjWgVefHYiCaivHZNHYz80xCEQae1eEKrQzXSlioU08oHG0zjYY9mHc1xRZFe0lNAa+Lc7whaUw3jpWUl7QHakMWn1OsDnMvGyWnOKSRAiqJUSNzzOfnJjn55u+bEPFTxKii/zRfiJL6n9/S82SqzvGMcRlzWqiOPB3WsdI6AIy/WckXMnczjKELZcMTSrUn6xj/u3pPvkOcPEfAlF13Sh6k5gAN3IiCscsPPuWZ/rgolpIsb9aW0rTYi4OT+GrvANcbC2fNUb3lwA10Jgz3LcF2KGqzmA4noHu1u6QXPWQMRWFDX/kz86/4IQamlxOUP1JaqBCrJeicAC3brIPR7wIt6qbfMmRIyHnEvihX1t6YnPiqumeAoOolJ1aslYYAawyumFelXQT9k/StlvA793LoyUFRhWkljuQaO3OshnNK2Ascx6JIVFYN2DlKyt9x3qCoz76wrSwyPgm8fPb4ZanVYF02HM5Rdqgv3q4SROkpEQZibm6xDxT+ZLW/KXEAJeRd2RPo4j/eZffaZp61eRYLky+xRRKoJff4LpN8QUsnKtHu4aq9rXnbO9ED5JJp4HHsGqTo1f2yJoINJ7DQdIxoqKDeikQ6FKoGwh21T70LMHXhinkvl4mNSRPV0712VhRH8GG3jV2iGQPvNs6caqNId/MpiFSmzQyhduhnUKDyK6wwrT9R3VQZAAXyCHuYu5PUovU91lc/4uCqpk29FyQwyLIXoCw0FnnzbwGz69uOfrQ0fDAojMTFimGam8SNIYaWaiD0SYsRRfR6Po4adfto22VCChUzkvIjqZK+0C+EKcvZqdVTIrDkYkGIhzSM15eip3x59OmKi3uqV0G5wERqZ0cpTcfaR4W5JmHZINYeyck9ev4KEQxtDV7vGKsX3QdUZqFBzTY+owzdFcMuTxo0wRpjZJ/AZbQL0s8kj3F59Hm2LLo8f/yz7Ry0tY0wBseg+1+hp1XJcDJNLGF+0pOdGUjEScED4yqeoteGSkizKqB4MTEB4NjZz+3PmK0kkuLVM36FfyN9g6ijQQqt768ikP7gPBRtw5t2fz7tzEleuldUT6/iSA9Hyt/PrMeVn/SebMQzM+7r6E7oc1xP82nlGC5D2DY53GpX013RHemmJJto5bjBlgoEUzhLaoIy1oTnpb+ux0Kg/qThaA+aCdP1SwRnfPseTVOCyVY5v26YAk52GVhh1MhVIFSQ6MTtlkkBOjozNJEcDpc8qPrl2KqmhgFORa9Crs7c60lReSyVrTwexrrbPpm9A9bioMqjrbN8dm7bD97XghF6ETUaVWhy+1pu9JUivMZpSV+RjghImPRgJ0T1wv0yGK3snxN/vg98TLzmmVfcb+VUjylIG4BkdaFVICc+Lvap5pMIsDcKjb6M1oC+mYqH5Aj6Oe40tlCaX2hCE7/hNuo8e3p8rt6nGhR+w4973J9d14DkmILW2b7fijRHbFnycvypVd2UQIMwQQLjFNVo8V/akbM05FK+qOUcRal3r0QIF2UoqA3nsbjnY08qgcqYaoxfaOIo8PrO0v2HMXId3LtdLZLiGye8gipdBq8iz+G2WHndMLqg8M6boNTeVx6YfiDzYARuGhqYT7XacWztOUrl2uDKsO3Mk4pmkOZeNkpOpuJ/78/5s7dcxE61Zp4e8IJTYbaVdBsMAx7m4NWuVx8Qi14xhmkDQBRSWJqG85xJbZUK3BwetsOcuRkJY9Pky8zP84bVUbhKqkKqabMq1gHJqV8trRipQO3CYDKrz0MaotlUmoWR5Hm3mQs8i3oeRnVPFxoU3DGR1peiKUTstQ1U/uauyS9q/m7Yl3ffgsrgVCq2tf30e85elZT3Rj3+T/wX+u7wiCL+mrYATyGu0uhRn2FzFBR0SrmZNDJYmXFgB0xDFiwx50yb9Yx7G13qm783vM/9t/NK/NAE0Oivbq6XvS7ENWRZfrFPGI2JVu1SE0+IDdTvCu1xW6jm6L1a1zc7w1E1JqB4RdW574kquJsyLrgYxBnfH3LRg6Hy5HRd2FJnTVVsNy+6LYeZsDBx9r7Jm1TvRfM7i9nCpaZqTw4dMDXYW7Bnb/RnYjCU8ScD05pia93AE112I3Fmpaski21yt8jJi4tvXb13buMgPIc74BPon7TPqBKU82ufBqB8pdGEqB7UK63fx+fAEJSF2PPL50+uapTzH5ok+VxpSIicgxSkU+kVDAZXAhhqVm8FsKqq0BYFFTpxh8cwlMQyNpRkXuBzO1vk5zufyvjXhxR8qnl6ahzHAGm39RP1CdR3EXLHDjxtkK6JjVVHOJ42kr5ajnOmECevvT+sXnX2dZtrC0tEUnRwOONvW73XnPbK4VAtQdK/h6S5kIluveQ2J9BUMoXxPB46SdWNYum3PujffRl/QlbUlDlG9FAOK7J9Tn99z78O3u0lohdeJx9iViKaCeXc5PbJ0KSjs4m7IWhiixmWxsCSrW9+FgY9IFsgzQOMdmBKpNPRxcZYuzKfwtfiqJezFkjQyqXPIkfSjnoEwEcL82PxESSSVa5fwU58Uoae0GZj1KMR1laNzeKi4QZuGwJLvgQg2m7HKU/ZL6TdSktERwsK5palPqa/GUEYQm2vWvMXvh7Vnji3GTbOuORtZVoNUBJC/mCV/QghsB+Ik93aqm8L+x/PpvHrcDNfi5XPri4gb/7oUTDIns5U4Lrj5BZk7hu1Qwi+PKoguQ0GGT4JGEf29+st5iKK88gQeq70Ssa9Vb6QjetWIVy/PNp5c5+uZn+YVV7jbuE9c/FnjSxm9ptmEJI8VOhghZ4BN08pW3yFc5A7V11n6If+HUENqFZIEIEhm2OiKGb/c1RBvJXKENxQ/DFZhEtiTzDPYl6UaRdxKVJNXiQEaCDpxG2lWvXbP9kmvYVoe/Ic8VEIFL6UFA1b3qvYaFUSNT4f129f9jNJGv0mZMMFbDbCgCUKp1nogJhI1hclgLuDK9ifnyBZGA9ffLDO75LHZDbatiTVt3Yb3UcAebug8DJDiZLNHe5T2in5WPqFnTKP9Zh1jz2XKzi7GtbSr8aVw1bzgY4LAjs+ZSf0TmnSYb/AMepRgHJE6MeLBl3es7ZbGdvklsaRTyLwryLTO1ZCBY/HI4u7cLDJDkVfXLn0/Ob3bO5077uPXQWcnZlyqUJwy0PPCZIBogQ0keR+Yoa1qXs83eXGCkyyRVW49+udrcUlvKxWb0QV8uP6EMk0ql66rLNr5ZkU+OiZb+Quo/VjOmGhRVmi+h+w84R1lkhFy7ExsqP2miV8xQIXn8g1FtXhCJpHuuOJyv0MxrDRF6k41yGSQl646XzFxV+sl0z+/eP/3F/XQp4XwjJjmuAOBUcoK5BQFjPyKh4+tz7n8ueUMsCUcv752rF/nd+YdKmmNlwyZ2/WJ+3PfiHdr1jBXFKDCheyyJK2wi2RzdNs4qkXwKsUCGuiEEYKDaVN2bH3tzoKCL3X2OQtzER6MMJjlWwMP8mEftnQkSE12PuDPO4Bf4gxZZr/Gr/iAAXDOWlirBZCbu13mPwZpTwAccJs+Xq1m3ExAwhJGU/qsOzJMY36rHunrv3rHZniZCz54MwpEIg5M7wOl+B73EaOH57E+2groAYw+DNR/cXLVURnhMZRN24f+MYtDPPRj74JnSi817+bwE70uVTI3XUX6ik2IPdX7qaM/kE8D76qrtzhVYuxrsEhKYaIqXV4ctgTNcM8rN6cJlcqrsUCr8tmOdqWb2A8emXUNcuymnrjKVhAsiPcawHTdmjb0ixaTqNqNrNkbO7uaxBXrw/gMr8OYeFHGCGhicvoZY/9YXnzHmjwAMYv7Omnph1AtaR6WjWko1liApvD399S3L6hWr89l8t8wHYBsDRvX6C5GzAykhyfWCZ6aPoWntRhrwWnpsT9wVROvTt9HzAjUEsUorQ9+g1Fa2u/GzVlXL/9d2sBgr8J1ApsCQWodjD5ZQQcar2Bavr0V8XXZJ3ypPHWGDjXGucV8vnM6HG4zb0j1H/d/XnsyS/hpCXv6c/AZv5b5FVjqhovb+cuuO1c81zwibn/nW2g7jg4JWjaDnhNS9ovwSEPQS7I+bkfhpdnX8Is6tb37x+32qnlEXJl0b0cKX06tPW5nhTldp6e+cj6K8YP5USbnbWsbcqyHikpsed8zVwk2BwQZ8uPns+bysLFuGFODLp9wloLklbQ261e3nBKeRrfX83s/6+UGUtifiG0zpQwlAIyzVyiwl2fN52hYFfJMdifZMpuGIzaGy1ZcUjk35tt9RkmT09FbgHnJv0lhbdG89tgmd+Xs0lJ8weTGNXv1GEppwdum7Hm8vpfv++7r6vXhirp1PntSqdzIa1IRPVmnvee+5rM0Atrn36B2DCE1Y3HcGhi+cpFigJbCvRHiaYdpb7RVCEtN0IY4MzIrHj8lBzvzOc9m1mtLYHXFLuZMsx3Nskk7Lezbp3ViEAJrA6d55G75uNRiZ1sdZ/dIC0ZqRc6w5F/SVdW9/ZvXCZrNRTEAGa2m0K5rWjsUeWozlRRCkRA2lEJzU8zQ2y7Baxys9vtca6M2lQvJUW/B4BgULzQTs1BXin8bNe6w9qaVezsUuopEDNWUQSzmYGAuw0gl0NajAMwas9GFMWIlfeTp/TKEyuU9xROQdvMeRAND6t/7WRJRVJfFQugDRnuZlF2Kf0mLTE0MyDkhQxKryBzDiMvL/k7vEZbKpWUTMBzXyNFF0XEeppLdFqmGXPOSqtGpkoaDQ09796Gv6+izLMcsOypfFSqzFygo5vmQ7jNahtSUlvwoSxEzL2OoB5NoTodtXKfvowytlJVfLS2q60fx2iozmD3Uhh+dUATCgQ/X70gY7Tm3aGCI9Q6juTOWuQJpnulj2nJR6QvXGFNAaqmH2IIsYSNcRyf1C+piOk6jt6XWQaiznZtvVr3P07uU9YxKD0dhzLV4BQyYksQ1BXLmyHvm9o5RldZj/N4476N69GWjqOZ6+WrpXN94K6JX+uKDjShTefRBV3E0BO8/arASQaeiFZ7Itj2szlJPMaNLQ2BnZ1VrmkKSYA7UPUY2iZyac6BLdM7Llnkr9DbX4tcqz0CMKkIcAYopEpwCn7hytEF4l8lwW8aAmmUNdHEb6qLwM3WnmWW9bNMUOqCc5HX2QAENNfMKd1g3pb3sUioGJmyHpdc0hf4ajQHbhAWj5z5zb9gDVMg13+SrtWBrSgshyRLVPI4xaQnC4QUjXJ0LPrLfHGIu7+vaa22ppaySlYb2ERoxKvlRgZmURSstTRnP8ZoZpWHQ327BCVk2H5j+U93NveonyjWmBCl8wS4IGw2uMXl9x7n8a4S4kgelwGtejQ1U+20QCHLk1a1Ox2oAABAASURBVGhoGbrudX1iKXylM96aKRnZqItpB17hc9m+6qHiseN+7uusq+BsKjJR4wkBgRqynN8LRxBGBcEBYGK7AoMRUnJf02Mn+JjIoji4mD88H9cOVUEJde81qjzGXGrypgXhUgv/LSZH4d/O5cviluQRbbMB1fUUI7Wlg0NGtcJMMIJcRKwkugZVQJMX7dK7Tywl0sZZZZE39webLQGl4nWZHiu3vuBWNqX5jTOWhaGgKbNxY4/KQNcwBwJj8dhbK4q2vNOqyKGkFHY5/WXEJqz5B3aN6mQZVrRHJk/VwYG1zcSkDuMMRKXPK5cgrhk/Zc8gZQXb/ChId4ldTWDFwbXzKkStq9fP2MrmfpSAAzJ1pLQ0d0es1mSoo3BNW448YyjvYGGs+65xBZ9hanWOV4waMALZHKTs3V03Uk9CJsGugtozzsU0tEQB+UY1OqcSJeh0w7wHR0m8emlJNpXRpQUFfqhlYV784AOjK4iC2sRyj6VXWIDG0lInbFwCefdqjmI3MtEV5tQ9A12x0UZ/3osqOGMbEBwzKOMAOG+4DaWxKCJFJyCp1Pydo85DalVi0InuZlYU7Y2PVyI6olXnQL9jjVDhYsq7lgIntsgYNua97sF40Z/3f8y3WzCPgeZvLDMX5h44DW6taEISOeSB94gpFYIrl3SoNTagTUQWk9gz+0TlQolgKEgdCiAyXbcNQAiyS1YFzZq5URff9BftBSre5IcCTitwasVRy37auicAh1mHlfTAlcbiQGDyNaBz8iwAC+rYlt0lTSrWWLg+dS8sVZ45oD9BpW5UC5YAxNJsrUsLL1QzmiMPGFlUshLeKNSemumiVP6TufLrfQCB1hCeZqoPZRMs4LB3H618k850hbtK9W3mUdcq4UtSZjXdMSvGL83adTSJGVB5tykzjVG0jgPTpH7X54Gk57q2VoZ+RI5qotNwQki0oz1LUel+8Ht8p7LrSqHJw9UIQkka9bZkcyQFZ/sN1zqDzFFR5CBDeZ5lqdg7apSYQyEbSuCoxK2nsYvPKTWH6RohCY4JVoxXdaLbx3/AG6YMMfJpcFDbmtXZkuCgWu7DbJHE/yXARfNteIYPRoIbUnKLZa7W76NG/b3u/Fy4PX5Qjh4fmQo7n2vbJ2e9DUgr0+eaW8Os16mrJMgO+bfojZQSWSvow5n1XlgKpo5F2jr2liIELPhQ8zuYd9bn9E/ChfA+36MlCENnL1dDhXQw+kcLhMHF2hD6OFxGv6hu8YN1Qye15+aGiF483o/phufp16ogvwZ81vzH/k5JSaQmHOST38xUe0ShTrJanxeWCfTptSq0C5nIZ9ajQBPZD/2X/l/H62spFg0Fk9dXq9IYWsr4Ph9131a+HPakRPPY+Rq1/R916n/b1yFm0mWxLfc137W+ocXnzH7mUZlRZG1HLYUqzk5X61OMpjZ2Q9OwzqxzVqbneypf/1dQcOiWfx8b9Rh1SVB9WEG0FsI1yl1QEhKKdRkf8Hs45QsX8u50oq8ov6zGZjsTsuRI7u6kUT2sryQfr/SNaRXCbL7eeNtE9/62wKLQpB1EtbjbgS97nxwpUOxeLYVpGBKCiQ5F74AqCqmClJUP5oP2fkGUW8rDG90WVL6evwt0+gRr+vtMj3lY3JJLUzH14woLVfg/GOVCqlY6OK7n6EekXdzBtlELwOu5e0qYzqfmZ7SHHvslejDFAl43tMff5DmCc6yNQrQM3qVglyt//5q9/0OBvmqUpc+rLNbP6n8rP1acrFY7MxF2wH65cLRWS6u15g4txN3g05yZUCV9yn9atsq1LGG7UjpIHWXZPlhKWRul+cRcXqqdiLP6ib0x1VJwKQi8oX4MV/ilQtE6DAp7a78tnByTrE7rNRtiRazn21hw1nwtnz6qcN3wXjl+eqQpxbjivu1zZbgKnKAJEhHSXAf29E6F2uNs8z1TU5AWHqzfwp493N6tnCSkNJMAlU1uyxToMxSliuU83BWnlCtULGs/2vpElqHgeNmDGpBfpQcxdfWIJ2zajgs31RtpyOznO1WmRhut7KjPAuFe5we11Lxm5LYl8dJQnGIpFZrqZWVTrS3VuwDTMcwRPjOo4RdeKh0zUGDrB8Td06ItZ7q6h8UKTYKSVwHsw6/NXt09+iwDcHOsO10dmmmv9AfNVsPaC9WyO9peju4xrjNskqbXol8jw2jFZZYe7yoHg10WIZJ5G5ebki/9owc8NBkYmy5gD3ZLAEiTjWNTIdN+vcNkWJ1g3y2mHftCLuCneddYCpxp1KApifQb226JcWZuyw/zoy2TNLh8MFAfltsL2hV/xpcURf1UR7xzZ+kML8WYM6mRAsvGBOgMCzksnxayU+qjERX3dp5JUBOhZ6ytLRhxo5doq3yHXaO45BeeuNvWY2oyqsizSCNbWJ23vBkfARVBW5M/7J9R7Uz4uDfW1AfPBWE6fo9+BLl+yBsCZqQ5gVMh9EZg2uhZ7OcF215FDtg9nbvxatBULVEFSUE5C2GbaTSoc9/H9nEoVy/ytSUXpIMzWl+8QsZ89XIuhWomDOMPn4k+zBqF97jKlqWnd0NTnYVpNSkINRus04Kru+pId4nHqAxCHsgCjW18Kp5a38YDblWBFsLehaeY2TXdd2s9e/7V/hkpoZvQsKp5YXJqMfjeQhj+WDyGxEGlKOrxUfH5lEscBl4O0ynBdy6+65rAPuuWveqnVec4I3XiRUKyyd5v/76oJF+t5vChfS97wpp9gbcBfA6+m2cz2ysdIxS6VF8IFV+LpUh9yEdjjvYXD59NdZfba+l2REWnHfRienXvOd+qFuur1qqmJazH5iw+N9Wb0H3ULGqGBYjDk3hOnzf0vxrB6Ldoq+RWbQ2HK/zH+bG8BsG17F7xQ4tu7219KC2QRSTZB/ztz1P3xKIyDEitdu+1zuUHeAa66euREN20ofx0azcG/fE5ObXbE5rpimJ/7Egbw54/WNEQRtFnlW4lDCkjDLNLvnDZpYbHlAQsxm80PVVNT/KokUu6zuak/LePELEZ7Qr/yeHvSCuTkoqgkqeitWKof7F/rK7y6tqWT8yz5EPdVL7dCtfskB/JMAQNplbNohu9GS0ftJ/G/7ru09XzQC8CM5QHrhnJ2P1ze0Rydn839NRZ+qv4nrWT7fBbsnNTPGl+JImCCgKcdGywoZftxJaKxjMpCGKPt4E+MWlnz37s/LoCaOUvOG9TJ4AXFAqMoBKCHRVu+bRpusbJcKMTKCoQwyU6r651zp31efL9PxU9YaUWJQ12d7Jaw79j/yG+fdf3nic649Sc7p1vkXQTYwLaZApQNl/gW/tohNtcnwzKufqlWFwCyjlhapY9nBxr3IUoIGY+gCtKQ1ochj4uYsGsrjmeqoR3yZT9aYjuNcK+X9Sqa+agq1ugsAinbjg7rElQ7Lj17RAgqPYNK0ewrQ5L2M4bImbWhn8j8+rwuaDmoIyselq8FN4riYjAUk9XlaLOgOWuqVHm2ZO1y3b79Pik87RXacibrnSyYZveq02HO/vieHuNnTcIMdhEugMwFJo9V4XXjRSOoj+0XNBHnYIryxpSZ40XvXHrMLXc92zB97xpNZUi0uTRYtm6li9TeY17gp2jGp79550oRgOT6l/5Zz6h7ilkwNJg8P0o9LNIM9kJsk+nyB5dkixEvBp1C/1yuRaQFIXwdBVXnbtL0KCGykOkWy19gZyKk1wikWdsoC2I+XLKIbpgOWvUFgx2lzBrug9fhKLywllW3VDDyRmBi2gOwGmTaYMUF7cH0Uqt1CHfFGynPQMZb7AnjjTD7npSGQ1dkwpTNVNvK3wCOZwEygoSPGTX4Rj9hcnVlLweWjLM9K/JMnldy7TAjQlrDpQ+Zyw1toaZ3CUVdoVy5qqxkpB7oX9Mkq0whaipmJPQDje9ZTeFCHkmU0tEPzPJJBRi2ei8roIHKg252M6jeBQRjtZu3sn7L1cIDibeZKSYBdxQB3sjLK9eR+F7QyMABdwZWu22o00pxBR1Gqteds1kBV3qln3JiLR1bpexJiZtdH/QojaPkouqKCme1maj9XjIKXprfXNxpiki9q3tgyxNpaqoZCuDW8PGTPh7q322ahHy/734SPZlV2XMt3tXztlku5sYGA+FqWOP7xPnvU3k6OrqWFzUIJpOEPrPlOskhYCzPPBeBSCBobqI5zUWnv7+b3NX8OOIHGwztSwVs+bO8xo50ZvzlCp7RmUbojPv61mqu9ELYMzcowDgcE31MgLtEI5hukpKFDBjG5L+m0vNJQVlz0AjddZYUKLjM5wptZrWBSkxgcLo/dEQG3Z5m5ygNZyYt/1BBWtZoWyu85bsFZSs/cx67KMCAA5kdSRHM+jl5Ve/n465AH5QeQ6zOHLJbtS0YimtI+YsuS60lHg46J7SkMjCLjH0BgdrZ05WNebNVGqomspqOyLafQd2j0ob2wYxsXJ4kt+OssWImOtSA0jrYQ2mHWoOvjKQmKGYechkww54CJq9PVB3rzLRNgSC/FBq6rgqXCLfMh4dCnHy9DnW5dYdmKVMpmQYKWQS3LGaDhh73nfSDWEtwKj83dUEWnCPcTTgfZOYIhVXj0M2SRro6hoezrCEVhAkKyN1mKcTGtEqN/78RaGxfz08hgDRwNYx3ZLDvG+dDakB2ijmZUd+lHpsX6Pk4U71z1VzyUGViRlwe2+cZ57HOs3+DjSt6mYtto+qwEzP5lq654Iyojko9KxeiL3clVMfeooqLCobb3bF8B5aW42JoL52xUsW/ih5kVzgRQvazMrxV2RGwd1SR+9GywPLYxtTYteYmOKcM72HAwoIOR8pJws9F7HczNb4PYo3zB3mB+8BjFM8EZQT6g9gprMa4NeX0YTg95S2T73aHC+u4/aX8wv5mT9dStRCzzf4NXtK8VSlCORa7opfEXI2LXk3gjB3IYkKpHHcl2NB8upqNQq8dmYeZtse24UCB4twFtFaSZaTEuetJQKry/PX94MjkIPuY/r0CJKFVGWYtaZLrHLVvfLgZNlkVPqZzDk1JdXgdYAYMid2C0caElP8yla07nv6NSQPakyKwiB8LyXJhXHtebK6Ru4vtK3ua86lVqmg9cXEMrKhboNKTgJnqPd+oZtRkqkIoByvSR8CLyWGJdDjpJUUKcSCiOK1GQ175x82vSVNYTszu+qR9rpWn2tLY9QNup0bW1HROq99lDZotd5pChz2HXwL2lSLZqMJKJFjZ0Uu4RGAEqZD6G+yTBlhyHHpxtxmcUppoMEKHivwQHj1eQGEUIFtEaVeoiKslDJnNOpFFyPofeZ10loJ+5oh9J8X3uowHmeSECTXUbQDcN7GPY0DpMIKpJ3yc7y7df0Yn/s6l6L2OObFFlrCJzphD+JxL3sZ1DaW2nG/9/VCWOVYimZkt1WiisQ2X6ZSTJWk0m9GZ+mivEiEQbJ9WuLGL+Y71CZSKmgM1sChXqT5LOjpiu7MrsyFDlEIGthC1Mp5SF3NAAAQAElEQVQwOhreZgXhvaxvg7ysMzYAyAzddUwOhnqZJINTo6+Rdh3K10jsnvLN3qtytQrtzxPpFD34b+aBMAOzpQM5QuBqcxXeRtmMw9t/Nle+DY1LEzVEEK03yCz00/zV2F3I8Zd+NazVviz0z/QkyUdlGV/oYWtupqSw2V/rbbursrMFlg0MLXAYPc5z8zWWbQ9ZdMAUjA0LXrSE/c1R1J1rDrBGqVd79zqw7aKL7KHqadvo3CrNsVGKxmFinnl7UnvWyrqe43nY1Tuxt+nkVLzIpIbptO9UtbampEZd/saTRKEcvN08fEzJdGNIYMCx5L3hieHnrJ/z84MPXatnmf1xDiMFq57q3vfoaTicMDhwDUTWqH0Lm3eVZ058g98brWeluDAmJxjgNHdKYo5K59emcGTeP1YRJTPi1XukiVDrH31MPm+Q8rdu0wIA+UHLHIciI1PFmKUhIyrCgmtp2DW+Tqxtn0zPncCDe/xD01xpsuGX2SLeEwoTCNp1/+D+4XXr7AEUXQ6p5GZujpmicRRH1tiiZgiYuy/GT9OzAdJp9oLC0Getk2oOj5+DkvgaAhyErpMl9iYfGzWkTS9QQ8EC9KCtPUmB1UdnSOlnYcLtshZOsjMaaGemejA+9DGF9h4Knn+QQ/FsIkMPHFvAXKOpnGTrOY3Qco7wuGykwLayxlUjRqSEkSEilJQK4SkbSRQj6R3sqUPt/MAudTzTX0qGVcUaVzpfRxIEMKSi4i0J61wnVVBNXqt1cMA0OcWMgikDGNHwhF4Z2VZS+2f44UVeCVWOadU3Iy6WWcmHYyQ9YswpEbmrbABpUMJicTK89Shkf6o+D3Wo1YQteGzlMzv63FbLq5CphS0E2HftZ+0j+wTDWgPsrrmsQ8vEy4WpVgMn5nFsld0ZNe1k6UzTsry16pw7ak0Nkff1uCr6irzUx2fpLUe8ARPoqxknLD/sG/HBx1yE9qZ0W5WIqsu2Nsx2A0YvuY8U7mZ24trrKqXUZ6XuiBogV3i40V7zw6UQXmfafWVClmIu6MnD7XX1yxraJvQ0xG0Z6U1L9+0uW4D80Pue0dIVp6WMY1mj0CmliYaenpK4U6cWLAbx0jHnEral/RI/4GVOV+RtkeFtpvSJuq7Uo6VxyAD36FUG+Q9XwC2EPS99rIb2Jt3WZaXGjUMMyOaCG7SJXLoYmQ7VdqbVO84EyzZj5c/e/578/NWSimo3TxF5BfTT9OXJl6Zo+zdCl4plV+cAN7UAL4R6gwPSHnoR2hFaEDw2x0Bcd/+YrVrMT3UIMEkL76sx2dJ9t4DVgzq/VY1x6gdzG+hAV3flZLFgLyxd+m2tNu2hf4mkAZfXha+XGK6APELZ95kp2eC6RxhdDRGkI7o1pYAUD3RCf2BHStRnrpgVINHw3XHGc2yAnnnaftkbu7FHgQWVi8HYF/SJLSvkuNRjWDKggY8ptQyNGmpAxsgRJXKvLePRav9UjWoF8w1tWtTtMRPublePOty78Zeq9lxf3O9N9W11Y3rNXiovH1MGD/wZDmTpgn0wSwZxMMdt49r7fp7I7XjpeePY89+D/8Z/df32CfPGvVsp6ybw9DMquHjBuM18rQzfPmkotwpOt0ipwcCs424xTjNAPeOLfHLUKez1/ySe4pdXzi2/0W/p9xSpmvH1eKScl4c4aSPDQg5lV4f3WxsrzUssUFexOI6xZJ/A1/kAx3uk+QIPIE794XrWG34J2ELnWO878l5+4bYd82WflxcZW8H7WoglS4+hAyH6MHLyWGy/lR3kvfvpPPVqatcou0bKcy0x+NP+k1T2cihAUJgCGh4wU+duH1vkWnB1mhvtsa/4XvYVBngLRWHPIM2POcyFjXW8pogYdUBpZ2gN6U7ifS+/1tQJ4zbeDr8G18zldl26Uj6zqlf6mwtCwVgck57+cf+DmnB1U8K8HEuxqr4GMb461gCjWB601NOwGIW/qTJVgOL17UE10JgWAtRfob5oCmIUnyK0762GQWN7f7u/5/k+81t+WtSBxOgIUmnOCNAaJeYLck+mWpM2BmFvDsOyHoMMA6T+jPH9pLGFSPU1JRe6VICGubLe9pydalELQy/TtgZfsNi8hgubvSVkT7vGDWil0foDqW9Lohdu5M/OW+R87/Vdaor22/P12aFxvn7v+79rPVqJR8oyFYb9tQNZVnv4BcrGjNOcQc8BE94OvQLKzf1iRX8N31arQ4Mj2G1agNpmTPD6zMd61YtHl3AjFOgZZfNay8Y5bRmGq6AI9YGL7lBn+bG5Z7x7ULCkKQzDXl4CGZ+Ld+D9/yRKVbVmX9w35XHcdlN3wQ8xC+jbsFqfr/Tr+05+OZ+EGOaTOiJ9zSd6IWPOyv26FKLiVTUsKtQugeNWNB+VLilRvEHe1p85lKVI6bw2+VAfDeS1+P6Uom5VOJwXCO8lLSaKgkhQmrPJyh7lWPN/LwMEsameP847QYkUfhZ066EQK7jXeu47LxZP5n/Jr+zOWr2ukVfYej7xhNz82exCYpLTBEafAX0QqAm9cvFcrCWZ5gdttkb1rTtAdez6HmiJxBjFznUYSlDoxbMjty6d7pPdKQSMzdfm2LQ4mwAoGt2M6hz+rpkwBVGwoY9pc8CZnP26UCOZ+yoX4r5a47epnrKVoQ+CCpgjwOCwZSAb6IZVmapbKxmlDprD8HcBSW8RV2Hp1A7h2ByBbTiWT9f5mimVpsJskhJAyTcy6ty71sM1dCNvjlvCayN+5/OdS+LYpntKsdIQUZCRviHPjlburHFHXHADm0tt2EEdipryYUabAaZb0WMvSjiIGlNOpvYaIQNnlJQWwoK73WHrDm0eH89dRykikK7sm9kvHjfpOl08gkY9U4Fr5jbkn6NxiEK0Yn585X3ZWmqxH8BGB71wzazJeO3hxEre1f5A63K/ap3CpKMcDW07vGVXs1vWRAhVXVJwFno4BNFkycqsLu/US5fxwmVoK0jpXM5l5jpBzYl5IceANMQC7ym2qJgj6bFW2wUPa2PzTD4yayu1ogpQmI2eD8lbhjesS//NmMKbcBpFhQ6MIPB8fqYvY9jQUlTQ6B6uSNxUMKUMs7zSelxBTOJ9vWacGXhuqmDaTfYf9gMUYZhV4yku6xJMI1W/i/Pd9rH4CVyr4sAffNoCToZmDfW2ZQAkTRFman05X3Q2WxuDpGIewCAmxY3cGNtbsZUQk1ro1AnkGkTPmxHjYQzcTAisQ4m1JJvMBuRG5wVJjNtobdJkeSKO9dvfBjVCFztaG1yq4k2uaCoA3GrostYkACqEUvGNqCQGrQ5VVyqox+OImLm7jAMfOswWREzMaLjewhi4O6/Tc9Ct5cEpgKJEispUYbQpEtIIDg00o775rLsdqjNZlVs7DZUKpDpxNVbu6iNzaeURGqdbDV3S1u7AiSTTeJT18gnV01WugcuthZX+Vds0BvmauyD8d0O7W+Zl5tMtHWcOhtTblISoGrRa46uu0s8TVIrBDLaPzMrOkUfYFCS11bMO9cQlDgFoTMebmkBR11ALWL0vT78yVqvzXO1eZumhNJWn57oonQZMq4PpWn850xyNxgKnJtU0jnkLLEKYGc2sA71mYkgEREsVnv1GRGX/IVXvdsAeqO3kjuesEpcAvZ36pEwAK0Ou/i7Ue5/0omqpPm+9MNeIxgMJKDjhaUxkvwJv19QBMJKv3oEDfztpX+/Zv9Ocmarej47d9YzKw4YN4gmo2I+n4iN44ZWDzLQCk2UMqjEaazMoSJUqSW0frjU/M96dClze1YH0EWnQU4Jy6e5rlVNMAfMKVEAxRu0sfETi1Vu2OcLL62qJHQ3HtOmOMjlvJ+7HcRy/4DMKUBEDVJqc4mTPSaCc4xJqVkuqx4uf9rtdmBnEiALJHOQomb+2hDXtbJpmtEJmjes4Mz2Fx3ikMnsB13otKfYIjYGG5KicpmXVqHYgORerO0cXhgV/nLprKx9OyflbnauW9AadA2vaQgnbVt00dflaZnHBqN2GNNE8XedUbiWZUnNHyxbslbEmKgg5j5GMx6RyDhjGJaOR0GRBqLHmQb0qmccmiaaC7kljL1ASByH0v8qFbX4Gv2fvPPJfVsdqW2lO0C6nAUh2tRds5J9w17/Qmn0MRxqmfoNbmqHLAQ9zOpSU2VqjBxMTJi+Kv3wX0znvCqTmUIuzHL6gCnNTVABZtD3aN/IgRBNJ0Nqi4qLKwU/encuGi9qzPWbqnJt+IO16eAc22e1WKwR0eQPjkq/+ynbqG7Lm2/M7fVq5BOlDjfMklVMZWiXTiR50QzF3s45Q5BAlIrNJLvD6klvZutEvwCWTU1IbWv+ZZjnndgu/brvMafJ1QwKFVdq8iYJnqqhElE7dM0tGODCZmzZ5SsO8NvDhCAHAkqv6Rgs9ZTrlZDhhfKc5ZmXcHSq3i7rWYG2VLxDQCr/N3mt0paAJbFjbm5Wgp+rtUAlRPm3e/IGBjMdHCcAJ/TeTnYq59YQBH9A1wbrY1fP+UW9/Z2aX6rgqYFdbHPcK062A34tyrGrqTbQMWsvEQLQFmza4iP+rVl6D1Rpj5i78Pka9N1BK1s3hpqxMVqiZgh4seofXSMeqgDmGQzYJiS1AH3jO2hVHhVutINVI+owdAUPAFN0aSu/KSN0ANAHu5ZKtDizIKaFUAuCYjfuimPWGa56N31WOpz6dwVrGVQMdGBg0/4qGPtcL4szu0CxAfvC2SKmWD9ENwpzGdyzQAyY0DTLCgC8sJ+PlBgoips1g7lSQc/FcoQWDGEVpIwHg/2Y7CyiVyRBw4HoN65QsuzE8OqwdhPoiDYDGC1+/lEgo2+6ec4hfRg60KAaT1mz0jindtGltUNCLwvuEFr8LWEHnolZyk/ValUYiHEprUiqZL/b3ZG1AmDH68vrp9NxVQm4Q/Qajj6IXkJqr/fSMpY7q+X7n+1v6dcd5XPhlQRvs7Sn59bJn9ffnPB+BLUOS3T7nq66HfLcsI4wYnMuk6CS4TcktALxr3eBFroaXwsWwNICWn2PX1sBkSkXo8qmPQWyyXyAvvSjRO1pJOzVPHIxhVOVX9SLdJpSLWku32BtZYM0If9xs61NaEvaUHAhpIYVUfoYWegLfVd5zpiqLOZfDf0YweXEdyjAhDFJCmoL0yNrWe86pAtfla0WYO+NCsE2q8Bcuiqj3QsiedaoPogi5q0EpjKj1MXPXjGN7e79P2IwqtHZKzB2VytAdWkYrOLhQLm5Kq0xuaYZvhCMAQIULwAYSiIJardL1ajUHTlAnc7RBc6lt4ypnLr7/J083kt1Ag40y32v1fcday7W945WjyGcpxPBxgio76pb/ck7HTn+E6p45jjRC12l0QJxQJkerLH3PnbXsSxOMwwzgo0Eub91LRqZNdK1qG+GDUUhzyDSzpJYx39k5WdZ0Riwu66sQqSRutArjdTnQdQOF14JrRJdpSvuTXpnevWTagx00vCM0ksUpFOToHrtAtqd+FtpG/AAAEABJREFU5j8ruu957qhYcxGfsNs1LHM/kuloXb1aeeDngktvUltz1OeUY47PhFjcnBS4wwKOzZvdnj5IsG1ZLC0H8QHiJWq+mAddhUw7c31zneN8KlTmprxYcaX/QAetcaq/befdK632qXjadnjajVnzcgG9qOTUoTeQ1FcQNmG1cD6sO+bj+BlofDSCozE9VhMFNFEesz5ci2ivtw2sUdkdV5zmZ1y9ozTnArwvzsL6s91N6eyX+O3uaeFda96FIS9UODsrz9FbNmbI8ZbAlltYdMS5ra7pVYhqV+5hGoFJ01cCZm0YinVvrgxLRa4crqHJWlMd4R0E7FEgAm649lw6frEeaNdlmKJcDziL/bG8T8Wubktlt1SG3zhH2MPERx2IvbkOhFPN1fGp+wc/qz+RZBVOs00RKQ86C/r9CJODFnSrwvpKleBwgrHsHn7YfqXdfbRF/jn0CmHTwwv9mVo0D2CxyB7UKzDYNkV2Y9yqTCgr/AO+vpMxefsv5vf59tbD0Yc4o6WglIEa1EKHH7t3QM/7ttUAHNwot1RcOp+uDqmKlrTU4FAsjJAW4OF0G1vVSyygfGjtles51AM4CVEK0zEHtuFFANIZL5d8rrulIxkaU4Z1PIVrmxXWmU57yLSJ1rbtWfL6ejjBWSqSnKtbisDVCEwBz/JtnmygwHGCaP2nE8Zly+TLggAxXuO7KVbJdMuk8Jikj8WMyZx8sZjWB2oSb9HDXaTAjnCVlYh5S1SPmlgaPBIyrXxEt0hBcr0/euJd6Kq8h/tTz7X/IpIby8+plQV4xVX3Bb8CAm/Y1abbBUGesISL+YpVF+ZuBU/ImDrQNe9GV0TMKZtySmF3nFsrBTvJw7KWeuVoyx53KShHu5VBw8dkbMyGUp4xRmlMJXpG5J7s38KX02L4aq58VaoNuZ9JiW5U1vUZKCT7K6b5Bb7GDOTCLOMVueIE6kpKzxwCECvdaIqqtX0JLQV1Rfh1j/IHS7a1zXgstyuG5Ck/Hf0WjJ+52z6zog05yFryF3vim/dWCxmFSeqCpHDddYW+2KAKIm0vywhKCCS9RNZMdRE6EmTZB7fzA1yjPNQGfyYtd0uKhA/WJh+vYyIWXFAnObd4uc6KgpVj1OrGslbBW+Bz79kbgxF/i2/ozpu8QZpsqjXF1JhKrka79bO73ek9V804K+REaHWGSV9r1Q04xJ5cNZf0/gTfYnz3QYElJjmaDgVjHjci4FxFr2sfSQLzafxQPzg8qhqFcI1/epa0QIwG8Ot9bASi/ODv1fz7Q3+tXe87L+NP5drp5wF/z/p+ozZhKU/G7pXPrYSQM+y02QaNHXz192pcWH5djDVlc8LyZknfayYUxp3tSYhkMgUHrzuXPyvPhV6kfj1xU3g2gOuM7M2ocY91qYmZBLmSgc/Y6rbRsCZbwaB7fMx1EUJ9vtbpNXWg/ZNveAEQgY6MxnU+gnjE0EjHigjHXJ94zQWsgVAaTjKbR/PQN2qhaAqKZiu2trxeVuej2I9PiwqChFMWTAMNmpxd2jwoQPde9zC43KWTAKTdgWKe1u8wQu9YS9DedR2sva7Hro2fwUW/3cxpVCkLRRDwY1FmgEVzPVL16vC6iAtGAPlc/f3gfDhACRyBdcmqaslNe3J780LxyrzYvqwvTzc8nC8q3z4pazrk83L7oVChtXf6KN3DZsM6LG02uRlpNwaoU9pZW4sygQWdemE2pfpuokq/XasTjToJVosVV9KtUUJbQw56tyQ1EAcYSkMZJdRizJpTNfO4Ziv3TUsZwBRTLqCsOcPC2deT6xS6ytN6+19vLgoKKzYnr4IYuN3FRgzeJjQCzcjLGTUYfT1D89vX5RhXp5JbtHtSP+Tfz8Q0PNUzWo3sOzzyPujElEw17eAm7rcYFNSc7uSo1jVjzzeR6DKkoyPH9ql+1IB3TqqE1r2uD6EfvBZAx/RMJs8QAxNmEs7x+rIroY1H9PPjGXd2nLQaw/uf9FR9lMvYVW9eb3yBc9usNb4IKgm03o3L4RxLkWHuavJ0QjHXtkmakmoqEX2Q5Nw8sMgw+hjNxh1hZrl6M78iwUoyU2dmbuRBlawpBk5xd33n7FLsJmKlq4VcfG1AxldCGZupxV1X+V1pOEURyeeyWit9VTrLr447FIOp1A6IUAzrnmvNZYjM2nvnM9N2LmyR2NkzHEbSJONW6KO6UfyS09fWBhOKHAez0V2csZcA86pQ1Fo1CBE0wbwsFaL2dgxQL1lW0EuVVMaNLGyfDJGmHvpEK2Q6hIpCvs3cCR/91JZ8DXx2hHqgG12lDpg/NCkbI9wBooDGrKqV6a2wJvSVtQiXgfaZN+J4t08JDobIz7NXfkeCE3dQtxiHNsV6irMFCmW7HWVIjU2hQyVHfnT4mGc5YERbZ+kQC1WeHXxskv1GYDzqInPPvBXBFraaCVt0B03yZhucXNBwpbR4LtaFXiYtj5Ss0KEEQWbPK9gcG3cTs3zHWF6+lzIPhVq2H88//zJvpr3A4pgyfskL4mq7tH7uwTPY0BW1kHvauuwMmr18NIXlattyVZhihWMFPFrRtKq8HnvUSbRWhHMZ8PYrtOk1t81N2TDDUh6yDQO9RAgHwv0IzulqY93W2k3ZYT8LJzxdbeGCyMoagTniS/50vekmQcFeGPZz8pyuolx29PZKTnWNZKTN2Vpnobgnqw/2sSzDwA0KyvwgtlktRTVKk1vuI7eyA2YS1lxTxlOx07JnrEi6kk9ovfw5/SNt2u9mPqiu460+f4Ya5my/5IsJ2x71YatOF2zM0qIqbLdMYyuy98vnPusWdn1y9jjmCviP3KnOeE6wVUVmAZmorDw1EBrFu4fzVG7dO2b2t/OJJ1VSEmyNKTW8n4D21mD7Qd365EyVG3YrZgl2rmW3wOUU1LEL8lPqD5Kq2YS5QyKHYOlUicGcoPAwgMvN3MoO/k/zrokt8nCapRidn7vVTTFA0SFr1OQGS61c/mRQDErOXKFxi+pmXRqsIQevJTlBme0OnVphGudcBSu8agLS7DhH6cIXTfiZkXDI8BazX2KbD81AvNMisdif0cSqaO0PHu0pxyQoiapOl0zDJ7xVk4d15hl7pP/wCx3qmDo8hVGRCW5ZtpbAHFhysWp90taApgyergePuFeQQaHxiqI4B6WOZBVUXN7AKE/fvHOuUYZUpw9yXREGTkEJUReGOjsjdzFqiR0b86j+PXq6K/PM2XEedfjoojXu7GtU/9/SQxHZVnVe3G2VUcJPXDCr5+Q7Bcp5/rCqqX2m0lqmYx3eNWHPhPSXaJ1ve2Flu1ax+q3+wh45NJjwb0KimZGjyeAbqk+zq3GoiXFRPayN+wUyOomGUyoRh27X1fcnrts4mBI5fGTvBmUW7LHRV3J6qFb14h4O4Gr1Zwt302UZ1Ibtpchp3ijVSmeNERoBs+zJUYbVNByomPfwmM2WZTn24lUKCkUq6xmzNjK6lxYJQcBuGVArLbr6g9lGqayb6QDHz2jgNckr38UJ6MYI6w1Ifggbt+2QgMOuT2rrrI7hZEwLLA2nesveMsUf2N3C0TeRktk9oziy7Vdu6AOdlvk5bLAUsfcbbAVaWNXMwaSP+3wCai82jQQkmlGgIVBaNrreaGwEn4FBBGkdllRqbA1bzQk8F5+rJRHRQSyl/7pUjS4OZXhj/3JOr8ZnIa6BdGpfW995F4X70XpOtDtUKEoXIGYUDqwJSQ0/M1do2pR3yZUgwT2qwGWprqf+xVZjGE3LEn3GgAIrCpIv/dAmwa1oJJnlkIjHHtVcYQ6haexZkxcy/sDZohKzzK9eQqaRqZh9L2nEWgBkOhMX8FHGx/1Z3M5qvGvMnjp9lR5EACuOX/lmIrW8guqzcOg2a8F/AOWFFpo8y5vC6Npj2aVBbDe6goVveFLa6Kf4e/ycZG1Ha35DGXTjqECrS8vZaqU5mELZGauyKZKjpwZyupaRYaa/7wobirn6CAnhfhUjaXt6Dwo2RCuZSnSrnARUCw6d9tVxz8edvc4jFwNs5yjgdm8N7zxVZ+SeUcaKmmvyTfAOxLK5FiNogXHQUx/gSnveVMqeIxAUkXQhBOOh4hLhdjUk/O4s5MiehKLywCr7pKSlts/W1ZOZ1QHcwfUTGrKBIsx5+YSXSFh7roI+QK68DvOpfk5/p79vAs0zS3PAA/4OU5/HTJHEZ3vs8QQNTQ1O4Psqx4jlefO5oSU8n84nLaVzqBiccOhQ3oIA1Zm7amNoHSqgQeqQNa+6BoxxvtfeDzY2mSm1V1X7tB4oLVHPU/c+cQR+m3xc3MTxwcJF/4DuirkMY0SoeYBi0QCRO/N0bc64w18exNFd0MJGhwbjWn6FxbBGvVv989q8Bwpt1xyB9Dva6rcSAQpefFf/lOn3kA0lbK8fWNZ17OJixJh61V1HZE/PafwatUt9ZqrNSz4pdYzQP3bE2jDsXqFfhO5pF01dcegP58ir7Z6SnRnNQMaq66YtMyu3wzroU8xeO6ibom01CrNv8Efeesbq6KHLZRNaZH3mCr9NeyPrQimPUsYqnPv1Knkw9aDNQ133cRzBinyL5HJ87HPPJcTjTulGq983KfoxajDR020Va8RRvPX0DLb4sG1vpQ+ElaBqoDCpwSwP3B7/M1wGiWJ6BDR5LFoCmO6/vLKiz+C88LPQiFTuMRVoY5/ofaFiNLtOq0koEZvlpDTQZmi/Du8Hn3P+x2f5g1u92mZcq/GD0IannH4xLjdFYttXfpyWmKNQU1nWVXX1WZHxabtHIivOlJTTccpOoo71Kx0FclAP7Kzrb0Kf0OSsu3NRdQlJhuM9I22IaNE1MLpPeNuc6jPrVDw1//H7lbQw0rvNwqxufb+eBmkC4AKWLQ+T8ClEM9UoFLriKkwpxpIjTvnUEuBJJEu0nlC0NztkhPbtPvbd68nV4zGDUjFi1zvzehPWbU5ZF89onQiZGz9zJ26OtLtutkmK5zn1Qsb0RY9I2Glr77gQNydKaigwa16idlqSujzoOl05Bc+4FVyxavRf44xtNU6IGveknrlYpsDbnPZD/KIKnUbN2s1vcU+l53v6wTw2ZTVXYp2h6FzNGKe6nM/jlZaw8lbKtr9rn6uexS1vNAhX0EdSuC/+XMriQgKzISXyTn5qtL7onBQvP5Rh0zxFv9EFUqXeIukqOkVjKRZl2RLOiDmazexhcsHujp8TfO6qT1Ok4mJ69WyWmsmcma9Wxq2fx/7i6uF3uNXrnQG3t/2Eauki1Fx9+sr89LPmgQAv9FEedMDffwM0MD3XrPALbs1OHVajmtEViDiDK4kkW4MxGZYOeXcOYhoDpXUb3dlaue6EEpHBvKJH0lrE6vccOroNM8gSe2WsVePFmRgq4rT7MGgARe5AF3zm81DrfQHHU6d8r3jg9Yx/D+rbXlhJK/IYxpywSF4P76QdBWpv4M6J6OWAGcT+GxBdIdWh8D2UeV0gFY7ceNO3yShEp1WyeYgAABAASURBVKawGW9T8lEKqcVv3BSehgs8/tx+X+ZLpu/bpHLEKYjUBk2/ZNy1vCK9t2vFPEp/YFPRast52TEv8hiqOVlTNX2QUxv9WO1WlrbdZrF9EvlVk07Tw71548abNinnb/Ijja2weMfidB/ZG6AhgySoVUDBTOTxOdr74Sqo6Pk8qAMQY9NWmlUEnPSB4UA1wjGoqK8r4vowbkDj/jxsoFh7zjOZNtS4CKoDt0n5bx/QROFD3VeZT0+efg7P/lgtfe2n/MurwxEwqcEAdAuG7JPMETvYplPoUgEjFRN4oz7UjjaIH2l9gLRKCEJUlxRrlD1CsWVWZmd/0zJj0nko1AHh+eJc1GyH0BMIM9OjohTlP7PuoH+yVzeX4IAunvdVpUEjTMYU2RUlXIXwC5/kIksVqSD24DvPk9XP4P15Q5u4qIthvjrYV/cb9lwlgEwLc91sN5gtZSiftTifUq20UFF0W8l5mbS3V27PVKT8zkL5mg4lPijoqBFY5eJBMzrP3tiFqqkv7P9ErqdRKk75G2n6UR4A3zDBbvW5i6X2ZIKlu6FmbCPh57X28qR7OTrgZpd0EaCJ1sz2L+K1T+1B2hTpG9skdamPyjj52nK4xzu7hL1mov//cn43erA78fPY9TUeB7ydcEgz/D5+xlq8fFTT7o9hc/pdlbJ7cMBtdqrzmTlmW30finyMIB2pn8Z+/Bs9yjW1QHTZ6nFV7GSh066ynw0dHCo6et7RNxllLc/QKyn60ydDerQs39YncpoSqqf54/NMMWfVe65V5m1Rfs0r8MGoLbcnUJ6MlLaGVzfRbYrS/K9FtdRWg4d4QYARcEq9GTyahaAslv9ExO2QV1zqjGPk3z6WbhiteU8rRIOZkzFQe9O7WoG7Aomm6TBp0pc+KYSIg89WUbY/fzlQ5IbbtUYGb9a6Qd1Y8/IKK3tq1KwmbFkIsx40RcVYZuxjVQ22oCfZ9lP5ky1NcBJ98GLOKd+wDQqRkTegE5JxXn3e2+F3+kF8hyK2WuDtYWnIeK3PxGUIx7omlgro3Fa3cb0SfckoGkBoJaKNdc5OnG2T+prhx9S0ZpSD/Wf3F+uA2pm17ldxhVEziw2hbQ6NncIpS3MLZ/hA2vfLfPFM7TwqA3D7Xet/Ez/tAvVokb7FEqIzd0s8gK22n7F/wNU5JaNQr/A+gQ3ky5vb8TmY0VfM3XZv+sAEDyx9tTL2OGRpiHitwKj3x4VFRL0TtyNdGNdUju4lrpxAAzIJNAnXjjbXldRhPVQS2nm+4Gj6mHU4qNHJzBF6uCRkBtaay52QldJOYCiERbjtR+/X5EKZt7DRIfQkWKgoEHmz/00bNZE4ShNjVlxtHlR2rouerkBNPYd1mNoa7vfzKJQ1Ak5C0VxX+EsZ05mcljR2Zk9CeLDLK73qTokQaga9q6RESy7AOIbpYpbGt8CXkt4UUWks3jP/znx+G99zZtqXu54HjKi6anfiyfXF2rjLZan5nqwaWi4dyEBbiZLWmPWEN8nN3sjDlOofpnTYTPP0GJTefMEdGtmjVzsTYmx0j6igClNdg25OQ4SXAIf1m0pB24rT2/nreFvmCF6xo24zU6d6j3IN67A0O1YZda5niyzZE4WYNROgcZHhI3KldfFuOfYQX40PIdr0DEfc9GhZijx2MuXCGzEmC897pyQNZxDa2qUWefgQ0FdfmucI9mUinVjTUdm91TxT+bXYSKumEM4n9yll+hnJ2RAr3ZVpt/6OlkoFLIyn9uzGJlXmM2jkRNrVbrYYS+/gGhVJMhi+Qp3MrlHvFJQl/9KkDCt7mmZd58JZiS4xdyeE2nbZpejg7Dqnzu6t5tbq/4juZbzLhIfiquWETN9+gjOsfefoqorLRxBAo9UMzpg0x/Sge9gsocBRP2K35WHVBGY1rIbZMsN+GjPQ6StbxUIqYsWphCW1ZXFfx27m4rY1oGHQM7X6cfUXdVODGR2gHL1mx2m8Y9YCb/MQq5fw4VplCmIEqwhVk9+X/SLPnv7VBQtAuV1L70WlgW6upG6q2VPpz5jgQZf1GYzK57zt4DjLqHBD1Ju9ZljrnJ9Hd9ZNhYsevXx4CZY1sHlTP9wYrdc7CPqFtd5or1i4QohJ9Efith4HX7hkQl/ohbt7ucSE3T/sf6tDyb/Kqq4qCLboZcrfJ9YJn+Z7KJQoiDFFq0HDKWoNnseRsmCi0QXwAbLGvnk/7Rrhjg981e3lUDeCPKkTuYuSjjyeTUtcwGdoy7Sl6GugPD8Haib1zMknQ0pOdTUr2fdl+HcjDAJypqWa1cnVc4m7sP6ZdCUzA8AlrhjnoBuqnCQVI0oJdFxc7mavIUAEaA8qQjahNgRlZB4X7htruUGvHu+BwMtBEjBIqwh9lr5QQY1RlsYx6rPJd5U9egayVWzoGg0SjBIGcmZMyGpKQC1e2mZYhL4zaY+66lzWF8pKOuOWDuL1t4y+0NZn+JRtXAfCBzt7P9/z+25pvc+KjLuVm0jVwl3exnMhpbUz61zXXvfrJ+v77TzI9of8ko9z7uH9IH6tMlKsw2kVNp9mlCsg7+YbwG6qLqCAGGCrb1/rLEs7HOE4hkVqso+vb9tv2lcYSFF+Styr1bl6uHY0W9vPm9+YPWTEda+4nCG3fqlNbUyi5q3iWOSaQmdX6s/vrOLFuUZPvXSutX7qjONlQb0COGyhDSD0r7rbHgUKntNne9maZR5jSsUV7QpfnDtDUHTPXzFDiN8maad04ggJIsIkN1NUQLcLZkMvzAUuXZAOVuRPpMNtf+Z77ycOUNEdr1GR8lg9a5fvUbAZqCvLj+FAqTWg05yazlT3G2VkRlLtVr22oFoYDcEW/f2poxBSK88jHwrXPnZbxTw5+6ktqAzQ9FFnbyshIU3HDYGisxgOfHpGXi06AsoGdiZGFRxL8GrW6VYvS1Xw//HR4idWuWu+zYHXrESo/VS0RbtLdUXIKoZeYUOCJDzUqWht0BOAjXq4z9rJBnQANpBRpYyyzlHv59G2BYJO1xRbjsoEO5guXnxve9q2i5MXSwHYDX1y1ZRZ0KLoT3u1A5eNduphgtNtrHsq5UNrV3887ytx5bl2omBvtDLKAJ0gmgVVrmS8R2AkcMx1bdAjWNfYKGtH3yipGbNbfcfocb09WgoptzI8hTNuVGhmPrgHiuXthD4+E0fR2JSOsL40KmKc5VI+SVcoadqJ/Qqdq1QTFxZhRMKzo+s9wBUa6rKoa2OGd8TtCoZmxrrkoElJ3zgcXxnio5Iz8vQ3QR8b3aMKqilVoG9cc2y+BfWkbvy95Hj5YRyzGTkkQLjVjdHqDBashu+ElG3ZG/Ehj9MxXLraqhmleTkvzAWVkcfcrNsaBrqAEhDAnCytCLNf+De87qV4PI6rEpnQB0+LinDaZVLhSVO8hF0gFW8XLeVFGodMx8IK4LZzqeDn6co32Q5DWGKyj7xKLMpj9sn9PdkFH6mKQnlxR6cMC3eqQ0yhB6WwBSUey+SDljx5neunlptrdqqw3OsTb/xz8SyBofgMeunNm65PMitw5A6NaLABb7B0yRQWOR+kEtHqP/9LfE4Hq7xOTHLDdqz0mGYf5YUslK4q//B26wDX1hP5n7fcpw7zeOY69CaUiMvXmtKjpNUM+aaaAf9BHhUrNAb1WGcYeXNWF4WWi+lKzJ/ER4jPoK2OVa0heJe6q1y8zHMZzaSJM6k28JXCeo71ed3bgKYY+tvqYJ3HRjBM3aMkDL1SWHdOb5WYIBHRX6CmC77pCetqjmohFFztKx+v5GsaRTYp847CZCrMVBGE4IRKjzOeZRiNEREuIux5+D1zlCM5zVZWBAS4GLWyletq42l7tCb72m1v4GcEgYIwbMvddQ4y3yC7e2q+2btb++dSHK7WL+KEF+QiJZs+Z5+95c9GHSD4Pl2JXUrdOslneEgMXK7VPbR7SpclA/XfIYSJ+gnYwJ7ZrPSiNH/5rDpLxYGmgP6Dr4WrMxdUUjfwM3HVD9SzlG2hE1EQ11BwKq01bYqWwrjT0DNWZuoAh7WV2uSa9ATaRwY10cYBWXfM8jE1K5nIZRWunCffmLHGvdzh9IsaPS3T3TCFaOpnahERLtm8WaetB/GV+XFLjDbGaV/YWVOLfS3Fa1ayRDUYpTHH2GXdLeR8zcftHskjknyMpajUxkYayw9qZa92HyepkBCjQn1CJNPaFOEre2f/dXvRUKwD7EYls7mhA6X5PotplsengwjZXDiBr7/vYFAsnJkp4EtlYgb3d3mT4JSVldqX36SKczZ0oAd0mk+4dhwrENfIrJnFUGy1m9FXEU9jK+30eEx5+gAkJBlk7pIiRisBPpuzW7vKtiGFFhTMLpsTLMaoXF6X044GqpFopTNEwEQ7CwbFcjmWjRTXr9qU10hQqWtjo65n4SsE3Y26LGdyspWOCXnK1shzi3UuZOCRGegjNHUwKmzmoDnAWEmtb+DKWGdqeopy7Ez3NGkHN+anEU3r9pPq0yDAIqttfhlagvO55N5pbvcHdv2Bu7O7uAZvr5v7SFAY7YI+7DA0NCOOoBEQQjNy7HSe7pE2nlDYmj4Ypxi66EufkjxCoEY15L10xdbQlMnL5AnpYMpaR8fMAkDBXQz4gl41b+bTRLeC35d4ezBPp/J2E2NBW7hd0w+Z5pms7ZZCfG5rKth+2duXpWrXMdroLXqAqxUhwK7Ri9HuXC6e4rR/M7RzAuAor6GKU6amMGnMy8Db1XpSIKNV91SMGPpwpexJYaPxLtJ/nEBO7dPM9iMFFaycJ3JM3wR+F+tg796l/JNXX2su14p2HBnel7qsjJycPpxmAQV9zAwV0BFq1wjaG3FRlDmUFI5JpkppnCbtDA7WEbAcKpHuoc2N/uGXkZDNktlNlBxoo4aT3Sc1tARoqAhZYzCfy7R6wyeKUZ97AnhNXshPY+XMqAxkJnw0E/e8O5pgTpdV30dLYXfWEW4a1S3t1Ci9qzjLca/yqj7qKuo8Wj8BLSNHbNr1MdM21uVQYyFk1daX1NZragnhfkdJgAopyhVzZW3k0Sr/4qYYqHLXoq1nAuKMUme/MgzJfaviALRbw0tY0f8E7JVY39c8GvO+pAopHXs6cyAF2of+D3ZNonaenVvDKPJqocOrQ2hvaDMYNXcw3AiOEDmDbPaQcEX39q3vTOPvNUAWqgmYkq3wjgsCtK1yJKUc2mV2u25flcfW4LTd+dTZp2abUb4oQGAmo2mvz56XbXmUABKnh6fu6c+0akJsfSrNBitSA4pSBmHV91PXbgUyLvkOBVLbyeqtHRQ0mGXWctb0noBkc94ERrDJRznmklWKScWpyNwC7+HAROLaymKCiJi4VZ1Cs5VmSSkqyTHjshX9QeqOyzZQ48iwVTCF0aVYR81DPJbawVY7+HDgk1fVqsaZrneS1MrV7Y++99+dm5t2DGmib6gSUG0ZdcdcPVNprWcZ/d6TH1OoJbowJBftZlxUPTC7XiA623dbAAAQAElEQVSB5aCfzaxpQAViKhvItXUav/pN5Qdxa1pwXXAJYzRTMgA1EeSHCFOcTMAI824MtumYkjGz0UmUCsq9q/v7dH6zz+7pwQz0Gmfb2LQW2RhZ5axV9sn59NwEXwh6XAc0ZY19dOQoyaspAi6vH56fN2akzzfO96qvjxjdxZO6cb2+eurND/gxQXBFWxR8jz2bz/eNhXTz+MBd+9HzU32bhUplJaUClKlMb+bt/938vez74UtBF2Sh9/gWr3yTvZbfrlWIUId8jvB1Fcaes7FV3mKkm1NMqlYTSTyOTXarWeWP9Wf5xHpb7Rv94JUpec+onKzBlpeyq2epod6/xyX0pihyLntJnIovFJAcyNMJ39fSirWwiAU3LAYkPHPorkBWJYBKaXQbMzjRuoq+pHiXWGVmurERvxNPjobKpSR33v0pTuXY1i7N1xA5Git1QzOmFr8XHk5LmJNvIKGFEXPWttBzf60aCAVFeBoe1FgbwIMRIilQFI5Zy6jOYgRan6r6/sfaUt5l+8qtaLQt+Nl1tXqc1Sso1XhaPtC3gJL3G/cHnx8TSD0+hcmaVLB3tgJXUc1nd4zcQ/nPoI5cPeDQInh5XvWwNxWNEtUgFYtcv3Za7eRyj/BFintXT/ZjfBYUZlps5R6eDGybehNKefS3IH35hCeo5PxJnnOQGn/WLbUdMwzYircbBN7vjqYwnf4C/xWefEnweU1TmzlV03CAVjsBc0RdF6WSauAZP3Q44I0gba3rsh/adeAP2f06q6ytzlx7WuySTmPcWB+EjeWeZ5eusNyFWKt9lEEV7TN3W/SnJdgVUJUZoA8mVERy8yktBtU9Uxx3zHSJ98D4NbPm8rlDFbQYONH1Kfwr/wCz6vc6j0KpSzSJwkN84adVpZdG2BpaE+3THA7scRYV1cbssSNC/nN/ORzFrvbQbaVZUFOMxEvN+xOXopGM9X6Wd7xI3RBSTr+gdD1sCTW6QTVEJ1fQdZVwjkO++4UCVPLXoSXB0dkqXIIFtXAGimuNzGIw2dU1Ru0obJXH171EUGOOWmV9u5NmvG73D9W5DEUBIkxfqvvVauM7eMp19AOcL6us/NSnS49Dd7zZaYMLmBXfYy4FrDC6N/I7eme3ZrIS3+Pftl1Ccq6CAlU4cMx1+b1MsZyTqRtuVbIstjA+RbVAci4rYyvA7Q6TwueeZ82+0Asj6QvceuFQgeWZ/LVRM5gJmEsQzQXK5zXyl1AEEe+5v+saMbGPPZXZp3AgeRNKZHJ1ovuMbymlFUIHYToMsNfwe6pH6+oRR9bXUEsJ09R/Z6PqoL9MdZYHUwY57IA4QIv1y+x2CSVXHfUeIVXjUx+rD+QPVNk++6xnfvpyhW3a8opmhDHCjQbZbMf/iYdXtGKX9S97xxxBs2hul+k28Yp3oZdetJV42iRWdIkqXOWfvE37Urq+uzLTYVh3hLk0fJUahjSQ7QOKOK2k65olwwJxJ771nPotpBJGahUabSdZPM3d9m01YWiTx6Ea5uK/sbtos9qEhQ6bXlUCwbHJzEBeSCUQG+tcV34+O+7yUKJmy/2f2Y/NzaZ+j7UaqEKnrJ/QgXSAIGHCyC68rspMcm3FJUHP99TufVCKCpISfowfZ1xJjtMdYWb6tGvEvtWJEk5wVcFDzWqoVMYO1oOrEQFc5MfuxWXSoa7X7Nav7ZVQbc7KtGeq+ADPxQ46SWieSAR65b7PtsrO+s3+int/uathdlWOYXNqgNPT3bYQHxF/UetQ2bDLn/w5+En/iOhTZ85XNZgnd8FIKe2+oNF7cYdCl6Z5aowMJx1vxXPG0pirK3LmO9/nPKn++Nxr05/ibgrfte4V6mQr6vr09ZFAqvfO79Tvmq+jgGjhZf3rWZx74Q6Cli+uWPaSLJSOTjowzWnXhJHn6/v17zeVwuL3Q8m42cIOX2DfNZ/DKF/jF2yNXWk/PZ+ZsG7tXnwwwDlsuGDim9kZyYMwOMcws6iWguL5T71CtjGx6l51xbDX5Ad20zwMlmgd1DdzCWob3q8qny4Qghan+BycxJ9jzLJZY4NXG3//1wSTT5/C0em5KEsY69EZ1U4lcrcGoNvlAY4yFyzPpRNfz7Oy0TNUj22vDf7a9RUy8bu18GG6VCwf9FXL9uWFYNrq9HikzcM3jJKehdkkDe3C6jM7bOrz+V7rUQnytGav6/El9kSMAxAWFbVj0pWO2BuYtUX2tWGb/eU+UjRP+Sl9bkyt/H5KYp4LAtEmqlUxyl5GrJWGArAxJ1gqdinRMZCEm0RJDgZzXX8Yj/u4xZjaKmSoLHv0X9tp2x0zIVSo9kFprFpmWghZFYRZmnDwGl0XbzYXS+UmuNAH7He79RISLPemKQiU7GeNXkjTQgAsRyzgEgq1u3uDgMzofYhSmY4Jv7qjFpnHUnzCXgsaaatXzPIxqnYVvYkBYjzmj+eil70t44W40wbqZAGGIjIZMSvzx64v14NV8DdMXV+4LrivmdXAsGk9jgS/arlJBTEbOBSttiv6qxh8gYtoQ96o8LFVvLuXciXs3YXxYn8q19Rto4bhMqskATpsDUNctuq0V+VHJDa6Jk/267BBvmDFu50wJSLneDbqaWYzuu85Js824S1Esl84N/k13exMv9MNtwK79Dm7T7cNpZSLWA4YxidNciOOqSG9q1hu+WEKAQoK4DEmPNuethLLcmUixNpM/Oujy+SY/0ULZSJB4RE/DYgBjWn34FpiSdObB8yBRnGOn+kjOSsZnZEWIUczNA50DKfJoZBfappbLrG6FffrXbImknUoOBpWQym1egbjbyS1CoAaISZL/G2L5BrdO/1hPr1yIk1jNKfG5s0TZ0bloCsoLOBy88CbXTn38pKQdMKW3cvCpQDKJaSVlJ5fhyL2kh4kk3nn9St4JoDOy+pzWcSSN5lnjpqbvViKiTeQUeRzQe5u848tg8b0FlQ2x61FnjnH5CqzT2ndeeWXN1593U7xGmyoYGpNusA51TVDgxIhr/ijXkPQgnjcJ0aGQy6hJeZKLLgveZ/rbgX8A+gJiWP3ZB5OhvqQXzeMaLqwepM2iJn5eF9rYrWz55iWZmvlDQwqVfBZ3VAcb1uSvGRCHlKUWM7MG4JD5ym7yv0KXx5kpGBfIDEtILWEKH/DFsne1GBNeUfISO5CjTZaWk1pViRnrDq6FC4DiM6r01XGCmmq+j1vGmaTlHAtnQxeFA7jjjfD6S6v0hx2n1Gr5pjmuGvKxrzbGjUkUSWX5iVSryQL3aNtUl7jq9UL3hza2KJQq4VBc9C4uCKusItcNHcMJ2cIC7rRAY2EgFzD26HlhtLxNFii2geQeLTc2KVad6bEvGFvGiCANB7KTXul6pgK6Kh3L4ITaFr2BbtajZuAVmF7liZNyobWbkEIrCmd6v02CM4JP74QJggIdrvGIlxcQsHvsq6BdmY5Yn2ymRWZMeMckK0CBVdwXW66d1wqOe3u9IT8SrcOtN4s31Yf8wJ1aIehevVWKLPWWMiUU5v3Os1KTumQulrJXs3rdUPaGFszqSCQ43Ncp5y4TE89zWfaREKq6c7RFjbRnLmbCsm2nXT5LRMl0lmqhgbqKdGvSZTNEfechhITSeSMAKjhPuinIie8jDmq9YKpldbCcjEtal9rSk7UUHgkmQxS9ZuQa6s2RWxvPGuP8mffHuvFXEGiRi/ZPjIP1xl/EUi1+E6sxt3QJlwUwSbjeXy3tFCBUdB798kEys3J6De+bnThNTJmBrfXuPDQtPrDEZZjzzgYCjsUVJSus9U1zcYLLJsXTwqRbsmwlHnalECRLpSwfAWoVw/Vq7syuhdLC6g6mClCaG6K0ZqhDlMUwr1IxbRGLogOmOnEQADNVPH19ecDCm8uTFBJaNJRvhcCupEcpFbOUqnWyFHopkiKsmzT6nIVf9yvO+KO9W90e8dXODaaewzZKLW/M0S0fEdhnbZTfjRAdCJ4LtQP8UlYK0yHwqTtyB17uK/CndqexF8tcl6irwVElmVOV9Ph6oSqhxAkl76B0JXsVqLwFkr7Q10LV7NvsazCLUHfxIPYDDWed2zNU31YCmO1FJfiy98dbacIEvIKzdYd9nF8hroFsM5iVoAxi+Ut/YrJDraaueLoAs66uD4xHgkbguOWkV++8Fxsx0gMSuQROphUV7iOK2urnqi5Mcu93Ta0mKCAtvdtnL0akeJpzijdYpugWD22/TVkluQeH+CCXXDCTJ+9ryCdYyz3Y1acBGR14DZkqcaNpjEAc6RggdzLA6NtVrHluS/uS5k0vTTJ/G9oa62aYuAOIQTWVPEJbm3UlES0W3dUe1FsyoG5jxq/vcC09/FOYl5mv83fvDJXjdXE6CmFf4LhKDu6HSgZG3WmrQA03QtrnGpDdrf7a5rAwHq8X4hnpqanDnf6Ls82MYW1QBvUuPrW6La0hJGZDZ0GQ8WU1N4uIBSZ2/GSPvOqvrw+Jq9CZbrH3g4HJaZqyZavmhfKqPAmqnROD4UaxglnLAsfXtNe6iAJL7+ede2QAowgbe7Hbro0N2EyitJOansoQrlDkwqBWguk4OEaxLwhL71uWyaeTm/fc21dlD1hZz+FS9uRWZt3C9XJ9t/k6cepm+a0Er5StKzXjHmEX00+zCTyivkfpX4o1e/weQXejIbv1m1g633nPDObOB7EupILILpOVSa0CK5bUs9jzI5HsL+X5aqLc4Tm0F9ax67L/Sfi+qzl7znbrZykFzgbmHQYxmeW13WneUb5Kiks6KBRFcdvAeDiir70+LC/8JoLCKP+zD1zK6quju57+hrAvBCKbR4bNaFv2S/enGTH5BEJf+AqHtiIWRtCL8JKxaoEwJ4ppGEv/oLfQY97v81peqxLBfBm0Eh2+cZKxTNY3bIhz2WXL7M1c5EXL0UVU5CG3u2Gy4HxC/w39iUI0Qg0SscgdPQbywyTS9rihtjGiIegTdbz7N6SvB+Huhfe3H9Qq8iZgfCoV1816CNtK822gRp+ZcxIHH3xEWz2qIOyoull1i0Z7tufz+SyM+sM57zRFT7b1uOYS7VQPw3dlyhnC8N19frXvs7VT6jrc9peUKpW1vUFFcEk4z1MfU6PyY/6zly3DW0L6jiC4M1faFo74NPqGVcuT7He9zr813kzyQAoxcsBNeOKI0HjDvXoh8ZT7/+zXJ0AyPfRXsiaQAfRmgJgB7ZsW9m2O9c6nxt+ER9OuPlypfTGkKZgIDjXKNj/lB7uhet/XP+Dh9KGYQyORO8zUhrqHmgZvI/q05Y0s58jxX96HEOmtcAHwRnvgbVEHUEqqhzWREKBRMA/4/9Al2k2rYOjty6du2P5ukLL0X27L812ms/lefkp01PvSoZ56Hbr83l1p3w9tfKodpEPzy9bVfi749tm7Jv1/+D/Yt03aL+n8sGojv08tn+FNGyZ33B30pRy4U2T0iLJD+amxFvQBqzenrFZrc87E9loD1w/dt0rFKrVXuoRQqtdNaa2RwAAEABJREFU/Vn7tH+J+9lC7+vf/Kh4gAEOal8zH0kD9agxjROkidbvmFaVUoWY+ODPooSL3U89ab93fe8t5gj4ufuEBkpPdbHCJoTsDBqW9L6WHJSmayXkwmW31VIatk+cvtJs2/vMsveWHG+a2s+dMoOC9kQbNljAZ3CfsSykiFWXuv2fz8/qWClneYR/RNMdHgxfr6vXFmtxZENgnxoJoNtN53B1+BP9xZP5tf4v+HgcTbEhpa1HkV83bYkOKZc/weuuYNvp3HXyNFMuNcxBjHHTjqb6wOedp3WjKdWpM1irs7hJH1JkFFcGjmVCsUMEByYEmIRJBZ7BvdAB3LCr3fPivtlqbTdO9MN5bHhh6Te2dcqrhixoxyhDJgjE70DyKqgTTEGVcXfF3jw5tgXkG9f/71/Lc2p5YZiT/fTz7bMHDcjcLcM+uj4tJKbRW8rFsPPOHQXCgGBjTrEGOkEtsR/wUpCmHuOj+6HlnD5dX+9zY+QLFDpycsAvCNcIuuY3aOmf+jgvvvuGXnwMv8pve6sSQp0BHCgxvR3bD8+Xo9V0ZkS3VN+z6lzna0jghVLcK6WUah+qw6p7BPrM90/RjTUBenWNAOvvnt/f2trmFkRR4aoMvDqUA6Hz/NcnBZtTubdLnxe1S7l7fRSortixu9Y/X/+Wx4Ud6ytxle3Tz1GgdnwcPZPJ3CZT8vMeQ2uPDT9jkzZqYXeef7+/93+//B7WEXxq/3x2Z/byurRtmpzURqKoCWsnZfdiGVE1nT2jON1N4dacg9pOKTkiJOq1Evbb57+99wOU29yA2DO75vr4P//80A1SDn9PaE3pvuTpueIrXhq559un1xNaYDBJATmXOI9ZPvSG7RoFQpbM/uC6oD6VjF/60860u643WMzRjat9Ln4Ix/wZ+jc//09T5N7/0wM9KLiLHZ2uuLUH6zUauLY2hNd1lgmHx0wVfjXu8juCt22TNjfY48rcpNVBqldcH7/d6RS9uSpVGqk9r63nYl7Pde//OS6Et78aMZ19K5AF8hi+kU/sjJNSLNd4uPGD/uArWqf3QIEAQgDU3kjyopw0bJqYtjnRX2FWA8xBP+xW4nfAF2kqIP2gcqMXVAr2s+qf7x9uKcpt045xG87lgtDqEsiaHNbqfqZKFtlzpCBei+ZOLMITeKwfA9zXWv/z+FhfgNoXD1gdIyS0eH+9UnpriNuii45WjDWKkwdXh9kHr0UzW5eF3bCfjHtfoRwoOBBfs+Ovq5t6x9Od85DfFX37p5ck6B+PsMUTwkP8pBGFu/vDCRjiA+PCXIT7+vuqrnc9i0qY10AKr9M7+zupyNt9eJnU/2KKVmnPgpcpy2wvNLmdWgywO05BiXHap0dX2qmtbnprOyiJGcx0RWrrrj15OGKFWIXT94P7l592bQDuuZ33uta6ua4JV3xfn1TTxeim9v/d/PcDE8XHZMJsDGpf/rinIyyDRpgMM8q0s0vwNHIqfZSCyXwEyIGPhWsG30xIWox5zsyfon0t+jUUz1OtLk2ME5QzdoMN+VmR7k/PyZKIuI6cnmldn9boUfEpTDn3OgpzgONTt85LEVXHdVu5u47lOMCAzxhLfxgaDBIBWGKSSECoMlyB+xEDZe8YCco7q9nqTj5+1eUm5ayy2ArXq5YyjJb4604Jsm0v5Vgz7wL2ngbR6N0i888o2W5lLfvK6dqmPAUsxKz+lUm684JSYxhKke5QkyC3KdGTMzUSn6uuSglP1PH9ta3+pksSEYXMXKOeU+yWmcd4HMqydRVTOGOnTVkhzrBaMDQ/tMUwwl4yXv4oKQsuX2lX4s64EELIogyKoUs4vb7MN7jR7WlTlvrrMy/IY0J1VsCmoR6vNnKc5wro8rglovWJzHkvt/jNKer0DBrV3OOFANzmHVVEgqnzGtfIZFI+zNYoxmv1ukzqMguEaarY8RIIPi2ftzJT2t4yR3NXYpaSX77GhvJJ2fSsVWKONW9irUCQNtXE48yhwpw/xcsCKBe6qLCGTjfRbfaXVpafXF0XSpfaZI9bwZDKXCMNl1dMDfKk7zQx9HZKYZVYW9zUq6/u1e260BjxbhOXisdg7HJvuwZyeMbfuocMjg8woI4EyscW6Kq999CVgRLVGYxoxKv2/Bs85nBAV6hMaCINEDKp8lLjG3eG8oPL74gNjlJaJQsyqhBh9jaxOLGeNaW2ZaWwj4jhBRJEkHfxPqXbQQOWBBZiLrZ5imE3rvcwY6CH38294/S0+trJ5vTbUyg6LC3a1moXOkJ2pmgAOrt3Pdsai+pJb7JUWqQuT/egS6OA0cczbymwlRkGU32+yveZUXV4haJ1hxkEB7S7spNo0n2W2xLssmJCtx3C6v3eHhosWza6a+mY1cdVtbslm3pR1lB1utspq9NKEjprpsWN76eenmw8C5soUu4mApqiwXagj5dz6csJ8fLKcu2O1FioP8hfRKuITfIQPXDDKds5NZIDUdbFbKJhKS0q6wkMX/kWdGG6jBnMy5qSGKmZ9LpWhQ/IBwqtqGbLJEHTK0x5G/BaU3/SAc1mj6RYy/o2vxl8F9+reBU+wAWAmpCBNOi/a+we8gClK4WoMQGU3ScVxDJ71acZ422eb0A2cBh5tQrOJLlKr3zLU10mF1sNAW5nmMrFX9xCSGBaLuamxdgMv20pDtvYFLKaMYs059DpixGhTdVDbQ2Qlhlz3BQgcBFhkI0ssXwO9rFdFDAt7myIWVAmbaM/hMUbxyrnE0ozffS72HgOupvdKpvrquW5oCmZQMv+7XDFWt5D6/mjgeQ1wRFKTHbNydLt3pPdYJsVDTB5VHmog/rYx67rWvfSRiuFgMaV44QfxQ9Rh5lj0AP2Yc5uPByFIW+DkS2Y6UP2zFP8hpdAdwEHTxIK/zutRjHzJg3heLVKczXUbXvq6So3miB3BffBkS66WcpTLD99zqlK8JghbrsvhiwdiE/99bXCL3cIwLG1Z2njMTNuY1Y+Ev6wmUZ5sdULYvhoOfnCcjkoKByQe7SL40JmZSeh9Fs0d1DFNIK4s3WR/a5Si47xrGmdG5SyF7zShlJQzjOqliG4mqH8pkRJLoU1fbpPYBiAccbaFYQ+SgLXosGdsuNK5tBrOIU4szbdjMtUFtulEupdWYXpvGZBkWQPhJE3sVn6wuY6nzl3vSs9pCMD4Rw5BpVtdJnGsqGXXCmaWvOsejADOgG2RFg52qjYk9Fmy+YvE0JjzbF8LUjNpoEJUZXjmnxBxQhWxxuJNVk4LZHM6ZZz5eZANJWSz+XmGsADLwI2wzpWhwMMpnESeywVk4DT44AL28tUr+mNa3zNZRrdyHkF/zbLnANmAdWYkRjORTNTLU0/3dJK2m19Lz0VTLmhwtE/4Jj+ONOQ8EfrWMbRXqqUyODyNEXa04k2C1OwhrFCjPqdFZFFYVHaGekaF+mGS77B0QmQwItQq0CZMRuQWDXIdEX4KXd34TU8tPQo9Yp2RtDdlvDx03x3jQlIyfLKizSzAb0mql1Hm3ssjPdmVTlw4TJ4CiKEWQTcumNagKZpaeFxRQI7gMAabMHrNpdDxzI0cQzCu+fUX/buS/dL5jNAjkjtyZlW8EuDa1alicYX+36TI8aI912jpppJ9rDbSskWtd05GtGj5sPXV8hcho1zRtOu0pOVuIlLK5uOpUAhCdMwbjImmvREQg8LR9EqKOJIYaFn/vnz4N5hsuXt6Gqctm+KWTsnzzwpH1BwsFnEDY+8Qrh1TVc3T8YGyksDjPrPIWtv3FatBcPo8jovEGp47EI86kg0bW7TCn5Kx2RX2ZgjajwZRWE8n6LhSvvsIE0Ld/Jbgq56xImJMKzBSkEqHtt86hJ6lybzml4sJ6gNnXkNzlG9TFtiFS5za+Qh0BFzma1t8dt2Krs3LCtQs1oF9ZimJrAvPPf8+vlVAQ9FR2swdluLB7GqTwoAq0GpJk3tB90QDU2MHWGxgHv4UWHrFvLDjLWp38hbkkW2BKAt6YF6G6sms7Jm0jhqLrqrMB6sh+vLyXpt+tjrnGdUXIqiKQcm+GFbGIUE3SGAmKfxvDjdwLJaYvLCaHMhZBbWHf7jWsCeQSei/XLh1iOGHsOv2RGOojlgxjY0lVdPQ8mBJ2z/uJiYCcv5Sfxg1t+IGZBlLUmjhkrlUPCe7ZsS7u0wP8DXslZJa1PiaUaWVzLaDG7D5bPM7hCVXKRZo5MJvgEUIukPoXjGZi+k2vvK5v7Dj5B0i5C9sihXzJo+MzegZZnJFtRQj9Hf4UR0XEXvQQ7GB6aBUE7pkxowslDtIsC8/NoT2RpdQJWXEofA07i/Eya/aCgheYSV6q0+s2/fy9R607pnXG7IH1C/gnx23p1mNYISAlc5HcbTMUPJna2gTtq37u+sfaa+ld/JtygWENDAsszzJmV4Tyzjut0uJbbaTQX7QOBYnqDkYXR7cU8b0j64F+zNhbsVrxnj6vtO6cj5eKbUGeJQS9acGcjxHv/t9S1ubUHKqPISZBRp8vXKGgq6p+kvZ1Wrjza15lM26da4pS3vYyMBngLgQLw4Eyonj24+yap57fRUd6HmHUSN2jO7cnDGBEwVdXINgxLtTJd1xcnJPk2xVR1IX4j1alyGsr9dGSmduOlsGE1pr3Xi+s9vnQeMsuo2YXZVWF9JSx+tVOJ2WpwMzOBadAhTUFTkNIKC6Ci2dir5EgqtswGFN8bGC43iddmnK8M601Vtf3JleyL2LI/QYrnWmCaO4EnvanRJOvdszy/yQf+O/Ud+hwpIcGA3ttLfHmOhoIaTxHRO7+6utyNcR4wmW5ENqBTC/A0PJBx9oVXAqLsmxEwR1TxaQTogvX3eeMgmEv6MV2NKK9PNUb3atWDiWFeZB0xXAYrMb/evHA6tF6hcrb0RDbUWd+1KJ0Znyl936znr9yi9fdapG7WI473/htC/C7+Or6sBH3WY8AwXQpOmXZxNyas2mvUGq71tmz9vw7hn6We0ULxT3a1AyG5DWpQF3QHvMcE1J2S6xRFnFF+DkFJwISIBm1e9P1T0NyWHZDsmupEzwFnzvfjc9XyQN8ZpZXyIhAOQz9eNje4Qemm8HOsGyko+12/ab9VvS9tzFLTEkMBoouIoGeKCh70PK/mnH0IhkgQ8YTWwrJhSlTvcuZTcASQIUhH6lZ+VizWT3xo9otNp8XnjUgVwuos6AMFheeFQ0ZLuiNcbxoyi2WP4M/XDuSjMmEmQ62KI0tz7W0/FwQ+1r3QBnY2TnGoboSFqKENplvxbw8YcERRV4letIWbBbjpW7OW/pHbKp2c0Ji7QBoS1CzGJgsXIRnHg3utufzFFl6GFOxgQ6tFLXwqKNMUTmi4lMilpz3RaUyNbybR1Zc+XEsU1ix4K3LjHWqNFcKur4LUhh1Cc1j/X+bnzWh2ePofjEEr+78JPZFhjzblbci2Mkx8y2OhUFHSK0ZnMo3aFjdj4PFwJNCDFYDRUGVdBoNZJTHVOPqZnOIrEDwkAABAASURBVDppQypBwYPSPsNt4in7psy3cNHScg3ErVbp1ekqorjfmCgPGWgW7NAOtHhb5LAyp9TDvwpq+rA29W5O+znayQv9e8/9JbfjxVgxzgZ/4V+hKDvJG4aMFqTeWlnWFH53f1Fb2WkXwGMxS1137v6yDquF7phsy78+qpwQ21bF8mVu4P+Xr/8AtGWryoTRb4wx56y1DzlnTBjQNmtr24ZWbGPbuVs72rbZVhsbFcXQZsGMomRRCZLMiIISBCUHCZecJHNzPHtVzRHeV/uce7m89/+vTq21as0wwje+MeasWvsCKabrCnJTOY55fixxklORJZUZ8BqrjCOMDiVr1VRfJYkQ6HauFV6ZpdMa4aCBqIrY+6vgsxg2QqfkbHXQ8kJBksKGV6MBI2VMtbSkfShVjK5DOSzF92/SJMfMZa1lslXAKgizPLMuAYYHM7BNmSRvW5TJ4kUrcr/XRywkRkrPyFy3XE8rHEwXOihSQCs1ClGKIYs8kz+159plo5VB4VVFB6dWKhqE0vij18F2K5lKIW3zFilKk7y1LVtNsxnimzGcBDRFxCnokDgJaqZlJQJ0kKBUW2fbDbClSYkyanWktVZ7SyltWG0SmyakUrGZQjYBLWJYATj2SPeNHNKWS7H0uWDzPE5VIYWTMFsTHS2FXjJ2dEawe3jU5GNCIAeyR9LTmYSHzy8Y36K/2kIYGC72Cm1wjU1m6KRq3UVTI7pPDYpuZXz+Kivv7ivpJRj8fdF02oeMakluujR2WUybaSs0CIWlLEUwtUlSIXkiBqb3KBcSoYPVQ5r3vo2+Md+svAeL7arm3Hm6WnQRK07pShbDqpQYOFAMs9Lsgplol2R6BchXUygjyhJeG/gTrW2ulYUo7nbJeUbRud5Ul94oTySLj4I9IhRFNE0pxB0UsLcIinyUmmD1AdAmDS76pqzcLK/ahrCMm1UvxjAhwUQ+inmqZMGCp2TaRjitKUtJ38y8Iw6VJ2BlCaZIUnlDLkFlWrWj53vKiYgKhD6ZwAZgQn+4/RFPqzTxXrkXIpGErqIrNKAcL8ppbLRi2WgkuKFGBmNFfKIJ70egMqAtwS1qJK1C78vQxQpRTLQoLQmTFKpCAWolfTQ1mtplpuQGOsgBFcoJTLgBLvhdU2pwY3hk4IUdNCo1HEj2sEFa8VSaCRzKujTdESsHwZhEvKg/iTlDyCmErrQqqzwBSXIjOmJojsbKmHSEQqsolNcAunAIB+okHb3FZPKqqWrPWNbofOBI6y3WUUxq5Mh9BASNJWqQb1tl0N6azfMQre3xVW/MD3GVbAoaneZRUwERoWxll09sgY1RzKqKKLImiyQCPCzcNlUiz8iBwXJSkzUotegW+zOr0jO5IdAIE2mijZ5PzVXXOY6xHFksxUS21o5djnSbYUmDtJCzL1VGDLhqFkCPuzaVxuBRsFYM1ImI0WSAgnlpxWgNBd2gvbyz95kTshYIfyljxprAnNul00FdM2Zw5Z1Hw9q1rFRKyaNDSXeQL5XNVTbh+rGWrCjRGqPIC/Dag/eNC2hWExpGOCDTZd3Et/JjWpSlGza+y6ZSIinkdGZNlRzSWnXDgAjz05MQUyrcCm1aD2gk0r0QWRC2qtO3xM5HN9koZ9OESdFoIRZmOtQWxj4hWSHbsba5Ra6tbYeDcUWVRu/GmqiAVHVFAw+R3sP2ywJXl7k1ZINwBLS07TkdwXoosGRrL7RkDhmCEiC0mwHfSulmmIqhMY82B1leEbvfxTSSTkrNmZFHyOmwaKYQo7HO6esU7kunxKpzYiNtiVOtqgjj+juqGtcrLmbmKs61+WTqEjT0JGVkIjxnKo0I0qPmrIyVeS0+w307sjPAkdUIkInS6Ixag/eKDVEKV7qYaRWiSZD14Kqi3oTs8GYlVi41UdTBQUSY+BgLBCRNfEE0RqlYg2pTD/HCRt/Xpk5fGSNDB1dUaHE6HddmrGcdnB1l7vRNZ8isYsy7yVCWNcaXVhhfFE4vq3uNkqUlCw8ay18nr4kWHbKG3sQYJYKx5bbJVgXZD5UGWMG8ZBMJ61BJeOGoyqco/HahJWZWhXejqGR028Z+MRR1BRc4sSDmCiZdSgoY6h0vKxfQ+MxRGAnighJIgWDySkxpVwczh9BMCs22mJ5rykKNIaRicb3Jjq5ufWMOMCYhWjCowUjCRgu1FT0xM9Uqo/3FSiUO+pjSohbLw1AbymlsM1EzN/WuObIOqUs3YRFEKI7ct6thq9giZ/TQBlOrpFIaClpfEjx5QdabNRWVMimFmgyVg1arbIbBmQRfuOwnfQ4yAyUbHVk0u4EhC7LfNsGmSGB3g54IA7s7X5TKCZmamVtEBqirN6a2qBikozXjP36hKWnBkTpN+VQp2j5WXGLLlSUts1CsGevEXFGT48GsdFUohK9hOCjDKUZ9XJjmIHUB3QOwtQh1aNYOUkuQ1ksP6ynDE7lluSdlxpnLFapTxbVpiq0J32d3t0YGqIWKWHVLSlGl2w5fG459L6yCvRc7m3IW00kFdDWahQnHyoRuO94cUkbiwLY9SUUIcWlIcbiCCCqjID1EYFUyC8U+TaG72WV2FoLpmNmc2SPiJpRnIgepJRJZ+y3FvlZMMj80OXCfloySVdM4V3udM2nSTQbDoAOyhJLnXaRZa9JEpNwQrkKvE8Ig1pbJDAMPiam8pmnMmTCU0YzYvUhHYhbRbXA1Vy2RMAthlWBFc+wXRZ/p93A5lAqTrziLArfyLc+wK841rb1IMhBaSSVqwYRRKkuzasSjJKdVohroXQFBrohrW/vYbFnRtilzo1FEn/yKJi5tVqu9AkoxzT1sNd2sCpurz6WvUM+sjOLqtd+0u+VRbZbVLGUR5JoSYQloDz5fbLtF3M04ysxOdDlIOwk9ZJl4YZJjUZlEs8P1ZDbOStPZwdueqS21C/qOKpJAXbDMy3lyNvFip3TtNXqZgpLCBwMgLj2ZVGyThGxaq+TWyzt5TDzpXMU+neCYBKOzKfmxj9WFkBUZG8RY5QyxC/fCBJ9weiIohtExPlQxE6GeUt0KayBLLkzfwH3EcDRJVZor6oOooCEZfV+27WSLQzZyKltwMcykBTylvHJNHLfFjySXWFcdkrRElgQTFQUyQaBLs95aMdIhGsXeEV1ZgUbDgeSFhbVVbCt1OoQW2mdpenK0CbDLzLTkjSlncSGLsyLYPbsHx5VUEXb0LEShKpGx56dULJW76pmIBFyWbeix6dEKo/hc+dwyxxlRwlHdeNMxpEyEg6dWkGm9pEOao08ylWziZgSiwlYYlL0GlhcIpERcCrpJC3pS0so4kF1A9lD+rt+tk3Rr9BlIIr67WLsco7xWaEFHM0PI4+30RNZzkudgi5kM3VRnSVhUS2Z4QGaUyyq6dfWRMrxsc9lWpMsoWWjewGQFk0XkpNCmglFEHTs2boqKnmp3WO7ejlZt6co1S1GYZU4v4K02xqgaPUQzsLpA2WUULFPA1O5FUzZhdplo78O0I4ZtbRQ3/p0vcEDWmuHOfS4o3mk6QNQLZQc7OfQDTBzcKYVLczEp4TR2p4pqWIkQH9FAqa22HNHdpU9ZXBTYwGV6ZEov6SrFKRXB2rF5Of3mmlvWWoCFAFOFDO9aTZPpLAto71qsIHSTz1NYimWr3BABWNnQYdzjDGWdWQ1uAmnIBX6o4BfrIo2mCuVkEs70iTy1cGM0IRMaVeZMF9qnAqBSyV418taWldYKtJm1JYTEqBRig4q25f7DTNYho7nItOTDq9SkBElBjWCWK23WLiXAVhLME0KyJH9LauBhs0l0V+4dY4Kl3A9J4seg1WqNECTJYkYrirvhXKm4jxgEt9bYF/E+k08DT5zCWWNrZaCypbJwmMXSfdGpKKDZ7EqEImPZ1sPKBTxFRYZCmIzmGbsI6Qc56cL79BONsWPj2+TTpkilC12yC5FzA0S0WtLodcqk26IYgt6090PMQ2wcmSYkvIfO1jbWGmGQt8TKJ5m+lswCS0aWCJhH1O+RxUbwkKxebaBrUx8OnSgW3fBtm7EC05rIslTZdh553EOsJq1xapN2Un1Rg1bV3O8S9iRsiXKEQEwbaM0gTHNRjCK8izKK+4DyRDoQFRLbqHkoH0XDyP7MCoULcYEqWFYSx4rZZlvKOgp9rZNT0ZUwT45GlkU1RuXocPeRhATZSG7NEqzIjSZWQkK1xFAkrNL13SFCUQzdjOnYZtHtvuVwt0kbYx9BYOkOC2MIscWqcrrUNE1pWVpCG62LNldMFWdmKJsZwaSxPOkEFYArnAuR41ma2opFKgd8tExDgqpmq1MtlpVK40/vh5WjCnRuX41p3di8URQ9s70Q5AYwTzamW66JOVQPO7dMqaegWmGYkpFOHaNgqtolzJzWoleJIBpKKxWEyiW3DIJQVVBox1A52XThHsOhVn0UFy4Gcy4593JdBk6GJ0v6VA00jd7WrtxRiBB5RtgEi9ITbUG69CZD+IlFouUaPleVTSM5GhMIwr5NWcM2DMViZABKMyqRTAEKMhUVruwnsadlT1PbJUpCshqUh0hRFFT6TkIW7mGyNCXO4REB4i1VnQqzWFKUmCgEVV6MSJJDq4LQCR3hLnwPb+8ymg3IyE19zS22LFfh092F+AWnMtlsyjgaE1GSoSJHMtnEii0r5ejsVEXljDaEWXzSZFGxomt1LMKRFVCgmYoIkAS5rxVea4IVU1SatAXMLIa3tHgSFDIThKHYGWK5B7qaMVZqw9CUi+Bisah06V7tWLVVShmrIpIaM5TfsbNAambMWXOvBQSUq5GelA2xpkrw6c4kTKgsSGRfebI+wCcmAyDJFQXdkrsNPWy1FMbu77qB5J2ylAyZYkfyShaZZsVshxbZ0boMG60N0ZZmWz9sh4OQAAiQn0bCjp5tEdrC4JEoUVE0eOja+Rljq7Y22Qa4bBGfhhT6V5VEUhoBZINAjFyQxhIlh8hGRwpBMYUEQWkizFFzqbOUJWvHxCGc5IEot3o0JPbysDYSpQW/IkdFB/ouBpNSRMRiiHF44aWvfcvTnv+y919xFcMHoUN0V9DQGhgTmBI017GJFTo5VTMiaLWhuKuJzdZYYi5yPIx50vZyavQLJZp9Ift7llAnY8ICmG2qle0JkCkhXOu2tGPBa0UdZQLepXJKhRXAkcLAe4v1sKWgEslZsnd0CWAqaSiMQJluYKZ6xJpOgqh32w4t+yLZSf6aBI10rJ4w0+2g509w2ucmZEef7VwakyJVPAyb9tPE+TViU8lszp9qJxddPRE/wZG1Ydq6HkK1tXmQdcE05sEW9GoHMbsSNtU+0mSmH708FTSUb6ZQacb7ibVhE/EPXHPZU174gqc/7+/+4Ll/9/yXvm6srajZp8BHqUzDaZOpTfTQbc89T5yyrK2CMDFmrRJTbVViE2RZW4ecmh3RHYxGGNJYPFi6uALJNpZs59S3ZTqpI4zgWNEmOSLWoBZr9dO5HJkv1UFwAAAQAElEQVQgu6nR1ZdSk7a0TiMYvagqTWkxcracyqAUqpEBnfWVXa0I0qnl2lI6ugkFNG+6MfuMklz7hKKAhDpOVm6Cq0PQFaOyqRyWGsrcawkDIyZoh8wFqwmZIKuQfQ2zyToQI0DPba2xAdq0lI1LrgdbmzGAGht826BrSK0irOBxBDZBZhX7JgOoRsNUJOCELWpBnoytL0XDqrS2hXWSMBFRoGIiGCGgSy5TJXrLZmlKxqwGtkDcqjJ16/14opyeJB9tFaYR6BUhQ1SRZIGVWosBpCMeKo5ec1g1dNRhguFHMYxdQsFhtjn32MS9wVUI0abDCXIjctV45xebrkCAMScnRXZQdqODe7mjynHRabxxTFmPY41BZDOHemvUzIrL1VLnDpUetW1SYJDryPSZRme7q5LQ5agJvosn03xRkoMntJceSs4Glvk8+KnVkUwus92/dZYH6EWIElzZVAXZiKez9ks1aaOaJlJczDsJJKxFA0KE1plbVHqB6DLftqwtk11iKQt0kKjaPVpsLWtADwwRoDLGsrAiGWATu/CO6HBeEVxFQyulyfxSKJctjABsNLOmBs2FC6ZoljSIiUiZilgHZQfDRb0mIb7OWkvJuzmybGrjna03iYp0GpqwinL3SGdEMbT4e6q61LZgW3roojaaUlP4TMXszN3KVVgFdCq1lYiC/W6Yjf7aAWLeYzusuZT2pm3J3mAGYIKZJjm0HfTQsBNIV7EqEzpGkqEUnL/XDrFVmBd1HnJESzAGdDOBDblJUnkgp8818qg2tUsJWFjJ3koHghg0tVZdwJVqiEjPYO0ym6N7J8LFuGVSTLFcLRUN2uuAdayuUVJEUwVLyMkazbNUQqiGcSxubmSEYivd2Cc0AFuDm0jbx3RJsiZ0GBihClomPXqfI06XeVSSRdsU8Z552LAwq5AlNAYoEQCtQEZYY0KwBGRFkr1aPAyZ4htndi1jzETbjDY3FMHRjRo5P7MydLa+Djk2SwvR0IJ55uSa5FOoTej1sspyinZenAuJUQjjrw1I+ArfJIIoiBBGzUa/hARp2QftK+5lWH2qgnmvuwNThSeTkYlBWsHpm534OBHl89tzLc4xIg3Ctcr60fbwr1DGKxOZLcsC8F7Yk1Zr91ABsUKXUHWGOoekkZtILR01eg4NqV2bpo7sh6ltM+eZJomD5NJljK46ACNpxUaSDTvtpBu4DqAzlUyx9GgtuFT25qIJoUbSQjW95VGwaktb0kbUwRnwWUgXn41bh1JgpIzivWp1lT34B+CAWOQsJllB5u5CmTReDFpuMys3MG7CsI/hJ7YOrJQYiCRIQjgn40IlFcCEFOEQvhLFzUWtLjSiFXknauQ7eoZUTg5R0OPR3Pyo29FYSdiYwiJqYk25/ZZ+am3tLSAo8EMnQlq2LiWFlfXStTKrz6nz1GtblWKUfeKiRWxGMMMKtgm2nCSX5inXt7nyEuXJHXJNVslqqUx/MzDHJgHxs03MnD035SqI4yqnIW60RLj75gONkawEyJM2uf5J1uY4BtyymY9wm9mqqRD3Kra08123ga3jqFhTtzXW/afElFA9b3GULYewUjQreiAqbanqWzgNR+6hHVUM+dZi7dPp6IStZUHWyRJ92XgBdw9sTu1wlBQDoVvZeU5EkTFSGtTfUMpeVNFi+EnILVQWEtFqIA0h8JAK02zivH+RNYmgMGitxCACIe+kiqsyApU2w9aUhIqICutQi1Ls/DnWPqIaYGoCRgT8kFYpnDKO23JKObLCjtmmW+2FfqHIdA3AWx6bn9Z29G1KJPcz5tgJx9hnFEoEsuNMM0RMYvRamG2FnGQ1TURCHEmynETxEVBL2e00xT6tcHYzlKkWNkx6T2OAe6RmtNoNZqpoSJJG3GSu6g/9k7/8tT941q8//Tm//ofPfegf/eVLXn+JSKBorPZSHhSWcQYa5lXnr33Us//m157xVw/9s796+B/8xSOf/syXvOotKf2Iw6naeqC/Wx+1mArPoqlCm+m2L4jFaggoC2IsBhjNerMmqht0M3hVbXs/4aX/D3niH333rz3u53/vj/7bTz3sLe94f00F+3miHJJc+LldE4tqtV90tVQpShE362Ln0IabbEKxwg4V/uupHc1A6yACo5ijjJiNycokE2vRNKJqK8xgSNKDmaVbiisNwBBfIkSU4+oQGJxKAvbazKf75ijplIwsuh9LizFCGbUtQMxpzaZzRTp4iIyuhyWHFqhwU50sI4OOlGT2DW02xo1knIUQm9I3ymI9puHmO3GmAIwni8tSpg0iAgkBHOpSwjFHTy4wQZcDhG63BW2rTpVc9poX4QhgY6bZij4ZjEQBbK/2tvdc9pAnPOMhT/jjX3rCHz3+L58v5WTinpnGLccBOCnX7RiR08RvUdKnZZ6JYeJ490mOnnAHSYwMZ3+Kki4oBbrV2NFeY+a6De5FqwYD0wQaW58bJu1Nwpa6zoHsY7ZDBHu1TUIFekc8q6GzODK4sggInrRZ5EeIzi5TbKVQ1xbacsgOYjk3BkqApYg8J5ZY2BJ6krUAaphCDHckkWahtgnjAVQ1i8NSw8RghCjBOCslNClhThYyxaKsQtkCXiRr7DJxwlJcUEqoGDMPUl35HVkmBa4llqQOk9zCtVIBQUGqimmxgdfW6CkkkWmpPAnUAKyZnTQ0SW7dBnQR2lZZUcJIs1ZQUsmZINREMvcp1zRNV2PIFWWAljQxgnyo2FdSAC7u8OnBCs2JlEetIoMY7jsDQxLdsgnuZZsr1kpyWANLEuTCmrRiVI4CazQYsp7M9WTKpBSEPrEEykybRXKnh0ck8YgaUT2TQIZ4yQpsCVlrrLWQJsYezk9omqJJaVRSd+7505SSBU7/EYSqgCIgMElh/KhqM2XuNhGGTWh3kDJtCX7sHkTuU9mXIpsMxznkUrBdjp8BmG3CkvyY3lbJySsNoztcBkoQxWMPUyWC2MXMbStCCUDAoI9igHmKQo3whC/JorRIN37X8KLPKmKlxiydRctLGVmlzZ12MIhT59SSaeIdtDzBD6UWKdq9W6Hpuz6ABknAgmxmFwk2K0uYaXkotFJhIShK3WgzOzbRSTmMHVr2JU1qD65LhKK0izLvjZaDvhjvMNJIcIuwmmLUTB6xKaWXiDSqLMliPLqEloLxIs96jSVad9KTjRpqaD2GrgdkE0ntk/v1NBeCQLYkMfEtcgZjuEQ1KUEl47u/e7BfKF+jLKrYZ0aqGargUXFEbYwMFMYVR0f3g3lXroiiu0ENII+xAQlajabJ7akeShotKNSaObHrFFBItQJtX+TQ9ltNKQZaMwBeFLYSL35jqQ1O7UK/uCimjsYJuzoikhpmxb6WUZFJk3k/rSzvtTZ6QfwJAziSHoFm19B9zzXgyq+he9kWMVpOIbCITbfTJasrujUWgU4iwROYSfOSySJkLb3zNmI0WFltB18xY63iQ5UYVmIqHFktBEkiYBNxGFSUmpQi3dOzgv5hfvDqK//v7zz1Zx77tJ96zNMe8uRnTJFJdZqp2KBr5dRgtmOcLWB0NEHpPfpIGYIxvNna9DzjIU0SJ54HZ/IDu/h9jFgHeZpLSMfSrNFuZrgaIAIXKpEtG9knRCyLLc5d3qipW+XRcr8nGAyXJPNBHXxm2/YP2ihm+7OtTiBkx5kGQEqbNBlSnANo8jZDF/C99koWk1xvZSKkBk1tUVqMgalkqQRDRkJBUDtatBFMG1ud4zroCp2QZvsVNxHZjkJ8gkpbcQIdEohEketIVAi0Ves5zFmSx05mEiUmgo853CqxH1Uwmo1U1phtg2+7UoLQh4BPBNg7MWd4UC4VgPYulsb6SmZswFrmaAkBIoUZFt6yeokQhOxFpkDKTLQTJ2pZMoRa3WQz8zSOpn1c0UZh2SChqF0YQRVwhOk0YhMMD8hIBkXVCUPTEqnQuRm1sq1zjQAlawLBs1AGUQakbILFkJm/gLlS0SqJXTr3acmbobrmhvO/9AfP+Mnfe+rP/N4fP+xpf71du7Uj9RfFlIAGjZgyCcHMCCJbIsfj+T967ot/4jF//My/+/u4IQgnVU33Bz/hD3/y8U/5ySc8/Scf/8fHOA2rkCR5X/qat/7fx/7Ro//0hZdfdQwygkINaKGaPZxDcvb0XtwATXMyxcU5L+mKbqIwNFFTiZa7KfRDG3r3oZy0g0xFkIhitfHAJgiTonS0EhWR3sroi1zEVwvwykkIagqAIlxSWgytBWtLmRbbVKprGacSuQyR5AixwnDVGOJmsavPlM3LQ1KHD3Dvli05jvVdzJUEw87KlArbjZc0BCSRzBdZRnFzltTtwGzIEejBvnJa0YAh+7QgzCiA9lYhE84SJaXNQtsGEiHc07c2qdxYhmgtYDXVH/fnz33cM174mGf87aOf8cJH//lzf/OP/9LnDTs81jXHsllbu7hG7VXqb/7+DQ/7w+c+5pl/++hnvuAxz3zu45753Fdc8vbYlsyWqBIW6alNuiozudGgfWlZHduKWtkNKSsDiVtqtP6gRYIvCbqloYVEc9WS664/fekb3p5FEbjy+vMvfcNbo9yjAkXvOExS5Cw2QAozmmcTM0g2gTAHmqUpNFPdJWGwDhUT6aGdLTGLdmB/QHZgXgq5ALCuZbcwCQ1oWMZYo2/KPGlEvBcWL64REXCOtuLeWBelxM3byvUwqD4ViemIkAJ9EXoRc48KbDdaIuGTqdYFZkG7WqsSzlr2gpXSJyta0NyuvPAWU7aQzWSOTqgEIH9oZt9cnICWCGrwuQvNZDEZloKMTEfxPVCTVu2zonQN35J5FSRcsQzNQ6ERGK1NYyPB4VqTe7HhMqJaURYuHpQKRBBxEpLr7CJCyormricQKSGNWaE9lwGhMNmyzqccU9xC2zSbQtlty0bZWbRUi8jIBGaT4kwoapdIoRBJEUcUpuGo2JSpObYiYXol+cC0pl+aWo5wxo3RE/InMSNZy2UwwEUEj7sWSJVwPPhS0a57ISgBa0KhjGFsR5XzHS7Wpi6z6CHDoESS9RwbxLM5Oi2Yu5GMGZTglGqmkZ5eNFCUThZYiqsjuqhKw37ZArLu3oYhiCyfU1AnzFobRuM0Y9lyhCoO0U4IYuy5WkdZpxwhU8UkhmwmG3AUauw6h5UM7qUP2IY4CVPNEkpDaFTjeE/bJnyrJMobi2nLMIX0aj2tNsytxcpHd4cR3QhRpE6Q5cJP+julVuiUJtKbNdOuSp1kSc0O7DYt6eq8oYkUgWXL6plqKa2s+Ykmx7G+rXVAHUbr53qOPht3MH3bq7pKZmuhlWAwtoZkiFDMF9LUK8KPHrMC6l2yUQepIjKI7G5MY6VmfSEFGoq92lIskpjSnAbveUSdMiHSyzdaSm/go4JmNWCR6uLGOlEubRs2WfqwlC9g7Zutw9R6UaTu5gjDSCSLK0Ru6cnitk6r3RY58J3x13Y0pIqSVixaSQBzAye2dGWQtIropO78tauAPQAAEABJREFUjckVPLF7Sx5zaEVWFqeyLJCiYFeUVS4G/oAjI0W4MPmALsXnwg1NY7Q0zhAlkalwuh83uO/qRbnN3pOjaV86IxEtfEBMOaGBKFF4s1RaI7XbJJpEMdknxUhI0opUnSm0VQBOpPUypvRUcI0Yi5PtBrplLhqtgQQUd+4NkAyOZsZOCokcs4YjRf0AGzEwWyADkwkAGMrECsNNpgZsQmdJbBFrepBwlfvC0WQCXq65qk9wPgS2iPUWOpy2A60JW6rZTONmaF1RIdCmrZUIa8mmEioCkSncsTtv4QDq08WFzCA8jjGJSKW3zccGocLQhL7gVW/6voc+9rsf+uhv/5XHPvART77i6mvUmUVRlSWZ4rw4PT3+xCOf9N2//Jjv+vXf+c5f+923vedDEJUhotpKG5EnlaPoJQgSpVqKoCdzJxpoqpCg09ssQCYspKl27j3p0UH5m5giltiHmmShMmRb+XRrOMeXEEWakVlcJWhVyzjksWeSaoA1yEnQlemMtp1AT5osDaoQpR4SbVQxZKSlEC4Nk1LBDIqL4xZEc152zXV/9revesbfvYLns1769xtyozBCJ0Z7IpFkOw1tcZBYamXNZoCSYnB27FyU2o8VmBL0waqkMKv46H6M1NFDWXH3UbsZB8EQ0kESFkohvM49W1jFqCu5rhfoIEvW1jKJKap5qk+tbHCTKeYux40bPIeASdTKiH8gKxh7stAEIko8PUktMGGn2lHaRsOH65iEgmBAucvoe4U4VQSN0FIrM9HDoUmnEAfWCk+njHIBV72NEYMCZR49g/Po98bdFecmnwHt/yVFEylUFUeBEeFJn22JGtgE5H1xsLSB0pk5Y5PcemSr4kShcY3W2Qabu+lMpdmKJXD22oyWiIodeutdSR3GbNIuBAhjOJirFcVaqkexVfpqsvXyNsOCxvAO2JSpu8jKijPYgiXpj5Z16EiI5+anr33Xu37nr//2AY95+nf9xuO/97d+79F/+uznvfyNV155vlhphJOM1WpxWTzGtumWuaEIIM1WFrMSRQ2NRpGqBEsdmkULiYhKWB3hK5FgKogVp4ec9/mcV7/mz1/8yj//21c/5xVvWE9dyku2jZgt0CVMGA3OJ67Ojgl95J//zYOf/Mw/+dtXPfBRT3v2K16JQhEn5DNe/po/e+mr/uwlr/yTF736dPMa3AnIK9/85gc+4kl/+sJXP/QPn/urT/5LXy24mYaSO9WjSSxb7PtELl2he6ypcGuyCZN3bYTUWybvtJWJ29wRxXBkC0iArrvEqmC4XNKzfFokze4irHKCqYgeZVFSpVSqpKkQCYTKTFAgIUyJapLaIQerxmiMTWRrOUeFsFOme8Rq5UtpZz4tNJkb2iHZJamqCnQJrdIERF6wGyCigBTI49pKK1k/uVgQXwRHGE1yGog93+GZFNEbmSI0dmJOjpE+0LUwI7Y5AcpnGZas8q0qSjkZKKEel+KkCr4xf5XpREr7kA0febzrg1e87b0fgDlPKTUfltqALijPv3r5JaTxzWecyYZuRfgb89qrJi3sptF4Uzu4oaMhmbn6Fs68rcQyhQ+YDVdcfd0PP+KJ3/3Lj/3fv/y4//uop23nN22iHSqqaue42T3TpCLnTnpoppRtaC6mMHPlKlZbZdiswcZqaF1H0wZaZcSL7GyEB2MyI4jtYIxhm8e67daISwshg4DIqhDscVHTYdqldz/sEWyCIWgmVL8WgoAaaTFT5j5eUAQLTLWiRlVtKbthjmRt2t0vSYWTLEsJC2PXbCNsEVHqDEdVmZb2tdmxiwvlcDQtMM1eoALnSxIhtCf74ofDE//mJd/xsMd/z6896f6//PjXvOFdi0NmVE5BGtRgU7aZjs10HZongZE1YtMZrEu5W56ynLWqF+lluyJQkVOJ0FJKgJdUyVkQLr7FznegjRKrSuF+foQuitKcLeeisvQuw7gmlNIaCsgon+CqaAD9OXDL5dvYuLdjFGUvRxzHtJ+Lh5XTdhWGcgj9CJESCQOHIJFJtNKaL0buJiXDElzKa8PcaqYVp6KlMp1A67V4REZOx1Z9hTlCdLWRlE6nCoWkiwtkv2UrzjUrUyttYa3U9jApNhf+EIaVc6HcGZF3Env1oYasoouIrfnWdQp/wxyOLJkmpWjCorMewndIgzintNqToEUWgDJJG8GFdBVzPr+XJjwUOQKLm7GghWUJfTeRjpQKSM6R61JbY8IZ9qBXz6nuu/NSBJwpude2FCoeWSrBxa1nMSe8t1qa9MJ0bj3SC40yoC6Y4lkukqIOYXosuXWZaqH7VzFBNvI0a0udKiVgbpoK7d4JG9h9Lzph2GMoWhgQI9DsYIY3aJ9cQLNzpgAWwmcFhxaN6UjEHSZiLJ8mSaldihaXxBTecID+sT4rA1Vtd0qFCZ/dpjXXTj3aullT1lO3ntGYm0reRuTmOpMCeElLWPp7VW9Vw71yJx9tsUCbwrgZ0MqNSIAaWJz5+0JnchehVZDvQDGRYq+ooNhWMkqHNBaQNBFVqNL1Hc2LvE0X90SFVkqJQIGoiogtSU/xAg8Rt4xem0lRBN9qMrmPpS7gpK5d90Ji1jEsFkUnYFBBqiRNT4lCUI0Aku0swYP80eoWI1beHI7JFB6MY+XZPwOIGDWEVHEYBiEzL6YWOQJRV2MtSzris/iOBM3sLsp/i+aBDaWsKxyIXW9VkkaBEKMJKtHdCfpR49SYqlkhaqZdyxwRmCUbGsRkzwdIbaC4goiRuQSLJ6iMuW1gczEOlVb7X4EsKI2idM5WLWwElDjTRDZQw4KpQbNZ/BiCrF0U0YIQLUaRASCdsSkDI9JpFi0uC9BlAnCYtZTSVFB2k1okxgcvu+bFr3/z3/F83Zuf8ZLX/MnzX6zhPVIBiOzvhMfz1W9+x4tf99a/e/3bX3TJ26+99gbxSEv0UiKf05J7d+ZgE+2keHSAcemsIZvAoAc0gl+67U+XABEZPNWa7rk4vDUX4oso0tpIwYrEdNKSQ9EIXLXYFvYbAZCjghunaMUs4YNEAi1hiCXRbKC1hGpykgwDy+EIaZONMiGz4ETLkI1gFeno3Dgk44APH1ql3EyrSHPVEPD73tulDaWbylBX7jK3vrkSixJGz5XcRG3KaChDtkFYpzhOhN80iLhCMQeH6IQFKJijXQbSQD2BEFFIA2U0EEcfVsPEkhVxr4JgMpox7kU3jtU3kRBH20yLXYBoYHfUsWMowqEx1dM3iwu1F6QSjNOwcmUncaqrN7I+wzOOUiwh2VGHIdYtGRtQZlQkMgvlHJG1pa6iG8g0vm1WWZasa+nQzRqdZVjosRTANM49LUK1dv9qSe+cmErxbAoUNUf5FpkCEWU2ta7NFJyNpAaqpdwliZU3TMDZpZBu2iiiguSgDe4ZUZmo/ZRKYZJUQbNGJdOzPNhECILCqxp8v4U8iDKyUlKTY1qGvfTNb//233jsN//Go3/pD5/9rFe8/kVveOuL3vjWx/zV3/3Ibz/9m37ukU9+5gsy6AszgAFIblGUmV9sSUa+FU100TXGMSyytRKt8lInGgQo01BIzdljW6RGF1uUrpWk0K8PnxRZNAvOBSqknNKHqagCFZnH8K2/8Z3vv2nCS9/6dpBbmtLspkZeeDSI8J7s7e997zp37Nj4+ne/74bzN+wcAQIKqm77+jPKl0xhk5AoO+GF1gtTfpptg/aQLsr+AH1iSrEcFa3jFKIBidlqGnON2TwSXbqx1lTNCkg1dZUpmtRIApCWOqVtrOjEqQpch3jzbTTASq01bUD3ypCMjMJuR1QWySfHJms35rIwxB7m02JTcmV3BbVDIFAC1k17TxUvmiUbMaou6NSAhDPoQFqBUt2bT4nwRKlJV3RUi0AQhLPqRgtK9jFbJeWVlEqiBYVyTColEpsRYi4V9JlWV4JjtKD8jo84bli3v/n7t6UI55eswUFNWmuL9isvu+4N73wvPvIoYaCIovQqY/ngxmXlD4FNtKyjNeySaHuExE77tF7GoenI606PL3n9W178uv181VveAWcoEFYhOHeLk2/5ui+7++1voyJf+umf9CWffd8Qms8SmMzV1pI7TVmKvkoZa5cQtNRJYihDBYMAwzE2seQ/gN7LJtgULAuSqUUzFJZnuzs9hqwlbMWoovFNmZRA0+ha0kRMOZKApSwJi0CuFVPmkSE2WobGZ+ElQ3hCSIgqoBKZ6ZSrySRpik7Dq7c+RAkKWbShaBaNUCU+yW9NZjMoo9jETQDpYJGFBLlwythbdnn9u//h797wlpdc8paXXvLWa64839eTWvvRGdtdCme5ITk3sYtSRY7cGjZRp+nIUKwmkaPmiQsXDynuHduUoj5GTfeLKtkP3HiUQK3poeuiSjzcI6YbgVFw30NqQiH03llIek1q7ermU7eyFZomxiW/TTBTc83ykhKpEc181GZz7lkZouOwLn1rkkgLmIJYimyap13micZotKNTGvuzJPd+ZvNaWJO1neO7iKkiSujq1nRL8VIHe2flar4uCEsUy0+LGk1FuSk52eTsD9VoVaEUfAOSaJTpBmUaUlXSFGCtnLvAY+0wICm9GzWFiEoI+CtGHkkwUexgyZZFE2m4xVLnxzZlM/dGykJOQ095n1YH6R1LMZkoWY7AhEi3flAbNDKNYEmdRC6rHlingtZUZm0rrNKwhcYxc612Kjiap+RAnYA3JWMs1vrsQq4NfrYpPUIZbxRxGKdYeK4EfZU6tjWXU2uBJgV6Y0Uwos/StegGKbsqxTdsUvw5wWMUujJXNLeSrSysN5GWLL8jvQFlbTM+oRuBnqvkxkBFU5zr4C2KCTRAV6GMA1qLZlEd6xjeDtpPFt3Dvj8Y3yqypM4iE5QLQqRqJr1ly2oFa9lP0viYQfa1kEHkGEjCZ99KZwPXn7VjayjKWbeq0+rHHGtxu6ccUkrcePIx11ZZhFalETrpU1tqMroiEGWrnlmZYVkQwHZVy+aHVQhd9axzm91iZVi7G/dGNDwkoqAulNNEWXG24cfcnOFAtaVZdVlVfTcZcNSWecyYleEiR6J8aIN6iyNi98aQvOaJkiCmTVhBFtSAajcxpl6Ye5uozKxww1QNdoRjnXVUcJplsw0ICI8dWE26U5IJj3QBzJRdgXK2oSolztgKXnGeSuzaSqukCpztMtDNDL2AlJqgj0HHoVFVWQmxHFaGKuEX8t0yl1hbzh7VubKgGUbr1prJoW/DMqYdoweNQ5moCWPeZsa6ha2z+aZ56nGcTvlCb6o7Tk71sEZPByqkwRccOzaWE9MQhmkgF6rjZrdpme7TOBSAqEgHK0SNIctCaHOXo7jZ8bi/etEHL7+2SkW7YCfihaceNxuCOSeYp071uhm4N5y8AanM1tWG6CgD+GCvbV65oq2M86jFVtYqPuHz1SgfULTiz+D7Rk3Z0ghaEnH0kBNvTQgs/Bee+hdf9L2/eL/vefCXf9/PPf/vLymxLSqTBVQAABAASURBVGQeGTnsYPGHCdV9wgZldg9MBtkjNsmpBbJzJZ8nwdkpmMDqI3JJXXDoMqhE5YxtuOkQYGQJTa9IXblCN1OR5sLmASx0kLjFRulUsp0K0461xCROrE6G9WXZzHb+CcflFCaLkTUyxA5z6IpkJ89JklcpNatWa8qI7UOkLbC+hy1EGECrxpWaZoso+oFCimGLRKWZ2NIMTWYY65BodSujNgIvscpU8RPxparNwpYy54jZM6VI/Vq5aNB8m2vb1nG6wh3daCOaQpK8dhp5RGP5LxTNQUIYBFWzpAUb5LRltqDB3fRM6nT32k5nrceD50lJpwxSmGtiTu0uo5RmZz83sWzhTi5N1wgJF0xBNGQrtRySA9SbRVuYJDWEBBHxHQmxStEJ3VCF2HMuFLtjotqEw3dzaWWPIHhjiq2jjj2PrXg3Vkmr9sedbYX4SOnTcsoxWEbwzBe/5v6PevLL3/6e023iI4+s+uBV1/7a05/18499kmuWUntU1dXn1//2S4/6oh98yD/7vp/52gc85Mqrrznq3CqK7mwQfgpF0RlgtjwqWZdc6mxoM5E0pdFdjLB/hD7RziZByR6J9GxTSI+TPZkzNYo/Jdzltre+ac697nh7ICuDEMlNrdTppr7H/U63vXXTiz13us0tzt0Ci7J45OVXXPfvfuS3vuh//8IX/8AvfP1DfuP67VphoKl7gPkQrIRklbKwMvqMR9AU2RimGhZWW21ugS5NYayl5gwZoFqqF6yQTJuoyeV1JEZAubwyyXhOsEpUdgzDSapNqyk7cvvMspIl6Kns8rPRf+xgipvMQbpExWaxU80KTpK7xkR3bbRETMbAYchJZ92LskiGwrT6MDWF7CrA8DGUjFKF0vhON+l7WqUUEB2zi1oNQDk+wVJnOOuWvQxMzkozAhNEfh/RxbvuecjYsgPqSbzWjLWcLOCQjzxf8Jo3X3vtBvJIZ4yJUbBWubz6Le++7JrrP3IsqqR6bJ1LCyFZJM4Bxn/oKo31YTkRWaRYjGVRlt0yfpzk7J6UJI1u8/PsFCn1Ek/nmiz1pZ/7aX/4c//nbx76A7/wHf/+Nre85Vl1PigcbYWtXMtLlyZLhwpJJjVzPfp6zNMqhgAUVgwXo1O0RMWIHYdkbR20r1draEJ2qqehwEiUC6VPHcfsxyhSRPRosmo6VxdRlXHwQ9tYBhqE8WC9mBVRqjlaGldW4iTq0CNwpCswDyFEMRR85kjaVWZoUDTEJbw8WS2kNJG2l8g1AquMrQZ5ULlVhVm1XtooBwG6XrNGb2eYnb0VMmQFt5HdOasFehbvHMxUxPpmnRlOraXmZE4rkdlmSqZyfjOSvRVzofiVXlZva4vQTVACtuGmQ2Qs1tTElaZIbSHrBHMnRXTsp+ye7HbP08AxeoJL1ALtaMheQgvVRdIzt/QVtSVD40N2RQmCs03ueBM6ExvxAc3r2XSisqywJCjUYNIOIkugbcIt8Ag9eAnv1TaGHGUCUQ0b06i9NeutSXUky8AUkgx1hK6h7pIEELt4yajEnuzuSpQrNolMUjxMCq3NPvIgjA16ZEMV4E3dWrZuyg2KirO8UQ2TRp3E0dOUVcHaJF3N0AVQDfowhQ5B1mKdYNBLhOmBg6ErzromiDEZpIv35r0CWSBY0qJ6hCXjxcZaIbMEq45NlhJuJLxLdMtQ2Vo77W0eujca1eAmbhU0XT20KDh6gyldnJpTK0ElCFV6LAtYr5opc0cIUAdUsNuWNF3Fm4K2Rgppyk2OK8EEw+EYLXLE3rDSMkGBEclhpbuKxgEe7FAGoYiYBNRBO4IKq8ORCX43gu7ZMHd7RpIllIttOgNVEAK8le9faIhKjiyK1gLNKkNwF9dQWjsZitBrM7NDE7NRySxMFLk7uXcz7WJNc9RchGcR25XV7FDVJljCN+8pSpuqaHmCIhlyTpEyQ+taNbYcRzBtSCARuscQoTHMYjl6mcICEEpRY7dNw0zJsEZUmEASsOqLjtGNHDANMLB1rFoZTBVxb+smEWC4QyT5sXo4n28HPzKP1iZnqUrraSNVS1G6K7AxqNSKVsgBtrhKSpRRpajpgHbaRVY39eJWuc0YmQTbMhVnZgKgPIFa2n666saoia6dgRAapGZNCIQEc6aytWL6qbppmrLHKngpMkHlUjJdimUnu0AkHME7x9lkE52wpKZKo4+g663QCblqGlZazQBMyt9TtzjQxHTXoDuD07tnr9KiD85qCCgkWecGV/rOFJ+yrYJqClW+OijKOmqZwSJCmoNzs7gDy2l0RHgQMGVE6ijYPHOnJ246rj+uv/jUZ2xJO5o2AQozuBW9aQAvFvYANoEQpc/RLAhzTnKQOVBJ5Ok7T6RmiItsJlNY0ekmO8tJLARonAoE4SQPpdVQJ790aMgI6WJ2ft2uvu781dfv55wOB+02YgtIIzLpiWQAEaNv1XOD89aOlTOcYZkocpNLmFdRn3GSAk57F+UWehkYYm3b4cBNR6E8K1jUN6ePECFiaFkMm7D4F8tBSxpC3VkFQtSzbHKYldnZHp+/bDZuvmc4ycnAdQfjM9WYpbSkkCkg0QADbcAwM5WzqbqbJ+IMWvoUB4thpxfFsVmCiqaxdK4tJSpN1LC3gluNJGkgBukR4mtkboLJGGkLbZNAyQZMjbINRpvPfGbtcYpFEzCwBZNGTwu1SZyX9Cp4Iaz7INVpBqhga7rq1JpDY1gfY+hwMdcpNZXbe1Y2J4qhooMCV+UNWkuRrsSo3LAVmeW1hQVY7LkEHphm9JdnJYSCaHyTY4eDeJgW5/Ym6ErI6qyElk1ophU3/7DsBmPmgh9Q4RqiFpzUiC2HV0glUKIpUgZBNRRvxDvgPaZRj9t875WX/tLTn3ndccPZcefb3uobvvQf/8B//dr7/4d//tkff2/KYjOh/ZOXvu73/+r5x0GCB+MyQy+9+pqrrj/Pk3QtalAVvhd2dNWLNFDqZOIoXUSggY7X2GJMb1SPWfSlFeXfdBIvaZ1NWqk+jaiEbovUuXGy9BMbo+n3/vuvud9n3feud7jtv/qCz/z6+30x4yXbnMeNs26SQ8XqQiv++Wd/6v/8qi+55x1v//n3/bj7f8NXn5w01dZYGtOvueGUxvO89vxpkO99ixZE2PtEKy1tSfgNjI0L6dZLT1rZ8LJMRAZhTS612XRlilmDMGi0Ih1ESZq1sTcQbhDx1M0xvdI5WXWipEgyHILGgosF1+KqFBajKbdAO8E4lCqbUiSWRgYkF0kphCBM51LViDtEeArA9NAlqrs3VhiUWBcaNUzGEq2F9JIGU+ggL5lJTOO9ptRZS+tGwhy3PE66IOcUrKuZNbMKG4Rs5GyuhKppAgUX9wWMUNW09AEwm0RDsfZeJiKpTMYM3HR0swvX7/jApe94zwfLAYFKWmmGnJ/+Ry95FXUDuGkkrxu0m0DmzHkqwgkwzIbrtnj/5Td88IPXXHbZ9eePU8Dtr/dWHJqznZ6v9196zYeuuvo4J86OiHzvVVfwvPa6U62WKR+69uorb7j2/HG97Npr+ewJoqU8UawYiKCkKesNp5deeeUHrr7q/eevOeYqdD7Ctg1zY83iKJ/zQ1de+v7LL3//1VdcczxPd7fI68/HVdecv/zqa6++4fqZJaomdMIoMgOsNDmdhUMILb1nXdlSszqIIe3K09wuP73mimsvu4r2+w0hWVwosnNAF2iVSMmAjJZma8wPXX3t+66++t1XXHnt+eudTKXta8rqxcIUVZHr6fahy675wBXXv+fqa686rhvASn3Dtdd+8ENXvu8DtHItls1geFLEr7zuyg9edtkV11yHG4+rb7jhfVdf9cGrroUYrF91enz3lddcdu01V159NeoGy/X6G67/4JVXXHPDNaRWH9KZZEz6hlM5Xn3+2g9cdfWlV19z3el5ZI0iGCQamFGLb0Pm0i6yYtcmAmUZoYuKzH2zym+grN1rsgULYomt1zFVwpB7JDHS9swt1zzGtq/yLolBRkTAY6+KvQ/yPITfNrVNa7XaBEFzYGJKxQCVSON6dOwWQMGtsp+9W4oRa74HlITyYGX1EJmtTzNptpiOBnSUqUlfXA5bID2FfnhRMfYfh063cf7YVm6FQumeTbekPKQrFTWhEGKsvJHrU7UsTLxJJLe4B40FtSAEnihBb97lWPRw0pGOoRiCBPkBFZjqUtr3b5trbYsFCSMAsWFxrF7eEbJnnAbMq7EmObLYQukszzhG5VYaVnvgJMWtVRtD5JAKThh9niAPFaMcpJvMBBmeTMdjykoAzKwdlGb72EqiYELhrXI4WPq4Zbax0JVieWJ0tcFo/NbkqAS6V7SiQUVJqduUcFqno6cMn8oklK2apxaLKgCRM/+KH12zSU2fvjrCssZGQCV6j8ZBDoSqMHaCgDuoQkTIhJAIvthCzZIe4Sz2AieUPSDOSxS9lC01diWMu4pANEavRWXoMMAiavPyFO0io9W+bYUaWg9pU3YwCq1UU9PhVWeumEcU7w1bYrFaGqXJouga2T24szI3rKIuh8RJYCko9oOfWZY9MUq6pOgsOyZmuVZ1pf4yJKi3RLAkDilcMLRgEOoa3WmdozHkHpGSaZEK1whMFiaAiILjNdg6sDbauW25uoUM+kW1retivMPPpWbLEIh0k4PKPhUoKYJBgTJX2Y7m3nhfIC5aejaCA9Cgo4xekCP9iNqSenKWgDmMjJqMiCS6uumUchHBGUaQ6qufrLFUEZYsrZQQvhGL/RRmLMdAFuVi2TQsSzbAUYzCzv2qZF6kAdnKmwqKlBMxlpA2lmxL6QF10mvhkrkP8VJWpOwKaVpqKRKZhRSgg88dckh0NkNoSa0rEz8gWRKuMlUCKuiyO0d6O1OIybWH9CNeL37zO//mkkvagPEOTz0kedx8hDB61rIuyDQjoLvCrIgCi8ymGcXFJpsRNy44VWcdLcHS1SFwrksZ4PfA4mgOmaE5dX+AMptQiCSzRvrpKUG7qDyzimzh/NbECrKWz3RiilrC2lzIV1Q0lHEQsIcMipSiq5xLnBbDieWYWV5kApaui0Dqogp+8JJBgoQFGPfdkYJIKOFDQDZproeZixN39WLdaJkcX7UVZkXVselRx2q66XQcbfcNzPuKLD7OWYGsaoAx5qbOMiHUAAiNAiOTQdhrCxY6+Iba4RDYPi1WcVZrZsGBGzdAPCiU7iRpuzolQMMtXK0wolBw2NFkWwKmVZ2Wb1AmQqpoI5LKuCzQkayPdhAcGgZEiZlktXZaum1aoYZ+iM7B4gkvKXMsa182YtphUlUsyyKapFinBJwdwncVWSp5jqqFAeJjMw8+3PPZwaI1mrI+92ZNOi2LIHHPgg1qqZHosOo1u7sQpGHejNYDBQ16FMuMJeNE5WSYGfPXCVaHGsS6K13vpER1j90t0iwaU6Z4Q4eWvByxxxW0hpg9/xWvuvzai4+x732H2zzygd/2gG/8d//5fp9C66h8AAAQAElEQVT3zV/xBY99wDf+9/t9Ph3iyRD+4d+89Lzf4Ick2Uv7YQy2Xzit9KRGQ9uVjJonmK3AeKcolFuRbsTDF49GCpFT+2PAyPQ9XhdEXHg3r1HBNblxYvYMyQrCwtAtwxbFwJ3ucqsHf+/X/+kv3f9Hvvlft97XrBmFjAsCLrw3atdE5Yna93zNV/zlT/3wI77/2z/xY++tIP1JQKPujWS9MBpwaXzyEnwAWswiWDZNhlgLPWvEbMgmYiCl5EJiqCNnzWyeu7XDWRZQPVx8r6IuQkp2wFAik4TsmIxgFlUW+E+5zjHq/Mpv6YIVVN8qld9Vu3ZDl1DhCCs9eBuhvTKkGFYKVRCMWtjHScZRQn54cXtQcwqiVKUvZSerthW2MTismDyt049utkMcBC4Fym8NaDG1HMIWtJS+EhHCy/ylbNlhGdHMDPyF/MRmO0sCDmLCBTcsIyt3ZzW5XBEPcrPX7jDOjk+4+x3vfOtb8HKL/KtXvh4BKbXqFlCvD11x9SXvuPh3X1/0SR/FYRdO+t/duLDv+GW44l1XXPrrT/7Tb3/Iw7/mB3/+nz/w57/6h3/lWx78u7/5lGddeumljbvPSE58/Tve/nU//nPf/CuPvOY86w8bcOk11/3Hn/nNr3vQgx/xx89actQRP/BrT/yaB/zyV/7gQ7/qBx/6sje9wxo9zlStgHu9/i3v+vFHP+m//+xvfuUP/PxX/dDP/ccf++X/82tP+NPnvGy9/mj0idQNr6p3ve8D/+FHH/YVD/yFf/7Ah/zi057xriuv+IWn//G3/MIjvuYBv/jVD3jwf/7xX/31J/3x5VdesbPHlMseUbbyJlvX0ERufa5dQobORdYrr77iCc98zv/+hUf9pwf90r/4oV/9Vz/+69/2S4/71ac94x8+dClHIsleKXHauHS7fr3+95/znO/9td/+hh/7ja99wK9+zQN+5X/+7GMe/Pg/fct73ofkHmDljxosWpJ4w5ve/pU/8nNf86Cf/7c//MtPfNbz3/LB9/3fJzz1v/zcI77mgb/2L4nezz/0t//kL6+77tqFC6ye/85ff8zXPegX/+b1b9tRO3v9/FOf8dU//nP/8Wd+sfi4ptvj/+JFX/v9v/x1D/i5f/FDP/eyN73tL1762m/6+cd99Q/94rf84sOvueGULILpeV+f/bKXfd/Df+ff/dzD//mP/PJXPOjB3/iQX//xxz3+VW9+Myv8Bo0UAcMw3T+85AEFIar8VEayIlqPky4niXYECVCZzKg0QTeoQYSlL8i7pSjrqMGULIqOobVUk+gI0creaqBZtjQymFvWGdyc85uRptoCqiBbVbizkUr4RE5oMs2KEBS3y3OIdy2qpmYOc1B0ZjmVGlNyHwbap0KRZylWpC4Rpd2SXr6Fs5+ulWSY0hzqBJVE92qhbbYWTVPFBVP2C6id5b+Q0kfJUjf2RNE2lguWHoCAbG4FEzFFuW/zmL6aRR8G2lEdqpOZ3fgcj2tNJBHmfoK/qddYa8xcgCF7qSIdZ865UYGgSpU1ZPPhtczWT61tIjROR+Bc+ZKs6KIQsM1oTm4RLIm8Qjqr6BESIRKmBM00uvgi0rhI9GoI8zBCXJWq/NHr6DKLbhRqypzNg7816C7aVusTrMNNBBcO1kCvqCoRE2n7XgD7zo39hIr1l3EBsYNVWbqwTgsH0ycwuCnFOc3cekrXLOFGRDYFuHMfvmtJhUNZ3Vda4vRqS0yVFDUpBKumu8ecfAUjtICh07GRXMlRhQxGQ2Tx4kJUWRtt3WCzeoJlvGWVxqbOXIAgQevKZyD2WzFkUcOc8JqpLlqSxS+RGtJKlFNMVrUpKrCWJqSrgyJpgFkCtJ7+VkHLTTItjQtKNHHzzeYUZx1NtWh9bc3HoWxkt9K0ilGTElPXwmTsGDDbFQnVooQH5UuhBReIwubTMwOSCnCgQlPEVUP3oWoF4uZz900DtL8JqEVz02SoHS5G3eioBka2OE93hVXKDaYwVZ10mpFz2wJxhsYGtqMQiVg5jCIleBNPo3ZDSvi+e1+VW+h6PMxJBtFCruPVaJgKP8gqWALIBD9SGiGjmyJTExJIizIKA4QWiaqaNEjnyInmjspdwJjaTodsjYLoeDYNG6ULakgIdmOzVYgQpqqy8E6srBnlWTUrKQ2XYMqs0ku6itFWfMSxTn/sM//6muuvbOREE5aksI8Y0I1TzdOSyiDSbM/qyne990PPe/kbn/+KN7z6bW+/bj2P0gY+TuIIL5li9oZ3v/8Fr33j8/7+kue88vVXXn0t1okV3VkV6g3vfOfz//41z3vVJc991RuuuPIq9fXlb3jLX73qdVfecP4m3a//h/f99d+/4UWXvKX1ZP15wWvf+uxXvPa5r3j9y97ydlecP11f/fp3vfBVb7j8qqt7U1UD4w1sW15x+VUveO0lz33l6573yje89wNXTCdbu4uGNOa1CS2sm7SIwGR0cGtdS5Y62sSyuzpDGIggF3b+NGfEij46bMV0WXOuLL6rM6hSnWVhiDLQEnuaUdKU3EbU4BMnaLWoBivTjc2YgcnErpas90nJnKktQ4kQ9dvkjHLJiaog3LJQ9qJl4eF59NxSIpQ1jUyha7MPpxOtU4XvMU8KaAamSQo3zcjdz6J8H+KLzoN5VxWMgqVlLZt0rhJCFFkVWwAQNDImWyQmE7uqTdVke5Su4CM5GqCSZlA9o5zSxqy2im2mae0oRnTWlQVPY1ejMHQheZPvDYzrECqhqQUUhGEtDZplaTpFnE7uCkuU7CqyO6kMKVkSqSkqxp6SSoGE0JkOh3igfFXblBnV2QVEqBMY5GbOYqUSgyJdqi55xwdw4/HFn/tpd73zXSaLymrEtpf9x3/2j+90m1te6Ocj1Xe9973rev6Fb3rT31zy+iuuu+FCe2b+7evf9LxXvemSt7yPyIlmSMyo45qXXn7dK97+jue+/rXPf81r3vyOd24UXZbZCSYH5b7Y0MELYvb3gq8x3/qeS5//92990eveetmVl6dz7ax9YNBpVNbr3vG+57zyTc995Z567/vgpYAQCNY03PwQ2eWWfuCqa//67y/5q1e+7q9f+vpXvfHdWXr19Tf89atf/3eXvH0fcDbluM0XvOZNz3nFJW/+hw9cevnVL3j5m15A4a96/d+97k3HVCdRoisJDH3juz/w16984/Ne9ZbnvfrNr37TOyMYNFKmWaoUoClg+RLhyskufpvYq1Rid5ihDrQNLaoJDIwjkwAxHHyuxR1KdveWBRGhO16xyRbcwXSWFbo4rZxB94Gt2UyLoDZISzTUnvsUCSFSuc2MLSWKAwpZxQhl7t+kig2JKhWyR2mDB5wNvEJalXBYCrbqq/fwIkqljZUDdJQrXe4DZgtH8p9MtbnAF990ZeCNI5WfooERxorQd6tw4/HFn/5JFy5f8Pq33DA154lO5gqs8nkvfdUN+7N+3PKwfOGNwzi4AHGMrS2QpeHFb7zkf/7iw3/7WS98zTve67Hn6ebx+ne97zF/8aJvfvDjXvGGt0GDYBgK/y/HMJbLPQ8/sr8MqwlhY9La7z7zxd/+y7/9Zy99zVvfT4LtA689Pb7sze988O8/6/6/+ZT3X3YNwRMTMw7OqIu6XvOu933vw377KS942U2z3n/VtU96zot+5needsPxvLCSIVVrDxl3YaOysQ1gJmi1oe+47PL/9SuPf+hTn/2qt/0D1VHrzHrz+y590vNe+l2//piXv/Gttqp6FhVqvvXdH/pfv/zbv/IHf8nB150eOZjnm9/7oae+8FXf/rDf/bMXv8LMpYta7zWO28reC+fL3vKO//XQR//xi175zg9dcaHlXZde9di/fP6v/sGfHZ1190Lb//N7SYoUQF7hwvGMv3v1Tz/xz99yhpIIw26IdtmVN/zoI570E497+ove8HbeH14Y+Z7Lr3r2qy/5nkf+zsP/+JnrLLQuLJ0mo30EQwpFLWS6z6ImNbrg6pv4lM1Z3apMVa2BT6+waEgPLqravExcFTCVXpwHPq1gcw4PTYpFUz4RVTEuI+o9o4v0Zu0sHkzhNpLEYQ7MUUErttQja34xweAW3n0OxIlgAQeqmzED+ty4B2qxFg0s3iax9EGgwl7WAddwPrRZ9NRqy6RbrPwqqnIYeugq/ArQvBawEkkVqt5zK2onSqnkyGKJQDCzY3It4XeLPBunQO0bsGVT9cqZ27YWf12cU+HCxdi0YFG7pZWbxWpgSWrCdY+LehdHcx/HZAgUA9CKuc1tdQQLXjDcITg22QYvW3FaEcm19BgsDrsShZxInNS2cLEG1/Wk9ck1vKeySvQ4Ks5um4y7CkK8tBxcbBcSMsqLO9Mtcpvh64w8pp/WXElwTNFUpJIpW2uzH+RgZ5QRiHBbAGARPVAJkrhuOaPoGkXyzBU7exR8jHMOOkSU6I4z6s4gzMWvRvlBxkwZE0vZKLCskgTd1Ax7KQ3Z0DdDSgiKrpJcxa6CJ+OdMWOy/BC+tvjSZ8cWcP7eWhsUaKYqyGnuSQOnzOSvFbIXVu5p12o7oZn95splQILTS5IMVDDk3nySiKsLEcGchnWRXEKGV2N4xUtIEsbXYjNuLrOwax20lIHtwWf5da6JjsyBgMrsug1yaI3aJnYbJ2fTdCMCjYVFy0lI5UZtpO5t0zR4i2PWmSmQ5uheh8wFHDi7hEbaBKYbaGDJVnw2PWccBdGghiZV7sd1O66T7IuFUDKywvneaI66SCWMZ0kLpRnCptgbjyGnOU7rZPOem6bXnEFiRCcV6e1atubi0bAPJzMjO9wkmorB1BddR9vUnKHeOkgk6qgaQq7v/EpIdZxNKs4rY48Jg2FnEoP8Sz9kKQqYkODlLKMlvTZgi+EpsnvuvC8hmebMYEf1sp677+C08ipnJYhhKcS3aYXJZgreytgSql7QmTKTDKzDrBNcmIv/r+PN7/vQHzz/5Vqc182Mdt18AJMonKzQzcQbQvNPXvKqb/rFx377L/72g37r9x/4m7//rQ99/Dc85BG/9cxnX+c3NKUKGp9d4nF/8Vff9+jH/8AjnvTA33rKO9/zofKoCPrFxHn8Xzz3+3/zKff/jafc/2FPfd073lN+/MWn/vH9H/F7L3rTO3Dj8YTnvvgHHvOUH/2dpyLj+uvP//zv/tkDHv6UBzzyd3/5yX/61ndd+q0P+Z1v+7Xfe8Bv/f6zXviaRXkz1dduf/Li1//Xn370f3vwb/3Qox//g496wvc+7PHf8LMP+65ffvgrX/c2PR627JN0NYjIjUqAklFLj9FFumXj9wAjwg6GZU7DKup0CtpFGcWp5fydYqxoWyKLaKmQOfxxYoDgiUuuGtHcjGxEh1hBAREoP6rSMxTRW+qIYJQ0uUy3wWRQjmbb4qwkWXtQizWhb0zIKJrVmYRTwHVDLfpeiZPlN5uIoqkuim40+ZSk3IdyLWtSC1M2siHoW4bBh7hJFrg6jA044ui61bIyG0IxuQAAEABJREFUpQ1a2eEHyRMpIyAjms2GRPbwg2+tjpmrz2CODj3IWKIrhslyEDsJ1ndD7b8NVlq6h1fBmh1GW6xF0wnABSkrycnUHAo1GunQ1BAlfIFNizpDGQhNFrBC6sbKtXZwWkzZ/ShKStkX0r4FkzGZ5FoqqDVqCy4WS7WWnSsIl4IMJhFIQAJJidqzicuxqpJyLpzKUJGhDqbMsdTR7nCr233Dl3zBN3zpF3zDl33Bv/miz7lNP3n/B676kUc+7Sd/5+nXnHKx2edtHj/2O3/wvx/2u49+5vPqNJhyzeVNb//gDzziaf/xZx/57b/+2Pv/1uPv/7An/Y9fedy//dlff8ILXnitzXVYaDNYS9lF3Ph6zZvf+00/+8hvfsijH/iwJ37fbz7p6x/88J9+4tOvu+Z62ZBr7V6s8eRnveiHfuPJP/Swp/zgw5/yt5e8UbvFoWdvN8rYP4lXQKPsVe94z/c95onf99jf+/5H/d7D/+jZFf7Wd//D//6N3/up3/3Tq2/g09998FXXn//xxzzt+3/jyU9/ziuvvOL8D/zmk/7Pw5/wAw9//AN/6wmXXX4VA9gpC5iZv/lHz/1+6mXWP+IJL3jtGxiAKsZI2kpuIilMhOFcEupcfr0cNQtkpDR1bWuxznfnslgLSFjzPte+eS/ptjdIyf7JEuinuW05peYwRz9WJ/VidpDAIZhTpuOYxU9QMzc5POGNm9nm2XwtUiBzTRwlTzE3zaR9yJxzbsEDCKkSUi7ZSlN3zR0YwelbajC9PbpkGyPGuSknM3XmWeXNKHfZamxDy1CySQXF945O75E1oZQjZR8Rly//rE/hKJ7vufzql7/1HfueylEC8u45r34923l+9id81C0OtIOX+0m6VsFMl9Hf8f73/9Bjn3zpNRd/ANy7b/Z63xXXPOhRT3/jP7zbeqxzvVnPR1xGphsTpgCeF7sK0rw0doef/sJXPOxPnnMDl/6LnR/+YLl7xdve/X2Pfso1GSw1onYyDiZyYcS7PnT5uy+78sL1zd9f/JZ3Pv91rw8mYG7EmkXAe1uHTd6/2iQzWECo7gcf+ZQ3vueDN5940/X7rrj6J574tPdd+kGZQfvfd8W13/9bT37Duz9QN4242cXVNxwf8rS/fNar3lh9oB+a9VueHG7qv+Qf3nfTI4abGnnx7Fe94TXvek/CTo//r9A5SSWMc3H8hfOvX/1GWn7hmu85Eas86OFPecFr3xwMG5s+8jyd/vhn/+3v/Mlfuxp3rGUHiH14SElEK24fYhYoQLOwp5ZMUixyjeBOCFpcZjU7iyL6NNu4Kev8fhLjpA6HNoyLGedVmEYzlgHeAW9QFSw6T7AelAIap4eDcCY5nRvmtAyuDX3wAemGcjBlNpdIpZbhRlIWE1pQRUMFyRSGiTFFYmtrGjdREEiCiYhNzaXRuW7ZjBPZ6oJjb8eTsZqlSIcN174K9mrPlWDPJhHbBeew5CKVUyVVmWSb+rSJBqs9KUG6dh9LcRrCZ85VN0lhv5LZ5RGrh28Bp7hg9nLzywTmgmgC5ZLJDRMEFVnnGVgHZ3Upcpo6WTJsrVZRjZm8cWXhYtuCtqCSCRsKmgTlP+zOrq15jrae1Z9sS++EhTDx3sMm61v1Jm0RHkiIgIwfIVLlHgg0saY667j5qYDh1X5AtKRtKBjSpFRcEEIzo4FtvOCZhjCZzYN3gjXRC8qQcAXSKfskoTryIBnxubTjaEfbUmbnThHBJXFGEx9tayCxjVZqsxgZLZKwy5jSi9VZhcANFIQKqpS20HdNtDkbqyJdoG4xlGesmS6O3bIw44RdlDJwjEzbmRDT3Duqd66Adpj9JFszBTulFMUpkIEUEIJwqVCRLmKViOJ9Sc2h21L0HoWKXiG2Tc1JA5Ob376YWYOgmteyubqXlJhK69AuLO8ec/N1WlXHbnpbtU1DEvWe1QCklmuKZtMy7aZNREJyLTnv/TQJ2oLWDdz7dW5q2OGOWbKROLvlVK+A0qNsAaaDBigLlqZ6EDlBnFgdJEZsS8yBaAjB6n7q27ojWcjaDWk5DgRAW9EcaPTmS4u2iA1jYOCx8RZ3a6K2nHg/xB77UCbIyEWLwVeVagwr68B+E0uv12LgknCKuNEzihWdo6IxO/2A4h0eKVK+ih+tVq0p22Y82SU4gDhyU4eFF8zirIGNY5LBL5tSa03Hqn7EuqZm9AE9pzhoWDr989M151akXTZ4EXrdIS+ftc0q+s0Y7Oedbn3Lpe8BYdvj/+rv3vL2D5LbPXuTvXEfcfaiKyhunKHwG9bjjz7qyT/+u39yybs/cNNzrHX6P1x65cOf+fxv+YVHXn71lYpggMjdzA/rKvKobGJuiJs17wpKdOv4/7M8kIgq+8gLr6uuve7//MYTXvOuD2xkBbAHys3Df/Chj/u/v/2Hb3rPB6+87vwFN+kXd2OveMs7v+/hv/uMv30JeenupDrqZuIEBS4J5amOPS03AZ+COER3j/mQQT3Nqi81WgwR2+nepZplY7PIgg076Kfepg5vrToLbhtuLZBJOCLHJiNrZ4ZtVckaTAkmiQRLzCq5dvUxqnVP8dDTEmddZOnV1bc4H8UtF7uQVrqf1pS3V+RoYdmwlBxKzCFedgbTVpbOkmNLka7NkkOZUlbYfc8ENuEtR8yj+zFPt+QOwHGEsIS19L5uuu1JLKpmhIJGb8aVIh1thrHbq6vK4IDWNmpSo2gDmkpXmGbX2tNokb0MERdlxmF1bpKYkboj495yY0U+QRFhaKZE1CwPBiWF8yF9luQMDRaRbb+svsnJrDE5pmLnWjRCZRm7b/TTu7C0SfmwyR4O01WKAMfBbBEju7UY9p41Ah97lzufAba/Pf8Vr7nyystZnwliKoPEPDz3LV/5z3/oP/6bH/0v/+ZH/su/+fi73fP0PE2sffT/02tL8Bbpj1/wsu/4lcc97+/fdMW11zM7Lgw8v85/uPSKX3zan//Ubz/petwQw1Vw8xuVy66+7oGPedpr3/X+G44bp0Tmldfd8McvetUDHv7b152/Xs2CiDAU7LvppDOjVRu6Pwy5qRUqxIFBcdmjfWN7Qrc4PX/x/uTG1o/4/Ng73/O+97zrhabTbfu7V79ec03Zb9aPcf7N7774t0nd9Ms+875QxsmRPoFtwl0S0FHSY49uzsLaxHsjF8HNEBJSIqGywbamlWhVCr64Kxq18fcFxjuVtxJbHaOOpVmVmfzmwu0T6KnIiOyMKjixWNWoh6KEm7kip+m4MrhSm06GvYqcyRHOb1vGabQVNqGTJnJ0QDlAIdoTS8qydxVZNbcW29Cj6UyBC0sjtkIUsRHNwSWgZ9NWunidnG/jOMg6Ly7bW1nQU88M24Rs45T9vPTKqz/hnvf65HtdhPevX/laqcpEiLz7istf9w/7L3si+Ddf+Nk3rL5POHupBPpaC2t3PPj3n3Ht+SObBfic+9z7aT/+3c//jR970g991xd80seykedl11z/u3/2/G3OL/zUT3nZr/30n/3k993ltrdiO8+73u42z/qVB73oET/3ff/1X65oKykOimHPfqoSA0vpH7zi6kf96V97BFtN5b/f7/P/8qE/+jeP/LmHfc9/u8ftb8NGniz+T33Oi7c6Izv3cmw6O2n8133up/7FT9//pY/6kaf/1Hd93id9zFkzuVB//vLXcunaEOtWvjXz/d6aIG2LxWJQe8pf/R1L94Xx97n7nZ72oG9/5UN/9ik//L2f9tH3uNDIexU+ION1Zf3uM174zg9ezmueH3vn2/3e93/Tyx7xE3/xKz/8b/7pZ9AGNq7Tf+uPn3s8ElogNV3ZeOHkgP/8pZ/7F7/yQy969M885UHfcd973uVCO8P1jJe/1pb29J9/4Isf9rP/9p985oV2vv/s//h3r3jkT//1w35inJyrKpxxj+08uXIQpVueLLe+xcktz7EG489f/NKXv+Wd7OJ5m5Pxy9/29S975M8wTN/zL7906TsZmNdPe95L3vbe9x6ljpAtKZBj95OynaRMboq4J2oOEoS9VZ09lZKO6bKFZOy8ssGe2Kw2Y1510BJjcjENmQANLRsXlEZwBQZmkYdEtUIHtxiF4lo43TOCrM0t3BGhyKVyaJLxWRuUNTmL9bKFssxrWKYGJLm14cn9aTWESYqEqaqcIc0YVSqhhlZ0zdaEJYqqSeNeU8EB4sUKbKgoHJ17JjpJMW1Dn9JRXE9IUImSgLoZcx49uuVA65suK04mGlfbJpvNqWuo1zDp7G/0Fu7FhYOLCU2pEjoMKlZ1kSzC2poaYaaS9JwZBW7Q0vZWpRdWQoO71UAdJrjemJecihxVJjGk35QXgS0lUqEyGk56HbgfEwOaZg36GOuQdVdJaEqIE0uMY3BA1Vp5KqBa6X0x5Uwlotq6oCGEO4HKABiMEk+6A7hpdd2Ve85jrlPWVsHwYqW8HIkTQxMtsFjRVacXMTDpiA6JTjQLOTvFYxdN4ZIqiVJEE18ghcbbl3UODlPfY+URrpm0IEvA4VCGtLXe2rAdwCS1sudsWKWnt5oVHtzvASXCLcFQ7WJL6nIsRBZLag/0xk4QHJOiV6EKHoVRaYqhzUJalLEFMIliughxUStjI73eUVFtKotm2eYWmTBGcYpOMFDGZnokieI/8d7n0tMGQpw3vrE4XUajz6KETVAOcbdKEkFJxIndOQFta4qs2mrSO+YiokOaDbMhnFfuGRsqWu7wZ0pGVYIzk/41LWm6n9bEeCsxgrwqSwmMtdrRNRJAVAa9IqsodFi0Si2owZZmfK4hICKLonWUdiluTG0pIOk6E6qVSIIGlnDWPoa70MOKw5HLcjh4azArk/gTDhfGywPAzlhjN3UTvh3fKhYgBj/glF6iQYlVEoKtw88JzjEzRMmGHBVcOTm+BCWWMC9JHp4j40S2znt42k4MZ9Mj45LikZvP6Tnp6lqjYGSOce9DeBkdwY3HR9/lDt/6lV944dtV15/+2h88I+ZZEaIpF1pvfG9lXZCYT/rzv37mS14T5Bkwmt37Lnf42Hvc+dYnCwdW4fXv/sBDfu9PibbUQnPXOdl+8Sxx7SswcYyYqIvN/Nh96/bx977bfSjq3IEtF8673PY297n7XT72bnciWHT9QiPfL7/2hvdecQ0vLpyUVFFPeObzn/vatxWNAJbWPoqG3f1Od7j1xT/a2detP3v2ZZddSr4ARMDw4aPCgntw13KilzkhUUYGipg3naY0m4Jt6uDmW4Z0bQt6k2amo8+d8xJTGebC0OWgnYSU5m3Zhh0RGU62GQe0OB2sAbPLXFBLisDIZoYsMIUH1xEf01toq17aUqExJbbyVXLfM6WpaTupHMVdUIhlr9z5SCDIuChFKSWxbTYJUq6GJlsAaEonRg4WNXglDQM5G1JM2RVJ11XLTEwkmXGg26I0oxRWW8MGi2hYD1SfIcIvSf0VkBXlMlW3ntEyyc/ORO5pw0tZQwNB1Qhws9bcWlEAABAASURBVNy22aiffEVNIwHEioaiWI252jAX3DtxKkiTNERKHEXDOUtKwoxGQLwqAaFt5qnc3UYEv2UztguYu0oTJHt4F2+84OBSpTfUCUqmn1/3eZ9xu1tcZN27L7vqO37xkX/w3BdeftXVYuqmq2IDnGMdldSIk5PDx939Th97tzuS/1TBU0Q++q53/Ni73/mut78tsXrX+z7wq3/wrNPpZ1246+1uza573un2qrtJRPTZr3jDc1/09yzG0nYzOOzCOSOPM+5821tx/L3udPtudqH9Ne94z+/8xXN3xxbMZRd7oZ3vXKAYPCCb8tuHT96ALRKtOJgIXWxXiKTd7pa3/Li73emj73yHZnqho5l9zF3v+HF3v/OdbnPrWy3LF3/axaf+NPVVb36H1JaYwPEtb3/3B6+5+Ndud73trT7lYz+Gi60yBOrZVsiGmZqEN2GEmHGSbtXPRVOSO+F71SiCJQxoLy+4a2UDMyUPm3ckukPdqZH0SyH7xdU38dmrhuhB+RK0JJIB4wi31F0ZAjUzqUWas8ImwlVDVMDyp4ZSSdrqDWkNapQgkF7tAK5xNIlYFVPMW4Ll/lz5QWpR4fICnYmk5bobLyZka0MsqsaHBL4vMqwLsWDtc9bGRZ1Dmu68BSmKG4/rVr/FyW3u9xkX4X3Ba9+4Hq8DUYM/+xWvrbNhd7/trT/rkz728hsI+Nl3QIqqZ8n26re97XU3/o8X3/tOt/3p//nvP+qutz8R/cR73P3H/tO/u8uNf6D4oje8/S3veT/z5txoLFVFTLAfIrwRK0tHspBYpe6tN75ENPohbfzVS19z1fUX//72iz754+7/X/7l7W9zy0Nr/+RTP/VH//O/pmMXZjz1eS89vXaNyYc/W9UF2/Exd77DD/yHf3mvO96+oX/U3e/6zV/1JTflyFXXX7+yXgHB+bNsQ5vcaozStlW75vrjk5//Evbw5JSf/u//+pM+9m7jFvbxH33vn/jGrz83Ott5vvLt/7BOvfaa9Vkvu/jr08loP/PN/+7T73tvO2m3v/2t7/+f//Wn33hj857LrnrOy18nSZbhdNvVUgLPT7z7nb7zP3/VHe5wq3PNPvle9/ifX/klbLxw8k6yGPelTm5Blz6cOEyQ0RpBkNqLSJVcGM/3O9zy5Ef/67/88wd/7/N/84G//ePfdu429tQXXHSEbPmB//jVX/l5n3au2x1vefKtX/elX/s5F0N/zfnjnzz/5cFfBOGRH1ZUKCeJGXBTW7q2nnoWpgaQ/qZmEDKbpQhoVVZkOKnilaEAw5Ccv1a60PGW1YIgtx5NXbiGbLnNPnNZtSTTWFM5cjdAnCIpUGkLE6WLdFLaR64D3iy8+Wx8hyjdAteW4prfJVQ9isuIxtZq60EPZBOdYilCW0E95bwwHZ0uyG60ZurcdHNEFH0YyuSeTabIVjkjmUaUJCk9pBcvK0whrOtDYcKXprHWr8I9E297vHEMo7HPcNFkVwAwwmV9Fpzbx6myNq7JSphqi823jZkwQKu405SAu/hK/FvoKBugGjUME2lw1Oa+zY0Pbbi8YbXm0oWQy77VWk97rN1EFmlLiW4VW1IvYyTDy1g/ygtBNKg9wiaXNy58WZVlXg1Qa9rH6JSi3EvUBvHQdK0kenzvOfm4hRsH1jMkd4ZMYU47cxc8BCUglzIkstK22ZwSZFNjcZ7IKpHsknyuaUeTrRlat2qMPf2TKAWVTlYHF5q6F08XYyS9MMt9XWtbjX5iO4gvgk6GLkOWxiX/lM+AVo4pItwwKFcIaUVKuVB1gJdROsmWUidNgDKeRZLF7lgEdEXzFJGSFn1Bdl7yTrR3Uan04mwHnaCGPT6O4EHPOloTiFUIqScruPNkpDZAuI1q+16NvE1QJ3PESf7i8KV0QAQKtvONq/Ey5XBa3K7Q2jl6csExujBByeBYWkY+NWexjyZCs2SI7UuIl26RCSoJGkJ1EHgo/eGSUofQnkNIJUUf6OyP4ZvFVshjK28I44YjoM7pqYrR2zgM6d2lORe7EKFw5o5pNKRCdoYwVRh4a7os2SyzanOdm2CnH4p8OchqmMoGJWAlc6qz36MkSjOaJAT7xtMwXYp2QIICDK5pEr2nLZG8WV9m66mDO7PjZnPNmZywznYsC8mWcXAZTDshOZx3X+iuSqebHIwFwyWTuT09W+lBtKuZgPEjoLSdPzyeanhIbbRp78FNx3/44s/7lBuf8L3wDW//y5e/0p3w1k0DeEFEbP+wd73vst//65dE7b23PXf40f/2L570U9/5xJ/8rt/4nm/8hLvfiUN4Puc1b3nbOz/YoAI5LIMtF8+CloghjKEjg3YhF7tQh9Z/5f7//Y9+9nu+8rPve2Mj/s+//Yo//r/f8/s/9q0nDMOUm9ovXNz99rf5/E++z+d/yn3ucofbnZ/5zJf+/YX2pvJ//v1XPfH/fvsTf+o7H/H93/Tx97j4vJzPGl/+xnc0NbIfuz8XhvNd0jSHoVXDprnSwD57m7wRRTTM1kLYzMVglpJIgKKLHxL8vVLBJjrVINz/VE9fQDZCMisAb1XKC3f10gRfqSGRNcEbFFJSmpzB0rHtdJ1uGWqlUlr8GIUlpINw7bjPXXGrZhTBR0hGxumpj9ONdckqOa5rCctEb6nKHCBx01OPGEcZW9VkEvL5bCtBQxxa9cYZ5xpDEOekxtp6HAQLWodJCcskSFbSWAUJ2UTXRssMollYE0eXvbhFIGbljJhz5spdK1keSI+c6xrHo/hm9Le4mCXcKUmm7lnheyHvW/SIrltrsZQuuVce6AZZU2qFOTrKes3DODI4Y5DM3MHulFcI4ZnFFCySyw2gdcFH4scluLOv4WLJQb4fsImRMBREWAPufuc7f/e//uqT0XF2vPeKq3/hKX/+TT/zsAf+5u8+5xWvvuy6q6ODJvCJ27pynYp73+uOT/zRb33ij3zzPW9/8QHz0tujf/Cbnv4z3/VD//VrD11f/NpLrrvxr8L+6Sff53d/5Nuf9GP/6wk/9J3/4Qs/90zD/vYXL34tXGNPh9q/3/j66s/5R7/9A9/x5B/7nt9/0Hd/17/4sm67L+z8i1e89oNXXAlJN7KKDRfPyshYC8fpfL/YyI8KAQd+hGw2Q6196sd/9B/91Hc++v7/7Y63usXeBNzpNrd8zA9885N+4nu/7eu+CoYv+/T73nK5iMar3vGeq05PRVJLn/3qSy6M5/tXfe5nLrlY8aewRWBNfUj0fRgQJmFNemP6n1PSBMmAS5WGIkevw+DKwXHIqcmk410/o600bvLXsmDtTHChUtJ+L8i7E82st9ayIcSTtYykSaOapMGQBrQEEwLhgFMTeUGFVm0JtVLrw5YmVuyFuln2Bm0gvk3QfPKXPeBo4zgO1U9M1UghAbqomExgk0rZrS2mf4ILByhNpbxkDWrf895qaq2LzK7K2KmWCMfiwnH06Jlf8un/6NZnT3auPV1f9qY3Clbfjs94ycXy9QWffJ/b3OIcNz8XpvA9S31mZr3uLf/AMLOF5xd/2ifc/W63770T477Uve555y/+tE9iO8/Tbb78DW/LttHPqmIBYOOFUwmJ0ItYYr9pudB44Z2ZKsKSIK+85K0XWvj+b7/ks5oyL3NkLSWf80kf/wkfLqfXvePdH2DAFcKRF04W/LYshaXHSZ/jtodzTfVCV0RIsj7ZYt6b72YFwyFEqALvu+xSPn66MPKT7nnX+3z0ndRMTEVxr3ve7X6f9cmf9ykf9094Z3zXOyjsbe+57Mobb6X489TH3OfeLNEKrpB5m97+1ed/xgU5dPyFf//6jC24Gf+wjbjVuXMilqkFE4w73uJWF8bzfZJYlYZpJBnNYtPZWVXhXttksyRnfVjcN3zZ533tF376uVucS0ioXnrVlbTubBLudrtb/5NP/0SII6tOuQ0fX/8ln3+hi++veOPbuZ3qcEHy641nseB5hisJJH0xZfnHcG3VhyxcJ8+ZDA0RLhS+AVySKiE1LU9RyZyfWVOmc+0XcjMTE1SiLhXG5rXkCFUqcHK8t2JNlcZEabPV2nOKRuyryVY6m3BQSidvMDVYfELLVAxcndhIjLywoViMuQ1jxZ8VtIIrHVLETamcDZmBKmQXUeGMSItNkhODum0R4c+StkWtPldfvdyFYwoMUq9kOFQgtTCxwEdHOdVWbZTNFTVDHRAkMi2VLMtMqKg2bSZD0DvQ6YxO9tKO4DIwty08N9XZF7dhLs03ES5RQU1awq3LIiBELB/SNtv9Z3JP6Q6a1GuDzF2Zp1ey3LgKhVS0GeVUoeWNAtSye5hHFraCw6utoseMSi6mtLQRY2cxib0qqWnYOnNzQuBuEEIdXBJ5mU1TupR1ySZrIwDUsLd0S66r2qLrphUUHULSVmWlI73AuHFDACbz2e8vsQeTwrsFoUfbsnkiA1RMhIxKULaryc6q2xg7CZfkrmUKUiFNVESrWXULJfePyJVBQzUV7npEpOi70B9SX5LEyIhAATChbIGgorDCp3pI7gSZEiHFrlb0pkSRvItVaJVxMD1DQdgsLGHcz2TKxjWhew0Pg4tmSFKLVzIRCGvs9i0jbIlqkYjIcAIDIs38aqMYeUii0maxajY33dQnNpXZLA1n8lC+qw4Y4w+03c8s0IEo20UzKUR304lLL+mQsyWYbhWlG/WJiXKEqDB7UCG7yDwS+ZFcaKQLT4yqQworZ1+khknbm6t4d9BJeBfiF0Q/G/Z0jIlwDWiohVl4plNlchTVFi1MBQbSCpgogap2gXGaVEJ5adjdUQRDRjtUxVBNUmUf23cLOMslqdBGtQaxzNh8EsxJyljNjmgGaVZmIg2UE9ShUVWWre1tKZG558wommAG3Y0CjRI3hVJhqSAFQQPwEcctzp377n/15Z3Dzpof/cznXnntdcl4nn298FbIEmIUL3rNm7i4Xmj8V//0M//VF376QiC1PuGj7/qfvvyfXmifHi9+/dsYKN5lcmN3oZHvUnvgFKalQLHlplMSSuJWQ8mRO9wbO3bYJNMb1pFB3y92mMo33u/zn/ij3/XoH/yWh//A//iyf/yPsvLHv/m/PvEnv++JP3H/33vQ937NP/nH2vbY8Knel33Gh+98PnjZFWLlLUP3QF4UByiaYqh25oZamiT3HeLiBKIKUCUrSl2dJ82XHVFXTBJUSJQSMbFuA9XSlVMSxTRJzhWwrIHUBCrEplJMUTQDGGQVwys25KDoUbJSqLf9LsmNoLtt1SeJ2hXEjzM16bwCFmfzdy0BZhRcA+K0LZtla7qbI9olTDwyeHCKuZcj3KqkI/cSLqbgMH6lFc2UYjgEVGctrDNzZ2lWmYkuVsPCyFEKZV9re+rCJSIrQyqQMqdu09f90VMFhaNyy9w056igYdhF6CYUSusWLsHD0TOsXNNZSbhAjNYG9TUGPapCMmRnCNNLJFIjhk7TGZkVAJRZkNCUhmRFJZdYc8j21OTcHZ6/ELDvAAAQAElEQVQK4YGiMTk9NzCShhndfcns46v/yef9+H/79/e4w20p7cJ5xfWnz3/Nmx70qCd/20N+87F/9Iwrr74sfIvygu/gNqmsdc4Lg/luADWoqhm+9vM/9yk/fv+n/PR3P/mnvvMn/se/vfPJrUbIrQ/tP/+zD9+oXHrlVRFCXEN2F3B23Pk2t/ref/2V97jd7ejHueUW/+nLvuSmfeGlV1/7qre/s0jIWWdjL74ZSmLD9HK/2HT2EY6s/Y9IAcFNBy9ZP7tqN6HFN7WTm9kiWPhBjD76rrf/+Htc/HuYq669/pL3flCHUszL33zxD1o478s/69PpbxL1MjjJIE3QDGxkIFkRwaQQldJiMZmJ0kRL25/TFoM0yKwE/zHk1WBMPfXUyZTamOlNtWkni1UpkW9NrYlSMXJVc7Cy81uJZTVl5EV7cS9dC7tCwNAzR6Vxg63TM3OvjDpMKTcaWFa9WFrM06aISwV5BjAU+zRjBsVo1Q6wAWkqMITk2mpKJWmeVrCk9wmrPOQ8BJ8mB9c4GGczrjAxawpjJPDh4xjrR9/lzhfgjaxnvfqN3vRN7/nA+y6/ioNMhT9nadT545FfL5w7cgVs8rZ3f+BCC98/6u53g/Q2RluaLiIN97n3xT+RYu97Lr2C+4LQKRbNjC0XTlWr3o1vyY2tX2i8+B6yAxLx9vd8+P9O9N7U4jCvnpWSNtrd7nSHC+MJ6geuulqKwhghEmtvJr4uGqJa1KS42Lx38TWSuwvh6subn6rwSiA7uD+e77/0iiCuHATc5la37GNJGQkxUqXVj33b1z/8Af/9kf/nG3/8m/5NP/S3v+dmONztrtRGMTuHknbGx9z94l/WUdh7L7tS3dU2JTv5/ezsjVKJiUVqifLfWfPFN4ECUju4HTceSWqEsJJqegrqZn7d+hYnJmhBOu3bx/e+/4qbVr073e42y8kSWqmSnON277t8OEYfuOLqSCLEjrpRDz8pXauKaoKdVFTUK8pIShJTwISMcfTaBFsVR4lgj11WzIpA1W4+6RpSzOYgyKjM0BatRU/vzvvoyGY1ei5NumrvZb1UA/vN/um+T47Yqm5oOHaDcKLYvtIxSIkqLgzqAeaibJbROMIatFXCJ41KzSxjJaDjPTbx01yPnsfK7aC5iIFb7xoJNZM2imcteVTfCDA8WsUQ8ggwnkzx0kKETLJwS11XjWA5wbnAYe0ciUIRmaFn5VFERSG0JilkzxNZmMtdk/5NcWZ7Ipx3Rm0Sn5l2nByduw/nUAeRQwkfIJ9bceJzqRLaqnumHcB7AhEGhRozgv5RETqwJFj2uPJFMK7wMs128MO504WPmZeMUbSHZYSVb+4OGrdrJ0PkhICoJ4hOrOEEgHDaNOevNNUhYoOxq5QmbssqLJ3UKpJqwjaVdoh+CxlLOzQs6mhOtDheYpBQDtKCiwFUVGiiyqkmMUiGZbLKZBnWRU/Rt6mylUBm0+hNzKqJDOsnzMeltwXSsDeJCU5kdzopEMVmMVKnShFKkBm+A7JJ9T5679ZCxWvnhTfWSlQXigdUwXgFg8kLbauP3VaaDtlNRwtYSImjrThg6aNt4p4rSC8pFShGoVP21G1T7p+C3mWP1n1BHpzzFQDRajbb4jniKHAU24CuNWRVmaDbYeDdK8U0ZlgTVUkP+CnGMccx6li1cuMAwERbtZaNxh8Tp14EWeHSO8Y5HBaMfTYSWhCQqMzcBgykYKtcPTafyQxux13Momrd5BYGOxsLhUhRUYIfpYdsB0ap62pjk8Yeo/8gpF1Y2tbEupu6kjyR3YlHO4l24qagiUvkwaL3o5pTMNlGLUa6M0papClPAFkKGMPeWindr2pOdNhkgkPEcCj3O16canSml3SgDMErCzPvIpaDG70Z5pUWoASbjtpyTJ6uK3kyFjk060FxKVEmIpSwUHBBIBSd0DV2ZtCsG89c2md9xsd/+Wdd3M2//UOX/+6znyNECjc/MnCcfv4lN+5UmukX/6NPrMJejzKj5T3vdfEXFU573VveScLrhsHA8fvZSSbwczc+GtMAtAkXDymlqLnhuOZo9P5iOz9EoMGo9BDCiAsH93P//av/2Z3ucHs6NLkOzeiIT7zzXT/xnvf4xI+6y8ff+/Z9iOQwbqtFbkHaXJgGXHPDDQRwspDspeTGVuBMAQ1q3g95UG6pV6M5E0cmiO7PLUKgrZgxRWilihEi0A6boERyL4oIi5UoXd8xrj3u4FFLx2E0i6FH/hJQzEr21b5JxpRj2jQVpgb4g0o2qHJjpJTEjgTFhgBSWFyX6ssQO3BuAjYm+jEPc54Ues9s2ypM4kJyvCllcUdIexFWU2cwm6BC+ab77jxLiDt3CZ6VZPCq7VRt28ceKWjPo0kURqwNsytzqEMWmNUSfovVD1sthaZOp2zftDENOgem8lEGZYa10k7fTqqf1Fig9C2VOVAn3Q+Ls/D11sx21hPbhkoVJleZiKBlGb2vYunchPpHJygm+8NOq2gEjk8Qz5NWCR4CBRNHByCAW59Yah4ORz13ijZ3MEwxjG7ExGnUTHBigko6rNmXf+ZnPu4B3/a9/+p+d7r1LUUEZ0dWvffyqx/zl3/zrb/06Fe+5a2G7CkaC+YJ5EDrz0advfmI48k82tHztre7zX3ufff73uten3Lvj77zbW4Hb+LQqpNztO5sMHDd+VNiJAnZ43uxsTU9OacEc8AE2g7jCz/1E3Hj8cErr2DAjKG6sYWfKjqEadYtO7/edK5lp+AdrHV23tQqVbIhUlwjsLGG3NjVujZ6zVz2oy32BZ/88Rd6tsiXveGtOfSdl1/67suuutD4Kfe6673ufudg9VpI/TW2yZ/DNjU6WkQuMiRdtpxbHrdtxRE1rUK1wrQ21DFohAlXkvTmzqCq8zdlbqbW2FO9TqoOiSYmxjeVcvUNnjoxXFqaGaSTwlm5V4h95TaxE3BRbPPQ81wHn0lBESGVSXsKoaiBbCrC8Zl5mnGs6cXooJSCjVdz0zotWzGmSdFIEUTxEAkIP1Gp1Ck2+RkgcDW6Z+LoLMSSHE81UyqALHXc/Fj99HCiX/E5n3qh8ZVv/YcPXn3+Ba99M38GYcs973j7T73PxzhqXVd+vXBKg+0FTX1S4oU2HA6H08SWjiSMtIyUt4t9wNXXb3PGbMKacvMooze0xndpOyxA3TTFQrjowcvjw1pOxi1qLho9s6Zu0bY+2oUpVbj8hlOoD1HG/ELj9PDgcwDKZb/nhy260M8sCJkQBruY8rXJMWXj/koYmhttMXIERDJnFqMr7CMT5qGceZyrxMoMvigPtzhhJZGxim4UoQT+HNf7G3uvPp2r52jJ8nNjG2jh/sd6R8UKwrcvqDf17RcDYkg9PW77twsv4cEFZRESR7kTv9FW0CMZoQ3aS0Y2Ia50/WzWMvbytxVKyyhVccvlcNazv12/+en5Wl2SEdwbLrwE1aV6JSJBbkJWLrKWfKut5g2+bROG6NTXAIEEOnKwHJ/M9ZDr0n1YLlyUKcEKCHW3FG0EltvLg6NvolVSYOSILn1LV8qqvcwyuXit5Zre0EZaZ54pOF5klaTBFSS/SlBGCxwKVhqk1OjRzaiJIQ6q0L5v56RNrUyvWItizKC2A0bhPBXJpKIftWMYNmbr0haF7CXKN42Ntoh4aHilSKnyVUznLtnpv3KiURGWk9JRCWG5T1gD40gu8yyDUCKXmNAK6J60g9uD6AWGk4ZVVYr1Zl2bqE3DZkH+oaqSedIkmIND6RzWBaeHmGNfnpSroOtoCpN9wZopswsoZfHRptE+6aIU6SZTfcoMnYAMbwNKX3iikJWRa+yVh4lIvtfJwBjFWicCq6J4VigY8efWhOUmjGGtEARqGGjCbiCklZAtwh5JwgoRGte0ulEQkmgkx1Qp6F8UD2jiEGwAnfDFXBbfRk3SSgGVJNYoK9Fowu+FBTQE2Ips3LYtyluzRsDphuea2KTlNoTjRdrQvk8GIEAts3gLOLAHL8xQKVLSiyu/00KaUn7I4FAhPAoROhiaZarSjbpX8zXDifSo6tSBVgVPuhIWDHo3GU0W1aYIkxTipygVcih7uO10r9JCTM2o5Jvt8jhOZmlYU+3C8fRd3DjTW2y2bdhcMi1tRFeRHCjBXtFWFTSeRT5puXjJZj2w505EOseQaOXTfctt85ks5qsJ9980WCUJlhVaqER3KH1BlNF1INSruyylpgoiXxU+K0JSpUxApcF0E/qYpBKkBGkG6RBwPDXs4d+jiyXoXyCk1JQx292A08K5VU6KcLVNepiiEdaGKTInci1MKqdoz52tClNpojoUA2RHMpK7p4G9cHuBBUN3C6eMSOU6GF6RkQJLkTBoMx40Q4WBcD4riOoJ7QMGRqdws0M0FsN3/IsvvcttbsnmKvzJi1751ve8m9cfPllJWgX8qmuvu9BIx775V3/nM77lpz77m3/6n3zLT3/ht/3Mt/7soy908f3aG867ZzLiJB6/XzirJF0RlmjZL7RdfDfswHrZWWJfbNw/BNDSTHZD94azl6iUtdOSLaStSlkD0TRmbq+45K0Pfvyf/q9fetQ//c4f+cff/sOf/c0P+pU/ePbZpAtvUtkZDve48P3snYBMtQrDJso4lWFaTAmy7BB54tCsjAptId1TI6IcbEkn9hvvGCTp1awFOSyVUahoAXFpabTPtCplo8UGG9VVFd25ApAmJpHms0X0ql528Fo2kCPKvXrLVOqh6hIWG06rprPbxpo7alB0CbK59qkaEr5blMgQ57sU2s5gBRbdd+ldbUjXMmUvrUE5oyKy70HWiW0yid17MX23ZEml19i55hIrivU10KMPjkg6ENVnaxtp2pWJwf5yMZieVQkx1RQ4fW29td5VDuJkhIikCMoyBWlNeqtmSXdBcEeBIKC0ALGwms34czvcbDqXi6K6ETQoW8kCa1sUn/21woA2A5gMPsXnaLWIwFC80iFYCC+zehCcPGU6rVCaG1DlEFHDnW592//xz//ZH/3cD//Cd/ynf/F5n3qHG/8yqgrvufyqBz76KZe86z1dLhhFY0wpHjceWeqIgGNLZrr4+z5wxaP+6Nnf8auP/rIH/fRn3v/HP+O7f+orfuhXbxy9f5JGcqxyBnH/euGVFGrSBU2ggtvf9uJflwF4z4cu7WKtN17fdKZC22hEQj+ivZSBI+lqdb9pMCGdhcxAEnEdNxPFp1+L7UjlAZu2z7rvfW/6W7iXv+kfjqfr81/75pvk/LPP+pTlliWN0Un1KMiUtrUTPrJKgoog/sGnKXxcWekam+pRMQXFN2qn02TXymerLPgi277IcDu2i+M6UbyTmYmc5jE29GzKCBVTNlmGASE8AjAz9mJC/rJCIii7yKegVWlSWDIXoJjXvYkVavPN5wrMRapTIk9hryKZS40wDuBAibaVUlJMSFBAsZDNoLnRkxyhLWqCATXGvwokZURnujGctAkqBtFIzC3mDtjMogAAEABJREFU0ZlglIubjvM+yfIv+NSPus3ZfxZ11fU3/O0rX/PsV76OA+jT/T7rkw+3vVWoHbkbZdPZyXY0ld5Z188a9rcK5Da3ldbRA83innbuHWevbtqtlfbW+82jHCIFC4nQID+AXTbOjh2OlqGpvDpr4Zs4agdYwhRKTqZUYj/2l5hGydHJgNq/AyKgbPo8a6a51MV2nB1k+tqTq7MTdwOWohNeEJiV4GaHZzhmyjF9q2OSNQhVjqGjWI2JcePgqhruXEprSkav1AskudBvSptFpK2sbrh4EMai6eLagkWEUi92nH2c3R0pXM4ty1nD/qYQSzURa7ysvelmrxJWeVEz5uFoBrnoC723FAORFjZ6g7cPo6cikl2m7WmBi4dI9Z4Dx0Gc1uKSQSBLvBiw2daNV+oqUEpFCnidMEnRqsWcawtGkf+ZPYr52RxYa24SkwlxQHV0w4mWak5JMsZRm9Qadswl2kn2BbUUTcjloMthLwkQzdrlSekoFmD6RBtYt2pfjGRTlISCrihZqgO2lJpQSuXJrEPVAoiZLyKNEtH4jYaiC52v4AoXIDSNgLDEHbrpIFFpeWwZLAdrNuaZFQt8aZeyQ8q5tU5Oc5k+sJ0M7uubstlYMRqFkVpa4JVmImbVWigIuU3kmqh1kyXQjtU2aSFa2gRatIcPX+QI0s+EXF1F3GprNZUwF2iXMAo93BhUGEXRnTIXCTaoDNHeDliWaQtzPWNBHJC9St3S21a1dfgoOaNS1tzDLOmwjQ9rjGJVTYdah0kashIJWifCayYXfQv4pkErj6B/m5FGWllQ7kGiGkolihYdhp403QVxMiAFK6LSenWhtyq0QtBHcgy1lVmyDQLWwFDwQh0501kC+Tx4q+UIblBYPTArGZw1wjcqFtghlLsQbOIpQbAh/JDkR5IwxFuUnzlqthk2y7mhN5iKKqBRbNKgmfRYZIJeUm6WRZUytSWF6AdTJqythdPCyqHNl/BlbYt3EqCZLk06lJKK8jvcyulOmKydaaXr0O1gm/Rt1vSM3IWyaFE8pMswGFwlFsV+V181Ekx6XSiBFk9I0UalxSnCqmAao6hKJj+nIHakJnadTG5V8pa1PKihrZPfqtY9nKxbEJpZAKm0zFXmrC2xAVshCl4t0L2E4ec3RJA5fMjQUInwnFJHs6qT7icNQ0nfoU0xZgqdokXVlRYq9iMVMVBMmyJjjlkziPsYOhqNmIyvexLyHSmAqxfQQ9osjQ1JbUbmVvWE+szw0CIYueeLEACpXmErCaFT+2Za6cJn4MXf+haiSrskU5Kc4cNvt5VekmfdetsZatl001rtuHa3VkO4nymR3fIbX/2Idmwfd8d7fv0Xfb6edV19/vioP3/ONv3GIZAmbRwSfd6s8abe/9+La9Z5rFgh54/rTb0isQdOii2605ifN51p7CU2aKc3f44FIRm2FlvPncw3DQcc6ZG5MgCjmcmo837dTzzyid/zq7/zBy985Svf/p6sXdHNZpxd0vmVmLT0s683vZVDaGoAQduaiEkZoRdXC22hNkXWUnZUaXg5Ob5mMYhAMmg9jlaeVtpkAELzFq/DBlvJLCvJtOmaG781sNSK7NKaWtdWblvqVrqJbKITvdBMahE+GJukADlV5FPucolJWNGirI4afHym3AA4gXIhsYywRHjCd/85I5Qz0RrFKRZel9E6siuFBtP00cMOGp2P2ZpP0yyz0kFdJi4WJUChXGorOixl0CFt+D6aa9yeEkKSOzXya6ryQdAyqrG6zhS28uIgyU2glpPBq+AG5UlaT5pakZrQALGX6kZ0oCy6RU1aB8OhhPI9sE3BOizJBuNzlDKC1BHlM2bB+zg/DkcRPrTwCLWipByS7cwdh6YovWjCikIrcx5sPXQ/tOKQiRO3E239MEb74s/55J/5zn//1J/4jv/1dV96u1uc4Oy49rg94hnP29xFyJbMjCOfsJx18Y2FwLha1FQNqvmD577ov/zMb/zGnzznRW98x1V88MwR/z/nXiqBVLt5j1dz0PS5V7JJrOXmvQ2lljdvSUjs30U+YiCs11BXJ0Nz7z97VckMFnkJAvyRE9KrNK1Ls4Ur233ucbdPuvF/Den9l135wQ9c+zevftOZDNxy6f/0Uz8BcIP0ZKyHQliomKFSjSWMlTolaPrsMskjkrxBWNNRwKTaggW3F0kiivILdbNgCPkr4E2lVeqqjckQVQHzEk5EMYewtZ1Ie4KIOCSp1GhWSgYZwOJNf4lHZWJnXqX0aouODksW2KDElBID63PDMFKIvYqmAtlvrU7Dkqusl02oA2QnMlJya+qLqgohgpiLoBlkuLfNMVHIMmol8cDJJavn5pVQwc2OSAZW7333O3/Gx92Lzfz6uGf/7QeuvJrXh9H/2ed8qhjc2urUzbazk4EB80nvfMfbn33f3y6/4krdUnZ3lVYSs2uuvH7vOHvd43a3lnZOMBQfoZ3KMwpVojSyzsZefKtWaGRv3fUOt7vYBFx21VWuwhU+TEyXCrnmOv5Qt/ebyj1udZtWjTdCRjD2NqgIi1JnTlJ+8fUR2qfWbDI7tiF5AmulPBhFx23P3ZLQnsnAcd0InBQ7Iuea7tdefd0111979Q3X8KNX3e2OH/4TzcuuvCo5FHwoHiuvpl1+xfkLcvh+t1vfUttJ2HI4XMxiNtLYuagvVSOkYceCrTeezOpYS0D1dWMbLeFZTgpCTfbjpi6mzUaIyA5JTrj97W7dW7vQe+31N9T0fqo4yjZrVXzomj3QF3rvdMvDraSfA/TC97N3YQp1LhnRnY+VnRvIqrZTrKjdUAcwoExesGgtE2Ozw7RDysBeuZW3BIeaFpMRQKlYcXg6PFm/c5Oc5tnXGkelQPgmvipvnTGrh7QpGhALs00XyS5pXlxUNMmC6qajtW6m1I6WZRaxOy2NOyROUyTCVFqquMANRA0mCu1oLAO9w5Yo4eqdmUQzmV4FQtAUw4snqgLmUMlsFd2yNW/m2gDrLNSuFoCUKzapkAlBqjpnQGSWlhBEUrnMgyXfopjEFKmtpInVaGkSltyBckdSIPzddBnCUyirks/kkpM2q1BKqClcTiZt3TI2yW3JOSIaa0kw6SVamzYm+DPAUpQtNCcoSfmCrCHbKsqf9TcgwUkNOiYXJaBKWCzn1AzGtkMFtKZpLbt1OYhmunFPGYGih1ONbBWiadZUBVocL1UFjphRPDMUzOXsVgfzNk7326UFaBwkiSYu3bHPbgY7QA4qYjIbnJBTY1Bq0a9MpYWQXa/JpqDgKDi4086gUQjTagKDa4WkQJpIK/QsS0qBUAARhQm6pS3sqsR+eEXQ7jSUHUotiypdpcQAEdEU2+bc/Bjcc0DUWznzMkNFxURYkucUZ4QroyRUYzSMZt2MYqrcZ66TBZLUANwlWKMd1LTDymySmRUFuhs0k1objKgSGXrDfqRKM+lUZmd+duLq2lyXUNsEM0HdWhM6aV7sRqdE8KT5zbuuIlwbIlUJPyjZVNtQ7WUd1viNn2gSitAN4eJASmkmlQ22WwgD71G0lTHRmequ4QInlbm4hQmhAkCtRE+kQ/vWxQ8oRUGyknOJbkLLRb3M2VIUXPSLp6SD+6eCCMFXnWgrlDGFuclGA5QXFj1TNuQUSjbQcoY3BGcBFQmR5PSKDgxHD6RKNhU1+iHcz5BCc6kaJK1VSTFutCQ1bERr6AqVpL3KiWK2G4MPHyROZou0//AlX/gpN/5X9a9+53tv+h9+4VAtsxwVjdcXThX5/E/5uC/77Pve77M+6X6f+Ulf9pn/6H6f+Y++/LM+mdf3+8z7ftYnfiw6kdhODsuF8XynGWqtevPR6ARbbjoF/CcstgI9dzjgpkNQZcWClVJ1U+t+keW6nXbWXsZ4MV/wK0975rNeccmMZPcw/bSPuef9Pv1T7vfp/+jj737xr+3Zvp/qTWiF7tcXXoXaClu0bfZpLQ9NOiUQY4iQYbJMJrs24uhn0ZiWGaKTdNVR6A1hCO45aCODaQCRKshRZQ5BMuAwkKAHscavQt6RHS25WyuORhOoUlkWyEngjLh0WWvv6srZWileJFRQN8cxmMlpxkNJHAYdnrJ7n8UNwID1EsIXxE8BgZZEYUOx0jAfupMhmcU0MxfRjJHR6cdO7yEqTb33qc3ZB6EsL01oJioN2QGamWmb4tixgqaxk4539G5dhACVUotEk6ksNywLSZgnN4wUl0LKe2KCXVHB01uSZjRAqpT54FbQaPvC6eCKVEVStZRe2n3Y7BqsEdFYhdLhJaWyqTK7WgjTkQ8XWpWQXDCJXvuaXBA7W2/LGquiXnrV1c9/1eue/4rXPOdVr3vpm9/GPDkElzC6iLvc8Tbf8a+//Cf+x789MMJ0F3j9uz/wjg99CLqpzaHgPc1Z8/5WHTKYbPuO/W9f+eZffupfXnl2fyLAx9z59l/06ff9ss/8lC/8R9zf74MvvFRgkizSuNmhrkhj0SVkDsISN3Xu7me6f7iFXfxCECv2sPDrTac1MVMtkrtuagRFeEbKlH5aut38uUOA1HOAFi2lt7lF+9LPvC+NB3DdcfuzF73ybTf+r8F+8kfd4953uh1DY4DARLoqxtjaHrLyqiS9lAkiLi20C6SnMQ+YdypWsgiWLto1WYs4t6k0S86gub03W3pr0gCeJjS+YJmKUt0ngZ6JaUineJCohsrMoF9BfpAw0lIHvCsXU9JbS8VoQWNMSwotYBMtORJSIrRPhRdwBNcu52OvGo6R7C2A/CSvdwsmIkcyYYuKuD9ipwKQDM0AkrIShqBE7AJ3FSW7G/jwsR3D1x6xfMXnfrqpsOPSa67fzmL6Cfe488d91N0E0zidHTeeleazE9hP/Oi76i5873jt298lyczSQiDcj/7yN75t7zh73efed0dBi0Pi5lEWRnxGuYqamIGDzsbvb0KqaKF//L0+/N9RvOwtbwubLltKJOrKa8+/+d0X/wuWZvbx97gLWS87CLsAvkRk6dm7Z89pVjfvAwFX3aPI5R+aoi5MeIaYheUed7sTBVICz3d/6LLTU28hGvwGBvX7H/HYb/yl3/yvD3n0jz76j7Yb4uPuetduF7W+4V3vvX7bQuA6PSdp8JI3vGWfdva6z0fdc2VBgFTJWcP+llmJFOaGFP26OQbszgR4Nju/bvx601kAS9LcVFI/LItjC2RNZK6xrhF3veMdb33u4hL2vsuv/MBlV4PKCHkWjXzJ6z9s28fc427LMIGg5CYtvBAn1ru9YmLMAZpIviV5xEwr1Si4V06W+ejmpobZWfcQoTalrWkRQm2t2KVqLRbhbZlbxha5OrjDCyWZ4cGqyTJe0hgpQRU5vvV1O8i07qHuEeKrIASqsEaTDCgrb7Ixbht0Fle21poWoylRWfuRFRHOp3fZrWxARi/WgmRIZ+S2JXJKuNBRoXYzaOa+D3KbRSmFLNUySxkBPsQ/mFASaovtiOlW0CpYQliVkroAABAASURBVAkwIsrDi4sbeCjjyuUgFaSZpFKYSXLrNmAizcFc39xOq20p1YM3eG0xJdjljqPUKvsuNCAOIks9qMCMCA0CtzjGVEJU2ULabOq7Fdxwdim25ASDhI7i8nM2sSLRnIao7D98HUxpGoolw+kCeUWxtPsk5zlsfKhnWdgcmdCiL5hSu8QJ3hIt6A3ddIieJKw4pEdapkdxzrr2PF107s8eKysZwJm8A6Q1VbVyvy/apoxVjfg2TWMzsFYehR5uo+ahcEA1KAMNImawgaWlAOZCLqSDd1cB7mdOWK8tezhRVWkMUtMOkkcaoELps8LhVKS9SlVWIaoBOZpsIrXVWRxEZVeZ/ORThDiA+5EtncUrI+lGwtNd1xqbdhIdfqj1HCF1nWVrSZSUEP8OCIh/OLYJ5msJLSfvViB1aj9t/ZidaRBC5yVA0meN4DKkomQcs8lpl0uWCZpAyCXx1ra+bPt/PGe0fmZN95jFYbNPbkJUU3k06WhtEkbLlWJocC2uJoxXl7FIbxwhSnC0sT2E1zyLLiQ2w2TeqhFJaVFtFqZGyiyaE4FIT9rcxNBaGg2kW6ij5BaZlFedP0guXHg5iq4VtmIaC1LSZCpD3xIF+lSootwivFYydKgNLfSZnTyajsxd7xIsM4ChsnzL6fL/IetPAHZbrqpQdIw5q9b6/n3Skb4lEkICISENfQKhCZDQJBCa0IS+EUIrIggKhr7x2tCICBJUFFGQ61PhXXuRx9UrXAV8IF7F7iIKiiAI5/9WVc0531j/Pknw3W+vr6tVNWvMMcecVWt9++xTsXn1BmsypMMAg0qVIr4b96WlOXylnT0bpN0ZWIGwooq8947NuK2s6xznfw4Xbry51D1yB72cpVphZOJ3PBSDURJPbQ/zz3jlQ/9QY0gZVW/uVeTERn/kwx+432jGT3v5i//45330H//cj/6Wz/24b/nsT/rmz/3Eb/68j1HLH/3cj/myT/iwza1z3V6v9/vfvWrqHr1Pb7pl9DusgyDZ0URSPXhIUHfdIQoQZRUdoHjG73iIogvSEcG4Gn/xv/zm3/lnb7zje9n/yGe/5g2//1O/5TM/8Zs//ZM+9N1e9KZxMlLbFIRufFOjPqgW5CxlQb9aGzSYplQwiq6glgW9rCv1VNRm4/Ke2XWycduwOekGRyotzuRQTCgnVzcFne0IZFh3b/ccN0kfjFgqIKY9kRShAPrq29jaVDHhXJ5Hl7wU28rwVpKAYGAoUU+h3OJcbI7wpVoCISt9YhRxloFyqHhZn80ni7W2jE1XK8XImSqhGEZ56KYHNUInQlNZBFlwEa53r+oWzqRZQUIs9vINaRU2tJVLn57VBzGBuWH1FoLKRmskugaebvelCjXdteQvMXm+AZmFLMGlFy0LYkPFwgY4z1YpT8vEjAolXsWecSPQHovAWf3JPIeJxTpOauVFyyBilPg6Wd+77fvV2xU2JJBUaGK1a9i1sNIvtBtP7a2VVP/uV375C7/t+37ft/2lL/nW7/+aN/zgPBZX9pXiAmWs9g7PfMYT3+IRmlnHb90e//HX/nsi6XG/Rqjx/hHWVD4JwbC/9mP/9DrX/fbXvs+7f/cf+Pw/8gWf/Mc+5+O/+pM+/H7j/VeaNe3qXAzebzhfW2jhwXSbPBfZ//Ybv3m23j0f86hHHhF5xN23h16o7kXGsvyf2l3trbmp/OJNj0KlWK06UmHtW1dpeegk6VkVI3mF9NSavc+LnvOmDt/3oz/xpn/H7D3f/m0e8I7BWgyVwgbfqudqJVFnnhGUzWrpFVuuDWPzop67NMc9sxXRfPh+5AMrVVEuQtk21F7Rma0198se2GZhKLK+slIyATIF0ithlt6N3pWWirx8Sm0OFitM3G1DPx1KkUsDr4eNFcXFDRQZ5TaarUIeKNEZa5Vsl0Sn3jk1vsUQDDrKNZND0uclm4UtebyqRNJAlU00K61crtJvHueCKO0vMLXWzntb3dyYq4voeNORee5Ajunv/Ny31wXZm9r14T2f/+wuhzI9FMpSy/1DQ2IhMt/x2W/5pn9v/Z/8q3/34z/786r5uupauf7uT/zcP//3D11CPOktHvEOz3gqZ/gBz/amIB5z3v7Wg21OWwKsJNCqYvenOF/TMByLr3iX559f755/+R/84//4q/+VLERkzj/7w//g/uW3Tr7grZ76tCc8Sq6LwBB7aro7aksFuRqVDorGXdtDL10xj36B9AKFQKuOTbOqts23eOTlnZ759Pv9/tOv/ebf/Mc/ZdVZXsZ/9LM/97P/9y//h//yazpo3tvNEx7z6Oe91VPvd/6lX/8ff+Fv/aPlFyhd/PZf/fIv/Y2f+Of3T6nkvs/znxurCNwe1/uNepUM+5o9YaqPJ3KxreaHDkfLM2Z273fcZftv/+M3y5Co8ziL1EOd778xgbXm7VzHrbT8Hm+8H6Er/O/84b+3Yi2sIB787f/x5//WP7w/RK+veKfntnYaJaivDx3JGPvKm2k9d9YOJVJZdcPWctc+tY4qlceQPnfEJaIFltXoOYo1LZc5oCgo9iSdYkwyOetzH+WRUarZtO7opgrvycvqe9gW1aBqu5r2KVsc7qO5Zs8tJuNATmje6eJgSQ5KxgzH6BieQuigWZp+P/IHNw6eemFUPshYUpkZvGZwHfremVS4lMbVSnyDR2VYgN5CP3Lrleqf2qeFj8mlPmpgudaTtYRgwI6tz13FRTfJah5cR6yxMleVZlWs/Jo1bNGTblG6DRnyoTjKQsCXEnq3eeNz46IAnItJZplW1pGIPCfbZ2zl8q8iirC0xrYle+SWvlEBbGXr2q5zG9kUTZls/VCbnBnoIxin10NTN7BBOd8bz2VskwA14NQabeS2ZhMuywAGVAHCgU3BySY97JwN0CHZMvxkQkufT4qGGptPbyMvI1SixI4sp0oyoIi5UBRUzECRBvS9uJdqFFbIa14Lo4ViOCvOuctSDls3N2BZzb5wA/PUV6q4MrgR1jO4lA3IhpQQLQc8q5n3Vr2l+rR0Xm1daw5FkcrQjdadnVTUe7JDj7lGHanFUyd3nDZyxajSri5v6Jfq+zShHAqKyW+b6AqqO/Yu/je6U+WMy4SWB+RTrSwAVrYdW3vQTeaslsXh62CmlSKwkRvSqMBGLUJHQBRA0mHBYvlaWtMsS62naoZxthqNh0PaOqpW+IpWigcXtytl05sE0QZw1DwwI5LLOfZKTuaQ2dQnMYsSuVor1FptmqkgLcasikLQF/xge7BynL6UmyDtcoimRS+Vrnu5iKSrfSKEt7H63QVom51iinNySuwrHUkmBKSoMU4YoRUvV9VklZu37jqVxAQZqR8MuXBct9srFR/1G00eYZbGZUDrkRjpwQ2nKHECKOwoJVoeawQ05OwxKhHpObuF5gXcwiyIsAglZ6YPffeED7ZlpuCahxcoHvA7Hgkl55X1YON8yTs9+0Pe+bm/4+QbPxKjM+/13/Wkx91vWpE/8Pd/Yl0jIaWePsJxLf7oT/1LvVrGNs8RD/yOn0d++/Y2Y/AQ9Exr4um+Kb0WTB4gWmXeu8h3tZ3HqrxyhY3s07d5Nr3xuVE2YL1KdZr57//zf/mtN/41s2c/7Unv9rxnbhcJRkNEbb5xEOT+oqM7+Ka288NCZdVJBa5LFS+PRCkLvG+otuIMXwG2jNNCnG7W5LTlJtqVbt4YrR1dWT3BQSDRtdTcxSZMMS9NoXASJOUsTOZWtpU9wgNJH25Xt/NmqyrmPApjncm1FgdqSL4VjMCaOE5DbUjMjPOjKtwSfGand/heaTbPwalZdUIxZz1Y8SDHNeaRMWHBZs121lah9Oq597n5krtVtnIrOFuwF9oJt8AyHQ6sOVVIEjiLjtE8hV1k0W9a78YsHMtmetAmxReylE7cAlvYnn5zjsy6Ye5OuC30g7YoIFhtHpII8gi/Bo9KZTnIpGViS92oAAGUniIN5bC92g5ljKF2QwOaw1S/Ooq8Fh9MXAOjEAZ26w4DFBKItLi+5ZMezdMi9PjF//Yb/+I//iKNWowAY8jbsY4hh3VWh5s97GG9GlaVwnMdQ433DyX9XLJ6Qv0X/+4/3G/U68e993s8dntgizJdB+u8mt509MW2DPmmBk106Ofqk0P5LcTxc//hl+6fJfB03XU2OHKeaXe/GRDNlXel+s12dE5OkKXeuCMMb3p4ZpeVCS2Hb2ysQjZ04ybHYq2E9PPUJz31eW/5pPtdjhWlTsCjH3bvfd7hbZ19oV0tpg20hMs7CHUtsCj227j0W21alo8IRd8ILkkF4Fw4Dhwz5lkCx6ACyza6DygJAzNw/uFhNftY+3W0qSops/I6lsYVJIDGtWX2VVsqHqVYK1qpMxCIdUBwraG2mjWvyiVVV6RZWFvuUgl3oIuaZNVpmEg1u9+4XRRmp8rubHaVjtEoarQtqc0O46LqaGxLGRbgIQFv3U3aq5viLnKvbYxYoZPLtgXoOHG98VmJSucjHnbv5e/05mJL4oNe/CLxgMOpsW/srXdWyD/5/LhHPey17/WOpq7Ag2N9xff+r3/wz/zAn/gbf/+Lv+sHv+Iv/PWx4uxMftiLX/i0Rz6mH46Jzu3eGzfcv/Y/Hvyq7/9/fdcP/52/9Y9+MsJUWbJOLjVKx7kw5DSMFz7zrd/neQ/9Ew6//Bu/9dnf+J3f/Bd++Dv/6t/9rK//zu/7Bz+pnjqa2Wd98PvRWjT2bfM7SGrXkbSAQaRB9OJ3PtiKJYUgiUEe9NCtU+zJ5t4/44PfT2bv9/9jP/i/fcm3/7k/8cN/9yv/3F/9g3/mh04yATO++j3eiTfb9vAHPunl76Ebh+osF77rb/7Y7/0T3/2df+Pv/y/f9yOf8Uff8Btv/pfB3/r5T3/KhujEA7vire7nMeS50m4pOazDjXa2vvFpTWDUFo9+xJv/2eI/83d+/I//9R/+7v/t79xeH2zGN/a9e7dyUmr0btazt+PTP/A9H/HGVexv/tS//Og//Kf+5I/83W/5ob/2SV/1zf/ml//b3Rg8+6lPeP93eqFKI+4zcr8VkrHgerSW1SHDVtkT2iheyJZUgCbs6r56WyJTVfPKdeXMjF5JHXTm1kpQ0kxVT4Rv7BdcWu57nSAJJ813R+vR9tFN+D3dYg/3UoLwQFvY0ppWuOyYZgs1kSuvOgpD6Vyd2fs5orJ0OYgW8KjUKY27UD+LOvL8Sk8SyHN8Bs9lTVPaVt5SX6Cyt+pYUiC12/Jt4DLYwso4wVSf9ILEAum1F3p56iuNXrIbaxvVVc1tHX3dNsmeGWhLp6MMSh59ZxyiL5CRgnX6iZKtJmSypjKBEXrVV7uk+CKI6VgNuEnfwdPdbTXTes2m0oDNuLm5WdDXaDXZsTrPpW9xzVVrat5zh7aF3Zvcl7fqmqxlaoCtwVhpjkYwEKNFbgmvzORUkVm0pIFiqS0VrNoAkzdRoQ4w5Olhz+BaYqsauJeb9g5aclkW5iM6/TTIAAAQAElEQVS3FT3StDvDWubLGvUo6LFKDgNzcc69Sq4kxKiXvuwVjaug/dMsH7wsRUS6a3toCaULJXOtnClDXppWwSkI7jmBimmF11o1B3KF/A3HsjZKE9CUYl2u9ljEyDbQh1n1KJotdRQFEbLmvgsV9lWeeRf4DUZJKa1N36PJ5dYNm4b2rCo9R+YQbGajiTwkIss4dG+sqiKYpUD5JpxuMBSlWqxasTIXczrL6e6mc0BUavwKi7nF2qcu3FZb2QPmy3uqFwu2ILuQObYoV3yiVe1pChmNOqllm2OUtsxCYNQCksxzZBKe1kfDUpQzbfVcbc42rraGdlKlSGTfAg3llu4lrooQq7H8iAq1wwS4ihHIZAHaZ487h8SF5VJp8NJlH8ySpo5nqKIkPLPay5RTLFTamPs89GuVs8J68DKra9WbZYsuR6yZyTVWAVWeZgFkVSp9EpW1qjR38I4Yxup53blaiQPKG6yzS6qLcptoHv1sypGhAGgC0VGKG2H4nQ9FuqM2x6a9HfmpH/F+b/pL+XjjQ3cBolVs8fJ3e/7W/H7zP/zZX/juv/Fj//XXVLnUUr/8q7/+LX/hh3/fd/yVP/fX/kEeAntO9tTHP+Z+Z73+lf/P//Gbv/1bJu3+9vV//8mfftP/uUynokqXbTGhMaJbLfePf/tLvxKieA9prFXeb7z/WlKQQ5o2T+XUI+9pg/GQY//pV3/t3/3Sr//22h/M9W9/+Zd+9J///P0h918LUrjdhfJ+A4pcN40XVs+pkPoUiaNUjFFOg9foa4liQlKxLbwva1noVVulC5jsmZGmP1SpJdOIoId2OypNCx5HrduBFYA7NgVbBaz77K3MCV+4my81Q2dutcAMyrSid82ajrBwAVD3wlm5FJGFWLmQ4GRX78rsgTNRsIhV8rK65isuMYyITF7D7hTFNB2uahJUs2TLjdKd5mzUwhSpL7XZNAZOBVeL7FzuSRbkBlpT5VyXGvci7xl3A4laPFnMbMiW0VJICnKxE/phsbvRe9KWISU8k5/mcJgKx8nFmWiqnOJcKEpYeXsVe8otWDuFoOW5BP0E6dPavGDtDGs63Tq5BSgIlWuPdS91azu0thjLo3dcnJ0lxzEZE1h48qMf8dK3f6b80SG3Xv99P/Rz//E/3ir8uSnDf/XXfuN7//aP/tKv/YbO6njkw27e8kmPY7dZPiPe9NcUxex/+KVfRq7ZPZo/6uEPU+f7x0//638bY6ru/MZv/vYP/uhP3G986NXy7gPvXs+X//obv/WGv/f3f+3B/448Yj34t3/y//w///W/P08Ab/Gwmxe89VObuXeKxPuN52vAp+JD0M6vb35S7qySTt/cJBrMqRAawi2MvH9OP2P+8n//9UbV/XOQLotHYB54rxc89I9T3e+m12c/5QlPeswTo+2phcYsKzLGClUnJsS/G7xFb3mSvqWiPNLWgKqcFVW/q6JyVgzOkSszqzAVCGO6EOvkmplawtNXaeujMTj7WDWodSB0o6iQZodBi2Ai5EWjtyUigjOxAhlQhnanuclHlhI7WJoCqphynmlSWyeasayvcjlB+kbdW9k2NEpGQKbSrmCG3qvTLNwj3RdbJn0BYJh06ADI8i2kspbI4WNucnGuJffwpkd61hbNxg3Wh7zjO7yp/YVv9dSnPOYxLoQpO/Gm9vODZ3YZaVzt49/3vd7ruQ9dRTw41g//k595w9/4sb/zz37+qus7CAFf+a7P/9SXv8xyR4kXPOzeAy9469+FNz7+j3/57/703/qxv//T/9+isP/PszB58laO9ns+4pXPfPLj7w/6ld/47b/0D/6PN/y/f/T//IVfLAUL2Hv7Pa/+gBe+9dtk2YKtiLrfFaI+bqcf0PIsLdFPCG88JzZNga2lipWlO/NnQGARW2Lzxue/1dNe98b/cZAi/nd+6uff8MM/9sP/5J/fDvmuEPAj3+NF7/2Oz2mKqPNdX/js177fuzV3WZ+RP/Zz//p7fvgf/tCP/dPfvA616Hj+M576+o971dayaQ8kEUWq8f7R3AotTK8KbGP9T4nj+kYQ+ZLnPkue3h/yX3/jf3zv3/qHf/5v/YM1rpsX77fevTrR2XbuN6YLVn2pJz3pUV/8Ma+8t4sEiJl/9Uv/5Xv+5j/8S3/vf/+lX3/o19GnPe4tvuqTPvJRl4dVWZr6qNedrbuXcAhRGRPKlUFI4OooWM5wC3M2SzKgcClf1MkCDexoNK/T4S2j4aASr6b2WSmapbrSeQEke8px7Qu9z66G6BzbiLbUIcXM2gouHwlJMROuxkIDqpDGpbmpc+7G3qq3oPAElHs+15bZYE3Lwba5FrpmMF+oxalcUjiuvi1uuZzybUIehE1YyKLejjrLATRjWmur9Wtro7NE00z16/QbqWUjWs6eoeW4ZXfr3EzOXwAFVGnMizlPKniEiYKG7LLRmG5smksRbpi70C4Vc7QBnVregzeFbmX6KhqW+JBe9tLaEb1NtoDXOUmrYkLfprdsDn33SpGkJW8cPQ9YrhNEu/S8WChoziIqK1fNpUoGdai0XrwELklAHvJg6Zrl2Oeqq8/pUWagAyRxPkp5Jnh9hyhuNMnbczaUeOLugT6XlsWb0qCe6p1jaG2qWoAAbsSO8mWZ+8qeqyEUYpZ7OdUBrGV1Llp9TJ/BrPJa4mpLbTScFWRqP2ArYdDF2R20MEQWohrZhLN8Vl9oWd3M1fNYvrQ2pxMdb947NfVX93lqpOBoRvXGosmhcJGls5ezHdN5NJN0WtACkfKHOstNbaOP5SMqdM275ABk5TLjskJnxb+csnS4x4V5r7CVb/DdpB2rafNBG0dmZa+8lOCV4k4/1VHSfK1NQ6BRc+O4bKlb4e1ilOJ2bZJSMZupXFyFkBPodO+tNssbQOHdDBJDQXCztdg7VR/aRB8CZCLeNmttI3uCqo5yf8GUItEX+8KWcukK+RwW09ewMUp3ntdhqQiRYSImDszJmnBZKRRExFKDUC36am1Jw7Zn7SaEWgt1S0DXIbZMPY62rv2sGZGM2rI2Qpfi22Z2onZYs9375t6EfWM0rrQ5bB26IK2YqQlZ9M27bxaX0LWd9pJVuhxJj0GONO1ZY8TSHkDSsN2UEitZgyh1jNxQrj+wh9SO+w+2Zn3jZTeZ5v60xz/hU17xXm4Cdv/8+ap76B6oyee81Vu95qXvYncWxorv+JF/+Lv/lzd8zh/9nt/9R/70J37dt//5v/9P1Pi9f+vHf/rf/Dsa0e3Fz3toCygrP/pzv/Cp3/o9n/Mdf/6zv/UNX/49P/DrD4p0NZ9HZVxnXatG4+Me/aiz6e75l370J77wm//cl3zzX/ztX/strv8JkmJ4bhaLlWlVT3/sYx/7iId2h//513/zc//4n/3CP/aGz/6W7/3kb37DP/2FN/8LZnOF1ZzQmN9prVQJ1knCDW/23G/SfJGz5HGtUkZsvPZTPGQ1pBhNT+4RF4yGpejM2RSUsl7d0VmS41Q3ST1s49qggoxZPIwSdXmr1sO3sFYObHrJy7y7eM2ipbU0A8hcOkxhdM9Lx91auHHrKYwcqAj04m7cPQs5ApkwLRTuSBXmRqesKHo9cIEWFUirJlWorx3RRvkCohjZV3bNTEol+i5qWfpCsFavY9drR+uSqXJQNb8UNp2iIuNlpBVi1ahSbDTQWnRmI1CEClhmQNmwcermQG2mXmphlTN7r5tWN8SWzmXq0pfua6HLv1qBI9RPWGBiXw2GsbuOKVB+pJgQC52F4orSRWFcR+WVfnQx7XAv72VO1xOt2K7cD9uXnVnx+a9+xVu88e80/pv//F8+9Y9916f90T/1WX/8ez7zW7/3E/7Yd0vVuHvIk5e96O0f85gnAHsz6y3f9DchZ8Qf+t4f+oJv/7Pf9ld+eGW+4G2ecTfifPnGH/qRz/i27/rMb/tzn/D1b/jOH/mxs+nuOSPzIKOjlPR3TXcvP/BjP/npf/h7Pv+Pf99n/tHv+frv/+vHXHfNeK/nve1TH6GpzVQ97jfdvcppsSueMgXwrunuReodYFDn667hfCEhki91rgU39+49cE819Gz/zQevv/9Pff/rvvkNf/JH/l6yaMMs3fDeL3he19vZ5Xxq+Pu+4O1VMZbTDJJtT+aKmLVqpyjpd/YpwmG76rNZpynysebyQ+U3ymQed+FMSr8c0zPhKXN0nZBBO9IGFSaXjKm4bqvvc+9x4ydfCUyUstPX0C9wsBInrUOd03K6pLyF4l0KN9pG7YoUex/bdu368W4LKLfXKiVtsbGb+SKWcm2WKUOPhslkjLZmN8BQWMartaEMkRQ9ooXEZmQ2jMa5Go50Ve0CvDu3bTWbVpO1SnjxpofWYPex27k8Pe9Zz3z8Ix64f+pV7/7Ckwcq3Npayvj95vNVOcEWKRS132z3vulTPvYT3vfdHvXG/xDi7HH3fOzD733mB730D37iR974TZUSBSLL761PfuVLn3v+py93ne5eFEfaMm2K7r7efynGhM/ck3zGkx73bZ/72le803Muze+fvf+qgc984mO+8uNe9XEve3FudXKwkHrXfHc9mjvNCswsdTY3kndnzpfIWCk1DFvD10Sm+mFCnsGjXeITX/4+X/KaD3nyY968BJzDgEc//IFPfr8Xf/FHvlzgVEOkiJtun/vh7/vFH/Hyp/0/Oj/8sr36xS/4X173sY99/AO8FDtQluvNUSBpzcw9wZPWvD/JQ6+WFJ6gPe9Zb/Xal714cyngoVPnmzoXQD3xxgdlrPupJJfYIen4q17yTl/3aR/5nKc90fhm9+/3f+lzn/lHXvcxb/+Mp2gWSWMWZfL+Kb1WERNiEA3KqpBG1ePUaoXEFBu0ApXGYRkWhZ9Z3oCujYqHgnrQT+6zECrGWOo0mAdmqIEGtkm/lkLQZDlbktMRfhqclkWrdKmDgnFkLSvNeJLYwE2jT36yTvBhHuzL2jKkGnIRgW2EdodehJ9XK303lYqEYFapoHcN0DBnqrPglxYg93DxlIxVsjEMpyYKzmgeTfZZAWRwpQ20FIySuIOgpQoHtp5KdHcB7JrWWzbPZmQ5kmJERs6TMJTr2bG0dFxStmRDralVem+A+rUmu1bWoF7atHpAHrixjDojgLTVEBbCU7FsueWFtVVxCaSt2JGbFQ06pVpJwFEissoiuRbkphbAqlIhSVflapttmh61YGEWTcMzqfKiwlpOeaGgS8QBnhYVlEUBbXC6Tvdc3Y99W7Ji1le5UJc8SKCmxhQXWYiqgZjt4OW6tdEq+sS2FBjrMmRuzaAHsSgBugxJdrpmyXaYLZ0sxT5LZc2WCaenlWdKVHZ1Cw3VihXhoC+YAOiXIO4mF5NWYJFwUL5am5AH3rzEZhclqZPU9IRF+TK/ZR195T6b5dYEDoBwJXFGY60159COl6mvnWyL24weqXk0ZDTMVlPfG4s0M81N12fhZCqcMpTgCepUXDKntnGGLMqDzIoIreeoLgAAEABJREFUOcrltjZWh4Ta+sy+wmD0Rkd5sEWjWVGsgAu6NjzXvnLHpbyvk+TQNOp2Pz7Zmb3g5SYQVVgFkWIbfaM5DC6kaLUcpISFDYCdDM3MtSz186kwar6sLD1wZ2Pktmpfq60iRIkZzGVaHeRSoi3YaqZpgUYdrm4VbU2IS+SyCqI0dziW0Z1bo3fIQGUYozWoVVgAhbBMVIkkQcqlPoI1141xZ6dtkRKlE3CPwhGopDn1kLBnQh86YAk9SEh/GoZMGRW2ZFN08OYHnbbrKUluUWdd+PB3e7e3e8oT39xDnxJ9wIMXv3zGh7ziFS96DtV4d/y7X/nVf/yz/+of/ey/0uXBXQNe9sK3fdZbPWX4Nn17p+c+6/lPf+hvj+js//Uff+V//xe/8DP/9he1/Xr8I9/8w3oVsrSTqWx473d87gOXXZ11/Pb1+Mmf/zf/5Od+YR6i29Xy5iPMz4JGcZDkw28e9ckvf+g/sFGf//qbv/1P/sW/+qf/17/99d++PnxvJh7UCvzSf/sfxzFKLGnKu5b7L63SFitIM5wR2HOhVmJlgFPeFu0s63fdNWUkIc5NUUUgk6E+hBm3zJ1okII5nSfv0yyoDXMj1QuKKCppobNMcMGVsWpQwps60ChvV1mmY5nXmQN0uOJvErOCHcZhUC/ocWI2fQ5GYui1emoXsWhLy5A6FC1MKbKh7w1dSJiSV2RI3imleKpj9mW+hKkQVToSSIonlcls8kDzrdLusbdupBKEIscjN2XdiVhj8rQZs6ABZYRVkaky4YhMRX1cWcutVDQSbS5LwYS+BhEnDcOpGxapJVEcWhPHlLWUvwKqQ1mlZPNaKhEbys8ZqZlP3ysCpcmDAg6WOMfGuEFdrJrYN8tiFSqxmk/VHPNcfOsnPP6bPv01b/Okh/5a428fUxL98Z/9v378X/zrX/zVN/8rPe/zDs/69Fe9IsMBAcuH3bP3f9Fz8MbHf/613/jHP/evf/7f/mIDPu59X/q4N8r7N2+v//Tf/Pt/9PO/8O//y6/tbpdm90f8j2P9yq/f1rnzeKhF7U981MOe+uhH/MJ/+pV//LP/+mf+zf99nUuNOt76SY/73S//AMMeiTsNqe3NxwJHmmT05iYgz4NB+/8b4I6uoMAfuHnkS3/H/wv13//Kr/74z/7Ln/0Pv6gh3tCkhrae+JhHPf8Z5z9Odd/ywy6Xd3/OM1Cqg2FA452xsExVHNFCzWlVRPGunIS05htpvGtjKfL0OgFsVIBgkK4D7dD2JmUAMIU6Uahw885NSLaAksrgJuVzk7lC6uCCdF2hGS1LvJpjdcblpnb1onKiJbqXxuiNaB5awmETtVgqKbqYC6cb7MRXgVjKZtnVEg1MeGhcbi0bteIFTRckh7bYWanETfFQyyIEJqpNs3Ea1n1fAXOcwfKMvLnZ/uJXv+4HXv85P/AHPv8vfPnnPfGJj3WW5oSzP+zyXV/8uT/wpb/nh/7A53zQu74Q5mUJW3Lx977mQ37wKz/nL3/N6/7i17zu417+7ig91hkFs3uXyxd8xIe84Q985h/+rNd8+Hu+86ve5R1f85J3+6ZP+ajv/YOv+5yP+sB7W19I+Sdl5Rbp8ZQnPPzbv+jj/8hnfdQrX/yCD3n3F37Sy1/60e/7YsXIOr72M179/V/xe77/K77gL73+s5//7N81rKXmZqoQvOUT3uIbPuujvvfLPuMrP+5Vr3zXF3zQu7/wE17xnn/8dZ/wht/zug955xd2xcknTHjtmU952vd+2ef94B/4wr/y5Z//Db/7o+5ppVIiTmTmWz75cW/4is/7vi//vX/pyz7/D3/6R+9bDyQyWsResVMqWtVC7OfpW+3gR7/7S77viz77Wz7zYz/hZe/+Qe/6/I94zxd91Sd/2J/9ks/83Fe+7OJbLpQYSnhpL2evfZ93+97f+9nf+rqP//j3f8kHvvhFH/iSF3zVJ77qu7/oU7/04z/08Y96BF16Ou3K+HOf8Yy/+KWv+74v/SwdX/oxr96axAWdR9XbPuNpf/kPve4H/tBn/5Wv/IKv/qSPcYKrMnt5/+yP+MA//YWf9mkf8NIPeZcXfvC7vOgrXvvqe5eHRfKj3u/F3/f6z/ner/j8v/gVv/cV7/JO0jFck2TVltxL2zPny97x7b7jiz/12z7/kz/zQ172wS9+wStf8vzf95pXvOFLXve1n/nxz9CvVWtUjoLq7Hrus57yfa//rO//qs/5/q/6Pd/wGa8VRxthFiuXthC2bDu4jrgOKM2TVgxtj6ohtMfrjlP6VOoFOZq6g8Ruq9s0lE5Iq1heV8ckA5COqkw7h4iySqeXEMuQnVnVom8yEHmMvC5K1uk7lFvnxC12jHs1LzFcFz81NS0hAzKs/GEJ1UqEjA9lQxYaxGQiOY5NgAXInAq55tL1k4LfbDacSvLanFlt5R6zczlDk6uej4xaqZhnNtmGRDBSU1mf3qbL3WHUuwW34bscB+TYAufYa+jWTMNmecFh2qXXEgMtok+zauk2HKMN9KWkFBJamvAYWtLV51rtqFZ2bi4pH45z+6VgFyIrgmtYTSM1J2t1jBu3fbuwbRq/CnOVwjbgB3yEz7vtsyuocepTfqgm9U23YeRzoeQmeDFurK3sYr1TUUbNOI51nRjQo8zEaMwQNQtstplaKmihStwtW6/akCCmedbe0S/ytZWuOsY8IGa51p5FlqQxKf3annJSRb1TJhFbrUuwj+aDtpYocxksjahRkHyTqMZwGyPrqsW5IEZQCznJ6FmXlI9GpsiSjb3hsrELmANRm46SiZy6zdq0s5HO9MvTPSxpwBGQhV6AmGwPTN1zUGqqdRQUtVb0kXmsOibFQTcZgFvqfoc+beUPHHxgxpahyZQK++h7bttoODyDaaLyGBhjpdYBayFYhImZQw7UzLyOtW5lfOFCCrtRoOzMoMlYiicFbidu4EZaWxuXIWFM76oSgD5CXvJAKpub7iHMjtUrZ2CiaLkx+yp9FK7cCpZIV5eMy4ht0tQkLZdD26JbbyOc2dwJPbthc9c1epvMYTFaasnF7lJBbDSpx/tevk2Fl8qgLAEcjcvP3WwAvtAP1QIqBZhGJzrCS9ujSjFVWFmqUyuRCsSyyygKuhhXrJ2z8RbSh/RFSDByTac0fB6cK9hW25PtvODDYi/bvdy1WprdCbs7CcXUdLK5KpkMhnAt1jSoq7mfh5lhTTkp4Ym4WfSi7i5/1is/4N6+udn9g1alMGy0nQ975L2v/tSP/oqPe9VTHvMoI/HGh0L1lo991Fd94od+xae86qb5UPgVsq1/4+d83Du9zdPNHupJ8hE3ly/+yA/8sPd8Z5d9NyExg/naXGmev+tpT/zSj3vlw/aunvdtm067s5ubqfN5mLUhYSgdndgHLtPsw97t3b/2E1796IfdIx+ay8iXvN1bf8vnfMojH3bvHOX2f//Kr/7n//rbJ9FZD1mTKR0rcIy4Dj442qHNB/asfWWP6VkFnNKt6WtJ3SWa5tpydU4oLYsz9HFr0UzsDnEpIWS7qdihG7RX/UI9tw1QxbwsbAd8xBn9uhqumIc+zxZj63PrMEbWVKCGNkj7TdvattXe1t4SBsICfUWXBUDKYhvVrvQZXBnJYe0KXgemsnhFZKBK9O7cNugCsCuIEpYNqV9pEBQxaCLbe6FTFkOhhnS7bk2Lqah0p1GPlKPJWFQxVULFgk/oN6XeHzR/EJLbpPytgXaUFGtN+XFlqS6MxrBSBdMGAHQIhTd4xaXGbtEkyQAjKgYrKDfZYM30cOPFc6cUfB37dSrankxAyT9KUJbyY6v0UrqfCXE6C7/01i7MZkdylMnVbAvtCgWrYq3KK/kgtaLGGBXPfeZbfesXf9bnfOj7PfnRjzR5i4ceJPRVwv6K137QN77uNY++XHCdNQcRG/3D3/MdP+o933Fzx92DgIa2qrd50qO+43Ne+6K3fprG3p0BySe+xSO+7jNe8+LnPtvN7h//+Od/QXhUpu5/VSRe8vZv8xWf8lG6LFH/+wPN+G7Pfqtv+bxPfvJjHotMq6i7WdT5PMzQmL1pQ10N/kbL+tACjlK7HLjfU43mnqqh7ioGOetj3+993v+Fb6d23D1I9OaJVubggi0Vk/d/wdvfnTxfnvf0Jz7l8Y90xSLCGCUwilbb7NKasar0M9GBueo26poxrWCtYdtsa91rIzdws9UgAmXQ3HQ+aWta3JK3RQWm+dr3ebPPZsuZlB8q4bOXGuSMsY/KJaVtqTrXXZ7aAIeXKLB7br2LIlSpwB5XXAfj/I4L0JDAQRWAylMyC3qHwXQl1BJGEHePgnRybqfAaDw6QrmeR2WsaDE6hmXZWb5ntTh2rObK3T5yHeP2iKPOVelWy1Az43Pe8gnP+V1PfM5Tn/qcJz/1Zr8IIblVbZF4whMe+8ynP+VZv+upl4ffu7oPmiWd/pTHPf7ZT3nK2z3xSW/7lCc/9pGPrACpyExYEbZv21s99Qnv/27v8PpP/oiv/6SP+fKPffUHv/h5b/mkR5GRKk/9ajcHFeRpc/kCbx64eZ8Xve3Xfsqrv/5TPvKLXv0BUlSuiuJTHvO4t3nCk571+Cdo69wvvVT9vUxxKJ1Gzf7MJz/9I176kq/91I/9yk/76M9+zate8s7Pf/SjHzBXUGCKQUs6Ht4vz3nqk97uGY9727d+9Fs95VH3bPU8FFKMtRve/mmPee7Tn/x2v+tpT3vqUyQvYhmq2Nn3diEvCoMqCVKiOHaqkIKPfdgj3/e5z/s9H/ZBX/vpH/6Vn/ahr37vd3764x7XoPhyNGhHmrSiL8qFeOwj7r3v8573RR/9oa//9I99/ae/5kPf64XPedoTLrGbrI0NQKEO1ZeH33vmU97ymU9+4ts++fFPf/SjuBanFCPKbx/+AJ79lo9/9lOf+KwnPv5pj3kEhTCoiiF+stsL3u4pX/jql33DJ7z66z7hIz/kHd/pst/cLnvEA49+5hOf/OwnP+4ZT33CvUc+IAmUX6c2bAqeFhZYmQH2yP3yLs961qd+8Cu+5jM+9us/4zWf9PL3fdGznnHv3r1mLuiVixgN8ah7l7d9S8njiW//pMc/87GP3Q27DxUaOVmp2yz3gA2VkVPCj0a/8dZMczgbN8clU/xFW3RlAE3kRm9D68lu2Ry2oTetaGzLS6rtvvZmKQMn3wpHH+jSMdLPKp5lZ96ZL3iY5VZoqYI+JVnonHkWiTPfC9B2ZnUfZvrc9ZQAVwir8gtVBSnSJvwwX50wZ3UdDTQoHQVjS+1wyuWKQtzD26An9poNR3AsU62pkTa1qWMXIl4XMmBa80XgUqYKSzKYy0MLqQTs5RYQuGwRbKmh8kTQetSpCRYMcMgVImItPYkFhSU5jsJMlDK8J5rsTixVtpm5oLixaKSsg5Qdw6XKBlTuUpDKmupP7+HbVH1DtTTkNktTeyhEIXxwbzICgVo6vWALzKLHicjKTKV3tmWt7NMAABAASURBVIv2ESKjVxCiNEbiGicslGecq/yh4hZu6QntK3rMZrNtSLdBC9lrxS1ar4166T1aC/Vty1jddMopPEm5k8AhgDbAg1kA0W1JiXsuVcIqJivVHeqJtJIS526DS7zQwDtSzCZVFaowsQWtHCv6mpBFuXX6HkDofAUqW0ojrpZEMyEahqnAbqVIU6C0wRpsQeqzlSDRNI1AMMNPgOTJmH4b9ElqIghQbRf0zWh6QD/Fm2k+sxQwWELUCW2AK9tMuSoYtS6oB8jdoZ21MS1LTnudb/qSlSqsqmgtE3bScEDabNSFRyHbkj5Ni4Qltrn2Y/pRPnUJ4ouItkYObTU0Fypc6krWcoocIrWAi6fwDC6xcU4UNqqVb+aeuviANhxHyDzL0J1sBBvQiR3eaytcJvpRXTRSVmFIwk1LL81UBXaf3FJgl8kBReOA1unMJSVF3RQ3tu6dbkvLXfPSRCNijlwPqlb6cl+SVVPMwAkofkhQvfQpaOkN5gXSgC42StNgep/Wylwv8id7LnFn1DTmxKYdNVY2jGbJczSoUC8gc1q89uUv+edv+JqfecPX/dSf/sbv+qLP7tY5og4lqygqI1RJ3vudn/sTf/Krf+ZPfu1P/Ylv+slv/6Z3eNYzxhkLyDoBd//QF7/TD77+S/7Sl3/+H/zEV//+137QH/jEV/3lr/z8v/w1X/aql777xcU8bGXJq7KnPP4Jf+b3f9Zf/6oveP0nftiXvfaV3/q5n/DXvvEPfsR7v8+nfOAH/LM//dU/8z1f+zN/+mte9sLntMy+QhWzFV750nf923/4y//Y737tF37kK77oNa/4vi/5tLd42OUtHvnAD/+RL/np7/man3nD6/+3r/mCxz78HqWAaiYS4MLMDa96j3f4O3/4i77n933Kl772g//Ax33I//r6z/vWz//Mt33W2/zwH/vKn/ier/+J7/m6H/nW1z/5cU+0tT3/yU/5me/4qp/+U9/4k9/xTX/7G75MyktRm8GA2OA4otIMZFD1v6O3bK2yKTol2Xum11ErVukwBzflybBcnFLcIic86VDAZSjLUowUZmFgze2ofUVnGAJ6FAqRzmVbrY1FdrStLi6hOVUMuk/JT2ZXgYpmbqtbnDuvolBl7lnutGbIqrVqHRUpFUYBy5EX1qXS1dQW7kmaEfsqjepIHa5zSIOMy03SjSZMMtay+ioJtEX2GX2mqdoule6MqAx4pKViXVq/Z7vkdpP7xkaIFJWXynUCWbRsG633pNZVL2E7NFY1xM71KDPE2YbwOgVLjJ7rUrXtSjNzqtFxgkqjFq5YPc5aayFm9q7aHNVytdRp9TMxL1BAooJrMbRnVo6ooWpwssaNSlOm4luWbujuj3nkwz/xlS//wW/80h/6ms/7qk9/9Re+9oO+8LUf/I2v+9i/9rW/72984x98zfu+9KZd5E3mjFEVHX6598AjXv/pH/PXvulLvuKTP/JLXvOqr/vE13zlJ3yk0Vb3t3rmE7/7yz/tr37N53zpx37w73/NB/2pz/uEv/pNX/Te7/r8b/68T/qZ7/yGf/6d3/DT3/F1H/m+78o2Hv3oB37k677sp7/jm/7Zd3/jl33Gx73g7Z/9vV/+BX/68z/p933UB/+hj3/1X/vqL/iTX/rpT37yI2u7grOGyaNv+KwP/Znv/kM/811f89Pf9fWf8EHvSd2A2+3eA/3H/8SX/sy3v/6nv+Vrfvqbv+GJl0e4SgnwAS95/k999x/66e/+ip/67q/+ji/5rIkWZW3mPuKR/eZrP/1Tfuj1X/j7P/JVX/wRr/y6T37NF3zEK8tc5XtlJdNaPO2NPzQBeJ93fPvm3holkoLW/YOST8feFO2omgtrbTG3zCwb54FkgQRoxZ6Q5C5j9mNpOIsSiRPOVR6hOm4KsovaRre0ksyiJnNQlxelbVZ6hWVoWhR81U2yl76ZZttG3MNwn7KRLq6UabmkPz+GA1C3HVMnZNeiekDK3itOTzzs9MERlxw7r6qJSdnEyKhKJCj9SGDYQJZJqFw6U41qSY2LRlXzVGflRaUGoJtdqm0aPZHToIWUsa/0aBXbmh6BIlavw+OWa2k2NZQpxhA/hQyzMG0vNmxNC4HL/okIi14d2Jbxdovbe+PWI1SKjmUqQACcLMSsEqsQlQ5upZ8xljPLkCqzTEIuKDncou1wdVu8K8HnzDNrQd4Lm4j3UzMyWcdmsak5Y+acSwY0F5pVpxawpRmXvqlwsZnZouLQWtqFoT7oXg0gzPRkOUr5F66JdEdoOe4/aNNb7hfzLpAeik0z3pyj94y2kgLgsyCvp96JPrgNWCBdX6RPpyrj2pB7Us0oY+3t0u9t22aMWMdSkZ/LQkjKxNPCSohP9DxRDIjOLN42Xwoy4ViuS1ZRZejwjbq9I6XVKlwrr1VHZSCXvANqaY625CyTKKQVNhYVs93Z4EzpASkCzsAqq6jYapOn/R1wmrCyS7CVyU3ppW/ibJkP37T252ly0KO1Is85l1e0nD3W+a+rV52Taj65okMCbmmd7pq5r3Ye1trQ0kkYgCyeVGiUUPR1BWb1zn4pk8XEuoZdl69lJv5PdTnQYNNCG4N51l3IQ12nd2XfacaoMqHCLdvGyRZuYPWyJumF8iP1zdNZtsBhVjxp0ZxZHvCCToTjaC27xFCXENwD2zCNKgo0SnSXfJuhWX2V4vcgMVqQq0FLA7gl1P0OZA8xYQbxwB701U1lRenPQ/37WvtcbR2sqvQ8GIqnPhKehTFiHBzXPtRNoWoFQWDRm5u7tFZrtJW9QgdW1WSFnHJspTpjlg45j7RMU36fq5W8CEtNToSheul6Sq/AkoyKC46gvBoiAxCZZiLRchGDKlspDDI2itrAJTL7UmpOplI3g+KOQxesJLFMDZpk7DzvsFEX8uMe0EsJpk3NmQNgALVi6MApgayO8kKWcbQ+3FDhEU0nTMkUG5bPaSON7s6+0YA24esUVWl43NS6ifQYwFmatF9fFZkRR9aozJIPcqc8gVgrx5x56FvrTTlWWIuH81D1uFQVsIzRPA2YcrC27l1bM7QhDaHmNs9NkYtY7Fw3QiloiDbF6N14C53cghJAOqNtQzsMa83gfUpmG9quuS/sTRm+dZoTYkdxypOfk1ywwI7YcPU8DUfWrFPkV2zX9LEUNICWxqlVto3SN/Rc2zFuhi6ldUKhIytY6mx15gcVHrGQali1VJLYaF1xV0nQLKtj6k737uaGbhRksChGIMId64ZLPneKvaosZrrs622z1XuZ96wmwqevw2s1DtgIm2srdlwudEuQIUEmLRR0EbLkhTeKk527x6WuLTM6x4Zy2ebUJtL2c4MpPmoDW7bmDsHbG1hEmGqdbKj8L6wxUKPrCvzMtqscjdUjtggunAH281UokyWhBdqKNoatBZRXmcyVYCb0Xcck1ulthMUSCVUra5WeVEACGT19x9Of+tRXv/eLP/YD3utjXvaSZz/1qRfldNYIGZPXp05NUgOz/OmPe/Jr3uPFH/+y93zvFz3v4dtW0JSkri2qx9IKKmfUNbHYVIcnHt7vveR5z/3o93uXj3n5uzzzGU+8qFwdoVogWxxi3FVIppWYaajGqWD4HtzYt/6Ct332R7//e3/Uy1/ytKc/Ye2JxqajzHSwJA9XQmlk7DjQD6TKLUxRljSJJg7Clozedrs1TqBb7VuzfYvdsUF1aQeqFGkJFKw6x5wtkPXlVOamhh3lylGuTWG5N+ceD7b1oK1BzVA9sRN0pOgH7DxKSrHRPZU8vrUU4qpSZAMrYynV9Q4ratnHvuHM5ZAH9IHd/NLYQRnIceHoe+TWhK2Gc551SndfxkSspRiKAufGpXss1RR5VCZR3kkdW3JffV+7kicLE3bNduU2L9tUQhcDucAjPQ6327LbK29nppzZaDceunwwOePMhtpGtZFNI6311uBz4PaqfU5pRorFHB7L5ZizOsQHYwaOZVFGU6nQK8xr7+umxj6Odk2u5mV6biwDnNkN3ZTGSTsqblfejpgVxUUcxAzNBlJ8L4NWS8qwRuS91m+2m+57nafI+aQnP0ab+499/3f9mPd75/d6l7d90pMfbZpBwlStKTfITs/sohrFyHrMYx/9Ie/9rh/3/u/5qnd756c95glhriV12bbYnvaUx8nOa9/nPd79Wc+9qZtWJBZKyYTZWBvcZvdF+exMsk7j3BwveNbTX/Oyd37VS9/xLZ/4uM7VMN0WGSjDuFQ40jQ3U280aBg80heBjVS90PKyigrS1HSnYcBYTJQahC+xYe21Wq4nP/qxH/U+L/nYl73kA975+U949KNxasFjmiirOP7hP/8XuHs04yte+CLWPfQbd3rJkSor6dXqiLhmKXWzOlSl0pQfhRSkqqxIuY00RI/pEHzFG2wmFNyrbhjnWmTsbq3QgA7dyaVHqfQoHWUviFHJGa5oFsrkS9bAyeTa7l6rEIV1ekhhtr7IW42J3CQwFgyqq9jBm1VbZmcZE0gWDV31qy1bo8XsWN5G6UuKBqpoaue27OJUCdgphKUZlF/eCKm9cWIl4jTjbCa1Kg67PnkfG46bYzYtcrBDiyaPuZa0eZpwKqpVR+ahPX7YGVpUQRqRStKBxt64WTOqbJp6g1cl8bhipoW+dwSwAuOQHSK6rQvWNmXeALh8brx0v1j5SL9l1/YFvTbC5H5v2N38jnNLxeXg5aoNocO4NvmFo2wYsivaKbp8mrbQdkzEyikUXuU4lOSra5GOSbK1bnQr5f+UZeLu0aJ5bjhrbK1RS4Fc8EWfhcJsZIdtmPc4byw3lmJzRR2APjSauWeixtR+KgXFynh0aiNQgbZg4CpFrqpPtgqTHBpqp/oJQmfdkHuji7GYiWOaol23e0RLMdksG5deiboDDJB1OXBTtrNR2ForiWDr2PZqLkuIwBn5PEfIjdB6EozBGRbddVumd5WyeW7+oYfK/xECiT7bvvbLtB5ooMGsUiilN5wCjL0BziN4XUzb64EH8kYjbFlBfMhIHUVtOg4C5W121f4x/DZ4FIpy+oQvQBJbpmSYG4rKLwtNRsu0XAwUmgYsBJgShL4jq0QwskUyYh4Yh85Ut+lNweJcbeY+xDOU2Rzdsq+ydYpq26xviqUmUjhqsso1YTVYPwXNdHEWOqGhcto3eYfqpJHeo+3DXNEpZocKgfXuvZmAYlXRovVw7ZcEHhVlhVasIELIs5ijYZmlebpD3KziisxZbaw9htciNUq9I0uKrTS4S13yQMSmILSl8thkzCBVnRNYZatykGmR0lxfYFUhVlVqcgmtLb1T/AyI2AFTF6pyR9GDPViy4oFIZAQzt7JGLC/9XrxMZ/pKX1kZeWZW2FF5tZhbxObWeiu5lrFmrhRSU5Ip1Ra5DCn2CphILXWphJ20KvPlfS6fiyl+LMJ1ZFrFQsXpjks8SYeLY5gZTu0yZMvg5TuxIZ1LXUBlldtyOUvLpIwkCH3tCNM2fh7wAAAQAElEQVQMOYmTj5Quh7FSGd8Jk7eLK5mih1fYSgu0WRJUzLam1qFa80hdJk1f0cI017S+upVArTDVxxwFwaAJVxIp4HezZsQsLrU2ORwqHJonQj2SWWJ9cbstCi+y+9jaIOTgFugFT2MaqhWoh+yW9bKzOoqqS62b3nHZJ7sEFBCyTnN68pykckLCc0tQU2mKAY5mazOpxhFtmeXmRPcQj5Yl1lHqHkIdMULXZi5Pp/TaqFrM1hq8QW5GtSgrazr8jHaHi7FMm53VASKtVjqEJrwnG84vfSniM2MtrPJiM9czztCKloCR7OWM5Jp9zBZZWRCs6oF+Mim8Z+J522guaPLQg5jMQ3fiFEnBZ67TcG+CBoOq1YL2c6Ul3rSHbCWLJaFlLiCFtoyCs5jQwq4XIpxL7gVrzkjBKBGrpbKrAKQgLQW5qvRR3gif2QnbTQZHxpgjB3z2pqQ1LA/otkOtlTE9pT3ahtazexGaUyDCKrsVyoak0CiHRWnL4fOwdUBsTsxDrlX3ubd56WFGzSdlp9Eb6RTJ5cbCBPTWVKLaFnJaWnHPsLUsUvHoeWPjhsMpHyRyr2GWNC/2WDYG9LoyI1QzRpvTj9VidRyXfvS2luMgDl86VuPqvXorbTz2ZX2a8JtV+SxNlyWyJEO1wNzRCD1ilU4Y0ywWS9IPggY5Eo0HGRmcx1ZT/d3klcFYRomJsiFvCbUqmLnKBnpG1yfFVCEVl2EpCU/hFxkVWBNj1bEqSgrtogtehmw1NU5ZIsOVm2Xbg5fitrn3KkIzQCYLZ7RX1MwtIaaUAeWFBmJBxYNxdjZKcVlYzjNAZWouRcSr6aB1uKt75EnHhaVbsabZVKN5YA5XURQfMTgmVDFh5zg3FiyqZbrwW0SfYhc26CslqSqPElrFMTgiUig1scBTrDmVA2CJUQ6rVHNA+mnpy/tgkw0I7ZJWbQWXVKqMop09LZEhkWa1qguxa3xBzocqjeAldDYyMemj5FsV9RAoH2mnO+oIZVKYWbPNm1kBK2KURLAm5WweJbiKBiE6nWzVNy3qcEgf8FW0SS4L5vBQSuhjMs7ugqIDRW1tVmGdcSomzmrSTPKZu+H8Ay/x6DSLxpBXXPDEGdxUfi1BQrJlQzWErbl0RFTpyQKX24EcUGoIjqox22xC5MxuuHjb2ua2J7dVXWXuGj/+07/wI//kof8rxUvf/m0euPcI+TnloaQyKxJLRYZ2ThYQh4QShpm26MLIrq6wADV72YJNeKIVO1szKZRaD86wt4XLgpcqjQ9yyRZqSWkiwBdttDbMC5Yyi17mSQpEBBCVSJUvbA51P6OIBDS1rG2AmC1kmN6ZrSm7NYMRxmAuDWeJNY31lDcSQl6tDhJU/jStEZTmN25d8d8WdsGBcno3a3rzWqx0QBFLvWwG+eXyWKzrUqKodR5W5Gq+vM+yWVwWqeWyrYbcxYiZu5GgU8xQWcYz1iTM0g3e2KiTVnqTMTm41hlXlJX1tfWxZwnaRvSzTQLqVvLetYholy3hhJ1QRa2ioIyhsgKqpUVhy5AdON2qR/nKfjLakj17J42KjfXpNjpCEMG7v8AGtZ8r05Eloy4Z2b3AvTP/jqVlJcwWrSoVJSuwFHuD+QFeo4biJrmSimUZfCtsmnmFV0GHukzBXYXDMYyxvI4tD4+JCJixn8nY0U6OuUMTxKpjaC2d02+rL0vYYdsVLeDUJCUlVNHh4ZA0Tkrivp80CaLEaqFoBuWEWCNKY5lGblojgoyqsiwL4Vy2YEPFoJ2hdY8OSLtHaltc0Swa0zGNMxGTp8uqGuGpiIW58qeGcSnOgGiwIWmkVYCSCKu5im739ILaTlgg5G5tVm5ZURAgbMK2KD3pW5zL0mrIDaduVILXNXXlwgnNXlINiwZoVhmQ/hcz+8SWKGDRC0IT1Hrja3oOZq5siG1La3K3jWG6K9GO1c+KtuW85GxrUuGUE1v2Dd0hdiWwLEB2qdUkdyiDnOLU7rcVU1Ci9aGcTfFGLIPknmBUIdWXesjh1uRkORe60BKiaWFkKrE62rY6pp91qUGbBYX5blY5WWTZPKnZFk3rvo1RSx7kdJZ0XktcK4PQTNcCqrTpJgoEs5U2qZeKLoz0E7upZFoL5zAsxySrkjoWTCoRcqAIRTsRTSMB2tlzVam4u5JMCLQ9Rkg8GnKS5L7KR3BorIvn7Bmqf1jDh0psoCQHqagZ06uEemEthlndGPae3rM8iPRW6Gv5EQ6ii3MsfWBZrraGjTUPG0dbQosEBktcJAosiEhJXPcCa291U6py5gF4WDtEjbqUbKHo08RP8xDr2KZttbXcONppTN5LvTMndT2IwxQTubtF3eiadijCMlDNyp3sqB7gII6eUwEsqGIucYZgZA1DXHq2m6gumbJmpUiaqls9hUXeUnvViIl51U8p+2Sf3ZWtThmoyFVrAqq2VeZBYdhSBCa2NVtkuCnwlmmYrjM3UHWCNJUATjDMCc90M7SW7UJuWHbGtHVs0DjdclByVdVupZtDJpxY3kfz2Zgt2eYO7OdMVIblbtN6JE/lYK6V44DW6MpJ8aoVAlDCN62OKj7yMYJVDbWVbbLD8jz7zkV5FaQgCG+UxdpSSZCNVP/VMlpUMQPL5D0KziWZpKgpacMgZSoCXgmsiDFmrEQW1cEF0Jzeu/fWuroWQkTKtpfL7xW1EGvlmhtDuem90CtaXZlLZ2uGVwFZmEhV6xXMdOfWNa9hWE5H0QnXTa99ph85Vt0GtDSEL9gwFfA5B7gujp0RHFKyGIAt5bScqSiMKsVYYE2JKrJTSM6+nLZlc7/QJZSGMIr3GUKmem0Fd7DDLcRwpK2wNdUHGINx1RsTLbYeLjqQ8ib12hI74qwKAVYCVcWGbs3RUe3OxybHVlqyzM1dBJ45t8q4NEi0oOB3h5wrzimHvOS10vNaZ3CXAtgtrInHeehKco95b8auydzkombtS6pwp4Qp1z1OKCtxqPSgxK3cIyKqMqWK6pbmEVyLVQSNNOGFRFoIyrXzxSrNyq024AKXjQbocOAsGpiWa+ckaqTWlEwMj9sWR3HEJvOixDg6bhEz6wTlBQIBm85BXJV3WpOyVEvrXMV21OaLYjswk1dyuaeiuzyHlXxeWw3JzHwpcVWRhBeVMmpCpXG1LN3HhrUxnaRVR2y22j7tMrmZO/tG12Hm9CjRUIrzqNKdoBm5wKKZLmdEsZdom1fY1dvSNrVpA9SXLprmhunGyTaEL6NWir/l7RRpFYSH5wlcMwPnd0mjjAWCyb6qryCX1Os8Wl69lhHGRjSFpWqwDudSXWxhJr9EMCXzwiyFdo445goZyN5zbzAHW7CrqhyN6xTzZEnNtlW/l+1e5DZLOCNa6h79ko6cU0HPVoLbC/vE/bkEJFwwBRyZkqqdFVhlxNK0Okjbw3KhKljTx9XnRBF7896cZmV37CvhHELW7+QjDQSDlm5ZlCC5jsg4kPL/2OrqMUoKVYlQV83bsFyH9hmirYC0Vjt5k/VA8Z5x00xy27pbB9Z1fMGf/L4Pe/23/77v+sHfeuP/leLDX/pi+axDAYpjxWEK3kRf6amlJDqi83A/Ws1tmc9mq3k6Om0vvWahAmaj97lvuLgeZJYaIyGCQ5+WKErVnhiIQcoOO3EpaLXryK3YZvNwC/Fbpz/ATJtzq7W1Y7sZ3ivloIQjglSf5dtGaCxAhMCdWVzWzrXRRJPiTsJoBM4XtootZS9pZrtZ0x8379x6eUHXGZZGmoZAPo2FBeTmIFGr1eqQp6ZtlKp/Vd1Uu1d+qWyUoCYui5IGSgHEYalfbaP3bW83DZtzgyLgtMZmsAITQOlpityZ8IleySTSMrrML1wO27K7b+uezwvgMAdIb94bd8JycY3C0RCG0reUfmQlT/sZgTV4nXvYRdMnXNTJI3d2ujIde5qHr8bR5QTK1M3c+161zUBcww7Vb+zudI6Rdwm1yqIUe6wsJbPyoJxQh5I4UcGq7ictnGL2xKys83Wn6ZKDrFBpvLYcHcNKdSzCFZocTQw34xa2LzZH7sgOsOEcAMV0MsoO5sSAT5x/ISqBddR5BdVsu2l7925mVRoupkyDl+eyTJFiDAn6grhMbAssGWdEG0EFNnItxEQtFKmZc1vVZ+8LYhiKfbttfqh0tyAk/kjxhsphfqraK10cIGf6DK60SFigR+0jL6s8MgtmkOzaVtXWyhGlNrUS3ZtZj1QzFTMDe6z9NKakM1FMVtMbvNIzJB2ZyizxItRrubhpSwtAC1U3kAaDDsrTAMTkjlLeZXlW8+odYspkUgnYQ3RUFFKRNCMZgGpziplaUyCVxEf5lTh8pjYi97BdlO/SJcUGbW3agPZ2cHuw/FZkjjXmuo26pRjFqpoGujijKXhLW84qd/jFWovGSD2sKDB+elopeXhshBZYuBV4DpfrWkEJ35xbQ29oqKqh2VaJlHl2pLlDLtrpTkjljUv1u6N2WjNwIy/sVh1T3Sh/p7dr2S22wLnUqkNHSGA+HLOX+WzMRu9oHSongpiVUTkbVvOQbGnm1Sz2mCbOB6SM6+BQipjb+Vd50zLbdI8dbVvdhsnLJNIpkA7h8SbjpiUPsZW36LVaVmdv1a2oslGqpHU1TeGpIOVCJRtoMOg4AmOYNpZnV1qCYsWxedupUjsTx0ytjDkUVFAmF7NQAMlO4VRENKFOCFl5N56ITAFcMp/FaIzdwhEaoe8W1SJkrWSiTCjEtRdL39ve+s0N+z6btAkBKMmnYq4VKrStnGdmbSKpiso128Uej9wjN6Y7iADvHJWHETWlqutIWTepyfzErImUt0M7QVar0Ut3zUm49i3XpULRRUwdiTG5tJQdhRmU2E+3E2JS1SDLFrk0XStVntiVZifWlumJtmgqkBjlg13xEulVpQI4jBCSztpy3nBt6tdQagVKzGp6HqhViYKeQSOVyIrK4tkY5VOghCgPcJXJX3URfl1J2ZY40RPOFCLZLNxZKCg/6rpDPya15l2n76alNXPPzrMS55DTOPc8WkLjpEpMBDlL+JfUJ0VDY9120iK0v7vifGUAoZDmhGR+B0ub1DUln0HTISMhCOjmzXS51yOylOjm5acHKkFhxYqW6XJ0ocjzO07StHH3aZfhu86WgqkjUNMNxl4pa/1YHmEI9+i9lHVoliImG67NjrWOsdaSI4QsxOQ82dZZGdJE5ZkesEmGiNwqtxltLrPQ2UmFsk00aRxtktLEyYfkrJhWQjxBoVjUL4JxrDPLVEJUwsIIqXmTNo0FL/RaysSTU3lsSlk7PKunbexmDEvlhwKHKogyhytWy1yFCQ7qDE3hOQySByyWQ1dk87ip2ayqKQygqMhEJMmyGk3iM/Hr2qdo27plbkhDMOEH2gENjH2Nvm5z3q54cPWo3aL1ujoeTC3np/jE9rHvqznKbJ2hLs2SQBLlOMtuJ12Cw8nh+fc3bCXGwtIUS/QVrSBXoLDtVfeOuhxpFwKpNwAAEABJREFUcZ28ypuY0rZQ5zRftpUIWeRhEvw17UGc+UapRnAsJyHHObYY20A/GqSA1SQ5rR5r1JqsgAiACKyuvqb7lom9r93kcFqlZQJhFZbygThzBxykvD5UUJiNcS9tH7KuWbiIM+5KHXdz28Cd3uHyCqoW6Ur3w7gq8mQ9NzO2Sy1dnvTyAEOoMZxzM31saXeB5z0P1vXUevQefR/JFG3X2pebdetEU0ZE51SqViGWvNuMtm3iGpthz9Iwn7WSR1LqjWUQ4axzGshGa84zABYB1XW/CqT6ShSss+dCY6ffADdVlhXNYY3c4V4NA3PEETlPuS937XJXb2ati2XJSxDF11F24JK+Q1KPWDnPQBgNWprrRAmU16nAfghGZVY1W9tGO/lPq7wJWnj1Gp2juk1eBnfNGzVS1C5AlSYRHYPxn37t1//Tr/3326HzMPIj3vMdX/D2b52mrw9mXGdKBl3GOkHRtqpDq/iG3CJdXm50sTLB5fRWzZOYxUCCE+2EIpHIyEqN91o9ZlsJwSZT9UJODu1gskjZjkZ0GaIqq8npAGEoY1mMHmkLLFUNvZHmWVhjxXHbiG3v7NuS75k5SjLaIvpKL+nTVqKisIhiQtOn0cwcfhaRAnl0HszQ+YKvale0wxkGzVgzlMjbNfvIQsliQRQnAYFWsinWCYXObmkDoh1xzqTRinNJfVix1l3wG6V81/CZLAZsmuq2hh08s64CMWrMNXPQ0ru1rZn7Aub59DZ9i7Kppffocoq6CL0IBnX2qDxSPkCMxZ64lG1srXFr81K3bZw3QKoU3rA1NsROXGCGG9olq0cwDlZ2qMSAueZcx8jMZVjwUJ7Q5XXo3Oh+a/1IpEq4WGrI8JCyRCEMosYxe5tbG5trusk5WkxPkQKc57GPunmQN7qII9eFShUzquKMjuVwcKvcw5jdypvi5uJMUURXpdCuQfnGKsMsnK0AG2jgGutYK7Oc6Ea7cNvsxr1n16XPCuf5R8qYPg4bKjgWmq4EW6uOrCGmRSJWzJlTH8S/Sl8nqT1olUakZqaxGQSSqKRNim/bq+SKuRVblk4MhGxhkansqwRQG8fGcEFZ0IAdbOqNmr7mvkJwGupmLdNaqYI3tgxtkK3csSWbQCwv1a0OQNy5lqxC62b99FgzLymkcmRczVZdsjm7/M5e3BIeBaxypDfzzdqmzI3esNP3EhXJvqQeODc1j90kn9Vz9IlNFT7LtIE6aa55rQFeW1z9GpROykoPsb8SC40wgbScVap+OapERobnOnMz09VNzBiHxZTFEWDBxJwIRrbMPc8ggmAOB8Rb30LlcqUHt6o9FqcuceLaSmJB0ZZfAjiHcEGlqDq4wZwSB+DQewfaGWyv1bKaBgbBsra0ey7LKJ3ptVTqGknVwK1FE3fq7ZPqma2OrhmBKvjCviRFLoHwcGjKogB22VEKONRNrZsmgbBNiZ03nq0rjNbnThUK+miclZpvpzpgU3zWxjScT4lj2G1hEsVmUErofcJiUTnATKWKt2Q34MKUhAAr7MHtJLPWObBUSFAqaAOMbJE2wua0qdBMUS+cIhNrIVcCq9nRtrE7THxqMISOu3MvEaBiypSO5O0SjtgA0ghWhuLZKln0xB5tK7tZdll2StB04/PS8mJyUd5DYVpXk6bErnJVglzVBMFCq9Si2/T9HNnMXXNlj6BCJAcsBLgKKecSTjSCwNiUS5NWLPNVWUydUSwqF+bEWG3NLUR2JJhu67LNZB5heVgcPWUWERoJ+WMmrxVnr9S+VlVbxHQbD+C2iymFpHxaJeZadegTyjU1bbjyaNclw+7em9G84DKbc6wVq7iyZkFpQ4oopCHlTKEoz6M0bRYiBdydSvimboXjHKVPrdzrNMo9zaSLjORR4qTbVto7mSVKY3qrbVuniI0O6RI8rE14aQbooSnWWlFSrjfshhu2xlYUnpQ9D3gskbdyZE4SKhuqFeqCUqiXoxzQimpueq9SkazAcMbGFnVz1GWuTXi6YdMhXxOIQk3YinZG1CY8rRHdFHSdFKXayYVJ0gr7sky1aKLcCx2kUV8EsoD0JdKQArxH3+BMjAjEcW8u3UDcudMuw25u7d7Be8m9TNwtuQLNWntkz4MctS20ZSqpoACuFC0UT1ZmA6avkoCKWY3VQmU4FLOcWVN7otoTDRpFX4o+cmnwhIzWEkbXWt5275sSKLTDFw/yKeQA+mr3Rr+5bjdDrWdUxB6r97jxuofYa62TaSjcQK/W1xaFtQbq2HxpeVPANHUKNVuHbUATB2k1G0I7s3Iua2otEZVzVtRU1ZFrmqqknHlYHfIZpsA3mCzllujZHBbSUtK0nqcrGTfTIsa9tbaZUdCWcmlAeyjFQTuL7qpPVnVSJL2EtsvzQD1oPqzT3BF0ZmNB0g/lRCET0CsYJhlsoOeqVHXLYbE8dI560YI6UUtghsu09np5lktW5ZpYAcxy6DRcao8zRTyxhWqNG7ZqXE65K5e28B3iREmmncxChaUII5M4GpbgcXNs3RpvwL3xDniUK2vlrSLtHP0SNBuppdEVKHQBbTUlp54pZ6lJxdYyTNHpZdBS3RWdsLgqfIYwU+XTYShN4W3bTNSGbZOtVAkFC63yxqZbZMVIQSQYVUfVksnoiA0kmsw3jWaAKxgrsIYQUec5T04pETfFQSo3eHiffc/eq7cQMcjKzIWAQ/6a7/TukqLVQup69japVVqhUR1qkSaq0xFcJ6bSKFgGKiVid/YqByEe0HvbL903LVGGabpAEaREn9YThoTckUEoaWLEcdSYCiVnJy/28Hs3AEg87pEP//j3ffcv+JgPbG24opQpxLH1vLTWa1PQfLrNXtZhYlPc0NAjt9IkIMoIQD5WqfZIXnUGszQVViJQUkmrbtWXELWCu9G8puFKW7BlXXk/Ra1FtQgXMVhkdF/iEExNcNJfmpgdufHqoTghuB2uKInYudVgzowBTdsoo3Ei9rAzHSsrEpXiNADnWa6aJaERKxCTq7BEWF/RQ1O2rDaqlqvwCOlkBOWsezaU/AWpQJgFqVVChnNZRrPR/GrUTGqW0HzSZCZmIEtdq/IoXYoUWGVMKvZ+e6ZZreQYGJMjKx3e0NzcIBzYjtRR2q2siCMU7Uw/JDYQkOk4J8SsEsWnczhTApJBm844Ebu49GmJWrTD2zSTF3LYB0oLN2fltc3YRjLWCM5QksSykZeI1tO06cmVY2LFvrHfc/aGVGddB9cqJDIMqWwl9KId04615eqptaZ6jEqFgKVH4pw4t9723rqqzY1UQlgnNnqPvl2bhZfBTUvZyFhZAupT23u/577fmJu498N66vrgosEB8eqF3aozKC1yocX9YKzFJEpVeC/n9JyUTDjTVipmnJsQHGZjQ7SiLRPHXn0vbmFWnlahKpWjoqxs88ZmiugsFVLTIMCoEwUkK4ACO40EEHeeF1pqH8COqqQfLu2f3cRV0xDpat5r48JbrOtJc1Rzb+YIjKqQ8aZ5C52LXoXK1eMEYhfjGaOaZJanAldYdk7BcAktVRaPYbdsS0FsoZtsl+UXWlNx6yYw4svPYn+UIlnudeO4tDRMj7Uf2v9fNrvp3TU7RGJRJHnAhCLzGPP2dl6PcZvHMeexeMAT0BbwMkcX1+2EDYoPOyvzPlI03tq6usjRJYhFziE7UdfArdXarTduoOSyFCWNdxFgqixHGykdAAt646wIHMuOlD6Wdtx0F0qX6KOHW0hTRkgGElJdrrxcrQ1mVK3lc1nOsissGs1pAGNR+aBYd/UBF5QxCy1lVj/8do0dWLPmipSzY8ycq2V0j60LMIDEKW8oFmKx236xm2ZbeZtuA5jGkmA3dtKpQMpSLIBWiJJfDHoWMlmpFLOaGKtmKR5FsS7rJYxbySsb7LdxOQ6l0EUB8iZTx4E5SLQGbVXlVMGKNKJC2PM6jyu0bUeYd+ybb+YGZyinCpXJOpDj7C21j4IK9eAeApAKP5vDduY9RgdkVUPZzN1cky4Bt5LK9zA/aW2EKVR2nMyUAxraNBeqrROeS6JUIL3gTlDvLQ9TwqpzGEgAwbFwQHeMGhXlxGw1kJSRBjYymIsjKqN5KhLuKEWuZmPJzkTdTS/6q27Lb61F77zp5n1fzXWHorgsImaoVmiUnGl79f3o+yyq+2UP3mReVlFRvI588MAcLmiaCiDoJ+OACEw1qWgqxKHLBLdiLVuHqf+oym0Z5AJwhRi8KAYNInKAsztEJc1K9GDXar12SKkZUZBlCYRYG2vXHIQyaKCvudcUrb14Jt6SCTiLqomrciGpw3hSUBkoWQLqnGIGpSyocKjymeRR1ZT0aWFUoLS/EB6tBSrAavOmUPYy6aW2Tt+hUn8qC7alt4pQ5VIUthQZvdxA6uPyLS77dmlKak7Dted1O51A77PZQczkyn6U7rZebDW7TeieU4USxfaOra+mowW1PvU1KoVHTkguO2ozdmvVXYYedAxDKhwJ2e7svbn0yhIQQPBrU1atJVsdvTVrpuaSVkqRvLO00byQtnQ9LxfVIyg+OnzbrG883dIYCEXFUYeKgclmSUMHYlnKePmKGpESpNg2aJItsGUzbMO2B11F2LM60e9MgQt7niBLKd+Mm3fXCw0e6SfelLfRLEhkYZXma6Cydzf1qiw2505NVavH3GPJU80L9fJTEX2z7cb7fvqNReWRpTlUZxvTJJYtxrZE4oiGte+rd52v0nKVwySCZNQmYRDiBwFwSEaa0M7sWrDSXKn+R55dLRdk1aw1KtG31rpv7pqMKviKCNARCuAOXU9Ei+kqId66iYNzOmmznX1AAKmjDLBu8A7dtACiFR1tlhaFVjCmsow27Sbypl1v9hCL3uB7wqqW1djiXBe21CS7xkfNKSLo3XaYzEuHuM7QcgS4Jg63cAiQSlbLafS0LcpCAeOVvsq0ZkIlorpjh5LnyjmjqjyBwTo4Zbk3yX+f/TJ1bexlrrvst75CRU0vw6vOHyoC91gXwSi/TjsELUq5eUpgyt3Krce+1+ZBDeTVeJBrooaOJBMt0hSajWRra+dVh+yFWFq8TtX+M/RhVczwWHotoa+U822DbWmcKi+NLmvIVTyqZsVadVz79dCOMBJSCC/ObtS4GTlWxpFYkao6tWVtaIbNTQ96k9oUwC0bU6FSjY2ati32AoXOUWadpvlFhNK+Qi2sh9/rX/9pH/7tX/BJ3/b5n/79X/K5X/Lhr34k7+kijlkLFson79ZkooAFBnugIamjwlmgtKtAdSiACGXn0dZokq3NlacFS8uUJZDgBmgZ7YAzU3Ij0zuxu94GtN66z0BcI/VWaT43iMPSeCuo6LXTJttAO6rPJel0J9AKLRNVh9kwbSUAnwJZ0CRAmLKNqyOERGHxSqRYt2tlIDXayRBwX0KF4KTpl5GDFmkY0EayoVonb0rbOqE39pbUwEyuqMwFzZUdATtDIzYKrk9ZLgw6ermOVpk4blPh1NzI58MAABAASURBVFxUnK24GXqDG6QZ09Ip75Ztcok0cp0IcpLjnE8zEEu8zUTMloeQlhaEWXko9nuyX8HZenhLMIBRa1ZBptIsN2i6dpm2HxSNlMzlf4CrFNXhfBC6Qo+IK6RMd9xs6JuXKp2PFBGe0O5yH1MjFhg66W1DAsfMuVQDSmCj4mg1Tf42yHAZinTUCSqpLWzEg8SD/braVfgb2EWC+sxlM0sFJ8NswCtblk80CPPMTdefOTNLJCBNdtEI2Q3n0aECVFT/MjfT/sBy4gpMAFSIpCX3RVaIo2QYbafvlb6WHZAU0ASlOTq56zQz28qdZq6XjY7UDxsd5kUbqorG7PRN89WJKVBVzGnagA8fumVrW2tts75jr2q05Dah3antkJQ2wlZiLKwFkG5osCY2R1RMy5D3jdXhLckj5lqzIWRAJUC0qC7lObOGcQObfAibEdIkCflXZuUFU1FWvRsSjZ4GbSxTtaFA1c51dlTpGJR4ZSHlNhKlTuan2Q7qW6TOKdE0i+3Gzdi74QLccHRELSoNImtiRcWE4mQh1Bm2Ds/RKVFqkJkRiiwtLGJGTiL0HcrlBmJl6H6zTlQK2DmbKYAKemRieYSNyIhJFdmZERM1tSc1MoPiMocQjCiULJb3oIeCwjPdUCkjQVt3R7Ama2hOQDzGgqwvKvkIVbduS3supE4tvWbVLDusZyv4JA+bB9ZhuTJzgIemEWRgm9wXfVTXdeEYq2q4zc2Uf1qdeLPYFfaQQxk4H55glNI5ZDb72SRh+KBpUjEbp9+XwTady1GaoA1y1Ek5AVd1aefWIZEGsLk0MvaUCDUSTrrsV0bKGAj6As+6UOkrXYPURVVI8RZ6SuCGc25EQg9xebJcIqHN6XPoQ5LR+5kwSYSJ7TKFrzkbd8pmyISX61UWqDcJzqsUX45kOFfD0NQACevYnC1SqZMRUK6gZXiRy7DMk17wiph5ZAahWROSpQinmcGVKkJREGY5GenV0/aArSVwek+YGunRDE0cANks9m3s+7Ato/uEaXjX7CU+GOprLKr38DZLCE4XTYONOIuPStCyU5LQOl7oTTmxW+saXo0sE9JZ0pVOBxzpiKoSdrlmFBx1oU6ulDSrnRBTbqmDahB2z77nuYqLt1wxtUlYECOSReaA1BIiMgTlPEBBSO+whixmuQ6dPOVfp5Agz1LyFraTVWiqQIAT0vlu2dm8WIozlBgxUbDZmtKrKI+Fv1GuoTRbnpO4A26hN7rO3EVSYxY5KlaF+jRVKvMKXThvSBcrIr215XBfm8UGdMOmkLQTp/LxQKiWlG3Vb7I1Cdpyeqbi4tQfYJvZR1imYhNmSQc8TY6rJJWVjCwiSHg0auAypjxg9JqqBi6mCAtsB7dDcYEZsRFMQEZTOd5JT/GomEAQyqAuBsj/0r5r3+z0RVMAKTEwCF2ghE9qYpQMAXBqqEJbwfMTSnRPZnAa1wlE7UIYMDhOLzRGI49qylQZktdIstBQ3grbUr9Jm2Qa9dBEVYqtpmo4cy8kcpDQosw900N73LIpFnaZEFC5wIJ5a93otsBKVXFekq2Mkl0alICoZakVTKJcZVkolvcJhkIbFdRJbTqmdq8HNKNSOytXabNrIszLgQIXcBZIZSj3HdsOBZRqtbCKzEie3dyEiGygstgDRYOTFjqiK/SyBEWiLzmsswyXUMUaJdaqpcUgeimIW8hruNBacqveSskO8bFV6gqjPFRyzvVWy1+pTOlSA0tibbpcECJ6NKDVyVMKXWWplugbap6/96+605ZrWootaaQVoACDorjs7I+Fk++4ZGpfrpNyJ5tyKSu8uAk17CGeAsqJExpKRF3Nwy2F3KaZrLazRi6shIaDGrk6lwHKojOIUW1VC2Ax8qwsqaSnAqWxiqUIMdhOdhhDnqyk6kWWJzBZh9VECpZOlcUK9SlzM99M4YvSlNWqDDNqrBRBiQww6BOm7xZKLqGBO2heUIN2zJlOa47uq2OZGh3R22E+BDOLCdKi2elLwVPhnunnjb7ZUEJgoMMAZBVQZvW0Jzzu3Z/7di9+22c/4ZGProAt9+xWIOm29bMLwApCJ6O4OMMkFRAIM+FdwRrGAZlcxUrnciarKuGWZkv06GQYp6liCWigoBAiKYi2FUx9c5SvtAAWcp0YYOkm6URnOQXI9BAyMKHH2dJNW0ExxIxCQqhkMZuseLJleJzGwrPsPKFuZGl8Bc8zceL0ombJcvkwTShmY+1V+4pTUck0w87zCteFxBpkuk5+C8KNQoIEO7W0gE4YTnLU2EnTYfR2odYxkxaylM9anRwor6n629xsd57yW2ej9426RHCpmCV+18gQZVXFgINdNCgIxqqW2UZknNvCZZmuAFavpqrrkgyOlSougyoxrQArQWOZT9NeyRdsNiwCKeNRzNQM+nLGiGzN+37ZNl2+Q6XTT8eKSzUhNya1unVIvVUJPd2upu1TH4WpHeRcYROqHBcBpl/MNqNV4XQomYVAXX0GR0L6AuTGWHEcXMPmqIwAlUlTI9Q7lTJe4PI5qNsftVJfjfSTxkJFydUYFtrec68zfwqpR9QJCXL91BIrlZGH+9Gapk35zpTrrI46aTMERF0FMtigYJoIk7thSIIsEk5JSz3SMy0XVAhcHCJm5hKLNbe8+xmYwpG9pXYcu5mjyRWRwIQE2jV7qH8cOLdT1k4NeUFc6MVZ3bSSF4GK5ktLJzV/Wk3HQg3kYJyLuDokTrNCm7TQYW15q2a33FbCdNmUggxNyUrGMoyGFIAJJIIIzXenDsgItmm+GtN5R/xq0Kp1bWsUaym/Tr22TrsM8yHXtdljNVjTfqERbGQvr1OgmlZgozQy78gligHtYJqZchGtoJHE3uyiwFlKIiP2I25GmMBXIRzTOaLmba4rohQ+P0y//UH8GcvOFVBrnOi9KpPDOHNfa8vpcpcFK+mjjjwvjQWj5LDAlIJG5LlYrKh1iIkSqO7VabQW3YZJMNBliWt/gNAnIMhM8YiWogeavIRhm9GvZodSxI/Q7D2mMnCNOfTbRa2BNVGBGuoEuwavS9oMSG6YLMUdglXqIxmntMciNUAjx+LynRdv9xa1YaHMTDlJKclo3jd4H62d88Mi6dEd3bgphFERIqYmMzwSsRV2tK1t1ju0m6collci6cTQPLvel+Rlx+q3o98C1WXzWryWrteiTNvx1sJrSroxZT9DfO6Ew5l7KS93QW4DlyUQhExwgcJdSmXmSUWKf4i6FIUL+0xkHCos1fZou4TeMHaffkPeSEsVJ023/Y50DS3OJg/Rmu4ZLiudl/Z71ZboC7bWnDGyPGpTzNoyY7tu7bd3u91bbLv1BhMw7XS044olmUXbgF23NS+yoVPEjBOszeoBEzNrn8clD2q/fI/YfO/oMt2W31X5rhVb3oqA2mx5zlk54If3FT1WG9ZG64vN1KoQCpMqT9U6sK4VtbItoXeOxsMNCkYedr2t61LWLl18dWkzQobPzmaLvQgqppp5bDU2cTy3Oftx2DhszhZSODaDGcoVfman+AquPIoTSt3WT7MqCWXHwIMqwURaQ/Scdh0KOrrbnoq7D+NBCEIpYhNQyiRlm5rZs2iefZP+YguzgYrr4jrMjuoVhAp+Fkj1A11vNAIatPU0jzRVmSpN55fL5Wa3tg0NUujX8kVbkrfUlL2lbqKZJWIhDscSkgUmqRfQqnu439Z+q6yPzKHh6wJhEs3MopHMRGYBgVqI1GiBsKTyrmzVxlAULQdrhuGArbAoBqwmzx92Kn0r39HgNbhUsCmiCBDB00Xx6pfOvQuokidkCS2v1J5JswBVNmaf114PdtwKwzpvMPRVFmvhOjAXT1Og1AAXtGUIB8Dmqp1njq4BoM4F4hQpsnyxjdJjBm7HtqaWEnGLWjPF/1FITVxZcmTZPu2BsnvAJi92fT96Hwohq6AVba4Ms9lbdINYm75WHq6bFiuApK+sOlYdV6t5UfJtWFu31nQh4BPU4KZv2gEt247igeNqY7ScrgmKEpADLFlJT/gwXKmTmtJFOkxzsgIuTsSP2zAplzZgc2FExpoOXXlwM9nx4F7uaI5NobmgJPpDUg7kRCiUqaAtaHxNUXzApdfcmVZcU9lClJs1PW9kIwK1RkjFaxy68juYgBnYLbuqDYDCHutmhR+nmAJ03y6tnXSVMZVWRhAaF1U56zSDplI34SA3xt7ZNkKH+EhmtcNTUmOb3MP2gluqluLGrHckY9SVWE0Usk6FuEfbsnQOooWNufdpLU6ogBQ8rxVBM7obTak7CpXnmpfIoPInllqMkE3dWei5HKeWYlrN1Wy1vWEzTSE8XRydeVm1EgUr0wuKsTl37zetX4wblDVxdFxdQkAqeopXnY3WYK3DPT0leAlicqRW9JuY9/IsWA4SCE5BvFWGDIEZtFsqcNQ5690uN7V7KlkwOGQg9nl0ldzCSO3lBUmHY3o7sh1Dm4UMlcutrNHQsjyBVtXCcJhCPNdt1UAV5Xx4lj7CWNs2iUWMJTFkSV4XrTo+uxa1DamjiZCVPrrl3lLtqkgtlzG5kX2zuNghGtKsFOelJOlefuHahWlNFVOL3Kmh4vvqcWtRCdkHEDbLpgJjCo/4nkqAfvASvsleQqhGj+gpYlzd5Fdarqh1cE4WUzHvgpFyRrx767Jaei7TxcE+a5Pu6GJcaaMIp8og0Mu6QrtI3Y44CoHWuKs9sEVJCk0slpV2FNGQjInj6NdxU+tmy233rW1a6wCDyTaX1xFrKEla9+5sviQAZKZ+d1k94Ktl2uqUORgL+pNM5TtJqyaQ2SZbNCewF4i2uAuLvurqbO9szWAtAQcawJbh6yjO5TZ5KV5UFs6EV3PgiJwxC0enaqSmhKS37h11M+SoXDx/6+2YPidindViYI1akYlcglYCmUIJ7TXyCO0vNYVvvracFlFjlS/cLGGJQyDaVQ5okM24jGgyAXIz2jQLOQn2Za4SEtQUJfZNozBGxkxE57W5gjHqbKi1IiqAElfOBaQ8lkGW3o0QpSblHHJwsiSgKDGXDerqxOZo2k24/DhY1+LgRttbQ/lYxxFXFTSl/jr2um3tEGVXzZdQD3NNJkatLubSkPIPyhyr3Htu2kVRhM1a8tGaqurksY5YV8yBULHamptvy3SpYcvkfrtWX2hGlZm+uSkYpQCaW6a8XXOuiDI5JiIqTD6kwmBrYkUeFffrYbpDg0slANAGq4T2WqY9SNWkdGjN7N7qZju9wWlFrCTHZscND2eoF1xq6iVB0kJbDe2PVjvPKAZWJYeYQujV2tKIAEOmdHjpYyQrWqUsK6zDSiZT3cxStjWuwU6i9SU1Nw5iIgEIC11vwnMWV6PKRPQRHeGt3GFizsL8anZogh4UeV4mn9wVlY3s2W3JpQjpb6vSFbimgzd29VDG9ti2BRuqKGxlW7EbmkSAQnFFrFpRs3pqWYOV6lDLEhY5brCm6wYJx1Sylpx1+FZbD5N4Ix4KAAAQAElEQVSiW2Yjw7Txa8ElY6TcooE90hRGUBvw1dWx1ozImKY5Wys0lYHSVu7EsBNdOgjaodO9JNLWWN5WmFayXNQ8od/yCUeZUjtHhUa2sg394taJIkujTZOncVgervydd7FAhY5QpzMdmJJQMguoxdP5nLRxpj3QUU1OMDO9htfEaRRwg/Cx0qHUWqlPBEJ67gZBbtV09WMJDdaQBde1TIiWNjyWuG2KEx1S8gwAVIQUhZIXZPRW7lAAPWJLqJ6Zsv5Iq0nXPK3i1FNKT/JKmSl+PRPgQKrnVKdeJksDh4IJoHWwRzG51IyYGWNAUdQ0cM7C1KmsFA9mW9tUKPcwFyt91V5QaI2SIUu1D+I7FMNaY2GtqrE40gM9hUmcQViJcrHDlS1MRMKPojJxTfPlbmZO9U6lcQTC5WjQygomi2YC3Gr3w+OomSjIZraUkIQVGfTSF71aK3gUamlllK8uJrHXaV3eREyOaXFYsarFcmlB04HN5FRPclUOVHAti3N+5mllAosyUEJi6S3FPQ2gpVm1Gs5D9rCBrrkaVD0GcT5YlQ1ra2NzDem5fOaKqCojmW0duE5MaUeOyZUcoUNRmaLXkk37keE6D80qWwp8BlYiJVtNIpSFAEJeE9SqwdPLcWixEysoQnOtWDnSpkprNahbp5btrFo67U0NFUbtGNCjLmU7zMN9wa/s16pRy+Z1W6tXRdo15DNWw+xYlrDVLZt4qRiIVUixUwtneKLoBaQ4J0WzylUeY121lRYFZsxU4KNm+SobxGxaGiqDlYVKvYRS5cGqmeBpTL2Wj6PFbAhq1pZnZjiNhug2lfWsZiWpXxp2QplLBATEep2diBUSys5UAHV6ubxoHtkRhrKlEShxG1aSy6qSn1YmT9kPMOrsRoBH81G1inmKwm1Ji146A/XNzdhaSRuDtWb6oBKTCyiBaXRNIx8JFw2njnIAGcZhmULUS3VJZxH0iDZRw1ZqKpCsYgw5XK2lorn2ltagDcdqRYWVdWUOVMrjrPMvtTcanemMMzyGZEWVWKyd1nwr96yuALRxsSlHFMJJHls7thyn1erWm7ULVF9hLPPsoguS70oDCwqoshlUQVdalFu5l5pnai6gaVJfuZeoCWqf7/PAsggPFdUQLQvbwM08k1ZbwfSSr/AGZ7hurSW1yVB9UWt4EZMamLrhEVqXPcpWMei5FE3f4Rdre108vIloHItjsFZWKqNTASYWxLSMa5i6lHzOBMIRTSRYJFfEwjRLR/oqzVKSbKtwY5LLLCjXCtZW1NKTYXZ6Okr5tOXaauxWog401UecfWISh5A2h8tws+j6RLXQrcQsVSp8oC20ssbqxkbHckgI8GJWKbq1KpYyVLH18+I9a4/lPnhZKv9VxkULUQV5YD6pIIOnB2vlCqIoqyTyDrveWHI6QqjoLJMjmierKpPni2e4ot82yiPqUyJPda/y1CANACDU4Rf5TKuW01YCNrFNXCak2LTUdjrO9sJpoLISlU0SLogKTVwMtViSsSMbl7fVraCgMWaL7FWuTBB9eg2ttC51WGobiCmS1ioEfHVFryYzbI6+0sj0VnXantlT9oKtKJE5vVq7puTYT4atBjMdJw+ViEAMLW17mvqVeO1NqeWNu6EDrbBl+Zz9vLERhmqkmzm37t02KogJU5YLGFEASKMopJhFCW8GLE6ItE7NAjnnlmg0Y6kqrkFKNwOeCci/BLOfli25HdbH1vLieQ+H5RGhiLXeuume3YmvsTkF1QAqyyJ0Z+RCuPfmCuvaKdhMpMYhZGKSg77SKwBxIPQ+5JGhZ+2LNnbkJizKc+dqp2HLIuAa1RTBSV3qKyvknZzdrF9Wb8NmKqBYct8BJBFtsa/WNU+KCma60zaYZpP3ZR6Ktqd8mgkdEjGgqCBUrWakFo55aKKIE1plp1BPU9QK2TjFygNWfYk1agJkJBYsGSmjZx03haO7yqlvCV8rBQRYTjSYpxGkAmJmARZn26rv2IyNQTsgt1pQGga1kGu0M7yW17QqgAfiWnGNOXhu90SgY9vJ3etOASj5UuNEuKywoZOXYRxWEq97NVSd/odVOt14aXQ9bWMpG7bJe8emS7HLPtHPmxgVGomxMzYZ13gQcsdZzWEsX5I+NDlMfxTopbwyByQHeVlppauPduk3TUVImOpwqDSKDpQMFQEjIMmUoHFmC9uAnWVJAe86Z1OGOQvX8ityteydrbWAMIvaaAhLyK5iXralpgJN4QMlCk5WUUvTuf+8iJZW24V2s+ttun7uEL1ZwkZYuePieijMsm+AtgG6p7wvc7jGLutpUIql1uX0MpVnbcEexMxt8IZ22dgNhsw1MhahyS+RN3MF5xDDrVorZrCCgJm5baR0ES4fJka1W2xH38TNnlo7i/pTgIfpYsCB6alCFs2V42JiJ89wlZEQtioUmqUjqVGrUqKIPHfMJUMBRtkqVTBPkanKxmOrM25nMGXBJ/tV6E5KC8pAayIZqDr3A5o6tGDqmmoIhoV43QxKYmeZFW2FHcOk/YWS8A6vK9e0FDqDrNCM3hrRM4VF55dOZZTMa7GsExOVjkS4ot+3Vq0PYkVoBfZVHktLfy95KBIzFA2gx75P3zOtdOtOKVCncUBhiLWUE1pZ15piQ963tBuUep75tAU3Kg0UFKLEmALmI9uglZSU929DyOISKUPocBQPZgJFPVwXqlIoVpa+C4ngKpwGMoQ5VSo8FF4v89l8iqQO10JkqPQKLSmtUvk6tcmnd9LE+WmtMJKjzjKapRKx89ycikl3bH2dDlssueigxFC78JtBslopxmy1DF8qTDNPwNFnbVOU26IHCl7crmOXa1CR0v3yKrGWorB0r+kyq4k9X7Nl9RQw8yocs64zpkhoAy0ljb680g7jtTMhTzXvsqPstpu6oOg9oFuyxNK0R7ZrnP/j6TWVorm3YzdZYBw9DnKpXlz3FlQYTrmGVtNSUSEU6VrNSnIjkGIPgliLqmobq+kbZp7kAYtIy8Q4e1Vp2qgFTsWUC6l1fMkmCgpTS1VK7WMzAguYPcunx7Hn3DSNO3Sret2rpbVuS5oeQtEAKG7EKEb2tC2L2ltFCJyWQ7imP8ZcS79albvB6oD2bHb0FjdG7e0YMyqFzuNuLDONUc1LOOXuXHNwLGah+qo9zjVpAxTorZc2qFT1HZdKaUo3UzevXUqotuHmJi43Sz9dsJF0+ZnBM8cET8ZFrkO4A4Kke8TXyxHaNW8Knjqn+YSLqwEGaNPF46rqrN2qKd48nFKsAY4ge+nWb5qtslTMlsaoCGb3YaIcmmPhDILPsiFBXyFZG6v2HBcFSjHbCG9cO2aP2FLxt9HW3Neiz9WwTI5go5QizubBiD15s/Fi2Jpcasv2kUICrUzsSBZy9Rh7yZ5pICdQIMXiAR4FH7Vfm3qGZt+pQMPXcmTfy24A8chtYR9lV4zrdpt1SNc4Gb/gUN86CgJXikkxllVuZo6e6WSa/PeKWfJ5oF2NV2LOkXJoli8069vWJaNrrQMSaHELv0nqhr5jZdpEg2KsKKxsVVBBJMEUnRtjFycmXdVGaULBA5DTchUivcqJRZzx3cbAGEtqHKV4SG2lUnA58iaul1qKRSpwU/BAjYbMVA5pSvMrs+qcRSkSCpFTPa1KUC6szZMbotvauPaMjsPaSNc0o+vrOZMnVNWaCY472XRBAkjyl8wbj8slegvLEZXK/1DXC9FOPPRUHHjjRD/33kcXrybCyH2WjQVWKIBKht4MjMAS+oLarWZbA2vyyFqBuxIQaUfYKGYyAwuQZ0qqU8FZdSy/DYtp7cp2BVdr0E9wjVAH3668N3KLkBMmhTTghCkkWk+aNxcXN7n2FVJalRF7QfphRPAAtWyXSZTQOdCrszxSULCofIAK9QBUDY+bBqdHNZ1YfWUPmIRbLVRap/IMijEZq89J3axZhzau0srN0lZ6zlnHbHlYX9hQjtSsF2ZjUgKYyJT9puTLfc5W2cw27SPNrQnb2QmRyzl2sgNn9bDmfoG5SApAoCGfNHhNroAcoa4EcKk6uXJ5B5To0XPp0/kmScoFpcayCDniFtJsncmqjoSMaHBe2rzs3Js1Npsdw/OIlQn4Hvs2WhsmEIlecs4bvVWzYbUaaXQvN43UC4wm0No/tBS/ECFVO9pu/XKOORw6ZJfyMPbAltiGb8fmS7tE7TdLEQGqFWzpXR654ErqNzm3In2nayPJVlPRXchZVYLQtrbLA+uRrkXFtbG5kx9H8kGL2VfrubVwV1UL08OTknq5zwuPm1LNWiWfVbCxes19TdWMO9m6Q1sHeriV4ioD0/apApANUwiDjEYwvIQZ+nSZJhGqhj/Y1tVUPI25WaFVdXC3tkNandkSFTZWhNIwg4DbObIVexa5eJrXLD2Gz4U4JWmerVcnbVFOLGeeMoNZuiUM033poJc1FtvgpqniSpXUpqSopEPnBMHsHEagHOZiNS1GqlPLbnb+ApGRNQ+sYUhwmQVZTNw9zs8FFYdWJsIeKKlnpa3JmZUOb+6+b9x2g++5NqxmpJBVN/Pu+pR2hZY7AY8tS+nXgZNNpBjIaJlbZcvBClBIzzESTTVFGsnGFTamzaTID1fHzCjM8GPa0BhQCg0P1VKU6SEjMl1wz01BQ/S12opYaynLzE6N9ZJl6xqK7mGW0aPO/+hkdE8n3ZLQNAnCSDshtwjWYbbKWW6Qj1LfGTYtU1atQdWq6aagB+0clPDp8thlTJVtE0FH9GuYtH3aUPMM+VdhB/zW72giPGHZvNqGJqUg4sjzHwBCZ+9t29vet24ucXHOWFr5IlVQAA90gUNaA3uTcoaNTKV6RCQMRPPYWxgJ9KFtYJak4pAyJMWswjKsrnXVVrqSmcFssfpUyMMyzaLl3Ma0GMSSxa6CQJG4TtzUgqO4iiVM8lDuFiETgYxVdmUXNWRkxFg67yEkluURFiDFCJINebHoyiaFEFuWQfuFEOXNZC+TE4Bc0OHn50Gmt+amanlTadI0EK5t9lhnacywJu0laRSt1rYdZnBQ9ggF+UisZRXnF5Gwaq5Mv6Jdq62oNnND3RguDtdE7qbZtgY3R4ltWj9qi2iVrJI2rJe3QEdANW9O+dyKjerPFmwa1aZrw9Bzlc/hWDrJ7KIgMgZW2CxmAUKpGUyD5ENlg23V3AUIdq4XCplP9iFy3E9wQM+Qa2HtKDnhWrZpS7GTLUCwNwcsgchRihTUIaHt0jRfMkMSLFCKMoYJK+VmVo1cgSNVa0pRTeEDF5TKepKncyACwgmoKFWkJqnqLbYO070Z3w00/UEpFDTYXnDXZygMmOEW5kVLZrooLGtL6/XyKrqVE+eD1ZS7Td86YJWUOaM1pqtjX8OPpWwXgZGVkFql4uppRocZ6CQRhukRPdnYRByakBdnbKFfWLkvatckwG6ecAWEutis3uGaVU3Uw0u3s81OUItcrLRagg9kxqpaYNFhzlbY6VvraOnzwwAAEABJREFUzm7VazkSFZBjW3AzON1N3YsapSyCwgMOs9E4vCYRUYZUp0qfmutYuB5TajmjRK/sK/tMLs3nfd+0KrP5QM6aGFlBa5tC0LaU4bXWEXHNpWySuEq7l+qal6t6yMUbaGGh5DFlmQqsAb6wiTaICR6qrr2lNQdFSOOkLfmqKV1SiJKi8pwcK1PJVa3SV1bNMHlvXnlzjJsxu2vuljSWdVnwZr2HyQ11BoqJloW1auVapzIaakd1TS3qlWrVKxtSOVL6GeqcCzDZM6ccyVaKBAQl5QJZxmk4KGUUwzTzmbki9Ci7NUyUzviiwmLLclFMLeQSJt2QarfskYxVmUlNwhbVJDUN2wOXZT3kkAu7MBeiKCxNl1ZOYMPqFuUCa4WNtk/jsJiaydH1J3str9WZxiwvaCkwVtORtMGYvriRu1k6D2o156pRfdbdDgrhMHivDjXAJ73OpFqmcWKIUUKq5JJLWVnzjEuzagaWaXcooQcT0itNDezLdjjU7Rw4u83ep7uVmwCeklvZ9NEFA9B0U2OtWU/fhrUhb66hhLQVARuSTDM0KNCy1058kuZYawoMggwN8zbclSHqWUDEeYpVeo8VGdpFShAoosxorpq2nENjA9AhDpr8TW616XytzLt4KVrV26Lk1GxyH9lWkEhWylm1oxtsF7tO61LgnbWpE+7lCmyD2bnS+Zm4GZiqWTyEtXdpQTa1m+mIndWqrdxWtYSv5Uv1ecryuulr64Elr7gIpXFkJWbiCB4QdKDRzJ3enHrkYt5dVHhPthDzLDXJaChQGo2EucMrPHVko7cSJrRse21NEy6FsWR+HbbSCgTSUjmmKbqxkSYuYRpWrUoT+eha6de4zLkterKEDRo8y0K4in536dCIzYMeKyO5sCV65b7EDYUmps9hulzX1WaeVwEytgdbZUCxPHVl3LxcbCmRPFtv3TtNNrV78Zr0Umwc0bVaRQZBkXAzeTOje9omhpWRp7XEQl8840cVb9uhVVLnbEozTEDiU2nphYw+rGXn5q6vMIZ1y11yrCpV0Wq20E/BmaYZJw2XxQb1U9DvJvF7VDmXkRj7EfuD3KJvziKiqlRVh2VgVWqbWMcmQVB5RGc6VQFoKRrUIsEqC2tZFJfikFWsMg5sI7xWqIisRvS+ta33OgF3V4wz91B2aUI3OsDkXDYmAkUnbmRO5Hhb1UKozkCbV7YjMNTJRR7dwBo8hQFMC/jccu5hm2HP1fKKOTBS51R9TIUuUxnnjWYqTY3ZMoGsjWMzVQJjaafUN2ZXnHqBhM5DTPSRPlcysQuFnJSQNT0l+NqRDaARG1zgDdMjLqtfyjyRR64jKifsqO2Y8nTBADPyfDsHgzvaHppjMg/UWjFDNjtVLU9HrUFvrK4Z0zHNhm1ovht9ZRx1EGsr3wS9TH2BpkmUZcttdB1cTdFJ5zlreYPqSbj2nbZFqARzGaZn9muKs1RNU/wX17BaLmbK1hlcV1xP1hSoBQvfYfeKotvWLeM2xTir5DZXVZ7BbKZiVnutbZZNw6IFtiozOVhVAa5uoWmkvqhYOkGXAM4+e8krcQIEIYeUK8CSb2xKvYtiLu+tci8YK12+X9XHCk18ARCVS9caAxVNvXML6FfjHmXJwLagBvWWlfBlwtp2tD1N0beKrKkEXOUaapD0U4vcuKklnzVBuoaW8KfMW03Fp0wVyAg1lq9qSIm4bTAX+1pRIiTBcg/uKzpUstKysPrKfUrP6qVXtJm2Ej7hKce6aaoUa+EyiGws59KkFH3isBCREL+rkIRIrdCjoO6Xkhbr3uE30TYaT1u9qtnVzsvQKNCje1qVypheKbczQRliA+ABqEmWO713d0MaBHYYUyG4p/Uo4aaTLvtSqMM6cbHSamILtebSkzmpLDTFT3rrM7W6rUAUCpqnd7guD/vFeWm46bx4GCvMsnV0qDhDCcs1CglL60H9crP5Mow4Ale3KXRVyr5SqmZniH+e3RHyJ3ZQJNzQrI24XOVvV7DhVB9m89gqz2y2xb2qoQiusEMCEmhbRvRmdhZiphd9uEDspRTqrH2yLwN6ck+/mX3X6VuzI4O5pFBf7nOjMkiEooDqMOcmOTgo8rlO2MJbW6oiTgzkaoVWRtqClGKg8EoJrFSsJNHMM3e0XGxGO6vr3vNmD90LdzLNpnnoHtYW+iiNLyNkgSgp0rGblJTFkBEazsdpGJZ7txtrN5QRZhWaYTNrxT5aHzDpiwLYpjGKstcIISJoWrnF3ikSq8N5KBAX494VHSkPUdG5dqooKPAp3zUCYmb4mUOHCxkzYOatb1pVSXmeSco4q0RTV2IAkrIpkooRDHCPxtgoQts2cZOx2WokoT+x9bVvoNRUtUr6AIsWLtqtlmJR1FvEYly9hiP1VXCEIxO6tJpUATHNXTN1sgzVu7kSYB+oShkdZMGtth3bmVATun6+lsBuCzKafaRnVs/oh2ZuhW02Wz1UTmGQ3gMWFF6WwhsNs3PYvrh5tMvKxuWKZA8gsUrgpKNFVWy7DUxIn+qTQopq6Ta9BO4aLr8KYJvlClh5emWb7ElX8yYNnQ6c3iFpdXbU9sEP9TPAw+0w3gIHQganqxfV2rJZ9Jy6z9LmCmmRBSWSnbYn7GQkQznblrZTS6HPyFqBiiikfE4pBwrHAlc5y8Fu2SHRXLcaG6sBrRZT1fNAU2R0L3qpdAp3LKsQlt4kJIVaNwZ3a5X7kpSOwgrLdGuGljpO82niQLwwcHIIQTAX8YBjiTfRrXWQywxKzyGhVs1j3cacbWIjzHhQo5vl5poW3qZvbLtTGUjNYSXLraQQpA0Znc7wZmd2Zl+q7PACjeqhJEk6qilZHYMZMl2aNhuMKMfscdtyFEKKyE4dbqdL1mXDq3qolOpe7gxPOZWLkm1baGSSo0F8Dfep9kPcZ7Lgq/woTgEhwAikCyMQc13XNWrBEoSSrYWQMq2HNk3Cs0SJ6ay16B2Xju6nBfK8FWVeveQPieUnywo1Jz1hgBXKjF3JYReafu1zzbMSK6tkA4JcbqG0UFZtJUyhGzzFRXE0vC/riorZcp5uWWmYp8mu1j5EmqdiENYGfZjoLUGHsJh8mbK3DEfD4bE0X7ijw72ooZxJVYDaAnuguyleKGvATjdabrmEy5QPa2+qgAkzJpuZszOcQYfoLK5TdgvNqvUJiaY0siwr5Id4sJ7bVt3gSS2Jc+LQcfBapxdYFClaPrXKtG25r1NsLNg6DYxE6dPdXiqhzdnomApxaJs5l3xlcxMJ0107l8gxuFRZT8rL/IBNubsobaBz7sjO8yFugAWcgYgyUAVLm+02xl5ra0R30aucLcjJkOa0eJYvgaVhw1lnuCJSujlsm9wirGa2tBvREJqr3SUzkS6UKmsy60xXNYl+rP2Ydl0YkyJB9ZGWsSVc2d82VYHoQVVrBUvRDhyR11QmnQulH1ljFeDN3BoJ9sAlzIJVPIFEcZUeUdKMVzhld+0mFU2oj0KMUn+cMdzhG2CqPBefvQbbqC2sa8hpbwZmKolW9msy1FOB9iU171UXiNaqoAAdqwaIM1XRojwEQOAvkCEALEv9jNiEPlfwmvI4UauruXMztL18q0LmUTjMVzVN1yL0Q2ln0YsmOdFM70AYk0BZ6TZ3bg+iX2ma0jVT2fJcvtCqOdN81o7YerZWrCVoOqepIiur0me2kKrE30lVr+gc03DUBl5IZYWJb1hYq615ZzfcwXGavtXFsEGTu+SqxFqEQPSky0HKcerCxRzyrTIZyjF2z8bYDV2Y7FSQ2KQ820zmom2At5R9hFe1c3r11/zcnKo5W3C7pj9oFHCT5lZ5HQaFgDIjme+0jdZTuCK3EnqgEhnQwrV0ayYO0U9C5qqlUuXuYHXN22QNNXuue6v2DCJgQEPrQUXOevStFDUUR8UVuhCKMjksQMvm4TXhmYQJzSkzzWOEvjYTG82aL/Xmsko/m8tsYTuqz5AAqVRfpjO2iQeaUfk1KI1JHMMViU5zj85soICJ7EBC9hDu1UxUNuKC3ADatFi+Qq9cC1VSu7YYbqc1CXWqh3ZPF4hg84IVrbpBwXCSQDrFszQhAiuVBTS6Jg7ZQhJLMrnsl3ZzY+c/ybxTjKsmXdmHiSpKiVkoJWnahX6WGPdploKOVO0sb+HbMpbgUvUlW7Evg3H4NSsKmrIhzRcFCA89rKqSKX4us+1HbDFvvHQ50RvrknOv2SSgO7sZKjdDrBAs+XJFW+bWZM/jeqnbi5x0qOms4L24oxlczMtreTqBa2rjpbKXKmQ+ncXTK1ZCnBw19ft76WMJr5VqUCRSRXOJolzb0MYpLahvS4ntZVB+ukExQ3GbdsnTIoSoAW4h3mZvi56CrG7weYLGZKn6ZixNJqIIlN6aeVk7whXSMkWIZSUGTfMsanVCheyz+ha2z7KgpXJQ4Wo6gyOVGyWF9Mqt3GP3NGbZKM2YrStb7ca4CbHwdbe7XVB2HlUI9tSZS9LLErkiMNKmvjDv3idN+CqBUVT2BVdtR90sFdLSxkPupHhUnmw+t+2Kc/dknDsOoga3A/eCjZr+/8fS36i5jvM4AyBAUnaqZ+//Ovf5pvtULPFn4TOb46pKbIkEQZCSnbdn2EXl0E67/sh/jETvIdgx5qPnBX47gkBa7vXVdq6JAvdgJ3uwJu+seCudiMeR1OmCWFF30VsBVyYITevRHqLqvLxztUeIOHxludTSLvUfOWWGl//Q7pfQrmJu5GP5VUPgc1TggGqvTe3oFo1uBtZmiqiZRA2KVN7uttBK/9xVkek48fbopmERt8c7CB6KkhZz+dzI0EOcb/ZTs5tfYPfMGDhL41Yd08k4vCnwKhgXARVX+0JoAE2WRBBakLuGjWs+1/n/+J9FUZbSV/88WsJ6vjZp1AjLDqabBKBfandy2p1qH7Ck8jKpKtQNGuDoO/oKuBOMlka0dZRSeyqeByV+7Vv2FRc80ip/YBff+tNHSIwU51gNibGrxVhgZE52s6/TNhgbneGCADTFpw6ZPY/t7CQqXJu5Z3muAZ9IPUd70EdLsPuxOTNP2xfx23HGe/yIIITRvePTK44KlJfAtyokoBH9XHPWDMcwTLSmpvXj/vXP15Z6BiBeyxvTSEWcgTSAdPpy3j7UCMFAfjx/FkxkMffYc1ZqaUmMQgIJTG3D1yWTmcz6s2tXTo/lNR0JV5jjXKSeRt7kBTc44E3R4pTt4TL4j6hQzG62SI0ozjAQi6ansQuPs0snzNCmpsOUc+L9B4Ur+TdHiquDAttZ5l334AeQjVDmqJMM2rLwuWRxptJzy3ApQseQvGPszhP9vey7KB6pMqhWCpfhXlx0F252t/WOygVdXH3fNZ8zKHZeLJ3lzODvK6iuMC9kjESxbHw0fSSzg56i1Vs8LbpupvXhaXSOnj8r66zBdn4vQXbpYCBtBRWCbMhU5GKZLIfBAEd7bHy2pjcg/dbZ1A3/XbxbQIAOG5h+oK9EeewAABAASURBVOCJXj4f4lMTB9xhz4qH/uw538zUIlKJHlnaym1Po6RsiJzL7Apft9CvKFMiBmdbaiWygDzQN6iqHNW5kg/2X/BmNCcG0PJxyrrOWE7Nyd7nbOSmcsPWgn8N0W0aVcpGd9dXQryqlnEpnfrVajpt+Dae9JMxz5r0PXidw0fZSJHTqvCOqSDsavtsXxtWSr7oHRec4QELC2o2edWD2QVU++icLY1s7ZefgRbu2HZvegNQm6tpKKKlyPwyFVHbmzc8oGDt0wOHRrvnrARFRLR1uihNVngFiPYZO+yz45n4uggYvx5t8izknFR+jVCu860zIPg+rZS1FHUpphwujQZcOppBj1CoEKgwVUSSZHecFvC1jbkAm1lFPpw9JdewdqswmKbic9wPgcAE3MuCmC5Fu4mjT6GcHOu+aMvp8sqjiWXzN5ZqKp9PPn/s2etrPRKEqfeXidM3F1WUg9+QxH3WJb/LWrcGS8Eayi11RD3ezU6u5CXvijiclxvJYbV/Cwet2PjyaGjOl/1n4c+yr9+Hfl7aWui9R01BEeLq7z3qVD70pi5BipGdkioU7+Fkm4L+QDP6Pv2WsCD54rIhM0axj5/lT8x/qN/sPIbEfd4vg3449wA0xsUQsG4TezYnrGx5xVV2jbGRNXmy6l0l0a5ep3kIqpPhRVbV6HJoaSuqTK++1EsljFol/hSbM7kaEZn3/H7udG1szUSYqHzxusM+CAULWNo71uTEEtzl4s6K+jAjqdZaO364PhftqrYv+HUtHoKxs7bEd2jtwErFmlGprkGTpg7ZA7x/oP75QzjKPRlPu8ogYH+5cHap1qvf0QBYUxJwKVjXKR1o2B69oXRlZjFaDVaE6/7Y7zI7zmNTmNZR1tvyy53TpAYt55pDtYQ5u+tPzzPdhrjwz4VbygFBAub6I0dd44XXuvwE3aQsGcgj6eK8AlPANC6M9jZ9Sp1O+WhvLcH6CuhEHLG7XCkVvsvVbgjiRB3tFDkGmtVADI+EkH5K9kkLLU+Bdc1alKjPMtmqm3IFMx+YqCBcDPg4hzqbSnSjs/7+VFLwZVXL11IcL2tJdfT/SSqOuTi6mXB7h88ZACRc5HnDCtF1CWdsNyUN49nRGepeq4/3zs6n2XNlWDo6zOwT/jHGDCUb4w+vtZZ9XHs/xJCEWN+Vedib+YUemSBHZNsAe1Awlvu5ZXKmJ7+9t43qNJ0b0+okqkdNtlaRpljpOOmds5DrqrlQM74RVeR0zHdNighUzqQBbCWEgXcNwKlM66OgSSuPBKpP7paH6HGmtPh06xxld4QSpPWotU5vrfX7JlaQfqn1ZRBX+f3AXkvkNn+dqwxd2UVg8PLEZXW72IkrlpadaDNRr9mrWlFiRMrDeCSqexjto9P1EH9fgyOrzRGlZoR4HeJgdAghx0UqtCQVEKyrn4UkHyWXOpWcbAiQKRBTe8E+b6+1BusYizYTVpfNcph74xoaXTk4N0Ti1HSrFqe1CUAzUu10+dF8nSBgaEjWd4rVEEzjSCqECL6fuZ/y5+Bwdrzm2Om7TNqQfz+MescrMEn/M/HSgs3WYpSTor3e390CWM5eUAJm3UKLM62OXJxaDYfpgNOs4pQ/FZV6dMc7zWAYI13p0JcwoT+z0GFjw7Fo+6uQgzqdkg4Ul1E7X6ooJbhT8l8+uJZft9tlghkRE1fFDX68QgFmqwPNgAZxKL5jkpiy6aiSpHp8z610DHqY44Vo6MsxdneKIFuP/AbsZ+SEuJreI4bT17hDYNk3awnL+NmTuXseU7n65GBGg5uEt84JPTUDqlWXI4tcAhoYaDzmkJvIGFv/t6+l5GvqDV2QfRc5ZTtjG8pscA0/Mk+8DWFpovB95l4dXiA2FJTi2ImtsGsFIzASganxZ/tMGGVIct7VJqn6GBvTI2leOVfhHixXpCxQgRNQ5vAoMbSGAiNpwAV8oHxTIWOlX70+GVqf7/nGdRxtaC2M5Xtlf475fue/1tzaenttuVCdcRLMcCFRbNI5IQ35NRAm5Q5/KVUyYkkkttgW5I/ZMszdbSn40gu2x1B+J7kF11Jtg6n4IoEkMQtjxrGrEXhFMW0vJ9OcYmlWodkTiu8T83Od+6fdA31Xslr/5pURZi7ObXbpmYJ92DdUHL2s2Gn5iB+f1e9OSbmhX25hExzn4Mb7UlAlSV75ysrYNF1i4v2jbtLvGBDmTReo2Zt7l8IK7hWqQ/FuaNm0OTlPtUboNmG8pG+uhmguKf0uaA0ydYZz69IJYrWZCLinl5RrT1GWU4vs+bVSoHrwEY+tc195LWWuevgdO4WZdjFlTYNDLwUkNMuVhZ4ugUzqniXVPNCjAVBudyOBobt5mCssfXJoJShdrohRS7hMemsmHWOt+sCaefshbcECvKlkHTvHdtrv2DOZVdPVbRA0rSpJSG9xbbsfrg0b8Qmso1ViOOAMC9d5lzJjpk++vQQqkQn1wZ4L5xpJJSCC6mLed684Giy8agQ2V+h7Prclz3tmMOutFy1eUuuZ/Uw/w3dCyxsgp4YrFMkZosrt8TkickZExDWq/dJTyVxIMZi1tYWl6MJP438Yt4YtQA8BLxlVB1iwlcPuqWd9M6RHz5HyxvgI8NvzV2lX+nEsU8iaVgpGQgNElGFX1fdE8gfXPR8XoGx6+0VKpiXeCVSwZkK3chLHoZqLPbRSVt8GlUVLhLCoKG1MrQVWerRaAWAFqZ+GPWnPUxJoE1xH9kYg7F7w6MT5dp9pGYPWE2tm8mR7nTUKLDu2DpFcvfj8g/4pSdv+sjoSCBY6hLJgx6dNHkeilobCxe0lycAxPu8B5ebbe+cZlHsJoWO15DFWClJ9xNHXlFV27sxWQ/26/VGfV8Jselii1+/y1U3OsDEtQpENtA0Ehn/PtaxITXaN4s2Ocz5n/9NSHtGcbnS5Ga4VpoLoDftajIiLlJUEn9QQkTnEaAOQzRxPfXh91J599PP1nVRpWnyJL2yPl5+yXSVme8SiMtYAL+MCZaOek1OyaarLfUox2fnbw4wKjT6Kc089nt8bo3l3xTrOA6Dysn2th6qu2XhO5znd4z5rCfdAn5Bv4/nHZtnQoKS3P+NCIQtDPkAKWVdNJ/yE1wLeFrn0xN60/6uCAIIzoKnJlVkyDphGAmzzMr2ZNeeHqSR2V082vjmld2LlFDR/QSaImxMt3pXc2Z+RULemumTchgtzZdtR3AY37Z8/qTvlL09r9huVVeOkMEENd4hRrsvZhFlc9u5AQn4w7+vM7BiFNC7lviasipuj5Ey62Gme9K4l363Hm/MMnzW9eswObSxctBvAM0qfU/b0Dg/r0VRlQX4AOCcsI371e5FXKkgYezx7FaCaeLeiywQZJXr03PMcPXyotJqLZcxBHYfSbMud6sUw6ayGrdOPIqIaCJe5Bdi4N9cpL5WIenyLk16SFVgK5+uegvO22xOirq45WqE1rdoFmWvmh/vDmvgb6OgMIhvCA+np1UVDody4BWaEXCwUQo5n9PzSewFUmC+EKdk0J3465RjCNNYqOQ135bfUB9UXWoLIDa1vWX+bc2mb/7GDKBG/k3PGzrEsq3FBUyjT1+FKGRcl8GsPv11bIxvcxsf6VG/MYX8H788RV3dCzblQJYxUrLyVpYbYuWWSva7v9XniSnoPFBvH3qCP2bipLV6e17Qddd8AUFB/0cialW2prEiENeeoiLKuFOFr4tMX1b1twdQCFStgtFg6ObZ63flZ7+2s7E2PLKowmIDSKwbawIY6bQv3xXPPrNOxFVAWUg6dcEdd3nfAQQxGBFhJ1A885XghtBPVhuFdLPvyEZ5j8UxUBocZc6Lg5mYOK/OXfwU/UF2P4rSBOPFmQC5feAeH2JRDE8Cpk0dr/jWpNet+53dq5JiTTtD1CnPVI7XBcwH7No8g1qKJIDcJKxaw5AlmEC4JMiBNSVF8SeEG/0wfPGOPuyDrbelT6wuA79FOSobkS9NJs/edfiYlnsCEs5cla5/prVBxKVJxmOwmywAvRmpbw4EGeuP1OvChRKvriDFT4+hAhzUViUa0NKqRyUgsAxeU8b4n7lyWF8ed0txvz5kZEgCrPakl/boQ9goVjRsyCv2hvS6vNweYQY/NmyHvM6iOUTNYtkzt4OpcJ99GpMEP9Ez7SnoLP9uNopTQ+PedCRkco3uDK+E5ACJkZtH9OId0E/9Xl1SBaXR74zMjXDfgqtTSqlZtUOGMZc1zUMcVgpODdfj5ak96uXzL9iz73v61qL7VV5DRuiqRCkNdpRxCO9w8q1qfXAZgDqpXKw9ma9ZVekx3Xw63pNrjk7dGOcVRfXB+vP/5xD//8CYKTLf6cb/Kcaz7/bVtb+zdidSphOJfrJsHUH+w7KvzOs1fcisL0oXXXl2YSYWGeubsTMn5mivsA97VPtVQXzDytrguF89v7YsCV55zzjPPrpqUOK49S3v4Z6tFXNXK5dORBdSG+l1DrC3wUhb0JzCgxrDC07EHp6eP0NHNrg9/fuof1z1GPwL9Xb8yO863Nd2tXCr539Le9EMGghNo18+n943tKyfK2EEYmv1uMWY2bYxKdC+VEkQUv7RfpzasJ/iGWj2YJgBjUgcapSt8bL4DNaNvT85MQp1KK2VIskqIJmDcsFyTlJCjQSgbbUwmt9W52R+aHmv8Rf/mdcJrFt6Ls72P/fXbiuQY5O4kkMYtoXC8mhO5loTV19jVdqU6CE0pxAOBBBXCEtYXzl2h8UqdcHiTw9Vr2e3Xis/KiFYeT9Z583Qk016+7+v3Z53rL5CNmU5pRELqMBUxrEHjCobLHkSRtxZJ0IuqDLMb1xoRhqEAKfNfxq9fW2jFe+rSJp5gXhfXbYD4M5X0VL4yNU23z4cRNBq0RPQAwZ9P2X2OP6dypzR1lj3je9vRjeLX0AparVujNQfc8Ach0LzH1lyAp4/0kyxaqXYuVUaf9ZKmMHT09LaSDB4RcHt/FCSQb9Vq3CjHVz2rvqbld0piffLavM599bIewUT40s9B9bzQR2TVrEwroi7mfe3LK4ZsPwzFsH+K9+MiwQHiHr79ZNW7XRHj7zkYxNz7c4bfsY34nTjy7D7L7MK6cV92OVZy7VllaEoswg4ScYm74Q3ECxKQufI51rtMd0Vfsx3Ut6MSb5dpHybP94XA2xULLWVqfMuUcuJDy1z9hKuWsjQqmIEyKa0nyyXaUuJT8ILgFMWGEaa/rgV94IWZl9qCMnVt3DnXzGsKMKUyLRv9sgj+n2VNcI6AhYPij96K0uVflTKre/W7SWhkOTomveSklIIiwTC5159Sw1ULMa7//y5kCig5wqAmc44a/VGzPqwBTemUjWfn7kr2OGScW+EOmbTBmB9Etq5nZXZyZhEv2z0c/WBc08S9gFEkqSGUhnY1qhXsUNC5GemCeX/i+vGQpEy1t9taKBCAEM5YgTP66Ff6J3nPibf0t86qEjXmtIgF1ngMF9Umxv/amAzj2mHPAAAQAElEQVSmD8zsPXNNG6a54cc0ezDorKe3SFBgoVwrF0Od18CwsjfNeXeprZTNcXVsF0aDhY+F0KFeEq0lAlCYajCiYNJDM8BDnDcShSz/N4/OcwqVajiKaxoarhFQugz8i6uBLj7gBmWw1bBGfHc3FImXj5sWjGWUPFQZZXVg+SJPqrVwTz3COokZcnyRCwiktWSn9kW6YvM9nGpmWkmzJIM0OYei6heXogElqwMvW22kcpuNAuYvaLkDqm2k5Yz/O9/g1v5I/mpwBD4nau7uNWWVCbxdGbGS63hlnFQJzdSxaac2CTra0QPFVURe1Vd5mHe4IqwKqDKgz4hp7xZoClEPBZ0teOa8wsO93dV5hiHja8rQMrKvSAVbZuU+Ds/md9tuE0IKUHQutpiCKWcoOmo85VjagTVtYOJiEM0lZxHpLLFk7T2eoueFWcE2HQ7QwCh6eyMKQvJEbxSg8qmrM1LJgITgFkqvjnFPn8LJ1K42i8R7iO7UzuFdzr+NSrwFdnZJyTicJBPrmfUFjnVjTLHSqHVK6+edFuUU/HcxiBj11Lkc8rlGYXAa0FV5ppBPheXyumHqmvNdLDhKyDSQQ9aIUpPipVEFFPZA4Ys8gxF0FixHZ5mrucquNk9g9/RAaXMU0XijWl0YneSYToiZFwl1lRhMqbTeuPWeLc+GcVVXgSZuVeX2OjpjaGerg9hQHb9KHcu06PwcrdZhDE0QpujRPUXIpZgUazCfy84FTXVEvLUKePGe+MDvjCvpNcY0jlf/H9nZ2hSphmSnBtWCOgtzjYW2MX0B17gdrh477dl6VHCdCuWtNEFgww5EXkEG36N7roK9PAjpIEe3Uldq56qcTHNgHaYsuG2DpCtmdBKaMH9XhoGEfy2/FmyNuOShmPIN6YmaI2Alu42yaVCQ8SZRgfmoB1D7h4mJ63wilx1kao3rIejdVts7y/GWyEU6lLpWYv1vsIDEBZtR0cup9CVTc49iiqtd4zAmhBqHdGsSDPhFVVEyGto3hG32o6g55UFFQ6cZJcj2I+LHjvYDiGOe9mYdktbgbkhanZ3Slim/01fvBY0pOWlXhFayA8zoPbbNBlpt+t1oFKd92hrCzj2YgXEuq0UJAnBxrHMgIcV1IcWiSTjQdyKO97WhSsYbMoWRyQHP9Cl1vlQXh22TlY7REyLlvc7Mc6yO1H1A4A4xMBd17SjM9ppGyc+wCWXnwoumBTOBMxiOXcHrKovXQIHj4/7mABsqAQgpqtggOWMKkq9JYbMqqBD1qxQS7F1Dqz1TBA69NZ5TLq4cMFmwIWTG2v0sDerX4AiXuUG6d7ZbQqVuGO+5D2KrsqjMWo5+aznCx3BRZjisnKfP49UKa4WtCCeXH4tEQPFB+p6dEHtfyx2daxQ0sueF2TPFUQrlKY+dAppGuHFRcHVfdPVZPMb0v9k1XV50x4JY1BgAosvbvBCcdYV/KOGxMaMioUJNm0YJyif7biUvN+eI4Hlvpsx6GI/5gQbNiGRWUg9oFZ8I7Bdw63xZp1TR5+0aynHLB6hc+ZpbdaAUOmusymUSwLhUvjjOMY5ONDE6BHkUAHS+7oH6zG0a225QIIqbhHg3dMsHFxTZK5d0QEyLWV0qO8NigRloF0r065IGM4NH0vZII1UUpRGMD21VQ+07CR8uOS0ZUSypqYAqXvN6nbnGlqwM1f3jAf/GrzzKK1xB09sTVyMkrTABHiXdPReLbaAOlRyrurLZdI/4uLwqKcpMAeWO20SPZU1WYYQfJAMqgYm1c3Bwlcx7mzcUdQiJTcbsGEmCS1fLDdakctzI8XHmjVr5tpuNaoFRJ4CGzd8wZQgUrHMyEwAxNrPiXCvduoTAb3pEB/OSJRd8e7WtsU2v8Fk2V6boFGptLzMsjY/N1nxTIEZdGKKj0zp5lu1LjSOnHuWFtYZaw2TDFfounuRU3MOf0Re/74qxgGXmUBXIMr6yGOrx7mbH+htVUXDodYi8rHzt8u+XvV+SQ8C5L1dINS2CrWaV/8y6qOQLxC12jfenyH5KDuxr6yvRKt9iYQoz1qBaSpy8d0vdYnA7FPE4Llsf+0T8/c9d14KFmKYA17m+qbtahT5Is71G/QZqsBvvGzvnfToGNAMo47k+Qnbq3qnygSR0YBvW/V6kH0GpYTSvaWrImtQfeyPYdm3tSC16DFlQI6lWMo52nAvlM06zsNv+toucyldIBp8GMFAZgi6z0i1ilz8HNR2MNbfhkjqBRDuU5VqmgzG35kwUZy+vRRg4hH6NB0TxJa+c4fu/T8zOswPbQ2ZHhl6nCv2lqwFIdLRM4ovIUZf6jOKeszBy5PWxCplqtjOXHZFaSLKX9oPo6SnlJdueWr1vez72vSY5LjNH0yCNy1ldaEV027h3OxrXYHWN9PSE/V6mb7vDK7SZ9EtzibO4qVRgD097jZaHS34B8Ax/j39PdC2fi1Q6olTuIJc+rQ6RF3oAMbvqQEEBYX6ZKArpFa0IeSBlrMYtSJP6IDxKEbiMxhkWdLexjsXUaMWt75PnmAFiG7pqiXjkJr51fSdOWytbnOVEQBWZc6ZaLV8eWrPu4pVgT7e+E+htEqmGTyxbsTRNJwolPbS2E+J75PiMd138Xvha6UtRQBDWor5yuVEfPtdqd7dWUviyT1X6PGsqYF139uqxKTpsOV59TJBuBjcz2HAkR8IB8IXHgCuD1T4j7c46gkPjwhXjeaLO5a3d4tKpIgil4Q7cUNh0WbFuPl3fzKqjeF8d8XNwl9KFq3gnRFaefKqk1IxuU9t9eAo5L0FT5d/tz67n5Bdno0WGWyFanvpznFpj5W61qiibu4Xe4YsXOvAdZZ/zwOtd6pZzqfTEsJjxaaEkb8NnmUPJo2dM6u2ACTeodZ9QM/H9kE9bTVfmPIdfSXjWHf5PQE/zp5RN1Ksz1Svn2/qivPKB5Ks43VEuPjZm5Khk3rSyPYEynelRjzG1kCfmWf19H2Ojf0oZiJf86BQ5ZbbjIv+uW8J51bPyhDWlT3v3KbR6NZszmRutPI6SSwMky8N8bB6jovbElRbVEuvLAt4YlsmDzFPSXaZim3rnCKOUtpS0ra/g7tW6rFXwtNYGS9OOeq9/yi/M6kNV/CaeYLrh9czXd19taD05gIrMj/u+9BHGMWW0UVlv6nay/sIZe2HaKcunu7pGDGEcufiVf+O1oEYnESsG2yfyoW5e76PWcXnG/92byRgJ5VmKqjM2LdG3XU/yOUXtqe/HlmQjBBolSGz3gcrvTTT7WJb2AZCfuMwXYp3l6ksjuwlvHU07ee3+pAyHHUobr3vlN8ehptc2M2pzHb2YQakiW/Lr5iC0tv8s/FjqgfzP6avjg+vTkviwOHI9C2QTaRif5Hns90/kb8wv7JW4RLaOJgBIV4prR75TqVI7uKrXYJU0xk21o9Niq5KAtFM1j482purlkBapl/esrcDzy7313PHna/98+T9PBo75Njvox7NWcW2FtHI4gFvCIaGMKYtyl5DRqRm9OTF1s9Wxfq64I3wiv9GgXe2RMeb9AW7Q33gHzOLOyL16Bw9sYNkqhcv2tVLpCLvgruB6pKf9T/x+7ke3n6KpNbhkwmBIjmI3lhDM6d7vhmTIFJn6I9Ic+Dj/x9U02KyyUt8cY+IHCLe1oPWAZ/BnTMsOWp4RM/rG5ejLx1+ZVa7kAb1EADwYl5uxIH3WsCj4i37xuvC5uZbiRQ4EUq3lGCU4dczIFPNrT3wHD06/j++nASlpIiCn7Q1jqGWZhUK1yWjlT1nclhQzt/yImxhfo+8bfSVc2KdfO2VC8raQW/KPfOnm3f5pclMhrkj33ZbbRz3kL3wv7Q3KRh9217Pn2eqG851zJo3pim+UBOWo9SNfO3HMyjGE+oy9/TnJ7ULabXT38ODtY/ZO6ZW8nnlFCsV00S4NeklXI6BU5OLcD/1ro1FKnj5TUU93Hv5p/xIC49ihGlbOXAvt+qbtPcpODOk2FwR3E3u86TD5WW2KpbMrR/IHITrshnAVUlkZDJzvC2EddzM4SiB/1vxj80FJ332fuUXmiBT8Sm92iCnwgJp+MS6OwtFYqCqtFCUg3VnRDpQevb/IS5YfcM+mGuwJG31r9xn6NGcD1Yu4fG7jhbFeyIv7YpmybfYZXH/NUvC1EHv0NUrPmKGkHsrlcF584zKR5XnmFPZRjVLMfzCKyDXGmWbCm7mHdd3n81Ofz7gEXoMse5xPQKD6pXOuxp12qcUivs6zrOgF7jrf6unFFFpl+CqNrTXzY4ztbGujekmzj01jOqGcMnERyuQovJV/MU+AaoahCS0aZ6tS5epynSBUI31jVJfWM2yaIkhXVAoEU2yF75azsrXXxcye2JQ1NCCvAmJU7t1PYJZy0drSmPuQbZWs1DhQHq5uQ3eNFiicilYbD3Ghmi37DX0m8XarKJg4GU/3bT4tU8rMCR5Bmu2y2T4VliruuiBH3dMqt15OxpJkE6J82jzdSoMxplBAiARZJYQ2NE7Pi47tc6kfxkPMgWdKXHxoasAd8NDjNAulXM3ntSI5QblRPjIqMe1/VS8G1X/825/90mXTw+kcHnahB8qsIPrqFepLYRQy8HgpjYJDb6J0LgDzTzsVqcQ4oRDndnzemtCsq/mpN9P2apaeb5BuboTOQBsJg0xBZkyWukdQWoJp38d6AwlTr2nJauVoHByj6a6+f7xbH4ohLQodejPzTfWgDBMG+szIVQsoLRFHzdFBVhH5nhlF7mZOeC7Bt/EhRIHVXFWLSmthoR0q+gf+WPS6SGkIrtWkR7+jzzDl00/Hpm9vKaydA0zNwHazHhWWt1nTS7WkvfbH5K4AwZ4pKOEaPxKeItrLU1GVse3Ci7GC7YNJq4zU71k9d2v1WbBr6hWghqzo+2oXx68OxQIkxi/m25rupEl5BgjbG9aFCuxiH/iR91RUjIa9eB6O0qyzJk3tEd8t9mJ4F7xLcjgTr0UyOD3zGGfZuvxyJaGqRt1h+qRnvT5taFuOlkkEcMFcTaWUP+iLpwcicNRtiQZAyho/Xle1TuSoncpNT50+jR1dQfiyuN2CYWWrJsYcn1ETXe4AMUgtAElICiRzUDrJMyaL8hnZwdF0W+rAS0YV7oywgt40gziD+YgOTJvrlIkiuGlAWOtPGlXSSielHoWdqEHokOf97Oc8OfyOPx1nfPl9zVqvak5i/iycC5xSWlGg1dxP3jmB5dTN19gobuthXUgzsrvRzYJVgOFwdd+1N76duA5dZaMlRjdRCe0P3+PimBeGznuZlkf5U9abYD7SAEaBOkOXVMkip/xVmdKz5Fi+qivFYi+NAXEOtVfYJThtA9JA2XJJpo2FflhlD7R3NyCDG3Mm52zTpF1RQ/Yr0uFT1A2JJZlojZm+iWvChCRDKAduDruajg7LNijCfJPioxBLdAAjP0CpSCpId4sgxr8ZX3h62/qa75cFwvKyDiE2lowCCyLggKmKPaV5F3njvgAAEABJREFUMu7RNLVGbQbrsdmS77g0ikJUWjeUCUm/USLItQIIFmxivSCFzDbWMT9k+bdNo6zdu0f0rUov+ZR3D6dbgoDEtRzLknO6S/bmECVbniUrgzhp+a2z6/VHwpRzrVWt9b4hZF1+xPPZp05PibEZ6CWvhWsLCw45HInWF+TZISMjDjXOaM72FydJpA+8VU0dvsGBmcU72hor1ZqVQW1Uzg+l4dF4zENqx5pQVYWsGelUAAcY+VLwpSeke8/M4sh15fqWP4pqEiz0XWQVpseODuWiiQb7pfzTMmwKBxqRtBMXVLeWtko61L5YS5Kuq1cdQ9MxLxCMzlFzsIEifDFu2AULt8/qz72VNUkhVbeapKBDC0bshkuyIlzNCTMFtNKYyS470NK8rH21pGJsKhXzYE62d12v/DujtVyoxfE645WcN+tj6js+HTxmCeqf96g7hPQ2ZzT12viIafPnvrQRbB7UgVihArCe5cfm9Onu15ChlBGTFatRBItcrWZakWVUnLUoUtTsyfI+MiRFtBhnTF9St0Md8//Iok0uZoDGNR4ixWL0oYEhHO9ZjLawgavJgdIrbjBV0IEeTwnKRHt5pZXqSmWVM9JojXrfIMPqhvYnJqikG83gMD2qvDVMhK9tn3152wzea/qjmd/R9k7FV60c6LwtsxCDpfQw2+vNeyoZo/Mfddub4YhU4oV+xBfb5MdGGRuMWMFndIgFUao+sRBrKlA0JHrriB6XyTOVeFOsGOp8c7ccsWU32e/FbsHQxzcmb9eT5XUy8rjsKcZ2ba0Ff9CybfbGHE7dQFiPkGmt6kr5EoK6Q4icpeA0xt187NNcu5cit8Qb6N9sNOOx6EUsEnI/rdv4E0fky1VhBiDImqrZ+lwAlLYYKf1VGZTybl3WEB0KSylT87VrE0ddz6rAsktSJ02xnvSCio7lM2TTnS7R3LatW7deISZj4QqK7Nx3bSqtydex2p1L5UcRuE29FwqdTIHScMY0lccJg5V4n7cYxzk+ephwlv/6HCUEDKqcR7FbGpSEmUTLSshaAZJe29dNxhxjdpqjWGHC5DXWC7yORzltzfJC/Jn4bejfOoytga5rIG1wNVzjC5/U+9I9+Vy71s63YV2Dfww/VqYlTgXR9LlMd35DAcM3/Bvx657wcYM3Jt/inJeQ1nqgtcIm4lJvsjRuoaPz6usinO801cvzmf3pusuvMnF5uo/ygxsMG0a/McsMJN/77MiEYpnZzS1mHvCBfdVI9nP18xPpHm2k1/K+fczYPmk5jnX350db+PY6SM0RhoOYWXKggzRAeRrcTYXquqR2OfVMa42BrzZqxHsQj+FZqB/qvk/x38MV5j/ONVIyGiq0+q36k/zSld2BxHoeO9JyUt2q1Djmq9b1tIbjp/E/G7qPodiyn75+8vP+Hx0BUn3l01hTg4feAfeWBa2Jv6gThQvN66vsARhE3+Ghm5eXAOhS1HjDdbGUJf3RZI6QdlBMni/rTMj8elIFLmxPU22/hZhNYwBmE/bc+Vx7IpXDa6hibNVH/DGchhxw0e+Oa3/8a+uBnqPrTn29KsaxUSHNMVSQESok82KMOdTO/IAjkO8270L92Lz33TaMfpVTOW89D0qOhkBLlyOOz/tB3DxhXwjwhvLl5/x0C6Qpoana39lZUAP3NzmRfeWEMNeQxWv7Z2atTfsd1kNrd1DZJjqd/ZGO/afFcV87r5PXpKrnGrsqtK59zK6F8eqD0S1TX7XsqHjcrFwDnLLfE2U+RnMuaYbdSKmgMQU67aIZjBoBiynrijNO8MP5J7TVJUe6msHReu53lmuvVNXmcJcsbo22Sag2njRh0YclQJqVnO/CGwx9hkCyv3LSNg/ju9lFgYM3BiSXdH2Ik97HBDLtafQFXIMh9eMvcabGrTC6Z9BNwxXx4cXv2DM+DhhKVL8LNxNsnTwrU7LxvmLdNBX/2kJPWLdZIZzL+HGoX8ZIAB4dMYFeeLuEZP3p+UldT1twCwcBjOgvnIrdl6JhpqN1fg/k2Yy38JRp2J5KylYB23p/jsKXkHrH7HhtSXRrEITRAbdXjNeywHH1HjGcqeoVYFzFpW3jVxl2M/c77NZvdkCGnOfCWbhumFbl5aCzKUL5+ptRyBGgJPLPjO4NKSmgW+i2UpGwHvikHHOO0qAtEmY+j68kOVKqIUMm+boTXKPa10QWc7bNYVdDDeB+ej2a0XnldpyJJ9VDZyeOSDsalq+NlLfVo+jxVuxUzFnqigItOnNBrFjYKuX4lqTm9+KO2PzkbdLMBBV3IR8vfZ0PlGfdR3rS1fze5/fCVuvYXIVbubML0ks5R+gO7bHruaJJK5cY2O1dgWfs23yKWu9VNK3TbH0nHvfDOH3tc2NfrGsmDv1xf5Z9L9tmwMKYbKQSBHx2fQSJ0OMF00JjqZIbZ90Uk4BifP8S1pv45pyj5rNZD94d0j5W5y5VkTdQnI6Zm+dyndszB0ibHgKtAWIyr8lsjlY1+8ev/1n3TQFjTR1++zp26e6Bq6k+uHKuoZaBnzP3lDgZGkZtVP3n7vkBbo4HbAV5tZzo2+OcEVt2X9SDOO0xfxyfeOb6qhnJhnyfauUYXrNAS+9BYYQqUGuSFFbby1SBhutnXz/f2xujKAYx/YPUcU1f2Sth5UobVM1KOF3RlpDjsax7ZhmUVr9xA++9f05vPTjYQtAk1+0ff4uWgnHnWfXFfMcOlH376tsrWtEobgdbnsYoV3jTMfht+56rzsUhtb1WR+Z34zdV8Bcz3LBs3CxgkcBTUDO09ovmZu6aBqGQh2nMaHn/KsfRpbTZ6+TX8fJWZ57MPbuhMe0DKRGVRL6AVMlusTq4gY03sAB9qBG7w8DA3NU8ytH05Jl9VHhVuvTuhlfFhXvNFXYpEi1AdRvuiM91X9fHhqjBF/HUZfNxRqNPZu22WfWmTW9SISDZaWdEYu8q7ONZvvc62ha1eJrkbNVM6hn3UfaSd7/iIWlhES64e80m0cY0b7v6c+eK0iUtL7hr1HR9cqKVuuWy6g3ODKb1BgZp73PlPxY3flZcthw0wBrxtesIf+Qgq+bbVrJqy3jNq/yLvbrkXZ04u3pO5zktufpA579i+Bz71jydz+QjBbv6rKx8pZrVo1Sz3jgljDJBpKWtR2mpFyXQC32d8Y3VeImmuy2aM5VgPYF4fv35rpylUzd5j5liI2BDFN5N8EPhwCHeC960xnrUcKxmiQTTZ2R0u/w+jW/2me/g6zwfanWpN09K39PWM8YB98xXtoOqMlCRe5USXZhHzXkeiNshXy0ddb+n6ik7dcWYQvaiJvrwyvz8HjstZLP4NzAsFjPPb37lZQhXJJLfUx/qSxKqS7zbpINq+4P12+qiwJ6FCV++TJGjiBNIcbhv5uceKKWj3451NT7PrGfjbENfcIdPY+RW+YxLMRm0oS9LfOA/8HVEh3X1WxVX8wNbILpHWnc3V6+xPovHo+cz9TG80eB+4Tsu4+1qn5SrcPqLxgG9DcDBoMXAT1eeB+dIcVePqQhwAa+rOLk6Lc6sJB9Av5UK6OrYGpgRspq001p3bQR2gE3bmiRKWFD2Xpw+FojgbbyCb/jls6fqXQSCUpLZvEluQZN/ykma1RunXYyrlxBF5LobynCwzNpWvcIqUozrtLy1QEO5LdRQynn1p4wAjKFpNA05VsINGV9rRRgduXAWeqE4Gmi9oteaCA36DH8OLkn6i9HtTLmNlg7tCdGFPMzjhFOUire2mTY7UO5iEuoiqVEDkrJMhYOwMdNHF7yuOTUPMyPLs7tjnovyk3jtH4rGKMmFV+GqvphuxWhgV+Uz0LswUR5aU9O71H/Ujy5TOB1tns4GW237WElkmYMNk2VS4lnDdfiOwuL4lLE9DpjK6o2zWiUOiUOt9DUEjKJWy5dyumstRsDNYK5D655Mex2NVk9PbTTKLc1yos6n92IvN3d4dcy7/og8xbN31MNp6DnwKMhQkNlzpMwMaxHYa+xu98PKd9f8VCsSRKjR5qT1WT4w67BZrmCaVVAdTYj5tzjfhCAVmUhZfoKtDE0pV2EHviGiDT06j1Pc2Q8qSxNW2B12RcQq+FNz1Fpl3uxwbZ2UqqUA22FNlYo3lmCrZpelQnZXvVm7jUPXHct5eSwqu9SeaQAagRu9+KYgGaTpuilFriofZsXudXAl7q3AOjkiQB2iUjLKw37WaLkD/7aqwtCOwKDlxbWTFCJxvoLhjnC7wGuEXIuYoqC3cbxhSTHbfQi1K+WmB3Aljato1YZjUObQ4qz1ZMo1rO+Bcv6XUJn5TF5Cx63nUe1NTHXrRzQPg6Z6V2rsyFWNUk2YKg85VGuA/71/6N3SOApBV6Xq0DQoxZhBDWRr8+80cRcSIVE92a+IHEPhQxd6Jk28uFHbiQ/yRnlOsfMuXAdqcVl7csviVX7RVoCmOktTxAsjvyf6iT4le+8Ef6CeeFV/Sqw0agzQrBWEz3CbVQTp6mJjPA7Vn/lbK2sB1xzzYozaT7QIUpGUx/bRZuRVdyaelvOP3W63KFNX+0Ktuhsd3k55A+etyAbYxfdXPOmVIrGjJ8ow0XQsg5GE6Jje5qkUDFQOVmlVoZwOOM6+MI72ZcKuhrJ0y5ddiLx8rrYlGnT/xcBrsMo7fRoj2rOmWffCdaUFbJlwlbYhgyK57HK7tdvx1WZtQxfMtJ2yYXrJp+YYbkIdTFTwCZYg+YAN4u+M2+eOWu9/T2y3/dwtcqbfKp0+WF1C33qblftwyPCJGallz3tHUy3o7bbXXfZDV3XNkj4sXal47ziWucExwfyJuVa7O8OCIywf8KIzR21lu0w732BYiJolh+NPj/AYW2d0cI8YlhpoJWVIiUdsoZm2Om4ZQ/QoMd0JnphiDw+vXPfwMpgnqQGo7v+bKmNxFdS4Y9yGWSpu/XWI5TLR8MxSSpV2u/P+dBj0OobnLnnoy3u5D6f6vHqSLqyJCVuXR9hbU7sslfj+Dr/tQmXgG/sdsI/tW03CqrWKo4zii2qya3BNT6mY6l2cUm+haBIxY2a8fH7MP02NlLkKaJF5y8lqpCOXrepIExqFLGrtkhIv05qQqkxWdE2pJXcx2xJWFi2nymHHi0IoB5Zj068Vcc4RodbS+fZzNLcGo4j2msPmW5qA30eHWJVnr3WNhLro0sqgz6R+udUVAy0RvsfVBsbS7fF1cGeHxtjIEGv1GEDA2ObFoLnZkAAK+utU55XtZbSZOrvUuzUi+IS2ApMk11qh3yNragrdZo9u4f1uStAUru7OqtPJOsG+DbEAsl7+9Jxhvo1+I7m9XS0hobi5aGsRWjN9KSNamvYP+6JkOI3aOlii2JDOcqRxjGZCxD7cj9gfHskrV2kv8UAnml2SUI2dQZnCH8xMNrSMSJ3kQ/sSybJ6oktMaIKcQeHkvVKPRBwkDD1LR+mNsaDcWjUh0gDKZqELU8G6UUsVfhpH50qjqUIVsR1Vmv5yZkcAABAASURBVKphOejmJF4DaGjBqRKo9qk1XOCFwMTp1blG6c31jkmJFzBqOxGBMA6m3zh2Ul8SHGfdxuAV7XEK9aT2CrZHxChEE10+Q9let93vE89rl9YOozAQUCSQRR1uZXNwyp61av0YlJwuO3VhXwbX0IqnrXzeS6PRbkdgOo9lKc9oQ5m1WYD/JK7ylCzJ7/TTfcADU7Buly2FjJnipLL8IpEDozLG6iVzYkdcxbRWtZ9cS8oIrVF9ojNME8idyCMK9uUZ0ZqWS53lTEqNNtou1qfbnvKdIijuOPH5apktd+nmiJcBLeky0he4GHB/D4sZJcDYL+EBXe1FigXp5hc7h/I32lgofQOoLS7ffo26YI+04mVTLj2o8eTfAU4uKKTSs40zswOFV0YqnmSLVaIdbSOPN/kDLML0j5d9Ajc2Kx/MNjt3zIexqNx/BjcEOcY/wJso1bKTq+0aLWR1+RO+wZ7G2VBD5OyrzxoFxatbO4eVPWjhKcHSPZOAkDYwvRmJqHg98zOpAtVCZePtlOx5aUloBzj69/6lpgGgiSZlf1aaj+miQr/6sop5vZCaNXS3ZUHqmFmzP/n4Q7wYMDnVbJFCjMNIT8YZcaajs1KAoxi2OF4j5dhr+0HvwRexZ96QhziBQzR4MKeqOg142RNQGfyOFZfMvJqUchffREW7YRbfOrSumtmaHdPREQyY7G2Z9EHQAjB4eEFU24GO+Rv3OywHPShCLE2/kcg9adTZSSqx3S+2rJli7PbS7lmLwsNJ6Ugq05b2Jftj+Nhy/4xfe5am8XiMbOHc3DdUHRPpC1Bi+aI2LIUxiiVawpQUW4leNRdLWqcL/YIbSCg72odK0RQ79Q/PR/UMwaaQg2XIEOAuFhW3TXvqkNNs7Mf2r2ehXfdwujOqv0B8Gdwbo/Vh8nA28cjQaEPyIjObWMm7802uUlWWYt1apLEvww+4poBUDIpFtCkQDOrivgjTy5eProq+ToDQsL5XaUNyeCXu6ZuzykJXpQFVWAtN14h6hQIEGApeq83opJy1PGaiKfCxorQMr49f+jb/47jIa3gdxME7JEkpdebw1CRqWAc8JpwLveS6a0FwSjUldGktPU5I/8Xe1VIzeciNl14/HXs6J3ueymox0TFtL7bkPMOvvAh1YMUr/M4GqkUsjy71mFJO6A4RWqaluHkzr35GHCgXIkQJlo4I/xvB4/MY9IrRL/10d2EIXuYm5/t6ttaXri6mcb+2uqcpjsD824/otDTf7g8iKc00+ZgpMDWdfjCqoIYmcUZU2VEPmsvFvpymMmKphs1lDIVlY9WG1s2hNiChTECiIMwvYjkhAV1Ja1NWN0WpauWp9cDUQPou3OVSlnz1wjjczaHKHTNmMB0AX62mwtCosavdKsTdQG+vCccYh8EKtgUBP7VOLglZ8pBI8L5It+V12YEW0H5zxuSq+JSYuStcZTV3jzXO3F9ee5xnrk0r8aGsROO2uV5mrMA9Jhg+66buLWxF21Xm8uODv31jSLTbKIoYcUGaUJ+8MzXYFF2iC4dKSJS/Ch93hWJxbmrrc9AbPO2VnymvyqPHtwkRddFBFthSh0dLBYDkpxg/wrDe9ZeSpYxPvMm5SLBo+u0APUpwKWf7M4LjyLvqatkItKHwHtCLAeXoHokrs1U8IZeQaX1QV1caJM1DpmaI8JwFvmngbAWmrOuLMV0bAmYqUrtGyM/EGSXDuuU6XmdU/Ti1CWLY62lnKnuUd9l7Hfexa7e+0vuISPIR8BviQfjn+4G+AztOrZUVuflFl/9NJzHupBpC+3Q3ey60JpP61mMAeEF1Clu5lhRFUYo5NskcfDu+eZ28VitjGzgqd5vjeMDTJvNqgeLb2q05GJgClNlR/FLjWKpQB6BQuCM0zG5gxXFXVjsecrcM428FThH61BIELggcZyLDW8QuFac37sPYiC2hSI/RZqr5g5PCxSmo02gKXD0lVtGnxEWaIyIMFMaaGsnBc5FUDFZtZ9iWHs9117XgzkQVUhYVglUp8Nc2gJEJYRxQwULsyYfJUgqOMTAhg2tvL/X+6JDIX17Uqla4rvfi+AQnitavDXkzaUTlWeoWPFfNXRHSo7zZ4H15RZ/V4xajuD5d11vrYolODaNxLiKGHBP7IIRTP+iiPve7+Wvhfse+pzHZj1SmNpkgYSwy2Zty6KJgtaGpOjyVOZzWZVAmbEZX9FbUrxHPmroMUNxaB8jAurCaqx1aWUZIRkPZsKanK0BuYzZeV0fvRlaLpjO673yXi5ftEkqHAWj9g94ZzUWd+ltjM2dytFU9psvkiIAlAmimmkWo19aWrPmzM76NXyiWy8+lthbtXr6ede8VdQ0E1d7kuMFXEyrNlbCKyStHAyQ86YCKdtHX0NEZ3d7m1L+Bp+LDdmtbFNkS3bzJWDSHMiF5azgQtC7fM1tneazgGz5y7p3XPlTorTz4YyZKmkq0Cm6sjJ3UNEULaDLaCdfUBo71102CmPzB86H41LPXdepvUi/2bRKIHXI7wLxq39kxwXJ55CRCn7VIz5tcOA0t1NrWZL13Mm99NFw56Rk0KefKZLaiqUrdB+ae92ubR1l0jUAdr1c2crjtOf30KTycx+btnonXDtAYWuswvg6LoINGupj0Fl+td1Al1iEeQL9H8dZR/g+mKU6NI0Rj2IajxV/fyl07VHvWhs35DtS8agBQHjXaMEZQB2jjlm6FboVJxKVDEAwIYGmOJr1t2nPb/mpLlKdbsfW8qRXkx5HrzUfMZXVphmYKwNMfNfuyUkmOqTidTdtlZ+M5PMicUxv8clKbXhExOhd7WDPQlb19j3rzZfeysC2M01i2Lh1Oi7T7MfXEq8xGPS7DjpgcSRNX9qoiT43sB3awQegmOneUYpUld3ANrp5Fp92tWjjBX9q3lSix/K3ZNVlTkOvJkXlRNuNla5x6Odo5fU2GoMQzPAP9o2ZYj1xcMq1BI4wESpLyb4fCLg3tvbW2U66HP6Y29rd4JK+96rv2c5XYRTRm22j5uD1CwscUuwCMzxB69xaIVv1DS/T0cNCapZhnuifV7w6kiXd+kO7mphQLVJhkoCBKmQRF2oC+aJc13niKMtoaFW5uOk2XY7l8abugHVAMTWW4PeuVWOC6NGYl4wlu9Yp4PAqOE9PRtNvniq2svNTNvOZIVcsJn+XQ9XLbwOm3KZ6x5ipcHDOA6JnZQyWzmWNSyXmHS8M16oolCWUDm0hvenGqVaeZp6pSc9IndV4tIpnKKibCwrt6J/ZBZluJEsxt0K1dUKKeAF9wKyFpGUZ5CPRqs7nDfj5+i1VhK2EYeIMEI3EdrlGNgF02RSpiYUzMSyKgTBV1Y8zCcEaRrt9xLfQlTbbxKdsMrI4or8JRvqe4+rJZjUVzJ4wAMXp3mQkaYqc/sLGg++slR9rAYPTylgczi9EzJb+KTPs/CU0sF1olsB1DG2GSGa0k8yyTSAhzmMJWaBIu22RSgnNcC7R78D8Ig5320sbgSBuIUSsS0YTikRxV5kvXHXAzmLUGtfg3haAx5JinRS42qavOxWXDhlKKc5tWvpputYn6JXKUuG/O0296WXTJMfGlpDtzsA6vkVKhoNVs8sLcoIo4TkWd1e++YzVDUDS3ec6cgzMvDq5bFEseJjvz2W/DEGKtW+cl7mpxMt5TnIozV9KK0ItlVoGRYNRjpB6C2PEuaS3M0NZqUGCCI6PnijTXGdMIpcN11oaEtbFuiEQ07PibEWXbPEY3kaaOv/Y4ityo3Rj55UXeLXOaPjBgwKbbq/NQdbs+tbrleYtCLf0ZacWzwYM+k0d5v620Xb5rr05aQdhMDRCJmjh2+UR4Ufir7RmBsGL7qiWB8AcMcsy07RjBKeGe7t3yppwEjuMQ84LjvFbER6t+V9Y955qeZiVL47UFzbIUa5rAFIBSuDV+ILrjrUwlPVPDn15HpZKGdJaJC4HX5vEq8ac6MOJ9qTfWUPm+M2xHK3P2ybgEShpS13kaG6IfHpp1IVc9V5+LdbEDFSK67IvP7vs7qAE4EGh0yTe5paL9zhhpNSFCv6iqQ8VWfc14EslT3iVMbXzMHu9nJV3/CFUb+DslQ/qL+UpjJbSLm7Wrvl1brWbPa7RNSlNfJWXJoxXbmN5Di4z8Is9stb9D9RCnjc5Drc7+opM+bFzjVRm1YN0v/LThiAbiLKeDvBLXSAcN5gSwdOSxt0GVykelDFHjTHv3S9fR0JEG1aX6PvgoMQXzquht/duTpaVjGT0xSuqwpQel99nihZzw4HszAOj8jEAhSaGznitzdQHdENBaytaxrjmVPd9R18T5zjzm/bH6UB4pKh/i17BfttZgba60CyHC1jNWjRaqyVRBjfotPN+zXj0HWThSwp1x4/LL9sLXKuXvCf/e3CZ6bpYfYIy4TFuiH3wj9zsXDjr1Kxpi6zPU0uIvS4+tMRUTYLntm/ZtO2W7lLUSM/nEPMLoIgOKH940Yymw5VOGLVrbLLWGeYy7MqcyiqMTbteapeVDk7nkvcWy4EFtq9VIb2tNHt4zd5mMN1Icy4RS3EdRfq5ySnD21Y7sQSkX98Yn26nmXjQCAdmmX6SqJRdfS0yrb/SJMF3wu8A3Qw0OVDBKXMnXoGE2a3XcjPutc1W84kMEP0uLXN+J+Iafi87xgVvGqhWwZSZatIrBayYPjrbeZUhpu2e36tGIm21iZsUQphEnDebmWNqVrAH1T3Z3zCMdapIQUmir+FVxIfUIruA+6/bxKFk9pPYcEzzAL3zT+ubbpC/iarWEXJkzkIlqlHVfVeLL7CxuY79ES1TTbh3Ruc5e8/W3qLXx7MeYg1HHq0Cr6NZx9pvqRyb8Dd3NVsRlaykYGc8UwMUVzSlpkpiLs6ZtataZOIU+paZROI2u9nP4m31mgHEDwBpx1zWnSzU5jAs/cf6JvmIWW34xhmNLAVnG2q0jTi9w0S70ykNUv+YVHnucNBk+Q0p9uKfveaRupLvEEro2aKCAhPjGo9ko5+W8G56TpRIQyC/eun45AKB5/k6bsZelFvRepZw8WE+aCmdGUKksazQGyZPr6/dEhGltGPgqj0mLZ3ybAD5uJo8zVPit/OXTuTtbdOgmTm960IY0vc3EN5kFdsqxQg9ZbK6Z9eCTa83lMCuqWPw095g2rT6mr7a1mq9Rf/gu27cJoMJ0MW5b2cqZl8FKT4mlGXab38MF49s4tMBFa6ucK5LdtrFyYpeVct3pI0rACRO9lyQqIZVyUcOGjpbAG90KA9tmX5VrSvURod/wMLzeblm206Pom2J6SrXBeVWwMMREznpdX96L+Xe+BkJDQbvsvrFWQjraiVMsG6pIiVeJEM/tzMWv+eHVeb3JmmaldnEtogby8tdnYxk+pK2W9+mOJz/PieOsNYpJkcSY9435p/ED9cQfXTJYmKG9x+kFRYnJDRVXPWv3lVwDZ2sPqOLTY4GXCAE0pHdenDvAy1I8w+em4C9BAAAQAElEQVSY/XF7Lpu4xg10J8OV4pnZnVn5BmqEm6RO6VLR+7RmuIRS84z90surlStRj2nLMfmXCtkgE5JJPvOck/tMC3SLxUCi+jSKIdeKyeZU7ekv6mGpMiBuZhYm9LsRQ96GJcmYzsk0723/fP3zrNAwUWSCnRKcj41K+65ZgpAO9c89ENhspVmGJ/nefm57Mc7A8PeQAR9QnnChDVMnqtUA/WJd9Qh+mp/3vwCerS7mkCZFTqvXir3BB/NRX1s1BnUNhDDDrga7F+cac3AkSNrr2gpvyg5mPP3tdPri2OU+W+UDHjA1c8bKaMF/gH+ER92MKSzT70y6nOmwNzT9ECY3PdYbaj08Ja5AJ8IGXuPGWLaW0Cmovc6ZVBdtcWMG08i+INdigC2+3d8Go1Y4N9LXsfuhJSFpN76du6TAE9PKZJKPTbE4x0oAJ5PCH/VqnfVYpg/W4VVaBLS2gXuxP/H2ffGRT5/friJAcze/wla0eUKHlZMOEqPECMraIlpxDT09jktPGMW+Gzt7kIzhGkRDQNSwEgExLTKhVwM5mgB0UxXsnlofghd4TVvPtB+xqIT+HSaOX+eQEVB1AmMfmydGmdjy22+TcPf2KZd3PRvRYW/cBTlyGeRhnINshQnlUoUwC+VVrpu3TTvBZnS5fRm/ag6j0Nk5mXhkIfwvwPM+kHJZ4JQBP+MLgPBawz5T//Sz+K17v1se2E772/oVsEJK2AG70f3390CZuc+5Sve1AlX6UQ+Yp+bp7nAThVo15uGcu/NtRxy5Nc2vaRx0MfPuvr31lQy0l7U+QAdxma35WN/9XWdL80QY6dqN9f7UrOMUj1kHx/r55N/KNYqXoJpZ4UliW+xr8t081TVjpysPh8Z2fj9SXk1pRyccbnhPVoyZjjImenwQdBtMVWKORvdkodNaYRcYg/9p/GTaVLcyU1Bs8hQ75To8I05ofI7MQELEyJjNQIYX2yLNS5B8iJ56Ms/pPmM62cTEF/EwDlmNrlXwUfJRIWuapxRatX1h++ZcJS2EtQdnERfLNJgH68z1QJ12IKxJARANNm2vX9rhOrbazCmtO8zHy2N/sLXMVfRzYb+dDdeT/mQ/tVu53xXFJUf6hiuWSAbN0vyhCmhakdcTpZm1ZHnGz6w0GzlESihVLuGKPOGCcirAOntyTz32JJ4mYSbku7lVxX7VYpJH2ag+xu2XBiiSz/69v1+ePG/XME2SwO1ajItaFMpG1KufiZDuHwADNTI8X9Qoz2uAHDl+d58DvqNpCq4mrIzb+Lhv9TXza5S97si6sl2dY490pQCF7o5yYV4BfOxdgmjyZZCV4UVfZm6Ecy6SrlxZ4RIPAPFpfEY5WLjR14sCW/Tpuz+1FHJgpnmqEVRMmeplii2YwXQvta5RwtFZnb7qs14VyL/7QKGX5CnHy8YhxJB8veV2ZLdXjOpOykH0uPS+LQubOMOeJacUUYC5K4iRyW5IqWgADgbKoPoqfzVqOlvJOYvtd/P2utCB9rOZqSqZAbrU7mZ6w7etareiQC2jOZs8HQdXXyEyxkojNTbyGIrvzkGC+sug2txrjdZU8d3IFYpaa8xLI/gp48mWXlSLy0CGQ7pxEyuFnBpWy2i7q4KE2DMoZmemCzQgeMx3yzo0Oqv2ob7ZaApCWlQ5ymfsSCCzxKor+nwEOoLLYv1c9nPltXas9Hv5Z/matkapVQqIyCCOv6/b5sZZk+paFyaYgMIDh4J+mV+2TFPu6ZWiHiNpuH7pMl4wfVvymvPPHAU8zkNXpnLjGa16l68V4XSNT/p2l7I7AnaTNyhvo6Jxt0EbjhqKzmpTH1+1XgiSI5Hfzl+EKqJdG/0F+KHOF5TcHgLCrOmN0/NN+z79+xw9ztmG8XcLeRnl7q9Clji+IA0Zjqe6RzuVYokIOBN9Lu9YmDU1I86lk8T6tWuvVdLfe1TMs3K4Y0YR4QvmuJio7pwHumO+sGQ2k6k0r2WfO8LFa3bpholl0Do0V0qrVE4NKVbWaudElneHgjJ2r654E4xWejr4t5HfZ0xhuz5xYI5ekzdL+fQAbtriuiwWGel4PifvPZ8997guEopkZx+VzYY98D/Hzg47+q7SfgTKh8tLuaSPTzuO+Med65O3l+TEEoAVKuV4k9EzZjKKfoW22j4eP2YCV4oHS5T09W17Gtkx745amt7ItG4Volrt9GX7c+/1GV7Va9c1DXIYkiCpAlDCMQW1zsqpob+cHPN2I8er16Ftz+87JMpvdyrX1+Y83IeHLAjmUlkrYGjJ4yZtdK+s6dCzsp0KoeaA+++hCUIS55n5kieG1s4iJpt1PqhlMFNe7sHKXv1QZsX2hV7vhGM1k+CMmli/jEdIlW6WU7+TjyGpFF4rP5/HYkxUbp+yIaW0vwdaARYicY2FL8RdJhhKRa7BjGRZPnkJT1+dd9XNpCvu2WgtYTP1snQZhHVinzjSON9AJhMSVDTvDT/jRMihSnh9zzoKeuCcxb7mBKrROaPGLTaAISRLOm7A4dRK/lwUrTV3t8Ap5qyrNc6b3jBQr1HycE7l0RKium2djgMpAmqfQA10QbQngKvj6hVuK/AxfsCLIPQaAYBmHGt8aJ/wN8KGyDYlJHAHTBK1lYgGdveTSolK4nx+c45YujJMNtDYmEdkKW2GWLCFHjt7qUTs4BgLvnboPkOV6yWpHU+iLlmhbJu63ozShjquHr4NOWlK/6y2SxTyaiVerFxpnhf7ep0lXrAUe6Cb1DEu6fLDvM+5vscf5djx1q4gcfao/kzm3p5gMw7ooK+eu8YLVgQWc2kJW1TfTbYLpGiVaq2P4cgGetn5sb7JuKfvaoXYqER2pueooJhoKPCX0yxK4sNlWOtcL6eYsPRorKKP6tChTwzrMMkuleLRtEm5a4MKvb2GT1syhheUXHAMWO7KsHtT2LTp2w9GxaEM8yhR52jDlWRDNXLuObdxhUQ5K+vutHKIBXMLpPEYrTUSMYhCJFiBufS2xdDYLjuHVVC+DH9RoCQQ4JzOJ6G14RtHozjpsnpf4QJotogACCTwQKmGJxd50dk26m/D0Xk0kI6KWdHXjBUrQxvZ0+aJeKDd9JxqCMObQ91/6BCLII3QlIuptGkhBGAJ6sZp313RbQWVbeI1W1aqnGoC1A5m3UVxfFB6Ki+pdFAyK/Ub1AK1MPBWAIjd16GgMX7wz2e0j8PKc9lePxv36TV9BeMT83PPP2E/xZ+97q/c+wG/JQYvntFd49pa6tRoDBs8Ld4+3foGzydAQ/i6TQlzCd2914U1d5xb3exqmflu9nfFc6lxzmkRbljLLZS6aigexovbWx8gm7bazsb+UhVrWRyFbPHmMhBW0kXojcJ1tZHuUkJEjxISlj67IHlhjEaGEmWW9A0vMdBSD2KP98D8rDvvH4sf+qfthoC43Y6r6UcZ/Q2bcLUgdkps48FZMQbYZj0+6YalMU1rQi5MaRh6B4ZNgFjAzRMsqlVd/AZ//foDT61yyvDoDQfbJm3K0H632eHera92niZbnpOKI0TT9WPzQ4VlqgCU/kkurOvCtexTvI4TbnY5Q0vWnXljX/HU9aTpfii16Xz2qadE3Z5WE96TW5+S3BYnJLffUhc6jTNAit0ScRbmY9ETXqJkWXz4uaSgBVg3v/BHiZ9YzAu/xmfWZPjQ2c40jmh7I9RfYRzOe0CrzRklg7JOTNtD/t5a21oAVAUzkUpUdLs42l5fmFQ5hdipS7h8/7Pqvg3xOf1TJ3wQEONap46wsYc5PBnzvfxZ7CiRrMzxyEynzpjcL5bo+hy7W73mBac7ijnAxn1Gj8H++uhEtSbeGbE/sz87rz2aUK6xPTMKopGN1jB46QHcfMauFn+zTKkjX0BWkAoZCVaRZ/ncy9w9scTBts4+9T1nzxnqBvX5eF7S/U1eaBC9utRHZAE1gmSlbRbjtlz9Ze5+eg59xzoeqeMKvd8eoF5wBv8CukCjLDyViRxlCtHVXYprdK20VEDrx1LBxuCiJsqjcoQ0sK/Pb6wvq0YLvkSmVVpQrEVhA20zLOAQOmENl9IOReLQRuqIgMVEtEt984XscDoMPovpolQMgccxq4YGI6LSUs5l33x0kf5VpHTBxUCMnVIoLXQT1lwtlUJffSn30scyuJvgpGpS8/qD9pV5nRRXa/tUl0lD4nZ13lqQXuVWsYsoM3MOVHx6j5HyfNxgI7+7WyFoc1AOjeCbgYMw07ONdfGy6/1/5vKeMKlUtJgMdPdUYuecqsIxxWomzKlg/u+ohZJEcBM/nAUO2UtR2eIbSg/oc0f/48fqZPdDoQ+Zq9w1XbHaKVL1dvcroFn+mlvD9WKX57fr98027zYUA1ic0LR29VXZNO51FaUxDxVXYcQHLJXQ3Vavvuw+Jo3Yq+WIFcuUM4EFECWM7i3Ay0Cb8nXsTtwmL9OcY/aYP/6m2xbk2qeQx6uZ7IMULDuXnYUkmkH3IO3oOkaAdYKIPtFpLBsM2PDyqGVUM0A3KaOKsDEJyXPMymz0cYSK4zSC75ieHHezcOcVEDE1qOOnXZzSmg7SAG/a6K+inJFVDVPTGQpCpGn/2Kk01FZmCpwBEn5Q4wMDK0xBb9N0oPXb6BZs16FGWtGyqvOC3lRqFFCrwl7QK0cwrOhpeq+O4KsZxyzNDbqbWJE+2+ohjzkaTsSYtYkZs7LenrrfUQ28sSclHENLcnsBVIi2hlpSxLbGDIKQMLZTFlUCcmzVkDFnLFzBiKBdsLWCN473ZhfaJmk53mKkFHaJc2slzQrYMzlMKY1Ldhyc7snHM203VPHc5uqF3QbJGg3F7YpCHx0yQ44pd8/6G8/VqT04jHn1sZz8olWUl9fHxAcGFOZMCkmpfNqs/O3iV6WdMyVXLxXLWsE4SVBRCMH1vOusSksNr2tgwMW8qcktvvCaPu6ImVdEkvN9Lhc2YkaBthax2nJndkSJptsKE1+63pxX58Xe4CnvE3MWzdYlG+np2AGqYU4ZHoyEP8VldOXICmNoQq9kbe+GaDXtVc5BZ86I6fHBD3mPu5Zs+xz+FG76cqMtRSkbMJu5R9kLhDjuUnvCEO/26C6PA5QocV2+6/qceM+0i7uXT+nTqn96bkDexMODqhGgvSZrWn2yZM3DPNxkiPplGkoZzp5OIsMUa3svyLAm/V+WlIXgIbqdWuaf2zepSxgYWwKH2yhu2T8NinJdXYnrsTm2KYoGkc2W6b/NHe/i0kCa132ZmcSIrOrKycMs5UA6akuotZZ8OagsGLG8tNGKICeYEe3LrsWLohGSGx1CxEtrwJkRqpdijVENaXmtbOQA7lyXbLUPFXmlErWFAE8gb5zLesU4RdB1a6iPtgDXUOdMNhvKM+ZlWtmHyGs80wds67h7rQmrEYscC+oIIPBOm0Q/6CM+FYB1Rw4VeL8XyptRtppXzkrtUF/MwmuIGfubRHkyfiOUhpRJJ0M6Eh+id86p2vKQ5SPz5dF9RNGEiAAAEABJREFUFe70C0t5n0sdKfbHa9GNBIEeKUnbpWUwy/aE/0UNBS9RrsGVXL3kqE1qDZYrSUqZ0N7pt2CIBVmSy6FroDKgcOEgFaHWm5XmZheF6Tj1lBR6a0Vs434DA6oFB4pV/ocLZtDNEqPHUJDQ4FvTez6dl2KsHGxA8trzMigwpnW6CT7g4QhsuDsG2eLGrOWIzBV1f/L6lOIX3cA9CCo65GIaazQJb6BnvUbl/Sq74XfYZWW1lQfTHHO+cyH+3i2fuGNXy53qrBHdHM9S9LeNgngwBzMYBwnQjFF+gSr3UvNccywwynzoYeKj2eZOXo0b9sP+8b1iK5LpmYIYA1/SFIO7K3tK6FyWUl4AbBIx6hK3bNbwtEgBnREE36crj3KfGX+10qarMvtQKKyVb0nHFE5NtY2xnUJ/AChFimIX67Bkyeue+lRdkZd3wEMnbRAcKEnJVsqVZxv1t7ypt09xstHQkLdOZBmnXoPosniGO6vPRh0FJXmrq8Xr2dpWtWXpSva0tyRhFmxlF0OOaL96rgrnpQtjaEKkk/KmaKesezRdWz+Wqh/W48mol7dqP62L6G/nY62GAjfXz9hQyddYHd5EQ8foJ8qi/JoxFbZuw2Q+5S3QjhKubb1h3x4xOfV0P+wDeOg23kXVppebwF5kQJRayZYyO035Svtsv1r1XzUH0ZBvUyRnIjs2XLlzGNVLJQKUQU163reu0Bqd3gpunKCOuzosqbog379bWjDPTyhhLaYMKr0WMYdvr2jaOysx+kzXnHmzNi5Nwj/O5WPovpm+gMWK1natfOO17poufJvMF/ajDjXf7ft75df7T+d+ttYnDgnkjOoW01FFac84K6R+Ro7O6BzjrKALzXNGDyzOt/LsUohmzsW5W81uos1sYOBgKqZu8ALN6rzb1B5B4xmqf2A0MRUqFbvG391+oCsu1pNWe+zoBq+WrleJhomCzUgCujxvq3dKNEenXpduKh7FrkR9zrqfWygAmTwKyXMMFr5ourMTN4qahDq+3W1uVM8EJIOxKLIpINycR2U5POmV6OdQznawiNi2QIO1cZxcFrBr/MPrmkXlVL8njO40dYOH81vvf71BO9S+Sju9M/0MdleWGW4TmMtgmnNdMG9opLKXqSmup+qf1Ab5jRQXa1FbxcTOa7bem82IxEEVT/NhPbpmDcLfUiXCak1500F1ImVqtVsHYQQ0/c2b3r7L7+rrPeqz8uJLX0PW1yjRsjNjzEGyj6lPvP0D4N/cUOTpGJUcqrNRBFVeiyJB+po5X6bYbQeWUbTJILXGf6YV9W3uLZV5riv7mp5Qw7x+++rn0mZXPcz20Ek9Qb8MVmSP2kjfXyrlKz8m6iU5qYYwUC9Bn9RnzXJu14YB++H5Iv9ydWg5UDJakgUdWOgb7Wvsnvnn4NNxMdyUS1x3a58xqj8RapG8ylcGvvd8bzvSNOJq/oy6kihQLDf9Y1rGaMHxF5OYvsosfU4gTbZ/Vi87GOnhAA6uWT168PPB4zjEXyZrOK1Mw6wvqqM3aS/vAIhhNh6x0RlThp52H+2iQquRjTj50H9oCut1McAo2DDNy87Upxi/JlwuBIC0LjsP5fsacTLSF6HGPITk4s1dOKYA5GmjBtDMdI53dLm6g5oEOk3Pyb98ozgj4z6GsWfmd/qp9hmR4VgcmdhlNYv4uL9fkFS5vM3ac+mRszx49Fq49SvFQMj4ltrTTlkey438QkuIVS+oRgbkgQq5eiSVNKuZ7DzTW6PXaXU89I5SCgFfdcUxwUwLwaDP8pYQlHIF0Fn7gdZQJU+9vE0vBoa2Bvoq6hL0yRv7s/qm+Sg2G2tGOlNRU3hs2vpEjxirk60uWtK5D0OT5mobQfvaVDhdRkjlpqHXTKm+vewu94Icq3G/rbQFzlEyzZ6FvC0v4w27d65zlC5Zqrlz7AG+gR3xTeoLwVM93eiu5pmVvKg+fxELYwPlfthsrZ7IEejlo6JIPUaKkRi9NMYd7hrDkfFSFrsnHbqZU/ZGQ7xBmIT6iEE8YJm/5h15KcOJNxKzCYnxWkrSAeqx/sar/mxMohU7Eg5edu76ep2I8YD4qskzBKm2eR1oBWTS5YQc3KNeoV4tjzqgl2wsqsI0BDwy3AYElWyFPNAv3ORnlrJofvXiiRnZLX/vIhf+Tv6UR72dY1PNw2eDIpLUXnLDazqg6u6ZEbdQECWkEyVAdpmbIkrwS88V9uFa1HOr/JzjT+IIluRVFFWncBShTm5sHXKqQGfN8ElmrtlX97XXtT/eV0843ifkn4YpELsJ0Xau3OubnXPMUs/o+iefWwubsJqfaFGuPFlfMx9NmMaIdjzTyR5JWcJ+rknJtEGJV5feDqB8i51rWuQvD3MxCUz37NKarkb5WUY4K+wI4A1piwKfM9VVLU105qrRU/2bFC+yHIc/oye87kxgt8QdNGWbtVGHmMvsavK1ciTwue5tP933GF93A76z0rvs0e5vXg41mmcqtUIm7svv2+22CsjHwNqnPwOYb/MHnuCwYJjFWpZKOD1BUaMYEEAJSwpQRrWGCT0UqY5ip07cNI4T+jXpc2T8qMrwMrxNGj/qRMRbsOp/Xf3kVGu+oBClmX45VlC1rRa6ZXLRrK88aivDa0zrdQfKSAciTPLVZDUOse5oqPBUBq43G+jJ4V6hB8Rcdd9p3tnsTTY6cRICoUK+7WtvU5VahEZ20dqofi5fuG2UpitWOOxut1kiqCdyMDGtUxcs1HRdjX/Lv81rHm/D4EhYJh/TklVyCLl/TyqX1cEnXDWhZexJZUwMzMW2mafQyqN8WENiiS5pX+H0+EuQaDIfBWWXu4p2fPd7NQw6nB64boj4vBcMi2P3gb7U8C1nuT/2XOuJSZ5+5dAzo3quMZuwjoD5i3bUr789D1Gg4ujWkJJFM0o+wIZg0K5ZTknkkEfjcIC010RURhb0Et659qEWymqlzfXtyW1UDBXld113LU8qQV3NcaubWIhuL9Zl+57jgmGg7EmFhXy9OSArlHOA6JX5z1bgcPD4ZIxkqkOszRXNdyWjyYT1aGKY33YH5SevHGulDGS7omeCGxwl5FR/N36/VueiYg3QxNig4hq/FvgpOBfdXzCGTSvoJ65Xnqowe2rtfBtHMBYv6XJkbtepzGnpQdEo8E0Um1D9NFnjCe+3wRROW1nAHT4ACnJxmVmQTqY6MIUJcXDp26AM4wJfEP4yoQnK2IU2L3pOYJxmCtw4a+b6a1OqHQfJBqEyTM/sAmaMTQDCMkof21h/30sHQw0GGIC1edJb5tps2sccy4aF75ld1hhybs+g3izgwps6NfJrCOiqAVRybGq6Mmuy1Mk+MJnB3xcXzelLXlUVruu7mRTIoML+ureYEExEzWWqYVwaPzq9igtwtpC6pDWGpoqAekMfRoK5G6qScsSCkgUTanGQkElCH0dtIIRzqCC8knnIIo2QO+2PRO7MCwgCAnZH5ozih34P+13M10j7iL/jwIHpH8LKj/mzJk2xp7YzeywVtgaU28Firyi7SsE1pSxKCSyYDHV5DRqk+DG9v6Z0q3FzyB4cxn4bjGYoDd64Gm5UWW3TstFabGzahFHk3yOJGE3sjXK0IO+6KI4co9DamVDbWXMuPIY9pfauJqRkSRpuAKdkgLPkwoZ6ySQMbEYufH8q7zS2aNPVFu4SetNEUC6R1idyW8XkD56Lx6BQYjzgzuhlSStKLUG4mRKr+1WZFNZD2z7JycC5o3yZy3eCdTjKF/SJmnej7qvoA8j6K0ZC/2ikU56kjEY14eEyYkaN3OVTqydE68C0tLd88cjCAp02bpso04mCPPZwHBBxtJEJK1Nzyd85qT7Wh53QQHkNuYH7Qav5KKK44rPGWWC2tbr2s5AA2tlcslvWlPWe61hp4Uw561kHnrDG61T5LrxEU+mVJue0pd5CJMBhM1F4CR9vOrosde+YVERviL27TtP0upQOysHZQhHeWmeoeIl3XGG3Op1pPampeY+B/tEHnuB38Dhxh1807xmk2ssjb22TsHN0+EiRyu0JT2evtku737NEmUwUhHnoIx5FJGEoQsGVQ6ZFhM6mwww+RTMadWHiVgKhSUU1DGFrMnW8hkLPvpbU0ihWcVr8WjVO4xnFbgUDi6yxBESELJoLCIBp663dg1AX/cQ83up7GpuYwoyfmc3S85U2Ue494q+JF8y7PsSgcr45e7OloNMDjopu4sq556UXDTQ7qUlY3oqtIQGt7Au4TM1wMVeNqQEqz0beYEzLUCa2hCH4O9ZR0xkpWEiomAgzYrVyn5RuhmfWlh6WLsjbDsztMZQ8vDVRii9rjZemq6f9qIbHodcIIkYx6l6CrkhF2ah7T2tidvdohBCxy2tE/hk+CoLdInh6oCbk3Su31545NRtdkRZbN0r3yvvAvpjCDtu3i4hQtXDdi0YSZkLelKeAzKnKO0VtbutX77pHy7GpQHJGuomodzkvyrDb4FMqIWhsnbU5acko2f07aYaS7BX1cTOLhj9jNZQxHNR30AAU5IzYVNF1cC5STIYFl9tdcR0Ghq+WRr/6eHa8E8R7TF1e5hWUU2tbZ2wrK0hjgmx5sBzqOULz0BKLPdYZPBqhE6JGZdQ5rbrik3Hyaq1HK3B5W1SFV9ixle5zQUJo8iXYRtY1qzbr1/rxp10hKW3vlZFv1FS3KosrRMCYCQLfDmlG2MBUmgP9bUyydMq9RJSuKkQXRp1/LcV4uBkZnSbFTzYyqXwXyCFgzWljK0faMn1QLg86Bcy8CW2gWyOqvUYHIZ9llS89++D7TUhdNZhOdKEpYJL5T+U/KWtO8zE5SjoITFJU88hejoZ3sG8NgpAvfT5v0wCCcFExrV+gCvvzVNSz+I2/uF9TNrq19UWElCuX7zvv617xc+EKWbSAX9DOJ5k5J/PRdxBZkH6vVyreeZ2j0uYsjltLULpVEv2m1EUX7YiagRFLgBRCOFyAMJCkkKSgOuRL9QrdK65ZFhcuukJoXYZoNkZort42WJhfcFfXW3r0HffxEEdybt1RYnSPtqvvZJl2DF42bKQvmlb2ogzrpBDDTcPGKKq1FdSGlE6Hm+zAk9VXIkS7SdgxmkzTlI/MGMu4NSThO8P2MuHQatgocCD8dBt5tEVcU6GI//bqquwmbICcPidbVujpvkWjAsTj2hO8zy9rJIrpmfFGFC79psPu0XRKtfLVgoemvzm4mJdn3OlX0h9auhyf4/nMJGTnsFshevh4VN957iqHcoUdnUoYvuwGlWDXrX7ffKLKBFZ+OG09GLj1dZWEMAxvs27R/mAaAQv1VEO2dwvoMqyRoy0oVCrUEbAblS6zzOaUrIkj6xpVmQH+1+RMTy1/m83SvOOjPjEsxbp7iRrTtgsJsa1UFITdYnPURRNi5OrLbHXEqK04go9zm3c70u0MSia1hKc6nXNF37cyJuKsgrNcrSjuO+ID/2kZosHkG9BmZnzUS0OTs5MAABAASURBVNFad74fCRLPVt+hRWAtMyr7xn679HXAIiCJEg69dGJjbSXT5NnQCqCXQrWk0nxFr2tgpnC74xRa3iw86A6KfLWSadFWnftMzqu861mr3VzmXFM2jq5q94Q9mNJPDoVxf/fJt35CXkzI0BldvsIvPYi+2xX54IL5FEvymXvyxzbXU+tb8cCGI9w+YnAXtldPzp79BSoMN+2D69N2ORA29pm8oDYxVhNGoW2c1KwSGZ0qr7UiQvuUihoeETKqb4V2jgR85X113GOsAU/G3j/7G1pzsWWAuBbNXLYTvk2eSqViFEybN71OpZr9TP5W7dNVL/Qn+XztaMJbdQ5NuKw+PTdARYIZl9RUOF1srnrp5wIC42j2MRR9oMdpNmpGu75th1gY8fW5n47nmf4mZ4/WuO7+Oh77dGqIUl226ra5/LUyB3jErdv7CyMhoKpyK4+jBWxeULhnLo6hBv0AT/McmQfacoklzSrvurV6VyzD7dRzvmV2+3wW7E5RXLbnfeJ8pKxN5onWPUtK8KZPwM7aZ44csjCltsZz6SHAyJWJAAeGOhUZk3qOsXJUjRSvNSm4rFq1XWo26NVSmQJ+cXPgJXGJwOPNGcGz+BlGHYtfxOMGB0ydobY9MwJwIz+yBQnyYhGtnd+YlCqhEWDTDqnWMZW9W9+BPrpbefoPzm/0/lxz6fEhkRlqY3QGaQJBza2S/hPaTCyZNLy5lyXFYYPQHd3j0gDGw0xExqexJCaczW8qX5gIX0t1N2biUWIeMfQeudgr75ifxXW9fXPDvqM7W3YKryLZJx+lWEoTOutzpypHrkJqp4iSg3MUklieui9lc9/zjdjxA7fB9Kn6vm7F71qIW0ZLEbUwOpX2n12f70trVPmc+/QSCr0lYfaymcjDB0xvsah+YxauFCzaBedYSyuOWloRzyVN70D/0/gnEBeAgmS0UcnuOUpYOTgeva4q2p/5bP5T1/+8HCwOnvy+hYJn2QujGxiBoVyprfImP1QaatWJZ9bpiMGPnXXvYeXMHIyKtuRS1etnd548o8E/G/8M1kVSsKAa0fCNVyCtfENLSYS5JAZOnf5+n1QDg8MVcgDL+nNlXM1FyIw2cyWn44DrBwdiL/5C7nQ7V5yA6G/DYIxwcomWVlU7bRq5s7dKdY0k/mP7n+5P6YoReo3mBVMukopOw+vUdVqcj9u+eAilkkMnwykMLWMzX4wW614ojQkRsPmrLGq2iiNPZ2eJqacnB1Kbta0u3a/Y7s7JsL6i3WQepRVERx9XkgGpRvSrrf2wflT6OKyKokAANq2meKI2TyfimKdZQjUyQNy8PnFfS+yJkPauyLoK115ItLR8npbMwBhzM8clICv9mlgXdId+B2638KDFTJy9Og1A4P/Kinrbdg1vmsR+ZfvZpSqa7Kmpgr2pjo8r03A7c83co75JV9I5KknpmVu90+1Jf55r/5LaT0wtq7FsOVawhGKtHiVRahpPUhI/yHxXHQX+nU/2j0FyNYCTC1oRuCd+26ua4kVDudtmdFNzoaMZCe4qTKjWv1KkJ5CiN4MD0/myBUSMWDR9Bmu6uwQC6h1LmdHXfMYEdVabLCOu+Ny8P/bx9W4at4J4J3j7j9bt72Pnu8/zPb7rA0jllKUX7XCO3FNRJpmKMm20i3vrWFQP9EH9dKvkei47d7z/ZcVTzzcPmD82P15G0QSOBGpQFBfz8jEyZUwEusqmgEtwTH+U8UA48EbUQ8WoJEfLh0j8WNkNWzaJczxbDUExcwzHbUBTxVYK3B2Xy7VleeMO3m4hXvNU7W/u2hkdn7pcMjJspcTTRA+P7tFV4wpOdtjqYmZJjov/adOqo+7x0J+b+67UxNZeQ3iU0ga7mOwT2QajkqVi10O/Tx+Xxq7V96oR9j5KnPEs+6IkfcsV9XPlNbBGUWsBN8d8tEao+VgiipfFdd0eUgZsyiAGxi5Vy5qxV7b90j0zmJwhQb20KPWa7f1A8e95GsOBSVR5/W216qoMebmw9VvIDX+BsOkdIh0FHNgZO6ix5AUznVMsLI8M7xqenlJIqle/0QE15F316/O9T67yNR/WwhZrO6LxE+f+eeqzv3e1App/qu8cDtEL/eEEGngDcfBqxJM40jskHGto1TFeYyobHJ4dp1nRjO0zrf1ItUWuS55BvQv7EolRCa2RF99uKaqetqdUh4rCprPw7Ok6oQMF01wLLT+Ikybwh1QvF4RMh4WHXUm5HbiIEOZB9VZW30hn91F5PeehiZLFVCowz+ARPxSVIibok9OveIuzTWm/vMISK/mB/Y/jZznWe25fJTGSBqBFlJCid+Io6L7bvFX6lO1RefLw2vMpxEX1ojfyeNm1ctYFyBOMgEiQY3HS0L9tKJJNlgOawC7kximW2IWbe2nNWJiQ6cEFDUff0zFpYn1AGNpnE996scmjOjruU6Fc8QCFTiZAAx1GclxTlG5HKaUK3UaqSqq6pl61meymdVGJnjaOrBA54hvKByi8Ap9O0Ag5NPK9WHswkDmYkpZbraHYiTl/zbVM7GGju6ffoSFVmZlI5yKvgAdldeZMH6HqbJTAHbMmJJaTSIW+5aNmvocaxt2+c7QC17sInZkUX2/hSEbcUnJWVC2lxe+kdZfqC8Ua1LBaeEbksR7LMttwQQYpupxGUvrw+/g1mvxW1SV6t7HXKROGp6m7hq2pBTm6RCSm6T1zdd/nWG3KU9NAE51U8DVdk6XUMFusN2xGsnhJb51mZ/RZs4MiaVZopijwSZ/2Muy7v86W2ifoEosB5sPL4JaavOfavtpfh5k8SbkbAfGiT3ivJXlBczWHl8MviH+jyc+IVSuQTZzWGooz/cz55TlZrnubdsDP4DHB7RQSCcrYZsORQk+c/iRUR6Y4UV/ikTmFCQUqKN9jKtf62hxidK4f6u1YjLtinMdmY+35qXe3YVDTZBpm2Ytrpkc3LDuu3b61BX2+VFEjTSTcpV3LM1Op9tWfmAuAsjeKpA7rAEkRa3Cwx85oNb5YZjlsWClqkwBRWbM395aX2JAM3cvWl9eBjQLVbHjmqloDdXctRnONzDrVvuW91pkrsWRSZ7Gsr85XQZUy1erJmqiA6GNG6hGIrFCmNaEcqQnLIsbtlO5Vkg+sMJyv41mm/iI/H8NFd7WCEqYbYaXv8j6tFSkVk33KlY5Xh/2bCgipzL0ckzCvRTqVO90fxsDUTs5Mt6jB6fmSOzycV48DcpK0fiG0Vg49GLg+ecch6mVV6lJbiW7mqDp1aKSRBGAoa4OOIYn5m0gj3sPUPAWBlFuCoYNKW0tAK2fw2lW+M6629ZbY20pU0+0gZBH6I1g2UEvDCeKdM9dwARBehdJaJhLTRim9xks7gfrR+zGV2eX9syZM+VlPGQz+GclLVOOb3C0jmlpwQBoMeaNCtQTEOm2iceVETWfnsNzDZkW6rDoRclzZ0PEMvpAEIYoNuu4KoRX74ACP9dez7ZAjLyqIufJce6/JFbVcucKMZ+n3DDXR3IZTQEFv7IqxEEGVmKKnJHVzNN0mN1909muCcI4KottBtQ2jAHKv64mQGR2HMXRZgIzoiu3Qu76yXjVhwIQddFpuU73igafTaZ/C7YNLCR+MBpZzrHi2na8AMOniMn3Ogu6yHrPfWakiIhlpl3aaCMrUQDy1cEq9a7SuSTYSEbVu6A9qiYR9zbnt3F13phZHWShl3cADbtgzJl4tk1UtYQ/aYIaXpZKadOfjdIyuHa09aM4rUAIt2G/ILWx35WJLmZqx0LqYY4/9Vf60hJ7lW2HdUEWCkJeuGmVJb8kR5NZlvVPWZ7JZj42+QGmqT3DJFy4JTnrgwngpdI2SnOAT5ioJYErgeom8q6gUy7H++Mr74rpgGpRCVXMkWN3m+JwfprfGWdo9cY9HmSVCHSwbCQirTbt1heKbPCKhscla1LDooy2h/d5Kpshun1EN04/Z+w4L5gKuv6NyYLdd8GvMipNqwUrem+7RKK6CHaipVhfZYSin0rOPYcPf/P/QtGVdzNmtNj68l0Lz0751wN+RnokX398lC1h5tF7OYGqwk0qyU52xrGq36ok9XmU7j+aNaEFLuTWWY9Nm29TS7YFSXdArBm6Hpksw1RtDQQfTdPKWrSFepqNHILuHiYCtQFhHSyyDpMy3QSdCNTGye5YYHavGtykk0+WtcOqYOBq9yLLJ7jN9eh6tVMWCwywWFvkZqrxrsEdo72I4YFM4No/jEJ2E5uig8lvg7nq0ip2h8lURbWFJHdV2pE65er4tZnr2eHdgf2Lf9xhX7qsfP+ObcQS8ejoHAltajObIPKk14GqsEb4ZDftJlf3fW0GawYprLlUXR4QotqqR1CZ8uSgMLxcFO9/iZBpyps5AQMW7BlhTfcRBHYFZUpf4oxHiSiaJMXmmGk4I3jSnI6nnc8DwOlRZBdyMFkVPFZ0uXML47crnW3vbbl2VkXbUy6WEOXO6IXrbWZ2nv9sOQkYCpnzvpqodE6pUqhJHDSpnssLGF12jfCzSVto1MBt5reYemevOUSPMN/UOeEtZoM3IkLtMikexnJinIatG2uKsl0+IG7rcSBVerkOaIicaOtAigdloTdbudM+U0nSeLBVC1kl70nOS8138syDeR58KXZEvVyKzsWrWpr3UC7BAPG8lB8rRpH56kJoyU1oh7gufjuuYtoXWAAgoxWVBUOmAFe2Q22xD8WAKBqXfhl1QKYt2HJQo58wCVWLa+baXVrwtkMSWW/fLLh+pL798tWdNmaZeru8fLvYa8VVQQKzBma2P0v2sYwt+gaYXRzZsUsk2b6Ummvcz/pzEOe5jN0ZlgvQaG0LRYMrtOAZt0K4EF+fV39s/HtNq/VSj4eU80SXpKBbRnOpgNTUchehNJ5kaY/n1vfmkaI4RhyC0ebVnxazrzbfmn36Xb/0+k8qi1Uzu6S1TrUBbXkbypLvH4mX6Fe6Xf4zL2qzaGwaUjpk+fh51QChPxHWP0O6N/Tv15Mz3+Fapt6r9UvYVpylrM36gNU5ABGhb7k8pfGfbHIbaQqPBx/hbVrlu2A/583I2aZB3V3QvIUf3Mv1jurw+bmsNl6X/HO0o1PelsnaYQ78PoM1EKoOT6idvDV/pShwCZjSlw1DN1pDqproFZojRNvE+yjX4d8h3+DUlkfqsdF4zErCI24Uegym3SdpgAVbKeHs26szUKOf6UdqI1Cz2zMCo0M1UKDlqHiXdmNxaFTRjhGPTNE2ZrL/So+u1SJ8Et8pDta2htefZUxJXq9JKXdbm9Ls2xKhkYkZA+GZYQH36NlweV2gFiBMxHkBRZXOmT0yppP9vY6tTsz0ffbslpiMMAo6EUgQHCfWXT/s9Ziv0CK6MDRjmxv7Zo/MXKQT2huJ97O9UDjVMZH9XpW5XouRTIl8QHbgMenZSF8tcc9fixxUwO39yroFZdp8TqUSXgDvX8ivUM3TXwNXjGKLIDWRBWb/Bf0ZS4xXNAAAQAElEQVRQG3g1112QxQhe3mJjNNAW/arbVJFqDMqQZNNUW5ly4+JbReLJZ0CCzgz71Q5zzS/nK7swYvV7tPGsLt+wB9SC2AWMzxWyj2vMENQvMBceieiKCx4DSBNAQWR5+bJYGiYy6MV77B4NIlv1kYP3mXv2RGljrH2xnnTApW7hayo63q2b8QsWfeGo9dnofFPYeywfk2Qqo+Mzt25XPkR4mGaVUwS0BklActDecwPLACrxouSy9qv77nQZmQdKqyLsyLFvitztrI/hH0gXfskyYTk8GMkLMq22o66jU9jB5/6pdRm5eG4XJFDcT4DKI6zHn7RTepHiQ+evhgqsYQWFE47QSYWm9qHRsFHHdRtzgnItqXViWhXKC/fiNZflrSQ53BbH1/jd75JJNtcgIC4NkoUymxDhEmjfJouKZo49pcUFNQf5vGU3b4HLfsnt6EcD3c3ppEk57zd4ISVo3tQRA/wbfrXNxBqhaoc0VyF5QGjcFyjiO6ur53iez/kGjvrHNj6QPzJZeiTytidvEF6QOHrUWbsaI0cP3z7/zq6zCCzU1WazbATtbzo1b9FXb6+j4xJNhOqHGrhqmds2tOmcccJIjW0/2S0nrhBaZQIrailXmxmNCP16WyGkZaZrtsrcjs5CBtYg8lt/2xk8Y148gnCt5l0q8DC7oNeI4O404Gr3CjmC5tK4XOZnErm7xSiR64JKQDxAcB+fPVqpf6lPBMc0wVUV0MjBOTiJbAGTasoHVWGQrskZeYbilwDll75DhZN+4GoNbZQ2NApGSQRBfm74ImFB/wC3gWoSxICEtDj2EEovFCaYlWeffkgdp0dAdC5YuhtgyG1HZ/TT+/t0P5pzSUHdXvuFRhF+bT08mvwubF+nQwzbIdBKdT1z9L6GmZ67Snma8zqzTftCU3+bXyX6QZQtM5FmNuiaU09r3ZnxNOZgE2miQ/VFwxRRGchbQf7c2s1/9UQAwIiotzVdXDosXVB0WiEP7ok7g6VBG9p58lvs6t6qqc0WRQ5QJU750bC3clRYWnp6WjHIlOhcs9zNNH5SV6dep5KWTY12GHHKyoD310H1YW472wpdnmnf71Qd6hHElSR7Rj/OibAzk5375Ghcp/KkKNMt7XrF0mn1eLaS/2JsTHLKUaHojOEWcE/l212CeNOXNu2Cukwn1kYozXVDA82GHP2OhSZGgROXZuO0cLyV3dDvs4yyPYtvQ1WjgBimvu/Rs4gdqBfDtth2y7T0pdE2xmfj++Dp98LmnaD1WV2Sj/mtT5p6jeCVq6dMPbM3zqPAQcHrRZH2f8wyha/NnCFOR0xyemGFOSan9pl5PPbyvgSgRGg1UyyrLZYK4x1neGjHF/wKgyYCDV2AXiwZ4AsGkbHy9rbLR/ZIYOhOLTB11bnGdNW0U2m17Oso34CUgTI/QROvqm7Jt/u1O/Bj/tjavZTVrid5MkYnZ5xNq+lDIemZU9lIA0IMDATKFP8T+Xxm3vUwWrYLgyVXrdlTBnhTrkZGFFTBDl3jxDZM4Z/TermpjeF+A4nUDNkBNEUTQVapDr1ziVpKvC3hQealbUtI2KMy2QUbTS0vcaDZ7y7tp/ujRtT8Or5u/ULX90vuL8PJzpfqCNhV9OdN1huq23OjPlimzfQP+zNp3eZ53fmJ8mnkdBlwBa+3iJGtpbra1V52339mqaOmH+CxStt1erbClvCQ1Vkz1cNd3BozAs5SamEG3V5PSKNhWLev96bqGkeKXGuSbst53a3mYXfggoUSJEj+EK9XbMMX0MbiHB+hBXFq7YmtripyNJaCmrgOrmqLkZVZZuQSwAbaW2IaNXIPxmeFBKYk9gy6TEWgfhsbq2nulE7a5hvzxNjCdcE1WFbGQL2j/pbNMVPIIVCpFifMcvRe3QMtO09gbkRskT88g3wzrMZANWJu8h0t95DmouBjl+ivXjvXcyD2uysTVfbqZbIhuAJgxX57NXIakhn6Euq2gEy5KXRyiKBOHTIJxe33ueMb69u+t1cq2E+pSfgabdquNNrdoTud+9TnqWsfxQbK12pl5sRduEh5Wql7rA+OC16jUkQomN/hb/XpbC04p1tQFfAq6WlRoU4BystaDneRewxFwsgYd9FXYcAC9KzHB2E0qdL4df/aOuOs0Lz3f5Yxaowb+AJPjbbXBQz5Bjyua8mr7Q6Lu8eqDYYxjtzWWsOFWaOUqy+n1ffg95f69kLjJshlZpJynqagEBqOm62dW0xb2hDt9deCgzfsYtiEwY0hlbwRQXLx4VT0/Ax/wAszqK3mrnSCWNS+wvCNnf7l/Y37F9ri9nemKZPrAno9faWmml8zOnrqaNOUUhIENVxZHQfcoe5uDlFgTF+NO+HKX6NtOsr4oHU7fh58t8rSEnYQWxVx7unP9Jrx0vACBrCkV1AwZRXvS6QZUM6m2ZgCY6G0WRCa3poIBUnehFs3RprrMuguQBxnt3qiGgaTyiK/y06QU9yPd66BlelvKk20l8QwLoUR1eomc4pFazjarPvqjEoTGL/e9KiaejPL6BGxcPt8IPMqBzap8tvGr/OhMtCp1XxHNnqmXUQa6NTUMe17L2FQbUVXSCNeKmHLkq6fwRGAIgXFzG9fd1y4rsFdfe3CEPqpCQ3Vp6QqZD3hChU0Hywvqn9G3R9bl0mSVLYj+PnAf0DnGGq4ezYETw80rljyhfHAdUNamEHvKXWHN2L+yetP3d+Jfap2zd6dKOdxpuI7bUffXXw/33M9qUC635nWXImXdqvxHkpFK+zzsft241tD9ItCyKr+TTtpI8XZzfVjtiisDXFIYJYUx4KS2TS3uD3iPT8Ydr/ncVx7hZ7oxX2/VX/GdjPnzRCQ1gWA0Cj8+qgSnetT6x51H/ySG9Nor2QD6VdfH+M/UfJtnPYuK3g7UxTZar+f9XPm03vVm0JNTmZK1mDMcrvGraynacCSoatuU7OG6hz2aM+RHjMLDTEqEqYIHkbV9czadCGeBocuVUCmBhDM0b7zaannqAkgXNedQqR9pseppcwhv+g/YLlIxrXgURRVzV0r+4a5YuKIU0wDNX+7wAjP1CRi+lqpk3MBRnJpU+akh9/L6cneiBe/Lp+UVeRMC1/twW9fvycqBQz2EjLi1E8YLkryyH7OK7/yTj/V+tAmvxgZIJx6M63XVOIt65OSXLfGQmckW1Kd59wn16jHN8AqWchBgvT+XPkzynFCulJJAqrv3WA/Pt+Rhcv6MprGkij6VveroA6qTKKHWnQGz6hU1tlWrQztzCO09h1XnFYTXI7lNMhZ62feHnJXcR7wkXfneLpptz0OuSKNTMcRIlWZIIFQbnHMT6yM5SJZZXEEzUINI9alU6BPOAzKWTcntS61dNPohsBoJa/WNbZITuOMe0eUXJvt0eeB77iOfwB1NwUNsonhM/7wetTVLr9+pHtecmQDUweUOg0a5JuLIQG0TsNj2brbrrKrXRcurhinE366mpz7Ho/usWqfaoxaSurzwTzy6gt4paSoO17FCkVrfliHf2eprEwFVtdnVDtAPjhP5zP5Gqsh5QrNM5mKXSyMTXMPWpKDdlr1zXnGkgKTppTkakbSehqcC5QAFLQKWvGzbI1f865Soc3l8dN2VDJI+TJ3hoRpK9ebwNUIkawcv6PAcsx9cD2t4LeL9jUDFCoV+YCb/FJdO2oimh85J8xKLfBzmbt8NBUOS/gQUVf020COKIxjF9dlb+NcsP/y+2+dP5n/nfPn2f/95r9/nn9///x+f+vfPP+d7+/z/J7vf/v7zdyJs2v/7t/v98/3+dO/+/z2e3w7/+X5395/fn///L///f4rA/nfF7/5Pfns5/nz3/73d3+/9fut/931X+Xeu54n61t7/3d09fz3e/Zz3rOyWfu/5/f//f7+nj9fGftT//1X/18dz/73+f6Rxe/+1eB9nt9n//6X+6mzv+f75+jak/+d+nPO7/P9fZ5v7l/sZ/b//1zK6JGJ7/N8z/6z97/7v+/333wHPrt/n/6z8/c8W2ee57/f/e+f/efsk31mduZXQe3f+vPNP9/v9/f3+f2v9n8anOfJ1/N/v7qSgpL/Pbl3Tv3W/O+T/+97/vvT//3ivz3/W/iv+O+zv3/+e/bz33/Pn//09ldI/nfXv9/675v//R6p53f3ny/+lc0t19CGaD/z/ZXhc7LOrvxz6nfvs3/z/Ff1nXnq/Mn6d89/A32xkVn6sP98/+zvL/N7+o9s9v7TvXt+v/Xf0/u38r95/vc8/+3ne34146tLLzPC999/598/+e/v/u+/7/OrdOZX70//d6DjT82/OP/O+d+tMfX9Pc9XTp6v0J3Op0/Od9ef71fq+v1zzlMlOs+L+ew+O7/7+6fOb+3fzv9q/iT+Pee3nsT35Hef/f+e7//++/z58/z5Pt9v7qe1Ef727E5dF2IR9+/3+d/n+e/30Zu/lOSf3/zff/fv/z5//is52FJs5r91/q3ff8+j3//18+8+/37Pv/+7//tX9n+/z/f3/0fHn2i3sSvbomA0AJKSLVsSKbn3/f+vqr3cqu8lMoFoaqbWPufdGqMenaaSmUDEjBkzAkjKa43tk9nz8O3Oxm7522323TwAZEYF8M7ieUZ25pcdhvc+j3mM7fCXGXnvu7HbYdKLb1FNM8aMee4jhlnvNl6Q6/Hy4rtdzPPo8zxv++55bIEZ7sxi5+Nl3kIMwDF2w7bddjOQPW8fdi/95WU87uxpCxO9b+fdGM/etx5bT4vIYfHS+8t2zDu3efjYWUA5z7D38oIywo3Z5tl67/PO5i3O+24L8zM/PfF2hsPdGPN2N3bb2O1Gt3kX43nEk/mT20KdzU/LiJx3/WnbX1CGL454Xp7G7hkcPm/n56cxP8/95aU/P+zmbZ+772bbbW1J37M9A3/Pl4xtzE+23c1z33Xcfcbnre+247lvn31+HvPYDZ9nm+eneTxh+i7mSB8ANffdvB2+tXjq/rQbjy/z40t/3L08zztY7PNut+3PY37x8Wyou/70PL/sxvzstrXs3bNbjJcZYPpz78+jP+22Y7vr87yb+/Mutl225pDHLrzPtHvO7fN4BrYZs8bjTHdb2859a/Nz9PsB+aB0nh59+zIgrd3zjK6AkYkInofD0XY7757m7cv8PPcnG3A693nM2zEjlMXvy7x7XoKyGaiex8tT37307bzdjd08bw2nvfcx+nN/fu6PuPHSvfdd9J0Nm72/YGh/2WFcbGfebROEbJHyeZ5hYuxezLY+dsPmbgj1ebd9mBGCP1t/tJfHuT/swKHfw44973zbffcy9+1zn599O+LZ4yHtse9282637UsUY7e8zwY2Xrbx8GLbPubdS3/Z9T7vwnbdnrYvT9v5aaAwX54Bps+zjdnHdhe7Z3p5ogcMhxdY3G3Hctdeejzv8nmORxuPtnvZ7Z6eXx7G/GxLSd4Pf9j509yfR3+Zd2PuS/TDXnbjuc87Wz4D9uNu9xi71436EAAAEABJREFUlzHPZmgmj7C29ecZfm23ixkL02wvMW9z3sYOOHcv8/Zp+/Qwv4zx1Of7MT95x8DtvHvejQesCBbbXd+9zPPj9nH38jR2cxheA1KZ/QUta+R2J7sn2s32st09v8zbF3t66U9bYOvb3p8wdZ63ZsOWDUifuW/jecTW49nG8248b3MLQC/Rhz3n9ingeve03W0Bwq3bsG0MzO/bGUB83M/9fjue3Z/Dtj6ebfvi8IIJY96BRn/a9S0cb+fcDrD+Yv25y/PIHcLZ9efXu8+zPyLYMW93u+Ww/hK7LVxsgXWed/NLnx97f9llh5sX2+HabvuC6FDXO+9mW0zx+SX7DBjhT27b3rcY8gw7M1CP2efnASG9vNiuA5g97/xxZ0/PvnvO+amPee47+Jvx9vKCN0T8/IzQdvkSPjtM993j/PTQn0FRz+1u3m53L7vdvQ1Qt7M+9/nxZX6ZQcF4nmcg3aZ1242xBTTY3ZntLF5soAAXaE/j6cV32zE6hgUgPPf+OmY878Z2XmZa7oZvsZ95BJK+LHDP3juOOfsWN3z7/DJvd0/zQO08Ac5T322xyufTzgAIAfvLNuY+xujIwIs/vuwet9vtM8CP7ei7ebkxdtut9W3klhJnDy+7u3ncv/T7F3tEEWJv7sPGrqNMXhJZmLe2fbGn2bdu2/DZty/94aHvnlChY8As1vfZDLz37bxFYN0XWvrLi8+Poz+5b7MPezLfjZznJV/5hK1Ij90uXl5i22Mevfv8/NKfnsZ2609w8LJ9fNltt7Z7iZenBQBS8NR3aAzgd+v92eeXGcdut9u+vMwvfTzF2PoOZzvYGf0OBufxtI37Fx89hnVzt7HbwchAeDajkW1RO/E8x7bnPFPfRcwWc+8v9vLsL1t77CDwfn553j31p51vd37/NJ6388vcDQZxjHk35hfb9Xkeyx9HlWG/NHvO7vNAXubRl/RsB4b1lzH3eTfMe+Qco78O2I3xZB3H425+3G53u13vY+4GhNtuo887x8R4Mn82cDw/7HZ383jo8WDjeey2u757Gvbsu0dfkO6eH8fTzXh6muft8hdzluN5t3ucd3Dx/Izc2G7rPvt4Gbab+4hhuduN7W7e7ToGPD/1x93uBRZ2i/0+j24+g5udjdke5xku5peX3ct2Brx46b61Lep47tZfwh6jv8zz88v89OTPL7nsP3e8TdopD03D4/BYRmxn+Bvw/vDiOObhvXdcXCLaRUfYz/HytMT71O0FfOHijCHz08u8MIMJI3az43zePb8yN7adXra5fbHtbvsY24fw+8A2I3uPGYKP2O78eTe/jO3W5pfZnufx3OdnDN4ttGDYmB2Q5O/F9Z+r619317/vr/7eXJ9d3P25vPt9c/vz9ubn1cPPq8df1/d/rh4vr1+urp/Pr59+XD/9c/n0++rh6u758vHp/PH5593Dr8u7Xxe3f65u/17dXFzcXN0+Xt4//rmDnUvYP7u8+nt19efm6u/FzfnZ7fn53e+L+x8Xt3B3fnN1cX799+ru9/Xjj2tcvPl5e/n37urs5uLs4vLX1c2vu5tfN7d/bm7+Xt5c3j5d3b+c3zz/vLz9dXH16+bqD2Df3P6+uv9zef/3+uoP5t5e/b25Oru9Ob+7+Xt/8/v27tf1/e/Luz+3d7/vH/9z8/jPzeN/bh9+A9vV9e+Lh9/nT2eYe3P9EyHg/OL5z9XDj7vrH/dXIOT33dWP68sfV8vdP+dPfy/uzi5u/tzcwO+f+9f36yWos4u7v2e3P6+v/3N3+c/d1a/b21+3dz/ubv6Dj7dXf67v/l49/b66/3V2/fvs+s/F7e+b+5/393/vr8/uri7v76/u78+XLDz8c/n0z+3Dr/v7i4eHy7uXi9uXq4eX89vbi/vH3zcPP24f/7nFyf3vu/tft/e/b+9+X97+ubr5e7Mcf66vfl5e/XNx//Pi6dfNw+/7u5/Xtz8ur3+cX/0+v/p7fg3Yv29ukKAznN9e/3q4+YHE3T6cnT9e3T5dPtyf3V2f3Vxd3T1c3D//vF98/bp9+A1yMP786s/N9dnl1dnlDQL5cf7w8/IGCvlxd/MTTF4/gMm/F/cw/hvXz+//XD1cPjwiC3+RoPuHn7ePv+6eft4+/HMBSLe/bu7O4Ovu+u/N1d+LfyXxcHn/9Ovu7j93j//cPfy8v/1xf/3z9v731eP5xcPF1fX5xeXZ2fXPq8v/gN6HJbNn1zdnFzdn53dn17dnN1fn51d/r2AZ7N39uQPam39unv6DXF8//ry9+8/N1Y/L2x9Q7+3Dj/tlDEj+syC//3F5/+cayO9+31z9c3354wa1cHf28HRx/4SM/z5/+nX5+Of+7s/97c+b27/nN+fn12dI393TP9cP/5zf/XN298/FzT/3Vz/vkfGHn3f3v+5uft/d/sbH25vfF1eAfXZ28/vs4efZw6+L+7Pz+6vrx6v7hz/3N0uMGHwFMu9RDstxBXE+/rh7+HFz+/sGKbv7ffH4G7K8fL56fLp9fr56eL68fvl78fzz5v4fBPUa8s/7h3/ub/4BYzf3P68fcfy5fPh7dv3r+urH/eU/90tQgAoGft6Coptfl9d/z6/Orq7Ob67Ob6/+gq57RH398+bu59kT3P2+fPp7+XR5+3x1/3L+8IjAMffX+dOv8+efFw8/z2/+QMZXt78vH35e3v1zefOfm8t/bq9+YvrVw9nt05/7u993d7+uH/9cPV7dvFxcPv85e/px9vTP+f3ZHYh9BrE/L+7/ubpaiuXhBlT8vUJdXP++vPtx/fTP9cOPy5t/zq9+XFz9c3H54+zy59nVr7P7nxfP/9xCHle/7i7Prq7OLq/PL5bjz9X9z6vH3xf3ZxcA9vDj/PHnwv811HVxfn1+eX15e3d1/3Rx+/z78v6f62uE8/fq8ezs/s/Zza+ryx+31z+giruH88eH89ubs7OrP+e4cvOfu8v/3F3B1I/b+19Xd7+ubhHjP7cXP28ufl+jju7Pb55/XD/95wa6fbx4fLl6erp8fPp99wCQwPPn4vHs4v787G7h8+zpz9ndn4vr39dXv26vftzdwOyv2zsI4+/ZNQI8O7vC8efy5p/rh1+Xz39vns8fX/7ePf++fvh7fffnFrOu/7m8/X329BfH1f3v5crV39urs6vbv7dLM/l1df/34vHH+dM/FxDA/Z+7RbF/bq5+X1yfn92en9+dXd78ur39z+3j/+fm8Z/r57NrVOjT5cPLj7vnf24efl3f/r24/Ht58+f27vzu8c8FhHe9cHV3/ef+7uz27u/l3c+r2z9n12dn18D8E3m5vvxxC9nc/kJp3N/+uns8v3/5cfP08/b+783N2e2ru+vrHxc3Py5uf549APzvK5hC8S59+9fN1Y9rVMr934fnn9d3f85vznBc3f++uf95fbtk//zq1zXECUg36DYI5O/9zeUjagct8fHvFcr/+uzm5g/Ue3P39+IZ4vxx9vif8/t/Lu7+ub7/eXv36/Lm78X12fn1n4vbn7f3P+/u/3P79J+bxx9XTz/O4eLyn2so8Oafu5sf15e/rq5+X99f3j1eXNxf3T1dPLycXT2D7bOrx6vHJyT3790Tcvr74v737f3l48PlI8R8fXZ3fX5zdX6Ni48/7+9/gK5rpPL698XNPzfXqIuf1zc/L25fj6ufV1c/F0Lufl4+/L56OL+8O7u5RVA/7iGJq38uLpeoL+5+AOH9w+9bgL9awJ/f/rq4+3t+/ffiBmb/XtyeX978vb79dX1/dvt8eftydv785+Lp183DAgDJunu+vHn+c/n44/ru5+3VH+C5uPl9/fDPq9kfuHJ+9ZrEx5/nz/85f/pxhdyB5Ks/F9d/L27+XF7/vr7+dXfz5+bq7+XN76ub3zdXf26uf9/e/bh9+HG79Ld/cH59//P6/vf9/fnD4/ntw9nl/Z+bmx93VyhqgPxzdYuPv2+uf17d/ef68cflw4/L+5+I+vLh7OL+z9UNYPy8vTt/eL56fL56eL64fDw7v/99/vjj4vEXBpzd/jm7+3l+++Pi4efV88+7x9/3d3/v7s7vEObjPzcPP+D94vbX+Q0E+efi+vzs+uLy9vwBq+fTf66R3Mcfl7c/bi9/3UO9N39u7y9unheWzp/+Xjxf3D7+uX78efXw++L+7PLm/Obm7P7m4vHp4vH57/UDtI2LFze3Vw8Pf69vf15e/7i4/nl+/c/FFXL39/76/Orm4vz27+Xj79uHi4eHs7ubPxD/7f3Z1e3Z7dUZsnb9+Pv8+ffZ09/L+z+3Nz/vL3/f3ZzdYoF7uXh8+oPxt1cXD3eXj8+XD09nZ9d/r67/3F79urn7ffGIADFmofTh+fL++fz2GY7+Xj2cX2GFevh9g7q+/QvBY/zd3X9uHn7dPJ5dA8bj2cPtz5vrP+fXZxfXZ5e3Zw8P54sSbmDq193TxcPT1c3j+eX1n7vrX/c3v67v/54/wvXF+fX5DfDfgKhf15e/z29+Xz9g/NkDXD9e3j1f3r78uXo8u39EpOd31wjw7/3jn7vnXxe3P8+vfp5d/bi+/QdbhXsU193vi9uzs5vzxebV3xtQhCuPP69vf8HF5eWfi6t/bi9/3t5f3jyfXz2c3978vbv4eX/95/oamFGhmP7r8vbn7bJA/+fm7ufF9e+ru19Xdz8w8eLmnytkFjJ4vn3ZXj08Xtw8/b58+gl+7h+xnJ1dXZ3d4bg+u8as+58XT/8gkKvHv1dPf26g0uufWG3vHn9cPv85ezw7v/57e/Xn7vEP7p4//by4/+f66p+7yx+317+uLn9dY/Ddz/vbH9eXKJ9fdzd/rq7PLm/Oru9/3d78vLj6cX73++zp5+Xjj6v7HxfXP86vfp2hUm6R0PP7R4Tw5/L24g6iffpx+/Dn6vby5uHi8fHi8f7nPei6+3HxhOPXNYr3+i+W7Purf24uf1xc/b6F08efN08/z59+n9/9PL/7cfbw6+zx79nT5fXL+fXz77u7f26vfl5d/T27/QP53UEMd2fXT3/Pn84fnv88PP66ebi8ebp8fLq8e/lz+fD7+vYPOLm5+nt+9+v86T9XwHP/4+YKvn6cX/88v/lzfnNxsRznVze/L6/+XF6fn92cn9+dXT5e3L+c3z3/ubhDOSzH5c05Yrl9/nH78OPu+hca5t3DP7ePv65uzs6uzs6ur67h7vGfy8cfVw+XD09L/7x+PLu8/glWwfz9w09k4eL+zxkkcfvn/ObvxfUZjsubv9f3fy5vzs6v/15e/765AuE/rxap/Lm6+3P5+PPy/p+z+x9nj7/PHmV/avut/c97Xc6Xj/W/V3D+/3NM+w1H28dFTNxr+3t1f9X2V6tl/HLSplJw7E1lb6/t7b0OnjDm9cCsNu2tpmU8puAjbuHAyf9zvE7Bx1X9r/G9stcqbE5N8b7/3/ETPML+3oKh7GNwaxi2anV/sQ9ICAHHK4bV8r4HMG/23uyvcOzjCobBy7/H1ExFVGoAABAASURBVDBxMd5eQ8P7/wMPt5aIFheYBSMrGH8d9i+Sf99fby0uptX+K0WrumBe1YLjX6j7i8dpH8ZxTIuRZTwIxEccr1cw8n8H4O6/x3+v/DsGw3Dy3wPWcLyCaUuMi4upLRQBFSCtJlhYpuPjcv3fMW1/D2P+54CpvenNHpIywfuqLYD/xbyPSCdcREQNRqrqch3jV6vF7KotqYRlXJlez1v7r2tcxLFwiOltf38PppYprxhgah8U4S4O2IeXvfJmuTW9Wdhr+7CDi6ALH3GCj1NdfK3aVJdcrOqSWdh8TUpd5oLG15HLLRh/nbI/TavW/q+0Tm8Wj22ZOLU3QLj3eo6J06tTzMIBvS3X66pCwxMGYyRc4NjHFIzEeAz774EBK4xZjmXWYhAT/z2WkDEYs/4djGCntvcqjL326hHW2rR6vbKqC/N7bfELzKv/iRFGSlEiauVV/6AF/OMd7l6NvHl9R04x8hVhxcm/x4IKADAABTLVhVic4/gvn3UZj49tSROm7O+V5Vg1nP/3mBpqeRkGjxj2inZVy3IFBnEsFxtgT2VJzR6u/Hfka4Dt/9/7BJZQp3h/Pf7H6YIWt/6d8i9CyHKaABseceBkHwMQEcJpDVdwYNaijWmqKqvaXgfUJYrFzjJ3gbq4WIICSxi/jFnutqnV5SMMTm0xjlj+e7IYf73SlsH/c/H1Cmy+VsQS6SuBiBe6wtzl+Hd83avL8Tq+vb5jVluQwBQm/vdor7eWd7C9nOMuQsPx7wBYbvD171xwBbSvHpe7mIXr0wIP+HEsXOEikC+dB+nYX/BMr+8NhbNEur96sweDGPPfROPiv1cWO/C+THl1tJwv1vZBFM73FlFhDOKC5f+qFzoBQrheDmDDRByL/f/OWnD+D2DY+d8D1/89n5akLLGvMOx/3LXlBF5WrSx2Xj8CJ7z/9yMGI6F4bw11IczLLXz89yLQYgpcrP6Lebm7tzh6My0d4A0G4ECkeF81WH4F8MoJZuEiDljAALxjwH8/tsXO6/miuv/7FsaACvCPi8vxyvm0cI6ROAB7eZ8WDGhZOMeBizgWMK3hZB+u//X478f/t/dlGDCDbTC2JHrVdFH7vwBe4e3/184EwEtpLPDqW2QKNjFg+fjqEcOmBRLCx7HfWtWlwyzY4GWa9jFgtbT6xQgmtqVnvsHJtMz6L2//c76U0jILwJZFBwYxC+/7uNjgbgIYTEH4r2gB/vUiXODu/45ZzpdEIPv7OMeBJQDvwAy/7b9+X2+t9oHw36gx4PUAckxcTeW/jia4wLG4XpDs1cX1vyJ5Hb+PAThg/NUOxuDAXLzjWHoFgP17ACcI/HcYTvZekbzWJkbiWEz9a7P9ewt+G2J/vbW3j+nTa9ZgbRk2vV7B2rEMxpj/HhNib7iFj5i7HCt8bMvJv6vS3nK+aF74zd7+fls+/m9QSAHKfJmLWThwdzn+9fU6cjUhKQvUBQyutH0Aw4HBOHCxtTdAiPMVVtUlR7C2WjWwujT2V8L3X4ftN4TzekzAXPARI/cxANaWY8kOwPw3I/9byIvlJWSM/98Dw4Ac03GA/P3/tY+T1WvKVouLN+ixsD9huYS7aRn2ehfAVtNS6bADAcPjorGG0F4PJAtOMbG1N/+Gj1k4EGbDgOl/YLwyg5GrV3hTg82pLSvaYhBIMB5Ha4vfvWWlnsryvr+YaosR3J2WtXu5slysy8X2ag2S22v7i5LbfvvXI1I/IV64gKMlarie2pLof9/h6L8j2+uUaf81hP3lesOwvWnBtjct9mFnSWtr+5gCR/COA3j+vYJ32FwtI+EITXUPtwAJ77j17/H/nE/7/175n/cV9iEwuxxLf1jVsvgC2uXA4P/7WAAAzD4KDQaXAwpBpHhfltqmCsDAsIwBJLhYxvxLFOysFte4AvCAh3cMwIErU5Ov682X4/df3u+fHK82m7df1u++rdef1pvTo83nt+uPbw8/HLz/+O7w87vNt3ebz4ebk+PN6enm68n6+8nm++nxl83h5ujt8cHb9eH65GS5++H95vToYH3yZr15u16vPxxuPr579/HkzeYEYw7XR+9PNgefjw+/HRx/e3/w6WDv5N3+p6N33w7XX96vTw42m4PjT+82X9+//Xq893Gz/+n0zZfNu5Ojt+vjdzg2p28+r/c/H7/9fHgEz8fH6/Xm8MPp228n+98O9z8evNkcvj1BFB/efTt5822DwW826zfHm3ebk+MPp5tPJ+uPmHNwcHh4cHz8/uNm/e1w8/H45Phksz493Jxsjg8366PNMSJZH386Pvqyfvd5s7fZvD09Pfh4dHhyiAHr44Pj04O3Hw+m03cHH04OP28w8mDz7uAIkNbrbx9wHH48fXNysr852l+/A/KD45PDo6P1+u3xh7fvv7x9f/r2/fHh8fHm6PTk3afTw69HoPd4jT+b4/X66Mv7468HG1D9Yb3+cLL+/H4Nor6/P/h+fPTldP3h9ODz8d63w/2Pm4MPpxi8/vbm+PPbvdN3eycn6/XJ+vTDuy/r/W/HYO/NenOwPny/Xh+cnLxZf3iz/ngAuo42B0frt+sj0HX48d3xyen7T6fvvp0ef/9w+PXD208f3m5O3x4fI6I3X98dfN0cfzhdfz5+9+Vg7+v7/U8HB1/eHn98v96s18ebzfrk8PjDwWZzcHpy9Plo8+X9+su7919ODj9vENTxl7dHHw43J5vN6enxh9PNl9P11w/r7yfv/s/m7Xd4/LD+ull/ONlsTo9PTo4/L1eOvpwcHx9vjo+OT48Ov26Ovh0ef3p7dHK4d7x+t94cH5+8Pz19c/rx7ccP7+Hu29Hx982bbyf7X0/2vp28/7A+hkY2B+uPh5tvp+tvJ28+n+6dnu5vjt9BKp/WJzB+cvj++P3h6Xr95cPxl5O3Xzfvv344+nxy8PXozbe36y9vIIb1x9OjjyfvN8dHm/cnH95j2Obb5uj74dvv64Nvmzdf13tf1++/HJ+cHm5ODk8+Hm4+H64/HJ4cH78/PnlzfPr2aPMO5+vD/c3pm83J/vrk/Yejg69vYf/d53fr9eFmffjuw3scbz+cgsdDKGF9sD55B/bef1nvnyBNH47A2OeDg89v9z4evv98st5fTVicDvf2T9+9+4TCWR9/Oj063hytT44/HG++rtdfPh6cbuDrDTKyPlofH6/x+rAB+Hffjt9+PH67fn908goVgXw93js5huuj45N3pydvTj/A/dHR8WazPvp0evD19N3n0+MTzD9aIxSwd3py8On03VLspxvY/LR+9+F4/3T9dg1aMRLv6/fHx1DSu9OP0PP7ryfrL+vN58OTL+/xfvjl4M3ng/3v7/a/vX337e3h5/eHHzaHJ+v3J0fvAXRz+u7D6dvPp2+/fTj8fHr8YfPm4+Hqy/Fqc/rm5MPBl83629HR1/f7396/+fbuPUIAzE+nR0jul3erL+/2Px+/+3Ry9GmzPj06On2/Wb87Xh8cf1offD158wn0Hq6PDzenh+8/bQ5OTw9B+JfT998/vPt+cvT93dvPb/dPjt8fb05OTjZfDjff3h1/f3/8fX387Xj/y7u9LwfvN+/Wx+/WR4dHR++ON4fvvxxDbO++bo4/H77/8P79yfuDD+v3Xz9svp9uIMVv79efD45P3m02h5uPR28+H++dro9OoJ/3b78fvf1+cvj94/svH958Pt37dLz/+fDNl6P9r8erb0d7397tf4HBw6PT43eA9O3N++/otycQ+bvPwHl0/P1g/flwvT48As2bo+PT9fGH0/XH083p8ebD+82X483Xk6NPp+tPJ8dfNqjog5O3Byfv3n46PvxwcPTtDfrD+vO795sP79an7z9uNqdHSCnaIHiDi7cfT999PTr4tnn/GbfW65Ojo9PDw0/vjtCmPr19//Ht8YfDzZfT9ffTw/9zuv/99O3X47ff3r399vbg29vN96PNVyA4OVpjypuDr+8PPp8cfzxdf14ffnv//tsB7BycHOx/OFh9erv35d3et7dvvx28/7Z59+V0//PR3qeDdyeHx8jQ+v2yxHw7Pv5+vP5ysP769u3Xg/0vp4efTtYfD99/Qht5t/5yfPT15N3X0+OP6/WHzcGHkzc4Ph6/R120Ur6v1/9nffB98/b7+v33zfG3kzWQfD45+nb8/tshrrz9dHqwOVxEeoysHe99Xe99OD08PtmsT04+np583Ww+HqGtvT09BLB3399tvr3ZfD9YfznaQFKnx2+/vjv6fnQMSMeb46M1ODz+fHLw9fj4y+H62/Hb75uDLx8Wj1/WUDiO9bfDo9MNWtYBBPb5/TH4/HB4vDnafDw8/vz+4Ou7t18PwO3Rl+ODr6d73073Pp+ixNafTzcfPpycni5V8/Xtm08Hq5OD1dH7N5s30Co68Juvh/tLOEfvvx8efj1+e/p+dXK0/2l98OX04MuHt1/X774cb06PTo7fHR8fHB+/fXd0vNmcrD9/OPx2sv/taP/70btvp8cfTg9OT95+fo+V6Pjk6GSz2Xw43Xw/XX8/Ofh69HZzuH988v70dP1tvf622Xw+WX84PfpwcrgY+XT4/fT9t5P3X082nzab05PDzeH748PNBn9P9jfH08nx3pfTN18+HJ6crNfrzTH+ro/A3unBmzXAHB1sNm+/nu5/OnmzOX3z6fTo+3oDhj8crj++O/h0iLvHx6eb45OD048Q0uHHt1Dv5tvBesH24fDDKe6eHB+vj443n96dfD04/nC83py+P90cfF0jZWsI8ss7hL8+PT78cvrm+8mbrwfvPr49PoHwjzcfT9593Lz5cLrw/O14/fXd8dfD488fjr59fPcZRo6O1ij24/V6fbw+2Zxs3p+s354cHX1YH31av/u0Pvh4gkaxXp+cbDaHHz68PT09OF0ffzo8/oBZ79eYfXS0+fT23VfodrP/afPuy+HR53fY6hyfnB59+nB8erJZn5ycrI8/H73/8u7dtzfH396u/8/79efDzcnhu/Xbt8fv3x4fHq7fr0+ONseb9enm8NP6zefD/U/vDj5vgPnt5uTt5vTtx9P3Xz4umi+lfEC9H2++HR5+PTn+dnr4ffPm07u3m4Ojk8P11w/rbydoBegna8gYPH89Pf5w8v7k9ODjh3foYN+Oj/H+6Wjz8QDHolXQvjk+/HAMNva/n+59//Dm68n+N+Ti9Gh9fLw5Pvp0fPz9w9H39eGXt6jc1Ze3+58PjjfrzdF6A7wf32++Hm2+fth8/7RZ3L1ff3p7/PEISnh3+ung4/HB57d739/tfzva/3D69tPm7dejN9/f73892vu6Xn073v908Br7yebL6dH3o/3v7958Ozj6/n6NEtts3h2tD46P16cHm2/v1p9xcnKCjx8ON99Pjr6cvDvdHK9PNsfH70HRp6M3Hw/3vx6++35y/Pn4cH349vjw4OPJ0ff1u++Hb7+sDz+eHpycvFkf7x0f728+HH/YrD++P968fbdBLIfaxYM5AAAQAElEQVTrzcnB8enb9dG74/Vmea03J8ebT0fvPx8ffDo9REVD4cdHbb15s/l0sPn49uTj2w+bZbH4+v7dl83mZH20OT6AkU+n7z4dHpy+fXuyhuSwVXt7evhuc3T84ejk++H62/s15PFlc/z55N2HDweb4/drYD/afDg5+rQ5+rZ+/+3ozafDd6fvN+vD9cn7g6/v3kEqYPXLyfGng3cf3h5s3r5bvzs8eX+IiZs1UK4/bI6/Hm7+z+H6+8nm+4fDrx/eflq/PT1AEa2R4tND5GgDkj6cHnw+ffPx9M36+O3p0dtPBwcf3sLI0fHm4OTD22+nB/9n8/b75uD7+/1v7/a/H+59WRR4+OVgjVx8Pjo8PQVZ8Lc5OT35cLw5OURS1p8P1h/ebTaHm+NjMLr5stl8XB9vDt+dHLz5/O7g++bo68nJ+mizgbZP3n88fvvp/eb03WZ9dLh5/+b0/dsPx4cfN+sv6/XX4/XHo83J+uBks//h5M2XEyjk4Ns7DHhzcrIvXcWFs6QSSVKkeHKmcgovr+U/n6EpZcVUmYQUBzsXEzUhKsbTIOKIkiQ8Fd5rqsJEQlyYJZkKjAtVqpX+/V+bpEpPGo6/rMkSWmMSmzQLERMlEFQKZZYqUnxaRa2ZIlkliTHDpFjVZXKampELy0qacnHKmcwwrgpJydhzL5klqDqp4Rglhsbg1/9cyJdAaSFAMN2rBEtKWAlvFJVTlVoF9CyM68kOXkwTt2qV5U4JLiOru1ICEoNONYURBldVSxEB9iKx/HfILUsSgSnhmJhWlC1ZwUpVKkECW4VVMb0gsAzFLBCZCDqCR9JOxGupMk21YaBlDedwYsBWGnuSK9JKRZKRSDWGN1gJcCHcuE6ipTCYpBR40xAVXl4J4xbCCc5S0ig9yZ29MwWoKaqNGpFmRlpkELEwlSalNS6KO5ESVIS1VK5wLR7pCzZirQzvr6BUogYXGEjLGBGRGtQsJ2fp1bLMTFbJdRibUwzxbJET8attKUKTSEOijDg4nYbX4U1I8Lep1ILEByveWVSUVYTpX0ExV1hLLiFEzQl6URaGClg9meHNAALJCMgWFKkmExevbeAu4QMV8sp9lb5yK5kwBAtEr4xlKmnJxoH7FM4ZulwpudhKxDWrhhRchbRsKl1ql0KqwkhrVRYRRUEUKRNnkXDhEGLYxw0FlWAmhyYKl1mLNAbtZu6ZRMxSSylVRRCSDqGBuKqQUE2ME2GwQbLASWKSVHHY59RgnZm7BIqPQ5mUWTRFuCgMkkiA2FWJib0SxdIoigdGBAhZBTevwlWkFZWqVNgZR0lv1WoN5J+IF9kJaeUiqrQvvMetcGHWIAWE4CkFTQUmixt1Q46tVKmqWlgqMeiAcw8qJAotN0WmML8QPjAxQ/nJylSUVVmYRYq6AAtcGCsxSxjG4ZbiXEOEChuTM7RNQm1SgKqsCYJ4uYOOUiKmolVVHAeJR8tY+pXAI6wNmYcEMeMN+YIfkNCKF0mYDBVbrhIFSecyBB1UiOBAWBTxFaWoni2z4gLcEOfg6MJDeebFKxKoRMpcBEcqJ0UQipRdBPU73DsqliRKLbCAsJMDnCvzpLySmNKqox8aqmmpIBSfGYtoaYo0FNjs4pikQsh2TUp86iSG7ESmofUJCVppc0p4V2IYT1PKogHoyWILbIpBmVQY91MXNK1QXTH0SpBVEmo/3aQTuQpGwZKE45ZIAuzeVFalFNYCVTBk6IxaUzGdXISFpHDTtlAIWyPMXRNNGtJmBcciCKkpwSAyS5VrYaEkBGQUXKlon8rYxztZHVgdmEUSyRQmYiYWZW2pNMJtCZYFL8ABE1pRTlgQNIQJtdKLDBGHcX59DZLBZIsxkVYLBDXt8armSrkmGosWAgQD0QHAZSWlqibhENiTgTJDNJSzR58HcSh5IVpCCEESAnUXyL8UZ4lKVIUICi0WzUgywS4h2kRACAbwk1NS1HFPSDlK6dxmwrtEo1ROWA+YEdR3o1ATdiVWnzj2FDlaPiSySbDCNUgWACSJJuMMyuABQbsu54FuZKLGpUehmBjgambjEAYeFhQMAdHyQSX5NbxgGJ2RvSY5NWoAoxhJgkkYoqbolkoCA8KIoQg1lqIcrdBKdOJapTQIJYgpQwMwRYvKVGVFPAU36DcXQ9pUa6lckkUo1TGD8JcBRubivXVTc5iOUHhQjaLEwpPyHnPFEa6JThjAg5lLritYIlTCJL2WVGmBUgOTHPGqYi1IcykC0CHCDsaC4QXNpwaKnwquVlbhltyignVoj31UisavV6xwKBGQEvCUCjtSRtExsTXOqlzRPXUQhzE5ExEiJJKUAP3LAVfsEQSyk5lgCludsSJIw5kD1eREEbhTXSoR8qMlCkNTuOqhWTQ1iRaWsMIpXMpKuCyscxBuEG7XkMlKFSmyIC1UKEWc6ogyQpZxiUg0GVSRFA5kninEQWqqSAohG5iFuueqyDXSMWpGI23IBEtEIS+cvIzUosqFCfsf/IRPxiDlSlQNTdGYvdCYiBVIQHHlRPOB+UpwtpjtMfVc/nf2Al9jlVjiYxJGCPX1XbV5qclATSIC+Ygu53hT4AB00tDCThJUWLUgcOLhPEexaEJ1oqzizIKdw+iVvOCiQpwEv4UQsnLACmOw1MziUQJks3IWevUqokJFOVThBBlJqpzodFqZCy+8wT6DDZlIiMhxJDNRYZ5YJpUWUlG1I1GlRExSCAnURjArye4pmVgqmkRtXJuAyYlK46m2UhWOK2UNL2b/JoErkFslZ8kR2I+VtFZSShAbgZBiOJc9kyllCl45qQMRCyUJR03kZpnfmNEjrWDKojdN40WKbCwhZIXQabMJC6Mnz2PXRyf3yEFkoI00Q2M0mENxjuJQjANPUqeMhQFRWApImROrWBWimWMUiJ5XQWCA0UBKCaZFloz5UV/3lqsViCClDtdI+hRN5wq/oRarQG1GYRMNhEd7lEXFhLFyYzmNDO6EI2cdlskhDWSuKKYx1JBnhTvSoJVpm7PsonYqxLXSRFQjM8mRJ2d07NWLT9ZL3QlvsdRq94Ukn4T2lBoTKDWKHeVM4rpkTouigInNGa45yisU7MwD3AQ1WmSx4jZlbcGTUYU75pmLVZWqhQebkSGzQEAswgAblEqscyz/EfnLiJ2rM7JMy9LnxDKUopAoEeCbxQ45MKiLIZ2OzIppQjjwZhN5JWlShJlbUkttJFNOzCuWwmRKc0nVBKeZ7oQisVqwTqLeqRg8uFXasaSvVkOhsbpjjANFO+nWnKasBYYxmHJuY64Dzx7mvnQBmZV3zFBRMhH0ZE4dBCVZumHXA5lFzwiKBK/WI9D4JAkboYh0/DUiKqnNpPTlsgI34gldoheDXfNio6Eti0Rb7aZpRugjafbsxhnIQw3JwdTBUqpkAaNsTNByipEMUmuFpiDs0ShjeNgyM4HZY2u0E5qlzNK6rkapIDhhLzsvi/S8zFKMLciLDtmfFQVZqIqteCQPDBjZ+LVvhi7BEK2EGksGVDUV35/G3jRHA9HNjdmokaFFkLYgjpwBHDwwuqRIkKcMhIFq3wu0r0x1HxRdKYrCT0GCA+qiJQxiIa7MSlnIhaIW1tairAYqVRL3s6jh40KvdRruEaA9KZ10qIaSMGGkgVQOF2d4AXSiQWNLWyudq2ETHAwoAEyU4t56TDta9oAatRnaSyXWgLwxjimruhZXFsnm0WYXyEOcxBX9iHrjUYx9LjEqW1NDzKBjZG4pgEubriYqxWJRmIBRfFIhEWNOIuFoLdtErTgTghpgyaIMl+yZIaINlBJBZgjYGb7VrfDMxZpK89Z2+zq3MhgWhF6lwlbCKnlDrJwMP5HkvfauPXim8AwaRgafCnSVUm3oLiWyJNWIOhap4YRtkA0JblhqiJEdnAupURmEzkTA6LjfiTKTPUmMoEGwwhbcXWdqg1qkoA4CxDsDDFRD6IPQD2ug+oKDdQm1ZDASHDulwSlEYAhTEz8IMVB2cauAOURN92iBSfCKBoVGL1vT2dqO69zqXLgvD2Y7p5kzVYSpJk0QTcYgt8Exo+MUXpbzac+mVdaWhRfZJBOiFYZvNk7SLBItvM6sCJwIwFiQw4LAkRVhoxyoVuggQrMXCsEqv1uWBoyG0x4oMxivITLUeo6RgessgXmzyDY0gmCVKlGRniQUWGgkB2OsdwTKQCLNSvVK0oga46XQhaGzM5xjUuUs2iUysA4hghLOjH4Pi2JCVJJLcBram6cSlojGsFNFRURxk7hVLo04WZ2UMmtiYEYs7Wp2CnqNeuzt4ymqvJH2BvOVMIE4XJzrYJmXAhPxoqjaKNtRXsbUgzKg7B3njhLMQqpLxSfYJh2ERQW3kQGtrFzIsZCLJxkT+NgFKJBCSBLo7oRMoFCWuzmSEuN6ipGHD4Tnhh7Ozk48llTRRLbstYJUPaY+Wp+lg6hgZWpSqkwRZUDIhBAV+hg0Z44S1jxbJi8+EhGxKuYkASFWUHDjTqMzBTqM7eXYj2RJQ9dfcAQlY2yAIfA0GRcngDT1jvCC2MsUKuCuMMhAdxso9nSwXt0Y5VrEa02eIH902sRtlxHqXIORlSXgoADAQPxJpuS1RCmcWkeiVMM1fKpbOMq9hLAoGKCSCHxFd885cok8kAjAmwY3TExnhnHmaJKryFVaU3qFmtQRRMmlpjgpCQ/Ss9aXIjvhGaHCEBWilGSMwkmnGIW5VCSWce6JyGq6jsD8mRwEpoQzogy3gINF1JaExAIni4dEZ3Su5X87pLu+QrGHIZu0H7QnHBSoMlxJajMpqMMVHV6MJSgVXonhlnIIoRTAAJVk/EWdLQleUa4omqK7uLBMwpqY6ARaIDoUZgHDXcghE8p04sR8clCi0CXYJh3kHWtNzVSMMPQzztm458Ira1v+r6sFSwvmqnCtjQqGs1maEfXkOdXgeoJXZAcJZmXZo4JJsDm09jqJNuB2qhFVrGgwLUCERXQiQeVwCzQ75G1KaanEkjkP7kPSCkdJVyb4LkjpzG4McgFEcVsS+4adciDAhGxrFYUENWeQn9ZBSaKsi0pheT2JJqAQIFwINaB7sld1v2lTnnDHTREYdZMe0pPmkfhcFmCVllkEGpMQy0yMtunLF1YN3/6niJBVQaFVWcKQLAljIaayK7XrCkvhLDlAIdLMKEfdZ0axmo0xTMNFzKfZZdt0O/GuLDW99DJIkkmoCCmoYWR2Sq5SWTjJmaFebVKzycIyItedy8AMYVFsXHcF+9pdAo5SlgVlpRQSZiZmaCsgjhQmUGipCI6zwEsZjt1Adx2pyQIWxAjFh+ogrshrZSTDfFCkkPKkscIIzkiIl4k8KIzFkUMIJoSEkF9pJFMR0hG8C4xxLpn0Sq+QCM7DMjtxMPIWIs5V9PUOcjpHwhIxJbz3DlRNqghioSUYE4YvZDfJE1sC9IwYmCG1lkkm5bZkJJBDN49gNtHIBmsMBAvANKFZwQKEcXRJGQAAEABJREFUl47QcyKtBLKFEOWClCiJiVmJORJgZ3EvxCIkhQTAybJ0r2TESUQYLMIqLHDi6d2HDXaaSBR3HazSqAp2NJLJumg2lVIlKxZ/mpuFOiVehbIwXpKQGS7CGtoscIgEtgVas7JoEJEaKTYXW4KEY0EBg6LFEzqjOWANjVdKMgv7pKRFeClyCqRwdutoBkEr1ipNyuLX8XAww48TmXMOSkZ6UpBxm2cfu0pYVXgKNUcReQ+bw3cdG3/rCNotsPcgIWoaRSLRaYoLw4KzYTGkuco8lWiTSmXAEKxrIaklFD6tzn3KoYUV1RxkmYM5CmoDAUOm4VDLMhhDqJAbhZEmki+LyGSEm+FiUlCBNNFRS/GSc8mO5XCPZ83uhNzgKZiTiRnoSnqzLEMpNFghP6xS1sxWzntW97wyD5EBh0BDlLxIJ0g1CQuyOlZIRExLgtk4E1qoosxqyR1YhKaVifZIjjGJCznOw5HELESCtA+PJMSZuqQ14Gs0IkWH7AVVKmOIz+oDtWioEAh7sNJiiWEXsKkgHvysvkjSXEGBMilxKwowIqwCb1JN1BnyMHK82DO9uDYrVQsQRI2ckhEGDCVjYxMoWdRYlyQRIUxHdliYopBVQdyikPbMZEHszh066Oxb4ZmQEwn4S07SSCHCTKDD6C40KNNSBkeIQzhR2UiHo97AHK4msE7BLSUIY4yIGD6X4hJHXynEkTES+AIhN+JSmInIg2alDntN0ijmRBfu6eihuRdl0lVtTSsTj8wB4wtHTQgkBaYGMCXkw0hwlaFo6xB/wXAFj9SqC2U3WtonmulUGGLIGdcCT1ktgymDmGRi2cORpCCH8FoKFJjRR4ix/22OQoFZpN8RSYhHGWh7TkrIBsRBjWqRUoqAOSpNpDLOnMRSA/FHYPJImXUJ2QAMU1kzjB3MeqrAEmZR4cA5U83eaCdkWeqo0xDNJMYqtrfYoxEa1gpR5bnIjhwaVtaJpha1hIs4VyJoIeCMuBgrAp6KTJO2wpWX1EBjzhnCzhIYQNIJDNEu8ZW8Ys9MeLkqxOCZHTm0ZUlKY1xf1OKsKQhaiMWd3HIYOxGnFe65XINaVpmr0ooWdDoZwnj46IUAB0PVSymNayEBnVgG4AsdhYNhl6p0JsMHipopERQDtpUiM5wGGGUdrIwVAlJ3nAANUxNjOCZn4nS0ykxlXrYKDEOsIiSwi/SUBI4EtzjC0UZZVJBM5CLwQC6FSCkLJYmAJFRbKqlwTdVkIIMK0ZeQwV6L70+5aqF1KRqJwjQJtCNhhq7HYS1fdx+LQdBHRh4IKKBUJmaWjGZzmXdYlm0AXV3cRhJ6T1inXWcLdS4pjYtoCyiuClehIIG/qJEF2ujcouKAkoHcLW0XiQWnE6U79lUyC7mk1qgT10l02bzkxNbAGaCBX6NhZGyi+HpikBKrEsLhpUxGxlB0PAtCpWBCpnpoLOQokaBtjdnDg8EvO0fEbGk9cQn5MHJcVwCvzMs/vhjeQoo20SpSSmYJIkPlC2QEUfls3nFFRSFeJQX1g2OnmcTErMTVvaG+rMzKHUsDY/vqJTrBAg1G5FkCzaLUUBlJkXCRTAFjSC6Fp1kaOo1kQkBCI8LQxhJFmLhBeEEpvduIQRFqBDsZIoQGYCVRMOiLqULLmg57IMCZk5nRM8RNMwh6rGbTiBIkaIaNEBGJRkvHaiaD2ZNaLKhYmIQxxyRSmfDt2kpDEpcaIiCaC/miTdJMGE4JK8Owr4vOfYQl0jzjUbFWYswveO9lZYxiwkdZWosM9c5u8JOSVtywbcqBXJGZk79+hxKgixCpR6oZj0EMkGJe51FGOk8mdXA6GdNA80mlrqVTtZSt+w5jRmJHzWMqVtWDwhytBpM0CInxSonQCoq5E5iFBICGcydmlMlaalOuERKhCWIqAgLgzKSEjpgVZoiKk5g47ohKwGQuaemqBnV7Lb1hBNoEAC7po8KSTCnB4cIJ1h2ShclEthL8E8EBjCaJhoA1xUo3qTZhjiA3cJdBPbkDsA5im5KmZAxQQjX54JiZTIhVmuD3nI7v/idQsMSpwZRGEIOioTSQwxYUmUSDZCYySu8Lg94hOg7FEMmawktSYCUppXiFIU5RCyVrPJcY2qmQLjlPYg+dU0AA10ZFYT+IcJ/BO5JUHIRwgoclbMX9wugOLi0SUKu7I1gUEakiMi1SKEAjpscEqhzZNRBJpksKIypuR7ILZ6XGueeBrHMCtQjJCvw4s7mnQ2Y5nGNEe80+7AyVaHVo2VEGL3xEBGT6ugtYMiYE5xqLiVziTObKjoefVeG9KfENLUmkprU29peyEhvoCZTKpXJVIWQkaDANSm/ecRTfiYN0o+oycdEiVMVFZisGILuobNipcdlj31c8eBNRYv6Md4iSmkrlUljBEzrEcDKH9UJIEReHcAiPOXCtxIUTOUHMwzGUEPjglVemveA9p5pBnEMTCMOEkcrgGixhhkPgSSeFIlEn5HDYuFCprplLT+rhzrR4whhlJZXc91x1pq4RQjDAU5aaBQF65CAbkkEjaReEV4IgJhZIERKPEE0uzCk6dOptlTjqnkohCvIRyI4ocsYTl5a1wGNNV9QFdVt40NCJCFuBaiESXiggQ1AcnqOb9UCt56hJYsrGi1CI4aBSqnRqXUB5xp5ncQY8U/GisDUKWr7MNbo4OVEniAXtuJMYS5YsKBlCzxasMkQVYalEKSjGbAgfQqEkgLVsTKKUOkd2dzQto0wmktRlYvFWYlWiEKzmpFbBhjIzSEgeid/IVWeJMl4rkooSF0K8faAt1xQYZ3HmroCmSWAUcFTIFCvH7HhXQjy96FxABnRpWYNeX+EcBgFpCrtbjuBwApUSaCJE+WoTzLih1OqCG8AyM6iSNEKqKWfz3sldfcmk7GpuK42qnoXBKExHwhTDWpK6Rgj1ECxXJpW0LrQxXsnqjC+EMipq2CChgCPBnIHLUoVlr+jeVJBhouzF8dyYbGw2rDvylMCzImwERyi7JFKgREUzNDxNeq/bTjsfw5KCmkPPiN+tCrEo2SS2JygqJE1hELwr7igjqTQ18kxsesaA+NSADZ8tdQineqFZHVGlh3UanjERMBesEXPFBC4u4gS3eMtMIFATxUW20D5y57i2I56pggYNLKpAQuIEFtm1kDRWwAlKh8eo05gqSWFndDlI4ZWXJa/KConi0dF6xoCTUgPkCklyIy5QIAAIR6VEh2GBCbQ8TtTENlEOq0roaY2kYgvaMYqBmCiR/GBGI+heMpWzkGJUSySSZXHNSqpAaAGe2XohF4JfKeg8nZAEca2OaBjMQZ1QhKgUJugCTLATvqXKmmPFPomg9xWJErvJBxYOjAv003TYwANrl3AEw1SltNBp4HNm2OInyDsHRoNRiMRVohWphRVGIA1cE/SmICpJOihHYBeGHNAg7VySX+MbxFiIzaonJZEFE6GR0YpAZSBUcqZRlnnmNtxTBjOrcxiuFgLJdSJsQBXbmER1zFNkWWZOWI2iCrcABEuCbhAOCaKuKhXe3MOoMx7XAEckUGWBfTKTF8e+ngXyyQVn0yjNsBYowPXMnj68+7Awi3Q3zrlqr8pMGilONAqZAgeu0BIZwfgUMIEqAn5OrSxwy6GQDFQ0E2cSwudSopYoKGhirxIr0UkKvlRaMSKBNU4WFUhDawolkjAP2VlzrOxRcZcpadnEAAOLYCRrC00TcxrZh+4MMKkGl5BIdsSus8poSlXBjlZirGsrykVu8DFJCvCxWCtWBKpbJlJkRpBhPuKUULVCUdFMZ/RdbYqK4gxKY3JmHCMkkUBsFzwxJcsS0nI7CmQiiD8ClGIl7hGdwpS6LBXKLIXwp2LHM2wX81ZHr+FKxAYKzMRhFeJCSjhG5ajArnsoZGoyrXjZZYgwIfOoKA7mJEECvc9uA9yLEjfBfYlgH5EBIdFq5LSTMmBeBoMiYXyB1bpiG42KtqGE6DJZgsFmiSDuzg6+8F0lYiQKEYeKw1IdWLIIkM7MM6fhOpJFRJnmy7fa2HnDF7qU8FLTKQnKjMO5kuOBWIiMGGoBAtCXQqHJGkKJdtWoCrV0EpfJ28qn/YB6XmGwkBYSAVYKJiqqBbQu9vlVT8wFOGAoyY3TKnFhYczg0DAOy3SwQuBm50iOpJVIbBYlHDIQ9tVkU0sFfDwedWJjddHQFVnjWVBew6MnmZEPl4zChBYH++gEA/kgQIVXSoY3DAo16BOwKPHG5MGecKSapEFqRJ74S2JFAwczti87cncqjISB5x0RNNdFO8nsYqmgYIQb4okspFWUknl0AKqMM05oHJ1DhASfJVhDyNWcCZquKynK3cWNJRNTwJ/rRFyVIKWeGqHKYJ6USExisHkHR8CbRExEshxMqTkLhBTDjBETwacnG7hgZSo0lAc+e+RwHqX2vRYrJWVBEl/1w6Y5Gs1oU0IZTh60yxyDqJM6ZFi4TCZ4hl3ZUBCLm547i535Dk01nFVoUpsUcAqlCgBgASVmLpIT1jthVEawO3cr5iVJKdHi2Uzcq1MlxmyiXPhokkBKdWYkKbG07IlIa6Y1SKsv1MJ1z9koVVA4ORHWRFbRZEbsWAAxDJtuJFGIeVQyyc65E5lVHOyy4EYJrTnVRTJKnEgsEYDpINoZLdvCJdkSpoZfCnP1KlwpOJ26Y3wRipo4i0CE6Mgk5mEUjtRMnEq4puBAxdKTXIL2mFdaysSiPBUpwmzw7FpSUXsO8aGpkiz9r2ohQeNuHNWC4ImMSrjCmBSnYnA/RE2nLCAVE71X9kaiy7iKXKIqm+AX2KSBh4G5xZBF0ADcuXVpiRwh+IoQJFCpAwkZRj3RiXGzCTXj1VAUZhKqbxs8oO9VU7RooB1p7pnO1GFYCJIv1apg1aYIpEOTilubMQ99HBwMYi/ElbHgg1DGDmgpgs64h7FEhbyElUwsCHMj7PVNBwlUaRkSCC+kphABJY1ZCTET40+hqJTCIqWWNrW6KlIIsCMj0kkolRIPzwsWNsItCdEZT5J7VVvTWpLUBTVHyDzWxGZSeYKlttfqqtXaBJJhjoRVzC8U4qSDFQMXWRXutc5cg01wCrEQKQt4SHivTAUYxoDwIysRMoWEc25pGGPZj6aEIa5GntV4j3wqO+HZfDv6gOKDFX5WbCt3/JYE1rKUnCIkHSaWedxK3deyZ9R2Idi/coqygFtLdg2ZTFsQBy2CGepdOXGfi7A2KkoTl2lwQ67NKYgAv2RoOJHjx2C0o9HTkco6sMSFQvfiA3nMFA/hQgtPQkQImJVC8EXUCuMhvy4on9J3+NpZSF8HFgpwRoWlakoNYrRWFtVSadk+iktK9JZ4rvDXpK4sas5QqbVFzxbFspo5yswEVySikKlEJeiPKSjT8MZCzEtJJpTeQRpDdtiyUZh7H4jXjLsnPpD3klmSeTB11bnq8u/A2XMg+1qjCpEgNEluMZVAbRcuSNysZeAo6sIgv4SWLFWECdOzHcYAABAASURBVG4ImiXurF0RVCoRvEgwpXAWGXCH9pvo2YbPBI0EeAUpumfYaM/UbcYXfj55NEM6OTFXMRNrdxFwZJMnpAE7zliWuldDUohQowavUrE38DIw+7/OIV6GDSynEwWKlSxBUi20KpQlhtqOeqceTUppE5oucSeBGpSGZC6brYG4y8LpwGdioxrEoBdtSl2CFD1hmUjIQ0amqQ5OZ2cmlWCx0JHsxB5YBikowrvOSV0ppTAzvtnB6VCZq/QlwK4lKnittSM1mmgSc8igJDaFMIWTJ5aJA8ljGEajsonGXkVfljTuTtBqsrASQTHoEiNjiAwubKjljBgzoV4zl7kWI4cR6IkkSU5gDjgh0oSJTI6h3RhBKqAiPeLUnaFImiBk4eVWkBNeQkDGHFhnbQezsAaIOXtsYSG5UEwFHdqtFmcKhKWeGEKAhAIE2RRCJMYMNATCaHSFJDyEcQN6chFXI+lKozJPBBlxsVAHN1UzCxIhkFxwMJReWdRZO1E3wt52NzryPmjnvvMxMmG2cSDODp48qY+9satiBLRVQpLIiuSCGEgQEpXwlVnbmg5Hm2qD1LhGkTHxvOIZi14JFUarBV+1owsiYAa9RjEouUqrWgv4JVpgK2vBU51n19FXTqWoTpXRORG0SRg8KjmCYwumLMxlVWmv+qqZrjxqJuWSRKzcXIdWWFWm5jw5FAXpZ2BAEqdisipVxmJXqgraEyLlysLclgFJ4RG9O34fHzMVSLgHvuoOR1EXAopgjiSPJYEDEXGQiIbqLGVO6caoZrM6YhpZk5lXzJMinsAsdCVMVFIBZ05GNthMiZHiORirn4y6fL+arEnphJyks5s6UoraRF7KK26kBeRkgDdyFyKmYBs0d8LK6hqiHZhb8CtqgcdVRcVzMvXg7ZiGQTlsmQZtcmGaJKcUDlgiEoI9CRbSqOSVWZw4mZh8Yt/HfcInq+nFHReHRigLK9UKUbhXcZXk4EK4SApLE2ZzKgUeXsoSTY7CLo1qCVhBy0Ykgt5nGMmMLxz3pDd9Sd0Fo0YwnYrgXSIZ4JqrKmXlXnvH8jG/MKIhSoJVMbjXutwmgKK6ilVbJKq+5ezuNrKjq0K60LcyiQVqB2BDFDIyac4KzTlbLaXWVmmq+JLTmomOQiGsRvhQU5SQL0kwRMmzIwevXU8IcXupVipLm6DcbKtSplqFJ6ICp4UIs1M5hGE8KFvk5AuU7GJGacGQVY6WkBUp2qULYsyWWSiDfEfQKiqT9/pe8SZk0GzQjjk1pYyGIgYXcIj6miwKg5oAWDDJAlvMLMhtsVTHdSJSTmFnY+4tqEWZvEAA4shTFvRT4Iw5w/CKcChUwKWJZIGxjMyEnvFU6yVSKZR6Icc3AVKDCg5DZ50JxcVkqFKbxo4xBfGS0CL+YghOSVRL5VWxSjPjrnGEEEoWR3LJUEIqEmMrcSmqDG+sQ1po0dIUtwwTw3gEg02SQHReS3JNRGFMWRlOEXCHLsBop4SCkwpTISWWVOFKXGQXEI2DIxipWQuL4j5RErgOcFgppvBCwXhzpZloIEODEixITWUUDQcTFdZCgtsJsTDGhaa20apXokrZyIXQyp0xGOYRVyphBhM3kkosKHwiR5diMYgHqWFQW5OwdowQzwKB+wy4idETV8alwS0FvMBSLmkjoMx0NJIENGEphEOZSCSQiKRhGTuHqjMihMBTchjFgGFLXmKjAZ+IODCZiGGbSoqmENrCUDbmUFwHWh88kESYx6gAxQz2BACTiQQUkDROTElygflOgWeSpU8FC+4hckfkww27O6CpFBSRRKHsIj3CCYAHuUOnzVRFTNOhXlzHT+ZcipWZmJHaiDCb8ZYK+Q8WZxWCNrLBIVwFMSqQDEmJffU9HsW6x7AludY4KnVFAtM5hKNULdg8VhqCzM8Yy+mkwIAt1ZRDkzhzoduBoNC0UtE2M71QdlxhuNfCAju16kq5FdLKiuUK8RMRL4O65la9gxAGXU21OVUj8lysp1IKgmBOVhbR6gWPbGVnaI+ISivhK1zinRNyOBTZHExQAYPGRIViL4HnuBBEmX0m1B0x4RsVdVfrZJaeljKL7JjnAHNTUlswM2kEeWceLDhM0tBXoEsAWkTdxNjR2OYWgG9UCBAJLUBRHag5o4xYHAhRFSulp1gAYousHngROwiEQhGmZyQUZUSEnrlKLwKWBB8jkyRR2l7YFzQQIXVfjl2QSQcp2qCa4jKRcGnMjagl8kxMHEq25MrFXN0knQtRLcRwkAKZkC1IKDxy6YK7ruFKwaLRBFUcAnAM7aBqHIM8wobkEFhnJqFgIvyAFfKBFp5DfaRjLIzABTFRCkfDu7AhHC3UapZGXJOUmDECNxPQMkogiUshMrEw5itjfEkSX/4GhyM5yoBGKoithHJyT0a3k1RdLIqDQajYRh1OSXCaCvuWSLkHj2ieIKkgqAzY4yIiC3dsBQWmjubQiYaQZya0YIjbiQezVUbKEK6mVx4VOySsRqLByAEsZ8+lkWTJSig8lPKgNCEqKispdcHilIHMeuQIHwQNlZlqsihSSbhDqDoeiYqaBqtwqIxKIZiVoepUzSmMkD1KJopIi4AlbIfxZWZoKuY4fgZL8l4IGEwt1lpHEbEbD6AC+YKlJcEpjC1MLL4zOTsXQwhOyg5TIWCldCvpwsqwDoDZbTXHfudmAtBGw3QwgogJkXsSNnAhjCYOsPBm2XuYWyesTORUwlR2Ak1wBEeiSLBZWiFRyohJMIQ8FRonrZV0j0sxLvj2kxKRIyLiMQh8OwcaWLREbmOIWqmZVYLbQttS+iAakzSNPMh7cmfORBY8eTiHBWWnpUZTSOCfJfGlDkQioL1HYlUNlRQlFlFoYBW2Yuz5LWIE7HKQ4E+wB6aqLxElMkMcHs7uGritlOLImnZnswjzRM0UAxmkqgx9QCgoQZ9JB3KDgkggJSQn8dGX7rCcU6bIgqhyKSiuqN1rzI17QawG4wR5IUidMAAzKLmmFlcaaunYOrO6EDUQheKgNB4MukruaWuAikdhs8wQ4oKAa8umIWmJPtHTdwi02Ovaims8kgxZQC9A2gLglurwtuK21wBBWlKjQF7q4hLQBRGkw9ZwT3e0GO2ExY6dcceY8UVIDicQtXAJt57IHgA0Wrb4xVSgJAfw1w2UGbkTIskVXHEVhABDmbScMNK6JIOGCK5oInQPKEFph+/yicawwArqu4zRhdHikjBHBKtIm5ATYpA6yDKCQkAKohUnfJojty1BaBSbkqZZgG1Q6bpvugrIyEh2VmcvEJ5nYVm+tmOIiIEZnIZyqgpPTVRR59qzQsOUrSX23aQBXpMZ/b1ghFEOCWgPPJAQXEEwEsxOlJGGpDA20BXrFZLBLuIyDWphQTO2VMHkmYPHYHdG3tQWDUFfmLtIxYfPZj1Q5LEolp2x7tdgRpcq4gUUJuInkLY0j4Rj4kTippTKgEj4AZsCvj3CMABZHtk7hzNGMgLiRLYCD5kFOe7GYTWDxeuqTnt1WpWqpIRYoWj0CgGSVnDXacWkSlbJiDmymMnSzoR84ihtMJpyw/3qdeKVKhY8WUVddVhfqBR8CTvBOGc07VidG/VqYz+tBTZ6SSVDk0sWdYRLFDPRDhml4h6jex8y+xIGDIJy9A0KDQDikhWqYB+aGKDhSX1O2jJyXzKTIgGZ1ag6KETpLEEENJWMQotKsrLKxlAnRkOsVUBmUkuDSlxi6zlbDtZRJYWJkhyXiaO61l3NGfUdLp3KrLVz8cigdCEr4ZMTfhFasDog94Rqmgib/ZSg6MVG46xSNKt78VBPxaemrMr4GB21UiiISJmakjAndhUzYxupLhxVgsWZl/ATHcRVaalFNHYGCo5CDgPA4phJ5MshxITDU2aqhm6TBCVAwfg6plfFb3HHRKy0MkH/4hAQh+JpmXhfJlYQ4AkiMnUQ1vbqpK/unGNQjIwdJZ4hrCOsATANXiDRSRAIFCTIU3HC4hiDUaSDqsueNe2NDCbV4YZhdMCuMKsmHqxz+dcj2aO7dTZUZRi7aefJRWBlNulWrBcJkmWWMJKNFpMzAiYSR/SZNojQPvF5EQf+AnkkodN0CAYTlbJgoqGOSnBNS93twqMLddaRtVvrs3aD/cJUmCWJUsNrj+K8+EkfYl3cKZGzqWSbtFRhhm48cwzpI90DvhEn7TidCbgkO+08dxLMDMsrywa8ARBIXorN1LuhV0BgzMs2H4BZeJlMAnzQMdpIZuIK4h0WPQZOAned0fAn0ymyRU7GKytiFX7ngpKhnXHuso5RcybqAO8ulIRymS1ejHZO0XMp/mADTZPIlMwWiRvCRZqOwjuhbZZOzQixJaUXwBku6a0EBFaBMyKNYuYxJ0JWQtNoRViWwgZvSz4yiRkmi2YlbP5CgnqykYZUxycoOKk4ZVfzKRVYSjqHqYM+nAQB2i5yt5xQIJgQJgaxMglKHK0sGYkTlCmFeQQAo2o5sMdwT47XAspUVH56ogFrzyKKXQAF2wz9iTK3xO4BVcgV/VJk4iSPpF45KoKwTCcavIRtFMj0iLLLasEJTL3inSmkZGVXYhlF0AkS1A5uxpVCiyu4Rd0DIVFS4CWyrH1EkWkB4zQL7qKqyyvzIJ6BOZBf0mSwAymBPhvDxjbw5cnwtGIDw51gq+14FbRH1IS5MtVINaTNBL1l4AuwwRIAiGDMzT0QIwwXCvJheIUg+BZl5TklypkaF42SKWCBqODwkJ6LRLxRQnrQ4p7FZFISTygyks01YkqsLX3i7SSzFpKqpGSSwc4ePFKM0SWAnCwGR7Zsq5gqrTgEhTr2aCy4gslArTLXUtpUWlUmcbBF4WSkwQWgi6hmKTkxrUgaUSHP4p3IhlIviBpZigp4lKGJMg8Oz8VIZlIlahFqQV2YVKEwkhTaldw2micKRc7MUFQ0g3TMTMaAJq2yijivkMrBaTbb3MPSSFMiQUUl00bgwgT+rAQQURZOZTdCDRA3RtYGHiBdeac5owoaeUF8sWwsYG9IICZooDRuAt/mK6CDhwpskkDLnISnEGjJwakMYifihQKc151CV8KRZRc6W7oTchqqQchDOAaXZCLtVAYuGfegThkVBQIfzb1GFCEIKwWG0Aomxe5wCtkf2oxQyDQod0SRrJ5tZ7KzmNNUrakU3iM86izBMXKgqgXTmRaBThHggBZIPaWjz+lyPSvyQloKNiVYhqeuOpDcumCOQB14JGJcMHGFElIVZJrocpFcGYKnAJhge/UZWtlZBjMpctNlN/Nzp0FEUzIWU0GSkhNsc0+fYxgTlaTWR+1WlFubqkyUKAoawqS1kdSkZJQ+R6EFooxFLskGNQ2pQirE4qTOJVCxUCAGVviESIf4iHQ20j5KtxrUqCq8injhnBItvodjj6lbx5OvRLBYsI3MwbRV2TUeyhzSEssEdFAR+JZiS4bgIsdEMSnxxLaf3uYkI2wt52reMuoyGY3W8WgUoNOIAAAQAElEQVQx5uDuRHDn3axvc+wySIoSKQWwZiDSQhjDxmKwRB4leuFZ2FxyxlEC8TYv05xlZ5qSCCclUDedaDijczGZCJGUwQ3UQDF12JS+2AafxCwuOmtu3cdM85zdMyR5spARPnwYkQEZZ+FQjkCZlRr4ZMlzkGWQz4INpXNqDYEyXQJ1zRqihPQlSRK5E8CQs2vyKisOgfTT3HfBMxJYqHFOYehgDk+k6lpmRETMCRWkVBdkjjWpGOWLzrvaHaIuDSEKRlGyeCEqpeBvLwQmnIiYWKM2L6uAiN0y50pboUGjhy2DOEfudjRmDi8lsjgxE7XMEtAxIwndd7vcuWL/zYJMYQQNpZkkMJaHUCBT6Aacme6oG9wAGHBgxJ3BNDDI1Gph9CH2sWwhrLI1hkZco+tikJhkWBnGndKW0OGBY5SBVhklS6biOqF5LE58RwiTU6yqg8YFNvBJC52GgEAeyIqJZhKpyzTr1FNoZPFUECo6YKePjCROIs8MJ0ryQd2ZInWQWtDO8LmG48tTPO5UJ7aaoZHLLMnVJKuqwpVJBaaX68DpmDXIO8uAoaDeCZGnIQjkLrWVRJpBEFzzIEbj3HrMlHA3exg7oSATlYJMZRqYJxZke+EhAqiTiFWkCCuAd6au6cpAQYV7pV2TXsRDPdAtNaNIQktBYYMiIJfQgnGjwvhMhO0Wrlu4D7fhhAqmaAIyLUDwivsqTbN3sS3LDpAHjTmx6SYf2LKQEl6VuRB4dqGoyqgFXPz3yCSPJHEFEEYF5SAyjVgFOkUsKLLAMaBOBfE4d2ZDSoIxkWRg/eDmUpFQIWQqnTAW6zp2U07gSKpOE+1NOWkWZmUm4lQGNFFRZibmJGQVXkGhWTFb5dLEUNxigXLIGLFox4PA8jJeJKqACWoshbiVWBke0YIhKzXIqVgyKnbHOZe0MgBFLYuzBmlkpiTXZELotDhxGi7D1ZwX+VKTUgazLRORHeJhaBBACvVSJnsIpwA8ATzP5n0gRcTEAJSm6Qg1tBCBCMVABsMNn4hLiEbTsYKqc5gpGbqgKEL1cA4qVvesTr6wyuKlJpqICOUroUsGOgsiEAZI18moLRoOQXojm1Eh5pLcolRnYXeZDamiRAIaFwY01oRpbC5elRHikYtvMEwOl4Q2QCWJmZ0TsBLOiYIFsvCKG6nBJVIVeDPwXUAtJErEADsaNkjqRErWyDMkoyUJUWr6UhoebKHIkYRxdMuA7mANhllJmoRWT85kZSkywagpsldehZA7I2QGITGcwOhSW7P6iK3bzuduHrAdJJIsFKRGksWEDAgdZyYG441yypzAl03hk+VreQc5OpCnIEAtWmplRRAj0rmkFlj0Ei4BxjxhUBhR82I7ChdR9PyqZWJBJYg7ywA9UGEaG5XXOJgDGiLKMDiDdMlXKqWSU2Ctxj1lk4jAGtzhgrCwsIoDNGghRBB4ttGZCVCiDGWXpAh0IuGUQkVIiAlhC8/COw6jZLaVUFGhqoZ3zcq0iIV5GUt4QWZKWSM4gxnR5SQ0SeyVpVw4xVhep9iSAc/MyHTymWJY4KsfszT3qE7NAoXoTiMJZcQwy1HTwIQSykIrYWmIMjpJuPLgHBTGHKzJJXFi5Eha1oVOJWShclZVxlSaiEQDqLQm9FISXtmHBjWnOrI40kbEDPPLiojeYepoyFYyNJNFYmJknbQz9fT0IDM3y+E5WKxWb6J1TLqrlEK1hyJtAA9nmWOwBYkwakARHfAkwTCE2yJUe2lRm05alQFQdSAixAfX4RGBsYhfGeAgI0Euhoqj1IGMmIMYJYTtBXTIiRdcFI2qQ5UCWQZBBCzBkRS4j0tFsjSdRAAD8XToS5ZHpHQRA7+IAhVUOhK26EXwqoiLkjSlMhOhUtIdNklIKxcExgioLFd0DpmN56ED2nTxECdAJsARIFKRYuDNm1shIi4WxQgp88T2KAwvD7KUSHWrg9Wmlo0JON2z416A4QZ7KZ5kXbErx08IGvCcNILCaGFERAFdyCh7imUBnwJDHAR7rCTK2khbElBmsns1VmNysmBOr4y6NBGQpeALdI7GvcEiXEekBYnkEkgmQdyOmRpZEiSVwnvMNUiTiOBQkfFI5xiaS2NDnTCjDAurMDITEN1EXJmEgxVWFoUHBItZslxUwEGrspqJL+kL4V66JVLnDtChnmUsYaSlmUa0sBopKSIFXoRLyRq8Sp28KMETJmYaHAo8Z63WigstYjG88UgwkUtUXWgRJ4WSTmhifeLRaFRyXpp0Z56VHVRwcAaFcmh1HIxUkgY3EoAmWopnZHRiaxyVKAnjCk14lEIbSZATCCp6oQW8DmiEBV6iKJhED8l5ZB91wFunNhNCxACTRbPas/UQhF/wRCzaeFJZnDAHQRgB93N4F8BblIHwACkYi0saialQVTBTKFp2ocBnSn09REmYhUhJsLXQFkCehVMrcQ1f5Yy9VsVMzokDmFy3hse0cAObO07UZTC5pikTw08rDI+5OJiYkB0X3POdmnOyB/TIXGENbsIrj0Li1EbWCGxeoCaSlBKVU4S1qryCzEWNaoRtAaH31WWfkggahskYiFvJyt7SKoVKogCREfetp0FsTKQqS+2wJxa1XfFoXJUnIVwxos6EV5LOogMDiXiIdtIA92iguLYghvTstfsX1KMiIh0k6OBOTEk0wAVXCw5nRN9EKlEBQjhBrhBdjBq7MkcNWWLBqHAKzmwQt0HtxMYOt8zckAcQQYS2M5folbwsWUjK8ExHCryarFyaK16lciGuI7QPJJi9ZldHTUniNJcMK4SuKNGACRqEJQA73IK7obAK34WyGGKHUxImxl9lUazspSanYQWIsNBAqCPYgqJTxMANwJkT34bgFp6ehinjMS8xS2Es2ZBofrWnRZQJF1mcJZQCXsHCIO6hQlqYMcQkrfbe0IGTRSTJwx1pZQ4tFJicroFFCpt1DySDiEMC16KE6dLRxKMiqUDI3EWsSCrBUkGtzS49OSiTHPU5RkQQghWnYtKiQjPB2LAi4/jIE5kQRRY3zYCrTOthc+76gAF3+CZBYCqz4OuoZlURmaEiw5LmktEcGcaDp/UB1dRsinpGnkkoJUNgXalz9IRbRGUpOOCL1RidAEAdf4tEK4RFZFnWF3V0ZsxmUAzyc8fhQqmSytQoW0KTDEucpBLEFMk5JLqUVcoey8RcmZxoBnElSES5ZlUMdQdB+JjNshrO3TFAYUUolLvgQaCwrUruQa4i0gpjgauYOhNBJfhucoZtpVQqnMBTCK8MW7ijjsnTyNZVvAgpMkjOZJlMpOgdxVi4UmNTdPKJaAVtVKqK9SRr2KS4MQdvk3AYBbNPjadVaU1KOoN7aBvrjyJ6L2Bjz2mCeazcdTLGS+pqpat93tOi1IiIhUhYEg0dS7VIiM7aZi2QFaHmF1FJL2otdaVTIVgVUcKVkl3nwBiBCwRCuovmXEpj1cJaSEOEJi1VRZA9At8BGCZeMvc998IKruP2vtY9wgMNkHIBSWPw3GnG+0iJrGTIJfY4qFgCvdBAHXUinVpU7pnYZjKSnFV48atUiTQlaQIDwq3Sv/8yD1cZxExTwh53r50bT1z2BNGZ1J51G9ohsjKnDBVYqwq9hCNPgs7KS+Kq42bEQG4beyu0CmoWaIiIk8PFM7jsyrTVtuvsW3x31ymds7ac4GzSyNyNMRuySJVpEpIpBIwhhcbuQT0QGezSqvIbjsrGsJpEBDNJjYGBCAzGxIFyXCZSwo1Z8gxrqwnIauK6MNfqMs3USESVq1SWZlJmKTthCygY44BKBp61rHpiYgugmHPeUu4IdTSjMFxR0C24AQaBzuSSylp6AxEhLbUwgV2EospUM0rCejoNNI+Afj2Zw9qyKJKsiCam5rTq2JyQwuRr4SSxsCt+8blvspKqtdWG7332RFaBFYWVuKC4g8Lwmim2wY7ZpEoo3jSwlCTJlVqGWqTPETuMS1++CnVwrJI5SRZJTGJJgU9WoapciAkNiEZUYMMwIonSSYNhHwezyXJwYiIVp0rYneWeWYUu2DySRmJEYA1xZC5SiGAcsQfaOxExc8MlYri3El66ahYvaBXeeQwQB3SKXtoSFlwI9hR3sRqkSqAh4IA9fIGEZzRoJjgYjwR7yhWDKTp5h3kuTILyrpyaECcOAnlMyOMqy0TC8KVAIM7NaJUoEhZEVDgyBiWYxYg6LEbsgrdZB7VhqzGaJSdRKA/IuGkrU+WphOaAwrejv3DuCnvLNlFpUYo649khtb+o73yKedV2RYwpWmYhKgHINIRmzVkDi0tve720AkTIJyrR+0KvsKPs9gbtDxdzkNAtwDql0qjZhY2w/Skyu+LYUQ4dxJY22KIq+1TmBovKUrgGl46KQjgdWkHp6XCkNqZ02HOCaS3QYlKVvieIFgmxjB3Dbo4anbtjZyed1CJHLJaEswrDARJQGX3IV9XHFL2lVRJBPQZTGNFcdFtXW95DEx0Du2Tb0XiZ3PYdTAsRMaeoF54b7ZpbdW2zwBtlkKY129bexVwcVcdNFJMyE5FMLoLtEk07qjtJK+mNekVWuSA4F+s679h6IThBMpmSWJi0qFQnR9IdSS9DKydzZAZ+MBxgAodWA5bgAZbgkKRo4WUiKigTqQkDe05CXEhFIMeGGTudtkG9p+9G2e0m71MEPAeRJwUJAQQwSEa1GXBbGDvMUTftUaFShq3aZKV4ZmvElbIoAxOFFJGKq8hfFFuWgtSSlXiyVm1a2EGJFMfFgrYXaCPQi+SigwEARMRBjIhckiep05QF6ymM22svZZmK1hIwwKzEe2oKdJRm6Z2noVPUQqoUxVHWgBAqEHi2wTyLBWBN8EJETNRUVqUW5E2qMCPTEC8xchROLsqtsDJnVh572VcDa7V6EwhCVNSERxmhQ1TLBD4ENhlsZNNUppDshY2X4pIIwVDqRMiiBrMnaWWZKCoZaHeqqG+jDPBcojCX1GpNx8SxxzQRmhmsL1q0QkhQy0ApKWouIQMNKiWwBHExC6SYOy7TgJ+EfVMJ3Ge4d8puI8KZlLkxAifaS1v+gQkRcZIEGVNvhGvcog2fPLXWUifhKsZKNEmdKggCDwzEbObYz656rqzshbSq3NQmGlWFimS6zDvJzolugk7kLVO1irRaWYo7M3lDD0o0t9A52IbzSE7kDwZIBE1xhEPXXImaEfIJs1GZVrAEYMFAX5CJlQiuOo1O3WEgdTVa81JBedvLsoIOlXZsc/Qc6CJJwWxK8yRD4ZLgLMJ6HaOSlDpxRfdG6pKFCMUs3gfNBhnzRLE3yjSvSjCy7NYRDC3ccrqnRdlSnQn3iNAJIsag1xFCtKQjC2shDZ4671vuUa4kWpGpZqu2avOKMYHINGfY35XYMjmRojcBmnhpo61or+5N0cRWrI2nrHVbIPk0+BoZEUyF2mSt7DgMH53raEvMLgC0m2nXdYdHh6AgEuaV6H5oc4H3DEayQLuzrpRodAAAEABJREFUMZ6+0DP7VLBa+msBEO5r4YWahBBQao1HI/REapRNishq0F6PldMK9banWaQTd7RwawJoJMSVqeXizXeZyMLScApqwkGlqw5OJLPOu9Vu6FDLCYxRAcCyU96Ke5gMzEfLIkLSKRdJVZoBRkliKQeGA6WYlq+VhisHlkfh4BCaC2qeUPw2aFh4LC0JVHgWsIePGvNUchItkCO+Rx6eSCAR42AANFJGTtiEexEvKK6kNLItdDiMQ4WqFFFJxVSnIJ4J7OL5i40yFtTAQqQYClIyLLH4L5cbaZNK3IAGj5eJIUnOIzKEZWIpRMSwilS7YyKj3oiVYAxX3YmCC0ol1VwM9rOqlETMOqy4MYCCPWLgIRRaIgztolmSsCetgng6GgjIYwfT4ImcncVS3IgSJkmqZGkzhEUCbnCEAAeCI2MsYRzAmxSJ0i3ik/iKloRgBzWQiIZZGODesRPTeZQ5FD0AIYjssa5U9oo0xMRJNtIjkSJQzVQ0UNEMAsAtRSUvSkrJ8N4HLjviEoSWgBsisQpuoUoTUyUqGI0QiEM50aoUyglmvCeLNOKyhINWke6W3gm5wtCYGJwyD8me0V3TmwBPF8N3I16ZeW/yqTrJ8OrZqDRd3A0secm0YBmCIDwSCQWcyrVKLUSZ6CLhDkhEQqKipSiXmsJBGcsbFkmTxIuCpHO1WqLw0PTEBIytvMIjeE1aCPAMXO+WY3YaJl3rmGSecOIuuOBekuEHR2HBrBYizpEpjEoZSqhAB1o2LUO1Gxaa9CBMNyMLRzaCWS2KIR1ALSzKpJguCFeICgxx2Qkvv0JZdqWGInJnIpWowih3GYjN5+q7iTzFusyGQzOSMGzAviZp4AMuwawh85WyAv+AuhfGOIlHCGrBjN0UqZkRPwtTBYQKsjBC4Y8ZZIuREsnCOvThFChuU6yasKxMKAIplsDGLFRwYCx29dCxJFXNIiLG8QIDTECUBBD4SSJUmBiWBVOLSimeOivvhOaqPnFi8dAe3gPt0LkqMj2cejZPKIWFkG3nTArlLKVWrYJDihPDjzAWI2hodh9mhnEUnI6O1VSKwHsdjq03sgjkjM8gUsxiuCd4DyYEQ06WaZwIVZs6MxfVIlVYSJNLNPxkuAzE7YjZW+kKgpM9angRLnCHAbMlrHskEjRERhGSJTvJnMosITyYZhzh7MgvNtWDFCgqM37gfpAIEBCjp3ZWGGLAQ1fOQWACt1iYktITB85htxBjbVm2FKGqJVtV1ItimHhIJpMn1moLWAq0hQQnNQEwqUZHB8MAooBPQTcuiFArLJMTmpdGhASa4kghZhCWSlqltIodkHCmkQXUKMy1irSoaoknihxRd7I3k6I/hPtEuK9Z0WjFEDSCJWFWVkkpgYRrAeQa0iAqEoQbqNoxKFwkCspRPHFuIJ0xHETpQhCBDiUWSuFoktVEByWWMZ131XaKgkyDMFIC2CVf1YT3pSVo4ukITR7eCIEOoznIILCKYvPlKiWBe1gsyQwXTCSQbmCLrWHqwMg9CxaSJASjoH6qLGIqVosDF5YI68ymNQEPUmbiMB4eo2d24hnduoVMVoRqlhrKCX1gv4cqziAyTV95tiAGmmqOTVhjmBdRVSY4dg5UC5PAFoF7Qi/RECkaWGfKEj+jjmgZK0JCRrktumgslqmOSUy9hu2Zok+oUmoacSSD+kqqVbVF0aGlo7/Mxb0xQfqsiiKwaoshqDwmc6TKnUeWXQpo5EykSQyPBCgIMQ4g4Ciyq2pFFxfKLmFikr0QyAtSgGXcAg6XBBABnEFu4JwyYBh3LCliSUQ4I6GRnoGItbVaa2hzRtgJS8gE/DFLJavpQoGL7BEgKwFdclWGCmKoLnueKyYtwXBMqoIf2SMXbyMxicgYmljahTNFpuROzLDVXRHXRqAwDeM5A42kyiTaFurhCNPTPBP3LdUSolr6qEK7DmzLdfSnIZQVRUCswsxkSeZL/1pYodcX7qKlFifJWMQE6DlJFiaGUhnJT2kkgBeZ3gvvVAN5FSncJAqL8OS1zaq25FkFzyuIBpom0IwnhA5ni7VAX2xCBTUblEkYNJJQhNxM2NmIFoGjRoFJglHyZXiJBG+qzMKkxIW4BmMpkRCu2WpOBSFtwUYQA3bjkIkIN0jFWZwQmRubiWFph2IHJFJrF3jUAIWQWXUv5HASBEUSu6UN4VAhYcrwMHZSxh9HVhAo0KMHByMCIcWvyPC1kiQlTUbVeQmYFPnFRLbMZbZlQXkJw6Tm8njSBHVOsE+hBdmmQkWyCjBywlZCEzJKHa3YJNZKFvVascgVxUOTV4+FdIJFDXgd6k6Gy1k5i7AikCBPSwgoc+UhndIzMzhBHysDeraKXRfV5UMy+NUoGE+htGSfYYrKZKviex4lkjlYKEnCBnmvMqQkXkQImLSSFEpdwiw4jcBCDORcM1B97qCSNLOiTMcAjMQnXQJnRjGyGivqgHQUdqUsJgAtkUshIq9FI0oghqB0KFoVa1ow86KY4AyKpdFVGoVCk9ycnARIhFVyF94JLHV/XY44ggZlz5iNfM7sEZHGbDV5xWWPuMFSOGcyAlz+IktRyRXgZ8auA4QS4UbEYtJK5OR1yqLJLgCOCzSSAvvzqXkhUhgPAWb4cS/p6lmGiKU7BylxsgylLjFneDiZpQVZo9Al0Z2mmVY7guqKJSMAoyBfssEG0qIJcgBGgnOGKUJ+mMKIdk674F3kbLHrPuMPvsqaQwaUpSRpmhCtGDIcvmxqOwWiHJJOBCQjEmsNEfb9oSBSIio7YhUSGs3mKXolwJ6H7dww3OF2roEV1MjEVa02n0ANepbHQJ2FcWAQlk2s+BY8tPZXxRCoxiiSORRbgIGPBfuF0lhLEM0hO8Lig9iRHFjwKDk4AmbmThaUxEyLhlDVLClEloKkuHiSKVnhgFIV2FGbROCLnXLowO8TXj8s9UAaS6aIkSqxPtw6ABe2ROoTGXUBnBJRYADUIEuFm5YJ19OCZlfvkwxmJq5OeL7judGoJMIlWIJgKXIAHhxFJapUeKwEVIZGIucxos/UAx6EGuNbqkkNUSyro0gtVUqlJr7sL+Y2XlrMxH2I91wgGOko3DXnxAzk8tUoOSUDpKsQiwYOJcT12hkztBjtOfSUyYgpoQEISSFJcB3BOGOah3cxasFCoAPtQW1qu70a+JWHRoRhXeExS6KfOteme3vSGuhWds2kEDf25QuLTomUdugrwWJaYyviQcVMcYcRPTzgAk46qjQwijIoyZ0zpAZNTmjs2kUlwf+EEgaVqeKphuFFqTZlfH2yQsDeJNB1CPZBlTA3RZtxKYFKSCbSzEIgDt/TWMusjNKkBTPEtwxJiV6HKYUW5sqoOldGrpdeTZMMIRMKUaJWCGUYSiMCcMlKUpWQMELeE9ojgkJmvJMwob7KilvVVDFhLDUuQRzikd3NumlHPNklZpGhDUpShqNQEhFICNjRO8ITDJEv6hzJA+ihSCfGsAZA0oYWfJM4NAaJkzJusRd2yRSOlDlr54ouzOEFyAPXCI5axhRD0xIz3cVZhnBnBJgePdzYlwnNIGghUmMwwa9hD8GCK+xoVKyTaiPIjlBrVvtO560PSxdckGXJM9GeMlItKg84IGedV41WIhMLNjhVskmvMlSzNtECFh39oe6y9awGdYAsYraqndVVBcsdegwWfRFm0FWYsNsAV6Ycy2coQwWKWA5tSlqZiwZKZg425CS0sKCBpJILE0kuBNbIUgyhlJZVSoml0MPQfIIKFRVYClO2qVADemViIV5qnImcyIiJlaUm44GsQk/YX5W2mC+4jTg4B7qIUGUpEDQjAyjBHZKD1SBbekMstKywBSu0JQGOYf6iBV+sN6KJjGhLNJeM4kAA3aDKQpH1DA500lQGDmJyy4FlW/4tgKImAh0FcatttWp14Xss2UcYLtYrTCJ0ZaiocbYgSWSghgt1xXRyNoINIkQu4FtzBiOiOkgHE5oCe5aRAOc1x0R9omRhmiqOUcpMqAb4BdeoHvQDs4TcOD0hyBwAzDPgsKQSjqDAq85cl0bkRmZMFoQyGonf105EKekipAVpZUbdW2u+aqNC0Ba98BYQAdha7ZWDk0Chh4fN5sv3baUnI7AoFhAGlca1LnA8I8I5EYCzWLBEQMojEyxSpSwJ5mHOoUwYTQQBhJFB1JNmy+7EWQtGecTSSneFgWvmxH0cGri5qrYiZa6uFaQxLV/bRWqGouXTIHMZrhALHLN0KbFPhO/dJWC5GNhNhugmianNWpaB3bg7D2eGkWAkRS00PH14mnOQJJqIVx/iuTjLklQZNDYqBTzQyNeSVWIVCVZSroUrfooQhdtwcyiDU5S1KhOu0gjAJU0qy2cC7YngpqiY4QFMM6lxqngl52R4MuhLu/uw3WIS3/8U8BAgoyS0xAtASrSoXByTE6FbeNlL2Uvw5S2oJQli9coOFJQggQR1kvjMycIqqDcMLINklw4EUkkmzikdhWJFssIGCfJNndheXQnlEgHuoo1bAk0UQXFwIdYQxmCbMxwiJUnKrM5tWQndAyIATZgznDCUi2AOYeF0d+sZwygXhMRS0lVCJJM4qBAxEQk7oqosUgi/u9DmhILSBXryEHCQUnMpK7ESjgkYAA6CCHgSn5m0kiovF9KQ5w4+a9Rqk+QUVEOUiKCKyjsuOxZnolwavbIQUCQz0I+uA6LFQOWlQyFIHgkvSKpPGCYOjc6EqmBsVLw6FRRrTlIaGo2quBLkxezQWMkiXEKW0sFnUS6UE6piCUNalJWwNKY9gqbVNYKCcslwBTmU4qJdpRc2BVwnbGsBFpuoSRZFk3taegRybIUGKy0LQYMAXNM5E7GxsGgN2SOwy8RYppZfd7FXJewvGhzMahaOLLpyNAYYHCAE/SdGLwPf0e4qigzMpuCixOIJOgqS7rK1DPSeGFMSCru4cCQN8+5EzIVRZATrxqWvmq+KMtWBtGRLEWjJw+bIwWi8ZEqRWZynQJxSSxUVTvRB9pTUxiIIiZkJN1I4WJiFWB3OUyMhfVRgyQADu17MidkEmAVTzLkHd5JENhJyIZBHRCI0EVVTHUVIMSWZs5acKhMUS8VIYWWp+QTlJB5woVRwG54IAkfX3BXqSlBWJVRSV55VTUoaxhbkxJolloyUGC0GOm+15EEcwYEEZFhxbOJwAfwpxgb2cyyIJSzEuSa4ZB6BzHuwgTx4Eyqr1EKFiHO5lGAVzWMM6bMINjKR0FgpjGQoLQxxouAbk7TCVTmVHFEM9+jCfbU0G9JkSHkUQz0h7wT/ZGzIlg/AgVZJw1bmLZhIB1UDTUpDsdQg9x08cAkoNQEiSgiLmiYqCGYEDSI48dnAbxDjdjATILJQpporruKyuJMvoYKLRJZaai7R1uWdX+MMHx6RSJwQi5v7GD26ISZiJ3IsZOGYtCqL/SCzPgf6GdqYAFgEIsnlBQAKboamSRSWqqpcE80wtCeP88kAABAASURBVLqgpF1qopRUQ2kukVAGj4xBNIMiQm/d51gVUwAJ9Uj80ZIVjZ6scFTh2rjiW7cVOzKAFTWKQqDEPbR37TPvZkTUSYZwrxHVwTQiS9adVzyqARlVDZEB4xAPkxbmZX8tpMxcktlqjIJ7CJ+Ii+BqUV1+KjsKysmCgyiWsJMSp1kFbSFUETZVUMHFy4oUrkPIKUFJ7YlNBNmAfdsTWlWhWqzsuUxFBPadcwZjmCVAQ1xIlQo5++takMYegmTFQleMCYkhE0IoBheJEqYI4kzIQN1kdDYvw3kE0RJeAbYhGlSYBMmNdMmxVI5Be4OQjU5ICJ6yakLbCWMU9FoaMJAMSVfrwunMFqyEZkyai86AwFI8CwF8FRHmnrnznFm6lsElZo1tSgdQeJfARJJUQMlEbAbZDk5gxQdNovSSNgGGUiIvSotlFfxsRouZjkClcq2MR060ahCWSeFLSY4kBwQhKjAlAi+VAgfCZzDlg0CaRxQZkwzswnGoeKFsnSeDOhkLQ3dExxxIjxSGFRQyzcndOPprIAaikiS4pFTSqoRRmtEs0VmJJQouleTms7oRMXFVIaSWKi9iLDpYZqdOPCadG/fiUYIhCq1UCvAzu6oLL6cpEpLgkbULDeqR2Csa7ldhIZVk/CzMVRmdUCwkPEGLIGsjxAFAEu6FcBF/lagRN5M6gN2IDQTRRLQiqgRPqYXRrco8ZDaNKEQEKArDNvnAMM0iUdkmZAsLEE9DNBYqmbmoo3PLTpFMp8m4uIq3tBYQHfglAW2pIgC23IIpLpKFmIPchThgTSk6Wc8glgnFNQMus6OxcGHEw4NyaVug2CBtUuKiBCOCqLCRQtNSD1xVmhYLqTbCvUdgFniaou8F2k9K1SiLNaQB6AxCcoYxIskuSCINABOCEtLFoHtexgCWhBZPxpLcKYzjVYi4HxgPiJxZmZCYJF5MqBW3NocmE8IiGJvVsVslSElTFJcRISdMZLoEiCIYweMPpS0BNskqQxkDSLQVqmg7gMSxBQAT9VKycBYndIIgS/GEYNzYwaRBFzjNQAYbCwXlIm2ky0tC1kwMwMI4hxyb05RcRKkmgjPmkDpk6lrnlqGz4Ll+RyDEBYohqRSIgJIyirKUjByZnTi4UAiTJw8iJ0HtOFNki1wlolfLaq5sVDG2EuP3UxPOOuZoOA6BMY4ARUgEiQSr8+spzHlILGHGLrxLegHUIgMg8AtJqVNUpVyuLfkqlOoAg/wRwQKrUEpaxmyQBlQ1lqv074vFibNgPNY2ND+klZQxAnsFHCJSBWqw2ZEXpcpYwEJzptiG7QKoiZLqyGZeCeAMoymrZqmBzQjAaQVFLhTwyLQgxTSJKKQVTyO8R9nYQeHSOgkv17SiLjW5RDJoTRclozInWWBEcM1eeE51YspFSExROn75MBGy7ypGhJUp5k7IomOO1sw6TNAoabC5whD2Cp5JwgyuM3FPcG9xLyxNpCoTMS9fvTRC8rsltnIOYKIC1rOoKqkIYQ6He8gu60zqAN6IqxRm8oBPX6h6XQ7CLZ3YhJNg3tWDPQi8DScoOAbIykpoYjDtZDPlLmkI8IPZCAmnFBoNNGXAkrfoE5b5SroSnYLqbLn8o7FCLEypFjqsZa6o8BDurCNbYS1EkhJG3hlHOMRT0msAEZERB1UaGp0cJVIHsimImBuoEeBn6jJD9NyXnQnqKsAmM+HHNnI3s3XSvvTIPafKqZpYY5yW76SYpBGKCJEma8UpLUgdhVI9mRdec6U5EeQhBGSKAYKwATNUGS8X/JCC2nOUXaIvMEdi/xfh7IhE3WQ4QnTOSIi7MmYUctzIYqzGlWBIhJoHjimyVEbGTWiXs2FZLlzwu0xru1Gfve1oNRPUFUMCSVBkWS1oMBgNXkktrVAj50A7TiP0F61a4ABunGSoRmUhqBu9TBboKky1IKQkTh8Z2AARsQpNEwmJ9CZ9r9qeRGEngh62jQy/32rgiUEUE4uISmEcSplcdzr1thpahqttWUZW8b3J237FD6Bw4kayH1oHtp97ya8PgEQWJXNSaQibSoguW9USqBet0eAsk8AwMkszv2okFHGCZcd1ElKhVgnPGEiOsrAIBiQTI73JmWpUXQriq0U0JAZDyCXqXlRFZY7ad0ovwd0rB0YUKYr02N7AMtQpnQh2S7LAnMkwxCjQ5thfeS1ItvK84rkyIUUoNiyJPc09KAKIkXQV4SjUW/aSEHfnpfQSt9W5cpOyj+c9XUUTZ+II6YnNiUCcuKhVPdsIyWQu2BW3DKURamMvxirnmrtAt+3Chr1crTWRjJg6VwMZbOk72iFSh1oRmKIqUnABzco9mTIsd0Y709SgVeYKBEEcQzvRVrH47wj6k5AFHoljBvLGRrrobRKw66KDJkEFyWJg8J7nXvAUAD8RYWukOxdzcAIrUmeWCDYjSkJcRI2YWQmbi4QM8axewSyTI+RsU5ZCwqrG2CFQDML6i6rmKoS0G9OOseSQhq/csUdqUScujYxjN2IeaircuKrsBSiUJqF7A9lbsRZmoqXqgZ9FCymuFFwK4kxhkkQqndgrj8ZWRQpDnIS6LsqAUIS5JElQTRcxYgvjYWX25l1oBEEXRF4mahOVWhqXV7JrZoVOiYTYIy06SMHoCPQVJiIGT9x8al2jM1IRC0lMSJnDrIUs9pV68vDJR4uoSF2n7MoJL3VCO8gkFDnCUAqiOb3DkhMi1VqQRxpzxDZ9S+5ZxRoetsQsOUIbSRHhgoph6I6QMCKCgHfCo1CIp/foFkYYskdlj6K4c/fMJEkl4s45lgtGlonIEKwqgTPGfIwKImEwQQEbkEI06EQDBeN4VgEtnlFa1imnRvuN9goVS7Idc6/qlUDoKmXljNwXOC2yZIqlDx7mKYmVk+BgsAxMBeeTEQ+lqGVZDLBnoBnAgjhQM9gO1LA6sMkDk67kIFUsffAc4EaBtwStZhaEZuSkmSyEH8LcjMsgsC81IUOEBJiqHa1osPWYPaFdQ3EUFvTMKVngTi2CYCqJ8JPSxEcRWmlp4H1mm61zeJPYU58qaWUpXDBEOHOEjyUrnuQQB1qkJycgMTEDzbZjgLekVWRL5HXkqtfJUAOhPthdh6jDlBPyOQb3uRIRE1U2TrKhY5BrUlNRacyKNcgQFAAnk6/C9xLBAkuGK8NTrVbZBbuMkbTsYqeU4o19IiRJNKhGTEtimGBBg/dMX42QjkpQc+HcX1ajnaoHZfTMmUaX3hn6dPFsvqy8kBV5qANpgPpUZpk0C7wj2MFjLIy4juQZUIiRvmIhtFC0YptiS4ZtTPapjVWZqw5OjKSR1U2cIkaPYUusKw4IhEtCFaEZKlSJRECOBivnkpjURV7EJCyqTMW49lKyLtIA2ZjGlLlgAbJEbJT4rCCjuoozh1PGwM9GOm1r6yq8pLuuStVWIQyDjoXIERPRDl/pycxAQGWkdKEx2YyGBAyhgPmm9NIGFxMeGV1sZh/iXlVEVHhVuRZmbbxskwHcYUiYiIEpvFEqSYh6JnhAJgg6BQJEPiZ2JRKM4EbU1HiJKFmy1KgykgYRwglUgeyizImTAs1SzpIug9W5sRZphWTRW+cYytbEq2RTYq2E9ikrcEBcubCWWRhfkPQxW2xNdlJIyyQ8KQNTYoQyR9pAyhVMUKOimNkwkIic0jLd2YdQZ02CGpJxhygJsREwTawt8bVZwRymXmhXeMgUVBi8sLsQsyDptMK1XYyd79zQrLMyKXMWw3PwDKwo9wRbKcg8eSObMng4LxoEZwrPJhHsOCtUcT06WSfA1D2SFS4rgUUnEbDK+ME4Cw3YIDIhnxL78k40AmEDnyAfkUJB3Iln4iFQX0mkw5xwOCnug3wTjWUCF+YgCJld2UgMVweJR8XAVTo2qwqRIA5NpBv+seCIQw/YtttEJEpWJBV3g6pnpVl0EEBC3SRmRAAkldAHjAeWoq0DUJEhCgY4E9QjZGilsBdN4eUCz0NGJthhYZXSnIqb+tyAlZyFUoIiaCCbbIiblPDqwYmgA9mr1aV0KmaUnZcCE40iWCatpexZXbkiq9ihhSdMmXiX0X12HzA9lHzpGZ10TGIICJdjTtnKoom5FADT1JpciUouyrIAx1vSmZSTKgGMk4EtiVGbcwurliVUgQ/Es3MZOlGtopILCwihYO0aLYc6sTE8kwslJfJdyUTBNzMxDRxKYF7w8kLo9Z3dYCawfGCIEPqdWUNz0Gnp+6DWiTzZRRvllC4E2NhBreAuEHlgC8yOstPCwWqMd+RcUbkldkKdyIgNHZYiGaBwCHiCUMYubfbslHgynCkMQzN62E5yW1DbRuyT5FIm4cQBIOE6M1pUpURciAnhV07Uc6VWB9TAVKys5sIWNXtDRyuR5DvB6rRDrWWgBTIiWryJkQwAgzIYP0A/eQu4FfAO0qtwwUWmRUwh4SK2/I9Om+6rNOQMzlSGaneZk2cSjFDVAuIYK0kbXhNEDppGvvZU6ibYcnhWJ8SBBwUmAzHB7KUlF5WpxapZQfvzJTdKy+S2zdJBnALfWJmpgaAczVMwWzSmwlrJNQaK13acu+XfP5WQIkjusADXuDw8rbhMXV+JZdgcMAsQpJYqDGWClC3lCEdte1Qe1Ton+JNt0wFfqEExK7tYunRIkhKBnMKujOhrbfBKTjy4GjY7Ar5FrbYuzYYkJoKYklKSOViICkoPCXBUDIgsAKJUJXCfvKBEgjvzQLI9CwEwkCGykL3ESoTZaBaMvFNwJsck0vjVfxYMAKWrgDY9fTZ256yCGoKUeYQY+n4XpB8IoQYmY0U2CJFQViaBQWd12GdKQWJGYci6E/Ktu06xQ0koOEWWI5p1S3MMVUarZGFTMBidKQhXRJKIEEVbcQNQFVL2lruGbc/kNFmohfSMwYyRLIV0Yl4pN9VV1motnAcB8w5AwB+ZcBQRqfQ6JRMuguCPXHhuZdd4ZkxwhIpFxfekV+TXKHvl3oAG6hPdaQ40MMkSVZ2Icy6EYsS4ElH60N0QtFnvtBS6q1JTElXiylRAmhGlBdkAywQUC5xGPo2+172FVM7kDqJVTcVoyS+TR7pFUCDfUQjgxRbCI7MHnHmEJUIJCZqC90ygYCIhJgr2PmU0oiZZijuNbu6W4Squ0D9nISoUrVSeVtam3cJuco1aWQslW1ifDfUhc5ZuE1lLmpYRzEVyRT7ZzH3XfQzyNM9hnEFQGA244BVxgROVwW2mNg/3PhQNwdRDx+B0JCK4MTrVMs12lJ0kWEVYK34mCT4inC7e1b2EJWIOFVoUruy4h3GiKtg8rZA262pgcYgMIgymRAZ84tgjm9JksSSe6ViJmDRI0aiYCkpCE8mFMsmTOg1iV/dqpDibChZVB+Zeaafa4WqiAY6Vg/AnvAtt8cVmiu/SX6hDNIwvHVZMTUKSuRMGphTC4WIjx5AoNYowM5XIIlZkRzoLS+LJBs0IuXSqSK1JBBvmLlpP5SxrHyrUAAAQAElEQVSexSAOSgQYM4VDLG5AzjJYZy8WmggFfsecwyM5oazWZTVTIS88hJYXc6pmjVGwasFkL8gfI0fFGJKalbeTzQrmTYEHvjmw6U9xbrxoPLtEb5m0WArmQWBODTuvFkhDEQeFJCSOe+xwVfd42ovanEh60C6BQznUiUciN5CNMNYIDJhSlBN1yj4Xi4brLUEs7TH0nZVUvKTVSKXIhMoDjhp3zsHGzFL2mq4aExi0gAKIq6oyYWx0n1EZZCE9i0dEN/bOSZTqpaU048lEqEKKiTwy/IipmnBwJhkTg9eRAs85qG2r2D7R5FV2WK5lmG/dB9iLEFC+CK8IvWHGw7i2EQWLe5inM/kqAkDDJYjRiKUwfiiLDuYuvjxYNsMiGQpBtahTTIoZPEXd1yYqJIkAgqOwEZmRQ+phKe41TKJG7HnuR1YEQjlSByWUJYjZxEIcc9MRGYPYbOFiVEKAwQUcsSfIUPQHJeLuan2VWy1GjVAZgyjJQUhgWhD7EgQUMWuZBeyEY0AUsilJTBiHK3p05ZwY/cUhXTGtziUBn9Ipw0vYxLGiobyjGqRJ5ozgkoigYSNHGoKVvbEVYbJKwWD+JWdfRkgRQmmJduUgAkg82tSRhQLYwcq2la3ICMNNSqHAqKEJ0rTOi7flgmmgaXtxLqUxJDURF0plFFJySEFqqFebpRsc55L0Xn1bCVs+GJ4I8g1iQOfdLnc+O0qnR27FdsgoTdlX+PZg9pIJkVfYpkhSx4WIkljZ5qgJgnZziQGNpOCLuCZLZuZAqsFIIWi7MoHTWqQW1Da7K/FqqtM0MTTXWraigjuquIOEUE1GsVVogXrwjuVl8hnbMyViLcHVAosA91ykGRQucLUQSTWppWooMTkLfuKEea5lp2yxXFFVwW88VGBocYjJwcaOz1MhHCgzQjrURJKZWAQTVtn2UiuicjdZwp1dsGwEpxVHz5lTd6F91G4lM5kSO4WGVuClpyclS61Rlw1lJ3EtXCoQcO8+z+lOEgmItOgeg4kxRVhXYE2TOdhBdlQyGM8gFVOdmVPxu4PWSgU1I9FyHaObQzQyStkqY2MxRMKhB5qJRiZHLo1KYR1m2ZcUiTAJ+ObRBSMwIdg8LZ3ceWexm2MX3WhrudvKdpYdLQVDTbKIp3dCOal7SQdjLOhqjQr4RfloIilSiBlschAcW1Y3JIu47EhnIstI6lSYJsFoZaLAVYSFnQ5FiYRuzNmScZbL4pdIMjp+Cr22jQjPDEnCC6fbjJ1nhGiyRmaSMaMsPGHblawZMMCFYIpwFxlFjNUBVATPplwpahlCsMiMUS1jZYGCY8psocLKKoSAMALZIlDAw3nL8iI6opGzoN3FLBHKGM3OOTM0DZuDIDpOLzqUQEUCJz4ZSaeFcw8oAVaT0CVqDnEjysDtkDkBNqJGKIWwUBY2TVveBtxCaCmIuYNwKjAMU+HsUViAmV2xsikoR4PGkaAJpIFyYYa/meuYYLzGLD4oEn3YmSWpLgQusbFBSDuiQY6ZUtAwKiPizB7RKVi4hHIqpXimCyewCCIg97QwDRO2IOqs2HoQNGqFs5AqYfC/JeZYnVmWz0G82JgyFGhSO0FXhYglgsEBsJVwRWkqLHVBD6I0SlcYKwIiOhpq7DQMBkCru2pKzYW9yEzSyrxslUUBIYEWlwyzhUlEmRKJ1sKVS8EFOOVgHgoLmsqB3McgCyGHu8iBK8GJg0RNKRm3TPATJ2lobxFllqnLFAxNqacAVoYhUi5ipZmUwEo4Cy/zSOi1i+CDzoE4qFpvQJC4U9WnYvstJywEbc+IdsxzUy/FBfOQm2UIWzXXYeihBb91nCon2bBA2wD4WE4IsJxKR7TQAxENWpaOQLpxBhUMqS4SRBCTr8jUIo0TGp8lHURwapIMEQOLlKwpSgnMk9OelIIWqNiCSzf2IEHQkQsAN2U8tlkwBfzCaAyOZOLCpQr6YGM0lJJWLIsTsysNJhMO1oAnAtWMl4N1J0rShAoReBMUEQwa06A2SIzDuScRp2JHVc2X0sIgLoTmiNvW2UE+ocUbI8mFk3FoIW0kLEgzO0N8YdhKcCgeMJDJyi6JeAanUyFvhKII9SzUa5mx3PE0gnuAtV0KugVRqI5WTdUdcwnTQSRhvlgo1r8xiAxptGTwRD3VVbhRlgwyvMiSkok0YEyYpQIhBQNYGrlTYkREwkPVJE1ncQQbRRzi4hgaUXMUVCHhJRhAVgIjhYkxnZzTMmAJkYPTnIpr80DNSXIiUGARH56+yFizJi4Ts3qpCSJGIpjqaKBUDChxG9wHooR5Xjor2xTeYlk/sR5wiJBMmYvpBcDyledWaBeIR8kKWQ1AVc5JEqwjKMoBKZBQKkUQWy77bmbSUE342IvebBQLRB94QaLKlCAKgUVJr0pFwsNHjO4B3kMyVYI1So22wpK6QCJEQCCrBDZGDZkbksbmoCy9dSpo6czOMI2+DcvLfCQtpj5Wc9Y51RiFvMBEPrknOXDjEMxBiERL70pdaCIPdDM1EnCvzCzRxfoCAbcziUfiA3OocOPaqCEjjKobqYN8YC/TArJlmsx1YYY5UaHEycE1MA9lhHVp2SIxkwslKqaoizhPQk2ZFOGm7VkAPI+ZaLiMbIHapmTtQgaFgJwkPKROgZwnuCMipsBNAq34BY4zigPJmxJfhSJq6VyQJxykkpOOKa26S3d0UBgUGCekgGIhIJmyCHNhlwjnEToLz6yWhM/4jiANTYyJVsIKuQtQG9Mo6LfVe8lRI0twcUDnxnhpqHZwSoow8DmYnMRgMCMx3Yf0kFlz1nBZLjEM9SxDwE4liA/LIlOwk1KDTnoiiSMXepxrKjGkWgGKSSB+GUWh5J6pgacMm2CCXr94pkAFFh7IcxIF/vAgNZyYxoxiaAHBAfnisIUszxdkbEadeBYJYYJaE7IRAp/LplCCMn3JhqZH9w6/0Aw0nxwsoaHSlQaJETE49WUCXDsSiSvCUQShohYIL0c6k9W4GFdygXuZ8cEAWwkmyJlIAhnCFXG4WqAULqta6ySiSYUhZV+MCJaUIEvQDRWoD0YsZpqBps0ARUlUWaooKwQRA5W5s9IdvQeC6oH9FpW0KRzCDkcEWdKFjRE/w4oUlDOFCXhs6dBHDuKOalYSComh2RVBArUqaeWpLv8oGebqANtURFkkl6MGo5kxJMShJBpSOhdMpx0Ui69QgpxicHR10irSSjA7EtA5CApAKBzsASwiwsJZakojLsyEG0RDMrArqZLKCYa1YcsPEhM3DHNRtyZlqCKgkbDTXJWFhDAeVrQyT4w+GrBHAQ/Cnlgtk1pnfK+mI7hYrHrU2XgGwRHsU7WmM5PNFElwidqgspxrZpESWiNc0Kh0eKVRklgEI7JSTEZlMKUEZjDkQpNmmVLx4KOB8Je0kAbKm2upQTLSjTrn4KSEFFV4T6mJwxC4W36zkBRBFqwhjZmJKLFYDleMZ2wh3SMjk3nBG4l2OYQGriC7MSiGJb7KSDQ8ByylxKqtLLwYlSwC4MSBB8kKp0wYoEElQSEYrJouYYBFpmS1BC36Ke5qQYHrRQLTQ2yU5XCNxdNouNwCq5zXToo4HLsNsompojVQ81FzgAJZxAFDi3JHNW+Uk5SJJu3VhvYMLwMid+jMiqYIAEoMygzhLtSJ3fAhjctcaC6DuNd07E9yImsEiQRXxucqWkRYU5PKIq5Uck7s0w2KytoxzH3q896whr7rbHBRGcuYOeo4W86KVSqyJ0W45Hb5mOQAVpnaMBm7OYfBLJiqnmAhkRdDOiwKqzb1qWKTE1LR3ToHtJZCJROlP5xG8piIJpfqpUBaIkRNaaUklGTsIlEJgiuIShCXIAoiBM0QAa630nFERQxmGMmlWJ2cp/CGEUxeMxpDvIzFEI0JZRGJblYgLhZjHqyuzJzNiQE7QgMjDLmNZrTY6mVlDDlWWujkEGKh5RVEGOqDIsiFuvKOY2fenZJFuE7U9ghEI01G0rXMLgM8eBjRyBY4nJBTQloxKYkD0pYYTa0JqJgEed4JI/M7li6CEjIjFN7M7MkZCm6gEyrCpJwqzmSoMKVaEpARKWyGw+2s2hn2wdQO6qmVy8S5InDspLlwm4QhJVLoNZ+cCek2dYUkMFvrXLU3HehQ/18W/kaxdR3nGUYBkpLTNef+7/TdjS3+HLjPl+W2iS2RIAhSsrNn2kyjNcsJ4igg6C+9t7I0VX80CJIJ0LfmK1AlkEabOVX3MP1CXLHXCihWkap6yu7XFLvjHD/V1Y/Vo4LDY9/aX7dU4lB4X5OA7E/rHx5MKxifveDOAIwqqVZiql0pUrF7tdpYOD3SKVd46TRIInUZlwUYGpNa6iP63ULY+7DmyZvS7zzN7DN4aDKNXF4u6a5lSoTSA8yMsUTndKkbIdtK+XjBiBiHk67Lwq+RfuY6uMacrngcFhQXPRn93fMlE76LFybIpVxFfVMPjB+Npmni1b20VKt7rKNqOhsd2g6d53SefoUmQp8z91hPLF/BNbQhJpQ1PsUegDkoyi1qi0D5kt8T2m8Q1xETXOpY/r37v9/+PbidJ0w59UO/3YpFa7iSsw5kXAEcoLOVAnEvrWtEn4UK5eDTz54mbKhFXdT5AkbzMvQn9lF6BLGgX+Nl8bwQXlJFSGsFUUeyxK66TsatdUrzSeiGBp/xTQsk51vImuFxft0GL0TC0bLbsFMqnnD3JSi3QTV13J7Fx0ezVeZ7aiM/w39c/z81NKL0NWKVaBZ9UWsdcwwopNUzU9KUOJj0k67cVa2WQDGlJhXnzOi53/OF9A9RDk3RpCwxhddOIg+zaF77mqVDpAjXzQVTZV2jtaeU0lxrjDblJRWbIRa4rUJh4n29p9vExHJZGwrm64wcDlbaalsThFvHJsLbA8pWzzNSfOHQvkRhsv0764v4tvYClaVzQ3VTvBqbG8LUxzunm9ppHFb7b8fdWlms33IRRIKLdKeJ0Z1nnzFll5PWqtyT9Q7UStiRwmFKcqGjZ6dQnUJVo0ZleyXt4Dzyd9x7h/nI70mRulrVNOGgA7AZ0aJ2oHcpT8NS/O/59tP2AMenRrDzRA2lgSf8qX3f/jzWzoi5mJ/OeGB3MBea1uOguOOIzFISRMfxyWEew1cKt2WMtQDVZwyszG5UnsO741iMLUhf2qNpiVlNFxkKsR9Stwm38TC79fWNbrpQES1blxLcttpdS9+s1KQx41TkrfpysjVPK/748rk+z/qRKq6NrQVs9aghPWkpoCeQNhAC8237BThjGJOg+xpVfF3f2UqB29nRZq5/wKSLCZGJkPeC0RbdelWzMCDfQI8DUIm1fg8SinuqdLUI4izLcKZJHOvcwhNKRZmuWYPdB1N65+Ocq8uQg+ctC/ETPnuJec5AawNTHBDwkkcCe57opFyFw17ha2y9RhS9bJO6NiUn1ubUGNhwZXxqx7CFViyh4B02ssqnqxTy+F7h28usoK67qNxw7QAAEABJREFURt4w/Ua45dfomqtAV2O1mTkWx21MrPrImGRFWRsgYUk/l/MT76A6numE5kBGpIAjqIba6KWpuXATX+7kmrUoky9qvpbw5GSPYZZ6RhKpGikFBmdH14y1SByTX7wvTjWacEds0JUQsCY6tOE3m/FTlvY6uVBSnMV6R5ZMP22PZuL15fViHek1b21aV7dDnLzzyEEpVyKHpKAOumzSxCvevprIAqwDsxW1OLiIEGG9ZgII/Sxdq6aYAF7s5s3ra34PckrELnCfUVVOMd2Ppq5BTKIHE0oglEqqiu/Hs3Xy5giksY1QknChtx+Tdle/bqnxVuU1UAQKJiUUVYrEoKH6XtR1onS9LnUIdfLpco3t4Nl940wWOpnJPq6VM1BtI8q6EY/tR8vpFbaHPwWXExpWwNZU4DifQO5WMfFD+mKG6O1Sl6ogNFil7RIr1PiqEyJURNCNHmjuno+1ysO2zzu6IMNd16Tbg6hyT40EdVGgFf3zEQLAaRJfbnyXVe/Jq+YDvzSwiIZXL7SyglqZkWMy3VOo0XX6a1gkSGrPjM6hz+D0HHVWS6GYZJ3IAsQ58hrpgfgDKdQSgIkGYqdI0rcvjorHKUDqAZ4Wv87xpidx8q3ThTFribZU4DAXjDThktMC2a1tSYxdxndJVGLeQ4xPcEgJREhsYtmPixFtZDZPWAVntQgYo+Kwu5HZ3dUK4HQc7ArDB/Phuajy0A0CwB6dVbchVxlTPcBSRIqe7qfWMfpGxKHfvSslUlf05rBttsYgtjCPAhN2cYOH04RIhw+9bErRm2rEFkxTmuvhVmIW+mfuy1pysllRe53wO6g5lH0dwZa6cCsdPTL60RTUNQfIFhU/M1KkwR/zh2wYJYiBqAbE6xqVKnShBoCMUjhaV/usfC5kKAKawa2ib8X3qWPzKAXHb0NCSYcd1aZmK23gOL5eX2oCsElf3iadNSjiffVabauTrLbJ4H156Q4B4En7ljI9MKPBpr1qCrjfEvv5tc/pba1OaayYo/d+TYebBssxE5Jh2szn2EIo2Cr24zMLijcmmc9UK3JZLcv0TKIVpfqSv/yc5nkD+kvT1HktNmCoq2V2eLw1S3mFRVMc66KSqwugjB9tm1ImmGazvZergNSDMcqt2Qbetf0YlC4lectys7JLgdYsWVTcxnTkgoIsil3PiUo/QoNRhsZy5kjAOmSn4KNjOAqsx5RL0qzolVF3pH63JcVBa6SRStIYJ95fOj3pLa4xXENrjVsTWjPGrV7SUGAFZfTydseoq7ELYLjHcizPN5+TysDRFWvEI2K72XRw9VARqtgkP9uM7WFu6XM4pxSL3cMH9hot9STfs5bFu6mUFOQY+AuOpuAqlcI8OiMd/RwVJmF+LA582pxglHla5zpjrUufL3eWEI2AtKeiqXfeYtBiIM3A0pA4zRveEo/jLa/SJv9NxVYCW0FCWrYqKGdEjT/v1tnvGeUCrk5f+3l9iy3o1VOt8Ggk6D1WGGV5pYfcNeV1NGG57Q3fXcK2xICuCC2bNoyZTfX5tkX7AZc64dP8jot1eI1uswziQ90MAwq7iQUGhsA8rO+rBqO4aVofdg86pW0eNfU+4rP0ZuZlgg65pA98yq3kVYVsBDkiQKZelZTwf9LVFF/WSnu9R1l2Axa0u0noNbAccRIVuiAhlNkXq2u8NIwqoaML6nsrU4BmzVzABoXxJLWq3z/AZWNQfOpzp/XgoI4JyZjgDlK4qzFjbaJLgtKimidGlG/bur1zyCEGnFJi6D2Q8jEHnT1DwI1Ojchbd0et9qbdxy6yTKynkbQ3ZKxp/2opcSFcIyPTnWMJb1MzEIVHfMONsbf5kk9arpVXTLjCjHRXO+oGa5glDJWu9lnmBnvj7N22etmEGBk0rF3B7BkBtyS0BB3RZ7WGwmHzDmpBIPEq6zZ73tDeHNrV9AEGqb8hvsd3amAXq8UvtGNvSTvgXityr5fFkTQMs4+FbuWT51QKrZeD3lDomxNgw0WzjwC/mmufNNXBwC1CGxYEQMLgQSoK5XwwzGnpnBmWS4ywQPSow1F8DCiDjjG/Q98LsKo4j8mvaUfz47bEkxxrSHDC6vJaOukiHBZ0GzujFqzCoHIuaW2t72pHj+cN5Vi3kuoFhyLUrbBy1p32pHiiu/kiXDNbLEep3Ztg4RmcqdK6YK2Wx+UEHIoZHBenRwjxvhY0VLrodliIqQ9zsSXQw7xpDZHs6SPtT5udhG6V5rvwwLo5bEBaAktbkIiy+c78wpPGHXGZfaa36eM0+9Vwa+J4kfPBfGrM0EtYJD1eQ0kBF9070AYlPhGty176pF9WQmk2XDZLq9LVHWOjERAJWgJmCMWu9a2+cqV8/V30wRtYP+eu7392aiW8oJZ3ZkaKXkOm9XE0l2M7w2BjWx+HgKxQLrK7NYKHKB9N8REy6jXWw9Qbh8uIlHOAR+LV0K9DXed9WNBap7uEiq/NrOoe2ea0xCCPu+GHorRMvxqFPK9VaVg0PYE0uXXlaPH5LFw75rrOWs+2e7/VIKLVKgHYgn+mwp55dVMdeew8M1mKfFPLpkpCGaDS/MaUJ0ubgummqkOurWhG1zukt72dAeKKDCE0Jad4pBDRFAQcuSZdo40zErZXOzM0TVaaBlfQTpXmg1PCOoPb8NAFQucvFKnI9GM6Fe3RYROk0S5YwOftuNaPnUOkEKme3doIjaEc6C9gOUQB07CnPduHLStTUb0aFOh1tVK+sAJmaBUYBzojU6HUzwYco+wGn2t94xq6NLnbXYkYxyI3knjrUTLCY1RkJAZyAN7yTRshmTKc0ZKPGWng1iYRz2YtfZ7+epbECAmBYhIBUiaOOsnikapk+DYtoXIi1kbdGBORrpR1ELtq5/Np/O/wk/3BLcZFUzKyl0/ueZwqXkUHe82vpoJuicLHjfI23urRikvJDuOiEz2ss/2sq/RxLMIvNS+NhqLA7X4iWl3447MMpB1MQTV1e/36c0sgUEKPyTjnhm6ZvpC7vUzJtkwvAqjEk+ecb6lgUVP+l2R4Wqg5phiVJmqgLui0i/A+Vl/vx/2h33S1pYHGmUYVkDsqlD9jyVgrUbY4uin9hAep9Uk3vj/qPsSI5UBteWUZxoC/k/maGvSCVLcpArbvy/2D1prA/7r/i3zYKNG3ba7rtuuRD+wwk3geE72xztr5QXx+Lx6ebojWYeKnaDRYY92wW0ZmvEQ8IQjxQK2UPvQcpLhT8DMDE0DusWhOdVlTGREyAZwAd+n6GXyT58STdkbfQ5Y4VZ5zQStkzZp7n0fxrSpoA5F9sIrvf1SM3z98gdmF9egqnuHt+B09K+7crsytg+ub+g7XhHT5Dfvt/i3FVj+2fkRAuh4GZT/TX/RNHDEymgkMbmmkVVkWttZiOBEmUqv5wN4+Jm3gHTln/MHn6/vMJ+uamsZ9bB5GKx4r9eb6xL1DS678K5Y5wiQGG2iZ+wW/duf6amJLNDOL7oB25VVpL33kUi0svoZE65AErYePDuAgmzWDURp/L3uW0Lq3UUwHQLxpqfFpjuRAaa8/Zhuv/t7w+/Db+ze831sEiYMQgj1CaceiTLmHXmbu17ZLHNNFCNq6c567S19bvciVYazg2tGfn+96FZe2R2TY9yWWUxYDJ1In25WRRqn8ebn9BH84F2coYV4pj9TG/OgvekVdlm6D6DcP88VVuJpoZv4V2mk1lmWPuDG68TPb0/AUOhFjImRY4hc/bLkJGbMZgFneRe1yoVARK3HdegiTC2nr5A9uUYWxL/04BWyoqpoCatB15fmU+vZZT+/vaEnqtmRQsjf/lEnJsv+csQdRJIDR5TYK7GjcJ3+2euLDK2sVXFdwkVe1v/dCbGV6LG2NqTPyQLALZBqenknzsUDsbaH1aJ0LGWdimqAvXnwHONvs8ZAU+i/WdhMxBD7EFfws/lvQVDwprA+swuqDEyf5KGTIFNbrmw2wGvqu4Xum+qUBQLD024A9448pSA0eMLnyRTNCHHGjv1BnwgPcNd+b+Vw2m71Kt3u90pdF710/PtepyGcyx9ovLlPGLGs9FY1RApxl6AD+KLCI+OibUmhzuzm0foK/238D/zm+kmt7lTVZTpD+wEvYKpVBoTZvfNo3Nz7/6qO3F7DavALlbaVmGH6u5fSACUGOl5nv+Xzw2biiPHqWFxUznbNtNmgA58De5dF1m6BqO47e5B63sqRrBs/av9f+7zI9LhrPGGlwQo7QorvbG0tprMNWGUDyUasZa5isjJFdxOjnRjWLVoJohJtDfX6HSmV6mALED2YbTJ4FeetDYxNL1MuFzMyb4srHSj2rCEKvqmlmR8HgGde93v+FkvbUnlCcrmAmVD3f9huTkaNGssN/LC54KFmMQaThtNL6DI6swnzE9L/HfhIBLeuShhrL82BEHF0DgnNJ8T5i1raWWl1TIqFOdn3y2t/RTqplT7asqMyf2c+2s7NHElKe9d1uFfIyDYdfIGvu7HtgFktJDSSYYwUO8wUGiOrNpqzsfDbnZ3FfYa4b9JJhM/y9ZlwT774bX8dh2Wj0E16FUQXV2AT78uylLe598vtUKQYBIm7x5kDSEjN2ROzPT/3bQ2Sjxbnifl3xKYoVAaeC0s3XOZBpKAL16oQTYvz86yb4wEUweT4sNyS0bKuBOF/gQHGT11Ja2DG+5uP9g1718NQ8dnQX9kXpUMfBmZlOu79x16plfbV023NuyDLZYsq9g7BRUamllokC1JZ1tEP7NzMCrTn9pAname46eL64y9X+CntgUqStIqdnhHReBjD6l+8kdTYb6bikAbt9pUxqWFXmvEyoeteutc9GaxWZ/gsgre4fjr5I8r3KPE/hF5PSAI6wbTBGtuxcONpsqU1NMYPPD9MwFjRboRO38UheZ3gYt/tZIDLmd/H/Lf/VouEJyezxzo+lmpjvwSYWJqb2PFfV1WMbc/EO+7rdikEDFvb/4J+wLW36DGf8Ppan1clCT19+LK/PY/9OqdGEY1pc1gwgDBPKHXOr8kf2XLMEV4owJ3NwP62EYhEel/lFxfmsuZtTrz/4gE9NdcpabN9rGz2nqkZ7HT7GpBvXGoM3nD37mShFdmHcBLeKXW+uaDBHrukNaBPQKiAeiNRu/bZJP49nQrPWOnG9a2N6ny7lJh8hXs/at4KyHj5uLeSsxj0UQEq+TIlf7yuUG208RLRE1uHuy53KaOxZ4R+QPTVZGNmi6Km2fm2O8S7q2mol8jh10d8Ur2x/8E6MfV+fe9u7B/VyVU1TEVObilqVpv2TjCpItflum7Y61kppwzjqrVoqNi3cPJbFNZKBhD05fZCtPhJShjHowTCqQve94hsckw9cJ/B1PK7x008DrTgUdpvXimfmgWy1g56OAxYIvTYnmjMXJO72n8d2YU1yFVxjfKDdyYaHprKINMsQaBMUtTXihJWvQaSyDJPZMQx9qB+9TSjVt8+7R7BDGxAklFOIiFaiyX0AABAASURBVAQKHKgegrXY4CIviOSgmY0cAIfTmquhGKE9EDEA3EQId+PnaK//dQUZj5uSa8u3w0plUS2PBUne0dRBlRWdscY3+jPPZ2bFwP9qZKS8B6+Uu6VS0aj5Y9OuySWbNiKdk4qqZh0RZZaNqowS5yMDqJE0gYGZuV1ee9123dCyP+imIh1a2tzUVNVwd2Y96k52sLTq8WR0Ge3aJuZtLLESn4zVRpAEVsM9ln5MCIBbOWfqWiA21kudLAtSDUoS49YcMoQC60vJVKbE74hJGfOFGLP49LXB8CesTBzMAfMVMXqqmZrRXrXVnc9zTx3n8cjX+h77KOM1mc9TpdEtQ0aOlc7TbIE/d15aNUZnN7UZmnVBibDVtB6q/mNkz1ginXTXHWt8rK/JCy/WcjYYZX68uB59DFjj0tlGcuTNrkdtB8Sq/vmqlVYYoTSeqd+H74ayt8VeYqkrH2HN1cpg+tPmWa40asTZa1QiwoLGtE7akRz1VpG9NDvAgQHbb2VM2dmmrcwGvCWC4HdpEdNqjnlq1eiCwcr3fHb/g60xpw+Xojq7KrKtH9MqlsL7GhcN9Y6yMStmy93Qi5ayJ81LLfk+77opDjCuYU0XXJF1Xv9Mt/aGGGN7irlqoteauCwc4WVGMGxJHS6fB66Ke1jdqfRHqzfPqkLVA3u0+o8EOARUCtblbycbVc2jzPjwGq0gkjnydfqZUKPcz3F9TLKLRpjDTcSJ0m60jM37uSs6hU6sttvQZpTc6LBWjYaZc7ncPsSZUbg+SmJcvj+qs1dH1+MuTRRbuS47VKue7+vCUKE57Yo/h2Py0DG8TDbXmAPV5/AkpkC8L4LmHaoroEkNnLVN1WWMEUOnezLZabpsbGMunKtmNYw0scHWSgCiEA8ifY18XWXuzvB26HOvnhB7d4y6Akx2hG8EUfm2URApqVAS92f4NHN8xGBICTHvN46TnvfPaDsxFnJ1FiU2d/v4Fm1Sx+1GM7eLtKLYO165c4xoP8AvTtv5WdriucF5OAkqSzJi9lIyL9hArX72eXbbq51QuXath5607C48R4/3XZvQ6VntUU5ovoZjAaqAK088M82GD6ZDz3nGnOrZZTYAauILv8WrmHeYEViTcbfKbJ29S8Ed62d1451gDT0nImagiPSBiIDoAbxVUksd8XA9ZXVAmbsM+qbLoMYqPm3aOOJMS8tv23lM7Qu4Za0TpQSXuivSWAvm0L3jP4c2i5dOT37J/7ASDkhQUsAEjBLqz2i3akif409yVENTVsMhoT9PawuGPs/cz3NLS9Q8k96gOy8X7mYN3nMW8DXK5Kf4efwd41YtWELOHX4tLrJHaVD+qQuiONGNSnva0iJNk4n3NQNTn9aEy7idelPWWe81oPFChnV3dsrO0XnCx7Srj4pR5rT0Wbu7cU8rvV4dp/bpyOnvwdE28dvKr9OWubshoH1I4caIRXlhH5sy6CWDCd6OR31G+GGWakzh45qV6tBaINEjholn/KRjlFRKePhl3yG2lgCZFqQqiTRYrozesx54mSkRuAzuoDx6Huj+6kxljDrH9jCa+sYTCT/75fJSwnJSdSDdK5LsyepDccMRRVo0uk9rETeJ5K/vUIw186XSCnpYrrjOvDoqcXsOkig20UKo5aBylF9SeV1GK2Wg64zXeCd75EJ7e/jDeBw1GpDICkoSboKslK8BG7JZe2YBInlwIBST2Y1bZzbtMjnwyp++f6z0HELFmzY5XSSWjiCp6V3dqTnveJg5RI1WypcRKp3vGJWLqo5GGlxJGQlAtkwo7MBEkRibZWZLlWrWMGbn6ZQGlaR3fMMPLUcX7KRSayNrdJ9WEP6aalhkrTyhc04hIQNwa4nEn6MWRSh4TezZ+Wij6/upVUf3nejWzYyOVp/OtqYjnBNxc+mzAoDGONKoJYLKjE7FN+3reHwOcEqLrBwMrN8IxamPMsOR005/8motsRr54h/li52DbIkxlIw30uQ81rp/eDyO7zPij5oeAx9pf2ADo41BRfFcoogMX+FmYq22I4ygG7TX26HP1yiagjBtvBv31epdL6T06ZH/8r6t4beebv4j/8kHmR+2Op6UQGWbQZSJmUmSC4KVY0+1WLNKmzGx+tc0oBYpzapqH9qY04RM1cQNLo072rfeN1qdb2LcR6Z1fk/vnj1atzxalGONOo19UD+okLYf1g1/fCn2aSCtkwrgLe2L2ojGgqjo5XT3lcuLwJj8utC5lqtusIYKGjmEWezWF6o0gGLg3TJsBGPe5WLhON7iwq7aTCq7yp0Kq6G2c6/zn9e3zslmdxS0OF49TrEzz1SewuifZ3MKqBHXYOJ9OZSMT1J1qrSnTc3Tb3doTcoRbrEusdxmsjEqUFjyon/Uu5fPeH73Xesb9f2cfjkxn/btWrBF7THF40YTU9JmDBxeHd2DLp+Sn25jOt9LmJkn66QEOSX3xbhjfy9/1gjzRXWgcQmAvxnP7+X351PLZ7XShmvkVbpbXu6dSyamoZCjj53um/3dfpabS6HpolWJrtA9eJ15MHdc+bPnX2O1GNL5mTebhPnLubFp6GAaK1AbIF9gsGMYsavAlIILiHcmTAkF0GyzXsSHcwWWMyAMS5sM65rqVm2k9YP4jlo54EkXR1mvROn8XPVv87Jw7GqvsmoOJVxcLoPCQQe7mTnf6v/WORst6t3M1Ub9mohaVrZrAu9LQQyMDGoJurqUWcLL0KY/XI/LyWCgF2GQeR0xqh/TxBxV4dOoGs0groJJb2daO493MSnv7M5Cj9qE+/nY/A/9MVH527itM/rIpAiCfmbu1NLy+ttCIJbofAODd3vHm7kJg4g+dX5Rv8RT/jx6bhdaE5WWkKuURRsrRHP3MnxqrjNeJNzalTwXd5jEKWX9JG+FIt2KqnlPdx5o0yazfu7Ju/T5zPXYVbp12HwrU0HN00PSg+4DnbDQJ4q90Uf4MteNmK0ehXOtWcsRph6EK6FewznvLGEZ5mS2qGvLiNxe7oDuQNbPF59v+52YSg1XHiZGPkrBx/pe/jVmE8c4rfM9YbmumQ9pfBfzcrRpP3KB+q2hKVN9GpKQlbM3eGVYm04Nun3aayxb/syxuT58wY8WAqW4735+tQ25S9qgwfwsu3uf3qksOwo1KUrXdIyFefz4+sdY42JGUbyWX4r082ykGDI1go1aAf44rx/S/V0ThYW0UnLC/fI3LmmvZciwmJ8Efy1//W47V5zr4/O5tK06pHLyKd85VnTRRiqexIg4KHFVra554+gUQHgvpL1Xk/aY1XZuo1lW5NEiAy0ncMwiF2AzHFFNp+nTn0W6LUPUpgSvMg6heLQzSmSz1QSpgEc/MpU9hWpyZI2yWbAk2rtXZbDM2kno5Wm8Dd+30t6lRu2iJJ3SCn18uKkcRXtoD5NkldVA/0SALngIMK62PdJA0lR5z3fQ+kRIWHY964Nraax1iQIVpn5PybnQnKv6bcvz9n5VEaTQjgZfF+1Z++7rSDkXfLPcE+IDPmM9a14rSSQCHdQ1WYPMPJo6Ffy6eMJ4YanAR/E+4KM/RJAEs9Z5nIUNaJmUxB6rr+PZ9vhK9aIILPDiKCUH9czpSdT06E93nzmFR29UP63zBuxw27ssUnMWhOHi94NUK3Wu9xq2a/Et6yZg5FvMiwDnjNoFnicTfRaeT5cpoHSrj5cafAzXG6ySX4pYyhiR5r1Z/6DXTM2xaFurjM9Mo+CNhZTTtYKu0DHb+hO1Fq5PxFq9VV2mMBROqyfhIZ+Y2hpJbIIDsEcRQsqRudDfGZNOQYeY4qfnU/ip0T2gXXgXdvajWtfSKI1r5ES2PV1jTdM/H0deoyq9EXMDX10uDKUJjer2NfvH4uP0n+D1x2yO5xnRrUxPg/K+L37+9drJMuSCjHG6mil1lGPAF3uP3hWHp/mUqydCK6s678d4XaOVXhs0KzAHrX/SxUx2/9abd2B8sM++UncK0oVgnrbEVZLKTt3h15QzzERDPFpFOktmBo5WRlo1uIxkjFlLG9Ye7YvjpjRXu4256MkndXtE9Y7f8VsN2TTOrkwXbKpUJALxu+rr+XUr/qDVFdToWKJOlqNrSVttNHc3GQ4jAspbUciy0tALhvTzRUvPxkfPfTd0QU2EgGvnmQ/nqes8n9b2/yGkN2ZaKRbpwd/oHqhdyPq1KB231b2eY6fxhDgpJRdhCx3QLFTYLL9sJFiroQryFkkDpXHZQf+e9/8K7NxGESJcZ3SznXKMRRN9tlE/LCUZDx4NO0r1ACKXPIBW3fri6Gu4FqGvgDW0SztjkgoYKFZB1SvMOhCd12RQiU7d3Ty4ZlQq4dza2Yi0YUJPY1KC/TSVbS8rU66WW4Q5zcXz4vikQYrDBq2hZLS6e7FLuYAZCAZ5edsqlYf2r28eTgtQT5/Gcdyg2sLUWCRdrNfFWZqro9H1xqBeKJ8aVqNcASBlGuPolbmeNEwACxa17HbKWcFECNJxuk++DczQlhypRIkwWVHZmSy+RyvqXqXOpxjdzMrjZZmdcysnTQ6uNnsWlIFsUdfCaaY2t4Mw4tX1WvwndoyFOvXtkojSp3TMjPIwR56Eqg1uZa/e83irkLwZKkYOBJTvaLIcopkSCnLGW6v7kWm91cfGM++dSp3O23QvrLDwXm2B7kdZKOO98HVUKy1n7E5mQknzkUx8/CWoekYS6Voxn12+e4xOftjLarReGAavdwqbX4X363cp+YAN9LRPLuutOR7vuSkqcSm6j59jX8Ttoa2VEAzElA9dMBrSDeMRru6nq0WtQrWh0qtjxhwM/WwxYQV7YabNAcRrTSc4Hey9h5vHDa4pOnReRhAjYZBanpUnu4IBR3uRN/l0f3seYMzcEd3rHiTO8JE8BisnitYaoTFV7CSHhpG7IB1EDV4hW2nGGkb/XdUFsaw8nOoCG1EZU4pMBplsXRIAW4eCCK/xEtLhHuws4PdEqpWc8ApkpDziXRKCLpYBs7c5LCFYXet5VLE591tZk8wHJYrkZuKh99u2jimYg11wJax70DNTL6pNmkG6yhbaKZzHtYZ1vQwq85ouCfcn58qhon00ts3cuOFj1sSMTOpHpL58TLmPasHw0tEqTdepk7xVFS1stZCXPeCdyEzmN/B1K0LeqBIBz6BF9zauBO6q+0GWCFc+ktBvFShrKiHMrc+H4CBEDkzX7pmbGhCSSDMq/IRJgvXMnJouWjkhB3Jc0BX5gywrXyZNKyHyDzhJiIcZK6BZbXhF52gkNU9x61IA0sTrXd1gxhuemjhgiR7NhwaAi9quSFqMlC82V0Er+buGooSoo8sxOhiH/iXKTks2oeEy9Gqn9YtjPbv602n19Dn9ineh99QqXXQjFUywtXgzrOMV0uNqSboxvr4MdaVWwvo8/ZaUWYz0Rk6R7QBnKOuj5XPu6m+JT6gncBv4FpkVBYpOhyhJ4LY/St10/b0kE8Y21RBdl7MHwBAlxUvUJtUkM7EtAAAQAElEQVRpg6RouuXFKik7Wk7EIimyMzbexYBEwVuOGdHO2l14dc20IMLbKLMOEQs/Zsd8aE7usG1aj01wRpHZaJrSjwOmsyg/WssAErbk3jmuicJBwNqRLtDulEhcILtUJrq5BDsAh3zgnVpYGmzk1gUmLSd6LeyYlYnMyTNVDWDwF03bO78pPb0sD5QRrrOq/IzWCUl6FKRyCY7xuJK0uv83pmcq1yBostB4hz7sCRkglUS9k34Ui1zNtP55c/VcNvqjjkPAEn67K5tCKODSXI5qTmnSwlf077SWr1JKulbbpbXqVPMXJrWIAgqeaVzxQr87iqzunFGemMuOE6dx9yW/YbEwhoEy5GazWDYqMdAUxCtnSfhOzQc28GkESKXCREUoVUr3qLxvQ+l8O3sT13BZp9eZPiWpiq3XC3OQnDZ6hz0qDz80BTCipd0oE95mj2JQpmbibMulzJjfsTJsTNkQe+gyO/OmZPrBnLYGV7yiXHTZqMUn1jP7Hj8wxcPCu5g9R2iW2YchwzpIgBxs1ocyEMb9ZMhkSYfSIGnjcoxIXslt8XEXaYV5FFBR69Glgifkom4BarwRjewmpQ8olYgtgjkpOBMVkRtnj7heYDRZBR2tl5UUOpP2PPbcAyWvBwPZD5JWUAZZLVtIAmjP5D3WDkh8r8GhgQs+kLUG0zGc4UOMI9wDGqFTmv2eypSNUUSapitKURv8LWkOPMU0olsdm9MT86qofN2+xG2iG9kzmKFyrmp6rdrMbluwRWzHZb18bE3zHeoz3go8XjfTJjEZ2NDfAEGHkz6kETpRM7+Or2muvyXw/1rNUVqtLZ7mPJJbFEKgJxocZYI3NNIHBFqsuUoLClFo6QdagZUaH2mGCuxB3zPFQVSt1F7y0DvXuXEyu5Xp0wlt6ErxPjkqDKXBzpuG7un3jbZAZZRsjOaqAbFs4v3B3A3Ns5t+82WJL+mhKI3KW7I1VTkr+gjRaPb7pzFF5MXarqt9ZmoUDDWOMDzQioGs2VUXexuoAFAjX0LZNpRinPSGpyG9rZ6rn3i1U3PVRJ5IJb/XANc8l52XxdypzQaKLM3FxL3j2aEsKpdl+8alu0N8x1Lz2ItYtsJVGdvYu84lq7PS4ks7AmYMNRtTLsej/CM1QYKZp+s7qrcES62Y5+75Du6S0DQAaLjqWXG5Rp1jozKpLOa5+DhLAG6lN8wGPMDX81nVpsTdrtsBdROskYx4E8+IU+Msxo69EFYy3+GzfEde8Q1LkiDEL3gK51vPN5/U3aTOuV9zcQIFiayFGUV1LP5avV/278HqsaH79B7dgd7sowjID8QRes3xp/zhlve9zubXRm7IGR8a5LtXP8Je2eegntVn1Rn768Mz65RrhjEVTA2/Fb/JZ/LwOcqsCFKger/qu3hvf6tfBCz1Wg6ErBy1c7wYQOm5JrSrS1qP6MZRQPAZ6oOLqlxUj+9qvRJChAPxZtO0FtyGoI/azuU03aWsxhpfGfsb1zFtoS4wBi+u0mB3ScXcW6XqSmFyWHJGOHTwXOdcD/07nme6ZkRCein3AAavoAW0RhKRWd34QNnoIYzU+qM+w+tteWT5tM/rCgxdN6e/07Ri6IAMTKl5fB2PYWy41lw/8A2o7cwK888P1gfzUWoYGEcTT1afxOluPozf2edNXyFvJWd6Mue5H95nf7G/Zs90Pck7rSrwVuoV9sPavM1uWi2d0z2hFKAARacJQI+SXnou8ZTeqTcloaBR2T1WEXXZ6IvDVcMDHLcSfK5QGKfjPnY/637825IIStwuD228JLC3GShaeaGNHjw8gceZmHmD0or/6Nn303drWmMKj5R8/LillNjnuTNvLZGPIp8lJVytPiI+oM4JHqpX8e7dtQgMX3jC7k+vJ+eIwTdmhyEj0j5taiSsXpVRJ6hmEXG572E8HufiFO2GPf1H+X5snR1nXR2LM1tB9MHkmXmkpqF3yibEhY1l9TmTrfAmpDWO6CUVuwAI/bQsww5nAgTtBJSNtG7rEbTI2sfj2RoBymf5lS7ji5SmIINamhb6cjNTwLD4u7Wllj9dsfNA8nfS52fuTz4f6fiVgqFzkLJghg8lXCmp0A/z1rIg0E9mvmMEC3Sodf10/IjZLu+36ia2hKQmq37AQ56yVBwjg7bK7BHD6jfVWvOkRwmDz5d6ljTlLkBvt2He3dln9b3fm9QBRVETytyzOMugOMcoGdVYwQ/8C7yCnoSSgrEF01OB8OY7GTOqkTxaNms8245ptd9rLbtoP+B2+8jyjAT8KfNyMCJ8mdzUjOSUv6e0FHC1HqsqQSlQOiaYSw6H3eJOAPIg734Ugs1sx7UMQtHSQl9/aABP4TdoLf2kKZeWcBFqV+sSH+ezujiYVydQpdcnZo9Ffrx/DPbuLV5w69WCJJIbtdFBjcXV+EDZEfgRQdQmD7Y6VvkCgrV4vI7+fev9LrsmYybkTSzKW+tLM4+59LciekTjNm6+Nyon7c5z9/GZS5NsVFQjrAJxotKeisTCIph8+vPFeoQXx4gVviKW9w59fWSL7gYxnBxNL1bNc4TG1rhCGcujvqudnR9ClZOJMeDCctKMc0U5atA5al+uJfP7mUeK/Bm4t6CYqVdrMuvhrSSZxDOa3HjnlWKrJSOvmWvETZtrVq9v96nu6Ygn7EYiB9rHmymfr3tyYo8veZf/s2ZenKlM1rfPN5+s40qHheHi7FF/gpVNE5owenkjCtS5WnOWPW146OftPcINCqTGEua81h+4Ssv2L3GrUl/qAcxp1Wa9q3NWvGtInHtKnCDZraOnUiKG5ZgcQqYIbR/2NUuV4rSAWxoPlUqNVZZPrhem8qksMs/yDE0GQLibUPcjsYqDCxbqdCqPqO+ax6aTyJWlzdl8cR4FgVkQsTT1qphrY12b0vsQhSnFUIoIIkdbQx3tZrZthnIf2qWMd68bygYy5jBLs5CiyLAWL8M+RfWnKUCMVDVaEcTT/+7c9QgceSBSX4r9KyZRufr974BEzKhfZ0+qKjS1HCq1uZS98UlgxhQCW1wVRemgiqW9iajbKrA3+Yx2L8lyYKQ5bM9iRebE5JrUmSw9JfvcvB4wfcZlUUGzGF+L8+aBnwf6HlqLdfhWYGMKKcvSFtSx1JZwGT1o7yczsfN55qIzopZuWxST1o3zstQJUpDKeqRifN9PNlzsz6Q2E/oyyZbUpLrLOf0ueX6PAwoX2hWRQ5mQ1GTphj/mIwQLCKE60w+fg/tBpuK0OSMu3dAQlXn0KkmQRphlMXVrc6DljTeeu0pbHj/ufmyf9em16XDIB+2VRY2X//TH88qXhn6LVgQvcVGugOFeYQ+n+F7q1jItvPr0BISFsvJWfUxsG+zJjTSFR4NaOZdB4b8dk12NR8cEZqmbdL2onn1ZSjACUqDCi9ujHctH9bEhntBpZ+I9Ct+b+TUK3H6N7x7lqbXfXRw4bdGMVKndCho2M1ZpKBjBNeZiGopF9GkCc6haKhotfGxX8b3dPJKUtmLvVfPHNawHlcpDPnVnCSdOK+Fe1Do4hK5DzsNknyybR/skPvJMxX3mLmpjNWnRFqH04QEK8qCkf5NVyXj8SoSAzl+EovhxnDk5D6z7Wt+4vqOKMxev/iI4IzH1htlCXXa2Op6NsnKoP55PPIcHU0pXQD5a4u7lvkKMmRZC9rJZ+j3wE3Yc1ZPP/W1BMviekX7zxretDIjebot/MQcZFxWPqC/wkEqd1NKDLiqmR+BbEdxoUWSPHEJvRomR3T3mKizEtIbrTn24lu/P3r433hhFxBdZnZtcDPNl5qFMmgh5BV/12gu0T0II1qr4MdvB7e4kRpYnRcaLdYZ9lNi742WU7SH6nynxvb0+UCPLnSXp+XILx1hDKR0supszZiShvlm/13k+o12B9ifHmdJZpPYVd9iDaDWfdOVhxIolVpunf5IroQ7+n8/tUbyIf/QfH3ujfNobr3nq4dL57fwP18FGkd/c399gT2D40mz600rtmVsh4RFH1sJmaJsWx32PmkC3pft9IXketYxTQznrSy1+nsG0raO7fI/jPEIxrGm1n4t09Vf672jh7MLfywMrIE2UzeOVIT1wemagFrBg29rtYB1chpCUEcc917ZQ9wwv8+oQRnRDyROE8oYC63Pq3J09CQMWfrb97Cv4M/458ck2/Krw8uuZnriAGZHgqc5HrdAtkEOnyTziTj5NwD7+Qlkz1o/VY8mYz47PhkTsv1hf4TNE1aU2W3fjlkiMacqPS9KPvb8zl0Y0jCEl4ufhv69/ZtxBDTQ4gJbettu/7f8uF5RD3JaJbFqtoF8z62RrB5OdmrI/dv0v/F/YZRY0fmD/sPRhS7zhB/6kDryRkiFGP9ba77g8z1FxaxvANh8da7ddd6y39FEW8zeeHXddv1ASUt3hCgQkSuHwmKWAyofWoabxehm9uzeOcUhAP80z/qwZMycHSpySJAHoGjRFKhwwDJvtqCE4QhMcL/GBaqDBgheMXhH9iqCRwEErL7PRu8txnqj3ruRSPCZCA4Ba6hZAqe12JVXWBs/g3LsfzVuzlp6s6Eoe5OmDYayIS5By5pyWyiak8W9By+VbVP7Fbi1Yn7UirvH1pRC28F2HTJsqdhqwtq9tduldQQk7J3VzWQpa48rcRoy+IA1cMDvDL5fkP3DS3mNRgbW27lj6uEFPdVc7TciM2DpEstsIR3h+oKge2pNKLrME5Rk+5BOtXbD0e+Ppc9tzW6nWgAB/UmvbonlvZqw522694wCa/DKgkkFulwjTXxVdxPVexonpPTTIGn+fuu9+vn3+H75fzWbD1DAwzVJ7iRoNBMyo3jBs1f6LPbiDZtQOUcdkDUo6mF6tfdm5pk1NaQ8wmv9BLBMZszgxsr/bfayzK+vMnAvHlcVcC1Ix3gs9xZEZZEwKyetKsWkr97/DfzVrBgM1Iingrvs8Wbc0Jz/LIJx9DE03BkvNeRI8WA7XhxjxNRG716dFpIaeVj9w6HL5PrG/SxtjxXCuvLcKt9myOtt4+Syro4SZ5iuVzxd3jZBgj++ikJ577kajlp8t9Yeguj7dd5zvqlY8h6gYKEoR0DEKaFEuEM+zslHO/MzbpZQHHXSlRFE8R9Vtk4PndJ2D76it29y2Hio241P+kno/Khlmm2T7vLUJyr7ZZ1/uIoXefYldu/l2HbTZcbSzA3jLQwTDXRxU3v2Lv5eNJGf7YLcYqakqYa89Mw18e8REdRE5ls0XR0Hqea3abPew7bPE2rxuNk1VmaufVaiOQw5O9ykJEJzxBXjfV2ck6pg6fMq2oAnkGluky5t8D4NO/4yFZn+Plo48OBndJqNCP61qlYExQGYDRSvNvFGPbjHnfj/OcLb5tfaPb+FRHs+ZbBASChQ5DjQoLC6zAJ3mAhKlEfNy4aARaiE6LpkKD0CJWVNic/39NEWwr7WXhY1KglzmmpnAo8FwEc0oPclV6qfctNVD0EDyY2q81QJS4gAAEABJREFUo9L6Avf0gxG6hdjH5ls9+jblN6CCaihUu2bpcDl87YlSqARsNX9E1ZvFZyQg5W9MNVNPV902977yxzVyMReUK6rLuaQKgYA6j0rYBumANV5HvbAWVXwO9uhML+u1hMu4IpxCYFMmiZWWrJBmgvJpKgaCMa3lBHQ5tWUW4yEa1zE7b5GUP0g9Odf20TdMULSummx2PjXMUWquL/jMZI1WfgMWnx8UJaqI7xVflWK87X1T4hdoubXXVkJBp3h04S504lRkbTvxAn5PemvNYizaVbwkKXkOtgGmH4ecluqxrLgYsi+/b7XCvm2T3G3Ro4KILuhVOM+rcyVcmRo5VsaQQnwxbQRCGSEq5rhpLmf2pFCwXaz3WDHSpUGfGUxlnbt7vslU+nKk8DfJazR+goYdiM35+Tyu2GqeJ+62Z3B33cZSjkzDnuFdLXX5oq+uEdA5WWgSCya73of9hFaYHv2+6t5U8ZyRUK7VEYaWlgJDXjKMcZsJ1BJyXZJxOMsFbcZbUVqdnK5wvz5bTw7hEE0q+pntNHdyjypz1O8UKSeDSQpUS/8oBxeWpEOLnNVvXhKa8hk9AR/KR7ayTh9zUd6o4QMxDXOhJOiVUflqcczgOkPDW5rCXlZ6tz64Yl2Xxwd+PbQB2DroJp37D/ghPqM65TDkgW029ScTEl5yMjLc2idVPY8iMX2UnRme49WmsL2LhRl1lChO2xc4lqOAgRH/FpzgY3MwSfEnuDpfQ2WTh5SdhvWiHufpdwfbka7xlegXgdxG13XOpwvZlMZup3iybpYqaDDyYdKxDByVK1eCgJJWq5RIDTgDHegGNKh0eQY90FSBHaRkJICF9/Jst4uY0ugyDEzAQVcinxhNsSbTXWDzpYf7rpjSW7juL1OrGpMkSHPtMsBn9+3CnkQDuMKv7RfbxKNrl4MiB2raEbANOguTM6PR4wFV6eydoS8ofkKV9J3ra5d2lrNqUJ02cvwgT/dBFd5AB0Z1ruvjsZdM2qq9xtaMi9fU92G645vEjBZTnoV2xbnkzCnBaFRNNpoTzhVbtuzzmHprtGxPm3XEbCvtnVb3e4NqrTe4n/xtoQA4Fs1Vzr4wy6g5M4WRWuySZbCp29xrZgueQifeFw1u0qqlraeupzVj2GNDDxMj+lCdOuGxcDncMb5bAW4bUx5lZfVE91C9SBwVUtkVl1fLtP6Gt6Q5qq2peNQQjG+gctXWoEDCAAly4RBiFnYCaZXM6dZCPPVYMTJSl2gdDhUXpleeq44KDz/9BEpAH9nrceufgb5l2BmcDTXK2d2uq4+kDfOK69jqc/ioN/5uPPpywgeCrybcya5/DRm5SuIyFptKXgva0Nc4pUPrZ+F2tIsruisLBdeeUok4pjqqRjRcUK2V+UcZjII3cA5u+TKbMDOnEqoia4wIJczaxWITShvaMGMOberDxpr9eI9pgEivsnp8SuNjYrUphO6sPkm2rRIseqE0jZqNvxeHKE6OVUe/k3106+iLSobgTc8aM9kfsXrfema52LvabsFUHmqF4nKmtzp/ljZF8+23vCGsHI+mJdA1ma1Q5c0My8CB04BrOvAQc0yJ3j16m/p00C8JIMtkBxWQGZ2U8ZuoRdu2NaSmsm4J5kHrsqXQVk51dkkFI4OS5d+fZYqOonDcaKRhMcG3uReTIoBp7M741r7fBgp5ckI4QQEDD+yhU0YHXTaP49vTI5DKXYthdQxdhw1MfaZY8CIoItapebQL6TRNRYEAdtXV5Q1/2v7rt6cXYMpaooK/P15SuQ+q+30iYaz9k1pSUneIiHGBe7b2P/1AW5wos1j2YXzGfgrvomNsHwEZGW7JaOg38wsxRSlJHLBufB8r/pjtDZM7aEeXRjgUqPoBgO7TOr2z9bWP6GhrzO3+qh8TBysnFDbYE189VaC9IU7j1kzdTbCCdFB16ZBeCiWLoiqilZXufc8n6dfoTy2ljRjWrGPaW20eXw9+Rpl3BNGQcCQaisqVHXMDrWxdBT2lB9lX1L8o9bqxfIcrCBnssHbv5uk5dQoPXOuA645Pw5jleXuLLENJm83BK+TLbY1NIJdm9DP19AFhmks9kwp2OzSa1ci6/8DswPK2AQtzrOSU+FIiwojdZbAmmxjKEvYPf9bsELFvx3saOl3giTjUbBkaIuGl8xjliRxnj8xXRU1IODq7oAUvzGNK+4ejV43GzQLElGnrjBWkA6by6xie0cKwDt16Npo2w4aVxpALWk1gF+ryua5xnQBLI3xpcTVHAB4teoD5y/8TnopKp7INba89QGd0jHU6cl5NWx3DgUEvDiKFqBmYC7BUVbJ+gp9Qtw8UZwzcEEQu0kezzIzmbStNRIzSMvXNVJPH2OUmTepr/e/uUuh9WA96ZE5LV2jmGDPAF7wwmpWWuLXovpZ77NY615fNCoSbWKdNXYa3TAJquUeYGjLZ5aXE7ZIIVJey09sh06CVKOfxMuQuhPItUthtZ/h9eahWF5ZqPCfOoLHQzjFNl31Zf2kLvuu/yYjvm5cMATYNiWHsuN+XPaJnRuOHKbtjUD5FtZXIEmHWoeG6UpbmL9QNZ2/eOx73B0jnYAh3XZCNUeUUssX98znn8zSTpTCOqSN1NLSbFiTVw60OkaJHyabPiCm84BWZHbzOLHrtiQj6xrgP19jCtaEwpuVZFIP+uMiax6tnXKx6UsSAVA5gb9IIWUw52m9OKr2bA7lTAqcFH7bpF/VH5AMa8ejBZYr7UYdWCeImQN8Re95JkmGFSJdcetkJ+RpGGhoV/Vw4a1TTv8f+u9evCp/cGkN3UoX4ZD1SXZ2Zomb9d9Wv308/87SLI6WOx3Fj7lbhyJOldh3Vj/qc4g9e/ZbMOdU5fuhlYv8cZqEpYJqbRbmc17GiNf1cbmsvfBZfpSBsXtanVp1LZkrIJKXt/pPrSiEF2vi4iWIOfczTutRa2jAcsXiGZcqQePGd8MpghZ3wg6nMVmXdqG93ifGxgIAUnmEuFdS6musAGjcy15ADrRp5EimVbju7juWNPJLRvH/ySCJP22l7BpiMeK79/PhRs1+gG7R9XF6ttPdkKy3rbEs9GsDggR1UQy/TbIVHO8n+5tzFdDC4HR/2MiURwRBnfgGhZAFqrtjhrM98fZ7pgZmu5ZpepEfA0W56reY+vc8sbRvF0hiLiksXYy3TLeo/691q+DZ0abdRQkfNB9o1/cLo1jOysv2xd19mbKPCBrkA54xNqiCyJiVkx8uwgnBCdmWwcBqpXDotRuhowGRDvgAJqn4/uD/e5qwJPemqGvYEadTmbd9nfceS47glaglQ/cqpX6YpYfMjkMl4LIFReqmGaPt1P43XlB9JFKvwXiRWzzV5zRPUcprPySyNk3riKJIohBSU4d2aEzBvYbehiSTpTNKMOOQNPocphTxL4ZNfu0QSH1yjjfM3hLdKjzn7tDajz5bQj/mxSETn1RJ9q5wJN2HLg3wS9Tv1rax+WAmdO7zTpnzlvnJbNwSbJcutnIuxa9cWW3hfgVkOB9Ew0TkvRQX7QpU8OLHO2srfqlm/Rd2KpUaqR46UgRESKc3WmMh8hd7C8JDZfbTE19dPOqzNytIsVcbRjJqrnt0npjkDZc8GNiWHqbmUsTiuYEpU2EHID4Hunh61VLv4amMqplrquxaXByBlqWwL0lgtaebh9P9pnEFoXQuLxctGJ9ltR5udcduMy2JbFEXXsJIsdzf3MFmuTfpaERdbC+1+UkNaQPfBFiDvdJQXTbmaDr85BzWtcSN1HjAxgIUt7V29nLqs9eR5UzAjqrvQG7UypWhN1HiZc7j8ua7zm/Fb9l/j2+13Xmd2R0t5DTDNH6URhuP2YOVEz3C+ZrcoirO9rsAySTPW4/sOa4ML6zGo0QxVdPfMKeWVGUwXotM6C/dRgsVjuxfmOc9z35UPpuSiY9rxCn1GNfTBXLTFLWrvxD2HyK0+3Z41eWQyvcZIrGi3GTFX1rn3s/eYL2tlry3alsagL54d7cuIeBVxuuZMc8SXIjB5mIdQzr1NXDRNFwcFa798VAExL9DWoEC7NYRg5nX0Nrd7VpZyb3s+y9f+CITq6Tnnd/KOzHnQ5V8PFSMmUeWVoQbTUdg56Dx4vqbFzeZxltzp4U6tw51YxZVuaobbbpctXe5gucrlVA67N/kp7sfXccMqj2fFoYOmsC+4n6uf69z2iL85JRdC76pEy9xZ1/CnuQnHXdC+wgyKe9lRclur/fHriFJCF1fhKl4TV/oqVcCv22M+arUXesRvMseF0WjBa63NtSCuhcfO2g/Xt+rp78yvmWJDxFjgJWJuKDQAZ3gLIJf4EA6zhzYhBapMdXkw2Z4n1On8pnWPzxByoRlX2Me4RhqvD30bP6qgUinN2xacGENC2UM/Z3ScGfHq9DULitQnkGtaGiC3rVUm8RqOANv0qt6vUg/tKZ9cNcubnlg3LvkwYgeWxoyIp9/LakUhHt13mh6D4VJWWWhhFh7hbtUXIhQlqNRdmA3YCKXNVzsWiY6Hq3sNqPhFmLa8Q4KA7FRb6rT1rBQ60qn+ldp6dRINbZwyje26YFYQWM9R6WDUYMrGr1z72/x98L31W51kWduIXUZyUaRVdVWqghqn8L4pgeKN/fS2pFhZlGBq7bKr66q/CjicL7TmPOscdpcvPR5j0GbUaUBT4GM+7iqksAzI70yjCqddrmYA/XRDbSH1bvpTuOQKfsiJUmgrPW6bRx5qbIz2FiPCZ1W+VnJE3cS0oCKDmrhAH3Rj1LH6G+dm6kN0s84owinxG6AJgGMWzdyVWn0coI8Y9B7DEJD11uv0S3msuRyqIxPVa9LxbTF7VSlwUZ93CSlcOB70Xae+9KOWE1NQ8HLGcdQPj4kIQ/t7mFd08QAj+ry6p2cw4FlUEvDZbVqautTHoFkbFdPdg1YLLP2dMmtS/X+lYM8MhyQIwLusi6dZtA6rwBDo0UQd+kC4LarcXe9mMGpoB9aaKT+vpQm6MQY2VAZL2AxruQWKUyPfI1S7RiohLhljUNEVWtxDTtAXj7xbELoCNxmJ1IS2N1x91LC2ynj6A18unMNR0T6Xw6GrrsAtDaPokc9A+977ofSh5vGizhYFogjKtjJM/aVp8JxEZhWzdAO67nm7mpDL/jDHqqNej1dse7ekaVXRDCyjxISeksoFoeGjrL2CGsApfSR4t6jZziWcpG2L8Fgk4YDpD9jDZpfn8XNUAiiY0ZbOQtYF7jl9qqVPlEgx7131hoqBKEzmQBHXKCXLyqPAt/k9wSQqlTV2kq2ZJhyCzSnWIwImFnR6PE/NOYMDYgGL0/PUOEeZYaU4fN2N1gkeVkGlUl7wCn9j9HYtvVfGVpcXAf2asm+r/wsWAcW0mmvczsLxTn/pYlAsyFlky2rJZz/Dcadv6UG3CB/WJ3SfAnWSDGEUMK2TdolDYVt82lKwX10a6Ai3HXuJaEOw6OVWFi1W1BwpUkwMGWt0vngAABAASURBVH3p5YqQ1tI9IeH51M7e3ddgU5ipLtCCygNoAZRUsUAqQgCjG8GbKiGMUS+IFRko/DyKFBrUq8/mY5M23bBipFLP1uSQBc7Y0yKXVtgjMqyFcE3bi7yVzw31VVGeGi4QJ55cj1Yi+jKEksfrINJ0KPyKKQFcDnstFZV2G42tuMoIyHupI9nXqC2ElNdzn7pzRPu9/Puxs8ywfPYyp3F5/1A1TB+ZnFWyn2blKtq6521stzT4LHxD9bOV2dZzNvve6xwhtfvYc9u8cvuzMW5Jlqo1CcjqTrvQQEsD0tzT3+ozXe85lFezBrCxCIYSoyUYmXXqgZ3mPUgy15VrT3hGlJQzBgJeULOAiAeze1JMxXq0ZQhlKK27K/rd1u6Z1SJvo96JVGZetzPCNTbF8UqeU5mJLu9hYroB1Z1sETP/tzHR36m2gcF11tCaL4RaPlqRo42GyxkyXVUq4pE1JfKdUkRNW4OKd4/oaSrG9lorJXdhO/LspNdleY2wuxxT3tAEpoOzwkHd129nz0vQAYvUJWqHtGZBlQ2jO0RVIC3BB0SDp0KHUKzTf0XjpENYW1H1wzmixoYYiUB/I7n+vBekYSaEYeZlCXDFeOEYlc5s5SuVznnnowqn7K01Ro84mpkoERkxPqPNli6qKygCTJlOXnI41uZpbE/E19//CJLbY6sQxHCZghwoKlhP58hJqqmWGKPnB8frpoQgA7KUoROdnfmc6jOmSJ6Ob1FLR5O4wM0Is3BnyqSlVECjE7rMP06bLUZudK0RoboEzlG5j8hx2N/vCB81LVWPBRDoy05oUYyXLwFA1rSYM7Cb6hYponqhXWwqcdQFpoQ+r5iMw66ezBHqgRG+Z/krkCWgHAl/NKIJuPbl3NobC/pjysFYQR0AQg0Ii2mEAOVhnRk+4ja9n2qVtrSzjm4ajsu7DKJP1L3Pd5+Wk0e1tifZwkOUmACToE/ogCpK2MSfCX8bz66zUpkwcuRGy0DNCKiXRdml9HcX6pGzKNcoepXSIhe6lvaT1+7LJnLHuaiupPBKyS7invjSH7dudiuyVhWFCpEDf56tJjmGtbGXm1/T+4ynD3ZjDUzqnhLFBamCE0H/zOxp5xhFmN2cGlFaSiEwAxWFmUki+Pt41LFUFpu9Vqs7D42P+9HwQFwQJ3zxRrR2AjL6utIcmel5CVfYRBkz+BDfVC1SH65eIdmEwXTEqBrdZFz+CcFvcTrD9AVqBOeBDgiq994tyhZ1PYht+iTrE1gf7ssE57iupTAtgrDmFEYKxGP2zGouRa9LbtJKORo16vuP8qITCtOsyBpDhzqoN1s8IiF4gHC+G8/XpBfjtedHbJ+Mc1PLjhrgFCEqkAedJB0uK1Xdx0Z0VHZnl3puO0YMK/YRJ1iXaIZO6TIqJ2tSPy1c+PG5XO869RDmqvkAKo2J5/FMSiQ2X/LX+/G5BV43vaVKMJAtkxjT5LFNpY0vIIsORaNE8OrWLoEqgZrBwLuprNgq51gi0w6EvDVnFeJp00JZXS8dtDJLIu0t36dPnjmzk6rgCErTHapDYAibdKisCtP6O6KgcPo9uijeNBM4Du2FxwdAs4Y5Vq0QxZMaw5u+Mc6bQKdibFOIzF7PiczuEpTzUUzTPZ1GGfLRDbccsBZbAgbUceqFdGGr7cYxJlgdg5jpmgLnYm/pc+ft9z2PHn/KU4CX+Q/t87h4UKMdhT97cCWXn7jua/1uyz320/GBqejMraXJn4v/4vqYNMyQHO5cN0QzFGTLDE/5U/6lPeH3iofznq6jHXE/EtRTPCCkKh1yGQUcTX7ZgF3Gn5V7n16tbCqMaLk1X84wrWv96TL1I7Rqj7a8VcQ5MjEp+3aPnW1CBP8af3WX2mvrEUpn9Bd2JNY12ADFwg9gOoF18nOn/07f3acqK4EaAIQyiFSCq+p+5tuscL/e0s2W4LSTvP+hVejebrCXh7H7CT0cWeM//ER+Vn2uiRgy10gyo4SXFoCPKVUSK/2s9ahHvWrL+tfPPxu5GSjaH68fyVA7MoFXeOrFbbdwzJi4HUxzSh3mhj0wldIxfF3+Vc/vf7GVgj53McdHhMU2D80CcuYNNk93jwESFtkKmxg6NJCxsRavpTm7gcx5HrkgvWg5nEfBzpTsDQ2aZUqoe685+62VYWF/8fkqE/Dk38yd/GDt+Ez8nPnRYxq2zX46vud8339dzcf3sxX8nA/yQm/3WLl4/1ReycgrToScRpXNjapJ1mz4orDoghkJ6Nz8J25GKe0H/cUoT9KzHlBsq5kUYXf7fTiJ7Sticbxn6VPTlQmzIGNUImM2cGtZlzIMZXTgIj6shduQBucoE3g0gBPEktw+mtXW5TN4X3U68265X5af4EZkxvPF07jRYwfRAZ0HRfP8CXyyoPzmpdXsmvHitCaMRCgl6LHjngp2NzupaNnwxn4QwmSvW5SDaIHSqGeQm/2Pysam6Sutn5mrpeNUSfBq/qhN+F5aHH2xULqDeK7SvdhEoHS80MSv38AzcepnJJWr89Pn47qdGUfYrFEwjeeMomvyCXxhd+MtIdG8Net89m/4b8/3TswTfOlrXNhuV61P7s9c0VfV7vec6gGbr8p+tXu+9/pKdaROfdycJELD/31UgKZFo/8dCU1ayOwjGlkMjZFwG9CN0773J02EiVIl2jHVqMJkN3iH916kwQ6v/yDVflbHj/fPJ9enjcbbFdT8qqLuHs2h8hand53wwsZx8aP9M2W0e+X3k7Xuxa+O2QdX0W/MtyyVr7bL+DEz5kvtSnO/7PqJtdxeE5w2NDq//D54V7fdHar/gUiW+NZ3dNpcJfIJ+2FcGJtnFFiyvzi3qK4D9GzUv6i1QOcMgClUcybHv9IPtekQF/lCgb/ByUXB+E5wLt9Lv0BAjXNuyrvMaPFJjy5p4TpyLZJcSoor6GoS+H03gb+9ytzcY/nrGDzvn/KSUj1Mr0HcFY8Wjz4QHQFSYjajU5yU1qrlj5C7cprH+Ev8eqa3DbXCfELGP4hIk2aF/Dv94NvyRMgQu8/wzC7FOSxdNaikQFW0lkG1RrP+ubTFfaaSOSs1TAxpBxCzlg7Ebmf1aPaw3VSczrN5HGoqkOjdESvhJ0oxu0LzaMSZl1DFbgRm1OO/vftsPKRSsUulM0ADwVmY4mSokVxzXwDrqtkz0v6rm/lVjQ6f2lWhlgCnb/ytPIfn6ft4tlcrhkmmFIRZ1tcac4ddjm3EKIX5rae+yW/YWT4X1s61aq5Pf/4Vr5pl9dmq9m1tYhCC9WhNCGsX3glhwia4kCpatDKHhI7OGQyMNitmSneWGgzFuViG4mk9UnH9TtBkLKwmuu3cff8+59tZOBbHP8kP52MuRWlM31XKUNnozu/+4NkCgig5IoNLz6Dqsx/VwvF1c1c77p5TgI7JwXSjCl0mGT2f7g/tEwA1YuJgjUbmPVlzN5vKwGX42NrjGtPTb+GgyGML3JstVRgxWkbymkJG3rsqIGgNHAAzPPcSmM350WHmNE1xiVCkDV6C8llIXzBKFdVaDjq7RnTiZCuKm/1sm71tmfXgGXsoO3v58p1YSk6pucdhv3vYgJrabf7AHotnv92PoFf50ZJjbrYXLurMtGjhqY38+eDavHpd3yXpcqad4kVHiwKO2W24Z0pXRGqH+bbLJ0TrnXW+LJUq3S+3bQ3TIGMr9GuG3U9rKg1Loc6u+3POJf8m4sgwXzaxv/bzuIYn86CzWD2DtsBcfj55b33f/a2Wp5Gh2IU32TlvgHSZXjStYOZaj1vx+kN8B6fhPlzU4vUb8YsmvxvHMN0oNOZe+P4gPxhTzV4DE1mkCLfDEHM1xS4b6KRNuYA+n2z1n72kQK0Vq5hGGVXogNp5jLxCzYBWqqXxViPiZqt5KIRG7LSVvY/WxNKiieaZ9dsvYIUUHm377qV6rRH/wmpWJtI2y5xESDfm0KuRp55b+h1RDT7owiGn3GqZuq5iaTNzW5S07cf9J3yZdzMFuaHlB+Vn7AzgCXtcfF5e4/iafantUyzSLdL20VCVjOu0sBhkGIhgrGj/y9wLQRSHUd6LumeQQltkE1j0EAQIGfuMJJTIWcOtWGAlefr12BKU0fTNFb57XUfR2rxRVcjPcWVu1HorEiiFudJCmswe3aIkW76852peKd2RQ/B9gQ4agx/bP/hZE4PXMEBNGqmvH1EKUdOD0CwQiTVrzcf4QQe7beSipsmHdmxOVxfxWNzuWlfrCIdWwEVDWz4HXr6LKrBh3t65D/3r1tEwlGIYmDLMYyFo/If6UVQ93TzAKcrNKlwjKLbk2BuO2NzOLaQNDPRjIMXuLLY0kkqKhLe747nxPMmGxcI2cd2aNsZRNC14cyvjcH9mPQNkryznPTwF9Oh13E9EcXzKVWcZd5tYaE5BUR9CWZ7BONVd6jNH9T3TIMJpC/QxpGk4B9Srdd4JEDWjfc9tmWRzlXig5BLL/Al+adoDe9bSXJz5k0KAm3RGjZCgY8xkN76z/uPnfo24vAse9ODZKsUj2gzuEUB8az/ngkrXknIs4KoaVTVn9ViZZSxyMWehQkxRQHhLi3ubrT11ZXor2XYX7tTAGUPvmTVkEWNPi7zHIcn2pLiI5mUMxxCJec+lNDciDYbROcpzLZdT/DR+DGv1urCsTQTn1GOnolQTOVe1Sna6ptqyma0d8be1tYIXrNfAipnRAgbI51EcNfMYy709yvpUCZvq329YOpPeuJQSivB+yKnyySDs4/2PFSWv6vczluNVxoRI21DcAokR4ToBJMmLe8NbkTGP348ScQRQovyMbeVYQm5FkN0NssW198MWeqNyolPKGVROJi1L2Iop8L44Ph3P+DGR/4189EFvTfG+yHUN851Ep5+ONlM2x0NZ4DNKpEpSiejtbZcE4SNBmdmG+g73Z6tbFa0gqqa9UtAuCvFIDO7OoTVQrHIFqhP2Y+Z+mZtR10jJo+xornXZjMOiZhX3U8qDgQEu6HpKhsd5IBbuyht9m4heBIGGcgRRPbULV+v+RUF4D0RadWsD8xz2w3VcSZB7gTxUPOG2FrdRrsrxSEH+lo+MOjvsuB2xGvRuXfD2MGCr909650yoAW9f4USNQoXHWCQVzPtJA2sC000c62es0/eQ3Xgjc6Of7WWg2slReKOUJh59dTXrNhMNriQq204YX+EYXovgGB1UoGirV0ElHU5BYSspH54tYmygZOhQhPDXhELTIVbUODGir4w6v8fNotcaiTZ7bmE5O05s6CWfyiyFEqbxmvZGMJOteKqn5TfqGvxg/ve38lfEUFiGlaiWyGa6B/o0NQIshnzKxINk7CzLg/PVSl15HS1NqjDbOcxq/YMBbm0GSUKshY85YDR7HfkNfocP5mnZ9IvuHLOEyA+Fpbrtp78135EOMH92tDi8YlZ6MDpjNjYpmIUmdRegkj5j91HL9JdhCfAB79UZdcyOWmhSZqk4oEp4097jmdayE4nRjMB1AAAQAElEQVTFCYIVPDZtil5cSivamfqka5hqsSF3xsvDbY/5DCmCQYbRyDQlGDOtK3aQVWpIQGlwhG1bn7WkYIqOwJEDwJqXxgOEjJHUm7hsLwvu9tV7v/5WrHcLecn/1Ij+3M0faKTJ/Hn8LlebzZ+qdWpu5lHnpxvgzOUp/yZraiSlNf1wH9POevUbquFDXpCD3tEmSAAkbu7x/fYUpqsmBl2Wo72TNTaLUxg2+wVkyYW32cjRiAb1BLSii/n4/COulx/XFZlWosWcJXKkrovcJvq2ijSbT9uD1gFPyEWuqoHPE0ofULXnXeo2pkYLFmWPID3aOBDQ2/sRjQBF8ZQsfTt/WTqf6o0A0rLjlFu+m2BiGnVGvrvdB8rHp8XvIe5x7RtUEZy0qTYTlC7K9UOIi4EeoCmC1VhtH4wRoNL+nKck8JoZjc+j5yepTeJTzAFGPz0xtgzuZ6i1Dl2wGZeI5OvrI7czQqcFNKRQnr/6cvTaryqC5My0JpPwhzxhAP3rEr7IV4xkO8oUsaWR7lxiWu+W7ZBoT2PErkZgenWtTleUpbTiqXmygb++ruLo8Uprb0UKFip7Gi1QOUiYlLBy1lOW1EMCL7jVjiesKVbptLAVWE62QRXaOvUW9kxmHdHdy6GwLOE1plhIyc2R0E3/zJhrxsJeVEX1VaP7D6OHCWOgY1S+FepumE+15Nng0IElnYVgmHqikq0cxGOm0W3AmIHlc3h0BzI35nGeRaF0+FUX3yR/uMMuKH41llb7Yfe0xC+0IS2B1sbZhu2hpSYijH+swJxaDcrxDf+lbt+u05vmWJLHPag3B7C4SN60HO2YY/WMuHwyjuxaKbg9bUUkDQvYHDkADIluaNTMstFXkYHYrbWYXd36AQn35sy8vSHji/2EnPs8gKTIST8P824FFTaLabo0d80Bn8Asi02G4lYutMivgbxwJM/uqdvQGxGzrdW67FKuNccuuLZG15AlFX9cz9jc/VQ/zzlZD+cOiRPDAaXCrpmEGOU459q+thsGz4P7O6Wk4PIRT960Sc1VRqLwJL5p1SJRe8zbVN0YQRdzMlj55DdLTA8Qo/LRA9L/zfz00Lz29V3/vvbvbJoe5wZXhXeY2ocaAEbsyZnt6o2+iKvDU6xLmzHX6svTI2vVUY58uox0zZL1nhi3WeK34RMKiqNvtmsO1QpKdp/eBygeh0rlDOuvRRzOIw1rMrYxzEaZbsbAWKLTC2yi9Xu8Reo1n+vsVVyDsIbM1FiTsp6rFZQHbTk3aqGX5tqUJILW4HEecjgYnRSj7xA7vh9F+ttRR3yI2mdv1bRaxS22c3b51QpiGj2aSE1/neKs6Snko+MUajbgshla9a3TFbAt8mP8fAI/4hHtU04NfMOrgj28vhNKqqxDgViIWAtOGBglqE/O3fYLJjVA2oH+zAsgJvQQbj/V5zl1yjDQEm8xb/DQJ9g8EpZQQ69xYWRB86H398a9/LFI5WPeMZ0a1YGb64vr0eMDW+JGDXI6LR9WZ9rd6269FR2Jaf7NMZHdMtKDv3TFcbuvfuzohJ3WrZj/+v7S9d7qCdYaL67fNuVGeOLY5/Di+Aet54eqybT2y/xyOtKmHTJWc+6qVgBG6HlB3KjHBYPz3iHG0/44YGOtxNwzKVEyALGZieddBKtkD7iwNj7zSNXEGO0aftq9P5X/hBFlp/LpkY0+fBuL6PgWtb0bN0OsMVOiVW5r7VgxhkKx+A/8mbeHEZdjmxIDYVDNIDoIh15DtKE1QwHogWnuSG4h+eTzU71naizHR73cT9POzHdwgMI0mprukpgzewaELbHXfWE2sbqtTsf97DeWM91Sw5TiVB1oCeiyB/zaI1PQ6S/iv9Hmko8YxbekA609ZCNSkV7GmPaphOi9Yq5rVGo+qg2CE1aO2gLhPdeZBQ8NSK9kKehQ7/le/GKlrqnECfAMbyUVGljauiEGhhe6HtvtE8X9xedJZ81GByXRLhVBTR72szCk7H1yekxmH89miw9L0UprC7wZcBu5Q6vWhuPwXcuPrqNNLiWIU9XKBfEgtZ8q4eDetB/2FnMtmvw5RrGf7GZWZj3TR7mUPt34iUcZwYyycdD4e+mSYCeZZ5Q2DA1Ud7Ki/r7xTvn0mtefFG1Qvufz5+W0ogKpihY37BkByz5K65pa1TFDtXGj0jLVLt6B9yu4mbmnvsAx43IzAyKwAiKSBfFmgjfeuc/EoXDR9I/kNfix/Lnq89OucmjEGQrJW/kH4ZQlTeiRcJ63YJtIbW+eUS1cyavMUhxVAZ16X55HX0R5HcgMFHAXq3NGX6P8/sP3Zz10MVlzVFdjVkMYqYL6+ONSvGO0ZdUkpmJuC+hhz3Lbgeuya/mCK5GPjRYoOinXQEou0xiV5SuyrBYkePNT9j9aLAAD6OZSXRY3lPspC5q+HLfHR7obSWBfyz6Wi0p/SdTb4VJd/Nw7pDIFKwnqnA/Zg5w6YvU6tp+YhxJL2FualuIxOVhvvjc+YYqDpaVsqGkNleSZUfSG2AorljnNztrP3qXxNo4kqnteQb1kpLLZ/Dn46bPncHV+MJfhCvu51udzwdGAaNAvwAfeSoJIRm6MUf2COpF4Rgn4gyJOeG50I9sPUFPEN2e+3I9ZSbg6O6OiG/+DPF59yQFlb9NnUuWhh5hiVhkbE2weGcy2BmGkykHu1IlHAtZHhQa+L3VBO2Fiv0q7HB/5no4DO4OTp9XZ7Zl109ubutwog1YU7IqVtoXLbKTTQK+quc/33N95jsJ5y7OKNYrqYQK31W2nLa/ID9XEDvuxbG15/NnMfznv18UY9ZcJ6Ykpx1gs7EZQkoVIEwvqOXozNr2lIxgun4tpLS6mFE+qD3SOhDWQknGXhPKPuKyEt5bfcdWEATYFmcsGOFD+mMCQTUswp9vmPa+MoRPIsafiEMfmqPX7y+Y6ub+thI8m7V/DHXPbtERFSWv9XLN/Hvt3n59TEk0ooezTmIQ9iHJ7e4xnW7UCVb0YJU8dqaEOqJFsmLdHmQSwb440f66ueKbRFsej9VJPfeH/1TYUyRBcmkm9b1Pe4W5uWhoND/2/jnswSMdxG3L7/GBcKjjrLiuwjfmyH+H6xxjEr1+3x1BWKMNF54q19ruoeqGf7vuRGKyO+aijlFZ/521Wi6ZwlBsZV+RyTZc/PFJQL0rzD5Rwg3rL9iWk0v8X8/t/bcShHIgSzoTE1yqxUVmrkfiVUJLtSZzTR18YngdZNmISXiUchP/5OgrdJqPGJMHsknJ1z0PdS5y6h6rt41S72Iyrl+vDA8sKamaNBsBvizGIn1fApRRQ3XnY3tKkwINdPK0+URXZ8TQ7mym1jZV7mYnYpqZNC4WestR+Zqft9ND5M1mSiM3oHsMvcE9c/jLiFGvdRK+WmamFI2aV/K7jWeKmvR6ftFP29BqtalKVYTTM8fjOckUNOZZgl2EvxEVbKKIxxGj8tnljX0pvSzmhzJlbk8lIOkxM9AGf46Vbr8eRFyDO3BmezEYboe7Pn3kW72apOOfr6IaqyTtfe+YticKceh5TgMh0MBDk27h9wV14YEdhKTgO35Q0BmwYFGD/2Wla6frUsK2Jo96mkIBqpOhJWMaqeKFrKv/v1YNMO4+PyCy/SrYMhVEFhpW9WOxedhuLoJnqJyjS1lSIY/bYkKVv0verHIpSKbTmHEfKgtjHLrA5kAH3l8n4swSbjlKZNetM98gFq/vwzAgBMDCInVjcxu24dGbqsX6iz/u+acdUjtesZSHTloScyxTQ8iiXu22VVkbVAasmn1Fn5z3MKdRjefg0VVk1UOtvDBWTomuTdyxXcpzweLkhMBkYdYrFmVEWUVmn8h7JXbih4WNRdAwhSwTQwCz21eMJhyzruk6+lFC/ZAkDsWT4ktnW54NabrnVbq2qOWpYT3+g1bQ92efK9MzJVNlmlnzYjDGlvhXYIuP15JgBK5nJO6sbRrOArslvV/YRIUf4iHDNJ9Kg1QoYh9NU9kxah/1/ilJ/DnNdm6aaHXNgnG0yamhpp2CytZx8IVZxULMSulfZgyXaKFDKxJnvM89Bj0wcSOIailEsd2trqbMj/6AJCdQWVLzn7qe02td38ADN0kjv9oan5LZH3DVvEz/Fq52wbLWSnBovYwWbZqL/tDfVBj3Tn1xPR+7Jz+RuiK2WlzTFkkCtnjUwOpXGQ/az1q2D1v3idv0WTggRMAyxowIesESmXGrq2xCqkM18SbLCKnJeC67cIrP71pXWZPkhzWTHrq3NoRr9e5ibkOQbs7ngNCGDLyOpRSfbaqJH3Nf4vKTOiFOFyHKU/vUfOT2AJGxIMnukn+mkuMRk8+SVtU6NnjZMWcy7UXiBsvVuQV5a1ZMqnPaicKC7skrPafMppbVF4AeUIqg8s/+AiRpAeRyEJKqyZ3+dt6/cH4Tua7ncIHMjjuFTU11nnfMp8TduzlhYQfg6HqkwE/EgSm1kullQBOaX6zu6WKTp40iYniKlOT1dSdEe8AjoHUc3cofnkSNL7WdqAYtr6br6jUK9mMu/5lpJ+DCOGolv97UWfDVeioCXG3Hr6QFCrBqfGXkMTgzY0OtE14p6t/LgwdtPU9DjsXV6M92PMU3TQGqKHU30ReeMAitS2jVSD8k/p1d/iUKjS/58zLda5EK4GcN5eTu6a56CUjp4rZzMJ0WUjUaELecP9AVACfU7AIKuELURf2FMd3VPlZr8p8417+okJVkP9LMe4J5W06tf5Ne7A2bxib1M0VZC+7se9oNRBZj1wkS1pfSu23z3txQqHyLD4eHmwrXqWkrsjGDO+OF8vY4Jtc4qCOUNgZKCdEvh+lsXQFGt5BOwodhqABxITb5pl49joJfpHCPe/I3qTOiZh+oisu/6I8iNDtaaUQl4ajd0DiSP53RDUl7SWhHpaOFpugowZv7xBGVRn8jXV8kOzUxOdbjRB7xNJXNrLzQbWPMHtg2zjLoDlYEpiqCyUJ0Jc5BruGcCvaZ9xtqGfDO+X9Zm2AKmimNByFLNjAYrCPpS/8uRRqN7UtOILXZDnJm9zns0qznlLGEFYdaYruLJT2nbqjMcY8svvOHlYhiKMGjxl4aTGjs9tZDbNFYsTB8o0DxAqjhNViaYio9QVnSMkmRtOftl41g91k+MtiCnkeNCC/CdozG6unQJoh0GkTZTAtkgqBcg1uRVdeY1SmsC5W1WAr579gHfdG0DHSMTt9szn/MY7uzRfjmz2Emk5msWfHSI6eRrzlpB6KIleXOOTRvT+KL1CkyEuxsNCbQQVHUrOCEAMnG+2iFCEYo9XaXgKr6ZwaiEnmbloIUM7cSCFO70YjQ+tAUJ4QUiLdEDkFgGHHl933dMdqUcSv8P/IYdunn4v2V7ifdR+ioV0hpYvx1DOA7qgZ7lDr7Io56QxBhMJO4HesoQNdS/YPn0q6EBXohsswM2elECMB8zoX03WE0NhcKyxebIvD9yyQq0i/Y3SLyyn1cbwQAAEABJREFUvwarZmfHqann/K1cjllBI4ds6JPLLh21WVfUW8OGB3XmrlGi0Bn+LPs6GkJa3kBGP6beOpZKQBlSbXqHky5hPX3OnFO6NcpU2XTLUmrdyqnTOar0/8CvUfaWSglBp4UJicENVEZleXBbvfcq/kAd8E1xNUSd0Zr2wB9uuh4Xmff4jAGhh2UfJutI3l3GrFDPTAFCip8N/CtcB6+mA5rCwn6o2yw9LDnNO+MZKQIyEtneRUsBgq2JEFK56VedosKwPnQH+gxzhKx2nLF5Gk9Gf/ecGF/wRZ1V5HbDQW7AWYwccdjaSnmsU/9+tR0Y2K6tNCsRsUfxzoFrLMSWt9YL1tYXPp+mDbLt1+wOgWxD5czNJh4zIeLAg6qUUukdoQx5nlF7MFRXdWdh0q2X2YXQo8ELuECHj8i6QX3xl+NGmhNhsInMPQmyeqpAuAW4m25e6pVawD48vsqdVtvPJ2ZdwR1vzMr+1+oJnr0OLrkHbrdn6eoAw5aDMd2y308rHNmX21atNar4jm8FBLl3b4EyhpktsMOO42t4OnveFMaCuxsImgui6TfAN1EF53zMfuDroba/XTofXgvHFd1h397JPlV5l56bnUY6RhT9nP2T62JfNnvDRKqPngxsn0WYh0PGZyR3TSYeWALglJsuE0twCsp94O/l3Z9TP9kf9L8QX+VkQt/JmETsPLIs9Xxnbppigb8ZVruaFl298UhpzvfitJ9a93gNs/Ko3kZdzT+zPilR+TLzwAxC0VXrnQLXb6vmEFg6xgyU7UTLEah/CNR10r4QGjTrDWu1sna1/ZS+79y2Ny/Otgw/iy2Jr/QpHDvjepcUsdaULHhNev8nBwfWz+rfqW9J511v+vI5mcM5VFCUuMsG9C6vem8dqkYvoXSJe79guhp9rKRJES+ijBPTLgt5VWllWhWn95Orzpphy8H3UYezUUJmtMUJuvFDPUmOeNxuo+6MZRaKVxxEjz9tT/HbfExF0cvaHan6mcBtuKXQu3aOjCa7cDDlB5jumJIw6GpZeKZ0zl9eMaefx3/HNV9BoEhdpN6ym32mc2QB4BjG2ZeEi7kosqG0TMi9ucC1qq89tOu4lKYUuTUQIWgYIaCbokTq78f6Fg6/d5dk+OK5Zyo7pETQeqbqqb4beg2Ut9Gc4zXaMJgyvGtdI5NFxcgeI+WqCsqMYG751Pua4nukmMzfOI9UBDH8/heMal+fQ5nnAG/Ew2DKisyqN2S8ar3gETE6HOEdc+ap554SIxLVzSDVgefH9a3FA5t72ROrJXOG9EErUuIxp8VuFanwv+GmYDn8wuxRKUhNE6Vnv/Riad8h2p+8n7rPep6fLoFeFBS3i63mJcPjeKivKt/bot4Rnysu5QKTDeVMDChZ7mbhogcYKPnKAFfCMYZXPitkrXdLk4fs3K1dlO97Ip+wXCEvwFv1MWldY7S1FJ4RVUquGJNNt1Yv8erC80ZM9Va2nBjoCq5HknyP6TdD06ORM8jp7FZ62frktLjclqkLPgu3T6EHZ67uCwKNJm9JIBHftHOYGFXK13ma87XRo0Xl5z2yT/K+V6Vm2v4wNhqTZ7StwwkcLW+bWDnveVuQd7SdB5lpdYI1epT7U8+PtxPCYok1inY71OKEBHclqtntgHEMz5rvJTOUgxA8tzEXmcRNQtrTYZ+llVqBaaK4LQFQ9DE74K3o+n3sIWb6QBmuGydVC2edChm5lXo8yIeiLlvsrnmuas8ArN3kOoKvvEvco5y/RjXirjZr0yi8LxGBNO/l868Yx9hiYKGWp632H64LQfls+w1FlZJexMKs0XkGzXerCi1CEx71rBXJneMsYHJ29lZA4MBgpU1Oq3ly5OoePN2/hdNGcA0tll2Xc8GoZD9LcDwiLrsMyqJB+oVsdzu03pxlKsu/+zuXyNYT63F/y12Jz7d9qzFEy9p09PF+mIcvOBoJgMdeGZTNTAxWaqjYECejvJTls/OxrnfsSDl6I0NU5Qy1nKZsrcI12NBJMW0my64XaIDLLL0QyLJT7wLQ3DAHTFHM0qgWZ3pISw5L8M37gKqf57C7UOec/N59F7/bvtf10PJ0DXxh/RVud5/EOVM1nOHBHDTID69rBUOpmDvn/q3+D32vzovjIRAlF5Hz+UXcraczihfthCBkVZ9WVWKQikWAXbEYUlGZrNMpI2VQ0eyFH1PalO7k9Ow+rsn3KJgcpnm5gyj42MJlZodHhZrM8SNVpHce1M2TqoPyKfKxyfTJC7UfLIU0ej2QUQuuy1SDwHA1VW6XSepdtKG3Tjv0K3ooIIcojolX+0p1ODV3aJYYL8XRkKdgv1NotCWTWAM3vEEt2EWFYDMY0o3hXDoYFz979maEUUQjNMNYZBm7Tp5v3hKiOJYH4nH7XnP/FC+qitTkuu9n+hxMapKog+BmztP1yNurEpQV4gF1VcFt0KQ3fDA+IqpdMJZtm42HdVrjgRi6piSmxiCZy9rzQP1RCYTgvniX+dpjS0whM0beMCnNDdDbTqTFwEZZg5/z3sWVyB1i1ABa9GuZkzyqcupYPutQj1T9XHwWnz3fnzl6smDNBA4gAApCLnLwNftPzcK36FRsmIc6+jac6To3+lkKHCoDZFH5K5ghfH4+/ll0vIHtknRhHFgrPcIqocIwwJP1/T75VUAjtvOy26e7/Xj8CaLGe9xgDndzd5JmUCj1dL5ddXMuqEzl1T0CjqfwHd70USFjdXKG0kPgQzUC6XPOeubfSBiM4LCnIaP+wJT+Xn9ID1IbmVo2qmHrptx5rp3LRev5mLbbRypsQJUguEs9qq0mqpcd84Tm3P2bmZWTN6Qtf3TPGQTWx/zjHVLhyEKCj3eK11GV19DMFMvIJCzNv6Iw/e6RL2S9ifLwvRhyCGTrxn0hQ2l0VDGHMya+tOvpTzxxlV+I4XXOz31fz6NEqqkPIG3N4gvgEL+O2yY9lTjqYpOgKRa+pKCXqwn7iGU5hdDHy/87Bozx66yQEmljNI2eiRfEblh+JZ+KEovzXXNjauTpqvzJP3MSErkJPUzVeG+sUWVLMz0YrDaTv+YodtMS+TdgHDbUTHs9Qt5qbYZKomueG89jT3ueYbevMcVp9XvqPpq0LYXWomnzLhGPq8OaFvQiZJSy9sC/5ROxLnMt3qaW/6gIkta2oBhQvx7PCoXr3VACBffQlSOUxHwi2k3LbSx9X6z1tJWslbk4dLPdYRNsouzJ63v+3W8jjQ7HZWFQpvZzXy0UnQO8kket6esop/84+/H6tf9u/wp5j1eYars2ewWwgU8wtA590j+1thpVUjcmN0qadGoN52fsQ1sEDeWoQM90wu/czzGVtbLvDm/J8WgMoAz4jHi6WRVHx0Af3dtCtTtORhnFvJ8nmC/Lq2fDzJCOe2fZAdVKnpnnEfYcN6ppmDPHMrlslnK2abQ1K7iWfbbHVr8RGttQPIfXw/VYl6qtulMLr8rkDrsX67UTYvQ02/4Q1p5RvAHFCPFJh7nJg96X4vFBPMOHXir7nbO/tu7A/W9qJy74tn9jV9G755l6hKZJX1gLJPQaMoFp8AFq0KINwZk/CAaGmaJEcx6r8/baZGnJ1DQe/VI8K6EJtMZrdbEuzIfaycGT0YI+8kT5OECm4c2aUTjUFCSX9b1NSm2JGB50+XSTJNpcMKjdSLWyhE2aQIuPT88PH6p9soX7aFi3hPYJu5bUiproERrTB00xjFQgBMlpwd1F86j4nLRzF1Xl+N0uXJL4KmjkIWphZNTrUEWYR4lefbaNb9TlRVl+nO0OcAqmJiXeP8+Junm+vG/cWZn3eX7z6A3EgeiIPFbHK7VG2l3zBhofmrXx3rz1O3n/4v4q8+T4z/B/A7HV19R7e/ESOsaXaWoMoMCgIMF2ywu55Up4iUrgdk+lViCNvuzzM3bd6tLoZ2RJ4rZi5PJcLUJynjkPVHnH5t53Ww4lBbdll6swWlVHy6ZkhTMseM96euewKdI4UsN9zffqr8/JLh1UnKsmzqBk4QRu4osnVb7K7FLxC/WyL0JEilBSgkROa/pMNqbM84qJMSXh1QIMMK8dB0aRa461lJAZz+LdQKV8KlfC+NAKgEAfEdsWy6/gNtnos35zJwieWLfMuIgZzKzBRwk44dWNu9TSKRY1awtFO9HkM9ZNUYEqDDTAonc/P6glky67aopwyKqloS18OInT094f47/w9bnO5/pilwwPggjrKJ7Bf96ld8NQRmalb9susX/GxEbLu7a59bR/C1nVtTqXshjVa0gIkyIxi4/sSnD3OSJVdLINGqCSWjNmq2zDPsKik5n+3Kv+2/gqE7ns1z6/eeUTAQoaNVXHhjZ9OzQRkW6PzWlmW5Vn2nQbzmIC9VB5cNpEfFekHOXZ0Qxm+L2j/tl8DC5qMA319UZWK/FtUlSzHxna8FXO0kQ1IMlj0lrszDqyH7Je+kt1IwxKRddFGwHlwbq5xZbNi70W61ITQHPSe1yMU3OArh4dI+JMCXYk+q2I+54zTXw//XuNVhdFFtLL11zfdZcSdY+Xh5pTBRuy+Flqiq92fx+WVk2en5URLPVGjRnhzZiz8iXPF+kFIspc3gW+Unucm5VIOnbgYxES5R3zlZV44XGcRyuQEZyyZ+z38f9Sk3I950qbA9bEkPa2mN4DrbnSa7Nu9O0GN11DOLiI5W5uQA2fsMesZiYHYlGwR83EjiyFpoTZj3Z39oH9pML4dUFtVLtwHzv9xsOe6JHFWfZs6mILVMPCfTXElN6Yexm7TF7LjVuXSxjdyoyDTme5koUyHPKmWFqCPI7y8w25x+zmkqckZhoP5ks87Ke7317HVOBeQLcLVIalkgFl3oqeKxQ1xcdXHe4yuLA8bd/0u6/usFvCCVtC52KxrKeBx/2rprH8cEY8TYpXRcwlr9MHLRxlys6qurRYSc4dIZyXzmpw9wJcprqm2GMmTjqPaFRVclRF4pp0EAzMNW2pIICVuj1IPkSZKj3ULKrq5Oh39S8694OrtomkZa8UYYrbM37AfwE6T3uVd9qkoXzAlHcNg6upmnOHVtLit/rpmuk3FVYPKqfdQGE3tnX6HJdxksKCGI4DH2DPaFZr4o1RJuSFZ1s5gTexvTq1Yvo87/3IxpiDilG4KBLmXIXPeNDhjdFCemQwRpujuqjgZODxShJq6SukmGm9Ci02Fcl0pf4JPHfGpa1FYWRrdtXV5eoyeFruZ2wMokLUo8469W5/36wfV3q0Xt0WBacvCyqDEVhrFMt23dS/nWtyBsrNoZaCg0kdnGed1F5NciqIcekAtHNZrhUhS+TiiA68/LVHYr95MY0C4d7bcPksT9Nytqsd0+MFjYB5uT2YO61bM+iKqQ6h5zDVZuzwkUgbF3q/9hbcqATJudIrhnqycSd/ez/KdgNs5fmI0/I1Zu5uRGOST+EZSwe91joWIgxvpFV9IGL1Qyr7pl9QBqre3cTX7+dzf03LlArerVXyOhEAABAASURBVO1q1yNFRZA2EkCbgAzyIFVzlWJa9Z/jqYc7j9VR51g+UMqXeexr+1oNH2WmDlpBt8bkB6ZHsy5aUNH51vDRoDV+xYprj8fk6tYKMRB2S6CnJh5TR6EkwJfYJGro8JCUx9Ky49AK48ygZHcCL7MWo2xoTdFfQlM5Ea8sgm9gNuUsuIK+KpeliuMs3KSN3q6wPVosfCDhatRTepz0iGjNMrQOh9r123TCaILQeVl9XPdNY5JBvFjMBXpwTFWZ1UcB6Vc+YAldhS61J0gjl1CCgFT2A93GqGj0tOHdq0C6yrAjXt9bFw++KX8Gtzf20V3cZq9K9sHq/dPby2ya0/5wFWUVUKx2QaU2ppJT/FHy3VBcR4A3zVWxZ+dEmZ4tXLbXYlykt+J528x3GoSJQCvBxfsapxlskipI0fwzteZbfu6YJgno5Dnz1PQ7PkebA1OKW2jz69rrYyw4rsDjKZOQazDq1B4j0+puSrkQKmwDdemOvn8mfzgL07d6eY8o/WLV+z8r+jH/0Bdtc3m5toHvs4ru//OPVzH50B9fZyKPhJw3zuMza9k2ShYtL8spnb1AIS/o7lT63j4zoBUlTD9zKXvVGtjrwX6ifzeOmwT8HqoSpSBRNSn7NCiVms6RhZbZwnydIO0N1QjR2aAuTVUmpJt7kEBhELDlbqYIcJe9Y0nVmxIFNwomOlnHRgqfOd1KWHLeoWaMZU6C5ZCaGpvzlwevoWPUK7VneHvPsljcnzFX/clKTY+F2WJvRkDJos7pFqFM5T1FPEJobnvxR2I4U9KV4W3jVz1Xlt+Q54F4W2LLSLcV49ou2hT46IqmAIcKa4yHfR6dUcOq5l2SgBa+9Tx4g3rwBtUyCVeuYM2L9mPhaotz9XE/rbb6RhzyISYPIMc0i952h5w22qclKdDaptkz07CZq/qq2WPwRadR16xbAn0VrJ+c0eg9WgBjVyzlqJ+c/GJuVuMlbIABNVgkM7Vk8unW06TiTPhAy6cXb/TX0VGj32SpJ4zyTLwfvctkajh0H1eXgTyptmO25Zp75mRPnWImPdsMCEwYwcixL+xu7xapM5C1HowPTZQLhikhY8l8/Ku1UL6NpjSOIWGZC2Wu4EWZqw82tSG2rHkmc9Q0GkUJU+3t0WmSo69Stbd+/1PNnWpJ+rIxPVuulNcj/XA5VZfmcM3sEapycjsWJTeojfC8V5WRmhZ2dRPFXyq/U4reG0YbJdXI7o36+Ax5nJo3Kl+tomSYu34hSAJoBX2/HRSl6KevZBzHmOIriqA1vRPrXsyQq+wsSH6lxuTmC/r7gmn6SFfjypTbmLWZCRBoxljxWWthRztEyK2k1uBLjQIpZ6lUnZmagrHgfWK+QWVS3SGnSwVpH8oGNWAub+HZEs9BPz1PqvJVoLG21puHFBwjouoq9entYXbBIL480+7p24+mHrNS2RLLM6xRUqJ9CxhoOuwGK4A1UEdkfq3TuteMEjES49Ms0tcsV+DetNHcQR/1mLnzydOATirVprdJ6ct6RqXgbDVpfW4wCYVYp7olIOrOTB8hGn+odc17kC1Xtn2FX+3amq1S5sZr9tP7O66eMPfbG/x6w8ES/xY0t9gWHixHcoQwGDaavo8UlGaJ0beQtzABnI4uUxfR2VG1Cmgczasxmu2Ny+9PZxwo0pZ8cDfuh8+t+FSSeJJPqR5jPu5XuH06lDiu3doM+THeFANNjIkrzMgpqaJkPGBJQRMO4xAiSE1m9A6zO3elYFNeuzE9qHppc3IF271IjKNxJu76HOqhrNsGl4mEZdd6KdccaVjT+cQ+tqu9tQT/Nn6RN3Ns9kwIYML0vDVdm5HC28hF7J7l5Hh9gbJRMt5vwGg7zVUymog2KLCc56sDrV6BD8yGQpaP6Q4WhZlJKO8NpLeeO/1e+F68EVldR0q4M56jIJaQWB57E3zSB+HDNWlqOarMObwHajQjCNEg3/He9plZ8wj3ZOMOP74Ll/Wr3pxO0ToqWqjYenULdAf1eFvKWYFtveUH07JAnrSSANsO6s7JEg9XfDyiuH6JL3E4Crt7jhJrO3zDKGGXbEz3c+Ob/OKVm//lQep/lUgDNSuztUtXPmYuPs4HVKeJNwi4L7gLiarinLgff8TjKamVLFMkfXDS+zjThEM/m2+mzLtNEwEO5Qmd00Xd7KQfuNLsu5dIIQIapw6kw01zZy9eYX+z5MIFM0QWpRCvs/rQsgTKZOle83UqocS1bW86hzOQ0KPLKjEi7WE9wiPEmomqGrbxjUKyjqGaiibqMDkyXTovbNewajyDxK66NIn+ZTztVqaa0sb1mSwxZqOu7gPFZ2ctbmi7Ql+li3c/Z/qsyqhsya9lYt7/4I1p0piqFc0qPLCjsFcyfum3DtnD8UqTylSB7RooLklhX/da30B5GmrrbNqUgbQYoZ1HvUQBy8T4q1NXhvvavrZw9US29g7WrTKoEKfIxBSlpBFJPsvWZaE8+GoLTEzFfTLrd9SDFKsitlFzuaiiIIbFbmWT3DamxG3CNnvN6Z5va2vgbcZmK6bmAjYgxK/tNAwkFKUOEJBjnauhPiLvwvgASkWoHw7qAOpmyosUPka14zKNSXGr1GYOICR1bLTEd2OgTKZGGrbTLbwvrwXdnEGvgQrZjnTuetHGq/mUUtX+DSv18hVvvocFryGyrQ/iyXXueaQnAowyPmtalbMxDmAs1fC9RRjRGFmc97S41B+FjjC9WZgFwWpjz3uMeozWyXeKXI04n2kDbe1laiavPZmu5u8IuneavQU657E8I0WWG0KAvJzqpBW7lSlS1NWvQVG93/ndLvDzNG5XYOIzoQiVsap/6GuJCf8Wfk8q8Q3xe/LoeOvNza90aUyreRXFP02A++6ugqKIwV8y9Ue5IUyMUPbbBGJeFpdx/0DJGDcJ7xm1tLg9vgZAOdYYr/Y22jXr0y6TlFFL+KPAG6dQ2ENZsng1cdt1nG1jIkQ4GlbKXehlvqx1WrSPwZHapF1btUDRbkxqPc0Eeq264myTdu62+1BUyZT72G5NHEFKe6zvVTqkg+b24DaqAJ+OwiKp9tOSqorT9anIlJNpyQqYoepDC851T2j9+5rhivWJFQyAkBuV19JbQ6sccauNSRVTF1rtfWGd7fe2tgGPjNZAQ/DMGQrS6ZjSdLRaqjclSlmiWLF+Rqn0kaNtFhQShbju53P6X6k127QgYrkYW6TPDFBb7ZZHDaw1xP/B9shEGZJLDmW0XwC6WozijvENd9nwsBN+3qwSE3EipqnB9/R3xYSiL39SbUVKhHhHdqhnpx9sJQQ91qPfU9Kn0s+RIcjIPrxUxWpAQfV8hRRCp/5n64w9xyoFUzYoEkOFWGX1kDc9Q4SMP6fP0Z7qWbxfJ0NujAPs9pZkrtZYcXvaRK2cTmcfMdQg2qDv8FR/qFm0FcvjTbcY5p86D+yX+8aqefMC8WWsl+Ga6hZlnEIXLC1KKjJKj3SQEAweyYijDkbaxl7SEbqZMyOv3SJj0srsIDHt5kv71ohyHk73yPqkyMNo6s3ofvueK01yoI+jmyI/paHVCf+N+H4wn7quvFbpYtxpTTiGLUvHcS9FIG/J5hpfMBfCagU4WknAR+E3is64NJMwLKjnZ3V/R+vcWL2Pan1eLiy5CrEMn2su6/91/agQz6PUaP1mh3vM8oOe595ndonj/Vgc2tBowmadrAxoe8RI4svW+JppccVpDAjTAfk0m4VZfCfnex4mtBTcjbDCk3kwlIJVjmFUCVBstWwpZqWkl3Q0PbIPK5vayA0NiYJLL8DbLV7BmziVR8WvMwu+jlLVPEBNzrnzPKcqbcZyWqSWEZC3d5LemqA3VC3l2lKy2/GsbvsOfvsNUdCF+c+fyzxeBdgWjOI0EL3trClvULTUACBImgL8/7P233H7VcW9MPydmbXWvu4fIti7YsdumjFGTXlO6kmvxhJL1MQUTTSJMTHdGjuK2CIWNGIvKCCooKJiDyA2pIjSi7Tffe291sy8331jzvPHc857zvt53s2+r7L2WlO+852ZtfeF6GpuZRjjqEVsEmkcL7QFPEgpuIHzhBZ4LgtiMWQRNcqOhXYzUTUhcEgmVKgtlEg0dO5BGFbKrSImkXQCAkpDRLCleQxFL4pi2iRb6aWGTZoiSnEL7c2kVA5aNwErdZLh6WOIOPs4722aGzdfGIJoVJ+g7aUPWTJ6SVBlAFxlqyOpkFhDGUjfs9gkTRWp7oagqVJkVRGDEXKbWYS0ZAEXLPNq89pvRiEzQjkRQoWRZJNAVYVYbTI2CqN1zVwzBaA3povKEqERRYJ4kEn86u7BrRE4ibqtwLhfyTmie3I8cjUzkgezjwwKk6HUuc5HOkv9ljUmhwb9ho+cF5ay4KdiIYKeZTAhBbF+ZWyrJjRciOBAG8VAlaNl506rhdYQnuq5mocy9hxLhk6CsVoRDbpJvCzTOF20Js0yRZXQHMhFaB79cZEZxUVFpFVhapt2A5eH0IYymP2jgg6JQPknlKqr5QxvSHrRIMl4KSpGFX4HRALBwEJGKO/lggVi9E5fyyamyStngJYJxSIMHaz5NqKOYqNpSEUXDIQTPIZCkhEhDxQ8gn+EY3XZglI6NTknW4qK8mpiCLrlkknvBGLVUJWfNaUNrVDeyIwKxoItjldoJ2EwzCaLqNO7wKCNQjxqhdFOZAUVZPALxFPHyCE0PpJHRIZHxEAGpKJOORUSEhqKEAEPLzJUNI2rczWvChttUET2idCZRpXUREoKkunWFJkY7tnFohlLrlhhod3OfdVD+1ktXGkpXYqiKWpYw6hFaf/eE4voBLCx7QcpU9RNBtCZIuGUMdbaY5HmVvv6HxpCEnVm1ZBcmg4G1FaZhYrUOZhigklyWiD8rQR18AFBNYcxY5AqIrQfAcyIBYOFl1H0bYVPRuubGae4gXsjmGcO5paHYRgZLWGWIkVlUs6HKsVeHyhEoTQhyMosIECiXdcanRG0eQSWlS3EexhSAmvOqHbRGeKrogE6oCIEiUUpioamqxKF4HVECCkijFUKi68DszDEw5nnPkcuIbHOHaCuIc4CS2hRUQomdh+pwo/BNGYQKZEqWdnGNLINWUllLsVpK6Ql7zkZXQsxQoaEkXTBsBRnzxg2UrZg+8ixIzqhcAEnRXgEXDK5u63gylxjI2kEKmg4WQaVrKICM7AXq7pwPpLuJAEj8QJIoSvWaacKLSllhatn41yfkCUHCAcXubK85UKXfdByXie4QyUUPKGZGZmE1xYXnxOcjqWUpU5ehNyNsbBtDi4OwpssF56Mc3XECNq0nhzlzD0nNM0cQjqGBh0dsThNT4kh4fBIIVHg1XQqtQhZZemMyliFBC0H77sw8Q5komUidGRYUl7xXoIOG4iNSIohm1KdBGzOuqSGgVqHCCtVeA3qwpiy72jfmcgYkHRUlUw41NQCUZ5czpYxSiwmvay4FwUP1vqlLrzt9JJHFZoeAAAQAElEQVShIqwiajAk4RoWfSWe6jDjrZ1Hp2/gNpBGDlgGoyvUB3H63AfB6gwH4MgREcIggn/DWR4YLe/Jr0W0aJWwHDwVjFWuVpoRUVTBBC2iCkCS/tIaENqoPVpHCQ7a4DICxS2B5kJtLr5EJJJ1sLZRN27rrYgSgSzO5kLMeU+6/nQxMJCQUOReAo3IFc8qacaQ5UhRp3MKVVohJdXWbT+YbbQTJcUCXMTvoigFVbORMkFgfPWByFoRMxXUoJtEAIBCNgkDEuHwEI1WWC5XBmYqXEEOsbFjIbLTiObRQOHKi/RfuCol0xYxzq00TtZjqDrtgIgbrzJiC4qjcKlkEej1bqxqB9PDVwaDIjI9lvBckndbbbhGZgIoQc0wS1Y6k+C60ZNh7Zos54zxiBjCEUlRqBCtYpCqaIIptYGFg4yIFIyCbjZADTVgCaF06bzA/AE0ORbikAEsiS2w132r6Ca9YhvWl5JedCiCHBruI51xswh04shIpBRWOhZQKEPVPIumSEboCBURgjw1h4a75sK3VAUqICVZ9lw6P2aCVXdQiQy1UMEKIeWmqhW4xmDIkCx5xCmGZtTgBohBpC4ask4Nk8R6UKsyAOyXXvhOZUtqHya+ppMwkcuCMlZYNJECCFYsFaCfI5x+YpE66iQbtNoL0rNsS1tYTZDm9J1mg54wUKHp6gGHDSSS0aE6pAsAFVPUDCiVmIAngExJwmXpRp8hbprGQTJJDbXaPpMd0PYi0CXYtFg4+F1krZdJ2lC6ZwveHpFQa8aDBBm1Dj5ayQap2lJpIO2RDEOX0YUYjFFA07tTRmgQIJpZUwrZJ5ZqolokFD2pJiySOqMQbTJaRDSLudbFxT0zEbVqXX+LU4Dllaem0heYSoHQcwsBl7UKtbakLUNiZBvkfZLS3mOYaCFtDEM8QYB1qC6SjkQm5cbMkjdv0PchdJ6/9vXzjjvli2/90ElvOvp9b/iPt7/+LW97zVve+faPfO7YU//zq+d+55IrL1PM9CLLlJiaN9791pwshWHS4Eudh/RF0CfrVTqEgShp9CEwoDwRUpe2IUlURHk/U3JBDx9cXUQk2+ytd95as9RASihFZCSc2IGsKKYiwAiNKBkMwmAG6VzLaEiWIvHoGFvw8YX35Ge6KwG6r+LKD2tgaRGy8zQZtYmK0tit2v6i2ypLA0xVphJMbAwFeTUJpad6ID1yzOm7qlt6A2N0Bryrj8mIyqybbZnmIrOORZYIhpRFdJHYKjxXcQRgZ9apq84pQytsDVQVYbXqSqPdHdpD+8hBARJhEALI9Vb43I1VqakUkAiS9FC7SlK6IekM+ACkbIbyW0h0mV1IhqwGBXWsQ2PsSTP3tUh2VydEBq3CxShNrSbqiOYxQapRuu967ywaKBOmDdgiQGlZNCYlw7bLMuZArxYbzU2oZVMqBCK7CB+XeFv9GNuK3nas7rSVngIQUx+YF6Iaa+FeWEIEo0rfSeyotirsY5vsLUbraHye1xnsIL1K5uig3jK6rXFxDiBTFTzWl8gpWdVCaAYJiYRECmGCS84IZ6hRq1hT5tciY+E/vFyqepFFgy61GqxYaElANmOQhl5sDtuOHE499MGGcj3MotZUYwdA8VlirHxJERKfogjzTEvZ7VUqtEYRZ9LnrnhnKJWySoaOBNt6TI5K8xzi5iiBpmICSWHlbwkqtGGMXAZ5sg1sIeQJRCLEBan8zi5TTVspZULZ0NMotLFRuSfpxS5gmjKoMWilsdvVIhwKk7koN8RuvNuQbeictpiliThkhCxsY3V0baD8ZBp4eg8unUpKpe6qUnjVC3om8xGLgEpUczXBqEGDbrnmEtl7WsQUrI3IWWQB3cHMFSRPpyshrIltQzIKU2hFSlBU1aRYLTYBaybQxsFSxua+BKRZIX82lVDQCyI9LY7OB2bRiZ5vZGl1iU30feqVSJksyYCxACwsQSK2bWW7I70OOow+xLlhXAsNTaUcHaO6CM8luY5afG7YlbIdMrYp3RT73A5wqYSMPkAMIklm15xBI1mD1vteYA7ZxZTKzCslm+TkRLKlZurM7CFRMpJVZwtsxUJMDARPk/VCZUzZ69K1u7iLxmCOKWeJKCex8+wgeNeY24K9o5BGoWAaeWUJ1KXIWom2xq6RiJUQddQJdRJ2PikJSVZWIU+QspPWshRJs5CWUQe0R9C6UI9kb3FIQBEmXXwxiVLCNvRKqKkrxkawU6RN2iQQs/fFRy/D2qzCG2wfKjAxAXcsKmDbbSbTHq0ojQHdcaVDm91h+xf0gE3kgUh3hmkk4wQvZqVWo8sdcL675VaXLcH2nkQABa7ebZttUWOhbbKYhFSjpeFT9LZAFnqAeehgAAkn62IJcpV6Yh4+976NHAIeVdCSSiyd23rZYixlCe1dGTv2jqXEYBqL0KQGiltStsnc3qgUsR66G1NnwQ0+nG7FJhb4Mutq9NBZMoTpnmZERb3ZoroAmcMoKyx62d9zHoM1Sbw5bFZ4erLFMqSADegQLZabypvEoCDNxEgSm5aMpGBViAii8lTblE3DJFJhAaqfA7siSzKN1wDToqaum87CKAHNWqVs1KeVO4aphoTryJq8D6P0pBCQIAH3nB2ZWTFqdrKXjwQbwsxDktk0otQs+xItloyZbpoqeCLCB7apM4tj1OKtitjKEk7qrpxrDmF4LVXHmgfYO2SgDphychbNSUbVoVV8M/nOhH0ibWD1Wlhmhc4wkLlRemGF8olBcrLogLAr5o6jYVEwvoM8CsMAE5rEygJtLEsQB+VIrdqmnApzZwmMxZJqc0y2iHIzWHMUybQxQA+24mxz2x34DoqhKJAyvOayKbFTosYC1q+MIEmhQRINLZ1p0iXJjmD7MVVFjdx0TAFz0YiG0SRCMYxfpaYUQgTDguJRaPuINQcDFIypEBhOaAtdnYXafMqtubuYq6mIJNKTAUmrjBo/JaxXrHcH4qEjqpPIQrZ5s2WSBcOXYDBztKHayY1G48umV+kthcatWAKg8K5wUaQVcWPTtXRZCQ0TTbHUlExQPz8F0kNIYQ62GlNbJusywkdghLpZTpApbYNWuL4gmL1ukq65iHQJi9UcRbUse94oLfPinKkhwihEKcGQBmv6nL4bsuWibKBBJpDEIlgQ0GyShF6XxGAOrVSAK5WgQ6pki4jeExA+IMr0GIZURORw5YymslMJSbB6MnpSg4uTrriLSFHROmh+SkGfhPzzZHSSTOup5KLT3qXItqkaNJS+rgySTE0gigwmhbYxbUyl0oyhAl4A7XU4CokjhXdpCzQz6xCeFM+aMmhGdM7JARC1OSK6gFJMuJyznWoKQMT3pRbaS3G8zAEIcTG6uko0sVCbCX1gWXKMAaebEEjSKmVice8TlZCXDJOQ4eNrZ5///CNe94uP/9NDf+IXf/nRT/ijv3zGPz37ec9/2eHPe8lh//rSw/7lsMOe9g9/+4S/+sufe/RjfvTXf+OuP/HTP/W7j/qlP/zDRz3jb/74H//5rR84cb6669LEW/TNIBYrGnwn0UZKJBEgIwbE+YnAFdF9qipAiSyELTf8aMMltsk81TTR6tVc6aPXMurkZipqCh6uGQwqVolh4FcXHxyRVLHKVyTp0oXOrROlAyOtm2bRoMst3MYoye20WyleJiIozFPO5BpKcVtcukckw95dFZwe6D4jmbKdhSRHJlSjksoYI33RcMlmqJNSntMQeB3LFCFDcy5YWG2EQd9a9Sm1xJoTOqJ5rv89dEhR0xACRmc8clC/pW4gLCkioLGOOkLQYYQUQrIPTtOutJvOQ0rCVMQwTGgg+x4rVeuiEOZTKoVwBsDvkpaUlaLBT1oTvifGHT4DxG/ADMJ4uLPnmoNCkN5DAlWkKlR7smfJSrHVdMrIRWTdd5UKbriE2IFJm9zDLD6SGyxanUKsgupUOK3CJbeJ3ajdWTEbgphFL2OodDUaLNIKWhOTIK66pmXSHIeIIgPJmQO0oNAx8EgiqXznmSkOJKurKKd1iw5aE5oQ8MVCJMMgPBGuMdN/ZcyCYUs1rPMS6UJdKEihRo6n0TmT9DpvZczZnbBwTqyCSpIbJZcKJ5FlEjRjajAAbGGhAjO1RgwR6k6HYeE0zFEzuUh0qxJ78AcUIrSULtG9EAxR5nV0RnuFZbVbRI1zBJYMmIKHrBTcKWS4gPontSIUaOoUNImwmgwMOhsFNMV0tcxoWzQuGOyLQehClO0iiHIZnBhFC+uYYlJ2FmJI5eaFnT5LGYJuzDaxUoSWsdGhik+cA+vM9ZXT2ZPRClC9IYsyuhAqVwxFwGrXwmYWSbbkIL4lfG9xlsob26lYLVCLYHGLPoLoKVehEYLhQXrmcDiCQZ+HdbafMCqbFNKQLYR131wytRdhO3CIdNVIg26KrKw29iS3TBPnShVeqTDJEBlAJGDamy1TpkAlmTyrNI+1LQmPHE27UlFCUppaMwEyfcnOZJ217PfS3YKWi+6o7uvCDUGM9ECxrC3QBu+NwhxwWb3pXQbvMZh92teHJ2Y0CYSNBtkSdaEJqU6jkxKIpVvGFMmTBGfzdF6M4T6wd0iOwlvjZRhXUssiyQq1CpMtQwBbBuMhasVMKa4sM+YFESEsh0qWVLMmhTNAUzQokFgIpyI1hiWqJ8O5Zh3DLEksiMm6qdzYPCm5BFRbU4/vEQ1rUZtEibWJUCgEQtulYM51UwptVo3kCsvUUSR3zKs6EAim6ArJ+pE2xiKsyoie0WV4DtHQ1dTaUmrk4DF7LMihA8Z4ySQozRQTeSBSuaC0uXKZh2/5U+/wEYCYyATZUWxotUaRQfnWBZ3eF04YJTnDKiu3sZwFU2xeI9gIOAvAaqpEsAYmPGU9XalaW7XCDzoEDJFmsyhVwWqwfg84pyuk8B8OQqW46whKiIFBSOi+ZlHKtB4Y/KCRZNC29mEaU/XWXAwiUtSQYuk1o0a0TBEfcEdgCRl895AAeVgCAsm0EO4aLIVkyLIo+RF9iVgVKx2tJTdZ2txkVIGCEtOaow72Z/LOhLOoVxlRzQ4fiZQsmVVSZaHX0iW8Dp06iBDglLGCqumJnogsJRvhNYz1m9LC7JKBJNC67I3FXJ2tZru4M/gD6cb8Bo9cJTKhhIKULFinUwc9wtqGShQDiDDLxETDSIDkgChYlLVmrWMHi6F75kJOOG83mJAOpMKbREHIcOfmjbDMjoVOJo1jNDlDLGSTm5JTgxYwcOFUuGkCAmAmYIxQOiXECCdbmcDg9qKmFitaVaoqMVRWE2LWVROGrNm5K3TTbdpIVdBgZnpFNFsndDAZq8MGMGgNhJurKmYGMDw2fL0Ug2usA67Eg8nrFFZSxTQryCVnmaiLVhHK5z10EdVF1YtwiqpwF8fyBwigBpuy7MB4Udev6yhUQ9sYBi8IH9I7dbdAo0gIVbOj5M6wwinMKXHXJUqihFAs1UEN2PHc6TahqRUvsgUWITnF1ArDleskTtbatAAAEABJREFUY47THemMPDSpt7hOUqYNJ+11j1EVxUTUIS6ckdmTWxcMIaQNKHyX5l67r5JUUVNtttxfR1exgGaCcEpKSWnCzhJGAigUoEXSfd1zwksdKMNBogZXKLiuD6UUuEiGs7AaC5yNYTFWRjspBtYseFpyofS0noUCuQJ7KTuCK2GJ4qnsDDOdoFrRagQgsiSY0xpVwgRpjjLAz5lFaCADk0Vl0tYKVxiNUqFqGUPIPxrC5SyBlSWJfU9kQVkS0TsN9dGWsW+WycGwRTjHWGfJ+ZRBaU3FqMaLzhtgH1VyCp80uATLePdqZtOEjQSRaXMSHBOaLF0hiSpqKAXGPm8mWPmx+dq5Fz7ln170K4950uFHvvmMM07LvQP/i2Pv4vry3W+fc+aZZ3z8U6d88IQT/vZZ//jIv/qrCy+9wnvrEDFhPm+mxcriNKLthrmH6YzNkjshDVVpDfElygE6CKiWNmVuRmDMzPVs7F5r0iyZXhgcIw2YjLVEVLjydNWllG6tJ5+FtpjrskyL26xYaqCgKSZgJ4WVmNFWCd037ICoO7ybmvct281YKqmXG5XKGIkMmKcmioBxS4ZsO3Jmai+q21a2jMLsuc1pxs7CMLtNXmsYGOYAWA6pUVjkUpdsnNf5yDAou6htRFbIOQu0xDRFK2h80VE6ckbGHueLmQoAGak9iZByPrAmDtPZMgLqELJX+RFzYBsxWzDqrVHk3tWeRLzHdsHW4VhQtzKNjaEyQAPpzVlVMGmBtC7qZESrIGdNhuTCqAyPmbW488e2daohRo6gr1JLoS9LYngm26HTkJpDowvN1lJMDUCO5CvdPoAhC+rdnXMsufVYMsLD5izL1pzKnAeVUjwrq+5sStuIDvRZl7mN0WT1lKsSa9sySKT5qLmdsL/J/iIu9NwaiqqpFBpoJgjEagJAfCFuOrQKbxdnFW4bCANWIlUI9yfcLSVGJkeDWky0JIHP9KGDSa86GlgP6RQZYgXKPZIY5oph6tSzDKz7oqSRQ3yEM0Qj1QUi2lANFZiMz3WiRLbk15LFFotlDXRMWkyplYgObnSWAhBHl2Dx7pOPjZBcJqOOAMNeZtWZv3WQCQopAgMIPes3NkgTQCDCtCm0V9tSmtNIp0M+AJVCy2RJBpDiIFZT+Lid3BUVosy5ITFkdS0EuXaTgJKQWlM1mZOmGSXFXAu0sQgTcGHhmXy9qpWwLQ4WNoIqgxKilrnwOZolkIjQ4dZTnOZoNB37ZOwoqmiSTHPaAmCaZd9+pomilrGzCXIcogihiEQfucwZDOhSkKyl6O59DIk0wsFzoHBSjDXSUjXLNIS/9Am3tzstjKL4c8JiWOjByvMJqq16K6lSqSmja3IKGqxIsaLElKWnp+52ZpNSRKhuVbYlOuFoaZPaJMqZssmchAVBNCpW8WWMGLHlLYDUiJqUpMLn1aXtQAr4THneh7lBUrHNXDyIFV2BgjdUXeBu8JZqXkpXOtOWhthxmUK1izC2BdGEdAjJjJESYkLLYUIQhPK6gtOQie0oWxcn44HB0Qoyz0UgtHYR65o9WUq4SJRLxRm25NUmMflSSHFkMp6WVCHYUVaHqnVKa6qo2muGdhEviSlhnVrcBDBVcg4pOQI0IFJM157YKteBsGEzSW1aixuCQA3IymBAIN3SSSmEgocYRFRAkcRpRORIxsKlpUXqnL7t2xFzlrGGAcWCm0V6mZkqA0wIzypJXrDuXC8sSyZAl9lKNjQ8urG3LFqWjS4bIpzB1DAV/tjU2rqUPms0QH31x9VKawRBFaumAK8QeFZeKDN6V5KoJ8J1xbEXHTB04+xWaAxDResAAVgZdQulQmSBlBSTFAUpvo2667pN6QIhyEygZH62mqWiVM7IKWHqXnSutcsEqX1T5knSk8nXxiIxGMjFrFfzSqIS5eHMKyxhIKBCh2YffOgZayPROiqIIzeF6kPWhggb2CiombAJHKCnoyAVQ2yR0rMsVXqTYZUWVFOtNnZk2UQRFBVoERqHxpvm/VJ2tWSjiwNYFsVishBuyTHl0ghcTbQE/YsgbnVkZt9i7Ca2InMRSCKdxWDM6IkAkCl8RcIcZEX2AHceNvvkg16jgmlFSJDbgm2VXpDsjLPpEEQiIyUJftrgF5K2O5YcnREEBGJpBfzxYJiHdgZVfFlB83kbHtTumZ1mcOpOlUKWqwQXQo01pExBq2BWFGRQxzKkp60GgPhbwqoyp6SA5GDtKFNnZgjnGqEtaaoLal805izuFZ33aEsvNHZY35Z5q8tcaXMEqmOHrxAhHCXcpEtdonEsa7JcXX8lB1g2gz+DDLIpJ1sKKR+Tqk7TYrWLJI0FgQ3QSMIsKYzvlLIZUkXYIg0kYVATgEGHzXlbtmNWxMI4ku5QFp1tWxy8qxOVUiaERTAv5mXqUZnQxJ4ZRemZk0tkiWIoUsPFWd8XaFSzWlVBm9wFruHaqZQJXAJIcRJm9Y85RJ1kqAqzI6qOsDHM4U4jNL0bKax0X5Alo41VUEC2wEJ3OruxNytl36Q71TjJaR5RgFAN54H+OmKW4XXEAWQFYwOWYoQL8y7cO8llQhsGQzLWMqYJpRdkLsk8hpM76UOHWw+NhBhqgZlHyV6SvXERUFxaBuCAphizamPB4lwz13aEWb0k4R1K/9pI3mvI7CS/BtjMCFhtLhVSgAbuoKRahLprRmqBTCpUCwbe54ptq52pyaAWKrBeSqRZdxJLloGAhIiUEVP6ateQkeGS7E7EP33ryxJMAwuxYGLV2WSxFGOpLhXFTFAUwmgNs974CwUjzfXvP/kTj/yLvzjhpI8tM6NALv0vzwc/6MHP/Is//8Pff/Qv//dffsiPPuD+97rXHW5/hxvf9ObcFHLNl7/8hae/6CXXXHctcj2UB7SIslKx143iqqEqkAS9AWFdMmfkLskZdMEhrhIqLmwcFOIlGOQhDgJGV5lePDXQnOSSlitcFpIuGSVEYapUAz5cITsRIh260t183doxTC4YgpUbWkBARDgriWpRMVllDHVmdlgmR6wYDBS/Ij0Uo7orqxsU1bJZaoUXW2xK5jBKA2uPiEbooHODdRAYaludRm06GctMU5+0m3XCIKml73nr6mghSBnIRWgDDdMMQ2ooRvFe03e6Vt7mDp1HiWBEvfi2kMdLSiTdJX6F7AtIjOD2v299dvMoPdnKGBYHgjUcntSy3oCESpLnJPxWckiCfgcy3Ve53YVIejKJtXvxUSNKenXiynBGZl9yGekwqIUaQWKEi5qx0Eh2LsTCnFRfJh1Ts6nVoKEYA07FYMNjRBhg8aDRNIYYKMDMUMsJoD+WgdUb7qli4abX54K5iauLLqWkcJdAfWWyVqQo6A91qKi51EwB0NkC2IR6Q1ayhDOoTylijWjO0PWhjZvSKsdIcR9CygHmLP1RfEFdko2RrBYv0otIReXOhjFbcVWgMkM1CT3lmozRe1/GklhqHVNLseJu6AK6A87LXPr49ncvPemLXz/5y187/4orukjqjtI+HZY0IE3WIUORMENY7aiD3+hTZBFemMg/RwCEj7J9oEeB8tJe4JBdMoTKhcND3HPxMYg3I0hckgt7cbJepNPowqkCiEAK9aWaGvVJA08WeVGHjADl5AIMegsBFCJpIkZVVDgIuovyrY8Vh3kEK6NTWSaTY5WuRbVmscFpXvpSKS5jiAxGTSu9Cb++QpfFM/gdWG0i/glJ75lOLZpGtaSADIGDExUzbBZxFBjpIBAO84IFigp9SM1ABGg01yrfE4hsjHcuo4y+Gj4MnE4JVODCSyucRKchmtBurSY0YXh36mXYmRkYnTa5SFfenWjJbFiKDKPaNDjfREWKEVRNlgnaI0pFKOgl5pq9lEVaUL5oS1cCwhbVC0i5pBhOJnhAgk5whk5hlgotaEWbUlAK/3GusLUVDbcB6oQpLTbqTIsQUS5i/FZJ5FkUqJK/MNKUZhuRlW7qqpxa1llM5YR6lVG1q0Uy4KGySM6OmFMWLzFUyOEQEyZZLaJFTIVoe2QmI8QCGhtdqSQr5qA/xKfCzRkkThUte36Ix9JjrEzTxvo6UGhURVQbaIPek+kkxjLm6Nvs2xq88dBNMxbZ4E/7kbNhNGWWFJP1oBcR4VQ0AAFIbxOithYQCZMoxvIQ4iCJvY6w9T+vAuaGCBFGp++NKGorSaMF24wFKk6rVEEVJLS0kMr4IZPTJwlTQuiS7umROpJE5SeqhwY7DscyBOsbQaC/Y7XQRUeRcFdq4Maoh3dgmZC8l20OCTIjWNBIU4Yze/NoKYYaqkPKIFjpJYPsYJ0CEQgO1eDlhZOkidh6zZhlOUxUlIZCBNASVrtognCFE6emUpOOSmQGeBMYYDCF0R4KF3MUiFKc2tqldE5fwddcfeZ0dWKqaRpFuXcRy1U6VXJEFSZGyRJDo0PmUBckkOsg6eRcLk6DIJvC/qGlqhSD1aF8lYbYGV7DbRA1TakhfEw7VZ82sGZG51wkbM/SFfgE41A6QNHhK+DDDWKka2q4LB2DWc1knpe1RjurPxbRGcaw8nRUR3EGWwuMRpgIEd1JQqsOG1VSIUgbaKFmNYpsVZY1+gKH9FUaUYyKUXSoJEQcGclBKcFosiqKjBqLIRiVlJZakYKRWDJ9hQcC9Swz7ZEGK9UACsBY1BeLYc4QLAwXjfKwgAySYgNpJjSQ4Qz+IUZa0hSpQhhSWzS2LPN1EtqiOcrI0hWDMiUXMFAx1IEOulI8WLC1w5e2LC0GbchE+DqPU9bTLUNIWNRhleEwBZWRNyJK08WGqIvAtIhsLGu6+ZKD5mmq0OwhQ9xLjuaDIdIx6KlgC7aYmQSBa2Z1jnqSVUBEjzFypPSQEUIDMuARWEK2ofxAlRjUTbj7FLvpvfsY5D2SQHfVobpGhsKEXvjsHoiEQgUE26oYA6+DMICdNzO7K/dV2Sv2Dk729FVciATSgwYRQV80Rs3U6FRH46hPECpeak6qlFoyyoAyQkrfxBmbFGLXOC6LyYLB1RIewTLRepZIdpSSezZ7RywZ3X1oCt3JBOlFHTUXGx3DaTaJOukaG6QPy4WLKx0rRjYsukc8wAAhttmTK5rXhn1tqq1aJZEGag+IQ7uAFGGwiNaCvuh2mXY7ePMyc0ZQSDHJTO/rmRKCOflIINCGlhpTG62GCUmu6JFLuOcQ474k5QMnnfzMFz7/yu9dif86bnqTm/72r/zys/7m6S/8x79/8hMe/zM/8VCddq6/eOODD/qjR/zO3//ZE4/4p7856rAXH/3Kw973uiOOe/Nr3/naV93pkDtyzmc+/Ym3HXesgoiQEHxnLDcm0yLqSYcHpujNhibJldy79MgkzH0gBj8OYglJzi45dPgYjIb0kkuNXYutxIJgDQmIaDGrFWrUJpHSrTn9nIykizVkGYqkNIRjocc5QIs0oQR1oGbdCFnGZUWjikoYjWSoRhuhXQQiRdm6LUkBmLCxSNeSpVrh83LtJNIAABAASURBVHpVSTYT8aStSox7nQYdTBo0gBA6RUwEKt4s1ER8U7w1qZnGXGFUF2P0OTeFkpoGRQZYIGMupe/sSN0ojPVinnTZQZBG5EJf4N0AcJtnzLOFNDEx80a5W+FOns8XiKd7el1hAiufV6bGWAjukJLQVWk4MVk/SOQasjV/vC/kt6bSe5aS0pToMGUSvqiMqlGI6hhYeW/0UTrxHbw0lXkDL7QLyqxjYMlg7SOWJbuHE+FSsirfNRp8olKvKRNQFSnhmsE8UkoGddBINuvcuGwG83kJbJdcaF8OBqmbuHJatBgbwsd9AUyKoK4G0DPJMECFB0c8ZQsZaUk8S3OpDoWEKHOIXLWF2n1FBg7v0IFpxBTRhlk3XZm5snRBFTCABFbMVM3FZ11mIwd4hfCYJPUiMbDiuTopheH0SFlCfZh0o27/5ne/8+evOvIR/3bEnx521J8d9taXvuu4xVa7ea1Ul+apmkrfdiQ2zRvbS8Ha4Eb6jOZoQK0mbzv587/yTy975HMPv/DSS5M2ruWEJjC0vn9gGcwjpgPhAm9X4ct12+ue8rIjf+sZL3zF0R9IpcChKmuBGOpO1BpgRGTEnNatylSnqWyM6jrbQJlFZmDpOcbIWAQAg1ZkiM3ZFmjPjuTzYjAYbjFrbBNjiIQSGAU/se1EhbVedC65KyxYZe6K7SjOzSJFhbcYDCWZJEz4sShFroq4Pn0Z0TOoBiRTLRdcee2vPPOVv/w3h33kC6dz0dixZGKTikFubZAiTjOLUL+CR7oSneFjtaOF6TDM4D6EuRxwlw4sSawlTValAEcGIqiT4ZZoKi38XSd+9mHPeskfvvjVl1x3BWRgLJjj86ef/bC/f+Gv/e0LTj/rm259NqSsgJIXiQSEZljTWlUsdapYq4ZuO+YFy1x8qZEtaw1oCFSzWjbttJgaKITkVWMyeNI4ESDFgkg15F6FF3pjq4MZHvxTXs9Uca2hnAeGWHJdxhckKVZmPrVaDWmoBROCFkBkaHV+k6ZcuXoRaLO1eQ11kijAaBgT8YKPTEYph0mI01uoBIsjpdA8cRY3pTE+OmcizIauGIdkDWu9KjugyKIIVQXDFBq95+7IWXgftBDc1VNYCZmcPB4aW2D2Ec45i5Hl7qZZbGVdOBt8iaJpjLeZYdpIJeiM24iI4Rgd2jnXeGHjxp8mbbfURWiW92SZ0V3GM0kzX8TDSIcBSWlik2DyRJzz3Yve+4lTr7zictUh4SIUszA0id5VQyyDw5keLILsYyDm9LANQdcRhFBTzz3/4vd87HNXXnk1YyeaDiZWHyTSGMmYp3XBnlXmaW7y+a9983eeedjD/u4VZ597gXcZiaRcyaJEEcYCmJvI1p1GBPgwTCUn8YogPEPMYe6XXnT5Y//1Vb/2Ny9960dOcaDDsC40HUm4zbmlDkpNSLpYoCZI+ELysMOoqBvXeIzB9M9kadjZYEPycA23QAt3TZ071sFlgEWEjFE4VVNECoR6AEfygPArySLEyxnGmdaVJOs4R7gHzVlZNkjfkRRUS7GyqbKZ2Gq3qAtWC6uTxABFQWu0klOV1pIZVM3aeuM6AZYpTOyku5yYyjUBkRwiTj89baw2cYwzOEt4391nZd/KNfSDFQ2Eh7evLebJexvMNi02Vd2YsUsKUbbhwj3KWt5bbbJR3cAa2mSVhqNIL4jVEmDbe1/GcJo+WplNFzZ3Xl4iR/eSnU+2ai3AFPS8em4czYVeDvQ5e48+4KMQWYeN1AjjRavgXoWc2SarhSgFVF2d12HWq0QzWFVhalCH5q7LPHK77Sd/+T8/feYZaskVmXxaUpYm3mBIIT50X2VRVn7dlViyz+GLR2QkPCyihOv45vnffv8pp1511VWszFvJXQV37bmYdAUrW66y2DEVQNrS/RmvettvPvMFz3njfxSkZjLUU9PGABMOJe6DE0tKHcWGZaxsn3XMkjNyLnHFcvX7PnvqV84+L6Mz04wyZp23iBzcaPAEVgme1tMYT3cEsruvwA+Kc5O0DAiGqfOtO1EdEHrcBnhSO1JDlRczwYyLkeAXYZcsSUI7lK5HsNiOiJ4y0sKbRs2oi262Vbl/ZWlANl2sdLVQgayLXBCirM/8gJaUmY0WFTPSipyRqRjrliurx9rC1TpshEYqLc5hFMJKutaeGSx5NUVCaHBSYKbTNQApayFvHi2XjXYaXqUWSmZJER+5Fmy3ZEMnI9m7JRtyClSudWK5iC8YC3pwR5C037ERmaRMIKE0CKnICO4Nmq/MGa5L+tyDVojbxlNG990laLyJlZWKi5QAnU/lepnN2EfI/hDJBO1evDMWPefILcWdcdbXn/XSl1xz9VU0iWfd7HvS4x530jve8uK/f8ZjfuvXHvbLv/jXf/gHr3v+sz/+9jf/7m/+1l0PvfdjfuNXv/zVb37k1C9+8OOfectxH3vj8Se/+V3vOfq9x3ziM5+56x0PoQTG6y3veNv7PvbRL37tKxdfefl1yzaGY9C2TYSx4C5K09WVUDT3kq4gfWN9IbYLS75N3dqoU8/iw9ZCxTkeK5qIYkyHYksx0pqOghQsW9GtpTPDXIqjDNEwWfe4A2XrZbBwsJFyhVbNynyzxVsMgUJrNsaRRX5/w35BH+akmHKbUGfYwqDrMmWn8JRAcbMsqiSDE08kSWmFuzyUARlINogFQIHWwgIlsZO9ydKwFMIQThni5NwU3FDN5t22WbdhI7WEtNF8sdmxMF4G1VKllKKilGnQRiqjBqCpM+qStga/G1OhbWvbVV0yRizuoxNTaVJ36rRTd1SblzY25GAYvM7EVwusCIoEfeJb4XdJH8SAro3cDJ3capiGypy6MLeQkevT6tli0KRo0msZ5Hwdor4XJSTAz5Jdg8BD1cokJqYEMAQEi7uG1moRXnIrS5nSBJaqEJu0mAoC7gQ3R42wSBB3n5pP1o0DHUktlQnjQ2JZMHaZSRHBNaCskpqp29CVGgoHD6aSdZ8GH/n1UB91dM0lMQerbxIuXXSaUbwkGEzzJtbcSucmzqaRdsXV2998xmEPedI//vNr3om5yTBAPCN0SOmTjAYvRayKYpTum15btopCn4HZe7I2Bu1WFPPvXnnZ017z1k+c8c3vXbd/0+rO1FqtZooMSg3NvrK6bKv6lKjCA2HCUjtHLCO6J0TWC5vvXbt77sWXfvuSy8acyRsENxEiv/Iw0GYte0wn3zjoqu7SL7j0ynMvvOSyq672NpIP/wvY21yhlUFpiLosMXpPEjaTwZMiaTpcl1l9VoSqwtQlB9SzIZWmMRlt6ZKeJdNSaLU2IwY5TVmbKZgHU/TNPKSHsEPOI8I7RQTX09BoPk/JEIhnpWhKiTJIXbQmKtWzDISsLHAw15xzAi2knnPhZTyv3t2f2k1oLElkvueV0j6yyoQFo0tfGaRKDsyRswSnJGPJfHQ3CrZgdAxdZSQSkTJrmWvZbdgaYeHtRHCwu8646pr951182Xcuu3xhvUdmMDzaRzAc51x0yf55S+/oRmNZpta0nrJkJpK+cPs47+hu0W2RhYVes1sOzRDqzl2RLdahFtM+sTKCDMKSwsV19I0TqeJeI014Ld2bDw5ILOt6G6gKlBxiAQFd4eOLsc3o4pHOIoGZ9AF15EZ88oAjbBVvfTRGJxWZcAfngJWEAlkFw5nYHisHSM0sp3ztzAc+7TkP/LPnPuRJz/38l88qrjVV6fbQHkjqHQKyytREcjh2/V+OeOtP/PE/P+hJ//orT3/Zdy67xqOmMKZwJaxYjdKEhUq1LDqQngFmaR8ark6ygFEBa1SAtglqgbRgkD0zepK2woHJpmIYhrlKwgrqRnZqNaaTx14fDJbZVcrw0YV8oHV0b0U/RpRFKsU3qsxOKZ0m8XawVwzprIHX9Cv/9s1v/ce3vPNpr35rMPpc7KmxAmUSjmAz2b/aPoC52ShIMpJ/yT1C0Q3t7X7t1dc+/d/f9vdvevffvvqdtpQaVSch+jNGZx6xlE2qFcx0z4qotGi7nc+78LLzLrp8IZNUV0YhQqlYOV1WgghArAUpXZprEU21sAqzLOZq/FF6++2LLzv3wku/d+11UtnnYjD5oIObgzEv3ZclKEPQNj4Zt5gOL56tM7RqQCkjKx8bjw5uJaQXdVsFMPa86Gue1rVr0Iwaw1iGaRKayg50I6lC6REDMUedcxrQTIUbLzV2/+1k25YLayjpgqBWlFBzWfdKGSOBDgwQ7ygdgxGJCEvQxhBxWHbRTqnDpCtC0RwyrEdZsB6CyAStGEVY2yrXYoBho99UUzYxlV5Wo6JAnvnvR/30k//xV576rHO+fYF7J8WiQ2epQyyc4IfEKrDnkcec8FNPevZPPuk57z/li76jUXhCVCEmUhQ8RQBlutEDl6Ar4F+uDlIfTQ7aNkCBamYCZBqyatL/1BzJtSWW4kMG0MkpzbJKyEyKo/RSIZbU1CCTlklJ5NUEm1z3MTZRJFR9abE19d56rx869Yy/ftXb/+oVR33i86eFp4UUQEByRSdMGt7mFI9limWTXmljUsmozpK4xQhJ4HtXXfe0I47+pze//1+Oeh+3Ww5xmm9FTZCc4zOzwlXZJwdhwzbygksvP/eiSy6+8nudBZSATk5zVY3SBv+8lpXzTaqQDLxBoveJDAojQIkj3nnsPx357qe+4i2XXHm1FtaPnoP3ez1aaAHFCJtGhREL1aCvRK0SJh1atiLufKObBmnGgC+tUmMt0oqKqKeSIIIw9LJu8cdUUKaaU+tVCQHhhwxFiEYOJ1wWpaCJTcLiBRgVUZ1ZbbF+Lim8CZs2WmpL7PQkgYTGeiX/U5coW2Br8JZZ3GxUA/ueZQXVdKVORt1zKT7TKDH+00owwUGCJ3IEofGtY15tL6pFa7VN0+rSerbOQHUpiY3pBrpDHVujhZJR4ZVZE0N4q+Zz1wyTFUNgvbOL4eloQ6atb+a0JdNFwgqDHJGmvZS5GO8eFxs9F8ggzVDMrBQtSinC+1sJyI7JTkNpgQlaQnNpZOGAAOtfSnb00SOXkKDFy+XXXfH0F7380ssuw95x85ve7PBn//Pf/fEfHHzDG4qsq/aGQUWH3OY2z/vLJ9/5Drf7jcc/8Zce9ehH/+mf/eHTnva3//j3z3nOv7zo1a/dO193/Mc+dv383RF//4LnP+yPnvBjv/Qbv/zYJz/teS859mOfuPzCK7BbY6s+MuECMWahtaHNZUfRigcyl9VIG6X1ZoMhQGMEiooWvjNdJbVImgxhVLGMvsS2Zx/ZM2fjAxBnqLRL25VpQQGxGM7w1IRJiIooasnSBrQv6YtLOlmWXtxBGfDFIssgSWz94IiAB1V4jHAVnywFud7uRc4RC81GsNAxUYL48q9rLKZphW5pVo+GUdFt6eBThi3jKB0alJK+Jp2hF1mEghIhZO9SK3OGKCBbkVplAnacd6y0iJQqgn3J7b4LONm1JcthjYTkKlbCdGvqUIHV0viIAAAQAElEQVQ1VWYXqmDKrHS/lFnMcxTMOxhNReinVtQp2ySN3B4b9DZsoM7SFqOt6UJR4pYEZ0sMxAJ0YDVH0oRAG0JyuMEVqUTIOSwUrphov0pLynPdBj0FeV1SLYqlCitHcUFYx8brBpPRYzFJUS/agesy9yPCspaYilQkfUVoFtGaUrrmECZLYHiw4EV6BVpqqA4tBCIBgsP6O0QWrElGxgg4jcm5CHflzbsxx5WdbGj2EkOVzFjFp2pvWKh2xP55uW53e+VV1yZnbTFmjDQ027/d/91LL//2dy8fy8hYEK5J8+rqixVoLMK5mewrztxMaL795M+cd+mVJvKHv/STJ7zk7z53xLOe+/jfmbqzWwl95Qy+0smMEBoXLsgAZmCIJlGIEnSK5DQVxd4RowypoxYvhIi1Lc1Em9pEigzRnm1RA3Hbm86XVARMh9mwlBaMBiwzMtwpmkFNyRRmVTjgSP6DOcoSxENU6H0QYSF5TWnPkuImOUEmsYRFlaxqhUVEjPqoTSgoJBadBxYmEhZqWmTquQNn+com3ihYRKtq7UarWjJQ0Vw1V+vUqRciQgW9jJGddw+4/iD3liJjAmhPuHSmeBbCoMIJC7JTXTcdrabVCFqVIusbPImsTd0IoY/Sw/qIhaQSdBajwqJqgyXfekpIruhT5N4pxGL9YIaN4vvhoKMITCOJUDpX0V9ffFn4OHKs7aBLDLhzUks2N22idA9LwENGJl2godSeyQh4BDg/OiIEZAeDRawqJ4duF9t1nT33ZoHO8va/7qDsS2IVIh4ZPHyR3Co/SkZbEaHVOpDrSq5N7+ApA2tNIcPQPdYyZ7FMrBr0WrWvVpKnRgPmXK7dztfuruf7P/E5JmNNLRQaI+ljR4J+CJMoSy/qF11y2SlfPfv6+fu3c+/pLNQBoZeig3xMgTpqlEKqGh2hgwxQliT23YYnBVqkmJBkZcemabOpbWOcW9xzCWZfTX4zBtjJBYRLILF+z0lVurFhECztEdvZZ96mrDoVvECxFKyMp4nV1E33Rp0hqvTLhKUGScCu9eWKa/cDuOjKa67dUmlSC+bG0j2lCoNic9T9UWdR1yrr9qQUEWJjWF3jfkG2u7tXXrelkAuvuGr3Oq/RKjFlzcrqDI9pK25MGEn+YwApLcnp68kglCogQbRujUFROsh5AAOezVFgl1173dkXf+/8iy9noxKZCaMRPI1YI4v1ELAeVc4mQSSHpCd66CBuvjNFLa7hIFmH81NwuYC7Rh3F4MXWRCrhFrAhLD9cCkW21LqwIMgYSkfCtQy3CuIgvEwro0goxLMOMDMi1oSqkI1KtVChJpHBd8FawEXEwGVj2Y2FfHSutXTanQMRcJVlE2MjwQsRmbvJXlO2jhgOViwbozGGVAsg+beKHqtS1sfgupp7YeWCYIzRpbLIcIWWuvnxex963XZ78ZVXffGMrykrF68ilEgtWKFx5siIHr70j37htOt2551a7n+Pu6YiiYJ0ZdZCBIzWBG8YFcOIrUFFaIsSUggyU101NZknYlTfoSheShhXAz60L0KHagZPo5vChjGzSzaWrBg6gHWN7RTbSSkREqGRhbqhXWxWc6NdvQZr2JLWGQ5Bu+SKa5kKDDGpQryiU75vkOI+gg4PaWvibNgfHNQiKbW0ppPQl17Qi0mh41ft39KfCy+/ajCnvJclChIMdcteh+sI5uHQHBZ0Ixb819Gzj9VQT8mR2TkeMJ8qQ4NG/53RI38g6qEZAitaLrj8qgSu3r+9Zv+2tiK5Sk+fL7r8im9ffPlV181ODKQYpAnTV0M0oGjGVhMbXbQsg/ddRcLqbBvutW3SqXEC+ytDIyJQTUtShq8+SUzKOiJdyGzPxZXUXsk3pvQ2sqaJlASb49r2zZFZXBpECSVNL6mlWlVYRnFY1xwixAigYEkH3DAUTjrG4MeC651abQgZpnMVRnxYG7LB2lhRiQX3Mr1lEkOaJr5I9qa5qTnVrNS3igPFp9uc2oUoemZKonETGTAVE0JKuiVbs2PJhe5ggjdZqi5G6TtFWtEYWJYRZBq41OvEdaTwRpK9pg5ZPdMKW++EyBMRKxVTUVm9CnrsBlSxBggBoseqgwaBhyIFkZoapszlEA6+4HX/ccZpX+IHnje/6U1f9fzn/OJPPBgMJL//P85TvvDlj5300f/H8P9k4GY3vtGmFV5IH9/8+lff8d53P+kZT//VJz7uL//t3z73+S+N3S2CgSD3REtahZga6Ji2CElyDesb14tI00KW2oDJIm2LnUWnYQoMMBa+ZN/N4ew6yVrBmwefBwcDxJ8hQyaE/yQYd6X/Et4V3HRsVbtjRuxHbmcd27L0ndl3XCZiZE52MSBVkn44smuf0QVcTFOQKgnDXDBX5+oYw7ZLWbZNtrUO+uQizHAw5UiFEsG9MYbztOEaIpzC3ZCyreuuSUcZWedFZncH4yksTW1GXUBFoi4M37p67I7cP5JtA4kao7qzna82bzyNVaFrYWloFeu+SgtdHfwtdGTwH1qDsQglEhodWywDnReCriQIOmWKqNI52zMiOcoxXuFQLVKqhK1xMZQaO0n1gajiU6J0KKtVYpVPTWsSNJS9f7UuQFUyFhu9pldfn+GUnA1bxWJrBQt4umYaVnYKCaqkRiESpMeUPgV2Evy5sYbqItazBJrIPjKENzuF3ZT21ULtDqKzJNwUJkJzaVPyTZZatqYd6lGDhQKVE6IhzUpY4Y7Jx7pzTBSXaQkmsHTrYMNSHaputoqjqJ1NW5irwUKFEUWgbzjmk7/0d6/4zX991WVXXcs2uiC7CreoMRElJAvFZFJTnUSBztg/9y+e/W0A97rDrR/5qw/ddwMq4E3sAg8bZlS1aJmjbYfNAdJv5Dz6PC8eaSqFFcWsSVoCzhwnxBSGnuIKgkMZQwrtrcLb0zTT5O3nhi0nHYKs62yQXaIruUI5nCN0Gbp4LiBYLa2qiLKwLNnZi9VTBApXSaawD2pL1MbRoH4nXXMz6LGglLVwlzl1Fh+bpW+2QwfxYpFrY0zD10TuVqMRpchNoA0vMnTifYmoqoFldsdzX8jkzrx1+NCI5lF7ZkeGQacwRiK3IwctAA9DMkiOgcg2IMHyAE/RJKpZu7ceGOtujORqVTfGacMWEkYIK/9W7zLETYflIn3kNrE0Yb3WBqtpalmFXcFrUuP1Z2W3yFaCj4rUrh8CApo0RxIKt2AAc7sNFvmFtyrb7FvIbmKmeBVMiuaiwxWo4twl8IFstZE0IRiUjJKjwdfGhaRwVdEQOgbV1KSr4lRkYowaP7DPC2YmS92kNJHJRsGQCOGqQVunQjEUJb2zfCgtkfAc6F6GrIW3GCfnGJmzy5zkCGDVpk1tWpXmOhu99/9yFyd++auXXnsVWhIoeiysZrpLCHuOJQjx0Jonn/7Vq67b/R9LEBkeNaWKFbA8qAdxo4m0bUAW6Fo0+CC+UmUt9JWFe3emG9Gm3lixm2m04tUYGaF9w6Y5LJaIHkEclF5rkjRBqG2/yLYmqheDCWLvcE1MCdZNFjYUyazorS5lM3NmFCTrrAt/edqhIgHrUT3wwIMf8wsP/qFDD/nL3/2ZGx7YsgWNYubk1nRuE3Qq3CSASrq0wE7qjmYlFxIeki4K6M1udPAf/OyDf+TQu/zFr/38AWUDBwbNbZkN1VCRIJuU5ska8XA2b+NC8OBIldFUVGpkHejDdwd/JJKlyLYIiSSve8+Hf/PpL3zkvxx+zZW76MLdh7uAdLHEfx2iRY3/RMoiVbghjZYg0sYiyWljkLqA9lK2RSkkSWpPhbBAk2w0WYPPZRfUQMlKuRZZYiljlgw4yKlVSFEuokeeMdZ4pImZEZFIX5jeYHmpAvotgEFaSgOqhGnSRI55UrI4uR5VsonCCnSTBEDNpPIsJiqx5ws1h4xcc4z5Iy6VLWDPPuExaRT2X1YTG0CGgtnE+4q6az4zlvMo22rLDqTgp3/gPgdw/wp88LP/SYybtCKcTeGsMGIOeM5DvnnRJd+48HL6/0N3PeRWN7yR7qYtq1SDKR1CZDgGZFTNDWlrdVEWKROMloRriU68Gp0qqXUbZUtkNAVQ+jwyXTxYvbBFQk2LhlHeUF8m902RWkSU16qwO0JZFVlvPESCvWLKhb9ka6d9DvHEeqGXNlvxn/3RH/z5H7z3f3/A/X/xQT+SEi5LuqurOXFZVJyyTUstzJFe21I3wd0AfTJhNcQGDHy51U1v9Qc/95AH3uMuT/7Vn70BbZkZrZE+fMRChGpiM8AAQpCJ4cJSTGP2zmwu4kp/M2ekg21vxDoPwkVgbHUyqeItgh2ZxGjQP/yVn37QPe/yhF/4qbvf7vY1G0eK5PkXXPywZ778F//6ee/62KljtmULZ8kgibypG4iwdWhAQWAYjYhMD0TSkCgYBpcMBqUKnRCtxUsbyb2WZo/V6DGwGknfEGyPAYhYlcYsAtEn3SkTvgic+xSjmt4ztsBCxHhSPiUEjela+uoznc/itcwbhHYZi2KuZduMu0tOiBANsWE21NKMiZFZDSxtNaWmm7qqFqkti8ES3O/N8F3J3cAu3YVAVJE1eyECiRF8o+FcN0QH1NNcLAtnuo7MDpqbTF6IhKhrhZgI5cRQGrjMMlzpKC+v1wQjuULDwK9keNVQjbQUSS4kNUWClsGT0ZuSQOQ6QHgiBRyWdBVGZEgUxCRoquWkz5/2tve8F3vHQQcddMTzn/OA+91779v/5MU93vru98zzzGu//VsP+/u/eOq//vVfvfgfn/m65z/njS954duPOOxdrz78va894j2veeW7XvWK5z/9qX/1R3/0+w9/1E8/6MdudctbcQlP/m5zzLEfeMLf/O0f/+2/nHP2hfSITFUMKYkKWmRrxVWWJMjqDAEIT55JxMLRA06OF3fDyqgUSdIpk69I5SDDp8i9awwB3/lKWAFiAAwgAM5ULmTiSdnqOriOQUeUTkTD1Iu51VVJAQ1LmifB+DJHoyhUkJRnaYqaqUMyIzR5soK7kT6QBrNVJpPNXRitRXQWXag+QyDFhIneJmenJduKhAig19uO4lJCbGRkLsgxUgZtAxBrdeEokKtTXCAGsAQVJLdaa/otuRooMGUVgwqFxCrJE1xFBLg8YbS3py7JrKPq5DSeDqarJPcZsKACX5dgtZj0hokqSkmr0CmNH2jPUPR1Bt33Bif+sYfqQJKCSqdgoChKz4hUiqSBuhaF7rkeQLiwjnC35so5kYSUl7BaEHSghTXUklXAXjwNNEeRNMAkaTyhNkXhV4iJAPCePmRAxiTOfobkIE9u5rysRgZnJb1xWgYVxqsZLJGyKg+FOfbinuEagZ4YNCepkWK+f6YDXLKaoNjTu3chRZOLg6nIDyN5n7GGhmpMRYQ2cFmiL3HN3nbtkNvdksTnjg4MdMYqIxMO9RCSZwx1IT/I0BxdGUumR2moVQwQzgtOpT/4H8ca4UyGLJtANZyi0ZcozQAAEABJREFUNKm8QUuuy0oyX/BfB8vpDNlSXTAWGR1jgXOiCvuSCAcZPtogwXWJBqNnSEjAqmhFNowEgcqU3COVeWp3WYFDOnykz2C3WjVwN9NWS9RQKtj+aSbt4t7YMijaaliBQJJnKpEfEL6G5JoGhhTig1UPSHmOCtXTmD2HuAjgFw3qJYkIaYJ2rUuC8CZA9hRxUQcnyRjSIW7CfOaWB5SvEipZwGqZkiGrwgqpiTq0hbZkhKE0h1bi+8cQdIUCBevr9aM0WIsYBwPyfV9AgcEUILCJEYLkJSFfqcmHcK8ilK8iuicoRDo0KFNaFeIDAYOSuWrgK9sYheRI+sOFnBcVfKV57kihjylDp5GNX0SNkoOTV6uUKsA5EUZxZMeKC/USf2YcMaAOznHFKExT7mNlafAitepGUSPCIwP/dSweH/7i52ktYRNa6V3Sk/mT66xM7J/9I1/6evDTfy1Z31nrqAUGN4LDU8iDrutSRnKdAWUWJC0vhCZHyfUhLsUs0BlwMMRjfScA3FaCLXK9KLlwYKwVQ2kG62BwLgTKba8UlRVP0NAiUjIrKeMZS9JVFyyqsycfXSBAMJvCNIm8ErT1u2j7xZ98wOFP+/2f/eF7UQiqZFUhnCCdRFwUomIiLbjJAUNomRSbWMIHhjW3Scv0Gz/7k6/4qz966P3vB8kVyy5IxUrSpNgEgmgODmaClnXnA02sRzJL2Rz5ChAnWXoyAMvIpBqkUb2D0GA9wi3XXRM/96QgiuZHoFoVrFuCQAoDrQXc7NQiBZke2ZNtwroqhFYFY1RAUTRUZqldmBTFsCKDXGUqwyhNBWwhwTVcBB2kCjgTPBy5sPwln5LRTMrlqgQ9i/DMLow0JWEgOzghWC6CzoOyMGVMnA7A6B4z1CQ3rtVpbQTdWvWCRqaw8AEjM3IlRrCAyFhZL6lcTmFMTeFyyZC4XqaIrJU/nevQLXnzwOJG10re+IYH/sS97wrgvIsuvfh71ygK9sSshgv9r4CmxUlnfGUZQ0Ue+oP3Ms6grz1oAkJ4UhMGnepYFSkahM7p4Dfxgi4YIbKsNpCNCoxkRJK1gmzkmZKMtYTzFUgBHSUQ2DOCMxGgTpBx6zVFBHVTVwR3KkW6EdR1DgxiIA7CiyBQgnHzG9/o2Y/9/Wc95hEHHXgg0lOG+CB0udewBJTIU8DiVkMqVWbAPTNKiooIyIqi5RG/9LOv/esnPuS+9xCHhIVYikRiBSH5MaXwW0D4j0rg/z44V0RoVawVK68/KIXGYAiIAonKnuF7A+SU0IUfuOfdXvnUP3z8L/1Ck2lFgGLl/xYJ6h3B1pMYDmRC1q9sdQsd5Nd1iOOUPWiKE8xV/yAAFlBpyqReNTM6boqUCLDSgpNFtGZRSpQ+OFuC7giCUmMFGIkxsDjHU1zoQgLgwkzh6RESXsG7hsS6SBPVUUIxtK85pytzkCGE2EXJLxKMNihHmDUKCF1NApF8yi3J7xSkEDXTqqBNSW/7umUIKGqAoRJlftVl3Z4UkoDRGJs5hXVjzH0sPrZqw6ZhjBsNG8Jy0tnJSeoQZxSZHQkqdhmLbWfdziXWsuFRF37jAqncQUrhrFmDVZzxJpPblr8CjJ0yao0h3BnTr6QGEUFOgQY6Bh8erL8ZXSI5YJdet/27578omTbAZmffs5/xNw+4733wvz4uu+qqL3z1m7x+q9vf6V+e9JiH/8rP//Yv/Mwv/tRPPORHf+RHf+B+973HPe5197sdete73ONud73XoXe/113u9Nu/8N+e+cRHHf6v//iBfz/iHa86/ClPeDwXcjlvdT75mVMf/qQnf+gjHx/bHO5DPCTdMDNqG03eQzUm4NAlsKQQp22M3WRxawsfw+ZmpA0FM2ZlCQOyAzpJmrK86qbYphRTY4ovBJZsARTrm6euVVWND7dkjiR8IjWJqpCNuVlf1XxiJ2aIa4EJV7rwzrl02RimjbXJtFgiTXKqrjrEhlax0ngdXpjZmr1xwbopaIxK8aTikLHaAKXNpYa0BOXLRmS13kdjFVGfmrYCaeGbZTR34W34FsuCEYwmreSvbhuLjTqfpCBlKYVnX91L9CEj1AWDrgUf9/YJCWtLmZbK/ZWIowIsqmoAqqAJqkqBqajmEEIMPgPZzTLTrWBdiwbnQ4gd7YoMl+RTS59qFElBAerisju8a0+iIUHd3ocC3MeIWl/Ah1l8YtXWERAjGlLKMFuiDnYCspFrBsFxpKirjZKL+hodX1+5z1hUfTJwW2RkNPjNKq3npkokM8EK4BKUN7RRbM4KJBod8ZjF95ecjZbRZVj4BG8Smo4Y1qN4CCC8zs0x46jUYQy/UAQoSFV7RdcMGjaKr2EAj90t2VpralFRY5YHDeE4z2K8424trQyVJaw7d00mPTlrMWcCE3ryLvqWkQXRVwFKZ+ygMG5Gs3iie3hPXzRGDWgU6w1ZIIaK1cQY2TsyqBK+urx+oASncxpWwMiKLzZ3zAHeMSBKibZix9Cl7E1f3XTU7OpDQTCCtTOV4kSGTsG7wwVt2YtuyTChJhrnmwn7mm34c5lCRHvQMSGkLWZzAqtYargFhLmtuhiGcl2IOfOnCh3JSl+EDtMSgTvIL4tSsoTI4EDOiV3k/gxSO8mBmEqacSezVOk7uZSx23dH78RnR+kw9o6AJXZKVlGMHEC3ZPAyDW6kKgbR426MOrMvy+48B0MU2GRrIjRGw0v2ki4Q6pNmyueF/G1HSbfkfRgZzmtRgNWn3NMKuK8Bdu5EyhIS14/KgISikEcqTCg0hkWyFJ2q7Ghscmycv8u4eLJLbJfYDkJZcojMWWfX4So0o8K4CNVWpZJLTTFEZkfyoeuuR7gSbjq5NCe6SmYEeHW2wQfEfUQyl2g2NJBg/srCuIHyIKBZwcrEv1FMqtQCYdLISnjhUE6ISWht38cAk3Yz0iGkSNlNZgGAg/dNh9zsYH54xyc/1+dg0nlqipjn1LWxEEkRTKd/87tnnncBpz3gjrfkK8+0QEsiRk+9k8DJHJSusXBDPIk2Ybwg296X3t0JhlbUnTQNHVuMLSvIEmBk2ZadxwhSr5VhraN6Eln6bphbLLpd31Nrn3SpGApksalgR5cdJkEwBrl/6O4unM8il8E05CkZTVSHFsrOYeIQFWaRsKXk1saWsnZM902T8VZXd8SrzAvmrc0xZS+1wwKCEcGczpwZK6tGVlgynemhhdoCxjwiBanCF6oFYwUgQLjTqTQrKCfwX4cjuBOR1ZXOaeZSSbNROqao4sxg+rg3WXhQY3PdcRQS9PsXhjtYQIFkJINdh9Th1DULYrEkgTXNwLo1aviErCqgsQl3wj6qMWltlGkrdQ7hfPKphtVeW5Spl81sfC0kVPGUBFk7mOyaQ3zwZXVYgogUrOOdEZMRsqyuZRnQDK7BYPaNtAFUxaQrBBwiHQRO28cqDUvkbgZD55ZNcnLVYJlvczfiJL4UttQOHgmKxWDuLc1mKyFEFrLaMyxyUhhzdQOre5Bnyf/+Yz/AddfO/ZSvfX02jAhJcnSg6FJakt1y3bGf/TLn3OzgA3/4Xoe6SZiHesagY9kTrGiMImGgBpMhOiiD1rtjdYgrvKzkWvc9it7ghJykS0EYqEJbVG4qdEwCBdEU5QD2Rewsg2U4RzLteGEgZiT9wlRLlSZDMynDm2YBoDXKlDIl6uI77gJfesxLLOlbwShBgCR5iIXWAenZB8ZIDLQ1sfhpN6RHSvQm2yg+RJDKCGn3HHS3V819KjullsJNuDHyaSxcqiJqqeTiagvN4ZmuERpoJrp6J1W1iA7oNuvs4kticZ2HknYxyszK1sVTlKGipR6DLjCASj4IBfLkm2FQt5EJJSJJJNo9LJMr0hlvFFBJgfM9xIYFETHbGlObEgBOhSe6WFdJJY3FStG6+lSKQTPYXefAFpgRPcVAXLWu2NGUwVVFsooQHdBBEYGlVMpKwNQpo6z5IioDRDKcD/VySWFtnXMaVEJ9QoLS/sgetFaTayl9NS2Q3dKNTWBk77KQc+AK/gVDu4NlEtZTTWpPXW1URmdYYkJObuvFRZcuYxHyHxK16mZDD2sEVkYse2iM5D+kwYqjr7Ep9CSJzdapU1wH2pCJDitGzZnbihaLCTlu2FNji5aupALTmgxZvPsYkaFGTBkJYGiGhIeki42u419f8tILLr4YAIvlnzzqEb/60w+hUn79X53f+NbZF1/4HV79+Z98qJqKCMATif/5wfGAsALf4IAD7nPo3f74Eb/90de/4vDnPOuud7+nin7ve9972r/+81Ef/FAn9fpIGg1fpLvSRIot0IIgrsNi0HRGxQMQLQr2atFMEk0sUQVmach0JjV95RS6rSUFXUgeluzKpRphwfRWlTZk0616UlJTTCaVgghd2U66a/yhxYYXMLSLbGnU0KApvRTZSNmotiLWUiV0MAgVpQpLmolNWM994iJzIIcbaNkYElt6w1UTV+nwact8y5V0TN0peZOjtUqZVKYylBAEg8+pWYWejvRgEUZJ21BJhVd0QjNvbLSSquCRLMxSQ5TwOGuAw7oXJyTMG03TodJha11UChDKyFJIVN/4inGHzxl9yRHpSn1wNQHaeqYixXnE4LXUEBkFw4aWBT4wZuUFSSRi1siqVkplSeqM6xg5B7PDErZJI9qUK6kasRl9J8cGmIRbo5KTpJELLk6NpLcvuiw5J88YxFPQ6VIZUlNMlDa4dcSQpKWqCYVPgk0hJpZurGkpQno0ITAsOYV3qgu/a0bQ0bGihNKhxJtr6lRt00TF0lt4zZwMlVJRpGhBTukFe8fEOxFe0HHJZd99+ds++PK3H//5b6z/Htdwf/UHPvrSd37obR//BKt1+gQvjJCBOLkjouRp55zz0ncc86pjTryOGyHgK9/67suP/ujL3nHiW0/87EKZplAlfRYB43b1/v7p0756+AeOf+l7T3jLR0/52gUXLbmAc0AAGRSLkUqMSac9w2hlDbMQY6KTiEMRdvlV1xz36c+/6B3HvPgdHzz1tK9td+fiuTcdEOxJs8ga7GJCSufFV1z18f/8+ouPPvZFR3/o6BM/9ZWzL5yHkwLd3DWHVpgWq2Yi8FgYcydtszBXmWZSTHKZv3HOd//jxFNf9I4TD3vXRz/xn1+/fP8MUOmiMheCkhjb7ZuP+cjL3nrMF047Kxe98ur5lC+d8fJ3fojgvP/jp55/8WXMvT6NmACtOqpyKXnq6OHfveTSd5/0qZe/7/jXH3vCGeed3ck7wfUH3wukpVrKaORxslZAhwo7NEkt3zz3uy97xwdf/s5jLt+9+trt/lNO/+pL3/uht5z8iTnGKsF1OG+k5dJrr/nk6acf/oHjePWoEz72tXO+vfu9xfen98zh1KK96ELj1kX8C8ZLYDmSBiLwX0cKpK66SSsrGLqcfdF333HyKS9+77GHvff4T33xjCsvv84kVkvFpTpv5ECLswxm/UJ6W7iN0CElRHyGkWUAABAASURBVOhFHd6WIBoaJZObBluIa+bGyk5rTD0+CCZuKLHKGZY0lu2iE/5UT2MkMyHJlxhJMwUwQM3cbBE2HA6mjkAwD0tlsPmNnCR4VbyARnXywbMMxrxUqRQy1frrD/4hU/nOZd879cyvR2qgZpuyTBD6x2BkLHHiZ08fEYfc9OB73u3OXMWzQTYQhbHKJpV2BwvRsnvGeee96SMnvfDdx77svcd/+qvfuPK6/YmumEv2Il0LHyDodskjj/vUv73zuOM//2Wu/eb53zny2E++5Ojjz/vOpRJgHbBk9pl7kI1Hn/CZF73rw698/0dOO/v8a5btKDELFtNgVFSNYK3F12jSHlIRtV/Xr/v3Ez7+wved8JEzvrUMfPWcbx957EcPe/sxF19yKadJ5NtP/NTh7z3h/Z/+cnfLNJOstUsZTIurr7n21NO+8rKjP/iydxzzkVO/dMWll+sYp3/rvJe/+8Nv/cgnRnglcCac+aYPffRlRx/zwVO/MBBiWOuyuo39WHaXvnvWeee/5UMfPfzdH3zHRz9+znkXSYRAqJ0nbVVngkMkpUmpJmom1aKcff5FL3nnsS9913GnnfNdztyd+6s+dOLL3nPMe0/+tGZYMuKF4zwz0rfjssuuOOkzXzr8HccccfQHP/bpL1555bU1tQwxL4KpmEJdarAhbN0J4JuP/dQr/uOE17zzhM986Yxrrt1aiLHdLmP07ViWyDz5K1978Xs+9IYPfkTFRx/f/M4lR77/Yy992wdf/d6PnvGNC7y7UvfImJF79UCmlCqaVRYqNTJbsY5IKVYYHKYEb0C3k877Cthdhi6dnbHDnbhKOiAeWIJFyEeOAUCaapWVAQklts3RADMAkrBlmC8Wzo2TVQifGlbOqhmbkJJFOFGxthXKHtLvfc873OkWNwZwwufP2J/70+bKqmQQqLsg/RvnffeCy78H4Gd+4N432HfgQGajqaJatBMGXHXddZ847RuHvfuEF73zg+89+VPnX3j5IJ3dhiO8q7Fw46QvnP7ytx337+89idl3yVWXf+jUzx/23g999Munff2ii1/8jg+99OgPnXL6V5uIge46oy9g0Ms53734pe8+/iVv/+BnT/8qsEVZgiW+QNRSi4OZ41fOV73uwycc9s5jPnXGN7bDzzz7/De8/6RXHP3hyy+/CtCzLrj45e8/7hXvP/biSy5Dj2DyVtPGPWvWYtdt+8mf++qL3/bBFx/9wY9+7vTLv0fc7QtfPeul7/rQUR8+mfF1moMci7/mfSe+4OjjTvjiGdnCtFuGCkRUUa6+Zv74qae//J0ffMl7P3jS6adfvf8685UF2DskWw4bieH+j6886sF/8LcvfeO7EYtnkCZLpsuy1v6UTEbLUmQoPvaFM9jUjjz+xGuX7e4y3nzCKS99z4lv+eipizulnnz61w/7wPGveN+JV1x5zQTu2iDQimpZ6iJ1Gfytkz2eEjMACTeHLwxnc5ItkYNqUxOFeHQnLGt1giFbRRGQJXWtyKxHGJGkHtggU0N1kBxmvVUaG0Xp17LOYPICDIsYg2MstGKmm2ImLIFAhJAMg9N5s1eTFnDHo0LtklYz1CkoM7VoFFlZJ8Tr+g+DSnmOXLdyDuRm0okxNFunMuvJRwp0lz5jLJEDFlRLT/jogh63jJbZBIwYkipqWT8JoqqLSQbtHYX1NwhJ7ZtpO20WnYgVBhOFFhYWHxFPbj6XbG6qRmdNMInsJCaKzVSvSjEBRyePMlMSFUQTREv4ID5sha7NJ3zyEx/62EnYO37qIQ994qN+T9T2vv0vX8741revv/aAex2akPXz3sv64f/gL6FS6s886AHveMlzH/O7v2V14gOyf3vVyz//la9ZQAf3m6xYnSwVJOUFQG9kkrIvyo7EPp13bDuVUSWJ1LQsZRmevrrIkGRhdSOXx3prGRUCReoAFpXBhgXbB7uB8pGpYp1uiEnZ2VPodozqW4utJbt0rNc1En3IMhhLGSbOQSf+DBud1kyjHp+5lZhGNggD4eCQeSPj1Chkzj5rBi0BZNGawocz3O0G+FhlLBlLd1JyUURB2aA11DKnLWjzyJhFvdRWbSqhrdMlZ5C5p8ag20t0Sec6lR0B3SUqVgX7DDSg2ARMEYqRmuvTDmPlAWbl+qnDZsXckC2kjmE+w2d36sxcum53RehUpqCneVakgXAK+ZSJHAVsLKvdFroikZCM0rN0FbJoqkw9mkS7hSpAYUgSF2OkEMkpkjZPjIOskZZEFWlFrZiU4MmQMnCeKMlCxEVzj+1Ytj6P2ML5rHOIQCytOWnenVaIVdiksiqfzEtbIB2im2H7AvyZTugDNKPS/gxJlSQ2IR40vU9dNGvS+iIw8D5+WWPLa3ROIUWThhkFEnSsx+xjqR62XHj55W857lNHHfvJr+w9Jx4eb/voZ4489qRjTv3CdTo69fqEUZKLCj3PtP7N73z3yA9//KiTTr1q/5bD3/jOBW86/hNvPPHT7/vMf85Bv0lQ6lQPf8/Hv/iI5x7+Z6846nUfOOnIYz/5gnef8LDnvepPXn7kV791nqRZTKUbY4oeI1YNlAYYK54GYuQY8PAPffJTD/+HF/7dv7/9DceffOSxH3/ii4985LMP+/jpX2E8OV8ArU7gclOzTbtbvOlDn/7tf37Vn77iP9587MePOu7k5x79od99/quffPhbzrngIpEOhRWyWiAODB/zMmZn4BpUhaOKPPXL33z8c4/8vee87nnvPP7ID5/y2mNOfsrh//HwZx1OlzmbmRXRPcZ2Wd5z8mfeePwnTv/GeZ/9ylmPe9YRf/byo/79gx878viTnvmmdz7s2S/70CmfLuI7xXastpV6sUS/fL7qJe993+889yUveOcH3/KRU151zMd+/9mvfepL3nLWdy+jOzwFIHY5j/QAH6a1CYQqaBfpLJtFvnP+hUcdd9Kbjjv5m+d+909f/IanvfotR5148js+dsp2u6tL8DcKNrz3f/rTj3jeYX/yyre85kMnH/nhk1/wrg/97nNe8adHvO70c87XIYykdIsxgelBMKgVEGVT2rQxNfmvIQ5KaAx2MMfodf76d779jMOPetRzXvW8o4858oSTXv/hjz3tNW959LNe+tZjPhF9VJONShlqQ2W/2owaqDL4sps7uyiMZ2IRGS1936gTWjTrU8liJWtBaaJTS25TFh1BjheJOlR7EUPshO9k8E4WEWAGNM0dOEg3nmIklBM5zO5zLIu6WzLZQ1QpinVrVPRN+uRgTezZuq+eatm/HRRy3e72/nc55BYHHzQP//CXzmBWSkWU5q2OKqvMMS697IqPfvErnPxTP3SP0go/8LTUErUkN5+i0dXnM88+988OP/Lh/3bEv737uCM/fNJrjv3oH770jY/+1yOO++QX0mdTzzaw6dqGDfvAp7/4phM//ckzznrtB45/1HNe+dJ3H/em40/+2re/vTZFAwTfvujyfzzi7Q/759c86z+OO/LEk1953Ece8bwj/vDZR3zpK2fxahZJ5lKPqYMVemfalE2NnZrThLohid57yhff/OFTPnP62Ye97bhHPuvVh73zuDcfe9K5532XyPURH/jMl4/6yCdPOu200eCR4JkjlnHKaV95wouOeMrhR73l+JPfePzJT33dW3/zWS9/64c/9ekvfvWoEz5xzKc+O6/TU5AS/t5PnMpc++R/fn2oxpRaxzTmOuLSy6969mve+fB/evlh7zqWFH3xuz/4O89+xT+97l2XXnkN9g7l3GgMeiteuMNvk0+bqKweecFFl77p2JPfeOwnv/Gdizl37v3oj32KI8d/5oykkVuVmcPr6Rnv+dRnf/efD/vLV73tzcd//KgTTvrb173tEf/wsk99/rQpx8plUjizYZD2J3/uzEc/6/BHPvfVL37PcUed+Mkjj/n4k1921MP+5cXvP/UzIwdJHsNjju7y2a+ezZL19o+cevGl1/zda9/9u//C0JzwhuNOevl7Tvi95772Ga9/1/7dzoYBcssrVvgmQSXVmkvZyrStLVupaoyK8S4GmwytIdz+GteEi1OTzmH7iywbcFug0pv7zqKVP3eP9MxoWTaUtwgIixVmWK2w1W2w/nPXAbghqyRBQ7L8l5Kg0x4S3QhSLkw5RI+c6s4D7rn+219fPuc7l11+udTATspGqsBYz7qf/MVv8M6yFvuFB9yfD21quilq25mU+Tre/5nP/PazD3vSK97y6g9+4sjjPv7MN777N/7+BS9/x3uv2r87PBdHUmKxT5/xrTcf96l3fuSzp3zprEc969X/8Kb3vvnEU477zBcOmKYPn/qlNx73iTd86KTuxbkHCYvMoeADjQ9/6UtvOuGkd5586g2myjHm9ygaZeI07kq3kYst14zdd5782Tce9/HPnPHNFx/1nsc851Wves+H33LcSd++4HyS5zuXXXnUiaf8x0c+fdnVV7vYbHVhUbEUWz51+mm//6xX/Pnhb3vjsZ9407En/dXhRz36n170wY9/9qQvn37kiR9//6e+YGOIOgrC/eiPnPqmD33iM1/5BoGcZGzmWfbPi4/jv3TGI//1iKe/6m1vOvbkI084+U9f9eaHPeewEz7/xeCmAuuhraKojH7hpZce/6Wvdo9TvvLNa/Zf5xvpod5DkjUWLIhWpdZknWVVO/WMb7zh2JPeffKpu32Qf8d86ktvPv5Tx3z6tOGsWPjcN855w/GnvPG4T112+X7rKIEmpepOWaa61CnGtFJtdyWczV5zVAylL1tj8cUci29JMulpfNQ8ZGR0iREig/USLHs9NWCC9TlTgUgJdpjc7JKVLjOrWtPaUFJ9MRvQnpIuq7v0hXQTkqh1MmgDLRxnl5KwRBnNUpsFIYyQudugBUsKc5cpIEWztKHTAouBXODdQlpagcFGlx7UQ+anNEnax5QZLOm6qHhLVBBrmqGIJtwMLmQqG7taNQsHwfd0UrJWKZsslS67BK0NFAwC1WhJ0hdNsYXB3+v9GwXppxQaFoPdYQK43zNwkrjJAp0JBmDmZt0siqKZV11GbmckrQRzlKNV8tIrv/eS171x9E47b3qTmz79jx5/wM4OP/9/P88775zrJxxym+//ZH/91/+fXgOy2bfvr/7wD574yIdz4TXXXvvsV776qu1+Va2ijV55sP8xqvCFZrN3hjZfE4YM4H4st4GFexwfOSJdXcQJXEEom21iZX0nHdA82YdLkTpsnVJUJrEGUgLJuay5LVOUEMYw3qfYVszhoklTpBbOyqCNAikiDVkDI2jCWGLuufUcLPgD6KUHZvTBVGKmMJzZRrI6QgppOUAlIcK1EqAXIugtFj5pYKFdIsagDFnVURudCPqzz6NkgguU3CDdi0UQD9YB9wRPG16GT8kMgCzZd+XSK6/tyo0rKECz2lLU91alZ4ZHglakKS8PpbQVOktVFrwMLaxKwwiJwqv0Yk4YVCQCIy3EwkT2NveZSZSDUEuatdq39MaJAAAQAElEQVSaFhVWd8DcGkUBogIoxErRjalyJJII7IUuh4ACJTicWQdpP9MNt3Q1V7rtwTIzOE9GlUXVmegaGo6AetYZuhQCP7oOGW5r/gaE8sBJxjuvnMOGyDp73XuOzAQgRCFZXsxpV5HVSMluGFQxEHPGEu6xtkD1qOEWzpUzYsF6gT7vyQGwaZUGsTjUyR5637s/+H53v+1Nb8RxVXngPe70kPve9T53uR1YYY3UwNLVO9WJKuOMm9/khg++910fdM+77JtYL3DLmxz84/e7+4Pud7f73f0QaEnPiOQe6LUf+Njz3/GhC6+46sY32PfDh97xwfe5230OuQ2h/8zXz33Ky9/4uTO/SdMsUhxr+tjqPQ2QIjCa3ec+eizv+sQnnvO2D/COiD/k/sBdbv/j97n7D9zlDhdeedU/veU9fAVAXFydeSFVYP317z3+sPeceOV121scdIOH3OduD73P3e5yq5upyKfOPOupr/iP8797cZNelTk1uLS7kPthOYqGcCcjtvWPnHrGX7/6naefe+FOqz9459s/9L53++G73/GGO+3Sq6594buOf8fHv4A0iHkFF2Lv+Ohp33jG695x9e7uAw6980Pvc/f73PG21fTa7fyCo4/76je+XdZMBndvKtwA5Qv/4wNv+sin6d2NbrDvgfe480Pud/d73P6Wp379vH9843v3hNEj0mdkOuMNW8ycpjJuTmBXBgMi2Dv++d/f8/XvXnLfO972wfe7273ufFvW7zIUY7z+mGOf/R/vv2AP+QcceqeH3vfQ65E/9Rvn/vVrj/rPs85rWkqQEr3HWkX3hCFdeFoWxXrfff2gJhNo9nkZy3LhhZf91Sve8smvnK0QKn3Ife76wEPvfPC+zeXXXHfYuz/ysnd8lDf7UjZgW1jTPylOZZGp836f1BHRjuS54secU7WUElFBLJk2jY9WOn0cWcJ1eHcsaYOE4qPiIkXFoJI1s6BoKqNqqmW1U9BbLsQrkESOmWcCK8KNYVqGaMCSLZldelbGLVhbiqRFNQZns28DYETuHNB+/ofvyc+nnPnNC666PE1MUoSpOcDlic+c8fUrr93P3PnFH/3BeWZcOBdh6UMwC2vIVP1z3zzrjw9/6ylfO2eq5d53uM1D732PH7nbHW+4b3Ph96553lEffNP7T+6os9RO7yttXCXw7+TTznzNcSfd+IYHPvAed3nwve9+s4NvCIE3fPWi7zzliDcc/4UzW7H73/n2D77fPX7wroc006+df/EzX/3WM77xTZMo8BL0TlUtqvSCMJho0ZWoFM7zxM9++U0nnHKzg2/4Y/e820PufY99pZXsyvt2XgMS8FiWZY6ty5ynfvWsvz7y6LMvvpwXyZwfv/ddf+TuhzBbn//O4977mdM4yFMCvXMVaCe+f4hYJSg5enhcs7v9pze99wOfOz0yb3Wjgx506F0eeq97MBnf8+n/fOl7Tvj+iqRqYRRV01gcU7eB3WAMZTM1OvvQ+x16qxsfxMnV7IH3uBMpd9+73C5Gi8HptJpXcOLn/vN5bz+GFZdUf/B97naP292KVGGY/u4N7zzvsguk8DZ0RAeZ+IGPf+4Zrz36zPMvOmBqP3TXOz7kPof+4F3uwJ0xM+Uf3/re93z+VLKkiBvIDBq2Cr/k6t2/fvXRJ375q3e59c1//N6HPuAed7npgft44YQvf+0V7zuuTmItg2ugY+9fO1FTaFJfjwyimuKs+l1yNl/qgC4CT8jKfHYLYUm2QHXVoRQbicFrDVY1hJ91wBZu6cjE4A/isSMysUJiNc/JOCmo3IiVJJKEAD4qXNcsYICUfceSVYeJlNvC5vpzP3x/0pKKPnjKl6usUlhLSkpz3X/VwicuvHTITW90j9vfemO+uiWZAkd//Yc/8g9veS+BYigfeM+7srTe7qYHd27rT/7svx31nnksKZZWURpkdeR71+1/5pFv37+MH7rrHX78Pne9221ucbN9mwfe/Y6U//ULLv3O5VdoKW7s2tyRxHX7r/v0V9Z/Lf+ON7vxXe9wCBNk0N8sbCW+kA3Bmub8wsV754c+9fmjT/rsLW980I/f+24Pvd/db3xgqzZaWfXyuppJNRihk55x0hfO/JvXHn3+ZVeqrFXrx+97NzYj7nae8/Zjj//S1zifp6xgOxTrye9Y4YWqFhMTxuaEz3/579/47guvvLqY3u9Ot3vwfQ/94bsdct12ftbbjvnmd9cbaS5KWWrJInrQvhvc75Bbq8g9D7nNDW+wj6VIBZSdSBE1LVIMVcASnNl9YO9gncnMu932Ng+6193vf+dDmBQcvtOtbraXBfc48AYbZ00sHa177YP9HU5pYP5rgDd0zdfPsFFLcMNdI2XEYBPln4/F++DGQF0tVbhQyE/JiGXMDg+VsEmVGJoMkJVIQGJwm2Kyt8gK1IqlwZFBUyFeytyEvF9kYXwQUIiSdbJav25eTSUVQ0bH6DRo7QQWElHGrLyLZkfQMBsmQy1RihXe7rUyLLasLwsTiaXEE0ExtNPL4AMcq6WWomrIItzGhHqWJM7VoDU9hvPeeTuCKViDPlVJNrwqQdRrSpqF0fpFVl+Zcha9ghgoYOs79RnAbxpu3uF0l1wqC2pGUxSIQMO0t51l09i/mOE+MpZEF3VmqDhee/QHzv7WWdg7nvSYR93zrt//CX5v4H/+EpnXXHMVr2nb3GDfAfzw/+YU0Sf93m8/8IEPppAzzvzKW95/ghYWlvU2oiy8uQqdB1i33HNmTC1iA96eObHpuQSvSBcjLiGwYOy0CSZhvUvWEumaC7QT8TLBJGsuggzVNAdpIb14b4McCvAes4whHtW9dpEF1jEhVRlDrHmRvAfPUkzMIIh5jIVnRvBbaiYgEdnTh3vOGbPzpmE4oW5VvJAYKJltoXgywEKktt5ar8XTkiyhiBy0zxlwRnsTDCNJpMyrTCcTDMpsp6mSXaiurKbQvjZ32R05x7XX7v+7lx/x9H990dxzgfccFIWQXES7Kn+O6XR2iK3+yHrABTMvKEtDL9VBc6WlVKyakL5B3yk+cUVgBc2wNQxWBiMoAEQikXxXVJNSQhQ+hTO9wYNXImuiJdSMDQIFMGZLgGj3XkZIQPgPwLnprjkDpKmnZCaxEdKbhXhqy1SyiZXk5goIhfCkUT5kdgpShCfYzhDUzB9Mc+tEYBUrzjh55Oicw4sJGdziSHVtyfA0SQPUMmt36SN9iRhOabCRMsCwSAajy2tzOicgKYfndl7KqjDve9dDXvnUx/D8v37gUI63Up77B7/xij9/+FMe8Qu1Sk4yDNwCDjeLqqyvYj96v7u85M8f8W9P+L1bHnQggAfe5y4v+otHvujPH/Xnj/yV2hrVR8QnzvjaGz/ymT78/ne67Wue8biXPfWRL/3j36OW5zzhdw/cmS648pp/Perd11z7PTDGEClTK5Wi1pOhtNzzZj7rggtef+xJw51N8RV/9vBXPflxh/3J4171tCcc8dTH3u6mB3Gvz/n0Z0BS+BEXXnTpWz/22RH58z98n6Oe+eQj/uQxr/yTR7zp7x73Z7/+06Z67sWX/8fxnyrJVt41hw+ZGUjCV62w5jE9O668+uoXvO2DvMe4261u+qonP+o1T/79w5/8mMP/6gkvfcqjb3rgPo9428mnXrk7oxZp4kblq96vfPvCQ255039/xuNf85ePfuWfPfr1T37CPzzsV6vp7tLfffIXydSMQBnS4lOnf+W4z65bvR+/152O/Os/eOVfPv7wv/iDN/zNk5732N8J91UW1qq4aHTLYSncg5K3mcKgatLupVmaXj9zjPGyJz3yiKf+wcue8shnP/E3b7DZJ5Kf/MpX3nTiJ69H/vXPePwrn/rYV/7p7//70x733Cf8FpG/8HvXPust7796/1aVdNs6tnTqemmSyaFBPkox7jm+PxqUSbByJNG74IqrD9zZPPdxv/l6cuYvHv7qpz3qDU9/4n0OuXVkvvPkL5x59sWOGmKOjPSVeCSmJlPHTAp9SF0CXRT0oklKCJOJ2YPVo2VgXrAsAgJMyndZgp2suGlUiFGCGIsO85EbNkwDdREmAFKSHwMUnYCYiqzyraOxknkXAYoNi8W6MzMsNahEbBbdrXDlKmwHAxC/+dM/sil26VXXfPo/v4JMZFjOxlAXiNrbP/k5AD9859vf+ca3CVYQfgGGoUOYo+m4cv/+Zx39ocuuvvYmN9j3z4/5rdc85Y8O/8PHveZPH/P6v3js/e942x7x+uNO+cSXvuZrhahOu3XsyQA31r/+wB846m+edPifPfrlf/HYH7jX3WjTdbvzy975QfL2Fgcf+LI/fsSr//Jxr/zzx77mr5/4kic/+sYHHnD5NfsPf/sxNpYmoZZZdTFwl+8gEmQz6UkFFLNquPzqax/50w9kUrzsT//g5X/0+z966B1VFlVfr+39RTBio3tcfZ2/8B3H8jajmf7dw375iD977Iv/9GEv+cvff/XfPOFBd7/TJVdduzcdsk0+/mCVIdzfHwE33EaOckvime865Yuf+8a5vPTrD7zfkU973BF//KhXPuXRb/i7P/7L3/7Z3S2LJa/AqXSveSARIUHTB8vU8Og/cu9DX/4X9PfxD7nXXTj1gJ3p+X/w20f86SOe/Bs/79IY0sD3oTvnkivuf5c7vOrpf/yKpzz2ZU9+xBFPf/yf/PrPFpXr5uVdn/5i2AhlPuHiy6558duP5cPxe9/uFq972uNf81dPeOVfPOZ1T3n0YX/8CD6GYL4c/dHPXLvd1ixNGpNXqRXo7l//zsV/+4hfPeIvnvTiP3ncS5/66MP/6tF3u83NePHYL5x+9f6rjQVvChZT0ic9U5m2Ppdlrsuc4WxmC8a8V5yH9V4X6AJkaBnrrk80xaIhJnLYmUYhIiDDraSZC50UDkumeVgPfgCC2lk1R9E+1VGlF3KY5J+TOjMYdWQWiSrcdiwmowQm1xp+91vc/O63uTmX8+evGJKrrkSkKc69+NLzLr6Cl37hR3+gaWVNRAGU0uZPf/Wr/378yYTol3/0vq/+6z8+7MmPPfwvHv7Wv3/87/7ED9Hij335a5/48pnJ/anRKApYT6Jxmxsf/Ko//6OXPeWxL/qzh/3BL/1ky/iZ+9+zFeP+/rPfOEsnY5celpDloosv+/Yll3HZT97vnrXwMWkNb9KBJYRY5rBw6ythOYfnlddc95ife8i/P+PPDv+Lx7/yqY++151vpxidGchrNJn9RLUQpfDLr7juZe884botm7D+y+//xque9piX/vnDX0oyP/3xP3DIrS675rq9FYgJYevHUlUh6yeinJnask0X79//yvd9dLv0Gx2w86I/ecRhf/XEFz/lsUc85XGvf+rj7nXbm5NR18+XGKaiVvmE63lPeMQ7nvmn//Tw35iMmxCtgkKwCTfDkiZi3Dg5RvRx/Vq+WoxW9O8e9VsvedLjnv5bvzwV4+CvP/iHD1+z4DF3vPWNR1t80/ZIIgAAEABJREFUGlkWxzxsnku61JFtkFE02SA8wJi11JrKIQpYgwuSsGtnEtCQ0oRORQWrnkYXMILEecqYZF1HhoHNR/mVP46GDt6HzCnO/tyqGj3MjOGjOxNVcqHyQPdYdDjWklNADLBunViX0DqlpCkgcBpjyc2QiEtydqy6PThGdAo4aQJ5oaWFNk7pzJ+FtOPChA6lgSIgfE11aonSF8xb3uupVLEJMCqvUgQ1WfphKQk4kAKxxBSoDMVkmJAae1fSC9tEF3MhiUSGG4aUHIoYifCU2X0eS8/OHlKpQdl0QkYgJCUC6srD1+XpK227iH3+6+e+7s1vwd5x5zve8XG//Rt7H/93L5nb7VocDzjwoLLHgP/dgv/N9dbqM574mAMOuAHnvfTIt1z+vWuEcKA4jHVRHHUoE868l3nUbUyL7Iu62cNJk/GuVusKbQmxRTXMEixi5qYiQbyixNjk2Ld4bmOZfUkfGJncDC/qoS4rSk26WiQgEOU7wXPmLM8cg1PmIgtV6DxJnyKVEBJ8A5tbVxKj1LVQDvChvgYjRRK6e8TMRmTVA8L4UY4Fp7iM1FURoMlMIx2kdatbVO6T3TOMdxTb3DiqQbhcF0oDpSBUePqkSY2wFMnzL73kaS98wY884pE//Ou/8rZ3Hn3KZz7znmNO3H/t1qNTWZRtoJuPaaDSOXAJsmJUn9UXm4NoCP0d6QvzJiWL0QMVIRdFOT2lo9A3ZBoD467pBIk8NmihLK52RIhJsSmz9bA5YytjYADOOzaSxvswmavsFp2tRCmlJhsD3VlRVzcb9XqNLjEUTERtoVOyViq9rjmUGbleSFlhQW5itMEQZkLDilclNgiANzdsKlEytCOGrc+ijZlXRAVAlAqtCpFEeHExLVJTuHQIxjpMhcaFTCqwfSXnhtEa0mV1NoKIURDAHygkg7LMDCHmWBXsXeLHGDl1mbyogUm96tCaObWojXtM2WDFa+TefC6PxTJMgSL0xPdvt288/uOsMLe96cHP/5NH3O4Wt9iEVM0D9pWffdD9//5Rvz4VPeeSy48++RTGKVO7KOT7+pPelBAZtOmjn/sSf8fghb99xM8/4B53mNT4TGOS6X53vv2/PP43b3SDHernVYLRM23kN7/5nWV4NX30f/vJmx1wIx8R4gfs2zzql37iwfe8cy125nkXXsU+OcbYYszGskL3ihQNI1dYuL5xzvn7+zhwMz35137unne4bVqZJaTYvQ+96yN+5kFU993Lv3f+lZettVCMpnKE5y0OOvDwp/3+obe/eV29iM2O/cwD7n3vQ27LS6eddW438K7D04HxthM+7pG3OOgGf/eIX7/Tbfbkpzbb97M/9EPPeNgvcz5PyiAsXmSwLhAZqGpCPJAuGqgBOs2J+JNf+r8edO97TWayHULmel7ry5HHfex65F/+548+5LY3J1/S4oB90y/8+A/8w+//6or8xZe/9YRPU6JAmdAECXtHMddNLhumPOXzXEfXSGjSJEQ5/Vvnceh+d7r1z/zYfacDDoTtWCt3ucNN/+YR/52PqBf3M886TxeGMxI032sdKJS2vtHkdPgi3E74YKItafPC1E3pUrKaaHJ7QloPk143nTlUlPyFkOy0IFLZFGdkVxe4zW5L0v7VTkGS9CJbKWMyNamEbuYsZn4urCSjzd48CmlGMBGsV0zAEg6hqdf1XfrFsy9xhxvfjM+A+fnEz5+2e92u885pEAOgyVfOP/dr519UTX/jxx/QrC6sDZwHMH9YvVUQ0Pd84kvnXXK5qf71o375p3/knq1xG0EMdg693a1f9Ie/c8eb32ge/up3f9iX/ZXTM3oZezJw51vc5G8e+Zs3u9FBTHExluk+ln7iqV/+1NfPVZG/f/gvPvBeh/Bpes6s//GD97nrn//2z4vIaedd8NVvnQ2EmLqt2EeXdBMt8JBlZnG6Xv6ht77ZXzzs12504E6R7tguYPJa4S5KdZ2Q6y6Bv8EOyGfO/ObXvrM+JP7DX/jJ337IQ26wr6Aqp976lrf458c/8s57/yMHLikJS65yZ1z4fe/UWDmaMjGJ3nfKFyLzLre8yVN/8xdvfdDBRscKDj6wPeIXHvy4n1uf8XGFMrnAlA02yUWmVCkKurKHgFMakJx2/alYL690sDF2tLdy/fiht73l8574+3e91a1KM9M4aGO/9dM/dLub34RXzzj727tJEakFp5973q7HDXemv/zNX7znbW5ZK6CjtfaAux/6ew99ICdzp37J5fuH7nTba6kcAo2Rv3r4L/7iTz3ggBtWrUsrcchtbv7rP/EjvLhdxje/fb6Iq3QlGUmqmgNw2jWpGERGiWQbnMBtHyTJUiu9qrPSi7t1WsCXaSvTwtJxwJimpdiuxZKswFBaTn5CIEovhI0kCSl4gepXcYvLwp6RCFfmVvgYurvlA7epWmnJ3PIcTpBrWBsEh/H/yfvcg6vPufiyr59zYVzf6NQ9+olf+MpuH/tq+dn739v3lqWYIMZ29zXv+9j+pd/rdrd8+mN+47Y3PXCiy8CNDjrgaQ/7uR+48+1p0vs/9cUlogOOuD5k3Kw/4/d+7e63u01V0iSlBAvRD9/t7rc8+EBG9LjPfXmXxoqAxU3a1867gGVZgF960A8WQe2CrcYojHWdvNXBJ4kpVbLRcp73v+Ot/+R3f+HGN963FoCFPJfw9KRgXoS6tCX4GLTO/pn//Pq3LrqMkv/sV3/6Vx90P1ZmqJRaD7n1rf/hiQ+7/U0PXhfQYiliBYIegxTmoERi9hGxiJ70xa+dd+kVFPLk3/i5H7nvfVvdlOENeafb3PLZf/SoW9/ohpy/nqlIc+bDkM1UD7n5jQ5o1bu4G+jLkm03p5FUkysXuMJNSAV+WE+Hp4pKYxoUKlvHsMK527FskaSZlizqtboks1YGl7hbZJMEBmzWNgs/9MHHCITdmLc7YpoqbHBhXFg8qGeMdbFArZhqKdm0cxrSQMzNc5p9Sn6UGPDZdWxl9SSTbAvCMnff9jm8l4AOy26x+ABjMUsuwCrJIUvmcKHQqbg1HzUG60ZxKT15Sk+ZAyNcMpDrKjpOuhpalbZR7m2oAJQSqUSOzsISSu+B7kkzluD3NI5QD0UQCDUpqoZagnIJMYdFVUatLIgadTPYvEP4zDc2i7Re0isVGGAYilWielqnYrgzqauT2/w2ZdRMOJ3F4rLa3RW9IBiUtrMUG4IAlhH/8orXuDso0sq//NXTain8/L89I/Pa/Ws32hQzkf/t/P+TCXe/0x0e/mu/ypnba7531Hs+yC0+8aBlThB0KmUqolUY/60x7TIIQ2vCc2ogP3L1f4EuJWNHlh1sG9uWEKoJuYORMoZFgOW6y9Jj9B5kDZJ4JcdR1niJcYVgQigDr6tnXNhz8NFK7JK6pAQ8c0gyY1R9p2CSYp0PY8qk2lQKNsM2XlQyi0PTNDWEutdVDEZdvFGh5tKCG9Jc8RMZIpre1qowYkpXJHO8M7i8AgUyMslylhDmzTKUBgAULISp5pfPPvuXnvQnb//ABy46/5xr9+9Pzh3j75/9rN987B+99u3vv/Dyy7JEFtoCZqdICJmD2Z0/wWxDt3QyRXKVSiqFeteciw4oNfMCdWRfGYUIAjzBhTIQQvwUUrJZKGLE8MH0YSabGkJ711iU3obMqX1k5/UY0qGEcKhmMRSlzEwh9TZwtkwzytosUUcQvFKr8Sy1qe4Qm6VznAZ1Ylq4n8hhriVQRrahJqpmITqCqUdYNZhgFM1TE5WnWIOQPkiYQTcpbUGuZrbMqkz+ZTEN3YHVItkyGtxsUetauJ4SuVdUF6IQA3vH7rLANAvd8pHpQXWydwVJigXXmnUhXpzVatbVBIFkkaiCGrBgnejrklRZDE5VkaxV6F//zvlfPmf9D1c87mcffJOdg2SeVCYtyq1Fkf7Q+xzKewAuPP4LZ0SGI2en684Rnp1DFF5kLMunzuQmDPe5w61+9P6HcsPR4YwJsvcut7rxLQ4+4Ps/jdY0uhWzVNALiopvXXTJPLY9l23mLJLa/vHxv/uuf/6z5z/pd6oW3198sOYgTYTsSCVXJIZJ/Nh97/3ZF//Dp170dw+99z1UyBFhLoXPtsR9b3/Iap7HldfuZrqCypQjPH/sPne/4YE7Qk8iU0avrpMccrODeOnCK6/hTe5KsYyLLr38jG9fxMH//oD73f6mt0woK5nPuQzsV9zjrqt8XqUP1WgbRRliA61RgTKqSlHiovg+VLjn3e7kFQJICDNgQM789veRf/zPP/RGN7xhoJDyTPZRjCj85L3veT3yH/7saTmqxsawaUaF4CGFDJ5DOv0YY3CEZwC9Wt9XbGqF7AMu+t61l12366pB2MG2mHe6w23f9Hd/8rZnPuVn7n8/2cI8dgTkTCvcpJhG0T2EOxNy7jbDMhVkTk/xXupSK9lnOibp3AKUFlnUWzH2qhqqaT118TVbc6QEws1d1vpC12kjJME5a7uwaMwnejJkWVJmPiBfCn9n1u683FpMUzf1OrQyN2hDAnFAq6sUYN7mvORvPOgHVeRLZ337a9/6loR7AImUfNcnv8Bpt7npjX7kLndMVp3kBQ6wYmsxph6u3e7/4Gf/MxP3PuTWP/nD9yD23pZhSQvC2s1ucdPf/IkH0OJzL7n87HPOs5gZ/6E0ADzufshtN7yXj2JqjIcgel8+/Pn/ZGG83x1v+6M/cFcr5HHGcF+YB/OD7nen2930RtR18mlnrRYSdZbLXKGo0abedKtYYNRH6cC97nIIewizoTZnkeyhI0vA9i6uFCLqUoq3dsJpZ3LwZjc84Jce9CPaxDbaDFVykrjxDQ64wy1vwavrKeRFohIFX7/yL7GVHIasdtZFl5976ZUce8R/e+BBB+ywaAVKcmJITb3XHW/PSzxV1KACDWW5N1UhI3dKqQRUQhaXhdDj+0dJJqwWtqoBRRpxWq889H73vEnbUfKBPnGKx8Gt3f7G64+9zL7stF2J3U/92A9+/FXP+sjL/+EH73ZnJsXiYyFwqXC77yF3oKB5xJX7WTQIGzKFIzxbrT/1I/c282xO1Q059bzbzW4yFfUIPvZYZYQbpbAXmgwhmGZCz5ROFAviVgRMvya5cd2ZtezCeywGPirpOiKddJKsMqbaJx3G7IvBmtM1XcSLjKaq7BIUJxLhALVkIUQ+hF0KEZKUNpIByU0Gew+x6pkrfpmEMlvHjkip/+0H73fwATvL8BO+cObK4jFGLlfP1376zG8A+MG73uHWNz545Jg93aGJ8y++8vpi/sif+8mpbYrs6VlFY2dTf+3H78tVp59z/tX7r10itumRHOCl6c53uI0pSaZ1rACg6GZn+pkfXud/87uXnH3+ReGESdhHjv/8GVx0nzvc6la3vTkQxlrmREhSipQClsCi9JsMwt5xr7vcMRil3Lp0V6fz4bR07xog4SAQIyDykS9/FcCtbnTgL/3YD0LcGNeADorDTQ6+wQoU0LIAABAASURBVG1utt7NcoKBsRLNkdn59fqT5TC3Pfr2Y3tC7nTLm/zUg35IjTYzHcI0Iv3AA/fd4sbfv9uB1N4x9wxrWifGSzRSYnH+CFI0eD+Qtmdo0kaoISYb1+viK8EA1wnDSsM5sJ6Za3dYPQkps9pSxZvIDTZRJ4RiV6PLXGOZvFtwnidvRXJgUKnupG3M2sYqaVz4RyFjyBhw5xwMJmOtbSOlAXzITDQTKS2qRV2F2XpnFCPHEn0Z2zmWJZYZA4kgZ1lVyeh92LczptKJ72BpWgAGg7Y5CTSW9LnYXGp4y6UmbR3kw9RbW4zVEX34SFqOFRMZ6QgHoLDJdna00jLlVxFdU1IlTajIYpGxrHmjdamlSyJHXaBbxECJvsleaGHWrUwdFUGZCQwVl1RdGrd0aYMxaWqTF52NLUlFigkalklGMVOp4Fiu0xBtBttb3415Gx6mvDxJ7oCalsgRIG6opYi897gTTj/t+//3jg/5sR/7sR9YSU8L/rcnObV/7wHYDfbtqNn/dv7/4YTf+e8/f9Mbr1w/+r3HzPtdZylBx5ArLUXpZNNeWGkS0jN6ehcJQo7oPfj8zsnTJPARwtbBxk3EQzghFKz4LsVrC62GoqR8ggnI8DDgvGlgGZM5pxkMhm0b1UAyNbLGKCRTQSrDgtAuuos6rNEwcFJnNEumpRaYcoDTWH+wZS9pqaab4q2ObGMu2Q18vEEEhcEmRZ0pFsqVWofootlb9MIPa4bQ/01EYxwRLFodmToQgb4OdUVvDPj+bfzDSw678orLRfW2t73dPe5617rZh73jnHPPecFLX/Yrf/CU17/rw8u2K8PO6rxvuIW7ywjjbgppJqZF1TTplVUK0iQ8INGRQwZv7oaPWDIWqtesU1pNgCcgPPjKb0o5TPy9bFDBRETobxgtHTCEGp+OSOomcsf71GfrlJqL6CB6oFV0LXNJbjbXtKnBe3QrsVFMKmJC1xMaYmmBsrArddks1shpq9pybNyrZwZl9qgM5gJd0qRIqTIJSoKplwEeZYziThLIYNBWbEOMIkWWprtmfEyRrnSLL9wB9mpehqCTXOATZW1u2AMA2LfZMNAjWe8xazrTkwquP42GZ9oC2aIPoCvceGYPjAVUuEi61OTPMusKAq8Qytr62B3L4qedvT56N5X73+X2THwxhE08IWYZOxUPuef6r3Ocf8XVl157zbBB74NArbJWBb4U9c28Py66fN3r3OfOt+9DHdULgTUG24goXdybL4C56BDCf6+73vlmB65E+re3v++I93/o1K+f873reA8MpuBNDz7wtre59a1uduuaBzbsCJSjsibBmCVY75fqOUXZYLQ2l9oLrlyuO/viC88467zTv3rOl7/6jbO+u/5HaQEMRmCUyDUE/MqTnuo8sQ7Dg0eyNaEqVQBbzoZDxYqcc8GFw520uO+dbp+2kM3r2gQUbC8re/mdZ6qkMooqvSnBjxCIiEEE6TlwPRsA6hV6T/cKVwgCZ3zr+8jf8653pIUakKwj+ZuuzR2q8uC9f5GGyF+5/ghsDSr4/sEA+uDLQhUj/fpRyyJMCylR5b/90H04eNZ3L3naS9/0gZM+c863v7t/d3GUZu12t7zFbW95q4NueHCKiKpNkKpJEHeV/Z47J0qRoMwsNatVxETDOFNXrxI5RCIN3HVMfZnCN5CNcBJv1LAWMmKwIJfiXplJUmcrCxAxlCYhRUNtWN2qLOpDiJD4MPcSQnqwhbWllLRkOerCmpAQRZkCO6xgMw0DDz5RZvbd72535tNQfv3IF79SCwx1LO3iy7Ynn/Z1Efzs/Q690Q13ssqypgZnwVMgQPMrr7vqesb+yN3v3FQnBe+MrC4m3T17Lz9+z0PZx3aX8bXzLx8qodkocZWx/uUAEcrBSkfs6nA/+7vrbe2Nb3iDM86+gDdOXzj3/M+fe8GXz/3O6Wedd9a3LxIRLvvOJVdEJ8+ATlNzqmODRReyyQB2oIlzrj+TZiiCRUc3iWnkGLEEkdq7XFzKMEm74JL1X8K55U1udLODbkgREsSArTs3JZXy1m97C2SNV1dd1e4NpDB3upfZEd8493yOmcp97nI7TDHqdkgfc7KhzFJTV8s5IcUgzbQ01SpRmOcLO9g66NBET2fUkzN5sostKOlmQyw48P2TrI6KFeXMBVymyu9rSJh9oTIVn9gP+jxyTusVJpfvv+ZrZ3+XD8q/8K1vfeFbZ5110cXflwWnE6wKZBa+rxZFhCM8BTTczKqqcT6v7x/RmS0LpFuRQnRBCUvWpVXGFlUEwhJcXQr4KRw8PXWYeIFq1pAcElESKpRatTcJVhdJEqs42bktlC4oaNXNgpnAeAPg9FZGk17KWNsxv6OkCABLUA8/wEBAkkD2rpkVpR5yq1vc7da8H8Anv/KN7bKIRzq+ecFF1/9Pkn7hR38wd5iiLjYEHpH/+fX1399jkOYeXzjz3M9/47zPn3XeF75+3he//u3Pn3neFdduqWY74ntXXy7IGBP2kAdAI0pojVrHjrG8co9ry//1w/fipav2b7945jd3AhvBZVdc8Y1vr/9ht5/6oXsHxlDiIUEYsWSQnibC3bZOOtSudxzDQ3YDM7ErYhOahTakUvJ6KsTSa4lWL7h4JfPtbnnzA294QGT2uWJuDGUxL4Rmnb3+cQUpYvPM5rZ+B30QkVoG/Nr57O9cCOBut7v1DaZJBAwU98bZtDCCSRbw4npKztGHI9c6NjXbTDIZWoD76CmTe4ySkGDCOcTXyIgq/sfBhrHRJLlHDv8vsSkYTaNUqCGD9l0n2DIzpJhJqWqJGNkj6dlS+aRHooqKlDAVQFdHTDjVopZtLb3W0qTVIeYDRBIMcmrOhXAnOtmiUa0XPiKgkQIKI1cNfETNVrRkGTIhm0lRFWVpLlTBbVgNNhrDmMI20QxKVi/SQ8Id3mVERPYaPoUIjQY8QZeEBU+CNSGNG+R5/7J08pFCJ4zEHpwqNAKqQFF6xI1wYIlwAYy2ugg319yDyQxWIwrlDNB4OOgObBRJoTmOUH5iFBBCQabRBC0nHVP2gkUwswZRHsLKsOIKKSJWIZoEKYRxkRjrYtSaOllWkpLllje4wVm0Ty++4upXvuktEQGgtvaEh/9uq5Wf/09Orrrse1dz5sE3OKBY4Yf/v5y3u9UtHvSAB1DUxZde8tnTvyaACVQkZYUpjM5ZSh1qrswt0skJFstUggOctc4MiBPwUGemJTFmADNLHYTAxCnOSlnh4mfeCimKpqGXWKnDSsLKPhhkCfCXEQULRUVOsp4KASzJpilys4j2mm4OR+kmDBawzghFMA+KpRmkVtNSRavwL6UM0pwF1bwkV7ByLkM6SlIsPXQ21OpuCUUKBMFvNXhFUCGmakWFagIZ8EhErx855YunnflVAD/6ow9622EvetvLX/zQH1t/fOfI9edFF1/8rBe8+Il//5xvnnM+73CGylzJI0lIkcII1tJoZUuYitDq2hTVwL8oKSpIhUqKJ4j6gkgbtbC+EM89Jg9GAcJvmUrmrbYBavRaJ67NoLs+uTRCA5fiQkiIrY3AooMoigggkFULJAE6qpVv3bENzODjlR6WnKZZJPYWG5MQ4qs8hdASFwrsyKFBaBgb8h6024yqmURDaemScCoAOrx7x2D7SsEqmXN1NSOwTgjwS1ZZauv0gzzB6h4rpQVZICGSuP4QqGrwhIB2/I9xfoG6KQkyuMuTLp02DgkXRdBRJuBwpSkFyxicznE1MUjtKl3pIffBHL/FQTe4yUEHQEdWpyhyhqBKp4lykxsdxAkRccXuNU6GUeZ/2WWApKbUOfSq3RnA7W5ycDJ7hiU9M8a/lkoO0FNeBBLiYWQJ8oY3vOFfP+JXb3nwgVfv377hxE8/+ZVvefzzjnzqS97yjhM+feXVV6Vzq6Y1tGY25GS5YV5CIzXMRjHWKobBJf7zrHP+9U3vesxzXvsHz3v9E5535BNeeOQfvOT1z33Hh/b0IZL1LHumx/UDe6+MXkqGsG1IwKAiK7C8lj7UoZDLr9syAaZqNz/oAEdPYoxhEHaxld30hLMBBsmlFEijnTFKdHOymhgjxZm6QgV7M5uSc7Am4FnTEN++bL21u8VBN7jhQZuF9VPY2mkofMEYvlje+OAbcimRv/KaayGuthBujqxnF11Cu++NfN94S61eqYA0+a3/9uO/+mP3q6pfPvs7f/+Gdz36+a9+1L+++iVvPubU07+RPlSCFjhpViI1I3TxSmHoJM+YzJtEFWYC61eVUaVX8z3+DaKSK3dKIRHQvWxJo4wM0l47cUwWOKLtwzq0T8YGo5JKgavdgIjbjueEsHTX8CJRTCpUXDBU+ZtvlJESDISL9dZ4upYO6ZKJ6w+PyFoPPPCAn7zfoRz52Be/cu3+jmi9t5O++LXLrr5u39T++4PuB/po3yc/p8E93BF5+dXXXrNdOHL3295aQP2qYlrUFKsOl4MOPMh0Bfbyq64NNJXCMsr53z+TAQEt7CHDbZ77JVdfx0t8KvzY577+0c898jHP//cnvPDVf/SCf3/i89/wRy9683mXrP9j9yuvvlZCCV/x5Ean2QC9cFAYQRualHD9SatVMQSymqQ0aSFZv389iVQZytVnX3IF59/yhge0DasZKJwYKoeSlM0Ygx95JmKAMVFk4VeeKUBJsTDpF5NdwME70w15U2cc9FiBpk51leuLFZcgJdIEashGH/ocY2HgOU5hoi7i4CesB1ctGOHJYRvKcrCOAvRgS5WZwRoH5vc0mDp7y0TAvaVaERhGZvcvfP2bzzzy7Y9+3muf8MLXP+EF//5oni9+9Uve92HsHaZZbTFzN8n/0lsSNb16aKwfoVWl7E0HrcvQBG3hUmJI8JdcXMnmKJaEXOjiXLG0hKYonMwp4eZKYiWZybTYZNahwfqz5d05O9tO0QIaL30vhZyZYBGWIQiJoMfgX4SsRkE1rSR7dgF5PwkR7c6yayDHILogFll6WViLBbTyFx/4QypyzkWXfuXcC0OEjfojXzyTHt30wH33v/sdYy2OSvpWpudi51yy/geLF49/euPb/+hFr3rcv73hMS98w2Ne/IZHv+jIx77gDS9794lcyPOqq6/mugL8F2ygg6apRVYvI3r2HssdDrnloXv/I5lPnPE19KE9v3LW+Vdcu1tNH3rvQ4lQigY7uYlJCAlGykBFa1H6iOuPyJTVf+TQgJBw0OuvrK9UNwgqS6Lm9WS++cE38LYWEk+iR7lMAhbeWGfv/Xm6jGGOjP9bkGsJKJP70mv2cxbbUBOhPsLP9YsBFUX/h7uogxTs5ISJNIMZw9FEzSxMF22x7s2KImkx1mWi2HvH3tEA+k2doUGq741BiEgjG0kb8QKnORrMQGqSVIH6CsWQHGLJDTl5lyYVOgEUqNRmoJ5QGUiW0hTao8bQVnJkzf/oFJojQcJzLuWwUroOeC/dZRTaJaJqsKrWmrYCraDzCsjBwDY8AAAQAElEQVRYNQRalGqGKlKzFGhUz2nElFGUeel9pNOpXKmaXgJKgwdKsGajqqgwoEqCDM6uHI1Up3XCJyEBILMqDeFJr8wlO+VOpoVKIZleBxsloAoTNoOyqLpEIjKRxVFWBzGLhIoimHEb1fXj6kQK0dE5yNCB2W2kpEKkmyRdskJPFm8z29eUhQtblaJrKjl/MxiTjErw0Y2WIt/+gePP/fb6vJDfHvjDP/J//nMK57uPC767/ueJDz7wBrXsyePo//tT5Gce/GNmtizLiZ/+FGq4kUwrOgn66kS/eoPbLLlUCascHxKwNToT4w7Og3frvonSokjIwttFJd3WYPaIoaFqimrBYh47KjsmlRVv5DLb2GI7sEUuZEdvm1l3OjZFbFO71a4WvC/ku7LAYxnCrfNQ85qxMyiOFIgMUINsUDcxVWdwAGWcKoQB2UzZNiLrRqPMOW17m1n1BvWRLy3pCcQresEwSIj2FHEaqJtiG7OJF2k97/BtvU0q3gPHfORkd6+1PvmRD7vpjW80bTb3vNP64zuAZ/31Xz7id39Plckap3zy4w//86d+9syviii0jjK57KBspE5EoArUUmrEFN0spFpOrVqlI1Ymba0U4iYA894XdISXhHIgkYPUlYLI8GUJ347sQIaZV+XE6kvr3XJ4MMk6d6XU1lBrEd5CstVaMtoo0DJ4U9DUJmn/H9r+A966pCgXxp+q6u61zxsm55wYwpAGECULiCIiJlSi5JwlZxFFFBAVBJWcvearmCNmgiRBlCxpgGGGie/Za3VX1f9Z5x347v/7vnsv+Lvfmj57r92huuqpp6q71z7vGdMmyuCY4VvMw2eft5a7pW2Jglgq0Mw3xEpGcraR7OwQeikZiD5lMtdX9WJeRtqcOfNRxK7JLGQS4ChbyBZ9sYXcMsEk3kyU5qP0XskdCxek5foyUmfTUYgWNdU1RlmP9drd8kEdJDWHcYujJMFavf4QhRjTgmmxEqDeriOoauylCh1G4Qoj+sWMA0R1UuFD2gqpwA4nz2S9qUnSaBJiiewjRu/gdBRZamEH9tpdHEX3rEvWsJQqtYkUosJlaq08ev8OYTEJDSEEItAKbvHw9SvYtEgOybj1Da/7mic+6AHfeeuzTzrOIz5z8aX/8K8fe8Gb3/5Dz37Z7/z532ZsgxC23Vp2W/QaWtNUqkjNLGQAn4j90d+989G/+Lrf+4f3cezX/6Hk16fiDXVyYQpfAuuulDUsjCwXp6mg6qbWnPHIehZZIraSS/VB7oGHrM2+SlXNXXQpsrWYNyMNFMzuLAQmNFuJAkpc3AIqogqk01OqyU4szP5i7IQmYYSGgZzBeiPymk7qpjsjdaG+KeQFila6CJxgd75ayyLV2f9wsZBp2NT3NlhYVWW9BLgA88jEzHRwvz3rAXd76aN+5LtuekErdvnVux/7/Jfe/Jf/9PiXv/Exv/Caz37uohKR3BF577OPLg4ZyJ58GUA0laIUU3IIo407Xu/gLspgDptRfDWSsYkRDMhxqMd2yTFWobPmKCOUvYJGQGgFaTiSNRSdmFO7qRcQLC19aqOt8jTdJDculXRfBJyZ/t5A22K2le58ppDbsYK2GiuwlcrtLre+2VH7pkuu2v3z9/ybpxzq2z/65/dG5g3OPPns007ispTEBddcPElqHzmH+zV+aZPR+/yQsJAaUkpRq1FaAekL7B6aSzDuVoWukfJ/vbnH8BGHDs0elPF/Nfy/3nlESB1WFKIuwq8FmaVFtqXPbST610dNdCkF5lCLNgWg/HRNq4C8NDFNjD0rajEUSaPRszBsF4xeli29c41KTPgdTDLrOBVZ5ew10itNokdnDb+2LUkpYLspsZXSQKIKODvb9wrlJSSSTlh0dB0pwxgtWJhMw9gz9/phfYsMzwzyW2xvzsNNTp2DkzQrzasswk5rd7ZaTaliZtXiv//9Pzzml1/3+//8gc9e/NXdZdWQHf7HIiplsmFGqnBiNnGSxpNJN259xFflEgSZ1WyEuTTmECvKFOJDuTRuV1WcxkLobA06wBwYgiwDhl4jp25tWWM7baAmINlDxtIyBCqookaBvE+xEOm6DOMa0BVdJcWAFY0Zfd042EiCDpmiVWP4YtYRA7Ggc8qxSd+JVWQMxZK9j9073Oh80nvu/rcf/KiXtiTe8YH1ueEtr3etU47cxxgqwjmqeNFhh+bB6f63ZSxew1tJuuGaztnJRN9Zxr4+8zFCFSjjAbe98fUBfOSzF33pq5f3Of/iXz7Ejzc85/TTjz1e0wzSTKoVUSVDlSkEDtHEhCjsyVLJ0ymNn8YYnRmiZ3ZZIWEjBrQTUyKNZexRvKiEIAwqbOE3a843fK0/x4xYyCp6X5VCWYEUZhKZVbfAYSEHdvZjF9Mh194jBtkzgGt6ryNgblRMm5t6WefljyCa9WIekhQJCVFnTzQkx3ahrbo3GqDOwsusUYtrKiVRvTOJBkPGCjdkWgIyM8Ci29xbp7AcLbOmCISiyCFGk+aQ2BXpUgmbuNGIWZx7B0ZYrHPZAIcVaqiyFGVHkdogNnoygLZtdA5SWhMTV6Id0anaNGkzWu2EiwhkDnRyMJmO19bCb1tQZhvDtkzIwqAvaemcOMliJkETYaKO0QamLtwrrEQn5aOM2dypjoFVGsA2Y8vtIkVrFTRdmGARBJK40p0pRDCbUL7mGkjZJQbtNgyzMFGlkMFgVQggEciB0WUAUFWrKut0jpgNi9JoGalA1NQNYEjAmVnSWWdpmlXUxGotk2Byl91cumwZZGVSbQkd/qmPf+bVb3prJseitenB9/jhtrfics5vpFx2xZWckj0PHthnprz5P1VudL3rHti/n9I+8rGPke5Du2sEjTRPDYu0McS7Sw4xNmSqsEjVMhmkZpaMNXyciIKgk5UZABOXE8J1HN0Ao+HMfsxqUsNKRHOebKDKcMqUYdM87ZshGFyo5z3aIUoZqT5G2uybmcxbzLt4sBtI00KZkQMy6Aou6GnFi7owJOiaOcW7la3VYeZlbWcLoTN48nKMXjgHSJegUPJIE05nUdk0AVS8xFB3ZDCOQNEkiV9x9ZWf/PQniNjxx59w4fWuzRuWI44+jq8srbXnPOKBv/OaV11w7WtD9Itfuugej3/y37zzw2RVbZaTrKJTwUURCJovgyY58UoJzmzIurAeKSY62fqqCYzEFvSMcA7QCrADVGi86vAgi1ngEkZ8G0FwiFMkwZqJH5dQX3uL6KjKUwV9pB21ZwsCIAJ6iagM89wQhFQbRVwYPF3rqEKsUjhaQgnlyOhlRglVFJeKbAph5qopkwqJ4Z3XQuKMiCEhqw0QhVEAHSBBdgnrWRVVY/LecoGsUxunSW9gnhlYHCjaxCbUHTNVwd61b5rYuXXbDKUJzBFIwrS2kRdGIkrJUEdwgECpATfmxEmpgxm0CoEQNoIXVxAvyLUzq3JTaAoOzfOWi9xYBQnBRF/3Il479PKrruIoVTli/wGooQk4DnuXDpFBf0nKTjVWffGyr9aGqQ4+MCbmzjXFIapfH5IZnBrkcQuzPOPYo57w/d/1e8954l+++CkveeiP3OVm1z9iZ/rK5Vf+7G/80dv+8u+WGlcX7xFgIMQwONFf2UBIe37sU1947pt+j5uYk44+4kk/fOfffMbD/uWXn/e+17zwX173glc/6X5UhkUUxnUmJZWfrilhvqgzVNJaKS1NrmlYRXMGQ2/7y0ZEtvNy+aFDqvRyFgmvvuIeprmCxlEZQVNo0iK1i41UhhCN5bwURAcGh7If0J0r8RyiwnwCheRklS1EPsegQwPYy+7cAi07sA3a1Xt/6IazH3VwgyJZq5lxCItqmlEWdOjXtadu9Fp2RPhIL1Vve8PzX/SQH37nz//krz/5kY+6652ud9qJkfmu//jU417xus9fcomsoCiVLynMU1oiazqpRa8WW3fqKVRtSbpgdtlKWcRcVQ26vtMGSSaNBcMRXHcW1cXUq7mJ1miFkURTVT11UDsWQBDoyRTU9gLSPNHDxlI4cfPQmWnBVjDEhGSTIquBZFEgRXY2O9i7jADKUMvTTz75hmefzrrf+/t3dh3/8YXPfORz629h3fcOt8ogtOGxAHtTsxN17qzNprYnF1ftXq05MFxGJL1piUro+u7uFYwgjjj64D4NB5Qg8eNeEeEugWm9hO/pv6+SLWvL437w9u96zfP/5ZU/+f6XP/e9v/Kc97z2ee9+7c/80yte+E+/+Px3vey5r3zig4jMIkKFwsWd8aMwJ3eM+YXArDLWHwslBtJTYmH+oaoyrqEcICJVIMXixIMbAB45j9z1seQIC1FFGjux6XBZzBxCLzAUDtcIuPfQnawwOzg1Vl521VXzNgRKvKupNtUCixDWYb2CblOqvJsyi/SopfNbzpoq9LKvbjIDKBi8imftQzwQIpHydfjZFsyl2KfcJ2nCfdkiDzfztSd94cuHPv2ZF/63P9z2ceqxRz3tR7/nN37icf/8ay98z8tf+O5f+rmXPvQ+lLEWodTSwdf10zU/qektRnFXmpzhQD/cpCubqhmRyVh1o3WZRZ2BShU7CZoCZngDhBsgNG5ah4pLBKUO4pyZMQRDNGiogktcVzhEgh8ATaJlHDOEaw7WkTh8JcRTBlZlHRniGlE4MqjrmHXlHtZppFBP2DDsSswSfnCn3e6G16GUv//Qx684tPueD330C5de0Uy/88LrNhFLkNpcFD0pVQ+0lQ/HHdx5+wue8I+vfP7f/9oL/+mVP/svv/QzH/il53/wNc/7l9f99D+99ufe9Ss/c+sbXI/KisQ6mKJZKCfoqhRDkgFFmnEZyjvf/IJ9rXz1qkPv/MhHLrv6Mn59zb53vOF1687kBAM0SEgWOay207Sth48VCcIIXiqyqaamFO/k/eI6iCHnYyMQkq66KHqeeGBVPgKahYzkOapaqGiyuO71Xl9EkJyW9CyQtWL9EXWzhUvPgany80WXfrWnR3LzvEWMHMlTVidGbNsrOTaGVS2BL32ZufotY+nkd5NYV8cM0NeRvuIBEgPicNbuDYdRC5NSS616jaEgnHXJsgzMoV2m1CK1iAmkO3cfGpBiosUsrWZSq+wRTu/N4e59O2QeZRbMJblAOW1ehuXWGHBKGnPtb0HrG/ctwvxPe0aOoZ6qEA1yFZyDsR+maQoy3HUsHnwWERhr+zrr2rOXEJkrehGGJxIcSbklOnOEiDB5MzAABxaOMzGUKnu7fm6HBpk8RKAMsrHQPId2K9uiiyVV1tF3t6MvicF5rEO6IDWsoKxTqYdsB3rAhc+oCptg4ByotGI0cEFA9OwDvERT0C24E8sRMq9gTqJMIfsUzVWiCvOeo9NFKIqdKhtOB5oVIYTEXXyJZLYGM79OUjT6eMtv//5XL7uME7Ccfcbpt7zpjXjzjZf/vOiaX0I9/cxzvvFR30jPE4856sjj1n9feNEXv3TJV6/g+qtlAZkhC51KTGJqEAAAEABJREFU5yCHGB3I9+BWUldfkGclQ5EhiBpZcijWfJoJ8sEN6QNjSKYWqAk3CjJiM/rE3UK6dHCSKXxCKCFX4aWISZYSpOPIALh3ERWQvMyXCyLrQFtgiwplS/AgHOqcwhlLswb3+1QngXAsI5cMUjHHyNW3svUy8+HM4GaxWiopzhCT7krVqTWzBaf01Ehhq3DegA+PresWGUQjgl8RxBJX7F5++NfwzjjtNOqHvevoo47ae8fcO1W4ztlnvOGlL7rbd99FzcahKx/3rOd94EP/PmkUqhsRvTtjULAV5cRChvYeObqsWi85Fh7zI2R4zZyASonk2EiZmS82gh2JjbBRlPvtUkCQHXxwbBlSeScAkbXshhATbKKX7RxzLMSxQwdNBK3KTM49wKgZHXNguyKoG2ya8nufzeDuuhWAZq4rF4ekA4HsNTk9JmtiLZX5SpJHizVJSnBRDNb4nLGFzCGMTQ4jPtXGDsbOwE6sxEAR0kdGjQH1PRcqVre3DXiwQ0iCntBMeqyAhhZ+pByW7pFh4sqZIJCgVsl6FitemosSaEd4ItkhQ2MsHotLhGiG5qxb4gE2Y076YllsoeJsvN4p6+9A84H0ly6/SkIqzERUAcpQ9PSP7v3Wbyt2zFFHS5qacd7DpS/cfu/mmA9YPWHv95Q+cfEVoWCOLAJaxDm3u9m3jkwO4Y+78kMw4dYkomC3SM08fv+xt7/xTZ5//3u84ckPPfO4o0bk7/7jey/fZV7at5VphqYMGd22S91ubZ6nrf/Zuz6ciSP3bV78iHv/2Hff5txTTxyBGGFSWDgdy2TWUExricaPh4tJShFZ7bQAY7om5HATiq4PfhSnH3dsUSWIn7vkSlFZCzS07Japk3BOgNYREtBtBGHI3IUdQj2E0mFIK25lkMPX9Ox9hHvQk0mNNmL1enu/VkHkv3LJlcSj9CY5iaW0USYma/nk59YNN3cMRx5xJJT616K2zsqfKfnwYztBYiijmTXgBopPYce8bMfS+xJ9N5eliG5aaxeccfaD73zH1z3nYU/4wTuYymcvufzP3vt+qFXUltyT5jRl20laNkQWrjqqPM0qa1p32Q4dqInqYkuBbzqzk4g0WX/7RRTD4KUkF+cgrNCiqw80VZOaUcM0Ll+5pzzjdszFF0lPYXbUDmEe6epb61tlrQMLN5ANCcEaapuQmgXY5NftX1sM8xRoWb7/Vjdvpv/2uS995NOf/Ov3vJ9n1+MP7rvFjc6nLPQgz6nE4RJSYlUqjj84HXdwPyv/47NfVlfpovPS+qyyy9mR/T+/eNHwEMHxRx+R0smqxSr7sxSz0WS7iaU6ZKj5/p12+jHrb0h+6qJLJx1t47ajbdpsdKcqF3hrakwfvEsFSdqHc5pDCrfeZJ4kKlJQKJxFICmGvtFl8i09zEUn100c2w4XJ6lcbHvOqSex4nOXXnHVVUuPstSJwIWBiWrfJGw6XBSJIeQqOMlelWQW6i7Fs5x5/JoBrpzHV668ivZmYgUXsUSiVz3sNWBEzpiTCTVdPQtpZBuXFuzNIVReW/Ie6yVCoM04ljN2kbFW8oc67QNaE7bR+dIXywCSTSzhjnnbx/w7f/ceVh17cP8vPPSe97jtt515yvGpfVQmD1Aye66FPbpzbbURNGet4Q+nC3WwTiOwNnwtPMREuCZJBvXPNZhXp5CpBu9chRfRrgod3IJvZj7Ukii5TFTPdCjYoFgE7qxRo6umWNBnrHsnLJTH5MvmSdCYRaV5ajIDO5USoAoK2TvUM1NYP5y1zBJ0NYbKEBUKrRZmQxF1OD1eC4fhu26+fq3x6S9e/NFPfe6v3v1hCjztuKNvfP55yZkWxDL6oB5zWJ5/0vFsvfzQ/NUrt8VbGaqhBUW5dkJTVFVK4VtxM08DqAR4ZUjMRebalspQE4ghVfOUE468wdmnscOfve/DH/n0p79w2VVHH9h3m2+5IOgLWfq89G3moKUYA6NnkDTeV6WE7uE4ND4v8QlLExB9tRBJIVRrG3/Ew3Xude7t7FNPZsXnvnKZXDmouSWDXnaG7Swb7RObDpfCfMVIFPqRaWOtozjCNSn2b/T0449h1ee+etnWlm7JJCYmyNDOOFp9wVaWoQApnBqpI8F1bCyd8c7sNqZJpApHZM7G7OcgqRx2zWwcjVRx2sef+BqCazUhVhlqpMkMnTFlTpxeaTQ4odLHmxYbCxNKm+aUERwACS402yW3vSN7zdgR2pLaOEvt3ri50e6UOKYlNmMd4iTeFsM5l4wGJm6TtC6yBR0StiT6CCzJYCiZhfakJVo3mjynD9ARUrHqNLecm3KD5qHKmqo6QXSYRFMRccRcZTB7oYK6J11IMjM9BBXui/RFuf3i41dYr1imrsLB1LhTodT1d3VMCiNdhnNwFI0CejeNq5d0vrmpRGEusRQoczRlKGpXWbCu6TkGycVeUHOzXmw2DImQdAjxXhKDHzKhCdYnMIAO57tJQsyFxjenZ4sxIi665Cu/88d/jL1LRH74bt+7MzHi9j5/Yy+fvXj9vVv2PfOkNYHy5v9gOfbIdTnZzsuV86KSlbTAkEzpwfQFmsTUIBBSA8MkGK5EbiA9S3I1Ye6qkq0HeSemYLpmFzEGlcBEMxGdBA8ZzkE96QlLYzucrINEbobvjJiIcUh48SzBCUWqmFp1qwMWK/1znclQqxcl6O50GcAFQHWBzCVmyzkzOsyDRBKyU0e3pLqZymlbNoOlyp4801GMrjcK4ZShiFV3RYpFylKkm7iruilnKc1VmALJaeD4o49KXHPF1+4Y2YerDuzb+cnHPfwH73pXVb3yq1956vN/7j8//5kUfrnOh3R9G71j3ZcYWbLHLwZYdGeCy63moLxtaocOK1SJug5DcJMXW/NdyVl9aBdQYe5TVtWiOEOGMbFVDIXI4MFoKmXTdkw30JKQDNADi3nQbzPamjkG5SFninJaXVBDWy8sNWGc0s3mhqX6mCIVJmuWKWBQNVHZi6wxMiIlE1hnSE0t3KKR+yHTWtaNlRKWgGaIqpSmlStF0tnpgl4dRJb7vxZL40egUvSYyjwxAIVDZQRmL0MKP7BEZlcdroHqWtzWTqxnSUTqYKNaNZsMZbV6OMGyNCNQvZDpPYXbVfZHevhC0ghMq6KWG1zn3H1t3YH94Ts/uGgRaZusG5lqqaZ62aVXv+9j6+9hXv+Mkw8c2DGQGCK45pIk16Lr0nbs5GPX4+u7P/yx7aEF3VZTE5nUj1lxBO/2Bs02LTpZ1me+/G2P+Lk3vPy//6XTgEAMAioOOf34k+94k/XRxiWXX3H1VbsKhWgaHaoyUntY99oZG3r4XxIfc8TBs087WWiOVTO4rmQLJPYuE630wpCvVaAaDZs2pdYC5bbBnF2/blGNLuac4LQTj97XVvz//n0fmRk0uqPCPEbGxIjog+quEyiIOJNCYORKRddMdb6SZF1tkARrN/40NbYRiz1NVGte/1qn72uVTW//hw9IrDsjnsunBpukN7/40Fff8/H1n8ZecObJm52DY1HM/9e8YMgyGMyMwLJQCqAYBXNJf+eHPvaol7zxsS9788c++3kpgwumNMCkoPzInW4z1XXST33+80U7GPB0UQhTnQoJXLtpF+mZIi46W+tcaOrGigmwpjD32TujAFARk2YyFeOz+Sn4HJQfow7TmQ9hyuilLyBvB5NRKZB1XqxX5MjhMrI5OGkpdBMg5K7OkotJTHCVEeIdMUpgh2p0NW6Ntti7MpFsz5XFN7n2tU46+sg+/E1/9Lfv+MD6B5Hu/u3fUlpLbVpaFob33higp81au+LIg/vP2Tui//l7PnT1dlDaiPQxMAywBP7qff/uEUfsbK53zmnBIWkOPSxl7PXruUdqT4Tv2688J7P1nz/88cuvuIqWDIIrZYEEOIypK4xGlqJKzIZJBkiOJWWrkjJERlM0SmARwShCHToQwmHV1PSayUH+DGUMzFriWmesz90+edGXP/35ixrUZDW5qzoNpZPcKY1l41nDs+fCDVFSMFLgRT0zO8486eS6J/0d7/sg97zsuEQupLOIUrWvCYHBqD/V7+SFtpkUFhV2MgiEt8rswdnWkmpB2FePGzjhNYqA9+bBvMBz9OJcFLIoKGQdA/iQOaJDvnjp+qCTzz5OPem4RROapqlETJZcgdnrLswKY8o+kUmcYpWN7k7TOEAhqVzQssteZ75YZlmNHwQORTitKZ2y1q1Y9cDQZDITbGUMHShQpTR4SGQxr9XRwht3ZQJ6v3vM6Its5+KDa7YOSyfNTWi3CcqQNlbNqAJxQpYI6240sJvT75PUyugS8t/ocDLAmtdG/qW4iJtpSZ2ue62zzj7hmBH5J//wnr//0McAfPuNr3PkMTsMR/Sw2TSqFBtVzzvz9KokR/z1ez8QZaSJUh8WBUbq7CtAsLGpqapDsGpHeRha52wLrebmYcttUYsuBKvU9p03uwF7vPfj//m2v13/kt71zjjl5KOOMYAc5nji7QMR7CIpEGO91xjClYF1LEm3loVpSJllhoWUFIWwZS2pmcLRQ/S8U9eDykc+84XPXPTFGIYxCff5oBudajIY1/6AWZXahICFHq5JpHsgpdbNtfaO7v/2yc9ceuXlWmuzyqS6U4RLjhLUPUU5iiuva3CxnASqhmgZaja0xiIy6zR0Z7E2KsDkE5ClSii+ds0pPUaMJRdubajd2kDzw2BmIgVJ/TkqtHeNXrJzDn6NQnREQuCpgpLNopCEUmoWi6mgmIgJhK0CtSyNRqnolF7dZUihqwMRIVzT03QyMShSmQvnYLYcmQ5qlEECsQUoFdrMaksIlhHbYA9NtDBZJkStVLmpSAaSRxOdJjUVBPgMv42QkZFsG8CCTAAi/AnyFGFLJCNHCf5glixVpCp0kjY5SifPwU+ojQ6dAx5u5s20iirEOE4gWSAsoowMD+b+sU9zH1dMRsKARPfsrF9ZFUWFoWIk3TIHv73qQCRIAMleJIg8PEfva2ZLMpN2GLTIjhBEpS3ZMzzl9b/521dedRX2rmlqd7vT7fduv4mXz/3npw73Pun4Yw/f/J96TeDYI4+gtIhw0sTUBMXp8whu3lKjTmHFTLj6luIgcIjg3o4UzxIxJddyolFVp1a0MpIkh2qTaAaRjnBKCuIQXNhIC42Z8Fsd1ToXBlVh/bBlsc7tDflJ/htJkJFFhSKBukbo3MylCWukFanG/Ed8EWqwAkZXXYOnRbeeOQqCDIGpa0KGlmg1p8KK5E9UW4uZogkqTGg7tZLs0JEmKcwdUyofXG4kp4SqoqgaQy+xd/ER7d47hsfll37l8P3fvfPdb/qd3/+Tv/3Hf/34p4qVZz/iId9+69uw6aOf+MSzf+HVV82HvIRXBMh1txFlVMmdxE66CQNgVhlVU1UyaYfGGm5tialnXedNR7AERcJhAV3vQiTXd0CQitESG5cKwmWwIlXAqpY2eZlGEfFl3dgOsSShu5D8orQAABAASURBVOgspZdCu1cBZDW66zLXMaqrDvVF6Z55tJFUPJTwJn3VE9m9kPs2zzoWTk4NVNS0lqnUZmpmZdJiIQBGGCcaxaCEF2UQa7hEVrcqRlgKFpUOcaTkOnWDm3IacK89D+lhlMNSK3GUwzCwa5Y8eHAf65HYLvDkdGIqExGApUgKvWctS2F67BKBnrFdlr0hWcYoXkqZ0KZs7bSTjv+W889i0+/90/ve96n/dIgSayeGpFH++l/93Wf3/nTp3W51UwVqSBnXoM8hVWibLU10R7/jW9Y/63fRZVf9tz/9Z+4gMzwkkkbA//S9H/j83t8ES4gXVanFceiqQ+/6yCf+8N0f/OxXLw9AdS1YOaeXXHklhe/bbI6obUe8tUUbUmpmAY0T0FgxOfLAfgBXHdq9nGsSkujbJFpw9fbQW/7iH9nEEmtsibh8XWsVUSoQVkKSXgKPFEQm2ZmludeFru5HHGx3uvC6rPm7D338X/6NGamAM6Qnk2PsvvbP/oZNa6G4SYqtCqqvFfS+0aPkAN0AHHYbgNKsVSkIeEdkSpx58lHfcv76z71+7x/fx9Mgg4GtJhzGR2vj9X/6js/sIX+XW144FMHAGz1oDmUBiSJSSxaBrJ/2KkmeqSbLUfs27/v4Z/75I5/8C249c3HtUaAJ63L5pVdGrsYed9RBBo8jZuQcOeaMzi6N/k+SCMIJIyNUlapzOwVG7sZ7HR0Od0UqjYwi0nLDjGOjFNdpybodKwLJTukDHuZa3AxGVQERZOGWqDPeXAU2gUeKqfWNQYUdp2gCIVCECesb1U2miBIovjNV7F3piEUDFCZHHXPgu7/1hgL8zb9+9AuXXs5Q/M5vvdmWWmiROplshPIOjwKGrLuOqU3ffZPrm8gXv3rFq97+N27NCU/Y8DZG+8d//c8/ftf66PrG55x+5qmnQgp12BOwvkRmMppW1wpcqYRW3PXWFzKDf/mKq1/0tj+/qifjmqzqgJMCOt72jr9/5R/8xejSRDfFWkM1YaPQk2APkV5BOLFelK+WUA4dqllMyHbSYm3jzwpFT4tUudOF15uK8huk3/zrd6gOHqaJoQMj/AMf+9S7ProedDnCMisWxHY4lWIFIBKspRs7zjr2hJvv8fC3/+59n/7SV7eR3IB5dMHYHdv/9o5/wt6lhiprAofvozxZhA97q0CgYFn184P7dwBkZqcGKlGgJmprO+vXIgSSDzLcd91nZj3UHQUFHG6Tsph5jSMPrPnt8quu/mrvsdlghQAVvsxX/sY7rgltEC7iggxSkVqsEvjBHczNXdeJxFcQr5GepBOzUopz9zhN86Zt+ZUDSEeqp66UR8WZXMlregRdrOtmkSKe0+xTD62KqXGpJ7UzOg8nIzbzsuO9ZlTfS3bBFIGErLQE3zyxdyXvRamqC7WqKlYEZJWOAq7noUyJAohBSjEtyyIRktaktp2dg3e66fqlyh++50OXXHn1VMvtbnytpJNzAARPd3RdgVDl5BNPuNm1zmDlW//63e//2CdSRxqyidAHXS/60iXP+dW3vP8/PuqSmiidUcW+UAhJOSZZ1BfMMTJn3q0rNlBvft3rHHNg59Dc/+5D/8Het7n+9VpvOqtFEVO6nURxmEC1GFR4GUYZjq9dbggeBzVAcFKR06r14VaOmxSNWUbueNMbNNND8/LWv/6HxcdM7VcXI8t43yc+8YFPrn+bbm9QgrOua1Hb+4gEGOucRaTd9eY3qab8mvq//dHf1ig1KjlYBLbRf/z3j/77Z9fvqAGopVqX5KD4wpcv/sN3ve/iSy5tGhsZBiatXERSqq6zUG2un5zSVBR7V4YFSBhm8mXfZGZr/TKYPCQ3FTsMFHAAwyDnITlMeytzMeYDKo9MVoExWUqaAVrUdppNRTaKPaNk9S4g1aDMXjsaUtEnCeNIOvVQlgUb0abVhOmdq5JrBLvJKtu9eOd0MkGnprYpqArNveV4wGZtWZROc5M+GWoL4b7W2rApq6jRI0QVnrZw3yRds2OAfBPCIsHlmM5fibVB3RBmAFNgE9rCKox5l2/UToQkDrinD+cLVJXNoiCyakMrRjGUCq3B9iV9hswcVkSVSg8jK82LhoUL49ciK2kxsETOiY5EMPyK9HTE0jCEgiLGSKaCkTGAEFOZlAA4+u6Ytxdf/NW3/sGf4mvXt93s5qccv34X+bWKb+j9c1+4iP2mzc7RR6yHCt7/HysJW3mxysvUyLLeOVYnpAyFK73C7USrpQk9i8zuwgzF3BYguowymkvqT2kNYh5CWAhSsTErT6uzdG+ZRURMQslIx+H9RTG1VqFGuLcyhiYsTX3dgy7UYUCcXsWisgifffAJB3MGXOBUDNpBRQRNhc/HpoWgiwq4n4R1E6e0rkyWxseF+yAMT1SARmmoWnKlZWeT1LbEtA2by8idITu0zIcON5hEjo9//vNv/OO/fumr3vD8X/7Vn37Va9/KL8dEAfzrv/8HVubiXz/x6V941atZw/LXf/93P/1LL/vxn3jevR/56Nv96L2f9MKfv8W3ftsJJ6zfg73jHX/9O2//C8JZSt37xSqtYzW3T8qpUjSj7MWVZrVQ60O4P5w1vUpU+GSD8WOS6mKMLBgUmFyqjajObxlHa4NhkY22G4dFECvZWm4Jl6pIsWLVkGSxEbpUQQswR1ZVEOQU7huGZ89YsK76dBmSuK4vSNfRq2/NeyJnz3lgJNx8lGDDEHfqJlFSBIyqUjaoE0twIoJDNGmmo6RbCUKcGDCnvxlyqTAFhHUjt4t3MmALWUoO2iwaovRoJvau3d2Z4VnQtcwmS0L271/36Fwq//xdH7zqyuXKK69C9BIpxE5qmSpprsmQHQQg+VO5wjYKo/RSwVxqVkbolnaW+sgf/p5jD+7fLv1pr3jz7/7lP3zlK1/dHrr6k5/7/DNf99Y3/tU/ZeYtrnvu7W56UzAQBmUG5RwuoqbrFn0HWu7wbTe+wVmnsP4Vf/z3v/D7f/G5y750tV910WUXv/aP/uSXfudPhq+jRLIp6F5uyu73nbdSkc9fcvnjX/mGv3n/+3bHdonx5a9e+rLf+8M/evcHANzo7DOO3TloScpE0QwRBwMkVMlmlda+46bXF+ArV1z1tFf++vs+9qkrl92rDy1/+64PP+7n3vjX7/93SmC5Yn1okqJpyb6swIjomdwHjA73iKRiDvp8bUQYg8WKV1Hc9863OjDVQ0t/1mt/87f+8h8uueLS7Xz1pz772Z98/W/80bs/uNedw1YnMcnXik3p+3LZBFQyKzAVLgYkz+GeJGNIAZemJNvRu5rpI3/gToeRf/Irf/23/vZdl1x++aFDy6c+c/FPvvp33/Tn1yB/+1vc0LWnOeUCgr2rp/gwXSJC/WuVMJEqnOTa5591+Aj0hr9450+/4e0f/fRFV277Vbu77/zIRx//irfQ0ZtWbnHBdZAa0SIrJSy+hI+CnFQmTfIkwmavc7RB2kKS0w/FUB1MLpWzdM1tgJHLwAPZzDI0ZzCYRLxYFusNTE7cNYYSKgR1pwFmTW0jYQDFhGXnObJ7GTKhVBM1sGmlvKfkah0RtQxZTLri8CViqbUr5/cMfOfNb9pq8Ui23uaCa5149LE5GEMcDlvrWH24jNQFRWNqd7nlt3zb9c4jvd/45//wgjf9zie/eOnusIsvu+LNf/a3T//V37j80Hb/pj3xR+9CoebdwpHjsAi6XMeYRjDTT1JEpiHtW290ndvc4Hxa94fv/NfHv+RNH/jQp66+4uq+u/vRT3zyma/+9Z//nT/71T/+239677+pKy+oiqfNZm4pIirIhVAels/XTPYjMkwfIfCuQQ1Yf7gQgxJcdeq5J59655usUfBH7/nwM371LR/7z8/1Zbnysqt//6/+5YmvXB19uD8F6RQ6OXjYO1wFRsRiPkv0HZvuc8dv5/Hpq4e2T3jZ2/78nR++/OrdfvXuh//9o4/5xVf/83+s/38kDqJL+Lqrum0SQkOZAjuznLhhlaQ+x8F9+9jn0Hb5m/d8cPfQocuvuioQKEBhf7asRQo9EtWbjkb6LdKAa1qHaTaI2O1veB1WXfTVK575ijd/9OOf3i552dW7f/aeDz/iF974Tx+5Rp/dQzOyMFLHJikRe5fDPbuTFWWsoRfVktOvbQHppOEitkgXDLNglgU64K1E23RrrsVkTDTJ2F36ysONRSmh6exXk/dd63CWEmYoBaSrcAISy9mHTTIqHNqDed9YxUYxKcLNeutWR5GsUO02ZlkG1fUR4V18BYtUMNnDizuvRRfJLKa3/5Yb0UF9kOB53snH3+icM2UGlwmkrHSqYiYqTPL2gO++3RH7NozxJ7z8zW95+59/6eKLuXzw0c/v/dN7H/GyN/zpe/71F3799/vCb6qHYw8igEAFXMqIsrh56jAKo9F0h9nJJx/3Lde+5pfwjzu4/zYXnJ/JVKHOBYdjqrhlWnDZgW0W7Gx7cRcQFexdAi2plJicyUKbFBpk2LtoaiuyU6l53OCcU779huez+nff+YGfeMvvfvrLX7hi3v3iZZf/+p//85N+9c1zH2xiybnnnMFPFMjPgCClRFef0S+81pm3vPbZAN72jvf8zFt/9z8v+vJ2iS9feugNf/xPz3vj7/IswSaWKWEBeFx11e7DX/Kq57/pt575+t/YPTS4l6pDlJwYoOuKGyKHOnjwmrzaNWrX0KJl9VKNnX3N9ur/+d8+dtHFX71y2V66e2h3g94iRTIUVFCT4tZoJwZZujLXsYXedEbgUtMnrKkbMlByLRLEP3nP+almZkoZqEtad49BpkQoUzFGDcmFL1WmVptxToxgsgpBQjKNP8LJxIU6wFgZrKci1KwCdIHzKzSDpxmDUSrozgTD3MXAnCxTio0UX1TmlsEwUfD4tD9LE5XCqzaJFSoQLHjRNIB1ZkqWLsOXXQQfxWhLkkHCc5uxK7LAdJQ2S4swRlh6MAi4hYZbCpggfFNcNy6TTKY1RjJelfxE4RSj+FAgKMgwoJJqAc4hObnXZVgPorUE0YOaVKIxUt3/2x/84XzV+qUt9q4f+u7v2nv/5l4+9tn1D3UffdRRRxw8+M2N/GZ6S9JAlSjEPbAPtklVDSjEsqpMIBCd6DI3eBI3XXkhEjpQZ6sD5owV7+ZkyaI2sxrSp9TmRl+LQInNoOvSQ0bZ5LQxK0xVNkyXlr4zsnWRreTsumTp5Iq4aMQEJ5wxuHsv81xjWzHUyGVPDUHQAdNAgVZDremZPWURTTVzFSRZw5kdwyJt0J8Cp0YIF19sePWgVyNpEHPl+LdPf/Inf+3Vt33AA7/jPvd95gte8NJXv+E1b33br735rb/yhjdfvvf9ySVfufiTn18PkNc549QDOxvsXWKF7xGxLMsll1zy53/zVz/9op/98pe/zEoG1i+++o1fufiqBpKZq4UBEi2ippiIKIowPMaUUblLqbm0TpDEFW5SqOswSBk2dUxD6kKFy1Dv0zV7AAAQAElEQVTplglNtxjgOtIWFIdAh4B53WPI6Na7RQjBYDipVDbz3pBiXjeLbrrkSA/0gbFVzutCcHnytErkkw8FrScRXWwsE+/Wf4WhuWRAFqUoUTJ+fXTssqbHnkjinaSOduicOgBUG1XSOnLxXAblQcOE+tIhwJASbA2ZPWbnmM6o7GV0DM9Jg49WqlLMWvZPUw0vdVhxyQjPW9zguicddYRHvuS3/+R2j3neQ37yV7a7V2YsmYAWN0sD7QO6lr7+zm3JmV/TUJiYUW5VCvJwIhDIa51+yk/e9/uPP/LAV67afd5bfv92T/3Zmz36J+/2E7/wh+/hgSFvdu2zn/vAe5oJ3Zy+alrsa5oZJ5QCM6v79x985v1/+JRjjuQa+Zo//bvvfvYv3Ooxz/++Z770VX/0jlOPP+bEvX/BIsAUvaDTE7e66XmP+6E77rT6iYu+/MTX/Oa3Pu65N3/UM+/6zJ9741/83Rh+/TNOevT33llDxZFQ+pgBwUDrDcsk3OS44ZYXXnA3PkcX+eCnP3+fn/m1Wz7q+bd+zE/9+Ct/4yOf/eJ3Xng92sryhS9fojJqGUU6P7LQ6rkv297noSO4OGMhBMSNbYBzQ5d1JU0pZ51xwrPv+337pvbly6967ht/97aPfcG3PeKn7v3Tr/qz937k5tc+x4TWgPZHF0SoRplCG9NGT4Ww2SSLhF4jmpG75KbHxrUEdPHcup5/5snPu98PHn/kQSL/E2/5g9s+6edu/qgXfN9zXvHH7/wQkDc7/+znPfAeTWtFlOpa1VT31EQkJ+05mDhIucN1SEazyTAJwbMffM8bnn1aRP7m37737s99+a0e9YxbPfEnHvbyN33oMxdtannAnW71LTyoxKZmNeZviMsQmassLegcJeADOkYZi0RAAuRlxCwaTYuJiiEVXCf6wkjpvWIQ4sRgrYKc4DpeS6jOlnx60Muq1+oColYl+LyilJgkKly6e1+WXLc3JqVyME1ZF0tL0jQj00ElEpC67Y7Dl0i09Bw+OnZxzjEn3Py8M9iiIt/1rTduQPWsDm4DMRiPHMxGZA5IqnKbUab9Oy+4/4/e9NwzuZD+xt+950d+6hdv+4Rn3elpP/3i3/jDS686dPSBfS991L3OOu5oGQt81ti1WPWnFFOpInXkFDlpLdIIVGk7T3/gD9z2hudzpnf++6ce8qLX3OGxz7v1I55z7+e/4k/f/a9m+oi73uHW179g1UDQY2BEW6pin7edwf2FjvRr5HOKNZF41GTOiCFjZltQMFtYZE2dnY9rwPbH3eP7bnzu6Yn8k/d86Aee8ws3f8hz7vi4F/7km3/fIWefeM2vJHR+tUyNm+jX2CgJixQ6A0aJt7j2dR72PbdvxT71pUue9drfudOTf/bWj/3p+73wte/75GdJdULKPsIVI8IzFgHTcRpnCGZFzJrz5IuSHXe84PxjDuyjP1/wtrff5jHPf8jP/9ru6IwCsoUSWMyMxkoNA62ryMmTVq560DzOILCadocbX/idF15XRN7/yc/92E+/4nYPfeadHv0zT33Vb3/8ootZj73rc1++dETpBVn2Pu+9iIDkJxWd6oE6SnHda8HYS7dCA0bEyOR8UAHNEjAklf5sAwUty6a3Ogt6RgirghNIpvpscsjKFtwvloDaIHYtKUWyanKVgxRHHb34Iu5DOB57l+ioDTKVUTAXLMJoGyCvme+7dCrlnotHh1MxdTWx0Mz0UOaXPPu0k25wzvoXIyjtbt92IaKKA8wI+3awk5AtcqgnYb3hBWc974Hfx8cfu/Pyy//9r7736T/3bY955m2f/JPPfsvvfObiS691ygnPfuAP7J80YwzuOSQpcP2JxbCYuWgYNzAbl0my0JqEye1vcj0lssCNzznthJMPzjYi04fGECOvJmHqK0WMVg5bvMy+2kfJLMO9CK0OBB0hppYqZkSOjWBVE1YQ99ip9tT7fO/1zzwlI9/+zg/80PN/6bZPfN53POVFP/XWP6y1nn7c0esAwHv0QeOhCjlcpdAqsTps6AF92n2+j2ZG5Nve8c93ec5LbvbQ597hx1/0ot/80yMO7D/npGv+DpANpg8jV75y+RWXXb2lmCsO7c5bRzdbtDBJxCjJ5cHFMwXZBpR5wtmThaSrwullMRx73P5bX3c9Gr37Y5+5y1NfeouH/sR7P/IfqQv5nc1kmrI0GFVUpJpIF/OVsglnbAQ91y2S7swcgdlzsDWajMlDZ8e250JtgZqjMvgYckaNlDzUXaBTX+GyqJOUptYMYgvUVWkEtyYUSGQysKZXL2phOixnVoRkSTGky+glxjqBsGcuTJkZmvM+6SjiTNV1Xb+GUmNsTdycgWNJ/5VIEl9b9irEcXHpXADnkKH09CQ0wgeIdLWi0jxtpkEeKRpaA+Yjlxkz1dO5tVELRgXnWBKShTKSN5AGtQpMSY4L+FUZFReI0sSF8h1NpESRpeQgwISUXQtZQIhYLMEobmbTtB3jN/7gD+jFw6W16XbferPD99/U6+c+t/5O/LFHHnHE/v3f1MD/fWcattcpIhJOJ+Uohqk0q5vaYNoRQ2ZgYYoIxksw/S466MLUFS8GaAzJUKTDBpEU0RSBMk5KFjLFQNkj3KOLmFRJYfIhD9cC0mQaOwWlCLNcF4WUzBJeMRi8JNveELPApHMtc/KBHlywwqzJvXU5NNvuMvVA2kglycnSuZJ5nHkVNvVhfYbT0ZGry0YaJay5eUkOKSGVFHGVIbF88jOfedaLfu6HH/TQ177htZ//z0/ga5fW9rXb9X3p/Z3vfd/nvvSVz335K6eduT5fEbUnPfIRr3zBTz778Y+7613uev6550z7Dqxdv/Zz6aWXvvjXXp+D5kfKGOZETwKGrBbNojSVIiGSIIhqQTRoJtHobfQaM1kHy04t18DZks+y0A3VGRarTZ5BkUuRda1rQbN6zaVZV+Q2sRs5Z9IrG6/izZd9GG3vjMNgBPUwyxKpczGvtiJ8WIPkpE4iJPWqyI36jg2bgmALOnO8TYimCASdHEyc4pDV7dEzF0cmMXDLIEl8KHtJjOq+8bGOojAQesZ8WUaLWXVE1WWfHmrYFcyKoGKFvCwXnH3aheefeeKxB6QtxArWOs+2SznhiKOffM+7nHfK+uUV5zr64P4ihNUpdh6YFywhg1CVkPSy5GaUG5171o3PPev0E47LnDqIkUQxMyoOZNzuhtd5zRPu/wO3uvFpxx1lKpTJDfr1zjjlyT9615c+7oEnHLe/1u51bAt2WznhmKNvcv7ZNzz3DJ1s8HhdYQJDnnPKCb/8uPvf/sLrHX/kNUw484Rj7nmHb33p4+773d96vZucf8aZJx2bSbuDOIjovb/7Vr/w2Hvd8oLzOCn2rp2p0aj7fdctf+6xDzjppBNgEmgLdgYRWVIhUael6Da9Z0/xp//Yj/z4D3wXh1RaAhy5b3Oz8896wf3u/tz73uvG55/NcsWWzO2CuZpf/+xTb3qtM6mShYM2s4wSUYfHKScdd+H5Z9EoBsosuVuXTmSk3PlmF/78g+91s/POOrgzUcFWCud67A9+508+4j63uv61bnLe2cfuPyqH5PBMdy3bqW2LDW5BGH1kmeZRRx5xk/POusm5ZxKkGPBASqLg8Mrq4re96XmvevIDv/9WNyEI/yPyT/nh7/nFRzzw+P1H6WJcFhDhw48/6gD5cP1zz9jfWlXhw4+05cD+6cLzz77p+Wcf3E+SBlWxkJOPOOqXH/egR33fd5x/2kmHlaf+Jx19xM2udebPPORe9/+B71bTVQ3DpsjUsNmJUqma+NB5EW7+ucRMsd0Mb4eyXA0sSwpXwCXJK6FtLinNmjZbSt/KdlZPlZxsXW5Ky6wJLiPJdWlwJ5ZcYLBemRKdhRIHI2hYeoNrMNe6l0hJZ1yPddfG9hHRuwzXzj1g0Tzp6KMvPPcsQnrw4BTkuEgZYgGD/tgdb3PTa531nTe54ObXulbZs4Z7T/WIHGefcvxNzz/jBmef1uok3ARAFZnajz1yesWjHvCE7/+ua51y4v6pUj1VIUPuebtvfdVTH3qDa5+9tQiLrOuABr3eWaff9Lyzzz7pBC1G88U1BaZZF8ghPTgd+fyH3fPp9/7ea59+0hE7E/auE4488G3XOfcXHnbfB3/Xt2vtvfhMq4MjqXeHjmA3oTQGo1zvzFNuct7ZZx5/vBIJh4OGj4W+B0TkOqefdNPzzjj31JO6lcVi2BjFjz7mwEse+4Afvd23fn0nRy/f/gbn/+pjfuzsk44HsJlaSglMMTgbLjiDU5x57TNOFZ2gE/ETgdW4z11v/bz7f/91zzx1p6048Nuk65x28k/e5weedY8foP7Ual/bSEIimfj4jjWVZqcPVgNQUizt9BNOefqP3vWsrx2QeGhZ4C4xNbvhuaff5HwmgKNo81w826zmLWBDzjn1xJXV55wqkEI+1U2dpqf92D0f/r3fce7Jx5sqgKP379z82ue84MH3eOaP3f2ma6ieeel2e0hHeJjb6ccfT9dfeM7pFaXFPsNGGWCSGbnZ7NzovLMZIMcdcVAz0wZsFt9qH9x8tpAmNEXaUBnSI9gW6uwlfWSfLbZmQ2oNsSWXCFpTESVN+OVp39exf5SdIc2lmbbJwvjYMbYeW4neshuVJ2IUbr2UxZjRunv21ECpUSZI44pWHBk0Zhm57fCU2sCY1CYhoKCGu93qxjc5/6wLr3X27W9y42QQVciUw7DVXLi5nWclFMVQ2y1ucsHLfvz+d/sfUko1Pfuk4+57p1u+/An3v+7ZZ2aWEQGNM08+lmDe8KwziKECplpNtCIL7RSmAt+L1Jte+5xjDu5XkW+/2XXX6CFq6iVjs+Smjw3SVCAD6GopgKQ2KTc457Sbnn/mKcetvxcTIlE9NDKGOI7Yv0OP3Oj8Mw5sSvZtLGPp4llOPPboX3rC/e5+25udeuz6b4kBnHzMkXe4yfVe+eMPOu34Y/hx32ZKKSt2dFjDDc45/cLzzjzjxOOZilIL1FzKCSee8AuPfcD33uJCBgKHsJx67FF3u9VNX/aE+33frW5EEp536ok+kKGpevKJRz/ye29/s2ud/cDvvP3RRx6RhiFIUVQbjNbaxcQgqwe7n3rc0Tc576zrn3UqHSd94XI/Ru6G//i973Lb61+LIQOAyZZBtZnJCFhJNB/NduvOrDsuDaBoEWI0LLyGNStT5faDq/5wCNMCcgEOpW2dBPJ0fhqWNqVuUtRDJEuxXDf5io46N8sWVJAkEGjDVG2yXuqCqbu5Y5j4JLOAe6k2czOAOmIh7lyLB3KRxOp8aubR52Weh3uiiBcEbzLpuqWijzJ6Lu4R/JQ1k0TdwpdkFVM0+bL2Tw+qad4tHSB0kkygZdQ6CpLOT7eUSk0VlftTnZmWRCOFRpWsTVTZi4VqS5lpYdpwhStjoWSpqEWZJ4rxP0mJ4JRwMUMYQQBTNj9qEmZT1QozauJwqEitf/EP4Bkn3gAAEABJREFU/3zRF9eH7ti7vu2mNzni4IG922/iZe9f0F7FAQeOPIoZljf/Z8uB/RsK3C599q3SuQNIL0qvBP0UQBfCG47RWSzWNiYgmguhA3gGmNOC/TIJOtQsauvSek6STYWZp3FMiJcVWLA1ShWI9EwfQckiMolNKgoDGnTSUoSpISwEVrNsFAz+xjqFSYLDkexCR8Cd9IhFhYrSKTHgUQaKrNPxHDqP4h7AkggJKVE0csnVmRDWZxYqlb1yL7K84bff/qOPevRv/sHbd7c8m+O4Y4+7yx2+/Tk//uNv+6Wf/91Xvux3fu3lr33xCx/94IcePPbEzHzZa193z0c/7l6Pfvz7P/h+ADv79t/qJje+/S1vce/vv+tLnvLYX/+ll771F1786Ic+/FrnnsvWw+Xtf/on73zvBzzGKOkCuMkCGqTSeUiflGqrpBiIpWuJUKFDyDzmyQlDjQrTPltctuk8MYYQCANRF5mXdUe+jqGZBCn30oJEkxDuHAcWz2WNrNS5YOahYG9ylTQXW6x0tVE0q6ilCB26MnmAy5KOsk5SvNVRLXXNEhXRcghMpRmqMs0NGandJBA+MmaWyFVl8ApVFws6DzRRh2Ervg3dhhGDIehMtykqYZJQpI5si1TSC6wDZP++nRc/8h6vetr9H3OvO4exT5U+ORc/YuT9Vje59q897f5vefaD3/bsh77okfc6uLM/k/rNXSIjcyCtMe1mRvS+KfUnfuxHfuXxD7r3HW6NAK8Ap5YqmNDLcCTOOeW4n3zQD7z+aY96/ZMf+7onP+b1T33EK5/6oO+/0y03UwE6rakVUYmR3Pm23/KapzzypY+5//FHHSFJYRQp8KzuZ594zM8+6j5veNqj3vzkR735SY987ZMe8qT73O2UE4553I9+z+uf8aAH3u02XZl5Y3ZCV5tOt7jetV/xhAe86amPestTHvPWpzz6rU9/yOuf8cCH3+O7jj5m36jdaUtiNWFkLqILs7NqWq72YSC4qbr3He/42ic88i1PedRbn/nI1z/r0S9/4kPu9C0X7tspr3rqI1/9lEc+7oe+RwQxct++nZ996D1e/9QH3//Ot1QNnYa04NlPvHHBvff33v51z3woTYYnZF1QiQ8Tg2W91XUueOVjHvTapzzq1U971Gue/IhXP/FB977TrY7av3npYx/0uh9/5K2ve4EK+ZQzWNbRVFc5X++x5Ai72fWu+9onPOQNT3jgWcceSZZEOqTzSWUrxDKDZiFPP/245zz4h1779Ee8+ukPf/UzHvrqZz7i5U958N2/47YHjthQQ9IvhPGc9Oxdbn2jX3naA17yhPucdOxRTNNJ6khe/9xTX/XUh7/uGY+41tmnhohAbGhxHHPEwYd+/3e95hmPePUzH/WaZz/ytc989Gue9KiXPf7ht7rwBmK2EJXio9HzMDDx62GqdvERiR4SycamY+W6EhZ2EFTlqrjuc7jwZTB2piLUAkxsY1Zza6EtXBkUiDQvZL5SGIUifeWK8OKkGe5j+BzCo4/DTNywDRkDWOnB5ZAnFO+SngDtgiiH3/DaZ7/qSQ959ZMecIPzT01WjWrMZcK8Kbe48fXe+OOPfNGD7nvEwSNHkTBwVKp6wcN+6Pavf8YDXvKoHz36iCMqtHUGXCBjlNjs0wd9x+3e9KSHv/FpD33LMx7ytmc86s1PefTTfuT7zz7hWFh6taUp51GUTT3i+fe/z2ue9IiH3+0OUjSkOrGQTGMOGJoiYVPdufsdvuWNz3zEW5/12F9/5mN//VmPfcuzHvOKxz74Nte7XhGQzwMpiXV9RsCiF26ZWAhUI0V/+oF3f93jH/LAO9ye0KasXyG7mpAE0KnoT9//+17/5Ac8+kfu3BOjIitM3WIcvznwlLt//2ue+JA3Pe0Rb37aw1/3xIe85CH3vtapJx7azgB2Nty5YHgZy8TYecEDfvB1j3/g/e7y7c6EK8YOfGEGLEXueOsLf+VpD3/NUx/1xqc++s1PfsybfvxRd73ZTc88/thXPvbBr3vSI88+7VRuJ1ZcE5LFxcZaZKiQRSvaSM3yHRfe6NVPf+hrnvnI1zz50c++3z0mndBxwlEHf+nx93ztMx7wPbe60OE9c66IGqaoOWjRrz71/i9+1I+29CkzBaNg3/6dH7vLd7zqyQ96w1Me9KanPOh1T3zwLz76Abe/+Y2OOvrgG572kNc97SEP/qHvYiYWi+p+z2+/5Ruf+KBfe/SPbWQjoRaiIxADEmedc9rLnvLwX3nGw297w2vXIF2Cx/t18uHa6Ys0GiPM7dlSBBgR88i+kk4Nq6uga0OUDBtdZ8oULaPYUGUlLM2yClRZjC8IcgIepr2WPoGXZFeeTRDM3k5WaQT2DIUxjLnBaCJ05ei2pIRAU4sIbcsSi+mc1sf33u4mb3jGw97wlEced+DIpJ4SQ7hLxjLKQunOgaM5tJsMOevUE5/+4B98/bMe+ebnPJbl9c9+zK8942GPu8ddTj7uWMFqiSfU8wHfeds3PvGRv/zwB+zYakERY05WmrRngw2UDlnkk5+9+Mrd3YM77RZ7v5ol1bQxY8A0LMLWNEaTJdIVvaI3xDH7973oEfd6/VMfcK/vuPmgtoCoiCZDwNVvdv3zXv2Mh/zak+5/zsnHYow+fBn0GIXp8UcfeM59fvBNP/7Itz75kW96xsNe/YyH/8xj7n/qySdsud4D+zabPaeTJG4VP//Ye732SQ+473feDlktJ+ZUGymepxx19HPv/0OvfvoDXv/sh7zxOY96zTMe9cwH3OOsk0554Pfe4bVPfcgT7v69gXAdPqFO5ce+83avffzDfvDW32pVswizYQgzFfkRg45SVanios5Eces3PvGBL3rIDx/k4aA7hiXLogfb5sUPv8cbnv6I1z/nMW94xqNudva1ZS7akT6YSSVchYCKg/zKKaMNqUPJmoLK08dKPGJHnVSsrHhyZCSJBtWVXVYQ9JZEpkKNW/mNgYvInqzQgsmhnds8mWmAajGzoM5FXIsMVB8ts4VINxrSCxbzEfxvwDOAURU0qXCnBVZzqfFRR1oMkPC5pVsnSytrfA6DiNqqkM85FlDEukgXbrO0m0bRVccl0bvHGJpSgmsC1DXJ/5wqSxVmNZo4vMTggmFkJc1IZsYsSKYXwuUjMZKHat32MebujkgGpRpQQK/YhELOm4dJNA+qAvpv1LpMtkw1WlGjPpKx9OwDPnz83p/9VTD+cM11u1veinhd8+EbfvvCl77ie0LOPfNMkW942DfYMbFvmth3RJBmqrzt1F36DF88fWYCYx3CpdOlfcWjmJQGYTeOoWrBW3j0wKwyT9LVeNTk0L5str0tw0SwacmdCG/YGqgWWsewZay/abSUwW+oSMDMNWPAi0UpXTCndKw61XSj7V64CJjveSZ5Bf1gZZhKJRM8JUYmMkgfOq+GkpLgGmDkXVmMjg5Y8r/KaJqVGycaloh0yMWX7T7thT//opf94qWXXEyLTzjljKc/4cd//9WvfMmzn3Hvu333hTe8wbXPO+d651/7wMEj/+Qv//zqr659Lr3ssosv/vKVV1zmy8whR+zfd/QRB3nDkpB9+/ddcP65j7vnD7z1F170wHvdS2tj/bJ76MWv/rWF/cVEGTmCABnuxJkxlDk5CwyRxd0SMEdZ1LIUFIPq2msUpvk1mky9ihRWZ2pw9YixyW3DjMFmEIyKleVI/kd8CM9wUtPHCO9dtvO0LPsid7wxonIWSdkUxv1K5dBMAfXDoF+smdYyVBZLqakKdlaLvbDQnqUPJla42SjMEswESA+xkIY9H7I/va5JD8SEKBTOqF8jrdMcRmZjbNEvsaqdiJ7hijHZ0jQtkhas0niXVFRntCQdZ8iAEFnOWWT/EQeuf9bpNzz9DH7BooTPERLCA5iADBHhQWUjVB3DcwmCvLa7LajBOE+KUbKvjxxjzuxMg57H7z9wwWmnXXjGGdc+6aQjatFlHmsh90QBnvKZ5LjcpAiTXTm0wxJb5EiIWJv4eGifxWknHrj+tU683vmnH3Pc0UwCRqp6nXtdIh0cytwow2Udk6iG44/df4NzTr7huadf64zTNgd2gIQmbRnCtXiuuT6BDShcuWZXV5NJUSVtxKDIA5v91zv99Bued/YZp5xYakldSi7T4rUnGd+78/Q2wIQ2ebSeNU0ZVKhdC3cY5GVTacEIxwLwGXpOUc1rhs5V+z6vB3Du6cfc4JwzrnX26UccuWnqm+1S5q6RUhFTWQqfLYrH0MG9gjMAJZlQYnDXT4NVRFPNTUYh8ZSO7ja8dXrYXMqAuMSxRx684IwzLzj9nDNPPG6ncefiDJpsmc27IkxZ0kqu7g8heDQjJs0NYkeGxRCa1qWKTgySoD1Oj46DtZ513AnXOZ2STzntyIP7jRcoxAGyaSs5J+bBY38hO+hILTPaFupKzUuVZjnl3gG5onB9rEplKkcnsYUPQTIoN5FtAFhSFgX1nYOZq3qt2tbFwqhPeLIHhNpxBCRFQiLRgSGSrDAs1EfUy5RSNIScLZAahWZ61gCchvJJpw3TrENi5noqpOZ6qDKBCn/SMmpsNciDKCamuoIGLWjNJgnu2bEwFnVWukRGzX0H23lnHX/9c0+54IwTj97f6CkzBrfwGrCOKWITQYRzYRpnOFL1NEfhQWtLFNg+JQq7U8Hc39rZp554/fPOuP65Z5x83LGl1QHGly6LqvOMTHE0WMPhvmomgtWZRgM1E+ksCkLfWhV+h9gaSaZTmIWCKCYAEVpdFWN39/IrriphJx559IXnnXzj8087/eTj67520ZVXvfcT6+8m3PjcU2kPh3C4o3Kd6xJL+rzMW58HVrcx2GQoIjc7ZcXh/FPPPuPEtm/q5lvbLnWbLVbtGLU60CKrgB4YalSkZEw578uFqQ9JGh23b3PB6afy26fjjzxuUposyyxz6AhZF87hEKEyWU2aoLiQKiqqUsRVlhjzCHYa0LF/2jnvtFOvc+app596zL79WggUMIaPTuKAGIAxbCHSg/QKgunrJKPn0tFdakpDkmW5ILpkUp2iVjdiCmS6h2dPMBRRCzYqrUMG8UiY5LSB7VBE0vdIlBBGW2OQc0BV1Jp8LYo2SbGImJfBOBTESlgh6yrWKyFcj7dVBuGQqWCiIoy7LRMGdZbUOorMJbm+mVij54FZ/FB0bgUhXowPNxZIhAEVCuFCj+3InFNmZDQvVM9L307bzjxrQWLFMUcfuP7Zp9z4rFMvOOXko6YDmYzblWBChYQ2MekL2FGgVVWzEJ5hnMs6NfKdjJ2QEvnX//JB7hJvcd1zj923QzgNkmppymSDhrCcA/OwwWUOruIKiJbQ5qNg4SxRQLWr2WapdbVT6A7XrhgSmevEJQXY3Y5LL9+lU44/4ogLzjnzgmudfcrxR23Uv/DFL37oM18AcJPzT7NGpgKL0HNr/lClOWWNT7Ql6xy2dBs88eKM4469wbmnXfvck2Fxmp0AABAASURBVI478shNhy7S5xpL1aGmg+lkXucvWkyry7QIiZTQgHlY72BkZPFUBw0uIPtaYhPJnAxEAiQtx0IsdLOx88867obnnnT6CSeUtkObfGSwzNC+5kY+Py3SQb/0ZDqoBCSTZLMxhBg1XyZxW6MfBsILXf+bUvcJKuMmeh/uVCLJD7Y7mvRaFq0hIshV9hbLkowcLvFlEc5rkrW60Z3KJ7NLKfQueGU0cNrwoosY40N0Qd1aGVNLqaBDBojYQDWXymlTgKZAEdmoVtD/FEAse7UkghJTcKOzbnu1JTtAuSEKmmuozGKqIGBLoodBqjCDeMgysKgstbgVGStS2pmglqgQ0wxbfAXa18YlVkRDECIOJINRATKrV4Qa+WPZXHRgA86v5sp6GssuwygmIyQ/9LGPvft972Xt4aKqt77Z+jdGD3/8xl8//aWLV3cAZ518EmjZNz7yG+kpOOLAfnbcXn3loSu+GiXQ3G0QDD7mZcoKmiOengTDU2G1SNlABCTJ8M6OpEgwbYCdurgrcV6zzZoE2eqZC3FJTeEFODIIpgaRcgRy3XQbPCWHcnPqfaC7Lkkl0rvngKWAzc4JJFy1S6E7+8IsS8JODZspSDixIRBVETXRBg5jjXZgmMCC0ZucHqtAc2ujta7GCMCQL3zxyoc85Sf+4E/+lPl+3759D7r3ff78NS+///fd+ZijjxKRr6N+1aHdZ7/4pR//1Kciok3Tscce+23fdqsf/J7vudud73yDG1542umnH9i3D///l4sePHjwKQ/+sWc//vE7e61kxSv+228QD8t1eqtpRQGGFbbEKochRMkooqHJncUydd9sebiWym6hxG8AMG+WjA/hPTVMCS2lqCKx9LHkLpENZX5uw419jChQrmqYZxmi/LLCufK5dZMgWpKmXMeoEPUSejxzKzLzhh+qC6OtrK6TRcuCjaOyO0y7RIyhfZRQlVZghXIFUkoaw1gAzssXT+uQKJEaUXVhTqjqqhAbtTEyVXKjfaNJOyKFj9PcLbvZnGU4Z8BMBURowxReXBAWCi/Fq0E5C3MtH1GwgdmJqZHaFepBVy8TYkoqYZ1zrpoOLV52vBgsUx1AKroQuU6m0SrZAvw2G96bRFH6pVcaFpKkhJgMxWws62cmCp4YckQkCwYljGAlESUVVaCj1+zNg0CDI4kBE0jNKIw5NTGjctym8RXh4b6AIJsTG5Um7AFOaVaC6yTDgDgCdKFgiC1SYQ0qSMnQ5HIXSZ+LWKQy76cDoTHUmfSYKmvkDqIOyBKaLglqyFfARqlZFSUpLBRLSIeLzkZ1oaAjWGAuslohBJm7qTRJUU4uY5iPmkmDZCLRrBMv5SetWdLRI5lP2uDapjVaY6dmDGOOHe4BHUWiJYs3fgIBtFSuELOX7hIDyBTVWo0/WUuQKyWpUCJHCpeeaKwKwwihNpITkh1oKScPphThStRT2ezM3K7Wm7qAfqPvnBan87/sQ90LoACKeMuZk4XVwaLo4i7BVoWlsH84NCSDezp6ClOJjagOpVLBZoxgYccCBhIxpkjKNwrfKxWyo/z6wUoVkNIqxHQadHZmBgxqVkytZLbQlgWpnJElEQlRYahLAT/EorkUMFS2wEyVqCXo4JAqwSXM6Fb2N4GBWCWoo/sYBGCojBJIGoyqqTaMUWbJGSKqW+NynjWd+x+bISPSYxsxCwApIpxsDGcCL0MV1YUptqTBJCmEPCJu0cnIEoMaCmmkEs4FGBCBsluFFliBqFILqaC6MuhNRUru5Q4110ZKhIQVpoYYAiaWYVUKKfSS3/qze/3sL/3FB9+/HTNUIFjSP/2VS5/8K29bhh+xb3PHm94g+RXVwhZBm3T/vuQqkpokYHDxIUcHQqzXOpoGQMZmlxywubcRjXqoiqirOWGM1IW6uayQcR9TCxXKFIlJxtT5XgamjpLSAG0SJRfR4fyGtPYwZOb6WpMZSIesQoWY5BBOG0wm3rNvxecJfVJICuHtRh1ptmPkcOaaBFgASChWN5Yc2rP0qEvUgZSaPEiYQcB4YQUpTGwiEjSnqNBNQIyEU/vDsmqs6wFnFJillVB2DjGEiIuIWUmeMqiuhVYnCVWYfiiA+9rt4stCNBeTmAqnLzYUDl5CMUOsWxlmqlCX1VyO80SCym3pEKV0nZwZmxKM/nHkguJUNV2CD0bYkYBsgFIJsAf7gbKJeZDtSnmDVWtOCbNFI1kTTAS25OQkZVKbzorV9YAU0EUKTIKihtVJmJ3HjlxIzrQ29AA+9sXP/sn7Pqwid/mWG1nCKBJYBEOFyLMM8ZDB6jEkQkn1wVxYNUQDytVZ+6jum1RVS6Pt4QjllDABAuDMaxIb/efe8rv3eN7L/+aDH9mNAYMCOfpnv3DRU17569yBHX1g3x1vdn1BhosPQxSgIicJkzGQczL97PmSfUSr6t7WeowyrrYYHkSCblQ01xoEjpD2hahRzCDLBp8VIw0oodLN+Cqa9EFqRoNwmDlFZ3rQfUHMOcPEmUREhT2pbpp3I9U8Ze45GHZkrUaSmDl8DMK/wDq7ySpjwCOAUSyLgB8waA05kEVLpo3UANMf0pl/BjSKgh0laaiQnykdMghhqDpy5BgSMKooPpTutEwgiKVABEj+qGtwtg1DUxyMF/FwBKWnSWk0ahir1s5W0yrYPzIVZEo1irKEdFJCimaRtJB0dSkpqfzAHl2xKCs5pyjWyWJMfHY4h/jSkdvM3ZF9yYCmSA91z6QKfGPlQId2L7G7iaVKQgizCSOIyQfe0bvOvc5L+Apl1l6YeRns4LO0UkN2XCZKzhGypSrMQs0op/zJ3/zjoUOH8LXrvHPOOe2kk7726Zt4//x/fiLcIXraySeAkH4TQ7+hrgcOXPMlAB8nQWNpwnS4EGs6bpj5gs6C7FUx1WzMCYJBjiwh1Gdj7DSPOmCR6rPlXLCtsRiV9iGjVwRIm1BEAl7RN1j3qGxzUk+5FkUNLvyzx7KMvnU+NPEB8ai+rgs6g2lgybGLvpUxkuG1kogODtPQSmb4JpyaFVMrNqlskMWRi3DhlZRRZUwkSeZgnrRQ1YbYJIeNduUV4/HP+5l/ff/6N9FPOP74X/mZn37qQ35s2tlJyP8Nwff9+8f+42MfZeXNbnzj17zkxX/36296488892ee9LgXP+3Hf+cXX/jWF//0/p2Jrf8vRe2e333Hxzzg/pyYrb/6lrf+8Tv+XpCowXhssvKJdg3xxWbYLFzZipWwaYEtQFdPIf96GMMaFtVkB3WnF3b0JIcJcqtmdXKxbZclkWoOIdEH0qskeVsnFHZqyYQ7TbJTYJJgpthK+CTERApcnBOmZg/hQXGR7NZHWRy9U97qLlt1GV2VcRbcpWw9+dWAgsd2E6/pBtSGTZMiht5AiYAs3cYoSVWxUCcvFboRMWEEMeAxqZsmyqRt0+qmhdKOpHkYw+auC5MHlWtggpSWYPT7zoj9szIqOediQbmEiqt/WWM66wSjR5ig1kwAel+8q406lVL2Tbox5WMYb0ou6xjCJE0JXpEtk3lfSEMUF90ll5KXqm42m2lnp5YCyb1duUdNSWKiZbvItjPlYFfIWiE0nsFhdUFh8l1Npw7OyuCMJiijWVKZqmLFmdo6YozAvCYeClq24pplJ3d2fLMvSi1TCtc4BptxyhRsVSnLuMIwD8aojEJCLZwDPlL7wiPeKoMwr8gkZWhOFbREoNRtZki1bpXxDgErNM2kCIiCQioVhe9GdkfRZcLW0pfQZVZfRi7OZ3IzdqijSBWmBts6+KWe7xu6M+9kTttWxk6RqZLqpWxRtkm8C6RY1VrELChSO5Rco0+JZkmJocM1bCQp3nSTGxmChWQeItxD1CkLYW2zmjfJGhAPYFUStYDbh8DCM3pdF2u6KkxGOIuIGOGuWFRDLEWcscXRgmxL7PjYEZ9UyE4YAbAElGgURWEg0j2ZW5Eto4ObV6ITwaXQ0UcZUXyNKHiWsBK1pZYxZOxqzE3mEttCKDGza6pVGMmJNR46+BmtadVmvpGlWdfJtDRQCUm+lQKpbFf6PpGcODMLsBNtp2/aMglBMMTOHNN2acuSORwuCHNCODHpSaQOdweEBmHZ4FDJQ+yW25pD04bXnnWYiA6UJafBDTZXOcaRTvDJ5mKHLIdkyrAgCgavgypVoU8w6Dah+FGoNiGFDBVAC9X1SLpixjLnMqSDI8j9Kr0MX0OsijRTTrMWE2hyhlQGzmTLPswF6UuOJSS8JMLVGSgYunE3WVBHEsRDCz76hS9/5iuX/fir33qfF/7y0371t57+K7/12Je+/j7P+6UP/+cXiundb3OzM086QVx2eu4EWpNWysba5OsmXgxpAQicvrfqOi0qs1BljG3GIqtmJl55BGQW06U1r5YR2mPFAmKLoFcBIAnns401eQdK6iakKEKdvETNkmUzio2WMolUXUQYYz4A7MGn7tldszeNqgmyvBjH6Ga/CtGI6CPXrR/nqpCS63tiCibR4loYFoXpdcrcJF2ntRZHW4ECnERoLRXJKeYeI6ECQ4LFxADaCOmZ7qqYtE2j2VZl19E781NYSpotTWlCikLNISEMJrrGKXJeEEMlMzGEdAqhrzAAw3pJI1Vl4cKc6t1iyJxcxyoF0ZDOoILkqLat2GrsZi6YuHqVotY8pj4w5vQu1AJSiJ9p3RSrO1PuK32qPRimzrEkaFjTgkKqcaaeI9KRodkrKMBH9sGkGwZtaApuJCwzxYO8jYyOyAy5+IqrPvKFi/70ne996qveetnVuzc665Sb3OC8RMGYgsnTUyh5ODEjmEUgQlSHamZto5VRmW4kQYhFA7oQiiiF3YBcD+BQZXhzXqomrpq4erv7iYu+fNEllz3+lW+534te/ozXvO1Zv/rrj3nJG+/xE6/8xEUXcyW/73fc+uzjTzJSSuGTphVFhWt4po8uZFqENqEDGcLVBGbDtCdhQ1ukUtNEG9JGVmJiWD0R6TNoM3LJsZXFAUl6shAO9gAyGZouGDSHxw3dcrUKBODiyXejUUKWDFbxtgyZFvJRVELphOxNBlbpsXAmrtSe6257HtphnmLBhJdWkRXs6EOHU4MYgg6WCBlkpcHKsDVuZlVnwHahNsYsPzB74UgFNEHGeeXU6QRl+HAsWZl0orccm0xbNMa02OSFGvbwMRjObrkoC7yVZZrG1NZdsdAkDa1d6qFA72K7xZjXaEny6VpOTkL0xBwryySTZNUFot1Bp0AIW8qC2NJ+0RBQYA8Op3HMa1GorYAIR9+ms1FAt2m69+4jktZoL8pTo1pphT7N0Fg0ggiJAKQS446fQikS5macf9mblExGOv2bAXZHbq/e/au//3v8D9e1r3vBEXvfXfwPdd/Q7ee+uP6WER/GH3/8id/QgG+y08FjrxF7+ZVX2mqopCAsUIfVZTKvRjRT3VoI11VBYsh2F3/3rvf/1K+99r5Pefo9HvmkezzqKfd80tOf+LJOR318AAAQAElEQVRXvvG3f/ud//K+Sy+/WnmytMyq0KbMcbOSxWQqiwn5YBLM7gjyTU0DXCOL8krobBgqAiWXQ7g4eQICrUwiUelYgZgV/kcSCn03QgbIhjbYh+phOP2Wpql1gJOhiku6xCKrP8NANdiRzPchv/CWt73nve8GcJ3zr/O6l/zct97o+sHp+Pn/Uf7l/e8/XPew+9zr5te/jtYaQriE/Z2k+5+MOjwEqg+4+/ff5TvuxI88wT7rJS/72/f8SwbBhjrhpSBqJCIGq6pkYFHRzLDsVUeTRFD7giyaVtTYCxUrcJgzRzqEQGSX4tbQVrQGconsUEgJI2XBUBiF3UCeiiVzZ+EWYPG6BemPNFJ5uMeAD3OOwh5Wpq4YioXDco19pWKLxxIYQ0YOlK4tmkrrrstMrwmTjo6E+KAzBABdTi5hTTpchjzFTUdpY9oZ2HFMnCJrMteFQ0Oa1VaImtNzIE4knkTSUB1Rxtb6Yt1tFZtcv4HMbumaQghTlEYPQscnr4X1Q5gMXDrVllz7QjUhXKYWzdlG6FKcB7IUjtdaRLlxR7TQklQq1s6D+dtI2FHVSW0JMVHOpoOLtBlUuOqUYUJn+b5MHpYlF/g8j4y5lqWURcdiy1LHsNpHzaiiq6tRNAyuyAgZoknXR4JZ2TGCDYO6LYRSZvWZq/QEF24gsRFvElWFYxco13qHaYDGqC89RheEKq2pdaEKZhZcAFCCjiHKqUF8QwdkUIfGqcKzBxOZIibthIm2e8QcvmBxQRq9MSy8JFVET+HpOneLoIkUU2rPhliS5AgxES0ESVLMrWTlaQZELVIdjFnv3nsfvqigGinfXG0Y1ljnciYxBKB9ynZk9rB5MAYU8HUWOpQcg0IksZLTeEfrkl4V2VbZluyFoEbXmAU9aI3BLCuyMeBMOQwR6KlbLV0lKK2UJkY2WyLJsVTVqCUE0WefZ8LqnL7Trekd9FhZQKwkzZQSEnOQZEXBWQoNAJRNWcSN2I4YPM2jihiuuTS9jSEemRIhVKJ68IylucpcE0CKOLgZJ+oiCRGgiBRRU62qkrVnHWrNcipMGiOTyCT1QAilEBF6ALxV89ThhdAmmzKGkYjIkmpRlDJLukUXH5KhLowAoFqaZIAoMKZ0ZJWoU0xVSqlDSg/P7MBQYBUBphfFgHs6BfnQwGFzcwzMXUeIiRYqtE7CXaiYprAXBA4sSGdJdV8lChpGBenMCFh0pCwSvQjJnakCFcaiSuzbaS98+H0fcKfbnHDkgY994eK3v/MDf/DO9//9hz92+e586jFHPOHud7rv99zaJhiJWqVWapoA/R8RndPVhAm1zq5wtgQ4f6ENQcOStiXYXKJrZDDZkIsiZhChDIuooKd564MCOmSEMmTRaZFyX7igkuzJrX44ClYKNBNV43h+FNfw1Rnpk/Q9uEEo1LS1xocrKqaUb+rrU1JC05nRPGwx8eZoi9gWZRYqhxTAIOpqIbwBuyE8xoylKwcZ/UmtaU7Gwl0lNcqR1N0QZJYl750xxsUhk6EtqgEE96Sz54jiUVPTfJ7mXrepncJAzIK1Sr8ImtmmNT4MIoacYhmD38suNIGIJ4MZAs0QgrMweJhra0pL4VjoFKqjymC3xZlKkj6w1EbHFYkUd9PgswQVYCRhQAIiJqUKT4RVs/RM7lbrjk81V1ozApkXU+kTpPDQRsaEWeheEJllU+hQStsmtr6eXsSTjQlZvfk773jX3Z/98if+yts+9eVLjti38/AfvPO+qSkvlNIZmmGDhyAauTpRYKXIVLzCG4JaZmaA29UB2kTzI4TOFihdTDtZK8EPAtOsxMN7HDw4vfAR97rPHW957MH9H/ncl/7wne9/+z+//x/+7eNXzf3M449+5j2/78e+6w5Vi8CzLlmcYBC/kpSeIzJG0PeihqJek/PFgOw2GwxmhdG2ecLS1m0RN3W2MgAdweU70ukX8dVBM7Cb5lkhJRt88mgDqiC/MoNUGigukut/nFqYZPkYJxbGTRkQiag+VDK5YmlFCsZQriZJegW1ZxNYmwrRpabTBoCQ6iIZWC9KNnf4olgMCQEVcCtpTHWk5BgygORM4jFUF9qsQqdvYpq8GmfvCi8Jo/Sl0GJ2ZHcOWTQ6T5oGp+50Pi1h9k5oGVrDNDldQEKUk/Q+ctBmwjFF1rEUeGgignQJzLVupWyHL2PrEQposomGklg9yuB8ApAF8wBhWocakqGzqo9k0BFlQ8XqsjAfYihTqUWVohZB0sxMyuaLiopwl6FEw4ZOWRsBsWzmFaGMK7q8RLI5gy/wNYQNxNszB23KT33q0//xsY+vEH/t53Y3uaGIfO3TN/H+H59b/zk+A+OU4475JoZ9w11PPHL/4b5fvPhSCfKoCKlFd1uX6sJUxAza6HiUOdEXj/6hT110vyc/6+FPe/ob3/rmv/+nf3r3+9//rvd94B/e/Z7f+u3f/Kmf//kHP/YJd/+xh93vCc/55bf81nv+/WMkJLby63/wp//+0U8zQhUkPNakFLBISQNTzqylCyfRqUhDEu+WyX1KQc/IcBGUjZU2ibZIiFltExdIes09fPGcbQzjPqr76KNvZyxLkWVTclIVNB/8woyRaOj74JxeusRSur/v3//tLb/720Tg2ONO+JmnPem8M05LfviflEu++tXDLaedcNz/otvhPv/PVxF54kMecPTRR7Ppkou/+IinPu2P/+Zvner2sWyd2YBx0bCR4JZRzSXLWKbuO722pSlhyjRRbhmcm6aEjqRdxVMCGJojIjwytJR1WavkIwMIwTFCEBKU2PVwpA1yXcfcZMtcukmoZ/RYPObIXckZ2bMgJpVNkaImo2FbbCSGu/nQ4K0sYdE5caF7RpG5pVMpXfeuPX3hHOnu6xt4pRSpEjyWtFnKrvJBBDdedYFSzhJC9TJniSW8O8WnwQhZV1/T6SRhxoQ/lNmBswsjzWlOVnqCRRK5SLCHQFQAjdS+8MEVJxGps5WZkd9FPBE+MPgtweJELV06mKAzlKlFCppoQ1XUWkrbQKbMjWdZzLiLGETGZ7jT5qymZZCkTEPiG7WpTlY3HKvG56C+SdFRaMrQEBsGL+G2JqxRmEzXlgbCK2J0QUAckD3ta7PKn1JYuRCOvkXQuk514WDUqNA/vdhW+eCbaTMVa3Yy8BKk0e29i3eU0Ik7MpgNmsYRzsa1Q0aiQ1jJQToUIeCLxUIfozPHjSK5I7ZRZY9YF41uMgQw6siRIpQaNnXbWyaXdbSl0OoyezDAooDL6JRhGcNJcBsyGXcb0ca8mXvpsYyxpX2ZolImmsz3dJPeaq+lT47qoili1NVDfIyQMUjXAXLPF35ID0BCZO0IDOQWNK0KUJJHZ9EuSAiVgXHp1AVgVBTVwtpBOSN08KGGyopNQQE1CA4g1uwJGqwR1Vm7pJJ4GMLWtKBjXZVMYckg+XOBdljMLJKzhrdkuKYWD3MACo2qo2AIIU3WsCq56ilt2vqyS+NCVwdFsv8SKdQmuEaNOTDogcUqRwo4Dhyfyc6Sk3TTSKUDas/qqTpEHclprFf04nsYcUKuWjrYErxHVokGMVVqVkObcmQMySVlINkXI3XxunhjGSFDM9oCcym1ytR8YmiMhf+FJ2eboFUEEDeQ8AuW0TNnSjQ6BMwiKAz3nppkrRRSUCAW0XKbsjtkTiaNkdTR3EEtVytFgTKNstNJBRmDC3ChE527kDKJbgR1zyUex0z7HnW373nrMx770kfe6x63v8X33epm97nDrV708Hv+yjMe/gPfcYt9JJm2MhVsEIUQR89l27dbLAFu56SERgXJPAOuDltEg0QalfVFx4TZqOAi27nNBHaomKDJqiTR9qDeGAuDb2YUggYEM1068Z8WT+9dZRZaZhZoWS13EDug+lC6GqVzXZqLhNaGdbkrCWJUJ9QWYnMkOUGmckaFSA4wHdPd3VZNO9KZpJNvfaCPusKaRj/62m9JrDRTbLXwPsGoWRVPSSpOe5XkXQYNhACBNe/sIraSzPNuEQqkyogU56qaDUmJi9LDC+YRy4iZQlFNp6rTVKapVZkQxtTlVLAslYKJU87J3pWE9nBgTRVVCygNJrmxsc+GxOjKHC6hIhipTu8UcWGTailmRaj5gjGEGZ2qANQMUiHsqTVVoXClm5OBPw1GiC1gm2xEyN6oRj65VamNDHOKWRYfmUNoUtDqGlMjVRi/pxx7VC3GcpsLznvRQ+994XnnFM5oIUbmgu6VEKzgqM/qYYpWtapn7aPlrDIYgINdIE41Ezk8eq85FMEm+ojVKLWg0I4YZEscd+zBx93rrm977iNf8tAfvse3f8vdbnXTe3/nrX/+Efd+/RMfdfdb3WKjxqHIKBIqPLe7AEqsACLJhbh2rHiTBdo9Zh/8Hsc9W+o+CAMgaaZ1oFtwaBLMEMlIDUyJCaR+JHSELa50ABQoQ0sSYIR40C56pZYsGuvEKmwPG6sYArp+tBSmolxlmmSpQzadK4KrBj3QVJTSYCr0WmU3IJi7aNQgZzoZZpzO1N10Jmu1Ma4cImHFrfaYaCN9Sp9NMcrab20KMEaEUFY0BF2wJgwYP9VQG0N9rjo4fs2VAgg8k4ivuKUxuVWj57kGLSVDMokRMjM8F34MSpdUSHAZ9oJAjbHJ1ZlrZkeXDHKB0HWVNTyURjRiYzZKztVducL2wOFLkYSG99RCS5RJarViDT5lL5liprU1bc2tejQPDQzO0FMX2mfNtCjIduNbqSqTauEoKIUmVjqPSqbHhFZ8x3wf+oZe/9N3fcB9sM+RJ5zK1wNHHHXD61/Am2+29DEu+synOGozTUcdcQD/H1zHH33UYalf/PJlmE2XIls6fOq9cGFkU0BSdCS42mQMfuf4oCc97Z3veddgxlWdWqNu+486ljelFCHFer/oS1/6x3e+8+d/9dX3ecTjb3H3e/zA4x777Bf97HNf8uLLL7ssuR54l4X+taZg1kgmQoZKchZAKpT+ZPgFkIydRRnVnjqbDSWfktUp4H0YrIJ0SmG89CELqZruzJfpS0akmrCHMUQzWJ9mOjG5ZRE6f9kRcmj+xde/7tChQyLyyPve87rnnEnx+J9c7HPp5VewcT+N3dnHm/9COem4Yx98jx+lKI69+tChp77gBf/4vvcvyzIvg2RnaJmEMFFuU2h5JqoPQ6oK3IgaYmgQQ3WuB/PI6GkKlPSanTnQtZDHY1gPLjlFUMuai9dIYjCs0WjSiYjoDA02J9dKqUghm+dCGSKU6CWlcsKYFmlDaiS/rSoOugTSUzoHo5krJ+H8aZESmR6RMEAr34ZSja3IzNG0DZCwoJqwLCFcFlpKsSFG5/teDuhLjgEwl/fuPLrNS2T21CVKHxpjR7CT2BmYXBuKSkkQKw/OLRmVYY1eOAbUETyHED+qrCVtgjLIUYfbiPQC54ascF2QGMjZBJMUyrQ0IQxLRW6kTllsyZpbVtaa0yaV605uh8+QbZEZ2Oz42AAAEABJREFUxD+HMv0qlD5qxSYrBjpk1LEYNaeBVqK0RFWUiDKSOiWEsqDCQQAESb3MMwzc+1A5raU2kyZpiVgQIStGQi9O4P7AOI1Dumfnit9dOKeiNJGW3Hi6JgAb3Fs4WaAwEWPuJDCly6YbIRREahRgymxpyhepoiKciCWTczUZGx37Jt0pKDIke299nmrUTdVW6GidhkyxNjpiG0niCcEk4pNyKczS07dYZp1DRoo73KODPM4hpJS6mFiIODUJk1G0Q3LQtSGH+SJ0dN8ZfScXEy743QfINLOoFBA5d6oVmpIQtnQMFzLWiZ4ZLfea3TKN6cm4oktwGRkoJc1mGbsx6EErWSq0CnWfG3oiRyCEKCJBZvosDMkaMkFbMy0TpFJ4kirFsugI53/LUp1jzMfoPHT0rXuWrMWJHpXWKK21MhlVXZZc1j8tmIk5NEh0D3WPdLcSUM2kXTQGSa6Q1qmeDsYLvRgDS5c+r9MMjKSiQqOpNB3gWqS2JGzBSi8ZAgEkGTYwHTK2OncNSAmZ3FQMWshGIUyyzhFZJYqmRa6QBm1aIIvUhefQUDiCodpRFqE+VDRccuZwIYPSEEHhw5rXJpNxZPg2vbM2pVkxKbRa0yvdHipcP2lf+FjJQX9idmxprAmKtQVtjs1C5mUdUsJEpK8jyVRUjKZzM57ghrgG+UH8MI4+YufWN7nuk+773c99wPc94V53vc3Nb3L8scfSoJ3R2qjsMgtmTidLxBIYKzxShPoTOlglWBohIyyiUGktxSqmadi0Koz0pKOEz0p4R3RTGNm2ZC4aWWCt9TbNNi1qmaYIZGfj2pm9y0ZVuMeQrTD7hBMxGialI4ezH8aKJYdZdp5ckIyJJcuc0hdZDtnYGmQzMUckrHMHKkvaAEFNEoiuGTOo+pAmkZShSQ+ql+LGASlw7QhhTXLuLKRJWAmi6QU90gd/AqDezFl1eMzGU9kItcxJxmQEQRVNstCcaJ3Jzt374qP7wsm6lFltEaLn2sQKdNQcKmzDeqUEohdf6WkZmipd6RIEfG1jx0hwRJhj6jpt0XZTdh2LpVNimHWLgQFEiruMHpy7r04NKTllKWOzLLrkyBi6zuBKOnOG9R41U4f4Ij3ChWYYK/boQLhNSt3Dfy65LWVp011v+21/9/PP/esXPumFD77HDc49NQlwSdf02rO4iIJZAIWhRPNiNVNEitCW4bYdG1oKM0yezJd1KbLLuedFt8GtNp+EmUckPYK+CdU+5SqVsBXL4485+o43ufFT73m35zzw+x7zI9916wuve9yR+2QVnXAIXQdMSV1mYJGKUkA1lJlHzGtGCZHV21l62ILS2SxahtUB68miw5IgVoIqNW2TYPQXRSutZBGKHlLH+ptNNRtEE4FcIhmo8I1ltZp7bCAO0jh9Uctm0aA0GSiRHNJrMnCRsmb9KKpmIlW0CmOrWuEdjwZzFYeXhUUyqlNCoQRKZgfj1CJ8DAgxhegeaUiDBqgAHImR0JHKGSVTIIKCqMK+BtgQOl0X1RCQOdQ7C4H3cBdCGKLZKuNGkcbxLdl/A6d8ng9MICkFua/6TovG9XGSpcqgMqTcqpEl9mlMTWg9clXGUYZugIlWZqVy3E3Jvs48BNNFJtdJeQFU2VwsVLwai7as3W0Z1HP1RdOmWTFMgkaRLoy1dFGYKJchZCQSllWjJWoU4WeqINJNhg3IQiCZvwoRIQuWfN/7118lMitXXLz+ZYYzTjrh7JP/K/9A5eJLL+N2lniedPrZhXmBd/+ny9f/UNWll3wlhzLPaldalV6DtqXQWLodYLRz/49XvPrNX/j8Z6nFDS+4/ite8Px//L3f+tc/+8P3/+5b3/P23/2NX33FS3/iuQ+85z3OOue8I444kszofbn0yxd94F/fzxz23g+8/4cf9tgX/NJr/uHdH/jCJZcS5mLFUhD8j3AHIxq8Dc0ABtkAseJSqU7KyOh0goaQK5q8RgpnKEULa6gPVpaFpLJQjgiJ69ARI2IrsebjKgCXBHVXElnzb//l3fw6CMBNbnzhD975O3nzvyickoUdJuqtwpv/QhHNH73rXa5/3esdHnvV1Vf/5MtfedFXLhXwP1LUM+Z1lXCu6gKnEauVq/JrFHkkt9+7IBr0SidKqWkltFEc8aMQtAj1kK3rQk6j1hAJSBcdbFYpU7adUTdRVdSZlLRFK33CUNcYDWEK5hUOKUSUgjNBJsSoA6QglSSGsiYPw6ammXJLI/MUsyywpRavDMod8eqwrpZ7EU85EXDGYGrlaNXC+UEDrYsBdFDkWHML72nfuq/ta8W8rm5MJJnBFlPVtTdfSomSUYJiwVTgDGKWVJIXMii5lCEb1JItzKJCKmPa2YG9k+1QExUZwpywCpLK1LmXr+EirgBcc3Tz3tJLZmr2KYZ4SE916AjtQwMr49gIpGSFbrzZbMGd1kLuBs0LOim5qDdUcWq352yBSlL98IG++sL4wZbRBuOORAYEVMLEajDp0Q1Q5h7mfmtQcllKHdIWyqMK8CLByjUaeGeWKZayCa85MsGzzML3xdvwMlB8zVUmyX4GKezsBhThbKoKE+q8i7IbQtYB3Pfuw2jYpvYuFlmbtVqLNu1aFtRQwZqVIxKJolbNoJy6LyOWrhlSRxTeLQMRKbSQIGrLplb4zHIUGSp0x6rq8AHuFmTFzzJJCOQoGjwv1cLBVJdaqmmhqvw8QFw571oQqRGmIdPsO0sSoXRTcDkQHvJsMMiobVooYwKpEBYCMiUNXBo1A8JjPQSoo42gKm4+eOI2qHDlblNwyWi6LrIqA+jqKT4Q6721rEKxfcTi4CFkkbakruwOUTEhzEvJJaU7gCRuDunS+IoMY9w1jWkiYssqXYhsA1/DEVEgynFL+uxj9sU5zXDxrsHN6XCnJDq+1NAyUuBBABNco1qUmsKtYevOhYxTQ/gf7Yq6FaWJVJIKEBLLUrKYi/BziaB5MKCwlCI7whNDcEqKjz1tgj6YEvsck3Eu6emJWPsbOTntLLV10IuukuRNbNowyegS7GljKb61pKYyJBO9cF8so4gWU/pbS0/rQ3uvIzer2ML5xhDMaTNKhKzUo6mC0EB09M4nYlgUzjUkOSskQ6ekd4m8+pxjpoJrLZDQJKeraWqRLDpkUnC7os266agGkQJyVSm9gO6FMHu4GuHOTrLQDh4jCp3tptq00GGVAVmF+3qpJFuUPiyiNuUuZx1NddE9fORwIkw3BTOULLUesmlJhq3rAnUhr5a+jW1nR8tBd0qPDbTWVidS0C2l+mSd2yYjAErl0oBCL9Ky0Ra3wdRasm5yYko0oz1hUWzslNhH7dZhElmaS2HXJXxBDgk1NO0NacJpqwMmsMKYJQlgiZSWrYxJfIMQMjqx7XH1Mm8HumPJGCZS0YrtC90sYO4BIEjCqAjFqBGkOvuFp8bw3mePhV6hlpxBRQI2pAptyXWSYZ5B9ehnihcXGSvrSiy2HCJMfcEIW+grzYzuuWXFABCUJV7MVfkp4ZAlOGPMfA2hUzgFfQqISankAuF3T09xNVU5eKAe3Nmp5ENmoQ1Aj9jGGLXLZuWOmRV6xgCngkJB4JycOZIuaI4q5q3Nm82Ypl5lIDGqdWZys2QOksUQddE6VLmBbkY5NNI5RxOUKtFsEDpQ04jw3iNH8FwxSUomwyLWqCNY9FdRp7Emhy2DKJuieloXJtc1l3Dh3/RamfoHJUIqtPLFQJV1JLUouQPdSYaOY30aDOniUedUUoQBRiQpl1YWzSZi/FwMdYKZW4ZB0sqgfREy5hpD6cwybVvttZA96BO8EfyUUCXegkH/yoCvHbMrlXVx0Ji6QwplmkZoDmJB6hBWagQCKYCWkIZUyWCn4Tl7Ds0oriWLoniUOW1BTWWnnITMGa2FlaG1gxCNzUQ2hwxBUqCk0TwNTEM3a1IksrKKx1zVa+WQDLAGzVA2yRWkq0irtRYRUK+ItYNKbNbJnBYiXI1mGIPAotS+MTAwp9jX+qZ2CXBt6DMYP1QBEiHLApmhs1vMZkvj1GBmBpGoGCpLYhZELDqGDoSwEj4oIvr6zjQVKzscOQBIlZxw6Xb77vet/5ih7T+oVgDc/KY33dk03nyz5YtfueSqQ+u/yL/emTzBf7Ojv6H+Rx+5/u+H2PWKKy5LWYEpwgdcWRhzaNJRhk86M7iYny7+6tX/+N4PsPNpp53+mp/9qW//tpvv37fjmS5aW7veuWd/121v+dSHPfBPXv3y//aKX3rBM552+mlnsPPXy2c+9fE3vOlN93nM4773oQ9/0HN/6i2//0cf/tTH55hVQ9UVW8NWhac/aCkmO5NOnDTVx7DYLcmNU6CpgqAK0iGR5LZMFfQ1SWNAWkNjMt5JN8ze53msaStDcwws3ZlXFg3HVy67+qWvecs8z2bloff4kZ3pf+egxNW7W1xzyTXv3+xbyoH9Ow++54/UlePr4P/46H+85FdfVc12WtUhTO2LYluNCcuy7ETZmcXm2NPddEZdsgYDgwEzFRh3XtwvKRNFavIB0yJVpBhUBamSIqZSAYF4rtEIq7AiRdihupQ5yyyaXG/QhquMmAY3s6kMGCJHoesCEF3gJEeYxmRcmlDWOCyQHbON6U7wQeMGKN1s1zBD4WvmJbaiTbB3FUcNhdTYTJSy0By3gZqNqoXTAG+IDVqRqdII3ZQ6GQfbnEYFhmTXutRpVp251Zi3rUeFiYREIGBeuRYUDRooVcDtckh1lEytyQPLmLA0+JRkQ0FMIk3X4UvvsTtyHsm0UpETstDenstcvAdoUGylz5N7cU1Xzawd1UWhOlK5sMacS89t57W1zjWTq3btvW3D5pQx1vlcNh2EiaxmaoraR99yTfe5JxJW3GrQBYYhWAIIaiINtVlTZZZlGkIGigy1Qd3VWpXCrQIrYjBYsabvSaTaZFOzwggJdIqft3xuPBIxwjuwTUVmRdQkNfa8Q64YpFALrFtxdYEj3OatLQtioR9gASKEpG7J6KsmRVkfTtqImHKzsunL1OcCd4wF3t3cq6MOqgJ6yKRPlpxiMZtDMhoJgmaiAOfo4kDSRs0sQs+BS+pgHYTxOcG4WjSumsJ+Iea1SlUzL9ywSiYfT8lmJkMmY5BnJGG0zEb0NDKGLjn1thmVRIN2aanVzAS0diu+dUSXMtCcUAUX5p4l1LhrN+0p26Fj0VxEkgoLalKPiDHHvBjoOs3CGNXZJ0Aa4TcNlCBsroW0hMl6T3QooqUUACIoCmVL1Sjs3LASrIjulLL+5iUnGaqDvFYzLoPOzgYm6whl1xKCkd1l66VnpbFogKaLdLc+sHh0SGhCfU3nlaADlobByIiQ4Zw1Q0fawg91DtpPf4xQD+qWOlGwexGfDNzK5FS9NW+ONktbigxTXqsvaE1kDi1bUWfQLkAXDdkEO3BCYXoTGkB1OpsGRkd3FppS1l/vNXKSkq1kVfq8iDchqwcBg0NmzdEyaans2RSvQo4AABAASURBVLUSc3eKpfU0NyHRUtwVWsFvLRokZ/W+BraIuKqrdOHWky7G5ESds4hAakG23EK4ww4HQrSoaSk6FTEReqpERihJM9ed0EmjCtkdjNJMHdAI0LJKrokqlipLo66SpKdsPHYy9hVrtZhBZBEwqrqMgS10V2TL9CycKpiqxKhh+BqrCST1Fd/SXnRtVVszRgDdRzFSaq2bTS2t6lRRGnQHZYNWYUoGDMmRCc5oYmXRMrN6ghavEaaKqdGZMnlfQ9awVcxgANiAEHJCLUzv+0qbpDZRnkkIfhhclAmjjFmjOx+jTM2stc2mbBqN6CNySZpU5wpsAkUCqtxgcTLsXTSxAMXZGxvkZoApngguyDnHnBJtatOOcXaNzHShaRuVZglBgOAwl9FT6UhqE7UaSski3Towl/DW1eaatCxFuBewLRmcpUlyRF8nmTu2CDqvlMVaOi2sUqoqE584BetgRi6cuckKJhBEUZNQTAYz9ujhS8wkgQmqZsNqk449og1ggFYSA8sRI2NrwifpqEVUmB9kakVVhZ2yQFMsiZRlEqKuOSZlJg0Jg1TO7WSLyig1qY51J1K5tb5bxzbKMurCBABJ8aT1BLUpTC1FU4V32YJHQmySSWI4lqVmTkVWqnDQEBlqOkzJ3C18N+dZ5tQuuhIloTNyZigJvaTDW/dJsjCuJ9KfmKsrOBey6ZiYAQYRFn7tKwMa9KE5SnrhDKzJRMCHYAQV8bF0+gFKr2MxLrlde5RBv2fHGOkaIcKZ+UMpo2FBidHCa9IJ86qcjFBxq7luGgoHA31dwT0ATIgmi0XPZFJKIAnIENqd1rxOwmVAmc9BNehMydAMKhcz9pwmwmlQkTRWkp0ogCXBbUCbfX0eaD10Hjb76ofF1ddZQjkxfCJgTB/CIcwUJJsNyiKXW7KOVCMquhjVZ4/hPsDZsdBGKwF1R/DM5ZEAeegwZ+YidSiTugzWclz4nsISi7EymVmFghS+mhLESTl2KMkQRv/gg5/86Ha7C8Bq89FJxJvf6AZgdOGbvr70pS/u7h1UTjnh2G968Dc2oJke2Pv39FfuHpqlRwvYttTdNrkwYoXxJ6ZZa2qRq69avvylL1HwzW5wwcH9+3nzfysCJC1VO/u0U3j/pS+t/8DmwJHH3O+e97rjbW9zxJHX/JrZl7/8pb/6m7962gt+5u6PeOw9n/CkF77hTf/w4X/fbnsiyQizYHSbarGYyjDSO224sTEVbilZMCYPmUWWIgz6ygHVuWx4I3dk1VbGytwhoEIWxvwVC+g4Jwe0d/2Vt/zehz/yEep/62+9+a1udiFv/tdFBFceWt16YB+fqXC+/3X3/1XrHW9x85ve8IZf7/En7/ibv3n/v2hNaI5gaKy6DnOSHGCyUZrQnZysFtpSSP3wiYHMYFLtCnZoyBKSmmHJfDsmdOIhERQyqvZii9dYinKdBTbC1KhDrJunepijpFB0CVJcQwl25xNAIWILciTXHGcCcYJKuGEazJzDEGKZVJGLRBU+Eg7ASQF6iEpVS1YyAxixY4Ou0UGFhKliCAMHmUK9IUrlwUlTjL7DVNVqy9ISwjmKjCKRJYdI2uBeRLAG83Cu5SqUAWXtXOsoxaHZqyZpUV1lCeFhAV5BWehOGSOLSwsxUYNthNxJH92ZxBCW4Ay2wpgYoKrGdNGz9CCeFdGKtcaBoRlFwKVzkqHeJUJoe45MT9pVQ21hfwxZU+M2c3fIIZXRlJa0cB1j9OgjaUpkBN3f0gALq1E0QE+MpAK6as4Genm10w4F0z9yTqFmIrKRNMkMH5FLWJea2sTUKK4S7kHHLZCEaRoU8BSnoS7mo7AaKkIXFWikugsjhCU5rY/MjjrcnDUuEtrovCVi+Ihglya5kdnUu1HFqeakQ7mDnN0jAiHMGlYhAstifG8wEw0zLzXIQi9DbFGMmumJmfCnctyeHWgDuojPzUczV2pSfdSBjBHM4xJCodxrlRUAYCQNnkxKFO2mu8wnCJplxgeP1LlTI5XCLbYtNIZQcY2iLlQI4NbFqcKIsgZbrxQemYtaFKvCXW2fnDzqUA+IoKhYuHFt2qYwzNIYCBPcOnxYEP5m7ER0kTCVLAUFkJBIFajSRRMAwbpxKa2Ama42UVEOCC474lqq0JWZgEtRMEn4RJhox1BxKMkxZUwIYxdokjhmIeneyXaERk5ERZDkIlGGUGbUkpP4DrphDFZBJPemVd6JOaT78EFXO5WigjR5WnyTYZ5Ja2kuUtlRB+CariKDuboUtrsgi9A1q5O6cwr6IUrEtEBnQVcdVFWIQwaEOTkjRNLUCuPerSzUQVL3sKEsGijclIzisCHFjRUuBuKhoMtdvSsZKsoHGSNbSolJtWhFbhiA2rvlVnV4id3KB2NDhBkxi2kB14pWQVHiIU4GgNqCiqUnJC3N+iZ7W7puuQdoziVSqIaMSO8q5BnUaC5hApJqGcEA3JZRltUBjZgDZERxNYgK2C+T3ImwQY4rvYQViWxDpwFLL+qQdJOUFZXU9TNzhJAfrRIpZrbsY1CKVinETWg0UaWcntYrYgfJNVxXGRjKXBUWQ5hShsJAGAPDHNW1dAtUgH2kGswSDWirRmQCtRV2R618rYu2RcCps88h2xAqIZhr8dZqqZuiPG5OVDHpGM5DfUrHhjoFHJs5pzgs0FBT61LrQhVSXRA0upgo82NwflN+2FEp1UW8s0uv0lvJneBnojcGUYdYighgilaq2hTJAckoFnPhQQy1CRk/QO9aoEAlNbtG10HkJ1E+12RQ2eiI2XLQNcLXJXMpCIZP60N8jhirT0SFU9GRXIrJoBAFZMhI6i+cOviBFteRRktFaIpwCI0nXVJcGPM7iH0yNojCPkxdAMWkGqO3hqyHyCxzmbaWtAAiq1OoCmQ4e1fqD4WpsCssIdHBzIlY61ZtgJGSrgM5ZDhWqmaEwmvhISebs46E5qO+SIoj/yevhR2IJFXSCGYQ6cn5ByiGTU3BEG5zMsRFqIJARVUKxASZyXCmEmsU5SKy2u7CJQp9gDHI7Nh1s+iUkJIkKhNDhoD+COaJhemKWua2CdOsaNB3ZRiUvIJJ1DE0PNk/jCFXBt/mPblgJ6qyCAhCQHmPkpzD1wFYE3+4rv7ZRgRSGPq5aAyEYQ8MjjBbz2ulAEY6LtqcsgNLkHd9KCcf0L7OLAHpGdsRPDmQgSMFQqn0L+nYue7U0sW8i48SoqghdYjOmouRcs7eSMXcZG46UvuIwdVypReIDVe7IJiL5bIJkq/UvcBrY1Rfqgbp40L3uUgxiKC7LjHNqAFYDGO2H0XTHJFCiW3kZgYzwlzadkfSCPoMAfCvH1y/TuHN7mWX8PXA/gM3vv5/5R+ocOzHP/M5vqra8Sedxpv/T4rgyP37KJm78CWHFqRhKHmXIhJWRJm/JsEkVq6erxoMZ+Cogwf2bOW4//dy0Zcv/qXXvHbp3cye/ZhHPOOh93v5Tzzrz9706te++Ofu96M/ctpppx8edvXVV7/vX//1la97/f0e96Rv/+EHPOWnXvZX//z+mQ86GHo5C30rTn8k87QJQ35MwVSSpD6JIBiGRZAedV5aDDrK6xiTZwvRRAIhk0hVLzKqRIUUGVryz/7uH1731rdm+P4DB5/8sIcU08P6/C9eKezyq65mh4P7dhrpw7v/apn7uHLxr48mpC9+xa9evt0dJXNVe5FgcRJ2UVlKXWS1RwQ6FW2WGXPwOY7TXw5+Wv0EBtk0ZJqFcZJceZYpdgVjIJYou2hsWAAfydiVdDI1wTwiDBoI3Q2ImaKIc6GqBrV0jCVyBnotUGlLTnPaDA8M5IjwbVCVTKzIGnP/XOaleK8ypLkwyYhv0g0JMG7U2WWAK8EiY4Evoi6rIlPnctO81F5X+um6skhPLOzqqaK8Qvg0kHMYRUHEISkJGck+KWHC3ATxXBWTPgpXNi41g1LmoLowjJYk9jzMt9W2srPkjjO+taQpB0poU1Ndn8MwCWUyAVfJDXInbcemVpsU7giU6zGadD7DLWn0TEuUhDI5FCY+qVIUVdGqiFFb7iKbR25HH0niWhjR0XkIhpUw1cKuoQbHJPNUdpvsVlmapEJ6YB7gayQZbcrXUJ918OQzBlN3p+3hwBY2R3GToULRmUAG2pzcIzQMPjOa+LyzVtCv0IQQPKQYP3Pzgwlr5eByseQylsW3PEO13dg3M5bQmk0qRmdZlc3Gq+76WEYPngOHKuVosBnUuDedtURXXficVqxoTqKbmKbZmHl3ok8+SiQSKLCqkpkjfQgtIZ0GBkgZbqILFtgIS6rKjUrxRTFoWpcgBXvSPFop3GUJD+W9zgQGzMNZO8xB06Kk71DcFspHa8GQc7ZNSTVzaeDTSi7i0lECBnKgcE+kth3Yeg6mnUlyGig8nUTjxHRuVlaX9WzlWYRB4BJZ00puQvnkGKUvZQyiGyphxaw0+CYXK7G+NXejVW4GrTJKgm7aKyZmUkkX+kcMgeAXYVic8KwpWRbewEQaM+pSnYoZqYdce1KVMpUyVTGj15nWUsZqfEGoFZVWwyz25oFJiHUqnfQbxF2Dk5gqpCYqaKDocBkeSfEFToZlJicCN/pD+m767GV4YWDNgmVPjoAWz9D1DwTRKAUEIgIb2HjfGfMOvTIqlpqLcE/RAlMy+mKybBZWQxpCkMSt5RCCSH6DShOTghViJpleM9bQD6mCndp3pnljsyHhNpj0XBSlSptMrUEmmq2GIrkvvWFrMkNdzbON3maK0pSCVnTHYgN2oQZYr/SkA2LZRZ/HksFsFRgpVJQj2Cq7A3scBBGVsm5ZESv4g69QSWX6dC8IAm7TYtNcy0y8OxvI/5IutuiUrZYSFnMl1buMUYmW9KmGbqrwC7VNtoZSLElOq/SWwrXTP93nZR5jF7mAuYNUcvElB0Nz6zHIeSsLFSEt943YYbvartichDBkXWqWntsFC61ajQgtoA94i4QAppCcs/cAN0aRpICQQmldsI3RxzI8M5LNIwhRp/WDInak7re6gQiWWRZHMrJnadRlkaBniW8iKYeBlyILcgloQqFa+JhhZ5JW1gqsgGdjVw7p6yQ5gKiRm4SUwL6wHd+05IM3BTrBblHNJ3ebNeaJZg/Sr06jSpSEwamUySiKWkSrkldQUyo7ui/L6IP6YEkgyWdXm1G7c6ly6aROgA+0RsRQnymGMibdp7rjIuwg5EUoByJTskNGFOyZrUMlLaNhGAXJSERwjqRCMg/JxTSgSLaq9CLYiFpkAlJRVKksTEKEUy+9Bbc1uoM2YV/q1EtFakEp7JNIgpQawsh1n+mpHINTUGcq0QyFYCh3t+hj5LzkoFRCkKJUSYa2WatPhh0FvWggr5XPN30i1pUr315Rr9MW0+6YhgsSQi6MdI8FVDNLrMOnkrZaPCikQTbqOzImJI+7Sj2Fw0RTQY6AYz0jaXCBNhqiKlx0GB2qaRRv3C8E6YQM29OZ4EYkZZx1AAAQAElEQVQJsdQqXopDwRY0BpIv6vQE3QqQFYmYszNFQL0qSeri4Z3LVM0Q1u9phAZlLIJRwz4yBILV+AQZvwC9ydjJhccLiu7iQxfHMJlNu+oAJLMqqokCHuGq5JCCSg4VmWFz1tFJX0MSEXCGhTqU2csqKdPhaJ4iNIRdhIKoAscqyVsDRkOQFLYoJ15xZyaB58z1yhyCmtVgSEmA5KGRId2YnYoYG1N65m4ydBn5+cF/X//el9YpOCFwrXPOOeOkE/Bfur7w5a9wXCl2rdNP5c3/F4XmnHny+heKv/zVy67e3aoQliI01vlDnOnRaXWps16+dOmqD4AzzjqHr/+zEpmv+63f/cIXv8gOd/uuO3/fd3x7UqzqUUccecubXfjMhz/4L9746r9825uf8tjH3vLm33rwwH61smx3v/DFL/3Wf//9hz3+yTf/3rs/4QUv/qt//pfLr7yKcKeri6IkF1JRZhUL1sbqFyoPeoR5tDsXV0WCERoIyaRfVSAWWeAkKxdgpiek+PD5+b/8yqX3Yvb0Rzz0Wmd+Q9gempfP7/1hg6OPOGKzaZz3v1Yy89d+879/+F/3TrNfE/Gxj3/8zb/9R2PVl+Ei3Akx7mQIS7oCIgFVMTNmLSm+0l5HMMGl0FH006hgJrSydhOYpIpAuBzJKqS4kq4EMBVMBjEg6VWGET0KT4ZUYRRgjRGA4yBVjf/xHlZVp9Y2a87IjAhnRgk+aMASufU85MmwDQtNWB+lJ30lguROM7dY11EBQDPK3mvSJ+puNrR02PCQweXAhLufoMw+EGtMFmYkGcW41NOHa2gzBMG2OrJSK6nyxUsu+fyXLxGN0lDENbnkLQMUGYxe4Ursux6dCgb9XtagpenrDoVm9EiuSEFucrkKW1EFrRBqwyWSSJEyPKbUlBpEksiHaqbSEuJErQKOMcg1YQ+tXbnzgLB7qollqWOq3N0Os11wYYjAEGbWSjAkhDSetOyztgM+nANEFzMXgsiOHXt+5SttoaLZo+yBglB0ojXasi1joTuJMzcT7BZIEc4fjr5En9O7iq1ebEYF1yafnFCyny4MEK3GqQXUhFN6h48YBCZywPkuQYU2NMEstI6mMUGKFJWKQN/OMdgRKoSE6noG1hVtTJZVy1DtBkANIiMzQvlVxQhLz9pjWrjdIIhpEiKJxNBwA4T/qaaWdHI1qV1RfuaM1a1Q/bAuq7CV+sJck/D04J2kKEx4FaSwS/MVZeXU2YFcu2XoAkWqjKhEKnIwfUvhfJAqrhhhMSoTSFoN0+DEjtaT0DWVVtnNo5LV6iDRDaIK9jNiWjrE0BuZrxChuqjIZkHJhuHJne9YSucXv15j0ELw4puPDKJugxYkbRnOj9z6DQFR6Eq3pDPAGLRRwQQ9iU2wmqDxkORUxn1KE4p1czEvptYmbTXJ4VUvSsviUUY3Z6QkiVZdhdamgnITtLUGLIoMJYAGIodYHH3U8FaWpInpMgShrujaF8tQsUroVGmKQYVvpGgZzNDuRZamSy0uys8aYjMHSgqctKvsLlZk7RbSF9CaVUApAemIHgEPAT8F4dSoxVPGYAKLoI7khYLOMz4TMZcojoYsNYuiRRI7ftK2p1rJNETVsEm0sHcZmYk98FA0i0mUIqrCb0JHG2R4uHNR99hGDOVwVaAgii6lBL+Yk7p+VqBEK6NkF2q25CApRRNM49zFsID3ytkJ9NrYFV4kplKqtiYTZMe9rcGRJZGSgSSPCEyRJqnZaVSt5laYBtYnR752A9UPzxiDDsqRkVjrE2MIkQzX6JRF9JS4FLJHKYx6KfXXDiW6uYRsseqTs2F2dW7fsuieZQ64cJKBkdkpNnddt5kDPLHyiOoDdZFGH8kYyYPLiE7HcrirSA8hEPxqbkY4hD/afW+VBi9qSbSW9PTAKiHXSLEIg5oUCJLVGTTQAZbOfiOEd7myZ+0GwwqYpUYG0sW9QmrSTFt7L+48UpUlCkCXe7FQ8lUQQnzpzKZSRbEiYTnrYUw0q1IX+jTF2anIMCH7wI9Bq4DgCiaUsuqiFFAZAcj0HD1d5lR+leurRgTKfYlBphbIJMaEJNTMU50pBCa0RVc0uzKhyEjhktrpSKBl1uzFYyUOUXGUjCIJ9b24oxIuoOnB2Jzg/G6/MI86JWfv8MWwzisqARVAUiJsMLzFEFN1M19NGUkvJWBD1GHZM6mGq4ZpVhnMX0A6GI4IVtYZZRuEERr83DmZuw9Dl1iDXLSrJueAmoipJCOmDimz2ihCq0aUheoYVAMy5gooKFtCRJEGpKgrUy6lpJHGSc8UqhCRPmJ2notLRMFgUZG02k0XpYo8C5Q5hUaQPkKrDcLIxUhNqBcbraRZJi12YIHTgNGXHM5RUkPLCM2u5qUl1Veq5Jn04UidQxkv9Am3xDSY7R6cgFYJ3cpeoNwMSU4aqfQjb9FBeRPAUJYiqJoNOc2+4QElU6UBLRQkC8qWB8SmoE3gno0KgwCkhVfSeoquykNTWApF9jXRx5RLiTlHdI7g6P2JfUE/CrXuEWMGfVhyM0wOSW6x675NRm4fn/zs5wEENwqj8+b2t7wFX/8LJTI/+rF/50BTPfHYa35pih//D5fEMUes/8/Hyy+/7OrLt+C5D63w8BsMyqGe7rl0msPcs3z6oi8fnv2kY485fPP/+nrxpZf+3p/8KZtOOOHEJz/0gbz5H0uIQPXUk0988A/e9fU/9/w/fMNrfu7Zz7rLd9/15BNPKlxiMq+4/LLfevvb7/fYJ93xno948k+//C//6V+++JUvLX6owCdU6mZkRp2hQwWNWYeES6F+JMNO1zYzBA1qfZKtkVxlMNrQRFuSDoF3vOeDX/zS3iHqu+/y/Xe+U4KExv/2+oM//8vDfc4+73xTPXz/X3h970c+9pbf+HUO3NnZ4evXy6ve9LZLv3o1VJu0HWlFmFal9qzOhJWa9EWAMcRbhVZwuWUXiLpmt2SaGECmZZaQCkyQjajBUIJPlWLSXgla424gZ3b9/5H3H4CWFNW+ML5CVfc+M8wMYcg5C0iQLCIoOUkQJChRQQQxXcWAqKAECQoCAiKiAoLkIBkkB8k55zg5h7O7q2qt/6/PGXzve99796n33i/9e2r37q6uWjlV9TlnmhxTqonUA1uPIAQzL1ZsSKIMl5KoKpGFLTCLBMKOyIiSYlMQ/r0wXD9qgR+QtVxABsTLTOguWag4eSG3jijqDokl1o2OKOBQhkKQGexraBAbuVfZQyoJYcMypgXlUAWR2qhGECXqR+vHYmaADZ7k0WdfPvQXf7jx0WepIh1iA6QSAlvI4KEHuZXM5IYAinwMAYoyasaKPKa+6uxSXhk37sX335kwd0qsSAhgLSmXylwzI/UWyhrxjnYwWqtdWHBmkAYS3cEr0gx5dm+FcswuWPXkUjpvSe4lFK9MCDVGwkNEx9q1dq6zsikTaKEeZ2zjKhHj6NTADAyUigymkJJwxuwWm70sXFT6Qk3NZQSXAegyZKrIJHpHOYiXwjKE1GEkOXcRioxD8LrXhtjR1mZr2VCImDt4razUTIG669I2qW3NDKQQgTYUbmqGTCKQdHBEUcJubB9RvGopJiaAEDdDRjQyhfahMkOmMmcJGUWcB0qgxECeW9K5Dc3Jbl4mTZz52luT3vxgeoNbGEhDSMdGFUetQ6q9qZArK2orTWDIXNmgHWWeNnv2S+Mmvzx+ggVXB2mwW6gjEV6BpODWcw7uhmCgbtpW0gwU51SVPNBYbTBX05y43+asbak8sTYWySjANMhMuCCVQwPgoATwIB4KhbbE7rl7EAt4SuRdZKRcMAd9PbWBbHhfAJ8TzhBOJ8BUE1XGMFeFtOFMxWMf641i5K1DD62RQd3UHQ4bakrbNoVbllYAOnupGEJxh3EJtY6x7MQF9s1RoggQhEAFz5gT9NE9xWBs2OWaWesovVrrELhIbqykUqgx7/bf1WHlDUco0qyWzhqhOM8Ezpw8mSarjCSQB29h8FpQus99d9y4a+742zHnXfWz86+877GnnAYpGsiIFfdUo4OgyCTKgAPv0dQW9z57w5VTJEa0G2ntAOWaSl1cM0El3gOtJEBbzHLG1KKx1VAYogUUHyx4edgaO1dR6h71yLAD6YOWi5cI+guoYMiDgraoQ0xpiDsYuBEVNJAWe1NntC+/PuHNNyZ4kwKpGMxLuVUYNrt3TdtYNVWvzyNgMAZD9MgsZE5GsI1cabdMZ8ioW2HVInUda1SHkYwJ8oNLxiTUkiVPHV6rS+7lVKc2ej9Sv6YW3i7sVgolUg0UA2I7w0JIOqmJe8VNHeda3U+RyWvPsSRqE1kX0iJGJbUPZkx7/YP33584CfcaGPYFLXmiBNgikSoINgtjJIJ74VQYFHidg1rNpEJMWTC6BDIoJRaOhWBmJTdNm/rO/Z5gRivmUTnW0SrOkor1LTecKFgIqlqJuKhpBUuPXgLY6nxRW4gAYbCTvZETXKBnbU2DIiamjmhJRNBNHxHTYOuaHcpSdtWWGZMbh5AVQs9qg5r7mW2wEqijcq5b4kZTUiKIjtjYcLTpnUmTXnzvg3cnTpZCPcTK0sXrpkjfoJpMmkAHIUkoMaIDe3JqzUsK0kAv2CfvIaiMIB6pihdZMUSinvEIwmYKVRpE1LH2kcxClXAVTapc1M1NikUjBT3ORu6UWbPWDfAASWOQA7tR9CgWs0GfNTWRGtUm95L1DLgCcceHt4WaQpNnz3np3Xdef/edkCFVsiylSyhEpVA2YDDyxh1ygW0QGWvSAB8KiAaWqO3LxGmDL7w78eX3J5NFrnoSI3e+5mb9TLmrSoW5e1tHBZrjUix1NAaiCrLD8wRcRpxdWmGEFBcghBcOLX+KlJaICwWYFgKLDUFkrVUGwNBQgZFC6QtniuSwUJEcrODNaiRYHjI+JAWRieY0cdL0F98b/+7kqVmVmGtzyZSSzJGCOWyZEa+d2hAswnrZrC3cQv/Zo+eOYgjahCCHQFnZnYiBlBALA3MIzl0fEzvcS5lJlDSooBrMAvDZLFnpc+sho5WYmoA9QG5DZaEWDp0lOiyzIYJkjBJEKcIsShV7zRYJZ+8ZNE2D2QfhrX2A6TIBO4HGJhYhuFOODBtkgiWIFDIn74Ao9cSi5JhcDQyi/uhJDtoGzgyg1jj3JbRwb2KIvXKoyQIVdliEdWwZqQFHFicABpOsTKFyBnkVpQANwXAcUhBFrIzC4HnCuO7ntejDY7stNv/w8p/7njl7ztTpszBn1EKLjhqBiILL/4LGtOCYbqFiqe335yLaGByYyIhNpUCgDHFl8z6V/Pobrw5TsMJSiw9f/E/Pdz706MyZ3V/y3Wnb7YaB/0+HdSiIF1looc98apPTv/f1q84549wTj/vC7ruvuPwKzIwpEyaMu+y6vxz87SP3OeyIQ779/TMuuOSBhx8fN3EqbB8Nma5nFq04Wa7YOuMh6DZYZ0zkSjBh6VwC2veYvW7f4jJ97wAAEABJREFUnjD+qjvuOfvCSwAchrbNZp8IcA/c/O/aB5OnnXfJZRg1Yr5RO272CVz8a23uYP/Es8+bPXs2pn/r0C8vueS8H4HD7dSpU/501c0mFcOSaoHbd4toTkINCSKRk1PbENzIS9QYK9WaYOfoRhxrCzRHSEqwU3FhCp0ACjG8AeabyAxhMxdOmbsU79Csu3bgURiZFYIhG+XOgPsO10AsMucAocIWkrdtaVsrBk+Kzop/HZWgIsSgLEAJH6JIUhODBIQay21xuAbh5A4GlRmxIjO6U8nqcDUhRzzi0khGSGRCrwuRG6ghLySZAwg1RDkt2UuTc1NSq8X4kRdePOYPV8wabD6ywhIZkEneHTftyDMuPf2SG/PcFCkwB+HAiFQVd9ZijM5KFB1eUYk8Ow1+57wL9zjurHOuu1costQFSwvECtcuBqRMbcGFeSykLSNCkJCDbk/FIUkyCQgMVYZhJbVEYkm5qJAWsAUDZlh4JoJkxbwm0kimuART1GNTTSY5sxGhIkSVHToBGbkT8kZ2fLMQB1Jm9WilKlZZERJliaqRIx4zsdYhxCqEQJSNB90HmZKLp6EtOefkiMGYSVpSSBn3mDdr7tyTLr7q38784/vvT4DenRiNmOARNUTFFZOCdCstIkMpCfpw3LuDucxixEw0a/asU/94xY/OvXTijJkwNmoN2aMmB4FZYLXdIAVk8Ge1W9DCZ15x017Hn/bD8/+YcqOesTIdLAVbkJlJQhYF4OxsRTlh24IjUxAz5XzTgw/v+ePTd//ROVOn9WuXwNFUMhkXRiJQh9Sx8uC2cBkSOjG4RYbr0DhEE51iKdFchVnAG+ikSg2FmELFVBDNs0tWLjXlmlpCK2ZJ3JhBfZ1ybIuR5YrgU7kUuFTDglcExKmSAoNzMKviJAxQ3bu9bNlTQzmDCY4pBix8s5KLMFcCsjCBghchgjy7QaUwaOzUoBhBkZKCTeEk2gp4VmKD2WhTQrYQTWLJnFMG+dBtdnIvDpMigQU5O8AyzLaNrQGAJAmOMaUYzpG4IhYGh3BugmVLSSL9GFoVk0LBWAnHFXc9duDPf3/sRTf+5aGnbnj4yWsffJJy8cKUPZhpIu4zNcINM65bQ7oA9uJghQw0EZl4I61JZoZG4ZKawZcQzn998oXDzrj4unseo1K0SQRbQ29SRaAzJiFTTixuQq0BHQydmQ1yIHYj2CI4zuYG6+9cysgNBxgfkipGhj/f8/Dux5375dMvmjx1dmlMknXC6/QHZyMyguvgC+RA3uwE+DB/igxde1VgNhk2SUBSUHG0kRNeG1ZKwkyGD06eg6F2JQfTgOciOUqj5IJ/RDnkrIWcFRVPw+CbiYm8geZKZrFuZEuSnD25teyFmR38kQll4SJaXFLrv7vqpv1/cvr3zrwwwRhrlOUxA1J2BFFG/qvIQjcva2rqftNrc5VYHbjI1Kw4qlxJBdmlrjiEIf02EfrjRix3gjDEek6JCWZDQKrFQE1W6+YDknLUWHWV7gjXSDXuGSIz5ww0rVITStYMjhCleh6Qi8AJa1Es/6lDwcRq8GupOde4qh1r14yCn425IyIxMxGzszgkbxI8qgSDUMGbkxqkAT+z1vr9tun/7NKrPvfz04698M/WJOlDirVxbciQ1QCDviDqXfARMaoGC/YMuGmNGtM2o4s1UOdJFSYRBSoMXkiIxEUcfgKj7TQBRwmKLlZRNQ8Z+1eN52KlG1wRRxHGwoMxyiq3QEYihblIgWtHIyVAzg59ukFa0HSCFg22S24EzsXpr48/sfdxZ+1x7K+nz5yDeTAOQIAwnULnAkkdAcRYTJw5Bc+Vk7A44oz1EXNMbnvwyf2OP+MLP/3V3H6/Y6JQl8sGizdqrYBhKaSeSdkjCBZldYK6IW+FQfSMe4UDEDk7EYZSXazKqWpZi4oHCKMYFEiwIgjZavVuUxELb4H3tmYNBIs8Y8ZZGXIQaUwah3UrBUaRQ5EyOdu5N9+954nnfuOcS+fMbikzWfZcPBlDToYRxMVhf8r52ddf/8Y5f/zjrfeYWIwUzMGIw/NdMnPGVILNCHcGrWJahTBAEpw6IiyYBgFjVUtgTIJIICYhDhS1YsNiWjkRF+s+ljvAbEbu5KKkIDoww0ACUiEZVRx6rOowA5ChxsJRXAQ8cRaBQANZRKgjAcdBMuTnHc2BPLpJK56UWhiKSAistXPPYG+cicmEXYm6EyiAwRMBpTAeQdeRcsVeF8H40mH2OhCSGllJyaybkb0q0pNqIMZKnNjM3YDXR4j2tFatJs+aYRgKwENtkSWXXXmZ/1aMDvX9o6c5g/2Zc+Zg9GrLL+OO7/+qtuAiSwyDnjJjemOlSdZ6KRHy5lbYKuKqk16ek19+tftPXRZffMkFxowZnvI/PT/5zLPol1jv9KlPEIwHN/9uc0iSeMEFF9h0g3V/dMRXLj3jFxf/6pd777rr2EXnETZ+/Ad/e/Tx0845d/9vfHvnA760w8HfOub086+79a6XX3kntdmkwF6KI1wQahGuG6YCGwilqkgjF2WErr5xfujZ57973AmPP/UUyJl//vmXW+of+s2fGbPm/OS0M98f1/2l6R0+/ek1V/73fuyN/t3jkhtuee657s+mbbLhRp/bdqs1Vlnp78Pd/cq//OWDSVMzPEvhToXgFlVyTaTZgxSVZN5mR4QgU3b4ZgLr3lrJRsROnhUuBps2kpYABDLJ3FJpJGcWQwHfwu2zSi5B225Tocbc1ptsRUyDwxncMAugMNDFCFlQmpzbpin9Qkm0c4uaOTIhLCtE3FOuCytslB1+zzUAZQvI6DIIpAzndSJyE3CS3FMuZkJKqM1BNYsbgnptgiCoFDQKR2rI+snbNharjLQIJ0UuTgXOruOnTT31suuwSvnmHluvv+aqDWvf9E+3PXjbEy9feucjjzz/OpkAXIqV15XEWkLlUpELaKTOIEWElEHUUHPytpYSgwgjQKS6kyPBpnKF2OpChDK1V0pw62YH8cCGoaLM3TwpTAgwoVhgIjSSLhi25OCa2EmjdyUmFcWaoXXEDZZS1BMQm6OM7eMBp4oykiQCFwUUiFEIouPoRaERDilUBUgBHlwEyTH0sa3HnXsq9yqNiM6Us2XSXAWPkS0AAyRJ5gxKNVRMQWAErpkfe/rVS+98+NbHnr3ojr+xVQL5CCtrjW3X0mOPaqoNIwQYWanb0utb7BP3zZKbQHxBy0PPPX3VfY/d+uhz19z7CAyYchObFJPDNjMDPwlTJAqKD4bXmnFHw0fNVCkkZJBCdjIIzoWZCpXsbdchmqnyFKj1bhXhNjyRDKGcXCRJbAnWwSwkAMxcWsFKvkCMasgqHsgRnokNcDvdEIvC6juaWIzVWUqkUnESKzB7dmI86BgH7565ZIPwOgDeSmkd8nUumEhUjEvpGoh38shtpU2U1MG0XmnVkhXz5NZkaQuoBmiIt6r7oR6U2qXWIYaYFPaoMHzcm8AIxDmKR/LIWcCJOKu5cgkgmjmbt43lvlgKbBHy5qbxfvaUmZKjKMmeGy8NeSYTk6qgkogdgIq8R1S5S0lijXDSUCRAxSbZQ4aIXYQC+UDJI8jg273wwIuv/eKq26bOmjOiCluv99HdNtvgE2uuwqWSJmrLQOeptdx0eivECcYvSpk1uCJzaEdK8WSUzTC6R6lmVAmxFRpUn9rM/c0Ndz3w/GsnXXpTmj3brWV2ht5Ah3tgASzCPTGET912bYmCGBSEqiRShIiSebFkjPgCZbAnheFnszb6UET8u+UQmVPOuXjDBPE0QGelGBXq4BAGwvQ0mxpwMj6VgFR14dat8ezSGtZSit0SgHAq0iEtHvHEhAGFnWBEbuwGQVLFVnlQyKW0gbIW7wwhC1kmKlaa3DbFwXOu2NUpcWmNyJ0cZ4IINQs0XJXE3C/RstLfD+iMYuaedTJyBe/uWZKxeWEvHRsGTDASTGJyTxkLI0qgJKHE5J54TYRm6rlmr5kj5eCdWNwhKhAhDSnCOCgW8QH1kcW1iJOSsHKuJQcZcisl1RK0ETAbyasg2LNXJsVggeIN4pBCsAjqDg6Bo2olVWUhFAP17lFYOzpLhxz2KgJL0SBETCSgwUPWUDRwDgRjNyuEj1kHcfgDzVPuiFflgVhhy08rUENZgBymQWSErBnN4fvkmTtVSXAKmYMZSzJNBoAQtOOhWyNuAogERjjUSXpto1CZCrm6BUKgz06kxBH/qsgVw8WEux6WihCKOwEUzsRJObtwCQj3hgVdW3K25FKYC84F7AszzTvYMTYbuXmkVmNmbFjHSFI5Q8BSxYxYRSGDEFgTfIdK5aoiHwJgKg7f89Ypayh1AFsAmJrOPNyZRFRZhUytDZZqoTpqL9BA8EA0JHciEs7RKRICUC2uJLlUTakkxrrSTmuF4DKdNMw1ExukykbGngJBRCrInp3ZCBNHiwoyUsEg6jAQDstuiZpiZkUhVcRWyNSVOUaGOP23N9x+z3OvnHvj3e+PnwIBo9onh08UEygRGoVc3Uio9FIaaEstgRTJEZzBaCAVNomZ6sQ6xDWRRg7KNeO9SSUKvYbkmA53Irwjr9oscBeQI0ZRq1D1GGWEV+CNvagYg2qyVKB/K7iOKlWlQ1YPMUW84a+IIhOrCg58s0sxb6lfuAkwgM6+YIgRdkQNzEe64ZUxLBx9eHmsDiVwACfQvjVcihP+KUttMCyqFKRxBdDG3Cdp2GC7no3NiJNo0piiNIqYCw/XRiQTMRl/MGFqLkYfHp9c56Og8sO7f+572syZk6dNx5wlxi6E839dG/3h65o5/aYwzIzIWZm6hAHtS5+DifusOe1zr3cLleWXWmJED+nuf0nRK+90P/y26MILL/ovUM40etR866310WO/cdjtF5x98ZmnH7LPXquv/tEqRig7413h5EnPPfPUHy699N9+duzOhxy04ef2P+LoE/94ydUvvfBmO7vfCT+6x44FGBCSYW2MDmoNVrfWqquPGrPAMN2LLDBmiYX/94J9d9z4w3907P0P3k/uyy+3/DcO2u9fVuir77x/9gUXAPuY0WO+ffCBAwO9ZZZdDrd/b+PGj7/tzntzzm2/ZJTpMH8RxuHdkKyErMNMFOBpJfVTmVts0EqigHBlMRBHKt65lzt8lrpkRcU0W+WmzKzRQxT4Z3RmIx5yNpdSiLPBf0OlqiiSq9AGccRlFC/RUgUv4cJqRkm8UcmY6bD4htiZNJagCbDctC2AK1QFipFUzEKb1YboT0TFg1tFOQpTVK5dBlwxrlKXCiGWcwhozBSsBMPMDMqQ5gfE6irUWldVD1B/edm1b0+c+pmPf2zbT2ziVJcUwfZCY+YT5jqEupbGmmSQAayZn3ju5d/dcPf5N909Y7BJRQroaC2AZYNJFCJadIH5S3K8yh8grt0lsw8RzELC3DMKhADGlhhlUjYSjTF0eV5akuJB24B8IY5MYA17Ekc9UUA4VAHxGHRIHL7mXVEAABAASURBVMgieEpEmQgRmprMgx76qo1bKpb6XhIGFKmIq4DgZJCfELIjttRyE9xr5kefeuEP199x2c1357l9KjlZvyW8QWm9FALogtqQmVU4srLELMED+4DlniXtMY+IEJCrmpX55+vVQTB6zMiRMAViCRRqlAotQybEUC0JTkoeCOQzfCqYZdCO4QK2KdoCo0ZGFWYeMxJFT0GUIIg9kxML47blVLhBmuGecE3MkEcHvfswFQg4gllXYFBiAf7gGJ1hd9Syw5gkubdmwYW7Sd3HBHbC2Yu3zBaoYh/wEnGTAwKXcUeGgAQiR8AWAqEiJKJBRcylNW6R4lBfuKOuLyRFwatXU6b3f3/H/effcvtzb72dYYMdAHJCsgIcDC3ZWy+G4UIxSICPkAGHE7ICxpFRAGAuhZMpbArklM7AnaWIZ9JSQmbOUrK0LRsNHdw9IC4B3BigFwIuajuYhcwQfZm18zHURELFC+jCRCdmVlHHbSExEQqsRABbDMUaFfKMsS2JWxU8qodglSatikBowsW4SaFv1KIUBuXiOiRlEUAO0n2Fxv2WR55qUsYq5fzvHfrzr+5/9AF7fG7zTdR70eWFt9767S13nnfrnTPTbItSqmCh4hCriurKZECtHmirEa0MmAQpxINZmmz4B+4oukmlcTibLDBygAjSFIqKYsm1XPXQw7+97bZbH+7etDAsSbJRKe5cJFKIJHAKlmJVy4iABOaCSvQqWlSD3nKSfsM5YQJ9eHQjMC2URq3EAo1YaJPCA4NRKCwtxJmSWyZyNYkNj8isEIxUwigMUBZYkCQMVy+emMBUSeCtjlxX6gHlDltm7HHULUVjCN8Qn+rWYzaUTRBrFmlxRa1rdjIyZjDOOWJS5W3NTV0RCTdOg0JNzW1UaBErEPfAg00Lbuafb4BjnTJ58X7T/Pnee39z2x13P/lUANGwg1SFVrr630wLQUhe+hC5qbeRG1UYUSSC2ScPjcWO6xKh0xrhnIt26DIFiBgNErbS4yJuoDZ7Ll1FnKUEygF35EmrUiO9UE2phjRgrFq7aGFrLCMGZA4unGCsDNqJcK4dWyKtaOLAzgEO09oQ2hCco7kWYXg3iWgUETfOmXNx57bSNkJNzF4FWIuy0IeHRzfNYEh6LuiGyw6JuHtuRKYG3CwwRq+VlERbsQS3BnGe2FLwJmhWMUgoNY6NUCYjGIdmEXaozaUkTom4UMVRPErueRtAGRF6Wy+AqGQGsVeqzJhccYFJeUZWKhzEgjoROBSKpAoeXGAC5gzq5jYNEY2swvwLjC7gmJpJkyZccNMdF9z619fGf4AEF0lAk5M7CRRjTQ0Jw8hDKEFcq8iIAgBBNJiaVNoEBpmRs+CaIMaw2ymWgZ5q4+BUXDJAZYclAHAhEg9qgYVL9NaakgYp5465wgIanRjcYVwCl2xMmS1Z25aGuQ1RQ6w9DrAQa585YQZJr84xNiINJheKLYViZENUDp1KQYGWCzKzQ/bsjllMzkzKUhHHReYfzRBLD+yxJMlFk2sjnSMbFwx1HyKhoZKpCLhqnTLkGTreWd3dHEcbQh8WFwr8IIAnKD3joZDV5gPCFYytCh46iJzdixYSJBc4XSFYqrJyoFI1mQdbsiQI31w6VqwLmRqYVbUNnANEA+FAullEqQNYuqIDSZScRATGWgSGX1pxXAil2lkz7I8V/LMDHwDDfiIzgyNQg2+RbogKOYTIDLXRQJ96mYc8EpyTFGJzM+QRb4Xb7mc5I0QOrVnGyWnC+A9KKfThscNWW354+U9/T5w4sbR9TFty6aXBJC7+i5rCU4ZAz02tcXcEMthvKB5yiTgTsYSHX3g5z+1+FG2VFVeIQel/fcyaOxcPFxozaqCLt7j8V5qzDIwcuf5H1/j2oQdfe/Yv77/6srOOP+7gz++z5uqrj11oIYUrGN769iePf+/Wv/715DPP+eyXvrLmjp//wjd/8pNf/vYv9z346vsfTJs5OycjAHIiF8/VysutsMXHNxqmph45pvp3uRhsmpvueWCvr37j8aeehH0vtdQy557404UX/Bd/WSjncuIZZ80dHGTm/ffeZ/hdytKL/h/+ygKw3HT3A2kwQQ9tKgXuLxVrJGLHEoGThjbC31EvMmXvTNxJlVRc66S9wvAFmAqzGEI9cVHrXKpQXSQg9FRsdaQqiBJULJQ5lCF3iQInI3ECBPQhbWiV4EYmsOoEWPUA6gBRYaGCsADn7ru1iJWQL/DAUAjYB2vvj2zLCNMYGDCxmg3JrVB3MBV1kwwYKtGUE4Br5rpwdGbHv1hwSaDelJAryKOiPKdgEkoVraq5qujJV1675+mXFltg9H7bbRkYdsg44JR7ferTP91vj5MP3nutlZYriJu5gYyw1/TI0y+dfdVt5113++AgaGZu1bMgwL4xbuJ7U6YL8/orr5CoATtkrZIzsjoCFEKlkKvD5ztRFPBKSLoompEm2CtrojXsJakl1ca0ZNdsvUx1YmmYgKLOuQc2ozuKHAruQuairor6wj0zpxpsQSzkxRFVQFrQQpgAutiVTNDr3nJIyN969+PPnX31Lb+/6c7+3NZaycmzw8T7nnJuGQAjc1dMkQWGLLPXiPxOljg3pSQTLlIXEqBfa/mlTj90n5O+uOcB221WUKF5gU6kESrkoF0Z2EUNaQGIhQiCJhc35RbzNaqS8iZrr/jLwz93ylf22GfLDYWzsRV3I8Eo0ayepMneEHJVNIdxWmcI8z4JlkQsRQbMR3gORA24jBUSSGdnnWBNyI3ZO7Mc+gxNDb3aENClFSMxwYBMubgZE4jqiQWIlx1WZ5k7dgrSd0XILgQSYGfFvZgVPHU34s4aQtHg1dTpc8674a5fX3/702+9UXqhDcEqIR6CRAz4TkTeQtWxKHyqTlaXDL06qqekTSHHP85wCoe8KrUKL9pD6EkU0MYeitWp1Qx9wLlbiITInPAypFCLZlXplM+l5IK1FCjU4mhiZMylUypndjaPjKIQxTrFYtElqoQAFyVWhfeZBxDiibgvXoTESSHXUpdUwW7M2T0wcAM7WxGjksQKsYDAAlumirjOFhGK5rSvvd/9SfqN11h59eWXZuac2TJmswZ69s03zrzh9jOu/+u0uX3DXkPQgtpxIEqtGhECQA4x1ImxJcQC+JQzw4myETuFLKNCffJhnz/mwM+c8639eigQqqqp6xJDjnzJ3Q+eef3tf3ngsdzPVAzWVyBrcs9ErQTjChAkq5IEsK8apIBvoBMRLmRmwKEYVDpk0B0RxtSVsjKCUV8911RqL0xg3nM0wgqokxyDvuzUEp7Bxuqi6iyuPapr0p55zKYJ0wiayZxdiiJO1+oqBYQ2DLMKiUIfN0oqFHDC2GCp5wZzIAWeUEonSBLvNAKRFAYNVWVUlcLg040ZIKnGHDKWtilzHn31HSZab+XlCdLGAMemVnPhnQ+cdcNtdzzxTMXUY2Bjge+2MpBlIEH57ZCpO8fooQfaFOsmI2VKzv2sbY7FK2dlZhUWdRlgrTyGEqVFfMtqOYiZt1SSt4kgIIKM4dpunpWce5grTrgy10LKYKujORPwFUiYS+aUlHCwQ2KZuSVNJOQai8DfCoIqhCOqxJ3wm0CGpSVXSqEQoxoqDPiSU7RG2E0CaVdZAuS85qCCpXKStpR+i408OJY4R5JIDv0S3hJyj2lktN6AaTRmmD94IMvauUymYMLEibxv0kDVOYIDRSZgLxSxwIX1kHORaBCwVlmkdO6TPLeG5G2ldNPrYpHVRTGLknIbKdcOyGLKpuAbmzKxDjEwtMVCLF6sPPDi6+DlYysuE2IsSoXLuGnTzr7ujrOvu+X1Dz6IEYM5EyUic9bSkUrZvHiIUvVqQr2N+UPNzBrU7WrgO0TW2qXKpgbJGyTigc3JE7bvbGjxbW7ZcqGEzJbYhSCRFgrP2RH2PEsm6RPOzB18S4WaYpmyU2ul5Yz+WmIFhYGs4GCzgpKMmaNSJeClzZ4TuREOKTjNax0nWoIIa+WwNJbsnRUQxOQgU762584/2m/nMw///HILLOIN7I4bkgbqMCECAiYjb81zNncRt1JSbkwy1xQiRrgnLwZ7U5Bp4hChUnFCJ3NSTBeA0eAVeUwesjGED93lLClZQQY1Z6qqKKKdQKQ0mAX1Aj8lppa9sBh3kw0g2d0NJ3GSYmSJPQk7a3Sui9fOrERqblQaKSkWE3MPBZVXUS4C+WjB0g2r4BQKR+wWpIpSj0ukgn+WuAAwtUQ5aWe+QNQTRxjlCkiTuSWHBFiRyTgR5xQoG2szZfpEEEdDx0ILLbz+R1cbuvxXTm++3/3CN2augIUKvv7L2uiRI4Zhw8k4MqJ9NIPgJWloJSaVXDlXDzzd/cwSq358vfWGx/9Pz0xUhYBHcwb7LTZ8cPUfaQBHMCEeNWrUpz++wZFfPuiac06/7vxzf3PSCUce9pVNP7HpcsssE2M1jCHNnfXgww9fcOkVh3/3B1vtt+9uXzvsKz/70TnXXHnvk0++O2li4y3y3O6f2SlW3fg3337z7qdecIe9Ds/+b+eZc+b89W+PHvqDn3z7mGOnTp2KB+uuvfbZx/1kucUXw/W/1m665/5HhwS46kor7fuZ7YeBLL/k4sMXfz+/+cbr4ydNhFO7S2OhcRkUbZWY4T6pp13IxGDnQHVPenWspUs9DI9vE2y2kGAmwTtiYgRQopADQYixkMNQW8ZQQWTGCBYjTQi2lUqAYZt02dooUzFvOTWaGkkteyZiJ1RbA6GiUDccGras/TY08G+37ikGZGSsCJDQF2ch1OgW0KXkCgJIxOFezLgtJo17Q7kP+RemZJwTfFmYOBaGTwpzrmKuY4fMOzkkMqe2zf3f3tD9VYPt119zyYUW4lJiyRU3NfcHIu24wdrrf2RFUE/sEIQ4sQEfzTvcGIZNgUmy96++929EtP6KS62+3GIsqdVsjFK+9bqP8gXUtwgoiC0t5QShkQf3KnsoJZfUUJOpD9GYE2DmkrF1KFkr4TpYzYjx5sU9iePOKiFSAlPArZygUq6NK+JIQSDUKrAwETMLMVnx0rZcBiMNVpw4ADdjfVWc/n64MMKWRwlqyqZOPhT1NCsCOCUouySsEsMg8WARvHlpmtxmAjhGbnMzlQ0/usr2m6zdGykhALppw94SjKjP3FfKkbSmKCl0DAI1R0ZlhMUu5FJ6VCoOVV1vvu5q22/w0Qp6U8cUQo5RKJCIuRIHaUTkRlQgJ0+WcDvcALFlcwZNRtw6Q6KeXAiC6YRGkU2rEiqPtUpgyGd4IryhEo5qQZOzcSJP6haNNSvlKhdNLZVmyLWRoyUTcBcIiBk5JnAQUk2EPTHUkSExFQkmvQIN2zAKYrIAIiiLFxRmqY0eQYiCCC0mrVPSnENJdck1Q2k15YjRBFxsVJljuVojelpkFICGISUG0ijMcDWMbRkkCNBBDqgNrJ8JPsUuAa0okbaIxKpqKk3ICdmPi4ORADqh8Zy4AAAQAElEQVSzR/NAFguFRgKBL6qFAqFLS4hI1MG9zhQKuoTMHVOLFHjZoDhEA0u3UgeqIwlI7J66ZaPGqN/XtuGmpVQ4wYb6KYErWmDMfAGwyQKkQ4QZLA0jl1J3sGh2aikjaUI61o2BEFEXlKptKsBEiSoiMeRaEkOsFBJhJwOcLrrg/HtsusGKi42tIoOYQrBgaRG3OsDdxwG6w8c5cKdiElBaUBgZTLYQBeaa1TMmwZAoRRinkkbySBSCEr64A4SPCxWFY4gJHhevXDrzkhYxIAfPcMUgAWNKC3FhVmCCj3HV94hwnDA9UgoC7WQpAZfUdubrsOxCgFmh14laJhCHC3VSEWhVEC8g+QxjKFI1IoPKLcNUhLzJWlpgRSxAuakhwIhALKTITCz4x9Ehebv7kadmzpk7/8iBnTbeABQHbSQWj8DTDcdHiaKUKibBlfdYaswvhtBeXAPyOGdibH37IBNexjaUG4aFwzbQXXEekLYiqwThxIpS69pkhiXAhuA3gQqZMQwR3+qlImf0hVIEYwy0QkGIJtwau1MtEgMhQwngYeVVmNI8Uo2kQBfgkEiDSBVcNVP3HGd0I2hYIiuunmvvHK3iIiJMFZUooIJaRBYHewK+P2xpQEqtcGWY/FAzAJLkVUKE6Ls0rbBx8KzeF3dD/osK4yGsNr1kL6bmCHl4RoHQXEupkksJuVRWSDjHnukAd5PANCK1tbBOw8c5MyEsmCIEEHTAzmReSgvfEpIQglaYCF9v0CtZlWJQNbUMrnqqr733/nNvvhuE99xm04bYYjRIRpSGDsuYWRxT3TtSCtXBa4FCLLedxxNgGcbY0HBisKrMKiCCSqFCzKxBBV9FtJ/DYOJizC6VhUgMPFKMUJbnTvVepHuEtAZ+PRA7wq1RFoz34IW70h/aUOtRjCzAFZgB3WFTnI1zkCZo0+naIVcqWEWkVsgjE7ASD9OJs1TUC6UKXEetYwhSCSmJkjqI11IWHF3tuum6ay67DLu6keMfMQSkpR5IcQAUCWFGFgqR8QKfXVqiuZKbCEKsqGREAopKxELFtXDHsEQK6pFohFNtXlyaEvqZG8/wSavU6mAqyRzFTlNqUtGaYoDcxLkqrEUJ+s9sRjk7ZKfFmSiT9E0yC0uoJBeyQuICxrinVGcLyYgtBWqZC0cT7qopITGL0CYhuDozmPJMJalR5yFesyOocxIm7fzHXXIgzLDYcgAu7eY4SSnBMnsRkAhaWlBnnLMUc+f03sRJ9OGx7jprjxnd/Z76hx3/3PdLb70zPGHxsQsOX/wXnafOmjUMeb6BWhXLfmPv5EyFCJGora2F5sqt992PYUsuvsTqK62Ii/9Vw9QVl+n+4O/7EydPmzkP8v9q8D/eD82jOXF2XmiB+T+57loH7/XZ3x//o8t/fdoV5/zq1B8fveeun93wYx8bMWbeT3NZ23/9jddvueeuE8/81X7/9t3dv/zNvb7yjW/+7OS7HvwbeYd21ozp3z3m2N9d/Zdxk7uliBWbPG3GA088dcYfLt778G98/egfP/LE4+5exWr/vfc645ijV1lumaF53dx/9jNp6rRzL/pTQtYP8XtfPXzMqBHDEBZfeOzwxd/PU6dOueQvt3rFhAhF1BbKrlmVRSKTCjmTEZETYhkcTEImVHeeipainrkTklIXU3ASVmRrrjiLNA4HIDZnK5guhKHe3cfMNZYr2aV496A4PEhbk+ylsGRjA45WyEQIYSgYByJjKaKF3Sl56Je6TcEpEzxIElNhAwDCWFFhcSIScvYkHY0ZTykjIFoWTxwMHoqpZtFzzEXMSRAJBH1OMEZgkWLWWHnypdeefO2dEVXcat21oiNWmroDHamFkJCha6GBOsQBrUZEryAmIwbyrkVHCi7KHKJMmTn98VffGjXQO2KXrUcMsPY4R26EkBlN2SstWpEJGZHjxAZxK2kI0pUQli0VR8wtzAWg3ckR7YPEKEEgJFPOzILI0XYxUEWdY9bgSLBCpbij6EAV4dhdrUmrWqhyUoKeMsIwBJjdmowAyMUrkMBunaA7aqg7WBjJvRYE5J5rxaHiKMTOpZAXJmKLEGcXQAkkRkBNxrlNVDLQsAuZFpIiDjvATM1ixTNMKKIGd0AhYUUjUozLRIUUlLJEnCESDKFA2MJIteXKJORQ5V6VegM5YoYjshcWUpFAouRKOQBDJy4aOsS0240TYnFnRM/s1Crid58lsRAxsZLESjsDHuAiNHx4KezQozNshFormQuk71BCktxoaYVhsZAxuqJR3RoqX0vO7tGESy0NXpdUdQkRJHaZQksmKEVEmXkIC+wO9BgxJFlg2cEDe087BTLuixSDBNg4EEsA/xUuTcSCcNQIYSWjxJzUYPMwhNIZKzOEEVQImYvJ8I8I3AeIz7kujmsSEqGoUolEAAuZpYSSq5R7fa+LSVYG98hUmaQwVDkkmOzFYCNOgIhO6MbhTQKhwCjAXQv3gRhC36pUUIgxW4lutXIdgCEK0jgL/Ig4MTVEBZAwmzy3OeNaIIwE7CWwIVYYJzzyDh8eUknGmamjIbWek+cZs+Y+8NQLr73xVoB2rOARvjNejEZTabUYyIZzuJI5W1FxAX4maC8Jnhbu4A5/tLM0SMvQV2upY6uoJKxfWgNeuHUOsOOspSBeeqFhspRJ1Sk4ARUNH7g273BJcc5uWblodOgkMHy4FC1FArRUZ44IKFYThpuLQVslZwYTbhSIIPgMKXlxT54SSLEWHQnhjEEmRGeMMa6UFLgaCkliJmHiClR0z8ULKxMwo9NMqY1sQV2EMQL8kgtBjV5nRIGi3uT7nnwJMt5/q02XW2QB9RbxJIgDBn14WLfeAYTMIZdaLYYStcVUksZrz0rFCcwgfFnm3D1BjFcTUbLgGaIGRHAAHSNaIEBk4dbZCd2hp7EnyCmBSZJQqVyhTqiqeC5cHObgMA+gSAZNkkWGUsGzuiqBUgIaIgzLzrD26AU0MYgBubANJWPYlWEExiQ2bGMXrJPhROxaqdaBgF4rj6qC4J/VDZTRhwcGmkXyIOQKbliIQBD4ERIxsmy5tAVfZpbZIGdhFSVQQ6F4jTBVWmnBjxLctIvL5tQ44hs7gwVAN8TaEDgoSyHUsDEnpC0nh5bwopMFGbcjgAEQU8y4lJhyVaTHEsQQFhA9zIhMickR/ICjmFi+79nnB9u09fprrbP6angeiEOoRZWGjnkj3aRkyUUpS8gxFhUyCn3XvnlCkDdM7SaIS6VId52F5VSshWswVAKI0Urlid29DZYDOIlaopGzYrIWhAnnwpnFVKWOoY51JGEIicQgLoqBKgYDwSV4iF4x1clDFzq4JUcUMEtaMjOGk5O41QYjbj2kpkfeE68y4UFHKSmzKKChjPDAoUIHETg0NaNEpVXsZVJnYMzOgSpiJYnEdUexgMrIdYiVYY2tpAWKY3DlAsmagwRRQBUNtbFmJ2PLdSlwOIL0JBhLR2khakporSoOesCWiPScK/PKuWcEJoy4RfLoVa5EaB2R0Zz68COCUyTVworlB+UcEGCiSRAWj4FiEGIpDrMhcyvJC8TVSjQ8qIijk0Aa0JINCYYJwy0wkrkLRroLZeVWqbAjEcQWQNUqTj0rPZO6cBevnLxNiI5sYoiVFTZ6jbMzLIAsk6eWX39vHH14bLPZpuFDC/uw7x/9NrNpE+eBWmqR/7Gc/Ueh/GPj3nrzjeGBY0eP4mSQAFMkNedi7okIlnL7/Q9NfP9dDFv3Y+uOnf9/s/r62OqrYWQ7d1a3KsDVf0FzIicuJKNHjf7ISivttMXmx33zKxecdNztvz/70rNO//5XD//MNlsvtsTSf8c8derkF1566aYbr//9ny5OCSvt7snMGdN+8euzP/ulQ7c64NAt9tl35y8e8pXvHXX2H/74xttvIyVhxAbrrvf703/xvYMPHDv//Lj91xpCwSXX3/jGW92v92y12Sc/vvYaf4czYqA3MGLk32+HLy68/MoX3nzPKyVmbQlxmDuvhBn3mHskalTI2tj0ud9vB1NK2SALhsoqOHpG5ix9aiw2XBVVEa85Vww4BG9rKbjBe8Vgt4yx8AiC62jL0mpsQs8FGws1I9hyzTkEq2JB4FVEC7bOj5jwT8hUU9TMuGJLIVHVZ8XunFsWUzZ4j2KQgDBXsFYMROGhBxCWXbkLAaSBnBR0ugaEVcqExkS4tsS5CQWlAhIGBxLQfNujz6dSlh67wEeXXzawRAQEDoWqwvGqB5/e+ke/3OuEsydOmxl1xHOvvrXjD07Y7ocnXvHA48DepLzXceft+IOTv/ubCxrrL7nw6F997fOnHbLnR1dcCqCReCpyJ37q5bd//ocb9zv67F2/fcoePz752Mv+/Ojrr4o4IDALUxSuJEpV5Z62tTai3SNlTWZfPeU32377+Iv/chvi8JMvv/zTi/+8249O2fE7Pz/0pPMuvPW+uWUuAjRTcfJM3Hfuk/TJZ7bNn+6+/1vnXbjtj07d5qjjv/qL31x0yz1z5syOnmPOKkVi+/irz2334xO2PuqkGx55DpTMnDO453G/2vrHx3/nd3/wPtwgEuQQrMvIrWoKtVdBw+MvvbTz947f7bvHvz1h3JsfvH/ihVfucvRJh53+m1nNLBV+a/zknY46dbvvnHLXoy9QqQtLqnKumxIbFXvx1TeOv+DP2x150tbf/uURv7rkygceb3O+69Gndvzh8QedeubkOTMza9vwYy+/tfX3fr7190968PlXHDRYNBgTmXmD8uKRZ9/5yYVXbfOjE7Y6+vhvnPO7mx5+JEsC/cMNVbNAwxpKXVMYYK4GmGozJWbIiDp9kAkEZoTCWZ1leGLK7Zvjppx8ya17HH32Z4465fDTzr7w5jtmTJ+B/OGUHBMJ8YtffuWtXX5w/A5HH//82+9yiZQrNy5sOVNJtNeJp23z41PPuPYmM27b9JVfnbvND39+6FkXokoAlt/eeM8u3z5l1+/84rU3xgWNEZmEyQifmghkwt64X1FTcVPXLqJkyDGVh2i9aCGQugcrCuIdPga0bOzFUM42qAVAnmvIZA1wMUsv9qrQY9JcvFvFkgJkqJnVCLIsEIdDJoJT5kQxMXipyAIxxMYA9+YH48+64sZDjjtrl2+fuPt3TzgGInnoyQwUMTvBYyhz8di4loK0Fau7Xnz1qAsu3+F7J231b8ft+9PTf3vDze+On8iiXFEYEK3Rwq1PvbzND0//7HHnfjCt22+68eGnt/ruqVt/7+Qjzvjdy++8vuexp233g9N+c9N9NHR86Rfn7/jdk7528u8GZ8zk1LSNnXnZjd8866KDTrvgg5mzpQogNJc6I5G7CSXhQQu5H3mO2/d/c8k2Pzjlq2deDPbhR7+/+tadjjxh5x+d9MaE7n9MfuK1t3f/0Uk7/+DES2+8r0JgYIiOKUiGY5i2KSL1YnlCDhS1h4FX3550wh//8pkfnLX1t08/9KQ/XHzzvXOaZojG1VFb1wAAEABJREFU7tQhrrRBWGNiz26NZb/nyeeP+sOfdvzJadt87/S9j/3NGVfd+ObEcSh7SmX9mAbVW4b+OIn1uUXt9dy775x42bV7QALfO/VLJ/72d9feMW3qdOe2s0DO1JFCMUgFC0kzL7vzgX87609b/+CUbX/886+d9ZsLbrx9xpxsHqgbrkJRQ/zDLfdt+71fHfiz8zB7/PgpZ15z0x7HnLH1d07f7ehfn3LJze+9P82TptxjG/XDfT73yy/tuc8WH+9bTslO+cON23/7F3v95OwPpnV/ZvOup1/a5gcnb/29X15/31NQuDsMkIpoqbRAYslKIZiMxmgykFOdLUTVgUp7xMGJCU0EnOOCGwYI1sI1a7dEV4eEQ4UjKMKy5syFinGigJDRxpgRtZPVTTui7Q9k49abwdw03hoVrkhqrYLUkXA4SbKuds+tlAacNMkzk/asqpJyv5hBU0gkRgY2c4MJwhE0yr1PP/+931647fdP2f77vzj6vEvuevx5DpWI0NABIJiP5IbtiRFG8MZ+KZfc/vA3T//DtkeesMtRJ3z7jN9ddPMDk6flBlnQc01NLNnbbrJHoUqMedz4aWdeesshPz3zM0cev/dPT/71Nde//O57ZFVAnipZuPF60GPDkij373n8seMvvnrXo87Y8Tunf+HY80654rY3J00tREI5cCvkLPzQMy/ufPQv9/rJKRMnTpo9OOuC2+760i/P2+GHv9z+uycf9/srnnzlnVw4QaGW9ttms1MP3efbn98tSkiD/a+c+OvPfOf4b50N1ygg8fRrbt36yJ9v+92Tx30wDvtuxAh36OcAa6upaEmUsrMzYzCak783cfIv//yXPX72q+1+dMoBp557zjW3T5o2Q6X0YgmRWSV5LKaIjVJyf+6cC2+491u/uGjH75+yy09OPuaCq+94+OWUEISIAny46jn1Z8059JQzd/zuiVfd9SDewzz6/PPHnnvxTt8+dZtvn/aVX1z85zse6reD4LoyCpBV0wRPFcG2/Kk33jz+ouv3+OEZux550rdP+90V9z3SOIgHmV3LFAzkojaDLBpys8KpL6UfqA8QXH559V3b/eD0L/3y/OllJlVZhaLSudf9ZZsfnXj4GedRoXfHTf3NNbfu/+NTd/rO8Xsc88vTrroRXjlQIgxKXAIsnJiHBFMXqlqmloAtNZSNsnjDuUGFEowjBkX1kcHryALiK/SJItczI5omSgATgtoAt5FdieBhUgq3pomDUc2lAmvS2RLGJiNh6kiQyCbSJG1TgOVbQjcFYVYCPxjvaolKlySdtMWSohhcDJlAuG6pmutVabGbIIY8kLJZxk0lHQVMDIOjwo5ZDu8xUyMtZEWyK/6FSEBPTsFZ3/2w6F9w7KIfX3cd+lePftu+P6F7ObPEUsvEasi3/1VQ/5t5bnc+9jTGLLzYkksvsgghTzIb2A/SamiUM3tj6eKrrjQrVV3vusVmzIzx/07bbrNN65HdYub3l1/xn/hS5d/BiEdGHKpqoQUX/NhH1zjwc7ud+oMj77r4/PuvuuJXx5/w1YMO/PhGG88/ZnSvN0AfEr/8CiuNGDXGrUybMf29t98YPxGvPaallFTDQgsutMPW21x45ukXnXr8emt8RAOMHBj+xfb2B+MvvPJqTF5ggYW+f/ihfycAPTGG0WP+xyVQ27bH/eIs9xyd6ky9lpQQeYozljziMDZVJioZMYnaJOaixpTg2xhBsFPPhuSAvCnJYLfMHsSCtaFJmOYYbKiBgyV1wnPKJIW54A5OxsUtEMpFCiLq4gSQ7KzE4ilLToEaJiav3IQ5kyaqrMAXcQvsBgCNwg0LVGJgQaIzmC2FUYgUK4yxGS4zYBqMSYDAiU2ZGTgJIUIZ/UqlIsQjXHLIjl2OZla+7/nXiWjTNVdV6eZxYFYmieax39q4qTMmTJ9Z3IGi7afxU6ejzek31KHwidNnjp86o3t/6Eklrr7MUmt/5CMpjDCuiSQNll9ddNNXTvnDn+9+9OV3J4yfMv3NiVNQoB98xm+PvuBPs6bNsWIOyVInP1IPIL8Gh1BKBz2wTJk+c9yU6e9OnnHmNbd9+Vd/vO5vT703edr4qdMfeuH1ky696cizLp89Zy6RqUOoLhCg24uvv73P0b/8+cXX3f70yx9MnT5u2vQHXnz9l1fdscex5/7t+TfUPHiJbgj946fNAHdzmy6jmvuEaTPHTZ0+ZdZsgXaMEmcPGWIi8J5oSCTUlHbC1Olodz/2zJd+ecFfHn56wtQZH0ya2uYUg7g5aBs/ZXoDq2BHsANZRp5z/sO1txz2i/OuvO+xdydOHTdlxj3PvnrsRTfs/pNznn3tLSCdNGNmawxL6Cww5Q7I1OktVi2cI+CQEaXSNudfffvXz/jD1Q8++f6UaZh159MvHfP7a756yh9nze1DHWiGYJszFZgJjEzZA2ecoQouHBKrdxrOlDObicFCMKlrf33s2b1/8usLb/vba+9PHD91xpNvvHvO9X/d95izXnzpLWWVrDFVIceSyoSpnQHM9YQEUYkIIWRTItCaJ06fAWnPmDsI+zGndydOGj9t+qQZsyBb4ACRE6bMQCttiVkD7FPJI1lkEVWkobZGgreIAk+YhJhYCkG7DA/ozI8tdqprA+y9Nc0UmIIXbQuDb4wkcuZuSnctGC4ZKREXRMyizKwYIpY4tSAQvUOSJS1OGYyQZpMk1KhdevcDB5zym/NvuffJ198By+9PmnbzY88d9bsr9j3hvA+mTQHtmaxgnpSgZdr06d854/xvnn3x9Q8/83an3+nPvf3+7265b/+Tf3vhzfc1SVyia1WomtOkcdM6hxqWCWxv3NQZ46ZOnzxj1qy5c8dNmT5u6oyZH2oTooMuJk+fpTlrwYrMX3l/YjGbOwSE4kAGbuRcas06dhhuoMloMFszeQYcc/rkmbOyxCICFsZP7RSXixERthgmTOtu58xuQsMRTsGlYlKDsGAnnQRSoEKQPF93+wMHnXjOpXc98ua4yeOnzHjoxTdOvuzG/Y45/a3xXTIl6mRYCqFIZSZRnzZz2jfO+O3Xzvrj9Q8//fYkGOqM594ed/7N9+5//K8vv+Wu3JFqJaQSLSs5S39OPvWP1+973DmX3v3Iq0AxdcZTb75/3q0P7HXsWQ899iIbO4O+JFWJtb3y/jtfPPX84y676dYnXoQkx02b/tDLb573lzt3+e6pdz7ykuUgFjUHpThnbgNhvjVh2oPPvPT5k8/6zU13vfT+RPS8+v7EP97xwH4n/fqFN97nzFp40VHzbb3BmiE6vJWKfgCaYajTZkLURDTYdkEPE+cMJtAhmTiRGGmAOWXThNq6qrmqYgiRtGKplENgUmrhMMGMjZAXincxW+rEIXMdOVQQuWGzOwWj4DBNdrLs1E8ldzmIAmzLQnDqHFrIArdRErtZJu+r9qMaM1NHDXUHjISxbmLHoORu8EsnGJ8IxpTCCf0kIMyACiGdpMwdHPzZ76/4+pkX3vjIs+M6C5lxy2PPf/s3l3zj9AvmDA52QPFhqMnZwGgP4n3h3ff3/OlpP//zdbc/9dIHU+ZF49OvvX3v48546JlX2F29UGmtgEwhFy75xrse2u2Hv7rwtgefeuN92OGr4yZdeMf9Xz75nItu+GubHIALg5xC1k6YPPWwM35/5B+uuuahp96dNA329uxb4/542wO7/PjUS277K7wbrFBhSrHpl/HTZrw9aepLb729789+88srb3/89XfRAy7+fPcjXzn9/FsfeZwdZZ6OqHpbrLfO6BEjKkR/byZPmzl+yvQpM2dDsGBuxpxBTAFVpRQwSSyQNMyAmINAWrBvhhCdmYaO2x9+au8f/+rC2x947YMJQPfE6++cdd2de/7k7KdeeadELRFhMnIkpy6z/+2lN3Y+9rTTrrrtnmdfA4p3Jk+95sGnjjz30mN+c/HswbkQkSF2dbHAhh123LQZv7zkJuwv3PDwM+9P7nzn/udeO/7iG4869/LBpgUFFZXgiAZNyYPnXXXLwaf87or7n3hv0rQJU2fc+8zLx/3xui+ecP6UmbOHKIU1WcndJTh15aLwj2KczaWEkEKFWD1+6gzEGZNSpLTsOM+YOwcCeWvi1Lsef+YLx//6vBvufm3cpAlTp7/87rjf3/7gF04+7533JglSLqy6zaVNnbmKATgjfKoDSCOMKOrGJaMBcxoI7UAwhbmi+igIEh6K1zl3v27KqU1smcmGxAzJUenkzgw9AwkhNquZOma7Q66gvuGShHE4YTw6LYk1FbUVm3fge8w1FJg4tpmwLDcz54YJibpJgyWDDId6iYK1kguAYCLnTG2/5FRMsgRiB0tFMurARtCjxCCESyGojLushXVdLRQDhdSH0yYaOtZYffWlF/3X34S0qUye0e1jLbXIwkF1COR/yenZV998/63XiGiDj62z4IJjiMlhHMGTCFSYO0fwh5977MlnnwH6j6251kZr/rcXAuj5n7bFFh6709Zb49GkSZNOv+CPwzEUt/9XNvgqtDN2ofm322T9rx+wz4Un/fSeKy695Ndnnvi9Iz+/++5rrLHml/bY7bIzTjl0v33XXHPtFZdbbpUVlt9gw48fsNdeZ/zsmBt+d+7pP/zOhuBU/hMk/+s/XT5ndqfK/fb47KILLfjfCyFqWHB0t6JjDURwbRo+Hn38sfP/cHFkQ28kq6ghb1qHURgVYXhTqBLSCPWE6wpnrF5KMRilK4k4sZEXomQ4w4MokmMtwd4kQyEqljQVTcSeWTDUq0RVEc1EuSXvR2oGuAwI1SCAOasxEldwtkRtoylHA7VMCCbYRuuCkZYQy1D+Ei+as7SFWvilsDJ9eDAJWnEiKnUhzWJOcLyUCP5lBHYlFlVsVbWFWUiCgN0WT1L27O+Ng0/MEebVllzUU8rczVA4HjkTA+q85sVzu+xCCx2z3x7HHLj3J9ZYGf0x6FGf3+m4L332kO03r0y5IOP2CtB1XEM89WV3Pvrnux5uc1l7+SWO3Gv74w/c4zuf2x4+DDe/6ZGnf33V9T44B+HccpcjClADI9CCGcgCZ+o+QHTjI8+cf9uD66y45JF7bnf8Qbt+c7etV1y0U/rdz7569d2PM3gi6SRr/s5bH3z9l79/d9LU+Xr1zh9f+8cHf/ZHX9xjz803GDNyYOKM2T/43RWPvPQGgCr5yksvesyBu/3koN03WGU5IhpRV9/fa8efHbj7wTtuzhWE3TeHMgtH406rpbullmEFGE107g13zj9yxOG7bAEUh+6xzcgRo1RhJzb0EKdC0pBiI5Ik0e0PPHn29Xf1U5l/5MCB22x6wpf2OPoLO231sY+gML3wrw9jNBrqGWm9kizyIRC3rpyjpNBkKjfd89QFt9zf5LLwmFGHbv+pnx609/f33u3Ta37kpXfG3ff8q4CAxkhjbGzZYHPFqEjOml0LiyN55hqhl0Lj3Ne2wXJdIAtMIzrxspsXHD3fl3bY7MJwRqQAABAASURBVLgv73XMQbtvvtaqVdBJM2Yf87urZ0yaM5BizMoWkCyHhnemDylV0SKrkQBJ1nlljRO5cFT69t47/fTg3Y/Y9dMAhVnbb7DWTw/c85iD9lgcDgsjtawhc8+oR6QshbiItIEzRSKG6mGskSEN6YoYOBmMBMmkQ9d6zKU2AztRkFBIcjEc3TxWAgFO/eytWXGTILHTjYozoSpK0i/YGg6D7sh1OTC6hRmPDT3FB80ffv6VUy+/Ycbc/nKLLHD4zlsce9CuR+39mU1WXUGFn3vr/e+df8Wsts8Veewccdr0WUf/5sIHnn81CG+25so/2Gf7n+7/2cM/s8ViC4yZ07SnX33Hn266v9/ENsVU+KOrLn/swXv/8PO7Lzx6PtC54UeWO+GQ3X56yB5f3m3LlZda/Jj9P3PcIbvtsOFH8Qjt67tt85MDPnf4ztvVoSci1UBvn+02X3ulZT+z4VqrLb1sKV7EvVtWZIfQKagNRBIwopQxfbi51GJx5003OXb/z/3oC7svNn8XG1daYpGf7b/bCQfuvvXHPsqFGcL3gpJ6BFHHU1VAbF9Ln/jhZ188+c83zm0zrH2fT234swN2/ekXdv3MBmuOmzrz5ke7v5gPLNEJRq4tk/msOXOPOv/yB4aksfmaq/xw311/dvDeX/3MVsPSOPXymy+/8X5QzeIUmyApZv/15TdeeufDqrLRaiscuc+OPzrwcwdu88lFxoya3aQf/vaKF156MzJJLBaaqbOnHXvBFS++N35Er/rsJuv+ZH+Y0557brb+6BG9mXMHj/7DlU+99S70H92qwgS9Ek2dM/id8y+NKgdt+4ljDtz1h1/Y6VMfXTmoTJ4156Q/X+lugY1iyxUqTGWpPQzsu8OnfnbIbt/fd8cF54M8aO3llzrm4N1/fNAe662xamas5gh+Gpu2Z1ZH0crQgqJYYa1YIrEQFTRzM/bEJdNQLG5zMJS+wcJAlmgukJaXJE2SNjtmOAVXKQpTzphLRIGZOBQscRFcpVgsXOde5T2WykJoSBsyK+YNBpNTcU6CndCqYTwnpA+lYiUWLArhRaEgg0WXikIgVnKz9oIb/nrZPY/AfZZcaP6v7rrFTw/+7A/23OETH1nhgedfe+qN9zqwROLAbFqIjF/9YPKXT/vDcIDdZeO1f/KlXX984K6f22zDMUMB9sfnX/nE82+AEleDCWEJBq+89vaHYELuvtrSi//bZ7c/9ot7f233bVZYdKEml19dc/v1Dz7caGglWPY86CdceD2K+6j66TVXPubAnY/74q6HbLfpEguMNvPTrr71BrzU8h5lpTY4JEjUFjvqj9dhK+1zm673swM+d+wBu++2yTq9GOY27ZnX3TZj9swYghCWrxIQGDRr1K/stt2x++1x6PafVhEi+uxmG8AHkVbGzj+qcGi5LiXkUhy6c++Z9DJqCoEUMBgNyxKEyoN32Py4Q/b+8f67br7WKtVQqPzuOZePmzqnzyCsqKLR06+9+Z3z/jxxxixE7C9vv9lPv7Tn9/bdZcOVl2Wmmx5/4ZRLr+0PDmK51WRO2jkwgF91zxN/uuvRDVda+nuf2/5n++0Jx1l24QXw7NYnXrjhb49JJHhzHBATv+XBJ8/6y53IKWNHz/fFbTY98YDdfvyFz2y17uqvfzDprmdeBig0KYyhVAWuoyFeVQQxs7ATEwXSCmOGm3eSpJRy9tahP6IPps36wR+vGVlXB2+/2U8O2f2ofXfadPUVggjS6IlXXJsNKwtrzJqmNIkzbCnidV7jsaFowNWBJ6oy1m1YHhZ12MKgQsewQSMMFytinV0JSeLQF04IQ9Y9ceaioAuKi+bcasqhbbk0yDWMj0XkDkoCcRC7F+PM0VmIFFHMc+QC6wYFFHAl8AoYH/ULpU6rHa3MXKgzaGDhaIIgCnoU5lEXDUUlk5VOEg0LCr7knoEIcqMClxZgKVwZpCEwQnGGZ02ZPGNO9z/sACJ9ZLllVDoOupt//tM0/fETul+mH7vAGBWw9c+D+MdmXHfr7RjIott/clPRmlRYWU1iIoGeGMElXXbj7W3bqoYD9visdj6EGf9eU5VDdt954bELY9AV1//l99fcUAzWhbv/GxosyTtWuK6q1Vdcdrfttj7ma4deddYvdt5mi5WXX/7fvrT/1Weect1vz77ut+f+6eRjf3jYwVtustECC8xvxP8ptL7w2hs33t5JePlllt1np+35/wg1hDB6ZJdgdGDUmP/jf01zzh8vvONvD3PlHJNQxjwmz9lStky4Z+tyuhSlHIxh+qFTXvQqFjX2lr0vDp3ljndsfSWvhKrIQRzeUuBkLmKIA9yy9l0KKixpTNpCxKZCndGx1CQDZD1vq9JKmzRnRpHmmqm2hHwCvcIz3IS6vtCSGFMHFSYryF3RXYvjhkg1VmC4p4rNPGEBFm6JWi/JDIuRjGDQSb2QJS+Zhtj05NQyNaKF5JUJk+a2KaiutPhiDiaZUbIBdqASu+FOQ4cTk8jCY0bvvOHaO2z0sRWX6OxQRbZaZ+3PrLv+ZquvUnXssWtnGoEpMKPOwAKjmK+70rKnHXHA7luuv+0ma+619ca/+reDNlltRSK65dFnpk+fWRszgliuig0UqzmJGxUIlB1EYxjazMH+np/a4PQjD9hn240+8/F1vrTlJ8/4yr5jR3Vavuupl+e24lQThZz99Ctunz5ncPRA75j9PvvD/fbc7RMb7rH5ht87aLeTD99nkTGjps9tTrvi1pkzZov7Uosuuv0nNtjuExssu+hCQFHFsM26a+60wbobr7JqRhSGQt1BAuMULSmkCaGCLsNgtNWWWuyC7xx20K5b7bDpulttvC7eKzqo5nlPMQBDs5dCNmfu4Dl/uRs9i42Z75Qj9j9s95122HCtPT613k+//Lmj9t6BRfAIjcmjDIVIdtx2DTrOKolQ4Q7OmHv2dd0fPFhpsbHnfeeQr+y243brr7fbph8/4UsHHPm5HTvhdxOQelwDeTDijMYKU4FF4ou7jpaz4ZY4EIwKVgmtDs2jVZZc5FdH7P/V3bbZZZO19/jUhr/45kGH7rylCr81cdoVdz4oDsswMu5VkHM3Q5yDuOBLVQX7w3CDeYyAevOisXz6Y6vt8omPbb7mygjomLP6sktuv8n62220/ujRo4sVAszc2UsmT5ZzQYJMoJIts7VUUIK1pER4XztQRFHrZcYU8ayM/sASjMUoaAlVYTPpU+yDrYq6wykPOpq7k0qppVXrO3bX+tR5d6kUNUuLJ8ERfeHHAeQLGbEXv/quh5psSy+84K+//cUvf3azXTdZa6/NN/rVYQfvvdlGzPz82+8/+eqbYoqaT4ivu/fRJ1/vfoL3KzttfvIR+3xu24133mzNQ3b99B+//5V1ll8am3Pn3nTPK2+8C568pSUXWWi7T6yz9cZrjup1dC672NidP77+Lhtu8KnVP7rQiJHbbPzR7T6x9mrLLt5xQLTFumvsuNE6n1xrNdERBfGIaLP11j7nyK/+6MAvoDZi9wAtdEaCbKqxX0ewhELbvTIoYRgGxUKa4upLLb/TRhvs8PGupseDhceMwmpnl43X/Mhyi2bFMk8sQyMthxRripHFzVKZO2vwrKtuzaUsPHrkrw7d53v77LDrJ9fe/dPr/vRLex1/0O4LjMASE8BInQYyVUbQ4WV/ffSJ1zppYIF38tf3233LjbffcL1Ddtj2wu8cBmlg9G9vuvettyfUZFUptedHX3zx2gefUOYDttr01K/tv/vWG0E+X9n9M6d99cDFFhg92KaLbr1bSonCIvr4S288/26XwX+0324/PPDzn9lkg50+vt6RB+x2/CF7oKKaPdhcff9jUDi2QLlJ0CXQmfsSYxc461sHHr7H1ttustZnPrnBiQfvu8Waq+LRy3jBMm08VdkjmWjROkioAq+/+nI7bbzW1uutAZgYtuTYBXbceJ0dN1ln+SUW7X5OSp2lcSmwBBAV2INkYbzIajPnAvQtLAxo3UKBQRuGE2oaDDMl+BE5McyMGHZpai6ZpHV4ppKqRgkxMNdmPfOaXNizBFRyLXHCPGWtKnzcFRCCU0Q0h+5Q+wmlWhutcgc1UoFbQDUOQgHBXEN2VHjRkUNqCT2sdN4YN/WiOx8iorWWXeK87x14wM6f3HmTtT6/+canf3nfr+zwqSiMR2jwpY5KrLW1/cXVNwwH2J8c9NkfHPzZXTZdZ7eNNzpyzz1OPmTfRYYC7C8vu23arNZUCS+WJLw1btLvbrkXFeKWa6/2m28cut+2W2y/ybr7bL/Zmd8+eJ0Vliai86//a5uhLKEs746fdt8L3ev9fbfc+MTD995p8/W3/+T6R3x2+7MO33eZhRcw8z/d8WCTUlFCngT7mI6mIqce+oWjPv+5XTbeYMdNNvruAXsfsM0n0D9++qxn33lXBJdUlF2ZPETubb72WttvtP4nVl9JuGNwreWW3HnTj+28yTrzjZovc8yZSslScsmpZKdWomvlJu4dIKJlFl3oV9/64iGf23HnTdfb89MbnX74voft8CkVxtuVq+54EFshkqA1ako6/bIbps2eu/JiC5//9QOO2GXLHTddZ/ctNv31V7+8+8fXA6g7nnzx2dff8oJagwT3Qw2yPWjrT559xIH7fGrj7TZa94Adtj3h8AMW6P7ION3x9HNIyVYHkzBnMJ917Z2YgWXMOUcc+K3ddoIv7P7pT/zssP1/8PmdB6qAR2jqLoAtFTELDEKoDloJrs2LIzlhzHDrMlz381pthgUTozObrbjEIucccfCXP7vt9pushfR98mH7fvwjy+PRc++MGzdzijKMF5EmsnFwWFpRN7Yk1kbPwRFcU4zWE+FEJXljlmHoISEesRgmkDgDCqkII81mtwLSYMCBHD6F4SlaUQNKgonDp8RS4BKZgrCKQKnoTK7mFXkFT6GksKNSuGCSFHgObD5VVSN1MaFsIt6rFNNLsWIUKA9IE+tkvWRRKMahai822VO/OAKaJg/QvhAr0HLuzCFQLBwao5S52+0C8TR5GuJPS0PHpA9faQ3d/dOnCVOmeu5ALbrwIsydMv5pEP/AhMGmueO++zBw8UUX+dhqeFXCIpG54qQxc+0e2B95+umHHnoQY1Za5SObrf+P/jDbcksv+cOvHdbr9czsjN+e/7vLrig2pEQA+r+7GYwbTIbOQ5xgJhyQ7hT2yPafSlsu9ovzLrDUsMZD9v38AmO6DcL/HkMQGTXfSPTk2dN22W47XKAtudQyIYR+v3/UySc98frzBroK/CwEi4GswODMM7EIibakc3NoSs+1F6PG2oidC3ykGrJZ1ULBHKJHPAtc1bEXQ0AQLFFTFVwFbkecM7eNlWyOuSwhh4FE0QmC8kodVU4ma4tleF4lLlGsBC+Vu7TkgyRNDkYW4KegQOFhBFoVNRnSJLUggIhUtJKBSkdIUAbxVNSzwgGzCSEyJyQvyq1iOTTMRWE3chyYX0lbhwmqJE1vAAAQAElEQVQzZhpQCs8/ZjTmAIiCRgTo3PmgQTZA0zXGXqtFLnBVLHM+jNoF0CyI1Uqg3M0SefdaFmjmzJ0zfspUIlp3heVGovxxKmpFZGQ96oBtNlt3paVXW2axmXMGK6+CwWzQpLswJagnIuOyFZACALTGskt8c7ctR6pm5qwVc2/psWM3WnVFPHv7g/GDfYhZyXtPvvLBo6+8Jcz7b7XJVmuvM5Cr2GgwhFH+2MorfW2XrTD+lfcn3vf4yxCCacSTQAh26O6airBpLjEXhpbIyYs4eqL2Nba5zhCnzSNpP2z6LrxgIeCthaF+7pzROjj4FJemSN8E8n7w+ZcnTJupoGqHzddYeSWC+IlgpfPFgZ0+sR7yJcajhapQD+G1DD1HB5q4BNyylHuefB45LAgfsM2nll18cZQiwh01orrjJpusv8pyGI0mUalm7cg39sJMGpzF3bKXhlGoGyevDRArbmtImoaPH+y904pjF4qtSwuhJ/jGHlt9Ys1luz/gcfUDjw1iolJhm9M0w+MViuyYNeYSHcVerHO3buyewjY8AwMFz86lSNcJtsmStt0dDIadYNguJRFKAisNecvICV3is1KK52w5ASPMgbRLBOIInhSUREFeDpqUMhcIIasgHTIUXRUHAKBjogqaK2YZ8CO16g0uqHXKahyKKqkGVYbKo5QghYRLd0e16xvvdr/KuMxiCy02dmEFeGWUKgO9+IUtNttwlWXXWn7J2YODVEJMYXBWuvqex4Bx09VX2OfTnwoCIQQJFLksNWbBo/feZcH5BuY27eV33U9kwcEwYsLc4k2/7TIReaCmkr6EDotmkexkECAggoWqiixK7CzmiA+JctbizilpMi5iUuV6oAz0UgT7UDdMUWhAfN4SAmCC99UgJYLx1BEd85qTmLpBTJGNghU2T44ii1WtGkB91tjzL7/x2vsTIMw9Nll33RWWDa6iAWGo6lVbbvCx7TdcaxgWlNzR6D5lVnP1A0+gc7M1Vtp3x09zpL6hrswICosuMuaoL+w8LI1L73ik0wDq7uy/u+3ettiayy+137ZbzterB4RqSVaalZZYat+tPglQz7yFl74ThUlIXx9apdRBNlpp5Y4QLZWlUGijNVfddZOPIaREpdQWcnODK3WuGoP+cP+dll5yQQjYeKTTQKyq7T++jjLnUt6cOD5XCGdxsNStB1NWdWXizkOYPjy45a6+IhMmVDrgiypyWFyqqK9wRHOUNKmfc2phYUTALJBtlWPtEhHX6mpurf3QUQUFBieH9XZNCiJIoFKxqRh3Ao4hxhiAyIjhjEYIsqjYckiFcO/e7V31wWnPvPaKpWdK3cEmEZgxSBJGm/eJc2c7BFYMVKAKcSZ21hxDq71bH3sO1VVUOWLnLZceM2aAVakqgTTK/lt+8iPLLE5DhyMixmQxP/nmG8MBdt+tP7Hphuv0QqyKgGogXWuV5b+26xYYjgB755MvtzQCUbqy6tr7nsCCYf6RA9/ec5fRI0dGIgRxSXGJMQt8bddt6xjGTZ/93Asvqzt88vUJk3MpKrzRyisAOIka1xarlZdZeq/NN4R+F1tg1IzZ04v2DX4uwNa1r+227bofWcWCZOQQ+ADpDh/fuNLu8RvvvGu5aWkwawPhhSJ1q1gEAvBQLu6mF4e9VKxRQ8QVobTIfYR+BymdRxgzfI/Ju8H4HLXvLsssOTaA7W5QKDSwx+YIlUvg0bX3PxZSEhhg1oefefnpt94Hg4fvvM0KSy3GtTPDDdu60m/tseP8IwfmNOnBZ1+u2WsFLYzpaB9bcZkjdtqqqrTUXEaw1b7CsgutvdIyePTaux/0GREM4Ht/e/b14Zyy36c3/siSi7JKqWJWFdGdPr7Opqsjy2AGicBgmbJT23CTIfmK6p5zDfn1oYgPWSLyVKkLjD8wxNHNHdmrf7jnrkstvIgQszAyA8ew+Xpr4lm/bT+YOJFJOEauEKQkEsGsEe7r5ANtqfptQJnv7rFQLJ5Dvy+dSUbjXuqGItggGkYmi+IcgAODE1ircuRcQYspeBNKZgZhdcQYxDs880q4IkGDBlosgEA6CRUeyFq1CoOBEYRAiSxzMi1ZBLGM4VMCqQ7EUgUS8aypqUoD51cvUHPOCLFcZa8S1Jo9FYfc3DsHYiKqjAaYqhg0dhIShzkZGIZkBMsgzVOnjG8GBzES7cknHps4ZRou/rX297lLLrnUvwbhH5n18FPPTR7687sbrbf+2AXnQyJUEVi5g101DSXbnHN+f+GsWd0v6u2z47YhQLL/COBuzDaf3PTbhx2GKW1/8FfnX3DKby7oww27J///8rnmtr8+/MTj4PZjH11j209ugov/obFwr4rDnXvusO2o0WNwnc232HoHXEycNPGrRx3z2POvpybmPnMuqohvKN/IExUrsEA4Vy+z4LVESYVzUpRdVODGVGppEUCGoLMlg/PF4kFE6hjqEIKyiGmdNWQuYqVOpW6tSoySqSCJeR/m32RqE3ZcqDVqlXzAtNdG8WidO5jBN0IhQZpS84q8hiv3yHtcEOyskX7ifhZkLyIlwkOGO4O34AU+1oj0UXewCjsMLwNHJkqVAxQjr8S+14OpalAzEUuYMadzLmEZNbKH6qd4C6ZzQnRPyXK2AsBdy5SKNT2nHosyd9bcdWtgESYK8HzuQ3gos6yUtjRpIOiCo7tl5F+ffn5u01cmEZLAvVBtvMJq53/10N99/dA1llmOlRlZJ7SiiaVwVwUguw8EHmBSGjqWWWzhKiDRS0ghcmQg5XqhkV01NmlOXzJ5U7zf3P3kc03K8w3Uu2y6odaikdjIBpOnFClvt8n6C4+ez9zve+6VYl6MzbkTmQ/hIIIplBgkcq1SEZKHW7GmQFMRVLmyxeAsw6MXXnAU86CwMZMyBxeGHdG8oxTJucdcR45PvPI2kM4/34jN1lmjthy9MIxEaglRuTf/fPMNz2GyLNQKGX1IkJAwOYwg0t9e7v44x+ILzr/x6qtZRl+uEXIVr8KoCjr/0MoccDIIYVFmyIQN9RrId9hKkZIlpxozKZu2XOVumEFzmIWGhR87bDEQB/LAheYLcav1up9Bmjl38I0Jk5ADinivqjEYzSw56PBMljk3lnGL7q5BQBHGq96KgyNItevFh0kl15QGLAEN8mGWFvlCtalDiZVrEIY9cRSPXJRLEC/ZU5uBSTVoDFRzrhmbdjlZ6goJcNAGSTVT6KYGfM2TnrpqqRwJjEoSLPzh7UOidaiQa+YRQsg+FYwASDgNUevGru5LLty9ZHvxrQ+efvE1lJ8EB4IiIi22+NjTv/nlM799yKc3WM+YLcvTL745bloXyXf++IZVGHDwU0ILGEDlaeWlltpg1W45/dCLrxrPDbEFuqohzaWuKiJyIyoEE5JIHGsn8QQnxpOusbOCIRIOWbSEpJpJyVi4MPc5I2JBjOAUoYeqXCJ0a4THBiV0EPDhUExSEWNyygU9w41jwVAvIkYBmIDaOJlkoGOFXVdkz73xOrZYqxh22njd5AW4iZWyUsuS+e+ma94m6Re2V95+b8L0WYC/x6c3Cu5UwItZ4FxZX/Pyyy66waor4OkDz77cZCklvD9l2kvvdf+fzKZrrTZyVK3ghErIVuGZ+2YfXQ2DsVH54vsTWzLAWnxIL022Gx5+3MATFeEcFdsk4Ruf3/G8bxx81J57QBSwiNzJErMhPl1+yUUx12BRJmxUIo0eFRGqi/mU2bPdU87ZMzMMR7lwQb/AeEi6+UMfFifUMWbFC0aSiVtoi8J8rdQG9ixylipxtwcQmBQ2iohWmaiJoUJsmYzcOBDBvBUGIClpyeStUZOktGJJkgUYagsn7lxQCmTeFvdG2UEBGyuIo9wQ7mrnKlFIHD1oZ0vkjKyDJFJTvyd99WyOfxSsMLDHCJujGGEd4Mw6ZtOjL3avL1ZZctG1l1tJci2p5xyKxiIaNMw3MEBDR8AkVSW698kXhwPsDptuoBBJgrTAV3FpVdptNl5rXoB95iXgLiRz5zRPvP42YKy/yvKLzj9KgbUQKBzIDFJXWWLhRYZ+EPHRl94mpxR9obHdj7pA/jc99kw7CHMWMCaYH3SfLTY9+4iDTvrSXgv1BrjA1lpMwRO0VZdaIpAoMyKARK6ER1W9ERH00gczmsHsibOVPiN1poZKXyVp3ZIY5nbNyBqlwqQmksFXcBVmF0g5u/Qd5l6woIDuCMeSYxfAGCUfdl9wGquw5cfWwCOEyrfHT3KmktODz3Q/f7XYAmPWX2UFUwd4M5MC5bZjRkUsSDD+qXfGZc5UJWgct2jL460dk7N5hyORtMJlgflqPJowa663VOYSD9ojz71m7vPPN2KTNVftKxIvkhRlWFOGMfCYD3OKiwEUta31S7eFmDU5Zwftog6T/VACBIsg4TgQw1DSdaCrq7jE4mPJPbpWFiQTFV9ozAgEaVQCU+f2C5FD6EOCYSPQxoVEKKgIMeFx0mSaiIEL3LSpxSVxYNICmXbG7aWFVNBdgsOWI5uUKEaARywudfZaLVbkPWmwtoaxM3QjkJDDsRJ1Z3YPoLhAZFY5DVQUA4HaQtkxALRglFN0iR4J+2oQnRmEEAuewe+CpYGSg1pGdqrd1ADO4ere2alUFpUCObiSCCgBswi8shF5UaWYKTYfTMWrXswDZnr7nbdPOfe8oV+f7W7/2c+b47qYiFkLL9AVr7j4r2i3P/AgAl+MccfNPxGpRfAVMZPcat9iQ9GmzJzx+PMvAvWIMQvu8KlP4uIfbyL8hc9s9/2vfa1X19h3/MNllx303aPeH/oLAf84kP/3jnz+jXd+cd7vIN7eiJHf+OKB8w10per/mZ16oW57A/29Xr3dVtvgYsIH7356/TU3+/jGuH73/Q8O+c4PHn/xdVhiKS2RVeJ1oZBdi1QeelwhVHGi1OSmtImRZTiinzhqriiHzvsrY9GcuW2Q6YgZS/2i0pL2WZsYLRJyOk61UeWimVpESy/4RsyAd2LrDWVmiezixCTskUndMQUewcShUEA0zg6fQCeGGTkBSHY4EBnhgCN20Fo4fXYpxEwgC3Mrth5nZWPHxG4PYj6LPVAUuKgWivCwzPCwWbPnAg6xg0r4e3HLrTmiL6O7++ALzRL6UY8CCwf45odPYiQdMAlEQFMQX4OLuFFJ7ahY7fLxj6nImxMm7/KjXx7922tuue/p996fCplrhCTrwJBagdA9GiGdeipIzR0baoXdmCkCNRqwtR0nVcyBzbohWHiJ4JE7RdRCBummZ9/qfqh6kdHzLbzQAgyYiDmcs+FfgXixcFp76IcNPpgys0WJkbtUhX6gARy0TDAFpP8SlCoSqINySSl7AiJBFmQEaVEaOqC0WEqwNnJTeQmJgldM855Sd0CVMKc4bnK3tzJ65MCSC42pDXs4CRJylmIE3XQDhz4thOYo7ql8SBC+WYzJcfHG+13sWmz+UQuOHIk4CimwFoWlDnBdDZnCEJCCbLrHxwAAEABJREFUscYhiyIQJ88QKGUzDHBYbQHR6mTuWdyhBAFjQ/NgOFG4Fg0kgB3wrkByWWnxhcBxQT03fRZMxwSGA1DdDCQBxB9K7AUZKjeh3yK4dU8IyiISDPbulon/joSiU2WONOPePcSmmYWsQiFwrFQFbhO0ozIk1H8eORVUcRkehqkxUgD2TCDegLYUcnMlg0kHC5X31HqUQSMNH5VrJR30gsoXdbxKC/WwMxUFlcwF2vdMlj0leIpRwCvTTOr7bLlJHcPUWXMOO/2Cb5x7Cd4AvPDWhOn9uUVdBySMYB5ZcuVNsKfe7H7MaUQVVl92WchKiC1Jk3SuSWFQ5+ustCyomTHYTJ05g0MJLBG+bmArod/cC1GOlGrKIkQwpYgvPELLuXWQGz1U1mkU9oe50B+Zkbh4icmqZNHbQK1a0WRcwBE5Zs9rhcUiuySnRKVDOvzAoIYAL4pauktlcgzCfLLsjClS+5sfdG+WxvSqBRZaMCviCXkJ1Ehp3WAWH2KBMlBkO5cX3v4AwCGN5ZdamOE2HaCKWUCsq3OANJbBAEhj/LQZqZZXJ0wYbBphfvr1t07+07UnXnjt8Rfd9PNLbz7liptOueL6391yFwYDycTpeOvLsLdNPrrSR5ZcBJ2/uPrmL/zsF7+48uaHX3570vTZ2WD45IEQUBJxI6h6hRgDu+YOlYuYoDDR4BSyybxnbgg/JrmN1laFxCBYYytSiGBa3ezuA/kpGyjB4ii5uAUI2RJTcYfGObirFqnNsJWBLYhOYehszdvWc2vZWtO5qWqRPYqEThJdDCMDaQh1sD5KrMUJPLpldyApUELCTREjdnIWkYISDyrIgduoHeoCpTkzKRGJU933HniJRZS4cigRZIfsMTNGZVWL4tHBgWqOXN54H8UVLTt2bF3VxYbEBwkGQVYyBsh5LTAqvwCvefbND9CF1chiCy4o2Sl7RoihzFwCe494OMCOnzK96bfFaPKsuW990AWuyTNmnHT5tSdcfvXxl11z4mXX/PxKXF932rW3zRjaJps8bYZIZOYVl176E6t1S9nr/vbUZ4857ScXXPXgE8+PnzKtnxoNMqJXV7E2An3CRs4OYtCYOGYCW4GtplJ7Aa3oR0PsKxCOIXqRQtmSoRZSwzXj8XADGCzgvTAjRnZ26iQQhveUFaOAyR3fw4OJgJbNIG0iE8oCZbkvv/iicP9iPmHGzDZo4vTkK90iMKV09l/uOPGSv5xw0U0nX3TjSRffdMIlNxz3p+vfGD+ZiGbOmdtit4FbYhgcOoiBCAiku4Z1cZu7M4FEcgfhLpbZ0hvvd4pYdMx8Cyy8cAp1Ys4lG/asraEOwtB00McwMFwbPmhFvCHqs0C/oSLYah0i+tHAqLIH5v8+ubMabE6siyCQBQVWEYDHeIejFIfjA4E5DBnkdQHFtOdVzQAECyxCKRQkSSLAGZAoOTo2j0xLMgPFxQHGKFtILB7gRNzBpg4eAJIE1ujQAgxaWCv3yKYEmSTPUIETnNXVwbNjSdSGxmAGwAwoZAbROVQEQwG6fqZ+loRc3rq4K0OaFBurB73XkiAwxJioKhyk0shV5FAF9HPFbYVikIEwG6dAHFnwQYXBIbRaQaCJDC+8iIiZR4zAW3W69Jprv/Dto6ZMn0H//DFx6BdUMG/smNE4/1e0aTNnP/p49+577EILbbzW6iKsRIUEu/Ku8ITiRHc/8IS13Tb2p9dfZ9TIjql/ihLEq3133v7EH3xv7EJjMfHJp5/+/Ne/feXtd7cp4/Y/2v4fPB8vxI459ZfTp00BjXvuvMuGa60GYeL6/9xG12G4s9/v77Xdlr26h9s/XHbVr4//6ZabborrKVOnfPu4n8zoz2J4ZyH4m6hVkaLCxSpiJe2CVivwIQ8FdY7VBW4bGBoVOJNm1yQRGcMwGWVvaRtOfUHy8tK5HwYSBjITd1NIieFPxTrfUbgl3A8vSiI7cmMWQsnmTJJhKUIFZGBnoDaG6XArkkTaWMOLGpYSXAQRxQGdKHnqW9NYY24ETMHhzVwTRxFlDk4KnyRsNJh6UR/+UQWKJAEubSjm+/2Ghg5RCqGbDzNlgatGeCz21YYekmOz1cArhOWMSMrD3WQGt287AUVgIUwLSoLYRpbMPrfFJl/5zKexlzaz39766HM/uuDaz/7wl186+Zxzbrr13ekTIAwKmbgUhJsSCqJ78WQls5eMPNd3beehITJ2Q3IAXjMGMSGRlOGnLA3UlshnIxgRLTV2ASMrJTkNMnbOlMijIMSRzzfkbrMH+03DMfMAZe0ImGdHSghpTS6D2TIg85CIa0Lut2AOzEKEMw0frlSCgXTPwn2vCgUmx5DuMTMFSQHmwP7+0B+iXWjk0DKAAZWgRULNmKkl60YPfbyVjMLE/3sUTmAbOis6dbDFqMUWwI4j1GqEkl0UggbFxv8NCPURrB2EkXcFbFPUShBn7hQuyOAwAgKGLCF1iqK/HwybIarMKUO6XJiEx4wa6FXRzKbOnCPeQU3kNHQAZVNQkdeI9EkV0rWYh54QwBfVbCqG1yQRUqHhwzlTZ8UOop0LhqMhmcHdOBBhlSIRFRo2CAq3zMW9ZDd8IRWJmjDENejUNzWPkTWwuEsyKsJFQDfcgQr+AR0z95R6QsDpmEoEDOrCzuSAlwULCmiutEyDpsUkuNXsVV2Kthutvsqx++267CILpoL3b6+ecPnNe/70rAOPO+uUi65+490J5ArwFFoLadrcLpIvNGq+USPqUDN0wg70UlT6kY3LqBHdtrS5T5szp7DDc6Wutap7MWIcubXcbySDNefMQlrXBCjdM3Bipo3G7CKONW8lwJyhCIeMcckIFB4d74bbkq0tAgAwk+TFfAhAd7LSgwAEFgzhQghd39DHKvVAQbIKsZO4qZtgaiucOoQiU/sJQxdbcEw1EL2uslTZOTl1QtPGMQuP4V4m+JboUwbn4ALSGDGAAOQCvysBUhCnaB6Y5h96xQppzJg7SyJNGcTbEcftfc++cvldD13610cu/eujf7770cvufeTP9z543UOPAxra7FlN6NehH8f2Rp9yyF4fX23FXtQX35v4h1vv/9Ivf//Zn5z5zV9d9ODTL2G33JQMMnXQA5yMuWjQyoDUA/hCJNU+2KXOGvCEvBCYqJUiJy5Jc47FpDg5ViLdgOEPeoRRuYTCXDBJnQUW6eoE76q0q7+CFqqcxBwUsOBRzK1gDZJNchWbirvVEMOIYdlQMQclZlVSUcO0Vr0N2gZuXMzCkKNGPIeRB/eYMbJ21JZCgSRkZssemhKbJOCCusOJGqZMHFqNKVaiytI9YHcyy4Qy1tpQTFvlNpANR5VFF8B7bwBsYQBMFhivrR1JqJs59PEi1upgn2b1O3tYeuwCsbAWdQOXQs5gIBQCsr8HWEopZML7jNltBoyn3nj/z3c/cuk9f7v0ngf/3q6+//GZQ3/gDvV6MKmsqoP+8ODPbb/xWqMH6vemzrz2gccOO+MPuxx1yldP+z2u5/SboppjxaHCFgRBwwBN0BW5JPeWsGffNpSbD58QMWSCCriqradStbHq1zHDew1ipXkHDDRmYqPWNYnCeNRzRRAdS5W8l4BJcLLh8ZgJZ5KSmbJwW0kC42NGzjccKifMmJtdGqMpc1qM/2DazEvveejPdz5y+V8fufyuxy6759FLIYe7Hnl7YlfGDPbbtsnUUpkHGzOIlYmxboyUoEiPrYO07gFR6GWs1LziKYOdIhZbcH7C2xxluLVaIrgklEtKYHpoArNLYFfhILDfErJ56+AT2gLLZKmUoYE4wW5bx3NjIjTCgQhOgjyOPVYCPgkEMOhHczDZN+lnh8DTYMmJPBr1DImQleBRlXf1kcOYKQXlGKu6CgQy3drEXrLCrJwiaQS5ZmolOlTFlDDC+mJNpIIMQkJWMKFKWg2yDVo/oVQoXV4TYhXpiHLK7o5RDJbMDWFAcD8k1k63uWQYRsZOVCR3fIJzzPBZBLrICEUxFFEjJQqqIdYhwH9CrMkrN0nFMoTVmjXsppClCNVklRcW86qE+MHQj7nHGDf/xLyXD48/9vC5l1xO//yR+0Obx0QLDIXLfx7A/37G0y++9P74bhdqi00+PrqH/T1prWoZZUUXnbJx6/Lnq64ZBjR6/gWZefj6nzszb/+pTc898bjVV10VEydMGHfsySd/+6TTXn6r295Dz//32ozZc4466dRnX3gerG2w/ob/dtDnGYEEN/+bJquusOxG66+PUS+/8tJb4yae8dMfb7v1trh99/33T7v4UgmMJAs3gMNI5KDuITuCkJYSiESjha7WTRjiHpUYNi6UYLqdvWdWeAGiTJssozxAyKECL9HiwYQFEBizvBbAZxQuKsxaqVRw2SgcPBBABMraJUAyJ4KPMWeUMaIkxExAFDyzNwV4C0eLMUd2PCAqbCZuwQz+wlLUs+AavZWUAOCi7ILNAM0GOMWd4OAZ+ygSiQnTh0sHImapmCpS9Sp4VEQWZ5CoNHSYGLPFzEBsMq8TTwq5kRXxEpgDY14dtApM4IGkCuHAbTb77XcO/v7nd9xsnVVXXGwh8P/Mm+/9+oY7Dj3j/PtfetEjZlPHe8EJYlOEFifEHCdgEtwByVBDxKvIuqCmzlrkvz3yKBnpJ1RD44iAHJMb99bcEVVAbZURBYWLzZslHgKLaiE0+vthyObZC5ZYxk5CETwRBU91yVXHJUij4cMIvlyzqwAHlIY7z+TDD0nFEAHxpFipVAjdDuySsYfdCccZBSpl1ownw80pELToxn+HAi5Qjlrkth7uYyapuTPazraYCCmsoMalD48wPD0Eq0KuAhF3MmNVGpJRC0sDEkY/G5cCc6B5hxApbg02ZrmLy7jpZ+zwFvCUk6kTM4TCw+NFwXcsBtMCqxWJqgJE99AYggBfHopGZ0i/6+0+UHHIQRNrgmjAbKuKGs9Qidels10npFuHkRnDOB32ScISolSRAT17lzWzCwnUp0pMQiVQEhBcCrwHFBbqDpBZMRxCOEVz7brIFA4GtyQVdmbIreWSIbIu0yFteUUGkRlZNttq3TUv+s4hvzh0r+03Xnu1pRaNKq+Pm3TZnX/7ys/PveaWB8B8xyUNI+ugs5KriVrFHl0E/qW1wSuLdY8JCgwZDq+BYyDVNkNEROwYTQScjVJXPqoaE9PQgV7zMkSOFxcXYoWImACfvbKgBtLV2chb8CiGsY4dK2hxCEB3skY8g7hS2It0PcMfy1ogLmGqCJSTuIN5Zi6slgWPKDpQdqM9Rqo0CgYJFeVcU6n5QzLJYR1JM/83acRSMUXqlOaxUK/hGlkwaUrzxgBVj028g64iG6224tbrr7n1emtutd5aW6631hbrr/VpnNdbc5t119hm3Y+utOTinUhBfYrLj134zG/sffa/7bffNh9fb5VlR/aqabPn3vfMy0eefdkJF1w1bc5scliEaXb2IegEMuFZME9R9JJ7YspCQwcYlOFII+wAABAASURBVE6YjNGFk5UsGfInYnNIkz48OkgMrQ0HTqgMHEMMleRKknAiaYsUkwL1uAMHM0F8JRKgqbJGlUogWAQY82wEtbJ7VIsVVTWXyACTo5QeWc04iJ0UMKITsISaYpRoFXUDQqfITGYgFs2SGw0fECtDkZWgioJemcE8/DqrYYQDKRV3OKhJwbnjCvOcvcTEvUxkwhTIlW2envCYCBJrS07OQ3fdSYkYEAl2LoLZzlyEHVC7XgLdzrUB/rzbNZdfassNPrrFBmt8er01ttxgza279tGt1/voVuuttc16a6276gowNyTeYGXMmN6PD971nCMPOGKXLTdcdfklFhjdb9MjL7/xkz9e89UzLnh38mRWIQ7EkTvegApfQARWifJQ6CrkDgKHHgm8LdaEUAiZROOQKbRed+5S5o0h0K6SXFLLUBezsIiSR4O5d+MbDQ7bdusgQhpQHGUvhbLhEpEosCbsDpTiRE2LRyitw/DgpcbOv9W6q2+57ke3XH+trdZfa5v119xm/TW2Xm/1LdZb/dPrrbHJmh8J2stNKJ37Ds+gUlemMZQYCzwuQLCwqOFnIF8q4gAJDHUw3vrQgHQbMnUMGpmViWToWXdyYiI2mFmNM0PI3KmtECf31nOxD5nKsU3SpmKliDtmEQ5hcma3bDmreyWqPu+REeMh9rViyp5zcWQIYyUz3MHIxIUdrhHbELPVZj3hSkN0gaQ5s6tSZATDikVN4DJGOZBJjtaExjVJKRW3WJljnAkjVhXnAmSNlKQxaxAbcA6kcLEsGIE3IhpTCW2yBn4mmVCvkLk7CSFvUkVBicGEsAiIouA1cU8oCkAbGXWHMyeD4yLnUC0JClA35hLg5pQMxmUdTgGrknIoTsBTdMaUCZjdtu1td/4VF8NtypRuPTp8/Y+fZw3tfknV0xj/8Vn/+EiI5Nb7HiiwYKIdNv+UFYiqblwcvHngNuTB3tPPvfnCyy8Nw4yjFhi++JfO/NFVVvz9qT8/YO+9MT2ldPsdt+17xNdPPe/3s4fYROf/Z1rTpm//9IT7H3kEHC252GInffdbvbrG9f+qBZHhR21OMYRdtvr08O2VN9w0ZvSoY79+2CJjx8J8r/7Ljc+8/AZHsdB5T3HL1GRu21BSKVrKiMA1/K1wMepXNKih7yEn49TWbYJqQ0IM6In3iCt3USYssQeKITcj3CHE8FAd1ERPtcPSBa5JkRGAgLJoSKiva4kBoccdLgBXiAiLmYppyyMTDzgBdl1c8OKjnUvYvkB95TEjucBBYFgMx9OqpkoTO4rzlGiwiWkQASFEq9kHUCe08CcYYuNVa73MmhR7Ev3Cc9lGjOgk6e6DgwUBjHFAIICmjMRVbNh5iZSDSgiVSN26fNhLmbShGuRKcZQBXRzgaFwjjlAtElWIF5t/zC5bbnDq1/a68MeHXnbM1/bd8uO9oO9Onn7cJTdOmDqbIRRvlBoVAwJlqZzqECutiMKw4obOsSOBhZgM/RA4yVA/9UNsIftQo2pBz3uTpkVXKZVnwcrRCwIVO/xPePrM7gfo5xvoaS94xSYIM9rJEdOIwJSBGFWNxMz4UEVcOxgiaSm1PNh6GhpKVJhyxTHicZ2tguy8DJrneY+ZBMGUQsOy6Jjut1DmtpmTJVZYUSYWsRha1kI8D39AegpcoQN0UHc4Ew2x6CRLLTAaXeOmz0R6xS23UA9TKeYtI8Dg2VBDGmtrbmpuQXkovZAq6aswUSREaSc1Iqccu0xtjUE4Q/NIxN0AjMAgLvCFkf1BSwUi4VEDFWcOmeqgw+ODiCBfEocCmkVZiOc9GjLNJJxVfKjPh6cAsbprMO55EZg6KblidEcUe+KUc/LkDKdq687V2hID11VVh0qAgEJDWBtFNSHiJJhNwSik4m3O2TJwCiyPhg6oXcmqlqqWuWUQkaA1Iy0MSWvBDRk7g5OaYLuUM4YxFrQlmiO/2ehR9VYbrXbCV/Y854dfvfxn3/j6zp8eO2rE9DmDv772jmdfeH3AvS4+/9ALkymzZs9oZ2VOyM3ItgOFB4rUJCo6dXZncsI8esQo88pMybkTnnbiEpUYwBDBo6MhPXrwhikPcQAKA9Zhjs3KlM2ykwfiXuROCYJAJAzdtlqjs7bQ8xKkldJy4/whBCIrNtTI8KoqQ/3zYMO8E2OcKuMfuXgm9dKLED/sgwM2l5dYsLO68dNmihOqoIq50zlMHkIjWNQ8UKX7ZoTM0UNvLCGN/uzCpQZyljZKA7I7m+vTtOnd7/MI8+h6JLvMP1/EtTAdtM3mp3x1/1OO2P+kr+53yuH7nXz4fifhfNi+px72hVO+uu+nNlq3AD3GESPJUgxrrbrsd/bZ5twj9/3Lz79+yqG7r7PCUrmUmx97/sJb7yErtRPsBMGxowtEJM5oDISRENwRQcvwE6IYjQc8VQaRxZxCbi3DcooYy4djCPqwRJCdq8CLyAnyEGFX6gA5IUpa372fBVdwNgI4IulJGAgyEEpdLPZDPRcb+hxR0jbUQqO5r94P2rLCIoDf1DwSw9e0Vxh7+ViEuGYYjdQeggYOlF37qW77Few/4h27lyhtke61HkrDXAeKQY0r4lAQHniQbDB4CtAv+I+aVXOQNsDKckNLLYB3KTR+xsxSSwrsTNx5Zu1NZR1nNHwkCF2KVD5fL6IHAZYLBRryXwIBnThwZ6bTPwywoQ4ceVSvGt6m2f5jHznx0N2PO2TP4w/5womH7H/KYfv/4tAvnPylfU/+4n4/P2T/A7b/NEkLMTuEn5IwrbzUovtu88lTv7rfBUcffs4399tynVVV+Ik33jv1T9fR3JazQ85ODGK6BmXBM6wqhtwEqYC0rrv7sIqKMBNGM/TVlgJLoT5Wkdw5YDfGYbqxyQGJvZBwN6HSfiWDLIhGGKHiqGgxG9dEJbNBINCxGZWOBg42q5mbigHRmAEFXSO0N7LqZLXyYgv+/Ct7nHDE3scfvt8JR+x/8hH7nXzEnicc/rmfH77HyUd8/vt77z6iGuEEpXRwAF5Z+qRuVAnVPeFeLBoJisEzgh1yhmk1tvT8neImTJ0RLMFOwWEIoSd1z0NgAQdDw8kR6otLPxkKGnOxMKLEXvGYMiX6e/DHYFPKgRNTn/+OjQhyCxCIB2sGSomZ2J2Gjhw0ofCpA1WVVeSV56pNsc1CLVOTtIE8u9jeokBgzgXIOLNmjrkg88kA516VK8nZ0RIVizBgN5K+xBII9qvcArKokkZnhQsXapVyzaXnVLkEaQLskplhgeocSQOBaTgNa4Oor16EWALHoBGcIOOGthhbQrwlU8vBsuYi2UtLnY+XkDMZcnXTcIFCrVTFI0mEc7BXVuqSoxXKhUrLnj0kLLNs9uy5U6d3gZ6ISsm9ul5yiSX32mXXH37tMPT8U82JBvHykijUIzSEf2ruPzh44pRp9zzc/QWYhRZaeJO11nSqYYJCpJ6rAtd3Nr/qL9f/HVozpXv38vfbf+FizOiRR335oD+c/svlllmGmWfNnn3+JZds/rl9Tjnv92+Pm5BL+Rdg/j9tytQZMw/74U+GVylLLLbYuSeesMQi3c+8/eN0fmL9dZdbdlmMv+GWmwFtqcUX/dahXxaR2bNmnn3RlU2jiP5tzqVt4U2ZKMMd1YMQGgfmyLmmrMFd2oatQRyBsHNU5OCkmiQSQ7dZEb/EYP7wDkMXKTvcD66acGtEiG1sWbgoGRxdQ8OE2MBE0YiLwwGikLAU0ty6txQYQczEWklJc4luKggknpQM00gL11ZHjkU8cePkTpVhgjtnkMAmSIQhM9w4CBggBB2gdxPKUozTyIGaQJjZtGmzEAtqR0gG/ejr4GebZ0JGmKKGN7ECSgsZBnSNgcS9FHNHlzsuCYiFQpWtzOnPndsMKlHIpsQDA70Vl1383/b7zNH77kRE46bNfP71D1xcEKOCB4QZZxUO4KdyYaKCDwZ2TZy6m8CMwRiG/OjSPSDKhZ0oiq21/JLomTRrzoQp0xhyRBQphEfK5IJY3Tz+6ltEtNTCCw4MVIhO2dhSoA/TQAdfmYMi6DMLs4IUZAyrvDWzLrJjCNgEDBJxjSULYlVsS5Vz5RY+hEQdTOgoSS6+8jLdb0xNmjV74rTJDDUWYQpW1Nz473IkgrFVFAjl3Yd8AU1RzopBtvjYblNj/LRZU2bOToVTiYCUAMBFMe7DlrUzx9axHUWareONnSUTLKd2RGHnAkm6UTbPBcqbN7M4qhHYf4HAmBwkMvPr70/AsKCy3GILcc4hwXgggW4KlhRE5rFosMpy1ZIU6R7g491hLWUiVG/5Q6GwU8RImH4kqQJxwAxAxHMjWGPjlBy8QrJwh5AVhqzSMlzK3BNEV5sNCNfq0Z2K2JBqVSzgliSjL4GojkI8AXZrGPOpxOzQkVoyqIuhkiZL6zHlgRKgMzcrUCPcg52yppamzWmn9vupw2CgakSPlh47/8Hbb3bW4Z+vgs7qNw8/87o2dUy9tZfvfulibpvfeHeitw4hCBOzKVPkzgQfe/lNIhozUM8///ylIPkBVRI3YUY/E6ZkyclSaQrY6ESH/uGmzOoCTlEwBR+qSAJrrSE6cbJSSnZuGT6hANe1UDhaVFIfhoAzMrxy5kJeiCFodA03pu7OyWFhoJTg4U7FFaS5EqkxLz52QYydMdh+MGkKw6bNYDnoJ4dHE/8dSWBYLouvucI8abw8/oO2YPeMmDuKSAE8QJSPvdz9TQhIY+H5AVlXWGzhEXWVij375jsMTFKcc0eQwXn6KkmjcJCiKOgJ/E8dnDm5P7ffwiiAXAakXnT06G0//rHzvn/wmssuDvE99MKrKVlQBQfgDCC7ZghFibwohViiogwCKd2DTgItc87Agc2TKgCdshu0IP+9rEwiQSzZubCVDrW5FFLEpwKahcwVq4FcqgzEZkIuUB4CmdbMmj33SyI2FFzKBM23lpsCAXGbBFrgirWKUaiIJ7K2sAFXTlzAqlkBecxeENpLZQlXLpy4SlZbqcBSB4PAU+swACF4Xs6W8GK0JGeI11wCSc8ZkmERqIRKl4CG9fvuxOmDTWuukD2mkjNBVA44QzKiDrIE01A+OhRgJ86a88H0yRacIBX4MQyPYQoMjoYD7BJjF9A6QukLDIxcemEomh59/T1pDLGHPJBRAWemZgTeLDU5p4YtBZvWH5w1a+7c2f3onagqrsf0Btb/yEqnHr7vzhutDWqee/v98RMnMKhk3H3YWIDLAd7YUeD3jBDxhh46eSZyJep2gFD3IrySsHit5UP+2MFqQSCoAEccCknQf46WY2mLpqJmorFT2xDMfmslCRPjzt04u3l55b1354XKpRcOFeyJ1hj6k4mvTZg+ZVbf2VW6aFyM2yImbAE+01EHGRA7tAtoaMVhGd425nBFhIkgHJVgMXhGlAxxDAh98YW6dDBp1tzJ02blkgsVZlM3hb4tsejQcBLDE0tFoN4MA8ykWYJXTCrO4IE+PIwKAXDLAAAQAElEQVRYNXBPORYiH+5mi0zKwbUyUicmDKPhg4UAA5BiVKlBpjKxFCAgYVVWCkNWSV7Mm0J9sswdVwwrA6Vd9Jfi3Jq1nLAEUDUBAiWrZFg6Skm57YhhYRaBSWDxB3EIgBYuErKLu0ORwkToJXbo2oWZYOwRWkqB+zE2gjczMVrtKVrbSUldIYxWCrwEtV9ys5izZuUEobl5cU+FzUSdMZioNlPNUhlkgBjTUmtSWCAXJR+cOzh73vsB3mC9DS4/75wHr73sFz/63kLzjxkW1z9+ZqK5/T7Gj6g0CnjD5X9ye+z5l6ZO6n47bbfttinSy6zMXJdUN020olIG+7OHC+5hxG1K7sOX/+oZHiCy8TprXnnOGUd88YuLLrY4Ec+ZM+d3l1yyyxe//NWjj73ytjvfmzilFKP/dx6vv/f+oUf95MFHHwX5yyy55K9++pOVl+/++Dpu/502ZVb3o9IYMLLXw3nMqPm23PSTuJg2Y+aDj3e/QbTnDtustGL3J/zuuOe+vz3xQsnE+KTMfeFWucDJBNozhvNoieIxEEkpDtcqBOcIBBeuC1eZoOeqr9JKWywTNOqYJMXUMzwJluwUmlwVh8VzI3BLUyaEKabiBM2UDPciD1QqLpWHIMHIM1ND7iEjNiVJ4hSdI7EqUTQBMAFHMTGAuxeD+8OTXKKqMrPnjkzu/BzhUINLxYjOFJwiUeVUEYKDUF5hibFB4JCOzc6YTFvkixTw0DkQR1FgQXOA41iYibJ6HyJBJ5qSKZiWnMRyKEXabMmoqMoF1931ya8dv+WRJ02aNqMuKLRCSsELB7OPrjJPieMmTiVi4iBUsQkSEDmRkgvSa1tSog8PR8YgRyYitk4tCH8275kmUvOafdM1V65jmDFn8M93P+jiOWquQpEoTDGXO+9/EgWlMH/io6sqRJattMSJVTpJAlYkKMEFEY+gXaXuIjiH1KHkwsHRwxjYtV5EqrFMpQW/7gwwihjaPRr6gEZSMeH8kRWWANJJM2bf9tjTYlYnCm20oi2Woskmz5g9NJ4YmLu94NppHo5hsgpIpXajVbs/YP/BlGn3P/kseiDiBsZYYEbqRpNnzNvNyUQg0gsRKMvBABPQQUbMSNXWc61gZbkaSmAGnD4P19vjpzgUoBbUSYl7PJfSHU8+i9mLzT/f0ovPz5TZaMyIAfSgvTNuMoK5xFbrLGrQ/OyZs2c1CY8cYLNmYzhEwQ1coDMbwimySFExDQQDUVdxdnJ3gnsUDR5FXSDwkAQPiIt5S01TmsHGc+uBGe4BhcK7GGIoXHI07XGtGo2RZHIDayAIgR0FXQIzVYqxDSAwUAEJuXCToXhrK2XMqRrWRtxhCASJCpUHnnp2x+8fv+N3T77v2Vc6fog0u7oD50rLLxU0gMdxk6d66WatsuJSiw7tbl5824Np0KQNJJxr6wTg9uq77z7/1vsYv8kaK1eBpaBAg2KK59RvW/Q7meVM8N9OY6BBmYJyhwJPIQMRh/yiwvVd2VgY8E06mTElxreSMRGjS6Swkgqm45bmHXDVQNwNIbhZVJHhB5gbwXH20ucuKEFTXpT7BFaVEkRItM4qy9dB25SvuftRmKGllFNrjStGNTZ56PUIoEEn2P2tqrDGSksPS+PCO+6b3fbNwVm3Jnd3UXrlgwnPvdP9EjCkUQ8EKrTEQgstOfTS5pZHnpoybZqbWSkFH1QTUL3ZxGkzSzYFMe4zUrPzMSdvceRJJ/7+L5Rj7g+UwQGfOyCpqqoRqyzXhZQpM2Zlo5alDewd06AO5mwVN5FgU6aRISAhyKN7lIGrJHESV6FKiWNgDcyZpIgId4Ooe6pY/2UIqpQMa2VyMcKmAIACBEmSYEGYjRAowK05jDCDR6Hi3rJ3OndOIKsqqrmCriovkSEFbkXwdhbmr5B76qfSzzpYeqlEz6SGWq20iVLj0ueqoV4rWI9wIW8FA5hVA+hkoCxuRuTWOswrFYUCcMuUSmgAiXFkpQwqqBavN161S4KvvDfuuRff6urfofDhwqSM4mTGnHk/Ku/E7AzhfPyjqyLAzpw7ePn9f2vhUp2wKorBVBLbzY8+PrfNwrzZmiuplEw2atSINZbpNo8eev6Vtz+YPMAWg0P4EBOoLwNpVjNzTn8Q8BNsivmbZ1y09b/94pCfX5Az0MXKRYydKMew2spdAITXTJ070yXpkNXT0BE0uFJiN/GgFjGHOufCQydzOFjnwBliIaOaeaCSjnAVDOgam0iD+BGGiEvWNNQORh8UZE8qBYwiSRh9OHz8lKniFZcepAQ/Q+jo9/P9z7wCUAiVyy81NoQstW+wWvfn/hCx7338ZcT8AUoVWQBdfUSHiKXIpMkzjVrXlqHND4EDiFCfIX5ol0tCcCcRYvSjMYt1MZfXXW0FyHnKjNm3PvZMI0hDyCWNwxgom+UJQ78ugfFgEZNJo0vPingqXmAibDzgcYBijTHDjUvQ0sXDoeWFD3cStOBMkdpArXbQyzxCSIwCkRIF9yoROA4syspMkT0GD4jLGJGdWjZYd+lpElQKBdaM8QaujGBzxoTgqgFTgRQMkgtxKwhEAVtXVojNCwG5ukStALhRbrg0hmQaclHkQiNjQSph9Y4IAErMBYQQuk26p+LwOuXkkrAmVVM8p0xGBArQupFBCDZE5MSohDKctag6KWe1Ln7U5prhq8RgBaIwjPBISj539uDMIW+pq/jzHxy57hqrqSjI+NcaM2Oiw3jx9Z/dzOy2e7v/PoVDtcOWW2fxjgUo0LIVAzbY9iMvvzhuQveTbLhFmzB1WoEicPUfbvONHHnEfntdcvqpX9p//8W65Qr1B+fe89BDR5940ucO//phRx9zzqVXosR5f+Jk0PkfxvZ/EYAHn3j6kCOPevb554APq5QzfnrMmqt0gRW3/337H67dfe6UbrmI/gU+/GWkT26wboV3lFYeeaqDVtf1EQcdgAFt2/zhisvJipC5G7RhDaHEkcxUIiJ97gJl58MhW9cMVlw6N+GQ4NciJOoqQSQ6MgFuhSuhrurWpNIKElF2d+6L9kWSdcGHEoZ6QLxAXMmlGJBYhOewM4Mqga8am3nTp36rqVQOb5POixWRijOrAfPQyEAgqS2Ixl5YU0Td2OM2htar3ATqEKI2A0a2rJKqUIKAYBIKSoFp5cXGjh4xkEt54d334MRubSrJUp/bUvXJzGjoCI5IwkqMQiwQjR7R/REI8DV7zqAIACqAN5KbnFNyy166Kme5IABjF9x2L+KxhViECwOm3vPYvJ9+XHGZJdpWilXKkYkLITpQRn6HyzTmRYaQdydnhI9MhMiHZ6X14hBt94Qkmxll5nVWW2GNob/9/+d7Hrrz2adcXCjiA9U+/eqbp19zBxGttPjYTVZbtc2cWkAswDFqYAD9pdhcbCgws5fiht2UnDSbFI8ilYRaolBlFOYJBFNAEPASqCYkiba7+JBeJpLAEjkKb7TaissvsgDGX3HPI6+9N85NzSUTg4Vr73ns/udexSM0K1Iymefijls0cYqZgpOYf2qd1ZZYYFQxv+jme7vfggNrsBVMSXLFbQ8+8spbGI/GjcdsFdFr7487/MwLDvnlRa+8NY68sEM8XeSOwaIgbyQsYozIGJO69vNL/vL6B1PmEc2hX+hPt9/37NAfcdp2o3WqoCwkIksvukilHZO3PfrM3NmztZBAMczT+4M/v/i6fgZIYhe2LkmIJW3TqBBUuikzZmP7gBmENBqKVhoirBAZNieyxMIaVWMk7qBmYiHwwpw8J0tmbaQUFU7nWcCQWbbSwL6lgKCgPoRDCMbT8ePEmYIxE2xeDMQ7njgH8YqscgaB0T2YMAXXmAfIIWmCC6y1whIj6wgtXHnnY1Omz2WQ2tYhqTqj3ko5E9HKQ+WXeVlg/oFtN1oTPY++8ubFt93d5uwgRzx7fnfipJ//6YZpcwZH1tXum25Qt7kuBWInh8ioV0FLZM6kLFUE81wqbmtp69EjRtLQMX3uHIrMykocTMXp8Rdf/eYZf/jVxTekxigq9g0JYDyAbc6B2evgvQCmfAhAdwpUiYu7FFanMN9At3czZ7DfloRpVSkMOWaC+YLwCFlIC48SdHi71spL4y0lYF153+OPvfBaYzmXLFS4lNsee/LGR7t1LHCogcIYhRaff+SOH0rjz3fcW4oreM2OQmPcjKknX/WXYWl8bov1nPqesF7XvTbfCBBeHz/55EuunzRphkNlLCIdD0+/9u6XT/7tMb+9dO6MOVXyEUrrrLgMBj/84htPvvAW/KR1dyOQM3Xq9OdeexuPll50YQkBng2M9KFtK3c6YVilMGSsNcrZjMFDDdckwg4nMbhDRSwcWQOp6EBdYQzsVl01C0yRc0as6ZA6Z4ZTYRpZZnNWlSoSjDdxbhViZRi19T3jZUFRypHzCMpVMcoY7LGiumKN4kEyI+YYtXiEUAV3KOhhKhIKR4NW4QGlLSURE0ulFEMiSs7F2dAnEkAnLpgyUyHoA3ZWuaMfsmytwvoht24g3JkBN0vEDNlxg/XGjhqRip1zzZ0TJ80oTBYIOmu8f/HNt77wzjgMQmMiM2S1uP7fA+xdf7v38WeSe8uxz3Vb6dNvv3XWX+7EYATYT6yFGj1nMljvbp/acL66QmT4yUXXvjluYk8yMIC6QPbBpEk/+f1l3/j17z9AIUSOY71VlgOu9yZP/8uDT1gpxBYJoSPOHkyPPNf9td9RIwYWWWTBosxmUmCYQEgikEIuklBPigv6fd6TIUkQd32kXXCClRdTSIBovoEeJmL+9NlzgclJ4TzSqR4BBaV0KiBRgyO0II4ggWLoUDv1shveGD/JPbRgPMTMetmdDw2vwLffaJ2BIJFAD2+58XrDEfs3N9xx31MvUQvhukDAxnmmnXPJzV855bcPPf8ypG0eDcobAo5TT0tER8hIxKVJqSn2ITvSDRPjuMnaaywPObhfcseDr739flu4SWLFYXtX3PPwfc/NyykYDmp0QGOlAaGPqTGsglO3WAgIjCoMeQMnsTCxkgcuOHc9+MBvKeObnCUTA4s5d/f4FLCR1Eu3D0HEDhlVYCSYKsyimDHGOqBxroVrrpgDbhFfsUGawtAbbUxCfia4AR6xS3aFJpRJADe7FARv9UygF/nYjRSD1E2sM3+V1mJj1BU/3rpAhuAvylzRTIBTSMmEskrDTsWdQQo4hEWxihIxDiGSyDRCykDhOiuGYyhLpoEiFYtGDOp6CplJK9p0kSBSZFeFIpyVg4S5s+e0g92OYz1y9CrLLkX/sSOXAgDmjvN/eps8fca9998PsKuvssqySy9etMD8YJtwQIi8oarPAw8+8VROYFZ7A12d9+p744dJwqz/eHPipRZb5LsHff7yM0/7wde/tsYaa7IIuU2bPPHehx761W9+8+Ujv7fXYV/73GFf/+7Jp1909XV/feiR198b16b8H0f9nw4B1cCFf7nta0f/+INx3Q7c2uusX4O61AAAEABJREFU9/tfnLzaivP+V7t/H12T0oQp0zBm7CKL11XABdoaK68031Bt/dRzz+AWbYfNP7Haqh/Bxf0PPfjGW+9UoSLS1g0mYiVS27N+ZQ1Zw6HP2rC2qUeDtfQDN4UQ9aUpcJMaC4LktWvo9cJIFQQ/ZSUORtFIXYnEmaAH4RzEXbmFyxtsX61IMm+L9g2eNOy13mU+Z2YhdKIYw0ASZe3+dA+gMCeNSSkRZxBPyoCS24K8r21NrVhiTBU1C8iIQE7YuOvlFLyBrxZWlwj/RUQIhWrVJRYbu9TCCzrRc2++ZVVKtSfxbpPGE/6Z4UmHJwSqlCrigDQuA0sssrAyoyw75dKbrrnnqdsffCqVPqITsHpbGay/tGt9ZKUt1lmdiC6/7/EvnXr2xTffddNDj1x55/3fOuuPZ1/f/U/tay+3xPLLLdm2RH0OmUBaqSlViI3siIFZBTxg/lBjYSQO8QwJtUxtJ5ehB0To92INHLuqvnfgZ+cfOTBrsPnhH6485nd/vvG+R6+772/HXXLFV8++aML0WaMH6sN22Xq+0SMgd4eUHfyUxRddQJjn9JtT/nzTdfc8ecsDT7epSQ31E/cLwiSI0JoD9mEcuR+6HELbwHURqxjlmZKjgGtLaUHb0ENiJg/kKtD8/L341Z23qIO8M3n6V8744y+uuv6qBx+7/O6//fi8K0+94jYUPsNTCrFzZm4AbbiHC4UMMCTEowbqQz/zaSwSXp8w5dBfnH/etTfc8NCjV911/3fO+sOpV946PB7nULgqudYyN8195MVXHn7+tWlz+hAZcoc4FjDIukShSMwyUAiEf8jOuxOnHnra73/+5+uvfvCJS+999JtnXPibv9xdzJEOP7fN5h56FMUrHznfwFYfWw2IHnvt7e/+5s9X3fXYtXc/ecZVtx1w8u9ufvwF9KOpU8UuAZRbZbbQqBGjehX6r//bU7+76a5r7n/ktQ/eNSmipCWETJVlNdFcMw9QiFCzEHQK6hSrKuyqKjGpFMXOXcyweycU18YO2VpJpeQs3CE1JQCF6AmidIhOiaRmZkITca7YepGqKka1IH3mhPBQeRXAnFIZSB6SlMUWHbPvlt1/0PTgC298+cTfnXPVHdc99MSVDzz54z9ef9T5VwEf6o8t14VhZ6JCJAfstMV6K3c/WXruzXd/4zcX/OmO+264/8nzr/rrQSf+5rFX344q+2216ZrLLI2EoDBRx0JS2+z9zu4xG341wFWvrno9qqpGuPCSY7uXnET0y8tvuur+x69/5JmZ/QTW2pR+e/0ddzz+/EW3/+31N8exVhZFwLQxYovjkCzSD5SVMXte8yKEeo8q5yCkSy60IB689N74M6+66dr7Hn7oheeFGxIpgMRckYsVTi1bi3MQOWTnLear4+SZc77x6z+dfMkNf3no8esfePhHf7j42Euundu0ANU17GZnyJhF8oE7brre0P9P99ub7/vBby+8/J4HrnvwsXOvv/ULJ5/96KvdcvqQHTdffaVlrc2WMxvt/PENtlu3+z8obnnsuS+f+tvzr7vjxoeeuvrex392/lVfP+OiV8dNenP8ZGoRHA378Yds+6kxIwcmz5r7rXMuOuGiS65+4IFrH3703BtuOeDEc15+f0IQ3mmjtavavAuPDvvoaMNHwXd07oynYbbgEg3daFHg2hWzkyCYgSi8eBhwVqpoZK2LjBlNRI+99s6ZN9wERE+++bpUhDVscGcvoglm56VKFhMLQVgqjgjVxWCIz1unxiiDHFLVnsLG3VtLMNiYvDbG1lkMuSaTpuRkiakAAEtkJQ0sNcE2M0dDCeqDqo1X2erYRmkdTlAwHlJ36v4RDkS0YrAz4cBcC1cSYCLYhoGjmycEgWIUCOkNGs+sttjY+Q/cetMg/Nw74770i/N/e9NtVz308IW33v2t0y+48I4HAXG4KayyaCocaz36oJ2HA+yPLrjq2PMuufLuR66+9+ET/3g1zGPCUIA94rPbjF1glCGKcSqS1vrIkvtttwlkAxSHnv7Hn//p+uvu/9u1Dz586p+vO+TEC+59/vW3Jk0tTFyM+/65T2+49CIL9lM+6fIb/+23f7j8/gevf/iJS26752snn3fnUy8y0ZbrfGSBBRcoOZbG3WyYPDMrONgZnHgkvIsijO0eiktFqIIlmyKheyKDoHOfqL8IAhM8n+iq+x676JaHrrn7ybfHTZBIVYTovJYc68Q95wEtARDg6fPQvTtp6qFn/vbnl191zb1PXvbXx4/49R/PvfmuMhQqv7DlBmxMFsx4RK8+8vM7jurV8J3vnX/598+79PK7H7764UcuuO3Wg04989K7Hx4/bdbcZlAUtEGfHbXDnxA5Rq88a4FNZPcyDzF1XLG7GI8emO9rn922Rk6ZNO1rv/jj2ZfefM39z/zp7sePPOeKU664rdi8GcaBNaDsqCOFwBZDFi+eLQ8ifYGlqIGGDpbOMlKRnCJ1eAhHYfLsXFwRQowN1vmhUzmV3JGXCNS5maQ+OsA7RNtYbktTLEEOw2EnkMbMoWUtyqaUJLb4ajS2tVqAQbZVTnWbKBXjZAOWUW0QNEexzdKa5ZKNW9I+bL9SF1XqSCZjAm6uCjFcwhIKA8uUDFHWrWQxBzPF4XTI0XDukAScmkvrVcetOGFmlobwhCzAbrjkiqkiDRzg5rAY9qyEKp4cgbUIMIGLWHmvphgoMNPk6VNp6Bi1wEK4Hbr8F08gaPRQqRpUmEHPvwjnfzXt9vse6mMDnGi7T39y1HyKUAMnCRy56rJty1V/MD/48MOYPv/o0RusvRYuJo17b+rQfxaG6/+UBh6dZZGFFzrgsztfdtrPr/ntuUd86Yurr7Z6hJEyW06Tp0x+/uWXr7/pxuPPOOvrR/94j4MP3fSzn9vl0G9867iTz7roz3+575HX3nkfGTSX4g5g/ylE/dNAPpg05d9+dtLPT//lnLlzmXnX7bc/77ijl1xskX+QoLlzB1996y1gXWaJxUKY54ejRg4E2Dbi02A/pYynvbo+dL8vAH7O+eyL/kQ5ktY+EHxApEIpFxDWKbdV6ZNnR34JGcGDB0oYMO2RC5dCAJUaQ8nRmDQqhhTo5ggvibPDxhj6F2SoiqRyw1qj1jKgLHAA9QwfK1RnR2glocyxzzUcxsgrOIrAUSoESSkKZ9DQK7EqiMTBhCmYD4mjECGaGJEa+rongMSEJFUTYTBVmrvfx1OSXASpqmAeO5uWJuYsWWPsfXLtVSGQR199d+ZgX0RCqIPEwrHAx/FgqIETR2YjykbJZaM1V19rhaUA677nX/vpBVf+/vrbHb3gq1SRQmVeWRlZyQ8P3GOb9T4qwk+/+f4ZV9163O+uOfniG+566qW2lI+tsNRRX9p95Hyq4NGIMyHiuBDY6ngr+EYtgN4h9ARmTMy8WC4ZJ4aDsQw/U8oVdhuLlWJLLTb2jCP2W3aRheY06fqHn/7JRVf97OJrrrz/0VmD/cUWGH3sgXt+fM2PZEgPtQ9xAJ8Vb7nhWqsstSiQ3vn0Sz++4Jrf33gfN4GJjbOVtsBv+403LZuxBycdRkpmoViduWcsiP6B+h7c5j11AgMJ51y8Mdp8vbWO2G2bgaH/muOPdzxw7B8vO/mS6+98+uWVFl94i7W71TJgqrr0HKrT+CEQI2dKgUBwEdnqE+setMPmvaiTZsz63S33Hnvx5Sdccd3dz768+tKL7bX5+oCAFiiCTvKGLeEWzQu34MMgN+4PymAryQw2o1K6Hd0hES44UP3wC7sM9puL7/rbjy68/PhLr7n/+VdTLksutMBPv7zPgmNGSfASUuP9frHD9vjMR5ZcxN0ffeWt4y+5/ug/XnHejfe8MW7y7p9YH/3ASOQSSGJwrp2rer6R+2/7STga6pjTrr7l6IuueHPyhKayDNRCInFoqIB7YRk2AGZ3fBiPWdkqdoX+oS7ohFLRVHquVSWd1XuDpT4yn1BN0kN+s2qYBqPWtLUAPMbEGUkR0UCsclaqjLWIEKyskFshMmUirk0rD/zFz2xxyHabBZW3Jk4976Z7jr748mMvvfqaB5+cPdgsPXaBHx/02SWXWJg4EtyLqjHzjTn+4C+sv8pyxfy+51/9OWRy/pXn3nD3hGkzsbw8/DNbH7r9llpRqakN2hdvCSnc66E3Kk6eSRJxIUJEMOhH08rLLrP5mp1VPPn6u8f+/qozr75tRrIsnLywCBPhNGbkCGI2YsIng/K2SElu2cBKoHlCIByZigmhYIFjivjen94YhWabyyV3/e3Hv7v2jsefQ2jq3hd6pRaoLxl7BMCQlFGypGr9VVc8cp8dsVaZMWfw8nsf/dlF1//swmtufuzZxRccc1CnVsLBSORuTM6Zx9TzHfelz39ijZWHpXHSZTcec9G159wIacyANL675w577bAFQZXEAmaC9kbGow/adddN1lHppP3bG+/6yQWXH3fhNTf87ek5g82Gqy7/8yP2HzV/j1FHVLLmisufeNBeY0fPB3e+/uGnT/zzdf8/9v4DUJejOBdFv6rqnvnXFiiTJAECg8ggBAJEDiIjQCBAKICIJgkQORlsgkROJotojLHJORhMNMFgDIggsrIIEijv9c90V9X7Zm/g8K7P8TsG33vPue/O7v2vCd0Vvoo9v7TXX7zjXa/6yD+ddtY5W8bhoXe6xZ1vvv8wykrTktBQtOWzDiaF3iYZ0G22Xu4uD4k5CL2IKISOIR19QpuQkcTksNvdbIdxmFp/w8c/R0Zf+eEPsoB9pBW+c5oHc+ZZq0UKfam0lOR+pRVxQ2Zojwon+DGV6CuDWnRbd5kVLknLT4p1yS6B6nV0VPFSIauaK5WNyCFTtIVllloHLSNgvWGeNGMFXYG+GmPOhmlRRhIWTEvRlyvUMOtKtoV75ZrKWNSkq+lcGC4NbS6TVD/kNje+1y1uwCln/vrc13zoM3/5xve86J0f/ZcfnHSza1z5dvsuryRIjHHDrNLCp4i997rkqx51xPYE++GvfvvYt7372Le+571f+NcLNqfL7LLjsx906C2udY3opbvSWwHn+42j7nTTR931VsXsV+dd+I7P/Muz/va9z3zHe/72M189+/yLSOfVj33g5XbfvTqZDJfY5ZKveMyRf3aZS9I/P/fdHz+fzvO373v5+z753ZNPpxgH3fBaj7j3HWCVPg/aMZM3OfpMsNWInroUdLOZL1b4gCNF0pUQCwNchUUymS/c03fYqPe/3c1p+jN+c97L3/vx57ztXWf96ixtga6g5/tQpQwamtwwBJEHBABT5bOOvPvmNL3jc1997lve9YK3v59fiW9Plc+9/yG7btkBCVqNotHat7z2lY99yMF01/XcPvKv333237z/WW9576s+/Omf/PxXF9tYPe8BB9/hgGslGVSQDYlzmNA7VphEZtSUMoYOYfpbTTOdES6LlXHz61zjEQfdhjXl1+df+Def+tJz3/buF7zzI/+0raYcduvl96MRbY4AABAASURBVMWRGmCUJlkx2xTMH5Y56Da/crQuLXtQtWViTSsUWlpoQGS5BXQwDkQbX28VtRpFoPjtYdJVerdoHNEnF24z1pvwHpICpZt5Y9BqlFBv5k3gQVAsFmUckiZ0+TaIU6xUrjFJ9bX2TSBERqlk6Z2RRUemJZR5MiGhIqoC0dE4JesAMnRokwUZR1gapuJNXXtWV2PARFI198GzzPCJQV6WlJmhcBIDVZW0hXgahMJAjTdnyeYpU3B29UxRxzC3VcOYYLAK782Zp5/923/da7M7/tSDTGShkcvHf+3fzPzY5z5Pmhe72MVvd5PrMyZVbEFPoSrVQnuccuqZZ/7yV5yz77Wvc5Mb3pAnxOabP/zxcvJ/wt8yDFf7s72PPuK+H3jNyz//7ne+4tnPeuiRR1z9Wte99CUusWXLDiLi7pvrzfMvuPCHP/z+xz/9qVe96U1P/Iun3+V+R13vrve+5yOOecKxL3zzez/0hW98+4xfnXXehRdF/p+A2r/Tej3N7/nkZ+/10Id/6vOfC/cdL77jEx720Oc+4TG//02r/27Ff+fGGb86+8ILln9bZp/LX9bMts+g/NsVMNNSfnvzdje90TWuxjej+NinP/2jU04VmFYrRQorRA26O2NItMHWOUQUjcWmhlJ1GKxAje0g3zs2pac2ZgN4aLiEo3t07gpy5kWoxmrw0VrNqeosNbIKS9qcLFcijAZgcRDNDgYloy0LojIrDBUYtOvyWJjKhzaWzuXKkHQsCnVmXSUNFWTlTHMUoBqsKlDRqnSRBB8HhLlpdieysnZrgSWmsutBN9iPPcTZ511wwg9PF/Dly1hkZBbwDbnhVfZ5+n3u+vh73mWXnXdZJ/gaa47oMm/ZwY595BFH3OaAPXbfZY/dd77h1f5sB5OKoRYbSEKdXc9oudtOF3vp0Ye/4bH3O/yWN7j65S6z5+678oXuITe93vOOuternvznV7zc7lWkFuaYpCgu4JVCagfLG1NCGeWoO9368fe6611utF910S6+zj4nWlTIbfe/7tMPO/jphx40DsVEqDfRH3u7zuX2eM/TH/70I+58u/2utuclKN4uN7vmlZ9y6EF/9/RjbnKtq7FUMSWjLHwXS+qw68V2fNkjj7r3zfbfYzdO3pW6mAxWlAhqunjQth7AZJjtinvt9aT73u3J977T5XbZWdZpMww6DKNyl2urS+2y+5PufdcnHnanfa6wJ6awdUiks2EY7cg73+ZNT/7zQ2+x/+Uvtdueu+9yq+tc5elHHPT6pz1s54ttAbAxDhvKVCFZV3vusceTqdd97n7lvfYEbW9M+Jkmo9aH3OO2r3nCUYfcbL+9iOTuu9z2ulf7q/sd/LonP+TON9vviUfc+QEH3Xq8+MqleJdzfvcG5DK77EKDUXzae05tvRK8gC7uVfoB19rnaYcf9IRDD7719a73tmc86n63u8k+e12a+5PrX+lyj7zrbd7yxIddc6/LrRKydMDZs3tOl77Ern/9pIcfc8gdb7DPFfbYbde9L32Je938+q959BFPP/zgo+5wmyfe9653vMkNwPCQIaROKmvVu932Zk877K7XueJl99xtl+td+XI773Rxh88iYYCJSk0Yskc0wEVckQoNUbdgkBhR0az0cMwVs+gc5llLjHyRxq9kko1CUEhgSN0yVeIpyJRpLusmE4QeQ2oLF2GINs8Z0qHBJ8oAQmecTizEYhKoc6gN5dGH3Ondz3rMg+94i+tfee89dt+FJrv99a/+pEPv8I6/fPj+197Hs85qWU1VxbHzjju/4lEPfvEj73v3G1/nCrTvbrtc54p7PfygW77nmY986B0PrJUKZK8219pNIYyxevjtb0qT3elG1zDmCMhERBiY2mmfYYv85f0Oftidb863y2S9/1X/zIY6OwXWZx11yF8decibH/OgK1/qUhpOLXLN7OEonsYaKZ4DYTAZDr/1zZ5+2N0fcKdbzGUTxl5DShIYXOWKl3rF0YfelqGx+y5XuPTu17jiXs5UI1rCKF+kumhDCVkhRyQi5OBb7Pf2ZzzsyNsccKXLXJIWvMnV/+xx97n965/+yINudcBT73v3x9z1tjvtMEiye7IIC6+7X3y3lz7iwS97+P3vfsC+V7jU7ozB7Wi885kPu8edDpAiNELoIIWjQrDjDuMzHnzIm5/68MNvfcBV97zknrvvvPcld73bDa/z/Icc9sonPeSSu+/A3iLLEoJ9qPvte433HPeEJx56hzte/+p77r7znrvvst+V9n7QHW/xlic95Kh73uZiq1WRgZbWkFvtd92nHHbwE+99F6VLJX3LEexcWra89O57PuGwuzz9sLtc94p7RwdSKIZxTQboHk0wWVfd71pXe95DD7vpNfchl6vsdek/2/PSsUwSbgiEH7MVXlsymdCJW3JnDASRVN6zZApvZWhKLyaokq7N+YqETRNnCZ2vg+yC5aGYWq2xMq/mUoWJOzWy5noAd7bNtA51pYwRSdYZcaeGTHqlWqo4vAu2H8ET7cEZhkYazlLS6yCjSZWCxuKBhmickDyIzMaqPOa+d3nJ0Ufd5YbX2WP3XTnudINrvejhh73g6PsfcuubPfk+dz/8wFvIwPQ6ibdw4ijXuexl3/nkRzzlPne77XWvtuclduW4ORPsfe70t097xE2ufU2QJbvPpmgmLqBCsCPvcNP3P/tRD7zjzfht256777LXJXa5/X5XfcZ97/qOZzxqn8vtQYktxGAi5Yp77vWe5x7NJv6Qm1z38pfcbY/dd7nyXpc+4sAbv+ExR/3VA+65ZbUBT6MC4vxe7imH3vVp97rbZXbcJVM1YdrUmg65w8bqEfc86In3utsdrnftjEB2kRQTiAbAtCOChBx5+1s99d53uebee+2526432GfvXbZsbLP+MMdq7QVdx0nKZs+J73b0Bte8ypMPvcuTDr3bnfff/11Pf8xRB95knz0vvce2hPaIg2/7lqc86qqXu2J0k6gpEotK9CC55X5Xe/+xj3/sIXe81bX32XO3XTgOuMreD77LLT503ONuf8B+ioGSqLYddlg95G63f8IRB996/329G3oBqQAbRdk73Omm16GvPu2+d5F5BpsLCReH4gEH3uRNxzzg0Jtfn0DtudvOt7r2VZ582N3/+imPPOgWN3ryfQ/687vc5mLjlgxzFrBtQyC1lKLG8DaP0vzA/a7z1MMOfsTdbn8x2ZBJKEnU6cADrvvEQw9+5F3vOGgFmAKkuC4+OmDvvS7z+MPv/rTD73b1P9sjK7pJR65BaUQp0ZLBmyCrLOVwcIXOUialEPQHOrSam868hmiXcUbllmxSJIMIMjKlkxVZikIEGixYYrMwr48qWLiBRGEJCLQa+AJgYLjJ2LBKfj2To9s4YZhy8KyopiuVUUWKhCjtgk6FEMmDrsmMVbSXMleZx+iD+KBRzFU4i76SWAwZDKhIdk6ZVLUvFqA6LDSTdI4ZOPM3y3/DA8A3LwJFwx9/CGCFuIOvjefGivjHk/r3K0//1dnf/f73eP8a++xz+T0uG60kdROZJVLcnI2un/Dd75x33rmcc4NrXX2/q+5jpfD881/6b1+w8vK/dhDVFITIrrvsfLub3eTxD7rfB175/I+97U1vf+XLXvlXz3zEA46644G3vc7Vr7bXHntuXHyn37P29UU//OGJH/3Up1/413/90Mc/4XZHPvjQRx1z9F8e+5K3vvOzX/naj04988Kt3PH+fvp/zQmd4Jvf/+FDn/KMZxx37K/PWYx+5atc/fjnP/dB9z6kmP2neHz1Oydun3/da19n+wk/KXN358mWnXcXEWw7drzYxe5zt7uKLvSPf++7IPSKphLKRsYm+r4MyiLE6zSpGKSNbRqYwrjFTtpWHBaiPkisAqVxXWk5zMVQ07Rnm3xet97nzgbE+ize00OjSXYwcApkaFrnEh0hMVvni64ITzA/GKIwTqSnzVhClnHGEJiQEzwhwkkcDKheVOpgGCAG3g5F8E+fbe5wBRSizmwTVXqgbfbwtco8WJOSLnvucom77H8dvsR6zxf+dSvFpe9KYzuhJfe50p73PvCmB934RmPd4uQ7e8acaKSy+44bj7nXnT70vMd87Nhjnn6/e2xsGUUcxYVYrJKSBDgv+5T7/dneTzjkjv/wF4/+5Auf9snnPfWZ97nPHffbf4vYGFklU3WybMx4sig8EEGWFRp9i2KHcuAN9rvXLW6y/z5XibDG1+MsA1TXVbJdb5+9D7vVAfe9zU1tg2/SB5hyY1eJQGJVxkNudbPnPPoB73nhE99/3BNeccwDD73tzXbdbctY2yBaoo6lDqOaggdX7Lnzbk+6990/+FdP+PALHv/YIw/WLWYmgzHJmRXLUphekRKO3XfZ7ZADb3Cv29xgl5136BFL5hIdUFdayXqnHTbucYv9OPbcbVflGi8sRCSQSqPmta5w+WcccbcPP+dRHzvu0S8/+tD73Op6F1c789eLt++4ZYOCJTuZzEvtvNM9b3Hje9/yJnteYjeQCqYiWQTVc5xjvyte/slH3O29z37Mx55zzMsedhh5rUa92hUvc6/b7H/gDa+OKk3Ynus3fvAzqsZvbPbceeeaSIgUjiigy40WI72CprzqZS9x2K1ueOcbXntU32OXnR92j9v/7VOP/vhzHv+Wxz/wgbe75c4X2xkMiImGpAhWVfm2d4RfYsvFjrzNrV//2Ed87NinvOt5T3jS/e5xk6tftUjc8UZXu+9tb3T9a16ZrloC2sPp8ZjF5nvc6npvf/yDP/nsY976pAdc86qXZy1LAc1IyZKdU0gnuoyKHiV8cX0EUhwGW/A1c+U7OU/JZUQyUjijpoyIFXvCqS0VKJVqUW8k4JpdxSOduiMLnA4Uk25LA6TmtskgToQHQpF0KQEipHdSZ9sZ+1xuz2PuddCbn/Dwjz3nmI89/zEvPvo+R97xxjtt2WJNqFpKeulUEkJPh4z1Rvte8+kPvMe7//LRH37249/+tIc98pAD99rjUmDNKZTaGIMiMqgOZhe72HDIra53xIH73+hqe/O5LuKmh/CkqtQSu+w6Pvyet3rPcUe/78XHPOMhh+24sYM36lV3vPjOd7rp/te78j7kqVPIOtGpLHGhCiimZpKWUuQ2+17jsJvd+Db7XSuIZqWaEctLxaAx9r3alV/26CM++aLHf/i5j7nrLQ9o4GM1AzVSOv4YfF8/jZg3HLWR9NTq3ntc5kn3vesHnvH4T/zlE1/7iPsddrsDLnaxLXtd+pI098G32HfjYqWFzK2kVCtaC8Yit9j3Gs+83+Hv/csnffz5T37Hsx72yHveYu/L7DxkRIveLWQUW6pkdIFQ3nKtP9v7yUcezCb1Ey8ggI9+5pGH3uza15ZOZ3CRxUrMPR0Qkx03VkceeMCLHnGvT77gMR9//uOPf/Ij//wed7ns5S6NnnBIs5gqm7Or7bP3vW57k3vd8sY1ajQn487WzHwO7Lw/x7U9AAAQAElEQVRlp3sdcNN73/xml7vkZabA7I2R7OZZJ8hMIIJLuOmSvOG+13jRMQ/5yAue/O5nPuq217u2OJloS9q/eK8x0RNazS7CjOdhE2xiThtpQ6EvujB+i6BYV3SrIiOsupEHYchsVeZV9Lq86GXiUwZbNzTQF0Iylc6fqxnjxASiAelJp67DWgZnJNOnZYBoCJ0AlFpyqKKDZo0wYoXaotDl1VEWZIIAbQITPdfCShuFsnN1zRte60p/9cAjPnTcUz70oqce+4gjDrzhtWzEda51xXsceJMDrn+tWYWMxhW2qFqWWaQOW+52sxs/+xEPeN+LnvjhFz351cc86NCb3WjnjS3mkiKq3Cxg6J5r69PK2UOWevk9dn/cvQ5661OO/uixT/josY99ySOPvPdtbrzjxapR2ErdXNEFQeWl6F1vdO1nHXb3Dz7rse899glvfdYxj7zXXa+7z5UdlcVHllB3Vbncnpe4121vfMgtb7jzDheLpRIzGiXQGcsXL8MhNzngsFvc6AZX2btT5DCEUrQ0N0WB9iwtDaPd81Y3+5vHP+aTf/nUNz/2wfvsfalMizTK0Q29L29TghSRM+Y/u9zu973VfnfZ/5oqfrlL7n7MwXf9+6c85oN/dcyrnnT/I+92i5132VHomrH8K76eGdJcYkaZ53EsO9z3trd+wcMf9NHnPO5jzz7m1Y896uF3vd2uO+0kWiVrbbYRbcfaDjrgWofeZv8bXu2KisxSw0bVUbtKx/WvfIX73nT/w26yf53ZSaIUicKl0JrX3nvPp97nzh9+1tGffM4xL3nE4Xe91f6rlVz10pc84mYHHLT/9QfuUZUHS1CtvZZZdVLlxtdRMhhU173Snx1yqxsfdMD1BzpnV/qCKK5z5T3vfcvr3+PG+zNEQb8KpzZD9Arstduuh934hofe9Pp7X2IXocJDyNBhDGi0ofRaWdEJtghUYAWmCU0TEwwpQ+aGxsgnVAv0lATghpmP6esQEdNaR4OgRevz5M40DhGdzah7LhuKgiZCqhx0aLo04YYEtHgxbjRk6KUsPItWiiDazYioJjHbtq4Gqiy5KsQcEK7MBaEMXho/JSLSvfOTxigaqhCxJGj0JhZp8mVAabTsbZ6jr8/++fKVH4CYGGD8+acMGZlBAd96fjRugv4UUv/HtV/5+jfmZbOLA29206KjN40EmJTD3UUE07z1fZ/8+PZlN77+9fe+3F5XuNzlePmFr36VXyPy5P+CkbSm6JYtG9e40hVue/ObPuaow1/xjCf9/ate/g+vfvl7XvWyt77kBU979KMPPuhuN7r+9fbY87Jax+0i+bT1pJNP+qfPf+74t7754U99+qGPfAzHg57x3L/+m3d+8ktf+9EpZ/Tu22f+cZ+Z+Z0f/fSpL3r5Q5701K9985sksrGx8YDDj3zHC599nWtcDcIb/z/H/9eEr37tX3g9jON1rnIlnmwf51+4lZbg+a477UjD8GT7uMutb7HXHpfh+ae++MUzf/PLoM08pXe6iVh4GbqMkw2dngpRz5iD/TpjvCAHSXYGsipM4iZZnYHOv+KmjLfRpHpqBFgFF7KiXQqdnPuVTHJk/hxEBxkGL4WzyFJ7Eya4zgzpxGXRvQr45QSELIeAhbOwt2DLtQQzoEpK7NgkOYVgcRpSQMYtha1NYGlgqkuJYpytQHGQf4DirKaisyQ99PDb3Pziq/HrP/jZN77344kJ1pv6bEsBjl6FHYh7ojNpzMiWGeQjrMtgXkCpRahBWoMH5rQZRiF04lnmFOnJvLHBl7rBtFUohTAbCL9x8khSVWsK5kLLqXgXQiGAqTGPW0GlghlCUZjSUkR5S6UyEaU2mEtVr8rmxNUVSVhEhGRj8UoVDJHqcybCNKxKDaldh0b4E7zdI5trzCRjxhLcu0ydBBRmUqrxXeaiIElTeGHapWwiA0WQpBqVd0RAWWWxk84JHqZaRIU24QrxPP+cC/7xn7/WuGkLKMS2EVfot39y8jd+cioX7H+ly5OXRKtssnqKMxnD6WQUu6EKqqZFsmhZT1sOPqNMDmGLGC7qPWPumZ0C/fI353/+O8tXtQ+6002rYVAdTKpiVaWoaHK2iatMXJLEQAy1qBoAdTHaj/UGA6+QZBJAs+wrwWBcLq3mPCDo/6IAiWXpKUAnwiRCd0oIsVHpi+9E52ZiMJfBs3YBy6vKshJclFzm4Ca5p6ZXKi4eSmtn0K0BMMbShNMyITCBUBxn30mkHdXUzGjH2KaKm0d1cBWon3KBGIEqgrCcFfPyaNGUzLbhmbwhqgIjt4X5Agfo3546LxEkXZNUYR2Mk9bJJmzuJciFQniPJphXhS2impZopTVhS72etDGGElzPkbl43dhB9ytWyqDFiLoIVACNyHSXAGqNaqkBmURSRQRm3UgGUVTUeC2drpAJT6Gk4HqlpgoUDknR2bV5Bngqg9YhzFqJBJvAJlEyt0+kS5GVBtEB9XGGKYdUF2lL7JaGMaFYUo7nokVJLLgmKS9swUugSq+2JguAyUZq6thsMXTLPruHhyxUuAAoEaWHBESS2kVKpFKOkqienOIURugC0iG8JOa8oLrRxemxiSLJtUiKRX9V2iuld/REMNe2yB5w0k21TDKJzG0CQNUh0Y3Mi3J554USpEifpbd0Jz8Dq/e2x2ZdpPt2NnCTpjqrrqWHNFiHBqh/JLm6l96UniCN80zDQiWY1PmM+FETTfIPtawmlQHcSYmtI4ZIyqCZ/El7AZ7JjfLcYwa6MATHIpWxw8LTPD2ZCkbDogSfAyESvCArCk/ARBaXSQmKkKpKuddAW2ofAU2SdwfPoia1tOzaZ4jnikYTVHCFpIMpccGDitZMqpJiSk+ygqKw2egEZOsDVU5vFDnSREYh+8iaMkSpGIkCFRMJLakDeS3FgOzCUmvfRkxtUaYXigNDp8O0mOfMJLQgNHQYU8C2aQQyoYSx+ImoqEEt+4C5OJ8jSwTPrAevlupcLaBOUtJKNltcq7gMVEjDi4dSNDCLFUroEAiTdqnMBoSmMiYlOh1ILFU70BB0Pq+RplKN+wixkkIbQJCFKo5oFWvFLME7pNNhzBjJjNCdmxIFyTmcUkbpoB3CleBbUKSspVWZTGZ6Vpj0WlwHx8bcbOqgUplimoOmGUx4yrSmxCiEzkFzqYgUbQW9BLmAPkWHSUA5OUxDGQFUy4gP4Mhu6UAqBWpSMopKGUEqdNs+a26auggJZBCsRnP72ESJaA/MJJCKUO06QFbGPCN1DBvSEuKgD2bnc/GCan0UD8lJCvsAUIHsNRdHsDlLGJQGj0gNWlwsdHHTFsEfjJwk9AjPJfZ6RxK1WKw/TX292eZNNh7N0FIhQPW+yvVqnDYqOWtkmbgQsiiCCP4oWgtGRU0RNwdz1Zhak2L1xIzeZQrZDCMwpDF6GbIUqoiR0tMmBQyBJmTWZJh9XLfpdy/vg/GEP/XYsrHaTiK3//iv+/zad09kcK227HC9fa8+CEsLYQm4j141h16Gt37ww9884QQAN7vRAVe+/GV33GHj+vvuy8vzLzj/n//1Wzz5v35kLuYX1d122flKl7/sAdfb76h73OWFj3vEm457zgdf+/JPve2Nr3jusx96/6NuedObXf7yV/i9eFsvOPcnJ530pX/+/Kvf/ObHPvNZhz3y6Nvf/8EPfNqzX/7mv3n3pz538pm/6PSt38/+D09YTj7/tW8c/VfHHXXM4z/0iY9feOEFELnufvu/9aUvevKDDt9xp//2Jc9/SOb/6+EFF2396jf+lbeufuUrX+oSu/Nk+/jFr38zzY3ney436WE8XcYldtnp4DvcnmfnnvPrv//oPwrDJIbASkUF0h1T8NXL6KmTMhDmURgFbRWxyjJgHHKDkdGKOHM6ZlG26UtYJFSxWo2rcbRxxGBzqT5YDBqjBhg95oIQRlupMohKamvaoiI0M9IntFk9WJeGlLHpsM6y2cfug6SQuFBmDhZjbKsTc0/WI95BAUdSeCbpbSnN01qvSBlqM4ViGDh0pI4Q5os2jbnnnpd6+EG3vHA9v+EDn7rwnK25OWBTclpTEGHiq7ONU7W1wZd816pt61S9ahRbRHZ6U2byPKU5nIIw8EtQVQq8qp0rHGtv3SaME2wdEW3OFrR5GUxGYXpyxDTZPLGxtSKwwSktE9lmls50JtVt5XUDlF3NkvXPjCqozCaTZldn3l8hRirLWosMLGeeMfeYOhJQ2lV7w9xyM30rfN2lT0kFV15Kors1Sx5Lp9TI3kSGHuZMzOtlWskRtWqtlQ+80UZUfaFM75rVOH0U7BBmbWx9WK/dNy9sz3jtu5711vc/7tV/+y8/+MkmX+rPG+f+pn3wC998/Bv+oXXf5eI7HHSzG+pKOGDUYZZEqqxVpl4mZw2BFMAkIMRuhopViPXEHNmSSbxIH0oXiyX5/OCUk39zwYW33e9qt7ne1dNMZBxQR1QVDWNLNUVugqj4MHdagkUcwUqzGmCarAOZa0fTnsMkNQVEkVgKX5n3XiCRtaW5I7urByhYQ3UZEsa5AdLmoMNFqbaKoSz+gElyDeuoAhMsR2q2IVptLhluEka1UyMiOz0jXCy5lKW6NzrwAB3Y8UApw4Z04a1asg5NcmYQdO9z9zm2URYJHUoMqz6saHRqoOEWZUyjx9Jz0oSakdJKfZSJOypZ3FZ4KLIurgTMqRMKK2Sd+zCtMc8MssCy0jUEDeSMliV9RNBOmQYKTVLep7Wv13OL1sVdE1zn6IJJKWFNDLOMDaVlhM459D6Gj6Ju2Frn9dj7ABkKdETWKkNFKW0svVLz9CYe1MAkDK4WqKSCzY7eM+Ft7n2KpIjK1U5r0a+0bdFcCaUV6YTewhPZDR3iLXPTGaHoyfwRxLaAMThUYX7qxCBdYrAcy8xyH2nMHCz7ncqMLithl1Ml1FNbSGeA9egB9gVOdyCDNWxrz81Gy0tVMPOJwgYpFSLEZZaYevhadA2ZkIEkTBmlz4UIdimuXEcPptSdfUlKpSpKo8VM/AuDYpA2oFfXEuzmx4TM8IZUq2MtqhXkydi1ajCqp15yMpmS6/m0DwNzQOGEFG0l+0bn9xm+mrs6xaHkIS6Fr0wDsJk9Cgb3OqRq8diYYqPBMuEdc885miMzLLK0qGtaiNuMNsIrUbFVythBKKEZ1E0perM2W1ujN/rErJ27Xhm9Vi86dyYSwSyuMq1sGmkGmaXNso7aUImVZtZOIwWnOCSTq6xEDDkXcvKxx+iptImkiopUTPTw6ONsFryGkApX0kHXYpte56jhA0BBq5uECP2FSvFEULCohilj3XprCQenLil7hk0kjqGWVdSxmWTMMk+27t7cO8ShDToFGuXNskSNKZkXd1oj4G25UslxJbZFbDQdRw5brUhzIB5dA8nCG3SKvnIvXXsO68HbwC9KNw3M4vQVoKvTgZx5h+4Q2wzvKemunRYxVQhNMfChoFMnFeSANrL+lc3cBmpIhwAAEABJREFUaHVLGccVxKg0E/K6FhbCMIGKmIiUVdpGonJZoSNOvUyQJVsxtVA26pK0ONzUOuNdaxThaUZvk6996uJhCcMM9ESPbI23QuGi6YW0dJFrhIygMbKQl1TqMEm0QVKcuWsFslRJ9i4WOjM6QdejF/ZGilRO+ZeCTWKzFu420EVmlbAAnUOdTukrVC0rjCV5HT0D5mSWIg73FpgDCIgodU9BA2aoF1Oh/42gujAzUCibMtd9Dp+KJwMEkb2v56CGITXVACj6mDHwTKhNJ4dOT9r0qaUbcsxcFddVpjEBjDObj7WvWFODrCt0JGsqD4kwhNKnmXtJVMQtO7GRdA2hojGrRaO/OboLZzGHBiMDiODfrp2KqLoquoWX5rXTRfiQD0KEn7wUPhamnIUTYLPLNA+zDkz7Xc6/cPknv6gKkK21bSd//Md4sd/2vuddeNEfT+XfrZzm+dsnLJuNS+6y89X2voxWt+KGrK7WJLq873NfefFrXhvhO+208+Mf+kARkpDb3+wmwzBGxCe/8M+ZyVv/Nw6y5whmEFGr9eI77rjHHpe6/U0PeMIDDnv9s5/x8Te95isffO/xL33pMQ954M1vctM9LrPHDjvsoGbpnbuLM844/ctf/uLr/ubtf3HssXc+8qjrHXTIXR/22Ccd9+JXvuXt7/+nL375m9858acnnX3OOeecf/65519wznnnn3rmLz75z199xkteebsjH/TwJz/t05/77EUXXWRqf3aFK7z4GU/7hxc/Z9+rXwXbPPmPAOQLX/tX73Qs3OSGN9xge/Y7Eqeddso0rXl15b2Xf26fJ78fR97j7rvusgtN8OGPffL8X19koRlV+6rMpg2WlEUgyHQX15K1pNGuoohawgYXJUfpYvRsR7Tos/eYmYt1kDKaoERw1AIuEPPkhSEMWdSrRAWMXJ3NQU2rScoGJneGS+lRknzBBAEwP+hQcqwhJWgw3g1JZAi9DLPASdPQUyOrxijB9T4HxWG66pSDTUfWQbD05EXmwkUUfm4M4XsduP9tr7vPd04587Xv+2RGptdsg69JVEzIVoUWD1VYkYEZDUpNrPuQyWTvzLFGCZk6m2GSJYshVNN4MDUXipvds3V+zKnuTJRWnFWCq4ivD95LM81Kehk0Ty74M4uCKQS9OhNcKUWk9GINEknUumANWecw+dhCu6WWGKwbYYphmmvvqxAqHOm0SSdWHhHOLn2GTik9QZstIWkUwspcy2ZZrcU8Mp1/ckmQQvPCFEr1ZREtJegSFrnQWCdYc7tDstSqtbiGY4Jz8wSKMwy3v/6+Zvq5E3740Je/7YDH/tX1H/X0Wz7puKe/7QO/OveCi63Gv7zfwVe6/GWsGiUgjpl9EB8MRZJSEK9skGZCFbPMtLeHUnkosmw7JUtZKcys0FQFt9r/eu946iOfcuhBY1mFjSwl7jW9SBMP60Ln9Fb64nCWme6OzAXasfTROv2xgH0dNezhm5GzLOVtK9c7VcMANbZoVDc9S4qJSEFTa6oOV4TMADuFVoPmsNqwZaIVs8L5tUwR6JCh7kEbatiAOqBYivTQ6FF6GueYhGGdnUdJL+gINlbdem6oVb5p1gKUZjU2VlF15iTMFpNhOUgtLSoFtw6RaGKpxZSkm3QYBh2GpQzWpAYJS0lBiMLKqDa4s10KUERdSFKh3hENGoPr0M1Y8sRTSZdihiCbKIFyBQjgoicE1Gd27z3huVwRnXBxT4Rbbpa2ttYpQIqFVJA73XGJRufsohgsBwkToWMVQqTSlfEdhA6zKKMth66S2hmq2kjYXYJubenWgkUziRdzCahgkeB2hnUYnj05d+7hvQk3oOGI9NAehhhUtmWluehUNLMOXpj0oNqRrkH9msoathZpQpUKuIAjCkEIX5RDLBQJENyjz9nmEM8qKEvhoDHVgz2pTbCW1Eh6igPQLEl9CAUgyyC7cZbiwyAjvQutk7TOXSfX1r1nakOdbMO5YxiUhhUQTSWg4FpHMikkZnD3U50uY6YpTCtDZ7tnlap5GWKgXF1tXXMaPTJlTvoDZbVUC75mYpsoAz2f9oE1r+5mkbV2M1IV8tM0EEW45yJWI+JqIiQDF3RIQMnFdJSBYbniZUimezj/NmAqw6x1rkMvCEWT5X7Myn60afWqXoLAB39MIjNYA1Rcs2sEeCQ0nefLZ80wmfmOxUu6dNFJbEqKM5pW1LFv0WkVOvpoNAlgc1jvEgEEaAnurUY0K+scNmPoIVMnht7Co1dKIAgWs0JWAd70ZK6bNLo0ZM/uSScmtQLVLMSJ6qcEKAuBnZefvWPdZe11zlGIjDGfESumJTbEU7FpLMJ7DqaDkqBTqISSb7E+qAuSUjnzgjMupJI2byCnJGzw7t4ZDo5QpV/Nlq0ETzSJC0OtB8+6gAcxpO6Q5Fyqx3iYpUVEcTd0UVQto5hkgpNSXeivMUVf6wzaqqYFIQvxJE0NN2esg4fwVnGS6tK78k1kNFJkqhykLyfeTAKmPkCqCK1D2ZMChK4Va5PZIix0KFqqJP1fR4ToPJd50/qMQGcwmkqRXDZT7L5LbdWkQpE1ybQrIySlkBCWc7gmJEUg0gvznAAJNOtN50hKmmAxsY00mw1ErdMbYZJFolKpJC5FvAphLyRDpxPujwYuo/8PikLITEISZEFGHBoCSttB+ryvClShZUKC4JEi2TAKhPvY1qKngG66oWUQFrTZZK3c90vQaB51UcaoBhhNZbBqWhO1uXUnzYQgaQtCtNaYwCAwOp+EF6fnEk7ps/lMj8fYXMk2mNbprhN/JgFVmCppi0HEeSfTHd0z0lE8mZCZmd2t9VjcXFvaBRfx9QV4ZGLdOk/+lHHJXXfdvvy83/1CwO2Xf+Lnj085/dfnnUci+177OlvGFdWhZqD9Qqnc+z71sac++9mRWWs9+gH3u+oVln/IkpOve42rXn6vPXnyhS9/+SenLr8UjOf/6wyBUJiAJI1WbeeddrzZftd8+OGHvul5z/zMO97y0be88a+f81dPeNhDb3HTm13hcpfdddddRegfNKhPWy/80Q++96FPfvI1b/ubpz7n2Q885nEHP+ihNz343jc++D4H3OM+B9ztnrc77MjHPOMv3vPhD59xxmmRwdcV17jKPs9+0hPe+7pX3eU2twy1THL+YwYlft8nl9+VUVcbN77efn9I4ocnncZLmmCvPfbgyR+Oy1xitzsdeFveOeX00z7zhS+ZL5qrL8Zj8I2CLUCBSBdj0tchDEzGtCyXMKJ0m8eKRyI1RSeUtevc6a0zpKUy8SS9uCsTQhpjCMyPbsomY65goSe1XiIL85SBjLKMsYySEdl5RJeMKhgVtQCiklZTqS5SeGXKtFDch3STjmUEzUbWqY0kfGL0RPoayt4jC9LTO4mSbV91t9b6NEspTznq7je48uXf+6Vv/N3nvwBzTUOuovGzaJD6KL4qOtRBZZQUkkFmLLPUBVGbVDfEiCjSovbOfMIcbAIFz1Gi5GzTjClrlC2wLaJMMohAC51lFWxGUAoRkp4RioETDGZuxSvxp8KUHtnUWzKnTdHmZC3PNopvQAevmAfBSFhKaTFOzYJZtgwiRYDoSVOp813OPFoYtSiqo8iAtiXa6EQHCU+ZE5PkWnNeNIOONEtqoV5MV3zeZMl/rIfpfP/ZeuYMJfhFc/DURmBURMYiG4MectMD3vq4h9zthte59C47qYAVNBOX2uniB17nKm967ENuvd++SNFcwcfoNXpRoWhhIAINcLhko+UBUgy1LqV3PjQfpG2RWNUUaqgDYAossy576b123LJb5MpNZpUppM2sltQeSfApvMg8dIwhRWvQM9LCx8zBbBzrwCbeCybtzVtOXmcZku0ahaAF0wskxejCXiNrQiixsiY4I1piJilhn9WNxZncaXvPUXodoKrC6SVA0zBM0tnMlaFS/jlt7t3nbk4XKhXq0YmrF+mFizK6e2sSkTQltzdJFMSprtW6oToiBiYsJEAEslhK0LU8syUCTrca4UZXQ7ogZMHXkY3bXr6QTeJCH5JcJfu7omuLWYhAuoXMbFugMsgwZlUyVn64ajPNwPKjiXaBlagWo0RVdoPFbCBWNEA4GGxhaSz92SlNdtSWNntOAvbJqCZamGFCBAlxNRSkdPFZtYlQchAFNHLUoP/ARSKFZx6YwcvA9qcFMrhUipSI2tpqDirSya3qjJxz6jF58utNd0Llpg20o6goTLxmDBkWHdHcJVyTwWMi2qHBWj0UCtxqUuEEwIcy0Cjr1EUX6picLJICdeQSCk6tkhoNBVUj0FrObXlCcZr3dIZmYehY7bJDymjQSrx1gBgVlJIQEaqIcMleCsNh9mlxECqdhR4xW52UHl26Satrt8l61NnKkl/RsUA0S81U93VvC09T8I9hpTB6C0BbBh2sS4c0k1kRhep1WMgA0MNGYpH0wCAouSgNQAygYlbSlA4xEwzUGnUVKFpQqEzSR2gLmqBL4YrBMToGCoPs6HP2dbZGVatZqTIMhMlFNhNrxwx1untdKYO8OlGmpIupLJkZFb7QAKiLmGjmmFHhsiQSTKjrtDXV0VRlHJG/ZrUZ0kUwVt0oi2rm0DnEUzIMWTXHElIizL0T5liYhDskciApBmSVuYDQKy8le2IKeBfpkBn0r77ZuRzJDQ1lwQAdrXLHW7Vz6Si+RGO49V7IUwlHpAQJJ60J+hrJtPAWncPRplyvfU3X7UGghA4PMC7DV8gBspS7bBRk7D5Q3Ogei9PoILLFZWDcdFLURmFkkBTFLIh0yVkSvE5wGZnFHNqX28A6fOrdKeG8RZLfRlZJDapJJDpKc3UxUGz0IH6l64bb2Et08wT4Q1OorTEDd8Faw8WsS/VSMRCNChlzyY1jxWpoRbmquGgIkpmK4To0q3NFlkAFijRFbxCX4kGL0kbQInwgYrqchlqHlSFJ0grVDEOOJhsGMzQLhyR9VAYwzsUo5sIqok19Xs/emLxyhxIbFDI7/0iaVRlGDBIEC7m1gnYqqmpY6DM8oI0eCBRAGWEMFdsodShVC28h+HBxXq4x6UUjVbroLO4M2TFcaInkNCPNMsCrtGpuQwSXU/OO2mCapkwIRkdxBEmL2XJLXMPhEV09uUDHjJVHmR3wVBKWypQVK1NLSSQPlazwQkM2nyPmpBCNbUcJHZa4YAyKeUfvOXc0z+iOSXOqaAN6IYkMoIvQjbZOEzXk4N15nnnyp4xhqNuXX/C7/6Js++Wf+Hnij3689aKLRGS/q19t6jK7BjQie7R3fuYfn/Gyl80XXWBmD7zvfQ896E4Q2c5uNQx3v9OdROT8C87/p698bfvN/0U/U7YJLcH4gED1Upfc/cAb3+Ahhx5y/HOf+cHjX/sPr3rFW17ygqc/5uh7HnTX6137Wnvusddqg+09/vBI78kA+90tsbL35a9w8J3u9IYXHPt3r3zpIXe4zWocfvfwj/x5xlm/3v7bIfe+7NfsllsAABAASURBVOWudsXL/Z5KRHz7e9/l5Q4bG/tc4d/9MhaRBx56CJ9yvOPDH75w64XUcok1BGNCmfoDq7VudDMUVuBZt1XBUHq8SIfMPXokVyOcIVhMZBCmnFCwdgCuGSU8ugTTD/EzpoYlsTGKglETQKfD1ILCZK48ZajVrrb0ewiSziJLUasJXjShYKGy0IKypzWYgCWFoUN5RNwyNRzSnblTggTNmGzoloNTJYZXbzo7a5NQh9QEUtAgO+2083GPOPKgG11nyw5jVk9xhjSSIpMGajDliplCl9hswiN1yapdwAm6EOQKCkYuyr99YF/Zclv5SEUwTUEGjyFkgImoAGHSdSkZs5CRq3ZY69pniRRFKbWW1VAGnqfyFsVRFhfpqXODT471bOmmXozdS1vm9JBwM4BtCQQpQvGsNlkkNDESq8KSqUWIn0hhxiYkJBkErqybrhsQvaKTCMg6qrqpgmUhbU4KqXwaNShsSne0ziruIC8VFdRutQ+mVcRUxeDXuOxln3u/e739qY947RMe/OonPOANj7/fO57yoJc85L5Xv9we3qO35Nsf5fc53D9hAAlFY941sIjxSmjN1FYqhloGyh0EAQkJkQjJXkAt1QIEBvxUUTPVSuH7AgmhSnf2EjIjocmCWwHtxolKKuZuPRIGEy/wBTVkBBBZxJVl0lIVvNdzzVeO3VBch8YnXOeeSCjMkpaASA/ClE5fS4DEbJuIGqwbcNJMKgByxBzSIB4kHBJckNybdPG0prWnJmdqanVUiaIiOSQLxQydU1pQRC7q/KFC0SXMGkCOUjrIbPmQpEIj1SJ9Llu8IFKCj8Odq1OQQtFEAO1RpqSrKJs1ralFHSUM1LyIUkUB6KkWVFw2VFbZR0wifF0NV7KqNPsgMhaQ0TK5ptAPJYPu0S1QugpHkBZKSonUCerF2AGGIiHaK3uWskyGL9y4OKN78orcsTRqxU2W/9LFeDfpgaQuvRaOeTsxE+LBBmPxAZ6RMvu3sBBdQIbTVFWt2qi2KLYs0k4Doyc6JF1sDmVfSEckEyXEqU20lwHFDJAumE2C6HnmnJ1u3DX7oFD6VhUUlVpENAkeZ6lBzTVd6YkhnbagCeecPZtrc2YcAkgOxATFogw+jD6qDK410sCQ8yI5hA7ELWlVQ5ioongKSNViSbVKD+vp0QNdJEQBoeIEOzWC84LPWoBekHxoQFXKJ9yYdEMqe5TKVCmBTJKJ6HOwfwM66ozCldlimCgA0qQr/WyZWhOFjMQWRUUgIdScQufQqavnRDIzMAt1p52hJW3VrTiF1J7WhYiZZtVUSEADkpZK101nW1kVZbnXKIZbc5qKmgS2aQgeSc5i4mG9S8xpHC0phUEHFAIgjuzoLrSyC1IBRf72xJCWsVwbpIqSn7Bs5VpkTu1J7LG4FXvPgkQXRqJiNWpdDWIVnMC3RJ0bB0WoRKIJAuBOgMVsyMKZqiq5TUygGgp6gUsmxShcFA6eVcWgmpF9RlvDNxGbDvoi31bn3EqKMrignbNnPnDahA4jCgMkTKRarVaqDSupdblLkK0VLnBaxc1oOAc9ZnOyKSxBe0W0hh4qQgvEYEaf0WVLPbGLDQGFRe2ldp5IMVTllNoob4qEJyKkqI5idE8LCH2nQMCAsUKkVbASK3xJ5iyCSi/l00hpXA0QBepvorZQ5iW2JU9FqlDwdWmTck6SsGZWoEobpasiGHELlIsUGokORDc4igqJUQIGs/viOZlJIoPQUlkHlBqqXWim0tJaiVnDSZGPQyRBKIOxZcYbhYp4mrtRVk1YgHIYZWYw0FTqCZfgB5CcAtUiWgXKOJSESjVuILxGAxO3kREVDAFtCIrexRhbyeLMVz0AtZBJiDWvpHKypTCZjEUMUlMUdBCyS/J0EiOHUG+6zX6mdBGOAEJCMmiZFBtFBuJCRtwdYgCtxXgyOh/1gnqIuVrjzprhXxePibBFZ0EYBCB160iqZBVRkYootHL7g29U8qI/eXdxqV13IQAcv/rdPybG8z99fPXbJ5BIrfU617xGj5yjdqHy/u6PfujZL37B5taLVPWIe937EUccWha34dzfjoNudbORORF45wc+8N0fL/9Kz28f/C//Q0BrSdKRgWEcL7vHpW+033Xvd/Bdj338o972khe8+zUv+9Dxr3nnq17x/Kc/7XEPf/hhh9zrHne8/Z1uc+vb3fIWdz7wNve5xz2f9JjH/v1fv+wdL3/RsU885ob7XmscR5L605X+3Ff+5YJt/63grQ644Q6r8fcEzzzrN6f//Be83Gefq1x6999+q8bL3499Ln/Zm9zgBrz8/ve/962f/CDEheFDFx1n0ZDGhMvoVYzemRubZKs0tGvjzKbiJiHB5YAK6AiljLLC8rsTGZUCSclZckpGsTDQCryi1eysvqODEbjMAdwAEzGHcjrQhpg1kFIhpo6c4S1b+tLBKJQcmQtIUUK1aWGcM43kXL0P6VpaFE/pVVBFlPPUEozKxuximdosY2mIfLDkW7IYwuuuO+/09AcdeusDrt9BBj6CdLmEs11lVnaH6HMGi2SCfi0mXaJjFkyKZArqwp8qUZgnqFIwSzm/mpjJM6JEFtKlOCE5a1+rs9mairK+Ld1n6SEteo8+ZTK5VJSirBhaA2NvdV5zWRYnUk7xYOYyJl+e9FW2IjOkhyPmwDxbupmoQN01goW+bSgGwoCqrIlD9+qJyGB9cm8CGGTDcwNtI+dBuxkAEWBssUz1ZFZMZ5IuoQMBzkFyw2PL3Jnigd6U0hPjAbUoaxJhnbDRnJlyQYYg77j7jte66uVucq29b3iNy1/ykjvqGGnrFr5u4LYrqZRAyVIrdWBhJl4mGzKUWEWumgxTHcKMU0wUQlAN6Tn3hTlFC8ABfqqIFaHXWBIJop/EoRPfwqaaGywbmI9bQa89BzfS8gx4aEudEhMwA0L8+RKK4Skrx4oY5kK/eWfPxdeJxWs2jTkRPbNnWar4UljIKypBWyu8JGEi7mkeLXLybW4snilE0jqZpa8zSCiXKCvZLSZHdoytbKSOjAlR9aoyaK1eyXCeorM1TmvQKdrU1iGzlKyqGwmA4Uj7JX8uLUIx0OzdSk9DK8bh6pENi2mMgoQySNUZqC2IJxx0PBmMCMBs1KHYaCRTCGOPBRtittjBTbw4TDLUp4pQzkc13p+XWEsKBaUGg4RaRukmvWIWCcCgNbXQo3p070HvkUSaxViEu3k2EJxmBpTe2TJFRqAktEiMEAvTMLFM7nRoCM1SExwSBbaScUP4zSyqeNUQKNdOSLpjAOQCGWwYrBQWZiMfL2gmLcPd2bLPhY0D8eXkRsiNBsvasXIZQ4aUAW0lc8WkJEl7bM4598XIQ1I4SqfbuqksVY0IgLJnNGRm5x6SZtQYvVW+2tsWn+ZN+NqSI8icZkXLOmVdAqc0HyZfzb7hfYheEKqiJoNWM9HSZZxphWbebYY2USfcxQloLs4pElU6g0hUvIvzeqD7pXu6oq/Eh+yG5IM2jG6DQo3OWeZmU8YsvWWbWzSuZSgRInXRLpKLLVvxTmfNWaTVUepYVEgsEZG9Z6YnPNiuZW++7o2tbzJZNs0cWpagpQz8ahOrRnQXc5WgNmGphToWDIWaTebrEfMQrkpwO6yH9DZLi7GhLBYFXEChyJJZ2SZR5w2XbBaMQS3FXKJHz2kaNgmpdOhE75uLtFFzqJHVqc6kMZd0oxeNwpVEvEsVFIOqwKvRABq+asum0RihVqsM1YeaKkWymiijWwslYkaRBpvDiIXDVYKa64wyWVnXOpvMEMa7iCRFllLENkQ3xIpQHyoc3bSjzFk8xYK1VUxrLyO/L2OejJaZwZazDtalzLBWFUPlCy4tA2AAKfOnpHaKD8usDIbQHtuSXoZoGIOSvCxlGKRUKPGKwk7ewpOYJaQtJrIMmMQg/NagWBL8LdSlWfYiTiEKhNprya7a1dJWtVClaqTJMZauTK+5DqrNza4JeqGFkOJdHWaDrZTfKNDrQtqAMIIj3YVO2OlLzDYjWUBqhS1ie5r0QUOQRKJNZZ6NL/UzgUGsilF77vfovqKzgSmEu+3qamJC0ugBD6K5cvZMGzVrTadDKLp5MGUqnSZkgQCTWKNmqkR1CFQ41bTaUNdzZaTN6J2jZ5/gXSLBPwQ9eGq0gmfMzHTUQqQXmat4FaYJumlmCilmmsZiqg6oA81A25VmrC2UmNYHvbQEmOY0gy6jy3ORqgL6WyoVFCZOZBEMQ5gwsmf4bH227Ag4Pd91DuI1VAyqIwyqMVhW4qWm7pin7IsCqmZj4ZeZQ5GBXMIGyjKX0mu1oW7UcRio5NQpK3gQ9E6EefYnjEtfYrftq88557e/nmX75Z/yuZ7mU049hRRWF9/5qpffS6mx0o3yfR/72HNe/or1xLZA/vx+93vSQ44aiQrn/cG45G67Hnnv+/DGL3/x8z9/ytPf84lPZVBR3vjfclD0BEO87rrzzpfba8/rXvPqB9/2lg+7993/8lEPOfbJT3jZM578ymc+9aVPf9JzHv3nDzr4Tte5+lV322Unkf8yTVvrH/inz9Pdt2zZcsdb3pyS/J70v37nu2f/+mxe3vi61xb577M86tD7iIh7f/u738OWcfbqkBRWz2mdvpachtxUJoKe3ujzK7DmO1Nnl8GGwnwgjJM0g0kOwKAJmzrfAiG8qXLn0cMADgabMFuVWa0ZM4WRE307GmemWRhDy6O6CGsBk0+oNFhnJz27ugtDUZc14OGgx7hCFaNgqWLRHNmZg5hoVcyyakoRWelCpzCEpecS60lKEVWjmIA5oTLBz6AgtZYdBtaiQUxllXy5qzKlrMOmWaa5sUdsJXMMNpI1J2ub2ddkyvVNxi4Vwhzg2/QpQOEfSJCyu05hTalituJzjV49UlIgqkY5TGfQhL02t876gAYw/61z+WwB8SxTZ4ZHlsSYxL4MZTQrSPWQiRNZSCQ10tq0imnD2IdE0b4oIC0HJtWWNLTkjGh9inmzzzMRq7BRSKlCixoRbVRO00tfBtbhWzFN4RKKyTBTAEOtaqZcBltWGbWUeR7cNzIqlUYAU9BtvGvMVQlvmUIniQQN6iZe1At3OtyqTDNmti+9SheuLWA2TiKtiipC4cPhTu1lwcRtsxmdcIHIdd2yRWYk56yDfDThidlZFLp4H2anF/FCiw0GEzBCQkuoQFx0NiNR79E7M3T0GelV6QaDsvhWzzoJNS4VdVSjNC59HueJDSTQUp3+oAX0xMWA6TSyUoSQmZQiqcIgKF0WQARI6icd4BxZHDNZdwa1jWEsQ41am1LdTPLOUkJqF0sA0kQYndztYJqH3lba2WAVJTlNFnVnX4UBPAQgxrXHOId5JJcXSVH3IfpCcpC0QbSYVRMjyiU6MlrVbuK2LFcKqSIsUaNuUVkBRqISDsAXyMDWsTFS6LlQgfB2dEcLpU4o1gebap2JS6qpoiiEP2SZmYs+kJI2hi7WsZkXAAAQAElEQVT6ztHmKXJG52Nx0GVJzJejdwgbhCYZPZIXnYIUH4ZWB/Lgl246GkzhpoHCtkWWBqB0F25hs3uGS/Z0HuMUZUZkXWvpEEoRoCnR6UcMxCFjmEM6VS/dhigly+BWhIRtyrIG84RAu1SOgKI7M+Is4Qrq3UUQs4UXCImzU63JDU0qL5GUIen+kUNJYfgr4yEHsVWp9Mp54Lt7J7olVZui0X9hEdYzZ2eXHxlBLgKAXBhiW5oMIVBJEes6NLpmFoNJUd0gSdHoufbOOIjFd5AtS6QNBYM6nUKMUrNFxaRIFSpaQfXdZ8aAIKHFSq1Cfy6ck5OwsVMwmlMJX0/pqakKCQi9QsKgaNK2Yr0VNb2UtepknFNUCgKk6cEjlw4+UnSGSJFCiMvyMGf1RnI5JA0LM7hm02iCnpEJDWqcSgQpMAroEEnQSBddWvBxLSKjoi7yJKrUDS91gpCsUUs4/6Ajib5QCU9BCAlLkHSPaJ50h5hFmihglKRaiFLaPivWzhmOFOFTytDU14gGFpHCrUTpc82+SubnqNGVpVM3RZct2jR571Orm1Nlwpp7ywiq51NHC4CBYgVCjQVQ0WJKVYZCq8pyW6sI2EeTr3JWGg3Za50GGjDmtO60afQ6hc3YJh96RPcOUHUCwBUxeJSYlGlUs5oXm4qtTXiHMRMLrOrE0FyFqaQOVuswSDW6WgOrF2YN9CYzEWgJYtVmGrEMKHQ4wj/nPEmjHwxpIyqV6IP0itk80lsQPO1hmRZ0EKsBkVgQJ9g9GLRJnxo9RlpJw1nCS4FZRGkd83rxTgUsESaNQooJTSmNmWNKUqrpq5QMljqNkPRABOGIkEihEIpWdFppI4fau6VXxTaUC70lKVctOTANE5TlJRrmqax7TOkJpCKFzgMohNdwAVEjCfoo3ZPB75O27u5EB54MkypFOTvm3ngXGaHStYYPpOVJKMd1DCqFwawe3tV6IQISc+jUKKAWCdG+BtYJem66o6sswhQQAJVBYpUoLgwtjyZ1UzZapRIKILjImd9BkUVc0ZKoCNHIcYdiI1P7amYFM1pRLLL37nNP1jUR0qf5q6xQB/A1DZMudynWR8vByFYlsp3/619i25GRv/92ZduNP+Zjtx0vtn3ZOb/5zfaTP/3zrHPO+/lZy7Zn36vuY6VuDNxi5We/+vVnvexVrfdSyiOOuv+j73dfM/3v8jr6iHvf5Ab7q9mvf332M57/wkc867k/O+PnhPq/O/l/u5uJxb0CQslTFGr85CXv885/7fjX75144onLf991ratd7epXvPzviU/z/N5P/lMywQN3OvDA39//P5zc/Pr7Xv3q1+DNT3/+86ed+QsBNwrVgc7MmvPM5KvuwUyTBb5hUTm1C0NK06oWZdgmb7nmonN4hkbaEsQZAaRgVKmZnJMZyihLvo1gOmG2YmzPky3VqTVzVgIHYwipIZJc0t2jMWwQTCrJZCKiyUFafOnuTTUrfa8NtVE4kRCuNeSo3F1N4o3Xodk25lZmz56bwFq1UWzDoDouOdiV1KNLT/EoEyzIpUIZh1xL+clEe8HMoG/MhMm0gBk+l3BwTcJhyJoMZjA5xUzJ2eEYrAhKCFXPCNFuLM25AIQgfUmRDusytJI+utY0qQqhnBk9lx+0QbRGxcaSZmCOaShMIVXqhslYwHZKShfrkICIFtFR+VM6XU6ZT1TZs1NY51bAWrepF5YSJ6n0zlQpYKKrGiRlwrdKqwJkTF2ntWABIyh4hkcH5pJNvcuawySXKqJa6lDKsFQ37y3mTST3EAAkGyS4sZlUtWWZuF5okTmK69htFWWQQUyZWtd9mNZ12rRomkWF38lQ7gBA1VnTROZkzvXMmduSqfbGFhKtRkoJ51d4wkBrChdrC/gdMWvQJ6AhkRlCdCGFRAWNrlUmyZlzJTWwGDyR9BVx4XVJqTGUgHRniawZxBIG5rRVoRUkInsDCVnmkOSR9CE6cKfo3AA3Zv8hiKOyDSGalmJJVQI1KAN9IkHja5ECrWLGDxVRNX7KCDeZ6DBQJG9Qn+xI6pUBcQw9aqaCjUWuQkdbYenFkjoKyE0QY/TSY/HDTg8NDHBGSJQ+W4aKqRL3CjNRmLOuR+0J1T6ijzyZs3tzJeZJiuAcVeIi4r4ozqTg3WSqMllJFAMVg7buvROJtKrCgm/qQg1Ax03RzBLUiPTkt1ibWOH2r2gYnatPXdEojbqz2cHk2CS3hZEX9UXl6GpRB6eIdVadFd0otRhR9Si9l3TKY2uAvtsiJ1JmCEuKUBJ+Iq1jmNNmR4+M7hKqWVMsNbxEVkGM6quSdVA1GojaGRgyUytTswihMyqmcI/ooRDJsWZddbOufDxX79VB7wCtVTobTqmAMk3oMFdNmyRmS0Y1rJbKtWMbOA8Ik1YJNEFCus3QGZhFNiXnlFxs6XQ99rwMk56QsOxWhL15sRwRlDpzgFitNhSlxi3pBoGkzwSxylK7VvVSfKQkvJ+ZnhDX0hdYCElHONHAqDIW3Sgmo9ClsEohrxgQpWWuNaaSlKqKFwpJUpB0Z54dM01FVJLWWCEZzUafU1eQE9wyhCgkDbGabIhRkpgJdbQejJ0qBpXwiLkvjW0L3QybxZxUYXA1VAWFkSJ9AQuUo0OpgVJ9MqATl6grH8g0ZmQw6CVHDX6HJEEHVC5RdVPmXHhG5pwxM5kMFM49s6eo6+g6ygzWj40k5s6MuV5CoAsRg0eA/AOStM2U3gLRi8yjdtU5R5cNNwtfZjaCHOpdha9Z1vMQPoavWo/eScawCK7ZMz0yAVOUIWSLLyCKallVVGSSYw5OP00tNYc6DyP9mcukzjow4rpJYM5cu282n9OTtOmQKtS5aBf11ABQFMyejI9Q1bFCCyJqmwd3R3GsioxjKTqUVEGINWiLrs7aNzVgrtKWm0LKjJ8Jfc7WxL2IWC1QxJK1nBJweNAODaVRSIzOjGGWY4b1jt4l0xiO4HduCC1FlR5hMau1YWMa6yQ00VrbXGMOOAtmrJTWDdUGp7kyBVSjSNTonBA5u8yRIkGPUREwPmScmdlRskubpS8GNQTFXYmUJtoEDU2NGzgfgvKvegydDzApHAzQkC6AArTyVNHEOJZ/aa1G0XkAeQm9YUbQFZBkJTUlU52LpNSQId0QJtskUxfpRAGrASQVOUmfjJ6zSuYS57RQZiSFxzyRJnKWJPwqSXB5Fz2DNwefCq2WsmmyOYoXz3SaTFBdk7KSvWIc6NMMq2nu4WJQUVKGRmiGRGjMBurebQgrHkKcvIuDlKg8hDJDOtBcvWUE8x+vlkf8GxkXXLSVJ3/KqONq3PYPf511zjkR+aeQ+v3a0844g3sMXl77KvvQMemaPz/rN8963gt9fVGt9c+PPPLhh99nQYIz/ntjGMdXP+eZDz7i8NU48vlnv/iFez/iMS97y9+e/otf8fL/Hf+TCETEez/68TbPnH/o3e76h6b96gnf//YJy69nudbVuYH5d/+DChdsGzts2XL4PQ7edop/+PCHhzGtMK8xSRaICFOnR9KvmWFsUD7O6hNT1sR0AOU6zYWrQ1poD7YjkgxQVn8mo4q+UYJfLdLr52R4qGttxsBvXSfPJj0qiad0rpUQUZ6RYJpmgSeYyWVmMGVkRkiGZgi5wiWdCUhQAOuQgKabUrSlmW3isSyfO0kwr2Q0a31wvu+KlRWUKlLIjJmF4dddGryzhdDUREY6emA2mQbpo8pYbDWUoWLQruHoKqhZhiwVprwYBBaYnSRKmDFhsuhlBCe6hhERE6klRK3n2LNmVmBhNafPEZ5gbq7DPIgUqMxKgdfzMPWhxbg8BMZkl4C29FNDJjNLRAYgakjjj6yGEcblFSKgVKLMw+gj1sZrAtqH7ObgY9YfY+gNYtoa5jnTCVXviDnMmUDDU7oP1lYD398T4sLnXrobC2OLWAyQkqKp2z6zMM/PxQnkOnXdxtZUPIsQ/UI1PETEB8DEoEqJU4auGykbqsZpFu4twELRS2njkMaugj6A0mVRqAC0DYkk3UvNqgwboaUg6auinZUoq6TaMi80hFNSxw6TSEt+0OO0pUhvtCH9LOahec0urNlDkapG569eLDgN3GO1pPBZl/lz2vJDxSoV7pb0NTazXEqW0RFdQkxRzWl8M1FkpsjSYVJXlpPFHbuZj1Jr34hp7PMGGgHRBWjMEgs6g7RS10KjsKkxaeqTrSdpAlBVVdDpnfbj30ZNSlFdwNPelt4eEJAp61RP2tLdOJ93UtIXn6FImYrkH5Jss02hLk6ntNLDZLZtDhyEMxaSMbunz1UoWnKG9JDoRmVBz8sOWozEqG4Rs47sJSCCCpZIVEJSey/NAScgItQCy8GfogLaTiT5g7Cp6YJXF3SNpZWhvJYWGDqGLJDBlZ82INnhEQdC2oF0t1ybzUVS5gq2NA4SAgaIKXR0BoXUYjlWH2lgUQcmyNwNzSrMTIhLOAQhyNCESC25KlHRqS868aPYjLZ0IFT41sMnEtI60gvFVmlVirPdUcCiOINs7DKiqGgYFSo5qBrUGeug6AIGaNIUQsKAqgzaBgmQTFnNpSaokYkwVESXFXwEJMKjw9sQfYyuFFdUIAULFjnrPAs3UbNlliojtRt6DNEIxqAYAtoU3WSxR6tAAWr36qlNOsQLYhtHIwzOpDCLEF9OUzMdio2mRQFJ+gFTRkrjIhZw7keUt2IsuVFjVZ1qzKs+bfjmQCcbo1V1U4glV4vDAAVKyjC7teY9KAP1MNFRyA9CjWRpibS0pQl0rFoZIiRnYWohMS3DIAO2E6LhkhHmU04uk2mvIgIVhQyx+EMQzqplVCN9cpUShDi9UBWZo8xi3WyZKqkS1LIKhGdAy0hLMWU4kkf0nNMbQRu8DBgL+I6pe7ItL64KPqiN5k6tk656GSyMy7VqLbXKoEE7F82iUshCkj4WRvOSSZdslKslN3zG0KNHbQz0RKmlOGpjlzpPktMQbfRYiKeJIQcvBStZBi2ZE5pLS+gcw1qEOzBm/lRzgkEcMnwtMVuPYZax6diVBqzFodE5HdHRQpzSwYtOOfR5zBhFVapkQSoLGJHbkj7QBdAbSUYmoKrVZKA5EHOyX0/P0kqZTBZJVYvwTJlXPJoR0JyFy2bxzfCZ3gcroszGJVNJYIbHEDJkqTD1zJZsSSRFwR6eiHkKoxeZ4M8yM0Bi1j6BniAOqalUK2GhA4WuZYLQQ9wBhBd35UqDrII5J9Xhnp2cbBgKNaE0TmfJkXuk7sJFdIeOdAsfklbrCsYR5aNiUiFmUEs6YdkarIgUbckFFaWWqmwNqFPtvpK+kiCGpoyIVISiSGh4b907NK2KileqoLDU2oliqxGqhqoUWhItYw6PTBFXEQGTbyWlZJYSF3WRkLGkjR0bkaYWizFl7t56T/oxJ3oXd4nw4DVStFAHUgzJcM/m4AgCKDksw43psHubY5qmzTV+d2Rionv+7vI//vk/ejrUu2gttwAAEABJREFUsvvOy79Q/KvfnNfd/0fT/lP3f3LamdvnX/lKV67G0/zIpz/3y18t/0fEXW9/+4cddu/C0OHt//FYjatHH3n4S/7yL/a5ytU468Lzzjn+7W8/7OhjXvrmt5/6/25XiMj/xPjp6T//3NeWX59ymcvufeCNrv/7FeddcOHL3/SWeZp45zEPegCEP/+H4/Y3ueHl9tyLj9/30Y+ce8EFInS6JfklgydV0pmLlEmV+YcFVEpKQFpKn5jPoB0RXFJ6DgklJ8aqcqkUSInC+FO4oDMk1aQIA29JMNtymcEKqjCFgCSkNITHxKQzlKEagyaBAHI5PFLYTfESgCal4eIIY7w2cJeUDM7SKZkJFCaulGtGds0l15VAhS/JuqYA8MwwRbFYFTbkAiYGckuGeU8Waess4E1zFrQiS5obtBgEacDKbWw2QIaS46rJ0F17JBSoZlYVICyUzKkXilIciKgy/wk5KokQWdUAVZJYBBIqapTPbNGshpv36jmQoFBbicIHadJLn6RPMc9z7w2AmIh106kGeQrzP/MN82AB0FUSRWSoziAtKlpgK6+rHFY2rtREsicHMjh97rkYtach2V4xq7KxKVGLDjagWFu5rxwWAu++sEv6Rk4ekU5S1Kkh09OD2XooUgyDyAJ3iinoDwYvOVt0LtRJrKmKDCoFWgiTdZEZ2mBOULiFmyMRlWSYvBfzUZjYsBwo+Ta/gxBWiEYpTUrCSIFdB60NqKO6bKAMpZqqdGWSFaZyJIvCgg4m0YBaalGJorOStQfByD7Ax+R+dNuMniyu4pkY52HFfVRfFdQqOoTojD6bC8VPE1oMBZRIJSW7J2OQDkTJJJyuqFAR6j+0WeaGiUD0zsrBeeJiyGKu1lUzNR1wBYxOEIOiDBqmMzEUSiIgaiY0qZojg5MBkIRYhxHYDk1d+EG66AQ2rYYGWSPYGnpju8BeAFIhIjmJs2lwqEqhYkLmvbU+R08uDxc3oYGR7FXoXhUhHq2zWErIwE4iiwDh2cMjqGwJmo6QBW9lJEHFcigodhpvAE0Xo4jCkpqDGovQKbWIFEkR0DidNiAChqUQI7Nr79xJ0m4MfUKY4iA1VaR0RycPy8UE1SRAMoOgLu5bKLkUKIRrkRAyLWIixrTCBCIkrj1zJjwkuEAci2HozALVrja7IbLArc596TPMogxNanQ2ETwHuxOXCF5VB8OXnsB2o28pfWDDPHZu20RCNI2tiAk8lMyRlhRUHQBTWPEsPhe0UW0LdAOhghCkZFBNFKRFN4oaQY2lA67Rcm48evPgcC5KIAw59D6mF56RdQjNQVNwBBLJQBXrIo2PB+e7qGo8FOFgWK97tKDGoYMXHjYYZVegJIo4+S6mTpQIzcQCR02t7gnv6p05TNBdM6qYFBMb1Zf3+tTGqxKq7DPmKcBO0LyWFHaopg4Bo8D5zsyqa3EKhG6YRRdqZErkjDgqEQMn0640p2fM9FMiHqrB1Ms3Qj5Gq9Z81GkofGJgYJpT74A2KHWnMIR0TM5Ooxole9WQUcJ0EnEnTKoJkwAfcwrPFFqhq9RV2JCplihCYhQVppAaYHtpHcwSWBnfo3DaamS5kErPJD0zDDWYVYEZTJlJ2sGsGrykG0cXWidQmhWniB2F4jZJL92zL18YTkrnG61XmyuSvqxC94kGbypgUKahR+kdyAyNbjYbl5SM0qK4V6AoDBDioL0kPUQjlYlglmD96g0kp1PWSQoszLoOoDJO6SFIje7e5vCWZAJA6FRIECpaMPlWgQxHw4aVoY4MRNAZpLecerRYFlFQ0CthGUOSRiWQ4TUnJVsaNLGwrINoSQtUz+okwplkE1QMEYnInN17eKKbdkUKFJAmxZlRR0WVZVDkCE5OCg/2LOa0LukJ+QMB8TSwREml/cuQVdWa0X9MJqinUD9xlVDroEHMi2YhfhgEG9BV8grZ6fXSyMi7BO/Qv2hEBTmlp3ozqughHsgAoy9Ro9LVVWYht8F6HboMATpTliG1dhO3xcqLXlzXIlsHJl0WaqF3laUt6WGgcpGrjo1WSxS6+iSTS1vlIiFjtec00wCLb8BXOqtNrUxe1zY0BhsdIkVcdV1zzd2xehAUqQDV0C4sFdmo4PoPNioR8Ztzz8efdgy1bv//6c+54MLM/NOI/Xb1qWecvv3sKpe/NC3rsf7S17+OzB122OEB975nrWX70//4sxQ98IAbvvOlzz/6gQ/YZefl//j/1Vm/esPf/M29H/7op770Vd//6Un/RcL+x1L87/qUpvzQp/7pwvPOoQKH3fn2dRh4wuERz37T35144vd5fttb3uqWN7ohT/6Dccnddr3jbW7NCeeff957PvpR77O2VruoG/01pcSg3YovMc6E3G0j+sgMoL3pHLWtCkOA7/1FtLQhpwHzavFpJUmmx+ZCSavmwBumzBZMeVVlKKxa1cC4SwWfRkFnVPcYm5qOMo6qOiIqgxnSNEIY3yGLAw9jlC2Rw5TOkEu+CM5WIgWAKpZ6MJAiE5sy64QaRuGLI9twG5flMfeZ27jMNq5QRrWqUMqP5Cd/SjcsehhT3zw3n/u2slEzByyJweYaIp20xrXYpDKFBXXPQTCAYaDdmY2qTNX4BL17xiwqqsMIJhRYCpNgLN8cDMxJ5At4z9d+5JM/++UvF4yGbrIebR6UnS7b2Z5msEoRtHefo/OdV4d0oCfchRx68P7UJLwrJiFezGxRseiktWIcY2MMq8iqhCaZSa3NKC1H5y2gQoQkF8yoZh+CeGcSFLWiZgipoYPQaBZk2kN6ZFfPnjkVhM10h7QeG4I6FNGVUmSVpcqIKA1MvQNT962e6xQP5lSIKurYh1WXAaRLjtG7TOvo6+ZO9ahKwWRJF0Qx0kz4DO7WaBjJXoK+OqJviPOpm/WAd3cPBaphY9CxsO4I3UzRYcILPkgW6tmE5Z8P2HvUDJ2Ud10MVeihgiLd4CbOxZDlohtNqlrEVJCsVeJjS9sqssk5aeoWwYABxs7H7PjSB5Vi2qgBGrwRqkItWc7SW/Q1+iQZEosXqGmhqQqkoleBcP+vyof0/9JEwSfSNUEzjnCKTdx5pZyP5WCF4RQIIgx83Tche9LpCV4n/N2yQdbhE02A5tYLyCIsklWxs19emtFBCXSPcI1e5pZzd6QNshr5blDEEjXNmiKUzcYkCBOus95tmmMd81T46te9C6uppovTOeFa6D+JEiGtYWqZc7BpVM8Id+3MNWMlIcqZQ2tDiwhMMDJwhJBec4Zjopc6y9C60QPJXIZUTY3kFPGoM2oT6ZApcgJPLIW4YD0Ujgl8sUoEtWIJFFKjRGqhnNIQHS29g4YjAa/a6zhjFHoAU5k7rMiwUeoG6CUUL53gzpJus8hUfR56hgilkQxSWWQiilBi3SKdNuXqOlqyT4q1tc2h8abwby+6CMFF4c7V20ayLxs0GXfzUGdG1aD0Agh6+pyYOtYZkzVHk8wKiMSUunVebc5bWq/hZNqj0/MHC8tMby6bWSZIT52BDoKBRUEUrUUHRUWznA29pW5qoWdzPS+JIqr0OkJrob9QVAnqr12t02ptxHrFr3Wyeeku9A0OSmRSB4wbUqulEhZaZx7Qaw9zEaQEPXVdsBW6dvVmkJQCU9t2UCCKJ0RQyMUSQ8y6NF9kDYAiDJlDaXVwvpNh1tlAFjLi6GKTWkvJDuUonb6xgBshaJoTPwfLLbAdolobdHOQJkADXz0b3Kpj5QxZhxWWixwZAjZabiRDkUFGOwA61BzVxaX1VQdbQgtgkU4jKrFz5kZZgxMqK84AB4LKF7NBxbrXTh/k+q5uKTVLSVFbq6yVOwV2pB60Ui05GqVbp6xTO51F0sILQhEM8sl9kmlarbGl54oWIdmMbFZ6M9tEnThRaTpVgysfgJqxjs3E3D1a13VKX0Kop9E1WgSD1KNG59BuvfPLPNTWV+s+rhtPjPiLFjpAE+neOvfLc8hkQ6e4BSqL/TWLKlUKkK+kFkbY7BpMPbqahy2dzjGoLT7VIrtH6ZRWNzZjtY7iFrEoqaljlhKSHcwzbUppLhmLS1H/FuZWqtWVlAEyOJEt2QdhdkqJNF9ALtGGnGitUcSQEb217knpyUbrepSt1ue5y9yGoMiZJVUaZS2T1wVtVOgoWWvUSo/vQ1PlVnEyb+K5yKMh8GTOQi5eJ312LDmx0fLcEfMxYZ1S54zu0Sl7EqZKf1dJOJd21+xG+ApgQl1y8D5kK9I5hTGjMJNlAkWTJLbplbDNZXHflAYwOzLAhnXD7DStd2Z3GSrxGSRJgRyCxoPS1yRcsleKor3V3llaKnG2BbSgQKA68DmzdeonBIMvALzjd0dmTBee+7urP/LnUOpuO+/Ixb8887TeqQFP/6Sxnufv/uCHJDHssONl97xE+PzTk3/+vRN/xDt77bHnlS67/JoUnv/PjAR22GHLo46874ffcvxD7nfk7rvtxlXnnvPr93/og/d66MPv/4SnfuEb3966XnMa7/+/4w8ROOs35/79Bz/EO5e65CUPuePtecLh7q94299//EPv4/llLnWppz3yz1fjwPP/YIjIfe9+l1123jkz3/6hT1x0/qbMZiGjMiBDNbQgTRsjfZqk0/2ZDAZThropVJgTioCxwmZkRmuybtqbhUij7yeD0AUaHB7RWgajQxWmdZChgK8BGHud4alAMpOIdsWMSEgxFdNZY5Zk1mkwNgNcDrINk06umVUAK8LQ62nh/BbXDDZIKVyrkOqDqLXB+H7BYeiaLXtEz+zae0FUML+RfohBTNNEC5RxSlqK8JbrWWIzdOrM+BqUF1zntTeh4OBRRYwC1XDz1E7ZATXrFl864XtPftXbb/3w4w540F8+7AWv39zcCll0i1BJVYkioUE2+stzz//gV7952Ate+Y0f/Ujgo3kF82MjNAFpKMzR1Mbq2oa1iUQvhLy3vqgSCGRKwiKkR3qyXUkHc7gUqhrb+JnwuRCnGWjpExobilmlu4ZjEKxQJaurEd5aHUs34FwuzZJLDaiQIjWE8Ei6aIN0KJCu8IKpsGZpERqAHpQVGDs9ZSO7eXO2mdG6ShYlCuQ5hFtmmroWdt2ewuzeVGYxCuzBRso7ooMSAqWXDA7xLj6HSk+ZiAPmxASDqzW1NJNCkWKRj8IJUUABhYZsEzJII5HogZ6WAeuwSEJsPYboUpL1h61T1EinjWd1V03u7xQpgKRkZ97u4UmfUSO3PkcjUupSUzjBu3ogQROjiJQaZWhpc/JuIwdKD6K2jVoaGCvuTr+XucAlvMe6U3Ij/ZTZc2ZVnoUbTfMqvTrLrkVDNoTQX7OQ0jZ6rZhIHVGq8NEiZ/eOycU5cTYhzKo6RqWNFQQsJqUVBpENiwrWvPSfnnrGs9/67oOe+ZIbPPav7vjU5//olF9mMI2MVYwvIQyubLElGVjkTYJTkVYQ6JGsh33uHt4ypzd89POv+shnv3jCTxEuxn53gnSKldklcPQAABAASURBVBnh7tklQ1zNRZMmwPd/dPor3vXp17zv8+edPSGlZdvkPolnKYudkiwsyghVOnaWhmF5QHdIKudhKQJpQh4Bzi0RKyyBpAuKq/QS3dAKbW2awVNd7kn1MihbRjOYQI0iYZK5aRceuvhOc8mksJLy9R/+5BUf/szxH//8BRddSFCdukqPICapSLIooUqgMxI9mWUyGW6dF0LgkjmBnDPhc0+nlNrQiZoFSkDIRiMD3rm+h1AeVfbBqv/wmS+99v2f+OSXv1ZL1uIjnyQ+/bUTXvOej//dJ/556lIxGgq0iFCNhZlOZr1La1vX/W/+8Z9f+d5/+vS/fJ+sQbKBjI6QElW88CZtECKRtTOzLXRSNIDYdkibM9ZgqlGgqBkIHf1GJbKAWQF06Dlkit7cqcgq2YGmLZGFWoTZmDwVor4ssUzS1jBFGWSo1ZSKU++5907PQGcULUMoCOOOpioZtBl1Y7CJCqc7HJQviZUyDzCGiH8o5iwdRmh7z94EkKzoisDCtVfw1RJD17qzDninAqkDDSerxsXxjk996ZXv+sdPfOUEBOdbqgUpEFajl8jQZaBxmzIvb2Zbe5/p6SDs5kb9zBrYgGZ4q75VY+qarnThcPFmEbo4Oi8646ZRnQHVyipsnHTlxUR6sWYECpQcFDm5cK3O5EGMqYcTwwLYZJiqhPISYIraKm0qPbB1njbXWxVwRqVljJE1g2/0K5jTyDJV3Egrm7Um+cl/+be/fu+n3/3Jf6Ey5JnEORiVEAdBVjV6G+vu3LqQTZm+edKPX/n+T7zhw58758KWMpiuSqmf+9b3X/XhT73901+Y15sUWKBVylCkgkSQTgzVkZ2Yggm3QFbaFy+CmhfqoKrMJ67wPqF3g1WYpZSkTIxpY/5zeun7P/cvf/2eT7z3M18L78k1dQyiB05TGLRYgRRwLxUypwjEPLQFuoCOU4oyE+vgxZxdfC5bu3Dv0V17MEELeQQdbVEYCPYOrZQ1SgiWAEf3JHKkCipnQXGJsHu07B2zbwt4UaVWHCqmqAFlwHuIuKqr6CKgCMIyh+KLd6dhCSG14jp6ljnUU+iZXJUJJDFAhHdvsyvtZ1WHUTe4cSXY3XunrjOIbNLmWzDzNQp3yzXpAp6KteTmMIVNTCSrodq4IudWtRUm/NF8g69ABqrcGPEB9Sj0AAEGRLU+WFdQPe/UHSJBilCZmT7w347zf/fLH//brf/kmZnuvNOyUcnMc/8rfufjSaf//IQfLtuSm+x7rY2xTsBXv/Xbf2Dqdje/KTX5TwoIOtTuu+z0+Afe78NvPv7xD3vonpe5jKi5+9e+8a8PffwT7vnnR7/27979o5NO6d3/s5T/Hzz/te98zwXnL79w89C73W3nnS5OTbv7m9/7oTe94+106Z122vm4pz/tyr/7PZt8+h+MK13usje63vU44dRTfvbFr/+bBRMAlHWv6oaogoGPSO/ROpNysyHrsC2yxsAqxRLSI/iMdQoZkW2yualUG1SGWCYgmLLbZu/rjASKMAwkJdPo9pzKkTJYMdngjfXMtLgO630YZqykV77cKJwOrgDgaVu9rPsqY9ABspIcUfo0bk6yZs7IhHlhVtB5CJKBuGUgbYY2SlhyKJQ8hj6XaVJfh07O5q8kUwo4D20UBnKy9IvUIbXOgXCW4ck5dXDVLC6UpieZJeVS4Uf2Hsyo66JNtfvx7/7o4179t5874UcXrafW/dSzzolk4qzeCtHsOTubLW0ZvUXuvNuOR9/jwIA87jV/95OTfqnOOdpcM4tmBbWWDjIdQAMY3xsO5okeTum8rFAGG8s4CAqmYMsbWaZcBUvY8vapZXaNpjRNn2XuDNtC2y4gwZvTOkuaM+W7omoyIBVLDfIoSzJv6/SpUE0kYzCgo+Wq+KBJdkyOsQW6RbDhsqVrTQpLcQva6DMBx9rQRHpHa9zHLFlStxTZqK0Ks8ecnshlNOvNslWuN2BlVIhVJ5nrBWD1novMWMh4Fe6DTEZx9Y5Z4QtXRS/IUWsZBjWS4CrBDJ9lnWVTjO8ou1I8MgOEK70tNg/BgjC1D9VWrIduL5ctaPjki/lZSopRCmQggoj1NWGcmegLqsQGPZEoNjRYtxphuSmsz0qaIzJKTAWzIReUZ2AiP0o/bmmrLUF7pnXEjNkxR/fYitj0XE8ZcxKi7KSLTsPYsqFjh9EbGHJAFxEXTV1Eo+fC2Q6klSKlOKxxYcsW0lQjJbwuRSw3dNgoGxuF06IFXcRaKTD6Q4p/9hsnHHnc6z7wpX/7+Tnn02m5f+6qlX/co8+S8+CuEUghDxsUozQTyhmLLNR1FTImnafY333q82/++Oe+9oOfhKR6BJ0nM7ppY/2zEmSZpcRgKKoieuKppx//0c+85eOf23rRhRUhqySdFiWhZoxDq2WsKlwZha4UjUIrkYziKahsCAxKOBoYKpKmUYdm5rRcGukTKukLQ+H8nMBrBVWfa6E7sF9aDVhVq1I8jEDSm2KFoCgo0qs1dhLxjR//9PiPfvYdn/rqBVsZ/KSaXnpUysJ8kmVWncw6KoekMnwlQ30acj3qegs/21rpkNGE8goJs0cPD59EpiIEETDKaGUSuEhy06nVIz/wxa+99aOf+ey/fHPoaYvBrUC++N3vv+2jn/vgZ77WmnlZJdj/DSt2vqNuqGwJbHFZCX2lv+efv3z8xz//uW/9qEolGGEBBFSg9NYaNCnlNKwz1x0OL2zJibVKazqvy9QxyRKkSEMDQwCcJQzXbtIKNWWsIWf4ooEpjEromD44Sk90RAKUvFOxURt3kENAkmYZK0FvEMYP23FKwGlpva1ohABXLSgUAWqIklPVXoaMQZz3sBxcPAHafJinVZ80myzskqKQNdnUwOhhETkGBihXuk/o67rZxhno2l0cgpD+vs9/9fiPfeEz3zrRGjRpQdBNYaqyjWPvso5Ygqr3WLK4EwdPhImNWTQFcEniUGZh7LpsOL8v2KgYaPIkQtkDmZyCWdBBG9CQJjEqnaGAO1kP0gO7SIxpNaq6kp47c3kMHWNgTFFDk6CM8EH6FuXW1e3UU359v2e8+qAnvOQ73/9xMdeVKiMUC4qirjS6ePTIlrwnQvjb577x/bd87Isf+vI3m6uXSvkNbokSOogU6zZ01Rngxt2ZXk886ZS3ffIzf/+pL7StW1dFBrWS9qVv/+gtn/jn9//zNwhjsZSSzNoqdA+nit6kUWGhJSYqwp9K8VUtXfuMycNzyDQsk6BcLAbmFWGaLxKldLWuBDPWH//KN9780c9/9EvfNsiKoVoLRaSRVHkpAymA6AZN70k1vI+NFu4yCSg8BsFQSRbwbA46TndNBVjdM1kQZMY420auVDYoo2Ubkuak52gPWaIggvFB+KhMiDRFYxHHGszcJEq/UzeYShHlASngH9NqmQNcKWFaiEnVUseifD00do11nbq4NinroQZNz5TZY3b6JRiwVeeBfmdrL+pDRvXBvFiYKH8ibALm4s0oJCQY09XocGV0xnAJ+gBnihbh/BSj7AhVL7KAniIQAHRJDXAEOGERtUIGpawC2gbLIeByh6UOdVhu/O7vb85bmtHfXf2RP/e67BW2rzz5zOV/I9l+/kd//sNHPhYzDYM73urmhTi1/Pjnvrid2h1udYvtJ3/c5y47Xfwhh97rfW949bFPedL19r2umZHOSaec/Mo3vOFeD3vUQ5/5vHd//NMn//xXEcn7//88fnLyqe//+MeJADd1B9/+QHoZMfmHj3zi1W96s/deN3Z41pMef+CNb8AJ/5PjyEPuUUrh2nd/9KPuU2ay/qAUw7jqVt0t50TMnny1Jq2X7hVhCWag7GiRAfpu06ELGGOuc7E+IFYKK+ICJgTpWj3qNutl9Fw3nZq6qCr9SJPJBqQKkpqVvNR5y4oNYrxNFs3A8AFIDA1wCVSX0YWfjKTMDKDxdhdxJhGJzkswn6cwO/ADPUD+RTGYMNAYcBGKZpQNESQgwshc6kjr6V07s1pRRv1KXZmrkqVBMpliLfjJqMacyV67gemWMlnMFhQhv/eTn731H5e4uNKld/+Lw+/+6sc88C+PPNgE7D05X+YI6XPJrgkHJn7m7fa/+hEH3ujsC7b+5d9+6NcXTl1LZ5ZAMUkRsmmODIEzsZjVYrVyUDgpQzUbCbU5JNSBMMsqQSE1hXp18GaDds9sAZ55ZQKrOZhJqHg6u9JIB+2w4METx6A6ipjynrLozOKeEeiJBJPV0k9pmgJSXA2qIo6ce3ceQRMz9XNIGItZdZMokdor3CihkEfv8JZBH9IMLYkxwLkpq8DKMMhgrZRNrTQRVyomy1YFVYZiQxSKFU2zJbla/12RTwpSqmoVIa4eVI37rZ5J7mx8l7ad0hqZ9dFnRUgopKaVPijtFyESApiUgkVLqocu6NBwVricCKoosoRTI64WU2XhhXpmY8Wo4Qv8aZKCtOiKEElIEmq4qy34F6M/L3P4IFTFIORrqabseRRZGGroy7SaMWYSmhpiLXTxf9Y+CmZROFEAJGjYEjCA6liYsU1vRcJENEUSQg0a18OCflNFK01DwSSiOI1z7gUXvvRdn7hwc32x1fCgO97iFcc8+OWPe9Dee1yaFcvUER1zl54mBFcyXCMQSc70CI2yasNIvWzQMooUirQMAhA2z9xb6BSYuM7MtBYx1dQSYhJqs1gss5e/Yq4bjWSUUlJ0+sbgKLCqtWohacZAcLUIRESlGIr4IG0AqH3SVQiRSCQi0rmnTExFsBLh7scihWrAZnCDQQcDFYGlk4YSmSKqi06VvijwJTkM2QdP9ErL5yIiwChXOo2qmULNqYkNLsyA0si2p3jU4mwWpPa0pO3RffGYdEKSFEcXYQWqZBjIntFNdMxhFWMx6q4V22AJcWw/Ekp/F+ujoiZ+d4SQqHeJ3GYRJeXCpSlmpY61yu8mQpgUIDO5lyImbmxv4JIhPbFGzsgomkWLSYks3TU9JZEiTZXzsoV7g82F2mlHa9laMDlRx1JRjRu6UEFBMTMy7nCPGX0uvBAD72lXZHFRD6aCHCRGwwgtqSGYVPgpQWGD4kCDCaQqqRnEhAcyDb/FJCkbAJq0plgaZyvEeBapXNutrw1Tsky1RQcTiDCOxakl+fEmlWRixxzZ8bvDQbUd2sCJILy5IKeR5mIo0otiKDRSFPEiIhBC1C1CGkDOUNggMCeOnE8daLmQEOUTpUWVWHdkeFBLBA+PJFemhwilTUxFlUgqeIRIVyO2EN5iHSWfCGKfggItGC2/97OTT//N+edvXX/qm98bJKtVk9HEhBClB602h3YdnDsSmopwByn/dkhZKJcqxdTAdChVRCkXbR2FPiFKxyal7fN1wWqKnKN3ut32m8lpQ0M26TM6jUyX6VBYoiQGt+KpAaNGVToiGVd9Gpurk8A2NdgLUIQuq5b8/nYw4kq5FiiphnAWuFxNRhGodZVOD1JTtVQ0CV88VSxYVfo3AAAQAElEQVRFnBdkhsWAMhGpFpmukgYUAf0ftKFqmqhIhgWKo0pK9dy2swV5cW5yNuNtJfzGvSnrW+trelNxKVwF52JGtIBUmK1qp2dHAcJr68McxtwhFfQM89DekuHQNYJXYNxwEClPo+sWJQ1Lqfxp4TaTRA6uo5cx6hDUcza4zslAJWNCkQYMqfQMNO2dqOoSrgJ0k3mssVF0o2CjJDUDaC8OAiVRLKBIkF74kCRE1YuIZCa6wAeDGTBAaIW6XKQKDWm1G5ngD45zzr/wD67+yNM/2+sy21eedtaf+i8UX7R1k1/mk9plLn2ZW+9/PQn8/Oyzv37C8i/kXvlK+/yeESf8T4z//pSdLn7xg2976zc//znveMVL73i7O6w2tnDeNK2//OUv/sULX3TfRz760KMf96q3v/MzX/n62ef8F+ziSPx/r+ERr3zr29cXXUCx73PQXS69+648+eCnP/Pi175uPa15/qxHP+Let1v+txOe/0+OG1/32tfc9u8U/+v3vvej009xY6YB81PSQ11LyyHA3BAizd2nBu+QTJo/s6U2aA4qhjrMZVgXnUpEtgIvCDUwYzKSSkoR/kyuhDfErME3jW1VlGnQAGPUB6oPJYsw70CBWlAtmExiRK+ZSo3Me23sJ9Mhk2/7phQMIO3wWfus7kAkAh5kM0N6lRiSlwxRpnbnI8b8QHHD2AKbgy+las4CrhRztSmlRUBCLbSKWBEv1nVAGHsN6c5g1rSa6OucZm80i5g633iIzCn+mW9/n6LuuDE++8H3vueBN7nxda66/1WvUNUV049P+dm9jv3rez37NV/59k8682aSD9eBTB5615tf+TK7f/fkM/7uc1/uUrQyYyXQwTKT0Sf245hRHMVMrBq3KKUQLcrNHCwxITct+kB7iIwJkwRbQUBmyUnTCXNClqyIgqJL8hLqKKxBbDeQXfk4S4YQXdAmQ6nVhGceHd5k7mC72QMZmd3ADUzAIaBlaOmEu7c+9T5FEEKnf/DlcG18AVOqcBK4djF+enZIV+0AkrLIUDFqjpCKKDKbdc5XznAtQU1YujYmrUuDRLfQpUqVqdhs3jM8tPfSGstjC9KGpBbaNdlbYgZLYcfQdNUxzrIxl5USPaXUtGTLRRltZZhrIVN6NohUFMFgZSzFHDkh1pB1WOd9UxukKsTNXeEoqIXXtYs0JAjZQOKSmQQK2WpMY3QlM8pKCulFiLFwbjTNGNPGWGmukoRXpWwUW0kOaKPOq+rjQFtGGITy1QYKJFGhNaS4SGpsAxH8EZCAAZxonRYuplUKsJKglxuaJBupAMJEhpSKDl03zE38xFPOPOVXS4148J1v/cB73GX/a17lhle/6g42SroonSC9B4K4FoVkb4SV/btKVwTtYL2OKaOgoEhyPoWiZ/CzRK5arOZOa0doMg2AnAUh3lVcazdqwZnLoDpiOYqtotYkJW/SncCCdkhxmNdhHm0yghA2ZLEo2ba5TQJK5SbFLOIBnzBNS9i4piiBSQXZp1JoPp7U5zI3bQ62qc0lOyh9CTZJ7FXYdJBgEOGURDYJgDcWISWkJAbIEGRc+TJ00jJV6yIM1x6ZVFSd3bxVGdU3um+ZZZhRI4mOmpCSOJhdipqSvEC0plXGoEEG4RTOoS83p1QLz+WvCKKgV1+XXK63/ZUyJeZUNt3No/eOFkgT1CJFzLZN2v7RJSIpZJTlEYiRtNSEdGY+yq6CkqW6lV4kIEJuXgnaKD6iGdXqRGmoOVB5y+zR1/GLX577iBe/7ojnvvxdn/5iqBDLMMgG6CsUZAZrxuwSaSKUXwg+UprrPHvvEx2QOI0y8l02cgkiSDJYYhZ3hFA6sVCiiUW8oG+1pHDbNErA1WEuYMdGP9dKI2kGhS6QIBNXfmagp045ZHz8K9+6z3GvPfS4l599zlkrp96kysc+Y95Gklx0ccsIOAO+k8gsfdNyXb1vABVFdEySQi2pJYrSARHhTZfeUm3BsGhRo+w96YQ5C/VSIEbkhuc4R5mXeJqZscRn61wa3nNxnuLULtzI3Hk+zGk9s7qMbagTGVuao3JqRJPoBtmIulrvd5W99tp1xx23rG5znasICuUyr6UNtpi8g/DPWtsgUkyZjdlVb1d3+aRdQGubZTW3beAW6cDsEaIYhtRh4ZSC7Yd2ZHOm+sZ8sv0WHItDZFAzT89EwroUN1B6rV7oV/R/NaCkF02QdGi0ZpiTFjfRsOwaPCgNp8kyZXHiUTh3Gx/nQxpqJt7ryFkzZeGUCReaUUpYMfBUGAhIoQjZyGOeunfEXDNGsSVha6EEWUWLWdDdZ58e+7o33eM5L3/+331oazDnGDCUWA1JO1uNWl29+TZCMThYTqgwdRHJIVFDLY2KMZHQb5u0GQzEWd3F65zDuunUMPfoSYmmFptT29wmFESMMY+NwAiqLIwiSETOyfgsgmJFrECzJeVqVJawNxDaWImNNGcZOtLb2vvcc+qYHR7qfB+nRehGoK28O5fOCAXzZfKHJ+C0SeUOCORjUgVAEFEHwYPIRBGgEFBLrbCKWss4ctbvxy9+vZSN31/+cSd7XGr37Qt/ecbyWxq3n/9xn2//yCcvPOcsrr3LHe5wyV12ojLv+9g/ri9a/o//e97xtoDgv+QQjKtx32te/aVPPuYf//bNzzjmsde41r6lFGSc85tfn/C9777qTW8++hl/cYcj7n/II4952dvf9aOTT+8e/yWc/9cn8okvfPlzX/4K5bzqPle5713vzJOPfPYLz3zRSzbXaxF5ytGPvN89747fhTSf/s8MK+U+B92JMy84//y/++Qn3RjbTBsN2bpFr0weDOtBtEKYHoVFCEMIU+6gUovUUXTFR4qsNY2ZdOiBqTljiYlNCxhiUptXZ14NBoAYCaCGWBNlEmeFiPAQZ0RJBVR0rsKAaj3JkS1cYTKSFAAGKdJGob6NkcxsWmeVMuTINegkw0BlarAsTJzcLwX7pCzUw7uKa2lhlINcMxLVbWhFmfhSBLKkNQxdqrgVth7Uo7QsXVUtmPpNI4Jfs3bqGqLr1KbZazDktWtpAtZV7vRO+eUSJpfabZe9L3+pScg4pYAlYCy99fWPz/zlT8781YWbE5ms6yrLKEOdhTDVR9/t9pH5rs9/9cxf/zqZlzLgDiodBaTLDqwpr6fMmWlJWA4609Kc05w8AcKsVWmSPYLC9wWtFO2hQq8okmOXYRImLhPigGjmMzdglWWDFc0shfgLgJ7wlF7ct/S2/HO6Y2IL5oI2T7HZMEFoX2RHm3xrz023reCuRAO5LdHRsmke0js8QouMpkznOfncwgVmavxIKRRNTfkT1USLKh+QxqxtGljRmyzGlSFsyKGAbXJNpQEoZaR1ocBqvJPVVRtNMidxYXpVTKP7qrN2DWBjNaAUUEOaErkhNjSxzfTZJ8dEXWORO2LpF+aW0Yi49MoX0DmnRzaBF4jVEmNpWnoY24mSWRd+LCv0joFqqlALSTUW3wCgIUYPAhgyGKrYuKhP57OIoccQdCz6hlI2oIQmSTOyjIUBmcIBmrwrW4I5rGOpLakOoUXTZA4shhAhMm4CBaiISB80KruDzMXaiyxkm2vRBgt6VAVYPWVOujRN1OzUX/w6MgHc4rrXKoX0In0dRMMNPriXGTRHZSNBhpKQFGq+yhjRFt8smAlCo2vmmjFIQqCspEOh+CkSajMixLddAYKkk5AI2D5ST2w7eha4UUqdevdssXgvBctwsPQ7lYOEqQ/hEs3nubXWI9cAGx7ALVJzMYFbegY8hYwEHZgUzAiJXr1Lpxt6gpB2TLNstjI7kXEpaRpc1ienJqX3ahjMKit5CrYdpGzMIn0WxklXn8VJu3PhPJSZFLrKwju9SgyKqotdpJtOgq1BfjQtxVQlBGyWVYtJRTJg5jm4ufIpF6v7soXsjt8fBrNZiIXp7+8V0PSqYmkgEFOLOdEX7wC9uzNV/H6qQzW0tjT6TGh4SUDUpXZYlBFa1cHUS60H0DMhQywOXhAkbi0ITtHCabSbDrOyElS4n/yLX/70jF+cu3XTJBDu2V1zHmUag4SldW2z8jYQCWeLi+j0iC7RgstB1VNViTGzaMwxdT7IKes66jqlNfGOBELMKdYiKoCUTPQMBnCDpAz8mx7ee4/05ONasmgIhD6QNaBzOe+C9U/P/NVPz/xlOI3RBYOUjRiZPYwkOYqajcbJNvk2nMRRnYwiGyXv6b2iD+JV+ZnN5+atZ3ROoenT1BnCVdcDmgQfThNaZ27kyK3wdfOYu7Ucs4+1r0oORJUyproUiiqp1hTdOU+CNJK+kBmQXgpUhBOU3lgIl8EiaTDJffa8zHuf+cSPPfuJ17rS3mtH2wQ2M2eGE+hcolZBEMjG3KgRqTl+dzCpmiEq91pk2mgctEamyMW+qVwtFuT7uwUKEiuGymX47ZEttAW9ulmZC31Dkyske2bvilBTjiwyFWl1yCwkqZOiAXNIn2FbXXtz9YnCFBFIMKsz0hnZWbezycSCzNRbS0FW6hUGOoenC8uSLsipZCh68bXSEdeSzVRKF11HWbtuSs7Sw7xoFBNTCrPhrSYL94/P+OXPzz7XkQ0KelQVsRTO46AEvbrURsH4lNoQVK6t3WWNpOBiAfVtKlN3VaUSkTqbTObBJxY6CKombcqikilA0gsYN9Jhaw5CEZnJnU6Lxmk59tyYfWNuGhwhmRpOf+s9W2MGJwpRNGukNse6h1N2JpDZwThgICC6wzuooXMzROYh4hSQCiEkYux1h1ZXAtOEhkZImyVozdzGKaOJ1BiGeUPJSiD4g+PMX/72lz/+wb3/9OlOO+64sWUHLvvFtt/SyJM/brAcfOCjH+XaHS9+8bvf6iaRsXnh/IF/+gLv7Lzzrne65Z/0332RyL8f9J9L7rbb/e52pw++8gUffdtbjn7gUde69nV33mknteLuF1500Xe/993Xv+n4uz7ggQce9bAXveWd3/3RT7eup39P5/8xd356+s+f/5rXz/NUh/EJD33QxXbY8qkvfvkZz39ha62YPe6hD3n4EfdlaPxn9aXP3fXAW++627Kh/ccvfuW8C88fWI9aA5OcBd0yS00r3EcXK7DB2fmXltpTRFAsiszGJiFB3weLpUik9Fb62qLT7cMYT4pNZTBzFVgsYciSaTljmpMx0V1iW1ykKfi+KkqfhYHWxKBVZeBdBFVL5QyRgCVFiw3BKKnFvK5SDevIKZT1uIgOgyrLjVBATZTEhkdFlNI1J6w3heXTo3hAmTQRzB5AQpGjYqP0gemP8QphdmvMLcKkuexSCIoKmA8Dg0gRdqdWsqm6mJhS3O5OUUtRo1Y+dZ87uQQfQ4RPtg+NqJR0eQ1o5r3wzdK+V7nKVfa4xDkXbn3/Z7+EdDAV9YwUSFFlK4vCFJJEVTzh3Vk4fXP5pDG6QQUrQHvrmzPmpDI5ZBauBzMfhVS+CCrBLMxBmFKa9M7aWMdqVF8pnUEsgSrfsgAAEABJREFUAeGC8J69aXeaShXgoxrEypbGjLUjTFNG5JgUnmyJeZWqopRUioGF1F3mtbDwSK/CwB0XPV0dAjXeWfGHQkLgxH35GSK0QdKDkmVz6uMaw1pK1IjVnKtGZYLykWMMW3XodECjVZdab1Zi0A5pUwQbvuipXlTGkrUkBaKsG6S42Tlp1mFtY0clQ+E085XkmGD/i2A/MnksTbeiD7zplCBMMJQo0oWU4SmIsB7sQnyekucibB0qtCQssKCJAijMmhiklMJvHQxNs2ugug05VBFsO/jDsNAUT+taOw0CdFrIiS3IL2mIWBXfyGA3HHyNkOLBP8v6zCphBiz4EUx+XzltRAw9lP3YWmkD7+Y5tqychHQXX0usg3gpm4XeHduOi1sZMlVSIQjhvD6VFiu34mVxJIQJqtLSA1QjIdwdz0PMFh7d2YfRdbeRWkypoeO2BZSwK+tql6TvCZJPkCa6eDXdCtuOcGnrElNvOS8zPdiG6VpzdpZLl3UX74uvCNGPmCNnb03ZfswtXTrKbBaShF21pFVnzzTSvbrbDJske1KdWSfYXIap1lltFuupUzMnvR5cPllM0Xrv8Ka12YCyxBaNBB4mTcCpc4u152yO0plecqW9LGYVtzpDg26cIRC6ZTKFgaKLewRlEA/NluBFUtoBGMOzoc3N2wSfLWeRxdE6GW4bgqZRkpHEZbnt1vIxwDacjV8J+snyxJ3YkDnm7o1ZYpm07a9oipJcoyOEkL8R/eCtHDJWKQXMbchMVwsrye4HKmkBBKRJ7RRS6gisIsfZB7dBCmlsow5Qj7ItT0lSNW5Nkjk6TZiUbPJFc4HDI3toERtUGBZdMGfSLkH2QjGghCVnigfKEEZSxKxFTpQZqrQppAIQZAGYCJsk0Z5T5gBN0loydVEYBSJ1bnUOw8BFCerDddh+UClwfQofFfrz9ruJHCLGJOd0UndjuGqv1kzd03sjzRzDOaSFNOK1LXRMNUFf0GboQleMNYV26cmgG4PgGqd2ZGcHU8MpnkiRampgGhMo7y286BrRsjk1F1olUnuzbFvEq7guy5AqokqD9pxnummXnOsWXW1ZjfSPhXsiKFVQCwxWRrFFOta8jCm1hTYIfneIsXytgWl56HN2OmAvoiOFA6hK0ZmpD787QixTxGkfWuC3dzWAUEid60hysZgqzRvlcHojuS2pLnQO9TBKX0ur5lW5TBKYkcSmEYMOZurFnSUCMYfPxD1/ywaUZ05xEhM1o78lpCuSOBvtNcdCrIoO5NNiItVhkJE3lPiCWc5deueiEOJH5sGwmrTzLn5/BE2/cMfo2Fg7v5ovs2mQCo0QqZNmmCbjxKlQJsjZqeKgKFWSOmURfjGcrCoDvXDwHBh5vFcLhsF0Q6wMNZWtG29yfUOfdO46J9g7qXaTbq0NnRVgVgZTdwUhz0VoTfLq6XMQGnZQqZ32MYZTDPCRQqiOLnXdjbUwKKigIErpVqgE6SAEnaR6TUoKIe2Cbt63FeflIVrXNcosOgP0IxqXyUOmdOKGPzj61gvdafw/uPWfP734xS62y07Lr1L5+dl/0vczQ63b6dA5dFW76ie++o3TT/kZJbrFjfbffZeFBc//y0dAuujl97z0I488/L0vP+59b3jNXz/32Q+93/32vc6+u+26/ENhzKu/OO2kN73tzYc89GH3O+aJb3jXB3588qndGaP/5bL830nw3PMveNZLXnbWtt9Xc/Cd73LT6+37j1/88lOPe8F6mszskQ+4/yPufwRP/jgRd7zYDtu3mr8449TPfulLYoScCaAUZXpPlRSBVqkrVIYYtHWf5j5veqwhm5CpZWf6YyQaHOqqybIwkMoaucm+mYnBwllc2DnQ6ZntbUZZi7mwSq8lWWp6KNOHdwmmTZGm6VaChLwMTdik1gAP5V9mBUNQFLPKAFySVsKQRcVYby2YOLQzCTJoyZRZ1VbKhr0bawgDkU2vZq7nfsrZv/nWqT/7zmk/O+38sze9aQPfNooHxNMcEu5LvaCMWnDBhRedePIZ3/nZKT88/ZQLz79IMsVUNipWBhOyX8/z90464wcnnfGbbf9wxeZ6/u6PT/vuD0753o9OvWgrfnDSr0744Rk/OX35soU6nPqLX3/nB6ee+NOf+foi8yitwuvGlo073ei6fPqZr5/QNi8qggBl6Cldh2RyS6Y26dzTlGm2iUlFNcZh2iihMkrZAht6ogdAfeuoVtcsByvkyqU0kgl4BLuCtQgTFk3LQmNMPIro8DmDuBvBL5s2rNVaxLzkujoTDKQqVgNq0YpeMFftQ5FS1YpJrkoftVNm442Vlg1gBWfBX4VpDAimxCFsFbpqWHVmcTMpUCQarUE95471lOs+ByaQ5dBRQ8QjFn9rrQVLmUR0nV2pRJSQKlpsW6rFIGSUMQrXSKdu0EKnCCm9qFNLvlJP1sLl7xTpsdi1qo5qY4HVtNrLQPFSxyY6UyxtUlIrlJJKGbtW99CZ2d0NEdl6LkKpdLHJMXdiu57rTLXbkNBWpK8cG51lQpN1ggqDSrkxTiCqBhGhM6Jnn6V7nUioyCwZ4KdMoh6DZI1Sp8EasbcymAHuOdNmFlroMCIgNdDVk0Ejnlj+hGuoJCEeahusD4t5G6hYThq9htNd7MTTzzjh1J/ybSLpcJx48qkn/PTkE392+uY0F1VQkExVl2HG2C7yzVPO+tUJp5/67dNO/sU5v+4RjJNMIqRlUDOYuPGahJaRInS7NfQigQuU1/TC89tF3/v5L/7tpJ//6JdnXdBntgC8j20HxfaUNejl0NpGoYhOP5QmLWItvpZopGXqmWddcP53Tz79eyed/qNTf37+BcxH1I0qO23rkV3KRXP/7umnfPfUUy7oPgt9xk/65S9O+OnPTjjptHPPv4juEibJlMHgUmj0Nrc+9+bZtZ+3uXniqb/61kmnn3T2L7fydYP8gVYl2cwm9VTasUn1OgBj9iG78VY55YxzvvmDX5x40tkxY3Nz/aMzz/rayWeefgE5wgaxkTYP73nhRe2kn5/9byef8s2TTj71l2e31iRgFMRqs0IfDJBrbgNm+SAytCADeSSWy41tf2eRWYkPVQ6ltS23JZSg7en0btsmbfsoHtJ+cfa53/rJ6f/205//6ryLOkMAgp66zjq3kmsdtp70mzO/dfJpPz7jrEx6FYrIIMtrGwbTby7c/PqPT/vmT0654LwJ6zz5lF+c8JOffP/Uk7sHGfz87N98+6enf+9nZ1x4/lbdTLowIIKiIirA4kmR6CKu2+5QS9UAhRRIzoasgophqTNZCyNZR8gYvIcEUiQkpSSsd5Ij6WGIjSHNIJKRPWj/ptpVG4aYzdZds8EibbNd+J0zf/btU085+6KLuJbje2f86ts/O/0Hp5/hM8PRC8XjXYB7u5wd5udddOH3T/n5d0865YzTz8y5j7WMI2x0pfU16SEtG53Ka3WNs35zzgk/Pf3bPz7jzN9cEAEBWJbgJlakYrAcIANKQR2l1EGIytqHH51+ztd/cDqhjlR4Xnj+hSeefOq3f3ryj047c5PZkPO12lBIYb1uPz71rG//+LTvn3rKuRdeaJ7V3RZnnacWbbLzL5q/fdIpJ5x8xoXzJEWyZAjRphhjYew3/Pr8804g8ZNP+/kvz5nW2qYBvzsIW4aX1scWxZGQXvT8+cIfnn7K937ys1PPOHNqc/KB5vYV3Y0UpinWnUu334OatlwRJ3UxRMlePWrHeusFPz3jlBNOOelHZ56xuTm7i8/WmvbUFKqnRXTQ1MVS6rwBAtyVHkGDG2lN0CkLXfW3jFBLGVfnXjh970dnnHDiKWeede5mMiYqOuUGxYRCq/zyvHNPPOWME3/28wFG5DVr55czHTbntHUizt895ZTzLjqv93bKz8/+9k9O/e5PT163Th7nXbT53Z+d8Z0fnnLW2b9ORikWkTtVd1YH8Dvk6vQ2zPB15NSMVjbatFhdiXBUEU1ED6Lm2qvKGFbWI4d0NSTE1GrdKOPGhsmIORdbAsHOBymQMW0LVhuyMapaBt3dfNZlLX3ZSrJaSZg4qjRlnlssXbNUlsAcJEpJrYkCJa0A7VE0rQQgEUPCaB+AYLHtSPWwzHR177O3ycM5gZRpEQQlSzIy0siUGZkxxwjDHxxU84LfBdUf3P7PnV5sywbbUK75yWlnCJInf/S47c1uwrXnnXfuV/7tO2717e97Py9rHe515zupKs//zx2CUNvjUpc88IDrP/6BR7zzpcf9w2te/tJnPeMOt7vDLrtfajvr75544ktf8+rDjj7mYU971ns/9fmz/p/y/7FE5HNf88Z//eY3qebVrnHtJz74fp/60lef8cIXX7h1K+889MgjHv3Ao8ah8vyPGyJy2MF3HYaRy9/+wY853VkLmO0giQam/x50TXajhVVLCu95d5b09NAUCYUIK4j72OcaDE0fihdxi4gOJxlVMhENaHrHkuXWhYs9Q5A1mbwYFBG1R50iZ3NnPaI4XWJO8SR7UV7zI80aMwbpMWpA+ojgE3IYylCYCApPUTI1TBirxpmwzm6RcenmrbbQT//b9x72srfc/9g3HH7s64847nUPfN6rjn7p8V/81ndk7qU3sakNfR5s0rFnOeuci1757o8e+eJXP+ilr3rIS179oOe//shj3/yCt330gnPXkiVIX5Pinvbz3xz5/OOPfN7rvn/a8l3oyb84+0EvePNDXvTmR73srb86++zHH//Ow1/4puf+/fIPIQB43Yf/6aEvfM3DXvLGn591dgTVU6RKyn5Xu8qqljPPveCEE3+mvANjgogQZ3ZSU03iZYiCVDAbjTVrCWZnQhBdW+MbGUmIymhMTSZelN1tVEWlWrNmU1bE7BBeBmtgSdPISISEi0dv6V7Tiy0WLRqjzGoNi3xDqDEH8klNGKUKJDXXmqsKeg/TmcbiC7RCkkbNZNY2Zr0oVJKyW5UcNJiRaZ4C1OjKXh8t0Lt39+VvS/RibZC52iSsUr1FWzIkumtCCYEujmjFVTsv6X0AJHpxOq/kYv0BfDyIG9ypUbTIxnlExgKlm0x8AiAXrzRSJQC8EBPSVhHthpnIaBOEKZbkHAIPiMeiuyx0WxJssUUaZGbvkdEU80KborTS59omQQe8O+WwJN1ITU+BV/RBeomevbcW6zm7IG3BVrsrQ62MaRVSwtCKhKaC2glvKSACkD+WTzgFcHLRzBqgtZUEaB3RFCMYsjTkXbgBiwkUyBrf/pds/tQ3/MODjnvD3332X0iH4+jXv+P+z3v1Uc991de/80MVT7gLfaVRwk9/6V/v/4LXHv7i1xz+gtcd/rzXHvHc1x/z8nd898eng6gkqmmpREKFQpEQpRLu4IBICiqWPmKK+X3/+MX7P/eNRz7vjfd//msZdw994eu+8K3v9IhtK8ClCQIOVXb+OYLukiZ8Gp7awxzsq5Pt2pOOf+dDXvCWBz3/zUe96PgHvfSND3nx8eoVlWkAABAASURBVG//yGcu2ryQfJZJzmZCvvOjnz7gea99+POPP+NX5//bD05/2Ivedr8XvfGol77uIS89/qEve+Or/v7D89bNEbLFckPbgGkxSwh3aG//6Gcf+pI3PPilr3vAi19/1HOPf9SL3/TV750YSdEWMV0kTSmWK0JdyoyB31TReMrc4l1f/Xcf/PPnv+Y5b3n/z0779eNe+fcPfO4bH/S8177rs1+MEVpglhH+lRO+c/Qr3/TAF77xfs97wxHPee2Rz33dw1/81q/+4KRIK1YZG0ZYlRxlYQnwLEJ7H+C1Lm6w/TaShneGD51SjXNrg/WgMwgtbyWG384DTv3VL5/6mnc95LlvfPBz3nD/Y193xAtf/1dve/dpZ/6CkTr42riVi+aIl/z9xw4/9g3PeMsHgobLIJ41ZAhoyle/9YMHPvfVD33+a045/TTL+cV/9+77v+j1j3/ju8/buiaXj3/9hPs/5zVHPveN/3riaaWXwcOARdQslEwoHJ1fey3b3zMn5ctBvdLFLdVTGDngJo4SV6QmPC25NoaBKoeKZwY1nbNNZAfJBfYhtZBSqHfqXbYBzPfYVToJUAIpyq3SKWf+6kEveNPhL3jVmz/5xWUt8OTj/4Ge/IQ3v/3CrZs29eq5/T4/f/Xrc5/71g8d+cI3Peglr3vIS17zkJe+4ei/fuuJp56hZVCKxRDSnoxgzRCc8IOfPO4Vb3nAsa9/4HOP5+eDn/eqp/71W0875fQtIczOZeylzCIthWjCyvKHAinhiPKmD37iAc9/zXPf8t6zz936yvd+/M9fcPyfH/emB7zwTUe89A0Pe+Wb/uX7P4bZ5P7BL3ztz1/wxiOey6B7wxEveMMRL331y9//kfVFs6aoK5IJFd8/87QjXvaqo1782u/98DTRYALoujCF2G/OPf9F//CBI1/8+iNeejwnPPhFr3rq6972g5/+9hfiUV9Pgqd8/Tw4K6uxAP7Nxz5/xLGvP/y41x553OuOfN6bHvLCv/nnb/2Y/sDJHMygSMwJtsm83D5ES4jRG2X26s4Kwd3s8R/+zP2Pe8P9nve6w5/3miOOe/URL3zN2//pny/oPjMLdhnExrAVpErY0LWq0FpN+GIiuvcQ+jVdgC+8cqRnb+eDs86/8K/e/oEjn/f6I499/eEvfP0RL3rdk47/2++cfBoihk6+1Ka30t75mS884CVveNSr3jymDmDEavTKzBzdfnzymQ986evv/+LXfOMHP+Cqv/3wPzMojn7x2848Z/kfGb7xk1Me+rw3Pvi413/4y193Zj3PoKsxk7LyUElx7gUM9AB0QUhB8jXoYCpqvEqxKBJldm0spdNcMobAymV0s7Bg7w/qufiA6bYskiIBXTIes6fxhFYXEInkY2imcWWOVVdZCsxRZ9OZTJQ8a5UcjJUkmDJIKCU7pCuorFeurBRGo7NrE7Ze7FgmS5aQClhaF5utd345iM63brOHGy/h3PUwB3HtGMI3VOKguR0SVFktdeB7yN/aY279gq2bv734Y39cfMuWnS62A1dvPffsedmj8/SPGRdctMleYvvKL33j21/97o++9d3lf6O//r77Xvuq+2y//3/NZ0I4xMpel770nW9181c85bEfef0rXvSMp938gAPMjDJccMH5//wv//KM4467x4Mf9vjjXvK5r32DSPL+nzr+b1ovwPHv+eBH//GT5H+p3Xd/wRMf+2/fO/GpL37FBRdeyDsPPuKwJzz0QeNAt+PVHz+uesUrXP/6+3P9Cd/59vdOPoMVg8Hp6D06N9nOukA/TR7QsJqVbVuuMiU6rVFNBjq2UNSOMsM6+CPHxuCmTbjBt+p1eT3KL2L5HoY+r+mInuY2kN5cjV0YKoOXeSlZBGaJtbYp+tyjuTT2Xv8f6v4D0JKq2hPGV9i76tzbmSYnkSRIVBBFAQUEFUQMqCAKiJIkiSgKCKKiKDkICIKKOTxzzhkkqJhQMpIkNDTddPc9VXuvtf6/ug2M7/tm/vNG38z3ptj3nKodVk571+3L8tzMijgRLUUbTNgrdd3gX4TzdwV+1cD7jRaJKREpkhoTsZPgKMBAqvccy2pc9PUfvPOTX7v+9nsemn71QYTYt/S6G2879tLPXPGDH6uZuANbzVxzvu2ehUecfcVl3/357fcvADGYXKrdcf+Cz/70uiNOv+yOu+4mQHVPppAERv+7jZW6Uv67Q1W0V67KCFWAsOa8+WusOI+IfvS7v1KFPWfyxksuNdXCHC5knjmazG3jCfwFcU+oAKPrax1+80HYkaiTkDH3TOAdWSW5NKaeJZIrIAzEiOVkrWJOjRTacFZP7tKJlhFbG9yIDOeFGfZAiZM6ymoQSZEIhNTe+7FVIHAWJlb2HFXJJNyiwnYquYMXrsEmjJietCDGUiqY6exBxbmU7D3iKKWhxi8a0VikLlINbFyb8KTG4tCIeTAFQnObGWdRExatlxF54yyoSsBT71ioFJ61S6MpmpiyyXEZjTsubl2KqQntJ1JkQrIZ6ThLNfLiXksxQ5HpJcc4W588CHbs0mGwr1yd1GpjFXkEciCSWslokJrkEclIrc1dIz3sgDw7RN9VGTtM3WHA3pt3VKv0LAO1kEIp3vUehbiEmxlVQ0bswyrqi1RMKmloEkWykrCoHgWNuCcurCFKTYhYJngb4Spex9VqR16TM6qO1KdkSWhSeZJoxDhlbKD+IDL8R5qoERzeYen/oLmFFGsrUuyyZcs+8NGvvuujX73p7vsWTVelWPP3hxf95Pc3HX3uJ7//q2szqCOQwgRTMgwOLZgKigRvlRqFrYmd/ZmvnvW5795674NdHch+dGr8x9vuevuln/vqr66j6WswJInMqDlESsu4bZgngiYooLTactWf/Pa3h57zkZ/8/i93LVho0wY21dfb/r7gw1//6VvO/3QF1SpZUyJfbn7LSv32z69964c+/Ztb7nqC+LseXPjZH111zqe/Rp2LIZ65KqWGLMo7r/j8h7/107898FC1wXofWbLsuhvvOOaCT/7098MfyQCZ7NBR2/lEhDLBITyoEhkKKx3DAodVmPbgokXHXvbZX9xwG85o8fjosmXCgYum6ke++v0TLv/89bfeueDR4bAJowsWL73uljtxnPGJH/w4DJyXTB1CLcweo2iMoMoQghSB2SEyoG9oPNxW8kjWTEChmVC/0NCbCbuaYcrwc8/9Cw448/If/uYvDzzy6PBMdM9Dj3ztyt++9cIrHljwgKBOF3fmwmnZuB8mBIwM3HkgVMNmIos1bKBiGGSqScuyDolhePx//riwaEPUWk0weYQSbLgZAOEa0bKkEqmHlBI2y32SqkJwjBAI0amCCpKKJzzClLJRLjmVzBbmCHLVTZZjhNx7+ABxKtx2GXEC9OVMWYgrh0tqctNKTlyrWcTyVf+PzxDAJRq3y/vve2jh0Zd86gu//N3dCx7x6SUwgGtvvP0tF37m9rsWlmgsgBZhTjI1X/nBr4469+NX3XDLA4uWADrm3wun+N0Nbzr7sj/c/remiUYc3ovw0nl0xF3LpWFyEac24B8DzvsWLj7igis++aMr73jgoeIQC0315frb7z7u0s/97Lo/n/mJr3zwM9/8y51/X9YVzIbjIPt89Ie/OP3fvgUZN03b5rZpIVwMDo0hAkhqiKwIOLTg0UVHXPjxT//0KjjLYHtIdouW/OwPf3nT+ZfddM99wwKCrBCxc8QE0US19J6Pf+m8r/zwtvsWINMR0aKlU7+95a63XPj571zzBzyiaaIMjE1EbvC4vHEX0JQQSfRQ5IMPTR16/icu+tbPAGdcDHOWduXGe+4/68vfP/dzXxtQKgl8JwQxMKwKwlV2hDSuaoV697GVvuRqIxcge6zCWbj40cPP/tgXf3YNrBcw0SD5n1z/l2Mv+tSf7riDUxVF7Ox7KkunjRPOyUBuKCBq5rEFbGrQHxYub8oynp65/PEfP52pwLEjnEKYmCiUPTeRM7MSQhOrNCyNVEE+cLM+Ar5TtOPGJMFZuToh8VdiCpw2UYY75KGUgpprZbfQiMSmsAfEH1FKxjoOuH4dR1+9GpOnJG2jrWiulKNPELBhIgyplWCkczGGHE3R22s2CeFK1Bf4aErLrUxgdB7u+GEyZlCXtIJOHjlUFsYUQqII3zFEAjLcNwSdBnwxzGNadMotzZhIM2fNfkJSS6fGDyz8V/+fj8y80prrAKa5P/DIItz8L7Va69V/+POb33Pa8161z7kfvnj52t9cd+2HLr0Mvicpv2KPFzc5Le//P/8ZMHTR+fPn7/H8HS95/7t/8aXPnXjUEZttulmLCOH24EMLvvW9777pHSdsv9c+p5zzoV/85vc4O4mI//N0/tMYQex3f37lhZd9JNxmzJh50luOQRw86qR3LXnkIRHZd6+93vGmQ9rmMR/+p7FgYdPkV77w+cwI7vaVb3/XxCoVh3k6HLKl0Oq16wp2GW7EmiearI3QRNgE1RHB0uFtLUeCC7cKLzaBxxrKpCxJInm0RtlNvfpQRsG7gUupSzoeEQ4aGXUFWBKXVKWphpowE8JE1zHcH5khQCVRUEJDvhephCrKK5deYqwo9wgBhUEviXN4FHhWSGdcO+cxc8dEzHrtjTd94kdXjkvdduN1P3PCwb+98F3Xnf/us9+4z9orr9DXeu7Xfvj72+8iJEUfrLrrp9524RV/veu+rHrIi5733TPfeu3l7/7BGW87+EXPhQRuvPfBd17y2fsXLCxd9L1suNYaV1948m8/dOpzN1mfiDZae7WrLjzp2ovec+WF73nKk9f6zpknXXneaRcf/noMob339S//5YdP+tEF71xltZVNalWrCBtaZk+O1pw/bFRuuP1ORxAD0SRESCTkMIJgI8ZyZw4JyLWyOyHwQDRBZm6I80yaGavAgiUaoEBYNNZklJiapuHU9pwKcWU0Cq6koYLgJYhVBEVVJVMBAMibse1pODCCsLp8w+SuZuqFzb2Yhw4j6PMI4BVzNlDThDaRtebCUofGpgDONmDl4hRQE0oIpZ6RJvHjI4qRDqFWPUkdaSfSOwKyjDSymECxldyUMTxkDbDHNBUCWMu5sUxVgSUxRMQuFOxD1RNWe5IOrPYeXWW3GIwThFNfuynvh+2WCyK+WkoVMZwC4tfqUiwM/EgNMAbQookU0spZ2lZaZiXHEA3kca7C2HUMQZ2IUPf5OFOfiWSY1hhJ4L8EwlypemAujBtrKYhjMAEUbiZJOU2nfQupEHOHhJ3cEiRFfXipJRUD8RxsTIPyw0EFAVwMF2CFuDN4NozyMOaA5QFSgNkz1wnyltucv3rKUVde/K537PMiwEH75hnHX3vBaded895dttwcirNGnf28L//wm7/+Q3XfYdMNvgiv+fA7r7zghNMO3GuNFeYsXDp12qe+8Zs/34ITjVoJmJmFpq8IguhAttYsFN/5xa+//qvrqscma69+8ZGvu/K89/764tM+fMz7Xt+QAAAQAElEQVTrN15zFdRh0ytgh9wQrBDe1xCkJBIpAiSot0mz6F9uv/XUT35pybhbf9UVzzroVVeedcJvzj31C8cf8dxN1wfj199612d/emUgJLJnqwwKwH7QFT/41WorzL7o6Nf/8sPvvubiky8/5sB1V5kPa/3mr3/3x1vu8CqFBHEkiD71o1/87I9/xc3WG67z8be//toLT7rmnJMvOGSftVece+Pdw5tS0CmhhN0gPgNhj7V3rkE9UREJbKewGrNoweIljy6dOn7fF3/37ON/eeEp79h7Dxpm8id+cPXHvvdLVJxPXXv1j755v1996JQfX/je84/ef6M1V13Wl0u+/dNvX3l1eKcUmVMSiGKARnCtCLzaqBmHOLDD6U58wD1a2ADDnYRhj7HcWcNBF4z2MWL+cMc941JO3m/P759/0rWXvP/b73/ri7fZQoRvv2/BKVd8ZZkY6jgn2JhOtA2gErEEyuzGoROwxdk4BQMR4ZJgcP+Jdx103WWnfO3Uo+bPxFaYXrvztr/+yCnXXPie522xecCTpBN2YogKjj9ICJZMRck5iODLjoBDcF04NEefQbDBiSmgdhICLvNKlaLCpoAzmIxgYQ5TxyMByACdBlLUYaYNqItRyKiSIs9gWiKBo5KIbPGU9X968cm/uuyUY17x/GEx0RdPPvrqj5z25fe/dc78iWgiBPCGkd/dcudtDzx0zMt2/cnp77zu3Pd+49S3vPw5WwLyPQ8v+sT3fmoVlAkzwxZ/cf0NZ3zuu9gkI5V84rg3XnX+ST8794RTXrvH/NkzHli89B0f+fRDS5d4IGoDbCbES6dqgR3LQDYjOBaaFuc9Cx99YOGid+yzx0/Of9evPvz+b3zg7ZBkVlky7t5y8ae++qvfbrnuGkhVV5/7vl+c+d4LjjgAphhBeH/159vxkidn+AXrNCQgohTYF3JLOUkDxs/94rf+cPvdTLzTlht98d1HX3vZaT+/4JT3HfCK2RPNLfc+9nvIgURb1VwRM7/w819+77o/eMTmT17zorfs99MPn3jlR9590eH7rbfqijfe9djGhtiEbMSIgLBDHrASlDgordFe1bquP/aiT/7xjnvmzph4y8tf+MPT33ntRad9+aSjd33aUyHlL195/Rd/+GOl4maGjyiGwBXkYqBewAp0oQNN3Ln3Uiu6eDkabLf+9uDCw/fc+VunnfDzC0/71hnveP2LtmuSwtfe/YmvPTxeElIUUdB8tNyMeVrGtQp3rdYRqwWi4nKSiZhZ9dSD9/vNJaf94ryTnrzyCkT0vM2f8vPz3vfzSz+wzx67hDB6RIlTDyK7RFONGMJQcHYMhagxYr4gphpFb9hIu3nJBIPk1uEAEQbBcI4AXeG1hkX0AQ0SPsAzYnsnVgkxWjzgDxaCs7HeqTokwyJDEFBSsYQyY8pRyoQ37G1UCXeijmhMVFgLwgMppNVEbTDibgCbmQU/SMcpmAJ4zKJyldSNcoGliCFpIUKPRmlilJtE5gGCSsTAFuiHQHFHyM5KlGikbcINLb/G4/HCRx5Zfv+vfK676kpYDpO454EFuPkPtmr2mxtuOuTEU/Y/6s3f/fGPly5dGhHL1z7yyCPXXncN7p+85ho7PmP4lXrc/3/emHmFuXNf9/I9v3DeGZ++4Lw37vuaddZaK+Xs7osWPfK5r33toGPf+qIDDnrnWef//NrfYsfy/znB/xECfnvjLSeffW7fw/n1qDccGG5ve8+pXdep6mte8YpTjjli1CxPKv8RYP+TOds9Y+vVVlkVk773819ikxxRsfufLHmUlEdUGbWclWIOS88I/XmSWpXEgp1/Vxx+RirYvEibmSe8TtbS9sSWsaBqEYLz1iSMIqI0I28aUXgPzJ+FVOD0HJ6RuiKF5mgTiWggkwWLuQTgD+bXk1LV3HMy9ohENGJXrZ7hVZiA5Ap8BqKkgH7nUi36qtU0GkqTodf/5aZxqTNH7RkHvWazdZ/U5MmJ0eQLtnn66W94DaJqRCABRDRatbX6tR/+4s4HHs6qx79qjze9ZLfVVlxplHi1+bOO3GO3E1+5B7j5670PfOLbP+v60rtXkTzZQF5TXQ8xIoxN5jzR5rZJcB0JTdP/YQgtYXJIY1kLcTGOjlNXUudNv/GTVseEOxYsetQ6k6JcR1InmynU7x7MlbQUKV0tY49x1dqjtKN2+FchSfKIJQEzBE+MyaPMrbo4CKqBXAlRc/KcIMamo7aLpid1xlxH2CRRa1I/itJYRGUsYapJLaVA7AJa2IG6QwcctjzqiSfyVEN7pim1PnmfyIRYcsq5VeRLrkLL2BBLKTgzNYmICcdm3kfSom210QAOnULMoZSUU8MM501RSArKrwlRbIjV8CaiH17jOFSl7BIdhXPnbEykzJQrMgfi8ai3UVexOWYliGTCfLKz1HfRV+/CeyyeUM74knByYtRPzALc0TI1LEpBQpJqVk/MTKxG0BUVCIdVca6mSiReuR9z7ahxh9xqcEeo0UZso4muHdUkkJ/nBriogSUMOSBChQT2PqQm9pylyayUOXJwE9Qmy6kwVXhcNfKAB0xI3zQFSqvSj0vpxggHoeEt4RLGYmqUh3hQKSrRICDr2KfIp7jrAgeytXogoeWwTB3zFMkUZyVtTcpUDzBoo5zQGh0ZjeogCv7L7fd8++o/wLuev+XG5x3yuqessRpVmUgzXvyMrc87ZN81VpgzVerpn/l6KSW4p1Gh5ICDxgR5BUHY0S1c9MjHv/dL4F9zhdkXHPra7Tdef/bMPJpIW23+1DPevP/W669F05eqC0cQNrdqGgG1GiCAG5aGRw19/9rf4Wh57ozR2Ufus/2WT9VmhuZ2k/We9K43vHLNFecCxg9/d4Mr5oNTJ1CALqLNnrTGRUccuPVTNx61o8lmxrM23PiEV++JEVQWf7jjTuwNu9T0nO9ZuPjTP702gjZZa5Uzj9pv643WnUxpsml33GLTDx11wEZrDJkUq4QEQkepINmgr1SSLks0VqHMbY4RL8c7kdPJr3/Jnjs/c86ceRPStJG06kMPL/vY934JFFuuu/Ylb95vm43WbYMmOG272WYXv/mNT1l9JZB00dd/fNfDj3o0UhulTI9fFWoV6I2CoY3Hep2opNQ3ZGzmhrFE8M3izJW4Mi+fN2ryhUfuv8dzt5o3K6dWV199lVMO3vslz9oSo1ffePuf/3a/p4a00cGN0IcGFEKRmBTsBAwVDiH0+CXFuGpKWducMQH9wnhnQi14475wV+EZglDCvcaysLFTjeQmFiCYcETlNNCGHzFmEzVgMszpYXg8EZFTL3C2qlFbPDkxJ0TRSDSIHwjhrZyJE4NNRYrxlrUxME0crGKJC4UR3Cfg4A3lSR4lSViJpm1qckoI4mLWOjUFnWhNSh84eO/X7L7rzJlzQnWlFeYdue+LNlx9ZQz98ba/OXUszKKLlz165pe+4xHbrL/2B9/w6qeuvW5OM2dPTuy5/VanvO6lbU73Prz4u1f9sS9w8lFOzYi57U1KB6kEaBhOQ6ArQKU2p3e/fs+9d91+9oyZCKYrrzDj6H1fvNPTn4oxd99qvTXPP/o1m6+/uuaUR+0zNtr4uL1fivSxrOuv/ctNTn2AxQpNYfrQELVzkUy5Sfmu+x78ylXXo/d5m23w7oNfttE6q03mPH/GzD23edZFR75+xZkTGBqaQzFVgvD65ZM/udIisI1//2GvffrmG86amJyVJ3bY7KkfetMBmz9ptWEyUXXvekgi2p4afUyeLvDcsTamql/51XXX334v+DrhtS8+4IU7rjJrTitpwzXWfP8bXrPdJut7xJd+8ZulSxab9UFjgpaMYDFTLuOMGFpS4436DIUnoSZA9TDF4HEad5P0lNe95PV77LTC/HlNq6utNOstr9rlkBdth8Fb//7gz393kznnTmXcTi17LKDBGZwdE0REM+qOCdKMRzSoEgZVGwnQ1ySYIjqFeVJ5hmoW6CkwlZmrh2N74eCRME092oIsWdzMqiXvhCvyLZxzTLRMqGiqo4ZzAmaBGU8h4AbVrnCdCuyieZgRGTZviPGOHqKekpEg6I3EJ4Rg6+x49Gawk2CSAkKYNDyRIyliLie1BJklixJSJXtSImKj5JqVNTGB9Qg2z4zHVoZBZDFH/qUIgGYkJHYDVxzKhEmRFI4zNod+osLzCdg1AMSo1sQ8OWvG46ZDFG4LHnxsy0v/wrXqisM/Ojf3+x/+j257Fjyy+P0f/tgb3vaOX109/OIySN9ogw0Oft3rTj3urU8Qwsyv3GOPGROjJ3r+i9yw6qZPWf9tB73+K5dedOlpp75yzz3XXmttEQF5Dz5w/5e++c2D3/b2/Y457lNf//aDC/+XXzEByP+x9rd77jvpg2cuemQhML56zz1XW2PNE88891G8S2HZ5+UvO/noN020LYb+s9qqK63wvGc/C9D+ft993/7pbyrSCxwDKRGGKsMXCtPMAadESdqFGCKCcaoJOWfcy9jEiVlg6eFcnWJwgvAYkwfDXeBS8CPT7NwG0pnAZ3Jm+F0lKxHhyiGDm2FBcm+YmiyIHpklWwgF0eB0JITQqFWiNmxKsRxlUueEXGMSoEMiSZei1iCEv16VEObRR/Tw4uH3UFW4JfbiSG8DjFQ3eNIqb3vVbm/d6wXP2ujJSjgvreNHp7708+E3UrbdaJ09ttuSGzCFRj6wFy/Y9lnP2HAdIvrFn25ZvGQpRSfIt0KVy+RoqBaFWN0jSvHezLCvYg5spLBkaEyq6KtUaqoEwcJCITFjmzt7+F1Nd3/4kcUa3IAYLaKDeBKjkEU4C7cKoKWGMXtigYyYsuZGm+xZ+7BxRSqBpgqQELJlD2loKqQlKltRJ2R33FIVtscEacI9A2S1ZAWko9VB5EaDTMMR7RDJVELdZNj8KBHUJAINUwihtPCMB8VmBSjQiFi0CmKwdQGS3YhBL6UgRCTQT4WVPCGWt5Wy+QBagykaKxM+zlw5hTfSM/TpBTMESu0r9aa953GoWRiesVGAsRFzcEQwO0dV6oWRO6BkYGm5CHZUyRxWGppdpGIyDAmfHB2TW3ClkVEbTAjWsFdS1rZlHE6SpoHBSlELV6iKYZBOuAlhwTxPxoM7uLoMUnMOkWF73jALIHeinVKRYqW3jsJE2VspDdYpc8pJNZPRwE2BjkLxCWMjJUZbjs2IyMKJCkUxdxFBz9AiiSRJ2TKX5IUhAQ6YllbnWsKiBlSGAfBfnMwsDIoNgE450szRY5FcIbGmD4WKCZJEtvryz3+ztOtXnDX55pe+ELOh7ALaHDzGhmuvse9Ozwb6m/++4C8o4xSKlGB0DA3fmUE7Va2//9tdf3vgIfTut9NzVp43h7MwIYiAC5oxMXOnZ2yBIbQAVAbTESiHosIM61jrOJVKJXqTeMVzn33JsQdd8Ob91159FW4U4aNgMPlK82Y9fb21AOGehxeXvofsCdJUQQ/a61+w/fwZMwgyKOlvzQAAEABJREFUMSYTZ9povfVmtQgkdOf9D0F9EGNS/u2Ndz60eKkK77/rc1ecnEWQAmh0yIlWWnnF7bcYakdAcx+KqyYsS0gTgvWRNBqhHChAFXwTrtVWnLf1xuuJiipzkkGLoV+7+vpHlk7NaJvD93zB5JzZnjzDY6NCq3Pnznnzy4f3Wn9/5NGrf3cz95U5QgBpaNC/eQet8QAIqtahlwZHKMEBUjIGB9uC2wZFsIsQUC+f9pxNN9z4yWsIplSodPDunNIBu+04e6LFhO9d9ceGtIFXkE5N/+oXlNgnQtARGm6FsbOvjKnTjWEy0pjDXD2oTvcRMSVOYBXTnGBsAWM26iG/MGcR1H2SghW0JQ+2WrwWuL9CD1qp9lx6GKlVTCcKZ4hFO1MQhO1bhCg0LhOJ8iAUDhKC6MVlgObAj2cQE44BbZMAbkSN6Kv1VN2NBsMPmr4k3KOCTqMkLEqgehjYZuP1nvm0pzrIGjjnkDox0W6+3hoYu2fBw9z14hFkv/rTTXc++DC2IAe/eOeJidmQnCSsSW78jE3X3XLdNTHrF7/7q9dEnFxAe81UWi85XBkwcvEGMNFWnDNrm43XHxh2A1EgC8J65lM3WE7Q7ttuNXNyhkkD4QYFCT9tw/VyTlj40COL2CsHrISdl08HGCLH4Uatat/5zfDLWjC2fXfbfsbkCGRDGlYCill/jVWevuGTAQRNIKmh+R9uv+2+h4aiaL8X7rDSSvMhEwEwxIooK8yc2HHLTTAZzQtCU+4p0RNICXR1kY0kCsWXr/ydRzx743V33GKTQUOJFN6unNvRa5+/fVK59+FFN9x5r6hES5GgWaLaRJdANyIl5Au8opHaaOAg4lR64EVbf41Vd3naJuwkuWqqAnXX2Pu5260ynTS/d+2fu87dSV0mR0MtHYTkVxCFA8anjTcaEzJEc8Aiimlx4xZixecTDfFTEWx6YvCDKI2ZrEK5DWrdE1dBkIw+d577gC49Bs1BtsRMUG4qNVdO1gh2QJ6rE+JuRGT1Ntso4PUEK+0pEOJScbKAZjEBkTKRCGAwPtVcLZZPLOFS+5EYZEKNi0PYkFCjAinyYL5Ja5NrUqQI6ABZN7PA1wgO2JmXIMejkgI9RC2GUABBW6XoGQYBn+PeuA82y1SaKGKdkTunSGmQKADUkB5Brpk/ZzYRQCGkMm7u/PsD+PwX24rz5gBCuC9Z+D98o4IJT7QbbvvbIcef9Nkv/dt46aPM/Mytn3HeKSd9+twzj33Dfi983vZrrTkkA0zG0GhiMAXc/xdsMNCJidGzt376e998+BcuOPsjp79/75e9dK211l5O6g03/vX95573uje/9bIvfnXh4keXd/6X+gRVJ5x+5m233wqqnrPtc56+ycYnnPq+xY88jMe9X/6yU958xOTjhQV6/lMaE++y/XYJibb0Z517zle+/UPEQPe+Ug9zHQw8Ic6YZOspxrWWWqSWpqK8RLiQajBwGH4YWSGUA5QpuBtSQ+CGnLmEWMCHlSqj0NJKqkSoHQVeFA5nC0HMRZglN0RB0+SoAzgxB1gcfpZ/wWU9jCuFYz48TMk0F0LjmoOQc7UZhSBCMLuP3EYcQ1bw8M3Xe5IyL57qTvnsV2665+/FywCNio70xTs8c79dnrfzZk9VgGa74577Hpj+fe5nbb5hM0mkfRDChYaBaXGi3Z+5Nei5+6FH7n1ogWpV9AWZP0YnhsB3IGc49k9FyLIWGcLIMDJENFQKLSUeYkDrkiLCI4InJoeS0T0WLZrSirDHgGOR2SMZgo2jrnfUEEIkiKME5oNqYBhAXVMf2iNmli4cLxpANKAmsoZqAgtarBaoEHRW8ONR3Z2Nkon0XB2sk1ElJ8H4EKGrkUPQXGEa0AjyimcOZMrE0B/CsUOzBP2qcHJw0xNCH1DUSgUrEZbNGreMQmQQRRBKC4YJOEjm0ipKdaIk5JmMU7Wh7ICp9Kl6w9ZCNhxGXLFOJVqRNqCCmgr20n1OBXQR7MB5qGtKRTGQ2ILFHOE8TGoVZ/bE3Co3SVObKTcW5Ib9gGX3XDT1QhUWXwuXClpCuct5nFprQtQzuBAWYYaZEll1s969QDYEOYwoZ4FQqDY5MGeYhSRjTeG2stZq0RUziNUGYVVClqgONKo+AEVCGQRKIjVxzzRGQrDGo2GBR1JOruzBZImqcmiwk/RJTZMwLb/AIldh2KFhh5zcU6UmsIgInXiBNiqQcJEwyKl2UkwrnJQgEJFmamy0/MKo9I4MHE6177vu+htvx8hGa6+x+ty5koRQWeMVW/KazJI8e/NNVpg1SUTX3HSbJ+AchI5HNFCGFNeMWEf817vuNQ90br/Vpj7KnkYEnnoTbEEi0rRUMWqQDKN47RN1atVNaqRCaFJKVPOnPGmN7Tbd4GkbPFkjKWfJ5NJZjNmszRkQgAN6CQIlTbCgB23WzEnPKtAgZEjUw0AaQgeGFo97FWq1TlD89dY7PaJJ6ZkbPYV7qFmK8FhjrGIkCeaOBUSOEbJwfFMAw4hYiZgwzsQ18EC4VDSRNJVh/CzhDSywueYvt2BolRXmrL/u2qYRypwoZYB3U9pi4/XWWXkFTLjmxr8FbCR1ml0eU3EQeLOUTHMEbjENDb7GHQk6ssIFvHpUPFCEMLOCPEwianLyYAxBoNFbVBPitVdZae1VV8L4nffcB3lJJUSCyVGLHsD3gWkb2IVJRBEQOADGIHl20qoUjOgxLQf0cnDiRjiDJWFSC2CJvgKjeBbouHFunXmgTCykq9L1aoWTEYaAu688Bm2FvBAYQ0BPmO8Eh6nJPQGMJSZCGz6YtEZGBHcCVIIH0vKL8cwQvvbiY4fZOHtwrVYCVj09JwqeugJviCTeUknT3TRrCL8g3UWsUVhKILY0WTH6aFdTqWpObr/76+3mMW/m5AbrrBUpaSbJYZqgbUkN6mnMv+uhhf14GYdX6ivDimtqKDfMwlZ5OBoJzCKoiR2qgajxnNlbNp03wjzGMF6AVGpKZFZqGku5TI4g4WEogqETJoYug5iWX0IRVmzcR3f9TUP9AGOD1xAzAvOUxzjIIdDwBHudXqIt6YTwKP58+50Wgb7tt9ykIROD5KyPijcaYyZ9fD5Fitxaq6WhJy5FUErD0z0PPvT3hx8BNVttuC7DnytXCoMRUIfbtVdbEXi7arfe95BAFpohaNAGZSUXHswhReGA/p0iO7UsmqC0aboIa2FH6gFrEKqg1UJG7cTm6w2Hhvc/+HBfi7E52dR4PFADYWAah3GqpBAUC5xleoSoukNUGp4ByEHyY/2446rRM0yrIvorEEKsrC6YLIOL1wgOl0QqQkiyvTFSh9as3ggRtKs+hgwb4UFs7JYoUuaknEkzsk1xc3eWFJydYQHqlIwFJufD3IFckmJRAhc8QIZToB45rBWdTkoCqi1TjIIbQ4QR4WkR0tjZgxj/EdZB6sXx4ZAiBwRODVwp8OiGREpGuaPUMbnVsDEcIyoTbJmEuBf1lHObdJStSZGIJkhWmD2LiGyMA9rAzW33/Se8UZk9e06ajuALFv7P36j8/Nrf7nfs8X/+yw3hNnvO3Pcfd+ylp5686/bPnjFjEgTNnJx4ztZbgTA0dz/3kkt+9+e/4v6/dGPGSdV2W291ypFv+vqlF3zs7DNfsNPOOWXQf8ff7jj74otfceiRn/vW90qt/3W4KGbHvf+Dv/n970HSehs8ZbednnfCB89Y8ugiZsYu5T3HHjVqh1yC0f/cts2Wm82ZNRMwH374oXeefvoBJ534xZ/85NFl1Yg4oYI20pqiNlFzqVSQyWHGJSce3KYnHkJEjxice80VbqVUhYhgOcHwGB+cRowCLz+XOcIWVYQwIhZDpvZUiho6MB1dpCQc4iTENngcDVcEWe07xGEqJHDzOqboI6BNCniZs4mgAGJhUUFuHmCEBCJeJULkmtppm4222vBJFPHta/+03+kfec27zz/9U1/96e/+MrWs4xhmUiB+DdX+LQ8sGpcKD91wzVXg80KRiFJPhPpX2ZSetNqKoMkjHl66hFrQ6BhtLC0bYxLASFgGMVlCNaTxgRwwgDWEQMaJ8fpDGoVogwveHYwChxUD3UrTV0UBZlyqdiWPUbb3CJIIuNQpyMvKmiB5pQyCEHFLWO807qUOUSYLJWGqXLvUd8PRTxUc8jTI6KQm2gmBQjLESrNBZeRKTgg+FgaJJyYxRK4ojUcKCvGqJSB1LGMOUKpQfNuUlCvkbwiNRARIaJg+0MpMQwBGiqXchmQNQKXll2IkiDWCB/HwMCKwkuWjphbkbuFsPnSHVOZQ4kQqhsJCBqNAhBdI1STzkABGiUZ5EI4nK+IQSHUCT85iYJd4uJICggTBNFGs98HE1racJ5RBILNXRmaoYUa4DCoX3HXkNbm1HJOJsggsykt1KyRARYAiQuxCQNIgnhvKDSLpU+obx9ap5uhTHquWpIamakDlAx1EIJG0kFYUc23TIDgBiocUTdQw5epqvaaxNsYt0YREq5rSiJtcmDvwQrjwxWQSxkLJhsTTFlN2VvfGJdsoR2pZJFMlrSgzurbxJrEwkdDy8pQgF8ooqUwjkKjNu2V2+4ML0b/JmivlEUNIklShHymWKnS78pxZsyeHjcoDDw/THNCZMX+6hXKIYpH+/eHhpHbeRDNvhTnMHE79ECoAoDY8TcPyBRRQvpOxB6ekLaPxgBdulHgQed/3Uw888PCnvvfL4y/6xIvfdvoLjjljpyM/uP3Rp33t6uH8GGCcqYYjOvggWnSQwFVayCxcS5+rpSIILcMIEROKA4mCOu6ehwYiV5o5mjc5A28E+p66SDW3rpyoMAVNX8bUcR2796iDKVUhQ0mi5MxKlB+fhrlYoHU4fw6Ripe87DfdNxwXbrDKirOaJGTBPG1NGgSGCYF99RWHjcqCRYsccAlBrSLCABQDctImKZQrfW9u6ETLUO9g5+wiNNA5EMQI0kUwSk+wiYeQKslViZjMLErOtObcmUR030MLrdbexyX6ZdMVHtBNzwMQjZrIhMFc4BHTyRmG7C2TFvL+sU5tEHUwDRxxlqEJ+Bs0KawwdYY2qoBuSMUarW1j6EZxVCMI42CNMosyA3kdbFRDmTIFqtJwrsRGTG7JEckJF6RnAVctDBKzMZRYiFyosptLdSyAKZGHxOC2igWAgJWYFZBBGEFADOAeOt1NwtKwtF5b6zhsFCyWlg/h0wX0Oei55Z7hHyw9sGjJS048c9djT9npzac876hTnn/MKS9426m7HnPav/3iN5g8Hrb5y9gr6PKqxJmTQhQEGgzKsCcspVqF5EWtzYwydDglMKfpa1hS1QpsBch7VPHkdXqEpsbYdEjHAo4hsuWdDJfLrgBdfbk9rz5v1mjUBEEe6l4rj5flMn5iAVGIw9ac5J4FgwuvONnOmTmivovecdaFVI84EplB9XIUJM5qDMtj7u0xYqKhQW+c7nv40SVTIIwu/uaPd3rHB5597CIAABAASURBVJ/31nc/95h373DMe3c45n07v/m9r37PBV0PJdFDix+1ylJEIpM21ApkMzJqKxMiC8RREWKtBBcEL0x4DDcFbipxRwQwToQo0bTrrTT83tBDixYXA7Epk88YNZhIxAmmW9ysWlSs5epRafkVxPC7DPWEmfHyTnw6JZwWVQon9oErQnrrqRb3cCyRTqQjDVKSQECNVo2yIKaOM3YTDAs1LuOoRQKhIedouUBcBBEh1sHUjLmqm5pqVcnCI4mRcCNEBm1ggy3eqCRmzOnEx4mraAA7SMlgKilRrdYbmanDWZiKeCTQLAScvZLD6D3gNOFuAf7YiQg/QcSUNGRizAkcNdrntmPw7D6YWUQg4oIibt2VC3EFIZk5Z8/aJy1z5g3HG/T49cBdtzmWPv74z32vOH/+5MSQSP5y+9/+/0AAcd/68c+OeufJSxY+yCLPfta2X/nwBS974Qva0egfVg2H7iKSc2bmRxYtOuGcCx946OF/mPBf9BaqIeGJicltn77FBe96x3c+/YlX77nn7FmzIN57773nlDPOfOVhR//8mt/8V9iuVLMPXnrFL6++FqJcY7XV9tzpue8980wkZ1Xdd6+9Tn3rm0fNcg/E+H9ag/b/cvudBxz7jocWPrabrbX+/Korj33Pe/Y98piHHl5iiUMM+JiiYfidJKyRiMbShOMMbgKRvlYqRWs0lXNpqbQRPCSBwTu4ulQnR+qC75Ep4qB3cDPXBIfMBg8IHAUQAgG8FVGgb6LLgOBKLHCvIY54reNapvpS4HTwokwhg7uH1BjCN4pTw00gFAU74rdW4tIKgkGFMwfH/FkTH3rL/ke/bJc1V5xbzW+554HP/Ojqoy747PPf8oHzPvetBQuXBrWFcuVmatr1VGQiz4DnEk0ITpAcwSlyojSiydktBIL2yNIpQmw0YgOH1DYZnUFUUc7bUFcGpKUalNgHLjDKToIYTcKAkagw9yKsSTQxDfwTEagtolVSDSHz6lJq6hCWquSa25pHLCiikqo4CqUcJcINFIiyjKLNNlGp6YVL6j0XBpxslFLy1JIKqCGBtCFiMqpGLpwJebKkNlQ4LFmwDcCyEpQQVaO2XDVBRqyZG5Mc3FBtwID15F2gmpCowtYoXpRwIomSa21qCKvgYpbAf1ka0ZY4ARYzWcs0Q2KUAksCcywU+FFvQrEcxi1Cad/mEAKPQURBwGySTRJxk6MZDbASc8sOmxtep4vQEMJBuzJAEsFaSBxcdABOVAI2jDSTM7XKky5tBDRE5IQUaoNckg5KsOq9lz464h6yztmQNpRhBgL7AzUeEcDoRBKkBnsbGU16YmqdWxPmnLXVaIla0la0UdLGqvoUR2WuQZUgNnIV4aw1pU4VtWABheTOqO6QUZrCGsqYIM2ESgu4hHoa0mCiBsQTEwsxs7IiRVngqY60b5Jj1VB1URtJuAWWBDPAHigNdWZhCMMAB23sbU8TLhlqzshjMCpwSDRr9gxqJYSlMtZxoBdPPjEJe89YuPDRJZCPkDMJPXEFhUOXOoVdNNFK8+YAAA38eGVCTpYW4nIGsOklQoBJRoHqhNqsIDd3DVsKzSmJYIdUP/vDK3c/8awPfv6b3/vtDfc/sviRJcvQFi5Z1qGkmgZigz5qlMr2GFwVmabJjc2kJEJw6afnEhTsVPreoeWlXUHnqvPnEeObBjiOdRAniO2IHoPmbKWJomK9lCkuHn1yvK0Sgapx5PCYJAGC2cG/ODRtKl5kbDEAmTNjorUy8hBKXWlhiSQu3JPG8lfli5YsM1gZ4PkTaDlxk+CO1INWevxKCYT0oKhC40IK9UJWXZJCTp0B5uMzmeGCLMIEaqWixCzWrzRv+CWO+5eMQWJPZlShTqxgwqsKApBwKibVGw91CANjoCigV5DMWlWh8OlON3PxYSKsPjN5QB6B0ISiSvBkY/OuSiUN9qQ1NwJ2LBDWsKOBCTXUtJpHkhsFG5GgdEzVygCWmuoaVog7y16nEQZIC9iv99r2gqwjHsHUSyAWmbMXpVAVzQLbycxZMtHjPBC7thQJoJypYAR3RCApk8LCyUCmBDRtI4CZHoTdZOLWOnlkWUGPuT+yZOlj7dHHbwZTHEarWa0dbEDBKCWKzIGMNlCpiVp15sEY3KMzYovklFUbgX76cCiehouVjaiGV7wt8KhgrEIDGIKplGEtYs9jPegciE4MfjlkaY+VtOoKc9SD4fihUmHJPTduWTB5eWPvO5wbmC2Znr/K/LlUS8AWDDm00WhSzrlNxLx8fmjPqWPqs4PNgQX0w9BLqJXmwUeWDaZItHTcwzEfE860k+J+8dKp5Qumxp33U6j3syUiMSWwrUIwz4hknqKQ9WANsZgkyePIqQIJY0xS10jNjhUNrTh/MOOFUzBq+NRIEf+mBciA2jfSu3e99X2DY79aCWYEiokY2hYOBr21TM+n6ctFwU5QEQFxoQHpYVktQiZSo3EfFcl94mH7IZGbNGpyQ0iv5DDoUPKJUtupcQO/ChHoUFiIKwVAcQDz0BCNWPvBCF1hjYQxQn8l7iVIfZSl4SSe2TOBT+1cK45mS3hYjcrVtDjII3LnUnF2VgkSUSbOXJUHait17JFrakEISIhcoqGS2sqJmJMhBAmZBp4SCOWGkQm5MSbj3qWvYhCOuyvBoYzDffWV59P0lWcNxyoLFy9ZvGTZdMc//7H2aivPmzcP6/9641//R/+IPIh+9Ovr3nXW2eMOCVL33nPP809825qrrUqMdf+uPWXddVZfbfVXveKVz9nmmRi4/Za/nvPRK0od/AGP/1c0D1pzlRXf85YjPnfh+a/a8yWTkzNA9l9vvumIk9514pnn3fH3/4S3WAD4P23mPu56tGXjztyXz8fNZ7/+7c9/+d8ifIUV5j/rGdt8+BOfXK6UN+yz98lHH/6/Y5cCE/z6j376miOOufa3vwUZq62yyiH777/FJpsul8wf//znCy67oltqUQWeRCEc2aUtPOl1hDhuwd4EtRaUqjUGVnAilDpn5xDUllimHglVVkkiI+xsqsIpNBf4ojFcEl7IbKHmjdUGua2WUTEhJzhNw40QzlMHW+Sh0oigcFGjJhqJBu6HIJaNsyF1ouaodap411erlSNAAYvA3akhyvA9nWwm3/CSXT/9rmPOOmr/o17+/K03WmeFmRNLx93l3//5IRdccv+jj2QmoGxTIgIJUYqXItYLoaAjoPWcqeEYP/7Xw2e2E8lb5YYoSPtaKxaiFYpl4cs66V0HXgkAFf1oTtyb14IozH3blBGHVkQRqUyDBImY8kSmVtIoMoTd1kgc1GqVpq+QFEPsJMaMqAPxJFYIGatUGHiqhHFRrW3QKBgCpqChkjAjGYATqVJmRUsFUTI5KTE3qjOarBAoETGhpCXIWKBKEQJmaIyTUJYqtTD3kXufMIRTEilMMAQeaQIEgoyC2IOpeKDKR90obuJELM7sGsJMIT1xR71Zx0FJkgiwUhVgU2HhFGClQOGUjFMp2nWMtN5zU0NjsCluLaB6KgTdcCKUA1XYnInahlrYW4bowCtaCHGoRxbBxgYlEeeE06KOEcwZnwN1EQBimYoMtPUgi0hrRnKmrvbFxh6dJMtCiSmTKBbV2g8lifVgLHIAH2hXbpkxYUQ6IZIZkvSsnXIv0JtyYKklbH/cCEmlF+7rgKKnYhQQnuDLnSAvZGQmVGFBLsWx7wyrpobsDo5o+kpkmUg8GWVTUAY21LUp0jq1hcWMwCLgmmrVkUWuzj2Xmoo31oOIaTjCyM2uRJokt9Jm3A4DffVKGaxTT+LUMFMQE/W1wIoxY8aopRLcq7vgcWiYk4jCrANMzKVHly4rfem866Wz0TgmnFSEpnU+LIANQiE5GNWLECMKGo9JxiEFIgKH/Ks/3njeV380VWyFmZMvf87Tjn/Nbmcf9lq0Mw973bYbrTsNgyQ4RWTqNWx5Tx1ejhQfJ+8To56qARkuHyLI1eFz0QsEI+hEOWXwNZBFSS20VK4oDyMwtrxlb5JMiqCAbeDs44r6T5NDdLA7wFo+C58ABxlEsFAwF1EfpEAQWgXTDHZdHBbGVQXqM7Px8hw92baYD9tgeBUNF7DD7s3JlSs0PfQNP8DKEBgbQQ8QGBwZajFvfPg1GmIfJhExw9QjhWutEXAc8ETVdMHC4d/sZWgABoqdUxulGuEKCitunfWIfcHOPsBaTj6FN9XaUhBaGlbMHpq79VI7habUUACp9gi+oRA4c+8xFW7sqgSTCg8nY2EBZmfuQseMiURQXMCzknKbEBY7yDhTE5r7RgsmR8DAGPiCosRg122CujV4mERQvjMEDn45aUqcc6gO6Kq4K5jA0umWNeWUmRWSdZ/u+m8fMG6m4QPidcQU3E8PwrIK9rneikC3tOHqK5592KvOPmSfMw7a5wMHv/qDh+571qH7nnPw3me94VVnvuHV73rtK+bNnoslgdnKjBWlUudQIjRPI2iRAVWElXMgDpXMFRJwEgNuDKFh8kCdBwyFDcQ+Lm6MBWmF1FDOT0dI9Ey3IDLmAHc6wEcNmSI1JqPCjSH2cRL4r0/PHT5EXKkibiUMEC1asjSRtqI5kQCCSEOcWIl5mI0fJqZB0Aqrw+N0sz5syrveksDnSYXf9JKdzjridR84/LXvf9NrPnDYPmcc+qpzDn7VOYe8+pxD90Z76bOflskHCMwMCKWKdTAxh/0wgDfJm8xNw5y5S9LT41dgtlJqOSWVmmjK+378wKKFGE8C+TWDfhinY48x6CyFGnOFKw0LWpOMuUPDfMWPELzKg4eu6R8WYiVKoeSpeiq4zU3bNCPIg3NPbS8t3DaZwTNCMTcnEhmaZmoabxpY9iAHK531UxTWENCijIFuEHsy9MzsxH1AV4O2AKiQFeCXlKUhb6Y3IkKhiZFJkD1SNhF4bzEqFu6sjnwsbMIE3OoKj4BzR6OWEePFIEdSwmAkIzglIiNhVo+4BpZ8iEfT8xodUpYn7TOB0mgsGhDHZCwdQ691yuoU1SphyUPXW/Oxv6tA09f9Cx/Ba7Tp23/+Y3LUbrPV8PtaDy585Pc3Dr8g+/+Gdcudd7/3gouWLB02RW94zT7HH3bwzFmz4v89j2jFubOft912++6+C472V1lp+NWXb37/h9/52S//e3P/S/dF0Lprr/nuow//+Nmn77j99qC177qvf/e7rzv6LZ/+9o/6MlgMOv+J5h73P7TwT7fccc0NN/3gl1f923e+f8lnv3jmZZ84/uwL33b6ea97+8mvPfb41xzx5n0ea0fvc/jRt995FxDBxL71o5+ddcmlpSCl6UabbP6dH3x/2dQUhg7a9zVvO/QgqBL3/7lt2dT4zMs+fszJ737w/nsBebPNn/7pD5130pGHfOHD5332wnNXXXkVdP7b17/6lW9/x1GzdUJ9S30yIw7WmlD6eF97/GSfDicoGYU0CHae4SFcOQgeZwX23ZIkEc+ojCecRznJbGlHAAAQAElEQVTg0aJklmrf4mQAyVzgIcVbFDwIvQG/UiFRoAI+UKIRCCwJhxcIXzwks6wNYA6eigG4PgAgIQV8CpnYJDw7JSg7mJDOSBAPUbS4xcyZk8/c4imveclzL37bAR879sDX7vRMALjx3gWf/NHPmIg9Vps7M6t0pd710MLCMQadAvRqyka45L4FD+MLbd7MmVJdKCghDHjbgHN0h1P1qOZhgy/F9AzAxhCBNUQmr+EmLoLAj8wdHpDs0qVLMUOY582cIZgOpGpOiFGeIbxhgQnBPiNMEH8rAbJF1GEVCaeGmxYBxsktdzQq2tIEE7Y30psUN4QuRvyfEG5EGNM8Ioy4CnmiYKEgBzCVpClV8YL4WYNLE9ZGxRJnAzPkTB0sIElppWoujO0YR4hYUsR44uCoTMUhB3BBUlFkDOQ6WQ0xykEwi6hee0gKxmWZWIO8ihWVKo1HLs4O8sjwgYEoOXy6GhMDsqYGCm4yXGRMnikyaKeoKo46BQyp1RyGVRwhBO5FU3AiGfgMcydQU20I5aB46GW1lIskyBkrhFWZG3etSCXjGrAhJ2awJOSYSxRs6n1UpB00lyCtxEYemECQSDAISxBxGNKXBjPQy/BBg5jIUgyCBuwCcVAQ87BCCUiymzI6ItgrmlhFYcdjruNckVmIAAIIhSvBomp1GIeJUJJIkJQQoHgqBMcgcmL85+ql8Z4ijLCMa5szTV/ZawNh9Jw9adYZM0crzZrAyK1/f4h9gACDgdOCfBaBky549NEl04lj3uzZ5BGFzDH9sQZGg92Lr7PScFj24KPLFi1e4j0kM8iHxZgkIGZ6/AIK2IRySPRDOoZ6AFOAiXq2Yh/7/q/gkquvMOeCN+9/0v577rvzs3d92qa7bL35Ls/cfM1pFACk1rArbv5bi4ANWg3upe0JdhrBy0fZGVLFSNFYZ5WByPsWLS21SMtZqSFWyJSsc/BGyy/INJNAtAqbYUOEEZg5eXBQxMB/LJ9IkDQDUSgbs0VWXnXucC526wMPuSpJIwRXoSyDK0hAUP1DixYR0dxZMwRWFMOFx+WtCKIQdQGfTMt7hk9IiI0IAgNysELBgwKYOTSeIFphrgxyLKY9j0FPMHYiDy4ZA8gqc2bA20c5t8o4G0bPdGNYh8OzPMyjGkU8xpi4onp0I4ME4MXTs/HhmGwVwuyHjYdUEeMAPRo1S4Bj1sQkgAOK4djwhiZIB5EZfN4SlhYXs0SOWWSY1rPWAOXwRPgBlgrFiKYvqy7ukJ6KinOYgE40BCZKKVKihjVV5lrdS+HAHIrppaQiIyjAaNj3IAaQL+93yMxAs7IG9lneBOhjXj5IkDvujWS1OTPRVavtuNXGu2612QuevukLn7b5btts9sJnbrrLMzd+/lYb7bDlU7d96sZtM0tZ4PsM0zADbDcICV5CIAemASBomRCkM3gJzJHqWAA5YQB6DXINxXQH5sQohDNseyBoqisNSRsQqJP79HQsIBiihzLnJ68yH533PrLEHEITNbiWQJpNRGaIFoNDsyAhdtbl8+9ftKz3oMySCFIcxmJQxjB1+idgPETKBK3Q4xdMwpgsygqTE6OcImjNFefu+oxNXrDNJrs+c5NdnrnJrs/abJdtNtvlaZs/f4vNn/+0zTdcYzVV4mQMM3GEz8JU4ZnOJuzgM2mjKWFKjkj2OBoCocGYgd6GIBGJWrt614OLiGgFHJZotuQlU24a9ARRESdWlZSycCIRGmwQY0QMYxDGKGwTwqfHL86FG5MG6BOcTTtEf2lTgq8KIwRPW51bA3tywgcMrzohEMgoFO+9UyfZUiNJCPMNVgKjDh65THbUdOGGPKyVIKQA17iTwtZz9ME1mC0a8BgYD1dC3eJqlYUlpxCNGuA5OU4opSF0wzOoTQRbBx9uVIJ6pbFQp1JSogSD8aYUqtbDL6lxTgiB7ThyERpCT8OiSp4QxwLpvhZHqdAjLnnD0UY0tQYWMwhWVm7WWmnV5bIqjw410OJHFj700L9+wM8veN5zVVPpxl/97veWw//Hz2r2wYsuvf/e4f9UuvsLXnj0/q9tmvSPE/7dPfNRr331k9dYfdWV5r/nrceORhOl9O+98JIFjwwHM/9u5v/eh/8c6Cyy+UYbnnfSO85997vXXutJAPrgA/edds7Zbzr5vbffPRTu6PmftocWLf7Jdddf+tkvvu0DZ+997Dt3fe0BL3/jIfsd9eY3vuWtR570rnd+8IxzLrn0sk998itf/+o3vv3Na6++6rrfXPfbP/35Dzf85S833/yXm2+58dZbO9TORNffeNu7P3TxePpXhJ+8zrrXXf2r5buUNx2w/3GHHTSBA8v/KSn/ixNuufu+1x7z9gs+clmPd79Er9pzz0+f/b4Nnww58IyJya022/R9xx8HkH3fn335Zb+78a9jp74QToVStcZ7RtKM4rXDcfK4wMgRY8hUnZU5pZx8xBXVGZeqZrlyLsgniYLhi5pMh0rKwyv1lIqOekEoZcTZ8MSWouZSuXRUx1IRHkAJ4kzDuc2ALRkuZpq9SSyqRXIno5oaalSbaNgbp0SVG+TDrlCPEJofXNh/4Ue//syPf/HXu+4y7oPh8iCOVpm/0mF77Lrh9DHB9TffDi5rKWuttNJECyela/7yV6fOpHaNjhPOU7gnLkS/+OOw7V9hxsQqK61gtQsBF0DZTnU9SCUOFh9uqGr0CYukBGqc6S4awqOnkFy1qdQUCIAUtUCyhUuXYIqIrNDO0jHVKn1t3IX6kOhVwZYho1sqELf0uTpKZBRvYwpgHBEEg70gSQ3CO4Iu933TEQgo/chcHHkZ5UAmYg8mJIDqMUYL6hMVoRoIUlGZNKsSSVdr3/feO4cY4TCIp09uyXG+k3KlbITSL9fMgTgONY09OrPeAR5QjNh8mMOaVJysko3J3GuGPMmkYROOIK9VyJQCsScCospemauDKAxamMHwYEYNeeveRNUhCPccXURfw6bcx8qdMAlnBlmeEZrFrQG7WjvyflCBq5AAC0zXx1H6il6IkshEKClzE8gtGlUgHSZPzEosEIRE0ioC8Tvk1nhp+h6yKI4xFVEhpgDfDBfpA37htS8xVbmrPK4QqtdAFsEsakzbShoULVPLCpbFwkOD0jSBwpwDaLUkGcNCzdQ19woOCss4NyVG7mpUe+0IV3B0jVcuVM2rD3pMAACV8eBPxJyCm1AwBxTExlJIIUo099xFNzUNhyizBCytJ6mJnDjp+mutDgx/uPVvjy5ZIkIEx2y4anZWC7rhtjsXPDpsrTddby3j5AFSKuajgb9g4sgSvMbKQ81UPW7+292ppKZmnZ7hmMsAE3hCM49CRuxOFTd1kDRoQAdMLxY/9Oh9Dw+/m/q8LTZeb/V1KmIEQlItCERQGmP9Y20gr8NGEyxO90RQCRtQuWkMgo3cTI9QBFvNDC2ntMb0vyyf6sqtd92FDsFbtwmWkVASgdHQYxdiFVUuwj3eKKgQTNbJgIDCQbk/Nk1EFGUFs1HymqJXL7Lx2sPfj7r7wYV3PPAwuQBOgoiBIJRN7n3gkVvvG/422qZPWlUbpsSeHmeLSRqD6UE5Vh8jnogkhquSOCgU4CI4fjCXnGrOToI5aNXNXGsImGdrsiFS+uKli+5ZMKDbYO012TmRKjEmo1V3TGZqyRuD9KqVYhEYGRqbJe8yFZNa5bElGGB3773UChkwCQKWN8KJlWOk2mbEZnG2GjbAlOTCoL/xkrhj6dFAIzLJ0LQ3LUWsiPSe3ZQI8AhEMuMeT6xFtBe2xAxshhACIuEUlsJEjGC/DltXPFk49vQGT3uM2gY5pAZMj6MMX47phKu6lZ7h6IHdUBuoe8lh1BgZWsMl5YDCnzKtx/sWLb35zgUlqTc5tVkkkQgpWRYgJh4CihLpEAIKFMPK0LPj5KdYj+cB5PCTUog6SXXtOolxHpHKMIC1HLl1aqBSYU6SIM28fGiybUU1ZzRhoFney8R9SuO26Yc/loC++x9+5O8LHyKhwANzOLkNt3ha3szEoyGT1aePnov7n+680yQIcdgteRBTsBPF8vngVr1NPGL+b3ZI2kSTOfNaK8+aNTkBkV31p1u4MmhFHGHgZK3aOGeLVBByINkEiAF6IKzMA7DIVbgoF/hujPCGpVZzGueA7h5DTmrwGlhGLdJF24uCtHLr9P+8cu3VVpk5mUR7Y+h0IDYiajeVqSYmhX4Q3qA8f0y2LjALKB80OWoFevxywbbAkgjztBMShIcddxc0Du4oddAU5iqGIxAE+yLjKj1xUBVCqQFjqikRSrYW1uJIE2x9kHnAjqNHDiPmPsB3MEVyzKBi2g1RAiQDUpGgXFsWsOvFq4Nd4lZYkxDDJAkJwRsl5UBcq0whTLigW8Rv87CIGuGh6gmmnM3ZQJcYwRIiVVOMIWihO8HeI6Yto3AtBAfwkgiSEoVhCVBmjcQOSpVq5hXmrzR33hDQ2/lDbqDwG2+/E8j/xfb8bZ62+mrDu5pfXPvb2+8d/hHYPwL87k9+9qurf42ep2z01PccdZimJ0weff+dNnvWTBbBwHZbP/01L30JDOzRhx/8wMUf8XjclDD2f1VrmuYFz932Cx8663Wv3Ktt2lr6X1511avfdNSXvv19M/jnv2PG3PF2/oZb7/jI57/01vefscNr3vC8vfY+/Li3n33Jpd/47neu/83Vd99770MLFy5btrQf45XfIBNNOedm1I4mJyZmTk7OmjlzzqxZc2fPmr/K6is/+SlrrLfxxGjirr/ff9jxJy5dNLzBnL3SarfffhvKQ1V925sOO+7Qg5oMf/93ZPyLD8glX/rej152wIHXXHdNRMydM+fMd5985olvW2Hu3H+EvMuzn3XUG97ALA89tOCIk9+NeNenWgl5CEmlUC617UsOKhxVuEQKR0TgYRPewgWRoGnCohVSCoETu0rXmrU1JLwKPKpaz8PawLhxKo6CKjFSWDPqcyoRpUSpDuMa3BBRCdsfTdjoUyJm50DYCiX2YCOtnFQ4N54Vd5yZspuYM/YZ416WLOkv/9aPz/z8Nz/7w1/VwBFqVeYcrUo72Y7a6TMYD/ccxjRn9grbPXV9SOOn1//1TzffQU6MUgI4SRBybvvb3V+/8nqMPn3DdeavPLfX6oi6xHDjiaZFPwUPNIjkhHhTBei4TLTNMEQU4ZJUEpx/SMepUuvaBDHHn/92D+asu9K8mTqhXtSZWcVacoE4kD8YttASakJmSkFCiD1RlbosRQMxLcY9dYM3OnElQuUAdRMiFkmbREQBHwQEjBtldg9xErtUlkoEcUcPdRomMTGhGZFTKoCFmaKQprtGCBMPPwi/DPRSMruAGafOegNWPAapimak0iFgBDMhNjuUQhpqlWI8JK1BJkIlEzCBTAFshFlkbQ6yUJeWokHGBDPJJA0CQVxGsK5WDDlWJJQJERtMMVTHAcI4wPkgGnbGQq/Vo8ZAlhkPNgyDYXFxoqLu83V3tgAAEABJREFUo0AgJgJ9LtIppMEV4i5Y43WARUoBQ24UlOfKTR/ZkVBgvpiurqzEGAp2AwMGAYyjdMWtFIqqbEEBufYS45a6JCXgN0ks5SqKrACTjixDHkqcEyVy8gGWleBQVW41stKAhDOEKgRC3SUK4QJw10pa4RXKkBIXgT9K7ZQ6ZiMyoSqJJUNWyIfIYSgIsIUO6YOrj5oEMGhGYhJGWE1TRpDiHtP/w7u7Hlz45Z9dzRQiISxKOGXIS5d2H//OL2Bqs0b5OVtuoiRMEATATDcM1MrGqmnzDddZbd4s9H78+z8P09SPeNzUZdjV2RIfX3fL8IfFMAptuEdUYyMFga4siRrhTEzkMVyY1pXiHCV0WTCUamEPP7r49vsXYAitCqF8DRXHGjwTBQSX0NE1EAJ4yICNrmEsCMYSmJoibbvpBnMnR8Xso9/5aSVQYISz3CHi6FSx6295LBcPHtAzTl+KSD+SoVxw5Z5pLF7VQfogtmmCKdWUoUzgcNYgfsmzt2KmhUuWfeK7P+27KZgXJFQt15L7qpd9/WfgcSKnHbZ+qqVko8awfJoLUK3sKqFOHOhCm6Y/uVMyFYdCRAPjLj6MiOCQdaBieLj+5jseWPhocGZpKDKHRvbvX/P7vy8c/ujlTlts1LvDaL1Lwz/RIXrk0SX3LV40GGUKUphDtVquu/G2ARZ+ogq5CCQRnDTptDCDZCAMhmtEkkhbpkz9YDDUCo+UVeAHYR4cLKQe2QJrBDTxBFFDQhiy8FosakkOEwAOQlaIxNwQixAzgwIoldkawiFOSZUJzlqHYq64BJqROxEahXqV6FjdnVE7NtOLKayL6D36Cjaw+ZDlQIdBhzeBKoa6BjfgngiWPIwQmyfzRmj7LTaa0TbLuv7DX//JGBajqPUwETYphZpHl9Uf/uYP01G3hhuslhBQQ4cCPBFLlVowdRrk9MfgcOYakCnLtHY8TQ8Q6FJhbtSawAQm1tDlQwbQIFKH6qJdnnSIuLIYGiUPvOdhpgcXL/23H18ZFdxDuplzG6ILl/Z/vH04myYijpFYSpW22WDdFaf/gt9lX//xUmOHvoJTFI3Sd+PfIgnScDnjUh7IGAQ8dEFCmkgabmSllWc9Y4O10Pmj3//16j/fPISfnszUQ4OFE//sT3+696GHimtPuZO0cPF48aKu6ykkwW4gKzZihJ/oLBDcnMANmAZEojvvX3DzPQvYhuDclxJSZIKvven2m+8dfP+5mz1lYtQwKEux6vyhkqlmd9x9T3JDSoce+krjcVx74x1ddcCDbmqtMHxEkaS1aTI6I0J7aXrPvQGRwwGzGEW1Qo4o7hV7miygNkgpKycROKeQCTtsKciqR0VIr5JY26zQJ6w6wCCsvTjVZIjAsC4FezEsChkQU43UEXlxhEXpnY2ZZXATOJqQdBRF1KIx4CSSGDQAWQHimPpaCZCr65jJMZfHzuPATSYWZmIJZqIgoKHig+07sCYJsFFYx5HHorV1YjQLzK6Uxjrqm4kqI40mE3CyVS2zV5q1xsrD71PVJcO5EcD84YYb8PkvtpTSvq96JYA88vCDX/r+jyNALJ6GVmv9yJe+gZ7RjJknHnHojBmTQ+9/7EdVDtrn1euvux6mf/+nP73qD3/Bzf+ljYlRrL/z8EMu/eCpG6w3lKeLFy868fQzTrngw/c/tBDyWfjokj/edMulX/jKkSe95wX7veHlbzjorIs//M3vf/+Be+8spbg7sUzOnLXqyiuv96S1n/XsHV66+4sP3e91Jx9z9OnHH3fRB0674pyzvvDhC7/18ct+8NlP/PJLn7v66/923Te/cvXnP37lR8//2WXnon498uR3P4Jjj2nxLX7w73Ceph0dd/TRRx7w2pR0uvs/7ePBRxa/4wNnHX3iOxc+MuyLttxs889/+EN77/4ikf8nIlE54oDX7LTjzsB979/vPeRtJ96z4H7PIZmwGUGxUdpEDWJfhnFrWBtdiloZ7kGGWMN9oq5NiIKJCUmIHBHZCjYgGX5Y8aajc/PcZ+0yF6XCyazVfpSGU4dEwcjbpWIR+UCbZ/dclZAm4ZaESrd3sqJSG0aAYDFENDRnrBV0sdRoumj6wLl6rDx/3pbr4X0RfeOq67/2w+uWLlk2IkaJVqx86ZfX/PXOYYfwtI3WY5XccOJ43W47TbbNo1Pj9370S3/40y0xXtZSob77w403n/DhT4O0FWZO7vui5+WsBB0xg2c3evxiNmk5WoSwCCdi0lmTj/nXLXf/vVSLLDbwZoGs1LJkXrZs6p4Fw8vJTdddh9UR93NDLcA3yRVctAUxTVkYNJK0zi1lTllG1k7aqLXEhghU+9q7Vw04elAUD2PmxJm0sUY9MQ29VhAkG8ZWLUXivokpGSIYU2SyRMZQGaYrqLBMkKPoVM6G+sMVDPXCXROWujLRdRPUNci/gnwE6B5iSj2jwmBpW01CKE8KaMJdQmRPqoQw27tZ54Q0oBw5CmSO+z5pSBLL2mdoVrxJ3CLvZq+jsFFwo0JttTT2NCVsiTPnkSMSU7iBMkM1IUwNcXAmAmFIwxoWEElVqwHLGSpIFP+tApJwEavkVGhIDk1Y41W4D7JSwnpCaRWUQhrCOWs/GCh544kosafo2VEmAVcizTk0j5WW5agUYYQJyhlSGEiO3ENyySPIe60FdxSVo2YwlRK1JGkgIXrxwtaVqCaSmGCnAgFRMth6y7VhbylGVSeCCRfTANKzQFDaNE0C+2FOfeSpEOSrqSo9tjxGTeXckywz6Wp4VBDIxDHuxgCDZkK1jb5FyQpxmFvdYbONt1x3+MPuF33tB1/+5ZWLHy2MPfEUPXTvwtM/+mXsq5X5kD12WmHUThinEGcGnKEFBZCw0UReZeWVXjxdo//+jnvP//q3FixeFEXd0wOPLLrkSz/8/rV/HOYTBUGfOY2bUZfbcW7GsAFlghVwl3jm7JnrTv82y49/++er/vD7IlM8mcbKf77zwRMu/sK1Nw1/LQY8ddFTpiTQDhyAhgv2lyNrVfUABlYbeqd/xOG7OSRVevIaqz5/q6ei9yd/vPmyr39/4eJHCBp0eWDBkgs+/a1rp/9MM0YdQIi0D0gPcExBdcA/qWi4GOhlzBoa4lkVpczcmGajhjdcf52dtxxQfO1Xv730G9+/f9GC6l6K3ffwo+d84dvf+s2Q7l/ynC3XXX3lJKqkWVRZBlhEo0ItFCrRZm80Le8sOfWpCQFqt9JY34qTUGlqbbpIocun3fvwomM+9NE77rlvecG6tHTfu+qPl37rFxiFSJ+x+QYk1hH31G609lBlPrBoyRXf/8nD5ZEYdU1jDz/6yEe/+b1v/fr3mI/mxIWkh7ZJU9IM0yW6/e8PlNoL7BICCUkIFJaGAwByAkXBDL1EB2MTaZSxxkV6EOiSAk5qjfSNjhsUa7l4Q+zcxqA0eB6zKIyDY4AywCM84SeB1558HNQTkwAgZwnicK7QsxJbgSe2LSvkb6N+ckZD09cNt99hPBwdj4kra9LH5InBSOTiBijG1mcyeBe6hxaFaEw4qNhsnSc/f8uN0PXj6294z+Wfu/v+e5f6FChZPC6//+s9bz7v4ydf8aXzv/rVSv0U+bjJNQ9q6Q0OHUl8gmTCMwcADK33tMzAgoKQxni0jBRIhxHwWNlJSbkJkko1UiGeHur6Du+uO6NgEC/TfRSe8ENpiprxVps8+RnrD577uZ9e85WfX714PFVbKZruum/RSRd//q7pv/GFVa1Qa9FQv87qs3bfZlMmuvrGv1325R8/vAQRHEKtDzy04ILPfvvnf7gRk4eWwwmOUSn64XH6JwrcIXCB0gNeuMOsiRa7uHd+7Avfveb6xYur1wSBLlr06BXf+8kJl3/2wNMvuOPee0JT1bT9m0959rHv+ui3f4TDtdJT56nnHCEQDr4SbKmtOMZbzvMjS5e9/fLPYstkpahF19er/3zrqZ/+ZhCtMnfWc7fe3Ikl2sTNU9ZevUmKJHvhN3/ywOJHvUZ0cd8DD3/2hz+/7Ds/myaZ3LkYhwFbaCsow9B/9wIcelRG+B2PYc+d8rhpu5THhceFutAxZRw09VmK5BLJg7LUUY6UuLjUTjGjKRTQJzlTyq2OJgDfETf6HEi6HIOJpaavGp06SqnUkjC5Sq/w8oRJVM3YaYBApNkz8HHfefSmBCe3hlwLxokGoeMnkFNa60EQbMAioy6KTrSSBBKDN+oJZhIQlbH2yibYJk5pxhwLYqcmvMmhoyYmIMPacV/CvcI/YFtMA4WUDRyXyclmhfnzISzrpyQ3uLn37yhoKm7+xbb3C3eeO2f4H6p86Zvfuu+hoTxdDvDqP95wy03Dnxh+5uabPX2jDZd3/sc/582Zdei++6SU+m58+ac+CbP4j6/9LzgziJ75tC0/efYHDnj1q0ZtCwq/+NWv7H/s2w864ZTXHPmWVx3yprMvuujHv/rVQwsexBCaprTO2k/aZYcd3nTggR9+37s/fd7ZX/rwh77x0Us++f53nv62o485cL9993zxS16wy/O23vzpm2604ZPxZnLleXPmtIDMUj0qTIBl4VT3zvMv+evNNwPgEy1PzHjnMW8+bO9XiMBInuj+V28i4urr//jqw9/8+a9+FbAmJiZeu89rPn3emZtssD4e/7ttcmLyg287YqvNN8MoiDzx1NOXjZcK6ruUHMVa33LJSk5SQ4yUQlAr4jzMipt7EYqGqakiZWSOHQovvv1eQz8MvpI64o8xc79wWffbO+q9DyerWp0c1U3qTIuxBDVUEzoGl6QSiNXgw/BlTggT4YFUI6gYKVdKvQjqvpBpx2JQHRIIEZWlppEd8cpdnrzqiqDs9M9868D3XX7YmR877JzLDjjjolM/87Wpvqyz0rx9dtkuBQ91ea7rrrnScXvvMdHkuxY8cuwlnz3ojI8feuZHD/ngh4848+O3/n3BZJMPfelOm2yATCBZEsq62luvtrTrgHVoEYarRgwN1W5edZVV1po/uOFnf3T1Yadffvi5H7vl/vuQe6vHEOOkvfvhJfdP/5nXXbfYBNLhhoyQEiBVYlQunNRRG4uwEoRa3cpYur4Zcy7CACTMOTGLhOTCKrhjTarKlMq0FDiImSpMD5MyqVLDSSih05md2JwDYJi4FkEodSYdUTTQGUEzEiEpKFMVRH6tloBYCcG+qNd2QCgkTWHpYRADvAGPBHsxsjET3sVVEtNAxKsRhQTEBIP4MoTaoCLKnFpKbSbOCNeQH9LckLVgDXWgOFGbKGdqcGRFbBWkgiYBa5WoV+mzchOYCdsAVGYoVIXVmA0lXaRAJV5GVFtyaZxguRZaGJwHu0ufuFMhnxY3+MtMsCclaiMawizpG61tw0xN6XPpUhTVTKOG2zaYgoiMmFgURuc8IgIUT0apEEMQ1LuiRRHpRSoTxK5GSmGsHecxKZKEOSxHzLMPyaiK9425mpBlhtyFwCGw0oDP1MQpmyJJUyIgYmxwd/oAABAASURBVLEAy9FIUa4MuToETOQalFi5cW8rtS6ZNU8+/j/FKla9MoeAfaaepE6MZp702pevs/L8cbV3f/Zr+5998WHnf+zgcz6y72kXfPc3f1LhF26z6at2egaXHvMDqBncL28c6jHYAkvIK3fcfqt11/KIK370q9edc+HhF112zMWfOOSsj37sB1fNnTlj+QLKyOiEjVbyRDBEN4rqZOFE4ZD5Ps/fdmbbLFw2PuGyLx51xsePPudjh5xx6UEf/Mi1N/5tpeX/bMBs0bLFStACBz1Gigeb6CANSn2kLmDJZTlGxSy4Clexkqof8rIXbrDaikhkF3z1R/ud+pFDzrzi8DMvO+R9H/63X/5u3qzHiIQx5cawQVBAxXKw3IK44o2ZwgjxPOAFpzA/GwzKA9rRYVCTvPW1L9/8yWsW84u//Ys3nnPZURdefuSHPnLgmed/4ke/AknPfup6r9/z+UkbcqGArmtQoJ8Ap3IUS7VkkB+QyNBtjnfcwiWkwNgGnTtX485l7GwYHSYRrbvyvLvvX3DABz980PmXQ/JvOu/SEy//0qNT3YxR845X7b7CxGTDiTm50s5bPX3tFedh1ed+du3Bp1121DmfPvTsTx/wwcsv/94v589+TAKk4o1whqemObNmbrTW8CvrV/7l1jec/vFjzvnUr3/zF4RrBd3GAVuCCARatHGxzr0SAnLkwVYpFRioecDmvPRGPazYGpRL2LJzA30BgWYWpigc0JhXz2NvcAcCYSDEGG2EBkQ0TIZ3Jobug7CG2EmM8MywqwYS8w3WWW3uDLgjnfrp7xxyzidP/MgXFj28pLHIg8YGmPixXAp7Ie5DK4MP2B66h2ZE7uxG7vGWfV/8rI2ejN7vXPOHN5566VGnf+zosy4/8owPH3LWh393292rzpv9om02qdkrYhc0yTTYg1lULBZh+Ck/gdOJQUIJZfgwdeydmAMyGoIolYpHJdJkEtP7YwwQiagFpOJdRAew050sxNnh1q4eqse+as9V5sxc2vUnf+pLrz/nosPPvexNZ3zkwNMu+emfbl1x9vDPbLCII8goHLqK1+2+3SZrr1rdL//2T/d//0UHn/PxQ877xH6nXf6pn1w773EnVYa4vZawgtWPNfEpwduKDt2y0XrrnrjPnpNt8/eFi4//6OcPOveSo8+7/MizPvLa915w5he+PdXXnbbc+MkrzZFwZn5sPWgoweYg20aETRyTKsx58AAYiS+ft/LsGUumxm+68KOHn//xoz706Td+8JOHnfOp+xc9mpO+8cU7r7rqij4YhWTnp6+37jM2WIeIfv7nW15/zuWHXvyJwy/8xMFnfeTMf/suCEsqGCKCXJsSDac8mhhttcFwiHnzvQ++4fxLD73wk5/75TU9D8HaYEFJWdktWWkkcGsk2OCQQwh9MYO+Su4cx69Rke9gkZgNlvogcMUMdIOUmR13sFEZXE0JxlU5B4fkIo2Rwri4cKrKAXPFlo3G8GbpWQtLBGJBD1WJweJTDanEMEgRxHPcDZbqpIqUKgEPCpwqcupL6is29CrCAkxCAI6NT0L15q1h38tShVlATUlN0URABWNwsmKC9OZdAgXZCAgAI4UAxVabDRUhYW6Dd6H059vvgmLoX77mz5u3314vB5iFC+6/7LNfwA0amPzVtdeZVU1pr91flJKi83+1vWCH52y62ZZY9fsb/vrX2+7Azf/VrZrd/8iSNddcc5VVh1+WAy933HH7L6+68vY7bo8IPDZN+9SNNn75Hi953zve/pVLL/r8h86+4N3vPGq/fXZ89rM2Xu9J81eYJwgnhAhJmI2GJUGQNL7/O62UesalH7vqqiFFPTE8MTnjvce99fUvf7GIPNH5r98snRp/8MOXHfDmY2+6cXj3tfpqq5910omnHn3YnNnDr2T8/4G/6korn//eU56y/vqYc+3vfv+zX10jnIRa7rN0zB0P/tX0PmHdRBoree2o1kCRwuIQWiU1gXOxtw9+7drbXnziQ9/4+dLb/vboV3/xwOXfu/eo8296/hE3v/CYW/Y68d5PfUfFCIKG+1Aunhy2SdJM+w6wh5EFdgRckYvgOA73CkFkp56JFU9ECKI1UwEZQggMQJzJslpu+5xs3TXmn3vkPi/aehMQdtvfF/zqTzf/8o833nT3fSq88xZPOeeI16290kqD/1sVtkb4Rds+84MH7bvRWqsh1v/+trt/+adbrr/1rq7UTdZe7Zw37f3yHbcOLsyWPXOBGMgoJkctSEUAwVDhGAchsUVH1HkwI73NHLXjUn970x1X/vGmh5eOPadw915QhPz2L39b0pU1V5i94dprBHgJrrScadPso0yJlIqQD2Gjq4QjJS81as0dJQsmiqQ8apM0LaVJzy1Lo6wjQx3eB/KZVGPohBBtnME14pLjsEWSBDUEEfWu2aQ1yjW8GEJtQLLeQNtUCLESYqWq3uWAkEki5z61UyyIjzZs8CSRak1UFGwx1hDeD9QMmQR5ja4YKl7ADgEBBLWhlokIpipkzl6wfVVoVZmTJ3Hifkx9l7zPNGxTmIQok7SCGiaxqDlZDS7EjuTBxQUUUxZKZlGcijfGrSdWIWVhicDxefRUoR6kfoKdMIkYpcAYVYiRJaVGUyOJkxLjP+mFphL1aZhnBfrGGAOuj6phF0UtBMgGgVB2Z8MaZmymVHuKDnTYOHHfRLSVE1VJRQTzkCRgb+5wGJhIRIAL4gD/lYFYSGvoOGRMXtkiKqYEEhUxMyU1TYQLC6gnL8ggEBg6IKA6kr5tLCsmKgkRJMi1kEFL1Cpn8qan1DfZmiaGCcM6SDOZ5D5a6zPXaVJigzXXuPDN+++69SbmcfO99//qLzdec9NtDz26dLV5s49++fOPP+AlGTaMMaFoKz1GASgkyuwAQcEWeOXyrgP22mmz4Tjs7gULr/nrzb++4cb7Fj766u23fsdeuzcq0+grtn7SEqgjdta+aB17eAeV9Ur1WZutd8J+e6w8Z1ZX6u9vvfOqP96Ez9mTo+Nfvfv+z98eEKa6/qY77gUsI3IO9AwN5U+d6K3tqC0Qa/RCdegfflihqmSkLmYrzZr9viP2f8aGQ8ly+/0LrvzTTb/+882Llo1fv8u2h++2vTBjBayEBTLylrnhnMBkIm9LNJU0KISm0WIuntShsArVUPhQLjjNmz/7g0e97pXPfUab0x0PPPzrv9x0zY233PvworkzJg7cdbsPHLbvqvPmCCV2YTe3zn0aHFEdsFbCVVN1wzea9DDcyB4TVScrZ+YkLEq9oIz0eGwW7fHsp52w7x6jtvnNzXdc+Zeb/nTH8Gs/a68079T9X7nNUzbimuBqiUkSzRzp6W/ce4t1Vieim++5/8o/3vLrG257aPGSFz19k7e9bBd0okUT3FpSb4ga0Te+bMc15881D4C98o8337/g4cQGnjvnPgaPgmC91IpTaribkVQTjBlMV2G5GCqlM+srjVVcVJngJxgFxwXvKIWIK8OJrFbwZdMbFYgXAYtakCxqBPd3yEeZE7MIhxhB9mTQQa6cgoO98Frz5r/+BduhroVOf33Drdff/Dfqp+FVeuKKwclQgkIq7CNKQo9pFBbVmGGnCHV6nZgxev/hr95352fNnGgffHTpb/56+6+nrbEr9ZkbrX/REa9/2vrrCTAllSAOCiCAexObSwgTeumxS0CssOIpKoFVdtwub8ZwfIvOOEzFSc24xPSYgE0YHvc9zMSW9xGliIaDEXcTTvrWWXOt973xNU9ZY2Ws+Otd9/36Tzde99dbEUaOeenOr37OULah3yzcqYRWi7mzZn7w4Fc/e3oDducDD1/1l1uuvOHWBYuXvm7HbY59yc4JZAMDEeyNq9a+xfLlLSlsr7JFqdoT77bt1mcetM9m66xRPAa8N9x09Q233L1gIfa6b9lr1+P23m0CDh8O2pcvb7ISQy6RkqG4Dpi/pkLZUEUYUanLpz3rqeuedODLV5s/7/e33XXln279/a139tXmz5px3Cv32G3bZ1Dp3SEch/E1lHG88txNh6LlzgcfBhdX3nDLPQ8t2najJ5/02t2wVxkAMmwluTVRR8oT++z8nM2nzf6mexDibrrtvgcZzg3F1VCqTQokI+lJetdAD2WmVIOq1/BaXQtMoyKqGjYIMqKADsy9erDDQQmK4Rw5Uw4YlIQKcs6IqXFyD4QlD/gt8TCxJawQ+EmEW3CJNCYZObWGQgN50sPJohB1mW0UnLwNbUpilWgUQuTpS4ZpgB1hMKNEwMW5U+0kwXuTCmaBFsIPSOMmKMBG5c5RyLAhKMDBU7gnWAdgKUEGhKkuT1l/A0buCa9LF0GUD95370MPPYybf7GBoH1ftufKKw6/V/b5r33tljuH33KBaf/uT8O75rmzZ2/ylA3+ORQp6ct32RFrly5d8pOrr8PN/10tImq1O+974Avf/9kJZ5y7+4GH7n3oYaeefc7f/vb4pouhfVljnfVe8eLdzz/11O984vLPnnvGaW85Yq8XPn/DdZ88Z/ZsYgbLECbRcEP/o+v/1e/uF37qc1/6xtfd7YnByRkzz3jnO/bdfRcerPaJ7n/15ta77n3NMe+48GMff3TJo8y8ww47fvWyi16y684pwXz/58CftMbq57z7XZgHUn99w03kQgFvjRi+4HncJmLlGmKMeEMh7gg8kSsGC5xXmGnZj65ZcNi5sWDRvW/50C27ve2OI8976J0ffeRLP5+64Q67835fNvbrb65wlpQ4lAdAJBFaVTwxCRHkq8HqIhXgOFAeNoiXwCXVBRHKkoMCcmMjxgJNASl6UnivqAgDbqy3xorvP2zfr73/Le876BWv33371+++3cn77/GFUw4//aB9Nlh5NZx1s5iL08AEo8p/7tM3uuKEgz7x9oPe9JJdXvui5x76sud/9PiDPvyOg7bdYhOQ4B0RAjxV0AUCwf/JB+/73fNPvODtb4hGCKykKAK/96Bepd/+6Rt95uRD3/KyXQ7c9bnH7/uydddZ2xSkkZJPjfvvXT38u5ddt9lyxsyJIjg9Q4AOMAIcgh1hLoFOVxyh9WQe5s4UDNurmV3ANTmbIdaPIC9h1GOWghiFrSciDSIHsVaUukRFGbEOzGqqogicqsYSHMmGxhzGUZmYhxDWuBLWD9sAw10QlKCQ50A5J4rWQp3IEowj83BMryQIsUMgB9Yc1pqkItILW2FsO7i2MpAlAbhoiMEBaEFVikOgZkKcJMSNpsO1tZ3hONzDCwWq0CakqUqUKVEkUDWVag+pkNDQTC3YnCLUQK1yQBYI/gLmU4fqpmeaEgItwyZQgB7W7aSI4ZFb0ixAj0XYtE103hQD/2xMVUEQWKDglESVVECCpzpmq8BUpHUhEdVWIaBSo+tLh4zNXFWHfux+GoYLiReogMMQgsxqcEXZVUZcWkb6kRSZIjtR5aiUKivWgyeHwCyI2CnBAHATkdmQQitXR9lh3JN2yhV5NffSjjUHoay8AAAQAElEQVSZiGuEFAglOTVMoM+nPQnWbs/f9unfOe3tPzjj+FXnzG2Ek7qKiVPCPMjQ6xorzn3fG1/xb6ccfvJ+e+6/+44H7r7jRUft97mTj37NzjsmntHVtk95KFol2hF/5/1H/+K8Ew/fa3c29SoWFBKstsYKM08//FWfftchh7/iBQfstuMJr9njcycccewrX7rTVlv++Jx3/eCcd66x6nyC1XEZGGv62kQPOw2v4UQElqNJOz37GZ879dgLjtz3wN132H/X55576Os+ffzRe237nH132uEXZ5/4vTPf8sxNNio9Y/KzNt3wp2cc/+PTTt507SdzH+A+oJdUwd2sNn/hPcf/+AOnvGvfvYSYIQ/YEAd4fcr8VS980yGfPPaww3fb9fW7Pu+U1+35xRPedMRLdn3Rdk/70ZnHfvWUI1eeOYvhFkW0NKnmYTk5jFjMtboWf9+Br/rZ6cd/9JhDJ1hb8qH24iAPaCVKES8rz5319n33+PLJR33wwL0OfNEOB77wuWe+Ye9/O/GYo1++27xZo6AA+F7IjCG2i4573bfPPfGkw/YL1LLJIwd0c/I+e/38A+/65LFHz21GE8IJykwM48lgkLKEUghZjHL7kbcd/YOzTn7dbs97+Y7bfPk9x5z5pte8brcd8Piho/b/xDuO3mGLzRwuaaCruvfCY9Vuk7VWveiIN1765jce/OLnH/iC575rn5d+6cRjTjtgn+dvtdWPzn3XV88+Yd11VgV0YWcPdVrnSatecvwbTtjnxa/fZYe3vHzXHbfaBMZcrKuMilEqkVdDhCZTsiYsD1HBg5gpZcM+OSIKG8fgtC3Dl4Ip4C/9sECmuwk9JIWpDD5qgzEwDx5L8AImF2epCFOsmJ5IEpY67J8iuSccO0VvpDWPOL1212d/4viDDnjxjvu/8LnHvXr3eZOoKW1S0xVvP+zHZ7z9uNe9COACBldhbwEF5IaO2GvPn55z4g/OO27UZp+onMuAppM5M2ccvfcLvnDS4WcfvPeBL97x9bvvcMK+L/rUiUeedcQb1lxtDac2Oc4A0ogcMEkipLGkNXmRGmInH/iqn5z9zk8c/6a2aUbJR7D8zqtxSelZW27xgw+86/vnv3OrTTbsA3FfHTCIAkI3/er7jvvxuSceufcLOBsz3Jo2WXeN757+ju+d+85nbLEpMby24SI+9vC6xfpP+ujxR176ljce+OKd9t99+w8evNeX3n3o6/d4zn57bP/Dc4678NgDm5xdKilEqVRjzXlzP3TYgZ897k2H77HT61/wnHftt8cX33PEUa968Y7bbPH90477zqlvWX3+iiKM0CSk79z7lT8795QrTjpyNDFBwgDCio0PNEo7bPzUjxx16Cff9qajXvaCA3bf7pA9tr/wyH2+/N4jD9h9h9SMqrde5f77F9D09aTVVzJ2pBhiRH0ngsuHSTgaw1Ts3CNf+/0L3nH0fi995mZP+dTJR15yzOvfuPuOb3jR9me+4RX/dsLhr3ru0yeSqxH3eA3QLZN+KtlKq8x/35Gv+fjxbzz8ZTsdsOt2b33FCz958uHnHLv/87bZ7Bunv/17Z75ru802b6ESp+iBj1dZae6Hjtr3Pfu9dL9dtjv0Zc9/7Qufm5kTceo8FQQNgi5zMgKhNRH6BfFfM+wjtSZihDDtCmoTKGdH2dMLArB0QZ26JWJlFR7ElCPaSqOUcpPJI3p3cZsMmpBoJRK5QISGfukjeXAAZIKvpKQYSRCxiRm+s5tUpoC6KSYoT4SmiBLcc66SBTpiQGBxYIapEPL0yGgkknmSfYJqqh4SsCPyTONk4wjg7HPjEwm+SO4wiqhksFrqElVRJtpgrdXnzh5OuHU0g3CFX/2HP+P7X29rrLrKUQcfxCK11vd/+PJarevKggcfAORZM2etOv0rZ7j/J9puz91OU8LCX19z9bgvuPn/qpn74qXL7n9o4R/+evN3fnHVR7/y7TM/89Xl7cIvfuOSL33riq9954l2/ue/fuqFl7zppPc862Wv2nXvfU9+/6lf/ta3sD+ZmpqiiImJyVVWWunFL3jBu4877rufuuInH73o/W89+gXbbQMxtqM2mONfYzKCrvjyNz58xRVm9gSk2bPnnH3KyS/ddWcWfaLzX7wZd/2nv/bNXV934G+uuzrcZ86c+e63HvuZM9+z+irDnx7+jwN/eNHi5ZNVBLbLpUqMFbWwVm41cU6B+CjsbeImJYZfEGlYNhPEdW/LA7//a4x7AMFnLB1DyMSssydl5bkTW24w9+U7zHrFdpY4lJMToKoNDkqor0oTLljIkpRymFJQqLOSkmDEmAxd7oSFCOhV2DAeKVhxEjJKRQeQHMyEloXT6qut8PznbH7kK3c+8lW77rHjM9Zefc0kk1Hg+lHEUcP3lZBmVUgStZNp4w3X2X+3XY94xYsP2HPnTZ6y7mgS+QpkSDaABhq3EVMGRTS7nZw7c+7MiVnEebiaLI0iZ0FsjUUbo3VXX/PA3Z5/9Et2e8mztpkxMWnhDIKY//DXG6+/7e4VZ894zc7P0VZNCBRAfE3xVKu7GXuVVFlBmnOvzA2NCjVdzn0m44jo3MbmnYlDGzWkFjIEOSJiThoNV63FOo9eIxxSZAkicubKYUHqlKpJZfZQjpYoOWqtBmpJQsKsngKv0UpAoGiKjUKhUmOKqRMGQJXaJmoCM02i9wA4WDhraqiZsNyicGCc/VhjnoAEn8ZEQxumBUmQu5feepDEzIqQGkJ9wzCjoCJRpQw8YR6RqWvbtyNvsOfxHLWKjfGlUam4umiww8fCKER6FnOEQMGhbNtbdpDvGWK0zJ3aUMyakypepjloKsFRjUFIwd6wEHfE7sFja6dAPxZTtJMxmnQF3OJ1HF2VjsS00UxZw5U6shIWKOpqo9EKqai0ShPSZ+0h7h7KKixjUhRUuE9RoaesmgV5SRlW5YxCDoT1WcYDZ+YgswYkUUEn4fKIAG1SvboPKLXCJiKEiuYqo6ITLhBBeAoHgdAfp9IorKhnLhMTssK8mfPnzNbcSCM0Ms/umF9CqwdZhSyMN1h91b122vbYV+725lft/tynbz5vNJsNlpcChpMyM2V2UZ6YN2PW7JkTnKNjHzNodGWBTTQMw15/3TX222OnI1+22yt32HadNVbIMIpJmzdnYs7MSa0SY7daA1JsayQKJtbEgiK16XjCbFJCV54143lbP/XwV+5y2F67brvpZrMnh9OiPMrz5k6uMHNmm1KATfEZDa84a2L2rJHAjQkkciPepJLJs/jKkxM4i50xMcGchdiSjNtkqmw0oXmLddY98MW7HPbyXXff/mmrrDJf0qTq7JmTs2a0jYTVnq0bRZ8d8awSB9aHDLKEEn2ynZicnDeCZUBvTMJMmOJisEETwJdaUSysveKKuzzzGYfttdsRr3zBC7bZfLUV5whsPAz+VDgsUY3slmc3E3NnthOjlpVVNRJbjnZWM2fO5KxZEwLryKIjpRHHiEJZAisnk08oE3x11kQ7Z8ZMbhonW3HejF2fscVRr9rjiFft9swtNp07a4bCRhMXKZ3B4QbfYiGSmJhsn7bRBgfvsfNRe+6y17Ofvd6qq+WJNs/MK89r5s/OSQUcB1BRuAU4mr/C3L222/aol+yyz07PXmGFOT3BGoO1altTYoJ9EI0SqWBpsmGhQSTBDOuMlIXxxDU1veZOuRMH2Fy87WKwHuXBRUfiIlEZciEiAGX2ocSEZUoRWAtr0AQTXjznZCTmTCZiLGRGVnIixPLctvkp66916Ct2PeqVL3j+VptOzGhqcqMxTtlnTcyaTDPEAVgI0AJc1CCaOaOdP3fu/Mk5iCKIJjgxb1Uabthzm9JaK83f6ZlPf9NeL4JBvmzHbdZfa1W4d2mjT07BuYJokIsg4OA3WL0hg8cwTU42s+fMnDExC7KgZKJhFVFpctw0NDPNXGE0p52ZYzI4BcxSlQhUsbnObidnz2hmDMr3JNqgNe3seTNApwm5ifdsvcOUIIEkPGtitO1mGx6z145vecXOuz1zk5Vnz5HQZqKdN28mDqmTKodripxRx2XmnNvRxk9+0gG7bX/0y3fe63lPX3PVVS21lNOcGbPmTs7T1JCySmS2yQmdOSdNToxCWm5HMgI2ZxozJKcyMRpt9OQn7ffCHY555QsP32uXZ2+10aw5o2o+BGZKROm7Vw//6unJq6y42cbrdrDZVgQmThAESyGtnCQ4l0g2MTGaO3PeRAsTN9C59WZPRQA5eo/dXrj1ZiutOCGtNRnnXhA1XNLdUYbiZSzldubGT1kXO9Ij9tzl1Ttuv/6TngTemGNGHkGGEwL4Y5axM0JmTJGM5s3cdbvND3vZzge/cMd1V1+1EZ5wz9GLwQogfuHMBMvriNHDwS2nUdOkJproc3U1yZTwqQAJLjR6pT7AS3KYIZcUZURVpJTskZJQSlGxR4FpGDVErUgDAMRceFjrEHsTLcsIQHLC7qNQqsYOcCU1pWVLEJcDA1cmGAisCFil4oTHOKuP2tpqaQAR40OqyQ5phRJ5oFmtNjakLapD4VMbwI+s2iaQY22ERlDAkItH6cMLswa3vvZa81ddYy0isvHwNx9x85vfD4esuPnX28t23WmLpz4VcK655qqv/uinj04t60rB40qrranCuPnn2qwZk9tu/Qys/dNNN/3ppuFvtuL+/1jr+nLb3fd+7+dXnnrpJw9/5ymvPuKYXfc/6FWHvumYk04+/bxzLrv0wuXtggvPP+eCc0875+wn2kUXX/CpL/7bT375yyWPPlaC56ZZZ621nr/Dc0866shPnXfWDz9zxdnHH7v3i3Zea/VVXSSI0f6z+PruVded89Er/hHaSiuueO57T3nxjtsT/fO6oH9/3XrX3Ye9893veN8HuiWLRGSbpz/tcxdfeOCrX0GS/v3E/8lT3/fnXP4YteuutlJfiw2/jBOJSRvkBOfIqeS248meRzW3AY8Lsh4ZkBiFdt9LbW//d39xLq0yb/Xzj177m6c/+afnbfCt09e98O0r7/t8hpANATZQGFVPTiM4TlfJkEqIQkiGulQb+EswXAixIUIY/xkFjuUojEMwyR3uT8Jwq8SIClU6xM+WpiZ8KnfjqFOVaqWITNH6aFRVQGhDY5wqUC6eq4l7teoWbJQZIhNumVrSzBzshXpQk4UaZCxCSYWIId4EYgA7aZGmElqOQDxiZUaQK42MidEqHk1iSrplPlUKqDO7/Js/Br377vislebONnCaACmslNL13kWtPI5kyg3xiB0Bu1XipDZKljksFLXzVGkQ1WuY4XjfUUtrrsqkREKIZ5EiGvcUg1CtpdqKqwgZU4noEXzDicnUkGQtsSDXRE3RuUefZNw20RCxh5dwRK1pEVaTwuIpu2YQxKCJKMSK9x25C0tmGERuICgeUZqo2ohL6ihZEC7Q3xCYyVHJew5ue8k9MzRpRJ6ZJ6OZsDThMlJGQuyFeoHyTRitlR81vAAAEABJREFUcNRg50RJWJVJwX2JzrSPbN72nsYCpZcONCX1rJSDkzMLmeLOgKoGDMwjTLxm6l16gCVGjLYurK9UpgjW7DX1oT2JYOYY5LpHdME9wSQCRYZWt+DOMQeW2DtXcyYB1caNWQonUsIHZlF4dlTNvXKFZrylqRa+0pTSdt6Os/oEaQPrhDGRjp17I2iurZbBcGXDe6WOAYsgxgp5BCt4GaToAoglqMLNhTkR/F2Usd+UyFE0+qphSYhVQlPNQ6IPtYDWam+Oo8BO1RmFclNJLFxCMhQIOIzLlSJiIEcCRsSQqUQnEUzJRYnFzGuxGrWQGRETJJ4HoUNalbgyGSlgZk0NhxD2Vp2R4Uiips5ssBwi0DcpeUbS3PDgQS65p9EyomXkNVXC+pKbPmkAApXi1YyJcqRUEgfB4yKp1kiFBHq1holJmPAJkThneuyJcwyt7eEbbYmmsJbQYsmqJlJkbmkYXy1kWBLViGXRL6PeAiomslS5HVOunDjlnFiVSLkQdCbjLo1LWzy5ElI8jMdDqkMAtSYrJpBRsLuUvnpXxWLQmgwAICJWIghWyQXrwQF0j82c9v2ojEddnzsom3BJOIPgcSWvQiFJBjKMpTdQEqkL9VKBLGOQhJhBYcqcs4iSQBnB3GcrCZFScmmacci4aqXWuGVX9giuPlFjZKBw1EfTgUBHJUltz8QJ7Ik2AEuA3chEyxMQXnLJwg3nJmY01CjZSEojYcwdcc8qGCLV5NH04IEcTlEzJxRo0KuAKR8nhtgRTAaXrRAJMUXjptXISpauFWwNjAepCmipCnHVHDiPKUoB2iCMhG8h4jZC1E3cQqVX7RNzo61whiA0k0yEQEhOYBhmXNkcouEcKXWj6DKMLLIBfRWp3HCTVESDYWXJYHB9o1NJpwSxXitIoqJWBP5AtccXRxZKkLiwZiCNwtQVdhz0OPgNZo7QnnScvWtLxRazEYZhMadK0afeMxRpy7lpCbAYQgQJ0JD3fdfBkMzcobEmJaHsPdG48tjgClyJK4MkLapOCYolYMxBmagRbanJvXIRyd5AigxpFqw31kbaxE04USVxDtBipatdEQLXoFDb4IagNpfGqWHJkbA+YOweJCnCCwy+YiUeHlny8Nd//TsVedXO286cM0cZJivsWfqcuqwl55ohUjEiT2wt1UgImcopIUwGEviY0jLRXoKcyCCelDQnm8idtNURxxUrKYtOUMa7tKYZU1NIzRuvbfAQjyarTFbGgJgTW4w8Jgm4ilHfUWdUC7OZRKV2HEM1IuoNiokyVfuxeyGvYkXMKKmPGmoykkNSPAbkSYOgnPJ4oM+pEHXaF9AnBg0zskNUFhFuVRoKJsRUk+hHViHokbpmIs0cjUoR7oMLqCQKGsyRYS6kMJ3CxlSzOTgAG4Vzz6mXMO5hoplZIEQjQ6gwzIKneW+lR3SGFpN7qjBRzK8p4B15FJqJlQqHJRgpejVgsJUZkc/7kEptfs7TNqN/uK78ze/+4elfup0zc+YJRx0+MZqotV76yU/f+ffHCkf4478CN4he9eLdAAEucslnv1gNQsHT/97Wl3LtH2744Ec+/tq3nrD3m446+uR3feozn/jplVfdfvtt3ZLht+b+g+hnzJ2/yUYb77bzju9889GfOe+sL1x43kXvPfG1L99jkw3Xz3glSlAk/wdB/cenXXX9n9575pn9skefWLLS/PmXnv7+XZ/zrCd6/sWbiPjMN767z1Fv+8FPfhxuM2fOevPBb7zinDO23Hj4HfH/VeBTXV/HS5avOueSS0++8JLeKgfCIwW7kQexECV84cHYC6tRDlN4SCI4IlOMb7wTEHSF2ZPPGP5eSr1/4ZI/3ZY3XDPNnnTD5eZYkWpPxRCFnZQjKwKXJycKrCX2pKi0EGsEMR0hBJ4qnJSzIEJyIgeewBfwVvVevIYFYkGtXIWHAw2xSoFp3sSQPNzMtSI2IEgAxWC6HEkIeYEcwSs8XNCj8NeIXCmbZa8aVeCzAMUgLhH8uaqQeFIT5kqphpIjqCQHWUoypGuICQR5IXJWjdQgl5iYIbx8/kc/+f3td2+53lqvft4zKyEeMEcmQlwyCxBMVLUCXVAmRvrTIM6E/UoVqDe4OJuLCxoHbmsZoppjWgNcIexKnSD0OBGSEU8GamAmBn8gQL2DqIhJiBk/EiQcyiCd2CEjqwZBsPTc9JydGPYV1QiRs3IOgaoSI0PRiChLQEGgFVy0rkogQt0hHSGcI2FYmVwtUuXhhiNlbzQSojTVaoVCNBojQrlagwBNKWdKLdJN4FxVPQUCq9MwRAi/1FerTIIkO8EK9AYwgZUQ4sCHsQzSk2EFlBptiRFTRnKYEFKKcKdCsBDyJihRaAxCg4qEnYnRkXjU1DZRsgRYmC2gnRwiQAsjdVNxpFCNgSzAhLZKJ7XLAMrRsCf2gNyIqER0EVAKpyqN8wQhUUGxJlEEUpDCUgQmZsRFapXqzA6VMLAKRyu1lV5R4VSwBHxMrqwyoJekmq0Ra5mayDJoJBGBWglWogTWnKyqI2XXaErN3isbLIfDovReYDqFiiMbZZAHlRtnAFdOwpBhVHJ3wwwsUK5ZfRSl6St1QX1KNsq1kU5roEiBiYZCMORCAQkL3GSc1AArUEkLKVnrlnsoCIbOAlSRtIIpEqFmupFqzVJatpaQPB0U2BSnyg1zbl1GwRwgamiJAJbZ8R/XRMCTODVVR0WaKTeUFqOwDG1EVphcCOJDBIQbSuRY6FpJOko9xKhJRQe+g0EooaDVhilzVavegb+IsUTw4GcZhu8t5E7UWm7G2hZKtVgtfcAQChSqIMdhKk5sJJZCZCgw2IWYTKyHAqCI0IFuAkGe1AelKhHQEJGLAGoX6pUZ8qrFBwxW4J/FoRKCMr3KsBhyj0ZiINqxtC/ad2FQE9YSvNk8KlFVqU3Do8w5cZD0rgZSnDgc7m+ZalMjeTWa6qP2WCTiCprNCCA4QkKYOTJHwyoYBTtClImVSQLQIntqoFYig804W41aa0HJQz2JM0QHDwCUcII4icEkJEO4gKpwDSpEiPfMmgeyiAKNg5MFDA3zwIpF1HAzrzVisF8L9yCUbyBY2AObNRi1GyBRPxDmzJCVEYdoNEqNsir8LCnI8YY9UzCDZiJ3JqFI5i1staOAp2LMEerwRUgNnkpkk2RMYMMcs4ETUqqVDMpkiLNms+kQI0SB/7Rydlc3PBoLmMUq9OMRN3noqMqhUAfUV1JvWOisllQxPzhD8CSG+JqNcwU15ExVg3KkkSQUuBIc1SGTim9mDAdQO4JKRghwBzTQim4PHnh0wTDQqDdUG3KwXVMq3DBniQRDKDWseniEkRtoU2etEq4dN2XarxDGJIJZSLMMDELcZK4hjE7xnIZIsek6q+3xrC1f9Jxnug02n1mSSLAE7B3zIEge7ItJcatelEI5qWQBLxYWXiN1zqgZqCQCG5lbkRFJ69z0xtaxVZghKOfEYkSdeyEYQ0pOSiAxCEApGN9E6BpmJwkJcFnHIWa4BbCaSh1cDspwrnA4IPXhPYNxb1rBF3hlBxyIxWMwncwuTU3aKbYkZj1JdQo00FGBfADnDu1ppAiNcKtWzBBTEH2KAiYDIkPCxKJwTYdIKdRDzZhJyJUqKXrZVAuwmyWKUYoWFPSVA3HHu1QrxFBrIjfGttWs4pyxL8AoCQoSDY0e6obYMiivCQbbE8HGSSFRxTZPRQUIazjsWTzyds/eNqVEj193333Xgw8/9teKH+/757+fteXmL3vJnlh/5913XfKZL3gM6nnilQL6/7m2/dZbbrHJJlj7y6uu/PbPfomb/30NMv+37/xg72OOf+PbjvvYpz/9xz9cv3jxYzsTVV37Ses+7znPOXjf17z37cd9+APv/8S5Z33uQ+d98eILPn3+OR896/SPfPC0i09730XvP3X50Dcuv+R7l114xVkfOPuk41/30hdvtvFGs2fPgi0E8f8++m++4853nXnWww8P/6et5VjWWG21T11w7jM2/3cb1OVD/9znQ48sPurk95zwvvfde9cdgPDk9TbEFuXNB+43a8YMPP4Tbc6smZ8896xtttoKa5csXfrZL3z+0Ped+Zub/gL/QllZI/AJi45Mvfo4UDhSVBFhTSQJbt/wFNdHlmJ5mjdr1fcdTHNn4v7RK7479c1fJXZP3mXvXUqfirOxoXMi8URDTUMAIDRohMmIjVryJBUdKTSzSqLh9KZJpEgSqWeEB0qCmrOwja2Oi9cgYsDA8ulqnTlJFhrVXm0KTtCFdi5TRF2q3hhWMzeC0KwN7ikT0olIpYBvdzWqpyq5qNZcTDqPAsAu4ggZpEa4EQnOxjrgDcJrlWzqLnD+jiLCiVQ1AYO0xD/+7W8v/tZPVp494z377zln9iQLwjJXl4pwg9hA1BA2zYjfJQxEEFuCJAxRSBBrkFZ6iBx91CZGGFcSwbKQjrkw9cHI8l3yijyXiuaaG2aUMJIqgSlC5GPUGKxA2yiP2kCpqdOlcqoMgZJxSZBVB1TJqCHNhAth0YUgoCyibEKB0gk/UYvWSJGZk7MwB9YV8orArJCOVs516I2snpk4MENYklJm1krkRGLMfepLKsakHk04pBg9W597p06tQleKXiSeWqg3WBG4p1YoUxDmtdRjCUhAbUMe5EgRPVPBGLGQZlXYjje1l6g1whJbwojBNNqGRTmSe0vcpnakOUvKLEOvwMylz4o4XqMj7ojCGO8Tc6HhYkLeCoY4mStLz03hCfPGa2jtyZHeog+jKMOLMbDcABsxdDUlPG6kKndMkHZvPXIKpGGWxIbc2jBlpVwld5pggRAtEQ9KSIP+wKwGJRIoOYFCF4gCqYwszEuJKecxqBFosRKbwdMMoqvFq3tICFwqFWbwAZ+RCJiJchU1IImA5ZvXPkqFGfZKDh6JzKhCABAiexXYC4p8djVN0bSgCuCY4WJGyuwTDRgx42kFQpO1speB4VQFPgcZY1rAf1Dd9rnrrdSu0pjBToM9SSW8hWCLMOlFPBllALBpAplTJlZnN2zAEuFSgsaycIIglAqWdRxd67U1mJiQO1kf1hN7bbiiLmuLsBMH7Fqhb1Bs1HXeW/REQEXEsIIYAS5FomnbRRVngy/DnIpUJwd3yj207RAyDDSgbPg9E1MibFmCSDRUmYZgRgbrjBrsrlHR0zCIKAyMEVhCQiEeGiRBTmwQBsM0p607vKvjru+nCtgD5eAeTaIo6PAmWEkKcUfhJVIluKZV7wxslz5qF1SVGF4qyS1XlNIeTfSQssAkCMvISoyrF3yhKOxFAB/djBrIYCtGIKUJUwTCzmvPvXPP5lLhvF0q41R6rdBUqBnZVJEpfEEitaPeI8zBWA4hSAS2RzWAVKIX7sOjGsgwBiyQzqpQJGfCDOLCCTtiUjHLpW8KkBaicfA4qFilGAtQaSkaDnm7pxrYiHlP1cNS9VydAjrlYhRFtAhD1IkdLsJgHhUAABAASURBVNhwtETwJ0ReEYPYSdhbNiGLVGoyeLYzDICIK+KyV2CBFMzaAutC3dypdMkrgajgyImUPAtlDmGj6CFRKIGCU3BjiIQOVoCFmCnAp8K8uHGsLZW6cYBBr5ojT8oo0wAJlFQiYrwT6nL0LUkKhJIkiWGOAU7ISCGnXoFDiEzcBPJlfIgGmEE4ogKSTZwqIBE8SRzjTMMVxAQ3AEUeMEcrmBwlUMQ7RnhA1HIvDGk7BBtOCKGewhmyLZUpqSTFcjKLEJUma6PAEf1KcydOeeNeb977pY02QC1D2FXicLWSveZa24oADt56juCqbFC7RaoGfJajwjLYhMpE1NGAtxKcj0djnjCW8Mq1t77rzMYEgoIq0VRQX8k0ohkUWEtr/QjLw5SMR04TwSNm0AG94MtG1DephlLhtlIDuan3IrgFCMcb4JLCkMcgYCUCk9VK1J56ln7EdcSOdSX1Ncwq7KYZE0+BcEHICuXkOKWjZKHg0FFL9MWmCNEymyfqBLJ2RAbqQ3LXwcYTgzcmkdoALwdTkEadcBicM0G9UBw0nTgAFZZuqRS2iAqauVcvajBIZ45BzZZSbRpr1BsaukAZ+bBRcnbKBDFC9j5MakuCJZGRc6pZQ7faZONR29I/XN//+X9a6S8iJx958NprrQ3wP//lzx96+GHcjB9dWN1x80+3idHo8AP2z6OJcDvrksvvefzfSP3TAP+7CyPi+ptufcVRbzvp9DNu+NPvu64T0Vmz5zzrWc9+26GHfPzcs3/zjS9/9/ILL3nfu4496IBXvnCX5z3rGdtsufmWmz51842e8vTNN3n205+2/TO33nHbbXZ+9jbLhzZYb90VV5w/c8YkwT/o/8R174KHjzrplDvvvns5MmbeeuttvnL5JXiBs7znX/ysZt/7xVUvPOCQr3znu7XWnPO+r3j5Nz5y/jOftgVk9b8K/B/nrzBv7horr7S8B1r+8U9++Jqj3/q1H/8siKyae+3Zu+SlrTX18DEe/IVNxXX48QeX+hQKOmrWnD/n6eus/d43cJtj3N9z4mXdbX/3FDXXQDCAo2WTiUiTNeWKeAvvF3gjgjyRuhC7aTUkEHYV1kzoM5MwuBlxZTUORDDLcFQGXWbhEeQVWTIZQgLqtIwXvhMRymGKeGBCnqolTK3KXZJOtGdlzlmUhU0AF0AMkU5Q9ldiZAQdgjRusBY+zxwkA6ySxVSIAFPYk1TJNk08U4RY4VIZoRIBiN1FiB9ctPicr31vIqeLDn/9OqushoCJslCZANQ9cYxSyoHsMxFNxnMtHrVKLYMICGgAlsKboJGX3Pjw3nYooppA0mIysB4UQoQaRYpKRbnL4FqhrTAiB4cpSZNEmDNLQiUVgiVmbpV8iKBM6sQu7kOUxBKy7Ahjop45kSPLKtJ09b64+ZiiZ3IoDjIkZq8xhNoghDzQi0TWJ0xPQRRqgo8SBNMoADVQEiKFxQxaVwosFzf0EOCCIOpDwlUMJHIlLUwFhEXVKEQOoESMon0EmVDJMm7FWtNRpzL2MKOoBHEAOOGhiPVai4YzfsQDG8/eGiYF+RCrFXLYlIpkghbdg8ELC6dcUgM7cXIhACWTKE3U1qkt2rpgvTChqYRoQIzGZFEgIuR5I/OoWJci8I6oyRZ4K5HJGhAHysEdEDtmCZYCDgAROQ0ACSJjGLUFV5BDuGKQt5v3XjqHN1qEE/CmUMYiEOnRW2+1gEphZmkNRelQDyQsE+/USyKV3JJm4tZTQ6rDzCraE48d1HpxdwI9wQMJBDiYi/xnKHgtKCAhogBX+BkskVJS9jYgmwDpxJIYMAmyFgFjgKIYMHZQYtg6QPANsRhHYTfpqNTST/Xew6NVI1MI9VI7qg6uKffUdAECAv2QAWAro2JIYWrCAe9hUCmZm0waBqOiStE79Uyo0GoETFUQZiRcjM3zWKVrGNGDhCuUBjzhYYb4Vut0eTYQDJ0mjZyYUvYAqcSFkg9DgAmSPMRdHKJQ11azUhYmU1hvmKv32T2ZZNfEuIiMeQh7MVJQ0w/8W/XqNuBnsEYOkRSwBX8aWOeUok3RqGfyDERehTpNPSeypL2KsTNoALpslqhmmI+gV4JD2IYJYYi4VCSwtjZekikTxhEniaMmiawC0UStsNUucvVGqnIRgpqzAD1zAzlT8DTH0Vd3kJqNtFTDbsHIndWdwkvwuHAFXZWduNcooEzH7lZrWGCgz9bnrqaeWhJiYAkuQcXJQ42akIYksxCuIAOcUEcgsgwKgEaoDjMReywz5wDxyoAjSikpsw5658Aj1AH6hSyR5QgmdRafVoWSJXYGfMQFUwgL8NBqCrgnte5gLzQ5T1hpHHZUBouCxhE6aZQ4cWSoo+/IKxMR6E05ICekk0QiDPqNBvLBGFNkhsaqIK1BLwzmydkTl5FJ0zWpV3JIgoAAQd8zsbIkbhTogmEhoBTku4ZLVlWYegLMwAk8wmotUkCOtWztyPH+EWIKCWLGB1a6QU1QjnEPmirkV0mjJioshXV4YAImAgkWFZtR6CuVgI0IsI3AU+POU9XGVJGXST0q1z6sH0KzhhFsibgyO+iWlOAKVaJSBE2M8qzJrMFNJUF5bDxQJH1NfafhkA1nEzWog6oG6ObKVMyCOpTxI6p4mQp+K974CIMwso7YIkVtIUcmSNWdEJ9iyNtVEfJhItRTQCw9WXE12A9kF9wY5xoNRQLXXJ0hBTFWSkzkMigf8oc5qgne2TRB8HEuHhEGpSYOwAJog6GwiphQz2JaWSsGc0AyEnDEiLa69AkfEZ64EPPAW/BAm2OJeKudkkMCMJFqNkRuZkOXBMwnhUhRhueCNCUahByEKyDjcdZelCJBbSwa5CQFRgebqr1aB+EowgfnEWk2ho3oMmnHkipbLz7VFNxQiaZKCyH01I/ZcELRFe4swMWosYRgPXti4lnbbAOkT7Qf/+qqJ+7/9ZuZExOnnvgObC1i+nrdXnsd9Jp9avV/EfJznr7Fq1+8u4jcf//f337aB5dMjQEQGBDi8In7f7EhR37puz94/VvfceMNfwTA0eSM5zxnh7NOPvGnn73iE6e96w177/WsLTcbTU5KSkEwdyb+bwiDmfHMj/VgwmN3/2e/Fi1ZeuwHzrn9rruWoxXmV75kjyvOOHX1xzcAy/v/6c/7Hnrk7R8466Djjv/73cOLlHXWWuviD37gg8e/be7sOf80zCcWLlk29f1f/Ls/o9x144s//pklS8cI2VQDKaoGBbyjDRSHyoRwUiIc30Hx4KM81QOarr0Kc8zfe8eJl2+PR79/4T3v/0xFBqPIUtuE5jnBG6e1VRFC2Y152jw5hChqmFOFeyqHRyBS9BYVAcQwCK+kArhGbZdGJc2ow+/WCIVxqVpLRrwJR1CWMWmflIRTL2ks0qt4wzUjWPQeVRxxB+4+OHogzA1RmxC2qsChp4MUcuYENW20gnIwEFQJ49wL9jkoQ1i60HHkjsTATZD6QLIhvrKP2btqUyAo2hXnzn/1Ds/45HEHb7TGk7xorcbUN9K1afqXBBD7g5HRESkTozRRi9JTcdYczYhEWMCDiCfmzORElUUpZ1VunZCHSYpoaSmaUAnUBgxVFUfK6SiVpIHMSg3TBDukI0aVYkpKh2zEQ/nSEKkwNRppQJBBnRdICoGQEL651oBiS/YiHmZsjhBv5tFBwhHVxsJTPMLuFQWrS0nQNJGoC2Myca/QXc/I+EScEwsnjwC/qFAG4qBWHEENEwQwPJJloQnLuU9RoEll00RoFDEeNgFBCooJKQfScQYWQnjVZJAE7FOc2unevlIXoJYoITmQUTgEO5y4dUY1aOy+rAYUNXarhDxI1TqupREepMvKJMQswMdG5CyFmj5ydbAoApokiYIFcBvEjB9yir4jmyJalqjPFTJIAMmlG8yEMgWyb3bkupKDcqgmQArGxqIZmy4tubNs4+l/n1BGxRqn4WLYNyJkKd73tRY3iNo44BgOtoQLiBLPlZu+yTh5tdSzFhfqcBzQZmYc0aK1LEoslJOMcp5UaqUwVYHPwc+ECnTOTUNgP3jCuBFMNWQ+sco1shEOICdKmZZCwzbRWzMVtiyNx60bDiuF1EggD6aQRCkpR1O47VKG/xm5MfibEjHuE5Lj2MmQyAPnH94WVyRLnA8SUahEywYwUBpXERRVPkAGbOEEx0hhRMxMLExZfEQ2GZQtUW2jJHhQF5gykCPKKajpatsPcYWZW2IhGLYVCRJTBWmUCgMNBaAq6SjlpmFNhThAT3iNcS3F+mzdqJ+a6KeyjVP4RGpHzSROG1gcjy07wZ1YAJGpJjEUMTQAdRGXwVp4oDtQP8swxB6YS+ik6MXQTZkoa2jW1GpuCceaCWcTjQSzu1JtknEi2HxQz1GkRmOSIO4BGTWRJkQmE2XkSm+8g0cTREjeRBmJUh6VPOolWVPbxloPrgi0CoTUZsmR2JREErjPrPiPlRg/YQLmhDmBlNZRxJfEjrghEJuMp4lhQlRKo2gmQiY4ZAjM3kWpVCDBYjQOaJ5UZJJ1kkK0FthIJCE40+Do3CbCxUETY8rLmIuA15HaaFSapmCoEsm0gEakI4kWDCdQplIm2EYuDWfJrSXEf9j2ILGURWH7ICg71kLb0od0lcfGPdGYAw1goSdWZw5NkZSY4Gm9o3pn6AVpMDMThvJgFLVaKdJzGwjTmYpVixB4cpBiFhFxppggUqVo+5JqCeEKgxIeq/XkbNFYtJWFs02MKt5FTjCo6E3cIwAgnKAZh2Sho9RCe1BOCpZeuONAeEU/S2U2IVS+3Uj6tqncFEugptPS8QApjZHspAoVocoU8I48Ju2Ig4CnIFS1GBhwEks0iVqGnMiFg2GehWD9Jtyrl9z3CKHuiUpKY9LeiMbcVmlDhKpHR7XkgnQjrcBTVScztxCmkY/JkDgowAF71i5nEw0GEvAAAgQscT8QBmKFNFseuY7IR9Q3VqkE0k5gIs6QGknacmRG5mJWgz6VpyBeiB/2jDAZUmlUo3FrBoRAS46PQtL3jU9l7ii7gQaezNa0HUfPxahwEm0TN0RG1oFlcZdBDlKEi0Yog7boMnW5jLMj3tCIKWUurfcTYcqhYRSdeY+m7IkJhMKwE9Yq/FvMMHe4YQL74SSKMc7qArLVWUvA6iKqWm1LydRTgnEkKpr60Kna//Gu/t5FFmQJihKHHigkhQCFIeyrSAg7A8E0UOHCBsnAF5F/qCYz6Cp6ouiZO9ZqYp6BmZSZKah/ya470j9c1/zutw898thvN/1D9z9/+7ynb3Hwa/ddvv7KP9zw/O2f08L2lj//s5+qcsT+r9lo/eEPV//uj3+8/PNfqmb3PbTwwLe+46DjTvjct77/zwJ+bN1Pr/nNqeddMLX4ERHZduutLzv9tI++5/jdnrfd5IwZzvLYpP/CX4uXLnvHGef+7rpfL6dx5oyZ7zjqCOwi5syatbznX/k08+/88tevOPSIz39NdvmnAAAQAElEQVTlK166pmlfuceLv3zZxS/c4dn/Cth/XPu1n/xy6ZJHn+gRJEiiW+742213308ySoS6GQ5Yh6BiSiKe4LtCuAvGU73nfls8/OoXb7A2leQqT3r/wbr68L83nfrONUu/cnX2zJEQUZSy4iamUQmrkCJ68PTjEAgyMlepHOEBt0ItJl2RrqfeHYbgwq4RAxlsEsSGeJKdm4T8QAFoJlJIHaHCmeB5UgcXhsuyqCoNiER7nGWlbCrGSBml58HLR5xajHOUobSMqMK9chU3LiCmwHl5gKCEVYgPTlEpV2qrpWJSg1goi45MkYAwixQRQHI9cI/nrL36PE9WBopDKIBDEPM0ggnRJ2qtDqRaKwkVlcqJEGOkZwSnLCnrUIewIY9Zob7nWsVrkh5MSGOBEFqDi3jHteOuox4UexHqiaxSFLYKJrxwH+RCgiimzBIRptXUWDkNGR59xqCPE1MLDKWvQVWEVRBfMSeQPZIgcPdsRr2RG9JzKBM6a1HwYUQcPkTUXA1BlASER3GyqEymGHZ1ZuICDlRdkisDBKTBHRIHQj5nGSYYpZ5HkZtoOXINLala2xnebaRcua2qNTEQVkrOKhxMAoF2wbAXghGIiCpLyjBhJcnM+Iw+uj6sqCepEEHX930pNGapmQ1TkwwFCzCMKjVOIFGrSlHtlY3ZnI2EOAcl0GXRVGqBYMQirUaWaCkyI/NokFfYWLJKEIqiGB8TtMdUsIVMOlhv4aawenZqQrQQjUMsEokSxEK4mLRRTM6SswgE5xRIqV2PYOBUoLAsmnOOEZcW5S5cJRpzGFoQTDzB0IWYi3Cnw6cRnlhIsqeRU0vSNEqtOKwMLMBChSyVvq01g2xNrJJFkrF0oDQa4SAaV669AB2RQntgkJgSWdOjUIFlsQ8cG3QdjOU0CspGjFK8JqoUQQplhLCIw4KLj632EGlOjC0CdGeZqsLZPMKNxE1gwHiuiXsMsRVyYpagqGRVhjQrTaRsIgDPwSatkxIN5CVyMWHPBNkEFAJomEUq0TCaiGbGOsF0ysE5JCShcUmD3Mbh3rv0FjWqO57EqXFh4upcI2B7DGiaSBlX9WHfFcw6Ymz5XHKJ6a1nzQH7EFUlNMwUYmHIgfDpQiWzizLhCsJYJs54YrBpBM2HG5MxS81tbXKkJDI9A3v7rJ5MGZIjLBEL9QiCtaaq4qNaEmpzRETlSCrkWqwpQCJEmkQzQXishcPDmBzAevgaCFan7DzytpUmcVaCwShAggoW8BHecBl8N5HmpASX1cabNHg2c7BXQfxmpiYBh4KxChTBxCzKIlG59FGLUE2EC2tgb5CGC1ZDGsKeRCTlXqUyVTJwJkrApVTc+ugdUkmwNWM2CQRV6Ms9VU5OgEfakxpDJiVy9YGp5C5WGYutt2JRiAp0oplYQTNxRMC+WCs+QQhxocApRKUUuSFuqjclZHn9DBaoE+76sC7IYAeJhW0iihaQ5iElqRM1YZmLJAph9+AhDoEjlgEdV9OxSQdCIpyjBt46UkRicMpEGtXIEXTDlEKhhoA3KpMn7oOwhIowhD3WKK0YDIKUB+ODksgbqpn7pNjpYKuEaRyZCuxCgo0xjyhJw0lCQYnVjpA2hhhJ7PAZFhgLXEISw2MYmlJQCXq0YmtUU199mUXHZCJhRL15YfAGk3d8W5GxSw0OFaGUQBImQjfZuWERFYLtJw0eCSflnCgH62ACxMP6KmaUPDDAmVhDk2hKSYipWHLOzCkzxOXIESLgI4WDaJAYatMWEAYETeYmmbP3wd0wIqwE/o2pDynTPeholRo2iBHKMQEQCsOqYlZrhdUQDIacuBMZJ6qJXNS07Qep9VQrdURFpRNsvygSgR4SMMKsCgaViQNYWQLyklEMaMAftU54nSc9jBLW1CfuVPvIdRCHMIGQvy+464DTb9vt7Xe86JgHTrr4wY9/e9lvbht3Tg0OCZpwKsXcSiTjttCoH5Km9KRj0ZJyBUKzgMW7kDVSpXEcdfooSBDRK7uTWdTnPGuLuXPn0uPXosWLf/7rax5/+k/4FpFDX7fPTtttB1i33fyXD178ka4vuP8X29xZs97ztrfOnbeCmX30M5/5wre+u+CRRddcf/0vr73u1rvu/leAP/DwwtMvvmQ8HgOIiL7yJS95xqYbwwiDGFpB53/lFhF/ufX2/x9z/wGgSVXmC+NPOKfq7RmiSFAEQVDEnNa8ptVd15xzBjESFHMWA6IEAREBxYABVFQwkA0oWVBBFAUk5xxm+q06T/j/qgf9/O7e3cuqe/9fzem3q054cjqnunte94GP/fT4n62i826bbvr5XT/x+pe+qMIXV3X9HZ/YDWIL9KZ3vfeiC84HmI3vstFeH/ngp977zvXWmbYB6Pn7G1g45Dvf+QuchWXLn/WcF+DRWrvhultCCvyqCs8SKV4ICZjg+5xUilcdGd574623/Z7Vav0yD7hE2NrdBru/CUDQrtv7O+2meRLiem9jyTnTPMIsGbBJ+xAOTGM8N6VWaXKVpEx2+HWwmGdrOScc4fQj92PO5t6PJAEDCe1TaqGuCik8HquyT2xgos9pRqTBb6v6jHOmPKvea/SMHIc4gYTSytDqYBoaCOA9Y5uV3IgXneZw1pzc2QZG3FVm7gRgnT0iJtOcCgE1kxgJ8Uey6FSx4vC6K8qlWA0EcISf0Bx6M1AulIRr+ibiVBKRlBvzYiDwIcIkp9ZEys/wNiZy5xRsCOAQPhLhGJJwb/O0ISlcxZQjOVw8JIgycDmBOhcdsH4cchhyNAjVU5wzaUoyPSOqI9tTGrOrBiPcSzINs4xl3i+0nhF5CUJBKKOudXUomEWVqc9J0kTOBMaYQ8gRp2XKSkFOGA4WZ4lUSemhWAZBAdooJVNTKCiVElXSwkD9vJS51HnWgQSaAEHFUAIU9j5VQSirpRrJhKbzsba5NljRyOqCPJDAGaAJwYNJiMAoTbFdRDqceHKvIomEXquCmmDPHINCRJeSchj74pSfkDArpcSY4SDTQ0dilwSDoWpdxaHuXHWupQGgMMMGwNTI0ZQd2a/va1El6KJmFEAy0FkTpYGpAoxlNPAu1NOSFFEN1KF1o6PA4qqkTJrUBbbMqPMIxBDBxkmq6Kzr+llXZ6Wv3KvVbAlbiGyQS9NeIKqKyQTR0silwVkDppEJL6IxMyNpzjltYT3TxrQkV/GFygsqHRVJziBojyImEWCGe7JVxRxaJqFsqPMJMiuBqeFkTNHV7KoJwx49ClvnrmNAwM2wpfZ5afO+jR1DLjWpd1oW2glB+KQzkgX2GZQFWzcDjUyiyQXlBNaMXqGAgWNMIHMgC4+cmk/0t2QnhrapsWUdBSZKrZh0AVl2AjCaqZEcYlmTmdiKG8iThHARA5iIWFmKKneC/U+wa/NuSHXCZYwdX0cgP5kFtpFG2RL+TjHpEFNGaoObN5IRiEmVSrVSG4OmHGGaQlTFqjThFijmuuZiSgYm4QJclHkWjpg1UciFsnDCj83HRceZOHmRzIIok9DACBZg7S3YqAYVFQab6AEiJa4ZzGESBo8hp0gOmGOWkXTQHKsRzyXGjrIjsoQwADu1RAcSIadpemIEYc9bMxvJUdI2ClctsEMKuDRrak+dADLMJWH8VpS41KgzmgH0mN1ICFxCpWiZVkLcWqnrZFZZQRVi0uJA1kpHokIRaThoQTIZAZGIIAJE+AX3jsZKzoVAc1NVYHGpzRhGIZaFgqi5RTOiTCQPCD4buM3aSGAiRjIQj+buluHB5KQspWrt8NlIx9Qx2Cw92aSYMgKC0Ri9seJfT7SAAW6ULSIhYuGZ9B13nJwNyuJSaokZj4IjD0Mkp6YInRCki+fM8XILU5JmwSWJtQI/TReMioPgiQOnlTZZHbQkMacwZxmgTLYiY61JlZmEsjqsuRRbEABOTXSpEMFA6tjUQ8vIvbsQYQUOIgTkS0lfIO80s4N8K+TZqoQqaZdMQSnpmlGZVNK6HDXGUMeW1ZgEXIq2jocFHmdFtBRBVFwMiuRCjMNFgfRGNizhjIIllN5ydIhjiBjEHQUzEbvUQWG3tTh1Q7C5Z2CVaHTqykKE1SqVKsqCOinMrQ7Y4guLDEVHKYQ5mRKT7Fg7oo69ktcIxEvNIGZh6jR7caVRaCQ2EmnUQcCZ1JXs+gAZE/5wDlN2JuaQNAo3m5QapJoC3yvq0IsTWPYC8OHBIH0kChJiZ3FKgMouGC92+wApzp4UTMIs5ozolm2anXgeuY4IK1qaTFNScKkMHjBjjqGSVcSKKaR6OpmRlQCFDfiguNnI2FjecpNdfWNceu1w5nk3H/D9m9627yXPft+5D9jmD//6tj++c9+r9j745uNPX1wcxrSxBEwwmAIiK4TE0impW8CSlaNyqLAxNYFgErHU54mTW2tksXzWP/Vfn0R/viLi0COP+fPTP+b76suX7/S27Te4y10z87Ajj9zvW4eBjL8f9H023+S9276BmYdx/OT+B574m9+tgnnHtdZadfM3fILCLx962IUXXbRqrVm74YbrctXD/7c/QfkFl13xrl12f8mbt//V6afisZTywmc/69D9PvPof3qwiPyd5APgkSec9tzXvv4bh357nK9U1Wc95SnfPWCfpz3hcUD0dwL/6+XnXHDxH8479y89z33a0zZes1/1OFvANjuJjShh0yUV2k88EL5rZUluaVbPuxzzuZbufncdO2NuXfpaT7j/6i//N/SP51123ce+DK90TvOwFmPz+TyGZp4IIQgfgWmGEFSoV5oRQldQIBZpRCHjKeIQDSXGYtZFSI4crsbFSglVDsQvngReQCc7iMNToa7LIqVKag6CcohbYeilZJZGOjDCJRYhPRGPcFIxlehLFgLGDpkjk6mwF44et0mWYqSCDCC4yaTGHCUJYgAOwa5DBShGFhNKYk0AiCbuJSggQYsYAkGsIwelgmiKuNElYhzpUmqKCTxociuOQJZEZMTkhCakrr3VMmLPl7WNpY1JppGVtWqHOKSpDEjEXEOLK+oidoMlOSkXIUY8gsaySRiHC0mUSoJlhIzhTginQyC/S2JukaW6mzOmf6BoYCjPQlgKl5n3XQBrlN5cR0vPCHCtwVVTu5aTXgSpTIGCWwhmaDKzhvZTbmyJ4AwqMjiIAtMruTgl4jMEqwo8QiVbdWMUv0FMuIIagUQNcRHcsoea9q6ldCldUB2zGDQUbE4B0SkbTfcUwTlWwGdG9cilckGJQapElU10TtrCfVLS3NSTqwNUcgTSUySRJAyuMuon5MepFYi7gXsCZOZCNIvsIzpPdguH6KkE91NTTkBI0hKdQBoBuhvDBlVGYQyBN4KJlkF13oFrNHQlgV7I3UeiUSRURRRk80R1xoTBeJJbpVHJBJQIzCdUvEtimA4RTAB8TVOFSHPqcLOBYAgSJJScDQYxJiFLhWdIC5gZ4a1TgnlGOs9xgi+RSgYpOUO26VqlMquF2Mg5kAQDm+nNawAAEABJREFUA0Vk83Qb0z2TzRiblswgJhIO7th6xl4YOidiCYITVSZIiFImU29RnRhCmrABTTrrmKhTGPZu1UynxA2jSpsQgXFApQxEI+cYZKoFBuZRjKJRc51LLHKYYxQYCKhoIpSgg6Sea0lumY3CBYgnfnCT6TS6wp99MniVWR86G6gagzYBkFCds8xVXbtgRUDzFtxYncFtNrNxtIyoGqVANxYOJDSwQ5uIXyyqQYEpQB1KVAoJp4WNbXSbvgaf6iIDpoYdDXfJRJTkbNAO6pqB0ie8E9nCg/KiygDnh21jrsjc6nxevTElS4pkL1EmGF4zmGmpAQBiU2QWhgIFNNikeKEhfYD+KngSJxmCBwtPStGU3nmyEqLJ0koQpDsPnUeOkyyFuJJ2rKIKsLVo6ZhrUgS3oj7V0yUd+EhrYKuDSZwkS+xR4JZHKhaVUCSbUGYlK0QysliBWTVnCBsAoohU8t6tM3CVyhx9Z1VT3X1srY1tHLPBUYMc8EOYCzFrA8AAZdzXqArhEQXoo5ZghlyEmAqLmlBAWklIKqo98BVN1gjVxoWV+1JUeAJtQUbioH96nBRaEGM60RKi0bNULGQBW6kS1DMEkGEWo6uLWOdDtaUWNr1aIMyXBhmTKTcpVvssEKmRwL8WB5/7tEMBt4ZdzNAlJACxCcQFwrNjYixO7KEqAX0y5fQPxE5VuHAHEp3T4QoYMLakUcKVVLmASrhhtZxZYt9WhJUhvBFAQoBCYXyVkRo80S2CV2l98Q42DojFUZCLBDOEWLniG2yboSmBgMEyYtJkLHCFwTUba0tJqGOJZWOJIB0CJiYmnfZSalr6YDbYKIhxHEQxiHsJ1lD4WbDLlARKlIRbFXNQSgo4mcTJkmpcBi7jJIqikjCcFOJIdQjOq4RKAHCM4fOgMRNKdQ4nVqldr6IJH52xAgePRYZavCpBjkNHKzsetSYsJKtYR0NPI2ycItkI8wpYizI4LcaYwFJZeqYKIZFlOrhnWE9xTAM6zj5pwbSScjI75qBwKS53WH35K5607GFb6vpry/IZCIz5GDfcsvL0P674/A+v/vhXLnnxh8/d4lV/etYHL9jhs9d/9afXn3kxX7Uy56VTgQYSpKRIt0A8oxT2VmgQ6FWiuGvzNFbrJPVZT/5/1Zpn/u7s8y+5DOj+gW2TDTf87Afftfpqq0XEF77y5UOPPi6XRP53oWB++uMfs+1WrxHR+a03H/ClA1dBA/Orbv6Gzz9dctnBhx2OhaLKDJeiv/zng+j8/2CDFG+85dYf/vyU133go0952auwD5z+E0miWd/v+sEP7Paed6x7hzv8/WRfdc11O+6y59bbb3fRxRdT5kYbbbT/Lh/7zIfes/6669KSlP5+FH+BcOiPjlyxYvrBLfQA/pte/uKbb7kV99LN1lp9tUKT1VIaGUlQkYSS0CoRqwf76G3+pysIl4rfaTmJVcrCLF1Z/10vk43WxcgNXzt28byLWGzyBMkhcSfjEINZSw8ETiIvKV10vVcFeAo4EEyCao0FnNOpVhdkSw4mRwyC4xemPhQdichKcylBAJRCQZzErKG9zDoEFHRlIr0GyJW0zpoAL5LpHOyoSgo3RAGgLcqirGTCKUVYGVhmkpWJQWJLGVAqIAOWRGxDUKKG6InQxEpR2TpuNRqbEQeiCzMjTOElRU9TCEYkpdG7FVZGV3IRIi2sHQmCe7EyTV8WVCAxksyeEL6dPQKkoiqB8FRbX3KZmtYGsSHEo7aGtItIL7ogtEy4xz/tshMEQOKCxJm4KLTQ0hlgRnpOogDjkHUl1WBGl0MGhDGYOHjBZIY4mIndsQTfKGM0c8ra8YIiweGrDlLm7BaR4JgcAnNVJ1HYC3YnRKYM5TRV40lBzIIm2CQQ9BhsYxmCM3Mym0wOSzKXEYOSHbXerMxT50WQSIRJCGAEeEgdMKcdhi4MXIlZqYnOs29RKJiC3HLgHChMIUM8eWpgT1MrqzAgFSZl4gLCYD4LzsDGHM7pkP0i0tCkCEpURDp4bTRJtXARSkonCthO8SItBQjSwGxSoaLERE5l0jGAYz6FYQVgp5DRlHzDs0TiJf8sjCPHIIgiW59NeJI4eoRwJTnKrJjPbZxbjpBQipLOuHQTcYkrgDxgskg9yHsiGQVkBIqpnggaJFhBtcSJbtczz3ICGW2OglLDJN0bDwYeIw13QcEmMioHJZN3FEQZjDtuTNGIwwsZbIt1SQqZwkYAQIw7sOW9JUMfLAAfTjG2iAEQ1GMWUy0gJSD5AlKSKIty38GNhEbjlQN5QEpdmKbAOYglSSK1SZkXJNyM3lMa9AugI1wu+gTWdJ4c2RMzTYdkKL9JiCWbQx1E5IK1SZWC3dNhbDwWdegTlGeoswiRgiavsMKGCdSYsUuDoahUKSogiVKDCYUhth+zPhGklEOR4of0RlidHTOARmDn5y2Fx06HXlqRIMpMwAW1kjk16L+Nak1BAHEG2xAxMhgUJ8towQlxAq9IYahXxBnkzQysGyAGJliSOegH9QwuSLjA8pIiQFcZDBLqiKpk0YB8WhKTLuAlYYDbRMxaGWJRwqBuVU3uOArURy7hPfksgSncx9FHdzCZk34LdENE7AGPzRbu5KbeJE0pOkpIgRlMiQYIB+9BzhRTT2IdIluyqXo/I6mcEkHTxRgEAKWhEOwtoye4xRjdPBZcK1DTZJ3gomWXqnC61AxIi4EccJSlQgFJbZShpaVhjJoCGjhi4kqg0EQNzllVS5QavCS/BG6iQE1IzsnTIoSQYBcs64RLpVROz5JeNAgsEjASKwlBxppUsNAT35OUChNIYY2onuoFqwUeygYJB0tgsvhk3zWyeFUQ6QL80zItwp6ol91okByKuMJvCMDILSbbcMeyEGQp0Ujx1EazgDSmTUCow3aTEnZAlIV5xsST6BKPLM6VtGSxRF94jtF8NHaqLNwrl8lgOU2wthI4Z4jRMCGBi8FiBkcoRok6oVnx6c++ZBCMb1a4QyJB/OAiQrVwVVbmdArjOSMw45ajFZr4TeqsLUTrKSSJwBtNhlFBOwuI1BlpHWFnBiLHQtgXLBoPjRoMqlNB5qxgk5S9r63w5PKuAAZ+iRsgEjkxwLlTwjBFqhLm1Qh18BCQtVYBqMLJHmZpEyGTBtRYhblUmaRSRZmJ1WDh1gHZzKiMXhqU6x2zdoVm1TqxQqQBjZg03Eg1hZXC+yMiE1ZEUpL7lK7UrvQh2ioNGgE5ZS+phaSjTognuokSqywW1l9nkw+8auPDdt70xM9t+q1P3Hmvt63zhmcve/AWusn6CKO0dGWz+Um/W/nlYy7ddp/LH7vj+U94y4Uv/MB1h56UEyhlqj0Bh6ara2Y1IJsFyobCBJWXAgVyd6973PPhS/+L4hJIuv76608+4x/2H6qsggly/ukB93/vW99Su24Y5rt8Zp8TTv/NbUN/zzeWrZ//nH990nRS/pc/fFxU/zaQ8LBd9/vCqt+OeNITntjPZoBz84qV+Pz/YGtmv//ThR/fZ78XvHH7Hd//vuN/fnxmrKKz1u5Db3/b8/79SRD7qp6/+XMY23eOOPo527zhm98+BEC6rnvhs5753f33+bfHPZbkb5Qz4PxnDdvCI4477i+jL37WM++ywXrEU0eG43sR4cx0TxudRhGbcS5wFg7iiEIjxeIV059jhicvX/cOnaWQClWDI9xprTW3eSYXzWG8Ys9vA1zOSsxKLZWlEuOkfDonDRIifJmQJaeV2rQ2QGG4j/U1UZFxV1I0SQO5GYAQbgg5p1hqGkMPLcsi9WP0MUq2IA+mANgkZobjD85mGo1QyqSjA3NaiFk3oUZYzJZ9UIkgY249u3AaZQvOJLDbOAEQSYBXkiwmzSlciJMlqZIrm3BIqlqn0SWD7AwF9gJeuYBf9NoCJfogR5qjsIggMVToYB5Edp0hZ2TpjbokFg2pHmUcI8PALTvTWNk68dq10rUqQ8eGYm9ilAiAK0nPXS1CwsnIen1AgeY6jjTaxEqXBRRG9MYlsuQoPBKymwTgCBHUTUKo8IyToFzQp2kzwRE4QmatjtIM+Q2SrYn4ljLhTsJ35VZqsspI7Min3npvZDmmNJloD2EIEdFfkcGEa9+JKFBrjp17n8iZUwtwyelMEskT3woeBVYlXYIEUS6F+w7kC7kIsVZSI6hfFh3XIolLpzxTUVmyE+hRjZUQ/l0711qZekqUXj3oY5rUwAk9UipxV3hhgWuBMgOmEAWUYCksCkUJrKWQcw44w2ojeKtRiLoAH545hlsR7npG3aWV1WHL1HmQEVjhBBJLsVagkgbEOWYaw3aNxYp7jXCC1NlYsSSIiAnGowx3W0WERUN1N3cQo4LdSj+KYreKs/XJlJWdNVKTA/bFIUQ9UwEpM+aqlCW8ttYRcvUkX0yVhNVVQZrsCmQfiUWohgzGB9wzmuxxNgcXkCA5+zxtAOzshSpI94HgI6CaA0SKF2ldekmpWkpfuo5T2ULnzShw4rhSy8i9cJnkVrzvxjqLWOitLnDpIFyYC9NABCRjtVxIXiDqMhnCAusBDrDV6qDlFqOblLHqKJ4ZmTBZouqwYuMyspCmMPyuFIQdbOClpxAarM7nC2PrA6ZOJj5ngi901qNUEPjecqclRReFOUMeHpJEzJwMaQFDMocYrNwJeoBe01r4IAqfjMLKfWcLs2FWVaW4wCycjYW1FmiTNbWTLAXeTc5FuUMP7jI9CEYXiXm1FgUVQkWlE4YKS9bg4h1lh+8G6Xc8IBb3kDUL6rWh1SEhCmZKygjEBisEvExFoGbmgmJH0iId5dB8xqRaMZiBfs2BuZFaIhRoSS4SqKOqIkKNhR3rKFxB39ByPmobugglAarEATGgRpIwd1krSeesrTimjVgVkAFRy6kxILjBnyDEWoyLOZMlQm0OHtA8iJ/mRWahLNAfkUAuSY0pmJKNEs8mlEJEYCbhizwnmYNG9kxUweBesKC2XJY0Y+5DJ1xuMxpnjEK2STdmP0KJLSODFQTjG0N/XmSEGYGs+QjQ7hSgIIMmLTePgBFBDSGaDVooEzfQv8NGtcArJvNd2WjeeGJnJPcws5h7rKRx4EXPIamRWBB28BlRXZAZiiRxmAiTiOCLMMsRVMZ07CRpMu8IwgMBwKpjk76Di9UCJcFEMksjbRLM3jMAtcxorAZtVmUJgvk3LhBTi3AzJOZMzFORWQqsS2Kq2T21gWEiYlAkpDr9xktHkL2LALpm9qAGCmDMB9kCY4ZjZ45GeHPlJEo5BVhuVNwljAFMC2uXUagJYHVFZl32FbwCsEoA9ISVgdRzykyujNEirDRR7oiPpAwvgn2ViOJJDhkpsEnlUkpy8Wk5qE7v2PGOnWpG2NyyzSeTg0uAl1aKCQSVLm2qCqQzRBnr0VudsNJqkai1SY9to+WkP24ivuRAxFDsyB6iwpVCzSzaWMMrpSaBJEpWJuHBGTErA/FxpCRJ7glREZ3XisUAABAASURBVKTWXrTj4Bi9LQrD5Up61aZsLK1Vb5AgIEwA4QaALJYxF5orjaxjShPuVuvve4/1n/2Ejd+71ebf2/ke399li8M/ucG+O6772mfMHvuAbqP16M+XXXVD+8VvL3vH/ivPu7JxDYI/ExulSaQGM1QpE+WFqKSKCbBV6Rae/+xnllL/DIaO+snPmtlfHv8hN8z8kqc9+fWvfjWg3XLLLW/b6SOn/ua3uP87W9d3H3z9qx90v//nPwZZDUfefxPQo0845eenTL+Afq97bvmERzzUliSw9uqr/U3A/gcXNfOfnHTqm3ba5aVv3uGgb3/74kum/9wQ+O5373uvs/batXbbvnarFz3939Hzd7aLLr/yze/74Ft3+uhFl0x/PeyuG230mY9++BPvetsGeJHyd4L+T5afePqvL718+sEtjG++2WaveO6zYDbr3/GOeExrvzvvT+lCppQ8io9sDT7LiLKwVUvKYM0V87xm+msQuvbqJSqjYho6d2pu5rbmS/+t2/KugLbye7+4+RdnKnHH0s9kWc8dao2SZpmpmBDumcZUEAqRRiJdsecQeJVl70h5iVCXlR3hqJCIT4VUDxdD/O2SKrqyI2Af4Wak5KktxBEbMqeAljIGkQWnqTg+KdqU00NDQXcRYXKmMaK5Bxgm4kAw40w2QzLtpiAEX6ZMrGfrLGZjdACqRnBrMlJLbJ3qmGg6IsoQs1b4fRaiyrUQSnnBPZM5zQeaj5LMQIadYHCYFhBM1kqACw8VQ4wCVASliQzUEdxCBiqRtXrfZ6+pgBCImjTlsGCaQreBgtZnKzWkp4lbz4ZcZ4WtRqsTqQAOeOZuIS0RKHUVJJozRRJ7aoBXBOViIlGhuuSKSN0U8dKbjs6oD3IIClWmvmaVYMbaJoko6sLTRdAME0NmGlEIbMZoyDGFRLVKAVrkChQ0A3KfMIRgiW2UAD2vMj1iysIoDqCBoFnyjGBGRjMzJfaircISE3QwTUKH4qAiCEmEqNmUJiI6oop/XiJApaUMzCOUqw3TSdxRvSgHIjhYJJHMDMnQxGMv3BFXLwThJo1qJhYgzqIgEVMSDa7zhh5XicrEJSFoR0agAEZwNgEgKkKh0ghLEqmwQF4JDtgXJHplQplBE0opRQsMBROTMqSCAo2+iDIn5BejRwKSl56kX1rWmjTjlpHJakTBFAQ6qbRaSBkiV/ivzSPbNKV0XGY8ASQQXMBxkWGSQBB7Jbz5QFKtxMJMEAQMHfTAADBmAKYgX6t5TWN26JoC6gd5JVklOSlTkjSVpHOojLpCGOEsaqIt1Bc5B1QMWsmZ5oQQo9RHYZoUDhKFiBISU2EhY2po1JKNuCm4a9VD0c+gkqVQkay4LR3xTKdWSIlqThEDZlY7rkpYbmEZIBrzC2ySEB+EmsISZMIK8DERTljOfed15iSCBUFWYugkClSDDU1wGamDL4Gmhl07daTKAsos00Sy9oymoMJqtp6kggIBbFcNYc6JVRbRKlWgJye35ADczBlJz8DTZ/aUnYgUTRHiFAgAhCrE2XGgl2t1LY2LZWnjbGydG6P+cQomhNlZL1UZuIiIhYChiY8SIYR/hRWkQOQADdIhIdxAvsYasICsSj3A5NhTW+AoWab54WROkyEzat6WRo4wM+GQIrAr6pgK5JbG1NgJyWNobQ7Jp3J2GZ1PsVNBD0sQAQhM2iNxT6uuIGZn2Abir45NUacmdELwJwZ6ZmKlSThlDB7EBpwKlG4UBhCOsQRpdoU7Zq1cqhRwyoRh4EwicA+RObG5mcO2DIJILQ1WLZyUwAPr4iYOr2SsDDKz6Q9qB3SrCKUio2kbiQNgnZlJYVOCcBoWHj6kj5nYx8ci6yBqkxaSksLFzQ2xyYE5A3wwFyrqCwlDSo2scMKIdG6UVkZRMDYw4amBOGlVW1diVktfO3Flj0pj7RalzpnHQkZgkRnglOEBxtjgK4IDaC0MREJjmsUI9iHIKixSqKveU3RJoNGJrCQgC03KxGcGZbCmdCTMTMk8j74F9jgFxpoFIGkxAttNp/RgQ/hhuWnl4q/+cOExv/zt8b8955Zx7hBsTq4oC1T7CZghCKDTqDoVzpjQuPooxahPKpSZ1lqL5gQ+RKFMFlFnxJpKpVAlklZl7DNgSQljY6Dh7IgmgOyR0AfU5y0NwhlERs/0RghEkmOoh1aX3k2yuaA00OSui46DHRRFshvTPNWkIK6BRMTTPhA1RiyZiwylGxiRoh9TLCNBketspAq2kqmWYEliUuYuZcEJ9YOOQXOP0cVQCvBoakFQUm+mLQmyj5KRlplCpKQC9cB/YAAFFjKSDlmckA6XKS/0s/XvuHC/Te/0jMfc5UOvuceX37fFkbttfuI+635mB+4hH8IV199yzQ9PJSoA6C1ydEYboDdE0iBqxO6Si0LzJJASTo95+IPvc68tsXZV++VZZ11x9TWr7v+BnyKywyte/JLnPBswb7z55h0+tNMpvzkL939nW3vttT76jrff6U53XgXnihtuXnXz3/q8+vobdt33c2bW9/2bXvHSP5x3vjWoXTffZJP/Fpz/0cnN/ahfnPSy7d+27fs/ePzxP125cvoRqTXXWe/lz3vu9w7c/1uf23vfT3zszdtss+3LXlBU/x5KsBf68uFHPvs1rz3ixz+BHADqRc95zmFf+NxTnvC4CkfE8z+4TeDM/dAf/MDd8aCqW7/4xeutM/3c2hMeddvfE9v/KwddeOnV1iSkIuN5RXHaRmuDDXD4IBaG7wi3KSr2m23YsvfsELltMBwmkGWZLdzhDc8mJNih3fLNn/BKQ5UmDHhD182rtJJKkSDAcgrqngQ51kRc8ICr4Jm98WDUKLgYL4frO/yupyl9EnOKjpUX8ca5c0SQaflM4JomOEwryD/JRAyAjWsjJHfFoigUiCj9YsF5LKlGzwhqwTQmzYUaEgVBKmgsTCVJxtTRZuadA0NKF9qh14NjTliFIgebkzIQz4u1GlYJMRMBpQYzwDKhwBlm1ZdlLLS2YK5iyE8twVgJQo1B5kbUguATaQPzyAgoJUOICIVJR4SoGSmj1WidZE9aSQsZ8fSGJ30IH7KNzQKe5K4RmTGoi1IV6Yk1cjC8OR8MyYQM6X2UYcjBfaQYIEgVvEvxbCCjjJBXR9kHVegnkzyYnHgK7iiIaElthmdmZxgC4yO8TH+zcZQwR/AXoOWZ5DLyBW4dmwpxSDQfJ5wJ2WpR5BynCPcYGo1gN0FoV3BDJLhP0NCUmmdEEJElO74jcTeYJWHeQP3cu7CFTrQrOYmIx8YW4jI2GkJH1gkYwnqgrLFo42RWsIKZeCnB6iQ07VVmqDKkNW5eSKqmMompwuS4GJGThQUlS+ng8lgZ1pkphFOMsb+CrRC7t4iBkGrHKE2EwGTf82wmONVggkixUISx84ANZJVU4craMy8k8r53wtKViWaggn5gFAQ5dhK9RO0p+5yXHJDTyEtmpUTtY8h4OWm9ZiTr2BWTfM/nvvq09+yyw75fGWOwHGFXPmgEi3BXCtAyMUPMmpYtnHLepUGTVNV6cfFMfNDMUmEFpMw9NgHLSXoH0xCqapNuEm8KBlWIu9RurGVMI3OYtxoVpl4ghFkScnlp0POiDdYsWuISppXj/LV77feU9+2y33ePqdxVK+pEIAw7IRTe3oGoKOyVG9ibpUMI0jFcsi2zXCBdIJoVmDpgVSatyXBQJiZhKznWaDJ5JCRjUBBTLnNe3koFisIwL6PJYxMKTmrkA+HoO4xEvPvl2Rf++3t2e/J7dj/jT5d4gmfWwsI1Qm3kyR2pT+ms16Gyy6T48y+86uCfnnHFTbckB3y7eGEuVIQoOMM8RytulQ3jFEIoDqMzL5C3qEnnPp1VEJgvmgVMyDRJIUhWoxzYBoCanNJg7UJjKaQpkWVkGerYFDxksVmFcTCRmrHPiedZaFzQ1nOykycTLiWIA3pOCgKFCuMkS9OwjmTilEgmI4FtCyRbudboysilWdeG6qYEsQYXwGJygrd1RqAAwVDGrjbRbIm9hpgFZmrX9TN4wiqekmCPBK9yCa7UTUASoW/W+QINs1yMNszJR82hZ6pOECYIIlb3Gl4bd97JKWed/YL3fup579z9gkuuFykkoNwRHryvMVOarEOoVuPZQN1omiERTM3IDXcSSZAC2OyCNZlAUE2qXES6XAUvCQJgUo9kzKYMhvlGw1zOWgQmKD0QK3imJhKgtJG1RDFowqkA1FOZRamLJeeOxUYa8CmAcAL0mBFjnqc0LBzYEWlqiNIUDL2gH6ixQLKIEFeOwhMt3qINg8RYCI+Y0LQ38Dz2XWgllwgbkL/YHBMCcaVoDaoGnzBjbHxhYnj7MJm19CJ4qjJ0PK/UilN1EZAI3XEoiQqwMzrGyW0Ez7Uyg8rJ4+CZs4T1uRv4jjj8F6e97GP7br37l3bY92tv3vtLP/vt743koB+f+OR37/HiD+93+VXT//I3RgyQZYBNyJIMPEhKHUXmxiOeMidHmVMMnCOleyoiAfmsH2f9vJOVmkNiuJXw3kbBaypzTi9iXEKqlqqqYN2dWnO08Isuu/y7Pzv5+utuhsQzGwUMbFF0CtjmYwAHkykjlTkRcSp0kUk0UaW9l8rCGqaTDDNLQTmCbY2bxiCJDYgnvL2UhGCy9EVqTSVnjlxiZrRctBjdEUYR2keiwBAQsVAyJwURtBOI7niwTBcCfFFmhsVkT14gsEZ1jOohOXVirUp0lCKlzspaa81WX77ioKMSeZWma/mj73PHbZ7iylmgMFJNFS34DguRbF2Mk87ZJD1DLOEByxYWtnrJCwv4mwDQzTfddPxpv1q6/Qd/YBuw047bv/DpT2Pm62+4YYcP7nTcSadFQuJ/F6LN7rLBy17wwlUgvv2971xyxVWr7m/nJ4qpnffZ/7LLp9/MeczDH/6Ae9/r2OOPx9o1Vl/jQfe+J27+/97m43jMCae86E1vecsHPvSbs39rZqXUu99ts13e/96Tv33Qx9/51ofc777LFxYe+eAHvvVVL67d7G8mOCLPPu+Cl+zw9vd99CNXX3st1LT5Pbb88t57fvLdb7vjP+LXXf4Lwi669PITTr/N6jbfbPPn/vsTiRnz77HJxk/596fi5tzzz3/p9ttefPk1Qp1KrzigahJDegPVlBmamdfebDevYKSV+2zCmkbhFEk5eYOTOq/99H/ut9gI0BZ/dHL88RKG01lMfplRPPqWiOgYZWEigWFiyZTmPMfIltHwCbcJkAbH4slwjaqValTg3jZ6INuY6UCCt9wIBa5w8ZbZMuDu4kiToLMSV+MJF/KpMlEhBo3JYcWzOIkzNZKIyt4TYUaCDRbhFDwQOfHYurH1U0IAElLSHAXx2OHY0wpGegRoEWeNRBjAEgeQRsiIibxTbCIyEXRcBaCn+Um90QJKJq9M6dwwO4glBVM6KZVQq3SZWJsB8kqwjCQDVwe1QkQZ6WFjOjgwFp5bXIGMAAAQAElEQVQ45iKQFQKmBSEBdD0j1gimsgFFhk/fOUrvUtzFLZbkpSSSEANZCoHYaIWCS7JC8Etd4SIBoZAjizHokWRxLZEF8iaxqC2gABKsYhYDPRzAzJKhYTwpBYnBghtIVMHiLqggsUBClFLHWiHdiOQWHJi0pFYBAcGZkCKgEJsIKYMUdSLXgM6msEu1o15pUpgmDAb3y4gL1yicpXEGB2O+hWIvAvYYlZyzOSWwOZeY1sYkVcxVMqbBCfvJxDqZulUoWa0oM3VOtUlikyuoaVI0lSiduJlGQNoh5uxGU6ajgBmyEHWCrEYFnQRdcSNvmK0UzAnggpXiAOtMgAbumCxQniu+CzBLKg7oajoPTCNDlz5NTZJkgti1NJ5FdAJmr73x5kuuue7aqWKmBDOubELOhRLYiCiJAj5bwiqFiBJ3pp1l5yEe8FYeOUf2Rs0njwQD3At4gALAaMIMoAdlWABgCnOAl+JUvBZwTVCjZSXSkEjoKoFcIJAIMBZlbLOhaTEvceUNN1567XU33norq9TQGTZAzih3waIBHEiG7AASfFWGU3c9VAUl8BjSTJwoOeF1nomI0BAYDCJqQWPCQ+AexpMeSVRCGaxTNmYHlUzp5KZ4LWYgj1YWAqvumQSZ8MpxvOSaGy655vrFZqY5GXMufRuTp0kWxEj9wViblnHzipVv2furH/vSYe/Z75CRACQT1EGm0gIybCNs2xiWoXA7M2ylm7EnKxE0xoIVAUeInJwHfh86z1gkNwmmUE81JwNTgENOMWIdkar3ZZxpUyiBUWxyR3CeOdNIoHJgdYAG8aGQW0wMjATSTSbiIQ4EizEFdISLBoEnQaWCzAbtJSll1aiViUtKwfqUAOJkIWFRrpoERjMzRrf5QA10EREMg2tLacSc1iUpM5fKvVq1OY2WE7MpIkVL1UnHk9hD0oSNLXAluJQiruHE5swIn60oloYDK7HMW7vsmuvRyJngW2BKQQ2Qji4GMAQbQBRjEM2w6xyVx9LNpQvpGBcMFbxPoxNAgEiCKVfG+woBixBvakXzosxu5EHEltVcPRx3Aha4MnCo1I77TkoftNBQODqso6mY8uTecDxiTnCa2gkrETQKAw4MCCn6GcJqng2UTKCK0JKgQ6vXDrOSDHwAGBFDOj7PsbFDxtZqw9YCWCqT1hC1ybShZcf3IAhsJBwPjAAi0bH32iBWIqouOgqFBqiuHRVZwoDQS4rhYKzNiAmnUnhwwz6qCYZUG8tISRhlLjWqIBYPBdHm8J+f8NGDvnvBlde6++rLFpbNuqrQTrt55Up40+XX3mCLMBYieKQQAxZaENhuqhBZZuLYbXS3AlVgZcmY2PTBoy1ZIaRFCarBHIkLUWEYkUQJLyXQ5eROxbXWvvS1FoZRMLWVK2768IEH7/y1779jn29gnWAFpI1EWMOgzOYKnOoM1rhVSQDlEERCQjjJUDF4AHTIlClCcDYSjo4CSboOMRtjARQgEPbqOGAqDOZkDB5s0hS4oOSgGPElNatKx6VwhybEhaISUJKCbcZEzrGzYRak3Ccv4+yBn0iEiopEIlhNbJJrjpXmqsOMo4/FP118wVa7rDzl9/Tni++wZq0zqpmVpEjBNQOMBO3oMRwu1RIiyVnSuzAcT/UpT3zcYx72kAf/GQYdedxxDqH+5fkfdzPr+4+9++0vfMbTAfKGG29864c/8rlvHT4fRzz+zS2Jl61x2x/7uvqaaz570DfgMLcTWmR+44dHH/mzaWeywV3u+r7t3nTk8SdefOmlWP6Ef/7ndde+DSweb09rZjcs/fL37Zl8e+ZAMseecMor3vKObd/7vrPP+T34WlhYeNyjH73vbp889htfeukzn7b66mswy19A/fX9Xzpv580NN9/yqc99/pmv3vqkk0/KiOXLl79pq60O32/Pf3nEQ2Uy7tsJ5m+cduhxP1u54tZVi9/75jcs9P2qe1XdafvXP/A+98HjpZdd+uHP7EUtS3Dn0psucwR3eBJLhqbN7nHnjX95wEY/2u0ur316kdaJIQlRN0Ok7jpZEO+Z19nuOQAVN6246qAjkGmQJGigRBJxKVOsYYxiWuEkisnnPME9ZGwIKkiyCKJTAaoiKG6nTFRa1oEEAWuw1mJOCWseuI26SDokLAyB26iNmUGVc6ZcOUQoC7mIKYVYjXHBx4VmMnhOZ1yFrVYqBfx3JEqGyWRCQ83WJ61SeYxMDfQGF2tVhsA3tXnvbWY5M5ytKrDhXL8DO0LZGjIDE4KIlzFqQ696koYsEN42xIxG0MXSVRameeqI75o9U88pajLD8diYQjaIDFJIpKecgeGYkxt7YJeSY1KTrtU+u+mkf4GpSnjNgWjOxEpIPIURVjmNJZd4KcplujpF5AUGRxSvWlTqFKgaFoP/jGKoLgy1aVVirSKdaM1OxkLzvlvEkZbWOcuQYoQYS+CVoUgwo2UUmgqsBr4ROamgYCneoNzE3qQ24dETpV1h7rtaFG3aC1cd1JtDtXOL0dRBHyhG2VIRiZsXQyaCRqIjkixBDKNiDohWmBM6VphfT9AkSZ/QB3W1VQUJqZmMRFt56CbJJAH2XARCWsR53FipVbWu927BZt28yErnwc1wYkdzmlAAqHJV0O4BZASkKkK10IJQ7yRQFfqYeKxlkQtM0AI8tqB5EDIVORWSQkoo3iyaRUAIToRqzFog3QYTtp0JrETETFTJqLRRA9sFCiHAr0QQyXRHYEdYRStsiAqp04JpaZQTc5iGJpNcilgtycKBakcoslnzbJnAjTnac51lr7CaomNPQ4GdJwfAkHOQD9DWZHNzoiSQACXMAZMLQWkBo4FgkyKIDAYnUgr6GboVAXjy8Pno48AxaGGBpVUqlaA/wxIq05zbvoIplZm7DJgLOgFAlInBJFeBrAvA9tqW9dZ3VuAMDuHOqYfzeAzRFlsbh0iQOgyS86Qh6jhZKGgRgDGmuRC0biRMVDz6Yey8VXHuKbmQdTwoN2+CsmUiEGSQUCi7sgVng6aaUqvMlbPzsYwDDU1C54MhK2H+tTfesjhv2IVatbGO199y83kXXXXBJde4m0ITxT3NGRbS2JJBU8CG54F6TCBDcskUqHtMHzLGtBBoQ7hhH1Fxqp1AAXkjzpm2VrTJ0ivf7L2v3KvCEgjBgUGMkcG8o4I1LkmdsAZ01cxhaW0U8iKciiJtdLKYEHUmgjnsKUlApaPUptLSpY0IetWtErhvnUZVnqjuQZBNlA6eo/HcO0v0qQokGszcaSfEiQvhKq0H9wOY42lcRZkEypjMixrTIBCCeThJZpXW16GTeRcBfFEWRgYF5HO3gS34zypKcExBCSESAb41njceLJtRg6nkjAJ0cwYEron3CCJCIdyyGFTVugTHYV22Bed+EEXAJ6KaNNFXNTF/zPQx0igVicfd8Ai+iVpAiDqzOlMtHZMUCTgdkA4zMORwAGKjGWlPXUddcUGQlFEtuXG6BEwPXlFqRvFUEqpE2rg5jKEIkFtx05FyDvFoujfYe3U4rvlsbDPPZRQzphkQU0vLAYYJp+9Me+Pi5j6aI6wSEzghV8TUcKg5LEanIWAZRTO7HBYIcYDFkW1mjhRjsG4nSC6mG4VRcDHiRmB70WTMEimVq6i4jreOXz7iJ5F55zusufsbXnbMpz9w7Kff++j7bYbQy5M9ES4nVelnXHpBBgBzifkYBBjQ2pJgTYNa6wwpZ0HrgvQdqA6KkX3oebGneSUIgwJBT5ZRXaBZz3izzhW2STAuswauMgRIaNZRr1LYfLhp5RwEXHfzCl9p/ZBCibxhxNGKWFeoFImOEwVPJ4W7YrUfYmbz6o3C4BFeeOzr0PUNOgvIaiWqAZmgkCSaMwxZIN+ApY1mboEYmOGJUVooNlPvKfpgaLk06pwrUyHqCAF66LpBdEi2FEkYBTF4JM4UDWxuqGrUvnU14FyR1JyiCehqzDDY8eZfn3P+89634tRpl1Lvsp6stgBuV95ws1jAx5hBB5Elg5gucoGDO3OYPkZoxoJcVsFLWhhCUr53+9f3swkCgJx0xj/4f34EzL+0Wdft/O63v/L5z+u6fpgv7vXZz7z1o58896LpFyH+Mue/e2OL089BrVr1ox8f9+vf/2HV/f/x8xennbHXAfunjV0/+/C2r5/V8vXDvo9VUvutX/hcWD/ub3/7/s9OevkOb//WEcfc8nf/Fn5EnH72Oa971/vf/N73nXn22aABFD7pcY/78qd3+9KuH/v3Rz1cFXaE7n9Aa61956jjnv/6bfc+8MD54kpA/KcHPfhLe+z2rte9eo3VV8fj/3RbnA+H/eAHq7A86uGPeMIjHrrqftXnBne84+d3/cR9t9wSj6f+6ozzLruc0kOHKMmKWMRdgw8FTF08FpYt6++xSXen9RD4uNOs2JZrUCVlLgHfW/PxD17lKbf84td+ywpkRZwRZKtNOke5LEBCCH8OHGyjIp/APVMFCYzghM6TlzIRI9FOecadR+I54RPDcE0XYhURVQ5BHOfGILAPQzCBn6t0qp3wDLMAkCMQl62VRuqczNElYh9g1ELYrZUacFJ86IhALMDrJuKiDF4Qehir6xQHPZACI/GmG3UkArwJBZEmo4xigM2M0XIcgcQ6NVAgqAkQYAfkXQJgpWRyQnyfgZEhzSS8zIwXKDXCGjkqqZyCGCPJEkq/Gn2hKqSQCam5QAlEWdBZpUhCAprII40JO5zWzXKhlx7EEBtRI5bS1a7va9XUOhW1SEkIfoWysHAh7okm+oURbWXSFSh1psYBaoFGWudNgLdLfApDbWkZjXNaVyvjm1aGpVAjAxc5EnsIAmmGJTVm7BRVSBCzDbWyUTDE24X0gOhCoLKBLWNpWTw74ipcFdPcoRkQAqRkjdxwT6wMWsMkBwRfFiIRyxwn1MSZAn0jP2iTmgjHPKEM96SJuVY9ajDmBV6YQVnkc6LF8CHIkyldswlZqjv0mRAGCyGxEbQxqR2rPM1gVETQJQgSrHED7eRqoc1rc7YxE4hj4BxqoDL1HKBgToKrFCY4gDu5Mzh3hrhGYAREjEBTITZkm8NCMJaayRoqKAxwaFC6wqW49ljRHICwSglZB/MU91MDrwxPQ3lsVVGOBym0grraWxszMLNoiGQmuIKMjQk6AsVETZg1Ga5XaKBsrZGNnBAN5MDMMD1XMB5haUEhKaWVMrJak2LcJbySJ/lCD2HUkr0mz8rkaqw6LzqaeuOJTnwlkUmDcZDCGsAfwNWI3mVWtKhiPICwaUaJIiBWCIubFoegykQAjCxSwIplBWUUJBJag8oEvwQXIIQgjdPBG5VoRM5CVAqLpihVlZ5CJ6lP8sE6NKYELsqYTKM4qWmfVb2jLJad+8y987jzWmu94WmPf9R9777j8/5tda2QAYF91sN+/qvn7LT3cz+217XXXV/YhQfpHAzBaxEBevM+mzIMgsdaR9W5yCDZZEIkTBpEiCQgofRaayfSxh1fVwAAEABJREFUw6VsYEEtmiPMoTFDQwGvSGePSJr+RQgbzDC5CdhzieQgeLqU1DRuHhaZKlEFUx1WaNBTQMlNASFK+iShaOh1Lxa1EU8O0dIDfWNMniIt0ThgEiwkTFVI0kpEn1ygu2W19sLC0bi1tCUFUWht3EdUMoZDhAcRTSCUTcJBkAjU50UaOAL/TKWUroooJCUG1ObiyXTbFQkIySnaSiImQXgEAxSiIB6FBtiqsCZcDjquhTv2wlYD7pZOWIKJoZFklMkuhBZB6gI3S66pkxURWE5MFiCC5TRpXgcwNgAJMeiZ9p8jOxib1okADoUjZo1wshKEoFaiZuvJNVOIJRgwLeEJAaJBmBI0BOILw8xIBsNFED1UBXvSdI7wic6+UWcFtlRADYkyZKMQMzQGWlS4Eqy0ineMIZZMEiOdp47JAcEx4T3MXMfRm6NrTBmjtBQga5W8ciRlSDi35AGLpeNSS+9SG0hwOKMJtJBsVJt3I6i65OorL79xOgZ9wWMf9tD7bVEqlYQPdslCf9YWKwSLR67sKovZrcwywNaKDbClTAwpKWwCijPFbdUCMIAQYk4NKmqgnYEWfaTEEAqHgF62No2H8SRYdnBJwUDDTLL+sju87smPeeS97v7OFzx1dZ2JwX5yGhAVmgVXZ1gQEUFchVRGzUHTYM9UyTpvXYSmQj8T284294wgmEwlAolEhEjcYA8EPqJhZ2hGAb6ZhUmZmRCya40CIMKiGE7ASHaW0YlhAU0Icg0PgYGyhEgmTN3IXcS72mo1BXhORGNNygCE9I588cwLL3zBR9pVNxDR6k9+2J2/+7H+bnfCfXfNTah5CiWFt+YxwmwN/a7wUoVLA1pNmGaKFGJpnC2G0ub32WTDrV8y/ed6mDzM58eeMP1mOe7/J1ot5cM7bvee7bddfbXVAP/Hx/9sq7e96+vfP6JBRHj+77dZkb8sAvG77feFBsP5S9d/cnPWny55+0c+turv4b76hc9/zEMf/MOf/vz886ZNzhMf9/hN73Lb7738J6v/1+7FYdzvSweed/55n9xn38uuuvp/Hf7vPF9w6RXv2GWP173z3aeccQbWlVof+6hHHbTnrvvtvNMjH/zAUjp0/qPaWeect9V7Przjhz58zh8nxtdff4OPvPPtX9l9l0c86P4wj38Ulv8azkln/Oqiiy/CnNnCwlu32ZqYcf/Xbf07rvP8p00/ALZi5Yqz/nB28tjIcS4Uy0i6lCRuGg4HgWMyMpCSwnGDtekU/ByOiXwkU8kxW2OtdV78JAC3P1668vTfTaiyGvdjV0f4lWKEIhmu7ukuhrSB6FBIEVircC3h4sHG3HptWsboxugN8QTOiwqqt7ow9n1OVOAAoHXcZpq1E+0lmQOQ8IqzQ1rOCZUwPNDT2MfKWXsWwRQuLIBV2FTMKYGuMy5IHiUToaClOJgr/UALOfSI0ESDLUTg9AEiCI3MKQIQjcRGEukUI3ogWIQYRbzskgqzoRFhLmiZhtERDcx7yoAuEoivDEbz1tzdXJsjVFH2JrWluhCiJc+YFYWqq6UqZ6cM+oUkfchxTuNAOOYirVUVEMFN+BieTCpSi4oqU5Ab2cQX8xSboQAJqBRws6ZVGhXlGOgPxrFdGwGS5oUXGYuck9VIzNmN3IkymJIQ4cqMtAMeRE4zNpGcYiMjjKLoaoWAt7OijvPfOi2n9EBE5EaEtDMlKlR72on0vRDqPBABGJIGEWYqqKSGHODhbASWSZUAHTAszDLckdHb2GJsOSakGdBFEBmpKayhiQysI89G71qgrzCMAGerE5BwwGhhLXBunLk8fIFoJqQoUTwA2xOqE0UPKt8FN576UOoFtEXkCfKGsbNBafCiQ4c3YgXiYXOKedTGOB/1RmNzIwL50FtRJmEnrI0SqZOVAngHmomJeooqqBDIuM7HCtPwxFwJA2BJFlYpHgCZY0SbVmNVlZwW09LFpJKQI3GTdGVyrk2rp8VoMIwcMucUYxlIB2hLiQCRgVFcgroJCSsKE23BoJzHpoBTSWojHkFNG8MGJ4M1oU+XSmwyMhho5cA+vNhEDsMwJr4SFHKXuUyQcj0pLOm2CyPQDy8m3uAojFs0yEe1qIYsWhp5CwtjMx1dPeoI6+ihESnGYiYc2Uku9IFTZWUWAZSSMGqqRJKwFZLeuTeunMmDdQPNRq3ZlVSSwGrqC1UhjWQQFrKKNEkuzupWGd8yKxtTMFFiHASwapScatPnP/6R++34ysff7x4w8OKlRs/I/4SpmEmSJPAzzqJUiogUhQbFC6dgnIFYU8Sd3cmFGWfOhQRYGoFghS1KQUefgGuw8CUaGOWvRjDUMA4+RIw6ASPmUMmOrSewLhFgACQXKR31DGUJNyUHVO2DphKM05k9yS2TmDjBVYMkGqdlzdbJvKdBqQlsAfYkmUARqslcSBZqXZjNtCwEaxIZ1AN9dCqFksKzuY8sxkyIWn2JWpywy4PqMBC06kpYrBAXlj5LJU4OlFWtRlQRLgIlmdbGC0kdc4LGVeuI8FRqQteiRajWrB2VjqFrUCMGGpIF7FIvPCNTgQUnk5aY5J8xwUoyGFhCKkRFeUktklEtq5OmKJMkO0lwakjv2AgIOZil9CC2EUEIAZMGkdFnbLxgseBUvW80mTClUUtaZJ1zl8QqIlQEhu0NQmbsD6IjEqKYlFcgjUBQMoNFkAispiMWNNhBr17wXqY6Iev11RDJMtPcIOxK0lOVmAgmZZYKSVSmAqNImgI4mDCmRjE5CPRoyZ6eCUdPBOtQUsqlxJMSFCAceIvWWXSlZbp5tYAnSyxZAvaTiPKu7dbxtoPsze66HmsTMtAik/ygHFp1gaAQHgj4RnCK1ONiHGM3eNe8JDOEz1B+wPKInTtmBXpJIi80dK3xaDBScJAVLOCJG2jEdIvWSESqShUXcoNIkpJrQ+DTZz3yYQe8ZesnPuJBVIKZJFmIKpcKm2FYNmIXrIpTeHIE8M0BRcnEJcRZE4ww5EGQdGYQsIJOBYRArGa8vqU0k2Z1ZB1FLUWJusJQDoPQMbTB16TkKtRIhyoWbGPiEzaQltqyG1QhhQC/2ZhGBlSHRQk6lJM8IsFmG9nmHIvKIw9nXnLJ8z4S199ChF3KQ++0+5u7TdeZbzz9NxHDuZfFfKgcQoZ1TskpxMJElXiJcRcemCOZBikGm9TExY3f/KLnPejBDwNMtIO/+z3IHDf/Q62UutXzn/3lvT99r3veCyiuvubqj316zze+/yOXX3UNHv+77QkPf8gaa671l1W/+u1Zhxzxf/ifK0/5zVmve9vbbrplkuGzn/Lvb3jpi1bO5184+BAA0X7ZG178XNz8t9r3jznu4kunX3R59EMfco9Npr8u9d9avmryTbeu+MQXvva8bV7/g6OOvPXWW5n5Plve64A9dj/wUx971EMe3NW6ato/5HPFysWdD/jyc1+7zY9/chzOJoHrBc94xuFf2PdVz3v26qst/4eguJ1ADvrO4atmPuWJT3rIf/J7Qfe4+927roehXnjFZRkGt4CNI3ikMnU1y2wsM+t76hVnZVomA6cMzIcZY35YDBYjeyux2hNue2Nz03d/Qniz0cMxWCmIx2RboqQUQ7yNCQuFwK8T6a10ikBJlYQJUF2oIc6JqwhTh3nMBF8mGjgGdupD1ROpB+Ga8CI4UYxR0kg+CHZaRpifGq4UJUgZkUNJEQpRj8E7kzkzAkSbq7E4YnlgY1QQIpwWhUbE0aZqoY6QnCrMWuqsIDxZigVRIgzQdHkwDpfE4XbKPQOLWOuSK1YFlZxoQMxDUIviI6t1lZA/IA2si3RECzWg8mQX8UrJFI7CksbMRKhCqONgRqBhjJCA60bm6SzJKinggEflMaejcxucA1tMLZaYRRZ1oGrEHKkItepZHCN4pBSkgbEjw6CJjlbaVCWQJYNDIgK+Lr06xgV0ceITqvQMIFGsp2xonXLV0hdhSBhUBGUqEULsJDhWkK4CUcQsvbSMkXKePmIaYU0nvqDZh2trPiDRFOfaSEdiEJZYLJokaASxQ8nO46CD0WAcnhpRJLiSs+IIxQB2NGGpVGcpnRM4poCsRIiEkTZsNiUP2DFoJCXuA8oFC8wd0l1BQgagRC4lGAaYnEgoUss0KyCSccg299EM8snCyR3geShP5GVSJOGLM4QpeyXYuAg7ZoLG8CpUuKp13GAXNbLQdCUW9JpdLwSKksMsh2Ag8RQkQ5jD9DAJzSNtyMWgNlWtFJApcAMpnNJhNZ4MBTK+BQtrQkZwkKCwlm5kgCrkXXiPRGopuLhICuQGvVpNXuZSIijNSljhgYHa0haTGpWmPCighxAxiIeNFwqgMRkXSxtqJuQoGOKaIbBNce/YaiGQQ0sXg+Dprak5jzaMPgAXpAXGkgzmhfDgxI10KCUFyzBf+yYoDTobCFxaBKkKbAMcElUS1FSYlTg2cBNroKQHlwWCJ3KiEZoq1ClMkxP0eKEELRxgvesAgAWPaCraieKIoBqqCE5YroiDNHNjbp2SllTxLOGFByaHzkmEUxGaBBBWNawUh1jQycbqCGRaqKikToEkK6B0pH1m8VY5qyg4hYU54CVDmZnJsNnaFRCoDJkXston97BMIaNJFCac2YktE1rgWmH4ws4Y5GQWKAZzVYQ7JgVg4iYKqUapmE2iIeJoSiAfK5Qgm1ASIoQNjcLUSfTVi3IVVum4Kgox4p4IBJMIcQYEHt6MomWa45uGVR96cM3Aw9JBCeKjZEtJWpI8ZSUczCzJhbULqRY06ZUBJyFgimSCjCEaKfiHJ1p1MUlhEUmpXjuYIRtLIwFJHAhJ5BEUJEaAgQ8oP0WYwTRXnixYKIPVuULgVGAFGqUDbeEgAtMR2BILwKsW8mmtaIJU72QSEIYsdMSbWohN4UptpHGl0RhChfoCsXgGgYom5OqhjligUjDiAmohJLDIMpBMG3ETTEkuVDWZjbxBEgW3VQiGoarChMgtI8D1IXUyjdHN0mDHvYmOGuxj10JHiJoJwDhBPtSF/SsXJ4BlosxSGJGGaoyFYHgBAVYO5SalFRmV5prWUfTsSs1p8scMpAGaC/bFHsIAkgGLgOAgc8LVK2QL44MOXASZ29C5qnGUJE+eRwZMpBlyE840Os5CpAVh14CQE1OIEv/IRalAQ4WzG9FaFw1yIrzLlIRYKdKFQOnAYlnFZz11PUFhbWSa19rAIzFIBhuF50lNhZSSCbCZhCWjcsI7lUjSw7K5DjQxhgA3M8FGJwlO2DJWRgHN1aXWpAVrPRnERRCiIBhIkmZVmWURr+TsoFR5ZIZyZEy10DRh2JbXzrnExKJD/4N1w9jl2GGJBojxpIC5tEJQpbIxG6oCz7TMMclCyEi9xMUrLnj5LnbNjcS8+lMffqfdt6/rrYHIttqa09uJdA93JvAIBw1RZlWG1mLs1Ko6oVoqYYroQYNQVNFCBNxOq/Vr7v6OHdZf+vuzvz777ACvOfsAABAASURBVM8f8m2HCdP/1MUiD73PvQ7bf683bv3aWd+7+89POvEZW7/hWz86ehzbfwvrOmutufM73tr3/apVEfHpz33uxDN+HZmrev76c8Xi/Mvf/eEb3vW+66+/npmf/IR/ed+b3zjru28fccxlS38e97lPftIWd51+5fqvV/3X97esWHngN7+dmTi+2eqFL0DE+K/n/8fR+di+f9zPnrn1G7900JdWrFwpIhtvtPGuH3z/kQd94UkPf0gPA/+Pa/7WHjP/8QknPfVVW++z3+dWLi4C1323vOchn/vsru9/14YbbACZ/K2A/5Z1vz7n3J+dcAJWrnOHO7zl1S9XvS2moOevm4gQTx2BEMAIC9otFkYCppycp5OABxZNNEniIEawRKxviEdFY8nE4UrhZVz+wE37e0z6veXnZw7wlsKsUXgsMcLngYOZ0IFQUBohRDCuyfskAJiEUyI4wzNSTDtfoET+ksnfOIEoaTRLQxLxVHiYzesUAyLVPMYYHYHQRk9HGEvlrCw1UBYII1BKEx7IxzTnsenYxIAKA8UxHTstwlltAUXwzWSeMDhNWImiq6xCHmyRbA7QroiViYzKmAseQD0jHqQ29oY9SO/aCQN08VBPRpyhRPgghDstIgg53FgRj3t2JbBBAzJiSZbG1NKsRbZoo3LD0aFWSAEnWoy+bCQyi24hdCFtZlNNpkvMj+LOELGysGXOjRdHadj0tRl5YUIu4jmGiClIggKFaRIBp0OPJMmVRWnqopKoHUKjZTqBQZlRLUIECcpgOQq5BKKbQNA8A0bBrDKEOCW0zlyJEdwVRRR3NQkRBI+CkD2SGWQ5RUwiLmyKHaK2IIswT2nRtSwOImsKMjIBD+RHRAzqeBoCnyBFmXvhIoI4K7lki+GZ4E1ZZ0VVVGHESckTk2wQADEVgdxRc2jRvoK8qIQD3b4fkUeEakaBFQYgsTlNqnMsyb6y9hnMyNU2ao4pAc9QIuDLoUgDLgUuSHJSd3EUu0DFUihruoSDjiTJxCx2LqbknAnaiBJyJdRVHWkpUlJgi2rISdZlq0tG72mukgRw6hgnVO06gj920IAWmTG3G29a8dPf/P6T3zr2E18/6sifn37llddMaajrUsUYCBk4SQhPQuMwjr8+74KvHHnsbt84/POHHfOrM88dboXns/addBOtRHnp5dfv8tUjdz/kqKuvu1miXHbNTd/66S8+cfDhO3/r8OPO+s2NKxc5C1RPYC9cOBUPMK2ky6+58XvHn/aJbxy+69d/cMqvfzu2G0tnoBMNYiIGEzB1Sw4CZUErFsdfXXD+/kcct/vXDt/nkCN+fvpZNyzeRDoxXfEJE8hiwYsRc0rLCVUm+IdKIeMiyYvD/NcXXvjlH/1kt0O+/5lvH/mzU357/Y23EBuMqTpCCSkU6Aa9cgZRxEg+GUy12nGFRYE0UgUmO+fiS75w9M8+cfAPv/C9n571u8vmi26Yi6oDwbDrgmGi8bkfHPvxQ444/KQzudAVt1y327d/uNvXDzvh7PMmKET7H/WTTx78wwO/f8Ktc7KsQdAxE6OempFVAf3EGtoZfBnHuKyTXTjsAIbllPCRNsD+iFSkq5UVkaTYAMVlJ9lpJCcpcMGRusQGOKSn0kG3Mq1tHBZlEhCATXOZgsmJW4hHOhNV1dpJrcRLRihEQCXMCUoymkLoRUhLSC3Te/El2wXhy5I6DzZjh/whXB0Dccna6G2wgCeTkY4sDdiUksWzhCuBIJJeS2WQPaHBVhxziIXBCaapgPwuAL4RSmS0JJnCR9+laKZAhoSrioTHny6/+qtH/exT3/jR7t/44Wln//6W+UrMYNMYijuR49glcsjpAGRF++N5lx1y5Mm7fP2IXQ/90dG//M3l191CWYVVJ1oCXhnJkRBQrhzmv/rT+Z8/4qef/NoRe33rmF/85pyb5ouQESVKyLGjpiVdeMySNIUOnlV4wKSqaMyTzMf5cOZ5F33x2B9/4juH7/297//i17+6YbhB4NI02aqnuBZRVUl2y7Fdf/UtPz75rN0OOeqT3zziG8eecO4FV8xvaW5BGZqkzEwdZXfBxdftcshRe3zzh9fedNM4Dmedf9EXj/r5rt88Yt9vHfWbs85feesc06FdKOPSq2/4/i9Ox9Cu3zjqx6f+7qrrbuaoaB4laqeF8RoSJjidAlS/6trrjjrh1F2//v3dvnnEcWf85spbrqOcC88Lj5wQc0ZAhTARmEkEB1NmyGE/OW2Pbx355WOmAoOIvvmT03b7+lGf/NoRv/jtH5hGZZvkigGicAozioBB5qgxVja59saVx5159u7fPWaXQ370w5+ddsUl1+uiUCsAn2bu9sOTT9nzW4ftdfAPL7n8Wu5KLHSjrLIzP/vSK3Y99MhdDjnyqFPPSgdhqxoRrCMZQkX8zGKjjp8/+njEn+/+5MRRkPuEnM+/+JpPHHTYnt86/KabrlVql1x+1cFHH//Jbx2+6zd/eNqv/9BuXoShkjixEwCRQQJhaWPHhu0nm/hcR8R+ygKtd8y9UMdZJWph7txq82jpo2fCV0epRprBFCQjHIZSeOwEAsqER0tNKBcCxTSUDDVII4nh3qTwQiO3SIEDUpFpg6E8Xn/TJdt+crx4+sNWqz/l4evv/qay/jJRJip1jdtOwW+65vo0YWSkQM2AuBGAo94IlBGsSuB0yBxNMpky0EMSzNmxls02vesLnj79jA309Ym9P/OtHxyBG/qfu5gXli1/1zavPGDXXe5593sAz6233PTBXXd768c+cdFlV+Lx9rd/edQjdv/g+ze6y1SAYtWtK1du9/4PvesTu33n6B+fdc4fL7jsykuvuuaPF178vWN+8uod37nznp9euTj9MsZTn/jEj+64/fJls8X5+OVvfRsL11xzrRc/46miivvb34496bSLLpv+D5DHPuLh99li89u/EDOhvF+f88c3ffCj79h5lyuvnICst976275umx99+YAXPu0f8D+iAMVft8uvvvadO39yq7e/67w//Qn9d95gg/dsv9039/3MIx/8AJmsEX3/91oz+9QBXxrbCJTP+fcnb7LRXXDzv22ttYRzwNBLAZ1oGeLj5GvmSDqNYNvkifIymo2on5lLdOwVHodVtcBNkxOGruvOlt17euVlV9+w4vcXBFkKgp0hmmRMyD3MAzATICPgI5kZzXOcI7lW9zIFhSFtmOKE4UkIpGkhWWCU5Ki6M6wMVi17R3Ygae5zt4EonbEPkXnmKB7Vo6CPyEppynB4wulGF0iBjCTWnFsoDdMIZ/EpiWgQE4yzE+lTZwgXko25UWmjzFsOWGcu4UrBZEJzJWNh025kXeQYQStMLnuIIkjwj5kJ2zoeC1mVVPGctVSwWbTNhGeMIxnwAs6ngMLO3IpmV6J0LWnlyK1peNXkbhoZQAsh2hKRVpaqTAVkK1JfJCtLR9zjOxiRZIGEqQUbpC0qXWrfagUOotRw9mk8gxGuCvigLmvPdcbaB2rkbh46rxHFiJNYiHrlniF3Ls6JMBy8CLmRTOMUDk5Bm2rXxUIfXcdYhLXBUFQDZdRzVFgC5XRoz1SNq6dOViBpmgNhEDncOFompUhoQlQuLZvRCESkSkiuXfWucCdUBMjrGCCXyQlPBWgrpWYq0EcAUCzBTXYV5MGh01Yk05FQehO1MriOxHNicECTDYDbFI4a2TXOISwtqTEN1DCpEgTFGOzFtRukOEEAUT2UJJmUufOsjRWMVM6euWThiXJRpsJWyYiIoyQpFjciyiQbsCsNG1pxOBfVVPBRqAmyShcqsLriWVwq06zKrEIZQDnXOggnYJC5H3XGWS/ZZd+37P/1g398wjeOOem9nz/0VR/97Nd+8AsGp2XGfUlYXQeCog7+u/Mue+0eX3rlp/bf89Cjvn7sCZ/94U9eu+cXtt/zc+decGEFV71AY8yKKuegY0/62k9OueT6Wz9/7C+ev/NeO33z8IN+/POvH/vz7T/31Zd+cu/fnnd+YUhbRQrmi+iY7VtHn/D8D3z6fV8+9KCf/OKg4058w55ff9lO+x3/699FLtEK1rGNNiXtEqcAJU8774LX7fOlV+1x4N6HH3Xwj3/+1aNO2OGz33z+B/f/+jEnLtoYlIUQD0Yy6ApSrHjJUIJlpBwSmvFBfnLm2a/ac79X7Pr53Q89+mvHnvTFo0/aYd9vv+gDn/v6UT+NuEUzYMl1UHEloVTYJU49FhvPx4URFhlLdEGOV153/Qe+8I0X7rzvHt875qs/Pumz3zvmdZ/c/137fP2Sa66H4CBrTigjoarDTz7taz/+xS9+94cQuuK6a7953C8OPvrnJ//uto3K4Sf/6ms/PuGHJ5w+XxkUrBzKzkEEUzKebggqY/OaEDR1kwjBUjdgO1dYwtgaB16cmSUjjEVlAtU0tBjnSa49CWIcM8GlQYEY1ZG1sUeOCqcpkLVnBrA0cwSsnIH1kvCuwbONlmbiQl5oLNQKpZSSOgtihGSWLJU6oprBCSCEXDJfSYnsvjJ5wGrUY2RDtXGWrYBgkTnnSB5BOdYcFmJe00A1sTBNV8dStZQKuomS0mLIXGRCUTx0ZZG7TBhE9FAWBEy8pOwl1YuzCqxrgkKEImTnrxz63Pfv8YmDjzro6JO+dsyJb9jji2/41P7nXXKlZ2+EaJlkFjmMYaec9ccd9/jyqz+2/ye/8YODjvn5l449YccDv/OCj+37me8dZdPWIrnNc5jLiMThvzrvwjftc+Cr9vjip7/z44OOPvnAH52wwz4Hv/gj+3zjpyeOoyEKMbjzdCYINOCOfbKI8KzqrCvSWZ75x4vftNvXXr7Lgbt974iv//TnBx1z/Dv2++YrP7DvwT86vrW5eaZjzwWJeFYbcjzoxye9aOfP7LD/IQcedQI8ZffvHv2qT+7/jn2/csGFl9I4b8M4LibIJOouveGmg3584iE/PeH3F170oQO/8ZJd9tvzsOMOPu7UL/3ohG12/eJ2ex546dWXx3zFgYce87IPffaDX/7ewced+I1jT3rrvt984Uf3PPTEkwOhQ1XEVAdmo0I32bj7oUc8/4OffscBhx501MlfO/qkd3z+0Bft9LkvHXH84tgsPZiJWXopOKbqua/Sc2oGhf/0jDO/ftzPf/Fnaz/2jLO/euwJBx174m/+dAmC2io1rfr0JHII2ZEfhQPS/uaPf/ryXT69wwFfP/CY4w867ucf+Or3XrzzPnt/94hbbh0CQT9S1TZab40fnHjaN446cZ9DjoYRiSpL2pg3rRg/9c0fQYmHn3zGJhtsIFTMytDShkB67ZVqpSgOQPMhfnDCb7529C9OOOMs2PlosrLJxVddByK/cdyJ11513YHf/fGLPvCZj33jyK8dh/h20hs/+7VX7/L5c86/jEYk3OKVuEQN79KJxyQmL5PztPDG8GIZiCMZtlycuXVpyPEs7kQRCDaaCuo4hZMIq8Utw4zFWL1UK31CnL70ii7EWBWeAAAQAElEQVQrkWQo1qIRUWHStKAxMylUi1IpJtqu/fRXV5wy/Wp1BfefeWu33lpLklG2OrvHprR0lQuvnGi2oqGR0gAjnZFlOJPgqVWsy6ysrtKAhhupQLealZL5eU976h3WWguQhmH44Kd2O/RHRwXW4fl/rIno4x/xsEM+++nXvPQlXa0RcezPjn/l299zyBHHesTtR/svj3zYF3b56POe/vTlq02/An7rihWHH3XUez6+8wveuO0L37T9i960/QvfuN27PvbxM3/3O8BcbbXV3vCarT7+jh1WW76Ax+8f95MrrrwSN496xCPvscnGuLn9zdyP/PGP0w1LXvOi2/5QMu5vT7t15Xy/gw/desd3nnDSiWlNav/Mpz71O/vt/fatXrHWGmvcHgi3fw4M4cjjT3jeNm845LDD2zhi4VOe9KRD9tnz9S95/v/ln/UC6lXt8GN+cuKJP8f9+nfacOsXvwABB/f/23bun85vbRSRze68IbuyFNRTiFDjFFsyY1CaK+p6aghd8yZDFCfhdEQIloA3ssDKRTNJqH/qbT/9tXjqOZaZTMFlckiMERw4ByEUX84ayAuNzCffN7ioCZxpWkCFWJITgQcRUZhBkIAClSmZkveZ09bAhEPCJNDbuiB25MsueOa1NA0YeG1Sx44MASzhgJLKqUFqRDkFDs6CiDF9siqhhyZykbA7ysrMOA3halQQMHzIsbFNnW1JLkFOgKvKUiRIwsnBLymT8ITFm1AqFXIOA8KCUDBLVJqpzolJJWTpTUImUVDJrJalofDlXgQnXgucDI/1dEAH4mQO0aY6FAomRhTLxImJdzrVMF3hWlVRxDMlOxeXkqQUwiHgDvoRRMMK+ogyM4LwnTgJ8lT0YB6V5EIpTqjdkZRBAFgpPJGmwRoFSFSUmQICzoCcA2/xOTOxYzQAFFZN1ajqqKulYaChKHJedHWs1GRJ0IcpLNyoDNmB7wgIgDmyS59xFhaSQqROFBxkpo0iS5CARkUS6bz2UB9Y5Qg2yzQXTgJlxJHkgWIsIiH7DMqYkIppMa2mghmClGHOwZDvABI93SJt6shKoa4Jq8qS7pRNAT4dqreswrXUUnptHXJOT9Gz9ZqiElqjduBB1CJGGgMrjYtDYEzMsL/KiU/cOkHZ3DoGi0RMXsSZgKaMY4kUxqVSg/vMHtLoKlXB3EwRLkWnEnKCQey4JVyXXHXNJw7+0U0rVj7snnd7wgPv/U9b3K0r5YZbV37ue8d9++gToVou0A4YNmzSfvX7C7f97Dd+ff4ly/ruoVvc7XEPuM8DNrtrVTnzgkvfts+XL7j0quTqSKVdIRUAHyz2PuyofX9w3B1WX/6Y+9/zsfffcrMN7shMF1193Ue+duiNt94EsqLMYLmk5ZvHnvKJQ35008r5rKsPucemj3vAlg+5+yZX33jrh75y2BXX3wRoaNK4HzvVXmp/5qWX7bj/1399wWUg8J+22PRxD7rnI+612eqz7qobbt714B8d9IMfNzNotLr1MRmRdEU6FnGxFh4t6IiTT3/ngd/43SVXgJ2HbLHp4x54r4fec7M1F/prb751z0OPOeT4U6gk4V8QU4kCB0e0MUbsITO2JNgIiJrapw7+4fdPPeseG673mAdu+ej7bbHhOmtF5om/O2+nA797000rMZEabNEZXj9Np0xqRsu6hUfde4vH3m/Lze+83lI3QQuPf8A9H7rl3VQBPsmnmUFkkiYR6e6JiDomNVinKyZAl1JTSxZlkiVUmfAygl0ySxESnmy2ESSvhfuZdsqYGKgpiYEgIzhciUrCk+EJiEo8d24Cs2MR4hQPACFQQM0DbSQfqQ1LIJhKCdAAptIlWZMUms1kT5BtDTsUz2mtAxTh1ghRgGImtYO5VE2QRmBY2FWANDKn+GkZHEyE0cxIWnWJZxkRAKICG1mhkTObayScnxH3In0gmjOkxlOf0NL18a9+75s/O23DO671mPvd63H3v9dd110b3WdfdMXHvnbYrSunH2QQ1ZAcJU7+wx/e/flvnnruxbWUB2y20WMecO+H3evu66y2cOOKxS8c8bPPf/8IENDSEL2gkD+cd8k79v3abXZ4j00f/4AtH3mvzVfZ4ae+feSBx/48wHRIkHPQpCK4OZx8kjnsuMxKveDqy3fc9+unnXtxEX7wPe76hAdt+aj7bL7KDvf69tEHfOdoH5tCMBBG+K1t8dPfOWKv7x0DK11vzdUedZ+7P+5BW953kw0j85fnXrzjZw8667yLR/PBsMjHbIH4RrTY/EMHfudHp579gLtt9NgH3vuf73PPdddcLYlOP/+S933x2x/50qGf/9FPF/ru0fe9x+MeuOUDN9tYmG9cOf/0d484608XEOj3FjaG+82LsdOXDv/iUb+4ZXF+9zuv95j7bfno+2657hrL8fjZw36677eOsxQPgfZZVGAYmTVJMktCUe3+d7vrE+635YPudltFB0SPv/8WT7j/lpvcaX2HIRB0TasumGOywKinyjjbvj86es/vHQWWN7rj2v98/y0f+4B73WWdNYdmX/vJKR/52ndXjmGqVuUBd7/rC//5IcJ88u//dNKvztGE1D0zf3TSGWecdzEgv/HpT7j7hhsV7iixBQi3Qbhhq+vVxxjbyO63WQsmI7eSwUE48LAkw92+c+Tnjzx+g7XXfPwD74V2t/XXAcXnXn71Rw76wY03jwNXpFlTuAP1laTzscaYUkbp51SHSJxbNkuYNfnIo4WTR2exINQxc6gQwm0U5Bz1FGrMUmiEVziVkcgzKg1TRC9IGx1ThYwwKYkwv8Doh0rWKVHIKDJW8j7jlu/97NavHQMWdK3V7nzQ+2TN5WkUc/fBudG4yfoYQlv8zfliqQnfZQCM5AzkPGISxIU+adZkZtqTz6QtwN+9J6lwwsY097zTuhs+6+nPImYiWrG4+J5PfPJr3/tB4uF/uK2z9tof2v5NB356j003nqzqyssv2Wm33Xb4+O433DT9uYbbiXzjDe/84e3fdNAen3rAfaY/aLtqVUbcfOP11157zeLKFegppT7qkY/eb5edt3/Z87vaoaeZH37sj2Fey5av9opnP0MVgkf37W1XXnPdr86eNj/33OKe97vHZrd3WdLPT//Ny9/+3k/vt/+tS3+Z9x6b3/2gPT655wfetclGeC80yf/2grod86678eYdP/qJN737vRdfOv3x5fXueMdPf3SnvT/8vk3vujEhidwOCP/wKVdcfc0e+x/QWmOWbV72kg03uC13/kdE5v7Tk05F/7KFZZttsrmhEoBzKaMWi67PqiJIeCFpkRRIQknmNDQ2OAhTI3ivp0+OB7Em1+V320gWegC85Q8XwK+JYAYdCT4VnUyFavWF3mEeod5wUiFeJbsUoRKIM6VDSqlU0pHCUCsJdUHiWc2QzdQpJswxI1+WMfNSBbFKhXU26MzrLDpUuYw0glWRqhmFggm0zuDunJQlApg0iFWkFgwJRc/ecQqRkY5w8FR2URKUwzxLxl1K9VqHinxqGSw4945KhFljKdlxrVaRUsW4uotjTptAK5MQ4Y5xVwtXwtKKWB8tyZ2zSSQhHik1pUF5XniOSDeZTvEgRwwMCzIOEzFEmiyMoNQQnHLuCEg4HJkx9UQIa8qZTE6UlXKBfUGGTucKohC4GtUEodAmxiEFAVqGaBL4M9k908ZE1UWgZOwJxsA1GTJHnC+LIw1jMesZyhFq4IEb5cARk4aYIWuCfCFXokgbqrVuDJrTOOQ42LQ6rK+hNZHGGWIBXg6YRNaayAVSiUplhUqhUCgjO8oZURUW8OVZLPsxkA403IkbtsDKpEQqjWiMxq3V1vrmABtByB40snhXZCZUmVmIRE0lelaWvnG1kgkKeT7yEAx5apkLrSQZkjJDM6uiW+bcjSCJW9eN/cx7wYODUeYE+MowlpJUgyH4SgEryhgjJz9xonRmA0BiVsImgwXyjkLELISLmaqWAtaZsBhUQxbLSvSllWmhMzmDUOM05sAKDzBGkkSJJeigW+bjhuusuf/btvrc216513Yv/exbt/rcO167wdproAg78Ec/ueyqa5Q8DAoeb7jl5l2+/YNrb15x9zuvu/dbX73n27bea9uXff5tr/7U656/xrLZNTfdusc3ftCienKwE0/ogODXf7rk+Y99yIHv33q3N7/kU9u8aL+3bPXcRzwA/edecfXpvz8Pk3Ce4cznX3rNgUccbx4oHz+7w8v3fsvLdn/9S/d502v22f41m6y79giBEIFilk6JQL97HHTEz66/deU6qy/f6y0v2/ttL9truxd+ZseXf+Gd22yy3jog/uBjT7r00msgPC7TPw0IojnBqlcqMnrxm1be8qlvHr5iPm5x5/UO2HHrPbd95Sff+LLdt9v6M9ttjcLLI755/GlXDysdkmbCp7OAKyGogjW5a7U3LWCApuvmFSvf+twn7/OOrXZ5/Qs//foXHfzON7zg0Q/GwJkXXPb1o09uS1XtyIETA3Suakyy2QYb7r71K/Z84yuf8cgH0tL1npc8Y883v/Sdr3z6stW7BhMLmYfOhcfqgzbsTwzKTAI3xrDUTPAUDNEwqRAJjKj6qGGwEGbi4row6gwtGecYs8KzItT1o/Zz0xhTBq+BJlx6q3gzKnD3fpGWeVmgvueFjkshJ3EVRvXVk5DRONLKBnsjUDSJgANqqVx0VJ0XGmYIA0WlVlJVrkVKoT64B5UaBCcYpTi8tBW4dS9dsgAuw9kqtJXJ42jDMEKPsFPAHmkY3WjpKup9tVmxBR5U5oxgltScxgFbIUQw75LY0KPgK4ErJwqx9I+XX/Paf3/sF966zZ6ve8ne277yKx/Y9gn3vzv6zzj/knMuvxTH/zKj7HjFfPjYVw+/eeV8o3XW/Mx2L9trx1fu/KYXf2rbV+61/avutv4dMP+7J/zyquuvz1nJIhZ84DEn3GaH27/i09u+bPc3vHCf7V7xxbe/dpP1Jzv82k9PuvTGG3jG2ZGwz4IEZ2mWc4sGT28JWr9yzC+uufnWtVZb9snXTza85/Yv3uetrzzwXVtvcZf1YYff+ulpl1x2FTybkyzz6JPO+tbxp6P/ife7xwE7vm7XN77yE294yT7vePXOWz9vtVl3+Q237HTQ9wbopbTIwcYxpnhGnrhot9e9dM8dt9l1u1d9+i2v+eJ7t33w3TcGO2f+6ZIfnvbbh21+l6+857V7b//y3d/48j3fvPV7X/JMDN20cv6Nn54MwbdGQ8vFlt8+5oSjTz+7K/rmpz9+P8B5wyv32O6VX3j3tv98n7tb5CE/OfXc8y6jpjnOYux9LjlSQm8JYFSI3/CUf/n0a1+87dMePz0TvfnZj9/zjS/c880v/LdH3J+ksC/8JS6RBk9hfSbS/+zX537jp6eA5Zc87mGfe9drP7XDyz+57SsPfPt2z3nUg8EX6PnFWb9NLS7Vuv41T3sSJD9vts93j7nhhptgS1dce+1Xr+B9RQAAEABJREFUjv4ZZj58i02e/JhHFlYVKR3XLkptoYbtydwNKYyRwKlbRRslSLCeuNOqXFZ1/upPl778CY/46rvftDfs4c2vPOgdb37qQ+6LobMvvuKMCy8yyUh2wlJmoVSazFFxCzPXmuEZ84jFnGNLZI2tlTYvaTnlC2SNyOKQkulUIpkl0lVKds6ijCIncWRJ7FGoVcIAYQsDEdvgbgEi4P/AnAm7JOm9kKECsouvu/6Th+TYSGX93d/cbbGpj2QDGY7Z2ujR1lpn2q9jua2ccw2BoWmKhCgBcQMxSTJlP1Ii5NICKhkTGCwyG4sRUxIhRG77ipc+4sEPEhFAW5zP37vzJ5611evPOvf8TIyj73+qAePjHvbgH3z586960QtrrW52zLFHPe1VrznmF6eYx+3EqkXvdffNDvzUx1/xvOd1CFz/72XbvPLVhx/4+QM+8r4H3/ueEMeqwdPPPvs3v/0t7h90n3vf//bvNLBgqZ38m7NuvulG3D75sY8hCJH+z9eKxeHD++z/xvd98PdnnxkZq6+2+ju23+H7X9zvsQ9/aNHbbPT/DOX2zYiIE8848xmv2uqbh39/GAZVffa/P/nIr3/leU9+Ut/92UluH6h/4KxhbB/Z+3MXXjL9WeotNt/8Nc99Ov0nooPNHX/q6SeddhoR3X2zu210tw0XxS3NqcGLmLCMYdJCCaeNYAotQp0401LhhW8qpJKYCFeGP2HJRuvSemsRrl+dVzgqBz6ViSaTpyLaiSpLgTIYfcwqyH5a8WnCVsIlk0qyIE4wjzoiuxO8KMWURODsCCLGDKShIpVrT4L6kChME7uH7IynXAzENVEvIiqagYNgGruwMg+aa847JuR6Bh6SgKXQdDGpEmjuvPVphaoSTrLReuUqIBXsagiSOTcRUAHvEfdeqYf2iQmbKGR+xIRgdZWgVRfQ55AIAkKYJC68xGJwBEHWI/fG4Jlzia+MmkPvOA8MKmCOsnqmc5ME7xO5RCPxSO5srtlqIkC3DDNQwMJeNBjaoQAGfGcPciez4q7QIgmLFpUKZgW65SRp7KPiJCkBgwSvhqWLihg3LuPWMcI0uMmgBAWNK6eWjAVqlYMmNFmqzTj6ssQf+CB0I8F6iBixQX1BkSTqyCrgn6LP7CkLGrNKwaV4F9ZxVCYF/TGhQ+QP7IOgSeFkYgBTh6LHTEvCDCEUmzlRAfBijuMqCeJMtdQxwUJPVVmEWQBXgpdgc3DfOm2z9GLMzjEJpyYoVWMIRS0qpM4ZeFbsA4umSJJQpgRstSHhTIanQrSErIWYAH5h6bNKVNAH48N8pqUrMBF9xBzKKSE0abQsjQFlpagsCngMNygxSA5cKTuBODJGdwgSt1RhMoM4FocoVEC5BALvOj669Uvvc7e71ELBVmq9/902e//Ln8NMKL++/4tTJYb0lhnfOeGUP1x29ayWD77muffdYtOFWrXErM8nPnjL1z3tcZh/6h8vuuiiy9w0oHi6jfxnPvz+73juv667sKYP7M6rrb7w+uf8KybDX3977oVgExOb0g9PPh1bIPS/58VPe+DdN+5Z2ajq7J533eRDWz3/DqstA7EgGEbrnVeyGMbfXzAdkT7yXps9bItNlqdIci169003eNeLn4p3MotjO++SSwUQpaZOIgomB9PMytQL//GCSyxp7dUWtn/h0zbb5M61Y2bCAf89NtnkpU94NNBdft2NF1x1FXwkoL0JECVPTZULlRKKylsxb6m99En//OInPn6NhbUlhZjWWK1/x4ufdp+NN4jM75/wqxtvXuEC9QvRn1cwIbEUUKwAzUy3XbhjmigsiotxH9CK42DcBJpMMEGEYDeVCzApZkDRLqh3HJ+O0ftQpZFa82gjxMSOFQh5Kbg0lUchnwQJXpiTIxF9UOWDMSVRltTOapfCBPMGsargNRnP0lGVUrOr1hGUrNJEYZBJDoI0mQOzOCLd2FzDhYt0M5n1XDuiIqFkJds0i7HdUHYG21GJey5C0cmwvMy72ZB1bAkyndk6qI9ZQEJg8lLTksAMUeZCeoch1cjqqRPsljVHQIc7EOQn4GxpEdErn/jI1zz9sWus2Ql8gXztZcu2fuqTMAU6+t1FF6VOADD94suvatbWXL7whmc+4QH3vNvCwvKq2pe4x0brv/rf/1mZb1ox/8PFVzJ3NOuaxu8vmJLmo+9z9/tvuakURIAkoc03Wv9dL3zKKjs894rLvYQXT0416lphyDehIWwkhmbjr/5wPkh88GYbP+JeWyyrfSTiT9noLuu956WTJTf3P06W7Cp564r5QcecYB532+COO23zkrusv64AGdWFZas/+dEPfM9LntqpnH/Vdd/76cnSU1bgAUOTkAH/tU9+7OPuf//V64ImVeUN173D2170bPSj4SzgI9s8f4O1lqsHAnqt/b8++mEP3nxjDJ136RXz1oAlQq686savHHsyxPXUf7rP1k/95/XWXKuKcM0NNljj/S9/Pk43sD04/BdnQBncoDkYxOTf6RTw25GpwUmFZbY4OiCjGSIa/K6SSIgFDYLOVW1y1qBIunm+uN8PfzZv/uDNNtrhRU9f7w5rwG7IbY3VVnvTc59xn02mV0mH/vjkiLCkYF1YfdkHXv4cFcGLju/+9JTM+PKRR195w82IIdu/4ClrLAisu3QksDplIRVP8zDoJotkN/WtooCgJO+qosmf6Xr+Y//pdc/5tzUXltE8uMVaq81e//QpmkEmv7/wYtVkYvGC4DZlAV66pmBsLq1pWiEMBlEG5oL56lQdboF1sF7PcMdoRIY5PIkzJDhVApDZa1gP2yYP4VHhuCPRKAKoFFGa1+RCUogrqzIn5SBX7XHYeP7l4GiNlzxx+b88IEcjz8yIgA7cYkSS4q5gwg3X3gCOE2cVy7hf5qVvmWwJbSJztOxwm5NGUpJrFA7FBCMD+ihKiELLVtcDdtnpfdtvt/ndNkO4iYjTf/Ob5239uoN/cKQH+AKS/8G25uqrf+St2x2w2yc322QTYr7uhhu2+9BO791tr8uvuf72Y122sPDuN71u1w9+YN311l9v3XU3uvOd77rhhg96yMOe/aTHbbrxnUXlr0Gd9puzW4MC9LlPeyrzX4/8n+8j84RTTsE8LeWJj3oE/Z+WR+SvzjnvZTu+6xvf/lZbXIFVj33Eww/74gHbvfwFyxemH0IDqH9gu2XFio/vs/+Ltt3hoksvpcx173jHvT72kb0/8sH17rD2PxDL3wDqwO98//AjfoSFpdR3v/n1tVTc/8eWmT87+dQdP7TTMMwx+soXv4grwYabzAdvhkOJhnrEuCUvFl7Z82JfDJkoqmTpghW2Xwp1lWdFO/WqxmJe1u7LGlMt0i6/Vqa6d2B4IHuuUp8CCU2H5AwgXJHVeuoYHlmD2cWIjBBq4JhoQaBiHNMR5MgR9vuOFNmsZ1esJy1cYG5YJUPAzIakBicyJx4RtjpCFEvmZmHkjjCWFFRMJNCmyJGEEGBO4+Spxagoa5csruMoA2Ge0iqCS5ezaLPBawNKxcudobS52DBGDszBXIiFMtzcHfLkWrKKKSVCAuFAcW5jw+wAS9MERpQvsaBtWdrCmDJXwt7JekbpPxDPXQdbMFmgon1ScVJXQhkYkYg3PcgqIqTaSOZWxkHHBhFERrBKMjcXH4s0rRZdelIOlKM0zFLHFIAtOStjFZCPqiCNsTSyBs2sFO8YGSlFcgpms1bBdw3WmMJtkJp0DOydl37OMgYPKSO2WwjaQr3RbM4FXQ4pd5KzDto2kCIlScVRrAhBagShoe5RpumuKpViWUf20FFBWOA2pSVhslAvxLiTQIwlpxwTwm9EGLdJKJA1BUeKZXFNUV5QnYVKTGpGyilYrQDCYJYg5BGVBrOBXE4hx4mfVmLOHJlGLY0lNFzZuGZ08I5eRu0c57yWKW6dWL+QRckdx+HRgikKZrpXYNeuoRVl0cqwXGyLhWDbNoeiPCSp5gQc3BEH6aDN6jxrdCW70pQGyxgTNqRNeXSHHTP7rATqJNBnA/iA8yA9EeRCdJd117nrOuvSPGjIGMjH1Hk+bPMtttxw+omAU865YFw5wsitjT/+zR+S6FH33vxem26mAYMmCiKIcpQn3vfea6+2fGx2ypnn8JDclEho6Xr8g+7TyYK0fvI9Ue3rOne84x2X9Ri87IZbJaJjXzmuOOGc6Zc07nfXOz98y3t6A8cEYTFBQ+0ud1hjzeVTcAD2gUbnQXJxZmNRYKHLrr1xvNlpWLC2bMxCwQ/cfLND3vWGb757G9SOwljELmxVUirRLGMBpqXJj7rfPY7f8x1H7PmeB913i8I64wJTKeQg7N6bbITP5nHrLYtO2ZThpUQelFbVoZYepQBBykkQAebSo7bconeZMbbe0D836FTzRY//J4xddePNF11xBdQJMAxto4uImbQLbCCdHJUouKWlK8PCRhoJhtQltJTFmAeCfetkr05l5NnAXaNK3hEi6MiTAYxGMZqOUZuXsQHoMGRrJqPVlh18VAPVcg5uow2RjWCotSZ8CE7sMTI1RUHpLCxUNfCcPiwxyFxYmKFRZoLpZl9oQQm2iQG8vmkEKYVwAlpGJzDQcArjDKzCgoDoMxm2NXDBWxQQz45d1ThrrnNyhyiWRfZzI2uUNlWNQlKjdqGa8H4pjO+0dLnY2A9j71GItSNdXmV5hUktgxnEAL68dkPfmYA90b+Y4hMfdl/pifrJGIw0R73LGmuu2cMq6IobbwVSsFEb3/+uGx/1qR2P2usd//LPD3KdgflOZCGiz7jHhhv0tUTmDTctamN1qDuKKsi66Jrr54uNq5JyTFLih9xn02996PVoD77XJpbZEpdTZMlELusYE93ckKoqUiLRFTfcPI5O0tuk4ySlLTa7y8HvfsPB73rjo+61RQaG+PeXXH7+ldcS0eue+tjl/UxTC5U+IQTOjMc84F6bL7ntj395dmaRWms30yXysORum9wpqoH6LlmZQOYm6661vCtEtPld73yHNZfnZEKwcsvCUspdln6kYhwbFN9pUZUzzr/o2ptv7Up55b89qkD7paSkU2O2O6277LH3vQdAnfbHC1rMhV2SuGOcKo3CZJXHhXRpuJ3RbDnUgLkUwqOU4B4wZMTe1CmnfnxpCx49zP5w8eV/uOwq9Gz15H9ZoH5G0oWJGyYuLF/2b/90fwydcd5FK2++FabOA2zR7n/Pu770CQ9D/9d/cvJRJ5582MlnqsjLn/ioLTa+c5nUNqZ6knMyRR+OYyfGJV3tVCZxYOVSm1TVJYELXnomevw/3bcUJWCPkXwgtvXXXWPthQ7DV9x0iyb3LfpBFEdZXAuVzqmAiWrzhbRlyJJVFmpX+p5rL7UvgvcsVnjQbmQ4NqTBLdVIiEBJ4N2JFIZqIZcxJUO7QMAcZaKAhiheSulVeyb2Eb7u1REywxkxdHS54dzLbzjsFyCubr7hHd/yPAI2hnZh1dH13BXqO+Ty+IsAABAASURBVOrWWOjush7mzC66ZhBp0IBAGlQ5debTCpUUNZax8+wAmmks2YIDc4MsI3CfxVwgq9q/5hlP/e7eeG/2Yl2yvBUrVnzwk7se8dPjgeJ/urHIEx/58G8f8NmXPefZXT9LGw/70Q9fuv2OR514mrvfTuy/O/+CT+673zVXX/WS5zz7e5/f7/sH7n/Irh/Z9C535mn90sd0M32d9bvf4VvX1Yfe9964+W+1+Xw48cxp+d3vtvmd17vjf712cRi/+O3vbvW2d/3+d7/FzPXuuO6HdnzLF3fb5R6b4Czh/0USRv/OlplnnH3OK3Z4+35f+UoMi6XUZ/zrkw478IBnPvHxfyfkv3/5z395xm777LMKzhMf97jHPfyhq+7/l8/5MOz11W++4V3vu+a66zD0rCc/+WlPeGxF4GAkInxphDIMuiXDKFrfucLNKzEHUSh7JRJ8qSn8gZEqESuM2b2XVGXA9GYrbrhhMDOkWoukRGdQTo9jizYyuRavAj9iJiZC6JUoQZxkiGylkY4caa6eHWmnyKVAKki9pWsihEiQ8yREKiERlrBqBv8mZEcigOROE1sMkmmSDryAPbMBJIoJNuKRs4kH5eSiYDWRtyIQ8igclQB6zRBHGBMIJIUrsDYA6zmEKCibOrELJYOIefBIHCTOXbAAP4kQylFWFzQ2i7YYCDyTdEmYWDSYORIFSGmhjpYEYTUnc0AlnF8i5CoxW3CGSlYFpwLIUdQre88IOcjjLMKqpCKYCiBNBkBjKaQlGURGILU6R5QxEUY9BdsAtKD0cORZT8di6QCHMTXdwQqhiNJoRM7GSkDMrtVIA0LmDGEnMjMfAESgPAGtToiGZBRGzkxIldQX7osLI1miZuJkIYJeErNoyHRKzObm0gCUKUPTFrzNRtRcOYmqiAoMwJJGJ4O4EVtH4EXHhKMKV2VmsSXcLpqFUzMEbMSk2aQkDqFQCckkj2zG6PPCJB1XrobslEpErC0LhgR3NcE3cyEIkqhjK7DamkCQFUIIp4hEIp4zD2XCx+DG04W8y+iBUoWwGWBcRA4xTWR7A/3GBIkRLoagyDM9JLgSl4C6s4WjHgB7qU4dJTCLZATlSCCfyEAozIABYakxSLFwo0ZlIj+jI97yrhti8FpUZObMedMtN1953XQy1c0WTv39eaeffe5Jvz33pLPOO+V3F59yzqW/u/waTE6iK6+/bsZNLCbK0EXErCYVHitdRQM55rTqmlswQUzjsHjTlddOwO+96cbNx0gJrdgCooQAW6smT59AMJqEMyTa0+MesCU6zzj/4tfvc9D3f3nGhZdePawcKXx5p5vdaf273elOay1bBu0RTDIz0AKf5LBGHJtMPzpUyBRNg26+8dZzzr/i1LP+BL5O/+Mf/rj0h1gAnChZEZugm0lGiQsEoVKqGjDxSIIprJonmAlwXmAK0AI6g+698V2KgFi6+upr+6BCmI+B25olVIW63CZ+QNVSNzC5wdPgZ4mdSVpCZ2AZEQ4YINZUF2YYLsPuc2zh1AjVF7EnWrBGAcUUkpga6iWywrDBcwPQMcchwrIwzSRQgZeiAYMnd8RYc5TTAaOtkHBCDUE0EjkDbALvmGxMKD6lSHEly0g3Rlh2E6bKUtPVXD01IJq0dEygYYxxgPVBQFRJMEwTpuYCZ2cak1rziRV4Bbpq4FhKZ8oIw8yIteLYakHyt4kowtlHNrzrTsiVJUN4IsDK9C2ZWIRZIiiNmJeWERVm3DqBhzIqJENSbhuaj1NgEoQNg5Y0m7JVztnNt976x4suO/nsP5x6zp9OO+fisy+80jOnNfh0izE1u8c/6J7o+fV5F++w51eOPelM7FhWNjPmrl9+tw033GTD9ZctzLy5DlnIqRsQSkFHRcYjJbTkJzxoqnN+d/HlO372oB/8/LTzLruijXOM4aXlpuvfeeP11l++fGGR00TPunB6ewOjuucmmzpcR7kmF6MY3UdbUH3klncDMRdfA39doaQdwfUZPWiZ4TEnhuySjDIcoagvgqHKqtQLVeg0FSIzTdgxSCBc0kip1Nr99vwL8bi8K1fecOspv7vo5D+ce9I555529vmn//a8U37/R7zDxOhNK4db54u1GBdEwzAEoJ6zJtQQHF7GUFscB8xEy2RYymDFTSiJYDJMqy7ghqW585l/nP7IEOi8ZcV46u/PO/Ws83551gVnnHP+L889//Q/nnfDipWYPze/4cYbSowCsxibRL7qaY+/513Wv/6WlR8/+AeYcL9N7/Lcxz9CGNAxmImEMlgO5K5BrExdyQWN2qUWZszCGmJLGsgMZjQ9Tl+FRYuYoHjJloJkiTQ0DRAhmkkj1B+SSQCZM/FCzbKNkDRDEdHBrWbwETCD862OFTmha8Ao4bDpoIAVEiTFk68nok8CHxcO+LpBdrlAXCWtI1QhQNNbzqiUrh+1G5Pc5toa5kZ665p113/5yLjhFqBe+w3PWnanDbnMslbWpiW7WS0LnfY9rbH6eMc1CAz8/sLRYNJkkZmCHpwkd6APBpbdQGrMBMIy2UxacFAm4i21IX0e8OIcoecZ6WytNVd/2zavesGznkVL18rFlTt9+jM33nzL0tP/+Me6a6/9sXfu+NmdP7LJxncFsisuv/RtH97pfXvsc+V1089Zoee/brO+v/nW6ZdSzjzn3OXLZl3fIWT8xyXmccuK6ddgVHidtSYB/sc5/0XPr/5w3k3XTvvv+2yx+cIMJxf/6dxrbrhxx0/ssft++6+89SZmfuyjHnXwvnu++vnP7jqk6dtM9T9d/N8caK194ZBvv3y7HU771a9gBOuuu97H3/32PT70vo3vvMF/E9I/fvrZfzzvrR/88Hzpr66tttpq27/65aqIEv8roouuvGb7D++861573nLrZG/Peeq/f+jt29VG3RxH4tzBXfoOTsxUJQpPds4sU6WpmnC/ZmKDaOPiIrBwOIMZIS0jHqbA5he23AQoebC4+sbmNI6IOYR4is6conAsLg5Dc0PuI7hwJCGMoBROYnidkpK4kGOXAndHnLUeMQfVRKq4IgCXxHNLGgNoG8WgkTPHFgahgKJYFmcKrM0A/FJVEGjAQVAZQTdOVcqEqRfABm0hTdw77BPMiSaYSMITjGxsLQfL1gLd5IS8SUwE3CAwqKb0TCIMqVhEMwRjzlLAAQO3EjEI6YWWlX7GijoDZxUlR2YsJybmpM7qAk6UjcWCRvNGDWQZl6a9z4KKYRI34caZlJqsUZQQPptSiJBG7Qwvd2tHAkcLgaRziARNLC5MpOzQHFSJsQSHHCQTmhgDUh+VDc+QQnKLagDIlNRIRnwzQrwuTiAaIS1AsnIWHosM6CNUVNAKdNM6H8vQ6gjdERPRRC07IzPMXT0qS2VSSo9swA89VZKJqRHyJAbn0UYyr4bpasne8AViXIkIkhLODCaIY0LaqpoyU7A7JzinUlM1arigknDMTFGr2pRHzoDoiEIyNImDITemVqzpaJDeAlIbFcpwaiyAC2GGIrUhWkN9XcEKp4l80bRKgW9Z1VmNNJmohHSm1Qq7wJoZTI1JJAlYinUgxyLdSSXACEhIC3hI9cB5LBFg2Az3PmkpOH2awTDf4g16UJPCnXAvTbPhXzoT/kmmNCCk/+eKJGzeOaRIR1RHlvGud1gT49fdfOs8qXC5acV448oBPUec/KvX77rfNrvt97pd99969wO32vOLW+25/1v2/cr1t0yx/ZbFRZmNgfdiEZiM5szzSq2n6EkLQxVgFf1T4wzoKXMcHPUNejZeZy1OQUspUZKg1y5cBUOr2uTFgAh9Vn3h0/75CQ/cEv2/+tPF7/nyN1+12z4v+8hn9jzkiNPOOz8UBtex99Tg5kxgG0vM0oY0y+kq7r357MxzLvjwAV9/6c6feeXu+229x+e32eMLb9zj85/61vRumXDBE7pMRagaPcGRVngqA+Ckck8l/zNtqKXrSAyj15ILNTolXnNhYc1lUw664tpbODKEvQAI4BI8zRBGALPjOlP5M5ggZhbmzBibW5tU6kQwAh5FWifEUqaTCCkJ4xitzWmI0kK0+QKEXJO7hMa4CFdWWJdA/lZZCD0JeZiiVuthHYRLYW1ViAo5IgkJXv3C+F1Tqs0q1OMW3hC8GNYmyeLEJuSSAYaMDbQRpcakJdDE3KBfGWrnXU3hsAaZ80DwYRITngwc8kvAydBUSIoixzEWk+d9bQhKWbMV4o55ASYJcEmYPUkbBAMAOzCWJHWY7fRg1hK1r8xRTyW48RkPPbcyxWKHJLFsqSXhbEdhP3AuTq/uEksDBKhdYyZNQULrjGZ/OP/yTx74zVd9eK9tdt739Z86cOs9vrLVnl/a6WuHDQ0sE3GEmkcJn738yf/yLw+8FxH95vyL3/eFb2398QNe8ZF99/7GEaf+7k8wnfRi4H4uiIfQMs6HWnGUvxG0JHqptbz8qU94ykPvBwinnnvhe7/wrW0+vu8rP7jvHgcdcepvLpAUZUQXyqojlYuuvQnT8Ppj7eULphBiRiL+UYOWjJL4DmtObouccutUwVs0S0gJawgkszSO6Y1nQqDZYFV/Zp+UrW84g14oXZcdWW3ONI2CXx8zXdz44mtvJqIbVs5fv+dXtvrkF7ba9YDX7fb5N3/qy6/f9Stbf+orh5/8G4yOzVbMG6JRgCiC41ggPcw8F5ymOIucaH1fMBONGdpJTHJYGhcupeptQyRECJikF14zsTxYvPNLX99qz/222uPArfb4yuv2/Mq2e+6/7W77HfijnwIO2s0rV0DzlRKOyGOut2z1Nz/nX5f13eixfNa/68VPXnuhkoglKvpqJtySLIwNeZzYu4xCzhIQtzBwAyT5KG1uBi+EaKeOiahCZH2dd8sW6wypzxsvjcCI0TLhleBaEu4CeNBQRBbrZ63v59nNkTumIYP3kiW5kGCr3ZlPNq9WJaZXeJxwJdMIDDNMBkcjpbBkEeMaJExROQvoHZmGRtE6tV6iS5ZWkysihPKlV6487Ocgrtt0gzWf9y/eMfaizMBcyCoRJheQkH2/9kPu2f/LgxZe+HhiWE5bbBYwmoR3ESOgpQpNTIoHxsEhcbAoiYool754IXhDgiTVECEjtdky2WmH1937vtPvI4KGyy+/7LBjf4Kb/ztNRZ78mEd/54B9XvCsZ5ZS2jD/7g9/8LLt3nL8L38NFf3XNKy9xuprLv2o8Q3/5c7K3RHbAEoXVp+kg7v/Tjvk8B8QTQue9M+P4v98/Um//eOL3vyWH//kWHcvC6t94K07HPjJj9xj001pSSP0D70uv+a6l7/j/R/ZY8+bbppc7sH/9PBvfW6vFz/jaX3X/UPx/C3ALr7ymjd8eJcrrrp61eLnPfXf7333//XPD3jEoccd/7zXvu6HxxwNLS/MFt731h0+8o7tZl2Z43gOJm0znTwjtBoXklKIZ/CxJZhJJRGao85L1wrCEKuVRkuP0rF0wnBKLzzrl+boscs3AAAQAElEQVSTONfGGlwdhcAUKxHfgBd+09KapMMTkNTcYxhhgmVKsZrcwVk4Ca6VJeHZKO9c2JnQRwjnlGPGUCIl4YFpPI4cStqhhE4BHguJRm3gcV5i6DIXrJaho4ZoSk5EURnMCCFOqIuEgmoTI7aJ3JKEWkE5iDPApKelBVZRMaljlpZIjAbhKFelPk2JpixiHoalfZl3jKiZhHpnKlCoCneYCeDJRFQ8aALKRgWkZE/RmXEbmTCdNBHlKw5tuA4pY9DIMgjqBgSXkuCUeZJFsJh0Qy2uzI6lkW6e4dIJNCeqLJHpDcvUaUZZgFuWBMCQXOQY0mIW3qGO6YJLc2ojgQfIdgrdFsATiIElYAzaKnuhUGmFTUQpFWBYuahWlhJE1tzNatgCy2wiQyyD5shw2UFzkSEUfeGFwoU1g8dpFNXJ3LCHhc6YGhlBaa3VFuqwIEXdn4PhpHb6WcReqHSanWB9FVJtUAnLyNPchE46C8XuaxTi5N5rVyqBNWaUh2NJ9CeDHeo1tBAra1BhkA+8oJQS2ioV4JlTGyx8kMCuwyypuUZMfEBQOFYeQ42YWKgr3s2iFHAHOqQ0kRQn8AcJjgSwOY7Iek7kfZ8zpkpVU9OE0UeJqRKkksINEkopCQGBvOIqBm0VINEJWRhSiCoJvKWmAYYX+n8uUFQT0CkBzLto6gCE8ZXN0yt3/UiTVaBHmEX+t01wReYiy1QZQz6YTSRCRVkY5Lh7MogMqJxwMYMNaVQjisMmiFZffTnIKqnkEHAgpwonGMRkNGYqBEhCruYLa662zode97I93/Tyf3/IfUT4ppXzC6645kvHnPS6PQ56814HXnTNNUZlVNSfRZj7pAVDBJKC0k5a69p81g47+cRtP/PFH5zym8uuu3HeDEDAwtSACfjgnhnkQ0YLdlcuXCa2mmU6MbEIJS9NJNAPRwrkcki4k1joqfQsnco04RaYgkApUImsmo/eyoocqiKQOVCt6pdaykJlFBEaIk3UCc7DPNZoPdSLuR3kowRNhgoimDjPk4ZkBEbUTzKfgbisxD3zsnCccmRDScrJjNok++l7MRbwFDAy3CXKVJLUKrVOsiIydpcgapAUmftkltBhaCT8FSA5zZWyYHplqQTXaIzoaWGkzoXBlGgyR4SkM3MtNbSMxJjnWFawqs6ClQETxlpTOxLl4pIwfxGChCWpZ62cQknkBDExRZekBP7hosKp8AZkhcgM14QGJBujEUxIR6oNi7EQjZPgJ90gONYnGi3a0kscjBAwNuIQnqJVLyf+7rev+/QB3/7FaZdee8OKYRRhWXUx9DbNFy1cq+If8TprrLnzNi/d+40vecpf2eFXjj7hjbt94U27HXDxZVdSgJVUleykMRvREjrEBVPszKqvtdrCx7Z5yf47vOZfH3gvRMWbV87/dPk1Xz3u5Dd+5svb7L3fZddd03WlJqyXwCTQF5Gazj4PRwKgpnAIjVpdey0VEyJiPi4abFeacaAHrVNENQ2PljhDS3ZBSkL/1GCYWCcEudagWVLpCQ8YqkVFJrRtzGEE7eCF5C8CmcTCS49//mRqIZM5EglH4cQnCZGWoJ68EwMmBmS0klSbSYOdU8507Pn/0UhVzPWeB5uQYvJfYZmw/tUj4z6IuSizJugOolE6LqvQZIQNMIxQQgdMYAp3lMrJMcUkJF0X4wxNE7PwxHogJE2tATNu9GcZcoTYZM7aiXZetHXxZ/IyUqzhkKggaA6GAz3ABE1IPAqqhCaxt9GHodngNncbW+QgNFcyJs6UFA0EwC4Yn6pMFX2RlCqEzC7VB8pplxXcOfeGDGkEIMbkrKSV4PhZQ4Ur33jqb/366VB52XMe06+Ggmwy3ppcW4XJZIAXN7dGucGHtrn3QR/d4qNvVHaeAoNPPKUyGDeADg+qIxW8/kexgYjWC1Wh0lntvRQtrGXCjKMBzCYKSkgwdTbbY6d3rX2HdWjpOuOs6ceWlm7/L32st846u73n7V/YdZe74tVK5qWXXbrN297+wb0+d+2NUyH+nxFhDrH4qlFe9e1/95l5W29Zhtcpf364re//8O1Xvz/3Jz+ffhbuXve854Pve5//OBvAr7ruhg/v+dlt3rLDZZdNf25rwzvd6ZC9PvXaFz+/7xf+4/y/s6eZffeoY5/6itf84uc/c/dly5a9a9s3f2evXTa/612Z/wsZ/J1ob+/ya66/4c3v/9Cf/nCb/Wx8l43e/OpXishfr7/86mu3+/DHt3/nO6+44grKvPtmd//GXnu+9gXPRUqLgGuGw42WtMQ8ig5wg8RRZmWDmQtWpBMyUWgfCieB76ny5JA5+SIi4iyDC+qopNsEosSVZXIFFeapkynhB4hCVUVVPHmIbI3CQ7xpDi0JJ/ABd66kfdYuRSrgBXuSEwcxAz6mNUIEwy2QRvo8Yz4dUihGC/gg8pKeYYmo7NhYSBrAz0brKLvMmkLRE5I9giERgr5kmmhDjuVeWJiC2fuudV2rHDLBFEQbKqbSRCwxPzriGki0IEpJS6PSWFw4WQ1xiJOmCxE+oxItxQHmQphAiC7JzAR5QBBcjCqzYETYARFlREqKB42Ed0ZpXWdaw0s0pSAWTKcSKSY0dN6wm7AWkyiDuVaadYTNQE+skJxAXpxcScC3RKGYEaPEUBVT8gLEBdg7ZormAagYja5AI96Eh4JAXqVLVqjAmawQWJeOVTmJiWeVl1XtiTiCwsVFCaXbAhgSlkJZzAhaCApVmUp0JoppbkpEN5VK7FQc4IQ0UqEs6pv2c+S8iuJpDJvbvKUh+qNug00k9FuDWcGJqYwEQyBRAlVFWoFEkgqLFlEJIZhLACdNX5QkniwMfaa6ElUnhkIrQdeI4U7Jyp0mwFAbI23kHAiFic65zhObR0MQdLMEV2lEytQl9QDjHCSALWViDa+KqEU4TBzspiVEwzDrUkEY9czFg6aL0UshgqcgwFDCfh3sdGnajMaMgWVcMi6wlbPknkLFootRKScQ01dKzaqkBQwkk6h19cYVIxEtQD+Ar6VbAFp00Mdf+awz99np15/5wOl7v/+Xe73v9L0+cNpndjpln4/+ep9dztp3l53e8EpnqIISmpmmk4DBjFmSWrIxxKcTudMYL30gAYZ2C7Xg6drrb9AlKphYg4WoUlYRxjIME8xaAd+5unM6d1oec/977rz1C07d+wNff8/rtnvuv93zLut7+PG//eNb9/vyFYs3N1VgVqGuZsfSRS8OQfko4/mXXfbRr04H5HdeZ813vPiph+z0ptM+/7HTv/CJXx24yxfe9polbMTAbg5LE1gXURKCg2dzHrCVG5mDZNVEYmeDjzFEiKkQPzetiy1XzicxrjZThISAL902Hd9SYGSsHOIGTwt0oamIaE2VZGJJ3GJKMhQr+BDAByoiFC9M1JF00nEtVjwhN9Jki2xUsSJLcY5EbClLlGJdEKtAliIGECMmU3rgW59tOcusSukY9jWFW8ywBEHwKRaC/IEP+9tQMx6NLFOZShFa6LxD7VNbwuSEXEmEAEaJgB4LRan0lFWNSyvss4QhalVdIiYQm3qi2kv0vWvn2fFYcyyZQWlgOmccM0ZF6ASqkxCyfVnmAmZCtkMJEgM+wXwgJDgCpmEqJ3MofBb3qxo6UjBHICIfCXEenC4NJSd2N85Uu7z62qs/8MVvLY7tjmss3+FZT/zmu19/8p47n7TfLqccuMvB73/zMhSMBHnAPItUIoQp5m5WH/vAe++81XNP2fu9X33na7Z71r/cc8P1PPxnZ/3xbZ/7+vW33ipFuVbiyXozmZxC2Uu4JsGosmmRhz7gnp/Y4eU/3+8DX3n36970zH+798YbZOZJ55y/7T5fvvr6WyvEKjQrHei9dT5HaQG7YWw1w4VZapHSc1dvunX6aZRSdGEBh0ZgSkgFS9C0StZqzEMCn7M2SmDHCIUwGKlE1VIhZlYVYWaM4RsVJ8UUqaroueed1z1j34/98oBdTv7CJ047cOczvvCx0/f76In7fuSkfT5yyv4fP/YzO93pzhsGqHUoE14M9jwJcYDVVK0qziwHAxw06A4yKDRnHaOkY+6fSUIyiMIQy2xhhpkbrLn8Zzu/6/S9P3D6fh894ws7/3K/T5z6mQ+f8pkPnLzv+0/+/Ad/eeCHH3yvTZOVauUiuLn+lls+872jVgxjX3Tl2HY79KibblmE4WqmCMkspbJIBIGwmL7D2J3dl0QCcoESQ8QdcSlMhIlLXdCWIzJjmhfBe4yxThqchmBsLjZXqAR8NMYuGIuUoBZAyRp41THUwHlcMA7mWsDrLRJK8Ml0MzKVXThARCNk0S4RL0DtONR54KSomyvP2Rs7DF6nBD2RD9IUDuO2zJCVM2ES0sYczFb+6OSJLqJ1n/vPBKXToJQlsksS5pES4UHYa4YkHsBSLnDCWrQQQfdwxJy5lLlmS9RaKa6ThZQyMGyHzFpLhILmnSPtZp0TzY0hSKGsPtRsfLcNN9huq5ezYCGdcvrpyH/0f/cC6n959CO+s9/eL3zWs7qlNwPf/O53Xrb9jsed/Mv/jJgbbrr51ul1JK239lrE/3hyUVJ/cNfdW2tFy4uf9czVluz7r9Fcd9PNBx562Eu3e8s3vvvd1lCDTINayjrTlu8fT9DV113/3j0++5YP7XTNNVczy/3ufe+D9trjTa94idZuQvz/768bb7717R/b5YxfnbGKEBF5zYtfuMEdb9v9onPlfP7V7xz2zFdvfdgPf4jHvu9f9vznfeczez7kXveREEXUKozQJ/DhWLoyBXbOqKFG18bVBcmmCxKqzIp/VJNgsSlECqcM9fAh3RGPQ8c/XUG4AGKhl04IL2k6ZcZcciSDmfUdwjSCEOKDMM4QnUJ5LNSYkiiCjdmVi0rNUhFGUnUaGTGCW6FagLRxJDk765gIBmFkxlhfCmlVAkYiJpA0srfSQowI3g2AtTqD15FpUcUqBzFbFmYsCiInQK5i2g/cBXNqMOFSuHfnLJVTpnjgbpmGAKEcmOJZU/Dyl4pHNUQg4OzmWRtxSwp4PTMLQ2wAzQjmgkdDRLSesoqKKidLKJmmLUVIyuBIdoRgYplmJakRG5E6dx6TzLgFhYmHZA2v6QhOygyChaQKyp+sDNHOQ1tOqDiTENokpWjWbpQ6cjVhUpkadGhW2LS2Jc4DszmpBGpiV0uxAA3KhEzLPVhnVAo0CnsNKsQsJbCcdHRJ7ooWrZzFjFYGqm12Tsa7HBvayLxY+3lXo0jpat/XsjCZSwIyLCQ7HNNCK5qimEFKwmCAoNyoOVD4xLkrRRUwDQ5aTyieojeWgdlEiQT69MgG3VPAVLgukg6Sg4azEwdgCzExEIkKqzI55VColXQsTg7TBCYimUaFggniV2wbYohh8MF4ZXZDltEk2LlzqpgRoZ4SLjSHRKgrIRVC8JaQhnDHgOekqNBo0u9spJlJZzQZjIfB+yqxiAZDw+7TUqPOsh9zib1XWgAAEABJREFU6iNizarOnPRXVxBlL1ILi3DJXr1etPS3UtZdc3XIFzPvuHzZuqsvx805l181lLCOrIIncYLBJoUl7LoRD0RjlQYgjMloDF585ICZcZdZ0ziBDiPASZlT67puvbVWR9f519xItU6lslJSUVDCRQmg0CiJotSxMtQQmpoEXksJrd4X3nKzu77y6U/8ygd32PpfHw1Q51x29Wln/54oAUnEuQuelEVGXSNhpqNPPAvT1lq+sMs2z3/evz18443uxE6TrDEFA0sNqDsqNbo+CvTSyOYSgwjIrs2YYNRL84iEtMuZRAVVsAJwPQ55/lVX3opMTnSX9dZKnmYC7/QNX3iU9CY25zbPcHQttQhzqG7BqScEHogVQwmtSZVgiggajNooiZpv7DtXYUyESpWJQSRkUqvyTL2yiZijg7QYcxOCj8EnJwZcMAplCIzUKFsmlGSZDt5IQ2VyTPgYYEWXwalmZSRydguCCTIv0TsmGQtDPq1A9Ukd1Q4dSWNja8qhmqjH8dLDJAG5qNVpKRNLCI9UBlJIp0YqfHtU8igQckMVxG1I8yAEopCUJYTkls2HMYdM0GOJ8RTLblRtyg1rK40dw/8owL8K5aqFRBBVN+lTiRlRiysrrbqYmYg1hfmk3/z+1sX5rJaPvObZW//7Yza/04aMa2IMo0FL12jNCDXEfOTWOJwZdisL2i/ove6+8av/7Ylfe+d2r33SIzEXdnjGORd0PCMr1GB8XE0LTemNS02wDuaGDBsaXvJQllq33Hzj1zzjCQe9f/ttn/l4Ijr/qut+csZvG0vrZIuNpz9xcfXNK6+4dQ7hwc8BtHogDiwkokOef+mURme1rrF8TTE4dCcEzgCGGqKBpkkXUqwkdn6BmnUawRfMKgjOa0xwOBU2YZ8WzscxJLgbkT63uPO6mHrFjbes8CE11KIsNgXhRFIq4X1OK2WREIuUVIogP+bgaRkZFDCo1mdUzuU9tqaARGxUSFRouiOccIGC25QlyaBOk+5+pwnpdbcuXrPiJu1SYOkttLWOo/TcdbWLLOaQIL4QgaIT7/xzRx9z5kWXL+u7/x9z/wGv31WUi+PPzKy193u+KaR3CCGEJHSQqiAgoHREEFF67ySh996L9A4K0qSIFxULTVQERFFpKtIEQg2BFJLz7rXWzPyffeL1w736v9d7uT/1Pfu8Ze+1pjzzzKxZ+/2WR/3yTWnIp/7pzN/66F9FD2XbL9uybt9dC6YZlZdlGpAWHuaqgYseAt1x4S6JRvx3DF3A1T+gCpKFWQG6zt+9IyM4N1XJKxRxESZZijRlWZQ+0kRKMZ005vRNcD3iqUywBoqmFcpLbHU0d5pKgssQFQ9LN0p2SRcbLmOEpwzUWNcWm8QYfB2JQWGBWBfjc89rn1v/RlG5wgn7jjw0gsg4L8HENmDHQWEkXpgWHpyQAxnihNQ2qkWoVxPFV7rQNlof9KMLl/15QHtkjxjkFEbYSHVIyhrwGHUaMo9RdSGueZub/MylLrn+XZFvfuc77/nw+jf798D6j3ySIw8//FmPPOOFT30Sb8ZT8T999asPfeKTn/7K137/3PP48X86/uoznz3//PX81a5wWeL5P139MT9+93vff9Qzn/OPX1r/6ZhrXPPaP3+D6/6owO+dwy3K79zxwQ993steduY3vvGjl7729a+//E1v85VhP3r6x33/8b/51F0f+qi3vv1to3fKutsdfulNL37+Na98RRHhx//048Ld7aOf/bwP/Mmf/Isll738FX/lVje76CMz5zNf+PK9H/nYxz3n+d/6zvoXfk456aQXP+3xT33ovQ869ECWfWHBDduZpnkyY1eFGMyBLMxecaYd06jDhnPBKCR5EUwR0+Kl82qkMotzjjGNjj4WjwGEf3/lBjZTHn905zeKooO5sodWM65ABjHxquxTGib3yo91g1IAEfAqV4FM5leauWJVhDVjkez7xFRRpibs13S4W+ZOlknNBAJYsrRQnrPiZQHl6TreRToXgeoldK1sQXmLdxZFdBaW1OIorNgjHLEkRibtKNpV4OvyKBKsyw3qbKmCZ5nLABZR1ghnRRTQYqgptLScFtZR8WGcsXD9XUY0ijavlQOMS31SwNrnZXLorkzdNl02OaqkQdzhToi3e9hFSViIUmEShKAVYrQ7UDKqexkwqIqoqAWQjFokC9royquUx0E6WrL+Z4ZGJ3JcWVkWpcgwbVlZoHIVzidHcsVeLBuLsOpAQUjmcIvVNnOpnR6mZkIjWe+yZ/PoFK+pAo6QJvQ3FFK0wFglpZVIYwu0aIzR+uJsEMARKL1azGyKrEphCKOIMKYzT7tgAF5TJkjFnJhalkAiYng0z2yiHQA1VRCGNIc1gdNrCc5NbyN7T5Zv7oy6q4e5EKuknlEZ6ORcjZpYy76ojbDWpG0xnAzELKHreP5OmSzttcscmOkTPaIKBj5Ce68UTkvoPgMI5N4sxmOO3ATWXlOSwxnawFi9WFdOl3UKf43oCE1ap1sH6J90IfqIpL8jZAhlRpbkdwCDpCw0jKQGZXI+aO7ouf79AJFmK26027cXXvCpL67L3EnHHV3n2UccvNn/2EMP4Yw/+du/v6B1Z16X4lwgdYbMXPVGLolRHdw4lVgjwsE8SAJ+ZzekZ4SkI9pYk5NXVn95uwCZ++/T4w5fhX/ic58/n71phdZUDklI2OjD3flpPcxDPKy//y8/cb/nv+IBL3jV18/6PsPCI0GHUTXvcuPrrCPZ4X39mwISqBPmdE/LqHDu9aROi535nbM47OD99zvx2MMUmoNFpYxkMeqehJIXeRSXDdj7jFKY1gFCOCwJbnhy0p6VHIbtaLRBqKQN3Y7cpaT423/8Iq9V01NPPC6wxmwNJU+tBxGRRoG0GyLr83p2DN8uMRYk063DPcBCCBItJldj4BfCDDaPpLR3wdaVhGZPgeDT1HViphmgoL27Ynx2SyHmsRqrUBPLolG1MwlcR1dvSsOG+wJnkJKkZU4xADVtSlYn0BB1TEONG4FWNJUSE62DcWVseIKkWplNlFPJw/BBHAOmUoWjJcBgihGXdN5G7plgsvM0zeaRVM2DZ2FFWABGZGf5a30sI5kkpA8ASRA4bCPXPzcDPlLakKRlaQXKlGmy8ZwiDJ4SjBaEw3jQr5zENwoqUGKe8t8vqWdxiNPR+Pq31yVvM0+nnnB8TIQgkQsbrxJjZQf2HknnV7P+4M//6l7PeuX9n/Oqr337bOQMFhNXyrVqd7jx9feG4ivf+LZARpexiHBhzEmlqE6QKXzzwU98/m7Pe919nvdrXzrzu0iV1LRg3MqEu9xs3ahQyBfO/Ca7+FHGqSdefGeqPPPej/81ZTBKKz2y2+C65d87+3uf+sr650SucMKx++1samBiPDl67xjDCZVlLWYrPYCstncF9NCzoQ9kEsZUpX9I4dXNNPEUjExYLnvCMYTt/O3yp3/9uSS4newJoiIE25L3FdY3gY3JvK6tyBHRg4KdRA2GqJWylRzLslAyD2pLmlJMoXSB1F1V8gIYyawDs+fJx6//IkX3+OBff4b0984gkOpDJCuqjarrnZHVYFrfgybgb77w5d/+8/UO7C/f8Jo3/Zmr3/Inr+KRr/7DP/u7r38zA9JTeXiSm8oGZp6IJH0NZld2yX/Jffrdo3is9uT6RMSYGlyNE1yujRfoAO3fu5bB4KYNaHKQmEnFSK4jPiJpbdQo05iqm2Vy3WCRMxhZOuaeVRTFxo4xxsFeYpDg67TB83P12VoVV4UAiLrGO4jXZuRO5iw5e6ltFnI9Okovc/i5P+xnrgVuvyuc6OyTQLgiEF581C2wq2gRymANsajh1haMLZgIVcALPYdjcKcia5PFMdZ7MiGJHSwFqqNoiAIkTwBqUmpMhZOZdAJBWhKSesgBh9zwp9aKHO5vfsc7215DjP/wxzRNt77RDd7x6pfc7KY3p/KlLb/5W7915zMe+dG//lt+/Jfjgt3te973gYs+/sxPrXcaLnr/r59FgPUXvssvMfkB/9vHRz/zD/d+9OP/8m//liOPO/bYZ5x+fy6ufM/jq9/89vNe++t3eOBpz3vZy//pa1/lmf322+/kk081M77nce1rXPPh97m7KQHnp/8HBwPx4je/826nP/wzn1v/SNXhhx328mc/88mnP/DQgw76d0j/jxjCXcqjnvW8977//T+q7HEPvN++vX+O+fwLLnz2y1/9S/e9/5989GPug2Pudec7vfVlz7npDa6thfc4JFaoRFwkkoVwTNksBlhfebIqOQthWjv6kD5YFoRNPt9l3+39wu7MEmanCenOYTnIZm+5tD1damqbTUtbhrWGVRgAqdmm1uctW47hmkwR7Kjty7KJWmLSlDSJwpGptCq5tF10rytd4LRnPRkqVByCWAfrpJhhs9cpi2Qu4UxNWWLquakyb2Jm3kFrVGMtykwHU7tbW0r3OliGtKTUMGk2Bs1qY+7YGbylCLZTVJuL8qDqxttuwTYwijCRWV6mLYz7Dq+tbJpuXAqNCo+WsSvRxTvWv83QPKKFLVIaRSqxlREYAzGSryFbt1hYo+rcjcsSfMlsQluF9ZDWSDrhYq/Syhi6JEIiy/CabGg6uFswXnfpzjv8fRn86WPbeusLsunU1SKkL5bbVHYXLrSF0yM0GZuEK1pKg4QMyW0gt0p7edkn352xW0u3EvQaLOCpA2hQGVWaTVspu2Ds3Z335ZJOlhxiXQvrerrLtk8uVQzcGKl1r8iiljH1bttm2yGMLShvMblQsVVpPJApXUYQe0QVJSvEad+wSHWZIbNkSCxufcw+irghdD2y0N0BkjIT62A0kT5863khpq1uclKZu859GBZQjQZZxztIJISsY3OA4mroTsFc1bgiqYN7iLlpaZMkG78qm1lmI32Sg1EiqdwTPoRLf9KfoijGMuhEN3uCPqqIdgiV7qrsluA5IL00nwa/E4JQFLGDeYvcug1XNZQpoCN1jDJCunknhgFpgb4nAV848zu//8lPN+IVLcdIqpP+pvd98KzzL+SAG17tinSQPWGB3eIaVzaRfzrrnNe8+4OEu7ZpEpMiMG1DX/6773/DBz7YojnQrVA7LnoIiHCL6Nk9+sI3lhdd4fPcsNPzQNObX+MKReXb517w1vf9ibhPvmu4MEcb2/a7H/nrb5z9Aw4WgJFXkgVxyL75b77wlb/4hy9/6BN/FzFLJZ6wXKDt2+f+8z/ucsRBB9rizL2ku1vHcBSUypVdi9eD9l+/IDp/d/ec83YLxEpwD7OUcu6Fu29638eojoezHSVsSCBEtXDjCAlEFxLIUsgtjlqPV7/3Q2ct54Rs6d9wZmD51tlnv/vPP8lrl7/kcUcfwW+qM3geg2cuOjJtFGTFPOtBB+y76OQ20mW4b3s2DheVTR0b3o6owqZJmu0M2QyYSTcjDbZY6BgnJdyTr1VCECNlJIQMYOypuNNidsmLKsNZIYhEIJYAABAASURBVJuErV65A07aTNiyubEkUrkFODQ0SNtkMMOaS0Bpi2oSQrDu0Q/flcHMZ/KkpLrWVgqLAXpjqY/OnCFZB+8jSK1gW06bMyLaLpbdst0WBhdCBKTQ4hWWJWU3dFlm2ZYpwaYns7Zex3aKbSEuhIgc4P59E7JvVGVNTtCKMTWrsqk6C3bgM7Yb2YruTZ2bc9pFh4RUTosiuiadYy+11muaUSLQYmwHb8DxVOv9298/d8DEsgqdXLa7F77lgx/f7YNXJWXT5mlMh+zb+dt//Mon/v5Lf/yJT/lWsC1oWVgFJj/rh+dyJI/DD71YFnThFRvsFHVdqoSYUb3LIQfs/8kvfvUTn//K+z76KcKrMhBsb0fvcdbZ618xoIQjDzlAdJvSLn30oVe65DE8844/+Yt//NI3ROE1uV2Aeovltb/7oe+cs97vu8W1r0oeFFtgu8lQcQLAgE6L7OfYRG5SZuXKN+9dQZLfQV97Zo6BkRIVTCde3bbGJSiGCOInr3TSxQ+7mEe+7Lc/cOY3vqtZHKXneojEZ7/0xcf92m985eyvoxLhNOrNMKAwIKMEfdswnlTQKYuSebjYsClIx5iN35Eks5gzeAXixLmryeVOOO4qJ67/XPjr3/fxT3/+zBYsgxZqmuI9v/jNsx/za2//my98TbMmi2vI+ee3X/3NP9xt/ZRjj7jjz/1kFdzz53/mmIMP+OF2edY737ubusTcOqM9QQqdBJTCdBK+ZGj8c11cbWiZaz6OgOf6mb5oncUIYIkeDroX00VXIIkSOUeWqE6PQAvD10yWC1fstQZ0yFrBndUaEpo5das+89VhgSBW3J+qIPlhq75bXUeFm6RgVJJUoBkSogNKjGBLlsaFDJpGWEUSdAjmZ57NNzymix89JgZT1tUPqRJJAd3LdhR35dqT1CYxwTcSahHKAhvDgQYsGkEWbYbNORRNcnDt0CaWmFhG1KbkKz/JOjkp3sag10uRRYhDyRT9lZ+/1WGHsgLik5/+9G9/4J//9QPa9h9+yHFHHvXKJz/6pc9+1lFHHgWRL335S/d79OOf9rJXn3s+NxurOR/86CcuatyvdfVrHHfkYeupf+s3M+nlRVcyI1fUL/r0bzy31v/ys//wkKc8695nnPH5L36BIy5x3MVf++xnHHnYYef+8II//NM/v9djnnTLu97j9W9565nfOJPSdnZ2bnurW931V+70zW+cSShF5BY3vtHrn/O0Y49Yv1vk9B/zoLFf/9Z37vGIxz3/JS/64QU/VNUbXe96v/uG9R8gNvvn9PsxVfz40394wQWPfOZz/tsf/EH+CLi3uPHPXueqV+xj/OGffOTmd7v3y3/9Deeddx7tv9wVrvS2V7/yCQ+8/xEXO1LbLAvTU7pKK/BM8QAS5qKeILPJbSYL76fA1owQ4drCcylQ0xKcF9G3sbS1ErrKUBfxwnfxvR/I99YKu/MTJycpMHoMFkwyAHxUzxoKiEsONlxctqdgYpunBiTXigGBFFGE+OI+mN8ObnQiXaPBO+0N1MxNRF1NjSFUraNaUHzGaD6WPX2SqnRDJUUovq59cx1gfYvOtKU6G824nNSQtZuck6agJDIqzZMsChrDlZIN22pyoSK40ZLVxLW+DKzCIaZu2ifCwDGCgJeIGTKFzAEWqKJdqDNZXXTFGus7G4SepspI7aP2Po20kCltQmwQUm1MdRFxobK1lhp61a2urQ62Wna1Nq2+ooVMiCChK2SNNa80SLe1gWDVKTFrbDSV40zTCgXqGNJ3xRsPgI0GqoAXCmuWzENoiiilUiYkVJaUrRPuJJ4UkwO5QLgcBNUmz9Sm5lbWpVEHEAwVX3zvqlCCSS1CUFXnwvsiE8nEBbUJGzlPDmbBp6IMdJeepSP4I7QqK6RSoI1OoEipViR4P4xO0PAgL2Koc3EwhSonRB30qntiqPKQBEGUUI6LRhnShWGMbtFGjI7e4SEOTdViZiGTSxlZ0ieLqWYR6heKQwiCRInJMJnopCrFRimNgJHXTpccWLUGqANUCkmChiE8W7AaifWUQPkCrAClgKYzxgwZT2hm8aBNPC+z2sbUkuzPEZEikEB6RDA+PE8RAOvtM972ey9+9x/+01nfP+fC3a988+xnvPF3X/dHa6d+1ROP+8mrXNqTjjY6etOfuMpVT7wETXrrhz7+8Fe86dOf/8qF5/3wgvMv/Jt/+PyjXvUbb3j/R1/++x/+3Fe/IiAnqEf2xAN8VU1hKmkD1g5BcdGDV4Cw0RR2g2te8Qp7Hdhr3vvhF73td77+nbN+2Hb/6Tvffem73/fid79v0Km9OQqlW5PJtS934hX3xr/wt9//vLe+54tnfufC83fP/f55H/jE58545W9y7MX2ba52mcsYDSEejlgxDhVGRKzwBde54qlE5HvnXfCIV779r//ui9sLzz/vh+d98GN/8+DnvPFPP72uJhRy4e6FMM/Zx+TBFNeQgPJghOlUCmPKYTw++5WvP/Qlb/zQ33zurO3u9y7c/eO//dvTX/q6H1yw3RS7x89de2c2jlEVYSrxHY9EGSjBdyJSDjrwgD008Acf/+QFuxdesHtBhNtGi/IwqwojzSLEWTbZofOEKJzYyUjkFMnYMlJrJ8GiyFFEHGKQSUjsLOHkUXhxChJRCXKA5pMetaYJipi6MH8yfJAuycxQepoRW1bkYsGrTXPYwEbBm6kVFAMrIlX4SOLDliumvfyNGCESWllWlsE9F4YiAVqZVI5qEFLCctTwuTvFpyg0nZ+pXkMnTLPszFIqZMVmtYbzIQW1JJsqky0Tk06AeaWTK11IggLtya7eGkLAsCccFz0E1ICAOmpytNKoi65QhYhohMe47hVP3TfXC5f+6Fe+5SN//Znv7V5wftv90Kf+7r4vetN7PvZp8p9TttttMTPRa13mhCsev+4cXvTf3vf8d7z7S9/+zgXb7fcuOPeDf/m3D3v5WziSPLzm5U8U6zKFCJAJRtaHDd42GGlx6qVP/Im9Rvyt7//IC974W1/6p2+ec/6F3//B7oc+8dn7v/D1lLD/ZrrmqScaPD1n3Tzg1jc5eP9929ZPf8mb3/2hT579wx+eO5ZPn/nVh73sLe/8009y/A2vcur1r3olM9UqULqb2HuImkA9urCWpU+pysKB9aFJ4JAsWxYsCCxGTqav5oLfqAh4p4J+26GHXOweN79+LfaNs8+59wte8+4///PvXsC061//7vde89sfetALf+P3P/m51/3BH7dkCe3pZAwUjJ9qag9LnhCkDeYf9h5uNmA8JRCQF3v5gIseK1CBHPtN871vcaMDdzZ0+QEvefObf//DZ579/e9v/Wtnn/+2P/7Y/V706vf+1Wd+9d1/wBIhDqbnW/7oA3935rc2U33ArW54xH4XK1kOPfhi97vNz6rIp77y9bd88E+jRBM00QFxWXOKHBIRVU6XTO5eLrIALhZSIriu2kWn+FJCzNZZI3OQs7bm8N7VrMqlSqUUF+vMVQqgfNVw7rcJPuvfauYYRFlHTFtYZ0RQhkqfIuYgLzdlmo1CQph+4cpsRAzGjdxZ5tp35j6XpWaznhLBroV3dJN3MQBL2iNZMWo5+4I9m1CPPoQepQuN2oTVMNYd1gJx7PTY9DYtyXwAnSo6GTRB+9I5O1J7ZgPfL7EJbEps2BtkCIPpYyq9amjqSmbme3AwIovQm0h6IpYURiOPu/gxv/wLvyAiXDOf+cIXfuLTnyObqOA/5TArt7nR9X/7da+81U1uQqhbW97yrnf98kMe9pFP/s3S+nve937axjbjQXe9M9/8aws55mN/85mnv/w1d334Yz7/5fVfzr7gnLMf97wX/tFHPvads7//b075wEc/fucHPfh9f/whH6NYuc5P/tRzHvuov/vyVx761Gf+3N3ue/oTn/yRj320sZUQPeLww+9+hzv8zht+7aQTT3r9m37j/B+eL1bueIdfefFTnnjgAeufjf7X9vyfnvGIP/iTj97ybvf88Ef+LCMo9hEPvP9rn/P044466v9U1P93439w7vkPfTp3KX/4o3gedtjhj37QfT/3hS/f+7FPvvejHvflr6zgE7FHPfgBb3/pM652xcsw0TAEbM6Z3BFp3rlRYdp01lyuglx1QsWbxjAk754J74UJk7t6zD3XAzIpSsms7oWVso/sLLzGeTKJ1N1/+vb47nrTtFzxkvt62/SlqDNRRcCHRC/SlcJnSghW17Rkxff0oe7KSjEgC9ADi4d7JhOFmahdZUt9ZfEcFEQLpuoVIZ69xTZzF8KiFdoNImuxZomofE/jlDkHukdN3KBkHZAUSEkDaEPnUtM22fdJzmSfCqhopscZrHRpklzMuf7n6oNZmhGn5NKBRG+1NVYl8QIXijPWb9ZJmGmdpMxaJpa8YnWDQsUeYNX3MLSiQ+pQo1RJ1IxJOug7vSU2sCIqYiFIo8MJ0+QJSGiuyO/py5CBXEwGoZNkha46KYMXGlpimukRZMV4SqlSMInuF3WnV+5ujFO7wzsn+0ajzlLmUrVMUXZKLfM8Zlv1Dq7MMBriy/Ate3vnJmpHB8voMJZZDF4Tk1KszIkCAsZ5zk1FD09X9UmXWX2Tps7XMoFLbxHyQokCK3gZjHMSnBKtjsGtSwwnniamKsqhklivu/UhrKUIY/wYaYiLQFUzJBtyFMKTXkcSF7KvrVC4CqhzRp0gZfTSRi4crZ2TXLjdJqvIHw0aP0lOG9SZKjjEuU4P6ia4ZDy4yd/u0MYukjUwU+voGa4GNmR0guaUSMnVnmGEhp5lMoa0EiJuMTaSO8KpOqXuBL0Ef83NQmtCuGapp3GOUAeiVhCFgHlwsqBjcrF1tEUQdivgfODyFz/yiscf/eYPfeLWT37pdU5/5i0f+8K3fegTXE6PPvSgh9z+5rPMKU6eyTwOOLA+7753uNpJl4jMD3/2H+/6wlf/1COecr2HPen+L3rdx/7ui+wMHnGHm1/h0scXb1Nn1gX2HlRSgIn7i2kWbt2qpdreFdAAIXzCkoLNvs3j736b4w47aOnjNz7w0Vs8/mXXfuBzbvHEl7/+A39+yaMOP/rgA7H3SCakzSY2WX3G3X7x5OOOYuF904c+dusn/Oq1HvzU6z/suY945Tu/dtYP2NY85OdvfOljjiULXPe4BAshogMMmzp7gutc7dRbX/OKJvK5r3/7Hs/9tZ9+wNN/9iFPe+xr3/7Fb373Zlf753+I5VtnnTUZ79Y76TmENzN69i7dLI0T4YMxpF1z0V+41pX+/uvfPu0V77zhGc+9ySOe/ejXve1r3/uBqd7h+tf4qcudhO7koXjWJB6cASQkSDIhutypXeGSlzzpmCN44Tfe/9Ebn/HMOz3rFef3C0pRkyKo2Ksbal3mBZsOdeV8QARFi9lUueKCeeyrkanuVcjJdYCoaCkoFrwcaxXTAEsC6ZvCUyWNSb3jmKNa1EQJ1r5gtiRopLCYclSs7UfNFux5DAgsAAAQAElEQVRAl5Cg+5aiYDWcNKhMIcGprBaqUhCqUEkmj7p4o/us98SggSdn229H6wZikXAEF45Wo2ta2L6MHXTT4bAAXZOyEduIzvwQtAiClIr1xXKtaCwJWtdSq06L2UnR927ZTXqtWVVE8c8PIfZRfChhSlpSTCpP7V1VTUqtbjVPPvl4Rs1U/um7P3jIS99049Ofc92HPvchr37Hp75y5g2ueBJ5zglf/97ZYwY3iHXHnnH325yyx8M3//HHb/PUF1/n4c/8mYc//2Gvetc/8/A2N7rUsYeJ+lxlM3k1Fv6lrrsUBwKaqHji3e5w+eOP3XZ/24f/8hef9IobP+Q5N3vEcx7xmrd96dvf4y7lPje93mWPPzYhomWx6TLHn/CYO9760AP3/+55Fz71Tb/3sw/91eud9sxfeeYrP/Spz6vKT1/hMk+9121LlcIfbAByNWkwj1rLslO2NloOECxJaPA8D75O5FQpUREZ0jOWTOcVML9YbUMZIEPWW17vWqf9wo0Jwlnn/vCpb/2dn33002/wyKf8whNf8Kr/9oELl+W6lz/p9Nvd3Id1l6BsZa5zWRBWEQ8iP8swUdn2vooGGMykbuGwzKlz3rhIK+Bs9wEsHdvxk6ee/JS73prbswuW9rLf/eAvPOG5NzjjSTd9/Aue/fb3fucH513++GOecrefj3XNGH/z+X9464f/IhM/c6VTrnfFy5IG2aqa3fgal73e5S9Nea98zwe/fObXseqKgczoGE6dnhIIMVM1Dts7RDGLzFomnt87Ax+BpG3gEjZoepjRib1rKqKqMU9MDiiGxCDCRaolwrQLlWGlnnVsmu6wrKSShSR6dcyL6W7tnRchddrMajvQ/QLKODgzDx3ipmG0SquLksatNCnOPOIlaJoM06x847kudntm8SrPsxoEE6OJ0mlYQpTKIWIS1tKGmk5SiqglUgSqWVIUrhjKW4J9sAlRkVllHihLcD0UYdKOcDSXQczdJ8lJYWIGzk1TD1v3Lxn3vssvnnDp9T8EPfv7P7jPox//ng/8x/1Txfi3Hhc/+qgXP+nRz3vi44886mheZ9f7wMc96Wkve9X97nqX2//8z9/2Nre78qkn8fz/dPzDl7/6kCc//V4Pf8Rb3vWuz37us21h0wl3/63f//3THv/E2z/gtCe/4vV/8dm/P/vc85J47E3eXdp++x0wbfbxE9E9+Khjz/7e9+7zqMc97ElP+cMPf/ic761/zHTe7FztSld64sMe+ntveN0TT3vgu3739579whcs291pmk+/z32eftq954nFiQJ+3OMH553/jJe+8n6Pedz39v4nxJNPvdwbXvT8B9/1jrVwjf5xhf+/mn/WD849/SnP+P29P/F1sQMPPPBiB18k+dY3vembf+u//dL9HviBD30wR5vmza1vctN3veJlD7zjHfbf7FcMkEjBANNaRNZ0KGhIDzc0SyYKT5tkoAORzPhqoUxSrqYhA8yK4AisHObXLZXZGWv9C1NMpaggL/jE32PvcfDlT+DyNalWw2wOCgVSnXXPNJiDouIiXWU3fcvLTDVl4gXNCR/BcsUkhThEomhaoiQ0XNuovc3ZWP9MM2hYL8tY3wExmW7KNE1VZsviQIf4KOhccwVwxKJrtUFOicIhrCeyLrFsq1fxJjSQclS6yOBc0CsomMhdMBQ+C++GsSxAiEWGpMf6I94NXdSFNUK1jFoahaVAC6YptIJZ70LMOAg6mPi5bviiiFQY2wpNNapsqCMmuFKZmtOzniUGa4WUFHbB07AyVDJTR9doI9ticANNUtBZ3XsIHwiecwaOlZF7qaiRmlCani7u5tA0EUtTNdBEomI6GAcUU6Obksgh6CZLsfU7o2JOk4dFs6WV3lKyV/DGaJ2iYtSRsvBUStDDsCGrhCo+ZYAl1SE90Nby2yDDaI9oioaMAe8pkRjiQxu7ZJrMDQZEAB0D6EqzVUat3aY+Vm8g1G2ikkIupHTYUDqck41Z3YrEitbsK8wxTT5v1AqjQNWt5jBwpqAKLKBbKYvWZuqSGS29kYgpAVA1b3J3pSWrRH50cbDdGySARamkXlS+EamjSi9JI5hdyrVBBoRQGrtHEU9tQFBowjlIwAdp4mKLVy5wVFxBSBtjgzrBuC+RDmpTFZSaBlc6LKoFqAE75fjjrnnypW529Ss+7z63/5UbXuv4I9a/JUKp3BXc9BpXfMlD7nHKJU4oq4k5l9QJY/aDD9vvJaff+ZG3/7lTLn4UWygOJsjHHXrQja96+Zc99J43v/61nMZpIPLA/fZd85QTr3nKpQ7Zfx/1FYEoktleZpVypctc6uqnXPqkY45eHVqDQElxieMOe9nD7vpzV7/8kXt/sZ6nTjjqsF+6/jVfctpdb36tK3H88Uce1aEQxo6/cvwxR7zsQXe7502uxzZxv3nieB4XP+zg613hpBc96E63ueF1uDkKFRGsWTQJaRQ+MpZAHzoYuYfd+RfOuN3NTj7uqLqWORy03861TrnUc+/xi4//lVte73KXvsbJJ1zYesJNxKAMODGODEGoumqI5EH79rvGZS593cufev+fv+lz7/2L7Jn238wA9s31cscf87g73eJ+P38DOh1wTtRgMbIrnXDxq518wgnHHUUSOeuPgazY78CdR9/pFle61HG0l9MPOWC/alKpoJHBmiB4UcULea6eMsRH9aw+z9gUZpxkSeYXbJ2va8EaZB9HplRVw0oKcocCab8MjSQDWE3gTKuR/GwkUylZlM5BgEQKRVqYZCmYVi0mrhhlOJZIB7URAzad3ZVlzi3Xs13UTbk7qqWqMMdLpvoi2y0bmnWWCoooszBYhNBokIaZKOd5iag0YqYRnvQgwF2Q2QA8qHA1S5YKrworA7S7isxAJWjJq5IDpWFKnrEskjP08AMPZCivfplL7tTKopsQaOk8UszyyiefcI3LXuqEYw4Hz1fCFVrq3W5zi8ff8TanXuLonWmVfMDOfOUTL/HEu9zmCXe9zQ2vdPLVTj4xQpe2jVhS84Rjj3rlg+9+75v89P/Ew+tf4aSXPOSOt77h1YdgJGFUNaC4KiRKxNSjZIA4H3nkYc8//W73udUNqZG6sPc49tCDfvKyl3r+/e/yCze6QSuTC/OnpiJKXPfqJ7/i4Xe+5bWvdInDDzYVDuesq1z6Ek/45Vs974G/vP/+TLEORmmIjHLQAftf85QTeMw7kxf1ybwUN3NIN7vyZS51jZMvfemjj1HhdtCU4kRoVCQuecQR1zzl0pc74eKzTSrMf80Rkv6LN7r2Cx/wyz9z5VOOOeRAWZVj/810ysWPftjtfu5p97ntvgP28y6jF0cB034TqItopsGNC1ANtUMOOuRql7k0jwNm1r5WSpPKFaE74pjDD2Y4rnKZE4zfHNASp0LHGDe++pVe84h73OpaV/4Xl+dip1786Pvf6gYve8ivHLv+dfFY2oUf+7t/vPwJF/+py5/4oFvfUGTuoQMrB/fb1Af8/I2ue/nLXOnSx//Rxz4dAcJpEaWndoC7CCqCpQlL/xVOuPg1Tz3x5Esco1BAOPKgi+1HAMmiA/ebBEOzI8S5UjGYbj9x0gnXPPmEE0mhvQAnySrOQQy6QubMTebkzC8hWoUqIJIqK9m8pk+RBsMqzRt/fEjSih3LqjzK3uAuRIHffrTaem3QZuhGkMdF4lXRk5yaKY6ndVGlGeAjzz63kgVanEfAY4ySY6f2eV7KzlanZcrBuEhoUhNoo5Hzs6bWHtxRVZ8HF1UmeIs6ymSz2CaiaBPGTZjyyiZmOJiNRTCDA4Qk07qEbPfaNNtyCSl3vP0v0B4e3/vut8940lNe8Lo3Lq3z43/WUcv0iz93w3e+8iU3u9GNaAM3Bu/6nd89/YlPWpblej9xpYh/BpCXLjo++ul/eMDjn/QnH/2oO53FIYcedtUrXunqV7nKEUet36hyzHe+8+23v/Pt93joI25/vwff5t73v92DHnab+zzgdvd5wGOf/ZzethyQ4Wd946t//4+fP//89U+FitqlTzrlnne927te/dK3v/LF97nDbQ8+6GJPfMGLXvvWt3Hwvn37HvXgB512118qhQWOJ37c4zOf/8KdHnzGa9/8lmjbUusdf+HWb3/x865xpSsAgv8yj29/7+z7PuYJH/zT9W/PH3boIbe8yU0v+OF5tG7ebH73fe971ZvefO55K3SXv8IVX/qsZzz9UQ+/xDHHZKjlxNaZeTQqYpbcEUxRMCYfhSUf5mNS7KjuSM45qjPZABnAVn3JntlrdpXskewQBCLrulI4MaxFYd3kvZ+8cHvBh/4Ge4/5aqdEatbJtEzdawZPD8Ew8K0uihQXXQBSPCIttWTZOL/557R1MFgsxIRFJ0qqyUQ66PpxYDTo1uZmO5BSVWjyTvDGmBStMlUxOpzDY/3+hfpEgm62IqRlDtU+T2MiZyaJSbpYy+IBoX9jppjR6X96gsOjUKMLAsmq07jjmZAlUVgLYEAd4HSWu3QxuEgLAH0urWIb3mhrypC6YO5eJIRXXd01muRW12VWREqOgq7gudWUwnqCxtER6lkkU8A2xFMzJ3HCacw9GjW577Tgc7LalELp6SHFTaVG1jZst3EL6qEU3qm2hy2JIRFs9EYUpBn1cxlEcSkDOlhGPTNCEpKruWFsTnhbbpk12EWyW5MEu7UUT+ssjUNMtJAQOYWz/IJbpxBRFRSjLYOCvAQ7DtU0zmxj6UuLBomyLrezuA7zJu69wCdVJJfF0lG6qIPdgXsQkDazLE9psy2zsMLTE6CokDnqlhSNEda7Jut7iE4wytmoc4+lBqSqmE1T3dQ6qXAKnZx73denqQvJTZRJLWpE9mTrJVxye9oQxtCjtDF1UiQYdSIwHJ2AGeWQzOsYJfRCi4umKKLKMCTYhPGFTT7RNPQM3lwcbcjSheKTjIAg1pg09GGaqOKaQXMrDdZsgkWEbYCoET+KWSPEF1OF5zjtdjd/zWl3v+PP/NTB+x9w2u1u9trHPfA3n3LGO590+hsf8+Cn3uuOJx55zCYJGZ2eJ6Fd2YGhcsB+++7ys9f59Ufd+w1PfMhvPPmMtz75jDc/6kEvuNedr3riJY29ZKQnzagnXvISr3/EvX7tkXe/8mWOg0i6c74MpqUx/Z9z2p1ffcY97n/Ln3WsEVbhKjlyxFGHHvSs+9/pzU940Nue/OA3P+l+r3nMvR5211849LBDT7v9rV9zxn1/5UY/E8mvzwfoJ0qaHHTofve+7Q1f+ah7vPlJ93nnUx/wzic/4Ncffo/n3eeXf+KE44voMLgpMg1eStIMrjC0UZLqUiP3r3KHG173lQ+9/1se+cB3POYBb33s/V/60Hvc4Cqn7jfPL7n/nV5x2t3v+/M/B/bBRJcw8h6Oz1OxUsgwEoZy87InXeJlZ9zrufe945EX23fDK5zyhofe7+2PO40Y/uaT7v/6R9/15te/KvZVnwQmSY5hmMhT7/KLScicFAAAEABJREFULz397ne69Y37ZJhFpsC8DOtXPPX4Vz30nu98wmlve+z9X3D/Xz5o335rCR0xhsdafsISFqpeSCK41yEbR4EKYx4jMybIRtUUQUqPDie5IiTXQhAlSDN1eENz6WaLlu7RnVtiZqSuJjJORaQSKNBe5ueKHJPHqoCFFBvRncH+ZYiPEEA0ED2Cad5EltQ2tDU4K4RaFmGmCN0vbRNMAdRdYMkeuZBJod7FgyAq5iIbgTEzHIpa6ELB4svSdyPbqNFzZB/MBfCRJjEjNwnWCakDNTgr+dtTtj2jifoElk6glRjstl9/+t1f/ZC7HHvogZ1KS2QhEDZa7qfz8x9wh9c/7C53+ZlrwRNBv0UGr5db3eAn3/DYB7798ae/81Gnv/nhD3nhGfe+0XWvfLH9d551t9u/5sH3feztb7dx806rEFEPO+CgB97ihq972N3e/sT7v+PJD3zL4+/3+off51cfdPernHwJ4t/FWtoYwfFp5G2F1aHMSdZfKb2b9oMO3tzntjd8/cPu+7aHn/auR5/+riec9ubH3+slD7/rla9wGduZstTMmixm2SvLX8iljjziqXf7+d943H3e8pQH/caTT/+1Rz3kJQ+4x82udQ3oPlaeIMtHyCD3cepJx732tDu/7vS7XObow0kFs5kHxEaqVnvOA+/6uofc4z43uwGMZRYls1hEdZLpzj/7068/477Pvded958PZIdauijrTGsbiWtf7kRm2Vsecf+3P+6Bb3vK/d72hPu/4aH3vP3PXHPeb3ajg6qitBkqkACDR3AVw9CtjNi51PEnvOz0+7zsIfe69NFHaHSFY4VeUvQW1/2J1z3iXs99yK8ceuh+UWyQjWHIIqOefOwlnny3O/zGox/4zqc8+F1PfeA7nvLANzz2vg+8zU0OO+hiCiRks5kedoebvf6Rd3/NaXc99rDDSPOx0iWKuIpd9sQTXnH6PV99+j0edPubbCp3+FoZDJ9YbdfroQmjuVXLU+94m9c+9G4P/sWfA0IRKeMKJx396tPv/MrT7nSZ4w6B7MpwCYBFMaTAXnj/O7/+tLve5+d+mo6KQxAiQ53rS4VXpE0RRU3oi04FWsC0zaLcImwnbM2dNcCGYhHmRzJzG0FT1DnnmRCaJpGo2iXI7uHuuQndoFRmgkoQ66ycb8M0oM0RLicfi73H9svfVI5EEVkFjBwWQyRGlUWkcTFZJgylS6BczzRwHUopA4WsdZaRFBMpCnMpRBJVpRSpBXyGmfNgfIVZzYUw4WIBoxGDlE/+tu4jmvzcT117vwMOxN6jt/biV7/6vo954lnf/8Heif+kJ5FLXfy4lzzlCS9++tOOPfY4IM/+3lnv+YM/eMjjn3jDu9z7IU999sve8OY3vOf33/jffv/Zr3ztI5729G9+8xs09DKXutSbXv7iP33nm9/xyhf95stf+Mdve8PvvfHX7vHLd9h/v/151dvyjW998x+++KXPfvbTf/+PX/jSV7969tnfywhe4iEi+/bb/5pXu8Yj73/f333Da3/vdS998oPudZXLXW6e5gsu3H3cc3/1Le/+7YjYb9++Zz7m0ff+pV8oZeKsH/PwiNe+4923f+Dpn/rc51gCDjjk8Bc/9YnPeNTDDzvkYEDwX+bx+a/80x1Oe+RffvKvaNHhhxzytEc96o//7M9Id35cttvvfufbrGcHHHz4k8444zdf9IIbXPsa0yxRRqqHBlu3ljogUlOnDiWVZb0gGqy9cw7VdHZ0tQwrCbrtGB7DubSs6VyGsmXKcEyJSTBrCltTqIu7jED88MvfHp/8RxpTr3+luv/cYyyRI8kaEfAF1GAGiKQjeS0ikQJn5S6UkQFYwVS0WrE6yaRaoaIh2WukpShkPVFFNmD9sMC0noepSgVFiTeJFrEMXxwNdWRRVvzG4rB1ixQRy6omvEcXkwddRbDJmrBaB1fQ0sKMZ6lBV20Eg3lMu5jWhoKomcQCURxq6lWChtGa0BrgliedGEnTymNgTfQeYwss4OrBqpLF28AYvMKldtOTSJUIGrhYbIs2lixQR1A0LRV6PNa4rNLRpCzQkcnVJuukpehcaVml9qVOrdDFvorxpfgowWhqCamdh/JZ3MDeDC5JVFOCDmmmpzd0T7oCBVL7gqU3WRb2XtPoNnbNnQjPIkQjq9iULLqUEg1L1+BKYKJlLY5Tspsf+0I2xEfMSbtkmKYB+mbOQSqQQR8pzArEpGvyaqAqKH6aqjI8PosLANIicqooKzNWG9CLBNR5pfacaPdwG6IhHE5n4G7RJ6ziUjVg9Fe6cBLpRzhsoLYs27CAFgMtHJHNOYZSa0SVVKtQyiAXiBHfF1God9lGeulCbLMGp5QgTkPIuMb2KVYkRDizWp/Vq1lIWUheZMVQ6WDN1+EMf00pSQcBgYpDIpIqijmXmGokP1RliIyUFoKeSprIEui95SpGuUA2OuiYuuwbuXEv++8cePLRR59y1DGH7X8xcVNkKRAmbXHCQ4XMCdsKe7/OZbfuO/rwgy993FGnXPyYwy52AMOg2zGR9NhI2WSZM+hQJ1cIyhgajYlOFxBjPZJ2MNGyhYiiIIzctJFcHSXkqAMOvuxRR1/+6OMO3xxIxCqrDl0gdWXhYptuGbSsWKpGiSjzzs4ljjn8xIsfeanjjjzowH0C6YHRhA4aARo1O9DJ4DSdFLOMSZdiW2hjbuFi++93wsWPuvRxRx5z5OFW5i3qVqiWtWqaMNuo0q13Hewno85ay6y2mixRSmwsZmkldwmM26TTJQ467LJHH3biEYfttzOZiphCGA2dLBlSpkqmlCgmMLAmNPUtwokCUvbfb98pJx57uROPOvzQA0LqgJi6WoAPTgvl7QO0KjmLqOQI5MIjrO3qig3hZFLy/sskIpXNZR3CIhOrwrmZNaTHCE4LSGpChsKlGJSMVfahJYaVkOpSlyqjcrYiJDw8naHjNIiglqiTFdNZGPMlbBsIRiXAmSWVqcmn9NU4OBRiplYNM8hmT3BJUIqYik1qgEAFNUvtPFAixSO7LN0XZok0VWRUx96jhhSkpAvFppAwERxPmR6L08MwccqjQT18xcSkTnUqjNdQayJA6EjrakOUy4hL87FtnVmibrP7Jl0j902bE4864tTjjj3h8CM280boFWvmVKSC2zal4WG9Ge8cXKDY1pz2m44/7ODLHnnM5Y8+/pADDnWti8whFqwXe71aSND/zBoAjTBBSbdYuaWrO77/vp1LHnH4KRc/9tTjjzvskMO5mlUjWUl3BVN/YQGJyk8+Y6UlDt3snHrxw0++5DHHHXNEqdPIDMdIWwimaRYYK4Jan42BTl0JFDE4KN3VgzW2WGAeUfrw7s3dgyk/zaNOPSpv3GAIt5aEkjhToiiMj1Jhmzzg0PlSlzzipEscd+zhR9R5x6WKzqplRWjyqouAjsLFKDV7oiU8VaVU1VlQCQwcxLB6q31YApmsXKbNMFQsywQxQyoApeiiBx+87zIXP+yUYw8/8YhDdsSCMlPJoqwSa8ES4eDVRTJo1W4WCgoWBy+l+RAfQelGyQITIUbGQMTkw0akOwgR+CIKtVSeXpY6etEo4pydS8agYdVVB2UH76fllK5JPZkmY3Kf+QbUbSEA00akl7JWHcUAS3JYc/OUJEipbcxL7PhUcwZtEU8EC1anBZIwpx2maZmygDYIZ00YezoofiaqQ+AuyRrbEZGbow4sR7ANxXmf/0Z2FScZRpKKKfROB+1ukqQEdGiOmXkf4UFNEiqxN4pmDwVqn2pYSVEGcGkMpHgtLtQ+IycDIDQiPXN4MN+IfkhGyCqKsCedycyD5/1vct31n//C3iMyP/AnH77Twx7z9W+tf/xp79x/ztNmnm57kxu+/02vO+NBDzr+EseTZ60tZ33rzPd96IMve8Mbn/3CFz7rRS98w9vfcfZZ3+GlW9/s5u9906/d4JrXOOjAi7EozNN8sQMOuPLlTn3qw0772/e996XPfMbtb33rk069/FGHH3bEoYfy+eLHHvOT1/rJn7/5zR92n3v96lOe+O5f/7XP/NF7fuuVL3zIPe925cueuv9++4usCJ59zrn3f/xT3/Ge34mIix144Kuf9+zb3fTGVPfjI/Lt737vrg991FOe+7zzz/m+ml3tylf68Jtff+sb37BY+fGF/7+SQIp84lOfudODTv/i5/+OVDn55FPf/MpX/OGH/phbvn9Rwa+YfunWt/7TN73mbre6+f6lbBCbKc0ydF2dG7B4IKOgSzY4+4u66OyTY+5R2wjnPp/nLd2SzE+PNuC7LrzvXQfzC0MSmTZyimYspKvuYoq01tOX3/ow5/DcYbe4djf0Ii15V4HprrlmAFjaRLNIrhGlrObSR2VWkevCjOOpYEmoqFVFuR5VrVpUu0mrzvsW3WypU5umoWx7Z7ojslXdnXWR6kNiN/uC1qWNXBcEHzN456pP7M00Alx690Whu1zK3fru3LczlmomtUJsdU5D1AukwhWDBW2apMxiTGVDpI5Rty5bEIswHRvlTRHulkAQWCzHlCx2glaTywMKIBqG1UlxBLOeTyzrCRtR1oKjcKJtwXKBFBtQx7oJg5izsxIdThxLaTzYzXWGr2IraMQERYRdVOVdJVFO1DrohxpyiNAPFHP6BkAzC92X1SCIwFHpS4Mw6uRTSjgDq6CuEBndudDxvCXpEisI6XPEDLpI2clCmpaV9GKZF66D0pZMXVGb5zaXZpmAMuzElG+4tvGGDDIAybopU60GAQxVKgVlcMk3KO2L9QuQWWWiqRWmvNKnHEUFJGvMSSK23LReR9QV7NDoEkhKljV4IbRJ1XPai4IYmqyURwT3C6UlnWfw+FxCWbWpNTQj6ECz8H09p+GaMDWTyVCwRwTIHFrZMHXmU0yDC7nJDN2vy9QmaaV35VdPdHOQygUK0b3pRSgqMgRJnyZTF48AGBGkJC56JNbFbnAQICAOUkU4VQU+2bKDUdxJauflYJI4xkjPZHyZUIjULuJcBivANHYa2uDBMK6W8KQFp3ZhYKx2tRbZOMiGrmtf7dviW5GFhKBJllasTGYVYSOzSe/lwq7bXe6QRsmcUqwDxLKlW+jkWqF02Y1GTMKkpb7Rxlb6sCHV2W0LcUFAJXUnjO4Zr0iAIFiJoqCVmzGkjdGCOxQY0chCr6VLcvfN9E2TDJHUNcCWdHjVCD4s+yykSYbRQFnlluI6EdjiUlMsQKE0udMIT4QYMVZiXCKpuoqmVXBgk+IwZORIDOK5ClRXGVW9GoPUQQEG7EjMwpZkTK1vlpRuGKWMglAk1JRddVoZrAbTUGM8dI+4vFogliJgtpYctS11acrGYge9uGuToB4pmhwQkO46IKluQvRhmcFpCr4zGSRWJaAEBVRi1qWuHKac7jE02wyfZU3x3iNaAJmazhjRa6CMlVGTNWNNp6bMalILZQ9Jh6MlGyd0kswqtKqSHKgyKsg6gVRFQVJsJMb/FWMAABAASURBVAhgcq5qD9lNY3mmmyKSjLMU7nzZhQL0BSXCWsPoEiFYbeIQ5RuXMkzT6HoqopkvGF4TKtV0rtM0KW9ccEbQJExZTGbROdSywGU4QzA2ZmWyUYLyM1WkIi2LuhH0NIiypo3ZfUYpWoSeMuQSpjQuPXOAY5XDaJKYgVVJxaCFlvEsJ6QHEjAdUj1IGQeaJylbOvcJIiB/GEgOnRgEWSYQ6WLBGBIRjeTbqlClbyFKYSVRoIISWYYGymIaNUVlxqRSg4EuqQjlPYLdra7//0nU0IKQ4mkD6GPEGn7oShZ0JXjWWlmGtoHM5FADQ2wFdFURVSNUFkuIVyqZjFXaMQWxanCnQzlk0KJQ9GQ50Eb2RJWsBpsyivQsvduylDYkkvRi1UhSarVNUqchE2kZUIgyR7vFGNuxUquBuHvQavJAtbD2mBNl8g/raGgHEQbtjkj3HN1bigMR8I4kirRV2MnogDYhXYLlSUiGNShJKaiApq2uyiwg7KvmoZlCk0KYNol0hRfaJyYi1CDW14k2AZUIRYK2OgFdBAuwCA0g1impAMGwJOmDLKxmE1+78gZE33pbRldhkIv7FG2uY7MT804WGaW7NfpvKArzSbZVGgLUpRKz+YEXs8seDxLpL/5+99vfzxyQpoZUDYpvwdVqR9YKQCuTXMFEhxgiWQNPOa6llwmbyhLNy1yzJTQaWl9abp1dIcOJhBOfZLmcUsVVwoXLgXJAJMm1meoOE29j05Sbiofd416XP+VUWvUvx+c+86kHPvFp3zn7+/9y5j/pjRx44MUedrdfee8bXvPipz3l5je75WUufel/+f6HJjGwRx911F1uf/tnPfK0HTZOPPWvjs083eZnf+ZXn/CoP/6NV//F7/32R97zro//7rs/9p7fesdLnvuypzzujPvc4/Y3v+nVL3/KvNkH0R+d/d3vn3Pfxz75Q3/6YZ489PDD3/iiF1z/mlfn+x/zcPf3//nHf/H+D/7wRz5CUfsfcLGH3Otev/nylxx5xN6fc+Wp/xpHZv63D3747g99xLf2/i+Uy1/u8s985MNe/9a3/s4HPniRgbWUn/7p6735xS9+3iMefugBByerz5rRTMPQHJapQ3WkwiWHjMAuciskYKikiHiiOwYrAFIGrCvHLExc9ao7O3W2Uj2VMk1YDqULs1zagEeF1MnEpvbD7Tlv+qPVngP37dzi6jKrTIXngwnFw9YrI8EjMBRcu8HNT20KR5NYJDsiYoQjB5Acn2HiZqFUkiiOuvCuANKF5sVAjsj0NBYHVs9k1ePqEkCoRalhEAuxhNHqUrNUManSzUd0btM4h2NB+fNgKoIODunOR2YT9TrBDFzdKqyE8dxgcWKdRE8aq4FqQ/ZsKqCY0rPSxixUPnTa5mY7dpbYLOyWKuGgM5BAKbIiwxI09V63bWpZASWI0yQqLGcJ4aksoK7m1Bspnit2LGGsz5Pn3HzauraxYkD/t5GLB+NorPPF5k0S/CpSMktzy17RpvTZV39ABFgui3opaVOywNdtqUuto2REy9EbpPHulkw1Z9VSYbOneWbkGKMvS45BfKda5rlwUCIXhi5oJTHVaMV3I/qAByKDsxxb5NbgysVBVah7RPUOTTUUaBWogAIYUmltir3unJHuxLbTBUkfkMXLdsyj10LsM1USYLEIctx30tk67pjVudK60bQvJRZMvc1cuYfyfXYmxKgI4SxBFE4XjWJZLWfGhq507S18cJX2IqHoGCMXEt62UluWYaIi4FoGFRiXsYlQywR6QlnIIetqlpZaEwoZJVpFCjsDq6rVhlmzXDTZugABtEBIBhIOsN+NvTY0oSJr57Di4w6J4PRCGZh02ArBUPOKUYeXEfOQOnTqIBFXQ9bFZugSMkQUWUbWrlPWIkbYXAyqgA2VNZvWANfoczR6bZIKwVC2WmNtQcKRGbSIp2vmFORNNwMxK7PYLA74kBiz6I6SE9UHnEmdRJSCDL0SbkcR1SKloIQXLvZoImHC88QUnbqFXpeiRTezFlNEoGc2FKdTJfnqNhwZEvQXNYc6fYR0XmSiWxgDORnUKJIuOqkTiK6AuCbB3vIMdeVKQaMFpWGTOqfNUcrqeYIQGSXNi8yZG2O2K93nnZBCxyR70QGmooizAUKVmLhjrSRcz+7O1MQwJU9NKMrBEEdnZfXScxpVvQYJOYiMGHEgU8qUZZIx1cVK4ylHGSFozVpoF8Y/kAD1TsLZRbIGH5ZCb0UFIoSJ0U5+SJWh7tKxRk3SaQYxjrAG7SX6Bm2y5tqWaG1wAzO6MBoUodmttmLdYsSC3hROkWQVNRkYcyZtCNITjWO057LEwmZsiXDVZE6JZHgON+RsdWeaCxEaJVM43QDQkXSWBueSwIEhnVKIqmmacFxdfTJh9jWDywZB9ghiPQpyQ5woxTWSrJCgniowtYIVQxHGOEXAWCqrxoCBBLOKOZ3VQ/tIH44+GBZdpA42YAowwdBVCB6poxUmKZiCDEAhvDyMD2pSktJjMNQjliGj1TKmzZBJummL8B4sq4ocqdtuiwsyM8bKWKl0zlKMm5mQWWSeUGfIpNCiMc9jM/ksWTuUpQd9yeZgFNYzdYRlGD0WLxkEkkjSw8ZKFlC6IEGdzltmJJkYEEUaOZOaUTIRA9GJOF9W8qn0YrzTwpJIdR619MmCSQcfwxsVk1CD43toHxoQUIsiAt4RTkuEWnV1L7E+REyoVSIt1GB0KgUSXkablsWac+Li3qgA3Tl8sjpJwSquoQ5V2koJKc74lZIzD19BbjJ2R5KxMQbRpNis9D0Y6MhMd5qI4UK2A+TLhFSM6JEuKpQ0Mb3nhh3vvASYiwUHJpC5kpzjRS3FEIbFdRlkD5RVU0kYDw9knwYzNhGL+m7NIcIJc2pJyoBAFDPIVk/vEl3W2uVJIvOyjGBqVz6jlqyFeI9djeFYkUiHJAFbRQBCGK3ItNn/xv/c7J71jg96jKBtSfMoDhlUnsV6nVzLeiFpgQuDTKdZNgLCVOSiYSurEZwBjVIY4ZHUG+70AI7I1ER1Un2adCqiogH1BKKYllptqlqlVkwT+J318574+Esef0n8yOOv/+aT933sk777vf/0vQptkoMOPPA2P3ejVz/l0b/za6/87de96l2vfvkbXvT8X3/hC971mpe/5/WvfupDH3zg/v/bf31LQDJY2bfZlFIpFKQzBOuBf/346pnfvMsjn/DxT3ycl0669ElvetHzr3bFy/P9j3mcd/4Pn/LiVzzgUY/5ylf/iaJOvvSlX/XcZz7sXnfhboof/+scfYxXvO1dj3raM849d/3LJ1e90pV/4kpXfvDjn8Avl3J02nnZU0751ac/8RXPePRVTjkZLqQ36/xQ7/A1c1LQxBbZjJiS2UKmGryyFltxtZFkaAcHWBchVYsl8y4HRUGqzOw5NmKcNTwa80iKJnPJdQxb07bAzPj0/be+389Z/w3rA29znfmwAwEzrSS7pgrV0gwynrkwmF/DyxCDxboB4IAABtAFgYhkDUJjrrn4cE+mCgdOacKqxVkRMbggNrjDUxeYg9MpNsGcnEryyySdBTwKhZbk4pSTBNaayroklMAyE+ErHusVA8QzR8RILp6pSJHB4aZrQbAA1wwLUQgoRx3WckVTIy2Sp+hhpzJLqAiUVU19FF+0NUoW4eqsBBHJH5NS9pxJCgpXJxpZkTorNgIaQxQEHqy6OlomHQ3aAVlnY6h6LaNQa4YjyIFgbGh6DpqRlSWpmLNylcLLIzAEoTkqlxpPwiEq63SqFEsVgIUoZ+nqA3ziJHWbuxVJdrU6ceUavItiJiMxhnv3pC5aaYrC5XXWNVZ9iTaEShbUoeKiDQgOTWFtjEJjB3grzPnIDNA6TadXIaYiEhLJTgkjJbl+cY1YFg1B2iAbyPW+0OFe12W3CRGsym6XgV3fE/euulVzU1Ur0ELiRA4CpOnCKMB5jqbR1mbiUgSToFCvEiKDTQCSjyVlACEjKW29MZZrv9GDDqSUqJzkyqt0BkokxVAVs6mxihfyBE5DMcaeUqGLg9PCqMmC4bE92McUvvFYlSLh9E+8KAPlydRt1t3aHtYGn5C0VkUyUxWmqpVcq31My5h6UUxD1w5jEA5+AslmVkoyxzmFBEryIOkfApaVzloqFF1saFWh1UorEBUUwe8mwgJ7KCSdX+ekiq9wU7opBCKpKsRCEgwcPQAiY0S6m6SlaVgNzh4ESWVUeHFmbXdEWHKOdDDYxMli1CHqligoVetkpqVIMauoFRAED0NOmpVG/3fLaHNNn3JYdqWuARG6ZZxBUYEpaYan0ImxzcFiYowf0TRiHM1HGzEiaAW3BOahLuZaAys/asZclqohSk8Lfw1D2XaoJwcA9K5IT954ZPmsIbWkcpJm9PRtBvsEcakeU9IgOkCgBrmRNKhIVnddPLuvYEpFzDpIz0F3lPrBfCVggT7MCV0IWZUZzOC+8ibpnas1ka4UD4Xk8JEr5ZMfNEUD1cUgWPFzVaqCUtWo4cII0GXp3gnNIlPkZHsOlL3RQogGg0mD1ZT05o4j1XhNGL+RzG7KDx3d6OzwPnTpBqdnRbyMXta7BKFSNlaU7pGt4sE6zhBifaSsz1C+kgk9zZNkWs8psBfHzMTwipwLbVhnD4crLwqSAWcxo6tsOseEYRGaBp1NTMKVyxWC0k0pDZFUA+Xnoc7MpK4Iounh3CLx/KxrAUgRpxMzgylzpiZog8TqMoWBD0kXeIb4UEpwjzH4S7Dppk1C7xjkyBExMnINB5CAy4Bwu5nqqEqz0wLFpYpU3lWrXTSTTbNORuKnUEOso4jASF9iBByZMiSIAFA5TElKJeJd0Lry+0t0KHOYNiSy02qFGGEuDH6IpElFrP5H5yee6dUW3vMgbWnNEG1VomAwqhdlR7Bu69ylULf5mNyVkaFMoYOjRVswElLAG1455arbUjOMluZKqSHGqYngT/KpZ+/dG18YyUhXy7nKVLSWMPVUYRUEdUSISypSVnkiQ7UNgYPSE9RuYOFrpinBzxzkQcA9MUR6tVZJT5MRGSPGoJkWFJtaA7SumCm/3pOdLnMU4oP0qIGZz56FEzgpO41UD43MILaeKUp8+mzB0moaCrjUbjYMWSR4tupYz7qM1X4tVqSULKoJjjEKwggsit1SCKykTVvwlCN6BYUkKSGkRlqmFuDA611F998h9X74nj9fdh3DCEG1sdE0A/GQhIYjm8ZWxq505ChtzFu3liUorwtdGbId1tXL7JvZZinzVsqulmYmgKyEhvOtlAm2MRoGrqmZAL2XaDIWuIsKYz2XE0864UVPf8qRRx2NH3n81Sc/+ctnPOrzX15b6h85/Z/3VnT/fftf9sQTrvUTV73RdX7qxtf9yWte5SrHHHkEE+cimzzWx0Xvf5znr3z9G3c942Gf/dv1b2WceOKlX/2sp1zxMif9OAIvmvvFr515x4c89Nfe+tbdLTmC293BI3GxAAAQAElEQVTqVr/58hdd/+pXFSHFLhryX+L5gt3tU1/88ue9+MUXXvBDsXLgwYed+a1v/sbb3vLNb32L9h1y8MEPuec9f+PFz73Jz/zkZiqLLwu/VigtWGEnGYLu0juipURqRGFtEgstqJNsalU3MLWr5z6LfUwnLic6SZFJIFJDiibKALZMKlY3RUqnIKy5Po+cO4xX12r8ne+f98J30SQm1MH3uLlryVBnLVyA3dStSAivzq6sXawqvSTv6PkcKWAmTrJejeAQyWJryx91uxiWIWMbzJGchsy5F52B2I3cjbLNnQUTxKyK7ihvQ426EzazJWpFFi0xirXg3Sy0NYl7j0H7LUpNA63IMmSnYd+eT8N113WRGeyMZL3cZL3flrHF0tRz0pxKMn1TIBuwANA431unykgDpARvcfI8fJG1EAVPal90u2uUZDpqWabqUqzLtBXZVgZpVq6e3tF2xQlYgVXi23JqXozeRJbFa+Sc2AGNGDpt13frliYVUaJbBNtWWsYlMDqQIntIpaXP8CJJ5LIC9FoEjD+7zZoq2PthVZpRo6FtuUFUqVOlUZbszLdL7u7qkkoB1ThI2BixbGG1USxTm8ObGJcj1jDtDcsiRAy90gRZQRYtIvOUZRMyE4felt5bpItKShmYOqxrDolh8I10qka4J6NAzT5Zm+YBozYTESUKkTXBgFQGexpsDgAJlGVMSyuU3bEweoFQIUNKEk2ZRZWrsGbPcP4qRRF7KvFQRM2hsuS4UEazEcWyzlkqSlE+JxGRWXWilGkQhcA20+lfV+mKdfUJZEHQWTEVRsHp5u4uekPCVelvCJ86pgQXUYZs0qhYH5kcT+t0QnIMnCcd3OWOvuzSF54oPpUMlRZgiEPTjEHpoq7pxEUrZFpqWWa0TcYE1SJWde/0EGKO3ms6wVKmOJ1BMSIeLjn1srOYLsg2UhIGZsPo6k2tJcnSJ1n2ldzsVJurGxOa/NZhGkO4eq07yDZEeCXEfEgb0Ui22bTMs9fSuKLrYrmgx/BALppDI43kRRfZUnPp28lbVZlyLl4ys2sPJTOCNvM8wYlCI3lHdI7g2s8M12S+pUkgEiHpJYidCz+mDyK/LVPXHTjNpkQqVbFpSt78Vor2jjbQ01ro4nQlczdzCHKNbfAMEMqgAoyfxGRithNc5JWL+dhEmwIEDA1okklghKEW0UBp3Wi/S/e6talVUmee2kzYAuRLwMlugNp6BVjIxMcYjT9wehUl21560OK6MtFo0/AWoyUcSty4VyhDdBRAVjNI9gEb2GNTgPGfrLDHJwmYYj2DFbF09lUcHkmm2mRR5lAWk7liUnYgdTKdZqCAnT80ismEUq1IpQ7K3qq0whrNJDF3ZdqXEnVSyYqcHQoBOnOpxNg3sLPCngN9jB5LRshQ2keLZeXgJJCVYWtCiEXUtsjKEKzY25Y8zxTQmiwJAhJ0UBzmOa3neUVQVITqIZ2EGpAWpWyJvzOKhDZniZUhKyxELXIE9Tgq01SEAmlcapNJpmH8gpzn6JEJhMwkMTq7u5EeLcQFI3tn+VpvQLlWkTKDy2Ey+EJLCoxbpspPOpp2p+M00UT3JQiqEq5Ab549uWjwTCUQGWPEduQ2xVVEyIWwnqTvGBYJoTjSzMGYmaPyrDPVyqRzFd7erkUMnOtOkw1tSnCoJ1piJCxzE6MK11MHqmMDrEYLQQMpGKtbWXxFVAUMIPOmpwRMtRbUUnSerBSegQ9J0ES1DI0u6QiBTlkmYiHNpCs1LxV9I7Rbl2R2t4Fdld1NLjW7tNSFFoAOaE6yFfKdwTQzYayYIUFN4sjIHNKHjiGimSVsk3UqdZpsp6Kmp/a27udDIm1R+qCaImIhMiS1AYukc66w9viF2RZaV9jh7MRcySrCv4GIGtQMWcaoydOQFNDyxjeO8HQWrR7acup0dA62EHOdRUtDNPfhoNaYdNnRbqKOOoSYzGrcpe8UmmvqzYSzEzWCigbDninMGp6QnU7rg7Z2YcwmZxCbIcDqk/NJl9j/JtcE0D73lR/83seEcTGqE4jGqkuZF7kbQk52ViEXAVBE+UzgRkJHckhP5aVUNaXDyqAqZmHyWRrHSmQE4KkDFVL4O8PM1wrTGTN0JuAqOvlYUQxc4eTL/ObLXnr8xS9BTf/9yM///Wfv/ODT/+rT69/2/u8n/+u+/tGfffyU693wKrf6pT/8s/V/HPu/M/SLX/3anR582he/sm7PrnyFK7ztpb96mRNOICP+76RdNCsi/tv7PnSru97jbz7zGZ456KCDn//Upz7/cY84/NBD+fG/1PG1b3zrLqc/4g2/+fbBCgSkj/POOfu73/0ueXLA/gfc9853/YM3vvmBd7rLIfvtV8g2mXTSnCMVSbqth225Aqet/XAJn7gwVRUZxS+cwb6EQwuZy0LGQ1gjMepIdVJcoB4yQPJihHSmOiYhvYWPCCOjjR8LrRo5luU7L//t+O7630gfcIufnK9wEmQmzjl6emgEnxE0CsWVtOfGSDKZEFG17Jc6W51pO9jnh9dIKwrNnFiLUy2pEVDpPIepitAHfuawcC0oOxabuXNlGVaaaHiP2Do3bBiDkEXmSND+0LWmuktpNse8I5syT6olIUn/ISGQSuViBIoJbMOTpiencQXEFlw7tGfujV5BDveBaBrdmsqYU6dg8SwsaN3M501seExj0sExLmil+loalJKE9S8lYidDlAYOPtq6lkdopEYYA6CrLWUKm3qUJTSFW0fNUG8V9HcRbYHhAYgJTAd0OFiWkpERVVt/RFnuh5sLYUcO14ZNUxZ28aV643dtZV3Pd4YUFqMh6tztwXoPb43ibbCQQ1nMygjL1CrFxCBUMhaC7MStUl2Qb0E4Rs5DZ9WqrMitjmEiBVUwKwoiAukCHqKUESm0ibuxCzE1M3oLxY6iIgQWboB4Fo61GrrpMY+lylZ4mQNAUxiNpNTsiKVH2w6a3kNDQsFQjKmYihET4TlNRGJd/to0elk6MZQBmIt59Shbm7Z1ytlKFQHgKjkpREzAFX+fjIqx0qXRrhBJosNrfE5xDpfAptsmUiOcEA74IMd4qEPaoG7GaKUK5ddVQwq484pqwlVw/UCnJYXWrl6NHBiQbkj6kKQdwiWiRCjJAFJSGKIpxFCU9wCaju4hjS6BkjOGsmsCaSGdWRbigVivWOQ0vK6RSMpi8MMTbQR6w2ggLDpUqZkRoW4BSKPUzisuOawMI2K1MwMcQYw2olOlaB4r3FKK1VJEOJmgu4oSJikBTZEQhIiLutSmdWihFTUYaB2VthmJGkvS9uTeBYxBcjin9LAewjiCn620OnmZeC2hTie6pw/C5EMvkl7EJhoylbWyYIhkqsikhmq+qaPMIzfYWnbLMUmYWKnKWZIFWVZMWvIm8DaU7BPMZonaorTGOQST84jH4sSY5Ap2Hlh9Af2EkPJuujB1oWuixCTglRAzk8nIc06nURIpuWVKWx2yIa3mlIk5NG0wz5hRClPBk+G0DIiFa3gqojBINlIihFAXoprSwmh2gUyZBMnVm7pzA1ipzJP9zNaY7bVgnmzmjyQTJktKBVYxk8hEexlBFKMPIhSWyfJBfxkKoz9CjsEEk7Bp22y0Vi/mUIlClFVEVmpJgC+CEAx094b1wSBIcYrXoiZaUUqRooGMHsxkhhf0i7hJCCU0ekF56inbkL4gBhUhStKr4BXanwlngQqFmnN8lzEEBMZ8ECvLVEnlzlW2KF109lJDMymOa9zKdpsKI68haxn1McKBBMoYmi2mJUp3SacGqVWqJgEUWMqmgSRSAFZE5sxZE5qeRF118HySFaOucqO5LZQZsjLfe4xdCZbJruHREV2c1qdgdYImq4ArE8uJSVhQYpADA2NASlIdQPcBdUzBWOx9Sp5aIcxIb4HmCof2YfSESVFSkYQ16ZwABesdEyN3NXd1LJJDFLWwHIkFJLV2ndfFTwg46x+nqwppwCWjT4OcCuUtM36PVruXJhqiKSxInBtJGHLUdIaB6/u6nmUx06mWaTKjfEY8+pCuQk5YZ3yzO82EGk9tPC3TDDqZT5PbjEK+UIIXbZpsM0Zz7w4Pk5xqTAL1noNoh2S3GIUhWxQd8GbCZGyqsChl8JXWJj0itmUQYcIBEBUuAlOVUpgU/KRZN76Zl1IWLQmaY3wa5LF1IXeALkjJYc1ZZwWqZoOHGMRUtHfCM+l2Z268XcmqMjvmKrJjmEQ1Z2QJRIB79Wglg8goGCBVO/Bet9QD9sHjB099w3LWWWEFmDOm9NoHKxF6dIyUtSYGuaomG8390XcoSbpPxB8QWmQxa59kHSNjY30HMdHwQc8ry4pm1FwyiVyW0mcCjszQSBh0gllixBJ96C5sNy956DG/9uznXeGyl8WPPL75ne/w64Xf+qM/zpVfP3Lhv97bb5/9/Qt3t2d982u9t/876770ta/f6UGnf/XMb3D6tX7iJ379V59zzBGH8/2Pc/TeX/amt5/xpKecd/75qnrda1373a95xR1uduOyxv3HEfz/eG5EfOgvPnm7+z7gLz75V/9DrDMPPPDA297ilr/9+jc84h73PeLgg3fYGVQNpmWUqvPGyHoSnnRTuCpXXSbVlG3D3DSQqcnMailbFp2+VPQ6m+4U0ZSevrA85IANSDiXxC3IWbQsA1PqPvDmWxFogmmHWXIWlPBzPvaZ7/76ewHYsYcd9og7mIC6Iak2tGzDllG3qYn1oZlrNs9Z5rSKklplVi18z7KhzAjJnANMHwNrzGyYi1BpqgAQkTrZZrLKlJ6GVID+SQwuW+qRLFdt5O6QQRdYlFg7rJthhlQpKAow7WibmFWVSsF0owoot9pO0VlWKBQ0UrWrLmJ8OyWrQvPeOqsfRYDORLIouFqAix8dYTeX6pKNZhsqrLBSFbNJtCCLDJdwU8EEUC2lEJOYMLBpbcZ21gXqLWK3o48iXk2Uo0QpaS1AEfwUUtyr7EbdZYV2n8YyydrcePVVshIISWcQMwSOArNUBX/DDbxbuUQLLiwR+xD73OelJ48Qmcxmg6Uj3MVpQuP64DR37iid+70+rI/1nr/Rd8KCHEAMSUrXItM8rAxlMySohqlgIhAaBZ2HSdDtokVtIjghxpYj0ylrghceNqqQksuEMJHEZsG8SAh2Z/FZQumiZ+mYe8zu1pr2KA7CpJPryhcMlUGcs0uEusjQgeJSsgKEg5jFFJxQMk0WjUX7kE4vitCDChGrWUXnyNqwfsPiuSC2E3pFZsBDhOJsGlZdKjCpaxk5udcUMIQwFxtqQzQzkS7wtNVR6BA0JSmFCEdYBFOXhtEgmTlXM2ti05WixdJ5rnCEYVj4an9JmS3NBmi3StepKd0baQtyKMHsnLMO30J6wFlJ0igvpPCFm6eSbFtiSSxAElMLoW8qjbYmaBE7GXgfGbyugAgwhAWgjJDmo4GvDBS/tRmWyGmSzSxlk1LamjHEjqk9GZ1LjTSCK9hl+wAAEABJREFUVs0nGjNkMMxaZvaChSWHs0pIDcLdN8qllBhyuLE69T3LiApG5urCak2ouEkU9RpNB8kYCz+7GI+sJW0NuCNjacFdBeXAxMADaiKbSWq10OzsX1rqQM0ySyHBK+gp4eARWqAbwaTQdZZ1YJujZfOFuYnhOgIuEatNiTQnFNuNbQvXecEu1/EUaTJfqPOgCGWVzMIT4DwkQI8ZIwWiGCZZH6CXTlkyFZ0qSg1BgZQKYQpZ4RhIgpEvfELwB0Ol6bwN3l7YgBp7sEHSqCKVORM9tC/w3YHtCgm1SUUWrEOJV1eWS3rC6EI2BYRvDW+SFUHTMkW9VszFCrFZM8QhSKzVB0SayKZFcqjRCyFgUsp2mi9M20Z2J3kNuScdfKzSKKjIQJBpjacSMbK5JHQHuU8wGXzStlG2TeJCbqRmKs3xKVwC2IoOjsewWNRdMqEJYU+3pAeEEI6oQOUCssnBOgIEOMYtWmkLcwYwK2IcQBlmDFrsqJlkyJZi6esKeuWstsJJWzV8cspQTxUfNaLUWuqmSFEBKNmE12kZUYoh6Dr1WoRmdCu9ZgqzaTQP1t0gRbVPqZZKXaitlYVZlRgpA8zi0b317hlgoSyTJqZmO20zLzvGtBvrLHqlkWieS/eelDkNBkST9J4VG1p20bApo2QIRtPoJpggTC9tzsIJdra9ETeazNCuzuTU3bYDnekH4wOsWBzryMgEutUQodc8BJXhKtsybdWW4AgvMSYZK8tCp6Z1JExzVvrLa8AAZVCkWUGtUqqIFSUWCO+9k6cLrA0JOtWDwaTxajpVWrLJXmWIMu2wOLYqMYvsAGS0NHhDXzJamiT3FQSXXiUJwdAYtYnqmBJcUWybDHZKCFzNMWVqDppGD9nNG+GKMdNKesQaYJsJLG45TTrvVCsFEFCNu1AtIpnOqK6VJ+CQxEpSAonZGRIP2hVr8MfQsJXPSEWW1EmEi6QWKEHdDKlNbEiRlL1ikK6V0UIIguzb/ydOPeCm65cq47vnfO+Vv7u0pQMEQwMszCG+lkYzih11GpOl0fvVHIMZVBRVS5EC4dKCruACmRGV+ZLcsY9MWjVV1RlRwiOWgdAVhtWrAbfsU0QZhkEzhU8BT0/ifPHjLv7ypz3jWle9Kn7kce655z70qU970evesPBe54+c/6/3Ni8yad+GUb/o7f/B89e+8c17PPpJZ+79AafrXPMaL3vGUw4/6KD/g/n/1tDh/qzXv/mFr351703rfLdfuv1rn/3Uy1zqf/jrQP/WvP/ocz+84MKXvOFN937kY7757W//qO6p1pvc+CZv+tVfffYjHnXJI482yWIplinJTNNG7oiyK2+lDiYnT1tlFqxrpSZKJHxdvXhvwZUUXvNTYvDdkNl1TlUgSL1YGVigKhZZ2RP2MUWfsJgHOarkKi8apIgKlnN/+N1nvxU/3ELkqAf+/P5HH6HLkNEUPltMU8pOjJqBoC89RxdxsDxNFaYQGsCCyZQRQSlgfTeOWysaxwuYYrAamNNrLJIOzpU6p2xCJkDq8MI0EulZaB6oSAYKok5gMRSFjqLNJIsWVUsNsNpVDJ5KSMKya3bjezEVkcw1Q73wOTR9CjYaax9cnR9HgriKrLCFik6qVgpzvzD3HS7okJSopc/aOJ3DK8wCFMsDgKiADYPO3CMuVtoAm5+KpLUsWSEIzkeRXqaeq+uGzSTFBCNkLEWi8n1KQMLCZk60lQiadEVV6EkkQKhisFKDRpVQg0A5gx+TNg4nYElzhbM43KlVaadoTbEOTaEgVmebVJTGZxuD0UsKYl32aRm8O0Ql1CQCHnzDQ0EhVrhOV0GBsPLRL7bX4K3nJhTEsUStGKoEVTTTzqjYhGJR0YosCR8M5ijuJWhyQVomuZiInlzEI0JFhD6mN2QL6b3GmNLZL21qTBUqkNx7rtYr3LxTnKWzmVVehtAmHsZhypEOWXRabLNOZlALOesDg4fHABfiZWSMwXPpofBJuimtqqkSkj1yu/DZE2kC4yvXKAuhRVV5QjpACVLAxlg4p4B5tpb5BkBAOMxSkqlsMlSpRSLNSC4jdcNAzqyiUYqIievaGYWJUFB1F/5kaSV77Tb1KikBRwYJGKIwlVpMaBGIm0QUqqc6Eq8kiiAxHKuRBa60Vkby0JHqyXEQCcYJxtUpw0WD5k6VpqUVsM0tHIDOfmFQbV9N0aWuLY/Qwa2CXHSMoEFBW+igsr8S2oU0QELENbl6DqIcg2nqWoaaC7fIhfPgITlKtik6ubB+EBkCR0Yi11hIKtJy1OBNwSCadNctYhppQR0A6HgOz4GkSfRWibH7nEOyZ/YgDiagvx4+MmguF22PWK+XbBYO95CAELMoYFnl+MIPZrlGRZgEVJPMxZyDC7tIs1ym5C2JbivAPaJrDwgb8uzpzr5Hd9PYfkW6TqFk/ZSp1KIR5gO9r0Y7lcoq29W75GCqalET4UmzCvZcylhCaEOOHN1HbEeMkQRfRWmtuI7Fshe+n2xYcRkQlq6Rsrj1RFdvwikt0RShFK5CqgY69zBb2NCKAM/AM4P1JqR4KK1kcq54ZR+5zWiiCVomgfWssPCD2cewjELvsT4cJAgiaDCC4twlOAYTmSpUC0grGDVhSkMQiUFVtLdZaRAqXcFjGWOZ7iJdCJgGeTeY+0H1NABY41nG0EHoo0OGKSawagNOsSTKRNx466N6Uo8WRhJlCAa3OqIaRYYYx6LDKaxYYcunnsYTStzEUTowgHTogESmeHKPMCmK8bOnu2dyhpRJdEJWiVKarWWYk7gMaLgy/kk5OXryk6aJhEpX8dUugGDyCdUYbo4XCkUfqyXCh4eNwSHF6+xc/1zZ9tcxDEpdUUBxs9B10twdw51h0+QnESgAs9A6mOaiZBZvnTg1d2jjOjRYZwRCm2hKiDSll1A6mVyn1mgMFVMhkoQjVqPSE3RDYuJpEFWiNuk8yawyr5ntIT7oa2sgBUwMSW5oc34KZGIFQIRqwWIxRRQGIhENMYKFqXBKXS9LuNngDkWLpCgPmlFTxGaTWkx1ypW64tWcju4WXGirC5LKeXAqK+GCcC4ngx+DsoUjPWVAlVFMuhYqHVyhesiF4JKoHmpVpzmkOPhohr3aI0gl6KAnI7lktYVh0klkAmvYdhrNWMXY04ikZGruuQsEDSpSZczKO05jLl0ZYItRDIc94W7l2MOo5Aev//3vvusjIxwS1LcGpubgBt8m1xm1JJ1kOaGpMSfLEL3uZk4tOqABUBs9Vua700GH8dlEdZ6lGBmY7uHCdBF3a+ASnYowd2nRh7UovaBPfLtk8dzguGOPes0zn3Gzm9xcZEWNRvKItn3R617/mOe+8Afnnc+P/zWPH+y2iwzbf9++i978+5+/94Nz7vekZ3zpH/+eU7hLeeGTH3/UYYfiRxDg+f+L4xVvecdrf/3Xe1s2m51nPPKhTzrjwfvvv/6/Lv8Xov6/m/KZf/zSPR/+6F999WuXC/6H4F7rald73XNe8IrHPOpKp56qpqqu1gEuCukjuAzY6GgtFuRwBOugTrOYoSamIRjMQGeb50hJstlMIYDkyGzMPcy9KLOtkM8ckzVkyiI5wUlJZVKDNbAl7/VwRRBIEfBC+Pee9Pr2iX8AsO+GVz30l68P795aLlEVSjvFlIFTTSjHtOJLjWYWyWtARu9j2brvJpzXxapkQTPdFmYyz7AsgallyY7DNYK1LZlJBghFDmaMByuGIY0pVqrIepN01sp6WGrWQsEeLX2Is9IIoaEfXZwVgItxeCBdpIMLy1otaEVGL1zshPePJM2618FeZ3WIHXAtAGWECzFWYsxdRM6c1bMht5IhUFn7WMmF8cFI62msRSzshCGSS2umhNQ+m2/2lDsrh6CiTFXqilcK+JDu0wgTyASpKbKU4RRlsEmgGWqKOonNopuAuTGCoBKlhUIdTl4IutRhc+iEpCE8L6MGRXVJdwUrzWRs/0ZEUohiYzFJn0ysWlRr1CtjGy2GlDHVbrOP6ouORiRElPZFaoe2Dl8MfZNWUQVIBimrRpkAjhuZvfEXqCaT8CIIrrcpWZR9lrYTbW592kb0afF5N7RlBIZKV+UzVtgbYlFzRpYiCtVyFzE6wO2mFVWzUiZbSSBjAxTlCsL7ztnRQhkn2jWLqhuGAZWGVqE4MBHQsmxNYsPS2117ogtShknqSIwIy6HBdOtDaJZQuzh/sIhvfewOH/RTMYMtTs/goKp1Mxep7uvgZuIzrxelZhfAR5IlYFDmpZAo5F5aqLjmMDjqFNNOECwxKTDNwv5GU4ugRPJE5yzlCYc2yQGi1E3EhN9RbByTyxrETa/7AptIo04BRS/cvauuEedvGqdngLJFapVp9mkeWgEFCTcmTsxk+qjYugMfpWfxquBn8ZJj3VbLUEf2GL0F20EPc96P0BSnXHB6OIIIeXcCnS1pLYCsFTHnMtHhToxbI9Niq8Ksadg0mTupE8o5MvgSkkmJXWtABGR6NO27LCwmUUtM64GismLEsTpcG9hc5PCeS0sPwi6sC1yE3ZfBTnM90LMOry1qG+B3LksbYzSaSp8kwfSbGBjjN6y0J8WtdJtamd0myzINmV2KQWdTEx2iTYTBWURaDlYLm6DEPNGAka6+ciqX0Uc0L2MoMQtElO47zecRqh7ae/TtaFtvPZgJhExCooA80MCcwo2wrggzN82UxSlCmFGyFGmQNpJQ1q6zJHkguk3vNXPWSbkpxxSuoOJlyeYDmmA/kzI81p/ITAhcMBDuLWNUeIHycx9wpHJAG7Ew5gu2bVoamxiOpeFZQ8xgmjQvGvWChIVMkJmzsDLCSZmGBd4ZWnR6hybrs6LO0DpEXbQzqhvJfRoTIkJb20TbsVEZ2owAIIlpjH3dd5IfKY2mJSOmtF4zeJliWhV6TwL0rSUTMZT0KXRFAgY1LamWBFNZ8rSpMI3B1C10pHRW11Bjao2C1XyP4LdssVTELBBLX9nDqCVjPUDidKaiWx2z+sR409gdz/XPP1jNHWXxb5UJb5FCr0ieTcOGQSqV/iAAljSk6FCO6IJeSCcsU/QyVMm0fZKTpGrGunaxWjEpifLw3qAGnSRUgmzwIqIrRikCxaZytgUDAJ+UNyBdkcLg8wiZyIsy7ZO6k2IIupbk/qiy1NImhQlDkovFQtlwx2hStlKbTQGxEVPHxO/El9oXI3kjSXYI3Dat7vg8gQW4iossob3JGKRf76ask6VMoiQql2ZSrwCk+AhEAzwGshHmNHhGEOAywmg4KBwwtblOM/ckRcqe2YGSKEV0n8nqS6ec4l5CoUK/+EqZ6KKhmbw3wPM83cEduw6n1LDgtn0kwi1CegZJT3cy5tE0t0jWUSvMwpydJmY3b7MHgSijCCcJwDsOfJskdSpKlxpCykgwxXKvEHoD/VsI8IwtgRkV2/VL+ojsQ0YrcEEMHpsjDz/48XcHkG2c//Q397/7mhWCVJF1wlxYrrydFPcAABAASURBVLmOMcY95mXYaEyrIdlDR5fepTEUwjiLD4lGISEijCeUhM6QSPMkHuaEOQpBdu6uGm1gCEDIzEuOOoZ4uqXTVi3gy0oL8vGQQw966WPPuNPtb48febj7O9/znl8549Ff/OrXf+T0/9u3P5a05YL1H4CiiForn//9xznn/fDBT37GZz71N5xylStc8SVPfdLRh/+4f+KLoj70sU+8+NWvZiLPm52nPfpRd7z1LUyV5//rHOecd/5zXvem293rPn/+l38ZTub/s2mnnnzKS57xjFc94znXuPJVQoUp69KdHWxlDrn2AY8AmeYBlt1lyyRAqhUzwxRc+TR9bi7eR03mlmTaGNUzK9pGlglLkrmDHVN2Fk9OQ0JTJdecc5mCUCWkibbSuy7IkcIT+Po7P3Duu9Z/M7ocftARz7qnMGPW08aZkjNiR9rGlg37HhFmLaQkU5VzWYKENcFHgr/evCzLpveZzWRPZhNCy8Lln3V4dWqwqFGADqMRHbFMy3a+cCkLukqnK2u1EQs1EdGyEZvDNYcwyyWTrnZnpTEuNKnZRYYyMQNJ8JiRoL+4CHEVEFdpNCLNEhPYFjCth6VMqqVKVu/qNB5lHuxVBVvAI32MxdlYBFimlLNYkWBJkimzeRRLVuEOtD6C1QndACg1C7JKzMJiI2WCiWmaDmpWoVErRBxYwBaM8nJohCjGRvrkIakBYwFsNjewQcosgxaw+naeZ6SjJAT03j0XyTZLr+HFVwgiCUZgShUJW0Esslo+Oaa+Rl+qeM2ubaiHQYuJamZ4EARkQfKj1pSSkeKhI2RgBI90LGNsl2X0XlYInJxqbrtRdg3bOXj0TNkVbBOLYzipgCHauRukXzyIa08WWaehmjxhqrQhJluEIAEToPDM0XW0nRFVRBW59o4bEaN413DNKNpRe5SRyo86E2TTkgqeHfQNYwiRjiAQMF+BlmSIOklQkjDophi9L8GgZJA5xhxNdKyjBpeWuVKJxarC0gQFHG9SNU1FeLePKyd7ApKFXod0yWHhKUIagAK7kPSpBvGCNmu3dRWSDnVUgH6NKphU1PamiCFKH9pdFxHu/2Maln2irxTAAxYoIXTArIlsByEKdeaQw+Ai0YYNHmCWAjtlnkqdRee0klPViZW7TF7pQJC9KhkavXgWLzIm76UvEgvPQ0ySQ6IyfISFSRm1rCfVRtQ+hDgphUBNPWLdBHSivdqgEigCs0yQV+QsnTWHBq8OCCWS0MgkdsssPrkVbvZUvASKB6jBkFQdVaIopopNyTq50Bq1weEAPMAqYz1rV4tJU0swOYBgxAAWSy3sAMsQGlJlqA4aBlgShY3R7uJKWNRnBlwboSfkDWS7RoiTCazKhShEEUkQ2M0S1mNkrDHRIlHhUybP1QtrGbCVmGme84iN6xQFqE2M9GDFEiBTMmlgFgSLku5CGOkU0rDW1TsbTQaBXBo8yQqTFA6xpjO07qXLZARkDShpE2rYe5hIKS7iyGDilnQhvDZI1CplUkovmVP3tUogSV/VUGO1BGtMo7ORxc3C6J5zl+NNVVQnyX3iO6ob1IqqWsNsYSiVhDPmR4lC5CF0Dglw9mBgSStqNtEAhuTIiORrMkKzR3WnT+L0mqQELwVXK48U9E22GX3O4C2JSQfYrs9JXbSV7bVX3SqbNjgFEmeiKRHwjM7Ea20OdoatIGlDyTFjMYoXkOOBUVtOo8wuJpA0D76maTNZMjsy6J+qidWqdRpTbUr15uvf+ptGIisRFpo5QlyhtWiZTIpGca8hyiEiQaMyFVlNJ7MprQ6bxUotobUzgbNok2kZpTUdayWWHqN7ROdKJTMrAqZBuhZNCaCLD5A2NJqWq8gEJ0mHc+VsnStmaTaJohqXHJ8oPScGtQt9kkURvOVVRVS6yu4koyZEbchKy1BSog/zLl1sKdZFHZbJWqvI1RugrJgYP/SA070hSuJ2MIxIMm5E9GVB8BrCLGuxYhOUECUTOmsmecwLymCsVHBO1eB9D+Z+GjQqxhSoNJDACUbJhcHXKpgmgOVDlQBA4PzWLC2ahLcui0tAmemppYdtx953KNnXgBcnJsSyjlWSNBoXvYrXkj6T9DFcx1BAckJMNaSmVJgaowwwI3rGAqpYBBdWLNMIDADCUIrUAsYX2TnUIidHYcVPtIBzontYRmUOyohET4mka44k1dRNXNiR0Ojjbnm9wx94GwD9m2d/9ZeedOE/nYmJgVb1qSQ1EPWt9QWkn1DaGNkjGX7CSOKvh1AXrzI2GVkiKy3I0dZMZGO3cA9E6aZFFRmD1tH5TCH7UC9UsOZFHUR2pQdUTIrQl6HiIa77yqPPuNcjHnS/ielPOXtHZn7mU399u/s84IMf/URk7p37r/SUTJrVHlNZX/59v2efe969H/vEP/vzj9C7S51w4suf9bQjDj3k3zf1fzXq7HPOedqLX7Ysi5k9/rQH/fItfk4ZiP/VjP+ga32Mb37nu29+z3vv++gn/MRNb/nSV73iggsvvEi3WbncqZd75bOe+Qevfcktr3Nt3gcJad2WEPKYrIjBtEGstA9JtTYx2WpO5lPEhFwdjI7OnBvDka2yuIikSUqGD/MGRZfSoQPM4uzOmEmydMEQXItZ2nJswufqZV/Dvg7rzKY6Ag1tnPM7HznvgS9OCp/q0U+7+8HHHa6iCTXImsJgOmoLgWtNK7KaSyrw6qyeiHB4KoqJMelLxNp1Udhg7JOMDpuAiatGNjCTSPFM1/VDp1/edZ0uzvXYlE2SrMbLkCrM+F2N3RHuAD0HoaDfedEnAgBhmWCp4yz2HqDkASRtE8FkXEpqDvUFCJhwdqQtRAgWpjxZRsyu0x6W6+TVmYxIZEiANcbo1jIJVz5wcWaSUzgvRcmMzK6s8yPRuIIU0BZJqyEVXhM6KQx8HWHZZy5RSkjS0wBahuTaUBpiyEBQmnoojQ+EcBCSb1yHqApVi0YRB9dFacNHW5JXnC7VsEn5QFKxRfFUkiIRQBIHEmxAPAsXXeyKkpEhirXqy6qvqTQ1lmCIsn+g7eqslVkQVWhpErvtEtvdXHp4Dywpu0W2VZcs4qaROTBcWMpnZ7gCCTTUzhUvZyH1qxg5bINDyCEshmEZChMzNfJgBCcnsiQv7Ba0DeMuqJqy9hQIgm3JQHAtbSgNDJklJLhYyJAS4P1Ndoi56OjT8A14TqRrpoTItujupNwueCVtIEJ3Z4hBnQf1ZlfroAZGSwA1TJNUrnYqdLhLxiSSVUaRvbUHZSRjgfTBnIyMAWLtJdsGfCdkZw+TFJJSjIYaDRrInkAEOiPE6NTkNoJmiMzivAUrW8XCuZkozKTYKE2SCInE4EwRUWFYMzyzJbipaYMxHMQaVcDRraQIkSXz6zSL7iSqSw2YYOWRSKGQSJEMZNKZkaHmag2MmTcFfZSS7A7WAQUydZuWqq1IhwwqaBakMxevmcELTdakXZACqSskQBEeAtWyQriZZFaFpE+dreiQMqI66Ag0xpS5elkNM4XSlhipoyInzoea2Aa22Wahoyssw1hCwmWBDiBFs2hMOWYnccJpgVhjApai1Tg9REfO0E21ahBZ+SyAO5u0KbFjwk5OAwygULOviLgPGX0CdZmszAuZhpkz8+bF561vFiFiCiU6Gzc+aw6RRiIV0QlOrwqYGAV0kLVjEXRQbTIGJWvxAq+RlS6HGEpJKNPfV/VUzlask/ZMLPBKAWqZpp3NvK/WOtUglM7QqTI/ShVTSBiwSQYNuZpGK2AgNBAzUdFVTCbTJBORIqpaABUnFMNJ/cmZa0VpIKluOe3woFqdZBKfmmhn9LMDAQHpahu1fSu9yo4XBSBJ0ZyuVFdktQ2pEQDoGAMWvYDlYpj0zAAV01pZY8JgoDNv06BKE2vT0kScfhLn3bKzYo801+qiPjCcJWjaKmu+KZwHrUq3xUsw2Uw5LCmhp/TQQWKgNIvG4qRCEyM4xUxDa4bFEq3xVFbLfZYbgREbM0yiYoKaWXmiVNKKUI1uzcnVeUozukxjmI2IMHSLLoOQz76ZhXc6UrvHUNGqSj4jHLqgjCSTZHh2WQ1P976ugRVjn7RJHUnQChjaHdEiK4AD4kXIHNrbBwZ3OM3aIm2MxumgsWMqi8mIXBHZw5108oYe0UdmR7rJwj0YlMEsXa1zePGhXjDmYE8f0yZsX2PTQXIwzG7GjVyfPKUVrHGWmqieGqu8Lu7oPgZjlWD0Cupkm3meOZch4m4iGHLFpDKJCFEX1je2KIVBGyXHKgiMVxW+q4hKPxfTJmgpCyQAFZIkVTrgRJmlz3tn8StBjmmS4wNz6zuxbLgPqB6FmHoqQtRynmCTGpdEBgahLAsMGknkqgQsMBNhTMCcUkCU1LfijVqQkJUnlaxlddtV0DZQKGHQomq6XhmBsabVrCGm6xz0qS9zMOlbEToyEm1Cq9zesdub+hCGsppztKLK4Q+//X43XP82yPjOD75wv+ef/c1vNTBusVqpo+pYxylWdpGfkiV7tVHgxbnS9qqNjqQxtjkknMo8nWBKkptdtCtABOkoX2jkFMWyZpLzyREgSAJGuwyq7GSHGJaRvXn21VYvd7vjbZ/06Edc7OBD8SOP7539vXuc8dBX/MZbL9jd/sjp//y3vvvfv1Ex+3da84Pzzr/fY5/8sY+v/0rY8Ze81K8//xmXOOqIf+fc//Wwd/z++77wxS9wzE1/5mfu/Au35pv/3OOCC3c//Q+ff+lb3nXXhz7qer/4K49+2tPf+4EPLLzNsGfWzn77/9Q1rvHipz3pPa96yU1+8hrpLOMyTZB5qKYa+BluXMnAvBQSazbsqG5s0jIV5n1ZMW/Rt3nhGNscwwRWFDvhk6usHzkLuCiTe7I2wSchXcFsM0gdUiCpOczD4c0QIhKCtcxGZpz3wb/66n2eRxFSy2Fn3PaQX7iOTOZT6UaDUsXBoepjas4vg/d6BA6uohvmsWQUNsFMiqrJtdR4P6DkKOlgPfORzpqzO8uWN3Qxw7ME2/uRCXYfI5i6zcqoNac05Y0hpbFSpZlEFuH0GCNFnDDM1epstH0kcgAcZpvQuSsXf3OVBMz7PLaFPavEan21zgG58aZMZREaBuKlGUhCAPATy4El1w/msFdAXC24i1PVQmUgXAQ1ohGrAV2COmgATBWlDJWeAR+zoxLu1WakiwBirdQtsnG5GIhQ0Gs40RQhvLFDwNJoUtlWroRcElanWc541GBxBjBpluJSgwH0VXYmyJQEknUyY2IUQndGnTHPWlbYdem8xQRypmHQTRKtIC0k+jSSPT27M9Z0wZLqKKoy6ahwNQXpqFxBnXDwNCu7CILFL9zCS4pJ8gzPVYm5YDJRz7LAeoGWXqZebUyWU5W55lz2/jhLKitq9ckwa6lZjFuIkdHDPRIM5tZGq7S6sRH2KlhjzBeirwyoc8VHjBQiVyKVK7VFq2gGsqeH9GFjIT91iA5FQi1sSuy0XrY5DtZzAAAQAElEQVTNeq+BAsBiKWORyJ6cgyQxI7DmhJeGebGSEzB5rGiTBjzEiRG3O4VMp2DO4+cRGZAkEi7mQqI5h6AIyCwAwhfji0iiOFkVmg54s7YbvTGLaRWoeo0kEJIhUOYR7WqCwe3ODorJnFoXLk7aLXtJDhFIpCwuPkpKSRWX5AGRiQfW9o3AzQnpyGbb2GwxbcH60kRGZYqNOdscWdSZdpbOTjIDMgyt0BRaQj3woWzzkGlw2UMoffTMBslgICu8xFD6kFXSikM9zZWK0JTZW5Cz6SxKcwhDjlhhspp1TL7sLDEHwGSSSj/M1OgYqCgJw9TMXJQocqZfdDYzheK9MFKTNwFSjelnRqOFHja4WxeBUC7R57MmiTFosFQaF8yI2KY3OkGPYQNzl02GWmSRVAlk10GFklDKSS1pU5pAhxrLVGAiUNHZcAk3vjr7xrz2tCa6mkPkgkpTOl865hH7YtQIdKqlnRACZYPPtIiWVxHpEs0yqmgRnUxrQQmAIQcgyRH8pDATo8NjWO/Mh4B4LTqBhgbZt5SyNeFNhDaqx5Tr5kq7oQv9GWHBw2XQMoBzKVURTKYpQiRIrk4UdJ7KThEGlEO2KG3E4Bdmvu3RnF6PUVrkEJe1W6LJLEgBAx8CWBbLWVESSGW+iJiolYIqYXCeRqRBBZLgbbdefC1fojWmyJ2GTfeNL0ySsfzdmd/+3Fe/+fWzzhEnBBBUCtQoGJlkWglCVYRbi/0KNhWsKlS9sI7NqfOQqcMaONgVvWYW0OYW85KbhcvBBK9YDQElpyBZHK1DOq3U85f86Kf/6UW/+YePf9Vbn/vG93zzO98PGIZJ09VzTqNNuDCxZTpkhlK299HQstKHNFHZmrbwMbpgUBFiGr0uXn1UGbBI6g3qrQpRZVzLFJoYw7vHWl0gVsEiauSQeuQw5iLkrAsveN9fffa5b33vE1//7lf81gfO/eHCr8AWgPgKMRpRGOJUkQIWM6DFtvUtgwc+JFAWnZqJFhZotWTADHS8NKXnAiGSSrtIX1fSMSBDuk19s1+WIiKZFVEzTElEjFVNrZNqQRjZVAUQeqckYmTChm5SJoCqKkDYQ1kI3DJLZ7IQSNUUy6gYWjs2mRNF+QrD6EhPZ05agbJekcItS6MJqSIYJs0sIEK/IifnkAJWl5AG79qcmbdTwEKayJHiQzRoDasSdUNpVqTyGjLQPZMmBzIhnDRh3olqy+x9XjIHEnyIQqlxQA0QcDRZ45KwINnTQMtQh3OdJUqQCIFp7FhuuD2rQZNWOSmipKDEQfuOfMWD7acuByA+9aVv3fkZP/zs57ey7dIlnT5SolcbGy4Hk0CVys0p0PhBkmKyAsW5+oRnb7Si8LJwIKrqlDbTGRmT9cJrYopJRNJGasuSWsE7TlYUXLxSDF6TgC8R28BWS+Msu8PNrv+iJzz66KOOwo883P25L3/lg57yrDO//Z0fOf2f/FYnLp2rDSvI6+v/5vcbZ33/fo9/ysf+Yt2lXPrSJ73y6U888fjj/zdz/n2XuYX7nfd/kGPnaXrYfe5ZjHzhp/+E4/vnnveBj//VU1/6qtvc636/cK/7PeeFL/jTj35098IL/sWUw4865s6/dLu3vuwFb/zVp936Rtead0IErhOMqY2dIpUZxIoBSxcSPNW44KcWY6klvTRIKxUF+TgGa1g6k6RKziZWRese2cRnyDzUAqqZ88i5m7pCBfxc3NMSMFkrPald0slzNYiIdjXPC/74U994/GtZ0MTskHvf8sgH3U5KGUKeohtSICwEGZIdrHtYOqsL1kdhZRjME4uqUThuVVlLlpn8bgW8leAlvPQ+N7cxJAboKNZiEkOasJj0yMV63cRUzZR1bi8TQ8Ey5slyk97UByA+zW0qUYPFhrcaVDiIFU5pQGE5aBi8cemhpYv1oWNkNuRaQ0QLmIOZyZsEHjTUYAjhT5rL7AQvEBFJr4M1ylioYoJUYVXjEkD8PLlCt8FRLD5iQ5Qni4qqpUxJcENXuG0tWSx91E55AY4KnsPw9GHEfj1Ds8DFTUVstURS4VV5UBf915AyUEZKZkLg0J621pWk16WIWqVeWNB2TMNKk9JYHq2kKSJlDPNFm2ePETKKRimhJmu8gSQYpk3BgEYq5RFXmLjwoQqRSGAo7bAUWslzpsJxSuU02bFyoSfX3NliEpTUOYPNhhr6ZN0k1I2WzUErA4zyKMLFyAomAwyMYxdeyFwkurnEoL02QOuQJauFCfgYgi1y61yyMvhZhREkcThe0kQT2kOWELYErpJ7AgjdIrqVdHKIH4ZhW9FkHYREDIwRo2c4vYXAJQSqweSaMstw4bLl4YJm6xwB/Unq58zkJb7oImRadZlyZSDIALU1m0F5qSKRScHgG5mX3KHJpJD01U0ZyEHjAALt8JEeI9WNTldsak4qipo5Da8hosVpnHFApoLvkxEkPi7UI2aQNQMgMLoQoDrZZnYh6bInH4GWBHq4LoptJRPDpYDmsscwF1lUmpZBOSigIFmNB4hVJoNCT2XFqinR0DRblSoQbulqK22QDIV5cv2jzhjBw3wUhAApDvg6pJorhYf6UklqKGieBuNISis0SOP1GZoMpWfyYBYl+CORtbOiRGoOyAhqVlEeBWkYhAaUzZkWYlGM9xNbork6a0XyFEoLWxKhDu0izo1WoVQLm52AUAsiVIhIVxmUVDxrapTSNuqVZso0hMlGAyCpoGdOSyIIFq+SuUOwQOivhLDoFXO1QHj6OiaLZVV+ySNzcCkghujqozomKOVLDbas5KEllPrnPqYYBsKwIohkILsrI0lxIq4IwktvxkBGOtIdnJCMo/CaN3RuCVQ6pUHEg2c9MPia/BwpETYiFwcNLcab0Do5c5ODqDU8YyRlOuDCIeGSzklJ3MoAnVQGePW+aClGkRwcXbLVKbiDmExBCIOecpyKwWdqDGYRtbpUlFpMDSCM4qncqzu778e+/jd/+Xmveel73xdIGFJFvGJrxsDAgqlCUVijL1p4sa5aOILBMh2lLFIWmkxze1inWcKSw0IFU6gUFxmayveq5LMTNzhjj+9+/5xHvfytD3zxm3/tjz7yR5/41G//ySe+9PWzIIXGY1QdvG8kqSvQrK1klxPB1Wog4WJRDTAB3SU1LKNkiHtvsQwNZ3JRJ9aSSWUTbRZZ/Z/VJG2FfYSzaDE0XjRKBYoOJgPSEJ//6pn3feGvP+71v/Xbf/rX7//Lz73xDz/KBW1QoIVMFETFKwcjRJI3/mYkMWC9kthqdIODFFFvJb0mbZ0KXU8CUjPUHSBOoTnEh0sPZr2uSyi0mphKIEIapBeLqbiuLhexEjP6LEsqmZLOgtNDgh4orQpYIhNkF43C6okGw5bJ6RiIVP6YkOJDIIxO0GG1NWm3yB4etEdUQKXJoElNvlURAbc0A5wDOjojlG/FXLSDG/mkC540OLpiQKjVErRBDVoStmrjbKEJiIAHzczkU1TELJiy2tg4OyqtUBUbSRIIpwVJSu/FsgDVXbgJUnrISzLVnElpRl2XMTedlzINfsdk0yTGGGXxoT2UNxvS6RTK/hc76JIvf6he4xQA43P/9O0HvGj3019pY00OmjSmOspGU+fUSWqmePZR3OccOnfM9IQnNYEm7gzZhGkyWbcfJsoj1pWJE6dct8llCCUZP29GTmGTl02X6lZZ/xvrv4iwFBpcauQmzHKTNt/gp6702uc85dijj6aR/3JE+Ps/8L67nv7wT/7d5//l5H/uGynTRQYM94ve/C+ev/TVr93lIaf/+cc+yjEnXurEVz71iVc4+TJ8///k+MqZ3/r8F79EUde99k+edMLxfPMfe+TXvvntN/z2e+/28Mf+7C/f+Z5nPOw1b3zj3/3jP2632x814/Cjjn3Egx783te/+GkPu+9VL3fCvDFIrsekMWuyriopNCk2vpLHAEutvUzbUpqBCahcBtAd/Joz2hKjMQ2xMdmgVFHh3XNl5rG6SoQuaQtKqjBKXOW4Lmp0k2Wetlobi4ElJqjZRrVocm3oZe1/yMb5nN/966/e/fnLl79F+/e7zhWPfORdK7/3j+gsEC0wqhiPCWGloS6RrGBumcLxubZC1piwQndyNjA9RDrQB0aCFYiGVvZCJSTZkG7Fm4QgxcC7aHShBNQFC2Uoa565gT3p0rU3xe4Q5rO6KuubsRos6FvddtvlJFXh3IEcvCK5uK/JHSFgelM39dOKxqpAaJLbJ52CqMoAFmWxCM4v8In4jYgezdsWYyssBAZ2n6V2Y5mWdJUuoJYm8KRzgqI6V93I6oZb6XPxDaRmISzdKD+W9L60WJYijb28Cetgi+JhmlmEVsKQjMsaxVW86FAdbLhSxK2GTZGTLEgnlENYdQMShE1FStFqF90VSghUwfoLh3v0oIENgGmadAuetkWmBeqYJGqGeOqC2WNSrrpOPiAKD1kFrUQEfXXxcPRQuiMb2eyTefLN1KtSDMZG2gbLxAGCztgbix+LnQzzzEAkQjqSTdU2c/ExYniGZChoscAmt03IJF6pQQeSU5BTglVzZ6lzr0Z3k6tNR3dxaISFS3ozCWJAD8O5l5syJu+TtylGBQTUMGQ0jGW4L6JburxvyL6upbHHnKKYADVa5VI7IwttqGOuPgtBgW7BtdhG5EqoGCO5banopiFBW5oXb/uk7VQuIZ5MU6c4qCnbNBg9AB/iYjFWESIBpR8yRs2hHKuQAp0CMpJJ0bZ1uxDIJhhVy8SMmWooe+tcWo6tlG6VoZvEECVdSQJUDQVhSQmpaoaSKVvErnkLz+5o1D6IRcmojFSOwhGtrKtZZobDHUEGUtDKPE8jwEAV0iZXD5RCZagEamJSn+YsG7EiClMvvGpaRTYa+8rYUa+cFMkAJDKzB41gDWmIloJmMkpkZUw1vbZABAFg3xZTNEODdIaCfhKc1eCSSx0hAzRSh1iKSbE0VhssGutKTVR5MUFD55r7SrLECREiH2COlVc9NFqxppMzo5n9dePTJtc/jZbgdm7aDesuFKMiBCOSflCiB9CHNrddQeflZAwlRUbVJERT5ryg7CKbeVSybOxQIMMi6wA23qUmkGDovCp7HhP66gZJTAM7KXPSSAcDoaLEfDEWQZBvaN22nKEEC6NL62W32xLZQFIyuDXWdM2Sw9KHgyi7LGPeLlxNNoAViFFVTBCh7Yhkq5erlpTqqZwTwXxcGJuyjE24gJorWhXw/eLau+Ug3lPVqlKkTtiorP9Qbk5OblYGMdkAbk0Wo0+AgvWP86VjYY3eqpNsnZWOFrhznywsBgw7anCOa+uaBGglDQpLXQ9bFFvL3cmiTGa88s9H6aiZlp7OrKijcgBibw9PkCtoUopHpEPCWWSQISAvxJACjJLEImeRSTABBHYtri3mrcydpEYVl7JE3Ta98AVve8+ffvrz3f2Shx/8i9e5+u2uc41LHHVUqokqyIKcljrtTrJNbLt0bvqXQ7nM6gAAEABJREFUTfYpWUpzUqyjFBxKKKZauPJXIN09PMSzICtiI6SLsyjZAh1SFKaKvrpo6qK0P3UMwTaEDYCNmNXtnHPOeexr3/bFb37XRK566Uvc7gbXuu31rnHAxQ60aVMYJBJuFp2lFBCJ9GYuhqK2I7pTfSe3m2W705cpFpELR144fAtqmw06166F+/c+Ek2QmbSGS92S2lScmG2EHG2RW4llBjOUwVMurrzZg1izl7OERnf4EhieSk9LWpER2hvagr6r4VzkYwbT1wQKpKlk1IgS/ATGGfxebV5kctTudenJ5mcxcl0k0jxZtJVzyPHNKoFqirSqC9aNUnOqMFlt6aCtsvKz97ENHWLUXZFFgCJpALWOLLSWQ4tu5zps6sLgUF2uyTa2iF6Q/DZjX9imE66VARbkGVRQAIOUECCxajUsRRctu3ympUOdw2Z1tQyudTNYDJLs5Ya0O/Ed0Kbapv0POexSb3yc/sTaJbevfOvrt3jsWb/1EWwptEBmpQ6So6WGWhiAId4VLS1TNLLqsKD+IqRp0VzPmYB09xo0YgRAS+CTDC58wvdFixlhgHTGmvUCyoLs9BogQmVSMwF4sFCplNT5Cpc78Tdf/cJTTzoJVAXw2Tb7Afj8F794u3ve+/XveHcfgx//c4+ybzWJNixL5/P/4vjM579wl9Me+vkvfIF05y7l1573zFNPOlGEHv8vJv3/ufRvnf703/99212/tbjuNa/+b13/f38uIvg1zsf/9rPPfdXrrv9Ld7nB7X7p8c98xgc+/Mff/u53nSn4Pyrct2/fHW93u99/9Yse8ou3POZih1hjVReEiZdstmJnI/kYLL0s+ZYqJmQaVHgjwJpLCDtWh4xENmQfEamZBlaj9QaVw0g+GVMVzi6ewkUHwQqNbjKskGlZrPPgbhighqwU4YBYKL9aVXhqcFXNM//bh751vxfEhayXeuBtr3/xdz4F+01DJMJAy7vWFKjSPlG+MC1VQjOYvAKgBVjdBikL2gcTUcnVlJYRbDIKmDjcHM0TjLPEvbB7E2Th18G1qm6UlVRSbITShpHUyhVnRDYaAdE1p4JiJ4kiXTTpLqUg6Eq3yCBC7CVYj9ZRgiHEQWO1BMLkHByhdB5ZRasQHON5juqSGTCkmnkqUyzWpQSeRlsgKTNk45gHgOyzN9Y4pPqAN3VUS1rNe6HBKjBoXNLYAdkinXprN3UNla42TMcklEcQxKwomzz2qVIiFDQwfdgYJXxKnnBIW0qnuqClhD1CGCwDK2yoQNgJaVJLkhezZS1DcljCABkhbFQBws4ngRJCsyogjjnEl4qtshhznTYF0ZTRRQYtbaoekJHYzVwkScPSM3wNsAOq3cqiJb1oF6A0q1tUfhWQg0vAGpk0zu+gmoFMgoY9K7Nt0XcRHUEFqeBjpaiIihZMtZgavCLICXM2R1w5qyjIqIg+mneXBqKpXuAmqaZCAmGObsw/XjbYxNNB0WQAkqSKjXFRQJZ51J2mArIuNaNKMnEmEzPIGjNXCa1h1afCO40N3F0RcQZYRVRABImF9hWbgAMhUCv84ZVACrr5MF6geWl7DiZFQ9mXaOfnAJ1ZITGBWdosYDtJEojBI50CuTaYcoWZkAW0VNlLXtjRKT8zA0FDSsBc0oXiYkqvOYrEJElJk6qmRiKTCRKhZCLlkjqp0tnxV4jlzCdCw9UYZTfQxuiNi0toRkmqiFVySI/sFKRCoaJ1jSU78a5iRZiwNYbGIr6OMVGaYx7FB2uCQnhUkl6Hrh0FI0L1mVQvYeTGMALp6jpfWG1hAwRC5pDuMWi7uCpFEOSZ8gkC/UgRW1OXgVyzimK8SpuVwfIcyC7SRJsV7jRzAwoIAkZ5EV0cNcrUijKulKqKWhRquQbWU9KNVaQiiXyENp98aFLHUHODiou1VIYYdeRE/A29CH3nVPUBJMkVlmJdmYllBCSzGmol1ABdSzZhIlOYODhxC2siXbMb+7nGgqUIgmcjZJvR00d4l0E7Fmp2OEKkKQDGYMDUpQazo3tZeukOzqi+WDq0y9TnulSqKAMKJpJJmbSYKJmlaQNTT06BZsjYUywiJKCyRgmiUSF9UBOarIVw7eP+ZA4Yx3sydFTtRmUhSV+DqSnYe1CMe7CyBkNNRSwfPZmhESszR6C7drgLj8bZifKpr5z50j94/yt+5w/PP+8CRJVgHNRQSyl7Itcn1zWp3YSKRUEtwsk0JVONltMID/ftwK6w2y6rNs6QUJWiWqOUlAKpLJMxFK5C+1EiCbJsPRtEksLd4qwfnPdnn1r/0cufvsKl3/D40x73K7d9zJ1ue8njjmL2+MpWdGBQpMz0O13Vw5KFb/ZSx575pqQJYZIMFVUW37IXMhm8QgpQla+xpxkQDcmRwfh2Y3STdDJAheBRUzaPwWyMCO0Dn/jcl7/+3e+JyH1veoOXn/6Ax93ptk+++y/uV+tGVAAPDwxBF81UsjJSQRRtozaVtBW4hHlM4bxREC1zWXWlSWqB79iYtKmOzEDSBgQQYmGaKinfOOvcl/3uH7/id//4y1/7DrbSWxu5TXREkAsOgPdoqJqcakMiJ84VSxQXjZTh2b35cENTYR8Qpas5ckXPhugQOgHpEEcRrTYXMaYC3WKou0vPvbGz+aYOm5srDXSg0QKPFcaGtuiAQquszE+UMay12lgCuQQnzYoe3tKHwEuG9YytDGiIomif6hAztVmzShdu11sUhtjFBugmozBCAVQlxgx4n2TUyhJjNJlOp1MwRyY8k+Pok9IWrJ+9Ibemw0TUAVrMbGAcFpWOst/+h1zht551sZ+7hnAJbP2cB7zku899x/bcCxWw5PBtRIODkVgLVkA9yWDm8uRtDifHdcfKTNeltMSFyQ1j0FVutkaT6FZTVSQZCpbLwE7FZBB3OIuA6hBz2twFqTDGckopnABNWEhCW9qRRxz+6y987vWuez1RZeh8e0Hd72Kq2nt/8vNfcJ/HPuVr3/of/gsOIvAffBy2M1+k8YLd3Yve/OvnzPyjP/3YHf/7/+p4lStc8Z2veumJx18CexT81+P/78787ec+d9HE613jahe9+f/oebssXzrzm+/4g/c/+IlPu+Zt73i7e937Ja97/Re/9MWlkXD5r5VO0/TT17rmO1/16sfd/34H7H+gh0SbR5u3be69jP7/I+4/wG6rinRR+K2qMeZc394EQZKBYMaIAXOijWBWQMScQNFWMeeIiooBM7RZTC2omG1bG8W2zdrmHDEiktnfmnOMqvrfufHc/zzn3P/+9zz3nHPnnnt9a805RoW33qpRY66PzTDxY0P6HH2e1zE36cns4AOG5YGMhqOHclxv2hcVcw7NB3JQWEg2xqh8BG1kfM+py7KeLqvrCH4nWOHqk8QkINfAtGCFKmxjiLyUEiR7T13anibC+M2O+byL/vyyd1187OuzuxTb7dH3uMLrHufVekGr0mvRgautaHJywjxLd65kzIWVLOlM5gIBLhlRZDaWAHoWmQ1sbbPDRXzMvrVHzS6l1yG5dIsapCqMZ5Fq3DYNYxQLuPeJyIATa/qGtsoqVjXKGLoK1IRwrVkaPFHVUXIwqugtMWPx10q1YVAzmZMZV5BF3GyqMg+YKkI4zEAuapig+lIl2fux5hXu6WaFFh20VgIw99qmDbQt6COaWKZIV4FljJ5bptjoXbPR2cw5Wd3LLEYzglAyio1wD2JVmOoVoEoRFYqv1YsyJEqhRCm8kQKz6pR1hswShJK+YJZcF7ZxLIACZSXXwsi0mk3RER4+Nz6h6lmz8nZ3ZeBn9pBboUM36hSIyADhN7ejwgaggkGfWnUfDFZYZsCAcasZ7IcxTL20daC3yE4VLS2TPhQBlqu+6EMmBpfClQktNL3AsV1QOKiwV7IBmMyn4jH4oD6s540phqA4K2nKKTCkChikGCRommxI2ZAqks6mmDRRjJ0RoCUIqjfHkLbyQtdEjB8wJj2gXwgGCk70p7EyYlNJd1MMtkVki9iglbqigHbn4BjnYq2IQ9ClRtvqzLxUTSJJoEnCsp7GPm3RHLWYCAdy+IA0BDlgJXJlk41TX1E3aA01YKS3PdThKV2xHCkCzjdopHosUQUP7hWIQqaoywrJiSvPIbSqlUFEk8xos60TzNKePLgSziusS8wZiQ6jMJ+QaxmyVwthtCkPOWhQVm1SMrSkbIRtOIY+h3Hji80N6RWDYAMyZhb6Ew1dsqmnkORRBgjQPXrLHtQWEtDoEhxCXuQMkIBZQ2Qbchs/YXGT8IWSEw6kpiIHxIZhy2hbtqhtRGcK1po8VXxmaSsJTYEaTetkBqsD8wh9JgNn6spL7QCZnuCHRKkcLhzuJJyIKbXQA8tAEPTsnt4VUrMUArbW3ESfbO58KjOkCoQjOcbLHGUSixAKhsFrWSuJUTwBDU3ntZEytvTt+0ll3gBpS7oPk+ps0TFXcaXiGTrByKc073VNfdPkvqYvLoyjMtJh7KBDlwS3JJKRLvShdaa/dDrmUZ2LA6mj2iB9Up/gDc1jTuleAfqe/IHUyVdMpgU+adCkY+7FQ4grEREFAUAlRPQsS0ZPD2KHleWGoChgIqoqxrEUsHz2ukJZyVjrhthIw5PISC4DjD52txksb1SYLHjuKWRbOvhTrWOVTnZSMggTks53EMoMM6IZaJQhswkzk/D1LtI4d1I0Jkja8N3f/vafPv2vJ3/6386/ZJPWJUsC6WMZKpR46ckNGaGBwkaTLRYb4eoarBZejNbQorKAQY9C50yiB3pQUwaUImZVdCWsGy7ZZnd+OdVSQWCDrSjmiPCwlGX39+s/nr2N0QHufdtb7LixNaolAbV1oLtgMgpP+CLeNJfvKca2YVwwk4sj8zRIkljLek5KmacpmUtUlUVU65BlBAnW6VZAIBROjIXx1mDND42sAeXtDKGTzE0SliWuylosf3rWH5mXYy2HHXTTjaK2DCLCkfPiVpsnnzZz9uR+C3Sn0OCiwsjLkL4lfQRX9JoUb0VMCgGVS7VhMUtUKv6P+kjPdZV1cEIsNAa//8vf3vyJL5z86TN+85c/mXrEEmfW7ELwhDmByZAi5smlpmYACBFqM9FSYMVFeurcLaSm1Kjh1UWJvsrCc2T2Hj6ndmFt3CjNBk5witLmtc/IIKtRmxqFtOLbDJNg7iFrr44CEdlul5jqmGVjLtKrCG/wIVjJnjnNst7MTis6UycztCWXMRYnrNA4hGYTk1rEikGN6c4iCcyusSlEGh2JkosGdzC/gsNLhSq9EEwFbYAPAVPAxPhmhnZHkPFhksbQ8sX4k8b6wuKKrIIiurGxz9ufseuxh2GsiDj3xA/98f7HX/Tl7zG8IJIFKZ4JpImDDldaAhQn8Tk72AtUkxFJ7TVSiSwt9CRpOIdtIFstVVaOBnFCJbQubFbMg0dJpRwCHFDJlNY11wYWutQEI9O8tfSMy+66w+uf/ZS73vHOlLCANW/bZ599N8TfcjAAABAASURBVDa2ZMS//tvn7vuof/zMF75Ezcut/zf+DkO9VO15F/39v6q/9OP/8Tq3dvJ7//nxz3v+ueedy4t3Pui273j1y/fYdRe+/594tt7/+Mfll5SGcXW5PZb/W87/ROGXijrnvPP//Ts/OOEt7zrimMff+UGPeNJzn/fRz3zm/LP/eOnd/9NXPv65yQ1v9IrnP/+fXvKSq+57BalitYDsLTkXzCqz+JJmIkk6k57RAnN3FxKDb5nUTHtn2Vybz+bb+cg5HelgnRPyW8mLhTXiYqwHSGEd4PZ5qcJWK1bCAp6c60udqlGYTJxTzas0eHpXyg0BQu3is875+TEn/OXEDzIdMNbLPu7el3vSfUcj+RurQSRLMcLAelRk6WES0hJNi2tlAladFTSaA9j6JDPFmus8x9S8O1BtuTyClBem51oxaesCL2Ueh1ypKHSZr8yvLEwY1jdBpwqmMAYUS01oM+WzwVbM6QmYLpmu4BwpZlmUlT7YNJjwFQMRVyIXvka0IrksDAMBp/ESwgTNYJ45HSQSTOTSwTfMSAkgq3qtIoV2GTM2w7pqUy58oOnqWhIVYqZaVcH6qISUUEkV1n36CmY7XQgTsSBKAyewhsIFQduKlMrLrBpzSzY07hzcUboul1mvGreC6JJQHUQNkGT59RLeOyskb1Cye/PJoy99ApTbSlkzRNB00y6Wi2kZC1YowMqw4hXIDGkKZ9RUiGWIAM4KJpzDTWAuDmmKdsh2UmYaTahSB7oHI25NSJ+k/CBYEmKBBa5aFu+5+k4Wc+3sBhgakaEvp7gGoyaMhYEOqXSARPKAN9FJrRn1LYppVu06rFXWFuxqGCZBKFQLQ6l0iCYXFcsQXyxwHYKAFmEQKa8nZvWgVYNEKQ38MswieDMGPq5iUnVpiDSAwoqERngIba5rfuniI9cRQkucOiwBmECKgJiEmARqwyqxEtI1VHuz1ir4gTdtEapQ4SkME7DYWiC1NKsTL0kv5EAiBA6CkE5cfBkniiIuGeHsXTEnbzTJSRlxRFiSCEY7oNG10/f0WSY+pw5wpCh96mRD9kbRBS70yWkDCVdhRdOkq3hhIQlN2GKaZgEqsoqaMn5w4jFkSDeZDK6SClW6L3wLkdDaxHw5wjWde6IaoYzkJNGM15CqnNQsQ2YBBMSv8JrohOxJgmiFjUKJXama40OWiHQN0l0YNVF3yHYRkQL6AwZlnqRFD/CWtSizr+Ycu4yhtbPGVUsJYBaZYVloN90MowCaYyWGGlW9omt2b9Kb9o6FgwjkUmSQAMW3WFoSApFmYQbiIkoLZ8tuEZwcPSRTUpCRRHBJDIouBnAP3AuddHGkI8J7tIyeEcoJmrwxB20A54dESod1KWD2aOHHVIWqCuUN6AOZs3wEQRRLY9mRpM3h1M0LjCtHEOKAOqtvsE1HFWyUPpJu1mBwY43R3tA8G7IlP9KwgqymKgNdNEiRQYWzzQMNXaQXU6VzrUTTeS1Os9sE93Rk0IpUI5opXnsQJZoJIQBYjtTuxKoIKHVAGk+FFggDQQOpmu96qWvUOSR4/Jep/ClCSTSGxmcJLFFdhPKvNameJqRnA0uUpZjyT1WRBCF1Vdgw0B3GNudmsS7KZSPNUToKZVC6ghaHCfFm8AVu0njLIomdDS1pVFnPFMHx2HOnXbiuMTiMnnpYcAu5zmCzuq7pJUkvEQU1L8A4KjkhURAKBz1Lb91nb1POk2qUqrWYmWiFaCZIv6Y5DaWrgZ/oB3+qWdTqgDI36PSAsCFlMGERmBbqUqPsdtnRSpcM6mh9an0dvOUUmaH0r6hXmVgcOjo5A2Otz57oUEiFFbVC6vEZIDRA+wlqbRigRaVYmHGYeSGftC/PBl37TFELLljMZ2ujS+aNEdpohU8zCRTMgaIhQ4gS5hXIBgovnehlYdTGyNq7qDNVGbnK9T1KEdm+ShuSaNtCtJbSQ+AqKajmdaAzEzHzBqY6rRUh4nNU7wYNnktaaE2rogAcnEovYJoiSnejuJPGkk7LzL0FPfKWyAwCw3GqgqIQ2sGLlED1unLd6FIStiSZZbIa5KyaGAgyfAHYPDlXC5A6e230UYsHayphggAtgg+lpcvoVul+NxEZyrIprz35FGBwGwFFdIfVPZ96/71e849lj6V1nr/6o98dedyfT/jA3EOr0QxHuBdzqkgz2o/so1AkqMoFyxUDisF4gbYs1bL64lZYmcaxSw3lMJ6hnsVFwgiCIqxCmQsZ4d29eWT3muxLJlC/R4CGy1B33HWXlz/rqQ+93/0AtNZ+/4ff77n3frvtdQV+/P0f//C45zz/ma9+03n/L/1PIW+0/9WOetjDn/vkJx+w//J7dDTpvz4vumTb809848ve8MZLLr7ISnnEA+5/4gufu9sul/mvx/xPeU/8Ltrkw0bsutseZuV/ikwKycyf//b3H/j054557nF3f+gjH/jYx7325JO+9b3vrS86n3d56rjB1//+3HeffR9yvyNOfcub3vLqE+58y9uUUoeaW1ZlHMfC6rLqfLCpo2eNLBN5TIqqKgkBrgnopQNLwZGITF9OTbKBiWLoOsydW10DEghfbkumaVGpGiEpABGoZLioFualwgM9h1nHjhGyMj5nbZK9d6SLQvg3z/vwv//hHs+eP/ctuiPFLvuyo67w5MPLRs3uw+wsFkLBQMKldiXHFZGsm4V1jmoleS9TgtOr+qCmXtFSWOL7FOk5WI6jSDWuA3x0FV662xzSkyWaaSIQpGTS1RksqBKy/QDtT00VZSEYJy+TZ5NIKDHQFII0demhElAFO1Xq3/5IL0fAhHYx5dwyk5OoQsECZBJZaJ5H8pFAj8UZIe4cZwgI2z9iBtYAlJ4AEzfFmd7gbcte0CEStbhCxatwXcsC4aA2JKsNHU0CUBFaMk0E0Ewkw1m9lbkXz5rVYkQz0Cfu6GKase4mQC2AjSlDpIV7o3dYFYxCHZ4aDl6caXvSL0H28O6AVlXWupDsqM7hUdDgvK+x6EZAnfYQyuUzZ7RS5rG2OjbanMCySscoOqhWLp4FKqqxAr9HXvwC5VWRAj6LXKUoamfR6ktQSsCtcGELkHbgStprb3xsLLOUkFGtmhgyxIPIoTJUqktVNc5oiXWLxp2tE3Nx2mmEoWNwQ5TmpXVIQImwVivVBmmrvt5Ir4A0QjHPjhls0sfUQUzgLedZ0qlNJMELIW1SdgnegwGWlsGmDZNJI7LGjqq0iWAFWeJ8biYSntOsfapgqKFw6QRXINkpLoWw1iAMDIFED5AzC7FSTTSHEgNQQpFc1ABaQHcaDTd1ipI21LVamyTXiBnkRvILCiIkoHZon31e89FFg5KXGLqsXAYGcDbrI5KfykRxtWc6eoIBBY/sPYlGp5vpkUJ3EKRClNz+am4rJYCIMjfdJGxUDRIvipQqw6Cl1kKY3SQzNPqgbaGfVtEBVlNNjANUtGd3mbJ0KdCC5UI0ztAU3mWmaSbWQRqmZh+jl+7pvXOREz6NSeVBqZYEuyD5XHwjglp7iPTVKipvMrtUXdn1KRAWk7EJa5vbkaGJ0+CzeZQODVSplS9ZI3gOggLQbQtfppYy5LiaiTuf+I+xWbkHDJuoSlkdAlQqItZF1hGTk0EutQ0b66FORnJXQCQ9pHXzrk2X5F1QS1m1HCO0FK8rRxEQIwla5TXFGMGU3iMZpeAnS/VERPI0YQPBtoJcHhMFmnRVBBBiIwM/SdEyio0ppWSOygcNXrr4DAqM4mnCwRY6zEn4VhZkh7oBQ9NxYpbyTY5zr0rTOotGBKPAAjJ3tC4ZTOqhDmLLt12DlNpBMs3u8zS13jxIhKLQdAnXyZlvPTMyzXPolYh7SRg1poQCSCSTePsb6UYEBrAcsnrBMos546Am6sV6XfWhRi29ijPDFrFIzlxOkeD7aItAfk4EXy89a6AuaHVkQ3aECqqCHJJIItNm1pViZnXFPrJ5gOEaJq2dnF22/2sKpQcLclksOR08PBUmUq2WIcAFCim6jYWZ9wDtoFS+Dg0bE8aWhl7gg2QJzkOqhCqyZNckQ5K3nN8EFEkiExDQQmQQxprdjNINEJZBG7uU5mSWdCx4QEPK1MdME3UCZfwpRVxLSkVyjhbBNM/YfrghLIXVPrDsvDk9c0grxaSMaYx9qTNs6pgb2ixLvUw40eND8iZLoliBDHPq3JHENbWJduXqX31xRI02o0mGBoEUvo3crpwvqUXHWuuy7A5LktNj/pjA7OQ00RqDdMZMXT2kkWFiyvVKzTQJbS5IQIX5qgXVMKTQCRWT5aBrjugqPgzUBVEGMLN3bHekTqBs0RRjkFJgxDokU8UMBAqYU9bGLYCKxjhOozWjPEQZ08hPuswEneFQEkJLqoYBqhZS05OJQF28qV5KNxbiyiG60JfbVCKWDK9qNpUu6ECAJloRGbwzkroAJ9huUjj6nNb4SSOrswxI0srt7yvl2BCiHFxdazNxs7LTfW+31yeOL/vvAx4Xbfvrq079zb2ef+4Z/zmZdsqBc7gAtJTSkZIx8Axkj4llmuQIQTB0TpNES3hGS/XMNBlLsSjZ+ClYqWHq3RusF40h5qGtEQyktWQzoHWTGxWGgg4W06qlppDyO27YMx//yGc/4XEbG1t6a7/52Y923LLlBje4Ee3dXG++75/fd4+jH3fmN75Nhbzyv/O85tWu/PzHPOJRRx52lX2u+N/oPetPf3nwU5/9nlNP7b2X5X9s8vjn/OOjd9z69/+m5b8Z/P/0YyYDtF1Ibn/9f/Ti7l/6zvdf9pZ33fXhj777gx/61Oe/4OOf/tRZf/hDn1naYGY7XnavrbvuwbU82/xfa9rYadd73+1ubznxlZ94x0kvftITrnf164xMN+tWvECtFe2KZJbYKDIoKiLJ52Dxh0XlqEFrURWTNthkZY3a0xQaMkyyahg75aSP2GTKg6XEK1MgRLLwxZOt3pzr9Al9ir72nJN1mWq1B5dJ6aTgpVCpo8wULWLtr+f/+pEv/9sxJ/Y/ngMeV738vh9/6Z4P+IdYdARS0rnrV+7JjMVBWGYQA8I4FBKtTJOtZ9/0aYIzQQGpgELoE9WVVKbzQBJkEySYSCptlKXucRwxGKaoa9d5yly7zM3mFpTEOl/cBObQ7k7fZK3ZzaO4iAzIVWaBZ2lMwUyRDqIJZ4JlOGSCzLkoZKEeZFXbwId2yqsBQBIx2+Q6pbqoQDVKBGdYiMJrxCpROKpHsM/yLCw3VZu5I0PqrCuXGt1oQTKjqRXRIc0Q4qGN3qjPShX0W6Jr69nYstna6ReyzKo9e8ZUOCOTqtQZTyihdUIzig5WBzXjJZG15GTcVFFa8mLBgk2kUqcHP5iVIkuxieBqwzkmogrRCMyu6cqoCAwpk/OsU9RkFa0GRpHC1dY6zjI6CodZL6TOkK3IHMJ6FogUFtYuye6fPoq4wTggpgxW3eWEgFhysexrYXGcJOaKpughXPLolaipqlVnIk5zAAAQAElEQVQxOqDC9ahgqEIEPHx2726JCjEs3VQJiEo3DY0FIXeJKICYYtCokqrOpJqFlPfI5gwOJKCLt+m19cKeyhGc7kFPygzmmToIPWQehEt5mjfJDlIsIhKEZ54NQTuCDQZ4b1mYGC1vQS1WUSzTeyaH9QELTxIyZd0WFl2kQ5KwAmSuzN17n7AcmeDdWbSpdq4AhM8HGrimcwITBhrRUsJJR4ahQ100OG027YN5JXBVdSwiJmEOc+XVQoOqcgJAzaHoBcnAqpKqGaqEfRAIk8/hzjfCS6OABGaIsjlP9EBz6SEZKRJqaUkJ7JSIAsO6nCFGBBOlhpHGZLR02psaIQ50JC1QV5kGYcFzAzzFg0ciuxCIPls2M+qI4AQ4rYgxpyJe0uhQmYWGzBkz6IhoYaxtI6wSNZpelLmomeE5hc1SXEtC24zOpPMifZC5aAvG18ucMpvPG/O8JbIoqcN5jDRLo/BeQR3BuurWoYSHwA6EtFdEXdCt6EOEpiBME0n+1WB1MKCkpKYP1OODzzXW8N7aQkhV6Cix/Nf5VYuJGqQkLCimLJETvklpTVx42WDVjKO6GKNPdMRHkYKAOo0rYxitVFmSaBbJ4HwiQKlV6mDg2iEEZEL2JEJaVawE+E1ArOeZd5K2OpGWblaGWgettYU1cq83AW+DMgVK/000RUMyNFya981o685HKaK0EzlEsOgJoNV11VeUpUooQCg6FyyNTCLh6VgOSq8SHEHDRMIski2NaS+yADI4r0NUpNiv/vSXe73gdXd65kve9OkzsP148CvfdtBTX/KwV7zhgksuCdrTmfzbbzBUFf/xgx8/882n3vFJrznoCa985MvefMqnPjdN2xpikt4ZXzC+jCWRr97ytH/7yjNf95a7PvEldz72hKe+/gMfO/ObLTY7yGC6T4MVnUVfWSUyqvloc+2X9Kee+La7Pem4V7z/U5dqffQb33rQM1900DOP++czvyE+KCuxGo9M/c4vfnXc20+9z5NffacnnHD357z2xR88/T9//bOOKbJzrqoynnz50znn3u/Zr7vbsSf8y398909n/+VVp5x28FNeepdnvOzXv/+rzaNOK+kMqE1zO+VzX3rM699xhycdd5enHPfst7z7k1//XqpYDRkaLb2ozQcde/w/PP5lp3/le5S/ObU7PuGVtz/2ZQc9/aW/+NOfitSi3HgOpkNP+/h/fOsZb3j73Z714js9+/gHnnDS6z72L2f94S9ML/a0LDkLrZaMiK5wRUp+7ce/es4/feSQZ59w0LNedOiLTjj+fR/9wc9/w4wqvva+jj5fdOG5D33Rm273jFce+7YPUjvP49778YMef/whT3n5X/909thNdWRQgS7uyuqKwYsxby1S57jwvAve+unPH/P6dx305Ffe4dhXPfGEd7/7E2dccvElgEHYtSvjJev8yc9+c5envOT2T3nxv//stz//819feMpH7vDElz7wBW8498ILbVSoeQLRN6eL3vqpzxz12jf+w5OOv+eTXnX8yad++VvfdwkGdw70IF3FjLJ7Fj/nvPPf+eF/e/wr33L7px13x6e++Kmvfdv7PnvGOi5kdbGKXrMNGqtuQy/VNTNYU6IXmaptz2QlSJsFE6nsVA4RFIi6e+u99aaiUoQViQOWShNqTeoMmZJPpDyVLJZU9CH6gA56K86iEZYhiDBfTC6Ts09Q2q6etUPpS1DqeOV99vv863Z5yv10C7/TwubXf/ybQ5/3q8e8uv3pT6oNRNeQTN1EIDtslroZso42yzTJzFVoptERZr2WXkw6OxVhpzPWvvw7yRIJcdVAb5lwcR+zjX1mAye+zPNqoeocVSRWxYaxaPEqa2DdtflGtUfd/x6vffFz9tx9d9Li17/6+a9//4dD7nzIHrvtnpm//sVP73/MYx/29Od996e/iAgO+N91CoP03+iKzP/4znfv+Yijv/H1r2bEzpfd4+RXHP/IIw+vtf43I/9nfVyN4+X33IPS/vyHs8457zy++R89N6fprD/9+T2f+JdHP/e46x9yryOPOvoNJ5/0ve9/7+JLLsnwMow77LjTFfa50q6X22djyw4X/e3Pl5x7dm+Nt7SUy+62x13vdPAbjn/Z1z956sue/7Rb3fKG486riJAOUrggVcC1p8/gs48+S3a2EGUETymqSeb0lE6irXT7+sT1OgtilGQt5wBjxtRmOlNOiACi0IUuUBEJy+SP7qU1RE+eJNns2bKJt6VVStIvOykvTB00sIJXUhi6rZ//wTN/fLNHX/yhM7PRXNv5nre6zqdeueVG1+qxWufGNG5MGxuxWCoKVCTS3KyJeAYyxsbVib1lkMSza9A4AGJiYDs+F2HVlVG1FA7vkWJZeR+GNiAGkRKmHdrW6W3OPokzJRG8UtCLWsrQoN7DJ2UgPImFcTosmUXsXBpLkGmM4dZT2JK6du72N4eYLegqLTJBqTJKNR/SBdRAP7RT1sxJC3obOazWo25uRavuCgdcsi7xY747E3yxGgIWmig9x5ABHDg1QtoVmxpr2ST4wmjzc4JFq3vLIDjNJCiuOTx8ThfVMhStktG7r12mtrBAoCp0Gc76oUiAjtfKygDrBByYus+NQZ2GkFrGalU0M5fepujAnqsjO6NeZ91Akj+i3YVLd29BEQOKQRgI9+yBoCOsx2J8pM4BdDmqtVGjQF3KBJmSPtbsJVjhTZog3CPSI2dNN0/pQt/4Vx05W+vLc2Vkood3EOHUQj/gLVrEIkBEoDWhKREwF343M2ivMqs1o5iSaaD7Rhs75lZnX4UXhUoKzXfkHBktLckDjiXtoqbWqNoHEqlPmfMifwNqNkzDuE3rthzX00ZMBWZaWPK5elEpfaU9mZJYDgVhV8/RmX0+AGIhNcw4jqoj2IQVqCkDJaHZaUQf+lxyzpDUIXToJM4MzCLEQTxobgbLOJZDPGvMWzEPzEkKt+IQaJiQDhWDTBusEDILrw9AHY3GikgmdM7SUPgGUMNiDqeKgtRCrlCKbaiYgKMhMOVbKymDotZSqklIm/vk2RcpfMprLDKqkGWKdPJAy6w6R3SbXRvoUoimIcbsI2ZeYByjOdQZrFTur8htmBE/z5i9ZzbyyrwOXqQhezdHCdoiKlBdKpq4IsfU6kYliA6Jrtrp/KAMtCgtIlvWHVMXWj2mjFAM0ivCksJMpHKUJgSuNUeNkXSOWdFKOr9xLjSFTGl0OafItkQHokC3qY3bJp1aMs+2h2BbqZta1qIquqGogM7V+qrUlY411NwFIREasRAlTaMIrXeWNdNJsEnnA2QlWg+mCBRBvEnakUWkW3dpg9hgVQfthfVXMMGmcfBhSBOgdhmyCog8H2ZQjGSIahga06yyhe3GzAu0AGiFBF23YrXUodiQaQzsTPQRg2Q1N9bNkhHWo84ocyrjRh9Gwl4lRSVScxZby3LT0x3hlA0kYplTGZEsHOjh3dEIQqs2F1aXVepIfypjUmoqgQMHhspck/Z6Rs4UgkWWhFToSmQjZGvDKpLkTSxlsNcKklAUMaJt+Iz2twsvPufCiy5ZT5zL87yLLuHH8y66WDOUw2ggrwJz6y9732lHn/iOj3/1e38698JzLrj4Gz/7zYmnfeapb3z3RZsXMTpJeGhFsoStf3DWWQ86/vWvPvUTX/zBz885/8K/nHfRZ7714+e+/aOPfulbz7/oktCW0pNgeOkuAr4r2UtOKA0XXLztbxdcdOG25fc1qPn8SxZ7aNLm3KC0nv3sxtmTP++9H3rIq//pQ//+7bPOOZ/G/PrP53An85DXvPNV7//UhZesAwwZCCdIL81zL7yYMr//s18d9fKT33fG1/54znl/PveCCy+4mAt86WYpP/zRzx/4nFe9+rTPnvnDX51zwUV/Pu+CT33zB894x2mPP/GdF19ykYApOvcM3uI5ceEGMpN6+ZGnu1stqzpULb8/59yHH3/Ss9566me/82PKoeXf++0f/unTX7rvy0/+8Je/Sle1iKgkKRgSiXM3L3rWu973qNe9+/SvfPesv53P8b/889kf+MLXHvjKt73mQ5++eHNWZxlxzJ0uUOMFl/wdmYu2rf96wUW8GEGCg8EuzNBsZSFEAhLMVk9Z+6e//O3bP+vVJ37s3770o19SAs8v//jXb/zY54588eu4QfKsjdnQSm9cFy/VctGXf/Djh5zwtg9+6Vv07uzzLlyzLmDM4KZu9bXv/urezznxpE99/qs/+w3v/vFvF370K9992kmnvORd7zm/zy5QN/Ka2qPgw2d++45POfGNH/3CV378K7rG8V/64S9P/NC/3P15J377l7+tKFW3iFVXIjiJNea0d8TarTF/04rAKMlrtBrdsMDmYj1rk6EHuhNRFgUmhVXe7d3XzXtP68GiNFgUjVKhpmKDlKIqaEW48BEgl75Z+1ox8wPVdmmN7ye3KRjzHsEcSNvAPs858uoff+kOd78Fth/rD57xqzs+5c8ved/mn85uGrCkci3iQ3ZF8rMyaiQMUzyZpFFcSkoxK1oNJbWEIlzQhSVHtUk66IkbwZNQUZJyEFfzLnWKcZ5WmAqdZoYIC2no7Nb7kqTgG6u3u+3N3vX6V93kwBvTwPP/+ufPf+GMGx5wwEG3vGWtA6987t8+d/hRxzzxpa/68S9+Tad45X//ecm2zTe++/0PfOzjzz77bGq/7jWv9f7XvvLOt72V8MP/slNEDrzBDS8V/+kzvnjpm/+/r5H5l7+d9+Vv/ecLX/emI455wj/c9wHPeMELPvHpT5137vLFgprtttvuu+2+51Wvtv/WnXbhSv+H3/363D/97uKLLqBkfrzC5S9/17ve7fUvOe7z73rHSc969j1ufstx3BJZAIsuvUcihGUv1XttWdihTjmtI6aWbYJ3haxUR6wKJwXJwRnIjtmxhsyjxlDFqi2FxGDGRCOpJHPoUrvykFExAqRPCHzJAUATSK5QgwSpxWW2zMKnymOPlecwB8mfPvfe//rln/z8Xs/9w9En4NyL6JFecfcrvO5xVzjpibLzDtRW1MQE1Go2VZkze+sebM0ksEoZUoJrm/YqsSFmqEqt2H5EIEMSLTOyD5IbgiGhTD8rsw5sJzx6JsfZjDpZzpbOrolFxcfsTPEKZZ3TAhigQdc7jSgWgzHPTcFdDRO4+0AzrTiKN7Bxqi7GZXulYEJIGgLOnodp1IJWj5CqNITqhAZmClAjMWW4poyJ0qTMUAbICKgVZXGRQWbts3CLEOb8o4ymYTNizSU5ipQqQyy9/DBZZZWoJW101LUWLu1eLajVTMxBi4yFAC5SUipUGJHuSOb9rCv4qnotTTHD19JntKbhmRmZgJRlQqR7BJ2j9dJTelRJo/2ahDhTQXZ0ej7POa0lGjhDwevgAPIOkiWiMEDokzjDJSmDatEUXeSWHho9oucsOumG81aJYWSQhJ0NCytQwmpYamm20csKOmgJlA5dYlqHNoxYVV5FAf1rOpH93AAAEABJREFUiUarFw45t4eT9oaOUAiIa+cY5sG4iqKhk+vMVtLVheLWmaRf0nQx2kcatrmRwYxcHYOYRA7CxslKRSHr507nGOFBUaotLyGrpU1woafUJ+pEy6prndQmgxu6mCzhqBKMi4oqEQUFy6iyKjCDmBbO0R6OiNGwGki/Tc3mkt1AMJZhUESit3AmfZhqjSIgHZEM1aRYC5Oc3rSs3Ufrwyh1yzCULeZDkF6MDkQbGJ6abTX6xgq1ggcD0iViQKN74dpE5kFakckZRUtxhkIG5hyfcWkoUyKliBWKTOey3Z28IQkS6MspulF0y4CVUCNgCQthqTBZ0mDhgCcrGdmZ0kKS+/Jprd7pQHS0gAiIFEmcSOcY0CBLIq/g8BbZI5U9f+0VmWSHllROSnhqN50Fa4AiUyHgi8uINpQoG+TX2PuItS0kkKbaaw2lmoU2aWajmM3WGhOWg0VNaqpGGqYa61K6FvGaKWuVWWzWAoghoWFDaFHOF8GidgmRgYxKS1dxhquiCsMmQy9lHtH5AAMt565rlFnCIgep1gciL9EojGUCnOIhc5feMwMiQnwYyuUtVGrtY7aakU7SFbCVHIxzSoIDm6ijKxqX/WTR9aqA0X4JATg9k7zSPqmnSNK5IqEqnEv53UQNVefBJ5nW5jFYWY2rwq1Q8iF8SWcptlCSoLXe1pFdA1TBAiETdEYySefk+86SwY9RZBixZcM2pBQXLhvuTbOvJK06xtnK2oSUJuHNs4AQFkgxmIhJ0uhUTBskpDL4i3TIbMVVsf2mbJ/AiX1BMfe47B7HPfi+L3/4/e924wM4l+eTjjj4+Ecf+pTD77RL1bHNtZC2vIwzv/vjD3zxGze+2r5PP/Iexx/9gKcfcberXW75b1P//Ue//PiZ35IcRFYJcgG/POvPj3v9u375l7/tttMOD7vTrV/2iPu+6MGH3u5616jFvvGLs57z5vf28DSJtNn5GqkR/FiA2kvNxx5yx5c+9IgH3/7mi1bgCYfRngcc98gH3OTa11qDdQkXr9vx7zr141/9Tw64ydX3ffoDDjnuqEOfdNjB19z7cnTxvWd8/Y0f+ExvpAPmTtI75O8unPqFr3vEg+54yxc98rDjHnnvK+3FJ86AxU/P+tWTTnrPb885f4/L7HjUXW770qOPfP6D73Oba1+1mH7hB798/ls/0rd1tCxiLz3qfi85+gG3vu7VqXqs5YRHHvaKhx92wiMOu+KelxEJmP/hor8+9k3v+Pavfk9nDznwOs9/2L1e8qjDj77rbfa8zE4XbU4v/+AnP/2VbwoGz42mY6pMF6+ff/L7P/6173nkLa555WcdcZcTHn7fpxx28NWusCdJ887P/cfJH/0392o+rMr4+CPudNzRhx11l4OonecD7nirlx5z+EuOutfue27VStbLGBiRVShYLGEd5u0z3/jWC9/38an1K+62y9GH3OblD7vvcQ+5zz1vet1VLb8/98IXvvPD5194sYqmKmnVhYKX85R//fLGWI++y0FE4xkPuPdlNnbNNYMU3/nRz573jg9fsG3z8rte5vH3vv3xj7j/8468902vvh8gH//6D046/bNpRdgvJebEh7/07Ze+/9N0ZL89L3vM3W/7iqMPe9FD732XA69F6M4+/6Lnvuv0bds4tFSGHyzhOYOhqhEsyioh4RGdma58NR9ZYQtInK7oJFCXKmWohcwWDaHTmqLCWhJczNcFfUhU53KkkJQEnLUhmX7Bv0ARqcY7kGUKZ4ouRUHUmbcsPRQAaWkRSpaib7nh1fZ789OveMLj6tX3BoWdf/E5b/7Yrw5+zjlf+CHQoYEhkm1gJlBVSgXDRpFiCtNweghauF2v0IIAWPIdXDoFCSjtyTKkWHCc1aQEGWvmCnOxpgJIdZRZmP+TJ71JEJDC66wGXBqutM8+b37hC+93z3sO42qe1p/5/OfOvnDbA4444lrXug6Abdsu/tDpHz78UY955itO/Omvf8sr/zvPn/zqN0c/49mvfNOb53k2syPufa93vubl19v/aoDgf/Fx8xtdf8uOO1PJaZ/6l+7ON/+/zvU0f/+nP3/v6R9/2JOfea9HPOp+j3ncW959yre/99315jZOIar77bvfFa909Wtd+3rDxtbNbZf84uc/ueCcP59/3t94l+dVrnTlB9zn3ie/9MUfPen1b3re4+96+1tedpddAJPk4sfyAYVaCjxSSOkl/I2hN1cNnqyHM6JxqV9+kKu1mlUSnHOKe5kDLXqDz4qZjCqWxVRMSH+D85XJ0aSyG0uHOiRbgoqCVEEpkO2pAoSQSVIUNaWEGpJ/aZvMed53fv2z+73s9/d8/rZv/QyA7rCx6/3vcLWPH7/7obfJgGeKwQqWuUChJImMlovRzFckFGlUHoEey8eogwzFBiUMFBiJpDkJSzC5aCBHqtARN640dQYmoGfM6UHRzASl4VVjLF200/hApKYbskKrSR36UNuoLH11XKRwUfGuLgRGBJkpHN2j9FCIKD8uPY31LiRDMG96M+9j+OBuHdI0CIoMqiJChegsQyo50vkQXq1g0jJNlzaP6+NGtsK9ZM9u4dQCNmYMj45EqqgYOEXUQms3RRUpnmOwO6rRau8lOEE1eccrAUEnctEX5YUzU0G5Aa1TKetBmyE6PcoAT6ebje4EqVLKmBwiRDB7gtUzKdYYhaQbBjH+IBwMVnM6pammOthiYEJoBgCRNOFTnBlJKESLDUUXmzOLh1JuohewCZ7FutNjpEoUQ1XlW3AEJ9KsNFZYE3puWlgMZbGCerpacg9ZkEP66K4AbRMFvZrSnUwGm7sm4csa0qCuRilFDQnvMQeSOjkLgpjh7PZS2WlRP/vfKWxSm0gWWQxQHWBsLDRqcsAwkyeiIst0WmeOyiyBJCKzIRkDmAAlMfTC9YNvclEIBVQ5k1MkXGRa7qogRSSJtGiy7cgppIuBtiXnRTBKaMZrieLLFQ+PKXmNPXeRWimX8tG47nSVTlMoyehEma0yGpZiLtJYAwZE9YykoWA0ZNaSpUJqdn7dsprXRsne+5wEwis73kV7d9qeBbPWnpItnQkW22Ugsne0SVsH3csQd2kgGTTEkm4wkUoXZYhLZ8VJqbEsdOaMtKyDNEOGIzq4kFWkZUYoRKBqJRfAxIT8GETUQriE1Q5rwTEELdV7yR5gD1wnHWfeEvFAD42ovVVxIcbMkQzJbummZsqKYO7qE/dYjNoMsl4QAtDsYSRnLap065Ml7XZW4FKCgQpVmCYKUEFPtDpvGOfRdFk5/dPKj4M0elOTdnIwIY/JfUJ37bDgZzQpbiY8KyPdsze0TNdQoUshukBYhli22EwOLUUU4Y2nZ3RB327KMs6IcgaZn0sPBhFaGkJXQo2BaeCEBCkN7SJ8FO1KN3NoKc4QiKbAkBKzEAelZTSyBxhV79tRAVI5oPfomVNqs0LDRYlhegG5ggKhDbPH7OmO5ZOpJE3O3plkzaUl5YCGCRQypI1axspE56gZXHWgfPGesZiVs6PN0XtL6VJRDQQnsqQIQDE0iUI7hwMIzpd0ck4Iv6QSen5scOrV3Xe4zME3uv5dDrwBu3wO5nmb6+5/8E0OuO11rjEKSdVNGy/ynD0ecLubn/jYhx5225sdcqPrPegOt37t4x+2+07LL5Z/8T9/sjxAagof5hav+eBn/3rBxVfYdefXHPOQJ9z97ne74YH3uflNX/GYhz3i4NuY6td/+uuvfe+nbkasiQIgQZOYfjXIHLW42TX3uf0Nr3ntfS6H7cctr3uNu9zkBgcfeP2999jdwa1HO/Ob3/38d37Mm4fd+oavO/b+97vjTQ++6QFH3O62bzr20Xe4/jUj8yNf+e4XvvUTEhzdfM4Ios7h2GPnHV75jw997JH3POSWN7zzTa+z05YRMs1xyWtO+/S5F12y3x67vvGJD/3Hw+9yyE1ucJ9b3OTEYx78wNvdXES+/KNffv3Hv2bUy1AOvsWN7nTz6++357JDY7bc8cBr3elG1zr4ZtfeeccNKph9evunz/jln/9aVJ95v3u88Ogj7n6LGxx80+s85rA7n/zER+x/xT2J4Ws//K9n/fmcEOXSklX+5evf+tIPf865D7rDLV55zAOPPOimd7nxtR9y8G3f8bSjb33tq3rkB7/4jf/80U8Z1zqsbnPANW93wLVufPW//z/rrnelK975pgfc/ibXGbeOMYBZIFDLAsZfRRIsE9um9fvO+Aq/jNpv911Oftojj777HQ+50XXvc7MDXvCQ+zzkDstW8HfnnPe1H/xQyYuaUha7sP240h67/tNjH/bYux9ypxsfcLsbXX+Hja0M1MXrS0780Kcv3La+1j57vvmpD77/3W99hxtd67Bb3OxNjzvq7jdbNrr/8rXv//ZP56CoqFxw4SXv//xXuEHa/wp7vPmZj3zkvQ865MbXus+tDnjeUYcfcdvl34n9zdnnfuMnP0vvXPBq0n4NWKPhTA/u0qHN0RrZbg0b3WryerqgebK7gIiaLummEnDwmnsIoefKOhSYpRDg9SpcE129DYswmZfBTpSYZ2Xw1arVArVBhhHjiFopooBrfh17JSc1UjzTdQ6JWna83x2u8MEXXebYw2XHLQQqgXG/3Vubs88cGIDIco4xDLEqqKMIn0kVKQmdc7EzwUl8O7FKJhVIF3SFVJQxi3JNY0LQdphIEdWhBItnDm5DGsdBMlUUvK9qUHCxsZZlcsxtx631+U845uXPftrOO+0E4Eff/87pn/jE7W9+kxc8+Yl7X2H5b0XOv+D895526qGPfNQzXvGa3/zxf8c/Ydx6f/uppx9+9DFf/I+vuPetO+z40qc/7aVPfeKeuy1ZRCP/V59X3fsKB1532ar99Oc/+9I3vv3fq/vreRd89HNnPOPlr77LQx5BO5/+4pd+/swvnvX7s0hNDt5xp8tc65rXuso1D7jG1fe/8KKL/vLHs37wve/88azfXHLJ8q+ZiZX9r3nthx155Iff+qbT3/rqlz3tCXe+9S122/MyswhLRguZhfvvhpiL9CJRhKUvkRIipKgNXVazbWQdi5qkTskGolNzIFqBsl220XxEL5EC9eINc1/4XtILZ6gkFy3tsIkrF/ODAnIzszGVPEp2AS8KaY3RJmMn0ylBESsIaZTaVOdBc7D5T+f/4SEnTJ//DiIAbFz3ylc45bn7vurYrZffHY2rc1UYr6tgEK1g59fHPld3iywdJaSKB9yTHZRM4ttkXktAyiijbZ/bo/WgOWZpFmzZpsRa4ZyoyEz0hMMT2tzmWHUdvJZ5VbsNHIVZ+2b6tvSuoUqfSqVSMTDtlmkzyhrVZeAfIDS6gc8sZsNM7rXuMvXVZltt66UFFvSKhUWwyswNUwrx58KUY2KwFWuCjEl4So9sLAJpIoXrrckyuy8uG9SYxm4e4T6Jt7HpiJHfry+WiQyNXyGMaDWD09cSMyBAenqLnHr2jMVlEYfQ/cnbulLmGswAABAASURBVHPxFreR9YOLIorD+uJhECMXCx2crZ9CLEuZfTXPLHNDNRtYcoTVpEeES1A0NCD8W1MUOq/LvDYypKqsaCOfVpUxudBDBeRXBj1bzpDUAWVc8LWIzDl0Cu4cugj7Z68bvQxNCFmskwUNHLwQim2NdNdIi6I+SJC2padQNtEiAL2gac6K2WVuGjFo4dxCG0dpK2tFpaTZbNFyrukbufzuzgbJPaumYjkSWTgxqoaWYOPWE8yAIkFeSgEi0LqgWe3QOW2CTFwYa2qKbJal4CJQp3no2waQJHOWJad0dkzGC3QZdGtIsQDd7+ihylC79OZ9JrLdMipCQOVNhC0m0zy4DLCnmEIm0QYJCklLpxF9ArrILAgSvHcVl2EJ5eIRqI1RMsorHUNbcooDc6FdpzPaw1qWTb6GZo9kuZ9oV+YS1gotmUo1PTOiUX5wx9Wq9JrdGNWAJmrzyuD0FAJIr2Ly3ghzsiyR6OQAJZLnsd2YDHS6PQfmGHoMQYtJXMJIt2AKGVLrJiHgcBFTtWJayDY6o8tWwUSzqI8DxtWoVqk3CEr1LFXLIKp0vXmsu899CVkW5o6rNc2WSn+d7sXCGpUWmKATJBOM1eDUkpuZvB5JU8FEbbVOxu+G1U11EAMzStragqtq0mFtQdISZxGBatVhpaVoqE0SzP+i61VpQ0kNw1RlvdLGk1ui2YkCOjMKU/iUrfWWNEFGkSKSQs6gayZ3PbrRGEHSHaPlMMAKgRilDlZYEmYwdjHNGQmUWmSsnbPpxsyKSkp3WCeTuHCk8PuxSdJDm0tAOovmSOuEZQZTl+6QhKJAq5clOzrn1j6PWFc61JpvOpqkcBJxoyCQPo6cead32kIJXYiIJuGigTlIFA7qZfQtFqV5bnowPL5GNhUMUmsu/gYAUdanrOIsPGYC2iV9nVPTNg3ea84hfZKeLa2TguHM+0I+AbQ8Rzo6Tx4Ny6OPdfW1RBcvgjFSiLb3mSlpy2yBAx0galgOYbzJcQxJAMhYltXlMq673xUefa+DN1ZjFbo6R+97X2b3m1zjqrz52z/9pW86DSExfviL333lx78mYY++++0OuOp+rHNC54oMoz3k7v9wpb0u29w/943vL2kqWcSNWRw1knYFKBjSLCaGXCmMsnlKMdZ9DMbBLdv6XZ/5AoDr7LPXsYffYYctg8BqjKOXHcbVk+93930uu/Pc/QOf/0ZsszEUmT1w6cEN2LWutp+oJuOW1UWY1l//ya+++YuzhmKPutcdrrTv3ilqJozcYHj0Ibe/3C47rVv/0vd/irolhyriKrGepksFborMY0UZRat2/d0fz/vsN/l4HXe80bXvdsvrB0gQJYVC6j77XO4p97vr1nE49+Jtp3/xa+zDVcjX/s7PLr+NcsOr7H30Pe6445YtIEVKAL7zli3Pe/C9Lr/Ljmz033/GvyNmBF1h1V9+XKodnuramLzMbwZwKRHisCDiIhSVhln0ulfd7963PPBx97nz7rvtHDJ4MrJDHeo9b3XgrltXFPW9X/8RLJ86VXBN77zC89Db3PRKl7u8glTwQZn9XYb8wnd+/KOz/rxlqMcefqe9L7crhEYxwdZ1zCccfqedt6wuWs//8e0fugRjjp4HXu3q97r1zR5z+N12ucwO5FcoJmI7brnrrW++88ZILd876/czmAquSfaPgu3Ykw6DdCHW6Cq9RK/aBqRGhvu6M8uAoHaFSICvLBlc9Jb0cCrQKrWKUoF6IiZKooRehIA4JXSJruElYQyc2Ib5Br23LsyI5SlRSqqJsZgaf2qm9tDO9E6+tLrrDns+6cirfu0tuz7xsC33u+2wz249coY0ARaDnH2D0il1gic1TZncYgxc88Z1wWMKyglyHsxQhAAGrUnWKQKs2CkUJOCPpssCnG7W1GaMPUfxUZlXZhyEDM2uaIYuMPcxbAe7+51ue9o/vfnAG9xARM8//7w3vuOdn/niv590/Iuf+6QnXuHylxeR8y+44D0f/OCdj3zQs1/52t/84U+Zif8FR+v9S9/45r0f+ZgXnHDCeeefT738LuLUN7/uAfe5+zgM/wsU/p+LrKU87IjDhmFg+3fCSW/ZXE/0dz3PP/rlr1/z9nfd65hjb3Gvwx73rOe859RTf/aLX1x8ySUQKaXuudflr33t6171avvvstueP//Fz3/10+9///v/ee65f2vTpqptrFY3uv4Nn/2EYz/zvvd86KTXP+vYx9zggP132mlLFvQaXGO6+8wzY9K5VZKzo/cyuzWkB0t2LbAC1cXm1CqlmAgDyfrs5Fp2Lr4qqWS4MLa8VNU3DKNrpQD0JjzDDWS1KAVJuvTEnMrXSfh4jwxKLiosKiD4yxgmUxuiscZQdILSJcWCuUAG4UqX33KL60B13H+fK77t6Vf97Im73OS66Z2rsqhWKyVpDdkGzlMYlzdjboDpAvFgw1el02ZeY9557TE23rC00pfBAP0OvngWCVoc1qGTppPz4h2EaZ3DWuoEZqinIk2SaeG0kXUrV44auh2bCHOUztZRK3xACGQGJtDVGLi2JIGWRRPFAMimsQS+uSxjEwgusIS9wIaW6hm9t4gA38N6Za0KtsOqUvoY8zjnuI7SG5FElGSj4S1sliHNlhchqzQyQ2cZQwZNWAtv6R6ucxh7k77uyC5G4+GlJ2tktOjJUicJB6VmA+MnPWgRDWXbPhTJQaPYdpsDIRIqoN2VFBKYIUGnVLyoa22CTcu5IgwmXPUFqU25wZ20e21LuKBDEjcpGqIeEhNk7rXPJWm2ePCjS3OVgEYP+uEIau5pqZDiZKG6lmYWKRqoFCSatE3MrRiM/uhmlokWU56nI71EjBmjpNFu0igUmWnb46ucYAgbVWtR4l4d2ikQNnWdZptT51Kg6iItWC+lDmwsQGSIl3TiWkUsGR0nWEptbXGlN7BAUqGRwJWBt1gvXdTabK5Ck1uiibggBKAoXxMtqkYKz0hEB8dVk0GkeGKNaLNQqLqVYGkHMkxDqRbdCWVyFC+aBDfZW2oIDZmar8nCvjLnd4AqPTK6BK0DD7c+G7UYDRGyUdLFZ/FN79OUmIUGSbjJulgbhWPnbtscVESCVIEk0AlzVy3CADCUxWOADyEFtK5iJkGszENp7EAgJGXvQrML9YcgwGAI3/NMEjHY5c4pdJ4GeUgnsuCKRnJLVYEKJ0PXTAmqyDKomlXYKmVMEsUNrWQvmryOgXjRoQhVGCmg1EdKeOYsZe5MEXoggu22azdhW9wKIkF6kbnKxTM7rZg1Q4LoAgplRCCWBrqKgULd+7zOPlea3jKbR5vQZyOzga4xj04S5ghWFBoVo3ZeX8RxNMUKRcoSeerNwuCr0JEsgJlkCVfxACtXB62ITOZvL+a1eJVUIfUNHK1Bo1yEVLDIsmDAuCiMwoM90qQyMx5FQrnxMjdxQSyusMYNyTcUv5hVerCWWhdYXf6UrN6FaHRkigAiAnY7JVS6w0N71p42I+fQ6GESqpI59MJll6emR7ASdqZIFDpl4ABxvjU1UZFCg90IF5zCYUlbsqU1JVr0CUCby+bEvOxMslQCteHYMnmd++BtTNTOGmE1FQpP2tIYFNkuj8BQAGg+xUj0RHD96UYoM2blIzcwJyOh9C/oT+uY0lwrP2+fCeiS9rVLddUgcC6X3rjinrvVcSQTS5qANwSJXbcu3yScc/GatiqXHOvf+clvp9Z32Fjd7PpXzwoZBBVQR25ayRtedfk24Pu/+9Pm5jpJxKEt2oJsHCIQ4WGZK6tbyNq/528iHA2lCbmRybbqV39eflH8kJtcf4ctW1nVgaUrLUo4+q47b9zhJtelwT/4ze/PPv9iUwurfWENr+Hyu1/WZMlmgQfVo7BUffVnv/aIXXfceqNrXS2wRIFeg2HttrVsudTgH57156ZTSgZmYFoNpCEFCnQDZeyqCQKo3/nhr867eBuA+972RhlBbzo0UAKkYF7r6le65vavib78w5/1NhX3X/zqd7/76/Lf9979FtffaTVEtyCXtDq5DtvrMpe5ww2vTWnf/sVvL5wuUlaBMoxlZLbw4nJG6Ow2i06QDkhmzUVXSiSQCJFxy47/ePg9nn7/ux10/aupIkkFpUkmpeywddy6WjYqm1OHI6YQ5uAyEzz2uOyufUOzmBnjHUBnGL78/eX3Qfbe47L7X/mKQX1Qr7pZc9J2mR3H6+x7BU78zi9/l07Gy5477vD0Q+/67Afe58bXu6agBhbsGoFN3WGHjY2R+YJtnQxkYfHOpOpqTaqnZoIu6JDMm7HF4EH+SAo6JSc9depug3Zzp1AN4aLRM7yrT+prqGeBAoZEsPbmMoH1M6r5kMGiF723bGwaG7hyKzOGjs89pzl6m3lzspxG4RMzmGihCtofJbGqUgeLUcbdxn2ffb+rvuihK9Zns64IRQ3fmLpMPdrU0KLOsdAvkF46dC4+lXWXJtIgwjndMC9dFY1VSIKDPIw5l5K0PeDdZ4m5yLpotFA+A5z7wJB44T1B8+XHTIrXUYfBag1+f+2Vu8xTXvWyxz3m0Vu3bImIr37zG/d9zOOmef7gSW966mMfy+0K9fM7gXd94AN3uN8Dn/3KE3/9+z8kNeJ/wjG3dtaf/vL+j3/6yMc+4chjHvefP/g+Ddhp58v841FHfeRNr77eNfcHBNsPatycpvMuvOgPZ5/zy9+d9a3v//DTX/z393zs06985/uPf9v7nnfyKTz5nlfO+Oo3zvrTn7et19vn/Q+//MPNbnzbW9xCRIZh/OQZX3r2K159uwc88k5H3P9Vbzrpm9/42uYlF0cEeFttj72usOueV9x7n30vuOD8H/7w+7/4+U9+96ufttYyYtzYsvflL3f4Pe/56hc//z9OPfWjr3vDUfc+fL8991a2AZgTMUPXDGFPLhRkjCFQ5mRFswymZDRM0Zq0KFpQS1JhROk+TqkTOAKFlEDy4XuSvyaaSvq2yLYWzGaenFSMk4eEJW3KObOVDI0qJOUkcFfWyh0iNwKmxa1Mg/QVhyQ1SBTW5KwjF5w1TVZRrZajTAFwQdvpyYde4aQnXOXzr7jsXW/N/Oru65hdAqPISkhRbSkdSMBFOs2xbrppMpXs1jVzYBp0iHexLEUHYdkRj2R+MWy8BjHKmPgBChjmVWt18jIxVWKAako2snj0OqxHybHlxuSjN1PKJNFZAAvgQVPQEnOv0Qd0AZeJobchZ+HXi9awFF6qEYikEaFRraqa8rMCytu2WCpmaWUWnRXc8figXmQ2b1hPmDqcNnE2phLL595YaBgYuOQ8+LzqGFEFG0IwxS2agLUSPmN5vL7ZZTPqFOqLrZ6NNzfUNwaUUrKt0EepFVqJHzpMsDzkNXVhO5zrZGuwSm7rFRaENNO8Vw/rhqygYi9lrsU1ElycZoummFlgRgs6TK6kBCtki96caCBFexESzZPwZUveh3TXTmo6LSndWJ4aY9/m6GudOyOeKs5t4pbFHjIXAAAQAElEQVR50PUq+mpK7iY1VKwW1dpCYoriXjUwatTMIq2gGeHoHa0HKYBcAKbPzrgyAfiNdqHbU/gsc2cLWGThJ3IAVqkWI6J607UHBxB2oa1Zku7PrUzQtDTh01doEHShdJnGbNVjcOX2c5jTJrEZNtuqmUlFRa46gVpHcakpYzrzSlIjydWVQYwp5N3MbWFYElcB6GUtFWVM1YIUtgPjPNFH6i8iY5faVXi7sCVYEQLnN0FbwrZmGYsMAzOBziumol7MR/QVo4TZk2nIsGjKqsVGp5HuFqEd2BxsKtZgfR6Ds0AFmZVJkoNaKYJkcjJQWK6WYQ7Giqwe6qJRAUEYnJ9ITXKlgP5nQRraqNPGEFsGXQ2DULWN61HXYj1LQoJeV1fNilAEtKl11S6QlBHg01loaTP4NSRX1L7hbQSjshBFSALPQhfQxVt6z3DIlNpdwkVaWixxZNCypJVBtSILd8kbMjEAG4SiRNmSuSXJAJhkU+mlhAhoDE1ygRewl1iB44YCrohKeQzmykmkCbEtYvYyozQd3Ve9lwmYS3OygfJoYYM0T19qe4fOzoQCCGUBtVrIahPDOoYWFVggZbO0pWJlojwGlmFRwtFAGNLJozqI6dj66AvDB7hom3Ke+xRJ3UnRHK8MW2XKFGHKtkVtD3KiVic1GXTibZYbVFTUhLGpKXD6vJTWWvtKYtwO6FytFQ0zQLHIblXWW9A30keZlQAFMYqqOQ4ga0IDIZorV0tADGQRK8fExUCGVCRopQ/BAOsWyNAZJo0hbAcvtUpuwbyKySIyWSBUCWVn5zdJdtAIgYB0y5XqWIoWihwxjXWuG8J8zrV3n6Nwl+wjDcZizLqORM56ofIeOVu65UzcIC5CmAvNonyhonnmEjFSFY0HD/egl02S/ZFLogov8uSPkl24nmVRGdUqE5nu8RZnDpQwOOn33Z//hlcQ8abTP/+Cd572/Hef+oJTPvSCd33ohe84/fh3fOw7vzyLd9mcXDJtcnBaFyMrRPtiWdRuQ44yrsoOWze2cCTPObzBp2hzhqf+hHuG7tX0qnvv1Q1OvEMzIQV15Jk3ufp+nLXZ/A/n/21Z54zpSRh5DRw2CLagrXItOSXjN9b/3G5w937S6Z992dtPffE7Tn3BOz/0wnef/sIPfOyFH/jIj373J868hPuq6ZKU7ZzO2JwZURDbjTTKcCw7dtf8/m9/z8Hcclxpr73CTQh6LqoHkUEIWLnG9o3KuRdtXnjBJM1++Is/cPxY7Or77C7J3hmNIfOaamEZpgdcZdnXbZv7ORdeYIPUYiMTRLeHGQjW1myD+9ijeIc22FrUAWnJEiitAY1Ww1ynzf69n/zq9DO+9JL3f/BF7zvtBe88/YQPfubcS5ZtFYA+Dczk3sHw8iPPQM4qc1W3wSlgknkt//nzX/PWJev1iad+7qXv+MTL3v6Jl53yyePf98mXvOcTL37PJ3999t949/yLLlHGaQG7UwAwb9u2+e0f/OLUz37tJe/6xMve8bGXveu015+6/P4YBwOiUIP7PM/L0pEl0lxAHJIpnaohZF1f16mJp4gwnuax1Xotk0RkI7KBoQvNpPmRsbjPsUR9FAyZyktMaIGmEtsiRawGbKLW3qae02zTuvRthevCwOQ29e1KFYQghcw3E07hrTRUBqICGcIIIIpGtaKj9JprzTmkO2t1xlLcW9Vu2TKa9y5IS5OgPVFYgsM3IjZch04qgc4F0yksQbeQTr0G8ONSoEVJ9ERmwkPnkHmWmKW4Fo5BpVPFBoyFVawrIs1ytTE85iGH/dOJJxx4wxsRpEsuvugVb3zzo5/1vGtf7SoffuvJT3nMMct2BVhvbnv3P3/wbg955PNe/bqf/eZ3iwr8jx2ccuHFl/zoV7/55BlffMkb/+n+/3jsne7/4Ke+8EVf/dbff89q2LrTrW92841heNtpHz35/afyPO6Nb3nqK9/wsCc/44hjHn+vRz76Tg98+EGH3/+eD3vkUU9+6jNe9KIT3/C6N7759W9/y5t48j2vPOgfH3+HIx98xKMf98TjX/2+0z/x71//5k9+/bs//OWvF12yrXf/vzbXI373xz9d7Ur7sfD99vdnHfvc57771NN+9+uf/zezRBSqZ//5D+f++axf/+oXhOXSAXvssefNbnjDpxxzzNtf+ZqPv/2tL37aY+51u1vuvssuGWTJZMG4L4xKsZ6jzyZcHBlUG2q1YloY8n7pIss1k7EsqrWoLURgnFKCvExBhAqqDUWqwUrhAIZWMlgXWpJO6RZuLP3aRxEpQyvDhOLIQCTcA1gInuY0LF0lOCFzuT4jW5A5ZiSrVIF6LqQDIINoLV2szdr7zvtfcbfDbrGyIo2CXdBV0gchi7t4MG1obe9B1KfmS9FJWZZOnUt095xTNrFao7qw+WARY81eezSjaeCRab1Lw5S1Sy0ggyVcaGrRUsaaXEh01a1CiwyJ2sMcBpHkH5phXIV11AWmDpmiT+yFWmrz6k4waDDrBbVmEA9wfRUmYlkPJZWHuHElVlGhUO0aTT2k56C5stiSvWZYT2uKVhB8zTLPJQHREAXUUlRNlJJVGxMRPmsgBS5GwTW1OrYDJV1puVUhFuCSxTk6RK0cmKDn5AYHwCSr0l1hm16HXBWMItz+0PFchHah92ETYlLvNWjVYgf4wtvg7WDJQCAZslRqK8VYsnvtXjnYNRtkouUMK5lXpRSwxHFmhsBStQ+lmUKU7VWNtKQYEHCFq3SrQQMhJUVTSkC9SZ+lyXZz6pzmHXCKcsnu8PVyu0OoOkQSmigJ4x9VNyPA2SRnII1xNxmgykFqbgjxxJwSCeU6oLMtVOlSUZS4ikQYfLCu2sMd3SJqmLA6Mw5DcqC7aGuWC+0EYZmSmXPBVAEhNMHvx+gnaLGRBaDZcDCjtC+Jp3x+Vy3ZmbRKHoijZhpXmrasT6vFmaKSkR4RIFBJClWPUcDVQQeVIqzXoiCOKSYoRctYBq2DDrLcSyFHu0cmBeDSgxxKgwuWP1kR9Gd5L4XpwFaREVK6rwvZUpQLW2ZPJzG6JVRFawiXQaFPKS0UKFDOFTILoLTRs6YX+oQslKWaakFRScaIRqUfmCNYL0ApIpopixOqwoJECZjQW/YIBzxLj9JaCVdxQaCHsyv3puoULUHFXmxGRF9uagj9jfDePSPpNd1hJIuFKWMh6MLWzCbTttJeaJ0mGHjTaqZaBEVocZOh6WpdVpMOLQ2kt1N7hE4unKC2smWwFbVhwbyamvFvF6EGV+ZKusfcti//3dj8eE9uSnxL8MuASSitl5hr7wqRWjGMUi2ULgG1mfWSvbB0NAZSxQTQJtaoYE50oKuG6IJQkx6N15J4SQYyq1Fc4QEVGFJlJmDCyNO/3I5DKiXBVRuGdaq3KHMwtBoZndym6iwqVQFHhgd4QwKawXSQpDneClUpe4KuHjo77SD1UR0WEAGNWYWugs4owxmAoKfOqL0YU7IsWQUjY2LVk/ubKha6pMGk0kzSIgtApK0OwOCdnEMIXWIVr2ymiEIXOMtyURNR9yJd6cboADKprjAqRSikRik0wZNi18swuGgPjbCczVwKzzR6xhM8qAieTl4RAxUevHjp2SEsCGlAQPhCIOTvswqWXCFDz7lkaeLP37b+6Be/+ZEvfO20L3zt1C9+9bQzv/GhL337I//+rZ//4S8UNfMJZaxDnJuVSdI1i8M8GbeACbYXcGEMwCPcsgvjsLxPO2+zJ7AxDrtcZgcR8F4KMytpkhppLDvusHzJA+C8iy8WJBNAhZ+Wc+6dYEpShTAUVcXSz9u2GHzOhds+/IVvfPjMr53+xa+d/oWvfujMr5325a+d9h9f+9Wf/8qZ67nNmxOaB21ttjGwkGGBAN197sgOXXect/1fKtuLX+uMG7VWqWYqFULAaIJKXvYyy384QDMu2ZwFct7mJQB22Bh33WFHemvSIQhOIs6B2XPL1q0cwPPci9YZ6g7GJZcI8Ro9ThSIwCykcP3vgdQaqNYZD6T0rHNXnz/zje8cdeI7H/6ytx///k+e/u/fOO1LXz/1C1/9+H/85yVrrhXg4SmUlTIiaQgvID1Kg0MmZEuqrlNvf9uO1e/POX/B6ovf+OiXvv6xM3l+4/QvfOsjX/z2H/92Pmdu21xHBNkB7S36R8/86mOOf+Mxr3zrCR/4zOlnfvv0L3zz9C987TNf/c62uXEwz6pWTUthpJqoi9J5/uSC3KuiIkuP2mDNMyRZsutqtdpQ3oMGDUuoBbQZnyuOvZiggMvdDDSUKAat6CbOWIOZZWlFCv8qExaT59TReoleJKrEyC1H1RG0RzKStHKwPHF9CotYWEhdrkNnFmvAPCzFqqppWnSBc2nzairsjGpplTWIRXrp1ViiVdS66iyLzQWmMXD5jJSgoiDQvJKaPIHkWRSDKAt1Ad9qinauplpbgJWejULpWrMU1IIiYk1kndJ5kSdHE8p6gwMOeOPLX/yko4++zGV2Qcb3f/C9Rz/9mS9501vu8g8HfeStJ/Pblb323AsAv0B4x/s/cNhRxzzrhBN/8bs/8Mr/r5PR/cvfzv3+z3/1iS/+x9s/cNrTX/6aez3ymLs95BH3ecSjH/XUZ7z5He/g/uSiiy78r6fPl1z4yX/59Cve8IZXvP71x73q1TxPfsfb3/+B937uzDO//b3v/fJXv7rg3L+m9/96yn//nhut7/zgB6d+6NSnvfglRz728fd5xKPu+fCj7vaQh9/lwQ+7y4Mf8aCnP/+xLzj+iS854dmveh3P55/4xpe+8Z/45nEvevndHvGYez/iUW96xzvd/a9nLwXovxfOKxme/e+kHLbueOMbXP+hD3rgu15zwuknv+mUV776kYcfeYNrXWsYx3Trzq4LIP7iZsG1kOHOHJFFuLZ0sSgmhdSzEPSifVmMU62zE1+ZMWmBZNQBs6i6LAVDLKyxWkarG9AiJins532miDQJQyzh11C4gTetK8NfQmoXNAQTHapVSuX9iSwQ7m/4CHFhUyYa6yTFVogge+Y8i3MgTBEVXmrH2L3S/lazi0hS/DBkXQmKTYgJ06Ki0nRPnxpPsG40FYKQauYonAiHJVZpq65kOp/tcHSYi4DHnNkJstNDy9FiRCuN45jOw1IIUK2xIRAhkitpQ3aEZgzqZZx8XLutNWJA0WJcGJp6l+guwXyfvKxTXQEu47JApEj0oD5WsMVvhERwI2nEVNxlNp3gM5OJF2kHIAAWEW0MDImBAjiwMAickmYo1aqWmlqgYow4qFLSlbAIrdVMY/NU0JWIrkQ2Kv0SLmlWrYyFky0g2sD6I1CoCbVmVzhTuGSwQdboGmlwKc25B0O0oBneMIXOC+ARBopZ1iMPRKQEQDlSgZXwZG/RhmWWUnR2KWqjDlZVqkURbkqdlFDPYqHDrANfB36g9EUORNRQBtovMRqEVmamAKrqFTPXG51hs0Xjme40Bzw4iCti792zXj+zCAAAEABJREFUlfQSCZoqJQcVS+Hy0jTYzE5LXHok0ao0yzAotLpUD2lIR8J7ad3oqWIcsRq5lcSCXYhZltLE55yyzaVjaBWwpMnGmj8kY5KJ2jAsypNcSLA4dlLGjeKFyWPzID56H5xXwaSgQss+QsditaCqF+0VSZ3hjfbOSZpFLy5bQlaZIr3lFGjgEQJubedR2ijzqDmKDDCBBl2hBdBSqg1j6gaiCK1fZ2xmnwV/P0RqpNF/CTCtC9W7JjfmUWtipXzyJLIFTIpQpud2kzlZPJov6IepajWpwo/enehyYSMEHEmRwMx46YYzOfvYO5+PDQB5G2opShNDkmh3ZlNaAiWF8VdaJ1GKV0nNpNyZy3oPzlRT0SJedZbiqUIuZHfkLLZGiSigXNM5dT1JTFJaypTJ2tE9M0iHHJsoHxzTy4UhSAKQDKgtdFYmA7FKN5piwh+d77IYvyOeqnSh0iEokJjRSnNyjRIKyvJUf1AbjZRXCxt6qaFqxgV3O6WaYa0+RzS+i5GbVYaxWxKwThHkn5I8MwaXYiiFtE8BQ0kVmkzWtbijS2JIWzGmodZDOa8SQpaUoIOCVOE0ZA/pQMqliWopqiqjysZimlWHekt0LVENDIy2zL79nKF9NmqyDkQiER0xm3QSoogoMqR3voJveVIpOKoLOgcNOjNTSMLI8OWPBzIlRHszuDLsWI6gtvQiAQmHaOrQisKyCLSntmCcdQQGdJiD81KG5JdVsmEknWaWqVkjp7toCsglL62NrTP8kSKmXCdFIkK8l8Clh/NH6Jiq1M1qP6BJaW3gMxlZqhdztaskTHPUZHHI4IztpySWM5J2x2Knbb+8vDiBNKH1yKDTsViDS49cA9NoTTMWSXvsvOOdDjzgkJtc9y43u+5db37AITe9wcE3vsGdD1zOg29yg9sfeN2tWwdGq8dC3S5NwdUxjHUjWV2AwMSNwXbRNclH8FWgogUQXHqkqdMWEp55Ogc96AsefdG/jDBB9bTsRmyXC0DCO2VX0UFkqKlDXnoDl99157sceN073eQ6d77xdQ458Lp3ufF1D77pde980+sefOPr3PnA697mgGsPtiU6YazShyUQ2+d59JkR7lxexujbL21/qaPVFasrSyvTRNyJSojw9b/ooxMVqksVWGb0KovtM3O0a9JOWsmhnbRabpMXGixtUy7txXJ7uSoioACeFV7Cc+E20x0r5PYyWYTfTc+f/NrXX/yBj//wd38y1Wvtvdftbrj/wTe77sE3ud4drn+dLWPF9oO4GlHhRyvbLywcKD2V1U5mD3Lbglm4/d6V9tztkJtc7y433X7e/IC73vwGd2F8b3KDO9/s+jxvdcC1NKR774kPfPHrL//Ap3/6h79Us2vvs9c/3ODad77x9e940+sddIP9V8PfVZslE4Bbv7olUamyQ3phlqHJws9qwfWHYGhCZtGuRWwAxgRXDGG6qQuTofSl35IBXmltCohd72JdCkfCsSSbzzW7hmkOBilCfqcY1MzAbMzaYahKFRXBoSmTy5w5E/50QhxhkiIMpS5JLV3RBSmqqRoC1awlh3FprShCHJmAhDkrsOZsfSrdx/BKrTWssuDOBjdkprjrlMVB6qJIFwYUyEiEaBPMlauUspYpxMJNeBlJYyAC0n4d4M4rMUFpJ9Khs/PTTlt2fvT9H/ChN7/5ZgfeGMDmev2xT3zsng975Gvfccp9DrnTp9/zjqc97h933Gln3jr3vHNP+eAH7/GQh3P7wa3IT3/9uy9+87v//NkvvuEd73rBq1/34Cc/6/YPPebAu9zj9ve9/6GPOOqYpz3tea981XtP/eC3vvvdX/32txdftPzjvBTyP3TutPMu17zmtf/hVre+373uefT9jzzu6c9888tf/u43veHUt5102ltP+ujb/ukDb37t649/0ZMec9Rtbn6LLVt3uFQ4wbrwwvP//Je//PI3v/3Rz37+vR/96IzPf/ajn/jYqR/58Lve/36eb3vPe970jnfwzUc+dvr3v//dv5177qUT/y9ed9t9z9vf5raPfeiD3/qKV33llPec8upXPPNxD7npTW+0+56Xc355XhaKGMkkBFUJNYFX5syGsYbPkA6hVQyDpjFk3mb0GXNoMDriVlwLp6QhSptbWzcSCoxkDVl1Fn1TxpJ0rW5FBeLRN7vPgSyF3aWVKkzOuvAWWMqKJEXPke7sICSh4qUEjD5KD6YEmphKGdyGUF3WD3Ul/zOclGKyzUObCimSoZZWlCIm8V5nGycLZzYMqUUL2Lt0runeaY4LP1pqjajBdTRSDapatdYoVYtAG5DIqjFwNpvjQVIkwSP5KuqFSQiS1kSlV/cx3ERAK7lrazVazbXlbA7vQ/OS1Nod9DgFPIQHYVEzGJr6JrI5oeVnxmGJgsNclvHEG1k6lInaeEGXceKZwQMODqZRTPOQRstov9YiGiGODnWoUGXtWjbFmooJczNKtAE+QivUxFuNzcKlNcMh4Zw7iUwmrSqIYBYJliJo8qDOFOGOgA+OU1DBl1Q65clyi5khjZTadGxaeCVmoTHLCEJbEAtPIrXNklTS1AI9g1jNlV37PGnMBW4mXW2dYCNHB1jvbBSYuuokiL6Ec/K5Z+80a1bMIixgpqB8W96iC72n7xIRljJKNWGvMqqz7StmplVQsoMiMrpJG2uwNqfBF5+o1hjpNvAGSVgoWdCGbPDWm7dAGNTEREKyK7C8M0lhbkwEIz1BXisB59mEMxktiFOMZ6McHzSLqRRGt8w5tBjIHTRTCDRh/CGZgTqz9eqGdWf95VUbhCdU0oRjoGG1iTbaHcVQSmJwiHS2W92cr037TNqII1OEQeVECvaMNKCEWEC4UnomJ3D/nWWGrlOiZ85MUUZQeDB+/IpuqmxKBTXbEADo82o96HpwPjc3pgQYC1U2s1mE2lhe0KgYgiwtcspwSzPTMEBnHo1guVZwwhplm5e5mQfneBQPWrDgHYswo8Fd2eHbjO30VQ5rOUW0THPb0pkAAkUnxVcyV51ApRxlnnVyzCluyGWHWbc/TAeN6bmUOCxlijDFJG1dMKvSrC69aw9lPJVWcEOXKCwBq96MfQ2dV7JgcQREIkHSF/IY21A2txtPnoY3GPvlntrJAJYRdVQKlTZrnxSiSiSSszcb1sE3AFLSbe7grDTTQqIkt5jFJSgYJUst5I9amiYiZfl1sWngUquIpZ7ZWmLTfY3oi3QhKWpDsFZo9iJpBhHvOjcVx1jSNCT7nD6JNN6CddCw5CdDGMfrwhVndqUvI9bF1qVOXFmgQvvEkl/tEJgUzqPlcJEAHfJkgPhhoiASwuekBCyD0rKbhZEdCeldyDkESBLNGdkkZl3EiRd3bcLRQR56DBwOSQppJRtj5CTuLLHuYKDoMkD+DfSxFZ+N1yANZW1jsuBjMGGf0hdyNQmv8FrWWhxhaEWC+ddqmYqK5lC98OEV4ciei2TJRXNX6WoKHcJoHp11cmWJXpLNbiasPJEbXaypT74kMwPLU6DqBVP1jmS4DP/HYQKyBOvMdcaa5i3ysBwT3XcVqzuuBn7efceN44464qWPOvTlj7rPyx59v5cec/+XHH3/4x52/xc99MjjHnr/Zzzo0N12uUyxolkR1NGMZZIsWQu6EPxMbIwryuFpcanHqUabc5eNQQSb03z+uZfohN6yAbPZBO+JDvnbhRdxFs/LbN2R+IsFixA/8hSA2DX4mg54iSVNbetQAFxxlx1f9sj7vvRhh730IYe+4iGHv+zh93vRMfd90TGHvfCoQ1/8kPs85bC77bTa2XIYSlkNtjnP2H4kj16SXiRqxGVWi81//Nv5DtY8RsCB9AB7kt7CXM/fbttQy8bWMcu8204s+1j+dygXbvZI1g5HD8/O2ZnE8aKL/osvO2ylQklkSMTfI0JaI6ynTbI4ji5CEeAomECtYWhnX3LeGz9xxtT6FS57mXc+/ai3PPsRr/jHI1589OEvfuj9nnW/e152u1gTpa7BxApBTmw/zFS4spDCVBCL0FUZttYFq/2vuOdxj3rgcY+633GPPvQlR9/v+KMf8Iqj7v/yo498ySOPfOEj7/vYQ+8MCXf57V/O+6fPnNncr7zXbic9+ag3PuHRJzzsyJc8+PDjHnrEU+53z8tsWbACGOpANySzV5umw5EBCE2ZybYsGmPByAGNKSi9IxrQ5pibsxJz5dSZOcYuwRTmhfkpTJExmeqdglAkaXVAnStiiHWh7BBdNiQYRVeoW/qwsbYt23qdU5okA8ocGKOP7ujdGRMwbZipORXwaWsOHuIgAYk/acU6LYOUUWUcY6OkoZgP3YeW4qJZV2I1jCHuKN1NmOwTWASF9HFzUz7e67RJwtRpJ8TSI6IRv03JKeGSPogM9EdGiZWGanBtYFWZQAOJXTMaGYQOqrnwR1mWVu4bgnGlV9tvn/e+9jVvOP7lV91vP1W96KILucc46LAjXvi6k255oxv+2wff+6Rjjtlzz71E5MLttw458gG3P/yIBzz60U9+1jNe9saT3vq+9//bF8/46Q/+8+xz/nb+BRds27YtnfzGf31s2bJl18tcZr99973lLW51//vc+9hHPPx5Tzz2Jc95+ptf9bJT3vC6D7z5De9982vfcuIJ7z3p9ae//e1nfPBDP/zsGd86/WMfPfn1//SqF77wWcc+5QmPfeDd737wbW590xvf4LrXu+Z1rnONa191v5tde/+7HXTTYx5y+Dte9fz//NRpnzzl3a944QuOPfro+97zHgceeNMr7HfVPXfbbfddd926ZckQ/J8cMoyrnXbcceeddtp1l112v+xl977iFW94wxvf6XZ3eNgRRzz9MUed+OLnf+ztb/np5z/9zU+e8uYTnn3sIx5whwNveNnLXLbWrRKDFZKqZY0coKUMMqgWIuzhTWrULS6lQ8jZzFiUM0pct9DRPDZDWJFZR0eJqsKhnhIxe8zhOXeZobMaORcIQQqr8Jy6lIrZ+yWzt2guCBgDGkXmWpuWlkqp3D5XDuS77KJtKFw31EPXizGhhYNYYWSYMGquxr7amnVFFYAAbGcGQUkXetHnTNZeFO1aptRNxTxYW3H5tBBFhmSONCKBnsyGJEVWUlamRRw6NXXXmqgiNhqBEnPRhhS1Yqb8YxAKAlARBHJIYSPIImXqhaN6yNSSC69LTkNOzOdsmZNJHznTFaEddA8SRi38ASzvai1SRkLjxWZVRoB3VYUwz/CZBoNFjK3gMLuuA4xTSRtDjLYkPzWFIA3bSk7KeBSnZVQhUXJNY6I4gVFlZ+6VUY2eZL03R+u0aiyyYk5y5dXGlX5igKCdCd01PUOFg+bEHOHeebSJIW0qTWvDRsQqooYqFSpN7pA5ZO61peWQIHNSYErWwEAujkLfLAFJZA9MPaORbprmQRfL7PDlWijmcZy21r4SIpwVPMFBlk1pC9AkuBAiXfiuW+s2z9Idyq3pmCLSgwGJDuLsoOMz4tgAABAASURBVAnbWQAMklaaDl5WOZB0YiUHbZoTlKhYyFClEJQqLO0UD+1ioYWhzUjM3ZoPnqzJMXCyCTUgPLtndMZiUB8yzFM7OLOruSrddR6t5XKN3ltvJTgQigKzYFeOTg/aegEgtUTZgKxAP4Ss4H5xrqWVMcMR2wabBtuOMO91hSoze2WxAoMcSPSsc5acjMlZoqs3Da8ZA8gVaMviPhBZdaB7RKTyDlNzNbn1Gb1LJ7Jz0l+Zm0vMlt0QQkd7dobQJEsV07IgAElYU0RZp12iNqcE/yixhhG2llQxJ+HMZNUnOgPcoKWUMCat9CCxZkRmSqo7m5IChnHK3gkuUY/oyMUN6orSWnSndXQSBQyOd8wmvYxmY6mF5QpIhy8XVXoT60xegQxdtoSagh9RVEmsWZlbwzrUBUZ/tPZgfoRTodiUtTEO0s1Ey0orf9TBpJKsy+/QzzJ2jIQmAY4LFpzVPA/bTQb5JGRFR2udhYoSA9mFJYLLWscYUO2tMGe6qCoGTBK9zX0BhENd5sieHqCUIFPMTIMDzUqR1cDTx+Kr4qOArtUZZXadHDFkrqaorW3X7TwgU9a1Wh+D1tfSi4hJuLAb8e7Fmg1dBnQjoOIabjTHXBedhW/RWRNVlggg1x5T81hCFmJpJSVSuzuEgFIa0hI1MVbUlTJMvJeYdfQ6kobZe25GepEoAeryWSQB+sqgtXGhtTJ+s5dJpfNr0UEHCHO1C1jhnWF2s85VYUCppEzkrLFJvhcWkYw5o5eGGjr0QfnkiiBmn4eIsccw1bGvhqzqfW7TOjpTR0fISlKzBZ2n6TUGl8L+jIxTm1jRckyIeYCHgE6FydTgvUt0AydvzNjoXHEhAc0yFNUKyktGMTtzhNM4eTmt9jr0Yp2wNV+uXPpXluRo26GITjm8FZfeQTN06R792vtegZfOOveiv150fi9EhfE2A2RJnQ4wGqhdKpt+DFsEW5XlIVSoPUhBnVwzGCGXv4sW7eDTcpUslDLvv/de1YwB/tHvf99d56ipW9hz+uhr7ZueX/3RLwFsHcruu+/iKr5ITl659NRkfKepL1xmme8R19nv8rz1m79d8Ldt6yIrzRVkI+oQhJRpzEalikFXVCPY4PviG6vKKbS4Zq1ZiqPEXGx9/Ssvvl88tZ//9k8gQlh4M0f4NC3PUFv7xk9+xYmX3XHruMMOHXmdKy3/BDP7lh///vcTFAlLZ82JyBSY+dd//HMAO4x1rz135lIp5IEt/vAiTw0wCbpvJ1OOBWOVMbNQ0BBJvKPKT37/l3MuXH7B7PkPvsf1r3H5HcaBHhj5UbVXhoNiUMxMwoIkmBJ/v8hrwXEidHyQpQZ0EKvFwZ/96a/nb25rKuuu25gsCNLQ6jQU1sEgYwPqaT/+zR/Pu3j5f1M+70GHXvMq+21sqQNjLV6TJZ6MWFQDgiy9D9M8Tl5aZO+e7kyc4PYb2kJEi0iNIhOjzYAFy1BM3ckBLb3UJchQbK+bISCXkrgvFkQocUxkBjTJfdK+M1wlwjo9JTeYswuxFhlUZAJhFMhSnoYuukZxRZoTIS69lskMFGmqRMsZpuB3NBOoVyiHSiyaMIgiUpJ5xJVNIcGPIiqoImDWOLKlbyY32RKo3urcIQz/kHQ2gd4wbdp6Kou0pKqYi7Sl6QBiSRPnLFZlCOCJJrGusq51bdokq7RByKVqPgqLuRWWsCmyza53uvUtP/K2Nz/n2Mfvc4XlXyue1uvTP/7Rez704Q96wlMuuuTih93viBvf6MbCQo7/u8ew2nKNa1zz8HsfeuILnnvqSW/43D+f8tlT3/72V7/oxcce++SHPPzhh9/n/ve468G3vtltrn+DW1z3ere+0QF3vtWNb3Pg9a9/zWvsd7nLbVkNLLu0NEkhobHOHGd0hpzNyYtisoKaJjAH+9gQvcZVr3DYwQcd+/AHvuJpT/rwa1/6lXef/NUPfejfTzvtdS96oRln/3/N3nP33W9+4I1f/NQnv/PEE09/1ztOP+Xdn/nnfz7jYx8+4yPv+8hJrzn5xS945j8+7hFH3P9et7nVDa52pXHrlhmrsKKDyNjUCH0OXoZQ0BSsxSZjMa9kEdfqGAPFBWugoWQUmUdtG1wykit+g4jVWoeCIVG7qdvkQ+sDQ0kGSWV2VQsGHnNP0sD6bPOMcOEyAZ86P8ANUpXjg1mQvWQ3d++Ye0wRAQ0bexkzN9JVGOKQDmH8XTIAgYkjMtcQpylppeeyCiRY5WohKUE6SqEGaalzpsAH0J9ET2Y/K2A3WatsFp3LiMEWPIbIIl3VuVgGY0ch0cDCzTxGDy7ss6eTljVc0VPmREuL7YmlEGNEmVTLw/61iGeBIyOiozkiqDhFRVi8nP2uDka9TbS1VbaVxqBmvA+CBe8iUFOuZgOGVdoKI8/RUSbPibGw4IhMID2FVsyDzQYPlWbWCL10qZOZF1nSqaoDPWhqU85AzoluIDAEUyqU3Z8nWnJRskhNShdIB2aywa1WGbh33dC2tCwrEEvJZkFN0n3sbXs9qAgTSzXX7EP4mMLlfYXOtVJMpJjUAHsmy4jogjSoEOrGOJh28rxoWWq9xZTScwjRRHDMABkC1ikbCkCK2ABVb0lE5mWwRDWuWxiH5bXULHUe6kzXO2JqsTmZTMPKZRRKYqEiTUeZrMyTUAypxZDBgjBQtVKVDpWN1NhcWg9H5tL0mBOebuSl9hRflz6hNZDGOUgtOhZjaAU8NFNddEZ2EHduVXPIIjKYVq0EZZxZIVsRyHafUiGj0v9FD/XNPd3RewYTpUVtrCSt2ow6CSadurK8kqZaYFV1RBg4IVmAN8kKz5Rsgza2q1xOwDTRdZZNWVJEgSUkxHXlOjq5kF19knlNZ4VgoOXiWS+tV58MU0WvEtaDBIqe6NBZSgecBMS628TGrQ+WKxVSdmyC7Udaa9qcq7pL0qsMhpT3xJPQY5p9bt44el1KDoqaJcKIBRBKXcWDBne3zawtC7KWhMAdCBjCjauZbmqb0WhNh2zGkoRBdTGWBe1BhhKkqAE5q0/a19aaEdKMSo0Vy+/gmYwubDqk9fQpGbZm3jXC3Sd4t0ZGcVvIeiMFAq1ANbclZNRgdYOXtkeqZzZEgFD1aCmNRPdRp0E7lKRR7Qx/WSXGJQd5Q7wYsiaqCAqCJS4gcwjJ2XNMXQkrwDBoWpmLkEp9dAwhzFGZgAYeWtlTkFpqkktYZNYEVSEoUZHZW8ybCI53WdKl0yFbyJkyExozUWUSD2iSnpkBiPLkz5kuaCllNNbKorCiw8CNBi3IOZNUjRnMDctM9RjmNrRWeyfFzb13YkqnMIorNqFrLU2rWCEJw9ABdR1iqJ01S9QFk0lXplhG8O6UY4Bl06BKfqC6yky4dImD6WKT0criWVIlldiLrGt2LSC0YQIFipM7le8BEniN7E5eOpEfzY2FVYLy4IRIoxE86aOQ0n0YQyundHpYepAhzrgiyNqWAmptOjqphO0H0e/am83kNrvxCRpKGCHmIumQBhEU+k9/hUIStVjZPncBEaFWtK6Isnv4pdf5yuc/JVKIOSViSCxx5nWeptTg8PlGV9mnml68uf7Emd/sUT1rhKinkcD0qPSzzz1HI9ETKWqLIVCNal3LjBbYNNmE+NQJOwUzMhoipHRolyJX2vNyV9lrd944/cvfPHvbJYHRxEbhj6T48865+HPf/BHvXvdKe/NbHccc+V/kAEncIdmrTIWqAs1tvtE19lWRcy64+JNf+XavrFYybZnCooBw1TLbeedvK4ybOHrkZsR6npdEpxLYin9kFbCOMFx//3132WELb7z9U//uHQa1FM0E/0r/yvd++puzl99AueV1rlGWgihX2fty++6+C8efduZXL9h2yVCxUWVVMrV38T+ce85XfrxsbA68+n4777Aq1mtd27Am2TiFJ/NDQOeVSlyQJlK1ENSuNoNLC1zPvWjZpXDwlnELto16SZVNKx61zmtfz30Bp7knF3aZ2sy0Uw7m2XsvrRVv9CEHyRV0Aze65r689ds///Ur//l9i27igIdMgZlk3rbe/OsFF6YOIJ3ULrj4Yg7mufNq4KphS6FXjHVc+dw2qZS3IFDSUBXKCqeYinLFIvQ1RRIIDgBECmJsVOHTLIFKptcKU1OC7JouLNEsh4Yi/NijN49IpIFtBU/CMYsyV4IFtRVS0gPM7mFmN5q6MEMAJKekBt9JqkRWxGDzaDNL5YoLulGzcKCE1M6gR2YQeZIze1pnaqSTVNGSqZncQ5NtEs7XMNURNoSMjFZEpC9qOlj5CgvinJTQiWnN0IRwQAIhmQaI8+qYUSORCGTn/QEwkcBSD6a+6Ot0vhS1NthkZKRxMPE02tRZLxr5q2Q8tu64wyMOv+dpb3rD85/4xCvvux+2Hz/56U/e8u5TXvba1379m1/PWGzbfvn/6uWKV7ji/e5zz1Ne/+oPv/n1r3zak+9z50Oue6Wr77p1B9pvwuhAQBOtRJE29CieJfrgPsxhDiVV64az6ibd9ZQWGkF0JbOso05SukGKshVHZSg7dPG/+8LL1s3IgBUnSMwrwfWvdvVL/22Ay1/+inc95C6ve/HzPvTm177vxBMecK973+RGB1x+7yvstcceO23sMAKKzhhHZeFTpyWuTaWZOKGe2auMaiI1ecGcthjNI0FKupFMkpJeEpYEW6LDGyivMjiIksEPipACGWuOY7fSgmOowzlag5FzAcqgNJ8BznQO6MucFNEI6xPWE+XCSBe1wZTBFrYDOtNyTU+SC5gkuyqHmJD4AIWKLMgnjIBnlWIoAGOZ2mbtbJczMltqwESoLSBJeZSSEo4uGmJVrWAZFuG9UW10jllJ8IHzaGpphAveBW4CrQtOavzYQdr3WXoXT4GBCIdRbroTOMoUAGHqbDpWYDCRASQshRUJymrDopAyW+mrwr5piXoVOmOSBIjqlisaZEHPCLT0ADXnKFybUc2tOsXRxKCqdNJtVBmgNGa7h6kOcd7jpdx+Q3QZzaJZVEnIRZPQKMZdkAmGMp35w89avJROULfboigVVjjCEy1SkrbJCDERuFhLNot1TpuxvU3NAV0LMNRk4BnlXr1XzhP0Xum6N06EUutgYeBVi2kEc3rxhX9byMx60BMeoN4sSviRRUIspSpqgWkOJoNAIbQdSc9Lcg5nAkj6VsQH8bHoOJShKmeodinhY7o5W+GYPNwUgxDRkg6EizYC14PBpaQk0UsXAtRFZtVmkFHMeIFjo2fyFAKTadrr0GTontG95xysdubVlBokMpx0pmU1Yb54GCE0XZXuiZqIhlF++OBZc9Cg7crxqNJHYzjIZ7S0DkoQSVSnMhkaxySh4qIhjEc6X7tEJLPJVql1O/cS6QViWSJLS4UII6A0JIK5WjVt1FypV7aLNks2ybCEBQ0UoQu2oAB6Fggslhu0MgyAIYpEUaSoQ5PJxmzjqyZvsL2cTJ2jFRU8FNhIGTs2Q8KjAAAQAElEQVTYBWFOdIIpl05DNjjSKTA8aLOqZApzDeEqXpQqiLSJUkNXn8dYr7AuwiIhtIGm0baoYA1gBBkYiCqF+7KDmxKRYjIOdSgqFAJM6TNISaeapHiTXqybzVbnYfACZNCq1tORaQlKo6NB+yIAYlSHPixbBEUtRLvUUMJhNZU6CAaDpu6inWeIe1IculqwdnZKVAFQEkNCVcgWRaUR0pZd2hDFPBkiOkRZFTrQipjpCEfaOOpGKTVNnLcK+U5JyQWGLU66pvKS9iIiSTG0JCMldZU6AtVhLYrTsQ6lOWJjqUWSbnXLVmS2tPCRvXvyIgBCpgPMGHPGl8KWemJWDMqXKMUZpaDYDj5tI7IxaXgJCO3SwaV2N7gggobAS1mrMQTRw1NB98HguclisLioQ6Oq1xJVUsAS1Tw732ioqdD1oYQIAuqLK7k2xoSQpjYpbqoK5jDloEMcwvaB8BjSBpHKmWOdR3HNXALMeuBpCzqEQbketIpe4SLdSUEzsKYVAYuILZIYGe0iTJZO4S0w84d0LMpmd+KXI4AkdWKWmIlMIByQQhqLpSj1NcdCC6QWjENwCaKz1Xbc/ltAAC684BLrGigcIHSI0nkVKEZy0C5A6HSFGegQ/n6sqq1MTeSm19z/mlfcMzLf+69f/syZ3+idXibmyVqbNy95x8c/+9BXveljX/nKsjw7LbWEdRTXoWvpiaWYaGS0oZS/i87aW3UQNORgY9048rY3563f/OWck0//9OZ6G1ufmk4pl1zUXv3+j7M1H2s57KAb1w3RRR61LKTnFJ4BSNaSpSoJ1FLiNte/zpX33DUy3/apL3z2a9/u1sOIGX+iXzy94dRPPfQVJ//bd7/jleXDe29J4COx4AxXwShZiARV6BUvv+ttr3c13jrjuz859fPf6HwO6DJAi9nv/vzXN57+r1Pru+249e63umGRLkgrer+Dlv9q4Od/+MtbP/7ZbXMDKWQKyPkXX/ySd3/qLxdcvDHU+x50YyOy6YpUi53+y78WcD6/r8iqi3LaIYwYzEuyX4IEaWDh4z577MHCAOBfvvad1rDcDKP1fznn/Je/6xOUz1ut9/Tu7h1MFeMVnhEMxZzZPJP1jicEh9zyBnvtvEOPfP2p//L1//xJbZTVsT1dL7h4kwIfe8Lbvv2jnyKlRO67525Gu4DPfvM7Jb2wvLNOGc76y9nHv+/0v120bdHiJAG8CH0TZ1VURE16EpQ8Y6khvamzt+VSWYExWf2GUWSlNoALB7yHZlRPpYgomsLh3fs2JoBE0xmyVu0lO13xbJktvPfgroXyauYwifYB1Al0Dm5onei1gpnzWB3Daog2ojbSsoU6NJzEyQE0kjZTFjhYGhQZwuaOUPaYlV9z9G1Dn5m4MRRm0KhaWb5W5qzdWEmqSEh4naJsBtY59bqOlSfX4Do2DlUGvNROvxbZzPuWyVkwSQGBT0Y7DXzbOj+qKCuNagQfMVnry6o38WEcJgxhNCHEHKJSyh577vygexzygdef+IYXH3eX299+6w470q3/O+cB177Wk445+p/f+NqPvfnElz/9CTe6wbVtx41utAtwaMthqfOdORwEdM5c04Cck4QSMP8a5plLe3ouRTAYxVqCTrfiPRmG1KVMZRgULJowU61aNmTcAt1S+oimiEgqs9RClZTTd95x6+Me9oi3v/bEU9/+5lc99yl3PejWe19+NzPiA2duIixi9DZ2YtKz8TnIGjll9IV1gCNKYJiBuaBX6YsG3psIOQ0SKZlK4nsPFZoEBs6wrliKuYf0Soa36MmZkhAKlHWyteT+cswmMzOMHvfU2UlQyUguo0VmkSYe9COHwmR2OG8tAjAMsjC9KJTa+5h9QDeVbmWuOhdpJaPkIpKQY3S1wTCGlixKg0VAho9oKuuIzTmnNXRTuAKygbEJZZtjW+jcNPkoa3aiCOGMCrUstJemwh2EZECrBFEQkjZPJLaVrhw76Gyl8SG+SDLyZeKQisKHLQNLNoeH2USztUm6UpQX2i9NirMwCoWakMuqkMU/d02E1tlkHlofptm4rIOQJSDg8uaqrfNZUa4JW8LAZn6zwEUU4Ckc16HCcu2SGRwxrGQgh0A80mtzIdrWS+lmg+pQTFeiG9AiJpRQUwbnFi91AK+k0iRAA0PHuBhukizHVrVwbQaJ3dZCT0vBWKi99ewLjRDIDtZUcylBnmrZnrhA4S57mnWeVWdRBt97ayxZSlz40aSl9aSVXUobq1vtXnojgcVdPHyOubO8NB8yS1EYpDFpquUoWl2Lk8Aa80Lz9bpPzbpUlh+mEcLoDBRZ4QOSbxJd+vKCFCxhKu5V16vKLyWCnoUjXYUQQ7rkJHSrq4SYqlhAZkeb1ftYsTIUJIiWhIqIipoVHRJl5qkMVLhRYSYynH54p53SqVg5T3KIHJ3GJScDCvGkNGqSESQRa/mkOiPm8E5EBo6DKFO2xmrgql5Ssq9yGjHVnuSm0GajNZh68Mlcg/caqVYS4ww+6eDDb1nEzOwzCNRiWAKTkMzDbOZhMheajExG06OKjgNP2KqyIUlNYj9aki9WWVjFNCW8eF/oUFQqIQ/wmNXaOPhoQllwjkItUTmMN+kFyZi2kGAWxICl36T3U0YnZCRXkWQf7crGYm7SooM1wNxUVQbCPQiKohjMqM9z7oyFaAEEQCx/6IMkn2Tpasihdjayc0NjRjvnAlWCdSVjFlrHoEtJG5MSOb1HBoEJkjM2ehvbZO4FQp/HxQkjDqwbGlGzV+kqZI9oDup8ylmSwHagMcVZuLK6jVKHqqVSVbKw0EhOCEZbYAxozitWOerM7tlF6NJigSYq76Jz5GxKkmo1LaamDDW4A6RRRCBZCrdTKVhsYJPIhCCVk7JyU3Mi/G51FiPaa5OpymQ6WYmkS2ApZeYbklllNbU24XMHMqcmIqwFOK1rpqRGMTpe0VYx2ewIBZ1xWEOh/SozJJNDxEC+R/PIFkPXlaMqjfBSgsEbQE9YVVUdmq0X95Hi03WeyhylpwZxoCxxQsTAGhX7unYflqaVVZaaVcGlbNVyY+bTEJInWSpjuUfCILpARBaPekajF9KLeETvHjTSQEuoxZko09gdDjREMg+0pc5JxFZdShSRkllCBHwsMPDG5HXqq9bHZP6qhGWICkwka2AImkUOtdndowBYDPFMEG+TcTAbVEUsAWRXELSemowCRGQwqSNVye67X7aqcMwrTvvE+8/48ulnfvvcizhOKwPDq1hqBkyis7hk1KYbXUYR3X4PvGO1Vv7d2Njy9CMP3WWHjYs2p+NP+ejTT3zb+/71zA9+6RsnffKLD3vFW//pU2eee/El563X/NaVIGRnwKxlobEFAq2upadlx7y5xvYjujrXus3lK/LF3UHufqsb3/Um1+PNj331e8ee+Ja3feKzp37+ayedfsbDX3Hy57/zI+Jy6K1ueIsbX5tiKNc6knWGoy89M0tio+RqA0PRksOOqx2fff/77Lxldd4lmy98x2nPfu27T/3MVz9yxjfe8rHPPPAVr3/Xv33lnAsvvpDlvthsNiF51v9SW3pirZiqs/hqmmA8+t4HX/0Ke3rEq077l2Nf+/ZT/vXMD33pqyd+8OPHvOZdP/vD2WPRo+95x70vv/sgnYyYIu96iwNvf8D+NO20M791zAmnvPXjXzr1C19/28f+5WEvPflLP/hFUbnHLW5ww/2vnOlAChBS9thtd369winvOeM/3v6vX/jwv3397D+dTd0pHeggLSPAykr6Vbv6fvtc/8p7c/D7vvj1J77tnad86d8/+JVvHv/ejz3oxW/98g+X35HjrYsv2cZlj9LVUmXhCS/yowtzFr2D37TEuucsu652ftoRd99hNZx9wcVPOemDz3vbhz70+W9+4ovffvOp/3bEC/7pI1/57tnnX9iaWzT06Xr77n2tKy7/oNRbPvsfT33TBz/w2a998Iyvv/idH3rg8Sd//ae/pgqeF12yrSUjTLoyvz0ggQp2ZA0yRZlpTLTSmnYRGb2MzvVTOaiQytG9xzSTyo6M7IGg/wBdEKHpE6THgjMDLVoRRViWWkqEZ2S6uKgBNVly2zjnavbSod3BVYTSBK5IDeK+4BuSoRZijsF98F7CxNXdumwPDpgOphkFs6azYoe4c0Hlu6RVHMS4oKoZVCiUgr2GB+gMPbRI2pSp4lKcaoYSNTmYwxVIICIzJFLSejePoHauWJkkIm1LjCY6LA2W03deNbfSBV2jM48LlwauGXQ5YUkLLHfaYevtb37zVz/z6V877ZSPvv3kVx33gmc+8dgnHfXIJx718Mcf9dDHH/3QJz32mMc86Eh+WSFCizGOq5c/+xmPf/BhNzlg/9123Vkx1CSG9KTT16QziUhmdc7BV0YkZw3GJwXIzGiJSUEn3Oc2zXxgQbkyKKGroGPSkRwuQhDYadBJDk4xESuqfLywQGgQDuprMAaQAVoFVu5xh9vd6sY32GWnnT1qC0pj7NypM0HMSjTNDge6KCH0WNZZEbUSNnCohbPyRsjUh0nLYigyM4RmidIggau6DpwZXXzNDK6URbHSwraF0eWFmstQmUBV9BlKjTAGyBdpopkcTS0dtdkYlRaERYBsYkgZl6qiUOGZWmAmVbTQhUwksTBaKxxikhKz9+aac3GSjpJGsRFWVc241FFEFO1EQsDQePMMyW5LDeO0IJkjPdM8l80xPaZtKM5FRSukBhSARh/aAo1CFhtCi6BqU6MMcRA0EVART04rY2IQ/nX6Flmz1eyyyKYsnpkMP3TK2qO6W9L+omJp293rxlskNitIy+boHTGL9lxRqQCGjgXNBPsRrxm62OQAf9IgcIp2mlEHqTQ/bEpbLJZSnZ1fMRoX5qwskYurRgnqSQWjL0uDCC9kL9lUMmvNUkMlLEIykBmdMWcRYDRb0M6e6y6NgRkSRuanzrK07gj4DO8EyFjM6BwhMws6IEtQjcaJeBHSpydzxpqUxdBMalm0u8oyhkZATI2fTEhGMo6+GtlM4ZFFo14qq2uGJu1HRkawHcjo6ayvDheQeZwnIuCxeO2w2aU3ngCVd7YEUlRRNcf0ijmrEwUP1RkjZCAnxFtnhe/L9WptEL7pmdmddcZo53YJhVSEgeanDJEDI7KRC6tKFE2IetrMfVTzaD3mdCcArtLVECitG7/VadRV5oYF31KIlGd4p1uiXdgu6aaSWAQdY8egKRb0P0ZldmRITuoTc3UlMi6GA4RmBmYJUg2MU9SeNb1KKk0VQRo9cWRaz9I1Ai1kypxZxJOfAkrhWootEyhP1CrPoYhxvPRcqld3VjnxvpjDKlspXcUlZrT0WdGrwYq6IWhXSRMADDoXM511yEJDqsvQzbqJDI3w8ZqAumIkTVbeCWpN8ruC1YsrZqiJl+pqZlh2i5SvEiZUNYYUiocJNKEzvZRiRWqlYkWTWGd6WohCaQgAXT445ebKauXWh/Robn02uALudQkggAAAEABJREFUMoV1oUSCgKQm2rgSr5QvTEz3IVslYrP0WTyYOsLRDI8LI004YBBTkQpqUctiopJC7GOJwSJEXN0EIliuIiBpnrSu8YoClCI6m2SBbaSNXUtHeqAzhB1LrqBEloAGhdB3CsL2OAaNWEbOMF/0QuBprQ9JF6zYMFitqQqqEQMK3Q4VcrAViSpOeZSfs4H1RlrSR3emGnNlUbzwMpCITmr7Qlra7JUcIU+qdlNANFqJSXObMAaKoqhKZQLxXvtcnaQmOCApKV7oxuI9NZDcoAWpJkVY3el1hlLdoEpu5QhOE0VJVOIg23FVWtH6ujPYNL8WoBCVkExq5/SgDuXohNBmnchWB52zIDhmDs9sdIhRgmpKDVeZ65CFhnNST24dAQh9cNPGyjwIipgUkxEFQseUUWSQ3QTbDxFlP1ILtylMUivKwlC6F3Sl10NJG7CEiJkjGYWzBdfcb5+DDrimAN/77R9e+r6Pv/HD/3rJxXwqjVK2iwRxSMB6DD0lhOZPoptAXHo76EQEiInl/lfd99VPeOg+e+zaPc78wS9e+cFPvOh9H3vDJ/7tp7//y04bw9Pve8g9b3cLL9aAqbMIaMCQWZFjFeWD5Bg8fRzsUsmSKAkLVSkqmiXLKp/zsHscdusDTfX7v/nD6z7y+Rec8tE3fuKMX//lnHGoD7rjzZ5w+J1H04RES+4kaeylooC0jIoGxoq3hc5xGK5/jaud+OgHX2G3y8zdz/juz054/6ePe/fpb/r453/5x7O3jMOLHnrPu93ihiISVYPFoo6bMwveIrJnd9ZzgiDBiCNs18vs9qrHP/SGV923R37h+z972T9/7IXv+dB7z/jq+Zds7r7TDs984H3u9Q+3UJDqml2yW9nYeNYDDr/Hza4vqt/+5VknnvbZ4971oZM/ecZZfz1362p42J1u/dh7HJyqLTJAf8bs42rLlocdfBsVOeuc8179kU+/9AMfOe/8CxXpyJaRJAJNTYRkiK+2bDzrIYde/XK7ReQXv/eTE0792Evee9r7v/D1Czc3n3ToHa697+UB/OL3fwwnu7UoTJ1XeFJQUCgB5zOLiZJyoXEf7njgDV76iMN333mH9dw++c0fnvDPn3zJO08/+ZNn/v6c83fbaYfjH/2AW93wWkwfROyw2vqMB93nSnvsQhp87js/ftn7P/biUz70z2d+c27+1MPufNXL70EtvzjrT0wgl2YlrGiyGCA9Ewn6qyy8riqSRIckkVGwxAvB4EFMBSJqStsrx0TLyclpLdW2lNhqXEcBXc6qhckyhg/mwwAt6CXXG7ZZB65FzbPNyKmWrEsgQxp3RzEGaTeHTizRXE5oFRpHNUyTtsZMrdmHMFUlrYCwTAkIDZPcUmbmWJ0GaUQxXAhCSEve1goZECJrZTdcdLaNrltgA40clNxoxsc9OQeRMA2pmYUOgx5qG9AHKgmbUTchUzrlRy3YGHKLpS2QjOJbMI3qrFqGUmQlKIEya53VCChUGOXGcBfeLWaDYYfVlhtc7UqH3/Ggx9z/vk86+uFPetRRxx591GOOevjRD7rvkx/zyM+//60vevoTr7zvvgf/w+2ucbm9ZZas2opx2ddZVsGmW4SLsbDE6EQlMQQ2QisfH8YWzw0W6g6GGpkVq2EadJJsMUPYDjU+hPFBlCsExyBnE37D34Sdv8ysBcwy6/zCZU7eo3cjchQmGLuYKVdzGWXcKGNhYazNtzSvSLqUQ3FksCr1HB1FwtEhgA+aG6OOKxs3hqGuWB8NxMTAvTAnzxVzjdnIQwZxWoVrsHiDAUb1IgmNyVpXBqBVySKSmg5xrWJFTZOUIBecvsrIC0VRxihVBqOXmcrlxpP6zERqoeDIli0wm5Y6rsYNE4newwmQCweCZFhsWFiRGFRWfI0ojWEN66BnyTE0dBC64CrJQhAi4NmTdEE3CxmDvVkz3gylUSKqJbRARSkluHHMaOSgbNCOYRT2mdCEQxt9TIjFsBKrlB9YSthyy10JDiiplKaMKIQTtKGskzu2mJBUiOUwnTlAOAugv805Cp3C61TqzNTMwo/YRFxiuW1gxii40EzMnG19mOgqDTUhZyVKulGmAIIEJpdp1s7ECIwFla5u8cnaJubJMoaqdaXBCKQoydO4ZPOUeeYygYV3dNwBSKcsMP7d1TSqkJ1s1Dy56pdsmpy8hCun3ucp1HMglp7eMSMal9MNyWqxzO25WueWJhucrQHhIAmvyhRAYflItLQqhQElR2q1YWBHX3uI6XoY11GiKZrUVKK+QqyQVQtKYZcikkbnWbrbFmEIcqZp0QyxdKOdtKlmow6EJ2Ltfcp5DndmhC+RawhP8xiF5meW5rWZZoUoiAER7YKJpSKlKGoNq+CjY/pPAGbQqnRNL0mvak4F66wuK1LGkuPIlCAcnq1JMKopIqo0yiTGeR7mpYJCIiTJfHdxs17Bp34ZPSf0TZnW2qbSe2mp3QJlW9TJi2dRdkArIsdVCt3VUeawSC7ss0XwGWrJAE2ErW2jD2aDloKB38OPYuNCqC7EIybqpvkFRWTVq06DN1lnTmLrPvB7q57pBKvFYp6WQcoIGt0RRKBnSd3Qgf4ykC3X0dfZ0qN0Zf23tega0sJB2rQZPoV7QxKXIRi2uSwWZwK8lm3ybFJiLLEjfPRZ0QmaMoikuWokjwU8tVK5gRhsg5ByLjTTkB5oQW6LFE5HbqhtoMJLY0BVSp1Wy5dk6IN0SqL4Vk1Sxi416JMkyCZNaOPuqJBphTrNQxCMEiFYnvHPhfSiUipcSQ5YAMQE9G7J8HWVTlPoNawFms8evJuWYp2JMluZZXAzVH6ckqNZEZXsEmP2bMtYCxA8PcokA09XM0UGW2VYSSwNqCZGS8pwm3uZXCaWuFBJ0QxlbHyW7ko/F453syCE290DssS8Ma9X624dfTuDfZW9sOjb2kjhgSlQsic8YAhjMRNIKaSI9BVYTPoCfFWRnr1n9KZtm85rmcJ6kRA6kE7pQ1B+0Ly5m8wYkFymx61RV6Qw0D3bYiUIjXSCqiGlRe196ELGQriC0XMB4+vJFO+1g/FEqgAFTFbQKy/JsDDsxQlxN3BVkgouklKkmJluiNdsHu4EaJAUVhjSxiy1QxoqfDSGThnK7IGco0xRg+mq4EAOM29GNSVskBopkyOIOKLqXJWZBwqodHEglukCkbTBhdoKWhHWJQFWipFJow3gAJDcRUdrShtAgmuCeT6yr5LGhXOwol0ie8PMfiFzRN9S7Rn3u/dj7vYPV77c7vvsddmbX+sqW03J0qHWow+9/TMffPe73OJ6dDCJBLY0jDM9zv4PN77eM4889FlHHppaNnubfU6ZRunX2+/K73v+E5/7oHvf9cDrXGnP3ffbc/ebXeNKjzrkoPc84/GH3vIWKx2GAigRmSM9M0S62FxYDDUABl7222+v5z7wPs9/wKGXv8wuJXNVkn3YIAnxhliV8RlH3uPtT3rYQ25/s2tecc9999z92vtd8cF3utW7n/WIx93rDlvAliyVwIYEYq9ddnjKEXd/5gPvdZP9r2rSFA3uaBURkLX7eg6/3rWu9KEXPfE5D7jnXW507X332J3nLfe/8qPv+g8feeFjDr7RtZVEY+gEMloUu+vND3zOkXd9yn3vlLEGw9K5WqFbZEn2D3vvsvtJTz/mZY8+/N63OOAql6Oo3W6+/1WedOjB73/eYw496MZsQdhUsFD7NKavJOpOu+zw4qMOf8fTHvqA291kf/qy1+4HXHnvRx5ymw8+/ZjH3vWuY92aaj2yBTwDrI9rOeoed3jmkYdc78pX3G+vy97i2lfdcYeNoB0RDqQy2MSIcWjaJ0660t57vPvpj37yve90i2teZd89L3vt/S7/sDvf8n3PfNRD7nybxx56J25jjrjzHS5uPcg4wd57XvZZD7nHM+9/n2vvvR9JoW1jCNYsljyomFcJaf9wo6t9+KWPO/a+d77d9fffZ6/d99lz94Ouc7XH3/OOp73oyTe7wXVsLMWqsCWycpWr7vfWFzzmH+99+5vsf6V99rzsda90hUcccutTnnPUvQ+51TGHHfLc+x16+G1vPbfZ0DlhNSh5HEPO4zyNcBYCxRBS52ox8icfQGzaPCEmYDYaM+rGOAwcN4SWFFO2yI05LdV0w5QJZbAUSUZavWm0FPcaGJgl5sx2VwnAeVVSIKP7lvQN6u5KFdGWDNTezGekCVTnUJbeyNAMyeQVFEPC02drk0RXaqQKgUHUlPWtKstSC++duZ2SpsF6uFgMNR9ZS2LUtpJWM8y7zM7GzVwNhT0MlKK6aAeQWkSW04CqKCVUVGFmMKW6XPJ9Sp8gTXUq4VyFRtGhmDWTSXifRtOMANCjprAejlZtRWUqRSmGhaq33ifPvriCWmXcOm550L3u/uG3n/T0Rz86k5hZz8FRFp9ad+YU3TXEIF4Qucilmylc6cDPqg7pRAmLhmJa+NNQBYMHPWO/OmfpHtIILcXT8opZbU6u3Z0ycxCoS8zCRChwRYhI13DSvYQwGi3QQfYXKZXg9RRxHTmNoUqQ4OmpPY2B9GrdSlgBy5B1EQqCpgCGIrBMSdQo2hFwhr+V1mVN2yAQpeFZQy1EU80WIUphTdT6OMyVS2nNCcaSEU1LMIwiGlLamG0jciAsykA7kmzMmkppGurgMllEa1ViBA+fvPXtHQ4jR+PFRLVmcONakFUoqZs6g+lztJlozS78/tPnlC5GU4l5kV6TI5OryIJkbWpNBmCAcNEx6mSPQJAIA0tI8+hhSNM2WFupDzZChwjxllygkIIw9EFgIpLqic4S3lM8SQgKUogJBhVdBhr40VIEPEpSrZt0ZTQ6ZBbrTCCDihWt9EhQTIXN34hYFvpFXqdid+mzZ2cisdd2OKRJpgaGlnUOj6QJmYIe6FRdTZUtR3UQWAZdiogp+Cm1gRAqA6QutATqkB7LO0wAV1Fqk25omdEShDORhU67SrPmttacLUKDUofBSiViSN5wJoXZAvegaTVVWTq6zV5JY88INAgv0fYAqSSg5FRoUbVBUamWdoFhAPXZpulM3q2DcjQhhYu4t7qEQCyhSKHHUqhgaug9HN6WK2DKyKrWldWxQ3sHIQKontSc6ZPIrD2ka8oQMMqSlNKVgBV02qFK00K247fABpuKTts3e6XQw7ZQpyaG4tvR627hbG6HQaXUDMtOQ0KALKwLQ1TjGxhckmda2PazpFRwXvUcZrGJM+ZcqgtA39RAmGDqUoMatWYu6gtU4SZz1eSQsDKNxYtEuE7zaj1xn8H7CIYcRdpoXVVFjfZ4c4KQbpqDhQ4do4QW9xJrdRfkonlIHXuYzwUhZr3arNF763NjI2ctLVk+1Omf9mSZi1ThwGpapFiYWI+cpHmZochauw6NF8GxGdodU/O5k3GgRvWUpIlmopFoEWtWRFKqY3ApLUUa0IR1iwkJyU5rSofEIlzDhYJmiZ6ZQJB4VeciHQtXs94AABAASURBVEkAlR46reXKPIgQkuQQKVKGWm2oUkoIt20t+oygRwo1VQklETF5egAIZHfzGF2WkAcEJqYGlEwLKCMXkRJZgyEV15gEc9jkmEWmZIJ49h5dW1QWyDCqkaoixa3MpbhKuohDshfri/U0P4gZT9VASaqZUdewHgXMz4YWwjk0hDqxkp4yzzG32emQ+7JHDEZOUyzoZGoSD4miRJTuIFVq12qCIWNYjI7sCfphIEIEF57olj4gB4AmAgvEmSZ9gNdBlLWFliUH0vZId5+lr6unhAl51iUjM9VJeS0wqca8kTpYMSBc1r1ueuWKAUAAY30qnZ4gJk1m2kQ4VDIlIxYMq888Eei0nkKsLNYRPIawL1BHIrNDM0pJNYc1Otg2bKoyS0o6g7SYjepBtmJMUWTSHIWC4rRQHeGHhhSnnSa8EDA4TWyCtWSQBMlq7YP1snAokuUn3SDGqQ4PUtbSU6OpNh0RJbqTyfj/8PTnP/d937cgMsacc619nvf34uYKrq5Suoho4kZxS3GpEl1pykVRIhFFFK6SKhQ3mqCiiBQ/ayLxg0RQCYLwp/m8zt5rNsZ+fXBe+znPOXuvNZsxxpxr7f18GjmLSgLPUAoRZvosHzrRfNJOMRtv4zqDkV9tCI5mzz/7n/OX/vP/4X/H/+Wf+lv/53/6H/vv/CP/4D/nL/2zMAZcf++/8V/3N/+ef+Nf/1f9ncqkY2BDxsJyi3/9v+Lv/Jv/jn/zP/Dv/OsTI5dpUDS6rNb4F/b5D/2Nf+s/9Y/8w/+3f/Jv/z//qX/if/vf+S/9V/7jf/+/6K/88zEL6bKrLZzwkz7NbloqFJ5cp6VbW/Yv/qv//P/k3/i7/hN/z1//y3/xgd3Or7+qPynN0Cv3qvhr/7K/47/5n/oP/jP/5H/j//pP/e3/7X/3H/8v/0P/4b/j7/iriqtHAKzK9fCn9/UXf/kv/Qf+xl/7m/+uv+tf9Xf+88MGFB4K0J/mLTVneaLUGrf/Q3/f3/U/+0f/M7L2f//v/+3/9T/6t/7R/9Df/1f+uX+5nKo48l4zOgL11/5V/+L/xN/3b/qP/9v/mr7ug9Wi64W+aWHQyTX27/63/Gv/yf/if/T/8j/8r/0//gd/+3/1X/sv/6f//r/3L//z/nJOa6UdPOmlm0wzRvh21UP/G/7Vf8d/6z/zH/k//k/+8f/HP/1P/O/++//Vf/Q/9vf/S/8F/wINuGhbKpDrGnuOVZa6n9t/8t/7d/8f/gf/yP/rn/4n/pf/xD/yr/gX/4vUOXFa5inxsMoeCXNR/D2T5/OJ/+y/5+/53/zj//D/83/8X//f//f+1j/+D/77/+X/kn/huP31f8O/8j/y9/21/+i//d907bUV/dhf+cv/rP/Yv/Ov/4P/rn/r3/lX/wVuLrAAUHUT7hfHVTP2VTA7/qF/37/tf/KP/Rf+T//jv/3P/I/+W//zv/Vf+M/++/49/6y/9Jdej6O2sY44E8zAX/zF5z/3D/yN/8V/+x/+Z/6n/9j/5r/9n/9bf/Pv/Zf9i/7KFfM3/o3/mn/g7/u7/+bf83f/4aodUfAKR+hxWy8fKg94SapHnW2e5CQjeVVeWpBL3J02QdKgtTeF+vaKeFxsarJt7G1GVyTf6Rr9Vn11AT2B3mxp0DQdZULrSouySW/d395uj6HfK4i3vdd0S1Oh3rEmtykWzOZb0DRCJe9P2VOKEpwZdJtac66WZMLefoeBilb+eyZ1lmthfnjclYFCIcrnxBSEcet52+iRIaH249BcSz1K/B0g5ZwMzYKbmDF7l+oeJckqnkSNeJ+laYUqR4YhivuZKyfmHaoQmENFCsDJoMvdJ+FZdZ77OXd/j6jeqfXBULTDv/z5S3/1r/4VLXn57ge0nLNR2f1tyBZUW+J/qdWYDbHQocszajOtPlmvMKALBl2LrX+h5F17I6oZS6fPaFOOx6cXpM4USgoRaJ0xDkPtNrKZokbNl7DxJlvJnKeP9H+othAFCAzIBIJhGlFTCUu7HtrXlQrKWALbs3E4xxRlY2RNkiclnOVwhWE+Irp3mx/NLYEbckyfl3hyNCBo3kDTZABpyp67cKVGK/lHowJmEEqpXJQ9mygMFBShEAOxJKteOZ6KoH2NsJnfcdfw0JNuE1dzSVzVKRq9efX8VC5lIWOjK4Onlct0awow0zH4sfjEx+1HJ4tt8VDKNC2wy0BFkuV5fwb7N9NZ5A1qOXsk+i0FuZ81xZpqTCGHh5YqOKXVSg+YRp+p6p4mZpG2AKmUZj6uuyNr6FXCYTilZcgT0To0X5Dud/Qgsnbj8uVOEh08e9ercIz+sdPw1fuUd+45P2f0dxs/BzPJSDjOjNa9M05JEmsUI2ZNixJrAZdNmGBfXj9Li18BOi//Aphe9DQV9MdmrwMoY2jCWI692VV7ge3sC3PRYi3+rLhmW7GtBVQugsoKsKrIUUSQ+N0OhXUXDuqeOgjaco0seSGcYzUzNd2WyTxjFH235dNdScivTVkl7nZx2ePdPiMYRx9pCheVOvHw89gquqxGzHLTUsPttJYHZvMYekE1oPiU8aXGqUgU9aq6ujdqR1swderNFoQA5oTjMu6kHni79j7G+K2jNKXJe+xVz8HSk+666dVhGjsdLI8SnULiaEsoys2ir+gf09ISpnEx99tZwnVbUe7YXGvkcGH8PQAQbSBPjIAeKrjV9w809LV/78gQIMlqkbe/tr7YT0UdL6AWz0/mrrR+6L+u9V1LQXYCz6iFh7Yfd9sN4trYuPz8wQwDPFQU3anHAbqzUG2so+Tw+9XTVVO0UXFu4wcCepNSxMWzDrwY5utyGV3eCqTQLZ5PH71khMRbGJtYpKsDTEIN7Tna8BxKOKqLyJt8jI/qZ4Au9iGOU54Pmp2cb+ZTpREPmd4907DCqtFXyId5OAMDmwyl5maX0zkz6KzKnNRYvClzTMKcLrm5IUsKXECV11yD6zDEf68L6xrTQrFu37fE+LiarLjutWrzeKfhAINTfZ7pb+MpfR0zBMdIYhRDLFN4g5V9dV2lIAYDVim8xd5sqkKuGhtAQaWQGXHBE1B5z9vUrCeYsGfeyptGI0uD/b4EQbSIXFHumj5RyuW1eOaT86FQOd1PnVT7NKg0bUzRZ1Jn2nPWcFP4NadmyPGrLcLXR8uZTI/82gNhWBTBhLuLercZjHC5pUb4mWoeoXNtZWx4swFTvig0envTutCTrbbDPhCHGwy2d4baUFJ3Qv0MsijF/IiDJWcYNKpcm4CnS/PB9iAngD1+qZgEtQkf1/ZJ4IhmQIUr+4a+eKse7DO0NULHTHUH+PRiB5RDyyZH2CUViZUq5b1odFWZPyKYD9SQV43IamSXQlUWGf2YoI4+fBcXeZUiRo0sUsUseM8Z6zC4/FDVpOQxkp2WoYbygNdAoAjl7fNn0b4K2WVbIHRFnNjHra2Csx/q0QXzQuz5WVrsYmYlVlMefBXXMyYIXMZN1OM0Sgk8L9oSYBqVLaVyARdL6ljTQ7Eyk2+b27DA5htDAt+Xeo6G4EkoGX2qV5ADjTJs5JoCTJVQBv7k+rmv6wjmZi+vFVBpmvH9Jz82s47Zr6X17u1OGCUSXqY/0bWWeHu4mxs0lLH8k9dKtRwfxcd6HI+vsUULXnWtWgNrhZtjWiJFJrCkqTbCTbC7PFSJ1vbMalmgLNxCzJybDNR0nYfPjTx1JnX39DT0NESGxdwHvDRUOY40k47KPN2O/iA3zrRgyQdpLjkoRTyqBUTDZXy8BEQjhh+F44pqdNqDJuAhY4bvNbd5kwuzrG07lrzuGWXtUAn3xxjtdsqU5U8L9D8Bv4p3K4AcYQWzXgIMooOJ9yWq4zcgeCWxUI6WBsd1uSnp9zUaN7r/ul8IlBokBLaG+TEBU8zGcyr7ntUIJSUzVMqDL/oL5W3ndJ7ijAXbKbHld3CLD46pPlTTVmKMK9yNPu0jQVwY7a1fYHyWsvJvmTJBd4/dE1KJ8KZzozdu4WhpypLbREhqTdQyamEdKy2gom6Q06Z8vtUPzxnxOEk2zRk+8Bo0UnU3WoiFDzo1fCRXEKTJwdKvobeRU1ap/mi5PF2WqnoeUIfAeQ+Dsp70fkz1IUjKD7T9uhI76UlJL4dFFOUAkFEH1EYycBu0RU8pG3qpih7psM9gfDzaWTPvN7xJtTJ1/W0mNuBTmtR4BSN0MGKpIatO2m77AEFSSItEmtJahFpIaVnOaRAGxedXMfT5Gl2zSbvbujWR8oBV4MuQrhEK44YwiF7eurLG6JExesDdSKJ8NKmpE33Y5Rg1J15UJIp1moCmuvconSJuyh0bcBd3k4nniZte28ZBAprpNEluc8IXaCXllHmqeIczNjuBZgoPTzmVo9KobdgB3zQrrc5TtWJcZ6iMxjlvljhB4ayYgmWwotooc/JAnUA1vBjLl0Uw3EyDd6ULh0RB+CvL+gJpg10CXTtmjXJFpYhM+VqTpfBao62H+fpwZWHubJuEDGm2q0E8ytYBypEjLDHATOM1MAOKlSwrWTB9NFkbqhc8XArb2jmOoieUgCZqymhA7tNLRkZC6hnIoYzebl/YA11xwkzjfcZmiIb0rXXQj7//Nx1wMI576j5gbYtF/YHgWvG5wsPHoop9A7cyBFo5FTrVY6uyns7s6iTKsvybeizFb84XWjtBc7cl/7I+rwqMtLWxN5ZQ98up2GBB5bYUnM/j59kPogl1+PcIQ8AMNCqY7v2OGVfn+TbzzXg6pkDVN7SDMy1crNNzoK+Eaw+j6RAyA7La6lta3O3V0ifrOirSx+xUdBnQChbCihzgQJVaIuFGKCqO7/bVy8ypiGwEcJFnvLUAXpuSkkEd3rpwmm1BVdFKDR+zvq76w6niFhE32OM+/uNcqzsoGPH/e1GOM2smB/NC46V71tAGylunHmQqiZ4SO1O0gvWYaNAXJrzS576QC270pDV8FFm/wTEmLizHh1ww7Ulc138jWJt5Ma1LbirjTW3ASvt+1znyUJhaRhdrZr4HNlVi39owQe2IDqrRAwUuBKbQVTfzhm4GOSNZNO2E/2KVp3Z3vOAxhGBsnzKfsbEX4Wmh/zy4j52WVPOnz5/7wUlvUYaYWLTl5bttkWH0IWVnrIdTpNrWvLjKwYV7j/4ob6xt740GoqYh3Dg2Ba8Rsp1a7bQMQM/cne9cjRmZVYPAoLq17LyN4Qvc1LoHuXKZU9BcnLe7kUMcSAU5Z+yZ3wVJttECMAiIVm99Fu4FZRLXKw/JSKG+K1u1vQnHueObViANdIyWUaysneXd5gwGOrI9JTDVu4zXzGsh78g/Se7ozp5GwBxh7hb1NrNQXThaipi0zqWPho2+AF/QcA3uCczVLtBd+yc9rMhU6DAH5YyCgwOR9356YWz9vUVHKJzd9NY5pEjlqOC0GH4x9/wOR/i7QEA277J6bFRoncamgT4upMzGpcZoBi1iX2uHW5NiFq1/A0WyFndwj/1GSOu7AAAQAElEQVShXjgDdk7e4vBgcvp5fySP2JBOlj3BZzr7IZ5FCYGBeIMlxEsbpRmhJb/KbswqmAHQ3ssaQfGCoa5PcUI170aBaW4m+W3Muk98y1KhsGdW5k8+jmckB+Z4wlv7m7r2sfUwWhNp1xj7NVzEzIUnXJcn3b5Grbc35RJnHMVVZg84kni1Am7VXcIEB6pmaOOiEQOoC7U2MyGYx1qABmc5r1DkXIYoRIKlsdOJfG96Ra4J1w1zVzDdY5xNJxeDkDhchg0V3o6jMLywafs3Fsstwt2peXeK7tbgeAMZDrpbUbcMBuZzzL/RX1W3nRX3shI48YVQemcZmyDgbwCr+Bl8Vvuesbl7f2cd0nA+bDGkFHDcHwVt72fJy8cN7z9hpd/Fzv3oGDkqrJo4BL0YHb62LTOsEUpOEpi2qf2c685VoLceMGH3eEHcC1KBD3GWQqncJqzX1g1N0/I59U0hb7oNdMsl+HCVrTQqEhI0HwUvGNxqlHc9k0eosWgH/khBJ3jbrlpqiz14YAPbiCUOBbHmRhmeNY/NPXVL7E9xlEHFLljJGdlgKZcZUuqhCT2q4Er1oUsKR+Pe/lXTPrz6XaIQOOT3cJ7hDbtpN/RHLj/DpB1TzNQI8957tHwDA+XIAJAyLykpEK1wEbqcPvfKWq8lNYdF+7RFt5fOPBmKUYugm6rjW7qxbn5vy5un0Pqn2gtT+BIQ1Cc06yVNZ2nNETGwAg7sOV3PiUd9XlGiZ06e++id1Ugo5EmRHDuwljh7nGlKdpSINEBbAYGAO+ZMt+ErkL0UMyFzINyM2gNIr28JmRIWPcf86EmdmmMTi/1jsxZUxXCiRln2e62pRC8G3UAlO02MDM/46HnbjcmcaHx6lpiAe29ozkAolHUZlTBHs5VtPV0lPs7hyLpPK1Zl89XA7i21liz1nowin0uFhjMDK/mWzmFOORf4m/XDo2kn0TVAFV7Lw7XCP4Gtc+m/Etp2TdLLAIyRqnZXcenyXh4RprYwWrJbfKjDDq16P491I0yjaWbhJrfdzzflZHBEHU0tTCis6GuFuAHUKaoxmM6V9zV+9fKn7Ztq4D6MZe1UtN2lMYYj5HZeUT+r1+dAz0n8+GjbMpBDJ8XtW+mKeLe+WDHu3OexkkDDZ1vGNA6Pis/8bDbFBy/ig3ElbaHe2TvGFzBEMn5ZpIDXRYU6JkF/ZeIRkHp6EHaoe7WrR4UxtXAioEOjZWAV5W/XqBo+Qz8mrhpIK63vVZlP3lm3We4de31oP8Rlg546TAFCE4c5Vh5Nbs7PUoTOdpTJOqPAknIq1ZRMq+E9qJc9BfRp9dQO5rDGu10sdbmXX2Y/l7mr3uM+1tqBlcq/z/1w0mS+yAG8S/jpvbEa21+cNuS01e07umW479H8fvbAh1Ngy38TKH0zmKun8cPeghOti+y2qeaTzAPeHrUBA0elE7jc/whb22J7rOtjtqfF8+lI+jg1VJqoB6OS+rKeyu7T83aCU6UiUO+rPqu+l/9aP9kqEs/CKc2jWcELMbPY3N1/abBrjV10tS54jxUx8Syqp1HL0BoCCTyjLo1Rcmf724WstNu4hZWRbuGwmRgGaQjrj4yWq1W8UgJUUo7xwpQ1veZyXkuT9qnPM9EQVH0pKrfe69mflsI6T+kokWuNvWqvDHMGY/da8KHE8Lu2nfMbnsHIrCRoOgG5jYVLETUGADMoL+++CoJAY4TJcHm5TfGVicaNBqd+TsX3vr5fywOZUmpbrPszczrR37FTAtE0zah5VtD5TCUp0Kgm2yHSS19alB2iwrDBaAXeINr5oM5LkFH+Z64pnzLhbQla8nrEV3O68Dz7+0SXsfva9fPzp7XUBHThtTeq5A3pEKeZeP/nlgase+YX+6FEY12XpWbuCA5eUUzB0FpTBRomprf6wLb1QV8tX0cGT2sRXWnmplRmss537ge/Ds6ZfuDlF5YrLVmts556iVbYcjcyGGIGeB0SZ+pR3ZSWn9wBSiKfqGVFcthqbl/6L+RdM49f9wqI7TGblYx5y+QIKtDKmLt16zTSmv2WZtuQ0FgVlPIq2t3zZN6pv57nGdAlh3XV8rSZ6Rl4M9I+di3sPZ89y5L98Nz7/u47Ka7vfm4Fh/A0O+6iigedU6tElzzqfAZqywNWWGzzz6gMxU3ed+mxzFGz6cF7lJ/6DPZF+wOtnXLfu7QQPOoTu8etsCCZymKv3zFdnC0FzCifW/J0qci2k2H8w/wyu4Bg09v0ooHMPx9aFZUjwGxXFpnf4WNRFkP2FCQMsTJ9VpcS6zHBsr2vK2Nlct1qkKOBPZTtFW60kbLfcnYY26n0z7Iy8SM8Rr6QqEMtkPfzFsqzov6I/mEvOfN6aFWC0xVzmxe00Fz2isiM8EHZPn7lcrwUlaFmBjqIWq3OFP649TsaFppCmOJS7PwedTzlVsABptuefufrI3kvVZzIq6PiBY9Fmo+E+HZiVR8MggGPWeGndfjSR6tbxQr1iE3neqNVReprt9uoyD2xRqcxsOLb+sOgl8LQAHEN/GHc6l1CnXByQ8V78XwkIrAs/jTrT886T6CheDtmJI39q6+7HdDU9WFsHaZ4GyOOdZ769NQcTAk3/9A+nB/OmmbNmylER+HJaJ1EKPwXcLQC1cLsRyh0pzpXC5yv1+PpMWYgSpyDLEU8sk4fC6WxypY+evbrZUOkpi7r7Mzlz/X59RHm9Sq2e8LnB32d25+v5c3M12duxXteXk7345jPZfwj8MO6GKE9UHBcbKhuntXpT5fCHDzSTFAKXEi+f0XQ4lePPrfUAW1Y367D7npKp0+thjW6m2+mmNyoTVwhFpx6w/LRkOfYc7NkcwEXhBUXZtvvtZJWNIAghyqe1Tv4Y3vHno3Dc/A0nmGTgtg+GONhHFw1eqhBUZSAiBOwU1NZOVmrGBIUqDai8CfG/NflR5qBxmhhOH1ujNpsItL1lFnASiCqP9Q1GUKbthGauZddH6xI5HPufJ6ZRmysT7rWzYIdMo3HhMe+3K+fo2VilFhdSGFiy/Jz1drPfBQffpUaThfT/d5R6/K1TUuze2bwF+VKZcG5baQELIMtWGAUPKre32EVbIdkMCXz4Ml5WsHhge6I/YHVG6je0DSha9xrtiokbX35U/EHeEl2zew5JQHs9pcJQi6qH/DmPMx6A37wJFtJdVhfw1V74YL2ObbV9xLAgXj5uaWM21pG4rWjfmSrVRk+E8dk71v1sJOCzQZDJDW11eB6MZfliloYH8Fw3fjcsw6Uyuhk2BlWw09f314nHc/8xr2we7Eu/d29sjFYjnCQbdGUWq6cgL5viBjNorpXGnl1XO0rqQvXzNbUAuBcQdKYQ9hcprFH4u9vI5HTOcnM/aTXoJXdT82H2DHbR2yghMHAinxo35GHgC161A/OfrTenClobVVvNPmAnCLHSmVZCrVasLwnOLaUJFareEAUcWOOdVs+g6ffM5BTfzMci4x40Ly/MXfgKACIzRJHVEIkBl1XUepFvkPvN8uHyN/1OTlzjxR09eHciazQh+OQUTkFCMXHwfdGPvpq4DSsG5WDFv6aggjQUrI8Dagl5VAJo0uEv88Hzr7Tbyl3hhAIgGoR+8DNsIx7fNdlfVGsiJCq+d7zVb5d04Oyc/9uVNdotj8z2uCozys9HugyqRgVs6LzwE/Y59gl8ykyJn7tuZci+Hb9Gnv4avJ6Ygv7VkRTzt7TO9tlbQB6+pxVEvNCLaXxrnZvw925rCRPEoCtzWvh2vWxs9Hr0G7DmS60QOzY29dHNfEdiZCB3rxXPnYPz3vMeZSgUBsV5IQ5GAN3xGpfbVfOLpOFC+G8ZIwXLWo31K9iIXRf59qpzJf9zTm3d68mE+L23Dhq+tq6nV8Quw95ohCShu/e17PXvT1tIKoWauu3+MMYzhpBUCLY8+ySzej+SVyzh+vBfKd+xTp7t60yPxJSnnp6NJmDF+XHA3tzbdvLtrMNsJh9jRvxBP/kvANHztuq0RrQ/oYxjqaKb1JcRSzaNo63orpDkoC2zd/bft07TWuIlmzt+kbaElRLWuzO93mPZs8OaEsqExg7aVKtyzXmyev76yfvNf3hx2OlGm1NSro9/ibLuYL+YW/rl5XtJTOM31/MEuucC9pfizdIKe7F7Pli/uRzzJve06wWXJ88V9+WX0sVdAvyr1I0kuKR14OP7lCR23KZEYBJZddNfOu5+/6FenRyRm///4NJrQByytgf5x85P2ecq64P1tKeYfEWzQLIrxAMwTE2DXtcMn5Zg9E4MKFNqu56u9QxUvPPmohpa9S4btK+6YpGJVzpfGISUDBvMxSpRT/aarpqaOkr9BoV0rf4ZIKlJd4/0Lsg/pzz6TSf+cP7L3w2LHud8hw4xsts6PZ7nVx2iZiJq/ZqhGqMh6bimmz+BkOV2ef1sMBg462yLMWxcYyHKHiHi43U7i3D6JDOgNNQA3aDCq1FU7oftySmq/Dc04dsUzjX9EZb1pN8bNSYuyefEr9/ID5z/cA/F9eq34BVNr9nvp135bey1a1hBk7n1LCbilzApuwDMzIlj0AYjKA95l9G/vkbR/8cWu90AJvGHzNh5iBh1ljPRDW8xs1G8UatV+A1sNk+f+hdzsenEYXo4wLJka69g6n2Zm7gUIwU+8ZT51H+ODUtFxSqe7DS1LOy6hm0SpzP3n+Cnjt8HH55zpW5hx97gyifx+UAcmrktvjArqftfr6nf836QuriCpNXGX4CtWo+T1357Pnl/GX4tvZut9WxelFCP5Wn68g/QOHGB12d3vee7/LT3o/wU9Q2FmfPBIVedVUd9WOO5h3YL3XUFqOygaZ/67qfNbd7mv3O2BHXqB+UP4q5uezFoQJnSRDHpT8VutClhdNmKAABgVyX1xVCVigsDI/NvRSnPTmpTsxi2Ow1K8Y0Ea7usO8vft3zNGt5R7QsyxigbnzN6xbW/cPUsbvxRX8LaoCoIg6z+LV1QqK4BEzrdBbqFckfmNXNM9NDtvFLQdW6Sy4vSAnnrDLbqs2Pn7UPfJjp55m+309skY0UARyuPy37k+PXPOSvi2f9jhKG9juR30nRdqtkLqpHaKK8amYrU18DbegjLc9VZTWD+RNRsrFmxpC2+m1ZipizrMvuW73S8dW2+TLpalOIzKVExpRRYtCDch0+3Ief865NUL5J6aRRA9lBoaJzZ6pUWF2Et9h27XTWSN+2+L4MQ5/lo8K2Q0WmFUuT+8gQZ+QkztgtQY5nvGX2ifaYDgqvrmGRaCBfxabxWfzFqbtxdEXXODiwe6xBKeeysQGzu4fAdGIeUfge+kn4KdW2LsExIdP6EQqD5VIlFtvQ2fYOk9bqtxlZ1pX5oPdAHmCEROUaSxPK5FgpNXc3KvXgGS9FUkRTP22TF2utFXPtVGMjY6X5Ea7dyF5wLbkR81YwadMSsnuvj5ksKyfsxXBU16OaVx1NQS8B0M+gH/jTXiWHjvjQPQKGFeRa9wAAEABJREFUSe8RLexdHfkypJbLcoOcTE2mVa8GZ5qdgsOZYw/qjswPbb+WlLQVQjjrplL+vI/KriGccJf4UItuo+lVJtJmqsVZwQlEJUonTGAqUklzhPOe2Q0fM7p+t+CCQGmux5c6akvy8xgOIRJ11QYOcw3CWE3fXalZjV3rc3zn4viAbGXW8jvU5KzWdAulC/BVxBvbqH9NFitJmXTZFlBpkjIHGH+74DHkcDBgYmfHvHxiaL3Dl37K8ByvZ81tkVg1OJh0zA5ba1aVfRM3WxeIZAxNYVFmD85529zNN8Pd3Bmhv0FKlWYH9ogODXZ7qRltovC0jhmIxfIAPFoyg+xWn6fO6R71Uqx8m6bCGAnjmdB7z0zWPDcUiRNuidKMkmCUUJlaqlwUgQlqHREt3rNGSBzWg9L4FnycPjXVwEPlM+KFrvMznV1Pv9oZjF6v3HNfMFr35PTwndczQDtf2lGYBNvtvX/015f3rVrRSMoeCR2ucMeCdkW8mFp6JpOAFHuOn7oMwZJ9OF/DNfM0dHPVz2abRIbu/2/0Zg0XzWgQxVQ8OSiv8QFoWmafxQxRxHemkeywjBkKnnIAGqj6V96m13u+G3UjReQzLlqFT7O5DOYjiN4p8qmedufJUxJuZj3z3kYkVDszbgy1ZBVH0spsaO7bPAyKZZHGt0Vt1sZ92XPt/rhmNYXqZLmNOU2jWkB02qij3zU66gwlxHrw++4FPR2ReymnFgwQcp3KEm+YUNdYoE36PF4lh2z9Xj0SLxuZElqWDUwOX5jqtZiQzyahnEGtAnuo2OkvWAqax6PeE2Zy49VX12ohdhQvwtRn1LUQI7md6VM6UOBAREOUqZg0sTFa+XW1akbpR9DYOaMk3/KcqMEzulVJohWLSDON5CMNP2OdztOsBjmXRQhlR6jnqPb6oLUY5eOepvgX4cqHgQkzGsZbc81H1fd+tzV7a5/wyIUSnLqkBiRcR0MuFMGYCv5VuSZLPUVAVv8s+AWIa39c/r21dKji5IhUDQakzoQ0LuODgjXtkPqAWSgJcgYIIsSDIontF2O1E3ida2oLIpUOIC7Bo1/izMa8BXSpUQ/7qBps3Nzo1mpd1PklqyPC7Ho8HvOjSwpAYxURBQIwjtxg8FIQb5J8w1Fy9LZV9HJdLXcNcYGNUjRtrQ2KEhmawhipy2VKRykUaxKaQP2sNyvXN2gkQgxLeSOG+CiT5qvrGuXZzt42LiAsWg1CvhJ9BJn5qJAUNwQfsJqu0+fU5DHYXttijxNLCpDe9ecOXVdzEXRciQuzN3y5t/fwd93r7XeooSmkQwkW1WU+pJmOAQsoCuF+/b+oZM1pHJ1zWSHIjpbA8xzdNVlRx8qKalGtiPhWWjJ0wSmtnTdjxQRrKInrYD0d2gRVDs9Ed/A420cfoDDoCtvIoc8y+QPRLUQU8GxXYBR0p/vhjBSo03i9Z3adxDywwzjQJeWEGHThHKujZtU35iiisSbUWXoECN2u4E8fzxt9TxdFZViHSslT2m95fFeRoTAQ4ro/6R0J19dGjg2gaN6V4lDb5crs34rKmUNlmjVTKHkW6jFc9/J8qXGDjnV0x/Lx/Jiv5TC2oV6/NhmjZxx38yRTW4oUBa+/GX/Kf5VXQmOpAAAxc+XZStWl1R4h0RX9gpmYlECkHVx0Ka5cEWlW47p9P46mLKUykaKJEeja4MbXKB9C03WloA+wJ+w2PJgZUCZKDoTQ70PnhB9K7L1+R4bGHIJSHdP6KS01g0MNBl8WGKBUsE5atRccMMipyTh6eic+aTs9zqpbtcNcXYvtAREASbaz+7X8pJ+Sw8Q85JR5XWjBLWdZne+WpXULBc0EqV/DY9Yvd/qBh7nT2VaIsvcgbUz7IbgrvuxHGFZLzGMcMWehEQa1mPAHW3dQmu0DYJrRWqikkx0MbY+9G78xT6iVoz/EdshWqesztVQa+eLm2f4k7ltrAZ+yLiKNNQE6lfoeBAWS/izSn3l+5g7TGGMahPB5mDnVM4nXo6php/Zo/jmxck1H6rd5jyWEn4Nhqk5bKhuJyFfHKpjraDQqc5T3cTPzNfapoXoA0aYSZre1HA9BDAwqEOgCNWc0faYJGMbG+h1kcPfgMq41o9WvX3nrIg1UzbpoHe/ptoJSvYzOEXL9wA425/LeO+MT2It7LSqpZ0WSAMkqb/X+RnjDtXYo3PakawnSjdGgJwaiGpefixkcetMUJN23Xz6RI+RHewWYKyvNuQfZkp5LSM9htk2uuLesPt05R5Qv2EexRv/qqqc1SBlyTHLMkH3Sx9aEHLRyqe4z1X6w0uysObKPgna9DzK1jIarAZzO0yeRxAl2iFS8c2SCQUlQB3Dm2zhGhByakJ43QtRA45c8Y6bF0KiYsOuFKpttC7yIS815z5gNOLQK9DWyNCnnzFZlagRgUsEB6qQ2M5iGY3TIRWuarguH+mbfz9zvoPvBu+kZ0LAMohU9EKYrnNzoFWM7GMs83PLC98p73YodCs31OC3Y4EnrKsNjGCjPxzIttJJdykxhsFsXHtgXUo0FTdnsLE4VbGLR1n4sbggjzK7ZT+2uy+pa2D4MG0LpuhbRnpn1MGqZLT3VsysV+sGjkqzpjbXr3XilbjQlhXp0qXg79NgLJt1OANuUZz7Tutu7ZRvBYb807vYLl11OrdS4y/JefC6cP5764xcUWWf3AB1+x3q3ZkvRre5QOtbPvL+QMhhmhojaPB8c4cavrqNUkXaCafWgbsUmlUXhd/7o39FglnjeOgnIBLfHRV/mFkZBMYp9SkSUArl9/hQU+qxx8YGZnnrqUT4qUtPSdV3LzQeXDMvV7tZxjTrlm4sYFfZj6DV5TdPlFAwYEeSCPgq4M4NpjaKCmmf4hT/jT/tXihokMCiTyvj0zGlmR+PjMnsmcW7tL7ymtZpry6dgkYbqxtPe8kNtQDEjZeTrd1/8bHEhrtHHHrhKqI3qfz+3qCTIfv0FZ7tro+pbrJxA+3jU2o3Ln6VdsLWbPEye+67nsUrHUNPfupXHzpwaGZRJOLFMKeurVRFyWb93C52Ush8oL5ecmMt09HKoStYiDYPA7KKVyGAruzyo6rD6QEvesQF6sYJt0GvcpXL+8bH1MZClaATJnMKZbgpmWWCZnplchJojRYP8DEwZKQd0jOAVvkBdPqonjvXN/k4dNCBizqDQohgURX779djn4avxgZLIa98e94zWqCpoMPUOJbR4NlNh1+CxOvs05XrvE58XnGhweqLtslefzS2witXecFwUPhMb1KPlNeSg1FouXbnIxXFAZ9uHlpzC1/OrbZIj6UbKsaN8nXdp8y48Uo9kM6YravOsW91HX7gNZqLGnq8iRTyPvz3hmZaglDs0GaURhnRZONbTzjJLFdJNPF61Tq054cf8+MsZNgT9kg4d6430zLrhiSbG4VsHR/T+xhIAoay4tl+bIS466gk9NHgawkVIqVV85KhLfwAPwEThNJ7u8u5LSKJhSWs3rNOR2iaemKetG3oJlqTnCT3T8BzXKQV2AeGwxdKybAcotA1nQkuzWHXFXVKmkjLUHLCW87MX906jcNlsaVEWaIFaOFq1fY9dDIvtRhPHMRE0wvp9K/DbT5/erYVfozQmDBp82bo0kiZxzMgh+7intii+O/axuVHiWxGawqzB3fEnWPrA6zr5k+oagKwZB513nu/0PfF0nF5acvtR63w8H4FBa+9ej8aucZXKhtb17+OlzcZAGb+ly8j9LlqO3if5KiPktgEzLE/pMkgDm+vBJ6Ft4FoIN1fSgHeZvMY5BtCo9I+q+zHczgxN80ar/3eOBjTspOkJAei+7FJknuooc7T/uMtFrEDcTCLNj6+b15fX0+SMy6adISzC13JzG5nGA5w18/6nftEX6loZIsn2y8x40o41V651PHJp7o/j06+wMWKiSzIEBmYTDq32Clm9InUVB8iuo49usJgxpfialBSWjUyXDM3WBYUYPWvw55f3GN4+09XZGOjivkUlGIFr5zJM+vlG3pQRytVYcskNBW5U2ynr9yHWXXrs+BsF26XtUAppeltYbPhVpgoqryPA3awRmZa5hV5jDV38ydebUOAFcNo7w2YT3nqpzEG93AHhabdW9dFSn7fWgOEn44/StRjK2lrazB7YLzVkCcag7nWrPkaZc3x4zV7aCoWLsrFR7VjQQZccRu4JyU95qhesMs/2yV6V+zVg6X47H0wU1nfiVtnmvmJ/Lvgnn2tuM6FpWNarx2QMerXMu7dqdvv9qTt0R5I8vMDLuRG7e+Vjp7PmeCOK0XAJIWtlR6qEAQLNTL/LVIJO6ChKB3Ny+hv4tSGcj3MYZrEEuINp89g01Ss7bLiau037jC2O5hXLHgqWWMt0HV2Z37RvX+LTYBsvWA6SBh9QCKwz0vEiNfVimyKUhuawfjV+uR0NbXNpStIBwviJ9XHtrzR0pvP0c7reuA88Xc/u16msfiAVB6UKzplKlNxOB58FbQ7uz2d+LhF+YBnAhoU+3i1plBbQ6MeteClqcaoFkvp71zQamAnSDLARGqM2ZdYcXaDb0j/GmBVExJUZN6fZGsnGdJfawFe/iBaiY1+8lXcWNXGPJqXhvT75hg6v2alyXZgP+Clb8st2vRdMK7eC7RqKd0osBKutWyssQjGFIUAT2gMIlrvrIdpjx+XYbOgss9AGLprT2K9TcbsIkDl2PEpHeMk6hoLSyvfIS3pkOJZESa/anewxGLTMFPyt7dZ4MKm+aZMzmegyjo+CIzHCcSSVZuboW2heKZwGsvugxgtWMaV4QNDgy01qtxhNaFAesee9kBQV+iCbcqFG7iX4a6RtFFSGVDQGNSOyAsoAq1TECmVeYzIvcFk+LwzgMrsk/8WWmWnoZext3I5lI6QhvzZqHKA+YQU1YIDE4K0dX1bLetnoH17ZUI4gKfAglFwy0xU3o03fdVUI02etWV7Kq3yhPt07gbNqFqRIFjuRLUSNcAzH7MCfZ3nbpvlsuB+zMlOvsm3QxlgsvxCiW7wrCAvntWcZgiDRgnLY1dM5BR1voNLHOezHVLA2I29O19S6In9GjqQx70InVYIrbVcTt0WqKRJgg4IEMxzRz6fm+6BlsgzmiBDK/MNTgpThE9BdXJmmqOn7Sm3MfHo3oxTzG6ctKUBcD9nqBWEpncijyYTi06jKThyxUFDIRkUBvY2WEFFukssoR4WsBSh0+Y3cwRc5ekNk6JsbvOCHpoWpS2dVOsPVcBl1Nelnze2jVtsjibxO2uSdGKLHu6XbC7Ntdsz2M8gyWZIBLm0xrrFPb+/3Py0v+m6wZDznM62Sd2V1enS/jsxdZwm5QkPxLpPOtvV7yO28VB3O6LOPlktXcRhUOxNDD3IDe0AblVIvZSWWu89LQ1VUkzl+WqrkNHb5PsvTwI5d3HRqco/scwxJewbP9PNnRtXIdKa9z2UZmL+AaufFwCwpy+gqS61l/Km1aikJLYl9y60AABAASURBVK3nQR3ksZoLSkVAEa5Iqb5npztHs0f2SMLQDnZj+g3DiKVuzsf0VeAXR4BrUfE3B5RSFeMDzm9CXEtH4ZNcWeucOajSyiBV27EL2mTEKmEqyPOJSpXNKMx4W1mvOCZ5nfLjU/7o8HmRDdM92mNaZDF61VCwjn6Nq197dWXODfm0yZ7KHBQUoHe7hp3K78l8Gj1vwybERSr1KWpcmV5KK49rj/DL8JiypWIUq8Yaqw7okdms1aEgPw+igGOVI3JElETLGBhfYQoQUDFvq3DqhcIcpjaX5qlK0oB5o73OGxEcmm7BIvPxTpfAZFckRFvDWlCmTQJt06xrTTgobRpXMxIKcJDjxc94KEtksoHBFHAffl/33XxUt0ZRan++9uTUvOMs20/y+JoVLV2XONEmwfKNvU0gONbioo3GxDYKkhFUVTZjgwOK9pk1WkSh54FruzsCulhMDeCbgoJ7cxqdeAzl1Vb2QqnfPW8085CtSqDQVMj1UE0GQsMqLbWRrMYDKL0kstDZmHYOaVh4MzQEWwPExjErwpget0WNUINz3BU0uOCQ6VekX9p3XaW2Ljve1FhR791r2rzVbuOatYagD9bUfotOYQili6lVWQAiQY1wmMKQIpo82inezvSg9mzhPzZ/8ISqQrcqQuRlSLXmyLA2AemFnW9k3IErSqkstx17mQhQZspHZQi+GF0Tf3H7dY8maraSmarOu5/sB6xYto0b9rYyM5IAxtDzEqNgY5XvmlVjiv15z073qP50LJhSn3fSwku701SGvM54v7oZZvijXgLZJVsGaRw2dRWv7iWwSoU7MhQLi7qyj/+IF0a317Nwf66zt7r849lIL5hCyMG3cY8nFK4oFtYsXMWLVLn0sob4pttZ9vCtAxj548q3RwprQhXYaylZ6BI+jZ/GBsPDIowf8YJuvNalvXZFBGe4ekI2z2BYbzbH7rymXTAvHyyWzbeQKbBakKyFoIGAG8uJ97N0eYrPcT1mUqxWLrKnhT41IjRh6NA06MWGHXjpMgtGs/Bl7vo8b4Cy3DbZjYSPB73fVRu9ZqIUTlLjyuL4gpkgpI8RMzKuaEJrUovKQYBEjJYCWQAMg6menhnTV8V0EE8FCm/3vcokDZlRGRa8EXrekR2pMDWvKMPeufoIPXr43lB/aPOckM3lx73l0kYqddLZ7knLcblzAu7vMdRexg+j5GUMgg9KWPywZlVvJpQP1WgFE6rGmppOXfOhmYrG4oiVp/u02GMZxqbZLwh4tYd2xTtCNN+vJiey6cjXkAuJsVPK3jP5jpKiAXWcGfe3DE0NYjwBg7VerwDKcT55r+dRn+zxM7KTYPJj+/PRwdnfozvQELvb86du/fXK4xlqyj15P1T3pWQX9+fUKkGPR0uCCEy3L/Xn1a8IUkhUMBwocHaq5WwR6dg+q+u6z8/pDxCkLfISpAPxFBo02OY7Pns+/qz+k+5Sjnpna4xheRFHy/XtXtKy9yJoSDYH6jKLQfRJ3LWevb7Bg+N4Ljt/xGyiaFoCCpqmn1nn8HkGB8B6BaOIBS9GqtBfJPafrv7T1sYG7NjzA8QQw5f2OdVsbJqNmsmy3nssxoBPIfR067GBQxZzVFEtoQtLzlN5Jg+zNXa0Edu380+Okrq/tO/s7q11UsCtPH/M/NCpybi/880a4tpxhSlvghrYM79UN1fp5ls7yCt/zMxdC9QN0w66HDZzgREz2rDMu8d4YAJDGX2WSvyqWl0GqTl7Z2sr/2N+7Wu562xBTwz2kUgBKy5ZogrPI3HxdDw3W77KL9oPeV3DqEOUd4ckeqaPPRQVRQe19Ka+2rNMnU/QO88fCx8l+oFAQQ8E8gjTyx/TU4/Lj185fkQVG6vVXSNwGT7kJ8r2ab8bzWX8C0mXD6aSmh1jLEgQn+/souamtbbHmX1+nXx+dT8WuXw0u+hnx3w+E6FJXfBfFfkMEp/hHzDV7xfTEPIM0huX5mmwDi8YuZbtbdrQrzb+Yn83csEW4KYBd/H+At+3rdTHjgrNNQvArHziOSqVF0Zl52aaEn6C7YzFayEAdbgBERbumxHcdCESlRYP9eAymlohQ5WtyBO1HL5duKsyMueM3bbvte+P5afO7rv5KMzvmmMAw6BfgELUAYAHrzpUSeNEuM4AsIN1rws/6h9hdNbsOcqWAzmjdrrrq5Zxl/bYg2Gboo+ABx2i1UQ/+2O4Lls/ZkGsTsHtr3iasgKy2a0Gne5qNQPzs+xeL6do4yMjKCB7zXfFHfE1k/+Rw5wWV7/Mnh14tdIwEanKi/rKB06pm7CfWOfno+hoUNZT0w8FRTyvQfxazFgwUo4mq/rWACtsULutWajL5sOyt5v8GSrD7D6fG39R8dlhf6B+ppTgOBrKyEwij4Pr0NsT/fXnG1UkFX40XB/QAg2oUTSOJ/Ke58nn6ewXY+WoeHV5Ck9Bi0mWprSPPN3RN5+yZ9YwBoMSf3fwGX+OaSEFx5prZlHIfqFFbTW3lfEMDjL7nFOjXNaAPrlj2cfnmrGqx+1dMhVBW4usyUGXaJVJu8ZNM2jKd+LUbuzhyrEn7Sn/gjUjEvcgBC1USNaHfYp4ANnD5m+pgfINlwROVjWeVqYK0mLbx9/Z/jq5pmxKVb96tMJ8si8boaxTecqyojkjxNEYEiSd/ROivcSuSmtd9K10Zp67z9fm1+b/e113r5qHlhKHGtAkqgapMNfp6xd+7rX7UgFk5xffL371HNUGuXNfvzxuzbxFwcye4/Vk1v1g0gYbe2GV7TZDcW7YzQvLLpwrbzs5ovf3WS+p4HfuT/PmKGcVPiW+Kt06jn+vnX+Ql+J7cO5VealAlHTtB58nru/F78oblcXJCJu10vzWQn5f3SJxGiChvutq4R/QBS9cNrJ/C4aMnsjCIWvv/sPwSSiRMeNy7VQ91PFV9MeEkkXyZ2QvBfy97tvrJg4gGpt23GrBwy/bTh9SX2ytyzzGJEocB00nYddqTRA18IJbD0UBNQquAJ1nNSxHChwcWsJKgULyIgD/YP3h9hNYH++QM6Mp5jKFs6C76dtHcK54/QFzukTRjCGtbmidOJ0kJ7wojvzExnZN12B1Po+H67wfDp1hFooIuacDM/oS8fhCOblGU2PX7ET0DmWiCJtH/KL9TVvfAVObjOCLq63otjP1te/z/lZsQlGGL3z01P2idif7uuPn5j5Fkax4PkIAQx9axxpqX2Fd2qB9a53nmq/PL/BP9lYUCgu8QJZaqSs4faOK4rIKSgCo9Kj44KXK3Mu1iFg6Wv4/3JsfgFrmef90/yS0kqs6rz8sPoaozrvr0a8ZhfC0PbwslpaOgagDWG15IBg8D+dX+w3dRXmDEAmjq+OnrpPoZ3SRT5+7O9Pnoc2JqCGfiVKmWOetHIwKMRo/SVm5w3Lvtzl2DHArGq18t0cq0lenpVFMMEXyymUxQqqR+ocGjH3t/GPb1qVVWCn6ammzYPNnPvfB57TfjacMiqyoBqqsklG2uJGBm1aAP6XSoyR7BMZsMppqfQrqUOJTkqOOgpF6wqRxttrQOcgZjvZG9D+sf7b2K+k8UBFyExo6NqlfwVHE1d1vuU01snjiys/u/XlR1SzW8Gp64Ug2A+1/3I6z3GepeHQvusYaFGPdm729Sf04yue7eAvPVWoodXjXPtTtAmqc2JxrlMo9yMe8cQ3WQJmP1uoK1gVEe8Xn2X+YygYLrSwFx+Fp9FDlLLbh90iD/dbCkkhjJaMSpRBgTUjlcWK10zBIJjQ5AAvZqMbdeTK9KsB3DcaMLihJwseg/iy2BmS7wxXKxCjrKWOq4K+lPkJLtR0fH9H5PFBrlqvRwkIqEKvRwYKgNFGk+CsVNa5SX4w2XZJbFaP8SGejENPOzGEPhjDUB9iQv7CGt5yf5t38Dn5l1ZPU3jFLN7APpzXhXn77a1qf39hbEZaW2nSlP6NftHaDWZO+/BO4OldnBVvsLgfp055pPWYTgkJbH572ql1KGZGIKpuctsx4HpXvkT6tTqNvTXKFu15GxmYcpcTqzKNB6n3yrUvrhuqgugQ2pTE4FBKyUberXU9BhGSfX6M/9vXTrerNYTbrtWdjTjM7YQ/Xt3fehidR738eC7pZuaLjQl9+No8NR4C2WRslOTOpRefdqgZfOUVDUShw+jtUCjno4qM0OaAIjTlLXoOzNX2ia88oCHB4UpNcebygVAIK9oitZ8WjW6y9TK3tuFCEJDBZSq5g5eQyi4CtqQvadhHyf0F0LadJmUJlivVMK3sk2AK5CBiGpnMP7Fk8i/WGDlMiGisiUCJpAIjQcFw0Bf77er9TsakvnGZiHpn1kU2jXbCF0T/YKIBLgNXUg6xqRS+OBjVOaj4gWPG+CwotZw93iuOxSC07uSx53SJH6FtpsWPplWrOxW3yQ7rhqPCdAK2MBN8UTmVK331KzLBt4BqmVqklcBFLCHzpD1a29nVLTfDMKH6Yg4Hfn4gN8TTeqBytaQPKi6Ni1Ihn1DLLV0vOorBVdyJ0nJpPTZJ43blIdREtO9EyWuY6EIw1bsUYukxOi1CUoOTLcaZKDSw0kfZWHut3VoRE81ZrR+UP5jIu0N3NjBD9ttPm9NxndIfUaVQbr3YDqTTG8DhTQQiO13MoUObSKaWIPqy2Q6GzvblG/lthS6GXcKr+sEcnSJuw9jTFLKXI6p6Mqd2kMD6RqjQD1vIIObcBcpSR+ywsvsC6TNWo551EpbWqUv4EMMZqGi3B4lgfdXjHm+YM5ZTlNbI2eThpHFcijyRYONrJpCoOU9btB55In2f3cdPUdVQnmEBdMT9rrbio8qLNS1ybW/o+kBqUEbW9Mhg9Zu1DV/l62x6q4paPRkFOdDgrbIwcKEK0e/v+TUoq6zr964lb2+QnkOA7RlWO7jYc7KlgmhO2Wr0oeANVtLQFvQae7jCFKOChZY7X+H69GYTH9FsVkodX+3MmexRG2TTL6haS8qkupAxjJDlwQxbsM076UISOAToOUbKYsG8jWxSM9Lcc+zP2Y9gSrbmZORCdSuAApzWl3lDdFICwpe1Y4U5XvHOUrlTYX2rBXVlmz3gmVUx8evU4WBYqKPUoxeIYKH90lfjtuoEs4FZk+tWp8pCA8OPX5jZN5PQYAdM812SrZLW+2ai44DlEUhiupvlUnLbk7zNQX2md4nw3axnGkIHhsA0E4rdLQdFlLXk3MY8iw4CEuYLK7jz6GWhsGfnWhdKKB9q+OR96pzPhoziz7C7dq1q+uo6XW0arhOvdf1zAdWX8DFaNijgl6jNer/OefqEeGJtUD6x+8Fp5ABGcxtKeeInTYPko63NWP9oxqwObEmtXQkQLWb1BrxBMTFJCdIHywA4+WVc/bg0aXkEqOqqYZHHxEMWxmS6vDo4H3LKZqsdOgeeDEAZKzBQwWLAuhQtiRj8QKWLE7zc9KDGDWJQRNiR4jW8FptG1oQUpB+zBIKeER6SyjDPrvJf1YVVgAAAQAElEQVSVDmBY6AvHqcXhd0jQLdEE6cIi2A6SZovLVWdN9Mz03KPSe83i7jjP5k077aVMMonHqJLJSKxuZ/M1I2/6hNImVF1NibbLmpJ0VI4dgXLr2/MWcdvuFQjwz0UgxI4S6aMaxONVAWME/L1535MfdAgLxxjH0KhRypYABrqw234mghCCQuEw5HGa1VaJeQoq/XNO60n8c9BvxLM1Wl7YkLnDvr3vXPWLeT+tBW/E1pRZwQFCLw5HX9zNaP6Q30b2jFhceNYq9Uv/UHnNGhErdT4vfjzZ1ahu5dPegqFSXp7K8p5ldV03P3fvRyuMkGCrdMvxNR73tiv48bzmWBZareDpzFI3O90lXprZU3bEzZi4neXm+mA0Tkytd1C1tTTabSU6rI6aTBVyGtMxDEkhQnWJ+DP/U66hZKV3BYYGZQ8ShDEsQk7Mo/21z2PSNi3Pfn6xtLHIptao1sYhx09LopWtGJ+0u9YtfQCjl1dpwfCrO7qNaJ90idbGDTJqs9hX9F7lHHnXGCjlYgr7h/vAMI0eCPHBwugx6zVjgyHnvSfRIrl6AmA7euF8FK9MZfERCEr1Aj7Nn2dLmr83hvYG0hsVnLdqFl55qg7joTUBd1rYqJKWMLEen+EU+tTJukVPvRW6Pvj8mO8C9ef2+2DUSwS3DLCDLRDKoYNS2pHpURtJH3krHrDGGVCpVWRbNQ0ZczRgJhP9OB7GmaiSsQIyRdlQZx58Bi9zzrGrQBXwVLq/fdzptI/bJ2bTo5Z9zb+/DTJJoKMn3ppFi4eYBeWG0/v0BVfqgAk4yTrxoG/gyIWev7//e0qUIsXIhblWmmW2V6rnW8b0lfBbutSuaeVQOnGqs4oaZTs1kswDwYGBWRePzOfJrOcVrWiVezEJVCszojH5xgtV9smpynEh3OkpuPWJXCIOCRrFZ9JvVq+zlvC++uv4tsDxPnFkMv166gd3/DzYN1kc5/mBuuiYRRt6gJkg3BveiXXKn66auzpHH7pO36fuxj2W5QMjZ+1Yfnly7nNSXabfZv+Cbjyv9D6N3aUUDVzMvVPEjQS2VGkFUTApz1Amzfe1lmngGFToGX64Tq+5Ibca1w59oxc2dehm7VF5cDZo468VVVSwzbMjYRWVzJSPLm8hFDOfMhWHGBp72Gn5mI8saDbHVFnClDNTkCCoWG2P9PvU5GnebUcDoEiSeYTO3fNgbpsEWvw8yJkv55gIim6PQ9XYnKvrMqwAFdhB/arqkv0Iu8w/tB9hhAHAUYW+ajysQjcAEnCCAKYZqjS4hk526TiQu3m8OM9P3us5yrbYt77Dcvj2nQYft2dfeW28FNS8bENtpWZ6xkBJgJD++PtknUfHjOZjQulM6VqO9RtHCyVNxh7bx3gcBRc6+5e71vPebFsYo84iVndI7+cAkktOZ7ci/B3CmEHDZFfPF7BiJFhuQf3UW4T1vOqe2GGzOnef4IFknIyiQ8lA76xZX1sHPbwxWYrWaSus5TNnuhVe2CGUb1DyP4Yz7LPRl4eZbke1aPlzOkUnH+lILmB4XxyiSl0P5yaet3AWfa3lK+hGjqgBurwqAHJBeOagkko1047+VMZPx9Zmo6mOyF1aKy4jw3JDEulw0KJ4tdq+QkzZLTdwnB1WYbMt/9Ci8NDvQHbMuTgfj9hrlhVxTNHrx7xMMSUf6QMrscfNZAO/XJV6ulR8jcvw44K9y85jd0NNo57P1M/wJ9zlJEZ5zBm7Seg1oBRh4zGO4SjfMP3OIxAKW6aooj0Q9q7y6dNChhrC0oh3SnuT7dlMk3rZXL9PtHQCJLpFmGiYSabS32YEG/ZoUeduc30GQtjvcAvtY7Skhj2mqnpyqsx89s9Zuy5iQwBFOw9QHGCUQq/MN3R3riisfs+paL7OmxiNnij6M7g1YcamzI960gXtfUrwPZzxQbQbTDRyohFz7b70DYpTENmEnTXqpZrx6uatl0cOAEHiV1JLiQ97+KXIslEgAwO0/2zlPcQo6AIrqW5YnR0t9OJITBY5JuATM9aete7Tmd9+dNydqkxxrK7nuwe8oSGvLjbzivTrOZ/zXPnsPFe1yUMBKREd80ffpR6F9RUOnNQaxM73ej+ZNdWBcWJmpXyfrLrnvI83oyEMFvlSRKWKrpZ3HpdiWwlAL5WHeO63kzYsEX1MHfOC4/2aifvwvqN7U0EvznLbpr/dwYnKmdMxrVrzYqumyqksH+A7OCizewm7epk1ltVowV+dbsUrR7S1j/IpRTHvJofP7KaMaXAKcCG7xT5AncDTrQzltkezVtvl+GPxx+Ldm3TjJGrGT5lUMzBwcW3oRrwuyxUtgSR6gCFTbTHcbEJe43lW628qDzkYTedzCnVHqGfftkV931Wnb9gDTsFSscvUMR6xEBZj/ph9ZzQOzF7Pfd1ff3ESGGOju5+S7xa4tKXwTM7Nl35tJNugmiDFipfEh1i04Bj+/Eop70xPgyA9NG/B7XjcI5R2+XZIVy4fSXl671bu7rtx9CeOZz+1nhXfTelVP9EPlMW0dCM94Wl3zkJzslGA0rpaKC0G64IpmLy67Nj5rnObssf7Gr0chOF91ck8Z+pr544s1wQp1PDYPKiO267v+nwRwknQb3shGmU5H+gpel68t91aZm3RFUJYKXrFpeA4JbGe09npLwq7513cJDW5bhtocGgtEjnZI7CmRpk8MiJSK/0YH2VjRAa1UkohdJbKmFGIVgdvyA39bcQ5LDWPVBiGcQje+eH8QVuCY4BuTC4BeVp6eYx/4jwNnaia8/VHFzkjJl5MyQ2wjZLZ6VW5y3bLPRRrljRj8i/I0obWtHkrmKEN866IxIw6MxpAwVu5HQkC5cwfF0Pl06N516OdFgOCaMmG8bK2TyvodwP2rLfGrGxpopGOMG4TvGC3gwZ5tCCUYI4liZD7W7UqvShEyrmpmgy6pPQ0zdgJgS/vJsO2aL45PvVMq5ENtNjuT9rnNlNTFNBa4oleFCXHHNSI3vasOoKtfvnz9W/iy3y8APrjekSvmMM3tNpeo4hNWI+q/9KAJbElBMAX1+17diznIigsk96+MzZsvSlTp5uYmCE6R3VNYYenTwtllurHEtbqb/fi7Tjazq5OI+CogUaPwWKwavzprYx0fmWvOfTMYNNK+fHj3OOKtoy/EL9sKV8ZUgfzY61G2WUlO7BJuHyidphxxehpivbFrfbiHlRJoluxoWWdgClvaUcoe6t1tkJDgeloF/aRegxRseCfbq9p6pKNYlcLUDWozrwYpvr45fMoRFxs9dTrakH3kBKYYoLyFSSnRtalnqWrj5TR0Qkq+tQ2UQVe8Co59SbcFI0sJDivmFx4TqsVIWlVkydLucCcbnNg97hOQCw+aWWgwXSJ1ktQ1AQwIIYsWAV5wfZoFJrzlWXl94wDl4XZgpYHpaaQxa+K84DNFsJE4UV5gcukiMthmn3s6LrMjxkioZsfSrmA9PlOYO2VfimksELUiUfWANCD0kEbStQ0urT3sU37YX06mXVa+urWVGIp5zylO7ZvznnYx2cGLNoJCMOMmC3thro5KaBOiymtaYn35ZzN/PD54L5MHexQAm+zZo5Wssp5nnnyZJ+5Tka2XPogWwnisCWERmk4oWLrXVhJ9sK8t0plj9rgolyT+QZayFLx0vqwpIIy6WBMyGuQd0+3ehcmOGYDR6tsvPQxkkKvxhTeiAuYhGBeUBCduHO+lXfm01NtTIsSo6Nu9UP8mCo4aYUaT/ObgNPggE0ZWn38XiZU5plRuoMydTI94H1d2AREQi1VSws4PqCI6TvkWUEHJc9pTEkPhsFYdaTeY/qTGV+h2HfaXbwbrSrQULap0xneSaPs3iyFguwPOm3Ghj5uut7d7LNHcXhLb6Msyr7AYwaGgJkvqvwLy1FSodymRy8ZdEkT10q3pCRvq3ENrCu7swXJrRheoC3S1sglcnuuBdDamQJGariZAxKwNJlXzax5zbWhHa2P09BtYRf5OzMqUUyHkpCI3VTgYxxGI2DmrgHrpJ+eatl+IVEz6zY777iCFboL74sYb6PKt3yOlCNAQF3LIXUQ/ZbRjKnXuN7KovLCd0VHEO7LeBGjvNUa0NbjRSbVqmrYzmMQ6ljKMEa/SLVaApE6xFofq+oa4WrEFdhLXXXbb/y7p72VIu13jRzgQQ9qT11DBeF0eWfSE87xQDiEzUQXq6aU7NCwDJHs1uA966r4tM7acCvtflo0BIzCAlaZOiQ6Utdrfh5qW0askmzNRgGVwmHAJbO5j7237lla6Vs2GG7WIsCmfEE7CvMnBtYsR2pvgy5F1l1CjqP92+W1XUQgSEtnegGCbVrQcGokQ8s0XdvlodX5jrqFxMgT8bJm8uyAARyDL8X8BRMpZ51+F/SMZhrAIApSmToilWDJDMaHF0KRFz3D7KVSpWPlPsuURogiwQ+bpadLgZpRw+Jb1U+GQJw+/aRuqoRHlXhZFstNMhEOGgf1kJ7kPEuVhULQwtwXQeWGUz5KZyDDJVIXX94g4LwdkCqnZ4hxo+mnR5yxTbPHjK0RhaPsA26qj0ErSJmVAr+khKC03+ybfRTwWw4a4gPY2PKtH64CwTQejnroYcspb+NzWVEZFxNUFLJdyO/0t7pVYz7aRMy7KOt+9brHW3kYUNN55qQ6yO7f26Km97gA6zPThSlhUWrcN6c062gS9RIt9ozQ2RAIY4PpRD1zjz1h7QEa4N2ua8tzX+Ufc3eChLRVNkWWQtvwLU7scZzgXFOf56xvdleSkuSz7ObO2QPjvNF7YzHMwi6rFdLEyK4uH8u06no4qYojrRkTu2F8DMfYHuYdVy+vmbpHdyL5xHd2uiGcEeW6yXdmW6WG9HQdKylElfhS0EtR2bOi7M37LeZsr+GU7nT7/d+tWevCR6HNkZ62j1AIn8z5VTjCEQO8AA9+62hTaSMsbJmo6o2EzyEKxBpzlDGpHNuUZQE3oDbYrQumCwWLtqsFuplKTCOdZtBgAQa4/jGAMJq0GwJQFDqFaSm1qacq8WDEc1oePvdkI8OepaXT72O4E52jyIQM5HkalMmZARuR8MJ06osJvL2Oe1Xg8WqO821KbCHaMNEu4z3HBhK48qTMWIiC+xKdQZ8QyEiW5Zf1qABv3T2mAorPs1a5FdTQ0QKQ4EruNheCNNJ81uptui9Z8Oi4jpmgk7c3FGVRjcaAGJtmj1LO9BlTRFNVibl1vl8INW4AcMJCdLiY5R8GfZtUr3eKeb2N96zpFc3V+qIJeB2M+qNSFfigg2uNj7FDWGefpzM50lgILGZecy57rqUDsd0jNPjIjvBNX4umYvnksdOnmVjPXKx1jf88UrU5LAqSJoCsec7kWIbJxCT9wJ7SXHq763sL4Dr7nJAuKcqbUzHPsl+6lLTOpYTd1vt3L2uzobpmKBG0orYzftzKp83C9w/3W5kC1xNbRbacCyOb+ZhC0e5IB1nS+COofF1UY5gIl72aGQAAEABJREFUUDQKnsGSqKeGLe6Lv/rnxh+NH4BmrywUopR6hiP93JxvR/YqePj4an0rKQ9NmAJmSSe9SacXulHCX9oAHYD72V4SkmpzBuY1Kq6G0PDpPRKA6Y1kbIIm4HURhlcNY46WTvU+0++DM9YsipAxusQ0ph2C3vSuce9wWU+5e6b65aG0cpWIaox8i7pw22iEem8LLp1WtrnZf4zr7nunvp7BVCzvz+f5uW5brTKenYm7W5WM0tLQQE83iqYkbak2FDSjQQn7O+ouRSqwkY4q71YL/Hm0x2m1IlmvXnjk7TueVBhQ8pG2Tnp/uR54H843cHs0l43iOcUcebkkBMl2zzB/B4ERdmjoN10DRJYOFZDHFWv50lkXcsfrwX3PXVVzo7/et+EAwkglYwETqj0ypQFZz3nq5DyO+pkXJZNgHcfz940r5rdOo0/UbZKTm9OE3SBG3g0o4mSd+5mjFvFElC0JpO84iU5RMCWnIEcZazJ7HAqERPu0+xs6YEDqoEQ53jtmx5KXX4MvXjNZled58tzzSIy93NzdjHS4DjMIHJEinGmFC/AEBQcfxJkr+8ceCE8CPe9lS3Hemji20LbmbPtFvyf6/kiX8lT4Bmo7EZZL89jj+onGYo2mGLKe5+nSP8WJ7+NzRsyjG067JIXZAnLUd9yFPLOQPS+jzhBz3m/AEqzOn5lMq9OJ1N3hCe/a/aynNQtnxfzEXF6X0dwYUg3MFvenPKrKTmoF97Eeu8/8qpSYl2RY82LP1LuEsIYg2qH++xg0au6R2mnIzzP7GRZ78bv5vZghol6ZU0FeMCremV9T2WUK53ckL2mpb2MqSeEDUvyhLbx8HWy0gC+glIqngFC08wUgyo8dzHE1WIGLlHfwt2qnMG39BE+w2rxCASz3spV087ku/tC8OU746X5kX7OOuOfRusG5fRItM7PFm+O9l7voxdAOATM4lomSxFQ4U3WEy1od2+F6uLVcirQ+5CkTVHnmPtDdcwvEBS7HCCF+uhYOneVxfD10wOwZfLPem7xhm09oRoj1cJOm3JrXSK3N6LTJkdGWicY5DTnth3pHyTus/ZgVjFAFeRChsfc6t8I7c27F/kxXGWlLm4CQT+9xmxc5TxkORbsUWPHNvNUeuhVPbCvXtu1N26D642btVb4GPmZwUP5WoAw39BgZ71YfXiPTtgrxW0DJNdqDvaPt9MtRh/dSwCsrlB/z3nm2c+/lpqe5FwdmopeYxV5Dw0slWo5fYJ2m6OxT4bf39zznmzG2GSuyvY8cnxPn2d0eynYo2eRTnR3B/vGzQUC3+nz/2yo0lh7g7HrEy61SjWqJtWZGHsnE1ABssBD1Ig33dl14mXhNWUMp6W6tnw2NqLbRBBY5mtYSdeANmpIVvu9zgIYnvFEwvK+xeXblMlMmiHSriKnAOJSE4kagTSigFnJZn5/+Rv/q55xn6gayoUhnKpkzyaPCLTe5QVOXhdLzYdBsqItrfv85CCRffO2hYBo03p6gimUJyncJaiGhNSKBUtIXa6L09DdhQWqfMZLYW8bCvmGYq0fhmva5u8b9Nq/N65prXr1Oc3Ccj6kWomud41A+s01kt2X3V0Kf8azVOa/Comf7s3iMcPPtvtdasV2Um+yW2vkwK0qoW9NdOcHbV17bZv2kfdzjcvvELVkjnjP5dJ1pZTx+dAZvPFR3PyHtlwkizmHfM1lqI0ixmuJEDWAtKhSBSnCEl0t/SlWbtcUdXKSun+W/PvvR0JmVz7J7WVKysayffpQ1o3yPlmu4cp1d/DkRaTbDpqLYSfWwOO06AxwRDDxjadEmY14licsfLpgjyK3kVV9t5APPJloL2A5JHH/0e0Vn8Rvq6wizl7ycqulUH0tUZuGkFg9HQtmKSm7H9bHYm7ZhS7yb+arwozw7vOiTG8+OA7fmrgotWTbjaHUXhxhZVnvKlcODsjVUncXdc/QSxgVMrG32M3M95b8UkT/qJJGBE5CyfGRZ5KxU4JTCcW8/r12olee+z77HxgiztkhKa2X28KTWsyq1eylkekZa/hq/QSkqWYOvgtktb4YzZ+pPUzfFwegl7APL/DL/IX/G8KxMJB9e5R/4NVB8OCZSq9dMEOK5ZKOlmitL5wZ+Qh11hBVd/UKd8Z1/5XYV1FgPgBktN1WyuAXhvE58/9pXvbLaof1irBMcK0J5/aJYM4qGFtFUsM/cdb5qi6WnQAetU5RrGsx7Gdw5F/zn+QS81GA1QqB9O0pkKVVk5S3R7VkLoUGrYj9yvDVZi1N80n/usxQau+YD3V+dAJurNvKSds6tq0KFyzIstXqsBr+D82bYzOz6VTrBxwDP4bwc/OqRHBLRnG72n6Z/wdICF/2qzzo/OH+gttw+Tz538daf2YTjAZ9mqgnNTU3mxrZ3ReV6y6hkpVXd0GbI+PzM83POpV09Z/aMQICXif4wps3jqX0QYpGbrnfGC8G1v+vStjHuz0oPtEzBnuOCu+/0wfIr+JlnnYdAmDpBWDXuYRu4b/rjJow/+bj4qe/ddz3B3kYz/eA3XTNooN9CVJeYUCTx0yIwsZ+z5riQZIG3EmMA4QS92enPiZRyyLYaDZCYZU4C7KDS7ar0u/zr841psLXiH6BsRGKxUTdPEphteP/pkyRC3SFq8NZAvLeI1Uq8zgKMr/SAY++1dpvlsz5mn2UTLlxqtXtum01sCRCDUW0kvoy89rz/eR31A4ujOnyeX/X8Upp/PP05ylcURhxXhuattud2jIq4rRECFxz9U+TFUiTj09w9G83Jeabulb+3Agy8O+PLP0Yq3im1BfSiQgOVB0y5GKSu6jNzWsBxzCBlvEWnbWGhZhTKFltQe1vENYxfU3/q+0jO80hx0Ur/x/Bx+yPUIBDDRN+cszzdD/3XUPLlvXj/TF3lzlDrv6neSzvOcZEwbFuJCx4GyKH0BtAb6KdwDzqgvHxYMjhQ0Cvb39MKfIha0MtLUBm5Zx3ur8UX65kwq+VCE6ORZ+cduLOOwC2Xt4UJKFmzZW+D66keuYaUtMPjQ0pxT51nHH75crAHz/B0TFnIhk4YclRsLtlqwOX4MVWPMRcfRy+lOCNoJEIaza9ZS+vxTRnl7t73V1KGXq2io0n33civx9fXIyz4WE4/rD9RjMuPfkGBjoLBqM9twKdIoSDp17fq2yKEYby8fTIsXXbnmbnDxR537njM/Eh7y0VPTkxSgNo6XKVoMJBFfRAusObCuqAM7WlMVszjntiFn9J7kg2lNFfmdU4M0JMK+q6ph34GZcfwXa09RpuCmtGQt9JzJgEpCAtie95YY2rTwqVA2aLSq+JASVKpZ/PwXe5wvCvSohaeeJ55Tj+DYzbNdXrXd6mgI4mkiHy+85S09OyVobxLYSlF5QqjOuVw+Jx13+tPs25GKgBqIHRWvWfNLNcewTnWxuZIWYvnkvUXm4UwobT2ACwdjYFKFkE3gqbTKTAsaMva+1gfYmyNGmu4SLBVNBXl4qIrSINDM0tJyeI65imTv4Nf3na1eo4qRkY0CojhSnpty81qn2fNY91TVqr9VY89Z1T+itR2bO4VS9KeHbkvyaE9whRvyRECJedKRumqbL6IG4LhdPcoSCqhEQpv90zKR8qTcaCfztHyxhSn71CTsw7vYBe0hldXQ6yX8h+dxWf8R31ASXTX0/ddv1DfXWcJuLfU6ql+nq7DaZF7Y997HXcx6/16x+izMNflSszA3LDk456Tpgro0HndZ1rnVlnqZuwz/W7H4vi+2/H7JRFDJ7cW+inA+8zcaeeRpIImVnwoSEAOTUyBg+nXuKvjniq5HNdu0R9awxcg5HE6b5svpcWL7T/DPy58/rj4uSQMAcVbDahnjk9rzuZo8bg2KNy+rrJz/9r+NXHPwaRhKCZT4JP0a/PnWh6f6vV88fT36PLrebfhIR5NcLTzy7qZ3JLJ86ZW2WBG3Gv/2vaYhpyPp239rCdXSWyMRZqsHaf6A1PaG3iBOdNYYwvmisjy8rkg5S7dwQahoqphH7I2tOGIhbVG+V2YPRYv5mf4OA/sFOqBkqu409vL4sk1uWycMeW/EXp42pIqlE3n2s82zQPFQ3DMhyF1aePyaLPyQbmyntYYtTHfs6zDhqovc4el8wSVtDk1oNmPDjSo1G8VDKRT05T0ytO6OkdRzekhEIoAVhQU+P0yivA1iIeqtSwvt7YRTHcLehx/JMYz8a2lM6l4aXvNFRNwpVqlJdbAS/ui2Xwunh/va8mu+dLrc8V1+dqx9rq4PuAeBT5vltrA7hVDqyXLSb3aldLXT7l4nIu1qPMJ8UajKSnvFkQzdSS+URZObtcbR/2pRsjaXg463k73KbjOI8YbnmbpDjN3LBTtrtAeX58oS0QVXtcvClTbFDksDcVGq0tAYUAMWV7ah9vg9WL8sVnsjdGZGCyxRxu39qGrPv68qdNc46GX8O0xS/XONiR5KlqKahJN7WnItxtaKhZk+bFI9ei44lrrxxgEx1AG9Oajpe132CfF2ksyQPrYng4hYi2EYwUlaI2tlibaVEq5AGsAI+2gU91K3WaKQ+NasbC9r0aUmbGvmaWRbkpsNKtdlzpiLkBloVPdij9oIUUXhRvE16BlvqE3WMnUjU9yUZJ6jqm0a7L5dfzyvllvw8aF0SPePS2H4tU02dmLYk7duNi1UyByevKMOp49gnXMxqIV6pKf6kiPcsFZRpiE5DmaMY/ioB0byae1yJawhM/Yo1T94s/G8qqpZ3b5qoUmlZ71+CiBevUUor7B1PcRUuTLoJruu5fgouzZjPfbMzbDOfQaf4FVIKpIpFnZHMFkPuG9aqiENEKH1mGt+1kzCxOExRoP/cKMmunha1uqIMXjQRvS0cNJlQWGCdUzCECL49LoQbXMK+iCJNVZ3Y6Rsn1pHqfRp9WGfk+q34bU2VI1lqeQOk2a+KSZB/1akMRkkTlqg/ojlSxWh+XWwRdR5JtCuzP8RRewocnZk3NqKDgU+DvU9LFhBmzrZeXi0d5p6a5UD/stxYX2PLhFeA9Gw8U3ls3WDMlCYmr1/zitZTYmoOxmojusGDMO124nGHKZ45XedITLMV2mRMOgq4tP+hlx99YxWiDCwDfX9+OMKAvr9+mLpKZL7QpA7FKVpQRt4R3NcYNw6Gw+9db9rDd/TJcALd60kgER+uLeo+oZrv694UYgoj/6piofMdYUOAVrVCmScQW/h0KUcmdtXTNZqjl/61E9L3Q1eAXkA6gjASgr1vRk4xH5ITkGeJVfRsnAPzPr9wYXmoSroC0r5d0SkUAHyidhIwG4WuvEm6k5CaVaPa+aFzsK4i2qdTbY+0VBxA9FwhjVcEe6VcRC32xHXdfZXsEiFDWW2dInPdWxk1DE9qLLLuAQx/pZOrx8DV5+qwTFj5mrA/8chLop7CDHtIm4DY98FiRiLYZVg+LVcamflFVVMtPvDrWFprxlKZOBzF3h0TTpX6QbJIIRYzaRwFMAABAASURBVENBq8ythXrGpCDKwXj66gWwHO93ef3COx1l7cMNlz38fo0w0rLdAjo2GFCnG2eQFF1v9pt0vusJNOhVjuAUechGiYHuKUHzGGq9Q71eVQKbUfQaxYo2FqG1ZfJ1ADNORWog2c7xeC2d9j4GeUGK3iJBhM2yfA1BV5SsY6zzDQCyMzRIF68RswJzoD3RXUxZb9P41eAR5BLle1S2CqvlWmahuWMmCsDFBjrNdQwdQYZ+XMrW4jUuUPRR88RefWXw9fyeVMlSW+3FWWFqE807UWWyZ8MappPG5XQ32XxMBIvN5WYUgGQvKt8b535vlm5CC7VQZYJtZhmhoxUTEJX7V1/n98pVmO5i9+5N7AFmtFNOAtbGcXBaDY/0jlAErg0B9k2ma3CjMW2Q+aWuZNWdamkyaDFuQMETymBQrVAFieR9eLK1Ja/Ris7mJvaCu0zpLbbRghOdo9slaVrbPRH52oswRaJVvYoYCBwOG92SJkeeVFIPTuMeS/iQUs0aez2YO8wOLUnNMCSRrSRGecIG01pNJyUANZUzdRwjfOelJWdVwnhUE6Dsqto4x7NkplsAQuHqW8/IJOSpNks1HGfZ2VQc749ZbXu2DY1tPhbW/L1dBIYvSDhO0Z1AE/6+wU07iNhzudyfdb7Mm/3lum0XTI1h1fK2mqmqyVzZ2h5YkQZ4d9za+dJH6hmxuHV67Ni5J0fqKheqy14/sJJAhmlo6xEkdIxNC51EA1zzMkNh/JaE8KHifDgaJLwVwJSMynijB/XaAQbI8Zvawrwx7dYuY7ue8ixrFuaxTpW1aSA7iTQbtFHPsM7yWsvCPZwXcyGhhjtpT3sppB4qMXGKZiLSomfZI1Qr6q07TV62Guq3nWoVMtW4TuoRxZTEIsID/UHuTD/K5I1yLsPHVNICcOSxlAKGQ2tHuaw1xXs1ZliUqFZR8tzW2jzh1Gu/IjVAwwZWCxUNDihEnb1MjABv9anXNBqTRImxTi89Knjs6AJ4YA/ieJTQdoNE5QNgOT9mS8lKSShnqWf7xfFpEkNhGDJvVN1yu4VcvhfMxjirWlgDBSO4MDHUpH5j6HcuBkPYUPpTkiYzZlfQFBGP2rgEwDH9WekH7qpgFS7Ud34BX7FT66GlEQ5DEU1HX3wufANKk0BMwo7N4Wn+4rwXIgJxHYa295J7L8HG+nC2Dc0My6Fmsc7HeqmuXFVwqRCeHVxYA5OSDhs4mCcte7USWA94UNWpkJ7LbtlV8ON82rSh8MZ1jXYNgM3DUWnQRosXX9AQzE/m0qrwUCrj5Icn+oa2Q/Yt5TmKa1Pof3IiQa0h6GBFqMSWolDk1uFJb/cxk5IrSkW2kjvVC8Lt8nWZakLygCCfGbufOMcajZ31g/4sxrWV+qvIaTjHnQxaCUsQ4Gb9ZEOF6PPoOiEmxwsKw0ew3G3nqCLn9peZnO4qoDdbHDXVoUL5MGRMm/VrTxAiTQnbtKqg9PPQh1HK4KjcPlsc3OjzluV31v14HXJDlHAR/lL33pw+j2XNGDxgMfIKfGEvPYap9qIj3MxdBGBJ171mXIS+2t1TNg/U6zRGIzDd+YjXM37m5fwGnjGFRu5oW60g2yfdjqxxTRhW90sjegixcbblQCX7dH+JmwZg81xW/uO57RjI0R3kH44lALXGPJ3y7XMUe2vL5RpgUxQlSspoAjB/1sNdeeFZsspbSjN0WAk792isxPvoKVHeuftZ01QPU6cQcYpMCGkzuTEbvuiX88IEpI2+V9YbAkBISyqXlmNkgfliY0EoEZov5/YUq4nuifALV811T9xqAShMe6UjLQChY2KIWerjArsTT0MC0nXxR93wNBgCFsvcuOSfAEh3qjtsHzm2JoUtmk6P0FMnN0nMn9Fu3p32sf3HXJ9e0qTJU2v4HvuJUfEDWn4HKtZCtnTc9vVPh/5EatBLYjABV0FlNfAwD8EDxKRLmrUz1ynh5NbGwTMtjdypDza/UVloe9APdc+ckyMUlGQBs2w+ji3TqxHJnWXTw9a9CI2OXgo29RtA2mhPfuPlTystqH8x9N6QFB7geTSRLdsafWhJYtvvNrqsLmutauOQY12/CtdMSMX4nn6mphXZDS9VH2kgiflMXwb7GVw0fVAat/f9g/mBrZGpI1uGsNy4ZxTC6UmeRjneSWGvk4W6qJ0FV2uDNK4ZOxgGoJXyd+xUz3NarwFLNduUYBc7/Lb5ehXLSGt29q2eiSR8zp8f/Ymb268DZUerETRm0bHbW6IHv6zCE3hW3ahfhaN4uTnhCiaDY2Zrx/4pboEg/8AAbE7G1B6+lmDVlimIj87PoIVblXKkeZlR+gAgFaybqrBvXcdjQhmlJbZZ2PZejmV8QbWWjt6lx2RavXyEAzrHYrihpiYauYLblZqOXjOSxEq5W5YX7y1YXD9ZJj1OsBN6tqqKsq7dI5LGMzltJJVGehxbvQxShCOjuCvWdsUGoKZhYwG7qCjhbLWkydWpR85xv4aUMN7oa+yu+j7agdz6GdC4edl4lXz14vPR39xdQKGlisqXNCkBy4kRhM5jYrFMLUAWuiUDusdn8/NhuAIy+KoXD2PCHnhK7cVFHbaHQkTSMxxRVV6zz1iNJo4MY+WguurMtDrMO31G+lJXNsuj/CG7NNVrpkZaT9QIg9t4ts8ygqL+jdZFgo/h6bkP6kk+mmIH6wuZTIzQUbFOUbCCH+xLxdnInkyf+lz1x+o/MFsjisf7fBqLariRrRhLY83KlZYbsADSTO8jvv3ixFL7HDwzSnFVON/0u2HfWncZ6R9en/r5pABKRLX8RvSigC400EEJbgIR3Asb8ER/rU8kpYuf1rO3x6ydvpQ2FNfpwYzLTVvTj2nK9MnU1qV1GnoNOyNLDEDJ+GFLe7O8GPlOFf495X6WFdJbEKwq7WCWugd/ENcoIuU8H2CrT4HZUE71O8YF4eDtNmQDCaaxNRfzqrwrkFNlVZtv6ynwdjvKozFHqtUxLy1yiQ8NKozPzfhlukCODehHoW9qw47Pl9c92mXGmrVv+i+bW/GuM3aTuUx0MLSaoB+5gpQ5JT8HwsH8gQAbaVWXB2f6trpjnkrpBpWGcVJBlnxvqCkaAULyIjrqsSOJtReulMfKnGesRmZz5gYe21h7xbglXZrGgGJJH71YtF6rVTaPd3ImOOu9L4ePseWaDEiHg35vLX2Ntr2aKG7CBrvOxiGmrRtvGD8Zf/TLBEetgFn59e+J38qoyQX+MX4927+bz5X+yf25bRe2xSVCoSiye7psFEIm84wq6ZAHngKe19TuUuwzPs1CDZXUC0tHnvWMVoTdqpnzhzbFXZrW7QVXHTeHoPnA9avY2sumz8jADPvg3PlklwKorm8LFWBxb9f2YFG6GlZF9nXoBIw0Zy4/a4SAUHgo0Ks9wSAkDRIwmLkzQp453xzd6M5Xg2hcMAcT89hzLM9Mp7G2SZ9tGMV1elVyEq1Dp2E5fPAeJUhtBD9rDDGQtPnUlH57t2MciKYXrMcbYguusTn1el2pyaAGEXqR6XbgR5F0pn3nxbOlGg3HyajCkEYqwdH5bnZBpoQNF0lrvckS0IdaYk42NEi9xdyH3uU4Kpe4Ar6ymQUfXYp4HyKNynkEvZKZwMQE+aGqaNOj3RiiQ4WPZeE/i9c2U5LNlk/lGUWlPDBVlZwJMzxKO8mSgfFlynOURPlwgZeP3EjfBdXkCUU4vjuMsuE567TY9iJc59Rl5UixleheZZEIHGc+PunUtTgvM3WQyjqg4F5AHhqHSyGABlMMKjba5IhSj7bdeKfTa5ylG8mPHILKVqMt3jmaWctSfxnTBOG1p+W78chXAYS7LAKSM1x9bizFY7BtDS7qr5qFNROT5j0L0tYjTdk87GKhHmTZPfbN3RlC4rf4WmwoyvGRTYTppMberDuQ2hvaU/hmp8bgbePis/2kcgMkFbVZCm4T6j7tuHtudlLjq0ct1xNXpXaG4KRo68GMOc0UOhuaLDPdukw5lw9Kxe8xbVX+dPfA4BYb6jzc+jjLJt4ZMlnzmqDwNRMX6KslXEHg5YCCAsTZABoPL6P5uKV5myxhe4UNjbClS7BoClnxy2e66lWeg3S+JmRjIOGfzGxd4+iKbCU506NX65P4vGQsSFOml81Lj/9p2a8lxwaZaBE5KMqthdZbqcJf0KbEIF0qaeFvJvFJHjv8gscoyjeC7lYHkC56HlDsCga+90wJJoZoAmVS9QIuqqLkR84kYe2iW3HpOgXbVIN6VHfUlVJtZEc4wjS9e0xJfQdqNlkE1WbARQ93Ba0AUg2t/L1W6ib5kxO0MT9C16MRCkPk/F7sTtfDlFFFoR6z+qCnxoqvNcx04xClf5OvhBuA6l7x26Tfaaf44qvTRev5c3hC/rhKWGfaoLh9uBM+musLpildYlDsmdNNqAivEUKC8nV/Ou9Wx1cUb+5GKVIxIDnFQgg3quT3n+Cpi1BXuTFa7iotwUyr3MxlJVYdVFWpbehzAa3VRN9NmXbd77u/OcH0j6u3jcfiG6aZsAASDuyAS7zambq1raQaP+ZJPG1p1gtlNmezLpNyG6a+9khRBO1hlGBwOM0UQUGQD/IeDDBUmNv7t4vX7INKAUpgvcE6y1Q6v+cIYk6a5IQm7o30amsIW3VeSHMP1kNPvM/VwVlOd8RgDUjm4qNQfej6DkoaYQNTFC+VJ3k/0bdPWTaVW3c0HKZKFD1gE6V4hxow/sv2jVWyM4LH/XIp1TkFBTzvwIpRDj3Keyxu87SXYzUZG9WmTutoBS6cGxid/HO0o3HK0YqCXdLz9bRL1TXI1/gwlXGT3RBNw06YrqMgON3sVclFcxAaZCyjm2gNo3bRq8e9JCL31kjViIqpCl6wFi2t0psBAXIMtdAxZAW1hW0aEFZmgFkCpdBBG7djfPuTJsEGrrxPWQGgjMVyM0Ljp1NbzTxSYM8zL+4TOevbrnGwcjzOhsmpKYTL1C2GWii/xiSNaotwa2eKrHcpG+Xj04EGtf9Z1EsF2VojanoUxBJXCraNMzSAIF8b+lvliiJvCFN9+LB1SxtS/OL1Y753xZXrg+VuDIwYEeRFKJ4SAZLO9bLT6NMxuJzhClgrWu0xVXwD7ehNu9bYHiFsJfQN5XwQSYcBGOIo59auxN9scxf28DLfvy/nnBH/Sqje5UMAqj+9aHG1L0bQadHKjiayFf+IhO6pZkW0x9j1O7GXkB6VrzQHyjVbvyi5BOmHUe46o6lQRxplo0VYJXopPWnja9+KW3hoAnrUQ1PNs2oJ3dyc4JiZ2yFGsipKfyZ/I2Pvyv/J+qNwGS2moh7kcYj8lu5z6eZxU+yj+DvNBgpUDuJtWmaIkVcrdLW6Qy5kWMMxxAET4AhVvRuUU9vIUo1ODjnycaDFSUi+pQSZkje9g6XznsvOtgk3yiQYhKpGB8z4njSUow2KhVmr+nJEv4qnaYo+Cb1nexonnfLt4xuxzDvQ0o8USwTFAAAQAElEQVSmCa+cTqpnSzw3TUwxBhdL3fByarSNKWYqf+i6hXzqA+VjwV2Q3dqIec6UH+i3j14tf8kpUFoq53ibF7RgKEZ9E78EAcFRZqn6fGAp144J6DzSKFRdEVHVZU+z8LJXT2fisX68dceh7catZyt5INnnjueTOsphegV7A94yRQUzrTuq44dMROlMGAO09hDneO1DgEYLHck7MVU0g7DXxXl3FW8QqTEOLIX77Dk7227M0YJRpTK0aYua1b/XhZEHo60BCGmgZ4rzoKu7CpCTQzzCaVC61pgT/axK03OFViObaqWLgxnICvTiyvrpc+E2oI9UKc5ciWJhaAd6arBlTUN/H8xez3OpyEdNQdvEJZDl9uQ875Oau3hUNsv84lx4yr9ntzpQXDonOk+UegdeQOy54/ur+3n8SKlQnxjVpAVHSerRmXOUv2AzhlYjd8VGAT+jJ4vtD5QNU45UzLn3ve1IAP6rltbq6VJCkViHBjcL065PIqtBD56abARmsXWvMupixHr6eu5dSVrun/z80dcltUkMG61HsZ8pm7tQysFewufkOflUI03g+RxTEXRzOT97VCP4sfx8zrUTag72G0XnDq6hFne9uyAsLf6wUhhSEcZRa3BhXfZxOX4+vD/4boiKGcKWKhflrbbVc3cpKdDpOkYqxw/4F4afgEsYuer5sbmsrXvpONhlVpSMMudVx2B8yj1HWciLkLXHXc9xpRYtg+9zLFTgcA7Z6/3vdolE7ewmb0hchfRh9I93tOYIfiVxCd+3XAkQBi5l10fhQAyAT828FwT+ek1LDNkDM4sFXOQerBmyGKVHzGzV3LDNhjTHXowLY5WStAR+Z/76lcoHzlY+NlPSsl0ZUQsPcQSy4gewjW4tCMRomacuZNm3f5If6DZoycOBwimWXEJFqIVdWplY05BpzjgNylRaSk5fAsargs+K3CyoKkbpCBGZnatsSdAKXWkXdH2VXfAd1pb5SvKg6RPXsbcXTKtgtEayFMHTm9g/7R+vsLvWfa6WDAlpB7vcyiTs7lGHgQrgFPW5d+dK3bhOR6SMF2aYtc73jUAaxtL6wLR+s7U0tgepFUMZXNYLLSlavhknTyJbqqtZqfqWiNe6ndpsPMLLjk93zdGX5/J2WoHSFJx0qP4e//bc/JV4shoP8NBvxE3hlGatQaV5+t0jPs7NI2KPCINFcz9vT0PnAG2BMF+0zTE7c/W4AsDIAnzqeo6ddyoJh6mP7ZM/p3Zq0+JSmNyd9B6OAzajWB8ffDx8L1mAwtBq/HjpkVjHWwdWZdV89sPQA81s8pBmQ93HR7+t7FP1M5JbKxg5gmWHDPRce9ai8MfgtnOHBgJ6AAN38Upey9VS7cb86vNYzxvWhdrz68KzmNvLKVXkKWk+EaWOcWD3w7uhaxM9JZo4XBL9HewobIVIO+FCeGRifUMPZGU4vLkbXhBocx78fjGkCdfTk1WZmCfiF1xtrvKMvidxT5fmpA04drdnELT34lQLIs7lakKzT8+RspAhMKF1dkojWZRn5U/pYP2aOG2jbHsVr8Jn8vLnZXWv2oEAWKDusHKqR+zLu6etCtcsM1zd1xn92cQxzXq6TxoyWK6gyoc2pJsMYeRpsNgR2UaokdhF7B7Wod2hoenQY+gvXjWvk2tOYGJgqEGnKQyUglHBNugq4Zc7c45PacBMi417STOiwONcq2OZuSuTGe17Dyh1cZJIsidMAX+R1QlOD3Kok/7j10fheaivCDShITOKY7zTlatbrXX2nN+spEKg4zE8Mg3lTHdn/IyqtSxLnqRHQfFMPIT8jSSff3xb91Egz6Li4DOrMbDesJWsx2/ZrJHtut7+JuCEhXnaz4mfowg/ZvpL1XpO41fX95yc5/BW8ytgutElJpdKYPx6U2MCR1cAugmXHmZhDOLG6ZfNT9RmLmq5kZyiJb88kr5+gw/6iWc+X4h5AZJvwe4WxpKLo8A3AVjENpVVGIBIp9kszNV2ya/O6RBMsxiunrCsLymjBb56VoA7aUKkpIA8EsxU3BnnFpiPUxdtFC+75+HzrAeOGN8THEKy/r6uPut9eby+OvvJ/ia/wJ/wdg9acxcuvTOd99iTVx31+yssLv8YrjjBG3Xm7qeXlmYwwEr0bXm89/BK83YtmyO2XVvZVk943r5kvG16lfqsIy1jvhe/m7lgUnIPxgm3Fywk16G2fW9F+/SFvHDoZ3a3m5XbAzvVuB+7v5Pfqa9sFkbsZSLHKlb5Pia96WsNUm0vBPFZc/vztfzV+ahCMzn9tfnlEJ2G/ogeUeCTNnT3y/Dj+HjarvKSQtTe5slRr3pHCARTdO9yp1itpstmnEMoqbHBziUVibXsemqKShQOsKdbG1y/Pce+hl8L6RZO3a8EvqEGgK7uibQL2pBctjboGuXC/4l5E8VEQ68EpOUjOsIfZSAN4KaJ48Nj6+zru/fX9xsp6rG8BedmGJeZBcaIxCQfKCJP9miUOt6T7rmjHbbqEyUkh33Oue/R1uuZPlG1HsbtqxVhbAWm6n/szr7xHOuxvW1D8ofGXiyr7NFxpgWZCsfcSYJoMwlWi0ThZM60B3/CfmTaFUlrEMA0ljCsWbettm0IM9VPhDetFOCI2nSMG4xKT78dvmg+tfiYdMEBvHuzaCj4aaLMG1rKMafz21KwTeuqY9Zgt0Ton9nB3dgorYdbYRXmGykd1Ytk+DGM2bIQWCRBagV4Fu7Nx3dNVLMmFaPCsHc0R7VaZ7KrXRp5tQaDKuX4c5CHVXyq72MPqu2mZgz4cj+/eWPblA5BOKsRMCHazTolKXJ6OOnsICCuZwjsqzxaAc5RI3izAn3wujgSgo0DbhS6/TZppDqHZdoc7eJRqLKm/Bhpo2FD77jAj+sLAUVaPbIy61gUTJR3Z5zyU/E1/+432RjTH4W1yggxE3BBkVsglKTi3+d7zbNc4c8tgd5gziqzsqqWSKoATcOwC88wlcmCQkC5JS+U8U3g21nfY2/r1IShETpg0wrHnoLaDMzWYqy3RDEB/Q7XVx0l850jN2b0ZQyHLWCV5NpyAU9buQwL8yKtESBmqut0PT1TCTUPP+lva7imrzmGOlxHlbNeKG2CztklbSanZganPBuKmUARmvLIVi9qJGE1PaMch4SOt/+bpDGdWSO/ktYc2eGMdcuejWwpGIU182Be2y300Fgzy8bXMISN+AeXQ5rtx0470WZPv4eK3nWiyMNXd5a6GjfjG56rhQkoZvhkvP+3ekfRDWx0vjTcDCWmgEKPjEqlLRpl6nZ8F7V01A0rLryRkErNhHG8es1qrcMaPxV273V8Aao5Zk4T8sI40tx3Sdio1Lxr4o+jJv4ixti0vagpoAkqvWM4faVK3JS0Voxnzq+D+yyOGQ9wxlEL3w8fE4wX4bpJuVTsSsfVkrbSWNnacr6mZ6q1SonxN1EZZ2WPcuXM9t7+CIZ0jPE3aS8QlSlpf9B/gDsQ5oENd82jRKrilSBnMkdiuomv4eE4a3evRlf0W9+XT0g97a3jEBLdKFbf4KrYZ8lWm495g2OimzPdWRKJaHrg5QpqoDTfyUcUm6ca5R+GH+daMidmugH9HNbD1wcYhRjRqG/1MpDjJ9tazw+OErKLtowqXDaqtToIWHY18o6qLQPy1TOWGuTu5vRP8+fuKNmdhxKGV7vinpXHcbSiJJWFhwU9aG87HCpKUyz0M9GwaUgclsowgW+M1ld97hZCcMPE5DtThgAf/dNGMVTaZedQqx36UQBwx6tA6DXAY4MrPR6T7kVoTF+413wdT4jQUSqeowXc2hpWDhjgqekNB510fYfHMT4msZyX3TR1iByWoTV0jAbTRNJmDQiaUUCyTYt0p9DU4VXiESOP9nTO3MhjdQKtEtI0MXwf7ZpKrk1Ksi4+GiG2CO2OOmRFuWMzzaddfkbOQhKzq16neia+SIPp4ECKqayAtJGAZqsGPlTzomUU2XaI9Go/cw03EMuxpvs5/dwzqfzbxVCMbBb4CvvMaNoj89WJHjQ45tAhKE5Xnnqr+iRPjr6+S4HqJFxA2eusTOEMMINXgvDTKo0RPFLCuieeulQtVIJ+Sij34nGriinzVKB2QTtfc33bGlbjR31xuT7bzXW7bE+PEngfEShEGXEB6lqqFTT0Iqc575BpcQCNWN7WkpLl8+nzORlPQgmNgs6y05YDLQn2a62vXQf7qXUUlQy1OFKyPUMUvcpPzZOttsJuDSgStvju1bomrblaIbHNBCDN5F0w80Z/p6CgOA5BO2i5EI2gmRpeGmFqIR0syWDPbF0RnO/Yt7PLntanVfTxbsMD3mNiDwpOh9yN02Nip6hHKS8I/WL1eKWfEwpejaNmxBTxctvWY3xf0KspZX3PPGJ7RpVDYl+5gxbKYtjpkHNL8wnDJUHzjeSBAuPDuq3P7Hh+4rlWGVAp3E04ivx+wS/lXIj6qpXYkLnliNLCm4c0dsuLBGq20NKLgBAuvSv3fYaJaG0BAtv60tbFzVjDeesmCOqLAteM8UnUKdGFuIF7MvU1ibQpNBWbcxgz0YKjlf51z6dwCTlo8qoxsbhXhzVepFvqKqF5poZKA2oyStqqvYsHqX0jmJIo1I1O+LgP5uRzV+UE5zIpXto6X38b1hd1z5M6cOitXFSq3JY+M0oGxsHJzKfw5PV0tEHrYcZJu1MuDVw+FztGVDrGMKSAsPag6NH3t27ROVQ6iiFmrKQKdLOMZ7n6cnofz6f7HrUPH10QTooB6lFcohvIPOfUM/0QCg81XS+Utka+YHBf+1pYXpA569aHrWtAMZN3sY3CrTW9RyrewwUzdzNz8ndwbSYAG4pXh6X44E/xQx32bnLZEGHdIytLk8yMcl6tCy8c5OYAo4brtIvhdjW39OrX+DrNM9l4gK/N0VTHx+fHNMp2k62scGrdFZlmxyQExzTccBmjqdh65qy8P/X9wfdj2n4dUPUnaCPq40cbigu2yasgO5MichbO7sdZy3HFLMVZg6RXy6wTFhWfxDVpalF54y5+x1phPj0PTuK2SL9ylvpHZ+AEc2mmUocALibq2N3VMgy8sRY6gbFJzm08DIRdZku6zZW132uaDD1+wwJIYqalj2u4YYQXltrA+dRzvZi96Tyw0a7EaFBX/e4800cBosK+m+rWDSgqG5oUc6REdjBDAZjSttcAa41c7sAyWYppR6toUI50P9RI47jDolVBJSW6W9MGenUxD/h9lra7/aguGxz7PZsjONr7BB4y8R7t6D0qQ2wvmHa7dXsOdX52YY7ljIQvnWo2EuiqSRm+2bICX/QLHhrzBIQcfGJmm5r2Eda46ZhQ69tjLhxtxmciHr8eXrc+rE49s7y0Oj7NM4M+tDYI82jtR7rg6VDrWG8AU8Lswuyhc0QiAYUqmoCZ0vTBKExRol9Yxz3Dj9mxqVJMr2p6ZN4DfF9QAzVYV53O7DsnW631MUXRa9SKA26F51brWqzQSQAAEABJREFUaaEMMSAR18oS8qu1pZ/dChlosnRkIm9n6Y5N09PibSON7paOnL6Xi7SYjGlF4VMzSv1djFYLurIZC3NnNCIxAtHQUz1kuyTXqtdmwQXaU8yusswNFdq7glj1tEwMdMrn3cN8B0PlOmEzrRl8EYZ2QsM6ct+NgtAcWEkzYnIgWN7t1lpG7ak+bPUkzTMaaeY90YL2HmQ3UU7KR41CtjFCQ+HetXAuHwRO4Cu3TUdryYLyX0LBkn3yybyRB5UtHikf+Eg8Bv+06b4oBFYpLFHwRjgiX8AYF2PbXnGF5Bgwl+fLeOkDlFI3mCJF8bE5FqWHxVc8F59tx4AwW9w2m9KmjGMIOHGpyAQPPt1aUdzLWO65VopaZTxP4SShse2N6qrz9OSMN135p9txexDdZOrQxd+jDnKoQF80JWoJhaUi+BzhTQgn2wNyANketYQRKDbajj7V3Sl4lNSrlNSY0efpbQx3GKcz89tz2N3SFdDvRLjA2Hx+1okP+LngUY2nphTJWp+wpchv6IGyJaazcQpTFK+W2zUMhdUdaIxnRysJ6vSQ6tG8ii5z83Sfmizl7bv3pSxG9uYUc7R6YKRlUFEbdsxnzbVKf1l22qjQFmDD0Rfabf74als2ZpowEAsh/MU9FEaDyt4rQ/d/BfZiLZJr2d70Rb+4AosIVzz5hk1Trzkj3JUKVTO9b6zH7L6Y7iNN07LryVNqQDYeiiZurvz/sPQuCo7ruA4gQFJyqu/+/5/uqdjiY+GaTburElsiQRCkZKfnDAtdMVLd6h3NNmqNuMOmgiX/zzt50xzWVNz6vSEMvNqEvFNRz/LcFICWoMZ0ptITGC+XtNAmHg9WAW+lSivZT9awR6HECkqZCIzy2v0t3C1qR21T3RbdSjPVVD6Ta3IEjd7mUoJsZENLsmvJYU1rHvhtU6Cs5IwSWYyEdf1vTFC6Svo7SGcVqOk+xusQj+Y9G2e7uXqEm4gCQ+SkWV5Rl/MahSXS3kbfBqJeGEIIemTs0ghXIAkNgt4MURQDQLTaeitVR5BBWxMX/XLo3bMqd1uMXeJkChwdjUqk7sdlRNTLyzHecR31jrH7wXN3fZO/x06anEFE2+PWq5crTiwYUpw7FcjrskVEp/i921uUQ240Xiudi1pFIzMXuYZepIAqSSoL+gAaOn6OkgQeRDJqISXV0oomKiYGLJFOmL1PgxY7psLKXYmogxLLJVBd9hxtfXImzWt/JiLBZ6Zm8L7+/3VEzKX+8kJcY6TBOAwdSet5Qe6Rk8fmayNiRYJBwLbnT+iua+As7TwkRLXpR3yywtOoNtc3PRmYC3lNtRnC4ZBEXHVt2rITWGKjR7StGe5GnDI8ogXKobo+vEH9Cdf5cowAGrHFhKQ7lrMerKPSmtHVYBmTmiT8UyMnPUEzBkh9TrAdMA7eU2Yw/OVq6AIw0PZxKYFdyefhqUnKoJpGZfGMH+g1SieHg2heh/EsHmZPaiF6xqpWHWtN7qQgelXk0exz/LQiiXbTZAy83Co4PlHtd6HP4D6jvqhc0wt+Ekc2zWGjmh48yepds9tYm/Re0O80xIOflMh5ccgaIqEpoSRCH/j+GEzK0gDdsU6E+n4KG5WAPx9d/ehB4gjFgL7MTVGMUmds64MRgxZhcUGezxp9s499x8rQhAdz0BgLMuQIUrtohiIWhA7PFVrlZNLci57QYWNAFR74S81u8sSUy6s7zKp5WjSIHRuVHIClBBIyAbtD9Jvw84EGARrSygF8nBhU4qSS07riLVPA1+e5KneWZVGdrkrVBY48axqXNGLWl1vArYzJKcrSPvd1//rdT9st8QAbsxqiiR4NpTtuEwUzQBtu09ZLnbMw9ULnAR5ajejZEepQtMa8u7lB0/6MmdbMCS+u06bd0uPwxcvsX9tPvwtnpE+P8nQvyeGgpCDl+575nrkT3UqdvbWs5K10Sl+0Y/pk88JWLflWvNUK/gjcSsRNfIHTQx7zZ/vNrpN8lAmCaBn0df52G0Kr6YHUw+P9QHHVyOsROikQM1DSvObTszgd2suNKF7e25UWM2P7lvZrSkNxfKQfifHInfhTIk74cWowTN2fX6XZqvlfxOM040IrN5La+ztnzXGZAQymUMXOeJva6KhgFMX00oUILfWegWegCp0UVPF3untKoKf0tOHukzYTPpjG06xBo9K6qLQoUA0uWSglWTQMjLR3hmAnpo49r66hmeCBIQV1PjHmshkzkcPm0JtER/cagKy2zDW12ID2KILnAg2ix8+YdCLnBsUFaaJdJS/bmuRye/zpmNnkZ/D3fTS5xvYo7yIrQDrV7prwspBHq5x59FdOJOENhexxQluceNrvYo2GgUlJogpp0ErkNnp45cjhfXg/7yjFsRf1BKa5nrctLnlSFT8ryi/DClu2PrMuOdGXFmooXW7KhNKGELJ3AWBZKXuX1YcnTPGcUtXn4Xw5D84RDoxSDBrYjJrI0hNHr3vOXeqc4+OhzKSQZnjN5R3Q5JIpe1XyKCHG1oJEMxGZUGEwtVeDyKLsaphS5CP+uINxkfoaQf3AuKc/lX5uqFVCoIJfzddkBTJwK/f2Kceg3z81yORIAihXb9NdynsNek1AxXeXpAiQ23FpA+i0tp38pAXMKKgjypfTnW+a7B99IcZ3h/sxT7mbo82yogG7DEUObBMBzcL7y6qU636zDWeY7/FLLtp4yNtNmzOl40s8I6W7pE9JBaMWqdJWgczc3XflI647PQXqMm5zrCpPLQ9Dc3GtC7AzGG8yBYS1Iz+vMJ40kQIa/IJ4hY0sn8RTlmZJ8Y4Z1SysFt6m8LHR1/iX3V6tDsWHf5I472a2XNAUq+y9RB0iaSmnnJQfsRfLEJOjpiL+X8cszDChAMuvs3a7K1K18agJSU2rd3P4wB9E7jWfZUutxPG2qe5KQpICCJi/3KfeFuq8dgiuWv+guwrKMN4gkMYerfTQLXBMrVAF9Va1Gr/jv8jUyeZGBBRItxqytqvegyNd22+ajI/LpzpHWh8zuFHFNCuOveUqwg/6sfPYY+uO/TpYSwYVoRYcQDC8FrjTdMTQ6Yu2gfUQFaO3ZtbOmwoDCHAKo7/W65oIhjDcSqHagrJhTRsNCuGUaHyVx4ANyN49XW9zaUhLG6I0Bz1jk04ZAusdasM1RbvHVb3ds0o9cDgGDlarmB1H508rYwpC5mtcHGLbUrmJWtoRSmlo7tMpYKSQpfSnYSXka0O5QWO0qvfH4QBPOZ8tuexqKu8K+pBzmt2kvcQw2nfSH0PZjHI83ZL2vmkV5BJNpoxhHV6PB5zb7QdKNUeWkOq8a8zSz9nneZd0tEtntypX1f65bLk5zejGpTdiWJRYaT70UlaT7IROLn12LwUTK/4mmDhqvo2q8/NoK3bLz9QIP5zalFFCMoUNHPNeHHl6TOXOY+qF/WUfYDQ0PFw6xDxAm8N2y3gOU/lToO0u98Abcasp1NOaOhoqBZR3qRHHwJsm082CzCK0nFwlx/AQoqRcsIsvQ6tKx3sPsynaGrug5qPnOvI5wNjCz8eWhSlZTq2A3SnYlJq2j7mDC9CpG/OtUuoVJh7rgrQd7z+OoqEdsxpebtop99W2dfsiuQ9O9jNyZOSK9nlXrN11lfbXjICLvuvgGhIx449KO/Jyc1vD1VTcM2feDj+A26jeXIHGwhKfFk2eUQKKYnNbffhKIjxcy+KOjpP2zc5uYoWFmxHlr3D4Gd/Fq+Eclf8D8mHUWuOT0FbF8ig2sqdr9P4566moWbTLgypOok12lYBRh9YswJ261tDz5qdEFKEF36+vfu4YAzCaNcYR5jTN10r65hUDxesfUPz4W44Hgl1QBzAbzRul/VBkPAeT2g1ruZjfXHf6qGt2ejIyuFkXk3affQ/ax3wkcBS8+iqJ1ndeCx6EdjK76QUFKavarXUVAZXelUpTqX29AjG66bfqzUZ9p785UijeIIYgrTCDoj179ECgXOmkouzN3ioMfe9w7cdcW5qTVCRQRATM4Aa1jhYr3mAhp291cW+lnniULDYp1ahL9zyiqwLllAGA+jOesDOTpZnoRFd33z5JzVxuyxRPjiKrM93UdbkXwmz1VkEVHqFHz/titZ22W2yPrKtr17YWV/pAYZ2SA+u2yv4OzniL9jdroVqw66z1daZiabexgSach2JM+PTBrQDU8CEfXfXL13W52PWkT/gyMXYZ1eZMAbBeJ5X2SKzrn5aRQ+9wLI2Ay0l5jVb26mg4OMJXpgkcqqyYiUd3LjotvzA3p3Yz6x7H7fH9Wc8P1CtaqzuL3cLeKvmmcZetR8+ad1Gg0zBmMOp+ya+29UeYhpmDdqpHUIuYUNxuT61vulq/1EDYpHdyyPpY7khZAjYYumR+U+6nXzctJJ1nxO9Muh1dEm/0Y//SHRxnq0xVTzNKiObLJ1oGA2APDVqCF04oicpFBx24WvPQLn9ue9bVArHsmLemT6d120vgABJz58wbiw8B64HqInUCf59hi3Klh8z3o2ts2GjSNI2KUQZ0zDymbYvVREMZaz0LotOEQ+ObLPPHWXF6vf+1TNVuDUViTxJKJaSt4zVmNKVgHyhx4MDatBfgT8//Te8eT6ABeepRR+QtsrgqHSrqCa4VI8bM9HvvgarSIaCAkBOAyajvuWIuKK+ukrFQys9MTr+84B1qau0ybXLQwi91XVT3y7InVz5uMkoQpg47agW9mmPrgaOlgucKftba0jqVbgOMPaP9jKc0FC1cKjQz9DKNV4MfjTDPwuiratEtYXTVJKArCDf/WHzoP8OrHj/3jHp+jy2nD7x7ydgHs6fCRnYauVlbIEl5xxy+99lzNmowaWciFYnQgQTX2AVu0Bft/386aCLAv/VB+76VjSqvE7hBLSXe4nd+PD8+l7UoN0T3uxR7mcFGvFmRj+GVPf48VpcamHqQk2oGQcZhfEcXTTUkASkPU6Jm1yw1ufaOiKv2T9m/1Uuh9pxBUaw+4c/Yd6AETqkvtYaPQwCr8VepRb0wq4aNFp8zA4Bu/dnntfqDvpCLbZptoHVZn0Ql7h4dnbS8/CyHdM2oWaM3TiEYUXaWVZiwps0Q0xHJ65l9amlBnXpzA6nB63/+mfP+A6+x0LI3pESChzxAgyfsWCgORnrk50rTeEFQAh/fQ7tksaT4c979E2zAJqQViQWlj1MDwL0vhRPzu/KxFK8U1emuS9bcnVEPRVLOmTy8sR5VIraYWXltqDpE1gP1CJmjEmZGKvwr81+pEoocrIbdUiCVSzrVhcpeHlhEQQGBEhTECaiP+Wah9TX8fJJeq8i20kmXrcySPHImNbOn65n5b+wrVoAR7uSWY20vBayiy0/jUUA/9Ovdrnz0JYBE2i8bVI65bW3TEsMhZ/ms3VCVioycF3ZZib1WfLC+OP7KZKV5bpcSCPXyUzn3I0xDN+xVWElDvHbXPNFfm8Itm3xisF4BRoLZLOJvkjqSjmQ8h9QozW8AABAASURBVPMVYYJNAO2wBQXgrbyIcKremwf9xdM4q3XYhRVhuHy2AbRKtvImRD5H4tomKPQ1Fk2tl96bJwiD66Ou9V+TmPGl+uS0W801x6woiN0qFWGBtz6q2gPvSyvdfVKZwMAalnaV/0gYtWTvGHUYjLroPN76qIGuM6TIPcm5wXLHz9i/Wuu58B6LSXeaDiOB2VPb3uWP1tGPbnZlsqUHU/tawiy+IdktjOOdAfZ7zGE9fF7qpyn0gxJp3WYjbagQvDewyxe0kfAqZB1DITjuKOPv4++Zod6rcmY/8/Pr6zblF4JGPhutNw/7vSVQOs0ykNekmgARoBc5g2mMptg4oT8KTWvwqKdQOmMFXJd0uI9jDDNP9tHf7z2pjchY+F4//xf7Q8nw6a5s2Lzz4NPGgaEb/ShdVq/X9tLyc0vXF+xncPWYxphwjFKuoDcF5IXTlPKtDtnx+Xs8p7b2JPNYpVt5QG3txQWjfB5xb+pCvY5FLb7ND3h6TlfzK2IUpdnCuvgRC5pkmBold25DuWSH3c9LlOJxvi/9HK0A08OJ1T+7rwVaqQrda19C0RrMspd0jraC+Dj3jNqrsqCY8NqetndIjEY3SKevWbL5jokyFqB7izfua9OtM6HCa9N77KQnYrA0EWFf88eQU6l8uPsyh2NQpLkrJ9zypvbYd4t/7fvV42DeQDXOVzI3kGKvBdQVjrMNCrFtNI56TVof0/ZPbIoyXWjLZ30fV2/7Dfby8M/igiJIdT6ZVmr6ZGkBgpJ6acRlHpTJmRiKc19sotK0erTykpotKPLaVtM9KEhnYaZmpneSPzp7da9oWyWoVEqpm8I0iMsy3NYPp142Ri8ZAA6023LIa/XRLaO68mPL1FmX1iOxTOGx14GiduoJsNkD/zbVpYBUOro0X5Ki14jgWO4fITmnO2+t4tsfsbzwEncZbYaEjcrcC9rg9iNEHoCxhenR6oEfiIT37xiHo2uadVCigRinvyYw0Tq8Gbe/TtIBEHqNeKjnJ5/dadNi6/FWOkERFtSO65/5FdyrzUYDZqBRjYE+lFIkuHZ+8PzLuWqC7R9IM4ASRq6ej6m1zWVSjEj+nfk9Suu8qckYYYTQGI1gNfNNARbiU7EnUHZq1wmcMSVmFQIa6bNWb081rO+sR8s2VB4Ff9w7SFsEGufXTo6pMFwEjOC2Pj608wf/ZdphIZuOF+vGaF7R8sVlK6Eb6+t5f2pjMKl1i79rUhmURM6DSTK1kbGXMIjooEOCTPGTpoDKHrXPXH6vuM0KEkGaPVzjO0xHuPIQ1Z5a8OkMU2LPzgM83anUfQofRbTdYmEcKUfj2frawicsgstwkVdB13tV6tH6hxUojgqJMxcRK6E1bIAT6/uzWo/it9AcnhrtIUrrtXDF0WJ07753qUTlq57rVS71slHPcOhZgnajYRZlq7gHlxAesQmFLXA6NmejFwqUR/0osX9APZ+uVmk1gUUExglqmJ+JgrIoq5+wK/bHFXbGjD3B82/6Apf/HTGkWHsPEdnXFLuOPY/Vudhrke6mQTJuhN404pl993X3p1MRRR48JTl0gxzvhHh4tMpozvuuZsAWzge6KBcGKrVz1jxrMrxHHQY5B65Po75F2kSkkkq7yAvY71G6tHTlhWvEGCVs+FAfgIEBekfAMYvaBd85/M7+hRw0urrVlzXkCl4uXqFSFNM98q06kgj7sdGyPtsmlhn9JFjKy0QqNrxEb5yrLGZftq7ta3ss6U0pxM40dU/02XP+vWSb02aVSRE13THlXpATiVSkTFmVq9i+ls+588nneZ89SEirh2gjFNS0FL/Ovp79Oa7kYH5Rv8xDNdDkJGbw+vqLSLF0H+rrqYe4F776+uoSJ1C23YwayOBwPf1JtYclEnsNloLmisuoL0o1gRz8dewGhxsqkgH/RLbUyKi6jFtQ9hyvp6uEA2Cbo4xHkKo+VMU+Fu1jni7hxYfi4ejJUv46tGSelqZUNf6YH796W3j8G//BSyvpBtozeHL6lFobDqkKoh7xWh4ANDgJgTNgj4X12s/i/Q6uGZ0Ngg42/FjfnNLoGvsW7qkblT4qIedfLnuqX7sDvXzNWhMDlvXY0/we/x60UnVKA+R7jO2A6Q9W47rn86jOiotqDdLY4NUpepAJ3o1TIgTFaUxPmiyXeLhDYALJzr9RI/QP7FRobBQEAxy9aqAo+pJLt9JghZHw5qplEuhIZq83UrWwjMwWeahGw6t/vue675/OsO3c9oJogKBoHJzub/VTT2Xb33m7jv1f4grElVgFMdj08uCu2Q2Lnevn8WvaLDm9QfOtrcqpnqMzA462TmlwIFQXHgbu4ZWzhdyFLZEjUwh3UPFSqS4qbaq/SIa2B7Yz1hOrfEdT2+zVTXQbC6IxZmT7py1Ex/DtBWPvtpZ18U2GOE8RiFatcEyMbmCN8ChPPd1Zu7XYlNfUg2/jdyKFsw1ZaBH95fO4PK5AiLRWvHGWtZHeLznqVJflhm7insS8mhnBowZDkIq4se78mecnnhWPGAMmqXc8puX4JM6N/PV+fM6gylTJ9qyrtQ21V+BIPI1Hyqzt9XIxNH3M+rR6ZjgEJ5R+K/Itn8dUpNNFO7Ab+NLOWs+SUuZhCqDwYYCD9djVrsYrbXESlrY1SdErAq8I0KiRLWCe6cJiXUAGlZxJquNekdcuu06Zap35XM/9c+aSoiprjjrXPdFcMIsYY42fWmiGPbD/um4pp84ixqPXUtQxiXz0Y9x6efr5xlfZ6EeFqR0TAz7YqXzqjRpHDLVe7+9QtuYcniTB9C6bd7UW4hOeiFmUgn0GN/uhhN896OGUyiM2NOQQ1bQcxxeTMypPSUNsRS6b9nkgcMKYaiuvlFsWZgzmNB/6F/w1/EdJYmTabRbSmFIjtDu73b/0g5kNufTXZBOL+lzr/TSUGUtUTbF8nRVpONn17WErMx/MqvZOYxHuAjwVBTtj6lyHs3fvGDTnlFcZnSto4nlKNx79MWjroF7dZqVlEwm+yBxlMPT7zzPZ7mMfVxHutXaErJStb1hyPGvlvArxga+0nTLPe8/RdCX/lcoorbo6EsBQTUZQns+prTWrveXHFtcP11XkY88jo44aEepEKNHqEbaaGrswNsrquCZLMOPFSQDH5rZM8Ws2FAo1LliP6+ocPAd3WrciNxSmUsuSaAGBEQIHNuEcoh5xE61U9OqMuWFPU71A5Yqo/uRof7m6nCJemXjh8jUyJltM95cqswAMDS8Z6UVoYXJgBk7bKrubVjik4OgkIdyWY8dYjgmdk+x87MO1GaapWiI0WKmR+mnQQOp65Jp0yfW83WuhXXG2hkNcDTm9kRLP0hhIt1RVc4N+fGo4D/kVCvUFaSfIcAZjcn3L73YhPOynJUQlb2milRSmHaFHUdkxVZ9hOlUGVWKYgItp2kBoCL38T8SPyV8v1Od0HKbJeGA0sNzu2WMLHnQTqkq+2movoLvBQXS4ZqvSvgPF+84UrMEjp2ZiRHQAqtWeR06h2BcRIorerZAmB1D1KHEtwbKBpL0PBa82H6GUmtnFpo8OCzX7EuqlNjTazlycxeC4xszI70NJ7k2VA2+k81qnt2v9NFe78XOmH7gOtFuW5Wmz1nOqNJRqgnu5LZth3WJEnCFovoBlMDE7Y0lPD4Sqzxzjdowqv0FG927x9bz5KQaWc4cph1q2UApKR0tWnhqbJGhyDz/06h5nf6Z/DvbQ3BhLRNJsiFcfIYKU/FtdUk5KoATdGjZJyC8f8Ihu1UdioNuD03l4ZpXj2DTY7TV9rAYTJmLWXsGmLIhF2WheMxfxWfgEnYReCkr9QIBAqNfek7Im8vFHDGCoYSubGP02laNcNG18l1io8ffxa/aRPKBCI0haDK6ala2wanD8pD3Fx0ZbAdst8rk9fKsO5EKSjPU6paMVTEn5rdnaCKlDyKDK1IzMPxKm0c2awXBKIbqSd5XauTxOSPPVugTJUv4grrrAcWCIXlZupXLoyYKe0+dznlPPmQPif2AqXh/rKOWlSpzN6oXv0nL3KMVK/Vqu5fMCN1tnCn2Ado5gDls8LWjtiL4i98woaU2+DloFMgI8GOCVpkPKPakhGL7aefVe3sjBYwZq52Bq+aYO452ihYlKdMnKangiBHtFhXIvWbSS0V1HrFdkR6vgECWor/BI3EY90clt0gmMAE0JuUIpsTWm9c/SulGdSp6Dq1YcpWagFyfgqOha3ZarjlbBFoWLLaIcyXNQZdFLhwtWe78ZdBKaow6oeqKErbUijwx8Bi9NuZibdN/T19RnlE91E2vgablXnkF2aG/jLZmgDaNkXAHr0Shhp1lAe1n14pBcVLwYKi/SqubaUPAtwMj0PpzsEctsjphmIyEdKMr3MGFUJ4G9kjKDGV1N09gzddTU0+xsfxSpTLpzxlQ+9RC1mS1ClmYgJloqUAho7bSvdV/RHwk+pCxoBbWvvLpmD/r1pmhoin5gBPdgQ85HAZT7E10/fZNPet6DylEf8OmlhtXIfPVUqBl0+cAbPoUBRgAVv9CoVei9ssLOspYMSV6kd2mWyEgqaLmitEVNbSOoQ+lRSOGxl7m3jU2rlkAveBbVgjuld1kB9UYZrvQqy0FOgm9r+9BDQgtTE4UFeqNjZN8jFslhqyeemDIveJOlqv3xcpZseR/jo85aVhJRNYjxUVjjirOXwfVxdwXHTaoxrf89LFMV6IrsJ2swtBa1mlsUiyLbnPBqLZLzVmTPQT6rewldUVTVdKMFEa8nETLuCJ81Iy06ykf987a6eUpiag3Lqiw0cJxn84m5DTclWHVCbtBebefM4dGTnxr5KLF4I46007aKn7KVXOZr27LWQyJmQoFenMulaoiGPNIzm1Yr0mMIVlMPqbO63eydSRY4QCl2un4OjNRQAIM6nirRvdydKggXOJ0vXT/UHKWpdcGGo5eshKVLKcyH/XZ3mfJ2z+XijGg8qUMwrBVs/PSYToI13jIqpxQD6rPw1hW6+mudOgpuBGzDr5ZCQgMhftBNzRVPqBJeygWhtfHGSrg4HHvguiYW4UpxVePO1ROyV/oRVlQj/zmPbqQUhNyCpngjataDETnU32wUSPiCGaCAC0pKv8h+rb9uje3QYqoHPtDcUJLfTe1T70OnOnU3kpeNL4F+zb7edBHPm4NOn+cjZsdyfka3HQf+7i7k1LRw2wX/mG8zJYHto5VTcRyVaI9iiLcVqHci28pcKmAMBcrgNDD+InkvtqMFwQg33yHDFvoQMvqurjM9WfM0TrFSU12mx90wPGdVeokPVFiGY4UC0vRF5cXJNeJZ/VDyGNze39331Y+jHLCJtmXhsUZcykxjWkMxsH6r5lK4DciGhWy55bL8mbooZ4cmXR28EkZiytjboW+Ul9LG0EOLlzezJtOQLwHH9EABT/FVkqjo8Dv8SNUMDhWLn6rJHrH4M1y2TXyhcQZlDd3sb/yhmh1zBQOy2Decz9pnAAAQAElEQVT74Wjqg/dO+4w8Uj0qvR/D3xqggGdG6Rg95/7BCCrUz0zWBmEI90Agd9+RYpUB7uYn9aQ+jaUUpOYDpkBCrJDnujqugU9Kmo/ZvIq4Frf91SLS1dswM3zfapzmwnQ3/R63KNMGj4MsAOJKz9frAtYjloPm8APeQOVQen0Kp9XQWw84f2Y+jR+fn41PuPz1o4HmJTDoM9BxE1/rh3orDDpLHkian/KdxcKoV7hLApuqculDYQzm7YHUTrnSjkBqog/MOfJ3KTMzKUZuCMFOrDyL6ZbSkELUFVASdJn/Mh824N66Cx8lqeZujoryZx/dSsTAnmCis6pOq9LQF+jN6ui3J2N9+3rwk/N5sGVvA+rpWGh5kYbUT5/Y+qnH0D71OUJEtkk9zUu+sJY+EF0lJlJBw2vX83ly5fxRPI0EJ4nqPfd7MR8ZA216Rwk9NspGo0qDmaOf835sgxYzP817plREUmF3SRd7l3nSW8LaZmtUV112yKJSy0htrSU5chaMZmTYiGdwWLO+/Mm958PnRyc03Hw0v3WjKhijoDbHaZh1TB3WpADhGu0ys5rH/ezz/NxqT1Rnd8xufJ7xmWHefGNjHkOpiBTDlU/g0N6ciy24JtCour7TvjceoDfduCiEYgrSAt5Xe5YQw9nbQIfZwETpzvcdW3irC3k7nrC0oTC83j60tS79aPg5Xffi756vt8w6L4+FzRb829bBdB8J0zFB2ltsC9jOj4f7AnUYB5i0lpoTKpnvM88Nvds4H4kylHHRCMqFHmrYgx62g6F5JlryqN76C37hicBI3bqUMOXGyS5q+6Gf1mMPUVN65buNmDLh/jsGqpGekJx4lfpyHMSTlPX5a+eJvyp1ZFRZS/tzaLBw21QJXA8lKuB/XREtywT4xufGazl9N73J0aEr4/bSsipWGhsn6tazi1ffyzoI/WHnyvxMXq4z7wnCFOFwF7rngUBQtTOcdzzA9XQ8Y89AmuLImchVHIAGOIdoWi2vMP48eDujcczlI3M/ZXdipqX+1W35N1O2R4N0XvFdVjskHSKdSRTW8HqwK+GlqFjbSsQn+YjrSQm6Z9PeG8FRsnR7eLcKILpmniNiDeoi2G12LEezAT/j0ytqR4er8ez0a8Q3Ugq7gGWSQNkUdVN2EZB0ry35UUKeg2nYOGsNguF+SXJGGMc3/GewRwi7NNDb2AAIXdbnOnlLIA7K3gfXyr3ApQEKJr+sAyusnqCTVumVMdT1auloLG0p0RK/JxbNtGJvkGbYdi2tALjUp7peRkwrL+5P16eUY7fYNG3Pl9WrEzKvdf+Y2oqzUPXcc4P1V1OS3I2rhawmX1GLzh5B74fxitiMoxZi173imObYDJ6W9punvAnEmev45hqtjrZIqIZ1x5iXokR+F+7L06+wrYXrIvXhPPi179eqZOD0nKnCQRvPko5XUqpdXSxmrRzkmyZAnNd8ZhaGXvR7rXMtcbhf6uYZ3AP9EWzYrrmeQyY9W2rAkZ1Sz4a3xmgcxMJhH7HSbUNK25h8/SBSCivOEKbiuewxyP6xeThdtHvb826syYvg1dj3sJE+X8+cwvE4V5yP0hP32gXgJEpXop3S9BGEnHhM+2yqaroZzWukCmCh9934b3CAnoHIX09+tFOeumAGo4/F6Betw+6I20wtpTYs7Jr1SROTcjIObGg4qpntlnYdrO+yDG+EivZhHiivb8fN+D5aEYXAXy0tGmGWMpGhilSQ2EE9Rt+EXoL2jLXevailJcrXBYa1tTRSTWUX1YNhGioMQf2mFHey+lhkqBhkrd3PWs/2BHhsm1+kGkb8lG8s+RRz4gOA1YrHtVmIWzzdBwXShiJ6D2hUnuSTb+gG55FBji18RI1OAFNP8/v8nDvmKZTM1Bk77Q1ZAIPL1TN8tUP6Xst8xMAe7TpVLw4ElDLnOBVtSKgzEkybcMChPJXTUo29MGjPE9TjhqNOsIvuljuUrKUJzS6tdi2hf5j7b6EBpQbyENAhy0Wt4r/EA9bIoM5TVxqV4r8W3LAkCB0YrxEE+R5RMzCQAWj4+OBKs74swJhFLsOCdhitJhxdiNb5iNmL4SGYKHvDjYk9IlLUMbNbOT3DmlYa0ZBhGGcrADQNHVWWLYx9uSzpmqLU2DNPzhfn6/ezTgkYTfUGPSO/Bsu5oLvANTtsRbhixRAkAYLt0fYhxJL78wJ/I3y0DmigUJTGDqddlrOn0KOUKj2WaEw6zp7ZBfW0OORjUzOt+VHQ4r14jXOgVw2Ctv1Qkgwz+TQoZ5eZGs8iUJ69m/utUT1qe+CtcSJLuAjZlR3ayIUpBrOBNa06slfjBzAAj+wEXOXBO6hEZuC2+bY/vWpciVUIFSm28gLkLaoVDtLSoNFizBclpIerEELPUV/tQ6TO6DKsR7sbx/hIPzXagzTyCP8q2+VCMrtlgpr/aldh+SjvJlU+S0U84tmGDRtM9dwlm+QQRsjwMpNawnQ92WmTGI1UxuDoYNk0Glro75T3GU7Fmx/pQSTEqNC8uR7IhFQJWwON6cmZbBXpAZPKTbz+pFEp4VJY5Flxcw7kska7Fy8NbaIoysOOkQ4QNBh8mschXjcRqVHwu03rsJe5cdau60JoSHb+5n2PCu9r7yIgzEYYNcohm9YQiVfngtxFwsUWrDVIdt1+zb6NNJDz0jaAUPiANuonS+5a8EvJQgcksIo+IV2hNe+e1vQ3FpBpfZCtABuu2VJllyiYSxKo214dDUYjZwOGaVApKAIqxHNrKRUb8kiZntHU2iuVl66uApbZZVAvLrwnpovq3q/F0YugppoJI0uFZ5NykBUlMVMUC3O3LpsEoUmt8puzWtWkWHXAxntEkbrbFHuYM9r3kIrbKevTy6CcxyyW8IOAoGaqCPZIiq6reSkY2R9dko9XFgkOCdaoYLTQt4KBXhpSSDUAKZoGCWxNw1vcFESnw+VlxCkr9VYe2vkQOcDQBWyNApx1Zif3Ad5/G1NaRuJvbiStX99joAlGaR4qX3M6hYAkLM4xbzJQckwfqoSezqefRz3KJRjHsIzl1AQdZWjBEgqakzPWRJJN1XCZnSR+x1PLUsQrWROVOZVCrp/MRp6pmlaRYeFdrhf0GvxlEgawpdhpGR31I9dAchxd/UhjnerTT1Un9Aqby+HCApL9v/hD7wqPqw3kVOOIC9MLEgAL1f+DrB4hp44JTaC36ru1yCgsNyWlJZ2Je5wtpcwufbYqdrMRPY6yUWA4smjHp2XGwhXOzolTaCSYRl3LyIyBzL7+MK6xP9RZwSkZLMZQq5tQvIyW6Jk0HLDau0XL2AASgeL0Dm9DiBXTIL1tY7qs64dcznGrkOzItlLvUfLNLKgFTOXfnOdqwXmFION8b4cP48w1CKsxpULORKvZ+LxZMCj8doDq8dhPK9NSglHc6WwNq1QfDp91jZmIpVIOgQAwfD8zLfpFTfWo8VY62voZtcY1o693NBIgcyLP65Bd7kkT5zIgN1dNnB6oeTB9yjpH98CyL9VrWwCbrKlnmI2nJ19j6jiXm2/ykr0pcIA2HcKh01rr8uea0KpCuRJllqJTowzGjnicYsopcbn0Ycgye/Z69IlC81qUGoYqB4gnvAw2/eVz3JpMmzfe6PZJaDUgMFGDu9UEbnqu4GXuWG37yLrz6aiaqWI+4kNkdgFgsKkwOdnlld6aA4CwGKeQkvSYKxAD5qhZDvSHU97VJcLYhzzmIkGuTbCVh7Q+zZOreidWIVpLykjFIxogxTka09BPVGH0CiggOdE5g1DRulE9bYOASNPwKQ7WOz+m7EynRgNge/Tl/ISxi/UYS7tOpSmKvB1aNnXeSszJS6PfUk7IttJrfFBPV7ckijcSkIqJRdj0Qi02qCn8trYs1h/YCjdD+X1CfWoawCzjtlgO1cse03ZDW7CgmYlAm4VxZUEjY0Z3LYKReHkCBIR2tIRz9TgkvHyK2ZZjA2IMDh0TesshoITsEbsdd1Obu/ccNPK9oPVhqh41X1bF9BrdN2OrjMs0lZqLxhQlm6thgP4O2EpiuY85aFNoRWVMV5h2RAwJFKA7l88FxWTvTE2ed2RJY1ONY/7EZ3TH6IBlz0Cif5QGG/oScbbQuqdZD1e6yKHjz8K3+2mlae7AYz5mHqMHP65hi+I1bBlm2GNlIYHuUUDvPVA9aPU3pIXOwDVtqBeUuV2tKgFY7EJyHS6MwaiQp7ie58o0QQ3kNnwCYZ3RagnkuKhkH+KBH2yQFrbkXOLrysUKo4iQMYzeLHK39VFNQdludFuVV1Oco/R5ShNMIJXNNJ2NMWfXe496E6mm7zKoMFlmGqxAnPDgrFYEzZIu4YCimUS3SB4XV54mmF2K1/QdxEMr+PKJcFsc2jTRsnUpNTaKLe18570rzBI2oZugB8zfWNodSvPbXeaQ/fq3H4gE2MDkVdGU8juCHlVRvXqiQ7hz5qTdx7t8SCyrEb+KU+nmDCWoHMoNRlQ4Y014+pyqYo0/DDXFJ1yea3qq/+Kudzpl0kBztyhpYrW/0YuQxdY+Sxhu5j39eEoCy+nL9Bf2Kfs0f+wN44I7iLK5V+tuSQaFpCaHkp/2zmoLZ45Pa8HtPl+cr5QAkHgRTBtKWYIpDqCiz2VY+Fh8TBIprIPQvVu/3DHHJqOVQ3AAWJubE1L0yezntH6h0o/MBdeqFaOHdYhJG3esq32jllaj6tI5QbtjpmUMU2U4upPf4XukHwc2A63ZdZ7v89xPSy8Q+mresFzXYwsTLE5HWyQJsd1iu9seK30X9xov8BlTnzlqA6F782+l0KJn4E1zjFRxLRiWJtYUrsN/B+huDJHrPdp2Yd1uY7CpFfd1cXMv7NfEToszKodarLWEsLVQxV3Xwfbt/kF5ymfedWS3j+HEOv6v58fy42eNcqBEyOWGqWXkjB76QE/T3CvRJSwtyKoiubIe/IWGe/oocnfjj7eWJxiLUhiWc9GYG4/SWzf0QP+cN66Cph/ZVafjJtGOG3t06stPpyJNrwFMzA5C3q3aS0GxZayW5WUS36+XvtGqdHyBt/51EnPN0hITl/h8au489SgthPhh93C4xIkIvHwCg6nJw2rDWGMXP7kdcZTnYevRkVhDepS+mdim5uEB6ctExQiEeK2mCeYF/FD5VFsbDtbwAi8xMzmKv7rDesdsX3sWh0eeC5KI2X9lv505R5oWt8Lzi7yl0IZe4qIkfdao9Vykw7xggrseLvRrTFc0km0qWUleAlbE/QyUx6Zcv7SZlgc+prSpYs4X8YufL22sL3teBaZo+9e4vll3fW9+C3dKLN+KO5lZqMKkSuOiLYYJhnYJzxqFvI6pnyCY15WKMUSjCIYypLvLFi/YEF7bQzOKSj411Wa6fVB3MZw57S2AX9STlTl+b7s/1kopPdyh+UVZ5RIPqyUf1jAbnrOmFbf/X9BcqpwH89DS7FAzlODlgGkK9D5Y1+61APBuUVLl38wHlSOda74qKQAAEABJREFUaALRMBXWyHK/JIuhSFBfF/rnpukqbTt/RFgqUQ+ije5f3ddTqnt8nl2/u74rM7JVehr2FJLNVXYNP1ZqVC+kHnS6KTbxkc7j+awaAV6cy6CfBQrNnaq9oMS1PvBNLO/QMoK0rKX1fJS3V9GTdiVciRmUJPOZa6nFjjchu2n7hIqZDepMu4MzVvBXz62H9Lob8AVTnfh1QxEvMYx/lTt/xazC/9VT1ou3izqaDVdKJ2Vvk+EAWlHKx6TqPl3asCFCSrxtUlx9cTVs/Lxto6cU13TtETtTpRMtG2+bkCxKLxywhKBXxI4L72tGeXKKg6HxYETwPCfu77rFYCq7ZJ897z90sJTp5IzTYiDrXZo/jUylus6uZ5uQqS18WG5i++m5Pc+67Tpvv3V6GMJUyOcS5XjQt+UTT+/iOjZQE0c74FCn4BUZMeU7hbCfja9LB7IA058ya6gcSqJsaOVQDGl11DIDmnHKlFyG8j0jEr+nFbIGazKLLk4FUnE/nlovK4hmPfX93v+p4faXEExRw2OGxk/ND7BDtK+SXNVGvLhhegGBNghKm7L0WDUF9kad4e/Yby2ZhL4/vVv9rB+IoN35fuX/TNzut6+nFd4GPoYPR3A4M51VSJVNXjzLNMSmV9Fe+qlyILCil6fPcZ0EW+yHfha9B5bvHIcy4AI5c4DyFdxbTCrDJkD5O+eGGogy2Cr6HseYTKFeBM1nrIdrRIHphkfN3KDM1FFRAzh7tJ+V/yegPPWn1jW+Z4gnXZ1QtBgZoBZi7po9khENMDZWae1AaDqncPDKuW1mkA9P6vIFaDCX2jdDFRPHrsfX/NB+zH/G/YilI1bTXmGW6CuRKmo7T2m1UNQgZ68Tdrqe7PPgfHHfPG215tlzLpq5wzWwW2RtNMRebtUtGbqkIFQ1YmWm8jqzJBsF6Xkwv2AKv+aMTkEvV3wqEfnWtuEL0+kQ3m0ei+yZ+0Emh07bV38WttykhfRVLcY1oUIJ0Y60SCsjB8G5YmJdoVT4vsK3ayDzeP5a/facMSLsNYtHKJklgH0Wc4vIhBltt3++Zjem856jzjJuZgx9vfXvjv/7zpWHChV2B2+9E6PPIq6/6VgWq1yqXfet2xmpigR4uV9m6wXzpX+1K/G3ZjQdPrjko0fSKa1X2TknkQMF18ZxKZMwzACnPWcpXEjq65wplU69PaN9XH9rtYz5SJRLmZ+ScG6NmkzT58dQUEvQUBVIxN7cMRzlo9ulKkk9fSzaZcHQJsV9RtJ9mNOAqOoLvZ+Z4nF+zW+RZhvhJEFxTBYECXfNPe/ezNL3ESlTk798dBwo/aAqM2eO2QmXD+yHndXpmB+fy2HkeeduAYF27J7s5gMcuE65OTs4bAVFq3fl4kBRq2dDOb/ZR2iql1IvbO5iRx1ZwF5CeptFrLFdMCXVZOehMju326Efrsd43FrRwbo9z5QGNVCoojR1zO7dZ7eWRKgYyGHfXo8/qWSZUngYDzb44d7jbz+awgTps5xLLR3vf3XlJpRiDlw8buCCGZjD51g+ipl2TMzvu/YxH05Y06oK2vdY31jF9RKznvGH0wSowMwLPjDXJxksv0cv0OiAkKKr+ur61Ex2qRMcyaaZNnpCoZhGs1qcdblKDIoiPojAh9zdq+Fwt5UMQSngTer0031evo41UFqjF/sDiJGUuPUG7WgUayRJvVMGhLYMt+OYNSU+zngbOIpAlKUSbsaQdxeX1FLZ1jO6vjS+EyltDW7DHRDp8REwJ60fq9u7Ro1tHGAQ+q0MVoq43niuKJNRc5blrUY44jVSD4lb/ZWVxDM8xZOjP2mtCigPW3t5bJjyY7f7YXS13bVvbq3y9yfO61GV69tU+RRhZdLxPF4pbkRGxktHcrJ98praCms0hseY8HKxwB4q3XWsHkwWNKaRtigEilEnykTTxIjAL61oKyw8itevLe2rzY/X2A370md8iwXJA4I0Lt8cvP7w0upjHBuFbdqwLCm838tvLK0NrhWurmXA6K+ZCJDlPsoGQTZZAILcTqO8CU/NVEm8MID0wJIHa+3b1c0GqTmmsupXoBrxSgurzcGR2bd9R2E9ffFx+7adZwBYIDYhY7U9Ly91AgEfGM+ys7LejelpqLdMhSeQNZoqNViLhhBYhaHTSgDAoqpHiJ5E69IUoViECnoZGG7cNO9h2vQYG0GEPDYxKf5hRHs9guhsrsGMGPuniWYLLiuyhbdY1KGTR9Z2pOEQxTDbH8YVe60PsaZxXsC33lv/dP48uRQQCDpazSYeITGYlYRgLLexMLgkZOdEJd4SS4i/aHE5c2YElu/8jRAFOlNntAfoL0dVidZ+18GB1XSJhTZKxVx0zWzRVM0hQEb1Vcn3GLW7KMaQDdSJvBdUjipKqVO2HIlXoHoylmYQHM/xN5EKXjV86nmmzkxSZdvHIYJha+g8VECqQxS0yiFUf4dKqGK0ajyPzouIE6uX97s4DDUVHhTKJ/fRXmzmGrPaT18l9xzX0YzDXSOfTuUhjMYBmEqoJvd4ptXDKtnyMSifCrAVi5dWgHOZwe0JKv+kygVC7nTZEE3GMlR31cw30Q/4JyvJ8KFVyJzd8LsjJzRFzpk9D3FsxkiKVHtkDQPViAvHKvv0aPc2KNn1UzYtTAONmYO+W8e8FBiWUXa047+1MZQG/Qr1O1queZERyUnMU5ZffRCoL+wA4y3ska0n3K4lXZVAnsDDv1rHOojJmefBc1frBfmpF4NjKKGr47ie2OdwXkrE3FhnsPwzuGauKk9VWo8dQ1lT88ghG7IjmtzoHJ9iNmQm0Eso87+hNkV2fCcg38MuSWE0yNm6G9dD5O0I1BCidQwScv6O2GrIT7cCJOHqHx+2C0I3ORaYl2rTqOE3J1Mh3rAWCnMMC6vbmWMjhT7tWZyGtzkFtzk3FNU7dOrVMg+9ZLoHA85QynqVrKkoeKGS08IhoUeZ7GGeWd+8JNtuK8QR2VRh+bhJ3/pJR7bl+JgJr4mxAJfJOtu93bgxNnfzGWuFxR1wZeS9SyndZ8ZjnNDANtNs/9rRXbf2sW9LdZRXz4N6VJtogtbOs6kFqilOQf0Sf29MB1rwBUQQV4zbqORK4pglbfEMb9osn2W9zK0v1Hr3iZU1f9IlWxlMIqGuTDs0LQ1ajAFwEIUt8y785oI2cx8dJU13tYGwNb5BwQv2EqtHp1teTVe7q27NkaoNaTVxuFKbmsQMWHwTKjpbEU9PFjLRDRuGLX4+sD2z+7BKudLM1DwIF+ikcgDkSP8ncTeew5NspbWnF2abucsWS0AHGJUGjPr9JHTzdZfeGxoSxTp5vYxk1SrVWiq3jnfT4hCe4COmukbNutRAunVaBGE6u56XT4w7te/VvoTKOthdCqdmBVeYS+EtS6bGo5r7hfquyFVKhiPe+o3I35l0jMCONHwIGMJd+2FDq+i7bN64r8EV4Ys9U6XGqHFyYSqJzKlWzHZPDElHSKFEt1WabBvf60Mx3VMn+0iMxh9AneX68spRHDp8vGs9vTN90rw6enjwMvC0dZNQ+z+FKo4iXsbLlDPf1Po2xHDMznsHoxHP5KlKoZ3ZfNtRW2r7WPNVglUeb7LthGV4CiRmpg1wxTaAlfpJK+9UqYMzisjRGx0jPw0tajvL+5QooenEiojlvua9xkfmSjlv95r19mCUW6417sJqfaB+rIjYoPzLsh7AYZji4sJE6aQEZUuCakLNy2La3xlPshIoalnHkY7lwiccgYSgqAWsdPH/6JaxPuQH4wP28ak1tk6oNfAUh5jVZOvylFYYDRgbPQC733+hAqiyLvDnmX+no4otvznR9wd51fwjllv7EsG3j3R7NH0sUOaakYXmjJgTRb0kL7qmwCi3g6QQKFjReO6Tv8WnMe/NudJXwMJEWKyAOo6VLilSHriC2TaXyzv08lR181KisWqJGJ0zuNbT9a5Mz7ReyAFshHWDS7WbL9uQHvpYih3FwQoxXdH5GYWk8SwSLbyh2T0zhYaAQDybfpiXbjCZfiYyN/Jio30aNk1lh/LyYfwzbfn+CXOr5otZUMWUmHuwW4oX+3HrjoIhctzk1FLO+Db/8Wv46d6iq6Bg4CV2jkQ5MBSrhGa9RHFinmDCn5FEvWfIXvAL+6q92jwomZoJ5w/yZ86/fpYyRyv7eVSPbQe8B1rgvnpPcakcHuvykQCnOGf6hoa5RDyqAH3CMWs6Bqkir8eUriDCa3kvVw4XQv2hiFT0vJH3KLiO7a745sJc07vbG6CNUb/b6Rsu4bfqeVDj2fJqj/kjFkaxtC8j7QxS5Iy489H6YBQpw4IgAWa2DXto2TxSWecSQjBbaojrMvchkK34+oWx2ZKqToHlXh611m3rEMNDPsKXWA+uTHa1HMUCF0ck1EZ+vHSiQ/412u277P3POBFq+LN8bL+rwLOqbJRT6c+th9NAmdvi9cEncnXZoR/1ARMvCsdGYT5ADuD0q9dyGqHNYxb4nThjSvtUoC4IATVRMHzGZF0Rvmm9jPQ9uCg7ON1HvjXPBzYOBT8f5wVfCP8D1C5FPiXTAIrJLCsN5rhLGPUbfHsrbt2mbYfJcNA2fTfVK+NW+5VzGXhG68X+vflIPDY71MbVGTXUT8imVFsFhZEyP1R1RioZKn47bVUumxSHkVN6SNYlKfQe7dReUkrxGiRH32UXzNu0PfERHxkxK/hJRA3ewDFqQtrSN5WHY6jVWEnP109WZqvlaaW/20a0iHHJZGp49ygJlFQBysmYejOtwo647i6pgtDInbY7BGhgGtmjwNTn7CFfAGobcL0HBKH96ZVjTWCm+8/6DySpppTRsKQa88+prY8ORHe0mUZDxYE0KlukMVGYx6UVulMxu2OabCMDIqdHD3A6b+sHMyjZl53ZI4pit++BA1joGAjpES22C0vdEo/wvfTdY1pFvLit18v2sOE3VRc+zlKCeFh3qEhmWMvFhIjvU1VTcrh8kZKY8ZpRN1q4TV1zWol+Y1N5YlNxkjdHjbpwSluAtDkxxyujxZdKx0sTFUjznMlHzGdjyrssGp/uSEASUZ0MJCJWjrfsl6tdLNZLy3B0sRW2rtEIs2mUUm0ZvgyfOvsp66EH9DBiT1/TwRbp3Sq9nUS/VKBhfTse87Y3/yzwDE/jae391TTSOwOKK4EZqi+vBVbpXizS/E3Ux+Yf+5IxTpFquO3D1dw2+2WunLdJFtPEKOCe980YR9P1y6DVONbacIWjvFVblqQlNAZJ0uX8KZRk2xSZ7J4zYkETmn3cNIDV8Y54ZgTiGi0ZX9gREBXvovlgOddHHF0s1xXv2igjEeaX0mwTmOljOC6cZUnrGF7NS7OHALEIPUaMsW65LPDZdvZuOSh0CkIl0jL3qShYiq4pnLJ6yXQ0OC/xxlmX6TuG5UmdmjeSObLR+rS0Fs2eMQV9x9x+Bg+6NZ+CTLYuFbKFik6FJvvQazROf2lmsV2xmwl3th6qPmOLiH8AABAASURBVIVn+OBP2GLmce+OrpXGkooGoptjWVp0kBPSpsu5HqGofUF2ajoNHSjOc577nKcmSxaVUslPJSa/K2eVZK14B+IUARm+7tnfoVQvMjDVg2NqjpCJahY+6audMgAs5OYJnkQfqMFhclRx1W9s4tAgGjXtZJQkXFDilNUxGwBWdOXqRI06V927KwTEV4XLsKKpOTfOTXztalwG26Du1t3OvJw/44DWypWtFmFroGRcoywyv5FfzJNyBPnBgnwqx9YUoT3n8Cm2u694+5rR8LaqXYyyYFj4+EyXZOYgTWuwW+rY0DcRs5LOoLtmehelduTh3JolhuTtIHPuVDGoyuJqeoGSikgVL1yjZaXXn8BbcgYqy37TZOwq/LSSjCnxJ0WRb+ijAF+cE3OYb/0dGxEIl9l7WNKRhUkjc6C1xW9MR9rqJfTL+7L8QJv3VHuAqvC4xAXQ6ObOgRKZWsW70Rj10Dsmg4A7HClrgyr4aFPBH9sLu+d6cB0EknLC8F6uOUM1eXUCVQFG9kFqufm9cC8bwb7RhbOl1YYRmrocEVxeixn2esmN4+dtVfZCpByrFhI6u+oKrM0Z8VasBvSWaBL1ukoVd/JWDzqtaDnJOZRKR/VYh9Dqo8ZSNMnovDalkCrmcQqEpAbFbvONfFbPblPCbHapH6+2nwwfyhH0KNO0iMxKX0Of79S38CS9PWiuwEUfEd6UAvyYMoOyKtfqf6tGbYaiMlbEz8KOgc3t/V2tuRu4Uql9YIczL+xXQOlsB6jBP8Gframbur/R1HGa5EsfwWoCbWJ3Q1BsQTYTItjH9eGHfr3S8i9QU8QBNULwUiyfydT+odUMJGQ2D/bN/TjRvR5fyuVhJNa5YhZpQD3AL6kHt2Wupi3XqFIAUs4YOpgaSO55V54LbUx0zswRIP/svkKyhBHjZtHu2t49uh14w1ezuO582wnPUDnVr9FkItib9+ITfAMiDPLBD8cp4y3fSjf2Hygh2CjMvaZjaBNd8eR6mmWY1fg8vQ+90eQ51o/yZVpQEI/h+NTMgT0izY7jWTqnApAgyMDYk/b8WSw9Sz5A10rzx9zh+4Hd71xqYLTJoj/wgSmKF6bW/7jTKw3l5ZYSn5dKg8qfDlgDX+ajcjVyxb5ccUFgc2j3CvVRNqwG6sU+DFl5TL2pfonaPhFQS+Df4oKitVC7wQBFFE8udVh9UiiOwYJ5r8gQENEVo4+D0vboqEcopaDIfCazJaBRcencte2zJhRApNLiuJ3tm2ab/TO9lJJ9ck1dIxGG0XTqQqsjY3nbOh1d7O6qu+rx5oW149Nqps7pR7VT97QSgVE/7RUy/tEYqWGi01Ut/ub0mSo/y57FIWcsRYkZvHrfeenTAqSz9f2uXw3u0hiCRtcMQ6mSvotHRNnSJaW34BBMMUzcXL+2iwLNHp+218cc+jmdWjuVhVeDXbLTQbwZmG51ooaGar3ft0AlWRNZ1jpNQt9QcKVbkW0mmT+TxWNEd5x7Z23C44062nfOVZoOCSZqmbpO2ZVYas7VQvFbo00nn4mTHD8T35broF0Rl/sGDKcnFdSIK3RI93zanwPd7vrNT1E3Eh+czQlfn8Xrmnj/6dLqs+rw8Tx2FMBV9ElFPLKmZKc9v/ErumMpfFhNn0ytw8AN5Im79tB5iZ3Vbj1aA7a3uxmVBD5dX1R52WrsgQTD95WcAyatW8TMeJehcmVKMLU+s6UZRfJ2XOAAX2jp8yTGQFen3LOstKoHaywF41n+djOVezLHyg1/r+nOKcRwB2NRJtBnZlJpgqezP71EyNbUynPy7j5gMYqqnfWixKh+TZMVqUsLgl+PmXzevtrcjJvCXjnnkRgWn20CpRX/dd8Zc1YeKofta+JS5k/hfKN0Sfz1mrRMFlHy6wBBc7rUTFKJCq0t1IP24uW8YN4KktNqqKp4al24bDT0tJYqVtNilhOCG8tjaS1ZOX7PpLd2ASuWDdLqUTCo7orsfebKDD4yVDQoZNH6gSjqdSxayT6MHDZadIi7c9aRiV/G13Yqbb5/7G0XHhkyetc6vaAMgIbhtn4VEnlMy64lPiY92t7k5hIx6/i+tUHSVjAHnLXT1kM8ABubuLTGPBTGHsUXBLxTNtdCyMNUz6OeNiIBNZwgV/AK+xiWG80BKltIUEVrrTEY8zH749t9KvLG/Z3npIi+Fj/mOwKhlqM9gY2yWfssf9zOoB+p5NPbbCVwJrX6aeboaet5q4JVnQ/vB4+0KIlf98fzDUWEGBpVB31PpbTexzGXy6VQHuALNdQHJlrGLwrDzKfr4vg809WKxpSltcYdwAzzWH0D+YLTnlOdp8NGD9OEr/zcWV/dZZSaSVWMZviMSgGv3Gnip5QXl66FANOvE1nWOi4keHTxTD2nTuW0AhaNSkfsZy7dm82DufFkp4qe29UTjLZKJYUqnMzzTrA/24p9viN+1mhz2J/3i8N84r5X1hoG1JWR6kh1vuc8U21E/Fv14dfyF+pDb9vsne1fFUSJTQpT5mRLGFSpUIJZtm1do03vLBWRCheX++XzExLnXZlalL6H2aYsby4hbjRTvZE9dkQsKTRGLCYqRUHRaVuyUwCpzU6/MbVp8NyGMrao3HRFESZVbd4uGWtJ0pXhtGea+CUQZhGxF9b1UPs1aUTR51w1qznAd1JHmfK7kfRbk9/EvEpnV5Q6iAJW53rKzzJcVGWpMtqLq0YDghMxoSpM/+b1IMaUordO2T7gUDl5357Joy4y48oabLilvOKnc+GJvqH2KTbCzPRLH07wENOwbr2FUnbmHAEtXLk/x0QKskpLiAOmKcqpPrNNMW96LLoFsbr9l/nLO+PWQBpEkSbgNEaBwwRVrfoPW78UWMXdcVq/ZxYUBYro1on+nP45UofUzWioDzlqQS/mTFvTQSMIHfRWrKJ+PaHmZWgn3os0DGaG4l8UtGNsaqiWpBY1MHRI9v0kFPQpJGQVJI0BEvaceZ6nnlJpMBfHbY6n2siz+Ltx70HTHniCjaCazVyJ9WAeq9KMNZjS+ntOPQoX1iVlOieaXqKO70y5lkbkMeSiHUqRMYwuRmAZb8x482TAR/ocgVdkeFOTUDRnabw46K6MVHEg1DTN3LjBK8z1MaQiCxyLNgzE63zA4AIdAei0OKIiMQhE+oxiE9xGj4FIM53qQRe6UcqW7PioBcEFyTRKYN9hNAi17rlZ2jYM5LEBjeaBHxjf95NAplv6ahLMy49wyK8mdI4+7YmQaR7IaYMcu07v515zuxVMp3RSJA1b9y1HQGqJMkrSA7cJKzV/n3eoj4KV755K9G1dC7Y6rtnxMjCgaqfVx2NvuG68mAQG3rWRTnBeOD5lcxNpDvx4/TNZh6zqIMzGAbwhCV1/kPs8aqHoO/CNeiDydEVDLkApUEKwE0ocp4AKNg2QN6WLNsYZ2ktUG7XQKEbz4N62leb3nOGMYKJRM6UwJHX+uQHUkuviY9OnNey1vMZM8fZfut9JwFneK2YWGzJRXrUM4wPgmKZLClAq4WVSKWfzcTuzMP/MfmAb6rtSeh08x7K1zVRDaiiPwrCBC6E92+bPB/8+FdEKaiAHF1Q05XosepCovL4VieYbEKOv1Z91whROVIbQAafZ+RKERYaxtyuAqrLnZhZa10YWylo/AV8VKDtSiHLWxgQmMSSvUaDNXb5rR8oFWx0Fu7HQebJSHeA8D6bH6YQDYsmd5evWt0yw35zsLojb0V0LBeBlTlQJzMhEdUufEaLsK/Q36jtzJmqMFNiuwXn4MFPEY2yE/Lb12MUTvI3H5F1BjIMK6TY8DS184wandGJtRZz3atgKLD+X/brdSoFwhWt7Mp8N3zPraLqNefMzKYHyf2Qn+EzXy65MmG0KncIa+vBCW3/RZzRo16jpG0plPovfhePCDNpy0yK9wQBEghL1AmiBVWRLpDNPA1w9VnKmwJ3HSVccpKbUrg67feWsauuB8gRszGXKZM/mSKotqVTOq6+afKW5oUoOrxm5wwAweUqYqJrnnvuE+BmWB8l3g6ylMlALvc/GfaXWSTgoYtyewIlpKHGO2UaPGEZpwGCVrIs058TWdCtbCpevYynTC8b3dAfMGWu4ktb9oO4phbmDV3BJqX1EFAaCpTmbZZNS+fKKPbbYZgVvUALkdKC1PdveBCSbHK9XA2bH8ZCgb+dlpQov6uSa8LFOVk+PmEwlma9VTTMN7zGVnZ+2s6wUkBsUHzWR8BlBK8aEjfKVB8qHDUUAcyQJvJuAkMt9bKfWNTM1CBoH3jCB7Ck8JbFy5GrkwYKIheXLuDg2h3ow8yZH/DENYrdIiHqltvvb2alF4tqxbFm717Zz+ZF0L/t25JFSZFi5wftLwfKbw9OiK1iwKmpt0BbhSGHzmmYP1AcsFcsoEaRZeC+T0vB2p+EXK+GC4dbhAwnTb/BQFkcwz5475ngrLhrY7CRSBmZ7aute8Q5+JquPDJFr5urxBPEiEsL/TRUhFxWhxhsETBJ4M2vVrEMcWsXmtrUkD7WJQgPw4ZXD1qeZgTpKg+//L5TSpqniXYuBLkAjUsnPUUEYEDYLpYcLix1H8wMT23x5L3tGqnfpweUcUOoZ3SYAFwBShbpsBKihbMkhuPbaPx9+Pu3U/NZTDNVlo4hkHzvpGAuII6PajoWZC7wr0qf9oY5obN13VSh+ugZhNGf8jdLt/ZBTB4qp9YIrjyW0LbEpo5AjucsyFIiJNUuidHLIx1a5DFGkWRer8ZB1ObZrXVdnP/1UnrD+8XWZj9sxJucJu01FoXCbWgGpiGDC8Or5JWLF3msxIt0UzFQpESOGGZ9YHzdpG+fG71OViRTUd1rQWJiepJp+AFioH8w/jClsHQ8Ky7ig2BW8sqzpXvjLTVp8DQdVKF/4sflk7YQsovXr0Zspn4qnWH0Ud6hryNqCRDx+NMBEVAPuaWvMCAUGUaS8CJ1ZO9PnadQx5Jo0PuJJOlKgQuyAuvyeMX3QUUqw6wx7kNqHtBgrRufKE7/DW61tG6X06ekidcAbRJhtRdZllewklK3ZYZtmsARFqpmoMz8WFdC68FZOnRl1Fv42tKqCbqYWvW0UnZjtMUUnRpFrdfy4RhxYp0I497zPXnuBm5yZhI7GFNCyXbi771b4pl2+lrdHvJ3AE7sjanXrJk/4gg2Ny2eeMyOeNfuvICdbaRMU+5MkOL5G328aR/3+SVlGxSglm1Az9cN4DNKP8ooO8JLoRK2JIPzpn0NIHgTWIBoBqCntlUtSrenj88B+v/z+D82Q8u816+l1iq3gM3fdUfeWi5HElCdgGKygJj8K4XnZGGZaP5r8Onq2Hw8cnyPCll0fWxuSDGVGDIxMDcRdlz1vHGoYsNP7feo8EeTiKHKNCeqmQ9Ytv+zfRN1KFiT/uYTB08WJC7gSZlw3mZNjX6iVjz+4jknn3jXzCJ/OUk3jNgXaNr8mIbDd/aJfu7nnu7R7cuWGUlj+Mc14GF/YAAAQAElEQVSS1UZLZdiOPYLwe+akzvfGXErMyblTnncr7kuR86lVv87v2OThfdvztSd116+uFXAORUCNHv2PXt2t2B6KH8AAmtz3yJ1GTmFGE3xzNtIkuGTkXmfN8TIl6d2qPIBqqNJqrGWFZVPM9mxNNPPBtqXHhUsKgsbEpO4h/F679TR2/ev9wXX52pLST/rqRdsXY8EkhwNvtYj9/iP8Dj1ZXvMgSzkCB6ajz3fyP8wJvmJrh24kqGAV0Gn1UGSwF7AaIUkCGhFdG40ByqscJJbTwnv8fiqrLGh6ZOR39ZNP9jM90waYu0lNdff3mbvEGi9Ssca4t+hpq6fZrYql6deraUpn6xlq+OuUeH8mMqGTx5iUecmM/UDGOSVWhe5pf3QtJervFYVtZ4USJgJa4YCKywSJ7sn9nc/J1ec10ZagdsHVrYUd38I9r2X4mJxLmwde3FSzGHtXGZlSsp7BU6hDjR4VxI53U1HJ54sjZuZpdL4jWss2sGiL1ESexcfYvbw+7helqOFiXlEvD46vzxG6xeNyL/wGTRcl1bTR2yb17E9OWu8AGXiCXztjB0Hf1sp+5W+xtLFXTXgN1cb9SI6K2qEbnVZngR7fNSQCBotq7fCD3eVs+5A/xE/z1XDFM94cDQu01mIuM11o7lyhsmrsMXFBjM34TNEbCzCyadRobxP50bMMbjrlhKxsqs37U9RxBgZhSO9DS2l763OX/kyaw32F7yUhQdZHKWj86qdVRcIlhGXpKC8sxozuXJGiEUIUEmVazVX2L2E37ClITbFi7b2xdlFCvoxCj5wn8aRp/9SNPOiyaojRxFTPjJw8fH5VA298Mbxuk9+gOlYOUn/YkNPmnJ4EhgBVL75g23NpDkSHneEXOIN5f5TANt8tSLqJsoGyKE7NHldXh15c5IIDah7oW9TJuMbGmOqLPvkT+a9GcX6mTPO9MfT0fSKSPGRRhoTzSfXKLikO85lYva0WCEX8nb6NLcRGGKmzMNBa6GabhRrgZVxDKwBJbx2CsmbWm8kuMdXD7qtyT0k62lJEWTayxMizAqYoZVDlN8eicQG6H1JZo2AzMWhEQQtFVCsluEHhFuqBeK1yGzqahsYaCtE0MhV1vxOlaH+0ZfANOAm3Xniu89ij9FNDZ8QshET+5KIByp40NVVKNgixMGho8CLCIIxrdD95miWkdvqPgTnIgrbs0uS36v7e9/32goTioBrniuJOn2Ep8SsVSYuvccNu22UBfazkbeuxdagDouvNCP2C/yOvGlPS7ocpu8qpPaycLlkZkelO4RXD8vzcNfJtqFbO8eyZBaVQTJ/Hs5QZViOd+YGoZh27H/R5V2cvRS71Nd3cNEu4+lElwcc4agK+10TU4ttGRho6c47n/UEvV70JvQJWvkmmMSkCLej6rjmwiSXLcKGQTNLR1g6JvsTw8gyqvnKeAQQEEWZRsw8vNSCtip/VsUiF6+7L+sfCR1PlrAyVOLXKNv6EwfHg0riG1QkrWzaSrkVsW26mxEw4w10/SbmcWlMVpS2j8jwqDA/lWcLtEtladAWO+9d+zg5b7ZbmWqXn/U+HLdTEedb8F3V4BsqrYRZ6RsPSffxlmz2mBiOqH5gI0jivkYwG0wpNXM+6z/XU55ldbhWEkTYByeSoIESTiJtWi7aShFV7vfLxU85W1Ok8VsNposF+VG2hiO0q7qe8HuUAjlGKQRcIoTWamUN7inR/FBfMx2KGJ4XuFL6Yw2fmP587DhRjdydHuZyneCeeZ/q94CxVsZpgN8TEwChMSg1d92f86frJXPL+oTR+sS+pC1j6SQGQHrNPzU3/Yh2dV7bXGT66cvfOWkrXHmzUkgOKcDzgKTagpZD7DW1lrNqxLAJTqKcqD19SoLg8hgj0BV+9+d31/ZlneWHXrHHjDl9StRlbE6UM1swg18U3Cp22MrcIGqs6a46utk1qJaWA9xIo8AYfF9KWJekJZ9fxp3D3qbyZ97yruDVYWNVajbcIjnvtDO8Yc+3gbtdXMxowGpU93VPZTF3G1dgjfUCkmCFiJGjJYNA1ma38owIi9zqy6e/QQIm1uaFligODXppOqWItuIWJ3PlDa8q6LMkv1piu8Irju0C1k0IgqD33fKgu+0cQX2vFEROjdMA1S/jaJIKqS2kz0YtxV/sveYR+3O3pBn9HASNe1WrWKhXtBhYk0hud/Vp8/xqg/k16Y8rzWZ1GMexU8toLSIAtg2hXwVDy7BvzUFkEZUe5aUrwDHE5k3+WoBZ7Uj+eXkUj5KA1PKOePTnD01LimgGVDRLCP665SpXr96z8rrx95L5hD+OpLV3x+Lx5FtrW77PrWZkLPbJRsKZoIg0NlDKXYErt1ZlVErZWhhac0cgx6io4IIYKRefhnHjVusiFWWxF1tYpIDEj+Z+cp/vYPKbFhproxVAK8wyf48hhyyIkOAWpcrE6C9RDfxUSkmOKVaWyy+UXfnJNLgFXADSnnnXbkm7WqKbU66iIVuc+47U4G7Uil6fhsPRqtPkd8aWJEUt15M0K4A3f2j7NrWK1FPcn6oTyXGPTrrOJfqwe9zGDcbwaOo0JIhxmE5hd70Uju6ueVgPQG3JgLu6sYYMBxdHb+LfISrwPjHLacmwGeok69TS8A6VT/dK5ZcflxDFieOrB5EhWnDPTqbEzjjIYJPfaXTid5TXic1vFvO35HP62PbOaH6qI1YbEDiehWIT8S3xL0AkVz+NVrS47X7OCKY7KLlb7rU+LKstlVDxCpwnJuXWTaPNZa+s2cimpTbbNUE3hnKxKjTOAy2qvincxuYlnJps1zLCzWAt+IXat/Qo/laHts7fFh39Qn7Y6Jm/Vb4TpLT851sP2OHGJGb3t1QoRBCm+vQVaQlMkGrXM/Br+PB39aqK4jllC59WC3Z2hZF4UJ4/ZY3hHkWwpwvoVk+KW/keuEzY67T3Kze2fO36SLkqhZgMlxDo8lx1o49IQ9Mjeldf0NjNu68UzgVre1+x1fkJ3zRUuSjWp5v2BAnoqvXonL5Q23XtK0zJYNB6fE91rSm9YhelC3+HfHdoar9XidWbVgBbhm9zF3cvnWuLwQVdL2u62IC3pPSHRxuNzez3Us62jyc/Y07grUbdRzpoMmLPNMIMS9SRMhm8XpZhW1pGwsSXVWPOpOvjKDqdBpznMAvjMRCuPBJS0NmWm5n9lYVaUHOJV+3vaLCnZahwmrI1vsClBZJ2qKeN49uRIhQBuq/NWlZFLvigPuqYzSIeaSq/jfwxq+jfzGbSbwso1LcBQBDPasfJrVibqnNAasY9AxTVuUtZLFsYmounSaB522iQ8TZSpFfJuNgDFIo1esGhnGh5Mte903Qj/mHYGJZ/TRDGIAA0QsS5DrKhlsw3CLUQtBU7lnLvuO9/F9TaO4538LkmmDNsqGRwgXiB7HgVFma13mDhQV8izxsDVMjuzm2vZctXKoZ8leSgvCyrOs2R6jGDTHlnEgubAvU0JxiiRY66FLofe8q5mk15f4BDf//f0t+eeGg60Mdha1lEupu44aQ012eOldy9iIQLf4FuRuzpF4gzLqazudT5+X8KQ3W/PslYqH2dRwq9u4WRSE3yG+piAbNKH1v1SJk65aRLf+AhB2fQ7VCkU7WGpnhkl7EVolAYVWqrxg2hbwyhFrThMppvEHnVVhiAM3qWvlHK/wYJBlw0gPBD2esAjbII0ijZK25zNvrKszxk85iIRVA9a3kvTwFEDv1mF/72Yve9eZ94uMSb2H+MZTntLVZuqJotZugJprhQcUY4iBgIEq5mjtu8lZ2p4VJCI4DWvskNx7bHlGMUjC3WIDAGyOHbduGoFF8eQUGpHicOYtRzGQCUA5SBn0k3iiOF1ej9gHnM1pYkFZ5vkVzMjV59l16bqarrz9EyRDTlUf/yIignD0hl7c5WEF69WllX0o1LTfiVf4qLD7QPX5Tl4dxil9tkwxl46xrWLUFoDWkIeY3mLm5Xj97x3GuzjyL1BVxDreSU+QG2HVobQQppWvcpF6HRJDwodAkLFQYxyIIGsR7XkgNe8h04vCIIiHUyv5EpIHa2VJFVaLrztEl9BBiXn4XtqzGl8s9ZaAcPO2JwV7/8C/qf6p+bz9FUgVsFPi3FGDrsAyFy7SOmn+VVJjTpMCwLA4tByBZaHIwRKzf3GeSwf64fvbUDvZbH4KtApHSTt4ShMKxMkM7iF655kO6347oxEiWERGo4xaoBDn+X79OknK9+9yjEwuKH13ZhvzGKipYQVvj/hanio6aPCVayoPbPaMK9h7xbDGD8aoWCsLJ60PDH1YXxqxd3T943S6dbCOL5oMcKiGMakJnuDqLNxrz5LCZxU33yOlQp8Na6p8DIfa/WE21q/IN3qnW45Imy5mzkVJwaoxnn6qTpusz02r70dH87lEIH2M/zkfE79oEJ52phQ3BChzQWDrVQUnOJZfPaFj38++PnclxJtdfFEnzgV99jcsw/2+2jB0T3SMJqjKN6KqYEkBCZUa8vk2HlhaKNyeoCX7aXPSkTemGeb7qDsojHqnaUl/YZ9a95FdpKYHruPTcJj7MN0HME8qDQgvK/1fFZtbZtdPHRIO2oXs3viJFSZyLEKo6mf+JQhOyRUYR7IpMrO6pHvcbNYQYlRu+b5Nmrobjv0swvvR1HuVJIAvkckl7Y2d1jhf/F9wSJ8EWG3X7k2P7N+Mq7HxTzd3sMAozU9ez1P53Mms/MlUXagttxKy1IcgLQ8YJco0F5r6/0jKUJzJUo0+Jj9om/c7O9nnvDpjx3t39TNLdteXYm1GaIWz3oHnjqqB9yhhGB0iYDSFDVzex59gxXfC7/BR8zVdV2xf4IKfebN0I3nl/xWnO81/21ZRPdYQinAYPpJe/I6/TbN9Ra5miSknNaOyZbv61KRNU+jrFfY9YmIVbPm0E9ZJc24NEpZU6Ye5IMntXnJxD0oYKiA4EyQUO44y59L5QhgTf4oUlOm6DVVfNqOdqePtNur9WE3Ps+lBH2gQoN4vMP7vZkwuEyMdU3mPMNnVJgGeYrb9teuRwdWmnJBw/7pz3YYoPYm4KlNwimfEaqu6EPM2AJltoHOYNq6R9svQ8B1WpG0G67FFT54YzOq1Odj5jbD10i25mbN3bwPjzYwj825CFGECP1mGQnqRzht02TqIb9z/cpEqqQOLh+/7N0y7ntfiW2ztfIFKbeDNrhtPbe8+uW1xM6LwRWd8V0/PUl9im2Ml62AJtjERy7VcT6usqxplKK2NLEPGcZ4z1W6NR1L4IBHiHRFowj5Lk7S2rQxievpdT9z+h5o63LTHw9dzaHyXpTWap73X5V/y9M+iH9jWu2m5ht46UWcAwncG0H4NBW8Wo6aCfSynPVMnAkRv6gPmXc9BSmE6r8lJB66yB4jpUzHatq9rUJvApw3CHynE6L1gYAJnTGXl1kP3j9lKsfWQ5EbAjPTMJhrSF+XfT5y0WZPWT3Gg13tV/vZ4wAAEABJREFU1XjnDsX3tfqfPx+8ZZLPaA2VsF4TGbLE+Prnv73uS/gVomlLhPTjlhutpoZH7KX3s0y7iZdjRf4eeaBKEplvcDPKxJ/Lfj0oHIWSlt893x2KVoGo1SarvOmUAUFMLfGRa9KafeTMR1mJkrumeUc8YYlypGGKU/oUB6GrirPse/P+zlEubVbUigeePV5QC/IjHo01nuPdsMkQKgXdrdVTSltmyoEpKProLWZGvlMKECAEPpDauFKQ1lEpQ2ODvfuE8mWtUaxeLHU4Mg9a3luA2WqD/0JVp1BlVdQ86BtHF8WnQl5D2gXzQDq/zicg/rVueBpv9hNSTjiWoHGUyjP7d/Yz/gxSo7/Xzn/b/nlcBAK1o3QrbFu1GHbMJja4ljnb+uDkSZ52krI501XTbQXLyr7vOWoJdSBk/2f8GSBFSBl6jyQOa6DZGd/5fOnS42NIa2Ko5LymSvo9EtrhN6/DT++gqN41oV+rUzeP+WDaHO/SugrkWDw7zgftAjQQcNNLIEupe7pufU3yDBuOJXA7xx5m76IXiixvXMd5ZHRRz0zs85T1fa66rxnDO7WxWYsPkSrq79d+Z/7z/DK/ojPd8mJrbWqPWjZhDWOb6p6aDjTm/b5+pnqqauqZnm7VAOrbagJO4sPSESzEoSn7pVmtjzRKkm/4uGMeUYaXdhc68Xf8LHuuOZMtEYXYnAd22tET08IDZxmqzXgt3aZzP7mKa5Y3WX8Gp4Bs5uNVrxzgGHG+FQW8zZ/Fs+q2k/aFthVWa+3P1lZ18e3JO9sHlFct4rY8RCZVGdF19bn6u/yheKvU/fn5NrTY+EXTsSzA6D+NDBTV9OssOj5GV0+xe7nm37EUxryFnBqm8Aa8Jx5GGqV1WtgQNXolSxmbpQtm8iUuWrNypgcLCs+J9yvohuuUN0M1MLtsHfXPa/le4c4mHna2aVaO54tSCb7X/Ie6tYP5xXwtHoVY2hgpl3FLU7OmF2F0aaK3MygpyPWtpGJKMBHOWZSPYddASeNwV1FSt++i2oC41Ejhq3ZldPRBlwdHGV09l05PDlSGvRwRcpg+ts/FjElOppWaJAwg9DPML19m3u51WW+k65jjOrPGTAnQ0G4e6693sjFiCJcE8w+5tfNxaWAaxrXtc1ksiHPWiP0w7TBdTWi1RYm0QzUCQIaFq3g4tynSKbeKkZx7yrIiS44PPYet1qtDMaFDs+u8wuZgw3+K+zw8T1bmNE0vhxB48G0Sdcy+HfWsyXBhgIrHyLdRDV6pNwrWI+qNYxOAbcw/n+XCNIAOkRCkO9wHwZR0ddh+ZglYUvPVQliipmxEr0KRH3KM/UqQ8754MCn5GNKesdPdo1ROqdKId44LHhRFWXeMm9msa1YUXMZFiZnswg8iYUPCCSzDT/AKWigik6pfxaNyIFrq/ZDq4e6NzQn98pPQ8i1AUea5qH2d1kB4E2KLVqnS1fs327InYuik2kNEjR3XBqLT8AgB2nRqRLe5OrHHmL+fuGCBZSLgGL492sskfLCGep73qm5frdguxVq0rC5hrULDDHqIkXqUu8TCuexcONOKiAcoxRzwK4nW66l+IcaMMh4QUBNhNoBJBCJniUVrN8FIVYcyXj40jI0skFCWhCrtHkh8PuMV+wSf5vPEc/s8JlfsFHHMO+f5Cq/LghH0NowDLa2OKoVcWIvuPr5LK+gHaz+XfVWB1ZTmE6fRel/0++2ePGOCIVCk9VoTMSRwaNUBOJ+a3zN3ATBzsrXy3PruALSIff0s97BW5G2pWe2uQdoQEyx4y3gFx2a4rGLlBJKqZDf9aTfFAF1/zad7zdK9SPpVsc5roRSb0wSyRwZ7pFCz1iUumvClSQj6q08vkHCA0KvAxhBlz/FsTdN5hmuzfnkswXTzmPDc/jiUoLKBU+g5Ey0RZIwqp5gJ3ZTOEVS6I1a1aOg69YqmV3GV6t4I/pT9P+3/YAZparKkgQtzsS5/Ruw/3UCNkmA8Ye/uxlfPGlw8H7b05ZAlV69uUwb6DcHgl832uXyUJ7FaEXyH8yESkFpp/jHffXH8dm/lvmPDXGF9sgK1Lf8P6bzbVROFtEhugEO2GhGRPZBOMFuxtEOvMJppxBDjaXPi0W++EOljPntGv7P7oKlpi5qFwaixHM0cdGsu3ZJKBL95evS2zWCW6aNnKnDVi8qIY+RqxQhMNpJBurAPOsUfozTNiRj3HOJNs/ltK19XlA+5O0Bqwlgdy4z7fLov7+2t6/Ogdf889oTdhGzMlE0qYyNzXm4qfGEqqVEHyHGexXSLwHabVn+zk4o8OPLpdm/ei/n+eYwlxObBbnRhSr/ECZ/itwwKsQ2J0co27VM0/dUkDvVpZV5VjpnQVc3T0cNShcuQOekEpQzThbBm/R2j3tK7JywlJaliNIi9RJgrQRPvgh9/hhdEcbFSeO/QRLKM6gfZ1tgNdg0aTiOFl0x9HNHUVdZp8g9aOWus2/VOo4fG6+Lng1iKJECHDOGI7xm664xlj9Iq0VKgx++y/5QLtViZNAuEjaP74LlDa8Wk3MTMEr4IrqVQ/AkfM1I/cqayxBqX+pamywzQ4w+8qZeNIjAVS3IrE4Y0E2/76bfJNmRjUC1uPfvHlF0McWqq9WYATWqURqhx1pAtAnSOU8aEmJQ1DVMqYI+q72dJ/+umC3mjO6hB5vYXRC92eA6yZVJLCJlB5UqDIah+qyLNdW4WfOCVUUnObKquEQ7s6Z/Cz/SWP0rAdyNLQdl6u3crlplWNX28r+k4xMAgVd/GR29IGMveFj8u9DFanfVGRbbQFObW32HP6hfEJOc4leyYE30Ag+LZsZYeVH1CXaedVN0QpKVmElDbYKCi2h4OZHgKT01qLW160lUGkBOD8EkNLWhqXeuCZgMz1TTQ/Z39RSehAL4mS21WiOI69AMmIGIutxBGaCus5bRhRlekM3awsrQPEbezH/BNo2Dd5XezyDFFNALrGGObUGwRxKM06aMxqAo9MfdbGrlRCjLTykDTbJ29kvrQHU9b9gutbLpuqdPkDmCyi9OAg9tdKgl7o9QZHaJ/GLoyEbHGV2tJMYyGD6YHD3CMtUMF3UKDCjw/c19IpVo2nVgvGeeW+NWTAZS8uqXvtKuNonBNSZ2zdAmTZpi9UjriqXpQ9QISt0LjxjANoDITqZ/OxXFGYY3J2BsKwTeSBVOb9Gnz4TWzHllrNfmHGN232YyyPSbMXu/QbRLM6FVjY+y3uUEskBrYJVhGN5LKC6zg9V5BjK1yuWebjSR53GttJ/U9tVf51gC882bUXSu6qacF8xwM4dsuDbaFN0ox4DmuX3gdl4UqVWimoMjaMRfGVAiPUuiOz0aEZuZjWYqWA4kX3uaKHqbWo8MpVcteycoIs6CNXWXa9bD7nYDk1DUQLYRD5aL0qL2lrUfW0KvKG9EekP/21q5Nb/8xP88jKWv1mhLxa7jF60BNfJ551/QjgR1TSIX8oh5oAfpgYrWkpTagjDTFz4TpjPn2tUSHs2Byng2knGuxIGA66u311dEaFSoCDBTFuxCpFPGf4X6JVp3mcTxiyTaMVGfxJoMKEGiqjPyZdeQl6pm81d+VknvEY0zbmHNZ/eNZeENr8xyi9EcB2/fljWOcbQhr2OCFLB/W0jJ8PMo96bCm9eXYa9zOxClXDR7XPsDHhG5prtJOJym7NQAxpMQ4x/jI/t5uy4Ux77tSNfFmVxgLmm+iQNAKVFcpczkPpmWqsalyuFIH/ABP4O22IzrierDu4WNVJqS8Fj7BkBPrntxdn65VQFmj821XicCL0kQQ0j6ZH+oB02/jefxOP2UU4AR6QAOsVEZqrqprKEJ4w8zG9RSBE2nWi8rjXty7zB5MN5cuXcDCGOZNZquBPUIhufxXc8v6Jn7o2jKTxjcaUJLAs1E++CPu1WGBSamQqQAG/uASNnrt/f3Bfz94PAvfEsuvv/jH+PH4EbeNuu3u+WIObagaGuKt4BybMo4WP1oinhE93gxzt21rp07naCjVnWt19TnfKbHfbYKgLbta0TuLJJaZjQ0HMXTRptzzgTV1cBbh5ss3/ZO+75FuCQbNIXb8aPHQdIiTXsglwdkY4KuxO00WL4Pz5QQ5BT/Yd36yQvPVgmFVjTHCwvSI7dH3A2Qr1gGgWIp8K9mnYmC+6JewpfKq1Wk5L9Q/zMcQmBgLGS5Hc1RND1KllZJhnqmOhrpMiNZHe41T+gYvb9GBCirG+Jt0BtNyeuAJn142e/ONGArSqIZxpp+ZJ1Xwdv/s77+PnuhqjlzWrLZFCCf1nHWLXbVBwIGrhz0zrzYTo1O1+/H5Gs5wxkXfLl9Q9MqA497X8/ODWErT1X11cs5MPiztcEQKEtfBz8HnMX+cbQWjqAx/KcxjIs1th4dJMQQnvY53Gwyzh+uEnJ8WPXhawr+c60MS70uolAi9d8gqpRDzMbRJx9/5+T1XSV8c37BP43LTaiJtSwa32R0rbdtoTqGfNMyCbDAAUsLTw21bAftJ+vfFgTmch2KyezqVMTVTnGXHAvRRUBB/I0tqSbwOIwmLnLfyH9u1Y9YGN7Rh2B2uW77a467IGzf76/OYMtAKwto3oJjDT6yz6lkn623446QWVeVjzuXHfzKvp9QZFMP/e2V+7jeN3+ZXfbrhk6F0zho1O6eyZEOUiWxAgJm5JxdaZNDpTqnCFX184FdK10WYssd8hzdq3EgPizZoYUXr3O5SoNbbxwghucEXbIOUlAV8Wka8i5N/3KXPXD1LZ6g8AsOgaf0TtPISMfhAWVOw1tuw3HcwVOrij5zpu3TUwSieAapl+ak8rMcfrMNX0FUYEAtqJFFlj3JXM/nQU/IYnTanLY9l3F6B++J8jCpeo9y85uOhFgHgR56aGYYfrB/llpZk99h5RMJx6wo+hEKcAhrvS29yRFVQSDoTKvGzz1wHkT0UlvriuedpCi4LlNWZFk4hbZ6M7g8yfhz/9uh2zVyGE5Q3LIatVbZkXXrdRZbgQ4+loFoYdcVSlVjDfq3v1blSa7g2L/s7/mX3LonT1KtdnmokumW94qzr7M/Z9mh4NLwxN1/C020isIjXqQqdDXvbBEoc86Deav1EX/ZqkCm0wFsjTjogYzvz+j5qtjvjwofaQKj12WM2H2ixvWwua/d2hbaJH6sVOZgDEhSXtEf7HlNdS7x2d/9Oy7NYX1MUsaLnmvl3tEz3tPg050AvoxZUzj+rT9o6FnoOLxnBUjs77dGwRNiz8L3qKEkq0QXqecSnvjxfPVfKCP5EXLYto2ujVaLjlT4tfh35Hj4GLrg8+CzMRumnh37N1xSIEg3w75hEJ9tSBzjSi5CNuY0w+0gkqDMzqpSJlt6k99IkjkMsoKvO0TRKoYg14U1rqBBFySg9gEMFuAPbERfsalyjclo2F3Kfx0ppehqt56+/ZbrXas0ANHE4rGdOojCQfj+/+fPNz7HN3bbucHXbjny1ezS2lC5pBesAABAASURBVAWSIuexPt24wRuKUOEyhs7ki8e8leIxO+xBgwRC1+YdKirDzu47Hj3OVPjWbWVK7iMXhAHQeCKJ5hj9iqUqthlo2Ogxye7+TOqnGBDwGpnQpBZlStE99rQDK16njm4xMCOjMuEc8Wiaa6ZBNPGAaQzMaejJzgfnGCbMl61Fj7GVuL79OYfzQOUyXakqFEU74VZwYFz41Mxz8tYYMbA5qNKpGk0UcszwsUhBoJv+LNv6+UeS8CVXYQ8cmmw2hFiRz+fY6U3TLT5NFW5f2m0mtzKoINVuyTZgmTvDIoCwNlHeZWlhSxRS4XTotkby3swYBWFFETL+elrzcMpBTVep7lDj9MuUgQHQkA5WeUCORyiLWZjzRggriCRthjRQ40vK6jG0UYbZOfWypmimpZq6fRRBKVdnbIDRYgvNMTlfgyga6E7fxuhWsihX7af5TW2GCBo9NDpGLNB6WiEvVc7UaTRtWuGhuB6uPKasxdG0lJG0KmVcIKuJHBM4fieyLpOu3kVZC8nJzKenOaNGumqvdpd2wD32Kmvk9fXizQVbZIASG1RRTNjQ26Z2qw7haXNHV7weDSDRXqUlYPSzmyUWIHxeyqzHDS8hLdHb6RTjLyc5LUCCVOBZUWuVrRp1F+DNA2REIyC5PKXyMVu9dvvO9la75FUWGjMm41XCYQaT2xlYizZkAo8o16KgPG9MCGj4Y/bFi9RV8GBbHby6hpjromy4wwiQCkSGRYQ++BPra57d8ZQyWDIrosr9UE3fOJQt9MxLnWOzF3oDDk7LeOsRUg3ayFd3im/kLlVdT6Ef+oh6uIXpnYkJTQ3aZ4K1Tpu2DU/dWfKwwDBdlg0DvSBtYJZRkwUjZ6ZGWx/FbOym+MwahROkYXe88ul30nllPpbQLQIkpNJUH1Ciyr+mCCj3lC4Nx0tAAaTbWTwLj7M8hDyitC8xQofCzc6ce6o5poiaNhwo/8jTeQqnCCJkcp6po65eMpcxY13TT6vL9XRDV5TGGvEqWG5ikoqmUEMMFDpVIlnVZ/pu07Uexfh3tasx+hSwNYKqD4CNdO3i+9w4ihEKRwkySexNR/nBRoSvob/R3KjvnK9gN6o4FSifNAHnPaHE4NDnjbwE+YECR5NY03vSmW/oA4iKBg6k6BEHk++t/EI3cQwSIe9AK68uGrSX9vP1vNfIkCnOqeyaLKYUNOYulB9Td9MIK1kehQ3yf8SI6zm8u7+V2SWqhOCZdR5vNQdXr48JUVE/aM9hN6hDyNX+jKQu7umrkiWJpgFYm7HTfSiUfBuyzKzULhTmRYWovHTXIq5FLeAvUY62z+bPx/VsQkaq2UlpQ+/phCfkkSxeOV7p85iVm4OrqB3yAkTu8/7bEYTP5bFt6be1GUw9wBfn0+/hZuOmXZHgKhdKGKwFawpU2ywWvE1OyczoB/ylaekXnECZa7aXRY0i1z72muaDPngxu372PK1tQj7r6R+hfYn7FrTS4EU5RAFEiIDgmIES0nZ9Jmbg5V5h1ohMTAPdmxKUqPTWZUC6qpkkxIOqkDZwWRy0PumCSqXRrknSqcQ33QNtduZ+s730VcGt2NpbozTHynhbHI/mdvgyBb97lobX41Z0mY8ElapZsHfi+XKOSUhrUr9obS6lGCl9cqaVPO2UpvaJnxO7zA0MGozDKSsh5rYZWbMpJWtbh6Xx4O0zZxRjsUfTYKZfRjgV4nSzjtexKkLWoOi8uFqfSIizzub0MoivhGoeLUmWOhBRUJIcIzwuk+a9UP/OifvtKO9/A4brsVdnskUTTNkf2YYsPzUtbwBfRG1DeszeWE5rjQYxQ7QWq/AanFOj1amah1Y20OHV1iO9TQrNSG6+y8W+m0PqNSZ5lx1BZQ+7W2RQIdmGBA9DWlVQOcov6z87v+Ka6VYUo60+frerkpWNRQSFymwoeUpKosVBH1gKx2EV3txaPnh+a7TgTXscwffD81iedSe7evRMA58Tn/MxqKQtt36bKsLOw3PQ/ZY4CzX1tbz9qG7wvoiIdjasZWoAhVV5pbYewzGIMkhf7BNvMyk4dLZJsZn1pJ4CHZGj+ycx4IpHV6eh7XZIQLbTfAKzPSWh8j5kzmtk52gnK8cyxcHIs7tMtd5Ovsls16TIxVpyrZo78Gl60cmOqo0xD2Cf1jalH5FMCbOVxTyYMtkkzmK7mSDf7F88t3gzpQ0iN2ZU+isgEEqgmxofFYHY6Uo8ylXG0/uViIYYIZOFM4q+JZHV7W8DeTC4aeeaupQ1NfuGPYrYrTE6Rqj/sFlBzh1Jv4m758luJz7WG22nOv+OhqfZhFlmfNMUz5gy/STvw29bCat7OBhiIRUnM/PmXdexD3jpjveHJgPdrw2YcwEzXThCRJ0YqAmt50aqM57SKycbXQMqfJyx108TzS/20z81cSrP/c37GYwzFlxj3ikzkjjQ+VLfM6iMSo5OKS577pzzDKdW1NIGyyKfOfdzzveUBDlV8mMMpy+IMUwKglNQWTW6OXxYRyuz0gZCpgQ2NWnEQhlzRZoleGAKel7c4yoyl93pqFrWA5qRQ5l8WW0v8SJ/eqp1Pb2G6+zr3h8GrqhYlkKDjbdXYtkJU6YTMjP2gvhkX13bvproF65tO+S/zoNUxApCw6Cd617fuFIr122ubtoCLStKOsagKa5o+hnvcMQYVQOWEKfOY1nMiREBLw8vWy1Kmg2pwk96PcF7MaV2rROS9P7kZz2X6WHdI1Peb9jwUU2GOuPrxZEmjlsVycFq2pTcSkhqEGKSSlbJsj7heVQOLBnAJCv7TAnNcUuKPVE878nnnOfIoM9aI1FfBJTrPbxmDXZW9BO655pH1n2tFa6EDCBpfIGvq26goW4Mk8YL1lCpQYs0pb/9PKoHNb4PGdTWUDbtKjFWqJqW72kjTGo8VO97FF2DE+8hLO6Ls0e7Kdoh3hLs6WLfnG/Ys4Kk+mfYiDHFu+3suT3VbfvncHcQAzHfR4UzzzOZrwiDVDsi3Pn6luwBJrxHwI7GVYs0Zl2dl3FZm+6aYsxHBJrKCYJ605/FJKxHv45K1BzwJZpLvKiPnk3NQEDUaeYF6b8UH5Z4ElvyqMXqYq1JVNa55RhdGtfzL0dsAXbCeh1GUzW8XFgi7tFiqxaZb5GXZTNRcqpKkDj65WkAbD3npsgZYy/jdn09tiiTc9dkNzBioX0/iEb5KOn6LTWJRbE1lVM4tza0Nj0jw69ZTMhLkUNANmJe8TGll99l9/JyGhWlLmJ6hK7Qbw73OiEuW+1hs836Qf3XQrOeE12qqYxM/w6P9V54//e7WXNSC9iZAyaRaB2l9RMfpPfDu/yRH3/cv4Y750neHe97c4f9a7jEfqTc1Ib+cnexYlg+GxJIjUTeN87Jd1sgWbgy5HV4f/3kgq191bWU0m4f1erZ/ajYHIL0qJHXqrGs/rYUVrLJHmhxep5ufTSDBXr7E6rCAUB6UdCafNgq2bIzdht+bY7O/KIy5y8fHj2fksdzWe5g2I965LntfNdzr/5PteGGDYVzqa/IuAXNAUliajByOP6XXgn3+Z376XMPWmnqayGIzlXHGzxWqUQIrKGh8/NmRX73ua4Ji6c+5xXmDsRqTSXZzR69NJ6hat89+8kuxRXFvRtb9KAfziNU0JSwgvez8J/Pt5klLpybCJQh1+dcau84htFnBSBw+iJ2P6o6BftTiFpjWzVh/naC0cBmFWesjC0fgLM3Ml4C2L5sNOttn93zTJ94+uce3WJscBTarPpGH00G5YTO8YGXhX4Sr6aJYtFyjB7Owpw+PSrAOVfcUCQQANob5wCvXuaSHUu8VlwRUuflUED6p1tJNcnX68iNhdkaTk7rANvYysXV3EdvSYX8cBVgU2z9fQGapkVDpwOsYA50tOFoNUMc67JSdTem0JCeLiAoiOSr53qedzkYbZdbQg6ZZ5TKL0zUpdwMCArbNmodABb5E/lx0dz35D2Voqcvr4+V7akQEgEef1rBTGJGgRShGUMVvnqGyOnJXEftZIAR33TU1HvW5lGny8Vu/XWMXmgsHUOriwIuotMe+EFg/KpeLUc9ShnFh72iRPhcG3bZXC5z6lF31AOWnhvMNvXF+kHrznYm25VYNCE1PexnaiBzMy2ye8E8lkQx8nh1f9iLeOPuOCcEWKwXUyP25A9mTcxZ+egqZGxu2pfrnuv0ko44JjnIQ3vPBXtf6H3nugVvj7rf0Vfv25K7aUCfLpFzUmuwIAFpmX5b3B7pY8hAgjPh9nH7ga3vkdevjFocUh10TvbbqLL0Et5UMBJFn4vPP9zRz+m7cNa8G2qoGERahOmGZceQ7BIdmIaIEVO3Te4+qx8qh5BeRvrV+9HUsmdsanUqyXYbbqdC5y5ciThAnUbVGGvNKEQeoaszPc63YE3v5BFwikyM47l4fqxV15zUbKXyGUreONBsq4kG0aKrXyFcwwVgcDgwJec9iQcKdJ57Z/5w3IK2oS7e0eYVoBc6Tbum3soLXKdnIP5TgVltFYP5PF5dWibSn8JT943nK99cxEW1M2lEgelIUxTsEgKuo2RA1nIwwHrc9EYDVvcVeMUcYjRL3WFpAAS9z5S3rzGJX9GkRMKV27ng3pqkrnSAp+s+9tRu27k0se7KN+V8i9qGkBkxJC6U4Gml1e20VRokx1ixbC0E25mozJbE3soCyEI8481uP+1ay3oLU1f3dL1aDo3qwhk7UOJOpnzzmUWapstWwwYqNn+mEmmWFmJQI4WzjkLZFGm9DFTu3atZ99QXKMzK2YmI5PXQa9o6r3newWo4Ycyc56kjhUYN1colvrRlIe0KVFXOUYOZinmspLVn6jHtGFJZxHtKwaFHqZ9ZUxsH8fQ+voUVHPgoBIOH9OglidIBNDl0G5vST9nI6m/XA9WqJuY7pEzbEsmFEL1lqJVth53smsnq7IyNdfmYp+4c+lvQkIGJC3H65OQpwV6ckEaLJJaH2dYUEwihnQKtPTDBjlAaYWe8BJOMsav3VRrPUXZnoGimC0dN6vZ+X4KRVXKccj+Z0Pp85Gvcx1a+SVjTiuIAT9o5VHi6WFSRryzrlnGjIYL/jAuvztucpkJc3VIGJfwmexSNOJthCuPo7HTDjy3tE0FFAtfndMDFgmNMdLyeuG9dsjFqZ/SEsN7F2ywjsIMy2n260nuWZo5sS/nYuRd267ev1n4a0QD40kAzu1wi8sdYG7hAjXTFyjYOICN43wUok+5Y1ntKkuRwFlVNYicphTX7f8FuiXB9Far8COx8mAaR9DT6gEkZrVbQ3YMzqgfHaKwcLqdfhmtm57AUuaR45eM8zedUiwyl9DLF4eLB3u3Ssah3lR52Kquet0G02gt5TDVjc4YPVnGPm8rR2HJk2g8IMQksUcrjiKpP9c9ghfkiAkALO8/40VsCUuFN6TSlF9LcTChlnaIQ6f74pEIyyQddXoodcFKmLHufb5S4ORZ4V/QLrw/KSThsawJpSQS4AAAQAElEQVTKOc+WqSHEAQi+s9vqdjGR5ePRdL4MzpsjR0v5zWzooxvUiN2o94q5qAbZYu6P/mPn1bx2nReErCBcksU4FalmSzsB1RHpMSpNEwmYTihVxjChBGZGmlnb9oaxa5pGeLRHSYbecMBytrY5auKFpEuEjU/NZ3q5YrTRhLKNE9ob/HT9sC8pg1LESRffLVKe4hda4yUFPo5c5FqC5mLPRo0Br19iibE6V3NbrKW/5fYAp9BJJOdZrQYo3BR5RkOzR7LoFHNmbTo/1NlBDDfpo6iaa6gnBVvNUf0h+7kn3/94Ql/1aMHSQiFZxIhwuDV0q8B7tnQGhgE+510lf8EbNUiQiAW5r+Hd8Yz4kzd1s0vcGmGVIiDbZpTERWwwdBr/H0t/oiA3riMNo1hISbnX5rW/93+qe9p72a7ddtslkVhuyPOnVVkpiQQCgQBIZfWZIUojtVqoqBZ0pY66iwofJJTiRD4kl0QkS+hi3P9mARaEdDUjgMlGxMmajjUtYHcdby0bGncsTvP6MZclF0MOkEDOQVzTmAgTo9jawVUKy0hlbIKOVBpVJ1kVqekDm65Kx2gcmUTdeTZ9dn1O/pPdS9SRNqOPYdQYBp03WpDWPpADIyVFIjmQFRaohs6oaqV0KSYlS4ECK3MwKIWyQ6yNDUei5npKY+laMUxAEzOAN6Eo6sJUhFEV2OATXkm6YB/C7IwBjGQ7pEfckQICggj3jKYShapaSV/lnQOzplDntErEuh5CWlmVC4u4pAu5KA8F1VG2lJCqFiZ8rYiKYUXJKFwy8CbgcAoMiQJPBnRW2QpFUMyJP5f0wNbLncNJiSSIEk6YB0pJ1S6QlzrBPA/CE8m6O9JQSeJ0wXRHS7HAn9OkC4KL5mbmkWSE7HSihblHZCBOBObBlhiXFpSkKoLvskaSsbByYuLcgUwabCmSUCIkDDGDHYSeiblelmXqtgnGX5iraQYFwwIh76IMJ1RgV4akYsERwR5BaSWckzEgQsmRs6EaEm/8vGSPRWXeKJYGGgo8Oypq4YxCgnjTKXVh6cyRRtE1olqgxqQ7CGJGfgbS4roKSTGFAIXFtUI1qBVzcqwSHuLBCG/RtDFoYEylDGEH8BSlALA1H2hJNXgI4IXNwbQY4klMjyBjBABkqZyFEsUPtXtLaktBFyupYMq79+4SMSbsiND6ULNIRYN0QmiU+Pvo+uVICYh+UJpWowYMgBLQp6cxZQWkNZ9MhjEjzKCJ6DJjBS2ijO8iVDEI8qFVNxNnLbEpOQ05jFQxR1G8KDAJRulWFkYUFKkh5Ou0DsK3yuOAcbWTNvZeZ3SDxoRqM1YPah4duEhArAtRELGbhg0edX1YGtIF2YFhcGHOyVV0KAKHJUjNaqzL3uQ0pHCqJSqeVKhQRHJDTufI7MJdB8JcwSI9imyIxuhKzaphcW3MzqPp2Eud8URASlY8lEJgBjkw6+ZNsBumpIThEDGtnqN7dafGuUjMBG3XgXWril88ZuekRQcbBh14FDyU+ZikUdNH4sq8YZoSFCqR5sCrdtbc0CKMLTWTqqiilyQ6vCM4yMXYxFmKZ7U2RZ+YigrXTJShEm9wRtV67aHGgiMgSFlj+isQYBwCDRrNAjXSOSyymS4unbCogSTlQgQ6k5E2XKKqOQ5okJwK1oUVEkVkREnEykhEybk0Z8KpyFDWAao9CfsvYk7YI1hkEfwiFGwwuxRhGhnlyEpMSlKVCsprwPJgSGHBSHVMl0zIBcWP7oDYGUVsjkuphDrp1E0wBQCxUCfLmnF4JgTm0pKRuK6NsmnvA7piLwkKRWggKolA0Oeql4FkZPkbFBlLT3jlIOsZLcwN0x3nKVk4q5NYRkDssU5Ri9LXfVwUarDvARikmrUK2CKHGWyj5qVYk3DMFdCwujAaHVEj2CANGkFLLYmZRLDeW0AJCraqU2HCpDChBu/EQFtFcBRhYWPCFgbwGEGQdB/MGFxYSis8K4KGiiSAl4g4XQQ5aLUEnqWwxHnm7OuxIG9MZtqX6q1mEhGMpBALK0B44YbnI3SITuTMFCxeJ1P+G0hXMSpgdk2PJLGnBCCOdVKdRNf2wZSEXkEoY9yuaRPZBpJ3hjMiIwoG0ujEz1KMOFbA1BkbIO0FEUWqEwEK3AWUxELKa0prEMHzSICqHILh5blWfPGYo8S6ZyQqFKTJA3F1LR0igGNTEJiuYYVThGV9D0mwGBJcaVRRodRsNZ5F1q9FdF0HkjKJUvCDj4HfkkFMWZNrDH9T6izGTNE5u0dGTLZMMRfuLGgflp5gkcukw8RaeTXk7D4EgzqObNho9dpyEwgfuJVZ4QZfbosvwZ6VSRE3AEoWQjdB3knmLtY1HQZ7osTVRVkEB+i1Hh1+XTBDGWSFWuO+OAS/0lvBAZNLWhL0EIYsjSRKhZw4cQPvQ6RmkKMqvUpgiHjPjGSShBVWTIaXcCXDSdbex8XKwpyVqIbkMixN2trrRVTLkFpciRIm/hoRIiQsmAidBKQKmCRaUS1cuhSmmgu7eS/uFRvGpW3ThioyDLgr6AJ1aeoQUXUeKaeSAxzXxCKAjIQSxAJWKLkhFE/lHJlGZpTHUPpQO2TCGYDgyKIqyC+YJKNVXa+W9YqoD2S65pSNQTkrQTcMyDjx7CY9tDO6kSAgTdZIJVWZKEcKUYopaZBB/m/aKisOkvAheQqdQsaIylbEhFqmIwADzUQrtlRhKRBCsmQdggcDhQl9zcxoe1mcNBJjNbOmjlkEW1+FKIScqseQoSUxOGgJ6i7MXIJrs+yeBlWXUicCf0WcwtIdb2ZhBGpEKDPNill1LHXA04cBicAaNqyGFHzwIFSGIIVzGrJwliTBdDDBFaNYiSoODlY37TNTp8G5eMIwtSWyoaOEkgdlQClePDIDmURlUianAk3jWDKBkrJLNqHFo4clOYsX9cTaRwQFg7z0QnPJVbewG8Ap6DoDsuWGgo6CihJuXi1KrhZAtxub0UJsNZidI7iLmpJRWHdeEswxsdP638o6EaIk5dW+rM6T0VrIMSpGpom0MBMSh4GYYh2AuS5UGlSitILNZBS6EIkgMUJdZNZCMhUdh4IPmIqbRCq4z5xEONDHEtOIvA+tYZ89BOJsrWYbS2oZicfutRMy4QhgYB8khsIiq/EhBOqHiMIgakH6UFJwVthZV95SMTJG8g3ZyCCSlHmgrAXVU6CwJKUo3cZuhRpxd4J0OwshblZOpkxhFCZPnWlG3oT6uIm6sa7hsQoxsDcI/ouSgnFoEBvUo3gKXLaGboy5yFa6Y7aIUXq4JcZBYEokEqY4Vog5JoFxFB7rwFpUchUbKoATLywUMrjsSCrSLdxAYCNaOBaCYTBdhKkg0xRJa7gcLNgAQETKVQRWwNaSkFuiTUIkS4kQqaRT5shW2TJQPITo/kavyYVqMkclV3cmKonxg6QQvGgqBXotp0PUDVrrEl6JxjLUASurQtWpkJkwEw7lKMQlWQgXVxTrljoT4qyWExG+3ywIBvYQA6eyaArud1xpyl3ZjaKzArP8DSqEkd4swkORWgm7Bg4Kz4D2JUNQmNRJLcVd/pYjtUmzKgleLOtMYeXQ7MwILDZGk6u4EkZJGVNQNMiaQcrqyovC5l/7OBtM2IIj4RVWQ6qNJSBxDE60NKU1FwrJjagI5UlyIzZQL4pS8WSXQJoLg1rKYAcGVSKsFAUbE3VOo7lTa2xeGaAJ1Bl+iIUUZDM1yS7qaJa1esVuYanNsYRLsoI4SHWdIysSRKroaDnmmm54HUimrBuftCGQLMVZqUM46d17ZNOag1RmfHEPiZDUJnXpq84E7LprN4LvIWFHaw/t4BApo2QONB37qzihxD9qxXpJCh1aGRatyGluWkonwvYZ8bBUqgUQmG0VKjPXqlI4Szq0M5AMIMGFUxhqD3UOqo0qujbVcIF/OJkiN6yjMOKROdSYSJOEAxZX6TaTBVWVWYMEQiOmZKKp+IAEx0K9UydeCltNGJakTElDT+ZsRIuRd8IdeGyc+GhESDUpQl3pE2CCaS08DDRVHrngYyERIvizCYrDoqsLIA7aCpAp11KHzaYg85FAYGQ8EFeCIpi0UBUWTmYmkiAikERdNUopIiMHUw9eKJGfdSwNlIAjIlBywAVBDk7BeA0SlRwGchFyqiyqK6MpLLFOAiHKqVAoFcvSkzs4AHji9VYSXCL4TqUJtiPF18CCNcEWILlJR9tspCRV6sqcEWwXratZCSbllWz0fF71gkxNxhPXgQflqqlKRABDYIEACWB7xhx1dYLGoIqCCAYPiKAxJYsWHEApEQVrsq0pdBKBdNCMsQMg5D7xg8PQSUwpqwwqoziTcAzQUWUtgIiGOxCXFBycIoT81ZoKnH/zRtVpiBicygqfmGDcIXk815mKCyYR99SW+dyxvhHlWHyqpuxEaFFJnrSUYR4gMnYOUefBC5zwKMg8wqhkOBSTtiljZs2gMIJdsgjC2rWC6UrYjtFoiXWxLBVli71RZHdeZMoySBkQXPdi4EuMnWTOaO7UrQTSsO57szP/Kfqs0C7gWRL+QdvqqVBZioNyRInL2OQl0Nb08PjTY+koJqpUoNJFuCkHc6JL1GiF8ERnZQxZOzwPoqNjLlmnhmfApVbTFTmqipXqVOoIBkpTMlAuW8lJHZo27J0Ij15/iDy4OA3IWbAJ5otXYvW/Qso+GjZOYMGEPBN9BCAloxbXDXFVWh9FiLJh+4htCDMP+IEm8v8EUzIHN+oZS4STZUR0j56cVtjS3c3d0ZnZYzDaLDyRc21Y/xJ219iFsxabdBnEiBnBeRUugRUS9dW8GNoJUVAupO4VlcqaOixUnrvMBq+lCxp59r5QLEwAQhTB0dRNG2wqGzMSwhymaSP5KJ1Hj9Gl0BBck0r0v8M8uLl2QvIwo5M6a3XFCBYvEtA6VNENUeeCsxwEuQxUG0y7JLvKQmLGZSGAjwQdAfcSFCBZzQcPLJMtopNkqTwoDBtTB/Igs8xGtHDBJqpsNjSqC9GKP8rg2H/3iVOTMkAGF8rC7IV6pdU9Sc1BQ3GiUQphahUUFFVOYe+d+sLZhkJookyMlKUnRunAygRMrXOHdWHmws6Ezxjm2qwsvbRkrlSmMowjF+hRw5PAR4S6i0U4BNDTkRzhP0qLaEhJtPZdlV2FWxaOLD3qEtJgWq0P+G40ArKwIVpRI8QEPCroMOtosuAgQgMJC+8B/hLkRe3L1Kx2T4eEQcpIOsQAiJ61odCjMKb6Io76CuU1n2ArsZFDdJmYZ6Y9xNyKG/4YOBDRkI4GwoSM4DAjdxNaFOFV6xwsViJX9gaCNypZdK0VJRp6HX0YpDJB5wxijCSiRHBHrFBGZ8PX2USOHHF6dg/vxi00mYkaqp2XromkFEps+ErkGDSmVuW1tBr+hKMNZAGpAB/+vN+DGAFiPy0F9bMUp+pcvRQbMofuY7fBWUItETM2F444tUwq+C0sSlV1GyO2AODldgAAEABJREFUGtSYnNk1UpIoUYokzsgiZRKaKtwmEw/rkSVXaTLBj42WQ+eaCAQVpBZp6UGGIEhUgmM9d6ZQEqhN1Quvos0lHd9gtRI0kBbGUOoUqcElSRPPsEvHRkRmikZmnFHFanEp5KvieQQKDK2xKDce0jaEhJCqpA6rN6w2TXuiKPHghRpbMuYhfeAoSJItz9Y7U2oKB0imrAUds3M1q8ALAgBqKDIU2KzkFTromciTDQE2RqPBXcmoOhVZw4O2qGUErf0WomMaCHEDH/JbWq8D6aRlqlIEVLuEcwTnamEM1PoUVhd3t1n9mawHRAqflpSirGwkHdqDO2I41JAaWQPzGXlAqSq+B4x5RIxZs5X+h/qctiaUlWKAGRqZR5ahpI6IWY3VTakJ9UJOvB4shj2RIu0kGeQJb0IgySKbpzORZqEswYrxGBfEHISmjcIRMmC0shKhC5WZ6Dl9TkeT6YQr0iKbelv9FwEaKrWUirSBeRMzXnKtHZEYymrYqGP8MjLuC6F3hCJlhXMKr97RVgoSkVQIUROQpnlferRY6YG6VIi1VMNfS8AWBywLigzxLp4GF5UJ9VfxOZq5WTrhxenaI/Jv8vEpM5kJYk1IFUdPbsSWmoLtEW5mBr7eqJxFWSsMJ8Eu1OsRRMGSjDZT2JS9jDlMQILr7h6LZK8SwiBVDLKoEaPbQCKMjyXMKUNcyEsiflYm0RSshQrbTFAjj/gWyHylfiM5wpnpAJyl99o7LUQd5RlG4YwIEy8KgpgJL4CjCnkTkhxBKrVLaYTsA6QoReFWQX5QRUxKa8MUpiqVcqSl9MZuvWQnyoVqJ3FQxa4CeOJWbSnpqRXf0LVhARxPd0LszTuWhdVXdpamvMAk+WpAHB2wEvGQVsRoYIFMnAXQhZIMxIGXzqv2/0RZUrgkNq0F2TBi6sHRUsPqENBeELTnSYTFs28yNjqMOmBvzMwp1BLBN2GTCA+aLWbJGInXr6KrldGxn0HIlMpNZHZqC4eVkMEn7gN1I0K4U/omHdRzWkrXcZHSKYu2Oi08IUsNGuOelBYEDc7EliVDsqcBYgvKrFwULWNc/58hLKSon3TuSlEAgUegHopKaq7xeLAF1o6aaC61qkgtNJQYGNwVxhwYj0hqAR4LKFVOJUwFiEj4TQROXQE/a3qFVSFSclGlsejEpWQKQy6O9jUiHKjVCnLefeikgn+pQ67gtGC1wGBPQSQCY4DAnMme2TUN04cE6eyUizqi6iLoxxAmwARLX2dqCsEJIZHAUolL1IzqLhZB4onuBKui4KGwo6ypkNeadaRRpZCUFHEqvoYqRUJBulgnt0yYJpFV2hm5Jo4lST2KUY3EZPgk/FRjiXSMMBQe8cpAUgbh5TQ2lENQQGbkzpiYwjwiDS5MOG1ZLYo7sWdFBaR3hEjB7CqMFMBFMhELcWUdUwdXitK9LqnwSpIyMAJJ0tVSL5YFjJmGAYURAEs6WMUcQe9wtgbPiD60oKR6BA6DQxmCNJwM8IPSGJ+giMQf/xbjRlCPaJj2lo6eBT6EmRW/kSh0zCJUlaigLs2pObywO5IEjKQpSqoqSoiFwsMRagQT4maGLyYnIg6hlCyMGVRJiUaTCaGELdQ7u6WYTx5D4gWE4YIMgT4BNKb1X6syc+0Qv3ESdj+EcFI7gzAxqBRJAKnsWCAM2nPcwG0vPSEGISLAImhmYCqJwaGagJQDiWQlZ1x0CKbySrNkAmeKM63R4syJF42ZrHMkMRIpJDWQKEjGEIZHYzKWnhzIEy0ARQQ6CQzjTppnJ4JruAWjNqC23eCJOUlo0KgchR3ARFbKKMg9LeGdyAoznGK6ZWTgc2HII7N7wjRFIEAjRlcgtxBjWIqQ7pFp6+0ggIHlEsYIolcHm7iB4ZkhaGqVJDytrw5Zi4pyEFCAA85VA0zrK8F/VQfEWEMTilQLRE0pGQp9ABKJFlY1XudTlpoblZGxPvFfyili6eYdkrJCgQJg0MOtaAxk4s2tN3JnZ+RHSActUHNuO09Gg3txaJ6c1UuFCkUIURBioyBOlUxntGfzIgCfUXDOgWGIS7gy1RoVz6QsnYpxtcrL6E2QXVzdKBYlY0oHAUnMLJJSiJWYECs25z3Mw0RsKPCRzOKCW52ikxODKxY4xd8FCXkJJ+oW1sha8WXoQJcEnAx4kR1fHTSkC51g1Sw1YvOxOP50K/VvC49kD8QHwmAcSESEqXJWabqmO7XaqDmlYr1Q3hStCLWseYMoS0D9wCPawGkQpayH9bVmJaJ6SACUNzVPSN01Izy9JeKRWE1JZVGW9UcJBOOGBzs4UdVw8Z7drbGTZkkuXigxUEgqDSWhBmFT7Hsge09yJ2vS50IBpA4aKwUalpBKDmpDNraewXitYNVBgXhSjwQw5GFVI9LOlgoUQiSRFBhSjTRyjVkYHFnVlhi1hg05EQN1iwa4sIBpTIQGAF4wmzUJ4JnFlY08I7Bi81K0Axaxmms0DnOKHmSdeD2Cgzr2NTZmH6mxtBTkW1AbUH8AxkgVLHF1AkEOtogzowjWRqy+Xmk9JHHHrEYCE1JEykpMKA8SMBEdPXLMMmUdudb1JoCDygwr5DBWIhKXXCkHVoVkXbljhFEY0Fq35rDOBXYx8m80mggP6hUGPB4sBeCJFtFei+k6khhMZBqCZDDCSSBYYkVJKsCyHvigJJxlzWNoJ2miyIxDBiFCSswu7IwMdmMzAK7jqhRYVsIOygujHLtpGjN+xAky0lQWql5KF57dvRu5ZHJI99JJ3clNIiSZKYiok3jidyRaUMGnxNVgFhFeOab1BhFS6YJhlKmZhM8dNEKog9ooCTFyShCHUkQkAyoslCoq5JydoQTpUkNFmXW1xE5If6KRBKdK1IF5EB8SqTOlBYpio9JdYQ2tcNRSVDgyYT8AVwUuE4szFupK+fexDb7X1tENkaxOCMObEzqk9QAfoGPdr0DkqJcWuXSPNSYiocLJRPgVA2XVRtQdzpA/FJsIKSOaDAnnCJyR2HqQaRDuUkF0KD3KrsiQy5qDlMJaRBF0sicyibnKhmPlJbAxL7NoK0zswk2hNMdbSQ0d2jAFZhODFJCY1NNoXcTSE9IpysykJHhPcqttGVapQMVGbIx0VoI10hTIwpWQNFGtjFcQZ7JE4Vj9LSkwztE4sXNH2OLr+qOp6HfeCF9MyOK6uCQlR8qqJIcXUGHGfdEMfEwriSWSkiiCwa3ATyFB5tTx+K0lWQktAAHX0aBTZU/LQH/zybgauclCBX9HiUkI+2tKbExqhwmsa0ow3SkQrmjie3g0by6VVakk1ovGDWBYjInTa4QmJ3q2lwC32His3ASlBwMvwqVsXJpWEhRgyGJm2d0zUEDwQgHCUYQRLgGjfQ0ZZZQAQkFOiK9EMu5TDyyPY6cBjPUUaqpdB6/AR1pDMnMJFBw75E5UgpSYRbUQowg5OEyIhESllFJ1Fbi4UDAlEaaxk3F1rcnMCMHDTT1resmOCBnTEbFXuO8lfWBimAqkmZWrwjDGAsdKE0xB8OmUrI6Fhzfct9oKnhiBhkid2dAIxSJJk/Cdet023liOzkgRLUVW7tavGXQhFQE04oXymXimIMmxiAj+sgYoWU3GRKakOgygY4B2qTAwEzgEOzZGH2JmasmeRbgMVguy1rWb9KzpGuZOfSGfCX9THA1ffSwjrZpLSZJkSG1iHgsPhQbGoz44hvYLE+MXZXB0zpmhFY5ahcakAlqzt9rRiqgLPVNtKWo5tBgaHipaQaw8M77cbxYtwAgT6EdOClmtbhUrWmf1UqOgukCF1ZKCNYG3iScuzuSG5EaOCCpSbYWSUnTkqlpDilMaUkIuyDSJF6jkueiiAwoJdQddokaxvV9qxVNwLAmqGdA6miaopSlTvVMuxJ2wNGEEHhuIaimjcmUuSAkxETE+dGh2lVpmaam2aLQiWQu0WB0kwIUvGTO1P2QeFXaUsWUae+C+CtFAXIkFFllBfJRwNnRMa/iVzThDqOPPOetTHg+JJxkwt8aQYWk902px3VaaBtLwnCWeh+iFKJgNDbCIFhl5AKnKKPaUqmhKyHNhKlIRVx0HLpWKUqEUzvW2qqLAGpZMwitBKzcRp9FzDC+JcUKUopGUluRJlWMsMQgGM5qoWIH6ZWECUwwjGE5JpORYhbEfFhj1lr2hZJmZ0KeHbbIwLQheKZA1BADAqYi8MDHwrQSzVUpeTZbCQyGGWTzhL5weCUCBm0tqz7X2SAIqRSuSlrpEJrWipgVXqMObDEobCvRNrK2OgMCDIKsKx9BAy/LMMot0rqybseigLJTQoie+aIpYcQAApkqT0WQbIJDxpY4SiOlJHXO1kS1ioI37yF250zqHJGUQUJcIjTSpVMthMRRnSAt1FzWaeurM9hwG/TsNjpovNCs1LSwwAPEQMwsTqxMmsjn7miYG68whUNnIUkWiSGfkr5aYJKokMs8DMxkAlqVNFpXROQgcUiWa4CAI96uKMIjRdE6H8o3BNUhBaaYWcR7m5Lmxh6KA2yDzUBYdHdkuBTkg6hzARAN7ZoCAIAIJFBFpoGoxhhiokbTEQmCLzy3DKR0hQ3k+Ra9sqmGIvkQQBFGVRmYVNRkAoRC2QKUKqRi60MDPEy/VjRfxNrWsUZKGJUrDh9TKED/ehYwzewBPraRoOx1gXIWoCLEqkbBjp9yGutZHktvsjA7ahsk2wzxwU4pEeD7Kqi4dqvBQ+qCz6FIK9rbB2NYzM4HXwjkCSChzdbQdLrFqgWgMUsq1pP7uvyCBudoC3+DKK4cgB6xJVIiHLCXQobV5dDA4R5l56IRXgmBPDwoG7dIRrQuur8aJirto8igpQ3gldK5ci4RRAmgiDmrBBONHGpEzSFC0NSFRnNlC0RIQKJA1I6ZB6ihlDNmYjpkwE4zGl4iVRT1s4QXrUlTqBPvRUyyHHqCARVwYhRggm2WNfP1yoJCgIkKoFw7sWXmsKZjkuFKZGAZc3YRRSkksJrWVupTSWENYGK9MJDIGS2wiaam5EhNaU0eCI8mAaSEyih598WXubXZqUn2ALiK4GxnizyTJVIZbXIyOlRhGmJUFUJkU9wnM4AqtJGAZIhAjUisUQMpBCHBdlLD/QWLgkQmvyl6LaTWWSGS7pSAnZUIn7H/lm+SOxTpWytybmyVqAYM1XAXR1cIbzlE8qfe2YOMVYioNzW1wGpmLsGYZMkZzaeZ93SsjHoiCsDMnX9PBYqP0smICTglMzEGjChVlnUirjmssxFh/9XkusyO7icR7MKLPgbiqcpaWbJyRoEvI//KENBZNkRaxNLG2yTZJKkFLosboizIXiVqoClESQiaFPaAZrGtHaNqhUkLdVapB0Le2PpApykSVSKrIQERMeGjThNp8YTIOC7PWY/EKIxSd+8JtoXTcVXeREGFBCAv1ji8lDUUSA2vxaXB8I61oZEFEOqSywW6z6M4sIxekfs1ko26y1Non9pGgb0RaCYwAABAASURBVBMsTYMEuiqS3SqaDy1qEZbLn+iWQZJFaUJXb6gbwglMgksCJZV4EKaaOQQj1iS2YE+tLEJJwjpy2TCAztJ7goGh0FZ0ghkFDQlNaTNaAi6TQhzyKtgm52QMm5zWO+6RIXpUB5uuPbohhSQV+RCUbScLyhSK0MWnHhvMNWbHFczJKVtthqkYxtyH8rvUWVbiQ3vWpCIkIggtE/xCIUzog22cqA4FZmqjKXhgQUlFuLPjHKxWxnoumZgpPJayEa7kTAuXyDFk6GnOmbhYUZvrf5IilMIJxUjWIYdKBbORZocG3IhQ8yaRYdGIXdE0NxJ//5oS602b2YysRFOeuZoP0Qdvoztklp29MQVwSimq+N2qoFBMkoLU4KygAVeX2mPIVrTLhnAQ9vlYyUVm8QUXMdKJjTiZWKS4Th1p5BEkchdiEvJkfD1t1APkolbZsiCrVEnUgTWbGZTUR4sxrKZDZawwNxANQeKFZCQecIVWR8Ik3JmheOtKWQiIRTpnMLIbiIBAjJMZtSY9SiPtkAsXYpZwZeQ8ncOcYMbA1Eruqsdifz3DhLsHWcJmIIziQitpFA2rqROSq2QhiZ/ugpJLIiaUrgECE17u1N1brucSBRYU1zWyMsMJFeGqVJgkKPEQ6SacokpjzbG6Dog+Q5M0hMGJjDwoQznskp0oZ0YMQgKNcHA48GhHLajCQUJ6iTpyItxUq9qELIVSsknMksYiWlVQ1nCC+SCwZVlIGjMOE4KTookxyEAlAAEceBcwiKSASiAGusEY/LFkKpTJOPNaMYCKhTZemcxG2AegEcIvBQgjMvZcU6R9vSUpUrUIVQ0tK+uRqq6AQHi05qiqWHRqsmJ6i2xprk6VZf2HeYzQ1/iAA3kuK9rSnBcwYCQMq4uKkWL8GDpYQZDkyRwj5dCtOMuajiRBl53Z5yDuFWzQEtGDLddAvZKVCCacM0jkoCqpIBa7jZJGjtC0zFxa1+zKVojQo1bzgAcfMoooMQdEmsnBqjmUHDSFHXkgEalFpghx8zA0djOojs0kEnLJAQ65eA7rXMYpF5QHYTXgMPxduGOWM9pzDWgmB40aVESYVnTN1pQ7vFb6m30xXh+43BAPZThgI/cdPOTKEYSQOB8iVnGsea1chTVpfdU138RCnLjEGWmIQhJ9BV2AakQCtbIUVl6LQRKxF85Su0wrgCIornB2o55hmikwU9NWioh6kHlqcmWC28x0ThvSC0XhwLvCorMamiAERDBQDB9E8JGUwRirweSAC6jqVZGwEoTiJWjLnSmU8IrINMJHjCzwTuGU4exdwsVRdwkadd0wjlVUg8i5G4dXQ6TMlIWRS2dEGkSRuFvcChuhnCtYySTQVGqMYuxOwUgzgwDPjNYSy3pkEDIVhECEMdHH9CGRayc8Y2vW9Q6qx2AJGwR00+AuzAREgKDOIoqDCWkx5QX+cUqp6IWzjc8NH4jECfvHKoKYGB5XEJTpWaCQpdFiHKulGDgQVlFWFU3RYBbzYo4vFdak4Doj3aIklAzwYAkHrXlkSZYg+GLkEFLw7EaWYeRJtta0shRyUnKAR0sNWnFj66SKqyKguwxUULElNQSbQKGBUapB0awH7IRwVHgGm4Q2xCXK1PGOS2pFfaOEv5uVqqlIikvPsUtJzsyFwCyRUB+wX2OQwIWFlQloSNhwQrom32aimTKVtchQcJ9wgwbxlaAIYqO1twY6HVHicBQiOntLJ7iVZI7S5ylmpWaiHTFQEVVk1JXnJBjA2aCJfkDKcGVAKcGraaTDQZJkUhDBGOEDSxDMinSulNBtzaKoIUqHQoRAPEEbJpwhtNSxDRU+MXAguOUktQQfhVIpcAC3wa4QZJVrA19Sn2loMZGxNi89IbZo0i3MOUsRbAmV3c3Sg5E8gANNlUCSYImDLjoHMDGwCq3gEXxg8+51TWaXxIEgKllxLOymGgiemQLtJBLsOTFIVy6IiIJl5jBmrILBySVDavIYMgkcpjKuWogbZOZpeOVsariaxCFMYvAFvpidsGcrjUsnBXgdS0V3o2Rj8iTKZKjVe7QujcBmJ/TE6qJB1AgDuTABhOLE0YJZYL8SmEHEWSUHZARORVQrFnWWkMG4kEtgz92NAjpkC6VUNQGQaY7aZGggLoWaS7Jk7VI7ypAYM9k5evYluLtEatYhCyiAO6EkJYhbNIGEi0fNKNKRaFYXuM+sLQqI9swCETqJYZ0fylKlC/e+JLY5SAgGB2CBBIm/c+FJBxMGaLjGGUSH+hSiNMIbcUpKsVJMKTgonICCNdE9QISOpNLHvoxLK21FwJVViTAyojOZ4JVKroEQ06L4ggd5ZUbqTc1APrvAoGwRGDtrpxpdolMEeUAxnMYKvMGZGoQyYF7HJCTT0WAZbZcyxQlOCruSEXNTBzAoPgkDVZeqNmhI54zBbcqOMyaYo7UCoTOWSizKPEqpOJDn4lqYQVvhrppUmWuKsEopPOBdNKJ2p9VjrbAAjUs2Z0MIy5olQgSRFEJRnQqzkhLSqcULkiWoCKBIBlxpBnoTohkScaxkAy14I0rKhv20MGGkEToSdgAwxSh+gKKUVsqyUXwDi5FrAWVxUoP1WJk0TIwQSR7ES0nisoji4aundW4BvbrJ2ihCGKMKmBAiTQRIWGO9G8qvUsOhGLmQRyZQEeOlRtQlQ0gUs5h9jVFTKVHgVVh5/RgrpjQiZ6GqUllXJBFFpRQo0zLN2VMTPSALGzPsS4B3KkRUKYdEEGIIFv7W0BrIMeKoHKgYGZCkwClZsBuHV5faR8LaRgODvAwWD2kEoQlU7SXXwoYjwR14dGU0UtJIYfe/v0AXeE8hKNVYOmEhY1NuhTqhnUWy0V+rvfrzSM/IoZEDByFADRJCHRE6DWJXcgVxFmSFMlS7lIYPKwIEOaJQrDGoiEx8dpeWaMQ9+tzbnI4vebEB6ElMsY6o2A+QAx/YXClCiUUa/hHgYXcriiXNajGuPRU8DpWmWkvdBG86o7IjNJSLCAQ6YK/QUDtziUW7D00lSjIRBKYNJJNQFJh2Ys9MZ5olu1QkGT9CmUTObCFMIhHVfNNawa5PCJAdSiB8r1KYARkRYEJi/10tmAxxgrZVWuBe0QBHdl27AQZXIQ5qSbPWjsY4BrBjtictRE0omNUR5zZp64UCl/tce6BEIDXR1FhxVCPq8M7EK6BUi+qxVdpo4AtfFkmuXiTFSAInJEk1kH1rZA3NxohN1CBQ5jJxndCuoRoHGA4NzMlgci1Uiww6gD/uROGhRiVF6P9eRmlD2NSoQkyJDESFpqSLWFb++9WRp2AqOQ1Y02IQGbJ2HrCIQ91E+IrDKQJwfIXJoChLxxxmQDDUgK9NpoM/CtFesDpKN0cc4UaEQUkZ2R37mgBrWYWTxZ0dEssl65yQpzSiFLBWJQZiVCF4kkJaqTDWnjn74h7GSHw1KyAIYwbhooQWIOsPtvgDdQKtIcVr5fWrWAkyZ1vgTmYmq87axZ2MMivFlD66rc0BX8tmMSmGveLKIHCi7Bb0OhaFg0K9uBeuZSw8cRlcQJsULyOBuK5wxcxEyj4VdH9nB0FL1EUYayreiIlCcxVmrzwXsSpSsxYGyGVpS/au5BvKgUiYhuBxJuxMqyc0PFgbso8MCCTKNophXFHm5HRNI+2kYJdQONhdhRMFjkjCXyO9k1AMQpiAyKEIBxZH5hDThodBig5ccEKqwBggaVm4/5noeUttHBceIitnUXBo2Ig002iDeqr3WHBCQOaqM0MC6LROAW21cJwOBnBCnSPIlLxE18RdQg5qGhPuA4tklEgooTFKAmGns7k4sxIVYgmUPwKuBKew3D2ztHFahmpCCNiSUSo0RRaSLIUKkod5mMOklJSO4lGkrxaFRrT62geERJiYgMiEAccZTcmZnChSwrRFGvWUJQbH3pa2HJUWjeBUkkIaTMbeKkFD5CMtoBpgiZEe4WBFKowiquggBbEo49/gOnpVmfCFE5wnMVWlgbz2Vv50fAOm2ZlDGmIXsOX4DVS5EHiuyVuiDTMgoMose+BLJDFiTzT3RgS/jryRuJoPktPIyLSgCaUQwRlFBcMkC0lQLckDEWGuNBpbGVBqIiw8cAygGvcwCeJNdcqW0jzFMTXLuksxIYM9HzatbNJ1HU6IvRqzlBiHLOtDKogi8EAVlId79wYxJDOGF0IHI3xiNkLTlnBwRpBMKkcWCVlvCinz2HGUzUJTxkg+VKmlahb9P40yGOFeZamIhcMI5KakB/UoC94l104Y6tjbtKTwZDBL6FtpqSGFVYkj0Hi6aXrhUCJmlqFyEdyiCO6kzroKE/lbiBrKlkZpTDPHUihKLUUGNHFlYvorlPV3gjgkiZpynyoCkLJaTNARPSIyuYpg0YBWQtnHXJXH1iJaY/xNED22G/eUDu/gFfPhqRBiNBi2IE94i0GoTAL+4RVkZkRgBQytQpUNHKOFaZQgNS1OODICBxEod2CqA02VVFZO2CZB+mfmteGM7NuS01CHMg2iRVJGqIEa2HX1UGIOIiPJAP61tAHNRbGbA7HUTIKduDMOIa4kIJMS7RDMMppcMpNSlFRS3BpcRwf3uIr0FoOdzjVpM2atcEaRBEbMRTpPJmNKNRpNN02KWYQh/R6cwvnX2ag0MY244NVi66TiTWiW2kWodswlEEEoQmcJYHHJxPSohXioUTdZtlVgp7K4A3lmBIVzGkYWIqkpJTUJ6eiUHaVLybKKqatTaMlN5RrKM2d6aPMNoBQfiok29gVJR2DxlypXabX0Sp2kkTwX9ZGIg8GydaKW7JY4iTamqa9gGoSoPJM6HteM6sLQDCXhlRxOaZiUhjpEdkgKD0JQB+EmUyQgk6Gf0qSMWEiTx2BlWitCkB1BS+WE7qggLkzeMk/EWNdjIei1E5BAU5QUxoYVngbOUrJi1BjOsdacI8uuGUnpEGhf20eKZpVegDWIIsPIG+dcxYFQclDktZF0YmenoSUOZA0bn4WHPkrfEMyHLWaGxCJSC3zpwrMOniiuqhHu3XpSBAX05EyUUg1LyGQ5uhTiRvxMuSQy6KwSlV0sNUS4EEkAMyEDzkw4iEUUbTawMyuZQjkUK9V5TMVRqBArFIqgOWEAh2qC7CTMH/DTYunULQnZ/IuskA2AhSNBYuJ3CZt4dQTJdkAQ6hyLWFdLWGWSIlwYwyORRDAoWXAoq8p6OMbPmU5EXBnfrigwcVvtcJdIUN2btD64D9wqY2kh7xoNmoW8Mji1BpQNd5ZImFEgfSs2PG7MoBMflbC5HTqjVddGTBkENCyJlUWrVeFCVLMX7akWSOGaBWJmKUrrf0BiSeaWLXIRgtUeGQGoEAuJs/j6VKa8PmWBT8xmMUKjKekKNyJC7mwm1tndkxwCMIBwQpAchFiEUzWUABjWLd0lAsu9Gx61E1FRr4LY4Bt0BQezV0caVaC7Nfa+Wgmsb+MaDyfSXqkIhohIGtgTakweYyxdY88+AAAQAElEQVTKLiqVq2IFCJ4tWpgj61oQtuCDc0RpVowIrJAY2O0O+RrWFkF9SQ0Ta9QNZci1JpxEUidpoZH4DgdOmUwSSECDSjB6cRlYJiXQ40qmScrwWUQpxK1ESmEqjki9yHoCxZgQEGn6AB7cA2tTOoWHUw9QnSHJhEMSHBaEi/HIs4NpQcy4z4F9RWXpaECohoaZC6cp51AAi9LYggINBdRQuoporVKRVZL1VVSHrKVXXmqmkhaSSsxC66xFEPJINPBqDFbHwKpG0oUqrlqKBVp/Cya8JPDGAVKTzbFLjWQmoSB3s4hO1BLiJQ2pPApXFU6JJC/kW/PBQ3AiAsCaSLJmijuZ6xIMYihCuAw6EqsTr/8YL4AtwVUKXnBoSUDUlHIiVVIScUIVDH1NBAYArlNmEuYmAR2BSsBMiHLxjqbMFEIEBSrumBCMWRHHojSg3yQboeyEpNfuYuIMZ2UKgkTxPJIDIWxy5t4lMV2K1FKHnNiG7IyAiCMghDJQGUy1ZbRu8G2yZDXRlTkADAKY5IQnQT+mpBTjNEImCX+GpEpeM5KrowVySWCG71V8GZFkmJsIJrh17gGnkgzS0WbhnrhKjOhAEoUYT2pK4hk4hNYrcJYumRK0BmNO1DkBnSTx2YEl1nMiWjGldzb0sFCqlBVsgGNdpwQFM3IuAxUODlACVIUiKWKlCjFoWMA1AVJRZ1Acyb4e8GUpLlA0bDhTJIggdSu9U3OZhQwSL57UkesCiwKNck+yELEycBllBTUwpBspxOsBVwPFgOGAz+490gqTDNpFZ5VO3t06lrBmjqMbmoA4nLOLElMaN+fOHAUX0GqjiggUZ6xLivVcWyuoxN+Y8LyPK8g9B6kNgr5nlGtOTMkrN2FwSh7ZiJ0rnqFGYoV8rI2tj2ZJtIg0GbB1AqGK+Aazcf3OJ9iSjSiR6PDmBt+1dcXGpA0c2GMOhQiEkxgE04e6kKIga4JRETBbnMRTMDfmlIUVOefAkocYVlZiEzpC2H/rgpjRE4AHHrkkobHBcRIidSaCukgqwR9s0uIAp/HXCbEEMg6mkUOS1Y7EypcI/AcxkkQDPjaJHJvUJhnUhRvQFuvFXCisUoyZjCTWgdABIULBGtAgggAVnkYcwlxWQENHWJKJvQkFF0Kt4hZgqjCOmmAiOQEoCSiwMyyhCttM2F05UbLCQQJIpiRGYxir8cRaFHJJgiLdLfyvYNyhZEpcTQI3mlK8DlFHkr8JZQ5N1AgLcwroSHLmBrCCdUIJ+YGcXFsvbsrJQhgpnSkIQc4FHwO1GLnCV1lvchbupVgdcK1iDiiHPNREDVkfSw5QqEXM3qwvvCzkQTCwRlqKFiFK4iTBYcwBiDNFJ3CXAMB4gW9cX+fBZUIgoQiYPRj4BZMMdavFmYwja6IhJfgB41VIlVmJtTHyTQy8FAMiILLEkUHBQKgpTBWp979C0LUb1DV3GGBBfz2g5eIYG4FNRlaSUpKrMXiBCUIu0S6ZsrjW1IKMF0dswB+VpSCpBiJzKdFKRhUuqTUqrIeXBEwtIerBhmlUAIjWF/y08Ejci1pjGLMWXqlA5SFrhQIHehXGCXEJ5iV6wwxJGGHcJLNwapRgV4iUQLvwGheYkTKoanI2DEqot1e1WlPX/yONHJIuMVO2dEd7SB6NJpRW6+FoGCrjqCOjdlwtkiiIbR06YzlhMVTMQImahAfKBJ0My2JDBUmGgk/pKJtw773haSWJmdRFFglsmZfW4bgzS9LkMTlJ0N/eFXDDTmhq5pkwqqmE6sAVKVQEdDpF81wahQtwoZPjfh+6I4F1NSlimZZYWzrqog04FRL4JxNqqdlKZS2FIQr41U5oCR5iA3ulJCPBAtrDMgy2RMmRE1NQpklCIm2ImQP7vuyDuLADKxJMxNCJUl9L3WuYBhVLda0kIzjmgAEixzgYSilRVaoMouJMcA04BeB4IhOOrBK6aipSMtmLgIOBWUWYC9po7xAp91FtLDYqog2mFTbhxRgjiWc3FmJIJiLN4JuIWeCNkDjgsfSW3J16iBHj70ZeGA6q0YBOmZgRVtOViBjThKywldJr7biMRqkdsEglhQhagV6CKBFbQQ9pCA2mowkJUmqOLkDBhdFiDCFa1+eeS88kwLeqWYpT9IB+ogthK5lMxYLSk4P/0kjVszRI0XUdR14KDVUKF0VkkxBWqKIBMiiqSGWY8MwQVSlDoJRzoeyoS7GhBBYfKuutyXgAB1UDX1OMJTgyG3VDRqFL4sSaBUIrd+EW2c3TsBGyDCejgM1lfScP5V7IK2hwDzyP4l43CDsRBhgkRu2sK4khJ9kTnFoO4JlMJBXQCMs/b6SMwdqD+jOTM0siGCZ8RvorfrMms1Vvw2oSSiRrbEtYd87OaE86mVeEHqTQFCULWzAIR9PGgC4ShHaQo8lgIikrZhIT6qyeSgE6WMOLN9BenSWKCtrLuv9UIhI1FRfiCCXloeBN2Zh7lIhCDjJgTTiI09hRFrAxcK9gxc0NAEiDmcWUl0JdiVmGIEdzzaQgccJ7oqrTJa0Ww7JFzGZDtyHWREFMrbIBLAmbk4GYQlKMJXxIGllHklHQaR3f8eFTorzwzcvgvuk6LEWdqTAgeBLWoUqkiuBcxRDE0L1SFCDBofDvA7wBzlrPal4ohQUOiTVJM+FWkF2lGGAGpYIQUkWGccphBCkJ7oKlc8FOwzMSoa42lPHZ0TZR3tk2HPjyRQZh0qHzpvFgjJErNZHsIiEaXCiUAUFgGHwysaRqlBJaEsligVlWZimiyiVQQI4vVzGVjCJsZm9C4JnwMpQahEAkGYAtREzhbEZihL1SUSLy7E6pLIy7meaz0lxgDZOzqutINCkPrEqh3GQ0qhKlkoy8Mu229JiDDBYKouiqBrsR2Xym6PimurakRsJdUC0FTlgXYOAVMXGkJCsBAQKbskyZAySWPHtdkBEoy2F7xReUzbIFB6kQWoUWmE2mxE8kuRH2MohITEoOJcdKZZ3rKS3U+nrbu8GArYmvTYsRkymbJKVT99KRjMoBHBAAUVJKiCBoVC1QRhZmnJeVVHDRQSVE1STWh0RHhTmnEydxIQLKAP8UQkpZ/t5yJIrC1Wl0LutQmGLXEkVERZWQ5UTxo3LYS3X0wlFY4LEgc/gkSD1xFNSEU10b2LJkICxHuxts1I76I1tybvkHyzQcalpJQ+0LaIJ3t/DuEZFAmkQmwFj+RulKRuJEAVJQyVYYnd3J1zQg5S64T0GMn2xKM2wz7HQGe4ttWowhSpUTpYPQbDUWGk2lJzSrmiKqrBQJGHMjA8kkIpokyE2KjRH4G40amgHG/f2PsBurAW5vFI3FAnH0Smuwgu0QAuZs2daXS4/E7cATDbSnjv5FKswc9PclrOoKPSICkjQWGNZipWRKyRgoVLCD6sX7pht2NxXahzcs1WurSyGqRBjsBKtaEjgHskKAzh7DcxQIvoMBtiG9uHMPbV6dovKi0oEnc0gbfSmBh8gA1+ADY2LxbgmkLhQlWRjaURM8uPHChFVrZQXsjk6T0ZQ2aPKYVIMYw6IMyetD0dIBRbgw2k5ZIwVewY8bW0qIQJ0wo92lh6A4uDfui5irEI9EA7GwEHGHyGj0rJGcxOu9kVkcta0GHqAEM4rQTNgtztWz9FRDEggvGEFRhUYUB0lB8D1UmQq+TRxrLxoEOSdTkoRpwGYQLpZQQrDY/XV8MOxCCbvGSGZiZuBEFaUoKE1NJdKEejizYoQzNaGOUlLHWCBJ7O4iaiAugX6kBfAjBZodIWZ2joWzAYWQVMUruZIWVuhhjA4ZbGiNvXCg96wOBYiDMEMjNJMW9Jks6cgItFQ0ideO69HETCmBJtkbtyY5U2lWEW6C1E7WuTOhmdC6WyCxQRqGc4qkINWbhM8xpXri5TBM6mXwKutHWkR6layoz6iZgxMGRqL7jUQkSVh2qKkxanNDpUAWSRFdAyLHk/RCNHOAxjLIsHabXJb0P8xtI7zNNdCOamURLfgjjE5d8EcebsUssYGjkgM5SMqEEtRodJ/YBo1hzIL5I0Bbj5WILM5ESqayRFmiwq2wcqwZ7yB3XHVvRLjOmI+V3rWmVpaSTIJrHFVUsVkeakcWrRpV4kJEBpWEIxqohGCxBaqLlllbYzx8laWXBqbdu7VGvni2yEbsmRxSUzTDhIyTKut2kHEbMoWjzPBnw01V4eIOM4KpHtSDu6pxZhIxk5SoU9aRMVoZhsOlsGD3oGuiuCzY9Kgg3EqgAVEagUAxmF2Lx722eZy7zpxQPwu5s7sKoSvlKMkcTftSEB9GUAA4ci5V+yTYQCKDMvgIekg5lfAyzyBOFkG9jbXQihdmk7KwQgqK7jY1FC2FYENHvXqqcxKbUpvcBcrslJ1gqSWqBHIrptB34gsYTg9JUgJITCL8FGYwJQoILM7CztzX3Bg1J3bkzIsTGXuriZZQCWWDcSKaOZgjtMzwYOuSC5eWdYGJYiEexIb6gHfwvQKA1sYM2FMU8kLchISHggHVsYzEhlDyKIVwJwtQZibeJ17DD9EeYMJY4u+OMqi4MAPGlDBCUilWshfKlqmdB5ORpFARWw+QLCTiWvowcK3KJM3wwuYnpPCas4kAOSgxp9a1wIpvkGishg63KzlGkp7kHtS5JCGA/L84m5HjlnSpnWUtinV8pAQ+M4twwcckSzAsyTAUHBTMnGvMla2SCBUOiJAFeRPSFBbKIVS4NqnOQ2VEiotoREnmNbCz7xHZjTKTEBfCgwXCZFwIW0exmgxWh45xqCjMUw+GSowbc1SyAuwRuCKRA9LTJKzMzuCujjpMitQ4Y4AV9gGsDgTkEoAzYkwbSIiHFEX2mWenhQLuMx32VVIEcsumGdxrLuLGkWAMTxmxmDSvnHUIro6rayiBSllTKho8INlKNBaMapw9sXvmYApUHZtVI4UQVHBOwZKJj0BXA41Cq6TiapB3fSbG858HAUYmDBREiT8t+DoSBeGRbvDuJCbQp/z9PJfs6kVcCFPNxWMFTykJByIRwEKionUgBAEAKASZCfEagxJFcEVhtKVEshIPIjIWKtAaEbEwE7vqMrAj35Kojg7hGEPSigHgkTAWv1AtqJhSeFDIxh37z79lKGWNaUl+Dl2SWliggboVsKRCABqFGyBlwDDVzMFVUhkZnHgcaSq9yCLihPszszPXkpVXuAKlE6kpr22gJtaCGN1qOEQEXCSkgtUPHGkHd169YVnJbN5s8Z60lGJaVVlrI+zZwgUxLpkBq3//k4Qs4dRN3TkZHhLY3NUNOcOvRZ4p1ruSeCGBSU6aqlHU0AZXZRvWSFsbek3AZhJXlCm5hlKKgz1znrO0WnwjYJ9hhxgLfSJmJlyxIVbqlgRGxkQEXagXZJIX6DAnt03nsZfBvUYjMsxPphVtDC3EoE6EIDmIj5wlzZOX3PQokO7MbrxImmKD1Bx6SyHWLmKsxCvbQUyCrCgyFza7QUcG/6i/VwAAEABJREFUUQwEQmoo6pi8YBox/FKWmGuf0VSIK5IA2+QmiWyv9PAqABVN6COVnIRC2IlXN4QoZGM0dMJ2S5LBUyfpAbIQyVKsqaLPcQ7UC7q5ZnAKQFTS0VUaxJSWqMjezWBYqtIgvXJTNmbcMui2SBYiYiQug8J7hHMw2sXARQmylI4RVmXBUEpyUMZpIjmSi/dVzk29aVloY7iYtZJWCUBQJGbtNUp9qlSU1YiNkPGqdRhGGcdE/FmYldlT5oUXl2Cu1ApSmHCkwYqsWRC0ihSVRdRYMiGKnrP7wshBaLJaZVJM7OvOuRMRhlQaGLsqY5jrLhaYGoN18NWSZyI8IKcD71CTMxgecD1gH1WLlsfovgnYAaGbCnLAPQezmuplXAaKoSc3JhcSgTNsPoMpUxhBlUi1Jm2hjKBiUeagJcMFNxkzyMR7ugRhURQiYlJNjHY3i+Xvp2IdSQA/pCxYCKtyJlmkwU/hmlG784I1lkecMvRBkNOqJw4LsqQgUUktXkqIsAQmDYHVwV0pKvlEsW4AjGRJX8hnRgaIRqINychCJUxmybk6wCarKw6O4EApgvBnWVquNIyZ2jSf2VsCAo8SA6KMZCu+jA0lYZxB7BQd+VA8jqFSGB0hE9dhMrnUIiV5jIKOCmQN6XMMz8SCOy2DzKPFOK+dS2tSmZPmTgg2aZUJKiIUfKBHEmPFRW4xTJjAi0PiC2erkWpcA11bCyfgOTAlaBZqXPBF4wIJVovSofyRFbWF2BeiWXRhjtVRyzC2DEd/W0I6JUeSC/2lR1EsPb1Jj9qyLhiIbWdr1VriGx8PyGIpOSv4EVupr5bAIpqliFIlnlqpbWTCY6pCrBZMWQiRMK0iYTGuC2srqGyyigBxQLyClHERQiXRFAkkwQlVkxCksTa3JE3mJHYZA1sy9MEsPaXDmhKVBK9TVw2oxRiRpxoCL2bcmFwCVpERWirNyjMxBMI2sK17AQiNnJ2MLdBcNWVgHiQYSWSfCZqU9EGXUfukvUrD18y16JCqptSVXCWEURmwQ1mYq1PtXlaCRh0nmoaEFgmPICuBvWaqFKKJCHLLNQ2U7ibxDGMZ4q65xh20lkNJWfXbmWbPxShTWIciG2RZCSXCoiLgE12gPDdfWkMDoAIgg3ZUoGuJKqo0CsYp61+zgcBK0CCUTME8CKPzDQw6mf7+cabpYKpOgOOG/iIJ5ihJ0B7CKCxNKSsxuwoz2oIN8zPl0lGZBboArMm59E4OFTq0ie1Y4VnpmajBApYvqJGSMb1QVEYCK3pEehaxqlGSGf3PI9hTkqEDjUzCpmTptXdi0tJlhTL7H8QumYMQSyKdzx5L1E5jhx4ykr2Ouh3qwMyUUBhDUxZ96eikjFctpIFbygAUGdF68+iIuKgUC/c2z7ksMpvMXeYsTVD3ykNCgsAJPJFILvhwKeSkHUwuxR2ofByxFBVrhKO1WJ6X1tYv7Ez4j8UPW37bn25zzEv816yThRgJeqt1W9yeORYlc8b99pvab3nGe491gGeHsaX/Wfrz81+ECz3DRWObyVo0aTMtvxpcLstvh4W+BKY6fs1ui7mvo5ZuP57t6Y/99jbjXp/nPjefW1i0/++S9fbnv+fff/609hzULbMTGSEp3ak36gu1hft/bP/1/Pnsy5yzo45i9myePVvQkut7/524CTIAz7sHunD8oVwoWi7dzbv1lYznpf95jj9/HJP67PDx/Nza8sf/zLEslvasMNKfrf333H4/e2vsnT1yzuW3Lc8Nc/vy3Ofuc6P5Ny3PAUg9c1kPW6L9156fcbfHc0uc99YXX36Hw+fcuc+0LL01hG7RoQLD57k5OPK5L3PD3Tm8hy8erRvuAMxi+LjAnPW+tDYbaHz+0/N5zueIOdqS7U/Os/fezOelcSzYqlksM8+Wz0t7xrzuCwav2emGU9hBfGEOu+uBStC++NJbXyANW8hgeV7m+T8kz2eYWHz2mMGG2/Nsv/7Yf4sDx+956X8W+9Pjd+QSvflz9+XZ/izPfzAyefacgWG9Es/L0nr37q0jxgUzbWmtRVu8zUtfAH5Z3OYGjN7CnZBxhNxwpyUtlH+M/rQEyt77vODVO6ZHh1vL7tEC8/E2x7P3xdvzYrOzBaYQogq3jPbc5t+/2+///HnJPz1m96Vbw6wW838GTT5H+0P2J/vSZ/8zg4S2pM3e/9DyJ54bbplFc/vT+3NzDIrfzX42+93lT6pHWl8ijAjE9qXN1jEK481weenhy3P2P2nzYrC8WD4zZNz/YMhvFFpr1v7zPvdoTa07TluH9J7ntiwdtrxFLM39eZ6f52WZw4I8IjHXn/8z++PLc/9j8+yGvoNaWszgcaF5xvQ/Ykt078/+/GeZ/yDaZ0covgDoshjImZ9/9+fZmudi/mzL3Km3tUrxeLwEPXv86basTpbn1p8X70vMz/Pc5+d8NsdE/2P2bN360pff87I0iLWxhc/pa5EgJW4e/hwLkP5pz39+IwXZo0X8iT4b3v7AECw04JwdqsjnP235M8+LPy+B9wbywnDrj/e5WV8ccumAa7YYLa7NAjOe24L4mkdrMGKzxdyidQxv5t2zOwiaUUY9PAy3Vq0iF23p7mkWS4sFjCK6eQnU8zx7b0suq5z/stJ6f25t5nQc3TAXbXb5rbi0OFl3ZAZLCnmSL4KePPe+wKvBPHlgRLSl9wbf3Z6XecZbPPdIzG4YaI6TbksHvgzz1vqcbbb2p82/l4bs/FmoAaXFs5E1S+uxLD731r15eoBngw1rzM0x1Pv853lelsDit3RduljL+U8+/06bffVh3XyOQMA9Z2SyPzdMeO7L0nv/1SB4foZHb8+x/LE2W1ustXU5R5C8tJj/A1J0UOthc+Rv92ccS5tndHpCo2+IyOPZ4+cSz79jpblZb7HAQbSl9dnbb0fVe8/uhjT+6c/Pbi0QkUvDgGX+My+/GxYpWrotzzME3Xprf3oH6sjnpJmXJZv37nPvs/Vn70vMPi+xGD72Pv9Z+m+PRZrHbO255Z/1My3REfY8z+15DdFbw5z+h/uf3hZblvjP7NmW5/78Bwxk/222tHhe6M+Sf+b480wQoRHWNW82P9u8pDVvz9Z+L8vzEvNz/nm2Z+9/Ym5ixtg+dI9mHpCLd1/Mfnd7br3DsGOiLd6Aqnlr9txxHceMHwOe9fjTu5ulZ/Rssy9AOXsA6oJRGLDM8WfJ381/9fzjYW4wsjy337/bPEdrOJ87oLXfs9tsscAcQPc1kLb4gjHujRyPByEEN4v3Z4BcYp5tmQ3uZ4vnuS3WgfOP2Qzv0RebbTFv5u7Pls/N++y4ukQuJC2ydX/uS5+f2zNuRG+cz2IzliSbGyJ9/tPnP70/W+9u2S2Xnov1bn9imWnpYb3F0mLu3s0SZ0lo7BAL+tj8py1/VuKWzDngyEG+Lcl9rRT3HmFz77+e288+LwET0Z7j97P997z8WX4vfVlmXxbM6s846W1d/VuuAWVLX+DxT1/mZ+Sk+bLgWJ7nee1ZKBCAs7609jxjLYg1Md7CbFlab23ucDCvv/sc1qN52u/Wl96fcWdGZqxBC0a9+2zgzAKTl1zc/3hveM3++4//99ue+1oOwDf//rP86a0HxDSb/bH+p/nKw9Ln5kuzcI9I2LXFn6P94Zhh39gtDB7nefmDD7kstuA3rd8hLNZ6W+f3GVQsywJDHkus1+Y/85/njqzFktGhqSW8e2Lps9Za/9OozWELsYVi4ZjFZve5e+BfD3PvwebZzBePOeg5AtTY3NtzzHOblxm5f55tnr23tJa9xwy7HeDjv0CAy58G0G3JXJzXvWO4c+vUnnOZo/8BeeltXkn4PS9/O4TPMz2bz96e2zK3eWlL60sHXrNnz7l76+7e5/an9efZwN+fmX43fwaE2ePPzNCAtwzHA63PiZ1noKs6L3Mia5g3ry7bahy229KXsP+W/qPZf83//NeelwW7QZ9pyXj29RQgWoKoBJQ294ariz8vMff5j8+LL727N41nhUt0nP9wa47nZ5vDgHf29ru157Ysz8syY/Dz7P05fDY3zLTebPk9x58Ws3k3rBLLQliezNB5xDr7Ev2/2Z//uPVsLRpy3iHP9t/cfs0dym8Y3rovICj77A1Al2Zz9MW6OYbPbZnnpc8tW4+eZi5/fv358P3H+9uf7+6e3t3cffj6+Pnr4/XNw/efP7/9/O/64efnb3efv99+eLx7//T04fHH57uH6++3199ur7/fXX9/uP52f/3t7uPX+//d3v7v4ebjPab/+vT917uHH+8eHv/9dvP+2/f3Dzf/Pty+u3t8d/v4793tvw/f393dfvjy9PHbr69PP7883X2+v3v37df/bn++e3j6+vTry/cfn74//Ht38/Hm9vrr483Nf98e//tw/4Qr/97ev3t8+vjjx9enh+vbu+tvD0D7+fru+8PDzY/Hb09Pn29+fPz+4+O3H59vf365fby+vf30cPPu4fbf25uPXxHI0/XDf/8+/Hx/89/Hb/99vPnx7/3N59u7r9/uvn69vb6++fj97t3N/btv94j6C+Z+u/90c/cZSO7vPtw+ffj29OXb3advD++//vxw8/Pb04/rx/vvPx6/3D/A7+dvTx/v7hHav9++ffj2/fPD7befP77/+PX15seX67vr65tv325vHn4ils/ffn67/X3z4xfufn58+Phw8/Hh7sPj07vHHx8fHr/c3Xy9vb25e/ry9cfnaxy37799B6R/v//8cIsB9x/ub97d3Lz7ev/h6+1n4Ll+/Pj158frxy/XK7Z3335++PYLx7vbn/+7Rxbu3j9+f/9w+/7h4d/19Onj493nhxscnx7vP949ff3+8PXh4frh7tPT3Yenu49Pjx+fnj4+PH6+e7y+fvh8+/jh/vH9/eOHbz8+3Nx//H7/5dvT1y+PX7/eXH+7ucbpzdP11x9fbu7B8/un7+8eb5GjD18fkJRP3x/ff/3x77fbd99u8fnr469vP35cP/z6cPtjFcPDwzsMuPnx5em/Tw+/3t88frp9+Py48nn98PD5G4L69f76v39vf75/fPh0f//t2/23m9vru9vP93efbx4/f/v55fr+y/Xd59v7j4B3+/j+2+3Hrzdfvt99v3n8cvPj3Q0S/ePd/e3Hm9sv3+6ub26+3dxef7v9dPv44fbp39sf77/dfby+/XR99+H65t3Xu3+//vj/ffv178OvTz/++/j48/MdlP+X0tvb9/d3/96vmvn07en6+u7L9S2Y/3L9/frm9juCevhzc/fzy7e7D3cPCP/d0/2/d08f7//7+vTf7ePPb99uP998f3978+/t47ubX9f3vz/f/Pz0/SeYR0beP9y9//793683/14//u/bz78l8OPjj19ffvz6+PCwhgMv13d/0KWJvjz++d/tr/d3T+8ffvz78AMF+/4WmXr4/Hjz4eb7u+/f393f4PP1/e2XNb+3Xx4fvz/+vvn53/WPp3dPt1qpvakAABAASURBVP+7v3n/7R7gr68h9aeP33797+7n/x5+/Pv44/3D4/8A/v7x34efuPjp7vHr/e33n0/3//35+PAbsD/dPn17/PXp9teH+58f7x4+oaYeH949PP17f/vv3e1a3Q9PH59Qyw9w/eH+5n93N+9v7z7ePV0//vp4/wACPz0+fvsJAfz69HD7/uHmf+tx++4G/eS/m6f/0Ek+QCdfv3+4R8E+fn34/QG+bn/8tfz9w+PNJxxPd+8fEPWvdw+P/z7evHt6fHf/8+v9769ff1zf3mPA56e7bz8fb34+ff/x38fvP9/fPv579+Pd3a9PgHF/c31/8/Xx7ubpx+d7RPHj+sfPrz9+fnp8BJ8fvv38dP304dv9u+8P769/vLt+evf1/vPNzc2PHzePvz4//Xj/dPe/h5v3txjw4/33H+9uH97f3H38+uPDV5h6QNV8frz7cnf35dvt57s7fP56d/P16+319zu0juvvN5++3b37+vju29OH28cPN3efvt59vn76hCt39+8f7j48wfg9aPwfivr29tPtw7cHKPDX+4ef7x5+vEcIXx8+I2Xf767vH74//fx6ewfL19d319/urr/ff3gASz/e3//4cAv7T+9vn4Dww98o/vf957vvv95/+/Xx+tf7r/D4dP317svN48fbH98ffn67ecDpp2/3/377+e/t04fv99Dw9fU9muf/bn797+Hnh6eHDw+3azt6uLu+ffry7cf13f2XR+jq9uPD7bu7u3+//Xh//evT9a+v9z8/3t//i770/WaFen33+eb2/d3Nvzf3a+A3D+9v7r98vb2+vfn48Iguh7jQZj8A/+3K8Kd7DLj7ePf46e7xAwY8/IAI/3f743/3P/93//Tlx+/vjz+/fn+8vn34+HT//vHm083tl+vbL9+ePn//D5u21u3j9e2H+5uPTz/+auwR6v1yf/f+4cf7xx+fHm6v72+uv91+vP/+7u7m4+3t57uHz48PX+7uUQhfv95f3zx9+v748dsDGvjnm/uPjzefnu6/P/26vXu6vr77+HD//un289Pd9ePt9d3d9debj2gX3x7f3/z38ebXl+8P17d3nx+Rhaf39z8+3D1+vLv7/HD7/cfjtyc0kLuP3+4+3j58vHtCBX28/fHp5senbw+fv95+/nb/6eERQsXx7cevL4//rZyA8Mf7deTXX/9eP6HAPzw9fXy4/wjY354+X/+6voZQf7z/dv8RsL/9+PT18cPX+w/fHz7fg7dblP/7+/X0y7d7aOPL3dPHx5/v735+uHn4dP398/X3L19vcP3TzeP7+6d/H37+PX68v314//Xm3Vc0h0e4+Hb3+9Pdjw8Pjx9vf366+fnx9gnH9c3T97uf378/3j7+9+3u18dvP99/f/zw+PD5J5aw/77f/4DZ66+3X74jR7/e3/769+7p/eMP5PrDt1/vcNz//Pj0+PnuFoR/vn789+bX/+5//A8N5/vdl2s4/fn+5uHdw937h6fPj4+fH+4+/BXS16+P327++/j1v39vfnwALU+314/otw/Xa15+frn+9fX66cvt44eHm3/vv7//9jfA61tEen19t6J9+P3h7sf7h8f3Tw/v72/ef3/4tGr15+fr+0+3Nx/XBvL93dP3z4831w831zc3K/7r24/f7//9K7x/Hx/RZD4+3n69ffh29/vz7a9PD09fHu++Igrwf/P0/vHp/f3d+5v7d7c/sTp8frq/frzF8e3xx9fHX9c/fn18+vHh4ceXh9/fb3H8+v4ItPfX3+/B1Zdvt5/QFq5vP1/ffPz6+O/Xn1h9/nd79+7x+4en+3fIzvcfH749vEffuHkAjZ8e7z883X54vIG0vjzcrZr3QPo+3T5df7u9vr6BNYD5+PCAkR9vbz58v/l4f3//3+/rJ1Tx4/vHu7/Hz3ePv94/PP1dpL6/e7r5fH/36fvTh+sfENuHb49fb9fNyYeH23+fbj4+3H6/e7q5/Xl79+Pzw/0HAHh8gCre3z69//rz/ddfH5CX21+A+vH+4dPt46ebHx9uHj/ePK1Z+/b49en+7r/fN7/++3z78x32ABAzBtz+/HD7+P7b3Yfb248Pd+txe//p++On7zD4899vT+gJ7x7v0R/ePz2+v7/58BXH/efbX9+f/rt5fPqGSL/df/5+/+ERPfPh/e2K4dP1r8/Xv66/Pn29ffz0+PQvLl7/h4sfbn6s7etxZewbtPfjF8yib7/79vDu2xPY/vj1x+cvD5+/3n24v/nwdPflx/3Nz7/h3wDJ7bv7m08Pd9c3D5++Prz7/ghg/97evL+9wcUvqOvbnx+/Pb37fv/+/v7dA2A/vft69+7r7b/fHv69+/H+8enj0/37h4f3d48f7x8+P958fbq7+/Xj+8+nTw9P729+fLnD0vP748N//97/hIVP3++vv6HV/Pj27f764e7r7e3199tP3xHj3ZefP789/vfl6+PHr/fo6v/7/uvfb78+fPvv5r/n+/9+Xz+t4OEXCFe/D4/vwQySdfvj/bef/948YPX8cHf35fvj528/P+LKtx8fbu8/P95dP95/uX/4iIhw9wmL8u/vT7+/3aG33Hy4u1kH/Hz8+PgAazjePTx+un/48v324/X3D99vPj/cfHm6w5UPNz8/QOePv758v//8/fYT6uIG7QVjviGoLz9+fX/4ff0VPerhw93Dp8fHrz+wq/l9/eP31594f/r34eYdNi3fbj7f3H2+v/38AAt3X25+fvn+9OXu7uPjHfL178PPd3c/Pq/83OEPvnTaTMfttN+Nx/36vtuN+932sNvud9NxP55tx+M0HTebw2az367v+IxjP212m81+s17BO04Pm2m/mU7bzflud4a5m+GwG067zXELa5vjYYPT4244225O+81uOx5w7MbjbnPabuDi7LCD390GBqf9tu524267OWy3+83muILZ/H8IN+NhGo9AtR3P9rg1HWBkO+2mcT9uDpstpuw2m/0W9icMO9uPp91w2E373faw3+x30xaDN+NxM51ttgdMWb1MOD2uZjHs/w4M2OzHaT9NsH/YTafd9rjdHnabHaavI4fTFmbB1Wa/Ww8MO26HHfzutvvt5niYdrvxsMf07XG3OW2m4zSddhPwgNKzzbi63gHS9rAdYeq0BdrNcdocxvGAkbCw2+DWYYNgpwMimjaHaXOaVthAvgLeTph12Ez7aThshuMeyMc9PozT+bQ9IRGH7WmzOY7TaZyOuL4ZT39hYMphO+1x7Cawig+7zbjdgnCEMx1367CzzeoO8e7XMdMRgzfjfhp3m2m32eymzWGzOa4pGBH4YTut2DbrxT3e19Nxtw7eHPdbhH/cbo6b7WEHZrbrZ5zuN/vV8uaAz9vNDsa3m/0GFlbkuAjXp932fLc9bqYDju1fR3+n7LbTfjsedxNMnXaAgQ/DfjPspw28HHdwNB03q67Otlscx2mz30yYctgOp91w2I67zWa/heXtYYJx5Bop2J5gardZ37eYPp5A13bFc9jgfXPaYPDmr8ft/12BL3yASMDVev3/It1tzo57XD/b7taLO+DEMa2ft3A6HHbDaTed72FzBPOAsd/h+t/P+IBjDW2z2wLwdNpPp90wVMXitIcwNtNuDXM4TZszMLObzvbTCWjH8QS0e9jBsd1tNzhO+w0ogq/1wJjN5jBtQcVxsznDrfXYne33e5AzbQ4YsJ3wfthNx9142o6naTrBC1jawtcE2Lh73G4P2w1iOe4wZYMwjxtcAdTxsB1OmLXZwP5+CxjTeneEnnFlQtXj+mmzxfh17m4DVEfo5zCdDpv9bvpbKdMJ11cL2+1mWpFs//rari5wC8kC23g/bEHXcJgGMA8vK6TdOvIM7zuwjc6wPWHuGjK8b9bP2zXLGLwHJGDeTKcNRk7H3Wr8uP3rer/dbUb4BSfHzea02x53m/12OmzxeTpO42Ez7Terhv9e2YDhw36DAcdpc9psDpsNAjnuV/IPmw0uHuFr3Oy3W1zf7wYcMHjcbk8wuJ1QxZh1ArDtAM4RFN5PyCk8jtNh2hx22xUMBmzG4zQdJvA8wcI6fbPdjZvt6nFz2K3wTtvptN0epu1+iyvTaT8dtuN+2qzHBr7WPnCcpuMW2d+edtvDdrPfTYfddIRid8jLtB+H3Tjsp+GwW0k47TaHzQgjK6otwt/sN9N+vTWeY/x2c1znDrh7gP0dTtfAdxu8T7AAPEfA3kHqWzg9TNvdtFnvbqb9Zjgid9vhtBlBwn7a7Debw3Y6wB0+TJvjZgtggHfa7U/b7WGzOW4R3XTcbva7zWE9pgM+bzaoCxU57lYGzva4OBy3OMbDihOn0wkY1mNz2GxOu+1pv1reTeNuHPG+crXdrKYmNOS/4e/G/RYcbnebzX6C381+M+zWYzrutscdAIwgYb8dYRAHxhw26yJ1WBGOp+14WkOejghznPabzV/A02m3OW23h79ejtN4REMeN6fN5rib/oazOWym4/R3DFxsx/0axXTElC0ubs7+9rHTdjpt1yo77AZgOOymHbxsRxiBfVhYMexwOhx3434z7cbNbtrstwh5u99Nx/143AyHzXpxO07bCSGM51sIY3MCnmk4bGEQbGwPW4zf7Hebw7Q5bQBsOE0jGs4JV3bb/Xa7mzaH3crGcbsOO+63x81qZ2VjO+HWYTvtd3+P7YTPx/10AKTteJim02Z33O1wcY/T/bgGshkP2+G0w/sId4dxwjBYPu62B3jcbo5bhDMgovUiHE2b47Q9Thg2HiccGLA9YNgeiYCjzW4LEqbDBr42py0Evznu0BPG034Dg3tgnjawtl+HDccNjuk0bRAgjBwOu9Nhe3aYMAWnp+32tNkcttN+O512m9MW11cXx/0Wps7+vu9xfT8et8PZdr112IGizWHanO/2iGUPhBvcRXTTYbNZKdpsd9uVtwPepwljDhizneBiv9ucdtvTuDmbNsftBBf7zQZTcBxW1+NhN0DzIrzfgMzxtB0POPbjbjfut8MRnzfgagN4OM4O+/1mdXrabPdoRNPmsNsedwhknXjcbA6b7W4DJCszp+0KGxhO281pMx03E8I5ojnsN2dAtd0cp9XFHi62G0QHbKc90K6mTtNw3IyH9ZgO281pvzvttwj2OG2Om9XFbgsvm8N+2m3X47T7u3LtpuMOrvGOWRNuHffTcQ87w24aDrtpRQtru80R0W2G/Tjtpw0Q7na4OyLY03YCztNme9zi2By3G3g//MV83GwO0+a4mQ6bAdmER9zCgP007XFxB1TTcbsaQYyH3bTfDafDhGGnzWpnv17Z4BZSDI/77WadiwCnzWGzPU6b/TRg8GG32e9GwDtsxtP/HfsJVw676bDH9emISHfb025zWI/ptJ3g9AS/uzVNu80Gx2H79/pmAmM4haP1yn67363HYbc9bDeHdQxqEO0UukJGpr+hbf4Cmw47OJ2O+80BRrYTnK6f8WGzOU6bw7g5TOt4eIdnykouAAAQAElEQVSd3Xaz321Omy0COey2p/102gxHhAP8O9iBomB/i8BhdjWIuzi2wxGYt8N+Ox222/12c1x5BrZpt5lwivI5267FgvfDtDni836DwI+bDW6tpnbT+r7d4u5pg1vb03572I24CFM44PE0oTcO+824220Ouw2mH7ab/XaUZD7HarHfnF9uT5fbFy92Ly9PV6fzi/PzF1fnr67OXl+e/3N5eHu1f/Vie3mxPzucXe0u3uzPXh0u8OHqcIkPl8eL0/n+dLF9eb59szvh7uvL/YsX25eXu6vD/sXp4vLs4uJ8+/Ji8/Zi8/rF9upic3G2OTtuXh2O/1xevL26eHWFIecXV5eXx9P5bjo7Ac/+7OL87Oz8zfnlq/Pzl5fwtz0/21ztj6+PZ2/P929fbF+fH95enP1zeXpx3F1u9y92p8vdOQwA/tnZ4cXx8Oq4f32+eXu1ffVif3F1DhRn5xfnp7OXx4u3+4vXm/OXh4uLy/OXL47/nPZvd6dXm8PZaYcIz8+nix2OzcVucwmnL7an1cgRUK9O5+en/cXZ9OZq8/rF7vLqeHl1fnV1OD/fnBDXxfbFHgxcXBx35xfb08Xu4grsHd8ej28P+zdn+9dX2zdn0+vt/uXu7HJ/cXl59upi++o0vbncv704f4XQdrs3++nV2fTyavv6avf2Yrq62J+fn1+cLq72Z2/22/U4nP2/w+U/h7PXh82L3Xi137w4bC4uNpeXf98Phxfb87enCzBwuT+83B3eHrZvj5uXh+3V/vB6f/5qf355OF6e7V9e7P653P9ztnt93F6cAHjzYnW6fX25vTqdXkIDl0gNjvOXF8fL8/3Z2f58d3axO3+xv3h7OP/nYvfmcvrnavv2bP/P8fBmf3p9dnF+dXE6Hc6O0/kRbk6ny+3F5fbl8XS+Oz/tzy6P56+Ply9Oh9PZ9urF4e3V+Zuz04vd7ursdHV2/uL84urscH4+vbia3r44vL04f312DkG+erH552rzz4vd2xf7K/i+2l/Bzv7in/OLt2cXrxHLfnN53LwGyWcXL0Hp+cU/+/O3x4s3sH9x+Oe0+3+HzevDtB7H6fXx9HJ/cbk9v8KY89Ors+3lYXq5O/5zvPjn6vLN1cU/Z2fQw6uz3fnheH44e7U/vN3vX+/O/t/+4v8dL95cwCy4vbzYIcPnL4+Xby7P/3l5+fbq4u0LcHU6Ox7ODhdnZ2evLjH48PZs98/FGsvV2eniYn95tX39YmX4n8sNCuTi6nRxOlzsdy/ODi9fnF5fHd+8OKEMVnCXmxdn09uL3XYsRPRiD6iny4vN5dmaxLfHI5h/cba72h7f7Pb/nPZvrg4A/xL53YG3wwtk58Xh7YvTPy9Ob18cXp/t3xz2b7fnr/cXF6dzyP7Fi4uXZ+dXx8vz08X55fnV+fbN2e7F5enF1fnr0/Htcfv28vT26vyfFxevz3cvz3eAikFXVxdXL85fXp1fXlycH86utmevDvC1vbravLravr06vb66vHhxfn51dnk4e7u9+Gd3/uZ0fH3cv4U8Nsd/Tudvr9aKvtjvX5/t3lxsX57258fj8XSAcN6cnb86XR7PzmH97BJRnL/dXbw6nL++OH9zefHyeHG5B+bDq6vN1eXm6vzwYnfx9nTx6sXh9dXx/11evLmA8dObF7s351vo89Xu4vJw8ers8u3V2ZsX28ur7dnZ2fn+/LQ9vTzu3l5Nr9G/ztAEzv85HN9enL2+ODs/nk7T7hxQzy7enCPwi/Oz49nZdHUGNoDh/Pxif4bSOF68OV28uDw/vzy7ujy9PZzebM9ens4uL48Xl8eXFwB5+eZ4+Od4fHE6PzudnV3uri63b662V5f7F4jleP52f/Ea72fnb89Pr853p7MJyHDr7QswcI64XiLd57vLi+nqOL05bP45bN8cTi/Pz99cXby9uHyzO3952J9fIClnV1fnby6Obw+7l9sjsv/6dPkKSQU/Z5uXZ4dXl2evLi6uDofXh8M/F8f/9+LiLSycI7rzF1eny6vT2/PT69PhFeSx373ZofAnEPX6xQlsvATay83Li+nVaft6v395tn1xuXl9tX19sXt7jhhPV+e7F5h4fvbydPYCMoCSz4+XL/aXl9uLS9iBnI4vL09g8hJgwfz5xQl0HLZXx/HVafP2sH+9Ob3cXaB1w/4/hzPQ8mJ7cbY/O7/YX17sr1BEh/N/Li+A/O1x98/Z/tUlmu3ZxYvT+YvD5cUZEdWib19cvn1xcXW1rl8XFygQBL69ujq8vNi/Pm5f7fevDsd/zi7enp+/uTq+frF7cbm7OB4vj2g4xxcvDi9x8XR6vcfI6eowHc/2p7PzlxcXr6/OL89PlxdHEPLyant5vn9xicG7y6v95eXFyxOycPFqf3G1RRO4eHV+eH05vb3avL3cXJ5vX5w2rw7g8+z15fnbF2dvzg8vzg5g6Q0a0XZ7uTte7E4X292riwPuvjy7AKKr/d+wzs6uznbn59P5xe4FMovj8vjyeHYFnV9cvj4/A2+vz/avL07np7OzNYTTm4vjmyMiPb46HF+j21wCAwr2eH44O9+fnc7OUVLnL7ZXF5sV3vnx8nRxcTg/R4xXx9fnp7e7s9e7/eVuczptLl5cnF9dXFydX1xdXpxfvDlcvNmfvT3t315s3+B4cTo/Pz/bnmD28vLw9uoISl9eXuD19oRKPL8CdefTxWn/4uLs5eXZ5dn5xYrwEhp4fbl9dZjOd9vT8Xi2moGf/YvL7cvL/cuz46sjyJlen22uDtPVHu9Ae3pzdf7PFar7eLrYXJ42b/anf7YXb4FxB7rO3ux2/xw2b49oIJuzE+Lcn+23F4fD2W53drY/O25f7rfI/tur7Zs1L7s3p8Pb0xkwv7o6XJy2p8Pu/Lh9he3BxVpc/xxPbwD16vL8EsfhxeXm5dXu1fnZ6+3pzWH3+nx38QI5OL54cXx7eXpxeTy7OHtxefH64uL15cXl1fnZ5fbl5fb16fDm7PzyAiV88QYzNueX+ws0OpQwQnt9hYmXr48rpW+Op6vj/vI4Xe3AyeZiu73YTS8vtm9fgLfzt2frmFfHi6uri6uzixf7szcHUL3/5wrHloiqFmjmzfnhzeX05mpztbaXzaur7cvz6cURcZ3OT+cvL9fj1eXFq7PL1/vzt/uzl8eLS1xECFebN2eb17vt2e6ApeD8ao9g37w4vH5xeHG5e3Wa3h73/5ztEdE/V7C/e3t5/s/h4s0Joe3OLjYXxx2wIfaXV4dXV5tXJ5javTnb/7/L/T8vjq9foA8eTperzben46vt+eXh4vzs4uXF8cXF9urF8eULzDq9vTr758XZ6xdHgL+8ODu/uLi8ws/p9dXu9WnVwPl+f3Z+fnl1+fLq4vWL/aszdPvji/3+5X7z8gjextf7w5vTCdcvDkfw/OKEPFxcnZ+/PD+9vjz9c3bxz/7i5X7/5nLz+mL858X52/Ozy9Px8uLsEj/n568vTm/Od//vfP/P5emfF4c3L6DGw+ur8zdXx6vDDkv/5cX5i6uLi/PT1f70Yov3w9vD4c358eX52cUZ+h4awuEFiuR4PN/t3hxwF6vw9hWgXJ2fXeLYv3yx/efF7s3V6c3VBaK4vEQ5nF4ejq9gcHd2tTu7PFxc7k6v9tvXO4hw93p7/mJ3gX7yD1aH8+OLKzB+AjUvcHo4e3OxQyyvzg+vj2cv94eXp93l2ebycgtD51eYdnZ2cXF1dvbybH91ub+4XNfH14eLt9vzt/s1m/9cnr86nF3uDhdnh6uL8xf7ixfbM4R5ftq93u/ebvdv9uhaB0T06vzizfnF28uLV5eoJGwat2/Pt6/OprPz6fJy/+rqHPFBVP9cXL48uwA5r3aHNwcYP397hK/zl5s1ihcnRL17e3n453j+cncEqrPj7rhDjzm92J3eHnb/nO9eXh0uX5yurs6g0teX2DWcXp+2r/ebF4f95dXl66vLt1fnb17sXl1tpRQ8qmRxH1tsXEcbxlYGIzXKEMcd0ahK0gubZAvqkcQpykHFUyIkJF25F41UtzBPIxZmduJYJyV7aFIhIpbELYoqOXIpUskzmUwpmSlTKMbkEfYimaIwC1GJGJcYG5Wuow8ayqyZxVLgKQAmy5QDEdwY4zZuEmeyOrERUVbSSry+hEUcQRPxIrQUsgzjtIIbKsnFmd05g9MzOlOkuwfQpDMp86QylVLrSDIkYUYnnimNVVi4pI+5qPfRnD1AEq8GgAXmuPe0RYkrF8K9tMRUN+7MXagxmUgXCQkmZzdNl0KlkNQMJVPc5dQI7b2m1UxNoaBQ801QDRGqJZWDqCdGao4SBeDYEkErg2dNWA8qSrJ+NBcLMdGFZSFCAikRUiPuwfiE0QwSMBpa0TEVs3hKuMJ99mSYVgwCZoksxtIJCSuFx2FNQMKiLRTM9H830/ACo5yARCTEgCWh6klhayhKThnZUhbjbrkYTkWZBsmiuWp1bKzGfxVG7MounBxDWskIyUbhQQ6nyRIM6VIi9CTqUZ2rMI8ZhRzzQgsURNHhnOGzExGwikP2UIWGkgnBBxMRMCYZiaca9AGDNFBTNpjyzotpY8lcoxCkF3lQIeZIBoBVzRIJNmFKuOioPFLUYHUVYwi6JpXUygruhiBNxAFzGE+UgMm6ak+lEEWnJaizZ1m4dGEmMJnCTWMuvaX3Ft4dCuck0YFUgqiRewt2DCT4ojFTMVdIGdlzAXWzZhsyxXXu3MyRLyVTIcxPj54+J4qGgmCGDb8lKtHoUrqGITG08irIKKKCKmsRweEQGZWZacFEE+qIzLw0K5DSrP6svbEjDMQvwi60cAQlrwSxWCmsyiJZaMWcVYORtJl6g1LymbKxuVoHuk7hBIAJ2oJocUpfFWQxOCiCFl0kkjw8cY8qJ85JidAsolAIcVAGI+mEuJGJIgHvxTSfs/Vm1FwCVpxZLbR1tWSMV0pig1PPcW1YI3vpPgAzqzCyTd3C3Y3hi0mYhTJXH500UhhBgq1KVW2ihJ6M3AJTWBaRmbghqlaylVgiGxLki5l1kFEiq2cx5wgonoesRUoh40Sva0X/IlzxEcOIZuc2W+swz8oDxnIZgmFDhUgdQudO4ixcQgshPsi+m7tHdAbxFEW5KAka1ZBlIMHoYHikTmnu4Ip9kQQhxJ5rWoMdEyoiHxE/blU2ZbwSmKgwwxElKBc1Ghw6NGmNDSXK4UwEvhgKzLVWk5DE7JmehHsM4EyB1wLf4cqpbAXJDDLjxdIimGOALsE2OTmitBHEMQIEQSVZoDTUoaakig/C+JcrDUDFBHQMMytpoUKjkmaHz1QNXqPukQsBlxG1XMJQMWshGFavUhREQzUIuCNOS7HuvVsmDu8UTUAsIVrgpTRwTM3hTyiUSKvypvpUDLkUvFzgVDhT4VHISLpJS06VYaBRqDDoEkpUA4UwDYEk8SRlEAHF6GMSM2YBWBWRpmjgHJiDichlcEA2VjTEPWDa3fPCeAAAEABJREFUOBFvDMDZxCwd6LpRBKnxGFQkGR+zNpKQUHEhgoCw3FowsiTPVHuIEJeaNGQkl0boHhXsuQh6Tx8SgCXAOw4vSkiEIk9OC1Ej4AifgibMD6rm6EJwSqomatA/8Jgjs+BjPeA3MCeXlBYDddHO3MjS0K1gdoVlAZeWYeHeLRIVFUuL6J7oET3ZIsGLsqBDQJzwabH+jyEIjSySmohVVsqNt9qMI0FUIHSF51WsycSaOq4KXZPUGxjVGqV6aDgRqkuIhIlGJmxzCvtKa1NaNLo4krwuXkSEIQXE1MF1cKrUKiEsFiQ7xyKipZN0Z/CurEOC7IQkeWY2MJtrnaWwD+liUJ5GBCc2FK4AzIqZJgHvz5lgGx/+BoOyYTiRkqTJvCYlqHQsQLTmjkoKBVRAMEZO4lxdhUajobumjZwTcSVouBDhYLKwGVSzw0FKyaJWJZRJSTCAemC9aUEUA+5oRyrcpaUGtFuMOKpwQeqQMutkS7phUjoPxCM8iSZxcqQLUS1MMC/MkimOI0AFiHKpVCoJMSGFKsKN6Jm8g5XQiBo+WBbywqa1yZgxVlO1SFwTMBcU7oYulzoQgubsWJeiYzaxUaLIfY405SwwDwrGQpWhmtmjd3AQxtJHyiEAN4SDuSOLEtAzcazY8IslVpipFKkSIs4EZYi6FuSJxVYcjQO3GIohMsGhyYVVtTDiJ0qDcWwMS4tsakgnQYzFeumm1iKaR6SwDAW0rHE7KA8JhOOMXxxLt96WZgapkxowWONm2Twd+cpgCmEih9W6WFo1FPtf97o0aRYLB0pAo2mfqXUycgjZayE4jVht9s4UzCQ66TAASq026VIYC6ETgmUBdRTPFI0tYAJME9SvLGu6KsfIOViUhbhXbhPnyLhDxgQDwMc8QBTEaDI0Mk6q5Og5LFGCpSpVLIjimY4XJeGQoEIpwkRaljo1xLYgg9kIaFGjQ7IICTOiz0BekEmfCUIjKTQNMYwdG3WRHCsPlXjAaKRJyYgSPghxbbQrt0bphaikKu5wKPkYNAooGnmYRItmhafMGkOJUXLK5Mwm2dcAvHrWRCqTIgIqGHLcZh1xRZlrUPLQSnkuuf4v69lK+GQm2UzCSsI62MOiBgn7EFqMCVN9INsOs1Y0QMQmroQcpQwIvDB4bMPYGK6j54xkRyWZhLV4EYBABhQFzeI4UyOlUgQCnqmYDFQLLPSx9QG3wHvGgjVQDOotg7Io9AXlLOqgJoQRmpH1sN7SmnqMxFVFx4hti7FDoR3VuIQbkl4WGXpkshFmgnrWkYqACOYoRAOXQogvc04My+oBkXEgLYkyy1xLBN1EmSuhjCJadAtIMMNjzrqkRvDgXAJ5TEbiCmcFQQFQRNpZmrB5usNoJ21O0Yk6BKGFxxpTQSfoSKO6oEY1WopnlfVuJllYIhJWDoWLlbBpEB40B/IhlwroyxIOIWhtw7BU6ZKOJ4Y55rnNfWlsDvFMShpGvfvS0p0yOCNQdF2pV1tGszWIQPdkG0gnGgoJWcABNZQ9DyaKeJkdYJjWF7MGtm3r0xqpEQWFKFWpbJVbookrMuKjRV3VRKjg3nvrtiSoJio8kKy3GnikJKZkMOnIRC+JyqeEXjqGoOkgLbn0mHtbsMk3Rj0NKFtiseDmkUvQMxH4wkjmLJm1Z21EnOg2nbO7eJvcRyIVOKeatEkgT2rBPZgby+JoL4AhQgT3sIXfhTODBDagQE3X7gjGmyAJaGK1sAyFJ3LpOS+o5fQkB8lQY8NDxExLY5NAX5uISu99cQoaiAvj4kIbN1b3IVrhnp2pFULGs4QODj5LFUnCLc2cKo8qybQ+IBCSxExAYy0zI0fjoROhNiZPNCQSTh4CEk2h4PDCYING32jbcgAOA6dj805thU9GgriyahQcHEyEY9TE6cqIcKkj7i2cjhiD8EaZTLAsNXlogSPWeZIaLh4RCfEwF6rMlWCTWxSoFDkDY0loOzKrzAPgOZkHFg3vILpw1swhowYJhaiVoY2Um6hjDmPRUoO1q2NnydkRoBMFPCQxSSGpVITFS0DnrJQiaBK9s9ugvRJ4ouq04TLWKRmqF6rICgWWwrH3qbdx8TJDziN5ZWKJXKvY56RWpVV2co4YO20WHvANBkE19Iy76fg0EHrMVGNaqypTOVklBg1lqxSa4VAuOCzMjBApWBpFoER9GahPqKZx3cNnChEnIaWomAHwU4eoylJoGFxrs4x5EUvgr1bZMBgnnWsXcYZgJCQdkZubNQtP+JfMKjmqTkyDiCq5cEPSFooWlJGUmSAzeEUckHFDE4TeMjmVSIW4CqCh8iSDB0f7s7/1xxqICAFjgTCuMKzVh6Ftsu1aDvMovnVsFEOLUNG1IgslBTIfBiTDUIW32Udrgzt8iURWS1Q241GgZisy05C+y2XiNQ8WujClt2xz8Mxq4BhZE0VczJ0GLHXaSJZk56g1x1FoAjfchREuIQnAzNBxRV4lqKZU0VG0aDhZkLtQDJ7YYJAyYLmACkqEzMpCDKCMV2qRWrUMooWKcGWVEDdBaokY4zvlHD3QCuacgjexTrWoC42dCZo1Bv/MFulkSexJeHhEgCI4g1sRg6YphISTsPOczTokUAxZk2QOZqQJJ4R4aZVQTSJBXGkRMDgwrdwoyl4DpBTWymXUWgfWbdKI2ISJhdCtXMKtBS+hESKeZQlZnGl9pSrUhXvDslTvQnM1mzptietQwGPjZbbWAC0I0XnnyOgaTUgabSzGAE8AwEFNfVZqyY14Ht3G5ywzCiUAmtHsZ16CnzlbxSrRKq6mBHAw/QUqYxRtjHRlLsKzlIWGxD3UD8lEZaBaggjVVokKkmfAArLDsgeZS4vSqDbCO4NjLjRWqvCQ2XrMva85kRwksIgLkwiV2ouaqrCWVTWNuSeQIR+SxOkZTcnhrg3UinNEQYqEiJKa2YzR3dncoxt5AByFcS5EkFwWSg6L3gSkla7oeUKRglwwp0JdpESSIgqxqSJnCzOyE4bU4QnaEUvMI7IzE7z0fO5kiC2kEqP5DYl91DCgOSUr9Upty7ll3hpvOg2zZ0tgSnSRAJUQZAsxBMaBPm9MmaIJdnHUgq37uhqTcJJgEIZAWaCJ13hTM/EhGMKoXGuBeFKQKkNMRLqo/Cm0qDqtLJr32ebZ3YnqyFo4rLiNFJOaaDfFHdDrzXghxig4YSwLHRocw1dtQNwUTS3FC7kSIU9VpjHqpvmYNmA8S6D8XBuFp1lYuBexAUUjvDRpz5G4s64YQgkfGcqQjnmk9+QVcBFCXZFT9qSu6TVNxctIWoRdqHMGIfqQNFYHbwRiGxUpJMWAbrXqUh0XGMkkJROfqYOAgFMcSQLrFmYJpT5jK0pCFGUlG4ooHFI6o2RgOiMSqek8tCzpJBbwmoFYhQxXRqKBq8hANAZjKy7MJJrQv5c/roYcUI5JurYl95xJ8STR4BHjsrMn6lmosDIpPLMoNizDpGUYo0xG+AOSezc3fMdiXaITNcrGbmpWAqRkYRRNZa/iPGYoZZQw5WCRKOpFkxV+MDPFmDtnloBlLqRAAtdEGF0sETmHOCVx8t8X4RXO6cwmbBTdzYKTSrGpNEVg6dk9ImjoWGPWFGb8nwEoRUmJ1IVTPFbobHjWlGqlYtUs+DtRQXlqehFWLfBOKbR6ghHYzCiZFfaEXIB+YB5EOFQ6yezaLTrgGFqEUDC+aamGNaiwqChxiVK7oIdxpCTe0pzMmLuKQUWFGEmvVgfSwUMgp06MbQEDS2EAZviJXFuFGjaAA6LBHKaiwpXRDlmA1COTkJbeNBtREzZhh66ywBkxWETyjMAOqREbMCdnAT5sCMw8KCKBDzFwCJGXMpdhTtx6jjDcJc+VCvaBu5hBoYm2J9SrQ+O4i1Kr6QKJeJIxIWuBdzgr3AdcyYTeHEz1XP2xrD0vUxonLDq1yIbcm8gKmJPwYoBSXYRbmlvLNNIcZBgJqzjyER2tWpwhpZK4JUVEccYU2d17xhoNsXolqzhJTk2SJWlON0DiNaGimjI0KRgGUirMUA2qBiTKtBY4cVh6RHA4dgLhwOOGX7DoSlkz0H0Soas21siE74APwz4Btc5kpbhyDihvJIgoAVqImE1oEVjFHMQrBIBgCs9ay8y+QkgGVTQYmtKCJCUGQqWuFEPjAsUgEHEqrMzFUePJAFssNpFFEmkgEgReKLUwRoFi8oxG7umpPKiO8MsMTywujEOSBkc+kiJLIJtrwkBF1jAKY6SYWZiZWI0FC6A0kqBKKRyrK0KabQmsgwyYKlIrJnhmeuCNiDTRRLowEY50CMGIHMKmgaVQTdYuQ+AMIF2HhUr6VLuigbBoHyfDKj6s7qKCy0G9rNVmMGUlQYPQkmGURAiDPDGoVkJPEjQVMjSjmQSQcBsQSCnDIW9cCkKIDIDappIj4COrDLmB/sCgCDITIzUAWdeaFLGhOp5vCjOv4Eec25BW05BTTU4OjCcxRJoAxatoBThXRgweg0G7ZhQmYRViIYFnZRfphQFfCC+wl33JBVBXmjSAl2FQwWNAAWtfEaEEXMVNUFMGTyy8mnANcv62Q6YkTRpIVTlqCzWoO3EJBzYqtVXBGhnSQ7OrphSkRjSUSOAtyMgX9mD4G5SrsOClKoWI0z2QZyyPXTQlq/iGvdaAL4JjTmGnNCL0gNCag+SgVBAgSxCDKjboBN1kIdR/wbZsKdQqhlMyHHCs1UYBOEXxhkOQjyirZRQUtEMmGirO0hAaETF5kYZlCdXClZRNY2GYh6qh3CDpVJyLCDNAm1KwMsnIMibX4IJAcJtIwwqZilLWDIG2mBgjCfsGgDQOJxKWUetQig4kKKcSZpkIWzCSJXsxYyIuGMcYhc8dyRWDd0IYUSlH5vV7IK6IKxDW+q1M85KpQREYWl0rV9Eha+WJGJu+KWgELtw15IcyTdhZiPEv4FeESSSTk4SorpghWEHYdSTG1CKcgzhrojgMeTCMxSSKpG6KnCuCJ1RWhjG2ErJUZCJhX5VFlddoWMigskzCDZNi2KhWDWE10rWKRZMRKhNWPlsrNZ3NoGYSyiJeATPFktYXs3Ag7+sUNALkOcnXCHBbqLP2ZKKQjHU2SxRZdYonDqSipz5LbWWICnzwmeFpQRkrj7CS9H8MFaa1MUeghToFUUq2dNDgzm7sJMHKWRSwojXgBR20ErP432tBmjywFFbiCMygddPUreFoOC1KWtJrpEQmCBLH9ikKMbsmFsNMJB5jiAehoUipRQZOdTQ1Y5YEE8qVkcKBkD5YA0j2xQOrnmX6EFBlIoGUzOrEDbA7wlizhQGZhHdjS28ZHZSRF8qCd144HcBIBChaXZllUsS0HnAnVMsaXdYSQw3obkUN+1RrlYIwCHYFPT9pIEZpoydBKUYgldilSB2qbqsoAkbueV3he2ZGInngg0CIkjDRyqgCewNeDFalUplHtVo6U3pwl9IrqlJFVZlKMBnpTHVO7LXyregAABAASURBVG0auhcCgy1misq95l+ecd2jBngiZEpYKYnAQ+/kjVCXhD0LkapLtVKcNDyd8DtZGOwKmYhJADQJSWVSI3j25GC8qtQqhSVDnEVEQR5OhUggbcW+DtLQSNwjUbYSi6DOEDlp79obS8e3IRT4vBRrbC6dqmclZkEj0hVwkkOhmmsDGQoKy7N5dmMC7p41iDEMoyzJGOOIV8+WQWkqPRGJEqE3Ng0ndnejhTQpEcc6FY2jJeIvQZISzoZRmlQZrVBqcjXU7ZyxaEYBwh6cDq/sQiGYpOs+3WggQ+dNwfN/SzbNVMvSsqIIwSIGKzo5vjBKIu5V2pRemdSUmxLIWdlNB9Z1ggRWtHGIKnAFgRQ810csQejz7kHdCd3AVPGEmDwHtgHsIhBLkzUaEgLBwF8KlxRNVS06MmBQBisn6m3Fn4NTDQbrE+Epy6HMFLEqwRgYkKzT31cmNlzVpBDWuIJWxyaBprRG0Tfhit2iYYPbCH2bMJfcPAkjXAUWEAecD38/OdKUVQngmIUDT1g6gPSCbsLJUhL8yxjDkLUIayGRxMwg77I4FpqqoUNRqciXBLQbychKloyiiA9ZxJEqKxFONAdWSA35a0myKzRondF6h44htI5E24JbAw41kTZKH0U3WrRS1GxCZqmNhz6wazRava5zByH4YU9yVBkkGVCeiIOIADwomwL+OynFhqI4Ux959UgE6MHMgMXg2sLcViOaodEp3Uk807KFJy4iO9QyF+YuULAYDb1sbax1jFHXr7c8uKFJRCmpDEyYLh5izo7ulDAGoiIplyQ83cFfJgEBaLNRWvWFW8veVje4rMIM4gneksIpLAJzmGygFGLiyjywyxo9c1bJSWRimbJMVHUVCkquu3WNGBH12qA4EhxUwpdN6+WUjErKVIlGjhFnWbhMOm5Kxc7CVqQNMKQqoSMbpw3xV9eB5HOnmMUXNZcUxrqL9LlSrnKymWyx3qMZFkq1XmTRseuIkZBdkaT1pRI6LSyN0IoiGKJco7MBcRlKeGyhGBdEzan1BJlMhKLCj1VGFdWpbSZbNcTVtUYNGszG7HU0Fgux5ICM//IpGoKIQZUiwc7WjSITKxRYYIgqClw0w3VjbwoxCdyRsGD/IlwQYWNkOjkDjIxjavn7gOnRiS3BJIGXskguQ2t4lreOlBbnUgZBK9XiqW7awSAowAmqFSoBCgdb0ZIsCT1AJFVIhwBrXKkPNA+6oDuh+6YwMVeB2hCyF42BvUDzsCyYz2AKHTDQkgTxhVFGIWeMIA+IYSi1lqDaALJX6AurZFNyogRnRK6Gx+cAEu+UDmdcC1UNlSxltRuCJa0Dsv/9ZgAsDBzjLHVRooHx1wNQoYqc1GqwjPg6NJzNsiUaZyCXFF61D/iaVDp3AKc0kgZpdvSXoSAwKnBbCO0FqeAoNaaBiDiz9l49hDWTg8nIgl0pGf8cEXihJuzJgl6mRStZDcTYEQJwjVxGeI+BUgJzwQEyiJIhB8GkoI3SE78ysid15gTjPAiIYwohJCFBJpam5yhuA/moMlAtXGstdR0naWqpRhwUbK3QUnWJmqEklbDODAwzCMshjbHGIC5Muk4XATBfkVl6ZiesqopC86F3/CUPBcKWsCtuxWMkUWYitpAlq5X0AuUFwKMAqGVawhYsxVwSnHvFnxeGmXIprQ/hTA5huAf1UJCmSZqZsKmFkAoc0Ft1URs0h5o6imvtVIyUM5WMSsTIOBMn1Hw6O3NUpmFM2kof16GJRhuE8ci9ZQYIpjTQFOoEK4TxyYSSLLyAL6WUhMepOP5kDUmAwt7G6CMh/54deKWDNOHKuW5dYcG4OPJvKpIMpoSIarp6JILkwdahSWCGzeFMWQZ0T8Ims8qAg5RUfGCr6+rqzaJHisQwsKLV+Mi9IqmVF82GIbm2ikF9CDSQSLgrotiMCrO7zeYRyWuCID5CV6VYSDwArSYxrbnpaeADIKGU4lK56KCiBK8FWYBJAlfBkeqJ7yqGLbRkysQIZJVuS03BnwellCUSVZRei5aEFKq5EvwIuTJSiIZRuRdUB3GwsPQB3EmosCq+/MOfZ1ThNIWyIpmgjTzSe4RlODmMQswoVGSyGkmX6OweROiC6CWEcIkAyDiwqiV05+m9N/CIHNaVG2aFZiRdo1fruiaiZozIHIvQUKMC1cixUR5EVYQkAUqICGOdOEsh7FYSf7OHHt3JsLtYgCogH1cMLJycQOVSkL4UzaJZNQsDnVMEW3In1EeYhlTDooEvAUUpA2OG1OJVDE0mQByroVRsJDhOGigqcy0ylBDKFY8qtDPRWLPiZuKBd8JuXaoHIfQwcNAkrCaWWx1FdGDJ1ObS0IWzqjJA4/ASqaHBxUQD66Cuz4DEBBKYE/6Mk8lrWzciFDW5wJxIcDamWQgsGLqGgX48eEQQ6KFAwY7FpsjRlY3ZFV8slRwHlFjgJRiVCU7W1jmklKwB1om4JAsxZoCWqjygDBzYA1fB1iDrZWftkibmJWkIRkrd4zmy56g5/SVm5BxZilbSqa/x4ye5Ri+0MkileKmZjJqQwkMpkpVJe4pbpVbwqVgqCTGBVuzvFrCHSnLFOtzDnR0IDH+1GJBC0JLKuQ6XJAvunM4cpjyzLT57X7gvFM6oO03gImUvM3jF/goAsJUqyhCaEognMEnWa18wEvnlSkVdEg8A3Ys7ZbZSl1rQu6wYaVNyjqTWeWnSDc1isfTZAlfSuGKMR+RC7JREMEdjHVCBhWNAX/FOnrDAYYFmtSyCbklISJFCYJbwEAK/RMI6VB0G4iq+0sjYDNaJZJDk0pyX3tLbunCjQ8g2eVsSVZY1ewzNJrMxgkuhUgor1+pbsYkS4WP1W4xmrBXLRF5USUoPSku8iEgpRvGNpoxMA+saAbahm0UK5tkSZDzAokohlkY5l94lOIBzUiPyltkJXmWwOnQm6VBQEYL+sJZECzcUciZlrMNhLUcOkFtEVYfQgrLqlZyIW8gC6w0JU0HyDUBF+1hz4GlKHYkgaq+WCk9G4ehsyIAEbPFmkHGASXWlOXkRbWMJljU7nuTGiFYgJAiOWhBlAfXM6qQWApTszJHWmhlox7UICYqegRBbls4DpRKy5o52IGTKmDwTto3AgL4GX+gAlbmqFBEN10wBbOLF1W2UXnlJ7obJIgF/VM1rroyEmelMtekAL/CkVApV4YqCthQPZcFZaVJn0UWpUUB8a+FHo2wG8GgpmAxg4QlqEudcWZULk9iaBmI3+OQEBdQjZiaIPiCOOo4DVBcYh81eMiYJ9FIQRWcIgJSjIBqJDI9A7OJFWFxxc2CklTvwgCzHXKosI0lJpmTuojOXZyqdMGRhb8gEzRp/IvGlGSAwEW04quRQSh0GrRWd1ZUtHd9uDxkDC5fs3DoS7Ljs2l1cFN5QzUVmoXng3EhOKHzJTP+rJwomJGzwVKcgVPxCxasGCVTwN40RscBWIMPo/cMAXzVJcZ84RYEpqQAyt5QuHAhOg5jM0CklnzEKxnrwYiBBSCqrZPGsjRQgnA2ukS5LQwR/uD+Huo+CJSVVrA6Bv7NywTIQf1iDKxdlVkgze/OwLulVwEAyrHKkU7ogTCSOER51FhxJDNSB7zWhN2dPMUF1OYwAXGNBLgKo6dnIkolZyCsRgSJkVLQRuzNE7xmhnLiVzMkkLuHSC5kkIhdnEA+YBFqUFuaFyh9Dtyq+gO1SigqSCb2gRjRGduZw4iZqgE9d7bnazPG3heQCt8XKkAN20VRRNggAQ0ksaH0x6IykzoQUWK9pSk0IWCvZqE2G3tjm6M2WTLSUJmmBH2arySOKiMCfWzFIzs2iu4c7AII/hiV07soCuoiYCKlRdtOciRbK4MRViTVrNbDzsdKjEPiIFOqDtIHBvFIVKVyqKvZ0dUhRpzBdSLDvA6oshAPwF6Q/jQgsqgi3Svg+ItTz/1gi9kFESw2IIqTjnMVRiwO6BGAx1VrGgQf0OE5xEoNYlZmFlAyxiBDWo0l4DBYjD0IEABupQaUUweKu4k19HqgjP0AunLyKBhTDY2IGhQTopL7U+P8S4anPJHNVYLbMxZAUBjSpXQKOyJlW6TBxADaMlJ51CVmaU/TCjXmObD0skq2KqcBtJHmEWWttSdeaQyGh5A6mHfu/UpQC4irSeOw8mtXmucBQJhnxqqrSwb8T+oMsOEe2OFWdw2A41q+VZqybyHlogAaYp0BY0TiQaWGImeE1SHMtQurFu2JP3ptYT/SuPrQYFhUD44hTiYdBeVNBKMkgRbTkFHjQmZF1SxjvgcUmlBxjkRwmQv0OnsLA2YUWAuEolEo5dNGcVMZUESURKiHcK3vFE9yA0oz0dJRAGlcMIqIgbIca/DAxpSASc1lzpwxOM5g8CS2z+nMtM0zHVLgOFQ6YjLkl+ogqLGUilR2dHf7KeitZk4QSQOETiEkZSeCQtXgyvIBswl9DC6wxQhg0hwzomjNq6VVinY1eWQYtWDwhkKxY+7gNCZ4GkYKEBsLjqJFF0BTKwmzgLtwNhVIKS65F4UgO0+CEnRc2bNk954W8MVlSUOUoqPKajhIuwoPGBsaVw2npfXFuXpYss7IDC5JMBE8rlUvaYkbuhXmozKOnWKee4ipwCw5hRLoDoqxpE4EGoOggShQWd5BHWXUcp7GMRVWYiFM0RJGUgVmCoZga4wC5CNZ3GbIMJKzMSvSXz+7Rg5zx1dZsEc3+bhSEHXa4IOGW1qTPhf5UeS66oOHJJmWsIvBDQ/fSNElalVmpwzrUDDqaoByQw0IjsxBRZCqv6sGJEuYCRSVm/CpB2jyW1j1REGjHhYVsoCbckdVIIuRY8JczPHRJctduA64mY7gpBBVFWYrQBv5US6gQs3IpshnArxZG5MoksMTVuaK/+5KxSKUB9OWEmGxQZmwAChLrG2cFlVKqjpVHZU1Ut7dm+OfqNnbjtNTZpCUnU0B3GFSZER1pB3MZlDoNPIqK5dBwSMkqOVQkIKlSFkiUxoDcgkMwF9gjOTzTKBG5Y+UrwRKFqKQSvm8fRirY0A6wGhVspAsJ5nKJGCIScZkvEQvsVOVx0s2g6D0SmllZRCsCZSqU2tEJmAkNCtZ8hV3Jh+ySLMDIaEatOBQfHTkpGWxSm4wzDat5IMxk4rqWIGrUQUNi60z4Um+wjJZmmQhDVHUgAfHcWqbjEikHU6RzujZ0P8UVQ5hh5EarT1ZhBKwDc0E6aiqoVKqcE/sgThHWItDmmyu3gitce0rvhGoiC3xirLnCjUamUamwaJfSuCRY86D0YC8Kg1SzCISWGsxEJrwIJDyKV+qVogC+AlFobSLdhfHHB57qMJUCLDWMw8iJSYglKVqYo1arE750hqrgRdiInYPIOcFDYDQniSOQHtpJG63kO9gXgSFi+E1JTVPqOCPYJh0LJNA2Ix5UYkoak7CcZVlMOxxDifBPhqe1MWgILalgUjE1uAah24y44lvykpmLm/d7qJNaAAAQAElEQVTIVkEJEpzh8COc+Opw7FkSdUsxZdQegq94W1ImCCg0S7q4cQIj6CA4ZTCXolIrmCJdp6awFh6ZB0qhkjn+PShgGPlI/DJoC2a64CKYSJsDeivy/+fp32Lta77sMGiMOatq7fNreEARRKAIHpB4CuIFAg+RIoFiiBFCKA8g8QS8ERGTGEwCRCJAiBGOHZzEEGwpie8RxCRSuCR0t43dsYl86e602xc5JmDibt+6+9/t7u/sVTUvjHW+VvZvnXP2XqtqzjHHHHNWrbX16aPqeo4ciMF7+B4zltXlqsankkv2JPDVdKW/TG9ee2oMY0SObfZkkF7lWaNLGc4P7w/NN4/GYYodmyoKvkAHQIVOtP4CSkqx+wI+6Nd0HzKD/T6Zb/Qtg0xWmFU4BFKFUKlZCMmbZm4Q8oX30uIDlVQp+xkevlJdznUdgDyY3DyLEW70LsnisFB1Ue1B9NbxEj5zxUksb5G7HE9fsicVmYziw76Vuo4y2QAp+1MhqVjmaCw86w9zor1teIVWJ+nOTIoTj45ydFrrKUQt2NRqWvIiU5ldsYcEwhrOQaX1ASNL0KtpcFGPyid6OW4OPGQA1dUIzuyvM11MoA8tYR3Ge1IUAoGOyu4AhmON5oIW+iZTVwTsyTlGSW7lQhjeiTzR3aAD1+rxZJCdjuPdgxw+1sv8Q1elB5TA6BdgDreyUp1dt83WWhKMRooYVVorBSXUpDEHQyaLkgo3Pey6A1myU2JSvkRaOsMzh0rDEApIRjSDsS5lmjYQ3TftuKoJR/HbA065WUXx9hGcbIvIwG1rvyYGBvOxxFZ2brEWipTEpMZShSGr5k8tW4Ddq1KYBEwMo0wcjH4DkpLhLP9VugoapOqsNR6OH2DYAFpG7YKs6IihLIgDJUQ+ZYwNr1bGREqXhpPAlE9LMuQ3BMuHygAgwkpsVu+mWqQQoLPxgIQZx6BbmgpExqQhqWIcZZJpKtdxg8E6X3ne5glDw6E0bPimbcuUOZ3fqurLbEFgWKw961iUctNxupKGqYZ93ZjRBM/wG1eo93CWs1wcDRtzuD3VFxoBTbYU18UOKp1D8VvFUG7LV/krzCKsTneWHHOiyYPM2H3vEdKiThu0ncDitHZsPo1Cz8wdPVOElXrF58R3A/cEnACSvmuFhsZZticjlPaM7myJSpMSary2madPZ4Q+zlFmT3VVadGPqwUsuljpo+dsWj7MS4swbpMAUc2BkrJMtlMr9oQP2pQdlObKghw26ojsqS3RsAD2ibjlt9ADNF7EKAxJIOE5TLuF8g4J4X13Vefo7SyTsxwrzMkSURf5Gq/JKSKg/HQp8DYoyNKuU60v6/vTiqFFLMRmS2d0UeS7baPTg3JuOnN5DUIUi5l5c2xwl75kTGg++lgnxZ+MlETpmlrjKzFGJL6MW3czlXCRlBzRV8Wc5RMKk090ivkpWEBGsaudys5mkWe1TdEpr7BS1psrm2HYkgxMWlAQclmXuXFVf5QSfo/agmXlSEcYrTFZioQyRhMz1SiM8qHcXBzThvsSJPVF89KErgjB9obZhF8lzC9g+aj1qjGqcJSzChFW5a/koq0pK4/Jh372dv9000gI9MHcrftahdrtNbN8KxmRWYE6lRtZE/AFjL5BHckikl3dCBiExL6Rr+ZHK2OGNFGeUPMABZSSA0f7TI7S8IJtszLl5IMiHmNzBORBfF2Ynrpiklmwz+l9GLSizHSgBVzHgcqkTrOb6C5EtfYxVc2HwVaP0oR7+HfTZb01vcKlC9WsHZWjPpxWlX8QYxVIgyx95VPwHe3QqefvgI+2qoralZ8rYsqaqcYqHs+dhRSwmP35MeJj5hq65Xj5eUxaK7mQdn2bp02ac0ztOsSq4ttNMxuTcO929otUysbzOXLo+fS+rVNDXPWQ0I1xx+DzIG8And0A4eSEmwzo46neSk0ak2IqonW7UlkdUEraDBwDvHaabhTSwi7Oi9CFJLQ+q34CfUacyXaDwvfVWFWj1WB720xc9RT7KDVfTEznclzDxjCYgJVXeefle11nrXXlhz+46eTlc7kW9evBK5jbRzgVRE2rtXvdMHZ6bVbmQe1W15LMZRsyzifo5z+M5ex1mewlFMaZoqrECOfgfHgX5gdCzKvGZc65iGHpWmMWhSaGl011rTh2gE1umDrtreWLJTkM6+n5UZA2+7KaKKrJgJAgURDbEK0MMPU+4QlG+ckJgbTjWtkoMXJUO8q4TW09o6VetorI344zpMs+rC89lHrZfAsH6sgP4OOMWZdIXqQLsWNRd0ZW6NtOIL6vy9q5QwXbLRAPT5PPrdj0bhTZY/CyJ3XKXq7SXWypznLlvd5Hy2+7+hjcFltLHdVHups+ldNr2VoH402IKfmwval7OW4+p4OePVi2rNcoc0WKGEL8MacZ0S3akH36ffRLQoJPKnfX9WX/+HybaW1VsfcupFv5ULRQ+EL69ZXY1sWqErqm2ainZ1igK1F7eM0FMq1ytU1IX8PQE6VN4AtUd5y1bAublN5r8tugL0op+fK6XM0Aqu1rn7EDN5UhpTZIwD7Kvh3oyy+VUmV6hy5n9d0dY8ScdytsrmXPf7vUyzAI8FR9xtGdUlzE/ABnVVpLZ8DTzxWKl0rj24vXa7lSvDgM00DTn9VrwtVkdnc7SSOGY86cI2wVHYDOv6wugwG7scPPk6IqUZ3vs0MdNAXTz7b7llKjx+Hc5mYq4r4CUgEVx9bCoCtErHrPPFrHXApslFUH/PSFcIdgCEp7ae/RD4HD1kXdpPRz8cV6uXRyA8pNo2GDNr2owpAETEXQarq6wXxtvnI6V9lIqnj2XO+5sgaO0jQwMb4lR3eNxNwce1ksYo7yp9LgWLp2FJF4wjBzgP14riowUnFh1Lnq/YGzZtR1b/U16/TAtd3aaLOmfg9iiAmXZrqH7IQWH9u9tlnqonV71GOicMGWT+l3WLP0GG3vUdsZ1D7xFXNkdu077veuqOXjta65hhkoYKXN8YlrK/BrcEChP48KxVgCpRbvcXvsaTl1eTTI0q+n0yQ05D3rzBJzZ6493dDa3/abcQBVmj5i6lvVZVO2W6K4i9oPHKs98b6Uamg9G12UZxcvruK7wbeon2jK8cVeSnSP7itjZXoYb/ouiyThNF9mw+mzOEOHD4njeG7qQVl1KReD05Ws8I+7P/bd2Sr0e145XuLaSAesq62KuNvvvjStXUTZJC9iOZ9XU+O4Nk2e3ejtqFlwDPpQH2zcg7dX4AQ+N/Qia157fGCPceNb8LJxTUHSU28dV5d3N8TZrtbuIeFR1tJ1Ux/3Xe8dEWC6bVmeGWtjxKj2jWhIF1WwG/apyPoeCBX+IkWXaeqWVd6WZx2ZSFvBIW9mIc4NUkj16dZ9f0m9NYS51GJMd5klGshpYAcrvDmwLGeHNUFVQ6aafdYxu1UKYlACvNk37bbrPMRqnAiW1Zm4mi6vqJzKRNXkudgfxh9yrJV8bcwkGuf5pdbir5bN5F0jpbU2APz+sDIjlJXhTqtVR13TtzLQ6uDe/nA84IOtIEllzk00hrJyp5a6MdueluU9ruJTWaAlFNQ6JsLi5JiH18HrKHQ0FLmPYUI6Xgfj7MxPbfg7aWELaR4wEbA9RFezW2VE2zVvvrZWpDW4FsaXYrr52Eun0qwzPSEytlK/rY91Aywz9aK9Pmp+W33NZkGpityd7+yzHeUTFK+r+xpHblLZqdYLSBCPHdmkWZOnO/Wzu96d1bqM6Y+fqG4j9BXJII01va/FOTl6r/hunXuKVLOy5TGnCvkdrR0ditVyUrO3cwf7BqC0zJ7OKmYfpkwoFoj0ardzXdv9Bo97vxaWye4XnAcQM2y/qwKWUL6yMlf0K7EK8vUO5DYTifRVZFpyFIfgj+6ZNmIo1LLdamZii+06wqo83X1MMyV+tJzL8SAvD7csWj1CAsmGHPiE8MThKZFIlVkGavW0flSYXvfFWOh5aulb5QzFd5Y/BvKxARVZtPUjXVdGGRiwyyQ3mBtFcpmIttHwzDM6HB7NT5xQRZ42lYO8rdvn7k7gsU2xG5mhEdgy300Zl7OG2q5JEIBBLQnerSRwjh5DiHZRJf1Oa+dY7ePk+u5AOlJFPPajn6VSIHq0aI93nq1ENBj5TE2ooN+2vmt7Q5lvXCGSRzbfE5+vuu0m9+I9KPbjDOb1yL29hfhkqCfnbEx2o3RDf5clDdMooHgq7ZO+J0rrkU8A0Z2JLlHeRIWgdItGXYws9UYtN72nkB8Iho9SKomIImRSTdACyls0GYP3Kz7n3kpwCkBBRLW2a+vVyw396no10Lxh71pvQ84b4zjaIdfqSvU0ehvQSy4O7RRClKJSIW5kTdWILnUe9q3rbR2GMG9OYfAYAqkFSztFsLOpKoFGpP52kKcG6GY9LS5KcG1W7j2HTVM309Su0L8WG008809kHCm1a0coaX3MUh77IVqE4ahaC91urr3xYACl8EOPfKTaciXBZirUtnhI9FSAWagwkjal7TiWMYpDdim38QT1iFI5Kuse/ErSQSvwo6K6Rmhig42WO1A2NRDW1X2aB9QbnRWaLN9pFZX6IKboDvOicHgEa8MLwzBml6vmHXRLIiye0NIqH/gAgdaAMTdfb+imgF2Pe+HrNgak4UroX1aXdkV3RIrPbjkbfs2hLAluaBYLM/xK+RUObMeZLgiQbALCb6enKBYRUwhlVgwTaSMx21aPJbZ0TuO4CRGlROBgHlUOBwqjVW5Blq6JuRpZl3DiJGqnnXwoak3mmVZaPsZQdTiVO8SsGycC1aNNjFjJoihIeglWEXVobWZ0ipl+slWNOKgwqdAN6LIqkUs+eQs8ijRIa7OG52AtlunaaM3KYp3VKQyaRRmKrTcWMiqPKKXUIcFZiSOw2+sYjpxNZww7r3EvLVMemFs8+UQvxsBmplwnuipQb8MeTGsqZXYg3XVBiRwUtubs8VSJg+0yn1mtYmP4jOnwRulEUTxRIMaDuImvl2gXZH040oJmHe4c2bOaI8NDaitdtZZzGQlFeZ1+5fQa6RVqmzytLFYj+1lzgIc/a+gzspA3Wy21osY514lXaICYnAWqPdSFdN8cKtL25grxL/3VXXg7kq1cSd0TpRZIGLVCLipWqdAbIHVOGMNaOQl7zOpjkWYaerm53oXJTRDhaH3EBUwT2ar3w1bG0GK/KIsmtqKzNFASqaoz8EwHSgD1uUvBAgZcBaP+DUwnBzkNdKpcWyQ+htCPey9nOookHPCwkfxyhd48Et2AUifogZUtKyr3pqFg1RQSyhEhCGV3g7pmRspWN062qjZVBxYcm7aLkTBt8nuyn/NEyrUoH02anyXhQQ0VA6Z8qL6DXQYYaG3D3S/N72a1FozMTip8tHIuymSkALSo222lVSxX4+pnzRCtrvKgLivLZhDc7KKEWUlGvLKm0fVPwuXCNG1dNMWExbuRqU30ll88dM/SVH1Qbosxm0qcnAAAEABJREFU1JCkdqWeVRosPXOm4LTABx1c7mNOmBWcujfGYq/q2WkMoNJSdAv8aN2OXZMaJtuqMlUzSjQk9GogQBSZmNVfUbAEkZ69Quem06jg+pROeYNcJOEH/Nqwip24LQ6PCkc+a1RNbWMEU4aymAVkUvzuaAXR4gz6Edr4fqTirqpQVjCEF5zppq3z0WN8yRXdXdWVMCF86EB2RfOk5MdOICqjQo5o8+LTzx1KC1GpvHWzOA+eLexntniAi0jMGFYTbRo39GCjKekQEJAeqVyfqY7UZaRroI7GPHrrGpEj0DErVsFKY/AMA0fXkIVWmSzlqen6RwNN1dFPap5s5vCeAz5Q1DkFQQSREYxUmLRxTPmHvDvGwppwQlb0J7yKmaz9LLfeSlvfyrvpujyVXAkS3Nirt3Uq5zVowxbFimd0hNCXdStiBaB6me3W1Bk3wdLQkctUBA+FmcJReieiqrIQyPbPWtGrbfjjCuQRf6RgG5WKEq5wNNXHl367+UiM0zp1npPjEFKOpEGN987GPrbvpzXQBuZMX2kjyiU1BzBbkEV1m7xguJHqcK/Oxe0IwWe7P2gPRsJ15uvOB7sZWUIvIwSXCdejnQ2GISH/Wd2qApQl0FAAhBCbEc+Hoi5ruOAXGuh+OpG7surQEETniVMlFXZS7WZqvohoRpcSa6V3hiCiGucwU8RoETfoVfLXbaDnyLLbKj1JIaBd0306BlKpxNPF9dc75CsyMxRiqDeJEoOgkI76YDlSKiZb8UHDhNqVXzdBuJHKv3FSbJ5x9PTizSkrkG71A3SNEMBEpzYUUlGEtVDyyVJUhP5CSVpDHFiLwXbNgWBioqih0R0HKjc8pEU9VEksTngDhnJ2mZ0x72VhMuJNNFUbZ0oEzloGB2hM9kO+3EDYUArc9mGpQkV8Z1db2yp6d1W1etXV+SEfGp6GxnPeM0YqjqCHYqcLCwFRhEIlQ9SETT0oElqWIRTK1ADN1zhvWMing5AjuBwpkgJK4VC59GIVRG2LfBVz5snUs6fjWZOte46hR29DcIEehanhluqjQZEFwY3ku0RYZT7pNqPbXHxdZ83zbEfkG0jWE5Ro4UZrKATWrQd+FZTYqq3h19IKF+sufrJVDqaWd1N74LNNZJTSRU26WMNw0YknSLTJ4Ck1/apqkSGNdSfAxFNVChgkzKRVPVW/u24DVFartZeNETd2MXTONF+72s5I41GSRUT0RehfVVZFV9njeKC9lEUoPGTTYGZYnZbSkG6Gn9RZ0qp5ovapDS+1hyZyR72rP9Hv4TG9fdIGvcjWKrz0vl1xnJPVclJ7YU8mFUh3M4pv2jaZvxThY7jNN9fhIAnmmSpZCU5b4/6YWLI6xqNPs1GchE/46Ku40ga0gmvTLgCjUDVuux5UbLByoBsI6eTod0dD95MdEvlCL11CH0v5nB2o9+53aruzcjkvkM1uRQFJ5kzy4viAXamVyUzofNgCtXZrfaKoYpecKgYHDNxsbTqUHBu9JmaKPCOkMokKqrF4RkEvT2HV8WCsqPyMfm98vnky2055ty/yo/PF27EbFf2lGMZjdzatv5jprsR9kNWKfMAv1yqtLcyLU2/NhroKqwG2WbcVoKFSRz9aHaHyedj/9PtYY1ARCnMjYVVGiXOf2Pcd+5R6TYrgXXmihFR8ubOHtXvVY7aEVDzaEE2Yo8dF+7jG6wNjfWEGIP7Xhog0oc6oI5uXYfQXi1Biuhgt3AKxol/aqxzOADAeTeksy9qYePjLjuy7dUrg1VOsBRMHMnLcb46jwDQUiqkiH64EMk+LA/jDYi9LGlp4p3OmTz2hyPC+jRspWYwnri50W/h8S7s9U2Jz2oUn3sYMXUvERp1ibbfNWWnVCMvNTMVZB0LNcsRiCuxXBDrb58K9kMJTIg+zbWg6X7fPVJlYJN9mMb2U4zrzvq+8gU5XOosh/POCv06+Tpm4KrKblaN6YBDjIZ4QN7S0UWkqS98umh01Os0VMoLQqxXLnsihKp8wwLZX6r5rwFR4bdmKmgtwQntx3YbWa8W89uzjJ3hqlFuN3p1ndwvqxmMv5mxXi9K8Tp3BMF7Xa44XzXEq33fcd+9EZk/067aPM0YrVFW1+gs0TZlQiWCm7hTmgyHA4iz1x4SdUuUtU3nw6kV8CQ/H2aYEogKy9iJeZSPMWuSYYmZ/KWLTjwixLBSiPenCiWy+D2OLUiVnsBWA3qiZnIja4ZArFYIdmT9Pq1FgX/RpdiPBAOgxxx56qge1TMWrqCojdlbIu8JXl1DyZlM3INB9h7FawrmTn5PfSVN72aFMmxFc3WLAdEvgp6mjQQZtY+Tu+mx8jkoF1mvkM1pphuYJCBS1AxOCgK48yNtaObqPtol8pkejdVW8qTs+AzUWlkn9fWhDj4Z4vnwOihFoAUgRNVTPpYx3zqgV5sEWmypJBW5VZgkL09NkUzkYTFuJSRVUKwqv8gF3PPFBuS6f0IGF8FAGNDdF/iGfMnsDb8h4saETGeonqCelY3E8e5Y0qeFq6IH3VEk/Ca1aO9cNNfIpSSyo1xcCGay0Zqs8xuQXAkHoo863d+kyFdt6U/cPulpTTxhS2RnRK5TbHoFhcl4TcSEWqlszBKxPuWSMNbCqXngeTltKzgYIOoQ+m9tqe7aD4ev4s77Zfvu+vdKHcXoXpCXfvHJc6dZQL0l7GCvrfIimcOK23pCAl41pajUjP7vumMrJpNLVVR0dB3VUxHO40eloyQVaDu4erWHySNEBDUbvspPeYTymtuA77f32EOACi96KZGOErxruU00VVqJX5xVaZlYCMNOywNKydnWuM2x7HMsQVjRJ2MynMlebVrAPwYfYABtkN+vA3/zY/cqzNEu9vVTlLQkNnjaElFmLRwvKRZVkX40Zga0sKgnVDFEuY5BAEnPXdbsdEeMlZrFrKL+37hTCeQbzCvhBl5V7dqMlwMIAnreE6LfVRmu4YsiIU5apniFy88BS56HRam13VHRpnrIoMkJBoQ0SeztbwckL9sO/9lvSW9/BuxhED2IMp8kvtG5lZ/g+fu++s9UYUzhqOMxLvkjDmsPGg0srxFmmTEQpBVvThxOwiJZlXOiX0Q3V8lN94lnD9OkJcDPUiyPuEnJW6hDpKNY0ZY29Z9Uw5xi8TCv1WZ9n3Vw53eRfQSluOdKXeGdkS36oa/BRSyvnkpTKNgt3I2gKb5otcuIYThzvfXVNCZ9LfdH0cx3TNl9vAA1k5itj9E3lRUGZu6hM5D0+73nn3MN7tvkjfZXn1CL6HfEJZpEYVBHrPTxtOhQFSEngPuMuz7G6X6iFZFeJG/qGSt0S1Q3WYAym3hRE4FSelKDRLTDSgLn50Ehtlt7ATTwCrWV5rZpqA8DzBd/28bZ195BgLBW7lREVSpB5l4v1R4qL5XKwmoyJ92ot3QIMjW31+vezZN7TSpmEIVWedRcKVBqmD8MsUSG0xtNKhauDVgOBoSnL1cnbxdDooM43EpauzbEr9TiFkyxl0LJW1pCye1Ce0LRCdSa0hX1vS6mqhFyFKEOPNT3t4SATI7HIsZZfaoSmzL0kH6ZKIk1FOGXP8s7SzstqAKvWsgVTw4Vqyqi+BHSURQ1NdXOOmTYKLp5sd72PWnKNG9fmaE/oqchVNmEi/jVvkgXfKqttPPAGJsOpKEfUxRovQF3cDDradM3uYWdUzqCHjTbhaId0od+NMm0810WlR40ixGvAFBOSXjQ9PqhBTYKLXjUcP9lyH1q2cZoNk1VK62FF9EwulL/0mLN70Q20egxDrkq/oFc989jdmgDQac84YdJRKIxStINRXlsSVEB0UKaoX9ayFF29x9BeB+wht1hmKs1nRqfyDaMCQzuhuQbrWCVVppQQOHonlrQiyZcO9wNUVL8Dn88Qy8epTRsfpvndBeVVdmPPIx6X+QdeV1/qnm6kj4YjyEwi16rpipW7XYJiuh3Vnt8lWfVHnhdSsDq9qiC6TDqHVWeLqxPSJ8P6q3/hLdddNo+N26lOGeApCmDnnMY17DJb5FhlrxYbZWxoQEdCDfEu/zzcSdLc1hoT1J0BCiYtUAnlrWKj9n0HtllvizfPjW4zpV3YOJQq7Sdth36f9Hep6dEKH81nb5KHT4U1AK2xW4EdWGC5XV6zNZtlnsMkZZcsxnTaLG2eeqzGzDatbx7tpbGhuU68WkwDDx+t2BYXMcL7TKTZVohH49XOJQlt1liH1ZCzTd6mGwQeBUCaUNlz4Wqwyqt0qqftxafge5dtLskpZqVQ87jBzJ5aafcYM4xAWZdT0aTzmKsJtvGuvsNUzKTTRDEGwpEDcKMhJ9MBc8d0rMVhxBPWOOHv2+MoKA4NytMZjagRGAkwnq6FyvZK/QnUl8khle2ureyOGm6uAzlSRaZx9sQmKZafsEzrhrTne46iG8y6+FiOI177qNez5GuVj0M/lGNAZZVQhenDV8Qotx5DCoPaDZyaMcpXyiLYpIgoR+l8QSaUUfng4wo0M3DIZku+26jGFpxpGgejD00aELdidPgeVvOx6Pl0LfYEHAqCmgB6Q2KaxHI1u/xm9TEpkZE4Fs0beVR0HciuA9VetihSe+MQjbdnGFpm0rvmgI/JMUCHzN/eyRynxs6IOLo+F8bK9khkVvfDWp222+bBlVF1a12BKzOtQNTas5DsB7Oxl7npGQLFB6AoMo+HevFpnIPY2IenXJUZyIrqEBOjs8V9WaXhOZ+VwlUDuEhCXKFfMdeevEeE3bQyow5YK+8C7QbTSMWl0oIcjBjk5Ys9rCkVmFV1N07Z+2uLASqPAa2tC2q/BXsCabSXz2fzrzbZ2Ts7mm1kkRtI7MTJB6yVpoyuWXpDKkvT3Am6snaXB5QXMdlB327lappzSP9iOjiD9l0/PS81HSKiWvdxeU7WSYqvSlpjPBhl6sa8baRZmx3sOJ9Vb3kYkWOzRbToE/SoC0VFmhbpacyBlJnuyYclYVe36yz0Yd9AZyCUnQSIQU6bS7Fo9bjQ0zjobHaUlumaymCJOB9jmQRzUJUJS0pSpVRaRZQ4g7ebmt9rYlkxG/ImNC0QxbC6fSdKPulZM8K9tfOQiScxaSFVttrko0XrcpCUeHWbUdcIiUHl06gg7sT7eNUUMzPKz+MorJuyn2oDq2z2EA43bU6+ARrZ7N1aIzzPCpXS7hDH0aY5UaceZEbJWmjCmM7uYyVz5qZl1CzLi9ZoKWA0rAzPq6HBcirA0sW4DT1jrDOswAiqBe5cWUoK81QUpLVj48DlBBd1H7on7mlJwYUWmys4aDTUxIHFc4xISpe7tUJ1f1aHVGZ7uFRm9GnmAOEsy6FekUrUJnMx9TuVswyokd+F7h7V3vDokcXW1WY445rHPtI+DqfSzCPQDYUrGeL5qglpgS4+VbE2pmwZYlixDuMtC3O0XV3Q9G5FX7ralBeXE405s85guotK9vSYjgvDa/F09V3YwHalWSK7UZsNb3WGVcIylEQAABAASURBVClp2ho2nDbVqUtVu/iedV/7/YrnW/Zz7MCQnGGaZe1QaksLheJFi9Fu5JmdU1xpydUO1mXRPn5ofIwyi3a2L5uT3pq87lglJlNZliaj+lQFTjOkNxGpQGmEsa1qPXx+8UMKqLXeSwOcpLmRaBSVUfl/MZZQpEGlryhZWMdngQjp7FhXKaNGrdE+p48Vdh14eqjZuvMkditSWS3TTW3GRoZXtHhE1HybnvYteit5rfPozFL4BiFZ1AWe8tN+lsoLQja7hjTm+1ADh+aq+2gQjs5SC9txqF6UfZXWi1pWDDlY02mgtfByQfA44IDEkjejijw+Srugqbf7qntVsNjHShVAPukVNbulI1WqoHSpp+xkygpxEZPJRjixbJlf5W5zzLEGp6I+OaMuS2OloWbTngahCgm1zw/aVWOUQWhw1GBbhjD8JBE00XutdV3mJNrFUUtsPVpibPVrdYimak9dL6CsXdC+qj/WIr8Vv+0auZ3poCf8KNhYBk/DpldfKCPqkjgk7PeIZ300wyBUnA6lRVlLOTO2yUbgnF1HN+Q1ILZ5NrfWUchHm+eSwWbuwTMsiwjNCISs7zlUu0kPeW8HlP7GQLo1Kb1SkqyWv7jrDv2t7mKnBSy6TucZnUrQuN923jqT2i9cR00zvMHhfQ3MCT5tFbdmC+QxVCmAHElimKIzGk5zt4RVZjVh3qJFkhq1is/D6kftbks81BftCbaCMsk3lTuUjFWg3+xyVyx4hHnYMTquQz9ksvE10BQaTBGzXe1Zy6s+lXrT9YxAmpcLfQ/jsAvjW/tlaCgCzJtTS0Arf0MquBbWi7QVWG8b9xgxvB7/TQ1R29QCm5B9rCwJGnOXAprPqQbu4t7j3rNyEtLusHv12yAJVH0ERqIA2ZqJqYQ1rVf1qxSYEIGAd3udOjtHiK82hUaavbA+6vWxV0e98+jptiLQBVyTahTv5lESW+geWtw5X4WPeopoSTrjvWbxxf7wnJLokOEe5CzFTWdN7oUzoHRZO4MN6MuPM9Zt12cxs8U0BIdk2Qp8jOQVRQSW6p4T8coeJcIn+tVHUCdSAWmEnzbOIQ09ViALs6gnha7buNPRaoWZTjfaUMQABMAawAU8t0ullJ1W78tAtlisAZGlYR3V0TTo0CAN4ZuvEGPg6BzimC6a1QwEVHEb5sDV8brvj8yr2UKiNpzCect6Glpi1lak9K6LkeqhmSd38B2+IYWrnyhTpBFQJrptYrwwCOqEZftm34g3+j24h3TEUo5vCKzG5AQHu4onVu6L2i7qet04B0UFk9pqdIdA9Rk7X1GTZQMU4pFaH0wNmHoOdnVbqAoKDEfJU/XcreTPTEb5hoUxulprxIbMrbqsWaycjUsr67KcxFSbpZ5fzx6z2Y2UWcCYU8NdOiFT8xVh29Vz0CFFPFEbAoiQD5FWnR77dfbHnfbGTtWga4Oo1Dq3eUrLfGVfUkVlQVGiXJk3Fe1CDmyzhMvZolr4KWoLVRne4aX0p51Us7K+k62aOJfzw0xq1om9myc/cn9TGlWpXG+ftzADZE/ShXrADfxKXVFyF6x81/2Og3uu+Pim3Vrm03M8InfdJz87ow2QhVeb2KL0BEA0xkB7K6E+bI7LaozWXlCqU1lg0ZTPmH4rIod0Lj7DRh3m3XlE3QxfDxyFeUItG8MlPG1drWAz1nSYXEH3DCoA2TMpDcrfI5e4kVsfIN6RUUqDSM0aT2KZF7Yey4sbnHGSigkdUoTSQLpRotHsG689PlqOABK0SQVqkRnSsWnz5NyjQrXRBlFReWfcBCZhbDmNRgkWuyC63qFTqeGiRrkEIc/H9ZSzlGbXEvqCE0nczK3Vhj0aM1HxxUpDc52ldWLChitr0nFZGhRSaNlX/eccsRiQ9hKS0YwxU8xq0KFU4HgPpHEUJ8I6eYPf0Q6VQFEOuczqVMuNPSQlhy1/QiLQenlTjtfq18qLLR9HsbtmL4VqdnKWpHI0hXoJiIlQecYOrY2uiJwdvGtoTLr1ku6jrzbX5m6uc9lxKl8VFZ63VGFMB8e20veE4Tw2N+ZRhhu7+lR510wYVDXg6p6hi89naFn1aSYxzRKDwtONt/GeLqbTqkf0KDMsSUi8pEGRQzVn3ZYKcBZXzHkGD8SdHKAftk3Nyd95bfmmvhq+vpWbcjQLK4sogWPAgSm6rVlaCnzmvOZUr4IVHWWjdTyNTXXfajar50McM3UyVQDaXGg5C7TaNkcSYNMCA8XrsT1cpCdq9KfFZ7c6JlMVE8OPqFGa29PHV5ippjnBgr0jKzfHmavzmzpMByAnAzAbRzKZsKu0tIk9cxfkNLauggKucLLvc973e++tZClnPHf1HYguWCryM5SJiz7GqOm6ECglbmQjC63laGVeJXiaEYxEstJrWF+Adp51ok6q6qYm6iJcV4Z9G/w2qdWTXmYcoNyxyxxG9PGMmacj1U42WkYVGsEGWGRLLEvVRwpEhHhoTPTH6jm0wPY6JTHDQMHqolW6MjnLDa/K8WCHLrc2AwqwOk7tjLAyLRWG55opal5UmU0vPa31xiY+FbK7hEQt3gj4PXGIqEakp2K5XOrpTnyy73VCK9cQldUC0GqY8TEflKq8HsIz7Ghr4XehuqflRFIMZNvJ3rVvS7UqLvoaY03YgHLMjsk9UMhA3eChQ0MMipvRfWhiE7VGjinGIPOUg2rNl8hFreJFn2H9cn8Rk2ldM9oPOk03NeUdI5tUwK4KZWtW66qwGop+qDyxN3DghPvA9F4EqMvIHim1nLFlGWidh51ilqt7tU50srMAzJePAf1jieCJbhq+kg2qbdQoLAxb3xS/mKzWJHmvBdkqCZOqtq0NjZIEZUreslEJ7MG2Hl4TQaQ6wrNIWUo9InpDnS3FhOVzFcMxDabAPY5r0S4+Jc58rIDqNkY3GsmoikpDyF9WdSLawwY5Znod3vmw4OAEkrlxR6mHZlKLzo64+4SIUzgJe2tRhPbZRp3UAmIBFmW1eFwVdyU8Dpg2skcrmHmOvQs3LIVRFFLJ5nBrw7EWYp1mtG0BxDCnNiyv5dPUQRyJRnvBjxwpKYJBcJl5DvU9RADhRWLAZ7/0W0YLghcn+jzx+d1+B+MNKNgMFwrQsysT0oAvrsXp4l6gEOQ5minzTqwxppnBiAJbGuN4PjVHYxquyTWnjylLFjU2NK7alYtRymdVnYid5+56NPR4oOaa1Wof5aCdwT2ZOn0Qd90hjOmi76HWop2uhrFaD6s0SazK2/Sm8BzkkeFD3u4xHCaDSnLrDAboEtCBiiFB64HvX7J2yeYTFHchxd+DRYGpWaaWoVQxSArnQWsDIsuZvnvuNpJGKd3fzXpMCEZLvEeNtB8TM7haHcqBmXVVzcGnzz7Xl9WirZI5Mruj4mSofYqDcuXcfojUiHTbRBQaMh3E0bqqQzYN7jRiKCSNkUKUWyhtGn0kxd0n2DDQwZW8qofA24Eykx0bHQbNxKH064ZarkC+qClpoqpm8WqVkMnK8yM1Wz1gwvzog2itXDm83fiAafPSARWUFgM75qHl9lmOZFYSSOUENCgnZTjuKto90FIhQ/QYFSnv2ucOJFq3bD6MSrIqvpnm39/S3t2Vlu2PxLqJMGWws9liiiWimKoJeYWA96QSm7NlAqTpH5Q6aa3Kvo5xQgUDRIoMwZG98kmbsJkSqVNMliONbZS69JeQQ8gfCEWzh+K10PyA4q+D2hbHMvi0Nv3JuzxihubIqOpictAG3L6MCH21Ww1xCIFX1Xa6LOJ032eHcoYgcPXwfWVdyZesLOBBIyOzYlWSWbPL0vRL6b450qcwK5fXkXPm7HwlhtKMncjHvdIaYAP6JWW+0K9076embbEHgHh+mYe7JNvd3uaC0j5KzcJZhsquN95bW0ahZD2LQ1C7bgXJAXvwF81NLnycOT6nv90O7CsIq+ZBpph05dKsrZvZz64xhZdectNVHdGnurpRoYOttFzk0CExWGik4n8Usj02pSVlWiabEpoH7ahxruqPGjxWiZIb6qrYF+hHW++SElW6xkg7G/K3m9XUWCt7POmX5Ri5vF2+XQTAJQbbNsO8bJQ9pqKfFEA5aBLWWscn+URXOJRl1IbvYB8QDZkLLUtN85q24YpP59u0Iq/NB3/ySPaqnZHorJNVGoECMlvHQR92iWnpuKc1xWYjQ1tGnpiNIau0DCCgYJaPSwuSdTLVjfatlbZApxvtCVsuAoDsuBY/jnpkNHSJrCoommVjTZMcuiKjMgcE/8hBd6DaqNksQFQKzxh0pNXR7+lwercHvKg0NtAGTMhLW3ypAMeopqHstWZyuNvLlMKc8RaGFlveee3yyvaj/IWQhEdr6zjezjCWI4bQBDJngsoFnld3VmdzY+2xtmSOLghQ++rXTBcsD1Iw4/JWM3dgyNxsGhgcECaDopPV8C62plvSOHRA0Ty+u8u6We0o9x5TfBqMkBFLx1n+nmpz7g6fhXm0zaMV0c/vUT632x4VRLZpX7Gl3s6BuEr7ZcGYpeRAl91iWgqTdxoFAOgqpi0b1+RcGH4MhxRkTVCIKnrtKgUHxvEAuDyn60M/BKF71hyP5PVb8erAQA4LushNbhH+lSZ0eJoCtB6AggWUlMpC6JV3q0Gewt19w9BeRho4W2EaJzFzyKRnK8zejl4c5k4fCA7dSoFtLXFtuJ7HRHlSpItVhqMMVa1V/dYf62vxunyNli9Rhacs8Gxpnthk1jgmasVxEXo6q7NpXSZbHj4NpjxPnTytWsvs0/z61AU82qf4gTZ9HcZq9m282dHV8fSPfFgIgVp02kVbYpdGiJxFLNLN8PJe3r4Mc9VweEOddBSSFmBFjWPPAhIQjnefd9WdQ+tA49KYbtapqpQAmo1uRMJu8qA2KYc6i2pp4SjaK3NZK/RJKu0jtXA0xVHhe+hHa4Sbc1q7sATXsdUgqwZ7EoCznvsWlsKMuG98btyixm0sfwavLN/Je1doR+f7EiPqOqEJT1R99L1D3PIIoUq2AEd6ba+Uo14Sq13VXmB3Czyi/cBTUzSi0eK9+65+Hx3RKW5C6YMtzpddNvQMa8TwUc9OaCr8rrSDJekEG7av173GBo/yjpuut0CiQwNPiNOsDgXbmls5kEvRK0Op+pL7QoUqfrTOQ+IDFTCTSq5b8coeTkzfrkZR7G0VpLJQuSrMmm4co91TTjIYwdyVQqOBnaSliXqGZZH0y6arPNTARnRlCaycP1ajKtDlQEvtqzDMGxJejxClT7gZlYrMFOS7e2d1pimROfoetbsh9638ACY7a7ldo2CnhayUCFazimhOzMmplxuUoK9DdYN+IAWUpkF+WL0cpoBhQa+pxpcvPKJfjNlvVxvI2uk7VuVEW2dnVn3VE1uEl0dkPyt5LtkSsSzmPeLmOe/dn0eInjjNV9slCnZ3dYMmx/78HpBvX5hLurAJfojTAoEGrLkULZWt3vKDzDTrOXo9takj78z5AAAQAElEQVSp7V5mVpOh71FfM9bQDlsII5TCzIt0zepCiBZ7udKvaCnXStVYGN4EdvFkKjo+4OZT7AMfnq+ZKn0E4rQ2Aklrm7QBQHkRZ1oHk/t9MtSv20jxh8FhrZ0MzvvsvY8ibqntdWooHy4bIIzmIXm4Zvaz+ozK7khrZejdupsZmyPUMzR4lnx2D0racoFeCX4uvF+9rzqWpVlIdhGKhwaxKflmerWVW17TPly1lozsKNtmuVoJTCWkbst4hbTJMKlBp1rJjp7JcXyGmtEYitoSOq9frUSbyhmhXKWy2U5KNthMpb7e2Zty7FykmQ0TMe3QuWn4aC2LRzsAfc6h7CmJugYJW3GOMjukd1+wV9pM2MlWjKxUqNjQ6qjWwsNXS+puzkcv3fNgKPOIg0YPT19F5gIuTOqUytLUBRrSvUgvR4uV9Hme7YXd+myJSkoRu1AAURP4KB+cfKKRIRoJdj9gYMUhIv1wprKXuULR1SxFljS5shKhgt4qqxpNJiHi+mwLdSiWiNd5udMgrcC1PbUQ1MhEFiTMiqzK8iP9mLcLDdzdxtVDxeAPREPXQF1denbnJ+RBloWyparaFYWWX/hpKSXM24YP1x+cUtamhT9qYcpxD6EviXY2lvu6rl7zdmZ3yxkdLiMlaH0T0fbYfqxDzMx++NlrxCX7UuKTxRHTaqjj+qC5KkS/oMxhzhxXPmnELHq4SbRGecFQu7gZ8FAdD+Wi27us+yuqZBVBg7EVu1DgboWZ/YQKepV3CJkBSuHVsg9ZE/CGaUpLSbZb6egeR0vmpS6C8+G9BunPiIiR9Qof70kdN0eKDy4bpgcJbaf71nFaOUOvx88IG2cwHjIlgtnVOAf3bTv8Piv3C/USrUqm4iuLOeu18OJYOR1DxIgDM31w0WJS7yyznoPLbQwK3Cq/Uh+8XpY2KvzJQ0aY57Az8tjetKiBfoqD+qWkcMzkx23zeKsfzwmvtvtgnyxp7EgnSIkTSrNkAtiEXc3Z9AIqkKGFkiVhu7WxYIAyrYbsbs5Uglp3i7fFJ6obcmSwYpuHtHqJPngVtk51uxWGDCLSUQYSzobcswuPvLbh9lRECaLRm/0I2AbnmmNYFSOctZ4IrWwIFC2Meighssu1w9oTezJ72ZmjMKl38EkxaVaUKxVXtRyzXJ0QefkhInadY9GuMXiudlulaiULN/zmSnNAaJHi8NPryMXycrR4ivNgtVC8rrRkWQaRNuuaOXkm7oWjIhuBeaSnWi1RizRWexgCqjeiHtCrt+MNqgj3wnbbWg6MPSHOSApiANssh5f5MEzB1sq9TtoGs0bjKnVT3Z29wPmQWdWZiqxWlfYsbCuOyBmxcC/WnD7HVF6f3JWzc4+ISZvXuGZPBqPUPlov+IPEV9iIUloMOdAL/kSyOzvKm3y2xK+7L3XqbGvpJQY3FQ5O1wFa2Hq+KNcvXi9/9VoaEtYtd1C5bSl134gdp6QFP9QDg1cty6XMpQuYo8kU4Ce+hukGZZm+i4BB2hipHQfouT2P2WmL4kneeBgOSGahFFT2w46YI5YNnyOHiVu6ixOV6lMWl3FesFXBDiDxJF40OslZR19pe5lkxXCxfVeeij7HKmroDmnnCOiyurIS9XHOqts6GjjDclDERvauTitBnywl3KDzlF4Vxly9vJ26OqyvkS/H1XPontA8r+4ZCNjBKFHy5ERw1JPUK/KIcU5iELLOJLuiKVr6njhkVqGPYY8v/+jV0vwcVqaEIhSzWligy4NWs3q2q2l4c2xT6UEJfdy/bcTwFm1WDLk9mTQ1Dl4GjW5hESmJ1WVQUo8Q+RNVq4is22IgxCPgpRHaVNW7bKODRSgpTVuYK8esMc3MJRfopwSoH45VWlZwYoyc1gtipJC7efd4h57AxMkb2uZFUy7oZmyzLM/jdQzlHipUxaiGA3kFDyJRIH2NJPS+RYpOVE1IjkoFNEkXTnrWaEgWaaYqDpEemLumCmNiiJvlED6vngW3MeboD9GfBb2EJge0LT8cR7fE79Sawgy5h8gS0KuhTKqooiqikZqod+Q21Yi3uavj4WVco4bLKqtX6ZyNQZ+udmqCYBrZyrEWksvfS7rwFoDsJ95mTI9HhN39bHzVohRrQwVQm5a2Qq0MTWylTICcen+oNg28pcSekGRrksprl1IImU6WQzsOVSiVHOGqgVhZShj5Yc8xjO2MURqsrIT3EUw3AfsK3bV2jkUtk9R5WUp09N7IGB2UGDsqoGwgq09BGa9gxzMM79a4B583jCNilNos7PEdUFTTYNaigz0Auql2amZ5mcGVBP2RSkSYskj3e9pxErIGPi4ExRuk02u89vVR03K0Wi+5IeqovnA7brMjWH2ge1+HYi6TFREkj8dn1bIz8eXYlLaL46vWVL3WQWVE7h41NICuffLz7u/eCrc97dXqqQ3muZAq6JGmRdF2YXdJ0Nt6++ihFrygN2ojEr6tM64NNSnPU53vyM/ACag5cR87dIxJUSdee4O3yFde7xLnre7VJpRjml3oqSp/LgMBK0hNrs2CJQfcJZlZiXOEBZWG7Xz3OIJcaqkcxh4mCWHSLIoVwC7e6hvqOjN8oJUOp0EGyzzgZw9oPUB/fVJxnM6K8ioGzw0tJLP8lWvqhNkeJmpgLfndsE1/Sh5DnGeKY0O75KAn/ZfLFwEr9aiyFiRTlvZSHw+JTCeKo2zgIhda4djedu7ZMVkm9JZ6Fp/5LKhLfoXQuAVNZ/byXKKlVLK2LucEvJxn5BEG1XqWWhCjqwAWFE2XHYyOCzEQOlHvUYJPuNWgqUDkAnUijUnvauUpNMPF4rsq+8kNT/bTRaqVjVfnA9ZGmHoe2AC62e10WGMG/Q2/jyPmxIOWNNmpyG5oVhBtpXzQdYWgl48c2irPLZwildRZqG70x6BJhUIXoFIvq52hr1uw0w9egSWMwUqSbd4PMTRy6PMoTAnmXXpfbbslgCYCUBM++hpkXw98CBYE3rwI/zogE8OV5oH2Mk91E9hUfKVI4aBjOucszu7FHqNpysQBMtnSSbYnWBJud5T203W8Uz1BhyN1XiP9tN1ldzMFzNhAdXe14gFMxtiHCCl5XjbkUguJXzV1KqPu3m9EEnYNn6JbWKdKVfHP5EilqUen4JpOX6XJRUDpUDayoQ9VJlvIWebm5PgKruDdYN+r1In017FHR3ekxxknmtUDY/qHjw+zBbjVA9hdOfdlRoNSO91cELb3DeYcD9BrLO1nVuNppbF4Rp2dfUqg7vIs4fWejsux9P6hZaZimWyHXt42Zd6QQlE21CaDaOGtJ7buUVg9QZb3c4VOvtpWtqblGu+5tHgmqIt75r5KUEf3SnFlzWuPV3J4LkunNNetx6BqLCPMrIfVQrKlwT650aWArgE9eTJGjl0zHjxIkYhua/czXADIBXWwUH99G95OAS5Hzw7L4D54A9IJVbkUGCaNoKW8dRONh+NHHamGG5CBASjQXlQXfFJbnax0pCZBiYrSZBYJXW0rWdE7ha6+UIrvOV/uqfV/bvmtyghZTLNzeA5UeOy02ILHRBSkmSrszaMJ2+wOTSLk9BDvwK4upCEHc6yCxI1kSpBPNEa1EAxlSyKDUiUEE6WIXUl5QpRW+hM4gyWS7cHOJoqjJDoHlXG+rZM9EiN0xbbrN0z6pWRzLdq0IAW/5FlyMb1EEdmm7rT3k7mq3bhhvctuBfL29a6lmPuQsby0C50p0mgtYTeGYMBTM7QdRCVZKCEzr/mhCnSjCYOa9xzhk7TL++Xt0JSsOt315JHQMvl22yb8Z4x7mBaKbSeUH82SOQ9D45SO9tL0BqGEm1gZuugy148K0r3HVdp07/kMWBagOop4L+FS4h29+qseYMHeyB0h9tgNVDRCbx7g5ul2DEGFppMqyDqsjVE2IQZ0HQ9mu82Pj+jaee6SPcUFIxxl0luVZ7MLMqw07sGTXUHAcuE9623y600Tm3I2brfPaTUgQEjkQRQTyn0omGUxIY/Cp5bTbmkMjQwrCQmGGl4TSld7ocxk2FtwCDNbzsuUeukzumUat3ArE6f4dt8Ahp01w9XgkVW7srP81NMwOoJ5hpabjoExMYYmnDh3RFi32vkwzFGqYfZpU7y5zNe8SrskYdJzE/VvymSIGpTGSlrD+up4KmRwLWhPZSPF0JHze3s82mqLtg6T8df2pajLuqS35xjppPcozuMjh4uTqezEw4BGZW1Fq0HBte0qCYHoSGzN6etwCmbNomoIynvLgFI8bXyIECm1cVMptnnqdc7oFLObfobDnhgu+AXV8tFqdM3pl9ellM5qb3Ty9AxbTxfXaWXvMKJKNFR0RPfdLHdz8XAWvoJTQKNr1I3+VGFzeq+ndkHFrQTPfoZxEq5r9prjY83hg+SjglUxsvROo0LLRgDEo5BHDDkTjEYpZW1Xc0WPsCtVpwrJB9DQModPx5t2w7fZA7jg4IeNV5eXEn/u3XcI8G7eRFTrArDcpjWZ0b5r7pZ9wQ5XBSltLKDZwNQYDDN3Pk/7vB1QJqVRvqxMzVLMIRUk3ZtWWa1AwtGDFMvwaf408srOLT5llYAzRM8w0B3exfqSkN6J7Fsbk5jIy84l0zmsCIA0uvVF7W0M3ae3VmE+a/3swkawZBULnACmYy1F6ShXG3l6Q3g9noBCJnbh6Z6bOAZA0T6/3dPEpGmAPjolemeycTBuUu7h1ilLnRoABZx5CiJSsXJZL6Aq78wIY/myhfGCaT3QBY03pbIG2+SVQZVMK7JhkgJRFDT5FgV3U+rPMyPHqZkcBeu9fb+rlc59ukqnUvsh2nGmboXd3Vw2uk6iGmgYbTzEdVdWWZXLi3VD0djqx2MTMSpW96WreIbPSR0Q6k7TvC7ldY+58RG4qnkEdjU/qpfrOeCJVaqlYoJZFtlgY2Z3SIP3m9/ls9mWkYGzOokuoATC1O17AFaJVkV0YrQwcMBm5YWWIcXi1ZMcC+NqqYaEDdZqoYHJniTznu+wUDaEQGO4Bjjy7t7hLQ8yQeyHrI+S+RytSa2cDVc6lIGBHqbl+VRjD2vVNBIKvRsd7FbCSrk57blXqNVSzKGnmezZgIq51qgeGgrIZyBv0xKiyKo7mlnOevRLJzRhGWgAWOynbghzE3ExrLjarpjzvnC8A6oxG6Y/AClF9rt0Q3Rkl1FIduk0nVTdTvb0UmA0a3PlxocsDz5TKaCAPPWjjGLt4i68z1A4FnMcl1AYtj4xQ3bRqTlPUNIK4U69z3vwfnYMRkp1yVJ/qVDUT+QpIRDK3RCyAiAY3ULW5BPQN8BNIecZR82NSvosm/cibHj6dQ/f7NNoGktjgWxC5jMqTiaqWZayWdOgK1XPSdF3NV2XpShq9YcZUKUFyFmzWxX45EkWpX+ffS1ew+1ht9Qz89316a2vKmtcxLexX2vrEZ24FffdTiMdz8cuO6ocLDRFcpjQVPW78QkP8zkHXyNU/Kqmul42hto3fp88twAAEABJREFUDJkzzro3I6tNYW6rzcx4SBNEF2dW7dUsikka3OQZ2dh0jVIN6rG1uCLgF30O95dJvj3EJEpVoL52m0k5wFapSNysQ24fNfkYaVwH69b4VVg6NVpML4eTPbwcIt/QnXgKE6TbekEC//B8ibgRtCpK1+IUDKjTIqVMlivSS78Uu1GooTBGuFLcUCM6fY5n0uOpwjKUSIVJx5kVO7WuCMQYlGAdR24meLWZMtmt9Dp9AFpCWalZfrJ2VOuDp6Y5m0mTegJChKontIEtjzEFoNBdlfolNi3Xus0Pqc51JUdzQkWMYaN1W2Bjz75X1KgyxVh7UYS5pPbwaCEn6FZRjAG6eLtwLp4xnv419sPLLj9wk3yl0hxvjM+OXXnujtvY1YjuLlUyAvPm+qx5hAPmAe6sA5bJp5miu3RypCRR4A0iHaGsNuYb17u+be0IS+HWbb1dFFTzuCpq3RyZKGUBXamIrsGlf3FVvMqufAGj/ZSLHYUyJLC2AZvQKx9eUVou5LOlqQuai5WYuaEcsHSzdeooIbKv4uwrkIKfQc0jXBh3vt79LfsyUVI9Ew9EpgHsoSP8AdMexiQQYZo7W1FCpeRzfDPlxXHIosInqanVRBs5FV5nn7CzV99ziBrh48PHrn2Q96s+J34FFVtJ8tW1/CyNm+XAw/tWA67hMSZNUuOoPhK6kuRyGtbFgn02gpJRnsisrCpv7RDvgbIe3l+zX1mjj1J9UjtJK0NbG/IyNS6pBtDMbJ0vL1y3jUPj9hX9ioTcAj23WaBabt6RIpidcr2hhUHMJjNDMUsTQ1w05LAqm7dQmGhJZJKm6s0P9tVu+Ej/UFcqUdMC7AqlJcXgNJtfjYqgJwwVK/BRfjXScnuUt5l0qKMMAZSQCUklBfBz9/3Zz+0LHteaz6C95cN2xud5Zxvna/gHJe5obfn06FXPfEUaSGC1vUDlXkS1hAhvd39U+jAGGbTZvsbgx8qXlRWOqsXGfoCAyepOelA3E4u6fixioaVhKk+35Rks5eHeuqfw64XXB9cPYX5ATZ+DjulFyyyxvjgml6Sxdo2UFjWxE9h1oz6J98rj3xXvzurUbFzEMiU4Pq7+uPgSV+F+S0U5kF+B2eZ8t+kOP49smwrJGtWNKMUxuw0w/cCC65guhYrLVZsv2nLacE7XGEL5beyb9xlP3UJ8GMfsl5acD+cAlQDoxVK6Oke+2Z+JIKmwZqOR6HYlBoXh6t5O+fyO+E6lOA/maZ5sOHta29oQyC4NYrrs25CpXkN9T/EZOIkhZCavkgdRpreXafJVe8YNZHdbs4Qj9V6hh55Rdd5zl6WSN1u8j8Uh5XSeipNZ972P4tx85bgaMuABBdwOvighH2lfVRHZjxm6U61i6a503uo0HGi62DSqu6a62nxWzusbbc3mq2kc6pRvw45CtVfRWqvzO3Uj/Y58p33mOqdPvjf6lvM2U97VAC3aHpb6KDyWtGY7+32itE27xbdhwZuuzL+FJjx3A42JFqDG7k4rM/mUa7EwmhMPaA0rNJFIhZZ34R3zpOs8KPrS9hmKsC9F0m5ke4Q9GQWIAf9ovFhD99FTceGkQINZglVHtFxt3jYI13C9l9Ifgd/RURmjnsbmCAemzWnfhnj1VZzCrukLICUwF3D5Pq2hXavGULvJb2L7bczOjKqGW1x9RhyKNmyHeIiHSlZWbtUr3cykoukSDVXFs4OZEPHxVF1CueE8auDakqicnbGYF/sKrrsr8H40cIJ6RBe6m8VENO5bX0auuFdu9+ZQP7fDPp3nVD/eA2hTCsS1+m/0aFyjLpfQZWyn5UO7N6xjMESXht/28ZbnGn5gKSY4B5bzNaAI3Do9FWxWp5ajchrXQw6KaLjDDZ0i5x218zk7zL5OVqKDXRrW0dXImed65+vm6m7jnt5yZmHSNEanSrbF4p2MT6u7ockLwjFy4e31Wco+wWvN8cIjyOLKJ/skFDwwSlHTYSPHUn9qQam6FOG7DIVhsFm9TP5r6wzpWRaYamu7fAe3GO0uh+ZCwDpbLm5X4+tEVAtelq42xbWGyXezwKTf+o1An1WxOvRUZmgB8MUx8RT+uNVeF1P+bbnVzNF6XJBzZ73v911xD8QLubgvxgWIblwLFBB7+zpzBgA2PBrKqBTwPrhPB3bxfSIyWVIBrJLSaAhxFapToSkUESWvuCv3jt60bZZabIcbJ2tmjghPeKl9cUSatBM0yWXLLyUfiXson5mSavZK6IS79fByf4aCSIxTj397E8chPqGXDFNX24S33l+gWurVLYWY7xE6anaZpyEZqqzWuyAn8FF7nhvnnWoQV2O0GQ0OeunvGPTLpN5WyYR1o9BhdUbthUNju5cl8NaRdSJDHykZV+Wp/dmxBVEDUnZH2jQTDWjFubs/fX3OV0zHKPE/uofVNUAb7WOHDN65774P38XSoFF4jCPFqmRZAENdxWwhX7WdAR5SCTiGBjQa5YY5pJfhJu8Aq6u4k/fbU4rdxrxYDhLLeEHDOhjiRtntJ1pPGetyTmJWub5HwK2t0HZpU01h9GAJGYAhXop1Fa+97P3N7o/mhIQ32UpW3q3uki2v5YWKUTZL+Gd0yYu1SYUnEMUzrb0rOt8m4mFqBKtN+nhpOq4S/RP+CO5zSMxzj1l0mEJogUjv9OpRPktWfRjd6cy2I6g6TEFb4vX2EU6dGEnmQFNx2trXFeOJGcKqzRO6j5XYomsszXoM+EuNFsqhvM1qNk10O/hgPs+syzCNgzZboB8osFPebQ1W9+48iIOjxTE/K88ZHupHdgmuDOeMPVI5mJ3Lc8zb6/jd47ZxX3bbK+Hsjjq3n6//yUDQwEZGt/pUwMNcmTnoM1Jr3IPFhN2i+VlV6AcWh8GVJy0jts57PLujTWmsk6hnEACCCjKRKggFACslSSW2jmiVtHsXdliftL4nbrlMLGI2M+WvRd58tX10X9y6cHGhPo70tlGkkjV9m+/0LijSyaOceaRH+anaeCfVyxNAO/c13i50xWrLwUTLyh6dTzZOs44JA3CQRxrzG3ObqWac4b1HnUm74FcvK5dRh3tD83tfoc7ZGl0295M1hQtTn7lHq7O2WxpLAXBO86tsSJkHTDJ9KC3C+Yl4IxOJIfuW7NZ7pjM1vBkafd1zbcnd6N0rBFqV4WexFoZzpbanqU5O9rZOVXneyM+2z1SnGBqMBmkiJJuJA+nDWhoUK+rUJ84tpIAZMJBe8uom2QwLU7nrxq1OKXgNgY02GrFaHObDbQ+FA5eGUZB/Ma+/bKK94EGDNQhV0Smy/AGuEjAOA2U2wVbBPA9+pDuOY9CDj03QoQFNkaK8ocwhzzZKwFSIA0BWCK0ePaSqHwqvWdba7kzTHhzNVsxVRAOdQBBKWbsK35NWORCGYu/Wcq9m9mS3REx1afgzq71D0jE1BgsJt2RuA9GPacAAhwSfTwGlpGgWOto0XQclzBvquww2usZRVkrSDy3HPZMzMRomv9o/Ld1bLYXZSkIoN3wA0iaxzFwBj4I8tiJ/DrKpsNo2PA0QodNyPrSn9lEH1SJQU4BDRbHruvEKm6KSIsS7pybOA4t4IlIxDt2IDStY1ixBa2NqOhAJFi2hrtuVTi3FZrNBHaUKypY42x3OKuhfwZVDDrZH+6YYlwWSMLT3UYBZucHTFjJD7xzqcKImE9Dx/Ozss6t3jlJoarq3+xFflPidQ+Ko5pdJOTLIDsxggzTQlEv2qTzVhW7B76iIg5B7ddUqOU92547YWd2tbRKhbsC7/KSS9nAntirUOORPHaUTlHS4qHChIZF8qyOEVVO6EvOqwbLyBb+AGZpbdBPodih+6qUmX5ZBreeZyUhTfzzK+07qoWkcsQKIaCohk3viSTI43E1n3UU2QQh+CjfNNPiITqqvlRgG2WksEykF9ZvvjyjrQ7HOCo3wp0GtNX2u7tm38pSrckSXjmYYE2o86jRy8DRczXu6lni096ZahFx0UfTtRJ/uDfUilMm6WA/Y7hdCa8cQ3iQDc9creqEFupnZgUokCMNQZlXldqUQXVRC0KWratjy1g0xTkzAAVinM5Ww5oTPxx5oXMu92FCUrXA12kbate1qU2+wDmbIpX40rDXyMWppkkJ1BE77kaOWplMI9FjERjvTLXrtM8+21KOtg27oJnss2hOfQUXoNoe0uqtK3y7GpgCuwsoeOCbARZUsN4CTVFdsWmve8GbBN1WgqIdijTGtJ0rGE6GJvklz6WiBSr9Iv6NVQohJug8qyoaAlw8Z/bJqpvyjqktYnWWeUGwz8KgVqXHdgmdu9UTXUQliwLy1gRma1RolxWwBnG5DV6Hc6q93kc+2Xz+7q7zhioqiF5bmJW+nx1vucYjbuN2O6xLajTadl8H7EeukmmE1pcm0kSrtSuvIusXZ2327ig/qS9ZYUFBPOWZOhy/7cirMNeSalUQ1FAkErOJ5T01p/+K/hW8iXaHfrdbf0Ku6T8W5uzZxrolr0SeVl7DeZq1lfE2dETCaRH4jb/IhGhd4bVxhVkOBQaSQdLqDCPkvsAbPwl54E7vBlnZLDVaXQDc37V5lV8NbcB8WClV9rPaVW8AHiyXImjRKIKCgaZCmwlXP1nTlrbWxyw0LoFGOcNefERzy6N3O1pmnJpKtfJ1H03wQn4b6Io53eteT8ac/5lsUNqoEJastc+giMurpqVEoo4qj9RtuPaxhs3Wm0JXxpKDKkKWowiq9niiq1NBrPyfLuoeYa9N8OT+wDMtWHnEEWlG2/Mu2XCRKyYPCls2ARfmJ2WnPWSspOkeFP+5UOn3b1lYisndqHPvJiDmtFfgzJlQOPtDL9JotjmQBCDmMR0zI1uQsijvI6oRuX4S/IqWpU0ohB8ar3b7wINPSxll+X9habPJ4pPZKIqAT3Z6tbS1hG6L8C/UT4gSG+EGBioGtEjWESBhK/B4dBjFGM9ClwxzPkt2A1AVvkNUdkErFBBrw0i9dbVFGi8tTeVGD0Snp4kDV5cP5cl6G6UlNWFU6jKcZ8M4LKcdd8qz27YC8yI/lM2RlWm/J5Ot6NouW0Ksf6kqxPACmj/H0byX/ZpaQunLMiTAtuxXilT2qTJhTH9Lx7CqpVSSEU9NKHN9HuRNiHuMGA1XQunzEpxl9DqyBOWHD3AzShlIHBYzZWqT3gLB61egS7bUBGa0SWLaXrGhUyzVM67kNOT5deRqRVJZDgVIOyUM79ow0/3KHML4NgUTmzBqVIyVIIVjuXrA7/K30KQ2PuCrV4mjjuWalSaZ2QBXg8z4kNblWUaKbKOqPbBNBHFKuMJSgeJ30A5QerFxnmpkqKgmBhyzTUcE4rIAH5wFL5mbw4hRsCVXxN8TQGLIZk0l6UfgW11hXO1RtU2bJIdncGgGvtlK7WMavBpjwPVHTbBUZoR6628LwtY48jGn0oYd0JHuDHHBTpNskDVrBOyF2JXHlAdprUBVfRZ09bmdAyXPwYYGNUUbDfh4AABAASURBVLDqkmJMetJJetS4IZMmIT8P+x8zBY3tVj4EeFA3kZC+zaYNPWJ8jZJlV0ftPfyMUZcu+aDR4SPS9+5oCVHe7PGtKkrI2+IhdsuoN5ui1FmabzPykS/N3AQYpsE9M6hq1HbDtOQ/wNn2XDFxBeqtLcjOjrzv7tOyrl2XnEvV3U8QREEXMrKy0c4WgecV59Ji6tTdF6bRJkyYZCotC9EKXCw1lPW7sKOlqWbtftjyw9ddU/ZhkDodKba/5jVwrO55Qk/HdRJKhvVBSUfC4ZkAorhTs8sMfdl9zc/XfC+PNaI/UuqngmvMiJGHfchqRxtgpAJgf1h+WC24STpwzTVLgd0CqbCjGaJb75IszZscbmKtUy6dX9ewUaF0cS5IDEpR3BB9vqENe75r31E65fpu+H3mvq8+A3TtJcEJ0bkf0mV3cV1n6o5o0GjD5nCd7arMndjFIubgB6FdkD82FIV4YJXMiFbr7yzeHlvlKIK6Lb/KBNC+QOxjP5X1It0KFkdPKLqc0FGZ98470M+/hFIm1IkMtNTJUHoT69TlydUlPvQT7i1Eroyet0n+ofWEiSpZYUOUlbXWG2lxXRR9xqvgretCVitryFczbJb2dmd2Fq3cOc3pmXZOJguznppLWNLM4BP0RilQ11VX8eIcq83nHBIiPGKghxdnN4UHINUVz4wjXD2RZi0zLHWewUcX4rlCNo3ROKUmK1MjWwvmpd7hQuIPG6cplUhTvY52YUZZlovsp0u3rIq0U6mUwQZipqgGY6gtdfcZfs9LFdknaIcKnWLsDipx2Y3Oa9R08+aIMN507WV73zhvInR+XD4udRHlsc8+0Z092xaGqSf31CehBmaTR5Eu46BwmeJRbBp1zb5Yq+Max2bQadDy+0NTzwiHQTSK3C5Ytx41rcnpMiGppGIrVHeyw3jMykeP8cUfFALFjgKduwWesRnRd8a7dPim3HeXgJiRtDIl32DrirkOlaSas6Y6Sr96L2if/eiqHS06LO1RQDdRozH3nHvNphDYaOWQauhRrSONMuKvnh85PpCvKamb2tUNGeJEue00CFEZ4KAEkee1+6Omvlh+mV90NjMQ4bv9Ng+xVI3n1cKTIw771PSA1+mKSgV1jRarl8Uc5cselh9DTmnXE0x7zhctRGFJ4cxjXRydkwXv8JkcypdXj0STrbhVlNyHR19Zv2sKutIrEHtQj+c3rshLyoEy9az2gHQC5THAN9ed132/znvKDyzgRcKpGxcAj/n3GPcYp9bI6TBINQJjs1JPvEc+2Z621whnuaJsQEoFaG5j0l1v2wjo3XCQqh89IyGmmY6VqgQzN0XZiEaBbvBuJCuX90tDFINV9qoY6ik8MZSwWKyrqIxdOApx3DBxnrnqonohPshvrhtpmqyiW1oZAzlG0LMlldAZBwee/jmMKjqIAgJOjJZOzNRmzymBjrbE6X4wliFHlT1bgLV6jjlzag+s4o2kViAHxdJgcYi6DbsHdc+QUrhMZQRwl9pCdLxldTu7euQG36rSmhDm0ZiHrxgKTE8qTpX6oGgark47rn4ETm7zTwp18YRlFKA4wWyB65qmODqtQqiqmOxgyGNmIrIzO7JCbULO8PxHA+uh9CS7d404IzZT6309P6HkMkdv15TSh2LDc7a+2Du2iI9RXy2FRMZQR5vhqwu8++mMWWfd+3rHvJ1RB1EMeIltyS5g3c6eJtaVsgoo5tZeeKi8S2QqeYUWuGyT0VTD1GG8v6IQYtaxBPdkLGWg6VnzrrEvuz8qvxU8lMDOSm07Hi7arG2o1aVP6EU8uW+ldjMUM4T0np2TyghUEfpDsnPhDH30oQpLjA4yW4GYVIsv9bLcIIlt2nn8Wm3WhlyHN9ilrrCro8qaOD03/JSOE3WTMVyFTG8fZVoLRtcwRdQS4pFg9qOwpVs6QStTfs54UKlICvnZqhNLfRdXz6YAVFHK8wnce0hHTGshtQdpo5lZEVXJprlTmh5rcs0UKwHligkYVcQ+bMhzXPW+4nPmpmpi15NKurnL7jih6GB7rkcOBm21Vm51kfTc3QcNtCOSyDFs6nHFMFJGokMMReJOBjG8J8rhrVqkB9CWPUN8DNZAmZVfcBkw0aVBMfr2/vQtXGTOkqgsekTPzKH4euwSxwZgjbqmpIvXwTogUFJXS1kPC/xY/TFEamTnXfluP7hSPp65SmooXPTQYc0nCh+uKBIMQ14BL5RStgwX7KrXSi1JFAbrcs24FobCUySz4duGdFuyUPlII6qF0fO+6m6I5xEcpVPdutDK3kBAZIr6yXP5UZIJNWgsO/5sDENM38D27Ks5p3FqANgq8rlcZ0Dz80jGI5cKvgQeozh8jrXW1F5iBudpuRL71q2jRJQNm6Od1Rmnq5DgcxjkanzkXElp7HirQcmJOeT3a+x5pnevc4+4aYcsShFdjS7qVew0wIhJ5V+aiEhpInqG1tp52tNoa5VfJ6ZIkAzsOpCh6D5iX3GuEG7rDcv2PrR8rnVkh8otHbc4V6z2hAEvnz0MX35p5VZ6/OoqKYVIRWxOwtpdKX2Iq9HnCX+gxiK14gisPny1xuh6QMKTA/IBSxGjFFSr1/hZaigZns2wytGhVJZgS9eur1Zew1zu5E8kRudJyxKmbrYaRUUW2gTGagxBFR81E8bMWYrs8RksocgKLynu4XiWD5ibK7eU6NqrZiu0IOhFZ9KikVWl09YD2hd37opPPhAlY50oYYFCf3Z4qjlSYEq+cPiEOkWj+KDaFVO8XxEeStFzaKyAiOSZ/mFYzy65lXthaIsv/yAewnHE5nKty64FpGd2y4WfvG4NDlDKLgVclEGYTmwrdY7drBoaqh4qy3CwR/WK0G/CbU5FP40y/yT4ZfNl/kF8Q15UV43siBIHNCt3lRmZA/Ea6Uup7qjMIGLQ9DHiirfIaSW5jK1lQVfK5LgBpozwUIu6ilRWk/HkwwFruv5MlIa0VdnpBvUYak5ZRh0/xwpGq+m35Aixq0mMafqoBUJzEGQ1kJCttblEYkhWs1Inj3VYSRxyIYcgGO3dbJYyfKpPIcBnIVM4znwCtpTUNVb0PfIkhGe0zlFAwjw0/YI689Clrd7uqx7M3vATI4tAkyEhSAR0IwxCAxGXahTBVU/nNQywnXikGJWR+vosQvSrdeHdJfgpxzTvQU3i5e2P+Pau0L3x2xg+E9NMLtEqvgm91fiyur0+HYqOULmaTf1FJ5qAtzsXsAIiS3STZj2FmWyMc14Jey65Kq+EuZ6yQLcFqI8t2rMUMDo6TyAOAYjM6BOp94NY4gzmRWs8s4x7+CZUn+2Z3alM5aj0LDRTTAoYGlCJ852MBJoyZqHB6ZHcmoQMCIPGupfzQDcKp6qzdbvl4ltcG3q2JPn1VoNkhVKAFFOxTg2F093jHI9TdkKdgGC4q+z01zrUVQ0wmptu9FQBRU16YIuFUohF9iC1SrPw+GFRePVWv2ns6W2ExVAtp7PdUEPM4SErrLdE6re4C9VyijwPosSDopMPcx/DNUmEm/wKO1JQbQcjTLx96637Nwcg/ral3gxBEKRh0EwO44MhLZ8pUMNnmlBuT/mE1p40CKSNec3retq4g51P9b9Lq65MKkpeTdWpLhFDb59I2P0cbhzaADJKLSpWcGSjW0aUjgrLEistEggFGWaFcYJ9upUxM5G/00I8CblfjVnnsvs1zjR4GvdiDNOAaENJoc4aQx68c1VaF+AVD5Wp+ClUcgp1P+1dNUxmzRIjMNunsgm3x5I1CzxgbvN7zNLqx9nPwnTBvnFNDrGa2EBqTtIPn5gAej+qVkueJVNKrCAxHSl4QEeJjgVca1AP0l5W00QZW8BGa4B0hYJeDeq3d7kiHPRlIOGNx/Zp1knqqHQvIpnVx8SqovShtvTwQUai0RpQgd5Z2WHQVZtuGNBFP+mpDEg+uZ733U90CyHEI3LcserQM6cfdRSimtamVUokPER9WRFMLjPogwAO4YHqmbe8esfEGOakE5TPYidN1FVdljZkPkJ2IbSBO3isyzSkZFBEShYCn0fMmOQAFCX3anmrUuQlxQQRIsjoOquDFi2uYaJXH1uOYS62lOOrUo8srq2oyvrMjKtfpgeR7tmQ+CI7lQXZm1CzkAQGRab1VpH6MBvQR9rd8loeAtvd1jRULitHVzL2wL6GZJrztJdf8Cu1bUKythwpF+FPIVrDbY/1yXW3N4lhraesjoeBNirtMJGhgXDF/nQWQD1GJ3WA7ZCKKQ1R/SDAslH2qvYSDV1HgCIj6oQlV5WSOoXD6pGs9l242dUBwTJrt2rzNnC57Mr4KnM8PYckvFLSsISqpLulpLFsaXwe7G1HhRGAPCuueiAMCLaVxMsLQkaTn1YfXRt2l50cWYRRSxENgKQaKRUMGEqPCaxZtKPDvYw+YromqFmPahg5aAZlS9CeXCtf0/Ca28Zb1jw0w5/gEjh2bX1UCYNC6VZ4QglQrsnJmiiDRsqlBNQncGpBXgzPeZWx5ZlVL6XN5IO3RUp/kAdJm0mp76G3M6pDK0koD4Aoaf1aqMVSikkNHYmx98rPQfWbKkUaJHq0bhBjjLAFahATJlXxQMa9hLDHgBqjLTF1g58Yb7pM9xRC1uccW3um+njVeBm8kfW8tBVsge3oqnDGsvRW7xT4AO4+d6kcu1yknZfXlXrKqLsiXwM2Oud5276Bt1OtuBq6bb1LuPyG7WSqEmqk1SfO5sZqzIEc/YZ9lgUPx5baXIlQUZiNKOyM5rmY31y5VCIaTqw1v018m/WSsodsOlyvUiMqKJPA7S307VDlPju2g04YuIjLbdry8pX5widWHa2J65vx29WX7iPsQotIddMcpq5p5JB9gkLk+1RUGvYyUaRtCTtJvvoaudC2J+61beyr+gqOh8s9RKKAtfqjvSwvwjOzXLSU7gvSvCiXuvO6D09WojVlYKvP2ift3VaBhzSQyjft07LE+bZvn6/r84MxO63b3Vys+ojG4PuHrvPN5GbiSFObKqqhDE5wDl9Gh6Me59/Ay9yZ6XUgkYvH07pfm3wPBk534HmJEWu1i3jHDhYE0zuGbSrfYkZJj2W1LtbL7/nK0vdHr7O+9fzW/HZoio2NSbh+VlM1KRRnVNY57/O5K6jwaUuhAt20dutlbZ4lnUCIB8biWsOLPGDoMqYZfemCvaa9Iv1NnJeNNRZ8KZotPRRm9zWas8KrkJqvjtMjJO+tGngPO4MosRtVkmcqvyEccrVqgXnw1hW00V42R4/SaxysLL/V6RQgmnR1rdagyFGhxzrUsn5dtYaRql5/KymvqlmKD4CZanGW1Ok9Gi3OC++eu666laK6ugmPelqiMc2Oj6Kawf3Bt5eiIXv0k/rrnBFqT313aMYH+6VV+Bv7G/DRasoDNuRuxJipCKZz2HCf4ok1ZOZZkCCgTr98XibI2KPvARBd5SFXNWfZBzjbywYoiZF72h7c0g+42kSUAUBoAAAQAElEQVRltAjDHXEs94rzK7/0Kz/zV37+8wQnteGY20Y+vnJB/HfsemtPgoIt9KQiUsZbwqraQMRAvSy0BwKcEGiMVKa7MiBehmiazCV+VgpZoTWfeVW93oupwbyHONXhxydtDrPxsvxmn97v7LjHOuaROPdz8Oyn2oK5Pd4Ks6epjcj1ab/7qviYe9geiOvEilQXSAvDtv6u+yTQ3YOQomdzlmbVDFKnA/Hu+O4kFdjdPB2NY9bW6xd/If/yX/7Fz195R4lkfKyao37Pv/aH/7Hf9Qd+1//j/9mgUEx8OK7SsrPV7Ng5n//4usmSy1boDZV9EiJputpiRX/tV66S7pTsrNcuv6tuRqv2Ow4zZon5NYa+4Rzd0LpQjew2KA71Wah18qPhtRLfwj9apdGLqi4oK61onnqOPsWCWfq8Md7Vn+c+ejQ4ZUA7wtnnS0cQQA2rU9jlEc6jeVvEQa8dq/OlDDLUhzJpBx9Vaiwtgxl3P7sEjqrrTiqOGm+fe6JMojSzZWtJ33Kycx8JT1FgUhbaM4jb5Ra9OfcYNRRt66xysHK4ckJdqmxltJqRVhR7gyFCDWkJf9sVGGWnrrMvqKd3H9/bJFkrPDzHxdACFaOL/Mh/++d+9h//A//qb/z9/8pf++t/1Z5sIZrvZnNZ9XXu0RroHsQx5zXt49o2paft1cPhozCTToeJpAIORnBAQnMHBSAQx5Fzpl+3KTjTrrMug8k2hJwqQcf12te3+7VKphooJbylvzTqBqZW+1WL2hI3ysGJZRA4ZyGLSXmaDJvvHKNxAVdWp9C0MSZhdNIuhxrQxVVQrxy70exh2+zTlK5q36ty3FIlCMABVUF/ID74CIuGngXzaU/bwBxPY0hr2i7UA/pLRS4S7RNQTs9ojmSf7AwFBLV+R4m4McUS/ZjVgLgcpAvtXGN6SwXlO3sjt6E4ULOdWM8VeFYqK2uWut/QhQVvuKJC63yhs6mYbbIBFkyJlZUqT17wjxgrBpsElwuIW7LexCN2nZzQiw19taFHKpfBruqPk6tiOS4fbqseJjx8bM77mCrW3GwoB5+gesmdMfwsD7W7C+ELNlgPUVaSIa9rDvXtKwh4j8Il+EVxdsUYZfbL+/0zv/SDREgHp7QMYZNhbCvLshtdVkO58AE0tT3wDuriq+OHMr4VZvkvvfOv/tLf3FbPEEM7lD0pxkN8xrB7ve75kpkcFS5+fF5jjnxM1bunjhNQ9ornfuXbsROnHDFSqdW77st4iaHmRzRJu9gGRHnnGvfr9Z7ztFX2X/j//ez/5vf/y7/x//iv/KVf+Bvq1Q9YMc5KWnCUQkBo53G6tqtyR9oc/jGg9qaI2yD+cCO3M+eAO5wArDE0XPmrd+68U8kUhA4BBcTQGKC8aBa1MFyd+igaVL1QdYzYUMtSyNLdQqoyICxUSDV0+1X39zFn+jjXfE/bNMhlr5tjwzDhdkyncWwn36iEKBLb3pdTWZ6Kv/EQ9tHvSx3UkT5D+EvtE92tyKV3lSLOZDXyGNMv8HKLNiXBW7oIXCp46VDbp680JbpcudxLK0o10W1sag7ehQCBaT6kEZtN797VmVHa+TJr7jPixrhxHZPjM9QtTJzV4J69PRuS3LMa9QnbWmmo9M7Ck1I63ICSmCzc2mHTKZdaUXTUQemDyHoNTgP76fAEXrQXIZjdAt+3ycd4Q9WmlsSAJkmLORULUR318JmGnNMxRol7DnMoH+5qrprgoWJDoo/zLFPRtWsjc3umCbG93NQxbRqnUybsKh3alObzYZT6EOwoGmvLZL5bSrKUN7Mhh0pVkepw7+zzVmpMTmHC5LaHHcKyHuWIDkNpAFqFm0Sa1XAsAJ2GHjq4FWyNTplsFEXt3RJPhHXnwlsafUXNPKwbeNts9yZFLrT1nEfGZpkO1bNYqu6jpEbHeT6Zpb3K7EB9Y7cKAUQYwyFvZq2PglgypyrK4m6eNCZItPHs0cecNa7E94FOrJevqYaq/mrI1Weh+fQVSSQV6q6SDPtiiNGW0Bw0iHRVlS3JI6we72A/NqFPRR6FnZVUQAE+7UWIDIvyxMLaMY5IkADxONEwcyyn44xKoPG8ZqV1sFIYQvm6ZgmHYDYnzdkaCeVSwinDI5NWgVCUDJcgTJgq9z69e4q6oG/tOebgusbwgScEjdF0zi6P6Cqlw+gmTrcSqwI2ayojcjjZUpkNiNOTcfKUtU+IP+PgmFzL5iq5yRZ9BhvuY9bwdKSXsSgVnuMdl9cc0TPUxN/KfvOJuK0xiJkcag2RlPoCLvXv5TXAVcMKLVwFlrDYhAmirOeF9vP+/E2/+1/6h/53v/df/Nd/TC7HSe8NPMaaKvMc97PhLvNei9earsmEkrTbAtlqym5Rr9ofwMKwHDzyaVbuwZFNR0xyDFEmfYrhyrT0cebYjhzdI2jq/nqTx3qbo6Y+NHXBm+/9/vW/7Z/9e/9n/+vf9X//EUUC8xzYg/fEVrbR7J7MVWY9DDn51mGmQuhMz6YNB5WmM/g27E7VRPZO7qIq41bV9mbude6lBQTBbDuwEMn2UOhUvguHBQ+3no15FAK9cFmzs2QWNublvtwNbj6ExuW2zeClRUfOQvfqedrSjCa8VdnRhGNc5YutpFsjxa3Ku4KmPmZu9O5nrUUvk6rLyFEqqGvELAWKTKtfOp9//2/77f+1//lv+j3/2h9qdLHpYuj+kT/xU7/vR/9fP/Ljf+aN8W67G6dG1JKvMUKSIEa1o4gyhizX4ObThu8UhsRom8fw2f3ejNv7TKspMOg5SFO69BsqoenmCxzY1XFAkIAOUfmkzAEnFKU02lNX2HwNeyk8RZ3FhMPoxCxa6wj1W3RXE7sRLYXma+CarV0fu9Q88Sw9leWtBON5GbGyhxwvgzYd1Uig5PkZrloQKqqBzk+DFjzdOEKJMykYM376L/7bv+6f+hd+3T/zL/y6/8Pv/At/9S8HCknmJB7AkTgbdQSpMrOUqpG//4/82D/4O37vf/+f+T3/6D/3f/qVv/kraj5Pxl3xB5jSIA3uNh00DOj69LaS2s/ArTxMV1ZtUWy6kx3FEx6avctvLlUK51/+uV/6fT/6b/6+H/njP/eDXzboVZmRhe2u8gkRZQVwEwyljMrDbAHxWfLspdPREYYeFM3iFCKJvNzUjKAvDj+XvXkFL8A8y8/t+zAK9STFOWdzqJD1meZ6XxbUNr/fd373dhHz5LyTfqSJOSWFedUacDP2UPIqvISgzEAK5MU9cLxTqa3Hj8B1zjgjj19Ht7OyoapuXXMrg7Lc0Kdu1CjKarVn68IsjEJDmw4Hx2vWh8dl1Cbd0PQGG/Bpc+Jq8dIsDmvj88oGg5bu7SZHDAxlDikllpZrKXQ+C3Yr9oLJHjDYU9xevWYLpgG27ekk+juYQtE9qw26Muj00aKDF+/X3HPemO8UgeCgceoitFW5sQs0c9rwHC620Bjd0sVcWNcgR6N75OcH3pe/B/QiSz1W4VSfzTwqHRtmyhIsyGRJb6vUFKDEa+Wqz7t+4+/7V/+B3/57/8Hf/nt/z4/+WJmrpe1BOcyMUlLOOoVT+jJRsx1j/Dt/+a/9A//07/wN//vf+Q//jt/9x//cX3z3jBwJ/Mr+/B/+tn/+7/2H/rf//B/4gzwwbX63JjpqqsHwue3rstljwa0sE1EUuyUDOlKaOPil7z7/e//0b/+1/8hv+ef+rz9WgAKOOY4MVEVndQDZqGqilVDIRnSXT04prPz7moTwsEB2UxQ9ThktUJ7DpZ+sGX3dnKEbAj3LXujZqX+KU8bVLIA6qN3/7l//+d//I3/0X/yRf+Ov/uDnSwwKZc3dV9jc5Bt5pNqixeNST+CVWr/SmKLQERbRkcrl3fPuldoUlYOoCbs4X/3UQ5f65Cef5cw5lHIsJ4fRDerJyxTpl+/R6rr3LG0ExXcLIzqC2hZFsh0mGBI+BF2xZHVGhHhOXKA0XaNpnOkrn48S0vB25YJdmmopYIMph64QdB9xD52GVU/LqSk2HnjOIVwxq2YYw1HESf1OeNJaJjV2pFWMzdlzaLaXA4aujKOHPnVitXTQ9Co9ln6B5PRevO3pyUlhpeIfXLDR1R3S5vBAQe0rAge0XIiJWr5ftj+QVwXQ+pfFHbyPgInFyTHa2fY0vTlUg8JS0VkarbMQtpwj1izVygR0XzKgdPU78/MZZ2ybVD2K9UMLalN+pfYbbQHBg+QmIKONZgq1pXcxX4KuACiXyNU1IVrsdD1p2eY51FV6VIw+1+m1WRs7Ogh4waoIPHAphK53sOdLIvl8VvBUP9JJI/VG1FJ7NkWjEDzFl3jocu1kU94aRzE0kCIQQSYpqUMsFA6x0d0615jWa/blCiUB9TJN3GCldSzUCzV1KQzVp3onTjRaaXgPjc6UD8x6lqtVvmAuhTQxwAtf/Q18ImamfEdXKVAnxU0OC+GD7GlPT3t47IeB1Rx4XoKokkMVGoz03KZ3Mfs23qlePVwkW4p2YfBZ2rEu0DagoinDkyvDAVrdUVUjLNPcbSV8k7LUonNcvV4x10NKCbvh2RW4ZNKOezy2nv/2pTM7gGJOHmXfBQpsPGu2biGy6vArT4CWiI/RU3GSUI4Ulci9Ye+2vWflYrv1UKAcTz49mrtYGms8joNoUduADICtQycO3nqK+1Yh1gf6Q3ybUUIHsllk05LzhEkZTozVz1eEj14bRiiaHu5TSZIeLNsA/TRb7tdbenT0gg/KrLXxmB16FY0kNIy9qAJb1Torp3XADc8xtVvZDZ2aLTcAqjWjMA6edFRBAMcQKKSL+MJMjnDNATVcfyk4BB3m8lfI9x/7qT//B//Un/nJP/fvrLDVPrJcrDyHHlpsBVoRm3hTtlwTKQKzJO6n5qAlaWQ07jItcffUSsvDPlJV12kmvAsugQNaM+Tw3nFOa4ogJUobkmQq1B5MU6Ayv5qXYRHGAVw/+/M/+Ol/96+dzD/4k39231o6saeO+koB20BR5jY5lBmxQxVVp57d6zbd25uraEasrhFBldcuhOTKTkpqSGSYtHPsOR2t9wHePd702zRZSjyrcgpgkWhYENtK5yVAk+sWC6wyH/NjjGd3TMjjqLnaFuQbM4pxss7jrkZhnZ6nxzZPs4ckzMLzhZnQhPoZs5VSGKQx9zY//Qotd3OOaVzdxuaoMSWGMunoL/38L3xP1B/+03/hiK+vJFRFVuLrJeCn+wDvbpkeXtfcLw/jBEaqMB5TU5QNmV5aUE4akD70yD/ssReBKE+Mau+nvtysfcacmyhWd8PRs5UU7iehaI/0KNRzmRnIejBkzs7JTQY0fJAsGDmF5UIPhQcaerKdgzAZaDwgQaPkIQDGnMgh6K5Hk8jjhq8XqVmtgOA2NFoMdD5IDXTXMTgUt0JDDwVgJJ+fBPdf+/kf/PCf+Kkf/pM/9cN/6qf+b3/0x61pKkmtOYoLko1+0HKJVLOSHH/uu+9+94/+sR/+6zuExQAAEABJREFUiZ/+4R//0z/2b/25OLc5U0l3kZCbCtYUguCWsTEU4hAsIPtp9DrlABchYFxPuN2VvQOQ5jqXiGt2eePfeyk8nRHXB2J3UhvyeFEdC3pgMECDFyUqNMy/DrBEe3eimo84UzWuN8okBzSkSw1zjvSZXFmjA9yoTdyEuDNfZi/hzyzuW4J3TUMB4QxTHiSt0kvSapOTdJPl4TktIe57Vqn/m5YNqSqpeIbiio/O7//vOSa0ivCZr+YqpaF7gJ6jMau7LGkSQIAHbMUFTMLZHd57hb4k1NhosVeST9lqaC70avSGgicntV25HAs0lNGkNGVzBGRbseMhWm6bFoL8qNQtDUFmKwWlv9adia31RJPGqDlUCqCklNNjdUwAG5UC6k0lVlyhyHRPH6ihCzLfWZAubNClUjWehARVDXO96NZEU04hg8rvYtkjU1kv4DRLzTb0Vic0UAvmYMuLklwTPZQGO0Cwyu8uNTNR1BDD6NbT1j/2Z/7CD//kT//wT/6Z3/ejf/Rvvm85eg4mM1stUYCaGQh90nvYv/SHf+KH/8RP/+iP/9Qf+ok//bM/9/NdykRbQ1v5f+v/+1ey6g/9+J/NX3lbVIuuIsLs0KvLMhW2zYbJpMkFE+owr94Xt2kxwc/8tb/xffv60R//8593RnliRFHpLFRA3gC9666gRMmS3UgocBFlLmFb3i6Hl0EkqzkS0/G0ynnGFJCmKZz7PU7MhIdRDwoCULjQoqCcMlkKip3sh9Rf/REIA2VRFkrFhyzsrh5bW5p1ZS+LIQlBxs7o9+BplPLfidIRlJzr6BTaiGVQC1cnMHZ3Snje4Gwb6mBHra6X7+VKXavGpLroSA/hhRnoOlld78h9uopgQNLqGEhOFBUD6mRrF9qaUKZtBoNkw7NYTyB+PUhazpztBfZzuTubT9cPN/Wx9kGb3pKtKrxfbIHsnqem1vun+aoUxDwBgoQbfNrQFPhjr6HBVZrnEA27dOfWXWY11sbrjRntpFOjBYOMtiglfhLTOYc/V7/80xDmb+j52Yp2s4jOynxwT9aqli11LNwaOOQ3TM3LDoYSH15lMJpjAFZiLbdarJU+a7exfF18zXOZCqorGXqIdKN2x1uIOBbH6HKLNcUJfAWmTIcCvmgD5q2DS6E8vaoQN2vXjFipwCUHP8bnXJ9zAwdmlj5KOX2jgw8rECxo739HaJOElIaC2GJF15MAhL80ClWuTRDZF2yFVvFrunFEqZAkuee6CstyMgwtAWmuukWMSM/pNa0cDOp+G3dCCHJiDC5za0uyiEbyiKkWRX15Dc1wt+HjGVrs6OpNqRqdbbulPF9lukpamG2VJsxpRmNPncyBsyJnaHlXMG0Cp1SpT4k8DdD7q7AODAVurltF5UhRUdg391ZCbvLQFKJGMxWwNqmmnb1bcfq2oe+I70ZANqqq0RT20j14lmVa4vHrw2AzOYNG5ErdMkSThBP6V9ZgO23YM+AYbpF2sTnz7fevWLy9PwfejoRsDgyDe7WeSvh2bX2+8/1e79A2D0aMyXEZXeUr6mCASLZO1Q8B0NHLVVNl23Nb3Lk3NzzItBP+lo6hkUrIVnADHA2cjOw3VtgFM0TWvXH3SLPwDlQ+yc/2M0ctxzSlZ84aV6r+fULlEx0luUdcwcmppdGtZLoZDvlOqsvsKALuGF7VMntylrI4FJJgNWt7v9mBlEfvGuxp1k3odRQEAkyguHX6crtII4nnAFICcKPBlTDNQLmuCIBu1NpkRYie00MqdOqdlXtidP+Of/kP/pr/8W/+r/4vfusvfH4mLASlUbjLo4dRqWn13SON7kDU+IM//pN/9z/8v/o1/5N//Cf+33+xVaIp9VuwgfPALTQEg+xG52Fqkt5SVSXtaH48Jo1lnnya+KgxdSMQs/4jf9t/8D/zH//b1vBf+5/+21fVQ77MMkUkxYhfvBZcabPn4jGcgR4e5gCdVIfB+Ok//5f+nv/BP/lr/kf/5I/8qT/fSaF5rtkF5VsE+XSbA2638y6oPgVuoEaWjIrno2q1QdE/kJ4hFqBqKDNOXLOfx0KKjE1EW0ZU3vp7FsMeaIq+fZj7Q7LyrK9qeeCHI6H+8pyVdgEFdFUsCcglYtuQIy84pJMiAyyDXUkXt9tOzdu9B2b3+lv/1r/1e6L+K//Z/5RIqZiavvFlV1QAs8BUbG8FRcC6Sc3tKTrc3lON1A7Yz6EJlEKKXckI7O5jmeJVzQgXckCSkGa7E5SM1Tx2y3qWeLCH9SkbZ9V+RVlDkeApwttEjkiVpks0FpDF01rlfQwfXwyxEihYwujLx8dQC8Ug0cYGQtdPaQVSybkpLQ0rOZd76CXM69YiIcIAE+vLlKUYpT1EWy1CK+doqXyUPify2unCBOWEv3zfsvH98Qd+7E/U7ZMuvuH3sFv3UXZ1qf3MNpGX49/8ib/wV37hF78fr98Fbyy5lWpur2hkDmniNHf7CetNxPG+5U3EqNsrwSRl7hkbpRclACatzHWcstwSpSiRAx1F3LTAhJCFQXmkCaM1ffN1Yx12KivV55j2HV1Qe4dG2owaW5vx4hP8nf0+9nwGdRJGgGgz/fG0saHG+2qtwJMQHKCbMRlab/6n/9Tv+i/9+t/89//W35vNsRYEp9TH4OLfo+08hvRelgMdsK7HfCoPFC8lWwba96E+rPnzqVEqcVTYqauanTfvT4YiQufgXlYcBOntwpvOElUdZCo+MDLqaa3pQezZ95DgdpZk1tADgiG2sJraa+ixAAe2+a6rz/DTCPFWx6ufPTU8yVLWEl0UVFNlzjoqfZX3ffqgFMagyBci5QEa4a82k7vSsprqtb7UjrI7VHoV5ol19zrbRIKL64t2yUD6SQViPZ5VqES5pJRNwdWoWcoXSkay7xQAPa+7Ag/KgJhs9BtQxgCt+5Pa8d7BW3GLOdzoaFZmW/aLNfvoywF77625On7mF/7mH/6TP6U3RF6lu2wzGMhu6uSC/vQPfvnWfYg+fn/Q64UcfazyP/S3/Af+c/+J/+hw+y//HX+7ok/WMwOufHqZsV1LpzodGlTSWgYvltEaXnKokAf+Y//hv+X79vX3/B3/yckXVCl3C7lDQNzX5XONMO7NHV7y74KYJ3Xf0ua23D6CH2l6Jq6eseAzCVq59dOsGujGs3+PXXVIJbWjcD+pEi9Y5Y7hSSGNtsKvvqxswscV49palNxUueLNRs/BMUgY/uz/52d+7W/4Lb/m1/+m/8sf+Qn5gXct45AaTHoZdawTiT7C0AUldfJBbMvO5W81liKOMxvCGRsqeLEo21RnnBZjhM/yiTlbz6ZkhqXAAKu4oBVD09zMllN/wOHtplCguonsx+Kjkzwn9JPYYVVcB9fN6xgbyZ19tv6ERXSpx9qbz3aEZ+hrU4Vt0s/Ddiga6yYSruiH3II0fMnRURcx6UahVKxbHQMDcJq5XmaPuwhI75WEw4jRCrfVoGMG0Dz6BbXqRx3eXR7wozSOBufIl4lT9aARNVKLh8+bpt3hMaSGC/i4F2/gHdSyVLjZb3QoCzhmlTqecn9T5a8YL0CoqrCzRBH6qYHhCqrQoXzJa+OycMF7dS9vdYxXppfTVo6hHXK0O+dSOOxWlmUu1UreGffdjB4wB2jYVykWcPL4pd5TotK1sTvDRIowcKuHZZ44FTfy3Xw/hEi0jwtJH6jORqvJ+ttHulknpeoMAvDEKC6MCV8uKabqAOraXa/qV33FZUVpyw+eJBTTGY4ydg1IUHzuB1Oht2kz5jEYbKCxFqe705+xGo7TvA23egEfGqoFLOr+6jbmCruXIozu6tMdhnh6Ngkz6Hchj9bMC6+Pfq0ti/IaMi/BGwqHUL320z1T2T4f3Nc0XL5bg9K8cGWPjqqdKbdRT0zKc6TfnXvccG3sSqzguCdVHDY6JzeZ1YjqyBA8yJ1BGyJdg02260xlmpbgsn4Q93B+I79lXhmjSnQpwlrgwKTMh6/09ktlmaPSAjMlCQADvEQm5HteNdZR8FlyOZqrYdC48OYMJVPFy6qBojzYOFhb2RYLPHI1/WPaR/a8j2VZtUiorL3jrrhZwt8m3qBNzuryp/HIutEHfMEHOZFWpxFyrBEVWku00rtyLGWdltkTJZN13rVvD/V78/Ksuk+c4MkRwvo1wS3G3BxHOsoSay/6i5gta+gSw4ofBNpgTLNSfDOVP3Rb6aLMvCIMG+x2UBRSUbwnztNNTUN0mFu9GktvnUE0fulXPn/m53/ws7/wi5mQI4+2dyqqZvXF+GY9JSntxB79aMZ3sX/m539BxzukH4SWVV9ndnigQniVZc7pl+XVuVLtSf6JrVzO03aaLAjDB/IJ+bVtnoW32tD0f+LX/3f/yG/9x/6b//m/0ykIOVtHf6hdEMNn1ytDizYTDIw8L7yv1h0eUQMqsIbrW5yf/blf/Jmf+8Vf3nFLpwOgtWApm1rhXKfWvOcrW7a8ejffjVu0XJXjaNviV89prBHHi+Lwsfy2aH0a1sMSds6O1OYgPqsjEgIkDHgGoX2O6/VDH/7yBE5UFZyUhxYFo7RKBdkg+Sh5rKKjIFlWCieNJtDWp+vdlZp9n3GnlRvchof90Hz9E7/h7/tj/+z/8r/xd/+dAPEsjlemUiyBQK8JfFCf08c9ZVth3Ibo0emMmthizuvTOkBSRqfTAnVj745jUx3ijHlj3KVH6mo6nWfjvan0SSf0lCv2ZE29dQkHzxWVhcxBRKikxoF+pwNlfROb3dmjllZgvUWGv0MNeONV+Ga4FsYYk7yMS12Co7vPrlPgoE0zkaimwgruUJggMLvZir3kR5xauIezrGzWnGGm8p51v+KtbT2/s7jHe1cic34MfSvxmNHPL3x3/9Gf/OmSKdtAKu2X9dQawIK8SxM5/88/9ieP2g1+9dU25N5ylnM7VLXcetBDNlGEcl8CGRSetdWsdOfjRKeqJGN/njynCo0lrE5V0H7VWXdJX7/qAUpPi9bTimihF8oVV48pSAh1SxsbelgQ9137qMm0AIYVF9XetbzIAdB+NuN9IuKwQ6u/+Q477/FkpWiONfqadVlO72lquCm1kA0lj/Hzv/g3f/Zv/OCv/+CX6crPHL3mGVNy4rZ10jL6LryVlN5VUcoIBVHRlryDStPCuRAGCLlChujC7kazet17fN6yIjbKqkjTFOXNlcyczNEQoyEzYJthior++lSiM2rcvbZrXAZaHDmnYioVtgL+rPVZnkFTzPj+78hP1UDqJbgKkzMxKQeZ6i2dEtWEs4XjAYTIyjLEgOBIJMh0xV3Uqd282e0Y7o5pWV17W9xmu8c5M+Ky85o5OGfPEo7ZevwGzWX6iGQE5FZpa9U7oksvCy1Madpjzdtn2oX8eOgTZ9nFwhhjrTldMDNKt1N1h1U4GiZr/mAiX8PU9fhaT8cHUN3/+h/7k517KvcVw+mGJAROq9p0c/Lf+PE/+5f+xg80+PuDpHurmR7Ah7AAABAASURBVGnKa43f+Pf9t/74b/5H/tv/xb9LQglphQ7XgTZHk+LN2pXVdpRbl6EBr5h+XE8ylse/79/P3/br/zt/+Lf8o//1v+u/4L0MBZVVRysHl/tlayguTM3pZ9tB1+J+Vc6oLr19PQ6Xhc1TV/VL/hrZTK15nGwZpJb1jmHn4tZMfEZ8d8RRli2bQ0cL9JM1OL8P8/ldZg2Btpk+ttXRnf68247rai7mlIj6a335wXdv6ZdF4iGOE3h1yt20m/MtKFA2Q3KQehe9xtRCVS6cXyls5Sx25S6EPKVXOuPxvwqr/EpFzQFRajSju1FalMBVHfI4dO5qOsz80r+JFPZQgVRBVuNo75uRokxqZQdwdBEON/0lRcXB9a4lBN1ZjPQOSjbV7GRbukkm53XXeGu8C4SmAkS3oRq9tWiPVg48ga62qtxsret1XfCht92HdsM++ZQJvDmCV2r7Ho70h3y1SS10snWsC5aK2oOXFBMqRBvOR/x5r1CX6Q0vQ3v1jNsRa5bOBHEEMy6FFVH3Odr2HxBc7iq8474FuIpVnhrc9fy/f5CYvq7x8ao5qrvrVNemfTbuRmn+MAxX3baLQtEwdLbBPq52rwh9qQkGlrjLULlWWArTWlLZdcbaw/c09Oh9jbdfbYMeNj45ugdzqS7GTqsjf5hdE8cSY4/Zy+CNbaXueGf0jn4fVEG8r1oqXsziejsk9HP1TQFQm9A9lIMWXMGrMftMoaZgmCm6I+Uq8oFFSLfSBCS6/z9j/wFuWVGsDcAVunvtc4aMZCTnLAgiSUxg9powoYiACKKicq8RFQXBgAFJEo3XfM3XhKJIBiXnnBkYhjhz9lrdVfW/64Df/z1/ev41ffbZu1d3hbfequ5eewbgq5EgjmLYA/QRLZBWpDYGIKUJwVgiC25o3hgGh3sj/AZZWoaLALwPBmrJaILm3iEqkNqUg5kHGidTEDUJVFkyp0CcjQqsdJLm8AxRgx3KLIBduBUPEVBx4jwRLwCLrTaqU/JeYlCqKZkwUSMZKDyaeig+m8BLwmAUa25cG3KZamARnBL1xj4VzNXwQi2BM4S6HZjUIg8xwzzpeDLRkmANjVcQZnN1RwsmJAmZjK+kzkpIdOZMeEdB7A6mlLGjpBgfdLjGFBXLqyLSQInKUDqfFO6SdBhnowDgTwxdIiQj85iYvWQrGhLIa7NhIKsaniJKi27Aq6ogRoIpQaVRnobCQfgXaohfiFsiSsRZSxb8Zg/3QLQqk3VDS70lpEdzJ6biLFW1MhsM8aB+jLXF4AzkIMk6iTxqE2GUU0oSQeQYXKW6wm/UZOQs1La+H1p1wIYhsGXg3If3PAzYxs9qoEpICqUmhAExlpUMOWhz2NIIzXVcs5gLGVSge2xihnW1DI7cSM5p7KNBrM/BHOikMTTwZzQJNxujAGFnmgB0UUqJLI37aRhqSSzlET955oULhSA6QiyUNFRqZCwBDUIJsCkh2A5rW8mhMxodFIb1yj2B3J1hK0JSCXiMQxGlLmriqRNq3BxPp1Ibh7Kkf7OKJLzINCcMQOIB9Qz5NGLWwhlszqiTiaJrkl2kkXlprZvW8fBaBwsPErUM3WojjE6uuZUJpllFCGyo4VJiopQJY1ukaa91rNQkjMU2J8kwSdU51yjeEiCKgANgOQIEAmbAAgp54oG894HCO6LcM2FtHepUvUHEJM1rwFh2wWs17asM1tpgMg1tUjmF0L+vYBKJjkfJPME+LYaa+qVhsM/rRDgn5Qk7gbGjORqZZCawS02OPNPg8QY2Z4FqlXqaqQ6AorNhhoaOuWjuUu4IIa3VfU58WhzfqRBgYLJMcCI6qFcXNu6DeiuEYholMMR7bTWaN24UQ/ZBWmXpETEixXixAG9oUgwVKDKjzJoAUoaHogSGiI68ZeLiAmgJ58UpjzWGCONIk1ASB6UMdis5JrJEMBqi6DQ1GRADwjVT0vKzwJx++PcLLfnIljEpFIIgAYcthe4stz30wDV33Yfx66+8LF7RNDgL87hWESlKB4MmiSkn6lJkqJRoSftUWHkirZQpa3ilsT4OKuzMouBJLTyUZhnsNkbICFMhf2zB4yuLkipGA7UpgpjDSiLGvQhQuTWqo7Sg5CHkLAMZAbXcAA4x9tE9yAx1Q4I/QbWX2kdrHpBexFMJ7rAy4pyuNhDhwURTyA8GEhXuY9h8a9KMm0aMqsk5SI25RRvMprBiqtbQiamkLjLAEtU+44hYzDJJ4ZKi0xa12SAGNpTMyuEORT0VkyRcilDpTGYGYq/QZhCJRGODDQokCOolpOMyE6grBnWsyHuOxC2xdcpJmXzwfmqB2yHkRBJJLNHAjFwMLyHgqac6LdMBHBLHapdQv2M62IB9njbKkVzL0KmVAKAkeGRQUQ9FML2WOic0stYLW+EmDIJ5itbhfbiTVtJooo0JWdGFych+9iBvzq0S1goyQv2zhmxmF+M8kDR24ugkdVgIcktpEKIRUAAQHA3GiBCkhTrS11JiYiUXalQo8KVvgSmAoCTuChXuG6whWn35Bcz0r9vuue3W29AX1cmGyB5FomRBQqfczP5wyZUesdpys7NZR40wHr85lIMrCbYFPCveJcsgNUsWZVwIFEjdOGGghBppzygdNCAQMJpZUi+KLK1EIponuUyIMJWyAk0QrgpZwHFiaBVKwopFs0Qjqk2cxIu7tiBjUiXmqMZICYpAHoYLo2GujuA6rO0ST7TTVghhdePKbJ11apgcoSl44tQRMSYRLpYguGgwgxup1ZnoE2jktR/Q6Yk4MzFGogVxYxYC3D1zrTJmfzBFNuoadQOkCRQRusLU5yjAFWtFHaJ7GebY+pwqd4ZSOUWQwBdjHqhgXCNn+BkGiZ5FcNDMikRhb1wJg2hEK1Mg9F0AjeQiLaeh5KbJe1UHugW1J2cijYFt0GriYFJHozQRMViPV06wdQipNNqqAiLXnDwXQpPEnrwlsLWheIU381pjaN6Auzl4xM0TM6DhcG6NDHecuJJBqymAdliFBAkPGlibJCgiCoGPFV1gmw/sphirnBADRiBQE2rwAIMxXITDzOaiTUsMMzJMuJawFMTM0YnBTJRj8InYIIoG917clalLURRcHQQSiNvIMjYS8IiSoWkwh1DmyFShxknIM1eJPtzdiKMxAe8GrxqiRClCDPODPESIVURZkiTIEVegYrBXmWEfZMFR2GesFsLWUlQlckI+pIpZmorq6AL64LjCOHa8wClJmUoIWELhLH2UATNlpL8Gc8vG5CDxTFAmF4qRdExZCTAKjPNIZJlNYCAleIYJQhBGiOYgQ6SBRkiCKYjEiMODItgNnKbRSQCVLTonlQDRWMOJcMfNHVJIlVNmhXM8VjRBRChzziEyJLWkpFlgTrA7hyHYlUCeIRwMATQQANmVeFq4x9YwslTO8KREw7HFoYTVASv4iuKawsEH2MyuBG+ISTRG61VGB/CY3eGYYTFjJhOCtdFImsAOSxLJx8jBAh+MwogwTMlgExNgEFVXaZhKmkbbcmmiFZMas5NHs2gBZqK3sQnVzpM6SkVpTSCAMgUwYkgGZ6DdWRlSQ7lVtx4FgrsgJtSPZAl1N49hVg0hmJLIZbSZkTcwgIWokSKGrMQU4WbqkmKSqRMXBBmSWEG+7CEDUU0UHLBxxLcG2ZCjFriSmJuwleKa3DRMjbQyO4YDX8ncsSQWTHaPIIFGYRZCh0QIhYuYICYdR2FSEvQEOFKNmHBZaiF95pa5JnGAFUMEFh9hwnRCR2qexzyC6QqZhi304GP4YF1mYVGav2YnJZDEPCyae+r8m26/4Lo77180/m0WWPbPm2699Orrrr7j9moWBnNHEIADAb2cBosrbrn74utvufneB+Yl0fV333fxTbdfcdvtbs2qXXLd7RfccNsDTzzEOBczNWgc3aZhqJdj2E13tNqC4bNHmDk1kvsfXnzxtTdcdsMtdWjimY0vu/HWC2646Y4HH3JGDSnWqJrWBn6yRFv0xOP/vPX2i6676Yobb3z08UdQQC1RAz+SzdX+ihtuu/Sam6+7496nzbv1vgcvu+7mi6+/9SkbBiQFdCIZyKD9yemS6+6794Kbbr7k+tseuH+xzym5CuOCgaOJreJbWYwcEg2F+qIIMDlCBCvEkzSlePyJpZfedPel19187R33PDXtG5MBdIDbvDWPZmyaNOMP6t/ipUuwP7jwmlsuv/HmhY89JOCSClaTaQBYYveMFqFh0WLhY49dcvMt599ww79uufXRJY+VwkV1jB/IE0MWbI/m/nnDjRdce9PtDyxq7IQ8JLA7nnYcr8YoJnnh4qcuue62C6699d7FjxrcotyM2DxT4Mu6JxYvufia2y697rYHFz0aEp4Y5Qb7xsyRw7pqXTNwEtJ8JOzsvC+imVQsMeLVEBueslWtlCsTg8ToZ+ggV/Ac+YqUYZgFc9SRFeHYQeWIcFAb41ipYJYZD0ZGhKHIBguMDSdGziXCbo26sIxOaGQORm5P8I0cYQCJBRkwaBLEhmRQ2EGqxjTmrJoTaWDBYGSGE0ahkTApYR5u0vKzM6/deTtmuuGehbfcda83qVUHyz5vzTgQYSH6+5XXPTXXr7xgsvN2W9L8BYksoDRU8NjBiB955senT117510X33DrxTff+eDjT1VUGSoEY9la7rEgwzUJzqFFOYk+Odcjsy66+vZrb7l/7qmp4va8aqJxDhFeg0d7BTzhAMn4kUeXXH7DfRdce/t1t9/71HQgDiHqmMfgkDvVBx9bfP61t1x43a3NGlG9/5GHL7nhtpvuvg833cIMTBLT9vCSx/912x0X3XArMvShx56kCMY6hQhi6yvpwUeWXHDdHRdcfweeHhPRkml/0fU3X3DNjbfef2+MwIbVZD2+Bppoy3XO7nlg4cU33nzJjbc+8NCjoDFlIxROxas5mwF9jtQZlWrAzVQiURIcf4EkASUtvXZtXPRb0spClgS/Bgcx5hlN5GCOO9IrPMRDiRMzUHQqLsVYQsZoaTAmQYtLL+IYJEVJwaEOvxIjWYzJhClgGME2x6Y3NUIaKnLQmvWNeqJBm5eWOpwlPVfCugz8qRIjcJHERhuDOXIwBIelGJI0LsxYGQBSLTqUiafOmcmHiErhHKImjCyIZgaXwFBLhI1REEblalrhgGKYSklI1qYY5+SIAjOTJIdXzn1Ez+GekkMlurnx6DiGAPvcGP1iDS5GBBG9ftcdlp10S/t6zuXXtwaP3VsUg5EhXYzbPaK77l947e33PD14QMzwLoQ4Yfl1Ife47Jbbz7/xljseXAgVHbXiFkO78ubbL7r+FtQZjzBvDzy06NLrbr7w2ltvvueRucEdLsObbDAIXuBIe8H1d2L8XQ8/xMoswiVa6v91+80XX3PjgwsXjyKr37Vw0cU33n7BTbfc/fBDDScVikLYm7hVWvjQkxdfcxfK1y133rt0ivIdGY4z3J83l4NALkhO2oLvffgd/3wtAAAQAElEQVTRC2+89ZIbb77lnvvm6hzAxmotDrgHwn2hIMa0sfE4012rpdo6axpwG6knYVavvOV2eHTV7fd6BAbf9sBDF15z68XX3No/2eeBw6RqaVqYxNzve2jRZdffcvF1N99y9z1LhqdauFMyx6L2b12EiylUWZIg/BEIJWwZBzkqtbiFNw9qVJpnCZlwpGIQDzJQcwZPnLNHGg+cVhNjO+JFwUuRTFFKoFqLCHgTjoQEq6ixBwiaG2Vx8IaSEzIt8AtjkDGRsXeuxAg9CD9mq4/rHrICOUvws0dOuaB+RyUZmCokNsBdhJMyMTkRrMZGEL+CcaHGFRJ14UaM1xAmYUxLjslO6gDasZPKosqRRi2OyUJGUoUaEhYQaA1CtWfJzl2L1JpC8gzRDDFmSmZCdudMqTg8K4wI9jjPINNgFoICuxqkMROsIWYVkoByGtqY1cGOg0pHtQgRwxDE1AdcbWitea1YqmE05qA580A0UItoXePOaUZiZkIdHsQzVwwfBur7MuBxxsAG+saQaVCrMXpRIjJFCHlhR1wLMyGFo1Tphi55qigeAJJgqcA5hoc+/yCBNCHmBAFEw8C94eyvLSdD+YsCMjAxG1wlonCPqcVARTglYxSM1KRjwRcT8Ls1QhgH4qlwj+HNYZ5EcGLqqGWE0NlbmVrpK9XKKazE0KmnCaBjbZZbpOgU8rsAZBAiiVInOiuUuxCY2WnS8dRCRE3YxIAYLEMBnGKuT0gEzhNMDg4BKiRkqk4pGpnZQNGEIgTYh2NYTbmWCeeinJNITlHAoxAClqqRwwWkGX3SFJDvkI+7lDVzniTOyYPGgBNSxpUoUVMdUkecSFRGAAECA340ChqHByhau2iZSDwFC+XK0jiYQqlzV6DpPVsjdDm8pCBqRHhIO8AmoM5wFFNdOagQ5yCujlS2IHLG2kGkcIIDLBncoJWEQ9jYRmVYhkSycJHIEpNEnUTHrXgr8JgonGtopTwQaFDI1JzAtsANClJA56CxD5RagiJsQUuTVEsEbhOBIqopaRg5pOEzEJMus2AJ0kL4FCKM3+KZQwjPJxh21lrJMQN3SCHETaKnwIawJaLM0CUIhLJlQqRGjTA8k2SH97Uf+gESzJI4NqCc6P9cjCDVeseddxx2wrcP+fp3/3TFjbg1tPaxM3966Ek/+PLPf/vUOJe8Jzys73rpalbuFi1Z+qlv/+iD3/z+mb+/AOPRvvHz37//m6f/1xnff/SpJ1AzP3fGL99//Pd+9qeLhAZF6OEVCWJ14fXXv/frZ37wq9+/6bZ7Q3xMDuRts7D44e/++sGvnP2F037OvRe4W/2jJ//gsK9++2d/ubgNYY1J2JF2nBc+9sSRZ/9w3y+dcPA3Tjv4q2e858tnveOok79w1o8XPvowF5JES5cuOeaMH7/v+NOP/9kfaP466/fnve8rZxxxwtmLHnssslRg59FP+x/877nvPu6UA44/++Djv/+er5+13xdPOvxrp//zytu0As7URcqWEip1tOAWNC7zxZqyOFAnzdwef/zx43/2p/2+eMbhXz/7Q18/85AvnrXPZ047/X/+OswtUR64wLPqQ6VmEnTnQw8d/Z2fvPXok9/zxe8c9tUfHPrl77zz6JP/61vfveWB+41G71DDfEhSk0gsevzR//r2t9/+lW++/4QzPvTN7x564vff/qXTPv/dXz7w8FNECXUxqpNao/ah03582Enf+9UFl5ED7Aj1pjHv9/gSZK3pw4/MHXHCj957/Pe/94fzGBkG/7AMmUYgDvTTP19w+Fe/+9Fv/mjRosfM8cx1GuTCwWweg5CDojOoHMzC7Fm8dFTw/a8GV2u9D70hz1oK68DB5KVUntRhxocujN28GVXUHmJiZHYH+Bps48jExSKxSaaOcqkd9wk4h8NsciMna9hFgSLoIWUB22GP4arC0w4iVCCKgsKAhobn5moBTfCAFFoBfZVhIK+RvE1kOsFEt9kmE8opF6IuKI1Slk6nu2y96bIzM0v74a//vBbd0WiYRoU9iB+Obpqfemr6v5dcCZXP23zjmW4B3qAZI8aDxzxNI7PmJ9pTp/z6d/sc9Y13f+m0g7921sFfOeudR5/48RPPvvWehT1pTyCHN63GnCiro8bET8497y3HnXDICacd/s0z3nf8WW8/6ls/P/fS2pD40ECRg2RglB/GR4lEi5c8cfxP/2ffL5xw0FfOOPgr39v/S9958zHf+u4551dymUlYMYi4uvz96hsPPuHb7/n6dx58/NEjv/3j/Y8//fATv3/cd381N23NI0TuW/zwZ8/6yduP/uZBX/rWYRB1/Jlv/cKJn/r2T+99+BHj3JB4TH+77OoPfuO7Hzzp7Jvvf4iI7n548UdO+OEHv/Ld//7D3wPgmDbSIcRM/vKvGw7++tkHfO3sg0/67ntO+PbbvvCt937te9ffuZBFQqJS6lvyIRyqORqPpVPE0EysT84daRJlQkCrNILL4cQSnD2xK4jtOSITWfjgUc3dR4w8iYGvoeaCzMDd1GIEGnBgcHZRE2rj5q0wIEcOofGE0KSMGlIgy40aQWFQE4EWwZYHsz3ca1BP3ihCHbwCCg5uO7JkLMqZs7CappbFWMkE2LNGQIAqYUal4EwyX+eHRlaxlMHhUE740xKwkTCdl+cQBTmJjBn0DYij8dErBogP82sC9KNJEGQ7wJAhpGIgmWZn2OABeD0cSe5Yj7264cRVKcMcImHebatNIOG8a255ZMnUgxlUMO+GXrGjwZfTZH+69KrHlsxNku6541bNoYkomKiEZqwDA7ePn/39Q04+4/t/+7sU1CykdL/kySePPu2/3/+1M/522RWLHl70sRO/986jTzrsK6d/8CunH3zsae877qzb7rinI2jPwdJEpn370IlnH3LC6T89/4IQ9CP+1D/5xFFn/vgD3zzzH/+85rLrbj3wy2e980tnH/y17x9y/Nnv+uJJHz/t7IceeiC3uP++xZ8+42dvOeak9xx/5nuP/+6Bx561/+dO+t0/LmX2AK+CYDGQ9Yn2Qr+++OoDjzv17ceefNiJ3zn85O8d+o0z3nn0Cb8690JrTSKitajVEXgg8nQzjhBXCYgzSAwj5pLzRI37o7/zo/d95cwv/ej3YB+G/+AvF7/v+O+99/jv3//gk9yXApIQyJIuuv6uA7703bcfc9p7jz/jsK+c8e5jv7XvUSf//JzLEJiOsYkz7HesFMuT4M6sUM+pjhvAhDQHHTNUElNN3sSNyI2hDb9JghJ+VGpLtVfvJfow1LhWvTXgiL1HIk9EzKAgFkZipEQlwp6KGJv70hyLiA3N6yA2v+rk0d3spN4YR8BWYyrW51bJW3WwjjF/FMgMbMILoWhTJpC61q5GUB4SR0JcDaKGnAaFmokZEEnRJZooJR4zFckK87gxVyEjWG4oVY7kQUsqhZHlztGbzuH0FUpETAL9g0jV4pqRb5lVTfBVVwyCSJVCOXsWV2SNCsDsIzdJjOUoubMMI3SGfCaE2lDWx4CjajE1TYa8CYk6JkqtjfvQGkwW2S25izm3Gm3wIXxo4WZKoKwFNzUT3EYAxKY5epWGVSkrZxULqZUJosw0HMsMJhFAGwsDCoSqR4mGX5wpFJYAkszWlR57Dwai0ierGsYsuA/UQ7FaweEkoubutcZgHB4oBWGgPcyukISuZtE8zC0NrlZnqc/aJ+4pbHQNMUgUiUd4MZ0ogsMkGo8OkxALhzIgoyBuiUzNAB5ZyjVhBZ0x7GdTygmjnALqKJxIiQvThPGtyAB8qtCURjRDwyEapjbsCoiciURMu4jxwCQeMc4HAtAghq0yERORNk6N4IYJACIni8ApuXJyyUm1S4kz3oMg7iNLB3HkEZicgpIXGJt6+BE5USqkaTQQYj3VKsh7Fs5KSV11UA4W6BSDKWwEgBiGUYSRm8C7lEKUBUHQcaCGMikJizATkwOIyqN/ox8u6IINjJ6A5UIkhKnCAfNcSQx7JSpm3Bz4YUvggcFM47wAplQbVSMJgON4wTyT3CSzFOLsyLrRbHVMIeeoQYMD4oAp4H+Dj4z3ioLCHMGmzRVlzwnBhjGSSOB6UeOqPHQSJTF7QahYnDoL7E7whiVxygFKK4iYNIGwoB2xs9RElQNc9CCIJaLEAS/x3gPoCSiSS5o/o7mCF8hYbMeMStMyBpViztq0Sj8obnF1HmgMJCTR3Fw/qdQNre/7nCApqch4gygnRU9S9SSDUhXcEG34UKLKFKASl5SSytPjk2oZpyisLJOZHTZZH/0XXn97naspHI9+tIk1PIq+vpn3rWHzZMi+xKBxde/npv+69W5M2XWrjWcmOYQgjfF5bBGGMtBImma/7+GH9vvi6b+65BocV3AzJ2WWhY8+8cvz/3noV06//c772YndPRy3YBXGoCXFURAOasmUioAngw2fPPn7J/zynNsXPgJ78uiLPrZk7tKbbv/gSd/53fn/ZFKKxHDZeAxbJjxBag66cA4WE0TuwQcePfAr3/3+Xy954NEnVARCmvs9Dy8+5Td//+L3fiE+KKIlhPiR1StvveXg40//6T/+ef/ixz3AhcQii5546g+XXXPIV06/+Q48uWRRlIJClm6+Y+EBx5/1hytuWPjYk8wj1EH8wOLHf/L3yw768pm33fOIV0E+uLHLMzhpSK6ZgtrTJKRnLnFByDZa69nrrrYyui664XYc5GAXC8KS+0jTFniiiVsbrb7Kpmuu0apFM7PWfCxwLhYSTIHQ5kYJmSsovFxJhsZD8zYYLGHKoQIjgySMkQzAhxC41gKChkrWkocGwiVKnE1gOgUTPgi5UCANRNWRIoH0BJHmGznHEFiwuIYwZcVrRAtG8TekEkISRhgKx5vCqtpxr9bCAr2On+YyFRrxCmRiqIUobFXVhMiqioQQ5xFG81hhxRV232pjzP3zlTcu6aeFiJ2q0xDSWEP4nzfecufCR5jp1bvsVNtA8xc+AiI4UVzKkBctevyDXzvr1N+ce/+iR5uPVISahx9/6tyrbjzki6eee+kNQVAX8D1YobsZH/+j/z36B7+766FHqhuoC0vuf+Sxz3/vd6f86q/zGoCPIZEcH4Ij0u0PLN7/+DP/+28XPfjoE8So0opY3P3Q4m/88tzPfe/XA/ARMUKxyUGCSWgfPfWnv77k6ieWTktK/TCMNktcdduth3z1bJBw4WNPBM3nHQu+UfnVRVe+6/izb75nISqPMNU6FJx9ElIBlkPhqDEnVcWKYAiYUQzsp/3u9x8/+8fX3XX/U1OMx21Z9CS+irn1sK9978pb72tRBs/ugoePVlsdWmtIVoVDxiPtyE2ZgDmOIsDdUrN5bURMHGgBVinm8/xLQhbihmBdKeSanIo5g7zR8P2OJXIJwN9QPkiE3JO1HC1xpbEF+lgV2UWFozhCUVrKrUhAvIN4HJojd4KPDBzUSPvwFvgdpLCKg13DMplyRbEfa4NTVAIY4YlcEeAYJgAAEABJREFU2QV51Lw2hy00CMGHjiPZiD/paESxlF3xFuxPASYmQsyEqKhAORFwEHMJB3nRyAhrF9Q7zW85QAeHjapO4mzBjbA/hKdMDeY54sfE7s1pzmg6jKS18Dfv+TwiumPhI1ffda8UZYmAwHG6udsTTz7+l8uvxoBX7LT1ggWzeDO2oHDwKrFideOxZ/xhkyC4kILYxg6ia+964KCvnPXnK258fOlcUqwUCuJdfds9R3zzOw/et4iH5DYJw1IWT4+PIMMfwp4mmPjpzj/+66pDTzjrqtvueXLao0KLyCNPLj33mps/ePL3rrjpzoO/etZvLrnq0SeXqmpWXdIPt97/8Ge//cu/XHYtJYpEJkpAxOzkn/35yDN/fvktd6PCg8bg7dLaUKiP+f7vjjrrF0jiVqOvWGcQFH9adZibN9QG1VBC8A2cU5AEGaDS3DMMUsgfh6uMxR89CBUFiUWK4Yd/OOd9J3738pvvenTJHPwvSeeGesv9D+N4c+xZP/FhwGQH0TvhpMHAjiiMzRh6o2bsf8VJgLiiOzklRRvxQQC4GQAbGyNqTO5gl5kP6Ha4DkZzpsCsrOM+TXRcZvvWiLxTXKBYahzYXTQKAztAIBEmgiwUatR5BVMJvpPQaIAPQY0hEumWAQoShQtJl1JWStghwtiiVEpLOpA08lFqcHgKz8gWU2u5tezowEdlH3MBdA2L4EF5wEQtAWEOrOF5L0iqHCURdmPizDItPJdGbFLOE+kmA+W5kKGGVyX4NoRP1fvSpqgn1ah3ckcKmI5R4WRwxtQDW7UaNmArEr3bYNiHSJXRD1Wh6m2IHs6iqDlOAb1l5y5SYUVGCuCKSoKt9zTGD1yZWkboYXYjyG1khjwhDmJkSiHtMJ8AFyPzcwpORF2WkhNT4abEFYBPuHUUuAboMwxhYAhNqQn0R0BqODXFpqS0WMZ8pg+pzYaeBPkvyiTVaBi/W0kDsVtPwxTPAsSGyM1norGPNBDyLEJEzuNSToSFsNAkBU4WHTcEBxhNIYAKRdcG5p4TAI5cU9eXNMxoE1BLvZF5sAaHoos8+uin0QKdyECF8TxtNPSB8tdqtKH60AIkxYoaFAQ6BXVBWYjVa7a5iLla3IFFZsNZnJNwICYyooauydAgOZTRmxo214VQ2wMFz3Jf1efwztWBYxPHU0ldIJPCmcVaGoY0up4Mt+dCp56thw3IF6cJkwo7UzARDWo1D44ctabSuNQgq84DinASTbWQCyslhBe42wzHBJ8Z050cUpqmxqA6Nw4HPmJKjXkIvEJFSt51TWZq1RjBMu5AFWXwpkYgd5q5GXuIO7KP8CuIxqdDIgOHhSe3HA59asQGFeDFkHAcFcQAcEtUERRAMyggEm0pu+RcpfRuA24V0QlRhk3cO6NusFCHAtdJQ+oDlOYdAbDU6phhgXgBRzgN+2lgtWBqnnqTyuxE8wNUJDF4DwxJiHPwTPgkAl+WZUZB0vkb4U42gHxe2ZZ6i+qlMWZGEbhS0THABwAxDqeZbhL9BJA9f5tt/n7qsRee8oV3vHBn3JmU/L9f/NjfTzn2xP96/2SZBZy7GAmhpBIB6bTOqmv86Ruf+deZnz/23W/AeLRTP7zfv8787J+/8uFVVlghgrbfYiN0PvDIY/c9uJgGYCsy1Scfq5ffgu047tA/b7lzSXMbj+UJOXbfww8vXDz+k80XbrOZFMLCT8LjOKIxFFIJOI8lYPjGT3573yOPLTPpPrHPq/543McvPeHIc4494qC998AycO+iR4/9zq9sqayx/Eq//8qHL/rWp771wbfT/HXse9568Rmf+9tJH1t3zRUn2hZ09psLL/nL1bcw8Zt2e+6vP/+fF516zPlf/dRx73zDGisuX5t9+Se/v/P+hypRD06wc86UJo4oI3PArEYxjacem374pO/f9sDDqy6/7Mff8upzvv7py874wq+O/vBrn7+dMP/un9d99/f/ALk8dZHToief/OgZP3nw0SdWWW72mHe/8bxvfPqKU4/90xc+/t5X7KnMDz/+5On/8wdEvpAiyNOhHf/jP9y96PEVFsx+9h2vOf+bR152xtF/+vJH3733rjkpdsmf/94vl4QsNV0aM4Ojoo1OYmImYiQFtVEK09MXKAnJYrb39uPfU7r34cXX3363ep9oIFQXSjfd98DtDy7C8N232mTCpauMBDHz5oSF2kIaceDyJtQrapP3RnNWh7bU2hwFJe0mOqvSERcjMRBgyjgWI2xR1cBhlTTDrVDP4qjaXGZZSzKWRmhKA0ffDzHgAWPga/MunNFIyFFUFALEqGOecYJ0N2cXoeTEg1fz3unpy3Ly2YiZRlRpcBAGtYpM2ZEtsxYdvJKwzFxQlh05isrAqMiDz02nkLFkQDXl1+25szLf/uDD/7j25qIFOQZ9vcucUXX6yQWXYORW66y59bprAxW8R5MQlM+xWWtz/VFn//DqO+7Fcv62F+78P0d94LxvfeZP3zjyU+943WorLPvokukxZ//s7nsfSqN7MIVB8XOvvOrH515k7ls8e/WTD3vnRSd8/qLTPv/9zxz8gq02BsEgH23UVVmbUsiS6XD4Cd8FE9ZYafnP7vuac7/40fO/8emffvJ9L98BIebfXXrNf//xH3WcwyKFSWj+uuGeBw5+5YvOOf4T/zrzC7/64oeXXUCLH3/kM2f9FPRbfflljjvgzf844eOXnfGp/z328He99PnM48H49F/9MeogZAe//iWXn/mZf575+R03ejaEbbb26ud97TMXnvq5/3zbq72FcaDinXPxRaf9/m+I/t7P3erHn/nAP0/5woVfOeqYfV//rOWWeeiJJZ8686eLnpyiTpdK8D08rAaKA9fOK1tz90BnuDG5KLgpzNkYpRO4BgcCtJTBzpKI4VTHSaXjSOKSWIqiDhLoQmGo4ugmzKbSh/aWghICrzFtbD3FQDEVxiM2QuEM0FvYE0SlnCad5JyydTIVWCcsGjPFJ4oBGDxxLtjXNCbKwRNXbcn68D6wXVHPRKmRDMgSGlIO62xKfV97qyCtedTqBg9RdFUmIlmEIco8B01UVEYBDN/J1X3ckilhBZz0Ij1hzUfOqKcWyYKevpzwB2MLa4d5Ei4jjkAhUSMk+aT0nbgSROtM5slkggfPRIuW9FtvuuFW66yO0T/56yUYYJOIJC2XIUEN/+umO297YBHMe+Wuz+3bM+qcoof5YHukTjPuwgrcq44wKbgmik/ooz/987onp8OR73jtH7/xyYtP+/QfvviBt7xge4x/8LEnv3fOeY7leEpSidnH0fjBG0M1QWiMQtCB9q/b79lug2efdvi7zz3+qAtPPva/P3XorltsiH7Uq3d99fTFTy459DUv+v1XP/WPU4/90xeP/M83vmJBl5v5Sb/8k4UxYkpYf+UX51zy3T9dGBEv3HazH37yPZd/61MXfPNTp33kgK3XWwsB+f2lV513zQ1G6kHCA6OeQQFRo1apZwTBpRCDEZpAmIpozc7O/uKLn7rkxGO/fcQByHEMR/3/+ynHnH/60RuusxaKGsvcHy7+54m/PAdLycZrrXbiB/a/6NTPXX7ikT/86EF7br0pLPnd5dee/D/nxFTDm4P9Hpmjm9Q0W6N4Y24Dc8VusUkoR2IrpIlSIKYIKXnfWhuGBjc111waixljgwhEc+OkYkHmTuhlYCxg33Tg6jKU4qjFoMFQ0jRJy9Q6GSAlQL0uIo9lnrPoRArLZMgzVjIl0midD1nmpOu7HIWigLXNsnhOBNNqchIXxmlTmqIKhOCZQKVskRBbsTZa04x7ib5En8kzYTSTs0lqMttoxhtyhltD9cZGxkM4KWiWO+T4ECLCBhxYWUU44LF7jJs/hZA2YBb3iLmpuKZAuipsqg46w7MJDYX7CdsMsM5BpRHE6nyGCxASdxZSJU7C4/Tkwi3GcIQWla5wlz2F5IBHoQwL1JGnMEYJkKMolap5IIkqBHcDyxCF0PxNdQFoSVoQLHZyAtWCHKExQBhKYYK4wazSTAjht8ajBA4HlIHaYYAQtSyELVFjF3cxzaRMiYlDHPYO7OACE2QYezDJaKkQFNJ4GROpUqLIAesrViyqojXxoEQsLmkIFBjWFuotpdppzRLEalpcOxZ88Kb9gKNBmfZYt8HpkOSIU2KzVsHPaVhl6AdxJFVJwYzYUrgJN2VOjrs6vkgySlK7NGgyWNS0NgBvU3FEzFWyk1KgvrOQMhHiIgI7GEJhCgWkRnUoHAESYgkVSqwqSsKUKZgasTs3Z+Dg41i3XthTMqwdwXCWmqt5dpsQo+iGktHgtU84zXlto5sInks1aQNbDchUjCJtrNiiEuGDawCrDguIBptSnQ8dN0qV82BsQ6VaAyhIAMeQfkhjXjIPCYMTg/DCQdSi4VTHZEk8MZa40SvjUlmqYIVRQ6zNDVYwN2AZIxocQg6I4DZkGDHGBmDzaMDUhV3Emngl8yAMZGcWh/2C1BxovKKR12AHl6smY/JwHyI8YQohEuHkRnVIrWUY0AxY9ERDJkepCFKHGHNqTT0Y8hVConlUeBUDN9MRAa5N+so+5bCMeE5mupznQ0aDQrRFghy01hoD2sgtBGZ4jGFAP1qEwVKkACsUwSypOLwrmcL3QIBw32n0E4PR4LNH9jqpU/WBdt1yiyzy+NLpjfctGkKaUzjf9eDDDz/2xGrLLZjtMpbAO+59GPNFCDS+4/4HMRhTtsY2CO5ET0ACctGYtWjKsFAXLX7qxrvvR98+u+20z+67rzSZTdSttMJKB77uFW/dc2f0Y3f40OLFQ/CAyo7KK4pONKugMTHgmjpgYmkXX3Mj+jdec9Uj3vAfay6/slabSHrJc7f7+Fte2+X01LS/6KqbAvEZwTZKxGPkkwdhze4NWxP72fkXXn/vwpmSPv7Wl79uz+1XWJAT1/XWfNan99/nBdtgNaJfX3T1408tBUrIv4tvuuO+xfjWhT/59te+4nnPWWamc6XlV1phv1e9fI+tNiaiq26967HFT4wUCXrgkUdvvX8hOt/8gh1ft/vOOJVxjZUWLHj/61/2hl23R//lN9+5cPFi5qQuhTJgRCdMpMRSODO6OImMnUFRQwhJ4C/dYVsgDLr8/aobAumZB9KB2a65+ba5YZidlJdst6WwZh0fIWfVpJRVJSPDsNdDBBHFlGjcHBYzqTWoBoNj4WAmewogTiVGSlGLPrwKNfTDxA4RhFkW1KxRrSCwYC44RYIhzmbU+mqOxZkEcaoRNAQ3bRijQlg0iYPJKNAaIaepCSOJ2FG0ifC2JGwvKbOIKquEMshKkSBP4Bj6iPGwBlFUJ3c31IKGOiI80GzONH9p2HM2XneztVbFp3OvvhkSVFAdSJDEvd+z8KHzrrlZmF++w1YLivY98hMDKbBtJYHN7vTXq6666vZ7mPndL9vjiDe8ct0VVuyqL7ege/2Lnn/sAW9ZaZnZx+f6b/zojzwluM6Btaf/7nXwbEoAABAASURBVJ/Pg+/PWnb2ywe/bbetNptgKZW63rNXO/KgffZ+zmajAvw0FPSiAfPpB3887/aFi5fpypFve/Wrn7ftcl3J0dZbfcXP7fe65222vkf85oJ/Tp96QslSALTAbLT9X7jLIa98+bKzy/VtDA0LXXzdTQsffyoJf2H/t7xyxx2W6ZYR4pVWWe6g171i583Wx5TLb75j6ZNPWgXtxSIxKf378pDc8GQIuElkeWruqZN+/RfcfOG2m35q/zevs9aaCNKk616z8/M+t+/rk+rdix47/4prkzU1EyIF0VIGLUqAMxlQhBka8h61UwjWMVESSt6yDe6DJwINIkQ8iSvDmiRBPDScP4agnrRnZISoSE6kSgzuowkFcqIIRifEPaJ5OBEcoPAAI715IzBIk2cVzBMxzXieRKUFzkIsgZBPeFwYOYjG2+yaREXIoYZZOLBOeKibmKfWkkWOBrqxO/gcimOLaiXkRRuiAYNmGhH4KaQi2AAlCsXCwoJSahZ1wKLGDbniQRo+LgMM76GVYAN+EFoD6aJx8DgxSkhpJWPrSjlxVu+wHSBnGAWf4a0WZUxEs1Foeu0eO+H9dXfce99DiwPlBE8JMoRlZN1Pz70Ut7ZaZ40t1l2nn87h/dgwW4wIIMz/jF2EPg4YnRxvReb7aLbLX3vf21+7507LzRaAt/pKK3xgn703WH38UvfKW+8EXixhUpHMT48nRrIT4owMigEix+4NVl35uIPettX6682A4dk323DNI9/1ehzOcQ9Z+f7/ePF7Xv/S5ZafIY7lZxa8bY/dX7XTdrh176JHH75voVowghv0qwv+hc4t11njMwe/fZMN1heXidK2m6575AH7ZMHaFOdfcyMwTYUzC42eYTiB6TFusBXwsQYOKqgjgvoDvhBVUNU94t+jIxi7DSfKHhN/dFjynT+eb+7rr7HK1w7bf/stNoe4UNpi3bWOO+BtL3nO5tDyk/Mvv/K2O2Ma0tdcLRsxYBXGkt1w8oiCvZAHmBAkoz3NUaWgXgIDjRl1sIF0kZEt2ExoMOiDdCzFCiI01Jj2VLFeszZCrF3FNYM+5NAT8z9uBFqYG/ug5ootPfaWlt1TDTVSOOg4IStWClN2yEF0WuUYhG28iR9IInHH5rozYwXVopLDtjZ/EGcO7ISx5wrI8xAH6w2rEogvFCW8xLxR3FwGhuUA3nM0+IchCEkgh9QJwQA0M1zzyJMYJXh1qo3MIUihJnygaIlYI5No4OnqJHC8Mh4AIUsyScgFvE9ccprImA05UUbadO6KHRPPUW5IHO6KdMo5GLIZG+USXkYbxmBIIiniE6pKZskN8xMjNlSZx5KhZrjBLbAfbIG4KXb+DjNH7IH20GyIat7cKrfGhv0gGigQ5hKm1IjNk2HrByAN7qeesA4HueUAFOTwlwdmmyHKjKQTQDRI1OyBJw0y7Qj7SGLOgqiDF5G8EQWjSwi1JEqhrC1zr9ZT79QTlLtgo+QMU1saQ014EdLMytREBhUcHUJhZDNqfXZ8M9rEzI1MhUtKBY95OLxNW4WTpfeJ18ItofpqFzIJJeCXRJJnhScR1qCcc0XukMBCgZdBQwCVZjzaIg6VkfAIqKPQjnmGCTFgTi10IG6YRRHuDEYA8CBlTok4kTPkR5sSG5JXxZJgewyPaTCC+CidJJhkFI15YOvxHCq1TNxZIpCoI0CKuFbw3YCxYeUxCI02UK1kDQVYIzITjKckylpSoAbDQWBNFDHmw5CkHx1IHkZjJBzjNRgWmg49A62IRiBcZslJGOsHj+IJBANwKTubgcjSCuBXa0yGLHYiDqw0MT6+yzGmLD198XiNwCRCrzuZhfN8CsDSFgQWtkqQTwzrWQLegQJcG7tFWJCwYckLlYHFlYOYnFAG8A6M8YFRt3pOTVoAOjHAHswtKGBC9HAKgigkhD1iYMzwqsHhycaKS5DeSPEe01hVygynjtqEhkI1OSIAQfMNkyA0UaSwLBg+30vE7ikGIiMiRjd+e1TxYWakWYEpg0wRI/o/Fw/YJ4RUiGux0oLltlx3Ddy75Mbbpsw9E/h58XU3Lx3qC7bbYsdN1p8b6tU33NxxU+mV/fKbbsHg7TdaZ4VuQli8avU6SkInQbtkzYkj91Of9rCKEAMRZlFS4RLdxF/3gh2PfPtrPr3vq2ZmwQWpCG90EjJKIIJRGpyxz0EN7CVaPPbUEtxiZodtXqU1jgjSrTbe8BNvf+1n3/Ef226wDnbyiBTBAEdmUyawpUTVZj7E9DeX/Avo77blRi/cftMJh4D81ogsl/y2l+yeVB549Ikb77hXwjx4qy03PfNTh5z50QN32GzDvg219UZ95IZ02BPrFtFjc/3ix5eERWV6stZ+qDCviChCDQFm5I6E2nfvXY9656s//fZXzZSMpC7BajAbYymYUDUIKWygVeAae4mYTdgK+7NXWnmHTdYjoktuuvPJOkVEiM29//M/r4OIjddYZY21VovJhDrI7vB4t2NSlkDywDkGUMwJLatnHQglv2BAhyygwfvW9whzrmniqeNQGTg83JUosSN63CkXQijca2/Rezhkq1OiQJ4QCwfL1NLgY80KGF1BcXjNBCU5KBkWQPDcRluQ7uHwERxAcYRcptBcsRhB+wS4SYoA1bMFkqKEjFWHiRE8aW7uWMkHEyMJZiGaTkdeQc50OqfRXrnztnj/j2tuvvWBBzkh9ISvaSDrj5ddgf5nLbfMHtttDmDDHR/RzHiwPK3y5Fz8/KLLq9nGa6z8tlfuaQl2UXHqzIHDczff+BU7boPxV9x27x33Pqitwcq7Fy68Yf74/Ybdd1x5pVWcwtmIAxHvRF7y3HE8pihpGulO4MYvzr8cPS/cbvPtt9gAIJHADIDOk2V0v72fj1t3PbT4rnvuhyMcI7HRg7b3zjsAttqs94aVacqyy7abfP9TB5z9yUO23nxjAjpYoCIRF5lMdt0GuytavGT6xLSvwb1pP+YUxDzToBfFBpHVzJL8b/+8+uHHn1p2ptv/FS+eaKEKxTSkcLFdt9lo63XXQLTOv+p6Djy2qxIkymksyBJMGpIZlVaJhUCZGL2n+QvwSZg3UJg7JwVdSIwJIxQBbo2w3FOtbWiDS0M3R1LHmURgAvitFJPO0kyMpyIoESwCzoI1viVvjLVmGJyAN1hFsDYCYEpAQxLNCW/GwgspuO+pNpCzECXW4OzIDVEr4popdSFlSjRNhO+IBFyUQVIVoRw5+yQxpTIoNwpqEdgfDGSwvzWtiRs2egI3yTk4goYKDg0WrdoQhhrZKEUoAyBhEhIlXECn1NBqXkeZo2gJUmccITsrHQsGu2B94wpWUWO2mA4j1fGKfHreNpuuvMzskn74n3MvD8MKgIJD7PLww0v+dcv4d3FfufNzJsCzOrShsTCCbh5uhFcYik40DSIHuZj/XXJ332azrdZbI+o0D5aQtW2yzGS5LdZdHYPvwaFozFJumQyf/92MBHpQmdwA/9i781abziwzyVlLcUXMYlhtmeXWW/VZuFdyftUezzEagFWgakpo4R233Bi33OPxx5eMGw6rMOi/9nvTGR8/9DOHvLWb5L6Fxciyom2dZ6+y/morYfw9jz4uEZ1ivcj/xyNW0ZSJSwQyQjCHA1wLOBm4OBrDTMF0NPzK1sQiwNMs/7j+jlsfWKQi73vNi9da+VnY8lttwL5Bx/Kzh7zp5SUnYP7nf11JHqlRgplGmOzExCwSjGATWSOH0ETO3mJ0slIxRy3URkLMKmAzzFOggzBTDsHql6gRlrhwqxRViMRUrMs0yZ5BhMDaYsQwaHCuHoMnrzM27SrWNpjBlSM8AiV8KG7igcgYjsCpIwhoRHWgGFA9e+EpzCR4nbSxIOlhs+HbkumMu3KmopbJmPBsXFoJh4RicEJhNRNTYx/XAbbsLbXG4bAWOaWENMhmpa8ytdEUfFbhAGcgOGqj3rwX946jyLj/zKmmVLMCCxREhwdUhQfohg4jaZybl7ma54YCwUpj7Qri0Tq2FJTwrsEkFdRZArJIBmHn4GaMgjk0lBRndi0tlabJEqLm1KiJwM2ghkrlQjZWB2X8EW5MgzC29D18a0ROCoGlNmkeESwsAiURTiFGilo7wtBYKFRhlAYWNS40rmAY0JpVs+bkRuCyObKdfZRpwsTANSsWVMfZKIwxP5KYqnkhw9ZoJkdia+FTynM0M/ViIhg/iPShbYgAIRrkkYR7eEOMhFicGypIDTInDuYmZmMBJukKJSxwDAc0DE86xn0DjId/NCgcxIG1FUwALMZiOZMmlsSRAT1bU2vJgFqT+T0FJwohEgDDIRFaiYPwy8UpCUkiJxwJRxY34WAKoNpgY0sldJJjAjNqr064Bwiat2bJPVEUAE6Boe7IqCK1S01UKNQsRc3U1ABsDNRaNikJlQcsyaP/yUTRSBBX6hAVeOyEtdEqiQeSMhEzC2XBB0OXE2zvAD7sc7ZgxioQqWD1IGYiBRipKaBppL2nihi58HiNRnHyEGsM0mH59aE1NwQumERC2YhCRJMkZSw/IswibtlqjiEnKx3RhOA1oqgtMLqKhQe1ol4wF7BUQx4FWcI4VlEBvslRrz0hnLlZCdeA9A77/q5BciVyhudBHk9fJFo8FwZWrHm0CrIJF3SZG+F8BFcJ5pkEayMlaCqsiVGxsSx2xAU+SxBX8goYABuFw3sEGoIIom3S00zfdW1GkRqGseMNCGbgzRAe4cGe1DIhpJgrpsxKyGuegmVPDyfEmolMiaASL2Y7bLQeEWEHNleHlmiuDRfdfCt69tpxazxzxZu/X3Oz1SmQaP1wyz0Po+d5m20SlBlqEzlBHvrGBquYVIRXXn651VZekYh+/I9LfvSPvz9anxwY0UO5auut8az/2PP5/7HnjqutsozkFokBCGJK/76Ex6iqAJNCQ95h4w1w5+b7Fh7/01/cu+hhqCBRmujMcrN77brz63Z/3rbPXp8rjwH0EgiPGcZTgIWlCC1ctHDho48z005bbJIzzikU4FBFRdNG9Ow1V02qQ7O771uU3VT8WSstt+VGa26+8VqpUycED9xuiQeVOpOB2rzscOBMOZ618gprrbwCur537sU/O++SJ59aqqANu4usu9Zqr3vxzq/a83nLLViWCB5RcGAkGt44WzT2RiCH+b/7k1NGG7n9svlD0R0LH7nl3seaFfL84MOP3fnAQ5i+5/Zb6GzXK/XIDPhqTAGnJIzAAYJnE7cJt0QVK9TIlFDlBLoxI1rhHHAMOplDGGFMSXLISDvIIdCaxgFMrKgZUYUGEqJCnJFQkYrknFQhAJu5PueWEDHKuD3+Fm4czQhV1E2ZZRzJJkyFKWESPCCKIKnCWBhaBMipraJc8sCCZcbhkWMMsBnqmPIUzKqwhyjHzEw3LwLPGZpn2XOHbVdddhbH6b9edXWoUXasFs2HP1w6HlR23HTdNdZeOc3w0NrTs9hJpsmGycJHH7tjDTxsAAAQAElEQVTjgfHbsB023agkFOBAUKCJjJvTlNpLnrtVVn1ybnrFHfcqhVC74c4723ywnrfl5uF4gIni5EySJZfEOQnNXyzEHRrfef8Di594Uph32myDgq9SZrq+m6ndjJWuSrf+2mti+LS2Ox9cDEBY4XagBw1jhXt1BC970zb1VZZddtsNnr0tzuRFSBAkq9h7uLBwKQlT0BwepICc4MFsQM/TTZMBk8ju0iLqFTfcEhGrrbDcBs9eMzmhQWvjVrWS8gZrrIpZ9zz8aG9Vi2gOESdvbg5r+kIo4Zq6jmeVlRw6KwIIYgUZaUOlUgL5OkFsiJg9bOC+18EFJIAmboxtkpBLOIURAEWwlblLwvBktCISSXFUx1RIk5NUBMUoGjQmgViywIZEB8bGIlp2Ew9pqNtYcFFkBvOxixIGVOQ7VZImAqYOneKwUUkNeU1wyZgI2gAqTEiENUeiSascIc7F8mwTJKWTNTZvZD3hCZ0FkXAQjpFhDK5SuI28aeJYOA0WZW+IkxIJ4Qr8wiQzhjnWvDbrow5wDHtfNc6NJoN1LTQkhI2gTCalYGprlpuvvfzKL95uPJGe889rax8xCDCggc+58F8g/8rLzu66zUZh1KphChoTRAgG+LTaXA2CxYRO5JGyqicG/DRey8xM4Fc2EIeoqo2ZmLMm3Huir85kLIgCMWajjziYh+zTFD4jcGvsI1GJsZIzSzCcHRQL8Apdh5uYt8xsZ1PHCk7hnrDcSpmMt3DXg4wYziRu2260xo4br7nhqssKPKxDb+EBOEKp5aTzgyOMuWqM9WbsQSd+CZwLcrxTpkAlS9awD4AmrOOmIsTPGA/rc/LM8MEj0pU33QGqlJSev8Vm2UO9CkyU1BiN11595e02WBsqrrzz3iHcR2c5cGE30YKrk0O4Jah0NsNBtR+kj/ExL0yZr2YkjQIHZcTUCVukYvigLGxJrbDxPOWlKY6Iaik0kyoGx2izkoFO4eYURsSkIgJPgowCoQZ7osYYMBZX9YIRxMwiOREkIeUDQ2F4MzVjCAJ0QxfGEBoINjvBHY5GIG7NZrDbEzekPYtQy1olBQPUGjREmMD0xFpqFtSCIbtlkuQCQYZRAVJFAF0JCjR2pkoVz1wlUydSlJNGSdYJ9lKIkRO1sB7cgHHk1d2wNnsZAsZIM6qNKYRAZkIIEbnWqSnehpgrko1CRxHQRkEWAEx8YKwblmMMDaWg0ReNQa2F4xc1EEij77LhEbhkzaQT02woC+ENjGjwOQhoC2OGM2uWMqNauIZZ80DsG3QMwmA6bncOgwcmjnEvhyA1ImuNmomxUHZGjJtjepDGSMHCUmBYg9Hi0TVlEyBHQjI+eGFQZKyQDv00TwDm0RF1KkaZI7SqDtjnZk74YJa8dRZqwR7A1MIoKhEhLQvzJHMu2EgyF2Q7VWMfbyq7JpNinjCUA8gYZg6UyEUrQA5uxHXEtrEh/kZijoKUuWUJEcznGdOZPibVgB7CZwGjlY0QGB/c+7BBvVf0xmgl0biw4JFGkkGaUxuMTKgXAofISUkY1oQndw0VT53zbFCmgdiGRIMSyB5qjYa+kkfHPMt5wlIC9jhyIVEkA41LFxmnAEHYoMe5N8bZ3diNGweezw15qFwDEAYXEVDLCWRSoU5JlUidJIy5QXgRHKFGwLBKcQv4x04pCYMZINog3swx2ME1+NuUg8iCmSkxwTFndyLHGNPWcljOJEUpZVfi5oBcmRTW4yNgii4RqIClZDAjNxmbqwrqBtBPTRTsTG3I06ZNSZKIoK4gJ6CYVKA7UlhHAIyyaFckJSXkiQn8FCLiQLVACQgH7MwOo1Vwd3w76kmZukJ5hrWDLUACq0I/7c178oghGEUTThGupfiGQoGIwyyBbohnIIA7xIprzEWC2nCwSBXuIEsctaVhSMCSUSLePt3Mm7DnDsyFV7THtlsw0233L3zg4UdE+N7Fj1x31/3rrbriJuuv/fzNt1hQ0s33PPjg4vG/hvzQ4sdvuucBDN5tu80EHucZl0nAu6flMgOVRk6Jl11m8qE37LXSsguemvZf+NHvXvKRYw/+8mlf+dEfb7jtvmEwwT43JQQja2RtIjXYnpZBzM5Co48sgeCmt754t03XXs3Df3bBP//j8ye84QsnfO5Hv/zHldc8ufQpBZzuFN6YnBGUIiP5erNaE0lJJSfsFGEDmHfcj3673UGffs77jt7hsGN2eN9Rz33PJ5934Mde9Z/HTYcxYR978klRZMJoRYQ//uSS31105SdO/e9XfvwrOxz8+R0POOr5B37u42f/crxNpJkJqaGx7OzsQa/aa/nZyWNL5j7737/d/SPHHfj1s0/81V9uuuv+Jb31UVwnlLuYJJvh0KdnEweVQBo5ORZU+XcvtaINFSgTq++x7ebYfHjEHy++2oeMA9tVN9yz6Iklwvzy5z+XSVBN+jZMh2HAnqeyk0hIxi+WplHFq0RjbqTG7kmIOwp8+9wFAVxngXIaOqKkaSwEKeFQ3lKr3A9p6FMIMeobfoPVQW5KOAeGiCAomSIRWBaMLAlYQ1FUMqagDknUFhWV3EgJ4ycus5W1srmPUAdmIaXcIpDqSD8casZKaEHWzMzNMMycCSexgYg4coqxKDKF8NzQ0/yFdKncrbjas3aefzr7+4uvnOt7VFbsBC679Za7H35sktMbXvR8kUwpRcT8JGICMOPbR5YsXfTkUrzbep2105AkECAGfZpLa2JMK628vAJUokefWhKEImv3LH4U45ef5NWWX1GIzakRVgmdaO5KYRXcRYMkyoR18t7HHp0OzSOO+v6vdnrvZ59/0Gd2OfhTux3yqV3fc+TzDjzyVR/9KgajPfrkEhdGnaNnBMDNEKlZHd2E3iFxVe710cVP/vJvFx769dNeeMTRzzvsyJ3f84ndDvzocT/4DYSgMbOGAWmihgygZzwmRVnwcLdgj77dfv943L3twUV7HHbkTh/4BNqu7/34bu89cudDj3ruIZ/9xYXjAW/ptJ9DJnfFJowCONS+1WoAAWFXVO9cQjsWEqRfTzEQSj03Yw5VlsSWsBxIELtHs0B+e5pI7iSlHFKcEnEw4CZ3j2AiZO8IthOCD02wFbKIJTiBCS0ioAqfWyInidEpIUxs3HqHChNy0WaKTU/v2N6YN4ECHhwWGJSEhKkHVbQGaQF3OFxCODKHjgYBKXJD+D07AqiFdKQ7kyo1pimmuptDNFW16uN4EeGU8QNjkwZzaq2rfRqGVFtCQIiIKcQDZc8J61ZUA9NreMuG72uqDC5DcJVxrR4wimEMhngEETXQkVrnstf225Wk9y1+/LJrbnTnMGl9/6sL/4kxL9pm09VWWj7C+r7h49h8DIu3amPSIaHGPohjVARHyhexsefpn6SzyhOKAn/Mw2oEQJ+/B4Q4iIEQ83wHAf88oKBMmJT0353jGCZ2x21LVCFN6N+XNQV8HCziUNtsHIebETGH4EFS4hyeobvWW++8/4Qf/e4DXz9z1/d//rmHHLP9e47e5cCjrr93JO04xVtUaOEuJ3xEY3MBAQ10xKdACpuUBl9MtPokeCaBkYx7aKykM4ySAGPM7Jb7F6Fzg1VXWG5mAbKDklFhl1RNvNZssc78l0JPLZ2bC+cu0Rgk3AtUP3QEnAnkuiBegnBSw1YF7wuFEgdCpBHFUCydfIhxnzVg8zQOp9xqbs6ounXCPkkxIZzwU/ZM4HhN0ZRRyRs7Sy9cmTRkYlyCZJ5PZrCWTWGHBhViYaJM0YUniui0dmXApsoxGvk2yoJEViPtMaupBhenDo+G+tZqbRQIHClRDhhhMCUSNU4Wge05GfwmAXQAPnMSkLeJOxN8dQtzb07BjuxUkqUkfZC3SD4CRA21gZg9ixesTf3gPVR6I8wnN+9DK0VhzhEclDiwOSoKV3qPwciAaRdYwRwbB3DVJlGzREhUGiCLp4TBQTqQGLOklvKQxbBp64gTKda5BAfbtDSTnHwGiCGruWNKgJCHrDjC4ayaCPzyhgQjE8YS6ZlFVFgZroVwCwuwFsMIkJE0zj2lnhiWBPXALLMDAiLSsA7BJZ1wQsoQnAX3GycWeBhOTjihTmrHlobwqoibwFak/BiPwMBI0Uobur5Nau4GfA+RC2anVESxuVAQHcEXFjWZnx5ZyRgFRaxPjO/MAlEWfEPZT5HZDSZzwhPpaS9tcDLqGjCnyO7FWAcavbYURoGgBZ7LOIo7NXM2lwp3larg21Q8SOA+SiVtHMzAKhyraxA5bK4CCGvrp/iFrqBq4ASihJw3MegCOsIdCYRYszltbcajYMMxM7SZ6ZzaUo6pFMqFrdrcU4ozhrlZIMU5lPGD8FfF0yhCUeWpUW8GWyxzS8kYxFWoRuxZuvnGUi2wIrc+6lwMc4S9HxhkoESHECI0Y8QwZyCaCxuocjQdmkB2c4Seu8LaMby27IB06KO5OhKXJkI82lSo5E6RQ0ao3A4ajWwAw32oMdf7XKtT90BqYRcoXc95YEiQFBkkAISJPKlrkrFjpLxx5yzCgdB5tEp14BoCVmI/N3EulRPkw/tx/WgdUwb1IqwRGJWIshjA8GxmQo0omtSqzVNAJrADLKBASMBzEg9tuOFCNQKtMVOEGpoYpQGybcitYoWPQchn2QsxetFmyqT1qc5pj1dPxPzMDSJjqoRPgmE0VF/qcAOpm2rlZh7eBBDjEBnjAKIwjsFytC7ZfCmKddd41karP6tvfvOtd+ZKF1x1fd9s+03WXzA7i8XvuZtu8MTc9MJrbm0Rt925ELc2WmOVNddcCdgw0KMU8YxkyG8uKHsVuHe8y/ZbnP3Rd7/6eVutsvyyzezyW+767p8v3OeYb739mJN//4/LbAimDtVkMnhxgILZY4MsI6QvDK8gN6rr8isve+oR+7/3VS/YYLWVMeK2Bx7+2T8uO/yk7735E18+/We/f6Kfs1keSz3oo6iywRaAINxqBqFk8dLBHFLJzGuz/9dm0AaphKeSjXOTMb3Ou+SGd3z6lKO/86tzr775ocef/D9TzH0cSsSKcCEHGLu4Fz9n09P+89177bDFyssuaGYX33THGb8/b5+jTtr/6JP+dskV2Q2PDjtwj6MKPX2BHcWooFiJMioajebhVoT0MdvzrJXJyiuvgBCg8/Kbbm02l2q79LrxL91tu/6aq6y4UjTTfpBhCGnNQOAQiZSDUyYrVJNESzzgE09y7WTIDgY4i2sOJde5lpbWUoVZa2IUlRBPadwv9WwVlaazgI2oBiYyFBmk1ajj84diFZUZEQPQmYdsyOUKM4KcqLH0nqt1E5I0dgSYiaComvpUhykcIiRBHWEWa9pcHRmYASlJo1Ipm3OKVnxQ4JUTBGkwir9Sg4aaZ8vMKIWIjamCgvLaF+w0U9LtDy2+9PrbrHE1++0F49/pWm/VlbZbZ0Oepr7lvlZ6+tLqXV8Uk58J/TJdJyElFe4yFeRkQ9oXkhWWWcAy2v/4G92N+wAAEABJREFU0jksi0OuTzVkOS23YEGXMxNzKzoVJchpPLrBT2uABwZtQY/hBDKfHe3/J/eWDjgY8lQpnp5PBNjAC+RADupIu9wZdX+56uY3HvX1z37nF3+/9hYcWf/faRkh6pQH0qrq3b+FEaO29E7IpWksWTp9YlpxC2n7fyT832/8GYMNmd5TDMRjgHNwiaI8cSrYUvTEjVjICzcVCS/VtOcGnF0qgeBBBo0hJiI5ukIzxBPLGQBTNEZN6ixmGnWtEGFRcedpoIhb65H2TuSNAOJgiLI6d95AAeHeo1KP6SqBrXCHPKhQFcGSqeuiTDg6DrAmOEUaA+ItN5PGMlCqhMQNCzjJQ9ORqxQD14EGZ9NA6bYIG+FhJknN8hBYJVKIFJaZQEnJTbSPmBrKakincC5yAlE6kswd0pA12EfvCYnhCCRREFmS4FyiiOfw3HjSUhq9157gu1kY95GnQK96okGk1tZo/gK+LnW7Ddffdt210PE/F14aJajwFTfffN8jj3VJX7LT1tkVtZhHVRhCHhRWQ41zIAPHLvzECBbBwUbEhf59ZWVSCUroDhqSLmU2mr9kOsi0iYWQzHeML4LCDUqido8zxh785ObNhjq1OiQXqngwovAbd8bg5BlJJQmp1qYDVrlRPjPPzhQV5BLMpaVL6hd+8L9vPfr0H5x76aU331Wb/Z8GukIQGljsNCgyHx/mWzP2iDzCaoSNFQir49qvEeogBomwzI/EixM7JTKhgdvQzBydC2ZwAKBa3HJuhOiou0cd2Gx2ZgYD5vphaC0KxQLWYhBImpt2jbObYp8QqqEsoVIT1cBEZHPx6HIUhGfElmutDjZwMAxgDpygQaI+jMlAowwrA8Qxch83CxTCrt5K76l1xWeJkC/N8zR18FY7/JEimaTrO1raDT1gdevndOkcD23gaBmkAzYSnnLL4DFUtNT1BUROWQUESA51Hl6lRhmyVijtlXtBPWJsQwzZAlneB9xKhD1PA4/FlYccg8CjQVqv1bwFPHEDOhwo6kQBCyBpPjBU3FPv2DEXJnbjVhlFsQELSughDYCqhTMBXxbcCFWbSM0M8plEU+w+0d3YPUy8pYrXqtGz1WhEziAbXphJE2mWxONily06cmgNRXqgVhGMU9hDHGbJkYkBR1ovbrjlpXEyYbDTIVNpDLqKB3CwAVNAVVeu6qZsmWAfzGFqOOOPwjEirAnAjNExUNO4EowjUqCfPLQxB4xkT9wnm84MSG2mhhLFPfuUsT0TwONAJEFVdEPtrHXKiFWuMZlGahRCkgUiIwAIQqIl4BETK1FSNLC0RnIYFqiv4hHeEK4ACjlQ62qLaeMetUFRvhKReRqstFBjAcGiE1N0oqUWxYYsPahGXQVGlixwgwhDiSRESRSABbs44EoA0LDlYsfGQhjiYK6adI1Kc4UbBFswU7zTRgRjvDkckOQQBAmIxWNLnvrLJRd/8isnvP3QD7/l4A8ccOQx/3v+RRgZSFEWG2FMmUoaeR4tWt96q9PkQybOkG0Bh4fBxiluSPoE4cU9GTngCDdL4Zk0M6qPu4/9BCtQOSnqKJ+YcIU1sz64tQBMWYeig/pUaqOK1OHwFMCKEA4XciZPqppYlBi/E7M049ZXM+itXKNfWu+5b1GiSG4J5b7BXWKSBBhj5BHBogYqgBicghHcAsGBi4INplpTewZrEYIf6lXDJQB4wB+4MYg7OydlYJzaYEN1TJ13SYwijOYvURVSdiED93iIEjEhS5g9ANbR6EYyjLIsEHghlkI6sdL5bEIh6ZiE5i/IliZh3KBq5L3Od48vAdtcckQ3whUW3JA/jbMZCi/cxSRPzvJ/RBkEoT+cyEmCJ7Ozm86vf5fdfHsdhotuGDfEu261iVpjTrtvPf59g39ceYv1fuG1t0PlczdZv6TUW6vVvA82Qyda4Gd0lgnSKdhjozVXP/qgN373Ywd8+b1vfufeu2270bpF5Zb7Fn762z8741fnmCU24DNf4+nf5kEKBE/7hitQgshZl1tx+YPfsPd3jjzklCP2P2KfvffYeqMVF0wefWrp6b//+0dO+t6jT86NnEquXWNtEJQCfsEA8ohc0EEqfPhr9/7W4e867SPvPOUj7/r6h/b/xofefcrhB3zr/QecfNi7v/n+/V79wp0rodra/fc88OUf/Hrh408t6Mord9rmk29/1alHHPCtjxx02gcOOmivPeAiGmwMBNqI1fPENlt/1aMO3feMjx36pYPedtBeu2+7/tpZ5Ya7H/j4t374g9/9JUtN3FIgbMhIzEbjELUklnk+LAgvOgkcB/HNcKQRLfk1O28Hs2954OE7Ft73xNziq2+7E4NeusM2BPWNyTSpwLuusFLkqAA8cItABknm6k25psQJjyQ4MTETYSi2SYPENAykzUYZtdR1EBm0ugwEPmGUMKPKG9MQSKuAXGniVQIumGOuuyplkSQuUlESBnRimJE444qknqTBVid2AlzNozrNXyjd3JsMQ7JGoEsWbLo4eWCxUCZmIiYkG+KYmCWjYkoVbkh9vLZ/S4Gpg0XzjdZde6M1x7+w9LN/XDKI3vbAYuxyiGif3Z+XXaOGtQhi9KCFUCSTjrrS4SPaFDsScuhToMMi2lTHnUPrp4F5RAtmOuQMZy4lYfyS6bSiRBEkogRDnJEZu1I8owK/2IgGyjkTUUn64X32OuFD+570obd/6yP7fuuId570kf2+8cGxnfSR/U/7yH6v2W17cDQixb8lNEpTLk0Aok2oFfVb77/v8z/4+cNPLFlmUl7z/G0/9tZXfP2w/U94/wGnHv7ud7x4/K9TQFE4TKLE1IVkGk1F53wL4B9NhGS2JBEYSNuuv9ZJHz7w5MMPPPUjB5x6xDtPPOId3/jwvid86F2nHnbQt9530Bfe/fblFsyYYzVpQc4qVDwXyx5aHZAGum0ENVjRkhNWMx4cVOjDwUUTxAmwkLByUkoKIR6IhYaXQDdmjygxD1laSHNyB62FiFsEynnD2uVGODcjg/pSBi6tZZw4UQUD25QG3nquoCyB+xxBFMJMWa0UL8LUSIxQu1mJlbGWc6HxcROHa5XcUC4CZPRK3jiEUKJ5hgLeaiJ1EZNqaWqCpASuoplnuigJqxthNJILWaBsSZAOTKIhwVQVO4lErkTETOOFXg/UF4KRGJJCFOkhk4BNWUnGUc64CX8d66E7MOMu53EyUc8xTZGKvnC7LSHx0pvwrdhDLPHLi//Z5v8DdNtuuJHEaKGTPz2F2bEGpMK5K5rLqAA3UBSNgHQVQgDRgZbhazbK7qmS9FiSwlDdArfQ2F2wKOIOgoPPaHBgxrCflGRwAx1PNw6Zz9Jwn98uFNSOZ4wBoAIvBSMik4AtCl/np8FpyGNXxPms3//tJ+dd1jzWW23l975iz2Pf8+ZTUHuPGGvvuqusOD+cehXsF6PV1lBRxz4jDrIsU4kKWoBFgY5o7s2RwN4CVebf6oLgELOBGwGP54GnvrbK1bixox4qsEJMsip7emLJ+C8ku5w7VnEhklARuErBzCTkiVqWhmJMmi2pSXWd87Daa2vJQZWcaCItZ6PchkzARDGHXRBlAA1mSiLO5l5tmDoZ1LDgPnlUMYBMY2zJYLqOceVekzPYN86j8GjwmInxjhsRFJgxjvnWwsW4a1JMEsODUIXBoXCDuTJEBnhOScB89GXGy7h2AFqXRmqwxgOTK7aEmhWlsWdUNpRV7GVwsFBhAgokCQbTuAuEQvVAgUVNj5SMZ6qWAUtg1yJXjILb5A6zLcMkTtm1kCAzbUaiUxaCBoseRdZrSGNtlIZAgDx8DuCaDk7UXGwwrjVVU2dAFEyqKCZYppw9s0/YCldiqyM0TlXEAwhmjDQffMBT9aU0VKvUt7zU8yC40zKxIgaQKWLKLs7NaS76QXpXEicZ4Ieqlhwl1WKOKjTTd91QoGPAUjgfhdHIHAPbECha5myRPErFacBDqEJ0lgDCBGciAgqDcHyr1WGHCHNuIVi+PAxqE+OoqdOIwZsbc6C+CJETYo5FmltQMARLQGrhUA4itMaMjazCTm1KwFHTVHlqrQYnSzk0k5BUaKGmMuQylYmzKI171c4I9wloJjZiB2Erh2WRopKJGKYmErUitcMIlapjYRWmLqSL0lmewVOZWoohzEQdxBLMCjiBEDI7gU2o6yIVK2AENZk+ZT/67Z/efPCBH/jkx3/0859ffuVV19xww1/OPeewz37ugM994eYH7kcajewZ1zJm0qil9coV7+A8EEkcRVuiIfpqbWgxGHtLqeUSHdxNDdq60pduyBrJstYxTGajcZYkOk5JcqhQIk2GOhckfaU29BytGI0PaWz+gQl8aaROzAy/EBvoZKo6EfADYjJLZoT1khtv/+sV1zz06OMp7L//53f7ffC/br/vXipTlilZz31Ey06dmOQaMpi5ISQSQ+Ghc1MQ2ZWVM2wKGukkYIiP1kXwaFPqhUF+I9hiHC5mqXkOQ1CcRsvcGiRIQcZ7qU0cYwlr4mzjBZVk4CFAQ44QIQ0mSDFyj+YUTSwkmFkpKetECVtPzmqSGgvNX3NTRIYI8ykyyGtM9Myt1OCr52pZvIzYKPgO1BKKBKkQM4kE4x3NXyyRi0NfQ9GYgw7Bo67dtt4EN2+8655HnnjkilvvWm2FZTdfZ00bWt9sm002WmnBzFW33fnUo0tuuHv8/8dvv/lGAKBOaZirNiBxAnPROAg5nJ1hgQBjbEv7hiKz9oor7PW8rd//1teccMTBJx9x8BZrr9o8zvzj+fc8+DCFcAiFEjHNX+yOHAIwCEsj5IbEoK1JJZpdttt+y3Xe9opdvnH4vt8/8uA37LKtMF96y92/P/8y6Yfsc6JT6lw7LkVng7veaPCVllkwyagPsdIKs7tus/6uW2y06yYb77zxRjtttt6OW6+745br7Lj5ujttveGz11zZW6U698O/XrDoyaU4pXzhgDd94aC3vO2lu+229Wa7bL7JLpttsska4z8whZkSlBoD25bFCJxBgaDVVlrlxc95zvtf86ozP3jgaR/ab5M1VoabJ//23IWLHqE2pL7mhrGYTS40zToIAZ2m6HwGwKTR8ZzYnHvvNGy9/lprrLg8JvzhsmtvWfjQ/Y8+seyke8mOWyUlGUOag7osnWL3nCm8UautuUtocmEPIEkOBBNxjjwJGR9Bo1+wCUR8MvWJq4MfXtiUjEx46FLfZc8TImZ2DhTdXoaGVGVPQa4ttCXmItnGp9ehwhwcYRZ9lWntvE86B65SUyhL0Rj3LMwjYDfhDoZbZ57hO+zAO2RKomDUyhHLCCdYk7xmaVQgJzVSV5YSXFhVIAQtwsinOrQF3ewrn/8cBRluvOOBRYv+duW1i59aOkn60p228VFmzW6wEFPGFsLgmsgyy8wuN9Oh55YHH24ybTHQEFKTq3hqRMPCxYvMYAqttNxy5KLKG87/i97HlvZPzC2FDMBjAmuDMRzN0XF0UmQAABAASURBVAd58DIEb41WXWaZomruq62w3C4br//8jTfYedMNdt5qk5223nynbTbdcZuNnrvN+s/dav11Vl6hg6NVgzBtlEDOMcKdCGlujdr0p+ddBFqCAMcf8pajDnrt61703Odvsd7zNt1k58022XD+X5VgGpMFVQRGszGPlqNzbMVDrbGK8uxEn73SsujENuA5G6+7wxYb7rD5JjtuuclOm2/wvM3W33XzjXbbbtNdt9vkOVuuVyahUbXNkRvGBwUhC2XeRHYoMq8IE0cKVmNhvKOqNKAKVDBJtTGMcPiCe0yYo8108IQNUitcE6HmT4NsEOkFicRE+JXJYH5YGpDPhaqYRSVwjAMFH+vQkPEZi3kdvBmkcmbErDH11IZwaVwMR7sMaX0nfclGcHwsh6zEYiX7JEkmhUA3pM48oZiJBe+AHRLFOwK9Wwqi0Qm16m5GyTl5ylZSaBJVwSQmojGV1ZkYvgSQ5p6hNbMoqzQMoAi3cFzgSqoqiC6nAS0xdKHQjTbWnGpK1QSbjkRU+lrHuQSYCWtXzfzinbZaZdkFT831f770qnseeOjSG29nohdvv82sjucrJ51n3jhJxHNHOQtcLZqZMXDsr4xn5TQnEDh+xI8wgDCSGqUiKUMCVcEdd+aburNIsLRnJCAWaIlQVZhd5geNL8yiUVJAUw2seDzSBjcwTT0ZRSCxOaRjTpiN7rFrOu2ReTTokqV29jkXYvwum29w1ocPPOTlL3rlDttgYXr+VpvssvUmOJ/j1tgQXSkNBQXxGD+PP0CT2MmDLQd1QXAGUqMnmwZiV0f2jgOJAqMoxi380FGss/Jy6L594SN9m7LVbLVrPus0I1JyJkn3P/wIBsxMumWppJ7DFIFwIjHDM8qEyAjBOU/EhqiykAGSSuIeVJtXbsgJl46045w8uO+5NSFlyiiEBJIIZcJb2Nm31jCLABTRqKU5pih8A1PYXVpScF3ceDCNYPImdcCWoBtScRSbRBCYOUlyrGYD9xwusK6WGATGpdSsa2OVFmxc3JwkjeEgRoZFECwpZMoO8YNSU1XOaEkKM+Nox0ZkxEHwuGgSzuNbzZLHT8KO9E5TkUQyUoOZwB2QIjmpB4Ix+AAmMWVNOWG3k8k1hyvEhjSYGqruPLSh995G8xmkNvLqQ3BTZA8wYTHKNXQYseLEzLCJRZ3EgywQfULyRDT2Bq0od2bOYVkiE7EpPCRjwughEE0lT1oLQ6qIwWsoDYEhJtQj922evRWmZuYSlD1EHC5GUxpShBDDE/wOTs3H5qFESsxORoHazAEzhSFx5AQQJFaGxxVhpJpa4JYYZo1ymaJJeMADq8R9YnA7Rm47Q2YQUeDP+MtaUHM4wQwtgdvKLDkk+wi6OnPLglgCzmgtDa2Yd0opvBhJQEdIC3USyialV5ma+EAJRCZiS4S66wpHiMiZEFv1JCZUCTbI2KW4RTBaYBWomwEDzZ+CAHSDNNyeOEorU2JErY9mbs3ZdBJJpXgqVVLf4o/nX/6Kgw//7Be/eNe997RaRaRbsNwqz94Ab/Cl5N//ce5bP/DBX/35HFOSZJJhIlZYidYRjGRyRVE3Y3HKTHAO9ocYqynbfGhNVClNPHXMRA0eMDPcGUeFZ4aRJUcW7IiIKoWzg/0YFkQO1yzIJExVmAmNGNKJCnlOABFoNuyF2JySYAa1R55YvN/HPrbf4R98z0c/+uJ37Pe+Lxz3pVNOuvPOO07/yf80cVLI9Ggt8AEgh9DIImd27oI6N24oJIa4AjrOnEpSMNAdONKUvGeg1Mbb7sjoxuQcGrCMicJHcgVMZYHB4H7xhngKRzCh0TiaGRMoaSMNIspKmoShpIQDCMlGQjA19wyrWJU65Y45QTqCafTMNTspQsiiyKQCpc0pRoG4zeEalQOMqbgHqIgMrlIkEqUslJLCQgIahIsZKvGSyMdRyB1h2mbjZ6+y3II7H370x3+7sG9tk7VWXWXFFY0SDFj9WStuvNbqj88Nf73yhhvuXYhhm62/DiQLpVG21GaNgsaLCUUH5Cezv11x/Rn/+7fvnXNBG5yJFXdIcs5bbLD+oa9/OQZDy9W339XTiHUNDmJ0ouGdEJmmyFlF7nnwoe///q/f++3f7124GMbgLnmAf+uuvvJH9n318svMesR1t9+pEcnmQQcEJXNSlibAJGLtZ62MZ8NA66JrbwxzlF5puQtNMCrCUBeKYfubgoq7mt/14EMwY+O1Vttpm/VJDDUCWy8P7EDmUApwC61i587BTOdcfv1pvz3/v/94oTtzzMPAPJlkbPsOfd1LMXJa2w133QffwokdHf9u4weH3wgV/dt3jFANDUsgpguOWLtvOf6/CP5x9U2/ueQaj9h6w7VXWXm5YPIEiAhlp1p4KAnTSIvKcIdqi8Ehd+hiwLiQCEQa3A1CyKu665gOSmCwtylV6CMscMLZeQyf1MoIqjeCRzlYHTJEJRUCvVIKgCzcSKoLYkdEXYRWQoGorA1qPKJGNAlnb6ADBJgKzWTFYAnKDRZbSl5S0zEoZhFOTECRxT0rAiSMTPHsRkwkRMwYYZQQOJq/GOyWTIoJuz5n8xWWmV3SDz875/xzLr8Gt1+x01YrrbTMvKkBWIjRN7ZwwIcPvuqKy6z5rBXRdeH1NwePCQ6b2VA1BIQ2swuvvaOazZa86Vqrk3PU9OxVV8V4B+Vuu1UBNfY14oNlx/HGeiLDXTQWtswmvM5qq85MsCbEpdfcaqajahKHB6SqWbCOBDVEoymWKRrgIAyDAAJwkthlXD1qpMHolnsfxI0t11vruVtumAUllkSChTAmwDzcm28oruRBiBhKyHwPXipF7UgokpSU0+brjf8++N5HHrvn/vuEKlNjT9q63IoidskjE3ggQpkjVy594GlvYnLWUOYsLFA9n29NCISyNChZcs3WadPsGBOiTMKBDQCRUxA1wt6JnM0Fg2vLczUjkQg0sRB3MiRAEZ5YBj+QkCMCDI7Nc13hFYSgyeg2E4hFQ2AZxw3P8CMagtYgLXg+2pbDMtm4fgD7XqyNVjAbkMMiW9QpB1KflAgiw1gMHhJzwD0nqaZ46iiBu7COPNAV2PoRCr0pBaLDWCaDyIjaqJ5bTRXMh8XSJkzYh6XCNF6QSoyBUIlWgnWMkjT2yo0J9Qs9ecwIwirEii7NKY1TCTgyCwO31VZZfufNN4TG31585S/Pu/ixJXMrLph5xU7bU/joBtBL8ObpSSTwF+xyMCJwoRfINCUTAm5iWDLQh0FExuQUHFIELDGUIeHxHpFralla4uBnegKdFON47H4tPz0Mr0whLATqQCHoDoJgKG5g/CDUg//CsBJMgvvIAyJmLlk8rLnc/uAiVEsietXztl11uRlNDvp7IAI+PKMZN2nCLNiPsg4G1MeekWKSIcFBwfmgh3euXSu5aTHAjOSNZ0wRBBEbJDG4a4m22GAdiJgO9dqb7wjioBAmIZKATfHQ449fc9cDRLTlWqvPTvDwBm+ZTM1TuII/xbiQMuoZS3OIHPMgS2DJDdaBtSfFqQ8kZXzGQFIaJFtkCsUSlFDMGhFOXS1GPsM4yOeo7th7RE3CiSSFCAkmUFLVcc+UYaLRAE4PRtGyQy47SSMmrH6Ji5Aqu5AJS7C0IHcXLA0RVTJ2oqYeDKqachSyRLCeK6eG8oScnzo2qSTinUjKqgW/gAlVTEKSiHMGqg1MwkRIkmAY5wlMURcobW3isSAYT8cyUVAKON3EKsFiR+BzN1EFv5WQOxEIcvQ4xckw7bifKS1pBFABSYMxB7lOxhQpwAxiVhKN1PW51IJ4GdJMUUW4I0IFIyzc0ZYazQ3Z5oRQAo2bYZMAO9TArKKETVUWSHLuKnfOiWB3FOUZ52yqzt60DRiucEAYOEXB0wUJJLOyieJ7GBCJ1KiYdTRMxlh4x6FSKZmUxF3n3axpV0OMmDOhkTaXAXnQkcxYNzN04rmKt5lmOCLnAs+oMfdOc0xDJrjYOHDSbKKUEvBVR9xAx4r7LNXR1I0QCaqJrVMa/+lA6RmnDh0IldHcapixwy3SkI6BVcsxMJb76s0hTRIjjGMeYOxQQXQigErKntQkBzERZ1YNxgQU1GDCRA8iCzWDgubWSphgIGM0bqFGTsFSJEKtMBIh7GccZ2UThxcNJJBOWUUefvipj335xAP/66O33XIjpCxYZtlXvOzl3/nG16749U8u+u4p3/n6V7fefHPBsIcWHvGZo7500mlPLnkiqAVVCxdioY5KmiaZC5+akcJoyQyqQEWCZ4FqNSTvxQ2sS0zZWIeIgeBfipw4iYpnwSOhgWJorQ0WzVkisY7fAkRS0SQm2qQzYJiUA7Odo6VxGmcdi747DwPVioA8vOiR/Q8/4pJLLhz6HueuJ5cs+e05f664RfTXCy5+BA/72SFhBNoaA0AmhxVZU0ohWpUHiSFFP6aDEndME1IsDU4JNPIwQnpnpHU0sp6bqVMiFZq4pIFp2mKoWfJsme0y0pGtOk0jN5mFfCIypoq0KE2RS2kuUZVwCAVNkWNEapEai1EEop4qMZErOWoGttg9wAapIAfN3FiCRRXYAHtibIDQPzZuwNu5RYswxA0xAExBuY11OxFlEZXZXMbBRK0RiCTwIYVmIvFgW3HFFTdbe/UI+ul5lzLRnttvxbmYFhIBXLtsvTE6T/7d3zFgy/XWWmXlFRkhnUg3y92MIFSj5aN0DwBAA5Lh5rvuPuGXfz7u53+6beFDYBCzZIokoanmTsaxRBwMJ3vMaYH9E81fwRQlU9elnGDykrnHTv/9OSf+4n/PufiqEZnokk2oZhrUQxlmEcE7mmQvM86pp5mGrT4ZaR0mtXWx2rNW2HXz8b8bdu41t/7ln7c01Afm3GmiIg3S0l8uu/7u+x6y8DEeIKGM5mHp6s16xzEsmjngndN26a3j33yDmTDShIX5xpvvPOnnf/nKj//80MOPiLImpvFrhgQCqAJ6jCUMRK7zKPbfPRSTGDpD+DNzIYpx3PxPcC6REqQTTlpp9603Lyp3LHzk1+f/C/dftuPWqt1AKGLhOjjMG2o/mLNQgSTGzdC+VqsDcS087WiayayNOQMSj1MwDyvljFoprak1HzsLe+KJxkyY4GxZ+75hZUgkEKuiQkJM0KLKCQ6xwwbyas3mGk1NK41mo5IpWeFGLRwvDr47ByItDj+BiMILppihfhluEwELCQXcCLkT7irR5UiC9VEkciYRY1zIEBQ6j2nzqS1dMoWQsUmS1CWFPb7qqsvvttV4qPvp3y+54Z4HhflVe+xoyGEol4yP4/j5Hw+sY6zclp2Vlz13Sya68Z4Hf3/RldwiwskoKvsQdz3w6C/+MWK+4RrP2nzDNUhBA11v1dU3WXP8L65+55zz5+YexbdiLNQMmyStrZ133U3zGigI9IymsdryKz9v43XR+YcrbrjsprtCZylP5jliE3KA+Ofzr7nnnsfNM7IAJQUYYzCaiJPPA7AfAAAQAElEQVQSJzampiiKCAMspbl+qA0qEa4iymmCpB+uuOUuTEELTrCf4UJQLzwz6cZOAgpsIlj+MuZ4t/dzt5vk9MRcf/rv/m7TaQnLIYVAvI6NH3ho0f/87eJhQBiFXYNm1CZlgBGIWwqlyIQFwTv1LlUW1JNWA6nXhIMpJSriKqGMAseIHwHUFgPIQgMVPD8TRaZ5M3LOjSc1OlRGfIeIotoN6NLAc76x/AjkPS2hC5vxyEZABYWNO5/AYJYQ8DJGhQCdsVZKxcrdg4wMmJWIECDcb+TN2zSsNZcaPDC1TupMMoWFQYR0witDB9bSGEiGRuZmyDSy8a4QYdEha2aju41xm1pLtaHmSCVupO4MCEygAb63rrk2TEqYiiY2gpw4ueaaNQA6sh+ggvxUazLKHCzCkjKCYWyoAoyJaB15ggXM7vrGPXZQ5rsWLvr2ny8Mot223nTF5WYoT2VmSB1hFMbPN9A5rLaheh0cI8dO3J4Qacs+lPHz+DO06nMU0yIxYULZTTIRYh7v4YcR9NSSN3lGxtjnuKwhsvTvYYQsD1Ebsgw6cRexZ+5hmoUqfB7GFQADiZu7ESFoAUHBfZUaiBi6iJZOe5KILqB0pE2d3n7v/Q88+iRuJlEQLBV2pL7+G9iEbjxlmkFBRfRhkDAnSAPbFWOxQHEp5WmHYtQbVbH9417zrjtsvcryy1Tzb/3mvCeW1MbFE6Nc11b7fulpv/3z0n5ABX7RDlvXwjYhzQYfKKSyOHSwaFPQEFJ7cC+ZlCjJkd2q4l3GCToQtGgefYD/HMwZYR+jqcTCZIMPS63O1QieTambiCRqxjZAABdhUXLJjphoQVY1yLPMgzDChrW2cGTOFEhjGZyHQKiBPlVNgeqYpUidUJ144z7cx3eE0CvrwKlJMQ6Eg9MU8aMmNkbCUZOTVNzLTVAThEkZ1cW8qzbbojhTNIgz0HvINIibuzqJZB6ldwxBPDRYJM3FHcKV4UlWTpQLo9gwNaIgClIhForEho9UhZxUBUhogWMT9kmqRUxJuGHh1NqnoTKwkI5IMCGsGOWWCgmQQdPasASRBS7CGGFNqtlTGpQrMppCBDFI7MyWoiexQeAHSwMwSoSNWxAbYpCVQDBOcBqdBKtnBu6MimGqiKtEck9hQszEoD0NlBpBSJYgEbC+z2Ioj84RGM0GvAhlyOCe1lBKSTNrZ6IhJEoYxiTClCjlyB0ptAAXbZEd45kYsQxruQb8VrggPHAeqjZzRhzDWZwAfDVogPbWSrXMnkUUL8RewruA4R5Soxhld1C1CQ+qmE1GMuWupoLkHXVmUuEZ4hkn2M3qkkxBzIJEJXNDpaGGKsGNBWBCu9Sh1B5P6pLAbg/sSxpWekCoKXKB14mIiYJiqOf89bx9Dzns57/5NRGllPd4/vO/+/Xjv/6xD+249RaiCpnP3WaLs7587P5vfnPJMNXP+M63D/nkUbfd+wCy1x0yKBJXGAQiNnWD/1jYrLAKhUuL8ZSKvOWBuDaN8cGJS4QahSHswknGWHuwNY4G00i0pWgjGjUTMxfmnABtYE1NpDhpZ80Qz96SQQisYFEuHUshz2ZLHu8/+uWTr77+Bji1yQYb7PP6N2604UYQhI9o999376//9A9j9iBiApnYa0ODWQJ+igQoj1f4IkLaKg2DDVEbwz1Y6AL0hFwRIgQ1ckqiQEHwhKTgiUgUFmnGrUGDJAbsikzXgXD1ghBjPlXhmrXP8JN0MK69ed9oYDJtnns45mHIcIcU2Bni4RENV00RWapyhUA0xCKSaiYs0TzDlNOys7Pob2bX3XufJ0aJmhu071NzJY3UVe4aCQLBChqQzMyPx5Rr7ri/NWirQZVyEPJPrSR9zmbjPg/nuxUWzOy4yfpuFlw5G3LuOZttvMxMt/ippZi+4+YbUgTstBFMAuY5jyHFLUQpyEIsCe/6nK2e/ns1n/jOL6685e7p3DS55TrcffeDZ/zqrxg8U/LWG64LAhNHWMx2ozvox/cPgCmM3ZqnuvFGq220xrg1/OGfL/jdeZc9+vjS5tDPDy568tSf//nxp+aE+blbbGIKujFspVGcUIz4I2ERYAp7x167wanpUI/571/98uJLHn3qqeZcLR54+PFTf3Pu587+5XuPO+uOOx+iyOHdjpuMONx2/8Lv/ea8x5+YOrZYMXfnAwu/9t3f/vwfV9D8Ne2HSoDVXrjNFgu6gr5Pn/mjm+66o4+5UIfkq2+/98zf/g39syVvtc46Ae4XQZjQ83RjYrXUmRZQkfnpTgtrPGYxB6LmqvycDddbZ9XxPyGAAc9absH2m21SDRAbMaofBjglJJnV6jYoAhaCysiegjnGkgqoHEshe3OiqmO4GEOyRSLTZFgvUKvz4POFlakIepsLVQZFO7ZSAmWSQccwcuhuFENEL17VrTSDKCQ5V0hNXlgTi1ZiWIjdQGtp5BjW0SQCJw2gwROMzVXcBZJgHjtTTTSIkrKyFE4JBWcuYFdRkkSRocCmgd0sTSeTZ/jGwpTUU5CTVnndC56XVUFgaNhq3TU2XW9tpCeFg40MGeidbwEllDzYiV65x47rr74KMvCrP/vjry668tHpkpYa6u21t9535Jm/uHfx40nlva95US7kOs4ok2X32XMXxTZx0WOf/8GvFy58RHoS48VPPHXq7//yiwvGgw2UwCAhT0xK/O69XwwOLJn2R37nJ3/657+WzmFRZ5/GAw8s/tZPfnfMt39++DdPv+/RRS1z6yiUnr7aGEgmJqS8ZEPbfqPxwHP93fd/7/d/e3LJVBkG2e133/P5s37664uvpvlrzhAPVMcYBrdKszPYkxJse+ChR5W0hbkiALzFeuu9eNvNMONvV9541Hd/fef9CxsHgJ+2/tLrbz7sa2cf9Z3/+dbP/8gVRoBU0ig10zAOQk3FSGdgnq1pNTV0BwYOiq2vR+fUMalQn2lOdADx3aJv1lplt4kEopc5ULCiOQmDKixNFI5mOIqFwWvHQ04WODRP2CZkwswggTIHshk6AzHNWTpNSRMrhBDCEMnHlZcIgudMKjkXowlqgydS0NH76M0QgMhECegm8c49N1LLjbEbz01KwywWPM+YFrZMieuEe4THxHrQycYOQaZIwozQIQkWQ895SNCmEAsAsrBHNEBOYUKEqkSJSFzCi2EezXcKxTySEF2ptuCIwlwULmFb1nw6zK8omG4aQR5UmbfceJMtnr1686g2cvt1uz2Xskd2YiOuTP++QCbTRm7ew5T/0ysy7dLQJWYZbUC/R7AzNdUG84sgr5gYWOMeESch4RgtD5q/xjst3Az2hM93zb+wOXNzYexgCHvIKBTPqECxCI7kkQZwzTAOUZmfBLfNySQPa62x4mrLLUDn9/9y0WW33DMNcdYlS5ae/6/rP3zSdxbPrz4AZCQ5R8EG6RnZhFLWsSks5hBpSfssU2GUuwrSC1skm3S4zxB+4z334zwSMDJYI6+5wipvfsHO4NeVt979te/9+t4HFgXgaLHo0cfO/P25v7zoX5izx9abbr/pJqO73CBWtCasHmxTMXyl4XAZZ0FrllLrujYeQ1hGNE00VBhUTtJTDNZASSM15kZcCYUDLpolgElkWSpPKlglmscWyu7hQ7g3JmOIYgEDLPq+eQ/GhpKzDAzKqothUHIr7tK8NSJJWVIH3wLrwzDgfq95UMDGStwRZ7WkinXZBmxax0OqslEeXB31PyCgIlERoahBTKqaFJnqoQae9UIVw7kGe7SoPQECJwkYCaI3c7SggAgXhzmoDkTY20EOcRA1POdquMGipEVVWViEsVq0Qi2xpFSK5EzoDQFsxA0E8lxNvBp7SzHkwBEfOoI7zxnDwnQw7I0nETg0OhbnUXCBCuVgGCKNxFARhkSDkmX431qOQVMlhS/jXXYjHu8KBTETKUXumae4gQ6nDlTmgbNHZiFBnoUTfAHIcNmUWhqrWRhhuQYGiRzqiajJaDBhbCCpAv6HBeDqkHqZDeMCF4eGJBSZ0nmnFY8UFWWDG3uVVrk1qlanXPtREZyWZEWHMn8kAdUMagcKckN0xR0esAe2GrOUcu54kocuT7Pi2cTSnOdSHkRMUpVRA0WDIC0UwgG2RiImghR1SqTCMiLApK5dzaWSRCPq51UhAGiIIGB1b1qxv2+pVIBBsAKIVuAUCXITZRiUa3T9/Ysf+thXv/bhIz931933ENE6665/9Mc+evJRn9pqo/V91I2+sTHxssssOOKg/b74qU+s8qxV0PWPSy7d/2OfvejqG7DMcPKA/0gZ67iNf1OD58EVaoxVLA0h1fLQJoGizpg8b7S0VqqrGfnTXlFAAm7hk2hK+BGGp9E0qlCgIYakzZGuOEGQgkHhnVmGDIQ1EE5LqqVoGno54Xs/Ovdv4353nbXXPuFznzn6A+/50TeO/+pnj1x79dVhAtpxX/vGb/92sQGLXFgomUlDPRiFmQdXEB5LmMwALQE5rA59w6pg41/pVAo4wmPKKCVkiuaiImqNuKc0UDbOoy1wxhr2iFgIpqLOWKVzgBREQbjwgtaIzIUNVGBQ0MlxUQ00mCTNxRB6gTpMc28NVcWcoTmxikPOfOMA3MgHnRIbk2y/8QbYIqMUfPKsX+x7zOkfP+0nT82FebbIpBEZ44Gq5RbJSUnWX2OtNVdcjohO/99z9//yye/92pn3PvBgUAvkJksJ3mmzDbGjwoCN1lxljdVWY2vJ0VAJaMM1V3/2KuN2GQO2WH9NLH3u1CxgKTUlT5iF5sEN70Vy4i3XWee9r35pyemmexce+I2z3/6Fk/cd2ynv/tLpl918VxJ+10t3WetZyxfxpMgqWv1Zq66/6vi/9PrR3y/d/9gTD/niKbfefY8hu7vyX+989crLLcBCdfR3fnHgsSft96WT3/6lU97x5VO+/9cLzX3nTdbbc7ttQA9H3aBxM2SVW0XxB6IAloDzequt8cm3vnaZSffwE0s+/8Nf7Hv8Se/84onv/tKJ7z7+pG/977nYu7zy+dust/rK5JlCX7nL89dfdcXqceYfLjjoS2ce8JUz3vHF0/Y/7oyf/eNf68//Z8fg6aLHH3czq77Nemu/52V7JJWr77j3vV857V3HnPzOo096x9EnvufLZ155x71Z5eBXvGCFySxhhRLUo9FTTB9b5KDsAX6xsow9RB6B/XsTMpAoDGeQZWdn9thqk6fvPmfDZ6+0zDIxhaOu7h3zTEldSarSAntTqdNkxiTMqiE+8gR3EMgm3Es3jJsycREmHoPOQZKYOuFiAvAiHKLH58ACkZOONUXLPJA0SHVwenCqRuNWA+WmVzOF/dDGUEaGz8CaenVsuQ3li7Jgi4CmmTlLy9Gz0dMXKMPkhjVFg5ndM7eUzEvAWNS+RjI4NyNt2PJ4R03CTLypwfmnZbDjtwEmuFw8b7fB+hvM/xsS9L7ieTsUmWkwk5toYwAd6J5vGgbhTYaal11u5U/u/8aVl13w6FjrnAAAEABJREFU+NLpF3/yuwOOP2O/40/d/8vfOuwb373qjnsx+rDXvGi3zTcOc2jnhETlvZ+3017bb4Fbf/jXDe889swDvnjqgV89ab+vnnjaH89febnx337gVlBkshwWGhttsP4n3vEfC7ry6JNLPn3WT/c9+sS3H33K2489ed/jTjrrj/+woFfuuO3qKy6jxQE3MWaPzZHDxEoxoVawbVd/7W47Pnvl5av5Sb/6yzu+iOmnvOuYM979pTN/d+nV680nDqaBlkNOAyfAxI123mQjdD70+FMf+PoP9j/6lK//+FehThzA7T/f9Prnb7Zh8/j95de+68tnvfOYE/c97sS3f+nEQ075/i0PPLzB6s96+fZbk418tBgqxyAKnIV6oimRBagSHOSsJgV0IzEKS7XlZtrcLRqGEY6siBejRNbASlZDDDWC2EKaJu8YQglUtdJSJuzPsDRiQMggPCjCn8JlEoIvHzset2URzXBQZhPEQpRzAslShDYarUku4KwTDkY1jDkmA6UpxVB6nh1KqQW+Vx1qqdzR2OCAY2eWMNxlILA9ESkhECIQTbkrnBKj+LplcRRK0BHrtWViJg3qDI9WE/aneDSEAToxQW/AewnXwBunpy8PFH7GNqOiJwIqUS5J3BS2MzXxkafAhgPJZoQVZ1LK03NpYDJo9FDTzK/aZfun+zd/9hpbb7pBJEiCJiLM9Pk3420hQkGDHENm8dhDDGYGbCcB3hl+zvcSEojxjhuRwzgY5fRvOYhzGmexsGDM2AJ9RBEUA9E4fuykeSHu44qD98ok8m9TInL1DtIrobC6h7CnZ7TjQxBDxXKzef+X7Z5V7nz40fef+IP9Pn/a/kefsf8xZ/7X6T997KmlWAKI6MmlS5cMPfGQySbpGQlCoQS6VM2OPAIYEkbjMtCIG+gF2q/yrGXXXWUFSPjlxVftf9ypBxxzxm2334vYwa79XvrCl+2wpUf89rJrDzz+FCTCvsed/M4vnn7a//5jqG3zZ6/+n/u8LmuHUipYKoAwuC1Vw7wBJGsxJUMxdNUckga0kGAhdglXo4QsGe8wtAe4obVRcwhRM8UYwY3ESTkHYTi7sqSiOsOBUuqtEnwggCmAqg/qq7chxCQI7EhC87uEICiKrnHxYLjtWi25M5G5NAOtnLLj0CToCRRXrSSBbnLSxskkSYz7yY6NJ81mqEJ6cx0iegpn5sSSSRnTAlU1TAj2E9YPEXImxyiKkBivQZzADaA6hTFIlJoERtcmgYewA/rUDQAgC6gxg6hdRzMqeFo/iSajxV7hCIGkooQR0IZEB6E7UjjCY7WRftDBIUOaSkvszNY0mipzyg1p6YV74WaETrPcnCOMtG8KGo2LGY3MgULiLFgOPaI2mwY1YSTy6ESlRK3jRvDLWAYN18qpSnGaeJAxTg+GTUdAnjNpaUVgNxu1YXRERvOJ1YLdMWFIOnTJkzbj1lBleyXgktXhIN5RTQydIqER2ihDW0OQCGrJvJEP5lN2gy8RsDhAgUD4lBB9FmxmLaaN+3DPA56g5DBtuI84IYLCpjDXrU3ccOASIwqIygSTDMZSCLuosRIYkgeOwayZGVlQzzyFuUxwR8IjrAVEEYcqzwjOVqQJHlDP0vsoE56KscN3cU7CWowgNGFjMQC9n//57286/IM/+fUve+y/iV7zmtd990tHv+4le6RSgv4/XAjLK/bY9dTjjllnrbVw+447bj/4Yx//zXnnUumTtsyWyIsw8smdm7mlNm5wJIg9sHzwMKaNMgc+qzuFGudG4h6ghmOrNmWackEXcSQf70swkbAwCzKgsQR4jnD3PSGbB0QmU7BwG6yvA/YdJE3K13/4g9P++7tmppMFX/rEx9Zbe00nXmaZ2Ze/YPdddtwBxqMN/fSLJ5y0eOlSK8BICRAFsG1eW20AfSBy8szIPoaxFk7cXAaP3mEuUWLGd44dYzX1JKZihIGozRDugsktpciIlnmtENaIEWTPXjss6yDpOA5zKnNVJkkld5OsSYmooUqBO2yILaOLswaAghTgCv0DVLFgIONnviVVawxM6tQI+mpssfY6+71kVyD32JK5q2+759Z7HwSCKgImMJM5Dc2hCIKVGAasODv7gdfuPduVuaFee+e9V99+91KA42ZMcEc4NllnzRWXW4aZXrDN5ilpgq2EMOUy0DJBu265MQxZc+XlN15rTQ5lJx6Y8UCpEooAbqExzMZGIWXmAGHevPvOx+3/5nVWWWluaDfe8wAeWV13531Pzk3x2OyTb3vNO17xghAHWQQXkwR/9G3/sdzMpG/txrvvv+b2u558ciksR/y33GCtUz787j223hgpfefCR6657e6rb7/ngUcf75Lsu+fOx+7/jknB0xNURpMYY1cDMUQR6HJDWaHAEuZ5z+23/+ph+22/8brN486Fi66+7c5r77h74WNPrLrCsp94+6s+8KaXLigZ6jho9ZVXOO69b3/uxusQ0Z0LH7nqtnuuu/N+9zjw5S846oC3ohPtyptvFzBGiVJ+x0ue/8X9X7fGSssv6euNdz9w1a13X3fnvU9N+zVXXPZT73jdm168O7I7HJBEgDWYPDamBgu5T9Q4nGLsIyCpxOzALiUKoRHP2HvHbecPI7znczZTjnArg5YmwoGWWVXUkWmcSHPmItADHBizJXLyyXhfwqQSD8EtxpxLqLfYvKXefAzlSJAMspq3FJawYiJrCD9C0QR2EQXlCCFP2VJprDAkOReVLCzEGSRqYkNYleoaXWEsO4VxURPmKBlTCUNp/kId1LGCsBQiDSdqBBK4ugWsCMdHdiiUFjxQs1F5lGIzZQn4Py+DQMLKDs3KkjhHfvMLnoc7Ky87u/OWm0djEqYSLADY6f9cAnQqar1VF6HtNt3gzP88eNctNqrNbn9wEVLphnseWNoPm6612pcPecs7XvICj44MDyZpRnwm2mzqPvKWN71ljx2F+ZEnl1w7ptJd9z7y2D67bX/Mfq+dBYlGrIQCPpMzcI+X77bj8Ye9bZsN1m4et93/0JW33XnNnfc8/PhT4MxR+77moFe8oGQTcEGwAtjTZjLDdi/WcwWxiUXXXmvVow/ad9v11yaiW0chd1931/2w4X2vedEn3vZ6dKJdcdOtAM4coeyCdK/tn7v3dlui/8FHH7/6trtvued+UAxxQ8RXml32qHe9bb+X7rZg0i1+au6a2++58tY7b75vYTN7+XO3OfHQd2+w1hqVHbpDBzSDSCGmoLBWra/aGoqWFtaJkCpTIlfyGCNlrQXeYLQoCgFlzpxyZAI1pj4MQ8UjUeYiqWAquF+N3VlQKKlBkVbHV3VkXrlNHVfgBDtGjcdYWh48922svDRSC+HnSBrMbhpY40dKh4SoCBMsCmBqJgK2elJTMmvRu8xRMpgNmeZScZJqbfAYHNvRSfVEMFiRXhKpSR5yCS1KmUKaUw8JgxOhMREzKbFWSrUJNJJMPapSqCQSZBPhCg+40oiNCWWvcUWyNLC0GYcLNRimuAOIySpFHZimQ8VMNA7igKYQhtC21/O2W2XZ8StonFg4Z/NkJlaZLJMIxqOVlBvENEmuwgk9Y4O32DL2CbwkyBu7KIu2DjcsrFpFcBo1n1c33hanxJGVMjJ97CBiZvwQvHefLw709MXmRtqGifcT+O8OT+fvMGALbYCkMTvynUnwm8YrpSwpwT4E+U27P/fDr9trudkJ6uf1d9531a133/HgI8jEE97/zj22Hr8DvP2BRQsfeUQDYIWDZKMACoSkJdQ2F1ZGMcluzEGhSiohilG56Eff8eoVFswgzW++58Grb7vrsSeWUMNc004+tf8+H3jdXisuM7voiSVX33H3Vbffdf8jj+HB1ltfsOOpHzxgrRVWRJFKiLFL631quJxNuqp5UBwMJhwdcwpCqBUKBq6WHZ+CgYEjuWGHkCRx1kbajKoTQusssJSipMg5RKgxeWOeKjdLMS08ZHElUUA3kFc2S46IYzgQZBlEII84CSISJm4UbbyjmGc0NMc5IzKLStaEqZBPhP1Fda/hvcIQZwv10Jj/zbkBsCD4IdAXuOCK2hjs5tW8wf5QMpJwVjEpkTJjiUrEQogKmXELocTMlVsTiCB4y0gYG9x74jYJ75y7MVXNPVrgPqDQkHBpBjetAepwJyImooA6Sh65AWfkYaeApZkBE2RGytQJjGqNrYkTsGaGT0ywY5QGQSbWT2pNoxVh4QOQQpUSwsEpOqIJKZO6S20cEarEEvBAvC/RmIOevozFR0OGyLA8jCJ4UEK2toCVEpMUMxoJniRv2TwRcSFKVEOGRk54byVX1Z6BcBVuxDAogGk4VFETliBUOG+11TkC5k4UJGzKJqlSbjQCoiSirFQZE6HBOm2cuSLylfBqzBUfi7fOcqmlg/EW0bzxYDqEuLCLeIzChRl6WZkYOcQBK0iJ2AMj05RlSs1pOjpFpuMMGgTWtbnm1VHpdJZlBjQjjUHBxLGEqqXsgIIQKkOEKAs0mESLdvPtd+/3n1/4yGeOvuvuu4j52Wuv/Y1jjzvu8PeuvtqqQUz/3y+EZcuN1v/JKSfsucceIrJk6dKPff6Yb57949b32ZtInzqTQs6IivdipqN5SC+h0SOg7RjXEQa1yFOApBbZiMRcm0qVDHj60AA5aMgeQoW1hMB4AjzEHIHgBRKsD3gbrohOJBMyf2pu+rd/XvXGDx5x4tlnttZySsf95+HP2XxT+r+uMV7//nj/woV/+OtfjamSeGQh0WAkn0XvgUGZJTPKJ8pOBKLEGObcKltN1NAUgQhnCmKbb2qwHcRzcidYTYhOwDluhhKVOKRBW4kq5ETERNK8C1gPESh0mjnpINyUgykxiaiMMkYWBlwMM9RvNepqZCJdbtnZ/z3uiH+deOwx73oHG7eBh5a8ShioGe967d4//Mz7D3njS973xpd+4l2vX2bca49cYABWw9pomYujQXaSuteO2/z404d+eJ+XH/aalxy7/xvWXW01GhkKUygkFsxMfvfFj1162tFv3nsPo2YArJtRpJgDGzvsdS+84vTP/fhT75+RTjCciDE3xIlLLud87dOXnvC5D73hZSAhejwcPEct2WvbrX91zEd+9tnDPvr2Vx36mr0PffVLf/jx9/7PF4945Z47ppKao+w5Gym3nNrzttrw558/7OPveOUhr3vp5w/YZ6t1NpBhDBn0bbn+Wid+4KA/Hv3RLx/wpg++ca/D3rDXiR945x+P+/h/vek1Ky5YJoCyw0oaDVNjQTiDEdJ+QiPLWNQniXbaZKPTPnLAr44+7FPveOWhb9zr8H32+s5/7feb497/hhc9NydlcRVCy+FbrLPqaUe866efPfT9++x92Bte9vX3vuVXR33gwNe+aKMNnn3et44776RjPvCmV08SJ002hnR2r52e89vjPvDjzxx8xNtehnC8/w0v/9knP/irz33iZbvvTN1MH2whErrMJP/l60dccMqn3zyP/rQAABAASURBVP/G13hDAQgHfonO/Oh7/vnNY055/3ulloSDMWfY7pSNpDI/NrfUI1ZedsFOm61fGAkRabyNZZ18jEIw+BBEHXFHRXUiqsQMpkpywW+RMYmokvXuZh6ZLZdGqS7VYdA5Z9cEyiNNyEQrqwdYX4NbS4FxYZOANUqGUKQUpcPph7s2lkCJrosZqZmMLDc8GndyClKXLECIOLnP97AIswrNX+5IEswSUVHOLKlhShXMHDllsN4L7qepg/XuFVmJp0WpEE9esPP255/+6ctP/vQeW25GMNSZmUgbkb9x190v+vpnf3n0x1Z7Fr4fc5yYAYEjtEzHHfCWf5561O+O//izVl5uBKG5ep+jZnZ8hXjSe9/1288e/tl3vubQN7z48Dfv9b0jDz79yENfsOM2iiVQhLiQl9RjQ4dZvvzM7OFvesOvj/owztuHvmHvz+7/hj8c97FPvP3N226+4R9P+vh5p39mk403xEptwYzqA+/ZdtxmozM+ddDPP/OBj+7z6ve9fu8Pv/nl3/uv9/z6qI+8Yvfn+Czj8ayQlbDX77njv0486qJTj1l3rdVGRKmFR6DiYIOcZeuNn/2d/3z//xz5vsPe8OJDXvfCrx381l99/iMHvGqv52y2yfknHHPhNz910KtfgM0pI+idUieT2XL0u958xuH7HfraF73v9S/+5H7/Qd4Czrtz0MrLLHvo61/2m2MP/9YH9/3g61/ywTe++EvvecMfv/SfXzpo39VXepYV7M3FhCk7JUcGgWJGPITMWRqq4GFhbqWQZuKEjCtME+Ycao0iGkuNYp4NN8v4fDgRBrEHDUSN2FQIwApphJITDcaB5+RNzXL1brAJV1DUxIbw2qjBZiXoIJyMUguaNuujWmC/J0RwCDZyqsIhxMQcoDEFQ9ecS58ZalgIOzqFJoONPj8JI2m8UPDntE3DpwHaN540LvOmjU5TQQfBO3As3AhLRsORJgalGHU5bDfte4WxZDbWXLg5VcL+kvMofvzBSHFFquFEygNzc7PeGYUaPElEcG80K2Cck/rsSgt+9pVP/uO0o487+M1QPdrPkkeF8awVl/vNl488/8SjX7vnLk4UYHpdEHWBWd5+680uOePYy8445iW7bFepMVFinZmUv3ztY1ecefSR79xH+q7VPDReftllfvOlj19xxnH/9c7/sOyIquEA5oPFyI9P7Pumy75x9GUnHG0IKPcsLU/KOSd+7tKTj/vQG14bAmDFnFdafrlffvmIy8/87EfeulcragL7nKxS64nqFw585xUnfOGCL31GB/IGVvCAss8S5ntsvtHlJ3zukq8evcNmG6H6EsgQDKa86aV7/vyY//rKofu+73V7HbHPy378mUPO/NSh22268VEHvPlfpx930amf23ytVbBzoKBdttn4otOPueiML2y96WbVikUiF8La52peXCaS4bqQMYqRMG+zyfo/+uyHPrXv68bl711v3GbDDVBULIXnSMvwW1/x/J9//gOnfPCdH3jjSz7wxhd/9ZA3/fbzH/r4m/9jxdllYoR4NNCBwuhHGMMHTpaSwWckmxAxWIxYFnd1DILchJIXTNNAd0fRcWRBeW9dGLaO6oaBrCkJ/HeRFjNUCwO3Vj3mIloTtdIl7HSIIJ3APG6ggpQJ5Q4V1k10UK2jNVCKZGg8OKEe4tyj4lEDDANWpWQkIZcgdkd3dUNCtUbNAoH3XNmbWJAXspwthAZm3ESysEKgU3WvtTni6ki5bDRTKRn4JaZBpUkxEnMeKCIoZFKqcrCNKjzAKUFJ0ABJGndsM9o6xoQ07XXc6VpP/RQfkPmkNiSv2AtWY0dEgzRYuXAIBZkEVis8gcpiE2qd1FI8K6kT0NGGJwMBwUSwLSCEBXEQToLiYUmrUGXCJ3xjBdI5E5CIKjGM08MxWhNBXRRGdBsXqyyEjW/KTbnlwCclYbKAPgomZu4ilVZKFASdobqRNBbVlISDyGC1hzChGkRHUTwAjqqpOmOEBL7n7dkGiuYURoCR+vC+EYIOUZ1zCdUoOu9qNizCHXNXcxqwcLBRX7kNAD60I5kYo4AB7Ke9leCEtTyop5i26Jsy8kPbrBjqNjtRkNC4hSghivD2kQdnN1OyQkkNdScRh5qkadAAXJ1RRGYMewOjIKwIDl2BYlcaSbQIDGoUzE0Dr/CZWweSCX4efWI49ce/et0B7//b386FkpmZ2Te++jX/fcLX937+9oKY0P9f1wrLLXfCpz6235vemAsOoPUbp5z2sWNOeGTxEsDtFMK1E0vwTSgIwOOHhPFEK/NIx8FSXwuZFrdJ89xCyRH4lDgr9uQm1HOYh9RpPHXLQw/8/qILvvnfP/7Y8Sf/11e+9cmvnP6NM390yg9++LM//P63f//L3y75x+WXXX7+BRf/8o9/Ofpb393nsI/s94HDL7/8kohYdtllP3vEh179ohcE/z+dMveHH30MnxO08Xjjd+ddUpdW8BI7wKoTlyQcSoy6OyCPM8EcC1US/LEifcJpdYIeRA7O9sJziYbinkmUuGTqmJSpiU4zuURi7Yw7i6zgQpC6czjxvFmqIpoEwWu5NsbpCwfkwLbDuwxeg0Y+aKs+hPXcenY8AIN9OYiYhhxDF1QoZ88ME4ksgTLceSQPxRJNjTZYe7X3vGr3A1+16w6bbqiWzDmEWDRzJ55cuXJt1Bt0SFBnq6yxwpteuus79nrxHltuMSlJBEMwxwi5UYdsrUSwD9UrmFbhZcf4lrKfdFYUFRKbSHPsHVqKGjxYCcmaOWbJUd24C5awEV6qyakwdZ4Lb7T+avu8eKeD9t794L323HKdNScTCXWwBRmRGiWgWwRzjXyV5Zd5657Pfc9rdtv7eVsuwIakYRjiGAHExddaccWXb7/dQXu94KC9X7TL5lssNzNDXKMMSRku0/yVpXV56HJfkOrhTlTVaqnIquTWUdlw7dXf/JKdDn7lrvu99Plbb7xOysVIxuiKBKxB8Ky26k352eut9o5X7v6eV+/xkh22fNbyM13ynCwr/M2JJ4xYzMfFKfeATcqG662xz0t3PvBVux34yl02Xnt1EXhGwp7BAgFISVhmSsIJh5HHFJl9wqDYQMqapIii2uc5TlNh1E0iRywt/eHiq5r58zfbYPUVV0qJsDASwz6UBq2NDXGoJlFJeteBoZCAZhZl4abhWHRSUSkpuAOcoG0vhJqGBY2NSErLk4qKydwCLChgpYJolQzNE7sKDOwNr8xEILYQZWEtTp27hkX46I+pGEsyUuWhkyFRI7hhihiMEhBoVkiAjCBvYRwgHxE6lTQTFx20mGSVnHkSMuFIYlbGvVxyOJCZ2B00ycpZO5WuETiEtIfpzcIHjj5r1oSZjXLN7jBi6mmpdr5gkieAP+MeuzA1pRAfWmvDEPi8zsorvmbn7fbfe9e3vnz3TTZaezIjakRQp60lq5yad4RQ0fiSkq614spv3GWHA/d+/ut323bN1VasRSorcJ8FOaRGRGvMRtlQtokpYO+Ga6/6lhfufOArd9vv5btsu/G6uVOgZCKmQoyWhCUpL/Ao1Zm0aTcufpIwn6My96hGm6y5+sF77/beV+z+wh02XWaZGawl2ngBpwkDMVEZK4sk5kxpQt2CyXM22+yAl730gJe9ZJ011yBK5uQOttSSsNil5Vdc6XnP2fKgV+95wCt3f+ku26y62kpW2DsOFRqXxsQlA/qCTU8L7MSrdy6JcHnEQIQyEQMz/GhAdsKO6oVgEZZi1oHYUU4co425phw8Q7lkmelqkt6ojjiRQJWQkOE3YQqTcyAkLXdGnTn7GF/KQbOhsyQlXKS6ttrcWpiHQ4syJcqJRdSEjVqzRqioNQWImqR1mAZ9xppZS4+ANgYSI2MJ5gkF6xgvnMKdMjWlZhTToOoURIFXN8joqYyLR/EEtlnjadSpg0TNW8MwJUkBDJM1bTVBNCbDrdEwTonVhTDVQx38cwiIlrwlNqGByDGUlOAL3mtKKiW6MtcpVhuGaMps2Rv44qlzLZ6JOqGcCfOnYYMPlBpnixyenMWEG1FLSUrKyooNIIbhW7HWevhemhakaY/aUAkb2UxhBiNk4mWG0oJo4gO6qAqCmhSPfKY5zY37oEJCEg3LY5Lk0nmR6MQJ4Qq3COZUFEozZx+QQonQk0hxB5llmnOZTPBiQg0nxKVZ+9wB4GUny+y67eb7v+YF+77iBZuts+6sowTBxCgss1hPhlmeTurSbm7KYU1iSAkF3wvysjGCZdECWgSpIKgfgvKIYPeAY7L8Ciu+5gW7HPjyF758x21mZrMLq4gwRQSpzMzM7LjpBu962W4Hvmq3vZ671bMWrMhUBtUpY09KbHAXOhiCk4QCOxxGFGFLNbhGa07IGlGELrSFoDpjXm6eyBC8YbSN3TSo0Jh81MOvyGBrwZiooMJIamPqSeaCpw4VnVMGPhVFKA+RhhRD4UG0J2wTkxsrmWqlanWQhnCoxAgww3ThTCwYYjGGHZQQttA+pIWQkQRM5RRjI5/HADBUzS2NLOvYmUY+NnLzimRwQnTJ0d1wR426oaY29WhPMyRiGKEEopqAEvMIUrHGjq1EpZEeuUqBGHYlKNc0KDkyi1uweYTVFD1gJWFcgnutBvjHTigT6Gru03Gl6yE6C8CWxIjFAM9gW3gILg2YQZW1aSLpNKecuITqvKswz1gaJ+Giyik5IA4ohWPGqkkyjBNYJZaEOKcqeeq5L6Wf5KED4KbUOkd6IXOR4kg1iM85FHZGkDeiqZMZSANyeTguc2aozVRzjdxT8ShKWZKEBLxH3sEGZQGLmnMQEQKkyCIqFl11TPh/sPcfcPdtVZUgOuaca619vktSRDGimF6ppSQDlla1obUMpVVqaZkxloplRBQjWRAUBQMZJEnOiAlBCYICEgoxgCg5x8u9/7PXmuGN/aff63rd/etfVVf1e6/Dufv7vnP2XmvOMcccc66197kXOk8LAoy5IzeVAU43Vz1rTubIHeHNtOvodWH0Dw4vSXqUtovskUtkdViTrbRll1Qh0KJLPVwmhOxpeJq7aIAr2DhGNstRbpEMobHODMdSIXx1Zb5qeYXXOWkNCtKqtFeNeCaXaRV+LKKsl7zilbe4zU/f9V6//r4rrwTwyZ/4ib9+p9vf4Ud+4HrXvQ7oG/8FL95/3+b7v/tnfvzHrn2t479qeMLv/f53/8wdXv/G99RM8BsHCUZpKoWaXpWKg3JtEKuE8xTvvkW84VKrHSIphl6yRbRkieB1b373bzziKd/4H37yK77ze7731re+26//2sMf+TuPetSjHvOoR933vr/1G/f5jV/4pTv/xO1ud8tb3+bbf+iHv+tWt7rVHe/00N95+N/97SsrgwF/1k1ucp+73Onr/uWX0vR/GlXyRuU97+WZD7v+R3zwhx//jsTr/uFV73jn2yW9ulQHAbY0Y1okyFlZoQUkTeTyxWJPuWyzwnzafrY1Jc/FYom34AqZAAAQAElEQVSQkqIFrSwEkFbBREC11EIEKSgImHv0LAEgmgqJ1d1ZWzHXfimSBLAYYBRD4bB1zCoAtK6ivdlo1BkvOQdH0IBIihEkI2hK+JuuTtNGtnutltmi2pGaWpGepQJTE+vQpnzRBq33nq1BLaWnKuVYBCwJ/qFtltby8EmU5SUuR4zIQOZQbk0KxqHeq/SyXSbiqLdAK60C3YIVkGSpgIQc2jSLTmoOX9AUI1/B2mtOqaBZDcYrZElMpYmIEaVpcD+jHBwWtOaVCTAoD2qLrFTOTJdIy2rJSVqjkr21nLQbkuGtdsSQxQpUq+0IumSB6Bgmq5WEVAawV1tMlvQF9QOiHn8hoUpKiKhVSM2C832344nMgFuBtCGph+JJY2jEtVp6DTBHVRpTyQYtHmdGN9qFK7JMmBukhoAbhiA7DQZVtwosmtGERqEQFeX5xre87c9e+SpT+cKbfgo+8NIUroHFtsBmIVWg3UMAxabpHjHpXnuxgVXU3LEcYtK2Vl1lS+MayguBcjM1a6PRPD1yGh0QYM/SYnOVcHUnh6nBF88AZcgPCE4ZvsgUXGKyj0yguK08FdUhAouUnYrQSF2UE4+QYtQBgDmHdBGtIhJmog5mCg2iyanEzsXXgcDlH4O01AHyxOQtrTBm/3LY2WZJUiieKAIW/g1ok9ZkqHdSZOFShSJ4EprFwSVb770dFrm04gydSA5p0XSdWowO46rD/Z5VqU1VT6I3hVluIhfox2FGWBF1KbgsuQia0tOINDaPdZYMnjMVK1OKnYd1GYKKurz3FY4XZYwJKrxHnFYMDz14msWNgUtzArk8pJAlxWQjuiyuVFBWAOsgSD4a3CBKX51G7YgVCAG66GiCrtEQbHa2LPfm3s7Sl5JDmKOF0KDpEnb3nfpvzDN69JYnq1PT3kVNFFDyqJKiIhBJhuIlTmwGjApeVjnEN9CqDpnulCRXg1iExGOoKXk3M1GwsqOqIMZJENTJw8inR+ZlAdIBcck4eFCOK2NdttVtdmOqcOCvyjU9M1h7J8hGHoIKNTKqHqGao6uqYGnutaqkoQ3pVW25uoOkXmhdtDqZsULYfVqPZhnIlbVcSFY4vGrP9BKpPnLryhishEAjMjggq1VuLczEOAQnse3oK57MMImTU2vaICk9oCIxzFUUuRUFV2RNLFSXCi9nyjGL5hvjBgNi+kyOSmzTbU6tJMF55EJKrbjsiC2NKXuQd+FFYdT8Wyt9TrYLVpNlo1A8uXtIz8pEMM61KmfRjw5hN2j8VQxNYlTZAQYFapKITCDCWZnMZxgYa6dUCgFDNaCZcCc6i5XU0CU6fBOnU9EMFpdQeoYFqWUyTaMVEIIS5jERNC9qDL50Zu1eeS6sQHjVOfhOOJ7DVUsQElz1okzcNJQReCUzQVtC0jmSHB6LgFRqQYACJyGZ5ovSC2/bNKP60TIYVq9s2fquNSWSI2s2sNS8g4g4uVGq7WBGVNCjGp+BkjlPCePBWJjCNgrMlINXaleZw6gJwhUU/U+qwxRW1l1tpWVybvaZdDJB4APJUtrMeRdaLVmUjlAHHOKigcoChRupIkAHNJ1nQoWmyuXgN5ZyksG0jCYogko+8clp6jK0Oku8MV3WmDKSoIAs4eVaFkioq0Wjgkpz6dnbDkB0g15UMrHaShoBuHhoJkQIishEIFBI40luOHr0EdKKW3P4YHEOXsbaC1MGQN8ySsAIdBS6wKromJVjG0lu4canplHZqi5m9XC6mklMHIg6Be+VkoCrZKVWDsmBYsdTQxZDbdWkOgrAMjlfITjpGEQEzmpFoMAmBo5twZxqE1MTUZQDAeG73siMc/EIxgwZi8bKm7MxNRFOnj2dz9N095x8enKoEHw6zBvOdU6qlxMoIJOlEhWzfK/ai7wm46UnwqvDUPbhnU2wC7uzE7ZZs9ZgjCVSMgqLK8Si7iiDA0UJWNJx4t6k0wVyL/3Av9Kq1bWdpHelBATFf3RJc+kO4+d+vM1i3ES/lCkXpDgkwnTXPsGFjCS5FdMb1UIaW1Vue9kZumgkrJddSOvMXWu8nb90jt3XpAx4m3Tit34DoiUVhmht4r3veu8v/9ZvfvdP/NgrXv6XRH/Na17zlrf49off426ff7MbqxEUz/0XHyX6Tf/yC+55+9te/0M/lJNf8rKXfvsP/dDzXviX6Zlapr3XJtnYv9Ykg6SB8qyeVBlqLaRboUM1eGlBp9RqWFed332PBz/4677nB37pXr/14pf/x0tXHl+A0P5/zrFtpy/+/M+77y/d+X53udPN/umnQv6nkyLyze88DF7/Qz74C252I15+2zvf/fo3v6XHGjK7oBfzwFsWMQuzqTKlxdhiHJHUFnJRjMq1kqizQhslVhWYzrWQGy8gayJ2WX5Zdb3KUsWFp8V2aaEwiNVlbFqLZTy9zsjoXipVfEIjvrmPWr3Pvi0qqUvbynq2TXqTIUelpEbI4ryGINeqolxGTyKn8lbR0E+ycVtohaqjoIKaL75YyuUzzHNwDvuJNulmPJphSG2aaikK5qXc4VPLN7glKydrFa+MrbYLZ7fwk/hAccFt59Ko1szGMPQGgq0h1LDv4q7sovCoIAIaTsFua9o5dK+WNZLl3WrSRLAMtjZpwWRTnHp1whGBdhHihGzsN1Wb97Z3fgMel/biLaPjiEPbAMw1KD1paoTAHpNJdrHG9G3tOi9h3zHJuJkO450fQyqSpOkEuIJYXSzslNjKDV5ecUbtJF26cMoQFTmCQ4nw4AeT6qSaoxvCuJ5IVmS5N+MjvfMAn98rvXhiL+QQ2TCMCVV0RQPoZlaUuBqsCzgoEFz3RoMK8yC+WvBLH7ZL0UVm//4tb7nT7zz+re99/yd/5Id+3k0+uYRTGpgggUiyo0gGhJpuXS5GdHHZ5z7di0BTaCGrPNloeII+xNBMTIXeYlnVwFCMrC7ZlLIqTUCQnUlYNvYcvlu/ZP2sdgl5Lnzg8PKSKVIfeHF2z35ySlg8uQAoF/4rgvvuVC+WD4cdZqnnmOBLCKJpNXPEXmtfyXoq5oT8Lcl5rGR1DtTKGiVGQAATaBVb1BWlQ80YRRzGRZTo96ZnKcKnLylRSIqtZmSFVX9auc1d1+5+hpRu2raT9qOJmykTdPiGCSivrqxoWkxypSRDE63SpGxAThwsZJyO0IFNSGui2HnEL9tgWLy2oMHwFrWMUcZBSz0sVEuVAxku34vqCSyPrIOEOk98ICuBvsD0QhL0XiUBODaXU4xBu3ttM1pAtEnjAmUT6tqgFBz9pYRXVi3KQKKrjx5WIPfLNc3yqGpxrUxh6wCgQmSi3OPM4lng6NXDs5OtAKqrHI++t1YXPq+I88kWTy1gj9ojGbEScMGrICWSHdk0+KaQZIcuUCpx5NYLzBMhn9gCZRX49qIavx1NWWfb3YiKx3TMM5HkZrmNtHScF84uHtJajC3GSbcmAKMFNy9VUqjqqAsp3l2pSSpxyTi6dNYukZ3XNbQ5RqZWJCdIirA0hA2IO3UKDpnirOPpLRnnpP7ztPj99mHb2B/APkxN0FbYEKMWqajqJ1OzEMZtFIvyUjUJMMQjfoiArdBSgVPhYsnJucYr2AusiQzAGALpWUHKJMuhkSOdm9ht19MlGXsR1lnhHBitVwPGKmHSQoO+N+RF1qm05zB27jUyJQI+xc9EHKWePaALkkQrTefAQmTRHwTNsF2AX6Cfup9kkUs1oPNgp2JoxdnnMXfuphql0yKHoosWszYPXFLVuvCxyOimmTLP5nMTOhqMPxBEA89jYkavpC4FzFtz2XY7hZiiDAWlNGQuJCcccM9S55bnUb4FLs56xT5G9sYC4ufmyD19J2+lJWDFJBaZjkMpWi3RQq3URJSR0zyalupBCIIM10UVxdTEEBdYJy/bhzsLUcs6eLANVQGUtrYwASASErv4NClufocmgAlbqozLW3llLW/TyWGxPZPqA/8MKlI5lvWh4jg0SFASikIWrzQU6WplmUSXR3ZacxEXT2XC3WUWBxOFSCU0oIVsFVewP8KBo9stApAFqmaL2bBLVhxUWAEm0ksoGkUDKyQbvHROu4Sa3IrLbBFG9eV2rtNia+cydWqyccHSkSISPL33dC1Z2ty6ahMh01qoUC2CYmmxqWhyQdC2hJFJj+oMQg7ri2aYB1URqK5Ni0qDIMRKOqppsibAuPZRZ81dQYskYtuFBGQxJnWzPcrDwxwGIQhVBlU9g+sQjAvQWul8nORBaXCI9MZ91y7mdCyUQakgSbZwzUk50lg4cBhS+6oeGAFGDL7KAgjkAYWoeQYpAE2Q9l1JSIp6a8euMoqKTE20MjOUFF9YFc7U1ArehHl9wEiG6rQx1VyNaZfjjwnp7lYbiTW64dgkLobUw8VXIZMxlEhJ15NZj43xtUxWpBYUY8M2wrqDtx69yhoLuY62BLZMVgyZ0yr2CSNJwaabIQIxSRNYKc0xvCpJ0RShSdHSyKEhm8SFrc2OQtYCCYopzAnpUcYcKmHFGvF65T++9k4PeNhX3uJ7H/SoR135/veLyGfe5Ka/c69f+6Hv+NZr/7/+J2jwv/kl+rk3+fQH/srd/8mnfjptvPHNb/6Bn/35Oz/gYe+6mpXHgqMK1In6CGXV8mBXqSHamZKDX2VQMJFUFGOQfOPb3/7tP/wT93nQg975zncIqvf+eTf/3B/5gVve/+6/9JQH3e+Pf+ehf/DQBz3+fvd+wC/f/Vdvf/s73eY2P/ljP/HD//7f3+bHf+IXf+ZnHvVbv/6cxz78Xrf/hX/+2Z95cWJTJ6L/8WD6I3O5v+ttx/890/U+6INu8mmfyss+z6969WvaAYEK2FWYWZXcVIQNjdQyJdqIkWNV3YzcJpUgkkwm2C+6lpWx5ao4NAuVrPakUANV3AvgrDJFZqzyZTlbLZUSoUXalYrg2qC7tizyIkpGSheodpPQBtVDCwTEP8HJgqUa0ppQoIcFjhDRJqBUWA8kTgAV45kG8GOAohYlTIEcpeCBKBy0GwfI8aPGPyxzDiecVgVr2fRyuPzA000YKOVYorSl0ko78SV6Mq+elQgmWzuA5MFmoIZKyaVwlQQtjGIafJe5dIkxkEugprWE5ZFlDuEJL5HgdBT5bWAXd0nPyNQkLybTSIClmlzGn9xN8ALRbwKmroupK+8ztZQ6OwIrtRQrQCcrlOZDkGm00aHQghF1GbJp8QpzCCk6AZMDm9WIaXG9jipmTlsJv2plS+Us53QziESIT81U8mIXCwoSs3Yw85AwC7rSGmCq1FTRLFpbKg4rb+xfyGrlHXVK6Uu4D+QShqjUyp6Qg1SRnrqtez3+6f/9re70bXe9z1/83T+KyPd/9ZeMdpJif9mkLoDeGkbPJsnCqmiRFjAvkkgaPVOE0aI56S9JR3q5BIPYzq3vZE2FTJg3SgeMZUJXMzdUBdSrHVSBgyBW1iCUTHjOFZEhlUC5Mn7NbIreD9EwPMquNJUVVBp81wAAEABJREFUpRbGUDQFrXQIhglpOaIEXwJpgRZMhmfNyAjAG0B5VWXWmunB/DeLkyii844m6JDwkqVEvsoqmnDNF2GKSquUULYh7cS3NFHpWVKcSoKKQVG3xMuAOENMRTqnG8fYqlGlepwcUZJHjAzT5MDMN1OKu0fxslBCRzH+QvCqDOaCuoToUpQpwUlEoYQjoXxHawF3j+DCEhyY0CTLzFKiWF9ma4B0ZhXCDisSXZrkxl0OY8jMUsCA5qqTY1HkHbI1NttWOSirdG6UZCBNuP57ZayQ3YsAQpgCKymIg3ECBMEghaWrC51YIJfDt2ZUa0q6hCOXxJQ5K2iE0TC2RIQy0ZrSq5gyF95XolIky8DjkHKfWZdImSrDSd78KRVipB/kSAvixW0RYxBEiYXZ7JIGapfJh5nKZi49mDuKnFSoZkT6CsJCRUM0pfLEepNOFrRIlwBGB8FmZIJRyrWZeM85p56JHFD2IWKSA++CSEpb3N94HupYqCmM9XJUc28RRa8UpLWO3qLbvrXcRIEWpINq3UJpFENiNBCRyZEk7g2njL21tJYqVgD4s9Iy+aFBzJUggUyY0jB/V8kqXW65CADqQlPKeRpa1b2MRZdIlEipzeNGpJhyyxJeEcC0rKt1k6YQpEMI4CDGqhiNlBcuWxeJbWiPJiXWlm2BxgQMD9KhGaIlBMBVS3Y6h6TChfhsLSZlJKx4shiAAMq6qyETeSniHJWuXQ4wFbK8piOKN1V9BF2WViohC6gVQCTAV5KFi5bDKstWEZKtTeooZElCBJ/VlTL2hCxj/MRXfTviVetIIlK3IHk0DoFk1fJ0L6Xk+kJjUK0wtLqQSUOwbGxRL4JQZNPL9A2gEY6XTUhIJlKEwgQDzpaw4FnW28rNa6tQKonmnJNUOFKQQJZFdg4+jArj8/LIZcrNMR/QcRcyTdjV8gRwztRiSGTgMupyIdVGMfHWKTRZi4BDpoibuWpoRJv82oO7Q06XAumF009S+9MgVTyZR1BHqMcb8Oame46C8C24tm2RJrxUeeATCetpVkoPDVIiUbqYplrdvGsJyJ6YwBim4mAnnMA7C04DpimtxIR/lVNJv0rtlueWyypGF6FkIMheoDq85Iy14ADv+u0aIhdefd8lggyC/hJFZjHQTBujqyVw6arXGHmFrYGdjoI0gnXnxS1/0aGQGLaQKlT04LPDpbny2Bk65bDTBB0itJHMhOYRfy1x6ARiVi0vkBskSsWb7CJzV27rwIegMI4AikMlKQk0wcGHUD/7sHUacTTQIJl9WCMFFLQWDGZiClWnK/iQfVM+955xQFNyKyMWPRUCyOa8GyYPvoofnfw1KXb2LtMl2VdYTsJL1LezJbIzEjTEpB3/a00cW3DUrOJvEShTuy05Lb+Iy1WlzD2DKEcSQJWgWbY+XTOD7EUMl1MxgJZiDuNgyeNXNZFR66Rh4tTaJf7hrB2YXu7UDzDISfq73/Pev/6b1zz0Cb/3w3e622d94zd82S2+9X4PefDb3v62qrr2ta59h5+89SPucdf/x8d/rIjgv8lL5JNu8JEPv9sdv+pLv5T3FVddddX9H/awL/yGb7vnAx/6xre83Z3ZSvKgbC7BTQzODT66NRutuF+wCwHvuoTtr735He/69h/7mVf89d+g8oM+5EN/8Lu/+7mPe/Rv3+V2P/jv/s0//6ybfvLH3/CjPvIjPvYGH/Opn/wJn/eZN/7yf/HPvv5fftF3/+sv+cFv/rrv+qov/dov/cIbf+o/ufa1r8Ml7H8S1pVXXf2EP3rWD/78Hb/m+3/kB37u9jTOAdf/sOt/7k1vwjc8/vIVrygFtwjJ5bEvUUixtE5cTc5lzvtuUF7Ho5xAefILqu7FxLHw6pR1hempy2baaKVRvVVRlHErQ0pwcYtDthFFsUyJnb38WPUO/gVDN5M2xUKKuS1agaKExsGmOFCNSueRltFXYM3KWdRzE7VGWSh1G9WyKJRVFMdibbswlkwvubo0sgEbxGyT3KSauYwqLrAsijXbnswOBZgsgmXYh54VsZS1uokpM6Xcc7CManSMIaotSue0TDNA3XKe1mruIDZWFhfSggKiAjVRMWGgtrXO1Y5scGAEK5Srx5Zru/o8ztF3acvSeqjuxhLRSA5p5DxksVOsyH3KObBDGF4B2xGDNLPWmYLokoKCgbterjencN5BVU1IZU/nKjjWGLNtx4AuZMTN2GaiCsxOJpS2Ti0uejYdmQxIsRpmj+L3mbz12BBNkmmVvdu0D2zzCz3IpwkTHCQlTGidIRguQfbS5VQRRyF5vnqr00VtfGQ6MOXQnVc4jYZpadccmpoi0+SssttKvySTi5Vs2psqUBBCrve8/+q5/KOue53bfvvXfv6NblSLBHQRXhNhJIOji6dy0TvVUCtiIZCwpWMXgrYOsoGmvvRS+N72sElybY2WpyE1chJI2NrJkKQoyZyo1IjO8Gvrxf0XjoeWFD4SRQlES3QWOSnyKoncuCRAmkBCwBaoZRbHxsRKpbh8bWAbtgbTbgcghliYaaxAFAdStGlylkZOvIHraaAqN+xXND8pa/FUfMi3kGvajtrFg2CozuSTbeVyA5kdXNi3bp2HttDlcmnpDGPJcLXRBVFJ1l2x0W5kAUnt4Bx6dq77XUK2Agu+z2rc369VZFOYT6/T5BNWMre4il2WJ+Hx7VxS1vQk26iTqAaT16pnXaxsUk2yYY91Pse6lJiGBfHi2ZJegvSJdRYp5T1oNzMh7D512+UUbVBNYRUaXGfJBljkhgpgZ4MoNtroqoYmBm8yh60aGQ1eFtU9LUlYePGPlVSZG0Kz2lmHC/dRRfNUS4bFKopbrbq1TU+Up0s453hhZSxEoIKW0hOlKEsWUJMCVJpGZyqrFgqpWsbRPiMjvGnyIWwbTU4No8/Bq2KWXZFC3cfO8QK+/Egrt9aAME/KqFrxruBioG905tRRVFYeg485/FsFnlEOsL611sVbJRhlnpAXa405e4G9OD12D7Y+QZfaZA0wi0QnKWIrxPfyS6nnknPqvjB5SyHCBwNEcFBMH6gOmADBqLNAa8EatxE4eSqllKwF5uI4SNLOeIDzEGbSGih5kEqZrNZlE5DKsQ580Rr5C5SzAbp4jPQL+IXN0UOhtfeKERQu+KaVVPRcF937SLUkj+WlRbcFaqlpjS1NQ5wOKUi10TbWxWhgYoqpJiknnrMmYSSiYW0bLnpr2PyM87nOFSkUWmgKUnPpErgwoztloa3paZPe1dRErRRzwPuMsQdWFGJl7UsyaWOv0549oChli95KuhTTsLqezRYRC5g1KbHKRjOVttCW9hgDupldjHZhg2Gsts2uwpjtjC1AiHakRGABnlXlxeD0kjpoWaXLkyh4igz6sNBeznj7Er009ksX08eqHl2r2X65azpQByAUqwNCIjtCWUWpq0mKkAZNZDXPkTQ7enJBMd3B2MNzF5ytLW3UA39OkJNXzVVrospQo6olvVjxrbMocFHc+U1NahFyKcVX4zUv2x0RVVLJc7kYVBPRVqFFdG7CfmZLbY/KOHdbTYXBTA4RGyIbhMHJWc2TLWKzgGolBxH4zlVeQGO2js08Ndr6GhfzZCDPlF+UXOq+tp05ZgaHw1hFFe7HUatamIilbBMakQgkWrD5NskGbdkOROsYEqCAbBWrDbKyUZ1SO0W6Gs4KBwTsKkVi/dh3SCmkCl7gKV3eWa5VBAnhNV40gZq1wbe9VJJV4DMjnK4Y4kq2Xo06hjUFyWDk9OEua9d9r8rkD3iK4A9KlElT1xLlYsTfdINSuk+Lo5foYuhj1phEWJoKDgowbYgqWfyMFJDLKbWsfERSAEBbRbEoPEyXddiW7O6wy+BKC+KqbmqWJ4mBfYh3DhMcEJcXKB0e+1nYVZd4dBeNUcmoo6IY0EraMaigZ8pMo88ULQFfAhPG5MoMqYqKdFJZ1TWPJtIASRIBMG8dRykh2dC01GAKTQbTI5RdcracXYopIoyo3eE7PfZqFh1vfu87X/CKv77fE592y9vd6au+7we/5Bbf/rN3+6Un/t7vv/1tbwVfote//kd849f8m8fc+17f8BVfUiLFk//tjoJc85rXuNttbvWzP/qjH3b5XwN793ve/Wv3u+/Xft/33v2+D3zta98SmS5FhotaSHhWkTEuibZbY/l4c6yr/C73fsjf/f3fE9eNPvVTH/Erd/nhb/2G61znWiGalzni+Q8cAhG+E1wORIs8XT7Bc//z49Wvff0tbnO7n7nzLz7zuc/5u7/7mxe95Pg33zjsoz7iIz7mwz7k4jofwvevfv3rrlx7inBPsDdKKNlLVkkk+0WbOGoGBmVzoMAyAxWHd0gJslVWglXQghG6ysJxhiGKJOdsHRu4p47NSBRPF1gcZjiCcBcvbf3os+bGCs1cLKVYPXblWyRLoaoyJBYv61n0rHQAuNqC8b5lnd2XH1UiURHmdbSMgM/yXSsGT9A1inC9d9cN0ql8lR0xsYDFT3kUp88ECx4w4XpB7VZHU5ZMSmlgONfdbSsVuluBOUFUGtVLVJuQJIaVLWqcq+0Rzf2kc9ty23ScrF9WtSBVIMIPAD8UkRFHOfhOWg6tQfeAWhQOOioUCbaEFbmmgs2Lu18OFZMmOqDNjiSwu5xROyRFlEcoyyn3jDPtoIKcHetQdi1jakq9IsN1Vl+sLva0Ro6OrFXXxaVQVE0IRpgBFqmZJrqXFcFBCJQnmkEUAhggHKihEgoQ0MJR1rx6bGmWdJr0JkejXCq7kfcAUQmWEEOlcxI90Dqnk3WRqTzp/CRFNhoxiDorp33bV37B3b//W+/5g9/xiNv92Fd9wc3pw+g9qJfwWg7OSSVrqs5oFXxP1C1aFxtpRydjqI0fqomnZZlZU1MtkTKIaUfn30MeJZnJuiUO0IoAUA7i0ANqBtYyzK1Vb9Y62GXFoNXhximpQfmUKGehVBLinIEZ4sxCJ+dCB/B5ECFC6yjyEqiFFnzyNGx0GQZKsHbJRcrHljaIgrgqSkrssD/AaFZj0dqO5ipaNZC6hGWwLmeli19E9lSpnqwGa1C0xJYyihRqSa/olXBGTP0UMje2IXAgT5skPZU1L3GOKlolWWbSGgCiT/BFzypLPUBRTrIgTXWMNPNsGWatdBRxBBh7SRgj1eK9N6snhXI3j6OlJMtJwbG8ceTRW+BY94L4YRByC4ozm8YmXCKiCji2d5eJZgzHVRCWiDSYLhJbolwJ9STtYFA5KLnauYcXK8Z7pu4WPkJ7NCSl6XPua2csIiUpFnTtQuLpDGS+eCrL01fmLFmMa4plHauuCWtfox82p64l02WViqiZGjMNvgQhKFU0ycZOIFT+adlWhA1XTpDl4mR34BhTwrIVFWt6BKEsyVRQw2Utu+XJw9bMtWakJ+MVYXHFKo+WIshin4O4NKg2dZMgkmIHp3UcL0MLqaMwKIterOQSVBMMo1MFlEnBpmRoJusoGRYAABAASURBVLc/RUB06t59F1JRQr0FKVipGZ7ciqbPIj3UZJcyYsiKhQnhPwBRaUMAiwIie+RY1JqapZUrCguojlKGrTwXyswRoYjBMvmo6BTO6IKKgaYKk+MOlq4gJKMCEeJH1BUdqU2WySzLMMMAmwIqJZm0MYTL1YaqzIhUoKupFXpJExeNoG++ad6FdrRwiO/y6qlpDd3kQnRrTYbUIBOXgF0AhaGsRIoy1ywxFSWvggXZq7uMxFCW8I5BJwucWzBICc/aVJ0gFQQGLNCCVWglMsE3SmjrKNgVdWmNtTd34VWJ0pSmEGE4VUFzvfpIs2NKgh3FmgDp6R5BshLgrjmraFZINd9IpnhuXu1YgEbU2Kvtpe50WYdxg4madcmhrraWBu3QLm0r2Vx6wOfKSWtV6ZVx2PZWBbcIMrX58Xskz4Ukp9N4JSoFlFpszXqjVDxyj1zI0lIFGKWEVDQpct+FpTtaNiGgxg/KRdI6qsSdZJRoNHOpqEyla8LqkXRBRnnGo7xSjfnW5tm8JB0ZdAxmq4xDU6iesioEarYIRfA9qeCji+R7JTTb2JaqF4xWapmmMuEmzJmr8P41eY19kPFSvCeCw1wS05jbGAl+Y2ZSAFMu0/SSKV1svjaCJGlcOiDJbjJzzHXy6FmGCoBoajR+6CpXCC6gJIlBBfNZNb2ojVhaBQWFoe3U+hUtLnRGI4KiHFca5imh3kHDqebKW4gmpIVxEJbQOYXM5xpTa/V0VVokNj6IGkslDAHxYq0hGOlsTtYSC7VIaJHFEkLIlHAwGYY8VrXRks4YS0MjXaiZyspQa11wAinNLiFdVMCsgH0tGZLTl5U37l+PZsnIWjNpovBWl5rzGQiwWxx3DqcSU4WBzFaieY2cQ89cu/OEfeDca2krNMgAenCocqlmS8YGEe/hzYNXJvqSKt5tYR2irMl4M+kplPcpNbO97X3nx/3xs37otnf+lh/4kW/78Vvd6V6/9vRnPeu1r/0H5hWAtPHRH/OxX/OVX/mrv/Bzj/3NX73DD//Ax330R1V94OJ/+9+i+o1f8SWP+LVf+c5v+qbrXve4AXjr29/24N95xLf+wI/93C/d54Wv+LvUJk01IXtIMVfUN7OzFMsCj3n6HzztD/+QsG7w0R/za7f7+U/4uI/lQH78rzle8LJXfO9P/vQr/+NLaGSM7YYf9/HtdAXf87j+9a5bIp/9T4///vjVr3vjO9931eWiOM2y/VhJHYmqyuBrhTi3MjLKxLVcmSerOD60Jf1cOIev1MhWZ0ikCXVWiyo1Q7fqwt9qLCyD1whspSw4wqC6WC29xkkuRg7JwCE6VpKuxbfsFtQCoC0EIYvtp2/RuX8gAMheOJfPcJ/LQaGV92KGDSYpFYVVkpaqUxG1ZM6t9pOuYVVarHAGJNFQxqhW0q/pojRNujaw9FiASG+5kEGtOrbAoAppHJEZRX5KIL2sR2NrErHgDf+Ws2NCfYlMOW5wsimkEMURYq01UkrrgSJEtazhtYXYIjWDF41W2d3gXrVIO5pp1xxYW/hWaQnWmqMHOmpDdnhWeAbvzzKrh/YF3VE7lD04C8KfglVqKdg6dUxppFxTTGh+gFcmklRWCxrESW1Db/x7oJYtTbMnWnBdF1NYN4iFC2tUxJsB0hKyW05FRWtrO8nYBLK5M/NkAC6yZ1FVJJZePIlB4bBgXQRIyNFpF2y2lgzPwlDcETAzZBz0xtq/9nWu86WffaPP+4xPucYVF8DsbVcJdghqNdgfag+PI1zyfLK6gBFGGxd1upBTh0ox2QRaAOQyf2hisim22MBtTB38EkpzKJk+YDjKC1JqJaoQLSmqwnf3XGGhm24XfWxNejGY4thUSQZ0AKEd0ANZS5EF2SujQjRwCvAbZwWLwemAE4ASMGpepB6QLVuPfqwZhJ2cbRj0VTpAS8yXBt+OATPoqdpYp7aPcgl1skGQ/CtO8tONbTyS2If13tW24H6lCyxTfBUFlAghMk8sKW+oZtqG2iDaTIX3znRKNVY/9RxgqbQNbdNNTjaHOipKwwSbH4GunfcBM/vqukQXl2tuxJZ2BI1BC/QgoiRULYUaSrUzjAtLHg/eGuhUlxkrExqSkcnfPCmymsxGXtEOtpmoKPKuTYQhFTgUkRCp3DK4fzLwE2vSme7kFh+DVFckiXeXxNGNFR4g0p6q6Bma56zk9mFJlgTfZ5ZVdQg4QpueBEbHS4VP+V2GH2wGZUl27QKUHweWFxetmrxHbe5c87atupKn1KDlWbJkrD4Yzw4Cb4bNZIjqgu45ztlLrTVlG2Bb46bfHCdgkzI49xRsP6JIASrbSuF+aaVHMDxxkLMlXpgjk4IXFMoSW9HcCe3kSgHsLeep+FVdDkk5xqhHS7EONYilnmDD+BIIeHCIIarW0Vv7XH33tlwyYcVsZHjlDLKGthgqmWelMPpcF+yQDmSVYxZAYxgE1EguzyXFt9twBixBF4c3OzqOMLeNUHC84tBD43WSxLQpY0o6l+RFbv80g+FPMac5R5H8muS5apfyrjDR8KijS2c1TVGo8OQAOm25nKcsR4uiS8bKqzC+HZ4W9IFuuBBrRWQwZNdojEEPEQKmMrSr2tRcuqrN3msMaW0TjoZl0xrmA9NIIOjIo+fqjJ1RGEorPZl/pkCAAFVI04KiotH2bLvX9JyrFqlkxfP3iuK3NTNkV7jacQHO2WBwnAjGhbAqtqmh0ofrWDiEuHnKWh6xp7OGx67bEva/gp6rUX7hJm592oW3MTf1LUpmcN30w16pQMRUD2tDluqedK/JtChIqDSgpfJuzLNWVQRFmbSBbtbQNBsppDrmajsJmziTmCl6Drvk8Miybv0kwkr2zLlbMUgWw+GYXgQMOJrWSUmxNRetgMCGbN1YK8LghTFEdTfuHYrPwKMUzQ5CyydqaqWVavGUMNlMIduJF1eUXJAUQ/K1YClNyGlD2XRqK3R6Y4eQBOmzaFZmHLKRQEiIMJV820lCFYGpa6QEDaEZpFtInzwXUkAxit5MlMO3xLajTU3upjlbU8W5vWIWhEOaqKhKv/y7hLOV++6Tz1N64ydIBLOuMO2jkypGHcXYTBOYFWsVwnTpSDmVKUTEGLqfinctdFwF9W5uKlUNKZreM1qSOqgk8fKSWBtdh8pI8EIaO0iydFSRXfYRXqL72GKwatiCqDyyY0pdq5eV8FPvItuqfhY4wDiGTiakVmXxskk2WZtO4vAwR5OjNIIAStBGXZys0XolQ/LWs22mPCPSy7CEX7tMP3gOsD10ioPlH+6IBTLBGsXKWRxpAaYouVfH2XMFXWJvMpWe1aqpiVt5VnlJuFDUxv7YESOzlao06ad8x3ve9isPvM+/+t7vufUd7vSMZz3rLW99Szldga8b3vAT/s2X/cvb/tRtnvqAez/xt371rrf64S//ws//sOt9SIry6v++h8jHfNRH/OT3fsfj732vH7jFLa55rWvT3Tvf9c5HP+GJ3/GDt/6BH/u5F/z5CxtbdK12rCB19nIo9f7Sv//7ezzwQWsdIdzmR37kwz/0epz4X3m84u/+/qd/8Zfe/NbjO6XPvPGNH/qrd3/0r9/jDx9y/zvf5jZf8cVf9Akfc/x/Vn76/+P4/yicV1/5169+LWA8tLSRaz2PdqmxaH1vrN7cXdwT4UpdXJhcmBlGYJtmoQFjpqIZyLEqBQNugUQtosUSXWi8Ptvm44Ib1+hKWxLgK4sUSDI1YqaszNZlpAygtaLQzKg4mRg0AxFBqQgt0xW7HlSyI8EyFAq+qdTA3IzbF+pSqBdL1aou03QvdYfuaLW6nKWfc8xmeTrcZdfoDdp0WdtbnwOhzpYUEQuskgyPtWcsmdHdTmlb5ailGiHpujKXRJCISqklfLPB1PhXzsjExIyYYLbZLhpYtsNjO4cuNYkO77CWF8LuMEdMVS9LKEvTdJlynlU7aTeVABZdxU5HEAPImFZDAVnk6xzs87bvuoLzFMcGsffV+77haOxgsRX7g1oJFw5EO7y7oujXp0RGyWoShhJhSz434zdDGHQmgoSs0jNkZhZDZ22jlotPg4sSBULi3OqsUjzhhBa9Uh25w0nipegzjS3Regws86M1CKQd9Y+lvrJCoNrYdBoKEQGaorkGRq+VXcpMxKpqBYNOryIwadpaY1zMRcM++mod1QShBw05FE2UgQyiOmfOhJZsEOXkucSFdlFM5zzLvhiPwdpxWeg4uZFPTz5y6nUWCbfGTNO8drFGMKpBejy0FqvlXLRX0tKUC1JhNUWnrWrFTiddRIWrmgQx+OaUI01zPgBFbpJDSrkPWPR5nCvZXMt7lYEYdXbwcySY9sNB8zXqSIJ2sKT2ws6N/g6SvTmDaMnEnicWUCw1VUMml8/jhvYwkkjhXq7vNWaJM1+swcFt1RFBIIM5oA0rgNYEUtlZ2KTgOHhFQ9RNzsUdBhW6kqExSdTa3qjWuQu8eqYlJJYmNR9da1MlFyVHhpdpmZGeSqk1VnKJttBCscMzIwcdtEfnIg5b2mAwUYTl3n1Xzexd2sVQLspiwolFXaefwQhqI2DPkVT7ogXWGL+sp9ABMxnC9cy4EOJko2EzN4RLCwzFdiHSA770vEOqSIyNocr7YLVkoVRyceId3dbUKo/O5xTUBCvK+VG22LbdmgNGjLJUdrG927mtyVoCbaAghz9FDs+BNWyK8RT/jKgNuamOJsYwxNusNglFGKRkVUlEX3GaYWeVII0UZaHVAq+YrI5iq7CgnZNDZ7Hy6DEhqXwxA8Uke1gtwVkuzmDrPYp91XADI4DR1kxNFoxgNt7XsHl1K+s8Vci0DJbXkZFG+o77glSD6rA+bOtihhTZYTO3YluQxk3IpLyZKPNzs7OPtbR2owLFkeEFcuYwQjxF9aKoWf/KaGRZzBFS3HhWo2GjDprLCPQMw9nSbULJtVoNocsUVvUUljIn8/a/kzj1ahziolqhvOQz52r7vAgSplnmIA1sMXvGUiLKA8YIIlkQVybAm88Wat6EffnSqqvP2M81p/qO2FEuYBOCiqixjaPD+XbZ2IWP46PSBd7UVc/O1PREt9az4WhDu8hqPbRP+jkQNHJnQkNafUnzNsJ4080klXB3empyanqiDZRpzMZgReHkjNmA6ggZe20LFpAqjzhHLKnmengGba5UCF9JSkJIqiu3sQZNYX+0CGHW1CyMZySpHpSKkwJIiih4GLVVKNJt4c1MqUgpSNBlViUSCJMa6COHOVd9EiPGukwh3/SRxC+c4KmLrB8uQpkCmq2tUgPgtBinZKvnQBoXQSovZMpOFQpZkYTuOtheIKJqTGrRAqR3HReNyly8iuN+rMT4sXUVBGSP084+NHvNTefQ1RDHxgKNDMuF6Jbao1epW06hn02MWDba40cnubxtGHtaRAMRHbU1jbWj+2A2p2gqDfYQWoArSeXBrAGmJdRGW5xMtELiRZqWFTiMlkXKRJrh1LMcFhX+AAAQAElEQVSfgnYYujhIMddIqZRa9JmStaFdyNi6iZH0ZG65Bhx8ZmRETtQq8d0UajS2zc1CISg9nAmgunqtxgjIykIL3mBHt0QvJSAYhPHBKgVQArNS9S5Hegko4A7Xy8OHWGOAHH+4shDzNF+GIjhwjCRGCV2CIR2QDiTUTGKh9jZn3ydbyznVVdWS7prmEDexjU08WoaEdJQZmnUeJCbhAaSINyX41X2X5bG78zdpYLAkCqvYivi+MWOa03J19uuAuu4hiyZQTaWJKZokCgiRZS0ppVFDsZWf3vym9/7irz3gX37zt97v4Q99y5ten5ljjI+9wcfe4hu+/ld+/uee87hHPe1+v3HXn7rVt3zZF33ix93gWte6VqkCgv8vvkT1w6//oT/6Hd/yzN95yK1vecuP+vAPV7OrL1161nOf+90//mP/+pa3fOCTnvz+959jaUZ33979nvOtbn/797zn3RD53m//ti/6rBuR0/9KvG9913tvdZe7v/mtb6GdL/3v/sX973qnG3/ap1z7Wtf4yOt/2Nd92Rf/ys/d5pNv+HG89Lk3uTF/83jmC14kVZTbcDmlHu3CShFUXhfyD7AOo3k2gUkrYaKoKaYITKuroA/ghBrIVoy3SRupVsn8ZwKulGPnbzfegkJYFzzLiIsF6F7HGGa+DalN0AB0Resw1RRKISl/jaQRJCVsENDVJktEc/AjdcrhSr+lGpASgR4hZS0JB6sRcLTL3YL1HVzSeFfCIZIpySLZt75zL18tXWOKnwsE5hmBWVxWUZ5tn6fFrnTgaqrVtBQ8woV7XmM3QpqVoAw8TV6GYEPJZITpVURmBWO1HKEUAxytjDEenQ4gMFEvRBYmkKVUU2tgQsR42Qw8isaCD9FSXNmkE+y6nrlYxo4IVr1GIeGi3pHH/04sRk9ya1W0AugBURP0RZ4YEJfprCq6ZYVnVJSjQjyZ3yIOSWOlOayEY4hweRZvrHOKeTUswRFeJluAiigESktsfpXTsVKKAxtCfSJmwWUs5pneeTLpyDIY5TChujBlRXpfOdg5z4iyo0tJZUKCGKpapARIEeDwiKRABE2qW41hxnZhkirBRHNZOIa6yCXDzjFU7ybZak/uR7BKinirKpE43oJwA5UioYPpkQ5RIqjLDlNiWUwtX9C0rmbWhWJHecRByq4zdbWE0VuoW7kFkbdaIykNVZhBG3kqAQ2DiY5qc9mcQSclFVs4eQXOKy8VuD5WAyKbFxMnINIUzikRqUIFCYGnzjgt39x7uTAirdVHdKHX4yctq7s0Vyn+w7kcdOQ3EjQJSKqlaArBs1xhsBJdcL7C07MClYUJccFxsUdoZRTBSOF48XfmKp/8HLR17KD4R5iwzDKJptO460iqhkElMyLHvNQDopS2lAHwpn3IpX7M0mQIjgq+GKux9vSgQOvy/BIsYC9bS9QP22hAG6WKnEUFsgKhjoBw0c9Qhkdwh1NLa2EMsh1kdJfNtVWTrtERA3n8lc5/tB1Zq5KMTSbFqujIDoYrCYavAOt1WFE00SpEVpS7r2BSV1lyD3ga2bs0CJsUyGOCUVk6BThN3GiC9hZ5Wi6TOvNG0Gx2XH5FS6RCQQ6TxpM592J3glcG67AvDCZmN3gX0nqgEbBPoQV7TRrvq+mAvmhNr6hsMQVVaJVGNEJD8EgPLK3VIsdSm2YBzqR0BZkEjcvw7ezcL5hPteTWErqnuvRDb2pCkEwOidH0ATlJO41+6jIEMAZBM1TiaLyYShZAHvksv8KtOy7qMrF5vJjA84b9JIBxs4mdtss4wJlwwBNRSXGpzbQCAw3GI+EaIYWsEXWCjGyoRqS7uesebXorHq3XIAXJoZYtlqy5uDpPskuNoSvpYrIi4XTGw8GhiqqG1FiQnW5dhwt3/1zjlLJmQjxqBeZil0Em5xQ0Q+AmCrNsclDA/FUICR6YQxYPMV+2zj2D3141a4gWlD6nShiRDqxhi0/95AR0Q+/Z2mTfPWIrFcBEuRzzqZZekX1M0yXFKkIJxPjTkcp2RppJlxooJ0bhLr5USrtyTCVAulBDTfgZMART0AAhFTNlivE9hXTUGhSckAEhO0QLhJY3VVNaK9Cyh5WpyPGLPf4wqjBVdmo5hhOlBXF1i95WMxLUmpioHPrS1ENjRmkSmpSqsYwEoriMM8moaSli1DIEg0ooiZcKWk+Q6FCrtkE2QaWEiMBEiKUhO9mjwEeYxTDpTaQz4V5rqa/O2FuOvjRSdi3y1EQ7xACVZkakwhdKCqrSO9uDCT2TC9cosjOWXGacFuaiEkwTyeEtD0NCVqnIrWrQpIEXSEhfjjUTa+/pvBE5pXHwOv7X5myqTcAbfKBM8qCnC40SbzFok4KIayXKsjMsLbIP2m56RCej2paD3Y/D2STLSxYtcgq77i4Q6MbHm9HVUizBScto0TneWDKQHXKGBSuhVJCR6zz3qyc7P2AijWiIU5KWpDSz2FN4c59eKKWt1eidGWkoqyUs5SWIBjVwN3dFZ9/rpzZOm1rPHX4pgwVFP5JEukfFKlpobaOxvXhLzxX+3JXaKbMlejCkkltDZ/QpObvsFzqVi/4sDdYoN1I+i/Qbq6ifTQogYgrdmsLy2CkkTNLBJZX6Bt9JZjvU3ZGb7nYYsoJ1GR1vef0b7/Kr9/rqb/6OBz/yUVdfupq0Xe961/t3//qrH3bPX/3Dh9zvZ37ge//VF/93H3q9D7HeeClF8P+7l0BK5NrXuub3fsO/+b2HPuBOP32bm3zGZzR2lMxX/vUr73jPX/+S7/i+X/vtR77pTe+K6bf/td949T/8I8H+s8+5+Q9+yzfyzX/lEZG/+Fv3e+0/HP+5y+fc7Gb3+Pmfvjht/6lNOV7HCd7LXe96H8p3f/RHz7jq/VfagrgUNcHHG2IMQVvTPky2ls3Y/tF3tbNRnbUAKe/rfMrYFCI1tS71PCtzzhVAqDiTqS2CRcIRQ8A2IlJIKo3VA77a8XFlOM8JHMy4Has7e7gr1shqhTKsvtDnqWKkGvcH0gDZuawMD3Ut6txNXMVbW9BFTSGSR8IDvqjQAq/bYq/PId7bMgveQ+C4GznH5eXk8E/1lnhVZAgXPi+kZF2u9sBIURHpKaeqTY8qb12hvZTABEBLZbPrqfyQ9Ig2jZ/0Mlc+lrSSQq9iEXQukcLG5EMmdK/c2+6nvXooSC7nMEpWi/IZKgMk/1GrCCUjuC+JDh9wjeW+dt13KZpXP51cOmAjrYW27CfokGqyW7pFKZkQY5pn2S4IsifH6SmJ5q056CN5nssrSO8MPvQjYrZ9AJLMRoXXuepsOrsudrw5+m70VoJsqM5zS1RrlRztxAG67b2kCfTwX8cfcxh3QmHKXJUf+MHkkGNL4d7THW6Fi0ruOUXh5KWKBAZqt7pUxj6F5KdVoMHqCAONJj1ZCDk1SmEd25Jz4LhBmntWgIObqqgVm2cswA+dDc7OoKUSE2yFwRGgX9jszTd1Q6oG0SCL1BR7O8w4pJavSx6MNUOOa0pxCTonpwYyUZKgoGTNlqtJKUEQiQNHXgWeBJ6rOBkQHsJeXCslpLnp3uRSh8viVxN8ECeOqCNpSOFRjoxYqj6Gc4+KKbUzZUZoXNmsHxZTSlSpJbPDPUUmkA2tJWsUqyIYjkDJGx1GF2k4QLM8afsAthRLlitvHWeBGixY4oARR9alWtZYJD+aZW8xpKUM1qNUaKK0uCeMjYaVJcATdMnfwF7iJsm23wApRmRQFrpuVdvO7fpy8VTSaMVKQysoxABKDtPWqqURG2Ev9agZpJVZBQleqas4vNpwA9UwweW4jlMOYuPmq4tagU4zRSiOxZAOwgBotVYiihwOmySYqKAMhMngdef5GJwMWzUWb053yWKsQnulFWYgx2mRQkyg6trW5IJnVUHzfdU2ZUzOD5t7n5fGeTIG3V3W1NrVktXM6nXwNDIyw3OFVwVprUhfuUceJ5SqqmPbEqhlHj2CfahM0A7MCp6nbojbhBuBpkJpZXRvfd80GyqSuWhLePSAEKA1gj5+VXnqpHnYKN2i22q+dLrH4q5+B7d0WEOd+qNHMIsR0NkQ4qJEIaBF/tKaTc9dvAXaxLaoT9KF2hO5LjdT9aG0Ra0w64K0Sq1V7eCb0qHxonfnplQzeD/gbAU5wjVYR6z8Hsww34bo7P286X7EJsLq4k+J5jA3xgr6c0ajG9g9ppAeMlyY2qIZOxs1Ikx8MbtgsiiV1QZ7eskAWpqw9GYL51O6jUyh43iVyiGl9LSJwcnAEN1KTyKmwrTUoUlHeSl025T3t40ZvSxq8CJCZCXfRVeXThPlTVPHYYh71r6gPCQMSXkehhbASbRvB4SUZEZZjKoYO5i6KEZHWJ0lngFIVS8CDy4QyoptGmpaVH9mrUmSVQWmfJEd6kkmT86EB7ISkoIDfZUlNj+MVWWE+lKfpUnVqpKIoo5EWUdQCL1SjrJ1uxj9dKrRVjNCL1x+0VZQt1SQNGGxqS3ZIFd020w5NwCalT5UW6uxxdaZhsIsmYAhWjmjqopCk+wSwiwsapDvbUizUjKzEHvLqRWAy8EGEjxEUIMbcJ3R1tp8MXlNhZuXwatH5GyzzGoWg5/HWjCly1DeqYwonVmeYpROnDK3SFsuTg5MBE2mySoOqVzpyiIzN108KVmyYNOab0M6aSJ8X+BgPTo5n/5L82HVTA/rq7VQpO3R9rJZ6RHHDKKXTEz4JTnqJcjFonrURKV4CqhIduUmfHmXGt1aM+p4mrpoQb0kj5/qs5oTilTLPFoUP6AxsZ6QZTpFvOiFAWrxH0wrZ51OC563dYRURbdMP3qaETF7WOMYaoPZlgTNW1ftRfSUHKbyaaJ6VzFYCZeDJsUdlDT2uN1X8k7GVWar7C7E6skUKiBVVN5M0gi1Gqg2AwvlvMTrtIQkAVZLMQ1OxEEA2FcyGyTadLWeTRXZKjaaNIN1gbYEK8sJfJu1LWHNI2GZDSsYrACuuuRNb3/73e/3gK/93n//sEc/+sr3XwngYz76o3/0e7/3Mb/167f/sR+60ad8comWSPHC/58dCRlj+7ov/oKH/PIv3usOt//iz//80zWuRYxvevObf+OBD/z6W/7At/zQjzz56b/LMx9/w4+/7Q/d8rSx+/HTf9Xxsr/+mz/60z+liY/6qI++60/9RGtHjvjxf35c+5rX+PIv+VKef/d73v2E33sm1qLAljavLX2gtIkqOleUFtI1dYDangEeHBlZmsKFvIH9rQeE5cZcUOXB0knPqhKEHQ3hWBYazlJnSEEhCvAdu8ZEzVVzj+BCFYK83HqPMXwXJiWsqp7KNkp9l3L58wQlGCLQomBKqfOMSMo0mrMo2O5KAupKORllhOTgNEl+gHhswecS0corcgXghUy+Bwg5QxfFKqpd2IayMS7XZGngxIDBQShow7GQCeuPbUDV6KFsia3eEgpH0WSy/0RTWJnm0LDDfqmz71g7VG4eFaRqkrmnMgAAEABJREFUek5u/sBxpBOlkkovKfwroRLhc3fPXCYw1i7YzxVZimURlURkMcZlgL1Buxb90owlvTLW0mCUFcJuLhAlKKgWG2g2yQ6k2mzc41MxQjs9rZX2SAu25MDxMj5OSJdVWNpBcgpWEEDTTqLWkD3TUjSGRCcpyuypSo2EuFlqS+krjf2iihkSpWtp4AcmwiGm4Lk0X02CcFoTFZGECs2DNBKnlzJi8aIrgQRqgQzFWjgOrkBS1QutUAB1vWYFUYfZcYI/Agyl3zB60Wwgi5fHZGrBOG4gNeG0DU47Gq2IVjPr3bQzgJoUjlOQyoKIpP4ySxk7RKVom5s0zSiKCwbmy1u4oFyJNglXA+YHgYWZR3EsqzhohrSgdCUYqijTIJbF1t+KsCURaLv0XQlCApyzime5Be68nRqyc/jqtmurOsk8WTZmCYTkvFscVibJ4tPLJ2z2Q7SGI3MSPrN2kWrodkgjk9UZHG3G8JMMFKAcXDG4M2SbZ9kRM5OetgciFGhdjgWQKzgsNfLoH1mbM3quftE6tiHEVDSEsKQckgLRIoHhNC9N+SQgDLtyGZlcoGZqodE00sD1LGGoJjWISkt0Nd3HKZop1ya4g3/YKhIJbW1ok5DaSybTNGvscZrcmgIdFLKmIErAkoOF5I4g1BQyi0z4rOMOl9upHK2sVSor2JKMlpOr0CrmJKOBYxbSuxd3uiKdB5NG+3LIeStwjpcmGSsoJ8KKfXWQjlaKRTshqJbKrtMrOsxgXtQaURXvyXKVu2X1pcqWBfau4hRw39ZIBssLuzmlVgydAoNQexyamsGOzNkJhNg8YkjR1FIVTuWwFAlIUb45uFVzvuclHtnMBZbcdScLm7NMjv09pUOzwBJJs0XZmqZS15VIQVi5FYNiUSaDiEz2+XPpXkydCAQ0yghxvKKoYtXd+mLtzoHVhdJM5iFLVgzaI8hWZDEdRKAK0D/D1aL9zCM0ZsywTKYof5PrVMYrRRuMIUhaU3RRHuQ4mOSB2gRNPGlZNNl1No1T5RbZV+qUBZ195NasSVc/1aUTLp3EVYtqTTXBEK6FYLIbxJIHtJbpMaQ1WFMe2QDCtiKxQYRMMo6LSiIKAN352mxtY8EWMyskKsg4c80p1A5hGp0A5NcZaXDWcQRWMF3RlOUhGcJQqDMnJjHGb8mGYxJb43U6IjblrSWDIzvYmoyuo4HCY++8pBbaTFggRWTZJVsjdx4g75rZOheKmZ07ASm6paVaoipk1/uxOS+kEAJ/2B8l0qWiBKVaiqrq3rkrNEakS2UZsqO4OzTswFKySufWOT6jyHOrRkREE7PIC0PqFyrDxAYTpqyQyyIgyARTLRKQKukB9pVebBPYhBlRJFlGsEu0BEIqUMV3lWJVys1PJeOcLkGeY8jcGnoHYyNYSWhT6SykzMnQLAVuvJI0xNLkIq55rErmomfYXsYYMDqM5mnd6F58muuYh4CSk+i7tLAlSQAzXAAdMFu9rb4FFxu3sURKhJ60jH9MWFmhuYQJmDu4wBEBeGlrJYigXVmMOMF+XgfGXqFRYMDgLyGuUPUGBi4oWMgJtukhcB2X0FeUrjp4hYPceGEXLI1dndc8dA+b3tc68Sp7Xd+0b00Hd0kV7rVD96HsitT3gHUZyiqDqVqJ7cnyMy1+9W09KQc1togqFP+AKKOmT7Ze/i2kFJMLkZH9iujXQNus1dDZMiuH2slOPcnoCXpZqwXiLVRGpSPTit2lItO16mLJxaoLr9ZMvRYJU6NxpFpGr7OS2EStY6HY6zSJkIvYsaTuzL5IQhjgwfu5al599XzA45/6Dbf84V9/0G9zMw3gQz7sw3/qB2/5O/e6x/d/09d/5If9N/hvOWjzf++jBH2ML/rcz7rn7X72d37t7t/+9V9/xTWuSafveMfbX/zyl2emqH3Lv/23N/io6/Pkf+URWb98vwfl2qX1H//e7/6I/1WKROTrv+QLr32t497p/o989PsvXZkyWSQSQhiGqqBG0idYTRhTR8HAHCEBCkea6wZuUIu9VS04SUSBHjmYZ1nefbFDIsEmWketKFIPaYKG+BPFoupHsXPDs5fsoMY6sVOPBU0ONxh1KGVACUu1dq8VjBP8gOratDapzsYgDpk8RNg0o4myLobJ6HKUz2baRHSPWufMs4F4aLARhkFay7rIHMenxBUhm7V+Gn0bJr28VRUrGbqnzBS4sKiOOEmSFraSUdIqgYLDylT4j5dMSe784E25L+byKyXmkBUarlIwsrt25IIK5qbnrXHLVSjxWayD3MtTXHIxK5I8n7bV5apGqqQJXXUVKwW6MECIAcpcjALZ1CN79LPQo/g552GrLGUrGzJMu7iqpxZEVCC0tqFZbqbdGoyLTTD8St3dzovPcI5U0oUKny+gp0sLdLAHiSV9sneC9hKGpp33Lw1ROR3OP9KhbZouUiUAD2hJD2lVxk9lEQwgh82LLU7aW1ibZYVu0ht6K+WCugG9IHS4teORW6MkfJ9gfvYqNtPkw0tiUuKcmkuCaLtqE5gVEyUOZNHg6NFsQkg2t1SZtayWYobOXWfmLsxUicFa2TG96VC9ADZ1wb6ELolWL1Sp5LZNjtucbLcQmVoLS7OGoy0xHgmBZCa8ZKlFS9EYKiNbj01wvDhmZ1qsC4asEyZ/D2bmoHgL6864y0QlNZfti/esg1KI06oxtUUPxXmLNWIJHLIasXPLZenQhCpCyEECU3iqVOrCaqM5bsidtcgrtM+oMzeXU9mFyTaqj9W6NzEmxWiatZgBrFJmd00P589MRGyaJw0ZxQ2G1ql8WyWJpPgwKWeaktMJV7Tiqib7qNlASaxAlgzUqU292IsBmHhkRnDVsDFtczQkxBlWlkG3YxewIY9kW9Pt1Js1j3a102awWMbWTBrIrKAKzh0Cmk8pbqgYv0wyzosItTlGtA0lcj7jfM79+JKM3WLKyqLDgsI3JlTdLVNUk6UT7z9f9Revec1T/uLlv/vn//E9V11pmj2qRygLnFw7id3AqJCB3OHH1y50mqXUQHHEBm0GOXmdlp4wTDheuGay5XKJNPNAHYUspYVR0kFBUkZgRGUKmEivI0r+WlRAlUR2UREJklVHKye1AJMzazo5PRiwKGSLagFdZjma9MPQYLamVTK1ODCWsACtGA8mKXTh2Sa1de0ns9FlKDaVk3B7CeU/ODwbpIeJw4n9kMVKtoGc6lQVNv4jQyBCLo7fVwxs0tijsFdeqjxTieUj85A1qts+tssd2rNgqzfulkuP6BmktCAx/OjRzzsmJNgZekrbpYJYlW5SZQ2hDW0Yzbe2b7H6hEaVcKibee92qq77EEZaUeKFSEI0yX6C8rzKXnoOnYuUpvXdxi7chAIpefREboKYDSPmHSNlU2aUQRjNFH0RUNpcHbWJMnnhzk1mea29akmT0eLEoiBTnLGkLzlN9FVHLdcH+ALqKLwjQcncOa96EcZgWk33zubUAClLsdzY/dOE/cQ2AzZQoOxL6bxtDeV85lfVmigEwSkd1CO48iwHLhnWSWwT5lmE6iYHxUrJLhGn3FtGpZEmYcDJzgLagFt5RrDWAYgk3bCKNN3YehpmZ5YxhYdNLqDoIQxGZhnTOwnMS0Jsie/lC1QxjPiYin2xUa+kyWAhwyAmfJG5pNVsJdRpKnOjRCHZGZpuUg1R4VHOEC7HSiQWOIABB73taqur+joLo67DhhXLQ+Iy79K0bFTbQjR5ThagKAVm1KXEmYDYj5LJNdC7840KqklcIFpY7RdYTZUmpJ1E6Zkm2FpK+RCDyzHzgs5AsbzlTJWz2g5lXH0XnSqrykUgpbIa/Ph3/0QrJSPFM8kc+4U2a80YGiIYVgElwYtMptN5mZD0csjkO6tZtZZ4tYKYRVPOPEWOShQ4mGSBTxl9i6iSlXIWYWhCOS2RxVB7QpPzwzh+A7UrDGKCFkyrjYCBnh1WTaUxHamUu8RiAUmtMI+NIo/OYTIVuy0+mcpc4Tln5YIUVAim9ABOHEfYA2h0wKNSk04a8wwRIPk5tMjKdPhhBlWqasaqIObu2lNokqNF5cJa4zSGIP1yvsAqhlfwh/40yEsHC1XptoxyAFXOHBNWpT/9mc/5su/5vjve616ve91rUXnFda77Y//+3z/1Pvf8jn/7bz70gz8I/4d6CYR4W2uf+kmf8DM/8D2//5AH/Pi//57rf9QNeJJHVf7q/e7/E3e5x1+/5rWZxTP/m49nv/DFL/urV3D659zkpl9888/im//145NueIMv/e/+Bce85U2vf/QznllUWLocaXDXXIIFLIvZ+b6yFncAvNEeTtHArJhYF80UCbCVjkQDughrxTpVZG31ftYejP9Qjqj3nuigLujUUrWEEmRX5MQhMWwN40g5ZAlFsbHV9JyQUqo1kWWLQKQWS6ezE9hw2+JybXYxDpuBmeRUgS5sUNY6me+GzdifoRVhMy2kwbSpdetDtTu3dzFqqSSTlda8W9F+KAADVc54iygcSfmXLC2nFRRcIhpKjMMCtkIgnACl7iWTDSOiOxiiAQwNCcXx8q4EX8bFfmtNtUkaUkC3GhG5+OAiCwvlrZJlzudezSRVdzRPyyXiekrdmjS2rAoAVRwOZkRLeCTb2zpyaFFSTbRVcVyQ5cNPAfSTcSazgqYgUa3V0TdNoJrN2F09NHbLXUMMZHPIaHmBYrOg0bBYLV3oW6rIXyV5KhE1/gI0Rejci1eRWQSILCHVZSK91MR0oHU2GK9iblRk4BBJMcZRHEm8DFWIOgXsRMU+W+TCesfGw8oUNFPHbzpyLW4DaAXKe5baoK11cExSHLy9WDtXyqahWmFElC2rR41ygvRs83J6uddFSoyc3GibMxyDJBK6uip8JP1pScuT4bipVU21SarbrlpKh6pKbVhK0gUoEXCf0IJeVMoC6vxLDXYdxqsNZBFYSCnl3QebatvBzRSnCC11Wi0kDhdVogiRg0VJyQWmEUs4hI+puHUKySgaFARhS5ae0Sa4LmXKLKmQcl5PsbCmm/YTayEtQzmBCTgCkM0wTIT1XhYpwT0Ri52Po7iaGaFIJo5NCZXmNJgpQlCumCEzekGkg/szf+7LX/6Y5/zFleezV0IoNuaMheHUTq9UCVMB2dA6zEARY0xthVPzMZJWvMM3FHuI1loyE5OjRAAyIcVdoap260ipiiRtnWh0YcxqyYz007po2Y4JtrSdEZMHM69UyMBLXvP3X/Tjt/uSW9/2L/76lavEETG4hpJcizCPRIgWWk4ptqEKyJNe8JffdKffuOWvPfznH/ikn77vE5747L9kbs7uX3f7e37Zj97+Nx7xJKChOgGWMGjwFep7m074q/QMOLNUZISUmJhCZVmRuEIJb4MXy+KYKShV025yIRjFsgo2DOFpURFpUsTpqmgsJvAGsIVaIiPTg5cEQB3TUiIyAhUdC17Lbfet2snYtK2auy3uTFYFbVMMUp7M4pEiipnVc2MAABAASURBVIskCDen3t2UoVG0Yo3NFZ0OtFihzmkKRqLKCiXoSGOknhrS0pQvIyPLnM80gqAAxidNNhVyZZVFqmcUW5fLgZwMlKWQleNjIarOmV6cmy1TwZ+qIXJZpJXFOIlKGgNCiwTDDliw2bQmxo876pIoQxBLpINbo3UK5Z1IlwwD2YHCqiyWJOEkfZEIJRwVHDDgyoP9LMD97woC3nNdypW0rwBVpiXKxTMpP3PIcQG+ECulUoQeRLzIWYR4sqXzrBORoPEkDp8ogmmywNkhNcHge1weU2vhGS/860c/8yVX7gx4OIojMqNcNMskFJVI9ireFUmrUl9toQW4ihtFkLGy9sMk+CoVDk12giNCxu17+NqjztBz71wKRKFSwjxKwSQNyeZOJ0kWNaAgudK1DbKskqXhPcwO0ktYNT41S0xIpjtpK4QKJTB9Vu6Qym6TizHReGq6oIrv+VMgKGrfLIv3Mn3PCv5THHL4ZUYgbDMlPTAgWsmXL6SDIF1kSsyKqAKD4CHMT1Mcq7x9IGFqmmCyIbN0T4tqpKQUaDwf0hBChIJEEUUHBxtVmMyeEDgBXBa3lIAPoKo5DOah+5SJ42+JV0Egojq38pHsaKlemK2yRyg358qT/FCabABZslhSQOmKzlbA9PMDBMfLpIX0VYOcotrOKDfJk8qxowDlm8JLPUU9sbKVlPbswlUhFzKo1bn7Lj6NMSnlPOO4FWEmopoxE8WqytZdy6ngSnIwNDtNnrKdqh0Z6URDXtSn8q6STbcRW1O/0NpAjpoHMoXcqQGKBPiZRyxP3orQJ2VaveJU68JXqwW4VBX5YyaFadoUg1yIu6zz8LNGVrSAsfj5K4hPIkHrZQEpvkvBoWEaQqiyudgFbNMAn7UOx1BvEppi1klYzZGXhq5jvVHJIs/lKLTKLZn6Ls3YVHE6B7ePhXPpi/7677/lh3/mVr9whze+8Q2Zea1rXvPf/euvfuZD7/d93/T1173udUUEx8Fg/495qH7Y9T7k+775Gx5/r7t/xzd904dc97qsxfe/992/+we//zXf9T3f+uM/9din/9Gb3/GuOFj/LwvwqkvnX3vAgyNi207f9fVfe/r//E9T/hdticg3/+uvvsYV16jMJ/7u01cEFaI2iz1Tmcjh3ajrZVR5samNtbZiTZ2Nv7HzN+uIHYDClzyeiwxWRohVWqtmh05HGHcbPWuTfYArsEOTBUM83GYXP4B9SzfRoYfMmPFwYArYIQwuzoi4J1mbuCkNa3ZlB3HDrsplvoolnWYsGrRRJp65wr2CQlx2KBGww0epwEroKVv3IVBr0K24TynLJAckPR0RRptBMTrY+iGm7dgfaBeYVE80h7GYAuw7i0WWC5Z6Kjkhei6lMVipSudkYkaA6JEdqS7Clr+4a2Lwim20zt1hayK2WIXsH2UQ1gVKvXQWh2cadLTWuzZxycgU9h3+Bnsqr3LGBm8E75DMLKwYvgYD8oaQkqgOZpMcVONZ8bDLbVzWVJ9VGUA24XV6nFAP5RImzt7SeGfaVygSQ2Q7jU353DmNFGzJb8fPqufEJPKjU9JM8s6CnSDYaKsKArSwNpWd1JaoDyQZrgARS1tDSYiQ9DNiT2jZaLAWZTO4ZsDFUpj6I6Xi2Xj7NstD6C27MLWnsJHdRjd+BTPQixNQi0jAH7Y5O3hrVTpj7WvyXmWie45DaatktpbDWlc55ELYkzkyK/SULUXMXT0rWqRMj5zIbEu37Bvzaz5kqkQTafR3tDgpvmng4zdrlAHIPreELakIo3arcZqKj5qdDRF2MN80Lyo3Tiyt2HZVLnZd4wI5ZHWCd0ON1XVtMXscRmin1RDXBtJ2ZD9sYYumus1hyyRovHpNjZ39vyHLV609Y7ljzi1Wz6IQd2x73/SKdrrCtiEmiKhI2h0qpiCm5GcJN49AkqHd9AgI2oEuSxES4sUIRqpkpUeR3MVtszX87etfc+sHP/qOj37a/f7w2RybCBRkVVS2YmSrGcqkxGqJOHrVADmNTTFMR5fWhK5kZPGoqkXdwgMT5bDwjl3gVUHYlR3RGe2BIsMrY6xxEVtvKodtg4Bueu4jVgB7l71iX/nuK6969/uvWpXce0vraoat5WnsYmcXn3TRyHHbF3w9/fkv/bkHP+71b3+3iFz/ute5/od80Add5woIUvCeqy6958qrzuczY3KARZVlij7QVSVG7KckZIKtnbnYj1sfbSGN6cnM5qUk6FBxuXZt2kz5AvvGJt5tyfHgU3a1yAHfkomhRitNrbU2hDmZwtty/iCJqRcGYVZHKm1LAz/BGxfzvTKsoACIHTGRa1ge6/rsbW9ZugszKsxXj7A2RZm77tGZxSocJxaZkcpCpHhKlNBmjrRNQWGbpTK8HuOiBr+jM4J1j5gJx/GiZ+JTUWFmqjSlIg11KlCiIXpW6qsOw1RW4uyYZ82zYnFGwQQkCU2SeLvSjgojxgXo1xELBwOQLgjN6RQQC3uoK+HmilxCDs1l7BAvx1ZxYTuZZoAH2AL2aWvfsLaOjV/ZUuqNqabkiuZXJlsO2+QEK/24UZRSpdaXgL2RURqCqaqZ6dkVzbJKUqx6ymm3nhT9QSXFEgBaQQoNzFZITJ2rOxvNJd/OfkE95n985T/+7H0fd+eHPulRz/hTq44cE+qa0Fk6o5bv03dUMGYjzzv8zPKQaVKd3htzxSZRy1BSWZJoCn5ZIjqt9i130zMVMhVRLK0MVFTKoZY46vWkfiGlvZVa0QD9diJWkHMyjTxlDdHWTbmRCGDSMlOjscVqcwpd62rtHD5zLn6UjrrgCKY0Sla2ujwXm6QiIapmfUhZhO5KgMvBXDEKAjFzUcGR9c3YNWjBl9elqLmY4cspF5rMTdhGjL9CGlI0KTRRUAwnfoSGtErLaClbNWV+0GONuQ8moBKkCpR2aDKTzKJJ7327uEK2rZi2vcXe1xxZ6AipAOuY/TqNbytXyQ6FrrJC1zOwR8ygfqq8qbPU0akDO4vtsPRCBOst+SdTnTONA6OM6aYh2TQ7MWeCd8DOWEy1G6uhQU20wegoV85dwDox9Z5BclFF82Vx6AxF/R46DirIh/oGURYlHQfS1RC9ex/QoWjHYifVpDVQXGkyRVKoVIcuk7zcrSSYJgQRpffKJlWpc1p4E1gXku+lPFmgZrGGTi1HSpKwNBUuHSVGVWZ1NSUPjZeiaVzoFMQs3clZRTq9aIAzhT1MDELmjWxZEVwRkQlUDSXlnBkha/Ga8MrWvTVPdUfNkoJKmVIFZtCu3SgoRocqpTF0nW9/z9tv92u/9o3/4Udf+JKXALjiimt8xX//JY/4jXve4cf+wwdd5zoEwJP/5zgKcr0Pue7PfP93P+kB9/3h7/7Oj7vBDYysAH/50pf8/N3u9tXf+4O3vssvP+WZz3ndW98xWYn/eTE/8Rl/8urXvIZjP/tmN/28m92Ib/5zjk/5xBve+NM/nSNf98Y3vuaNb1QJgTMhTNRxKPPOT1zJhDopVIhnpszgGpgRy1fIzD7RAhT0LrkkoyQd5Yli3qX0shk59EIVhAj9XT4WxCPZxrPRqKSYs25YHq3KFtj4pMKkusBSSlAslKZNRFOQCZYQZhz62sHCbtM2hVmJQarEQSXTSk3A6baEM5UYqDkRNKEppGaId96eLc7hkGlwsOgzYpdAN2kdEOXRZBMWHM9isVDC1zlrB0KXmaMlNx9LPXRXWdwLmLDCikulGskTlpMnaVmlrIxVDErESpTgCkCRDd4/kGVOxGBx2eXyExMpNUC1xLyE7Y/lRCSKPKjvoezpwpJKQxKJTK1qZSZaLOyhziXX7DL3nFkQpjHdEUtZuhppmYSYlVXJDWQcfyFRGem6qAl09E1MVUmCMLgqm+DOSjmH5gBOWZJejG2CnbDCPVaRfWEyEl7NbZPesQlRZYo7PbSltlS4kEjx2R8TzkhHED7Qhyo7ifWdXS+TkTPiFqFsTe5s74UUhYK/ShW9C1lplQREWeCsMrnQGIdU8RwJWCKBArURxb/84Vw+hJIrGkPMgC4YwojV3HggGaEwyU65c/VHES3oKvvGrunB11qy85lsdGoObcnGDZCqNtV0W1OjRKQMVsXssItyJ8j7o6RVnuMlYhPu2gAIDm5MHVkAP1ICIE8idBtqPIoAxdCU21W5mM0WwDgM3qmdfNUb3vzKV7/hjW9662kWv/3gklgVCXcrKEemBNWblfRgArquzAlj9YgNWqVzUrRcuDmKxd2VsbgB1TRdqtFDeoAS0K7Z2yE+2uXcLoOnxKIQYKxaSzR4bd8nFQXgvVdebUrPa8L3bJVdYKqyIFx0uKMgNGMCohpctQTC3Il2EYBGjzhpr6gvZWL+hywWx2KHnFc6Wczcyrj+YMHPE/uODGYekM6jSjrLiJbYRTSZId197HFKko3jdbg0g44GfldGKVqKZlpycBQDCa+Y9fDffzaAD7vONX/5+7/xqXe/9ZN/5Se/+vM/szDog+ePg7UUK4XzSDW5VxrtYk1NWothfmibUZISiY7F+wAJKVfUCGllQvGKCbPCGawVyWnYN/WGEhXqoKARtp95hdAWVIEuaTolAjv1wt1WbVqnVqMXjBSYpKCswhCllYjMZPBhDI5YRmXPpqmFKgT/DBHlBJfyEgPToRwnWYJDvAwNRBPQFZW71+6s7QLczEfL0Wajz7ScnW1vCtaxN4Brr8DlF2OeDbFlmTZRdrSL1FNpU1WTohfKNhETktnErAuGFDfcQhSlBn6UPnLrx9PXsdiNhdgvH0W5QCf6wsVK7p+txNGnpY8ZcFplAQTxQUtaSKNNRsMqCOUv3iZMTEVZiooJg60moYxJzYRLBKAJtjnSIUoqWBwtGgemAAJR8NPOVLEamYVuwao9qke0GY5aMRHoJoszOV8AeqtUqUZczidgqRSRlgN8B8gl1lQVgHdf+T7eCHQ1FfGWcXI3n1EeBjc+H6LDQnomLUlg1PE189BBlfzNG9/8kn943avf8talyebrTTXVkjFKszGabSXIvISaIglCbUJxJJg26akn1c4Hd515rwio4yABCOaMGWrCAKy4vk3ozLYYKVc8w7uves/LX/+PL3/Dm66enIbyjDyGyTFBaCanpAztJzuZNEofPlUcm/sI3kNUiHhRb81rBBMmVeCPgiCtSevahrK9OCljCldYVtcYGgY60ZYqs2qVzmgrlQQQnJm0VqXI45+QnZURUkCRBZmpLmhkJ1HTuVCKKYtTmMPGdy1Kw1FekiUCghlJOnu0dmYQCNXFy6m9aqgKCMrIwBl+CRpHD9jovlnJKbnOYgzh/ISHRAp/T9SycFslCY6v3q1Yi8iGy99GRJjT7IHoyBjSIHxTGWvpCsqLP/uJd4FxUKUjtDOoSshqmSyRsVhNioZU9oNahVUNrXNDsjXvPXJgdokOBtBEqlduIpsqMZ+m9mgMPik4v5S1E080nYqZ4csbF+Swbk1Hl6GDDAfkAAAQAElEQVTEJqTaVvTgM7jkZcyhOayZGYxhA8ta9k3VVLuKFpPXNCppUMRLXCgXRsA8SVPhJDYqaEqDoXhICWpTN82FnJkVDInLAYqiYpgCVNHQUeFNDdrKhmwNJ+rBpc6ea2Wu+P3n/Pk3//APPeIJT5hcVoAb/dNPu+9d7/zLt7nVJ3/cDQq0gv/zvbLqQ6/7QT/47d/8mN+85z1ue9t/fvOb6zgxzCvf/Y6nP+OPfvIOd/zGH/yR7/yJ2/zKgx72J3/+4je//Z0cz6v/i8cb3vLWBz/6MRE+ev/R77yFmf0vDvufn6RMPv8zb8LzV73//W9665vTJLO2qovyU+UoaAGpyp8OGUJtswLhwv3EdApMKJtqnraywC61KmNlXHKqISqWIhQCKpfy7ct6sqBK6dGY1VqJZFysPhcqryf0kJQGLHhJDGw43cSi4Fpp7C5FY9vOjkX5R7Vc7B+xyldRa5Qpt9NgMbWkJRoqZ2coQk0rlpQpLxbLESWlHBMUaIiGStGP8GGL6q6cqLLLtjiFFcA2GgXRMm9glZ910mP5ZfBWyenlAVnZUXTBE67ODxituCLQZ2omvHI3TJMoRoPKzJopC+TJaD0DxzJf9EUkQJdiJ1AyyMZcSyvMShuCg7Pr3PRqk0uxRQ7VJp18cUZUQ0kvVvUAf0FHkUpmEVKcJtaFaViMPHkeYubFJluoAEPVZJzZz6nnqNiTmRncfCesCqhSqRNKUSQjNmoDx9njEkTThMwKCa/wuhydt5hAlIjChg5TSbYWp21IiAiCltmLlIxly+RQWtsMo4myJap5dqKFJXr0VgPL/Bw7yVoMNsWd35RQkeGiC7qLMh0VQTdN9yYuSGqFzaouNteBEAAMmH6ocpAGtRCtaqbSW+C0B7PfvDNYUY4+dATiNjn2wVojCIdTmL9LO9baM869okv3sh0MUZFWyziAmUuaJ4xS8VZuSJQsUMjstAmfcDogJh6K4vTongY/LJApMgTlP9LCmjc5PlsdRQQEWprpGGT3UuhP3ftR33z3X7/745/IXEoxyuMXNcf+v2/IniIhcQ6cg1dyZLTlyvdFek2kpJi44M4hlqxd/CxYQuciJjV0bbI2LlW66iBqSy4Ohm1Dv2jojaD5VI3OHFYQqixxoxt+4vd95Rd81eff9BZf+YUmJDHcc7os4ezN0c4Ra6ZTMYgEZebQgFCJbZfj/x3EC4Amw55WqbbNPvaT+YkBVYg62H4qOJvYC2WYhsXgU7EMO4pACnt1XwPRBMxp08IRPmaVGErxP75ExUa0bao6a7eaMLBIUMotMd7y9ve+7h3v4fCvuPlNbn6jfyoJ85LsUT3wP9qpI5VehGfF94W0ZItpPXuhexs+NiqaewNBiHm3bC2471K0zfuxU4eBkmwikoGjywHFAU2cbygqTf7F5Z/y0JUMIzZKLEPZbROtqklR18Y674f8FKkBjS7guctXA2M6FSfNfAxvUhYwZthriDTVDdUiOW/2oIIB4xCpaOFjT2Oz4CZEpglvOD2OV1YmECZuulR5HwJkxizeTAq0HRuVPgU8C1xSnK32HuuiV2fd95ZyoLDSBq3ukw/h1VcKWm+dr6EyKjsHDPioMy3AEl2V/Ljbor7YAoogCLsGq23C6PBsNrulVnrmLCypwJ5t7hplIhCL0JpJCpqABKqSsl6+teTtZcL3EtKVZnJRbaCTx6SghnK90sTiEFXpZHdUdcDohGw4mmjjwFYQF3iX1UHdEAvHo5qzfRggqYiOoBbUrS1O4TAEkKI+NZd89if9k+/50n/xdZ9/0+/4yn/WLlazXYrJj6nFFKxAphqkJ6wgZZJDnYoXgSCEMs11vt29H/Jdd/z1X3/006bG6umtXNGtuBM5XcgYG462KOE6odMaxKAp8CjasGwEKFzm5775UqNLLm2JYMnFCD3uBWdxixduJz+NaMyqiuhzXvY333XX+97iTr/52je/lYzTZHNoMboCkGx4E1nCAI4EsPUHg7NcKA+N2XIHGWJEs3OuAtVIVi2he60SKbMalvw2j3sqkRYyEiq0RPscz98ljKEyMoxqyQjUKpLFvXuzbEB4TQ8+6sjDi2pVFwzhXYZJyIGPjkyY3ELRFlEwa8o/BFZcc8H7Ivap1bpIaqFx1PEnVJhtQkxuiKShRIuC24HFP1HgSwV9iPWmHapTaqUme1tWVLiWk8pmYsaryuLWUWpM3lnY7GQRhEysMybj0xhWQ3wECywUDLxCDn7LtDZwDSAeQ+PAYpBTjJYIQgCuNsZMipT0kLZKKS2kQMRVWDoupILkqPEUA2GUacIIhawknYRoqAJhstDP3jx4yRRdeO8zBCTAcLw0a7iOVKM9Jp7WlulZe4ARjtEGr0Boy3pZK1IjvdpIVojUqiP/a5DKtSwIwoNsheylExIattEObQ9FB18moOa7lFhID5rDcLYahjLKrIwRFZmkUpa++S1X3vrOv/6jv3Cn177udZz7YR/x0Xe6zU895Fd+6bM+49PIP8/8n/ugdK99rWt+6X938/vc6Reeev/f/P5v+5aP+bhPFGuk513veNuLX/ay+z/0of/hZ3/u6/79D3z19//YnX7zvk959p+//m3v+E85ec0b3nzrO93ljW94PU9+2Zd+2ad94g355j//+IiPuoGqVeU73v5udilqFIcIAzin7zpnT+nKSquS7NU7eqrkkOpmjWphb7PLwuC8SqE6y6icEhXJVvyKLZbEahEtBdGL4wiPImnUAl2bpQytbUBbJofGhK9OxRDIkGhFkSuXkBDOB5ItJVOhIDBWsvVc8Gm+bB3+FSUUa4QgjfoUvuGyFUdnITD2UEfs5ZN3VLtkWKS6NApe2KJ7sefVprltwmChATkXez/2QDkkounaejYrIpAUDS9egPBiqQkrrXH9qSgtNyF68dJQoAkbRkcayoTrFU3wwpTkzGZMA0TY1tkbsyBQqkBAaDNOZHgmdmGDoo9STfLgXgxd9swZsbt7VMqoZuhLbaqYoEMsybI0ydbUTLVUEmpgKwBZ7OJWogcvnNsxOMtkMXW9QlNAPjVEVshyeEbVjnXuc2kGLNIO/FGSqtFUj//HBK4KjVOZUB+1xCAbWgdjFvLJ9WCtKC8p0+jC+xOx1oS9t+vlxTKgXJxnsc9Wgc6LvAtdQ0SGtg1DYRJajAQBkl1zLV9cwILRgu1qC+9FcKlHEvmoZji6CAnQUcKOqEWIxdxpkdip5qPBTFS1AQPVmTLAFNxYjq42DNQwJ4FyOYzaqla6lWwzSQSIRAt8HVYrS7KpX6YZRZBMortB6Jh0LZMDXa7Ys4L3M7YfMwGHLOG+za/uZ94n0KhN053DiWnjUPfcazoT3zLMlvUwXjqOKEKnmcuHIgyr92ib2MZMemOdSKpIQRDZcr9o6+IU/RQYGZKTRbGcExlpMLHp7pFBuImGY9pS2mbYHA+VIxpgWJK3EpuoWVTE4oqzrliXtvMql3W6Rn33137hz33v13zEh18npteeypFJs9RRXNKsLDDubaH5kVSR4H5hjHkxFsstkFyAz1S/TGVERKJd8/DH3wqxJYMyU5UTanPnI7BaRZtd16k7EycVHkHthrEVaV0SOethrZUwhzCIMrDj4F+FD14sutxbcJ/iox97LRkcZoX+3qtWRHL0J3zER4lT+CZLA8ccVhPP8yiRGlZN0ZTvs8qqBCEyJXZh1aCYhiyKXdq0BrMuNsqUbUF1SXPpAjkEo1FIFs1MWeikGMl/JIfWUDNtYr1XYTkN092RmrKQy8HQpReYVmiyMYhRF6Wq2DQ3tFNeKG02BgKAqBUCQMxx6L1KGJztXULVvB1ZTsJZIlF5CCaSJX18jUwqWcxuIIqKoKixkAmaKGtlxndVxFA2RI2uOsBzqFUyk0Em/2pbshU1Fl5BAUVNaChg1ZrgwCneNcgW+DiDsTswqTMvCWnZVPqydrXYDomQimrJm4LJvqyWZuTjQMyMVBUgwEnQPTffVSM6qDLJYsmcTPvo4MIgohBNwRLMwLEX53YIspfuMRDcBdlW5Ijdno1aykRFeFdntYiigY0ghlSDIlRohRwlr7GHwIxZrE1KJVd336bIapKNLmnF5AMwA2A4OZn5dY3Nfuirv/QO3/lvPup6HwQpSZeV4gzWlCQ459YQcD1ggJqtq21dVLfd7bzAXtnzjMsvnjWICBuAe5vYdrNzgXeF5do0lXsAMA+GdVF1chW0XTE7Vx8GUGQ/tXKT7B3agV5EhDIkKA0vJ3AxkzayqSsE0MueoRXM4KnbiQJWRCH5Oq4Jf10e5JC9MSTiq+aEVeHguJBVPX0rlure2IqNK5TviBmZs4Sb0wJMvCMaVZkKD09wNT/K2mL2uet0jbJScyAsYuw+2DNGncy2ZK+QFlBp0o9/s5jwU6zkVLiI7DFVPGUtOZ6T1UolpJKj3WiG7Lu6q0Q25/6HkfHLvK0oJoX0LMZeHXnRdJiKBZBSBI+EQHiDZULPEFVTIatNq3deK04FtKURceVBvQnHGo5X0Y4T0J5ezEx4LS1Qwmx0CpZXlQDqZJCRcc3oIT3UcPwqUzpgBjxZNKoiVOyQVBSFN1Ep5zy6IqfTM4KIy0tn1a4VB4IESnioiVkyLHbPeYhCulGTieTBMU20S6th2Q3R22JZqrBlmRVLPCB7QVOGg5JUfgaao69g0qmHvpluCibX1LnlaQE2JvBxh18UNmGYFcrHX+mJVcyvWlM5Nemku10khhOwu/AhxWnmiGD/ZNWrH24pneLMKGrmvB73rGd/3fff8glPf/q+n1vv/+rLv/JJ9/7Vf/tlX3zaNvxf6SUl1vonfOwNfuy7v+MZD/qNpzzgPrf8tm+90Wfc6BpXXGHW3P1d7373q//2rx7+2Mf91G1/4Su++ds+++u+9d/8wI9+50/+/L/74Vt/9Xd810te8Vdk62M++qN/5vu+EyJ8/59/XPNiEzmmvPvdVyJEpxWLQg4hX25URUFBqUj4zALVVXqFaKujbKBa7FYbcywJ1oqastfKAYIdTqyBml0lxUU3weIFVSFBeCyCpmZcZ2FCoU1KkC1lF9kDMZeusIIYyhaEt0iix6wAO0MVkQrVbP0o3dHkwrGdw9YMWTvSq2a5IzrqInCNs7QplT1DYsJ2sLgiWMYOobJDEy0ZkzQRY/hlompcp9oqLAsGKVKFmopSNGsbuB9vhM41RwrCcRm48ORGScqyrMKShBUS5tCFos3GgGUAbFONPouRMcJWGF3soqmWZCoPQA6PVcE2wG7nxTYIF7iV9+RfcZe54Au5xFdlMPrFcd6EjTktEgGODKm0VX1VA8HTJNKPCzlKj/8IwtgmqkqZpFLBKMJjOiUIr8MoAB6oSs4rLgJZExyb/KqWK9rlJmTmvZ2HZm/aGnqDMBQRs+pNTI2xC9uMpgTnqoQI35u35EGQRV/V+6EqEIlr0nsCDKvkMiatlsF8DkjjCsybjY05KoCwlM0FIckuFSDKKwAAEABJREFUH8gqKRigMhhgoXll0aOCPpV2S1xpiY2TQFqdFX5kkJf4hJDxM6eZneqRZSQCbN81evGBjphlGYUjOw4ZMQqgydb5DNJhtSCT8HH55dBogzOzAcxjzMByI5ENYUySBCiAgESqVdsCAKCQU0hLqZIo4TKjRgtpjJR8HIysFT5X8DcdIoSgSHfIMV8u/z7e8fNScbVShiqbpyVgKq11sVZGwNELrUQsypzVEbHXmuqhDEM4WUpMpClhofgP2XXxOTIMRKNJosAEk/MdOZOv1bA6vNSz0eZZpo+EpFVmOetSl/WwK4C+zWqXIEtgYtqa5palHNuyuHQpg8aBxHUtyof+6da1CIqgOY4YihoAOEz4jc42gJGxeRVjh9kwu8ij5FQqQhiCGB88u6+aKVznbGTbygwltHIcijxhGpk4ggjr7AjJ67DDUO+A8h0+4LarGqTBOCuxeFTFYQU4MsFiINOAM/MVlSIqKenkYU9dyzIS/CnhQr0amcYxrfiZDi67EL4qNBy64jTXhZNOxgFRNeq4n2q7qNNJNiIpREWSIIMqQa3CDNkjFn1n9jrr4iYkxVxPq/gUoalznpBIq+LXhHxH3VoFAyA9PupAkeTXBqy14shyWaHsAlMZwkzzIj8NHOxsYiChbVjlRqHsiyzSxnGAhSDCREhSYNlkNcUHXlUWxfs80uGCXWUH9RO1AlPUS9goGmuJADgfuQuWIUWrOor5EC+cJfcKt1hCgiEarWejJKxFjZyt9oZLVktTKauzwU9io49jC9MEVm7UsK5kLxAmw8w204aDy0QLCPufefRw5jFVDjaQIRUUfZNZIPqSYkmqgW6Y6ggAljgFxiydIlFMU/O0oJxNIV1kZPaIlsx/Rc9FooWQICpMsjQcVkp1GccHHEgqBGUirGHusVpb2vc+5rBlXAN6P+fpXC1COKONJqOxwsnTNk2qHdVIYB84Ogmhg2LkM+gqPfZ954pSSfpOXldMdJejDBqDg6ZiN5/FvCjOJZVqIs2gViVZoPlknMVgbUFjaTFYRgfClg94BSSPH+nKOBDFGcFzWkVDx0BCiaTcNLpxrk3pu1EpOIJlmnFWlioHxCxmDlzVKXWf7uEVAm+H15VgTrc9Bks/wI+rbC+eg4cCAzrA5qMBIMuWNm9iDYNqZu/skowMPbJxbqEarxoHZGCPnBVsxR4sLEa6izpK3I/27iIQ17W6Q+jqsNNMG/v2AcWRphiKrWnrNs3cSpREBelCVToBEVqz3o390ax14aLSTII+Vq59ZszkQEVYWp9Jb+XOPhADxYA8lWCQKtqqtponHlBBdzNiEoqosiRSk+P5h63pLBIEkhrSpsrCiuMJExDaHLpLTNmTFVcewIKtKtfJRDNNELotFeqX91tdFAJpWmZL7Gy2G3iGB68wEOO62XaMvSkXjjAKQZ0sZxEOyGAhsuh+MqVM2IzkpgzGgGh3EQ36WSnOhBQMctF5Q6JXSF1ROqIS4C7JW7LjKIs5BVODYoAxAYGQTP6OlTNBLukbnFPlvl70ir/57p/5mZ+90+3e+ra3MR83/LiP/9Vf+Ll7/NSPXPeDPuhyDPi/5ouJKbVPuuHH/sh3f/tj73m3P37kQ+99lzve8hbffpNP/4wPv/71zYzSXe7ve+db/+av/+r5f/GCl738pe6uqjf6tE+77y/e8YOufa3/Ut6u98EfZKac9fZ3v6dntMta9eg1t+IjHnrUVVRpiSfTKk6ILFRl0QwpgXuz6u2QkxRMxE5WW4YFhcr+LgYqhQMbuIMAViKT7ri8yOG3c4ZIIiZ8F3acAX5LLH2yJMpN+LgEgwPZWhm8FJsP5LCrxyKvAYVIZwnQJipSeDOhZ6Eyi5+S7s7KhUmSjYmVJVFIXI7BDGxFKmqrd3LIqbBKy6WELPwT2ZcMt4HW2CkoaMYhF2IXZIBBaCmDIoQYGZtk6fGwgQ0wdgs+J96DsqetSjg8dEFW19jSLTyS1SHiomc7OpM0k6aj2dha75dbd6Um285UeEeZSrEPCErJgjDerfIKnVeMdcVgCQoMS30BsZB7OY9JspMnpESWHjc2uaeQa1Kky6QMTVpHq8RyLEnyPkMmlOud92LX6CUdqlw592ZTG/i5+ha9xTGxuklsLU+9aW9JRugsU5K8kD06GOpdz4l9ZS7nAM7Ymm2kHnDEnu44qLRSqcZ2sge/AJEiPt670COkJwSVrdKYwRnmHNoB0aQFXiJBihLwVaStmkozHHNNArIAWuieyq4uXKbYnKxdGnZmPKgqVNbgjvrSapeWMXNOH4ezBKpEKBmNqtXn6vsUd5kiR1vdBCZiJsMM0JqwS0IbjaGy7ysxCAdwPgnJOnIXVUm33AGXlZRYyhBpagAEtGFbjcGgUZsWV5c8wTmBIeQS2csSmXIutnOKqi2pjD98/l/e4YGPvutDHv/O918C8PdvftsdHvG4OzzwMX/y5y+ziebRc46yIQOpr/jHNzzqGc/9xd9+0p0f9Ogn/tGz//Ef34BzBjAtYmQZTPzd73rnPR75tF98yJNe9qp/XJfOL/jLl9/9IU+9+8Oe9o53XxUlkQgwdesf3/yWP3juC+/6kMff+aGP+a2n/N7zXvm353X16BhWnaoCUuzt777q9g988h0f+KRX/u0/9KGqpnrSMbTriZ728wtf8Tf3ffwf3v7BT73rw572xy9+ydve8S71zPIqB4qhN+R+fv9vPeFpd37wo1/wspdlyRvffuWTn/3S2z3oKT//gCc/5Pf+7G9e99aDVit0WK9BNrtw403lXLWu/ou/feV9fvePbv+wJ9/xYU950rNe+Lo3vLOOzYiZCAZDDggTRNqOg0rIPZjIpAR0oyWrLj48+Y2PPO65L7jjwx9z76c+Y2fBAPx4h4c/5nYPf8zT/vzFqFXhqDisXP7huyxIMJgo5kxZgvkXf/UPv/20Z9/54U/+lUc86dl/+bJ3vPddDJI0CTUd7c1ve9/dHv67t33I43/r6b+/qBbOW1UUWyDcH/bHf3Lbhz/u9o98wtuvfrduyA6XOoNHuqzR9k2m5pQltgafDK9qXiHT+36p7TuboDmpRRRhLSXIY4DtCQprMkprjh5rZI4jlEopZ58ZQBdh4UpDtgqLrOpRbakkUz0oLV5RTnbuo/omNhg5NYLQxZvCHefzLBaPzj4WZCbvNRgWEVDzNXRXq8kfZkSoUE5LSDKfxh50jKI2JzeacrzHf/yHf/j5h1JyT3vP+97n+/nV//Dahz71D+/8iMfd7qGP++O/eMk73/nuseJCZHSjwrQPkYGi9fIZf/2Pb3rcs55324c86c4Pfeojfu/P/vof38ANCc3ysKZVLO56/5WXfvOxT7vDwx79glfwIaC98e2Xnvacl93uwY+/7YMe/cg/+JNXv+l1wsg77yyd28vRF+dmICMVhNsa+nFGfVky2HCppa/6x7c/7lkvvO1DnvgLD33cE5/zvNe+9Q28PpZwti2ZV8sDnvSnt3/AE+720Ke9+Z1X+TbOndGTA4Han77s72774Kfc6UFP+cu/eXU7yTjJm696788/4gk/99tPfMmr3sCOELKJnIydvOqqdX7R37/uQX/4nNv+9hN+4UGPf9wznvv3b3iTE1yE+VETf/S8l9/h4U/9xcf83tuvvArAK1/7xts/8LF3fuDjXvTSv9mY15XrUjq7da6Y+4v/9jUP+v0/vu0jHnO3Rzz2T//8Ze987/vSJDdxBTUkuq48v++ej3/qHR/2mJe/6u/DnQ3hcc9+9m0f/pjbP+Rxj3vmC1739neJQhZf8aZ3vpec/8IDHveU5x//HTJd3/t3/+TnH/Lkezz26e97/yUSWKIuI/REAlsc6jmDX0+ZpwobgeDolVLKcamAcZMRztbQcBZZqKBJkLI6+p1nm2CX5B5gtphsbZKq1cG1OljN5lSwjjSDwtBaZ1fKy836qNUoZ4jErlN1AszjNOyGpZV0NfpsxtJkl7KpLYlXz9HOjjn9mGtqYlZlEBWRNISgiLmpFBMVSq4TlaNgAtGGakUsGSr0FsGKZxdMnlPVLqw9EVF0gyk4eWY5/5A4bi+QqRUWaWu1lcaBqmktRTjquAku2i3rbi0atImpgcVu6lolMKjwqCTKSnXPVV4Rl09AAKkCXwpvRbL5licNBZ4nyJk5IUFLVQyQ7xQwwgDdGH9FMzYHFwYXk5crkQsZtJpAWWVjQp0BOM8SCkQrWrisFYt6DPqCFrdPyzhMRY3he3GEE3ApRICRR1K3JF1a2n0zmgb1mtNql/LkhASXFuLEkQvhm+CZSi8PpBNSvPr1r/3pX/6Nb/2xWz37+c9nqGOMb/yar/ntu93pS//55ybohuf+74MJkRS9znWu888/6zN/5Du/9TH3uvsT7vMbD7j7L/70D//QF/6LL7jJP/20G37sx93gY27w8R93w8+92U1v8x9+8P53vdPH3+Cj/zcQd60L9vOD9ktz59rSekhJBKuaWQdlnJpHfSmqbZerp7inUUe5rFleh/LblocOi1JWEfHBNbN2mylOASqkgBRuAFSChg53lRGB8mbQZlUtaQp6uQgNookIoXhCy4TrMRrFQSA8jxamydribtZczJVrWY/LFdGj66FXOsQxnU0lFCo5QCPcHh6jtGyUnEzaoHMOVw4xh9IZu0ZtoBJrr7xUFdSwmR1Wjzs2fuQfm+hFq0JLum99dnUrFCM8+lkGzF1YjLnDXWeRB14MBo1iUMkfLwkR0qXCgkUlWRdL7da4zEuXVOKBS0bL2TA5jpUp1VXMyjTNjnciHdpl27gql1pk0g2rGUZ6p/iszAqCC0NWqZclG5OHZJIPbJARXfx45DPNl8wVfqA/IKEOiKZh7A5Y0oJdLMDm18sIkVeEsQlbm8iRSVe4Z3pSNcw0pFVZJSQLxANaQjStfsTAhitCMbQSJECmj588kEtUUWMpNML2Ro/JWTyEQ+hCjjBgyOYOjkUHaLMxcC2FV2YZeMcsg5bBvqpBmlwHA8csPWZZwDJpkEFlDzdfINZAemGGrFIcWSb2Ygt2F51NQkNk11wSTHo0CLXQkORe6V+FNtmK1ZlXOaZWUIpqQLcUJkgAURUpy7WlmykYmGkpLr80kpIZrpugCXOay7C6BClKwg/poLtKY4yS6Cv7qr961euf9JwXPfW5L3rH+95PM297z5VPef6Ln/y8v/i717xOF08wgFDBG978rlv/5sO+6x73/5Un/uGTn/fipzz7xb/4iKd9211+41ce/YR3vf89ScsiHNairr7q/Ht/8bKnPe9FL/+71/z0vR/+I7/+iMc988+f/tyXvOO9V4pCUHOfD3nys779Tr/1Uw94zOOf84KnPP+FD/jD5/zgrz/8e+5yv5f8zd+1vprOXqXoV13KJz/nJTxe96Z3HdW+tdWoJtVqL3jpq77rLg/44d946IP/8LlPee6LH/usv7j1fR71rXf99cc+8zm1XMgVUxl0Jx7rT4RaeXsAABAASURBVF78sqc990V/9w9v+uPnv/Q77nTfn33Qkx/37Jc86Xkv/eXH/dG33e3+D3v6n9R5byRVcyjFUVn5rJe+/Nt/+Te//16/fZ+n/cmT/+zFj/vTF932t5/6nXd9wH0e/wfYZ2NpJCJdibPwgVcmlsOXOjVH10XvVtVXdLL/4r/9h6f82Quf/8pXZRXHv+jvXvPE577wic990cte/VrmA1G4fJ6XjsscFMVOpkgRf9Xr3nDLe/z299z9t3/zKX/y1D/7y0f/6Qt/9Dcf+V2/fN9nvuTF3BxGcmRd91rXyqgnPu8v7/v0Z/3JS15OJdbiIsxS1T/7q1f/5u/+yRP/7MUr/Lofdq3otSqXV/mqmlHUTaj5AWClnbUd/EmiwAAJAOxvbChsYP0MWyEVJVnwrEq3DAZwfORQScqYEmUY4hRoF3Qk1H2UkwbGGUl+xXRo69KNjXd1TXan1qEse2b32AJSUS0kFnxyOnjn3VtBaq4MBkcHpLe6BueAyhNhzlVh3YVdmo6bhWiQVXcpZzm74HXvegd5ePLzXvLmt7/3Lo948rff9bfu/bt//LTnv/Bpz3/Rre7/yFve88Gv+sc30EAvaSqyCRrXoP6O9159p4c97bt/9QF3fvTTn/Lclzz1uS+95+Of8R13uc/PPOBhb3n3O6RBj2G2Uq/y+OMXvuzJz3nhK1/z+t9/wctvced7/9yDn/D457zoSc/7y199zB98310f8NhnPFvkbOBXNG6tiBmVKRRIskNIMvoiWA9Mae+8dL7jI5/4rXe7/+0f+fTHP+/Fj3/OX9zuEU/6trve995P+sP96imzZMmQ8Ykf+ZG/+4KXPPqPX/Cwp/6JM+cmkz0E+ZZ3vO+uj/y9pzz3pa/8h9d/8sd+6CBO0yvPOxmg8F7/lnfQccGS/bTF3735Dbe536N+4B4Pvdfjn8mKe8JzXnTHhz/lO37xt37xoU9495Vcjqo7/vKVr+alpz3/ZVde2pmAN7/rvU96/oue9Gcv/vvXv5W5Um8VJxr8hze+9efv99jvuvtD7vHkZzzxBS989LP//Fb3fuR33vGBvGfbBWtDNHYnmefzH73wJU993gtf9YY3P/ZZz7/FXe9zh0c9jdie/LwX/dLvPO2773L/pz3/L0OUNfb2977/KX/2YtbLS179Ovrl8ayX/+2Tn/uSP/qLl13az0AmiiNRncv5cOplVVrk2FOXRyGLXdKgVqUBald0r7H7QAzuvpubuJJ/QBY3uC3JClPajplsqCOohiFmpVJmaZ3fmeRJnO0bh2/jQ59R2WXp2tl/EDxb8OIWnKnMAhrVMY91VWzpqXS03LY8NdmqEQJCOHoVSkWEBcclBdUHbXIRa3JZ4loNBow6ZJmQw7RLasth0+CMMygnAFG8UQh655oVnjPWikgnHUVbwsCMd8V6lBvLKFv5MU8NMogghGyuRl2YzhRfnJd85tcLUkbvChuiG8ASr01wwQ88021ZueRCeA8wEx2btpOchmyNn7XvXecQDKCbCIFK0XUVkq/dZZ/MmJI68FcBvCAW1pZqZMVc4FdB+5yTrXbBFja218k37N9atFEutmOcva0lkaJHwL05D2GrkqpIhAI907Ab+ytOKH6Do5lwLpn7aMVgtJPWQZhZiXTImVdxSeTSxVhbjT5Hm6SLI7SDyxbe/Z733eF+9/233//Dj33C46++8r1Mw6f908/4zTvf8Rf+w/dd/8M+tEg0T/3fx3/CAAVAEZAZ6u26H/xBn3vTm37H13zV/e7wMw+9x91+5553f8Q9f/mR97rHA+9+l1t87Vdf+1rX/E/m/Re8vcYVF6woTnjHe9+H1qQpxZslyf1nP4t5CKpMtDOLKlzZjUvjfsnn1bzLtaKANJxlTXkkdJWxnwDZKo37vsmqYrWJV1RFEzETCN25U6yFVZosq8ZS9u0COfTqVpc2FkE/6jYAFlfuWrvNZXtRayXYlfUsi2D4XXr3AsoVTXOzGKe0ayg1i4sgpn5uOG/pp1AW2YZ+gUY9DyjAqKS6cOosyYQFVERMoawu0P55zHnCMoShxLP4GHWfjAR2zASgvGSOfsRne1zkJPi2qbRWJYVaghRoYExgydn5d4Q2qJBq6VkX6cYwi1921B5IZxdhBa4yJ7pGNAw5+XOkogtriqXXNHrmmLbtx5JO2DF0XWE5imCggt70BJGlcwqJc+7Yt0IDVxcI2Ma3FdyP6l5yCVmWXUyyMfJUHM6DM0YZ0mJZIUUX+E2XZGWtbLu02eVsuQKLvaSEfSASwRASdKFk0cH15dxjbQA3j63xypJjhpTzOruDdG2mmhyJs1M+QpDVcj8Ecq62SpdUpLofcJfWFIg4v8DeYrLtoQoMQBTSrfqxCXbZF++4iJg3INFxvDKPYZbSS1ARRmUtIzVtP74k2SJEVXjj3G1XLCAOo6hepSjmkOR16GDeTlWnmf2M5EPi5FhmOQgdW8oFxFSVRG0l1EBEuJMx0dZaHZmrJtWda0b5amt13Y1MC+AHzMp0ttIJF6QSasasWOzu5CuKS7QVz+Pksu1cLDmPKV358de//ld+7k2//OY3vvbFiXY+9DrX+oqbf8aXf/ZNP+mjPwZCTOWtXvXGN/3wPR/8zJf+tUd++sd95Fd/7s2+7DNv9LEfdt3zCu5C7nC/R77vyktQHWBJDoAHLeGhv/+nf/Lyv/0nH/MRX/k5N/2Xn3Wja1+0K/q8ovJ3nvzHv/mkP373VeeP+KBr/svPufG/+mc3u9knfmxT+ZvXv+XHfuPhL/ybvxfNagWSYR9IAWmXS6GXWp5b7oXfe97LbnWfx77qjW+7xjb+2ad8wlff/MZfeKN/cp0rTm977/t/6TG/++AnPWPsxUw6mHIDeBx4/uAv/uq2D3rCXOuLbvypX/05N/3cT/mEremlfd3zSc940Sv+lo80laksSMiznvfyn7v/Y//ujW+7oP1P++Sv+LybfdFNP+1617zivVddetgf/9n9f/8ZlV6T+kKrOboe1vlD2bSLXccBPZb4gkYxAoOk3PQTPvarPvvG//yffnLTY/zNPvGGX/U5N/vqm9/0xp9wg569+cZs0AYP5q/hkoZLNKvxqn9484/9xiOe/8pXd7PP+9RP+orPvtkXfManXDHa69/xnts/4knPesUr1qjZqBm9xZd83id+xId65gOf+sfvvfo9pEBtXb2vez/tT/blH/ehH/x9X/vFXgSO6R6XzrbPzXkngyVybhJGYAZ+stWEWrPYlPiFfe+E5DfeFz36oPjqXG36KfbNLrVxblh9zW154+xOuSNVcOyCpJg6uGMu2xOUrVCfSxq2JiNFFjfl8J66edeyrGJ1JRp0a007T9ASv2frmkPylDW8bF9YE7gsz5CWFJxYcVJdYefNvLHsmvkmoQpeV9l7nk95HmxGBYBL0O0f8rTHP+cvP/76H/KVn3OTr7j5jT7xw6/H869609vu/OinXvm+MwMksCJPOt9z6f0/cs/fefKfvYRS+YQPv96/+mc3/crPudE/+egPX1nPeOlf3/o+D37LO98C52xhz4Mp3/F4+oteyVuUudYX3PhTv+LmN/t/i+3Xn/DMF73i79mWwmt6E9M2UtmSmS2XdYazqZ6pL1x9vnT7h/7OY57z51ed90+7wUd+xc1v/BWf/RksGX588B8975cf/+SqOCgQ/PNP+adf/lmfQae/94KX/u1rXqta6LvL+f6/+0x+F8GvrG/19V/yYR98Bfe1rIfFbHAojyy7VOHMSr3lqvf91H0f8by/+nuevtkn3uBffc5Nvvwz/wdfj/vTF972wY+ZmSh8+sff8MtvfrMv++wbX/O0ceSHf/B1vvxzbvqvPudmn/SR1z+QqMDaa97y9l/47Sc846V/yzOf+6mf8OWfd+MvuMmnXHPrr33He+708Kc94/kv1yZKnVAiB1/g65HPfMEvPep32QH++5t82ld89k1u9kkf202vvHS+2+/87j+8+a1q9cHXGV/+mTf66s++2Y1u+DEcz+PzP/2Tv/xzb/yFN/vUa1yRre8wVmMCJQKJ4NqEELDsKL9cwbSrVReuTHna7RTDTA/hQ1sb3JCn6TSZgmmx95VjqelJ2hAo+4Gq0TBayphtzK1BGjt77jZ3mcm3IaLlVpxXQtXKMqyNWp2QM3okdwMCdoSIKC3a3rp1lcPwoNA3120qdx69MsM92Lxpt3rqRSlrSkDhqhtoODVEIZISCM8Ffm6lLJpWhCZVIShebqlYIWvmWh41E86AuvBZ66gyIomSgvGAKC+1TkRNrVlD66EEGJZnXS4JRQLiuGw9JZdGGE+oLEWZimhxgYqq8o4gFqmR1T9wwbxseRYKWqHiTbIJBGFazD0nJ8fAeTUTCzklHAEVU5yabjgWBc6fYPelIQGJqTZTneDbgZ3v5OxMiSQDO8wyEqaS4s1KApNIBHtgKJpmlxRYuC02iCW1QlkUqg2dYzOjCKclWnFZJ3UkSWWItMYdg/w/2fsPsOuqIl0UfatqjDnX95MzCCgoiohZ29jm1OaIijlHVEQMmMWEGQRMbc452+bchrbNATNmkSAZ/jXnGFV13/mBvfd+7r77nrPP3uf0fa7zH9/61hqhwltv1Rhzrv8H5plo0DszBD72xa/d7kEP+ee3v/2vZ/4VwG677vrMxz/unS97wQ3+4epqxp6/t/8jCDCDHVKHYacdd9xt55122H5bJc7/R1b+f5iz7Za/faNyzllT1NYr482wqZqlipO9gllKD5Y0q+7FvQIMMmyAGpb4kwguLTWExAoJVIhBmWW59LNDAFMzLVXZDYB9iXDtrbPQo8PA4eQWtPJeImTJHlIxyMjkzMzW0Z35i4JQLlgaRCEmbsIFebFUsi3EgqlTXLQJeNbo9IpJSIsEQTt9Bkm7yO/pm10tmU5MPoim0qGQhKAKNAPSIH1xKES5n7MxSWjlsjGVnkxMD6Yysxy2WJSsmMsH2sp9nF6JFfUR02pJfEiyIkSGd3faQE0ApXfVWXUd6ZNHC+ZoiEGqCGgKkw20GUl/nC4lPIVYea9zshegCKkBNI0y5MjKOEpyp1dp5nOd54ImwloldR749Mc6fO5UlcvSoqaiwqrGu0wMEjSZ28KyQByqKCPENAs/ePYec4QnZEgePFrB1NHnbL5AJZICdVasWYT7RfigMRS6HNG5uNETCkaZTN2gHcvfo5/os6WtMAy9MLw++xJ0IRB9CRLDkDkLZrVOW2lqEAQEaEcqw5ShvpyLhHYrC3+bpDmcM4qzvHpmz5p0hfahzLoxYezcq9yiO0ChXs2LCKyFTe5d1yLc+lwsg0NmNCwqetE2iNdG07VDYQqYdE2WyCXoQuYYpISUZujcQ0KDG6DCB4R2Cx5LCbWTCc5xVnN0AUDc0kh4AX/xVpm05PycBsxDkiBK+8rEoFKjCl3Rnpi57m43uOZzH3D3Zzzg7nvutB3lHHCJ3Y96wJ2f8eB73vAaV+2WTbFFfPXMAAAQAElEQVT2ePm7P/PrU05fVXvmvW//2qMecfTD7v78h97zHU993P1udj0kvv7T37zlw1/OrYOsjapoCeWwTa0/49DbvvGIR73g/ocefe+77L/7Toj59LPPfNsXvsnRm171oDc+69HPfvi9XvCwe77+SY845iH32DIOZ1+w9TUf+9Jk4qPA0JhaWC736B0eEI3TzjntJe/9+AXreb/ddjzx8Psf//j7Pe8hh7z0Efd981GPvPpl9u2Rb/7s1//1uz+xiNTuQ2t8trfIwK/+fPoV99vnXU973CsefP/n3/fuJzzqwS980KFjUVL5A9/8XgjDTW/kvLO3vvJ9n7xwmi9/id3e8rRHvfSIBz/rofd88aPv8+YnPny/3XbKTJ5Z/3zOWdymMz3VG2vCpnw1EbUo3OZYKayHeLqUabR5VfuhN7rmCx50p0fe4YZDMU4/5AbXPvp+d3vuA+56p3+8pg7kCPUv/RxajPBgHcvw8y7ceux7PvX7087cbfttX3/Ew1/+8Acdc797vPKRD3j70w+7xM47XDDNr/nYp8/tF6Ck2rj3Lrs/6va3MNWf/P4v7/vs17zA1d79xW/wjksEj7zLrXbbYUdlWYCKqShEeKdPc7UIFCpaY9QYHNJoY7WCMsRyWNtAjMY51a2GmalUQykpxmQH899NmuRMMJSoS4EOnrWDOeQSESBSxEpCaspG11VwqTD9PFiCQjnDffE4EaFgVeiKxSIZq5gt63sEsz+M9rsxXQDihM5CkjlIrgSjpokru6Mmb93IliLCgjVqFIGFDAFLbF4/+cNfjrjbrd7+1Me+8IF3P+bBd3nnsx56m2scxJEf/u5PP/zDyc3X2YLGtfQT3ve5H/zmT9X0Ebe54RufctgxDzr0RQ+855uf9OAnHnLLWuxnfzrtZe/5+LpPkqx9onmx/F9uku0dT33six52n6Pve9fjHvGAFz/wb2T7yg98tuaji6UKinKZGIS/2QQFUSU++MUvf/XHvxhrefLdb/WqIx/43Acd8oKH3v0dz3jsza96BRr24a9/9/u//00fgQqr+ojb3uKSu+xw3no+8b2fkN6K4kcn//4T//ZDWnOra17pmgceKLNlJzQMBL1cmqJbrM3XyOmDX/m3351+1sZYn/egux3/hIc//0H3evGDDnnnUY+82dUuz6k04+s//VmrcqebXO+5D77rM+97h92334b9V7jkXkc/5G7Pfchdb3Tlg1RMS0Re8JqPfIq1Ypfttrzq8Ae85HH3fsYD7/zcR9znbc943L677nTh1F774U+ff+aZFrOIpBQKYfvFn069wZUu956nHv6yB97vufe/+yuf8KAn3fs2VXXd+se/9t0iesndtn/W/e/wwgfc887XvTrnsz38djd77gPvduQ9br3dNtsw6sVkhBfhA/e5i0ssuw3JYNI5GckNjz1Zia2mWhpEIRzKBWsFKcaOBR6xIDASsC41BpXqQnFJGQhPylNOmAefBevU3nnHsixXlvPsmV21DQOUf7rJcuRZRY69WXJZZiR3OptoQOc+FsY8T74ZNDeKbTCQYdElSV2QqooQ/knl8qlE1wZwW3AV+ko3yGpVRzSJGNtQyabSwR4XZIXQcraUsJRMMP5MMuYNSadI+sI8klgMAEWNdJ1LCIkWMavmUNJlCPAef9W2bul9pBRI67zv6dE9Mho80wFaQaPTspnmkIM5n22MlDALb8+WGSHJgLMsL7cWHj2cEDSpKFVXaSUzC/f74NsQookpQOmhgSpiGKzUMhB9TmQ+S9EK04igX0K4x7resDVPFdmXslBMh1q0CAWXPlnfmphZVTMZxoh0xiBYISsIZ3eZZ9feKp9kVyQpkNGa+4xg3IrmShp1VxsgW7qyEMJrdPO52B9PP+dRz37+4c961l9OOw3ArrvsetiDH/ypt73xXne83TiO7Pl7+38QAfJAzWhA9z7NMU3WGsST30AUnqnmigllirpW7lcydFm5DVmsjlIHIcuY+aSQWCdhZspxsQxDFhFNEQiiOh/SjopRZOFnCKdZlqFCS4/kt39kE9mdusxn0oVrE7TIdPdwMD+5UAJBqlWBZCKkeMk+dB9cC1eWNg/TNJDLJKWE86xJqnJuYI5Yd2zN6EikMCMotE1B/mYT3Sw7IXNndZDQ7kkM2O/DEAMZaksS89jNTb6YwKSX6NFzhmbmuByhQiVMcoSMQU+cYMQwYFWjVreKSqvVakIDSNAVFpZsPfldxwReIqlalYB20XXoPFnONfuYMYiupJak/Ag0FhYmvnQBBLXn8gi5scAohCHi9wkaJWENmFGDJuVgUiQpT2SCUrsqyopz0LN34fMHGKekGAxYFsM0xyGHsiyUDOGYiZZUg9giA11nHxo2gpXHCkxYINZeHCISBsaKxUcJlbbCPtSkZN6VcQNaDip19tXWri3FQ7JleOudO24WNZEBWSTE3bbyK+Sm4tTJ0CUtTrqcc7GutAW8IiWQhIy1aiRKqtztknGo6fwuxzJ60nHw5k7QavYKmru5WKQUM34egvcB01LeKK0b6IF4orWONivRHRCFmBbeE66lz6NjCGNkhLaZuKmqKK2cHU4RAiNMYkANDI4x+shdnUwL5AzvcEodx6wrT/MWIVjsgkBEh14LhfSO5tpVo4Kbt2DSaEM6H9htCdmSwj2kgGYGZ4W6lVDFf7lSoRIjpjHWkO+f9Lt//fEvRfCgf7rh7W98jTJK0ylr22bb8XH3vsPtr3WlBN7/5X/78x//Qj+A3iUuEnXLq1/+rje71kA6O+HvU/cLm/z8d39ez9zM5E43uM7222xPPRAMg93iH650l+tdbYdtNk4/57wzLthKA4DkdZEo4ZVZhNXD3v+Zf/3reRfwruaoBxxyhYMOwLCaS/HVcIm993zRo+/Newmeb177L19YtyYWWXsntzal7LPrjicefu89d9shVGdoiF7rigdd+dKX4uBJv/n9eoMwDR0bv/3zX2sd9t5lx8ff7VaX3XcPJq9JLxn77bHrg295QxWh9l/85XSh2JIMDN2nBDYFNCH8NUiOtWHVAySR5qzSc/CoDFdy5tKsR5nIqqzKIKl1zYtxA2W4WbJ8+Ve/8+Nv/Oy3xfQZ977LwZfabyRddGZVvNy++/Dkaqq/+stff/jTX9e0qqZluOVVrnbba14pM9/7xW/95rQzTz7jrNd94osiuNsN/uGGV73aGOMQdRAbDbJR2qo0G4DKB8irNUhVFPRBWgG5RU81B7QRa5NJrWOVbaW9jLBByVoE77lX4tWkgIlfIqQRcuMZKYnOwgPp7AvzFIaZy4YVxgIy00lJ9Uz+7hoIYE4+YOiBSXKypUtETYthhV76hcG6G2Y5WDdaL+Cl6IUql+piCiHKYHXReWvRrVImZmswj8wq8dmQ3IbGy+ZC4BG3u8n9b3WzLbYi6pMn5zzo9jely5n4ye/4pURHS5ns5N//9ePf/AHX3OZaV3nYnW6745ZtFlOLbmy7cc/bXv+ht7k+rfjqj3/xbz/9qVUfwepBT9gHku34Ix605+67FGZozkPJG1zxoKteej+OkWzNNyLLYguLE32IYharIcaStcJWcsrZZ77ji99iHO9yvavc61bX3nko1ZlZumWbHZ90r7uSnMymj3zj23OZnAfJ2vfdZZuH3+4mxfTbv/zd577x7fXWfvz7P3v+errUbjsdccgdERvZGF5d7q71YgYK6zO5JPMoftKvf0fDLrnrTre8+pW3s2qDMK223W2nx979VpfafedtN8af/v73rIn8LnYsk5Zlv+Z8NrUmynJrvdQo+vWTfv6lH//SVJ9y6G2vc/ABY5ZkKnffZ/edXvSwe9K8351+9rd/9LMxvBB3wkoRwFUuve+LH3zv3bbbXoGaxuf9t7ruVS61+R3Xz07+vfjmvKpZie3FxmtYaZXhdzcnA4Pc8mJz6jyDomvJHJPlsxBRekxulpQiVnUALDQqXMkfkQ5ETdbAVBCTmrRByMuWOokGOcMnXO7Zonlzlj7xCOttiK5IVSsoNbVkaLh0Wik0lN/8ca1LnbQuFi3F25FYljgic4JMibXHhZ6TAIbR+mhMN5Kx6Dgod9F0jR7JyG9ttnXSwISglCoYIKilMx2K9OKhrTMQyIYMSjNXbSGNEQ8TjFpLVOUtXPSMGXxnmcW6IiI0u1rAVPi26ex8iqWdMDEPOSAKpdPoTspYbNaGlpkB6XydM1pk7xouqlGNaQqm0rIqLJvENIvPLC1VclCYd+kTF2XS68IoFJ2LzYYlFEL3NAxZsIhHoDu4d0uqVbGVK9E1RVpmRKYrulhHujvYK6JRZa6Mq/GuTnrBbNGL9GqNtzGDl4HMCfVEak1DsreEqZtSUgNBg3XqDxWaKEihluymKSqABjukibkW/fFPfn2PRxz+yc9/DsCWjS13vs2t3//q4x9733tss7HBnr+3/8cRYPiGnfakGbxdQN+q5EwwhBAE4B5kqBaYhCw8AgbhKbKSF0pmWIQ4SVCzjAJJzImeCw8AkSQLFWGlBL/VteyY0ice0BWAgOTSCqh1VR4gunVyax2rOTea12V/AUdDpAea1LWNvQg4FaQfuVgQLFvaRGbhBC/ho0thkoHM7BAegDVhwXcGl8b9c561r6t3WltFQ3S2geqFDsqIIm7hcCavda/RKyh6dvoER4GBmy3RSJWm6kGZKM4sGOCrkMFT+5xzj47kdAwpthhkkexABrQVbXQ/XSyjwguiepr30PTBpSpq4eLsYh18RxgFEDEtpqaiCi1C0VTAAUvC7Quw6zp7bQzdoD5YF+kZHiyj/ASefgxFs0gM4nwFLzEoUk3qAAoXKZFEK1BbGbsZRAUDokqvQqtbYI6cM7tqaAmBUrulDW6lCWiqFufdmBqiZMISKhRL5AqoCsIOHqx5CporPVymBGf6MkjbRIuKUg6db4MtHipRmbI2q2E1l51jTE7IFJ35aSFfhsAHYMXFBhcPMEDVpJoVLSZLoMXZqa2UWdEAiGYfvBWHQGi4CY1eXkRVUYdkFCiCAWpzbV2cWgGlx1IpKnJBuoDUJkBCTouGD7quskb3tq6NTMtwbQwuoaQdUEFSXwBuvetMu5Ne08AhsoprBa8U76WZTVUXeFPDimtpiZgTdJy8rp1sDVbQ4iIpIDKbFhsSM2Vc1KraYM0KD3oznfzY177P/r133uFON7yamHhkbzw09UxuJ3rorW4wFJt6fPXHPxHrxF4lOZ/tspfaxwkshVvAsot6Wh1YCUAhP/vNH+u0+awRM21vRe53x5u95akPf8Xh99tu220zQE0SFLM04XHVykbHBWde8KUf/Jxd1z/o0le7zKU0tXMi6B5MdJdtdrjD9a7J0d+c+tdfn3JmkhT8AFlegH+4/AFb6haX3ursxbullrL3Tjtw9NRzzl/mCmfqNQ+83Kee+4RPHX349S9/AFpqdOkhi0Fymb32MlUaf+55a5MyiBotZKMIEEPi3CVIebcCOm6KTAAAEABJREFULRIiLYLzvac37rY8mLhncroUSM0o2QiN12yWDDIH2BYrTNVU5Es/+Bk7rrDvXle/4oFCDdJ4+5rWHHngfvteZq/lLyx948e/UviCKZIyj7jbrffYYdu/nnfh6z/yhZe/+2Nbm++3+y73vsWNN5gh2dG7uVdENaTKDJnc+sQ4hdIIuIQtZmuP9JwjJmdhWk4/keqpxAFJRGV5oWkpXdFNtFKiWKhyNfuFqNBYYSFkijPn5mGYzZapxCfAYRSixCVQ5wITFwlPStAhYRBTLBAPhCMZK+2WpFAFSpoGFsClD9XB4ATtisQsOqWyDnc6knOR2ZDVhYmxGJMum4q59gZXOZjQpwPhXDyr7bjjdtuPhUOnnX8+EwAQafalf//JBetpy2q4/y2vV5g1JcLIV41kdsm9bnb9jaESqa/88CQaL6kCweZFstWNVRMldJkEy4ch9t15+dKSZMNK0vD7P53+o5///oe//N0PTv7jD0/+449O/i3bemZM6r//4g9nnX9hreVeN72uNZVQZaPkIXbafvsbHHw5vv3uL3+/dT3NOWWdUfFP177Sza96EPtP/MiX3vnpb/z7L38/1vL4u9xmY9iGYHkSJeFPEiZOAg21Wo0xUK8bwwDgjPMuPOWMsyRZf3JTZ9ltj53++cj7vfspD73bDa9DOli6COFWTr6oMV4dBMRmWMA++52Frgfstdt1rnJA6ayUNrIoQrmFXPpSl7j8PsuB4Ws/Ozk6aUOmEf1FzHUOvtxIA4TvVZYYYyy21w5b+Pkv55yvWRvGGcWLwti32QQdEpE9IG4IpX9MYVPeW7Na0EJh4lojidg47gK+4WO2oZNF6l5mMaZ3hpCDWYIBUVfLiiq9BjKMkxBFmTJ9lpxUQpBAhMKr+UAiWs+SLlCIUULUcG3ivc5R+BvkbBFR6rPoBT4mvVhYlsQzerg7+QMoWwBd6AwG8cJabcluCSzU5WuIctsfNAdaGBpJrb1q1Ax12uvIluFJC1PTXfqcvQkaxVXTyn0iK7t9btJmoybLNC71jJCkb6B33qJPMXs00CTlU+IgWQchvrGMF6iiqrB+GD0DwRTxLO6WtF0Kh8Ez1ZBhsNBhjnH2Yes8BE8EZqZGEUnIaGRoco4IQrTVMhF5BamqEMIZQ2aZEXOf556dBnUQ3DobmnYndrM6N44kEaZoa84kP0bDSr3ylg7eTLbCJhlcB0iFFrVedK42V3TlOtUAF7HIrMD9kABkYtnbxCCmEAFxjVyn8G5r4YVkiUCkRX7r33/y6COf9off/wbAJfe7zIuf8fQXPPHxe+6+a0DY8/f2nwSBHVeVlpx17nkXTufVMpvAmFU2+fJNCimlmgVMteWXVYzG54cRkZPnOryTkUwURY0sngsdhBwAb+gDW6uuKxNdeVLINrmvIwMJIAUkyei6TSlbCgZSfKFsT2mgOG7jIL1VSBXPaMw/2kUzkhNIfJWs4RacoIAyOXvyJavqKCzaSzYXY2rPzM6VwVi5FtMmzc4cK2WQupFl6KV4XWldDXzWNQDSaZ46OcwkxbLLx8wds5dguoKaZgxzDMtTj+wsuzF0VkSBGjgtork7ImgoG1HswbroW9F5LOVOZE2Ybpgts4raqLZCqa4ZBHLJnpVjkNAqsmGgCGwCBUBYPTGolgIrbSjrocxqyTrhSYWaXbIxG6ESoumw1nRe95znmj6KjFmHKAO9n4p6QQqSSGvhxmmULwpF8jWKzSIzoaDySHTOpSUZThv5w3KCklqy1igsjDOybRYqiAzGPRmoniYiIB/G8Grd1IFG4Un7IWAJsQ4+qV7lVEozIm9FZXSRzAaSKn2oZpzNpSx+ZQjyIQaXmqiaihDRKKNzfAQfEktR5fSQcNDwmjmmDKg1hwoVVeIWWgIVMIB7TWmNJM+OVjTU0shg81SBFK9DjrUUXTUd1jDepJWkBZwjoHMgdKhJGwQCJ8rRm8JtYPFDD2lN3bl/zrmcpiWFRowwE9quLI4YWpTeaEZVHSsGDVq1WI7oCwt5zowxTLigIse2VGYqytDgWh8ySvaavQgNFx0hAxDWnDIuaqMx6G3ofP6Ug9mPT/49+y+x20477Lx9aJXkemFQyB9J7Lf7bpfeczdO+Mkf/hwrgmGywISLLoYltDPvylhqJXWHAy+z3/6778zRN3/2y0e94Z2f/Ob3/3LGmUyVkLL9Dlv23nunfXbfZUvWYUp+51m5U3AqIIpiSXV/OvWMM84+F8CVL33JDSixJawpXUGzQjNudOXLcvSCuf321NPDGZmSeRFAUBWNoTCEFjGIDmrFRTkdvNHayBiSxSCdQFEXxMNO++s53z3pN1/49x9//nsnff4HJ3375N9EJhdoyDAPdV4ZIxGbIgCJkN6Wlk2lDZthnkMu7DnN6DN4oFvZaCIAhA7JqDlESjSLXponL2xeqcyxkgI+fWeHlvqNk372xR/94Is/+RHN+Nx3f/LZb//oKz/4RWcMgFNOP2tRnV0wRZl32G2bww+9bVH5zL//iA/7h2IPu83N9tppN9A8OJVhXsu6194MnRq7S9Pk89BA996DRaaR7uGkYWs92ixt5qmzZm5azluiHu4ZUXryRtibzMqTlfBexXRhHi0Ga2uqRzhvrIv7oF6qe+2T9gmx7jwHCqALGO6R2mXoyUc2IHJggSDtTVgtHAOxriZjFbOAJBGfqvRNJQKpwbZgG06h7LX0gSejKcuMYC1rmEFsGNsMkjw4hS0CczAvulB1AY9FbktkOTS7oA5SBRo/2iT/DltW++21EzClrrGUJCySOnYYt1zjgEsC+OWfzpjmliAZjR/ZVGniTJSEXkhSPr0DHaCUHjCkx+s+8NEHvOj4+x5zwr2e/+p7Hf3aex396ns9/8TTzjhn7vX7P1++4thS7Rd/OP3z3/nV5777q8//8Odf+f6Pv/L9H33pxz8++8KtVHHe1vX551/QF68SJUezw+7AQG/3pzPOOuEjn+WEf7rmlW54xSsWBY0JSbpblokcuaiZ1m1QNhDlFle/kqn+9dzzH3H8m0/46Ke//bNfnX3uBVxgZrvutgOzcqdttydLZGZNJ9uHi9bzlWGgAQQzI5H6g1+czE4p+s0fn/y57/2Ut9lf+8lPv/aTn3z5eyd9+Qc/a76Af+qZ53AqIyLEgbOBsVYGNZkvxhHWQLXJJIWDcyehECgN2iTZc1FjzrXKmktQxSJd0gmpSCFl0kzTVVto31zh6q2iEwgIMyZENjdahpnrkjqtJxUIi6NqsGAIeDdjznCKeVHuudlMoooYuM0W7pU6JtOXMnsXbtLp4Lyh5Bh9mDsmMkQiJaAh1k1dqajWUAOkLD/KSxZ7WkOniQlnNtWm1hUp8FDOThPWXKNmV8ZLrBY1PuAEpllmuuWD8LgSI5Q7lsyqW7PMKUn6KT3gOyQjyb3WchFGM3qNSXWizo5gpRmQFt0S4sVTptLnMk/FezLm8GXjS3CfZRbqEDaweIqRcdS4ED4wYC7GHbVr4RjnFFubNo1UJ24FIYlc97m1np30sJFlyZQ4WMvShYYJP3IEGURhWFO2mqTTlnCnQ1P3mGc0X+LKmhQzNHtBDp3bLE23LK4b88IlAaBhcAP1Yvm9ttrqqhfO0TQLG7wIKGAO3RpwzKhdNyQ2cjbGA94jIxPqfVj+7ksm83uSHiW4Thzf/sEvj3jW8087/XQqu+G1r/3mFz77lte9pojw49/bfyoEiint+fMpf/7zaafpGOM4ydhR03mYqUwt0lDIxeB+Fj2zzxZrja3u3t3clQEH2SxRFYMyewXdnJs2W1iKNGMCdGCu3TeTj+rmYW7cgdJ1FiXXunRHd+3zgCVhJaXMspzbE5SR3azVAg2VhCrUQImZNHIaZDLt7FzYFaqiqrVIGVJXwkypElvMN3gbrmPKAFRIZTrTNFUUEeE7ySosFuxYxhRQukMpUgMIc3oeDa0Jj4Kd/jlhKUY73KQXZkU2ShYrMDOBCBf0qefkYKL0iMZSwvmRJbKCC0W0oAxSBwGrI5PROiQWF2E0SgBNiSJZKEIQoglkgzPPunHHHmLeIjFUKGsuDD5gWmFdeZhymYNwWzTxgCfohURo69YTrcBHcHosf/No9vDeOamEFGe8InrM6bwTbYzOzDNGH5PFNOiaYihhg2Ec0mhOT1mDYWh1bFts5oG5mOWolN8Gd20urP7C6uA94G6ZQ08loll7ijsyUjJHgowZ0ZQlbw0W+tKrdYZdF9TYkfySnlCpjYKVBDUZi+1qDAnkwjHRgKRmMrgqDp2wuK2waitTThRyqQoGheroadE7XadZtJu0nrv31glHFyRZAbEIkzSBiHSxedk8Bo/Rdeyc0EBMy7JXrDtaMDwYvKjDIztSVBkZ0CzWZWcYC0PFOFP/oCpcKw3cuFodZlXCA15ZmBQUIEwQZJFxVuoa+YCgmFSQVm0TMQVEE7RwiKgucAsSquJvF1Gl+tKFd1o08Yzz1xw54BK7p3FOURIfliLeM1zGYttuWQE465zzGmzK0pvw49IS4olsESluVW0YY9ttx+c//J5777LThdP8+e+fdNQbPnD3Z7/m0Gce+/oPfOovp545IodEaQyxl+iifZEDygEIss6nbj1va1s6L7/vXrS6UzxUkoFLqJOFu27+SxvQnnPPlyhkn4TxI5uJEhZzbmW1ZkCb0Uk4h9gkBEsY11POa4tv/PLkJ77hXfd8wQmPPu5NT3zNu4541bse96q3v/yDn/aIZXIKmJdO1VAke5bmzHnekVoG8QUJJSjZt6SPmUJmFPZvDc9lfjSzeQnfMENUokI1LwIuMtdJw/na/3LOBZT8/V/99oknvu3I177z8a9+z+OOf8/jX/muI09861P/+R0n/2XZJc887/ykpyLU2BHNcaOrXflW/3CVTkGJm13tCje66lWIQVTLYgmJrvQeUw5TH2YmUA6DyAqTyuzoQc4ywNWkiLHQVAxFzfhBlMtBz7v29YD1YDN9zR7Ob4uiuzbe/nhJ34xbhvPmS91ZkGHdIhnXzCWjqaZ10iyEXZUGEzEtCquNSxspxLgyHoGQDGtamBwgSu46U3lIvzhqoDmhQo9aWg8NKEJJh1X6yLVIZ02KrYoLNaYFkMRFl0YMs4xzDjEWWZICdtFIQGe+X0UrF/7l3OU/hXeZPXe1JTtBsyifW5yhOYFE7rz5X/Y//8KtU+/dNUn4i6QA2jH2zoNtraJF5GLxy3Ash+iulLd8+m9+LvD12tsf/rr8R4POvnB6wuvf87h/fvvhr3/b41779sNPeOeTXvmOJ57wlk99+0dcM7e+3rq2pQ7mojdwyZ13eeTtbq5LSchL7rbzY+/6T+M2dRhRtCm2gpVIADb87VIhjuvADa56hYff9obs/cPpZ77mE1941CvfeOizX/aME97y6a/+cGuXWUeFrVx0Ht23dILDqf/RJIrMDHVfx5/OWuD62e9OeeKr33P4a979uNe97XGveUpsEgoAABAASURBVNvhr3rX4Se+88mvesfP//gXLjqbN1emCxpJ1NmxNCbTVktfklGbVMeYFxuaplMVr4iaiYs7IdZlYAqlQVRoAdmrS9YEXKSDjBDutKlMUQaTBUiQmcH5zEmrMhSrRlovTsFChGVNxBS2QOIhbpYrZCG72GfjUv1YRhebU5x4TiYJYYWbKZfspjOUwqbRWVkHlyKDaHUlEYK8pMAi3DsE5AnPEjbkQFA5sa7DOhwsCtGVIhPoJgHl/AKloUV1gBaMIrSR/ngDJuYGKwdbchaL2YbLBmfRc8rHRsGWIVWb58wk5SIkM1hQRUw9zR1UVhU0NJfa62IyoKqvgjWVzoO76jBhI7ABDCWlzlwG4R/Q4A4NrXOhk5asQSmTCm8o09U92mb6DolKIxL80zE37+wutfAEo4LSYC2lgFuzr7i2zJDJIobZrNMSmyVdxDxKd6DxnVSwuVI3BJpSetEcVEsRN8SQZCRhQK4adK2YpZBRETVQvIAgkAcaBpSMyjc6J5jPIDpcr0Q+o9N8Z7X21IxBUKEMLl8aFZ96+rmPf/6LTz39NAC3ufVtj3vO0y+x154Li/j5P3H7/0vTZNuNFR2PiG/96BeZCJ64NMK44WyorCQXNocCJVgvUjqTv2kkwCFlAe/mKS6cQFKJsleWUKuC+xqQyaIQFkqOhI4OJU/hJXvlkGdfGoV21MjRfLBJyflEdsM0WjDzTMhLV9AWgL+GpoWjUZ1Z1VRalAiKk0iQk4sJUrSUtBpFg88XMoVJDWYbTxs1Q2NJUDI2F3e8eetwkSxCfWFKm1Fgg5cSugglKqBJIh3S+MwmStW+Up5ZUdemU2FNVbBYKIGCUhy3wIYFmBDq4zoEk5XWomQV/nZcZPVA0wqUVrcE4QgkNJNl3gRewXu3TGDt/cKprxu/mYo5I7JJTwMfZOmG2UiD3JDaXXsnqoAoXQD34SQmntm691iOnyyFsSC2FJNGHDqiB3poUK+qg/tlshLNkT1reI0g6LZ4NxAZRlIlLLssckV6lQR1N04SLBdrQi9BQ4DcFCqsIuIw3TRKdHTb3PJFlgUC4SDVJysNZRLwEgDLFKsY4+zFe5l5b8sDFe2nqQYq49ssXtSV4HoGjQCyREpSCFs6ZhhkMLVSM4feZU5pVKrcbsZVFNRc15jI1KaU4MEvqDGzRndFcDhr7ZxaLSmah0fxeYioWXVBwZcCrSJ1TE0qNY4mSg4pVGuV7+maZIpnj+xMBuVEi6xgwAFPmZBroINmEzoBqvQtvRV3yQjhhdIpkwo16Wlinmp0kYAQDxfQIZ0TPXtiiTjFLM0XkQXkvMs8k1ocRVGjFIsoKVrH4DdHxpLvoBhVLts6U1SJQAg/XdwYHX6ORu9NGkgzAw7a71Ifev5jnnOf2177wP23WY3nXrj++e9Ped3HvnDI04972Ts+ySMfEQOK0PnuFwui3M4fj4yLeqTWiRHkeKcJptCgxwordtGEuc2jYsN1xTmbXaYGS1pnhANRxDWCWjYHkTONFwHlx4e/9J1HvPJtn/neSWeedwFEttuysd2WFds2q+HiyfyVDEpyFJL8tLSkgkFl6OAXQrUBPRLO8aHyTzGBjsRRaAJdTMbWOsxBDmAjdaWLNPDSEO3KG35+kZz8vKp1+00btt+y2n7LxkVtu7+92RgGUUnTNAuHzJJb+zzNXMh2znnnh7mw4hSBqQvPHbSwQsySp5JlDyYyKovlyZgWnv9igA4oGCxHw8DnkGatCDGj4YCoE8KAzSpgFaq9l5xdYy4yMdDCvBLPNM8aUIhxaW/mZGsD+S6CLulkMX+AXDi5PNAowBjJIshbJm9Mm+YkTwTWmhdmOt8580sycdF1ER0YM340aAFRNjqq/Czm4F187UVjyBy0Z8lcRpZBTVIYAzibgVmJWFw8hGD+M4s8EdhUZKqSognhHKczIRIU1izVjNJa771xDvI/LANWUwwuRRW1BKODzbhjueg6fXj+Aw791rHP+rdXP+ebr3vWN1/7tK8f/7SvHv/0vffdPUprm8zn2v+I9XabDPzbKwm5wf2XgacHNlvOmsxotfU85abR62k6f9qaNqFMwuMmiHQkaMTFZgSN4BOxNoe3WvURd77RR5736Pvf/Lp77bwDZ5x69rmf/e5PnvWG9x/6tFd+6we/BPlCeDjgaPhvrmo6Vh8k29QYc46NtWy30JVGkq4XNb6/qK02xiEIyGpMq5y8tExWBqROWuYqupLYon8LFDFPda8dNnmSAMsCepHcreDhkV0zC0wsbZysThr9ojhroM5sZCiftPN4gBaGoKmcrlLNWGkZxhBSeyUYpG4GOEu0McXAyUKiKrJW19qYqAmGviCsM9K0xiEpSAtyWAFZPi62e4XUILOUXC+GoomSlEh2xZSemSKqZbBRZeB7h3pSAnhll2BqTSHRQzIWfUItFw3TOSkZI7pZa6U37RQ3ByfL4DKQeSZDLVWh9GDgr2KEnDtTyx6RpesI+koPhdINWO5cFsuNGOgodWDOCyUF7w8LNS9+wJMp2b0HppRFZe89ZC1D3xQEniqkRUjvtbWxtdpEOnUVH2lSGh/+GhGQlvQqFDWE5gLMYebZvLiMOa315SEova3SVs6HPENBTVoxRBmTtW4YdDCtKp4lmeRWuzJwWArfuuZabAY1VnFgjtphHsboj2sMcw6R6m5Tt4U3NarakKZ9yF4ckJTqjDPUxMCa1JcwmoxtGNtqyKpA+PzKt771d789GcCNrnudYx7/6I3VchTmx7+3/2wIJLDN5o0KDfv6N77VL9RpsqnxmEpeM/NSAP5wGgs1g8ssLK41SDgpUllaeMfaHUwhnjcELuBCDS2dFIHCUgowkKFck6KkDcVAF3JiRrboczae/rCysiqkKi3JBBKiaUuF6sITHzt4fJws3QLg8XReaat829ADpGbyMDkI57RM7qIutE1FoMKteV1jzt6REqITsOZuKYnsMjdsncAHBNEn80miJag6JF3DpcGZfSyVhX5R3EAHuI9KVdWhg3dMkpJL7aqyuOZLGfDMJkNnkUNhvcAWzZHAjS2YPCs+TGgFTK8p+8ySszWlQyyZVuHZG3XmtIppxYJfPRQcbUv5iM7U4rBbD13MVCcSiDQwRUMIDH9kdAwI4/bOzdcsLaO04ENyJJGvsviXc/d1NJemGoOxYJak7nBXl4pRpNLYnEpZW6VysGpNbtGUFYxHa2pvPXsKIFKcRAlrNMYS6ohokYtwG2Ydp6iTWGNRGhldGS35RVUxhRRBNUmCQbRpFh8ruomIG2YFIMjFi6CbcwdP0AlPBIVPuVkWo+vsw1x0XqGtqDFZ3DB5n/hN8FpKh83pHd3Bqlqkp3qLuQdPf0RI+DSoKiqiBPceo26tnU/ql+DRNSN5xrANwcAQJR8gz4vHpfDYImYgi2kOuHkkvSI62alMdLQ6lCKqM3gPlGGoFaY5y9R0isFh8DBPcZVISDZ2VGxeCYQGarPSjG+6cB1I0E5CLjnE54NDT+YOHdWYtXATD9FUX3BnFDbFgGmR/NArmrnUWlQ5cNa559c1956Zn5igVopUScnW+oVb15yw8/bbcgMraIVy+Rm0TsU3EgONnSJ6T+JonYzEWFa3/8drnviY+37iOU94zWEPeuAtb7jvrjt297d+9t9e8d5POJ+dVZ2lxkI88EpZPNZWt9iowg6cdf4FiiXKQwO9IgZ0JALnnHPBMgzsst2Wml4VtPaintkbQ+ggJXtqFyRJB5IFyzUJvR2Lld/+6S8vfvcniMm+u+101H3u8I6nP/rzxz7z8y976hde9uRXP+7edQkfUmKtjEnLQQtjtAgACnJI5zHLuEcKMQyZlSmZverQxtU8lHUybMts4X5dp7SJojwQmSaM4DLEH9VicO6LtuntA2563a+9/Fn/+rInf+3lRy1vXnH0l447+svHPvdfX37011/xjDcd9VAoYaUQFXLG2+e//d3Pfv+koVg1/dYvfvfRf/03LM7SJw+RVm0eaoyVp5G0aOnzDJ8H86WOjmOU2mkD0QE4PbC5FgwfveECQDKZIukpWaJG8OuLkgTSWLO5LNkPdCWgQ83VKKtCbJAhXUqa6YClKQaPETGgW/RsmQw3/SXDEhExua8ZLOnKgQgkJZgk84PKBZuXML4FMjSpiZLZNeYhW7EUAkhznCkoMkoOqCXHjLK5ji6IxFJHkAVzza0F0+I1RxVeY9KplzBiyB7erzpYX2BCWzAnC+mAHMyM94GcwK2w1kEG5RGKHy9qQhyUJgypY8eqO/lxsdmbnqetbGPYWMlG5e+6GlfDihuZZjWsBqOQA/bY+RvHPedrr3zG50986mdPeMrnTzzqSyc85UsnPPXLJz7tyyce9amXPn6/Pfaw9Yi2McdG09XJp//11Z/4EnHcMtTTzr3g+Pd/aj1vBZEtcBuCGJFlcjECRHheT33uEvSr0vT9993zcXe/5ceed8T7nvGYp97/Lje60mU3hvr70858yqvf8bPf/warrVIpzZVFkMZtNqHPKJIVFht87KWLg3e73jW++Mrnfu5Vz/nSq576tROf/PVjn/q1VzzjS8c9/QvHH/XFE576hmceVgprgkbZFAHMvQ1TFNZdsLJmqV5tEsllWNCytqn6pADb0sefbFxG0kaL3tAhsKwFlQVUVmLDmrwW9xCgQFWLF2mGZXNvyQAurac2taZl9jJncbCwWoYiVVKVdEsN4QQJW4Iv0cODznctNdV6hGvKhmrVYJI4/dngzVKMg9SionBmQRopYKq8U5GIZAOUqePhYI2oKWPJYtSG0JpWk4TktJi9L84xPaeUKWzyqr1l75muVFdzXmiaMzW0Ct6fNaKSzKW0SE1SPOA9AlqL1UK/gsUvvVO7thhoPVI84Y1jJKeSsbksLQUMgi540EiCSNd64VDS/U7DMjwyHUp8FCXZyCyQCEWWznR1WNAEiiyzXAxiVhWBZ0Z3SrMkyUs25WbXuvQQFxENs3lkwCz4XjWW8CVAM7JmGXIYclyhmMyJKQbHKFSULhHRrbfSZkW4JVsSKQhxlrkhwO26cMS7NlhTTUIfyUdexhu+0DmQPbTLwhkDi1WxRtTpVO1Sm0hKh/zij6e+/+MfB7D3JfZ+7hMePwyV7//e/nMiIP+VWT/+5cnnnTdJlOiKOWz25Thi8CrkSjiDDyH9OhORGSDMxJDsmeRsCaZVRHOfM5yMMIVRtiB1SLHlzxgyMpdi0WlJIVVCOrNXkdWsCrvcmueU2UmmqlPJuWQnEzWXxMFihzjgEIiBoua5TFurO1luahqLrRzOoGEeSEG6CEVMhpkrlH2RLZf04KRkgieGCGEFyclbQwvTJMdT5+SZKKmK1UGr0CGm3JiyAahyWqYACQQ4x4xG9QWlTrlAMQwDZADFwYoq9xel/+qF61LFlSa2dfocESDsYBHJdFqoLuqwLjorzXeCKJ1omQoKZDWVoZlSMzO2ttQgip1LEzKWpCCRAAAQAElEQVRrbSgSwogUZcEUK1BJC1acUjYNEWHE5shGI0pJK6JgSWAZjcaVg3FWLWbiLE1ZPISbmnWHe3BiivQgTBSCLAUro/QkChEye/RF8hq915AhTRdIAk6lSMswiaJdJZGAh7Jn8dWDY5kLdL2X3ge4SnBnGizoNagobLnhohmTtF4mcieWfU6F/KjqxiZJbFtG7+5BXGitcFdoPTcFJzw3H/lMnWRYDIGoCIi2FNa8xFBVx+RnrgRAVxOSEeERjAS6WXIvkEDyKj0MCJHOI9Sogk4FAlUrm5lBtbTQioumZjJMM6Ys6yyNeHSxtCC/K++2OsMDIBdMsgfEtYYaF6nP9MGzN8kcVAZGh9Jmbu7OzUHMFU1kITJnB4Vc1BxoyBnLNp5FLneJXdl/8qlnrqd19pTs9KNImtBJOfuCraeccRYn7LnzznSIbDVygp8BmiR0G5ZVe12MQQ+JEEf32nLIajtuv+WaBx5w2CG3fePTHnHNAy7JJZ/93s/+cu7ZfKLZhtqzbEoCY9wGEsIusd1OO26zhZ0//f2fi1PqhD7J5GgpPTLws9//kaMqsuvOO6R1FMff7GF/9o7YnCroCecBQYX9bE0syPIyfPl7P5+7b7cxPvch97jLza5/6X32LlnT+fyAhK6cudkS0hkcUVO9WEGo97FHcZWk0EwJDdVmEZxLU1rh5hiZSQkhCAuMyTOeN2CCLt0cAYREgCFG00vvvvx7nl/85XTvnWp4EonOo0k2BphaLEGLlCsWhUBI4vSzzz3xY58DcPOrXeG21zy4R7z9M1/9459PkSAVHEhWpDBhDi8IkFsxTz1bSzQhc6oIlgZxx0z+elKhzKQDINGLs1B3Ia5bOrh9I0qEIbVk8l5nqSLVIbRLCr2HLL+Mk1RJdh2lmBYxEyNwkoNkVWrsvIn0DCuoVYoSo6XCugIaFL0s4q1xUa0GqzDDcqXBzWYZHaMH4Jk94FGTZTQ34yBBrrIEjoJKv5ZVmz+R0TR5wpvH7DVDFr5ujgjCmLIJw3D5S+wO4A9nnnvONFMelPZgM7pWeilNfv6HP3MC79KHVU0+8jLhx4sacxSaYPK58MbHu9Kri4cS4ORaRMUyOKurhZqEjS4rycvvvStnnnL2ebxHokfq3FKjNleH+RIKnczXJWYNZJdwyIVzP+HDnzzr/Av32HH7I+52q6Ly5R/8/HPf/AkCygDZwPvQTuBMKZlNMj0UmsXUUNKN4lNlKLjkbrvd7YbXfcXhD3re/e/Ae55z1/PHvvadLo7BRXivMnH5Rc1Ssw/uNRWsXZfba/nnaiefdgafXyxqBCkc0AQ34G6eNlcs3AWhyItEbL4yeckaUogZaehjOpdeNDKLNcY06QdXsG12ZxEfgiTVoBOSYkANqYHBZGARlAZWsQZaIBqpnkQpggdiSiOtw7v33rN1kDWZ4mI9EEQ3FwwIkzJjYgm2SodNAac4D0aKroZGC2JYyGbuS4geGakLWVVRlFTh8k060uaSS9wQNMxgppzHBd0RSHPho5YSAlqgq2QOkClK6d4IuhvFIkRddT3l1oYLUxwLr3jDPrSkqijAMGFoszWPyMVz9xZB0oYaE4wahh4WQR1u6yZTE++CDsLUaTojIq371H2O7Cnp6Z2iBHORtcZamGdVoQySoGUioSYYdK7Kk14OwAoyCIzZt0qpec7Z53z2699//bs+9MRXHHuvZz3jVg996K0e8vBbP/SRt3nwQ297/wfe9qGPvcPhT7/9k55676c942FHPfchRx592FHPPexZRz/pJcc8+ZgXHfWCYx//3GMfe8zLnnLCK5700pce9bzjnvuSVz3zJa886iXHPuUlxx710uOeftwJRx9/4rOPPf7JL37p045lz0ue9LIXP+W4lz/llSc84vnHPOboFz/kKc972FNffJ+jjrnDkc+53f0fcqf7PPAuD3zkE579gnd+4APf+uGP+nRhMcYIcDpK5rdmjAhaYHE6CC+Im1oqkuGnv8R8Rl7Y4iWvev20XgO4913vvPuuS4Hm+7+3/5wIMHD8evci204//dST//JnUxtJ1HDtM3oLlr7Bm838wnZupfXqmhyLdPfmNkXhk4KGbuCe2ZA8qrZkQhbEiKgSIhRfpVVmiM0uEfxsLjxQw5mqmhjItSIcaa1R0ZJM49gHDs5pHhVZOUOgCjfpEJKuzqWsJXm/YM6nnKuSBaL8IwKR5r312XMGlgII02VHKUFHQrdCeDByncGqulJsKaOZ0innybPQMsiobYhuIspj+6BVQ7D8Yy2HeqZ4K0x5XUoGqwR6oHehroYCMCeiIgYmeffa1kM0kQxLJpQN06BbVzkNOQ+yICQgCESFmzWKWFmsl5JaQyTSy9SGOTYdtjKuhjJWMeYdp0cW7WXzhAPmZfIFnVooAeiarhzkQ69SogrrcK0og+gGkucwcck0/ihCkgqADPC9pYxStngZumjvGWz0mFU6o6AV9bp4mImEFFENsQQ3JhH37nPLucE5Bp4fKTeytZh6rLOvI1hOWFJKzBI9enrLCNrOwwdWtFd0FllXnwefVDIHBsDCLFZqg5bBUddZePblCTiG2XI9ehuD2sfImi5QkaIBC6cUY432dEiKB8/L5gYUpMBpYvKEg8ix5apzv2K9J6EdiZpKPjRKN24Z6yZzD4ssCWIis7jnFJ3WZzJ2ieAaES7hbpeh5HhmkigofTRUCITCu6BTBMDZPBmOiqGq+oDli8GY1afNIX7bhzlpIGApkkYauitEleKJAYzMkqDICt69OtSQgimsZ+FRhlI2G9dGSucGGQPT8NoHX5bdv/zTqd8/+U9dHNKK9NG9NHI3v3nSL8+8YCsnXPugy3ZF54mHhvPzZhNAilhJGYKnx1Q/8rVvv8vzXv60f34riVXKmCvMq7nXtsNOO9zsOlcBcP7WrX8+9RxBr9Wpnj1spAMJOmvsvPOO+2/+2/3P/vsPL7zgvERTMFa9Ta33BvHPfvckzt91+233v+Qec6F/gR7suagt9jgqn62GJRnIKMrfRi2Jd4j84S9ncPK2G6vL7r0baVRNBvLfDMK7CgrgIBQYYEWEDFmWLX38ifRZOx/p50qgHFTppBgEjWZ00TYOplwFng7qVl8td2sKw1zcBzHlTCBBGDNFKg/K++4N4Lu/PPmUs/+KqrQ6sgVZhJmapmzpm4wkPz1ZXTz8he/5+KnnXLDb9ts+6nY3eexdb7Pjlo0/n3nu8e/5hF/o6INBV5qDdglfngzwgXp3MJmQHshJEAMwSqi6jc1KByIWTg49+YH5N0vv6EW0yhCCdcVUyxxjzsJfNZgozAQpCYVLzmBgwt1EyUOrrIkimRqhyEKPxTjNOzGSXlXGKrZKGEqxQapIIXBSdRigY0+dMpFWCQuQXeblBbSd58aezAVtGCSlIgdFaJmhTIsMQq4sErG5EJnRfZpnLE+YiKFE4Qn9ojGIqKJCxrjWlQ4w1XMuuPBL//7TlrrkrU7ckIZkerSf/Oq3fz7rPAD/cOB+OkqnX1348eJmCUZyXst6lhbB5dCLhtSrtKpEakgeL6VMIuFK7KrMQg+usP++JnLeev7Cd37IWqcuNSR7QbdoZLxEV+uMlHltvrHWOn/4a1/70o9+AeCwu97qVje+5j9e6XIt8gXv+uQZp52vja7IIBHm6+W0zVkIkxycZv/gD3+8+/OPO+TZr/zyt37qTI3N2C/H3m43v+ZVd91xe87+9R9OaQ5HMmJROnsuatU9e/A8HSkkyZUusw+An/zmD3/40ymFtIFNwoB5ZMvepYFak3K4I66bUiJnb7aUaKx/iExGl6yiuGVg88OiTpAaXS7uhohoGWw1ltWqWjVism6xbjxT1KZJU+gsmjgpaI5YD+v1uF7D57m09din0tdoU67DW080Vae6nlTB40HXQRgMsca1Y2eYJmhrwoJnMg9laypzx1qMDYyBWsxDm6Q7QoRPAjkzkKG2FnoP5qi4q7kISLPgCblkETEfYtJg5co5ivORBVmPTs1DkVGzli78srIWx5gxqCvhoWAXbC22MI3eQWm/RBvAXPPQnCzpdagkqKOQj6Qqx1g7yirLAGZVYOAKiqgRg0xq1B+ULD2VePTgJYxWhyc6gtFlA6qommHZMNXTCJplR6KFNM+YIQ4/7awz3vL+997/8U+51d0OfcwRT3reK05494c+9K9f/eJJv/r1Re2nv/rVT3/5ix99/zvf+8aXv//NL3/jX7/4xc9/7qtf+vznPvfZz33+Mx/9l4995F8+9r6PfOiDH/voBz/+kXd+6H0f+OiHP/zRj77zve97x3vf+/b/aO977zvf/753vv+97//IB9/zofd/6BMfZXvvRz743g9/4NOf+/RHPvHxz37+c2xf/coXv/+tf/3Zr379i1+ffNKPf/jxT3zs2S992UMOf+ytD73f057/8h/87DcTK5T1bhOE5k8sW1JMIEQbQU4AJJ6y3mUvccGFFz7zJa/67Je/woFL7XvJO978Jnzz9/afGQERdL8okIuZ3/z+95iIxZicyaIjTDEWD2kZ7puED3JYmbxA3yw+HLIWxkOGs4ebWhHWFyabpShkme6Zi4ZQZosA1AjAW1BxVavLpUOotjQe8VgsJVOl15y1eNRsA2VV7kDkHJd1ph+yJympXo375iiiHjlFbwKHJtM8KKKJziJd1av6IDAVXhm0ynqRuQbvusx81G4lvSzrXMErggItsSxgR02psygPzKwdifCCDrqDbARlTaBcw9nPXDfIUKAFSNoMExcmfyyFe1kyoBtrwjQTMUBYSbnPKUtI84igJK0wEAlowyJUm2ufy4TBS41C8VX7CKho8sRRRKqISQgLknBHLYIiImCPC9gkoKmWg8oA7pNZaH8u70saXZw7954Fk5JD1cF4KuSJV3yZN2TXZMUO+sNz7ayakkmzVLLUYlUVojOsU3CpBLhEsguUUlduNgvoL+3U2miwcIPKlB7mIMdonqZndguSxZRVe0FDCUCNLqXBJEQATk4YQkHtaZkDUCEQgkLPxGrXYQqbaG7nSI6rrGOielSyc5GuRKVWo81SzKt1hhtIUZa04DyR6AgJ2iqV/KzZeXQgvS9irXKelELY+ZmhCWpKQGHqhq60EUL3xIHWeKorQVdGpS+VxO6Rka2mrsjmBQlyKYyjUjgjS86Wa8oDhWgtAkto0JCiJfmdRY7cEhloCZirOlUVaudhgRaP4qSasG9QGbbfwi/80HpPJjY3tgRtNNPbXffgjWpz7yd++DPnXLhVBAIs5nb/8xlnvOZjXwBwyV13vNqVLiM8FAYIBns2W3bzbnyrSgyE74x3zD//wynfOOmXv/7N72uoSDotWdbMp5x2JqeuhmHHLdvbbJV+G1WxDwLOBBRDHW59zSvxwx/POPs1n/jiJJgRCfdwJsJnv/3jL/3wlwCufdD+u+62S5PskkhCyz4MpB0jAfZmeA1fwUnaZYg/lf6CuYCdie3gNwAAEABJREFUd9iOHy/Yuj7lr2cKA81mXlfZ+ta3fPpLzRdpnJtRMqwHetBnrgDzind9/HpRZ9eWmJJzxAY386SX80DVXJ9cDfoS0DmNZyStWVixkgNJQTSZJ5LofBiycdvrXHMsevaF09Pf+qF1iK8KQ6aiitJ9fvunvnT0Wz54zvlbES4tKO5T3/ze10/6tak86JY3uNTuu+6x0w6H3+XmAL7641/woThCg1ALLenaU7polJXZlhUpExycU+aptKnQFFHQKh6PinRylz0LharXHBNjNzQLMIRKHkfRbvSxo89lCh7Wwoc1tPPxEwMz+XIDl5YmDCXgtbU6tQWn4EnP1IpCFZ2hoU2zWjLjVjmMXrRpb5g7c74DfNrb+JbuAiBg2aM3HvV07qwZEUTc58oFQaMlaqRGCJfp2rPTQrk4WFxOgzv6jFy6iqkZO9lEFDmCe5j69a90mT133JZyX/+JL/35j2eHZ4qLdbF27oXnH/O+T3D+Ttts3Pjql8v0los09lzUstSUbNEjGecITYJ/0RADWIIyINw3GAOEpXMooE4+d1znoIMutdtOkXniJz538il/DkMfLKo5dxCVH538m0ce/88//8sfjaltzKL40xmnvOXTX+keN77iZW96zStsqD76DregYedeuD76XZ9wd+3daGBQ/OIudTEYvClEWe243RZS/Wd/+POnv/XdJMZRaUv37hFnXnDhNDdO3nPXnSPLLOYmpeo2W1bsnFoneOnBmVhgxl1vePVVMX4D86J3f/j8c7YKWTKLeMLJIH3Xl775/Le/+5xzz9aY4A1+sSWmmoNj6DGsA+vMiA4CTRVsoq2MTWVOz+03tmUPW/epjGDFU9OqsHBvnTWKlqPDCZOnSrAgJsZmNbghcKumCoruDpYGL+RoRIFUTTPuVl2E2TgzB4CtuTTXZuxj1GdFKOm7oa7uQumpCpUUsP500Qh1RGfAgZbRnO7VJLvDJINOWLZAcm0D9bmYKmu0ZI+2eCyycH2gQGFmDb2sZBhLNWXZTFIyVJsOG5ONs5WE2HrYsrVuzDpEh0zNJj4qSAuX6tzno9ZSx2GosqI5nUGMXkxyqFMdolipahUyZlT1oUQKUouC+V4ZrZhJ/zKAAEKSgC+SK7pUweaeZTorH0sotvYy9Zy9Xdjat7570qOe/Kzr3ukez33Zcd/8+lfPOfcc701VNza27Lzjjrvvusteu+/Gtsduu+6x66677bLLrjvvvMtOO+20ww5sO+6ww/ab13bbbvf/3rbZZpuNLVu23XbbLVu2YbvoI9+w/deTt9lmW9FN+/Bfru222273XXel3l132ZnzWTfaPJ3yl1Pe9+GP3eX+D3/8c17yle/++OxzJobHIEWJywpEDSJMyWDYcy7x1/msT331y3d94CM/+MEPZMROO+549JGP32WH7f+Lmr+/+9+CwP9VoSTvONT/kPLTX/yCewmLUlSRDckahrYQe7NIVXjBLMyGrJW5AmYERNGVFWtixdKCOoqN2kXWoj3RmevqIZnWcwATHrJoi+hrlv+xjqu6YaoB7gIcGUbhEUeFKTc0GZoNXWmeUkDxptGFVbWnMqmFdBQZqghacg9jMs3h6y5buXUKdPCVzgWTlqbDrNtMsTH1jQ4jiYvokEbBSzmowfz3NmYfkwwX2px8DxY/eM85WFyHxIbmqqhLDZYiaLHuihZDi1XQZTFNkSAcgtRJdC2YizaxLpHZBaHiogn+cbTO5zSj1y3YGAkMwZt6eHpqOqciBW4Wpku+hmbwQUE2gNUn2+A+EAFQUUJFuGjBRlmvldhZpHHDicxZfW0ekiBKWDxr3TsyYUINPBRmR1lEOUpwt9DNieEQnk9cDSt6bfxIxIt7SgtxGpOi1KSWSMTa5rnmcitUxmIsy7UulRqNg2AFLfw+pGrhQS45TRWdOA4iG1KGlqXPKoSSmkvIqskYYbQQliGUkDqzfLMA9Z4aWGVuzF5bGqqQhlusF0DdcrJcW58yGhjZQXUlZopUJ1AWqCaDlNGtNmV91ixQE9ATMCpYPox0GBm0UCMBAUxkhA6KZV+ovL1JbeaozTim1EyOllmDnbN00oU3HMVn6Jwr5+YEiEtpIgHyyaUPOZtM/Ow0SXsRGZJ7LaOkAERkHKwWI+PdsITEtFqYcshFGjAHHGJJBiupnZP2NMFY+2jzKNttuw0AfnPyje/+/PTTzv7rmWcKdWjuu+cuj7rzzU31x78/5dHHvvXL3/nZn/56/m9OP+sj3/jWw172ulPOOnfLUJ946B122m41SKs5SaeYpaUkvy1J8g+os40TD1mre9/shtttjOdcuH70iW9+66e+8Js///W0M8/51cl/esXb/+Vdn/8ml11xv3322m03ZgDWNveJPWwG3cC4pWIY2h1v9A//eMUDMvOtn/3Gs9/yoR//5o+nnnXOyX8+9dUf+twz3/TRdeuX3mPnx979n0xJgqGpdWJNEeDBwTONcLASuQeaDHM1xmxzdJhhvXfM173aQUOxc7dOjz/+HZ/9xvf+9JfT/nDaqR/9ytcf/pJXf/lHP9+ci/O2rtdDrlVYABoXXdQrIPg0LHq22dGjTFpbSUKwxDIrclVHEwEwqA8lA2gpsCqF5vXEcil4thLZDPNVL3vAHa99dRX5zi9+e69nn/DFf//JX8457/Qzz//+j37x1OPfcewHP/uhb/zgGz/+eZIY2X936pmv+fjnPeIaB+x3m+tePZLh9jve6FrXP/iAiDzxo5/5w5mnz9rWuVhcVGz5U7HwJjbquoxbs87OZZ3YSI4dK9ZR2rFkAsMorlZ1WOmqCnnI24dZ1jF2WTlKQhR9FXOZJ1n7vJUqkzkqdRImkhSHrTPWEQTGIiTAEsLbuoK6RYdtRl1xWuO5uHfhwjBzAetaZDYEqTBPE8NGPZHEmUgJYC1jltaKS4UUoREuOrs2DkyuCd5mhfRZoqkny87FbFCFqYBGQIh/ljLjb1fSEeMJK+bcfrXx5HveoZr98YyzDjvujR/9yvd/e/rZbJ/69k/u/7I3/vC3f2bCPfqONzlgn91qhEaH5N+kkAxjFhPAVaNi8fhvY1azaBKCBeg2ZKBEDEGqNpaggG2/2v6ht73FaqinnX3eQ17+hnd+8isn/+n0U845++e///MJH/zkI1/5lm/9/OR3fOErnbUo1CYc9+7PnH7u+btstw3vT5agbR0O3PNSD7zVjVTkKz/+xUe++d0FcLKti4D8Ai8BbRsNcsk9dv+nf7gyBz793ZOe8Oq3fOPHvzrlr+f//uyzPv+DHz7qFW869exzB7MbXeNKqZpYsVnZ2G7LFgAn/e5PX//Jr2jVn844jZWSPQfuv9fdb3xNAvudX/3uvi88/nNf++4pfznr9DMu/P5v/vzk17/3xe/75Ie++f2v/fBHoyTP6HFxKOAZWqQWFOUGkj2RebGRSICDxbNGF9t+223oERV94Cv/9sczzjjl9DNJCtJLbCaa9I8RT0FOVuay4WoGVAmMAA8LZaxZyySYFTyfmJbB6oZYEfKJy+axxCAUl7nUyoRTVAnUpjxTFMnVyJlFoljKWGUcUBQZtJcmFTCIKZmM5rIf850aWUwPOBrJ7Q8dzIremQGyMIWcGT0NGqVSMxaFExafJUDTUQxi6MYIywxT7k0bShtCRDIo0UrWCppP5JLIVVqbWAapk4KEv3g6gLd0HnI8nHNp7/K7mbqkSjftKeJNeAwwsQpUZbRVTJZzV0kvOQ0ySektZO7mnkVyLH3UsAC3zu9+/+ePP+qF93vUYz/1hS/EtJVaL2pi5YpXvvqD7n2flzz9qW899hWffefbvvjed37h3W///Lve+pm3vekzb3vjp976ho+/8XUfe+NrP/L613z4n1/1ode+6v2vPv69F7VXbb551fHvedUr33n8ce985Sve/arj33nCsXzzzuOPfefxr7iocfK7TjzuvZuT333CcU977GOuePnLC2+yLrKAT9H23udlz3rGF9/7jk+/7U3vOuHYFx/1pNv/0z/tu/c+qhoRH/vUpx98xJMOedhjnv7CY//lM1/+1W/+dMH5U7plLCw954L19372q1e89Z33fOjjDnvy007+zfIP6HmX9fJnPeM6V7ni3zT8/fd/XgSYKf+1cT/8xa8C1kKd27HY5lAqkklFOixv0hWRVZ05WUswFYoKsxxw0RxMa6TMkXN3Z8WBgPupY05rMBIqclNoNzQKMQMtELAqTNnX4tz5h2EYrdYmPBhTQ1Tv0gGBcjJrS+FmYZYydhlDlVmoKsxkpq44NaA34fE3BZJB0bDMytUpCiwTuXgozqM0l0K6y4WsFXDIMpOn3O7qtK+zrLm5s24EiIUMHmNwwOmxiaUNrBGzSdaoJQdhOSraBdFVInPZnLnRDu7Q0nOUeRhYh+k/FVXjJgddw9ZmrUqKaIj2zD5FehTQZi2iwqI2GoozJrNm40DR5NUSSVlggc2mmA2TyHLnwZlJ14EINukia03HFD4FH571cK4ZXcS5LVSXAuWf4NxFFJehCw8pEUK7ayHAKdRYEyxmnDYrmkkaidD4+IJoB/cVygREUWAsuhAqGCKDFpVMNQ5QH42h2BAsF63v/C2mlDSp0EeGXAMyI2e+8q4ghKsrb/WUIUrrqqnCetuLJzEDNFKWbUMzBshio1vyqEGPktbQcFXGo82g33MmUeu5MLhswuCSdFqa8HxTFGrmQlSkkUcEUVQNhqAMyIK3WljpIl2RJQUQJC+KmQujoFyjAaWdpDJZKK4Ad50C5eSezAw+pC88F+eUmMADDtw0zCiKM0At2tToTo3gVHHwg5iki3suqil+sTnZqRJGevYGz8L5BENvd71rDsV4C/HE1733jk99wVs+/pnFegQ6DrnBtR5wi+uPtfz0j6c+5oR33uqIF9z+aS959ts+fOrZ5+287ZYjDrn19a94hULtoLWbmrBcogQ9AQa5W+9Bduq83yX3esZ977T9lo0zz7vgxR/41J2feuwdnvDy+xz92nd+7t+Y+VfYZ4/H3eN2KNYFTqx8kcMfcegsEmEa44Ye/dB7XOfyl2b/J7/1wwe+/A23febLDz3mxLd87l/Xcztgz12OecShe+2y3UBjQO2kGzHiXLpKEooz60KFkPbkcIosY4B4Q5sy/MoHXureN7t2UfnDGWc//jXvuuWTX/JPT3z509/04d+c9tc7X/eqzD8AfzzjdAyZFoSXbrFns5lUTYNvSmfIqyzUJ+jO444EveAYNi8FWbqW7qxwPAIwnnMLLBZxOCWdxwNGaVXq4Xe+7Z2ue1VT/eWfTzvy1e+63ZNffOunvvDhr3jjV3/0i42hPuZON7nZtQ9Kx9bWTvjo5/545rkbQ3n47W6+7TBEkLYsV/LoO9x8+43xrAu2HvOeD3ku3TOyEZVVx0aSp8SJuSMMUSQTkS2UE8QlKYNeWlbwtJcFYl7FrWWfc9354kuAJZL5prAkEQlxNoTzSTFcKjppy8YAABAASURBVGSlMhRmBPOre7BMdVIDJHbSDCWhMxVaGG+WuehNMA8yKaKDZ8TaRRDCtLNMxn+oUqsoYWK/WhpDpSaJqlZLNR1Ei5AqSeItgEbPaD2d02kM1y3NKIklUZS9GCQUvnQvPxkkg1ABnWlhN77yVY485Nbbboy/P+PsZ7zlw7d/4nG3f/JxR77uvQzHxlAfeusb3/VG/4iu4hiC8V8kXPQTImEDj7PFmPteuwuB3hwrkapJuDLhnTWphKqa1MICHoJgebrZNa5x5N1uu81qPPfC9cs/8Pm7P+P42x/5kns/99Vv/czXt87tFtc4+LF3v21EoJWPfvV7X/zRL+nJPW9ynf333qNAheKBQ2543YP22YOQn/Cxz5189l+nOuQwiNGzxQhjrYXWHryzPOwOt7rl1Zdz17+e9KuHH//mWz79mDs848VHvOadJ/3+lFUtD7jl9W50lSuMqTVVYGbj7a579WpGRj36lW+9/VNf9KEvfy2449Fq0Yfe/uZ3v8E/FNPfnnbmk9743js842W3etqLH/DS13/+ez/dZhwecbsb3e4GVyOVCmdcnHlAEqmV5GgxLEVsHjSrsESCF4cGyyJFpuqX2XePq+y/N3s/+m8/vO2TXnzfo48746yzGCaOp4lYdj4QTcFSGquSx4Bmr55Dl7EzJVAr+EuK5gg2COXbTBSR3VjqxBMNaObr1dxqV64N6IDcUILKvLRuvO+pYoHi3byrupbUiiJpMiu6BCyKULdkF3FHcv8ISqEbQgxzzuw9MHm6hBjt72DVtskRfdmuKYhrs7shGDDqmkl+VW7pNkaCUXXojDGiilipPirjTh7VZoUp5Kzv7ujMMQmkU2EELaPFLeG83Wh0MtKSR4NZsnVHIxUbS5iqkEICEZhAI4BQY/OeOgd3GKFNTJ8ifvY5Z77oNW948OOf/Lkvf7n3jv/2Su8//P63T3z96x75tGfe/4gn3ufxT3rKy1/13k98+ju/+E2L3LLNNtttt90uu+y82y677LnbrpfYY4999tpjv333vvS+e/N1v0teYv99LsH3l7nkPgde+lIHHXDpAy659+X3v9SBB+zHj1e4zKUPusz+l7/M/vvvu/dlL7nPfpfce/99L3HZ/S95nzvd7h3HvuyVz3nmda517VoHmvOTn/30UU99xhve+8GN1erAS+93x1vd4qVPecJ7X3Xcc5905IEHLH+tOXv74x9+9+F/+fgRz3zWXR70sDs//OEPOProl7z1rUe89IS7PPTwezzicSe+6lW/OvnXFDUM421vcYu3vOKl173qlZOf/97+0yOQ+d8E6g+//+15W8/PsNYwz+mb9jskKpIVA7JUf21RvW3oxIe4VpnemgvfwyxFIrkZNWYQzzQSWEaZZCD1o/ecGy7WyEpTKo+jzDx2Oc8X1ptGY3IZn43IGL5ibVKea+ZZOOQoRUc+CVQqUUzgagsmoUg1biO6KqWYKLMSBhavLICwRTLha0A1UpjKjnQpHasQThaRhHCPMfDSdNamturzSFPgbr5ZLMBC4LVxdSBhCcoOK15WbjWEdmQFK08tXRd3WBGgfCpdGnd+GiC9mGZqgCZp1TJUM4KWyI5sCNowdB2cqzykQZzzhaJYtVCThcxH70Ofi85Wm1gTArmIywnRJJZtsctSOeeQlAVGZZHtstTbaD2mbDNmFxeBaKGUPqgMwYI2cEXwpNVn9B5w3vc0FT5/Aga6GpHZw8IL4CauEpZpnUbCQyZgEg/W0JY8VDglMZoFvHubeWxByowaMWbX7Bm0ALVDm/M9eIVoqFA0g1A8tM0dfQ3wV/aoJGjRXrnFWG290kaBhEhk5ALe7DSiw5pVFvkchcDVBnShApA/hVU5MnrQ/04TM+hV1GgE3oMjqX0BoMCLCGC9xCyO9Bq56j5itqSHa238pgFqhJdnwRkZBS6Z4Sk9VMMGyFB5isgiXTcHGiIKSdBVc6DsEMperFi2sQ4uJSCSdezDkAQDiWj0rKcRoN6TO5mCJGopcxot7KZMErJyYZKIcAEPxAxGF+lwv9blD3r07W+57647URpJt8v226A3mQNNBl094o63eeWj73+Dgy+3147L34zinEvuttNd//Hqrz7sPnf/x6tVp0JBDKLYsm250ZUPvNlVD77UbrsNYSN4RFv3nJoxKB2l3+w6V3rDkx52p+tf48C99zAlmcEnx1fef59H/NMNjn/0fS+19+5ZkYoG2zKsbnbVg252tYP22nUXp3ctGQMyaIfttjvu0fd/+qG3v8ql9+EXOTSmmh60716Put1NT3zsQw7aZy8kPY8xUBy1jte/6kE3vdoVD9xnb0+O6IihcJx8HfoB++99w6sdfJOrHATpJi7ZRePBd73Zcx54x6tdZl8aRuE7b7vlhle83MsecuiR97z9ba51xZtcjV+5DCQPS9go844bw42vfPmbXPXgXXfcudfBeeqVFPMyRBnDEJSANOHvyMHKP17x8je96hX22HH79K5tthbaJJoJ9DpXvOyNrn7QgZe6BCOi6FWiGLbd2PKEu9/uRQ895JqX3W83BgXLtffO29/4ygce+9j73vu2N1Aqy/zVKaenlZtc/YoPud1NrnrpSxqM5YOyo8eBe+/70Nvc7MZXOXg1jj/79R91GYomC7ugZGRnCocjm6RDDFqTidWSmbpCG6QPqkyPAlraMcEb6dIyGm3kkmwuc+eTam2rtW6ZRaFNZS4ew1zZVM0KnyQxw1wsXDNr9WHFmuQKeKLDg8WrhDDwOUQzVgV2p3StbRybDRAVoZCopiyDuqDAvChSRxkGHU1qSqlVNgbwcZJoQJgAiJToYFjBBLQ9dtnpZle/4k2vfoWNjSEUVh1FaESH1Fque+WDbnSVK1yO/JEwC6ucUdzk0Fvc6MTHP/hW/3DlS+26o2wq3nPH7f7pmlc89hH3e8it/km7IapyN7G6MQzXveLlbna1gy+/715EabGBuwvzsPvguPyl9mF8b3S1gzAnWHLHKUafWTXCfDFPCqESNaolfNnueP3rvO6IB9/+ulfdb/edZFPxDhvjVS9zyec84M7Pf8g9d9yyxVs/a+sFJ/3x1Jte9eA7Xv/qd7zJP0Q1mIhBJLZdlSMP+aebXvWgg/e/5Dd+9osw0Vr5pcRNrn5FgrDXLjuZi3S39O1X5bn3u8dzH3i3fzhw/913uPivV+29y443uOJlj3nYPR56x1uPpHOXYQbDH2v5xytf+WG3u9EldtmRYAyl7Lz9NixF0TZ6rLYM2x15lzsd94j7Xuegy+zxN1EsGiwdxz/yfg++/Y0wSjOkYJthdYODmThX2H+v3RvJFsV9UF9pDIJ6lQP2v+nVDr7ewQda1/QCFa2x2saOvMdtr3+FyxRTqt5th+1MC1JTBqvKOcszGfIJMHBJFYTEXJ0bIGwSZRPIKLFhfUCoS4Cli2dmcp9h9CJ9qQJw61k8C9dm4dSsIglETJJTsaaGyJadG3sXwljdTKjR6Ji4oBSYZHYWLC7I6FycqmarAStBbYqFHumREVykIFV68ElUdWZkW+s0YZ4kJktnOTThpZoQkClTL+tlhsrWKGsMs4yoZYPSBSPCJGDuOrmvpU0LCPyCYag8cRQ+Ix1EtKgvgNisGaSAezCDaYzQ54RhMScAcbc5OCeVqKajgkvJo6E772+Hk379pyNfccLbPvSRCy+8EP/Da57Wp5526vd/9MMPf/RDz3n5Kx76hCPv8MCH3O/Ip77qbe/8xg9+cu4FW3NzOV8vagLI8kdIFPbg4ot9wh9A2MmGzStFZHmz+QIMq3rL613rdc99+ute9MIrX/UaEDnvvHOP++c3fO7r3+IsrmLbaYft7/ZPt3jHsS/hnDvd+ja77bYHh9jOP/+8n/7yl5//wqde+cZ/fu9HPvyLk0+eLlz+30B8BHKbW9zyzS978Yue9HjeROFiVfj79f9zCPziD78ptUU0PpfsF5b1VFoIKYShzqwaIqJM98a0YY6XtQ3rYdXHgQyoQItc8xBcVkAxhFiPQWLMWE5KU4CntsyFHJVZwo+sEQ3RYYKxSlUWuDqvY+osHZ0jsy8XK5yiQ1sOjambvYc760siyFWeE4IFI0RVTSlKBi08YkwFUSBFpCiUlYTr0CaatDwyMQjrALTXoRczw0pRVcZio7BXsTbhzs69c6jcAdetT1SbKKgVoQpRJFJjHuY28LBAv1jpLEtk9VhM1uCZWYVlqfSQdcc0s2IM8AGLL548KcTM16Q/LeYQQIsYcbS5YWrSugRRa/SSCpEifNdFg7dkg9UShUsDs8DFEgYKoF+iVkyrRmYA4aX36oBHpCBEU4lLLaiECcLenpkKK57Su3gHn6uuIsbeePblnpZTLnBHzWXrBCxFYFC+iyK+8ihzUlvD3EFrHenW2ETYWwK1i7DAw0TFIhc8Ijg0MzBhvK+g6sTiXfLIVBzEL21SW8tiQZ1snJXIRsFcZZ1lDm0ey+OjZVuQOgl3nVh8h5kaBJ7OL5BYlAVVbIiiS1Aks1qWwWQQmo+e2jVN0ji/qfMB8CzWZeihAkDXIVPXucvsGkWiQioGhM3wNZpLKPF3+kfnjCcsU47W7CUX8HKZFj3gKlqEP42qxJJ4UOMkORnHGPVhMQcJTNIaMVrQ8XahkDThDFsF1/AUKKAPXLPMzMG7SQtuP5KixpZF7ZCb3uifn/SY973gKe97/hPufesbwwMQKaqjWCnXu/zlX/mIB777yYd94jlHfPLoJ7/rSY995j3vcDBP1YrFriLLvbZiz922f8Ej7vzyw+5144MPrlulbIVGRMmoRlGmOYgfuPdez77PPd76pEd/5HlHfOAFT3jf0Ye//ogHHXaHW+++7a514k281CJDzT233/GVh93nuMfc91pXunzQbrfw6nwN2bLauOdNr/OGJz/kPUcf/r7nHfHuZz/u1Uc+8MG3u+HuO22HXjGbBAjwkG37Oh516F2Pe/j973G963mCsEtVWoKC1H6nm1/7mEfe9yUPvYdYWJENkwG57WC3v/5VXveE+33k2Y/+l+c/9r3POOwVj7z/P17x4I265egH3vP4w+53+F1us01UVjsBDrrk7i9/xL1e9sgHXPYyl/K0kLFpdVFTl+JC6MzMq8xji7rvJfc+5pH3Oe6Rh179cpcie6xG1VksUeq47bbPeOjdX/rY+x9yo+tiMZTJsPiQVcZVucnVDnrtEXT2iA+/+CkfffHh73zmw1764Lv/w36XK77RZfQ6Xn6//V/6iPse/9gHPOQONy+VtUPJ1lyNWcda7T43vsErH3bocQ+7+7X222fVfIywDESSA8LzjIdn9FQmUQa/R50HUDvvQNQxqA1Qcgezg3z2XqKzTlcog6TFWAFa6tp1JsNUY8sgW2hyjNLTs4HFoqQKr1GGYiqDzaJdxatOK0wDNbfo3aBDXf5AqjdjtjJ9aJYb52Q3Gos+1ymGBknwCpFZiTjrdOFqkw4sAy4pullah5FpeTT7AAAQAElEQVTFSgZhg2biKgcd8LLH3O9lj7zPvrvuWNPL0IQp01Km3KEMRz/00Bc/+n53vcl1ss5pDuEaYfXQwBX32//ZD7rXG5562Eeff8THnv+Etz/r8KMfeq9rXf6yVWh3Qq3bMBGpLatnPPBuxx12v7vf4HrJJEV2ODMSNEjkXv90w1c87v4vedT9OrcEFnFWESpSZ7ErTRUKVPEVeNQsFOmq85Uus8fzHnLntzz50R97zlM+9dynfOjoI//5yIfe5UbX3bKFVE1IDNutnvSAO7z8sfd81v1uv8uWQb0H2D+lNS9xlSvt//JHH3rcI+91r5tefxtlBcKee+z6ksfe79jH3esal98/HM3KZIMXG4bh1te61nGPe8Dbjz7s4y954r8c85S3P/kxL3/Y/W5w0BVWnmAcxXNZkHSIkx94x1u+/ZmHfeT5R3zg6MMOvcX1eDIvRSrLRJGxlhtd5eBXP+Eh7zr68A+98Anvf94T3vKUx738Ife75gH7i9FndOTcfceNLc+5311e8ah73fI6B7u1uSS3zq1F1hV8vPPg29/4lY889Hn3v7sodwYJYBCWjjzwEpd46UMf+JFnPemTxxz5z09+8J677qwhrOGGIU1DGeQkkDIyETuqwIwhhPbgiaDrHCSJQJoA6lqc1RcQpJLNIHtTHGBEIN1rhoHVVvpUe8hiYGx1fhsRDGkoeslO6yDuAldKDIqiRFUBwFmMc29U6TVQgp0iEBVBkSUYNcvYxzoPsZxQ6lqMZOdCksaT60VdrJuFWldt1NAyPIKSopa5lp5LqnjXLlDTIZVEonOiqtxulORshESEeVfE6KaH0pQyaBkhA7NqrtEKeyUjyHURSg9ikII0aHVaK810MiqpqcvfPXP56S//+OQXH3unhz78q1/+Qsz/5e964W/XYsAwbKxWtVZR+1v3xb/n9da/nH76t7797698w5secsQTbnS3ezzgSU8/8W3v+vZPfnbB1jUdvHje/9QvQpwiw7i6/tWu9N6XPvcpj34UjZlbe9GJrzrtzLP+Q2QC2267zY2ufc0XPfGxn3/b69/7mlc96dGPuskNbrjrLrvQbCtlGAa+v+VNbvrsJx/1lfe/6+VHPeHqV7oCd+r/kPAvX/jSPx5y70MPf8qzjzvxDR/6l69//8ennnHmheupO/H7j1l/f/P/MAJnnbvcav6HET/4wXd5dgc3wFR3yVlyQulQKNPRbRBVE5aOGB2lMSHMYFa407BMNW2uIVVtMJUl42poQWo2FoU62Kj8CAzJQxbrM6IBDZLJDYxyWNtiDtYQ9vUwp/YUBUSY8Q3RNZ3VTlwwc6F4t7Vzd5UeCJVUMykDSoW4YR7RqQmSXRIewlfTFOXuPlMYT5ldbBpLr0WlDjKMtE8kwMkaMNUqRR3pHj7ZMo+rg7J6FO+F9bJlOkt2tIjk+1B635hgEQUAn9oUzRRDZIk2xKzOexKf2txkitWMjUxjHUra3rNCx2EFLb3n3JNfKc/h6QHufzlYGk0bOuEtVUsRFSQECr5ISqVRfFv4IyKMEhUzcK0shX8JH2QANHge6dVokylY8n1NcFNr1HEupUEJXY1SQqi79Y5IKJplZgy9raaJxT95UQJ1segJVZu2ghCKVwmTrpxvEoVjAfGC3AgbYQPMQhEW9GkWzTCOFbCoZoikaalCfnBmS/OkLggtcDVXFcXiDfcCXZM73K2CiIPzNDpf6ZVITQGscYsgYG6qqIpBMUqOBbxJAbsozoqpmRazImbOnaPN4k0yZBAtAoBe0v1MvqPN0kBUyiLSaGkAfwtAWjSKMN2cSLjoeFpCHfBEp0XUsZFWnXwyppcod5dmcBNX6TzZDOBiyV6dw5xdfJU8pCzxF1EDI6JIiRThxMUsKIS9Io4gpvR8DFqhgh2223bfPXe+xJ47D1u4t3IfzqyRJdnIJkZt5223udSuO+276847bOGWteCYCtQh6baS0UqXSedJWyetIxgeJMpgdQnPKJBEslOQqy1yiT2222u3nfbcdZeNbbbIaoGUWqsvJ5KiEASVzrCgiMVJZoNlGpRCANUy6i67brv3bjtcYtfttt1SbEwd6RNpqSFJlyObeuOZDcyebIDz1tNWoSswcjSX5iQ8tATlC8sNClKQFD5ubLPnLrvsvdsuu+6+XW6j0whYsWVcheDPgllFINV0NQgRjkUEwOhV9Er1dBRGfAmeeJRJyqSkAhHqyW4bdNCySoyBokmEdBi66iIHyVnpNChrYlAdbax15+132Gv3HXfbfedttt9uHOsA1abJGVZoNKaO1g0hxbBSGVVsaUstKi7SWRy8QxxVMApsNpmVlqRomMVGWU5e0XyapQUTmsWxk8iEXCPFWUDJdZlDG0NjGCoPj6pZubqEGImQhlTqLDCAmaV9kuxYaMcX+jhohaLZHNlpnBZFoQVU4yqoWkyKZ+3MAefhbVZvwAydEpyDIMm78Gv7huWiT4Yp0GekRya9i7VoS0ZwcSoHbWPxkUJFkwo0kdQMy6gqUiUKtkp05e7T6QgKoRmS6TUDRJ9KJILBs47quuMwXmKX7ffadcftttl+kWmu6ijJOKRIgAyLHs5fjAsaP8/unQbOXF1HFaubGqAtaEcgmDZqIJwJBG3TcMscRU1pIN9SO3L7bba9xO677rPXLrvvvN0W1qHM6B5cIgbNpt6lkwJVYUqzOeTBGZpd6TZtVJsYboChWAsjiKBh69Z9nTrzjrvySTuYlVXqLtvtsNduO++xwy47b2y/oashTWgZE528XOSLCiMslrbbtjtcerfd99tt1223rGRQLcIaYNqlziJJT3fcboc9dtvzErvtsd3223qJrfRaYIIU7alrJLcIKG3siRbCm6vIKr3A1QMt0WUBFXDJWBYOlRmjq6L77rrjvrvtsv1220lozFwtBrMyQiwUpS6/k2u1wEZY5aEhx9ZYykIW3ELEl6kc1zGkdAUFpaFDZg4MIVuCmxMDE11zndJZ3gEtXWTqQS6KqRRWkpoucIgkYJ5jOEuYhIC5u/nCk4ST5m7qFQFAwISGWohBq5TSC1xnWMeGYFRCizllMhDQFGmaKKk1VDw9oolmqbWagnGMuQtnN+H2Oq1rbDV+5xPUJlUHkYKyxEKlGx9frj0mOBR0nENiXVQQzAPLxTaa3dz7xOPYXJGFKiorGb9A4SrnLu7589/89onHvPgeD3/EBz764a1bLwyyJlPNtttu+0vsuefBV7zyHW97uyMf/rDjn/fs97z21Z9599vf/qrjX/n85z7r8Mfe55BDrn/d6x2w36X23muv7bffoZSKzcvdL9y69Zvf+rfj3/DG+zz6MTe7/8OPfMFLPvTZL538p7+sZ6bh5qT/qZcQQan3u8sdbnOb21PAn0455dVvfw/f/NeNUaP1w2p15ctf9sF3v/Nrj376V9/z9o++8fUnHnPMu199wpff8/YTnvnkQ29945123AFKvP/rpfj9X0474/TTvvf977z7Qx9+yXGveNDhj7/RIfe8xQMf+fCjnnXMG97xsS9+9bd//sv5W9dU8d8s+/uH/xsRyMw/n3YaFW6z7XYbW5Z/WveTX//OGHIdmUQuC/3HDpuXe5XKfOFm3YYMMh9mslThgtmWFGNhX5KkdDeyXqWNVsw2UIZgvaqddy2GKmLUxswM8kUDEgFfR2+zO1ugG6LAxl5qlCErjyLKRExrYTzma7fBStUiqqw9s0uHuMqsaKzkXBGKqRc+s6NWbS1buAPZa8Qow6AVAZ+8+9yVD8u5XHUGD+zFQxwyb8g0xjTCLQUp2l0ixEqW2kWm7OvuvblOUeaGPqVMrDq9r1tGFzjTyjLMZfQY+GtD59U4rSqGYhBOmYPW9c5aKJGWdSzDOJayEhmYDCpZQCcWmOikCSwTstwuTjrOUpGmoJIIYd0HA2WMCj/Ag4dD6m+mXKERashaohSwDSuKC5EEfC6YVkItGSX6wJrpQ+cuTC+DburoKOpik3RiabUUqyYOm1J7S58cHVlEtkjdiDoANjZdRSmpmq6cZbbekHmUGQJeCZlFW9F1KVOps6nTLUCJkHZCCVomXF5TxYqWzMplVIMppIGRCB2RvNuoo0gRECW4lkVE1hRzk5Ao2ax3oAcJZmqmlZJDuL4kzRO4Z5vWjkbsDVZRjegsX7REz9YRgKkOg9IG7olbTVoZQgfa0IIbBM022qdaW5FuNERMRDyXbSkgi9UQCVP6bF1WXKsWCh5vuZMmLeXoIL4ZRoaDW5AKdwkuTNBO8FMtIlVEKQMuGZV7v9Lidc+t3jo5mUkRWyyGEWFkeQRQVDc2+pZxIvIEEFAhvoom9DaDV0/yo9NaEsp0qmhDLK/V5rpyqCCDk8W2YphptchsfWavhhAptUGUkDvKupExkgxjiTAIVGkHP3M5H96uXCxBmzpAxCKje3TJpMMpgAlJS32amT0jKaFoHWQ0MTUhBNRQpq3IrZGTRItpjiVqFDuMUhhxoe2x6lId2mkCRGvLjTYNfU3HimUJjLPXhrHH2JMGI2jwQOxSSiTrVnYhxvSJzzfEjI7Njs5UZrhpuGFdfdY2tcAEaVmDpiYgIb2X2UuPIaXChH3ZIRNinVgO5h1KgubWxDpYhFQ2y1/QKFBiy5nFoKQVFFek9cicPafwOacpWZtEtQoY9ty6CGRe8SO5kzqjzLZK2RAfhDCnhQ4xVGwUGE1ZFDcezRzi9LjVPmdrNBq0dMiqXnIeESsTKxIYAhTNhZt+8GzJ76Mbl0bDGvASsBAAkQwWrQQN9b58UNe0MesActQjs6u4ylKW557o5B8U5r10EiaHhBavg5shI50yF+pUUAHmHs3JVNZ66Q6HBlOWc9oUOWcQ/1JaLd2EQVAD/1RIJQIlGYbl60+3OZUTusB7kPna6QDhlw7j4+SpLErTg6FRK7RGUJVY9JJ0j2CrzxoTNwT05FLVOcU9xJXYcYEAqgE6zLKRJjOLxObD7LDCyhsZ0pMr6VqKkZNUEGHR+V6luMsEX2OefevUJk7VwmzXAWkOCVlMEuZ2RZegYl1HXWuZh6SJXWbvU0zSvLKy0reI7qRrBs0XCRMxr4k665K6kU2BCt0wDDpZXsjyPIjxecKGqEqbCVOT0rUobAAIW6L1mSzkhrhkJ2ixqMAkaNWG5GiDlEGGAZUvQuOXQTUpJdUIWs5AqshQlsGoORutA/FUMpHI2YgNyw331ZQEq2dv4fQFEBFT6KC2EhTABZP5pB5IEBztpOoAs2Lz6DyFc5cu2SvJzvLazCmrt96TrCxpg3JNLjhqqGRNL9Kl0HAGUxS0DFpyIGRIsQx2ATAX5mTJzMJeWWyFjn2wzgcHtZmwFNAyVFBj8ylZHjokJbmYTYCyXAOW4oIJeiFVpfqyp1XtncxkiEQvyr+uaCV9JEs8XLJhWbKGzgEHM6r6WJndgtQmwiiuDV17pKukQhWDwLDsOOD68OwIZkAQtQBCMkqGpkqYnnr2WS98lalDIwAAEABJREFUw2vv/MhHfvgTH19vvRDAAfvv98B73es5TzzyLce+9P2vf/Un3/7mT7/pVSc++6jDH8ov3G5xrStdYf999r7+1a5y51vc+CH3vscxT3r8e45/6eff+45PvvUNH3j9a95x4rGvfuHzDnvIQ296k5td40pX3HaHnSiQ7ezT/vwvn/vcUc9//l0eftgDj3jy697zwZN+9ZuJNYhj/1NNVQ+/zyF77rEnV3/ok5/88+l/5Zv/bstEiJCN+15ij5tc48pXuMz+VkoskdD/7vxttmxz4AEH7Lb7HvK3my5k/vWUP3ztW//25re9+YnPOfr2D37E/Q4/8vHHHPuhT3/+5D+eElTw3xX0987/bQj8/i+n/+mPv6f4Ay996b12241vfvSTk4IbibJ4mpalbATzgDkA1nNGG901ZkgLQeQosZK0RENEdI1ZnWnH/dvTzMSMq+eaUUOqBLitg5lDIeIBTVh6ZnQPd6FEsSSnhkJ9WtVq1WI8mZeey7cXzjoiJrXGONDGMIi6GGgnVDoEquxKT5mDayKyL6IbeG4Q55wUSWR472jBi6V/Nhqfweo2N5aInEW8SqojXJJWIgtIe8uii4Vwk64SWFo3daXprJNkr/WoqaUDy2bYNVxTA9aFZdJEjMWpR3ERKQIBrUGIoqBU59GLXciM5ILqoBT6NlQeSAOBDutpktCEdM2QHhHsarJqpfhA94BNe7sk5+uihWq0S+3Cqp3cy6HgcmLeneZVCToglkp3qRqRBJA6IqidSlyTKwolWSmi1ul1Cr2vrIMhCMtksNIoyEJVUiTzon6ln0l1og0aiQ50T/dEWxhTTIXEKCz6hWeLyOCYeLKkEEQthcWX/MpiIIi5WOfifYBXk8FMUw3G8HcTFwWM1qBLODfEJDboUUJLVE1xjTUPazWa8Mg7t+Z9OY+GhXAxnFMEFvRjpnn0PRcTR2BIUc0kUCrREXOw8TwWKkoHJATL7QpPGSUXMwW0RIW3j2FOh9VTFhDAXy3hXsK5lHN7GLGNKjScmLkHNi+jJ17gRShGmoAwzl2X/8rTvEnIYPSoHjpKqlJf0Sy+DPGTiZiZFFlwzGR4zCjW4U1yDTRqSSEfrYYZt8eJBg0SpSoYZiBDUjzMaSZ3ZK9FVzJW2SCBSnBrBpTLvXiz7gjGWwQiA2Sgr50HKUzaepli2Oo8ySMBQrigC0Ho4k8XSCQCRLInaQxJZvpAn+HgMVy9qTknh6hjgXZBQWU24zwtKiJBV5pbb2PTMSopUUzFOHlEjBIrxCAJkZ7pCTBXJPhRQ5JoMSDCsNdYHg+UFHAGFu09l8hxGMhEcslM3BBOnVjyyFBVKmGQElF4tPakKwkNCDIzMht6t5irzEWbag/NJnwXri6R2ktixQGvy8sSoVSHOe3NLt7JlEbCUUdwZbTujafbTFEZKlYlx9JrmcGEVFq46ESosiK6MbObSTeDkYEgucON1qdmJ8UJEWp1HrqMlarmps3a56FNY7KKwHJhb+0NPBPPGUGtaSJChIRaKMrJVplTqco9aSyneEhQf/KnMzido72y9owC1jArjO2AoipibmUeldGJoEzCvNkQtA7JP0IV8BTOQxUst4+6Bg9o0skKiNNoalVJhSSzvGuwq6auPM2TbndIkM9VCSEojt/Ne7OAhQlZnzRj4bsCJKQyEUDY0zvcl3uVTsOoJUgLRlqUogrEWOtAA6eMdVCLFVUryDFpVwggHhYtMxu0BZtndKrivCJQAzkSyNnBWUFs+S5TaRTjAEhqcS3s4CIv2QyZYiQeZXakog2YB5oKg0gaUOmSCj+6CnmXAQkBVPhLmoSiFwRBrJJ16DL0JEESCNVlMHljLLS2J9nhydvBnPjaMzGLzLYgoukmwX3ZSteSoiWWPamYU71WKTWLeGUrrY5rrTxiB41jM5qpAmhLpRt0xzLGQE0tUkRMUkHUxF0yofQxCLAoTAGmbQfDNnVpLbKHuIBSRdWWXNeQoNOcGEH3q3AM7GEM6XZyNNGy9GBNoCZa2TSpTkTFBqkMi1CkdkjmpjsL7nBiQd452auMgYSZWGFYRkfpNC9cFlsQgqYkG61iwczqabQFCDBENM6opkKqUz7onmdStKgHZwQDC6GoMutGwwZcJTEICijJlVIyQIJ0hzpvk0oJpeGoydq9Ut4e96XYBq2Aakpd3BIBDQSXCLQCLD+S6/RpoWgb6he+/5N7HfH41779beedew6AS++33yue/YwPvf61z338ox98j7ve5LrXOfgyl96FXzuIQZQT/tsmVAERQExt5512OujS+13/Gle//S1u9pRHPOitL3ne+157wife+Jo3vfwlhx5y98sesPwDdwDr88/53o9//PJXv/q+j3v8vR935Cvf8o6f/WY5cXLo/2zbZ8897n/IXbmK91evfse7aQff/49byv/3Wfe5w63fddzL3n/CsR9+zfGvPPrZD3/QQ25905scfNAVxnF1kfC29YKTfv7zT33qE0e98Jh7Hfa4RzztOZ/6+r83niEvGv776/9+BD746c/Om9/L3f6WN9tz3/2o8Oxzzvnj6WeqyCgLG7MWX3FbY5oKePgoc0hyy8c8Bx9xLsUjTcCIqloupcKylXRzVgaFMmPhJIsKQrrLnGCSorm4owiYlQLuADpiGFkdtOSIXAVGsLxGtdSiZFsLLkhjNrM2JG8uelHUXmw2btZMTsAQIoDQHBpQci4yleSRgPWBwlrQdp+xbjYBbqxgvqyYh2hD77Wz5qwDkVKRBb3b3CWQlGcKCuZbvl9srmHFdXQbsygNgzYepyEsZLXTQZHF53Xy3imr0DBH8lE4pXdV4a3XOBjhpRLROdCJIhcslUxiRkfOQ5lNu4GSSmAICKTBelibSw84wot37d1zgUZXaisplZUPsewwQtNDKxvCIsHHo74oyCSIUNVm0tOijxkrFyVWq9y6gfUgARVujPy2KIKFTmG0wrhT0d8+bPRSC0FxARazkASgSeUT+LXbPFOkKIKOpWUKVFEjbZLk99lr87XQbJeSPJWWJfqLvgCk9+iT8zwevYBrhqT5hZZp6dXWFRNjM0XOLsFKLiOPgVFbNS4mZugV3XjU6OG0LQTJQ/5WCE84oWJIYzzXjikzEjQbaEFHCQPobl8Iu8C8WO6zz0tQsiiPsTJwpyT0IdxSCTn6RRkQkkQow7LX9IoocM0ivsGbl3DQUM9oPZOE40aLGdKqhKmkWCu6LkuMnbakJxpA/URNtCtmSk0kL27FvXnzaNlnJEZNK1o0l9ldch5iHqRXaUaVEpu2LA4L/dKG0bVkjRgi1WinexGpaqrU1xMB4W1GqQJTB5Ing+S3AFtb2SpbWl1BRy0mK49xcgJGk5ZTx6ZtmyBlLWGDN0lakxQXLhTUaZqQrg1WUKwOajRtAJ9euwgrQCcpQTCxwOFWnQuwAL9EmWKQQpKVkqsxNsakyYWs6dVosze0SSM4JehK0UV8qSClyihUKCpiaeLcqm2Y09aaU52dCSoggzSTvwUDiA1Ae0OF3oORyBAwjpmh4RWUYhgMNYW6bcwcJcrgGBb7tCO6M9CuzaunQSqpsMR3FnUbhI26PImdOmeSrQW0UeiaRNgcxkGFlKgiY+jQUSMtekh05VgH6cPfoiYlC+teWaLfQXaTVYns1hsXFPdxadgQfiVYVEMkTUx0FK4M6U7tkcV1AApCoGB9SfIkm2Jt5PJcKi3vCxgAsTLN0ZKAIoVJFbNJo8iuGxO0Z4fP0lnHkjYZhUKVb8MAFYWWKEPaKLpCLV6ti0a6yARrAV6J6OwSN6AoJScWsUFklSKsKuoYm0Q0U4DTkbMKg4/u2Zkb1tV6kapsUFf3mlEKQQZMwkrArGGMVAhligrAxeh009mVJSODwU8qr4Cxkynog5Wl3vGjukpWkYxgdXCZFgdFzVChlhU5dljjuHB18hm/+dw9AipihcwUlrpc8AcztTGNF84Olkrwy7rbFAjSk7ePkhkioCnEyeloMlf6VL2NneFUG5LGKJoMvW7U0VYEVqYl4RNupYs6Y5WgblalrvQKizgSrFlfe9+6lvWkOS+mFvBK732aGr/K65OkWCgXt/CWk7YLV751xHpEL91YmubgEdnDXXuURlOKsL8rQyhZzRXLbfXC98gaQYRcyxwDYLBMg5ioSVWwqSVrVjeZskSTpTAIgkzWPnvQGpBh0dc5TViHTgWcTgElzciC3megaXWYKiMRUQWjRUVX70FTu9SOIiGlWZ2DEBtE1ayYVC6NGYGWSxSihTBE1K+V7jSNLqSIAAR1xGSYBeJe26zcIJcopRbjxfyip3RFAMJNmb1LFqsbOTKfGVFkU3qzbgqnX87XnoyAtGLOijJIFaUbMkGnWp1f3hRGwZPEDAiDIpBUgSotr0ui0P3F9miDU1XpY4lRQUBzhdhSsBolEZ2BCkTmcW9++6OfeOSvTj4Zm9fuu+95l1vfavuddv3r2ec27i/Ja3Pgf/ZlGMbLXOqSt7jhP774SY/7+Jte/YX3vOMphx/+D9e4Zq3MKFxwwQU/PumkV73pzYc+6rA7P+Kxb3r/h6j3/6zKO978JvvuvfwH4z7/5S+fetZyr/U/a+x/WUcubNlmyx6773rgAZe51Q2vd+R9Dzn26U9517Ev+vJ73/auE1951KMfdaub3GTb7bYXEa45++yzvvL1r334k58yVX78e/u/AYFTzjjzQ5/4F1Jlp512uun1r3/wpS9Fpeu5/f6MU7s1Vwf6slsYI0n6Z4C517nJwcQTk/ualThTFFIhQxEbhDt6wMyFaQvKBotAOPc3FjlLQMAXvlJGWk2s0KtmGUvlhqTKhM50RRdhQjdhpVBZw9xN3QxhwgLhM8twBxIS1rtGU5lUIoFe1KuiqAaK87W6DmkpljUbgBALHVFowFxK1yJixTk3zCmJMwQiwnnS+aZCVESKJ00xFt0iXKsVMBVTRa25eB2piCgdGiW6gYoyWSOkB+ABVvwWhJQ7MW8qBujgpdJS36ymToi6YlKwYEB0VN2oycemRu0o9NqH8KHPBY12ukhPTZpAHDKBDBUaS0DcZB7hqgXVIlmjc2ioHkoPupiklZTRpbin08NJShd60pfNRrwohJEmVNIotNcaxbxnZ0UMCTHIKnzVYK7ZJBpLXI+m6IpGBJJXCKIIKoolq/uARpVQS12JrDJHzyKiUikuwS2E54gSUsIlO4JSKVtdpSt965bKww7RFpFEX6yOGeKqaqIM+MT9oXfpUYMmI4DkEsB6aPITxKQklKbBvQibYKGLhAMOEocSDLn8/byaNqCbZFKFpGin2y3QGwQwuHGVdJc5Oy1NRERSrGrXIj1Lm2We0T0JDlKyF2mabTHLraMGWV8p1xC1Bx1u3IhdJXHRRa4WFVGVUtSqJPdT4Wi6GB+Eo5ZSB9c6iREFNLdIkxTvmNboW526IUsXtWkT9CpQHuK1woildVFkWXyhWAHjX0umokf4RPSNgX4AABAASURBVDvntO5bkkfZVMLnHr23SVpTwsDMc5BaXoXHrQkePKbmTOMzCX9NxqTGQP2Ri8SW87rMyfyxDvOsnc3Vtac5w9NM6ACqu6XTrkxQVucCZ4rImGUgECIk08ARJTt8qyTvZXp3GusFPmhT4SoVpnLK0HxoYY22J4HgkCZJX8FDVZa12Vp0BiaLXprTOEGYkitthkyBKXqIo0BK1RjA72awkbqwtRcPLkuZukxZOivYGBjI74a59+UpW6IIBkZOlS9Dt5FVjE7RBuvEPyE9SksipcrAkFYtJYOBCClSK1ZjWm0iIRPCzYv6RvhqcpsjwwN9AZIOEyuoLQ2EJtgjVK5WTEWV4WbmmkGGoW8YH8bzNI0UT3MxUSiggdJzcBSFgU0FQs0owVgoVHTM1SpKVRUjmBxNKOdDdRAZ2F+0C7xJmXIVjfciWtoiT6txTiy2ekgShYkjVkNLuvFIyPOTYLnovbUoHSzYwYP0bHDTZkm++WwyVcJYFxsKwIZMJ2RdkrBpV/UaXsLFe+q6ovPmNPlBfYY7EpWAipkUp3qe5lZcgWC4fRLm+iLUqgyiKgyamMqQpfCumH2mRseV2UXccxbpQO9cLUGM+B5Gv9bKoXQVhFpTmoBu2Yu0pScJdJJzuRjTe21gG0WHUWAA844pZo3+09hsiEyUoNlDlaI0akxseBbW4JmyhwTHeo1uHYxINWN6yGKOZgjDw1ZCDCVhQpnISDDwAV6ZnVeEM9Ay0mA4exkjGkGnRETDRAgWXOA9Eew2RI0cJtUu7GPGzy7N1ZPCibJHcB4FcZwBhnRneOZ5mCfrPYq6Vo9KCIT+Ua5CNE3mmt0EKqZRootEEmzJ5SaFSRH0iGOQklFAQF0CdDtSmvFz8nVOXtxuUJJCqqUCCkEPhsyc65ykSAk342KJYArEkJxtKeJo5nMRp1gJpXxQcYMsngdFO0eC2QUx53RJFVcQv2ibbDSOqXWxltj8M2OpBumsC90grL/DMpDZuVs2lRR+7I6FDrRTKFisiImKE7uhMywoVSpYCPhIJC1n9Y6YoXMfc6lIow+lDaUrIgJzxmQuhQtUq8IyC9Wwa9Xp+Lnnn/uIZxx94uteN3GLwMXXaaf95aWvfu2DH/e4G9/1kEtf/8a3vO9DHvm0Z53wrg9898c/PfPsczpjevHE/9O/RHSbjW0ud5lLH3afQz70mmO/+8mPHvP0o25wvevtuMMOIrJ169af/uynLzrhVTe/1/0Oe+bzPvv1fz/vwq3/B3XsvOMOt77ZzTj5nHPP/f5Pfso3/2tbQpy0VKbmascddrzawQfd/5A7H/+so77z0fd9/C1vPOJhD7n85Q7ceeddHnmve5jq/1rVf5f230UgMt/x4Y+ddvryD1TufNvbXmbfvQ7Y5xKcOU/rU//82xBuEM793rQNuR7SS2feMjQqtcSobZS5KIUkd05pnRlhKFr4gKiWZjZJOqtiNsk1U1pn4bckg2EkUQFw6VBSLILZNo58Dcvg9s10j8Qs2SNdpANTggWZpYWnJtHk2kD1BmclU9ZBnXWcW+k94YlkBZnH7Cvn0WRMbs3iWltZpQqzf7UK1v6h1FUWPtIq3FLHwIbIACgrWs5mNEu0cBtZUVzSx43AyiJXrLjOLUqnXAxFsHTWjtqFu7qGCcdkzo2J+zWLZGYXVtvmLdMgg7LaRIoEpQboLJUGDCrpkEmTDiBpJhQFKJRaSle9qNsCxRNLVJaaBFb0EJYlGxS0LTr6DDRbdheVvmDLjX4GjRqSRbaEqC52pgRlmWtyy2spSbNks6ZBDFpThCCP6BWLdV0lknoD7nSozjBHS0xqWyFzOM9OM6snSpNqsFWneE7MrELLGA5+EAexHCGM/qoqTxzg1VJ7jtFXyT1DKit11goa2xZnLBOZ2dxbBGVnGbSANzsicJomYaml8z7XCoPAHbO3yEA1YYXPIdRsxaNGqwEFB3Re2WyM5ShZudWvpA/ck4SWpAplaRTiYCvRlelQEpEevgSHMtaFTxalawkrqSpQp/nEGQqEdYBIdcMS80BPaPSaSZsL74qTAaWi2oaxM2A0ILJGmLpJJ8ASalLBS8QVrXiMaUVqrcNqKMPI31LokMnGyKYWSiUSod74DhGgoTzYxUSKZoOmFJILCeEH0qBaGwexoaII5U8aDtTASMaTCT2J89bJeHjjyCD0cDE4tTXMU5/nHgkI9z4JIGnAaotvrLQYPe+9Bx90TqZrU0uMpoPwBFQX51r0nNfiLT3RF3PoYukmzRBFonouzwMzlNxdiW1jfcs4DePct+RUytyMC01SwhZieCwVR91D0Q2Ook1lNkwAFfAlcp5jWqevYTPqlLyrHpsMIoUtiVmL2lMIvJPVQS0xDTlBopl2tRDGpbcKL0JUaxPz0rX0qsl8DCHTfE7enXGyV8MyOK1kbcFi1UInKd6H7CN8DFSmi0nww4o+aIIOLXJ7AooR4N27WO90VTOwQI/K8PUkBxVIE5hKicg2tWnd5yn7Ym2KQESV3/FY4SwnhXTMoTCl5hUaVUHEJCtmdcqHdd69CDRCEcL0LVuzrCEOBkwI/oBBaek02NbCbJFgPEuqgmLAK2RwH0Cnu25AVpUUHVA2gnKtZ+0xsK7YFHChzJBKnHpMYMHeZGgKbR6IRnREC/BKQR+CyefO9REyg8+Dl7u1kWe5zqwmBeaVu9HpCZpZKmCSoXBTkVKUeVlkrX1tk9eMLDKXXMc0e4uYBc1K0yGzKpaxKp0uwWyW0X0lORJ1tTT6I0XFhC5XM1UwgxwI0tyiqzfKwtCwWmPsgXQs5rs3x9QZ9RUoP8ivzoE0bjvsAeepBOUuqEgfchpIfwJmyJToQjk8X5chUBCINMGQOtC9NBplo9ZCUtkw5caFUb1Hnb2sJXi3TbAFWKwuAtZIy246l9rr4GQBHayZ4i7dU2OZYFFVcvOKRNI8gLgS1lzVtqqcJAnzGFwkjC+NszhvmSV8C37uwux1iqG56EDr4t3QWcLB1Vl8rsyHHr0XVmfvWYKapHEOC1eSzs4r5o4gDCO5giSPizK2wJyWochk3mwgRxlc69ai06gsBIKt2Tp6qkfhz2iT1SkswqVO2NJ0Y9JVGAGtXSq3mnRdOOlm1NZcZqYEK2My6kPSV8j5mjMxFqWjHZg1Pd1kKuRwySnqhfNGW5P8WWyqNUyhAaLo4nRGGowwCrrIDMmAzU6DoL45gIIMSQiU7gBVrfB9Zs9oSW4tziQ0k+Ed56gNFBWQUEGNRCx7oSeLdYP0XBSVEAO5ghbZPbMhnGj2rBwgVTuSCs9fbz3yJS/8whc/x09bttnmUvvuu9eee27ZbgcpPPXgoiszfvazkz7+mc8c87KX3uEBD7r5ofd78JOe9s/v+8hJv/r13NpFc/6nXhViO+24433udId3vfKln3zHW178jKfd8HrX237HnSht64UXfP6rX3ns055+yGFHHPfmt//01yd3D/b/j9s/XuNqw8Ay5N/8wY/+xzP/V40GJEUvs+8+D7/XPT782hPe9IqXXuXAA/J/lfS/y/kfIvDtH//03R/+MKfsvNPODzv0EEB23n1PtQLgnHN4LDNLGNJYm3qAD1q7ay+QgR1MUWPdVFiqtbA5mEVIZkaDRhREoPdobZ49uZlI1sqZocbEX0QimFo8WTeRUOYh4BnL3YkIkp89Ze6b/5i2CSYRN1VjsWL6ZVSZjPNbadMAZ8kVk6VgpS9ZKuhKGYlkhVp1Yamikl4Ro4jpsqBX8ZKlhBUPxYyMYj7WbtVdMGFYlo2QGtbdG+ZN06jFJEDJro3WRU6IXhKj6IaoaTHQw6zNSqxSxsZdm/Jmr2hlzNywWaWHBJ1K4QUzQER1VG6KJdPSTVlbwuEEMd2D57wGPhmkW9rM4Jq9gRZRdeERgRmUmtCWCHERCKtulMljkqCu7jKn9SKulApelpLJ2kpFKXWN1eTq3mt03mUMRLuJU6CgmJYMRmp27VJitKxCZW0JSLLDZDZiF6q56IVCWbolnWVv0xKkgnZZLLVcgt0aS/xV4JId3BkWMLqwW5W9FMHxeYiuThuNG3hGFw/rFFiS+IpQkDEQQzd16hWx5OdCJT3gsF5LGqGCCCcMnf40hzpGoksAVJTLBcE5IvQwAt0Z6sFzSFB8Bg84PrlvJS1N53GMDcOGgls+ISmAJZCRDJJU6AjTpUu7qrNJ8vDspchQeK9hUaQrmmno2DHS7kCTRSuBLy4apmmCzStFebhj4vB8tczkOSXp0HImhFQXI6jC2Tw+cQNp3P9gJqpRgAoV+q91MusidZC6YNMtNgEMxWJlISc62TE7eMgJjwXrjChEGlAr4H4Ngi6ETQB+VFPiBXXO7UALWq9DlEE4nf6VKJ7kakwRIZyrIoUtUIzGFof0VIENYJiDCcJjsaw0xYSyGEYAfC+D6mAmYpoFnZo6vAcSVAQt5pptCHodXpBV2b3MVWmhs1MJxQRNJQfREnPI8iCyFZ2raM3Rs8xa1ikzcUomjHYo/c4EQpMZWzbUlkMEN32iw27JNDB+ncOSSpUKtRRetHBRpAoRTeVkOika0dc9OmV6WnQeqxUEvsMmCDljGUuTBmtSUhR0dioL2TFDGHhPqKZtrkwQHjeRKrIozEyPnNOJBcXn4qJp040WPN+wEESz2VkBJHyxmbonwRTZIxKN8iMbi7NER3BKkvtzLIkLRyxig85FMhsqTGljapsjJ4c3YE5rgR6Lu5LSDITWOU+FTokEBAlxik4Pvld1USfWbtks5+KLMu2cnrSNAUvffPJjppZSICZC2lRDVTejKnpLB+DZe8qMAphBiiAkQxkTVBpALSjkTNKs6lmikS+pEooAImiTuJMpbOimOlSqzEQui1zQBG4e5vRtWZZcw1NaZ9lOLLWw9hDnAqpfhNIvpMksOsOJDoMri+8MIntKD4Z0kG6gBsPCAyoEk3lQVCa9NZe5My4iYtUGE02CS4CqQxmETEn6rMSRYqyJeEUWMeU7hmrR2rswnHBJsVBmGmdTJLIJuoCdIc5q04Gu2kv1ukUxpBuXcQpDQLWbJAtTsm5ZUboTKoYRZRikjl3qOhaHvNBxGARIQbflCUATEwGFNLXJxkANivPoMfclt8Syj62NsQbLhiBpqweiZzRa1kMjmcwi1FchK8gIERhToW9GTng2CN5sdK8eBkAlUx2kPkLVDWmREokgQZy3GI2aaB8gbNKlZNryHo6FCFjcmxO0IM2hIa7BcS4Rp5TZY/bWFwWSsulO56cWkTSMAeUziPXIeJiLKf9UmhKZXFqwHAk0qLiK0FlRfigcRA/Wq2Ew5W2OlI3BNooUaA/vup517gLPEqiu2jIm98l6NweFsVG4+DLDc6C4KEu+94F2y9CrEhDzoMmuXYa1lhmRDbk1X/qWt3/h618HsPdee77uxS/86Btf+5HXv+aDrzvxg//8qje94iXPOOKIe9/trje89rUuue+lhmHkNDaWyBo7AAAQAElEQVQ+wP78l7/8nBcdc+cHPfzuj33ScW9623d+/NP/a3cslCr77rnHoXe47duPffEHXnfiUx5z2EEHHrgQLuO3J//q1W9+y30ec8SDnvrsD3/2C3/565mc/f+pHXzgZXfYaReOfucHP5zmxjf/NzUGk5oEB15qHyjThh/+3v73InD6mWcfc+Jrzj//fKp53CMfuc8eu/PN9ttsKcX4ZppmCS3Nii87BWtBhrOJWYqymrBMDB4bkSuRGsbEYF3O1p0pBzSUiYWpC4/ZEe4iAiZzGjOtSzAdAY+c0jnKclOY5G2dc9M5Zakbm6kZXWMprwoKwJK9zqwFmHtcUNSZ46BkSAkUQSRmT6flOfRkTWWpcpaY4kxfHhB4r5BeaQJyAmaRAEtuU/DRK6ufJPeP0aBIT87QdZHZOKfJ1LK7W2QFVEpXzMp6OGeyYmHZTlnMpNhygZW0BpSFVyt0FC2DJjerShco25LuRwooTLQPOa/Sx2IyDKzAs7SekeCkaMm3TRp/s7gmzJObyyhdrYfFbOIqCYqllaB5Cw4KMU1WTbgHARGKSY/gzlxi2Q46z0ucg2DlLGlqFiYOYREtfSqszT17t3SY8PYpqtCe7E6vB+ggOSZKH6yPmXWmB2owul4kNT0R3dRRChZ8AZbNgHRl0NzbhHkt3SU4sDCANYbNMz2VtdpZ/WP5D6yU5E2khGWqmKpGQVPa4cKN3Z2yk51qPRdBZGwNFBEVbtEyEyll3LU7ZpWmEi69Ux+6iAun06wEQcnNXTf53kGPkZxU3IvPGa0vbAYj1aumFKhWc7MGoVmFBiTDqaGASREbTY2ypYaP0QZ05dY71CwKzjPwPEdYF13QTm+tR6S06uB9rTaynBgt7gjI317nHpNnIzaRxAc9hQizES6myVSkl5wNvdTIsbS6mocyD4mB8dFhDu5Tkkzg1TQM87LWR3QTB5oWAtNzmUGQpKH2BT9TuqKmvLnxFdYDc6l5hpY6DMNoyyMK8VTm6dSzS8/aaQ9RH7QWKaG1gZunISH0pBkTSLOiFt7KB7/AGq2XkpXgISVMXWgOk70IJELTDagCPtgl9bxauMZMe9nvySWZJaOUjqEFt2oTgS2qDCjZEURrjghpVXtN8gdLpKvL2FCDcWOeRy5/iypiFV6TbNYBUoPi2uZzdbEiOgzD8veMBOG9ZaSQNTNDOoDegFQfxxwHGZSE66wfmAxto/mYtpF16GKLFQSJdytrsmlIWGgXMA5dI1wQJelVVyWfWhS0gtDIABzRSUSNsOATk+SThDaXOSrtlDKKsZKUpMCSnWRN2uiRqUqGElVH56dw8ZbJAcpJZCj4Cu+lzx7zFMECJnOSApht6qVl9JizL01yEu9oKjEoTOi+t2jNW7bw5ZXjTXKOnLxNucTIVSRQwbZ4l9wvfGq8aIhVXb5GNac6X01t5FMWp9/Cm/jCEC1BYiUTwhDVUbuMKRuSJEAZUouGigPhnhmZ0iEhUM5sSYJ0XW8uCxmV8eE2s3x5XzOqdR2FX2omRBh6zx5oaS14jFvNtUYV8hHI7hnB3yHUwXceOrksXkYQkpiCSmkEQQYgNAKRJsxsNR/rvFFiw5bTtWEzfFRYXGsHtyFZ8rpLIF1zGdC6smEkT1oOc5dG3ZIqA2IAf6sLHzMoN6iUBTDzOWVal7amcaVJbVZZsyON22MnnXomQ4GAmMFqF+nqLr3RzJDCbqLHVIIwmAvr3Vioau1K61hDqs2DMtJhSbk9JDZx8uhOIoqwutcKHXofJje6TcwIBKcFOrSX4qKSWKSKBrTZwBLTGvkeCnXuSCnmTkxp61xkFjLSlcA4URmyrwKrWEwNQMIQJeh8uGGCrpG8BE34ljcvaCDw3GAT4BB/vNREFS7x5EIq7qngx8wOLEXStOfAjOISTmlOawbtSy9SZ5iH1I6BqiGtxTxhatK7IESNU5KwcFVKERFLKlZw8gyNVFUxVaS1Xr0XR52VohTsXpaLdTrkBohY0aFo1qBYJSr/L/b+A16XrCoTh5+11t5V77mdoElNEomCgiAZyUFQRATJNBkkxyZnkCA555yDgCQlB0EQEJCcmthAExo6d9/z1t57rfU9dRodRZxxHGe++c/POnXeU2HvFZ4Vd9U594JbGqSClGMgIUXpldSKTtlWfGDpQqWVc2zCXKMocaJGoIcOwi1ZrZpNUaaQGvQMGodqtVa27ROf/8Ib3/LG9HHooYe++ElPuMplLnmG05/+bIed5cLnP++lLvJbv3fFK9zp5jd64oPu96pnPvWvXvGiD7zhlc97wuNud8tbXuD8v0HJuJ+6/9TPfPqTT37u8256l7td7ea3fcyznv+Rz315aY23/sO7ql3oPOe++60P52LpL17w7Otf94939h1AauT195/6xIMf/8Qb/OldHvykp3/w01/ogwbknX+xH7gzX+gCF+ClH/zw+9/5wQ958F/7/5MItOEP+vMnfvXr6y/4XeMqV7nFda55mpoip/3Eqct2aAtENKannYapFxmTDwMjhDtGyrImbs6QSVDKyNLbtG3FmToZWqIMZHGWXzY0Y2ssT92nNYYYgmQTnFklCjMtK2dnJooIb+mse16GFF8LnyQymMsynDlHeAQfa9PNAWEKk5RwTnRPBzyDxXN0xJpXmnoT5mhVzsnewHoT1H13xKkMgDXFwLMsPvXUISLK5EG51X2MwREsBMrkIMpbyAKwTRIdXdMd0QEDKqBBESkaq1kTG1q6WqvisxIaUy18jCIQRPVmuQirveQQpp5wjVbSIWsdXIYQAHSPpNrDfFgP8UzRJnXRqaG6lNiUoKTK1CSiEDTIVthWDLKd+RMZiqzI2cdO9k32OgY7HcqptaOGMx06Sibrk1qCNFAkYJSK+DNRI7wKrxlVUMyGoulKOLOZjKn4pG7RxVfNBVU5tkCUsDs1nqtWg0iCmbcjR/IwKZQKUjMicjuEePEeMoFgOcOUFDqoks8SJKkiPNFU45doAUiOMsSAjrThmQ3aMXtagVJhIy2LlHDy0BHafW0cXCJE2J4sLCQJEfCCxlAQaU2xUPSJMKlPktpF3DIqJ0ROwiJY65DNErO3OYlbYYkTqVroL3uew/mkKWESRbqRrFBWKVthacVYWbiZUzD6OYQGYwx5IY+QQnmEIGSMvZy8ml4lJE1AMb2Nvh2+H2MRONCDDeM6MmGUbRbKYUL4CS+XvTAk9eP0kZHUpENcavpUdo2NggRpBC2NfaGrMmUFtsgQCWpjVdjDUNYkkcpbPHTVwGppus2oEhyiJLIouiCQiBHeBw+syDSZ0lcAz2QwJ0W2IQpeFMKVrmz/SqMoJbplBx04p7Ktul+wu3gMkg5qvvo46ZN6zTGHUy/aHYN4J/sA7iW10gvnHiWbMDLY2lp4MMIpjE8yFAM1Y659LsneHq4ISylDp6a1m1EuyzqJFYswpS8lB+xJZaSuG4OpEC5KBObAXZL0ZIOvoRNqzZm2jVi2ujRzqKYljOTd6pzBF5QULwNLUMThEuxrVSlwqKulVBIdXfsIUEXy1oUaO9I5KTyZDLsOlTQXTVUxq1qr6qQ6G20lveQoQBGZDJXKaE5QC6NVMjjLlfLDCqjITLPSNcg8NbMUn0uv4BLShb7mNrK4elhjSlxKcrZAixD8FqWPNVkHTVA9aowpOpnQP5fdCDaTYVkV84Rp6l7bomvw+ZAICVU1UbSgTpGgJXNkFQ4VAAKpqNojBh9edfo4EaZL6go8LVXo+oFczTaMggsVI/HeleQlh+XQJXQxtnBTTpu1k0+dBjY5OFYAMggpGWvkWmjRrNS0NOmDrHIgGBg1YnKh9TLALxHXMWwb6EGG4NWMoCGhYZuaVmkQFFUVE5FiBB4WIlHgsyo7R5gMMKez9DipxOLuQ0kIKRrCDJVKO6zxqGFoglRR4Q9ZJcjRuK6cPbiAKWVQeAHDYyu55U/nOmPHfBNZPTOSIWfg5CIWU/ra0Uc6hkRYs9rK5IVZObUnGLxtstUChaJQS1os6e1aSpikR2mjtLTQFBsizKvaRbshGftWYDOPNFLIOnUFjkL2QkRZYVJlFPraXMY8903JzYAkMGLl3UvSd5IZNpTBQhhA0wxrYdu0hQjHoDKtslTypGi34qUS5KLQ9ERkhkttde5qW5hH1NanPiyIC0+LddOmpak0xVAGHqek9lq2RRcJV4ipSBHByODELGpztU1ZIRQ3OtvMZFFiVDRMOaxEnzNKRZoxqzZI83npU2vWRjQdy+xLJS2IwhSgvQzUJGMIJLl6lo3n1Fy1aBNxyj08EKVMU6HHWEq2RAqKeZ0GPUtj4p4pq5QKs0nKtJgtBs5miIV3T4epFDMBSagYg60zWDcOQT771a/i8wJVffyDHvDbF+LyQ/CrtlLKoYccfJ5fO9d1r3G1P7vnXd7zyhf93dve/KgHPeiKv3v5Aw84QFR3d3e/991vv/BVr7rVne9yyevd7M4PeeQ7PvSRnx1/whj+q+j9u64ddMABl734xZ/58Ad+8u1veuIjHn7x3/7teZ5pqmOPO+6t73rXPR70wGve6g6Pec4Lv/CNb+0uCz3oF0QTv3upS/J4//79X/z6kTz4r/3/PQT2L+0+f/60j3/6M1Tt3Of6tcfe997zNPGY++kOPrjyWThw/EmnRPjAGHD25J5MXzaqdEZHcmO5AVNFBhCRkGRgWR1W0yv4g9Fa3JShaRMzkokXDufolBKiPGAollJLTtJ1NJUmhXScVNe4C4H6ZGO2Nm9a2XSTsCHqmp5NY001UAa7Mm1hQHsgOT9IRW1AMkgi+DNLGUwxJA6ndJLe1BelNAPuU7RSWtFmMlQGauRG2OJ4Kp+p9JbJsxr7qmyoj23DOltr74qlWtTCTK/MRRqDvcroSU18KbHV3JZwRalZ94XMwYSBUHPMOSoFLKt8CQo4zB09Y6RjmHphGhFZTNvM9Duk6iTzFNyV9ahClLRkkzansEGx9DKY/wlGjZSm60IFXYpXyZlYA0Wiku6gPG6KsOxCO6KEs8Igic0GwezmhsXW1UeCw8rIskBajZyyVKkeOmRQaBXSJuTe2bxU1xKrIlkzatAOMPZMOhUxIB3ZJUJSoaUUqQnhmMDoaKHb1JECZ82QUiBCKxjFJ1JU1TKxbqY5laiO0osxrUdWpxe0gTbWmpEABRNXXUsZlZJcW4NwzQjEVmQbQc9hySnasemyz6cdgLAkacvUoQt0W+qu2rKatVYwYdbJax112oKmL4sL63tX5R4CpRTsVMyGoSmIoFJJN6H+sgPdCa2D3DOcCintAUOsgjJsMopJKXu7JuGsAl0BA3Idwius73y5VEQiV+dI6ebckcq6t9DkUgbdnWvhoXSg0rJuvexa9oqQAV9y6cl7mCHzMPOCNBATVEaMr35GIWZhfaNxJCWRSjEg2GtuxKOomKqQY4xOKRCFA8ygE69a8JQAgkAvQ/ebdYK6wxGZ0dLDeyYNzJYtoYCoCTRg8QAAEABJREFUAx19aFNzExfqqiLkETZ5mo/hY3/mkvCkctsVEaF7WQyNlBFEWgcmX0NqR3PadIZkw6BH9yIp5MKBHrlyYzboyfZCdMmZ8FC/kkFGsyc/V4fGorpbZTuVoZrSo7QmucRolIDal0oMRoYkUxANnZo9mX7GEE+EpVQkoVVwEAAR8CsoRpnqjk3mUwYvBXVbXVujBqbtikSYgh6AaUTpAfWYkYXST9GnyLpAtsJnOKHC6NOaLtz3YnljsSEl1aKWWtwIIsjXpWyN+Yw4tAnLJlx8cjCScuYUYxvD+KspohpzlkrP05lKaPJCCrgJN0PSVwpCFUj1UZFCe7GLrStHc6mhlXzTMiu9IoaFJpVlku2FYLJHYlJZqWY4glghmZcjsvMU8KKkCiV+ZArRYTnC3Qe9KZX404PCpae0lCB/5BQ+d9231cJlUfOk1XOYqhRbaSTNMXZ0TJpKOLO6q5CxJM8BERhE+dpxu6lOLxXQNKNrG+yt91h0rK0tg8xpJeM80H89mOoMg+QoReeZGKEDNtTAUhhQsQFKinhh9pUQFRQZ7HmrAhLKMQY4hNotI4eTLq8Tv2Y6WEEghBzrxrHQIXSDgLt05XS6SUxT2Cw5CwRQp/KSUdMKGaPEkKAkQCfUklWlCL2pbn0dii4SxaF09kGrekYL6zJRhylkIotcHN0Es6mJKXR23dFSzdKkC0WhYDR/yZUNLcmxVkToRak0kJO/USqyU49VESa0ovSWIkwXhmla6jRsRhFoT4qUQNSIycwKhW8RAy4YI7AMOC/xvrgm8cycNed1cT5HsSQDYhgUeFuM8EJ6+BbeYKh1nupGdBLnXjQLkKBvkRTHboZVR8FA6Vx+YJLVWgxrerIYhGhowSxlI7XWyTdY1sGSIcRCNkNmGrgytYyx01ensEYzhdMClD40IWlRik9K+qlOF6JmgBKyUgn80kTLqhcSCH5p0PoqUtxthOTI7BDYNE22/sZKJqr4Drz4L4AB/UMGOfGOjyma5QIZQSdzIcO0TiHoVXlS2/79Z9dW7zKXuMQ1rnA5/Ds3ETL/tXOc/Q43vO7rn/GEj7zl9U995MN+76pXPezMZ1aljcfxx/zor973vrs+4EGX/uMb3+o+93/WK1775W9++9Td7b+T/C8NU5EznP70h1/3D97+0he84xUvvcPhh5/r189TrIT7j3909Gvf/Oab3OkuN7jzPZ/wgpf+7Wc//5PjTvCIK1/id04j8qnPfZ4Kn3b8X5//zyDwk58fe8Rjn7T+VRVwjrOd7TmPfcw5znqWf9LukAP2cVnO0x/95GfWzDS1LkWjDGDQbSqSsbx2dV6x3UxbCH0GjNSWJmA46cQk4NUxa0x1DU4pxepUZK59YnUroswHZFGFoSSSUzLjy5R1GqzHgoqYPCq8qmsNTMIjzMU2qFM3yoFehV3JAmeGSNI0nbTMIZoiZtXKRkjT16VEdRZWMLhkLlE1tGiuR8WNGVMSEUBCBCkh8DVJLDZHXdvEMjYYTBwOOFPDqKNVjKzaNrNPc1IFSrLx3dp3dXiYtH26bJLLqP0ydiUXkyWTS57YukYTmOpU1CrSBKlMd0tOy7CRXXRMk8y1lqpsKJCs6QgrrEVmUsyRTGNe+VrKXRNDjXN6akdZc7WlUkoA/GE6CgcIZfUy9R1rm2zTcPLIkiExxmBWT0dI9qILiodmG0TAa2ASJUtCMsBhS4JjYm2qLNfaUhvKVliaVFCrmlmAyXSoEJJqCiXn9IyeMghzURKcpAhtQZAcRr5KjZhuUZvtc6PFaBlUmkMClNGobsjaDw4Zoq2g6dhDDLy9GJtJ0odQ7ojoaENdLaTKIOgaw3JMMiYbFAYclUG5VDnZiwaLkc7KSrI6jZuur51mLNRfA7xeZFNRJsGEIS0wIhvMRXtIhKeOUT0MAlFalUcOz9YoUzPEBlKpTM4jSm+1d/UQgZX1A2kgD5bYWtja0G6mVqqhsHMAN3MS75MnA6iUGtPc2XwhtUulzYFeInTAHVgcS3cB7bFbbZQgJBxki+RQwtJL5z7o+jGjVd6aQGrLzLGg4tAdyVnGjgTdGaRpmSZJK6TrnmtW+ExtaikwCzoA5aom6x+qjhVQKbOwKqtnb+hd+uJjd7+3/SQmWWabrOxRbMKHEA0ZIG4FJBMTaBa4mDMFlA3JAMNHT+oVhHZv4ZMbSUtw8c6wV1LssyaxquKqDeokN9y8xcaFDcZUlGZejTsLrSX7R+wOdrWDyQXkK5IpQJS2GB9GDDD60ExbCl9tRC4wLgRHcAw4dE8g+nJqorpCjWO30juffo6lSuwU4ZJy6s7Fog1V9uvUjYIF02CfiLfkZKkl+bymY2QOE58IZ5GY0ueMoqobKqVZU4baUqYg3qvaiklLRVFHbMN3IYz4pCkgTCICpYrpS0Zbg3pEH04LeM/0UOuoA1VEdMAYjznE3JRSTzkA8A6pbDKnAWlT6/MAn4JMdMaizDrMFdHUl6p9rmOuywQvkjP3JlPTTZ02G5tqoRxbyO7A1tFNZK7TZuaX2hrUozsStSZzq07bsjMmApmSI8EtEYxhQdjs0z6UqiJwdxKTRXRr82KbRXeCdhXSGaKoUJUC05xzdUeTqkCKDCgXnDxSCCaRaaS5D6VYyttAIgcDYUMoA0mcdLg1BpVLDwxSEMgySatZMAql4k+bSi1CxcSUtNyLdinNtQFM9du59doKliLMlxEBKuw1KBy1mVEKXSItmHswdFlsuy27Q4YpaRZRpkF4hQsGJPdAoZvygvFMERV9FmyqbHZ0XuUKqS4bp1OZiMasXIXQ9K21sR1K6RRYV3ZLRGZU1ugYLmNR+ETkHLIr0tlQb/aAJBB8nbmkp8LM6HJlhVfFkOIhQwCKuXcy1mDqYM7e8H6bhNkkWFV108bUmG4yh/bdmvvBpEgCc4KBPqvslKn6DvqmhzE+MmeLOVAJe/A7oTmBi63hCDqUKmhhL95z2Y7WGZFq0+RzdWYpp89IUptucE/spoqWaZY6BWXtRaOy99CqwtEESgogc9qOFy446BMajH3mblRqhxwdseUQg6mLSkoZwTwjql2nMaZYCmc0wcoeHJIQCJ+hlZqTShXhLXAI/a9pYSXwunhtQVprUpg8SriCy8+cBMbhJVfhILQSv01CmR8I21hdEWsqbgxYMVVTgKmIpgCTIQ2RakkpIRaJJAHCFC1iREiMmZ7b07529I85jfvvXvrSm6ny4Jd2zvvBT4750N998kOf+PtfurV3KtByljOe6cZ/+Acvf/Lj3/XqV7z0qU+85Y1vfIHzna+UlVrff/JHP/GJJz3nOde+5e1udOe7P+q5L/noZ7/A1yz4j2zC7bfOf95H3efu737FC8noete+9mGHnfU0St/57nde+YY33PH+D7rhne95z0c/ng/aDzz4EN76xne+e9KpdDQe/tf+/wQCmZ/6wpfv+rBH/83ffiQjDjvLWZ7+6Edc9EIX+Oe6nfF0h0yF0Yyf/uiHTOlpGgWpI4EIHVEzUjMsOEmC0VxsgPmv5xi8uDFMhX6tUkRLoPqYMJSxrCVVQ5PpEes5AIbYeiSpEACMQ50npsgiokhmAYQg1wjM9VNUdQ1sZhCJqm5wpjYZzjo8uRRUNdPKjG5ZDExUrAkehTlFkxlbaoj6Kn3RKnVTrUD2uACMc4hAwXNZKjo7nWmq1cxyZAwKwJbaButBUaipKHirJEgx+AgoIwBIFTVDVWodCMdYEJ3Y9WGjFyYvcGIx5bZmJmDwgZcYYSbrCWlFZAPMCuIlRY1amfALYWgqXZKlLmmSCpOUATjZinEIqYCbUhntsAEZkj1lmyyBU5vKqOZShbMbufbimUG2RoyppfVF0gNC1is1KOUeKT54OWkKEZ1gE4+Gx0AG+49aw0pSqBxKlLqIUztOj5RAugiJqFADapSiA9aDwGSIYDadDSALyLIKDGAkMi2dWOkyovXuaGJDkKzfST8A1mOFC7Uk+nUMa0uOxqpFZhJKvSWJsxmKec2sPFPAgvUQUxdqbp4W9GZYQiIlA0bHooCyBW9SLFYUWAjChfvoFt0QYsJ7NqL0RFAYE9UMllVC0oG1Ee9Y1yWkuwZMw9xWiuRAeMmAeNmYasz01JHaOa8B4RQCBXtbqq68WLCFFW0VzVTpGkiKOhQ0odU0CaHBvMkg+imYAT69r9nXPSh/EjXPGHvkbYBl0oRt3xjCnhxbxFaipYYbRsJXU3MFBIJbhppDxINVtkSqSLFSRNf2gk18ZECc7i+ITFG1WbV4aHdv6cM90SGhRs8Vo+DEaAQHm0ABzS4YJYLMiV9QCGiN1cUMFLtnRunCwK7U3IobbRFahs2uxgHiNYJ0QcxmjTK1lNFHLPQdVZ2ySl/XBnRYMls0G9FqaY2ciAv9j5MLlciEJKmsYjnFaWnDa+I0z7AA9RjOOI5cf4gGSkIjgBHU3SmMgipKiYQ7HPCCTsWo7bDVHEgyF80isKDnJQ8nLSaZCNonm2m4ku5sXmoHkZwya9E1tEXEYQSZrBKdTBDQFBNIIDN93cPRhrUwd3UKl6vrWAKo65nwABjgLU+KQiUGFUAkuW5EiDCvN/FRGmrbWyaJCr2GzDk3zYbVJuoaq/OtcdRNOy9A0lKLK+8gI8NH5MhKzlRgEgZgzhEcI/RRm1EnnUzUNDMZMCt1moBUVEuZlBKpWQmtQ4QJHGzpJoWUHCYEMvkhaaZ22rMiCxeyVaws+Sk9px4EQVSzGKxmKujcbU80ur5GlOyonpvOW8JhITI0h0LCGScSw4nF5MlINVoCWkAjW10tQheV7sgFWAZ66+FcB8CpEEnlQG4jxvDshF5gRIf0rSTNWy0SOUaQQRIVjGq5k6gpGTroxQD1WQ0nShzIFHvGSzLcWvSSY06dpSgQUye0UqRGKUOF0rTmg5HsjGmIJGMR4ekWMjXX4VjNZF8+6oefPPLb3/jBjzJSE0ZaQnTaaD0H2UNQgSkg3RoXH1Tpu0f//LXv/vgRL3jNPV7wqle8/28WX2iRnxx33D987dtf+sZRYxuCIiqIAWrYS3gR97IdXIXYUHRD0H6TUCO1rREm196nBRq8JuDcXrHU1ecoC1mjjBUX3giSQqdnaXCcmUHWyo5g0qugAYncFFzNSaiMNVkgmRJDUo1FTyYzwh/GYWXQwVCFKqNzVIqQFowO472P7jFkYHZQbsEeMa1wE3pwunvk0BwWgKyBXqYku0L8na/g6d1h8MwRtK2nnhYaZOChfWiMNS8xiEbRKFBkiaDlOHJLXjmBTiwSSWWD6QxdCId1oSKu2soqz8A0wHgTB2UHTCAJabmaFwmjg/GTvFgcvvSlr2BvO985z8ZBe4f/4uOnxx53myMeeJt73/d1b3vHv7jxq07OfIbT/94Vr/D4+93zrS96zlte/Lwj7nynS1384qf9eUmM9sWvfOUlL3/pre5x7z+69e25lnjHBz9yzHEn/Coy/+NrBx944NWv8P6IfY4AABAASURBVLvP+bOHv+NlL3rhkx5/gz+89hnPcCZOy/CfH/PjD3zkI499xjNOOWn9T1S+9/2jPvLZLyx98O5/7f9fR+C4k05+0kteedcHP/Sre3+Xcs6zne1lT33y5X7nor+k1zxPZWf9o6b9rZ3ou7R9MlulOWPSRC0n980W065ZU8YjgolDGO5j7aO9guEvsOKFscUgCwa0ayQTQHgrWAwjGUYCQBhf6rpOXARDkbOIqQoEDPCw0Yt35QRtIYvI1tDNxXpJL0yGSu7rYAAdDOciIRWcwfLgjhBy72FL2iKsqAxkNEiLErkPwpTAdDI8Y5BAATQ1hKk2bG7CFoZNE8oI+NKyL5VvJOCbRClV6iwxsbsYWVpMi88k6qYmrB+6EdsxYxlRcUkm5FaFSZrggOlVAFcNlXBhdgoxEVa/WbVAkQEZUO6iMkspxaRQMCRClWBBoxTRyu+Z0vbkFEFM4bXxGDDtMKq05jreA0JAxBFFYzPx09MiamDiUKsgoTq08DnbIHMtUist7tZTUjhw1c9KTMlcqAjavGWw7xtTgVVJ9R49xsAIoZcMchOoozaaRKhcUYKalKIDI7mxVqAJE26xqVY+J85cEi0wRrLCxmDFXHmPQHCOc2qqGrIgVYSaSN+Zt9MGZW13kPCVcS29utcRmkncvUZaghOkrAOL7SDnnsXDEkIufSFHXadzZTFoaQobsRljWlaHbtJ53WVxltJ9Q3aGlkHXKQqtQqEgFMosA2AFak1Z823tMcJb9CV9mzmQAkwileCwNZKWU8f6GBJCKVsX90R37BLDFmgAVLIUhyCJ4wBXBnOP6quWRC+zK2+w4xOaDoNOVoXFTWeUjVdbZjrqQk21UkAaYnWmRbVZKtJIXygVrUtu0UZSycWzZe7S0SW6mVGCQT2E1FIpOHhCsVKotWGwxYvg24RoWH04IrmTtMqcVludWymwyhcbfFRrE++OxvcZS5AAqpQ511Zce+ToGYONQXcdo7bOXQZUBRPYOWvk1MRSUBCSRGogUt1o7x5C34hYu2zI3NVCInOI92yeAynmKKTmBpTQKfhJCBqYSsCLSWmLTCm1T9Jm8AmkMRwslNmr9mLbavvFdsFTSXFC0l16K5nSRZxf6ZNnDUfS7YemiltSRAYEh7OLHfRa0RhTb5vF19cBKqNMoTumhXKhj2xbEQLgwyn2ADqmbus/i+Cz+VyhNBn9sQc8IxVOA8/DbVUNQrykh0mYpgosyybrBEw8NsScYw6fhBuySHIUf4DM2SX1abTaYh5Ze04d1Wl7rKOoD23TGVmkWE1mZVYo5AW2A7FMtrsjznddmuDLs9h2ejxFj2q5UUzZZbQIXqFNkBBRKVMxcgf1McJecgMWBgxfh0EgWpkNrK7NOjIya2LiDD4Dniaqk7Pnpvc6iDMWqYvNKQUQISwdfdexdEE3GaKDWHFPEVRAgx6T3bxZX6Kj9XkbxcFt1CDcdeob9ZkjuYurLBAqn4OeH5NSfUZXlhHitOZUXDGQ6dwBJpzW6tLZbaaWJvpXH/387f7sBW9+94dBH+Fr7G3NxYSDmXqoFEqAk0M0fS7co0rQybJGkIVGTBIbiZ295NnVVKxySozIZWBZsDRsxZaabnSMNe8wEygkQzIFRDiHaVvvNxTqEuz/Y0KB7HSnuYnPo173V3d4xque9dYPRBNfCBFKDokQ+njr2fsIOOl18CfhfM/ffeGOT3jZk9747g994asf+8o33/WZLwlDT+QtH/3MnZ71kge9/LXHn3I8DK1QrsoSjCRBIcKRAaaMUZao226spU706KGli7COdIlROixMhNmqZAcTaUUEogWcBoyyegPxySk7QRRmQLrvpNhnqLV4JexmsEJ+QsEptJirUZc+ZLhUtY2GSiNNzxyg9WfLyrwCOgT6nKNENGDrhHc4QuC8Ij4lKKwgia06GPArLsHzykiSSdRCqdJ2wtakMYWgrAqMoRR/QINiKZWLEnT4xIrLlOoxFKkKVeYDymNhGDNFqc5aA+P9SPNVpVTK03St3IxOzew6lrm3Qj+CxCrBBsIVPuhTFcJEwKNVYoMf/aP1jco0zWc/7DAhtX+1f/5r3/j+D38QEU7M/9XdX3mBSh9y8CGXuMiFj7jDbd7w7Kd+8PWveuLDHnzNq1xl5tssYexvf/ijH/3lO995j4c87Go3OfxW93/4G9774eNPPCmp/K8k99+9eLYzn/EPr3bVpz/yoX/zpte+8QXPvc3NDz/Pec4r8t9U8TEe+rjHXfM2d3rc81/69185so/x36X3Xzf/L0XgmONPfNYrX3f929/p5a993an711dk17zKVd/84uf+9gXP/yslPuwsh/H67rKcvH9BsvSsKYDZshijUqagI1YZFY1BEGBqLYYqDBNRINcAyUgPHUxBbWhjUA2BqAjJejAP8D4PuQcHurv2PsWyycVyJJNjsh4whkPBfMDwVEHVLmCa6RApUjeLsbBa8oxk1qLSYzQNpsq6SsEERxHQPXNJdmEeDPK1GLu4S6ZwEDwzKWNiCCtFthHZvQ6p1IHjBokM8cHszQlN1hrYVBetkcaT6AHdwhaVLBDWnKmsNdKZYqb0HfNNycmiWMhsspkwGSXUAe2AK/UQ0ZQSOSfYaiQIjKxyuxtEhKCbse5Sh6AYom7poALDJQPmXrzlaGPrvhViDy1OQgIZma2nu1pVwrdeQV3E+krfObYIiyFz/2Dit0Q1GpCfxcRKlMJuYCiLZExWuVMxcVrXkbyTEipiWGlb0lpB+SMLgmy3kYPDHOJiQzTqCnZ1Ct6bjG0JQhnOUUl1QwqgkZqhmerqraSXTElFpY4sSDCoh/ZcefFGspCrphXOLTaqeikya61iKeQqZF1zVO/aXWIQkapUi9fJEuYk3MWXVYiIGNRJyDt14/M0jEVPR6AT8WaxtaB8RXzW2EiKhkAzzMQK0sDaE8TUyCaoaqmFlXOSRDTerMIhUsh5Zpu1DnBVpBqgwkNZVYrIaAk2IYPhgXUTSAjdJJMMMIpGEXOpPcNlIIdG6jAE/UchKqSqgNBE4kEXysik+7ASmybSMwdJglBySo/a2U2SuaogEE2lhdNfQihjhmp0KpVFshRIBaEHVhlFB8hIJYfQkNkhtHZ0jxhCOawkIcoJOVerddJqIeKUq4/SYFEURsmcQnWA2ujwlUoHKQdDMKEpWtBnj6mLpMJHDh8jGf8MNicboZ/7orwh8NUcCUHSJZMTEiRhzCaxi9aDQSMz5sIuR7AySKKAOVeGUMEKLtEMlnspoK2odZWoIBgJCqrIqqq22or0edbHvHjtnMHhE0aNXpZc1xcwtqEROYYlJsRc2roqsxyQgGSWjAqvdGOP1sdC1UArqMEpXKeBOVs0bAIqUEwoYw5KLq4luKOEq7sNfg5kzzLGlFmJfDEwkIWSluIyie5Zb3TpIxEw51gahGO0iFahjAS3+9i7TfF0yjKBQmZz9BAfYs5wUeHxGkuio6prBIWVMA/1bdI4GoNy+ixSFNyV2KSszT4SbF+5K0cgUyVVXZjMV8sR/irY21IUFipbyK5Lo3cMCkInp3AjvSsNDhel0FMMKq2Ly+Lw5jFaZLYGHqY6lI4R7pGUs4hICkwYcGmDEyMlXbIbQ7w3KPYE5gOTFWTOcWagSQZVhYhyg1FlJIRmKWH0tBTqJuwUg0uQha4ypdZhhrp/v7/yvX/31e/96Nlve3/sbucwHYUVKN0RQzM0paTqpF5tlLqSl+Ehzle2vO9GuRs4LmCUbEUFUcNrDIRnOFbhXYietzIax5dQobOselJRiplGEOgZwDqdPkPJmw03miBtvVgEgtM2p0mGJLteL7wFccAzM9I6qBRrwXeOOuaxr3zHcSefuqn2+5e+yG3+4CrXv+KlTGpftI1+GhkqNjJCIfU0mIayVirUyupILgIfNFTvfCbj4MOBUQ2ooEAauWF1dkFkV0oWSphqZhEDnxdOU24EMxGh3QIhEeA8+pqUFHRColABly6a4hScSA4aMNW9ky0Z8DYSQV9ZEskzNVNVc00nhRgyFG5wRWjIMpiuVJSY+JqikCnCWfQ+XQOlqJayMqU9JJyD6RGghWXMAlGTlRagHpb0WjYM6kbpQPcLHoHIjRGkVTTUCKJI2uRE1VXDdzT3iVnuhM4tswWDySGC1QVaRkfwAWjIlDHBiyU7kCyzxBrzxUSoj7ap6Ne/930SP9MZDj3bYf/tV/x55bT92z84+gGPeezu/lN5esVLX4qf/7P7PG9+7exnO/x6133Zkx//pQ++5/lPfvJNr/fH5zz72Zn93ccJxx/3oQ9/4H4PfchFrn6t2z/wYW96/0d+fvwJsdrvf46Pqp7u4IMuf8mLP/aIu370ja/8xDv+8tEPetBVr3QlWpGEems/Pfr7r37jG251t7td9ZZ3fMSzX/yZL33tpFNOzUze/a/9/2YElta+8PVv3vexT7jW4bd+3stf/tOf/SyRZznzmf/sAfd7wZ8/+ux7q5FfIX/muc+2+vPudtnu7xuwe8MkmCp2+AnINK2+IQmJqANzyE6WUkTnNNsLqCWaD65xllq3UtuYMepk0zRtHHy+s5cVEkAyr4nmUD4f0m1i6WDdDgFqnXKunBXKRMZAJnmOZIwjLemzhaHtuslpI+weD1iG8ElIZ74Qr3u/tTtC2kjJ1IB0t2xT7m7qtuxstTaxNbsKvVglZW0JRrTmu53Pllqfd7FvgTZ2OT44hlFfi2Ed7gImJibe5qyUbEJizB37QjYUmqR68qGXdw7omJ1Pk2etqrWX2rhnicVt63UXRgAMZoPZmAozd44YydZvTbNsMiyghGjFyNMbWJ9czGOOPg0mLenDO6lli8F0oC2o1RSTySRlcpPGYkNyIbGj4wAsO94JX/cV7phl/YsM26szEgtsm1z67aBMUBNRsUhzFgRBFqUkVNt6JxeKICx37Mb2ipqbRi1ZFbMoBQ53ar8Mp22tZVkgW0HYKLkV3fbah2lSMR3AaGsNAaY5p7mbOSntVC+6iIZO2MxxAG8JXcTagqXJIsInpgphLaBXFWmiXWafN6m0AA2pgIZZVkmJtaR5U2KtGKFNCmayAyh7WTulrYyAewApbnUoLYYDApNUIR2kzl5n/pia1P0moaNgUSRtwc4aAelZfK1fLDCeM2JjUjdmNtVRp7bJOIDYskWQrtYoAEzoSVReyFEYNHXH+cS7lznKnEkVSBVdZPXQHKlbKQ5qVOooc2Mlo7AsTO7SVNrORI65WSUAZFiPeX9wRYqcEztZGUmohF9aeiJB2alaKWB1k41MVWfJHddplJ2uuUUu4CaFLrsU6RWcIkI9kQPUG1GT+AFqoLS9sIqrDwFRNBox6TcWAOcUE7GVYXUzC6NY835Ux2nSTiIbpCjHhgQ2jWCA0gAQYjAQjQ43L0O9RfBbQmVMMSYnEVMXiaQzJVi0tcncSUBIT2cjXzLMLqOzwIfQhAvmU+nlug/YTG6nBH/rAAAQAElEQVS6JAbWTQCGuW/BcOkgE0vJTChkSDaJhl4mzJtiO5shm/1ZiTz75lXqXHWFdTH6maupqBEYS4pYCxIYZs43cXW7kcV8gGZokLbktvVtC5plo7EzhTETgPG+qO0vup3RKqTSXUVrEk2VNITCVwoemU7VWgZ79ghzSYbCzASHkcFBiWIxTwxfoav0bS77e26FhG3MCrVJbAObIGjqu1MjxzDRMGWiIBMrXXPF14GO7I4YNZWhYQbqRT/Zqi8zqLHIbJg0NAeDTuj5XqdEFRcmnZqjokm2dIyl5iJYWo79mcSM2aqAeoNMgle7pRd3JSFSl7F1GQ3RqS/BUU2b4VXdpIV/8ivfeuJr3/OM177/5ON3QeoaoyTBUunad3XstwyaVyiZeE3MIColdkqbbStlWQoWUF8FvcV2p9wqFhHKTD88YNN3dmKu0IFwqs/UQ/Ldms9bLXTX8Fn6ZsVTEYWuSJ0PnOzXznQoafzaGU9PrKol4QJR9oHmsqCScUhUCUldsjptat6pZVpkRbToi/TOFx9MvVEYdrzkixIUFeEOKVKFwQcxUKJMmiMNMgWUxwrMazCMMYdWmTeKQgaNxXWAHkVJkyom9jarXmqnqWokUsNKq5Nboa9OgZpWcnrfJ7/Yxtip5Ul3vcmj73bju9/kD292jSv1bnRh8tsjg55zX1DalnSKllT14jJDZqO8olGnMc8OZaldsnUGR2E827TUEgYJMK5NfOyM4DAGxFzqTqkVJhABhYv0kF6ylzb4lltiyjBfq8gI3t+4bKCwHGULegAncRpdJ7YMFsACfNLDR2ZKv6yZKgiqqVMQigiEInZU9lURKT6Yu1UkBAuc3i9D6dgak9DBEsY5BG1nDO3iRcaO9FkGxFen2im+U2SidbTDGlOYe4koQ5E2lBM0BnroFhg+ii+GVsQ1iI+aZ00wfepEdUaA5SBFM3WdWYYV0DC0TIPyspYe1oMQqsl6z+AFw2SQdeq3v/VNWujgAw849JCDefBL+4vf8Kbjjz+eF6/3R9e9yXV+nwf/8V1k387mOlf53ac87AEfesOrXvWsp93rDne44PnOtzngwNNovu9DH7zPgx/0+7e83UOe9LTPfuVrY/hp1/8nPxVi5zjrYbe/4XVf9JiH3+zGN3vQ3e76J9e+9q+d/eyn0fn5j3/wF2/5i1vc4543uNPdH/G0Z3/sM/9wwsmnnHbrvz7/r0LguBNP+qsPf/SOD3nUze56j7/+wAdPWy2f6YxnvOMtbvG2l73odje+AV+x/5sCi5z+4IN499Td7cknnBKtjzFcxhBGWx8yglE0h05Z65gMRcxQpVTGEJNMeGf92OpwW2NIGKtWoxYpCUUIa01mLCmri/ZQH5PKzNsSjFxZ3BqHkli1nGyUwrIu3hR97LhXzzJgnjIooSiqSikMX3Al0lEyynpHujHzmhaY+UbHXJlCXAGBapols8kQ2a3qc9VSNM09KXFSSmFt9dRI5XBUaJEppWzJU4UX+TEEXdRZa5wJIpXpoldz1RCqFvwsoXVoSaqPIiKJ4URx6e4jspMVAOk9l6FtiIeSpI2wkUJUomvhSQ6sixdiJi3gHpAhTHM1iwKx1hrVnKppsSRDK06ZMwp6pQQa0JBODovwwTIGM2GjstV4nWWdcqoCKAMT7ZVDNQxSEusnmViS5+h9dJBwJuAhTmz4GaPRkFCJajEVNZcSWeDqGGEtZDgtmmACpb6duViNWBSDVbGqKdqDO3jEyQRRQqCwQo36ji7se6TwiropG+BRVNMlhrFipGHUJCe28xKaIU6U4CIgAoM+ISJYR3HS5L2S1Qq9bmWioBA3yzILdI9Aoo269alDaDHZiBkSc6SpTjbNNulg0amNBjJZvYTw9fAeAQlyq1WnCUW7jGDflwsPeccJ0drjSW86uCciKaeDqCQ8MVSyilEQsxCFKBUAvyVRpSuCJPrgQHC4YpBoEDG3CI6WFKHZSkRFJ/YhQiNmmdgcaBURRZlUirFJi3kESDHVhKDQ6MWVNLrEQA4SmuA7PmofERlqLIElyYCjIn14z2SIeYqOpXrX9FXO0HAZ4fxaXWOISxlSh5WAAeztFuPC08DryCJJRSJp04K1VQhoSwlTp1AEUAUmUhBKfZFJ4i1KY8wb8ZUKXY2wdR/SdKIVdcDoHU7KlZNBIdAkYpXNVKlGkaViMQyEdEgnIV7OKKNJ34ZvpVMCcsy1RxaJXOeKNFlfmXhmj3TPNLEihkB0gt+zOGPQ2ZlTNYwK1FTpkwadoWwKc5p1NkYo3ef0WlxqDtSlVR8ZugcCpArzHAUYDULbjSqYkupmRGQyxgZSQJf04mQdQU1dzW2MGD1yV2wRS0kJMafwOoZxJJAFVkzqDLUMWjlH9wGIhwx2fGBW9UmklmpWQP3aytHprDVoP4uJZtQRieEYtARFNoFBFGIo2sMiDbpDdxMb1rfSOhXJBFkSILdGzMVRRooPGbuIzgOAytFEGSGxJhcACXRKpU4BoZKqMQSU10ZW8qtkRPU4hLTSRLjL179z9Ove/4nXfegTyynNxkTjCl0cTOp1qigIbQMjDT6rFCjLwuwkt8B6mmvNiVeFOjKZqBaRTchEFYsET4RapQxmPFAc4xxlIRS6XRK0IqWgmEpJKYCRRCycVh9w+PWfcPtbPPOuty9mVGCYhaonG95fuGaiRKdfio0tvBMGurrxqoDE0iTreiKi2TR3DV2oSmYRBWpSSJ+Db551wmQ6hRRmGc8h0S24copNkUmqoZoUo4S6RuOgxkw/yKR9kxtRBwlPQlOA+MfKmBJIKJi86Q0yMIkXfP0H628P/fpZz/zbFzi3jwx6QyCsYfKVyN53Dhow6VnmlE4MxZl4TKEoFjk10kEVClwsS7gO2BC6WDq8iDBRWdWK0OZTohZIUUApVBlRaLJhwqVJlh46BMxALbCEdjUX6uF7vMBDT5AmVZKAwDSDp4vmYsWtVorAK72N4HO7AGm1aXU2CZtcqsf6+90oaQb6a3fvHkGtISEiAXL0hu2IRhKjCw1LCbgLnW+4kLGplZomQIZKN/SQ0WM09xVWheqqYdHggPAyWKIWMPVypbw1OhpV1RIh3WPE0BTBrHTQ4qppWqA7w2bShmRVJ2yRDGxx4y1FKHJFvx59zIlL7wDOevZzTrXy4J/vw/1tf/3XvHLuc53rkfe40wE7Ozz+5Be+fOR3j+LBf3Sn1rqzs++Kl7rE/e90u3e+8sVvfN4z73Xnu/zORS+mE58m4ic/+fFr3vyWm9757nd7+J996gtfdo//MKOdAw584hF3udttDn/Wox/29pe/5MVPfsKNbnDDc/zar0OUNH9w9A/f9M533uF+D7z5PY64/1Of+8G/++R/rVgIy/8N+w9++rPnvOaNN7v7ve/36Md86jOfDgao6PnOe/4H3O0uf/mS5z/8nnc55696+/dLkp/hdOs/pdC2+7ennuCs0RI9GUKtZWdp91zSFhbcUmKGzGDhZ1QBDomR4WuMKJNMlCl01rGpvdjAmo6YW1yYFxjLK0/vDFCGqqGKCGOa+ZaJS4PVWxGsdGCCTUnOXqA9N4FNpgTWtMJM75MkaxppidWUqakyHbkxl4iKCVQkKnQlbwkWXctR0FkmMj1MfWJbXJACVrLCnC5VjPOQChjEqjAnWIiMOdtOyDykds5fZm0iPRfpi40kcc4y8oVIrglW5h7zwgyU4vCMYb2zgTAEs3DsZNTMHO7h7jaiBGeJl2TeTOZPJC9wHkFl/s2yKiKR5FAFRbVkQRQqaFpYlCYhhKnqmQgXzhWpVcQsIryRU7I1cj6xn0HNpsQqMQHAbCxpqRRSO6QDIZnKKihDlccxBrg+5QOwzjmC9SuZy23tvXsghsJFOcmQmERK1TRzIUXJAYmUdVgWdS3W66ZPm7QV6gmgBpIGzqdYKRnGa0HGSiDUQTyoJ9YT3sgQuoWtnuTR0RKNzfXGk5gHRkNPJCUEUlSI2VS1aMA7rVSyGwGik6wopoJ6T7CpSx2Yhqxkk5zItdJ02C26xcbp44BYWOEQV4PQr0Iw4KO37K7r9Vq91KZlMACWgW1XtiTzbpQlfMQ2KGtHUOAlMSg/xtCxVT6ud6cjmevsPgULWgIgxlXNREQ9IzmE1TB7SrQKLtDSsiS9PBEmA5KeFh0yQn01klH3qlIhE/1nWK5oZAE0xJ3DDYMThE0cxYF1MPA0IMHYIIeVZ07DZzh9iJyaawt6wYAF3cLpSxS1qYQYHaZ4wnNINkNqaEL4U5LO6Mhecxiii3ReEogOiqolCGZJXp1ymROaNZKdI/iWhfuew5MQKQnFKDlNwmwSQn5CVCC9wosRAQTpmSWU8sgQtPBBA/HKxlD4o0bXyBW92OfQoE+2ob5YtFUYz7UvXd2AuiKRQZJUauql0MOGpSXTSJYKCpiyNibNSipZOqhUgfKMpPZCgVFnpgagQXejLmOigYgJmAat09lQaBEppqVOyDp6YS9BrshiUUvfEZ8HEY2OHBRIosAtPdaApJpEHSDOtOVADcyJqi5KyYUTMIYEY0c0OdhWuYVTREUdZXQdS44lSI6D6GdVCLpPGAICh5aSgNlguBZeTB8ZzFSpAHfKEzAkA9qEYNCgoPqEzSP7avQoCcKn7ojIlPC67k1GRIAjAjSvGNQig2FLqLjbUKNR+0b7xrqodindbThXeFKoHPUJz94ZxrVirqmawonrLqJuSpGdtlTQ7KXWQjlHhmuAgFB9gZiHtW6jrU5TvBRyjaTg8Fmwr+hsKlmFZ8MQ4WM474u4Soc2EpAEpak2KiscC9KMrKJCMdyTvevpD9x31Yte8KyHHoIYtGATdMtu0tYphYSpP0aVIKMcSYMEpJsOTIgiYHaUyTDB6e00ObUgWGKlQAQVY7XhErKoBvP3jtClgsmvhzYTSp3EtYI+IcKJCYgojCcp6RJB6fGPG6msiwB6aIqlKVQKp0anPlS5lWyGNgbH72zmmU93mghdBEPr2BCAPcV5F0TJJItA0jqMcqyOqfRbC3oCtVGwKk3zhitjo69B+rpz7qpoMWZkUepLGfmDXknhsZJdTeigvSczKEMBwYjM6Iz0RBSjqlYyzVcfJTGyc4ozTLSgUi1Hdg36ocxeqkMHPbVxOjEbUIerMuWhDobjEHqucK6s7kWPdV2BEcoSgkZDWjSMZdCJRiCYpY2IEJeh0ukgOWUqdw90F08xErDm0TESqy0sdTODRi4ilRfoAA5ZRnpICDKgrSl9qXNJhAFIYQgUlQICIMzLYPrQYgliLUnRrNHVR9b0ogyRoRpk+6Of/Xy7NADnPscvXjjw+J/2b33/6FNOWd82XOzilzrj6U/P68cce+z9/+xxt7r3/d72/g/x9H91F9mZN5f4rd+8/+1v+cbnPPWdL33+nW95i7OdbZVkd3f/X7//fbe8x73u+vBHf/v7R//HGYmKKETOdOjp/uCqV37ag+791y997que9dQbuNyizAAAEABJREFUXv8GZzjLyoiUv/O9777znW+71yMe/ce3u+N9Hvukv/qbj/38hJOEN/5r/z+LAGPyM1/+2v0e96Qb3/luz3nJS4/6wQ9O43+xi/z2sx776Le96Nn3uM0tz32Oc4jqadf/+5/zxCS3DglJLUWtrDZlsLiqq0dsffC5mAeEMZGFkaUd3JEwlQk6pbA3kFxCF9cYvDl0bPvIJZnaZsOeJExf6bmSmhKTmU6MRBhYScdwjGGZZiYGsNwhVVR1FYYFf3af2GmMrXCkmDGBqrIeLIrGPO0Fbg64NvAlf12itGF9TRTUQEezpSUbeCYFXekyDUxCRZW8tKkMW1yW1AjRHGlgAmASIq2uLaWnYGzCjXIN5AJzM2X1ZFJUEsw1c3TpA0tkA6mYcoBV4+McwqtEKmAaRboa2RjcVKnHCjWCSK4GAIJMK7KaGdjQVR3KVGWaYSWYptSEmwpEiQ/COJdmMfFScqomOlmb5qXOpFGkTd7m3ueW81ANJjXjtJrCcluoZw7aqDs602uoAEUSOlIGWJxoGEn6g1Wpk5NayHAFSogSH1FKU2erdVJZ/w+yEBorJxcyUFFJcxINYNWherAzrRoo2efgO68sLlg8o3XtfcIQ0IAZ4WELa7HMWedRmfizLz23wyK4QpgqS79DRqYH2zQSjGEZBcXYL1WjCUa6CKEgCbqSddqL96mCVJmUbVbRLApNIh6ZvUkbBko66CPL1rEVGRvRKgViyZrpAo1k/5EEy5svAxhmYwo25n3SIUV9Lq1Ym7XPPDd4SZYqdE36DKh/99I66MAh4uBzNHUHN8pKXxKFgDooZwp9KPhg0XjNoJCS4JyULWyogPAih9BI9IyA8guMyNyCurhbahGjLdMTbcCH0leSoK2KCwywSEAASI+MZcjoSo3Ih+45LLQ4dUnj1dCAkJiUwEZ8U4bxKJ2RUZpYck4ZEI9ISkqRURBu2Q2rWekUkryEWVGroEpKF+7oiU7+noSBopgMk1aLTxVFhFxHmEPTlCYzSkz/E6YXWSemQ5z0plJMZT1bGCcoANu/SX2nxlxT6HfhbDL4eDN1WB3FxiqBmJtQr3AF+1ifKXxAnTRqlMJ3ZIR3oCrfgDg/VyVVa9jUTHensS19aJACNSoCQQYdiSLJekBjiLYVZ1oTZkmQ6IzFhNpElMw5RuWeo0ow2IsQyEA0kUUwSFCENyJs0O6wBKXl9E3q3LR0qhmwJqUlGMhlDLqTDVAMsZAiKExvsyZT4dRc++gYg3wJp2sAsXrElGKwkoWhmJDkTQraai6UM9JWcVaaSDpbYBpitBwf1dDW1nQaNmNSNRfQnQSjgHBaQB2cI0wluj+nBTQE1wRMFGoySBEimATVtTYqJAWQkiwEnsMp6LqnE+A0hAiZbsUWIyzY2yi9s2opL9DrOGWVnSdpPmSkr9hJioivx66D17iqiOBFHSiDA50gGahvCii+ZIp3d/Z/bYiL8nKRrNG5TjCnCJ36QMgPJMrJVVGNAsLcpE3aSx1Fu0Uz0NPoLUX4EkRFIJZqhEroW0zmEdV9WjiUQKNQe2ZjSZIFn/KFTVI3YgTNZhkMQ7rToA46QjJA4YxHgjRNyTGGrz14Dl0dxxNl8k31CdTABn1H1EQE3HLVlKiXVtD4ImGTVked6FHUg8SUUHLW/u3CwZxBy2qUalrMCqba+eyLd9ZdJ5mK1TIZkMMZwk7YnVg3DJdetBXOhahoNRpxnZShiEIFJGKABk+KC4eNNOoj9CIntQK6KoSxsUzFrZII1EO8wZ14Vp1VSyB5SQSUQCCEL8iHriWSkZlhOqoSsj1gVHguK8Rb54OCQt9mOGhyNeMW3XrnhCDopmIkAkVqUD7rK+0w8hdFMZafPZ6UN908K2Uf9JiteNdwQ1YxVeUP+jiFV0osynpTJbgnyfGuQ2jKzGGkIvRtzwBUuO/xT27DEbQ0qWNhZouii2H0TO8pzSdWTu8rjgW9gsYZP/3xd5dlC+AC5zonP39p/9J31j9f4cUr/PaFhChlvvov3/7do773w6N/+JHPfoHX/9N2kX07+y56oQs+9J53/eAbXvWcxz32gue/gKnt37//r9/3vt+76c0f8ewXH3Ps8fm/zE/EznD6Q692ucs+/SH3+cSbXvHGFz7vlje60dnPfo5Syhjjpz/72bs/8P4H/NljrnHzW9/i/g974evf9KUjv7N/u2X6+l/m/F8E/k0E6LknnnzK69757uvc6d63ufcRf/X+9+/9tmEefNDB173OH731FS9764uf8yfXusbpDjmETvhvUvlXN5al7V0TrNlRmdSK1IppGlZbETeI9mD9rEOMWYx5SQDnl6loNS9Tl7kxSTjjtAzno+ncanRdo66oT1PyaI9HMlMwtCIwQfbJVKVK5PDB2tCi9qgIqILR2lWYQJjYwLiFBTi1ca4zP0kRlI3IRAEkRxnNRsCNGS9FgxeWOrZlhCYy128KnBHiKR4mWUyqVk0mCZGQpDZpPUpjbooh2pRZinMaoidFArrawI50pgSNlAhzty7TrlnDiFgsvDinADGlVFC3narTVFONIrswCfp8QExMuynDxdecp14oo1GGSEqRQvmpPcVYs2sWZiOKfRrqv0AxR6JRSxFCGqJMcIa+gdeqKkXZ54068cbce12arI0okrbhQ9VGEJyQF+uk3YwFrvfm4aOzk2Z+LXWqyvqsEbTghDFbzvQHBd/iKseYZwmRVIRixZb8itMe4F2Ya3U1p/8YoMRjZIJfSTEnBMcJ7WPWShkSIzvx9bHTnS8z2Lhl0jwdm+GzE0ajf0UaBRysK5JASUxRImskaAQ6pm15UJIw1mQHEuiWW2hXCFAb6kh3ugIniRlKEaKkBVKSamSLvjvA9h6OQoF2t61vGz1SSqsmSGM/rZFzLXNlbLiMLcYSwznBYJRwaA2ftAk062aUuetOcKloOoHtwrbG1nojVp6aAukoPUSdLi2KvW2EbukUkjqJbEaaRwoHa4K+LiVUWaCHSjddtHC2IQRtGKXJGCEtiOWIaM6HvNZjQqH3ATIiAwF2SBOq2zSmOemUKbwIWoYw8ZC2BC8hwV0SRqgwZRRqL5Njs7q0KYVhxSUSDCBobJU9cBNxYHhmgvadFXSANHGv3iwpNNbrQaUYFlQQItSuDR2ULHnoHfyhNih1n9FmpA26bDo/TMdGrMQcWTkqg4w8qDLlt6nM0zSZpKULKAEtJmX02ftqvI2Mkp3G7ACJG2bJSSmFFeppGJOElYW9xoJV0RAxtXUPRctIeqxKLQViqUZYlN6vFC9CIhzGMOqpQdY6IAGaV9fwyVDxLH0UoeFUYnKXruhlJSSKKsGQLZESRYSeVQwiRDGGJEmp8hnArLUkyClFqxjzaO05d2ZDjaIwZwqZCoz0gi5OkUFzI4g+CLSbrJRyKAKQ9dQod0s2ZQCEVi47UnZSCniGlERyrCoKmEiqMh8nui5swtoosmym7T5r6rScpFZMqgbhLJLwap0zNItE1bSN6E6WTZaKXFtCT00Vo4MD5JZqwR+6foTBEVQLEsStwbu7p3C5wFnxlaO+f8X7PupS93zUM972QextN3nss65yz4fe6s+fevypp7TVtrmaQHIb/s5PfO5uz3zl5e/5+Eve7THXesjTHv3Gd3z+uz8cQRwtIgfjxAWu93/Oay55x4fe8rEvOOWU9qJ3vv+Gj37Kpe72qBe9/cMj9ly5h7X8yje++cSXv+lGD3v65e/+6Kvc9eFHPOn5b/vwx5dlF3TEGTHRK4g8jnjuG65wxJ/f5qkv0aq8M0Wv8FroY+X4E0568Vv++o5PeMbl7vPQy9z3YYc/5bkvfMv7jj2Oj3dZtWRJJNXJZNZ77l+849J3f9QNHvMMJr/v/PioJ7zmTde87+MueddHXuV+T3zoy/7yyG//WFom3dgjs4V6LwgtJ56w+/K3fvi+T33ZFe76yEvf4+E3ePRTH/2qt/7wx8cO0Ubjq/jqHLEmkFVSMuNugAbDqrrqUvnsg6Rr5GRfOPK7V7rrw658p0d87ehjOO5z3/rele/9mKvc55F/8JA/P3XZlqwyjMLyFvdi2KgUaEIjY8Dbdv9bPvDxuzz95Zc74jGXvfcj7/jE57/07e8/8YSTkIrcCKMB+eTXv+kqd37I1e/6sA9+6gt8/EQjQ1S6aPeXv/19l73rwy5774e96aMfL6MSwhBxlcYXrtM6yiuY24d1rVEmGMg1xtAOSzMimYkI46JUAKVjsQiWJmC+5tNRZZSolpR5SCU+SDENPhYxW2q0bMz77qOaVXYlOpRZFmkN1kUU2NE4QLupACUxS86yJkZzeI/Ws3kOZihwKTchTPhVay21GB0hlRaTwVHbsSxUnDEx8zHmPA9suCoMj1KwmWXeYTrjfO2tjV0+EPPWfYEHOn3XBi2fMsKXyAwwODphDfBMgi2G/+jn69+fqJXTn/HM+FfbcT/+xULlzGc4lDf3b5dXv+PdPDjDGc54/9sezoNf2j/wd3//9g/8zY9/dmwS2l+69+87FZGD9u273rWu9p5Xv/TJj3j4ec9zXlVtrb3slS/7w9v86Uve8BZ2tP8+Sv+DUSK2b9+Bl7/Exf78gff5+Jtf86pnP+OOtzz8fOc+9zzPEbHdf8qnP/2pp7/wRTe6452ufvPb3uMRj3nxX7z1i1878tgTThru/wPS/3X7343A7tK+dOS3HvPsF/zRHe7yZ0992neO/OoYw8zOde7zPeAud/rQX7z2eY968KUufCGGxb+b5H8byLeFPNk58KB9O4f4YM4cYsKKQud09yJTlVmNiUHYySQyjQ3w7pjbYrnNyvYekSKhpZjO1bWOLuFgRCv9VDOFSYEs+Fi+2mAbVjov7uWBkjoGu3Hxms48qLkFG65EAJnB0PdFPSxQTEr1SXuEbwdGL+IHGHZCSk/H2K6PDplKq6VKZpJlMdbMlFU0HVVPq6p1TWAOeq8I2Au7pobUUJPUZKwDFHFMXdAmjLn4puZmB1zEsQ4pNlWnSYJZog1sA0OZC+sATPsGfQ6QCCDMm0gk+9nFczuiUWRmK8xQC0FED7SwlFnY2WgHOK6Jd2V3t+YhZjWCmAoxgUhH8SiZPKZWsYmYWVuwkiJSEKQK2QoZrdOIomHwiqJortNjkF9sBQ1wV3SNRYJCSLai9CaMWgbWKlaEqsrE2UEVgvQDtkDFCamKlIAu8DWDMo02ML134doje3EXcdEACZnSurQW+kISnDoX1JAusgALxgI/NYmWGuheqTF0xV+xTkaBFE/zbtGrUEGa3lKAkVi0LODdZr7dCcqNNB0oLacBUT6T1yV92S5tRFLwLelQdtIZ1C+Z26GrZtocXKGhNuzIUuvasu738FG9T5HiyNUkEihSik5Va+gUZXYu42k2p4dMMhS7Wfb7+qt/TQwsOTJZ3ZQ6m9YutbEDTK8YLLmam+jTWDZO30KhOKubLMvW+8jEiGzDPViIuoRKK8lem9fYl1sAABAASURBVHrvWzAhyZ/uLJ5wDGKCyYJiR+ZoVqhmd83VT0YBiqppgSmllxKwrnAbwp+zl50AxSkqIoapZJ0Tm8WnIUIAa7Z5ZO3YpG+kV2laBkHLJOFZs5pSmhisn0wAu33uvSAhlApNJcmbAzV8+Ni7ltHRuzTUqKsbmC5iu1HohLQ6HZsuQorcBZHesXqxlpDSI/sYgS7SzIbWxBxSYWXVSwGIuwhv9KTyGaOz7vOHhPOzM4lo1JpqSBGVvpHdqgtZDy++zPSfEdakxbTITkxFTBJogq1ioS8zO/SiHauEjPTNsDqqeRUyTs8Wsk3sGpbJ1vdBhax2kDsZU6QgxFIYO7BIDBcec6bymrOzL9NW6MgCEyFoYZR5yWRY03Y7Nk9azctKapgAqwpIcLbyvppoMSND4pLSkrI0MEpz1Na0dUFHtVZmR2HWWZ0NEIDeg3Ug5UEG6pBpF7IF7ZlDCmAVZRIjj5jQqtOkQzRBhpCSoF3CChXe2nAsRoYp0OpR3M1VtcpKYFabjMdQmpCdYIBmmMFt9fUlI3oRKi2gV06179v4VGNWpxVkntymkdLb2N1tjXt351TuS+/bpS2tD6qrajJKjB8cc/w9n/G6R7z0LR/98rdO3L9l0fzhsSe8+WOfueOzX/6yv37vlnEFHfTfnCF16c4Bx554ykNf/uZnveND3zj6pzw95viToFOaLT5e/tfvufvTX/6XH/uHo445jrdO2t1+7Kvffuyr33bbp7/4+z8/LiSA5Id2viUdu43NJx2cixThZZGkfp/6/FcOf8QzXvzOD3/2Oz/c3ZP/i987+iXv+thtH/Oij37m6+41XbGbaFtpu2N0jvnZybvv++QX//RJL33j3/z9T044hXx/duLJ7/jkF+70zFf/wzd+QPgZPonoY42Qj37+S7d83HNf/K6P/N3XvrPdo/+NH/3sTX/76Zs+5llv/+CnfKCDMWsJj+ROgVfbz5Svi/YobdQetqt9O+12W2SMHESVTCOSIPOTInEncac+tL80Xj9tP82ZemIJbZN979if3OUpL/jzN/z1x7/23ZO3C2d95ts/ePbb33/bJz73Gz84OtcEs6mbzR2ud7XTHbBz8v7tC9/xwWNPWSjF1Ka5xXe/8/1Xvu/jp27bBc952B9d9pIEsNJhV05dhC7pQe/IHQRXEblGAiyF7huje4TzMGqGelBOrMlHKwzNRqtLbJaxiRYsrmaoMk2YJ+iGUWW5mWSeRKEIjCHLsEFwirZZGqEqRCsZb+qozlixHpoGqCUs+KkykklQtivWs9GPkyW8I1KlFJlMlBcV2oaOltHcB+WVrBXTJKYqZBFoMnW+c5WiJAEktwjE/p+f+MM/fcqp7/lknrx/ZCymi02OYumWqQF3Hb0Ei0UGA+knxx5L0GotZzz96XjwS3s9+Ay/uJLJg69/53s///EPefB7V/jds555/U9LePxPO6PreS9/xd0e9OA/+tO78fifrv/SQUScdOr+Y447YQ/6X7r5T6fEotz4Otd6+0uf98j73udsh63/zuyPf3z0o5/ylJvc6/7v+9gnxz8G9j9N+I8eCCClzle61CUecc+7vvtVL3n98551/7vc+WIX/q2DD/kFIMcf9/MPfexjT33e8258l7vd+G73ustDH/WCN7zl45/70k//Byrgv7Z/E4HMHxxz7Ove+Z7b3e9Bt7jnEa/7y7885qc/4eADDzzoKr/7u8/788e8++XPv+ftbn3Ymc4I0ED4j23HnngyJ+6bp4M2FdEUTA0JJn7rvbqbcptE52TFhWRIumqkpgvo7pnhYHGUoAgJCSjJsSpvgqE8GJaNARe8VovPxoduzuiPXIslhlvjg1dlPJequV71XFhPsBLKwTiERjdpMi+6ibIzorIA+5Akcwkro5Zm05onyLe2Sbc72qgI1vtW1JgMuIAJpiaAmUxkvUPumdTAeUk0dB3HtENmqTCUKUwhonWCzQOloXSwReK4ScvMQV08LINQCG8wI6mIRMmsTOjIIBxLMoUlIXSPQSBCPI1gETup1HAIazxHa+FF6h7JacaZHAcgoFmN+ThzjBziBI6ygdAxl5o1jVj18BpeZZhE5poPtwu46HEZmKmIrKwqktIhlSaTWLdMVIUqMCl1TSVQZJlgts5QrBRnYVamsGGDPFrNbkyNnNmQgyp65KBm2UKallEUAs/gZW8pDrGCIsBIUDKFaaaMEcHy1pGteFSQuUKJm3lSBVVYiTpFZU8aFIc4suiIKPPz3DjHM7HQPRxJiuBUBQgrOnwU15qzYAqAFnK0lXVQiQFtvJaRQWKIIr0gSMGoukoBWxjy0zLtvaoL6SGNvHKA1akM1xzS07qKV2EXRtOk5IC79CBsrJS+ydVAAhFAJ5VSTVQTQujJjCapLpICoZrGT6GdKRL9soRqlDEQQxJQHSGRIoKiwjkaUhvmFrWTZyYKB4GUqwShk26Wq2MSSckcRI50JRjIVaDgQKwnYdQ/k4e91lEn11ohdB/Ituf6WwVqtAPnOu2IIeZFwlY/a0E0g6pKuKpWRvOYtK8OOzSJNju1dYh4mpqVkqJNZcnRokUsue6UnaJUySqiCln18AovNcpEqgWr81FAR8lSVnGTktAQYZKFk4qiaE5kMiLpTW2ERwKiIggnSTEBYyP7dsjiBVRPC7kFhAMJ68g1Tpamybh2JRcqKk7caN6amwj2NlMfNgJBWqolVUWBTCc0msrgKcWl0PRbH2Md6DXJbBFsBSS91TESpMrRkASGohPLVAPpWY1Ko8E9qUdm0PzGcaJUIwknOieZa9IEUM856eXrPQ6i6V0DUwpBjgqv3mfq0ngvaJGuq8HIE64WU+EuZtSE7RYDxMhIqAsYhSPTRk5UwJHragwcR9dV46ApkwyMdjEFJQFTSm74OrQXTy41gzKSS6E7hRrHogQJg2EhA+CFpKHm3jZ9V7pHwEMU3BLhnLmEjl5jMcrLiZoikQapdZqmYkZjK85+xkOecocbP//uh9/gCr/DqdwffvgfP+cuN3/4za570GZToDXw82NPfsAL3/yJr39XVK5zmYv++Z1u/rwjbv/wW1zvPIedadvHK9738Te+6yMlJ5TNYtYngZAMjjrm53/75SOvdakLP+5ON3zWvW9++LUup9S+2ke+8IUXvetvdlu/yLnO9vBbX++Z97ntE+9802tc7IKm+pWjfvTwl795dz8XpwP0hqQWKyl+DyfsE8JS7FNfPfL+z3/dsSedcsgBO7f5vcs/9a63eMpdbnG7a17hDAcdcMyJpzzuFW///Fe+xZfUkxDysZpXVjrH798+6pVvO/1B++5zw2s86963fOKdDv+jS1+EOJy4f/uUN7+LpgKEZkofn/7yV/7sFW/iYuYspzvoLte58rPvfssn3fUWt//9Kx164L4Td5dnvuW93znqJxg5PJmes+U/iUnDVQ8bweWpNM0xK+NvSHS/wDnP9Lx73PJ5dz/8Amdbu9bznv2sT73X7Z5x79s8/o433UzmscqJf9woA5jm6b6iPznuuIe88HVf/v6PDz3ogNte84pPufutn3LP297kypc6cDN/6yfH3vf5rzp+9xRalQn7LKc/6CE3vzYfc3zjR8e86W8+JW51qC/+/Pf8zfGn7h4wT/e92R9OG4bIYjmm0ec+5rZIOp2phs30DHoHYiS6ijMGMoTepuvai4ESHGeAWklYBuiEzpiRlBHJByUjhZnHa+llXdhYh6ZVXR8pWZFU0KE9OShUwgpocKHGwYSlWC0UScHWqY6SUgRG2pRuSOEiSlUxgnXDlrRG9o4A5ymUrk03peNVM5mU/QhZpzMHlA1kyiG9JXcKnUWDT3PSDLsf/vzuuz71ozs88cTXv4exG1WjqGgp0Dl7iS6Rq8y0L3lU+dnx6/+3ONd6ltMfgn+1neHg9d9N4uWjfvxTfn7p60fyk/vlL3NpkVVPHv/T/sNjfvbFb36bp7921sM2c+XBr9zv9NBHX+dWt7vHIx596qn7f+WAf37xdAcdfPsb3+CtL37+bW56kw0f/wJf/uLn7/LAB9/zEY/54te/+c9H/icci+7s7Lv0RX/7Xre71Ztf+Jy/etkLXvCEx9/oT67/mxe80DxvTqN/9NE//NtPfvIZL3jBHe73wJvc5R43P+IhT3vZq9//95/72fHr/yx52pj/+vzvIMBHRR/9+8/e/4lPu9ld7/lnT3v65770pWW7y/G/fq5fv+Mtb/HWlzz/lU9/0h9e7aoHH3gg/pWDcdj/1M43KRy/bb2PXT6gtUwwJbH54lHFEI9Mhrm1ZFhjCd5Rh4HFlrXJjRXJvAkW9+EjuMJZ//3NhO51uQ4dYJCSBYKUIg2jwJVcQhjLKRDohGShrV0yWK+S9ZbJwrN4zDyRlbNzHJOPzC6bRThUF3DBssBaUbbbQqkwMpj5Gx9U8jHiNMXEnrIWn+ZFyuIZe41MsElt0hel6ChdpoEikpOx2fO0OeskFcW4h1pDbDEa3DWSuXMK1iMzPhDf3ejuhIVTIZqoUECHoCODkqSDm0hQGzVH7Z6DZDhYrEyiBvHkQNPBrreKFtNalF9mPNJS1pqH4RjD3A1EgOgIhsKlO6s+yYpbDB0piRAlfGTLZMmBcwHpuqqzaZ7qtKObOZW5LtkQz9S7z8yuQnmKqsASNULDu1tbpj40RcAbWUCsmmQffSxLLkMG1jQ6KSzUXRKpItyhAfoIhoOTjY1GnWATyeYg+EsmZ3II6SqqMkFjnU8SIpOIpakbNdJOwyT7AJ8yCij/jmpdy2ukMGP3SVyKjqk2U7C/XjgecwppptL1YCYbsyoCijcStHwuyJFDSp9KK7muakwnmeeclQ4tK+cZUqvZNEEQGZwbagFjCenb9F0urGdHBXifMEIbawRtTiFRPKfGGW29GiGR1TDNYnNRwpBTNPhoRbzOaUVATQESIgzrg+QydtB3JOepVt2RZZNtH+2V6iX7NHI18hjFB2NRtqaDJKBYSvUwOgE7Z6XBTYZGZghtMAAX6TOyxopu8Hn3Iusbu95LW+oIqLiClu0M3qQwqilTl50haGUEfZpDoAjk4mg928hMMjRqMU05W6dhjGg5xla8lUiDARO0cJ5HDiffjCmjDld2GFkJCmQWF6Bl8UFPtNkkJxmVMURyFfRcqJKEKOXiOYOtYJSALhK72Zax9O3ITlbEfxiPMESJw1xXmFMkCx2BRV0F4ilIWBMlLIQndLhlVtVpohugl3DeWSBBU8rKQ7MVeCmmpUKqjAlLcbdF67ZOvXKtwsZFBqllRVel/dvQZdjWs6fQeUQMknSOqCswQ8RDa1IsAj92sezGoGMuBII+ORNZCkk0o+VokUmhiSb9OHrBqEHjwtNaxMhtesdgHi0a3GfyGnDeJmgEBTbTgxFAEVRzMd7NukylUyhKFyVGTdpYRGqkZqpQMoBR2SufCA1afcqyT7XSnBsZ9BgJughjT6hVQkhZYMxy6lJFijCxXqC2AAAQAElEQVRqkuGy7eYjwWy4Bw9MISaeQXEACAAOLc7Ff/ZJtvPYlrFIDqXhSVr3CE9i08EHHXiF3zrfFX/r/Oc98y+eCF/8/Oe53IXPf/EL/NpOsYk8GvgK4ivfX/+tqgfc8NqPvPUN//CyF7vyhS90kyv97gvvdZvL/sa5R+Qr3v933/jWD0XUa7RCM3MaTOXO177qw297w6tf8kKXv/D5znOWM1lERb7r45/pEb9xjsOec/db3vCql778RS90jcte4gl3Pfzwq10GwBe+/YMvfuMo8XDtbiTFa+tOyZNIyLz08Yy/eNepSzvjwQc+/c6H3+161778b/3WVS5ywXv98bWe/qc3PtPBBx6/f/tsDjj1OJ26MuXzuT4dHcjM85/jLM++5+G3vdblr3KhC/zBxS7yiNvc8FoXvxCpf/snx/7wRz8Fre+rF7/xwx87af/2sNMd9PwjbnG3G17tKpf4jate8iJ3uNF1Hnjz63Iwb330c18pRSpGslDEijavI5EOaJoyPVED6SIQmbBG5unn+Uq/c94rXPx8fOnBwYccsO/yv3mhK130Qpe7yHmrZjR3ZgrewPodJETniSjdX/WuD3/rx8cctJmecreb3utGf3iVi1748hf+zSMOv+FDDv/jnal+72fHveNvP9WTgoh7ueIlLnL1i12QJN7w4U/+8Ic/oqU/9tVvfPRL3+CVW1/zcr9xzjObRZZIcVKmmwsF9q7oE3JSwFaX7vSrkFBjqhF1+lXhfJpWjCdhSLHQ4hOdfWaJGLkil9nSF4wudEJCEWAMIUiz2jyXqdoElCANUYGhKJERhQzUpjNURRM+tC82Oq2FmmrgDk7L6lkgqsQkQ9PdW/dlrMu5rpNm3aNYimhFCFzEVw2qKRNMknCDM6xz1UAtQiVOfNZbAOjOfNA1fyfLUE2rIiVNXBEF1NRLUp9M06H246PXv1eupRx6yK9YqBx2htOTGveTTl27yRP3r588PWzvN8F48M/3z3zxy9tTTuKVa17hsoDgV26ZJ5x44neOOuq7P/jhbqdRfuWgf3lR5OxnPezPjrjn21/+oqtc/vICWZbtO9773hv+6Z0e/tRnHXvC/4YVgshm3pznXOe6zjWu8rQH3/+tL3r2R9/8umc+4c9vfcMbnO/8vyEilI959Cc//cnnP/eZF73qVfd+yEOvc+vbXfcOd/3z57/4vR/71Aknn5I0Dgf91/6PCBCQ7x79k6e//NV/cPu73u0hD3/ne97z858fAzq92lWucIXn/vnjuCx8+D3veqHzndfM/nHS/+rPXzvb+iLu1FNO+sRnPx8MHlSK0eGpoHsmJJiK2Y6t+SIzBuMvPGUwnFE9JHxYDhOGXkQM6DBNxjOSHrBR26gw6oE13DOwlViKrNUJwqCGatQ6qA2nsD+a3DjdakTNqMLUJPsKGNzMqK1Kr1NmUbZoA8HWaniIYqVPQboNMhDGy7AodanFVVKpB5OOTKMH+zFHYG9TitqFU7UkSKKp9FLApNFNF05MzlpVD/Qhg5knI8Q9Ru8ayarP3JCFybv1bBnEiwmItS+ksAFiy6NNbEhVnarOLBq2ykgCnhBQqgpQmBwSQfiMW4VSG6AA1L1CmKW9Sya0iE3C2yIcEUATmiB4ay3U62ksioFSAtwlhQOlZ7pnBDS0Vqxd/DpNOE89a2DKUmSSIpCw4bxoalD+lJFceyKNNN2EjBLk6D1aZ29ZneJYmc02w2ovmbWrjIpmNnSVQDMM0KlgXf6CuBE5XpxDNsked2OYxCQDdJvVZkLG/E5DCoJun9AG3UpdTCkVKEU1mUqyGRIZ2LjXpJijs+gkAMsk9Do4fSq12gSdUkJZrbM7pyRlGiFOBsqFV5PIQoHpc5wsbdaoZjpXFBslWY0GLbHuUjUtCRtW11g0UwSrl1MmRWHpKEKnY6UJ9/Deg41OQ1KqknUam0oKFYwtX/VTTdUQhhvWTRJVxPa81WDItQJpFOughBQa1EB6FHgVt5Th2T22XsaY0qfVideIkXXx6TOVLBD22nv8QXDgqNwbtMla1nqsFbElj3OQu9BBIAgZtCTZqBbxCi4thiJH1T7VNlc+qx8xguGwiG0nyVlUVMmriw7zrr6Gl6xq82BAUhR0SVoip0RxDdfh6UWkEET4QCNoTi7Iob2RgmqUNQ0MEzeFVpKwGDYWgQu6RmBE8is63VstdPLccIU0FqRHWsTa1BSbqqlBM+AJUSJPiWrK7Ens4ZxvPSyV0mRBt75kjESWlDIy6SoYKgnwg8lnAn3c4bnXfYu4QpzzBTK5TIGCFSvTFsnQy0gdRbIKjL4ilmBUDbKgVYViuo2hHiAigfUaB4pS5QFpsu6jepSks2Uiw70l3PY0Gi3HEuiOLjFE3WydSivKGg6hrjuuRR3SXYAhxsktzTNU+sx1UAYsRBOU3Eh2FPqDh48V5FwilvAcsO4mo9QeZUtu4UFESDnqGGwo0yVixRiZmoSR6PEQkbL10iGBKNBqhUb30O4AknJqAT85LFRRVFWm7kbnyo7sw0dEpqQWCuiZBImn2NuCW5IJ04WQ/jEnnfKWj3+Wd675Oxe64ZUuNfMSc0I4Ig498OD73fDaZzzoAC4eXvfhv6UsIauFk6OBs5/h9H9ypYtvKgUS7ckZGMih3zn6R7x/njOf/gyHHGhYFUTHbHbzq1/mir913std6Dy7S3OiEqkc94+71QVlMH7/9stHfu0H6+8+3O7a7PPPKywBGitKiIud79eOuP7VOOPrR//k41/6Ooi+EZeNEApgKuXBN7vuYYecIcYKuaVMVq5xqYuZirsf9aNjYkBjEi0XP9+5bn2tK9zjhtc89znOjAC9QqFqcvXLXPSch67/f8ZXf/hTkSzJj/VXPMnxtD27ZJKb8qOZ9I36Dli4TNMjRmZXPXW75WBJ2o1umCLgd0TmmtF5Z935KjFBl9ajjznmvZ/9UiZuda0rXOxC5xVz7cNWK8eVL3mxy17ofBz93k99HhkuukVxqfe+6bXPdMiBJ5y6+5oPfey4/ac84x3vbcMv8utnv9G1r6wsUs6YYQyvkQFaGckNrDllCXNX62Ij6IMC0OXDBAwRXtUwSKEH0i0pOqSKVhLr09xqDVv9zSOd9nT1BbHN5GdL8aCymiE0HoxzY2ooDgRdg4+/SHmgLFZdkcShZ44UgdA5VzcunJgqjBAGgGWZohJ7eEajI7YhXW0W6GqpkOBPEafFlamhipjBLNfCVkIrtUjawnP/V7/fvrsuvutlLlh//UxTepXOTylNiq/YU1k+kSmDmjFIRrAeOIBiduCB+3jwS/uhBx942pVjjjueB+fc+xUsHnzuG9/i5z/f3eNd7/8Ar6jaNa90BR786l3k0L23NCecsn9hX/CrByHpHf/ylqr+1vnP95pnPPlVz3rqBc5/fjPbv7v78te//vLXv8lL3vDmE05aF0j/csZ/yplwO2DfAWc/61lvcI2rPO5B9/2b177kc+/9q+c/+Ql3vMXhF7zAb5zukEMomI9+4kknHfmtb77yjX9xr4c97LJ/dP0/vtM9H/fsF7z7bz959E9+tn+7/Ctt/lNkA/6vJ8Os993v//B5r33jH9/pHn9w+C1f+MpXHf3977KnrnU65znOcddb3+pv//JNr3naE//4965GJAn1f65C17zqVY0xnPnYpz3jD+/+kJNO3TKqmjAKhSlgTi2dOczWxqt4L9Esm0SL7lvnB4Mb62BNm7PuZBTpvE2D6yS1mBWV0wQWhnZaYBrdMmWo9tmWHXY8ygS/mzpExoTcBMURLSFTY08BSiFkO/vCRmGufbNpVhYfg1lGtsqW3cfU+s7Q2ubgg0AgKBKFyuTRabt0wV6JimRGyZnRDNZolWStrbzFciScxVSHkdl6rC9gBu+rTBk72SsoX/cYGEOGcw4SkAH0Bj7YZQ2EIou1OnWtNrBpOY+cJGsBVUJJaA+0EYSP0WuRU4cEhmKYCNYtXdMzQOK1ZSzZmfhLldmYd0UUZMJqWNdV2xSyJilqxPnsQTVRi2wKuYuBEI/wbYxteBIlbCsWk05zLiKLDpYOcsy1LEAw4C7BeWzvTHYoB4bQwARuwc6CfU4rqKKwtWuoNJ+IlZht7FO2+G6ZORJhRWzSgiKu0oBgf0hsxoqxWpaSO4W9u/AJldkolaWn54hc0noWS6mZ5rQWOyOCCEd2jQ5W1TEZyRbDDqurhrp4qy2QLAmrvBRAqOrCsl4BmjKHkn0g6aUOAUVMUI+l04GyDzTugW3aIJ4uE6QUpFANfrATAQ9VSk3byXpg39n0fVjmPmSXpARVptk2tZpQYqeKwzGaLK77XbdDtvQMLqelqQ6zteuY1DC0jxzpATcA9KItclvQZ+ROz8lDaJrqbcYyrSHnhJHOv0zSNnySX5faM/Yjd3tSqyJpZahEImjBMIPNSpWKbDfZZ2fVDSobSRSpPbIoTKVnbnlthA0ptikTBcyo7gWrx86zT8jSCpaiC+snJZ+AzRDphQgMoM0S+xysrtCp1bquZeq0gA1tLAgSJ6PJ5n02m0/RdPQyUAPVRejHnlz3Ou3u07KU7RLN4SOpCLxIn6XtSMxqytpNVQMjk2RBc667StaNzkYAaFoPzTQOcfpt9qJSi0mFw5ksutZUUtvQQb1km7HlDc9o4p4DnhKQHNaW0iBLsWbWNZ0Ec+3faCIuPAlwXWRqgkCsgu9EblKrKrNKnzXmKrYjshNSktfQRdo60kEws/SO3sIpVKY2aIjQ1kLrVB/sQSZhPhKCtOnYuClFMhnFXVvTrWtPxZRWW9GuDOtYxU6Bz+jMUfsgJupS9vs8hkWEoBcKEbwKqpldc135pMVyYI6dbc5O11lys0VtI8MX9W3JDgmnWELVJSJBwXl7BKm2Sfu+lGlUarBtziXAoFmwOvRwZFYTPhWBwYsuoTS35WZetYkhOQTclPJOy1y7FOHzoVE3OU002UxpepLo0rb9VMZO9hyoS04LVWU0ci7oOmEpBZJJN8Y/fOu7Pz/pVN65/pUvvTnABqUeXCt6QkXq+c5+zsv95touf/Lr3z61b0UZXiIQjp+n6cCDprnmgUUrwyKKoMLlvGc/K+9+6sijPvn1I1tfSg5juel+tjMe/LR7H/7ke9/20r9zMY+CMYsXjjxtL9J1XnxuH/7clz3iLKc/+Pcuc+FB29IFpm4zgQidcY3LXvgshxyUiY984Zvb/eLOXHUaAZjpOc54WOZOo/1SM4XJ4oA6FRXPPHZ3f64tOpie7/THv3+/m1/7Or97MXTzXR275ksiQ0wOPmD97zF6p2Qp00Y3Sp3wzzYN+tUcdcqNJntbw26V7axLte2Ylt26b95wOH16mprLaKkDhQu1qCtovMV96NRkE1K/9L0fnrh/S6CvcsnfTkR409jWpEyLIi95gXNz8JE//vnJp5yCkhR9QM565jPd54bX4tr0Lz/x+Ye+7NXf/emxfIdz38Ovt7M5MLIi2UXQoYXuhgAAEABJREFU+WufqysoKwwUeSv9VMTCaEAR8pNhRF+s5OrcksFwUVXABBYgeEIBJhlTGdM8F9tR7MvcaTb1LKujMoAyncviZWRbZCTDI/pqp1GVJhfwPaeTXA3RIRE0pBHXHnUk2Wak+piZY7HnTiQQw+hIgqJRJ7fJWYBIJXSA+U8W+t6gEb3mngtaSIlAZIACq1BbpHVhPlc57s0fwd520PWuoNUYhoxGJiwZSlxGyYVYWTEtVbnQsXbqWFrnjHLQ6YU//tUue1L+0+VLXfiCdWJKxyf/4fN9jH+6zoNT9u//+GfWpf+FL3Shw860/iIgL/7K/UyHno7X9594XN/+4v0MT39pf99H/vZZr3z9F4/89i9xgchVf/dy73zJ8x96r3scduazcNYpJ5/4qKc89Y9u86cv+4u3HHvCCbzyv3MXaDnjoYf+0VWv/Ih73+0Dr3v5+9/wmhc+8fF3ve1tL3mpSx92lsPEfhHbRx759Ve/5S33efjDr32b29/i3vd/wFOf/eb3fOBr3/3+Sf+O33bD/5c3utyJJ5961I+P+einPv30V7z21kc88Dq3/dNnvfglRx75jdPUOvOZznzd37/WS5/+5A++/lUPucddznWOs0IZgafd/M/8jIhTTj5lmufTiH7t83//3Ne9MdREZ8b8Bow61TWIhktvlllVSgEbozDPkRKaUocZm5pgPZXBgb5sZGwYcMnExZrHKFyFT5s4ee65aVnYsId3dx+STUaXPmTbYvQQVlL1WmKaoRPj0rtHtoKl+lJIdRKlYGVlWjNq67V7pg6pmcXGbKyPQqkNAyNiS4LUjokhBRipCwUdFSHCWaty0O5KXbKG12gqW0gIVMT4PcE2gX0NO003TolEVMOmJBbOfFPSUsyZS8kPEarMM4mqUPp5CdceY8EIOEyVSwkYOUcsHotGm6iDic7wImuVNkpAPKIhxxBfhvQu1lJjIH3AHcOgllQBET0wViUC1E6p+yazMA0r76inQZhR2SlngkOZlF0cCWSJnJtMDdYjkuLFmqlD0jUHJLEWOCXVvsfVCkopJjKxO6fGmisVChxkUTkM2TS53FxSYkU6xT1iuLeG4aSHVmsrc8fqJ+6piB3NTZEZKc17z87eylcU4DpadeokUtjb28jesi/mQzzoUrXl+j7FulI7U0so7xPoyBQxfoZnpGQIjEYj8pZZk7yrcmlWqY2xztkYaPCt9tW5U7Np+AKWFkOaubEkBNLRBRSYdqYDQIICZiPgqx2IjMGAQlkV1VbD0GkklCineqLrcICya5dCsULoD1teVF4GN+Lv7pZOo5ShZbGytdppTskcK5II32OnKxOZApssMyZ2Xb2ORdOHImUVQYRhJqIDWIb1hbeLj2CHPDRD4RNGLT5Zn41OQBl1wAYhnIdVKEQRBKKgGw8oV/iw6OYDEZmgT6EMrHpgLKRNVwdFhSRkraCSHRwXBbqTZeOizjlDac+hMWaHraZJLZBCbKPLqiQilQ8ebFeEAZiigJJiRFiDhmrssayhssJtnpOqThllWmzqOkspc6lcb6kiJ0ml1OpbjJC9jTgOW7bKPJMRk2kRagjn099YE0JlHJFqDumjBGYJQ9LdWm9jv7cF3WuDpTpjKKSl0AWiinKiWSblI0rOuCw6YZqDGmTPMTKFOguE2WCSBt0GmxGMokwbms6WQ8cg9FkQ8yo7rUJbedjw0t26654OFFiUMM8DEwGBuEmfR67L2yH0UZLM6vTAAXRS61aalSgcGFEG89UwOipmYBqreqpNKZEw+ESc3TomQVFoMbcy6F+E3UZki3CAKmTOw3UkNzoIBXBaBuuWkcmRI+g2piwTqo71ZpW09TB62HBbx/JbMouRIFyT8dARXiTVjFNFqoZS6SXhVJjnpc5iRJQzwQOO49xY7TO+uvcfMxw4118/2xmgYRPcZBHtIpakJxc59zk47dSln3TS8ZNj7oLkhXUfKesPIQcTI4uslje7+uX3zdNxp+y/6zNfe6envPYl7/jg33/9yGNPOjGS8FgtahogjRBmmXX6ad+BHNmjf/UH6wuZs53+4DMcfHqCX0So6BremlCr8+YC5ziMM35y3EnbpiusGTQ4r3BPpfCnSSejjCYj1hG8AxQJOpfEyOHpgtju7n7m699944c/8aBXvO5hL3ntw57/mgc9/3U/PPYff32GwM1JfzxNxZUE50yjCJ8URi0MlbKWInII6lOSISTrc/11JL+FHinatXd0GoHBUk1lDy4AIl11K/KFvf8tsKi84O0ffMjz3vjgF7/lQa94y4Nf/paHvfRNj3jp69/19+s/exuRPz/pJKIF0P/SYH9wuUv93sV/E8Dffu27pnqTq13mN897LhPRGOlNks+NhhQf1QaYJhSCMM1UH6qrg0cx3oyiXBJMQnsDpQ4ubRgYpYkOHQnuFjqnbLRvOH4dl8KNFqTEklTGVk8Qpa4QzSmol1hHaSiDXLWJ8dB5X7mVknV24ZMWJSTB4rHGJgsHTRaKgBIquvmC2mBmOqtvfMy/kCYNWDloNIkBZAKeEcwyOUQ7SCJo8iliqst7Pk2A9MynO/TSv2ngTA3y7CLEMLRXZbwERQGflky1x9jdNtoKOPQQvlMjZc7+F/uBB+z89m/+5sUufOFznHVdFRx2xkN/6zcuxBFf+PKXTj5lXejz+LT981/75nZ3P4+vcNlL79swW/DwV+87B5LXemtpbf3xq75f9dZ3PunZz7rFPe971I/Wv435pSEHHHDAHW9+k7e99IW3O/zmBx+yLnu++/3vP+JJT7n+7e/03Ne84Svf+X6u4fFLk/7TT+kJctYznfEPrnrlh9ztjm993tPf/rIXvuaZT73/3e529Stc/mxnW9MHWS7b3a9+/Wvv/Kt3POwJT7zJ3e51+D3uc49HPvYv/vo9n/vakafu33LA/6f3zPzpscd/+stfe/eHP/q81/7F/Z749Fvc64ib3ePeN7nrPe/4wIe88BWv+OJXvuxOr8Uhh5zuGle64rMf+2dvf9mLnvfYR12NTrKz879P958ce/wjn/HcW93r3rv7/5uXvv6tb2usydDCPgAVa27teyHPFComVljlYhZh4EBSFGCGq4yyJkzTjDtVnywlg0WkjdEiPNeoCVMUKclKyoLfsV2ybbnI2OlSqX0XDDWmpQH1YDlWa2ZLyuKMZZlGzEvnsqQkqgW7tEJhQgqFo3jVuoqDGzmLQMCYDiaTcIsoSXEp845JsRDSL+ICsAgzX0RfAosIlzQtWWjEwT6laK7KaRFsBCU52EvyAW/WKbJgrG97ZlhlJ10mqUyigcgR63SecMamU2aXzlbFF5BFUk8FjMSSg52pfliNOidhdc1W1j0EEN4ekq7NmOMG2yQZ28BusKbDDaGDPYpFUs+QQFlbdjUZK3EWtMw91EJ0b6PtdiE9RXqZODQUpsnFJGFV5wpmDKZHlaT9kuwhLhZTNdPJoZoxIQketILlZAYrJseiiWwLWkXYabOqNdTFCWyukogHRqSIqE3QMpiIwbbZR28xmNmaGLgb27KeGIjgVHFX99VpCjCXmFlUHNkS255toHcJPuZcJqCSMpJDPelxxKVYVuGxN8S2KHVNzBalmGNadNPYPEelY5Qwixns89g9kYSHNEW3vfox1FrZhM4R5rSfL+SdMlIzBaaislam1bUHLxTRkkUxq21MbMpU1uaasFCOjbkNWzgYZBKpdHBolxwEC9wIHhfi27p0W1J7aEcdqAEDrIy0cGArdcEUMckQBaashdobxeg0dgo3aM2JbivpOYaOToETOpJoikZy8IZoRNQydNJqmJBkI/DkrkFnV5B4JoKE0TL4ZelT8kGjF8ZJMwkVqsbKKNkc3hWOIA+VBPcUAKowE4KU6JHdIxoIbjIaoc6eNHOkinC5sAGKiwjHlxpanZEmDJGSmLqrrwAHbWoqNkkaiRYbUwkDXwKU3kF1a9YJdWNlmiqmkhOQPUdf1VIzKYIAWqCN8EWsa80NZ1BM4rkiO6RKsSK2jgNHqyqnqQBp3W30edur87SMqksKKSW6lJZ1cI5TbM7yjkYUOJlCSNQBa8JhlJFuKiKWal2km8isujGawVSqY80gQMIQs4zqXbcu21QOFoEWk9l4U5CAJlNsmazTMFz9FOIKipYuPlYzqGYh3zWKyY6zXLEU5ZPz1X5AbTa1YgRiuOW2Zp+of8wV1UQFAhAxsmVcOiYn/vQLyi5zJgaiR5MYk9ssqgXJL0BEKdvwWPFgEqriJsHrWLdQuEiKrScgmYU3V3agj41BYzncJ0XRqjJNUkx5gsUQRbRaESj2tiwWO3PMJUumLCctu7x8+gMPOEBnOExVp9nNBoSKSPqB+9YyGpmn7j+FrlsoCifs7aMbl6WUyuvUdyToE2Vc6aIXeNztr/8bZz9z8/j0N7777Ld/+I7PfPmtnvTSh7/sbUce9VOqa8VBqzM2ZeyRWT/C2VnLsn/sNuf52c5wugkyh81j0iBbHWItKmE7YB+dHifv38/YgSDFTwOQs4jxOsGiiKu5zKkMBN4AiORg5JjDmvt47ye/dLsnvuwuz3zl49/8rvd+9vPv++wX3v/3X3r/p7544l7LVNTELGRwT8k9AutHcjo9NBzOIkbymFpjVCCraWH22tnR3WVZh4KBpeIoPchQukSglnLaLbUhMlr6MSet4BOoD37mS+/91Jfe8/dffs9nvvyez375vZ/9yvs/8+WvHLX+5370/JN22WP0SdosTAhZSrn9H17toJ0VhzMcfMBNrn6ZSpVzlBggSzpyDgNDX4ZwWb7PpYLGVKoS6jmlT8k0LtQybeppKRD6szDih/aIPsZoPdO9wm3VhAGQu3RaDhUBJ0oNoS8T/Bq+EalETLSK7KmYwJAMwjUNaKqKQUuWNZkYiuYgLbhIJxEmgpRuZREdQ7ypRxlSJNVAMUdRDdCB+e5DVNzXURFCGugcYRDeF8Zi77QYtmonfu4b/Yc/A7DvwufZOdthZdiEOglFICgRjDnszbIcSs9QKhrRIhMALQkI/tV25kMPfdMLn/MXz3/W7W6w/iVTrfUSv31hjjr25z/71ve+z4N/2v/m7z522vHlf+eipx38W58HHXrG026d+C+XOqdd5OfPjjvhG99c/2r/rGc89Fxn/RX/aDLHcOfa6dH3vtvbX/Tcm1z/+jqtbvGdo77/58945g3vcMdb3/fBn/rCV/Y048D/E7uInv0sZ77yZS99r9sc/rKnPOFdL3v+21/2wgfd6x5/cPWrn/7QM5wmQdt/yje/8533f+Qjj3jyU297n/v90W3vcNdHPv7Vf/mOLxz5naX108b8X/5JX+Ea4L2f+OwLXvvG+zzuKb9/i9ve4I53uf1973+fRz/mWS9+8V+9+13/8KUvf+d73zvh+PWfkqMu82bn6le8wpMf+fD3vuZlL3nS46//+9c4596Kl7f+N+0R8ZZ3v/9P/vQuL3/9G0455RRRvfIVrwHzMiYAABAASURBVHyNq16d7E499dRvff8HoO+7MAzBtMLkoQqdalrpJp1RaKFWlDGngDE6hjpDjVemIlWLjIkZKTpD3VMYvU7KQ2QxprcYADOLgAG1BrEy+whYlkqpDDGDkEUua1rMDIs10xQbmMKLc8sWSGiFVdGqMhcT5iqrjRPDlijOAQFIkLQVJIXZKOuymhVTDtfizDjBes2kto4mI81w9Y4YsQa959rqLoiQoIrQLZhCSNh7jgU+BpgAJanzigOZ8RyRTJLsDCVTMxEKgDpb3cq8zepDbBmaHQLUokWmIrNChefCSTIgnScqEAlLnYQpUZiFI9wpGHqjhmuXEDJYLCZK4OqjDLKXkTSGaayTsyx94z6bV3ZBNpKiuGovGlaJjNWUzUClLOwZhRZKo3jpBVEqxKSbdiW0Y1qhXXJvTPXc6bLZVSzSgIYRWHQsyhIsMAUsh2RTcRUxKz5Po1baQKaOiU2BLiq7w/vCtyhNkKZaSjWboAWZbABtG9pp43QFJQjhmGmTYs5DR644eCfYrjJ2IuaOmVUidTKOFE1LQZi7ZkJ7FLqeaBReQ3ZBR44VTAmdgq5Do2TZ6uSoYmJUXgSj6nbSXmjIqJESrA6t18Xp6pJFvIKkFiEKGdQcSBEvGZYZLmDR2DobE4VXjlc3WUKaW8BClEBJDAHAox26AHjW6XURGXAXGswhERyREFGwhSGPVXaBSswZM3i5ulcMlRa27GkXOcJHg/Qyd1VXZEVO8FJG0V4wTLymbrrujGS9l+LJxmvqZWVK/gPiBVx+OmOuihYxMxFGIObImfPbJDSF9rThhg5rqV2tF4PwC1TPwWDp0hkrPbxHEkgrmBHz6NqWFmMxxEqbdrMiShORsWpWpYIks+JrIPQFMYlUIUUnoVBBGaZNog2PHqsCmYAG9xW+ocuwjhqMsarVABnQ1BpmOjH0YjYpmKrOpUrRUdEmUBiZDKZkkHATKbwnqgUyARSFBiiwkkVD9sAakl2zpTjvc3BaDoS3BO0hEAJSVbKVsc0cg5IYp2d1lxCgiNlUUKqaFoYCQgK5ZR5aAzY0k4YS3SRXzTaHzOnZo3tpfKMoUwK0bkmUMDpsd2nhgaB/hVpAEi40D0UVHpVsyKX74nAVrL46msgiNoZIppnL3ITGlGyhkameBE5AC+VsY5aFV+mZuyGdvFUrzHpiSXSyc5hAmKw1g6f8jiBhILC3qYitdgIy9zJo0u51gzoPLdwBU1IfA7lAepkyJ8up0rVihK9E9+hQ7yHqJlEHPXjv2vrRnThP6DOVAwQKbjlkDOeBAEL6oAqGf9zSJQcRnAaEINAuOWCpV7n4hZ5z39u84F63uuEVL3mx85x9Mv3Bz49/5ye/ePunvOz17/6oR0QZMYWQyz+SCpu6VIL2jxcSSZSaaBSpdDWTKWDNc9BIgKpOVehwuRpqnSTAXG1W5qEhq2NJNS4NKvY2MnUExdcpP/z5rzz0pX/5le//ZERc+Fxn/YNLXuTGV7zkja58yetf9VKHHriuysjbo3SnsqzZLiRNIvSHwYsUHctIPvMZkdR/8sHcWhMGcOC+vWfoGegjIrvlmMMLXSHxT1tBF22KxvG8eLp9mxte6VI3uvKlbnilS//JVS9z/ate9kZXveSNr3RJSnXjK1/6Ble51BkOOdAG06NLDG58+P+Oj3/21O3CuSecsv/vPn8kehrdiOd7XBLqMJEC4QyJmBkmM3LWxZIJRSIJdQmON1dDpo8W3d0txlo9muZAxGoAlYSAiCBtjBxJKw+IqktpwadIczCIUx3S1HqVXtBWLsIfpQujVCwyU4iBjl5Y3mIhNANFIQZmSxftRVbRXXVI3Sbf+vMgRX2yrjVl1qw1pCr5cHI4/VkYIAoqCnpDUuehva/mOfHL345to9g717kM70HIaSqTFD5qq0NgCj6PEcCTcawUDkGL4b+3qcoBOzv7dnam+guX+t1LXPy0CX/3uc+ddnDa599+7is8ONtZz3aB856HB/+d/UCmr73bJ/0bC5WvfvNbxx6//hLX5S5x8fqPfPdm/PKHiJ7/vOd58oOO+PDrXnHzP7nBQQcdxBEnn3Tihz760Zve6c6PfOZzt0vjlf+ju4iVcsYznvESv/3bd7/lzV/4+Ed9+h1/8e7XvfoxD7jfH/7eNc942NnnaRLR7Xb3Rz/96Yc+8uHHPevZt7jr3X/3hje/w0Me9azXvfmTX/zqSSefMtz/j8r8bzOLiFP2737pW9996VvfdcRjn3TVm936Wje/5X0e+tBnvPgl737/e486+uifH3ts23tKQa0383zgAfvOc97zX+PKV7nHHW772uc/5/PvfefLnvrEm/3Rtc9x1rOWUgC6H/43be7x+a8deeN7PuCIRz7q+98/ChlnPvNZnvjQB7/iyX92rSteVkRaa+/+wIdlDIzItbgGo2JoDS2WwoqUnTk0TNVKFWYIlK4MvW5sSWsWUzCDtClacRdNZm/oaQGMaCGnlmlrhe9CZUfrpMoawQJR1964TpPyTYnICDSXNhjBmABDCFhZd1UXtlkZPbR1XdisMH+oGSu5xCzNdBEdwoglhF4kxBxTRl3zUIdoKB+czOzH+ZAI6aERDh+M8gElB1TIOi0jlxhdeCs6iECkgogkggDGIDQDGGkhGsKtFY5wwTB2fCUWQQPvWFJ4mXyqvlNGrTw1X5MlMsnONr2ythG99Ex6kaHXjBk2GQuW1jVPs1RNa6YC1BzVtXjAbVj6LKlgHleNpDSRI7WzjYSurGoyPW512ubUUilrshO3YKUI2sOY0LN0TE6bOeZUsxWYBmE7I4NGdM1BCHSYeqHElBmD18pIyQxnxbOWHBpDHRGWsqOxUtF0JKeFGpzNcS1eilgmfUItlEaHhNc1Jy9GomJUGDslNfgCow+N3EmZukkHSUFTK2a+4piw8mahyG3JRYU+lqMOrV73PG+AHiKz60Rg6jqY9cAXyRSpAl3tlcRPgqpliWorKrNjgzErphlWSiZFBRDqYW4mCkgq6BpT5Ez3Ywi4Ygg8la7TgUCGNGdtV8/aize+/aM2SdFF1CRNI3iBIkhWCaFE9Bhwk0RFtVaTFxLIVbolwFrYkKo6FSnVQ7mQCSejMBnFu/ZIR4iJWBnmA9F8xOjOd4QRPhWv+4ZNbqgljPrTjIYoMeoSuqA0qy5JV6InYJiQ/wBxTb6wGjVQU6ekO4VMQNESs3rhBbckW+02d1SKBKfmqmnFtGwojYgrhesShGJQoSSKRhGUHsI+PtoovWksRTFVtWlKFaTPi0yLbhhUYPhxngIWqkPhEhnhDZHIolFKhHk37VJyGFpk50OEzjF0zuE6eDGqQlQIqiuWWn3fDv3IlGuTOZ3hzhy2DrEBbWlULeciZiHFhWhJpTUTk6GoTKoAaVW32VGCxwmXbMY1k6xGVpoxRVYvWLp0EZuhs/SU7gmMMg2lpxVtJjGlzDA12n4mcpIMDdrUk3rSbWRoXar6nHQb3jKmirLXXtK6vkgAUFERilkzRdWpwwhe96TYtBMMMWE1VqTKEHEhTKuLUJHJRplHzN6rNOocnNQrna5wcZnOoQEOg/PLwswoIJVXaWJRCUctbrXT2QlCoJGzZ4GqiVbJzBguOTQHxHkmklMJLQvlTkDCqKFQ6NKtBquLCkAmJowq685Gz7ogU5hwbGlJA3MeuAmEnw5SRxQ54+l2ePqzE08+sTWPstojOCLDAisKeuwJJ3OAiEzzpqV5YUPOC+s+qU9KcDgedOQ+ko8iwqv0erpp36XPd55H3uS6L73v3d/11Ic9+GbXOex0B520u3322z/0+W98Bys4pvrfSEnRJC6bctBmIunv/uTnw7tbR6EaWENYq6l45PEnrfIctG9nLjlJVgzhhL1dIZoGEYLMELBeFLZ3B4jV86UwsPJJr//rbeuHnf7g593rti+4/90ee/ubPezwP37YLa/ziFv/4VlPfzDHewR9ZCxKpUhRRXmR8K0PCXJa0kb04SODd8QsSVU9hUZc+M2BAD8igvMKTQCsgnOJ5djb2KbM7pvE6ffeDinigTf5w4ff7I8edLM/ut/hN3jQrW/w8Ftf/xE3v94jbna9h93sug88/NrnOt3p6kD2dM9Y4h++euRbP/6ZyDz7GQ5pw5/ztg8c9f2fSifTqrYD3ddlHigwUUXSC5qUpU7DKqWSZC50rxG8EalDmJaNIaZtgBz49jfmIGrFRYhDwoWHRaCaieidnkPMJc1o6uCpO8YYsfoYE8y29qWw/+BYmVInDZqNA6j+0D4QI5PeB6GEQpOCbEQRANHUnBHT6HVpPnpXHTZxPuUGnR0ofSrbSTrnsIj3giS+OkpptYxCw7PY7qL91adAcgfuO+jyv7mtyZGQVY90FTEOq8ELYhmTMBpFrQQzAef8z+y/df7zHHTwIZzxuS9/dRWQR8BxJ5x41LfXP0L49XOc/SxnOHTv2r/5UcxOuzcI4WlH//Lz7z73xd6bmV3ukr9YFP3L+798RkXO++vnetKDj/jwm97wqPsd8ZsXvCDn9jFe9prXHH7Eg376s5//8oT/g+eUbbPZd5ELnO+2N77BC//80Z97++vf+epXPO1RD7vZn/wJV3SH7P1La330U0864WN/9/HnveiFt7nnva5ww5ve4YEPe8ILXvrRT//Dj475+e7S/g/K+wtWJ+/f/cZ3j3rhm995zz97wpVvfPiN7nDHJz/z6e/6wPt/8uMfLQubDKf3HHjQwec6x9kvdYlL3uamN33cA+/78mc+/b1veO0XP/Duj77+5a94ymMfeOc/vfKlLnHQAQeY6i+I7v343/Fx7AknfuzTn73rwx59vdve/pOf/DgT2QEHHHDzP7n+u17x4pv/8XVqmfim60xnOgtZv/MDHz35+JMZwgxrljAdyl4BU/BnKCvo2PBZc+ksOzlNmNSg02CQcepgZmOtWMI9u65JAJtURi/vxegxGqToVOaaVdlo1O46wIZiFIZ+Z7Wru1m2U/SpqTCCq8AydFnctzL3MjFKK+tKNz4/abvet/RhCtaLhCFhQ2pwAcAqbJ7ZPbbbsezmtumuYCu5de+jDV92PcaYzGvJLliKtql2psKiyXyizBYsDEyE7DJaL7s6iU5IpqMC2RiouVJMRSthElNPaRxN2eZupWcP2VKTtBZDVg49S+ps01SLkQQELUgbC/qqxpKdvUcj7yDcQJFatZrO0MIfxTQM3QCRijIFr1WADYExwWRGDG+NnZqw36qBypZxQ6yDRDsCgHGmRm4WFKbikbsZPQLUM6pUKdyF5cv5yHXs5vAks65Y0nPs1mk7l5jA3KgjqFNJLhRiKCIFmfxZlXfZNLLnqmAHlpIsG74t0ogPQrxXXerka+rfWJ3nUiuN26M3JxCWzNzsghfhmksTojoLVKWbNrDc65AUHZBWwVwvRWXqMjWpadJpi+IcEWoEkNMnUCUha8qfLE+pCpY5toc5GepsG9hOS3acVpTjGr/SAAAQAElEQVR8LDHH6t203a4swzohi0lIKsmLnWRohbJbL1Y1KEawdCtSMlyZitf/80+9pTg1rBvMFdVX+xegAqbCalmsEwU6iG9Gq9kBkEGLuoihlKI2Y1MxzVrMnOOEHi0q4ETH2G3W22xiBR20FC3Yalls9WFbomxBoEdqhDjLGgQaWcBY6qgZgr0t6QDknOtJZommWDRH4VCBaAbNL011zOITPWGEE1k+WXUbppiBaSTXyyUIxEa6xQDVQA6ox8TbqjVRnHoTdqX8a3gKoAYj0KsvpwnEIGXAkqOalW4u0cuqVK6yTUwgGSF0WDbEMpwhG6vHidAAPGAXF7Xayi0wfGyxy+WVaqm50V7GyGWgJVwwYs9NIXQtLiF3tl6WjAV9GeLbHSwTkaMSZAfMk+ne77QvPvoWvrBvnec+b3ySVECgjM8ZdTLIPLwOhpGDm6ZUlWmVCcSe1xjSPTZd6KVraslV2w1igz0APEtLYXPl4iOlhRLLIlkhKoWzFlqSMnlHNGLh8HSFlxjFEwJIgkhoiAxBF3oC+YoI3IACK23SZvCuFht6L1+MVw71ULBJRi0Cql6JuimdMAATL8p7OmmfsK2ym9IpmJVcI3ayTdWqKmp9gwUWvqGEUeipSajnQBVhnA2VAXOAyPNDE1aGTIPHgFhurEsuDa2Hr1o7SoJzYypt5hpN2kj0wBLMRZ5VoThto2fNLbT36E5Qfus85+T17fBvH31U2QyZE0KXWlK2u3Pb3emf+ibXFTj0gJ0z7jsdfU8kOf603aaQqSEbPWxDmiWO81OOPP5nRx13nESYUHhMFWc63UE3+L0rP+1ut+GsU5f2ha9/r2RhYP+TSLxuVMpSa1zkPOvvtP/0xFN/8LOTdiUX6h8je3IT2qhvv/y9H3L8r53lTFaKyNaIWq4i8ZtgNNdkplKjem1p0YKDuVcrszL49Ps/Ovane0uvO1zryr97wd84cJ+ADjxPrZDSLwZzfBlgtvWkhxHY5BXuSWQAEZlKzolKtygFmzKMJmh9EMVlaQ2AqJa6URqFPlS10c9LI9zY2yLpNTsW04V/fVX2uP3tWz84BlpRBGxZ6bbbgqDmvjB7UMVexScyHD7vP7U9863vPXl3ueA5zvKC+9zhjAft+8kJJ7/gr97fxpa5lOSZF1TUUgi/TEnHE4scmWOCzxqFUVykmTcdLi7g2HnyuboUD5rFtGyKbZjRNBcdNEIYZmNQKwPYRSiZMQLo6BFMw1Mu61ul5tj22N+9RUaAtwpE0RPNYyvBmudSFUqDMeB8Rhqoo3pW9xopIC7ZZwwzzmLupCdSm6o9ZRvCWN/zKvJNMwkT11UWiAcPbU1odMj83s93v/JdAOUspy/nOzvJOuCRvaE3G50JEyHJZMDrChiNnqncRDirO8/583+8H3Lwwec6+9k47pNf+to6k0fA57/5vTbWKL34RS9KknvX/s2PQ9e/h1nvnrz3G4fr0T/7Hu6f+cL6V0pTrZe+6EX+2Z3/0aHoYWc89A43vdHbX/qCxzz4wWfc+1P7T33qE3d+8MN++rP/f65V/rncYvU3z3vuG137D5784Pu+99Uv+8uXvuA5j3/MrW5849+58G8dfPozMsg4uC3LJz/zmVe84Q13vP8Dr/+nd7nNEQ945HNf8tcf/uiXj/z28SedkvmLsOTI//Sdcfy5rx35+Be/6hb3vt8N73S3pz/nWR/4mw+fesr6jIS8pnlz/vOc56pXudr97naXlz71iW9+8Qs++MbXvPWFz3rs/e516xvd8GqXueR5z3n2qU5QAx0Y/+QdnPqfvxOHY447/v1/+3dHPPaJf3y7O930Lnf/6/e/b4whIpe5+O+87KlPevwD73vYmX/xjzqc7Uxn+r2rXJlC/OToH3z8s5+3aihKOU1kYg2S3g2dgVvDSjAJMRiaCYOoBEowdWhGMkSCm3ZYEw1L0TExBElWOljvxEGCVUWCFUq2g8VVGA5FODtbhLMTsCJmvCgoFGBRLGq+5gSRYkn2YozxNZmEI1rS3GyMJiGcKbYUCQtFY9G2NsQ9ZaR1zSHsXZg9vDOFmQVzywBriFINCgZf5Xc+vJ7qykltTjDdYwTTwCiTlonlKSlaaCGHNKYzzmKuIGsoWCgm5b3MOgZaMpPmWEWUXES2vJ/UIKZ5oDhrP4doU2sZ4awBgwJSTAdpUqcV5QmiOgW7AKUYFihkv6qvvcIphLqGknOqeoGDkIPZkZYprOc1OFYUVCeKiLFdxdqbcwqfWHfpro2jI2tK5cWBbInhQgkmjzkVzt2iSxlqWdYLc1/WP79hjCka07MNsIM1lojkGJd59RAtgwM8qo+a7Bk8pAjoVFIzKUnqJHUuKJJCdgNUTzIhM9apvBpRkZbhsgTaQE8NqmbBLlF5QFUJKE3dirsNwrzeGrQr628UD2QZUTRQevLlAdiMbieCYlCzKTkwBtC0uG3cSrLK8GFbo9kCbsgZZoSfrsoqArqMhis9IYSuI9mBIHaavNjNXIToaomqSQ5QoFKVFMlRxBkFVL4KLCRZdKgqp2PdMiQ4jFKFcnHGCcqJUokSVLBCbgjh0FyPCyh6laJSwmwUJWdZ7UOba0iiiEk1vpxrp0krwzBMIKTAtg/brBQcMqSMVU86YhcWYh+0oXYCSSrJuaQlIXQqgstK24WaKfhMocSwJEsRLSk7AGVKGZk+SKPXwVVP1NWAtCGFhKkBmhGBGJN6nSC1YJqTPGLQ9hnpIM3R02kBdw8ZWH0SlvzJXYoLyYiuVis5JmAjYCcTnNhzbMMHG3pPII3GJ1pdQEmDZ9aH8lnAcF3tJoTfk6mQHUi481QF3FJjNSkgKHuaN6dSkEi4ZwxJ6EoZE6xyRo3UDJXg5nSe0UQCWrPMAi60tNOV3ArqbEbhh3EZlkhR4WzQnEE1HX24dzIQUFRmhymVo7vGIGGK7TzsyaYvE5JYORZqJgxehnCGpuqYLauZVkmLzO4xvJlEtRRLp4wmWouVQtaj9+bhsBGIWH8orzK7zpLUXBIM9QJGRVWoENNGDsZlBr0459InxgNA1wsdq1+ISA/rBJxpzh061iWrEo5kLsmQSCOq1ArrtiLOQT4yuTtobfIQMF69zCk07AZKMESTuvF5GEUv0zoVRCAs2a65ZCLzN8999rMfegiA177v47t87GMOcaUMkaHx1e99/2t7fylxhd+6wL7CNagXJiwqywlAkiXSETysItXik1/6yg0f+pQ/edjT/v4731qxnBSa4kERz3X2w/YmgQ+dq/Oid4zTrqyfQUGHKa78O79RTI896ZR3fuJzSR2oghProOMN5F9/7JPLCFO50kUvpKloIUsiVwL8biN6Jyt1K00KC+KIX9wTYbDRuOX4U9e/c+bgfZu51ih0B+rJLBS2f9tP3TbeOm1nqBLRNiT2iFBpk6ADVxvKPVNDNdbCGYixusIetmacnrSolklN1FAKipry8i92HxZePMpvnuvXDt6ZefUV7/uIq3CYjo62wBuEiMRRPzlW01Im16nZHMVe+6EPf+0HP55rOeJG1zr3Wc581+v+HnPZe/7hK+/7wpeEU7BkBtIFqeqKoM9IEZ4Hb4TAKVmI9tSRAoEqShXairqYeNWcK4qqEPh1OpADsTdUFDWTKUBoVK1Kg9O1kkWogAu3nMMrUoVzZdJeggbACHWoa64GXO0yzQszFygT6GEDQv+hvJrFxOizKlEVZlYhGipddEEuITEMdE7qoIJK2YW25d4poWBwbB0zsP3uj+OY9deldv7gMpuoU6oOmsdpyO7WXUdPZmyadWXcGeukETu1TLUC+NlP1n/MgQf/w/3AfTu/fs51oXnqccccs/cLWpzyox9+34fz4MLnW//JNh789/Yk63/z/s+OP/GHR69/pfTr5z7v6ff+IeN/a+g3vvO9177l7U950cuOPuZfrEN25vlWf3ztlz/lieyqOffTn//CQ57xvN3twuP/W3YBrVyn+Td+/devd82rP/4B937zi577V6944auf/pQ73/b2l7vkJQ79pz/jOfGEL3zlK2980xvv++jH3PJeR9z4Lne/9YP/7Ikvevnb3/fBz37tmyeefCr+k7ZT92//8n0fus39Hni7I+7/qte++htHHtkZkIDOO79zkd++5U1v+rwnPO6vXvmSt77kBa9+8mPufdtbXvOKl6etN/MGEPwf3DLzm0f98NV/+fbbPOhR173NHW5/vwf8xdve9r3vH3WaCBc43/mf+qhHvfqZT738JS9eGFanXeWnyM3/6Np0dXd/x998zEXTJCdh52gWBcFsKlrAWBDmkHQXdAizGwORidxkMDCzKAN4Us5ad6a2YcnABOawEoWJiEkzUtMTwUSTvJxikJS6mLVNYLPUjdcqhflijURLknQ+pijBeh0Qvs1xmaDMBkJkTbwAFJYnkY5keV42GHOAMT5hr+qRmCpYhFWYIqa5TgUGT+YvzmW7xWfHGIvEUovWusk6tSp9Uki1rmBo+P+Puv8A1CWpyoXhZ61VVf3uM8MwwBCHnHPOgihKRkAQJQpIRoKgIggiCogBFFCyRBUQFBEQlSRIEL0qKgpIzkgcwsyct6tqrfU/vc9cPi/J+39yvZ/vrtO7u7pqhWeFWtW99z7O1XnRlAl3JpyABVfMGRK9WdaKEiFMc2aUdrQJJYozrId6Sjof1cwJcAEasXSx0Ry7rjVhNmEzTSOLMzn20ec4PSfzG7OzMV33GqEJLzk5K6ikZuhWrDUxKbukm1UCEvAVrEVzTMFR1X1TX0oeSO5yK1CtC8ambpYi1Y1Wy6QmSWtDoUZ9JRQTsoqsVrVlwZDk8qOzhrQV4kmgjkJDoqEvHsVTB3RSS+bjfWDPm6UmFp0mtDiGE5xFKSuSsMukomgifDu1M3Jg9t2LdwqldQmz1ck00lLc5fTIPZKSo6hUcClSzWK96FGTDunio47gM3lz1I00F/vJlWwozc/CB3ZYaIsbJn1FAjLMuuWg2LJXiiPSh7ElB3hLqXRJk9hRPxmLBBeSFBZ3kR5IVz4P1uQZz82jhtIKKtSFKu9op01TVhZ0eklgGrJFUW8g+i5kWIPCASJZdpP1I7IxnjpAl6QneNGhRLR0UVcQkjyM1ggnJoRuK7rqojCjyEEyCx15LgO70XbWrGlUmVVgShBImzFHtUUnxVGVJjignBActkzuUX3wbp1SJwE2l+LEgbclaL4kGbogB06bc5fYIauIbY30Mkk30rtjdiYRmZBMUxclQK3P0qf3HGkBlu5z0a52NJFjyL7bOuEYEtxAJ9IpVY7KNx3HYGIYJpNPofHALWtuIC6SBChy9YjgWKorGdKDlh/QIKalKBg4dZWyebOX/Wp9bD3Kp5eugCC0kltQfMlUEvDh1AJwZ4ZQRhs9V+EZKyaxn1ISIg7GhOQA3Xtrw8fA6kkXSZlWwxSKIbKW6lZmBcN9Xzuf8DoVTGoLPoPZ89Ewkm6Ym1oIEdeg8CIqWQLoN1weOAAAEABJREFUoEiREKgl+7bOXUihbap2w1RhEkJJNSmtFKklKkWfvroP3k6pWVVpB+8ik26YAue2J/pKPRUbcc82s0BkY6KUYmp41WxNZFedxRIUA2w5iXYMyF6Kh4qXkosYx+isHjmcGKySs9ZcliiSmBlDPaiN0Kw4/DCIRsLVSIJAqUB1MjhUJmgFOpYupVotWlj+pdEjznTAlRT8fPG0L6E4aZWizfTsJxzc6jpXYv8/fOjjz/zD151+9Cjm3A1rYZ/69Jd/7Xde/eXTVxbTt7netUtBoYOzcfRhmyP2oQMlikaFqF7xYhdainrmC//s7Z/56ulZqruMdeW/N/713x5OwqUucL70ETEk/VgPj8M70pfIa13sopc+37nZ85I3vuPtf/Nu21uysMx15vqWv/+HZ/zJX/LWFS98vmtc+qI1FV1isuOMlkm/i41SGKzCjOY9di9nZg9JO985z0EJ2fm6v//HU9bTJUD/bnt85fNffezzX/mRz53CW7RRZtKclYevgS7gCmbMlDpGpKeTHAfLFC4+2C5qKU1km5DBnE1PZZwEI22J2pg9OfqwMVxmUK087zlO+u7LXox9r/+Hdz/rj/98XenTYeIpo0t/wevfdu/feM4zX/UnsWBdNFT/4V8/8II3/hXH3+xql7nixc4vvr/ZVS979UtckD2Pf+mfff60r4g4cp8YISGYEG7j5gToEY5wOhM7gz05TcM0tdBhLKRF0oBFaHexzG1syazVlY/VKqkwFsNCHbWnjZTUFBWFCdTA7wouoKXIgZUD+p0Sn+lBBxUtVkV0Jo7yyCHbGlXcbQTpUSZXTEsFmgsfNGyCNClVID5zdFX1glB+zeYQaPKOyeQ0CM8lKDGgrKVq7N/8dzj8nHiZi1owd0AJxkQmOQinBoSygQWAJ62ESCQODtpSK+fF7O7Bk/+wicgVLnOZY8Pe+6Ezfp/+lO2/MUmxcuRM2w8RHrv7rY/yrW/hC6ec8m+f334V+5qXuzQt+G1G/vlb/upnHv/LT3rWsz9w+Cf8/peRIle69CVe8KRfO+c5zsn+173uz1/0x6/iyf8nG9GQpS0XPvk833udaz/yfnf/vac88c0veSHfV/zkfe55nWtde7c7OCb20aOnf/yTn/ybd7z1eS960cMe/ys/9hMPufGd7nLjH73Hgx7zK896yR/8+Tve+fHPfD5o1mOj/7ePn//Sl5/6whf94L3v/8hf/pV3vuufyYVTdyec9Xuvc51fe/Qj3/LS33np05/8+J98wC2+//qXvuhFTuTWUeiylJmj/utaRPzdv7z3cb/1jJv92H1+4Efv+vBf+uU3vOG1n/jUp8KdQhx34tnucJtbv+QZT33lc57xwze74ZGDMxDjra+1y17iouc9+WRe/ut73/PZz30pI0UHhFlii6WSUhzCimAsY7CeFnRGOx0wocjCsJGokqYiBjMpYGkDCRwioSiLGJiHhXk43NwKG+MwQpxJz5LFD8M5LblWcxansTEP6U654PmQvp/BBMbejZ/s0haoqYqJkLQIl4rV+BzRnWMY1ZpV84jhiI4Gis08YibMmAUhFhwhqnUnuyqLoGaamKIiCrkNpkPhAlbabDZUZohwliqnrZSYyYCiJ7/BmekL0THzLH2rBZQiCWHjCticuSuNQAkmwtXZjzCLaqDwIoIa0qDVTLlgIoIQ5DZ2Tp4HIGpFzAwmU9SRkpuGPCXMhmJpliJwYCQnTadcbKwsMMxZw4d46hraXWhTqBTRmgXM2WE9KZnAqlCkKhQ8ZHjyIwXawtE9epJvtKAMkkX8QLhN0TpLGwmIK5cYZsjpMYXM2AXCQjymTJ+RfFq4fXNYEkEgZnqmUG2okUJKMJHzlhB1GXwRI3wXUpFFXZJuIUSpggBRJiO7WVmIq5uEAUqm29sAfnMI1eHau8GVJAAKPpImSxEBtkUhXDCtcGRBFOb8BIVBQFINSjfNmBjd3On4UNmkNnWEpyDpblq3PhAjEowpoMRQJGkAqQnOE3Z3yExDmHRFqItMkxS6XyngSIEVuuTm5xSO5IKSYjrn28HgXts5WTTUompAKUCuAMlGETQQFTVVMcGyMd1gLCpV7ZA+eVWdVdktQXaxoc+TjZIoChgZoUTW6MySJgzT1qLyRFSSfJV7JrfSUaiN51BqqmGa1MUC4lbSGtRAbI1RMl0oDyCSAIwXLubAnFQjCfaCqBIxYxs5RTlQBIegWpqGlMwtGA2puof2mYNAqxWWEhQpkEHKKQpQDJtaJjEQXaBKBkNTUgubREp37nEzG0cmBfIp2TmhSi4pVcTKxLKCCtoUyh9zOAvYnEL9t7BqnBxziaExkSDbgJKw7L2M2SKMNg/NiRmgn82cHEV2daJNMcwivM2+BKYgNjqMwhwgQcIeCjciZKRt4ipkYSrFQulEVWCqSAOaSWlAdRQCIPSf6kn3Y1LkdwhQcjsqOEEMSKKVyk+B2JBgANqclberillxMYUJ/2kogzaSkhIZJWtQSbFMk2FKd1sFrKozuoxJUBHsN+oOzSg6awkRZDBVSAhAKsVMqgHGsSAmIyVx+BFQIUXQ8ax5LQY/4GbTI5knEH2q9827TdWEwU8TxrnPevZqCuCxv/vqZ776LS9+w19/9TS+PucA3Pkm1/muS1+Et174xnfc+9ee8/Q/ev0LX/vWJ/zeq+76mKf+y0c/xc3M3W/6vRc977mVGEskzhDCM/uQHEXobZKu4ZBzneOku97kuiT1jvd+5I6PeeoTXvTHz3vtXz73z978E0/7ncf83ivZf5Fznu1al7tEUCrVTRp2HTauOXMttPVSdo/50R8+6/FHvnJ0/dnnvvwhT3/hc1731uf+6Zsf+uTfedjTX/rVo/tznHDcT/zwjU44nvW/iJZNoDMkQlExlVAGhpcWCzdblPkY/YzOkPF54olnvvHVLsu+N73r/ff45d/+rVe84fmv+8vHv+Rld3r8U//sb9/Nfrb9yqcCq5TRChYVftjJlkW8xiDcqRORNiEpUOVTuWxZyyx1swboOhFzHdhcWpPYpMIWK7yzNYnN98tK9O918xte8rzn9MhnvvpN93zcM572ite/8PVv/81X/sXtH/vsJ/3RG7/w1dOgMmlMwWmnnfaUl75638e5znrmH73Z9ek2Y/rxB/V+t/q+4w+Wr5x+9DEv/pMgppopdKRNBnA54W6gi9FVScQgldjPTNDjI4VxBGqyuVZWy6oocMlVZKZoZuExCCkHSnb2bjl56nDqD4BhBNmc2OimgqpWjGyYgOjLQzYiolJh1VkHoGbINkk6wAcTnlu+MeX95q4+gBU6QB1oSgayIyM9dLFxgN7COX2oDovMlQ0yNL1SzIAEO2nh/Wnv/QglYzu44dWF7JS+RvTFmmqRAlQOpjgurZN6RgBWS11OOvkCnNX7+Ozhf+nI8/+wXeLCF8LGA3//zjN2R0daPTaLEB87+TbHU/lU4PD2wcK8dHj27w4f/ujHvO/ZceELXxgQfOvPWc98xn9GeerpR7/pqPOf5zxPe/xjG1emiN987gs+d8r2xumbjvz/UKdIa8tZznqWa17pCg++x4+95Def8N43vfbVv/vCn3vQ/W9985tf8PznP8uZz1z5bo5+sO5P+fKX+TLhz9/w+l9/xjMf9LCH3uBHbn+5m936B+/7Ew/7lSf+1otf/oZ3/O3HPv2ZU7566trpY1+vIkF76zvf9eO/+Kvf+8N3/M3nPu9jH/9oRBzsDr772t/1tMc/7l2vfunvPOlXb3/zm13g5JMPdjuIfv38//PXY85/+9znXvkXb33wY37lSje91S3vcrenP/8F//Sufzr1tNPI/Ljjjrv4pS5zv7v86Eue9fT3vu4Vv/rwn7rOVa98/HFHeOubNlO93rWuyVuf+OQnPviRj/YxPEZmOBReZMI8uQb3Fb2XiGTwgLdUwUEQF46KZCWbFr4gDRq5Y6HhpCkGYQ62mZiONcS1YKd8zjaC60XM7JkTSSqSUE6Z4AyUOqWtad0xKYZIVz4eqYPjMskCxuFApPL2ygVTEKBA4EDru4omIiqx9LCVdMWdKSZ93RQoxZbaKJkmzJodacqn4cad1CbkmLIpBVXbYnfGypJeduAsLiyn59hzFM9SggNTZKrttfQQDCv7ilAQIMNSdLeTg2ollzl1daK4rZbJIax/+K5DddF6YNpqMhNJUdRSstpU99hbRJtMlcZJKlk0GgQiLGG55vAE4C0heOHTdQ5WMiYAk6cxnW5GpHvv4TOCQPm03hvcZJMPoax2NsDTRLTZdgyhiEyrgaA2AhUB0/8Uoj93S/IVjtCUhKWRTcjCuzXdINuHc3zWSVOji440rhvZ55hjnaBl+eCn0mkQq/eIoxZOKmaR4rTrCpvGFbpN5R6vapQGNtZry4RtZhSZUhwlQcmSAiNKpgzMo7qe1mavluB4gHZlMi9cOka2FUfWJBfqtZUUaytSdTFv2k0nhBaE6UYzoH3mmsPRldJNlEn8lxZ8O7U5HNFrddgBRexw31pEzDm4m+auMGsAkZgzJnnvwf0OF01Kk7NEWoppwHD40aBFBU1yocDF1brCAUsUFNGUypdwKSWNUpQVsnocXbHuZbpCTKqUigZuaoweAgzEVKTKRn+zSlgIZslhQEBiGzVbzAZJazkXTPoVUKCLsBEXhSpXZcqSHT5sm+uTFpoWScflTEasU4YhHlE0D1rWZqF1oIg1VO5kj1dfjDxZ2DqjQYyXYQWjyFEabKVjoTVvNhiFpbaya0LnLxmLxBEEAyR88N3Alp+9QCpE6KvikFATK8rxqWa6KGh2AmCSWmSYTEk4gzNBCYpiV9HEVajPlBBJStYghV0psIyaxIgxkgM5E1uULWpNBYwtMZSUTHOoi7GHViU73q0bfzMpNZUygMBROPrXfvsVHpkQpwwKU4IqAJLEHRk87V3n2iKtqjYJjhuMBFUJW7yYUbGSmwhaw5ouixYy9XCndgABkgx+0VVjk1nIOYrIUpdSKu8QAnOpUQ2FrsbRQuEX2zaktmUzHoS2EgGJpqeIobEuU0/3HAGfCIZB1+w1XIgUHwt7iRnqU3xqD8yJIlg47UgmnwRlzS2oBVJQKhW3mPAMp4JQSb7m1MpvMqiXakGIC+lPL9Ozu7Pom0MdjHMPdMT+Uuc9942ufFkRed+nPvebf/S65/3pX562R+CI4MiZ7Phfvfudbnb1KxSzf/rwJ5/5J29+wh/+2Yvf9NenHl3PdZYTfuoOt7z9ja9XCiHyfeR+CA4/KlBswVX30N5zSz6pqPe81Q1+4ge//yzHHXzmy6f+zhve/uQ//NOnvvINb/nn90+PK134fL98nzuddPaTUJrZTorif34Isk87GnVoXvAC53zag+9xqfOfh1Pe+E//+uRXvPapf/yGN/3jvzJQLnP+c//G/e9wuYudvJ+MnwxGzHEVBAigWAe70nYihWA4WM8as0fi8OMZkRM5BfLAO9z6enF+Q2wAABAASURBVJe9qIq+9+P/9vRXv+kJL/+Tl771b75y+v6+t/jeG1yZD6/xqc9/8Uv7U/hoLgudAQTtkAaoetBvVEKNTMWYnKFRLBvANIhe9LT9ysHKa88xY7rP4H6OrPMMUUDUYMxxFnTAc539bL/+gB+9+iUuZKbv/vhnnv+6tz7lFa979p+95V8/8RmWrw+57Q3v9UPfm9hM+pI/eSP3jUX1fj9w/XOf4zxeSxd1Pta/+AVve92rUMg3v+tfX/mOv2c4K7jvRcz0fdremY3JSsSklawixuSa4mwcg7FijRzcbxarmiojMVN008tBN+Z4JgVhvJY2DspcmIK8ZGZYCiSzMJ4YtAXKFGygwxGCKAyD2DH/AhQytIjUOlFp5g1ILoWdD0vLTipfpKsMiUG+cGx3jyK7wEUDGqpb5EOEqR8JilSmWEDMQ1fnVBNRh0/E50/3D30aQL3UBY7s+NJ6Az2NnLVUlQWD2VNDwWxYSmkQ3fiEQO3kk84C4GjvH/23z/Hkf6fRlQ+O2/6+1ic/e8aUg+OOAyR9fumUL+I/+nBzeWzIkd1y7OTfHz/1+TMoXPSCF/z3/d94fsKRI8c6v8K98bGzbzhe7XKXvtvtb8/uz3/h8y991Z/w5L9bExrsipe82L3vfMen/PzD3/qHL37D7//e83/ryY9/2E/f7Q53uNoVr3jB853v+DNtP8N6TC8/eup73vMvr/jTP/utZz79xx/28Bve4c43vfv9fuynf/YRT3jyM37/j970N3/7gY994p8/8OFfe9bzb/eAh9zjwQ95wxtfN1ibA2c76Ry3u9UtX/yMp/7uk375Fje4/sGRzaDHaP4XH796+un/9N73PvMlf3jXBz/0Ore+3f1++qdf9sev+MLnN09jeJ7jHOe85lWv9vM/+eCXP+eZr3vu0372/ve+zpWvKFIAwX/0udzlr3hsyHs+9uFV5jqlb8VCtdDmI/kM0Y4OmSGehqzY1hFs4bV68lOEq2xX85y2dq4MyugNBGm6uLcpLYrOimDX1IKodRXsw4dwCoLxq3uRLT2HMkRj6hzJXMlssqTtvDFTSEqpXsssNg3MgIP8kKsINNn4ncouxpUyM4ozD6Wlm/KYXAKmlz5tukpWi8YdDjWCJ1xklZgzsBcdS7W2pZZAc0KnKHPUntYLsjKUQ0loRomt4gyVqZkSnjFCVmacTGcuL0k1YQYxhch0zoJ4MPeWCWrsKtnQGkrZBBgokU1SNY2ycvlTR5+xjjmRGGKs7gIg/FzP+QrffTNtAVMV0/5M4saKi32NYaGLyiIinBnkr2qEDtZmUn9sGizB+tioXHgbXtxqLouANY0ot5O5sjouixqTsWyjchmUemHaTk2hxDyKSmTLlUpoKaqVAwl8SElTtVnVCyXIHL7laKAACidYMfucnZVodm09N1sFp3mYTqkrV31KYtpUmsGIX4aMGfBQoKUyT1vyQTMWiQ3sSYloiaJaZHHYBLLMShGAY9caNGbO0JhFiGamGrhqBdLA8orY81EmNFzoBdFpK7KFDAiJUfK5k96sR4lh6ChZmmqCp3PGdHTGBx+WeSRnhHbaDBihIyz2wIRpCD2qYG4EE1zQhsc2C7lPQlBVqolgG0xgLXkuCm5zK48Qnapc+4IxkRsbp5KASkTxWTGMILtjnbkfGBMCglWRYkAVr+BGCOkI1/SkcLUMq07cTIbJlOg69xgDpByBSGcQZiY1oB1LRk6POSLCk2HrQ2NKRhGtlFJZhHJDnhZEL6nyGpkjlaStaIFSt9DiNEguIgIbAfeASS00damhdVJLgVuGQpT3aIieyCwmTWUZWbeHJZqSkGLRpJc6R91kCZ02UjuJKggMtDDQBcW4I5qb1WUmIEnCSgB7zq49SLCmmaiIwtT4EVWkuVRo1WzQhXMSpEo1Su4Qy4Q6O1q1xawVrUYQYH3DEaKQSObHKRI1seu2rFqGCNWdkJUa0RNdQNTrqjxmEK01MKfnDAsBnVLa1KVndU8daqvlqsPTNeIAtrhZF12njFXKUXLRHcR0wmjCkT1mpIA6QJRTtg1rmcmdxsy5z1iVUYWgIKPQIURJLyGM1hxHJ8MzZ2aC/8Ipe0saibgkFYsMt8E2C70vtaoWlSyz236fzFlJzyAEBbPm5iMJfngQgTDeiDU1DAlekG+aR4WwIgtRhkesjHSgIkvFcQfLz97pNj992xtf9kLnvfT5T/7+K12mNm4x8mjX6Hbmdvwv3e0Ov/mQH7vLDa59hYuc99IXOPm6l7v4T932xs/52Xvf4vrXJEWhYIKZHt3vdIPvfvw9bv/AW9+kqrEvfM8Ipb9uWURyseXut7zh8x96vwff+kbfc/lLXOqC52G73fdc4xfuetunPPDeFz7P+cxFU5xYWr3bLa77K/e6zYN+8AatTDOKq/tZ9q7nP//5nv7Q+z7qbre+zXWvdOkLnnzpC5z3Vte80uPv+iPPfdA9r3D+8w9Xhhc34J5RxG5+nav90n3v+Oh7/IgVUSIDncQBAtULne/cv3iv2z/m3ne46iUvRqZGEB3HHX/8Yx9w91+4xw/d/JpXvNQFTr7iRS9wr5t9z7Mf9mM/eovvvuetvv/X7nu7n7r9TWMpDAJnQOjkC5xfuu/tb3+z74sUhJIO3ZlWd0bedDiMTRylZ8Z9f/gWv3Sv297zptfh2Iwark7jG+0lN77mFX/53rd7+J1uefYzHWci9DFKPyrOdrYTn/7Quz3l/ne68/dd8woXOf+lL3jea13yIve/1Q1+51EPuN1Nvyed/jszTrv4xc7/6Hvf7nH3u+ONv+uqlTHVdrEccZog5cducYNfut+dfv6etzvhxLPGLNqVrYxSmJWRgTEkosGKJurwYrPVWDTVjY4XTOCTMwTDkCWALaDQU6ebDlC1AKErKKrNSjEhpaIiCVFVKmIVhHzOIWOfss86Z9MwOjOj0+n+ltIcbXb1KZkkVEWbogoQ9KuMBFKRQ30Vbop5SXMaGRJwlKCLZ2oATnE0shgqfUYiOT9RxrYFaqd+7pTxya2SO/iuyzr5CIlCBVqkGK8ztE/rIYGiqDWZSTN8wygvdZELAljXlU+aefK/04qaCXn8P2PPfZ6TrRivj/3UFk++Tfv4x8/4jYLznP1s3zjsi4d/3o79ZzrugMdv00qrx+6efupXjp1841FVb3/Lm5318H8yefmfvX5M/7oxn/i3zz77JS97xZ+99pOf2QD8urvfocvvEBlRiJ7jpLNd78pXuPMP3foxD/7xlz/7aa96/m//0XOe8TtPeeIjfuJBt7j5D1z9Slc817lP1trOYJl5ymc//Xf/+A9/+OpXP+npT7vPQx/+w/d9wO0f8ODnvOj3PvDBD3CMWLnspS790z/+46/47ac/4RE/c9XLXkq1sP+/vn3pK6e+9W/f+ajfeOqt73GfH7z7fR7zhCe8+a/+an/09GOSXORCF/7hW93qGY9/3Ct++xkvfdqT73n7H77MRS9iVJOYHBvxv3G8+qUuemzUP7/3n8Gte1gwXoWRDegIY5jGFmyMtwIpDKJIT66p2OIQdWKZyfCfxbk8jh6+MsskaXZGMDNWehMS5hhGWvVgHiSbBeNI4GBo3Su66Ar1EEa/B9a0lfEPrVoq84YxE2WamEVlMmN+SB+IzpIlmQtMtXou0UvZK0WeyRJkj2XoAjUKQtLJnNUGFnTDHslCil7PysBn5rAYRWdZQqspCWbMmEmuhwjkGCPDmT8rqgSrnipeLVHQoR0ywUUGW94gJDKTBZlEshuBGbIGs1QgZ8vVsosFSJrln3gysQrVCEmOhUOdtRFMJI0gI9MBT1JLJmemYPfY53QEUSnC7O7FpKpqbsSGZixVF6ZTsTQdfHJ+RI0ostKDxEzvCK+DIpplzcP86bxTTJciEM20MQUjq5jVynki7sEKpuxzGapTR+gmLFWjQKYifN3Sdo5FvBQGYwPLOVlAyTSbaI2wpP5OdaAw5TIwk0bc+LDgNIMqzT2078voZU4O4FI4bALTkqvAgA+KUQKalmhASZ5qBS9Zhg8VuMo0z92K0jlw19uO3rANEw0VB3JG+AAGdBKBsOBWKxexRt8MuMwgCZuilkkGwilI+k9Kes1ZZU146obNEeWG0DlCuNbRVRAhwe+qzWLJXnNVGUm/VCqBwv2Rh2Bu5IBEzOFrn50SIdhZhPbClM2fqI2LBRWklKhh1I4aEEvZcZTDY0wqM0P3UcZsSk/nSj4xna7vMzK9iGvSoBoL9YEiqROvQd6RUdCbrqVMMYK7Yp2x91gd9AkD7QFJTgcsRCM4O5IRQ0+A0ITIFvsiR0N68jIKqchwhj7dNuHiQcTVyTcUGzQJ8MIqmk8MjxnhLsEL3kmmFU0yH5kTMFgV1qmCcOkbMTpyo3uFTNc1bYaBITtgfR70OFileLphFvIQQwVpSAZlAGGsyT6DyuE3EL/MkeZC1NU4Qiy5AWa6KBKaI2SMJWLB5sWgwQkFUpLOpsYyqDNMBCRWWpYqynMOAueKt/QlhqIHxrRJC5p5k16w/RFCiNPdOaU6Km9OaGi4zMxgaogJz+5YGXJYVXtRmhT71G4yiZbvLbtBNM16qZ1EQgRMRTCqm9MJKJ2jO+kJ6MXJuxPmNMN0RI9cJ7addSAMNLQmqBvDomukBLUQHVliqk/pIV3Cyrqz/c6mAi7ixi5z29gTGUq2UOMYc851dm5V+owx1EdVbzqMEtD4Gxd+44WBccFLjATJWcqSZSlaiIh1pGtIVW2t6CLHn/nI7W5ynRc+8p4vefj9fvpHfuDEg+pj9ZGMJAruppe/xEXvfwduTu71wkf8+BN//O63/Z5rne3MZ6KgQntjMVXu4CriKhe64E2ueKXrXeLSOytuvtpY6XSyZafN4qAictHznOtuN/7+JzzwLi/4+Xu/4FH3etDtb/X9V7tq2x7iQMhMEBwmfqWLX+CG17jsta5wEbRJ/UokwU4XRR45oje57uUffvdb/96j7/+iRz7oF+90hxtf4UrH7ZqnpJeAJjI9dcSlznvem1ztit931ctRzhgZjgTTEvPowQlnPukGV73ija5yxfOddI4SZqkahCtr0++/9uUfcc/bvPCR93vew+53/9ve+NIXOk8zXOz8J93g6pe+4dUvd5bjT8S+Zk8bct3LXuJm17jCFS95fkyKaUugIOgiHsVdyEzIWEh3JsbVLneR77v25a98uYtMVPNaB58cGLFJ0Utc4OSbX/kK17/MJQ8gtJRKFWJaohVfin33lS7zsB/9oRc96gEv/rkHPfkh97nLD9zoguc+R/EmcychJ9T6fVe6zI2uecXrXfUSumSR0dhrIJpMECccd3D9q1zuBte4/DUuc8nMkkzgKKIqohtKDPU2QpOYh8PXql4MpoLtYVhzhlViy0cD6kkLq/fhc+bWh2RITRb42SYWlJJVSJykAbof/7GX64gghF7vXpJAc5atXuZgJHA0mgh3LoBLGrATHBRbMfgKAAAQAElEQVRZ6Ak+fKzhk5N2RezAbTdlhrha1pJLCz5+oZaSKlEzKRznMxKZdkWYAJRDaewCUCWM93wEh5/jr3TJ1XRQfcKUjpiSW7Al0bJwRYQkg1lQDWFcqOfFLnbRZdkh813v/ddDGl9/+PwpX/66rsEcEcnO4w8OeGQ7/zlOKpQK+PAntxc77PlWbcz5p294E+/y0fjZzvpN/pAxY4932T7zxS/x+G2ainybu1+7deHznnzNK2+/jvbBD33gHf/wj1/r58na+8Oe8ORfeMKv3/+Rj7753e75M7/6pA997OPs/+/RhADIWc58wqUufMHvvfa17nunH3naox/+4qc+6TUveOZfvPj5z3vSEx/ygAfe5uY3vcLlrnDkxJPwPz+nn3bqOLr96NQJZz7x1je58Quf/Ot/8IwnP+hud7rQec+D/z08/yel78z3Of0Nf/POBz/u12505x/70Qc++Lm/97vved/713UldRG58hWu+FP3vfcrn//bf/ycZ/z6I3/mZt/3Pec/z7kY4Lz7/6Kd/axnPXLc8Zz42c9+wYNLLErCqDYXGJYzySUhLaQx2pmomDC5vjGBYRZlNZk6hZN8wpkRlcGa4iRm/BdQuFgw0lOCZEO22IsOdSkCYTlijSMG5ySzDQbgnAQrKS3AmnGOxtSxejK6xHOLVCFp8Nwmlmk7KTvUNoSvHVgdCIMbzFZIgLq4UJHNJYpm0zSZKdg0GjM9mBFMRLe8aam2aY0pSV4gQ/4Tt9zL6FzZkVpqKVVJWEuicaIA5qOmV66rS2Xu7GoeohRTwgMjAsxTQQ7K5XEXUh3pQt6DaXU/Ylh49d5YpQgoXE9u0xBLlAMs1Q64zhaRMta6spDP7WORugc4EpBihTnGBAAX79m7x2TNIiqCRrap3ZfpXJzI12MGWyrA/B/OWg0WysnUGczZTKolXGJSyRDhQJVZckgSUu4YkqpJbntEMJ+Gq0+RCekmvWTqgNBH+2bCIqh0mVxSiqRzlrqLq8hidvg2fzvXKqwQbYFUpRdMykHeM4PmSbiIa8BmlKQYXZOSpFBmkVkJXEQzi1KmqSPSndVSTA8RA+eFLD0L5cOGhxCZueaYHEqM9tQ+sZUOBEeHS6cXSs5tVSE1xdToOfcRq4ArbK98/t6CCw+HDGmz1lyqLmoGlslUH3Sh2FlaQVYRqBIuvsxoSXw4UaiDAOC/Eslvk2t3oExoF6xR+1zcd9zREkVZ04hI5iYhDdYSdVZ1ZUnc6SRUtUcEvdlSt+4UiUM4QyXpTkmxp8pcZDRwU8pOdQokWgA4ZCC9Sho2FiHJlrwDVLFdqdStKbUNEp4kXBZYg6iCA6SapSAyY4XMAbjn9gFjmLY2zVZgFZYIn33GRlqMX8D0HCFmVlLm5G2ASkWJUd1pDHPXYKQw1kTS9dDVXbSnzhjwDqKSGkRQTEioCOWDGERgLm2gcrV3c5VQ6q6xK2nEgkRpuFInpW/FDIUI0uHcYtYNAPooA026U6xQzh81HCBX6hmdmh2SJAs1+jR0groEJnQUrLvsFZTUBd1LpxqJQrFacGOXJmDmIDCmhw9FJGkKUUkBmyoR4QEROX3MuXmkThOREN3giyh07TkzqbopYx/FokpUZB2QyFB1IPlFc/J0hs5UDodD0lJqMlVVU0pZZHB7WWtmNboCizwftIGVorsmO9GD5DCC6kVcFRuSGRZBzqAnYKdO9qkg67Gp6iauSMw5fEaECFwNbIBQ5L0ksRaYQoQCZrgDrBGzcOyhhiVpuk0ydnMYfd85MYvKtmlhvWkxW5+SpIy1ySgiSoJaVEUMoZY0RSS3elTKyYY3UFVUbFIKEW3VdppNvAnRpaDe51idD/A7ByQsUBNNhTA0Y3ripZPo3DLUmjPMVUF2CufgbT9bU4njTnKRqO7GQTK9BON57NbRwiGUjZNINwq9RjlEN5beV+wZzDpFZzAgkiiJhVfwsVAkzWFcC6N4qmytGY3AtYDvAThGGpDux+QXl+JhsyCKTGX+WFYuJWQokBK0oAhl8CLeIDvANENUnaGhwiTUQqshjZ2umVSYdTHCFZOIe8Sk6VRsUSkigjJTRvIzabOqMM4JqCYqaXDh0LKK0RAIzkd25Q7Ru4aAOG/prtcIyqKGrPAqnesA0ZekyY2qCV1kBpgfu1I9Ik6pkxzMAkK3m9N65rQRuiJXOh80GOzJb10YuO5kJuABmfwGfrgCDF1T1qK9mIgsGo0WIIfsZWXmnzBJK6DfKTNhPSaOBNCj++jiqqNlinLkLMn4Lm1UTHVypbh0ERdKTm4Aw14lqA/VFP5rzEDVNzUSGqe9418ORyEvd5HwmOHpjsEW4Am2hLINUCOK7FGZ3CYWHTH7xc5//uOPO8K7f/Oud/cxefLv27+8/8Pf+8N3+Mf3b3/4+Gv9+/2+dxYQ4Bb8WOd5z3WOosrzj3z4g5HJk2/VXvCHr/y3w78wdp2rXY2F4zcOu+xFL0w42f/Xf/PX344Q8O4PnrE9O9dJ32TDQwrHmqre7pY/UFubc7701X9KXzvWz+Pb/u6df/mX266JfvK5z37m9176+ze43Z3u+7M//74Pf2xuuHHIf5cm2DxTlrac46xnu8gFL3SD61z7IXe5/ZN+/pGveNZT/uFVv//WP37Zb//GE3/qAff/gRvdiK+/ALzot570pEc/8nuvebXjD2t39vyXNY/4yqmnvflv/+Fnf/U3rvWDP/JjD3jgy/7o5Z/85Md756M1PfHMJ97shjf85Uc96u/+/NV/+Mzf/Im73+XKl73MiSdsP2r4n5SwlHKuc21/seRzp3x5Dj72Zx3G0KC/yjBzFZlWouxCF0lFQujSwuhu4MMyF+bGWfgQQjxMnHluES4oHAM1rnSlDjWXnMnAKtGxsB5RR4psuaBkNEwBQ3sFU6WOYlmV9M1Teo59rGv2mcPH9LkFLdnk4QrPLJFiYMosGrpRGcJQjsrM7HTetOlCKlKjLahNpOXEHDkj00yE/9hKAUo6CdgMG3TxyewRYA632TRaOBwlULIkWrd2lCukxbYIDkUv7mIpVXCgsdGCOTSBRGgxNUs9cNmpF6aqAmruqt295xyUxEu4YFrrUZI3w5CkV7SWUjfRIsqcOoMEqhXjhkjjMJdJZpGsooBS85Ae4bFu+gUg7E8uDhNc2clI3AHnOgiBJM97Gi2YLGO1R+6TfFwOZm7cwNXFMykekU3KzTlKbpNTiepU5ariVAYgq21ccqlgBT1cZGShMflGJGrXFlzUpgQX1mnOWTgidZGqUacXwVKwVIkibjuMpmszFwoaGlCeESto1Si0gDgC1EkkCHIsM7dFDRKaXdc9155w0Lp0FY6iaAW8FeBUY7mRxSfyqATl9BAfHFv3buu0nJrQKRpEErwHzKqj0AnCDYMMKrhXcTQMKWvIVoiVlAViGz5FJAuvTeJAyAvNYxlThJYlm0KvxyyqACRReSOK646ncMk56eDmoG0LoUilBCnbb4ytG4wLnchNpoCf1CSJoBWDSqYp66t0dqmUEgdVGl2bejrgSmGjs0YxU7FwnQxhpXy0rZuSZle6tUBN0ATUCilBhbSZLaqLsL7bdV16adEYyxtzc210VCT6wFg3N3bp9BZJUaglqqbtaGaQ3XQGFR/MI62I2tT0GnQlSk6GmCPDU5DVUMtkxQMacaT0pfrCAOAtMDlyuzVqR84yqQmIL3YjyYjzKKiJOdg2XWOKI0IH1FVCJdky1ClJtKm7uSxSRTQyu4ePqZgqHRJUfau7lYkisIHIXuxj+Bw+u2JVG1ZJD+pU2AMxERxdp+kkQSRtJ7NQZEFYcmgu0o2YicKbuBRkU9qjwQ+IRQKqyUqoSDFptjmPqw/1XklILGmczGJeSqpO0FlW0bGjxVSsJNqYxk5Vr0hLE1jK4MCYMdx6loHihCJLs7ZbysEipun0VaUcatvtERhQoQ2S/MQM4D1TjSpUfCI9Na3QbRCwCXHI8Ji5uWVNragVB01UZIMgOBoGK0IbAkjCn8wqmSHIJG4OOqRMMPlKUNyYxUbhXkqiepRw85lsBL0wqZt6gQI1pEjkDr5AldLRM0S1CKyC46QklE7VIydh2DxOwkRMqFITYRBaqdRP1UXGyNHZfM7IAB0tO6xLgZlorTBJsBsrcmj4bqKF0BxBy7oKxGosiy9tcBUy1zJE9iCWLr6W2WsMg1shdqopQtgxJfmMyidjxjc0VLS42hruThcfMFpUeqhPiyyiKruZuzl3yVd2QadHhk8Jr4kmrhGZlJ58iIKZmOZ0jGEjCghkDkl+M6JQJZmaSiYVE8soOnyZW2ipV1GhiIKAU90ZwIQ4FDDSQ3BSLEJwimT11JkYTj0G1sF0XilT9lQqkCGGVdBnHZTZhHSSOHIZGJkSqAAz4Fw2CaCbVJi2zZ9VtbKjLKllc64uOYq42WYYCD1RKGVkFTBh+uKxBUXKLCCItICF0QpVXayH9Ol0U5+gK60Rs0yIZ4QjpmJaE20WMJkoHUikmzjNMlIzm6BWqiIITtjnfjjlCfpFhSrgdDGRaSWM/DhmDmyNU5N3kV1sjzJTVaC5UZeVGXkymBknmX097UOfBCCtnnDCiZLqnoPOGBxqmzvmNBG1Qk+jQigmS5AgRaxDTmrHX/kKVwLwwQ9+4N0fOqP05+Wx9ozffdEpp3zxcb/xpKNrP9bDY0YkiQPVjJdsS2tnOdvZeXLKl7781VO3B/Y8/8b21dNOf+5Lfp/9Zznzife6449sCvHif23nO/nkk046B/ve8+GP0zw8+aatj/H2v34Hb5Var3KpS/Dk27RrXPFyFzzv9hef3viX2/9Gcmzk6UePPuPFL4sIXt7ipjc7+fwXIkZr76967Wu//3Z3uMfPPPJtf/+PvOTd/75NRGptRw6OXPDk817/mle/8iUv/oUvfP7Tn9q85fkvf5XaGeb7L1AwM087evR9H/7oC1/xJ/d66M9e65a3ueN97vvCl77005/+JMO/lHLec5/7zre97TN+9Vfe8co/fObjHnWnW9zkHGc9a91K62/jBf//CU40jhz7tagtsELTkWvm6oy3ZPgxZ6ht3LYUE+JzWwdEhV2cMLl4uKnGWgNLWequsiQX4y1wzS0FWhpTo2RR0apQTTTlSh1MEpNJgRWHNE8WBDGS+Z1ZjqkETBKCbUzPCJtTeG4mpiRjzDSWo8ZotloZkCG8XzyYy5CIKvuC01XXuet8F74vHMDHH5gZYD7KLeqrFSuaEoMJbEzNqRsb52qknsWZVQCxUmyxWkQBCpeDuYPrOk/7RISki+dw9BQuVG6WGTqTxb8ppxSdtqiRk6vxCVLvTKtFhJmvKPO3cyBbFcplc9ntK5tOZmWEUVw4uIKPoFKookxmeaQiBSvIshAlGykzUdwWCOH2yJkJEylRySKydNFRjflWlcjx5iQ9CaikqUAhGcZxLczYbaSKdENIKrS6bhKWYWUfElPgnKFZYQ2V9mX2LX1th0MrdQAAEABJREFUY0iXkY1tsnTaTmSAVTK6pWPG9hVTg0uAIyml+WyEG5NXVIkeolgW6ilGZymYXOaJp2mptvUvrgv3zhRCMQpCAaEBdxoVsREgnwrqBBcuq0Mqt79dhRULkz8sbEkcmayCepmrZoet3WbfXMKkUBvJliPZW4cL+ZSmjXcEhCqJCRAOieSKlq13da5ZmbqEtmHNBWuR1cR9ib7ESl8pU9TV6E2cV1ILto9IqjDUFA477EhRZKmztr22KXUznxA2etcWbyEoEnV6dubmJrultrqk7PpofQA5TQctO4KrJWNtqnohQfY5u4uiWipEhywdfLPX0gq1Amb4mpHkWSaw0A34dDaiiziEI6QYnVWURhq+ejrpOeDTta919iVdoT7VUYfVaJIbfyF1VTWlWmCXJkSoo9CdDJZik3eKl+oic0bSr5gaagY3lQKQcwDCaa1DBy+76VAadyyS1WLq2NPeFmC4SU+MnGw+Zcztx+EyqqbQA+leEKoze+Ye6BvmGulzrPPoPif3rTRtQ2ybxlmq1GZKN+KYJNaxVU8lqH5CkRBkkm+dYV2FtVFWSC1h1dnE6NBjLO47GS2O2tjnOpwxkVJR4cH6sgcFo+JWhogLITKlQBkz4VBstTqNTqehF6+EkyNUmyw7rXUD28G6s5uf1npvnTJAtk+miS4aC1KcItfVbaAUrwxKGfuZR0XHocPlLO7c39QIWhdLWoMabSJr+iBbAtqbjF34Ipi26RqWNachw8vIDOmgQ6yMnzxKn7WDogcNRappERiNL6TKlFTAj2SWPSpTmbiLT0cCJbP5bJMZYnVd3VINxFIRTvuqz0RuLGUSd6OsEUbv2wyqoxJ+6RUhEINtqSIYWa2CcYtFRYlD1O7LjBYIa12rp6D4sqy7nbcllNwSAgQyMzJdGC85FV3IaFUME85hpc/HWC2SiRS0o9JYHpxnKotqAzvJW6KI72zsyr5JL0jaUakUIeCQZcIylJrLDLYi0QoFLXUruLlnFu/MZJQlhPQTNF/t4DVkY5WiAYEIpV7K0VKOckRwcgODosGXHAfmO2E0jlxn0JvSIbQoJt0kTMKUYs0Sk+tHhgi7VNzThuwmlGJpriojZ9CzmNdzSuVASk0gRDaQ6MOMWYtDKb2PPiczE2ZiRPDpClzUi8/MkZhGM4GW5uMOc5WsxJO2xlGgl4QCSZ0ACc5PmaImraHSiVDgnEkQywYAYOE6p06GXtBfUcwg2kGHFJ4t1oxpsSbaYPjTFxOYvo617/dy+pBpMbVH6fSFaskpCIwp+yirIC2yQixpfk8QlfDMpFLUilsxQjqpWlrNqtxPjcbcU32tuWJKTs4VigHAQ6eASI+So9BRCAStkfSukR6MgCAUwViMMP30KZxSL3X+etZdmoAggziXSOEogqPQwnQ+OErAXK4FEE3dgDO//vWuA+D0U7/y6te+nif/vv39P/0TL//ts58ZY/LkWEtmQt9oHbs8drzAec/Lk89/+Stf/tYblfd++GOf/dz2qyDf993XvczFLsLx39je9+GPfOlLmzonnXjCN979Ws+nP/O5f3rPe3l5pctf4Vzf9o0Kxxx3cHDn295GRL78la885klP/tRnP3fKV776C7/+lLe//W28e+UrXeVJP/fQP/nt3/y5n3zwZS55KVUlvK9/05vudP8H3eNnfu41f/lX/36TxvH/7dqY8y1//4/3fMQv3uHHH/j2v93+UFtbllO+8Hn3/8em/4eUmj4/8ql/e9Xr/+Jnnvhbt733/W9yx7v87GMf++dvevOXv/zlYxzPc57z3PrmN3/uE3/ttS/+nV966INv+j3X2d7vHYuBYyO+k8f8GrE6tLs6uDwgU923PMAn3FoG0mMghgzGh4UIwMaAUmUi22ILGdJcdynMLeBHmHTMwTRjHK5gSmIuyNyS9cYBjJbBUj2WMtkHAu/k3ZFkRGrFQhuTTemFK3A1KYIiTG2h5NGT6w8TWWJIDiZbFbQFXBgTJYJPaiqTEIIrVQiF1z2lMz0M8FEpBcUX8Yjoq8x9RddgZuCEaZhNUE21ZtlJ29WmBZ7ee0RSMzB7MDP10MCWf7ZUNl1HLAHZiBozTIlt5ODEwvHJBLX3uad7OThtJ7mtqNVR0ltmjeASNmqZhSmI+nQIOcK7zIApmhBnJqcADSQbbZk1c7vLsjJW1dlUGqROzdk8LZDVnK+ZS1VhYVSqGEVhIJfkspVVhBmPIGWwW4TikjWHqIEwU+hNWUlVVzBhbzajaRsHmriBgwomYvRkas80fqERfcEhMa9wViYZOeFTCNBk8s4giJ0GiUktJJOKZxeAovMhsZIsZ6sRI3jIGhhKKItEEVhCJyITmFIGVwxCE1KRrKdERMUkd5k7zwVpEE4hkZHUMrCIHillYXmyzGTRwdu+oSySZFeVe0ouj4XVuhF2DWlJ0kosCxwIIVs2dLJGc7cZMcRnDVkc5skPNZuSIpNGFhJOvqWvLcQQYSPAT1LZggJWMZSNigjEt6Ch8WEhOkMnLe1SJLmpVwmhd8rKFa+5KfVSk0qSnBc2lbMlzMiSDFbqKpTZd+s4GH2JWVKM4tFJJ8FmqVpcFYtlUSIgyd4hgV2CSR7qrtkJcdB/RrJTyEk0h0iHDS8gixEJmSwOjdSFwhTbDLi4EoyQ8OJ80D1NQuhnWJJunspgsQh6uwi4rNMJRaJ4Kl1ck5UwapikJhAQN+cJRSfJRk9dNBndYkZzeiT1LSvUKf9cp3cnVwHnkyspQicwBfRd2bCmIJ6uHmWIDhZma85NCQZMEdtkygxCHTsjNkRC5kwHjYBU7Uo9LbUBVQLsnyQFpXZFuVGZUtc0PnWfDiQUJsagDRKZI1bI6VonOJxqJbVy0YBEkhYIOxDprCaIQ3JmKTvdVfpras9xNMOVU9WEQAvojDJpSSRrjq3e7AjfCAhcrAjjvIhIaEojYkHRacrwUJ+YnDOCm9vViaNsdhxZI4ul8b74mlg9Q0KYSDZPUVKiO1tQbEuhEU1DeJkyQwSsxeZCa2eTqCCzknQYDtE0QkfBcPhhxNHJCQrYlZPam/AgCPLSKEa7DeYDBHhKCegvKKocNLcZXBSmJeGXAklxXm+KE03ajfQUqcKPEnmrEIsQAqKBMqGpUTRUaFRwbxV0gqNpNCuq1ZpLyQIoSD6aBefGpH+IJw3jG9Kmey1DYRKWoi6aWSKrJ4I49NCJ7RalB8hKyBVBoRZl0W00HiySFbVmYPsQppYkl2BHppUwQ9R0JVeoqNIiCcxUhEAdoLvwfnOUUFMB7elTNEu1nWiVsqWHzv4NRk7SbpXJijFAP6rhmcGDRcA9qVWkUGbhJqVkurlLcpZNrg8CIEt6DW4IB/UNcJKFKxFk7dSTfh3gdUSIzCIusoWVa/DMIccigRQVh8oI0iipliU1u87BI+mmkhPFmTEwhwE1VaRIVXBGgOBo8EwQhEdEIrWLbiMV7FHqQHBL4Rw0MNs4ND1lSB1qG7HcVOU8MxDBcGBAZk3h1qytmStREWrhmuAKvuM3IQX2MEOEe3CFC0YKs83gk4ANaTAyMKuuTbooJ0YddPBqXHcYoGo6gak2Kw1gg8h7p0hTPIzm24ftt3NG08D6oU8RgnbBc8qRKpXUK59CmULBRjcuJRo9rw5sLkVUCPasIdUX7Yvf7Luvcuxn91/9utd99vD/MCG1w5a+HMeTVuqy5U+ebk3rwhSxnf27fxc933l49ZVTT/vaXx/m5de1MYkB3QAXPv/5aIevu8tLusULXvaHY3Se3/wG38/jt2r/+uGPnH766bx7rStdgcf/sN3+B252qUtfjsPe/j/+9tZ3v/cP3OXuv/dHr+DliSec8LP3v0+r7aSznu1eP3Lr33/abzzhFx51qUtckrdGX//iLX953595+G3v/eN/8Kev/f/WfxlJ+f43Wmb+3b+8934/95i7Pugn3/DG13GGWLnKVa/x9F/+pWf80qPNmLHY983af67vlC9/9U3v+B9PeNbzfvAe97vVj93zfg9/xIte8qJ/eve7176SsKoR4R+784/+3m89+ZXPfdZTHv2I63/XNU84/rhv6hIc/51qEfmlr26v+44csBJYmAZGSgZjjM9rsfQ0RhmSyUekiVsywDSjbB4rTOq+5d+owbqEDjpYuA0GfVK8me5wpgTmMQY+mFHYyxzBGB0D6tEkiyBNmXEU09CZHYNpaRvL1K1ardWlgM8SNZmFUphmkquUmjdyB5MCJMBw5qKsTQuacW8wRHy7he3DNDWSxfSwyI2+ZkcE2D0zQgTaKqy5ZAZ3K5SZtdOSTHlcdVVUrGzznJs0vkvP5kVzERCM4qvmoArbRcWqMqSQBfHJ5DqXnQ+bxkyd0Xy07FX2Ze5jrpN3azBpRXCZ8+HcIRqhISPUQEWqpCXKwG5KlZTSZ9szca59GWEgCDkAeo5IWI6YzA/OHRgVacnlJKFAFamqENYkXFGX3N4aL4PyTMhhFiedgXXWjsaNA8jHvJY04cMxrjtIpGIg5yzRuYIvFcWEUHXOzIm+ju5zqGQzaVJyk7XJ5MsQdAFT+czqTKshIsBmJyZcHRmqcylr4bO13FMvPp4foQEgEsPFnXoFuEzoWG12yZFc5emMlKhPdlaCr3vUHBs/SzuIdqTX6qp0GzVau6YXeDF6bVQvGruSTNPKXeIusHM5CMqLaJ2lFqqUIkurWlVqpq2hM0EIEOquPBHS45kHXTk4sBQdph02pYGFQhafpXcdo5BRy6Wx7N3wBJXpJTudjhoCNKBp7pCF0cMWkhG0CSFq3WX0Oekh2SaqTrWtCkqEaBaJGkNyH9qH5eBiTKMR1/TKoGRlznq47a2swopCZTbFjnP8KMZefSW69MwE0U0pDbVQNCBFB8pchGZhMFpUm0rqfcUeGS2xpDbLtswNh8kZRXY6j8B3lkuVqrukc2mm7k27TXgv1EgTJnTjnq2nDh9BGZKuJ8IiAuJTxXkmEtExe9njoNeDrAYZmEdzHCVDFaXNyg4HhfYW4u+g0/fmWdfU7tQJsCLLEswDUIJMwzfxBb5ItCKV5TvfJnnbD6X75gIcESwwppZahcp7GXs6ucicZbrNdEIOtCQn0o9VMK14GmUmCTgH2hwmIZCAcAxTR+paNQ6KtOa669wBLVotdaM2pKQyncnCq6B+xMRS+MW56QEEZtYhS6BqsWIiKMN1DvqhABL8fpgW5tR1Q8i2HDIYTxgSIRRbS7TELpZFDypa06wyVWmDlmacfAjrzIz00rMcDZyOOQC4Yqp0A5qYKdVoiAaoiKSzzm1RNHeJJcykSLG6vdkjB0iHHHUjFQpIgyGpik7PxAzqxeuN/gJdkLUEdukEToVA1l6Mrl2CQeciW+zDUWbR2JxTFIYU6hhliAmISoUpzchEepiQDyPOIYkGKeBJYLq5V7DHFlgjceoXxHiM2Ifvhc+M+gwHk1arWiu4m22pDWxU0msSWwbj0rsEqQlEExJaK98AABAASURBVBmgudMACKbJRGCM3IPRRTecdIQKYTMiPMVTsaUGLQJ2CpVR3dRqKK1kqTTiGmNgqEprO9WGaRim0C0EKlUKU+qS4Rmkbj3rfkLGaLEuMYzemEE0bNQ6OZ17IwCEtAkZoWgWRTWpPXJ1dPrJHgSYEdAUu6EWNZX4EosJhIoqillRzlMJmcncYH3A6YXd6WYIn4kOTkqiZEWMjuaLzmqOhWttUKkoCaqtEBWHzJCRNbSKlpZSHAlbC1adgW6ZEXXkcrqWoZqSrpHWZ1u7DRwSiR0mV0JeZOGMmNqVPUkflVo2uWfkTCcc2Dfw9eGeHg5Jyqdlwa6EIkleVvGecxAN+hkHbAIWCRNMKQM7c2IZKkxU0geZ0AZrWUZbmE8J1oYfIRQJlIxlzN3U6gvCAGhUxKIZRVBUTYyeJfCA5+HiofQhXoSL9HLqR8/4/fVaioaUTMadGWCZmNRbsoqb5YZl0ST2yeqBuzxyLxiS7ciR297i5mT8iU996lkv+YPkPF4ctvOf42z8/sUvf+Xf/2r7siytLez/yqmn8nisnfXE7Y/kjv3pc6V3HOv7+iOnKX0D+Mpp2x7j628DX/rq9qef2H+5y1z2e65xVZ58q/b+j20/v8S7V7zsZXn8D9vBbvdbj374SWfbfq38E5/+9Ec+vv3S/JmOP/6Jj37UNS5/xv9fCciJJ5z5h29yo1c8+2nP+40nHqPsY/2Hf/7nn3z0L970Lnf//Ve9xmkF/Pf4vPdDH+UW5fb3+fE/fd1r18NfoL/kxS72zMc/9iVPevyNrnttAvKdVYNu88/v/9DTf+clt3/ww69329vd7cE/+aRnPeud73rX5z//ed4iryMnnv2G33O9Rz/sYW/+gxe94ref9osPvPf1rnn1c520ORj+Sz4e8ZnDffiJxx/frAmQmmDEIg4vGC3SNbZs1oSLBINoGKZJMsI7+HhVWEMaUkQcOqmWH1JAeokhpB+czhgT82hzMoxDsFrttUjTTcltfNUwczXGJapXLlTmxWDMMoupGm8zLI8yFyVThQjzFZvsQvj4vJBNmaEooibF2Vg9eklHpFuMNsHnnegZmYzwUHcw2QtmKVbLQkY6DVxaqYiLDopTmDgksFEI6eldEjWYS4qUwlQ0pTkTF1ANylUNvJ/QZGKcohPKqVCHb8m9irBUsgKV0J5YCTE01Dgr1ROgmGGcyCRNAFBTNEWyRFi4TY/p6eFwiCfPwvnhCFrMPDRdmLWEQPEfYJHiU3rIquKqkC3fUbkqKZhwyYkxY52zeyDQQheKQ8w0YXyM5lQBg6N8linpdZpOQQSA5BciafigfQWsDmsl66rYsrIP7QEnF5cJBbSINpGaqoMAEHOpbUqh/0wEIZCgcj1jhIypc6XsSU4im7Q0YW7f6VEb0YGc6jNmYGzsioArZbIgZUXIdRKsX9tgCTM2ew9xB+UWMXFFiACWqU5bmKmUmjAdgtQkRqoazfqi2yJIHk4F5HB0ChURzU2fIM8maqUIeylksoIl2xrJi1DabEqZKiGJSH5XzYrUAD/sCktRNSl0iCIuLIMTmWUQ+pkxqaALNrvo9BqrZScjCgAIcoSzbkAVWdRJ1qaaa01KHjUgm0EjJEgiiwpCzE29watvYM+kPxlxgBWGoFSMJjS5ptINDoXWYdIZQ4f8SqAO+nwlc3IviQamip3aMnS3aulkNoB90iKg1JIT8Cl069lm15whFHnwpQFfqMncUPREBzXhqDDxlJjgQg6F2VxsljHTZ4fOzcimSHKfBipDX489ZCitKCKUBlsUF2Nw+uHOdRT1Uqg8TDixhCwJ00ywsPNpkrVYs2paSmiZU+cIVld7RICfVAszHqWKHphUSqVCLeiglEMYAmGDAdcFwzJtbO6K8KR8CAa0YkIcTUqzpaJKhDAxbtKLBuhxrDJnlJmkRfuFKKBKliFl0oehrpFUW21qHTRsAAGaW6ykNc510ItctoklyE3gkVjBvELppxVqn6rOb1oXqweShZAnR7HSCUuIOHkiqbJFKSharVVthXeGIUgxkQ64ADUYwQ2l0JXdkGIpmjVRp0vGyFwzCQoRsQmE8x/x4jdkytY7rAgo0GZzFdJM45jZhpuk0lSQTM7VLKnFCybTH1SS0wgKLS6pSmM0JC2VZJGbJpxFDonCkcwf3DrEzElvqFTJQmWmzKDx4SAAGizzJx3FHCXNwNykIpJOUyrSRaaJmiok2W0hxaMxJoypm3p6ZIgM0X1aHyoTZePAbiceyW1Yi9RZHI0RDX4cRt+AhKmIsUELwEiSjRKlgiVEAqCchDOQpUKKEq6YSrkVtE4ah+gktkPB/cwkmImpMhuFn4Sr89wgprWImXgTWktTbXAoNXTtTmepQljM+WSljVChauoV3NHx9VJRtTQinBIUyBWrcAp7VAZkArl5e4rDBKXolntNinAbalXo/D4ZKtR/iYMFWplJxEUhGwlMwK1AxSyQPjM7co2YwTEuWMFEImPDJUvxylVC6DCcQFKTokZEppAIEYPATFT4YR8j21MSEIXyW9JpWEwstixmlRoL7zIUC8LWdC4zpLMIFvMqoP4qLalIprnTD8UlSIh5gzNawtIwWW8sU3jUOSQ6OEgJFqhMqkud0ugZ2gsxWyR2GspbU/1oQTe4uYij5iw6UT6z/aAUgIMrXSoHyjpK7wRFeG9BmMegplCA7qDFJbldnMleck46Qp2+u9ttfuQsZ9l+Jf3ZL3nZP7znX0ntsMnlLnpBnnzulFM+/ul/48mxdtKZTzjpxDN/7/dc/353ufOxHh6XM53II9unP/cFHr9pO9uZT2il8Na/fHjbJ/Dk61q6z/22hznpxBNM9evu/vvLj330Q8cuL3L+7UfOjp1/++PFL3TBP33hc+9wm9uc7+STz3O+C9719rf/0xe98Ebf/V0izEz/y9Tjjhzc4LrXfvXznvWyZz7tute93sHBgbu//4Mf/MlfeMx33/b2L3nla77yrX+87X8h9H/jwiPe/cEP3+9Rj73xHe/8qj/709OPnm5mF7/IRZ72y499/YtfeNPrX+9gd8bfavvPS9fH/PTnv/hHb3jLQx7zK5e74c1vfPs7Pu7JT37LW970xS9+kSu1lXrimc98jatd/SH3vserXvi8f37Ny577hF++xw/d8kLnP/9xBwdghP3nJfj/h8IpX/ryOH37S9ZnP+fJDGfGAtQZfCxNcpmjtbXYbCVY4pTOLLYYmD6HyMpsMTx9ZbJJYcLIOpnxU6vIMS91Zdboqqer7kvbs6RhGuCaI1WammYLZzgmV46SpKm6g1aGL4vNqvMg9guJI7taFKbM3A+uNiDbNOEKTi0z4EmBVHWAj7aZHjNLpdRkOFjujN3B3pZOvtEybKYEogJHpNd0oHjualU9CCwjLUSTTWu4TdJGDrizioyhJUuD8oEiZVHpBaOoiC1F2m5mIWhtbtk/UpOjnauQatWxLKu1RFm0Nt0VNE4z7q/ONPUg1aiIChTgkLRMdOZx6dCt6hNnPd92FHL06vsiPMt14bOggTiqwwt21CdDNa2gLtFMKoqkxZDeY50+HSO3BcnHLufSZj1ulON6lv32LLBLdsFQA7RS7AHtXC86JeRKwkzo0bWubTcpCRXLmMyyipYiKEH5W5hG0SwQAwRcv6ayVE6sju5lTIrN+xVZInaZLUHIgyuocHSxLE2k0HkiuLPQ7EUmVePiRHvQalUoGugUo/UR3QdN6OATwl2PBQPmbgjLNIBCcMHNoznHHLmO3Zi7yarE1S1oGA6fg1JxNdQpRQpURGex3pAASHt6loE6FTnhR81Xir0tqlYyuRmwMKXakqlwLm6EIgqnpvBQzXd0PxnqSSNu/lPGIGhZyKepckwmfF961jApbWobziWpmolqYNJFqUgVVxwaR7qK285rcYAVAo3fQ2eCqaKWLKxmJcLmrDprXUWPhhztxQ//5MMSTvgATRrHClpNCh6rrb2MTkE8LdZKXzaPkqPoAIXILa51ZgvsEpqMOZYKYze9IIdlt0AZZelbFZEr+pTTnVsQeltAIALE5nLrRNd9ryOoOIGdSTuhKEOgTOhcCTo3hStFD8ZUjXWzlsAUWzcdK2quZnOCW5YpY5ScyYnaguJGjVigVUtp24azrJBVZcBILZUDIUpZAAd7lz3IVQhik85A4LP2IrwxtQ+OCFSPJU4rspaM3dCDyaAFtRFhZqz0gUE/mjBEsbSUoD4zXThtoO4tJ7OkRmoMRKcpqV5KgS0iB5qNLo2cITFiqzeoPZ8nT3OkSILQUm8RQhXAPrO7Z/ZqCatTaneKv7bSj7RQo+AiJWyZ5SBV6DLFXYhxRMpMdQTIZKzSu1HGqnQVEHiDANimNFkamrkGEQ2NNJO6pSmOcGKtfJq9hg8QcnZBKYlO8DIdCIrN1LHfgHWGb0GIT0nqNjO8SJggQoks+XE8TU/AfNubplNISRIsEB0Sax1eXehv9PJM1og1EILJ5iJeVSy1ZFbh0sOeMAQtAiyDSqW03CT0CfUZHp7kTddRFrxE0SN7REIl+e5kqdJQGXuiAu+Sa8NYZpR96NGpKxWrid00m4v2pUzlwBwVa2kdB2O0sfIZVUmuKblzkoIWahMTcx6CH24GbIU41REikoOMdNOIpChwFAnaWWuWJWvDIoIZc8bIdIoODVDjXNALBlXjEuacQWWpIocWbJ7QyqwSNkS7llWkT5+GNJVoyANYnaZd6PFSgiLphkBKcd00B0YG3W+Yi9YWS7GKWigbZ0hMX933wFGpXnewpUIP4DXorDuJQlRQhBMQSuYlZafYNdJyBd9iMmnBjQ6j6ZAgQAWamay0eVRTpTRG8dFbTI2MlEhNGn4wTGB6mCd4MN7MVE+mJzFX6wGPwOYS0LK5VwQJU7QyrUiCS+Zxgh1Usk2v9HYoUSPfWFqWg2D2FZ8LE4pLgTUThnVv1ot4Ov+BCSwr8S7OasUL9dpwph/ZGtXTInUMWZ15ULMR1CHwTUbPRlOpD0xavlvOhbq7pgh4UgaZVZ5khM7+0U/g8NPPcyLNHMl1a7MKVFHUobwgYQqXigSmmdf0ZUiNiuSQKnKes539QXe9m1nx/Wl8e8A3Gzj8XO5Sl+b3nOP9HzxjY8DLk8959t/8xUc995ceda5/98shV7n09uNSvPu2f/wXcuHJN7bznuscZzrTmdj/6Y998LTTj/Lk69rxxx9/2Utfip1/8ba3v+g1X/8LM+z/WlM62uEFnejw+//W4dznPPuvPvyn/uqP/+BvXv6ixz7k/hc8z/Y3oL7lTJFrXeVKL3ri4373qU+++Q1veKbjN8k/+rGP/dQvPuZW97jP77/mdacfpW9/y9n/9Tfom+//yEcf8au/8QN3ufsrX/Mnk84lcplLXOJXf+4Rr3nBb9/i+7/vOyVSRHzo45986Wv+/C4P/qnr/cidH/AzD33pH7/iS6d88Rj9E0888dIXu9jd7nCHp//q499Ru/aQAAAQAElEQVTwkt/5w6f++kPuefcrXfqSrS3HBvzfOr7rA2f88bpLXPj8sngpoUxEDBOoi7lqpuWEuGxxwlUnpyS/NKMdc2kmHohVLgGM9ppmoilUhwc34dTIGrAAOA8lsTAtVoGxvpnO5Xe6hohA+GEoM6UEKTHDbSl6kkwyLw2MyeVnmGWFcHGDC7unkjRnSEIdCFf2RiLBUO6mMC1SDWWK9VqykItyHUy3EM2qvHYkMKv1VrwsYLnP/ItQpsJVcy/D3TWZKCgzxw4uB33ypmblQ+UjRYpqMIcWCcqQI0YwyycpCBdQIaNNOhVtIibUvKlQMoUKVNVEhGOrb7PBRSWDC3qfGNQwRUm9QMF5JVmdmdRiC1qxnbGIKY7dil2AvWJGODAzZ04bsEGz0RiaoAKjgfYgR8FUGSapSmAWsMoIW6dweRAZim4IWKIJhPiUTWrTLOIGSRpxuwBqSjNbWpGmw9FXJNsMiFRV1nhc2qYU7whmWzhs0xfDlAty9OCivwRUVaxGMXK0CjVVLrK+E9HZwOf4h0t5RShowqlcCiR1ap2luhknilGkFWMKeQNCLWMU9FJH0ST9EPYFMYnYCoZhg+5QZFThMp8hpQtXJOnIfQqdMWeRqICREZEYiL358Jn7yYVpM9AM9znnwEi+O8pg9ZWdBTxxhYpkwKeHB3IGv3XnjlFXVFeAAkpJLZtUvOSUQQ1MORHk2htdUYU4ZHHfDS5LWUnSgRW6rZFuOVmOkQXZoITR6TxJl9Q0cpnZYlADFOqCDuw1h0zTbmXETHdsIPoUd5+D7jpnZKQLptBVFo1G/WfqUTLgejtcB2yzK7QmjDHgGRvYI+voVbqYgL5Ih2kg5lWzABlqQ7Unouf2oVdUs2JKWp40U9KHzLxlFkkOy8IpYZzKnCO7lAMV05i0bUrSilBMwNOc+JU2RGcEnCDwpnKSN9p9GzMjGaYzBpu4W4dSbKmORpOKz5Q9bEUBPQSmtWnjs2GQLrgHrn0qxEUGoEHDyZLbfsgP04tg8xARG6hTKHSKJcXdZWvWomqvWEUpWhDwMYenQ12WEUsOi6PCfkKY7FNYag0ThKSnBLGAIXLAnWbxGpxBEH0bY1Vo75FLRNNsdNKag4Im0E3WDQhFklDMQ6fjzXDSJK2xxhyaY1eSyqLSCAkSnU6fpyNETs+Rc/r0EdnFgpoMte4YofSAAUbYxFzN90W6ilPDTEtRY2o1ZOEVL2nQmhBN2mxLKOBHvGgoOU70sD3qXmtvvBsOJO0QbTNdV6yImeEBhBB/G1NHt0n1VWgplHQLVzKltVzMtWELl+S00RkZKjSeZ7pQkTmmMBwym2VhqmpqW0KDNrfiipkMa1/X6E4RM0KGW6cmQj4pEyX4WKUIhPc8wPw4K6QarLkfpJe6pZFZ1A1TM5gpunuPBNWnb7JxJ+ZJN+IAwxCJSPGeMZJDTPkhUJTFTbPwityqDM1Odq5OwgQvJcBQIAeqgVI8lJcxGdqB3Lw0EjOQoakYhWjmUHHzzjkbwnWnsixZFtkGONaBAYtSqgq3/IUxg6TYpCW0lYgRY+UawRO0ojtTkw0Zg1ZoIRUh/QTzGq2AlM1is1YQayuEZAKIIHIw56iYU3qvs1eh6V2D33fCjGGCg7AKG+J8wJYyUiadrThLEllo3mKQZLfOFs4aQjrBKJMiMNnSxt672eTTCmFu8+BtjeR6vsj2pMpWj+59ChGTMSrSrCjVlqwzalBMsJdAp0PWKH0uHqQna40pHLV5zZJRhiNypua0JNBVUElrFmyjQPgC7qCXSLpHdkcnpITS0krXuprGIszQm0Nxb96/vD0kJv8zneMsUJ+GUF5lErSkKhHSvURYIkcw+FK3yGcaUEmQuR9I38m8/Y1u8t3XuAZnvv/DH/nVZzzbY9PpGlc84wer/vqf35vJ4bwPEbn6FS9f/9e687IXv/CJZz8Xb//VX7/j9G+2CeGtYnqly12eJ5/+7Oc//IkzfnaLl19rR3bL/e58h91ux57H/foT/vQvt1925/k3tpPOecYe4wOf2H4/5xsH/Ac9QvT0PxhzeFvUrnH5yz31MT/34qc/5YducYvdwRF2v+8DH/ipX3zs3X/mEW/723d+DRb2/19sH/r4J37+159ym3ve93f/4A/W/bYJvPAFL/Srj3zEHzzraT9y85scg/Q/L96Xv3rqK17/5rv/9MNvcdd7PORRj37LO/769C+fsT+56IUvfLtb3fLJj3n0H/320//khc99zEMecNPv/q5znv3sDL3/PN/vCIV3v/fdx+hc+sIXCIirJZu0QOvQoMN7lA7bW641VyYL5wKecQC0CRZ4zaWZtwIDCxXTDJMQAGkIZXrZeiPoESGRolM1RbikMc3Mjq2+ZQwxLg+nhaHDA1EymnvRoUwlOVdnvwa5MOVFSFfsw+c4ChZcCBFJASPaMUZiMo3MnXbbrawwnEm7zV4mo78mtTMRmLjJAMtH8Pn7umUnl0WsphXnzko26dPBVZERHpUacwaGbPni6JSjXhxSRETBdNndfDJ1SxIARQzIiBpZNiSojw/kJAak6IOpeoctvRQRECapwnkxJRzIFJ/MpocXmmgapUkpECYylKm7rLt2sBw5MIIOdBWin7tC6UHNiUpydUubumBpWDSrDNEeyhQvzG1SgmYwnZVztDTed2qFKYniZoCokB0SPM+afsTR6BmwAm2qKSAwCt+8BGZM6R0+cgyAJjaxVlvd8cjygIQyg7ClDbFs2QttPuewSOplUicxQ9SaUpPztz2lG2pLXQaWiUK7bSSc5YxVNHgFTAqyYRiiCJT3Z3RCvHGivk29FWtCqnwkuiDL5oHJ8iUpu4hWQSNOSVWLwhSgaSKwpnUWh7MIzHiT9EDxO7vpahkR/AcPFgh92H6VuZ/RO4Rk1UUs5rLOwhpjCgI0rAiB7eF9chrALpQKMpU47AginBUzw4ciKzWzkqX50mZBUNDkOliVxMxVOF8nMLjNx3AhAjYrehEPA5TAZC2mpq7qm1I+tXvbZ+vJjonYQztqd5t7YGXHpCCzSJqkihRLMts4e9d9n3NdpUcdtqCIpi5dy+mGLlSP8HkpY2myVLNmVk1FzWnRaenFpdBCVCs8THTZtbbboM6Z1bOUgp1F0RQoPyIGsMFEhGZNMXoks4YGr6VGFXq2MIA8dmCoasYYMnrJGRskdWoLjgvl9UgWg9Nzprs5mqCabOWJFxbDFB6YKc4uaTvbWT1Iqg5A0duczUN4gVDPOsC8hViwDfFKzbb5FtlmtqEa3DTV0TKamjX4Ai1MNEIOc53roJgI2mhWDoEXHRiS0zIthURntIBuWcNTghdZEppVYRlZfGR0itOLBXsmDcl7SnVShCXb4URhWBtoRIgGcgZNu08GHaOKQDli0tb0oKxmxHt1WacTI6pCMjMzh0efsedXWa3ti7mQlrRBM6bMABghk5TS3BcdhRuolVovUyTpgYy6pWarUWhdTU1RRCWKIrBFnfclAWpmZS5lb7Iv6S03bQM2YNRywDuSJxjFOgdCgg4/C7xKZtAZJSAQEqdqfF8uIVMywiU2W5ilGblMyEyATiS1YFukJGqEZWbkSKSE5sSc4YxnwoMk3RCUQOuQ/bC+Wk46rkC1u6xdxlRwm6JFpPos3ivFrsEQjKq0SM6MNWVAnCJ6SBBbV4UZkfCKCToFgjjkgE+MADSseK3JQrtWbcxcVkN01FwVnfJElVBwIFAgTYrSLsmXonz0QN2lK2Z1b44K4VdSOw8dUfZeOzIzmmdVKQ2lOrgicoGCTavQJmnNpzmTMii7i1Mi1V2xVgCmyZ3PnaQoRAuMvKElUmn1rZmtYH5Rgp0AMVg4UUCuxMIjkkADq/WhR2P2CmK2iqzMfjrMplrmotiRtKj3tu/LOopnlzw90SFiVozkZMay0hUbiAykaw+bBNjXGD451xio0j1mUpAQkWhGP51CL4/0bIO4d5c9Y2WXpUUpEi4rHZ86B5lJn0LXiDSMNpLYzlRawLVltWwk2iu9VtZqkymF+6wG6kpMl0gRTVhAROlw5E6BB2wUJaOdRtUNleymfKMbxlF9DZqVrZUiOQl32f4xQc29dw8n0FaoNNO1j3CmAWX+lQKxgcXnAkop88jOHvnAB5x0rpMz82WvfNVr3/pXpHmuk0467vBNwsc+/IHT9yt7vlU7brf7ritsv+zxvg+8/30f+di3GCbXOvzd99NOO/Vd733fNx1zo+td9z53vUsp5bRTT/35X/v1j3z6M9902BUufuFj/a9+7fY74sfO/w8dTz2NL/vzipe65BMf+dCXP/tpt7zJTRp367O/5e1vv+uDf/KRT3jyZ79wxk/f/R8S4NuT/cKXvvS4pz7z1vd+wHNf/JIvnrJJcvJ5Tv7Fh/70K3776be7xU3PdNy2s/r2FP7Du3SJ93zk4z//lGfc6I53ecAjHvm6N//ll768/Xecanb5y17uofe776tf+Jw/evbTn/CIh97mJje62AUvWJkt/0Oi3+kB354ew/md795+pvGsZz3b+c5xHt/Swy6Uq29ycfWjkT1tMFaKehEurR6QVMYhmB8wDVAWzrabXWPMjH1uWZhkyZcjhBl6ovVYRq89bEVxFIFB+EENtmgkqpha4UtlEA5Iki6yMbKN698UJIRnO/Bth5DDdAemUCIm1qMrBjPPKlxcZnq37khjGgiVmQgxVCEjaAZ0BgVZgtyMwY7wXJPrhaSHwi0hwWE2c6HeUlyEL28NTD8682BfWy9Nc5GwOSJHZxvr4OMa14Ethe8q2pKFSYyLi0P20/b7pQ+uchgs5lf0zsLCuXzsZbhMJLNiMV+S6Zc5jWWKNS7QXepgVg50l9UbLTEJXED3SlbYZsYQJ9NSpdZQ2WcMeDJTqgqY28Vqpjrc+UxJ1ymDMmdUW5v0OjjcOnXutUyzyBSPCttBd5PnTOAROgUd2bkuQMmcyrec2udcM5i2EcYhAsqTw9CNJ/ANLqtWDhorWG0p5opt/KoSVYQZWzMELmC1Frl9I/AzkaNgcNoBZY+iQ6Nnn6NjhKRCi9RithRsP8e2Th05Q6aqBzJRQg0GrYE2xxJsnXWwKb8WbdDdWmqnripCP1RBGryJLx6NjieIEqOOQQewqRaUVM2qyk7TOK7YpFmbNLElQdL0yyxIceE6azBCKIdBosJ6tukeFbU1MiuDNTX4kcRmzX34iKNGMNnBbp9reIhsE6pUU6GHtvSWfcFcgmKCfGm9RaUBZpIKqq2w4i3c6RIGrs3gQ9NyYKWZhmEtwvrICidGE+KmZXCYJcypRG1Sm1LyEqBKApqRKybdqQpIFSucYaUOTCYGBaKRozjIXiEV3AOElEyOZyuutJpmmHX2CrspJjEpxFacnFIlYkrMaoMSQvmVNejiQzoxwarpdbCXgishFSDJP5iAtPrmbzrCchIAWobKu61D5gbeWnRVnSiJolZVS1ZFhQaW2XZeKSHtFQV06pShLwAAEABJREFUNeSUOTPAf65Dou/czTdumg3cDHGoqLKDYRW0D0PBpmgkna0IiVPSHClHGXyV8k2S0hRWdXzWvsxNcC8hkWVlvTMz6SZZaim6UytrVWYDj04G6qZZiWqSFZL3lSHQtKqWWUQkgT4ijpZYzQcy3EDxJmRPC61EBZaDk+uQhYkDYG4OUaSalp21So9JT6Gbd0mQIYfPlCy8z8WpHKYRaFFRMYgMzX2Z4HQz2OHMYHjIDl4lio3Y0YaO6DnX9OmAi6KYcDxMUngFqgQI0EBvsNA6a+VrnX6AtcoUmbCRZaA4NI2clbMkQQdhJttCtXCykETQDbuvM4clrBY1Yy/pMzvsp3dxJ9NqjNMsOltda4tSDJXiU4aOeTT269hH9zLobl1zZiXspsUWyx3VyyzpkDmEziGTR6aWTA86+AF8J2PHIc7UCe7p5nSMaelCW6guRC+YFAp1BdVPVzhoF8IHcaHjp3BlHUR+S7nIqXNSlpppouGF9hhkqJF0ry3Odp41pwLubfbqg7TFimBRZiIKKzKq5M7LErtG33IKM9fWh62QveUo5lJiKH1XCVh12UaA8FWtFkZkQrAFJXzKHEoncg0AOWoO8TGDCA/WO5gmTEa+Q68R1BB8RRi23ck9CCvyaKFdseWqXbrm6BOn78tRlzExCFsic8PEExG+XRDpoTERuRGctSqahtU1mXq46fMe0lGmSNAlZT2IWEIpHqUeOl0dYhnVGeZRLQtqjLYP9tMf2NlMFpNSaIdAcco7VQQiBWo8yQwnajOTMvKMfCzZL5JkM4lBxSqbgkMC5qpuhScbf2zkRFMSwpV0kvwic6Efy+GHBCgBE1Etxs6ISOmxZApFKOL/z/9bolAaffIo7oQmYo5ECMnSGMoThnISxhzIffP1iK9cUmy6gdIueYmLnO9R978vo/jo0aOP+KVffs8HP0wJrn7FKxKp93/koyyIefKtmqre+mY3IyQ+50te8cffatgNrvtdHMO7f/72v6aMPPm6xsz2E3e9w61+4Bbs/9SnP/W7L//mpK50qYtf6IIX4pg/fPWfvPUfzvj/Lnn5nW1/+94P3udnf/7qN//Bv33XxsLULn/JSzzl0T/7oqf/1rWufnWqTKBe+NKX3vyud3/Zq/90zPmd5f4fUvvKqac+40Uv++7b3fXpz3v+5z/7bzTWOc5xzp978E+89kXP+7EfvvVZTzwze/5DIt9+wDrGa/7y7Xf6iZ++2R3u9JwXvuATn/pUOgOsXuAil3jgfe79tpe/5BXP/q0H/tidr3jpS5/lzCdANmf99gT/b909ul//9u+3P818vnOd4xxnO1MeBkaIz/BwRqXzgU1zq1bFTBAMUZQoMpkKlGox0hlfmZrJu1wBaeyOLYVRI5JhWBkzJphdJ3MRI9ESmnHYkiS1ch02sQKQPok4A0HII4y5X1WYAFKFGBbd1lJTJatIpjlyYNTDJSdiRvGsnsJvSwgXn9zlupO1bbk1xZjUmTxdSY8TVbSSZoB1RUwJrpUL1jbWOrp5ckRABiIlxHJjipJJEb20WRot2rDluzH7XPuYPkswR4E0kze1qFYRASW3ZFqiwimAxNQ5TLoKHxVlZM7AhPC5ZCCLxFIG9wxamanbLpdC5Xym95wuE7Ur1SPB9DU6l4DZc8taU4TJaiIjSAZKjMQIIqS4JjefnXeGCIl0972sUVcrq4hnhgckrcRSUxWRFs7CASw8OlljKGnT3j5scFUgIFRKJCkZqe4V1CBDyC8XwW5LtY4YAJsim2RdwLq5lVqSHcHpoH3EKk3hI4Z7zOD5YU4mZdCOG+iOuqKtIaMTJNdw8UgE18+SNJhRBmq0zpzupNyUgNeBwiVLF4+aXWO/yRKcViBFlIfJ6rIqCo0JMQASsOAcJ/6kndahw2vQqbniWRHTUoLYUGTLg8YCKK2omoJHQYUojCix2GpIAu8CL9W3Kt8yVZQEalFLGM74yHCZQfiTTnsQwuWGlkS6EyVSNAJhQqbVRpHkLB4MyZ5DhrlAiDfV5i0QFAHLpQ6aK5rIRgGqWelSgSVa8Z36wpLGqX5rFG8oBwMCLaqlCrGYUM8aEMzcyM0iKladJWmV3GSXAaO/JtI4fhN/i55ulD0dssnJ6YHtE17m9lstOy7QxQqprIFYU7j8goVIepARo6JABUod4BE+nS7Bhwf09I3FAl8IicZkOeyHTKRlKEsXZhVRa1VacZWZITN0pkYKUiS2gpGxi00st0MHZqTVlK2YaRwSkevwPnI6BjknHX70NlFFtkYiKURDlFbhK5Cee6oMKGAiJB+N0RVAyOZASflzDs8+JVnYMOICpqnWIAulShfZY1k34tvT3JaiHOqaQdOWokJTU9pppMh7qSKi4N7KOJQxlxlEf0ahrsFY5TBxWFDgqXXOnffdnDaCGBR6PMtfzaS0EDNTK+w3FjZzFgegwd0Dm9ZSURdhYrOiZgaGLQHUsqKSGzg9CWN6gtykTLTpNeemXAXoLkkU5tThxtifx9QJo/xDbOUINk2SEFD4XJL+oOFLzIqoQlPBa8yWSSmWWlsKMeP4aCCZzM09Ir1PugeoUxUxI44Q2i7mJpmnZNTiJoRO6K2bdJRAeC1JAZPx5p4RITR+ik4uGkPLtJ3WAyt0JOCAIuTQ2dUnqmMB17oVcwY0rUotYTJk0tY2NssnHCBvuNALufWcraRBdMsnh3IL7+ZmhoRICAMIU91rF2MoZE7xUXIyfcARPsdcp89tRhUQXqm5/dSuhkyGlCtVIZwpIL0UAcQoE32RiNCHwjcaMmOZ2hxs9L0iLghQsGyJwhOB0o81ZzqliCRi0hRMrRnJ+zwMCcS0OdUdwweR6wq3TEmA7Gmg3MwKDZHJsSkx6afYRohSuERk+rTolePZEckt++JZU5TKuYlToYm5/VjDRlOraJHG2y7pIO7hnmOP7joIZQ2tXtVqUA5aPtyAzcyVZ5lSRI36tZzGuQhX15a1oSW3P1U8VGhSF1B9g/BLS5oG/ZGgAewQAFPYJUyy2pQAwwkApVHPnEIohfM5BDY3608ywkjvGnsBnV4Tswhz9zQLgwlqUoRthlCCqJAWqhOHHykmrQhhFWym7yD/Qg+ijrpJI2k6SwZcZg9mSKHqFCd8Oq3UBMUScsvv/b7b3mLbJHz2859/8C/92ilf+eolL3wBcjj9tFM//ZnP8eTbtCtf9lLnOfn8HPCK175+7fRtnn59O885T7rYJbYXL//0T//4uW/xIqKU9gsPvO8JZzqBk//ybW879Zv9INnZznKWH7/rj5ZS6FK/8qQnMe9z8He8feqTH3/N61//la9+5d3v/+DXiJuVa17+sr//W7/++Ec+4lwnny8zP/XpTz/40b/4ow/+mX963weCbv+1of/HTk7f71/4R6+68Z3v/thf//Uvf/4z5HPS2c/5gHvd840vev697/jDZz58CcbO/0z74pe+/PyXvfwmd7vvvR7yk29+29t6p0vh3Oc8591uf7uXPutpf/mi337oPe56vpPP2xgY2BzsP8Prv2Du/3jXu/frtpBc4ypXO1iqtr1yXaHHpxbForFAijDOIAfAklmEaolHG1sMwsDUINBUi8NIyaSpiUkAUPGUPtuYZaYFh0gbUXkrZbp1t9nUFhifHFYuj1OyB2ItfFwzU1zEATdMEpISKOoF0XJrDNvBWnKTTQqyiJhA1XcSOymNKUFZcJZgqbYlZ9JhBmCu3MEaFxuRQHpkeNaR1HrHR7upwZ6RYJCOzJWpUUKYNrMBhScpmeBnirHQ5/oGNKxFWFhkIXdSPRxQp7AQcNIzRa2lWoO2WRZXIWJNwhoRG2BiSpmpPfiGwjhXkGIzCkZTcuGFb/p7EmfqgYIp3GcEnzvr7KV3Xvjgp3NtI7YpO5QyRTuSuXNKgA0T7JCB4moemF0wCoUXFKmapaQVVatEMuaeTn36mvsQhzCZ7mats+jM7O5TuO/k4y0vFgLh2rWq7AkBNnOb9JKuiaRLgMuSg7wBqcLG7W5D02wibAVusQbl3hMoyYqxAyrxook8ZczkCkDtm81iohVp3LHRLLJqTPGptJ45k3fsAacbqEFooCBYkcQvt0tFAGPG9EhPyiLgOFp0A4YoKfYiq1DaqZnGlThUaCYDhB8aizTHKutRlhUaUKAGqitbc1tCWxHWVzRMdwmCzCs+0W49iTiVTC+U1bldpdVJFYRnTUolCjOq1XQt1k29CEwlUqbkQNIZ1WEUH0Q3o4irpCSE10myE409G9TVKacpsdgI0/R8ArqGcAchRtmCxhaJmARC3FNlVsQBlEIuw3e+XYKmkokS04bqviQViKnVZFmwGGhDBaLEejCPHsl9KzNkjJwxpwYFmY515H7VPqoSmanKYIrqyxEcaTT7VOyDUVWpFR0RtfeKXqQ3jAW5aGkJTe8yh8xw6NGUowzF4aVDCcmqCAMWaj43S6iKatCvUXIYMUUZoFLFgyIBQ2Oou9Bz17EeLvFIp/EXSZMRIJOR61HbB3c+BGYGck5zpqltFuYAGEsDHjaz+tyaOHf15dBalMVz51gCy2CFSgVkTT+KWA2EHyI1GSsqEIMUhGSExD5AsQSpDDrb3GUUiTZB1RCMNBx1vsXELCmFvl8Poi1TN04pVSl8QgJKP9eEQU2UZnX2dMmJSsJihTGadBb6vR/zRlVeavAAChRaD6kldSGQLsQYQ5QvTLyBxnGG6zbDWSNxVkZOyaKxSDfph3y1iIk0z8WjMH9vmk1gqpCLIKkzAEoxZsakO2qbWPa5zCFYs2UwZVXaL20o8aBYZrXVUuh0w3SFdsTM4ZMEWuoum6mQMlVyAj9Z8SsdGIsasRhEdpMRLbKMNEpCNiy8TqN4XG1aK0vBjkPoVTOigK8I+EIsAeFo5AC8Ri4jj2SahVtOy5CpJWpBMWQgqHu0pqUpSuOtiToG5aSqwlw2+fKFicsQkWC+jambbokUTrAs6shZhtah6TqZxKfPrSu8eso++sTATCN7SGiOVIcJDR4ddJCMaZFtW73Me1lV5wIwtGJ4nzlFZUFdwOSuxcUEdKNQEi3DbKrtgZWhO7kDd5soKmZ0UU1VI76JMiCzajNb6BwCytchSe0xhs3ZkCWrcJMlllqQmikhAiCJIZyHKtK0KppQHghUsEiW4otay1aiCQpgnCVGKYcdpOxKFuZYL9NrinqbscwkGNMtRLJCF1lUSs9cY6wYKaCRfFhMaBgVQA3Joetex5pYHZhSMxbrthuxxBDQMpRRCqShFa0JS3BWmu5l2UeZ6lpYUuQSqARjlezqrj6kT3YsUYpQOeodiMiZPulRDMA9aO4pOWv4Et6Ih8ikpmIbTmWdhS7NSaJHO4By7rOVsx6fikjSkUzWCtZ4A+Kqg/jOgi7cJQUPh/mkpNdYNTvMvSJJP7FHPvT+973GVbc/DfzP//jOxz7pty5z6ctuZID/8e5v/sNax+7yeNKJZ77hda/Nk9NO/Wyamd0AABAASURBVOrr3/7XPPlmTW507Y34Zz/7mX/358W+fuCZz3T8Na96FfZ+8rOf+9wXtx9n4vnXtVvf6Puuec3vYud7PvCBd757+88fef6dbec+57k2lwY+98UvfR1lVbvjLW7yimc8+c63ve3B4S/VvOWv3v4j933QLz/92Szxv27wd/CS27Y/f/Nb7/iQn/3Zx/3Sxz6+/Yjdmc985nvd6Y6vePZTH3rPu5544omA4D/3+epppz3n9//gVve4zyN/5dfe997tVVKr7RpXucqvPvLhr/md5z7mIQ+82uUua3TD/xyX/+LZf/G2t8051ezql70Ul3tlYkpnoVtEa81yMEtRJL8QzFM2My1nCc8pwdjXIKoR6MGwEd2ED4ZWJocDamFNuAi66ZYOqkeNXlg9KDg5wVwhyegFQ7IoTyNGMh/l3OI8GIRMh1wPc0h6TPc1mSkUpaY1lxZRIktw6WCF4weT5NVrnYVxy7ydbXBAgBSZpVyEGcjEGqzOtBExZQydU3k3U2Aw6g3OVOkZM5EzmIIFSrWzcDppMAtzcKIlE7CSVRUxlEAbrAUCnGckvxIvCiuTSx24tJfWrXTK2SqskWRouCaTkO5V9gXTkswc6RiCQZwtmTmFiXAha6414hniwZEhdZpkgdZUSxHiQxGipqhTirSeTFqzY/LEwVxG7WQp2qoY85zolLbhZMqFzMqEBQCaoLpjnXPAhfKQW6AnFdKkmoKU5HoUIEicFZoRGgNEy7EfpU/SodlLQkPmJH/m7+FwLyFKsYswuToA4YjBeYGg+JPweoHteuETZz06cyTN7qFTKyWuFY2ST/OJiCQB53o0dzqLDpMA1Q4CQitPpOpabKWASuSJM9f12cf+6OA6QpENSQoxY87uWEVHykC4OKkkRKEFqmOTVVi0OyZkePW9qUsBVEAJeKcLnbphE0I2uABOF1e4ZM6UdLo7xSX1KdSYuBwOmYmMYgTfIKQJhTaVRk1yrNwprnzVlD6lD+QI676sWbnipqcxFDQ2AtmTriiF+m6m4zeeb/RXBENl5CY3hWEp4ZSzrsIWAS58bk3pWS3oGongpAQ/EdzPuc0oZzhg0r1UhV+kkxCaQ7xkFoQVllkTVBUpW+P5YGaYRLIWtAZWSiIkDU6cxaYyXGl3fp9Z6cZ1evOhsUp0kW7ijc5M/drM8BzpDPxMVgZZp7ZgpZEEypWclU6TwVHkn1SiLdEqJW2RC1GPmXRj5g421mPhM3WmTIqj9BHSnUrVamGlpJolQhgCtqCZbi68Bj3CHLGB46kBoU+Qw6KsrZkyusGNaiswdbr24OiStqFahdSIRlQz5YZ2Mw6AgjQRAuC5URU3jbKDFBURJOUywuCc2DM2AFywYS30Iyldl9QmalDOzNQkSYgwvywalXSzgo8DeNtCiiRHZs0qWQHNGesAM14RqyLUp09BmECElAQQCEHgCIZzpKwVcxHuLiGskVwPyZQU5ek0m9Kc2kyCYigFRVFhlgx0gPmbRIQh4mCCE7ArMSZYk4XyCiWkBGoqBajIIlOlKzen29yEBOk1Vxs1u1lPZZymQ01r21U5EGgyguggCLTQKg1t8aWxZIsy0yYBDFaMIbkNlWRA8gGXLlqqNmBz4anFRal1gsPXKR7qUcleFGLhmg4oUKCGKlm6236WSazKKGX7iSstpTebTDPklIVzqSu9hg5FympFqKGIQBRSCLg6raoZNbbRKXUqH5XpCKHPpw2mgCWshOn0EZNUpXB2BiaVMIDMHdGnRnDgMg/qaMkgmsOZqZWu48Rc6Z+95CwptsKYQjS0wKnqmusEs2tmBBVHwqFOsjRYy9hJSNiAziIMYDMs4EzEYfxM+m7nadQUZaSBkgoRJqtdhoWPxKBjAZhDPVUZA8uBLEvUalqEqbEJQRGBhViq0HhWwWSoUKXh000o8kxtU6or6KXDZL/kJtjey4RA3biYocloPoHoi3lTF0LvTJ2qTN4ihyYdw8dcMffIESATS8BVhm4TMdW52EWkZRGrulu87YQGV4FNlV6TJneaR8I1YUlzEEnzrN4aJ8Fc2Qrvpk1w+1QnaG7J2XJf83TL1X1QfFDrIpw3s4/9tFgZ/gIgT9/3de/J+ofDWmk7OlbSNIlwG3PhdCRzeiCKSdkJKtgY27RiOGJmpgOC3XFHfvGhP3nOc58M4Pdf+cq/+qd33+62P/y4h//MTa5zdfZ80/bhT3yas0Xk5t93vd2y45jf+8M/QhImnn59u/n//PNTf/rGN8a3GMM5F7/A+Xj88pdO+cxntzcGPP+61mq99Q2+R9X2+/1f/e3ff93d78jl57/wBY8gqbOecDyP39DkvOc+9+N+6oEvePKvX+WKV+Tdr375i0973vNu/4CH/Plb/ioOJ7LzO9JOP7p/89+965ee/pxb3P0+9/zpn/kff7P9BlFbdrf/wR/8o99+xqN+4v4XPC/ttXnCf5Ld3/zju37o/j/587/2xA995CMkdeS443/gpjd/8VOf9HtPecIdbvkDZz/rWSHfAS6k/F/Z+phvfee7yPGks5718pe5xIBwmSr7unipwkiLQ1+lC8O7r/see4yuY9QurdcWDOaIIvvUscpkpBTP1sVYF8WGRpo01SbtcLlUEYFopjmMqRgGqGuO6tOYODAsuFhnxoQOM5QaYPHPRyDR25xlOBP3mp0kGrhLKYCMZLrPJaJ1Sraakja6MI+ns/5fgT6C0rPmHaqTeYaLaF9jXfnwV4fIrOm1A3uTsagXM62KkmLGxFX5BGbajNi3dW1rClnJokVrDTGkWtaGunNjaooJj4RIitDLmUByeq7Ytgsz+OWYYr0uvW7PfJnRZZNeDtwOQioYUb7R2BefIAwWw3xwxTNpXgiVB3q4AEsIs0kxtyVKqYu2wgyrRVAixYM6Zrd0iUhSZXextpODgmqUFrUJMx2rQZsCp1aT+TABgYjSWEVKTavuyzptnblGrIq5ZUWjbtlGrzmpfBwuTqOARiHqLt3gVcSKS1nhK1dI3XscdWLO7x4OZt9ISQWbNxFRA7GkckmtM2Wd2Cc8NL1435VuW41gBBuhNoBRBotFKRS4wHfm2gI2kXuNoy1GnXboYKjwkqmZHBmeLFZ9v/n49Og5u7vPqOmNFFnIzaRJppErQZPZMychxNAS2motRbmy1u6N2/KJQqJGm+UcOvat9wP3qpAmUcJnjomYdXql3x3VHC2yOgr1R/IzTbxCBTllDgvULAusZdL64QT9aFpPjRFJFx5r9gBtGjGEvsSoO5B1l3PnWzGhKDMXj0WSMtGoR2tuC3ubkE4H1P2qR2m2arSYHEk0wwa7hpZhbbRGe4Efhw2lyumYsMzFzPIAs9EetEmHTIiCnuCFelhBPc7sSBVTGdCRAhiKTNWUoocVLCFdNegZK7FVgNM13GY2Rw3JoBkmD7T48CVll8VcMulwTlkIqqIotl0KwQXviKBE6raZWIlVzqgDC1CqKT/IIgAkxBg/PotLcSj98kBLQwrmFOKqxqjYleNaY1lg2XI2z5q20KF8LryUpAZR6BL0vKS2rYI9SJkDsa+6Ngz1FX3N0TG8TTKoSogEoiLFtIgG/RvVpTpoHt4RXySOuBxMs2AwTEJdkshgAoM2qDJ2KctGwGTrBzZfC+LPuBhCczd3zakZBSaqbgljfG+TTCRs4yF0MZMWUoYxxUaqmMJME/Qq7dhmZDXH1iOlaz1d61HIAPKwzTBARWBFaq1SmpuSWF9kZXVukiGSi+gCqUXIHQoI6Y8RjIJkBmeuAj+ZiEz6wVxA/Z05Rje7lukyJkArOg3uyEgeOl0NTiHoSvMgeyGWSz30XLJImyPXfURIFUutFJShoVN32oDWoV3d63Yf1FCN7FCDCJyhGWBKkQvdJDoTK6PfO1c6y2mAiFhMmZ6UukCMpvSkbQYzohQamlYST/Kcm7owo2jkYrBA9tL74qMqo7pobSlLgmRcZJ+xB5+OYG+Utgkq3/JzAdhRtE30qmhNLAu40Ujwe1GpMZTiJEkjJbyoEo6sJcnXtrSx9GxMEe5paUJvcA/vOTvBdKArVoObB7tyHViDkW6T6pvAEDIjPdO7cSUPidztszAXpDpiIp3iu2oODtJZitdl0mEEzP4p29yyD+0eiCmZ4RaioBolVDRgIqJi0INBY2WNgIPvoPrQyXRC/1pSD6KakGFOhEtCIRLmaA6usUcm1zunibo79YAQWFsKc5/ZoDOKWISsnXHI7xIKGo//BDOD9GiuMoIrdVMRRrtJT5mUdp0+9rxrWhexRju1Iq0FNIOg+JreJyI0qcCylIXLqaWkUwpHpgwtXoswc9GQi0RR15A61CaKCylJMhomk46TpAaBmfQvdgZ9Gphf+Mo89bTO5awatv+nVbO1LGQnMi2ndKHdU6hz6kZOJ+pIQKCWS2QJJNNBBSnjouc732Mf8sAjR44A+KNX/MENrn2Nu9zmlhc5/7ZzYM+/b+vaH8fq+d4P+PDhX9+64qUuedlLb3/R+O//6R//9aPf/D9LOf/J5z726yVvfPs7Tj99+19T/j3Br51f6lKXOXb+zvd+4NjJNx6vc7WrnnCmbQvxjnf+Q4LafOOQb9WTP/ebz/7Ev/0HP8z2ure8LdxLrZe4yIW/FSG1cu2rXOnFT3niIx/0AJb1HPYv73n3fX/mYfd62M999NOf5eX/68aXJ3/3L//6zBf9/t1++pHf9YM/dNcHPOBpz/nt973/fRFBmte8ylX+4FlP/eWH/dTFL7T9Xzfs+c+3v/+X99zrpx/2L+/6R5JadrsfudWtXv7sp/3mz//MNa58pW3/KcL+/47t3e97/8c+vHnRxS5+6XOe5UR4iXnE51bAemoEA1kzO2SPZKmR4crIHMwhxUIr00jRqbLBns44hoG1IbMFZ22YMC8V7JZeWzeBiPDhfSmcljngfLox3HJuXDCBmRjJz5TIDJFkejMJUQhdmFncChmETZctQl04yfgpxbaMlBEyxbv4GD57xIg5mEooNcQZvlJCkpx9jjEHc0wYVIWpvoRtaQfqXBCSSljhCobFuADwjrmSFXEYqWtRlqNRFMLM0FMH6tQqQhlgkmbOhubRkqEnk7X2kIgMD1l5JBmsok6UhJhQraT0ckhHNOQQTooJDE3W/oP5M7gqq3nJ1ORSwW/gd29Lt9J5Fcy1RAtbWlMeICSjFKc0URNemUrhGYQiqmStvIkQosV3DFP6rJNVJOsPcGdZWDG3BkKmgthyv6cMYcuV+MHpC5HssqJFlQCCUlUIh6TMLJPTsIGuTo2Z3SVpLM/JBROYhW26IaUqk0htC+ez8KR1JF1ygi32uoQsdWrdo3QIJCn5gmgxW9INjD0iwW9lNJmLQjcP0jWNrqQSagGdQeZGSTeLNs1C19rgFUUCpANaF5mb+24uFKp+WCfPGeiRkUm1pAF8GWa1a3ZwbaNAJEyG5MVIEfO03EzvRmuYUz2vsYkXruJSOspIGylNS7hTAAAQAElEQVTAJnftZuSvY+qIEcKygZCkbxSlVJUiSt7IVMIqGnF4GW5JFbwI0mwIXIMLltCfKOB2K41yobguHDRYYmNb1AcNkeQHVaO9lAiIsyOzSi66CUqpILyvqhVFskUsUAOjhsjyDgJeIBVkGDo7raTCkRxOXarEVJZWy9raWsoah9YOgQFkJR5KzOiowNzAcgHUyZQ0TAHqZULVRKHMEbbLLGC3eN3k5iR2pKjJqHDabkCcPHMdcw3qd4jsTJnMTdRRSHQjqFlBSlZas7ZoMcaDCdFMTApFcMGemo1VBQgVZiARUsN0GFgrTnoEpaIwKZQQ4CYhx4xkWciyJ7NqcIjLhITQaJFwqlhCShjCNkptjuqdxVEVCUaIWxIuEomYNLIDzv4czMAtuFtDG6X2WpxwRGrSDW0SwpHcIUlqFRS4OlmJFKilWhSZTaMC9O0huU+WNwwVRYQlWFyFcttgpKdpBUxihJhoMnpTQH/QSTugJKlO62yjeDfQTm3d/LdAtCTV9IgZZKzCL8oPHyaj2tQKrVIoTvWwvjoT7TRid6xlcHC45KSH5QaOp9Bx0nmfAER4+pDRJdkUw0BFOIle0jilYDKiMcDbcApLc/KWgibLjJyZU8iY6qBmqWG8Hi5uRUpRV+nAxktTrLdlLEaP4kaq0huoWIJ5qM5c9lb2RnUKQmXCplDABFL44RyzZLQI7T1AmXm3KNOZEI/Y4BcL0QQkOQUggtopWxLkEND5iQRnu+psgqraFmErSE7gmpE6nPbtdBUO32gkIpiuOIPl79AWZiqS03IoRLUozQhxsZp1B75gVON6GghU1zqAnsM1+I6M+0pRqfBagqiibrM4IHMEJoclMkMkiwOhAUstJtUYSLpdgsnUhaI6gsfNdaiZh9OiVRM1vDnUYHRGITEGVmSgFlQzeEPSgQWYKWNa7xqhKtmUaXxu2CrtNpO2p75S+CUFMJc6TCI9+GQ0UlC0NmwVvWYKpS7hVEuF1uTKKAKeum+LhgkA7if2WwhUBXUGKD3dVDgZEWSWoqIQiqIGMwVkRERGWEYDFrUGNSkVRpuJ++gsJjITIhImXiNteyC16zqaeUEayIuhiXAZfY6cUBfRirZbjW/DWsXhxyEwpW3oNEnRFFKpmh3ZSGYU63yV2Lg2m/lEdtZpfYlQMzLYIKXYs2HdUZMxvv9aV3/Qve9rpXAv8VO/8Jh/+BY/9/WS17z2mc977imf+eQjfu033GO3LLe5yQ2oy6mnnfaCl70c3+xzwnHHfe+1r8U7p3zxC2/5u60m5vk3tstd/CLHOv/l/R/cADp28b8ez3OOs53rXNvfKf7oxz52ype/+r/e/HZX7/ngx573guf+xnOe/60oc/KXv3rq297xVzw5+ZznvOrlz9g18fKbNm7q7nPnO7zh93/3Rt9/g9Za7/3P3vjGG9/+Tk981nO/+D//fvQ3nfi1TjrCxz79md/549f83BN+404/8dAr3fL2l/zu69/yLnd9zK8/6XV/8YbPff4Lo69mdpYzn/lG1/++P37Bc//gmb915ctc2ky/RuE/eUIonvK8F37+i1+k+W74vd/z+he98ImP+OnLXvxixcp/kvL/9emvedNb6MYierPrXw/eBBVV14a9chFWrqtBx6f+TNMlBKpoaaLLFoIHmjskAxHbsyI+TT6SWyLBtuwrB4GfjWBXLivIjKwxq3ZrM0tODz5wS6ZZzJbJ/GY8bqGc1tGGLb6tOQFh1lBRyrZYNlMugm0W7Evw6UMuVpaiNERhxlVGt7qz8CuaylzY0m26kjkvdtFES+jstvL565bujCLWxhpWF5GdSGFSxrY+DJWUBmWyLSkKEwFo7eYO1w4ZzBPpPZkPkcwrWx4rqrVYsxRxpiIrg1mo5Gojdp5VSRIhQZ6HTdWKWSV5Lkb70fgwduRBYgfhc5AtfU4mXkDhZcxDLqXIjnKQwbQhdZCLMv3NfvqcvXvOUEAIZmlAE9uJFqmlqDahchDkpNTIbf2KzK6+mnumTrBSzjGAYYkjors4lFdSF6vLETNaVKbGTF8DY4rsWQnbkmh1Wk3mby6O1Yh8kE5yUQQrWQ4tNLBqsUY69K5sGUv3OrgseHL4UqqUIyg1FdQMsumAtBKLepXtEiBwTlqQaYFmIRIRiBSLuazeQurkylM2xJKaGbCIH2CwgndJjbbIkUWXIiyyUaYZikmtYqBXtuFlkodCTAQGVS8B6UqIEtiosnoK3qs+dc7p4WB5Qm8ToZknttpOQBnnkDmholzVVTWldBIkCeyr7xsfwgMQbBOUS1byY54Yw3096vP0EJdFS6VbK40hvdXNy3coIhky6cJmYdWlBqOPFaGIT5BGlawmpD3VGLZooJtnDMRQEq0uETZFZxlRGHxzXekIqlgkF5mC6SxIpGZVrVpBxiTngq7BejWgBiLgNUOTg2POVUHYoSk5XYYU2k1dNELREXMEJuAKNxkNY+FJIPheaDVxyymb3ASjaC0lD2QuBYtJVdQiZVFrRQwZPeZaeywzU3MwqEQdCX5ik9zVZ+lbTwefAmBVC7qc0SWW2Xdz7CIrPayUEEkQN61Towth77FFPHs1vaFbsJIYlSc5xHN4ritrKQm6FchhUwszsCJHZFJ9aVL5EH9nhUYyRuaMPn1wvHvwJGKkcoNKJKYKCSISHgJ6XSewg/44yz7zqMcgSnsmSWwlH1pP6yE5BZu0ZcuWTp2TaRjcyFWQbrCsT0hlehsLL/isfCIDMeaYWE+zflSys2tINRfrThkEoq0VFKPvZXJ+0k6cpgFxqGesHnvD2tRVcqaN4DxaVdwiysw2tK5CfTPFAzkB7puZfBQkUp0uGpazzNXGGU9dyUMThUGZ0KQzQgaZM5OTRpGUdIkVxNZ7HdHWmp2WUDoR44suD5fecx2+j+wkodYSll2YHwaIoa2e+xirsYoT2anV9PQ1dBstIt4xOvYisfB2wCh1qrBe3xU0jSKoMpd0m5Q8F4Db27JToc4yZrVcrDRtCuFY2hBCsZunMfiQogiRwZvWpFXX1sPGpHiI8D4nxctI2i1oOlBKGrqLzC0ZqSwIcgSyp5+O6ImYIfuQnnTGzKIwkKOLOZTjUkhx7seS63EyDkRURYyiCIdWkQIz48ufyhQNFEsRUSttV3YHViqJZKIhdNlj6UVHGS7ukikw5RMhl8g9KF+NuoMeYc1jlk25AkOQwnTmBEqGGp3FU0S1Lim7ORffkhDpwEERY51JPylOLz5Y6fLEyopVA7n5TJoFnpXIw+kZk086ZJW921oauDYfIdE9bEKoRkUJ583sYHysRQJgRjIPsbQiSzUxcXPF0aX2Vgez2SgHcFIiKLy1heIyQyfUUIs1JTo26AAmQmrpkVMyCkKd5A3WrJRmUhwM4ITSAIIRFCGnpeuc0nmMSiGHpU4DCsVxUDDVFHLiYmcwde7maB/6UcOJxx2QH5uskNAmUtUNQ3NqJmmwLSpChbREq6UIfS4S9A+Orour0LmCyQPhiKTUppPOc/fb3PJ2t/gBUj7lS1964GMe/6HDdya8/PftB2/wPVe47PZLLG//679+3Tu2v6p00+tf/xxnPzvHvO5Nb/rUZ7/5K4ubfs93mxWOec3r38DjN23nPvtJJ539HLz1gQ+8//T9ypNv1uS7rnKVy17i4pe59GXWMb7ZgG/e9873bb8c/+a3v/1Tn/vCNx8B8B3RKV/ZNj9XudKVjz98ufStRn6t/3znOuezHvfzz/yVx1/sQhdi51dP/epvPOvZP3D3+770Na/9yqmnsecbWwKf/cIXf/eP/+QuP/mw7/7hOz78MY953kte+qa3vuVzn9x+8urY+BNOOPMlL3qRO972tk/9pce88WUvfs6vPvYql9n+W8xjd79TRz5y/sIXPk9qZzrL2Z/1y4+9EF+gifLyv3sj8m94y1uoxZnOdPz1r3H1YSoqVqZunu86MlPY6Q3B/FEydsCSzFfNtvAtKcZocBnBWqwk86MMVy6VCINtwY5gleTI3OJnunhXDMa2IAAx0MYewkFc9wrS1Ism45eLRhEuMLJO62LeyIpLISWSsJLFUCAKg1jCMjmUYlD0ytxbGNOHwa7GUJICT1BOoCJbROtSU2qUJZpKdbOxFLQqlFhDkhkGnJI9wNwZziojmQm36k3EAtpnDuYFYFLjIkxFokwNEgFkKLvBMyZbHy0mJJkKh7aQJlg0zQWs9WabsQxVJxBzS9vFRSBcaKwKmqQIwpmoNv2YQ1NcFIUi2JKwUWwYFQM25QYygsMnPDCBoDwlmey6SWgip8yBmBNjxCRaM/uImBuTYsRp0yKtu++depMCakT1XjGYUYUlAdGDEE8l+Eb0K0CLeSaTS5AfaWlKS1lgmjpnRA/6Q3JBgJlWUSMBZGq60CAyho4hMSM0wwAtVA5uNEKrqoupRZYebaVkM8fwNXyWYeIKkFCAC7iMWXQVLrAW7padrWSvebh0EB0Qk4KpGBrRBhZPy4SRbaSATr+xncjJPqmp6pQ+3ae0QScpZSK4Hu5jduSgP1FeyaCDTLdJlIRYVINamk4hHXKYWpLLZ3NYVquoBUWDWlJBALI1R0A4cLTildahOY+Sg+U2zjhxgz3SoKWoKCclts80MCCH0BO539CjFEV92EYM5ijbNHUhD4gSKy/AAT2c7yikrULQwnnY1jlPAkMOWWbomjFkCh2RtApMpIhKyoxcfXWsGm5BzYV0RYL+J0SXbfqcPuhnmjAKLuZiHGThmD1nl7SUBitioqomfGBg+1q6bUQobAmpQpndyFCpKjlFhbQipXgI2alMkH5K0GemK+vk0IpmpZgZsmUv6Aqf2LSapGecx1sqsOANBCQQmT2dRxKL9ByOkRlIKNU1MqwG2ERdky04Ml0S4oYkV44WWjjMplqqZmoIjVYQJlOUXsihqgrM8D7cZ8DNIdPoHJwRSkLITbjkME0OhPPDPh2LxM6jOHwgB+ZMrvE91UN1GnMXvzHjsgP8RMnYjOkp5gJGo3MSKLDprEkYhRqHx3YMCd6YZJOxxGQTHx5rDrYYUOZGR5WQJGlYehVKeIifywS7KQT3iZPi8TpnxIjsTvrJKSabFAFESkK0wIywLOkbOf5TmEgFw0yoNcdR4iJDbHiLEFVyE6qSFJoST5tZJ1TAEcpcsLkC8vAzk+ZEkED6GD3DUXLuMCQ7cU8x2RrFkrmZTSl9IkfSe4iGy0Y0wWlDJkOKg0uBlU0CSd6ldHNThBN5z4SBJRZaXOr0QqPW4Qf73A0RkdHSTWgKn5oMOPpuFnIELSGRkT5zzYwYjPRFiphxlrBnjsEWc2JM0NiRSDjU6fcbNJg62SehLVvJCpEo4SQjcM/hGZs69JWpnkDOkqPNUTptQGtNM1JLqCXTsxUK51XD2EDnMxUtiQpoiPPVgzaxJoS8gpYivXTH0ZDuiJHWURw2RfZsqqMQHpKJFHZ3q1Obq1GQCFBdzARTR4SVpOAgoTqHmAAAEABJREFUkelOQGJSTXpE1WzoxHnrGAKeASN00kCZXJaPLLS/RJd1X0a3TYZhLSmjpMSAT3IaZGYgpoWD6Qeas85ZMEudBq9jysREGYfs6GvbdYbNKGySpmpoGXUmVsTM7rODXrUZZEhMhTN/V4EFFEhHOBBqClUkJSFFSg1qwP7QGKKZsc3zWSZDeMJK0a3WKELQw5Uy1dSqZzsTDj+nffqUNloN5h8Hp/Q9xh4SqEJ1i2TyQkDA0mxiIa8CusuMPKpxVJjJJgi6bwOzKDeU+cgH3uu6h28/PvT+f/2Jn3/MKV854z+XPGS4HU44/vgfv8udKisnn6963evZdbYTT/i+616XJ5/+zGde+5a38+Qb2yUvdpELX/AC7H/T2//qq6ed8RyCl/8/6v4D4LalqBaFR1V191zfPoAEIyqgohhQLl7zNWdBRVFUVJCMIiAIAgpGxIABEQwExSwqwWsWs2JWzDmBAiJBkpy9ZneFf8x9fFwU9On7b3hvffNba4buCqNGVfWca4fX3Hpv7/DWN+WZv3zu8191+TJ3Xuf20Hvf48e+89se94gveJPXv8HrHPA6T77gucdtwD+86EW/+ft/+DoH8OTfv+D5V1993F38l5v983c7PPnvb1WE1j7kfd7rad/yzZ/9GZ9+/dc/brSe8+y/fuAXffEnfuZnfc8P/8RLXvby15Twghf/4yMf9y0fc9d7PuThX/pLv/IrfvlQxwGvd93r3/St3uqD3vu97nmnOz3ukV/+9Cd844982xO+8kH3+8gP+oA3uP71OOB/xdZau7jq4JJffuX5PP9XqPg/IvOXn/W7f/Xs51D1+77ne17retcNK1YglXnCvKi5gQtEXxtWH9ksmI4SjcVSqiU7iqEEacvtSn4k9CzwjJ1LYV6hWG4V6SwlLOsmXqxfFTyUdGGBY7kBT6txybHE1jGrd65Qe1NKR5YmWBklpaQoJzkveICmXKGANYPy0yNW5p6sKYviLroONLCSV9cm1JAqR6HyLnQijbWEHqBbnSwsZfGBcbjxbiQ96FhvMCQifCV7SKzdqSIbxFhgIJG1h+7gI/yLah1QsBh6z13KA+Dj6zmwS06OhYis3habm9Jq8FWWc3hsXuKEJ3lKTLtY455L7kZJsjedVsHqWqKsVOgoRRWU7mArYIdcjiJqNoxNTNAyxVd58S34uWQ6ctY1qK2KFc6uU+HUm240DNUpkdWW9z2tLVX28VmzJMRiaA5hUDLBzQUytF301ntH62wkQXsq0zX5qNtH7EOyNWtNO1tETcGi+86KHWKoTcJkIr2Wgg17apynr/PMnWoLXBTxKxCGkIGrS1ZNA4SouW9z2e45yaE+28Xlk3lDZdlCo4stvO/egtrgw45NGUFiVCBnzakcVSAcOwnVa5kTOV+EXzTM0hIaIiSFNMmqRbScDUkYXvaSipqpszzCifJJAC7P8kqDtRKrUi4ObJkW6KmQRVxmWGY7a+ddbnZppdvStkg7AAU4aqWUUzs6dDRLajJNXi3SgOxZSlsUAQSN4wVQMaZiFc9kLeiKRhxIdifJ65+dtgD5eQhBD22+yTKTas1pBsVMHEByyWJB0xEViSNYqFnBLYSSokVujFvJkhUyq6bOScRt0mE5NbmgVzRo5Zo+1wpqF8JXI4qrS5Jya1TLs0gHVw7CZVCd0JtsyXzowmGkWUGiuF6QFAHd52ASdMoMm6ArqarWin05tTLhuzpkjsjTPHUMnDRPCiMmJtOID23V8hbkC4c3qz4CQrmV/GAgHe4pCYOYShn4EoeJyGiHgb1UuB5Pslx5DcU3YZBVvVUQchrURpyaD3VGaGXtaTsIkzrDb8LG33oJ8SkNGOtLjigL4SvofevZmuk2dKhQfFI/qdW31EuznaZlqIOxEUF0YbpAqwGtlDtehzeAgQcF+pVLQJoxZKUL9G5Rw6UWfMreoSpSAp+ovVqgqYstJwFp+OJOzEyPFF4S9K7SWh/ZT94Gl6KS1SMuiuUAahLaUlRSEAyrhx/ZskcPH1gq7hJck9N8FonTZpv1JsSPWym4ytIGpQCXMpcWpmESoassxOp4NnDhegr0BbBiR9fDzyx4Us5oYp3u0NglOvmxl+w0hDk8lmzrdFrt+FsQS0hnGiOFTWMrt8hIZigNiYpZPtuM42TgyERyQ1CNVvWqE0pJ2p1KJ9SlVKrz4hq1A/uucRbZQ1aC7lZz+k2GlThZG7yRkCh3W3lazG4cRYEyuO+q0ZMxvxTjtLcWsTB3bhn7Omr6AgcKupjhZLjK5snYT8LQeEq7NEYH6Aw4SewJcCDDATrnwRimwpW+5d7CrULoLxToJb0ItmAFeGeKKV3Q7DDuGFJA0U1qYKvpzIYARRMS+MI5/JzJxc9azQE+FgnEImGHMJzGqVGrJJTrd9+JhBaD5ai9aKii99FbB+fUOWMivGqF0NyiqzEQwhCCxUKdmC8tH1JjHOITlOFwYj35KOKkaxPQtEX0aPGBgoOgUIUR2AYU87vNvVGcKeTQmntGkAPiLULYCdKrEKk5pQhicVKVR85Vc0lo8CpjmyEHMOJ+ZWNwQIFCyRlES8Zo/PKbYmgg7Ny32ExGtuZNiRMoplibssm0nkXiMpKrF1oJE5XRj/YWb4wrL3veS5gBKBPXXLU8I7OQYhAJoteRYP0z5+osGbm0OWt3WXq8BoUXqRwzwh2SoIZrXbKvfej93+Edj78p/qzf+93P+oKHn/d5RdtrvGVRE49fdPzhK7qLO9/utjzk9sPP+KnL+86df7Vd99rX+m/vdvzV/Je/8hU//+u/9a+uXnPYzN7myt8MOb/yZX/218d9xTXn/9U7g8z6BAj+M6+rr/yTx7T7l37tN4qRfF1z5/JrTm9jXLPzb73zPurJP/7Tn/GwL/nm7/6+a8Zc7zrXuf9d7vD0xz3mdh/9UY1fxGf+wR/90ed+6Zd++Kfc8b5f8uW/8+d/ffl8/rb//uO3ufPdH/OEJ/7tc597zaz3fo/3/IIHP/gHn/TEZ3z3t/7gE7/pSV/7VZ9/73vc+gPf/2Zv9Zan0wXjcc2w/0XvrPI3ufFx97jc/+TKyv5/kaL/nWLX8u952n931iDg9h/3MQK1gJL0M3QKiz40srGPVYCFgle1HfcBHlzu8jQTUJENLupcEvl5uJ88+N7ycuVeeRBPNKWv3Hy1YqHnqsK5zy8TbLHIGVPySHaQpIJS8k342VlJfNVROVjE0GnADuFAgxYybaUuQdBAcZMUrZRw7F6cqQ4NWoxiIVloE6w6BYsIVmjNufW5Dd8kKuGT5YclfoUuR7AqKXQYuPwQ2YvlUilcdV/q0+gn7aMR7GaDgEGVXWEKXDKseF1pAeCwFS3Skg7mMGkiVRLOhV8/xNAl4XHW7pFOoEQEIl6++9kZGtA7llRkhazVZ3Y3W4bFcVVN0jgeEjzvF8Z7u9NWQ1lahWYeyKsGuyet8zSdPNIrXU7VtIl1GDGSaFjdl0XQhjBD5/USSa9M2nwyHUqXLDhMenCZawoQcexSWQtw3vPFsLMZ76/Yl0SEt7EdTbvR82oqdeAeQXx96XRbVEgdZI9HeRH9zJXBNcEZ4Pvi+gI7iEYJcjuQqFIrMQ1pUywF2/JBA3geNcq5zEWh6MF2HM9Rl2FzbevylnSehB5nudjtNDdbxImFcYnS42NSxcHNhj7ov2vSvwZteazmKlEpqSpA0b3FbpyASy8SbwTvzlYrF6bOCmEuiGivvnFpRAOJL1xrqURRAJFDqh2NgNJCuKSuSd0jdAtsTcam1o1JEsbOV3xTKoLGEl8KiEqaUKJzZWbRujSuAXu3XgTGlJ3ZRSpp9CwL0A05rulxYq5z0VI5t+5qdN+Nwe2tAEQur5nFnYO5M1HhjJWQ97Eopxo4N5eI+5YxIulPkhFSymnU6KnwjuS3rlZLcxeUyKADPYzreVsmk9M5qiUkyZI2CQZ9KvTACGFkYbNiX2tG7pGe4D0HjRFDtxSRKgiERNcAsjIclXCYozGcwZMZiJ36ya/K8rVQ+6HUpabO0t0k7LCtkrhY8kEqNpoqhx8cvAIeFL+l0h3rKqOrDl2tRQFMloZsJgJCNxwaRpbHVly8pdEAU6cb1nJrdsHFk/RjyBxRW4xWmxvm2Fe77M2dVVD5BPWQBpyusENU0Vpx2dG8Nd8GGbu6ujFuWpJkU0e1Hce2ICGgF43AmDLvKCMlsi/T5DkVUSlhkmxaKhypScQIbrTdtmmb9xb00oTjdYlVMx1DGiU2FKqYAMRH1aRbjYwWjN+A0UKlPHE63SPbckEJNKVCPIrqqVDaoFXgS8h+lCK0GLsjapHg8GpSop4RJF1CtVnDgfDoq2/ehqYS03ILeioNJ+iFy/CSQpiuPnLr3iW6wsosT8J74kU2pxe5qIOM92BgSxtkExmZtbhg87lF9GCqNyxBlaKyIQf703DKZdZlFQOfBSmh55N5mYqkJgjoehM/DiuDr4yqZbyZyUzCEaE6oS7C8tlJqZHKSRopBKPVNuTUTlJjlUyMkg25KVMllopvwgMz1Wh11JKiOlpIdExFUJ5aY7SxbWLmKlPpYrSZVipKIMqkFEWNDdVArlTJcptnWUGFCAAMhtVqES1bZxcJtSltEr6gg1LonkxZ40HjNR6HGLMsE7mSaUNVcVq5BU5FcELZVZNh3Kp3oaXZG1hqWhlC+S1F5RLm9TSnapog1TqSMMIYBEMG7QEzox/nMKM8SBSOSIoxgwL0tAc9umLziYxVlhpiIkCnq0nWVhUVkAh5MmwEhJfhErNyOkWSbESD7stawEw7ek+606+qjjbERmP+65EfvAWG+45gthFG02k2tadoownmolEVAmkuPbWptKZHVKOSXmpdicQMSQK7diVXrZTXVvh6w+vgyuvq3/1z5gZ9zTwwrwJzqCRLlnO3qmXo5DOhlRZgddeDF1G9rNWp0IouFs0T5nFfpZwLwRtd7/W+8fM++03f+Lgd+tlnPvP+X/yIy6T+FY3XvP3en/1lZLTWPvOTPx6cALzdTd/yPd7t3QH85u/8zu//yZ9x57W3D3/f9+nMkIhn/OIvoeq1B/DMf3v393iXd3n3e37qp7zRf+bbEk78v93Oc14z5tee9ayXXvnzXdccvub7pQt2I+GZF7zkH/n+72wvfMnLvuSRX/XDP/Hjv/wH/+NfSRbRt7rxjb7mYQ/58e/+jvd77/cevUfE3//DC5/2Qz902zvd+RPu+zmPeNSjn/f3z6fYa1/7Onf+5Nv/0tN/4Lse/ch73O5j3uUd3+FN3+iNrnvta6sqcBiA/12vt73xm1FVZP7DS//1V2c8///F7Xf++E9+4Vd+mZbf8p1ucYu3u5mRaXvERJxZJYwJAinynszPYqUo9S5sstl2T1+eESxxbvC+o+ZAXGkGYoLOipTMMcrGcdDKtVKCAsMQpiUhEmrBOAq/C9ATJ7Fug8UNWlTlNVnxUaoQNseyhPBAm4hRcqRl6cxbNn0AABAASURBVEIdvQUYOMoAs5i1JtQXHOINYcetR0tciG3CHhbOa7KaZbfo2G13916gzQl4sfACApZ21kTfUMbDKzppsnlWJf1j76gOGVE9i/WaQFQV7eKkZWwWPY/i2LvLKeT4x4TYiDobGzWorxFq3qPGrjqFw8EaDUpMW2VecqDTltpixRZIKgIrMSF0OMs15qh5/Ne8zga8WZmCoGhopvgV89Gjmzc7w9jvIGJdpVmpQcQATYEK/RbuZUjsyGCtG9q6brxQUVUKMyiAZUU0YDDP5EKSXtMRELCyEIbCNKux6EotpTjOzSuVy8CrpBEWgQ6uFFICGrLhaCsq0oW2oO2CnW3Ad187x8LDqcYV7ATWz+OAA7SmhdmiEtk5TQARUaCnb7p3CxvIPi4325VYNt4rLsaEqFDrCLTVjZ2u2N5ETFqX0XovUa+Klmqp7DKWpjJsbMJbWWItSD1OEJfaqrZjtoJgQDgQTVy4uNZV9FgQijSkTq3FtUEvVa6gOb5C3bUASIAvEbUS82L6tAWrpK84wTebhJLk6d6IkFjxIoR2jmwn7RfaugOeChkKJtuAc/VIPm6kZx46oiG6lqhKNg2u4JCZxTXCmfDTbBNrimHF/aTVkYxMCk0rJDTduCtMwViI7CKNoRK2Qit10WVS4GW27QyBDINyULBTFqKKBKCcJIsENGD2AyEaIF610itcotJHyMnVtMAOQLSkMnCUoPSIWiUT4HuBZq7R97Ir/ZqpLK0JGTdb8IsXiHd4lxBlA18uqGyRl0q4hKBnq0LKCTVEuTctyeZqWowANAFa5VQcpEHKChJ5iYWZb1LdgNGlmyAtUcwyH7JOlr3A+56cddjXXUf05kqKEHypOkoNzEy4zCZDJZRWpTZVmAalUXN6locQIt2UTwVMqIFwqKOloYY3i4bsCa6srYASKd12Ex+obYddJnOQJGUXXgavq5Ah1pWDgWBiM7bt3NoyyBbdoDznyN0sxoBtoldlOxU5YUWpHCZgNGkoJXphB2iMdheNKknXCOEiyLbaTiUXWpuw3EKTSJANhDmFmCe0lCYUVQ9ceVl4JSPku8LpSlUKK1vz6HBwflA+GBoxjk/Rs+muwlnmwlrGW1tNoAImBNYaDDqkd7FWxnDowoEfr9KQCuGiPGiPLD7WoM2gY7VpnpK3spO2Yar45qXrmFYQSXqG6LUuLLdWXWDUyEBJhIZzuJijo4Z6N2+1t6rGbAA5TSHVknELXWuRBJBDiRSAVtqgdEwk1VFOP3gsW0c7oovVZDbLg0WCcFveSRlQuHKK86GWzCRGXtQ4ZrZQM8Za+LGq7dDZUiy3wpitRW/RWsKCGFclTQCoWKsslrDCYiZbEPj8AlUj6nSGXdZYLUNQkMgj0HFAuynGlnVCniAbcOG5SRoYaK+qgKWMEgV9zFYsSGkiolJNDpep/CAHHawiQJZBForsClep6nRhNJjooTqrsSqeJmhTZU5UCAy6KW8edBOwoVoVEh40qjcF7RMH3ajyimXnwtIrh8vSu1jfetsOU2C8sERZobO5kZiN44CQFlxnaKUGmjTRViQiEdZuMkxNKRC+CwIlSNMgGSBFyjROgdFEZb067CA/umjfUzO56DkLVoGTRMi6o8TkVHPplfTj0o1vKGQBcH7ZK7Ef/2nUFKnNqpmZFMA5e8GFyqCebQYTqQMmzdrWe7NOMTiDWqTZ6GOTPtxGKcFW+PYWb/gWj/uSL32TNzz+INOP/MzPft5Xf/35Ne9VBNe8Wice1+zinp/ySZeuuvb7vtd79U5V/3zyNT/e+eZv92Y3PP4e/K/+xm+86F/+gahXD/vg93jnH/zGr/n8+937bW5yo1ef/J+y8+KX/fNC/Dl/+5y//je+PXibt7rp9a/8Oavv/ZGffPbr+vs5r7bk9a9/3d6Nhy9/2Uv5/pqbqr7dW73Fdz3qq37kO7/tzre//U1u9Oba+przd571W5f/6fhjYJ/8cR//jO/9zod/9n3f4s3frFl7zbn/+/dvdMM3FmEWxXP+6i/+92v/n6uRheVP//rZn/1FD89MZsJn3OH2J0mJqx2xQ3fhcwDWM6s0rS50O0pZjUQXK6lp6kFryajaQ84wt+YKCC+IolkpM6epHMNcZIolq2Rl8YPLTBanJbZCm+CkcqFCgiQH15UZUlnF3B/LNteNM1lB0FxKZbKmABq0Teuoz9F0euUOWWA9VVbwOCNmZ24iNs8OiFiBtdVEOehy9Jkm1CWhlmRhsjyJdORhskki4A5PCfSVp9j74XIX0F4BHdUUcQGfwniyv+1d9o4YjqaQLVmnBbDBUiEUGx2F6PwGYJa7EY11GEU13fzSZoMYNPBaSdhmdhra2GVUJ+BRVrHpHEafQ5M9ZO9cnJ0DZ5hjGLhYtQAiSwpEkDYAKqUJRkR32JIBbbRjYh2rBMYAhjbUVFCMWmoXC6FTosFHpuuku+pKVHCrjEzegdjkOXXPKZgXPS5OQbxdwW9bsnsxyp1tJpan84sHr4RXuZgQ7KqUdNGsBpZgPQrzRZdL3AG2ku7QFUjatjgWVYrYBEx7ATGw4gB6IXyBvuURf0WOXBu9lS3aWDTWz5b7iZhJqxyoIdUIBsgPOMr51ctpyaXUrqKUbwYoXHQSYO+IhjKo8uRxmlckVLrIpoOx4nuXbRMG6BDu0tyUAa8RDNNqcM1VfOa1+0zshn2zaaOCptLORvFOhuF4tQ1jq44AO/FstY6ppHLy0DWyz9qyNagIrVSqMdHaUHSvA0XM2Oik2JcjuCo66BCwoC6us6QR8BTyFNrQVUdZcxVP0cVHuwT/RCxLAhUCVwvdqvoItIJCiDC4UzDtORoXKNqYV2elMymtODkTSXkFtc1at1I5o2bwXDJo1O61+MQww5FnnMMmeuohPFk9qBZNcAExxjtXLK+5LFIJkVZJeY5K6wxzOYPuY036L0pNXWQ0H1p2DN+gncTVfvBWARMQMLaeboO+Z0rEyKLtAlRJBjTRrLqC9SZZRGgjL7WeJNKQnUWwOUhDp7ikFjRAaClzhb6JHOyinRyCiOJHR50SPZQ2RVqstryB+nSZLOJLYgFpBeNPG9l7WE6LqRzbiGlAIolYkRPEHuUiCSyVs8rlJufGs9JAOpwcwgIEoFVcOC6mt33yW6iVCpy0jWrCTNqTws6ZVEJRKlMuzkXfIFE9mtRWaAGEupW2pT0htVedKybOKy+LHPWq0fjQmgU+p8KRyXtKLmlrNH7VdCgVkGj0l3okudblKpPvOEwOngb3avg6+VKEt1zMXRYvmovM0owNQQBPBNOpwJfRIdaCTA4vreQdEdkpR+2jU8TQhHE2yKHRoVwVesayuS4qT1Ibt9JeZVWkS+ui9K6Wrdmmk1lbsdKFBbATgWUaZPUOPbv5tAoIyqQGMKQgc3L5bFMlhQxgfvAedecQGphJy0ZYM2m9pEPEMtrae57RORe20D3o8gbfrLTlrHU5mfuSqd0P7BkdrGlrV7BOaoEfskqD6kIkJ0GqJXGu/bLEaiLdlNc8d9+XLxEZ2Jo0Ib9r9pqDga0VeXnmXBGLxRCmQkNZIryfo52XOEEWaFFrZuwutJNiDbR8SngPGaIoVlQRB6EAJLSd9WJnBSvUYgB7sX5oVzHVJr3kNMkwL+z8+mRJFShQW+voUp1WaJaS4JcbLm9+8NCkHXkjSYGKrAS8iHw/slogJ8GFHLckqjSARpfSFpuhe1Qw8nW+HPva19pziS5GWFlP9ykgehbZaqllMn3yArEdBbEb67YoE2uIbdKbDT1ptVyZy7FoOBQ2RE5qPVSpyScxUMWBCa2YFVMre8Up1wXDEgnigeLElrxt9JHUf6bECcuwRgy9EkjigEF+9jFuceUvUfz5c/PqV6GSok2bGXHqNDPJptRMqVCUIKXtdYghzZpuXYce4lxrGsoYLhsm6Orgd1wXIc1Fbv5WN/uahz7s+te7XoU/5Yf++yMe/+1zLVx53fiGx5ctEfGc5/yPP6D1fu/2X7//cY/9tq/+snf+N/7aN7+vuO2tbvWh7//+d/yET+Bt+RVJr/0mUMP/7NcL//Glv/37v/9qqb/yu3/w6v3X3LnRm7zxR37wB4nIC573t1/0tY/e5z/7+5pjrtnf55VnAMDpWvx2i5l3zen/8S6Ct7/pWz78Aff9ye/8lq/5oi9451vc4tXXmukNrstZrz7xf3LnLW58k+u+3uvRgl/6rd9hQLnz/8Vtuf/2n/zFw77m629/r/s++++OfyP7nd7+7d/nv94SqKMTmKY0luez5ZEQ2ds6ivc4uF8MHjdRsdEb6xGnrCWRKuCXnM0EKrtsu/DZ3Ka9cST48mTT4aeV8Kv65tAwnV2iR1nYkXlsgFw6AMIqqKRSCfOy9zqKylGnndVAmfpSyTKjiWKKN4jBWgmLGaYKFwPRS1WrxormJREiXCmU8JUi0ZrQI5c+Y8zDlZIONE3rzG0WQpVMAzv9Wpo0LRuFsWSJztwkexeOhOQEDisBWi3qSX3bni2VCFSXFCFY9JcT+lYltTISlJkIlqVRXOZuyqIH0VYdnrVWOLuk6jAbwifdvQoOqgD97WWaBLgLiDP3UjWS1lbRbG4RrdYonuaoprRDi0NLTEWblEJEUCJB0xjqdcSJ/YdVrUudUnu0VoUMoZ3icohILmGdUdbSgBKxFLKCuIBFGiOV7DDnTmw5t1oXVYPl0iknaGPx0xVBBWoEpRg8HdlOYkayUagOaNduSvPawTF6UpASgCNkCRZNghyhFoCk4NojuR5Gk+ic1LF6LNogYqrG+IksMPYD3tUbr4wQ45oYlRAukHZbS/bkKGopgoMDX3OSFFw56IItypCMCCyiGjSshDFQKTXaAEltezvt0gqU4ML4ZQEkQFmWZLnzsds84r2hOF8knLaoiZW2Sc105jDZ2qFeanYso5k9gSth52e4ctZiDIq/IjBFGXBFBd8t81hnUFqQXXQxEGkrlJiB2Jjr4bAbYjOcRmwqp+wn4hOhEtUOOzToRDHeyVRSMx2Na5kdZHUVRKWbEGkq5jhDDomeq5SryEyrEC7vI0HrjOuIHDODffuIfhOxgHgKaP9Sd4u9Vx4cluNa2KruEI6BcZhHrnn4HLmF9pLiaodhbeikLUAKpRmIZ4OiVFyNQySYLbxKazPdihkvyrTSUdIOh1SrhNF0OPX0rM3B2qNMhajm2TL0iHuUwsCSYCOlZzH8BDadfHZUiavOY4AyEKRkUSet3+F7OeGneeDBSk7SFGoGVymySkIRBuLbjcsuPmpIiQ7rB5lHuZF3sRKzjmVapRZ1g+JpT4KGs/IFV220N5Fk/kbkF+Cs1AaYJR/tQ+cqmjodu9A1xfGzKEvywAoWsAXlmooeHR4XC2z2PfoZ3EgVYShUQPpGLMxoERvQ+MONTADTZ4Yv2rJE6hA9A3HE94gpXZ5iWWQUE4V2lvmqydGoI3a48hIBg3YlhmRRRkMe4EypVeiBTtPXUNlSAAAQAElEQVTcg3f6tTuvFRzwSklSLWlhgbEAj4oWCJLq+Obos0i+NpOv4CxvFa0anapK8+POxAhEE2MiZxXokfD5PMueSUim7sXPqkov572Ma84qh0zuMVlUmfB0ErwTQG8FzeDgov/HPuuqSKqljNKRPCWlXajiiLTRDodGNuXthRvjaCBaKfvCoggUM2tz570tkVA16czXjqCLJkXQlAQqPfZSaFY446FiSqbK7uk7xE2gqlm8ER2ZJZS8aH2KeHFD8aT7Qs5msbXaRAzVJVn7JSVLV1h5R44mG28ROBEEXLgCGJIpFSNyJBV5tvReRftAs0KL7w3gbSbX+TbooayqmQxe6FrYnTm+qLcRFYpncDR5N8kvaDQtd8swSdI2K4Tlh4CC9qr1GhamQTVszXRkoegYVQtgoOZFM0kj58yMVeFCp8Dkl9oq1d3WEmQV6EMPOiACFSPKaZktqhEilp5B+5syuCXEk5VMCiCkTAORprzSGDYhKRJCA2t6Lv6q10A0zWapnEHmToD3zeeTrEYhpEsrbzHpoh7PEE6VVsF0k+K1C9GLtzz+xM782384P++FvMdgKtjKRFsMsDXaZ/T8TESK9iiupNmODDgNSYhbZ+hN0asI4GKZYSkDX2Uhg8VlucR73vLmj/jcB54uLhGk7/ye7/yCb/iWpBLg7d/mra91rWtx5o/+7M8XDqc5cfT+X972bVrr3P+3tvt82id/yyMfcd9P++QbvuEb/Ftj/ueez8o/+ou/fuCXfsULXvCCV0v+2Wf+8nJ/9eGrdxjnB3/63W56k5vwzC/+6q/9+u/8j3sbnvm/tuPz8vl8DRQXDDHkOPVv/F511bVv9+Ef9J2PeuT973mPSxcXHPVdT3nKIx7zzXEFSR7+O9t5ny9/5T+96B9f+twXvPCa7R9f/oqsfwb835n4H7904zd5o7e40fG/5fzeH/z+H//V3/zHJ/4fGRkZf/eCF/7un/7lD//cM5/0A0/7im96wgO+7Gs+6T6ffes73uUT73mvb3/yk1/04n/+R+eufdUlPVkOGdu42No2VEcJn3C0BKSi+CtNrClTdEycVg2meB/CaDpGyIWcersQPkhqLDQpvGlwKZM6UhbN18XybW9jNmYfeyoLJMlf6CviPOfVM/e0JVaetcP2HF6bygaRpbIQsnu/7LxA8ZTJisbFWC1nOTN2plPmYYvwc0OeoJtoc5ez5+W2zqcrAmnNgmKwFBkqxYPlRkuY06xg4smujzx+IgtT2pnJbR0suCzbCNDCgWrwKLCYB+tGd7PcEKOmJlsuGkEQtHLJs6zJRUYRQwS1sKFDWhprMisry730xSLWhSX3+NNObNNtYYsgDiVCcck2kLQ1BLOaVzcxGz234eMUbLHCSglftaIOkySGxhC2NJMyoGE0RkZP4BGfC62G3ESs0+ZOGxbnHhJYXjmaaMj0lpfR9hqQHFgbwSlog12gBvW02NmuxuiwbS+77AgHiD6Un0XdREjSspSM2Wwz3Yi5sBVCelXn+ehIrkZq95issmsci/5qDFlIg1JVP+XWpFllP5+p9UonuZDalM3Gko93vamYDhStAX1fnqs62HezNdot58Y1ZUUKi/vyg1gkI6v7BrqUO7AXWOPZ72pykCsgwruaPgk6sYrw2HcswqRSsjzpqC10pwCexkpEbOVNpiQJqrrriOChyUKw9Z+qTlNtdpmGtBLJshSBCf3E8RJsJacd4zJkapitU/eTwRjARrXQQFKaJ/ZlM5lrzeWK7SIEoyiSHrZoXUVGpc0ro+mWXNbiPVz0KE1BlfSUixKzFhdt31YyzOKHkmqMcTWFsbHVVuDiLoHdtcIGehel0xVsmwlRhlBEeKhrb1kNIplZx8IZxz5XFD2xLbvYre+9+da1D6UMyeIASBOzYjXh3NCsjKTsoGxREaBobnNTRlNatRTbUYll4qdWm4k0uOQ8jEpMyFQJrUqXhVoKurJ1a02PsBbUKyeLAYuPIn1ba4scQtozOmiTyxZ639S4DBGTApbIbjJbMLfSs0JKJRqZF8W0R9FKLcjKolYt3ilXnGVdVmaynw9sHGO1DWRjoi3ey6NLmGZKRSErRYJykgKkeEIyquj9YYA2wAxokmIUnruueTofZfQke8P5UDMdqyw49LR0OzNeFJVQVHfmTTbGL8hkWi6bjhPGiK6aPnK3CoimdTdZVO5IDoPAwMp2luAtwlodPnrTsYk1KRFGpsliQJUjMaR4S0vHVqtsIhy5HeGLlXQFUiA5kCswF+8ClXEBX5J91DApGQvbTEvqzqwE3CoP0WR78iXn7C6jncpOIs0kVDl+Ua6YULgGR+WSDOJEdVABEwbVSpqnegBJTRcKLbq0zukOdG1bjS22jk1rG7UppXGqZIvLFruKgzkj1eOIUiGLSQYyOIdO+t20GVhVW4g5eSxUICVxbGAiyGVpl5Wr375FZzvidw7UgCgSixYBWhChPOMOJozPQ3QRWFbmIbFtq4+QziE2hAhDUstUhrXTSdroYj2N5iJaQhDZvC5gWw+7mNnIdqzRs3UyDEVlTOI+bCgtrRVrOoMkpuzhl0yuStmWlK/JEnMK4/f7NgKtBFCKZ+PZW6eZlQc7NM/mLkSoE82ZPhm6MrdciCjHCkvCQuySV7SltRoKMjmpRGzaNkd3AXEo9MBFND4pQ+oZch4ZA1Ujfcs5yllGzYpDGYbzXpd9zuTXUHtVZpG+Insdmb65NGyuF0s6ZEBOMgYuHUo6CHGS+k2CnNVMXWm7jB3CVJ8KurAil8/KXYv12iEuIsqMOUwXAIEi4zay0FoOWYXYAedHSiXYfuYJ5wu5TFYZeKoiQ4WJVV2qI5seXljwXcosRcpLHSpKTbj2O78NtXCbv/Yn8JCZGlqghRZhYDrPXZJ9y9GEXKDsEBqVhpIUpOxX+/mFr4i/eH4WEA6flk4BBVeJJi5GcPwj3v9dv+LzH8R7FV/ru7/nux722ONe5WZvceMbv/nxR7N+/4//+NnP/R83ALTn399aa0IqywHRvz/y//HVrPrBn/zpL/rar3/YVz3qPl/4pe//CZ9627vd42d/8RdJgTd5kzd9x7d7O0r+nd//vRe86Phnebn/r7bXu851Pu9+n8WTc83veMpTPIL7r729wfWve3Ht6/L87/3Ob/3RXxz/8DH3/+1NKPZ+d7njYx7+xRcnLjzzp3/pl1700pc95/kv+PFn/vqTn/Hzj/uu7/nqb37Cg7/8q+/xuV/0iff9nNvc87Pe63Z3eJdb3eY9P/q273vbT/jAT/jkD/vkO1yzvf/Hf+J7fOzt7/rgL/iOH/qJF/3jy/5tjf/RK4zIx3/UR5JSr/ynV3779z+1imz4j879nzKOd4x05G+e+/y/ff4/vPilL8/Ma8QW6iUvfflv//GfPf3nf+Ubv/N7Pv+rv+529/mc9/iYT/rwT77D7e5+z3s9+CGf/5Vf9dhv+dbve9pTnvmrv/rHf/4X56v/6ZqJ17z/06uunueJhDEvG/pWbUjXrkn6Mx2nSVnxcpUGVcoOuwz1nmpiIEkVAlYq3oZ0Y9YoV+jumYsCqEIsTKAs1dFWVDF7Na2LMHFpOrtN+CqWOxJIM4t6NZnYrEKtdCztwZRG8c2Y6jQTol7Gs44ICfIuc8A3K1UV7TZSt1XdQ7wyNaTvaMtNlragIWzMbFoF6kYqS4EW0haX6Xvp9I5SK1dxU2eLb0etaghVtt2KOHJSy+CStUJ3p6YRSeOsqsE5Nz18z8kVloMvxWqHX6VD5Ji+hD5zNrtKNBQIhxhns/iFXN7bYucQtAZRQCsj4KFRwhNixL2hGauVGK9DPOmeEA1DElaGAwwoYRStAKawfa4pXiWbd2JaKTSM7gh9FKUNi42gGB5eAQvwOVostVADlJGmLabWpEBrVI4zOJqqZCN+8ILrYWEKQa10tmlT1T7A5RrMMixCs5JehLhLBIKNwXehHg2VOr7TCdVSM+1tat+lp2Xy/onAK0JGgNqi+RqZWkcrrV4pKzP4zTMhBVctmlwAaHJXc1btTrdSzMFuKGnBe5heSyMYvJhLZkgJxIT2sqv2oLWoIHsT2qqlD3fULIqwRfvZy9yq2P9GaZNsCKOA0oqeyWZ5kn6y1jqDRM8KVmz7xQUdvbYEB5MTKMTCIZUM7+Xm1KiEUYIyedVKGtBUQYYorzrhcy+PrFDqF1rMOEuFkL5gcCVEEiJRxdwolFRyBA/50cp6bSpGcjcOiYpZWEmgmBOi/BFNzdWS76VcOs1uzshQcoam6IKiDKnNi01Ps1m1LcTCKSgdJn0YM5BYFBwUVBCFNi65iLAMCqEpWZJllElgvTiZwbVlLYzyuQln9WonpnIPkDQ73IXaJbEFbC5CkIlaJqtVKsNZLbQYKn4Kc4O/ICzLay2sajzAQjEJMiF12ETDiI9UqSIppWVJ0QIV0GnwJbxYogE497RKPLipQ4RTNOSYZkLstExCrcwEJK2moFoqAUXSHU6QoJ2QMIpb5i5eFRTLzUIZOk1A1EI60ymIcBIPSbElWgo0z87dgGdyLilnqgaqSG9wxrQ16UZKFuFFUPlpmxcthlCzIITDrZuRCRos8JwMkE17Yg9u4qwUkwZIbF7tsADWemrPlTLTAs2adYqgt4zXbn2vxlVfJNOmpXUmg6o3BAEXufJmaZrSBKB3oIFFd6CEmnb0IGeIj9DBlAMOZnXzbBWFdKtoLVsv7YzYYYmb8ZSTdSXEIRHEBxQjhHZYdPqKpLPw0DNoi9KHwwSgXA+YRKHqYDofcyAoaypSnJaLsePTnA0wSNNGO7UMJkBWZpYUD4tzjjOcS30nScpz8XSQapjJXfqrRhnGi0xXT2Z2plSWkhgoTi3T3IzImJZGagBUZGqqHTpKhIhXZ2/25HcTi8dNTFTLOtoQ68r9omEoUQztXaixXHi/XAwJyhog0gpQiU1jaMhRmvOQRVDzyLnOFr/IzmI/UJiJcXSJuywBS20wMahEEgag+JMyCqYgDHRoZUbINYHQVYSAhyt7Fkc1ZhcdNWMQhahXlIiYoSUpXlciEt7OictXTlQ7rhpBJipNoMx090RUSblEWISmSy1pXjb5tGhutusRTW05tE7CztQbYeY5k0HVogJZR8UWSjWNLKEINzizJw6YhTkHwArKHzCvh0d38hgEayEX69RBAV42aIE+Bs8BamVC6ziMm6t6ERch0pbazr1dbVwzdchmloYzcHb4SsYBqUv40PEkbVz7/d6RxtGEF//kb/tclRNFeaJGpyvoNVxkHhbSt9HXaczmmufOrKVhrn/2Zrf/27e729895Ftpa2ZhLVkTOYtyklZKbyXKOMmHf/D7fPnnP+Ra1752ZXznd337g7/u8ZX1sR92/CePL33pS7/nB3+oqmjJ/0s2Fbn+9a/3M8/85W///h94+o/+6F//zd+86lWvom3Xus7rfeOXftGnfNzHQQhRPPa7v58nX+f2fu/2zu90sACj4AAAEABJREFU83fkpV/8jd96wQtf9/3MaWyfdruP45iX/ONL7v7QL/qL5xx/3IiH/+4mt3iHt3vDN3yja8ZcPu93f9BD736/+z3w8z734V/3mK974rd+91Of+mM/9ZO//CvP/O3f/o2//Zu/fMELX/iil7zkJYT4ZS97+Stecc32jy992fOf+5yf/Jmf+rwv+WLexnzK/R741J/8mRe/9GVzsZpcI/g//X67j/jQG93oRm97s7e91Qd9wAte9JLf+aM//Zlf/Y3//jO/+IM//Qs/+gu//LO/+ps/+2u/+SvP+r1rtt/6gz969fbbf/DHz/rDY/vDP/uL/8h2zWDO+slf+tUv/8bH3+H+D3rrD7z1W77n+77zh93qfT/24//bbT72lh/6ETd5r/d/54+5/S1vc/sbv/v7/JcP/Yjb3PFO93ngA77s0Y950pO/71d/9Zl///znEofLl68mFV/Tz0uXLr3hDa5/83d4pw/5gA/6xI+5zV1u/0m3/6hbbaUy1dMDMxBMxREsQ5qSxe7D3E1hFfUBPn2E7Bkr+RU7hvdeWsXvRYIvZQKbVSpXB/vK46EHVS+TZFMUPdKsagbbi1MyGqyxZzLzXJhaSCpmepSkCaVqSXM+eWlb5sni0GQhICmrmTJRbXg1LnJ0F9Yb4xQq36SYxqD1zSBaiTp+Qsy983EJ9jAsadGLVa7US5vLSDStveuCRXScqe2YH6rBz96VCe5RayG8AhWW1TOxPLjaSxdldwEzKpW2VGbJ0dcd05FlaJ22XUJynVgsGYQaLC+xl1yOEdhkdB1CF7zOGQvnsrMULdFNTUQ8a57TWS8PJS2FlgImvduWetzX8AbL3E+RBqCQ8L3WuS6vdWbFqqzMiDpLnml9JIJeaIKBbslmgtljV4kqth02rJUUgY7qqzIIo6ENKx0OPfNMYKTxi7RmJhw5F4Gty+a7OhdhUUEu8V119j6l51KuvjSQzj52QFK0XlBXXgJYE2Hg4kL8kjBE4iWhwdJqnFwkUwYIektIHv6l0gl62pLLKPosM+c6Z7IsV6XVvFRnPuFqwmarLezkMLqy+kKXLmjsqHtg7fS7I1QsvDF2I+Nkq7WQrrVZ6lA0CUVG1QRHyDLdNztvNkd3HSlbDI1RUNPk1prAepmlITuqS9I92ppYYWvyphp80cx5xLqqMGZqcmXA7iTUUkuzWpEX2DaxYTlqsiG12lvw2pTkipWyhUkkS9LDSUXQtzAThYJ5cth6NhzPsiPTZU9cNlkdarHJuZF+iyteUkmrdANOECMwqjRRTzALlb2EFVOC644guwYnoFxKBr0/b41fAq1xmrkd9/lZVGxHKlT5pCdK7cKkTvBF8jc4IitpYG2yTun8uoHhAuHOOB6yOwMB5SGGtJ6nWm1P3mye4YydqGugiGI1J0LVG4GYkcpzWQZNmKtliqCM0GbspF+nX3VFs+yt7YIkPwtySmyVx8pFK2gb7agyQLTQocN60wagvGxZW5utjfEhHWlFtZBN+taNmVDGUJiZjiEXbV7y2Q+BcrbaSS0l/hUSIt4FjVN3mcwaDT250oZNtIteJE0qZIavUMcoEqlLkoitCQRu7ttZeZ5HqoetMAT4VfdFxYUdFZNkPOlRBkfpSCXT2fSCRZm5rYo20k4S9Kbo2glX5mcKoxLLlp/cRjVh3IEM+rkbJtLnHKs2SDPC417TZSqyFSkRe67AZAFRg2XnFStVgtdFOTzEAnyVMHwZ4iHLwACKmK0hvgWT9CKtxalwAQafabOUA0t2yKSU3vq2qXaKEbIiYpuqOXZtvCrFeMEkGxEG/chA0iOQ7nGpxcWlbJeqNgmgSpIi051tJ4pTTUspNgtc8Z5ti76hd2gT/hafwnP92zLbTEKgRcmYFVEuEBQdoeI1YqHgWlS5uqX2KHMIa+5CGGLzoPdLkQ3HPAKkfJxxqkYS24rmMdy3rFOKJRKIYlxWxWQGsPewZHEelFdSoSZ8E4GyeLVoY7LIyUbzKGVMVmnsY8R2YiJXISgsROmX8NmRdpqdrLJ1hi6YV4mkIYgDrwT2yFmLJgxfkiuT4KihjWD1UEUyOTv6ENMDQgfPNOHtU21JHGrRd+vZZLaKzknVrEuxADRUV5iqpNQO3TkSlRIuc1lkh5pKHz5GjuazuGrfQ5g6kGYyNC9hXujS7vOC+WWLvYBhRVXSfDKtN6epCKlpQd+oV7seIRCAXhKs7qDF/BWOB6HTtLKWg7gMqdHZAqZjzZxZwbiy24Qb3DiQ0xarJ6GS3mpTMBoF32RewuocHrKiJ4u2iptlMmlZ0GMYJQQDMVMmGjAUxw/zQM3e+Prtlm99iP6F3+MidjJgWARFF1MMQrQ6dECgUsZA0gEXAwrBNdXO8j9u8sbEoF78ct35hV5xKC/KlQ9GFwRcpAQMF1n1YR/w3x7+kM++9pV7le/9nm//nC/7ig97//e9/vWuB+BpP/Zjf/v3/8Cd//ds7/uu//Xp3/L4z/nMz7zJjW70aqv+6RUvv/MDHvRDP/kMscaT3/eUpzz1p35+LpKWR/9iG71/xAe8X+/9jW5wvb953t//i2uvcXD3j7/Ne7/78Q8uP/9vn/3Qr/vm1ymKw1/5qqv/+C/+6lu//2l3+5yHfPgd7vKcv30OT97y5jf/oz/78z/7yz/n/jUbrbrq2te53vWu/0Zv+EZv/qZv+lY3ufHb3fSm7/i2b3PLd32P//Y+H/jhH/yh12zv9m7v+ZY3ufHGNAXO58u/8Mxf/qyHPux9P+72d7zfA7/im7/lh3765371Wb/7B3/6Z9dsf/SXf/1Hf/U3f/zs5/3Z3z7vL/7u+dz+5nkvuGZ77j+86NnPf8FfPe8Fz/rjP/2BH/+pG1z/+pdO2+d8+Ve/20d97Ed92p0/7T6f9ZkPfvC9H/KQez7ggXe8z33veO/7fsI9Pv2a7WPufLdXb7e5810/+k7H9uGfcsf/yHbNYM666/0/+xu+9Uk/90u/dPkV/1j/8pW+XvjcZ7/oec9mM+GVa8B5ne9tnN7shjf8wA/84C+8/2c99Ru+6bee+tSf+KZveuKXfPEXffYDPvcz73q7W3/guNTDdFZN9pxajW2GuScCs4J6sCpLaBfpaMIUdd2ZaaIQacyXxR64dq4U5SjCiqMyMTnrSqOAAEjRhBYLjVbHAkvZdGSpWret4ULiBCZfxlZcO+uxHJZiVwNERczACrcq1jrSXE2tsRIpikmrtPFUbgjokYMsWcHTzXRoY0Ynn7ZOrdAOKDjFYMPKlPvdlgntsGOdWOBLWIgwVtpl1sHOwjIDHnlYjJlYJVwTJSBAg3UqZxmJQ3krWKIFLI7+VrRBij+yWCNbU+kKldAqZRHnliVJG5DzcFJpCHqEpIPIVfihvatsVoP97UoU4Al3xCovPxYVY/JxF90N5/QholQNQMpFvEiRXJ4hIcbG4o1UCXBF6d69jIFeHEkbJEUg0dTtksiFpIku3ZaMCRB0P4NtBOboM1us1Jk2GVEqJGwpWeWmWS1MubqSXgV3rnBniEPKsltuoLEoSIIxo0YoBFfeRAQEtESKCECcwIU0RicNUjA6QRWlVGM8BRq8pq3VKkdrvGPaRIzAtFyNgeo0UYHBFeGG2vhB3IvAqClZJ9nLRyZILAXr/q5rRxJZxkSkCDrnQlrOrfZGEzkKpIGLuyIEgVAscgeq6FqjqgNddOOJUIqakUSXBk5OIHEYwEqg+OkLV16cTnkZ+xGuOMgBR0lUm6XnqlmcIRCTVElByJJWMqKNIklhdYgpk1nmyoPoyMPBRh6UEe4tZJuQFeVzhs9gU6N8jjZCEOpZfCloWCzaBiW9a7hZyhXZXitiGdksLZXz6LNzNdHCqVbkjKNYwKwpwfTE4bQzTpytUQPeDjeC8TPOSFEgi6YKRLtpL+uhV2gjqUpk6b9qDsWAIAhIRpUUeoks6kATaZbaXDqHpwWNJmtm9Bm81WmVLb15JlH2EqCJDWVydElT570dxexZka4itFwokJuhLKf6ZIjElauZId3aKXQsztNAc+1LqBAFJ3hHyNWgvWj5kBhaZgXJlIRnXj1ruWKZOc2oUoIiSpXciczIGdiF8ZDyyikrDSnNwe8p6JvG4aeVaOGgW1xxLFLTpIwSM/ekueFAFfhToGqClYqptIFp2BO2CyYi6HEyJ4tgQQxMUmZ0G751PZnSCxZZKOUSFVSLshXloJGLtSbaStmjZtJ3VFbKUnEjalXlnuck6+uQwaUoJEIyaBSSxrs4jeQRa1LFGbFocKDFETtjNE2kKwN1chmsjcxWmqcnJstyX7mTVw0M91Q4MoS7hNOUzlaWhjHqruYjD5/6lB5Ng6WgBUMaGqIwusa5iGJ+KiH3nA5qKxFpoi3bKTHca+6smxxHCPUwn+BlSZqxo9A0OKokmmSzkFE5gAZtqlKplQbaVvwN7kmWHhonOdSsoIQ/SYRME3Tpmw4b6kMX+dkLyoRzcuNQkRydJUhg0WfWk4mKSmSBJ2ke6D0N1WShccHiBLoCI0RJ0FN3tL3apMd9zeZM5RSauwmFndP3iEVhYl1YJU9wi6LI1EzJ6OkmaYr0Ij/LcUDA6S3yCqSqGKObmBRxl8axaS0ZiGKHkcNvQYi4WUpHMSYQX8RArZsZzeJ+yyRygEa2M0sZ5PihH8L+jGkJMIXQRJT7vUToJlVr8ZMHEPAcAxpFpvrUmpAFYchSmB1SMGG4SINCUhigAPckS/hxhYZqZa3URAViJOYizB5FHyY0iy42JU4UxXsp7xatVxvUrVc8BDgHQhYZrSniF9V1V8akqLW0OBQQCUGKiBKAZjIEJIUQnXGt1//wd8eV1/nJPzc3nM3hfDrgrAUymnSDNkNXVxI3kxEs5ht9WCUBtOsff5/bLh8cQReYQrvIYTXolKlItxrEVPdiEn/kB73Xlz3sc17vOsdfvH76j/3Ew7/u62/9IR8C4B9e+MLv/aEfpWju/79ne/3rvd597/QpP/SkJ3z5Qz/3Pd/1XVs7bk5e9rKX/spv/kb5op3u63O+4As+5b4P+MrHfesP//TP/sYf//lznv8PV593XuL2qbf5yO/5hq//4W/7lvd5l+P/k+GZ195Op+1RX/Swj/igD/qyhzz4EZ91j2b6mmMi4tf/8E+/+Ose+7F3+/SPvss9vuCRX/VTv/hLL77yNyje4kY3ut9d7/TkH/yhcKdhD7zXvZ70qK/+/m9+7A88/jHf98RHP+0Jj37647/+6d/8+Kc9/lt/+Fu+7Ycf/cgnf+kXPv7zHvKEz3/QE7/wQU99zFc89du+5Tsf9w0Pus+9bv52b8vpVPqKV7zsmb/+64994hPv9ZDPu909PuMjPvVO/7zd4S4fcYe73OrT7nqrT7vbra9sH3Wnu/3zdue7f9Sd7n7rO939Nne+++c94hHP+p1nPev3fu8fnv93xQSgxP9zm50uSeuvrf+q697g9d/wjd/mpjd9r3d554/72Nt+8XAFvRUAABAASURBVAMf+L2P+bqnPf4bnvglD73z7T7hZm95U+TGWimuLUyjqVm1Vm0ke1OUssTVBHYIjIsFNPaUWSpgjjCJ63ikeEKxf1tqaDCNSqTCYtdgqxcFU0RCmMOHeZYsPax/wRX3tqF1gWRGJjwKVc2C6qWhUsoHYlCVIEpWcrMMSKVV8NQ1zVZANaULElJmTEYWPMFEzgjeUMcyyaZQa1qXjCm/LmQNDR2sB6ENIq1xomzB5z9ulVksqgYmMi50mvlRS2ZntcvFNJiomSywNF2wAQYgm6BRTnaW7F45UJqstTDOD9EOXkm6VsYrh0W8ZfKd9SUTowgPLVCgQj1kViuImmy6DGdl1WWlqG7ZO5feNRK2qnPJca66WrlIn6uxzcaOWKmIPtQ2iiU4BFISjfqFuKWpQU+pjIeyJZwyeoZWJdELiDMIEjRcFM0IS/HrJKO9YgfCKZHiV75Ejiwg08Ban14VOBxzUe6oNrF2LHg2bb31lNj5GJArDEzibdbYBqJJtFSLTdFNwAZIPSZSFFylqIaSdPUSs2gIyShX1S6qwKo6M1pns7PwCiz4GDMomf5p21rvubVz4xNzCZomvEPBaFAyh4dH2FRiIEmykxTFGq8J+0RB3bm8FVKNgZDjfMdhTfTASeQk2jSaTwnmxhLZS7lG587CWNldxEdxFcBPy13mTiL6ivSsqOAB31RFmohU0l1AYP3KgmCHnCGhDccrUW5YjZifV85M52jigTRiR+t7gxnZlxACggpmMe+8IYSthQgKQuek65GXZZ5cqsBJZOxaS3zCyW+UIDRIWmWKFlnO/IEuNE4GQaGrteYxrRQk/UVgS0Fv+0kun2o/hZu7ziMGfILPtW6DcFEZtFgI7Og+RpWxU2fUbrJrX7CkuuNxhxVZ0keOVoelKKtqZJeEDbEBVNYqnijtOKArEyFtYZAuSgM1Z6t9SHSB0FrmQ5vCdb5ykcwb9JWktbVtYGN6BoflJix+xE5wRb66ammTkgMIgVJfTJ+engf7pwTxG302Su1JQqjFkLVxvss8W56bpBgPISDuAfM1lg8vPTD2iXCUCthXRQHugV5AIVlSIce840AQoL0VRTdYi3sJIEXLkMrgpB/l0gFXUVNtx2wv91XFmyc3noYLWDT34DqyUvdss9rKljAh8xRE9BxzR7mMQHfQbZpjrY/RVOhIWVsDu3m2PTsFRx2EFC0Qux6hy6toET1iLkWOiMbwVkqGzFSnG0oSrUJksmDncQXgMY5XUB+S95EyV4SzxCwqVqoGw0yImEe70RNazWqgtN9iL+yBmHRPwHuVZS2sURyVSK6WszGUUK++24XbJiyvCm9cm8+F6bRIsjg6sZdfLvck4GJJCieRrxqF498U6wnZZ/HLrrk8ecCNabz7OoDoEAOjAsAKbJk2XNpOjhAdbkKbUnWJVWlVEzSCCmENLGfERBuLL+9IKU2W5pF/UooaIRYqR3TX8n2tWRHUAoX3CqoN7R5aTmuTdkWJV86Ck0JRttKiQA8B40blG8uReNBsJ6pmnK+2h6xzK1eVypouO3kem/hFq2YlmoSFdhkl9UKrPJgcpYGksoVe1g3KxgiHKGSIDjo5EsfXQmfhzijZWJQWaiFSHMxK8UqNXddlnWdGsjhZSbkeomfRedpCN57MWpnuJVUtlxbJESx4rbUy4pQteSlAIo5Ymy2RRWIE6H2vMkTYnrpXVpSuJm7Um/ww75ogqlmZZLOxi0kVXYjG8GhyeQPVA/PiixJXs+iWovCyc42MjuzCb+XQGSo6vh2JSLglFQu6Z2PmV4xIQpmC3bQu/JgzODJ7rlEwNSHUm5IJjBswQ+mpVMq1P/p99YbHf83+0u94hkOOxQOE0B9bmXgT5iYBo8g5WX6buptMOxXzd+vnGx9/nT1eebWshDQffRqExoVWWkJJEDhsEkWeArR92Ae971d/yRe83pV/JOoZP/8Lv/m7v6/bBTH43qf/4HOe/5/4myqc8p/aXvTSlz3uu5+8PP5Tszj4+q93nTt87Ed/+9c+8oe+7Yn3vuud3/qmx3dQPH/NNtf61d/8zcc84Qmf8ZCH3v6e9/roO931gz/xU271aXe/50O/5ElPffpzX/CCv/uHF533ec3g1/n+Jm/wBt/wpV/wyR/zkW99kxur2qvHPPt5f/9p9/+cT73XvZ/wXd/9p3/xF+fLV/OStPF2b/cOd/2U23/XY7/uZa94xa/85m/y5E3f5u3u/nEf8wHv8Z7vfotb3Oymb32jN3+zN3njG7zBda996XRRqmdn7WFaFUdWkCYNimtfdXrHt7/ZPT71tj/wTY/6oW99wr3vchdefZ1brj3n2V/18v2VL7v6FS/l9rKXvuSa7UUv/IeX/uOL/+mlL67w1zn3f+nJt7jxje92xzs96D73ftgDH/jAz/qsL3/YQ7/pKx7xlCd8489935N+7jse//Pf8W0/811Pfsb3fu9Pfv+3/uSTn/iMH3jSM37gW3/0SY/50W/7+qc84dHf8ZivftTn3u9Ot/mY//r2N3v9615byMuQ1jQvsPjoRKuX90hw9eLCYohQCc08MFySmeS4WJlptyC9WTYY4r0J8xRiAgGS+dVKjEeswdWmiI/AsdZfTGuOgIpgJMaOPgWsp+CAI5cd6bKWrhyZF16nYBmEurSJxlqBude+g7XVoQrrrWvbnJ3U08/pi1VJU9pRjjoKwKqi0CxRKE/4kZiitI45SsN4uGqswwSesF5c3m1SpI+DhW7rIqMvvXBcCvC+piQcGqFmXJ14Q1LyebQ8nbL3QobO2NxPWEN30VlEL3ge5rAUiLpadaE7WTR4zRWXi/wUoIE2isJUG7jaUUtshLQN8I7OWJvqigte4gIxyBDKjpmyg92bqEtZHZsIjNclpXkZcYGrijDWIhswtAYlQCxaT1VFGktZZgUkq62yXTXN2DcapBUkFN7TR9bg+KZlBFodXAcfpqJoAfjY166G7tvwcdKxtU41BpPiVOBoq+HJKUY3KdVYvxMq/KEN3dw0SypDfDozEIwfZtUuDnXqMcUu5VZEfihElcORwRkrt8vVOU6kQAq7EJRoxm6zabZjUloCrjrVVkoudNA2ZftZDVq1LVxEuxAMy9LKdvBBZKnsjIVsAukEr6RJjVbtJOA5E57PoDvDQWuR02S3IiXhzgjVlJngtSJoC7IHFm8O6IMM6IVh24JhBkDLG5skWipVyShaBYrPniIJhOpKXaGU1HLb1hjTOsToBkkVhqAQQsc40ueWJxOOrUXartKFZovdrSRAU+xCt96aGUWnI5h3GDVMGAe5etSZKFQiE7yN2w47WlTjKpdzu0QXJi/RpKgGMwiUyPaz8N5ZuFwIEQDWApZ54EemWbFWmKDM+caUpuolCE0DoxomYnLMOo5otWR1yGbkkXGULxJCwOzYhOFYG1vyAezyFmuDd3gBkb1k9IbTmGOsY0GzJWxmrFoOQEagLdUQS25oM/mcpVlTiuaACIAEOVZ0OSWXIoaHoCJmTteZ6kwLKy23BXoNkrp459IiiyNdkk5ZlhRBEDXlsspk6z7Mi6v4rtF5Vpoyqu7psValHHHsTTvGwMWwYcZKAYIgmfBd4jJqFor0UNBLZ3RWKxk5JCmwKH5EHaTIE5OPWV6ajnKxMFUtkJ0ik2XVtt63TbjAK6wSjoAqdU3My+C9DrKE/MTF1DYpRJMGAuIqMyxiCIOymRI17daGXbRxSuse6ikqNpqYNkRnEmSF9LBeRjAKjZebYAAgPm2Ltq2mjpB57nM2OEbkqIyUPVtwmkF60e8lVtqIfmOcYyWRNphtwKBlS+eiK4BVMWplrFjKZaEadadmSYH5zCxAWqbUAiY8GWoqq146IMjlPqNKkqxB37HlkUbQEGUPi35cSs95TloISRW33E0ChrKKcBJNKsuyOmgJyCDx0SiwaKCCt/KumaFVLQgqmXmm2nCfTu2FLHUcNTlQDmOgozzpM5C8s8YmxytNpLLAoUEzalpMjQPt4itXgdnFrkgfD1WSJTN0Xe7FR2407ZCBjGwzTudiAW07xPnNBys5cayySbzNrS2WPhUpoK3VY7UIFaQyZuJaUymG2mAOdayQIzTsCjS+JiEyqPIDLIn0TDB1dz3PNdMZhtJiItA75begRC8LKqoXDSeNLWfzLI6giwhWjUa2JjnQXe0sYEGNUgZHOCeEPpQTEZD+6OQmgy/Vcm8aEGo6nlv1Q0SJumGaeyY/2uJdpm+69zF7D9PippLwYNE2L5UmbfCCozK9LkPOkCkSIgw9Gg4vochqHO1Se9bVKQSAHlr65jtlTJyWklNSkqmSbSizpfjEQzKorLzEySg3XLrhG1z7FjcF4H/3opd/3y9au8C2ldIMENmcnFDFPRLK5LiHahJmCekCa3Gt6x3fqPiLXpavmqU9OUmcbCfStdS9VmQtxg9HmW2M0IYa7/1e7/5Nj3zEG73BcZPzJ3/2JznPAF7ykhd/05OfiiIDePSf3oKK/u25P/jTP/+Bt7v9Ix79mB/5mV/4T4u+MuHSxemd3vZmD/mMe/zEdzzhZ77vu7/ioZ97i5vfnBXvysV/ftsvv+ol//iSv33e837/j/7wx3/qGY9+/BM/50se8bF3udt/+dBbfer9H/z0Z/wMofznof/yY/TxL0/gH178kk+4zwN+/ld+9fLVrxKRi9PpVh/8wV/+sIf8ytOf/CNPfOwX3/8+b3SDG3zdE58014LIvT/1Uy8NlkoVAdkkPpLdk1VziDRlcXMhMQOkUBnYWlgXEC1TfFwaF+/4tjd90Gfc6Rbv+E6vtqG1dtq213+TN3/rm739m9zwTS9dXGzb1kl1a1T36mHc58irrnXtm739O773e77XO9/yv16zvf07vOPb3OztX3v7L7d45/9yi1ty4+D3e+/3+cgP/dBPus1tPv2On3y/u935QZ91/4c84MGfc+97f/FDPu/B3PnsBz/gM+/Nr4k+5z73fsBnfsZ9736X+9z9Lp9117t89t3u9pD73PsrPv8Ln/xNj//xJz7pC+95l8+44+3v8Im3vdsnf/xtb/0BH/b+7/Zu7/QOb32jm93khjd+szd9szd98ze/8Zu/2du/xZu/3Vvd6K1v8mY3efM3feM3vv7r3eB6l651LdOtVpOK1lyuPK3IBU2mSEkrlnprk5lcxUJSne3/oDAbgF5dstQKqVGjsJVuxNVXxm4I1uIuqjKQLMUC1ic0dLmyOUtEQSuHBMdcQVGOEdnTLSgmsldYp1ivwfwGwkAzWXk4keEqSIE2MX9T6ihJNFZZE8xat96zVUhUIKEJ4TrRm6d6ltMO1ippPehBVlIdw0cjeuaIEA8HXDgzmPMStsqWWLTAlnksnaWoqUFPuZn3VOEtyOpDc/TYSD0Vlkw3C+FtlQXKTdKsWNSrgqUhVED7RU1MRdWSPTIQHuksFtVKjPOXSCTx4If26oYmkGrAxj3rJu2kKbIWhZaANaUxzPBtAAAQAElEQVShDGeNKeKiAlODNRH2MG6pXH/UXIxwpVJwr2wK3RoRC2hFK1Ai0mbqZRNX4zH6nm0Poyp0mKERcEAglEIDE1RlI/tpja2a9pYbchgOE3n3KVmrxECEelMawcYZshMKMAQFFKlgQKWym7kVOngvQbNTJJo69bBQZxRNtn1vyXBWpweMUkUhm6WmwTtg1eEb2FxnIhhcJZBJgNeKFbOKuAAY5ZstThHqzwY/1CicFIdBBJVwlFh0pX8IsLVszpC3Ia1RoEMuV1u4RHRHcclEQZwgCuO6yaY2VwmqQlV2ZgQrkcOi6E2GVSnQWjUTMUv0UCKnPOZp0Oalx0OrBJaWDxVp1kAaxCX4GGEniS6MevXiYVMBDMUVnHrVAa4U6LYiPN1pw1L10UoaoBaQJTHVL3fbse0g6iatyanhQvOw/bLAOQymaiqmizSlyl0YLYa0jsW6pUk11dZdBsFmNJSTAgfJh9XhwyXTi94vFdp5wbOOKaNhmCJbOsBJZNQQ9BKBeAhXhMUsjCDo2IEwIWLLiiFJzjLpTQDlGLqGYMylSZVldK6ZjnPu4N1bpigzgiBoVaW5MUOHn7bZ+7R+hQARWGf4QpamjBIapVKZhKeFKCpdqBlSHHBkVskhdoNeXM5t9zaXecEFaZH0Zk1inlVOmlB70lOxw/NAJAIHU0jfXmpCr7ckrXxgHTGsLkz7ncq5WABHWOsbulUj6UaNodZETHILP6K4J0h02n44qorstqteFuH3rUXM0wzNyHdrRfmN+qhehB4keIIGCEhKX43gCSlpzVSlOJKYAVCs5g6fgWBggFImR1UdeGBIjZThffM+llkQBWJmHUPRE+JaAdGUru3U7cKkkT3CNV0Q1sMm43htmSaUjCsvlWSqpHAqyzQtAwNgC7UYVTm7RlKM9YY6/tJNYaCbXoRQdBVzFltpc8nFgyDdTUVoeWVpCJEw8BatMb9BVTpmG7O3asWAKCVHkxKJUleeNYh6wA9OFnVMIFptm21tsytWp6TQ+uSrfKex7BZ+3MkuqQMFytZxoFzpnulqvm3BU9Jm4Zz8IqKO9kboyzTVmFTVQQw0qazKWUI9m1dbcjDIjlfTSq2lVcKxPFITVU4Wk8we3oMMJHFEBDgc7V4yed95DpbDTLFgg2biXuzok7KorEn2zIHiDK9ecqG0SiGY1DVWDoa+iTdaJrZyy2D20tLYUIPKbdtLeaNQiyLE6vCD9cCT3yQYV+V5VjCnvRU2gI8DuoF2C+h4la8ULwW1iymRXAyilFtL7cU4Z+sqQwm3BGqRNuEXU0aWZobK6oPAiqoirAVagiCkMWS0gmIbtJueRIyTtBzHpGZlnQVMV0koyhKHf0EgoQF1w+pyHliQcCZxSCWdlVPRbFEpmAMiHOq2dsaZPhhAL1UlFUt5h8doqxc9pFaRErU0W0oAO6IhKoRpH1rSwnjZtbJoCqTTP6qkVWr6Rp95Wz0xRPXKxzwtX/EqAuWXZA0ha6A4ZtQSDUYv+wFBQaVKycxJeAauvF75sldK0mYg+EluH5UrkvfEufKw3JpqE4Xw4BT1rjd/22/9uq9467d9B0BQBeCN3vCN3veWN4cI9197u/q8U9Zrn+cZnv/lZ/3enR700D/4y7/h4evc3vD617t8Zmbkv/NvcL3Oia99chvbzd7qLT/1Yz/qR771m+59t7u11l5zzHWufe3bfMSHf8kD7/cNj/zKb/nar3ni13zV1z/i4V/44Ad90Hu868tf+cp9rtcc/O/sP/0nnvH8v/0bEXm7d3inR37+5/3Wj//I47/i4Xf4mNu8+Zu8ybadMvHN3/3kZ/76r1PCO73TLT/8Pd8V5L0m6WYolh0hG21ox6XuJ92NKTQ0WittKaQlGQmkhBr5EkWuzo/+8A94kzd6ow94n/f93Pve69sf+6hfeOqTf+N7v+3Hv/Ubn/n07/+DH3/GL37v9z7tcd/4xK955CO/6GFf/rn3+7IH3/cRD77/VzzsC77vMd/4e09/2jOe8HXf9agvfuo3fsXTvvFrnvLYr/+xb/6mn3r8Y5/xuK97xhMe9VPf9vU/8x2P/dlv/4af/Y7H/eDjH/2UJ37tU77lq7736x/+nV/18Ec/9KFf/qDP/rz73POz73HH+37iR937Yz7ksz7pY+5y6w/89I/60Hvd5sPu8wkff59P/aR73/ET73XnT73XPe5+r7vd/dPvdsdPv8Pt7vyxH/3RH/ABt3zbm+tmu62JLIiK9VLsWTu5h1lH6zCpU4E5iYQV6Uxqs6g1OGoxZ3ZlUTdOq2xydO8qmW6+rNgQlHWhuI7QZHEaPaxb6kXJVmhMIGkTElI4ZItAyVua0SU22TtSxJjvrHJB6axqsYzr0dmHb4ZeIglgRVvTYqq4lBpX/nNrSfkFw2o80c5mu2ACIVICN3FYipnIMFM1yRLPKgRayBBsGg28k8ApsM3WF60zMP166wXlPVX5ucl525YxE8eEkiDemYaiU5trRKz0YJVRUBM0iaLWbFwuXx3tnEoTWjvGirA2tKajb1osKyznAcnscMJmDW2THLo3PTPzpU6Ik5F2R90sV0YsFyYrhA7y1dgOZOUKPiGBVwVckI2uL4LFRcSp+glGHpPfYhIYkFFyMauHqYxsVsNqS2xZm+BKcibWFOKMVJViMYvLW51PGraBa1BnIJJ2e6YzDN6oAB2mKSgvZDRQOIdVKazTJcpNwxWM3CjHljEAybdWQqwBEy/Jov0CERQqky2rRyq9aQyRlFHbFPYo+IF+VzRFIQL8LU1waLFPUSkvFRqkF02Ah8UirpvwSFJQtLnCKs1QhqUEL4uskcyRu0rSCJI6SHzLZNRPeZDQWnJToCZsUoqLRBGIElq8yJMje1bHbBJGGUlixE7PJcuE1goXG0iHTjM6QVtW2nRdOJCwJRs1KPuPTc20GN0uGlOp1WayMeZDj+cfxkZWXgsVtAaA7GirV44aFS20YHsXJ5hlm/atWTOoIVFFk3VVkbTYZzil2J5wLF5BMW0AcCIYB0ttmY3E4swOnKw2hbrJGQhJIkXWdGKTm0tzA5c91Erz7Oxsgq5y5GJ29a2ie8rUKW2aRLpcXrqXzgGn1FNnNtZAYS2fmWFNxBTCirU8YvcD8gC6TIauhfEzaQQHcENRrycypZJMoOkCZagKFaglNfXgKwyl5a0cjDqzp52lu3UpaStAzZVJpaTKMaRUWzOyipJpMt8VOVq2U1lfCTffZQbxkMUlhIg1bUOY22DbaZromjZERxelOTjoEIiMvMK4oG0g2mZ2QV1Ochxqw5CEKM+L3/CzdBJdLdKyqHIHzlZrSwZGCj1Ywpp1jQPrOhe3ZHwLoEbKhUltdEHQioUoexXhr5FL9uRqKRawTJiT3hYkGNg1m0w6XSqeRW4cHwXamumopGjWjgtv4sYQBC/pVq2bDOpQIeIy+a6zjLwgKGBpaQd1R6F25dfbk6kQCS/6WyBOUcSnCcHgb3UrMQ8r/s7gCvNCpKME5BMRAGfkGbTdizIUguwIraW0EKVL2xITqIj2ZspK19SlFY+adqETFUKvs1oeo+QkNlI1mRJYMSq5PB7UUxowULk0KVG4NgdvKBo2qUZzG7iSvjK9OD01wUxoBziHI9aVSEIpNqbEGVVgi0gThywVPib1zaNBrPfTaNTa1gLoNMoIJ2FPl93rHFiJXtINSg82tWHatuonI79URQRiGl2LIxh1mth1U+0T3WOz4GN9UKRJHn7AoU57nR2Cm9IGFXBpwDJUjN0Mm60HlZzoqFKwzIZ5kIErZYnR1tZqCEbj1k0MC7JDkiiv0vMRmoKUELHuZtWFgWitQ5Thr8uljia+Nad0Tm/CkPFB4AZvWAxok9DizEnjrHWxreigVWqWFYaK0mdiyveh/SSdx5bBRwPjcrZzs71ksUfuyDBVlZ6MKf1vaKa0hiVqFyw/GDFoYXfZllstpHOYpamL5bQAHQVDXa1WYDpHNmHUELzb7MHsKBRQIhkVe9JDZcOsrokUd4tlHcZKzPJQCIA1gFMmkqROFDPziAe9aQzSSdYWsOhgEd5oatVAGmUv1o8toVqhM2WvmOwGGjTMTRaxcWhd9W43v9YH3BKA/8VzX/b4H8IK1hxnuglCJRuySQ6NzvpyKnQwUBYlHIXxFjfElVc+5wXlzKk0UU1BZXGMFoTHIl3RuX8FUkdbcaq6+U3f/Lu+6gvf692Ov0oO4FWvetVzX/RS6ub+a25//bd/9/mPefwn3ft+P/drv/Wa51+9/4u//pu359Vf+PnveNoPZearz7/mDh+0v8st35ln/vjP//x3/+TPufP/90bH2gPuegd+0fFqUTd84zf+nm/4+sd+yRfc5ZM+8aPe/7998H97d263et/3uv2tPvTOn/jxd/q425y28erB//7O+cotjYjc4m3e6jrXujbN/rXf+d0/+Mtn/8nfPIfbY7/tO772iU+ihKsuXXroPe/eB7sMIGTHTF3F2CtzVhgDlinDspbRbZ6Uzy+mFnkKQCzzuJZXEjVvf+sP+YnvfdK3PerL736Hj3/XW9zsute/zt62VcOdQ/UNbvB6jNf7vts73e4jPvBTPubWn3zbj/zEj/2o237Ih77z275DP12QutqOH9SxFDtHRZaSeGlrsjmtJBurKuCrZmAVysQGf0lgDqyDLZIp4k3QoMIB5gflzEsrKjm5Rw0ccmm6keWVViqhoDAAbG0sA1JAUqqxx4iZCp0MwKsFNuZyNZRmrpBJoKRMcyDNRy2JiJI9LQsm1U262alZV9MiramjyiBJ8mOs0kXDvGShS26GjaUuF1vRMURtNvOm0CCGY991X8wgKxNtdJRmwrOtvIBfKK2i2VLRKwbn0AHhCdNowsymj5ySKof0EMzR/NRq07RISVYgBhUQ2FDtneaKsFBAUyxMhaWBjvI6ZSIrQyvVYE1U5UBcwDEBSUFVJZIFmnPrqLO8FEhnDDwrzu4rUEsHh5dnIqQVFz+i28ruBf4UY3Klk4KTq5V3jhKptAqjeEmKAM+h/DCyi2wNxpMrlNQRqBTH05hwzKhZMZELLFoBXmoqRp4kVzV9UYIOkcGq2U/L6HGUTpA5lhAeo+izUDuhHYIqUE15Nz/KEiDJWphVwsiCPudQ2UoGrRD1ELKWFC4pymvRLTpckqfCyZmSzMrwrAU4A9IOsYwzA+W1kiA5WWDExCSaFY3VVkpCHsnAsimuskpWpmdWBcWohQ4GyNQOYmtbGJ69VGCoXmgcwPBmSh1UAk+Hqoh2IaGn+U4/VWEmJ4JFNFM9rAhHQzCVGAeRwKhsBQkJ1wwNRLaEQIqDWPpR0qQMjqIv3ScdrAkPRLlLRHhEaKVwnLLQk3Fq64oOqm8lhuMtDNFovljK0FAraWqNxFG6CJGomllZOF6KOlGrdjfAdrFJTMgVqvFmTvqz9UnCIasQpHWVVwkKzdm7SagWoiVUBzZmeD+Q0hArU1EpTY5seEoXbAAAEABJREFUXKFLoCIz1a+El0Z1ObLf0QIcqQ0lSCRtOyxYgijuSxXPFSdZS6OZ5MFRQo5SR6s5BwKTkLaEbIzFxgsRQkoowAOv2NVRLmncoqmbiJCSzRjpFFoVUQ6gpdiCrKxFhiTMWJ2OyMuujRsfdfAy0aCuvloPUxjZm5YiLjVbzp7Ev0yqmXXrKlqrKF2ThILkUYKGFBjKSo1DOugnY8iKIt04jw8mLwPLSnrjRmEEgx6EFD2jnV70V6gTBOTArpqkooVZGPe8DgfK6VsRxqzcadwVbKFOwqvS96ZIVRel9wxM8XokCpAmo3ejA61JMxAx1hausbtoqXCCkpI5i26lW65jq7PwjGZ1wsLlEJLx0yDQmQx8ZTI64HFXDEWrEOoSadUvYFs5z3BGMYouCoEKVQFW/EyV2WoKSJ7FeM09jnLlZEYDNlCYewQoX71bNk4Ng3fyxpVUVZTSIvBVsXJ3XWKMHR1ikKXzAcsKW6ZJJCEcolcalKqkqB9W9AMKSSOPM0Nkb0JVRKlpl4OkzJAJrgBpEVnvXsGzrWor8BRKpOgWAeGnQTgzaZNBTbmVyurInrxTRXNUlmt48+iRVhxBn7ayEdoDxJn5okQLpZSQbSRrqTNUdBeVOH5X+QSJSbZAK1pJwZZKUaMqrKn13tVallUKivCYuILg8D0tqi20SJFiRoOpgirNpL8mx0+KXqEMwBE9W4OoJaPL4kMNMMgQJabmS3bPZISQLczOQ/aNu6BgLTcPIu0lU3MxAyVKkEBWAMQ5N5LPtCAJqTzOSaZ6qZugG3V0yGD5LnChQRtLPBgslaIV0GRi9DITyiFWpFXCktwRngWNABD0EOqankhv6UqrouizXKO5VkjABLRBSJLwVKtxIGFVNh0HMTMd6lTE6hEcLNklGFvUQXBHuAYtSR0K6yWHOIEhjcBziGjAKnss9SQWmDDHoAlEpFxkKWtKeKygAQXKpbqjZ7Mk6RW6AGhpPW2o6akScHSQijWiwS35Yh9w91mxCxenHGAEgJYpbvDwu9v1r00ZL3r0U87P+hM9n+O8pwdZWK1n78u4LmtFFIqjkIp9HAVRr3fM4il51eVVTAAlNK0pscjeMFofal1y1DIsSIKjppTThSpc7/rXfcwXfO7H3/ojKeGfXvVPX/qoR33+Vz/q5a/8F/+XxXNf8MLv/J7vetbv/u4vX/n7GBz5r7b3epd3foPXO8z4qZ/9mb/7N/7pMPbDj7/Vh6oqb4f++088419J+H98aNY+89M+5aqrrrpGwid81K3/y9u/rQjje80JvnOfG3e4vXqH+/8327vc8pbbxkdY+eSnP/2eD3rI7e55r4+/+71ue7d73vaux/ZV3/T4nOfTdnrAp9/7v7ztO0yviABzh58ghWdG8lHHugwytBSgZnHCXjgXH1AV92GWZBY4nsEALq7arnWdq2Yl5/J2is2AlWKr2tL1dJaLlJMyuim6KyZ91BwNzY78F8msXELKgNNx8EpUTgLe0h/F5nJvi/pUhWVmz5gsDJVGm2TCdihlnhv4RTwUR2KFlastGEDrZAHO8yiRZtC+sCWsmVir6DITmLDJisjavAmalBogJZp9S7V0HPxjIjG1mviJnCQoBTfZeYejYsFazFrp6FFMvYSEipg0EdUMI6DYiwSmi1UJQIl58RUlkQ3sWGehqZxcNL1CTXvfTDk9i+Zn6pKRZTw65iOpoMvhRnVBqbhwY6FvzXPEarV30AqoHEgIJ03QU3B9sJTvGdhBqxKSTdYAAzPVptas3YXdgIWjFftjFcTLEKIJ0xJDNmN2YwguUrfZa8HDPTm0q/Ch0NYmL2qK7c4vXlgFdIEqnEWvHEL3Y7Y4K9GtUsjQYSD2IkkE6ihlMYu1TMDXyiOIQPGqT9k5NRsxMkvtTmyKzkRaHecIOid51R7pexYf3J19epDakDDzIW4ZSosL3pqY0QBzybjyNzglhaEQRzSbvdNUigOh15PjlL5Rl9TRlBqq2PPQkATkwuKiclBymaENNhKsvfjEdE9MRSD5i73FJK96DdRp5Zghk82izoljXNpKVuFTRZs0X9lGYOgdjUaKONESVWTHJBmliGau48G7lyuc7lmL6pUbh5XyS5GYoYy9H31CVIbxVmCZF0kQoVl0DTVk9a2EeakiINn3YTUYE92cLkov0+zlzWcdgabAiVx9Bct7d0CZDMRVW+Hk48QCPrqdKJvy2Xy4ajI/dR9VOWMWyeVaZ8NZtklUZTNjVtLV7F79XJ0xr3RoqYD9WfZFX4u5NiQVSKDIDJMDfdomPLiyVc86lY7qKh0lurztTozdxbMYcsHMVX65zrtdjr5zGA67G0yVWbdd9HFpYwEKyVSvtvgOoUZFnVIuLTAVk8EP9Mt6saTLqD5WF2xVrSIgU8h7Du7KUKirRlbW2dvVyaWHooluPpSPq9GgdF66S3lxZTzpOrmfsbUaWykj78JopfTkTTlPVmqggmJrikYJ+KNpvXqhrbLFPGZ6O1PWRAo5k18Z7VnVWtPRwKFpQvZHO3yEOoFWVTD6EKQ2o0eTz6i0Jube96tHXVZesNGy6VQ+LvYlTiZNy8VK1NvxPZc0lQ2lQGZUloqcLDcj09raxwpT4TIDF8Go5ygdJLd03Zq2TXVQN2ryXcXMNsEmcRId2MYa/WgFjKmkKDHgqKrl5r4Jh2zZOqMCXG648u8SSAzALEoKUeYH4pYW0FnDpUFia3HqGFpilAuguGlBkzS0MKkBdAHZNEefVmw3yzToY16KYKpJQiD0CtmEZRKK40SRPEtjAQtF8pRJNltZ5OBiQkyXlWJ1oLBJsZKFxWzIzqVYD8iMta/puVOCWfUtO9NpwAWeSJjICboB1FZKyd1wGmPEha5TOK1Aq9Kk7Qar1XKikjGJBcauJ1CsFwx8l6g2xVLrSHVFEatgYWJqK/ttYAbOwC65G+2pXc1rg1THZU0uFa6u/Wrun2yNzLYTvTwhtxI4qFayFU5T5Jxzwovm6tbaqeGiaUPTvellvouUMJeIaO3wiUULN2kd9I7575cn9imFQb9j6E7ZeQ4Prh6RRWwXMhOEPkRCCTCYukfdSA9q3hmQQmXa3FlfRkojNawOJ9p27iPNJKL25WslAlKGMlVlusjwsInwtniepoIa4aE8an1vRCiyFEf8Q8CXpsbkCKc+jJ0LDlQL1kyGnlo0D19Vmcdt2ikanwarLMeaVUnDoAb0jK47zepVGqBzwDR3IhvCok00om1uXHrwFNEXBvsYtaJYpMP6sn7uckRGmbVJmVIZYFakdUA945y5wOm+6z4rtGQDiR+Wu9RCObck51KRYlKjaAiSrpZS8QhOyUhZrWE06Q18gHohfmJkT6kXLHM2Ta7Omt59beUDLjW1jm/QtpXbilZeQbRIrcPT3DxoHZM1os84zewhgKrCdCeitXkZJHgknqxAJSZNIAXC5CCLRKQEZZEW17rxm1z/028Dvv7p8nPv8+j9la+0Cg0Xi2o6hVVTqzDY0NyVjCo4WqrmDa7FSdxe9Qd/7dYCmRLFet4stdF/WKdmRmyJciuIirtROqZv4Zeuuur1v/SzHnD/z7h3ZzX39Z1Peeod7v+gP//r//GHuN7qJjd+/evfgCp++beexffX3i627dM+8RN4/iUvefGP/uzPc+d1brd6//d7/dd/Q1766Wf+8gv/8aXcee3tRf/40t/+4//c9y1v8xY3ee/3eI9rRF33ivxr9v+d98z6lu9/2mO/778XMf03xr3XO739Fz3gfte69vHPo/1fQ+ryP73ila94Obeq7L0/8DPudftb3VqYlkjxKqJ/DGVIpe+uzlO1MM42iLxk9jw3slDccllNltMhQQpkYBVbkSk5c9C2s/Q1jx6rsxyUWokkGViiBjSXHqyrgEkKT4gVSEUJVNluMruU6EGPyhbZMpXCHAoqIW15NapWlQtccqUXJlNNWyVVtyXiCnLGJtqsNimCWtV2leQFY90R5SQaNSytMcOwUvKQp4dHtAaUPFGHEtm1r5IMCafuLFIUdUWLJMBN6FUe1qWpN85WeotFADOKhgjfuJIYxeucLZF5XpRn0qq3MsvSQzuHQqJsQRZAGXRfIMkEgqUAPbIvWEgVj6CanM6sreKkHOVc2tJDlyNNA7pycEYQ6SOymtUIY4pUphwlveibuIkV09KRxWGiqWzndPl0mc1uNpvZfbWInBKrIYW/jNkh4yALLaFBTlSVu8n0Nlg72EBKrVBGgcdiIr1ySw6UUPbELW1QYRS7EWbRse60hJ2tR1mlidMPwjTBZawyAmhhQ+xoLGm1DIt1osQyW8aBSjY6YEkHQTP18LtJdZVhEJFM8zAGTMFWl8poquhI33I/SZhUQqbZPozttw6QUym3872JdBbFM3TvfWr3rOSaz1cIdto2m0bvgVYkQqaiaEsnKJYeB7JVtC1LPHN6cQ697KbWmmhXU9rHUGslraCMLjS62uBpTqxsjFNzaQi63cVHaYOaKHVDilBxYmVFCmNcSGMUDpc14ii3xWvixEqPSS3TYnGwO+2DJlN1Gg7QyHavFiqLpVvzjCoviQM/aRwiHCRHNJXGNIWoVFg4P7yjTDk+iQoZhVEEoE/GjxIBK+1oQ9nDKCidNnAVWSZJ64AJjWqSZsVTmuzJzmInJe2YyCmIyOBgyg5l79yjdkcFAQY4Cb0IFhTYJNuCssW6HC/kRkYlkWjBOzAORjC7NACvVHHqFZRY0dLaFobALNEPoEpTBNoEI7nRZleFiEkYzYVoKqoXZyM1pgW7Lw2xOISJLK4miZ0okKXpLUNlN+UNSimtOVu6ZKtizKT1UuFkmgqIpEiVqiiFSSUOEQKubkxoR7QKLUEvH+G2xKYkGGdkCpyBLV5euvFKohCkPwlSRQW6jFuj9aJKCqYqkg9dNo0NFExV6hyXzO4ASBaJ5GtW+Iok7ogUXmGNAz2izIRCTaWpCDcRhLnnHlx25VLWFGa8kXwEAQReolRSampSiChvFo4EJ5Lq5ckpKEoKJTHEIZTNGxlhrA7pXN8yearSufRpK62YJUkGuRKswkyd0eYcNE5qITw8Z7LWV7mjnB6WDS+WN8sAQQ4hfYl+T/TsJt0IY4GIplZA3ZXUTmLOVc1sW5IUSU9Qq2aQwUjawzPBSVlJpVWMWhlxcmEgD8YIimjCyVtPTdWlQsBNU1hYapPotkCPJOCirkkf9l5nhlmZRd5YQVx8KZfxltlx5aWDBreCEf7GSM9qs8TTgGKg3EiFJkotxwma7H3uWudUunVEQx22qkNalnnomhquIRa0L7Mm3WF3Zq6gVwicBODU5dvufQ/Otb3UF8GkWjmHehhCiXQA7KElJVJWyYmipbTMDEfagIVCpVQO30C2ciGwF2blNJ9MHO2mXUJkle41XDqslXIHRfm4THNZEUKQ9IwKClKp5UUAU7KiJEM4tDJdZKpM2hKtckgROeGt9LFyFnoE1r5oItLAQu/qwYgAABAASURBVOB6lJIqPZItFrJKcGya2DhMG0ckWKFlY+HYj1amSBOwQsgRmuLIK6LotGU1R3NBWBBaCDJs5SjZDkarUBCExXAHfaFDq1PBqXBBmUnvQjW6pGbhCj/Q+C2hEKMqYM8jUQTaCGkyABQyLecw3ywHcBI5cBOISFQREiq0pgpJb762zC6irccmPgh3c9909p4G2HQNmlQiEDI3UoKfQ7IboiO0h3eZVnkQEaWYAqeLJBVAGDzU3VDgOeGKn9tKYbRno+H0aPKiFScuLJeUBpQwcjJFo+GIO89K6nGjX232zU+kki8+haELpMmFjQGCdZG1Fagoioo1ZXlb17v3x1/1oe8CYP/L573grl+9ZQyphqmYQCALnhIx3C/NvTkBAmG9uMmx9OesXOQTOVvlsUjcwzzOYfpLpAo1MTuKZcla1xrlTYvDmHRN7Krtfnf6pG/+yoe/wQ1uUFXP+t3fue3d7/WUn/yZ5U7JN3z9G7zNW70ld17wgr//+xf/I3dee/ukj/qI67zedXn+KT/2EzuzkXuvtV1cbLf+wPfj6ef9wwv/4tl/y51/tf3V3z3vjg986O0/4zN/9Xf/4F9d+ncOe2sf9+EfoqQJcN1LF//OyGsuEfjHfc/3feEjv+pRj3n0z/7qr19z8rXf1exTP/ajPvsed73Fu773/e5x9/ve7S5vesM3ffWwa1111WMf/oV3vc2HNVvWqjfTbMma5Wa+bVyJFe+m1zB2OCnwTneop+UVqoqw/CeXuR6kzVatZcNuMYdCO0QwdmxzyVHXih8i+4i9xzQSDxxQvShpN5AGijCJaskcCrUeY/PDnGyXC2dBUF403fNoVVLMsd5Fm1T0mtA5G7/3iRQ0cCmyNqYJNcgWuLTQlsS5r3NPH1XCR25rA2eHnGZpVaYUzZjSM+gDcK46y7raFqtjSO4Zu2Ml4OizehQ0VhMvSJXupbP73HaYD299NSsNxeoW2oL7no4KQ1JrUEu/lKdtbnK2DEqRHCqbChdFLas7kVPj2mpi1jrLvIx9T5ep3bcezShp0ZZqiqMuoat3GunTjwdKYjCBZgkcChSYfnWZLjIRpm/nsmVFGYEeegoZuxltZYmkoNMKPj7rB37nYdEV1MLr2JabzGgzZIGPoxiKErEsXM41a3chhvSoeEZEGtO+gkitws50LgLPk7HJ3m32IcNYj5Uub1lNNHXMtNgTc8cVN7yG8+bYrBjU4tqXNi/IbGAQtGW3tm3DurLKT/hy0DCGn8RobSC2mrzpkcokBja0HYM3a91g5OfJyqRwvLVjMcJQGmMs89xiSknkOJhjAKs66yDOFxZ8oCfWUhhJQCBbonMk1tKzstgDvFaQtnCxy4lLmEX7GRE01abSoCNB0AQlPfIUNYgiJJVWKVSOsVvpVUUXTsJAzaK+TbtaR7cUBpGoilXjDHjzuChhiW5b5iXgWptd9K7AkgjGgTkSzZIszhTqvkrjUvemTp0hzKsqIcApNKosfCzZ3CxBYEi4skslxjGBPeJcOUtktSK0dF5pFcOxhZ28tQBcq4ZoH1rCR0hYHoipWIxCTmDH4AATgSIammqHBcEu0PnUPEstytlpSjTUCGUMKVZKREuVBYM4qlIPOE4Cyo9VNee6zG/jJwHBiNEEx6tE6XaSG3uB7l7JMgbURindQJqwWOnstUycFU11Kk1Lfu9i58I5cQYiU11bXYKewkTsqFx83MZcoAQ2pigwquB9SHLBk27hxg5W5D6YoSoQi+hrHrCw/zOtnA18BLOqWnkmKgEKqb1klcGbQIXrJh0aJDNFAAWZpiS29mzYGy6Dnh0DYwgfnuM0vG97bZOrSWadu4QpuoqqlFmYTbAq0/7WRXsI75U9WTazVx5LoGqNGyzR2AampViqaUL2XVaJQ7i/hMvRE6etdaS2iLe2t+28Nd/oltLKmhSbNBhlUhui1cq1z/NaBEuxdR0ihfBaq2cxMVJ0Cf2PdDdPJrO46iL0DMROl+Zl3a/uZ87Qig4/sqFQpRaqkUbcqw4MPQUrKzl3nycPRYAG5R6e1CFTfa+5kJme5GQtYXGbx25W7dkye199W118gPEqDMRpnjW8gEnYkFFZ4LKXUlAi6MDF3OV89n3lcUnEDydY+I7tOGOACXqZLhXWRvWi6wzdgA9dduEYto7+1Qf1MVhUkwDVd2a1nBLEQ3hbfoZcVpnZYgEMs5+zLmfheJFwTaE0iUgXiIzIklMMmyYkTYRommZXNA4rlQMuyyW5IFWCUn4Sz2gZSl8ymGMsEypNzcDx5tqsNRuWo1H35JcOsYu6GFjhRsvOknPW3FmfimGmHVzPeVUgVYAG6cgGQODkVZH/O+SKAbjyot12jjZVcqBOno1X22WcvIaiX9LtQltXAIoSSjIwHkzkQUJJl2HSmC1LoLqGcEMzFiwQWzmd9j6cxptfOuipVd2PcAQoXMQryBAHhH6etLFcUEP2FYPOqLiYywBrbr805WKiG1+nQwokCZHWyMB+ZtWU0rKWJyo2irrkp76uKnTvkA3GRByMB4PnWZJl9HgygOjivZdwmSHB3BFiKWyfviE0JMhSJhBni1jRTqDhMEkDR512wqHVpQakeVdyeSd7qEEwupzU+uWhXBVkWOMTuUkqiWBremKx4F7T0ceFnRAtZltTJGFWVtLDWJdPzBqQGBKa0tyISYoIDdr0EvpYsJ33GGfMYIRRgCP3pG2618U5r0rRwzxVUxO6SbCrNNGXbDvIICjB1iPlmNlWvdpVjkuZjPtWnVKKtWAqLVIlCzRkRO9iJqhIBLHkLLhgpswIZ2fxWRHp8Bz5Bg+/y/aWx985edWv/cnfP/IHZGfHr83nVmFgXbFKfqQUq1O1Anl2Gh1XXvPybqtsyYFtNapsYNqfPcNpiyj5P/xAi0cQUeWgVEtc4RaNef//9u7f+8THvtt/Pf4mycte/rL7PfRhD3jEI//hJS8VlXe++c2p5BWv/Kff/cM/4s5rbze47vXe512Pu6w//4s///sXvui1B/CMQD7gvd+bO7n21/5ypqp+7Gd//g//8PevftU/3efzHvZ3L3ghR/4Ht/d793f9yA/7sPd5j/f4sPd5r//bKQK0Iyh9P19+yFd+7fP+DWuf+w8vesBXPvpLH/tNv/ebz3zqj/3Ej/7CLz/v759/jfBb3Pwdn/z4b/yI93sP6dADaYaXV0SSq5jeoqlo8XMzhr4lLwGGUgFEjyxiYxsMubAUnK0t62VNOJ35maz+vBSiS/tuMq2cP2RPktfGREPNsfzEpVCwtUuA1XY5cyGNWlF9ma1eoQdZssE6RqtO5RGV/NKZaQ6gZ2oVmAUsBax8wj1kXfkwkU4St4pG1wSahTNbUh7JUI0yuaTSRms0SEiIlEiY5alTYValyeq2xDJkhc9c3sWaNAPPYdtlW6IOYrNQSSeuqBZC1LRMiNWkWdpUlVMUgAm4S/GL4qHMfOtqGiKLCTENrihKNxAGNBAcb5alNEN5AfSk08gWciUkJeCLYUH4Ci4bMq0BQ2qznZOil7e2i52T8VjFPqe++koLK4wyhVAkkMq9poRA4zhxJFx40phW1YQv5XgwAoUUcMw0iQaxqVjFHOWqBx21sSVoHY5aOp9dBvEJcTAKtK04ishCymwYi2gQsgytZq3XYGn2pbNwlnBdIYtNjDehMc0CgzOn8UH4is7zRZzpLEORiENOQL0jjYBaE7MqqXVcsOKVwnJw3cyzFjKScaDLkgyytR1GTLiUPgAqj54+MsUr+CZV9KgCFcJHrOYNwYXEODxlgBmcLcRA2WzqDdUyh8MSypjuyKurKB999EZeAImpQuy0maXZUiEbfFUGkq+iSoU3Zs0RXe4WkZSyDgaCs5FCy3t1S26GQmmiLemhneolGJoKKHXAVCWxT1nkkvTSXrBIiagpzsQsadCuJmrgedCiwqY1bLXuLaT7huhVViqQEl9wRkILjWZkJaPsXlQoZAokaR1UraGZKKAkvdBmmnn4Th56+Rm8uU16jMXmLGQAGNOtcisXqzTdVZjmS+IgIULgxV0WmxxAb1OU67QlhoOEIgJakBHBCQPVhXkBSIVyL+maXkG9G32zsVSXqHi1lQjN4rq8cSF+RY3BTFspHEnvGq0pacKTQmFKPEWhdDbROISU3HtdNpyJjRsdIo4qZzHn9bmt1bBCsGdbLvTGxAf7sHUl7sMab1j7vrgwU7dGBnNslmUi+VsAJCRQs/nsUccl6KJ1NsuqwNifBJsw+0iFVtFcM2wPpNMiUSHvRqe6VmVZSM7oomwf0kWyjOwNBSVKsv4ItGtVLdaAmXKEmb43NakRchFcbcloaJpXuFTRU5owDIEWZs7AhPI2IBZWSQgzQqSolAYVhDGRpsxRhRYqZEHPrbu2I4aS9IGm0EzJEA9dALGJ8phJbDWTbtAqoISlSSm7QNFAgvJoeLlyHjlKjpf3ygtkl9KipYwqDdCkHRDRFF2r5gx3Rx2jutFgVYptxfWpdSZT1kJeFtCU5MwkiLYLiFlaUzSBQdrhm5HrLRl5ZToqDmxRAKyoVUDt0gFCVxYxdD+pd4U2nqP5otH1rHBTFbRa46jV1XM0OSmGFh8ggUFlTABhkrKL0WBAiYUbrnnJym3um58NK1pOCmREhd408kfpKWbpSllVTmuHlDRxs531KBvzkFtDgB6SEMLwKqSZ9E2bmgmMQQA2HF4V65Uc0NOuWfSutw20NcjlJrHV6uUizACGUTRMfXiMJeYUBrEFBqDOs03dZlcGHyJCjQok53hnvhSIEagr1oz0qoLTDm6MlTZHP8tYIM4HB4Q2QQCCHi3oqNCVolFciNfWaqiA1IROAadKbcfeWYOUb+GnWJ23JEesV1G2pLoQx7nUgzGECgFTc9Ml4tADBx1hIw2A99AOMQGNzCDKyWBaVznsE5qux9VNMcCKKQUlBqsW61EnxxwrsR9Sh+QA4Spjc9EEgcwQZxMV0D++Be1TgkoZYbIrI0Gkkg/9GuNeJKUheVY4XFuZsfUd9BCGJb1l8DJMQsqLmbAgbpUtzdDNOsMASZhrL6FvQROSzLAAcecg1aJbIrwwea2AHJZqxCWjCnQOClfZmxDs0KiA75phia7ciOQxyBMW0jKPrOEvZaMJmwLabr3UTJRTRKiYBi8lXJF1Tt3KLqUMIgCFbv20NU7MCjpTBoOwJZRzieKeHpWsLYBonG5ygxs+5t56vWsj80WP++/Pufej/GVnC71SsBQlDtCFonI0bQITGdcaNzq+VMln/31ddg210oZUhCCFjKngAS1XycaCkeQq3TuwGy0GgcReuWQ6I/2Wb/amj33kF9/90+5wOp0APO1HfuT297n/M37hmW/zFjdW1cz4/T/9M55/7c1Mb3mLW1xz/m/+7f+M5RY3O/4hZg7767/+y6In3Pu/NhG56+0+9lYf+AE88YIXvvCBX/ZV/3T1Ze7/R7arLl362oc9+Elf85XXvtZ7uQUvAAAQAElEQVRV/5Hxd/64j77tR96KI//+eX/3qCccfyee+/9q23p/8YtfnPthw9899+/+6i/+jNCfrnWdO93+kx//VY+82VvetFJUTKwxzgFJEzSzNCQTBPtJ9j6irC0+ycjOcDRV9FqDbA7ZQAKnkBLOtLDqw00nwuUgCYzFZWu5jTlqtx1wws90KWStXfmVWrhKiBFEL7jIYU2DZY2JtjS8T3KNbOgdY8NogGaw389kcoMjFSSGcI42klIrTSZYsjqaShdV9mazOjXpvJbOCtT36ktboKu2poAyzTRCDwsgBENURQpo0BPqopi8SG+sWiVdYUbtBkIz2yk5kvOLqxOmwOrYG/2EKJ2KrMpSKZJZjIp4FipELElop5aTDROa4u7F1NOp5lIlyQqVG3JrpcOK7pdZZNcUU0FL4sBRgiuv4iU6gTSaSkC1+ApeD2uzG833HjmShSuF1oVbBOfGUeNA/MUmbJVCWDnZQPZq041tbq8RfhiwSRuERksLFjipDGTz6hztrRhSLdNsVtFt9k4DUYw4f2kyoS7iz0rszgAOqVGiobFj7sWCoL3L2KRO6YO1ODJ3mVN8LzCnY3UNYVTH1Ny5qqNpiKqEZA33xtRXqWahDFlIQtFEuJ+c1bfzICtres7dfceI46aH/qeQZPS7PNSX1bLBKh50acaI/SKdTJ1Qll2A44XZk7GLnztmJ11QooB2U+2I44+NDcgpqRyMdxoCtbL2STnats50AZ/tLmEGoTaDDixl2ZJgAk2SIo4ZVONguxIkmrvWHrK41EuTlI4yLempfW79fLJdkS7i6Ksa+ylmSWrBCr0oggopsJKRZyQHRTSOWJk1o1e0brSL4zM9dCedB1cr62RzU2+I5oxUE2zVBlRBWR4ZLZ2R5JxkkHIvXAEpRR0SHNesb016g2gk1XAiMfSoDHZk1/Oa+7q8IizVamRuIZ0WAx2AVDZZSh0L4FJnyZo5eS8OhB4AqGxk9FJeBDYFk6JEs6SqBG7gqxKLsIr2C+jwtJz8ZWCo6pLnKUJlQmZjnkl2EeGMPEAbIMSkSZcSy+qZjZzKw3tIJjKoQtcgfZBKOTNlrUCk6JS+snk2/p6Wt7VL7gStomEd5AanqAhD3y7KNldemet0rtMSqw5hHhhvw2wuRmSlTZeZWE2XIWbVTgOKHlNZ0E0ap3sb06QguusRsLKiBpRGwVXRbcgBwVihiSI3mooJi6ZZKwKvhO1gBUUAJhDeqNTu6QkzPVnfZJg2hoohVdrJkFkHKSEdOGSWIgRJhB1ZrMpukdjBgCXfGmCNEkxMOBJYKGewqlqVYdooHXLNCJqepWsZCRpqakSMkRUARVkIKFUFjbSChFbwQkEVMAjCYrdchSmktZjKaQO/ADwsLF5uha7YhCtq7SnEqDynlQ+gYxthjLmhzKpZijhzvoo5D4qifC1tvtm80BhNcAnBhaEuSBM59dHHxhpYks2jk+9KC6FYTcgA9BBF1UhqC07gJiLFH8BSUG0x5QqNRGp7ULIRyNKFvss221V7u/Bu1lK3ssZIdA6oCGmT+HBjFZHJCU6xpBOawERN0KUd2pOvVXFNlCwxWrUhdbIcPTnUYR4qqeoKJzy6yWh6EdpK0freT9O2zEN8oVJ6qLUW24VvI08Npyxu44pSAQZmlx0SyWFxanuLhVUZIyFiq3QnVMlSY7S1LkQucaOiGmCBmi33VnHoJjpBy1dipbhqSTvhUFi1pbQUAMIfHN00cOVVEsSVDHMmA+eEijdalB7Jynhp1skndC/bgZlec6pHQzZCYzl17jIXcjqLSa8S69XsUBKBg3hXtEgRNPRC0xTyngPMfAhvOycVpVQ2Dq7YQ/dFVrQyrS45vBVvKt32TBChQHMwNk7aMWSsnD68jPZTQQlv0w4RSYoCGkI4JirgkecMFtaFUMpYKvzSex2XEOQcBiGiTkLefdVctUN2sUnRjpPnRkSDrwxia36sMOhYyoKdre+tl9IhshhrRxIcegoVAZgNTECBF+hcshtdFp2huaTCUD1QqNUqe7SWLat5SWU5zw9JwxFSHvdlJ3bgSWktrUunZOMOlLVKhYjsgklZdbwSMgmJqFPORF/0kiRxEr2d1Mg+6RFbaFs9S3l6AAwTuNdERHti227xdm/ytfeWZuXxsh/91T+7+1e+8rkv2aUjkzC1PEwHmoRViIdMNb10Ap0+z2QTFGBboYQzfEGzMZVUmKsIlhDJqqR5zjqeEtUhDb3nyKWZEezp173OtT77M+746C992Bu/8Ruj6s///M8+43Mf+u1P/+HWGrX86M/87Itf9nLuvPY2er/m5D++/BXX7Lz2++niol/n9Xn+6n0G6cW919guXbr08M+5/3+9csPzq7/2K0/8vqeCAXmNAf/O7mnbTtu4ZkBV/c1zn/9zv/m7f/RXz14e15x8zffW+ud+xt1ucqMb8+RTfuRH/vq5//xVCQ9fdfXlH/3ZX/isL/zSW9/xzj//i//j79tcuupat/7AD3jaY7/2C+91jze49us1l4yjKLmHH0BWDFQ7ohnFFLjseQ5nPCAJW6SF5WqerUrKCLm0QRndqy0gs1ILVjJKG4bGSXzTUjEx7UNYd0yLgW0zyisqyS1shVbKeeSHt6GiLL6RcGZZiQbrgqkQ58oU8I19pZHpbqgmjZsK38gYkktYMlHDkollmpKpYCagTKt3HUO6sNwG9V+TRhApcKNHAYsge1uoZBVShfapTWEBSZtmpYC4edbxgzKI8XWCbqv6rnRi0uIScrgwwXQNZ+x8enJxcYb7kZ3RDo56MT+hrloUtPdx7mPS7Kzal58RqwlrvHZJ06XmlEyX1WDK1KF9xVWAJABWgYxRvLQVR6W4M3faSpLdmORDtYmKmOiANhBO5nCjhXDm254rEN6XyyIT4lw1SQLZLK1nNMYAaEHJlVHhrRZbdNdgPlM7FK2pNq48YrHgN1Z5a1zEiSnv8S4st27KVphqZY0/ekiXuZL3DeVLZXUB+SRkTu9Hk1eRpJUlSFWQE4ICQjotkjSZxuayWLVxNcAqZakbw1+KEnA8AOFG1E59boa2dFzW2iuIFi+V2OLIKIupcoZ5qZR10qdYQ1pxTHNt6zhSxnODX2COOMs+a4+MEq9xrrGneS6tUr5abLJv4oQY0SWORNkDBLaWIKUwwiwYRrDtBSsta5osso6jl1g1M1boVJyRTDysMAagYEINIFS1zBxgz0w/O9NBBMXKzmziRoRA048pMajxssaefIkUo5YakJBeMhIjqbUUop2/sALTGBmO826LCgiU8Lpm2RSGISVKZ1fvKFFNJ0sSQVvIsiahGpoiyRN804IWjySyVkYuhQv2HmuT6FvayKbO2ZJKdigrPBqzVQo8L+yPCAbeiisko7aSVRVkK/XGspWjbNCMgEe4e3Femk3VaeoFBO3gx6pjT5XaluZEIktFTFSgtBCQJnpSPQ1rG+tIk9Sj2V7J5tgyuT6jxbwTjNwJo0mVYTZMgAWJ1NoOESBOQvOC/X4HXwKoQOgPQDtUfNDlpl2HYggOFPclMy2ghRLWxMV31X3rk11IxTxbSMkRNkmxUquWRTwX5IzT5caQlRxnQD7yzEJjMAU0cTHIV7RnRVTQ2l2IqiZhpjowE+r4DQERYEq5cGFWhRC4hqgokCJ+vBeH6GHTVmBT51LIVwStQEfXOtUy7C6X3c7eo8amvK9p4+BAImeXMkLeLtDpvMicsft0qkpqoUnqaFEHYH2zpqfIzSEUmqoIQrVp21TYGYW2r8oFMEzBdRpPQJr3xqqvw9Ss6Eilio9WrQ/ohlJh01pYPnfb1zaT6iTpJQdD6asQK/PKYFwlTEsiZHcGh/LKhps6h6gatTGshrURlTKAsqOlA0GT0Eq6SANrwCJ6GXkYyzOd1sXG8+5MCoWbzI5lOYWr4ZrGcOThkCzF4nHuvZIB8T33y5lMQjQxU2JWtFJ30gp1whpjL/MpxSDTVeSGEgghAGhfF+0BcLSWD4mtpR0RoSATpamih3eqB0pQX1VTNEyqaTZLkWMqIALAopRkWXRQGMK6nHrulqdmXLiYsd139YNcIqnDdUTraNrMhraGLu1wL8tpS7PAqdxSUATRjIU8U8g7bqVNtZnAiivXNNqQA+xJJIL1Te1kGA1NEUi3CqNbF71OQ1WTcF2e55KrxS6rnE/rPNhu+FhkeRFUYLVtcoaSnmeIl1QozHK0s9luR/E5+JgMQV9ay2a1BU1ViDG3yKCdJ6eeixvXIwoWxhhJIhMBwjQTu420Vhm1zjnZWz0EsDoM1IiKSoqU7oLZc7ZdM2zBJlUw8NGqTsE81i62Bcaeth+zqtS7zi7sXc79QGYt+MydIggGtKxSqI6LKFoEXXo4CVfzImJF2YtfNJ6s+iYpFjpq0+DqXGdK4kq0M8NXIiCl6CgaKvOEPOV5y8sS56LKrHRkQcBhaxyxKBpfKrFt57btsQpTiLSSDI2r9WwISdcohtan5d6K7YkEd5BdMdgXm7s22ECTarQWTo9mrlh08vI+ZfpWaiSyct6EBJeMrEg+y4MxIteaSKbuxHZomXSoiBxCS1sMjYEa0rfrfPS7v9l3f26/4bGgn8/8w+d8/Be86Bm/iUnGpSLVskQrRSfUgUt9f9MbADj/0bPTS3umeZS7h9OzujBpGiCLHeJG3EyBLFmOlRIkt27Se7Y8R8RcRwq0/sHv994//O3ffKsP/hAz2+f89d/4tTkntfzNs5/9dU98EonD/X+1vd61Ll1z5nVeveYSCCeBAP7sL//qe37wh8/n/f86/8+fb3iD6z/8wQ+69rWvk5nf8t3f/ed//Zx/vvAf/njBi19y5wd+3od80qfc+T73+eg73ukDPuUuj/ue73vFP/2Lf8eMwt749V//9h97/OsFvtZHfModb3/fz/nKb3zc/b/4y9711re510Me+tQf/dHnv+AFtKG1/lZv+Vb3vfOdf+SJ3/Sohz74nd7yJh1gy0OiQtKzPMgEMFUpVMqFx8uZYJ6ypiTHkYVS09jDp8sSEHJrpSqwlk1SQXY6Y6bCrxRyOEOrOQecN6CXSjqFmxyRW9LXQIwwzVYwCuM0YTEgqyTIi7Di/ZBXuDDuIlTvC7EXU0IOTZbQFGsqvWEzTskoJK0SbeitrDFIXj6lZimTGFyLC3RschI5iMcm5C7Yq5FSxTJFmbSqZ7QdzBSb2Vic5lEHFPQQXVlSTKfi3HIJ05cXswBzrq6pUUqCuR3h5mu7HGMR0Uhl8sS+cjnt1zIwB3SI0IqihZl7CCg920h0XtVF/5GzF5OxNnizWew1FmLZmjS2mcZ4IEEv+cv5LFx1wRpNIwMMZ7EUF8tGVAM24AQaX3QzYQJBKZpw9zgzkQtXoK1C7VXECmBvYSkYTDddwgmz8pw5qbFltaRQygBtCC5xuwnFIiJXdRoLoAuLocDpryrBtUnryRcTVRgTfDHGREN102QtSsQsO7fGAsoi3oiKfQAAEABJREFUks21+TiVdQWMPMVhnQoatEWvpemF4xmON4jZCJFZmPQexbFFG6w4GmCQiE8cwoSwlzB46iX0Olk/LKRzoJWMYrVJqLj2yka1wu4h0UKw2GJHLZMUjoiyCfEir9LX1FxK00S1qhV1GMKIbRQlj8itYKAZSdVbl650nNGYvOEoyxKlwiUNECLdRSy0Duu8ZNpIY2RNKRaSlokk0uVUlWuTvctZdJYguaGqzAl1D0m3xc4VDc0IKR9qTeVM9aT8qLbMXCG0LVAppUqslERdqwoUKCVWoqnlmcll9dTlTaEl5SKLdjatbt4sRV2qDmwIDwohlZUHrXg2qsCARavkYnJremRhU4NA0TsPaZgRdgNFJGdDAoCIALRSlLsoLatppKeDPm5eg2l89nBfpk6gaugqOXMLgKIQje8UV32hp1C/FQhUBbJoYjDklmrZWnBRa5uK8MKKdCJQKodRwoQMg0tVpEZ15ik7/yIsDFnD/4+xPwG7ptuqwtAx5lyrar//gSMgIo0dKHglsY3dVbHvsY19L6hggyD2YEtAUVEQscEGJCqNosaOmxi9xkSjRo0hauxQQWxAREHg/N+utWZzR31HyfPcmOfe/dX3vntXrTXnmGOOOdeq2v/5jj3Kzye0k+ncEosOpbtxM8ebPxHYNWzPUbIIwVCUiY7OJyuci7aJDXW8Tq8Ytx8Q1hBazD1mHJAirQ2X5XNoOb7XSRUxYIuup5il349Xx3lNUN11booh7OqdoeYTJSrQZm7GZmf3VZGVYZkSK4QZh3QQHenV5zR3364eiRIMQnNvDKuwrlwhoUg86lpDuVItUE7EPmwc1BN3nwr5nleg3tHdTIXWsr53Voxu9TLXzWAfO+feJQnCremV2JGwMIPTGLQa7tNIk2DspnFbXVVLPpfQYqTr0nS+sF6EX7nFcZ9hGhV5dTCfkIhDCKyrGztZaQfny2kvZw5uLSOr5A49kJ5Puy5WmaPn4TxY1NZEJa2wmlDxNEm41Akgs2UUAphHJVFA3Z6DIN3NrFiKMhdapTMQD17ulylvUkCLFFQ7W38YkE9gBHPNq+wC4iDUSB7RqmBU1q640DqqDWz90CRwjaPtgHp+nLi/KLFxX4amBFlmNNXrPOrQWzBSGVG0wK2wzhIxhd2e8KK3qNiZkYLVjW4ridIzJEEQdbBlDmaDEECHYgYk9+oZ8GUtYmk+eOi+Q6mro9qIJttSVaU7Dood1CwtmcpLVio28J7npWbvtgj5cgzPc4zpWgX9aOFbtS/eVRNmgBNz9Kn1zjsZl6kvJCzZqftnUwjXxg7Klg0cUziUpwt8xUfg0TaGK9M5PU5XhbOHDtDSskaKRodwFz2prn7EnpFHUsY9t+ECgjc9XRspskgK03134Fv9XPoQzMMV+IrnXvvWQc5Ba6Llsfa5+BL3hNbS9sRcukU5FQEjrMvB0dBYJSNcrogEik2WiPZUdm4WaPKMbtz6s4J1MzUy2qKP2rN5lL8l/QVw23gkJm1CmhyhWbehTerbEeUCod3Osr2tzXwSEw2lKDIjOhTxLb7Wyfu34OB+a7IhiVhGZ189lxb5EHRYwopMlMAUAkWxOnR67qVf8lHgXsgtTbNyxMb9S+8Axbu6F1d5qdrPMj+7Hr0OrZTITq7AJcuckJ8FedcsqnfulxnDoYUo1AgWbjADw453+b7f8b0+/aOOb/6NAKwv+tIv/8kf988/4hOvf/DFIrjE/Qv3IUJVT/WYeMvrO4TOOp1urji4y8Wn+TBawRqTPXIM6Yh2DLu/exgZlgUcwGilil2ImgPTWnXn7/wOb/0dv+YX/86P+5hv9j7fFDS8fkkvn/E5f/gTP+Oz/6//Xda7vcs7vx6Cf/5F/+e/GPb2M1/7c5q9z7u9iz7+yy/5ko/++N/4gT/9Z/61z//bsqkzX3t8y/d775/7IT9LH7/iK7/yf/n8v603//8fb3v16mf/qo/98//jX3w+nxFxXdcX/ZN/9LGf+Fu/+4/+CX/w8/77r3qbHiL/n8Z+6Pf+Ht/wPV//L4Le9ra/9Ff+8qd8+md87p/+01/11V+tap/n4xt+4/f+6T/hx//hT/1df+EzPuPDf/oHv8d7vAe1EpR1eUIZS9Wwww3exbygPJt6rK0S4zUYZu26Jn9V2PcRkEzUrHS2q6Ic8APz0UP8blfzD9Sm1BTKRF1ZV/H1/yI+Wk6kyUOpodqEnTSTpiNrpe3Wz6v3q9Z6wj2Il55HjxvITlR2s1UpbV7t2eqCDBobx86xty8b20dLuVXWO2Mx1pDaeQViJVLl4W0zH+4DhzxC+6jaRQEdbONIHIuu+wNXG1dNXA19NvhDEXKY5mHO8HEd/Zzxqve10dUKndbWKto3z3g+lqrr3lu4vdCPnKPcAVM7Gfcvk6tjDuO++np27xjZGjBIHy4vxmmgAMhipxG8D9eykbM2EM0yM2dTqUm/VBigGUTO8Occz2lp0XhVvAqyq8HFue7EiBXEHImze6pQbcDgXi77Y9AfrdrnmOABjG6TC7DRrb9oZcHTHFAqKXCvZLagyp0bIyyKd3fQlUYt/SpJcVV2Jp5mcRsrKk3w6fPlMR8U6P28rlcio5hEwVX7z5oFhaSgi68WXz3nejVqtZUarQ8bMCnAlXS0NOWDlb1XxVUpN4EWYGz21U2FqhmPFzuHtb89moAt9+jRqfaiRXPzSrt6SCzMfeCpXEPfAkhxUDQkxjHG48QxrpqvLj4Dz2KBUCK2ZWFN7rPDs/aOheDEMY/R04KMjXnZoR3HOGvM9AK6ZP28Bb+YSzOeJg6s272TTMEKE9XDesymjlGNcmubJL1iVN6fONln5bnxaEznTXSUsUTUvV05QU/0q+R1QRXlzMnV0Su5lWdKEqrkOO8AdC8emlnJtvBZ4i45ND1N1RamvJhxoDCjvAPVqVjQjd1d2PDLEGfPGfMMR+2uy7qOrpP1huONAy8Ys7zYxcQAtIJrZ8Vh2bO7q/c+YovbGKLrmOfhR6chu7RD7R1wESIuHHnqWR/X6GwKi22ZA6G4n0PRJeR2cE6F06vXzlWKHFnc255PvNp4EqrqPObF8xnnSt+2257yB2nd3Adocdeetpdhx1KMxBMioYVkmpJMjyEYXAarNaP9OqFDo9YFyTJQz86LIj28a2aO7dy6pWAUqlpRF2IDLe6XBXEdvjhXytoQ3+ChxsBL0/dxEHQ0ZuFYNPyHnU/RBq0mRLYI27BMqzaWeYlg8aZkai6bIkKEKkV6rlEKXrCfsOfwPv0wP5jNFVmlnVgQqGSluA36llRNbewBm5IbcY3j1Ty2BIoAcqbbGgh5iZ5X+8qyu7DqpTnpanLuNVIa2xCzwBNKy+a+xr4sL+/lCbsP4uF8eeFxjuFGtJC0wJTIst5oUXxB+7wZ1vQF6oVUfltrQBjwGDZPg+sLmyOEhbVaVoaA2Bykyw+rTaYHMH1PLPWyejX5fLF4UJu7zox1bS5ziS1GU+N9YojWI2PslHhRORhjbG1SrE7mFG3mTOPK7dgv57ajbnxNvxuAVBGZydZGho/C8bzzBPqwx9APor3SbgYxU3NG0KIkmJW8koV5dj+UA+jtBUc29zPUN1338Dm7xyVYx5VnlLqEjLQUURVdldWL+ug9jtASajKyqzbKJLbNsRydu3bv5iuIW12h02cfqDPSM9l6Kb9+pUffupibjmpK01035BLnpXrysOkQAUWdapOVhyr8Oc+EXO986nnyznU5lytQMY1rxhoq1V14Ji7wCbwi3mw1+6uEy3ewdECkmR3HtJdHHI8nH2/WI+3o4ZiQZuYVx2ohM1d9oGru9RI1A0f2SIDWGDDWoaN8bkNKHJ1TtCgS1hwY0w7joJ1NMU+InBW9GVKuyFA/x6tjPa3Kx5wPf8yewtCHYuLF2tmRCEm/8mx7KZ7LBGIQ7XZrq6C84H6JFFRmaMY0zjEqbD2Zl5BN9JSmbbofkzwLxz4HplWDmRYLKrpElxYQQytPDpzc57F9BrlBZcgKQ03MRnIiGq+qtW8IRYxGRV26q4qoOI5+EdzbujhDj5eMqXhmlqGU4Bu5m0RfLwxjoHVjpJhYRZGqBHDE7FQ1ebExjTYB7+Y6Os7qAZdbmbsqFiNmtZPA8D5Ps/EgaMAQsyiaDS6YioPh7GEpZ4DCBDZrW6UTU54ok6qiwv1q6y1+2mCAc77123yL9/pjv/6tH/QDMb13fPXn/sUv+BEf9c9+0ae++j++uDtb9DjUH6ZqedhtAHhb6OtE4XZvqr5EvqHLSLy2CVoaVyP10RWmY6uhuGBuXRptR4+566bSTAg5aD/4e3zXz/mUT/zgH//j3uEd3gH/8fUpn/o7f8Vv+W0qr/944v79dd/1682Xt+jdF3zxP2/9+k8dqoJf+wt+3nf5jt/hrW/9Orr+D//xP/5pH/EL/9h/+2czUx/ffhD8Id/9P/xv4v/En/t//1/viN4+7D/583/8m//73/wb/4suffP3++Yf/jN/xg/8nt/rnd/5vi/68i//8o/6tR/z4z/sF37Wn/y8f/RF//yf/PN/+ef+0v/8uz/nj/1/4eQ4dFf2k3/cT/zU3/jx/82n/fZf+ZE/5zu8//u3+Z5e+tr38FT1m6v5lDfEjg+3KV4378IpMTsaXnezhq5B2Q3DdkpqYLVle6SWkYqosorZNdET7nHYc1JNZGe1Eowrce29MrdOaLI7fYB00+tWUMGW9reUaofKKWRzZ7Ixh/s55PgJeXMkJ6FJcnQLQ7luy2ggQWmuG2wa0UBYByVTD3qVlz5oUNdtX6dWn83pEgY1uRmFksRYIDCaB1T2w2GUyDMHe0675eiyL4uGPtokQ/mSNzRJVdEYTn9paM9srvOdKpF22uFjwkFuN7WP7CxEO0Su8egmKsdaL7lFpD71CzSB3qkrinmkze6ZsEY1950PtMpSKZEf6KVfdfuEpebRBE7Bs/RH0e22bZTSLVoHy/VJYZmX4lXBUOnztO5ZPKBGOMQbas1U38nbLzVQBMsVUMbFUgtiiwpjymiNnbKGYu5eGVtYjVaGVVl7tx4jjkr2BlKwygIWCtPcIOir1A2RG8pcw+UTLLPgDJkOIi0wttkiCAxprYSRd+BQBpmJyGahuutetW4ixa2WWy1uObrOxqSTJwp21bEhwaGYhWy0SR3aFcXIPOSNssIw14HCWDlrD6Q0OIYQMGNEnBGPNiVeCWLLAEAt056KkFG2m3tYSvHpsgkNEb5q0zCjXkW7cISGh0QgYZT288nECzktbQgXu7si69ZMU/p0OqDOd6rZ3ZzqQndLm95EmSQCCJCWxuE4s0ffseREKjbvArJlgYQOvDbMdsdhPJ1zeOsQWyMlk7aq0WVO6NMAVJJWA3bA1b4ReC1rNyO8wzSUvRQ3rM3c7NGcpfADaFtKellfxOWWbhLhVGxbyacGMWSFbu3MZnKHX5qqfMnQIRrpwtxsVANFYpidNhyjCkRa6wBoMKQ+TkkAABAASURBVOD+641khSt5LcHAslpqjMXUfOshnWpetbbiscR8DUGYptBvBPdEqMvsSvE0kh4BTS5FQ5A34S1Nm1imaeaYPc4012SrlhZkCTVuBFGMaJkJaAYEbHBPhdxsG+1n+aS8MzkuM3VjOaAYJ1BNuWRQ0Sgy3mnes1KXNEgxVcMKumRINJQf1Ogem68B6Mo++hoIYxU6hdw43Ob0oVkjVPrpJHqwFEfXyOBdS9lVvYuaJiagOuOwOY5D98N1rqrNp5/XMcNP63PK41oWVyAKUr88btkJMGVk3YiHu/NkObrY2zru/ZUwswWtdqDKK61aeARdvxIdGu3E6aSCYML6xktTvMi+zYeGNZ1GM0MKeneC0dbt6MPUi3P6UpbghKbbAE1erAuyL+FrnNRDDoGZG0phG26oEqwG32uN2lDYETY1R7S8jtM1BN0dXGVL8GvgdjtrjvT7PbtGFVsOBB1eTYFD3d6KDF2XPRKjbaZhO+i3lQM0oCmRKWo0kD5i6JnLVAdv7lRydBkaBcqrdMEtPGlSF6SOtxPFpJWKkQ7IckuLEtnyDkcaQm+E8+aGbHArGQ2hwfHaeDF3LU1CgCLajAQQrOhV0J/uSKGxVkwNKppopvLpu6h0wnf3Ddahud3QL+0cCPiTs1t3CtYdxUDVHVOFds6Yr0OJxJ1PqapjdS20POtKnbiRA7srWvIs4Xbd3Y0BnzU8FSuPnnrW6CKSSkPuI/aRaRQXYzRsg4HanjHVvOIu2DarqQrbj+zjGiOO6hnU4S0pxfBbB850pUpsjaLWXImyK9sbU+DCl1BCLa8nqQNU/qQ1B0fCb59ikcJlLNn1tLEPS5p6xa7QbNGR+fr7mElMo6TdblmmAlUe5u2Md0npizTKxNzp4QVBMCAaF3oZ06ygE3KHDOFSa9XcKRMKvVdrKNsmtakntGx3a7+iE8JSytWdQqUbab79uOyxdbNm0+A6m4M1FbqV4UmEgtG0uK+ZEqtoUpJqdjPAPTzM0hwtZjCNPtJ9ibUO2pa1pqWGC72yKXYAa0nFph/u42F1r9HdOm19t5qg6MMwGTpcnTHGLsVArXq694crLk7UzcGw9CP1qV1jsgDZ8cE33v2d3uvjP+gb//5f6u/9Hgqp3vb8d3/4f/jHH/grvvhDP/Er/+T/9Hy+LZzY9Xi/+4sXDVhf8C+ejnV7NNBQskOdx00IdKa7OjIvpPYe9BM5sCyXl007/Rg9GImItN7mSZ+0+e7v9vU+5iN+7md/8id9t+/6AfiPr8/7s3/2I37tr/urn/93soT3Pvte7/au7/OeN87/+a//9a/4v/2fqfC7fvtv95m/7RM/93d/yvf/fj9A0972trd91Mf/ps/6U5/XilmfXx/v/V7v+Z2//f1viP31v/W3PvHT/mDelLy+8P/rx1/+q39NQ2wcv/YjP+LDf/pP+x0f9zF/+Hd98k/7cT/2fMtb0fW3/87//ks/9uN+5M/4kB/xwR/6QR/5i//rz/6sf/Gv7v91isT+Td77m/2kH/cTP/OTP/GP//bf8es+9Gd/wLf+1m+85ZTUt+GabKfZdB45bB2ltPeAEthubbra8FSFLvc2THHPsPtoTOaJOKFmYoMYnTP3yDSxpkVoMZ6MAODU4/hxSsJpVI85jj2RIy9njhNj2LB6IN9onGVygSP4CPWTCTM75AAm9YTe6FmFWYNabqWw6aCKUD/lRZFIt82CWlboMVPpGXWc3FNtEMii1GCppXAANLQMkZAiqwuqfl+lpzeuoYZAZYUU2BvZaMdwTuOgAsCIPlM71xIrjrbdvnpKU3UMHMeYigkqEoWOw7TRG4cdqKPWxAZs9Qj5FYgwaEXJS6u+NvLX1bvodkw/jzoYXbESFYWQF2rzOmo5yts8oe45U7XeMbTL053g3UmpZaYUFUwZfU480bvlLY+qGUqqxWxW2lXaIWLTC9N7TPYYt02Mq89rD6WxFXtly5ra9mxYJNYWj+DVlmiMdF0SmarILOjxUVUn5arWQ+G+9kyEDKGYEgDHmPIHRiWh7lSiIdtji0MpUptV3b4UcEvBD4EL7awwlklBj5riJWpctDCz0dqX82AeppYIdGYzRT1B26hrNV4NX+eE80h7XFopEseql/JTJJZRKDTHWm/BWf3IOG5WU319uwJoMM0K3u7NQrOLKZl45tw5Kw2LWCYyG3IM+uAQMunTrR2ypHWh5aJ7UFcTvpIhIwDMIfvLGfoQPXerlqxh+ihP2GNcp63zdtxgi5RxJLgjntcubZv0AdlcZkq+3s21H/deXqNFPTdqQyIBZg+jHj7NFPxi7WBvt6B3HWfNKTRH4IEqVc2dcZiWO7xMnBLyYPFoiMi5nNvp1YJ1aJb4hUFdALZ4H+EnxuGGyTh6s9l9rD6UgSBbUvNN5gDRAwU1MS2Cq3foFInd+WY+dz1bKWAbapzh0og3IGvklNuCdaMTMi+rZLmmH2wzVvuG7bLZ94O8c/DE/WogPcv0Xdea6xr5TKwUD2glTOI0kXS0tire4RFK1dgKGke1HuE+Im2L7Kq1Wrc3lfKd0u/YMbOFih0KRW6CuvF9aZ4GPcXIR+XJPVhmitoI13vvpikegz6MJul9y1BKPGH3P7rpdqh9DJt1oA7PF+JRnBuOcspnHQoXrxmUNVHOkeYqkZtg1sR18tXAYjeaKaFebkl0d40W4rSuTEYjB+zgME5r11mjuMWeboZT+q2dXVe6yj/Q4DZWEcXXPWucztGwlXXVvmTwLD8IE7YuudglFCUMfb+Uhk67vWZxQOoz7429MkJjNHOeww7AA50Crwl4TZa3Q6rZhqu0Tb75TjXqp/E6YS83Ye4miZiGW5ZvjJ4Pm0J3393nmBgAbypGp1KoqrkdiHk5pCthrN13DAmrOHCPN4Q4mdB99niUykSRdWnqKDuag4QNgMyIa/dN+KjhQWm4LMGbKkUWVj2LbGtE2wUz4lCmWKjOi3nNyJm0pqn5VJisNrq6Klr9cQGvhEZVNF4deDXxpqtxIs0wXovFNDpty04DQoQ+Y927NRcTzlBt80I/b5m14pkntfL5NnWLNkpnd2WzRY1wg+p+pqsCjpundpKK3rs9kJtqZ9VktSnaXX1V65Q6nHQlCTF1McluwalZcWaMSLuN6dx9sl0dtzUmvbWgvU5KoxQyVYYirOH9dh4IFT+tQA3osd1DuwF9bogvu/cz0wsWa9SbXlehn+CWI0By3wevY941U233rxN0XQwhsE4gCPVg093HzYaEFTNwPjm26NJXR92getzYyjpMuxfXOAoqNsQqlPsdXYVUGXEOPuzuARJISx6jvQJr11Iuu8+G1CUyjcfU2qj99outKTeCODE89X5CxDjvmgxfl796M9czOqDzSQWh1m5TtEhBtCoBIpMJCxiBqLq/ys91VjqqHe06zRYholU/ZX+5lnXdwXCVthbqecraFkPkmD0emWc9tag/utWC5gAHwCN1U1iydcNw+snhRM3occH08AkadXR5PmvnjrOg7/imB9HZKGXUqACESG81VphEZfheD6xjsuirLPeefJ7HskmrMcK1BFpahg60WrBb9VE1+xaJRUhF0ORRpvHoe0FRv17IGtjS+mszUBjtGgYUh2aXLGlsCGlUsOkc4+Udv/93ed8/91vf9Vf/NHvHFwD51W9+1ef+j1/6IZ/whd/xQ/7xh//Wf/en/1o9t87r4CtVlSBTzEZ37FKMw4siPZB6oVbjST7VCODRaA1Bm6s72cNwVlnJUpdoaPWeUWAAm/4t3/f9Pv1jfvnv+4Rfp+8cNOL5fPXHP+/zfsqHffhP+4W//M/+1b8p/X2dd3jL9/6A+18f/qqv+qqf9BG/+K/8rf9dw/6TxxzjP3vf9/3Uj/noD/+ZH6wBr169+jWf8Fv+8J/5b/X+a49f+eEf9vLGWzrj0/7QH/hVn/K7td/42kv/d28Uzb/60i/R1a/zdd7pm7z7ewJz+/hG7/0+v+wXfMgf/7RP/l7f9bvqko5//++/8iu+4t/qjY53fud3/rE/9AM/51N/65/4XZ/0K3/uh367b/Vt3/HrvNWmtik9d6nBR2l5SJpSF60iRrTeMyQh281qjpwSxFjw1dIECTZSGdWsaM+yhmFMnqOHUWpBD60snBaWiaR6vVLxgPzeakgNOtsP15zh7TPHNCqLrU1D+kVpu1EhMZkSR/1wGcPASM4sr2KXULR1yJbG9n0C7aA07rOGPQ89CFhb+//QHFpKIzSAQDM5ltYYtQ3B0C65iSFJZdVrEWmZhCNntuJtWmhuR+HZ0onGNin3o8u4zFLe2CbDmAVNOtK8hrtAi6XOpDihG167F6MLSAiyoEDKtU4e9XbkoAy1SAMEqQdcTW6aTNz9SO6yTNaccLLBlJmOxsV+pazoUx6e2uzoWhsA69lFWcTqsbbLxFk97oYg5A3lHLI+PA6atzr96IZOu6ljSSIZUJ0FqzuIREYW9KfQcMHVz0z9ThuLVjJBsmtvhoZQi5SOgqcP6MthaATYJmev19LSaaPYPncr9VmW7nVs2A5osWbRjmHjpHRCVpbOAH6bkZnmI48T0s8k/egcvYmqgqrathqoRIPk6GPW4aVn+CMV/ACF3iZkFb63YuwIE07SGx7OvH+3V+syFTuTtIE5vXiuaz6Xdd0w3AXESiOtu8g6Rhxmh85WBlapoJDM7bvGM8aGN72VndZpbEPqfcyOs1MUX8iN7EBjBpzjNG393OR7UPIAWgtoekoepoTRI3R0KtlNcY/SD9soVLjlNAxSHA6qCSNWdUoVPUOdXuLZaSm/IKa3ChIUnJnh3WYNE+GENJX0rJnrEGRmj7AO3QyNVTBfw7d8BxEz00syVZqVWEmyiiUsbM7Q72yd00XITj9lTTHY6Gk3vdRSgtV92S6mp1mduUY+M9VGcAMNHu0PkFIsRSTc2OYU1pLtvsOYlLTJhLgSndbpJG3EMBnD/SJj+uWQzlBRebeKODiO4XNQ5hCOoNU8eA4/cViZfEzskyoZsNxFj3qQNLeaWy6GODaOTduey5WTLoW720rsOJVOXRoVj8SICZ600/h4tI8QZtv0VA4IQN4w6aMOi2OAKtpDIcuFsn7HLdPIEsgpZEICnMrW4fU4niYHZ8LBTh1oombjEEOqq4AiA1j0+zUx7eYwm906behmireOquhczUuwTW5ApnVDKdamF2cIItsYw0swPMF7soIukcPSh8qAZR6UNxqpUCQP1nbfMBXr1L1dB8TK7ByAW5NSl9zutgbpIXoGWihh5scYU2kyJ3g37pV77bX2hZaYQ9Lk3DadLobNHY/JQwFCxkcTYTtLGTcvNwhzRHWhdOVAa6TR0NT2InRaAFLYuTVyD2wFqGkFExNj71FPq22JVgSySAdp7cLMQMkRMCgkQ0oJeMNNWmi1Rle2oXmVicwhyctmDfndtuiLLDP4oA1Tycfx3No7KiYdEKa0LDGLguxePSH22prcJmZBfpKcAAAQAElEQVT66BqRysM+7YJeRDif6VuyjnzUc/Yrprbw1Oq+p8TXCbD1FwDL2aPL1MnZLVpTc+psEKJHv9S3wlu0TQ4bQypy1TEgWsEj59jg6noVym4rEj2c9PNq29XdJcvZYyWiAgwbMDcAq3QPtsuqkV3qhbsEq1LNaxal1KPoUTMUmVFyalqC2X4DUzvS7d3J6w1TP72DQEskqZOR/Ww8s7Cvo9fJmB5N5Sg1VWWvBYoCUKDR1AQGCtVWsl+mOLvXaDzSTCdbTUkpHXKBI7WODAu/E3mf8KYshYcyEeZFV2+dM91vqEpsD5J1VPhumEOBTODQ0T5F56FZwvZadD1rWJ1BfaGiAe59sB9dnq0XlAETU4xU5ggfGEfbUJhSdUfWSl6l9XXWPaKa0g3mxiAOFzAykOk7ZxZl6t7oI/VGfIoCyQ9ORWwsH+RhmK1xMPTQeTXuecANpQEGnVahJCEHkfRQbqINfboZeDOUyxhj5NBlHZARFglFOVgzNfq1dNCh81bCUTUyR/TYBISj4lh1XGl7K8Joxq68lzYBt2zuZt5mKJp6oTRtOVP0Ux9Xd+it+yrxzzWAKfpJNcBqJZ3kZA2Z1F1Ghli0NLm1d3jre/ycH/P/+D8+410+7oNfvs034/DOyi/7ilef/Re+6EM/4ct++x/H69f+G3//RY/jwrosWtpW71AHvSx3V8hDWt+1OqBO0S2qbG2Lpbw5vK2lz+v+f75Sz68z96FHYTtidyiOPPzxlvl9P+Db/5k/9Ds/6df9V9/sm34zM38+n3/xL/+ln/nhH/49fsxP/ITf/5nf8//5nd/jPd5T6vg7f/fv/IQP+wUf9Qmf/G//b79akeqPX/SzfsZHf/jPH2OstT7q1338n/kL/5Pmvg4F3+b9v/knfsyvfuvXeafM/AN/6A/86J/98//eP/6nX3v17WP+rz9LUQFUTuFTXXwDyenzW3zTb/Dpn/Rr/tzn/IGP/Fk/80f84B/8w37QD/rZP/2nftbv/h1/40/+0d/0q3/pd/h27//yTi9tSRGnCepI7Jk+lozUrAWIwGvnhb1ncpTNgK02bvP2oZ4H7QJnp0PF+ZbdbykMugbUQB/QNqPGJCXkPpnuEHlUpwOR3dtyjVdrvklPtEuYBpy0w8as6bp8i0OSvu54ImPrgAWY4N0Rs2WD+sZFc5HaU+g+PUSE4qmWLb21oN/i4EPjFAQqitdlJYG2ezllSmD9YJ+9H3iOGbpB37S7EqK9UZiL48l42qUyMKtJSMENFCTgFvpitK14MM5HK4ic0ZAkb+G5+NmoksFNBjP1oev1zEBvbrd1jDB65QvDdQ1txKCN0/2YrgV4TkfljpJLNqe5O8saidbOah0efvcdPaiI2XWSap8oiwDEu2aAs8HWR5/N857YVb6X2peJVBM2RWsWjjhKJmIcgZs18JrIE32W8lqjtt1m9C3EO6w411W4/wGfeVwv5345e2LbDsviQD9IN2tHHrtc24Y11ATGNTwP8GHH8fI4huxVNHeP8gfPo6crzSMUwE0zgQegbii3u/0pejSuzIvK+6uYz5opWrk3+MrtUjUgp4VGoBBJCdgEx47gvA7kW+x1G3rhNqt96xhBUXyiR4sNQDm7rC6vGKjROUo/+RpWaoIoSFoPwcrWMimpaIgSqsEu1/SbrMopylzcH/s83vTWqri2sm7VnV1ee3DDcvO2jyYJ+RAg1X2bMggohaHRuYEEsI37YJ+t5RFvyDloGMo/XleKlx/Q7lXiuNgLWxoxFrVfYz6Nq2dhjFYXngfruNt876e/usarOpazh3HjCHnogzRiE0/0q4D6Ka3cVR/KZCV4te4XdqhVVBvTuVTO5oI7sLTxRGUi99jX41kvTzyeOF7tfurhOJ9alzjG7scStXmDAbqPxKNcPYBio3yUSKf+KvrIG4u5KIIx2t68LC4FCJ0hHy3PQBnb7GbFWryAYixlLFvBI0cTMbq9Jv1o1+q1oqAXQbRVnpu8GT80jjUfr8438jiA6l6pGuwWbL9vlF2z4A3tD/TWBu3Fhj+GnbApPzlGnK7Q4lD6LkYeV0uDIhhb3tAPXG/E1lcqy/o5kLOFwK1s0g5ZpjhvZnYgw7Rx6PYB1ZjI11FAl/ovSMm2KzJXrao06M99jg/O00HX6FcgzJx+f1a+4kQP9hRZVYo9kxVToMZ52LSia/RhfrouVmnjH2vtXIu5AE2pxi5FYpjGY3C6cepDL2IF+TStGC1QcTEbuqGtl+a5TVmHsh7XG7yOhxydRrMuiWDIF9gKpyQ2k9igfJQ8CLkZGoj7cnTv8gApmA659ntj7KXNuJmi7VJzWE+rNXCJ16NqrEAAoq9FovJVpJ7mXNg5osV1IQsER5MaCDUdBUQay30V1WBiu9U85LDZxQVqZlqpVWrlIpUQJaFjQaVXR0H3hxRRqgduHi2Q8q3UwCF1P3o9WGerbQtnsekow1IA0c4s30/Uk0oThufZGLAxpvkk9Co4MY+aUx9edDW8dX+jB9xvQEvZUyVBkxy2lq96JSAiyhQ5znt6A1yuG2JXX4gQbc3ctpLQ+rWtNteFXOktsQDwlWPVTHjBBfCSZAryrcAh5CrG7YsW7jbmMcY42k4NLBg5J08X+2UpjcJHjiM5Cp3IQAtKmjLhGaqYeBk4h6ZZi+gM7rwXVkoPddnucYH7sHjselz9xlVztW3xPyuHsGJungmwwrceEQRzjWufXdpMumEIUmvL5L26n4kNiYsNgdHN5E2VEE7HG6NVQX0Ah+zGy87Hqz5e8bxgYNJTJals8PZu0pPW1DjFwFLVj1AfFLnpJ6cqenDN3odyHFmiFt0PcaPeYgY/lH07cJ63xwm9PMlCrkToJlIL8epCLLW/GLVHB9nZLzneKDv6tuGjDscLeuTO3GAIpsnwAMWIfw3xZmX2RZWFy9FB6SF3S8vHtmP0sPYFvo19abHw+cY63vHVeFl1MgXKFG9p+9SMQrJtqEmmP9COlv6FmhziTpogRJl5A0s3l1fPy4ZaneatmeJHHfgN1qMPyzdWo9RyTUWnEmOYrGkpObFP2xyd+vXwNceW0pQ9heSj9dFtwWxLVRfwZrYMRXebvFZIWIQoUHPbGjYPKvtMdLHE+BBSWpFpKfw5eOmgSGsDhpLuXcaWAo6JaU5jjUjuyuZWHthLOcLjvT7oh32z/9dv+Eaf8cuOH/+9/D2+LpSLhn+Hby467kNGFJEAppdTluQVHS1sFLYKR5kw5yHWmfLV/YYUv7IDEfI3krMHzdMlToktcocuV6dZjNFu5xw/7Pt9rz/zGZ/2W37tr/6O3+7bni9vdPcX/rMv+uTf8dt/6of/gjJJgAKT6/kH//Dn/PAP+bC//Q++QB//k4e7fehP+vG6eRhj7IiP+oRP+ht/9+9/7cgf8j2/2yf8il/+ru96/+tn/+vnf/6P/ZCf83v+yH/zNa9efe2Ar30jAF/y5f/2j3zen/t7//D+v3l521d/1fOVlJVsKAsWNkNZtP/He73HL/opP+6TfsUv+s3/1S/56A/74O/+rb7VYS9ZIyHAdZg08LR6iqLX9dPSw6SfMeciqRZ0cyiDM6Q2mhUNZlLjQA2/fG6rdHUmpbUM1FXgvhZg3eIvDup0osR393S6GXlk+S7141hVDYkEaHTfZ4/Q/XlTTgADvPVCqLXV9l4vfZ1bO6OnTnTeVxUy6ALwmpxotiRMpzWRgC7QHDZ0OqVFApWmJnOLppIo9+LstoC6uRespSTN8BqWZuEVnoXoyjHBs3CEDfGbtzqKvSOzsjtNdWTbUMhEvp5YiQ5ADTSzM7pDF7t2i5281JWnGDlH6R6pTL5vrAODOHHDQJMNo8CLfXC/PuAcbvSAL8wrPTaw0DJNV7sPurvNGf5IbVXo6NcASxTdAY3GQREDdGtmoIiWV9W96W0JcYd3mibknU6q0Scpnu6mNARvOkzTzZTykYYYySNuSMHcQ1TdGLwltNqOLTz0WaoxI82cAgUnxjAtXbuhb6yrAQONZgpbOR0KDy11HGscJbZnl6z7XqMuR3gR7eghe8KEZCR3R/AyiBxsdnV3NBdv13JmXndUnGtbCDj09xqypWigD5rWcPSsOKrFADcr1Ew8RRFI2WtMQsuBjJcyW7LtcGljtFFDWBM9SANvsb2mszSq3OE6P50TMBQYYLJLRJ3g1Aw0EaXAvaA8zUqtd4Z0dEEzjNpuqQI2cvlu1rQmkcNq6qPqFtCKR4Uple20RVvicKNCti2kOS8BQ0OEp3W7NLpLCiHMMajwF/2CpzK3eohPmRq9ffaaUOHsYolUMVI5FZ5FW6/Red+pp08ZbIPAoYMZxZaM9L1DGMKVdIHGgFuyMcoezQN0qv1gaHrfi0nTspAYRoUISKqUlLqMh/XBdaDwJNOcPRnq+YlG05N2sa5x1QwVwYSIulqcoZptp0xqnBfVuLVEzm7c5qX3JVRmx5lvuO4hISLIAnZTQd93Z9DnQSUjlCRcHvtY9SDOiUPrrYOD8lGsntEjA1ioqCRBg6Ll5gzM1sK6Np+FCDBqFCbkS7vEDrWQZV63otCiPgoZZukmO+pemUxVtJbkWlna8FjRlYFiljSpq2pKB54vtVSWrRSV7cauYIyHbp7qUMiKMA7ILRRRUcXiAkYoRgFlSjRNmjvdmplVMj5IGEE2XP3bJYoRw3Dy3nE9GqZB1YF7sPAE83L0aKXXoav6wRbWNibYyiZrKi6vdhHrBh+hcaUxYqU1JO/s3P18ogSVsl1jl0dALFiW6U0baDYwLKaakoMHzIMKRd67GLAFJlv0NEpHILKvzF1l7V6jm6s9JC6eAwqzmDWutqDwwyAgqeDa9LHdCl21RY2BUE6pjuHWmOlsBVSp1cD6QHunGC+Cs+llprab7UV9k2+X3RUayytdBuuGSoF3mJE9DFC/GU4j07zN1ETG5JzQzctAOKQOAnJ6wg7ntDZAnZsKYFtfXqFqkQwrD7/O48IBvQpqIUdJEekK2Gh3ICh2A6ZkggVFr4zQykSqoYbFmKlCJcphBmlCj5FcUA0xIFPFiR4yASdEWiOiE8g58+HtjnnP3EQVIFETSlJp0kG6eOmOYrUuyv5hbhyeHOXmjx5H0WQVM8t3DWEBBR+JTNUbw4qkeNOPVoNpY7TpMrqHpZ4nzikDc2zX3c1km6pWJ0Ao9kSFR1qhbwzQJudxcV6lHG7zTeZgTS4tT9alynWMoElGUso5cjSQBIygdfhavvqEDXcY4CUonpmZV/cTfkHK0xyISBv2+qUEZHdLbWv1tbuenKvGSkRunc62ElOsHDvZ26zF3mm4CXT3OSZ1G6h4GqFRU4qYrHNoQM0SxTSKzjYbcLd7TT9JNAGJU7tlUSUW7Ui7I+y2BsNsmW22kupwlJXYKljZXTbXns/n0FyRpA0GjG1O3iO7/kMDkVR5Te5BwU4aeXfzI11tuLugfIVbIVptm2k3JhPeEAwnrJXuM3vSL680RgAAEABJREFUXfd0OJpmyOF1Gsc9eKSdOe4yg8wwt1EEay1YaYq1VRVusts2aC4zuGWdbnHaPs7i2HJdHQrYYA5dlxMYoAlafZ/Xl/yb+Cf/8m1/5x999f/w17/iv/1L//qP/tl//Zs/80s+4bO+8Cf+un/7qX8q/vG/shdlAXrFX/k/9FPHV77tazqZCovmY85holofk1VThqE6qDJpaO5QQR4ogmJDqrs6l2IYFk0tBkyIH5+bSEtYyDxCWsD0OObyw44f/P2/36d9ym/5tE/6hB/5Qz7wnd713TTiup7/+l9+Mbr1/u3HF/2TL/iZv+SX/b1/8kXA/3ny7Zfe/tPdfvZP+Qk/4yf+BAnyK/7Nl/7yX/cb/uWXftnbL5H2gd/7e/yB3/qbv9N3uv+39V/5VV/1cZ/0Wz/oF3303//Cf/7vv/pr/uWXffk/+MIv/t/+/hf8sf/uv/+FH/ebftgH/+xf9Gs/5ku/7J67Mr/kK/9tK4feZrRtuB549YIaElipl7OVp0jJiRVg6Kk6psGkAcNq6c9jMvV4Z5Llx37McmWmD0/zBunkyVYGaAlbe0actU7ZbJOo4dbUBZo+IaNWR97W9ZFaI3t3l9NhJ+1AzlreavLFBYr0KOTOig3LkpgoStrRBlT7nVDbVGAV2jFVKz+34a6WODHZB6DOSEWkv7SG5pgeKt0VAWcdJa8P46ON7YKWafLZtx8O1qzrxuMbZ6vIp2SsKrCDmIND3aVGAURP1qP9pcYDh6tDtWkHs7oXJIN05KgSZDWUjlBfkYdG2R1J6RfJbh0VXGoy6lEH7BHDA0jKm/XQlKdXVCCiMlLM8Z5PLzORSLoN+VZIMRjeqZcGGeNofQTvctTioUBoVVpKETcm3K8w236AL50nIMM7PEtDffTw1mNgv0fzgmkHD4fzbkcrb8DoY8yHD0XQMHAM+tl9hGnJyM7osoDsucQwqViytkbSD3tMe1g/2FNuQ0VnSjgyJ9T64gkdeicaabIr5dXQzKDij8FrDAlgOWNUaqYJFJT+sQdCnZ7NFu5eBKo0ZiBr2nN2alRVuVLTYhkFi6jq+0MqiEGYmMfbX90GO6CowDCISw3NlBV0tHLsCoBtTjMyqpeoraGnHdNBCzB1Fs1yD3ole4cKgGU+xnFqD2QjT8aRcWBNW8fM4WbQxkN5EWWKaPd44hGS9aZv8xyH/PkyXL4zn7yeXNUCoPgk0JzzBltCaKlZEMcttMXEURhWdLMeTCFqrX2y0b3ALn0lBIeqpyc4KPp0s4dMVAa5fK46Wlcsj6FUqr2KBLDQhGza0DXoUxtkxGca2TaMxxHzsfhy8dx9tK63lY14nJzHYHnLPmiDmtc3Et94aOjiSCA7KitE6Zj7tJRH7MPsBfNlzUdLJcQ8t+kbiYlnYhXSuykONnQXIA5FA12SVHGvy2MPBojt6ne9SAinoioACuY51M/Qx3S30+loCITpmqjQQa97VR22gK0rFR7pUbTmkWabnTa6Tipg+B5Y1Fc+r7ZGG0nleY4ANMy9tVdolN4PnTE1F9MFKt4KUa9mXKLQ3HW1w5ln5+iN1g2X9i6xO0JVrwoSSXLtNRRMwNmU7rC9t+fC6lrnep5rca3Mve3Z4myiuzPZMGM7y1huqpGmrqg8EOmZhqjOLiAV5bQ5jscc0gmhixEDW5S6fvaascdK7ZuAauNNCOxy3cdM5dsH5G3TtvY+ErNPQPG49Cfk+6W3eZV5Y1je9PTItqzujtSfoua2TW9ZQg49kK09vKB2qyG7MhoNEaqsufs8hPak4kq6yG6xqybfaiLYVhWRl8W6Fd3DHI9532zJQgLwMXwO6l6Tl4KpRGLQDuNosZbdpTfqTlRvYDaMEGwKjpvzHKZ2IFubax9X3UnvApIqw3bdN+RCitlOQ0FBpmyEW4nvRm2IhuPR843yB20K30xViZwQDhkG5MtHTpOFSIvOtjCracM5QNMIAERYFWNWWUtjL3lH2Ue0BuiqrOXwtPuBluOccC2RYn/pHuDcHLcgOoQVEPSxfTQV+WwhJeBz842FQ7m+KWjlayr3zXYkWglXovqoYERcUZcaQL/geLzOI7oArTJbqQFIGZfrY+RodLBS6kwJyacoOEwZkJPpHAfNQWvjZX1JRZN9WusBQEnwumEowWtTi5keQ4hcPfNY/SAPuMks2rrJ6keWylJRS8dJEjLdBmb6ihtGQzZ7V291w14T9Sib5TJ0dji2Mx2iX01VXsWOGb1A5dBHTYapnVYqoIbLS92eYYREVN0RvjgVKpmYUGCyprRBTQMlXeyugu2elTLWTG2WVng1DHnGVtOCVKx7qZd1447ZuMEdNgc8AjsYW0MUO506xmx7QDctIn6XfmipVU9+w/qAaI+qGwvXQLoz+lz6mnkfkZLNsWhLjtlePtQA28JQ3kAx12ytbOVHSmyd1k9S1rzEg/BGecE4OQ9IeYoFlnSYmSYk2NwxKnR2Q8wgc+w6NpyWB2sCaP1R8t1lxXuq84gF0igIc40ZL8BZOINW3qaVy9g3WOtxmIhIllJjA/qNqupMsnlYCwYl3GJX40olgF/+p//KP/vUP/FlP++T/tXP+8Qv+D6/8B98pw/7J9/tI//JD/5l//iHf9Q/+5Ef/S9/3K/5Nz/947/yF/y2L/9tf+TLf+sf+Zq/+L99zV/+u/U3/+H+p1/SWQL7tYf/0y9ZFqvFXg32aGeOCAbNnC42QuJwfUwJo7pSFdo5YNzuWgms6TvtziWRp/X0g67Cm6ngtlKQjXtAUeCVizEe3/bb/Ocf9ys/8k/93k/+Tb/yl3/Ad/5OY4yvxfP2N1/ypV/6U37+R/yQD/mIX/rxv+U3/55P/6w/+Xl/9q/9r3/l8//uP/zCL/7X/+4rIuI8jl/8IR/83b/799L4L/rnX/wX/ubn683bj6p+x3d464/5gd/nPd/93XWmY//Vv/7XfvTP+tAf8BN/6g/9qR/8o37mh/zYD/05H/Erf83n/sk/8SX/6l+gy9y/z3f/7r/343/9f/Yt3m8dBRM/XTTx1IKO6gEfYFm2a9/ZQy12z63oboo4TZfLFO5ZJSH60y0ISnDNFwnAIFL3oITSo3UBTS4iusAwWmAknTDJfJ184s6x5B821lQjm7kGromarVknMMpuNo/QgvdiAvCIUqWakmI2LI4VY23sEnhZhV6UuDH0xgAdYvwAj+3HOkbLfUdlt65rIzAYoMReiSyt0y2k1Z3UyDmGHXNgiA/s+2QVNY3ZYsCjiSpF5Klus4ftOerFJHw/l8pSRWsXRve01rqh9a96ayegmpDXrgqHDkkOvtR9VGBpbEF2AgQnMNulRy7nNgtnUQMg7G1RJm2yNBL6kUV9tQet/V3TdELxWNWA2JYt0lxGJ1otZFZM1KFLCbyqfNOuZBzoERAHSqcQkNArDWgqhb7d0d0oO6qnBK4KReuclq5mxd1RqY9Hp1YmNizhhQFQarINQE36gLgamboouw2KasqT3FjAgtYaR7//DHPzkc6kutjy/URHahr6rPZl2k5ticOl65BxEWYm2aVFMKv5KKoLTVlEo9WcemAPrIit1ib1uGtD4Tm0hPE2XDXZh5W7ZkcG8nno2lRD7Kcfmw+HO/forUhZcIUJIYyjcSSs2qSRBWzEvhmBsY1QP3wZGNYVO65dkW0dsxTD1RElYglrMEV3Tq+H4QFMBmtRYLXA6l5FN/82ZMVG0wzaRthBeDV2y2HLKCZajwv8yOOoY+wh8bQNdSstCm1udAqT96bEmAGZcTbRANygih8Yx3j9IGd6a76i2Yy0EkLzIpx0N7O3z+uicuG3IYKoofmTGqPHE8/r/g/jlbRBsZyna3tDm0y3rdxDVm9u6zQew0+fJ6eMEFpm9Z3K8yXTgLZKU6Z32w0zClsUrsbVjBZprZCShKBkmcFHzaPm1DsPyoIzpsm0iziljfDJ4cIo271HqMG5Y9CHLslEJ8dqW8zoSGlJLSJTHmQxIUToABZcIFR9o+P0px0rT5Qycqth9SxJToNxzxPrWu4wNXtTpnTDuheU3CEr+2CMyJEl/wqo/ZZuepeWEwEAHG4wGXHnkPnRley4aR00d8iJQEuX3mPqTAObuMpWdbW83sUtq1ND9XkDmMc4T8wRNrKDqS8NN6TM6oGa1ViVhRTasYoS9UWu9t3HwtE2RJ/aQKyKZRncCTXiuHKLr3Ffnxgu0BPFDENGcbeOqiikFNueNCF3BfegfO6BhHKkHMqF3mF6DrUlMTwQM2SJ27iMiVY5ebehyZsevdvihBGmb1EttA8Zdmu28igM2NTmY44WadwhbmKnSkuQyi0N5TVcqbgmttnmuKgbFQVzecQoO9IPmmzYSfd+fXJJfWoa6/BX05UOzq2NqUIC9KNg1my0o++mjIYi1FvyNk0xJcJH0HN2WYqXWB5p3pzQEql+srOfjfvsuWv2UvDDVEm05orMEpEZVH9imEoAnn2zI03ITJsi3a86FrJEwgU97GdepUX9UTVYJVL1PRCje+yc3JwbY5tuGU3Xbze4VY7Xr9v4rt4qAVORJm7C4kBQEJZ3hOWyjtIA9cV9CnmHRUlPWgrasOatXEjaqYjFq3K26xaazlmrEmFuBnQvdLJhoDIc97JTOGocdChStlmZe4pwlyTRKL9JH3JY+tAgaijf97IvCJ2Q1xIs1ZMyqIAaah3JoQuOxU6mAKSo1Ultbq7itfq5IjKjUEYdgiMA3hMwqHRm66w9ja8MS9kSPQGxLdPtth6+Xng39GnVoxTuIrubirngRW8CAgxyep4UQxdq5R2ZUtEYRSdBiG6M0/yN7jNClbSFCUBBlzqAFqIWLeex59ljnpaH1oBB+Ck7lj34PHkJxsgYvXhEDWhmaabHsD3KPDyhd8GXPR8wM5rAMDt3VSZQHA3XH0XiIjUgOK/QlwmJ+YZVeKSlYMsQ1O8btNQmhtaWuJuOTkDzRVO2RNGo3SVlRZ5HP17oD+MJO/pwVW+2tEfZF3UammhieL4G1pBsTYWc5dZUyg3UH5iGCa6AYlbMpdsoDqHXeZKKvEagk7usfQx1RLguiG6bsGeq8Xmym6EmV89LIhC9mE4HRm8J/8Q2RNeq/PLf9ke/5lf9/q/87D//lZ/159/8X//h8x/9i/rCL61//RXx776qvuY//pdOMq/px8DjsJdTxxvf7v3e4QO+lX/wD3znD/9R7/JLfsK7/caf9S6/5McmrmYOxkSqmrscehuT5UdSz90wWAefk2oOz6xACfmYOFq3mOOI4Suz8yIuMjDRYtENqU/sYKYUJ2qSLSKsw1nufK9v8K4/9od/3z/wiR/3l//o5/7GX/7R3+97fe93eqd3HlrSAfH+r7/syz7/b/2Nz/pjf/S3/p7f+0s/9td9yC/4BT/p5/zcD/zJP/UDfviP+pbf9wd+h//yJ/2wD/nwt33NVwN4r/d8r3/6Bf/gE37X7/6YT/qUD/olv+I7/7D/8vv/+Bd1k78AABAASURBVJ+oKf/qS79UV99+/Puv/Ip/8SVf8mVf/m/+/b//96/efJtOytF7vvt7/Iyf9tP+/Of8/t/38b/6+3zn7+LnG2EDwqYKGZ1n13h1pxVwjANnG2HtlqNKAlUmNJbiwMWylECGV0o6SlbguIieF8+cWjZhtfncUV2FrkOXSJsUiQ1w6bqGe6c2arhTn82yseEXxKRPTIOrZ6YFEaPc9mk4DlORqoGqmg8Zm8OPo9Rp8+lYqIgW93Crae53PlWEB2yO6XZ6PDyGBArup45mYp5lx10A5QXbxi1BSrSoMlNJD6fLlOwViBpWRnRjt4UxmGGlIFtrQSbrGv0cCrZPK3EqcpxQLQ1b99b5WYzbfxnMCh4xcDv2NcZ9YDZ8UtsQ9ZWbIwt690zqeOQ8Y7wGmILe9EsHagvAhsKPlJA0HmfVYAtPpO+aqgugWE2iBuRoU0/EzuaAI7zC1Fz1PGgTaQ3rHB2nCyX0uhNg1brSBgg5mhI2WtsRdeU7eme7B9RNrdUJbcPSWBiylOVZsllqLRu1W5BUMGYTnO2NuQTWHGRXUgug0md6T6uWCI8g4bqQrKLaZAiiWpimA53EdiQqqwIaqvtjrWepGwtbW65Ga+AEpHQyh9cJ2rXiCtlznw8/H2aQmpQyUGi7eczWbkZvlgaFjT3PxBylfQ+8Vs/nOjJP48MFHKxEhlUeBuqPXGJU63Bvan0+PMxjmYmj3ta7V8fSHqksWoXVudCrsqkBBdXXPKQ6Wgd1L1FLA6zFIGDWo8FgJKoFEUaqB5FV2FWvHxn61lajrc3gxNAU9tFjtJWohMM5ClUerSxxACfwojFC346eJg3aNDtqHNfRig1lCI2aPZAH+jCaE6BBANrG9nPxaOUEIjtVanMK0oiaoW5e5t0na1qqfs2qJGtQd+3bUi0yZQs1qUzkaVvD6FlaVzxgVzcy1WLOdJfWc69rPa+6YkbOXH6jM8cQYyKaLSoGVDSmZYToxPkK54LlHfGVLfJoh48Hoblj17HwEmNgqvXli5r8LLortK6suL1HcYnjVpGICugSp49KX6uX7GEpQlEM18ziuHBjq20G+k2LfhOzFLb62c5qlYWYbe0LckJJoLG1zoyT83h9iwrxLz5audXfhnRxOs26W6x4NowC4XM6lJXKWMsyH6PP6eeNPykPiBNqvGopI33A3DTxlQZjcdhQru+kcZl4fqh0q4ts3n3cy/FEySEVfxRKbWB5rdk5CXiXF5iOAEpEd9Z+ZmTeu/GR4GWsdgdHd8HK5qb2xRbqj22yQM0vypHDlB8mbauJBF3TOARQCbSKcU9vKZhqF5wpEYbdMrpNVdPkyi5TeyXQ8lQ2QgettBmrp2Od7mNMOx4cbnJUGbv3xb7oV/mCoihFO1Zr3Zgdg+FVFixF2FNE2s2lo/vMnsIUo8qxX/PsAXkEiWFptlrDut1qOL14Xv1Yyhhy4kk21DcUaO7KJ3XTqnuh1rO0syBribNVsi3bXnuy5E9XJJ+j6qFLg+MEHTVYTVZVq/+jA1xN9RPd/Zp4kMtDbSE6duZS19Aieyg7jSUY97LDHbzerPXMvMAwdrcvMWKo2donUuvJI8SOEJN+Rj/ilddW5prYNorUGznrRo4tbuKQVevLuMt3zuee8ZyxVVWUaGBFDQ1Gj2hrNANYtO2imsxBbVGPQ+GTlz6jCHZ5xJkpxYIGEKgOZs/rPK+7x0KkVnipCd5muzs6lfkLvmtUeztBoqvlrzDKdZiZzupkRtW2rmmNyZBypGKxGYFuDzJmrVM4offGu6CUa71vK3W+Q5Okyk2L5amyDm/qG7IKrYPGcEGXnQpQUOomLUX9RqAAIRVcg6tRnzFSPb+iISZxbhypRHdPrIddL9xukHYi9AVHVGR3I0Xico9bFy/oyZ7AxJxqIYr5rHL5OZjHLK3/UCEyFblRUO4dvjDovSrOYXfpVuex2qXVLW9CHUfHo6SclKIuKB8UCWaqxnGGssFCbqc0BA6gW0Wgc1Y2U6mJOwcGA1t6IfpoHg0fEWekNFHQ1gFivAteZu7KMjWiKJF5Kn1hUr+eaFguazNTEpQneTusB0qJLMwyygFQqn31BEoCjRI23nf596DoeZk9ZXI8rTajuMxyOObBG5SUL9OW08tVdTNSuVobu1E0H3LNVEVFXzlKzy3ktT3nN/r6APyd3sHe5a38Bl9vfuN3P9/7Pd7he3zrr/Ojv8fLR/6or/uxH/wNPuHDvsGn/MJv/Cd+03v/T7/tff7Bp73/P/tD//k/++z3+xMf/75/5GO+1W/8kHf7tT/56/6KH/tOH/oD3+lbfuNz1uOsWUXJtVVt5aIkHBut8I4c5zVmzPsT2m42fDCFYxle5VTZjR5D03ddq69KUrPXLKUo+2Dou0gcdctEoXtrMe1je+IIDhuPb/D1vv5P+iE/5Pf/+l/z1//kH/rs3/Upv/Ln/7wf+gN+0Ld43/f7+l/v673jO77jkLDQKQnu/byuN1+9+uqveduXfPE//ft/7+/8jb/510XCF37hP/29n/nZn/xpn/F7P/Oz/tz/8Bd0f/Lq+SyFQ3vjLe/wdd/lXd7z63/9l5eX4/HyX3zb/+KH/aAf/ME/4cf9mo/8iM/+Hb/9L3zmH/qon/0h3/ibfFP6G2y1TkMNk9QGjkkc6lwtAZZ0smyQUsPJpQrvVBXVpayoeO5+ldPXC643ot+4oLxO6WFKW126knPKmNqLYlh1bVGXPPI8+jjSPctXyGh1EtcBlUEgS+lHB7tYZQdoDXZbFPTEIGA82+YiF/ShtI2ZTr2aimHbTLfsWT2yPODwqcmFVM8YTmikximjBVVPaLoLCVyh9mh3LUPWcUC/NbTQ6BzZ6g6Qvyjbw8LPslGlVaz14CAgFPVw9d6nx73fWh6XXVeurAu45j2JbhPl2p5nSiituA7n1O+0YRhHYUCV7gqkkQa4D5oLcU7R0+Xd2faq/RWOtIPuZbY4o7XJ0m0L9oVr3dYTnhy3DZbpaFGSVCit0m+lbiNKVG+k2qmNVpAl29Y2k0SVYEb75rxa+hbxDfFVraXP1mA4++zxUP2sqRUoe4PbGMCG95ASDgUr8JhxzbuT6FI1gtzyZsrM7oYvp1LZYkH21YLGy/JHlzwIqsjPO2ovWAvVNAiqSQ7AAiwUu8NnPYaadp4lyeyN9Zz9FCogOqsWg57D1swYCRRbog4/IPusMen0R4lstvy+gTybZo/guWlFN868HatX9gShll8OYUdv07Yiu83gskI1sbm3VUWzQMY48zHxsDl7dD2qzgrriup9keUO5f7A0XnufTacs6HMRISWAOpO0oyTuDdJnWKuekYd4YpI+wxLpTB27Td7by1ou2a0mGcllcLumxJ7u+yJQrE4VCRhCV2/UIulYHpYqToYBh1OimeW2xqqzd7RkVJONUSl0kyQLVHJsivwNhlhtHePCQ5o8BrXc6qBvDZw0Z/HfOW2faZidgy22MlaV91fgtLWWXvibhqwrYCe/dxyCQ3DLdCj+6XizJxLvYHihEeX14W8iPAuDUUN7NFEjsQoiWYEpchOsgAF0Us7CTGEI3ddV2z1smzR0hbwy6jO3Q7Df3iZ+8uYb8DOVlycAzad7imz3IalRRiK5UjJMIFX0a+2EtVk+HgSz2QmskXX7nFhSLi3ampFRokgWxQzswdFqsf054OXMm7mA35vMyy0lOqwqfIc3gPhrCaEWS2BlCrxaLVfHKBVZ+yIUM3oEwEnfPYxLz62CTzccuqysMdheccqNSQInsWpTWK1NEqnDfRj1huj1MkpjbgHLGkl7R0bU0eKyjpjDV/myy3ug+vANhSNmNNDV9AJNGzUg30/HeUYc9BsIicUFFgT95BQ0UYrc+OiL3ripl5TRFEYV/lej9qnxuqiUE34bDcxm20lt4dW5thHh/YWc4it6JZspKirlPA2qSMFhhteVCeVBeQsSJYYUSasLEaO1TNjcFGN0DeklC4jTQv4bAQquEBNEDYrdlNooV+V/mzbGCVsRlSjvepcde6eZd5zgEAVSvdHOZjHUT5uBipL3pW8nCfU21nNUqGnyD3imDyt5H0Sp9lpfLQd62TOltmDMAAbdvVUJSlwlHjfQArfLKqbHQp+LAqfhgoXjoKEQ3UmdFbob2ceFUdt6zZSJX5QuZUYH6lJIPrkBLUGzJjeTmqkN4801HnZjGP4MJeONDKO3mdssalRUu3Gs+05Rg4nTHPpsFl+lDqgUju32BMN5YxRY9SBoGU1Kq2vgQXY61sf3lxZ36b17AFADyqtiVAh7iywCWbV68+iOxWeRp2k0zP7uXSlcJg/IE/cw1h2R3CNWWPe07GI5d7TjoPnacd0dyGi5svr6AJukbG3WYME3PL0PtqtDpdCeKtCfjv3vq6ODVs99pqCPvYht6UOZa+231cb90uq8FYHCQbsoiSuQKCcyHVClyAJo5iuH92pQ9NYVZllCgRn4yWuo5/DxCg3534anoDGjujRIh+mX7NzYhmFKZfo9bam4zAckldAFArIlf0sitVqg7pC0F9d2Cs7oVcJkp7YPc4xHjMP0/qFnpSbYC1qu3cNriNXLzyNl3fU1g/1rQN6OGSOsFp3dDm7W3AOC9quzmdJMtR6pRE5xxqWuNrfzGP5kPYOxEnV0YDrc88oV9K7mwK/j7zuxaOl0ztXW8ugc8sEWjy2p7tWDhyXEvFojhidhGFQ19KusteqAu8/lDsMwIod3hKkUlBcerPmXMdZ0+o9fvGPfp8/9NHf9E983Pt+3m98n7/42775X/o93+J//n3v80d+1Xv+ng9771/xk979Q37Uu/60D3zn//L7PL7VNz++8bu941tPCc0E1bsrSjaZBgzh4nCaAQR1JXfrpU93YIGsRvXIOqoIa83AlPw0pjUDUp0uLN6GYmIrzQKeBTUdaVpvChN0J70xo5mJVjjozdhjL6t4bUlpnHi8HN/2/b/pB/3oH/M7f8Wv/LOf/un/3R/49D/6O3/bH/zNv+F3fdyv/dhf8pE/74M/6Ef/8B/xfT7gu32Hb/Wfv//7ve83+Ubf6Ot9vXd74x3f6Xh5B5sHAD8e7/wuX/d93+d9vsd3+y6/5Of93N/3Gz72j3zKJ/2pT/89f+5z/utP/Y0f+9Ef8RGf+4m//pN/2Uf86g/74A/+MT/4O7z/e78cQxwrAOGUqr3z6BwoEUVxBUU5oqhU9Y1RelHSRYhc8TIs8ys9pOoEOmHp1ofh8XqwbCpj1d5UiYdDmR4tIlNnduOCL80qrJqbR7nS4OwBHXVXBzfqwnqFvDAWHrtmFFCQeaUlyBwFTxCsW8e1Y69nrEAaazQdKSd8+3tWjArNT71TGlrvNa5JWjl40IRAZ3dpPiTPlividncHHG1r19oRa+ddoqUh4dzEQt1DSw4xrIdvUOEFtYc6Eb43l+IUnNluZhTFO7lG9rE5cUDN/6g8dd6i1ACgdaEMHEY0qlmyrZF206Pwy9mRAAAQAElEQVQT5aGpdaycpXG4pell3mxjKjqWeKm7M0wx5sIXOoPiLCmttfwgk63o9L5swzc9lOqOdMvT5VidkGsp1tUuBlow9EtTigzOy3Ede0817rvtWGe1QSROayd0TAtDKskteu5LtwW9DaoFAVDNDxdjemsoUIRqdCsIGZDuWvhAg3CSKEWGqt6wLcvz7VcL8lBbJqqP5iTsnlBE9q0C5ZZAW+bRPSEDjBg7sGlpOgGTAAzT/UG3JJbIUQO9ByhbpHIwmugSMRjqWFQCbmTOeiCdFNWhzk/ujr7hRF19rVqlIsjbkUIiOWDaTEwF4h7DA7a2VXjTcGIc8ElrG7CTU2u7GZO+bS5lv+FBBq3qJqu9+ki+tKuZ2ia7gKxamUtQaTJzQ1dwyRnKe07eOpusmbI6kceCyr6oaRu77o0A5iBkX6poRh1odYVCRQYUSazufVnIVskxh5JA/bWGV1oHS0MK7KGPWcK7S03lBYvMFjC3ejnjGCVgBavAVjmJqCqRz3Dsk2Wu0cyM6hWyipqALFDLFe9p7ehhraaBgVZqR5u1l6FNYjcIGixkv0uZRIjBognFVlwtyDD2tBL6KlYb4UirjNYakEA4o5nV2QB6Mt2rscFyN9O+21yudd4ESvA0SiSoj5R5lnXSSuBy9FYUqDUZJ7eKfEZrh8+AWIklApDJxCiluK1lpYYsZLnAt5yD+oMSIaguBZkO/RRUROJirbnKFfqYhTOBlEX9oE0bdivhNT6ZWgI7Oy19XofwSXw3LpB+izyxs6I98ShozTOTagkfbBZQ2v7SDOpwbjmHH2ZTtALCPBhmTQ8h4BAJbWY0d7d50kzTvXJ0QVBLjUpjS/YprlQczoEiamAPpkYjitlWnIkjl+mq1lZrDdLppYsaoLC3i7v0qrNt5EQcHcI76dPohCEHYjIPjzJtDHOnDKhivDD0TsB0SAguX5ivFwIMArB7zQApPmb5MBwDw9u7qaujJG1MD5sXzpUaNdAOsbsdyj/zUXfbUd6Y8NumVUMtflSws6xUdGWSlEh5RtUiwoT5tkNSAzhK80ZrulymEhReeZrsli/j02t3t0KUZhrZDcLpxnFTXG3dtGqz3WO13gHdZCqnpqSYV49oIOkO+p2cJLexXEBwBC3g0KrM2ZiZQ+5oqfmQKSAYNUsz7+EKT/Tg2OZabm8hufCP0i2GG0bAV6XWyzTACEJ+Gc2QKWsKsk5YqUXVjDFzWN1nE57EUgsaXA9sbwUPqHty6CHXNe6GnbJxx6oqLhEm28QdQ9tO39VQlCZK90g9tdJoKK5h9z3e3QgQ6Oi2lIpVLiaH3VDwdtP6+tpusGtYzwETQ3CVEgWuC1AYHVG9wxRQqbs0zsLLrcaaDtMQGk0vDNC6xSQUqu3hCwpU7XmMMXmPDNx2s0a1gNW2zMkahA4Bakm4AtVSSiuUIKLZPUGHeTvSs2TWNz3kudNVQ9q6FIJDRzOqqiv36FBY4sKGRN6auwzP7ksDopVyTp3rY7vHSI+nVv3RG8iULWu1AEwmrKvRUmCJt2wZVNYPqmksclGsOJZjsbdGZXikEqpchol4Pba5DGEgm/pRxlQ4l2uTeawwS3iT0ax7vCnq12OtN/tCpVdR11XrBjOoWnWAKKCLrUixA2oYC9JYlFY8NCEfNoS7O6FJPUkbAz5hw6SBEFoxeGwzVfVlj8ABKgvuRslrsKyhqLDd3Fv9ADOEJg7y8PMt3+qbvvUHfPv5zb7h+Abv+pbzMKrDLmKYsjA6x+JrJtiro2I3W1l4qAdK7RV1qBW3gp+pzcY9y1Cs27xK0RWbvKdJdIiL1J5snZVncSq2RoCbFuYwJ/ye2h5aJR6Tc0zAukQNhIe6PjRM5wpSi6JRnVauKqym3t/gQkkAEC22H9mHroTzHd/5Le/73t/wu37rb/2B3+17/pQf+sN/2Qf9lE/6RR/+GR/3sX/0kz/pD3+Kjk/53E/51D/+O37nH/99v+tPfcbv+TP/9af9mc/43f/Np33qZ/7e3/k7P+HjfsaP+5Hf5zt/l2/9ft/iPd7j65/v8MYHfPtv/1M+8PttWDZC3KN1895n+7w9K4nBRV4T2yQixEYm1B2OoccgPQGKk2xLxaTEe/VpPQwcoKt2d/uiy2adigJr8Xoy27d5+5JP3KvTaUqd3eqQbmrHvvS3jJhe56x5JwkTPIZRveDRLRsde/dz2/X6NtAas1twdt/NKWhaj/x1uT6fS98kXauTrDmSZ/Ublvd/uwmivYHw2gXtpbT9UVmsB+vRHEIEh8bz2NCuV4JO7O0ryKYpYOtU9iJSTw3q6nxF7ddq32F4smHBUW6yovrSMdhuxuF+4PS82406iD66fLWkg/T2ynvDcnG3P6dMNLp73/iic0+qN8HAvS3CYYaTPM0ejgMF26VeJaWJpLl6XiUgykYf3YMUkTA5fkSNaGl+G0Ir3LBGdjSWM6ZLBJjbuKmzzSCq6XUc6/QYeI0GuhPpBw+DA1C0aLM2Fjs0S2fQVSgcmNNO+KOntt33UK0Jtanym8eeepR7BM7gTChlk3z00PLXlNq826S91F97tiAbVWcl2qYNpzcYwVzyI4NrIA/0WVQ46Ki+G+AEmBSJ8PRZ2ltQq51bjbIbBBATd92mW/jw0gQJZfbhdhJ+87mgvhcBiSDUN/3oqTtpBRGQ8rwLqO2893dEX+o30NRDNwO2Wgp5dtxQyBKSi12JoU3ClajXo88hq6X0b1d2XAwr4tZwCAA5SZq1neVDfI6jfBK63FBz7QT67lUsFD1gSphOubQ3Paa1E01E28Yg7FCF6pSRQb9AEUXv41AE2iv1KB8NNlmDKt/2pHqEAnlWb7WENzlepT9Dt9oiGaH1pH0Py+HW5spEQ0EGoDgjMzsDnbqB3o/eCqQPpo3W9nHxbpwpb8Pbuyv22it2QuP2SaWmuykhdfSMGhbaLIPATTn6DqxjbujZ2alQDemRdRPBefAF48yXenXW6zisadVWQiitl96GjQXPoADb7tmKIexEu9rNOfWFnEpukQpnS5Cjt/lz+X7VeKatsihucPfYd7FzkGdyxIESw0A3KoVfulQGB7U91zcQw0BblOvDer4IRCsiso9sK80V//fcVmbUWDYXfE9uxwKu7lCJ6S0rHdvq6etp19NLrsa9AUsuVhxMwXGDM6P3hau8xmlDS+M4SaecSoGhFCh8fw5JnQV3TvSBHjZ8SA191b2PBw2TNg1JpeLIBUtxkzoPONTmOemYR099EIC1T+Be4R2GnKxbkm42MB0uhkp/t43LcFnv7qT6Fp9du8oabAvlM4iSDyPMmwSknWliGf7sFh87iWW+OAWnpYynWTT0hBdqDQTdzMUDxoSW49PE28qqMHBQ1tqUqBZ2c8orVYSzix3gIvLo8dJD/IKbrMF0K5v3ZCoSzjKZuefqHC1qLptpDtmC4hjdji08y1MVTt25UudMsIFmJxL3q8aK41oz1SXQZTtsX1VZu2eMlzqP441+HG8evUaELcHRwbFvb6IKmSjNns+tm/nILLTshlYt9DpC5SfkpmrZPUSlFtMzl2vx4goBtKBgD7YF8FTJMkZfJ69RW0G+TrAplBQ5R/JsWQl1lcwVUWjRKXcKaff1nCvufyP86rrLOe4yViIDqYCVz7v3xD1l3cZXcqtx98PreKR7KnuaUti05SKoEMHsLL/6WDUYmLiZkj2x7DPOKktQvJF5aDVhdSmtWlBb2LCMSjsP8GhzNuZK34k2usPQo5XFUIfSreR0VQpdz5AsW/UGqPXknKV0toSxbF9cTwFrjBYfjWAkVGgZacqyNRwKowICkSb/MjU69CQaalHA8iIlabM5zHyADxtzzGP2RM+uM1vpODFPH8NdBgsOOmU2CglvhTNe2k+NpySlyKnsmVzOyHG7IeYYLxQh4nlunDEeyTNgwVL0UVb76B5qrce5/bjUOBaFTRm7B2xPDDGHgl/ABkuc5f2u9CHMQ2wK/WFuIpLw2tNijj5O94OQ4PqJXLha0yFCZKFgq4GGsJ8tD2mh8pCXkcJWqAKDLMlfjMcAzq0NA+kR59boKhrkc0AdR2bKDIBVHlmu39Tvo/aIUPmVCIXpevYIGypOTWcAciG22Bn+fGZeu7nnTIWk5S3Vh3LA9d3gWCdr7Nn72GrIzUuYcSRm272otGxtsIEBZcWtvCXIbFq7Q5IGAQvRbK2aGdyDCh9d8mUDoC4d1kfrVC0gHBmp8sHedzZ3jypDmm0FyQ01bbyqscqzKIG2BmiONyVlY4LCVE1Ap9zaVCJ+S1Yg5c56nOB5gdmgaRRJ01tATLVKbMqGi7nFc8mGC6l8EbDtraFOKMxdWFHccVSPBnb1zghVdNl1F+uU6KGloFx2eY7HWx9f513e+vW//tf9pt/wG77/N/lG3/L9vsm3+c/e91t+s/f7Ju/+Xm99vBWp3IsDK1oZZRAyyykK2830t/RLmX/9rtuqAC2U2S16ail/RRXHIUIxrGelJIBGNY0HOce04Xa4fpgGSmJIUzny6XyytA4hRRettXq8zgBJxzzKDtFH/ZWOGdah0xhIavHarSV5bAiZHaZjvtESEi2l6eYdiCyKUrHQKRGdokW+mzu08mpAq3zc3agoyVvOZubi2CCpzNswd5WaoEqwUjT3XlARNqJVTaNEBrocRHuqMbUNs9d/e0gO1YWUMAbCkaNTpe2vO6xBqEapyUs7dC/pVYsKJ7U6ElCIKnO14kpsWbgDzmw1N/10wzTaKJkUKZfglDRl4ikTGiU5lBRS2rvyXihNAAE4YKSEFa0oGpexprvbBFxKOogbdWgharSy5wVLalLpHZdXulaCdnb3DIVIz/ZYNmKeUB81BZLx2pQ7oVfJkDW9aR0yUJraLKmkzZhmQSUWpU6v8cXXHpseNht3n2qohNwUJ1hl6gVdNKeUMODeujcWqsiq4LB6GWoQMGlq78o9sDWUVGRNxRmqslHFZKXtHJtvf2CklabPXsMWhyCIMEWYfSaOKj/oSurrVpJAjNDcLqAgzLLdXbVUggTbGMygRGOdnukrHxlH9+iSWZuk1DqGQ5JDt6WPuj/BoMmdEzmL45odt1b2oDpGpFCLTjSbtSxCrMgdQNkgUr9Nr0kcaBRV1FJApK62dzvCJFzdxWaoBkfSYbxnB3gFZc9qjOZroKICIrtTrMraaAxFaUMM5GRNyCRRQHRt5ayad2wzlQDluckFu4itzCugXoe1PZLnxgygUI0kA2Q6UjgaLH8sIRhD7zTKpBTIb06IzZWtnpcqo9IApaM9zHVtHwjL6u3Jucche2VRSn31Mg+/Vwx5KCUk5BX6jQJ72vap0oVTQjTFJxkpChjN5Jd3MrhHL/RVirShsAmzgeEl/jTlWJwLd7o0x9hsvSJbAMKrUKux2quHmBCNBkXLjdevEgvRaMew0gbILIddD3sqpCEk6cwjg1A0ZB+0wUnSmIa0boMZ+353Ig7cUZVA5g3D4yE5EQAAEABJREFUOtE3EMVR6RracqVLUOxIVCJLg3SyGgGL4S1rHZ2BKIZwlVnzpeUGiks9NI9SOYYKYLTQsKGdaEbqjYDF1Je2ofLv8n35ftMow0yh7UwIGjxsihCd61AE8M2hNAmCdxvLXPmgszkbo+ueis2+3F5NLjOptzzTO70Wsc1KpUl3r8GYLUnEITvVkSKO4H15DvNhWow0XFBKtGfSiy/ESWgLoClDmtA5oEByTp/nsHPWY8InlA01WiVUbdd4RwwF9nRFYIJu6eokngf3ya1FqFDSd6Ft5jjvFllekXYb198s7q2Y/Q721Kfo8OOac+vJkjlezzXRr9ZwmBRCa2VOwCTdSXPL7iRFUt/rqo+20VTjKz2CVvRWdvRQfAdhbKD1YwzAMnHH3xxRun21yOgqJKBSTuuaMNfvAFZig6J6u5V6tggW+2Y1Sieoac4Yh1q/+prOHLgOX1NkCC1altWEV7HvQpN9UHdlFfdu3vNpdeEWm1mwIDpbDzXCrI0uGIpPCu0JDjMcybGGyZB5KQFJBpkU0Mv0RCoy01DO1tmMRCpSxeuDFR2RVeloN1C/kivsUnCe09pzKOoLFqovo4Yci2wCg2Ia6vWZVtvwBAPyPwDvasXVcNegSmCvmXdOZUxOZVaZ2VRwWUwZqUZB2urJbUzNe22/XGyjWISoerORljz2eLSZ74L2OrJh91gJA95iUc4Bc8eAqYt5HgOSvXN3FEoxOumA6SA17Y5SyjCvWyjpM+2OgWioIBIlnuYgCaVJmIeXSmkNGXSEjayTYqfg2epn6F7kK/DSelnGoqu/5Ns7zgV7DqwBDr//1DzqOIvipTpurg8nnCFKNUNZL93ZreiMxhYkYtDNdGvmY1LOaVrG9bRmighW6yMFTNBbhqwd0NG3RcVuaKPQyJS0r+JRUNRa/2rZKqSzZ29m1rIQXciNuGlw5IFy0eZUDbjet0YrMa6FR9uaWOwi0GyZr9IE4ek9xYpu+ZvCXiFrDVHSY1ja7grk/TOtE7OVJlp2K1Q0nEZVeB8a1ZpZDZQfJeES+qPgde9kVWbpWngCWNBYqJtlSSctI6xhOByPqnMpFEucemtbHYDTjPdoh/ueHQ7zY/QD+QicOaDK6iPqLVGH4gIDLpJwl/pMoWnoRwlYQ9cP9L08xSvEq6ZaecoDjW7TnfPF1C0PZ/qxqy+tDpVNk4lmga9qB+OAP3KOMCxaeeNMPBqHjXHa7HEsx9tGXczVceGZ/oRumx7bp7Q3INAOO9M925A8YS/H9LeM9Zbj1eHyiEsC5Q3YTdz28aQ92zKHsXlcpsjkOllXeezhgQG6bh/mG9aPmaqY1HSak4PRyNZXKKMxOFmzc5e9oksbmK9qPrflxtytJzwSnB/SfUDh9bNuDqoLDokiaT0On4+HveURj6OmCtjrHso6oUEc28YT0JMStQNdejnnW+bjDZ/TSUKB+W4tolakE49yMTElARE57jS1Hg6ttbKupmCqIkjN00yiZMTcXTOVFaPpxZP+gI8a98dxcjqPbb4OhSvc+pkYhel7zFfONwtXnk+JZnT55Xya1NxdhP5WWDzt+TyW1qNHzZeYR2tDeZmHMa2Xqs6KpLZpmhfP7Kv1ErWYoDA4QYfwVferzq/R3rpSSTuW6EgN8hOc1Eu6EoaRMhuN8pHDnsjrKuSkn33ilafaEpjJSiRk1s/HMR+mLUZLDa0zstOzama/NF/YUg3YAT22kTDRCMW8V+alBQcn+oE4VSXU+Vb2NF9WKB2LJ1fGcyl/WWy0WDyab1j5zHRZsIhR5SiGuqG8mKUpjBNbZrXTGHXYesP24bKwF64O73zs+bjUUmoh1h3P5HqD+ZbiQyvuRqeCK8GA8oCkZNA7YnGtue8nkY+2UazAjrrkultr4rJeB+LRJSHZOmyrqjjnxWP1TB6tu5/T/Kjhdtgc92p/Jk+vMcumW/t4clxAdbh6bj0lHUN2G/WN7e0ASHRzq9xavTaJGEynzvWirbSnWuslRfTkhmVSO4PLLmX/el2HsrV75y4sTS6yvPa4L0HhVndk7Kutj8FDpXzkPp95LO8Yl440jZuGNwynN++e+EKReUbcbbBUES2/DSp+yTw5Gl5oSlIOV5wcmG0zDk2uB68H00Zrt6eWEz07gQhuWvkBHmVH3AozXnZcPpqgqwgWrBDTno/xyhT9m8veDC7BI1CZyklVUfm8sFbv+4NCxF0agxiAudMmbcTBbTPrBWFsaHqrY8rWWEFmIYNAj3tLGsesqa4kWI6Hyt1RLLG+JbjTKFKwkdUs6kOP1OqcsikLThTGDqSphPYGnlSuwxQBnmYXKEOXv23PN/vxCi9ZD6fanFOa95NDOhHnEEYZAieGseDK/0g+ClO2dh+NKXKH9Wn9En5uo6Sa1zOS4EtR95BDt1RXVkXw+cRKdN11/gK5PMJwmeLQuv7mE29uvNq6V3khT1qtXlct5TRhsU0JGQZWQ0V8bLxkHK12/WrFehuUNeAFremZCrl2e9ZogLkMy6iUuWFO1LhFCQmb6DSxNNuGaLzOSL9KrM5KR5t1i0tQQzVlbJw7R+5mwGmn8zFVWCeUq9O2SkRDu6CZEF9eC8+Fa8kLhvN8tD+iPTlwHP7w8XKZLFrt6rurSgFu7ZN5QtlcsJQb1482GS321X1/dXFlpKD5dvFwRKat69xvvsRz8gmsyyMm7PChPz1228Zc/cgyZs8uGqjIXKvZPHqoP2g18T6Hv0w9YvcyCykYht3N5aZlRCCrOt3WnK+O8TxH2yz6HsBLzQfuf2ghGxpjEG9VFjnULvbZfAQtwkJfYwWw2oOkqWit0y5Shd8TJ8a05mu0t3s/4IP2sBD0s9NRhvRotrpnb+ACgfuvWofAGJnVu6xNb4WkG3L10irg3aPbYVljF/Ewf2PwYXobkiTyIcUu9+3Qa8BseA+ska8etfS+3ZaWT4xXrry9sSjW5P9tr/C2GFKNZpmz36jyUlMaqWdUEk69WXuFXMBBmMMp1UPF5Mps6snDqt7sQqpqSxwVg1Vowz3BkDNz5B2R1Dv9OEDTWJkyH16Hmc+CZFBdG55ODvJsP0Eim9uGtkxoSkEcmyIQpc4TWiA3uIxvOl5ZXmPD1uiLO4QnLIE6kKa215d33BkHDFW5iwtDBPmIcZSI7bZGmWzMBFsdHq/S5K0F+C2FR9FgtDHMTObVJDZ3n6WNJITJO45oE0oFrlb51NdMb7AfLuLrDkO13IQ1RC3HSz8eMcYmC0rM6CftKkZlKyt23ezWOKNdbef5ClfiplS6mFTZvpz+xsRQKE2BeqCmJ1V6bjphLafw7vPSliRN8n2j7aUVnWIXHUge6Dd6v7HXKGVRAXGUGk62SX8R6lJK7CoInlCTajaKrs/0me6tP7OVu6kklTIiDaBP8kF3dICiEsqmULeK53qJ9XJdx1X3Om8Qvh6y4dBLFKB6Vp6M2SnNF6eBRyllyQz0LWuVfMsFbFYfOzNFlC8NUuQ1j3yMnMqm1zTFMkw9REKGTLUL/ZDTKXQUYezpOUYTcjNaWREBWVZwuJepxLkTavfZpX1JDp3xrEkSR6iRWylzjdzQy2FeYLSozeDe0vvRtD5oD5un2oLbCT85jAqQug3IOK3PyTFTAamNnepho/tcIT0e3YfwxK5Y0bXdypU0crQIWR1XRa1uDMNh0/2Q/8yudW96SrByGdoI3EuDWokqtXZ3jq5hdPNBDBYHDbz73Cpmoe9wGm7dZqWY4KK/fTeTrbTCEl3Oaq3OXjppmiLq9JnmHi/aq+xrRLcOgDKUPRZmjqHr1mfH+ZRmlBPuGrHNdsqYVU/YpPGwpeeS7uueragyxdXhBAlhqlYuT+RU5uRHgmUBNz3Paa8Yalh4yqgDRp88DvPpPb1mmVE7y9NomJDm4NXSemuz9WIxGIG9S18Tqt8m3chJBdy4NXSYne6zbRJtajQphFGWAKQlWhGlDB9UnRSVBonX2+GnP+Ybc75B6U7y9Y1aSK2S5kKJanaaGifAfHE8ZIlx5d7SOWoP3JJ6fR1CbIeJAj6QXoibu3FHv24U6p8qPZtuw3fZM32VrvroZnRdobRURPfKjM7qVlazsGKu8A7tiD13rriqb1YFjyCbcuUJLayjzOsADm+z26+scBUXs1Uj3VrQGc4k2rvOa7t4UjLHdk89atAsVEUHeqMhRV9NcdgI2KXDISS3x/a6iHqSr3QOVW3S8II0c5Hh2DPLq2Gl3oVhgPRz0AQNRlGlTmFH2FwyK5JLoh4uQkDzmlpYlLyuzFudSlQL9HPuHlGlWGk1bEiV3rSQR3ILZao5UPkqMQOMjJtgh8uri0/mPRg90nSjYgpTZ18SMwg429tJgt5AdUUllMhU06C8OmA0CKh5q73OwkT5wfEG/RijqIPGFjiR1z00YYH3JvyVj+c4rml7YPHGmncSNwMVsjLZZt3YioD6nZLFkKoP+XNl2WCDPY8bATkO125KndUOpRRhTCcdFN9hHsLEqY+jyy15QBvpMESyVldqGJhovW85I6QZbaqtBGZVVadbPRzzlMdUVgxI8JIth5jxTK7eYQuP9GlmjgIaLDJ5I7EJ9Vg4ehAPu5stm9EV1qlT7NFNGxjcFCiKDaseSB2uS3DjRDo2dKmE4ADG7Sb79bkDHKkDzUxWFX0Jh2AQ7YZWBoEdkmTHsp2ICyovjZ7AYbumiquxoKPLmOr2nKZeOEeJ7GVj6bLslEIiBdaU4FuDaWyrZIcQudewEgs+B87ZyrFXUNpvg1nQnuWhE2hoKbVOTXXU8BgzcHQby1lSTSEs06ppHo9Da2pK73PZmXO0E+SRUN+gpEnWdNJEncLSLhoovZ/ZKoKQeJHCXaQi3PqszfsSDs/2Hk7HdGgtfIGJShZaHGY3MI1Hp+BpqPZB3V4DrQ+e2+2aXg4QCgeRvXZXNNdguogVcSJqOZ6NC3upr9WqXsNysjyrop7VemKsMmgmKITNvus79IsNceYdwtO6aFCvMNcucXmFedMVVo6Z00OZFoqlyWZlZi7kqFtehEDW8Nau1ocprChbC/GEXWVI6L5F3EZl9IVUF18DArBMfM3Msy7FUJ1VXZk30K1pdEqh8qilrNYrw0VtErUcvOp4isMg1/DlJ/rwmsMhTernaBs0YOy2q2dxuB/u0hM3aldWm+VjQC2SsCSyt0WMkNA8+4x7NgncIJMHhlMlZKEzrw/KI3yoT5Qoggqvbj8e5mKkRD6kO+TVKW413Q9THi1VIitMUUJrLHGHvmfZjJN5Wvm8LeSwXaKROqlUQkbEihaDVJgHC9lCQXfTPnP4odBkkrF7P3P1c9XKcrNz2jhGQiVMut4rCmtQqY2cO7QET6cdlgbBDPaelmOKr9PwEj11pyeslrDdTcYx40BSgFiLWnc6LXevZkCsT3kxtKkem2Ajy3ahe01IlE1JG+gYXS+gnlMqlgtx8ZkIAaM+s7KXcp6YEOkajpXcaim1WkicGIY5xnQAABAASURBVKaBpeU7O9IifMe5JKI6mcqMyv96068nshNjzVHG5qF4YyvEOjbuf9bvYkrlZYAO77s8OeQUZ3O2fEzCeutZhFDd4Th6ADqmfrRheb+adi/JE00zoZItgxAaOKq1iejae/UllObUvDPGyKFsnLBpNv3eFEtdClopYEP07eGvxlGSXcpzLctn1DPvIaYioylGWAy/Tt+npc5NL3jUXMphw5Eqsko1jKQc7lOZg/rhiwr55DisvcQzQPlDKbuW69hNPeK2uc5u+SDDlN5oM2/FTsNwuPZ9DpCwkjaHkqRQSxtyCBapEYYRdD01LSs3gXx6ZwVkzxZ7MyTJxtBCdfA4hQcok5gsXaaPhDUTsyiudRiAtiQk0yqiprSoSlVWNZwiKTIYaoML7EZ3vg5On/YNOjiucVNSQBdlTTYSsRAq+ZS/UeE3BDZcrUykdEvDeq+jbpuGGhDlaZL5EAvwIZY2qDMLx2WKWeavjqd0Be4WUbBRrvsYv2VaCmG3rZv7oXmFa/fKnXV1JWiZ3Tv8wnjC8srasKJxGg6jtYtxJCkkOTo1PLvk8+b84eUe8GymcnkfekMrjEqL6rTeNTLUbGSJiR7E6SAQ6IAkmjRAIi2FLppKntzbFNhQh9Im43jJfqk4cxO9UcnuiXZko6IR3VW7qKqvMo25/ZuInYGzwktNRLnfrTZ8O55ZYyWeFSsXVns15fR+xK3+cxyYxzjHPCXAAdWcDlKZCu+GXi11pwiMnbnyWDzjlqRc190cEpmj+MAYENLKbnWRDMMFhHw1vMVPWSvWVM7ShR7Wx323xHlAZWimsBKhy7RU9fYskVlokXvzZ07J1pmUnpQV+WYnu0RJ5O1mHD5fXnjOcul3A2mEC49uanM2XjC0MRiixLJ8NdPu92oBLqafmVFQbVDd6yIvHKtedo6dlAaQEnuFaKgAYE1PY5Ch0HBU3bvWJqQaYjgmXzu/17tMQaxUKltXcSAf0l8oEbvv3Ei+rfXjsGX93PFcuSNy32QMDl0NswU5SnWQVlI0qz3rZqxwY+b9lVZKrIqSKit4x0h1tCXJhLTmx4D6l1PzWtEPCztum8pVM0pcHwyJbMJIuVBOG/LbqoqTB8xzImcRLI2TRMyhfdFdz73brpj3bXhQmShP2bzbw60fNuS2BxJVu6GjASFsnablyTxHz+k+Z7segt+bx657TIEKOZVxCb/6njAtT2gBEsyWDbqUrKnS7ovZS81zn2NTwKaVHrNylGSaGDE8ZkuTUUoGVC3D9CkrGGHZUigBHa0fNMDY4gFWSmmjdc182Bz6KU7oY+Q8lriUlT2gctHITp0wF9BBzIALNHM7liHlJkZx1JQz0UgmHNAm5uD2scRZopRi2EW/nGrtcaIPDPAlcNY4zKfRNWrtsWqmZqeQpE4NU+Z0V2SCOsgh7hsKwkCnZkSW0LQAKq6TduKg9gZUEFqIfIrVXuNOjwVn9Ky+da+i31VF0IwmqyxaSSGntSTj0Da8QwDSDHoRebCn1KHozYse5lc+rh4F0VZ+FI/ASLqwYG5xfM8jFZtNkKGjOK5+Y8cRwfuqDdgs6H1RDZHRUr+lcDWQdzqhxm0YhDBWdGmttPMu+lRRmA/a0bTdtpORlgmN8bHOY+s78df/eLF2cn1a0tE22g8c4CyOODxPgqo4O/dkW98e5YpudIo01UDrpUsmwQldF3WobMYM3pui3JLAa4Fowix4j/QRrselM7bpWKa5InGwB3PgJkYhJ6m4s3VLZ4qAgBf6Smx1iKhc1YnWFfg0jtGFWhAFwgtNTqAgDV4z1P13hxoRNu7dJJGjYVAMZlK9Eo3IWTGyIJsNaK5FHVs5E6qaZodIbnrCUm6SAuXWYldiLJFVZ6Y3A5SGVXTT+qWVyGUVJfPGwoiqa6+SuhKVQwJLZApblEsRwtzK0JW1yrKJdK8eRkqaaWvXFdnIslb0O8VGsOM0fcmbNhnHKJ+Fg6W9mNfL6BcvaVxgJZE5OEz98ESNkBktO53inECDUFOo7amd60ZVmO9xLszAeMo3SPjDjkHeU3G/6CVko+aR9kiMhJm3Np1mQ0a9l/Fq9fTNigE6HG1SQ5fcUT93KTv1BPc+b9nT8nDc/8WvQfmNYhC6h5UA97F167i1fh8nTNyDLdzNwuh58jwwIEF0MEM0VncIpEMGp9KlEoiuLnfz+8bEoXztkWkluM5pGKzuG6sZOI8+jlKId7iRvjxDC57BghauoXZbSJ0bSEWlTN7CuwvSQSsg4YURU/npi72lsTKvOhoDiqGG1x2LZBoeYFEvJaM1VwqI8rJprpFQW9LV7KqWUAKKiPRsGY2uAC76opqN2ixN6roX08zWNk+aKYZxH5ZuxKgIXotr3+hgTKuYLH37ffamNSYgWAdccARwv2ixeEaHzHYZ7jBUQEdzQqO925SmschSQkAI8Smx+OEisSid1qWdaEhXXW7lnrRtCEdrxNDgIUtZcrGrpM1NS87GET2xZThrZMwShJC2/TAprNsziFTYB/GSmKQdKO+03qWLxgXcCsRdJxPxiJ6LenrOmG4NZbFh1g7Jjc+qy0OTR81RZlCAPF1ATAVj0hvYqTnlDI6ImUk2laMJjk6v7R0TmMRBM6iG/JJCTPAhN2aTbfLVpT9DiojSOvRwm2gqXzIoVDfx0LCjwF25d62r18YqFY9IDrujbg71vcH22/EhOLrUA6miP6cJtgjPaqz2wm1KDGj4UB3f1eKl+rpSiZOa3IcOU4DowLqw1ZqqWrvZJy1MG+bx0lrzBaafiUrlQbd1r8sFRYOUkkbVpCGGtUpRTJYjp5ef5o/HnC83mUTIOldKnFBiUs1bBmoeYWJUGWi12Ker0WYGM0QFKKKNVcXQt8X5UnncYlA7aVqpT2srTmO2MnXT1m3ZjBqarbfkGoqrq++qGTP5RtqhjjhNe7jwZ/uzrLqPjAMJW3vGmlUu3hrbImf4qONOoobpQsrSqPYNLSpYLkR7CzzkpOE3CdsyccmYBNbETa7SI3MloAYcoMQyBLFEYSyLsDjilj01RERZyIuczqYl9GpApyQAEc1Ml/Xs5o1YSjKnDbR7u3YBagcR8Xqrwr4Xf6udI2pWu+zImgS1835rzeiW+kOVDfdtCk2nsJGiQeTd1G+O4mS5hzGJDG8ddJypQhVGN2lptKnCZ8GbTBd7XBjNWwWTQ0SIHg5QIgUqJGqU6WCq7sYgxcqRZap8Dk51ovti+bB5HEPaVSZKfcdh7oSxUUAUuyUU/IeX+Fr07dyHsi8I3I3enjnKJr2h9LgY2fAFZm2rC/0sRgo6Zi9Taj1iVkgcS32SBV1b1ul5GGTlII3cciRziiIwU2t2mVT4Uq0uiWPYnGOOx/R5zh5yD5PFZ1eKYKE3oqyjFq9rrGAwg5WNuLXW7PJIDUAWd0BHBduAFpkCAIIuHLByC/0sRlincHFZFJZVwbpVBAaxkq6mZQ6go2+JXiJAwkQthX10DoxqPXm4gwHGsD6HNoFRzCACLLk0sNsibWt5vwwX7dJF1X6030/dKTCoBslW2MOFl2bEINwwpw83XQNXc8mAWRpbkUz6odFKVvZeVEaAe5p7D42s1Z27e6GLi5AYpSkpZfge3CYGbUMlkwv3OGz6VWKkmWjAhKClMs+ofWV0V7m5TZk3WQLaG0fegmfBC5LrEGYziLG9Y0WoekRbjNk+YJ5mNqmYjsNBBrCguSNshkHtsV3Eeco8AQiIBsTq3ZHzajy5n5Q+pybPo2zCyQkViUus24RyQ7N32yY2ZMzurK7oXU03DbXD1FXwKGpPZkFhvU3WrXooKB0GP/o8OEW9naB7Ky/HQ+dwWI0Mo5k/ON8A2HccOQgd7BJ5MlpepnBHOkoo6obTUmXZ3meY90khtjLeyfCOSC0bzws7TQpmWSW7nVCQPKsn+7Dwm5Ju6A89emgMrd14Iz3MJ4dst2kAHT5xAoakabOUIgB9eh/TKeeJLneTvqbxHvF60epDjqQXe5X/4U7SJe1pTVQC0k2TFbZEcQOyXaXZNGBk2+V4DqxDeWsp0JeUX53Ie3ZrKnOo2mpnRFULxoDZQgQyobDCanmFgmNJ+y8cL2X+7NqIRj2a566p4Wgx3xIFQtujcfKwQZVXphYQV5IdySyBLs6cY4/xut7OspMUt0KZa6hcyaRXGbQ1eTV52dxmgUYrYP1OhHq9OwF2DaYbujsrexcN5sY52gV7durXnbKRsN2KMCHbDxPrmGprVhwNBU0f263Q1FRtgHTW3NRAhJ+zSbD0I4hlEr5CGwUqm2S14X4vGe7pInTDskslmgWtsPqpAQbZ1rCAVdJWndd15FZbTLkYbo8YrqGAzGJMtlnLPpx1dM0ymZ7FUeWJtN7WdYTPvL/yVy9bTto8ODmonC27GUG3iFtCTe9bTHRF0pVQGWSx4G2jFWrJj5R3aHYpXdSAzqhuDdL8cmAe7VOHxpywcQO1G6HJB6PuuNxsyIN1pKa2SAMDGqCTunZvM4FBNxACZgz38CM1SUnQKWQZ8iAm1MJ9FszQ3rJOqIVUD7SZefmUhsJlwbYi4LYORvrWQuB3MiX+5d2E8iaw3UZY+eDL4acIxgEIheZQy0FRmIwT9KSMWOkze7aQtEMOR7ckvCV0wKSlRiB2pxCHn+lKhnUj816D9zh6Djt7ut5K6tG2Vmq1fKae5Tj2wqVCq3Rckfta/UqrRkhNQrUN1Y48oHClcu6sQJIiRR3TlExpINiBDktFfHuu7hKFRUgLUoTokggDO/burdHLmBT+NPnQ4E4vOOQJpghTugWB2cTmUgMHWW7XgL40KeUI6Kpb25M5juQZ1VVEAjLb+gkEK4qU88v0szIj6p5U8g1X6hRE6gt+n5zdh/LStRLaS2ClaQNvVyIuBaUMucAwuyWv4B2kHFUjdFQV5KnjttknagZst3qISUXcy7Q3uI/d0bs7SUoChDLspm3Wyl6XfMpzpMqFJtK7rGuIDhukG1s4+ux2aLJJlQfspesttcVKdr1aM9N1bQ4/fJifNc6UxhsuHm1wGGSHTuxSE+4lNkpUdd8RlFmM+bTHsy0yW0eMTN2suTg2nidNkkeje5qdJi9uAKrQ1UqQGf2wOThg3SpEGU8bPAfc85Qsh3Oe5SegOjAOr+kgh2BCs2jpvdGrhZCJSq86XSVyoudqu7obSWEosOCme5c2ZTwn6JRQR6PMVcYa45L9ITbMKoAN7QxeYrqS7XjO2iZDg0o8PBTAgi+70yGqoWusuqvWwC5mUqopDzygxd7UqOhkC0zeiKTkuwbA3ZYx1BSRmGmz8dj9KEzqA/oBJbBCoQAL4vhCbt/oREQvSJsLwgpMYLT8YrdLQrgkwmPy5ZHn4FB+7TXZowS7LOtW1rNaoUp+s23cWS/2vm1UacvhAhvE3UTLLVz13uu59aoMa6HmCG1kpiMhnGbp1jNSPrSi3f/FqSyIjrshWuuiBNPTTStrYpU9W3LoI2rFVbRQAAAQAElEQVTk2Hq2/jgwvc3gxCgOmHiue2JOa9bVtsAw0nFOPgynRnGOcdA0bTuEB3PSSaBcZ/XW9F6fDMaS5u9wqxy6sZADzDdrXGkSiDvFmcLqUVpbXMPTY1lsFh2HVpEBkGrOTxP+3orN3A5QHs2POkacrIHRUBb1oOQo93KyzdsUN3RHODutQR+D5+gHMS4bF7RbcBDZfedElY9tWMhCsBohUdrelnt2uGpI5woaCwkoHxmTECM9DAbV180cutGVd5S+ulapa/Q1M05pJPuG1Ur3HeFQOiEKJ/rYTc3uNG5nTdZR+YiaYsaOGNRdHgaNclSQEosRbJjRHUMhH5u+kisgYTRLr06FHDV2MdTU7xsBxSf5KWRAbswA0gltmP1FPqGnD+HKkjdlu072gCsxYVDgVah01ARHqaNZZIU8ZpYojMJV1G45KZ70I3EHGG7Vs2Lq+QqDokFpmqJYNLHyEOD2zJGlT24QmW40NBO6vzz7TiPKNR2LM3ncyT82eJX6cVxP7E1u4cQjcOoNaMfh53G6aXfW3cUumNUc+jqkaYQ3WJSLEnaRs6IqoPOeo3S0w5pWaKs6kAd1rURoib8YUVpIM+TflNZigbuwQ4SIGI4eZobZNSlHaJiwuyS/NvRqyFTL+t19qJcGhLtmqbBmu28IjWAe0jXhKul99L43b9jZ91oEtS9fuL9Egmlkcl+WC1n70gDLMfp0nCyiFvqyWIitG7snBLwVOxHqSNXsHigv+C6lVLmy1kljctyJuT1uydNAOWLBlvJc2zpn9+zR/djpGeImmjIo7syKepsd6cUT/vq/3IMHRrjRxzTzgu+cKaI4CwLRaqEMyDOKwK3gfVNV3nQ/VPMthLbD9+W9rHDXltDgHtqEqCiniCx2h41SOz6H08VhXVjaqgSSHXPFvLT+u2nXMO0OpsUHNddt3AqP12luc7ih2rO9QR3QOMvWGHUXylpiJA/amX6oDKXAC8eCivq+KNBos3CixQ7Qgy2LtE5Xo8Fu5EaFWY+z/I2lp9XhZ3EGUVZa/Yb5meOxj6NscB1zzWOPM3R7aS7MdIzhw00iQovXw3gcZlPAFL2p472cdrxMc8n7qH2kGmKlRVguv3ubyncnumiECiGJTX3eDkqRiu6AQtaio81oPfteByEkVib/bdoQZCqUUiTVLa2/XDXa0saWQZnUz4EWgxuONnjFsdeU0ypWqAG4rCulw4biHRqP+2U+DIeX2DdJQ4eoophs2z2eskNnikMNrvSUMBo2bZ5iQ+dQzW1UQEvxrnNfeL55Xa/WXt0NLQlvvMFjDjMJWYNv5XHcZ4YXpcZR8NeKIhLIrgxUiiP6tEOXWnkUHs21mo6Ddhr9gA06aNGAbvauqeSxHbDGJKcipOZUVqO9fYDShCYQ0pbTu0ZVRd6qbS5meCgMxLA8HnGIk7RWvHm6U9q571LRBQiNj5aHNkMNllvTYG7jGD68ySSLyObelipkxf8GhlJdrUU/LsQr4jk9HhMqgBh1HVdN7RnaHWhYtborQiUeHcHUMnC3ig64tJnuGd0ZdqV4W4FQBHswT+RZ7RuBWlhVSQ3UND5VAxkz+7ABP9LYFb7Dq1G4udLnjlXXuvaOZvq57Xiit77HO3YN1E0hVJDKmdKjmgIigU5KYUfFrLzht4IPxz67Jmiy3mJfGgA04qH2stUj2rZBJbC7owoWhwhCK6rK5B2AzLSSBy+bUWPdQBsMmkze9QtLabNS81MT1TvQhWQLbB/dI7qu0h7vTazneG7djaJmmKe1iLnOfPUSWibpqpsNXojWYtIa0RhCm6IBdz4SL8smhvvhPEYNK6fyr3PlfdelZmYXed+0T8DjZA+RbI3OtqGRA8FcLcq13PDuHQbk7dHq5fTzZbwcnCgUo1sHeupYUCVOxUQ9Yn3aftW1K53pg8B0+NTBY+q9VgPO+67ANT8q4wSdI1il3cFx5fG8uKC0X/24I5r+GPOFh35OTtMyTxVU1Z1HN9p58px2nGO8DO3H6u3dwBaFQLlfq9fS3sTCZtphmGx27rguPLufndkNHMWjyyqK1X6AD+aAdVtDk9xAEsMUR/sAB/wEHpe05ovHdR714phgF6JkFP6aXdrsPvMWSRpyBDx7tubPSZ8TxyHQOHX+8vPVnNeUR3puiT12YsFC9SvGL1RB9eHHvYd9KeMekbxFl1VlWVp2znBvJrQLUGmBhh7Aw3G43TppEG1GRxNGazfO0mEH7Rx2emvD490MQEN1dtZR4/E8Ld8S+ZZLAWEcmDMgqhK4nxWMTttlrzDv/9tGTqcP67LU9TYq/w/Alql5JlQbu4NIT9ZFdWK06tBnvNR1xupQGhAZXGHZVR4tXrP042ixSdsy7tmsbNwbIg2qHglTxINjKggNWbYC7WljT7s7ZQWyKiGT1iJfEQJNC+oZo2s66cpNjiPP1tZGvdOEjeaueOhaJ4ZP5uQmk65AcYy2WZgDnJYT4YpP5Io1zZqtZj/SoagZvXO/Ym0bNCWeM1qxKUQtgE0VafYudd+9mFstU2AR08JkgcN7eFHDEGDPI07r16sK8+h8STvarGDFgzyOcU4/fUw8Bl4Mx1A6sXdri/jMfkJWqtCAONb3tfYf/nN3ZSt7Pi+/QkK+2FfjVeFG5OEWp+BHJyGOFbvyOkkOYJiNA5KfAOB5xHNeF2XDd5/VD5iXpHVCqY4DeQTmViaLAKwbq0zVx46u2pfVcuXb6/K1hrQlJQFKD44LLpbays7DTkIUWqSJk3FiThuQMF8XPThs0GBK+H6tJfT2Wn1V3zLTpBhI9UQdk/bocdCmY0xtntzWSDHV8Qq57v3BGJxDl72rs2Vx5dgYDQJmpKNMlwAQxTJfx4iz212XUJC13onolgjFITWT1MWR80gZEhmtii/otnwcoCGjV+Yru7ZvthrImPV44HHyccbE+v+w9C+KrfM6rygKkJScjv/93/Ssr7HFy4E7d4abJrZEgiBIye68eONNSEsvT+HuvKktpfoCWzEpvWqEDyV7O9Lay4yKq/L07+BrPF4TN+L2z7gZ21R2qjToNRgl8OCZ0QTeIj1vu39XpksBjQaIcbANz6f63w09sZy8T97POXWnf/OTbf0aJl0Wy6rw6k6flQMr/e6ezDv7aYorYYaSaRHmF2bPAc4zVTKkGL6jG52nLEfR1i8yc1Qhc2S4Tnf2zGHfXU+fqbt/f/H74GmoXThb+fZO5mTqpD7JfFI27+STPn3RaO62YB/zz46PaXdFb9Z0KXe34Itgpb9b2bCo1e59FZYyYp4x6kXOcyXjgZDDW+sBSRC2GdcFV+G4Tq7AdsSfhtRSRp3DJ9fRlKaNwQL6LISp9QpHce97xWw6tZC0zfQgZWrD1jS7JYF6rtb6pStTLef7vX9wjaCsGSEckJqGQkiEjcLrGJqsUstV9Ij6n8APGBV9rt/y/7rOnOwsV8mUQ+Z5G1XN9lEocjSueI9CXg+l1yLSFLX9zL4QCxHghd5TznZqbWov5PGvxfMhLwqAdVsfTk6WVKT+fs8q7n60Pqgb4KsQrYc+c2WGBoiBYH48f9aodBcZBp+WMDoj5/No35CmEOy8FI+TBkMDnehpsbEBjA6jWJdMxO6I3anJfDr/Yx0rUD3hsWrMJKxsdSgMlQFkBsLMlB3HO7X523xkswpHrHRy0SUAfP5S0KYStMYCdiHSlcZ3zYpbJQL24DRvmaA3w+e6ksz+rfOdTowmqv9tw0a53aKQNm4rTFrUhleh3sMjNgWXlbirUsx2+piNQFxQlErZr8IZWJUr2vpfKApD8Qqeil2VWt85T7eNLc01plSzxvbQJKy1CLJar2f6u7r0F7+fNP/G3H40/EeL2t0zk6gbqvYbcm78LCpvV5e/hbg42yxgkDbbdOrhNPtq/jt7iWw3XNvjsiBQo4MNYdfV+BfxTz7VssCcBSqnpENjF8O52S4+plPpaa2Iy2KJwj8dSoZIgTpmSbQM+63yvmytivkiv5PCPqC3GZbThxLTeYHYLvMidVWRPMe+OXdWTgdUx6UAzhtjgn/rrIXBNVwJ8HRoAe/FipVr99Ya8/4wRh0FXVN26ip8ENSegZKfSRlnupmUQKf6XVTuoMC42QWLNCs5GTlxImrsnjr4Zn+/+ZtHGpM0DWLHAOoFEWpUPonRR/3o3dHB9HmdScYlj2JjVtOkfTUiTUBDbGH0Asd2ryVzMxjKJmFGGyB7nv4bCDIY2pL6eqdbc4OX8TWH+isk4P3VM+QB0/iar5YZa3elUSJUUwjoimswZF6HUmuoS53G2qqodjWvyyIL0X8pJxWlS9ujm9LztXmWIexN6KVfe2zRfEy2Ts1tAiLK5xXjULDIx10bo6dLLFZXSvjazoTsuovQNnBG0c/wPDgH0CcQ5jAzWCADpRY2816CbkP4/x6t/NBsU1gw4A1sxKz0BFEO6/Y5ktMaGPTTrS8hLsBSuzzDUSYmq87pZFA9dy+5VYFIJxAHSNrtFMDptlc7lo2WtT/E1vrVY1net9e7OXC4ga5ydyVBR20KzfJcVibGWxSfQqb7A1OFJ/oBb0zKUg1vWuElDu+LOqPIW/p0hUHKI2xG0t3oMFI5CBsqmOnqBJSEKhGVZkWUJyjhKxRokHNcpHGV+RGxQaXA2q1gxTG0GLCWC1WANgY2cNKMMMi5s9eU8wxKZm0Z94ZfMwDVCRASm3ivd8YQ0ypKaCE4kbXEkBhgpk0NOeNZNTo7GWz+eSAwhT49OYRJuY937caCChoxwzdAsTZnXN/eoJ/B0URdXNAk6g0CKEOYdB4dRkLhJUUefKC1X3LowUyAq5dhc5nKam2GkubSV1UDbWbD3ROEf/qNV5YeNqavHC2XgtozCMzGW+pvlSq06edrz1EnsrEadfKn63e0a3pTswdufOva9FtF113ES9wsOxv96rrbpxUWjaMse6gR0gl9XprlQRrel9CwaAnq0356Ha26SlTp/ljNYmNFy6ZM+RSnBf6gD3DoDS+de+Cqgn4NGsrQ/maYgpIBlUAqaaO1BRexjcTr7WCGDQxHrM4SbHBlRG4hnuhR6gLE3yFXW7nGaRMLr9OkyQCUhhKofHT+yg5TT1eDi3lUXNOwMfWZsORkHrLol60N8+gOFGONS5xWX8mL6TVXYkkXXZAEfDwgtU5YM7pY97TIgpEGpVQtcNlQwlbTre5yYQKgMEUDAGfBWuF3zuj0pgU3FPGwq452XpmtODTQJhVNvoIzkx2pWUoaXe3wd57Yfn20iVbleVwgjaUiuIB9Kp7D50x2lbpNop6pQcFNYbzINJNe+kJTqOumg7HofKmZgiJ8mgkTCcMV5Z9j3qFAQGCxp1s2XcH0M3pGLYCVz10ns+QpdLWfL77KyNhRIvz7zK26b+oyxlvb+EdUWokUurgbuVmtvIsW/pF02A29ndVzha/tsY2L2BATgDpUWyW6xIgKtekoRrr3jBXeHIpF77TzUAWJstcTkCYPDM2WXwAAEABJREFUNSencpoE3+Ux9Nu7bWo0REdjZKk7J1ILxfrYuOwSM0aPEdh1RiwwS4mi6mMf2AHY7nDluHVOA3B0XVaHLQI0BYSXuXR+tpVuTy6OGdpOQukXEsP/XoU84pOuZBHuA4fbvMmvVuxojLRlyBBdj8+j7cL6G1HKpZCBaXY0k4C2JjuPTD6t/gyyFTEkNv0GC9ZwlpHjyxA+49TVKcGG6yLohIIzqcFMbIndLG2QbqCdHhx9aFmx5Y7LcolP5RMD6Oe10QxJf8aedHUQkYJRmNMpUANto1NBHSVozvHMq6Q9VV1V2l3it48LEPUmOvE2niQSVUvR47i9ETTFJ6XPepT2JfqwFNB2NaTkAwlC4lGwNi83ZozN9eFHUfRwSkqAEAu2ftmM9zAK0WQOHnE/7PEa+YFPhWR4Rz56rhqP4XCyVNp9suEdu/bl9jHFQkHGUYzPnGIzGgumHuvlfKh2Ps8wpWDzVjr3ca8eic3azcIcvvK9d7xa+4MGVLKYoaRX89ZUFmCCPE0dFuQ2+IuwDVOYbDUcqb/YyXzed7ypgy4vHD+Ju850vd4Y1SaTPRQb5ESN59hh16BAXRgPfGxetKLplUjQNsJGgcxDNjZ7aeGyViM4omECgIvJ+spRt5C+b0XA5WNSucczk0Q7FA/6oWi7qVMDh5nM6You66vRw+xDd3MnOKqbcsXCCssL49OpyOWUmkSjszfuPVri54n9xA8idHJZzprcgoaHr2Ni6JC1J6adMhUxUhP9ZSIdY8ZZ3irCdcpVXmiz0VCJZQ5VdFLbFBNqZ0vDV4yJuwHAxhSa6Kv57zHlVt+fx+pZnstnMaItQGpmzKoy3R/WFDk0wATYbylQkTzjp9gyKdOpmPV8Q4u4wOlx97Mg2FxhQTeaeLCWYjIKQlOns+YPnC3aezIVt7n2ioH2VwYdJ/d5dj5+jCJJVEGvwdgabuoz07UQf0oZtrdj0C4seT2LqWg5pQ/KSGjSt+c363567kZKYTKyWkZkrwdGuNE90uRvHkuOsqjYXrkAxyrjzHypohJLsXV+BqOfnrmRX8vbsvTlDBosTnoXgNeOPcYTbLPC318MyIF11L3O4ejKsoHODWRSi8Fz7ud5rb0ii0yvx6xx1VwHWnO8rMDHe6ByTNbUvKo9z+gUWXDNxiRxN+/zXiuolVRIOPec5GEkr8ZV8bPEgHsH03ReOIxDypIF9Ej4B0rdMZyJpnhSVKyzTvrpas/iSZnEfedznu6iRKRBeJB33O3fZXeMvtVf6pUBcRN4DFnAa9PcjAJwoBHKUvbBNKRdJNQl/D7x7ZVaD9PwF5uYAgZmBkdFtijusRbxpd6iIPvJ1we2Y9mRjohRqimP4ryWl/I5bDMGueGBEBMKOgyo0vxOJVU6ZwM9iu2ZPDVo2NZB0/gUcu1jRpqSGQepoX0mk+PEAna2Ha/+TP8wL2bMsTewMpa7aoHgHvdcYEi386HRHfTo9Zl1UYjmjbsx4+OmiBrEE0vwl2LGaetyxR81fhSZWxrKoAG0teaF2bdV+kDIrLcJnzucpoup9KuzOGYbtquI38wqViCZ0yeekl10G2U4Oy2PApEbIs+cM7oiSgETD2sHVsANZyp15OR529LZ8Xyu7yeOytiplJSyJrlhJVbBzEsZY4MSXCE0gpB6+9jkptNVMPBHLWp81irFsUu8ReRiaWvqv7cOCtSg+KJtL/gBk90ra1dZd9dfjgYGUYmemUYLUTj24g5fC74P98OVYsTJZVCtugkGlZ/PKDGUmv5IjKnr1H6wRE7cpgByWjYlmOGU/E+/goIlYmyZvSy5yivxDptBIe+RcFtkysPI28VeoL0uSGt/+RU2gxlqUWoCET2R4ilnsmyKw4Q3NAtKh4/G1PTMgYjdV/viiPgEUNyJ3fJhzaXhl5+tpS37HW98/LXXlcxkDVXhk1Z/K05i6s3FHdEfcjXRmCm9o2JyQ7hECLYmFksH4Uq11+TwFoqN5TRzkxyFcsTYwDD22jiNLEOOV04XR8bRNGGfHlTVVPsI9BIfB+zhEHordr4bo64SRvLvUmsuZNnt725EJiy1Uu3l5pwhS5/Wtda+XE8y3TGQ8/fo0VCofHWnYIsAhGLtO1bvFz+Nf6gL62h1X09bMS3u4V1djWlrrFupUsq0Z+3AtXr5bTrw9bqJv7SPufIwsWZdzWvm8gqON+JYNNwYTpd5O1DJhIl4lk9x3o8T++xV8TEKmZsV9AqftUgJX3icvUKBbNPf2Gy9eg+pxjrdxow6FTtCFDDesuCN9dvrdu/V0orZoFGn89HqIoH0YLLj9EVel1+hHgJKCJlTwyYxQBKvarTKIL1TjXEruaOqslcSx+fdXWjctFaSOlAKlUP0yDwUInV9lvuSZkpgTCKJKZP9dEB0qRVch/bl0eaBVG3aijAIqB6XP8a+hutE3Av3KiVpOi2P92OTQlqsUYgPrmeuKmu9rMzoKr176u4+Othf9q+pBbcUbqvVYa3lSB4m0DZubUut4xt+uwmTGGCDD/DQzzsUjvFs3s88heqqeljP9DPzqnZw6AU3DYPWjKdxjuVNXdWPTauNbAtclttTYyfaLq4VS4J2y1UlYCHxzNyTDzKt29Em3+JLqnDkPF/Mqy0P+zH/Ia7I8KKsdFCDMWJHKsJMQoiVEq/x7+zftZ/1A4/V0t2CjMN8lg4FjYO6cR6oBrSlSWkCtAhfbst00Cd4yN8RVyINwziz3qKQ8lAc5V4DQMalCSrJzXad2bf9YH+2RKozwnaIL7qNlysEBu45v5lPTicJOmejFsY0uCEoqibfpSXhvEkfy7EeKtE6hnOmOwHpQdWLrmuwRnWyDiQwfC/Wlt1XO5x626xQHsQDCrc5TFchpotMIoirPlY/7E8ZOr7nJ2uNBq2Mf6VA1Oir8TjfxknK9WQJQwvAH2I5mIlq0zD5lBUYDVy5/bx4Wn0AA6ebLnCb1vNehy/gW1Qe2G3xXfiFHp70ySkxUeWn2TVV3eqkG9vgKrpqnkLpdA2t3Rl7kRd68X+eA1AEvnVy9XWdC8feU28ItXPedXfKLYOv1iEV1AHSwcUw/+H6GHyPqhLoAD+JNdV2NxMzfACltuslUbFqpgrgFuxnVKNaoNESZefq2kh26duQExilnGzaMXuokV7qQmiboihVBB5htqCc26uON2e+lr/bl8CBbgYgl2IdrUjK31mj5I3Jkmr2SDlJsTTTFoC5dbCuqauhMZKPRt14G+hD67+kUARZOct0oPzdHn8VyjptLfZ0KN8HOUIdpT2ASsLfPNwtUDbYA905+Zmh/1kxkdTJAWVXebOjDudE1jzZKgCoH3bZeue2vR0MlSaWz7TiLMbt1A6tVvfAxq3oQ3FoNvJomZ7HU08NSuk7jZJZsJJd2DjLHxgOVJaqXvsfqj+VNEd7t9cIphPn4RQovrxdgDVrlIEFzBxByfZWLo9Md3WOw7fxWljkZYJG5VdxJQYjkE3Bo172VrhOnqG8a55ROhtLKBIVjT5pjg5aQ5AqUfizhTpkocPyw3v1TQm+Wdzti+VWciVaeIyN0tU4hl5NfVUOjlHUFO+R/iQWmhNOoWgIhDtCTa/MHjcQ2ql5exFaAcE2YB38K7vk6TG/Ld6ZUtOItD8fZopL4lsjp/06UZoNbrIOEm8uTs80qDT1zhcjEq9I2kyzXVRNASLnK80In7WInIM+BvPYOuCh0UlLA2jUB2VslOAzcKGxM/HblgLv7fBXcHLqkPblRlap4JSPv8TrtP8RhJcGTrAVlLnrwiIAVTlqOHQz97V96UrZJOdAMY0mSifmhmsksnDAjLoD0HYzjIvaxVicNIGQuZYWG/pcJb6YzafsHmZMGbLynO56OeqyKs4bUo3p27S/X0cCzUPdXTS2lNWm9GLIP+gcoI3DZa6ONy4b8tSDMlPINCfhGm8QI7FmiSZDgN5yk6ZLb8ZtWpBWj2f7t63GJcpYtf1dO5nXnKhkPnXO3xZKQUHeC93Z87BzAyLR6BQPQmPb+sK98i02aDCpa9ObQmH94yIL4TKCLqaxXHGlNVjvm8jglOMNWVyJiBk/iNNxYNIFYV4jhlSW57Gn12N/pe7Ta5ry2TIUsjoxChoLaXGnl8w0PgJcUOhspI6BQaPCj1uZXAnXG98IYhdksUxSCi9a1eDWvFEyrWTRBa4tRjGYhG9HN6hSksYbu9+Rrbig7jfFAz/tSbYrjocscBRqrTXhPr4fe80rqMc6Z6pyWKNLwREyu9GPC3xu2aB0uMzcwsplCw0b8UnMiAQFbxTT1N3dlvfJ5+C+BXuxdDenpXLgQiGnahuwY7s1HVMhQoxuwxHC3h62HW5o5cVFKmr6WD+708ega0sj+KE+Ris8rTvJKYBFWRpZhDDBywxjmwhzqGw+EZccTDTsCTTLoyhYgBnNexlcnpdrM2jLlk9MequQ2unmmySgPEG/POAfxoq4SFE2ZcKAoRhCEYLrcqDvDoaBsrb0YUwtBUoYQJol2FSYGkB9lJzEKEd7ro8JgS8zhytyqmbqzRNacnPXrsTDEQGuwaqJd6GSXXGS6IPncE7buf1uPBLx4myfbbIWmMhmZvajFlGZlZ2LR2fxrolvelqkkAQBKOhG67fmsBNdqIZ6ZI0u08mQXQ/7iyXHRrmiXiZu26LdtcVZTD0vENZBjVGs6qqzembE8q1VgTMtsD0Yx0u6M2ScRiibGovp4Yiq6TN1utDjTbbgtKIQuCqesrz9fF1fZyE+dm1cjlAbgNr/HXZMj9dUJfJEA6h/ek+4NDmryPzzINMDdHXnUZG8rg16t6Sl9lILP+itZ2TPc0rPGX16U30Axk13M+hVYNFmmV/2cVwKoYNypYCsm4pglh6q1hd8VBgBJ0IUCVxxSiHgQ3q79mdarFGlK+2VcXS9hRAgZKhHF8RhzCudZSr3NZeJRANXY5O+RsriQnnCzzsxw8uu5tuwhoT+URBM2WS0mfy3iQXjeAGDjEo181cXZzzps5w0a+isKpfemr9o6tlSaM4cz4bFqIOA2kw9QBKjGdKKdrGCYz2lpibbZTDbNq4RBDq8F4XE/TU83Zkjyjkwcq5pu5sCBoxRYHXP85jUL42Wr45Nc/GPyc5SMeA0cQMPsv0BhURdQbfmlvzbHhZYvOCGJR9fBbZgpFhi6mFa5PCwak6iJao33xvkuKqvXwb15ZW3UOvotqfsmU6+x8EoS4bZg+hQ4XXFALjkq8XcfnpSKyHuY4oUaJFgw0JUaZduk9ONlA41Q/mK1Xu52wWqpZlszltoY03dZH6oCwqBnj4l2ZJtxvAgetDN14q/urOUqeV5WauX05zcnnalrePTu2dVOUAYlCQ15oaiAajYdeYp1cDdeYbdGxZm0Cd2AkWMPC3aWtQ6tHxk5XCf7R0WTnmoQo3myIfRBFbFOihv7hGitZ17x9bosm4ImwSpGO4o3d9lRwHgyF2PSO41z6drpXvtF1EGSjjUNik5sW8AABAASURBVM6dpT+vi0iHnh7di/KNL6SL1qZJBxrsqdLb5fFxiyAGVO1piyVHXAwFOeecR7sWieFZ9hv79oDl9t/r+o04suKYRQ/utuvACg9VxnU/c/9aPrvmozU53LXa2rS2Dy15nil9dHGxBjn5tS7tTtaRCGSPP/P59+z9BW8NPc/MbXzC4PtaP9e61rTNMYJQmvhOM4rlhdm5mWpzhTi+4Ar0P9Q3kKFbN2ieX7W3b4sLQu5KyzM+sE/xLyaD6oP7g/f/DAtcSWYadKd92zn9TOdYvQrGY95qRTf8SatBU57mdvRyI1UKbCzj54r4v6V9aMY3VYnTSlY6ZtuiBSMKNqkQwpaO7eaCQdAG62k7DzmxuVbIXDuk2nv3vev+9P2DZ/OBQqHPXMv3juV/FpbhQzWZ0cobgTBba302/GL7/A6qjW0OD1D+DvgYVJf2Ems1lofPw/+Ah4BZWHxiffbixaQkgkq2O/xj/NEiW6WnLxVdrtwA9Z/GVFMIezekWJZQBiAQBs2jiiQjvmsd81QOGs/N7xndvN3OWzp9WjgLqzveJ1/1tOez+CV7NdRuNpUzjHHGnoo6Hk8q54wRx8S1ytfUB0+k4oGXO3do0iq7fm3drrQA2WpBmCJLCRmnb/4sbmFVE9tA0F65NQEd+vEmdaMpHNJkPevFBVtsdmeixmqDPwfxQMKZp+y9DxAKzIbaxea4jA1ghBja8vx+N6GL18EBhDbVVtg9j74+6obwqNa65vtTex16Vl2JNc7Yz2XnowKRHmFVCzdNMe33ms0VHT9PrNZKtNsVm9SA8MmVd0/luGMHybmnqxXKTA11a/01003noysVVp/47rgt7qee75PK0sz4r9lDNNN4vyDyrv5qTbhUUZ87lhZWWTKDIjdIGjVapHzELW0caaf0NGvSGlG+TqAmlRp164tUHpjol+nrLrE6Zw4KzPAuzN39wDNjdDfS46ORtz1HcZyxpgK7Vi49zsfJU889JXOQ3INjgV76NS/VmGQ/hoGzCQhqTjtGELyUMF0frhGtq1ekJkJxczjKpYT5/o8RZPWpzPkeP2erPlxF7JzAlyfrzn5qjunCKKtrd3yIf1aOc/q+Md/F2i1l7Asf2ErY5PhTVFGImnD0j/Fz2c8Prv24yuIuNSHFbmLwIY/a7AUFIvVkV6/AJ/hZ+bNrbZipqnkZ1FzwgyEN8IZlj/CdbG25tfbWKC+ajgOkw7wWzz/xO9nWv/u51+/w6/h7JSSY9WB/W753LB0GgdBVI7CSpm//rvrZ/SHX/GzuT1w0wV2t/tJ6xhoaK2y8fPmi7oykUbuPj8jEInwNN91ekUp3XlAAcJ3M0X4jWO1x+/UsJ8c5aiL+ZHZKK4m4GV+Ml/mYftQ+jjpIKXLn78YdT9nplTAwp/577u9za25hOT4f23u7b9DS+7uexyobDQEb32pIqFx1u2RQMwm9JKWjIli1rNCVz3nmq1xap8NIa6XUYnjNaOpuMdocImt9M6pEm8gI14tWxhSb3nv3x+YdNuxkz0jMtloMaPLBz+DDooiNngf9/+JIXQCXSwwu3NdR6L/rN+0eqj9gXTtwLfh2rKX6bDdywhLztmVpgWKqMTZKbQMP3rIf4u/ztGi7J1r06PDVft1PSNXP315RIXWc8nyGA4eiV2w2sHH1yueL/l7r+blyK+y2/zqe/sl1Sb4TqOkWostLh2hDvdQqkhh5kw0tX+NK3mP+EKzl34sqaMEf5zDQNg+nYtn2BfnVpQf4Br//PMNED4gXmsYP/Gk+qto6IqjU3k+r96gCNBd4Ry5Oq/Nsr0/j8+Q+97KvrzMb9wffz8uPlOj6CUq/23A5Y4V+QO9CvsYRp2nM/ZkdmwiIYWL2zM/kJSc6Jz33W2WlRKpbKuGtAZLDmfrN71emRMWiLE0R5Uw8St3gMbZTZdcLs0qBG7rz4X3bqevGv4IWF93Tyfcaubc9sdtW0pSsqe5nPH3xgmLBTN/Vtz1r578twt4Rd82IFRm6yAhAorzvEoNCpmZS53v+13wPLU18SxbPtGSzGrHEB6HXgGfYZlRCMHnAx/DijvbXhT5761qTIxEpsW4WiJFWMTG1RrzSDky3II+Wq0fryTSaOWpTfsfu+IwtYDEvz5+d//r5PBXs3lM/wqPuU7uefb5xko93Lpu1fV8WWi1BJa9s1Ji+61YpGfpqfApLHdPu8eL4ei7cuzrOksvFMSk9YS1DP75+aBtzAZ9bQ5Uck6psifQLdUl+v6iK1gr24/enRiHJ62AMmPYsP63QtAgeURcRvt1CjWbOTA5gIxVJ2NfCFeYdmofA/oHKipRCREZ8bZ+KatRSV3rO5LdaNyK/OaPdbl3Bf8vA8cZSJIjlNGXMAacgL4foYny5jllHigTsmOW1p1eVOyBuZ3v651hkEG+9Kifppxb75cdP0Fa/RQJfpvSgeF7QKQ14pPM4pIfTPuVSMl8qilQV0qDDSApTL6YNTBIBsOjX4l4WUu8Ib4+UMM2kP/BSVjQWdIu1qF0V1yN41bzLT7qVm6qXxZHVV2/O4fSgIS+6GGHXFeF2yu4U6hgabfb1vT73XrMhuOGlc20y1BG1cQRTXkcuR1bGKuUCk0CBGigObMu3gAOKBnDKP0y/JdaUVr3n7UoCgxkqRxprIDgiVzK4c74HWbrbVy5maQSamuXlLOlsv7KoYacjlUYtcZvcgCmEump2Rcw2RDzjgqYYlFgndq+YNe5vce4MS0lYGZaLLK0Z51H11tQT87iXXR7bPLBhOw3wNG+LwLLeCthWmNC48tIaxDCJWNMLFeCiOfyMPU9XvZuUnn2wiTD3DusoeGE1tU/spx7wdseWUpRozegnLYfEhCM8Y4412/0Jf7Q/DSo0VRwOs/dtV9oSo1S+yjr1wMsqKAQpE4DogT4oa4wyNOYUVD19tCmoNj5kF9i6ZhT2VaYseM0MEv4MJJdJZ0/I53wrsxvzFumM0GzA9KWPTA9KFyvVdyvFQCNdtOJBZmmVahMRe6gJGOesBQdZUIOAmI4OzGos9lrUsa09KuK4HZOYD6SjbPE0taZdqJjTT52n78fvZ39L8GXfZO51/btGdfLkrsfnpcNFP7r79ToG5Wu8Hj6HZcof5V5Oh54jS336nKqEC8XEogUnrLiG0kc0R8ETqpV2MdHLZFKx3SaQRxfNlR+WwJvcI+SmTsmuWu51RPl7IlFT6LR6rufoikhJ73EzhOnG8gkboZNUHMJr0wrDVP0zCOd7iDYZ8FF5RPRWJ7IC543xnYIpfYSRsklraLMv5ii/Qpm5nrYmYeXrecViDNIgVJ13U8l3peG1Qchrl5RvKIe+jTIdlV7IuZKSD9tBc7NwCXQCTemsSajTrF2xHsdxiExiOIkqjLlH0NiSn2xSAXYS2QqYUleheIqlsmn1GriulJw4XR4kB0c0ImE6ykCzgVcrrNXrrQBF8tyrvs6nRQnxjiE5tEYLJNUfLlcvkeCqhCS4SH9YSZ3KNbl7FizaV6pYluo68DEtbw+nNNaGqL+EIlwkYjyHX/i3WQ3VFQ8UkQiMfrzF5Hj3aqHBjBS9MUatcXm+rzTfpvb4jGMZ5drEGgBRUIoOcWi1mep4MVqLizNmvj02nBonQj3chHTxwdEe5/89cx/WXIjLtalaih3zR96R7sv5ZaToszGfNt7Eo8bkjSXPRsWP2Qqm3t4FdzPz9jkSXadnmnYRXUmOYlPM3dUP8B3co6+gLpGzKQyznW4+pkLvisHSQYg69vZROswVLTAK16JVDC8RlLbMF/tacPWhixBFtdcTodsk9YOOO/fDq22NgExGjb78OK7rSC4J5SnZNzoPWqT7gunCqybVbEGoaY/2mvYhjFqGYT0slqIAhdWNDTsj8RPXiX+PXY8stURbM1Cpq0FZ1mpcRdXlHDUR4yDtPCZrKyx8E9qZ/Qyix0oHadDqexwpCb/opjCNc2RPibhMIwZW9Fwqp3Xa5A4U9+1drp4Zjzji+uD6lG7GDGLBmiMw44/4ilF66FQUI90cYy0fSpLplUvVZ5huwUedRqsXHYnlBZRtDXprHV5LZpAu6xrCaUlq/9i1ZefpzHkwQlJucgLdKytPwrIMOoCBVEq+b0k+HjeQrLIezMm5k6fjqas6ujgJ9D7zuRlnU8uiII1i91Qi2nMa8iNiLjnRQS5OUKoubTS5AEcEZs0hv7J265fHMT3BvlCXHHyJM5P2IDKkCXapbSefWd9wRXDSMjmd7Td2NjljNF825kci4SzrMI07R9eoGE0DAiYgOgtwR1778UsoQAGiV12ZVs84e7lkplGjCAyyOOptu8V42veoOv3tsS6346LJkCMQkMQpqEKmZNPGpWdeCuKOuSk8W6bDlHOzIZ4pbb8eycpQRkyYEFLG3qV81IykW3nVYuhPWVcMqWTQHpvUF3/zpjy15tDHzOgrYnlE6DOAtnqrDxhd51s5nWMPo2AqMOubOoECWuWmUxTNTLCRUc/Olv9FETiwp7V75bekiHmO1AFlIPlT/aOhczsfsylX7rLQBXpThnNy0OQQqFFyMQqumQnpRQnti6+YxIJqOeeVh+VGWpyz8lDWakrSwFs8F90WJ2RVlnOm3wSx2f0GWHB5lp9WSPOu95nP6akc0zXzkETSwXWuPR/doP585uOzyQuzV+Ka2ZBCYePee1V4v4RJxB6+1Nwvqx/e/5CrBzCal7oXdtQV+cH8JHb7jLfC1zaTuQcrPz2fB/zF/DqPHhitz0dKbBchPYAqUC4X5L4+ONset8e60/hIOj/me30irm37BzrAUmz3eQ7qjd3AoLtteznwu64aJxqlFf/en+fzKdttWipkMOoTCK4SExApGIxAKNG+2qPpKPfBD55lB2LgangbYR1xrJ/ptpYJACJ/H/ohEtNzOu/JnAKG0GuwgGUjm9nZ6v2jNP08/PfdfgKGXPxzZ5BUvOiKhYG3MNAmTU3K8Vl6DIesmdvszM60rHrYuRvXG7yNMvbZpNawQQw//R5Gy4WUOIEDtDChgBTwaPNc00qi+LrBe2XuR2UWTL0ffm88NSUvfjRDn0hYjAn4yB6pFHGMiSoZmnk7ua7TFsLRWfc5D0sUa4pTHEUJxmlv2ixRPGPi9ng+IpujkgMln7RDlr+YR0zaVJiSrbbuzQumPhsY70ROw497xbx5fKqf1h6m2vV1M68pz0E6IhhmUJOFOWPz/Q55bC62NmvydLedGrHbnsXJ1mf5HrjC9DAZtT72hotUF/k2SpE4SuegepNTQvdf3BxRYJh+EfEeewZ39cnz5Pd5vp3JBO6ubz+HhUX5gF67pWW3TRg9GRUKd7NW9V+TUyBOHsTN/Vg0XPlWnDpGDk7nMY22s+ZMnuoHXZbH33VBXKjyej/Dp3vmqOlfJgn1MPvUCGNW2RmKD3S3fjCgAUriAkw6SpnjUSt/8h3GD7gtZWqGt7gTLVOjwrDBqElcm3TZq5n2qnhoRdnjNbY2vxSEAAAQAElEQVQLMT5gMh++rP7n9mjG5gqIGG+6dL4n1zycVDnpCtfullZKS3RLY4MsmYBp/XxlXAOdFnhOK2/r6iWDHsqKU1ljO90g+kySFM1CMvEZe+NTwThaHIsOSVZ/jfhs+Gd6TUp4y1ttSoYVZBeIIWWudcnh4z5hOom2VNIB5Xlglr2VtTSN4a5YZ1EGFXlB7I+tXqu3txa94UzMmDKnazNOWQWXS24NMaeyzx87Zqe0V+7TNa04iGVKBd5kHLG3JhyvmeFqtVdlatFUQUYr4ExlJ4Vkv/bbkHvOQAs30qAXe3rQhKJ0W1fFdWrfZ93p6oYThxD10V/kQdKPx4GY07FSTcney6qpVOq1cOycANaKbetnuFtRC4qYeiylvU7M1/o7ynODhFoiCL3Ek3DE9DX3xj199FeIp5ALYvPifBI/d7y4yzS4gS6Wonnov+bfsW9jBjBCqApygQOIHzOSoxPZdz33ZGrgGvE86GYnu9GacQ+b3mHtPV5/5wdlhojYvi8aQRpoHUphLWCNi5feivbQ0tiU6tDornKZw9J0cS/73YNRKgXbvx6PBLE2GNTKzEvVfuiDRVxm14RsXuwNUKcXVYHjRjM1EFzBSwFo89Awauyhqj60n1HSIRALs6TF2Bl+bNKyjWVR5gq/MrNPZWXyvAoJjUYBJaaMMe4D6eJE9Ar4Ii9CajMXU3LBSs/apdMZGhAGTVZsqokeUSzL9RyU9C6TqoXdo/cI6tXNoeLgdljzlOjQE+F/yR8cxTzRt/Vvnex7LYTaT8yz+rlcQByS2mSXRDCIahXxDMPWsiA4rR8pywKkYI3qXH/DvQfpyjyQ1keA1w6/fNM+uVbS4GXT7CnvJ6oMVUB36+yK23kmB2q/kglkOn0dv+p83vUb8jSEXe5h5BzkUUMT3xf4E/PP8BPkCtPdzDL8vUZKVfYEF1Pym96N0D4g882OAJNvwso6Ez1Z/jTOdFrlEkd4CMGDgTUssAFFbbyMeyTSegO2mAhE0IYsqpHtbva0Y8T2jlarRjiUjEloP3A0D9BlzugM1Ypq1fhXruck8hgOqNQ5lJTnWnCiBbEalbo4ls1ncBxlyg7lxJHAnODjO9dmLEGiUCSZ0nFjy2QwnWcoSwZu3Wxir7Hdcw2uJs9n1A4UjI36sWyLyzclvFlfr4weJVUn34Rabq/PVWsP39EXVDOFyqc7uydpBXYhErJnPa3CV72wgiqZpor4EnmzoeoRRz72Z8nRmledSdwau7nWfPb4h+IeVrSvxW98nv3pd1/arILW1e9d9Tx+2gbiY1BtRyEvlL53TicsBcisNlrykp+peasVt6xADbW7kWI4343d0RPJDJYzZ0o/yp/QzajW/Qa6suzpdcNbQs30c5gtb4BxGMejVQAl48e6fTjak31QuzHKX9fkzTdSZ/jsp+bulMYxIs914eRU9mi0Jupmwndzy7TrocQjlWbJFZg0CV2hsEpz03gvHjUrT76jCQFqQ3vJMEEbb2LMyBiiIHL6dqRBBbqs9SBiu5k0DhsFZULS+cy7Cetqmd4eHH+BoigRPy8LazWvBikA/d2ToemzKzcf2zfWAYYC61mORjNfQIQ+CU1f1t7AIQTUjKquZctlmhCz00JuAboUEwOzVgSOIN66bMUhWm3Gu4W6+JQ+pPgbGQlnZF3fx04XX8+DHP3VTU9lAsnoDAkhX2JaVdnal1FOWmM9Hhd5AZlGPCMTahbyi9YfXf0cm0QMNQYNyLAxaWzpZwHWOkZ1oS6jJjZ6/cmEMNcPeubFTmuLBksQlN1bvfnTfXHEgxlk+R02b4CeFknXKIXzrCrqqhR5Sg/xT5UGypJhqE+dwNN8gFnFpeBawLANjEnvG+dh6wa7BGDaJ7vr7qmyeZ8mOQ5GzamlvU0aarqUyppRQNUiGD7CxekZdNREasY8GiZZYkTBdOTZjeW2zYOONVqiTR1AOKBU2Bf26GSc8Bvd53ipVhm9/Mj026N1dn4fJc1qiDBt88CmuamLTVQr6iUW0YQQn0AJr5y5TzT7MR0oZQZo9PBJG3UhXEYXM+vFUUk+7f1gbkPvtg/CX5lxZHbxbAcpzbTuaM6yE2sUhIuvHpnVWodoD2EspWJKaMQBlGuCpogi5NS32OAy0gClMMeLNtYCoWBHpDp7EWFmnOmuRDc0OqYleZc11+zBm4icHKrstYM6i2oBypc6fMwoys6jQzGJBStnxp+kzUUQ0nSMQGJayWr1SMHqFxZlf6Oiq0TH1/CU2Zk9j6ua1UndBUigBnDXMYMWVtPksPbOzqfPa9YMJKA4RkEu6mW5r9lOH1Kl5f4sTMjDveYb596/te62Fi0J73dGq6TYU5Ig8jULJyRz+ackTQeQPilDLmkAz1SCqQv2MaqUBEGp44jHVzqqbHF1wkbcj3WyNFmZ8c9cV22Tf/kp6IOgrdGfOzugSCAY2tiOnueEChLwgaORJZa6R2Nsj1h5P7AMh4f+FXH0sTdkkVSEBhKhzYS/CLzdQNX8cVfdiU/R6RiHRpMx2GfsBo+xrQAdo6AVIh8fbbKe3XqS+Eh5AqhJsW0tj2X2kj5E218WEtXS3M1I02w7ZhldkWN1KNLfiOT/jeq29bSl9PdK7cVFQpy21V15Jx5Gv/Zf3psUb+M6NXForzFlLIvZ1hhdtnkvpy4LOgQHCaS74hb1HYIUZ4mTGLNmDQ7ZXq5HIH1iioUeNLyVmKpGSg3CdzKfOT1K+61zSENeqA9UbaMkC8c5uB+8qjjjJ/0+cafsjGAMIBoKog2rEccjP8Fr1IMu6DbqqJ8PKP1BUmG/H62wgR0SmIh2KkBYTnDv/W6IHYai3xP37VrkRxA72zBGm83amCspDiv56LehXCF2BvvC23nWtJ2RmBVaaYyleZLT4hhrYMoGC1a2xi68zJHKeGwKD7rR7xBHr05TJ4HixKCZ6UpMS7n6GmMfmLaG5OnK7xndu6ZqQH/4+KTy1uAZmbj8bOX1VXEaVK7q3GKNIxzeRPXMPZa+4BTAjn6g9XdxPAAJEbAptGT6KGc5bwgme093jmbj/WIz6ry2bWJ1a6KayKAyz9OPzDPa8DY8V357XTfj26aWwaQY3X1cg5UsYR9pJvd5LAspfATDGIKyTCIUezJM3pjkdEz5jLVjHMILdQavipGans0D8SD62kbd4lyjleIIWWiG1ayccNpSELJAc+daWKjIZ+V53WHEG0/hRt1WEkZFTxzYM7rWwflpv1IRA4aZERkyzWfkfXTfhNSG99GNmiNjNxXGm2wCU4ayNw0uU4d+aNlsLalzhz9XjDm5NcSkzNGMmHPV7/U8WrqV2JLOa9l4sN2fpcirl+XiCwxvzg0JiMpGjRC4fiX6pnqH0hj06Jy5G4/6Xk2Nht9DoaJ2TlpUZA5eCMuwdoJ442QjD+oBmiaUCIXCkj7+VHE4v+zslwTrwarceJwtqLnj2X/gT+ohIVJzbYdfb0FoLbrtOdbyyN4+Kxgq614+NHR0W9u06TcPIaiKcpRGEevW8aITHZtcw0lWEo+onsK7O1JxAE9JXQHusVUWYyyjkWszgkr76U402S5JJ5bAlJCVd8eoVEyjzupbYenO4p2ttL9OeMRPY9E9NnS/oHS2Za8Hekjh9aJoK4t7xWMD6828roSfZ7Ierto/tuy65mIFH1OfXwmODa/aqyW0aupMi/Vt2G5rwyUATk9xjma5BpSMEyHKkDZVox7y/krVGVE0qGDtCry15ZXzthFhGjahRvt1aR4ajEmgFZky5VqHQaPQrJQXcmp3RY2aiAbZjN6/te/8ybMUoNbhDGgBCBObACnJq3G0blZUHULljksDyqlR15zPc39OflId/jvvtp9PrpPX0x9FLocuJ0rFgxa6thZijJaTQia6c/hMJzMMwRCNI064zHxRjZfImUYbZ9GVdt2XwNkTBTsw9vJya4FU/1H0Ptl9qp6q05UsdYH8cC6TAJ8p3Gd9Gd+Zg6yV6j1yPDPNbgeuGtcGAn8vJztcD8C+V+j9sZ3Fc/C0mkq8abYhOYCiNCUZs3Bs5Ibp0aAzgczKo+bKc13nQi/WivSrsTpjlCZJp+EDVq8z10nt1Ay/y594CUS/VNWcsoPQ5JISI4+do4EbRbnMTkxqIBRpmuIZa8HVGWnZOPyE/7vieqt0F9Z0tEYln8fu8wooSfYIb9bjONsP+fQ8Ddl7kzcgFrledrqUvlbkUjl03kNvBoYoGTkdcQKlPnc+nyNp+RU/FnrssMYpvZVL9dg3PaEJjz16UifWXKo4fs51+nPgs+DX+JvkXX99TzCH5kBgCCY9KT14jqk4dYhVpaFmH2zOJX+u6RR518EPLQg5rBqtLwODDLXrFFa3isxcxHk9gK8r9JcPRUjpBb/gkcei2J3XsVFDtUi40Vxd77JZkmNqGVgBE5kvw4PXA0K/j1KJlM414TUDk97ypXi+6N+eEhfPTsUvjBoUM4IjZnuQqkdFLVikNkG8Vm2t1kQlS8FtSacVsfT+KG6TqbaiQ01AZLe3DOw5P+s9AkfTbptHmDTfFJkNLg6JlhiFWn4pMVfK+ZF5h8QV2rY+aaehgDByUNxCBPMZTjrOklt0tYDx/qxZl3M3PEu5Fa9yI2n41/ZxAu69doV8an7HpE2hqczaQ9dnPNBF1zmX6vCUHXrBWTNdB/MMxbHSx6qMrg+pKsPcKvkDenhYGKNpalX3h/qjnLINHuLxSfZkCi44sfa1Vpi/+TNp6giLgmxGIWbcMGakzBnCJWRVkIY0UAN1nREzWlNlfkknXaonO7OT2vGFhrDUAeZv+gzgDSl8m7B5TSstYeWeLobg5rqyzN0L8SVu1+lRGRyriewe3MBvKIo+3uWs5eVITK+pj/W+4BdmL3A5jdAV4KZg+iggxFEXyVXUJTHMdvYHip5jvG3lshNITrOAxrzv9G5PlffRFU6wqZJKXQZ9tudVz/Velx2DYQj9aOD0Z9rS+r4kBOil3jXUObcr1g+v3XvlX4kkcGQVA2p6jAKuUAuugey1crlFpiKZzryf89yH38O7JqvmKU6GY8nwx66Pbn7ud2De2U9BmR9qjyDcThAKoKtsxnz0ZGNsBMqh3zMPH93RnBiaMAl3NvjYaOlNkrgWlyRjqrpQJLJBhFlwQ+E+9KpgGmfIxLrTT0/jNnSc4LNxQpCewXk7gjVQRBs7ppSfpeE5dWZe64mqhzy1Ze9NLVOL8pX7c7//6SMnNZdan0KNFj2COb0ViaOXN7eZFCXmn+pvjrZlNQAb0ebGcUkjS+vyCLN9o4o3eNxlb4O6b8A1GkXtGp5HW6sQeX/+JJ00pF2H+zv2YMqy7TS1jm+rzxInkaBCaHtY2ng+C2etFEfDNbUg5pbpLZUIMeovlUpllSv4HKuaEutDc43tWYTDo7GzPV/ScHV+kG4Gi7JQUj+anj05iXmmHqDcJmAYVvdfC1H9yhlIk8EWidR9WV2VexCjeh51Fu+HzaoXDQAAEABJREFUo5Zyz5siy9zf0uGd0wkfRmDtdG0VVvZQlBNBGzmvvg/el3xmHbUqlIynwl8+kRNF12tVxL3VWkzReNoeV6ab3oYSk/M1+1q/9yrMd+8wL+7XcA9awlJLbDnoSnQVtPs/Ryk4mxnRvNgX67Ixo8Jo3p0tPfv7vz52WbFAwN9ceS+MdiqXz/oMwzlkt+Ed0cRdrz+U0XS2Kyx1c7+M0WLZnuN3a8HQrCdwfjgXevGMHd2YzYLwgBelKh68KFpwp6k2pvfUlqdrOpA75Hhq8OTM4zEw0/J+l3fRSDMbeXyeb55z4224cXULD12zIDNTzUV5mw2IBtdVe10pGE4AKjNmcr6R35ijKLZhmy0sXSR3GEmNfvU+ShlCoNCJU/Ld9fXK3a3VkBj4NDBuYm014lH+HOJ/zzh0VVbH+0Qfmbre0vOCUj6HHM3HWoxdFO+XnNKlLSF466cYT6zzh7iergd2wg6ldo7MWyhTvrRzE9UNmoWvkIsF93QlZf/2z9PaGb8aCHQLrSvBBSekOFsvAnW/Zro9ZgcAIbPY/gY15iBBWX/5nVQPmRCfy+D7474WuDJNe4WaVXRRTP2WyOGTP8/v9S22KIxSTJFmeL+0RRsYA+XIRKQOOMZjNmcPtzwhlc38wv5/aqdYrdS4nO/rfHbrjuxnRv1lUlaRASwBwy29zvQz/mu8XYFRCUL1fOHf+WRLnVap+2vn22EbfSqm3UviQp08iYcGCKNfBDfqGkUBNHSyYt0TTy3p+/PNrT+i5d38Mo9XRluO58GoM3TyDB+wsqg/A8m43RnHmPIIG/06V886XC0KeCJ+13ocNQ4Qi2514dEaL1U79Mq2c/bQuJTKpjX027WwzmelhldnGVV3c/EdQ5n70ktrAjYO5yZTsVnUte7PPHt0P1OQf5HsShSocEfyZL7z6BTUmWTdFLcYKMa+uw6Uu7A3V3hfujhWFvazXX/p/PhZ58vnd+Y7fou85DoXtQCE1xX47FfD7/RUl+R0KA8t7iR211iOSonaeIQP+RAPqnW38p3nP88Khl8L23iNb6HC0/ab0ZIX8JlAv8/leMVbX/t0fqdSKaQvWUUrZHqbKTZJrYcFjZJlGMQhDe695kZnFmZmOz+xVsjswoIqN1j8qkYjgLI6kbPCzBVUDp83tZbgIcR5mrXGTRh2IALmXJsMzBbV9/ConL/lzzjnvbLX+CdjP5/1C+3HiALQNT3T1ukzRvbm+SeVQFrB7gXtNtpZ4P3MPEr7WmvbVjzUdBwl5MRz+zlMIv0kjritNwWATzOLdtv1FTApob7Er+ncoy4SIx9cidX4AS6H7UQ8dCXJZJ9VhifEW95z93TVHOJgwrHiQizAerxsiVWMsLaNaXoXWX5pK8tNFa0Kp06hRGc3vLhgBDnjc19z2yQbkivKMcG/q4J+FLrZXcjWXoBt5qv8rRiDX7TVsuW1kP9KglFyE3yaxabN68VGBjdt44oOayyfpUlIGb0VoCZD6SNXj7Wm3oPfk08+1ffd9mBJAcgRbwsNMVciXuce/Jz64Fm71hqwJJ3hIEYGayRRrVA49hET8E1v+BmeqWqyhevjuJh7TihVDprBpMQ1Dzs1rG7Y91q/ez2GNgmc04nnqV/meVuJhYz+juANehV0j76/g+/p03hsF5l1kDkzaX7EV+9V1xbw4901CnspXUpSQ9ZFGyzbaqzA3ojFa/NablJn9CM7D2W0cOuOtFeMa8Hh242EsFv5FgEmx1rPoBwrDz72GdO+wHQncZXO4mzRcfHe+3ET9s4H+ZhUs/oVjt+beSGXEujKWLulropiuAbwirX7InSnpi0s9JToJprQi6MFpfO3v+Nf/qRdVdbZ9zzqHY0Cmwt0SXCsy06tChuR3xbJK+PnzKeea541LytjnW7fdb27GYqyZ+M4YRNRao8fH3UYdZIT2sesVIV+HsS3eLf4JW2IYyc9sQbWM/fUM89DpTrUUtpnFo47emOAUUqehgb6FC3Hq/3w1dQoTLUaXMA1szUhsLcCKj6nzu89ecvMyCWVRgzHA2YwLyDRZxJWAIKxfV8Zesgm+s8zI6ZUc2/Q/S7rlLNBCdAZ3IOvglr2bI7m6xnLs14rDjMBCWVumor6+5w7s7Ui1zw5D84LWLsMu7qWMFTlmekx47rMHErc6jYRTX0cRPo65d+0b/KZXdrQD7vn9NPPidsNcsteBAdMs1Qrtglj+FiQDrzvEX5tC0eZHYNYcapvKHx9bDT2aJsS/hfW2ZhNuzAxrHPdvY8FbK6qXbLnV9jPXpdvcxvLDt0UKqRarSgfu0syy+OnrUdG+kp+en8Ym1fx83zt+d6qR1tuO8D1VNy3qcN2l10KdRByv2rpxmKg+XugymYqeYJ8KlpNeWwUuBreAyoWtOWzUym+lm1cIXZkR4eW3o+/O5/1NLIxGu2qdjG6XloCUtxzWYMUZwOTIynhHanWzSqT1+KpVIWeOaPUPQU1bDIHXaNXEppmUbxaa8STNm+XmI1eDupGwx1n8Y59x7qXPy4QK3MfmsjDZ0TS3mRl1vexg6jIWMqq4iNo4mqkcsLX2JpZSI85wI3qgj2ufYW+Kxx+aFrSIqvRx/nsVaYMRf7i+e1zjzGudV2lbgs1FPs/zI916IEH3zKQd+QPjppASPk4bdVRah95p3498FtaxrZyP5LVA4iSgbZgqWaxOFry/+icmDtwG2zG75qcUm6CCqgBNAee9OPRfmFtCqt46sb3oUx34uG68VO5qjxNeN9jrH3bEm/XWGAoQxiMgXzfmzyAAbLLuij9vsUinfNa5nKt/ZBGrObqcbT62RarpeKqODHa9HWNsEXbYCWuPnvlfKJm57OrZqL6GqU11b5GbhEONfYPtYfWZNUqHrMHnXygh4ghnjKRqXYssBf4AWV9hHVNfHjFtfDj/eOjOnS4j+2WFoABYtkKN/NpKzXtNzanq6eOPzaHWVPdIhT4+9SgJr8n2pi9zoRSqc1zW1vAr56tGCepqahv51MpObsEIIMpgGECticUnmy0iVKuVvaiVb7eAxXuKuyE9YIujw2nWyGWPN3bvnu+PscNHqPc2KE/+lCaofDVmUZ4xJ2AToODoh3MMSorJqDQzoMvqDdTaMcJNF9KwDZCL9MP/iy+J7zMjq3vS4W3KDKjMWArKc1aB9uW0jIpEt8LLeW4116P8yHSbBScPS1mOA/nRLeSHihCuXX5m+w5PaVYIPs+tIdKpYZMC9U0oGjMSHUirIbpH/UdNEWhA2aQemR8nNABzSPxHopv1FaY9DafGbYMDvQSBOAwZgzjY3vWtrUQHMWn8e8n57vjU5gzSpKNsAyqMvtRjrAwe/pqC7pxz0hxEQFfCd0/7IbguNgeNfgmR1gnYJtr+XYbrzSZDsHyKmvhjVnOi7GxQrjUl6Y8WtmaP/TV0leXUbgVuZkRlzyPYsIobEMoPzwNrR+g3OqELV8mMMEyVV1CkiEMaEwuCpS+jFWzoH+504IeLM6Z0jA/f9ofCH6QF7BBeStMTYtCCyHRwimljKbZUHif1l4BAsweNyooLra936CXEmJo91yabEGDqv7o5o0omZKfeEPSyGRkziRr/LymFUAhC5ah6rKzukPjglqo0rPeUffME9nrpg8o7Vz6JXymk3YU16Ao/5JAy75ga0lKpM3tVPVkg2Nrrf1DcyhTK++d39AQIILu7dZwwQAcoy2gUgmcm0dryjPTzYYJmBDEy8GMo50jGJoBo64JRmvkKB7rVvspsF66RICoZL+DN2tZ8ZIahhh/DQ2oUzNvud0YBcOlxIyFbqrtcsdAd+oqvZYYulByLSFgWXuXN73M8lR9z4heexUiQBo8sl2cG7y1zzrwoxn0cZqb4sKT8WhFmChuzuV1ubQhrDgDJJiq3SFp49uwRiVQmonhe4hZYUe0dk/jNXaAtwtohA0mMY9PRgLy0G+z63iGrY6iFYxJiIFohXxe/OXWLgHU8BlTaHMvAYlOdy3fY8smIqlsoQG2AuI6tqH1MszDFISzzXVF7HbiPv49TDXhV+EzyumX+QymYyqIocwYlN1o24dXS2OSeCwLkbHxLN4Tz7PPfbG1jaSvaYrdrCmlhDAvYRITY/Iq0qdby2Ift3vZvZlaGNQHXKkR/Lntyas65FZ+BQEQxbV5v2ZPxsn9nFXNBCXk2tm76HwTfdpqHK2Z1G936cBMgMQahgKgqDzGdKsl+TGoMMXePW/B0kPL6/oZ33aCX9+/9FtwJxuHVjIxsnKgFFByfJAzOZk4ShuUDyoYI0XxU9fk6rFJy4f9RX4hWc0MfcYy3wAeqAxdCvCxa2g+9B4jYIrEWuH2PK9EOkzG5zq5UjvmXs/4M9ZmY8Fw286l+Yu5WT7NmUKWns9VWdHoAxslt1odT0eP9KilzG60zmk0tWHENgYpm0uNGoazoKbxjjintLWTQBRfLMRV/OTst4JaywW6HWVLGRDd5k2XQwckCRv0tF5vDoRFRSygwHsmxhfJLe/TMt0a6cbl9gkpbY/7Q7XFGju0cpE7UIcKfR4S2xFMQw8DtrjZi/N+BxSwDvWoJ73y0wwKomnfoh14jpe5GCe0SdOTSaswLEDAlUM2+XaGae1xR/IcPRcJXRwleY9U1yprpIgWgLkI2d89l05Zy8qfQAlzYEe7y8b7pZzVkgKo3iBUYL+jAyI8Av/f6xr7aQd8RJ3Q6D2mtBkLkfDnUxEMa8Qo/aalraGPcwDSbdY+ro78hjZj0zJUA10VN4p8powiTUqYBhFmnsNv8y4o3S+BQRWexuzCdZOPQQqxC/sClQFiepXOg09bpeeJaZNotHbG6qUGkRkttzKy2BGFXYXJEvJFOAnpwy+9o4nUq/QMoPMqhdLyeHzSq9gz9LTPMSkOJYMNP75ldIzDbMVvwqfsGTC0w1UwS+5jUSWv4jHIcGKxHM9MPY1HGTCYTnNB8JUc5rhgPI/xMIomghy9lvmHWO3qU7wzW0/snmERg1YXmEc1pdDkwY369yqV4KrZU1LGNW88KuxmJrMURmmqusIo3oGXqMMFumxqKNaM4bxNZjTwav83vmADVPO9JliORz0W3kDrwxuyCHkvdrhTTdu0Nc28Egqg1XYpk9blboZgmvI3kxmTaxIKgMovdiu3rKq0nF3KKJUxAau2d+TRNQDWup/sTYT2B9qSZB57xpOLsV4Xi25yYyoaJbOrM3HMjtS+XAj/ohHDeJuZPY536/Il9RxYd4OdDaWo2EDaeTwPkcNsy5qnWv1LbC5+N45SVWWprKZoVTXum1aar0l3q2minuzTiegVkDL83qP7ObMnlEQpaQcsQHPaRb8IWwBu5uPVPsvx2eaabIvjfkjBYPVO39xu7pr92po8/rwr+/skKyG8TZ8d2kRDxGAt6rHhWqNOuq+KSDTYZkYHN2ahdPcca9RTapPiESFFibvqa/LqozLZXVGSKzW6qTTFGLsBSbnEkThLHid+zv651dVMIxIAABAASURBVBXIOtKSguLcbeKsj9WDzDNyDzAlhjbJmo1pNZGVZ2VakdMvqnrWmZ2x6YoneN7B0kFMOsZFj2F5hmfFlP4ud3VoBgpFDIMWwggk4IVLzNkEb/Kg1RpAgq5/JjbFqi0zihMTgcf9dnuAb/etO6sphghJTqk9UcGv0VgM2csrFFFLfPis+Ql+DHsZxbUze0bcDQgI2EStD7dBXOQ8x9UWB0hC0BCOcX/MClCy5+gB3iN1VgmaUqhup6UEHFMjd9utFhPEB+tT67qb9Sg2EVyyPzKmUKcp33R1F3OlMDuSHFifyPuS1HHgqQfYBGR6BBnH+7E8nnDARgbAQF189BiL4e1bGEiTIWTZ8/A8qySAZ3AS4osLtiPoIZL0XdW67/rkuTDmq+3zSEdCtYuhJVDP60Z1XQRA/YPrGoZsZyv+dGvQxLUs9FWjYwOqZg0Z6yHg7Tgb54NakYt5oVeWK4VVDVSuuZ2S7h7ucdkefZnc/fzMUTJa4UOs8me4bqgi1aErX507/YJdPa4I1RKsuH15uHlP5RypQ8YfQp5IkN62jqtYlsWCB0lgppWjJLWTnz3yZXjP8108p/WqeeMtvwZXa8ExP/05z+6KiBH5PHKmQUOZkkHVq2SxH/HYPXrg3dzRzTyyl06V9aMah5HmPmMjifS94llXwlFUrj+Tl5W1+tixbicm3qMdPnjPTxICzeWmoKOcUtBRV0d5DwvGoZm5fknRk5F6eq9sPTYNKHCzDqeFjQ4H0/rr9Rvz1WcQRp00tYOnFGGBA709xtsPNiT0WUirlDUT75eJIfw8/ElcFNLzTgDoDN/OTanC5kRnKG/tC3u5dnwOy8aTuuvRDUmiFC09bX81w7/h/w/9XtUguQoV+dAPR4uRqBBIF6HfWP9V5NkzrzbXGXFf90yWnbp6NkhEY90jkevzVE+Cz3iRWmuq8ny1wDPDa3utfiAZPdCa4gf4Ig7FvqKeC3qRvD7L9tVceaxKrjHL8dGF8usJHrtbSnyuKoc3d5OKgdDiyT0gRiRN9PXj+3Ptay2/NKwm8JA9Y5pRi3ONrXFLQ9MlSTEC9Ze3zdjoAtyV1vdgdJuyoi3E67WcoGRAM3CzRJAQPN5nZ2kousQEOt/rEOO6SiFr8JTWzuGDNQh/Mr5PaQeVy/kTFoMpSyLFy35odaZvliRR/kJimG3abog+0ZAKNmYvLpIPR8+U8RvM1UuEo0zCd0FWUubFDES6PfKiiSPCtae6emLosfYn1nVFaJ0IUKNk7ekq00wSKqRnok3iViO8n3P6F7jpMl5gLZawVUR18N6Wl4yaCdm1MuzAGpvcFgHddhC3MadjZqkClD1f416N0/1tNZwHmSzYOI2kiATGWM56eaKfpaPOFJ7ap1Q6RI1mTlPOXF8GOVPzPPN9+pw5zRsUw0OyDbPANQyD0V7t0IbDJWPYo5OqkCpai2S3zzorlMIR4INtXC/zvg5xXOJgteyZeMXK/HR9KvVBbhSFoZlAT1GxMJZdrpH6shWi8lGdT2rTdeBKDfE4v0QpEtUYmKpFKAlYVfHcNjdFqpquGNbTbZ/woTQeuBfE7W1IxeSSMR1X9LtsKyKQTi7jjDoo6wu8ISlK+XL/4bo0wPBsfDfHhDkX2g3CgAaqC+J0VE72LNQu4GE65mpTfdaJByz5KNhzzyOtzHcwu+lDjspqT16viqrqFB8LrSynoDt8TdDGSqysGBGR7NYqkD647Nq8hGce5XHejS1XElrvrZQ2OcRekHUkDz3VSWJ96NdoFQtssdd+uM5EQbVvGwFnr8rInumWFw4Vz+FY5HVhX97u6bLfPGXnhnBOP6gbqoEv2Iqqvb6jgmwJCg4XW+ZtKpdQtqo80xXWgcoZj3mNyctMBrDgH2jlo2CDKvOUTksIdZJm4lWQbuIr76nY5ygdISF5Y6dkehx32LOvc+1UynbbKjPhAgbdwjl1236gv2Jr4VeUGJv3onJK6aFXSKE68TSf1tLnNF8DZeyCidjD63GxjAmZ7eI7+5r+qbOfFFhl/IYfX8c0jArQp2B57Pmd/m0cWvoqf4UHb6UVXShCb+PVaqJaFaA6ECDBwjBfRiGqaHQRM6U0J6TBdlRgfEINwSy13C4PFWhjnQrIbGu+TN89iTKFRMhgVZ9Uy7DkPnaNqQxwgVeDKP2QMDNsHu9UkGZ0B11n26jdCl0jCIx77lDy+CLtkT90Yu7iefrV+5c4rnEWFHWLt1qdPon5kRakCHbJCnG5arfCe3n7mJMC0qTUkWOPYoWYkEsbWLWnQtt99sloAJ6waRG0oDmmsqoxuJnPtkd+XYzl+gNT3aNVnAds45hQV6LPYOge21YYNDQy/QgmUOOQOVq7QQ6aqkszmsF65FtNT3hox+I2pd+TQogGs5GkDL/zIVdHu4UJa8UhUyAUEySTOfAjbuei9JvGfrc0n5T+YMM3gykxjM+4vq/MeMqHGjgifUZh8bQ/KXokV9MFOvRrmX3g8boqzP3obie/VUcTQAUUJotlEAe/85x6WlpOQmC4Sr+FcGpGDGVpGpqyRT7zsjhy0eGz7E9DcqkGvKKw9NCjfgtad1xBvwkCLcf7Nk0MNRJrSit4slP+SrO2Fbura2YgLyC1lnXqHGp4k19QjYkNA/wiLlIVKgL/phRqVX0ebMBGFjQ1UdrtjrI7WKnMJvwZ9SQrJWUN9Wx2TsqjbQvpmXP65DndDcXuVL7GiWCZt+a0WAF8CBn+xqQBbf582J/BfgLqp+JO1WjCOWwKTEir6uyKtfC03Tot7uYNsTd7+cDkgbAmZlNv1oUXRQnu+72B0kXlR9A41MWSwkbnR3OXloGFjm7XKEGeiDcxE5g1Y/OaZisUGUO/1o8Mon24gDCGU4nE+3Jqwul6Do+eYqv9t/ux9VAyW12L40YQMproZ+qZTo7SVMWSoDrgZoZm5VYlaeM7fIAh9YNDH21M3SPwzJxSb5BRqqUugaEIoSN9fsOPq6TYisBp5s6Q2wQKxa6owYiHpnH4mpnXvRFs4PTkbfbQmqtmVVrnCJNZjzvoUsXVepaPz5iPAEJmhmug+TPoFopRGaOrvjNJS5hRy/Pys/q5emSl3Bvj9maTmjYKq7XczThnBYXHTR7qb3rpLEN7F00SnqqnVIIjwDBSGbAGRaQQKX4aqBdATSvF0aSGv01U54KlOJK4wUeoMUTP9PDoHTfnIVvFPgjIFAaAfJTMVUC/sniyNLiN+qfgGtbVeuGUn4l0kmWTViIXehEjhUPjMS00WmG2i34Domb1qAeyCs8bRVTw73SmzQN5C7TZmTe3Ix1wGV1EsRMiGVmcaq9S9xCL3/bf8VJIz/ijJorViDHKUSsoeRs4aAIG77ZUzmphfuCfpEhebH2VtHAMnWRqC66y0GQJYVZgf2ibboxDdT57Xk2nXsr4QINAJfhQNFiUXcfsMIFelCAN6gO3QasY5AA1cpKFgvI5UG1aEeZYXAtCzxHYGIFGRUvFumZ0ttdQyQOwrLdY1lKlh5OiEaIzB3DYp6+YD4XWuOCGyZjvstS3x0pHvzaiaGpn96mSVdMpTW9bHbIB+uMmHvoCxQ/3DXsUgohyn75qoo2Pq7+ix/wYOxRLmY0psGw0YV2UVl6WBmQMmXyhdva8buuNzIqcPKMnYqlQGihngcjlqSgBqgmfGb0nR/Rh2hRr2QgJP/f+1L7eUCHzCXazIZgUYXMmWxG2xFARvV0vFuuokSdcLEaLJZi11tRRNkX/hChIKFEnhdlTapI+K9mpHNzDnsGMPCX0Is3hPSieUstCgqBZYGx0/A0pTk6/YpXYnBrA6rBcV/mCUfQYldIWEqYpfuGUEPka8FnwffzCiu0y7CYANlxAGEweupRrYhyxdAc2H4cvDt/z6LY/FvCeULX32IHPItBxyn+zVOhaRahzpZt1PwS1iRLmghIzDZlKF5EtdyO0ZyJbGVKIIlyHKex8pO+B0ggTABYUE6ZtZHAo2Aua4D0yWERqqbmpTqKEOcsWqLapL7C00K65VRiqnfKALzdbJGUB8tfGAY1vPAslmwFYt0TyNOVZ5nVoUkBtJGKZuy4r1lOZrddfQgrIwa86Eu7DM7LYauLyuFxM9kU9yDDjKl8tj6JDg5SqsWYcvKGoH9vyWlYxTZsXkwvMAAOSeq/JRAmZHFhMQGxCRDY0ZU0pWcvGdMDBwBh6Rgo8DzTTp+jZ28Z/8MKaRr89N177bm0NF6g20ZyM8l0O8F3ktG+1HmOoMyy5qK6/HCkBiFHKD7KzcUaHaUeSFV3SEzgi2OmapBDsjN8M0/2I34PvnMYTJ/XY9UUxoB2CRFAvlAAF2mXFWRHPWknBIM1AHaQ2NB/O+99xuPKKXBirmLqKWyPmzQZIVTc1fiA2XxpfbGyLE0t9yQgp0fNm/lY/3QTiciGKu1aq10tDnFESDLWpFskJ9XGkVkYV9kx1wnvEBR7L+1OqgdZJmZsDqKNwbmTOqVZzH61kr66/1HDZhTvsQ3wwTh51XIWt3GpJm9RvZvnd8cDu8SpQEKlyMXGOoyykhANDfiIuyITAKoiNNhSsxk1BbswKy+Cx188K9zE7TVmZGnY4L4sNE8lj3s4aRLX/EUeIgJZqHmAgUkkzF+Ou80VpKedvzhvqUXG8bDOE+BmDAdeD68AUjKXEMXPBNsKwDrQD1w0857h4UkijBq9EcFR09mlog6hIQDfb13DX+8xjN0J+znlO6reozTrsZIxkJHL/I/SsU5xCGdSRLrN5zznQ1uVI/4TyrGcb5gqstIxIk998ns70gdMgdlUHAzgUd/F58Jy5uxLnKJ96Ex0AqZ+/AwYYR6Effk/XtJg029P7qfWbJh1I5/5t8ZuZ1Rhx3y4QtgpIVEM5utp+jnmvtCjZLyg21Jt+gYkeOWnHg37m/GnFiKHqi6MNyiVM1jduxfRMiwprbKMbtCA3tBrAH/TB6ZHRT/Q/k0SE/j33V+HSO54fv3/W7+e6P/4YMu0PXq9+dLcYt4y84RaRIe7P9u+nc5eq9cfP8ru9/rN5CmhTWE7t+sZBfz0BU7RHYOz/Vq79H1faMuzrMf53zvc75xeTKpOx3VSkldShJ0Xd1ZEeswAxSrhMhg1ZDHDLTHweUzkK8qg1tOGQOT1TqMHb/xQiRQukAncaQIEUdaqbtudknvJTa1Q1idQcTczGPG7NtUrbAjdZgy4OlE1pEE9Ya6GF+tUDjQNHoA9HQGv4LXxH3tfl+0c6y9Ot48Wv7EAAZpu5aoM2xlpgaFqmOdWaoEbDBJ8s3Xqr4SrkWnEuhwmVe7XNDOY0zjEdjYBuXzvsmL/6E3OWb1Q9PIgOD0nmAy5r9e6d4yJLsE3Ah1mfX4tvZOpvcZKtwnjtrQ39Yc6e1nOyPp/5uv1iP75ypjJ535N9qEgBpUk3P/Pp3qfUcDndUOyNrYPcCkKBh4tU2wETV6UBM7v2//Xn/xCXjfgYkm1qt+aF1Wmv+dmT3jVw8OmpAAAQAElEQVTKgvNcZhEap259rzl+2ppGqMcJqUFkuNEexEN7w29lJsa34Rp8Tn2OUMUq2GEZZ7n17Jt+U7mGXkM8BCFbx7bG+Hoi1HF+UT3DqT35k2VFcY4U2AnVTF/3g/PkaPpKNzhk0iDGnu776VsGbuq75+PnTCMTj4r0iXnWzv43qYXi49fPviJCjKQi8Xs8ZQxm00SPke7LEdpirO/xSpFZepbQ29I9yzGbdmXsCoFoFzs2sPbO66ldGp+//dxHzWXt3m+GXGBarh51qiUXZdDE0NVPxiV8R4v5p6vP/V99f7vUcVfaJvAZ6bgd27n3+L+Hn+N7XqrwAGLvLBufxe+HdzBhoCZGDwp1m1qv6BimNpZWQIs57RQM0JzRJmMoab5rw/2wH5MurNzGoIsVCijgKo2aGGNfb4pKURff9aJxUPd054T0otpCJdMSVjM9x6ausZDFFL04m/cPceGz6/9qfm6YkpojVbNI7QSfFuLqYc+C71qyhk5ZG4iPmShNP86HhOtcgW4en8CP2ScYn+FVVKxFlxZrDEfxJ9bXr9t/nkvT2p5C1h+Fk6RYaAlgoWmZce7RriMxCVcGbKQY93GBHMxrzeqG362yp99pJXMKWCZsddmkqPg23tfMPPAz0phpeTFgbBSKEnIld7OAhxqVw+rrPD+Q/qy2Bmzm1ceyhQum2iK7ZmSCIA1oXdNWwI9o8Z53C/05XJqggc628FGTBlsc4pk6Sky84fzMbI78QpUnpCIBtgyuk3ywbt3S9uI9UDampab9npUYYOZQpHyvWNsKfyeKv7NYQi2rJcNje7a798Zz2eOfu/4vtcjKbwfSZdLmre7F16IK93Y0qbAuG/OGYB3WYBGb1MUSEJ0VRhCpvOrot+y/UCcRfmzoCgDNakUySYWMRyicxJiU1EdMyXvxSR4B2D+MHzfbfiO+M1Ud0w55su/4d96FBn0oPKJJi5oq+WAfxk0O7Rq7Otg644ohTdqlTi6ZaLlAl8+zpndxjZsLC/kU38ZQBhiYsJnYYeunsARauxB0Y1QLkDpmDpmfxE/ZglExTpZ/oeLidp2Rxq4Ha/TUU3Vs+Xz6Nt02LJnh3F4T0uDH7/Bn8MgA4Pmw79Ld5w76dXldfprzHfntAMnXfiLH+uEca/SJnBmxU+uF+/Efk1vxRtFtLCh1JtjzDPXrRRji5CCTWZi0yvVAtx6r3VDNI2vxCrLcMb46A+/fc5MPTASVXqooDaPYayJVn6zVWufa5iu7w3LBrYuzhvrUU+BhFeqRZ+m/v2qtwITzc0nedSn66Z4pTrub0Yec0hidQ3XilCLGmPL+B2XMVJ2+VqO/mnuApOld6qjJL/LLflYrzP+ZmBoAFmUSi9RlrY79JbRw+V2umUNZNGA1reyiMvFmLTl3zPOTrW8h1MrFtWrvZ2kYfOpztJnsdz/5NavwcCXksbdOB9uLYv9pfF+XNRzp1xPXtFLtdq23robiUsmU/gSJt1gr2sDpKoj3YJhy/55TT8WMoy3apdTYrA/PEgNp/Zi2Tj0JqSQgOhq6dDZU75CSVhFKUlLpMGUylXPpUb72UWEct1mL5uSfKtBkpdQkhbRAws6a+6pjr4SaylLfXaUAU4Fe4ZvLVVwvkVJQ2Jv5gdRzC5EyaCQafXKyx6vWiPzz0NuvC/HDUSQOuLsReNtAZt8H3SgpSL1GekLFTjPpYg55Q403RK2mhClKO4COBW0S4DU1LUuingOVX4R5ELvw9gpqwxpKxtvrVsLtcD9aaLvibs+qow2uAm6oU4mvNzqqwwUsjOv9NSV/KUeu2z6dyohuU9TqZcoL4IT5y4soa42zd+IyGoCeV/yAECluYMCWsGCjjI9eGhPMgLIJPKZdkUvZ0VgaSTurSjCtBF2ZU/7suj6b1+Kyiap5shJNs033WGOfb1+3MvmM95nMp/O7tHEa/GN/LphLj1nKfEppLEYqYgVGXeEAv4NvR7W0F0CRX8g/THGZJ5QRPkp2W0KOl3VgdECrWEpHDgFBuai2HOZMzcypevhkdHo8Nv3MZM1c6brTULswJ5mJOj1PT3Nouaj1Edeun8jVM20397e12yStHGAHU/kYH8GdLZ5fibBXIcBwTrScilI88GP+mCl5DRt4W6gmCL6+yex3Q1vSHfqCIgRSVVcmwrZrZLZn2YGSZFJmj2bqR9otalG6P52rhjMwLhGKFTctmxh6u5fIX+24o57JhyJJIaFr8lgdfd981qSz3CWpxQ77Uh2Sp1nv6yncxlS4wvmd89XpFbb2Zb6gkBC3sAr+54QdIY+mtjvLbFy6jXkoH2ya9VhLoIG67PZ9w/Mx3AZRty7/iSs6FN8X+LX6UoHf+zof64vmrzlzSB6dIjBcxbJeN8NKZ5pI7U89u++NcYtQZrbNNuhFjEPZHjZ8zJRLicJfKpi3cnx95/rikunR8E1cN1xwTfkeLQt3qS5jyOrRbUulxKX0FRWVJ350N+JE8OakfmNiWjUZC5Q2ltXlx72N491Eql3p8DmbZ7lCzboKV7X1FPrhXX17jpWxYKUF/ayc3QwOfWjADJ7mPWpjPfJu0YiyHr1FbFyuXdp32SOvSwxG6IY834lwuEME6UPBUrIvunZOmTOiE71dBraGjPFNaxn0RO5QYELOUDY37FRk2zjetffDdGB6nVGJU7BWHn8JUnvO52SetuKSbGrWlNIUGk+n6Z7WP4HFNy6q8cvzOkaNd29w8MYLs0PP3JPMZOd1atWh7lfwx1o3kI0kClmoAlvb14wxOT2ME55Gmsw17UFlZ1Z3CjZmYTbLQ3uiVGVRe2/18wqbE0wDbEQxJQ7SwTWI5m5N0EcdFyFW4Vp5+4wwP46za6CNjz6CVLxuTY2jh2FsclAtM68qrOBouyqlzSbC/ZUQydE/NGVqaEaYHeAUUtHe41ixthYQtHKpNx45AtDjJzXozZYkL0M12l+isyOT0yPTIpjLSyZHCX1ixvf0hfIRtoFagVIssa6p9Rw+iTo5AiBvNaFs38NjeIiEYgeUHxYh0K/dsRL0g6fmHl0aXVmKdFjFu9lJti12CGDPTNEGPmRqZHjEZnvn6ty6Mugu/bjT1Ir24g5+2vxpKJeVEo4fEbfEn5vShmQvU+6/7s/Eb8usTqgkzaiBslPGQwcF2VQMExqC6RfHofWkPcfusWJA42YU46ms7E6862iZab3/5Ggj0G0NEKrWv505c0pRvSd1FgFD+znTp+TQ5bNL/7rhxNrcH/sg9nB6RqXvY5Z8ap6qnOw+LclbWjD0A6Bkr1bZgoVmmCeoKPvPqtQQ6QaHoA/etGLmeY8E8g+WwYNa0lUjBShgivtj8xhqiPyc3FnWYnMT18zqMHFxkWK5Vo90co5wNOW5iQd+n923jVZYO9L4mKlghEnX5tdwAwPQEKQRsbG8dS7HTq7x4KIPFYvwbORGb8FmfifvOZK9G4M6mddzosrwvjqQIU6pvIXlZc8Veo4yUiy/E6kGDDcswDsz5xxHc+PIhQ2zoo/PTalm9LyoXII/NULo6koXIJ45mn6NSJaBrxIjh1LhUhzlWm/ja+Kjz+iF+avbLuogxpSMT00o1BbXYpdOJWem9W9mIDJHs6gw0qAzBMDhAj/mPwapPR6fiht96/d7sTCVw3v2Y9qEvqqy1AVo/Zm/GICGCJVCFHxpmpU7wrmjwsv+KnBcn79LT1EhIQEJmzLg6FAZxpRQHrMSCP3lWLojTowkrRCgD/HCB49RvApAUTZEBrpK5OqvGJc8wGesuzgyp5AHqqPukeYnUOTI1hjJUIKzd2IXQyp1twVzaCXcRw3rnZgDfZRBjWhKsXPQh0JkP/SfaO7MleqK5WvcB6zXGfRSteePnxWF6FmcNaK826W/uKTN0ISMVQMVHtWcMICmn28+pf3R6znChdWFy6mGsmRXqv/mnLI66+J17bDlhBzsrjhtEkeRvmiLIJ0dbhEfWx9qsS7007ifefJUP3yP7+LZ/3L9Gw2a+sxj8iC9bu+9ZrxVsm0QPS481s3TqAaHYWaMhusbQH0fjyF1Pea5/Nf97n4OprUHaEuBhGi3N0XTZ/V8ih+IVP0sfq4XrizNcuxd680oCniyawRiwm25hTWQVZXt8OWLJPSaclPJ9cb8fafbK3CNEVbSEaMj7FyofbZ9L5w4yx8JgpgB6F1bwOJeV4q0ZdptiYXdiCKTfFp0vx7/UbbBlgApcMAY0KosL+4Hrmy7+/Za9hBkKRqaROU1G3N5y7sh5VjTUMMpqxHycTOjEwYCSMVvqCX2ofKzKtHZpbstjbWGWFzd0sdn5rK3SIrnGXFD+JYzkSVwAzYEwF32E5MS823+QAtMEEU/0DIXx9lbACCoc0iYGxGCkVVtftln42PjAzSeEZA/V6M4F9PKeNTGVjXVyI6SXxopMxv7Q2VPObhT+9/61ZqT0oIiXa19Kq7A9WnRmiWRMWFFa3ULEaRva2ijj539lDazVmArY7pjjNXLoTZEacFJdzN34spmHtSB37x+t3+1LYFeo0yVrQcsm+VqgntrQrxfe7rPux15WB2YXnOQAluYERdaH+VJfWLsaSuCa3DRrVefpVPToqYMUzse38qpAxH6Efxipc3RaE0EHEqHeozvvlYtsZ2D5PwBU7y6voE9YbMng2dFicQtJkyGZRBy9x7TQwqi5lvbNfHJFalYwaGeGNg9cxZrCZhcCKPLW4Xia+UXSrV0YEQ3Aeq6fsm9OkCueje81iFTUwWltCZIbh9b466txVuwECWXPf8UyW4uzMIJfoFvo4sUxNz5rJNv/tL6hE7HUXqSWr9/18ANbXOIHF+lO47Dffd1WlLgKKnT7PfLG26tTpmI9N2f5ZeF9hDAJFR1n/aFoGOizc7CY5lVkxODC2rLJQ7N3K6LS748S8nPQIYLLJ5T96lRMnpCO9Ia9rnOs0td8m5OuoHOYVRpcqV0u+/ZX7FoWk6lIDwZz5k6Y4dx2y4utrHG+2uTn4Wf7XuFOWgdy5ZS0UD2jHJ6ik/hmJSCT5sXwYFO8FlKHY0QhnEgNE/XWpf7ZSDbph0dTY3FW9jQiwZqNJg1fQ5TtgfutjbdEygVzfS11YI5AS2oyWovqLljKwUY2MAb7LEZzMhS6pe3QQo8OGeOXNkjmrlR15wt0O4dDv0bgXqTEDNWzW+6ROFQZy9xQEXTyxALI4OO8YE1ATdGYJmyE8AidrSPU6kEibd+QsUEvfhikaWS89aNoP0cRI4SVJnVOS3ePCzkJVQnM5mWt+pdLWXigQ4mtS3stFGwccr/NszdyoHC9rRXUaN9Y/fr8cUncDM82d/sUz067zMXJfSchILVuRFWaT51UXNqUOwaL65kUDnLVfMZLfJxrb7+aTV6syfhZtvRfuOSZhWwci/0tsWA+aF9yd8X90ql6oO+7okCbEqdKXgbmzQ5J1vR6+Orttkt/P1EtuegNcACtCyIKAFTjV2pFBUJUj8zGIWhwW8E3TqDoAAAEABJREFUVBwKzX/b9dxNU3TNilcu1567XiXcBqUdTGrsCTwbj8/34P5Kgqks03bsi36Be5ZPmDT60+tTLltZ86DLSgYq7lrfcVEuWa47cU8acZlQywVy1FAsA2+MvVAik6nWWGnKOm1M92y4S9Y0pM3FUWG4WrX3+i3re/FgnXeDEpgxpKHgI9HRfPRD7TP6mnI8T/e3NJ6zQQ3nDJpVwuG6i2ie6XbUx/vqkamnVWhmM96DQC7cy2BYytQMK9e0mHAFucYDoeVCpEjoTcWXJ6t7Znr6aN29cJsoHjlbzzKNWWlLghBiQsybLS8dQZkIAGPHmIojQAFqPxUJg6cv5U2YJlBSkW8zlRyc8AXfYcth1NVZbMNAxa9iOqzy4zhL4TEV1o2g2BgQqasPISmxEVA4KnMCQsqeQSs7B+gxYumtucs8Dclpx6h3hL71dNrquMq3hieGGIOI+cH4XVpVZyTmFUMx5zMiqICSVKDWYaQ8Ks5P9TWv9znk8Tc9p2Wdiowe3m9b2Wx/VXpUdt041D1mFhWZO8PN8L4axPvRwaBRQbhWRMgdSKHXcDH9OMs6O7UOpBQ/nTp1wp+Fo6U9IsmbI5ISSjBPi0NNF0hRrxpoH1rhDYuv8Zf5Sn/C8jI4Qtd3cT1mt0NkCah3xyRMO/BqUqlcIe2OnVHgR+2QQS1d4ANTIBxFYoIdRCg8BhYsjv3BOQE9Bn1Ug6hqZEFm0NrgHRnBm7igg1DYPjOatdBOmrvvtWItLE54UeKP08D08lyfRzyEUtUGOJeJXUCrOiDWV/WaFqpSuRARtt2izeS4O9Fv3ngsSh3gXHHi01sr2Rg130ZKmNLArpxBjytKjHwnJvk9WqOqoYW67bwC1byI2Oobqm/aLG8ztc7n8RTmPGC6jzZrqkv5mEHph02rifz6+Y25L+ZH9WPP2NMoYSx0E/OCWqevEpCEktRg4b3goEnGu2Ppmk7ppBnc9DFYgbYcpIw0DPCxeIz6Xi0LeOakviWs34V8e4f0IgqI2jj+NqqGK7ULs2xiH+1FxvNmJ2uYtHakKm8rQIRSeJin7h6O7Xa1rmCm1219hBXu6azFDggjFGd5jmP03TCmiSrgiRZeK1PG/LgMLbfYo3YLI9tjXPm6uAglMR5Oz1tzJVg9sjoYeNPYMWUNJM79oMpBjvfYTcW63UWWcgit1KoNEKPrzfy2UleyWcdUY9SWZ1LcmhFkZT3PZFJBjQSILVfm6FZGTecnxR5tiPZSfLQwC40sQ8+b/SzLQVIJ8LoVAAbRGfNsjsuLBo8NSm/tzrcOnYDmB0d3wKYAou9oNYQRN/uvWyvwma4Hd16prZQZxOMei6Av2WyyBih6IiQmKUflGof+pFJVB53OIb0Q49QgqHZ2Cy7vnpQ4qLMYbygVsjx0vGch0Upck+YPdklAMjZt0HjXKqBKNjjMtLmpq+/1PHYEL1aGZ2CovHBMK5d3+bv9zeJTqCISOrKhYwYszgFADGzqrXhFINLNS33IXZDWyt0vMwEueChJVDxAQbOApuyMfFJ23vSoUbgBMso1HebYeOd72UykezuGVvIOhWTeSgbR/R4Ys6SEKym2dvCdSoOgcSpN2497ds5yQbHRWpUXe63hRXmMm9pemAyxwRFHbgz9vF0rhm7hy2JBr6f05+k39lFQAvSA9/iM0CSf51VtywRP8OtCuMPisr7U72ZYA4kvqxOnpYDFuaZ9zuALnGm9vRGKHZHTbVDDOPaqWqrNrvFkHAxOmiY9tGbAaATFloB4M1/5iQOmrhDB+TFetqDgpjuZaY/qVB+fzOfkMZz1tpGjihXZso9ldi2EqiG8RcdWNiwob+OSgpa/lYYGbeBQQ2l5tg725lpc1EUBQMY7h455a8GV1/6bIt4FGt6Froa8eqcjSfzvVfB75un3P9Ncj81ZQwxzRikARqNm23GNolSJJvTbFBva4gWF6RLpbaolZ7X+3PGK7yEfY0GFANHZrQVF1WU0cOmw8NgTKqJ5pp+ZRyPamML4OoUg+mptl23RuOkGiVXu+YrzwBpm9FhkoMVITgu44PFlRNE2WWwFwXJQzlaboSif1V6CATnrl5vjYg40o/3tts94fc88j6JzNxMSD5nq0t688Pt6uQpvMtKzOyNLu9ifLU/zAOqnsGNeJvdGvL1JjJQ1vHaMTAb5xlmoVKuW8vHj/cm3ujSGYq9/V59osud/xrQeLFextvVoOXj/C3trWDbH1UNyoax0CS3DcPwFppkvOR5j0RWlHZ6t4QrmZbr7fJxSutiv1PhJ80P9QfYnR83UtCbCUrdJ8uoNDc0jdjmYsUlWTjZ7nNpFcSmiGV2T/xiOoRczXGNbXxf6Y1xOvdqQKg88SLfzmfwUrB0d0wqiEGlRyyCaqFyminqMUMKW1fYKFKQ8jKNWFlPXFmOZHOVIbMp8SgDKPJSHpdYAgdAcsGjyglzzOAY0UMjtRaASukZ9W3WgUYTsmWEKfVQX4Sb0bV2uuBtHWKs9j50yeW3/g8gqu2VB05diMmin8UpgURu7Mo5N4dx47pzud4d4jb250N5ojbmua4ydy7SLmLC1oKbwHg7+j6JRLMOOKCFGGd6mAgl4lrXaiSonDjf32h+4QlNBz7RZQE828flyVaywl+7sGRY4LbZdhEPqa8uOip/4bN+OtztOqhn2gR0tljDNQqrFigmki37NXKMP5Ix176mr+jp2ldvwIG98244YAWNUkKAQ71zXuONC6KTOwGOEU6HytZ+p1C3N2ENv05wnvPcKiMhqdFsP6+j1Nm4sAfy63Vs+jcl5JJNGPXrcdKXC5OyFa2Nf8g6BVdgQ6bDu8Z7VZUO9XkZ8XpdREUe9auaufOqo/fFs00P1DEAh+xJA2W4M8P4c8jut/k+krYYPQBzDM1AeVmI9NFW+fTpEnDVRMbULa6wUUM0oNmb73aowiXAHlr/MWc7kaLcMXCHHGPnV4RRu6IP9KRI6762HvfyUXWOXvZx7QS8N7B9785iScbG1A53ha0iefMzt3Y4uAsgxWrgF570G6uwYD6Y0I/WJ6ynkSaFSVsTscl+joKMnkqup2U09SlQPsChcM0tFaJ5SU0mAuZAR1n49ph34n96FoaNb0tSKf/e3cMw65qC0LWpDiphDOd1Tny7hg/JwyZeYEJnCu2dDcmlUnrlTe9ozmj3lUxffRFsCRwQInvVC7SzmH8FsMQjXheR7G61qrRkRopBceRTsatwaISwYpZiE6QNnpkoHWp9i4A+9UD0PdF6QBW5P6v1NZ+T06SfrFsAW4x6MJcpeZ4I9X/S95vYY34Zr0fTzE/Nv3Xt9a/77vncjGAfNlo1n8Fk5akL9VHV1WNibP+fTfo6jaawwbV8fq9IWUsSZKatDK9iIH7VHfVA80WOKg1CbMS+Llo/ldkkIAhTLGHil5L3SPDnFHNWJbkYezbVyb+VBCQKxs+1JnoPfXLeHWpxzXPWlGvCZqPGGV0OLA56ap+th9WByqxUggYGj3OjQ6Mhn9Y0qQiCou4bQAMoGh6Oeu1m0hjBxVByjVY1bmfKpOOVIU6vkIUrwRGFiClDM/JALMsLWkpAaScmxGWYBs8JMviasHWs/sdQj1CJzQCeH1+N+0+5y7V4fYZWzFvwaIVw8YbeYoTsm5vVHZfBiiPCpR4rNrMyRSvFpRJPQRLcR5PnP8q4spQOQFTF2aHXc2syI7tYwpbhN9eZPRxX6pE2q7/n78OCD+ci5rS7XBfH8ChFaO2NosFbkb4n98SHjD3Fy9HJ0QKgMJ/Lm8+3U40aIDnZZyoVPWmvIeq79/KB/FPJx3l7SWl+YhavnyjceyiKUOjK4hpGc2oO3GYhlAvTWLUQvEe4HXVLAlOVQ4Ap4k+YjShX4QESCgtmCE4PNNL4WI2X0LPuaGq8MmkEx6tCvZjSCMJ8wRLdreUW2yc20cQVUcKG9FzVrYihK5R3aAfF9Qnhf6MWhRGiPljyGvhhqKpUGQSmjUvHbQiETWNbbko7inOqEtykVF2HeHU9ZNTXN2IqHPYPpo1o6o5y45SptDKVYKU3NlTXTndLGxR1r1AW1X+u1WrkvQ5byIk/AGSW16BVBcGaAlsDtjKkrvnLqzox6nL+2H1tpOI500m37bB4hgWDStk750mpNCTgdksegds56oPK1BL7RbxdZ2GaLkS7HaJEXjbBZ0eYvUwkTeQuz69jUG05C5E5hDLp7m40ada6i1t6ZR8S4S+dOwJqgKNUboNQ9kw2Z0RtK5aggaSLwoFNh9tYgIonD6q7GHEAPJ2boGlZ8C6VopEsNK8YuaFJ6pxb0rMQUMVAa8L/XfKI/arFcbMF9zShtMjC2kyrd5RBYvFGYSCfE952cVA5gNOPS9XHWBUTKzArzvV/Nu3L058yQkuZmLTGB+Zu1zI1QZEcOd7hIDojHBaW9aqScMjv7k5f2e6KWnqr3WSIMPTmSmbnCuYEEhODsVW8A5OviL74Ez3T3KeF2Z/hQNWAIQGV4/vykAcwSSZt5WW3clmK4QJZDLM/4EYnNgTDLhjf4yr0R4zULY47ZXWpVcdTU83I3JWD5s3F/5rSWDavHMldqIcEqmE1freW/NVg6KyFrRTJV6IbJvQKhNoVlZiHCNAk10joO2bRWAgBHU1/D6RaGZeU25pqeR13HiyapumVMyhqesJaqHT5m6bxFedRL2rCMbS8IeWT4+85As4VVAVNGnZwJQerP8OeYFqLBl/x6HQWZ2Z1AGmBEMVMpADyETkRVNN/JiC4KzS53D2xqEUHl2CnrYa7Ol0vWcJA6eloNZxSC8AkCRidmVIfX+TNmZ+LMzr5s1kbEmARfGY9MuGO9MbN9H1et0s7htMesT8WSQIp4EQME0HgA5XFWtpbYCbbWnJ/xSVNl1kDqUPF1IONNLk7Wb6syXr2p390PFM10W8rd+vBaAnVpVTF1qPOGPm1NTbeJj8eHxhmF2mdO8Xu0azzf0nPMHsxbIxxRzxceIQHYfbF/akVd/Op6mZyN5Gh9U5HrI5TBf8SHEsMH9o9rxQI33+3I4lkQhKKV+Jeh4cKopsYBDQ+VClbPQi2IqRKKmmoF/6QUmsUnrTXajJaMW+wP0VIj4Qf+NXto3+ENdGJunqPpylyrIzUAC1KahVdHokcxLmUv7HPpvDNDNVLYiZVcww0snMVcf07gYMiFYDHhOmC6IDsncR+dMwXlcEZhP/g8Er2TwQlBKky3Ty0VvbfIKlUD1S+06mjpsTIbb1xaKLXz7ILS8+7rekqXEzp5j6dBdbLiinURQSzD4lykHkVstTfs77bfjZL2G9N6nnxqFS7jtU0JbO/DUdVghArAOHtT4yeKbCXBnpnbSu/q+VYMY/yY/Yvj+kbQJ1ABjqL+8uSZ9eUum9Udb6BsX74Jk6P5GkStxo9jwuRdwAADTRCgRvV84/d332Lts/Mf+GGsRd/gYplK/V1+MEZ5tTvWd62Uba1L36zvL+7fhdvtHq7d/S0AABAASURBVH6FHCvVexfhzhBK62TedioGF+DwKkmpWvDZeF9d7aON5tevasUmHgDq31iOPUfiUD4O8786eb9C3TlQ+VV2z1ThgXD+8Lv8LCDOUH9R7OlBE9N8fT2YyoNvz7dPo2BKp4RvV9UPVFrKrQtQI9XZiLiozYQ4mpmDvtnutd77CrP5zHixep00zRH1eOeOuC2ZXo096nSBGYhzgdXO4yy5zMrOL+oJHkc5noiU2nn5hXXlq18CjU41mpZ/fZlUZnNNB1erYJb0R5WKaZyrtOSGgm1K/2cj/vV88ht6ZGzDgJItM/ng/vI5opbAUmno4oAFzWL3eWoeabivoLscz8sfgFE5dC72j7ubgZqO4Dhv4y9Ejk4sKuGW26QE9ZzZFmHbNHt4N2smRmvriBx0j0hbYwEnrNhvIzw9jxIOFCfdzmsqyjmSFVPyy/ic5e1Bmog+x56v5beoFljdNTWX++XhSRGuariggno2T0S9cxKdxS5ziBMMPOG3zTckjOeZ/G/wXTDWzynPnikJrfsvClfD/FJSy65B023o+sOJ+n9qwEtMNFQacTreZjE8MzlCPqbiIbI7s1qnZDF3nEsSUD9w8Foqtt6ZPIWnCGA0/0SpLTXtqAYcSndACSyF8Dzi9SS/qGK+o3sAH9D0yz3iWtiq/UqVEOuBHw+YU0q743psf+d/1JY499F7yqvArOJq8UXCpl2qa6l80E5B7h9qW63gy422NrGgfGafFpIZOL6UzgZ3ReXF2tL4fJkPJYOU4FTWu+aCSkQ+eiQwZV8PrAGsVcbffsXncmIAXYFDAlN1Q6HifUlIg+fpvgfKw7ZcuMN6r7Xi52VTp+/hQy9HuXeYlHDPTKJO5eRRAa0znrSWUj/wj7trk7BNzXipnk3J6S/yMLnv8LMG11mfwyU7o46S6JEWO4mXHaoTvyKIETFBD1bgHiTAsY0VEROmu6Db+LCfqpxuDlZhW/H9D9a31lDdCmuO6Yzy3c+gKrrxTKkVQQFDtmlKjyGpkPoIxpl159ZDW1Yv5CwFPoWU7E2OmEpIu0brxGZuU4XGgoCpmBkI5w79xWM7XaTzbZMjdEcMNVXAFwQ9mWJP8Mbqf6IiP9lXYj2L6iCDh2fm1QzvqVqdGwRMsZ5Hr6qGudxZ6ZnliEwSf6+hWOUQm5r2ADlxKgRcA5TecCB6FB8EIopXzfXUTu1Jv27fsLrAxc8VagTWShyLu3zDlm/6Z0y9O4ZtLGM2oDK4KZJsLvqi7YXYEYw1yrrt9qC8V/e33gIOpMAjTJVdMeUjt7oTPSMwwoeZOcdvbSrt5XHPmOLA03gkPRS67JvrSeCRpM4P5ofcUlAmjoKWpKf52m/NRP6WdlMQV1zh9nYtHm91hpBwnoX+GcUc40JW9NmX7e3YSEKJADU08y4pyAMTOGwMYECExwozt8L1Pfs+uBu1jR8Z8BkNGkUEaAJpP8TfBZNsroOVqqg7z+AXls8etc/vqKCHKSCX8SLZhZrSabCMnAv1ozxgSxZGmU/00LAN1hpZp3Af91xWoX6px+Y8rWxe4nMNA05f6V6apDhGv5sOrTn+2pjOzFsCmQd8bX1HrWnJwaW2W1QwKTT6vZQaFfuYAftM3EOFHOA1YtQp0SfPUe5YYmIvrlWXH9D0oESyeWKeRj5Sapo9ex83lc05dj/KcOBAkZcur5Uuq0rK9sc+v7X/e8Ji4EPkrlx9OuosljNp02Us8QzH5Eg7GIPTTN22p4XTxrXzwpl76lvPKfkhNRknu/TzFTRNLqZCHiSmDFrZZj2+jjukbOaaW/et+vuWQQOX/I7Gc6QVI2W0EoITQnHOZD/gEZSCH3WThlxC7POTs/UEcV7MGm9Jp5ttqFyGamtahKYdrjDKui2HhXYb0dCBv97SVamqLCVZ2lTO8biJqNqN/YrESBrgAIcwDE/N3faUnbJSb6ljUP9b0UY08P5U9xnVgvJ+jBMTnuIwDwpsiT9nkvMQ8DAuU8ncyGNnnOax1l5+BTf6mrNQNjOdyLJsl8nizOjH+zikSWqnoTW6BVVyokBkdye730UhUy679c17Vo0pMvobGQW49QHh1AFMnW7Fl5po24K96uA8LtcvZYfKK6pZo7lyB6vxUYKwnL6ohqnakT0LE8W3a9dQMeqDGhV3q8FaH9XZC5LUdK7zSm/6hdXNk1DSOxWzD00NsZdniP8CXrwKRH+Xly6aZuaCiUvnRyREtrcLW3pnKOlV3SO2HebOCh7Y8zCf6GNjJr4hH9D5FqIYaiShtwHl7wgdV1Gr9KsczGqKRIyK4P95ZhTslgCUhaUotwHe5dlL5fWIbOUhc0r4dACl2fiA7/9T7qSYEH1WfHdeI3djBHQAQ3SjJ9g0JK3a59CPaOqi2KAa07/mddgPs5nQZNfEJvUxtT8/Hz7mCb8V4GJtgGa5+VwQA9Pz53Qw9b9PVm/3rZrnZTHkft468BVYbhzrcphZNHga2YBqjn61i/UstYHsRPz/WfoX9dZ1nWcYBUhKTse77/9Ov9nY4mHDXX+Gmya2RIIgSMnufNZq//TWewEnkNJxdMtq+qOSNJKGMsXzdL3LN9sak6u0vpTpM7pb1gVLBQN/7UxVdSZS5NsUgXCakcpWLxpHw1hvsk4sc1/XVhGBhUhz7bpKY1shagahzxjZ19S21W/jWJfHIlUZr1OM8vP8ecXEwrUi3CV1VV73dHXLtJVbXw4sam3uuOVpsb0PhBUQONPAQXJ6x6yP8WMStPshhSh5HaWqMZPkwOigSp6l7qd+3qoD9EwVT63Hrnvv71XflRNnluYR/MEKaCBH7DwDFJgFOWU5O9bEZbGEBPnYk+vpqFxxKMVrvhzIug04I9h3mRadWcQWohn1+iqwlXkvZa4VfWaUZuPl0VrR1QvcpNzp59XBIdIVoepyeM86kK8Z1NjUkoaHo+DO4FQqPCj2WfCJnfuSdHu9ZDhdlJhhQsZFKFiIJ0I9WjQTvZmOacnxWJX10b+/kkCyWlYP9XBscqxkJqBNWy10j+SkoMrXE+/DulEsh14yZmXMne1pViHT1kI7GMFAKiIF9n7sujuzTa9YlHU3chi5F4TQavIwb0cDU9N34s4RVsezhDW73yN1F9Y14vxmFCQA2QGRPRhi3JzQ57tQ6TbLYX9MuJRi0UTVxFP7qXXUfSjmrd/mdjRPhoyx4O+cQ3mcTHn/Z9ig/uVrqznSuyOj9BE0hLuZiFpdwPTCrKE67T4PJe9KQuC42y6Nu5z/gBj02Lf5O9qmKC/PtmdTxL8k0Wxk9eP8sP3drQyLPtBB1Yt01UgT+3h6nlKOjtkNP0cFpT2DKouaZ0ipJlFZmMfRLM2DQmntLS6k7BXsQT+dahuWaWJ3vqnF7Om4Gcf62DNzAAO96QWVhCa6r1ixAyqX1c0hS9IzuM3yL7XGQOlwc9O5BVIAvL+diTKaM0IcTI9mLeZG79FmVcrEC0NFZ60Cl6uceTBZXXUKD9FBaoPM4COF0UnoxRkdvVuXC2SjOksxc4AXhGkQeqY0sBnlW4ill8G8pxNa0rsxDgvqWjxrpL+jiOgg4N0UetoR2lUQNgOmUOgEH5Wkb5KYd2AGas/4OKF4AfawsxV+Q7E38MW+xWfDizFurxvnyNRITcU10g0VgoI/LUczAKyNKtOTOXfbn5LtvD33MlEuXn1kaJpVozFpIyL7ojucVkDa4XV7lNdAGwAE5NsIOMZlKVZRgrIyr0afkZB7wAX1UPyrUkM1KnFOoxoVOVTMPQ0bM5V2SMiE9mWzFdXlfe1073HAzDlhRcmzlR8wLYmKadKH8sUMlsxYjmXrjBlLffaC9aiSCNXp7uVjgNSHqdec7ta9k48SIs7c4MSYUFXiAYdmhjWaxA4vfwnqU8peOyekKyx/uTJ/qQM5UKnSOsOeFeVXC+EMqNXj/dVmDaL75QmTdXKUqOQLtx1oJXLkGBGyrSSHp3y1ryN7yxvNll7GWRGP3glUj6QrQm3EVkqLswrr7I1YpJuSIkUYD/3oPLwWxAtkSgkXDHutJUWNvSz3nevhp/bVl6UkU7c9x272Y1YmgRTv5zpzASEATTWUFlMu7DTf9lm2QwWniESKIA7uGhzDF/x2HBhX0fTs8b/SDeiTqsMVOQvw7f2x/lSxCel/5mVFNX260UrQ9XX/XmzXXt5+ivsJyJPuzqI+tMCbyO6jfzKibOLJ+X7nvv1J+SZAqO8e1oNOlRsM8/5rRbAK+4bdc7JPSRkm2U2/UUPRR61r4vWqRXbsQPXRCtWu6/F4ONOnKyvFBbSHXtejxhO8zQqQBUoDntQ3V7zTYQ/f5aGUV0dtvtbKIVEPbCZO7ueOvKEtAO7b76+fX+A3/enVCkUJtm4BacpFGjUVoFtpej2s/4Zf8AZrAMcE5qIUkMA965ufOto8YeMhkfwcv0I9bVpmq8QkRGJx2lMepUbGh/Pj37V+TQuTIu6eqa7sfIbatCk1CKF5vJRUFb4Pu+JJneyFE/IlrZ0BjW2Aglh1X/NfpB6r8Lv3s13BkbbWfv+iUYQoCrRRK858G8/42AVeEF8PcGAEl2FF0c5kCtkk39Mxx/vB1Ji+Z+Bc6DCZr+2pBsIiNZRkbPd3RzlAiY53o3/bKLWnJ4utUXq3uj/329rwvkgFSHNZvGhblmWumg+eh//d6yQfq+NCjlJLs43aefNbMld5TX4AoxPiAnqZwXFoojMz8Bv7O/s8Pl/z1OwVGrwDH9MXBQy74Hr8GdtwLaxLiNAFiWa+ePPmiuny2kjDqFGtVg2M2ULEbJ9F93bk4OgHw5gytZyf7f/W+gSB55m7wJlPp/4+dx2p9YMm7Gn77VK0SrMth1tiGkdr4udO/07d86387m/+tH0m1jvtaVZv4yfWC3dAhawbWn/UG++nnsYAWpf9pB2wbczFxnPpt4rw+rntKppjjNleJdZysS5Bio3w1b3uofb8GhSOehNv79M68f3PGJLW5+q34bBC5fTp1lYGX5PJUsyhhU+5HVjHJsMO51bZ9DdUNYW5vZ/w4ifP1QUY4D0vh/VuHaTMjDy7D3pUhceDH67V4RTIMqtt6ueYnL45xeHkqncxjO3+rtIQB8BNNIzHWAKkrgK+673LJ0TTNOddvvRM3Oc9YRORfv1n6kqEUwR0VgpHv9nXtGYz2vdQohh0KmHVPYeQbdtx/Rv71BPPUxOPfwqO78x/ORq996zLal3n+r+Of2hXMjsPmduPLDrybTkDptgrSWJdN3+OnlR8LY/K14ouVzDQOG7C8ihl+pTyh1VcWOHbTQLB38swC6ZXBu+oR3OGV3PTNLitMI+rEZS+ioAO+a6gflfd2c+0GAbGvag8yS2W5UKtHCo/AwFwb4A8az/vuESNNQmFcqzmKv+0RI6cUhv65tXzwRbF7KYSFQfB4cXZDIqRB5BbWpkfxjNqpJxW7CY8qrP94P2E/rK/iekJ9ZB/d0vdrgcO/+befSNrUklrgUFZzbRqAAAQAElEQVQH0mXEZD+/fL7r3Fep9S72B3NNN/qu/gJP9fOcu75pXwsNUeR8VVSKuq+HfNaUZBBDLSqe9Wl+ZgX4nP491B7pjV3KbJuxpnQwK3k9fuXyXGpuXn1xFFLPVOOIwVZQ2uLun/nZJ5SRW9g0V0xu6NpYjp8Ru3l4l3YYjosRjvGHSJORqnmVDVXP1S25puhQ6KZZnmF2zQ5f6Xg05JSdcqobCAgh097PhmIDy49acHrRR7Otcsx6MVXyu8aUDsHXqY3cIER0saYHzxGHU4nW1fCRYoSScH/chF9jiDFu859a8d6XAP7168BLpQKhN0vDAdsF2aJN63E+80aoLn5RNaCaGpZAN/b05iy8L9F9/d6ev6Jf4vltv9Mt4wJlR52lbcDR9vxZPgwbW4/tr0uIyEY/qX/f7CMmxZoCZavB+Dn6hm6cOt23K7yBt/nYNnwGJsXdfZ/skfBmTlYpZSvhCeEc1S3UPrQVpXhHPmCa5ByW254f1vLuD+oCNrAE88m+s54xmuBLz4rXpDOa18fzYsvwmf525RkkyFhrbxNJMVbWuZ/etT7+HheJRtUIIUWWM7FfhnQObUCIECgFIyHfql3lGxtzdf6k+rXMSxfk0uFRS7U0bHmpQzxt+ZjdsZ7tR6rwTPaYwemLn/CfCj+GGyjRCPH/XXHECHx8es04iOAEdDileZlZp6LF8yf9Z3DNmHaz98pUcKUat57o9pNMPa1/w5BFv9RWrWWuSdfrJW0UWpViFWbFgQqRQOTMU6OaEr8PSnbMbF9rf/zzr69P7c3LfIk6iOXUYpeuuHN3Xnk+0wL/xsQkhijOOZmSq0zbQNExFBp2+RrZAbuhN20kxA5e/G+2bVwfCwaAgHSzReaxt0A3bXHsvWBU56iNknpgIr0xRV3ahJGg5YhgdJJTNolp4TI3jk95SnyhhEnEyUGXeA8Dl9taZprg2dREswhapFJrVnQwzKNX5z5wRQDFpvNDmbWWXMraPJ0tl2pp1EWYJjF8lnGZu7rPx+GhbpIyMeIufbKZmqx5L34Zn/VgtT4QcI5RQDZEgVfxdKFOnLIvLKFgc/ggHlw9KzP0Dii5ayDgEFP1hq6Bo7wkHvVcAAPkoIgKKyUjNiLkb7AgjzSHK7g2PxhVWTfHCG/G45YEYeSeRXh69Ks7HwesCcDewQBLyNpKnkblq+qbATQije1oN9J1pqZPJ1oNWNLGjMs6AAWsTjFJlju0jpKaGAAVXZs+GIA/3fExFCnbrQpvyB38CeRq1UiEWZCCj7u9wAfW8lMtTQi3LlNgXIBNpaC/MlVLIWXqK/1+EgQo1iDeGHMzVY1Sj430jSgyW1O6DvB+gmIWZ93uClSpgMTkZ0EmUT094NpclxlDn6ulxql8jwfUwI6mlVNHGDc8RuSNzLvJCBvqH/MoZHPZKgkgw1JyxWsTn9BOY2zQYFMdJaj43StGJAEgjIFxZUVRrrStKKYbfy8aQZsewtzeAgm+q4x8WfdqaYIUGU0NOSoBB5xGmEI5guhBF+fgQKBBhI29ONaIvntb28JotmV6Knv5InuXMzAg+a5U4hQAMUHQbMIhI617Q11jGcxFjU8CSmm9vp/1pKVTm5IdmkejZpr80pqj4EYoXoKMJmxtp5GDFsa0xpTVE0I4mhKj7O2pVWkY/YNZhjaTSHcZn6VRcnvsvKkyuFKh4uPl4UtgWZDl0+Jkde+W8XcFrVbkPg0hXmPSQNBN0ZpajKvFyZueLFwNF7ZI+UxRnG2d0jyK/UUl4eP7MTsP5ja/Yx/fpK2SrXHriqw5OGfn+IhD515wlavslvz3eNN7prPVifAqE1WWRzGz4xzPIWAcA0Dd0qQGYUQrml1oYESLuzNcXMwqSEhWlP1bdmte5mjWhjYqgSv4LnQzPmQqjUcpJIYyOgzgGmESddBrwhCU/sGBbCnZ/MOqvqRe1uK026tXmT9GjYDBNMPk932WlI1SpeO1rva7dHVZE10cLd0JbxCaaO9H0jwv6zC9XHiPCgQLfE9MTj88t33LU5sDbDG5NH0gKtiPpUIBZYPuQmH62JyOmkjDIGOUe4txRV8NQGeXjwGCpDpIbRHko9pp7hfWKn8Dt9G90DEKY5qNw0AtjNFyqba/S9Xrnho2mDGW8Rm7E48OuUEHXude04XzJnumhwDpRnd5otbAJb8koVE5aG+LEtzRZ7iLlvedSEOpoMzfuXz1c6zHyjHRHsZ3GKJA+eq7ntNHX9L9mE+bNd4FzRR158woeKVAipU0dCnLS81s6MoazJAu6b0cC81KsjJbNyqySktx9FkU8nkNAJo2flG9dS1cwoPDO7VAUPjHq9Ct2DVwOGV2VDe8SjlBtVZYcaR9xGR1va045Bo5jSniIdOZy457SmKmaQeOsU3InboeQV3QSCi/ozxZuwg14WIr8rY+VkVyXCdIIOAXYvusKA/QjAp9ebk2y5K7vZTBFtygWZbba4dKxdAOxHuaIFGwbO1wvAsjbJEMQb7BJOTcXC+2K8pOZUQ6k3bZXa1EeaVPGo8iH7g8LbxYYAx9L3bxuB0mvACb2tVxuHKZDsqlGoTc0OzN2jvC51hixmh0Gxr+XsPOmszphI+tFjECRwenPSuyTeCs6E11C5XsxV6epiVrmyYMUzAfIAk5MlHGkbpbkVcbnkApLzpmIKHbzLvqK2k1wOOl+2R1Ofgxe8zSxM/AnDAClKf10FqnwD2SNGXButYgvCvqvFXdwGhjfHcnyZAKaDb6XGYYyHvEeOwKP+63TVkOB6nvy0+wUhH4zDbT/vCyWV188OZGpKwOsRPV1MbNj34JO94f5ejpypNzj6VbCTVVBNdlQUjvf43ERlN6qnJYujCBnpEHkwZS0fBdnMYUGxYroHFvpWbqVlZr/HR0Lp6wggLS4buDHRjFSY/ect6Nmkr2OGCibOaMcGFmmIX74Fedc6aR0zlocc0xt/br8SXbuhBCljVvX68p/ZJsjlD/jVUCS8Ie1yC0rkxhtV0eK8J8nfAMmxdmdvZUj7Q8ppi5yFBGMd1Z1Dyd/VDb3RnDcZxtdRkvcyeVm06IV9PaV2+UY/MuaioFKDqavA2WPs4o92uk/IJDN5CtyaQSoUN9pvc5P51bASig8tYsbqyQxuXC5nbL97MSPtMG9+EnsZ5h/69jNpQZX739mGJcfq3ty0cU3H1uQnMCH8tgEndPDTJat/q36DQFV1LDcs7lCM1bFbs3sTlLSC3kFYqyQ99Ua7NhP/SPHjv+RPrnxtWUhOESILTRiBuf7i3qSoSKGrLX3Du1S6KPxL56KeIBPXvdeqBSF/paxeieeMq/j0RosEXXTmFJ1aeQNq0yKPC33ntSJRAzJCG6osNzhfbcZ/NrTLHsdVFo5d46OTkSH2MzXOyTGaNehPmDZS78iJJC1jLbAZ054yltiBZAupkn8IAK8XyrhkedYS7MXtx66qwb4XU9XL8T3/nUe8VBBbpgG9PUs+OvYdy2yaqFkWYVr2eFYYwFsXP8TdE8bsfw9Nj/q+u/XrprmH1sn9HeDSPYrHZ7xLWtAVQxehtlaUJoxR+1omUzpc6EQm0E6d7qO4l/s/5hf8jB82jvt/Naz89Kv+qx/hZOmfRaKm7tZfA7+Mo3fyS/URLxvvhWS96lLuBjvogPcvlNv59dXx3M96lUzfru615RTq+J3/F7MMNrNMm4Cnii8qf98mXX/K+GhSDTULYZHl5hWeiT3dWEkmK29IeDz+B9vKgtiLOgYNUf/XO49Xetx/u5cK/5LdcjDIdueHj1T/ZVKjWLLUTB+tgJZd5gJeSP4XnNLQpeqx5x7XJJbzijwGlGXaf39lzaqNgrJm/3LvJufPVw+Pv/4vu9nola1YqXlajuAzXa1c+nbxrPXqnbnJDVgV2H/78H/x5scZ9Up8upQrPlCmg1TixSf5jRlzqll3qdhoxImZ6IEiCb8xS+qLv7GfWpQGrz89HfUbXTUbCZdU/c5vdC7rat0ttrXdjAbplGMeequEKqNsXMLlftdWNaeGAj0UZR1dB3DM03FuD4ifOPtQ/fTFDmfPYM0/jdpqza+fqjpevcp39zjrIyimsaba3r02e+mS3DV2pqWt/awv8/rN+yd2JXBeffxr9latCjIGIOuh5X5jpN5mam/8yp4BuV48eXwn0zu2qkLqPgtuAroWoVto/HWBkyhKaNJ3BT7FV7J3TeXXHPqF8M2nN4fFLMgPCLquTA+MguaSOEVkMfEbXq/pffK77cQ+Xxu30CMnne7EzaM/Zf9u/T7+M5H0YrX4EymfOSCQzJnnkKXxdCd4eeyeu+rcdSgeDSMmeoEbT+oPX3p9XT6YccaDJkh6N/7hGXq7G7xDjP8J51ZjfMyWXA1qyuVsbUOivsEGOtpjRhah/n2XcS9v4lbh+YGFPMWd13QS/SzNuprsU83hVPTlXZlNngGq2oBHlcElNsSPmHj8EDa+nqrlKn41g1MtGpq6WbbREiGrr6keyV5Y6p0PIrFsufZo4SR2JRS9gyrVzYHtfeP7zi+JPneWv6WWwvASfkDJd0IkmzsZyv/xjZFR8UZVq2RaYtQiWAOM/nfsOfyuzumYbyz1HOr/EPYrVkMOCRRWRAMnJwp8rKEcHPxr+pjyjPhrL1hnwpl2/mGjoFvWLM3o0G64PvWr8eiVBpfQw/Df3Fa53CHCXhkG9PWmTQZ5TNef9X2tc+QjI+D+dmHWj86cmkuDzE+T/iZ7BEajkSOrJ5k2xDR49jRAKk0fENaavCCRp7SRzPvKET0KGkzYf1s45fPT/I5c3AbHIR2iy1gCVReF/hVqGijhmLgbWxlIgJuHHTHNLlh/hw9MzYig1TAQv7Hznf4LMEFtWdiROGT8xPzKpmFdrmTadWspHgUtkuJqSmpCcQPesBHkcvkI43xnn11y3wAlqRs7RSfAOHXqlVaBOu5pH/fv3ff5/9e83tIm0+xV3KuwMYMTiYajuvFLhh9sG79psZtgFiTuO6rYeAQc4FHtSsMF7+uewThjGV1lk5yrjd4Xesr/XX6ugaTcQaygqtfbNawTRsuN3/z+3H6ooJ8zWITH7Ljntqz7ddWyeZ1g7BfQxLAPwqXl+uORAz0QD64fN0zSxM1Kx8UXJvrHAFmmCPsze4TGXiP2Yf+TuibPJV2A08Y+WaR2tGS07oxHOQKhWUuAitdCdYNJFmNXg9D7WotOwbyWNZ9m1ds6i9cyk8ovM8Tz43XD38tdftzWu29sFqF5J5Q325piZlbhgD173PPE8cJeyXc1oxtvwap1kigrN+8jKtgM8A6f6EFgFBEqziKRMsGWQ2S2Zat8hxFNoUn5pOuZoxot/v/I4JWwmWp/Xo5LfEgYBQOR/Qu3fXapS5pGvcS9yOsJ7xokzVvO94hcq/IBPteO87UPrHwfK3fdqHZ/Uvn68gVF3W/7ZdP67XJPm4zx4KBptnoG2q8qowLNsUeyQshz0WsAvm0lWAP0ltO64iNjzipAAAEABJREFUyyCw79RHnBneRuAi0KpX86fLNlXoyZWz0rxiJlqhg2OjYBInq1p64QZcy+/TXcTfdXpBAvuZ/khYjh7pG9MtUHMkGorTEPTRjFYOClYRVO/ANc+lHkDd9tQvXQtpXubB5byMK97Z3+pbehHXZN72fHGUensztQ3KwflIYgpMRUiEIum/8nJk4AFLMNtw7CUNTe8OQpEV7QB6lyPfI+R4ev4b2W9/uA4WR/Of6dNm9ABWodNT3Y5rtas4zQGt/58F9+HfhBmVST6e6tTnPOep05pzvdnp12aP1uqerD41ZXOsegkmjDZm8IGAHvCBH3OakOh+fDhELeb2pM8ymAuSwaNJSa5K3VSTl6evd98ouHEvy3DwDXXaSTRGA7vB0nF8UkEBspEw/UMMrY1W7ywbDQRNurPzjIJi9ZqM9VyfVpePNgdG46a0OOmTST9L/S7Vzjn49rlBFSTD3HgU11DKKF2rwWteF0hIJNlouVXUY4zIsJyOzF3PIsbfm4PnfJ7H6pHU+kQlOZD2xWworXtqeVM53hhrDSyzhok7tnZBqag36KYAZdL0lUXZ0CgXMTWqsYeC5TM2iixoHqK077mfKqVB+ZgYOPymPaJThZuI7j3QeRon+v2jhPZWnmVqqp95toLu+9TdyHErW+C2WSyvNHWN2/0wppady+uDtdLtMTvO1nJCAmiN9en1fl4mh7WqFWkEEIIrd3xSkQo5bAyC2z1zr/5K+gowC5hZPv5x/qxeBo5UWILk1hxNczvsYqWWkcrVbZ2ihGybMqSNaKzp75nnJmaFXSHNwatkTfy4vY6nmTAtEYIYUeE5SiazB2y3CsFLjEpqqE3W4HROHS/5QWPEVFfXoBW8vGvA8Blv8SEKJ6Ea0Y3ATi6keSqrFNJfx+9KxdBqR9MSw2qY0KmbSdk5AsLXh6KdEQiofAVP14riTEQncLPOVM5wInr5KLeL4U7xigZqgGQoxAY5jhJ3zSg4VSXztMDKsqZDhGTZM6pVk3lXgiP/rbhi06Ft6Nefe3qKVpodtL+k6/carpE0+eSIHzBJZoZIVCY1HERLN2qnuPrtTfPMk323agTKxWyF17ReaKlK/ujZgv1mZMNW0WYXdvs1dGF4DwlqwCYUlxlHoKNsz1vtifPd5wgn/Gd4WX7UDnzoa2rJbeWpaanqMdzAQQyu6BiBnlLG6eUg26kFb0xFsCydjw2CDCMNXaefUjufoy9NHOlutDwJe4n8arSHuBY0ENJ/iWUFqsdmD1wTuu6Sc11UCozaxS2UIjzeadnK7T3ZTFvlC0FEVpxcfyCUdyVYccsqlBQ/juOnFUIZpcDX44S3a0wbRqBHhiOTbc4VbibdieuVMARsHYsDCusi3DTnghr1KF4fJyhLhLLqNf4rOiC4OgdLeJnRkn4f77MCyzXUinz8Rb7aV5qVBnIGUzKmHDLDbn/74MgFhBNGCJ6ZL20y6wI2GfLdL/K39RENq145QtiYB3Wjyub9LnQ5zNnOMOhbI9GjKTUcWgd7y8/bVcfMwhxrenc71OTKFxT6CE0xj8zqMP4NfM9rkLBPg+WO9WO8WIHHMk35GiUOqQgOY4yL0ruGdqNrY7zaBVQD6zmpZygn44zEtxYj3BrVnTglmg51w7sTSmVPNJn+WNxa2MQHy6aVpDaeoDKv74dPUTdqMy/PpME2qHDEAkpd3lYzSILd1vR290Ao++in5n7slMQyUjKRPZDLgLkmEK2je86wna8Zs/WopbncxAy1AwpxcA8ezOsRM6inQ3EYVD3tczbUxcAZ+VQ5O4ECnqKmyZ9htvUly+8YNrlY2v1ZzzQst+EDrArWmvQu2LJYAPXpjBapSavapc36S6dFwHX1BO54brvTn8I8Zfn43PRHPBQbsp7D5JRMDb/uCpVPxtP+NUVkMzLowc48KkzfsKAggbfocugyXe7mumt9BVfJmvQXF0zkz0v+J8fURM+MtWgdxIyXACROrmeudK+pOd9SQjzxRreX66FY0KlZEEylQGPWsX07bnu7OCXGtmuM6EENh4F2HIzQTj9WHaXYiIT4tpG5y7AcsdzWh5KKsnPMRgklQJpyAD4jB6/FNXbJZfqgy89BJ6jGpFV+Fc1MZTqBWsG9Fvw6KWlPrkrv53g9tCqHkLrvxWWKHm/TygXZRJHpa1zfZGyWaeQzr6fT0zNvcFcF78g7zpF8KT3YGHrh3ZGYn97fWkff4WFHpw0F0TBWY1ONbKoWR2UDG7hOjqX1Wzr2F/zE1MJNdjW1P1cx4A+p3gg0cKg228+rkCmpCUI2igBQisezkNWDJKGkeZMDKHlrsJX5gmKBMqQ5mg5gIiQl78fwheZzTvF5WlbMFAhoj80dLaFgjMbxad6uZnDlukYeiv0g811UmxnUxB/2D0s7teC0Xo90P1+DHtO0Fr+cPD1lAAnj6yOPHaBf86JGyQQBgzpmmnjEoFGpsrAsDtoslfcD6dDviOfytAErGg7SWgHjOm5SwFh3ZO+ZHbPW8dVudoVzQypIihpybJ3RA6vPF1dS2BRMt5q71slnaoAIhNkQR+HaL+yL1kflt99wDkorxot4WQZYoxK2u0aIHf1hfvAs5R/i8WJfyk8MAjBFyOrpJzEgjQXRse+JoxN1HPeLWxEslo8gKPNTjW7DS0d3tSwo97XwaKOv6g7QzOniI8oCssQ8vO8X0/2yvFkrzlut7R2e4Q/xPNR+GGMi62dPLAIqQuGBaCFFAclTUA6fVmyDcWABm73VB9F8Ec5YvXxRjwtr29ep+1gJeFzVed+e97AIjzZTSntmegCa0y5nONZn1lZBFUsAZt48ktLpM2bgP/JfaWcGRjIerOw1SdyQ2qC90XpsVblWk2NSgCgLnSWGHOO4eNbvhqmdaD3saejllkuqUT10crRRViU+04ZRFUQlqnQytQ4EUtDNZ+kas6lrc8382FwkRbj1rJxItLKkwECTB2h/uiWoEZQ16rzcj1gRjxqmaRLAIreZyHYhlGu3dhHlsyhFCITs+WrtMgLm8lQzZ9Y90sz1zLtSrdU/cWtpRIXEocbZahxfaNxUIDfK1BWpHjmQrNbLiCC1Ud4vj+VO72HOKP5mq4aunA3Ip2CacO2WFKzuqbvyxnPwyD7KlS3WgWiVRlItoF0G6IsWOX5GklxwUyrAMzy5n9zn9ikOoGJ+cHLOy5wwlAzS+GYbXJjP6s+nVuQe2EDlclNNFND+xNY7hJqhaDLscc+zrPan1LQ3hyV0OrpUP3y4Dz8zbjpEBLeJ9zGfIATWVi301dc+20tTb8yXaB9ahpeLg8tmaXD1ZOGUQpBs6elMZYexA5eGeU2Jn5ymVeyn7CRqum0UsLII+OBq/KS/lX9nZTl6Gz5LQugZEDrcEKPKjrGLo6dLnyt/oq/BTmpEjXVfyZ3bZjfCKLnrUdpxKdlmJDsLyq8qkLqOEUFmbXhHota0AHQxb9Z3+GDuGR7OE1lLTpbPB3FBbj+ymYMGEH/x+viGfYZqWDp9GuqCegfN620Oeip4b3GTU3IZ5pdrMiRqWBeQnHKHQ1lAqMn0c3O+G/kOtPj2deaqV5FddbqfPoMv93ft+0J5u5UybUSV1XNNXxbbrs/8zEQzjlevQphzmQ2UgNIyl45pylw+dXcLrNPX+FJwogqA8UU5Xdl2uGat8pAk1DVn4AkXSQqKvfDdfeowcyVd52Dmey1KR70r959Q0/KM3IkLsEZLl8/wNB7UiL1pJzA12f0KwCALdAG1GSDGJS6EkJDrO/P7TD3wxjrqSNVAQT+NSYxefkELmT6iR+lwZS/kNVi7n6uOStwJ87AQM9i0PeudQxH27Ptc8moGXFaBokA9w4fXUYw+FPqRpJiVWSP3ppesr+r92KV9O5ELTyMLA4Mt6L3Am/ha38hUrPOc6crOb5+UmuVwuaItVGU/yRxvheBJaKRYHH8A5cymPmxhf1PLkG1jXd27e+mYUQBrKN8N6D1AI/GyDZv18c/2DxUHpr331fvnxnVXSGfS2pxiaXqUsS19Xs3MJ7hkKIcno9O9YZRSWFhjhhkVA1ZBocykvndT7jmM3h/fl/q9+nSfR5isxnJYZWhO9LB6ugc6pqCT6z3NOkhlGRWR5roIOxKRWhk5PVS6TV3fvu5noUxW5BNmx7Xl17oUJNdgJe2xVlGoR6hLqsibMqDBbyQra2dZI0cNrmweMtvrWKLS0nYKAKPmWvYTb7UoxK5F1caB5RShnJfJ5rA6KqNqKsrXo+L8ObhqtOFQWAZCOJ82wIMbbkeMShIigJKP9qp41w9Zu83L9OjiCmyupd0/nXxJlqP6oh9jKt0q+p9iuOGKMWvxggECWBiiB62C2mxXxMV8S1/j4B+zz7bY221buXhaBy7tAq3oLrZAgzlIncY6VDswRCGOiVK5whqoFYcaVCmc7T8r9gpeXi4+jANlWQjyvDujmRYOkWjLpRDOCX/WpyFqqLFKmOsznCCt9bvGJ0O5YWPaYIGrg2dlXgWuVb6nFV8Bz8wDG34cH++lqvD+Jb7qWQ/XV2k+xJyZQ/b2ubbKnDae44ddmAEJaSm6JvPJOkepeZlcvdYrbLRpxMCnWalinRbT3EaH0bL5HO1C6zlaXPqRbOhuQlwMEzJ/Ka1qdQK+CVaPXlzRF0YkRjLaVFMoaT0ehAGbXA7vNJmcFgMrxqBQ+AI2Wgi1o1bLj/u42UCcEQ86UbCeBUTR3xCs0aLB53g3c5Ef4l/11eU9VcyDPiemaFLC35nU5lO85TyFG7zfxE507h6bMLukIGXF6tEszs/Cv02/rD2g8ODbeq9auwlQaknXOf8sXjYzd6dMZ3zPuuV8BHkUhmOj18zVWAyuNe4ZXZE97LNP/mRYrpqlzHm3n+p8mlVK4rKh8kKqqeEpESoMgIiS85RyT/D2qAKfsQeTlmdUmi/FsWHmjbjHvs3TyGECpeH34P0vHKUajaxwIzexRPEWI/fjaqSWDzLRY8WlMR2oPXfMl8qcstNQFQOGZYxp1Gh4HfPHdkNnGmkCbGU40GHmqq0tCkCID/iaZN44at3K/LxpZr8XRxMXjSv65UFG1SC5+uJcVL028zf5Lcov6YrANkbEZt6/rSatUlwGvxI/3/Hv8K4zp+c7dvc6N6sGotwSOjRaisJQmjnKlUFnur0Uo4QqSDVdfcvl8ad2+cLl87FzQV227ctQnd6DZjmEceRZclWC5sdLsF0W3wQo9TPKsYgNLuduWpqiNiA48ZISa5ad5QfxHHyfKm1EhW51X5KTyNMBiHoXxQQ1UwnkBpZg9l1W4xAldmkH/jGlTy2erRFmJqJY6LJufQZ4fA5UNH4ey570PFY3eNPeANps6DMarJ9Ow0NhMwAqXWi7Il2PBnA6G1JBSqxS9Q5Xb7N1EJ2X3bFO2OMoKafS+LhX7A4Zr8ChNVzBqNQ7VCPg+G7b0ZTGqZTRKjU9lju1VIQYDO6AOqUgvPOnoNUnwR5MdTcI/NYAABAASURBVHdSUcpXv6VQHhRsCjlEzAWu0OyQ0aGcNyypFjwzMLjNnnOlbtifvSyuj2uTCsw7Xfk7j1pkQvXE036UO10wIVDltNKX6yIuiWVj+VptK52DMh7qd0+nTw6ycKpv1FEZc3aZ9uYuDKzH6nY8iumgmw71yRnOYIhZrWf4WgY2lRfAy61Wwn85v4MGbGH5G+m+ue7ZOiPqRxqZaUEQQzJFZdHf4QIC6HTPnKynWHP9UURf+i0vrHcTshur2pp0ctdapTOTUSnT6V07EbJkamraOHym9JdufwMUeWOpOutGj2N8MdYogMhRZfYp5cHKdyntH0sfsCzkwyhok9qvd8zsTVfkckOfki5aV2GG9zUjWuftXTNe57Jnx9nmaDVcf17XHIz1rFSEtkx5VTXrT42Ne9QapNjCtsT6tmfL9QtiXEE6POLZ5DJeZpLhNriJ231mP71zgmkKo62Otw7VlACxENlL63Q6EZrRS0/lIKI10OwZqm7mTglihDF5nk4F581oWJcjKdZNMZZbu9Tk01M4ybvxVAuqHFbuB+uh6SsTrYd3+cIah4rRcDCPqGUuwGp4ht1AC9bu9sPplao58bwSa3LZISVGHl/dC70MZNfVqb38Ztoo2NXYFaslub+0zWBq1dmZ7pNhx+d4VkxtqwsVaD1zM5ePVvVPe5R6IKE4Wk+688HRlziPqBiZC/pf3hkxrxe1i9HeKwoi4WGpbT1jCmGGOiczCh9qML3WxGUdYk3lJMPP6eddClmulPg86Id2gkWbCtT2dlio4z7X1CVwNhNVIkBXzPd4KAHmJV9ZzAwbSKXZ87RUKYz0Hv4Rq1Z3F/KGAi2CizQQQz/QI3ZZOtHlz7gWOlEudZl6jiklbBlR3+vv9O3PvX6FU64mXdWSClJ6kGNfTiXU882mRNx31tNT2dAqcnTv9iyFZRNEE4+Jmxkp/X8TzkB+rO3TTbv17W6/z34O5eYoFNP1886N5jrODq0at+RTqIIQLU1UUE5b4TYUqDelokq11Iv+Zq32jINCj7aFWUjRmttxOd3ETfUgYY3NPzXdsDv9+1y3pKS505gCxp3h2BgF0vAOsZnOX/h/z/V9vDSOFBvjafuxz6Mdghg1Cwbhx9aoCu0yWxxAcT9n7qpbqrd5m9g9ymC8XD2BCcMyh9nImzuc0rkGGmJV78mla7H54Vwn/Xk1NUizNDSB+SOHbIq4R43w2Ghv31/iN+wWPeYmjEO1IGOti3svXqv3whWt2mhormr+WNT20XLvwliu67RPeSTkh3yxoV0sPzfymVZ3aabbsywD5KuCIo/jgdqSOvCK+UvrM1mppGc8Ym3ttEAtu1fl9cyWVYyTGzpGyfw8xu/q2+q0koYfG4/T0qFhNz4PLrk48BIsiodZzghfbkGzXrjpNY4hjVxmRtXvOV51uX1UaklFYto6eJnBNrAth62j8e5j2NleWHgNpL0VDXdbdGe3Qx3Mlm8zpcbGMMb21bYfepJ0MnA2df8KJwA2Is1bQ2c6MSKqOSDbPG2nL7/enKwzfoOn8WCSPW607f6xz4Iy9jglnZJ6oPnHNWbaCNNX2ZthiRI1q5zSNm9OB2qxXeEIqQGmX65f79BF1ctc3o4zGB2wgSeYPZLqeg2joI4YNYM61kWA4xrkX417xBWAHnvav9RgsOlgmBHmCP1NcX3CP9m8u++xM7udIODvSIs1WqChJQXPQwi/TLyTZ538ZK4ZmHTq1YqiRqLcKOc9XSV5JW6be00tX5+P7RisW2tzS4hvZXdMXi3Gj9gpTZoZYbhtJTZLnNAu8pqWM1Zi0I0WH36wH/cH9nAOJ2mmmhUtEgyiz+5bKaie6uZ0eMeGmRFDnGseryeY2jZrhwF1vO6ZZoy8TYyigASbL205/YYXiAi+rcQmGizHyByWClT3v9apm4T7H+7PqiUwGqJyzNTiU/UqmE6292EnNPitomwfKrHoGmDAGst6P0Fvo7B6vIYcZYYhl9wAWzwAxyyNRYj97EiybdpkRrHISCpWh1tfxJrXjkyF08LNQ5+9ykyT8YSqzG1ihWokDWfNcYUt183QdJtFCTatz5nn5O+R3KbDWgiswIFePKPP1uTYlIuBpPQ7LUc60Ntu2AH76fn2JDSt38vdJVu6MDONmYGE0AdIHvCBPa86le/3q4wCFvAQMwk/E2mrfMEctPdAGEKT/WCpZUyf9tSZ93QCz2igNLE93UoJ0G+drGfia3YCTY0aGOFuSw0FH8NlRikkdHpiypQVhyBYt0Mn3eB8Z7ACstrwohf+CmpECaB5CHrUej9GiKpu/LHXhg4Hfdpnlt51mhATM91ShurIZpjOoyoLclOmLKFroIqXVeis6teisky5GjCL+lt5Pj61o8x6hJWzrS621WtRmQTpa8Tq+GTzO6YN6l3TevZzX/uofhcDtUrNr5QjxXV0eXrYQjiCam46KB/jfKN6CySGVlbp2l0IDeAwwN6zQFFu30nt0141sqiQxayhZh7211pKkofRtle+PAIRIqFlrHremMUR0eKH3TINWyV92+lz5yjwrqjXo3LyDREYoEHCpclBinGKCJZsEG8WeqQbPBQaMGcUoVTmq13lCggQjsTnazQXMbVtsKaJ91W0jIaCxnD8PTVAJepQyTA8hmNMM5ovmDft1hWIFud5AVICorljR3/E0KFp0MDKKC+tpHs6xiDH6PZWk8an13Lzz+BDhE1b/x1o1mhU5xscoO7rXV3Icp0qH/WkwpBj9h7jfNntM/XNfhKtmJSrKRGePRwGPIzg/B1w4bAEy+KYn85SwrN3T2jA6rowDhgoqHF6HXnVGbnzXHFW9xSzoURLB02tHk1hgiW9kkD7SheveK2YVBXdQm7+xPXE6nFO44j6W0v8+xlWiLZoUpiRPmlS7kmIiRanVaiEAhLy4SIupssrlB+fNyZhdtGepk32ffopXY00K+ZBvqxalJwvB81pRr2arktVmXouO6yl4VQIxybfClLeOeFukpNbyhs6WUMBhUkQyqr30XkZCh9Cg1ziqd1iDq7bH+3bCpqjuFWRSxjbUdeMQSyMlGstkQ0IyGLWZ44zR0KkgY4GUhYSnsJjvbxNvju9RjbUgHKdR333PH5GHLqt1vbXPDmHdVN3BVce3fcJRmbc9O/ifzHiUz6JEfNw3YOKqUGZ30TOyakkH+KgM6ge5a7AR+M5gltUjqBPOqlRRRnrq3JBHFFKHKzi1bg6YQWfkcbCuadMxVrIlIeUS2GI6Z+cfxWf3isZ5WRP9Ly7fowDqt3VGPnnjDTgG3DziTUxcqVkNFUyjQYN74h5ph8opV0SclQaD1lv6Xx2b6fpfK/H2Es2M7vfQlQQ738RgNdQQTBmuuV3JAXT7mQNl63wi6NyIbqqWw9oWhV45xIZtDZPtZrao0Wg1x8ikXUMd6AJ42gMOgRVJ4LTfLuhKuzNozVE/OBppJ461IN52a7pmdFgTCaUJUzriCGVdVP3nBo5v9AbJHZ5pGMoitL79jrDHk5Ba0L2NAXABWUszbz9ai6jwNdkpZxDfqAkrC9eOYkUgwzq8Zi+2tHOGzHt7zCp7Zm7tIgdrfS+ZQrJ0XogYw21VRSnbd4ZMSMkagP3s0/uML8c7wXtPdZKfVlQ6iComuVncRZUjJfbNnIMR+VtK3FhnNOQdAEbzvv1T7DjklqXiag5MwlrhEVMBGJTRE2O6bTWXHitZiuGAtwgkSS96dTYBSejXzIvhK6CA5t5GePwWfNc9ezBHr8sQBb4dJz20wL2lpAeZOExlsBDgtB2+4jB+t3Ps79l35kThmvbZeHvMxnnINRIlAY16ve6uxRsQrSwY9YaGMWnwvOGXuOmXhXaHm6LRRJWUhXUZ403/KEdiTba+o777ud89Y+/R7l+Roqqe+rbeFK8lKPo1Uog9Gu3MDUB6OfthutRW38ACXrfP1H/B7j+2X5MFVUnHtizMQEzLZbtNnSLj9uOpApV676fWTns7heWQSLOXXfUr/l5HZ3Rzah1eSdr+pkupX53XKV84DqI1jgqTdiUm2cwZSZ+uPbQznQPsHT3+vyu/i5UGGA2vWo+bY6fVAtQmMyKTkPqEmd7/1u4gsFROttQ8FElDA9xgwN3vN9N39m6z7Hd7RTGkfXuPFRDkDt+fLmWrW9JA6I55/uo/qQTqNDAI4WoHNbI5u75VIk2mk8YjZd+ik+pxVuD6FlTGiaFkwK7DtYr28dbQE00zGW9d5nd1PNPN9F3HmoRTXETh55cirm0DAmDDVRE9oTCntij2+RloArjLvuW7qdXjh/Yr1PNvT2LInlAb66O12Ndda6sH/ADE/ttqu4RXH97TOuFkqX83P0nC6fUhTFgZlAm9vsN8QybrPHUFUkLB/MYHujupE49XYojmxilIon76dMH+VvZR9HlVOUz2ZawR5VGYHJE3nei1ARVfUOs6k/z0rUYFWG2SAU524qWSWgHICcQvouqngHusgc/t/2kxTixGNaBr/VbMtMxs8UIII9GtapZmtUjzMJXePgXgjB1SDfiXQZI2kt0ZWRbvXwQ0rWJWbTWLRR42KPQfmuySh8v+73Wb+zfuk5d5lcss1GSNRXwhr0PNQWkGc+su1IhSpeNEK1X3Z862npeLQ6VyRsDF580+DSqZnrGC2GQDozt3VpY9uCyWZb+N6kf/WoQDeiHSiRrIIAzSWSYtOSzPm9U9d4VLwc+pacKWv2b3ZwJvl7NxAOBPpwHd+K/6EdUf3I7rIzn7HP7GSQsFalbXywtErC7M+UezvIpogQexv3hWmJuZLLlF5AusqzewlEKt1BLKqtG+zzwjGSgeu2uUTgJJqRTPY3h70iXMUq1IoSW76SPGcbxoJ6502+sx3dzc9QrgrZmgXpVqOn2eQq/iVMNlESxW02jDUXC29ZhpLw2WFjDy2N92J8n7bRYyuSTunhzPaCwbrwvaeXorljLpNFbdlssgstpW0YqruOfae7qUPGfovz0Csbbi35AleJ1jvpPbZ7l+QLqEWD1GWXxK2FqyZVDlM34G/CwlCEfhKDbW5utKxwnBt/k+0Bdwnaz1rr3dD/Dobm5L8QFWsFa9detQD9mWgtMQwZKcm/mNT025wL2xLotakRp/0DdoqtZb1H9rHXZxVmdgZnl5auEYGaawkv5bAkOIbvEMT3b+g1ICTlzYAOY1ba0YT7RFcDLybVhV7RF9h49Jfx6/LZndSuum4ArSnTN6am3YczIg0GqnOpiZlQuQjFMHsf520OGH+ILqdfGCI4w3sPvoA0+OkTnjHIWZqfieWx+QfnrC9KRUbE/QFX30+96MvLx01xnqPWur7JQhsTdo0ROZElqyKG7X7HDrjHJrFvMYwILkt/4KWRPSk8xX8NjPPAaAwfjRUsU6sb99fONToOC5tsXRdeF5el5OqvwGm3jeyzwauVOBCs0KKtfk13TrnOSok/5kZ2pSlGnL/KvxviQpYuREpbEck1u3Et695DzAAAQAElEQVQ63FN7Wg75Sl5DoEoVDayU9veWTxVhQ1yu5h4OzpuFq1Mxv0QtLqggtBl6xTzs0dKdiXQIZ1/9KBKLXnQV4rqX38vqn/NnTZi8CjU70V8oZ95nqeytHjtt91pfoRSvTGpIowUxxGOGHdELpctqNvmuqa4gxLq2IHKnyNBa0KYfxDc/5776bEmTkJTuku4cJcEIgiP2oXWyytpM5nbt676iRc75bqSz7eT6PZo3LZ2PmCVWXLaXWMrE810jJx2yAFGPir4/HF7Mj9/bHipVfaZPo0cCaCrlzfrbEtO5qKWJyMrzIG8NgmqsN9xVc6Pv8yYX3ShFIuQAhTa8ctejpCxCmJUjF7yw0cQB7+QX/YtWXTyK8BF7ZmlTNvpDY1Lnx5Tlr/XXj2DpS+i7RBfsoLPC8rOUcKd8IgffUbH03ay3xBz9akzyaZVNvnBDotHYEbFwJlTGK7lUeI9adaaU/QDS7yP4fxv52hwJCbZEu5rx6ln2xfrFNbNwRF911NpPRA2Mir5eRsQmae83Q3a9+Wk61gqX9DA33gKZBh5Oa+rbMoK2loz12gyjG6/M6BmY7A2qu8VFw5VDUwGX9EHUqll3uO460wuKWEyuVg7M2G4FRwVw0VbMEo0zJCWxcnVHhS6jMyPrbxopwOikcjquiNqq9CTBqfT0KBUNERjMtx7FdUke7MdxnPW31GxXkGs8hkbzRX48zcoNoW70nk7IUC3WchhIjCn3bL9y72f1sfM4W5RxjdaUowmDmSzLo3BMfcekCQjMO90J+pRrkdkkF2p3AlNh5RqkTw2J6qF37DYXNIO/F3MXomiAkXGNf9hbwfoBsqdLg3wu743yblnzd9syjZixFs8zG287l2m6K5RsilWpcAUumyu4QpzRyoJ7x7V3wKJydVJREc/g6VWPkubSZAwgS2YQBVp8dHTH1OW11/jW1Ra06bGCDZzmMBt63pyXa/41JXi2Vo0PeNkrsjP3gzPmpsOBoIdmhigjxOO0zQgaYIS6Ze2GNtU4L8q2J3mXFKsRkuj+Yewx5JxKybhMZteyuABDzYxMquU8/wpCsJdpkjlpu7mKMxybjLnj+cZdoSbWpof8KsiU3VmCBcrKMGlnrXzv45UdFWNJF+N55Idt0ErQpknH0RgqUxozxtqnd2ZQ55aZ5EH0/C2tmbeVKr1DDSW6fb6Q1DQwKXKV/TFNcRexNTlzd6e1AhJuQGFLjRqoj6PktquMOVCAYzG5+Kzrxk66sT2+FgnjhkftqOs6fk291bgUIQgILiI97ojDa/rKx/OXcyvIQ0XnFgiXFSWh1N95ZkueqLRTPj2CYE2FwwFLr6e0T0Gn1HXse+x+TLxLSQ1Ul66evkcxFS5i95EFb5hiwmikOueZmKM9o+KbrVAEH6oOOUuKg9KioP3LdA97BhNd3t3WJ0AX3nYkeTu1HZtpzAwJRjfznFYfXPoGn4rvxEGIB8f4vDACahKz9fnMZA673Tnh2JeYZHXiGUslADZuaaZHKo9L7SxhGQXEarGCLj4NZXA0sPH+2flhlOyNmUUwCNdYPN1H3MiTIKGEtbBvyok0JdSwVjDrvW5ULEUfGNpx3t5acm5WWFWokkePUd3qGKLlw/jx5nupy6Q43Po8p9W0LpaHcS/XpEZNFksL4i3olv5GvELZKHH7+upJxWVso40HzVdZqAXck0lZPwITTkW3rrEtqEdJ8zTQxfor0xLzeKK+m9qEYQt5zj5lKavIWPVmhTD7a+4+sqDuVt/i0U2IiHkEp81fBeB9TXvZp9bWnn/7OInF0THLW/hffXvh6pa0OOoUZSxKBgYDbLwFPuHVu2rPWL+VDt27fAUL6gnikQ0oQs4QEDps72sff7s7Wr/kvSUWC9mcFsZclnu1fAwnW3Ev9QoH6DM/pn1TmDgMh0kPwcvHCPHzLIj8B/ataF4Ql0IpMh+yAPSwNcwuCcngpk0mYtzhWKPAIWkFekxmWE459EP/tdFfwst04cEMapUwg4SMdutEihcN9xAggynJ/abvtdlrHayjKlNk7SJDdpDNHppbaF0QpApvcwzYqQTZ8bhxfdNLkM0K+iDXgxzTPFFJKTmmFlp8iroY24hlY5YwoVJuvo/fx8+w3sAYaUwDMWKPZO58dtZInX7658En6XZMdxb8fvnm0DbftdoPhI8ToCPBGf0KFtkALRHpcSw63NyMIQogpIrIgWpo51IKcGQ/RWzTDHE12E+Wusvo45q5MJe1h8aNfjRlBjAnLjxLgzRtRW4byux71IzaADLtIBRmu4AumairS019sNIXTQnG30uOFIDeMeSomQis4VzIkBuznk+eT6nQ1MiYIl2jAMeGXRPiC2o7k4CR7wEbMJdJZkmQY1UrxZMJrEGX7Y3Dr/dznciMKQ3TrAE0QQSKUOuJSdoZlM28Tn2953o6dQ7EtJ3kaUsLx74Ge563d2n5SHB6+dmXcgANrc4v6khmPRjNGo2wqd3jKZEv9uW1pD85UAM7K2e9HM6nxuStuqR2nTS4AdnQw475cpft8Z/8XAiTLaf62JT1kSaIUcCN1rnHO/0Un8knU2kGOjhrht1qS3XQE8TmlspCTLFoDfc0KygVxvKuj2GF11pjm395LEhTlHUZQj+jDXcqVkVt0y5ORMq8UYfgnJl8P1PLX2rmLUWUFfM9P13pOVeun74uLnOHtNYO7WzfiCVwHy7HK0oFIPLpyvY49dIlAjVyLkuzG9fslaH7AZaeNtckhagyHqzHPkoPtPNS9vGkV2EGLatGVV2tptJBsT8BjSFaAZpAZ033cMStYXrmjMKmeIJyIwjdksbqo9gkI41jiQp737I7UU1I6G3rUJFXKr7y6LACzmiy9VN9TmWf1Agr2utwqizHn1nJ17ufH7aWdJVYwx74YeTy2RBJdOVXqIFaKHUTLcfz3XmMgHnZB/Gvr5/78vbukaC+iUd7zOteP49fwhcGmigZ/ZiiE88X+WnsGyaJzTIses81iBbIZiaPRotp9vjMQq581p079ZkdwgJ+7LrW9bn4opVeXy8yLlZSLJDR8BoQhyqwz2iVcKo+tCXWM7k9ZiM9kmpfohi8cQ4eohel3/8jPk+bdP9mCUKOIZDgo5wwCJbJ60FNvDvOWGlBTe6gNXStYr7Lb2XFza33wo/zJ7mfwY3uJ+LW4wZntyRoS/GLuY/UOWY0w8wb08KE3C3mBxkvOfb4yiX6pYIjusbRbywRtVZdzN33fn7n+63ftC9igBUwh14m+zf8O2zJ6jF/0xW5/y4+kF4KpaHOiAwptpSKwKhqZYLY45ZuVa4GYRneYmWONj7SsDQwg7QofOyE3Wa/4Uc7nBihNNPu8XIoZPawgrWsxJVsQAHbogoOcUa15Dj0eyIF2jEXz8UHnsMZA0SLXB2zEzzrplyCslMmzE7bwc1eKtXJCBVGui688+A3ZUlFMfX0ubNZvGD/Z7OuwWcUWr89JqWL/IQe9tUq5/np/Om5VOZ6nNTPmWGs/bniJ/CDlBX70h5YGwEc8AYerw5yGZXFLWLLMdSAtUe3cWtjfVbwrULD8s2lQf5CHQR8R5jpzvgHzyXhaFmzq+tCrdddKjGcGr+fOLnp232jt25f7jurvq9crU2Wq/b5b+Vthcnr1KV1p17G+HjeeJ5T53fynjPdVm8IDABqaOSJwOXhw9Fm8DxI3RnPgNu5wZiOkjxLjNOgjMkyjGMaouVxLuM/C6dV+qO+/aXYGS1if0W0oFJpSjxWz5qj72uaCkyF5GVrzBzSj6xO+YwdRo4KZo9T9tOPVEcY2nxgbNNMrjQrINVfNetEH13xPQpmdS5BWOfRWQ4i/XPzSlFO/Sjfj50SF3Not+0ehfbjddkTgj3fvO/6qm9biyCFGWxBtBH9n0+aZ6p5zLL267iADFbJi6EEUpGBAlNXKDRWLKxlRPI8aCWIo453hDwHBzO6tLr3I5t4w3SDY6wb3a4L4GuoLWb58vi0+kCwLt577pcwU/J2+Gews5E5egnsZ2KXjCGsl93LHlmsQh962dLVWTaqSpMzTcm3tQEUrKeQcxM3/eEHUL2Fg9NUHjklQ1ASYs0VJ0zFMCg/AJr4urZMGDb3eGBzLogYwKlfmiszaXbWGlucDfexGTQKU1GP6/dInovl+K757kqV7HHUHq6hN+Y9WMof+Ri5KH4iK9QsEDT6pyjka9cKAeG6Ec9YabIqJloCHTFxzogWZXE+n7g+Aus4fCOyPevzuhsFDhtho7qfvsz12H7cjkrDFXPEE5+v292Tiarp4BO8RaVwMRZoyiy6bBQRy/yWQN+pZfzGzCddBpFRtbK0uIkstMhx50gD1m1eeGeoit58bvPYilnnSRFjqBZlP7QfY3gbH6I13d+RIAewCYXBGiHRF7y4eqBbhy+vtmW2YTvD6/IJ4lWlQR+M0tAEellDWJBnsjRVBrlFNbDX/IVwWBBLvGE3lkiqD2pjLIGaUdYSJ3E3jjgZxKxFA9BSVfVS0uYNYQEch4rD4Brk/JEfzoelMIur/UK5qkq60cD/BRbTFMT9nmvxf6uA8nL5FprPf/3vbimt3ZK7sNimfgLClSGuGi/20SrUx+7bpESTN7W/5qnH+nGAIRCglPFjs/6neWhdkD2aKslKXcqiw8zhLsMOXF2LAEzaPWXnhjpMoydQ23IJ9+b5WF008irGoRhmJtNTHV1Ah3jB8xk+CvxZe66Qg09zgbWQywsSOOoCPrCgatb/vKIkR+oudTc+e+kfp0tZxI1KPCJ/DXabAVyx9HMaaiRO29hxlPgA/LC/xO9j+UwfDTnllRu1CjyDBAnKyBhHpvSTtDZDWHwuqC5cLvMABYxZL0/pZlYXqdEtkNd19k4YuvklDyEp0KGAMJ36tx6uQ7HwLa1nr541GoC/FooYwVbZ5Waqku0onNZO35R799p8LoWRNwF3eoABBHphBOLYfAGthJPS0efgfeD4i8ghCtYjCa6sOLAM0NW5laBaM+7WEOqaUfGd4dP0UYMT+9UcNbaxoovQWjZqftjW3o0Hcm3XqIevSwPAt5GfOWmdsWDb4E76R+6iYW+11VagBaerxDfHUFPdsnVMArYzrybBNmsaxUu6Vbj89TP8evya2O0T+cSU7Iu343Mb7uFN1kiX7vIi1LqmVs4Y2TCI74a4d0BtQ62S+2GPKt/nckr8pAHoSqIALUolkXEKFEeEsJ6p00oeCVKVcYnqZ+a840tzoThHyoCCtd7dSzMnuqPQI+Q/R01x+f94COq6NpTFTk5aqoYUtE3reINXHx3BLRWiOeRzlGUohuk/TE1FajHmAj4vagrFDAecwSvZJpVGxdEvLC0wAXeEEjNy25a+81rz4+EdgjinrJOWkLT2pE0ZVPNueusAvF8gb6za6BaBkTvRInY4DNIroEeVPbL2wuAfDrDfBHDulWJDmCjDZ8TYyARKLhVFuMQhfY2PsVs/QXAEQR978BdIjySNUmU/IwVwlAAAEABJREFUU08a6GFKR79g9Oa0kEFmaX9nUYzMkGakKXcJsYVVEHFIgFeM6gkkB3imn0JnVSrRmWhDf3bvy2JrVdaoGn7bz7ggv54oEyg3EotHMG9AzKe0iPdFKHdX/EWhXepv4S9quQt5pEIapUIfHSCq8HqWzDik9hufMGeoZgqsN6tiywxjTFsnFmM53Q54zzziDWtmv0OF3dzCNrFru1SkdkGH7B7L3LdotIbdy9OVB0nlhGvDW6u1oItXldV3BJBKnXny59RW+DjVnY+k8boqXUyqcfjMx1RwAheQYQHs4QGOO9wnlFN0t34Ms8zczal2s6a9VQpYwgZAfWZE1+hVPToBs977NeB85f6ZEXdm8KaamosajKb1tOY0xGeVOxFsZ7nA6JhW6WZlYfjn3aHNBe02HjOjXdbuDZTGiWWPiVXbjtYmAwErSCDxYDV3605po+Vbp21acZQI7WV0XYWWZisf+NDsjaGHNf4cnqPh4W1RZrpIgD2dJzPPJNEhhO2KwsYMe7AlNyVCRG3mG47G9wwblpzCq1W7c8/Zkb5G8XGF5hqBWtDq8fjchoJn6ybUa+RV8Wg33+C4jUyNpU7r0ygSeJmGcTS0OpjaOitrrdEtrgE0ikxEMnSGRvd+oRsGPPAEOL3+v1FtPtAVkSu/NS1ogTcIsjmI8hhhVoAvCWU8iyXlQxICHKaqXyLH62Ojr++5Iajp/XbrPBB88TZROg9lX+pXeZi5HHlBXl4EVhPdoiZsmZKp8W699VFV6rKnFlj8/2CTy0UPu3AfaBvECP6z/PhjMkQoYcMxe7MKDIzt4kMYfGzL7Nh7WqpCn8JT80C5m5cHRZa0UTTQq4cO2/O3VM6ssQiVLY7riq6DkJ8ED1hsyL0yLz2LTEy9V6HA36ISV4yRXQdc7I7yRDXMlCmTLlyRxrRruc9yEUM3W6oHWKMKgnhP5dQzfezBvvEXoHWQm6Z3+ReJym/VLtlovKgke1Unjac7u/vMlDSqNBqHrMR8Eef15SrCBUZTgqHqmCO/fODVhL54twNtqg7UyX6erix0U7Znxjh7+Bk4mHRJeUYTaaIRcefnHj30W6W9qoFMSJEBGCUfqwbUwhRolpBy3rPe2+/tGRt2OX44MTa2el26rpmQa0oZgNNDLJq2pyoA+yO/ZFQ/jZYifJWtwhrINnwssN0uMAgCYrn1KtVvTw/SoAVVWWCMuB60/oV5bF9LKR12GZR8ChpHOVg0A6T8lWMWPmEImc3J8qKeg4zbWdESGEozZxR9cELum8pQdG1K7MOSVNpxUABizOUEvarFKmm1JlffqtZXYHSb9L53H58mWApRcBWGqPAqPqU7d9rI5KPLUkd6HOexSoXR2PCAC0q1Nn3VzHJrW3TRNCYvaslHWHQMgiKt5MuU3xf+xl6tSIAqPAMMNH1H68RoFeyv3lHidjhO2dQiSfOCDhYpoZyJQwqo2XEDHKM9pnT20idLy3J5KwPgmlFU85bvL/CNKCHCGKVm6bCUA+vXqlyEK4pZ/7tkAuw9kvX8Ds8sg13yHjYgau/b9z0oKMZcUUtG5yTPzcwciW2mS9RqRP5BvJCx2OFNo1Aroz/kQuDvFGQWkLmGst222cseQxGl94Ef7KRz+jOpZ3TW3S4kNkt0NyQWgb4WlwYpgaYoSRjcZIJ6GSlRV/RYaTJMH9iQvzOdrZcwvzGzkzU6r/SPoLo++pQQAytjp1iQFUr384TeY4ZQzhhzmQUlb6pwjncaYNK80jeQ7ZQL0fN0ZFvP1HSB5A4Xzpc3FNSB93l25pWlxyrbZHDM3CQ19W5FFkPHBPUsEarNkbNR9np7hxRAI3fBJLDTSoNbYZd8jupaWE+jmij9XeQz9we3sQi2PEtNNFg2npwUwww35wu9cOfUDY11GRF8ISGsDfJvlvYZX0bXfB3QlUHqHcq7OVQ7vXi2Fd2hFYyjBd5OUwlUXOryM6hpfS0GuMRNPVVZikES97tCo0UhIfY8StscxcYZZUASdKT0e+3vji9U9TWA9CyHokvPs0B44vOVmKZzHvIJOy7Gwkfxj+phnsrbdJd2NyXmrz1cJaaDQj3CBIcSZmOjBvQQJ/jsv45cU7p1mp+DdU9/J/MopuNzLDTW1r3jcc0oLSX2IFLCMvEXaT5ThXoEWwMEpNRfcrSRn8bmK8O+TTk5NWJ+FUd21cOFppWoeWzOxm0uu3eZJO3dbPQJHfpgo5fl8lJGqV55YlLZ6cBAdPQzWTuhtouVZz9YL9lHTPdf9lhUJ+iykmvYCO7duJ+s0yOBoC2U4Nndf/qEsonxaFe6lQDZmzjKAKPUoK63ypI5TNr4WuHqgx/O9cw+5S21z8YIHmGID/e/5NaTkOd8S0zNUMAP+IX+MssZIMWfRC80UzNHAXMijr3l7Ec1MSGW7+/cv3YeLT8Bcy6fWE/jqZYIwCE0Qcjgnj/X+eyBz42TyNHi9idwpSzVB35aDy5jY7kZl1YeOWSalZky0t6go6+5VQNBlxTnUHeAAIPiMSvzhR7v1BsKwfEbbMA7tsGIqXPyOW98CNrFWO37WUL1dzEf0e/QPYyo19EMZUBF87Q2HWNjBkHqDElJJMmJwt7w1dCqce7T+YRGrJprgvY5Hfns8xvny2/Pw/nt+d7zpA1lrFkZ59SpnnK+SQ9hVshu723i4+fRE/Gftfcn8DG1kZQSIR3l2q0dCuE4+8VDhUrwsrgYrtNcx/ZwhbmEel2xrovXj+2NLUmUhVMrCWwXf+p8oLhQD/vYtCgF/Wk7B/PAkj4TlsuUOSXY7qIEUqvan+MPqSzd8fTKdZW9xqLFl0SANVg9b9euZqajG3Oa0usqX25Y4lYZMpBviD9ZUa1kPKM/P2g13902faCezla/7blzMqe6cKjJrhAXzejWXEmV35pxjMugWLHyM7wRD9iQLRAjVSxo/5OXlRq+czAAqQ8xkGB4ufk2MUYnzdAlVJBhmxWpOSwFY6M6qTpPP0OFx2W+6dfx62vXAwOkg/Q86LRS8KnYpkXwWlyfjv/z+fz4in3hWvmKOw0V5AZF3VW9C/5c5+xv6akw2kBgct4XjFis6OPn5K1OwnkYQ9cQdsslGyfRNQt5ee3gdnfaGfvtn2/+qFXAzS8FO+yy8zCPiZARm/o7u+grectG69dD1HoJXFfbuvl5+Clsm3obHvCCW6bKm1LAmoLnZKo+nsPOGtzgoTMwbwPuv64PGU9MAd0YvPiuBVfn2lS3rxEaGn5sOTaw5KSQd+a3VLl9BueZ/JW3a0b3BCKobI58iICnJaNJAGX25Ge+pJ5syCitNISKetC6LMhoXX10BrCDJUQk9BDlp/tn9U+McvJna6RXeivydr8ZPT5vHtjHMhX0PIVzvJR03uPfCbFodZMPjbiInxl148jFCaEbk7okFxFji/yXrUZt+BbvoxT2c4ivIZfSa42XmJnFtGkUwaAD2hPhPnwe7VUu2ILgi1hWeR0Dc4iCfXNpVLfmwT3CvWjPWAravOkFohnfdlWbAsnyb0HNsNExcc3PlX4VSpTNy9Ym3Zku9QFp7x+IsPbCZffGr+H0TNPhey/5ADl3vM90u8PmcdyODLnPFzBwUErEN7tyizA8UoZ4l0TEYqcIOdayaMeu27dmiSbyhuh+Ave/Xep+s1jUHBBNflsw1CJ8D6WyKt3gHuivDJZnRvEl1dHp8iNxSKSBvUsVuH7dUlSiomGmKPRLgO/CJJU6QByoYyEzcryGj+SgkVGmR21xSqRXCwmT00uJaVxQl3zaqjSjwvpdk9Qw/QD9SmGCeq1pFddY39tSZ6BCe5QKNMbE6GyY01QU455j1XrVG+mj6yf9r/pbRPA0nzvmF9NodbHLSvMU9bPjYT51Tk2NEMKbr6/RbAlHgKBXKmseW1m9HvMqoWuPGsvqU3XP5BxNgY+FrdrXHcrDzCdHGwU+3VIrKLjak09RvAwS9uw14/3187uee3XJUB83n7XOx7+wb1+t6MvatOvzgS6u9VmXeoKR9+Kt548KUlB/+PsR35hDJpQapagVukhJSSd18lPUROp6q4fcd9UbuULUITbxWa1yV/9RwtdI8in8V60Prk3Vx+gN+puVjuUuEh7bpz6ShV+gNmDvGNnSUYr6S9yBVDxYWkrLIZbq7lIqvsNJVwR9u/bbUwYta8GU9wtYHQRbYTzGu73b3tgk/lcAEivjDL4zN+oIuvlqX1ViQKJ4+Nf7xMLb6RQDZ4AmLNLWLb2fi6ra4WFrgTRMGBwPK2e0Vo2K/zSnTcm//SWYmm3SGhSqb1kaTchABRUKkUqpnPBv0+7NT0hrWEZrmvqAymCmxlphUVPk84V+37iVnZkeAhoJPk7VgKyrfnUE8LpDHMTX7HaUzAcaqruZ+11joZcHLSipYAToXN3LafaOTdtpCgUjl3N/W9lBauiDyurWz1OZJUoHZqqBDlECKRqKzNGbYkMlKyVr4bzJx1QxOUbQj1sxRabZmGBQk3vVY/08iWwfOKwLdeP9b33yTNy2hrQBq1ECLFNcdIziMxkd37MU+JXaSrSQvxxWs+WkDe3QySnNR9Pb3zkLEAmFq7HnWQKpAe8wit1pm3YKDV1Vh2meZEOZX5rFwLG5G8+on6IoV5g0RdxY9QZKWBQ4efz7aJBWF2itgYzWaDm2R3YpD9bGcp8IXKQCdZ04ygBMESBfXg16JXjbfKe+ZXcuqHQ1gNmcosxZKnAkveEiCWxQObUVHaTOshs4kLSlrH7H1MSM0EpRVVn11CSgqw5tBEQGRhJ9Bo+eLZ6bZ2yoVzWenpxRLrBbJhTe9DV43Zy2p65TalKF+aLRhr5wVsFAJ+WDeOdSWBtKq9uIWF0KJd+8Pvns04pGy46gYtNDpaupcvzgGXuWZ3ga06zCClfaBdBYNr+k0B1OruolfrLz0RYrH/TNfljK2eE59nKCiNamz93cVEHRLao4AGzGH9ihwi7gdNVko7r1y0W7WttFX2RJu7L+lKfuzh8tRHZNbR81m9rSxslJgvIBdzhnGjkG4/IxWh8+tx3lZxYnUIbRz3fjWRin2Zjm6cMy1/70UsW6+JPVro1ZpjQ/Zqq3M502T1AEDaT2YWqHWT013tSthzTmdCpuLapKhfxftkTywL6Dt6vpp4bl4reqCzVogffZfGy1tWMu9gL0ARxdFieirhjH1phBmCWA569Gkq0RaFrRn3eBXp2cFDL07+AUG4z5KwLATCPVK6jXm5I80d9AysouSEhXwQtUEeRQ5wXKAHq+vQ8JsQs46ZvQfdzsj8hIso3hbyG+sn35QHm09pFPZHuqv0E8jWzCCPN0T7MECrg5qsAGTWItn2byjIHBWX7kXNMawBSqpMA+0hr8LsVl8t1Wie7SsCM25jXrWXZmsLi3XJpmiu8jLlliYVuvTC851mb9ry+4mnuJ8T/HXO8nfXzdmgqDUxGjmP6Av50FHqNl7BVJuMAbhzLfVUQOBEahxCihZWG2zXMAABAASURBVOoPlpyxI3sguGXh2XbWnGYWqzA6D6TGKNbRuFcdz8iUxJstDqtZ6X12qvic/FuOGLdbBaS6FVLr5gPJ8oujhf84UucWXhTCOLYkIgX9iGHRqSDpjzN72JBF4GdkvKQGb/gRrB4ZueL5rNwGq7FRcgz4E0WzRHzlPO2lk6a4c2q0wXi+cZf8WWMDS+rBNNb4CjN3ma3hgKeZOdnPU+cBxHO/8jIKsGhQG/Ctj2kq5PtZpVLR7NWwHKi3fh/cx56OmmkZUwG05gV0JyFzmFOlNtHkOMtxus+wddBhgvQkc1j1fpaQK6h3DOwhtDMAypy41rN5Z9fzp6KZ8j4hrYY6cyYOR914llE12ixxG6qRoW4uHkxZ9VSDYkn5Jh+1sHGRA7HV66GXNnyXzQW47Xm3D7uLKAFoVxqm2W1zAsf4GBSUchgYcjRGeCE+et6hpPtD+5rXbMiPEEyXauCVWm4UvX0hXlpI2ZArljsUKE0vFXQtRVWcNl1Evm4cvkM6M3mc5jxkYmBQX1bGxGNONQ+NOluyOM9WNgg4ZxQAhkqNA2MmfR6x7gfMBrL8FpHfnidnUlQui6CKly+AnkG2HVk96DScl8JP55KEW/tK6hGOAZp5Sjo2aAJnhMuFUACg31wuPW3vJUhlFJdf+hEdg5jcLLCmJh/1LZdkOC80QfexaA9rye5Amo0iZ7LuwYGdjL4XyNCbdmtrpcC7mztNdLxOIOUl5AKCn26K/mGKYhTQjTlTrQp2XSu+ldU+IgqGhlLVixNVWk0o2oNv6pljBf39E1BS2LYdfumX0mpB/cOYLikKKeIZKp4qzHdB8njAEd7IWn0qnvLqthYVV/S1m5BmtEIfAdSRL3gjuAiT9BytVAY9aKZkmW8znXqWxNFxnFpnS4zT9G/cR5zQDMA71ZOarnbYn2qHQd0KApCsY5phaGTP6WkTD4NSiAk8rpBFn7uiSMlzimf8jFKuINtMVTCjktaWrpxC/31G+OG8Xi8E2TCNf/1Dp9/VK7rQv60FhhAUfLy2TSzusKVMNO6EMvcWunSwpvz9yqEd716tJSljRmFZEASaXvPXqNKrFsO0Gp+Zp+uZlHtJ1Ag5n2XjTr1FwZXd8dbpt9zMi8oypA3r+tREz0wZoAhSKpAvV3APBGMpnRDX6fOsfOLk9Zz3P8zvypF2XwS6rqln+tEemsc8L/cPdvTCsAhppazapxb0DlI1qwPVeumh2q+a9T1s9XMudlitnWt3bPW127z+qOeJ6p2MZy2sn7XkA7m+j/1aP5/mgqvKI2rQPUqejVGB5WTXwaRNOWeZ+3ZpslVtk0MphRfdLMA09jvANgr9NsUoeYQ/E+RHP8E2n6Yi4+qWbM0w12qPlrz9xZj7ORtJEcw4WEVDlAq+zB/XhSHUDLDDvTmP5mG6MGiWFDosKUlwttoj1x5bj/DjlCAVkRdxGTmKoI9syRoQ0b5h+sdl4iiEE3jN3jX3489ZZxb0sgZT4U1re30eu5/pKah89jXmyhBAOJTiGnkhTXJC+Ir+WF52tn8FMrurbUZeoGFG1wZ1rQ4qpGxVeo9c0ekhlhqT57k7tYhKtH6Db8wocjT/Tb3arQAzIS50fpSBc+PkzkJO6zwEylD/UD9cZCS95q09caxjcwJNh/kyXlBlCWDfqAdAG1TtY9sU0bxuWhsxyyvPKhV8asAAQ59FVct7w6OW9f2KKAUyXEk/En9n9SP2Re/YYzKe4rDlArup/rncXEEv55WzH4TMwnqWsAXwYS8Fo121zpvARmD5XAzY7h0lZqrZNaNhzyIiaLbMIJFU66zI0KBF7aQUmqSm7XJwQKB7KtGPDxQ8prqP4KX1qKEYemYg80qOxg9emy5m+nfEQppUdBn055pgkpAEsNI/T1wSyWk8z3zLijBm4MQe19P/LXRLxtSX9orL+7K6OAKTX0u1MoALcm6VhoHEJMmGvUSFLWKxNsXUdD1P3U8pbXav/d0/zw4hseY+uE5HJ1yV1sf66/O4cKyk1XRSHtVCm8/IieMFSc83CH44uietVRUlxUl349OR6nWKw0cF9mJ7O00cSon39D0iW1bcGE64n+FdyFYv6+VQJ1ouZvG1+PqVWPhrz8dtdmM/Uuv0CI2qL/t66iPhn3h0iXu2mdsaN41Xm4UAexcq3395zvfkzczrmUvcrs6rc2PYI5Nvo56pwd0z58w39SB4ZqdYBFBP/A7fWD+9PnTQp+wXn7vWJNZMOBpaUpSXBWpEbp69Kthx0h+RcBPHo325XZet7aSJM0Vma4UJsIqmIUnV8Nt4/w8lwBr5R6u+1SAkYQgyZpPKeq2nV84F/OyJPQaqhuI2Vtd9nsqhTlEmsvVIcG6eo3R7copn5gFPdG9zPS7F5oM8/fR9lDIarh3XWpcUXLgqQszNeqfNfb9M5auUvqbfRha91phT2hRTAmGqllE8IoYjSIv1s3KphheOXU//PMfO6cmDo0eTqU6ulW2gsX096d1N9S6xK6JmWrZizbVxiUI4CXeN9rv8/nplzhKUgFoVcPpVMAT+gvJYlmkHfFy9S3zogM+wD+0erTiL86ohS6FrtqFXgt01Jzs1bOrCG2sQei1Ka01BrxopuxxjmDEafffllMEx1VWluYxFh1I1pZgfrTQKVnCWkq9anfSHzEWqzMOqcZRvGbQiFbVm9RzUsXq2BqNLwKp6WhvlafcWmiCpiNBetHRFmMxCSwxX5k56WY0BZ89zoVyB2DX6pQ8EdU2jXRSOr7+KO9ipwlQ+OCW4JQP9SdUE5Qlpkb7PijRTSMAIQuhNx9tmt0Vo6VxrtBrOx2rpusWsJfESYCcE2CXm4Dv5YAolEMrSI7PbGL3wvggGfCxeTNzwVi452kzVemq1hpVMpyldUzeyd0uudpnRcRB3RuX0pPgsTLIb3fRuWW1ELu2LMV01bBg6xL+80hUrrajdRksiqpRwNeQPz2bNW/sf9pbq6Au+BZaZfidFyQCj9eIgWxX+8/EfAyk5aZ85KQm3eefw4e7+aUq/jbkND2NsOy3ABVyO/Tm23il4X4IjxeioqdQ/BYaRaQGGIn/spJ6PoAHrrdY/X5UQzR0gjiJUVDYzncqrvYIB+q+TbuNnfFq56UZrCFWFRK/RGtHygls0TTlyqb9tmEHUGobRStISYn0MnXfahaVIZoo8vpS8qjVlrYPWagkSoipFdyphype8FCBtVgteLrxP9kjD3tx8cyHVDDBQGI6Sv4oXfhosXkycUsE+RMgmmA/yXpMfMEjNQ4IHrVVkjrbRliM+kFPtR3GjL8vt7DFNfRtkjj/5yf4ciYVjcqoyWgNTLlUdWPRda91r34hv2ffGyWkbXMSO8cXYfimsqZpuLVGMC3NxPpQG3qESIQwItGdbwqSJM0+eu+v7omW14r9M0KKBqhmoe8+JeDrq8RmFHBMf2hVm5IiScc77Lx3H96wPwhVBgvdZ5/Y8mVom6lXQ1C+eB1+gAha2Xul1sdNFku5v1jETQlw8lykvCgWKRpQ0mWaiB8iDc1udGBlYe/l2Lm9zBaoPbu4TbwSnEt/D37EHpnBY4wBLJq0hysyCMH0dO72y36sxUpLYRT2NJ31KDWxclxbbDORMF6agMPeCb5MJxiwySK/AE4qPvjs+b1eYTnuwekJ89bwNvIMQFtGnPGCMQKzhMswIdeoWwI66ib6OUtFlmurE4hN9z9OVrNCEHGvCi9eJ1SZNTZ0+DwT+jJVPryPqWOBQbtTEPc7GmLHCGku92uEu6GssGBaON4UOGDNMX7ZLYKIQbDDvoUz+Oa7unhoCBhrN4Gb+5oHUWZWQyrVqIXcUVVNP4F5MLKXUG4Aa5ku0krvS9rRDFQkfH6zRdTTdYl8fvy4GR+z1e344r4Ix6IQlhlAXU0Cqe/S5Ri5sEcHSNDMor35u9Whk20yTRtjVuObFF0hFV5AQdDUfP39STc1fbxayj1pshrzKb0fWHm6Ewp41lMy78776fm9qaB50Pmsehe1lHFprPci/6Uqzj/hTamw/vscwO2VLzaoZwIIHtMJN6wqhl7kCVr8/PVbwFhE8tR+5kHW4cOayZJQm21W+sOI4bsmhRs7MnOsCVx/zG35ELM4Hs9E28kAbBb5KLHLNgPOWESSWi0dBubkFGMpyTKkbeymZX+tv9v3kk3fKv7jRhuup58mseo3ILMpPOd6ApHsH4iz7anUju19n1+oXeeTYM1f2EleSlspDA99acZiNRhNuCnX07kohupZjY28L58ATBr7TbcQlXcxitNNMr1rQqxt3o6Ay7EnVccAXtTSGzaa88JjpQLrNsgm8a4kvaPnNNZpT51udEy/XNrKqvqg0eVAE0Rg9/lCPV+yGhGPCu0TUOyqeNtXlM7IDL+7DdUIDrEyYoG0cknLv806FNUWW8t+0NJ+OgC1f3F6c59H9xre0M+SXcZMp6UiSUMcXjiFHvyzG2ezBHfw6M5EpANrUPduxDR7whXf4yB1dM9QkCLNEvNv0GYxKiiLuTMnUxmwY8OcBiK7L2q3Fob9f288JpPsxDZJr/RrjgWW7ZDt5V39bbXFsKaJl3CwXN43UzGMcc18r9EYERAGLdmj9GL7XyJXJqKwPxMU+fikTDMmhF0jq2fxVx23S4n4cT4T6jqT0kpI9lUV9RKNH/6CUxZdICVHOgHe50r3cU/LS20aPjXXzo9oSLRipaZ/Zp+3Ui+oLPLRbCbVVK8qpied78bv8+DoQnF2jW1mzu+xb8pvyiaj0OjNtggOHjbqeVsWl31I/BGe/fX7KEz7jqG33fvtb2lRLYNG269qKfGRVlhZ8x9qbtsQDoNqz36fPM3yaz4hpnx0KpDm3VcrKN/gb17NlBlwQ5RH+gdvhdHY+VXdJO9syVrnfpnyQGvBqztMtATaEp716HTpifHdctHibbL7fpRIkfWAMvwQUYlpxXMSiuVKsLkcMpmqeed2oh7USdFL470Mt5AChPpqoNpDLc9k4hcglYLo5oUq7Tu1GcBhA4PH9TMhamUCKCl4HVyLkjsAFsWU+godf4qsiYba0zk7djl7JPW92OhrX4RoP+8x7l6FSPScy9/S1Mq6ElU9Ft2XXi3KywEMlSlwM5cWtFGjn9AHTF0NRbDwL7vhhXhiLrbPGkWnPEd0BC/zvdeAt9sUcz2FVzwM8gUO0LhxKTg4u0+bEY7EpCLqq6rXJhVpqLZsIo7Lta7hbO2M9KayLttx9BU01D/wC2pNjHUi5cxLPvMeNfqiU5LdVzNV3xqNFN92IpWP+sjyjpMJGWEdJVd2pDfn6ehQ5jvz0iTizqx2EuY5ZlDpN0DB5slSei1J9Gds5y2p7WqhNtLe6CvBXGgy5POPZhfkab1+lOnAnlQdInK7ID/ZtWmqvgrbb8XicnbOSOA4YleRtsvI89qhH8ajKzqB++XznTt4TPUF32xG+4NZmNxd0wCQhrRZP9E3oW5irNa56rCUSGDUj1oqgrKhAg9P2AAAQAElEQVSpqJ1zwHyoJQolTbxIS2qnpFF6bPHcPmfRuBxOUINEvkbXCOWcGinU+Ayn6V2cmUSif5UbyZzs1kCZZb/0tDWWGcwKaGgEupHjw9XQPmSN/AxCsux2m1dxjvLIcMUlpmzDI0JTSwvQuQtf6kGMZ4CkxqIeT612GRu2uuzO/AI3rBiAgV1K+VV0yVBpPO84TaX8hcnIi0+SRRkKrRxDje45bwf7tlcintRWgeDwKu2+Smgw0sflD5YW2cbACInxtdu0A2UidDKHB966YZSr3X1VOhs+o8LWpfnmZKvrpc1T2cI/2MGPR5QopgEWZbtHMEVqHe+015m7dDvmopWcl96neN7L+uqmKtMKwk2EwczcpPGIZbhQcZ5+oOPwLdOZQh1YZ1BYKWFZzAs0S4PyYQ4ICIyGQ2yfv8yzczFfdzQrY5kNJZ7YsYLXzGdaxcO6Rl1BPWexd9vbs4Q107ORFLtPrbZtG9DCvKxGy7niaoCyaEb9WzlUGzjSjxg4Y/e8KuNrOVpSrET2vECrmd3VBZJYuIMP+bx3OqfRsj4X8CljKV70p3HV4igAtoLsNeVwRMAYU6taGROR9d70OPo9Bt6UgcEIKjhvdip6LlKHm7gnzCALHuBl9n/B5UMHt8syD9eX+9Go0cb6kCkjDVGqfUSdafE7vYUpaEEl8lNUgYk0tHovJ4BtvmELFESX8rxGoacUSPH53jvb9u0mM94tmAswzGi+x/i7F75ueIGKQ2BpxNsDyltl+dtXUn/j3kjMw+6prMm7eUrn3MS4vhdSpTUQn+Mtu+tddvDlfJEpAVUur2uPKF3EH6d4acfo84wEiENLuM1yMUYvtYAPR+F9zNeOvginmwLRT4AGvVJi6Zpz6s5pzVbMLKYeCH4F244/936+kpUvu2yudS7eVoV7ukUEtqiVKaEyJKk7nqNayPulc94B748HZ2FAFEZB0uH68uxJd+v9metK49142qs3sdQBPBOyB4wg4fg5auolGbKxhp9+26A/E40LtaesIYb9YJ075ulF/lzcG7xO+XN21To1JaHbvCyhoZ74qFlZ1Qf5Q4aIoc8fYIE0UqnOqVL1+E1VAiYECTKiQNV3XcygRP7cNic6tcJxgGt0CJyVQvWxxf6s5/N5TBJy6J0X38OMOfM9KSCjllCXpeBjGhVWO0Y5vYj/a6joVo2W90X6Ko90SlbaaVGyJwi3UvlFJrPxQO/s8SldT+ppzlFxEwqgF3Pt49e4GaiOs8S70qNVHPloH36djGrrsFkxGqRZAkXxzlc/ilGEg+oXVhZoLcitnrXnJc40V5+WRhFta8y3UX9V+Jn6nP555vpCmzX5mmesCKEncjjTypOpWC/EBd/A4pEbJFHwATsx79lRZ0cZn7CU/k6n2lbJ2GBqsmxyrGVbP5rT8mFQEW0vZzt9ZXwevruKKu8Z6RN4bTe7rckxc/JqfuZVTEGJvkYKUQY+G/uiEol3HGdEPqNGgD2tSwgLn+S65WwpsuxTuCGj8bns+uR6e4du7luqC8oMT8WTn5v2VWXvGY/CpYMYd7BnlNljdbvYeGlPsyJaG5G89UvoxREO1Fo6eD4+bqvsetaSMuBHYtgHoQCD9VG1fckU1pQr2QpRhrt54EWHUJkIId3X9uVhtdCrS2t69EQ33gkpvwY3iJYYVNW3PPOa1FN+NclPYE9vkObwi/ES9GgdPUfC2QtrtSKyTAdeqRk4PUrziRl98VA0uZDR2joYhgCo1JitcUeDOQvaMHlf6ywo7xxIRJ/BctjGy9PNfCCrFTRdflDqZicaLlwGRQrYKNMe+MilSUiiyU1wWFROb+2dM6TosHSpO8YoqVTN6PpDQAe8YYBM2VWv0SYkxnNBlMCM8m2Z1z1Sn7zTVMt9BXeHvGK8O57xb9v5syfMMlezvrnv4it0ZKLTqYMjzR2baZu7Z1JdLsF8nW9RNYD0ci9OxCZdUUkyhLSTPhMy/R4Eo7fzRZ5zF2pE9KZtvrOgCzQ6ImybGSFyBGFYTnE/45ilAaJleS9/tqWMa8NTntpwXbAQLeIJZ9nRX8E/+lzVJ6da1gRiBvX0OfXU+Y7WTHY0LkK60FyYEg2QZq4zRE+dmemhdbLfz9me/THG8nGvdm89ZNFnBkQwrJSohIkvoQcycC4eLZjc1aYWUIPEKmVxSNsIV/qjpfxDeST0z2DeZieY0SPK3OCWMb3LgUjO1/IRgYggpRb6aShrkVrVOuWnezLlrOEDKh9i7vqhcL8n3gAvzoJ1R2V4aSPLn5EbjZ4WEnFGaDGUh70oUlS1zywxYU9pxetpNpVH9RXk1PAMDgSXDndzhszV209GqJ6DO+fkjfoCBcP7kiNZ0PRJqABAmvtC2MM5pTFr6ety7fJq0I2ElEU4u6+GVvYr3YfQivNjr0IwsqaBmBUlGVyYvXmF0FCLQd4tejilCSpkANPomluUcpVgB+Wl6/QDPsDziMen+5CPZEfTucN+ItueYUuX5Er7eeyT46XZLPOvr/TVy2e3tmMFOcmeM2oAzxX1YzCauMZoDYyi3c57zUHfdYTm0Rn6Keao7XMsTa4lWqt11f6Ha8Xyy0M5JIVDQ2QSsqeyTnAUGwxDVVrN39duPoVTSlb7Mv8s3/q1AJNjtEYRA1qP1ej7DKpRcCA4b6hIx3Fvrumr7qsyUlDzXeE6Z76zjhtfUE1CM8ep9tRIA4wRvrmdi0rZsDnJ6lW1mt7GCe/4kMvGNGHI2/oRbylchM5RwfYLS1owlUhxpqU8RrmZjVCaeuiSDIC745t8jndjjKMH37jQl5ZAKMJClXZuF2+3Qz8Gro6AofB0VU3XB1CNan2pK9oXWY5nq5P6+LQJoYAj/ruU8/3FatDQe+6lfK2nvR72jdRiUEOR19+ep5H6PHxrYgTF1MOV4jVNz8ftd/38d31+sZ+8+J8AWkO5Te97+9HWBW83nAsSV7sA0wcuJjBSZ9GkC+leEkzOL5gT11w/uEIpUeuEHtx9XVkuqyce3eSAPSaZa7VvfMtSC3lxY7my5q2ELOUMbuLxAAeTsGq+iOnuHNN6taHs5uW/UTdU83kjju8RJ4ZWxS2axyq3hy/rj8vDEuVKzzm8b8cx17W0x1p1kNqGXck1HKAkISi4gzUI4nAqACIU3zP2aMgWy3o6fWinV/Z7Wbxcg9UNZE2ksgMJn7IAdOY8hZbKs+KL+G6vVYwhxYlQo3sKIcyITJuTbAwFZOEgms5FbkqxzDCpBk/2kxBTCs/XF58veBeBEYQz/UyfQQ2mMZowRX7hd09OpWdqR3PR99qBz6rtqbUAKOs2hSs7zOYz0ea0Oqzf4e/DPBnq/rJYk40qA8xlAu7Qqwf1nmBgNuAYqn3X8zznd/B1HIVjY65J1X2++TypDxxhVePI5IgUKO8J+6/m7lY+Mry0xSeBBuW0F2HlLTRcD1lvI9PJWK5ca1y5P7HPCphSpOrS+rimLxExx5gmUzI2XWI7j0++FiVFT5FxYSSALn/KqqCXTFIxvVP8dpfq/ut1g90jwNKcc3lFP6IXNZ2rvlul4sNwU7xsYkYqq40JgwVnsW3UewYDbydMwc308H/wdP7hUzxqL954aTOED0ezTNJ/KhvfhliaeljPmEx6DFYrjSzqgGayRtHAjBLqVXN91daays8PdXudYa3GRoLZW+CRrVy36MeUU2xYwNlrRgacSp/YoNaw85xCQVfdRKHOapjiYToqZlQbiorG0E+bmvk5U9BroGr8/VUHWlA3WUyrbyuGUdybee22HVxh7srX7YK3SItZSwvuXIAhbbChAVuujkqPh4rXVr/TNUOHKWuJM6yZTbkKB5WCAFf7Uo/vC1AfvrquUXkqr3ciE1XuWi6eiKenSloyQzwuGed4t6Njl0wZ4dlKPEChanYh2zTCB8oxEs+9C9RyTRUGwMrpbNrou5hb8K10AfXmP8EUiU75W45NBDRZgB6rZ9fs5FLswzvYgv6KSomWxNK/xHdP79HMmOAsiDXaBTqSYp8i4mUPOIbHWEZlF2wspfJMW92Oe9jpqtpQu0D878rwwSuqcS3LN5CTgyYh2zv8M3vVMohzCvJqvbvhkneP3nb2CLYkc6e09JanZguTjFDuzZBR6eLmBo8Qvd/O+IB6mXByzFSBZxGNjw6kdd6D37GzNnZYMBS3jzwqa/1VGAQNC1g9gWJXT4LQGJt/9VxZ1gAGIohvVo6r13qHsellMZ70e3Yeky5Op4ZqAnvwrm9xf/v/Zd7Sr6NNlhTCO8R9RII/EjXkEBew09b3ilROWytx2AmbrWNjWziXgMOqJNksnhn5YiE07RlLnbuHzXJKiYpF4aOzOx/0I88U+65wX9fuwWXkQECNo7uRD+HEstZToyVtLrQ75rLbrMYgMQz0Gsao4Fvvba5bnvOTuQ/sbt1gTYtShveboGLksro64ympBwrzjYh2l65zVA6SXTeUM4MYqUmgaqA0d6sqYnGHqx79u/iwSvEAutfKy2qR0hEl8n9Z17wCE1PX4HP3qob1my5aj+i9RjeKK8duiHRKda8oVQQdL3X3jPYrqvL+8dEdCZpzKEE0VQxcvV1yG1PM/KMOPaP03ZTQYERIAvWgtJS12IL0MDb4O4hyZ6911i7o6qjbnTotn9qw5Jlsbw0fMVLSXLuNrYxIhSi8RbZNueiJNfEZdwzfMUqk65mTNzgDKvvY44QeC/KulAJEjxau0EVgopP5bb2GxKjnbNKhz3Tz2IZrPZ8rY7ViQmH9ITuDowq899ZRuuX5pJKDgVhQvuilgeh79Dzue9eR7G9VxAABEx4fE3FpARfstGkjzUw+pbWLvXAEdRo1K0EN0xHKDi5OhARYSjsn7ZkuFkGJF6eQrUjEzIk6++TqluTCHu0N5KFp7lw6zM2WbxE7MoOSK5iBizAdrz1daEFWQq0kLDfTdVX+6H5fnqrl1f0NB6BCP8z/pgX8tYWidiv1flNBuS+umMB31eN21GHaqchaTjRV/qcUQviEAfA3qCEc2jC7Uy8fXNW6OBC9L4xREgXXvWmYA4gTsdH95BwpZUArSfbUTMlk75mr3SSNF69PRrfqlphZU44WhDOrjkk8sSzcjREt115tR1nT56bsQT7N3UNkykb2TI+Y6xUZPFSIhPnyMEpP6Iaaj8boq80mg4ADAV35PvZN1xYnujQyhbmVVLkTNVl93p9m6qWP3z5DVTfG13jAZl5BtCLk6taCYTgnz+/d95kcjJAMSalulXnjHQ5AcUMEsRIz5lTAJPpd0gXKa7zMOtR3n9ViaAANPcVUM5hRQFOKuygYNkaKf1FjSKuiLrXxTXRkY+Si8c6UaZv+xKi3uwpuCZNmdvkZt7TFDK2JsjI67b7MLx10H59yg1mEib6hQnE/oa1GHxFutRAcs2aM8GJEeebUmbjbRJq4IuCwFLcN/UZM76K9kt5zdsqJqDC1K/YfG0yngFEvEYBiy9Bl3GETzKECKDP1IAAAEABJREFUWpxL1kxfEiXDIzIcJzJ1mWOobbWagBKijla5Ka8KekB3QFUeSgis4Pni7mIlT6PlIo82uCiTbACIBibsiE4zBWtt/ig0VbP/rLg8XKZsEFix3JcZ27o5rab5LtJE19SMoE69PtVpxubEHPpwjV1FbxjhLl2YciSlWI8G/88O/LXfVmNl8mWTr9zOY9M0juNQPoihQYaSDnCAm/wle4yUCzFW0DdMTzyz71m6YRQmpoCardFK1tFEh6d6ZyO6Ft0uhWbWZkelRypYW3KeprVEFYLCS/aa3AeREPbpOl2l8zZ+5b7OXunUyFE0ilq1U0G93r2ALrxCcIN5DZSMGcLZexqjlWfkGNNOXr7oqunF+eiD6RQeXepC5l+xOcdwez+RI74geoJGXDNBrGlrrXp1s/r96N0U1/h7H2+NwAr4NX3lRIkxwRYp1J3EmvERlQ1FwinycI6hfdxhY7qQ4mgU+l1TbYgFgYQHpRk9xghviYOj9JsAcFSBY6GCFGejJ7V6FIHTnujyN6JPInqUq9yJ1wEwch3CXRUpCDaFU+/LDEpOj59SdYrjDo11wmhh29xbqgJLMRhbzeMPOZV01ojz0ToVWphksW/rojWpFDMVW8rxqy+4YqJm9ka9+/OckC1L7hOf45bmMubaj39raxMn3N6X2J+YfikF6Gna3/FxjL6J5ZIsRSSKilLEOUyw7KaaVTy6Uab1YGR44EBD2Xaku5ktDE9m93fYMDGOMOq+2VeOeoHCFuPVT+sJS+cu+1FF6G9y677X935jmKvnegwnqkxOZ611cV+tpokNyYcxKr+HAemXVo4OoY2W+0W3iknPxkE9nDsnE/KoUq9ceSK1OPbBHIlHCbInR48aUhGNQvMw3+6uSHrQ6ZbblcVaXtvb9FAp7s98t0z0lBJYVq1w2CLM4DYWM1dhlSRPGmg2ih4kMfrHCfZnng3FSZWqHE3rcy6ri7xmXb0I4n2xzWqoPWu9UusFSOUjvF2cdhVJePl+aj9jVaNJ9L/ciI2gqUTDjk1DKc3yrkBLiJADmQZPqe+KpiOBnWcnV1EyVHWqtTFD7ihXh/jfa8AZG0THNbZdMT9ADhNal35mRRjQlbprn8Q6sUfZks9ElGmkLr5o/gwOUI5DZi4h+QpIZRNzDT9tW3uLXqwlIFMhtuQb4Cisi/iY2wRuVxTTxyR26VGzR9Gl25HjcjT36uUdVpvjJAedhhFQULEM5ziO+Cs9eL/FSLzUQefL716idsSgy3mkKYqYxdddu/RT1brINmq8Ty+JbfTc9RmNiaP8vxnRLutzjtdtfAQgRs+hYo0uyfTUdKoVuD22xpYodBbEbV+b6w1jTfeT5+nzW63kUxlwHgUy2bhb2bdNX4Ty0+j2magWsZN1quVh5o9IKaHlUwMpxIAVxMXyXNaCRTUULsSUzcFodSlFUoYTfVSDyy1grxOxCB8zmfHj6iVx+XXp7wxffaENQiDxvjhKFAkFhPX+nu4pyOijUor8on/VbTNKCu1uVNu8EaBV42CTOoF6P5nq87K4llOpj5fp8ZIDi7XsI8lsjbOxq8OxUHsqgKGC9ElagmNVdsZuWLHfDhJ8Qp/cy6UKm2KlluS6sq/mWxYNWeGbGf0OFqCuz7l3P15gbaTO9nS26ePgov9s21v8QKen1ZCkxhErrnODzuG35vs8Sk9DVqLYeLMo28QSpvyqGBXAvLhmYSjKFa9JiPvxdbyV48E9fc/37q/KB7lgG8sFAQF484X3tTxITE9jHn2x6eWzFy4DoYxyWvQoyY9fff4KEufYraTMZ5u///lNjR4Ay+fXUY2umlG/P2fvsz4Vu4y341467IY/ZQmkq/hveqrkDvpu5N19Z/YoME3Yt6IFL9eG4vuTx8WLntzUER4l8AwA+lCvfve/fEycep8rv9veOoeolZHzPgFyiwBdukWne/s12DNPzHPhWSf9MP16Ymmb/Dw4iRtMSJ564DEbSnvyK7a6S+zp2ciKWvFIF+ZEvORX7070mTysFJdDDGx03QRWZS/5cQEXJ1oXYKa6OVTUWbe6Za55errk+P9PxX8oyG0sWaDgiYhMVFMzEl17Q4qiNP//RZee7Q1JjVhAhtmD1rzdtyC6CkgT5sSJyARK9w6T0lFtW1gEwpuJ35kohgTD4KZbZV8yH2lsBYeUTmFPhvJhvEdwFmwtKAFWBE3uNZhqG5EJZaSacxGZpW0TP5ELFpVlsjE1shtM7a3kkvChScY5SinPwBTTTkmTdpYEzNPys89bXVKTZkJ6coUBHVcMIAToJta8elUDjxKBbabW+SPwEzSIbrtte4XPNX5mzQEbusEaXBQA+uwuLGciazgoTWGQ7th47SQFi4FStaNZqSWRSPBwKy4VEGsgCzce0/yvlRZFxJbos/SBKcNqoTpT5Ui4MH7L0gdBZjFfthU/reZWaSpczNafsypi8TEW6Nza0o2CxywqTNVk4AbI8YmalX4v1RZsQndENiiL3CxFsAoa6GBtDUbsyZh2pLHUoy3GOMiiWiUNMBF6h2x9MDdLtAJtYJMuNRe2DGVNQsOksY6IkKA1giwBX2/ttL55XOuksA7UJq4SWCUSlyYrYm0QTJJiEt2B0gf0TZsmwiEkY2XqlDkViDg8RtU8IVZbrGBJkcZwIIClIUGThfITpI2wJjAQrfRJNvMSAs+TBrTVER3oS0guI4OY69Zk2wbsZ6t/ugRNAYj5VNqLSatlyA7hybryJJSqoFRZpWBUfpmwQVq5grWRoQ4wfiNGejCVRMHwKKR4NJWdRoCalFZ5+EDUJmSiG6FcADS1RZdkMewNIi5Cjizaw9jjhGPatjZLy2XqS+/FCbVgJCOvJVwOVUPWyM9dF0U5pMQm0FAiO0KDQWKIJVqQqcJwDWtL5WpdRPcZMRj6DrJEWk1d+iQmNHD0SmaPsmNCdIzC4J41WCAWKT77zBpLc8owLNynwyXIoi7YNACiDDA2xHBDBpfTCHCdh0UT3wRkJH6OZTtXLNnTn+ggd5vZaNjqmJVOVjHWNcBVE03VuDuAKrBxTANcfGJ4YabfUlw5bPlnGfOMwb6EpEhkLRE/g+qRDALnAqtpeKLYYcRDbAGymAAws66m5Javc2lUbNzZWYFKRpW0mHMEKZclo+fYadAnffzSfTIRymk/w356bhemAbo0JWcSks7IIm0oogjbvFm4ukhDh3cCOs8zKxJJyQBD4KBXxLJY8CcZFUGPyC8lgDDVJsxPSk3xaq40IkpmwBUyKaArDZbqAIOhgBXaoo2+kj6sRS2SoggKvYsF/BzqrkTNzXOaC6VBYgEh7qvkbWEZ4lFps04zzZZaoHO1maJJvY4FVRCiV2JK4j7JbMkptsXk3OJtwGCycDRBE6neRZ+Ibrh0j00bjWCkVkjSQWtCYUWnkhdoYYYGsRAQzFEyV8Xg2EU7Pa6SjIilVreL2GzYSG/4Lof9uiNCJjNj/ulLWhghx9jYYkYme/PsQakeFqW0S2yqaQdGk6FRE0nirIrVGDy3lkYCJ+cUhZpXpbOraDGAApgX2QemATXJHdHuWjNY35dB+9QFLini5Ea3aq2UgVEIGLZSlJi4KYfaXFPKRswUpgkFOIdLt8/i2WiLN4ZcBrCUUOjQseQ8hssYzLmNSx+TDcRY5plmAnRxzf1FfGZFYUkqYOiU1kkWFqCNsrZhR7DpqpYhDp0nW3rjxQqvzin/LLXMMhmsDWHthtCHKUR5x1Vl8BWdbHSyhKUTAydnRiOPitmozGpP+iQsi4zLgkoChywhlqz61JLBLcMy2NarmXQ1zz4vQqrKUp3chS3aoFPVTsQ0lk6RLjELuH9bAj60QPCeLKPNkdsWAQODJAWdK2fkXDKHzksRsGWpMWTr0zIbp2qKkvlT4JfCBowLhFb2RTazMjqjL/P0c26zC0MhIRNpQwIoYzXRLCaxcQbn1RrQjiIWpT13dmIyU/SKFmCVwyIavE0iJeRJNFFRwilVlTk8xHMaSRyQVotjbL3+8VxisH3akScQ3Yr+XDsyt4VtMawiXn0J9QCFZPhKHjURY32ddrT9QiNWx3pgQ6ZyNx+TQItmafValwD+tR3Z7OjOhN6qoVQQsG2TYVIB2a4SOK3TfcB81ZfQbD2FAATv08I5KxApg0PKAn1x89GTWd6MhhVSAEGTNJRE19gYOoRlqg03njVUE0xHMYVYJecAaCWTi7lg9c0tuGVwK6vqTkIrZZgUPAnmWjpA8bz21UYTRYXCLRMqoGDesIxEoQoZkvwmfnR5QfNm3iqVaUTXk2ZCwUlKiwi4ZorGjkXTUM5eawUNDo4bUk7FNBvWGFv0IX14Q5jN0BHgYdTWi4hYCWhweq5WGW+qWN5KnAiZNEgXNc4omjckojMmSCJIXcUopUX5IuSzj5R6hEHMSmkuLZGiPasoLkkxmG+wyax1VbqWBNa8+gj6ExROHgkJ31BNs2TVGYboHsK08tpWci1g3WSJgBuoKCmoi2h2gbUgWTTAngZlPGpFoiDsAw0ygOIFoULxiF6YMIl2OkL2jBwjIgkRdxBj1rWGFFQgjcuTVMFHxRYINrHgbDRali5KPJaVc4SFlA5NKgAYv6lcS721lWVKf4UFWStVQi1ZzTTSpeakUq8qSzXOQwKFKq7FI2mRB5dDJGWRsia025VBsAhuis1zihUmwKGrR/VEuOIPGVTISRkEVLx4duhGddJJCDV0O8E3nhuvNkJ0yZ6L2ZIN2bVMkv+UNcitoLFJ3xAzwEyakDwgsG7i/EZZea+o8IUwFR4PFpTFaTts9UvItbTg8lzNBwkpZi59KUqHqWprBcmk60MoVSuxIuFFhjYUKzhNX0c5xlKeZJtMkxNdARTapHUTtchyogeBNEKNtsZCQD7oJruBuHOnphHJkclbwpaVVQVe2QRtnKklU8EM1TlXaA3rv62DIjk2iiaSE5pEZ27iwhs6unqTUiFRq9eiWK+9iFJambIGTdabbSjVZEgOQakZtXAao6pJgzuYi8ISwx2v0yopUcEKFNHhBBpA43OpGolRyTNj1OLMIs1QT6QxrJFwIkXrLE2rlEsARq4oGyDIyoyQXHUEmqvymUGDGWFrkKXBMY1qlU2ChNDVFtqmZkRbA8X42iKNcEjAUlVIHRGqqHTLFMF6FINPdVqBSF9tjdAS5o/Nqbl6FWC0+eFgujXQWfX1oxiCStLSZseggLW7JlivqQPVkmZkWLrmiPLgZ4B4aZNJq6OM1gEizdCU4tBDZXT1khGgx8hUMmxS9ienKu+kO5TdVS461KJLcc1YK4w453NGkBVSuu5EaIIRpObZADC3VVaqgFiPMuYGfNLHvlr7i/XdbOhaDmBGME2cjjTNiRKxWLgGhRaTmDsu5ySolqy+gnhRSwtIcLCQp61JFzEwIgiHLIlYAfYGYeAq1ENGZCXVrzkrCHAztIW70Q0TAspVZBYBrJW0bJYdrgx2n+g75ZiLEjXqmU4AABAASURBVG0SghZQDuYuTPpSWksFiSwVaFMhFKrClOaq3puYZLnl6JFK8cI0KKGpmY8kDtqS4LaQgNQsnryns9mEFo1kIV6moRMoqQl4pBRIpsrijD5rG6nkF2VPJQbCw6olXlGZAGPLF0HEQ8MrfSQ4vIESAI6V1UV5lEjnU9w0zURkJU4VILEOLWgoll4xhU5Epyv+HUHiSQaNbZzLoaJELROVoZVQeJPYlG7GJETZhFumHGznN1FZTXRJoAy5weCpVQoVwoiVRUk4dyqeCPtILSDBRF+GZnVpbTIWTkmKcppaBKJofWMYZS2wdLpCUFIqhLQaCJYHMguailHwdEEKLQjYCAxZvAYTFUUdLb1x6Nq1MlAaaBhRg64qeKcQSqOU8iJWqmgYBq+iELIMKIhDhhBCkVrdW6SPZgWaVUjEIAwc36omN0vNpC+S/OfVgrHoRVp2DZaUTJ1hLuQXqb+j1lKmqL5qXFY3qJ9fkQRP6EIUzUsZK0KrByquRZ8ZIRQgWjRZEjXovDmQlUVb6QIxkFSs9VCFY5mKCVSBseK6s9ojo0SSxpR0ykikb2y7o8ukTsvVdT3TlGRDci4tKTgxg4gnk5wvFMgrX38IYjhV6XzOnv+kjmG5FRulUULXuC8JOtNkaW2YqtAwAUL6ssryckcMBRMsgeRuxuc1jnzhPPFPrYQwJs3uNJkjks6D44t4VGZUrvM4io+3sT4SOSYI90koWl4cHtxlCZNMjAHCCOEEflQbRJ3RL6GFtA2SWtRgAb7O3EANWqUhEq0IH9kRq18ARWuooLEuBUAjCgGp6FgaVqaG6BDhHj5tKdnyMumcanSyvE8EjSU2jDPmRrBLHt9eNvHM4VwU15Wero2sjH8nFr8I/UzLUYI0yTKNMnf1ilAfWkmjHS2VRzYD+C07JtNaHrgImUo0hEW2oN3CFAyRamZdi5nOExYSI90lJZm79Mq8QCsEo4qrGcMDpZZi2ObmNIaIMOL0FCmlXXoTwubewjuEhaS5YlGiO2lQQVFnNhpXqKgcgsIktQM+KGqijcLqOXtRTdJErIvShhq6LAwGC9pmUb5cejJxCrogi9u4MYOS+Fq/Z0kQTHO1ZK6tUx1jzjF0aPduC7vX/64wWs42jUlrkqkLn+zhkj+B7QYw2QTofTK2oM3KppS2lWmQOtmU4C0qNDfVSEHYgJUKjzKQHEsolt5TuRagF9YuKMi3NXhoE6YNcwLlVmnsT2XEE0XkOV/ToXO2JdUp2DaZm4hO3ngP4pBSMTA8POg3YSi+dNBCbldfi6YRVqRi1jZ3VCeOJY+qRslIOMKQm4WG095OeSuZTIvGamsIWQr0leaoNG1K47sQcpuKVAoGz4HVXh1zxRxrOlMms12i8eib3ExLU6lHv4CijnBh2BVlupiEDKsxyczNagIBAVR0/eou/G2gPBYdyU3d5KHMlkbdQ5zJLmhNaC2FLFKzYCsEgpaqNAkGb2dpfaHD5IeWQxmfoUq+VkFnfkghXWZeLgRxye6L5CJI0+om1hqNCUDAhC3hWu/wwuxMzMFGJV/L+U+D9PlFIBzsyNBaGocXusoTRacxG/GNBdgj7Fy0uVqKCmazuemi4lpiVEoPig7KMqhMpED7V+S8RADYEFlkmWshk1BarjmjjWyVXUYTnzJNMlYDpXUSmTdsrraQJbIR7LQw/VmIpuDJwarMiVHG1CnpojtQpTZDmVdf0KTobDEaHlXRbJl45tKXIZXewLozwXW1VRhidaE/sWAhsuFmfO0Fra4QlRSa1pTbA2cuGXe2qxXr1AoBMUDRHd36WCoCBSkx5QGQOtS4BJEJr2WELtVDFBNsR2zHqAALYjwalY2KRCIrFgctKTIMQHQfvUiAnk1VxUwm7eawebHtti2E1hc40tXdRhHr8TP5m2SFTnzb0Rdvi1O3FCseMVbzED6Ot1k3Y9rxnS6NppRQm8CQOxK/VDypfBLYjKQdLnwzN4bJiocLtQnx8y6hVqVwKLGmKcvMpRRmFGgYXkw50lI2TQwSQV/HaCgTZPiYx5IsJyDghSoWvHWxNi9ztaUaNSXCU4qLQRlto8TFcpg3LaXBBK2D1YnCpeiBlRJiWSIXCJrs7FTf8BXx/ESXqS2mtKEiObzUYYOuGILwUotLVKcN241mT9Oxmap1l3JSaYASawF1J2zVhDDxdWYoS6f6Tu/G6lkbGZMkyI3RmvcoW2uHiqVNC4wpi/UgV7ySOExG+7KZqlkB9Ti3Vdsp67wp3RZG5UBw2kbkiVhPGqCAprTAxEwNzoRUSzW34c0pMHOTszDYKtRLOzSZetWXtKIsLfLJWD8Xi23Sf6nRYuEzowxmWaMDLtOymfgOxFoaqA7FMR7Twsg3muOdULGpliLtGGNwLyJmqzOIkm32ykZ5bWuMWRO6OYkICLqSkmtqCLZMAPqXQgD1CWLKESRcQYCuNRFpT7hkTjm1FWNys6djQNTscUjS5YCPaXbW3+4qJZ4yg1nAsM7QWWLE0Hm2JWhbQFbxKH5WFkDIIhuclY6U2pG2UeNp1mEShuQ/cVEJVRaRmoSWqGgTkr1jvTF1mUafVBtFyMLUbaM685AuYGhg4+1JTOwsqMOqlFYU9TsTgRTJiejSe3IO89JzmWQmfqCrCQUEXDty4uzVBb47jhzWQIm9uWLYiOYMVU/loAYuw6IlhZVCUYgEOUcZK59H5pLiizRHC9U0LxvFF1ZVcxK4Sr58mJZSzln/XFYBhIA1kJZHwKuDvwwKdzidI4YLfzYbasRLN6WguvTBvFlN570E+hJTsBIHgu6AjKCpYsNqGKm5FdmuXFEndfpSMjIqGf5CToJJxWRaUrcsHtulRsJUWskvi04hdLakZq2tSSiDW9AkxrwkGI1Xq/Wm0YmVMwBda6NonCdKZelMDhXvYxEWJ+NK5cz5MZI/MdAKHbnxZZPzRt1EpuISJatYtIwuauDBkERQiLW5FX+HCJu7Lp13PUuDqK/IWxs7Blspp8J2c9mUmvSQ5oVYgEVzsWXpcNNBUIosNF0HQTpC0os1iwQ2M4GUCNLWOhVI581kstHsGaggiduTTXvyRLmTE04HdwwF6IieQyiLwR3GOh+cUZv0jcROUWXZhNaLHNNiIVpQC9yxQBY+jKqMwly2gA5km6I98ere2phacDbJQFOSedA4CVVi1TYbedLQkquQe2N/kZ/kfBZCy6nBhG6DvtKlVkvLRcPhUSP70BaEYcW6GjCZ7qhME8VWU3SRnSHNEaBQ+QmdKbUGYivFhcEZ5p9lony66YpWrhUJLEU2Z/kIPtj5KPrkhBcCUYAfItQbHa6CskpZShbOjwljYi6QoJTSdLGN9A15HRCOkoW+KOeoQpqUAEyE/xMosIpG/BEN2qoZfQbIeCCpM6GKiSlN4SKLEIMiTqgkkapXKEnJoSFZIlWrfGHCBMmbg1ssk2APBynllQBGsSYl2ZHdFb4O+Bl0ZkrbwBq1AgWIpEhUC9eg9qRYAdHVrtas8UtMpVRQ0IxJRtdom2rs75XmJFOhlAOSYOZgXDU5VHSoDKi3pk2t1aRM3ADIRa/w1PRJuAqgr0yAby244gcNgLiupa2XalUZxITXndfFSkAmKqyjU6ys8lymYQZaq2Yi2gRqAqFrU7GMpkYQteLENF1zVrjQLZuKUjKRnpUpC9HCGkrnsqaAluVaNSo2NqSjuoCf5hAPZLRKlaAOqZYua7QcVr1LbymMV0RxhQ7h1mTSZiI0qFOvwVP4aIJliXk4c4OkFU9CEskoVhDqgsBNoon0yXood3FYVB9tlzkYRymtZm7KsfyjRkTAGYduarSVGYg1rlN6N2+bgY07l34kZ9IKFaaEEilUT5ccKqmkpzaSuql26y00PZgrQei2TX8+kWBz9mkxC0iGI4YgNIqTOT10EmtoggakCjsHRqAQhtDKKGdbQlREh0jQfApfIll8kzuMRCa0oACNYGUbO7J0BMXRlG2uUSxGgmWnh+8Ek7KE5kd5yHAsnqMie6InjewKYVlTchK0qVk2WF9UmWXmMTG3CuQLSiBS6wrVR+tJiput7WwNURhlmUKbRu9OCaUBdWBJsEKUVG/W6X0pYADomc6ZNMfptzQRlZAeaJJABSSZN161VLBqhQ1KlLbOTuK5FIL4STaZDbWuFV7DsPTyJko7VCCVLXVCTVUW2lK4sWgxp4ZYlmCVttpsDGxYBJynwJWxF0FYlTk1PQZTinFiV6nSHSAraSGHIbVCaGrQqdZKpJylHhv6jM2kRptM2FRhmmRTNS2qNAJCIlJqwsuSxagajVJFs5rAHQH30dFagmxRiEbrInzvsml0oBbRge6idBUitEhjeeLLZknlWq3ubYj5o5mhGEAwzwdHFilBt/hoMohtiggEVahopZ3Fgt8pyXRLlJjQI9o3NBcOpLY1gEx10AqFSSKGeKzEKhTAKalMZkbLgiqnws6g3EQmylf0VKiouHXo1na0d2UFpBZDE+om8KQeAzlE3WU10kEdNJHkAHEHsaQIigr1UnYguC1p3Kqkp4YTzsW5qZallLmFpppUinCkiwS6yEagvNIoK5mUoKMS/CcWXWNKa6sqFMUvcw4qhVYqRla4IKes1XnGHaaiguLfKgaSbtz1FK3TaenTLH0ATv71GBQA5VeSbsyPlFATGtgKoMc1dtqYumjTerQgKV1bYwa1pihEKjdii9TCO47QHmRVriSpVkUPuJ3NGerRaxj9pc0+MR1TUshe6OQwnqVGJlYpyYwaIcu2cnELusag7Ew29a6sFgvjG16+rlhWqWnEjhzFk6gNtxYj4fwKhifTcjQHn0BslOQq3HRWNqk0JdBoGS34CUFSMA2zhNGvhtEjLCBuGRaK2jhaZUoQPzimmZSnGp5VZth0FhxBa1AtUWhxWsgSlVVsmbS4MFoKCETRmgJEGCYRiEkZb01BSUXNLVMjAqDhA0lemFYnaEBIG9gkplCJVQA4C0XQeXKKZ7lLudINSZrniUoFKJu7a8I/tFi2Mugc+8M1h7BSaxUWJIFnPaQgpHMZamNI8YCTTx2pFovGsNLJrG96dQ1jEGjAmAhU5aqpVmObhHJChNbchBxxU2f6xc683eQsImbTprhL1RZiBKRUkrLoCKCeRlEAo8u5kYwsqmjU8E1wvZ6MiIomLWKeYMWZ2qSIaQhC1gmF5D+DGVQhlcn28CpviCYFQQm/AkvpIjLA5RsLGGBEiUKnkobsld1Bsoq2sg36Zi0XAiTB4MY7NwpGSIKMTKNqw9xtpMRCXQxuGrxnUGvPMgeVWaSRGqvhFGQgpkGGWYUEqgRhFjAvqVBxWlmFAq+EkNAqK4LZJ0YALVnX4fWYV67qQl/peaWKMEyYdhgwJVUGZVDqUlOITLAd6TvMBtNqUUpLIaqlxisxMBgma1AUbHDGllDAsjSiZQES0cNbVRNpohuxjfU+WevMsDAE8SJxmCucGFnhHiMHdF6bpKG4n27UyjroAAAQAElEQVRKuuoQuEIYuRIpE+aSgoBpFVJEmvYWTbm4Bi0qUyFkZOdP98FJllPDjmYThxUZxQ2edc5VskJTzGqynFjMjGJsIeAAHdJorfj0oaLAJvD4lFuoovWQNAqFN2EsWdNHWjbTja28wKBpmkSNyKULT1flWPRSd+MrunCUhmb0OcxTqkoTUzChqQ0h5VjFkEIBemVIygSzL1ZRfA/epqaUV1IC+mamPdsUjZ/GPPBNVhMjPLQ1yoPD0Js8serJ+4wMs0V0i/q/TVnVDBlA0MmVqZlEYVb7CdtmeP40rE/KyV/RMwv0T6JNQiUFUqzchNZUUhkXDqmkJ/+SmmPBGxMVobHp5hy5zqQHrclEoBtk6mqtBaQIbI+dDSaVbtYlUbPzRSRZ4hWCgDsXTg5cBxf6sDbMINipbEW7U3JsPDdJWoycgjfEFlW1eC3c02YyMEGMSkUhErKGIa2VaLDGcMyWakBZaEUmWhaHYpqsTTtqTxRPBIQdQgy6lhrNVEJBpKN5WCoNKqjDYlKwJ4oyFrShKloNaGkSTDYlDqABWIGTbAbWIAGweoOJpE/uPDdhmuqFKs7UliTQokhFcKHvbKzmBNgSm0GKS6kMlS0eJada9Law38UGJBloj4Xv2pKjn+im9R7dvGkw+2gzE9m1AjDVbuiWradYRnjJkM5fWMZOYAVHm4kow9GRHCDDFXzwkkazg8vv5MUwo9MjGmsYPaFo2ZA7wCStaVs1ObUVLNeOjcCmKfhiuGuRN7HWng6djIOtdoS/F8dEHiBHK6fTWRKktC0wQBi5Cd4AMA6kVUoujnmbvnWh20tufJVvaR2rdcTcdzD33GYxPzmRU1EGUoiwPt4XPLHNsY1BYSkiU/Xuk41uYtZFqHqdpn1dCjZ0e5DqRCUgoVwidds3/zRdiID51JdNkXme5cOymkbrZU2kICVK+VSblSkSValZk9iObrpM9LYSFMvHXkrmlWMarYsJtyRdimSY20LjKULTsdDk8RMxm5ELXdBqVjqqkGaEuALzwCKrLo0wigZprdVFVaVV32D6RZ+0tuNcJRZpLoz/JrSnQDRRJKzNsvmpG2+UD5goS0QjNRJJryQBGp1VIba0HdobWhzbDcwjpgNcGsklSANTOkoQCsZezUVSJaWpTbYztb7TrEMa+I+P6V020EmUwElQXjfdcAC5pQL+S23QjYoZjQXlSmd45tIYYkMmQDukh8jMexghyMf6a56dVb8hNuKkLGEnTOaqjMcm0OmUygAi+MfsVA1MS04M/VjNVzSTLkrDd5ryNVijPZQiMHUmcCbTfYK12Kkx+b+lo6FpNpB/KPKiN0xCTwUiLjlqMA+10oy0GTWB3W0zbVpX7rHXh1J6mMw2IWUtXXQwoZfO8Pti83YKOmWQJpRIDRiteFjAKHlBLcY61WtAFrCcIRmoaFrTpLZj0WwwkJ50N8vAZNzyRwGYEEwvBco1tw1baRQZIsQGimIpIL4+K1Iqw+lFpCkEyeKHAjqCRYGxgU3MTS8JurMgEr4aR2OE40JqLmydBT0zAAQ5bzSQGVSqKWyiOb7BspEyumoipqvDKmhRnQGkT1LRgA1kErNGQOdhC0OQrZCVXo/L4tqGUUmUWGxVWDxiJ9Y5RMP7jvM3qARrgmdTqAULNX3LKtClouciVEubokDknFeGMqWpglQsVoOYUCUjW6xBytVYRDY6NphbbNV/Ttju6EptoW2SsrqcXdSsGz0T6iLooyFoAL3iTQVakBq6aZBG6ciRK5LSRmlyNZGAYj1J48m9xSxYOCQGkpvUBVLJALm2omctBCsMmL3Y+zjXio9x0VFCT6xyYmhBN0HyYAYoMIh32ypJhccpanTdSVSRElA7ZWcZQgUoxrk4P1N5TzNYGglCEle1Jq2h84HS5qyffAfnMZcOoyckOsPlrgyfS1VLaKJ7TD9VvafasjHn9ORSrdqarnUU9oTPv96BViRKZ2zFHnWN3sJgMsqWas6ldnXGmpNlSVYWxUuHtKlsJQ8gRHYu3XLJTp1MNwDhKyWmuS1hMfaUsOHNIVA3rs902ztpANEUC51IoFyRYLAkOZ3tVsRGtZpJU5FkUxljVfSyZ7EC5UbLtAQNDPxottBxaKnwlEW4C9BliCw2UYWJToCCkY0la1jBoAIblCwKsVJ1laAEkkqLDum0DuKsSqTDfZ0sICqDcopWUFNyc/lkxBPPx9TdYN6AK1AkDTXVHZEmTtFhSmcY3jbAUwCHRtIfs+TW6hd6UWyTQQvochM1Oufcew6sh4QYLehON7uVNgVrO796A+iJmME2LTeWzMXyEI9CJuaoGeqlZUlwKR4iPEWVOdSgDbKTfPzsIkUNAxlEfjNBRRxYMhdxn3RsuO5rdXpND92kQDm1GGZtXk1ACTa6uJbIMIlGEEKTnKrGQ/sKMyPC6hWtems7tbFoqEr+NaBXqrpNtRZEjgCepDxhaAxpAUYhmKhY0LbN+J51jVJiZ6knxUXbrDXrE7UoLdM0GtPJjCHMIaV2GK3xHmhiRA6dn9WmMU2zSJKzYxanEoyszEUYaykB09sKKPFqi1EofOIdTa4RlYzFcM8i2QqUA6M7wnClwokvcY6uQZypV4noqCgXSyH7zAcqo1lZqaShmMVlgYYVz1AUYImeKR21EZkqNRxF3KOpE68ki2DBTX8WZEAX00UlivNkCG2MXr3LZK0brXFtCyQz2QX3SE3v5VNIi431bjtqooy9uSTxCC/aYLwzVoYy4dE4IEF9Q1ZauvsSkVmykqpLaRXLK50g0kmyE9Elqlha0nWhImmtTLMQSRgYEx2qi5ExqjGRxBUiAQLCel6bqBWJVSyFgQQi4JXii+VQRDNh2IlOF3CgYBCIqOSGxId6mLciPjBXrvuP7yrUxTARGKPZTvtoHe19UmhG4JrkmmZCdjaEiItCVCZtkxABlNQiOkNLxFQ6ZRmhmc3/qfwZOhhHbU0xBYfClXDPKkG1fZ5aNGXh7U2VMzeak1SnWLrfC7b+k5poShU0iw38roILQE+VkkVKJaW3mDazWK52lLQuJNpK1fGzZZn1sD5L25bN0caQCjQRk64pj8CMwpax8kbJyp5YK3/pspgTrGyQjbYdnXbENrWWu0r1rksjtTSdoRL+tRWWXk0ZmpxlOKpCQMFKO5PotzBZNkg15TDvk8Boh0i2CJsh4eLUi51h01pNdxbd8VBbMA3uQn5ClrQqA6x0XU94hxUtwiRBeiqSEpg7yiFd6CKhQsd6jCSZeJFAwMEoaKFh7VWw7I1F4d3cnrgYRJTDYk7fzrF4BaQRamYNc3ckFupdHWZ4KcPWpMqkHFUx7/pTPMjAIsICMbXHOIOjpQiLz1ULNADGgAWdmKlQJKpB2RiLRNIe+o7QQjhqCJ0zsa5mKFQmJMUKG6/NnBO7Nqoi6UW1TaBKWYIQz/iJZY4EQ5xcHna6ggOLbxgqOKWTJW4rGJTV141w89RsBuaxZEkU9STaopZKD0OMcNVYe5Ae5bQO3EqQ1rLJ4HuM3lNJgmBN0NCNCN94MOlJn7nJ3MyVhZVOcAkkvWktC0crESKNReDqxKZJTWidrrD0L1HLoDtVxsnZEiZNG8TW5w6m98KavLIBZcIoEJuSJlwSrZM1SUuKwEnS6lw/s5AqoiAfKafZZqM7OzWtSbJEjh4gNYfUTxUXBkI4WG1C60IEabZGtMhWxci7JtcQBmL2aRkbVmfiEwNjwbyolxREo2SOMfwfHT83y88WPyWWRGmSXtaqZUgGVl3YUSqCwbn770MbmnRoLwJXJbGOQvM2+TQlQ6UmIZKiXVh0DKg5fJu5OFjwtCwnIXdjZJXVpkWRU4jRPTYz1kNUyKw1F2NM5VZcmMJZGwUi5NLaiWoBpUlqs5Dh1FlIF44KLxJia7bQGlA/BFZGqqnQevKi1Lskf0nZ4XPEDsIQYeHUuLKFFV6IE9FgAqEzY7opedzVp0avQXSyLRWpXCXQ0RpR4ZYYwTgRQpjy6KSbuCIbZKeJWiXo9DL4+1sQpB3VNrWczBULcixjYYJH6bDmTC0z2UhsSjsTTCIZoFjgXGZmOp6GrD5xfSobIgOaDALLFdEvqVoXC882ytKbcRi6oDOrFVUV5MCjJxCGwUcV49WiT0snds6IwQtEACiXGr2CF+4xuL3wgJOV4UuMECd1AUtGZqK2GRUaJbRvTBm2FJaQeWBOLMSdUjPA3c3MhR4TSCZYgS4GKpF0IxKzkSrMctqvzIAcYOUZyFnTsTQsDJ8Us3DZavuHXvcJE6lLVSMQ2coIa9GWGhGeXitCw2Wl0oqVqq1xs2a66To1mbS4klgf5LZbm9Hm4mvn1RWFtgKGxboXZKJCxKLW/++A0VINDVbZI1qyGEsvNlXrS9uMtsZmEZkhLlqmYgLyzZfuc/fagIrBo5Az4FHNkyFoJqIIVGWQ0upF9Tpv+sIQtWlIK2ohAStMYcoPFZJPCKBKs6m3vnqE7E6zvKlrI9bMmR2kSamKiVgzdPUe8xTO1gDDXuEq5I9krDyJrEihqqxWMSVnT9Y22TVpxBShGjXF6GNlG8tRa1BJKdarpZGuTj5o8bWobAKdTvdMqq/GMqIgKkKuqpAzAy0FptF0KGvRamWjLNSSwrgSRIaeJnQRZgWFNE6HDbGtt0otMnuMZRmRWYCgFYRtdItQOlNLVSci07QUzrBJh4n2hRIEowQB9SGkDDq0Q6wYUHQO2uhmYsCIDGUvAp5BIKlgVC6Jip7YhKlvsqy0hAgZ0ISuiBschtD1MWBWeFkum/jZlrC5mle24Ttjniq8Y0xSm5JJBM3SdhKbwma9bkVJSwNL9izBOgoJKuJAulu0WVx1sCXRPBmexuJYWQyIKxZdRl9KRkEVmz5tdvq0Q1e9zyGrE02npk3Zi/UQSFsd4bUS7B3RBkVIuYSqM5QTkzM9V2/az800MzObyGPU5sGaNiNDea/0w0e5DxRfwvYIo/3Cd2JRK7+XAi+oqwmF8QYlQrCxkzoRcSC1DVZLATQEIfRCEmWU3sSm2BFMnCNiWgydsUMMdJwaozKwgCpMivUCrQoQrENBBG2oYDVjJTunCEVUn2TT2kbbRrLlsOE6a23VRywYw2qh+C6sDhuTDooLwKtYxlfXaghK4MJA7IADlqzFA6qt2dSgUTGQaGlTTSimRwOmVl1kpTLrW7AS9wXmyNwIQwNtsqC4t/dpbk/GZvK+qbbTW++2ERC8hEqbpDSJNr2WgBmkQSCyhS6pNbQ5miu1Fxj2OWhZLgPcD0ZVlHBblWFophuVTWabpXxy7Pyj088iGg2LMggFm5UFSQDiCIEmJugOwG2DtTIAgcZhvCDojBhEaPToHU1X/AuZWCt3KrSX9FBCk+mosASKJ/mmw9TFDM2kibARhCkmVkauWSmITkybMKtnJhEGU+XJwH+HMnfSRFSFnc6RtIgAixTFaTdsuAwHsEj6ah9KhcoC3BBQPIs4jLFML1T6OwAAEABJREFUQ3YAK3MiBUlbSBEGlngts+VC2GYwplZrcRM6Y1YmLjXgMRYbTudAB2hLrTmZFppDaynKDWuLqlbSWIg6ZGViC3RLUgQikB2RTk9axpTzEyTvN5iUz3ZC6slIkOUF7z73xTeRTVF8UP+lsElpIW0Rm7sOFpqJEk1MWqvWizWx05FAlQlBJxYVQn8DwdqwchWhVtpEREPElWookGihZwHFaRXByK1CKIf3HJPFNZjrtw3rbg3aBU8kGkQFLhT+r1rkY7CF0smdpoQYKwHU143GnCScLGUcDKEYmTpMFBBEZTF+QetYkgIjc+GrmJ6Kpto5nCOiBpAlkbq45rxm1fo8zUYRWroB62BVrjucGoQwQAgEZtLQtKxvU93FmR7qTVTQIJbWqHegDTFARBIckiNDR4DpDgVa5iTJwWgRiBC40moJ4RcnbIEZTET3WtzXkilJ6VjJIBkrf7BUkdez8LW+McaUUVJK6a70rGYIIyvGtOKKVRnz4j/Dh0SFbuYNizPTTg19Yinh1MyMjIXiGVE0KVpJoWjWWE0ILdaQpqGaZEPSFhNjJivTB8nx4sI8EQ9dgnYTdipTH6isjDLHxjdPaurWTFFKGaiyiimqZ6CIJlx7gpXLNNB8PWUNYM0O1gQvWVJynTZSPHUGBlowVNKF3nirMIQZNtBfhLkqy5CZMaClYqaTGgdyhngBSNCUjerGRMAQ1ALan9JVepduwaDTchZj2QjW5OEggTTy3oMpZaG0npLWM2m1ooSQmIxNc5OAeFmIikKkUCap0BpZwXuKQnJWF3+C2FT0EgzD1kCgNbsWrEUjRMwwzotylRCL6ICB2gAiEuop5RAfGgMVQnzRq7ows3hdYBD4J6OJW2qDJaUaFyMlpp2/zJeItBQBStM1CyHhrBmWIelmTqO8GjPWTPjDE7OWA5PPjcuaBUUXjbBlb9lamlTjvEXmUaR+TsgmCe4ZNtqURa9TT1EcmpkyDE1AfTEqQsgDiyZMjswsEAQCIRqizFoIeBb3jhatFTNVyxhOR9IHZC/vQpSpXhdZi+ni5is1VKok1yrMGWowQmvblGVZA+KgIqQ0cM0KzRE1PCOGYAudU5dgEksxpq2mnbDG8RKyKLa9tpMsNIuDRxIwRGJE0PY0RNWS5XAGt1MDKlMgJhM5xorE2C0jf5KpMaGauqiriHH5z+qxNeazkascr72IVReu6bqOpQmgUy5rtiS/FlFWBa+U9L6NPqroC+WKGVSrKm0wMNCUdWZIK5mCm3YuDGTH6FwTOrFwYTmIrazOj6m1bjQM9CK8uUyF9ZBK5q5m0tyeRKQ1SIqnRtlqG9JC29KnkBaL+QyWJ3PSjwKSf4EkYV0rJ+HazGcJ4S+2PYYSKKG7Qm02TEbXpSM2ue6FGcNsaw1YLV7/CwsVGBLqJY/VWor8EdUeOQ3asHpJI7MVrY82Y6cwWTHkfFRPB1JEEha1zqjYyKDIQg9Zd2JW2Qc96wrWenVt29xsfeOzyULjo6BBPJXhI6/hBR+ybMsramJMgvEDiqAIIdIV/Kwi2+ehDpkZFgSoUoUwuvYUyqoMDJ4UmWqucHBRIJ8ki+gGWGJpshQom7Cj1q0DUVM3jdjostO9GYpsoJ+solvuPUoEVFUayR6qEVsHMQ+nNmGjrEKLZcRIJ7e5cyIVe6KrK7ckzUEDBhlGn4bxZWQukazeU8ZmgLs/MJ0gQDPdkakvlPWvhYBLuXgwXalWV1E0oejMlCDAzK3Scj4ObECHNRpCsS1Q3SgJIUBMeVVkFc3LHNnDnhSTqEyqVdKSKEH0Kivw0c0zBdULLVUHpxo6+YQiU4lCaksW+i7Ff6AOBiW8KMOZgNCisaGeq0xT6at9goXZTUcSkRLDsJg6lY4UZ7YulGbaxCZi8MjLkJZtU517ZlkEBI9yTITR0VCJ0Dbb5JtmXaYsbGNx3xYLU9QQYUYHIKKbpKcm2FQpBcuOWE/dOEmOBtHHJUoGjNawKKol9dApIeEI7krLQsGK3gNI13KsQKk3SdUiVuR5KCAy0hay04MwcktHlCcxoYqGJqwWoKRUCQ4NTN56cM3jquJCTJitG2VAJEy9W4mpjAj3FIiSk0ZZmASTtqbC9DBiyQBqbGP85CpA39b6CB4BhEAhLUsGo+MJj1xGbLc6BoMi2Wc+zJIWEd3jSShfmVrrsMYYimRmZNJVwZP0//KYwslCuCK05pRtrSjToJo0bAwEH+T5TGdzh08iXfqEZq6aLRg33QmduA5MubHFihauJsYKh43Gcas2ivD0XPm0ZP5M/C8XkpIkzJVKUi0iDtoxMSAWwAhPoXgi0gNKg0OBbDJaphikJ1qglSh5tNaosDBqs1To5H2KaqoGtaoS1g7qgrroumwIAx/NEippSkCqKLwkCS8y1MHSW8MXWWIa6CGB5EvA9ecRrurSKyTnGPMSawx61E605lOlZRVx9FR+EW0qkUwBjSA7xHm1EZlYXGVnyC/OwiCb7l1INvHSiOIez7ZhudLDaRZN5M7WZmu+0+ZJqqXkoPOak+qTNAuxyo37NIYOl4yiY0LnRcCD45UjzBJaFIiCQRomxM6IzUzGNFmZ8mSZdpY+acq09Z0cv0gYUsOJn1KoIAQLkIsM5zzGi97OOeaRg0Gk8JKiDo8Ibn2FpJy3G4dkSzBiG5Um/LOx2XhrAuKPcp1lWmwH9OdRYTImpoLoKDhGlRtSWsAcDI4kIEgf8nPoz7Al2zI0tyZLsYS4ZjaIKMZOjJ25+ixkb2cqgOyIsOKBhEQl2mi9hAaGsiqUVEp4d/IsEtQeqGHddQPho6KUwU0X01WUay0tSgo+sA3dQgcxHg5iBm+gHQTFwdLNrUuUBohhuVSQcgl1s0EvjSKMa0YVb5AxPJYMoh1dxJizJJb0aCVSmsWq19ZA/ORI0GCgi28wk4NkNqDEx6DCkJNZ7JuAHdAFa0JcU3IWvtwYNYIrbg4N0VRlbosVaquxKCimDdGgObTEQ70EraF3mEK4kowJg3ycLCfzqS+bvt30f3pf1KoUrsqqUpMoy4bqiiyyKyYaYUJwB9ua5o4sO9hSFhjo9YwUGoQSCSiaFMsIWoFHqThnyXpJKLHybEYsgurok9A4Mbgwl6ySBbVbt9UpXzaVk8BMy4g2QAO2UtJKlYolStnZGxnKBC7Qb5dRkFRlbUlZI2utWFlWIkzg+6++CVFnKJcwhIqv4XVNrlSL1NAluzMghLQyqxLSEL+sIEToYA1x2i1VlM1C4kmagJ8oYj6GxFY0GHFZU35T2pwylMMRTasRY0c6LWXZnsu5TEY6xTWgd9tYNhmJWOgDGtJEjf+ayATOTqMhoOHrhdB5aCfeAZRweBdsjOGGsNzJEzaZ6BqLxg6JJ6iN1+MOetF02iPNvY2lxojwQbZYJbENJYsmYA3E9GT0vpU2Vy+u8WWbTNbzDqrgdsZImVpVd7GuNTXrG+s72g071ShA1yXJuHcei0VYdqknQoIJPQbEGQl/xIc3rAo2J/FnjHiyhUnrTTKjslLMafME60PaWMdNRXySN6ZprJwCDgxIoTf0DcaOzTusXESCRFmLEsVQUJf+RHvHZNrURIW5BHLUZP2UgIyKwFhhjU6i+88d/9kwR2NjdxJXjFETLAVftAZt9GILtLjocI4qNpESklBYrgoAZGoUCbRUOYo9wk5eVBSWQHQjXqIVWUV5zikQkTahPVHtgKhBpgTFerWlrDS7hgpRQCrpEa4ZGeUJUq8wvLaejy1lq6r1j4IjaYibDhOQBctoNQth5vRZcqs+m6+bAxpDX3yFcSNtsyopIcLCLVakIUweD1Au0S8mhgzBIrRFc7Iw9ZZbW2Z6X0iVkmbVxFQ741fUCc/1sCRwUVIu8FUHTYxClRDW9atU6Gvjj90r4MGNDvFPArWlxqGs5DVSBp1uUpMoA4v1SFbxUvI5u5Wt5haowVCPpyuIaEGxtuaoQDGoTLhiB6ToGmuGxYJWFLGocb2gXitXiRCud8lpGJ40KocWneA0cxOl1AlpRoC5HvSJAthUwvnLdtSyoJakHktw+zwVNsmTSIyJ4EZJNMca6gUgLiwQDZXU5MRIpCs0Zt9u+cFq0yN7pRa4DQNzr3GFQzhQtId6FSRLALRb4BRYOXEVSleKZ+wJDmm2iaLDY0u5WUk5qizmfcDIjSWSBgAYojPKl/Cfw+dcVlPLm1bn8JWoWirgx1TKrQT34TQAmXQW2tBaUirRIyeolvWCm40JKg221nTAiC/pQfzThQsgExAMMAoNMBFQ+HoybGmjeqE3ETWIJXMoI6IP6aPr0lhzFUXmKZJQrDJBFF3KzRbZBDaSpmSkQqhBUsKUKx2m4F6ntSE9RxNykiRLEmANtUtwtLhKatIig5EPyflgk4WL11iSGCIYfd2A8KAikvOK1JKoChqjZrLazrrCmWLatTVZXWlIi9RFZZhDZzOf1jphBRVvFVOuqx8JU9oEPIXXsYPaIAwjZLBI0EoFzaL9ZInWKtMYz5ReolkVQHRdXx50LEj3lWFrcw7ClaJMqNykbFYcGqRSRpoTuNbKDMj1bIRQmtH+gP00rhwTQF4vTd013MF49oxO8BCIEdyHZg3L0TnD6LfAUGs+T0WDZCNtIjQMDOv3TLisREifsu49U+fSLacnouaBMXgRGtGzKAb0zNKFN4HHJhGVwICyGtSOosFlCRoAMTQNaTGmEazxAi3mWblbJY1Ihpah0GVj3NKDLkvPnBAmVSESqig+SMoOksWLHCBwrHNUqATIu81Gv+i4c8XdFk0t50BwhWMMk8JoakbMEG4kvEgSK9VsUPNe1YP2oDzEIUAZwZZYQVRnxBjABcgqmtHpjVg3mwSEIbP5vONDM5xDDLKypFdMwaeSmgZsFhk9ZSUxpEzCCuoNg9KoLaU8i6StICzCQqUJcKQYq0NNltbKiZOXsXYUgjesFYNhTyehbGgvnSS4IrQWKxeFLrhaKEvPOpfynU6LMz7atsK9GLUtZfO/MlVFaQqTmvuVzCq4irW2UXTaS0E7zn120nLwSOIFSGrP1cTWy5uUCBHVBhBIx+LwVLrYawjJiZBGcOjjTPR5N23RmL6gWw4jLg18CJMmlmoz4RHCUDRDTaV432hywUGXR8nSgxjTA8JAN8RTUpSWdUJQnhZhZug9RCshIQSptVJb0zaJkFb5SpXI1Xeh2gwU0dVF+yI93RgzlDI7vLQWgUsydzNdYMo6AitYGJInrcswKVWBWjntM+c93WqhqVVSYZaiWTyikEwqV6Z6loKN+WiVylqW1E1WaS3EIFrKibIgogZpiWR1mhXrDoP6NRUMQKOTssqp5K/qMz/Kk95lQkNbEY7V7WzKsj0zLh1uRk/dek66dBRoPMdYCSPSdELntObCUiNaA9gScWaMNqWf6gRsGeUhBXHNIYuXw2Ndd+cUTkwKUVEEA5K+Mt1RXkCghiGbkDKTlmmAEc4ybgVECW4WB1El2w8AABAASURBVJWUYNOlG/NKAZoIRzK8ymyQTPZbccPZWkAiS8aomOtRtqahGliPRDdSTagtVcSEaRaqpQpXScbCk+HQHMjF0jtrZq0Gohgm0AhoclqF+Jb90qGqLYGBSPav44qcjzFGhmDFsmHy5B6hRvNARbAMbaqI4yJUJAyrrpojFvUUBlptlWpqVcQ0uewbWwczzYwI8GxQbuItIUACnCaulrTHrNnUzHTzeIWiSU6QlVXKGj9JRrggVKpXbYY/YS0mxFZpWTm4li4zoTOlDkmWW/QsbpqaCWlMw2NpUd2Ej6xmoVWG6kpmBdlHpljvfVLpyAa2iKqyJgkYuqYpokAXEYahc/pU5BgFSqUCZsqpgjKDmAj7KIieI0pZtqOkLcV4g30drRcmnqK0Ti1TA9BWbVrtamG8bJTZLFg5WJ+sRL3raG0xzSalmmr0rKk1pjFzZ4PoNJbg0PwqsMyOrHLIsBpTeksOSGrxHVZ0rpGanADGgboRxFZKMFRYPxxPSnYY67LgYpWKDKRbhGSGFCng1ED6dTBkdIoBC0Y9JGJC7Qj3GKCXBR6CdbHuJLHuCLtHEn0QLZFkTdvp2BFM4g3eajYdkwK2wrAIyUc6BcRB62glKwspFSlRIAbOYAjYCTiRFF3rf/E+qiKLgsQAtoIB7OQ8BwukWkuTsCqh14RZvGPQTUYaYRniaUv1JUVCpkHediFeTFgCtk6yjm7VRUkh0BmeObrP5qGEDNxM9UH9HCtlVtKQZi5NzFqKeVXGLMS/SbRWlLyo8iTYiPKkauf7F/7YMTHQSrUi2pt1s0QlsjQ5vYmbJrrQJdCZ1W3GqHItA2uRqVYrHckyoUys1V8KXEMth4qLJASBCkTQmipIsrEEQPEOFADTMBIphi1gqUJHTRLGAamZus7JERU0qBfbHcqd3djAp1xdXinfDSF9ZI8kunB45KgM5MyFTHxWVidfSDKW7E1oQxdIkGcV6mRiQnlFZYwocsqYnPu+JLphtqg6M6RHcWdfLlUSNCgRMG7yXVFSQl/D4S7MyxKEFC1NGlG+eIzRZ2xq6mDGU09fSaPkyaRb9HXxgDJkW7Vomz4Z3xlVc9f0apZq5HnxaUEh3ay3PkmbShpBJtwVkRjUSDN67pi0QiV8YPaxxMIdkTSYEO6aovoc8k/mNoVESGCNSW+xqST+WTKE6bUxVqrWappipwUrmxAI2pirsvSF80IMKNBPep/8rUZDCEAu2/A5OLBauuloFTsD9AKmS7et9gU64MI0EO7Ff45YgqJM+NFmb/FTLOgzqdCz2s6E9UWwLJWkvVeDTNL1MbWh4lZDy0FTBDBBa6ZKfraM0LFsZu9LKQ3Gk6bNykt9UZmV7PB5LDw8t3RPGRioyRORJxqNazrmxbexHbodPeaGRaQCxvfh6Tu6/DLFzs6ys/EVcRFwMgO8cWVJruEhQ3cWUae3k9uT1n7ZkU1L1SEgLEUnWkMzStXNEn38lPoJsMCQU1ybWiXplHOWJ0TIwrkz5FKjGjFMKhT0qg0JW97aLCspIIDUGtahrAc7Ja11YgIFrNrEwMomwFe9xCkgC5AIFcagwJgl42MsWsu0TV00lkmTJbukZAnxBZJVtoXNnGuaRuRXEawbHANDSghKxKR1oSfgYc3BDJLkZKP7JcM2NH6SetK4KwgUcyVKomv2ALiPnZ9U8mlCraEYyAxuXzhOtIhyNHOjYECwHpqgCEFACg3SrRkvuevw2rKm+cJE1Kp1bHT4BIeGK2adEk2Mr8a3O7locA9lRg9ldOIPuiUsBqsyM4VxWWZ5mQsDDI7AgE1bJvupxES3kIHHI4nnaC25/qRJRKuFNuaCQZsQ2X1oRM1IokVLkVBH22KzLX4SLPq+JiWntBnkMKhXl7VcqS1C5XhUDgZH6ICYhXBxEBeaVGy1SdqUdLbNuc6gtyk5U3OTMgOhC4X3rMkn818CvyxmrlkejTiojCZDdMHODD6X8GyLdIJbQpTX+eKls0CF21mKkwTKShpqs9TG8QRcoesXMF2SFJNgkJisOkg1akjqQQ0bYzNipzB1oPNt2+ZnU7KCbIQuJT9nHXNXxlxhLSZNljgsggBQwb1tLwLcMvuAuHlMMXZ03vRt11L6PVwXn5agLzKKGZZdsCFIzsrlEpGwlC66kWlH1VZbMZhYrYimDtigJkhIW3STNlmVDGcmxrAKg7PbVR2aURzH25bC5oGaC8FLjusL1UyiXZpNpSS6phS66g5Vh3E+bZtJ1ceNJythbiUHJcYQD6Ivi9TgFBUU4MV7gl7EfaA5KZRFYIpVktAAkrZGenK0LTsgY7WkW+5Q4ROMFlsWlznbPPXY0D2ivSVWiIFIpiS9nxQ75hKzz+FbW8YmghYAqwVz1ZI5F5u8zE3DMjASybMqpsWeDG6alYVqPMHjQcOh4EDXuUhMK1gU5/VwXkPU1RNuUEO3EuW0aEUHSwZ9SqHPK4dLpyqmKcVpg1EmEoSJ8YeGWq2cDGCGjZyCQxUliKLIxgBsQA0mKlItFrC4LT5QjEmrMmoqZFXQo+StNvo3i24LRZNZU5smqV4sKMIsUmqiqKZmE8RKKGe2sWDhEjdMq+8AGyxdks7wHcgU1UZRGnqraTOpPMmYmBtrWvC3woVvaz0nkJW0swkNfZTPJ9BeG82OFGRvOW2qs2IwKuLat1N7jD9RdMnFEISIGxCJdY9Gy4Eptc2h87KWHzPvsrSKJmm66JjFZ2AL22oWBwt2Wj55EtOTpLKWs8XcVqliqSiFmnTthicSOzqe9Fz/DyNSLAgfXDmUglrGZrvd8YXKRSaTnV6TEcDMHC5LWTDDh+5UNaI6AZ1nMbbhGlupLUMxNd2ZtE0J9WL1kaIFwRtAoL0M1bnyYAq1YVh0BKJKjKrA5+GcSC5l4nSPJk6qiYFqUJsOilVh2V92wE6INBFRXtPH4NBcCj/NZunI1hk3KxdLn2Sedn6W/MzaRs7UmNqkb0RbUTj4UQIK65UtFnUapfwZlDWt2Fo68dFoEU2RUMU6cjO1JxM2ffDRDZuW3LiQVjCBpGBpdK6XsbrBCmwZHcsTy52pJq0WzCauf5spew/ZDLbMsiGViGYCCZGw5moVJVEqGmZby2Uabmo5TVvrc9kMuIk30LRJbaNNmmWxlmrSIyldAyxQaIcqLJn1QZlDK8ShQ6YYpBVHRE0ZO7p0XQzVEkYJQDD3rKEpGh6PNkk3JouO4WNO9baJqc/NF9vWRDeyerpia5XG7JlRQ5QRasOmn9JmkUUlpFCJUZpozdvO0u1n2fCNzF1GNOREJbbxdftkVmgSuqFHJtpTJTkRucrIDLpU8SiQfrfF6WcTsqBvdGfHOmupSg1wk/HTZcEm9EkI1zMVpDBIm6nWbWyLTbkNk23LsVaygWkbLA9zgGGSttNKJg/zJPHMyG+MlLFMPk/5T8t/spZFQETD6F1U87SKxYo575ueTbIFTQjdlo5qTLId6Q0VuSSzfZEpyHJWmslFmcQFkFLFnNtJcE10kRnwKuQSmbndbFjBM42pvNCSCDpZRLGYiVBayJEcjaCg5ExEiGuUxkoaE1EhcgkBxWczV/E1Dah+MXdSqYLuc42rSlLTWoLMAVNkWFaPNKeLNmZJzglYRBvs4pd6bzJtWC04y3qyAkk0Hy3oQBG5CDg4cKQGt1v/9FpMwYMmBQqRWGW2Tk+ou2j0tIipohHkcrYR39RKSsNCaUKmMACriTYSlWkIihRi1wCMMba+jJV3pJEIS5bSbg9IhYTnUiM0olWgle/0YUiCV8nOlplSlBplliG+pcACKquW8hwukeLMNWIJUVu/jJLGUjnAMJDXvkrIBPvpiax/HNia9gZh0mESZkSCRoMya0HSAaXPSf2KUhaG5mVLkYcbzw3R46CFVndApaSKFkkN0hupbZBmRK5CaOAQ4ooUjELQ2hwcCDiKbWRXoYTusVpGFEZKDKsSbWU70AmT2RNvT4Z0T0sq06HdmxWBb546JyXRiAbXcCYdRTBA1RnI1T+hVRyTkEwbIkGXS7KgXXTVRGIVW6Hw5mGLaawO0ZHKBjKU1pB9beudmkyIVJaxuNQQiuQg4qdiPFpnmKYsZq2KlrRFWgrKi49KAElkU9ERsAgLE0Gam4VqMQZpEit+6muvVhEYIjEjI5M3mirLpmYpNjkVx9wje7FQbkolTDyLuZiDM2ygV/ZYTdawZMzMhgUdnaqmnrJBcVp3tQJBhYorLeRJZUmGEhIlXJUUR7QiN8FaMVlOmpuiX7qClQiBCOhXawUDFEaTtIlP7i2INgSL2tLgQupyZi/t1q0JRFzI6yIy2YgWFliWqKJbTShajFII5fQ5zBMSYiC2hQpNBotNHMC2Fs3WaPUubYJqhgi/xEQlC9Su6CIcaZWNta6F8ue3zn4VZGmxDsVa6piPBIoBWbRRiAhnZ+UCeKsVdQFoIhhbKRVrWG1lptQKbiQdt4QVjLOXkNlRUFFAQKt82SzVIZrcYFsgoUMxTNIUqrnOzJRYWdxoVIJks+gSEOZURrkX7bSU9SghiBLSCqxb1CMoNhly/VeYTWbJleVpTUrNAZYfBNoMHdAqjk+GAFVIod38jorBPf/iNRBkEHYCU6pJl951El1565TX6L8Kj5SEBkoRDLVmGsIrCMGKVm7S2UaqC2FwDg7TRVbPpdPrVmlOhkaV1fpMQzmqZgx3llSmVHByc7XsFKEEIJYC3bASaYQWXNSY7bpAFhHKksfRTRkfzVCkifwiUCnQ51F8jz/SgjiLax8wjoFSXVKqMFm8SvC4eQAgAnIFpckJrapF0Q5mIITJVxvVppNCu4hChOSwUcVX4x4FGkpoI8GrroNPWl2DuRud8S6tnCLJNdBzjmEb2chpoORUSVJCDYAU7S2jKWwUcSDT0mk0mtKaSgH/VXEK2BvGY2PWwQ2wKKPDbi0rndzUJ0R3WguAvoRVTFKtFxhXoRFEySNjuBUowjKqAjSwpFjUYkiwNukoVuHYRoRDRq4Tk9kSAdaiFKf0lZxJg5umdWafq5DDBNOUdGqrQfVIc+62oUnF8KwoTeniUxs6wTtiIiI0wUPoagoxFHcMp2Ue6T774jq7DhuL0jZI2mM0HPS0wL2mhiW0zGSjzNsVtqCFKUzBNrfORCv1HjyTZqs1Ra0r4TZlBohXqnpP705HBKQCIYeK9rKppNN6Wqc6S/9ZkydW9nKI8ijVIJoOpcrK8AiRMtK3qQt8gF/de5WGZDBXu2NCTjRO3KxAj0JjkZqJl3Sjf2lSlKBaEE8eJc4Yl7US2m4yms0ol4ANiBiMDCraRXcozzZYDRdGatWe6CHUDB5EjOspaxh3FBGQAjIlRh/cslZEzSnLIrEUQgCKVeWnaFobpiXZ+C1RIumSwellhmpS2oIocDRXI1rPqbz3NI8OMaGc0irLUsvVYwhKM5uLBDGHs6ulFiwZTrY1aTDaoDBVg/RkVxFK2mnuFUvwQWutSVnFLEavAAAQAElEQVQLTzjlddAWVawkjlSvKqcvxMaK1ButzcKiHRJJBJVhKC4Cji4MCz8bDW2oDbSDK89GJwvTgERNWSbhSjOijG4ly7wzICprXKIy0iurIsOjNKQz8ci5IK2rKEEB5UHZWXSq2rRMO6WT0j0UkIRCjPOSn8Zo24oc7QXdoHurK+VpzElXhNHBBZlVEG3aGqFlUJM1alAdpKyKqDWtTlMUNihXFfJIMIBIPz4Y8PFABHBH0JKNGt3upirCU6h8KImRtK9rGL+qGH2Aq03FWpoSgDT2cKwKeGhpgVCFC7mU1qz1zgcYsbmEBWiuRmNEuHCOHWRli1z3h1zC+6STiRUQOcY8e8xZY5VKC7xG+dKGt9kJRfEIrDV9CAWySE9AA81w92VedIs1yQvZ5mTqmQkmFZQip7Y0PnJiGwRsgc+K0ZtqZ1F6QiQYI4T7WLYo54so/hYwyXYK1pulaqCGVLjF2Mgo/m3EVHfgOxktVaMHppmZM8Ge+IZP+rWob9vMpz2oJfgI+V9av3Aaw0QLQ7JEUq1Eq8Sgm8onCj7eFhMlzX0TyXKkIMfJVaGjouDoDl5LedNFpJbAsoA1ZXGMpWW1qBW27dhuxzIjl80YO/Rh2/jj0ZijnIvimHTmfmdFiEmU2Mzjl2X0xbMItG1nSuk1KRUoQirBo5g18Qv8v6ibBCUXNiAMImbBhEkDSjS0FTjdBmxUG6Oz9rXiygHZam0Zf6glMdoINq34JFNS86D1USR5qHAd+dljEc/huiyS7iapxSXJBroXn2IaTeLS81/Q/4reXKoY9iBDSFJx9rYBZAVkyPpfAWSSYeG5DWRAWYF0lLJkGoeUZNecJJJ4qRCg4IZlbu2fJ9P/9vxH/H8lt6wIaBoYI2fWIBSa5AbcrKBmrWWSn60vAr6GxT/ImZZymXLsbOfN4hWawiAOyrfk1k5iq0mzoYRXYqjRW1nQye6UJOkjZrr/ZKESdFSr6CUTbGLOmDBJMo3lQRXTZCubQ6aZ4JTSNDYaqSOMB0WHIldFU4URRjjUa4PUSXRS3zQCAAGHPCY6MhClA5Jra5huJ5m70C7ICForWLov0+ybYvrKyD7XukA7I4ViZdGG6DX3+ilCbZDpsT4JD3JJ6LrMJT+hAeFYa8WdzjTpzk5rG9PGs0mfojC4FQvpNu0oM+QJsANpPRlWm5gFCURU0ukumAp9UYmOnLr1LqIrq2oAW7UCWuUvse2MJma6Spc2oipVuVpSTDxmHVoKFW9V/pElYpmW7KGCVutHtJaNksVC+cpXPSfUxpYN/SdoC/VspJ7UsqHVgFgyjUoYc6QK35jO/zW2v/jSmG2EwEt75eSDZ+VSslDwpuOJlAGgJzs1djREVxxzgQw6BeGL+hyq2lcwGmsYI1YAXVhZ7yWlO9V+sdYmoZ3Ub5SRJk1qg0B6SqSioW/QNsI0RhlVNor1BVzWFioyhSiY7zlkDM3BSGHaRH+ybbW1tegJQyBwqRCGfGuyTWXRLgdbigpCqI1/um6CuhBESWkwVPPANmqe1WMzmjkDAqAKG8IiVo1yiZxUsbY9cfxS9mR6Mm02MiktMWaw6KAcuk6MWpolCPjSQe6R1TU9yenJPG3YggojwMX8a5mqi5iIdhVVG7oZ4K+UjSMQy9AZE4cYA59bcr6Vtd6Km8LYLKA0no3PydEXWGlTbU1NuqLRv/WRqW1nDBpjspgkNxoFC2xQnQqNqiFZY+k/FxZe+gweAnQoJyC6qvJypM0+jfqlZBJDox2Gx/8UhsOxqA4QRM2IIDEaF3XV6gJzlW1kujIhOr2VJaVKMXp6x9jps0oEq1fsMBSEW6NBW0jjHpxhKWQXJ0USCLCri/yXtAmp+jNATBpACw1N1LgYci0FYwH4ag0yoCV0XKJnrNkvJbVSgWYIAC4SO5VPiuFg7oWu1F7CYlRkxSQ5KQMpg5LSscwYP1v97PXTajyKbsqpSzk3DhgFigaG+Gx804l5A++DC/PQcvPalBH2QiHLuckISFYlMSANRTp0Qp88NrVtEbZtbWtt0ZZQc0NYcSqyeylvR6P9kEoZMxDEdGvMmEC4aoBydKR6N7SabGgyEJhTtxCnHBllnCv0vXl/UvoLsOE8JbgYwAhshy/BB6KoSfskTzSZHegC65Sns9TCAtqdSChcABUxJRWpgZqzIydRehgo+oP8RYVGc4lbgpChEpYrIyACsSwZSUS0GMveSzkpY04sFebzL5h3ZDAmhiIZGw309KK76LP+kroj0jZh3VexMYEwtKpeUeQdPRPVtmO/dHkieMIxnLsQvAKIYmFZsP63JuIyBWWhGmGoIGpOi7GIVCHBftqcpEiOsPQJLEczaMlIzOHu5PN2emQ1qoqU8JoWTEN2vHRLdtDJTM6IGsm0MGIn2U3AsNBZ/d+xM88agZKsCUw5S21UU5gAus/wLC1j0vrvDVOHZYVxnZfOkjm2PmKBRklxhzDKQ0MECoa7EOqlsypf/vOmu2/Ss4mzeg2Vn2kLtQbpVBJDKpqx5tCAMuRqYEdxYTHGDU9k+kU3fM1v+TNzEeAxjBJEk8EES0xD9ZT11Q+4PCb/Ro3S0FYiVsmQW6GqFsVi8KkRhtSiwq6YBLzeKv4Rmx/Nal4dKareWhgaso+0EimTFGjKxlk+mgAQd7pF+AtMJol04TCzTeNCs1KzizQxttVQX4qnzdhsuX/aoSVs76a20dKdko1CmuW0EX2ixR8kG+HnKhjd44nG1MMkFYlu8cTCgCEYCig2XVmZIdhmuUjGk8DEhTp+SX/ijgoSUIs/uepUYmu6IAtJGcJZ3pboP4slV8HNsLUdaINnhvP3kyJgyVXbJRcpgiOTwtRTZpelKgVFsItR2qiIgRh5xSCuMSqDNUtdNRvdBaxoAhqJH1NljyS6sghhpQ/0vXODFwpWwTm3C+NGXm0EjTpWHyLNCpJ8vBmUKNagk1dfYCESyngzLGICk+g2d3GFVimJETBipyYqYKzYk0rfzIXOZI6oWbDQcSsO0GpUOgmUVVWIFa+6mvFn4la0a0rbCdlAuEajiWikOEuOk4ZdqAhTh2qUQaWQWYQinNdT09Zbs6aUmouVA2Aa6NytaCOaoUzdpiyFJ2jNRN+kwObctNix6OXOJHRhP9BVp8laE9s02BTGvWMXkDBNYlUgQSFKE8vL+AX3YmCoumDZNVVSagFClN6njUVYTqW6JONkNLCk+EnkCs6KNZhProhONDhIiFIv8kaF21HFAJ8P/B+hQILThQFn1EXxeDQqXDUCBlUTHgXJRCWUjUlxKkMkrWDVuAHmEmDx2MUrZaalN12EaisiIAJoL7EoktLByb1skq5Chk2OzeiNmVBzcLxVtVKhUcS0BfPZIEoOFPEZGTNpXbSkekK5CK5PdyRGFtxkNJ3puGKYUjER0yZTh63ISaWFWjazSc3IM0RCqlQEVWAOMWJcxmtUDFRAAxjcqNu2TbHpHAgVJexSHdKqD7StmKtUE7USyeK/5DxLWLr16p2qWkVxfVigaQxmmjdEL3QRWVGvSF88nL9I82GNz6xFq1K02NlMeUTRIJqpBptM0Yy8gIlZWPfWCUOCQ6oFKJiYZdlSNhyy0JRqNpuuxJjACqbaZWzIAIEJplzdHUCUFotDihSUFNU1ypmQIQoYSnjyTxnvnt5lK1yxiFQAdLyQ4JWDRWDQJWeKxOI1wpyVoyp4jlZuNkRmGQPboYOICERSdHbUUsgkox3gSbmaQ2MUUPSqDW5tUqOECJozA4AVVR1C5WWRuoQGyw3MZLWahFynLyFofWpMARYPME3oCqcSP57wSaJBp2itTFTorNDvSLHQaWgju2CaNi3aopmok3EQDAjN9IAvOkgX3lVKasOkYA1sLbolbQGP3Ihwb9Br2Yib0ScJEFykqkoptw2+lCNcyQBThwY/SLK2EG+rZGhoJy0RKPU4CCNlkCmPZ5U7eRTgIR1qutHNjkyT6AboPRmGJdVL0bVEa8Bds03N2G+5QEbKCCpKqLrqUI6ClUhaFYDQDCPabmUFccrQUpWNtTZ1NLYQuKJGExVjDoakSqJGZRSREunoDSaURqN0mLhooKWaW5uFxcOTMxvUSoieJJewRs25FkKSlTMFNEyMCktBviTJhtV4kB2U5kVvgh0QwbqaSDbQh9RKQ+naqparStuqOjTdGtUVDKrSRHnmZIx2NLLPWy2UoBKQaJncLGWmOzHn4Gib0Yy4iEg3ncwwsdpz9y60TFpMKtRZkqXF8VgLbfNhGCE+aLTQXaiyuzSEFtIrDGGIAsXVngbKRm3KZls2YLiu0xesY0VaJ9/gBhqbJqLgGbRbTWiLetcUFQ4GBoQJFQuwwArK7ByJ4RlLxMjZdrY6zdpmCN2k/SQE0iVcmOCmAikUwHWT972w0RDFjBakvUzWDGpFOCeVdTklnuDwqlmKoZVtmScEruRfiYsNnVx0yQqEIlUpP9fbLEvpafROlWolc6PVuchv0FH0IGeGTirBcGiGBgmyrhMu1AkOWbtCaXHJkhZblblVtCgku1O8GH6BAk2Ihyr4STtGIkJChRtG6mIvg8+AFIje/8MiUYuiUeZDxYsGlNYaNnHYWF8TWXgm608URJYmaQD4mGcdYgybGkrB30gqvEv1XsL1sC1EBD1T4KkBUnB1xWWs4NNQgu0tl2Yz392kMHCKQek9nZ+l5KWCsyXV1haTdWJWOkMgVaQaOl2TURWsus6xjKRARRieYGugHBWjiATrdm5SetF6A2UJYxUetQWWLk5E1RJTCC0XV05ZZF1YWCgr6UAFxQ2TbKpqPQmzCq1IErMZa4JyFpwbT6sSFUD5pWLC5ZrGBmI0OIhWX5+rJWhnClJSJHtb1/zSWjOAQ0SGrDQLTJAu0uhDuXOSlraCCEqECdUaeq1rqEVXOldrRzHwtTgWsTWEjQ5zSmbkoBn0QyQYAuEfyK5wY4y9+OSOuUkpGFixTiyRQBL+amhTTc25VhvFyZR44koYpGpwKZSZbCto5E7ZhknDgKvJMGy1Bkt2OantyhyMhaNa6gCbhQbDuAdoCWsZOzGsgpCwN9nJHC2KJeRFo9HKTKqzFpjLJkmr6KiNyCTC6aDJhL25CpFOnUSbCuhx0EN6ZtYbixtHNwjlhliJMKtcFg+fB9lQQjHNxFSJBMxYRdVUe5ixDtYm+hQTTF2QZQwZ0USaR1sWXdxAcL1pUTXXi5mor6oAxhWI1fmotpQuKtz4kDJYl+UFlk5bOCxRqzGsZilpqyWJJB29XK2ADCJOTcJ7rYINt2XOkVS+LRkcYGEWYu5R7IQrUqraEEvrAZYqX9xzGd1rp4iruy+zj3Q3D4uoSloWgmRgE+USqdwQbugHZSbfgmwhi1pJaaBF9OGaIyvYIq2vW0fjNF8kXKXoWpZKmg6b3FSsTJ1k5vglMYRMdOQspGmlqxZodBpvlcqqImsuiEazxZgcMc/zsh0/t2Me5Qm+694E31NVY3pSPwAAEABJREFUKWu5zbzk1pw88qzM8hCy36liKLWEZxJToc0NpQnWKc+xjVUUIjXdiDRlaYgwk4oeoDpGr1mHJ+0ST1pr7Akb0eYAi1FVaqTFyFVFaHFHMFAIt1xBTPo7ShLVClwLSEf0jbedGVQ7dJnbdpEt8LPlrJhLgzgQZvGWDg84sBhGkyE5HBkIeImzEhQ0F40IZ3PPnMg3LIqtRQin8SpzyaqA0GzJJOoVFeohKTD6uxX/ifgZOcKylP6wa2h6IqFRPXITY8e3Gyfdt+lLOiWkZoDmUTs8YyA5zSxUVuYK5YcXx0YEtQbKPZZt+Sz4Kfm3cGHzpJtVxplR5Zqj52LEKyzCZrEwOjjE59ouuaUkYb/41JLLztRzzbDKqHnOIAdk1loao76aV+tW/ucic5Sjsjh71ErOIASAcPL6zmDa+rqvKR5aw2KLHIunL4U50zOzVFzKo2IOd8810mPUGOyiB2t0WjJ3BKuMwXFIMERVMZZl/PTkrzajWoCgN09d9Ve14bq4LGnFcxFQFyU0lEQQzErloEhbDWAwg4IXZ5y229iOWAal00DGIDN8iVhomTvGyJ+5LOkz+kx6RDglz/Q9c0CYH0vZAioJ9+EyUkemh1eytv6U+qmYUZEafFkzJr5I9hFF8ToWqVGmrIZuwSTdRi6UwpBtkmH0pMRIZE3G/eTQti2ZMTgbTGShb8j0ZEDIvVhLMkPgGh5UZoMxal6TDysWypm4A8NzWXwOXwhbdR3oK08zK9dArsSmrOE6e/diYQvTpTaLt3DLFCG/YtD0dF9GuXswjrkKBMc7a78IyiTI3YaUoLUjcqRHRiRrRm2r2FjhtWRGeTHdltRZLUSS/AihD1mLm3PFYB6MxSXIMWqvkcPdqTG2IytZGzkLSeURmp5Okqx/6VFw6ZEtKiSHrFCqcq1TQcuojGRGVlN6FhbFDKdytpHyXmvQULmaGunlpGgyLRgwbniW7rNui94sIXQv3LMKKizIyVlD5n+wBVA82GNUlD6SYU93Rq3Ci6Asq6WUPLOnIssl5+ZLy6GSKoJEePJk3IUso5eEWee0KjGESZpQbVFRAuAMUMqgErpXtRSVjhCvtQRwjq6X5KgzjpktCaqmMDWq6EalajYEPJwoU7GUryuqoBKgAWTRLEJ+BY+VslULZpet1xxBUytpA3cO3F1ySWMlHCJrjCwdDAMEhKi03Hqx8DSMlMWnMbfIFC5Nm2VizjrHR8Sy2IjJo4VruGQiJT3HNsccC9zZxz1PpW9DfhZ+pv8T258eTNtkekbIEghFdgSU80u8KMf4lZHeIyZATKpnTqlWUjWcs9QB5dTiaFasTB2h9HipDKzWNMa1lFxNoSTUELhIJQ3NiOER1UTojCGkXApSLb2NbZDP7oQ4PGKpCMu0SKnHMCY7PMeSvmQuhQiiJTmPXDgvC6hiWsW/wwpzyRaEiWaXAI+yAhXVPDZj7sHItUrGWr00y8rDxgLq9px9XoQ2a5q6FKkSjvSAz5jpcjXWFZIHtcYAMZKGp1AMrwddmV23rjGjBsoZXpon8AlLryIcjliCRW99L0nmLKAD0TVaD+EuThyVSO4UpdIxFrKfEyNqOIeWaq5hIzCRFaMIVzo/tUbLdRlXMCwRVD/U2V+Uv662qLQg3qE5KlMem4ulMJGI4lk5Au6M4mPDQGwjRtSSHA8R6+hdNiYKoJw7+7ZyVyuKsfDIkjFhcN800UYGl2whMFmFxZWfiUKLtOI1JUBjNPARcBs5LzzVt+YzAR2xnYtzgtP4F0urudNWeLbBTAgDBBKoYBTFk9diaWTuyDFihCwqIfSMQZgrXOaU7VJLRNlcrBcYXsnOUeWjkN5iNjDONK4YgQhIJrlCpiCYlDrEmDclQyp5gyzPWHKbshQRUUUJOLQCSTUE0peRM4KiWb0ea4yNsCgh0JIumcV/ukaOJc8542fEWMPJ1KFQ6iGvpCJ5DNY1qXUCI5ibBA3TdIpPJt6OFt/ZSdYIOu1QZ0K3WtyHB5VkpzPEOt0CEZuf2X+Geg6tBWNwFzEPfttS5mZiNom0KF3h0UhGL6VSMjXRkysEWq1raMdsueQ/P+ftP+4zRJQlX7/e3H+9vft8d/3u7urd/fW724cPl3fn51cX59cX59++Xvz4fPF9Pc9/fLn89uXy9tPF1Yfzm/fn399f/v3x/u+L7z8uHtj+4935j3dXP97d/fh4+e3r+f3Fxd3l7ffz+78/Xj+8/3r3/uv3D+c/Pl0+fLy+/3B38+7y5j/nN/+5vKPGj9+vv9zdfD3/fn7x91fKvPz2/vzq3cXVp6urr9c3ny7XiR8v/r64/vvq/vvF3cPF1bfzi2+fr2/f3V69u7p5d/Hj/fnfXy6+X1zeXt1cn1/ffLy8f395/+7qmqZ+vbj6enP14e7q/dXV+4ubD3d3H749/Of+/j93399d3b+/eHh//u3T+cOn8+v3l1efbu8vH/7+cvf3h6tv785v319cvfvK9rt3d9855f332w/fr75+u/5483119vz+0/nV+cXV5d395f2P89vvF1/vL26+f73/8eH2x7vLb+++fv94+Z3gXH57OL+//3h1T6do1bvrm3eX398TrvNv//n68PHi7uvlzeXl1cXl9Zeb2/d3D5/u7i9ur7/e3b+//fHh6u+v5w+fb2/ef7t+//3q3cPNu7tv/yGAD9efvl1f3N19vrp7f3VFKD7f3H05//H5/O/3F39/vPz7/Prvq9vvXx++ffj27T/316veS0b2+9eLe0b2y/nVewbx/uHjt4cv3+6/3D18vLx7T8Nurt5dX/P6y8X956/3Hy7u3l3fMUbvb6/eX958vvh2cfPj8u6Ocfl8eUtsr66vLm9vvtw+EMx35w/vv377dH5PR66ur68e7q6+3V483Hy8+/bx5n/P7//38uHHx7v795d3n1fAf7y/+fvd/f272+t3l1cfL68v769vbm4vrx4+XT+8f/j2n7t7Ys740k6G+N3l32TXp/u7T99Wdee3f39lpB6+k5AX51eXjMLF1fn59fnFw9er7x++3RCr99+u39/fvOeU+zsC+On8+t3F7bub70T1/c3Df25vSPiPt1fn17df7n58uv/+7uaWlrz7ev/p64+L82+XXx8urm+vvj9cfrunv+cXt9d337/c/v3u4tu7rzeU9uXq+sPNNeF6d3m7Nl79eH/794fbh483D++vv32ihff/fHn45/3Vj09XPy4ubmjk14ubd/ff3n379u7h+6f7bxeX38+/fv/w9dt/rr49Murvjxd/X97/ffXwcHV9c7G6c/v18tvn8x9//zMAfHm4/3h3y2z9cnP//ur2P+e37y+uvl5dfb25+rAmwsO785VXTIfPV98vOfL++j0tPL97t5Lt++fz29WG65sP3x4+fL/7cH9F2D+cX3+8/v7+7u939z/+c/v9/dX385uHq/u7i2+3H7+Tdfcfbh/OicbFt4uLu0/3t+/Jpeur91+vP329/fr17vLi+urm+uLh5uO363cPVx/vri5vb67uSLzv7+np3cOHu++f77+fP3xbM/f+5uv99Zfb+88X379cPXy5vfrycHP+/eH84cfVw3fO+nL37eM1q8ePq4vvlxcPF1ck+d37h+//uf/+4eb+y/31+bebr3cPn67uP17evr97eHf74xMRu/rfy+v//XLx97vzbwzH+4e7j/d/f7r+368XP1YaX15/Pr/+cn51eXl9fXt3/fD98uYbK9i78+uPFzefr+4+Xd8xrVbCfL/69O3mM724v/50c/318uri/PbT+Y+P5z++nN99Pb/6eH31/u7q893Vp9urlbd3tx9ubj9fsOvu4ubhy/3Nx/u7T7ffv9z9/fHh7vPD9fntw8Xl3+9X8G//w5p2/vDh4u/313+/u/32jqovrs/vrr6Qpd+vP97df7788fH2fz/c//hyc3dxdXN5d3317e7q4dv5ze37m5v3d/cfbx/Or24uzu/Oz9cYXT7cXN5ff768ef/1/t3F3cfb64/faN71uxsG9ObzzcOX6+9fr79/vrv/cH/9n7srFp8P51cfaQYz7v763e33j1d/f7n6/vnq/sPlw6frvy9u//5ySQtvmAIfzr9R19Xt9dXD3Zf771+vvl+c35+fr2R4zFnKvzm/f/j67e8P3358ubtlEn29/fb+9vuHy9sP59fveV49fLr6/omJcHn75eLmE1lHVO+veFxe3n+9/Pvz+Y9PXx848t311Wre3fX769vz2x9XN39f3jx8ubw+v7zhxeXNKuTdBQG8fX/5/f3V3+9uvn28v724v76+ubu+eWBeLB7vL28/XV99ub0+v344vyH57y/uri8ur2nz+dXfnx5+vL+/+3h1RSZ8PL/7cPnw7urh3fX9+5uHd1e37y9vPlxevb++end/9fnb/ZeHbx9vv7+7fPh0cf/14v7z5d17jrm6/3hxQw6cn999uaXX39/f3Xyk0vNv76+/fX74++Lhx+f7ezZy4vuLH++uvr+7vV0L7wPBv31/9eP95d8fb398eLhhuaYZFxf35ze3H66u353ffzz/8fXq2+Xd9cXd1fn99ae7+4+3Dx8vrj9e3L4/f3hPlL7evbumwFXpp4drcubT/fXHy5uvl9cXl3dXjM7F96ur2683V+/IzOub1Ybrbx+v7z9c3JA8H7/df3z49uHm4dP17Zf7q89r9K/fX929Y9Hg9d39++sfHy6/fz6//Xz+cP71+yWNuLlnSp7f//3p4sf7lcA/Ptz9/eXb/369Z4H6dvXt9vz+7v3N/fuvTJDrz3d37789sH5+Or+5OL86v7p6/3DFmP7n8vr9+e27y5v39zefaPb11aeLqw9f7758vbu8oNl3X6j6/tv7228fLx9YY78+XH/+dv3h2837h28sR++vbz9T2vnV58uHDzd/v+d6cfGIxsXtupzdXL+7oQu37y9uSDl6+unuhhIubq4JxdXtt0/X399f/f3x6u/zm4evlzfvrq7e3V9/uL8mK64uHq4vv1/fPFzd3V5cX3/+evOORfLi/pxIXjIL/vfd1Y//3H6jR/+5uPl4/XB+9+Py7seXOy5MD+/v79/d3L+7vuby8eHb1UfKvH54d3H/7vL6/dUdk+7LPavr7ftv3z7cfDu/vL66urq4JraE8dsHDrhn2t5cXD18vvjx8fLb5+vrrzeE5fo9DeB5efvx7u7rt4fPD/fvr68/X9yeX3z/cvm/LODvL2+/3Fx9ub/+fHtNzg+PL5f07se7y7uPt9df76/Oid7Dt08Pf39++HFxd/vl8vbD+f2ny7vPd7cfvt2/v79/f3H38eLuy9X958ubz1fX5w/3V9++X95++3x19+7627ur7++ufny6/3b+7eb84ebT7c2Hy6sP57ckwOebv79++37x7f7i23cOeH939/725svV1fnFFQP0/oYgfPv87X8/3P39n7tvPD/e/vh0/v09q/HVw6e7+8/f7mnAh2/fvtz/+G8lLH4AABAASURBVHxHDt+9v//+7v77++uH/5xffzy/Pr+4u7z4dnF+/+X85j2De/7t0/UPwvjx4XZF4/LHxdWPr9d3H9dkuf7P1RVh//zt5uu37x9vicDfXy+vL2+uLu6uznleX59f3Jxf3n19uP38cMcdHfn/7v7q3cPt+/uHlfBUcf3ty+0/59f/fL78+/3V93d3d3TnPYWTBqylX7+953n77d3Dt/d3P95f/v3l9sfF/f3F/c3l9ery1/P7r+csMrdfH67PH+7Ob398ufj26fzu49erz9dXHHb18O369u6a1xdXX86v3l9cvzu/+nzDwdef7x7eXX17d3vznjz/fv3h7vr99f27yx8fvn5n1X13efXp7oqhvLq+P7/+/vHh73ccf3H17vySEv7D1L6/YRw/3d9f3N7wZLn7yvE3N58uHj5ersvlxc31h0saw+ubi7vrLxx/+e3d+Xfyk3uVi/vr89urj9dXH26vuIp9vfn26eLHu4u/193Lxc3niysG9MvF1XuuEV+v33+9+fj17tPl3efb2/Pbq/Or648X1+857Ob7l5sfH79+Y+A+n199urx+d36zrrwURRbdfvtw/+PTw/9+fvj74hvBufl8cf+ORezq/tPt7der+69f7z9yV3nxjTuxd4wI68/99ae7m/Pbh6939O763R1Rurm4v738fnv5cPv1/vbL3e2X+2vlE1uBj6NZyEQxBzIrYz35qMUTWVLrCA6qx+P/eyt82NHHu3ociKIEXlWsE3SVjVX+2luczuv1qRclUhzG23Xcegv28gT1CxVWUdJ6w/Za2ymmYHyyepTJuVW0Nmkp1T9eoZIDV/2rC+ymBDZURYb8n8z1nteQ1bSqdYZA1q+oqvU1FecWj39V03HwppKtqjQhax37+MkmusLL4kHD1vuiX1iHrbOEV0V3sJrNRg57nJAF2lsgBKuxbH48o5LNYC9v/7WEsx/PVQn72LW2R9Xa8H8j6/FYu9aLpDMChqtgsjqp/0aHTWwu6ucA1P+JWIVTlAjYUVgf4VchbF4b8KiGzbzPLJ4lEFnlcBQ18rNoxdrCr/WuqIdgVWVxEvjHMyEUB5H1ix8cxFaevE48+lX0iw0F4T9UAuwzU1UOpiwAJNQqfJWbKwU4UqAqq1lRpOv/OUW7sB5ZyZN+ZRVPgl3IYtuj9xAK5CTeP3aj1oMKAGr/v5NNlCartErelK2KORW13uf/9WB1gdeU9e/J6+IQgE4oFQtfHfw7Y/3kGPrCk8avPhdVIghEQcAZ6x+/OOxRCTFfZQnWo6oobxVe6wcb2cLzccRqNZtJUxrET07gYIA+rJJ4uzpMrHK9pQpw2nrJSetc3v6fQOIg/9f3f8Oy/g/eR2UcyfNxWq0CqGLtX8VnPcLyOJ1jOD2LA/+fFKDUx8FsYjN7i69q5P96/4W3qB2cilUcxwlHFBmRKLYSNJ6cyyEQNq2XAkpaRa/DhT0cW5wjkFVIFgfWI6D8/Ri8AR4nZGXxEMoFLwnbeq5doKTHPr7/oUE8VyFspSjwYANPrKIoYD0phGLYyHOdSdNQwgCs95y4dq7jCtSxevL/a1jvhKLq32EUj0IWSsieVcI6j7qC31n0iLZyEC95/nvNmRxf6yxCxa9kF03ijPVEQTiQbcWRFJm1xnNlXda/wyDrwW7e5jrn8Y/YS1EXT85nE3spQXkPMBBCs3nPjqpVJWGjiMr/p3ntk389Tcqp5D+eWeuxduKxd52anCXr7UoDCkHRTsqhCp6cxCmcIf9KA0eUQNbBj8as98UWUMTayJmgGY+js1gM/x0AwlNgCFaKUAGvi7IfR1L6/3Oyl2fxWEEv2ldYhf8LPtuohS3sr0e/CAVbeNLJLH7I2sURqARHsGk1qB5beUU5lEloHxvWcbylqOJwrAebOAz170HBQrGr3tUhCkw2/V8vWzn6sVvYS3WPkyjl8Zt9vAS/1lsOoxqiINREq2jqyoRcJ/5/x4DGUNQ6TahnNUQJtaw39HT9lnVxWYVxEOXSFJ71uIKwlfjKKpyq2Cmr8PUWWHFeGzmGZvOsRzNoBR7nPw74t5OfVLQCFUV7Ev//I7PYW1WrcKpbbV//CpzH5lUi/wq1nmtDsYPG/quHNyJCVjAvOGAVsg4EG5lzHFYQ8IauP879P0ZVqYDnYxuHEDqaxk/wSApdhdTj92otdbGbUcYqh3eP1q7969i1rXgQe3aBs9bBFEQ/hFqIIHupUcB+als/KYE6Hqf8azrbKYheg8sTqCjZ/ziDH+t83mI92M5zvaLD7MgSAc9HqSXr16MUFEOcq/n8eDyLk0Q4gt/gmLUPtJcSaCpF8eQsES5qlEhZbFjHr6qLF6tCjgfv2bN+/htNftL2Wj1fP4pHrsr4zTZqe9TJOzBS/OK5ypDVAMqkZGokaGyvdQIn0+BiC4dxLgdwGOXw5MWqjq3sowlYEV7NZysejSzOzXrs4td6gs5yNH2iJF6wrTiKJ4qN/EgI1j6wt6h3Pf+1jg0cHkVyVJasA5I2VPFa+Pkvizg3eVPsgUA4spAcS4N5XxTCGw5YByUHr3drY/2/rtf2x/4qqq/Hrqr1+tH8VWzxqh6PFIper1ZjVISpw1Xg/9WWwt4sxgWrI+v4QvFE8XrtowSevKJr68Uqm13goKxHA4U+8IoeCFtrNY4a2LVeceI6qziNABYdyUoeHFEoXnMcT1535cF+Cl6H6eHJ0cnp8avTo7cnx69P9w9Onx6fPn19uP8/e0dvdl8c7z07fL5/+OLwZO/ocP/o8HDv+OzZ6dGvb/afnRzsHp0eHZ8c/Xl69Nf+7p+7T1/vPT3Zf3m8u3u2v396fHB4fHh0cnR0/PTt4a9/Hr98fXR4crh3dvLy7Pj5wfPdo+f7HPnq+PDV4fHp0d7r0994nh0/PTn477OD/z7ee3G0e3h0eHhyenh68uzNya9vz56/2dt98+zFn0+f/fHy+euD3dcH+3/uHv7x4vlfL579cfjizdkhvTg53Ts+fXZ2/PLNi6Pfnx2+fvby9xe/Hj/79ZSW09r9/aOnL/58fvRm/+h/zo7/Ojj649fDV0+Pjh9dOTtZJbxeoTh68/LFH3vPjg6fHZ4cHJ0cnx4fvTnd/+vs8PT0+PDo6OBw9+Tly9cvDs/2j14dH7w+ffHn6W9vTn99c/Bfb/d+PTt6dnR6cHJ89Hb36M+Xh29e7L89WK9fP9s7IYhHB0cvnh/8+uvRi6enRy9eH+//frj759HuX692X52+PDr4je1vnj/7c/flX7u7f+4dvto/OtrfP9vdff2Cn4cnZ0evXu2+OX3+18GLP3d33+zvvjl6+Wafg1+83X35x4t9avxr9+jVyxfHL58dHbw4Oz38/fTo9eHR73uHpy+fn7z4799f/vpm//nr44OT0+PXlHZy8Or06PT06OTk8DXNPqV/h69fHrx9ebRiuHfw++Hz05PfyIo9Bvf5wZvnL//cffH2aP/saP9of+/V7t6fx7uvT14cHj0/2X356vnu6fNnr18+e7tHC1/+cUyndk9PD18d0ICjsxcvT/Z+Ozv87fXx3uvjw5U5Zy//OPntj4Nnf57s/XX24u3pb3+9evbmdPf49Pjg4PDN3tFfhwe/n7xgfH9/TjSO/lite3l28uz16bPfd5//ufeSQfyfs4O/Xj3/6/C3P188e/109+jFwdHe0ck+1R38/vzg9d7Rq/2jtwf7f718/sez53/s7p3uH75aYX/29vDpn0d7bw4Oydjj3aeHL387frn36gVDdvjn0eFfZ3t/7b/888XuW6JBAE/WlhX5sxd/0aTd337fe3a6v394eHhwsP/qcP/N0f7Z2YuDk6dnp3tvTw7enrx8dfjr2cGz10e7f5wc/nGw+3b3xdvjw7Ozo7Ozw5OTF2/PDt6eHZ4d750evTw5fHG6/+z1/ovXRy9eHT//k2TbO/rz+dHrvTV8//XkCTP59Nf/Ptl/enT64vTk4I+XK71fHeydnj7l+frkt7enz/84fXG2/+KPF3u///b8r5fP/tj/9Y+TZ29Pnr8+eHq6y+vnb89e/n7y7ODo+d7B7tHxwZs9hvK3P06evjl79ubsxeuTvZXdxy8Pie3Z0z9f7f55ePjni8M/X758/fK3k4Pfjo73To72T46enx5RHeO199fJs792n7599uL0xdHp0fHbo903L//79cv/Ptl78frg6I+Xh3+9PPp9f+/w4Omrw9/eHvz218HzP4/33hwdMQFf7R29OTl6c3p8tnf45+7h77sv/zxhIj89ff709OXu7wfHb04PTk+evjp9+sfB/huy93D/YP/Fwf4eM/Dk+OhorS3H+/tHJy9evnnx9PfD316dvTw9OXx9cvTH3sEfL56+ev7fZ4cvjk8OGaGz/f03z1/+Tuoe7/918vKPZ7+9efrsj5d7TPnDk93D/ZfHuy/+OHjxF3Ud/fr6+Lffj579sb/71+7em73dP04PmClHR3Tn6O2LA7pzcvrs6OTpydGvb0mqg6O3R4cnh7tk1F+nh/9zevA/Zy/+PPn11d7zo+dE+vDw5cHRHuP7/OTo2dH+8+O9g9PDw9en+7+f7L7a3T3cfXFw/Pz0eO93Umjv+cnub29ePH+7d3B2un90uHtCm5+9+H1/9+xg/+3J0V9rOJ6fPf/17PlTqv7j7OXrs6dnx7+9Pdn96+zor7NjTnz9khR6eXp2eHhyRJROj14enT3bP2GN++10/+mr3d/OXj5/dbj/5nTvj1e7v796eXK6+/qUJNx/tf/i9YunZ0//6+3u87cn+2/PDv5c8/Hl6dHeweHhyeHh7weHr3YPeJ7uPXtz+Nvvx09/P6UjR78zhU+f/sVkOTz+4znjuHd29PTVwcvXLw9OdvcIwSnD9/Lwj+cv/9pjyvx6tvfbyd6Lo939s92913vP3u7/9ufub2/3nr/ef/7H/t7r52zcXbOGhDl5+fvpwStm4sH+n4d7r08Ojk4Oj0+PT08PXxHAo5dnB0+ZF1Oz3w9e/H6w93r/xZvDZ5Tz9mifHDvZPTh5dvjqxcEpe0+fvWVcDvf+ePHi1fOXh3v7+/Tq+Oj3s/0/z3aZR2TJ8fHhq5PDP+jF4RHlH5/uHZ5w2B69+P358z9e/Po/+7++PX7+6oRW7L45ePrnwYs/9w7fPD06eXZ4enB8dHx4fPzy+OT54R7jeHD6fO/k5W/Hu78d7j87PPr1hHm6v3v88mhv9/Do6Oj16f4renR8dEI2nD0/PuWy+/xs73CtoweHZ7T/4Nmfx0/fnLw4Otjj8ero4O3hweuXh69eHr06fP7m6L/fnvz6+9mz1ydHf744+uv50V9Hh3+cHPx1dvTHCfF/fnKw9+rw+PXB8enR0dnJwZuTA+bXqxe/He/9dnS4d3x4+Jh6B2dnRPXo9d7x6Yv9g5fPDvefH58dnJ7tH5/svtl98cfuS0b5j9PfXq9Jsctl4ujg6Hj/aHXg8JCS6e/R2fOz44Pf945o4eEFiu6uAAAQAElEQVThi1f7z7nenT49PH5xdLx7cPzy4PTl4dvDw78Odv98vvvHs72/mOx7h293D//YPVrNOz0+Oj2kut/Pdv86OXiz/+L3vV9f7/736f5vxy/3jvcODvcOTg/2fz84en24e3r8bF0r94/+fHl08nTviLnz4unrl7/+cfCcPDk9PT45PX51fPTq8Oj0ZP/06IDM+WNv7+3Bi9dHe+vt7su3u7t/HRy9OT4+Pd09Pv7vt6e//nnw2x/P9/787ej1s4Pj/ee0+PXpwR/Hh28O9+nL6fFz2nZAaScHJNXve89PXr48Pd47O919dfL8zd7Bq+dHp88Pjp6+/H3/xetjNu6/fXXw16sXfx799vbg178Onr3af/lq9/mb3adnL17sP98/ZNFjbT/7jeF7c/Db73v7fxxQ1+6bvZevXj59vcuYPj872zs62X19svfq6OXR/m8nx0/PyISjl3+dHNGwP44OHjnfXu0xJY+PD3i8PDx6fsot0N7e6YvD0929Vye7p6ck6ss3h7t/7L548+z5q72Xpyd7r49fvj3a/fPoiOfv+y/fHLz84/Tg7enRmzXfDyn/9e7e0dMXxy8PT/ePX+0fM6y/nxz8fvrsNVeE/ed//Lu7ODh8fXJ0enpwcPTsFXc+Z4dvjgjSIeeevHhxuP+SifPqdO/t2e6r02eHx78enT07O93/4/SItfGPs/3T06NXp0d/Hh/8+fzo998Oz3aPXh0d/Mk0PD6i2JPDg9PdlycvXh49f3lIs5+/+HP34M3hIavf673906eE8dez05dHp3tHJy9/P3z6x8GvZy9//f3o2f+c7f91tv/2+OXbg+esTm+5aTza/f3o6Gz/6NX+EXm7f7B3cPDs+Hj3+PSY5+HJ/quDp2+e//rq5W8Hh89P9/dYGf54/uKPl8/+2n/+9ujp2RGNf3F8fPT74cHb/d2/dl9wHTzd2yfgx8z0oxdnp8/fnD59e/r0j9Nnr852T872z872/3x18D+nh3/uHb5+sX9Kwj8/+PPZweunz06f/3q099vrg11i+PbF89e//fr2xbM3+8//OHv+6vTp2cnTV3svft89ON4/ODh+fnD87PDk2f7By8OXz1/vvnh7sHt2cnh4cnRCGM9e/HVCI5kRhyenBwT29dHLP/ee/fnyOXdlp4fP9o+enrz87S0Xkd3d30/2Xu3vvd7do943hy//evni9992T57u/n6w99cpKbf3Zm/v7OjF6d7T3/devt17/ubo17PD//796Lezo+crdHvHr18c/HXw/I/9p2d7v509/+/fnz87frZ3/Pzo7OXBmxfPj3779fDZs0OavL/7x8Gz18e/HvOkwN2XJy+fHr3476PdX89Onr062zs9OXx1cvjnAYH99ej4xcney9fPnr55+fTs+OnZq2evz569Ofntz8Pdv14cvd1jaXr26ui/j57919mLl2eM4OnRycmhoiCJyUUDBmimeKrz2SYV3jK0HMnb0qxesrPotGD9D89GIko4PQDEorlIuar0Xs34KmCdNKptVVyFD0Xqtf6H5kM05PE/yJtUdkxaYx+KklICGqWR2lKoC+sznyCbUEtSjfOBrKQoQdf/xUgI22sSmWj4iFz/YUFlSITBDF3QoAKIKl2lXhMKGeqJ4euJcgnN7Bm9oIADIakbs9YUWhjAaOaKRXJuOUyraStYpWuyNwET0GdRSqKZIRY5LSlUEQPi2tKatGacphE0ezVcqGn973o7XGyR4jlEsJpbwOMZfMiU0HJQDs8xjGIrtRLJkdZErcqiwE+Nar5Kx/ofvT6pnNgQIJiW1aqawExoRJPsNZA5B5ZZfHB2USZPq0E3SzzYlis3JGmoVolqq0YsPCdnUHtEi8GYEoWSNA2i1FpQfhnNS8owZxwbQQQZlFk6KENWiauZKiAUViZK01qiQxogPNf2FB3AomP0QVQrV4sKUeuVVGvo4ubBZ3tNTInmnMGgmVCMWfXkiRYpcNAkpWgVMTGIFCiGTJFB+xgfRZNshoYEJzA2EBchNE5e2lhjDPaUZ45cyZ60ldpWaZZSpRHrf4AqNEtNVURUKnQNgEk0hFXIaowkHkkAYi6tFMUY0oqpl2pQnYuD1tH7KjSGW8FDIOpVXun8V2RIC5kcG8gkTddThHYQa5AxtNUor5CIhBcDS2FZgorVEaGJncYq2JJg7sY8+dxzWTUCxDTSY82XoMgKvoCRpIFNwNIgGKUEGKu7EwwlFRDKj6RHBSmlYCAoqZVLjUIMXZFjXqRrERCYV6ezjqgYUsOQlC9U5ElvCWGuEVm/SSzblDbaSwMUqKp1SCQvycgBXmSXEFm8MmErrxSiiM5ANs/mQ9wL9G5tNpQWxVikDac412SDUG7yLjORIXBa0DhF6JMYMQ9MwpipQ1ebfdSyyOBIjuBppRM6iwTQJJuGNYqz8J5DyxkBjujQnkwXNWe3KqzIJGB1nXqG5ULoRrahUyrDK4RSgrkuLdSGxpyxJGRRnlircUjByhpWQXSi3JOgD7Bdo1ZbhBU2dXYZAdA9dlFiMOJiyoCtMy1ogyDFU+YhSzDMptIs1yGF7tI5BFlC6UmEyiU9AsHiu3IzgvcoNKExSpQrnQhIraa3RWoJuBdCV52qTLsuwrgzCbqNRu/AQHCcJdeLjJ6q5EFMGAoGSmrljtJmPB5Vk5YJOH9StkqZpU5sTthg0erUVcLcIkfXNHQawAiXg4YNUFzSHOcFyh0Zj2Kp1Co1h0kQKZR4qaNCaXmKubYA4VZxrcygiFbVtch31JDIFtHobLCv0lIYIC+ZHdutLXMb7CXYBkEVhzqlrWGi90iuPLQPaSg6AzemKBxUL6Q9q16FeFa2QnPULOVSQJIowhH0mrRby2KJuOoKZNlICWKYWmUlVoReuDqIlHYQMWBGEZUYGVmr4xxJdc1r47IT1TI7OJ0yQYIa3Sq3hGaKepiHbqEDvWoH2KhuoJ2BV1FTEcgSMkZlJoiVoSglwezzXIc0DavgKIiLpvZQZgAyndGDzJVrIROfWjRTHsCqVhlMul4UhApCNre2tPAcMeYiLyVYlB7/F+PBKFCpruYWvGNsitZ2HTsynohPIsw2I46pOXosHLRYLWrMRKz/2/0s0Ajqgg9E0IISjLUpIVkqmApWHAU6s2IoySRaNEmJyqGBtkDdRNbAhKqF9SKuQ2yWVtbJZMEKdTEstEd19ZAgwCke68Fv45gGY+5o48BiNFdFVSkBehwiw2oogganGMVnE46l4mBlKRslCzBoTIkj0zx61E5Eh4BVDhPFo7cyKTBWBVosqevKaIAk11i6XJ5JAEpIxrGJ0T1WCzgB6xFZMSJ8VIZKFEAvaFkgm9emUoG18siqVKEi0Ja9RdcS9gY0inwWi8EdWFS6YmktecoEoUEZsgolzEJ2rspTlII2HZsJmwayxQobYQ1JAcCPVO0iLDtFrkdkEgB4wVMiu/I0erBkPt7HoN1DH/1ncxIu57cIDVZQJBEKgHThwJGQUOWqL71ak44V/ZrJXo4BqDU2oDPhUcUzVaDaRGXd0FpFA4RCrYal79RoIO2kikaENG9GQKh+PeGCKKncgXZQbbr4InAnZqaezIpIce2ulopSAKbFvG9ZUiptbYu1JyojaIRYpVaYRFFxrc5nKS/5R000O90UsunSLa1B1lpINtjsmo8+wgCViNUIKpeElqr2leDMcjFlvek7DES4R0Q6L0fV4jmPDBGV3GhMJBVCEakLZCkZpK1vBnehoibYSd3U1HSjMtWs+VPxswULUQOpWM1XqUO3wdBkIQypBjFRmIbkYmNws97GhNiREMBRUQVegfj0JXUulkWgshXrQ3IqRFRrY9K6FUs2+8qsmC8Ft1imjJ2sqaRlML8qhYcSPKsluKwqK15EobwjDaLFj07BhYTCSJnSthR+jmUZwf3FFjkXHfRZFlcHMoasizNsVQy6LHTOKTJz6DJvIhvVakkf1haG3ot5RDc3os361HPqhHQ4k3VpOXcMzXXL3FMLOmTKPmkHqZy6xrG0GB8vIbtZdTYbaVxj2yibOaaDc8lesi6I4fiJOWLREsOm0JzfaCrNcpJBXZsanN0TBhW0gA5KJkVG21TjytH7ys0oqQVBRmQuWZS5HbFdMuZq7ptkHuVGSoShye20Joto9vINtlXOR6bwrpFWXgHBJNpbKjdVm7ZqmbKovkxBV6nLHR5WtKmZdAU7wGlMCBUBtYCgzzU5CJZL8Fz7V2qFVHKep8Zoss6spkMRCpYuZakVL0o0PvhiR1kVupI8g6EBwMzzGiv0bRKh72QDOLqqDxW3ZMJyYynTpDuTThCqSCmqrKi2wqwGglkpWX1Z+rytZTCNgUmtqa3yDCJIo+lTERKSFbKWoV4qWPEZ1SuakvQxqlBAei2JIEImnGgWypQUmhZag8RAKsrW+HEwgFqNJ7UM3BVXuYVotZh03kFOQEfSu17dFha2KbOxrpiTgl5aDAEoK82siTYpwejibVpkWtAWkUQqY+rVRtqQmhll38J/AgnRpGW0QqQUdCl98dh6G9ZzR3UjMClrNT2RzRPQGw6oitUFAahOghrpj6NyQc7pW6YIs3YUHWKO9IFGRSVCb9IWKrFVl6WhlBlQTaFUTr8HLItVREhXlMsUxUGZMmduvcYQId2hDKa3Nas1lDzBPGKMJSppEoQHMQ7muTaNbAtX2JKEpOgi3HDJUC94l+xSVAIjLqMNTCn00RtmZe2IiEx+VBUQucJOiDetegcTWmkaUyNhQd80Aw7/Vw+bV9Nr9ctMdc0NExWmweTJIheWQzKJGb88IKVTw8YGKqJqiHiHC5bM4cNYJC2z9yHcU/ZMjZJtx7YLQ8MN42NKK5mYZH1rpVakdVIcbWuqalpmXrrm9zAJhiQbGFtgBT9MXBQOjIEYLj5W/2cCkerbaj+50SE2iTQiJDLRpVYyVUzNbUeN76Z0B+i8M46qCi0SmyfdhSwNtIxsk5FYwBQJJ/1Ysi0iw6M8MAd7CxmK9RAhG2AI07kxOFosJctiMqbuXZn6QnN8tG3WluUAyXKmFQVmRDEdpVwjxIs1aJXvXjEMuYGynLSmIARN0Cyqh0sMMCSAVRN/rLcCwoOm0tVYFZiKqq5TqTJak5Ao5IYlesoKuIdX0nxqibSlabU18o0VJdeKHVrVPZVUlRpmgKirEltK0Opak4aRRF7z6Ns0rmIuc2BLjjEhpSVnzTqtL4ZWN1Mcq8MiXDyjj0XmJXywsZTwY5rkSdvsKMtB2c9FFk9RbsyXnFDCIQw5MVdfNH7qwqpAFLJXGrsRqGwiTaAmsqm+M1vfMpglOaSSAZQEfeLZk0A7xsJaaOgijCExD1YTRlZMaQrJoKITaS4bYlYoW0qjmFstFubGZtGNT1NNhLttQnqkAAxKVUGC7ldnTHUpkWSQwnpAFWwtZc7MAhExyJrmbjqmLPPVRgoiJSPWzQFN5woxBKRTOsVjnaYJgsFK1SnY8H9UcQAAEABJREFU+tLaYkYEOjWnjFqtkNBpsZZNYUBbYeKsKkYftYhv+xIbF1s6aUcFZRStvWqS6FaiIFoisKbc5U6tFKRk5gCCVgIi1XqSgKiSMpHmom6gggoMt1wD18FuLVgKPyEFDSCrin8QZthIYUvwGrIIwgqdo7zgYRm0Q4AScVU6wxgkRhZtqfSdJduQKs7yXLdzktn7om0LzAbue3wjYtTcwtWXSo7ODqXIAd/asoBbry2iViEIiJsNm8omwIQ1IncaNih1qcWIHoURUkpMITuJlWAHnXvdabjyUR2udM8zWQ4z6QHWQ1aVBkEqwV9RLse80MmFMTLRFKkK67BJsBYgFTWRDXJioi7jn8p/1JbWBayrmGgq1QwGXYCe1TJpeNooHxkrBYTb664Z3ceO04XGPREYRyHe81TzxrxZJMlQNSICS9mgeKM4k7lLWQOkVQCFhIwmi4LiCT6osMrTPYmcGs1W28A2Qi0i2mVMWLSiSnL9NmiJDSrtqeoqBfybTiWVJkA36Q0ikVxW1EkjhBaDHIRpLk1sFB2yUbEU9kVNktyp9iacrqVNtMESSKqF9o1uaFIrVRRbKyXDCmJCNQ3xBE7L5ybbSRbJUduMn0JCrMyjAroNOHLhmZW6Wbyz7tbImn0R9d5tY41rFoWKwEoaLQNCSCilBC1taVOs29LifgC11aTDkxnPKg3R6IkpWBagIrLailYw+iLVCAu9ayVWKCc4ijI0TbMiatK1CXEUhnELWcA4KaAqhkdbyCo4QdRcpiV7ZAcEPaQ72oLmJVnNgF6r1kYtkSPTi83VJ9+ReVMLaigcJUJjZalMS06aQnQUxw/PjOC+k4usVrhXLIWAhUyUramNSJRVouAuoH38NYmwEKwosrg2Wjs9rD3Oy9QledCTbm2n0+MwWaRKTNG5ROvgfG0mXLKdDgaEnuhCaITFbtLaoIxRwyJtlhYitJ5yg7e6tiSYM5JC1yolCV1g0PCAB2emiSgUeJxohjbF1Laq2/r/sPQvipHruq4ACJCUnF53/v9PZ3cs8TFwn6l2kipbIkEQpGTXfmhbWhrYBo2AU6uCCrHH6E4RI4KkEWKc3+xqST+4/J8KWxx7G0XCdNg3Rl7K666rHjOa5aOewKCmWytmS6jltSovz9xv/5ia3pAfFeD9uNb0RyXH6WEZpUfL0SBZScdpMWQBt+HRF02vWqm7ihR7dKRbfom1vtKtou+CJLQ4BpGBW96S2aMGcWaK7fYhj5gVbeovnUdnS593b/lXedTvFeZVegY0cV1QTKAW5g+xTOZruvE2fl/Z72HNE/mghVsFN/+zD4qrfRW9UClJHvLS4EH9Hk61wlKsNdbDLFOjSBEI5dLFrACOtlnEjBods+Uz0Vk55+ovVH7eK8rxfSxmLp8fH5dRdhOq7a0cysBpvJLur9d5Ri0j7d8rHB9NnBmi3LQfgc+aEaMoLFlvDcD0APPx6RJWr0rTwVlhpttmdiqZJhQzQqOhJDT6s9qwbvvXd2JKOw1VnY+YwBvTS2MVsg5FPWAJSWa8evR5F1Iz/Iwqx3oWnAxwUTlvoPRSjX2o6On2rtHWNx1t6PkUXAle71zTe0TEH9R/aM3vsSs63x6ZUR0xGsoSBYYSiIpRdHwNo3qY2JztSpoROm9IF6L5re9pUKcwKacm6oNDjpGx6BOr3NSL7tEeVNuOkSa9ieRAl2KetF2uucImVjGFc3XwXrEAn3g8llFpAcQhr+q6bNL4pQ+9P8F3NQUx7OHa46vCdUMLVzA25gwIf1nMKGUj05mW7RqGZya6RlSN+GR8IQJa1phDFHfWxot19NDoF5PVNiVRSCU1/2ocwq3W1K0Sg5f5uIUZTDTAjMvtaVNjDSZXO+SwZVp74F8oTug0w+n8jmVlfCHv0xwhS5m9gWOtm86SQoChaEAl8NlhyfXc9t+Jopk3d/VPwf4Z9CsjVBC5bHyWKzo2LUlCY6aYb/ze/Zb2Yc6AHj2tP4zdLsL4UXGqhuBn8DPvBQ7MgCgpx4JhWsxIiObw/WB7QxHv9VeHQnLzcbtRdxLJ6K0xajOB1+u1lL3VZrB2LQxfYfyfzIq699hse8of8cE5Xwok7Vbw+pPssmmnDajpsOOm0I6wGPmJ0FUKyLqVRaQzzX4Z50Pbot2sYTOAAlXNrZPP+Bpad0+XD7UpFvDFX8f9t9qyY6jz+NQiCvkRkZOjrlQ35zSqxXU/PL7UP7jGd47rhmFWEdfm8vxV0rrW4L+ldfN7rLbbHfCGKdZZM6ubptybJhlSyXO1G+3ZB50538DSebvQeypdz6S7DX2+om8pN65ZBivmW1CjHjSv81WJwD4kR7p6T1dGtX8kxhj19yPIq1Gla6lq9OCj/dgf8sfrcZWXq9Npk6yYs1MtauLAL/2GX9/DPdCrZj4zI4xyiJqTzAFZLvszt7TIU2DCR+1jNBzyCrvSOXHx79XWUP3eWVrDqtkjtC0NGaZmEoNQ8WWIJtJsVYXOAY0eiPnfoGwo+MaVTR0ft84xYVcb04LCmp5pRU+jjHAZQHZx3tYo1MG0dLqcyhKHpWy1TVJh+bH1F8+ROvKrtXaWdQ1wB5dWWDD/jNfMi7xzSsEzOdp3jauOGEpNyt5llQ+IGaIjcgeEaMIRJt1J/kpZyQK6dYGQpw/eW1rkfLg5Md1Vmaqv87QkwlPe7SGDRm3C6w/yP0hkH53aHR5nw132ekjldwxmbV0mxLpyt5Wsjp/7ZHrL/tybgmCsR21gLMeOGFPhKZcSwAoss914Xj6yqoTtyZ+GlWLDINsu4tpavRZNt+OKkWMj0jKB3/EsVraS88kGNex0nG0HroxfN9FiC9Hv4OiqUIeZIaXxRUbrLeCImG2tVBW7zWjCtkrgivbOzlx9CyjXOEH5UUDLEBuhrx62t4CBPv0om2zOYoc0ELQ91AT3ZbWYcuLC0FCbHvXfVe70tL59Tx7kmrsnl7Q2yu+4fz3XjMCgr0MhebvZnf1e9O/Eaz8tKo0az2sFXur3J8/2ONvvw3rGHOEU0er/9VShIrGTLsM8Mm70wIftKtznjpdu5EKCQig5pG03YqaUgNKCL6E3LT89THxWtDZPqiC+bNxGNcYwzmH63J+ZjeXX66w3VY/L1paNFRP69P8aW5yZKQ+zv7FW13/VC5Vff6lvi37wZ+HP7p8/ZYIUd8X5BCuybd+tXqiimcEaJZQDdkoV0dzjUBCul/2Qi3C1IkoOkiOtAQ1U4z17zVpBd1iLnoSiaa/xzjUtdrYel6Cjbtj40/39H2FaytU9naViwA0qgb2rV8pT2PrRP/4xoVT6fo+9v+t9dVD5K3Xgy+dQWZS4qkWYHN2z71+LvxXvVbMaiHTFoWqB7sFNG5n4UViwsAd749mMJf2d4s1Sjr4P8GFc/Hn7Z/qPk3vd/W/NHKpRSiUom4QdKk50671SpkcoiWnlm/ZE/aCXhr3PyXgxtyrf1JqQVWyJMQNtHT1B4+zOSDHJQUCaMEP+hzcq7SiKhIopMqxWvczLq/LlSsRtVTtoowk+LdEAvEYFY8TqtLdlOCvKiSc1r473r+Gv4ZfrxjqsOn/nf2JYlRV80WqjF1fpiwjnQ26VsxrHSLRIU+ReDaS2Htmj7mgVJtyBWS/8FR2Kcd7N802eB8NKDZXNHLW7jVEV9eg093E1Mn9VAmo2Lbuy7P3RF7Ws/6hnuCehpvvL++JNfA+oDq9AsKjXzHS/7oAtUE+x3V/uX9oJjlVLZZp3V+Z/7zyvW32KnfUo/zQwZv2Jn8djm9qTgy6YB6ajVvD5WfGIf79SlJ1cvyqx4+9rpQWVCONqKvmpTHtVqCLDR4c5SKhu2qj3K7C18NRdt2NgNYImG5NTXTWQOlS3+iYE9+/qvxvS/qB1uAmdR8jYIY8YhMqTi4/3n5J8pLYRhmA+9v6x9LDZXou1amyY5LvEhghyfK5a+5k2G/eBj2nclZkbcyGuMWzvXLgmpFjJ/xL/0RQCAy0VH+LCugx60cRiI8AHi3jM1Or4zEgQfIm08kwJ0psFTeRhNq8BC/3MMXS7ECLRkHM3vcCFr5TCIBT19DcehopIGi/jN+23u0CXwzDuxroKJ41JBcatT0oSBiq403j7Gw4Vapn3KL4Vr6iibfiSJOdgjk9hiHJvKYLK5B6amqG0TJOfn41/Tc1cY2q6Ts/v8JgdLZH0L4bB1oEyE/cnRGYR0vjRDsYK+/RTHhq0/mD94Qqlx7vii7IGA8KDUrRYDc2DTnxssYcluppe8jRN5Gau0YI7cZpiTc/4dkjOP2KOxiCetnWMgD/tiySCvYMbxmM4pthT+CSTNiWBEknLkw8xGg1Icwswk2veVJnzVxK2NfaUEE2LHwPXL+1IO/KC/fjWg6qf/W47jjRAuRrH1yoL6KnERbjvTYX/f7GDQ2Wrq5nvUh9kOZw2qSX2PyLgQGTH7YB+qlePAcV5F2sBax4f1fi+z6p/i5d1iLfJypO/k4dU37kRZWZNy5GhFiCP0UDMmBPLfLkRIcuDEi4PGLrmEr2h3DVb73Upx9NlZxKK734mxfJ+1n6CCo1TI0dSnCYy1DvVY9vKZBJi+Mw6clRyn3wST1rcJ7WC/F2T9k8Po4HR6ed6/rqVSwelSIgB1bke9s+BIsm33yuYmPHBakb683loXL79/KWefguUireoNuQtl3tJDD+Wf7qfHu+BNm92L/r1EX0hXlOCX2Kj1h1TLZ3hWyZXd46yJUT9LHvC3ACFo/pzLxEnJCP0YrGQ1TfyxNv1F78va0AtOnnzau9bRvjHmJowACXljCkqbf2icmV6VjTc5AQxJyaFLLl/XV9v/VxzDdgS6LBYaTdMu6j752KdXvfKs3+5tMn5ufjJNnROT/V0F6pUnDrWPw3RMPEO86N4TD3qPnsYesslk/CGHZvfKA0aVceo60lXvd8rm//+MwCBeFavp9sqa1J89b2dL/qMnwkt6BQG/LZIXe+sVhUPlKEfu/ZMP0hrKNKuNTDvMuGW6G1bxOe1Rjvhe2XfLNdTFnUzrwbukZY5xmsq9mczvjLLX8//xbxUAtff9lIRPvPH8VO9SkHjrNRfveP899X2HTvX63DSUTYkcJFJtQmFYNpWXuxfM636GZzxPdranGXH7XosPo89HsQS1Vf0/G0V0swCgw5dMWUSM9A/0kVzZiMRN1YrX3rS+/1PL59prVEXPvOD79GkbigamI+zZWHKQO4eVykdIqVwRf5G4KuTEiWDeYw6wnrxakFJXYofRvgymj5oiGE5tsUWNmjLO/tt7QxZQIfhGxlKS0lRiGq97X89joIK3oI++sv1LlXiC21aSiratv4gnk7d8v+3/b/Yyx+zB9T90IprRgNELb66HEiqSM6BpEkYRYutsnAXgNBeJ2rQ2SxtoM+ej4g7I09i0UqIOxJSKWULulrp9/Uq7wFbSb4/d5Z8BqUr0bFxFKEYzpzO+ezPR9JQ/2zNP3zVPI1P4fpNlXWl/v8AABAASURBVLgsbB+j+wBU+7bmQ+WbWFZaZ91a3puwNkuMQ8T/Wt55YdeitSqFm7lJr4hly4dfUo+8f/SbfTmtzXygKxQX2xhSGVeKA7bhOmorE+xi9yV04UsYbOI7Emhhw6c5lE+7ot7qjn1EYMCCCsQFVIQ3ZqZnUGNJv1+YSkwvDEiHOShRBdXhaZZu3/lWbN2SfzRjbFE9I02/xwxrzEv5o0pMADuzswcyiKZGpVM7sNmlqe7cYWsxw1REr/jYTudUqgiX3PEHwqRN8iZD0NtmCiGsLQL6tKgVCvvsC7haglj/5ALFw2ylSBMHq6Uq0jHGITzgpsiWhynYVpg+Su5SOAQ1udGFLI0xEk3dx82VmmgjSyMYS6ngO7hq6m9WX/hoatFKxjscP/9UoXMDZZarCCVRUzd0WTZESJtMTAMFa6dsjPUdpk7RTf+E8PsowmlECP7I4733N+VT89DevsatTVAzUEowzaHBGK8hZKyhU0YzYjhSXOlMJrOgK2KtG5WfABWfcsQPFABXQMIzMDLGHe6uKWUt819iq6Zf+F2Ynb2rA835LZsrDhTDiL0WVEmBLDKBEzwG3Q+iVbsZQzZZcuYKUvpW23PDImwZnT1zu8uFlNQoMD1hR67cCHKgdGFG6TEf+2p4fWAfvTWnPcxl6Yb2rSPhkuLxKU0uDx1pyt7pugrMFAAIqSfV2Xr6Kilp8md3Re0lSLCCyGcPe5svdbnw7Qn+Gn7dj+t9wNayn+2xFtwb6Kx79Xvj/a/FmdjwptJ3l+4iXOu3ZFaWpWfr2vjjbVxabzDaUE0IFVzLxd0LIkcwrD7OFRtEIfDll/qNuhjRDDGIT/HxDXeUQ+2zD1vdYz5MmGx2jSIdUUlOLE0g2ucuQM9GLKUO1jhAA9blT8srHFN0HYawJjMb6YPvscxlpEEOEqjCTZt2tBnpmAAM6EG2aeRR7VrMZ3xIUKOakzPv9ZtfxTymbkGlkrEwI2PmoH5uPKXK/THlVi2EgoS+yMRtvDO3s1LrhWABGHxO9RukwIBQzPTsgU7+C7+gS6SQ61zpRzxoJwxBjtlSkczooNx/NmnVaMU19J6PBVgPFXKNZCkK1MB8l/+0egygckoEC1Zae+6SW5hc9vK0fYjSXL41N5EjficpO+weKEvfNu6aOFfqBVsD1QcV4Gn27MHWsNvf9FYY+LikUqUYzBqKYY9p2AjfbcVrLY/W4D+0It+XPcvNiGtZruL+BfM03qa6HEc9VP7Ncz/9PTmScrV8j0r+s1KWtbIViNWjuGZGTcjLA+GA+hQr19UY3Z8XurpqCj+FZ7yX5fbSMkka9QNyFk1zQxeOQf3yTjSXrmgytA0p1YSbQjNAypAT07sNZZWyfKEBRZ+wWRDK0WUalnMFGVY+OpuamyMtYgTfmrn8PpOhz5+BD2q0tn4lDjmjnCdJd4+gOWEYATm7x82vjJQ1IL/bNCUdh9MFqCwrWntEuK6PXWO7GaWP9aR6ASLGhKzM0gYsigp1fjjUsG7NQGkS3JG6E2pZqiZ+OBZtiWLpsYYqm+pnvjh9V3j/AKowmYsiwc85FMeHcvRCoU5Va33Piuzn/MOIpAs89KLk0EfeNbJHXlqkpFU+09vzsdf7yjhudNpthYqRm5wPjvs1HygQKiA49cHP8BfIxfFQsxSXIEopu+gu5vE3/dDusorHetu7FcGgoK1qtFQFkhPWP4RyJXRD+RwKMMY9YzquhjiwIJ6jy6bgVYsIo9nP5J5eKatCrdURWV+qsljmpma2Ew3h77ICj0b5q5aM0ACqHdrO8ArAR1h9rqndqc0aLHyFifhGGTxmG/Y4VEWkQ4fhAyzjq2n0gRYPypDMUmmgp6D1+pA4QX7R8RvJohArhE8gueKv2anG5BrEABq55nKuYsmv1KegWihi7Jv1x0rrtc4Mi3GWWBqnJqY2Nfm+82aXRuOr20iYKpkwoOcTbWEqE/d4qe/M01xXQ0IhQ0L0IS5QQkGpoHOkLqqHdOuKEFC2GjMQL21tzGCTguAWP5RB0Y87uD1q4Rhahe4kQ4+zm5WRFcOGNwI1n6URnk82ULfKrO7UJPh+HUnq8HQogkU9eedPqyiWYZm5j1YWLeCS5WeoLvsYATBGeZ0VoMgEfofnWqW34GyvJ2z5IyAqaWliUkWiQj9rymum2W61qoTwThyPqxBLczEKi9OqX363VcWv36tyxtjEK28b6X3O1UPh1vBQkDmn+S6rPcrKxjzVu+7TxztRYMfQafP43Z6Pi1eIGxGRMAW0B7sbDcw42r2g8aGsaSh6/856VVr001a/Hr++T1jbVE+Oqt+Tq20ZqenBIZvL4nFXoESSVTVX4ZQuAl4tSrr1R2Yeenwq/uK+MliS4OxClLkxzDzL37uLT9mTZvLZCvmMTYbAOi0mrLhQQZip4/nyBiXzCybWZ61MUbN9ck3rIeFycDd+LncaCMRgEUFFoU8aPOcLEYHvNpOqmxT5v6vvIwNlqxxo6O462/n4eqjin7wU+3/m3zM4hb5YYaOS+J/d1wqSd0J3Ka3TJZYpTrwjCPe0pbCOaLijjtfr2wRthZTBDPNYa2/96J2hjNomUXug2fA4IspY4Lz6dcxGOe+ZKfnU2A4fWNJOPKlFetar+9C+xzpj2ERZQ7Ncn1iqUKg7KCs0H+6C0a7b2XY3wdR4tPQsCgAnlwLQTHUtpErjsbWoxDb4VWcoPUnPmaPHkbzHr1i1B88Pfv5AD3yUBElEEAgoHPhjBPEXnqixpAmLsC7mWCs1OTXuZs4enI62JxBPu5EyQXRUT/t1n1U6GVhh20PUaHcYHJlX2O9ntCwaAbkQXdkGOCip4U5dK8lm3Np2czVjYEJFjavuhnJlt9b3tkvMzKxeP/bj9lAgCoddQNZUlTJCyDMDe2F7BVoGPtfecMljYxbHgjTekWT0m6J5JfTNGv4y77qzWwM8LUrMQCxQkC2MtsHHLNaMTwLE7MvnmF/4QK+RMc3SJFHxcH7IZ+Zn6pn7w3ws12CPfXzuCUN08mad6df4iu1hLUCPouTI3PL6/BI53mMsKmF9MC/1XnH25pnJgzfnAEMiiFUTTTacI0RTrT+iJnPOqTd7eqBLwhyuoMxPTK8ejbnqbc5Cs38hm/eKY1b4sv0zezVM4ug05aQgy+oDDYCb7fH2n3qDqc8FxRF60zP3Vr1jf2cdhCZal93u4oUd1dsAbUZo+FJ6co+6koKFXrTRnBo13AUz119lzW9+KFek64mTGrNBOdYTWYOGA0O+akITEDK02vB3AWVUb0mvg74FHFeS15SpAX3+lnPvrcmfXC+zeBrNHKTUNbNNFkSjPqh2BzLRIKhQoReTuvcwizHQ6PB91/rfhtpTS4otg6qC/oiLmQejBxfE7VJOTq8sF+VMkV8Tq9bPWT+/m9fQKLaSWnCYKnEGn35ZbalsaTN5R8CUkZVn9btSsJVk6IfShDHRGitGcj8X/1WKtSUoAGzcCVMkhXzxW+tVvwzTytx273SO1PGDUAHpQ04GUu1qLK+N3AvYqG56CSULkVipi5W0tM8M6qxXx9hv3Hcp96Ms2ox9jq2GVYpPBfbHVQCiQ+mRZYfp2LN/IsKNhFzROQSYjut25W6exE+7GpMsKvkFkWPZSv92yqpP2T9stIb+XX9fu9nVU5T40CMgOtTD1kI41XUXmnhr5rYffB8MMLNe/N2tRyIfCmA+ir2BpogamoVrSQhIIrHSfn5r1+VAEAZBYxg35w9S20LH5HjCKAlvNQuqfb82eRmlNoefddc6TmrYAZtfxX1eZi2pdblCRaYoS5ujd1V6SqSlx9SS14+vZ+lbBlVK4BJKpjpNbfsk2jDQn7ue3x3aejCkAxoBJ5bew6Yq5x2eHSnYJsc1UbXABWAGnVX17ac+BmBFEhV1VeQOyoL87vq+THL516Kz7KWqob78zS77c2NXmLZR2M/IbOHTRgvi4trb1jJ8gStZFze1e8CLTiiElhRM3d5XqQE0KRADd6w1/kybZO8V+zf2u/+o3BMsFQ0PIDeUXSIayJmcD+6mbfw8uVzkCAj9NVFe3Ul53ml/KpZ5/eD3v3sfrSPJZGGuSWQ9w1GQHv24hNIGenrUsn5G9DTI2aw/Pj/o1dKQ1Y4JF5bo9m5jud//Y8Rtca275WIGx+ZuawSL0p0ZjRrHaUqAy8a9CzVDEM+a9TR3T8NUJe9ib/RCGlM6UVPO23pbpU/VuDpvkEoRdA/4HotEQ3GXRt/Ma/VuOz//aPAwN8dEtiksg/0AayX2qXWvGpFXZWq2c0VoayCgAMaUqLQcVnu+oz0Gzwm7PyERGEeXhBlIld/m+7jismrLw/5tgVX277l9tJZ1t4Jj9h1eeCHG2GtO8EQUl+hUbU+gdl+c1IomAmjz4DxayECqr/0s++NG4sCT2pHQbiv00lIKq5HidIlC7a/WSCnJMPLi9saU9Z36EOUpXn00A/zC9K1UTkGkzgAsRj2L6qQ/FSvdyq0ljogOSUB8ir6w49VxdVXysNYLVyR9VlpAhuLgqJCTBmHeZd7R9IrK5+IprOGiu/KfchOKaA7rtdbd2es8rpLu9zIveGvn7+Zf86OEgOjFlpRH7M/6is9turSIT57kFYRSTCoYDK1iRFMneX68Hge9y9AqnjSkIxcGQKdP2vTMyIN7E/oWy0/ZjFsvliwMWlrENWUUWVRJGpNxbX+av+M9QdGeLgSjbGBMrl/YmUA5Wgq/f0BtvIx1cM97Om/g+pwPasRZ+8Yz3EjvX/QLV6bzZn98KHCx+i0A/NSgMIwyDe/u4u9MsbGTClSSv/JoibkZWT6K3ZRVjOfXMxS25qOJJE/7aauGGPARzT5j3ejJtBYpmG/RGAV0gSL/EaV6S9Uzr5fcFKENLJdIVmEdJRHSWCU9YTIFOYKq2F0KwJ4jjiVM2Kx1tr7R3i/s4/UYj33x0dojERA5B93TWjJR6ARu9ys3+SnNzNy2zKJnqua7rhgIsSEB5FDLVfKn/IHRtABkS/+rGDa+vvwKIC8sLSCtO0cEeML7AyIzAhYpBeCQaUPPZfUkduszheK2WMfTFgOV1xjLBYCL1Mmnvg2IGyz0Q4p8VRfSTVlZOJOFscIHKRHX13nW+S/S/Y7kg5TsvyejhCRmJgSrGd12cy4vV8aPP09s4oHH/ICbxh6O2sOUUaz+b+F3VCYX/U8P+EbEqmD7obT2r5Cr6kbmn+kV407DTAMKnl3o1qeyVsVmUYZM0sWXRLMvOyjgYGqICVHFdKzwFdq6ujmBZbkVAZf1hihnN+fq8NFLfkTODvgCR9msnLqmHYPQCgyh/C2+bi+obQULvBv5sEi7vU+u7FAuB3r5ZSipmabjmp8NlYW45mA6eU/XnXnRL/O1vJh/80bAAk0j99RGKTldXUzEgS6Uek28Yy97uikw3xMbjg2fatedqlYW9MQkPj8YbMQDuuXy+W/myRfve8+cK1usjGit6yRRM2K4uWZcAAAQAElEQVQaJsAloBzDwPrauaj+KFbpVfya35CCpa1iZVeek2VFJx9jSCJWQEsztSfn9Mm+6HFR0hQ9xS+yRKbaxLB5MU29cVRAvWxYlq0v9NTs2+w6M8j4lx2PnkWdbXh+lOv02B48hEfQI5t15W968nSfQSPQbi2I8HW5SpHyV9dnRmigQHGa+YFs7TgfPOGBzVlIiEobidIMkhA+xWlrIsYojo94HPUL9iMf6jjSxV1xLEpWP+tnRhPDGTEBBiEcDetWucq/gnc1/TN2GbPCnugnTsQNbzOVw55YUHgcKD1SqJkbHBgpvnLxffyXLMjla7irVVphbyA7q6aBHmtoPptWjGplsbcWuhlCgJKeSE6S9PmQERo0HV3RKqdEpIbqA6mTJ/pXI4Cy6phZVMEpQjFU9OueO9rWqPBoxsacyUGG8it/kFMA7sugxHzIFH4tVvhwtCs+dCIcC+3TuIORIfjmPJZuqYDUPgov5m1Z96mY6zjwalRO3F4XzMhc3SbD9lStSivY6zv3TuxMVmJS3na4RhWhz4PPv/MLWeamhdbMoVio+DEPauuNJdE0XaWX+1kuZgrFO36WjdNhRMEvvsq9NsLUXLBFa7ODudP1L0GzGsw1s8YM1MvMaNu4TYC6veE9BszK2UfrBegdqJhmBJ9tO7DCqY4fAxs21N4XuZ2PtU+VWpnNB3jGrqDWyPly3yvEgDbyy3U1S7UBWMtCyIiMccAqSrKzDndFkAKTMbpg33v1ksUQkob7QAGWd/f4HT1FWl0LHaor/c5cfXydlnFeQrRDTGbzWhxKuTWFKS9uQszjh71NO8lz8WmaQBD+hbKQ23Kvwo8UUNndp3FMWxpjQQF/v2xhmzmbwxoVO2ZoE96rRy77xL3BiQ06bEY6kvUwX76I6ObJfKkEBri5oez2YDKjtHe4bSnW3lKh7J49jEKMCagigXgHCVuUtfD5KhomPWgN0ELUlIw0FBhDC6ZxBdcSWzNS+/218yuAu9obGnGBNCuTq2Gy0t607CD3o2y32p9SeBVIhIVuNZ5n7b2ESjsenZZCVrRKkxgH9XYzgMDoRJvsiesLL1CtyrP5m/bbfseaM5uzles1Yi/XpPGKWqe0uE5406CJCsBJ03qXeXzuulSnkg3JIjJRSWH35IBjxqGXlKXM2YIbF02vcCErn+qeA6sRb2Mv4uAZE4qhv/Iijy0CjvGU3WOakJ2v+rdXW1mV9kwCCDxpIerBmkjGYf7y9xf3nawqFVO97FPQNA3eHlpyfjjLPGA+3yQ7br9B+U069mOPBlF7AJU+ULPe0MH6OkSJDIA4xCX7+zQBmGIxu5SiUhzbzTi1UXreJqWKZjWryMs3856eT0XdzNtXQuyq6Hw0521MfS6lxQZWa/3vMERYw0VFnulikPI4GpFmZe5SMFcpyCOjYuH2Uy9wUkT9s3iC8kGKelhGi1H5ZyulIz5lCUAaD1mEzOvo6Ssbhfwd7XDsqhF8riXiHpfnJq3p6i6r78+UjTRwEZ+BKX74+AUynd2lkQ2JDJNj3hKGWvZXD6rf8dvrfs9FrTWz5K+984f136ofHxplunTKj6Q2/HW7y3oxgdKUQlO+WI2bAsvhDpWWtmW8AixJfn7nUgNtDA5YYBDVj6IZG7XgNl02S/oVbsjiNqmwk3yBi7koBTPdbFhCvy9zRghmNKPm+5a59FEunI89GwEVh4gxuGvbQFP0qew15MSLMSYlunGP+/VP0wM6fJWErfIonpzf6aPaWo1wcacWzO97VYWtDII4bi+noByd8Olld7uy2jmVLHXr+XRIk61bxOjxx35QjhYWxBCYAhKK94ZBdFSH7JfWOnWIcJHVMgdlSki5bJamuStUEx9eWOWPO4w9WgoeCBf0EjZ9OTDOYdKqR4uUKc4sXtGHDd8CXBipf+JogclN/GyXrQ9RWsHh/Fx5bp/t9kO5G4XIQKvQTPjhBOOLOk6v90vXO31GhQGQTt2/KV4NaqfKtG7iqCYHCbRJwPpT+LJLpCCOMlwYdY9GM8pWuwUnAFcHEGE1Cras9ajsas/ydbClESAxlMEsU6mN6cS4FMQr3bTftJmhnbDZMLQw3sy/F+/7zPkD/FFT5jfFPyRJKizlZ2YwMK/QEcddGZSdf+a97Af4sbvkr7fpiVP7IoyyP6/3b/S3kVRggfrHGZciKfA1/ILZlsd0P9M3qTS3hW1/DDs/1LfeCzWvbKY5/I8ubM0RxV+oGM7IndPHrRQgiHGh1ew7YuJVylQUtVcqtQ6YFayL2jp5QdMbGNhYgH6NB/NujM1C/5jt5TRPmbqJHB76MZ9lvkbbBR1PlZXAUCUrNQkAHOUgvUvcdp/W6i9sA7UpPdRx/oPdeIpM5sivKvtjmb08w2GESATMqKZE5B781wiAA4x6jtX3tyGXRoXTdCXFZKStAcSEvnTwW/pwW+2COomGuoppURitp3JBgvLloxow1jKGuSQtE9R4lx3k92GgaPSR8s19lusp3UHXTPdd9XeXtCjuH86/fMgsKE0tdhgGvFMpzEuGqp7As+2Baxm105CZf9582X4oDHc+8a3CukTxVs5pXq5c6xogM09OJIasiBZP5+V7s/VyaZirK9T11xS9GPmVYsx4H+qrsxZXs823Qk5jE1TW7jbpVR5HnCjLlOgqwQsRZCMkHul2yrOisGfv2sz40mU9wJ25LbhHdCwRB2litbYZ+cNeApstiDl2KX3/VO8r2jKe5J/s0AOZERAsSI1ONaRNRauG8r/ubEVbbZVf0d87+bdQR04hLKEQZinWiWt2Q3nq5uR23U6mufrJOXwLSowWGPKgJIvsofiWSBrKAumfHDRIUXQZ2iBeIG31rrRBj9j0MWNoxjt3kIr0DymVAOBAAXui5P3A9EA58ziPVCF4UYqMP+MuzsQnCGlySjIidSrIbfmgNsaJ1+f3K9ZLjkbCNNgwWmZ/1kQoXFKhPubbXWnYtN3uqU71QZ6BXvqtQ9Ool65ikWbWISWtAnumYElVEfbmEwOKzSLM0nSftHviOwMWkGhSDqAv0B4aBzPTZ7q6ZWTJAWMFdpRZeeeqFP2z34yEXk1ZOG2nMdXTr8T9jqdFBoUDnu4XTxrPz7x/5m5K+A1FCr01/22+p06fYvpTz+KuHz97oFJj8BEpiriZ0hwNYs7bSVcYgnM90yFnd+HrFfOF3oC1rxtxCCG6/xqHq0JVOz/93TvkRZaLnEEOutKLrqW7FZcY4fyp60dksLGU1rB6bORXWKozR7uWBAamPBjOP+6U5/+Ma/lQ3wNMnZrTc8a6N1wH+Lz7R/3bzamAy16FTwxlJjj8zE4izNaznp+frT/qIxygxLBPm2HcWtHL5zX7sicIHNbgujwlhXmmx2Qodf5wjoSv+FWLP5wnfLmPbKoK8VIppMFWmj53h9iTQHNO89esnvE/s/X9h25XORXZlDoIyGaF5NY4ZX2NgNt40L3VKhfMEaDewurou39tcZ09ZiNKE8I3aVOWzoRgGJL/1IceKFTIHmd3LTnUHD4gUbXf+XNGa7UTYnBuiUZZKGOZ/eJR5he/ytTnvOpkqBnYNf3tnibYkAdRVDtH6tZd67leZehnaLZaT++81RyBF16I8qfhUCWNbP1Dh3Cq8fewEvPb/u+GMI5KJOxdPNxv/ZkT0tgU0CJkqZwYCnDO9KtZNedGY+0H+jK3+V50DXsewxP2aGERWy7OFsK85uuY8uRXPhW/05N57RdzOY2bbRrkkOZOSEyvojTS+9ltFMUYkvjT+NEBMO7d53gpgDzs215XgUrtMF3mDWt5NhlUaQ9fxC/FR3i5Ooq9iUzMbI+FZ3XEdX2l+b2v8AEwlXZOtJ7mCK1RCef+TDpjHO9MvfTzMRyi8qR/ivriycY+/dzmeL3+vqhubr9iYU7zl1ZOIQbeQcnY3y0xiWDT2XS/z38wLRI+A4XP5e5rKMb9SgVSHVlc1yPpHOyWfPBznud376JD+GVqEdHss6Q+B31M1lR+FeReNGqc++zoFazF84yEizUT8uoqA3WtIXohV1w+jaCNPxfbYray1aNmHfPhi3aDtgk5PnBSaZDwVvM/ymTFGm7kM7luxYEcIoyWoMQznfFm5DAJbKgOytEx8YMfct+ZFL3XovdSan94XdcFV6UeC0+Zn4D20WPm3CakpBxA5lqtUkw6atn7g78KtuAtkgA2UOhL/vo62zFYl+LVWgoALqLwZ7jMNrCzcFkZUMhM1Jn7C6Dtmb2xDNQLRC+U8Vvfs6fKX64jFmZ23uc9T+daDU/t/9+/1InfeZQZRNpT/jTMh0zO4byXWnCOsG9IskhbjMd9O5fKa9Esnruf+9jEcSls8Nf3/xfPL3Qf5/LlJXPVPIVfwcEZHRqYlb+lINe+7mLCs/irfo5rA/FyLw5Ip6n4Pxa7J33SctQH7vvgPsztTREnRuO2pTUFK8Z4jen7y6FvuHETy4pTahVwpxLH3eYdX8ZcRIZOPr5+wpYGK1HmF2Dm8/trf+/Hl3Vbmc6M/TC2JAT1qTtUIDSzcXYcuhSH/af5AKYBcmoln4pFgQKv31LLKMg9ogN4ZtnIXtxBtWHc4eDp9b+yF/wVm3yOxi9MfJdbZf6POVKcoCOw3VQjq13SteQctAjjHlcLeG20w41nx14Z7CV09+f5+zO/WrftN/yvPb/+5AqLEgJm9ZvzHva7TadHtAF7MuZMZtbVIRdgsa1V2V8hjB/wTlX9ndQFN+7htyA5aY7y0Ro/t73ap2mYFeWrRyNVdAAI8XE3KqK/4TOvAjgl8a6ppauGDQ7v4VGQhRGzYctcZE7t6of8YzI05+Q9XcURo+YU/lLz6JrKFHhHEfxTa/dimQZmeef0xIxfg+6mldwq1mv9y0+gmKvymUBh1RmeRMpNzi+sxygB5OP2867nf+5nG9weqnIohcz0L/pXPSXQ7lfNDSQhxGGxF3+21kiKnLftV9w6jFgulYyrFWCbwLV0clOXoLofUhC75T3Zr59fvG+99/5lt4scfJ1TMvLf4luTrZ/BJY5+56AB5Nhp8uZWdXF+Y96ZKgym4cW47bzh7x//feI1P/NtPTRTy5RXeedCPcrm+IJqSbQyrZI5WevW08WWMf4DhNQ/O6CSUN0i+i67bnn53q6uf+drFGf3RKsq6vj8pfWGBxbSe9Zp1nsnz8yv5fihQk6/Y8ZZlFSFGkVqsfvfiPYv6gWunhDFiszE/hekWFEsahFhpIM/i99/2gvux/k7dXoqif40bk+FyYOaQdmAeJL7BYe5VVk16/Z0HYxmDApzwSawNZRQvZsod3bwDI4uAG5Q5vl4B27XFeBKqIYpjxFfVQ7i7W94fWb6B6JiMIV/+Occ+01/q1OJyZyu1gUnQ20DdssFp9DHNXHxeWgrs06Wuv/rP33DPpx2Pvx/kj8WyiMMClLzAFKiz1B5My/Vvpb5s/c2NfBPTdA5xwyy8rZWJz/2cP8/j+cxhqfWwUaL2wAAEABJREFUt29jxt798n2Vj5be1tK9wvV+hfFDvGEyTHNlSBu1upPJ03OzZ0oZr12CGOg4E7+SfUMu2b3QWqV7SyoSskjdhuCxqwMrode11JNfm/OTr+G3voIQqY5rHJn9HXXGTX+In4IW/0Bl3Mu/7/ye86pY3BQwiCFH5Z6uwmRgXCfEtY+hReRJf1/2baDV5mj0gaoQJenPy9sm7YSS0YnsSgMXRn3HlFUMQpddcXQtDhwQJdRfwxgJxuQPYbbKRK2WePwgn3Xx/LZLgcrDnrvUlta5f85FlUpQOjRpTFwpBMAYNj/Ta0rJvYvXF6DA3vf+vv37zvmLVC/KVg2ivN6VekjG0AZgQWzaobf5bAKKUAJQwEN7GRdDSDDH4y/XtfZ1/7P6D/XHxj760N1qMJRL7447rDW+IghD14wujju0VG8VdQOfSXhV5Jnqj4oJ9r+lhGxSPute3Cv5KEejUDCAupTJFXEkiD0duWJ2cK3wMGPLmJRm10L2IhCApmMGo3VhVmErJTS3Z0v1NNP5aXZQPozz8fHTavPmCgel7jBRXP9OwaaRgw6TbV+lOZMW9Ty9HFYt4Ka3Q5dfNFC2JlzzrI8dHdXy9tlzZR9AV5z0Gli0u06MVIhlzUipgQjLh0d3sGaKa93ZDbo4G026IyHahK3AItjb6ifqD8tFV0cybs19gb/muii1WSmC8tAhQ9YDLUUiyHVCw7MXJ9bgv5kfjAjEv6L9ynY+l13EUDG5m1trNholtL7abRbbe2JUgR0aS1Eo1pTUKI3TcVYTksK6j5idJo0GM81OI8AvFbJbLEXhGGHUIPU3sanLA0/49dDoYY6XyknnFf+2HNPHniHSP9VqNgIMwCCNbNqCddi752o37qUB0z6qCdjl3PHq1f1AmRMN01a+JH+xx38qg1G9VKlHto+5k9RMuw640U2Io+YpVe1lTcqCuWaBQOnoEj4NImQkrdQAalPvTaEpEF2tT62uwd9Z1Bfw63GXAlEhqbraOdBh/GeYijpGzNmwS3E9OX5hHweibwAIwhi0TtUnLbNSMlMNWywm1bdOrexAPxw1pF3OtGkbJbinVcRg0eCKVn8+QqEkJTTCm3IADZV3cZKGS/tNF5ly7WPrM2QlegWPB9+KMQGxIiGQJmQVBYyGq8+UBT4ntI6V/jTcgaUfuTcRrMg/EqvQZvPY6Ou+0OUNV4Y1QOK5gI7WGJSjd86qqyY7oQqwF9FlzI6+H2qFqOyP6UWRKm7p81npVo/v8FIhspPd7onA/IPiivw638D72FnejiFaU31ysUhFlSIkl5cCUHmQ0G7afnKrnVHtqhSKoEZxQ9VXXILaMJTQil8mXREYe2XtS7c1m9gIH8WMEvFOPJho6Y3HeUUEM7Ko4FKjzuK7Vumez217+pPqDJBE5qt+dM84cud9zl2qoFEQz8yH2Gfb0aIU31AJuJsJ/R2S7h5LvtE35+vlSUC2sj+/wqeLJr632paikL7mC6U1tYMVOXNlFtOgGsXp6Y8MJaZIyMOUTZuuHpii0d7QExzJjWj26MCgi31VjiSlHAlhF3bRdGXyMjMAe6x/vCOml6euTYdyg5E1ESIi+QWSPDoGSrTGdH/s4t9CpoHZ8/bUjNEsqNMcFp9SM1rT3rQq4qPNNYSCCa081e/UYakNN0jIUVF/aMvm8XzWMVefRjfkTzaiO6ZgilreAOlW0nTrQd1u0eaUAAZyOF+tM3qsZZi+fmjW7SDExLJGuxiofiAZ95evkDwW5F0jscxsBU2JqfmYlE3NtYYN/PuB1NfR4t1UqdG9Wr2tOZ73H/E3wWPscQHimpF0MLekahmcz5rIHFZS2rGE9ZDKsqxawGa82ivRSjeimkiFgzWqUuxMSltd3wDiiA5Y02zDfsZiYKrYPfYMFJU0iRDOIWqFZCZLxA3kj+UPzp88O8vF0NQ/VG/2reRNyzLktpbBGgW89E5pB/RLwhTOUceRBF6oifnAI+nFFqQN/FByX5AnZHXdlKY/D3vSJd6PIXODSRZyVJjRO8UKnZpFrOt2Q0pp0ZomdJ91+IzLtU0bL1fZ6oWOmrTWDjifm153cA9PxYWVsiDLUPuV2yop4NOVlKhs8rsCxBo9JHFlCpJtD95G3i64ghceU3mRo4xquoB+DQ/TEIIpznc14TXsSSg1GgudxmiQaic1laBUGhwd5gm7w2Tf8IzVo0dwG8Vq010NZnn/Xz21nDg+VLLRg1M8x+8RguWx6DLlAipXcMyeu66W8k9Kutl5FJaULn5lwlQjVH7sZFVl9xgsuGhLUk9l/YqYoDs2YZTnGkvrJLPFu8yk8zU1KrFkM3NQbys/GjfULhA+HwlNMmQW1MSU9JUeFxeSjo0SRk9MVp86p2u+U2HuvSyXYid2zF6aTQCd01lqCAmOM3w0hpCgoYA5uJC5pxGACtQ0ZSQl5SUPW4DphtmlfcOoYd169VXTQQ+CLRpbV204E1GqJNkdlKKPGZ8wPORqIinDZtAMh0y6GEJo2nQVbypvnPq4+ckRoUD/E7C3W394jiP3his2vTNlOsWg4pDj616tJUBui1RpiCzvYdH1bvJOnZpTpVNfE1CVs6fR13DEAhxg6gfjFD8QYl2bSzsMha6osGBhZk6rrYCkol/hbmHTxJHpI/Rrc+9w2XNqV3ljpauYiTtXL2FgUnRpxLgSouvPI95bfYw5lbI3BRZX8Rn/ITfH0MKutoJLal6s9eO6y37WrBjzgh1nhageWFtnTLk1l5nRAASwwQe+jdtgOiWjQQkYnlBH4yeZcAnP/+1MlRrLllaXz973+TM6jWHfc26qHfW4ZNS86RpnrrUCEa34eNxo/hB/avQ14DODMu18pAhfGeophdF2sQRD0CYUlx5tabIsDuGGbdzb48fmv4Zuosxj3A9wErIEQJkKl59N7cI9bNEdAzaogFBTSvvgQt7lDXm/HQq8h3H5k/4n99PUCkROugpzvcU3/eYqN8hQJ87LefG83BVLzy4emweilEVJx7i4tksUwzG2pll4e6inqwhs6UzyDt7hhRA+DNP9/2/yjD76LvCVl5xvkTpt1wcO1+nrOZEuvO11HWXAQIU0qQD1AJhFnZMZ+wJoaacxPUi9xRm/szqXzYSldGaR1o3fur9Tzc9NB++SXtw09o6N2PI3tSID93ySpDFcvYtqN3ey7bqMTRtCi/84cp1+flXigEFWh8D/SQdvssWPBqkIvbjTNl3XO7uTVDy4VaVCNDhdsuBtxTZGTc3JmVootsIyWTVTUJAkxUJLBajSJ18fsWjXqvw+KK3Kiy8FBiJsjRr4tSqrMWTP7+m3/I3nmLa/fLvPaOzXEkQjHsBtlNweZxPm2O7b/kv+v5x1p87kRR/XHtjecIlBPz122l855EdSKswINz3lWg59GHZBDRV3gtd349//6tQ4+oOxezSIeia2vXwOdeCQ4hlWpE58MfSJvLvvYrnIUK2azAd6Ge0xnUyM531GWyq403ThGbDnxpxdbUQtqdMn3f4CiR5eiNixffBzav2m326JwdQ0DEJ+pt/cwx2basGLo9SU5moApcY59u93irfhQ/iMpHT8LT9mxVGmvCbgMld6Mq4HSfX3zVROBrL8A/64qXliAxRGWs6qtG61pkSMmYfuHXTVYUKWUN3w9fh91l1o5U9pAE3vSk+N7EJuEfpTGEW0qsUnlEHrRN87dSlR4INkdpYa7l75eAVUfcqLN1YNs23SkCa4miaJWjsZTc/Ge+qtUSAyyUzcS7ywv6YKBJMhtHQybLQirhQUdM6ZelltuoCBJK0r0ZjPVB1MmxN71XoQe7Vri7OSVsCM1KBG6Gkcu9vzD/Ez2G8rdfF37HgVrxrgZ5aO8LOFZHXpUL6h1xB3sXW1tw1jEprgbTIKQc9bPYOlvEz/VLqCr1bKLXunrwyvZl3DBM1dvRqfKoPjexDdgM9wZFWQ4Q3nmE+YaWe7JXWsqWgdM623j2Fth7ZOe0bnbQYGcxNHbMUtQmLDFqkU9CvLeqeTa2YR7jV6ounTvbTm/gG3Zvl8LzOuxVgDYnokbrRPP87Ys/x1VWVOl2KzHjQ/cyvMQW0E/IDH43DXxBRckzWmnP0sSH1m11qT/asDaQpjBpotgxsUpo4T9poX+BFkCtzf/eTy+pn5L2dlTWtZ648x4iz+pV8tA41ug25+qlZepXFZteOQ+F7G2c4/j//87FAwjLvWu6kHUoOzdbXogHO8dQI4bkq69MzR/CpvNYv8rztgcFVYiJBWAbQEVA5aUlmHYKzONUNacafMiDSoTRP9Y9qnPFD3AIu/Yb9U1bSaBGaSMyNCIiywp58aRyeyJ3FafrpLB/BxX3YnSuM/xJirk825ref6z4WuUEzpq4xKoHzK5GTu0dln2i2HlTI7GiX7yqnmaohiBYjZOKpuI+GbHjrDKRSHLeMK0rIXy7xz5l5Al3SN/hWG6bU+mMp+p4suow88B9XM4QsdJhhrwRVI5TRs3GzBWPRBUEasMNPyzoIl9CFNn0GTPICUPV1Hj821upOWJ/ja+u3daSBmz/y0dKXGEH4cv8Cv5uRE6wlCmCowIZw4YI8OGC2M6ox1+0WnCxvscmcoqMA4he4LSsm9uSo5aLe7ulamSPwd/+VKaslxNUtTdN0tiuLaTpW4qNQMjMFhq7TOuKpy+grZTa93IX0trp8qah3tjBkHdcT3G59U4KpnQXeTjv/GvMuvDl2qG78Vr1I85jro5pH2Lzee8mY/r7zcxr2Z+JuoqZ4X1oDb11q83a/56Z+S7q9YHET6I1jjNGsdTbYQKUIvgoFvjU/aO1BNdL8x10cjbbi64+NZb23UulwFSUeK0YehoNLqd24ezPckLHJ9tqfbMtfks/1Ze60fbutAKy2Tx0+F6jS9Fcm0uEcpn3Kl+9abFOVguU3U50oyabYzjQOMXrhUu3peoyWjFkqtz8ZIi6g/vLpVXt72jP0Qa9rzGtqCKuX0yXvPe6oSrsvfrG+iuVUoxTVomOmSR4eujmwxRtqsB6bUzz8URjHH4XfKjbqADVsetpZqQWFVD1yJdyIMyyyQmGNdzLEaRVNWaR7Yg1Wwo4zeZsKmnPn9Dxo8pvwI1ivezkejB20NXZv5O0i9kc3WitsH+JTfmBJsBTwNOI2g4ftx72Fni/TPw8IsDdU5hiASFUhni33rD0/Dr8rNVCIRya1K4bt4fJqsRt4p/UHpo+gfN2zyITdGHKkKAC9XluaknR7JpXcm3gOhkDOU7K5j8SpcTTdS/js5un7Hul2A5H+VlrTPh32gafPRAAJKln4r4NnAFpmj9mHGZRKq6kOR72FkxxX52hZNzgMuX5CsuvqOz9e8y3maWczrVWLKB2ps7mA5Sv+Y1AWV6cjrTM8VA6ju8ZZUllbRmpmEfiux33X5vPi2EWkKVOQYZ6jrJyfrY8lYYUdgDKTMvFF/nVIn5vV+O/umXTjMsK05N6XclFIF0XTeCdy0lKEAABAASURBVJ2+1Zk2CDI8PUTpUJKjj1bvLFFJ8eAXqoOSQsMaNbweBQLFel21iYZUIjgYAxbSKysVlRP0hiU8A9emJk/le+voz7ejFl0cEhh226Wr6K7pM/QaFmPWHntYG4eW9FLBwW0v2mJGv9FpGBHPofVjrb8zSoGrvGy9y7RQyDasSh3w+DmCPo4y63IVDWRy3HoY6hKpwhrtvB4TFX+Gz3X/dXRL+EdRUG8Ds1Ae6fGav4GW1kRKDV6KIoyYKHwxZc1ort4juuGJ55QCsYGA9jAh/lpnkMZWJ4BJldoSLGpBnvAmDYIGb4PerUbTGlamUp4e9/FV7jmKYeQmnsbqu7S1UT19VqG0uNk64BULPM7CBw8JE/NQIqx6ZVKkQMX0ZbnDSbpsQKvILc5AKHHYp1NSVjenWa2YTfMxqbOibcGIHqmLlUFpcEEJZje17mEOPoGk5Meq4Cy6/lnQPRxuAP+x1pNVlwJVEKWDuYROTApJlyeFslUA+KQtPtiAZtuIfpDqn75CVU+MFsBPfTa/Vi9u41i8sS4s7ePNaywbo6M+cicwz/BpPY8wG+r1Wb1K2IBjYtM9DOFlkWn5W/a+uhddyGVwmq6Z0yFMn/R3dRyuW66mpp7bomyNYeyQLXoGynRKSNZTIysz26Hcf59+2+7xygAww9PrthZOYwkofGac/USGfb2jfxWfC2QvnfIEX1XemY872hiPNhHeGe3PLClMrnF9Zch9oCHwCA1cJGW9re6aVlxGU8DrihIFZ00/Hsc0Lh6GHojtDHZYbV2zAcN6x4Tzgf8A9BRdHQPHZj5Qw8puScJELQZW8O4lBCVWRA/wFQ0OtPd8Cy8y55KJjRIDpHizkUvx0oJO6SeWSieU0k/ewtpBUC+VNH+Wfu/HY83y+tNXN8LVvGod/8o+ucujYmhiSSsIKHyqiGptYURBAyK9+NDCrd1K1CkA1d/PwZ/E9/9AMr0tH/96WRtv2FnU5uP3KnXzbaK/VVQiH8AHgY/MJXLHRnFftjfQgdnja/ZK3zlUmpfNMlWzSF2Ec1oS018DTVx4j1UCagpwL2eaTocjmKvfp4ujltGViXqdrwhvs/SQVEI4WB/B0xj6EBwQI+NuYmRfxtvfbvmee18ZGWXtDISNZUwhwEjjem6C1uqNM1NTBflFGgfeqtsyMT7qh15k6Vfcp95VB9KzWVt3Vt/pksxj8L3Yij9GK5LUBgvQOZ/FHq+xKlq5ErJ2wU5m3mZRkvammhHSmRsZhagH89OzBIWFgeZ+nH34iOW9Jz3vpAzZx8lGPvNadEW33c0Kb5/2TJtEQPmIH5WHNrStqMU4bQYjZX9LPYVArNuvFp/LGTWPZaZRQLW4QzaGaPUKp5msp+5gHscip7vqikWFq/B0ApPMUkjjpnG1vDRHKYf+aO3gkoXVn6XxacowLKWbkAxEkrHWSBlVXankjGKRygjWtJS0Anuwq7Tr2iHbIheiasY1ZrU+3u6Tc9QLv9hnwElDGWgaAuFLimLUVoOOJoxKFaYH8w4vfICI8UXfYmJ2ifEWR+Ma+ClDWZTdf6cAd3iQhvxAGvMLwyRQsyFbD/JUYeMSsfk3RO4E7PPX4vvqjVYIXSe81PSOsUdaFNh2VBAm5MJRPhmW7jcEfEQOcIU6yRJa04oggthuKkKTv9jhrslin56MNOunKf4CJBewOAFwWIl7pCk22LS2NRS7AQLgQEFVjdrb66c8jWXbEA9CCdGOWlMo59oOBVBAm+szIE6kTZFv5qaSBw/rhXaRurZnBAeIGZGXupAGUC0lpC03vSUJO/zU7J8qVRj/n8LTwvWtQsrYEGP0oG9qLW/rypnb0s10TwuM052ffoR/OvHWqfolcjze9Z8Ia56LV8LRLcf84ntWr5xhOYCZmlvzu9F7KPyyrKQ8XD7ESea7Gq4YTt1LrXxDE20tvUKKWcYf4x8R9mr2iMYE+j7xu/d1JUse8LWDwaChXzp4kM0sqx527n43LuWsGO0c655qtWq5T8t3rsAr1EYT6pwImjP0mPHP1RpCSrWQHNEoNTKcjgPkCKRctg9E9zeVlrNBh8OUFy2Ka2CKPJ9Om9MYUGfIf9efKfYVa3G47yJWSVE5Iob2l7hOdYM5gWP4YhmApLSDGYkjZVNc/ZDh6oGNoeHj3RHDXeS/1ZRFuF0VLL3ATJ1o8aDUOeUsIdiCuzosFfSWkPLrzgkV0wzZi9okXHsNV73rZ8z6uRXCpKu0Xt60Al7TmD4O1NiEmfEHtNZ7wDQhd8KLOtELSRFgANYwRslUe/uJ3IRnzFlSmuHX7Ld/ugVMJpKRDqrx2DuGEs0NaqXSAbpJygvEMI3XXfHDRzhDvuRUPBziKBPz9ty299o5Kt0ecYveOE++P3W+XS2h14UlH6id6S9aa4YvKK4Rz1wWFGnaAx50y+0CTHDaJa7LwcCKuJg7fQbv4HcidflGvhCMvqUik3P77Inb77+HHv0spxPO5rVOf4mU2/XSGv9eOeQsE2p5cagXvPvqIfspXJEnkmA9VlOX9yxt8lP4vAwSTQoatc68aD03eqff7HpbSWtqC+OpoCS5s+19rP2H/ezBj89/zG2jRnrpeamg29UI6gP7pQ3oj/0VaFMr6F8KPANiEFYS6GAq1UMa3uu/+/wHfv9RyVE0EjE2bCVC2XBqjlh4yD+DPQ2Z4hXowAr/QWxKCNACMeK8UR+SUCojy5kMjlMcdFx70gMozMt/pT/noi76eArhHSuNBmTPELaif3AfNYjJm3//x/lGWBOH+fJeQbGQCDb5k/Fc6WzE+syggVBI7+zb6rTv/0P9Z9yOCKlthj5YaLby3l1m8I3NMYLb8Mj9S76lBcXtmmzNfAxnGYtxA+Ul7hgKelmbFWxDBFEU3eKL7zp0ff6tT0p1wo4g0xgmtnMxodbbama00UgAMwnVTKlq26UOY7X9r0OKxWA3iRqgsaeWxuskj01xCHke0fi7+vx8kxWH6Xx3p3KdYrL+IJcMvFl/2fXTyq24qHyy/mR5d5WqYfyFJUnAFXvpj6X6fOxRtOzPKuVZlu4IsvtE0NxbeYYuDAv+zjoVZYMNhYP8dLBiguBlvJ+eYolu2R8qTb/df+v95f3dXubzAu9IrO+m1C48Kt5fXO0aJ6ezO7WD/D3Ql+X9OsDGbltUC7UE2rKp6ISHEo0At40INGhfYDFGdzxb+wuSAMdKiUVfijwL7eSemv2L+LU8kEF1DK631zmuLKF1Cu+tv694sFmBsDR5UsPfrcZ9vNIoXvjtVgyiSh/kHcDUacVAy8Ab8+tXmXsvzm/Vb3+cDX8u7LSMq15hhWUjkcdPR2ARUcKvzdEyJ7l8/EcV9gtNAEuWwd7J6AVwrMeba3yXxbk7D/t/xf9d/Hr9fW5a1+B8hC/6WsuMQthfVyQIkzsKjJCjZM5Bs0u+bLFsJJVm2+lf0rsCoKbXl4ghnTVzVRa/vZRWXVZJmX1MK3PXpyf2eZ4jqfhFz1P9H/FIBYwyESLZxCAOxJ1wQrsO6zWU9LNyBM0ECIPsmFrIwOhw1xLlnbSrhIo1QoPVU6++AlOV7ApTZz+OxEwLNFbT5YUyyWvfLaXi7ZlStNf8CkxRLCskjlsrcvaL+V08f9DaBcCAfZb//vD+ONdDrh6As9bYD3QnVhCXUvHBnr2o+9n/zu9z3wdHkzyuUj8ak2MTy5bFEg133vzN+6Jr71j/b1Ov0i2RWkAPIfr+rvkf+9TMNaYp2w9MW9z+/v88X4/8b9tPaIELARoW0EG1JKvmrZnZJpRQrVKBasNF/6Uds9xr/t/CnzURKixUGX4j/tpSe/pb1LkZldy8nYUyr6DOTn5tZSOCZvJlFuatJzX65piZncKgpEGB8OP9OZ/Ux6DRj4VH4FlDH1gDoySpEhQYlPz+zmqWBs+npfy7jp4PHSfDtoM9uPC0yMChZFI1MMzSHlv3poPOhk4NHDJmykzXbk1kC3btzJVtpmhESBM2ygc5DoCGMFA8BkCgMKk2Rv2VOTycDcWibrQxa9zxs/rPot40uwdzOBIrvX54/jPsaOVrXApOUIIb3Ir3uq0RatBtzJP+aj3I5839t+PvdMp9r5CPtr7VJbiMFZs/Dgq8tasUtJorSQr4YsE1nDeoxB7andv4DZ1odPXBvZGKVLOcTersv/jUes+cmfRZ3guJFr+XoiEhG6dOigeBj7H4p61EUPim9S9NM08/pxbKIrFK8tdozZkD0RdDM0LZaPxWli6QFtq7blFxxoehj/nzlKrik5RBfWdWdx4143X5pyCpbxioVrTu1XMLn3rqK2z01CcDxfDbfbJPqSudSdXQOFHkhcmlZkOJouNu1+brBpRpB7vtTbzq+7/v1G1izCCa7XHbM4EP5Foqza3sImW0NG4uqfLq/BKUEuWQaPfra5QdglBaqNCVMeI1XN6uUf8teIMieRnkf8685RNuq0ImIEtGN6r48D+Ld8cE/zzYChtoTAQfDR/MYd+F2To1DIBrSmW6eval391lcmUBd3lYg5+on659TTYnjK4BOmBctVyn25aisE1zCCZG80/K1QQ+JjbCQEnoUp0B8jKiaZsGGw5ncHq/7/cmvP3nDH9nKmQ0/kQ8Y6JwboFaJYqnFXHiuWO/ybfUtqA2epFtMxzc6YvR8FlOI2a87QG99eKLffFAzocN2aaQGLva39qFlTZN9fsc5vjIQrTZOLh7/5Tw0GqkGWWm7TpvMMFLSw0DYK1zYzpxh4XPB1y6Z39hATQYyeK/hSTl0Z92j0zLqycKpyP53PJzlbpb1m1Ac774GjagXFi6vcZXhsw8fBlHr5v9dosCWOPfS4mwvvvoxv8v49Yf6sYxl51x3cOAcOgprVZMopP5Mv96HWfBZcKlreyRLUGUZ6g1KI7+a1OEQSlmSaGvpjY5iKMCIgBlH4/3dqlkhBg+TzA+lOgvKH4v9SbvsUpV4VujOgFOoBeLozVMtWqdYtiNbaxhD9XHog9x80Nk2/jl9zFqUVumgBQVjOqjsy6s2BQ0sw7nDpepXHyf9ftFyMDP4nC652NcBW3l6jcxc9PvXJvcUHKjTGXjYz85/y9H/VHCfK9AzxT4HXQQPz+2Vmmr3JKSLNzVAhARz46gfPp8ivoQYqgkV/wdk1+tRf5bdkohkwddMrNEmy13F/Bl3Co26YPs+ceGqhD/la1UB7yOA17pFmcUUe1RgiQeb2tq4iO+vlxykc+Cq7yBphW2uIFLTJiT+Xt++zuPMAbN9TLNmd3gOPMDhxhXY3j4+6d0V6sLll/YCgR18eETrdt6udknCAOegm4qMV2thosp7yuojVBp7cVHwvtbczizZp7+1qBt9eO1epTKJ125RAuwgpw7bMEpo2IYQ2uhXx/VXQEVx8UY06EElF/Ps8/spMPNHtbDo2voD05PFThAWrddaSswjrAR06Q4kH5rbp3Tr/VZdxYn5G3PyPNMpkzQNIVe03eyeeynfBfh0TTYAAAQAElEQVRUbKV0QZFPp/FIBJDh9hwropmU7EcrlYZRmOcL1y98iizAyGVaRwl5UjzpzAlFjDGnGxzUPmZuD1TGUGkXpTOfUNvUuKRdFY2d61KaAm817E3NZGfc5G2cEXJGMTgMQBvZBTNQfcMfZVfGemYEd+QhtUv47Ztdxcqo67rYbOnJW6llf7VnNEaMreoYXQOclKigtnGPbjfUyEehjYiS/1buMY3JmVszY3rzLrwqeuZnyACFeqx1uV+3X8K71TD3ETG9ulQMpsX9VQV9ZzxpaYEJr2fFWo/v5Tt8m6mmpcJsBV2kMmnTa7AuPQP5uMQfWCv3KtFvyUhfDHH+oVi0pWj8vnZ/UUnxtSLMpNcBNAQSB2HiQgdKGtE+AG0QB66/I86+Wqi2UHGvDSW6sXF9VaoKgFXUgkVDfWkZs+9gCy7bcb6hyGqRJq9P/VPOS6XSxEvUUfTL8AwfCezzxwHbMJDC5QmOGGg8G4D8AGIBGiDYYfVMEWMFTss302q0H9h3eVNub9WbU2YfFw/rj98/z1m8yLy3s8dYD4V1RoY3kv886bS443DQLWZUsUkjGPV1GoeRtPjC1oJ2UsXiZvblzhDxgtn6kc0CoN3FYbcVVJSqBqdbA6e1IxWSFm2S9RfZ+m1NrkIa8bFQek21q1AvQ8RPO+8nl7VmuqGxgog6owyKNApy6S1gaf7XcS17T9ue+tkVSsn4vXZnXp+/JjX+qV9fB/uOHbsdLceKDC0OB0pMH6/ydpTilP0ZgxmcGO0GuGucv+U3vxOheVS5Zq5Ul1Ox4DjOvrllwb//XBd+SjsMgzgA2BNpOMy/0Aahbl5jUoVGELXmruxOat92ps7OEUDC5MeUn/akvm20LpMfdmHSeWlF2XAvt3HTv8es7AMPDtmUdxGLHNwvM4Lhpr3H3bwb15E0tAftURd7Akskri+VmVmZaFmxpdPt0Jqi49T0FFHy1HFHPZJXEIcQZgYwn/ERyRc38dlAsm4gl5fkDKWzW5Dq3y844KTDHVt/CaFEpp/hsemHeFTmQZjBXHBbNgtu+AkuOayuW2qf0BAazqrzZD6cx7GI5bH21urtJYQVVZNC5m/bX8G/X9o1dZhn9btxRJwSkz1HkWIG1a791dXSoYtIYRCOrxcbi+pWMeFSh2iRftZsi2hlwXrEf/VNoctCf4YJGNAdnZvvj91NZfmx/g8Ms3ElUFXe3uN1bI56e5n6gzLjI61pMoEfxELYP+c602nSYUIzfscSerVgcgQjCjaagkLC2tyV8Fab3nO3p0gM2S/rbByw3CQqspllVT46MRYCj9EHdmNQ8BvrPJHL1Ll8oMhb7bID/+dysps310mfGdMP+Tp7t9qL3G7ZvUU5lDIbhgB+Lv/kxC0100GSOkrS/1e3aitmH+k/1srmCKGUBIEczRXm1bNkK3uUlamdzjdGWSzciz74ILUaYt0ZqZgjLcCH3qAQTOnPfLzgWN9qHq63JaWF4aBq6lSdX6IWzETp3KkZeCkmnWhMg0NFu+gStY3KSyRiSvwKElYJ5LUeADTZLYkXeU1aO4MEWwbm9ujTEBPg1rCcTL/Sr5kjbbpnqj07xPELTegZ8KxJPd0KBx/i+TJNkZ+cRGNG+ynh7ul/HNdIJJC24sPzmf/6Vzq+EDLmbOiu5TzTC1Kc1m3jg2bmuX0HRc8V/ZjuCIziuCU9/+YH6bNstEKF2/JFt3G8rmkK3r0X1REOcUf8jBc2YFKJKwrS/IuThW50jg21EPiIm6mLeYs3LXN1s1t0nIiLn8w/1tLzmN/c95gyzhHQM8IKoVhbphWwl2z2TDeUdTrMMjBIkVr5FP60b51kml2IODi+dmIWYN1735P3uufWchXKE61oRt3XlJCuKAH2x2J70a+fF/fllQa1pW6dCO71o83Qlj1nm73iEMr30vji98579NfodDfha5zszOmayOb/BZCSTzGxKh6alkv/mfjje/lGUxcoKaG/CIAy5OYoL81qKawTYji846vvYRcnzV/xR0BvZ6wFL1ZhW4bnivo0hBl0QiUznFr+mdX5iF6rt4vpcIpejQJazHxhDL8qMCrVe32y656qai2FE8u+hrYaUOwdSGquikNJIgUGGCHWm5A2ZlXuEXgNW8fizNNU/PYMTHHCjv3oBvNoKVqXO+3pelD7K+kLyakkGO5pFVMTI6dVK3OdUnIm/RSujpmySaExlMSYlFyEoxoN5kAQpJ+ST+Sa2vPdlZBfYPdO8mrPZwLGhdFcTW1MIYSEayy03XmVpUVzlVEFE0qCGBgqfuIa1KymhIMz/LxPJrO8vfG0ntLf3d97q/H8Z/zfIOA2T9rviB//R9tnFThR9zmllqq7WJnUOi4djxZ+aDEBLe1Lgly1VcoqaQzDYq85Me/Kd93Lum231M7cZ7xrtS/tItb4Om6iDE51ITI6VI2c3+lX2PN3OqmAZKLiNBJpnSvHCaNBblshQGkhSLXadPXdrJpyzGN3+QHeYz3/buchrLCk37D+5hPLbBM6avd52DSJDI3ogfdoEKcBjX6ti9oO/pZavWm9uD82e9ElXzMCe/hDBQZIHvv9oMmP+qwSFMNNM8VpLqPAxNzFu6LcwKHQS2LHkD5YbfLU571Qa6JpLk28Up0PZD98V6lJQBPNjZKlEo9L6WjERgLVPaMTcsYgPSSlTNMYu94ILbC/e270P9b/T4tE0cp0ec3xuaf7r6kT+1DTlcCYjnV71YUnXDeKopLKC2aAwajOX/ar/rjlsylUygVRMyzlstEjpsQDVYRwCUJ1lr/rjnFmzwkcK/FcqpmWp9nZKxsKy3rtDoIX81YdtciKgZzJ8yFfIG2wYVr0uaZ81NHu15QbBr1ypgYg9Ecm/HdUViSxYDqCNN6ym4MWjQ32JV45n4FigOiL72LJ3aGDko+H0QxanOTRS12T4pRbRJYkpCP6mml4c0xjIJDWKFi3myLiN8FZagb0nJVmGtgzF62av9LK0cQqf4tvT7KqWW3TpMC+nRe6ylpWthJ8VT6Vdk5k2m0/O1SQinyVPUOpDEx7Un1N2JtQPoNqlNC1tg/Y5JrejQ9sxgfT/RNmDCECbaYbt/tOZ9X98kVzKKVQ1fYJUw2Cd+ovz9EWGlczZQ90o7MfG/qYp88NTRHLPWkoU+R0rBht2R+DuHWOE9ThQAQ80thEez0+0hpJpaiBbQidBw4jsWd8PoC3skRS5y3lk5fKF02zYAWHUqjfCUs8jqVdr/uwaSqI1BUdTTaiwdIxmIQucgSWrY9DAmh08vToF+Is5mqZ8YFQGE9QQmgZw7LaqJWtCwRb8dJERpufx2+sNn2kDMubkpuq5862A3thF5/bkfMWwaPOpOyWQRTO5GRyWmhA1XUpYTYyno7r1r4Qi9vsgWIUseJXvw3ZIrgl3Wv9C7yca5RFSimOMf2ZsgGt5amB+nKiYMYhHWSXXLqBfskCMYHe6GVy1GY531JsZTbFOAqUbGjZmDtTEHfgw9jS5LaklbgpzaLa/6/nVWDHrMIRA+bgQqfmsoeCQJdhnf8s0ktSvUQqlm8YO8PT1sVS6Uqz6dmey7WqKxICLq4wC0UdUnaxflt3tq14Pvzi+sipKTIvFaDoKa9e1fvMao7mxYxrfA1L6FQarjLos2rYSnWU9O7dM+pJfcNG6wUXRPlas0ze4QJad/UFNW5QOTqcStKYAldzls4HrICOhmWj+iMP5tT6ue0+VFjTifMPiQbJXBqSH9UgOTJ2a766zZMYsUqK1q1E9HAsxhaGRId9SeiDOlPTBtiiE3Nb8wcK+JRdJXCg1+q2O9LpZ6ktxxMYp/u4NWHEj8/2pKeSkHO6FAJVgp8rh7xjQl5qCKb52Va+jT+GgBjsxVp4g1qiUjaDMMEYHyN1OCBjuuPviKbN6HfQdEWjaqZh0KHxgsVFWyafI759sIah2NzLfAjqqLQzXeREmC/qsK9fqAHJKDzcl6uo/PPf3afy9LnQQtLwJhNe6dPLTE8XfEU9UpLnLFmHcHmYNn/aoGm3DqVtAHdzudMFI6DDOwxWpmK3a9Ko4JiRNoG71HCy8/R3r5GS5nFdUZ8U6K5pigTAGlZjraNnpt+t8sim6NA4IJXgpkbImiXCx59gOEHvCtSDq1ysPfZTLaZMdqtRmDL0JkN2VIVDHutZ+ZlwTS9JMdGSBCSG/wtO0aiWJPQ7c5c0L/rsaVONWio55q50R5lfehoFmVLWTyn5cjeGT7AqJzUfDkzubYCy7uWIpWLLmZz6omeXz9VvHq9XrU4faogPBLwGM6OpikNTwQXZ1snLucAdR/9fKJzR6A+L1TUefdJgexghGs2wfYxq1iVfP5j/CG37oBfJRfiXDdWQhofJfxMkBINsfNSMEzqVsNmmD9RnwjIdisAGkbQEx2FuutiYhBXCqp1FjoaOt+S7DFrTdaJLWVUp6Tvd1EuNk/Ltg5XUSMilKUoVeIsy6KXYa1rcVck22loY4RyFgx/jU+EHc06N/qSZ/Qn+KMKJaQZGCg9eWrkLlnlvu8sa7F+zF56CWMuva+fHPm2nombBY2hyaSWEKKpQkIU6uC+mzMfYVJx2v98AZ5CyYJmrZ5dUZ1400Nr+CamW2ls4nuhAyXYU/h0TyiFkbYISlQzml3Cb4n0Hv8Zja0w09nRBvzwyHsG2qB/rTbGQ45JI9VRi0ksWuluy4b8NiNxB7LWLy0it+epbKZclyozBMSEljNXVn5PeiCfCpGFld5wKZS0+dJXT10pOiigQYa2gNNsz5kOH+c4Cu0WAA10xqWCCbdZhujx3oLVnXtgvDDBOY1pZLzgERYNGZ9lKXuBt5MWUGsYYhRryMUI6X4Q534MN2YspgjnxKqmgN3cH/r2+UtCjiD6DkpSDSxesqm+VanW7ZgiiqUjFL9pMaeeoW97GB2tSyUgl6uH4QHC7vcqHXrYEVeM1cqZmUoa9g/BC3IpqEXIZGsLh5NLhUDqLCus3Sxu1GRv4QANo34fPR+G8rEGAxq9qLRcOSl8wc97HdBrzD17rr7kijgP+nfMX72ECMMQOt5jG/WaaK/7TfbOsS3rb3utpPPXhI/nBVv4vhoB9lSIAGIyQqy4E00iDQrSWcKkaU+P/ghzyGCVCNQK+Nfra87ZdU6eI1owof5QSzo4xKs8TQtBNfXjWbA1xW8vC/q+65Ao2HwiRbNDoVIafEQplT8g62cesIjRBlcTLaW/lETgF3ZBUj/41/tUArQGyP8Juqnm5VnCuMp7J7KqEFXHRspOzE6vakfSLud0lLLO9BF+Cpw3CKNLhnPBa6OhGocUaoUSaTvMbAzoqUHLe5mIX8tBQWPxiLSmEUDbXuBK1IVxSXu5zN8TfxY/V3oWfqVWFgnwkIqEgwGy/s+7sFnWQYzFg0bNGrGOCve3ukK33z716YB3FWcwvwLPUjMT/J3pl7oyndqqnP/LEbq0m0m/6r3wNiWjs5A+WSmggVdSXALHQy+WK0KuMTwAAEABJREFUgAKBfawTYo8G/zdM4Q6hjxRDcqUhIePtJXKhb0Lfi1/gb9TfStFtgJY6rNfWX4ucGN2c/dr723+zRIPtUTuirDGFTQL6s22RguSS1UrT1n7OV2Gl62Al71/N93O9hcxN+wg8TVUNXZlW7tTb1lPPrp9V29WIu1vxzLN10G1jLPRyN8VzZ3qS6mI41dJHzPwTmGU+t1w0q3toy0WuFcZIUiEvpIKhEsmxZ1QUQPbbc1QtHbe16i9HcNg95kP7Bmt8se8uPT0r5afwm7g1xlHYq56fjjXj30EUK5GVFW07TatU36quKyVSuxFh1wPo4R/Mf1MOu22KwSdVQ5hEa+SxeHVb0fr2s3PajLoufwNvFQfi2lyiCxBWoCrfRIVNxNS/80A7YcWphXpcKrwyokU7GiBqeYcCFjPNzlbu61+h2Wb84Xqw0KhblrIi34EdfJx0h4KtlRk6blsLolpjqVqLgEF4LWqtcU+tI4+d5/vPubWYjVGt2FrOZ/jnQnFO3mv3jbw6rMRJKw6rbRmmOYe693i1o/aWNWIDf7xWpNkAbuV+nYUr85Y9gnFGT1QHtriCkoz56+suh4XOYghxxlTfGO/Zpd/NaQMVny0jPbSDRADP5EY3dUecv+K5ovn9b9nYcl1It1/fHW6G5dTJiV0mLfQoDd5n11l5ohQTGrjEDBmBB4zhmLcpnZ7QkygJ5vmyfbFO70zJEzIEUL9HEzW2NVeHfKb9mdGdogGfLbM0CSyv56x5LP4f8J8KrgrihNjPWs+igaPDu/z06MYmNQmvPGIJ6Cl+t3LhO+zHW8QWSl9/dofNirX29uWzmbs6Bm6MRRi7oo6n2ubw8XHJJ3D8T8VjJpfdWa27gvT6xHYTryp38ifuH1NSDRYjaw69vvFk2bR3LnWo94xQoPszHcEgcQ0pIQ/MvnyNrZ41DdaoTBztTI98lJhRyEbABl4yPJiqm3k0X4lYphDUKKBLaC05tz8Q65tgNmM1BJhrrnwYDEHQVRlftpZpyzXRH1z0QGIByq3VZLCtiTacNVKBZgwf439uy3bYli3OsFGqf2AUGIjBiC2chNby6atAABSZREEXiXmIH/dN4525Cqez805rH5KKc0bAsXA5JyeT3rHhInam2zguIkymyk5SS/ZAwOUF8r94n1Q4otV/bP3g5+mnNcNZI7ucAoTEW1QMJLg/agVT2XzvSj3X7viuBLTMt3IywHw/3yzjaPtDVPmkRX3ZdEbB/zp+V82f69qyTMlcaWE8WC9UpnSI9kJ33Tq/6krufKBY6zJ1F9INyC/ESfzZahIS9Njbfg+/GAVANsIlEviYc9nsXnFjDnnNoKf3qzbP8tRlBZyMO1Eaoe6RoLr11oJ/zSVMs1TS9dkPUeye6s6cTnLIcHJlrUnVQauUtWZMGtOnDST36Z+jvQVUWd7lqruPJIOtWV4Lo6h1wfKSDcKooR97vSgHXZgaiUfJmOmehumQdvNt+2WkVP0kn1YFwKCXLOjvIIm0EsEz05u9B5p9E33dLnce7xNzkbIarq8m9xZi6SjOK2k4rd1SEB/wESzhGfFqPoERJc+trXibU6TZD/CnR/X9jv0i/reeuzfC8bBNqY8Xn2As4G6gQSzerpJ0/7HiIH1gilY4BwsemlESobmCqUIe7b75Lc1iRAMVpBa0PmS1hn15+cSxNM3tQpKH3Xl6AMxAd5faCfx+f68mGH2PR47klhbKPn8YIQv/lhfqfqaz/X6NcMUve+Y3oOMQfYaS5FQrKW5tiqcdEMnBMfmDFTyD+fS75i+YHT9K1IG94Nt+8mf1+q/x84XPwnd7MZrp6hkB5XVruj5janBArJ6dKrU0XijphJhtLVEirUykRXw9Xem50Jp9Z/K2qqLAoc1j8NW9rxrc60wzFuP2xt3+d+JvUvkwws1MkbQNgWiPMuqtHFhSNCjYx+GoqcQdnrUq3NLFFabT0VpWApPzrzQlChjE7FJYTT9lt7zO+vj5FtvRCzDSzdeyUOofhPVSydlceddPUdN9UYnECOHST3fXbTbJ3TvyGV/3gfAahpmGS6+hFFoKlSu5RLHbDb+r+ufXnxta4sayJaU3mMrfAq2sD5KV6zddvbUvKnmq5V/GF5RuvZViHbZK6OXqCrSXWIr/OfXt4WCexFOBWU0JcfVZPByIsT/KZZaNeqfbwMnAbvykPbViPEh5WUgvpbJSnYK0VGsBGgrJJjTiq7qfvKpk29Jq9dNtU+KY9jv+m7yVM7/QAvA25bEK90wlvpfRTQlDqbh4EcNl7qDrDdSlLPQLPw2/wHcH4fO3W7bAoffqY1Xtoz55+rxi0Y1BQXjzvhep8pnvvyc7PXV7KKfEzMD6el9CYdiI8qVzmCR6yqep5Pu4CguO7AJuQMVJKONVnHc0AsTIWc3q2ZppAyiI3jWPO/fC/jFoN/OPymF9rgVSaAAPMzWoWgkNiugvDzY0i4CPee09JoBC+R68r/YN7/ByqYVOtMK+tFP/MqPxi1+tRIrT1c8+bkINsXNlTRWyXQ9VqXrfR321uc2XwI6djjef8z/WuezX57p2E7wPK9bYc7RWH2cm83+GvwJmaExN/+Jvnt973le1YEMlhdbUVfDxtYPaghj+vbxUAFj8Yh53MZ2Vzd8lxEZ3ZdSm4k70mPIzNGK8RADa+luqCLZOgnOUHyniet71dpz2hjHcXRut0ewCO0aB66fxM/PEaG+6Viw36+rKhPcIz0AeY4b0Macu68xvQ1vJfobLqH+OoJy3nL6KfdN+YA+wGrxgXpzbrUEWnLCJaVNzGp956A//IJ5yr54uzqzR6vTtrq5r77F+oRYr4Y1JBL3YzpK46+HzxI+pJZE96G32+F40krOJP8MnZ6ut5nlu0iCVft0K2HuH/bRupqX/+8Ozp7fxJ3qL8NW6/LNhVG6UyIs+gSPgNlipNi1ZKk7n1V2S7A7BYuVkTbfneOVSpGQYnbAOq8cYgLC5qN9ZzJOZ0zOd31zMnS/QrHfe4um4zSp1rCq/jeqcq/GqEqVveEbtx2rta/8lVlfk76qDVP5YXIv8GXT6W/wf8GtIne3+4/iHI63EKFAaw3O07km9e3xBeE/f3zkvT7qblMN2dVylTQGMrr81R4WQxBfA8BbytCbkCMnC4arZkLa0kH+qc3QDL/F38L+7lZFGMX6DrzKOMpekJ5H5v4u/qLPaIsIDbv0zKnobukRCmqVu6NZrTKXtE8NupVFeLNHv1G/gevhe6/lMzJqz+Gv87RHh+Pwqj2vS64376yUg6IWJitCd+qBcQrSuwW3LcegaVIWP4Rl51bZynqKnWPkCk7pbLROtMT9l0fQw7EaJQt1bp2g6rXR3Hs9ekmUps7Df9bz235WWBWCd8DQr9VIWDljSzqZuVE0Zb0ogg+Gn9EX/A+6kv3ZrzpzfqFkDp4YkcKrqzlxShzX8Dvs91Ga8rwlKl/KucUSxbMqz9u0Nyggqcd6plOWkFK0B9s78zXzxVTnw4bgxYwa1PDEHk+r11lTw/+OH3mb9P8MfTedv/fTrc4Sm636LXitILQSemm5Bxcg5dTP/agBekaNeKSm5sNipdb/+A/jMnlFfCYC+lOJtW/0iEWeqVaTzoRKuHMXfbGP/EAHxpqgqvTWgNduoQuQ+QIKJLuXo4cghdbUhpm6qmkSowTFAzTTIcN/mysKokUmgNF2sbl2G0Up7J912mqxolPeYZkFIhkelXm0jqW7Dhhpsh8v6ggjFmXlvp+pKWovZcZYdh5BToTXSTdrCmGrGzsAAl2G+zUztASKHM+7gk/Zk7Mun+Yfzn/ezq37u73r/+iu987X1F5EpcFTg8i9bJs5vdHrYo1b6Y7Z6omGUAZZMeL4OYSJnibcZUsjNLqdVT348XvO8g/vF93xpqlP5d+bv+GlDSYplORpx7Ve/OWYt6HYz5uOwUrncPmu1WVYknountNq6ykMwMJv1oNfQGP9qwBQJ/8ISYUL74/7jGPAXzxcNuVIHxBiVLY5UnTaqtPp84hZHWbHYvtyDK6j5S7HBul1NZaQTTPNtSdYrRLe92kLpmKj19DL41w4IUaKZ9NbEnpeVCisBrt6LjvjyXt6nbldzdEJRBI/3Zc2U0U1GqCk+sEGSh6j6QEq0buPZc9EJKhTmLHDDTU2HGE23ZRQRYkCf7qKyOjshiZUoV+SrNSGWPzTpMPzgkTWvDmV8hrg2lwmF+syjExe4tMSHykSaBBi91GH5J/I/H2Ua7OFb5+C3IBW308jNVh1URNoajEoYu9TSK7rAosuiY3jzy44ikHORfeDta+mZwDOhhczlQAdH0fKdfvvNueCgrV9rSQ7kU/Mn+Qw1u3lh5SKtkRdXqsySo5i9+1m1HevQtNKU/TX8pUgFG6vhIp2r6qvjj1rlY8eIRkkV3zUBNyg6ke4TlvadrEJTHhc/9kVdP2fsHsxtlqpbNZtYrRQqCy+ope9V1+sc6kSy7uTMKA5ZKAe6/YW9VgensiGF/7q/iLrL3rDrvHuUXBUSR9o1V7icS9XZJv4YNn1guNe0lCk/xlr+y6/632+P0gw6HYzxR5maoHnLiqfawDT+Hd2VVT0JTbdvzChSTHWNuI5aSPF7T8+Zb+l36coQ7t7iObzHbbgLUYyyUWt9D1r6J5x8HCtABVydpWKbHF28GGBMk78DXqJcwR0RrUu6RpQbjDClI0h3muzsmhDADgw5SiqV+SK7INum8HvbN6mVGOFWcihuojv0tt68VX/bT0X1JqN9pXaGi20jkw3I2Nmr/hh/oOvKcCt/Up5vYKEZd2QSYg8Z0QvllVAvnHxnKs3T19F1qdRrAHSETzw34hd9IWrONydkWLjaomDdbIXBVfZQpxor6Ze8U3qBg8W7XImkeChliBDdFEfK2oig9mi6APXm6DBcMLuVu5P5i7nTnT1pVGz7B3tp9xVGMepoJ8KDrsLcYr12l31YZ+xjRc6EwVURJABnL8/Z+H38jafwQMPwCf5rvpwcTUZDPS4OV4Z1JHYvs+ixQevweaOv0ktFbZs0BPNR6eqYTwldnDalBqxwLDf/Me7+RHegfjE93wffsgu9Ktk58xFGeS/yUk3Qs9fARGKYueBfrxeZAE2Bo4kbVZaFKiPDwmFavSJ7n3ruWFFA6KJDB0V6P0aF/cHvscworEXX/u1Z3TvTuj4c+K7ETe8kuylLG/7jP+HPMnephDndpgKGGRTRDMdjjIDwiAIfBMu9uZjud1ShfRv6l1R/Gv2KfhfOBh635SClkoGyRuFozABjMCZX6Y1svI138EvtueNVCKPUoFuqFZ53+k5PJSzLW7V5f6NbRihwgqv3AqS6mFAIz82nNU4z/y/51trP1TJtIbJE3awxb1dGFIfCppkUoc+jVxX03ZLm4zU51RKqStVRaDPbay0lJIOicvYRITAfX9/M3+y/ZVdarHDl3r9oBbe/QgclbGqci0lxUOJzCGXb+aLfKmY+v6E0n1kAABAASURBVP1Ts6flmqOLmAfYQ5NqH+IZt94zjtbV1nzIVCpHgvd2ntZnU4LsZxivukL4fCRJR4iF5XSfMmRNZ5MH+8Ob5ifVOjjaeSHQbu+eM98gySAkFBtl3MLXsgfuEnGuucY3WGGkQrN9bZXGf8DMTUNDbNkCo2VK0sDc1pF9fg2v/BuzTRlV6zeoy8h/WyNSCpACnRPT3VKHpmm2DqJM5AyGAM3/jd5pwW0mjkA/kMi0hfFVQeNFd52FG5h/o6NgLXHpo8/snK9Pjs56geNQiNpViP9GlmDZDbHWCQiinHI+WGqJGHyvaYmzlYboYkECry9gsEb/pDvA3KNoL25NbhtROldXKbhoz/pCouHDC8Uk7nvd8zPngeL9bH7+iw3PgZxodT5LQWNtFRq2ubnPfkqkDfKwXkoOZCLgQ28dgjbyndag9ZgCwffqQs98tFHUiMYV9uy1/Zuq8FqUfrE7wCPrEFwfDQ1CyYUKtD6cNapA6zDEAvdXGjafDpFWM2n4XjPqqYrBYXugxyTrGNMEMBDqNvZZbpR4dkLteMNVtN+VDuUABTlbo0utidgYNKYtUzvD+ZQNeMPTdES3o5VogwIjHOmjhZd+MbMLPs1o1Z1i0/Jso+5hu5MKS0GqpS+3DWq9c0W2ZKwuTtqZVf78fpkFeB2zxDUtx1p4hL8NY6NV26KlZNRU+W3T8mdZK4eYw25PeNOfsie/OtJAtI20GEuqAE//c8mEwfmv2loWJUwtIOmd49P7mf2n7ZmWW/goCLW2PeXoRh8ygwRi8Gw++sbjh+4aJ5CNNbpasg9th237sr36x2Z96VxUR7kdt/BCL5mxH3/+xNLqsVnIF6rjUYsJxZTQJl1jB3Kg4XM5ktiXOClJNU+5WzEeumvX40vmVQYEzUWNwHa6ImpwRHq0sZ1gj0yhDbAlc1o9VgJSiVKy6nwFVYQ9y/8Egp9PEUGgMUPyB/H0T7Rm8Hbnbd7r2Ws0etfaqdwlUsSqr0xf3IRYkbv6+ZntMFoc+m+pI7G4ZDvY2ypM9qNUVL0AZxPF0jZPUu+56SxHiRk2rGDCAFOHUda+/qfVT/EO3ETPmelR544ZJHU0HRH+s3xRapgG0uyyum9PkQkMObAWT/mtguLw/y8/R86c7qoalNuscHk+NRdpq9fToakXH18QVULIHNbIvW7Smt8bby7MB8qHgBrdolqqfBPyHIavPCieUToJ7W4e46OLarrKIQZqI9S1+9kXPRAU38dM8uxjdUfPbarFWs/U8foN1CJU/qZuDR7T3oBn8m+lKJKIujUEChBSNOzDoUJAXQVkA5VvHk45SxZ8qznesLf5PZJ7zXXBx1b5uuJdrvCFJjMQ5h3K6TU2CcA0etaMwISDBinGv567bQcf7+WCxIu4YrulmtYQzWMDSsj3NH/pzLHRE4yrLA21r18igVYtalI5IJa5LOpvWxRs0JM3j3S4nDvcqBRSe6tcYjmf+rt+X4XkjRU6PKDY/cMn5mJcQguJwiGw/bUBZe4H+AM81fJMt73jP7OfbstPFE1BKfSpur+ndHRVqm/l5KVYT2B6eAVOpfTc9dNh6dB5y7K6k0ck/FooCAB1b97MPtPvyPiGB0wXgF7MZ0ssHrP+Lfo3UOagKMZcTXrn/vYtdfbTI8UqWRoph2CJAp9QlP/sTWFStAtHiTfOcu6tr4Kw0DsL9/Kmzd3r7IexxPRyX1CiE1pDdLhofcBt6p5y+rnk7iUvlDZGrH8bP5tRhYcKwNT0oU+FJ+PJx+vpKxZu8r7PqXhX1c/hLpi015LOYH8RAt7U1MQtz3mohgiKRgVkfKyoQqrJolRX2C/jjQRrfyJzftcb9c4c8YXH+yePn1PXfjNShjIHJ3Y/zp/xlYLsL/qX99fy1zm5ZfJ5uVUSqQTjtNoaypQQ6O5Geh8g6Rk0jn2RXev2EmgFOBgu2I/QnWUSJWjP1aqroWYRvhhfP8GIsEVuFU3qfQGnRrmKVOjdmLv7feR7zr0Snk61BOKAJCvercf1LGyKToRf83fsrW96Y2XhPfV/j441GBi1hfPH3h/FvAa7GFfh2pjl53amcnLEY/Ihn6D/BLXCQZd7aqZxW0X8fXRAMaAI5YvjNZRDllpOGIKxudb/wQTH/Po6YFdZdbTQ6kkQlkOFxuW2DBu1ZM/ARQVk1oZPCj0Na5fJCM7T58fOwxRsfXJ7QkBFoRpN/Pp6976hZwB4c35TqZ9SEyR61IXsy+Gah8Oygl0peBvjy+IkujDusSJsxQhzRh8qNJ+MKc+2+x2C1WLcH+KxCWhmJuqovzC8JEbQzARMQ8yGIkxFVn/ZL7Pn2FzBOdDeRfcG93MmYtUPKi9lTujYPy6gMACNTnZT+ZvdfApeZMvJd5FzRtD0GeKJnJ/voBESYjnMadRVSwS4NuAQpX1gZQaQn5NgOfsbuSx2r3XiqbXgprEfe+olhnCKNyiTNT60vHHUfEq8xElCe+BzmdVK9ZoJGmRVGxKp6vfM729riZJPTTYCquGBqLsaP/gx/y+WeVT31c8C1HOM0f9yIPwLHZpm1nuV77YlF9qmXJelsPIPU4yG75AQYWIgbLRm2c7lxyARs7Rcb6kA7YL1D4VfTpq6JBeI1ii15GNWPoPuwtwRrJ/On5KVpC0K6gqPAUbXFbOq5xZyUEYRmLHGwhorx0tWOLjmN3hcu8K3xKSyuY/7lQ057+J0XK432MpHFySmZdt9wx/QRHynoHrk1zu3GPRpq6qp9/nNn7cfhQ9jbefj8Qfa/K4o76tRGt4uGCDpYAO/mL9mv/CTpZLBy9UergqgU0NCgCSv/9WcVmRwLsrzrJQMbYm8GjulJA44NJXZbvxg9lbB7unVF97Y7asXlglfi8mcOSB83PRrFX64oXc+42kqzEo/1YeXarPTke2d4IHrm4Bqa3mj7BVF8bKz412aPPHWn7/z82tuHFe+XT3xDAdjRZaNuPYeZcQrA0pyDwF+A5Q5jaS0xbt4Y8ZV5w+uz2Fj/PlKAhYXVqYzjt7opfn4DK/hH37E7PnIQ0BdTSBN79rzsErezWZ9PH54LpDs1IIuzRuFWK3HhKBdFTCTmIN5WW/V7RktlveBGoP5Z3fb3tyLCrbBYuiAV2rxZEKvYVfpDuDYJK0UcKr8ABL7kStpxHeY6hpqM8CIHhi6p6Zkx7sXLvhyasGfWXtUCbmvVEdcQxEqwtXUcv6TjZGG2L0c9kOKCNHVtnSGBWdZ0L5F1IbndnfJvW3TU6Pk7xEIGuk+HaOGkqzm7dsKYprUZpNsAj50XYfC5P8wfxunbUbQBy25jzc5+M4w3LYYMs5m/cFsa/j8feZvWPru9lGpSQr49c8p+UP/sbUQHLsywi5t0vJaXbMcrx6ZL7PSshtzmYnK4gFSxjQtgRcfnKct3sDlOCpwLG9r2BECsSKM3e0zjzKXUvDzAfVr/GutsYVijh5Mz//Qr0SFtRRNaB1IZiGhGlxQamWdZd5aNv5QZudd2tDUJZM1rsOHu0RrwVJ1mYYyBGaRbVRgrfx/RNSwIxB7BKv8HmHIpXHjbWaxzZ7NCCrAoh8HoLmyltog2KG3JGX9TD4wmFGuP5Ca4NMz69RPqxFekaDLFobViB7p14o9+EJTdF+eZGtsCZEYNG58Q+X4GjpoC3rNCD/tTb7TDRVJPRj2F/XhtDV0QAIwITG2dBlbhstdtlVJ7cryoJQ9MExHQI+7ynAgSbDN2g2EPkGLwQzTXBrQmR/nCqpOvskKV4BypqPXn/wn61MOuNNNbm5NpU1pEdLnETXYDVXOnhE/ooM0vSVozQX95WXkOC9EL6t8RpHbkISCMnyrUGQJxDh00hRyFaanGy0iSrams+X8jkrbCjbW41caVEWyc05qD5yKQXRV68R8q0pX9eSHuTqne7qMgNiTU2tbFdEPlRexjSgQbRgXjjEF+pva1PJIvIHrUigb4po2/17mSkFSKwJH3IKVksEDmBtnj2jzoSmMyZF7ykG6Z5i2dRLgT4q62XX+m7Ni4lncD+2rcDSmsIZLASonB7ijdNr3hpO0C8vCiJEpkdTEv7VEjsRTZ9udncHf3dIxtgGuIQOrUWf5GLRBd5HH1vGtUIS3wpsCr6NRGsohlRdAeoUSp5R9jWTePonEDCd8YlQM7GpqSitW4ZcnoWRefxXz98ypDJnnTmoE6cEn2r+C6UJXZda0TuCP23YHp2Rx9CpA+EVk6ERSnm9xIDxcdBtnGkd/a6TaP8jtDDeZIAbGdqR1+rkmoWRXXRBK9V3VofyWFkXPFnmnvM7+6ptQTGrZ1qfq5GgkZzMZ2ZFgGw/jxZ8rrbQWOLQgwRqrsQsUN9Y2XBl7QM1UIqnENlkqnQlhp9ZAc4pkIZ0G2HT9KcuJ20/OylfYNcKxMK7jAkrVWnu5h1dYkhXe4mxc3E6noYVe+6FtaZ4102OGod0beRfycQiWRkAmYRrPMYcvPOAehShpldgo56ipuA9Wp36PKmVFfXdiEhNbxh39GO0Z/jkjjSqAlrk1BnBAuFk4aVPi3ac3RgDWFubBmvZI7jtLXDa6mTmpMBz3x3pHWZyB2V1W2yh7BZsRa05zRTqUXLsSpUBwbSTSotoIelBRDIXRlm096oY0odLI+LfyP692OjKqE5hs3lJcgSHb7J/QwNLppvqkskKvqUaTC7ZJF/G0JkoasrHWcAGZd5D6xBkHDHqxyJoZDl2DKOv2mifwFXINIvCs4sPSDSgfc3oKb6JbBsaWuumb02eIkg1sztODEbFTJtMjSxKjbBWUOc0KKHbhLW0ybnqXGTnrjISsy9BHuMJM5cWVcaR/IaCBaWrs1yyj9WKmozRn8E/UYWMFpIIz02qmeIDywmqt9D++OJ/BWwodo9Ax0u7MwjhlXFFBDmwZF2nDpL3q6zBreuPbxPwsUQwTZwBB5Xlqqlrv10dXmJgxr2DGtE+yS21CsXAAELpcG8k5qCtdqcoVSskZuiWVztW9muZh8cFQ2P8IHMaoLy6nU7NdO1l/IQ15MxQgl0hR45LaUgMgxEou7xiMyhwlIUM1RvFnT+v9TMlTiy37MIs/SIox4x+yutVI+YejA1SARmWY6ptzf2NOmJLcysN0mo9H0FU+hgteeNLdTDO9jZIeRNvRu4cAJIwoc0Vj1FBxrBTL4YfOeUl5LnELtqKsQC4rLlLVD3p7XKkRVmNossimuXs4oIrQggOZCKlMNzuesGNxtYeYfdszzAxuA9ZHSPcIUfmUt9ozptWdeW431CnUSfqpEoG5pLS4js/piPCld0RJkxFjQSqz3wM5hauPJ3Ai3/h0Pru52idR099LhFQoyLPldXp8Kur4FevTrSb56kNOsbGuewrZNKogMnuxhb6d6a6oraEZ1dkUEQVoel7mjdEYqi6hF4nQHwygt/RhaPoxe521xikGwaEGaDHxC8NKW1xu5gNLiqZulTUdrlbzKCNWXRzBh7IpHXcrG3JkvVlUbsJKnE9yAAAQAElEQVSur6SxYT3NuSLSzSnuNRFa8BIKzJgQ8YNSGKV2n4rqw6ksATJwaeXLoHoeymkmruw7IFuQA27FNUJvFp/9dVsr3z505V7gDcMPUypGalxhSqn8Qht9hCle1x2Vh2sXutFEZYlLQtGjTRM+jPpoMXo0ln6aV6MkA/9kPDXQiThaS2QNY92R8GzqGARVH3gE9oNsLYMcrs86a7xbG6lJdo9mPphtPTYtkJBTzKDSJ20klcFZc36kaYTTh2GQswo0yHYvxDiMjb5VUDTGkSD0+ytnt+iFo6EGrdf+vcCVsF+p7l7eXLMcfwx/nPuHvuEmBmW+a+G6rJmZg6o3emoHMQqaHHkAoiP6B6OqsxZEqEi0lNiiuVsZU6qJxk7/KeEXd3YVaspJm19EQYbRQC2JcDN0hejBhzopNgBVy713WkrIAIO0MUK/TL+oDpKq4vyMrdf5/yPsP8NtO4qrUXhUVXfPtY+EciQLgQ02xgbn+NqYaIOzeR1xvH4xBmwwyUSDQWQbg7MNxgIjiSQhchJRKIDIAiFASAQFlMM5e83ZXVXfmFv43uf5ftw7z9xrr9WhwqhR1d1znSMdMGwTy8hcH64xdImamLLslKludrQWEGWWIaxXySxQgzAiVAVZwCQoAik6TdWgbJFZcvRFuD3vnsI6w8ezkzVl+c6A96AFS/fwSB11M3b2Yd9UNilUQfghPiXBXFezeQd9n3D3kiYJG6GDlQeZsYCOC5plM3jmNnIrNAxViolmkSHqdEaqGttYineqbxp3VpQVlhAFNoh9GAY4sLtgl1J4bEGBGaTG0sYufA4aJspASampNV0xAlEsd8rYEVZ+B60PzZGZc6wRTnAUaVVN9lXdBAxq2mTslGVH5raMOsJUWxVd0fRFfJl83pFlxwbdWpc7bDJUSb1dz1m983lCI42mzn0lEkvKrthM63Nx24Yc6HWMSaM0gWXQ7kiytO5jeIQu+gCWQstzLTvIpUSnl0rWCAbTQpZBGgLMxgms/QAkJZ12bGYpbhVolW7lqjQTAhTRSZjDOaXzPUyDoWHljighYo0R8JLMkKgQE0FbZ9C4rcccY6vuUJ2VT9b2bUsFWddGaYsVlzIB1cfwpWNQnZZqpSgE0rMMTAru+jtDYQHbWvJEtVQKpLG6I7pJ+rJI2UUdAsOYUK3aqJ7ajRmgI4FY5a35w/cdlDhEdglgdRh34yErnpr0a47cRfa+zXkOJwD8bmjqrFSre7R9CIsWCiAtfBpe3FxqL9JVnJnaUwg9RISqIq1wID8hTZOFpSSEgQUYeUpcBPxaa2xrjCpmpF40REsCmVJ4j2q7O+1A2zfTPySDty2xy+o7wYpNNglKmI86J1d+Lqcio6g0NIh1RUxSKBld+7yTyz4ZjRURbUHzXmqwEkilNbyXiGXE1mnQvOiYycuodFCGGsCdjxBKEXJjYRRMuqjmqMtCYsau9QPTvOUnV7oz9zb7JijaWC9Xpou0zJY65aaAVkuKe8R2LmB8k56jJoxWZ9IYUeNwhMUgRMTMZDPJxAfDoBnCxBeAqTMgXDy7j0X3Nmgm2EidkjWDXUOEXEVPhnPZpXM9qLQoSoWIGNQcJBkjZxlEq0oCMoza2mZnYn2UDccFZKvpusBmSFZlNIdgRETvtgxbUgfSom/mZeO5w6TUnci6Dd9GcBq0mMqUoOU5XL1rrGW6j5FjGMYUvcEn0AEDBsCq3T3HIiM0QVAiwEfp+wcWlk/ZpPDb5mZRNlX0oBn7fNmUue3b3dnX24QgIyV3IyPSkGRV4UPFwjqTSYb4NMXaSB/IehY1rqyTeFURAiK6OHzmz1IQ0hL76nafDk0Z5FnCy4jNPNoypywzhxZhxmHqqLNypz8ih4/EyFT6TBuGO9OpzLKzK9NuYh7wzgIwkSU7GC0oZTiwJgd/e8wllyL0XCW0Dt0EKlRqRTFTSlZ39kJURknBSmvlb6CkVYEYe0kU1oJiyFUK+VuXbLPIVsirzPSULFGn0XYW2SyjbAM0bjtbHAjdFWGqzTY4DkHBwQHiLkuTZSP8JnEmxOSLQBGNpmdosGpN6g2maZKMe5ZiS5fd4bt9zNg6ZuOdXYITmcs0z3NkzLKwtC1aXHdq7OzznR1ncEGubJGOdQdAPBcGAT4i0bnr3fU2csfSSh+y7b7QUs0iSx1z+rzY6LqSQAaZCxXwco1glHJhLauAFWTNXrAVTg6qEs9CyFSFUKcNKV0siKrRuzUwAavG4iz7IjfDp0WU7BiFZTBnJb7SEbVg3yY2dcUhSsnCYi7CGJlGGlaFpizDQ+G23mOSsUmdRm3bZktG9owufa6+mNGBoWNuvuVgChhZ+XnRGTHaErWnpIdBINqTU4BFLNUA8ZE8t/d0koKfaUsBYbEeSqdnwQy6joQISycP/01FHb7kEjmIHVLntP0hDN+gtV240yYhDRkxvG/H2PpYGOexKFxKxg7Q1pJXZ8tOpLvoXCUng5CJuqvcRhQxgGpB5xbIkjl7X2SVPpW+L5dNZo2p5kEiVVx1iDAOFE9HQqVPJTfU0sgaposjmyq5Z16wmEeueNSQDLJhcUlyq3YpCKuEdLFljDnGIhGUP4xgSlJ7Nw0pFLdiCgQxlUxZAsMZyboBiQFxzmPSLmiBokLKbBUzPfIoHSWzNGwqEXVFJuVQKiF2dtb1G+02L9NYqmUtOimDJT0ZNIUqV+RJzCgLnizhCAXz2liPmwUrR1R4ldGSHQIkTSFFNmCF4Pc0G3eDqpkolPYLmAbdECB2xvJF21VYGYTMNwxBqlUl4yUjSartpvcpV6MHIta9BEHycF3RntpkpQSr/9Ln7XBSI5WMGiBfBoRLQTZZRPvqE6ApRUXNmDK9ykyCRTXGYU7VpdqYGmGdE9yKhhYmR83YF+MgszJBSAIFy92S0YFIGK3dAXbcKrUFQJWo7hP57zlqicbvoHiCMe4BPHrb9qn31vk23UEhvSy+r8dO151Rd2CTohBaE/ggWYhIiI6hOdhEK4wBEaIXQ4bTzJzHPPssmZOWakS8zLs+z8PTaYyss5O7zVhiuIapVNDrJqLEWJWc5NthStbDkYMcdUUGcqZiqIkCEskeZJKACRMxIpnDYsggfOZFLcVGanDMOnyKMPYCnERzM4yCtCE3PjYjKygCUnOojIRosCUBEf4kP9Iw3aA0MF4tlQZg8RHDyUztbiQjJ5N0nIewIk1UB5uVdNDhPnfsdl167Z0fSVx3ZrslRIVA1VIrzFBKqVOpRWDpmtBRdDTLmtKiNFe+yWKyz+qEjUUrUDJuvdv6Ka14qXztFDaYarZanxKLMgAutqgNoWDTYcKS3AmIBE2g90GTQuEio1gvxYUOWQRG9qE91MGQ+eCQDHGX8Ci5NHSR5H6kJyUh6VESWkLYLQc4q4UWQNZb1nGjBlsS1ai4wSSzE8VKYvVuY6nJI48Yh7kGZymtykzrFKYEHjTHM6WO0qgDFoIEgiFZ0F3GYI0DWY2R6XxjCQONT8zdl+B0elVi4bnMGKgV1RKpZNugGNfsRXrxyIBTbO1oWUQsWJM2kTuO5mGL6IJ0jzFTqC/UZsEEr1KUDqcqDe5ZHKK9J8cl6IWsfda7zEm+ZUoK+URIWgqVNBd1mjFE6DoqibL1mN27j/BBfzCIfPHE8K4xVteHclgnBokWrjRuFctAmQyjCqdvlREcgUgVUKNI8perutFdpbo0yDo4h4CrVTCI5DExZ2MBq2RXcWJhAYA+gsAjI+EcT/RSnLEPThBf6hI0MSSjgKtNF+kQWsj3wY0QkxZp5GAw6nyiqUWCqAnqSDIGpC5lIRQLZEDTQQYoxCFjaC5cMqtBdCVfBHt91aMEN+gEeCWE2C/wTqBSM4uAKBtFReTclxFbgPCgBIdmhkdG7k0zlBUcgZcYVcjlaImpS3WTKiubFoBrVVgCopyL4GFgq4OUkzSubpMaR2pSIt1wUxeSrGC9HFQGdBPCB8Yk0jOZpOS8FLQUHjBtABxQ0ekDo0++pHXUhVULrSpLRlNvyTlUYISQtgAZMQFNNUVGMm9XUQldEjx49FyYMq5CbEvXQjeIKInJpqErHEJ3lf5zOkMbcEI4A3CQzzZWH1wLEx6z5wxxs1Dbq14gxmHiSkRZxLQwgcZcduc6SKgB1ahl3QuI+xjGEgJ1GPlDSmcqQLuUGwPml4LMMXhCkkN7QZil2BgR4QS1mdhG03TQi7JXCJHNwRIJyoSQI4P76KYCjcQYK7EZSqmhyvAiBKQSKpAhg69Qs9KEwgSrWuWMMVbYFqafMz60SVgLuF8C61/mQv8FTnElxfDty7W4lz76kM6gtypiXBV9ye5Y/0hIc9nJnCJKzgtGl4AFVnRTdV2/1Dk1DUlCoEnSKpg6QACISvKDbSCTwxbCmKKet7IraLsXG1CyO4vw4p5YMhO5Mo9wjgIpabQ4yD4Q0uSJbVBQRxmYkmSfbZm9L56uZqpKMUlXLehNpGXPMcTpCKMWKJ2ctZY60f4CL1hMF10LZXRNbpglRLvaQOXsTfjGt6S3lFBTTcFAQIYhVDGUychpoeGCGOjEaEAUMKbKvPjiu5kjIbb+UWhyIHqtXLnS3NCbhkFDmhSur4UqhDkfQ+iLqbAP67CSbgz/gu2IOWmEcA6tLFBXXZrMBZ1UXdaz0w64n47MGi6aKTyJQMKLdOW+XRnFOXTOMlJhVow5WoxiklbWbJMb1aY4WCjQkal8G6xOyOCcrbYtJlizlUnpGkuJnukjzKUMk5TAcNtyvIhAzSFOGV21q1CNWkltIZy8uCxCLSZDiXJZOxxcwExA1jsrS/GBhRgbgRQJLR7KjwCNmakQXMyKWa7WRQ5hjbZqKeouviAoH8jVwNTIgmTsijDCzjlIET4AcFtEh0rIwGD+QZX60xcSKJEWxcDo8M3gdqJiiA0vS6flDqPcKtwSBDHFyO6jEwD2ZTjfDA/PAdlWdXJaIzSZpLGhh6KWoAQvCkQfV11/0yVXXnvpNdeMMZTGrXASClOUjuWKm6+75KpvXXbNNbf42OXwrcsgAmJzxVwGlSEiJd0kKQ8UPfjiUSJaogR1CGHz1HRogF66K4MwUjjRuP52z07lyY+aoqsk6yueQoz3wHcHBCKMgtFtoA0WM8IEGyiriyDJlvThyDRdBwa4xuUitvBZUlSyi4joAlusujAIWTRZg+pQy+QPBQlhDhlBSEmrUqSQIXWPRe5cGNaoaElIepCrDdqkTsmmJKVctik0TLWqtUpXOMpphsxbdRoaHLinhQThli7MR9VRLCAlSgMS68UCyYdFzGaaQetlgS5BmwmOAGphOiOImZJsY6CHODX1hWCW4KmWycLz2KKeQVcYiixiNFtVk7ZDuoIHba+rKPcxOqELqk7ViBF7JAy4q7stPlJna8N2etZg8RwGrWSzYGB0WdgtyVfRYsrHhcUy0Le6zBYsdymkcrbIjSQ0JBfpc257jKTDItwcdlaQ1kf0WJcClBQUFAAAEABJREFUUVMqIEfpWBqLyJy2BDBCxYWTtKAyB6VqnzAztkUiM1g6yEMxMZapBAcK1wjIqrXBqlSIcgTLBw+ovaf3ZCQzMxShKoAJj51oJRUpY2TvsZdzoBWDtFvLPw0fnZW+oBNVKTBjrIXTpQqKiiZizcBI5KigxlS1IptCAEWcpNIUUU6MTfEdTWj0YgtPKVrEeGly7pwYKiEYObp0FFdmMyr2TB2rElioobAlVDtJ0SUHbTdJmpHqnbuRnCmiEAaFJJjBsDnYtbCGGFli4ZoxebYRdWQd0DBCJXyxpI9NSClRIJCrUYsyGSYvlWRUDPeIkAE+3dp0b+t/0E90UTD4TmtdNV1oaBG+EyGs6NAkBMlo6pLTHDtzL2MZzsew3dTNhgiDNOAJLyCMQ7N778PTPCzTkYuSTLHiYgPiGUHmRXp6gN3bICJeaVuyTnIRkVnGVvuBFr2NLIvnwhEBAhY7tL9DCCOjaCKZ4TlAOSJI0Eu+mhSVkiWGcmanGjhJWMssTQZzXjUQIgISnotKWBRiu2amBQyUj0gKgkd2zy5YNGdjFnMehAUp+NDLqmnbYNqXtiHx5opt4V4rOMZrXeom4yDxnToqAmCzk3y1MPdEhkfs5TdoR0h4WZYplg1iE8K6Cc7AKHWUAjRNs6D7skBcWmBagjGcFq9br30kAQUYLCIRIqIiJpSQzp5gHR3Et0+TbzbRWnBvaw6BZDbOzOwpu1EWI6BUpUHXB2mGScRayrSIbUGMya2qWlGN5QAKdbXOCGWQ5kAWySogXbrw0euy6OK+aGQLsyyG4iaLqo9SRqlRShcfmMX7upMpSXV0OjtC0y2RBoYSKlVyg07m0Cn62BnSoItlSq2epBWZYxjJMeKgDdUESoslvZbgcyMVFjxRSg5XB7yJT5oW6dBZaldbOCOYRCUhrNa9q4RObpu5GIMbklS3cdVME2oHPZ4zt05+WvjGxsQSHLtYcsxlGakYlf4KvBQX+kK3ooTVZarbSTrJhi6SgjD40BxARkqn3trNPEUGuxgnTac0y9HcSwxb54WVUoxBS06KUZdlx2MqSrSEY5zgLsLYQnelbaXOKRi+s8U+BlcipyE1WXAaDSUHaIGlZinZkOrDMUeFTCyJfHwjLXNS1zKGBaMAoQ6pzt8qSsQn77plsQ5QikgD9iEa7dSUWtQ2mYX+zUWiFS2lQE1UiLkngivKIMAiYEMWSFnViqu7LJn85oYYVTGmHdZLDVSaxAHLcE9KqgayAAt8Gb3nSGUqax1ZnOzsnmR5ZllvmpgZNL6lFAKbKgDdUFO1qNFLdwm2tEkq0410wBgZESJhGSYkcoqGrLZAVcjE0oLlEyKCNAfoLF+zElJN5ac5ozvSwVktsJ5tI4n66FuuXzFV3dnRnU3dGIlZRIsb4WM2zMzNVLis3BHTnMBjzqo8M5FJ/vMoVZjKBFIssxmaCYm6Fg0rK560lR5RdTSNTfMyebZO/hNro2Aoc9iHJ4aLr3IDHUR2DPVACjSjkoWJoaS5dTBe5AIN0AQM2Ijyuw66HubCirgTQVr3QASEV+Km3e1jXvaqx770VR8+/4tgRoNSFLyS8HQnvpmewthBF0YGO6vCGVwLxryBN2NCjTpGLgxydlogXN6mgn26lOiiEkJ4JKuDi58HnBC2QbbZsOxKk5KhVUu98LLL/uyfX/HIf3rFl77+zXAE2y3rFPu0T2Sdl86UmcwriQEQhNatRmMlCvInUDuNxAhWDx1FWVY384ZjVFMLrKapGoPEHBwCSVRI1arsVAO1+ZxJsAUtGU4REAUetVM0C9UUm6Hr4/MeRIjzZQnrocn8LpTOLZesJar5sNlzq7lrPpdMIULB/JMkFxzgAkr/ZBSIMdJVZ8stdOs2NFO3qUFoERvVaoU1IscAtyQ2oi2Kbsl+xLADy/b8S7/89Ru/FUW7ToGqxUxrSZZvnPeZLz7x5a96+En/+CvP+cdfecbLPvSJz5FR0VW3Uz0gHz7vU49+yX/8+t+89Bef8w8Pe/6/fOayyxJMy8ElQjrS4WHOmKHsQoersVAsQuD2zEZJUQcNEZiwbHF1CA0BJPhKHs8MuXDMItt5u112R3I7GiwQ7E6TLDBdpRihiDlyhDgVmXSWykJYIxOKWA+WmZIcMMKXSEYZWSyUO6BcQjxJdUvlKzwVtCRFwEK18dHAElIiNJeM2T0SgqKoBuKlCvGCKKgpU5dpLlwHdU1zrEQawzJJGbJ42JZkHQC0FWltrNCscDgDOjx4ptAt2lZtG+t6s43ehTAEqmqrlVnBKtHVuy1u84iFNYZkGsW9ZhAQ/oTIUMwmtNMW2cxaFlGqpA2lQ7Z7nSGWQh25G33OvvA97Wk7smMETBMaEqsPSvss6HH0DAqWCquUx2SNecbStQNhMK6ptYuGq7qKtVyLlUh0cc/oMUYwrbppsHhWSRVWBcSqBuaj5rJjLmrkVpobTW2RFgSLtwsgYTGQw3PwfRGuJiaiVJa0V0hXE5b/PiU31zQjF0n6CCpiBHIO38IXRAbo0wRUJTHmmnMO5kJqAAnwNZyaGqCWhqAUAF2lN94KpUCwURLJqzuhQJqLiSGLrDoD3nOkuDUpGqojddBN9kVgCA2wIXUWHGA4CrFZbOl82EGawUcfy5LztsQyRTY6n0rDh9B2mqci1YR0C5HOutdbirnyW4Stk9mpSp/hYBaW7WBQuDBUQlESLbJisdFluDoEKxgBI6ScyQ35jOxsXUWUXBcZjkJKIolzX12pFBGL9jlGiCadwGY71l146CTSRLjftYVkoVBxKxQvkHUhbWl1vemSV3cSI+DMnaGIUAIjxm5ooUalUsJGbSUh4jDmLJ2ncSVoe2JNorpp0Yqjjl49LUXoH7jVYcRVF4oPSVFA4NDBAAXTobTdWreQ7BwqlZKGyuo5PHIw6XyR1SBOE4RmWtFamYOKkUwDwLOGT6uDrPUxKrpNXVvCKBV0uWXWBHpm75Juwq2p1FRxHd2ya1nSBqSbeClu1WxjRC436az2qxQBIqk8+HsSUTEH5lRfNjo2gpYr2FWCc1FsLXkWnMQgVaNEUWUwpNADeDrCJ1Wpwo07iodKYhpuc8fscMhSzKsxfcIohbY7mNqpaFWaFjVNYTZgwNYRhWCKeu1eWIXcEwEJQzJx2pVX3fAHJ/3Tbzzj7x7zwlf03ZmbBk2n6+TN6pRTnFilZctJrzj1d5/xt3/07H+4+BvfQttAtGSvsbXcmnbuZgY/w2kObIm6UGG0QIVayahcG63XkvSrFwzuzMVipdRqxV78ELQ3uba15mWzznQVmpqBjMz1J4QwlxKFxKFQ6TOcSBIG0aixlEEgoojQODA8oSEhrFBjwW7IVurQ5O+MOdGJeG3MeqxoiYNiMMGqVNXiRVypHRRWRRqYc+GSYlJEmxqU7BHmVnQM1teBAJ2li3SBQRBtguaQHosHj+xzMlNShwVKb0pIguLc6DytgqAATTUTo0uETQReaFp4N++MDNO1YRTpAyyF5P9YLTa4RCr4mGy3yqzMzsGMHSnchneWc400pc3FJUNCLVWyRE46ii5UQAgVtQhB44hKp12H6K6OLXiOGopsRavRNjJ8JWgUT4OogMKrhzE7YqUzEeNjGCwL+sgcKYMMhnj6Eu4mWddpDhrJ5XAWBsHXcYATcq2UWRCarCtYZE3hVE9zMVc4QzKVZGniMIQDpN2ijb2RPWI7ZM7aqVHKACCAotYsFBuazOa5SDeLKkkthSIJdFZPpV8iW8NQoedE1BKf/sKXfvVZL/+1Z77s5LecxX7jFGUhIQ/BEMUY2UXSvvy1y3/72S972DP+7sMXXOjr/6RhC9l6oyULtfFJhLYx2l5xSYisLFPqIw5BChMnlrFlzM4MTzAIYTIUQguZKjYJmsgEsVXvGB40RYWJgBTNoiKMTFEaJz7GcKykKJAK5vdIIYwgGELdoJ+Do6RD+F61mKpw4S/DCyAF0uA+fHGJrAqtXWxRQZWqOgE0mDjWJL9I/PSQpGaTopwMhomq2OYMoESBA2MsI1jeF1VqXRYZXRhrRVHqh9ighTnm0dE5jSZUtOh5zoVf+ujnLr7y6mtFJAl3ks3mveTCqemkXXY2ByIlID2Lu4y1CksyRY1GC0GMMljJRo4+BmgZqQRydHHOdponNB/CuXW4UQ9hk40mHybAssPnxNU33/yRCy86+8KLbty9cVgkDKk2UmQUG007V+xKM2ywIu9O1qsJnXeXJbWbCg0GxBMjOYaWC+cGz1NSJZpFsYCClGfq2zAb/J00PCR60FWkhQZkFjrPJslBg9vMKiLCmJD/BWiGhr2rMLl6Mnd9kRjsEuZqWkKCakQGCogRjUCOuvDpMLjJlRL7d/e/+YPnveDkN/z63/zdb57093/84pf/01vf+omvfLVHuBlNGYSDHmmTSaFJMZukTCamZVEui/sUpeRrznj3I1/0b4966X9ee+PN9AYsdstUo6riA5++8C/+6eQPf+7i6245AGBZuYZgIDLIlTM++rGn/Mfrzv/iV286sGXv7tyt1Gljm0m09KV07gHTTNEUNQWhLgzIKLFUwNSKFjAriERUiKiIDQMEilzbTYiCQ70YhNkghC8KiS1lTlvYkSEC4TBAOHAMiZBMNkgKpwgqrA2tQywohrMjG3ha6BkuhqqtSYteB5e5rJrWknOi5BoFJAK5FOcWkhYgWd2YGBwl1bBuSYmDp+vwOmL1AYCGKO1wnVMWl3RKCrokyU4gabMCJnw3OubBsqhBD2mklqGyKkJG6uy2de/hOVJVeUpRE5bueTJvxYT7HnrqiQ4ZWboUmpsyIj2SpgNFiK1Ulhzdc8uKVbFpJJ+H8gBWs9NPcToRKL1XXzRHWPFC4DJLElSEhZo1UCNA70mtqjJRkrZAXWHNtjGrlmErjF4rd126xiVAKSiKimzpJZQtXTFqRA12OJmq6jFClmCJt1QHXIL0qEIzmsNmpMtiGHRC1UZBljRVFVrVhtWFrmelloKiXcfIGN77PMbWZUgbIl3GCF6MwoBnDBuxSbTkurCMvlWmvyfCB9f/njSBkVj3G23NlEp7G1frusBYEaE0aek6gjArfwqk0OuyEW6F6sRIh7uCJ4isUYpwjMMDCzVgZLJWZZ1RHBIsldojhy4+7WJaVlqnM+6jC7+LpqUUpL5YrN9WhmER+oTQjMhwajcRgxuFdKHabwefv3xR75Xhi6KEUFwERAiKpfq20QNaCl5VY7JQRAzJIQj6Ay1KwVNGTVdJWQfl4M5Gw0fGAmJHFFvfineNKFzckyGBhVUvzUUVaVRhTcleoRCIN5ZhOiAzgYjFctFcYB40TSCDoHCeSFHNYoP8UpdMGxC6W0UN1WhqRphAShkUqMosC9Go4g2YRHbE+T6mbfAAUcChAMUQKGNELJq6iCRqcqUsbTQJiaBJY9hWbGkYrAxKqplIqTJttE0K4mnrX3OcSUivnlV9aU4vXExsygsP/S4AABAASURBVLLjVkKcumAKAcIJDQiCIOll8cwA3zbH1L2kC6hlqropZmV11Et44RClrwbqbFkKJq3Np+JC9ImYJle3YOpnqIRBqrO/mRapRcRUIHSJARCMIMLo3GXVUSRNISWDHhs8tKcRy6iIDdZ9a3WAmQBIj1UXpxVRsUKBwhC4CMyED3EbkRQOnnzY0hFuyCZSgHL0Ybe563FHXfz1K86/6KvvPfdzdUnrHDFLuMReqIvGRr/w9W+eec4nv/T1y4+9zc5db3s8UiRDWR40TB1MSkQHtsr8DVqTnM/NqXCpiVVKwIa2rnWAPUJCRipzt2WdaKKKKhjUEvOOjlZS6btSRaxSefAdgpAUkCtrYS2YhVCxIop2LcRxg6yL94ULuUG1ebVeOKcjO7M5BzIQQGoIpykhG4wfDEWgmaAyAaBg3IoQZdExMb4OzZrBisYxjiqYKqyECcPBJOTutqdH5qw6VHNSn0DfDCBzuF+VntwXkU2sFbJNWTK6IU1atdoUG0QBhqgXdZpBKyNUhqxGhC2BbbCM0U8bZiEWQScGpdBsGXSyaAaEygOsdDHWQawxQZ2uhH9EDiFMPrWkeU0WKz1hwzfBmiE0BZpSY1IqhsBYtpc2QodHDK49UQ1NzVSEtrY+jEiRHwViCqU7KV1opSyQZWSuvExKEhfrKosuXZa5EU9FMVI0C58bDZlpGEPjWaCEWFR6s6VZR4wxYiyIcNUuaxFOWY2kGU3MqCE6tc1BTjUIHUhU528gBIH1EiUPRq0wLqi54hsLklDwvRaRyt+LiBc4WupGYjMN7olcInHLdveLX7uC9yvf+v7LvvFNA7UtJHlHJrtHWOQkdVk60+eLX7v8pv23SHD1cym0JJzhrwTK1TIrts23wqiqQIqgAoaS0py0GVzKtlvIbuqwopOXaRjdmjoPV4xF1r0JwRoAcLOf3K0PDMFQUZXKbFYpBQmQxnCvfTSfG0IhrmVJkAwYiR6MevFQF6FdNrm3xScykrM30ot3X8hXGq5ikNVVilU00yIKlaRCyYmikwkVKcV2mkxF2NtHjgiAY0yygbL5NagPBxzVhS7lIHa+WqUwApW20Ps5hidtWxNjgpgY9i4FWqYgY71Al6lGDbYWtJ4SIeKZ7JcYpiIjSmRdQwNRgzBGXsKVWumYB7MZCVrky7yC0aVBhS7VFHOTYZqcZiaM0FyF5lP8ni30ClkkpeloWIxbIWiUljtFWtBlWuBRbGT1EFqSnXib8rjKEE7AJrINkn0J5MLsC8bHikDryDUk7EraUAaVp5t3yQFyhxYNkQXuY91j0kshV6kSEQleheRGKzC+J/hI94zhOffcJtwMKhwr2qeyVHMl7qIhyjSVzJZhivd/+gu/8/x/ePar3nDK+8+7+OuXX/yNyz996df//Z0f+uOXnfzHL33lV791NWNGWTlXZ3aoZlEKSDqO4tqirvq1gFH/9Fe+5pGXXnXdN6+6rgTKEJ3VOhDjZa9/+/7tcsjO5mm/9ZAP/P0zPvjCZ973HveUASm4cb75lW8/a9v7kbfZ97yH/+L7T/qr9z7/6fe+6wlTNS7w2ZwHlV5nbga1cDiMC6suaZlZkJMk3cyswI4uk3QC6+wiQLQRSacdyBRBQnkAytZabZMxbcBFVlkVvGNIpHTo2INS1EnbKE6mKVJdEKb0nBo5wlMlS1VrGsa6DuozUSO+ktPom/AdZZnXbBHVMTlaKCITY2g4qEw92miVAk174QLKmKzqMoy2RgHanikh6cQJi8naKJw8IMJ1TrJSQVIYxWFBjlRkUVWWECu7giUFBGis/wgsrQ2qh0BU1pcgKrRerDZsanDtHehdgo6ilVEKs5eYJDBM0KLUmEo2S02YiEw1NxoEPHRxG/SiCaNAsJRGYoT4omXmdruJFAWmkSWkaiutiVECi5lbWtVpslqy5KhwjjTSPGosVQebq6USs5AYGtCglknQmD1L19ETg14b8Zpq6CQKJmjFtjAfA0z8xLbW7cZ6GZkdPjBE6EJNLa41IYBCLKUKW7ibIC32PhdBImcCa+jqXjuDAJgioI7SFDsyT7mbcD62PaBr1jH9ekfPEdiaE88ck3BnwHrTIDuiLUGVI2JgAKPmshnccEQVbyWmmk2Zqntc3oIpVLSgaBgiXSRk5Nj6zKKGreS29CFUEogA0199qNNoQYqkWinNuA8shoI1Y2rm1EMjo269zgPrEWyhj8rxRRiDFWFgcmHFYDXzMqAZMbluMhU03jtqz8nrzpgsp4AkOdp6Fr71NQBCaAOWUQS2OitcyEsXk43qDmSSUY1yR1jQWFCuABh8Tye7I5gpyRYRVEkzYGPBJzwbh84g133B6EbfGdRpViF61lI0qS3UOCxS0Z2oCIQXUkbKom2umyVsbJFb3+SYWrfWR50X4eCUUChvSEeZRVxyNYNNkAwZkGEaJkmRocKgUjqkSTHTJIDESUeBNrSdzrTaaSH7xJpUFduI7UOWHERlTGXhohvzWFhXEy21hQ/vs/aoK4zoqNtoy0KHiYxqSp3HztxreEAXsW0WuqnIDc0x7coImEiAvAdN7yZLp7jMzVZ3FnB0VnELMehkbQNmSVHXTJBWC59uz46uQKWuYs2xsyTxEvRtzEtfEoKCPjmJx/e1GxYSjhYvrKeWyeog0kVCiJ0OXQEFGeQVW7GBRM6SDBwjQL8HLSEYkYQWHCMmk0rNLJS+Ii9hRR7+4B879rDb8OMr3/mRG7cyhlkKf5K8UyE+zKZ/OfN9DOC+Vv/gwf9rHwxzH87takal+pKiAwhVt7qgjWiBaqoKWuRZoJVBClgHV+8hY6l9t8SM1jt5vy/r1KsxLqieBoiqMFtb6ZVZ2YNStNJ+6G76Qv8UfRNDZibklLkDFJmRK7NLX8qYVUZvGIydd5m36AKKJgsoPVGAkrPJLpREJWYkHE/wvkiBK6InYz+tXshesBk8IlgQk0QTrx6FEEvnEpM8ZlRYScqGDVFnBmlIgtJimx60Kul5hOYoY9Q5A96LS8mqZfJJ1XQCEYILsyRTo5KMEmPpfRuxjh1jWv/TrL73D+znGokgXrRFpLjUUS0n24oydOCiKBxQ1auhSZKWtl1YMcWtepk6uSrVoR5Kd6sUrQArvi0llp15aTMk1WvZX2W3goWAKCREwM2QyuLmQ8rMKuZFltr6pqCQKzk6yAlJk1JzaqJVwQLDlBe1qAhxx0DJTdUd1TZQe2rS2EQCqzmSQR6L7Co3DluU0JICB7Wze8B6n2IUT+WSwa0POnsoXWSiVgvGGYBCjL9Wocl+fhKWfQrD0nM7YwGiMGo6VUympastmxJNraoyCrPkorm/b1chwC1zf/Z/vH5st2OZ4bvwkF5UqtUqte72fuswFdnRstOmag2iKikBIY5pzdQmix3tqiFaQpj8xSCtYNrEpGm4Yf/ND3vWC3/wT5/+lH97I2wbpPCI7sElL7P1bDAdkaxMMoLsgFCvF6oAWCi2RaQWpesqTIQtZtcDOW3pmuYwRo1LSBcaNTjAEkyPnrZIS5SSalxVczskOmii5SYxDeg8wpcIl1C60VuZWw3uMIoW8YbBZLfePNvMiEmvMW88akKRks43FWKSqC4VWgo/0DpmlAwvXBXW3WfzME+IjNQA8lY4914zbfRNLFPM5Jo1L5YTsPe/VyWFLMic1JyrJOXLZmg7UIy2pNy823/jaS/7/kc/+wmvOKVAFWZRIjZLTovaqCAyLUXHPo2NolAymZAykutjr+KbkpV2CWgTeAlnoJqqJ/anHciyFUuloKY6KaqqZC4+BrPABxEgt8eYdTi7GslCIBLifUreo9KO4lCTECwjYhukFmGQqkWIvtRUQQ2pfbRlLktqiJVo+2SaUDxyzF1iaRVW1nkABLycNrMCubgz8LR/pgI1pLahjAltSJmLzvvqqBoRb/nIJx/376+/5FvX75va/e9990f96oMf91u/+CcPve+P3+Muibzgy1//kxf961ev/BajlyF9wdb1HR+98Pv++G/u/Sd/c8EXvpoCMmtIxkiWvN+8/4/e8egjfulH7333448XXRESmjX6175+1Zevupb2Pfx+P/Yb9//pow455MjbHLxTCgOqol/+5lXfuO4m9j76IT/zkB/8/sMP2Tn88EkKXdzOS3enIQnEInMHGblt27kuIWq+ISckVPfiFCFB/wlOzAM8q83el1y2cLJiweDesSO6CayYTambnlUW4yMl7lQkKD07Ss8yC5sp01lvy4j0XJnhsAWN4eHWLXwwdBWy5h2fkZsGy/3svhXttQLJSmxaZbVtUaZHC9nXUbh134p0ycUShqKijBx1EL9ZAjJKmc3msO1Sx6iCNYNSVDAV1+oUzRW/BsGb+JUYXVvRdyaz7EtwTd0MLyPgHMmSK4laBBtmrZZiDbmZUWamPHYn3b+DXaoIgi0tApZZ0zbaTDQs5qqzMe5JKRpFaM4CkLC5pNCyYl51aaDhy1LmvtnKtBANL0zwaY629NWOwAa240WDk0CkaqCVIXWBJlLTWWK6rZhHpowA/xCZCXRbhskoQjhxAL4do/eBBbWbrrKHxxBGtEppwrCKOXkBDRCWZUKvIyUkExapyIQEEwiVRQFrlptkEREGgToU4NCIYDRSoCKyzishpVRTFWCdZ3RgSioiwyUK1r0BqsRU0pQq0MNHZ7UjimpBmUHhIhRgUEUbA/NgOpm6FNFqpuxgqD00BCgAX7FaLlK5Unm1TqcCUTSbjhaDFU3WRTWHjrSBEjw7+GYopA6b1g2BSmqD7Fg0G3RexUVU1KyaNKVAGUssc7g7VTZgyiwcgyI5SRQTKRvIZKa1olK1cE82GA8vlaYPaaNa0AVL5XhNDrMitZqVYiB8mvyFXry3HFUdKilc/bl81BHqQ3XU6rWl1RpW3Mrg4EL4EmniBl4FSUKEYkZ2LiDWQzxjjJEDNTc72tQgxWHE3zS0+hrmCFmS6WDarSws6iRNaHoZiQGiPbRkEuv0xHZ4ZhhQXRI2pIRVSJUkOKSOxGJj5pBhdSiHgOEQ7BMu7yGNFXVi6vaKmFymbsbyAiEcViXEIpuFCOsZDVddaGVSldfsXB21SRg6ZHZmUs1RNCUjaFPAEwkGkGGBuGsGIEkeZRmDs6qwlqzW0CmNkI7oAeoU8I9zDgJC+ZyYg0kGhovgZlpG86hD0C0TGikaKIxKsvg1T/MA7U0moA/jnTARIDmab0PURZgvQn7SXyWJbARnSa728WRJQGEmBBrhsWQuYI6rhkkasgRAixgA69rCyDIhhIw4vGCQ1sg7HH3Yw37qPlR7+XU3nnneBYsx0SajD0Itq6cf+cTnPvqFrwB48A99z72+484U6ZmOXHXwiWKhGfSFMpvlpEL/qJnvjQWzSJA1WkAGDfg80rtFb5GNajRCYg8epHqR0YSRADhf6VRzVYcTIelKxxWRiAUYQ60XEkK6sGnJlU/DGnEgeTDHGIvrDKVXhQldXFCNBARbAAAQAElEQVQSm95rjyTmFEPvBJTOKhrJNl1fkt0khQeVWnYyRZLg5fCuvrXYinOKrKnO4YXkVTHeJixFjNZIekRsPEfErqJPgcbIScmq3PxxCBKrs6KDvsls6JYQMl6RVLZKoyxbT14LRiixK8qYEAqTwVNCCCACKjJXcVBw4baSpWSfsJrVgiazsENVpAjFpkfESGPm8QW8XCOlC4ZKOE0R81SOVLaQKXVhgdqBbGAiFWRrL6wFQYbGYHonCLNFEUuVXvpSF+erEEbPpErRYtKglCkQBZ/oqGmyM6gYYma1KslSVZLuqq6+O7iPWMdU0UrUkqoTJGl1mBKmmi4BiqC5NQYfrhilNsNGUGE0JWemOnk3gRdVlwRneOgy2rpmMqcdPXMIvGXWNCEWkUHbqIB3QYgNMd/hMYZC9u4Lv3bFae8+tyzJOha74cOEDm5UJtls9nRxmCp22uCuP6rxJOMCaDAzY4MswoBId8gyNGaGIUoZVoPgWGm1TrUpZdx6i4ssqr0qNe7GckCiN6A1Y6IRBE1xlCGV9GdW0UOhHKozNQNypIxEAnSu9GJegeYxISpICYYtsQQWQlkgmpTN9BpA5yRjWLWwEMqWOmADJItTKm/GyZRCnAetjcsmVIWTsHLYqIIThAVqm3AwVh0WpXKJQ0EyCUsYVxqRSJroHo4QpKlMRaC0jSiRCPh/rkwO8bR0SAQNMS5rYkNUTHUH2szF6D2XgTapTpIKySTwwkL1bUFSdKo6KVklKiXrjtVWKpTEUg4zRBOSIZVGdfLL1brVqI2yvy0DBjGoKOh/KBmDGCyCsmrMYmHchdbVRhfuP3NCSiIHkaCTXEdRaBbXXxFz0xRjH3fJEwIFnuIh3q2v+QVY7OUxBHCR4C9TKdWk8rcNldF8bj5E1UkkbQIFLw4siiK9ytymTpsIxSDSI6hO2CM1A5m55lkJHiqvuPaGl77p3d3juMNu89LH/P6zHvn7v/Wg+/7GT//En/7yg//x8X/yrIf/yiH7Ntfv3z3p5Dd0Hani9GoGVpvAi5+w/kTHyoRw+cl7fffrn/WEv/rd37B9O6OABRCT0+Srrr2Z43l/94l3yGVGd4KpBYRVRK654RZ28f6eE+8mxYzQ19VQd+cxZQyiwDVA1uIewzFSIj3dOUPUoLoSoWd4LrIa0kE/h+sYNiLW0eQFLFN72NzhlDCAlBVDrOpKCuvxGgMX1kYXCmCYdYCZWrIIUycVYaG2NFu0UFIEzccaWkKvJGj6UB5ztlLGaoGHUvsqaU3HlBU37UVGTdYeoAgKfRgYgQB9Ug9DVmQh58NpQCQM7OIGchRxIaBZg3EVeEFIMOVYUugrQ+OcrFSlkuywQYUKRtvDOXNwG1hblUJMMMiIWLVyHeMsdqdpUbMoGVyh18gwK9bpWMnJsS5lDxkWjjSur9weEMNFwWxxFKKZBNayG4aIDuNiIcJeLI6cUxa2UqZEAS23AEF1EtiQYEzmMri1czdxZXBTPDmG3kNBnFVFMrlJjRiWi2FXlBOJUgadJpatQKqgjIq5bXen2UsvGG0NwAD5W3p1r+SqM/dLgjF1poEGeDFUFotgSY6kzxn8w/YCK1JK2PqaraWoDmUNtAxJjjHVut4NqsWyCgXAF5IVQ9MsaVaAucEbVOXIKDH25bJP+6ReTItwITH1wNJzXmSkwCJrlwppEy1QV1tkXdu7KWuTtoqmyT9wWUlGIk0eU0BDIOqTSQNpQOwJfU8QKJqUAwK0DdpO6gQHRjRPg6Vwn52o3Yq3VH7NYFDSjsVMpSoaCPEGoCJJLbH66ZkpyS25bsgakTDb1mkuXBtaFZ1CVqADCelC4+uSMofMM/rssZt8wLoZwocNbVpq6SiIVl1bwlOXJWNm2SFduM3ZCONGZ3oknVkgWcExZkPosQor+j7zneJ1ArvCRCAyqmdZwhbnKFSUVBtGs4FsEi2HgKgDyKS4CJGEkoeLxGiLTAtXAi5OtMwlF8OwMcyHBFxZbJKTowtmBRt0rhIl1/jLDBnQkjIFGlmbc3AfwtTLbjGrbkMQsnFrrpOAW2aFpNZYFWrw/VDZTrklZelIn2y3yW7LuRYn0VU4nFaLqKViKejqGJEjZXWB0pO0wkZySpcw57rkdCOruKtHLohZ+oK+aHJPPiZKsiLFihI6G6ldRgDDbdkWdyiKgWvs6pBrJtcv9RxKOokWZDIwHA9xlW65TdnCOjOCIwDmCG3NAR8jEcWsNuVdTFZ7aTVghhUxE6REwDO6xlZJW8WwX/7x77/t4YdE5mnv/+iNu7cIjOGlkeYxtrec/M4PDI9DD9r59Z/58aFlrtRRTK0hTROGCEMnycq+QVRQgZBIvijUQJf5iUjRb3dL1yIyNdGmUcosWARiYSVpXY1cxa4YB5CpSGRIkopDJSSo3Gx4jVAF+dtJ6AU6l+bVeP5klZo4LdIRuYnS0KTqMBbOTHo+gBnhtCohS8QYa4pTmFvrxGZdPvnAcMzsdaCLcLT5sk6OjMyStDBaDtqpgNBFlRJedrvuAgcktwR3Gbpg01H5RyYnVbU0lR1Tbn9bEU3xgVgCfcBZRnLROqxYg7UMkQizrKQMtFmtUA220cBYUQJ0nUY/RhG2qKAwIRqHyd5YpVc031WHyFJ0zS/t4EOwHIPuTM6jh8MzZYA3gCiRbUGhyzbQOlREYBVFzd34rcx28TGkjtaCfZnVU5wivIsvzaMN5Mh08RBAaFkFiBHIEQjaWOWjM+bG5B9T8anAUklrI9tjsJjoHBpWrViti0jviUULJ09Jbo21mtlSGE1DKkIto9ooU9fWPdWXMrbaXcFLM6ZBExnn7EPoBNRQVMIFdDuFb2VE23rhE8LB5yQzKHtqnSt5XwZl8G62SnvVuz5y6ZXXBsUBwT+sCTKSmf4/36iEJGtVB/VILgVzBTQKwYnFLTI5Lzlzq6PHSGJFbGjoKI7ihZhS16239sqWKdCSmUGvcmVdNZ5ovGjXVRYkk0AHyca3RDg3mhUiWS2m2tfTET8hQ0IMSrdZAmvJCSn0jHdoknQkJCmlHWDd8oJqWlJ9rIMY+gkqdDMdyU4K72nbbmMp0Yt61aRwySponMZfgTFynnNZbJEdr1OWokrHyJYBGWSymgRjat40OINBtB2tTVNrJ02gyeG8U5KCh9bF69axG9imDge76ZLQUjExKJlV4aVkrcIzKx8XV4ZiUMKt95AMS1ioLEV6UxQxBVeE4Pcr2YICF5dOHjskIIQWjB4wmZrxLW9FKJLMyXXfOtf0ugxd5hzMqxSX0pUZv8+jRiiFSAF4Jxim9GXUvt0wslQ8ptHLQjVIohcqSMbZmLTahy4hXUe2rhUcLAKz3qaok+qOgsR3jG2kl5CGVQp1B/gWSAWLjE3amnBHMTF4FFsydCAyh2E1DEkLPVpEzfj4RV+56sb9AP7owT/z/See0EKKp3aX3YU78Yf+0A/8zn1/nL0XffNbn77kKyCtkPBc2cFW3pkSKyQiLflsSjc1uVFwrd1LMoUZTZJv1HrAvx2R0ohWh2/BWrmCBQWWTgZSHLTY4Jaj1Wq1lqpkB/lgaWYt6yaqSSGlpKKLd+/o2Vwa1dCKLrEdxbeNWVK8mFSVCZyI9X2NTVkmbiAw4E6eemYngNoYbJa/6mQkV6QIKDfLWUMSZda6ZQSs65S5wdzCiaakqooWpK2wJkJdlAGvDFFodGzTu/aRhJLZruEllkKjxWmoWDJZJxinrH5od3UXibXWzsBs6IVGVBeWWSQEWXPsRDYAoyzzZjvIchnWQxZGNN16V++Z4dSiYwM1YYDr4li2w7ezMHHn1FCBsczvIqnJC7AzUhiDallaBZoPnV1GFmI4bTENF4RXnSdZmvABCsRsaKEQX1VLhiphrrEpvcWMHG7aFUOUJwNyWeBjwANh8Cq9iNMKn8RbROFHmo4AQpSSpvQphzBES3SPyAJUOFbGaynWQMsgDBVDALVUuEREZiKZeyaA1EiLgUy+R2iJuWEUSGQdqEPWMYClG7VyJtm5BFibEYEMBVZ7nbJpMz9JQDgrRCiSehKekQx0zJXBYoEhMcEEqSRjDWMEknqKiCZzJCiWPoErZISH02CoMERiFKjoYAG2bZCRYLyNcwiA9Cg5anqDmEd6DgUtBbQIY1VNiyqUbjuEzkmSSKBHYpoqjNAywMcCvq2s3EODBFnNoFGqAlOXmkwm1agR5mlOB4W+U4gNUW4dnIMyc9icGOqONKCA+jJpE3/DmBTOdBn0JYAurGABtoyQARkOgsW04O8FmNNTyMQFIlBjcprRICqlAgCWZL2tWoMAKOO8cF1G0IgUQNyJUg6MwWpQi8CQSh2sxJ1oioJPntbnzJrBeIukIiVUgSKjYiGmaUVYiUwRknPVMVFMivmEzrGrHsU62YWAJ53KNN3zmu2SyqzDYDy2EfNAd07TEDgDByDUlRPIIw+LXpxbyBGg5SES0M4BlBxEv5k2QSVyQruTy7RKCAQOalZnyktKcRUvI1ikNha1inJ9GKrdmIaaiMA2ZOYcVRGVNUYCS5gMExrjwQh7MWgRKC0Bub5E76WjLllpFFSJCjuJCTqEoQ7+LFtzlzWMIuxvxoCFTIqSHIMuzCuBFDCGGQPwZLmzGIJAAsRKAPYCkEFToYRXipluTIqCVyQ6fwlMUjiP8Qydh8wBskgXaT3aMbc59Pfu92Mqctm3rn3/eRcokhtOMo+Qn/u5iz572eVU85AfutedjrvdQm/VUUlwM0KRyB5fvvTrb/3AeW8666Pvu+BT1918QzG3VX4sANXT0gQ+/JkvvPGD55/1yQtV05oo865JaFmiXnL5Nad++II3fPT8m/bfbEhNCg1WunTOS+aQGWhbWgr3dEJ5xqBcftVVZ5z18dPfd8EHL7jo2htvYkQ4U1LMQWeJOE0UlIoVhyuuvvLtH7vg1A+df+ZHP3npVddEFOHtoJvFCZfWNs77zEVvPOtj53zq4uLaXK65+oa3ffDjr3/fue/55Gdu2H9joXaWFaEV666RRgrfK4hprk4OobGUGSzyiTFuuuG6sz/52Tee9dE3feBjX/jqN3qEGMREmtJ9a6GsIWMGBmdfd/1N7zrvE6//wDnvPv9TV994Y0Bv2N/P+PAnXveRj193401lEXXDqGd/+itveP/HP/DxC1lZ1IvTEBFBsKZ/6xuXn/6es9941jnXX3ezLEqcVAkaM4s7hJG2fOnrl57+kXPe+P6zP/35i8d2UXUK5XFkCLiO+WDIJQxpQp+uvO7q95z7sdPPOvvD53/65htvVta+wS3BiO4r50p73VkXnPa+j73+fee/+f3nnv6+808/6/w3vP/8N37kvDd99PwzPvKx0z7wsc9efFlPMogTAkO0T3DrmRde+rU3fOC8U99/zgUXfnHZ3ZYIIIUlh4UgV4hMlWlkAMczvftSc7SVRjqnjJAMS6/pMpLGImhbYk5dlIlU6DopjcFcoVzKKCShLxEjUtR9hwAAEABJREFUgvSAkh8SDd2cC2MiBCooLly3NCiV4iMKUoFK2mG9fvb77s4T4w37d//5LWcdAIcXFNrMTAhuQxS2Dtr7idXQVbFCkmINYWP2A1/40lfOeN/H3njW+ed/4qLd/fsH+ADUBuHuZks/7zNfeN1Z55xx9sdv2ZIPYA6e9sHzX/fBcy65/HKKVpHC2ipcy5kuoO97licVZrDc2cjcv3//BZ+96I3vO+f0959/0Ve+PgbrVwqrhmjwsEMvqTIJNNmCTL34a1e86ewLTj373Pd++lPX33zAoTS6aG9llAKtTHc955NffNNZ557z6c9LBXG79PIr3vShj732rPPe/fHP3bT/gIQL45quGTddf9NZ537mDWed884LPvHN69Z/zBAiwe0VJ64Ix4WXXXbKh85+9yc/mZHhcsW1N5x5zqfe8KHzPvrZL9xy06LD1MMsWI+9gqRujfbQPwj/EF7jx4LBFVLJpy3iljF/5rJLXv/Bc97wgXMvuuSy7lwXSSRwrDT/xJe+/AbS8kMfu2l3xfMb11x/6gc+yvsLX7sspKd3C6fEANdSXHfTzed89kL2nv6h8y75xpWZYhAhzTRoTOc45c9qDKjBc+79bedcwMHvOv/TJNVYXZrD+9qbAug7z/v0Gz58zjs+8ckDXAeqmlEaF761cIJshaxGWmJyEkQYH4odJmgQS5OkbmDAh6xZAbDW+kZHFVe2SHYiEZHpks64OPiWViyyV+c5PhRRCgVhQFxUTGsxlQCrXTgoeGkjdxYp26CNX7z8Gs7ifa+7nKDBBxPcQXdwI8CxzijHj3/3d+yQGR4f+8IlB5YD7zj/42/88DnnXPRlTuH9wU9ddMZ7z3nHhz/e+XRUmTTlM1/92us/ct67zvvk7vZAjCXdP/ypL7zhg+d85PPfnnLWp7942oc+dsqHzvvcpV/3ZfvOj33qde87+xMXf5XSeL/145865f3nnnrW+Zdcfp1KKbVJLWowgRWxUmVqmKaoyugMjEieTiAhmDUXzSGSoP+tWm2l1moNpaEa+RVFR2mOGikBX1m8mPRhOfMEUozYQYawmhj3h1rTqogoMgZYRgaVAeH8w/2eeNnjCmiaCwYQoRbr029NBSQTrCnraCQ3/6jJIWxMS1gw/CgBMH7uLFAuBa1KpQ1d3NfoalJDoSiAAxdEIIZ23pAQTQHS1VOGInjClChw6PDSB0MPQHQIzw3OKCDcsoOeBAGygIamk+bTHEL3IFuVLWRVlRyyQDpkb+3REIGMJq6g0tRIQ6pmgpfQELpWSxIwMTe4jqBfHTyojqxuOgTd3TsYr+jBAr24+TrZVV3oDR0ckklFihVxKjQqy6QMTzgxl5FCrFmfVj5ny+QySnia8Jw6dAyZ57G7rGpKMMwmrELY6dCl5rZph/FQx7ptMpqNCheMLKFcYERoXJJJLrnaxRApVEVZLvqwBToI9Ohjt1/39nMv/fVn5bU3K+MBkCgk4iw+9yV2t3yKs/7tu5aVGS3gGGXKhoZgRQboRCK3ktuq8zTNdWfGZu4ylm3yYWBEmmCq2YyWJH3qnkPYGjEhTGjiCDaPUZZsW60LYaKhJNUw75IDsiTIKuRCbaFDinOlJ9+sDj7wSPUZY4tlCVnmTXjT0q0NMOhcCVrmNKfO2rvwFEbUhOygylhG9+Dxm2AAVVED6oCkZwYjO9YIZwzGKYfDF1CRuNOOQQcj+8Dc+TBnEzKJyqb5jjmfr02STFJMkUhGJDg4JYstGxuVER8NtMV9KbHdyLZu5mLb0pdpkdqrrLVT+ph3l2Uels5wEu41mIyZc0OgcynbzWZb2m5y2yTqo3QClihrkKm2A7PpKGCrYZnoTxIIgwbR3tIF7pICxn31vmmZ+IQAqcPgiiQzicsCnvqpayPkJ7OdlaZJvzUfgZ7EibYtk9JgNVavIWtUk2RXZM2wTprnUtF51m2MWm5dx2gZk/sUQ3MJeKiSBytHKpQE1xyoY30mLgPZ62J1KZYiCM1sEoVIdgE7Ab5NBWMMM4aNTMwkvEWkhKzJV4SdMZIsx4Fu27RBs9bOUJC+YxM5bXXaonRVjuVLZDrhko6CKAbSqLsEBYrail/1hZMBuA+H+PqwaaLa1RSo0Dhii1VXMji0aFGex6wMmVymLKAbwvGu1tF++ad+5IRjj4zM173/3BuuvzZ0EPn983jNWecuw4+6zb7fuf99haJdFGE0TSisnv+Zr/z6s/72t57/D8957ZtOOvX0v3zFax/0tBc85d9ec/W1NzB8o0dfEC5D8PoPfuQFp7zlv977oSQJmDhwZAqTarYvfOGbzz35zBeccuaV1167Qkme2xZJt8pgBHUjpe0gNmXoJL2Usz71+d97zkt//a///gWnnvnc0976mH9+3QOe+LJnv/Kt1954c3SvM5qoTk0acy8/8qnP/8mzX/qwZ7/0GSe/joOf9uozf+U5//JnL/vPL132TQwl3C21WTHT17/v/Oed/LbXve/cm28+8JRXnPJrT33Zc/7z9Be+5own/vup93/KS09529lT2E5u2mJYkEt6pACmmbqspKglpA7oNTff8pzXnP4rT3v5419+6kknn/nXrznjt1/877/613/7znMu6MQXUPVS3FrWJvOB3X8/7a2/9KQXPOWVpzzn1Dc98RWnPuRJL3rOq15/8TeuPum/3/6c/z7jim9dnUvkNiPwxvef98JXv+0/33o2epFsUFpUKU9m//wXv/yc157+/FPO+MaVV4YPknjKWmGZOPfzX3rYM/75d07692e/5k0vOPXMR7701Q964kte8/6P3lLGaDVsJ6L2MZiLIvGNq6547L/+x0Oe/ZIn/ufrn3fymU/859Pu/8Tn//UrXn/DjbvWFZp8fNEEzzn5zL95zVuec+oZz3vtmS989Vuff/LbXnDyW1948ptfeMqZJ5321ueeeuYHP3XhGEys7rDRN2O28z//hd989ov/8AX/9ryT33bSq9/2hy959c8+4cWvfc/Z21hYYEVdqkrb0VbA4jD66GseKSqETjgLpfZFo1sks99sMZuLDkhPosrfw3jJBnQegVuviGSGsuwu1uad1nfKmCKFtyd6YEgRJUyTKElDMLRrqPQWQWf1ViH3vc93Pej7vyuBD3324g9e8FmbZLIUfl5szCoptw7TxA5YRLxpqkEqXcJbPvjJhz315X/4ov94/qvPfPGr3/K4f37NLzz9hS9/41tu3u6fmWMhcHnzRy74m9NOf8npb79+P7+Sw4WXXf6cU8446bQzPv6lL6WENJUdSnP0gaVDPDVpPdkr3m7ev/uy173pF5/0/Ef+3X/+zcmnP+tVZ/zu8/7tV//6pW//+CcARzTyLERGju3iB7Z27mcvedgz/vF/n/Qfz3ztmc897Ywn/tspD37CC57yD6dcfs1+hZQCa5HG1bW98b3nvPDk09/07g99+avf+JOX/NuvnvTyvz75DSedfMZf/ttpP/eMv33jORcE4tobb3nmf77xwU964ZP/9ZTnvfb0J//Xqb/0rJc89Z9ec8stu6WZmST3PS4f+uSFzzn1zJed8Y7Lb7jyL/71lQ955ouf+erXP+/U0x/zL//1i09/8cnv+EDfcqWeF9WBFlIgeiukCSRSBdW0QEtg9Hz3ORf88l+/6Hdf+K/P+q83Pue/zvi9573yV5/0svd/9NM1hwjJsLzt7M887+S3veSUd15z8/pFwcXfvOpFp5z5vJNP//Cnvzg7lhAGfkm5cf/uf57x/gc96aWPeOmrn3vym076rzc8/KR/fvTfv/KzX/smV8WldtdI5PBxqzEs1EkTvXzq85c87zVve8Gpb+fBJpAc3LN3Cy+44qabn3nKW0963ZnnX/yFJNkMJKewUmSYdWvujVbSIWuo0sSLLGG+2MpYNjfYRPhRuH0KorFSy7wbTfCgbzMwQ5ILC2JFC2PQvvWgFO6ddmZmzCP2kmoh9TsDwG+amsQUoov27l08aqam0BR+2uHOmjMBrvOpRNmtdrGxh5OH+vG3PeL/+sUH/p+HPvA+33Hi9Tfd8vLT3/rC004//TwSbJ323+89+6RT3vT3Z779lltuKcn1JN95waef+9q3/Ovp79s9sE0uHrvLq9/14Rec/KbTzjp3nQCc8q5zCOBzT3n7Bz978XB/xdvf9/zXnv6Wcz51a+8r3vq+F7z2TYzIJ790aawkZjWXIlwGOxTekAQqBXwI6nQCaIBFsID1XkKLNNdpSDUYPyrh4pSa9BrDmTVpIht1ohKERQRYnPsndZ8cPL5x49hc1ZSHlFpKxSb7vt51N5clcw6bk8uTdPNFfWRCpApqyaixbjNSHS20rtKtg8NjVgTlpUyBKZ31S20oOr1LsAg5aVYyqxXhTmjai8zKi2y6esfvuXWbMlzDeS2z5LYZO7NCDGamVdMq2lRLNVFnqANbj9mXThvYUKzsqE4c7xHhEKRBk8auNGDD4rH0WJwuCXFek42rmdA+aFCRV3FTCF2pMlCGNx8GiGC1pczG4aapkyd9UVlgZLUNi7RAcRrlyaVzdNnOTBhwxLSgDctsM7SXRVjVIRpMDHoBNSkqCjC/tthulQGmGAGztw/TA6XsqoQqjRDLyOiBEdrBDV3mKHKg4kCJZR8ZIasgdieMt6f0HEtmp3c0lJ6By4KlFDNrQlBRqxdNVWFjigjW+5rXve8b/+fFN7/3gqv/5r9EBU0YtEaJIR65SxPhzCuLaiKpERIjAHBYFvKRqSws5M5YoroV56aVHmuMXLZ8vCsh5OraKEnUurSFMaJjkV1Ln6al2Vb6LLsLlghf93luwm/rYPyTNEVWDzGG8EtKzoJRdylSKn8DJYSOpjsik1aswTJDKtL5qwZkSPZwic7zvEvO6j0TXbQLSB6LpTn7XGkhwHm5/rRMw8hYtj4GOogCeQTjXmU2GTVdCWEZWnZNB5m9sWK26fygZFkkh2YbQyz4HgFRKPFRC6G4uevc6RXgVGkpWANWcuWgpkQgSroMGa49OZ0vPtY3WhSb4qYBY0S3TRYtkdoXGSOXHBHD3MWFaDu1Jgk7JIYjSY/ZjKIWFtYYxXwPKxlkToRqVtXCmNEkgP5pqrqik18eilBJEwgAymRwojDQoBJOzKYDxkexg2Vj4WTat+oMpkHTruIQTw0tQdNp7eJlMFySAKXwYSe7Wzo/zRY8tXEs++oyaHqHiJhlWU1l2RhI0AwKGi7RU2ZRLxW1NJEpWbGiRZZBftArHUW8Fr72kkXEhG5ZmdUOMBYe3WUJDNZ71lYrDlk8nXGTFDXWw4Jswpkr9koUGQ+aqcxJtWq1QBOROXJ4zogFmYnVFY/Mwcgqp6tABDCoLJONUpIim+571C8/kIB+9apr33b+p6Q6g3v2RV/+5CXfYONv/fSPHnPIESoyldWAUDAUp7z37Ee+/OSLvnFlK+WOxx55l9sefezhh2z7eOv5n3vkC1711Uu/ZUtdbwddh1DMCtYwCWHYt5nbAunki/0AABAASURBVBZ30dzrA1LW94JUESkK4bOvnU5mICTnBi+CU9/7wSe+4rWf//oV1eyOxxx5l+OOOfrQ24zwN37ogj9/yclXXXWVRp+ATaWIOOOD5zz2H07+1Fe/ycF3OOoIDj7+yMP68HMv+uqf/t0rvvy1r1VznYZOIarAasaXr7ruz//t5Pd86gtH3ObgE4479g5HH9GKLWO89I3ve/P7P86lnljHorsdzOU0tzKEeHMyjTa9fv9Nf/ay/zjjvE/PfRx7+KEnHHfMHY85qpryMfnT/v20U9/+vpELcyrDHOXArj/zX974yneevV36vqne6dijTjj+qEP2bU4/+xNP/49TFw/sXZYqjGLogb0n7oQpRLpGkoPISHW3vjdyfZGh5lpVA77glLPOfeQ/nnrJldcctJko/87HHXPwpt2w/8CLT3vXK8/4QLgYg0NqSQrGhV/5yh+/5N8++Nkvmsgdjjr8hOOOPurQg8fw08/5zFP+/XW7u2M9RqxlY9zl+KNPPP5oRvyE2x59wvFHn3DbY9Y3xx0zFaMNpnrkIYfoUBVyqx1IvPZDH/qzf/6vS668mmbc+bijOPhWM174hnf94xnv61y5pxQTiChoSgzpbl6qlglrAmVggXavc2xmaSwfltEGixOzLUeNziwjtcMybZDatIK3CIhW8pdOFsw13lKGKa00BCSc4xWsmiaGoqkJloJe+6gHlpkieLvHX/3Ozx1zyEHd41/PeM9VV1ypa0ohDVrzQP/2MNKa9CjEFE45c/d/Ou3dz/zPN3/jmut3Wjvh2KPvctwxRxx80C3z8t8fOO8v/+E/rr/5umD0sh20s++E44+507FH1pXqOHhnusvxx55w3LH7dg7aosxN+lofkQn1FQzWrfSSMV174/5Hv/xfXvP+c7jnPvI2B935+GPueOxRm1ov+9Z1T3vFG05924dZQYy2brAUXxKvf+/Zj/r711z09auYsHdYc+foY5mwS3/bxz73py/8j4su+5Y7ItOZgXR7777o69961ItP/vxlVxx3xKF3Ou7o2x55qIjccGB+zqlve9X7zn3Mv/z3Gz9ywaEH7dz5uKPudNxKXSbXWZ+66AWvOZ2iRExCvRstp7Ab9+/+yUtP/uBnvnjUIbe583FH3+6ow4vq9bcc+Ps3veflp79vdmaERSr56yyCnMDYJe2hq9y7J0SWBf/xhnc9/VVvuOK6Gw/dt0N/73zcUaQcPz7jlW9681nn+uKce9BUyU921cI4g5S7y/HH8D704IO62sJHraLXX3fjU//x5H96y/u3yzj2sEPvcttjb3/MEfT7vC997S/+9dXnf/UrXZFAWSPJ36s1hGWolOIP+qHvNtWbd+cPf/6SmXQLSYmB7WJx3kVfWUgd0//1/XfX0jmfMyMRIyGprPRlFoRGYQJjjBG5pDhSrZc6CkOrQzSN5U8tC8hr59jMuXPPP+ieeIeG0O2dQPV0luLaU0cmdQlJ0t19LD46IgpzISFIg6tq1jpYsSgevlmW6rPI93/HXU2Ec08560O72EWBMhkmYFI+VELzww7b+c37/djvPein7333u9OP2x25FtJb//srnMWKesJtj73jsUfZhNUKcTZ++2bd0jWKtz3iUDL8+CMOu7Wdb+58/LGcddghtxHR2x91+F2OO/r/Fni7o4884bZrvG6zb8eTwDpTlRsLcEsx+wi4eKDHUA1+X8KIDBF6T/9E6GYjOuQSFAGi63MyyxVpiRDhzR6pWScXITCFsbHo1QcbKoWrQItnZfSIhaRbiKKKsZbx3VJlmMKzJYUmMoLgcl01E9bkpHWksnOvXQBODarpICoKMYEaWDTAvdToOlzX/1WqhIorM5rqlf54bbFHBUgm+ijuLaUylTUHNbtjRPNgusioaiyTaBEtSZUwg2EF3gLEnv6GVNSWtZo0QWWCD9A5LzVLSy1ukgpLsdzLVa3aWk57UqDeZRDdkaJdbCQtKU4vxMs6fN27Wi6CQSB6xkBJbRk1RcISxSBVvBmMb4uapZibdlWaJxy+gK+RksnypoI1JhQRLHoFQaglgBCOBsGlgnDvGPO6aaPhe4jYItMsO45aQZ+KrkuCRQjjAwypUSaTDW0mOQiiUBXcPUdEUjoYn4JVqxRU2Pr3R8HaheTaBBPZ88KqlMN/4rvL4et/euj6U9934LxPldYZt7Yt07aolGi2TNKl+NKcz+fStioLR2gWGQ29ZqQahyURETrFu0BqETR1xQDNAc2RTGFWu6ihlG46Q2aVKFKbTzoE6bROGAPwhB1Vhqoog2ytUKIBLgyzeVJwLaPRsRQjlqpeQ9qQklLK0MlhlFwTlokgN7v5kMwVqyHCUNFuEW3Bb3pKmnBUpHNkZCEN3Tm9WOEpO2qAMwmpAhMBzaq6USmSqydG+nUeRmyb7rZwRAZDib6gU6B2CqnSjKroHbM/0afcu2NoMqCMf2KxxL6wzTCb6yBBU4nq6oUVpTHDhsS8HvEOGPgFBXcRMNC5Dl347HtMZYmyDJ3nHBkizC9nsVE2YodphA6bl+iLhwSnSgP5nK6+SM6ZpI1EaKSpoolMkE3KBswOnn7m6ANb17EmgIA5z7BhNWxNkOxjCVmsjlopwGeMYaGNHwfjAF4VsJgSLdMSwvLB0PciXkiZpO05O2ZyxYUxZxgpZzEbRdQGXQz2h4ycJAyeSBdKlQrRsL7Elss0aJNVrUpOkK61e6G2NXpFtVmtspGsMgSppcW+Td9XubOm6BwzFmYgz22LW2iy9kxzaHhIugsRq0YZ0IYVIprvSBowRDNVc1gSc8lEsGzQPXDOBLTVdlJHEqEC1isl6l081imzcTCSs37qnnf//rvekQJPe//Hrr5xP/E85ayzR+RdjjvqV3/2x2USa2grVLVLPf/iL//9m99Nj088/uh/eMzD3/ScP3/zSY/672f+n0f+ws/utHrJt6577slv7tusMEFY9APbLSXLapKtuPVIDJTeBLQJe9dQsFUZLkbEmjZdeU1oyGI4+z5/8aUvO/293eN77ny7l/75w0972qPP+OvHvfZpj/qzh/4skfncZZf/85nv6Ziha3y//NWvvfR176CF97rzbf/pMX/wpqc+6synP/a0p/z5o3/xAUWF26b/evsHtA5YH0piMbHA64rrb/zKldf81cMe/PqnPPLMZzzuTU997At+/9cO3bcZEaeedc7NB26JILzM7cqWtC5lmKi42GAG+Bs+dDanE4G/+NUHvfYpf3HaXz/hlGc+7sWPfPgJxx5FuF/59g9eeunl4dqdQZnO+OCnPvy5L1Hp99zp+Jc96o9O++vHnHHSo0556h//0QN+4qYDW7bzFgHd0WKpuW/DaAICN3ELkLgISVm0BAx7lyqkgLin4vwLL3r5Ge8hKe/3fXf/1z//g9c+/c9f98zH/tdf/l8/dc+7jsj/fOfZn7zwywxPlWyqN928fdq/v+Gam2458bijXvDHv/66v3rE657+yP986qP/8Of+Vy123sVf+693faCQscil9zOe9qg3P+ORb37OI9/w3EedetKjTznp0ac9+8//+nd+kY4D+PHvOvF+P3gfQa2gYP3kl774j29597fNeOwfnvLsx73+uX/+qif/4U99z91oxmnvO/eTF1+yrLux4L4C9HsUkGZNbTKrEmUwJTWsoHKdo9iIykLSQx2kfVEUlzrDvLvOiwWzALwEaapVdEdz8rmy+oAJU0F0VBQkFCk4IIvx5CyjB5huLhEKVfk22oQfduh0+JN/8yFF5Yrrb/qH17+z7y4uI0ovdT74oEJdvJlBsWCZWaU83N967vn/9b6zGb7vv9ud/+Vxv3f6Mx/z5qc97lVP+NPfuu+PVLNPX3rF35325sBwLU/6vd943XP/8hV/9cfH7f3H937qnt9x+jMe//qnPf5+P/ADdHwJLKmzTGQqRFlNCv1j2CEvev3pn/0aHxPY7z/ox1/xlD8+5ZmPPe0pf/lPf/b7977L7Um2f33L+79w8SXG3LGBHfvUpV/9x7e8h+nAhP3HP/v9Nz71sWc87TH//cRH/NlD78eorQn76tNv2c8iHIkR5qQZnbr65gO3P+qwl/7p7/z3X/3ZqU991Kv/6k8f92sPnGphNF/yxnd97rJv/ubP/NB/PvVPTn3OY057zmP/7Ql/9D13ui1nnf25iz//la9jTDlako2WbLx+d9ndbp/5e7/w2mc84vXPedQpz/7T5/+f/327Iw5h13+fde6nL7zUFrUOLOADPDbyFknDyvPUMTTee/4F/33WR4vpr//kD7ziyY847W8e+/q/ecw/PeZ37nmn284jXvL6d17yzWsd7fG//pA3//WjX/vUR9zhiEMp5EfuceIbn/u405/9mF/6yR9GbdaMFP730992zue/vG+qj/uVB5z29Eee+ezHv+7Zj33+Ix522EE7V9+0/x/f8I7dbQgzaaiQDZRCtmiOMmBx73vc5XZHrHuVj174ZVZ9pr4nSYPo+ZHPfD4yjzv8kO878fYb+KSuG4ckXLOLkHk5ag53rnZAd+1DRqZ1aaPQ2VyFDBY4cRZ1tfCa84R5kkG+DrQeNUl24goxYYE0rW5tcJ3TDc0UrEki1CSpDShI8WFLb10Mk9fN2FfRvOS2RW+Zze5+5zuRcgDedcGFz/r311186ZWRVqwWJmBlHlhNaQZUEj6OO/bY1zz5z898xl8+/tfuzym8n/2Hv3bqcx//70/50yOOOJjOeGZ3ZzvvNVvbpFqe9Zu/+Ian/8WzH/7LbOT9zN/75dc++y9Oee4TfuWBP1X3TX//iN8+42mP/stf+ll28f67x/3h657zl6ef9IT7/sD3dIRIVw/lO3JjTnITwkyNLgppTczSYwxV6MZip4wCuj/BoSPNBTQqBOKmo0ggNbREUa541lO3qoM4S6OwrnXZZ7HRwVohksFlNwJDdK5T32wqH9RU59CoqvtSuBErA+EsGxJZKlilAfrP5yllS+BBq0NEAI7jkq5Zighj4uHas25VA+QOaL1sqAiD9glaARjyFkO4f/PMLFXrJDlZWkA7DYXOWAYrJkXUjOJbW3wtaYn1D5VOapO2psYaqrGA3YPESEWmKzd+WLPLcpR0Q0McBOyro61/eaVBp0RLGq6tU6kgiOdaZbcC0ifZJ5ljybE7fKRLX+Ckc9+WpM8g9YJ4lCqliVbQEnA/XtJKmNEozSZZVrGKWSPEK1jD6axmirnQTfTwiLCMKmDhNg6PsSZUWg7BVhQMPzL4RjVRVasU0QLKilUDB0gIBzBCogYSKAcRynWYIL3nsmiSXNTMZBSyNzLBnrDB54EOBMAj3EjJOOgOxx/+pN+UVvnc4FtP+pe44cYYw4enk0W1ylRpgIoX6fRBJxgvruBO+GXJXKtJAzbJdJUJozq1JCEhIrAWWhM0Eilb2MLKoIX8dIinuZfoVYYWU3JkrEc5KSU3YgXGkrHagWEla9XSTCfRCtCO1FgZ6jQEKRprYLhjcL7LAAAQAElEQVTx7eYqWUOVt4A2K+HhSA5XFxtZxCaQG1KVMeNPbrQXXdS6wSWdAZzgDSE0kaeEVqNROWMuIsNtsNjYtNTqZprJhXuMnCP5PAfDpXeFJ2JxwhgMoIqoOmeUunVsMxeMaI5q3HxUmkEXwooK/ygqrVd6RROrEn0mjiXZgmArC5YxkkJgVjOSa9FMqldh+pJPVVMDKmZSKwiUSLGhliMmH4qgWYwqR6wqi0dxYAE8QF8NVRsowT0ZEZqsHObwHsHCsSotzJstdIiIKUSSUzXIVMpJ56cUKExQ+SIlhFYaRAGoohaXOossw4e7pjXbVGtFkQQuHCmACiDQKtJUdqQUcLaGCOMZ6e5L5OBAEVtRtaQuqMBADMQYfKPxWL2lJKwXJyeUVoEtvMn+DuENSEk08EQXm6D7UCVErqxCg52amuGeEeiigyosXIKczQlZsF6EP2IZPfpSSB9RV0URI4sK+iR9imFLiod455MI80wZgcXHEiPGUrq33jb4vx7yU9X00m9dd+p7zjvvM1/81CXr1ym//8CfuM0hDTZL3Q7xkbZd/DXv+MDuvPA57ksf8/v3uft3mk1DysGHHvLbD7n/o37lgUXlM5ddfsa555HIDFXmsm+asHdZqo7io2aWNBMjjrHXg0QOsADkWMwdru66pNJdrKhl/sdbPtg9jj7k4Kf+0f++1z3uUg+ytHH0YYc//Ofv/5s/8yMAzv3iV6+8+ZZuMrqc85kv3rg7T6ZP/6Pf+J7vOHHamWBy+KEH/d5Dfpo7aQ4+76JL+lYyhNGcaQWbgKmWp/3uL/zvB/zYYYcezIadsrnfve9z//vck++vvP7Gb15zTWiw0IoUknwwpzNV1EaVNJLs/M9/kSPvdcLtf+uBP3HEoftqChe3H7nHXZ/4Ww+uZjx+fPaLlzF4MNy8u7zuA+d5JPdqz/0/v3uvu91xUytQjj3miEf9yv0e9pPfTznrLdAJLGloCfBeX8LJpNIqC78pxAARxd4lIRrIjP3z/n97+1m3bJe7Hnfkcx7+K3e//bGbInyMeNc7Hv2s3/7lE447qke8/gPn9tx1BLyd8r5zuVvlFx1P+e1f+al73qMx54EjDzn4j37pAQ/+wXtR8bs+eeE1t9xE2ZGeGWSQpugelSXziquvfdZrzrjhwPYORx3257/xc5tN88TInOf9//nWb5vx17/3y3e94/Fqmi53Ov7Ypz/8V2414/SzPk6CL4Nb/CXcmWaUK+sFAeEtnELnVMWqsVLvna2TfbyH9I5OQoXRJILDlJqzOscnEAlmkyjEs/Re4CaMEzRIMN6SIyQGf6WPMWIghvQos5nveUYxUDOk/fT3fO/Pft938fN7P/2Fsz73xR4+wErgax6xFUjkyAhqDrnx5lte+76PDI+73vbYkx7x23e76wnM1K7LsUce+siH/fxv/uwPq8j7P33xeZ+72Iq4IVh+VPfErC9F0cSbeVUvEZopLqk6VEYJqkzEFy+77KzPrGT7hR/93kf86n2POfLQFtin5d53uutJv/9bfD59YF7e/bFPejIO6N1f+84P3Zqw//DoP/jB7/qOfRN3qfWQgw793Qfc/zG/+O2Efdu5HxclqKsNt/5w4/7Uh//K937Xd9bpoFLaEQcf/LCf/qH73XvFgQMe8sP3+rPfesiRRx5GJtTE3e5w3F/8+oPZzuPQF3hQgbIIhbGKMRTg9aTf/PmH/MQP3ubggxO2s++g+97nXs/6vV87ZGdi15s+9FEdM+EfgqFsWG8JlC4gzurXHbjlle/6iEf+xHed+KTffOgJRx/BLNdq97rbCc/4nV867KB9N2+Xt5/7CZSWtaF+mzOUkhHci7gO1KgmFXHRly8749xPs+uPHvQTv37/H9l3yD4X31faz/zAd//Vb/882z//tSsu+tJXawqB/7bpAOmMsXU+1qib//W9KwJf/MYVX7/22lBz5jPqzTff8rlLv8npP/Hddz384IMlRZBibpMwUWuYsNqw0CzBEPbQAIufTzKakqQVWaJbdlsrch8++PXBSIZPk0FpxjWGw4qouEZGVw9jeeW2xPhZgjSikUJxLdqEyYZq9+zLCB8iaVWl8KZWkE1IrHKlHrRpT/7dX/mJe96NlvOs8vvP//dHveRVZ3zggisuv1G2kyxNelVQbxELEdIpQFBCsHdFiHdoz5JEgbbKtzsAWS/us9ZSz455nrF3KQmNjEiX0XWgQVn6lGVs7SbheScwgFgbuALk+i6FjUluZDHRPZuQjjHSxb041Q+hHrVB1FcrpCBNYSZMoJQZckCkp1B0JXq154YTSQmtIQK6l6JdysLNm5fucKVr3N2MGvRGk/lYZZgGb2+Yi7hIEN8cA5hRt8UWAdfZZOKmMj5eQzdD2yIeoLSeOQKREYGkUglFiNH8liLCtjSgiuWQpevsdTDkSIhj7a1oU7QSdAlhbE+PVSKTzVUWA1c/UICnpUKLqILqPJ1kWAA2FxtQZ4tTc/aIbWAZFEKVbJHVPu4WBu1HSSvg9zFmYsIwhAdLZHomzy3gRmiELxqLMqDcVqeNodxPZICc66a35jLh06RVDMoQ9Lpn+VCSHUsZYV4MtZoVWuycmqDtUCohJk7UC6cbUEa3EaaqpfETg6d8pr8GDgRHpAgKMEm2vVvrwMa10sBZ6JHULMwJjOGqUFYDaTnqGLIefSGRGEmchjsdiEV8sVxMXZASIclW6G1/8X7T/dblef7yN696+qt5/ttCFqiFTG4ta+G7W+OpMmltlkVyuPRh3Y2WGhheqlO6MzsYlSiqk5jRM0VVEYVL6Wgj6TAcq2pZTOYa3qTxLmESmiZaRScI0Rq57ucyvRTdtNrKZMJBEq5rTOkFFCCWK9+VIbOB0kVYwJpEqWCMuoM9VWmjpKx8MZACytiQ/iDH5lqWnerVQmgAYh1EQ9Y3uRpTdgybSjVIh29liTKIvCblN9aJbfEhi4FHEKG8QA7Lrh5DZZbs9FUQZU0aT8mkUVazEdiNyAZiythvmAZifAMIs4UBB8UbKC3YQljAywyU4MBI7b3NOc3ePFpklRBRrTW4Ma0bqosylYIqY/Gk+ppqTZynbh01xySkY8nRshfi0rRUZXXRntFleB20kpYKD15BYDhbJAdjYglhpIA0p1UhpINap1skkxS3HbcSK9L0DFBIFbQMy1TJ0DEig+bWSepkJhaDWwAVN4UKApZSipVJlYwIMoVyitZMG9vB7wqCeJTCMTn2/tcB9Ir0IflTFEPI79walqpBfYRFg41NZIfGJBgOXyJHT5a3acmNWyvaKiOc9N/GwNhazGqZTcNUHdYjZ4xdzB2edFMB3kDQIXSPHu42MhJdNae0Qv9znSUIDaDzHmQEP0ZZwvi8oEsPRA0wfub3ueddfvbe9wBw2vvP/4cz3ueR9z7hdj/x/d/p2UfOI5gHTiFf/8Y3z73oEg57+AN//LbHHT0WjFlicJ9dS9MH/9QPnHi7Y9n7tvMu2JovWKNyYDuzRQS8hxv5suXZKC1IpGTPehOWYLn0GN19Dh98ArHXJ3RErrzmpnO+eBnH/fyP3PuOxx0vYqmMi7tHMTz0x79PRa675cAlV1/XdT3t/NJP/a93/t2Tzvjbp9z+dscPkbAiDVLHpsUPfOedKIfHmJtuGdobZhmD7GQb7njMkff9gXulTr3UTnhX/XKfE+/Avlu28w27u+RM2xEKExVxgYvOwgilFBSZ++DIA8viuRWbq2b1ZI377rve8R8e+zv//BcP56GFcyD98ssvv+TKqzn4Yff90eOOPaoYo2WUE2ox2XedeHt2rbdhFF1E04xbz7UF4DK1yXX/ozBh6AJ1tXPtlJR0jT4uueKKT3zla2z6k4f87EGH7FgDn2oU0qj60Uce/NAf/B52ffTzX7557nPGzcvyto9/lon2M993j3vf8y5j4uYEFFqzk9y/8dM/WIt964abL/zqpavPkkxaFSBLcT5rVNv1f3jdO+gO257+u79wl6MPF8bQlhm7X/nmlZ+81Yyfu+9B074ClqwQhnnI4be5zc/90L1oxjkXfnk5QBZCyGthSiwSQkv5pFI7mMsmRThTU8l0esx4S2lCvD3gQ5laQ5VWOfhqfL9QLCGZI9KEuQxAFplG7siYQF22uHZPTg8gXbCodO7fPMxhQ2XZbrfYu0JAm1Lqn/7Szx9/+CEe+aLT3n7jjbtjIUN9/4F5bxQSyZtMbVHOv+grX75iDe6jH/rTRx26k6Kdy4OFFEJmv/mAnzxk3053f+/HP7OWG2Ysp6LcKoevXPG1LLXMG+ub8M3itQ9h1jbtkkNBBN/9ic8eWDoPEr/9wJ8sVkuwxrrR/YLjjz3qh+5xoop8+tLL9+93pttXv371rQn7Bw/4iTvc9kipw5uvJq0wykN/5AdPvO2asO8479MkFCKV9ZbwAUcddsgd73Db5NJZWdoKl8SNyL3v9G1y/uS972lqVWxyKTNzdtztzrej/bxv3r9LImsZVUP5ee/+4e/5Lkd1We9AiRHff7e7/fB3nsDOj33psutvucExz0p7ky28JRUDJEWIfOqiy75+zfXk4Z885L6bosU7azFCMuuJxx/3k999Isd/9PMXC5usqK7sYMutd/exOqRRZf07Pm8971PL8Dsdc8TP/a8fDpmWkB4Rg6VAfvJ773q344/2zHM+exEQrqAJtwpZ33p45zZHHvBj92Hj/rl//AsXixKvAi+fuvhrl193YzG97/feM5PRZDasv6oy9UA0cjB4giVlrA52NanC9X6CyihjYUiE9YShBC1ekpFjYdLkzgRVxczCatdG0ufiQo4PQTJroA5x8ArIrNUZk7pRbQzlmANbM2fOmDTJjYzaqSYcMfgdhDbT44854qQ/+90n/u+V3rfsbj/0uS8//VWn/+qz//GRL/uPd5//6f3b4RDOr3DDEhj0I9Oobr09dQklP7uWqJpcj2NtB9MuIABrpZqp7ts7kbJLubCCiCFoBAJFcyoH+mAXb3KzdksXGpjS99SIQFSENpiJghJbrRpIaua9hkkYGS5NISnmIsOQnCTSKqcxz2PAXQcKDxIJ5tBgrRhFmQVEr61glpF1CQ4Mdruw34sLS1wkFpJYOHVpOabsKnMX1gqapbKiGTO5kRYpXiWoHLpoQS3cXZSK0GXEknxhSVoSAcZNNLzltthILr9gcDgDGZrBva5JumcsGqu4kbLNqfed4q1BTYAWaGxenJ57Cg1s4BocqkarBwhhJoYLPRqkrTORMlZoNCQoOjlC3DU7fFm4xEekAxlA8IdvaWVCwW2PNRX1JDEx6PnYe6+RZGd1KmRhs+AGgHZRsUXXsVZc7kaZMp2ypSfFcngJljfnxxhC/jrpCiaNThWtDsIt4iouEVw6a2pLZm1ZjcoUh6aYGmdw4uBbKWKh6wbYXXIBt4zKQj5HXXKCTzpP0XNkrq/bLpHMTArBQGS4SZJN4hW7VQ9AOyMoucZ01Bwl1+Il06+feQAAEABJREFUBdJCLH1112DlxJP+VI88hAJuPuPs/e/5FOlLiFQBThTR9OLDeJtYUYgOK15Kh0jMDdvKjMw5OlOzm6CyfU0nwph1Tts1+MarjQ2iwTUG6JEPUdeSaqxtWqKXPnLOZeXdINaoEVp7n8bWZCsrWUnTAYWnjFQd2ZY06mQgaHpdhNu+EN1trBm6cEXYLbErvrVOOHIymA34UI8COsHwwYW3EhA0uisEm4aE8YuRWPqqnGPp5q7I1rAYPznPB0sdvSJUoFq5XJSslO07ZXDlGjz3bDSLaVAZmBxbIIpgsrJR26BOs5Wt4EDKMmzExMiBmy6j4Vtu6csMDaAAMtxnWXYxOBCxqT5VRoOZA/oVOUrOe//ToA7TUVYEBjK5RSUTFZpZSBRNKhtaTKvB5iSHsxtUtKHVrFPzNnXoWFQGKqQIavSa4SViQpgpyRtjCi7dqWFsEq+WjJyykGtRMcCQHMhqxe8OeiTD1Bh3zMKt727BrjUX5amoiepog657OgMQNZOi6wgM1awFpYHuL8ASksMrlgl95e5akGrSQKPd1UVodRUvtDncBl1HBAgMlQtFEEEZizCN4EV8n+ROrGybxXdlbLst3pBVMUWSG5HUZwVFhvUYnZKtNCFslLnEiPRRQQkpK+eAiFKktkpocxFfWIQYABShzZ5D2YhUiCaaE1SdaKkIkMY34D6DKDaRaZ+23/mZH7vNznTj7vKly6+eiv3uA39qXzvElV6af1uff+7iSz1CRb73O08MFwsU8jm8ShQsh+7T+37fdwL40pXX3nT99e7ZHVOrbEliyTuY8kv4nN5Z+wPKLt6NYU6roOuh3m02jAZYBkm/85WvXbOMwRj/8Hfcbd8C3Y3YJrEQeunjHscec/rTH/WmZz7mPne7XUGnhs20OWJz1BGHHKqCZZ6vveHmK2+45crrbrzy2uv37zKoVAihONrsofMKGJtUzQwEjQ2RAkneHMWu7vTZjMTQUTa9taV62hY6XDKGYlfzR/ceTH72q9980j+e+rGLLrlm3r+mffNpX/3e777LD9zz7re/7TG9ZY/++S9dSpkq8oPfdbdqYgxGj2CKuFKdrdFhP4aQudJTuBrtfeUC5pO2WShjO/qWx0eGH4KxjgZS1FnZZHzyS1/1yKnokcccfsVN1195ww1XXb3eV15385X79x908EEcz1PKtdfeHKqXXH3FdTfdTJ13vu3R37zhmitvuvna/Qeuu5HRu+FbV99goEUyIr5x5dWNBFfV2qJVBltCrJfXvusj7/nkRSry6F/82R+/x3cZ60MNKQv58qmLL7nVjOMOP/JbN978reuvu/aaa6+9hvbcfMVNtxy8b9+tZtx0/S2T8U+NKVcsl1G2gSVZd3whbUVMkMilcC2rqZtsEwtRTokWfOI7WFZFjd6T3gIShlBwPNclKjChmR7rv9WgGyrQVCOVmCdGeMscmzLqPoq1LFRDV4fv22MsgN7HTEJkHnPsUb//4J8x1StuuPmfz/xA8NmKa/ufYUSvmBdLVXz6y1+NTGVw73Fig9QUpqYxB8xYWY/Yt/Nj97gLJX/ua1ce2N1aJK0ST7bwFlURF3Jt8HeDlcER5masEFZ0I9hITJ//6vptJ7+OO+G4Y0geE40iS8XWZBY86pcfdOrT/vRZD/9Vy52c22f+J2F/8Dvugpwz567dd0ZONHg5bKP3u9easBdfee0119yS25Q+0xLeAilMQlsmWpGMZ2Gduc3BjV28d0qtOZqkqLkYgpZ9O5c51HKZiBHYIhzM2watzLQBI4FZVd3Nf+C77saupY/rbrrJa6J2lku28A7aGrLQ3hGfvviSzDx4qtMh+75+001X3LL/quv2X37V/m9ee8tV1+8/7Dbr95/X3LT/pmtvwW7mbuDbHlAMtHBZ1Qlp2ZHxiS+uD1lud/Th2xHfun7/1d/af9U1N11xw/VX3HjDjbeMQw5aOXnJ5d/ajoyWheVglQFVsYayiVrn77zT0Xe/3TFs/tCnLk5fNEg1nHvh+ve+jjnkoHvd425D0xnEAemF7xbkrO5lqFBOtmRRY0+did3kWkNF0gFkbaOqttgIHxKKcCFS0G+undbVRibrALMAWZeiUYjf7r6xXRf6yukgCTncEm1gCrSUBvrdYjBNJJccI7bC5Y5bBezr2OkA4wMctLPzmz/902c+44kv+IOHPej7v/vwgw/av10+eOGXH/vv//37L3r5xy/6fMxbISKzd5E+2V6G7WkEhColpTBcol3wPxdpy2xivbTJWGndv90hYhvIvoGDsqhw91QWYOKTzb1+zRVM6UOXbQ1mhXabolVtJptikAleZThjOQkxSeEn6iF4TuISSDZAmwy2k5MZiMUyuHNWegsG1UzRdBj3+SXW2rqV3EbvyyzUmbtiPQtjpEVYjC2UH4qHJiC8FUJ9JnwqPIq4ktTB6JoMrV3XPXMVP0idyQGKClmCfiw6tuojQ5IVu4i0LA7ehIkRjkHLmENpgM2QXd10FLAs5BbDCQq6Oi+mr3e4K7xJr2VM2qfc3eRu826e3GtNXkwF8PAejsgdzU1qGU17oahRkxB6tSCebYJVMeKbNpEvkkWQLRaWRd2CtEwiOKwvm4imUCMSAyM5u8mO1UlRNFTExBQbYAfDGA0oJyZ8RB/OHOY2eFSpqgYi1hN9L1zDtDfdFp1TIFSeHOAqC2Eaoi6tW6UXpSd37/ukVVeyeHbpkNBVc+zBRGjpavbEiByAV1s4Oc2ZQuIeo3eMIWHVViuSbwdpxI3UMPYPT1eRUtSE5tOMsKbZ4DWT2oXIEQ14QT3+6OOe96cA4pbd6158Gq69oVRHXdJ6wIMh7sn9vUq60KBg1clqScula3QhHYYTWhFCp3WFjvbSHRGaEDZE5oZRJCu0hCAyI5l4yrwugR66jMoQJR8PjZEjBZpGOWAUAvDhAzEUIZxltmal0t9IkmiW3BufIB7BlwzSOLR7c9+AT25TU7QwXz19CTohCWQIl+PkGrNshPkvBlOfxM3gogMlQil5IBMZYkK9Kov6XMlYaC+TW1HVHbMdLQUFqAqbwHcWQeTUR/HRkn4TGRQTkrFJihMB+jpHDjg1IVPY5OC0ETGD7VQ80hOeBD2s6yRWJmTtvs6Xnm1W7QpOFGRRVJo5hoUX3hIOIeOS0Vf2FTOYhLg7nUegRac0FZZxcI/tC81anRRNYEhwL4swI8VZHtS7BsH2MmdfsAQQHBgKPhoZyhFG6nIiA7tHkXTPHATB4U4aDdrAqrcpRVRGUlt3ep0CKbDiagucSpWzkZkYS3E3BXQN8mI9bfDh9CSwpM+qjClSJQQdPODyRyRoD5BiCeKJYMB9hRQxKLNL8VLRqDLdYlGizMH0xQPRxaIWVlLhzmtQuWaYrnEvqaBsscEeWAg4hQqQNE4Kx5TUoEDxRYM+81PuBEpUGSwmpkmECghByZUl0KYT+FDcSjdx7kmme935rg+49z1FwOv773qnH73H95i2jImnw/RCPFT8squvZy+X5KMOOzh7t2SZibUv3LKXiGMPX//dZERef9PN4VPGhrWKUxJrQFNmA9PBBSRKhLGZnSCTbEiFmY1iUaCVQxxYQIwuufoGj+Q3mkcfulMV7BJX4lFgjL3kzonH3v7E4293yGZjHmZ0OYndxRdf9vevPeMRL/jHhzz1+T/7uOf87GNfcL/HveRf3v5h7F2iYKwZwZa3eoyIGL70GBgZGR0jcpvEcm98NTEVmitwmEABDUGodhFPzV970E/96HedyDHv//SX/uiF//mbz/zbv/jnV73+ved+5WtXIZO+RTWGEKlX3rD+D84O3WnHHnZwAai1s6T7sFnKajiVYL1SMDIJwkAfY21BivHzPJZl6TyVxiIRSVPWzjTOrjD9+tU38PM84o9O+tcHPO5v7//4v7v/k//ufk/4u/s97m/v99gXPu+Ut7KX9w03b1Xb1bcc4OPhTLz8Te996GNf/NDHvfDnHvfiBz7hpff7q7974NNf/L+f909zX1Vff9Mt4rBRPSZyeEma5p/4yiX/8Nb3U9TP/+A9f+/B90WYqJilCZmOy761UoVmPPwl//LAv3rhA570kvs/+SUPfMpLfuGvXvTQJz7/Baf+jxm3HBB+K9DMi4QEMTYRyvQkMD7GYPYGW5K5DjJNQXyowHQUOtxCiosS2RD1yptzOdwEEjlYg6UNE5ee60QtyaOOkkXIcMnBoUqbWzG1sldqks+rCDLFoBmL5QgEPzz4J3/4f+3t6c8855Mf/cwXw7TvtbML1CXIyoVsXHbdjWy5yzGHH9TWDcvkoIXsSxOxaPBjDl//+cTudj5wy64FJ0GEHnESSDZnxlMUG0IyJALOF+YGSPvSRqFLX7t2VXHc4YdyoGVSxKiyPrfiOpZ5xMGH3um2t7/DMcdu6JDgq1deS9FM2GMO2cHs6ZErDhSXskkUPeaIwzkgmLA33ARKy8CtA4AiOSGJKSQHJFCgysG8VdTAob6n1xQkuLKd9/7dGUvakpIC8Aav1Ym+lOgtoiCYL644+rCDTWXp/ZqbbknK47j/uVMleHvIVi+7aiXS9fu3v/pXf/fgx7/ofk940QOe+IKH/tVzH/qU5/7c01/w6vedw0lLHzfdtJs9cizpM1tuvSVrjR1BIaJMlsuuWUV99MKv/PITX/hLT37uLzz1pJ//q+f/3JNf/KAnvPjBT3rxBV9ev7a9cf/+zI50/bbtCCiKNWXZjn0iD/7B7wXwmUu+ftO1N+6EHzhw0wVfWSfe9z73ZAVKIyzM2sxRM1pAglRpSO4XLQ1h5JPkkEwiFEGQmbhWh9RhJQw6qQpXaFJPrGslR3yk9F6Wzq2oyJSluQVLelGne1ZpDoPGHbYbyCB2Ottq0ULphJ4p1OFDYmiCMWRMudVbjfSEONg6lemB97nXC/7o187860c9/49+7f73vse+qX3ha1c84R9PeffZnxxd+qJwCIMqe+oAoWsQUjJIPFZqH/ifS1ytU3TQJlFZRtzawylFUTXFxd16F1+4B3LsXSkKdS5FK0lomdP0df8fldtoSzJq9PCFk0Iha6kzpHpHRKyISg+NtMxouRh3covTJkI4s7rUYPggki7ao7o3CSbQXjpEShdxLVHLUF1sRB0V3rraTP4rTDBBGEXVwQKSLRxjlcW9RAP4jLMOVDEt/KHjgr6xrZWuvAGNolIEKMDkYl64kAczUTQYsFBJgLYheUVwO1E9DTKGj6Tf7DQuAXR9iVzgrgDHt4pSQrizWObos4xY5cBCM1VEzaRobTJtQOWRZRmTL3S8qBYtlX1mrVpVrrdWUwtozhhlzMLRPSk5B00SNWXUFJ1IWYqA8jaSZBncfLSsk5SGYiwqK5KSJg5BJCOiDh2AkdquZLlmrFFaIxKiQ6IjFnjWzg39UEmFiLSBHZcKCSrQkTKqM3WCTtL/dRAxi8ygqTqiOBGhErHqpXRmU5AJJbHmzEAylTbBzWtRtDNTVjIAABAASURBVCBEyWkjMdexu4llg9xoKbAEDQlRhn1uwqeHnSpWDGaxxeuYWx7x0B/f+cUfB7D97CXXvuTU0JG6jJy3WHZhW5JB6Fq6jIWMJGRYDFmgUA1V8JdYYT0RrPHm+jqHJKyKTGPUJdYpYKdBikiVNASHdmCLMSIoIKqMTZKogr6xwUf9tqP81a2Ooevfa5+pSaMipnXYopizbsdmd65zr2slgS8WvTALDMngwKisGMwcGAECSx9A+mE7bKaPYb3LWhqDES2JUpqQRAUFqBB1YxqB7FFbJopMtXDLWco2yzb4lZFEpHbRXdJoQBfZuJQcQafrEEM1vgAL8gBitnnULTZddiKndFZCGrugLqkD6EqWDp5f+lx4dhulejXyPr2MoJhs0ptsNWaGWpMfl4lFdCRUYuLWYdJRy6623ZBdlyUQXusKiKUAG5iQQSFBdKQLMoe4g5opc/VjtjKmMmryNMJyAJhqVeakIIJZw/RxT+85BrrHQCxii5Twkq4u0q15tlwklxm5lSTApcxFBrFXlJYlkzNsFAzTztpjIWVkiy6+7ZhnjCUTFjpFbaap2HouQyqspRZP+Iikz+RTIR6M+ZYmFebHDBXWATEEB+UW0asEpZe52+6iM3mWPCOUImo5Ct0nFoBCSoKlcCex8djJaCWMm/Lo5q6ohZY0ASJzQGgewANjWZIoLlH4fdGom2zTgC+ja6JwydtMsUFsZuzsoi3MFte28MHgpnhlrBQC6YgFodCNPvAHv4dmAfjJ77vHzsHCwG8GmaLN64RS0/JWvaU0sBTMwiCqBoov2g+gHyiFtQAMah6YmeLWUSZ+YU2JSNXFbDTLWkVL0RLJYrZ2AQz8iMFXs+T+aSeKjp0+Wo6lYAnnKFW1DdwWNafIZpVGZyhfI5UwzMu6XhQKn5ZTPvTBR/ztv732PWdf+LUraAmn///dS/elZ0ea/j89Iy2F89PB4K7bnoy4tZumJtbhjE5gWqotDdmSJCnBR2RyxGGHP+cRv/WEX/+5+9z1jsX0iutvOvszF7/wlHf8n+e/8vmvPOPqq683iU3WyWrPpMyDd3YMNDg7N4/opItQVxjIBHYD5jn13IxerU+k/17jYLRdtpm+GugjF2ekv92lkFKZeNvYa/j/eBnMn8SBA0z11Z7/99Fzd4yinTti9K5zL1+7+oYnv+q0xeNutz3mz371IRPpxIWkDpfkHqXNecv+5f9d5q29C+0QDEQirJhV0rbZJFo7MAf6kjGXmDc+anehIds5Or2uhk1GzSGRMrij3heloaxSFewqMjhNcxmh2156txGkO4GVVA+JlTpWvE8yqojtmG6UiilEGIVVjmevNUhwQx60Ux/9qw+8zabNfbzi7e+/ftlOm511EED4FrNt1W69+2Aj4yXMZQgd0xlYMDJdU3esTY0D6PV22a6SV1UUwLb13hX0ShP5fgELxoIk7TJ6dJmzJD3ot46eppaF9BUVhfBCU9kADXwEUcrGysY31iOcsirzkhRiuZ83OiZFrSipLVuddjiJWnLLZZYgTwYI9q6eZSSfzk+DmzXjmEhSdK/LphJlIg+Hj/RBC8r/8HanUYpkypIrMnvD4dLDR9lm3Wqdm4KIZzOaDl4pRXySnFqZ+JG3CqaqlTLH2D//fxMpMuccIVwEXViQKGLvbr0zW8Zcesr+PuYRe83/by99eBu9Doby/9arwbV5TsZRevmp777nTqtces678FKTvPTKy79x9XWU+KAfuJcFGBOHbA1LEUGZciMiKeKCpD8mqsSlY3EcSMwpARZ6rRIcE4ytG0Yl2CwA2BRi4tJ8Lti2lRppI9c6miZVKDs52lenBGwQsxhKqAGKWZejrZQ52yAtaqL1WtxMgWmwuAwB+WlzpGJ3QwXKQB962Obnf+y7XvSIX3vJIx52+yMPuXl3/rvT33v1jTerGLOmBWu+09lbb8avm+5CZh1eSPRbm6FdtXuPvsAzks8Gv90hLoUORA8+almwhBCHHLf2hpAw63uVnTL21bmWLdRBfd0xWK63MVgOXFTQLEyXwHDX5PpIEGJZsHsglm3Co/ow1onMdSlsQqhU3cMXpyBPakLSlA24vfNNY2pWLZuQAlfZDtCpXh2FUjiruMsqjYzoKrGGZywr1oaFJxKVdKsL0dVU6ZOzBnLUjshUbf3bChuiZw1aU3RIUviAmMAqV4VCqnTGQhIuvmAZLrq0ukxFFC2YKNwRRQEcmeIp6WBe7oQ0JtIw2SIz3HUO7aAKa0zQMqFOUTbQAstShppzGDiiM1+6MEolyVIVUXHIgHc4tc/ErywCL93ppLWl2oAmJAXDEtgbOhaDO3TWwjgFIJL0caqYSm+FcIiUitIEoMVc9yJVs6ivcHYhEg0EHGtxkDHLMjAjRg1MyMpZmqjmUvuQse1B68BIEURkKIWlIkq4IIR7qQqoohioQQEIwVnt2HALW8VaIeJkI53LZIScqKQAzI0oyISun4TSusKJiLPuQrm1dDVRyBixOHcprdzpqX+A447g1Otf/e5r3/Yx55ygi+mew9RN13BHGmnLaIkINxartilkGlpX3IR9OSLHHqEhhsmyKodKhMUofWAJFoREiqikGiglEQRTKF8slAjGrOuDmT7Eh4CxX8eSJMH3UdYxTlBETaTtTSjDJQMiDG9uFVsUD8kEx2S1UaRDwUDGxlwwqHHwckLmYOAjZEBCFFoEpYjWAlWYw0aSf4xHhInvHWTTjIIpPcORWMesOeijMw0j2BRKEpoK6kBbwCHkygiq7L6F9AJGRxpffehgNmdy2mowvGKsGABqURQmfCO03YeNVZ2oCGsyaK9iEenDfDYNM7FSimqlAXQsO2PtDaosvYtQPumFmsSBEGukCDV7dx0cEJW+sis5UIFK9SDwgEHDeRI3wgs6gqD8Ig4RUZWEcpujnnASkM2lCzmXoAU+kmsTiaQRQhotY/G+mMcUahzGSYIwCQhS0YU8ZLEYttBPA6qkijhT3Ainghrp9cr2JX2O0akDvDg7ykDhSiBNlenplVWayyS5GCq02FNpB4ZQGdjC9NWCSLDW1hwNwyx5BfNfu1hyTho9B7XkSCGbaJTwHZkGpVbmxJSz5pIID8VKZCU6q2eLlwDhcxJPIpV3D+qCBNaNAlTW4TX5KhEMvgMplLretZa0RW2pmKf1Du4bLZUXu5feoUNNOWGdj6Cq4cbdwE23HOAAwrRv5yBQWiYfHq8tgLkYq1xYiKa4CJR/sF5eSpgMwUgbQcUZbDCQStJGm8iFddjwCB6JYpTUTFk0UbGSrXIiGDLaQJfP+ezF//Tm93J/c8yhBz/mF+77b3/x+28+6clvfM6T3/TMJ/zRA35iFYTV4kGNJaLFaudea0pRMa18SReunxa64vztzmSsUvgnzV2W5FkYGQRPI5RCDt7Z+fWf/qF/fdzvv+W5j33J/3nYL//kffgkmyv9GR/51JNe/pqbr7+piBWRiZEHbj5w4EAfA5KcyVsABjuGqeypA9ssvWbfqG95dgY4sve2CJeeKTWydLQUS+xdbhgGoe1llXDMIfv+66mPfNNJT3rz855w5vP/4ozn/8Ubn/P4Nz7rSWf+zRPf/rzHv+OFT7jn3e5CFW0zcTaVPvcPfvUdJz357c9+wlue+Rdn/PWjz3juY97ywj9/6wsf+6YXPvGNL3zC7/zifUNKYiicvFoO9Bf+9xlXXHfjbXamJ//GLxx/6BFr/UR39QClyiSyqQUAzXj1s/709Oc/9oyT/uLNf/O40//mcWc89/FvfeET3vGix7/jBX/5juc+/ntOPAFkPU0WtVSVRpC4PGWBaChSVV3RdS3FAeZwOJUIpHSps2RfqUQUOKwgdFApb8Zj704rKTW9kCHpY1DVUCHUJWHhspfbaZGrAwwjm9KqYe9SIqnDtDeJDeQ77nD8Ix66/heKvvD1K057x0d2t9u9UUhKKQpohqnwDXa3ixhI6aADgPDMtYwcIVlu2b8mSKu1bRqtcgFdxP9cK+3oSzOUENYKimZQB63rIOMQtRVidOtwjoCUTD5P4CZIqsAyLXuVXmT1UlWrrb4wYZcuS9QlSg4rFMYq5yDJb97dBSAitzl4fe6ajJrg1iuGjFkH0c6urG4Zawbs9VEyGKFeWCgtFpUA1e91bbe7ResolWtK4Nvk5NiooUKfDK4luHCWZaHEvTmUJVmFeK/osUlSCDzbUqzo6sLdjjvyLc9/3JkveNzbX/TYd7zocW994RPPeM6TyaW3Pf8xb3/+o1/71Efe8bjj0IxgDP22UspBKFJodkI2m1UOG3/5R+/19r959Nuf/edvfd5Kxbe+6PFvf9Hj3/aix5/xwieQ6i/78z+sWWwm5hy73pRna5yQAxR4p2OOvvWfrr3rE58LkQu++BXu7e541KF3v9PtRRicCikhPE2EJ7SrMYnNuigHowg3YVZYQQBW87FyXyW5AnHwIivkCWiKrXcwoAWoRWoTTLGu1Dm4gRFSUpj8lTIyg1YK0pRvEsnZBaaQ4NtUkBZSUgtqoCwK7pMgUP5xFXIynfwv4Jl05c06UbWUH7/ndz3yF+5vItffcuBDn/1iWqWVGQNBLeBFpvOBEaSOUWanoG+3A6AR8NGX4MYuxOf+7UxBLOmdRoiSn12FdqCzClIc0IFFY7BRmDxKCluAuRNdOMQBT8tRNVUhDVmT/sFc66JcnrJT5RhcwWuux95iwgkzC3wqjNYiHGuJGqz6xalashRnSYYUw/+kENUIkn8koL5nkjMJVjscXcBoZmFwRDSRwbEZfCchkRYB9OBTkUrdNfgQJCrEzKhB1cgLcaGDyZgYe5rUopwqIzAcMQp607kyUfm8rpgyrQIyYGJShMocAJM4lBYmJwgIW46SbpIc6iiiVaABDMlu4sYhMrxga22r63qnkQST/ezMUB4APPknZAzjxpwEGTbCOiUWEEvaLKlKxKGStD9oUzrHUxFgcF3lgaoID2ooONUiG9RUVESCw9ENURBqw4Tehjs4E8VyHcFBQ3TOskAHfeuZ3KpDvMmo7rpNCdC90AhxWi1dZS45qtCocMo0AYTRKBLkO0pGodWajPMaYod1YH2ez8h51sgdUcuqwQFpnikx2Gpesk8DO7NUryobKXxiy4daI3Z7DN858fg7PuP3oZpzv+55p46vH7C+qfOktD9TixiEDNtZsPFURwpf6EWxobnmELd0i2fngXEJ2aUxQjJMgqn1trOVdmBgGT1812OOMmihMN219mp9I/P6nHkSaglWUCflu4/BQt0tBl23km7h4lXGPokdSJViVdCEsVLjwuk2qVWaiQ7Z5rTIJvj4AisnMqaMZrLWE+mJreig5aad/IF1jKFb5ylMEa7SFYROlSVpt9Bc9pNAlrpvLgdnWvUx5bZgXqOcXK/hOjqYp0ZBGFKgRaO2hbLAEJN4LiPBCPRpjjZSFCiKYVxqQH1hLkJUWR94Wp+EzhQinC6QUolUh1NGyhyJAAAQAElEQVRF2e7Y2JATkmEDZUhmkkq9BEwhG2Azp20XzblIhOgQ1WCCkQg+CiB8UNlamEkwGXZVGQ2MLCGgxCwdFtpEmFS1g88kfcDpMReibEK/Uq0Yb7VWKgUCFE3Lw6hS51JnikZuepIsOx05chHMEduZj3KoRkQKmAxL6qLK91o8NDJEtskbXERVE+ousiacVewYtytrSZAcAfe9H/c0L8R6ZWZbYIwpzJR1qwvNjiLO8BUsheIacoJPkXSL5VLKiCpraWBot2G7HduVc57rwYXkSBtuPWyO9KBIT3AuREXBq0LLqDZqDuWhsKc4GlM0DbRul4QgMBCRBAIYSE+xIW3Q4wwNBoNjKWslxkA4he7dAic87ogh4soIpuiCE49dnyN866b91968jWkTtolQaEqJnCyLfO2qqzi/mB52yG2QiYzN1NgigLEwYBqY+CwBPsTXmezivfrAYlCZdjJ39eH0ZDTxtkJ552OPKKo3H9hecvk1cGSy/goPgEtZ5mmbOzP9dMYhOT23I1/3wY/vn/vRhxz8qsf/0Z88+Kd+9O53ve2xR9z+mCPvdNxhR3A3Rn0Aw5LkKstwSdBTNgoKZBJURalpRbNaaMGtF+NOBo0kjCqckVjg3XrWAThVE7p0ldzXNnc86tgH/MD3Pevhv/Dm5z/mD352/Q89ffayK97+0U+TVLT+toet/2HWG3eXa264SZLRIXsmsSkLljGcgdvT6ObBUPF95K0YIiTCBDY1mXbQmrcJygasV0p3WRYsJxy1/uWimw7Mhxw8HXfUUccffsztjz7mDscefsfjDznh2INPOPKIOx11zB2POXr9lxgZxxx28EETS3P2EXc84sg7HnXUnY868i5HH8nxxx192LHHHn7bo4+87VHHHHKbQ9O4wHSSwGKc/I73nP2FL5vKI37+Z77/O77D6IWv4LP40Eipmhvc5bjDaBbNOHrfbe56/JEnHHfknW531Am3O/rE2x1xwrFH3OHoo+54+DF3OPyoDT2IUOK9TLpsxDUIJskChTD6VlhPzUj7QfaGZJqyCyxKOSSombdb5tQhTJMOBgdEOoKyuMw08NkJxHItJCMxes1U02HqJmQ1Ky2Z7UuMZQTpsxzYzhTCW8B8CsugekiyRP7Gj//k991l/Qflp73vnC9duv5bEQ5b7RSpDoty4jFrgnz16uu30WkV6/MQ5keWnoVSt/a1vX9qf9Bm2jl43xCutuaDnMKtV+t7i7iQe5mNlBLmgAxNj2wdheaWE44+nIM///UrI0g8wwqg1BAZbGZa8FnV1rckgiPs7sev/6CCCXvVTfu58HcEmPQsFHP4toePS795BacV09sccpgLq5zy4603beZaOuYlSfQYqpz87V6DrBVsLjZQRbXCq9w666CdKnVk4SLiyRl7rWmyfj1gIgphXMSLyFevutbpZavHHXkbBoMCdG/w+hJggLkwdMGdj16JxO3ysQcdeucjjrzTUUff8Zhj73jU0bc78tDbHXHIbY88/PbHHHWnI4/mY3suCl5odVkl7P1wYY8CpnOT3KTe+ahV1I37t8cecdgdjjrqdsccedwxR9/+6CPueMxhdzrmiDscc9Rtjzz6sEOP8GhBVuWeCEBCilA2+CtN60796e/9bnp73hcvveTaa8/+4lcBPOiH7402DZHkKpY1g/2Edx7D4YLQ4dpHDYukddpQjUEWCRgY5syVtSOx1exEIYxssXmxPlM9Sstps222FB9YPPsaa6eR6qBFnAxQoQxn51wBRdUwGUxaqbmKGGEjqS4jPRDl0q9f/aN/8vQffuTTTn7fB4Km5Eh2CYaULjvdd3q3+9z1xMNusy+Bb1x9jexD7KwFTlKwd5EPVtTIbleMyQn0Xvvey+oT+uqP69J2vj1FZioIqNeKnaYbJn/LNrW9KfAQpgITeKGBAlGEIDmccugnPZSqdCw1ASfWvbRokxBNqV6MwIWKlmJCp6OGIjfddDTEFMHCwipC67PZ3KwXSVVhpYqDInd8ltxGdSdTyj4B3a4oAHdqORvtStYCMHhJEMtamCpUc0T0kTTdmODb1F3FTNYPKducZrdlXc0AmEiGyqi6FO3NB+NbFj6f0wLoQGwxZvegzNKSp3xDKGpJ+qDRuW/e0elg39mMwj2DapdCRnnHyPD/H1P/AWhNVlQLwKuq9t597jfDBJhEGHLOiIIZQSSJ2aeoiCKoCAZEgoI8fc+ECihiFhXfUzFgeKigICoGFBFMmDAQRDLDzDAz93TvCv/qC/7/f76+957TvXfVqlWrau/ug45y5Cg9AWEBjJYGa/oWvmWUG9gZcgq99+SGKawlz5xmzUomRGfkJqeZR8tYimugclHLkKymIq22VtE2jNNuxwWrFjOwK1nJ4GLWOoDkBqtOe26UD47gcEl3za1LnBS4bUehNLJXEiHDk6jcCFPcTNue2mHBw8BkR1bOcC7rYVlDG7SlEk319F7ckaswCCczGzk19VaEkMwEJII6gYEkCfGTyaiW5FZSc1N+pstpAqbjOA6zjRAtkQJ/hIoGUfrIUANUaxjMKntuy46o5hF5/ud+8o0+e3/qefyHt3zgJ39TVwLTBbUoFRQqtJVldBuHyEF4zTVcj6GbY4vY/ye5kqLejdVw2mOm1waZ9NnVCuZB0vaGTLqgq9jK310IHdrKyBjERIZmt9KOGiSXT5RCpWeOKmJfU46xa6sEY4/joNL0rM2rdsGiuVgYNa8In3EmKHNhJ2N3oTNUVSixk/1WlFISl3phgrpfxbeKOZkrVCYVFogUphuN/jdgyixZlZJX4uIJKNSa6jmNgU1lKhJcOVVXYM2MqCDyg9gifaHrKZYqAFVnKnyZ2NDiHYQxNaqJBu7VFmBk9sqR9GybyzGFNxfcRqmBc7K3bdHdTJZsFY4VsXWvk6xD81J3gEVqMjtOKwillyxWzVxJCS9Cs5rzNzLL2d724IISrAXsxiHl1auaZucOW/ZBxZemQDRoIPc7mkGM4fQoLACNaCkiKmdRUGkdbGYMpKSJ915jQU/pwUgNtUQe5hwoBZOulQ0MV3enIaSyWrRWkpqU22bqaFLaiylWqCTtMGQltJ0m7OeYDdPe1DpBVq6OZIeQxo890Oi0S7UzjMnAM/i7BKZEQBiMK7TCirOV3kcYLyYfe3nHWeaAtJ5Ls4MyvsP0sc2Fv81lBMoq07Y50skMSuZUP2aebrnN3KKKPNkGnbVzWIxXEv/9ysTclLeaW7awXqoCz5h3ufUte7MC/uAv/5FslJTWNM+BNozjr/urf93/59p3utmlNzo5D7xU2RgRMKdHTGsmQiVYrVrZ3T/iMSu0MRqv9OR+u6qQLnyITldyh1vcdOmN7970b28PUgW+DRGHEGa866qrv/J7f+xrf+BH3/z2t2jHGv6u9+7/u/x73OYWN7v4wrOxVGyx+5kqRD4copDmpdvoYAxnp3ihp1vs7aGbNf4IE/oRhHCtqbF5ekHQtA9rrcEWb93/4d/+43te/Kvf+3//31vf+/5KAxeFDY5qh+Vxn/vwZfQC/vXt/1UpcdQ73vJKssAzr/+Hf6GQOlQPLfctOqTC+UXcGR4gwypKPMzUeG5zvrhdqaF7FXdlUmoGlcKL0MpK95h3uNXNVOTo8dq//UdgI5cO6tPUkjXvxSk1PSx4W3C88rLzLzr/HOn+gzf+/Q0RMyMRnEL0AYvsjAXEVbJZcBXDkD9605te8od/zimPuN89H/XgT9IQB1cv9WTR9GTXt8OK5Q63uvLDMP74Df+gztYyIKaiSJ3JCld2qZSd/lLGSmclSfe1xxMhkWnie+CsPGiGboTWSFb1we4XwV7IfoA5Nh9r6FRJjQ6A2LbKzcTNqkQ21W2om+x9aNJscOuqhzhrLrlHpxsDZIQriOHkv7/BQ9Yo6SVKJRSmQlp/6ud/5gUnyw3TX/S7f0ZfPKSqFYMHMu52+1ubKpP7u6//K5eNlcZmTttWYmnv/eA1f//2/cbgHre+6eHcKFhzdhEOp5n9KG3Em4UUncN8kSLoGqn7LUwK+cLdbn0LDr36Q9e/7R1XkaMKsVm1buHTFf/nNa973A/83P/6hd86XbeUuPsdb9PPCvbVb/g71ckMWdKMm7IY89prrvvzf9732SzYi5bzdSOB/z8wCrZsLpCsCp1lOWtnlr4BvsPkHyl0MZM+VPXsCtWTtWuxDKYslbOzOhsdgh/ZlikEysq3N/377vow+sUX8UalVGL6irNXKrZOpSANd7jyZjx39Q3rX/7jv3k0T42pyepCOYsjBXOoNO2ijb1TG0XLCWdHmsBChArVjHHXW18J4E1v/a/3XH1dQpjfAkuGjpuE9QTvctTFylQ6hC2Bw8GoJaFsvTFOC0fD/e9110suOD+rXvIHf/HGf3t7N/2Eu92RpkKIC5VZEVLkbA3btyYVya8ybGpBk+SBQmpyMPDb0nJEYs9MA59WcInr3UWdFTAJDDJNQrE12qiuslRnIWeysmWmVXTFR14CJRtVCbggqvGEVrGLgergIzBNnrPeIHb5RTepquM2//LN/07tZLT0xUAK1VANqeXX30CZBwDhq6hEq6b4/74SMrF4ES7fpNPVR67JANNxaKJ97+bXb9TKfqlSc1P3FmgqrRlJL77drwE9pLFIi6N8Njk2YUWGrCrb0idbdS9lHWl4br7xIZEPeFPQjYk2k06bZjZEzrSzGlPPfYtqmgRDQp95yFKuz01mj1SUKEjwqpM7r1ls8FYJjaK5Jpa6O+Q2nl3Z6aiBe5JsCOw1r1S4twxx89JpkRRaproqK2fuOkhHrhmnvq+9mTBYZ8RNtJtYlwjdtuZHi1MlgxKsAhVqRAvmAColXWSFbCXCUEQ9wFWIa7xEKc9yKye6GNEgmx7ZOUpZrDqJxqbsltRJosHFCIEho6LSEaCFCW6+mHOhLIbpodmifTFTa9FtM4ZlxT9u5eU5va/RVumunbyeMcIGJRU6ozZEEKBO3q6thslGNqdsSR2M2ERIUULCeB7J0AJFKAgwNOyUCAMFVkFQbOaKTdpWVtU6c2XixNObWtcu6MCIWsyXhQAnKhFZTKowslTnO+QBeV44t2dVtuEwoSD1J9XPHW2EC7aEgOcK/KtW2uiJHyyrYy4giNQdoKrZoTNYz8wal33zY9otLgXwvp/57Q++9q9roBGYqGSddZXahoSWhjdJ7cW4K11Trnv9v77lwd/8vu/4hfmBD2pXWarE51ynVwoos1q0DtCD2kmVAVkyq7yKNhQ7vqaFhj2drXAQ4VDpu+sOggbVaw1Qr9y4JQruyZOGOadARLUTKgb68YPySJYigbJtqTNWnLGwYTiWItEG6UhGA9mrsdccVYIK7OTNzJCosdVJ2kJYe6eEFI/giClILaKieFV8EZygH0TJE3SmJlNQjNIPySxt4lmmuUg/5GK1OMyNJIuATk+8RiX5bC10JGNIpScwfTqURzD9Xrxb7uRUMIUgYydOHz2K/gAAEABJREFUQbOmvQuhpITDi6itYBqL+EFcxQw2dpmKB2hdS1rSYqQJWBKp7BoNDpp0WsTZy/YIlDm24FqbUi6yqa4S7CiTOS/amHv6mco0MB7VPMkcx8LUWbZ2rcVkDLOTbsuwpav2Q7RxVExLawxTpErWkhAVMabcz5mzM4owEmoPOziZkbHHzBCzaJbPDZcDuKVh4ELMW08RPuDIs9SJ9LJFRusHjCVFfSJYcoGFnMIEbffFXHLt6OWmm+uksKsZFgVv8RZmgvktExn7cGuqFC/dEXohAWxoE4egmEqpBlZVlymyogXniHWrOZyLGSrNKYQZGhtidbgX+E8Ar+L+ZNIVI2hFs2eHli/hsqaubhnK0Qm/5RU3+ajb7ov9r7zmdf/2tv9UlkHRGLdKrJz67T953VvO/h/vPux+90GmQAxy80svocH3XX3t29/9rrHEcqgGregfumH9/b95Ey/xUGdTdaiwGCgjBUdUhju2AC6/4MaffLfbc9ir/vqf3vrBq/2c4qRkJBNHAfzGH/zlG/7t7f/6X++55MI+xmpaY3QOvuraD7nTiFSphkjivR+6/o/f9K+8xKOJGrTVsOoC4Zli6JzANXUvHmkK06mgf178yAjL1Ex+UFMdvTcoksFLHV/x2jf8+p/81av/6p/CkROeEqkFPV3XvUyAc+dOWERZuPWll97pystp9Fdf8xf/+c5304R3rCIBdiu88exOj1f3I9j7lRTwaw1+/MA1177nPe9uQgeZm+ZqN9ywvuqv/pmXeLAJczSi7nzrK+9yi93+i377T9797veoTUgpWWe3M3nPdR/6od/47be8452FjdFdcqPDg+51R8b/F//ylpe97i9vUG4VK5BI01jyFC95xavf+KZ/QlXKDvKt77/6+375t063ebdb3vQpj/p0KwsgVNyk0HkINOiv5A43v9mHYfzYb//hW9/9vlnKJWIthqPcv7/vAx984a//9r9/4N1rJ1tJ8wnus9dgABsYSGO5ER9khsKN2lRnFMWGX52DxJ0nWnalxCfCo8rZJjoAZjLO6tlrXx2xZlsL0mKgTBSVNEvAWlL80GcNjwMHKys72aSLRniki85zqJNAW4Gj4Gh12ysv+6IHfqyK8FaQY3hoglB2yXbc+85X3vLsG48X/fZr3ve+q6wKWSn0ZLPwot999bWnfC5pD/4YPpJnW64l4mDFW1na8Qhvwv0O21sQKBYb4P1U8lmQHNZcXCjl+dCPucv5S7/6+tNf+4M/ZylrRWGiZqq/++prf+HVf85y2LZpbCCI29z8ko86+394/dI//cu3vPOdQ0TQqQVwcWvym3/6V/9x9g3Pp9/vPiOqb8xlEslHDklTGL/61sVVg2Hy5+xa7slKrgJra6fjMKUJPjLRo3yz3LK5SFFZ+4Q/ev3fkkF2Jurfm7KC/viv//HD90gfd5fbXXzhRYKGclB6+3BwQA7gJLTFx97ttvx21DNf+Fuvetc113D3dsYpOEOGvusDH/yBl778fddcg5YpLohF7eTAyXCiJS21EucR/dTsofe/T1Ph90s/9lt/eOqJmDrn/stbbPLav/mnX/39P/LtBpVNuIB9BDtcyxmK94yenIS8/NKL73br/fbpl//wdQDucPPLb3vFpRb048yF0GLOobCu7Pcpe/PtMU0mOSqUJbe3KLYfijFZllKw5F5gp9EECBNv5K9VjqLVI84abVWZjUWXLi2BTaNainICQUCK6mC/qo2BbwFPExrNadukZ14tNe1DWy/D4fz+4ab6l29+6x++7g1UZ+dlqMIFpGqTnK/4q7+99oajiNz2iiva5L6AVzlx2b0BPqNoOXwRN2wSzhx9+BLYGQ/DTrJxXa86cKX88AUh5LHOxfNQxJ5ZVWwmH77YSrt0hpM23WZaQij5YmRtyDKKVkWYgZWU8c8EpqCaCJ8TdxRT1qqEjaMYvHqJFDc3e4sgRQp0Te2TKwdKwO6cLYAQXtzJKcInIDYEyXJkATx2VhGtfERpGLKXSSVyRboILVqVa85eIUyZbtKmspIauRRhokRn+sa+yv5pSVnHSLQ0QxNUILbKtWk0rQaGnFAedZRaC5LKZeOkYpxBcnoKVJEwOhVXqVKIiaABw1wt3WJtmCeFBeybaOtssvbDNPaxhVNFuXHgQW5RoRmWlHtDG9m7j8YCzYbepDcDGvF5RtqKcVSLMq49Fqqxg6O5VCEAS1WqmqkTmdBC48d9CxMRFcwT40tDgOKO6hzNHWc7hLKkyCaDShTDKBN0yRMN3mAcAEqFzyYaq0aoj8aG1HlJctFsTfaji6oopCWQVUJ6ChNyBI6B4yrHlCLZxtXciWw7ydBSYEfHTalXpuzEOsTDKkgesBWNSaWCIe4kk3IFhnJb1xay4H5yqytu8qzH8Dw7zLu+7UXrVR/wJgEN58FIzaWlNBqoICe2dfChMcd88IW/4e+9+gM/9pv+7+8QLUY9ZlIlKbF2CUWI0Y6JLJYHbngrUoPs8bSQr4agV1TL1CqHbMx+NlGTJsXf1XtpRzGtpVkgLKVMIQgah6Uj50ymxiVgzkzKzieMv93blmMFl8UOtGLMctJqSDBsVhWDcaEylgGTRX2YZI0tGaso72tOoGbIMZ3PK/s2wPVTeAszmC0VYX5iIjdjWq2m6VG6ywJ0gy20JLmPYYDiipSEMHvoCjBew1rKIxgXdEvdXPyI4upPunhA1FmjWFQ664kcVUAjVKZ2aWpD1SCE7s0dR5unhx1MssXw7ulE0kg1CmU2G+u4H922DcoLhg7p2RkvOaylsilPbE1mjzIoIy9RAFJs3DXCFgoKcBaZYK815x17i+oSqhNLcO/OafCSGziGe7ie1PShSz9EsJAR4SHhtAh1KWq4wKQ3/lbTHY8XghfEUkSjbLp5MgCKaBMVMWEwQe80EJgoocTFG6aSNiTAUyG0btPYsplPEY+9AspIZ3AYnPOFtlrXtgsraEUgZsWuIWoSZmFtG8Z7rtlAmqQlrIrmP3JQiLtZSFCmTQCRCXSRDv6pTXU1yaFN+rBuabuBxMYbta3lVEeABJBGGuXBWQDNGM5eUyIicytxlkbTr/iMB55/WK65/vQZP/Xzf/ymf/iQn86qd1911Y/8+u/92Mv+OLLufqubPfJjP7rT4YnipD7qjrekHe6rnv8rv/uO975X2pyy/fVb/uM7/s9L/uwf/o2XeDDNjZirCYnoC8MMUs2fcEsXr6/4tE9dTN/9wWuf9ZO/8tq//9dTIq7x3ndd+1O/8uqfPnuw/Yj73/Pm+38GIUarj7njrWnzTW9753N/43ffd931NSiO4+v+6U1P/9Gfe92/vJWXeNxw/Q0SXu4SzBNPnB2iDBEi5BK6mXqTjxBSSNHQLs1UOUMyJX3r8zi2rd/p9re78y2uoIkXv/JPf+YVv//Oa9/vwhH6H29913e9+NfXObvZfe90exhs1Pnnjy950Mc3lXdfc903/cTP/fm//PPpZKh46zvf810v+c3ffN3+H6SjqUqu1S3FFLjv7a/kmauvP33+r73y7e95n8d2w+pv/Je3/c+f/PX/L4cW2L/EzpNzhwu++NM+aentg9fd8PU/9H//4K/eeLzhWjaSeW3+2Rvf/LQf/zmCfOZP/+pxwqEq7dEP+YTLLrwRdzc/8Cu/87O/8/vv/tA10cB4/+M/3vndP/crP/qyV33bT73kne98d0OL1G//mV9751XXEsx9bn+bv/i7N7/8r9748je88eWvf+Pv/cUbX/66N/7u6/76j1//93H9sZ36Jf3wpQ/8+A/D+OofeNHv/eXrr7numtDtuu2GP/vbf3zqD/+fF7/iT575E78UmenOY5p7yyrqzeGFotaLqceGcjW13rC0udjWZapmsbDFvZM6Um21No5U9kCCA7tCzyB1HAVN1lMxtQ4Lb7oJcme3+mRF6gZbpZXYEJho4PS//39YB+jJ3HQCM7F7UhmLPOoh97v72T4V//1SFCTV6iYXXfTlD38g0/2293zgW3/8l9/0L29VXoG97X0f+J5fftmv/fkbq/CAe93pPve4U0yTcGCjog5s+sC//Oe73vP+q0LqdJ2SzaCs29Z86cVIkmSgEn6bKy59yNl/8uilf/L65//Sr//Xu98jvabF6//l357xoz//rqv2/8OhL3rwJ4ctLuCt21d9xkcK9lt+6iV/8o9/fw2Op6bv/eB1P/xrv/8Tv/1HjOC+t7vy0+9/P1QDFxt+/u8jxLIOPUm8igLsJyIfvhgIH2uS48a2gDWdx4cv9b54sXXrvs+qD5/D837jd3/+j/70A9ddx1ua93/oht94zRue8wu/dZy+NH3MQz8FXG+hxaTERyZIJdPJZVOa3PKymzzqAR+jIm/+r3c/7Ude/Lq/f9O68oZxO73hulf9xd9/3Qt+/sWv+pMX/MbLvLg5L6bRuh6WhY7//V3vec973muZN6wRZVC99z1u+0n3uAMvveIv//ZZP/OSf3nrO7ajQ3H1hz74k6941TNe9EvP+7XffeUb37jZVm3Kf9c+W9FRnCdlb+/Rc1IpD/uY+5gqTfF40L3vcv7JQb1sSw12/mxmps10QE7Uh7hBM3uEFYPtExwpFDaXDSenaqJKjiv6usn0rEIvGaJcDwqu2Li5teTsPltBfVH2plAN5h5KDFW1TdqGNfbj0zh6blJHMDdUu5rYsCZmSfErZXcq83Gf++BLL7wRv1T5nl/6fz/5/377LW9/i891ZpzO/Me3vPN7X/ry/3P2/1HtyksufvBH3asnukMLfOBCdzxe989v3mpz22Ydh0QT6bYj4SXsAxWiQlgRp6fEsZ9O5ebQjHupKVhlztqiTsbYrwEioqplLRi+bMseNC3sGyZPY9QwoIF7D5Fg6ABlX2uPyezoDHGpAJI/IabZLRRb82KP54UpGtZFzbRIAkQ9NCFcTaXBCBb475RH4zpdK5hwlTFaW4aZtIrUObVSIVnB29zg/QkBJ0E275oc1YURimtwdV378M5nAr0JZyc1wEaRABuKA3xDOVgJH5edqAxeA4ituNBTtpJ7QMadBmlEZh2lZlenzZScrBcC5saB8xq8N+ctWHZZoVOiiEWhQGzh04OEowmkC28sY+na+yIloP43Lz6rZAQ2IWvGcfrc0mh8EbfYqnytGRVadL0sbofMZddaOUMQDkwy3ysPyeSYtsXasNFzMWe+ijtMLIJhKmJR4qrZui5WfDqunnMlSkRkhCuqt2rqhZpAgnLvWrKILLt+yZY6ahMNRjyW5IqaQ6oThTBApQMeTFFGm5tsDL1IVSkdlwR7mkmNSTWT5tLu2SKEzp3l42S6JN3KJSMJSHIzHCXZrfk1gizZBrEjjeWruORzP+VGD70fWGVv/s/3vuClx4g1mVx2sVIuUtEFjUOdFQvdrCjqa9/wT6d//g+ccvIJdz/vvrc3iU5rBTXk/lSZXqd4aDURtWROi8VeS8yT8CVhCRQYSE7N4vuptRNVVohK468AABAASURBVMXE8z2lqYBAg/2cZproLkEWFTll/QOKqPTpcUy7odqWEtWAzipLkmcTLVI4GkW+pHvTufRgzkwEIqKipcpdthyyDbiRLWlFpKlEVVkbYtLImI2716yx8ZFB75TfLNZVrS662sl+liNOg1VqKKXWlZbpttj2vGJCEJBCA4OZuRW3jx48I6YqjNhr3ZI9CEjpKarg2GiCIe0AGmVshfDyEAdfNSAnfOxy7OVsR8JkV5mGDd0ji1omh5QODeX+g6mXTEQWCabKaBHQ/Z+y2lHW6JeG97xnqYsRHYS88clO79k0tCIqGXh6aZQkq2o2hEGlG4GKEGgUv5rKcmHATEIrVwnWbzHJAT5Tacx8LBKtya6qylH7Ndj+O1k7e85iZDIDKTaNHUEyEo6d8iNkUyQKZ3o7Cw2ohCS9JKmubNr40qrc1jXmxkQS71ZUREGqq1rjmKWqgtikxIUHRII31AMbJS4VEqrVmu/QFDtdIG3C8DmROW4NrUtYUXChbkjt7B7lBwnVVjrQDm0cdNEyd13DnGwVTRU0kcU4+AF0jN6L26cToXo8Qrbcx2rKx935tk/9gocuvf37e656yk/9yiOe/YJPe+b3fO53/sBPv+I1fDZ2q8tu/N2P+/zzLuhj2GhNen3ix9zlnmf/Mbg//Yd//x/f9iMP+Prvecgzvu+JL/yZV//NP97xZvsXpwBMFCWY2kKUNQBSJSgYQ52Jijve+spvftRnLM3++T/f85QX/sJDv+n7HvwN3/3F//tHfub3/nT1+Pi73v5xn/uQzXRCM/OzH/Rxt7/iJp71y695/ed8xwsf9NTveNgzn/OUH/v5f3jbO/k9AN3xeM/7358rVR9FIoUnGLII22Fv0WVqRBEKy3Qnh5eLmlmCxYYGIVFMc7Kfc62zbWpi+dbHP+qmN77wuuP6Q7/9qs//7hc85Jnf+fCnf+9jn/OiP/q7fxHIZ9zvHp9wlzsp6bVCrwff726PuP+9afbf3v3+b3jBix/5lO98+Nf/r8d854/8vz//G8bI8zwYdolUK2t44L3v/BEO//Hfv+C7fvRTn/Gchz/ru5/4wp999d/88/+XQwGUY73B5VM+6t5f+5kP6mbveP8Hn/XTv/GZ3/z8Bz71ux/+rOc+/cde8vdv+a9LLjz/qZ//8KJO+LjDx81vctnzv/J/3OT8cwT/0y//4y/5jh9/6NOf/5BnfP9jn/ujv/26v2b8j33YA253xeUjcP3V1//Vm99KbDx+/g9e+y0/89Jn/vSvcMP37Bf94rN/6iXP/mkev/SCX/mdee1pr1Crh330Pb7+Mz71wzCe/dMv/bxvef4jvuF7PusZ3/f0H3/J3711h/ENj3pkCvcNpNTFyDaoeLCkxaCtpy5RliXkwkQ7D6h4r62zOniYs0ZFCaehjGkBJj9IgZU1vEYE56mIKt1AqsnkPhNwdhY3gsRe1UYvtTfebIiqQx80wsNMuZkMleSHgPKQMtFLLjz3dZ/9wJPReJoHAYhRd+xE0tAf+XH3e9xDP4nn3/Dv73jiC1780Gd894O++Tse9b0/8tI/+ysuDczj0x/7OaInnsZ7kmpxOOGz7dtx/Nve98Ev+bYf/PRv+N6X/t6f05cJG0xIOp+hLSHCpsL6LGpCvul/PPKjb3eLzf2XX/OXX/BdP/LJ3/icT33q8772h37xb9/6TrL9jC/8jLve9taVkpxd+dF3uu1TP/9hHynYn3jJZ37z9z7syd/52c/6gRf9zl6wt738Js/68i+40QXnSDmM4THhxLIfs3GFMlLAOw9u/G0Iw9sv8KdErdoIM1SC1bL5RybewGeY+4BKUlkfOdlMn/trv/eZ3/HCT3vad33uNz/321/8G++99npG9JWPeNCdrri5BUucCycj5DlOZqaK+21qv3NrOfqXPuwBX/wp96Oxf3z7O5/8wy956DO+74HP+J7PeuYPfvuLfuOt7/nAbW966eMf+cmqKSWRWHp/0D3uQiv/9YFrHv0dP/6pT33ui3/rD0lfYV1O9GmP/qz73fHWbA6v/pt/euzzX/yIZz7/4U/9js/9tuf98P971TU3HB/0UXf5xI+9uy9cb9hSaQP82TGJJHtTXwcmZYOMj73LnW507sCrNzqMB9zzDmoCWZBGrZk1JVNFatRCe3SrMU2OVC1YpDDamFKRGYrqQt5FOqg+riS74gQQipW8UJoK3hjt3q24ygVX5ax93JJbxya1nXFcJezoThxMkyVPw2dlwQ180UnXojcHF/vNMyNvdeVlz/26L7n1ZRffsG4/83t//OXf91MPe8pzH/INz33oU573Zd/30y95zV9SYOxp3/P4z7/owgGu0YQVuPXNL7nyJhfS5Itf/dqHfMv3f86zf+g/3/8BMz0YHyXtpchLmCkbkngB8nhucPJ+OiHQbLZvYdimsqygp8cNZy9SwB3C3jjAhSCNxVuaZIsNIAQpnM2DBWgK6WerBQdhHsu38OSyh+KCBwQg0qjZcxVLhBQTVh62Wa9uoqlI5irTkl2Ql6k0LWazQ1H8R1szsdG3Lan7FzacZSmZMp09xgiLw+DowewhhOvDHu6SrTFAbxm6wlwRqs3GQh1XMwa2CUon9Ig6ErQE9yB2EFvEtLgrJ/Jq4BFKkDwp2qheutLASBvVhmtbN91WMAjTxazrPg0qQcZSC8kpnZPRPFowEAermDcSY0b3WkqGKnODPSLKcdNyeNSWc82Nv0lagS9myAPRwGf9Ytx5NKvRkpOhWiQw2TKThNMxEyQlJMOyD9IW2qg+UQYjw0RMoOFIB4oYBQkOzlJWbaQ6a4LEuAiPoMmtyO6JZu+Zbc835azsKl4oaEklYpvYjrYdW+3YdGs4hW7ZXXr1DtKQydEcPDvWob40jDGAg+fJeuzY7BBiFqbRo/oGJTwEdBIX3GOWu8bWuPFmcKtgVQpBTlJa1rDLnve1/eaXAPjAj/2/6177t4Vj2cbqNV7cQjKriQ7+ohB2IFe98DdqnQR2/ud90sFsQJQsGKIjNBVHi2meSHj0XA/lwzhiJ26GJQSG2XwKby9SOLmaS3OoBvd8IV4VRbWUF9h0spkJxScAJMAQW5VpMHOu6UIaRUW7w465f/Gae9+SohVqowItIA7CUcMieQJZvNXkufTyFD4scKRji3YUC2OM7h6RzA5cZBpT6Rmo/XZvFO+34hDZpYwSbM2IoObMdRYiTSZaFWieTzKoybSj9E0ifJXVsRUYIOdaUfPdRPahO44UkJmyvettRM1sNvWD5VBkKSfOnOlRq27TNkhKppUMoaUp/bT6WvQOV8yDMbM0pwqqVkYxMQUkA0SfuaiyqLOERuiVxbNKrJnpUrmE9kRjcQnxR+pkHwjklrU2ikcBZRBGe9gXit5aX7Df/0q06eIMEKBfsCGiiWAuNVuFKdgK0paSBjREZWqVovgeGe6MjolDdfYgHtCs9LUiemWvElAnBzaAYiOccfDZkiyQb4FMwVFla1KS6b5z5b4yL1q+119TqKCouNDhvSe1GI7gIsMYi/RnVlF6E+oiPLBjx/4qBo8NGjakN5OsmozSiDS8ZsSp5mpEK5glsZOwmZaolZm0FIqwJ3VKY+mRab1ddvEFl190wXm97W2bJbI0ZetVTVAUw2Ca+vmf+HEvevLjPuaOt1l6u/b6Gz7woetO13nphTd67Kd9wv995lfd5hYXi0FoE9CKrvWDX//oz7jfPS88d3L9cf3ANdfx98XnnXztZzzgGV/0sMtvfOFlF1/Yxjlwhh/b5mNOkRARq9b2Wyxz9oixPfxBH/+CJz/u/ne67bllfPBD11917XXbnDe7+IKnf+7DXvDER194/rnKUbFoLDe9yWXPe/JX8DEndxLX3nD6gWuvW+e83U0v/e6v+PynfNHDLmOAF1/w1//2dknFnr520fnnXXbjCy6+0XklAAtIMVM2Ehk46YfLL94RYlmOkIkMCgmsljAXbkD2KSjxuv0VN/35b3nC533ifS4+/7x1m4R31YeuU5Vb3OSiZ37RZz7ji7/whEmaiSmZrS3j6V/yyK//rAddcsGNsuqDH7ru2uuuH7192ad9wlP/x8PxkVehrzI8DInxg1/zmM884/CG48aIbjiu5PBJn/HAp33hI3eEF13YbEH1rAyEDf2iRzzwh77hMfe+3ZUno1/DHF173bU33HDeYTzko+72E9/wFfe87S17jraabCrR73X72//c07/60+9/rwvPO3e6bldde/0Hr7veVO95m1u88Ose84UP/sSZc1KV+38n54LLL77gsjNOPvz77ON+kqm8/OILL7nwRq11rslzyTgnj/70B/zwkx5zn9vekjAolauuue5DN5wSxkM/5h4//vQn3PPOdxTr6Do0BzCYdAZruWpNa9V5ohlKxRM5gTpjRiBSZtm62yFZhCzU2flhKdAKwHqi8+Fo2dAbDlo8upk0SygPL83sArOiY+/YtRBItmDRkLhsj/GCwzAj95GtWKubwkVRsoSP+97lto964P0vPxvWl8b2IlXp6iFm8sQveMTzvu5L73LLm3VTJpcF4hG3vPTGX//ZD37Rt3z1xedfoFkMMVWyESG+5NM+9sH3vtNh9A+dHq+57obj6aoRFbyTDqfmVmlRHSJkwBPA+TcaP/j0r3riZz7oFpdc7JGsKeZrNPuku9zu557yVZ/98fcVrKhjZZQ3RP/8j//EFz35q+53pw8X7F47p+u87KIbfflDPuFnnvnEO1x5hXRgST/MNL/o/HMM/8YXnPfhDlEVWFM3mOS5k+Xyiy7kcajek/06T4QLgSgXMRRn8bj0ogukIhGquiMmXOBnv+VrPu2+dzOVq6+7nmpspre87CbPfvTnfMXDH2ydNRQ5Y5L8apdftGvp3LkTEe2uLRqstfPOPe2LPud7Hvv5d7r5Fb0ZjbAErj8eLzh3+IJPuv/PPvUJt7jicmTpJjJ3rXzOAz7h0z/mHpQcGxO/V0RkZ84CuuXlF5z/gid9GRNx6UU3qr30djamB3fkz/jCR3zv47/wkvNPtJnr4Iv55XFOl8PaWijVFCMdBBvnXXD+A+5+RwZ3p1tcfrtbXWoGaJtcosRURQt56nnDtDW6wpaWdi6SAhWQFDU2mkqUFZmnqLjeNMmuycdJfUClFFHp0WIOijM4qYmYhOc6g+LIQoH9392ZIECAFiZ5cF2syWDXxMazbiP6YYqyhD0zEpllXAoyO/Q+t7vNr/yvpzz5cx92m8tu0kyvuW5n44Mfup7vb3HpxV/24I/7hWd91d1vcyUXDDSPjhK7cT/5ti/97CsvubjAxnU921HRzWjV5Ubnzl1+8YVX3PjCRpJm6NYgTVS3+nBBXdiaVRE5+2BWwYrFjxuf6Y3KsRMtZe2KhDaHJGUUDk/xQIAvzsmwCUthC+AfK6kJuO8ElvRozY2LiO1BijdZBcQSk9c1hZTzF7APh7diYaMwlZyIuXZLXSbZgdDo5PAmYAwgxTFhS5MnAAAQAElEQVQnttIEPXrWmmPNcyLc5ljLZlANBmqyqabwE4QZZCGUCS9wmtKUpPaQsUGYwbnGtkWw5+hmNnkdYpU6izCD+UwYuwljJlDLKZQLoMIXgCAhm3ALR3xG/61IGGcd0Vbud3ACdP7LJbjGaaN0LKGkg5EFUxcodxSt8FITHShsuq3DY6kaJKAcGeJYTEdxr2ZMmIutIFu5bDVcYLoqvw85Fvh1gy6uY1V2C0x6AC0WQaFEo3Kr8NzICDbEpLQnlVBpmrvvIT6o8x7Z2Gwmr2UZatFN20Z9UQ4rZLJQ/cy2kd6iIadGdjvMigjTk5KVCO8Zi8dB5FzD6K4oNiR4IwMtDMwqIWemVO0NBnuRiib2ZDH8lrYPm0qwOLLyE2Cb4uiqY+SRQYokXD0Pl11y8bc8WkYD8P4nvSCuv04sBbQMAqPYq9QEXZiHmm957+nf/QdHjisvu/QRH8vTVmB4FICrAZDIDATRhuZmGSbSYSbllYSrJbanOpg98RhQXUYcBvfKCXJPggtpIF4WzUzSIQLsn1MtwEwo+EdE6G4RLVUMRMs0r+L0SBMo3/MGbcJrbsjgjNjZLabS1E1SJINCDQ7gOuMTxa0nYuu1ce2aU6hXkRLZUFsVf586VsckCkYEIwL6WdKGo8+QnI5cyyisKgqaA9jGII0S3eB0pFN7olWAdpkrTRWONDYrIX6ezfKk0Ir3BDOKbZKxWFsOYielXYN07M0zs9gUat85WEGFM6sQrAdfK7aQ4kjGR9oYwhjFhiqS0Ny/81poyhY0pqEwY190WLQITgMoWo1ajtKmISld21S2QYOo1IRxOchdjTvSjGRPEU9rTI73ip5lE0YzVkubo82mVTBOpP/m1YNt6ViDJ7BD1rkRRyOZHJbYX2aCxoSrJ2KWgCXWaFFQ5N564tBn72vrx+w7UwQnRWJBOQWpmYnMguSOBc4PmiSYQOQYsW77mNbKLFwiYyb/cvim6QTfN9iqNk2mWokUAI8gu0XxstO0ohwiffZytk9mgh0EzHymsIzBt6CLiuKnsMIyMVZVbyS+moQG7nvn2//Oc77p97/7KZ9533vIpAfNYRjaujVTM1BApAAT97zlrX7s67/6xd/6tc950qO/7wn/40e/7kt+9Vlf/6TPeeSNDucH+anY3WQ2iAQuPP/C//nlj/rFb3nCcx7/6O/6ii954ZO+4lef9Q1PeOQDP+aut3/185/1suc88za3uEVRf2QrZmME4V0IqamYi66V1Hmi7nmHO/zA133VLzzjSd/12P/xPV/xqB/5+sf90rd906Mf/IAT6MJvD4NEs1m2NuXKm1z6fU/6sv/zzV/1nY/7fB4//Q1f9X+/6eseeM973u12t/yd5z/lFd/35K962CeNouhNTJ791V/8Wz/wtB952mNttAyUC3wn0Wf7lPve6/ee/y2/8/xvvdvtbjNnO869TQW5967VO58inuNq6D1irLj84gv+5+M/61e+88kv+MbHf+fXPPo7nvCon3zqY3/x2V/3yE/6+GUxWEaEr5mzi45xGF/6sAe/9NlPfuHXPua5X/0FL3zSF//mt3/tNz7qIeefGzh7qbC5hSJcZdV+wY0u/I7Hfsmv/M+vff6TvvD7n/B5P/H1X/Jr3/Z1j//Mh3zM3e/46uc+8+Xf8S13uMWtzoQaJnsAVP797nWnn37W43/+W7/yuU/64uc88Ut+8Ose86vf+uTvfeyX3OqSGzOdlJqweHxLp1b6JZfelDl66f/8uhc+8Uu+93Ff/P1f/SW/+K1f83+/9UmfeI87Q2LD3CQvOv/k97/rKb//PU/53ed982/9wLe+7PnP/p3nPut3v//pr3jeN73iB5/6qh/45j943rN//ulff9GNLkDre/UZ2PI+4W53ePGTn/CLz3jSDzzxS5/7hEe94Gu+8Je+7Wu+40lfdPObXWamogoeEE2mg8JmuRgYPATShSNMQBaAIg72CrD1tWMqUltZd+5O8sRiDJelcPYS4GzhMZKwdjntwiSJmvClMDAVOAko6APVPIcne8qcOlNVPuX+d3/FDzzl5c97yqfc607sCJJFOZvNNlh6VLfk1mxr3/TZD/n95z3t5c972v3vc+cNCN3tsQiLVjA/8d53fdEznvSzT/vq73vi//jeJ37hTz71cb/4bd/w5Q9/6AEnRNpqK9myaPuQslxw0Xnf96Qv/OmnPe65T3j0D371l33Jp35KSYRUsUtwB+DK4NmxOtRUpYssbVnGl3/Ow37pO77xJ5/ylc953KOf+7gvfskzvvKHvuYxd7/NrZwFGFlZpCNEIUoZ3eNOt/mxZzzhF575Nc9/3KO+/7Ff/MNP/PKX/K8nPfGLHnbhBbyvkKIS2Os1o/JbH/d5v/29T/2Rpz7m0FOFXSAYHLLg+qD7ftTvf9e3vvJ/f+s9r7yVJLS4wPnIINuq7be/9+mv+r5nfNkjHiDiNGiLiirOXhdceKPv+dov/bX//fU/SA18zaN//Jse95Jve/JnffInFLNBQ7TOLl56wfkX/c53Pv2Vz3n65z7oE9wap5IDQUmFGB55v4/+hac96cXf+PjvedznfddXf/4Lv+7Rv/6sJz/rCz7vvJMbOcyDm3qhMc4774Llfz3u83/mG7/8+x/3Bc994qMe9dCPY1H1KWyf5OJGi33FIz/xV77ziT/2TY/53q/4/O9/7KN+5smP//Xv+MYvfujH91bC3u5W2T767nd41Q8+7dXPf9p9bn8bKq3FEnJYTb0khQvMfPt73y+CT77nHcd+b9GMQIEg10VmPNODH5hCTu57oxeu3VHFkBqYxDoIRlR3WhOHRNFaDi1laaqtrAhFa+yhSG4YqycloE3cMi2rRekWNjMcfAnAhsYeTtOtjTG09WojcLBobaKIphBcSFlzI/u5rR2OZpueZ4fHftqnvuTZT/3ZZzzxOV/F2n/U9z/+i37hmU946Xd+3VMe/bBLLz7f6G0KK+4InWjN2/1vf7tfePpXf+8Tvvi7v/qLXvC1X3blFVek2ib6hM9+xMuf86wXPeUrzz/hPlkstLElt/HR97zT/3veU17+fc+4zx1u3llukqv5RrhGZccD7nOXlz3nm172vKdeeulFISUom3AvL9bZsfoRLZT3SMrVc5a7zIboVRohGarTmNNR0sEbjROdXZKUnqat3shNRZUFzLt655TYqoKL2J5E4y9qK7BFaZJhb0iOJlHsN9K4M4qU2hz8ksFRaQkpmq+Z5aauIlYoYmG2mlRjjmtWMJM6hLWHWTgykMrMSq/d7BJ6cKbeW7mIR4JFxvaKHrRS+wuVTZJ5U3GhA18kD4CKFo1OkTSlAzMEkJzunAFn+UY012VTDU14wNUwBvqoAoI3AAG+gZess8hNa3VyoguXd80ycNMqfUjvoijxUlXIoYF/eDX3qaBHaVEdBUnnJySH9gjbaBEVUb57cdvzlAKeKCeHwW1/JHeeueU2Y8tCsZmNMRplbwVwMOmmieqFocGKBeOnJ0TGTE9A2lJ2gDLsSJEhjeEtQKO17BktZK/RrWSCNXOCw5jDuOn3DwNJcsZEYYgLbVquo0FOVPj0xTA29K21PLFGHwbUcJxsekh+r2ZbyOq5BkhWBWM4Mgxc+hmfcvKw/X8ANv/r/R/89p+vJN6eaG4WfByw9qTy0Cv6db/4mnz3BwFc9FWfnheeB+XbEGbcLaJXUlHc5Y0jg9goL1FBLJjN2AG6M0TmQUJs4yqYunlhCsEbHTITmTtrIl0wprWV+oHSqCEZ9KzcGZkl6t5p11JHMuogTFhNqOYCP7flyZRlik1lEVIj3Lny4rHqOPPIHLSpTZuMQZG6FY1VyyHBlQAQtEb5dFGrwxAbsIByex17GwNpl0Ggx1LKNjRrEVlskO6UhSHSgkqT1jAaLwjDK0imSSnDsyqUY8vc6qwoaaBRryzFjWiRyoE9Y4T3tfpqDaZU96F02I7JNDVSk9VgQnK9SSRHk2arrRgr98xMAJ8SZs5ChKp0utAiadIStGOsC1FmS7k93zSilRCpDoFWEm4Z9wp5KJw4pYN9tzgmhdt6qzq4H8LBYgk/xpwb/80tI3hSypqICiW8375N9yxabFltZq3B20H+1RRlOwBPhq3Y714Aa2hNxUQisHlyHNXbyqgHoIK4oi/JVqBKH12CBTqDAIo122gMaMWo2GVUrbferaOQqCxWm890DwLIoyIFw4VPa3hjopnUDw46D7Iay4o5itJZskILRBaSDnd1qTC2fl8tXFEKsyCQpaLHJuBESDVDo2NSgCBurpiAQHrJ4KQcM/ppymxbjY2VEukMghgle5uHZY7FVcJjOyZvjat1vfXNr3jwfe/+0Pve8+PufMdLLr7QWvPo60afDJvgIreMaQ7FUje74qJPvfddH/JR9/6Y293hwsN54IriC4UVlZtsTDiDFQNg5mrTmgkOEcQiwNbhCsNysFteculnfvR9HnG/+3zU3e5wuLBPfpel0GzLEcvGQZnluW1tHm91+UUP/bi7PuQT7nHXO155ct5YDn3prQkaCEgF/M0vv7wL63M0Nco3orhTaYGuKiWRWbJ1ca4h5g3bgO90RllmK4BLBgvaADCdFVm48QXnf9Rd7/Dg+9370+5/99tdeflJJ8FHbmyz+2aeYIY5T0u7SFx4bvn4O9/+wfe+xyfc+faXnDvp2lSJjuYgUGML4TKcqhokUA64xU0ve/DH3OPh97/7fe986wvPX5qFanFVdGA14Q2qDJApbgZ0uiXf161udpMHfPRdPvV+93rAfe5+xUU3NmmtkYhBa+A9NebEcWMpdFhX3mt98j3v9LCPvetD7nen2978YtMJWVmcRlzKhCwIk1AkIxYA9J0YlQPVSRes2IgIXbZsWxs1yKJYcvptr7jpp330PR/yMfe8/93vdOGNL+CYDtAQMiRFvYNL9YqdWHYUbZQkE4FEQRw9atE5dG01KXFLCtiSBEFFKKS16dSi2DyJisaxCA7lvfZ+sJ+CyA4wOR7VkHsU1SFdWSPg/nNa36xtKqpYEAvZVOmi9E4gzTlS9vV0yNY4uRZgEe7DOHIuyIPKGE0OvTrLdptYjyrzFlfe5JPvd/cH3f9e97zj7S44nBu568bKiwcoN+iK2Pqa3fpy7zvc9uH3vdcn3/POhwuW065JY7nbg0gIXyyM1N7EhoK70Gh+PM/sHre69ad+1L0++d73uuKyy6LJVI7lLRzHHLLGlNiW4Le83spVrrj8pg+8170fdp/73P8ud77R+RdLjraCm6nc21ETLqbTZlQogULBAiBpJi2EBDhy06PiVGNVEl3UO8tbfW3hi9rBmlEnFmqlahDDf78qnCV5ybnzHnS3uz3so+513zvd/rzzDpRuFyAoI4OZMEEqs+uq4G4gCtS+qxSXinUqdcqaPLfc6sqbPfg+d3vove/y0Xe83Y3OP7+wb6AisG66lkyQVgH11OQet77VQ+937wd81J0vuvh8ypYcrMfJHwAAEABJREFUp2lqwaTBbnI47763udUj7nuPh93n3ne75a37yThqMLrpLVermeytCpqy6FGHyCElrZh0UcJ+y1ve/i/veM95y/LA+9yJo3hNEmNCnFNj09KOtgvUao9wLhUnsT+FAbYYlQulB9EocS+GWOHF2SkjKE+XBjXR/Tdzg4qQdVqxkmB8kH/wYTFaLd3biIb9lZDVwAoJxUAuqqKmUCRCECppVm1AGAjfoVVN2sUkH7Wcy1ve8sYPuP+dH/zxd3voJ9z1tre8yfmLGaClYCI2Y4N2B8pEFrHlRje+8JPvd+dP+7i73u0OtziMxUt8gs1zK6yddc8xQlAyFbwjqX3dGX3rfWXL4hpUQimdmWTQmSRNVnYC8J3oBp0FkMPoQBdtUuDBq+USs5nrCD7439sNue8Wi9YQRh3wkKPuKk0QkDMpe8KLE7TPpuwSqWAUiebVIlu6xcZcl9IUzJP6FTHhNE3HugkVUdL7Uty/SiZna9AY9JiyJjijsleOQgeDllKmL9gtu5FaCulYuy6hmW3zxX2gCVnE0soaWQrJnbfssw5SB0BF2QTlpKrFxNwqBGr8VzKPsq0EzZXMDiJ8VqvMi8/KzDBsi5W1Epue3FN6rR1zwBm5S63KyjVhdAQcJgApFEGJOlnp+uET/J1WpdGSPXc7sePQlEBQuHFADa1ifzWXZKcgxb1QSZeWBLrzA/DC7BktoDUT3KhOQezaG+C2VsWMS1oNWmphfcrYyCCfyhx7bL2YqqO0yX3IpA5ytjjt22oSthTT0HWqT7hUNtiyS0RKcqpsyiK0FKISDwYF0xJwZ9s28Nua5sfKY8hcm7i2LWWbyYUDqEbWwzgpEgmICgNdhInUbG1azxNwryyNNClYoYyuLKpiGVd+21e2yy5G1Yd+7U9OX/m3zHQYsknSO2yWbZDj+6655hd+H0Djt7mPeVCSTzgr0LwWZ1AURaeDGJZmO4CCGrgyTjWVg/JC7IWwh9OllJYkXchVumG/5OSdgGxKFgcoDVEL0NiPs1WsepVKolWWqjewqURV0QA/lxgRT5Jg3UdLtXKV2EMtJK16xBZbJnmXndwGzgJUob0wIndcquCtsTVLbbMPH4uKSO1wQb2h2+S1pNcIEsArBcscmo1yGNite9G+Ogh/BITa4ZrGm8cBMLBIBP/VVkXKrappCs48mGnrTcsouXQJ7OaIrYmWqBu5qAy36a1qQDiZ1CkTViWAJipIpgXSdXO6YDNnx07ZmaMxTMAza6PCnSijcyinpGF/EZp4CtPtmtFwtgxQSMKQO7t1Ky21UF5Un8xUrSVryEwJ6bDGykAyO+6SWxNaFosmbhyi6agkm6gGoV4gBOwBLh5M6sDk7WKvNBctCMRgmmbqxq6aaZvqJralcg6HmZuZwoInGBHnkG+eadaHjG6272qEdrRSd1AokbLKhhCiNEGHGCRJGyu6K9eWBd6FfRZUmoPZBZTnMdgsggMlrIkau7jZhIL+xAq0ymROhe+fmqCJkNCSEotoNUU4uvZVvJTBajDCFr0lx3kV6U6hBdEQ5ees2qeX5BlROxtCtK0nyWwSKpVDvOlmRZ2wgTqoBKNxHhAFpzidVgkHFFPC3bC6qBCy9AaV9CExwPFWYuQghajy7HqHUl7SVVi9IGGlyNGKbwuZHj2zAyIKbV3MhFxlKy4MOvZpvAgBXxxOZAI3SN+0BadEyVbYo+emk3pX0dpEHEQzREy67RUppRWKcpA8lAR8a9uUAopi4u2apCNiqX1fM1QprrDarTDSSgQMqgxihNNMSBaJt8wGnIHjH56ZlN7CBbB1JtJ5IlGoVmkCIRpVCF1mBpJtvpbaO7+VgBGX0WjKDqQILAQhDcrVjfJTVuKOfVqwIKgOLioqzuKrFmpTNYAEMydJmIv1oUNBeKyDJrMYYw+n4Glbs9tmGoZiYA5GQ29rYVLdrVSlKQOmuVSgG0OvklZ8Rj2XYMYUORC9XChuhZLQmsgNXnCTaZJphS7VNHiKQ6fBoQUbyQWPTDOSyIkEXwJpsjc55bR9TW4SgsloNDhgCpJboVltY+OG0CsqOb46AjaPLY+sjF5k6WxmpczgNmJUkfkynWKUy74AMcYe1omSUSlMhVgpIttzGSxJgFuT5hTHTNSx5FRqNkZi2sStAuXM0WShrXzrrit7X6oWlxH2IxVYlvlERWmKQTpPtYy2T1vdN82CMNTuiqTAtKy1rsqhHFmSkFBJBRjrmVWvngU1MsF9clI/oHKZAikGKPuHKhHRUgEpqTLyDkbgFbkzsQ+sAjv2JAwEIyJkcHCSaoi0/W/hwy+thOecOl3WyWoRxt1UtNMGOEoE1nUX5xBCSL64oXBB7sCVo9gp1FkQ3huRjTDePvAyNcDqaMIXUqigDKaZXSo1uHJKY1ycPSU3i+zBvRaXKEDVlSNYSiG6d0Ds9PAXjSNBYafXLPWCK7d35D+Q/GyNQgl56R//5ek273rl5Vfe/BJUVq5SRRM8grPNlPvI3rxjVVK8wlZFCFeUrJIoKeoR0yrannle4CaA/KRtjiSFKVaMz7V4v5YQ8i6eDdZJkIqUkAeS1SXoExwL9mzlbea2UVHSBOyiDRtyZnloiKRCkn2Bf1142jM9gOoUTDWll4jiRoS73KgESFZ25lKktEIFNLoTXUp1Z0oKpghXbqRYhnCOCM+TUg4hmTQOh/IZlzT6LrXKJtFb8HruM9BU+5JqXtw8aNGLiyb5cSbJmrBKWPipxciCbsUPMgVVqqXUTOswo4yW3W9GsTftiFHJt2kFo8waaaOV/dAGWVTRbW1Ym85sNbu5GPZB6CgFlwrrEERVpuvw0TFqCPVXza3F0JRi4FRIYYDwqJKpgU6HZEsgqsbfkr5nm/URbHy00Gu2KohUJ4hWTcWYnoo5a3VlZxETaVCDOEVSyWzAvWNy9kB0J0bRHsx3kVQBECkRliIyqrpXOWYgkpeN8QgSQrezWZkScNq+Z4KmylTKc1aEormMEKnSAFdEC1NYwYThunRQiEdOENESZhDRtlimthyCPlV8SJKo1hkBdEJWSEGUppmxszVFOFk5v7U9dRAGo8WYp7VN+go+NqYOyX0tPK3b7JO7ObYYJ9/FtDEnQsyYxECpVdSWCBTy2Nc5Tmdz7M/gDpkDK3LjurAu62arh++hSTmLBGvRhEZJeK5brg6vSOG953FyE5pbpauWLT0OHQp46iYL4xtLO1QfvthY6tCSHUaw3OKSS/73Y8U0rzt9/3f+3/mBqzI3sbCRtYAyjMyr/8/vznd9AMDhsz7xvPPOt6pMOg117T2Wji7WYPtmpgNM69lR4EtgTc3CZ8bRsFF+Cx/XKwsOW2iEhRg1UFjTnaM2rRiizUxlp4zUjg0WQJJEszpjngjZ+jJSENklOZR++bsg2mDVTrbl3FpLCKh7jNitSYKaRNae0QNsyT5yERmglhGklGDcWtLKpGW0keOc95N5yFrWYo4ZmUdta2UgElvBA+pY0pucJnx1Od2wTRyzbWbbDq5soz5bz17Zibfa7q8SiEjKzUggC1e6cB0MBZoz1TdUHZMrV5bTsy+bq6dnbgNb71R8llWJ8GrEIaMBxoQ4v1A7r2TZUFvNibkZVqU+0ufqxyN/B0MokiFbJxkh6TErj9LWOufbYdvM2/QlcCJorZgKhNSmmS3yBHEehHriV3WjlkXGosOaCQLzmNvGxq0pC/rSW+/MB/gispOogwTTxCA52Cp1238pgoUhOKKOHXMxGywxKleASotYYmuxIrfwI46n4/R4brITd5FzEgsmGNIsgXRTpZANDg0dwpuWE7NlipFUGabLYqxwmdaOY5ljhCZqWqHBllLNjjRJCI0RNKBNrKtKqQefDy0+DnHom+rGyw29S9NSK6ibzAVb6rqpi0iH8TqYEtZQVvXEEnQCgbi2VBPRZtmbN3hzjqqsCaQYdJA9jKITQETBgaarIDWl858MJsRlhkYpwFzlCGuyP0FCgVVFydMRauXRCM3QpUm1ytx9SEEAzQKHKyOETVgJQHM8P5Ez+Z6+2BVbNL1h4AbIqdrWSKeVdXCjQKQiDIUzC7TlKOefMtmGHmVsrUOtouCgUg2u3fWwUeJikNaQLJ0lyEBDO8jJCc+RPkUjFMdcGSSNz0P6gfoDr4m61tFOj2ONJqKLyjJcBhluqmZcQTwSQ3ZTzgI0KTqrluhbKC/h7JWZxJUqnCSUGnKlxmojWvfhpfxXZxc22tPZlH1jc8lNwQBNRWgnTPgoACMkJ6W/Y+KexdeiWtVrZOs2+gCGz9o2Wl/JVCpzvSadFQQqzaqrpxRroQpVOS24l1+VyvfNMhsrllkJA/ltrBlMxJpzS5lkmEt5xyZVytKoph2lk+VfPrpYVwii2sYrJYPJqFgljoIJiVawPRRq0BtcqPrY7bnK3LdiRiq7bKazSTYlAB4JB8qSX7KhTUbvNNb9qEmdkuOchb09Esa2sF6TaRPdtK2pFZwKGiqR0N01DSIRWaep09mBlbjcPKRIBm9HmzhkA9W7m+8q0lWXRskso0xd2MK2BGkq4xQ09EXNiG+sNo7agj11nRU5hdWn6CHmmEahVpgL10lgg0znQf4BQ9bZAbeq0SCjWTcFxEKaN9u6poVpGBEPjzHBM3LY/NyaYxObphswi9IH2PAtyxoznedcRyiJ1901YUUFGES0G8pWQR60dRaXHUKWEC6ZDRVCUrnbij1KFbZtECHOXtaowOHSpuw05pyyBaqglcuuZRYBb0AIj3IwNCm1rLYlMQ8uTG3hDiE1ReYOyzqtiqBL7MyrWNO2wHYP69xOi6nNTvGwS8DVQss9c4VkSW2C04wtWFBsArIyHoVFO6B31ityIS5wMelrxIzMo8SK6ZyT63Xrez7woR/+jd97+ev+lqL9ioc/UNgvCiyoiWM05/MC0VFx8NmDAVJ85VvVZsVi17OcSyLCfUVQj7OXKu+8HZVb7NkQlCBT3GubXjW7+KGLWeWQbHuGyLUbtibctVAcZAMisey7zxB4TX4FsscdignqplbzrW27ZCn+YwkLPcGcaXjP1XQtNqiD7qu38p7suOK4cYpJMhut9zzROKdEkiyMSuSGmi198TQvEZemGCO7BMM2d6RPi832WJg3OEk9rD54RAxxfgsjJzEOuQwdMlQoeyF/1actqawSDcq2o7gqqvhM9pIMo0pQe7xRAvBTX4AFU2SN9D3U4xI5uHdYT0oUfcVI9HTuWsaUXsyxdEEbqJOaHdGMzluaFkSzWxiTUCmsqBN+aBldnOSD/tDok+j43qRalQnFhyZ7c3KPUzbZZAQoKpSPhJY0Jc2JZPqsmw2FFapCKtjoeFtBv9RwlxssTiWZDpmLVJcyZCOS9MrSY+ncWkUvar8ZAHWmj5cs9IAaIBZwexLKwm/mJtIpiDpBX1xtrSZquhCueMreWhfhmt+TnCdWQQg0SFcuZGzuXUNLpJXsYahas/TuU8ulqiCpUANE6C8AABAASURBVI3eVjkcc0xINViToTZoyVtuPUJDanJfRabUSwGylsW5KpAmgsxK192e4FBzgR+sjxo2pc8YBjAwT3Zmm705B24lm8hUHZYiUq4ZhVlwGkNSd8zlUq1NWke4zHQIexKUoyOT9KOSTSWpnIOgCwgsqjaPLfdSRW2pcx8oCq2mm7FpRILluifhLDVNGIz0bCNbbDLnFY/4pOXsfwB2/Ie3vus5P0djJmmaIBJJvX695pWvByCjX/F1n2foLZuKSluqmxCzzgZSJBxC5tVgLaArjVtGkcdW09Ld2Weoc15n4uVQRoUCMyUIiKi6gz0tTrW4nFVZJStaMgWVqAhENOdokTBEgzctJUyVTcdKwagRQ2nImGIZWmmSJVoY1XljwsucsBacZ5GNgUiZS5vaonJvbTWZt6YQEZKbWpw9rDoxxoYA+0WmzKAusQmSuHiJJYwVTHJJqgahJSh/d03OpyGk8iBeoaM20VPFMGkurdB3d0Z9EFf3WjZrzsBKYmqwIChapGThuvUY6RlTZIMmGoKimuans2IWaUaZmIWSTmnMfa0STu4yKsBljmD3QIFp5ofmne73HuXTGbiKNPYPGp1kaQvbKL8SC8CFs2cpUwPtplQPi8YgVqW7UInJZ8h0y71l2hDtMA1mYRQoGqMy5zo3dzZwZSsju4XaXUKLJ6n3skDLbEZJzQ4mlMy32kM7MuwildWKXHjyFQIedG9nOUHAIRO+ecyoLBVYCXmCi8TeCZt7A7NdYJOSkgT3K8yHsEbaTItNetaQ0l1zAJR4QpTRZ6w1N2XRI3kh98C0pzA4IywbaW0Kc5rFabkYe0I0BgjZjAZ57ONUqqMawEt0DxEOp72qdBquUhU5K25TlVSJkkhlR+JqtWWtqKPJxnhAiJlVdH5mQvhSVQiC2+wjXRBalXiQVMsm1Qg6w4pJtYy2baRqFlWiyDHqYDlAwOLQs9wEs8mmS/p95h7vIuBOJylFIazZhDccAhGUgLpMXydHZvKz1L5EssK0JCGzWDE2IUlfKgxzP+Wx24FotRKiZCEaNxswnhEJRQg7WkVBGsREg3yoyB5mL7XkCBVbKJQkhEAL5eVivpQUuUALltaFJnG2vgtYKjh7CcpIFw0RT+1U8kdiQ7hoeGcvoCxrJly1DKZpFcQP/vDM7j3Fkh6w1xVEpJDAnFT5cUow1dpqNPqpYkC8SnIyemUnYCf3hbW4XezOD8wEGyBvrM8uIsLirMWS4pwRSYcUJsBUQnSWztCt0iERsjlWAd+auPaglumqSEFpkk5O5NITsvcgg5vNIhP79TShJqbFREZRPWZQhocqC3ByQSMJmVXJhRcN+6uqPDyxRYUHaEiSkyvJKsA/NJuymwG5F+50kqu9CQFFFbBUNhaXJj+RWxj/ueQGVm4J0Eskk2sZ85hgGpWqyppaAerT+4jR3DS9WBPqME6gu8Z09viwXESK2qs0ZUCRDc4cSBdpXaWn05ajNsiuy31OGjgjImurmqIh4CWUYagu4P6gi2k1liKxpokKtMz45GIqStSqjLMYJQWRBhJEQYjv4Ccpt8gOLt+mrrIa64dJyAIbPR9w0GlqdDZmTVoSGKSXNUebIBKQZ56DgulgQpNO8JGXWkserA+pELKCiD1FiaT9EgjD1p2XHSe0aWumDWSkigGnuTSDEh6kkppVEwUjgu1TS7RUpPMTeihHK9GkR63sByLkmHtEYYcYHE5koqsWhQVD2M410cva2lF5WmU2SzFQvEElujKkDH3P+97/4Kd/+yO+5Xte/Mo/3SIffJ+7ftTd7h7ZinoomeqzbSLRE90FmiIlwVbaU1hE5VoiVMBQ1wxzMbFqmJCC7fEMoOdk/ZhxJPbchFjsl7rGSSOFiC7s41GpfhTxvSdyGkDeJEyqG1Sxy5wacIXzrWi2BqGNPXhwblI0EAXXBWX2sjI0XAmcw4QbEmmRrJ7cAkxQikEESSfpPcqm6Wq1FbyyZK02y9wyOcYtVmGGBUF46kVQBeKglJkQT9ZUmmjX3pqqSomk9eCdizWaJn8qvbdxaNYdrYpJN5toqcPUdBE3P1KUCWcmsct5xn4Xtvs3E4YrBYFF78HNKGEk+6ZPbFlruPsGRscR+6Eabd+ReVDgKynVPUrsOw5XBtXDnC27hIGQA+FfRVcmTluaZg0iPiRX2rCsUh7JQV2gAKEvZ6QqyXCpyTlsfwVIlTgkJMuSddtxaNEsCrWivByZpT277TsNpU432pDcd3+2myagzbA2kqAeNpPZSwG3d6poUDGJtpvRoDJT9qWUp6ino7CsJV3YxQxC1vYZlir7S1upJt+DBPKdSPI5ZhknNXDdPhqKaMqSZS4UK+NwpU6iSlI1VB18P8WyJEJKtQ7GxlEGlEp0xOKwjWEK412BlaLyIdgFQegaYnN3iuxTRmCYcD/fzMsiyLBVMx0LDoQZvbh/L7AqSGsRp0V2F24VQQiiR1EfBt7qC4SmFYHu0OQnllZf5ISWxNvc2N3pmgeNZTaE0JkWUnS2ij6RXDs3LmYzJSCh2JofLVepBOS23/54XHIhgOt+809vePXfECbFxOk2Y/3TN/kb3sxLFzzy48YVl7grsomO0D6N6mafmQwiC0wXadEKkgBzw2bzWNxEK3O864u9v9YON0bfWzQaoMTZJnXIstgBOrwT7Z4T4vSJcgf3zEH9B51M8RreLAZ8FFWX1DPJYUQb+aIkGvYnfPSxY2g7KED40mZyYHGKGT/rDJ3O+MgPau9kRfPuQX8W0YhvYa5rVaqPXWpAaKYqBVxlks+3WHhCEq2jHebsbA4VjCt7a2gtlSIJlzStBWJJJEvlIDNZTGMQRJNeFGISMUoSFdQcnH9b8BLxQjTM2HI6tBcVO1Tt6HH1DducARRNsRLXeTw9Xr9um9TWWoyow9aserYRJwsXhBu2WF02r8POMpoWgAAzNKK3Eipb+yYnXicSQ/jMZ+5LvYcnBeNZ+2BuGabkbIyCg2w9ke2ktkNuzTdWMtGkokhcdYZ/TmQIJFmr4ipSe+/QPEZQLl5VTbCIqEvzGDZ1bHVYqx8BB0KSMuEjm5SuaAaN4nnN1po1ZjAL27rWnFYg4WOUIJCOyAhslXM6nWECwokVnBAhvqUfNeaQGihKp3M65R+J2hWxHWJ229gReV3AV2FKTRH2x+SCvGadFiWc87DOvnq5hDRYkxxyPNS21OwpWlxq2jTMplrc37V2CkbI/KtJtRZKrkqkSisZnriSQ9srY7I1nIguWqNSKYdV65h13N2WmyecUI+yHKFTUKYG62uzvb1ZQqnZo3iUarQhtiBpwHzrXhVCgmT3qrWJbz59m7XfjfYhbFTSJtIrQwHrRe62Wrc8JjaGKSfDDmIcw5sOCfb6JBpFkkxDeMYx2hZgfxTwQQSM+CqiAgQ0KJ4MQIpHy2ozMekui5EKZSiB2qpWKV+CDNIGb6TFmSfVln2p0WbDCo1u3BHJAtCOsSoL7K9ppykb1dKEt1xGpoilktZdpYRhG72rXHrxRR99l9t/zJ1vd8HJoVvSPAhrCoeCSpiE59q5feUWjIdvRTStyKTQOLjUyyRSIVGgY2VQnE8f6NQ0FBmxVk0WQ3SNQbrXnYhq2PcBxWWXebFqSvoy+lzHcS9PFwTBMvL9eQxrpAysos4HYKy6jFqnZOyWEukUDHqpsVW4zTVyrxtFpffYuqw1eKahjIYcRIMdrAhSyQINk81oOA49NWySqxLHOvforHlbXMamlHqtjYbIC0lSBC0PnL2ypKJiFvN4VCqCAmshBS3Zd8F9KAtpUu9c0NXQxZZNbJutNiZSh6sxoAIEIq3nSV/373N9ZUaEG2WzjFDfFKvonhUkYyiSRgkcjZofRHgac/JJaGIxLIuk9lNZTqVvIiBkR2yVk1TCWtUi0K45WISUUWFFOXlJxhV8qM3m7rwsmhtyZQ73vruoLtlAcyxz5lAIPTqpYjSzmWsVOeWBKqU5ZnHx2IlMaKKvsnD9i5jpp0Sj1CsYSKvQpNg5sQ5kmztBz960DVOjKcqLLYshJDndU8BbobQh1mEIsSChw29x+cX3v8vt7neX2wdKetjY2jL1JHMoDWduepzgjpc6YCZQ7GdCpw5lJOdUePMoG+o4w+eGnNoiDVnQ2RcM5Q0lejJCL54VhiNyaLITrbIp88J4rDhnGCectFjG8bAcF+WYZciio6FzTTJskdtETCVB2qMtsbTSAKaqGju7qY2lX3TeOQA3u/GFj/qU+33zl30hoXhI1IA05kAIIYO9o2WWrSI15rJsS3N6NJhIM6TWUTIUB8hJ6RKqaSSO31swl4iFXXofCwMTzFZ7kDoApD0XKeaMc2XLEeR4oyx6K0KSKjmKuHWMQ45ejROKODTLSIyRLlVYgb6AUcbNgIqwY8x+jH6kZWF7KDNSNUZvvURWr/WI5BeJp96OWzuNmiwmLuEnsy1humGbfZYkkHC3ItmSQk/Vq0qxCljIpBHqYbKJnroSfYFLySC2iKi9nWmfwq7QKwwks9mObfo4XRurTEuGFgVIojIPpEhWgBljfTNnm9mmi7eTRcYhmgaBkBalOLuEULDIishYt3mcvjoipacuyKFT+kzjrqWOmx2nCdIkUe7ONCZR1RKQailtE+MyGgOtaVOURXBfDFVZWlta55kMKAutskiJRVccwjqbAVs1S32tHjBDqaRmUtFt2sAw67JjjSj2y8kmDkWjHwaOIWbSApoioF6bFlOYC/nzJO+QKbJPidINJySixo7ZwyPXLBd413nAUcxLQiaVyc2qQozB1AglXTCRbqpcwNgGckqx/aRsALn2Qpt1gloaZMlsLrLTbAHZqkIrLDfFEeKVrXzxzcjQkNyVVHSVYq4aQ6U7lNUWNdWnYUZ3buyc+u6VCKAEAEubAzuadhUTGGpULSXUDc8guzr3L255tGJx2NDetRscYFTSqqQmQ5y6hC7FjBnsoKUVIF3ZRvEb7y7pUpv06h1gdhaXA5gML/g6sW1Ncxm9WYc1Xkhw/gzbpDlTX8VpJtracvNLr/zfjwWQ152+//tfMtlUqkVIQd//g7/M83rucOFjH66M22tNrMVsIWb5mnkM94mcLTfNrdTTElqiSHH2GHNqSRpUqvHgX2VooZZmKsPURCo1ZSmKRcWjfMaMTIgRY9AYLZLRJVBqc/Tgkx+W21oypQdtF8aMg28jXGxi5N5h6ZAryUajMIFqhUjSkTNTXuIYtDNNV4spk7gGdDB/4lm7c1EYwEhtdvWDeW9hrbfemaomBMCAoA1mPpp06+JLzoWLewQJMNWmYtBCq/0N8+dKt2COhMEJWHea7sWVUap0VDadoi4yW/fFollACgxFGk6Ww9H9dPq7rz6eJjYhtrj2eN1xsjnMMUCle9/CSH5TbV001zq9Lq6/gY1ClbeCxpBywFtmOi2rae/SLLtsPKjfLE1TCpGRZ2wb84s1ddbOH4tNEcgI30cYUi0po+TPK/2YAAAQAElEQVQEsV6MVhgzd0iCWTi6cIu9MRoeqtVa0XQT9tjoiV4FzGnOhJkxfJeTxKCTEZvNGUVSFvpo0BJO73VYcDLAwclLpRqjbT1LeUSKoroEr0qCNywyWXe6DOvDzHakGoxbfc/HINVa2OnekqOzO4mzjAa3BvAgV8o8UccVrahP0RzOCt1gztLzIAmiodSmVglxWRML143wwAynqAp/8VQEWNOVVtmRjRcJ31LsWLaBPhk/tEVCJr1szdnHOIoAGm17ytrgmlqMsXrZCfqJ9cXQOxOWNO8iSLXqdD0zyA5zgUNlj2TgU9Nt74obL9ZWxuJJ6TZ4KFt5Syj1tfmsCrBZAFUp5H0Bb2O0T7QVS6AXKP8G3v1Mi92nZ4W70HkeWg5LkW2TuZJt3yM5sFPOlEkUjlp7EMsEwq02y5LQs44KJ6WRGrzfDl7aE9BrgM2ojJxKQdhjrJwyUspIkkZXWqz0KFQoYkdH/8fCnMLVlreIWmvZKj2scN+73ulHn/bVP/ZNj7vlza4gWzJVmO/sJzFOfFGndlkXoFMT2dNPeQpts7VxvWD6O71HpGd59K2Gi1YW9iEi4KSuYOUtBUBSjAnlcPPaT7LN7+zYZpqDC28tWmKC1ps0g4jDttIq8HxJc2HCNFsP0fB1bsc8zsyaQ5NT5GytCEFIFbuSQuFQpk9YsFGaBY7OSApmgRG+bCs4pjFRdShvMSUDknvE3YrEgq/UpEZ5uOiGtu2nQAEsrnxbJQQqDNswVUnIGY0FQx4Qh+FLCyN6H74aOLxEnWQuPMmqHzNZbU1YASzeQ2ChFWfpkIdUZc9YZ8H38q8dXTpYPkmvKAEDKgcTnomicYTKphalEeIlnn2yfRwLlEyxnkn2hgUbu3hlYpXGlbGGqI2skTIEpozTxLuINvXGQZXVS7pKU2X8fESxucv0Rkhoq7ArRgRDrgF0o1ZIxCiXCk5lrZPZSoFKQMPLi6cJWBhhqLpoYJSYNlawtdHFGmI0HxLcp/SMkI1LbHAO6uwlQBrWXtuooOV45Cff6ye/+fE/+YyvPrf0prM159E1u0aKbxlrBnU4IZtEYNXcWhJhQsJ5WMWCrVXqFrrOuc5jlgcE0XRrVgqol3E2ZO8yWiKT1nvOFujVm3W0lsqBtCLTclMJORSakpMGDjCOYfQrTTPe6oeyUUWktkjrQ7md6Uox1hU3Ou/nvuUJv/y/v/HF3/qkpz3msy48jPIMtqPNtaJ3VdtfpZ5yTIYWQCgSLcVwEGl73mXbwGYTpQi0UNmjQRhMxATArDyS1GQGpBXanguPFg6shRU60UFRNFNApsgKcDw2yBYI8gczWCvea+QBqzVXsrFX+hrB5poF0TJ3BO93V9OoVtHcjbKeIOAuWLQNa+QYumXbiM+JrmCVJqWCJtagS/IKPZcM7LdHZml01rZU9vDm5G3tkhBwYGVrQ9q5irEdwcW71pDjXiSkIUaiOb1X5pyeK4s/9+4L0rmKbQ1TPSS120HHQlmmikg1mcoueohoGnvcNcAr4epOMjmiGxbr3RZVblNbK1VJdrJsM7QM1UVtNuZjJhzK/TgWltK2GaYqO0GHy5whTo26orQwUrj9VYGrrto3Lj25tKm2sZhWet6ABFdSwCIoZVRmAo6u6CKD1afYE9lVKLwJI9rD3Ph81cje2ui7V2tqAVsF2Te1NNjiMo5ix4ZNvJC6e+lgP6C+o4BiCCLN0FSoHglVaK8S7EvViBrqXWd1deMViiZrhs9cuUxG1HRb0zZ6RVLhXG8TvrVMGgArLcH+3VDWE0y2QQlSpEsxTFX60aLoWe1pRqWepIypbdU+sYhYB7FRj31LnRLsJ8QO4mMfIsuTKhVGahxqBM9Zhynd6UAgMl18iu5AkKVb2kyjPywDfEyh0gs0DmGvP4E3moI5lHOb9EGsrUGrLEqiwU0mkAmrRmAK0QSF1Gm7UCG5Z6KBBk5knIiZVmhM40ySx4Sw42wnI05UBi5+5P3P/9xPAnD8h7e+/3t+vkrS+zWv+Zsb3rh/ndLueqtz975DZgUJFwTH8UjHBirRI0S2ZmvvbpoCAWQK0ppKO0RbUMyXkNxRlL256Co4MpjWrDPB4J2OS4K3xidCHpLJYlxMC5pokyZUqfVgCSxM3VrNo+g8N4Frr2i1iXBtRtaQ7JrWA4tjaY4TjxNZd91IOivAVTLFUtikjC6Yw2jJzHaDCQMvxpcFKGAglVx8MBUu5a3awGHIkH02kykMlfD0UM1Ew2Lra3UxxsUxHVlVXhzVFLyeimkSqrSsRa3xSPrI6tjNlKirBFkcFjZCKJBIkMzrbvjQxkenHtdcfc1V77/muhsm9fSu91513fU3XH/D6QVLG0OJODTDhNyx+DH9A7y+bh86rraodBZ5NpWG7NRXYecIZNjYF4kiUlzaHLaRQyuSJRtjDupUJm0rpwpUHEoe+ZvRVFfQJBsDGAILtOibtqpmcVLybRSXFC4lnf1vnDR2QLUSUWm7mIs71SwzqKmFmvNijRlt5vS9hRYzimbFbLHoeLRoTdBEgVGQIh7boBC0MwzGRUmE+TUXCzNrvSnJ6YBqQve4ggAAkSkZ7EVC7fEyjwLKUVkCwGSxPMi2IEcjR5bK6pKCdknjHEgW2xJPchotG3WkHsAWDfMgRAWxTXUr3dyK7bRYVz7JLkgCEe7DY4pEiEwOlJjFtY6hBxIUwBSd6JvVBkmxzoBUtIvxF+dIg/RUC8EERKWXQdoUys4bAiIAZGZxOWJ6aSIrO4Krni2t9YOq7UNKMjU8kzWehVCmgoS0xmgb1Q3C4aGMXqLr3vxaKHxknUQtCRXQee+8k2P0jImIMz2ae0e2Ks2CF0IqolAllBdBe9kKXS1c16wtg9cAlqTv1pFLVSOnuTkzpZUllSiFMuVCVFnc2TJeVPBIwFvFwjsfzFSnRp3hq5KgGJU2oYkouAqbkAMl2QTGFa2YaDZfuoUCfNDaBSpS2ErWFCd9VewSorL7WgtzF/0CnshC0hjpbqx9gyD3X2Dcg464o2gSRrOFqiQYLGBR1tLyoLkYgDFrWb1TCaQmqeCEe/ElXXrj56g1eCt5itjMsqz2taAFG9i+8neaYO7JzDR1UYYpgSwJ9iqVZtF0ZiX3aRs5YJ3xs1CqWSXSOroxnwVey8ZWBzaSFEGBDJAGdAYvdAL+kqIDtQZTNd+hc50zGA9AaGdr5RHlTOd0mc722UX70J2/8pJwiSqV3ay4Vg1exgAZE2VBOLpJa3QDXpQiigRIsdK1RxMuX2UmlKKJZ22BTdlNZFMEqly5yHAJOAjbv1vzbMkkzsxiPpGQxrnV1c0AHsRSCtkdMvVDyG9am+TR4sxx+HR6WU2iCyx2dSHIHaCcCGC25DZo9h2s2oQlGkSTqWgTujW+bWkWi3jTGIahtYR2ZzV2kkmfbL4xNEmfzZkVk9neRVrFjQw8EVJVYB1EetUs8NUlG/+Qoz2samB4CIMblSAUe3JgTjQ3k1ayZ6XmvuqaZtPSFK7tKFZW28gfrZdL0RvPlO8V1SIseFK4oT12cSFTrWsbnE9IqBJQujsKkJyzByQpUmppI4UtQOACkHEyUG4IKEyHgOTlzpFCL7/JRbe95RU3vjG/V+Has9EJMhHULHPQhEUJY8vdARFsUBklArE9KHYpmndbwziRvrRgGR2kRZCKagZj4cApD8k6CRymN1YEFcHawEadRtINhMjZDboWNB1nr0JFpQeoINBtiHlaZps0h8zI8olaS4iVDSmK8FphkW7aotUUZjO34C0kiVRwl2BmjVqQaTbVwA3O7h0COSc4adyZ8z1SKKaebZR1q9bDzAX7aQfrXxCicGhRWJwi+7Xw2o6WG8MGNz8HHBewrALikTNq7nFop14YS6VgM1/VN9nQUg7a22iylOrsmJxqGiQxEpVF0RgbW3dpU3SPk9ExzjF0dDNtqcss9XCHz0ZYlTQFLhM5aL+IsGXRBFC779ViE/esCtWtwtMTO3Tj1UDttKbkJsyHZQkTwTvKcgQQShn4cD6MESEfBSk6KJA5Bq+tWLBkxH2f2TV7Vwi3acrn7BZYcBZocqvoJVMFVmJOw4hUh5ayDfUuJqDRDbVVJn/xk6FpnIx5aIwSZ69oQ6wPBx+ct6TPrMhkzsm4R/Adyg0J7HGUZpCi4gXo2b2GZiLpvYQoqZ4u2ZvzSaE1oBiQkBI5i7GQSS3oTj6j1BJLWAt2Eo7tk4XSGvsrQyAhIJA1ty32F91PEdJ5sFCsTtI7RWRZOoXgkmgKzDh90BdKOtJIL70TBi2K0kaZ7uHKHnnuPquRwaHFPX9vrRgo+56HgDkDhzXlsADzXJEktLc02nOfH3rrf77/537n6je9td5znKfiqa6SVhBJ3R1vHDfdrF3x1C/UW1xKl+/7yZd98DV/LVlXveK15QGVG3/pQ7mWhkRh/8cxZHqYg9iott5KRRusVzPskU8+NG1EAmnK74TF0FHnCkuIFGblTFRIlUIodMRK9iY0yxTD2rku/OKEkQ6hkGqgROm1CVlNvqGSI4tYrIiknL7GCuZTRXrWcpz9tNQ1pW0i1NbUmtYZMcMaiYX2gojROV5siJql9r3RhrcKLaV1T7CPlS8194eP2FcPEReNYbUoC9qF7JUElHEgBQGoaet9iAkvsLNFKpuIVS0ie1LYriSSsjDQh5Cy6kiNhLfkWIbUcqcJHF6KD17/oTf98z//3Zv/5c1vfdu51q+57voPXHPN29/23ne+430fuOrq9fT0pPULzjsPIlqE1ig2KZrO/3rvVR+86oM8RqeaSPiUDiXuXUiototXBWYiS2zDJ5kThVLpfW1NtDcb7FiyJ0XEmqBVNpM21EyFCWR3HxvXJBDvJnoEI02KDJ47gbSs0SrgYtGHKPfui4hBUFYyIKMhWvChE7nlGCDI2ARKU3KryXW4pmBPiMz9oqNg1XvaQJpulNQ0SQFfWiFnb5pkV3QpRCEhpeYMItrBW0ul/o7sDskars5KAGk3MeaQiozM3Qf4YnqQqlAu/CzARshg+MbvLU/SDJYN7ETB+Ry9u2avAbGYxzi6bhMypUWN1IXeclVxoGaEzw2bi0sC0iZsK2xbbW7TWxBA7VRobOBdy9kXfirB9QGlJA3o2F8hoLCz9VKtEsYoagctIlVlMQmJBtNcGqC2rKIh2tLRBrp1rQOlPqMmQJfEwtIYRU8DYcxvRzJ6J7psa/XAkrKUkXxGuddfr96YvBATUB4qjE4VaMozDWoJ9foIaDIGXiyGqzv62W3SkTgSIGslIW2DbJxmraTplpjFoSJJjpPNyAq8M+SsBXVOdFmyDu5dz9IRwli44AW3l43tXUp1hiCIOzgGEtGyzNEIjycWZHfoWuq8E7yh49TguUgoUqV6205sbSxNTeLLhupdlyGDOkHxZQoTBrvwB7IlERb52bcdSbbVSsiClqVoiiSCHUWOx+6nCg+TabxF01MQOWOdKnvhYJ9Ada5ydQAAEABJREFUNiJtDX4L0tRt+FhIRSTKFXwHZtRPvIa3DuIAEilcvtWzyzwYf3vT0AOzw8RYumaZmAgXMhirYrcg8FKkjW0b2+yOyDZdNx81h2y0BMyqSibRAiy4PR+gzkQBRSvvCAYKVkaMik7DijBwCs00Z4XULq/k+L1tdJUFPMlC29bMlUv8OnDa5LQ15yVhOK23NAEoA6p1UcayaluhAlBn57U80WIWRsvGqkeqwzbhE8TDmoeN3DfkaEJqBCYMuvgSUlchFORRMGETfYa6I0OlujAYIbVFJ6OgxqkarbsuIK4iyVUyp+oEixqqsRsYrJ6oANZEVprmwfRE+iKjgy2GtkHr+1dYQ+vMam8y9stUh0A4dVMcm5ckdv8JSfHsM7uCoefJJudWlSMqa/aItomuzJRvFa4FEDoHRhzKD4Sshj6qj6HLwL7mevOwEsigyVKxqf2GpnOfyHS0Gs3Je+t64PuTTSkETNm2nMfCbAlPWT2PlS4qImYxNLqkIUH+ArIK1sYwUKyxOuKG1Va3KUqms43i3qVMHAIX2s4tfSq1pWtgm8KNhrtsnlGrVziQTQ8mJ4PgEmW+9UyGAMI3YrAOkbLKFvTRovoaPaOZ2qJmrc9iehEeqVvZZkiTJBE95lhj8dRaMSdFGIyhnFI++FxWLJu0ykKx9jltI8EABKraFbpfQJzmZARZ7MrexBtAPrRaT+kujBOza1K97MekahE+8tKRwi7a5ia5cn2zgsqI5TAbO+vQMlW68Wzr0cLJstngKilg5tMnEGIsgOwIOgT7fBOmVcFfPIRIN5FNLGxUPxTjwFJzVAgK3b35yjaFNMNhKRvShlISc9cTZwuChMmEQQZq1NTp2KCzxkwlZk6PnMwDrJsyrQYuZ6vDQ7aShGjp4tWSSAWcCbIvs0TIIEHsLkAHfhhxGATC6RGBKu2lhKxOkVHZazGQncfd/2ku26rUH1glwhfA9G2Y3PmLp2jDWLa2ZEAoI0bAmQLvlT0BAitSq7zNGxR48yFTcrJdO3+VHVWPVlT3jqNCo9rekzkRyT7eUAqR4spsEok4Tp8bfU/aTrGEeeakkvvUJZh3lYmaeSZkprQpa6Uk9snsMmxdzCLb6bLhEGoFCJVQ3YL7kzIy1tkXeddIypejHNj5VqFJSQD8obYbIxVSNSvoKiEm2tELnJITW9GW6LCFiZrC6vSNNyRVhUBK7b8kO4iDUx3M80CQrsV12fqYyhxPSIlq2ghpXvCq2C1UZW1SThW1blBKpdFsSU2pDSxq4iAelHqaYG/DJ1qHYoOKhfaKqNNFmXMDZweOV/3uX7z9G374LZ/0DW/+lCe95dHf+q5vfsF7f/kV173+zdu7r55b0GGukZ4zql152aVP+0I0w+l21bN/+vS1f7v+1p8BWO5wiws//f7GxC+hDbtfhFGVhmqSrAUGTaJlgXaGVWI+Wx2lbSB8sSr0QHcUu08UJzOAgkTVljxnbsIYdxJ0ggRZGXBIOTgW7202CeyTpTjJKkVW5ZoAQ2/eLJRcSotmyVPEFKKZgmltE/WqZGZowDTILHhRW2qjNQoY1cBtH3FjCoqNr4iAx0yGyKBblQrniKEUUUksIeLG5hRJSQt2rwVJXqc1FWgV85ZOuYinpAsAEdZKkBGC0+QQSyhA8KqmqoyZoIQoRVxkdnzw9ENvePM//8Xf/u3b3vWea6+/4X1XX7POPHfgOthuOD39p/942weuvfaa66+/9PJLJ1lAKx/qBhKdeMd7PvBvb/0vDuCXvJefXGCrSXbykMowBGpWAr74y5KxcTZa8uwA2TEjJzasj3ayYFgaozIGIFARsa7SNA2S0dI1C4yZmYbxbcGl3CJGJjcTphBeKvJDufGgXAL8CCsyhurMi6xdQsivblx8mTGhhgOZydsQZ9eFmoexWYEUKkGOUCKQMulKraORc0EyzwCpbnupRNRMqnzNcjpOXhVh1WGvcoqxpJnyJckWxEWOX3Abqz6E8YCvJBZ2XxdLKGkNSfqBpnCSNv5llFoUTigYDucxJiuCKJechao0k9ZURYt0lU87ZcdPrFXbLiWjhDU1EXykNUW8ZC+foBA0eTY0yRANnLgSdoqneQkqZTeRDHlvg2NTnULHe1Yae58KByGE16Gge8u9WpZSUwg/k8cVMWtWbrURkNRUNMUQM/RICxBy1IzYwEOd99pzkVAJR7rS1BEsY4jwLUqKz7SzFUxgh45OY2Xcbrm0Mg5h20i+mb140wm+dTIzhKdIQChoSLhcmaQxakk/q3qRHZWRX1C81pgD9zZXmz6wNnNYiOg+u0qCU5vAQFEz4toXp9bofGdMQ8pbVgvpPFVaoEbcc/JCGbKLCJCH4hIXjTiad90aC3JwCkBmGIt2LcbcuLfDiRQbn6myjfuKbaO1TfqGninNoW6F7tpyt0yAKZncQTGJFTLDKoGoQE4LrsUEgpDGIKX2KLALdwIMZBzOndPW+B7hYKsMFCOnH7jJFK5G8KYl5F2lBJmYjLGECQESJRX7xqRH9QiJjjhg9hZl4iXu4PIzUUhRh0jKAOVQAmYOEMAS4HVx7gNtpCp5lxHRY2pN0WRVFptgDtDiEmKISi/PtjmccotAtsIIaOZO/t73eyR3kIWtuAQf4CQci3C2pvXESHSkSSTNjb7KcKglCHwPky0J/DwHtpGkJckbZWItRAV7EjgWyOilI4bQSlZUlqOSqrYk6fubYqOYcF4QOS46G3Wj6rlkLkDrLYYRGyAjbCiUQgNiP2Zg5j4/ozSZfHYnWs+S2ay4XRgNQkeQnUHAlFmp4hCcKcBDPfbPAlGIACrRWop1VBeA/DI9JVtDkhJNFViiiDe4UjHXpm7iWlUie2ZMmlTj20Y8MAHDMPogAku1sL7fcKWx5HQM4yOpjqZSu9mTwijRaBInwrdN3CIxJTb1Wqb22eDiYLKdudsqZjB0R/c8RDYGDmadKacA6FGFQfSqPBuErW8rH4JrVkiGBWSquKpIoHhUGfnq2kAOemcYUvvIBNxATY6uXdRKeZrcrxGnhNA2LJBugz2sF/nmRoQ3W577xghspSkyjZhph7qTuZ9P8aJU9pSYsBe5sr2sU/Z86DTEAFDKloZlMBdH1BpFp1uZiQxIR0I5lbt+XRSdFAkT1ovNLFu6BNMCrjXkeJFmVmIsabrgJcCsek/mVVCGHGwDW3KrnkwvmBGzrPQknUbcyglSnYh7QAtiwuQqdtlwCJt5SJcxGv3ArCg/ITjs2/44iC6wIX2Q2Oi6ewhp0dmhAKRUASX8AeulNtcJYVeMReYQtphwT0QywgHu+GDFD+GC6cidTUdNpZVCwaANjMtKSm0KEuQpYYke0tL2ASYouFq2xnWro4sgCYSPSmJUKsUsWtkrGDMkQvvaDgU+y6ADF6VJVnsxCSI1Pecx51oIUAFNUndr6hQxRJXKdTFqqEb5CG+O9Jp7QBKaU6bHiuNxibmk9Bz8tSJjF091wW6tpUpppKypIcaYkqcts1Ji1ymiGnGCGsloCjn7Eb7NQa6N2t4teJpzJoQbLLWCmoF9Fexz0KDLLCToqor2Sx3qYeRKROqQdc6rreXH2riKd8mTqpElGUmqHZbCgmtgGShSRc069+9apYwngxQVSDCiI5aCwlGuCA2xTQUiqiJDpXdANsWx6QpjI183XzPXptNUhM+Ztg6Wn64Ts7bKU/imWYyzddU2FMNC+5Fdr+ei1RrKMrVEWKmHsl7rX/4zzl7+/mv8z//x2l945fu/4Yfe+tBnvPWhz/rPR/zP933Tj77vJa+89vX/+qGrr9sWvfCLPvnCR+z/Ccjjm/7jv772ufGBa6Fy4Vc8fNyoDyQfDRwWX2RqbDOZTgl03TT3//0ufTTBEDQXrFr8bOUSRL4JKQnztflqpGcbWGkF5ZWlqxwShyamTL3yTCXze0PUqWBbcnbOy1UxtWSmHUuPkjAdMgaVw1DRSpiOJiJQMLUtRtsORvICsv8jZKGJEBgw9kNpzUO2rK3EebqYxiKxQgslmQJoaStDaEVzbdEMNObKsgxhtks4IdhiSDN6UlHMZ6tAxvQMLgkF2qlGuAUVoW1Rxe6lyJNJGi2SHYgwIttzKhCUXH88ff3f/cPLXvPav/rHf3nPVVdfe8MN3dr973rnW9z4kjvf8jY3u/yyf3/Hu975vqs++KHr7njb2ygfTChbDoUppUVGrj89/v2b33bd6Slvb25z+U2HDq1e3mNKToIipBzBACsBehZlbJ1SVgvhchfStialKeICFo/Z0IRQhzwYGQoQvrQLNCXIoUiydbASFAqYS0TjzZ7T4nBTV6HnLXz66YY1uDjwM21aSSuV1JGyBNcF5oQUQnintjULX/q6GKyG+FBvVMie7cWyjY3dJ2148awIgYxNZAOTI1KBiq3qGNgiZvqW6RN7ZpuYSRMM1zHFGEvWNut0CsUQCmYAZ68Zks6mdhy2sbQkjeXt8CCL03eqlVHX5Mq8OKWC2N23bVOd1bNGUhds83MBi3rLMYmUglpclozBEDWsJ5cPby1rqeA+DU4Na7KUlsTi4JtuOoSPJZqm0kcWx2zETCnyQQcFFJCJXohkh9hzrEUWEtj2WNTkxOykhO2d7xkfqmpz0hLJKpgAg5mQCLCfGHO21ypjcdqCeO7mJSyYZ34UBIJ1sFLpDnZ51CZyFHZNnXO3yIEloewF1HuaU0UOQl/ETQPNfWR2lxatKVObW4tI01wGjDukRhVXVgHF6Dq4uvIRtLUuosVTXFvck8vRmjbDal/GTqJGszG6CU06caYSQxflmTj4HJFAFipTI/smbYrw7iBQE3bkmpSLulnR7SZyylWBy6nKiGUcFztt8IpKJCHxrFB4Gty1TCVKymZqbBIbtg0RRRmVIEituBrlL6pmnJcYVSo8pdNGSj9iTDSx6C1UqTDaMxGKVLPhtOS4qs8uGK31w7Dzss6tsoQYmPMYvo25WW1SyUmttUW0M0qC8FTqNADG1ARK5FIrsGmRIEG2BlloBtj1IllMEqy2Zay2eAzM3t2WSHoReO0BYecHfMdMSnaN3tx0s1yl2KU1RNOEsqexgrrzq5A6tcnv8099rhtXuWjAUA5BZUsqoSyNrFCAvlVuhVni6BAl+Km2YYlt2auIK+UM5Dp127pvzZURlCxSIpU9yqalqI8Cw5lV6Q5P8aGxKMf0tg32ib4yWVGq0nSnMbbpqwd3vO7hG7zatnAKexkrLdPFrdfCgxqBw7q3Q+ZZZ4iq8oRLhZE0D1QmaUqGImCeFrqQlIq9XEq4qWOZOLYVdSzW/CGDBUFVg9+0BYtXXUfpEGkG1RINxYZ0JMOCCGH3Jnv2EL7thAFnTGKKTpWQOvN9inaUhgbtJKUysLpkikANglBmnO8prkWiyypeWMHD0LstVNtBsYhK6yI00ZjptZgORsdm51rRaILhblVH5Ga1Eq/35XT0dZjrLByhU7rovmkZFGlWFWMiKI2D7LBUuLwAABAASURBVJzqCeQAWya/CGKOVMXKNI2TGTgU2mPYUeNYFcA+e1mtlxBGZm5R7M+uWUm9aUOnEV4TJecl7jKPNWeR9OIMGiiJptwj6EnZvk1sgRYdWDSWSg2JGKuMydwJZYj9lQr2DclguletWqAHwTnUEjpmNx+CBs6oUdtJUa5V4OjV/YhwgVpyH8pDtVigpGIwArTJT6nqhcki3kySp7dk5RCpBEbsoIyWCUDUpfsctUoPPwhXXYiCL2K2GZKJLEgpYoGfiI8enAQQche13q0Lh1fldK57UtOgaoso+O6MSJnAjETQkJB8kpQYhcozhlP5Bu77xpSomzIANf7i5NV0VdkgOwimsREAQRavlspZUrzZUTQB8Ex1a3ZQWVzOpkBEh/D7yNZB8S+ayqFi2a1aTquj2sQSOLA+wNY+kmXQhL2tVQoxRrHqi+8ERiFB4ZJTZOXc5uyliSapferIUimJidjUoyEAn54MXcQ5qPH6tMhckSFNKEqaE9PWRuP3N3bY5FBEgaYQaxUj1j690fY8dKecpQGaTeZB45wmNSZZU2otz6LZNMFeWSeOpYQfcsjsOrU5kW1dqaPZoyjukhG6uHAcj87K0xZjRtuKBRZHjQ0VAa3S5tn3xZGCmFOQrckQfinflx18lWQmaktZOZcxgTgc0EwL6pD+mAcSSEm3mWB3oxEVV54L02C4UeZTItmVEEamqnaTwlpzxyw4jKSAaQZDCQvh/U/fmFiCGMbkaEIjzQVy2TO/9KY//rSbPP4zxwPu1W58AWd9+Jhvf6//1b9c/eJXXPWNL3zbF3/7fz7kqf/14Ke/81t+pm538w8PyHdfxTdyGBd/3idao0Mhtr0pZZZLlEAbWMPSTbt4ydywVWw2y8hJG8yQEvBVV9/wX+/+wHvff/UHrz1dj1NXdNKk5LPCwe0G+wvDrJaGlQeGb7xaVVEGVu0qnGOO7sYEB+AjVNHQSU8KojPkpWMxIVHbJrXtpKlp2PDWpuoa2FxrNinu2dFU1EwaaYR4YQ+GjPGiItnRwXdgRroUdaWSkICAh2ilVPAa6wdhPMeUi4YozwNlCpgkS1dTOV1NrQk/SlI9qgZVUkcPghLhDEzs1yBVRsNbxpv+9d9e+spXv/Zv3vTeq67mLYeZfew97vxpH3/fyy+5SAa59z9549+8/+preenOt77yJje5eEcDKEzU0JBWb/inf33Lf737be9632UX3/iSG984DVCAyvQqhxYIjQcDSkYsPNOsCNIEOw4SUgUy4iXsOLUDFc4CFwlmJTKyOLJB1Bp6qwKjABcuFeUFSAbDjOIEscmpHJC5SzImcfDCGp6bV27ZMptAmhjncvQQ7ih3UnPv4Icq0/0SF4ODM74AtpwrwjXpR8QZ1socJhQ6Sk8ch40ROVOlyQSW7n6KdxfUFKkGu+lsDLQzkQjmbSqmpvNypRIn4WB/ZSbHm4IY3OgPDCwzjvCjuCPIIQ/RqGKQIjv3NONlDKpVUxhIBEmnzoOWC7SGUUyTqfY96gJ1KmGUA4TxF1fH5gsYvUWOaRa0A5G5WCxD+xCgfAufIIZkQpyfaWXHATaIIhUhzQN+GuosG0kpN55HpnoKnRlBaTnPN5dWpWDoBdESqSYpPMyluzRC5gBhu+IhSap2fmcnfaJAi8IMFNNPV6VklLGoQ/heB8eCQkgk8Zk4V/CO3Guvl3CxIPW7oRTqr6O6146wUhkmg8CyeVunHb0l1Z+KrbCFeDFDTHGXOVg3NtO8RmBPLEqrGAN6piFD22SH2cw2mk+NygIxAYIwSwyttm8G5qpUgRYoSBPW0dbdT2aOEinE7re2WF2YSjPufGcVSY5EuHKPplQ9uAvQDhnK0hd6IA1pMyT25YI9SxYuiFurLTmNsVIlZRpCNNIgvVQToEfIwtg0rHv2dbVt9YijblOnafSGGrY23Zg5KKbYlCZiArUo3VJTTKvLJnlMikFEGq9xjQxQuTCzbHQ3RKWpm9DsZpxebNAtUmiF4SGzqBKtkCohdvBF/lzSmWSHuWhChIdVgYWhNSmObns0qcXr3hAmltX2vl+U4EQr61wluha5VFt5iGyIiBlr0GmynkT4m57pl5gFO5SqU681UVPUGcBhM80uWDCbbMaFRCxZ0y7G2ZIpFBhpxeBtBXejmyrBLoGlgvJgOEgwGsvdkWSgmBqIA3WW1/CMbYZH7iFpMUncJKS4gn+rFdSF/KRoLUI33Er1CN1WKfbPKNl9gLpmrlyCJFVJbJUznYEKcWcvlaIdmaWnOY5qfD7DvW8Bfe3j9GBTkwE3qQ4MgEHAhJdLtthWbG6VIslQwGhJiqfkLFCcNbXCkg6V7OZxcvPBYmHSmGcOq9IziFRIUe2siWDwmta8a2ixtvqkYFqqETqdAjw9hWjJEbKjpGZR6EhlmVGV2p33hAu14GVbNI+OyUxJiWxSaVJD3WxrbVv421bBhjMwA3ao/WbDDbtQGKvC6BEhk8jk7C3EE+nhDIteTZYhvWOPgexUGcOHQOgTmjJVp2BFZYKnS0W0W1uGDU46i5+h8o4FKS70J+hxxsbZNc7DpCfl7FBahZKNdEvvCSutUkKRIP8iMFhjCGsltTyTrypADNDKEtIuTBKnJHyfko3XxYuC0aZtERPP8GPOY1FoTWA9JyRKwDBy90NI+welxqYiLZPmuZKrRe5p3s+LTH4kkknHZYxNzn4KsSHcgwqZoese69KTD/867VVMXiniFWUkKCEGmncZbsOIMJnubeZG1BCRVjrCFEsI85jlYGOiOSlCEndh6UO7aLPWStRBD4KzDZ9mmaQwqSVsD3y3OxXqc2zVIvb+3jnGcgcpReOZktjTid0upxaoLzKLLrWoYB/EOCsLxZElEP4tBuwRG2fLwFCxKjiQLIfdA9IiNImyiexWCL5lqWuBFgQYlVR040CxjtaA0mR3Y4UmY4vWHBbVkmuPLLk0H8Uu4CqMSZhUYYZnzK3IO103lLCiRwsr7/PYi+UQtcQ20ok4kp3Mk+tQi5AktNJW0EwGJD3NsMjQVKpFMmjNxFSNUEVyi5xbUFKSVVqbqhuvtQW8LTUrU5XdZPBa2vQOCEbS92pKE43sTldn0UzKay7YeoXlrjE1MANFMiewCfHQZh2QHX7AHCxVR9Avj9icjxjy2GrrUbzSwPWrkTsSsfFiuo6b3uxGn/GAK/7XV97l5599t7//2du+4cdv/RPfeOHXf875n3B3Pf+EtQ0GdNx4W7L+3X9c99O/e+0PvBT/f6+LP/eTzl1y/p40qoHl6AIC5RodJ63GEENTLE253HDrtPq2q1+24/aGf/6357/0ZU/70Z/9wV952c++7JW/+nt/+MrXvvb3/vxPX/dPf/fOd7xL5kRhjcqUdbJ7MCKmO7l25OLOb3DraNvsWwyUMEXYVDbpDkkmZTZmqvb7ea5XqTWE6SqpLMAVmyW/i8nDJmNF2wQuFXtQaiVdhPICtAjeFE1oEybCv1FGwoMM9lLjBgXsQY3ZhBGoFMWWZwmSfYJxNNMnRc+sh4AVscBUQRGoqe7Gu0K1IAUodteDLoACc4uzqcKQkBaZb/73t/3oL//aL/7uq974z//6zvd94LobTj/2Hnf6ood9yu1vd1vtjb62Wn/zD//kH//jbbx0u1vc9N53vl0TKqMaxFJF1Mze/f6r/uZf/v2602Nvdr973KkG2EmjOYT4RYlKrczSqloRd8n+UoFKgTEU23WkpiivQgNUGvFS6HsgAhrKEKrNQrQ4V2EQJIp54mo59/FuxZOLJx86GjRnn+tgQgXNRIXT6XnC1lCKVMlqlVra4uzHO0NTw3vxamEqjoO/GSI3bRWOuelxa9MXtm7dHLx1qNmKOFqZliUzIigxBRNrBhBpCXiKv6mGidikKJXQCdlEcyhzXciG2X1lSgBEI3TrwUcAQtBkhzIqSaHmOwpRmGIbOjcaLO5slVJFaVpUF84WZQSyVtuSfZQzFNqNK5j2qbyDbskRYHswAee5IGX0OkeXOLU1KWiKkf2BSOEiYb1sALS/GTZCFIeSEC40y7HaLN1JkFE1eFmduTqetXAk58dqe90lekpnNAfECY+UNkcF0YM/5EzBjLFRLplLmUnbOe2kTaa3o7N9yUB2g6k1dKOUNF32mJKV2SW1WmEUvdDVuRUHFnKmKrAHMWKfWyL8JOkMRzqUGTCJqq0ipJSmtRmMEYpHMdOTnScVEdiqu9L8QLXA4sqHR1i9KqPi6OEh4MBFti5TYis/S8sSjKskwMxKLuDOKU3IqKCxCyZirUnlomtTJSQgXcuHZ3MmuiKcymGNdBmWdG20RV54Qea6ZJ2rdtDDYVnG0mgCUAjhtVBsXHjWMN/JN5KfjZFHCMIQEuVhUxi1dBODQsCkl0CMPyqV67YdJ9edDO6eIKIM26sn639tNqnCIoImSLgn70eKlppxahXvcWZCwH10LBbaABEy1ORow4V6jkNtrNXh6HRuDFq4ufWoubHCLMpyh0Jdga9qLJOa5qBjFgVAuqxTK0Ld4ZjiIk2yw40Nt4stuT/Oa2bMs9L3HlyvZrJI7bURgYqSTOgkwdjftnAJYKpME1fqTllPPhnzkLCWWApSfeLgqNTAAW0xSju3cj9+aP3QX735n3785X/0Q7/zmtf/+1unurCTzK0fqWHcsB7/7O/+6cd+8w9+5Dde9U9vf0eQ6OqtWOxWwj2WTsVWNef0LTMysKXROda+dww2s9iAjMKxGLQmrJGCNkA+3erYwxvDaEwIIJ4NwV6slrOXC1jdARQcknue2S9sUFehGtRLIFCEUIYGKLOVi4jUIXyp5OckyBwC0L5x++5KYwANi0yVkAZKO/YJXG5lMgKB0I5oONYttwirSaBeN6QewbY7VLkPrcapoMkN4gA8Zds0OVxyqDeklBiLPMS9gGL9VHVVaQZBeciGZQVHpZp3O7U8rTi12noxKJtkoNqOhqLiToBi9UZ/awVbGFj0UFaAFePG/nOQlSKpDE4XTEMIZAKcG3l2TkpNu/Sldl34xBYyi5ZIe28YwwY7JXpg0AgqW0WXphiS7JBg15YUKViUVamKNJHON6WkgEdVrIDD6eBEkt9rUnj0EVH64VyER21rMoZuNVhNzHiXMClxyLojLm9JUec2W8wlo4HXiloQAcFIQ3aQZLPGdOQxgnGAYUCrypHhyYWB+NmkijP2+VN9A+lLqWYyRNqcse5InLhz/2nqPLhiudMMS+ZouUnlFFmZe41NiUtFuyIRx4qjlAuXpq4FwqfkpnRflmpnN4OZrjkHSCJnmUCVjhTbAu7N04LVuoRocS6HeoBmulpjOumKRU7fGfTl4F12MGNy3EMzpjUFKUnwLnATqW4sqITrnjSPZbvhsN7ANRyeCCTMoXlWNEaytNmeswjdCpt6dci+uhQlIbUq95uz+qqH61s/Bcu6Wxx6miRBMt1EOeTkIIupstiw5C4esQQrkcb23NGqzA6uDVU2RSV0AAAQAElEQVQryGpEGqKhFtFWHKupKhXMRd/C2NNsyRprqAewat8OB19GKQnZ0FbhRjWyrd5xNN0Ee1EwXuxE1AwyZMnam6pr4hg1mRLHwXl/MMx4W8f1t2eHjWpa6LWNOoqGm7JafMstqcxMMdN2aDgsaAtqaAkAh8xsXgPcBDRNdoIKmwx1I8NQDhQmgiyLkyYx2WdlVeRmya1yWYGmxUZvhFSDjldPKpGHEz/xMl07/bNTe7HBV/dtbrwnjmShyejWzaR1DLvgNjflvcdtnvHFt/ml/3nHN/38LV/x/Jv+wJNv9DWffd7H3s2uvFTPOyHm/+8hS7/sGz4vKxzMrUwJJKvMtXhOEEL4hxbLSHadLUFZfGg9fcVr/vjx/+t5z/7Jn3/9P735vR+8+h3ved9V13zo2utv4JcA7/7AB9/2rve84c3/9tq/+ef/fN8HpamjRaivIKfCTAvWGdv1x3k6fc/+yGS60aQWSdaN8MQSZZ7lmzN5TkCbFvcPVKFJmMHExA2bSqTSepU2bcPATZOCWpSzCBNSplzpuTSLZhYTVvsVhnV2CNOzvxH+agrOSoM00S4QQamIcUgpaIltOy33bOl+BZzDsyol+2AaByDJsTzBUSXFqTwFDizI29/9nh97yW88///8yhv+6c1XXfuh03W9xx1u8+THfN7H3ffetuxJEZrKfNVr3/Cnf/Omd73/Kn58yMfel4ltaibQEmA3uG3z//72K9/8tne89Z3v/sR736UvQxRqqAZ0IudayhlNOMlU6FygyXSi9n8MyiBneWgUN1WfJiHmbqSysmvyeQi5AMiteIAK40QFegmTCaaDLYeVQdmqtWpt66w64XqYIgD7T0Kcy0TBAOvWVKuA4DUAO6NatBZDnACTAOYSLlx0gu4Zg0qGVqooRKOVixCjBiPw6sEje4EesiV9NOVeqFmSgFHgb0gVCF8DFuAEE0AVbVEdxWA7XDmEgLhY9EVKh8sCa83KeKgQWHUpqqc4SsGy3ukSQgnr6EMZh2POq6+97od//VU/8Vt/dD1mnCzb4SQ6+zw3K6XhVQ4T0DcD4bs86bEsk+CACdQqgDSRXSuemplVQapEhbkWiCox7Rm2qpKNaleRLq2JtmRvLu0iYpE2S9mgXXeIDktwKqSD6aRBL5kYnGQaokcTOitZiaIEpVKEyBkMSkiZpihf6ADfgKvCKN5hnkgdBEwK2GVX6Goym9CDpEgmPFHwho1bLbLekvlxDc5QJZWpApd9bjIaKYZH34BDebq00CDEnQbpadaUJxJMfTH1vEBLUZUS0dwTCFhGr1VroxuGnqS4ROEkhy6Fnvfp/EuZRrdjq5Cae+6L8h+7mqRxRkm12lpuI70ZWTMzFZWkg6W4EEwiK8qS7mm4qfBV6QUUf6LRJWPn1SwWDCOjFW1K6pJWipA5eurk/aKMZkMEySSACGUYHTJ6epMo8mKkY20ZzKmQFKarb0mCRSAAf6M0SV9WRx6QS1abjJoIAF5XYu/8EDodhIMd3RSE0IUGFylwrUrjLpWnBPSZIs4WTPHv5iGQLsqXqfasw8zFywDGKvzTkAMMWnbDZUGyTKmbfTEO8tbQWzYtkVLmKwuJjFoLwZuMsVgbi8oSaI6cFr44hsM4TrH3hdF8DNic1//sq175o7/+O6fXnUqQarLiS6ptg6v47/zF3z3qf7/gCT/0Mz/+it//yZe/+mde/odgGwmIa3m97M/+4gue/bwn/8gv/dTLXvNTL/uDX3zln0oxLojufzoD5CcOlG3Cg8ikpDlJCeWuY9IGFU2n6rMiUhDKsEw71KSBUCsZFqoJP6oUtEQZvVu6hEc6/+xpMziM+5lk18pWTFoYnzjIjAokySkAJUCqQQScXpvmfrRyG6XnoAuJM91U3ag4uo6QTK5HNKiSAGsAKzPZ9uwrjOirXDQhKBQLJ6BeGhOMpiSETiSrkudX/qmUKqvSCo1NiV4CVt7d90o2LIu0pcpwZlHYbfbUIemih7LgvRCipeZFjOBL+NmZEOLaRKYJdzfJPb4x2gLbT9JaK8jU9BF5MuUQ0kEottNVo0oVyuGcwt8MBS77FiKchnmmgTeveqJqyUyYkwlLskjXFCYZpvUklNIAJnteWpVpg3VQ8c2ToHkdzOAyibukdRVrNFIiDt3EwlAtVGe2nNAMUWHiWTJQa3oQOykir9iE+9T/D3N/AnDNVlWHomPMuVbV/v5zDijYYwuKDbZgryh2qFGjqKixiRgVpTEJ9g2KqKCxQRRQMYjRiOaKfYzkJhrbiNgGFUGFA4oC0nfnfLtqrTnnG/X9mHjN813JvUle/fXtXbWa2Yw55lyratOMakC6bfR5RDgMJSeQZtHkqo/yCJdhkBmsrp2MyL0grJBpMT3QshZ562rEAHbIlVB8qGVJyiEb5mTuxCCm6VY4m6q8heKjPXDA5Cgzxcc2wycN7AvsFIJ1AqmdUbYoigQHP4olPOW6bK2EnjSEgbPJQ1ovIBjTZ2pQK+tQnBHBmZgikZpn1ZTJTPhgn9ZKmPneY6Z2rEMkkw9ZlFL4ZlNB19s4LSFBMOU3Ed3QFUcVHSEiPkngNo6hPEAjlUMy2wAvvb+C8shnyXCWQLHUwnjQVB7ExD6YYWk63XlINk2jBFMzoEOmmrgGT6qZnJDFdshDChLkIbRCtBhZSukASCeO2oaBOf14lxrmAIxgm94ko+QGvemg2AvMDMhk7S7gJX0jGaGEaSVpmnWIVHyUsgFZloCwipl5zjqjBhKkkR3d0QueEieTpyQ2NxEWZTIQTGlOum496DXoe7XUJaNZgoWSIdpmifomuzIqdDbPthDV52hbaF+Q04o06AOHAoOGQIdkGJZ1LsvWKgS/TOVIGdUUQp7DRqjDlQnavu9xSHeJMbB59DbRMZpvUec5tjRlpGxiq/Xt3vKGe3/wm3zlZ97xx7/hzr/0HXf8t9/0Rt/3Jbe/3z+8eJ93aHd8k5s+6YPam75RlCkVokrRSCYtmvZEyC2hoHZOyUZh5/rCV7ziKx716K/6zsfcfPOzLl/y/Mt93OENXu/u7/oOH/Ke73r3d3vHt3+rN3/9173t1f9o1Sv1APO0Zz7nWc99fi0dXDktJ8f0l75if9nLtvN2eNHNJvSe5dT3ZVE0ohJI1aZlkLsQVNhCMPs+MXdklSgxfUkeWQKr6Ntou5JHD2hLW5qZCk1UFUUMTa5jThJwtZlR0BcEm7Mo0BVtOlyX6qG6TZcAQZ04DoNmOY1pSFMkoY8j3FYQM1jQjfGQRpG/pBCEeEvNk+JKA170spf9wM/+/Nd+1w/82n952q1ngco7v/kbf9l97v1ZH3fPm2686RCR4pQqPH/rD5/1I0/6xVe9+paLdflHH/FB125Q+YOREiJgSkxA/dyv/PrTn/3nr7q8fOs3e5O3e+s7sdI5G8sOVxq8iVIwzaCc8NfMZSmQBebVN9EMhmoHe0P8OXQLOk0Sgs2d3uWFHLRZSFSmel1IGWQHNDOz6dIroEqvSeVtnJqMmGX7VB872WxZoN8PekdZTQVx89xVfk+xLNUVrivzvIoSpsDvrkJ7ClzALnI9RWvqYUKCPaRFId0T0QzaKRADKlIecoVuejwwa+UO9Vp2Q2c4dhU0VK86XvkXbTWta0e9rwTA4hR8RJUKgHWTHBrINC/VS+2eGlKaj6GAUto4eveld6OAifq3T/69x/67X3vMT//yz//m09P6tCOVXFc7c6sZKDjlgCBuzRt7dY6Fk1mVFJPDDAsE51Z+68btUi94I2G+4KIdC/CqS0dprWkxrSYU8oZt5abtXD8tPC1G+BDEBKi4eiouGTSyQzBUU7i0gFaYvBKkkjGI8EotilQ912YMIQQ03sxO4Km4QJbLx8NU2dgaiBQhtDbWKOxhM5dNLq9WDSiZZZVQfNRpPqB+7MmJHtCmtOdMxmyDnEumjLyyUvRbevSL0NuQzmpecOgBOrpN4162lQ1wP2JtFtpAuPwqKZyTob1P9mYODQ3O4ZjHDUjQcczpWwmbBGZbwnupmYp2yYfaZSP78OXcMf2YLKf1OKe9By2zz5TpcorarCxnXuzQqzOMyDz8rJ05Vew50wl3imWutSGVNO44dVsl6PCmwCiDZg3OaGFmncIUUOixEmuDuZOn1i/auto1oEcpW4dq5jKm1wzbxrKn4JE0xaktUybOXeHzFHNbEyyKghyB+G3JmqWiOizSBku7gjpJLIt0FYDSarXU7Bnd5CwQ5LSKVrIKYMo0LtVohGIlu7W4TMFuuXbNKfDqhLkSchx28QjoxkzLo1GJOuWvCIE6PhjWwyUSyutOijSZbUueC2dyFxMoZTADe6ERv/K0pz/qZ3/h8U/6pZ/4+V/3rUQFV8iGNC9/8OwXfPOP/txfvuTla/N3fKs3e/c7v/Vd3uLNVoVw9Kz1P/3BM77hR376Ra989bWlv+tbv/m7v/0d3/pN3wiYNEguvYwaNxdCpC8ZQigm5gcUEG5a61y/1aXooHa4D70sNcHHUiTVG1vbDt+aRV+zKaSZUiybq7gnthm1Q9a2rWWZNOlxkoxUL/Sm4qLmqcKVSyAJtinBEKACaK/T5VzOYTOmZnhYy+7e6vhJLHbJk6BeYxHdp+x2sqXqTm9jZbTqzsWaC0NiLY0fmOCEYqfN15BhotZQ5GFKi/IgpdgAhxFW02KPmBvabtqqqngrXU9Vi43eh3VAYnEtZxe9dj8n92azd7YTakWhWGURPuGqAjuoVXobepxee1tuuHbR/KQyIYw4tF29qilDm8EctKCEtsCoHJI6tE6celsXLB6ZY+rd7Yi56woTFka3MkidPKDtUCBM9nnqeXZaDSpVR9g+PUb6nCTKuteRPZ7CkRDGoSlgoaabUmtlKfAsT+WqSoD1CSdASxNXvEe6OMAoIBpSrPF2NXxWTkIWSVpTfGtKKKC6ZhTW0EQFMM4iyLA9G50L62JCj5saOZZjFEesGuUSxgqqesdJlmR5pXMYp2N0s8V89epQhm/KoPQxlXZoCA3RxqNkhJFHXWmuKsNuAKJcisPcTeGOqhEZKmeHf41cUE2XCkELejXdglSLPkOFOcNkW1SzUlGvviPD0ztcomVuTXA0hlJCGLZafKy+nTgVpVFIij+2THIysu3ybF+5u4pUK8XDWVYhHgBUHVg4m+ptCccmkEjQRgigyDakQqYSKD3iHl6j4ObNtOPsFEWOwlKELDvefAuO7q3TfQJTW91tVhw8S0QAoU8GdEIoJ1EFCDBmm1kzSocX2kDbJ3Yl+I46t4hlHtiCEiuOmiYKZwoRXiSUNkLkPHwPg3XylFw3rmr0YDNXo5kvwVUS5IrlbHIuN6+zYabH1rYt96xsCoeMCPFiU7WwoqFba23lcZjBvdytKCi1YhTHVF6dY4ioGqoqgnChm3Swp4V5Oae3aT1gjOQICo5ah0p/r3ba165CKWe6+meb00bJdgmxMrF1bfTW0DuWziYa4zTNDK9J0QAAEABJREFUEgjpcGtuvrR2asvSu+wHN/FHOwBZVIliTedOilx9KT8JyuCpLm5/mxvveuc3+LgPeOOv/oy3+tGvuePPPuyNHvKZc+k715QWswKV60NqLMgBA5SgLkGQaduYj3j843/+F38hNa7iw9/vvR72+Z/69ff/9M/6uI/4qA9433u8110/6gPe+1M+/IM+6cPufue3uMNfvfTlL33lq5/5Z3/x+3/49H2/dYvxwpe/9JnP+4vnvfhlr74869eU29646F02FoY5cuG8yDqdsUwuDmuZ3eCn5npbwTRlZihvkjGSe/QQlhpbTXlX1Y5CDY1KIgUAwJJQT32jYBqU8BIc0mXURQJF6J/omHVsCYrOMkuyMnH0W1G9pU992zHYYYSBViY8QB1XUqTGDaZbtZmkOugAb7l1+6Gf/Y8P+MZHPuHf/ftn/uXzn//KW2/3uq/zzz7lH37hZ3zC29/xLQFNgumLkO7n/dWLvu0Hf/SvXvySF99y+Qkf8v5v+5ZvCh0y5zAmiiktT3/2c378P/36Ldswb5/woe+v2BPENKYslyQNkr2oq3+arU40pIpEgHHVLO/N5JqBx1xqqPiqk44iS40KuD7VXeKWmjSv6oA1nalEKIO2PTPCx1xnLVmKxxJluaR5uht81bIl6imcbZdi7FCuYk6kzJICoKMa2Si8LKtv1s5eA7MmVdwsnBrLCShcjrWnVsP0OX1GTjAPU8OQxmNAWc/TEr1PtFmULbTjuUciDZqQMVhD3lm5K8gJAlIwspScc6bOAOhKrjSVI0nWEKtmsVDJGhyTEQbJiKKBDWVmboAGIvfKEPu2QpB4zvNf+qTf/KOf+80/fNXlkK4Bz7TSgINtHakUptZ7diy6UdrJlirkyDnmLKCrAi1OHZVTT/NLTFZMjL32vfSpa6TsVWVW6CjgMMBK1EwM7TaQhnHCXClxmRZDNrCIw2QKhb7O1sMSucO3bDkOfSJMdURpB1M+KuKqYvaEBzgEe7rcFADOw2/fgEvH3lwRmwRSAQBQKcElly05Fda9LEKDbE0SMZmbrKVsac2d5tkLfRATwgHK2QqY9kWjLXvj6C2WtdgtuBaWFGN8OffT3laB1ZOWOWsfLcuWhuYJDIYeVPeJbc4hBliYiZ5TwZhXVtluHjI2YQOiR2kA6UDAtml7eTaDA9kRMtF7ekRu8mcUIZXMQ20VULHYvtQUdGP4UIaMQmXTztHCPa0dZU1bxTk4uYxWswv3EBqwzqVZh+tsunD35opADqtdtk3OqeSxfceRTTI5rEXve9kWbaDnYr7UUtsyN9tGbIjRHCKzS20yokV05lFUszzdqMKq/KNASj31ohFUWIANQBVyeorLOckEM7gL3eLuCI/NxzljK4mxBtKmWRkIY4HzarrFJqsFKqwrhmXdqqeQy9pT7jCqH8LHtv/K7zz9SU/+/T+4+S9lhVZQdGrDm6rtVpEFQBhDbJ5HrkhLsH7mKb/7ilu31e0bPudej/2S+37Pgz7/8z/6w60dx6z4mSf/5jbmTafl0V/w6Y//is/+l1/22Z96zw+obnOx2XyITl7VprY62rsIOavV9CRRa9WVCumscO2FXAuwquwJMquuGDJEFME+xf4eJdDKBRdx2BkahTXqqhai4GUexmmpIJMibZuiIWPCoi11WrBYdQKt0MMtGwSK+XSbJd8zayYkH6XEM4CDpAA1evdyTQOQS8y1UmlBGS7KhEZ22mK9JZYZLVP7vGWXkTmprYbSmiEUNZxoiOYhv50e3kTIo9kOPQbvUw9A9MPtjFaqJkMRRxLRkHYYKQp5iH6UNlNiZysJibTIUrRECvlRNY25kC6sEFtsyshMxyzfo5+HoGkol43jql7PHBxBVU7AVriTNSPHphLYPRcZ6FbNoguCkl6dCYaraKnqmU0402ipzhQPK4FG77BlmA2DkhzwKMoZoGBGFSdLL8pxJDGn6xzlc6lcZF0dfdEoPYmZgQggFbs6RZ4Yp8quYZmytRAJSTIr16gogWXIxgOU0ieih5Dt2LtSgjklKkrTjtGw406lk5gumxZPa0kLZJHWmrZPJ7NVtrQIpvJVUxNRuacOmdg0rPelL0s7dSyyVw5hBoTzqee1tVbPJvGzhE3KN7jJYtmpEQ6HubAFJii4ypJ6ha6i43tVcVIglgCUUx1ssKKm6JPqHbIhYx6XvdriaNcQa8vey4kwikZpwIDJfBVi29Us4njUUmNtU3NkTqsjnbywEEcEhOkQROKaIOQEEwoGqdkSRdVryY+W1aGjz7n6vvbN9WNd11DvsXTKxtxmbXrilZlyZ93SMkvmSCpMxrW9e8h9/al6xUK2MpfRO8YWgngCZWYg1WxeVld2gISsVaWzuoRdgtMr9T5GK6n0Iw86tPRWbtWXXNosgOmWq/b6Mq1sy2Xmwuw9TbVE4kSoaTzKhM8ckRszSMoCzmgZhtE4RQR1u/wwwMWn2EP9kGpAMWFgDlEFh6huYctcTrUu5azAUWqIoE2z4XK+rE1vRajXsmrYHF3cTMyO6sPWrfd5YVyd1qApCllxnXkq7dtt7Qf5eneFvy0mNkXWkCZkd+0QzTwg51wBWRqxOnovMqoms6S8V2/d7A1vwO3XNOFkWk1bNcYpswvWqsN296KhYCM90p/473723/6nX0TV6y/1Q9/8kAc/4HPe6e3ufHG6EeKooC5MKprt9W5/e/268tF3f8/ztutx5c+f/1e//JTffsrvPfUP/vRPb37e817+ilfs5/Pr37ReO7XmZR7mVQsUv0QnFq/Fp5Y2um7kjzUlzQmmDocJ7RiB3JX77FmLxdpSOAOFrOQhCHATX0qmUtaDh3WyDRJJHrdqOL4IIFE6S4NBOsAEJavUenyhSlelkU6B4VZG6o4k9I86NOdQB13ycEaQMRO/+tSnf/qD/8V3/MATXvDil77y1rNd3PjP7nXPb/uCf/y+73KXZb0ATdON+iKJiHzId/2r5/7Vi19+ub//Xd/5nu/77oAZDZARr3HiZa961Tf9y3/93Oc8869e+rJPvMd7v/kbvaEip2EAK0reI0CNr8OZqw/1gNcPqAc6pBHFLBnpUFZAtHYImXKk8XAeJblkCQ0NcCYrr6aUxpS2Omwp1oh0VJIMZU+RCSFJlvmxYJrCIzqpcUaNrF1rlhLSM4dcZdKzcdce7qIphNNm2uw1T3NrNcJzeGWW9kxXM23rFcvkkjCfYqmpHFdF0yQLyZLzgZrW9rVtHVsqM1NRlJ2LluXATtc5wCyO2S5TCxKFBpGUeTMy58AYlQj2QkdaTE+dKafQZPfhxxQBoTFbzG2SsfhHvvd7ffG9PvJLP/6j/8G7vwfPe23agG6a8p+f9rSH/MCPfvXj/48XvezlAVQYduTMQKYnpERFqq2kzMiSs1E5YXkYlWUTKA1bDntLps0grLoLVJWLPc8Re0UiKYkokGYllC5ydEqX5iFk9nRs4NZMS/NeNaucxcYrr7xbVwG2nrFC7+gCKCm2Gb5l7jaFZYmFat8dW59TO2tTtqE3KldVWzyq9prR9+6XHYPVw9pYTUGEG1sP59n23ceEYFkquoDdLCbCNWpBLXPXgxi1zITsyuKuKUtOVrlZN6et7NpX9waYWi1rydareVpOiFGtZqOSpYV8RAldszPkuh1PVlUVEppjUXBDmzeCe3bE6nqOuWyH+cmwyWWzk1gikaAMPJyQgd2DQHXZUUsr6wJeASmiEb3YNZOBCkwiqKiIS3sM7alK6O/yU3WcC13l2I6w7ZFnzp1aLM8yrtIEDlgQ/qVPneiz1ihXcHqlsMzG4mRe5twSBUDgrqfR1l25tAC2W40mOu8ic0lmg35s61gY6x7LjlVO77MN5C7aVLVIHsUU09pUGW3GWWI6QMDUIweG4Ah0S4OM84qGmfon+p9143Gt5qoX44E8GTpUV9rY7TzrXEotFL0UvrZ40z+QOfcxz8pymWvpxC233PqIH/q5Bz/2J37il35H4dMzXq0jl5G2v/87vc1X/aOP/Gef+KH3/rD3DjqE9/GBc53/6M//EsBd7/xWH3y3t1+d4bWfbOsulF51y6ue9YIXqffu7/x2d73LHd3h0509j15RBuF9LstcdM0edjH7Wg5lfFhNb9msSFYZVBdZ7pDugkCfiC0VI4hsblBPmZiwBUbVlL92FvubhdgoNBvMJFdIZ2nBF43antqGHKMVzAZ6K+EK0LCYlnipNtNkQB0Cx7xqqdE5SYjqgLRWLkRXWnRWs8xklklOEioaI48HzB2EhYn2jFww1kKfoESuky5pNkbbwGCy4lRTmo68c5/N+7I21877Is+LbWKXcw+bg5Rdx3mUKZikeVMxgUmGzqrKKTsoD6rJtsxeEAidaR6CAAYduYeqgWyoDhMdbc4GnILrTt+YOYVnlnTWlikiFHpZL0aps7QG9wW2QHpDbVYtcdrgZ484ne003AjZNXrbm0VHIkV1Q+tG2aMqBRSyEAidFlf+ECqDaVHj2IuyZh7VpWbPatOYTeprsCGvSogCXjElW0zxyJ7b6goHWWBY+px9pgbDodmjlFuEMOsh66m4dQsyEzbLMsrGtKxEBaukRWehVVyMvEhRQ267rNbSJyK0KcecmsyD27SrrkNahrhUFAQGtbZWXBqaUkDVGSMMu25omPBdjpxKSqbUXsFRe0GmpilQzmPFMjE7MAuBFrZImFimYp2ac+Vqq2qDSFehwZL0NLEqBvfNzgpfFZCLQ/lAuSwueDGg0Kybr2fBurti4VSgwgawkRHO4bJBBVaQAQvCMTxiPfd1CGhKn1bnaQe4Mh3DxHU/U2geHgy2WpbS89jCg2YlI/RAoiQtdvrizM5BFxjVrbxEgZQCJYokd1FFz/6j4cr6VbJEdBjltCpfIOhwk0vdIaxUSzaKJ6YJwrQbBC80uAK69tW82UgRKyVQdKXbtrRz853cGFtt2cqcDmOqv4NLp/b4tRyrqbtbkhORAnpOmxAgoZZkRsb0gtgEsSl2FWkTURpyWWAnVgNr2hZS1qcKe6bqLKRJMUFz0hnKLKDp1DRWlhoGMa30hiv73CO2mTLemmJJrYjZx9a0WOzF6YB+ZmiIaTkWD+sIrVuyUWKiXKbnYUIN9ZStwGpwBxMd6N7kHjQo0qWiahux6xDFmmFpZgQpAUfEkDk9JxtDPO6g0PJicY7Sksvn/NnzvuP7HjdueeVtbnPbxzzsoe9wpzuZNZQQOShAgNBoHHpAo0pCf9krX60fT/7yhS/R+cKXvVzrk3pvuOGGO73pG990cVEqF8MqZ4hXPm0JkXGZcdRxvckb8JQjxezIVR4BcroXVj2XmEKU0NEoz00eO/IY7YBwN1ImOGhFHYXjUqMJ6Lz6M12SkO2AfODxB2FBmsGoU5OtQOjUQILUn6bpvGoztRQBXF3gEFW6Q2Y+9Y//9DO+6mH3/cqvecafPEOPKPt2vvfd7/Zz3/jFn/Th97i44TaQvcdAgPMJpWYAABAASURBVHV9lsj38Md+/y/91u/o15Q3f4PX/aJP+YetLRIPgFeCCbv1cjzke/717z/9GZfLbd77ru/64Xd/L1AmSmXJAp1CCrz6B+hLpgntQz6owwSLvghc3eoSzuM0GnRQzWWs63deSaREBFFVGmGgGRua0aubZupbA3Rrsfh+gXEyagQ1cwS2wCz1UyRBt0kf2ksovdN9mg0lJdhVKWwzDFqkzXSSzQeP1N0RcVhRS+SizNwK2r0getPpPPyRQZRKGRqqBxyOSd3YDKrmo6yLy1SxEbPQpE27gkTMQB1rgcntliZ5S/Qu1VWKnXoDoKUHuOEQxokWEBmO0eQC8SS2kqCMedsb+j/+kHt82j0+oCtkVfpJoJ8BFTJ9XJ2M9CEBtkzrkgc5LX9GBnP0eaZ+HJlgmKVZ1IUdCnweLm3JvSyzHPK1sa1LP11revFCUj7M8q14zhro4Qv74k0IlnAvlHBuW6tL1mUZa21Y0+3segIJJTo3Yk+bcPGwqRo2LQUFZmFA2At8GXUU8lTQJa5kvBalJZ3sEAhZ3K32ZUCuldhmI7jrdPVGo8jS2Vy+ew/VpWCXuUPTYi6ci0+zqpnQrkVskfOeVrmU8j1sISWiRy2pEG4942JCRUl2IUzEpkH7l9m3c8TczMM6q7RarGNqhUSkwlCU1loWXqwwrU5tMWGUxUrJEKqEhpz1oLSi9aVVww6F3gvmRtUbg/Z5daparZoQaUEjFIAIxRXRZnX52zPFOoTAL6zlrbAwD+YjxPaIE3ENOMGVPQW96jo8B0pRMG6oY5d1pE1M1KwaMbMV20KYw00voJYVJyuv1E5UEYQVO42qwl2g7IISBcgy7+ie7cpKp5wIP3oSOZlRWXr62Ca2rN0VMgFVTDhYwEx9gCjLXdZpTjak0cLXWcuIrBAChcaySDuztjZHm9tkHP8NHCUJw6c2bujRTtFb2XAOGWWNTluyL9EtnSLAsi1d1uHqEPMypxID0WssI2/y9ZPv8f6f89H3uOmmG7Rv2YzTMDwukXoLpjlv+nq3WyVO1rQh8wOMyRHQWqPeN3vj21eC+8ENy6piQXizQ6A5DxdI+RBKPp3qGsa9c1xELGCZtk08fr6MAxRNBj3q+J/BHnMdttbSS5J2zolZlUbhpifsU9RayrcL6AnoolrfWurRvGA62Goit7Rtep4dZ0Ld0XcZ6kI6URoIreDLaktfzPQ80eZ0tXrLlvJtP8JQihiMJaSVIDtdEG/wIbvn6Pv0c8mqnM5qLbhOmbqWueow7NBhTW7uuU7FWtWC0HIc5NRQbyXJOzMYRYgPsIz0cV5x2biJRDPFxNa8r8qb8j7DoUKeLJpCgqZgt95rsRQfUHo4QmIi9hKcZfAF3SU5PeCZDlwwrrl1MaSU7k2Gd87OMIKGgIW14adoq6aV2FwqJpvjsucl6gw1ybmgLE79gtQHMKp2i9kLS7lyCZiz5m5T07bi5Zh77iM3AMWcvqvyxZ4RkUxGE1WWKm1BSAj78jDMlmXAUQuxdR/mVVaorJQYaBxrocoMPQVka2DzWT6H6sfRAHmmRpPbqoCt9O3ySM2mUlW21HRFr+/eCZWQhWezMZccLeDREZ47Y6s5z7swMHPnarnEFVAytVuTTiy2J8+Vo6IwIBuryXAvLGP2fW8KGt3gzczNYDU1eBblUKbt0eZlq8tcxliwOxNws5MvymyMZQ6D1h4I/FsxD+gyVD/2iumj1gkvzrTL2S73KUjNISh3O0yRqWFVnsYtM4ZsgxmPh8GsUg1SEAl4j1UvEpYSdzSNyWIX69M0IxbaqbhmdvlGpmG30s/70+WlhffJxsR6ruUyqDc8e1MtlmRpMr3pMhXl3qevhROwRvYt7Pg/zVBsTconKa/poCexe2xt1lri3zV9xNISFGaa6Aj5sjfb0EBecy6NwXmJTdxYyQWdrU1XIHbfd84QVwIV4pWlLmaKmRkqS4tIQL1iau4o7rRQMVpoJOQ5qpktZWmMRqMMq9CuabGD4mRPWCCBFPH6hfuyNDSbzrqSrhk5a9/qrGGUEV7pEcapcIESK46cbUTtlqpak4GaUTGIvUPuySzZMov6jV2VYOaOqwVF31v6BI/sEGYVMXfl0mQm8hAQIQeKTREoGQjsJQq4G5UHUrMjJ8dueqAbsU9sIbzKWrVlqpRYQ1CygoYyH2hppuxbZOKsmJGXt8yHP+ZRt9zy6n7txn/8sf/g3d7h7SBFIMgiBQosaXVcqb2wRzzxP/3Ks/7iec99wQsFxatvPb/k5a963otf+upbL723WnuWR3qhSjoP3ZIxHYqjvJqVTG8hdoTF0NkyrSBzSFP4FJOOdFa3cjvGwVCKo/7KQKkESm2uS7UdLbrSgKueq1sen/rj4QGpW6pT37DjQj061Se1pS/W0au/66eGEMdgJqXpaKzKymf/5V9+9Xd816c86Mt/7T//6vl8aXP7hx/0AT/2LV/7pZ/3WTfe5nUhyoPH4OPz6kIAAP/uV5/yQz/9s3HrK1/vWv/a+93nNjfdeDXm8AF14KoQfPcP/9iTfulXbx3zDa+1h3zmxy99pUyHPiSHclJgHg1VRrUAxwfzSgYAtZE8vkAch+kgD/QJfeNw2wy6vj7FDsVWKffUTJoRx2F0NmChAmHWTc/G0RluMGpsj/CpZNRXJBfiRB7V/uCYCO7dRSxDwgPHeCCpnENEi020WEomWWDqFKAaAsKh8FcraQlf0pqbdcCibKbvsAFEVWWYduFTS756cmgb2y7KT4nFRDux2lNZBPBwjgXA6Up2M9PFKheypgjfxvTMbBw9Jmoc0HZCiY+uZpMAJxnhsTfpEZd7pVIAkFmQ4AwcX9IACsNS/cnFpe5QOUzVfaow1cyUvj2nVlJb0E9YGmVLsgKiEyphhsZ0CBhdNfPVe29uZovypUZkYGqCWnhUqeVggEAVa1scDvQZfoBIXBUsarPAKKKAkL8hVeU1F5yb7fBRDCuYeKRZNtz2FXk8aaAZHSRQmpsSUlPPVsrGReMFe+30nRq6ApDRTYG2PsyrEY6ReiSxOHvTAifMlj19zwzBq7o7O7QxQsKgWrpSc2yG7Vr2RtXknBwpZpTZbtAhW3Ldy2coVochvnZcLHunyp+ECkAlxLG1d5fh7Vg6ugkiLZZ58M/kB0FmEDBzzVdF0U0Ka3AhVXdYElWpuDo1Vqg1HECYmCDr9YiBrErJnD3EgaKTHbWat4XmFFozTUjVZiJJOSRWUpuwlq7ZMEURyPGr6TUxM8fOETRyBZuZwRpcBY+94EUr03Q5v6YyMViq8DGLw2zrPdeucWm5s7Q7mcLq6mQyZ5vKtJLrCkZFNpQiIa8AUSLMtNfU4NJfBxeJx25NmKvUdvFit320Xdi0U5PPEVVTjKBWoplbcKsj3gGK9914kvkT8jx3O8aSJYO5wLp8L9Cie65GSqHOkBlJr1Smel6Ray4Wa0cX3Uvcbmele6BmpsZDcOoNiSJCkVxLRSBVO+TY0SugkLRpJd0s1HGY0TUeUqlhGj+hHbqULdPa8LZLPjVL+LEN45Y1hmg6ZJYi0uAQG4KR8ryDjWUm92UN1Uc75rpRyebZHc3KIyNlFj1Fm6WFtwnTMr8XZ7aqw9sJF5JCBRlCgW7QHwlDQLuKgybFYI7a5zY1u44RAMnqJlWycLhHy9ZiwVz2aBss0ZBrRceQtQG56Smzc6G1qdbjMXPIPIqeBblfyjJLwURAZpKpPVdnVGIXL3fGFKRbYiQ1jN6amXLdWERRZk0BVarUpfpJpKQUrDinisQYBNE7mh/irwwzn+gHEqqfrVPiFslR/V3sMNMJTS4N0lRrwbaHeIgIMIWI0M9iBKfueyq+xTaTii/0p9PhHWYFRA4BSB8GlQ3MqRHFkCk6M+dMDWmTOasquukF8U6E1hoOsFp6heqSsFARNMjZktHTBRwUGhVTzB5ssONFsQk8tynwXJdHpFjSpjNRSuJQqHq1pVo3mZjmpOzAzLmPUjpXb5CwQiG8UmuZJEtrJMWpQExKpXl5S1UGIC0JHmKcU9SpzJiBc0BkUynzksjSTjfWiD6DkzbbIga5LJBxg7mXPJlRIWFXgtfIFiUbUCpCRrhZlZaKnizpBKYGx445MmJiolV3tqbR2mNaS82em2kYMo9R2FPTZYxZQupm5a47OkwnfYG3yhB3hIlYvfEYV0SJaFAgqqSaaJbuQ+FMD3XJ2qKUerqFcECY77AxqQqQ9NmaJA2pBLUeN/lBAdcKbJoYTmNRs9RAqcHh80BOTGY0ESKySTZ6aoJ4LHuSDGCiCgMMmu7cal1HP23ss2qKuV1+mFWboVExTd5qUQcd2TyPrxTBMny6ZffoXkbs7BsOroIpEL2kjObNG1wRsMwOdokt+E7fzNJkepYMH9RCaIzmqN4LS8IMSZRCIE6omuuySsgoLiZQZMA6ao1uvCZoJSXLYrouFgvvVS13US4rz4xdBqCisAX2bU5xpiNFdTppoq3upH76qL4fhvWUx3sJpglxW5DBqbiFT7IIk52j2qyVrleLbMzadp7TL5dltFZyRBNooBlzMSwNy1JihKqFtiijPfYJ//rXn/LkynjHt77jff/Rvc00FqDQ0AeNdtxQ08sgIPDCl7zkdjfe8Akf8j6f9/H3/MQPfZ/3e5e3Oy3Lq269fPbz/uo//uff+o+/+pt//sIXqxyFdBm9ukKZA4WicG9WS8+2pPcps9NC595qNJYZweblAq/HbBXGUhB1OuB1WJRZ+pYdBAxGXB0adnRev+b1XkCtGnAYrS9Cx/WP45OEHKM+df5XORpynFcqDinUnf74kle88pE/+G8+8X7/9Ed+9t/f+upXcm7v9U53efzDvvpbv/Bz3+LN3wxwjdP44/P4k4WHuyD/6Fk3P/wRj9i2s9z7qgd+3ju/7dsc/fqrQwchG/nvf/nXHvuEf6Mnmdue+jd/0f1e57a3kS8kjQSvDtlnhxskdFgJFl1KRQkNnZJVBxuO7ivB6tLA41bjrp+arwu16tTYQ54UkARFO14VBtlNdUDysVgdAbSetZzL98oJrQI2sSiQvprS6Uhnlbm19kU5JBcV3V7h+8DGzFYwafM01ZBSqrYYa44VWCC18hIk4R39aILrRpvejvKAb9Mv4TuWqCW2JS4bbiWUFCoOgeS+e0lWdYkq8VKZIIXaup5a6hpAYsc6uSZX99Zbo/V9cJ5hZ1KVdatlnwvOi006UjVIhWP64mbNaOCvP+Pm+zzisff9jsf+2Qv/3Hs8+yUv/LxH/+Bn/ovH/cgv/Zbk6/zy7//x+3zL477i+37k1XGrX8AWprVpNntg3UsGKB6DCAnMdZmuhRglyZ6ryyxSpS9rRmRGVA5gX81uaL5chF/sfjpXR9pRH0dhU6J0zZfAoxpwcEmt/QYQM2OUcpor+4ojyyuo0rMdhed4IzljUkUdRet58rw4QGcZd1nV1ThX7D60GmU5zCXUCqdp60bbyUAR2/m0AAAQAElEQVRn6ceHhhLcSaSYGNTbYtsx5atPrFNjlp2uQjdMnbDW0tuEdgk19rZvp/O2nHduG/ZzxQaN0tZEb6G30SqXqjaG78XdcupuJdxyLBzdvdBH6r2ZLDCcUM5CA3pBui0LNsswYHr+tFF98iL6umkBa22wediqrbiYLGLNZdtdVuQUgWDAlHD0oEciJIuj4ezjjHkeWY16PFGFLvVp0WbUCoglXW0kcswxYhBxQr+o4z/Kph8it35mbQvnStPZqmwfXcsF7OQwi2w72lhwttrmhpqnhgvHWpCvyOj7frEVt6bN20aPltO3WXvUiDpH7jH2mopNx95zaFKnXgmKAGK7UIIAEruwBUcdiXaCDpl7hGmJdm3iiI04JhC36jEWqhoHBcia4zRyHccPJ6csS+n0EbLtYhmnl77gVT/+n37jKx7/I/d55Hd/7qMe++if/LnfePqfTCEzC5PJeuRP/nt1Peh7f+ilt1wC+OU/+OPPf8T3ff63f/+T/+BPFJ9p7Y+e/4L7fPt33+ebv/v3/+TZq2Ht8StP/ePP+cbHPejb/9XzXvoKTfml3/+j+zzye+7zbd/30Mf/VNx6y5N+9Smf+23f/cWPe8JLX3Wrev/tr/3u537r993nEY/7lp/42UjlNGiN5bVXXW4xt4EpkoJO9qSqUM2qhOjRtrlGUVTBSjSVw9AuwXVp6IbFZ1cFCmqw5FlXI1s1Z0drtfpYtakBDQX9uHGhGjInqoaWei7WfZGyJeKoi54KBEkY2YwJC4RhX7gR26ixVW3Jufc5espMwJTMJX4hZa14fRhvtNJtHoxfFy6L6olqjbJ4WaZdiERHglaaqLbGutAdPeFKBYHt1FwOtH3adgamVKyOY0N7qk2IaUvvAos1RX7LJWuZQ7GWgzLGpL1Ro1vfWWfI5jG5B/cROUav8syZc2RGJjXabObpcl9viWWYs2hi1AbsZVWNwxkAvMzVPqNiZwoulY+SjTlqTKvzalvnTo0EKhnp0b0uoixKodkKqRiFTaWkB8ox1xjaOcCDa+SC1DZv7X5I0AQpIWPx6kcizsmYYkK/nH4OZtkaQo2J3Gdue8VcIvTT2NjVVKepwRs3uanZTaajeTozidHb3vuQQztwObRu2cZSFjiqQS4rsm6ZFzU6t5JMDiV7oSMuYlvlPkPmUxSBNaQBJibpkl5UHZfvow6eskZuU2uFbrhATNwxBH5MdehjIqVwaZqd8ByGMGYjQBMVo7mAqsgWdUo7iQNZWjazeEZdRl1WGxC9T5gnzqVz1cQSeIOeZmWmfJrNttW3U7PeVm/el2Sb5922y16jVbJASmer6oAlN0nebceCfhJ7dyttfvdtxlYVyIOobddSsrggVby8AHAyJwa4WZ8pNiy2L7KrJIYt6FOiE8yhu26LbUsqYMBmB5w2gXPVUNJnG7lWiqlLimSHfai+RzsYHGJyZvNcANF/jByorBASUtEMjeraHRmFc2Db6jJLi2YtraRoHMilxRDCKhNW7tGwuTJqwb5yrgt8oVe12Kx2hmYnRJVsqCUiczvbOHMqGRzKF6DlNDHNayxigipyavqElssZMTD2EkcPvVBOpTLaEesMzEpYtIZF25DpCmsRicgZDNCoPL4wu4CvSZYVeqR43+go2ChTFkeE+A0s1Ns4ir6xZSRhqIbpOGTlQvTF2SwUBGRB7sugys1saqjI4kJC7FUAxUGRmuFgL1nXvbW0SpUJz3lYSEmYpabuQ08HGrdj3SDBAiVk2tOfdfMP/tiPybQbrl176Bf989vedBP+60FAJ3QYYCR1W6g3foPbf9bH3fOjPuC97/J2b/Omd3jj97/rO3/aR37ou935Tq941S36UeWpf3zzT/7Cr/78U35nH1roXaFshwDCrdzQXRcAZLRV0Wa1hNNSNSsJFJGG0kWBArmoo3hcE2olSoeujjt9yaSrT7Vfff/Xj9dcFF4zAq85pLTqehtBneBrev7bl5qPG4LMzB/+mZ/7+M974Hc+7vte8spb5i2vuMPr3e4RX/s1/+pbvv593+PdzUVugFcncFxAx9U98bJXvepLHv4tz3/Jy+J8yz/62I/5qA98fxwjZOrhAkmB+gfPfs6XfdO3jbE35Fd+3mfd5U53xGsOQgN0ffWtD+DqA7j6JqDZNH3qqoQQSOLqn11dqPn/yylMdUJ4giQk44oIRhCQNGUoKkmxZ7JEP6upchM5d4RyHrO1eWqiawQwUyOUQ2F6PW3a5NluGT7CtFvYDaMxVDS8JcUCJ0vp25kOc5kcsmDF0vLk0y3KMxqKGkYoRvogtWSptIdrSnN0kzaxhYTOYKWSMEMV+rDaVIfp1uUhAaRoblv2KBPXFlFxmSoWopen4WoyAj6ubnz2ypbFjS2b8lxkbS951a2//SfP+d0/ec7lfqmF/6WXr/6dP/2z337Gc577opdKvs4/fM5fasDTbv6LvTbzubAM1D/lJ2taR3XCJmdYyuvqS9gyYCpWruyTKbIMUBFR+ufYtzhP7jLXKCdahaa3Y0hkzRSoheYqZRZWWvRqNdPOCjaCu0aYuiW2GXord42YXoKIc3b5tYOzdE+jkyaAkLOQyVKYmKrLyVFaADydWKAxrGoTjbAGU1VSv2pl5ESUlWn/FmxlRDPvON4dtlbuV+GDl6771Dx5SEhemqcowkxBDbprxxUexLDSY4zMVMHNvg8hpLVhJwu2porpBLVhVz2k+jYbO0bUtEqmVl2X+1csCGjFTy1sx8YhTPg4zBK1AyjT+sYMhBxQ8bWsPiF+CzgSnmhT8kbOzG2pXHPpocWTwspcUetCQ/5mYQyL2QXhYlQVsK75E7WBIfrANVtCEZ6jZXQFxtMFfxwmHxAxDNrapQ6UMi2DgWIrazQDrmZzHuwR8rB+jCpAKpGM8kgbu2278LDqja5J6kl6iTKLUmbK9N5cWTiU1ymxrUuAzigO+JCSQgDTONZeiwtjEwgpLUVqVek91s7mLbztAkBW9eIv/N7vf+4jH/fQH/7pn/nN//Lbz7z5KX/8zMf9+1950CN+6Gu+60e2l99y0p4q+Qc3//lv/enN/+XmPzvvQxpf9IpX/d6fPPt3//Q5L3nFqw+NLV++X2rub/3xzS9/1as1gLSXv+r823/8Z0995p9NAQy86JWv1IDf/tNnP/05z5tbPvdFL/vtP7n5D//sL0JoAc9/sW6f/dvPfPaf/OULRkTlVDPDMIU3IrNM6YMilRxz9ymaQcs6RkHyU0bAzM1bd7MrmIOtsOgk5HJAGCUS1DjjFSRJD7MiZGBMxlB66NYmfA7tuergd1vKuke1vVomyhSsCkMcQijOWWrsVkOzA4qa2TRlH0yIdxxPH6ychw8zJ23QZlkGM+meSu3VqY3CukPvMdapugnYnDh2gpE9U7oOppTKZbeDfEsc/HFMio4j2yyI7H3QJROqBk38oXZkp9I2VRpOmZ2T3FkhUWZ0KDPCIl0iBLbSR7QV7gjIyame0oT0Aw8pcJleIfwNHRRiWTVi5g6pVcqVo7qQhak3u5XOhfPQ6uUKDL24Fo8qRKq6eHeap0rROpuVrpnKZxcvbecRl0QD3GO2MT2jlTdbrC0ApESp7+km59TSmhEospgaO9F2uBIlGgqH2XrEymWfNoVmptOBNSAtM7FdDbE8KgcZkAPTtXBgmxquzqKCVT7NwyErcUj2srITrVtyzbmMzepcHLNhos/qGkhjt3KD3IurS1ZlYogfra5W2Apy1mG8RqnZeNiGQytyhKoiQpRrBndobKBSnjoA08eKbCbcSljFEim6QygcwFXuETu26Dt7mpW5WLH0vnhXaV+yNeEocZMxHUmpPh5kTqbIaMPdQ5PCGntLCaC0SWN3rJ5HWDlNml08iNBWO5SIAQKwSpc0eDrHCbFMNCkAhVgio0QbcGKJ6nFotWIP+JRfSgsTunBZO8lCkWGIqtiIWa7NiH6hzMuRl0SJPba7FusRHqGGAx66JpNO6hlFD3ejMk5SN4BSBFu6G9FrEoGcY4w9dZQazaKkDLhunuoHKNOASOyDUylerrlsFNR64jgbFPeBKvkitqXDDMhETMxhc6pnotJHHTdT18xDvCqKh+SmhmpiWM6RGNNExwEeTivilDRz0vp2JJqDYnihRviGi+I19cAGVCVOrVx0F4dUQQQbyMPcaW1vS6x6BrVTrT0UQR9ZahzeZc8etSdwcMNw8GmOFhnGuZac7RUOmBthmU08qS1rogR+mg23gItP17Jdm9b17LlZDMkNiQjzrVmYLAoobkMbr3G2yF/7rd+55ZZjnfjqL3zQXe/y9vg7D8oNlRsex3Ue9pKtbOV229e58SPv/j7/7NM/4Xa3vemlr3zln7/ghT/9y0/+zif81B8+42bOaNLqLPOCHRnM0FU7XErh5K2sgybFhlJaVhFm8iige+qGpK5Kn8R1Kwp/81CrbtVdR/v1O81VG/DXd/gbhxD8G3d/16Wgf/JTn/ax933glz/84c+6+Zlzu7xpbV96//v9nz/0fR9/zw+6OF38XROvtwv5hzzyMU/9g6fGduu7vPO7PuSB93U7nDx6ScoG4vkveskDvuLBr3jZi/N8y+d8+qff60M/UD1yFfi/mq076g+43n54iesNvH5cScPVIQRFCWQd59Gi0TqPKx6TNFQnrl8ahOwVVFVeaaLT9eVFkzkRBWULaqnZM9ompsFOEPn2ot45ja24F1QIFd2gnYGZCAltUF4EzyglZgZ9qnh1eNN7ml17OHbWjcxrdYgEoFTEEDPnLJmB6BYXntdQa+Rac2lzvUi92mUPM+m3Ndoys5+3vp9tR8Y64lrFUmXDPJUpOI6smgHl89RdmE8evxDwMIS92gm8VuPa2J3S6cMqKyNnVNg6+yoW4upwKN/tjV7/dv/ko+9x/4/90Pd7h7e+asanfvD7fP7H3POTP+h9T5KQW8txKimnR9peRtpqgi8xcW4YLoprtUm9yLN9zpmjkD3rVPLKRlSO4XNzXB6ldhf86IFmpS+9hlQKCC/jETYzNirfV9WZbGOuCeE75zLCoqocys5mXKH06tmWWI80ozanaWhHROfhqiKNUbaxpuoDcSK0aGJHSGotSSNAc2DNudbuJWbPMWpOSNixvstP7966L32ckmss8HWu/XKFeFMeymYIlfRF52jr3i82v4nthkURyEVLdTKbdHlCRQLRmV1WtS1Osm16EfrIzKXidA7bIva5HQ82a1YzUNNGJja0OPzNXnC5SZaDJ8Bx1IZBJF0eM/cT9gtU6xbdN4UlawEMGmep8EcNO5hEPxxrvYX7SBbUsFLPcFU5tsg9NdwViZM3s3Wbi6Ko5FACWABTw6r2giZStTyDES0PM2tUTIa6ZOlS2lGtqC4DURpVu0l0Ve9cvS96lI+T5B8jajVFydagFvS1qnm6/O26UCRmxtxj0/S6Nv0G99PSRDvFkDDDccgLm2PWntSrsoBWJxVpmtT0Vf6R2DndlgAAEABJREFUgAq2pE69r2/QS+qhdbvPC+w3xv7Mm2/+2h/40We/8CU3ndaPe/+7fs7Hfuinf9j73fENbqdd2n/8nT988L/64bmPZbZ7feB7fP7H3eMzPvx9bjyt0voOb/4m9/3YD/u8e33Y277FG1MuutDRqR7MwpYYaXd88ztI2md+2N1f9+r/EOwub3mHB9zrQx5wrw+99we/l/f2rnd6i8/9Bx/2aR/8ftcF3u3t7vj59/rQ+93rwz7q/e6WXVZObiP3ymSwQe8abIH5MFFll3Q/d2yWM1Pk1mPrnhzVgKZB3rwRfvzaJYJv1QYP+5CTcVkcCURgJmaJKvBt2K0ztspZ7tlWKs1ntv2MbTLknfEYP3NuCgXOZ98vF73qIcLbToU289DQYYpq62Vrsgtwt8W5irlnxL5PHWnniTFTfjXQioCKCbaGs+cl5rlS9m7bqrJhs1pMcEuqFEdLW8JRbbq+3VUcZjJ9pu2lLJZHhWEtm7W19N735NWMPMgKs9aK2oDImxAQYrHlblXsBlGuek0/SlHf1KUprdqyLxYEE9ZIqjeGi17GWlCsrbZZeyIh1OwUZiWflKEqL7bCDf2AYVm6SNuVnieMpe3S5uxYDMpCK/Ne/VqeVq7diz2wWiqPidKtvJHG2gZ45rrVGiR0EBeN14ydhLkI4wu6lSrNqWIJNuiwUkMsa1lDAaU/BgWhfO7de9NwxS3ajAS3osq1neFiMAIHf5S6FFyCOUx2eEpqkalHMNmPZnk6eb+QBy3GYWUENTitwCpjFEO1rwTMBlMtyXMdRzOc6Eup4le3cK8onqOdVepsab0t7tExOlSXdy2IZ9TEIfWwBnP4nMtk363r9590eMECMZAYqOjZfFw0GZOSPPs8r9vwiEDLA6nORpAsoZz6Q1npWihvJSebE71FO00lA5WNi8tGRFvmchGr9rvNTqhW8miOKbDKmSfGivKR8+zb6Jt8EQY51qnS1io9zbKP6DmJsbeUezQ6y004AZLGhJ4BZFjAkfKKsqsiMydKBi/GBSENlxnTxX8i+9z72I95Wu2WLM0yQ0/24MqiiCj3IkMTsbPGkrlisdBuRKG30eLMbatbI9JnuxjUsofV9obZhue0ysPTiFkVhUHOjur6WWeor9IEZpcT+1qjO3haw9eJ9Ty473vGAEDzZUTbIrcl8qKwVpNHVuqhCeL1xHLbVC54Ki4RJ3Ll0rkCPUzYBa00XDzOmloRgucSvWRQCroJzMVyMZTgg3ma6G3auvRQpFgHRxR0W2pxXGtcmlsWx0CGycYMHq5t0mLm3b05aZkJHYrv6DY5R+2hxXSWRU1PxaC8aGxmjVhDyTdVls2mKhF4FLcJJiyM87D/EDjmi1/6MknVeff3fHd9/t+elOX6w/FnOL5YxHHkm77h693/Uz7u0z7qQ5fe9HPK7//pzY/50Z/+nh//2Re//JWyS3CgIB9KEMrSVjCogjlKn/qDszRCzYVjvAZ41THtkC4dlDb9vebu+Ppvf7y65PWvq2v8zevrLVef9ne0X3X+149nP+/5X/CQh3/a/R/we0/9PfbTTTfe+In/8GN/9gce9wX3+dSbbrgRf5dw/PVReNxPPuknfuanxZG3eou3/O6ve3Dry1/3veb7la++5Qsf+g033/wsGfQhH3iPB37avd3FZvzfyj4GaI6c1Sf+u0NRkY/61ICjU4N0Hlf60xUFMtStOxyXx0cVkib6QKtIuJUpJsyag6opEIddWolmsdi2YHNGqYs+oEwsh1bFOoYmRb9OdKdLSR3dzPQpngJarFTV3edK1Zw4Io6s4Ewf6khqma39GAfT4c7Wm7s3ozGCpZckgbIZts08z4yDTWvUEkBKjKTRkNIs92RBTcQuN447mJXsZipZu+F4g9zTfKcFuskYKyyzVr1SQRTGbFnXBQEl1215vdu8/ud95Ifd/2Pv+X53ec2Dyj/6oPe638d88Kd88Hue1qZRlGpKu9xR1qmAopRmiUQv6Z7ADpG6VRknqDpbU4q1WSiVnvLV2Bb5NCfOO8Yx0RIg0OgX0Fql65ia3AmnKlcYZ8GS7p3LkuSuyjBxVS3MIOCADluhqLomqd5qmrFUDMozFhWRnHOMuSmmi2vVk3Hb1MKgkyNrgJTKobDp1ArUechNxmAoWKjqUCFPueSjNIXBCi8skFa78l33ObBPRVeTm3k3d9VHKuKZLewiXWYiVYwb2Q7TtWZ4C15orlc0G2i1e0tbSLNerdObEQir2f3svofW4xazF5lH+GZiQHHVfn7VRnGfW9UmMQQRzbG04yJwmbaTpXm+JxVBuNhs6oQ+1FAFkU3S1xE9ep+tT3hmlfilUiw1ns2j2WZthnH6aXrXylkMesLicAnhmM5JU4ByLxHLQS/93NebeZoeX5BMDSlOtGhtX5puHek5verAx7JxHhEkzG1hLzByGM5ak0mVfBusAUwY09s0Lw0trCjoKMmuHhIXgoEHGGUx2xgN89gNrZNNOZmT2GWwhOOY4lNo1xOu/ifU3+B1bnr8V9z3IZ/9yZ/30R/6hff6yB/6svt9xN3egcBvPONZv/esZ9nEx7zn3e5/rw/99Hu+740n8QB6UPm8j/ngz/+oD3zrO7zRlPHawULDZQ2E9LyE1r+3fos3++yPvudn3fODXvfqQeXt3vIOn/txH3K/j/ugT/rgu16c/L3e9k56UPnUD3r/6wLv+nZ3/JyP+5D7ftw9Pvz970br8tRUSjI3yZO/UPj7RA8ogFduwoReSbVcqiR3RAihBjYcocSoESLJnEiZpXBBWWb6Lo3MYbEzAxzCKq1U8dR0ZpzNN0WKLpCbT21UNueUcyLfLmoGLNxTSBdSKSeBnUapAGpmZhVItTjZqW2GkrWZUVHXJFTosoBUHkXHxpqzUpU5sqXsjoytYqBkNIvrxOkIk2RJRqCOwSjNJ7KB+tHX6WQL2DlMbL/eGRqhxJdAYZYd1dN9N9fz+mHgDj3SZPlsB4gGtGFyIQ6t0VlHkW69oyMUhXmgdtHzog9LGajGJbgUZEP6Dp9eVof9nJiTR7GwcYTEZslsTRceGswUZkP4l/JFJu6lsEHX3M20LyxTjgl0p4K6G8+qEL0Ri2dv8xAQpRgLaxm7dDY61Cwxkz25DLOQH00Z1DFWjGZoi/XeWzTfmo3uRmtpYC9zQgk4zaC4nmuP4wk75dV0DmZqCNyica6j+t54MEGeVkDkKamtdDuy3RcZVBN5jJC2as7oNihAJ5lG0PVltGqpxEU33cqnLhFZQlVPryHv4DD3Fael1oULooNJRkMyONIvGRsHUqW+pFWPfXRAjBXqJdrMYgLhlt6qNfVU6Ymco+aes2KD+k2B8UxHmR/J1iJ9EIECZLP73k+hglyyvRAKV6JKGeEiY0FukFI2IkZMOPrSl9bTKOnTUCGa6qlvqgJVh7swHWF7QXUiOgWNdh5FcJYIJm9mS9p0q0qZIIudrqPT+wCGlNONi1k3hzechF9UDBGkucIuj2G78rI1QFKEFqv1tfSY6ia0bNCTEOXBwKHFXGats6+5trhItARkpwYjLJIs0qhdPbU2WagITUE1F8xWAqnV7EIwIB+82CVMwZ2dKkkJAmWt6a+mh+gVLqlqBMMOzgISqN0JGsiSYeYp8eJFRotjO2VopNw0aJptJ45eilEiZZVLxY45ah958E6fKiJt7r12uV8QX8wluOUwDbBK2IRXSVxbvLWOdE5vw/TKGkOB2CqVmxIsp9marOkyfiYP4Sg9320XYz9ttYzDmQZhMZaYPglh3qloTKugWXkbymO2yqboeVDoqpwYltDSM+cRAPkF3Hrerr7/Hh8EDfrQUDl2XFXReKDj9u7v+PYPue+n3eNu77z09qpbb/3P/+UPH/Z9P/KUp/5xDTCqChoPB9yOlIWxFMOCV7ECyCJBHVATqcvXnFfzCClWI/7nHZH5s7/0659w3wf+zJN+bt82a8sHvsfdfuhRj/z2B3/JW7zxGwHE3+P4hd/4rW991Hdq4E23fd2v/9IvfrM3fkNd/81z28c3P/b7fvU3fkONb3PHO37zV37Jjdcu/n6yNeP/9ykLC5IlZPHXR5WQ/esbDbh+WRoFBc4AnWrTMDfFMtmOOCAnM6hRTjJ1+mwl0u0N56adQMtd7NOsq3oiASCLcK8F7ERX2ayDjURCwxKcYqhCX9ExxV7OrOkBUblPNFFAK6sGixREGdEc1qDKvOXcGaMrLRGBGVmqJaq81plL7VQV4jHT1VHQQdFJ/IpU4WxqcY+lnduc0KqW2sJwlEcuQLOCEkPpUnJIZWwCe/BcylYJOk5SZSybpYEafTQdfxWmlxothJI0AEQlo6wctWDiWMympbIPVfJulp1NFjrknRCwOGCFTbrkW+96eeE4m/yzCC+VcAF6iHY34ZqonORszD7DpjYQVc1tpas6UjOOKUq0AdW5KqZ5dafVwrGQzRu8SSeLhT7ZRxnH1KsFpId5uqVZSuMs4aPFNUbmjMqEDtKEd+8S6LE3TZzE6HPzffhWtuuusrT+Qr/pVgeJjjIgo2KPODO2jhA5JG3MGSFcHLYET8NOO31zn+6o3qItmm+5rTy7J22FXtj13lZfr/V+YbbyqOLBUMxCsZbM6RHLsD5bRu3YZ4Y8k8YazDHCBtuuiuwEq2wWtsKcxxOC9p5Y0wxmtazDlj1qag73LYW7l1X6TJwde2tTr4NG456Re6i4UgCxI833OsUUVegy3134T9eK3BSTRjpb1jLDxRRCuuBWjWBiBkYWkU5KVHGaXkNjTz9WhK1qyA4CBX0YypxCCpa7TdlVaSRo0H6ltBOl3E09g52jZ9IZTgGk2ccns9QqCdItRYwpPdDO2pK9hANMq1BUya2w0naps9Zi/9M//wvNf9s3fYM7v/nrt5oXw3z2ZVnu+1Ef9DZ3eIM73P51nv/yl9I0i8wORV+jr86e06den9YUA8Z6ZcvRUVl5UCNKHhmVqme9EwaExFbcEajwVuvKk2ByLbVT0+oYoF1LsmbXDqs3OI54sdIBo8bIiDIBmU0teqO7SLaSQORrZUnTO4xKARfhg11B2pccTsxumzclB9G7pRwsRCDYZjnyQNiCuq8qIRQH9by7u7VMhfaklhJwwrgILiXTFBfBiIpF4T2sk7czxS8RtGyDB02D4UXti1dl6mLazjbZ73Btp0zEtpqeG/cU32zr+rWBm9UQd6Zom2uFYPUOP3Xpk4U2gpeFjbIGLKZQMZaX1PlwDmEGVGbtGlXcROhTzL6luGZRfZaVIE4kbCoflZVwplvRCiJcmsnGax76baiX2ZiZY7dEc8imOWvbMCS3Fj+qj3k2SqiNVIGdWw1hyIBiB4VizqYyeaTBHthC9BeIWTlLta5SpiIzS2L3iZKGEqCaM4AdMS3CZJ9e2ZwKBzUv3VQj5TXTOuU6h2lrhg1CkXNUhpTbhGUIXdXsq/iAwny0ZTZH0WdVCiFOwAFtBmPJ2b0+4MgAABAASURBVOduFSmVjMpcJ5ZpLpRboYVh9hqGLdumoFVVbjMuQwXPOFc7Q3VCC0dXJJYWfhGSufkBSCCaQdY6rSw9om2pWl3ZQqvpkKs5swYRnbHIflL8jcM2eqM7WRY1w7bySYZlmqaFWcoZWOxQV991I9UHJFEk7GqqLkI0oMesMaVIGGHZYt2iyQTHYrYkWxUc2VDQ4rsbZkP0RJR+b4+jduSxImzgRs9gDdkNDrhZUwn31eqahbY9FHal1EpJckZDt+gmCTOVpxSpBKucMlEh9pwxWNES4jiyUXaU4C4shKQt4Tl9ojezbiX7uDauK5bVlFvb1a/D5RbCRcwuByIrp5yxJpZ4iGUxPUpFoYS72xA5RElPrOSFly/ND6GqO8aaqmocWZPIDnq1xnWhLz27folekQsQ6IPLruqQ0owgkO5HRda8udccxHQDRb+TSU5aaXWZfrStgHGn7/DMngHqT4GBdAGe8EBBJxOCwyD3FMGyiF5TP5TSFvBiiqAiM3brSXcZrjDlnMVCeISePtsgshgBKzSjlVdpdJctO0XMtDyov4WdW1127OWaciz70j9ahhZNhYaGoxY2hjddQe9f2FQHe6bPKMFdiZRSCBb9aLbEaEB6GyowTjB1S2e6ZbOquu2NN7zd27zNp338x9/mhmv4+x0EjpPCjmARoBny6luX5E23uc2nf/SHftGn3uu2t73ty1/+8ue84MUP+8GfeML/+Uv7DPlNOCBMC1dicHVdENtgurl+SmqR0Anj9SZ96bx+/T/r85bLy2/+7u/9gq968Atf8DzpuONbvuV3Pfyhj/umh97tne5CQa+mv8f5R8+8+Ssf/o3bra/21h7wmZ/xAe/+rn9rkmD//h/7yR964hPVfrvXvd2jH/Z1b3C719X1cf6/4+J1Kdc/D6kQkH+N49X98aEAHAxlgVf/pqFUDdSle11rQvaYNiv2NqefC6JkAQqTMsDznHZW2BWoiNSpmRJ08M8ymsTNxrke1Mtjm9xVoyrET0x6HkJQSgTrYV6mmdWhb4gLs0or5T5rH1OiJ2aq5tdYLdal1naUJKf3g9iGIRM9uAyVNdGIR800GRNAVBHsYieT1J80tYQycUAL3SXtbG2jySKQLbkGemlCNFPOS8j1kyhwVlepjtD19VaItZYqPFNVrM6QGKRKSNa0Nle3VcnKdfiiWiDwOCoZsWx12g9Na7FhhX5J3i3HUVMWL1L5vADrAqcSWot8TeWZVDYoaadaLQ1a0/acmZJBrFThPmMP7b0srAA5nwlz1et0gglQt3PxvV11yh0bWqSlsB863aflDi3dmKWlVjBoNbKRLKSnmo6Th8pUmMxI+Q/TCrTLMYySeA2cPncf0fZoUS4zIqhBlkTqH0DNmBgZ+5VfVs3mgsPomC3mOkffpnZujMFjvUG4RS6qagb2Du1WO4y7Y4QWUozJrUQCT61c6EYWwbyKTXFiDC3+c1ipWDdXX7USWZa+LWPkXjmlIbOdZ8SubV9csK0Lezuk5FC/Q2tP+eLQTtdsovaImVWQHaL2QRZCDk6WGIcgdmBCq0wurXVzjYwKFDxEiSOCzNVTGOvDEhE55lnWbBaXVlPpR2TNGsI24pz7hpiUHV4nQmdDufar58hdlqSC0SeX3Q/eLhNWrKqMkaW83Y3aMtWYh10oQomMAHZmVFVOmlRCR1QOHtLMPbvPdCFLNi7kAoOC9bq3Of57jM983ov/4vkvklo2WDN4f6M3ft3HfOlnPvYrPvdD3vuu0xFV4k5ojsRenQyvqohKmUCQdtUsR0QUgbpVblC/5WlRAiCLGT5AWVpTgmgL19aW3q5PjFkYaBkLs3miR/pePJyT6AW11FhyCJNapZxwa8vqetLVC4HevVt4nGcOcRdH52qth7dRLcdiW6/AbGAvp3l6yyZPda41F+yu7oZcbF8sxRbD8Z2w0mhfTtW6ppqUtLI1/ZS6RQFRrOyJJQrJkIwmy7eJbWAGEkwaunW9pBQQC7HKCpM6XCK38kJr6KJCcK3FrfFUteqNdGyUXDlrUmKo3rJbLaOWKa+XWDFO+9SesGRWJxoOYWRVz1hnrEMbB0QwqGIiyk5PWaiIpTDJhROLxh6olJt5SV1p02ZG0A7LhyZa6fknq0w14NZRKrQAfGFbTzydclnCrSwtqsISpjllapvNqgFK59jnvp33OREw6wI25IRvCT1yicpb8jw4bom5jyPlEkq3FiAKCKJYcoGTBnXjKFQIQLBKnvL28GrObaboEih5bDOR8jN2DL2f2KKPFE6yMVi7zT13mTPSg50mwRPukSKfEgXNwnxSEhjhQafchZQDgiHrFNVkGRTe3ESqbMiTEtYD9JKVZhqtwSZza0bxKhBtQvIqA1FQbigXkkyVEA3vWK9xFTlqxNzOY8YuB0qrhztLug8WFRu4yByZpNRWscSuDaLAUNJzt2UuEreTY1ZOuJnJUM6GfZ1F+jDK9pnMsoIdUMa0iOaj++GGzZoxVbeUD7JvD8+haI5GGTxnZnBm34TKlAQ/WRdXe0mgFS1dclp1E14rrAVLJ+dkJg51ZjTBzelQR4KC5ExKGVGKhNPqsDJ3hU1OTAjRqSe/1iFnoNS+vNqG+Lq0xRUorTWd2SW9oJjM4w8USXOjHC+tW9FYLucDSIhLkSLbsBqm+g1xNASSEQcVTrAlS3pqUKwztcpi+krxlhwiYCRGa9magtG0skGwYAYtR6vLmQrdiFJDhLTR3M1pkpZHdpijt3mC6nKSAldTbWiN3ZuIgkJPyoJWR/RzZ6iqD7dFOjkoD7zVuiRNwVsd7n2ufSzd0ttW7QyNKdnCRA7DWEscSrHyaFfBnkNJgCtTAqZrZq3BZdJFnYG2hY1UJuWQhHSDW0leLmEX6CcKdAXFUO6CbhjlpimSCW/Re3aP6jnXTAenUS+jC1cLKCM9H//jP/lR/+Rzvv+Hn/A2b/02X/8l//wOb/j6eC2OAkpxNlAnijQiD8/0xyLY3unt7vzND/iMD3nPd3vBi1703Oc861E/+jMP/6Gf2scwmEZLFSWCTJNXhP7phPqKVKf6wFLz1fX/ko/nv+jFn/OlD/7uH/jXY7t098/4xE/4ycd9z0d/8D2W9fR/p/+/9b/k5a/44n/xyL98/vPV9BH3/IjP+9R720EQ3f238xd//Te++VGPnnOq6Ru/8sve4a3/63+BXg3/607BW4KZOgw4zlJOAmRV6usIBGu6Kk1UpEiDTAv2mX3uvUY0MQ5iQWmIvtkSIlqlro8PzdVq4aZ01oo4TPXGNUK9YrshWpYjWYzjvG5KV6+uEjERMXXOzKqSkXousaXYda15WBWj1gDXY5T0V5j4lxWhmVdCMDWSNJcvkTkQo094usnACRVEVGW2vZQolkWrFImlfRZGqZbKGMECHQxoL6EBdWzuZZHarp/OvdVYcy4zLHNqZCVmw1jA7hetrSYImErv0hSidGVZgkK1UPZVnhCdI23fuG1z5ubLPC28cD+MG4NxDqq9cjapWwa7MAFHsZJEos2JLUvJNZpNQ1oVDDxWfqcyqTjLznD5LQcTEakVflBTGL54X9EhAfvYtsrRC0vhQA5kCUeZYn6oiogZU36atuEQZtEDCJRUWsWCWEr4apapSSIPM2UhJE0Vy13f4NG+a1ixeZ6adoW7YhFVob6g+iexETO0G/AO0GL4HAUZ4yDVVdq/z23EmBYqilhSLrA75ItT5qDFNWafk8duJuRFb67FImHRMzlLK/z0vfVxajI7Yi/tFof2kG2hlvdqV+N1CaMWGS+V9t53j9kyjckmSWL9qfYTz2tufdt6hImWDcIk5mEIUY2zmZZ1Mzogn9DlhK9YTtO0HYl9TnnfgRXlOGb4IHfsu21akBRBj9bKlzqEa4SX3EuMwBwaT23f2VexGBPLwKlM/LNWZaVlzDRbMTtHQQepF5YFOyzRB2cWoi3a9LIjs3I37N325rJald0Jei3EWubzXh/wbpLx/Je+4j7f+PiH/+DP/NLTnvaKeQu1rbZ+m5te5za3fZ12w7r33FF7SHJq8PWT2Y6zoJKP5cqSqw4eQA62dDPFN5BXzfJKxjBj3adxQIBChr6mE1ZKIWOsgtZVnkq6pqJeHsH0RJvZNwWqjqEwr1qA3qqdoN/l3E1H9YM957C9WB5u5QfFXqOndETNAhe2i1QBSotyhR7hnNZDpBBkaB6HtShMbyI4pY2dvpiuqFFZxhR2R+nZPPY+Q1ud5gobzeR5m6yoGBUbcyoN8jDIsZIu9IULiYQ6JM6ZrU9fNqhIoWNZbFmYUVvEGbVZnYENGPI+lyP+Sw/569k5e5UCHpDJNoH0lC9eckZhYaW00ZE9rIZeoy87aIm+5ypCygJReQEpL8HW2HoZKYgATdbE1gWHriMir1Clq2VNb6RJnY2FY0WadDcc5lnYzLmxdrdhPSVsRO1HIBRPlrVqohdJqS+kWo55oahPzl2VoWRawKuU/V5B28p1T0on0nH9kNuoCrNULppggLp1khrMYb6H5cDUjhcj2pir9mhZaTYaFXnYlOgoWc0GAdA9L0qPrAknFso9HNkDBXwsjut6aRS6qC47OtU4iV2xXKx1xSSTGahWGkdBIhDbRIMIJXP2HVnDbQdqytnpjJPVUiqbnW5OtpwW27TjwS1cbw63Eo20ZohARktR3JbsVg4ZNxLiX7iAUJQhICTE0VmAjTLOchXmOUNR7WYr+gWsG8EGEoD+EjnknKQL64yRtqtm7pY7Swsq0V1/VU0h2gVajUAR5ta6iTNCYIvcY1hta+2mWKgU2mKQ9GRspVy+bBU9lbPVazoGQgemxioWjGKVtWhey/H+ckFw7iMEmDShowwTlSXNg3luOCtDvBtOPbU4c+ZhoxTOtKEgV6YeGGrnrsw8LIUjEojhtbv4qHyS3SXojoXoACmpoKNZJ1sUphbGGJqhdqQJLCJz5n4JrT/TplLMLfTA3fZZGBuFomCHZzSjXj55TMtqhLM6Jm2LNofeccChw0SCw1LBaYEeqT1BpcCp2TlbTYtZGTEZA8Yy22D71GpgzfMGHzcQi0t2VYhesxaPC2dnOxJyXxm90KdzdEVx7KVVCdvWDgIqCDS2YdLM0sjwa0M/OJIUysyQ4nMtoQAvLsJXQrwUDCtUw2K1rfutUMVyTzThOmWhd7qhYO5wo6spEzEqFbqw2DG+7tH/8lu+8zHP+fM/u/Xy1n/7pJ/7lsd+3z6EsdD4e56iq0w8PqVIV/os/aWwLB26JOw2N932iz/7Ux56//tYX86vePETf/qnv+bR3/fKV7/6mKY5cpg4vs0M1EkjJECTdR6Xx41a/hecv/tHz7j3fe//q0/+9cy86cYbv+Prv+4bv/yLb/86t32tVL/0Fa/8/K/62t//veN/u/a97nbXR37FF/XW/paEP/zTZz3o675RaLv7V/2zL/jIe9z9bw34X3dLLSeiGQnQkF5piLKIjJzDsC0WXX0tzSLdAAAQAElEQVQ7l52tmIuVMr0VLMUqro3LRfSe2iAtxnYQcAnF0Kd7qPamKFFwoIvePlhaT7IzHKptY1BMnCztw+KoJ1C0swoYhY0tuIQv5a0k+2SSIfNcA3kJprSpGunFybrQm+/GqeSblxWXgUtlCYBGpWWzFllTG8EpjWPvI/pQXV6gnGt+FMq1nb3JECVX7dhGbTJ1X5r2/JJy/ZTCo2VFMFM1K68347DHLBtztaDNFEwQQj4hWXmsSPRSrm5LH4KGYR6LpWPv2BskhxRmZe4o1hYxjpcwWlZqKa6x9iQsMrdtDHmRWTbRwxeu7j1VqY4ivkmKUw5jpVzZDwSaZxP4UHwtTcACu8fx//9j24zZIrpNawlYoqfbMEj8DGy42PJGxDU7tggqSPtyGtTP2IvuBb1Jmu1tOS/rebUQx03Fqyh3g3Wg62dbz6rGwAFEYStZslzjcgO4Tqj2RVFi6Iz0qUWurKpXtZHLRlmzBsQ/LXa5MlUgImsfBwaFxMKDVkwWTH+tTGABHoKwKFXNYA1F5JKKzmROxKGlHTjYjmXz08Y25DhWz1ObvUc0nltc+jxTcxsFpqnIT0mbxsEMYS1Xm7nb2rw30MU7LayWIfR6ydpxsLXDs+XwoefHOb32NcINsS47OWbmLMoJMwc1PeVK73ZarK/MlaMj3MsMLkBX+kWvxbfGyxCZ9Rf0sL4Ai0Z4r2boRMtEVRzWW6N3sf9acTGY2D66Vl3g4ENnb/3Uun5fqAYtUIrBrMZajU7OrG3PUQXGgZHN0s1lUam3f+T7vvMX3fueN107veiVt/zwLz7lAd/xA+//T7/2c77pMU/4D09+1cvPpToCHnbbkGTPxF8fWWScOpe2mDuNr+lwkzVtreMhonf3E877rr6WOGlRkkiVCkN5jsxb97nPqV7WQUSXLmgnQxvp4SYKGXi8k09MQxpL7GafucxsNRvHwpKLADUCgHWU+5DHyok5hV/CdhNFfHiPRvOUIBYtM+ahXVT06Uu11rxWoKthx9S6Wn12Ck4ZGNkymqX3VCWazc6FfUZkSLs5s9lscl0/0jVoa6Dz0GMxfUsxhCoeFQgomI7plgWZupovpCKzD9TZOduBgGAQgdvWfIIxq87n3LZtj0tgUFRBC3UUQKjMVgslOsDG8CiMVvspxoVqY6RnWwRSrunaCNIgAFpLKlFLRkFGoQ22qTy0aXrMt2VCAypa1intdEyMQJ4XH9dOXA+kVNTPjN1iL+ylXDIzxdpoKIWmD0VD8S2NkDXJtWxVn+m1gKAvJ1aoRMhcs+3UYxEW1mRq6pVO75WIQTlfF6iGyXVrGuRxYeMEyO302ikCJCyEZZt+ku19xao8PxBC2mwx16QCXhBMmqUsSu9gNyuB4IiaqCGnl1OYxgJ+hlXRNA9mww8aBgpJwDgXXPY6I4uHT93oqMNCj1onLpgGRXJKHyGMuxwt+DSgpssmWXclMhtbT5WqBnhmr+BIIabS2pGna8A1aDXbqs6hVzgV6Z62jGzbxo08nlBddsozlqMJf5OSBd2olFhsd791hvJrzOTufYidbotZM1ut9M5HuWsk4JCQFEe1DbYZAoZmDUkb6KHfe9tS3WT9PCyb84Yxlypr6+xtlmW2ObHH2HA5eZZpjUJhwkctpfd+swKZa25tDtuNRRMYqLMUpE0KqyrF2+bsoWCyUvRurVxfGJRmzdoq57YrLWUoZswRNXD0bSYjjstsmNcslnTOlgqBhu/ZwxbIAzIp0gw9VriugD0romILnCt2paVOB5ij4VjNwcCctoujYcfk9DGFVy4ca0xRe+Qy4iI0JWYUgw19sfXCL1bYQrbVIB/KZ1btY6k8NRmn55ocexcUPciyaJJeGoIcByq+CRKH0inYNyx7Sdw+lz1MNDChgzRCnNLwRLnSD3GEzJs1oI3yhBjuUp9kEoYSqpGVuilHuBwRLRXWnoXRqCnugx5BAyfa5FLVlX3q11zDcK2XyoOGodfahBBWPOtwoaB/ZVmtyiuaxbKA4mM09QvnUqLZb/7Rn/zUz/yU0uWGG2684ZoYju/+Vz/wZf/i2191y/G/T4/X9hAEV1N49akPXZAF2QLK7g9/r3d77Nd88e1ud/u98MSf+/df8A3f+rJXvOIYA80kYdCw4zz+jMcBEsRrevA/94jMJ/3yr33GP/3C5zz3udL0Lne5yxO/93s+7p4frOvX6tSy8IVf/01PfsrxXzu50x3v9B0P/ZqLi6NE/k0hL3jRi7/sGx7+kpe8yMw+7qM/5j73/kT5+zcH/K+9PlAmiatoSXVcxQxpVZxm03weNE7r1XutbkqRg3uKrepyLahVxWzlkWNa7/cWw2IiFXkrO9LRVFZaanweD8gWanNWl87wPISLr5PUm+0cCQ0MzQ1YZM/0huZaIdygar2SB6unCj17hTLCBmyYV7cVuSAbJQAZlWAA6DBH15+0QkW9Kj3LZUppOdm7qyaW7JR5rIisKDnIlLRVEFiJvRJzdRokValH1XtZf9Wmj2SqYNOrOV2JqrTvdqUzmzZb8svyyhWk21SXQ4OXlicOvXSmytdWtUEO6aFL2wxCIpmDI8dESKjZSiEAgTLrQGmDyUH2tCW8ZWIW1IBmbFbOpElGVarUEFbgrNIZYATn5KZSXhVebFldhT2nXA+sIRsWrULddk+V87AsI017LVkPS7DKw+WEGWSRVEmhL+ieSwXmoTTN1J6FiFJQA0iqvTShmIAaJlGUUworJVY8w2GnMKYPtjCPxXxpjpWg+sRAOaxJsWPOKhZP8EaqU6E5qtuEB7WLgHRh0qJhtgzZacd2zAbl5p7UbDkPHmZqtyNfsto5TQsm5Ak7gGOdmCkHbIZliE2CWcyuELaqr+YU8fUBqKkyp2kRRJQCicP82VjeAMrrw1okSlt9IhzTc8wYssaKBiaxmzoITz+CohEKStHLpIiCoFcjrKDwz5hznzmDiUZrrXFpZQAUmWwRWiJDDptFHbGjdXBV5yyZlMdAgFa9IPuUPXmacbHNvmldd8nU5NKiY0FXGCHrOczORQEiN1Ca/Skf+X4/+NX3+4J73fOd3urN3vh1b+PG33vWc7/9x570Gd/wnf/5t37PI3qwU9sbdiP++pjKUd26N2eTnL9uJ3zNVT52yOQpEl//j34R5SWCKrfLGtJiQyTD7fCiALQqyaTMamB3a5J9fFFgaSegoEmAaSajGOPAT3qrEEfcJN5ZndnblMisCqWIV3QPtEQnJbGswo7p4o/VXimgtkNmt+zaRvieSnUfaYqtqUEiURmxV0yNI0sEmqgsHWZsJmdMEMso2ETbjVoaA8hGtoLo1/cQd1Ej9IvPmF7TeBhja4c4IiYcKQsdBvgsVylEo95nUuMClmREcUxEZrq0DAaaHr3KF1AkScgcUiJYgQhkeM41qocXeooaDdY0lEeOSJ5TnAhNALyqBSFgBjnDqkx302OouU9fkuaBBVgpAlpJ/F61pfaIChpRRxOifEqPQSossmbknCnvvKN1+kmf0zVGTQq7SIJmzdKtqCH9lGwO1wEzyU+BLNGyD5Tte8NQZhE6ihZHYXJ9Etr9TZYGSYmmSuClj/3wBOa2whd9gwLA3CSpmutGTiYsAqWg6oSjLAeqoliaUa2jN7QoG6nG5CyoxmMA+wQjT5kXWW2iUkWKSWYZDjxRBRjVYFapmdmJ1dldxjR5eirvPAI7Sv0GCkgheKRlNaAJB1gPSDjTEwaWQ/KAGeKipx6vGnaoCIhRbrTFxHMie9bKMqaEzaRKcFkDTRZ0AIWc8EjZJalyVB5sO/bAKJU+SpCyt0Xz1BqoYcXymo1oVKiMVTJGy980S/nZIIOzCQLJ1qXMjKqhtzrhk9Kb1echRqbG1mrrGZZ5DM5saT5hQ96FCViFVQWTSSNbq26KRpt6L5Q+rI4nmTaCql9bKXshlgRAyjCpmiHvYQbISAVjHKZOa6FbZaKLjMEiitlcv5Il1aHRKBOR1iyNyB1HiFmKIq5mYib3sh0WNC89z9ZaSuiE6Di6zU5Zb2qiwtucXbErtoCnSVdR4SoMDaJ0K8jKDW1ZxrLW6eTNV6uTcSFsOpICssiSbYc6toRcH6GqxumZHl5KaDUid4qvy5zXKsVVnFCLfN2duwMNxsmZPtGCPEIFgJS1XnISlUToqbaL2xFJ+V/FVocVvXrbG3dghgYWKIle4kjWrCHVlXsipzOoN0nHuYdvw7bNSpuaWQRKC2dnHREhfurnnnR56y1m9lX/9IHf+jVfdeMNNwD48Z/56X/yRV/2xzc/R9ev9SkNVscsXeg04Pqn8eqivfvbv82jv/Kfv/nr3pT75S8/5Tc/78Fff/NfvoBXo/C3DrW+puXqilefr2n5f/9rzPn4f/PEB37FV73i5S+j+Qd/wAd87zc//B3vfKfXVtPltj3k27/z53/pFzXxDd/srb7rYV/7pm/0t/8L9K++9dYv+9ZHPfVpf6Qx7/4u7/z1D3rAxWnR9f+2s0rR+Rv4HkQhVJkN7qCC55amRNC6bqQ2wpmcYvRoSJvEZJbSyXelgepKiIkzrmoXRMdpByOVBqmZynqEZqjNbcKYpjxSRYflULIBpfU6jpIFZTg2s6A3bSOm1UQVxd4lfAWWptO8iIBSAKraqqJLi4tuSt+G46jjA9LjZFfF1HCTqqZqylDda2NWISAL1pFtTxtAOmzpfVlWU02HVn9eF4OQlW0fnKNE9AX86w47QEjklUOSvIx+AV/7tDaYiaNWmuRmB6g0XuwoVNfnF3jUepUjpfwgRmv7CejoFM6Fs+XeA4uVra41MOEoUuVsskElQKvh8TKkCDvEC4gZLfJk0lYYEXMWstSuKsMoiY2jEFlqgFtJskPXsfVSCS1wrUUv9W2d0eVqxBE9a1a9psJAiZvgdEAeuGlV4jqr1dV2xBXnWUaEafPm2iOljiCTVuxBG4I0fcQR2jCTX520lnqLiaXkhIbtQFbKFBSRkErRgeEwZ5Ob1WbYCGhEeaP3oqecmpkV1F2rPLBBMGYf45Qyzq3ptZlgRY+ynMWR2jL1Ppcu32+t0kI2hTSWlVivAjaoxhp5teQIukwb7tUNTaAR6p+caCkXCGc0pIe3qShi7DgPbr4M/VIAs5kKVGk9K82bUVXT4viFAzUO2ZrLmjMj92CkPKnRLC7YTtEsa2rt03u82qIUF2GcipjbNDPAKhmYs8bUXBBwZE6NzCjoqNzBs/V9WeZxCwS4BfYdNVDCq1iyf++zfBJ7w+yFJYX3UnoLCFPMd8POKpu1ZNob3/71//FHfNi//pIH/qsvuu933v/TP+H97nabi/X5L33FN/7QTz3r2X/mBUMzl30yEMdBRB/ndR5bWU7jAK+MO/qsSguUhO8pPGTunGpOZPjuLVtzK5GulcXBNvUBaTF9iE6CIptX6wc/mqJtK7pGw+dY5/mA76gJmquQjYk8Q1DyTAy3bB2ltwarY126Lc4ug8lqTdkwjBKeIgAAEABJREFU86A3LOkxF1ntaa5lP5rsy755u7WZyJgTDBrd0CDPD9cqizoZyIEZ3MxnX9ikwr0Ias7wK5lC+njDUzpY4jlp+gqW4pdnj/CQWKeX7AAVZE9N3WmBDngooExx6IQ8mZq42LL0bk16Nog9Ucc073NZ0GQlW4Bm1A16k9HmIKA4GMI4tZ9dhK2mo4DZcqxxsKK8SoBBU6kQB6lyX3qq2sIV0BDroEnG6r38xFhrdFWx/dy2vWV21tqhPoMCEVECu3WuJ/SVtlYZo7zSbehnHWvJZvCipdU0jOaphgYcPZVmO/tMyO5FbSgTSc8d55XhQCICGwtXR5UQctCFLWQhZyZqb1U+XEKUMJiBYSYEVY5OpXgytJOTZ+polQtTQZ5suwzAcczG0Sh7AVKnummtRLqo3GadY24yksqnZAZKuKdsrrYzUzRag6vBfCCC02yuVF2YNWN3CgQJdE73QSvSpEZGp1epw2SqvOM+e5yXqww3d9fyKAPQ5DDn4rMfe/tBHgJPlarEFTZFLxzzVYdYwxmN7Mml2smWvhhX2SddQmTj0F5yRIZ+BImzQBO2GZg7vKQN2VkuaxohzgJRwrycgC/ECbUMWBCgDCtvo/fsS3fvPBioadoMcKKQWiKq79Roprz1pImIsRtCHmEhXWGo1eciCH3z2peMNWNJWBnolZQon+zVltIrtFXfsEUWpgzbCrE09mYCNLFlTlHLo/WotaLn7Fky1US1dUiICrRMbmtDV2DdenGNWBnNSjgwwgfWsFUy2U3lCACnOk23i/wfXGcjW3TGgjosUe8S3hXwQUyvuezT9pxDHz6HY4pF1sBioaGs2Wwt2NObuZGOvqT2jwtNKpGWIToj6UQ1hLmDx0Qo0CWPIhk4fFVC7KAFW5YJsWgZTZ6U7ntqLimbdrcBJOiwBlhmRUxENQvrKHJWFcyM1mb3MshoIg9dTKnWNJM3CGS3eYGrEMqIatHUNCdjVszBDDCKAxLZ4hBpZv7q8+Uf/tHTALze7V//kz7mIz/6Qz7oUd/4sNd/gzfMzCf/9m9/8gMf9F1PeOI+pga8lidhBHF16Evn1eX1D9pd3vqO3/3Qr7jL275tnm/99d/49X/yZV/91Gf8yfXOv/vz/yrk7x73P9aj30C+4dGPffijHrPtu5l99qd/6nd9/dfc4Q3f4LWVpqedr/+Ox/zwT/ykJr7ObW7zPQ/5Mjmr6795aszDHvO9v/hLv6DGN32TOzzyoV9zmxtv1PX/zrMErwh6mKArIw3Uhb66oRVb6OwejtFS3NVYcXMiQpRnVp0z9YJCdGUeiSLGqTEoFgJOiKziBKssylO5rLXHVUA0QEkxe0lsZInVnm317FQYELQjSWmywZPMEp21gZkpkXAcR5WkMpQ9vkt6wkIVIH3pcK3Ui/o1bGZqKczZkausKYeqmFmpmMpOGJRnTJN12kW2tEOoishS3Yep+DTVtNckQjFSzzZacnKqPkqa5OtMVY7epd4wXHlM2Xh9JZUNDldXwywtJUuWVxtczuUbXOtOmpSrnJRqcMooqbfmq6+sLtFFqEUlrGe4AYtpSahGeKkoYW5K8hpux0tSuopZCo4ZtSdVhyRwMlMgXsUUTMdgyZYleDFpkl21EUMweLbQK6hpCRXCjmo1NbwxASlcGCq3PQDNC3HCmxrihOxIw56RihMXtwtjd8XdAi0TVQVOmWL0FdSre5+bpKCgiQYY6xi6o01t1uK0hx6RmiqWxJbmSX9mZVQlgcOZ4y+ohwecadHg6/Q12GUzEpH7qC0Zdtgm8V4HUcAF3mPpY+1Du8QsPZm2kYpJt2oahJg99gVb71fCurCDyLOljbEgFlHHjc2hfWZrM7nv0oUUi/UyEuZZWoIukKdZ2rrGlnMqf1yxcsVvpM9olemjWjiPgFWKtYwZwsmtquVu+z7n3HPfeTl9l/JsUT4FFwGDczRGa4lmCQsiHOVVDKTCK+TCWjmLmTjOmuWbdmJA9YpOIQodk6W47JHneUiBfPNFXrDUOcwCrovgsY8bDl30ymOTM+GKRk36LmNEK77h7W77Xu/y1l/66ff6+n/8ic3tJa++9df+4E/KvWSX1LxGoaRBMawDulmi6Sz8dVfGwQlZO2fOyBI6R5hRVXJIqrTqVlqhejtON1EHlEuVhZEamPLXiNaMcoVCAEU7ZmdLbW9LeLWSG6MQ00p7olliyvHkN0odYLPWvbERltUq14SDTO5p+/Q8rJVsudjQK9sMU1tFASmDGkmlJrrsIkRABxYIUgiklH3RiN5wULWmbbBSKTKULE53dIflwBxW4Z2tlaQIDTlnlUqQahENgkqwGKNMTnT5QhnCWTKi0jI4p1m2la1rt6kU9kgJnXpeSKMOa/qQX8ZciMaSplYqzX3Z3bXLH4xQdtVe2CH1gFyXs9LLwZZ0Aq62akM2myi5H1Pkkp4KjzIolVYI2oTi3UX+AyU3oJVYO1uNDj2aBWR3wKtULbRHWo0n2gkUqaNSS06mtCvKuTP3ltMqDosS8qlk4ZraF06WVDDcWGkzlwgfex+TcmFIPHQQyh+WCe+Ctag+0XYxkWembAGny+JAVoU8XMrakf1yPIuIhMhg3tHXZBvBkZUo0HAUEOFhGlJqgdPNmtuSihnJXq0fFFbfoV6aKO8zI3tgIZQr0joiQpm3tz6VpgJbCiReNM7CjCjqu8pztjzLYxaQIqKrI6mF2AVCBggoxG76OLaPzuoaWlBq1RRDFAOCLBMvyTM4yBlVKTf0kmptvelh2lvYHBizzjlnSfsoCw95lM2K3p29zYatB6HEKpScqAnIhCIJKBqqA9Ha2dvuFQqowkf1Txh7HI8RhiabU69LwkEzwjMZjblWrdFtroBLNtXrxIHl3mzzPiiRxZRpAdvZR6172SywTSrRYCxvaN3cFliXBLXK9iPS0RPiSx6iE5U2zau5Y22hLDNJrSErlM+zY0qz9Jd8i2izetHj8A8gYFbmYCcX0uEWpoGl4FSTkWILBIE6yn3qLAu6xmKxyaN+DfHXJjCVGMxJS2kIRxFZyxingvsSWHeopqGs0GR+ordmevVgS1UB2iRZVUd1Dre16bsOj3cekbScPZJgAklF/FwxFS+ps9W1OMPPyliDtzz1YVQvM53pWaTIhQ1NK3TW6uhywNiqnHRzr8zUO+qKgkH2WNfd8R9TVrXxUrdbecBS0ltH2rKXb8apJbVZoW/QbmTbOPZl5iKFL3rFK5/7whcDeM93e5fT0kl+2Pu850889lHv8g5vr+sXv/AF3/jIb/+IT/+sn/oPv/DqWy8P9zX0tTgJ6MTV8V8vrt/Z297xrR738Ie8xzvdBVXP/rPnfNaDvvjnn/ybuv6/jrsa/D//49W33nq/h/6Lxz/hCWOM1tqDH/TPHny/z73+49JrpXzb92981Hf/0I/9uCJ10w03fPc3Pfw93vVdQPtbQr7v//jxJzzxiRnxOq97u8d84ze8+Zu88d8a8L/6VvoOGymCF45/FDl4XBXLqASl0rGlV66ZfY+mqq4Z7Spalagshp3Hsp/X2pp4vRhac7nu4ELr2ft0T9HwKE5GmNPoyB67YaNtpUSciOpQwba1tAiIw7IkD00l0pMrrI20fViOht2xz9A5ZYIkK2F70RrF+1t3XmabhmM5BBDTYuc8Yws/N58O6bMK+bOEuaxZME/YV8+Ty2E4okYdy3MKltBwo+ToJGSMbFShlIapeqDGq5PNWu+ulLO59x1rlGyPZnHRVPCXNNstgxiGkjjlvjJ6bImz2VgNJ6wXvTdtcJez8lVZa9Eq9Cb9VOuSLYAzc6MmNuidT/Wk1vWjxEyf+5rlsxDI9JIHoqHeuZ+HTyhUMsVxuNrKWsKLKt0gQYvRdj0pDkfY4Ymfc7l1a5d7RZWDzQhvgzapmB2O+wGIUVWj58rdakC7CZNt+1b7FEa20A3u0IUZzQsIqkBBCuksnSSsIdaaCZ4nY0MOl/UzWAPgEq2rQduPzJo+9tN5LBo4dbsRx6vDlhORwhPGNGP3JnOTenobeo1Ze6eKXVvPbTkTU4BcjmWIZkzIuN5hrabpPS+qc+284Votpx0Z0LJdoBW1EhoKHt43LoMdqWKbEkcLMgR4JauaUNg4i3V9Ut8Kh2uHYdtct3OLS3DEMcvco3taMxfle4ZiaQX9o2NZuDZxN4sJVo+DMTu5e6LtzVIvAU9cTtnabsuGU8210oFjfpSlTnjSRGHhGAoysQTkiBfMGCvOq4YT1W2YVzn1bnPvF+d1iWtsKzq0hdwJWFx5MWbY1A5xX+KsHOvtP/zm73/QA7/uwx70DU/+o6dL1OaxnTAX07r4vu/0jkvrarz5+S/czS4z96GtaKrlOEtO9NPWVtEra+wewvLokMtyt8TC4EFZFtxMPaT5ZEw7D7s84FayRqvYx1SvfFqqernvaOeyHR6CQtjV7uOcGSH/DhqPo2wQBkl3U2xyMM/YqjZJn7tfznUPH8FDbkJM0ZaHQDRsK/cDDJoHl0L3WIAW+m7Fqa3FvGh1sVIBDXIrO4JZk5WyrJRDpJOyikjRljXOTL3UVJCsuk8FZIVddJWRdWLdxrKdL4Z+5otgCnzD5rxVn9KWrHBaWQv0w4/JGG2ffcrFyMlxiW0bIwXHWa75ubfsoM1l331LKrIRVnm1A/OCAQkGYEZzn82jEthbzK6N6UAFRDAjZGbPamNqhAEyLKgwZBJpCw5c0ITwOnpttu25SyPOJ25r1ZrrRZwWhXeOgDr1MEVxDes+7bzjvJUyV4UG1qotuxIXPnfUbjV8bDk1Ls1Kt5gb41wxcmTuVPAVLvm6FUdjrL47ha3QG7HMtmZfCsdRpde7cqwqZ+BcqgxKlsMRm0i9ZrCVOP4LXVp7DFurW2tGDWCyM5cTc+3bcprWLcldlU8hBqEFyqFqEJk1SYV9Zq9c3OzU/Ya2XLBZP0TX2siLRv3Iexpcp4ghtghWpF5LnNM3Q/gRnIuWzVHThGXJ3D62tgs67vAQcEmMuc04i04LeK3qgukt0jMUtGTRgCL1M2A0TLO9Y7uo3RXQhupecAkWiKjZIk4jtf82LI0XjoUVrEtK5VYp2paDaI2Llro6rbFea+3aRbd+miovImtKpQCI2AtJudmoGSCr0rL6MIYU1sgta4dhKrXQ9rDaIi5ji3a5rrObGLYq49vGfovz3GYsZ1tjOTWuPFypYsAChTlPY1yMuMjpKGe1hlwyaNx610+d0WRdwBVC8Fy+R7dpCwGkHnzTzZsvbFobbTjnuvKi1crqqb0DznkEU6NleA5sxW1mzFEl7a6vYxeSNdoy16WchuGYsn5RDoUNncUZRKXsK9nshZxKxYsw+KKfWEMtbYTPNIFkAg7NWe4D1JlFpGsiQDj0AeQe2v/Az9UG60DCxBh6UrREQqJnz1hNmUwJSFgYE4VMJUsf8Cy05JJA5JBL6qikC9mprkkKzynON1ib9I121lDIVrcsS8hag7MAABAASURBVFGrscXRJnUjPcyw4LjPKeR8wgfnZMzMEFzMsH3Aqlbo7UiwqqU5adRfycKKELTKw8kITop0L33JS2995csl9a3f8i30eZzkW73Zmz3xsY+5/30+88Ybb6yqP3nWMx/4lQ/+sE/9zAd/2yP/82//7otf/oqIOEb+P/57kzd4/cc/4ps/5O53z+3yhS960f2//Kue8O/+g4z8fyz4tRPw7L943qfc75/+h//wc3L2dW7/+o9++Nd/7j/6pKb9y2snBpfn7cHf+sjvfcIPZ+a1i4tv/7qvvft73u2/l/Fzv/ab3/Dtj9SY07o+/Cu//G7vcOf/fsz/rhaKwgXhUAkUqQ/dIZRjVFMV1CZCKU8AsVnl3EXTQlNHaoIKnB4kHOnKJSOUAypApcf7kCBjQF0TvfTwkl1JoIRqNCu1U9Qt6+HSrCzXulRgq9L9jMpRAMRnF8dn5XZF5CpTqiiXynCVIFoFTFkkJ2apLIUtcP0DGpUP1QpJWdEn+5FUoRSmuk5Q2sgOph+VJPqR9hyGTVbpGurDYYBsANGsupmyK2X669x4w9EKXL76sieoJYfCw1QDXLeF0Hreg5pGo4ybLQO6ahVtnhGq/5GJQ3wza8YmVKiGQkgA0m2Ua82hBTzkuJJECcwhxKdiU9aA69aoAqAIlQouXtbluKFZqTwRIl2ygo6U3UZbRlurLWOxuVT5MXGa7d2qeStVWhvwuTRJkPkoSAYYAzOOeNKQ5DGLx6HeckJE2WepFmWUwnpYwJP1U8lKj97myrFkMgFja7Wavos5a0YGTfKO3qurrhouOECgrMIrjdNN3NirtLClM2CZhtEQBmeRszxKF2UuhMas0Fgg4TlOc18oo6Ge48ubu7UrvA/tVcQ0k24HuvR4HLrRil0hj6b7ZcYyQiEZikTVlfGK+6LdfjqUPOpqU8ogSEOzrB8qerpaQU2ALqmxww0O2dMVj63ZzkYFBThFdhOk3czdddmiZHEWR9ZADibU1RxQUKpmGUKXmVU7kFenHKjy5IFLyq6YlgESNC7IRdeaTg1jnNrolkqEKkuFHAg5MhM7Dn3WKT6AMbnXPPsMLX9ud37TO1Tkqy7PT3rKb926v8qa8nxKpoPP+Is/OzgC3OkObzxlO3IPxcMu1kUDbjmf244WzbCgtDSPrFD7cRLs1P4LzbyKM8+7XAJN049Qe8klpbE0Q8FbetMsCy3VzbTcTyGshNtrjJhz5ti1TTaMaKgmuaTGCjSY7g1cRKkUTNlhwtOZiZQtU45zDpnFaoBQ4UEZbfa9pZCaYp5EKIopyLS3YBexpzfQ9Y8CXAv1vtncK0pxmRIsVlpHX3LN0XNIkGVJWlpPugTFLocJuMxUNEiBJpoJSj8i1peQYDWy2AC5ZAqm8BIZKo3Q6UkTnCMjmWkza4+Y+pIe6Om3WkerZETkvitPGSxVAl9mUXu2GBwp+UkH0GdcryE90Pe0oagB5SgoIsQkYDCyrq4FFVs2C4WwwR2QnlSxAJNE8+oMZSoh+2KvzJIgDTTTgBLaURz0jdwFjcycE1FePCFW7pLcqlrCJgWAeYbn1BVhlT6T8rpUVLqXUZEWzUwqgPSKxgQB2Q6FV1FBUfX9aMkWirvqhpiVLkiMOAwmRRohq6KQdaBJqEDIOIIwn4p6dk6YjeoTPUM2SUOiMgo6oko0muGQXKcIZ+uu1YcyqHg23x27sJJ/22h6TKshNoHDY/pIHg/xKxRrwzAlI7Pk6MxzZgAGOxowp6ylgHAjrdGsOxYP2q7UOkYm5Gq5pbdwz1bH4t0poTFr5o4Ytpch6RE2pgqykK9RtSdEZcyW1natKjrbySS+ZTk3sxmwzKXCDJOuTb3WM51SeTBhyvxT1ZIzk7v5viDXTSw4tgoCKXKWShr2zoSnbADpIFgi0wFvowhVNlplh5KPVEeX7JEesm43EdOqSGkwmCiI7BpEN2hwwvQYt9kYODT5rLWG2+4cijchlUaczFate+bVbHoFF2j7oN4ShJXZgd5KT0CCHJKxRLqkZ6/JDC0JWQpAHtTF9EQN1rAIyq1WBSKcY6kiIMO39L1oKJfRCuUwm82nIBTogjTLCm5hVzodsLTJmof2I9KcdZylDqAqlPQbsBNi0y59CZu6ByZsi+NRP83pSspmeeiF7zodqkdLh5fxjLkJzx3LluvYTZkrv1kwLp5dFs1Q0NVAeTPJPAxpkEzuUdtB+Z2VVvAkR4t91TuKPX3SYR4U4eetvTbXiuo+relZtRjK2FNaH27DaiY1YwdHL+UlOe2FL3opro43vMObQarxmkP77C+//32f8Ojv/OgP//CLq/+G/XP/4rk/8G/+j0/+/Ad8zH3u+9lf8hXf/cNP/L0/esa276+Z8D/6ddsbb3jMwx7yGZ/8Ka31W2959Vd93dd9++O+/7X4v5v8H9V7fV5W/fLvPPUzH/Ql/+Vpx3/+7c53utP3f8vDPvqDP4DXu1+bz1ffevmQR3znj1z9J75uuvHGb3vIV3/EB77/fy/gv/zRH3/lN3yD2pe+fOkD7v/RH/h+oOn2/2/O666z6uA+IEpigoGsFmWTVq4nE6Vz01WKtdayM041Lmz2NtBrNO5tmVB1UWEziMFz35XJrIQOyXYlMjYviSbaCbkeDeXW1wtXKc/g3K9SvMugrDliDBx1kyK1D/NtZJ3pe1+z93SI1epyqKCxqN9IjMdRPL6hwwzWy1Atpz4PDdkHLqJ1NYGbz7mOVGoktLctptlcMDp3t9Jq1YHXRCphQWU2eQJP/gav9zpOSsXjn/SfnvyHf/xbT3/mLZdJXwVAkhUSH1YBzBDhJPgwtPSx2OiuQllQahMakgOzSqeAEjrLTFDNWgVyzE0PNFnwoGfIYHCqds2iYPVii2NGjTX2PrNP16s7X50rvAVtS70Dwty010jDZA9b6Gz9si0HiktzeSFhQtJ9W3toV2QeaKFipB2GQLMAtbDsGYzhenScsWeqHGdLtjRt9PpS3kNGR4xzbFEKG0oWm+fqdfyX3kfD7jE6IGDphSYACghjNs/Oow0c9M21MFo1eeBwL3OHgC/VtDVlvZXubfbcTrG1TGp/e6Cdc5c8R+/oEpIbzrPSdrbZZcZgO68Ivcg8BXrBvAqRyB1z5xyYPrGUuFCHKQYIXVS1GgvP3vdsY7aIzNoQ01m9zXaAk9O1ejrgTKp7UC7Z8evIKmMaFAyTOLPpCLfRKrxoQRDRIcMqlxZ0bRm6XiwW5BEdbmyKlaVncpfa4A4bnQcAAC0h/Opwc6b4QN3rDE+0Yh3QjMqRPqAdw4nTxasyTTpOgzVGt33NWAtLFmft+xyDc18jrg1bZ1tSW9lpGLK3iNLMN73DHT7mfY7/eeIn/c7T/vl3ff/P/trv/O7Tn/3bT7358T/9H7/oe56wz3n7G6/d/d3eTtZxCONaFrvNDRea+JRn3Pwzv/mU33rms25+/kuAJuh1qv04HQfwPdyHZ1nw+n+ZPo21ZmuhyHUeqbjP3AeuHzJoJkIuMenat8yKgS0iACPUlS1UZYRkFJRMM5VXLa31xMWAjFrNli6qm/RaNKneK4QDbXeVlGEVC0KqvSl8NpwpEFUZR80JFUiBvVRZFigdNsAtx4azlutQ046cVWWiig85ySyvPIWftLEAU026r4ooTSWlxkyhKWWTJqLMadBAm2ORgSuxUM5ZNoG1WHd3gwDSkBmGbERns+pzcquaLLPR1ugSVaTK72XlNip2UHNgO/uWrjVd1ToTINNb6JMwiaqeO8fEqDr7QNsappW89pBiAVOttgV7y0CxqguwXdrh5d5hJ9QCtlnYqy5rZBWElcguwyRCXC7znetup2F9WE4766lpwAKLwWmJVt6ty6gp1gsjzsXH4rm4y5qUUCihvJY2zfa0UPA22eapW3IkdEjrKA81qXR25Elv61VvqDAJG84Ww2cWDvhJRONkd8pAWT7lHga1fEXqybzXYJez0tf38A1CzFiTERa0cu4We20T+ySmkNS0GVpqOCJyz3bGOqjaWF57ZqieEvBOr/AoSwb10RqsgWPlNCdqkWdzdJttNXp3LEuui0AK464JeiKcIobZLGbI473aQE9FVWI6IHFJqywfyQ3JHNeirqW7kg48V+ocmKX1SFjQ7Yhyoe9YilXcgc2EaOS2z9h3A2Q0okViVArdVo17YavcfextGyK46rZ4AkuXU95oVtVm9FkGuaW3Ezh5iitGTujxzwk0mKP5sTPwYyGDlRnBJVKvjRpDqO8rt96Gi36cm8dunJIPGKKqokQt7KYIxoJyC9TM2sxHX6s5oIbdkD1tHfCYug9NBgsYaHoQxVxWnd77wiZr4CfBjjU1XG7YnFbyMQTPoBDFoRnSleazWTG9olcaoL+Df6GyylaKClAE2BmOPbnt4n6fgxxy0RRY73UY4cMyTdNzlSx6M7UvI21EZk7mRDjn6gpYDywJ9ZvQhhu7W3cuBqEkYiusmYokAx3eubh+R0/F7kytbB7hcgdarzU92aNSSIjtekyVtEiPM7hN23U2D4l2IZ2JpFR4ugdtkgEoYF2FqhxoMroWs25ytuVcBls0mQOCXt5DmwhrRoXYTObFqJe94tW4Om5zcbr6/m8fJO/2jm//6Id+lX5d+aSP/djbvf4bXe977l/8+c//yq8+7BGP+KTPu/+97vegx/7wj/758//qetf/2OeN1254yAM/50sfcD/9wiCsH/X47//Ch3/LX73sFf9j0v7+s8ac3/sjT/z8L/nym599s2a9y13e4fHf+i/e453fCTDdvlbnS17+ii95+Df/yE8e/70UPaV8+0O/5qM+5B7/vQT9dPNFD/36F7/4RTS7973udZ9738vstdb134v9f7uFpaSxEm0OySWqIGFpeVBVxG+ymqIRKo/65JkH8w+CNQ72zJV7szFPtl9YdOJYVjQWR7EIHkVrq32vFH+BVtP1unOfS8DhAVOJpRdZnjSwm+4M181RpuyuBI0j35Wto2EQVRNZ2l0hI0bO2SoW7dCsJA95OATWTtvYtNeMnpNVkqhAmCwmp8W0kTLpnHVObBuO8bMytaL6lusuIHD9UCGaZTNMiVuc73CnN7rr27y5un7hqc+47yMf9xX/8odf9srL1AqbGDKNbNH7ucUlZuThY0NbwjhBC/nWmmPts2mpkx/7gGp7CJu9KcPLa66hjc088v/ccD61vffgyrmo3JVP1ahZEVGwvdmxdhh0bfA25a/Kxh6CpxQIFUuYnITKdpVWLQdJABqnAlEdGgWUhblqBwhmsuJoFlYyZhC7KzAqLIWcux6fRp7TdnBnTUc184aFcEGXW2CPmsrr0KaPKXdhByFUug20GTjPPTPY6BeLLd1ZNkbNOY8ZI7gldiATKM9ahi9aNZIdOFFrhNlonERiIKYNtvNis4s5quxcmrVulsZzaI1S1BMMerrDDxuhldi0p/XIbtE8oYprWtJdG7mpWl0p1WPaHssGH60Ka6nGAAAQAElEQVQlHfCiz2xzWG203ZgtITtT+6LQ1qEahs/iLIlrEkzI4lpZaZimtWMRAUXWql3rl4AW0iWgY9iETzFU5mLkZExMzW7O1rOvWF27NmtVbSe2tfK0a6cTrAHRaTnMlW4Ca8W11C9ls1HhBA6T6uyQiVPahsXEcTDQBr0Mluy7qBAur5piw4TblKsM+ZJseZCmz+7T2i445gM/8SP/4fsc/ye2v/PM537tD/zMA771Bx/4iO9/9E///Atf/qrb33Ttiz/5H9z5ze/Q0060lbzmfu/3u2t3f+mrb/3qH/yxz/r27/mV339aKLBXSX7YAkzkhuMBdx4sKOXRWVtnjTni1gqklXEJnCIutt33ceWFgG1z7yEypPZnpd1Si2oU5FzLnDonWqD7SGEayOEHFBi0hKYbJzNL9LNycMlW2RLImBk13DYXJ1LshAWsgNKmu89oJlRqtAwogDPHXmNAsZMnwczW2axAZnmVKMWZFZR0nQdh5+xRZmQTW102Ww1xQVM63ShzmNqKtNFzrDMvAtqgRY0NQ5/FkHMezeaSU0GNORNFtKW65SKPsWRf4oQ8paQRRC09tLN1lymo0XHuHEZPs5Sdc9QGJtkRYtgax2uXsh5ohTTK+tEmlIN+MEMh1MSSo+VtpG8j47zhvHMrC6anPEwn5aNGZehZn0K6+76aXjFPyiIzuP7NblVkwXyiZ1zYOPV00wZwrxoQgok+y5WTmXSI52zhFT2rh7RMcCemgVZKNM9ylOxGlHCRrSIai4v6zZM2ZaNpXesapnMxWWIOU/wtI2eEfO4e3qpQuQV3yJDIPHoGXMBJMNQJSLBogCyf1VLidKLUxQpDJDDl1zrQi0J6UuvKflX6WbtjNxu2VDmGJhQhQEvg6+yGRsAwTja6JEafe68yb+5LpzX11VL7su8+LhHnkr/ZLC6QrWSuiipCcUuO0iJT88AiG6HtqUuIGw+/l+gthb4fpqrKmLnXqVUX2KJAk7/jDKR8jh6Ho5V7mFzzlutFagdaqnDJAsKVOdKVStOcWchOOJoCEtaxsAwlmLCqWczvzk761DSk0ODmqoP/H/L+BX677hoPha9rjDnnuv/Pm4SEaKsf2mLXoYpSrSqKOlQp4lARRDSSkAORo3NscQhCSEJC6iyOVWxUS6uqShyq1OfQg9ahaGlR8v7vteYcY3zXet72t7tbp+hut+7vftb/f697rTnH4RrXGGPOdT/P+4pqY2HbCztKUmIZ0qC5zmoG9mQvXVkCUTFTSI9j7lJqgeqYG4SDQMjGlHOK0kqqseiuwuxGAoWImPOMKnO6zo46tF1YfRepRdXiiiyntcZRMSrdiOFlLptPgq0qdTuEWRlzEeuUPugaAHqUSpIJKfoyTIfAKI0VE6LqbuPO1SK3rF4lOpusnGbLPV2oKFCW5oUTBAjM0xIzy7B5iFtnNQh5fA6C0UhRwnSmQ5/gAOGGzkXGVBHRYMG8F8U0BUOtsjdXt1hHq6M7uqIu/3SbgbM17Mz0lS2PZjubPnEdkmWJgcvIG9Y2c9zich03UxVGXjCNNrL16S0ktJHNnOqkNqYJCtl/AEkAJNxVnHhp3X3shslQkDNVUaHXA+53R7//20NfdLzBa7/Wsz/6ad/x4r/+xZ/9mY9477/66q/+mvcNu729/ZEf/sfP/MznvOPDH/n4j//kf/HTP3vf9d/D7227POp93vPTP/ajt/u/fER809/6lod9yOP/2b/+md+DqN/llH/1c7/w/k/92E/6rM/+9bt/8+3t3+YvfuXzP+uPvMof/l1O/y+H/czP/8IHPe2jv+lv/+2quv/97vfCT/3kt3uLPy+S/JdjdP7vf+3XH/eJz/7Jf/kvdf5mb/zGH/eEx4zedf777RBdxFsQ9X9aZoS46kp2iOyiVJaIy5THquSYnefyiZohth201HSIcyB0eJWpWiTPGjNrLd9nxSyfNeZSta1VEgc155V3TynWu0e3HBWbWdvE7JaZGpOhtYzYPYqUMacGJWcirPS+Yp05K9k+VEeKMytkF1Dne5CyTZ9VCU9vbkwV0pK80vZe02OuwEzEKq29+6KrKSWj1Gtf/Q++0vu97Vu891988we+/AMqo9IQkjtvLv0jH/HO7/xn/qSbAXi1V3pQg4zBmilBOqX8j6ZFRGbByzaZPmVQpYMdHIbB6MK18kQgpHNChS41umWMzKaR2vxlVwm1pfV3tsvRL7OZYGmaVauyvNASMsIo0ZOQF6E6El5ymCrnJnMcshyUqYUObLIhUkuHdngPa0VS4a7MqlrMaUIUQbIIwYzDtS7ojU5UIA8wAlFIUGuEFJwOEqpHZrzrt4yTAA2uZMonY7lD0hgxYx6Ruy5188bG83rZStSCTdRhCrjKclSpz/vCmLiIOiq2qSJOxzkjkaGWBo2OU5YKc1wSG1Xy1IdHCwIzFLYKQe/opDwH1EXrkKxVstlG4zZMxVzY2dz1uAhTI7DLBq1Ut1mXyG2rreUItMn0uHosmrqDeo+QsNQKJTyLOqkzYqar4kKtiblH6ZOQG7m8SXFMMNiK4ptfhToOnD2gjEBiHVmlgyRspGz2G9N2pZtipAG2dtfSMAU+rVGP3k40JDrUZDRkZ0xNpUF3JTMgTySZOEMLoHShoKZ9yNRsUolMxc1aijAGtEK7oq6YcxWqQzEyD/CYfc4H9vGMD3z35z/xEX/pT7/eH3rgAyRQx594tVd+v7d60y998mPf6c+9SWdrEkJ0VCt/xzd+g6c85O1f9cEP0rCXu3PzoAfcCXi28Yqv8KD3ebu3eNjbvfkfevDLRSqNI5WN0Xu/5z3f8k3f7+3/3Jv8iT9aJW6Z2t1yC5Mh/X733PMeb/3n3//t/vwbvMar0Ko8jm7XahFaAjUzffPrDtc6gRZeyUrVFXfWasJFpJFHsgRgLmptot9WvbRid7LXsKX8OutCjIpOVZhkApmIzMkZKhGDab1ioBiVWasyqoKZIt0Gu7iNoZRF06wQkBlisQvekgqtFajt7hRhnHS37umn9Fxi19GYLWlQ6McxVo0pTyQ613Wt65HrgM9ysDzzTBdpRxXRyQaNdWDLtkmE+8RdbRyJBqADo7wJNWd0rAbZhOyiRDV5GZSn7Ae6NIRnjeKQjWKr2FGcmmAAoYzScDrbgJ2f9Ax9Vq6lq8rGcEwRuZZgU47YtXnSe8PwaDi41sy2wwopskFvGBkXS++GNkwAj5yWAZwCkdEqRpznSXlkSzeYZMnpS86eQkpLraWPLoMMlmhlMre7hMhiHSdb0mVY+opm2RrNu1Nap90JmgHMmfM0W/CkLixTiNnO6gObhtWwvBVdTALnWahzVkUUrc6rsFpkGorSV7qq2NSJbtelhiVz76waB1Zwti3GBh9QV0xEnTbbqNFWa8FCSw4ZZaWnbWnJokaSozfFusS9NW2lRUqar9VTBy8hxxtENUDkWJg7ZvDgWVkBd7K1Pm7QLmKERBLWo98s86kxin0LpABSYLk8i8IscBunU7bELiGGVLNG40KrUn0cejLRhFWU1G/hFqahLtGNqmHmdZl9rGHihlQKCznWIZiPprwSiDNU7qVCCRB2lx/cHYuFEtKE0Kfo48vHwiWrW0H71yFdgrsdySU7awFgDFtaQt+wbQrx4HLeFvcljwBY4izxyqVVU8vPVPYmsmrmOnQFy62GsVJrgmgp3FNV1edVgdD8xuFUCCDLNEsCNV54Ka+OzrB+8lLyeKSYY4EqiUQ0X0RFSV8EJDavJpmpnPDNvBkoaA7aLm/Bpb2tyb12zsoJSTa4dFbmqkyJgnOJoQ3Zw6jpDuW9DrNjRy5JpAwWUDtcBNBTnEu3mwhej9sVIUdFbZvbdrQteTFsEuQLlmHIgpxvsa+8d85pTm+j4VLChAAT1Ju5yXKzVTxtIt3LW7RuhNdBEc8rRKzI28VZXWXrerRr4HzVRKW8TnlCZUCurMk+by7beRt46b3X+05+098kX+GBD3qbP/dnP+HJH/adX/UlP/R3vvlZz3jGm7/FX3jAAx5QVb/6H375b/4f3/gX3v29Hv+xz/yn/+xfRNyn8zeV9FtebN7e9e3e+muf9xl/+JX/MKp+4p//87/ygY/65n/4EvWD33LO7+nGS29vP+8r/8bbP/Rh3/UPviMjLpfL0x73IS/8pGdoj/Eyy6v6nn/yI+/16Md+3w/+gHB4tVf7o1/1+S98izd5Y6MC9n8Rdu/t9QnPeOYPf/8/0tXXe+3X/uvPftady3/9FZZu/T45nJQLOpQFLJgqAxCoo/xIV47ULFSo5FxsKKW8sBZrtrbTMgvX4r3VrrUdHEcW15IUlY+IidTjjdqMY3XsXWtgFd120ZOYGKx1tZde19WPaDXI7eq1Z+QCr46deu6XayCl0KeZEd3mlhjRE7a7OH/X9ArXso9J0jUMZzgIsizhO5U2bj3UDxxTQs/8LWXXQLO6qIJUqA0aCpms8qiWmvo6r/JKT3nPd3zie73dH37wnd4PH4zhSpuZ9aoPfvD//oi/+gPP/+Qf/pxP+/wPfdwfeLmXK69QhZJCeWAvXX2navdoysCZiCuxJ+fea79gb2ud3QWHcfVVLrjybrae5ql5u1WjvKQtEtYtup9PcC3Z0F2vTmsI51Ejasja4EqezX1b43IX79N/BHFt/tLRr2SsdmK6WH4emt+PuKzqnrwcKUPUxdIsgMA6RqjsTlK1fI9YZWzDh9OEbFGhH3s2OTa1foMvzVMV2yBr2LN5OljkQZuz1a3jaqzAKA7QBdeOtaIoXLNtfbtcxs3om+hlxbolr9S+YNsb920dNo/p69BSC2ZZjLAoVAORIsiWc2RQnRK6SrHAzy8iqrZZnMNKajXrWrWLnIieyxHH2JdaCYzcBCEVeyoWd2pexs5L6IaoeNkxdtV5ZEnltdq0FibwoZ6NFqvWCq90niuYUpxxi9zruDfWEcv9uOPrpnBBU+uBBkkSDL3BMXFM5VnlMNy58TYu0dU1WkXkEXNB2YeEXq2wVbVwm9uxy4RS6C6RYTJoA70vXFZonYGZPXAj6rcd7ahTnWZyXSg5Vcq6g9ewhRDKxmu2Y262tm2Yiwh+W9hLTxbIuZ2z3HbzezkOGUy0ju3Pvv4f/4THvfff+syn/8jnffIPPf8TvuDJH/TEv/qOr/qqD5ItxT05D+w7K2Gj3bzP2/6Fb/jUD//hL3rGP/ycp77TW74+R3nnq73Sg5/2bu/85Hd5x9d48Ctv1Ztf2C/Tthrjfd7hLR/3V9/2rd/otZrd9p5iO2jlrLHu98Dt0Q9566e8xzu92Wu+VgsYxbAKnkGXa6zDSoQ5Y75MpFnJMowT03I4945bswnP1XF0P/Ts3raZ/UAedpjP3qo1KmadZ6G5OUZTg1+59ppnZSqpkU76Qew+9cUrh/XUs0XXymh0tHZ3xGm0aVZMobuweR+zcTfcDsbF6TIU696J6xWRlS1ryOxjHVPpd/L6Muqm5rgmiMO4WgAAEABJREFUr8gdZRM3s9oCokCsxsUTlzawdduSI8zRO0AGxNjtilLBvffCJaOquEtKWSe3UdQ4FdxF3rJdW7dsPXCZ52NfGHT9Ssgg+aqPGjI4VbhKbHEsW7Qjxrx3w71Qntw03Az6ZbObFsNT6XBw5RQAWDu5eGPVzVzzi0dirqoFMTPMgjerhrhYBmgIVNG0admAVi7bI7VZ28K2Unk6Vj+OrTjKL6v3aKisrtQG2N1u1rhz7ybaTCFgW0K1DQBPWQmmfjxpy6tghAaMQkc2hmgt7aTKS9QRx7Wvq9Zkl2wlfjRmL6JanNPgWgPfKeti+IqckVzVrba8lWt9JgJ51LrNuprvvaatOXfp9+aKvbLryENGtNa4+TRm4TLH2BuW/tw9IlbJjbAOL6YGHdVqdqZykNYjYq+8NeoO3c29u2+t2UZorTGgatj28kO3zFRbLrodcZPH/WxpTAK3x5pxrt+DFViVKaG116ycBZtZ81Bt6KgN5T7BKpl93NCabyv7vQf2GRVGDLUkMwqk1Pagb11Eu1R2QFG8iyxPy2EKe6F2CCMQZ4oegHxIpCKvZ+6bmb5Wu0FKKugGa+WAsNY8ExAZVbWaV7vhaLzR7UZcypyZVsISYbG3GUqrBthqhnTItimt66iaqKWP5AXVRRB5Cx90h+YTS4PHCO9YwNLnykCmpmGJOmdAUBJM+E1hIDykgVV2LNwNf6XJFqEYjXRswa5Ot1fLwwxsFg2qLHLEkzcdd7S0cgKQXZhEqDGpdhMWsKX4TOTiWoLhXC9sVr1vbp3Rpa8snAtrxW6pdZFxwg5JsSz9BhMEzd1Et+Y0eRV1O7FiVcu7/VBJDQV3eqRneskpSw21ZCYKpsbtG6hLjSmUAwwqMEBSys5rMjU95VqCJ06ayNKpPs6J0MBKq4ZzECynSQIScoqVflRbbAz3HJuiAr1eqm9q9fa7Ox78oAc97J3e7is+45nf+mVf+OQPfoy+Y5HLmvo3v+Wb3+0DP+jDP+GTf+QnflKW68rLdtDe8HVf+2+88Ll/9o3fmGa/8R9/7XFPecpzvuBLf+03fk//68nfTPeP/MQ/e+RTP+p///RPv/dWSx++7mu91pd+9nMe/wHv2/wuwL/ZlN/q2jHnF379Nz/8Q5/0c7/wCxrzRm/wBl/6GZ/yJ1/jj+r8vzrWimc+93O/87v+ga6/2qv9ked/0ifc89/8RTvd+v1ziFKE6HSSGUlRC3qDLhuKZ6LePUWn+UkkEc2UmHNDXqxIJXGIzeGtanC3dmCEWzUj+oLt3g81FT9ZCUBsVDkaaaYJJqXBSEtjGSDmU+VFeZJQjnpwW61prSleN8xRs+WKqhkeqhfelKsYZ0lajjQoB6UVenkPjmkDakFDhkvFocKLGXnQsjVQu37Tz4Vs0qk157a8r24wR41lLXpZy04ZQSfSEbKXVGn00/GzooOBlrkRGzXTMpQL4QlPI6tYqXLEWnUuMWZymQVTH6pQlJwmURJOVUIZZg1wKzMlcCu0WpZHauUrPS6J3TFOr6lNkJD2PbGwKhfXkuBu6lVyAVBfSSPKWfBDgpI1ITsYumygXnDSWpVF+mmLE42q76oqOafN0G5kO0KlMlIWu0YrTiAEUJOLK3PWmsxqNDvJoB0ePaq5dE+Pc++UJdMzs4DhVOoZsmbUvmLmSkoxzJvZVifkPHolWME6NCfKZ0miVYkDBjlCMZEYRSkEZHuTrwK5SlZqGBqopUyvzuqepBiqI+L0vjObo3X3BuT5ihUSRFG7bTBVyGJfzab8cZFMmEWxTn9C9xVodW5FLrSCqruLrCyrNSD+yhkJrVq5kOA0ppZFfjQKEWZv0xV6IqAXG7wTwrEto+KEvjkubTYXQ6VWRFd05xKDVlrAys1QHcu1nOAefmQX2ptNxTnS9rS57C56bseGRexRp4CjaZ4CABTEAs/ccHTtPfrZV1a2OJoteC33VXYYwllmJ0dUGgop7Wl1OjlZS3gITS3AksJX17FSzyklOmX4DgvN3/UQtWlDcVLVYYSBblB4yqFXssJCKTe27KO5Wn4ag8g4I2OgSoiOEyRh6bMrKFgIq7nZ6mIAONkrjKeRJaqEx7KlkOuGYOZpsflo1pxwyxpYw3duUR3rXEPW4RmWGni6AgeJkr9cBeEwt4quWAYIa7RhTsPsmOc3eKHBmA3XgRw+OunQtRC3usIrsS3EC4FnWJHHooLXIS0Rudaxjl1ZUC0USEIAl2xZVCFyGWcj3afS4SwEBmlmo3VC/snncIPgOW8s+IGeZjjRWIpPoSyzBOikLdCZHcd2Gt9XjJWWJdqidoWuBbcUOpSV7iXmm1nK1lplk67ppd9iolJy51x+lO+hTYfU0Aft4uhOgZyuFRgPczEml+IoMK1UZKyXbxPtkCDAA5aeNmQ5T4gKUmeSKJy4shW3ZFPIrxc7huewRGQVSp5sVBlplaTrj6y2Hu4p1Jltpc+ypREgoZcmUeoqgCxDUfChQqLqvHuOMoAwL3mQ5nlalHDAKTCwaHJosYXIBpk24E0IaRbAc4hZkwrxpDL3og4pCbiELLO5Ma2knOlV5CIzYHttK87oEt7dBqmYVJ5WxoLSNmQjmu5COmo5VE5xBjVmIWQciypV27JOG4ZBVLU6ySnbC3YqsoYm4e5WLqDXgoqHzYWYWHtkIgGURfoBTJl2DkzbTTdUhLMnG0x/llWh5JOQByVH5shTbXUqFYbTiKpN5FjTUHJ+Zx0L2vCc2YWihpVckTqdSmed9kqm3HYv9IqNSlQ4ovHYWlkrN7gzdZjqhcxUygelQXi71Ems/DxD70WjxFdbR79dLhfFlyoTKGhQQHTXU0qPknI0g7mnW6hAWWOz3nJUjpUqh2TK2apc58Yt9EnabOla6qI84fKhZYSZIuuRfoqZ5icsWWpvypTlhbZ00CAIdVCaM2VQnMPDwO5lftAPa1Gi6ApoK7/LS1kLvQl40HNZRgSwaGkNYzPvQqZKllE+Juzu3bUYVtxCTkyczzi0SnGIG2Yyg6jN0dWQmIgDSnAB2dVA94o8FPTpY5o6UnpXCMK9WRsiJ866A8Dkm8YenBmrLX0ltksKuAOrJXuWr9TqQd74IDvSLEm6ZzWLzW2cMV6tUtGVPJx4qMgsFfQx2M3qAffcSJOOX/5352pbJ7/rg4C/6iu/8od94Pt9wws/65M/4mmv+9qvo7nX6/VvfNM3v+ejH/vRz3nBP//Zf6MrL+vx//lDf/DzP/UT3/c936tpPTrncz7v8x/19I/6iZ/66ZdVzn81/va6v/DFX/2wxz7hu77ne3Tr/ve//xMe85iv+JzPftM/9foA8TK+fuHf/bsnf+KnfvynfMq9L/0N2fku7/AOL/rUT/pjr/aq4H8tSnn/vC//6i/72q+Vhgc+6BWf84yP/mOv8nv5C2aa/j/pqLqbPKc2IlwsPxMJcqxBpYMnuWlsVq40OqKOiqWcoEHco52Ub0dsh4jYxEs780cblUuZvlNwTVTRiYYy62h99twzS/UUZq3XpSmFWqObLHDlLapbutYgHTCEbGol6juPnmelilx6xUoLjWjifvbavQ6NBCWFEqScQZvuckBaN3S1GPVAqY4r83DMamfhTykGvWgVZ4NTni273NblmmOnikfIVFtUq9QOLAryAko0ZROpZURtx9rWPqCSA0g7UQ0qGJoXhNJ/lVWao1xFTk8/LmdrohPnH2GvdwjCzlBxqM5yDXST8kk7XYr9yHVd0HIKXdVIE4yzl7zPrg9xFg7tFhZ25vJJvWQDz+JweMtNdsKErDpTlaqHqui5EJhquM3QIZQLKhYIWdkgNJrPVmpaWnNK3ii0rBLupQuVWODUqNNsrQ9qmzWChKezrHg3uqw1cqpqc/e21HWMIStIF0cEd9YWgYiZx1RUMCfXLfedR0kSt8ptX32pOh9eq1m2puk473m1Ed3RS0pwVkuralm+5OE8i7nYBUSDmOXtvLUmb4G90wwj8wbVrUabzQ+qV4e6aZrQPqH33b2yBmoUvNSPUXXQj/NBYd/SxkQ/ziWxFnhFsquQkzBofRAj2nYdFb46sot31CPv6sJlkcQo3tzWJqMzVN7Lh/fL5s1ZE7oi91Fix2x2HoxVc6a+pr/ibPmSC7sU27qqVwdY7sKlVXpExczM0phd1gkqC2vSWkjAyXa+A6BB0DhHnrT2W+0mznsFpYbCfY4yCo2tbIvWsllRB5ZGIKsUfzvIJHQQRGW6LLAJHIUJS6j79kurwasryIk9eQzmRbDo64s2FxCrhRY6wSUR5tXtuOlzU4OStnAFHUtTGkr3j1FHk2pgzRMUiE10o2go53Y5tHRaPVfn3n1vvJseC7o7yxeGqdk2WVvFKK0bB2fjETgWZ9lyPzOhB32F597WXvvKXc5WY90Mv2z9jrULTrxBFE8cCSBzRci/FaLMpWrM6ZFGYWCXrXyL6LEyLeTtwqHxJgHmNGrcCesMUoYWTk5btrXavii5o3JzdmdD6lH4xWPrsC7ETXjgaNIPR3mWZYoOaYHax7yOW31fyC444oRq0bzZNtAQdlRdV+6TE5idS8KRkkQNIsRFbrCtjFUrFaqjrLB5uKeUzD1MwSlDtUqaFjW73SjqxVULdrAlL+BFHrKxqEHQAg6S5762UVvDySo4exUqlSxasgBmyxAhKTPPKgLCjYRkVjvCMkKa2UGXMMUbSk9iQ+q7pm2Hz3JMlkZVWyWfTvG4+3JQRFO0qoUrHcWxiWOWAqd0hmK3egSyrHHIPvbIQeuniRUZudIn1VIqOAkhB5SYXq5B3d0o8/UBxPkyeqMYd98nFtzGgJAJ4pCfkD1gLEi/uKcQuwp1dC4RIRdrVkVUCs802AaSxXHE0LOI23UcRyLkRxfKTalzTnZkAqHiN8GJOsmLog66UVQhk9yDR6qH7+H7XSiV3jqARsWTI9SQb07rOVteNyFjgKN6sK1h1TVQ0QlRww70E/JMxiyEeQ0cPq91PVbs06/L54nxDgXVjuk6pohDRquQUJqRTlhPtCyd6+4kglWQsboBDsKpmApttmqm0BUDzHOMcmbqfFrfrbljyzNIM53CYmYopvLLzayG2WY+SoRZXRFkSSOozwdOApEwmaOh6YpyBUIoHpNrJ2UtqRJ2yJTIiozygCc0J8/8yGbr0ufoYbJcNi2ZZizqk015B0VAGyH2lVyZqGgjLwPWlSCZtQQisUONoNICFtVyWaHkOIYp/Q5UBvRyhjFoXNa0lQ95EymPCflpviz13GDTFEjqYk4HXAZkFz0Oy/La1OIWmL3UbClUDCs9MBZ8SaOifsoaq0RXRVYSIJt57sGFjOcxYraFFW2PvqQk3MREEY2mgYho55GWGg6BseDRkY3sJhotlhx0S72nRLtpLeamCAH8pz/xk7j7ut89d+6+v+y/aC//8g982Lu+09d87nM+7eM+5jX+2KtLxEtf+tIv+fIvfcgjP/gjn/M5//rnX9Z/aj9R1swAABAASURBVM8HPuABn/DEx37y0598z/3ur0h89/d+73s+6jFf/g3fnCkHJP5lPn78X/zUBz7lIz/xOZ/1K792/hv9N3zDN/qy5z3nyY943wc94P4vq6yq+vaX/OOHPf7Dv+6bvmlFjN6f+rgP+fSPftqDH/TA/1aUBn/B137jZ3/e58nye+7cPPujn/bGf+Lczv23I38fXSFKf2QQQQP1G0pAnYo4ZY5qYjOV0+K4EkXD5WaeJBRXM0w55Be7tOi1W+z0EBcr0VY5iFYnL2kqPyoPaJmmxw28LQYDCLGzqzUUoaSqQ9Un1R58sCvJmVlTPUI5xLhLhuxARym70c56TDWvVSlFmqlKzakSoKyArulqUumzXGZOZCBLKW0BO7MnFxcYnTlY3lCdRVqm5cICWDoKa+HICtQCVaZqI0a50Z3dszOapcl6UgCd9VQuNkyXZkkwzaq0EedgKrFteeWW51qbYYjQIJdBNGdZK6VsRJ4lMoejmRRVWNBXa2GUFXWAWS3NpvygZBi0PNIIq6WOcA1oVX7L1IpLxalR6/UyIE8FUZDstVarUufs5YRJyVk7GALXUUTd51DR4Wdxb27SmdI+kE1zkjgHpbdiSw1jq1ZQt8DOWDmrDp6+IGFV1hLSTmcqeKrYm2bCexkgqNXmtAXKTPl1ktHCqWigV4hFOM0y+HJfhDRLf7nZyjHFuEkyvB0mPqh7ysEZWMeJLCkANRQ6KQV4nDHmch4KM0yI2DKPxThkdmknjAKO8GMfeZXAcAutDWZfYas5B7rRQJQz3Sc53YveyJYl6+aIdBX/uERc6JemRdn5bGu/S/ZKodtoraD8Oel/QicQUnpXUqiJ03vVhPKll11MSch2KhS9gysgUke6BW0XD0nPUuvzNc8BzUJr8IY8+1pPc1cgW3qPxjApEdwKYuPytpJnp85sqYGnaWGeio+6mfg6WpkzG0q6CA0JuVrkni2k1SBHRg6fXUaQ5XmoAow4iKCVSJfNw9sBu1auFbUOq6vzOIlsLpFZraQhW5XmxPRc3Wp0OSzubwYPnJSeKwNComFt0B4d6z7v8yQUbGUCy3C4pfUszW+yvlWi9iNXMtNYJgC0QJonNuf6AKd2h3ZDaxT81N7mGitG1rBlGXMipylY5m24CSiswgzO1XNdFptEAIdWvBoYddpRRSuS5foFpQlDssVI3WiwFs6FMtQgZSUUJQFIQpOgXzKTRlJrm93a3gyb9Q5HxakXuaoCXLrgLCWXJLi8pQJALDBNqJTkKe96hh5wW8hqT9uAc4GW86jcPaev8rJyKwlSOJSnmVW10ErQmwcJaOV/OJYCJnuzUQwRm9ItbVvaz+hSlO8QNAfWMqI7m3YqJqm2ClGQWB21CGmDGXlQ2WPLS6S+eh5lKcEld1kiIFzGnGYNWANkvAAxcPTqXmao8w+ZXGErqecTWbWqzWyruFtem91GVwQBkJqSZomqqR+GLNARqRVnpTyUISuxgqEK7o7e3XqPPujNjBC1xbuTC5JxQPaulOaANzRWl1tJQ25QAtZZMu/eiWwzWqwh/qKSzDSEWTUvR9pa4sxsJXtCTeb2NAG5J2YCus+jTj3M6S2tEcOFpiYdFkFFXaJsCBYWg9hZtyUOnHPLVjNlBMw1CHJUAuXmdH0ZJoxwzjBvrROaTTQ0gdv0QYedr9bhW/kGG8WzdZWBm/RewxROt+qd26Y0X6XGM7FgKRdLDmiXFC3dFhhYijf8CJsKVvSJlmZhqrWpgA9JH5oajrKU/aGC4YFeZGeZJFLJYym7WN5zNJWBEnP0KYAgCpBp5V5NHwWR3K1YnmFibsqsrXFY66m9fvM0z2JUhGvflwVCOWOQkKCY16zYVAoQCm91gyBs2LptLI/JFE9QwKqcOWumLT+Fn/MQyCqZRbCPNUbKVZkziwmBYfJsS5U8GmhpJdewOg9DKKKuMSdPGKypx3crTqRhRqCiMqokBpCS3BeXQF4tV9axRBVxrCTRzH1aO7HKceztiDoslyvras9cs5aq3dGRF6oG6ftsvzP9TvY2trB7jrN7bXSHCY4dVP3NY8bUvKgzohQVTyR6pqeXKZd9nR1bCEElvFl2P5oK/rG4A/Ik1J7lOg1pzLKsSqLOes3Koi4MxA3UTn71N+bf+NvfIR8f8HIv/05v89Y6+e84+ID73f+h7/yXvukLX/CxT3vaK73yq5D2K//+l77ky7/s7d73EU965qfoK5G5FK3frYbW2kPf9V2++gXPf+3XeT2Sv/Krv/oRn/hJD3vCk378p346U5T43crZj+MFX/5V7/Hox37X9/yjzLznnnue9PgnfNVnf+obve5r0+x3K+U/j/uVX/uPT/nET33kh37YP7v7b+Jf7VVe9Quf99zHPOyv3vxm/+Ckqr7h7/79T3v+84/92lp/xlOe/LZv/mby5T8L+/36LhMNRdQJsxVMpCHci4Qlarq2D1gTNrMBrbyZY8xj3O64ymu3bqNnr6tS4DgQ18ZpIp9EJkbmRTmv1M1YmNUTDa6Wfhwxj9BFnpoZ4FGhnh+Y0EzoF2upc3ClEiY1KZxKgBobx6DRkAHs1Xa2Jf1lmZU4xUFFDY61eMw8aoX0q35JQHRjCzRmehw+ryoINoC+Td+up9mSYJD2lrHN8ChdWBQLy6cxznuAhBWX6ugNSwWdvUpzwYWK1G2AVt4g0rFMNpGpzNc4XCbU1z1OMJFUBYJMxSDbKTuPKH25ioSz97Z5v2jB27dsfZaMBSNoV5Wim8DNsjvRumrQCQwd02zR5O/MM3KQHycei8y0cb34cRPHzSohOmUhSdkGLLfd6/Ba29LzyXknQjih9brxumk5mhkpTxq9dfNOGb6qEW50WIktevei7RSmqk+lq+E4RlaP0dEUkgIJNuitmURDT927aSBkp4RpbbpBHyudasq8uNTFvh3HmKkxE2f1VjBQYTM5azB6n2fjqZK29CPtfAg2S8TAQsnublvnHeNFShxRXkerW2h7VCWh8gAV0+ZsCveUmeoCI5eJ9K3del+TGquLcC5iSVu7oN+43TR9WOcMGKg/FVynyDQ/hp0hlv40gc/c4as21MUEJ9GaYrhHxLEqQhGUCIOMC4Vwk1tc4k/ffHNtGrQCUktBR6gpQGvp7VIKzWAK+au0Cf2S1qTw3EqqGs/V1YBv5aNkY0XJtEJVm0lej5vr7Thufe1uk+zkDfKO52VoAbIa94HZcX7v0Es2ES4La4gfOzqaN7O23GOglD/OQtONQ08n4/xKCbVGScZqLVeLA+uoyoRQolszEV4ZYdZ7msmLM+2XYs9uHJuNbtagWbc5b0O1Yah2sNpINuWGlbJSVyobVzud2pAXu144N3rHTas7AgCVhVUsKC61WGWlFZAWH6US0jbbNvZOpqxDXWkH4DRdaUVoVsB1BXqVCMN5O/frjnn+u6k2wUUYuAGY5/DcqTzqiz3TatpanFARtWxdi+yuhYB3B3fi0CZCgaW+T+vbSLFMRmBtFTfpLQfhWsotQ7KcS+Gg7yEzI9fVoG8Hb13JECTMeZrg8jVRha36nWXbbowejmxawO4iigVahlCYoc2zfPTo47DL4k2JH8ob7HtNJZK4XgIt3GgEAhWRbfKOhLsI1XxduO5Y3DhkKasEsoovVIf1pdKdlkpv2cq8Je/tdW+P2xB+gAhA1a8Z4LQmt7IrJp7qE6epPD/IYSOVOO4w5oja5upF692VDxRQckLdJBWBKM5VyFkSLVOtZosQSzuWzDk6zlfBVhnXaSgwgYIb3WkRvuZpFqTraGfVCNAZ3sqEkW0tLz1aWyb8NLxJxjTtJvYsXZtpIXlRvoBdbaWbbc4tqu+JZcdqt9Nub9ftlMWWxjSg8Rx08ZO9VXmstee+Su30AESqybvZqrCokF0tjmazbas5nDLO0bAGbokdJQS5kQNVJkRqr01nGmX0S/M7FBtBSRRCbWGULrTDVTzFmH7OUpphhrkQirYf2JfQUtEZF3anJaknX4XqiI5jeS0XsS5oN5AbszeN8IP9oLw+bDswVrkXtSfYLPW7E412RmJVS4V0kbnYJkZC1YNyt86oWMDVhfdxW12Y+ukt9QIsxd4CEZ2pFmLrNKeA7JUX4+ZwD/gyHq1m45LlwdO3g8oUy3SVZ8zzJsLPNQhyheUxWm6eylaIRrLJM8pSFmM0dzdZPty6y9CeCl9ko1tuGTzmMedElVN+nGlgh4jMogHysXNecu91VOyRkUVoKvuFpi7cjuDaabeS1mneeLJHdldf4nVI8JVxYCWiVYq1SsU6cdgi1aIqjuKRrs2ShEeokyyrGDbmcdmPPoNEWZQv6tmECCh42m7UaAvaQROFYaHDDDJhzMHDqNqyelG06s1Oj1kLlcI21ChSmyBuKLL2LkDTHIqjCQ9KJKASLeclenGssxI1bZnTIR2DMNBgo0xzKKeMZ0ELyNFaBnzH9/zAz/zr87/M+w5v+eYPfLkH4P+GF+93z/0e9Z7v8p0v/utP+9An/IEHPxhV9/7HX/mqr/+Gt3vow97rQz70a77lW//jb/yn/yDy70bb67/Wa37d8z/jkQ97n/vd736Z+V3f+71/+f0e/vHPee4v/PJ/+t+//DZCqvATP/Wv3+cJT37mZz7n137tV2n+p17/9b/xC1/0xIe/952bOwDxsrx+4/b2xd/4LW/7sA/4yq//+rVm7/2vvMM7fP2LXvCWb/QnTSj/ZqL+/vf94FM+/pn33v27YY995CMf+s7vSL5sSn8zqf9jrv0WUpX1pRAyS9RlZSmPUYs6E4tqRVVB1LJpnqdzRtzH4kCCYUyrcigvYFP3CI2HlYmKMdVR7JZ995tZTZWJBTjQpjY4UMYtNF1ZdVzX9fxyW1k8rUVrZc1VJ8pV44gAWFNpKjHb1WxuZWO5FGYq73DXMpgcyVK5QSrPGR28kMN8JEcEauU4UtlfcpcGyhqP8gNW2ZaPazMt3GVfzz7CaznTyL1q39e+xzpUfeSX8trUXSJcZ74Dai1RJk8mNUOSy9VP8soMa2aDMsqPOfbsSV0oEEpXCEoeyvUIFX7XF1TLslZRitUiXPZpVEg8ZeMy7NS6aE2wZb9UU+1co+fWdOEUqxbEOpsObkN7tdB8ygCVi1HHiL2rvJWgQUzENVRuiahzFMoie0XzcgYhQKtZdUuVtlKAl0baXn51teZ2lC0AlXJnrOHVaaQBJGEEaKsUOUXtKFkN4XK6BauiyXBcIIOJZtHOEjrXvEZNbxjNm3lBajLHMlkzVbbjWCp5VM3baLIqD17TpmKqwelZlQRd5dMMblkn9IATg6FVz9FwtXZv3qzUYsv75Dhoh529jUAjB6A22CQI8qqxLbXharMcZQpUlbyzxuGi78SRFbZaC/PUDUHFjfAIHHulZmjJI1NkoNfVtU61gxIORHAmRRotXQOU5BF2CZlkHgUsyOWyjrFxbNm7NJS1bL10kTQCZ3xCsTKJJLNXeRWRkhflSDQVkC0TAAAQAElEQVRkA+y+wZoxrAYBBR/iq7LAK7Gh5H/5EkyNbExXEXCY088fFCnlWuAk6xS+JKGEhSZb6g64mesza/d2bTK/iJKAonpSg6fZ3YRziA3ANC9JVpyyoFS/t7iQ53XNKpSKT85Y6tGll94CVg3sRZGpVdopH5TbabpuhPQVAmCABJQRCT9kiss1IrO0kLa5OGUNnO6qK4O6ek6TOHRS0CiW0cNdusKVO6pVQtSJJtmyDJQkgyMrIlQVclxy9J2qCCEX6oCWH2s1QxeWTlGZTEOZoJT3IYcVekAy+9nsr93WIC+rb8nmja1T683VtHI65hK/EgPewBZAopKFqsiZ60opK704Q4tjLbSUYROYDtUkdpnpZ4bKBQbHwqhyn93irDPHIAzd3eWVZfaVbZUJQANGLO1hLkIIy/Ms/9GopzXQqzJz1dpbJCFjWCzBWq0WTlIHip5tc2tNpzUFla5J9pG+aHF3uA2wp2S7LE7CXDafa0mXm2VyszK4iukmtEqBLMyZ+9SyJkPKJKYkrVOhAtHordwmEXTaGPhPr8qCrW7HqLkhrEg6NCyxykMJa43CYNb1XhlbaXJsyQA6W4djd9z2gK+R6boHVhIpaOUOSsVT1DrDcF6E5mf2qjZorUpIWi5Ypcue0k/S0K0bVLvEG3loo9DvypSbfsou3pUTLekgF3HXnqWWqJxCUTxahYO2otXsclGGhkU5wwjjguwKGUe9n4Swci9Ja9th7bpsnwJmZa1psQ8qW9aAYiCqApbCfsaaoVBHtjl9JarLTpcK6dcnWY4q6VQ60Jhb995cvIvR1oZymZRCfOPqquqnMWhSoRjVLKxVdSTPNNtLRtao7NJblRDBlTdgieqVR8yjtI9SFU4IU/K4q7YqzaRErjqjY1i7WVufPQIrMqLWmhlyIu9yWPFWgA3Z2jIq9iUuXYFZJfdszToyU6akiY2XVOZVJ0v62GZaiTxp56uGPI+spYxP6ct7lnyUSNPjyOpzb4eyU9GAKldi5Sqlj0c3zaRB5mmurkYVOFg3kDJTyqqelz4rSR1hMjuK8ObWRFKRpjM7YUrsJhzd0azEMx/pHjTbzY5qe7o06qPRElwiAdnYmve6YF5yqhxIfRb0IvUrGTX26telcJrTO9YNZwNB2c7Ze/nm6X2d2R7QFVsyCmYrcq7KKnkbssv6tfVj9BwNW3BEA4So8CHgLo+AopgrRqXAELsOx/qGb/3bMkRr7oc95F3trlH6+H/Hwfvf7/6Pe9/3+tYv+6KnPf6xf+zVX0MhFDu+/x//4BM/9uPf/VGP/epv+lu/ce/v9t/H3//+9/vYD33sl37WZ7zBG76RbDv2/a+/+Cve5ZEf/EV/4xt//aW/pZB7r/uLvvJr3usxj3vJD3y/Zt3/5R74xMc86sXPffYf/2N/BCBeltec69u++3v/6oc+9Wmf+Mm/+Ivnv+T5I6/6qs/+uI/+rGd81INf4YH4LaT94I/+2BM/9hm3t6eF7/fQ9/nwD3go/td61V3HznTJYqo0oMQcYore8Ap6hD4tThHxHCAGdSqXYJyBmcreM4+2FsNWITOgqtpYOqDcdk7tLUZkW7SAQRkj+t6QjMgjtNtXkpoN3clYa2VNOqxTl3yjeVPaJHPy2LmWK+1KkpSaUPIq75ae5TU00GQ6ggGGnZ/amW1NLw6Lrcrkm+VCpWJEWqNegJxHIAq7tQWvUop6O3g5C2N3FYY0jdKXPlOVO1Yglq/0Y9WcdVpbTij/DNSnnJGhqVUuoU3waXnTyzq2XH2ffqjYttHkWQovqcYq1moVY7GtdhfGqSiAKo4QaMgV7YAeVxkOaMbSjWk23ecd4gbqtnfduUO/051uE7XXuq5QSeQJC+S0HVAnqCrpjcWcSJ0jwib7VbIM03vYWCqRs9YsBFtaF+iH5V6ROT1V1cFpPn272yln7dGiNozeL9YaaEz4BKdsXamCuGQOa+rw2lHLUESZBgqWbHLqLhOOE01dr/MWzqYGcLXIfqXppsSU5TY4NnifYECKjm7pbkZ0phZXavv6uCTVhKyMFsrk0dqRW8QF6CYqSg3TvDqrF1hONQz2UL+kYrkGjkuj/OnJ05GaHYfnjlTLzlSQKqAWujCd+4bVq1AOG9m0ZFsLAhcdNVrQ5kpRJYpZDCkxerhSxgHksrrdeD2fwMVxkgQZjL3UBZEGMv08qFf1VW0vkzBAnNahBuRJWZmUylTAm9KsaGnUmlVU5VkJWVoOt0vZjQwcVWr4Uk5YyEiw0hDGMxatdlOSoczKT5Hy2BYsiyeYlXfTZ3lGE45S5Nlv5jYmtorWpvUSBMr6Y9QaJ6GZcZnrIpofqGCCSSyxgRGiWM2z0AQq1+Sa0ONCqxxV+qZHIkQWsELdVT22sdBleHP5TOiMAqiAiWNJmoIAQaZuV90PQSeP2HU+e62WyQBKfyDQ0xJVXL6tPtIgUA9dtuabifdZaxVOszqtq8dbXvqxjdl9JddRynIh5j0BQS/JdlJSzAbMOpuWOtsqW6F4RpU0S53XabRVYS7EJJbY1g0ioeuiWM/h021G5R4eMZhNzyMG7HSWq2pPXLOumdeoGQmh2VY2fZsZyzIhazjKG41gWaRj0ZN+YhfZF6TeG1RdXUMKklwd4RpNlJtbkna0vncroVhXqhgEWmjRAUpnxKravXaIGWCCgaw4X0K3u11av0APL2SEGaqY06C8d+JSOaLaUp1pXcYaUvuAsdimRmXmgs/0TGJaO3SbKyGnV1VWShjkigDWAdkPoEC2YdwqR16bBZuungeXHLFS9EtKJNfmKYFFptlqVWJUbwULSccBKq4BFfYVlpBVN7NvKoDyoGmSNdGPJrU9OQ7jISuLsnflVgptJaiZKofYGk+jTlMq02q5BThhy3pa8wYz8zYkMTpn6+EjSRuTXbkI5rnoQ5X8mzkrFy2an24lcl/ziNoX5vIj+4QLn0k5VOWZtQ5kZlSUkFwqXvoo39ONTbxAGOTPKWyVWOPdbCv2WdCMWKXNwbHjlJ81mbvJclnmWUik4+gVJJZLUgoPd4hEMFo2LqFq8iWcgv48BvTMfm2iNnMBO1TdMlWgDsdkBeSmrCxbUlei20roKvJMHvFKWbTC87Q3qva6vXKfCuPSCO6yQwlyw9atnWVorOBcJZmJgKBYCyvkWakzh1/sooT3FTYXI1g68r7fC2dVC1mhMrsQWSoCMnjRVnoSZuI1LtlB3md7CQGGPBejSuHpZzhWU1wY1qI3eFmslrEhB6uT3lClUncs29OX7pXLWFiYlVxonb2l+TITNXyD5ghPn1Dz2FhjLG6ztqAP883ZZRgp8BvYClCY1HtayOpWk5yERrg34whvy31FrFhLhmlsLc8r4zDhdPJFjVPrAlZ0GLIJH72XCm9vpOY2xX2V72iHKJMmsqzJXKK39XCGV5KyR/q6VRNpCvo1WdNS9V5ffpVkO60kGV7/4Vd/9bu+9yUAXueP//HXevXf5L9VpVv/fQcf/AoPevz7P+xvfu5nPf+TP/HPvcmftr5J4I//s3/2pI//hPd6zOO/+R98zzGXrvyOhxz706//el/xmc/6+Kc+5VX+P6+i8T//cz/zsc961nt98OO/7lu/7TimrvyXx0/+q5/+a09++jOf89n/4T/8e5Bv+IZv9GWf9ekf9oj3vd+de/7LYb/j+X7Mv/vdL3n4k57+6Kc8/Yf/8Q9UrMvNnUe8z0O/9oXPe8g7vG1v7beS8E9+7Cce87SP/OV//+814N3f7d0/4lEP99Z1/r/OIcgLZ4IQxZPKongYk2e6WXZ1z9HDtoieh6l0zLPcb8RdgvmiGKllgapSs1AdUNXWlh5mjrMkS5pnt3BJL81J35JbQMsllkK2I3dVDgslTx/cuuAzY3Przk40povIqFy+ltZoI0RpKOvTFzyGHdJLZ7EXjThfusMgUh8NUupkg25WjdLFpFoRQyOteJq2Ru0Wqs/CoXRN7fIsVZkJZVYEs2R/0x2U6TDKNNo07uBurUglb6M5cZpdOTTpYGqdprLEDrUlb5rLSkkpqBTZgt4Dcj2TM5tOJRvoGV4rqoQPSDPo1mnI6ZGXjeJlZxPKKZ+AIqdb0rJ6pkZbXUY1hGdIn+6H4e5reYWvaFKQgROlAoSdw3rBpfJwsEGVEsIDWCYQTxCM5WRbpc2CXLvSr/DUFUvjirlWBDQGzd18EFTEcqUelmn/YXUE5kHOGndd9Ageh9oMBPppnWXEikSFUxJBUvAi2mpnO5T3vUplW5FxQLE9n+YajE6qb7dhfcBN53SiqB/UIb1Nmm91WnJuQfBkoFKeWNNvozlZUIA1KYHUvVJ0ULJIUFnlOYKcJxmW3svnjus1jiPr6rha5HmU6FiiQ9yqO3VFwtH6oilQh5BabtGMoKLua7ovG9ldlwyilgUl5lqQtXth8nRFmBtWcs7Kdd8hW42ZCg4RMNksQEzIQyHUYPpRNmEapybXLOVP5NJ7h15FiwGQnaOdvZbKhw1w+eWmPdhRCmWtVCgyjzy/LsqThxCdRFywpDsBRWpGrTq1SotxlcR6pz5AvD3BaBlmS9y1Chkge5Vhx2KcAle1qJNOq865aowl0RrDLCw5J1jIAaEE5dKYu8sYqgJxWU+5UCBkeBkgHCwkWLXkWDWPWAcylREitUw+DAfHrLGofX47w4G7Lwo/zZaeOtnglVawImHNac3M5HDNqKkoeGPvw8/65Sf2eXd8whKSY97pG7FZCXl5nR6FlGMsLeXl9xEnDhypkeYGIHX/JOk8z1VVw0/sgAJTnHGWSfipLELQ6BrMQQV4m7xZrcWZ5HEGmwpFeVUB2RsudC9PGrZqKmUVGUBpbTPa0Xh4zL70hcrRTsKsLIo0YsdSWk5ICB3NIG1mYQrVwJScblJjhpRdVrLSdXZulMfqxn76ZHLmAJIAiA6q0F+cbVhQ+eSF0dHcSAuUhlVjNQ/2WYo9SxdXiPaiDrJcECw3+Ris5dgbY0hGuGqizOk9rC+VLhqkIG0QdMnNoxbOl2J6eIWncw47Og/WFFCFVt7T5e2isszo3c16S2ugZYQKkixJmrmbN9MqoFka6NnJ7tKUhRUWO6FqvAZLjlTwiIxMcLKVjbpLWI1beW6UEpqptSYXJMG1n7Ymal5WbrCN3s85yK3N4cclVAIFtAxeE2uVXDJ0KK7wZdhr7auOA3NlaQFYaXnCOFvOZuVkUonoVWYoQ5hcnRD8zUs+mY3O1oXvIZ81hgCjKkX6CA2vVOSacenxA5BRrJAR5dg9jh6a2ebGdRNsMaiaJnw8YfpzchBAVFYmis2im/YlQX3KWlURoRwwNGentwXVIyciOPVEUj6l6ATqVkOTaWIHaE3srJqpnJ9ZswJYp2mSlVK9UcKafnLz7ObdzE+SxUGxQpakgAvFpY86/6bFck6X4s2OATRs3nuJJx6H/ScTlBNVLNjpolci9MHb0Ej4OMx3q2m5mIfgX+agzDUajQq9qgAAEABJREFUNLhDutzRVvY9KWs1XTTuGL1cNDjjulatjOKEqHGmNdHqrDUDZ7itCNUiRQ+aH2mAUbiwLOW/LSUpBkq66sbipubQ5bRVlq10OXsmV5q2CTBsVncSY8EW59GPtFCINFoLrJ7WlobWbZVg1Syo9yoosIRygEMiJdNtDmjllNsq7ZFHZjsOExUzQVCpfXrcrlSKCq3KkocKfAGztOhbhyUtNq+LYyOcf+8l33ccO4C/9NZvfc/NjU7+hxzkKzzoQe/8Nn/hSz/zU7/x85//Tm//Dq3JwfqRH/uxxz796R/wpKd/3z/98d+l3vvf754PfM93+5svfN7D3+u9lMeZ+U9/7Mee+HEf/y4f/MRv+La/F5GSo4sv/sZveddHPPK7vvd7I5Zf7nnSox/1lc/5lDf6E69z8kMjfnfHS2+vL/6Gb3qXx3zoBz3lqf/gH333fUC9xZ9/8698wXOf8WGP/YMPfjCEOn7z14//83/5wU//qF/4t+d/QuAvvfVbPfOJH3zn5s5vPvT381UCOko/BiWOEgR+fvDiltWZqufWkB3hWKaGElnKWyWLxtamCoSqu5ViMpdVmspFQ5nVYpVVGzlUzjIG0lXLVWghGTkLpSrRatcj4naFrzOfuw1Yk5Boaix59TqQSjtfUL3pRoILtfe1txUWTaaVMkNGlOyQNyoUQCVr6nZwmTL4tNJPk4bcEGFyWWnpW7aIo+HYLFXRmnHocqjgSbQqjxYSK9qsVvDhl803F69Hbi2USVp1jHKYpWuWpKsIGORuL/RptShN1bogVCPYg4EWLihU2eQTwlSPKIRYpwNQEIT6OEoalcS14axSqdWC9Ms2o0lwmqc6lW12lkChhlW8jdpzRUZSdddux82x+drQLF3STx1AWzauNla6L7lUW+fo1ltzkpAJztls96YWm1D6NhT0Ct11q43ZF21q13oJ3CQlXL6jLEC1lNl5erRYZQL9fNd0IcfwPtdN7sOzN7hKYlO7VK+ZyBMzokMzyqD4SqrKmsoZDygS05kur7iZ3TQflYHbiZcGjl68YGvY0nRKFigR5Qo+cGTdGnbZiznVySd5tT55I1aso2nxT1KTLxV+aHibKQ5CFDplMGnHiQlVRAeq5Z6281znhWg2I9RKwdgSeuSbk1mAPiePI7UVaTm2sDZLJOSRLWDyg9ueW6REbp3wRNPQiGjQEkykVq+NjmiyrHupSWFmXPO4rbnnvMY6Vqa0yDBA871Wr2AxrZW7yRXT9U5q0WDFwN1G3tRTNF5Jgdx5rpwR3mBmCS2oZYeAiz6n3yKvRDm19ZxrzX3WOsuDUytKpNvdMrCi1hIKZj4MyTy53hXEVtXXsiCWV+AUXyQswpHnjyZVFDR5FgtNxto5oDQ21aONDd7Me7PWzAg3BlzJODNF4rxkSANCOFQCNKYpvMdRSN1cdqw6tL6YKeqsII/ph76amtMwJaxJiqItm4U9RQcpLdHNlrQIRJ65YAJSHVSLOF05cu15HDETC62iU88f6oTbnSKv3IoO33AuiXjD2qpaBs9Fi+aElhItswFkD/FcXlHnoEcZFKGD4sjRYm8xhVKslaJimNb4giBFrOlx9Kn9RZ55G95rmBYM7Dyf65AAnBBahstmY3QRSP4vIRfWVzHjaLgOnl8NYFu05TH7EmhaXMwjMuKcn5DmWBmStTk1dW0zWlij91bWsvwkgTtv3G+o6Jv3NFsDIq28smUtsmpVpJK3ivCmkHbz1q0uPDZbLrZlz8NzlgKRzaZjiXbK8qlVmuAvoFANa/PVPSnRJfAxLEdjSyizFAuz3dukUJHpxQWHk47KFVMV6ZSS0cNbbnZm8iZ7YJa06GgjepcVVWcImnn3zXzQADscu2upqsiRF/KG53KT1VGeUOhQBStQitOwVPRNqqtLHQQmKrGAgxaGLEslxNG45Mzounr2NChrhYpbxhLyzrCG9EoXeJrk6RQAENxoUcWD2/KRnZTTEjwDecpK8XiVvuywcGEFnGoucH0N52TKyMN8Wgs3GeOpXYq+kW6dzm5xacdlO/xyRddQICMkwTmVC9Y2rcdHi27LXU4VdDOXrTjdW5YpDSe5gWCxiTnnmZJ9sR2ynxqZukoqopKNA9xpB+WNz3IJ6MY7I9oFOWyiIUzFw2xRjcTRnK27crH3M4jmrXqTMcmS3mFcZdfSbwk/EFdmdDZTtNuwi+PifhkmZ4OU5Ogt3Q/Y1XzRnXXDvCjm2jb0zt5cQ0XqHDktDyh7Fiu1lDf5pzcZLW7VElPZh4sv0CVK7zpTlZzd4jJw58LNU9FX5MNXuYArg96XaaCR3U++WUl8pS5VmJBNQRNy3Rze4AZraabATitBnBOySXNiA9SYoGSjasM81EusbDedxeoZVjO1hJmxcSnGve1rHcftOvYSmRYoKNbWiqbzY6nI3qTfU36njLNm1RVj75fVqeqlg5TWKhU3zWiBcYyGO8YbYX3auRFVmVbyAMel9i2XIpSQ8y1XYS1XmaimNQivyHmaVzjSVLOvC7/wC7+Mu683eK3XvPv+P/bXtl3e4E+87gue+TF/68u/8M3f4i+Mrnp4aA/wkEd84COf9jE//i//VWT+jhaQ/IN/4JU+8akf9rde/MVv91Zvddn0aCH+6Q//4GM/4qP+7Lu+56d/4Ysf+ZEf/7RnftKv/8ZvaOTr/G+v+U0vev4TH/nwe+653+8oWQOq6tdfeu93/cAPPe2TPu3P/JX3eOonfOKP/sgPHcfRe3+TP/WnXvw5z33xZ37KG7/u67i5Bv/mR+H7fvhHH/q4D/3Zn/95DXibN//zz/2Ej7v/y/g1jib+vjlEMTApe0q/RDXP8LNaRpmiRSEx6GpWVF4erWZbKg1i4UjYysy5KkRjXRGdVUrCqJITa9i0MVUBlXMqwmFrUbQMDd/pWqwoXzetrKtm5e3ap7q09GatiX2HOkVVAa7uU1Cdqb2vrKK6Z27LWrAVQKSZHroqv6FXNdZWc8vZa6nwIiFrcAutGKlu1sNa0Qp21o4EJlkXbzfeL5DA4j5zTfrygTaa5KWBTtua3Wwx2pXcrS2/Ie5guVyKWNcsFZINScK6JjQU20EcWUvFXy6WGZqroqqaQJVlH9doO7GbnG5sWnYr/akHFasQqpKH5B5Vh1saAcjZAAMtqaIESSq/lu8qcyoRC32eEBf0oVRExuDFbDh8XKk52rnU5UC7tabPV5h0zyoVGEJe1kWFpmTqMseFvEzgsL3OaBNh5xhu4GZGgb4YR2awNb/pvAO2pUqbe85lJbJcBvulRjd51ulCW02Isg1FaoBKHtrVGF7s2VUmRTKgVmUoynpDHl6h6U5VxDvOzTUTe+QVxzXzdmGvkh1Re0QuTUmEc5nt0Y/Fpb7gK7cj/XDE5thkSI+q2zw5UTIaJv1MVo0IFXhgngHYgA0F2JRxtIa9z7kFt9b6Vv2m2patU7DF0HImrbM3XEzmrkAdBcKGfh+pirdQq2YJXFZxNYYBsee+QrsoVoydfV3GcdNvO9dcmMfIdbNC41Gp36x15NqjRE3sG6K5Qctmy8nDMFNOb33vPchETcfKlDJdT5wvIltE7HM/brMmaHQnNbXsWBbV0/zS/cbsxvwOZhvTTC2qRuXIGqgRcAnNvbi6Q3+yWk5g4i6cvhzLV/pCoxQgCYOJAsM4iC48kroW5tO5GtlMLAG1A7ECzDAcgxYcR/foRa8zwtNjtVwUiIpP5LH0StWcaeuAAloNzcbGAdr00viJKkGPVTEzpvEYlNLKOhm0uFPsc7lolS2LKKeW8WU759Xy1mwp7HKyoGhNYAbX+c9jre12J8c2xjj/VXQtPxIHJGExNDQoV7QuV6o3QrCJJ95ll4G+kIp4Fpj9zuGXyC6+qS6qxS/mXNDGag9eS082vRoNOZxuRq29j+vKSF0T+neYN0dy9woHnFQ19iJIAJwH1p4eFJhKZyBRiQVzws8xwmEWj8KKtKhWdtMw+tV5tdiBAnCQk4qg4hEQ4HMdUfAyqyCio7bkAKyUe2ulZAZ1a1UeOK4IPUBZU1MW5b8bo3JFCAKWgn8grlkzHTIJdJY1ercuHVrHrFniG9B1sDZqbVgMruW1AFgQaSBE3IA84VIo/DDTbqZpEiAfdIcmlskLtMptrVYHPVJKqPfmhydsWgUDUVyg4FDmthOpSGRG1SyIAaEAmXLwmKssu1dvpfoaLhGVtUJmjdZuaF0yEw0pp0SCXuyl9CmTTSYlq3ZYljxpOAYWk0phHiJoaiuSxpkyxehz8/2CtcGat9lEDEVnpRxXTrlLP6uhURc8wFVhqbso05UOdksKbT2syMZWek4PGScxdaiY3JZNd5pZld+Sh0RkKNMGIeZ0SCvKsWCLF5gcxY7rUQd0zeswXK12OWG67js4BcRcjFOr8Kwz/IUuwyoOVXYxgLb8MnlZKpDuZiAd2Uo2qtqWEZ7S1OG+jNEQAylvTW3VZY2beW/eL63LvNV9bSyE5eRJrhR9aHKgCxmiiBBO3jvahr41zRbWMcXM2HEfyfuCC8RqCtpOK2eDmDjcUAOzzasfV49DTxOqInTGNT2cKo7oqlOxFZvpiielMU0k2jK3qeosjjPg1XTPluy8EkeeXrKD5lWewR5nA+5Co+ua7iyLwxWga+M+Zm0HmMYyaFd0lRVLtXNrLaxdM0XJnLcWoWBTbHBzeSTB1Y8YJU4BaUdRQShJKiSNZ/I6Z+ZKGRjel58uMKVb0dBEnKEodxTPFOABmWuipsZRMUPDeZfyplwJVkqVtnRjydryOBMLlSCruNLOvGge9KionLUmZNE64j/86n8E0Mblcs/L/J/o1cTf40F77Vd/9Rc/+5mf9+nP+tNv+IatNcn51r/77e/88A/8qOd87j/76Z+rKl35nQ6+1h/9o5//KR//pc/9zHd4m7e5c3cn8Au/8PPPef5z/863/50SssBbvOmbPv9TPul1/zftwfjbS3vp7e2//Omf/cZv+46nfvKnv/MjHvXQx3zIl3/d1/3qr5x/a+sBD3jAW//5N3vRs5/1N174vLd4kzcG7bcRVVXf/j0vefRTn37f3/h627d4i+c+8+Mv2/bbTPn9fUu43Y2F3llgUVzyLNEawoGkqK/yuZSyNOvIgekrRNXKFmcmVOe5qOtWpHU/iMisUF54lRIWvaCVgM1lWutlemTDUg7XRc8OQcdFSX8uA1TfwmbWHrHnnHVku6qRA9X27rddRQXTvLyhDMoGqL4K3vSCAXJBQ0WtcKblmQz6ME8XlLRwHqRcgRE9756WGxy0SVuGkFhVBI9SVgkB08AmN0JpuyCP2qLPqlSmJ6CZ5ZDNcx5TD12BxroEb2apwAK2ms1WS1Wo7dECkndKFW7SJwRVV2cexb3x3s487ZIxd9cblVgH5nHmdqXLGekr2RMSeOG8YMqMtSpj9Yhe8EZzOCeOaTs2uV1b9AFXmRooRwG6eFifOTI0i/uq46ic2RHadVOiq3IAABAASURBVJFcK/dUrQF4wueKYvYVgiSqzdJng35JHqWamApsVwSpy5yEet1MA1sfbWujhRbO4sS0MdkyiJIVbubNbdjW0rGCWad96FUE9YvJftBLZdzT0zwMOkoGakCyzuVpvy6cCzetIVLBW8yMKkjH0TCtrRqrPFjuhzHuwjOGtZvCqDIhDIT4Yk55K6Ea4ibzKm2dzUf9JxKrUGbyjxZoZcNHb93JBmsCRoOthmOzputpfbGuFXspQn2uEWVZ0pTISKuSJNFzN86wlW15h6HZutn23rV6S4uoXeBqjgsmt2w8qd4ItzIsJZBgP1ipFYowgui6spIBxhm1yjWpowFOEPe9iGrlDFO8VyxQg8cy7lbaFLdbHxymGDhS+e/m6tID6Cg7ZKqABEwgpHNtXfm4izVRPAQDHAV5RnOnXpaacXhprEpG3+TC3cgHWZdSkrgvy9NiT7p0OWSNosYizlcCByAoZXN0XXVmeZ4B6BqpuUBGWUmyEWbhVt1dopppaXF0JK2ntxDw11IycZFpKKAmeZV0wClEnAYrKSrLYlUIWKTGpQRTIqEU0sIBd8NX4kPBkq2aDHObUMocmUeGZobEQ8s+9pLzRShsg9VpyHY3kYhMRiFGxdamXaK8DkmV/BZwQSdBMRmhxm9Cs7mdYmTZcWiq8MCFcMm4m7x5tMKW55IJE0re2DNWgQkumJkb9MpZDMXpTrVLCS3K+e7RFDAXEoC86S5aZGn2sXJmLSA1RbomVY9nlWRWWRXP0wiXQTMViqU5KnWh2J5QITIyZ821VMzjWH7UZVFBPa9b1EhaSkbqc9NjXyxnUAnVU9DVRfCsw/dlt+lSU4cjegZCFmlJ1wupM/N0bTf7jm2KunK5pQIDpzyBfNFRlqsnRlafZWvaXKhYPDUWYXDXhayVJZNDMSwIngGlsusR/b6vuY6qVbofsXKtqCNxdewXRjdYY1alwIKfMJqhobZZvlJeoqM18wrGsijIcpltBwXveZwIsyBogZI9mp46qxahkhlUtQOGiqmESBG09C+1sRxOG0yFbaNLYkJSDEWfaTFLjGSu7tNbFizANLqF+YzMfe7XuS/OcxGsltVLlmGZFalfSpymgMiqyIiYsBJ55mazIAB5dF+dskCAM6UHdURO8IieslmOqbwpXMKWp+oqD+0OyyRe7hhGMx1JNYC7nKX2mpdiF11NeUAFyRgmykiWjLNIW4KJQHP3Yaeri2P5lmK4S4FWGGtm5iwuRyl9mDuV7ga/NLNGsjpiExKSeNi69n0fKTLoY6yFuF1rhewtb+YOb3TLZkHLsxEO6gYh4yqiZmCqz/SlCw3uelZgwpFcupGKBiS2ZA/qevrUjLBArAolRmqypuoZzZkxfhosTPupnFKGY9Ru2M+w0KssUWmVLaE0pzY7s5Nj0DdCK42wQ/Gdw9YY1YZx0yEQsJzqiBjlvUNVvpQQdsx2pB/w3eXB4NFsJ6QCHnU5UuuYrjK5yN1FVZtZiCBkXVZnyUL5kLNQK4f2QhMU4Cb/0RYgvjSEF0QOwkiQVblwrI46v79yXdEdgwlm8ld+7dcB3HPn5gH3u6OT/5kH6X/xzd70K573mZ/58c94nf/8v4n8shd/2bs/6kM+5rNf+NO/cP6lqd/RHvf+pm/4Bp/zCR/75c97zrv95Xd8uQc9+L+c8g9f8n0P/9Annf/fyc943ud/xdd807d/x9/73u//ju//oe/8of/vd7zkB7/tH/zDL//6/+NTXvDXP+BJT3/3D/rgd/qAR37IR3zkV3zd1/2Ln/qX9wl5tVd9tUe///t99ec+94ue/ay3+XNvSvK+67/V7/04nvfir/2Qj/joX7r771L+8l98m2d/3Ec94J7/2cD+Vub9nq7Xf5ol108q6ZNQoOlP6jMIcTKwVKaLZYA4qpNZcah8Gas3V8PuDYN5x6Irm9Jib6pm4OFWG9kSDFE6aUbfvDX3U29fvOzVZmY24DJ8a7ahdNlm43Q99YysPbThz7N4OnimmzKPrkxQCyuTlXBAel3Gyj4tAOvsWmF1oJZ+YTWok5/lBiiqXI3SnKUBVB6pNChv17rO3BPKNm6gITL3WFPGzapD/l/LdB8z+lw9yhMskLUsVU9JT+vR7NB6ZB22hy+uZlct6mwwrK/EWcmrNvNhQkKieF4stWZ4qBSs0/d2DATBdId7cw4sq+lx9Nx7U9+pVCnMQ51hj3acGMiQ2eq0Kc1my8UJLGZ6CvSj+hECRZWmyqBa6VfUNbFbZtc4K4VHWraUiODpM1b6XpysgBPdptUy8WDpJwWHxHif0pbHrJWSULAq7Ui6VJocoH50/bBc7gqJBp1SoXtb+aW6YyRsYi4exdB4lfLmLiRbacEp6vSS3XWVIVULCNlSzrq4tWIP9f8KR3SnDRNeSJRaAzg1f8BGtHaFxia4l9QL1+65eQxBscQHAVIbY9jqtrAVm7zo2Bt28rZyl0Bk6/PSr8JKn9YQxKYaPmsEZSngomFRpLljx6Ut86hYwkWT9WQKG7J7WXOokMtNqN4XmtnWfev0C2UFQCxJWrYpKgUziAFDxuaSYvWefqPR7hKex4p5yFHxw8xz+onRSqxZ+2H7Ets2lr4PNBxMAKWDEmknzklb0IHTPmd6JwZEbKxrreuZ2ifKY+Bie+cU9MdVWgxp1mRXFaXpOisR7uGm1RZNIIooWsJsWjzPQ51TmX+m15xV18C9Z657wpT9m95ymgbWbAgyC6F4iIh6k1gFWx/FfHc6Q+wjjtaPtnnvrbfTDkZf6OmOlpKwaIf3ZZYKeA9vaCcFwOa67cHzTVhAppqWtgq2dmPkCc3Z5IXyCFymqU1vKFlZmtGrGwahMpfq6ANHr2myN7n2fkw/eCyVBlNjRyv0krFy+EAI+mBJYyXPVdQBXMlplQ1Se95BlrJJgS9qnOitEmdWrZR+SW38Q9cTmGctA9LpW4AoLVoWq5BMhdI0CqilJw9zxoRJkn7dvWoFZafwPVi3pcP3dhN6kJC2rTZiM+0bUlnaeLhhgF2JYtewvcQ18jS3rGiSKCPTItuRteeMmFxVK1klB0h32QeMtqhnPhcsxXZm7oqtcrypnJRwMjg11mo73Ug5MHPk3AQtd7OlJD3NaGfFmXU9sGZkTBRBc5biWRIRva70WL1Kdpu7646ic23rRA2Qnq7CuVTY164I4KgMWbqQAXWHa88wDchtWbOerrpuSw7N00peTejq026p2rEv3sYZh3ArH0mJmsEoLxWjZuYEK7GuFnvVXIlQbHy1pjIpw1GVEwzrnm0sCkNClaGqQ5SQX95gRjOxthe4Yh6xT5GD01GGlqEnHw1eflk1sGgJyEplR8sahwwBRs6uPcgx9Z3JnCx0sueBjJQMshgQaoaIUr5jD1tzpBbGafTZe45WRlmbOPbMmURWrThlHKNFF0VcQ3Nst31bWv/ujNuKmWuQG0dvTqDkcJCyEsvgrbZR2yU0o7l4kVvZBbS1jmMuhdbMNb1D8MofKcwsoQBmWSTlqdC7BWY5YGpdlWuBk55mDdUZww55sKAbpsnK0LCYHUpcGqlLE0gr01fiLlysBfxoOLymZcyV63CRp4B+96iylS3RrRooa9lHN61SFAOgVuaqKtBpGmUQuB7IhWPiFj5HHWOJoITnRM5qDZq9BrIXHOmMYuKU7hoDJiMoyFcZBBqEkUqK3CWk6qiK089ML4EahMzaYK3MoZ+JpuRfJMwo+aJMCmGuNVQz++AJsYoUd5/HOGpodKZZdFstF+eRokyWKebFzEuITsI7igLnrqshSOCLK/yAEmDfceRYtsk3a+aNsvf0Tp7KDjlFCHqTZwiB3BFbYxsGod9HjM233v79r/0agDuX7X53Ljr5n3/o24Z3e/u3+ZrPfc6nfMTT/8Arn/9E/lf+/S990Zd9yUMe+ZhnPPd3t10hxhh/+k/+ic/+uI/8li964cc96cP/3Ju8iaqSfIlYP/OzP/OSH/onX/TiL//4Z3/GY57+kQ9/whPf//Ef+n6Pfdz7P+HDHvHhT3naMz/peS960bd/53f+6E/85K//+vnlkma90h/4gw99t3f5suc+5xu/4AUf84QP+RN//H+7T5pu/TbHz/3CLz76Iz/u0z/7s+596W/Q23u+67t++kd/xINe7v+W/9zzb6P2f+itOqWXaHS+60enpYpRSotyK7II6GIUYuV56EwnNaNmxaH64VRWZIqCLEMaiuGhNNLNqSuTpjKtQrAP1ObZneZAk2im7idbSkuJt32g9aK7ie5jsHWwVSJDOiw72piNC0ipSbeUHDMDZCQhYwvnvZk1KSOoT6fJp73QLdcLjmxVFvRYCPmgqiRxa6myFPKUWZtJb4QqVK2IysKSR2p+SssCVfdipLJYw6H+eo737t6NgswqUSFB6UxVKFUAWGnymIeyu9zgxuZks+WYXmZrWHih5KkFtA7x9AAXW9hY0APDvk+bB9a0CoPKU5RMImtZTWGu8ru8oqUAa17dNEqGICqOmXNhySpffpne6fIPSd03GK2b31Wuz4SKqPybiNCGb1o/Rl8+1uxaXjCDahgaB0lU0OzsgAjMGTND76AaUKngoQ6UlNtMqFWmFDgtVY7ToZdFeblVR8npDGCh0liN528X6FWQnaZIomT/qSDFvnJnNyl32KA5aAA10U5hJxRVh+BXVNxcgHTG8OkzKRWLKYq0Mk8JFjOOWgV9pEQmtZjEHD1sIImUJ+lrRsZ0X+Ylm6a6Y8Zi3G0TBsFnijQFQzEXM72BTeRrmhXZQ1EUwCYyF0vdYLY6rDSdgm9IQLWa0FZ4DyyFDvLQlIHU+ovFLMFCydMh0ePc5bc0Q2ZqvCLiFRoCeC2qZ2BlhiYa2V2s0KIJd1+ZVjkcTY2qR3iKzDKbUS4/rFAZuVasVbnKIhvEhRireEibp+iMVi5haXV4TqljHWeQs4qWhmrpVMKHV7ViC7W0Qu7JSI+q8JQA6XMTSGRWm9VOVYpjY56oE0JJLQ0mC1OuFVC2gjXddKCxd1O69Yi+0MObYh+IVTiYq0W0QLMUak7fmo8uqvRQ+y3JreaKRi/NLKsCz6NYRegzvapLiIywaT7heaqn7EUNxMDRcdtizyOP4JkQEiLfoHHW6OdRlSVzVpwuF0q3ClnCSuNXD6k20EKSC0iVJF+yTEZX7xzdh1ezEJ7KqNAwCZMQeX0poz4vQYO1OYfUEYX7oFPaYZZrDHi6o1hl8Cg/qOnLFFcRTZcs6YaTZZbmSwxkqRqmFVpaL2uACG8uy5HLM5uGp8AIMIIIr9BVssotNVz2pmu6/PIcQ/v6dEcVKuocWAjzIpIrXTFPu5wERavgkuqqU/kJSRRL5CkjV/GInGsvLYfmieYSH+RuqH4VvGRULeCQGDiNbGFcWCqIoilk8LEYk2tZLRfbbdGBzlI4pjZBs3YvLatMmFOZ0TxFgjNHz9GsTPlafqQdYQXBbK13p1z1qqXjvmv0xsZiZi58+D0zAAAQAElEQVSVR85rrevKSdXPvrQypx2QP5IqGJo5YTbhy0bQ6oRbqijTqraoUVmkvJBAzWJW5Fpyo0F4G9Gwmi3TdxCDmp1u0z3pNFpREPBQ2VJhLopoGGQ3lUthRsj7YSqQp4TSdilLBUggJJvqt9nhvkwpBIEEJaGbe5mvzMhS69q8jVKJK0jpOKAKMTIEEFYa0USVDQoOKVs0X92ym1nTrwEtfKPF6ggnXHjKL6zDVo6CJxQXyOaAWgINYMGC0B9WYQqICUbRlg4FhIu+dBfW3NDtZBEVgpnHQiVRZhLIQsc0cUlesBd7hyuCxgzPJdkEyAYYMi2PxsO5ElgkXFkmxwnAyzskcjCa8jmXVKir7rBlKDH+TI+VJUMrd6vZjC6BIWVE0sN7qsynLlqUi1llCypkLRJZyyP6eSbPSZoTdAGlzg6hZanfMtysWIFCi2oTDYrHxZq+gYl7Y12RgYTKQel0L5iXD+DCNRAdaVV9AtfCBC22juZdSTL3FcfpEjfrd3jjcVMxUMZMKG09qry0p40Le8thi+nQpaAXHDZbn1Qhbw53jL6GHUxkOGC0IlEGRbn129H3JgHmbtsv/sr5jcpljJvt/5mNimzS8XL3u//7PuSvfPuXft5TP+QxD3rgy6Pq3/7bX3zRl3zx27znez/2o5/xfT/yY4eWPhr32x40f7VX/kMf9ND3fPFnP/sHvvkbnvvJn/iwd3vXV3/117i5XHrvclezK6OWOHqtUP0CydaaNksPfOAD3/ot3vIjHvfYb/6SL/yur/myZ33EU//Cm/6ZV3j5l9eU3/GYa33p133DOz78kd/+9/9+RFwul4954od+2tM//P73u+d3nPv7ewAB6g8gwt89VJkCiNIVKK25HDQbOfoEcobdMg+Ve2iNUgmuI9c+4zhO3CXKcqlS6QYBY4jZceBITm/KTTRJg4pd7tFus93a2PvQjW4LvCL1Pf812rU1bNQ3mv2SW4sbw9BnXBJjzbH2FtGKDoNbsKEoW4m4a3Q0qIf5XJdlG5RLNve8d0HPJvXlxmxWuSxVsZbKDA89CtpN7vJS7bIG7zMR5nQot2q4yfQ863BVumBQjWsMZrDKFlsM7zc+BlKlq6PG3C5107vnJcsOQIs96Vq1YOmsBsEGzhhVN+Q9hXuiLknS1/DU1b5cuY/tEEKF64Da5mIcGatwlIUcDrbsrfQofosJXSFUGq1KbW/MYceQD9kUn5y3uUJg9W6bo3FVX+tmx53lN7ENdifTzsjhkK99a1v0dk3oNUoPXcNWpF5S3Hv5hmq5eCybuEAfhy/PI6aWnNtiT2hgHaWSd20rt7IClmMNOe8hFwIp6RQ8JST77Kbd2bbtF2rTFaiDx7Vfp+pbwWjQ/InzceYRqXVIbuO43OzbBf0eqpYuG7frsu/bDG1M0mw1RUV4KiLr/Ipjy7jh+e86SrWWtoM7amcctg5pcz98zPS544rUI+/bTdyX93Csxr18BpZFXK7HmPcyjsPkvdYNlT3Y58DeqIw4qpb8K2e/YAy6rg67vfi+UR7XXrg361zkXnBoqSAq2Kq82u113N6LpQqfOMPhnBs1buqu2mUNKgezZq40sPxS/Sa1+IbUHbJNA0oqBqsXRi2Jd0EBccJ5OqIz2bZylacazBmLXahNeXTP7exc0Hqjbl0wY6CP5m6lYiBDGndaVhGla60HiIx+zU2StmA7oq5zxYqMAzwSBTi5dVVeX2qnC/30Z2oZKEiWDTGwsiPbGXge3ffmR7ejtQMtVtNZcawxju4HZ9hcUqs8MPXhCjnkCZGMM7Gs2Ex5S4hLS+ZYn9Z61rYUuKJuXdrYOqnAYd7auneL44JS065aghhHdRFi2TFFe1vM6gvcgQMd1Xw33zUUMMh56FXAjNrjzDn3bsPZqHnnCgrRmN3KIv0IVpAFMx812iocB2tvSutKP11h6Z6sOXK7nTwObVqaKQbdNuuX1ruWzl6HcrzOAFDQZokKSChQS7nQW2TlnrXD2M+pxV5lhYzUTy3DuiHESCgSzmuGNPPuC6DqEoYwy+UtFQAYYrO6bK0NlzrIYuGYaWuvdSXvdZVuLi2LqIfkKsyx9c0uQ47vpf2bJegOH4u+wzNU9HhBE6qLEXVYhuqUWRcuhmrrsNrDcw4X8QXBHhQVcwPvpG2YhZhnymau1FJhXyLdkT1sa3mx1dayVSfMZT1pZ3hwch7Xwj6oMbbVTeempcJ241una6F42JRPBWZ1lNUpwctc4G1H2dUATkcBo/KmY7uY3WnWvQZqK15W60EuQvPN0FBugZYyYVWb03KGsUhHEEmzBpPA1bDrK5FtChwu64f4z3X4WkXVG9unTUXvTPMxLjcjrV932/drxK2wk2caKLWCubAlLhNjwZCynEQN3i0j8ghySndkwdbk9DjYZ2kgq9zZPcSfUSpG8M4Y/bAR0GuvPqH45MXjoip/p7UbWqc5Mbw2n16zHZCKdM6t8aJl8v0i78xsCKh2+wnKYDcoTl40Y5pij6v5LWwV9OqsG9ZIfREQ4mXueYTyJnprpuU4F3zRWg2rpswULGdeznPuHH1eOtxFKVTIFFwWbsDemY3HVFE6OOAX0iGb2mQLrjAV4XtRsLxAiiYRRZCjtTuqFLHZbDkRhxiXSJEXF3CrxZxHxRXjwB0ZtnelBBQ/3wfmpWIDXIaUuphSZqeyT8Myh9iDQ16rziisllScDBOCgtEancS9BX1hd1S2bdmdoitKjbPxgIXu69g8tVC4ObbLrd0ccZkxdtUpg9YXbOhdAxW1DMurHE4yUAV3E/hyEMQqHZHIvsyjWzWp8LU2zO5lw6EwRRtpHc3MokmCt9nVgrxpEFcTuuhkT0LeO8vsvOtROpTswinPQBUgjbqrVddxQl+U1iHQctZaS0WrQojpYP3KL/4sgMs27rm50cn/9fif+4l84Mu9/BM+8OF/76u/4kmP/qA/8IqvKAiu1+s3fOvffvdHftA7fuAHf+6Xf/U//1c//Rv36su938EwdcBXesVXeLe3fetnfdRTv/OrvuRHvu1vff0X/fXP//RnfcrTn/IxH/b4J3/woz/8UR/0UU943Cc/7cmf9QnP+IrPfd53f8PX/sjf+eYv+YxPeuwHvO/rv85r3XPnHoXgd9Bx9/Zc6x98/z9+6OOf9BGf9CnnfwQZeL3Xfu0v/5znPeq93721fnfI/wt+UXRSGrBKuaEfZe15pYqpylLyswsveolR521fynGvo+PaSsvJLGVfW+CxInjyr1UNjc7JiswMifPO4UlOjTio7IvyaZzus7c0q2QtVQar5TkhIksSU/l6N88A55KaVZWtVJCtL9phFgYtDPVbd8/MMFeCZcuCDDDztpn1SK6Q+FgmYzAzW8Vgbq5ETKWvc3KT5jOajVAS1oBM3rr1BloASrgiw3USRiSVi4Ik3Q73a9mxrHYilOh0q3Z3ThkgE1s0X+azHIlSXaw91kSmCZnFCq8yg6rS1RgdWic26XBCAwiVMvVZ8wqyyJmKQE+6J3r0bqN1qxEQqvNABmSFlyq4N5fxRSbQCyPPCkyU0UzyO0+7SrXIBGu57NFI59qY3ZZhGXezRa01QKKzWDNylczlGqfnIVktm+4AFV0lWRXT5CJSrR+64YSbRkS1OKxkXTEN8ErkoVlyhOaoDkVDGBw0Cim0w2TB0bi2outeICymp8Yst5AM0FpZJyRqMcXEVqVF6ABhoC+cUcqC+TK/NkikPJfvgLqTWduUyOaTOIETaYI176Ihukgq0CAQjWamK7FW5Vqam4waR7ZZnKiUbujHY4qmI2V+ptixmW2kLZP4mgD1MqZ52vmjEZkVC0j3Maq3qbtyWnlB2Sd74IkhK3i+Cz/1ycpYkik4l+JejdlJZy/0LJ2aW3dDUFhHVZnkA2DJBa1ELRa42IpdypzZKhKCwNOJ0fTUZ7NhaWspoXfl12befTguhEuMj12drHkaG2s7O7WyK7Quk++JmA7tS6FFQS3LkCMFMXr4UDaVmWKFFZQV8ltYM8Myl3CIdZJLYFhRMarmRzdFOzdUM7RtqsFOm6ozFWCwJWWRJSHLjARQpDnLdTfTAh6UdM4oURezojJXKJZ5WKpAZcBW+ZF1LG1bVuyFvSIEHA05kEIVid3W7hEdbIBX6bdZg+JrbRBdqonrqjkTmuOpVe2AO2SVBqeHQmJQdelLaBem1gmg6HGHPmAFrkJmCC+pK1g2ZzNv9KI8KgMUY6lGCqaEXkJPcT6mtNYuqe6rjdW08m0rHFMyhbZgarLDkSeVujwoQMqMpVWiArghxLHR0EnZqykLIGzIw3SomJeHTmCyQ/nTLBujlWzI6TVHsTWNZWmdeVxDGSLQtODo8t8N7ukNjWwSgE1ikkiNLtY0i9bTh1gMjau0OcmVXtHa9H6owrWbxkujhBCVhQgeYXHIP8KJllnX2vfcI2bD0nQA8jKrMTcL62tZrma0plpVFOJDorIWS5FJIS9aHZGyrNw0txbWIRaVSisvdZrdOEiXtMrgjBZSLN9aKujrmmtHKGw3yJuMIatMnq7kgsQge4XbhJChJlRdU2YbQjhY1pBdZrnKds2IoqE5O8wLOgirsKg4ao8sZRb0nWYgcUb5sJpMWYUja8oX0MjWaVpUj2P1WJbi/lxzHlPaTwyL6S7eOWaz3VzWlWfzGMyOkgAEVN1ssS1ruXVPjjz/bh1sZ4UnNoWqdFaLRJaVnJG/kbYwSmXDS5s5oV9eSxdT9VhgsCjDswpMc5DieFqGLqCWXGWLGNoUzytqNmZnDGAL31Jb0fCY0r4akFkxhfxSsDQ1TlHeO/UIryONpkhiJedCrsY1TCUyS58FlLR4w2byyuTJCHT3bk4U6tAhzCmWB7Azr6YqbrFwvim1IPmYI6vBaa2GHaJUo/YTm5DFcGp33mzIO5jZSFiekO7mO0aU03UoJ7JOyqJWSzUVWUVPPTRwT5QMn/dFFKAI4qIfc0t2uNHv2uCUa9HLfLhyuLottytxVJkmgecvnE7RhcWxlHWoMbd+e2mruR20w1DG3rY7Q6l2dAU+KtULQ0ImPRqNYTwc1dNdjucQCumSTlLiiUllHmvCD+BIHKxr42GWGkDJT0V51nHkPBZn9FRMky+9995IEQLan47e8Pvj9YoPfPknftAH/q0v/6KP+rAnvM4ffy2BX5k/8eM/+omf+Znv8H6PeI9HP/bpz3r2t3339/6bf/fLIuHvZLLw4c3N5fX/+Gu+/Zu/2fu+x0Me/b4P/bC/9v4f/qhHfPD7v8/7vee7P+Qd3u5N3/D1tSkiNdJ+J2n/5/2IfMk/+RF92/OID/vw7/3+79ONO/d/wMPf+72//Lmf8Wde73UAScP/e14EWWDCxCXl+llGKrWAMayyygEOF09bcFt+fulXzaspHajRpNZYzQLLam+VjlbokRfkpWoYHU50rSACWmS4eCy5jiRUEVSXeNvG5FhrrDlq3q2qeyhFKfV3c18ZD2ixgAAAEABJREFUi0gT4opjbRIHLtRETGY4lECFs8pJE5tuu+rZUsmwi7XuqgW+Sl9ye8k8GUeYqyApRc0KKLajTLmlc2j26UyDNW8os9XbbqVKNUdmX4UQJtGQvoxHV9LHmlA1mckiIOEIZkosADLcrkP9VnVQxq7Ka6kEx1EIlKbIj/JQgcH5spIMlooQAWNeGBfU1ug3tA4FqOgqECpC6xxQPjq3dmIeh+/Hpm8jUsvLnUzI9+18MtqNLQ5bu0MFKcyrqRzYTAgQmztXJKlZsq4ijWtzmEnNTIXj4mg3vcZ2SIzmJIUq2JbwlxN0824mE2kGo2qoireZtXDT0i7EpGTuqCMqClbmUPgXqIoXzmVqPXs7rqOuDTHOqYOtiEJ4rS2xnW1bdbxN1wZmmyr0YKAs1XjQRAsTcIlWcJJOtCS0Zj6cu3s2wnRh9cCWHNPG7qPaxV1XFNSuqSbm8JLrsovJPIjbra/thrx0aj5k6YkeKc64THPFONRdsQPBAqrD+gT2jBVH43W01EoWpvud6O43OpAbtGppjKH1uuZqNW7b8N46BZMgaiIzWlgHWqvyRQuXgQuW6huxBEsWmL3nuKBvAT9QV6vZObehmVxlc7LWZCzoRVAxku99puNgX+iGroWMRQH6KVIxBxyEFJU0iaPSbdY273DZgRzhPbeqS+mJshYqQnG7IFuLakw4YzXRO9dazH2kno8n4Ms10U0Bx9GwyDNt1IBrUnsAmRiJQ/Gc7JldbiKPZXn4qZE92la8WLShqyG/poIZ7gpwEHIIDTKbwOB5yHiF6vAZdg1cF845oZido1bHrFqau7RSYPW1Wgin5FTlUz2ZaYpilSFkiVnCloJ19LkUjo3UcZekjq3BpVjglE4i6zhUuAoMV042Px2uRIUtOb+VYtyz4VYYRHpYVy5kaphZSJFXIBanwue7tEK0KisY2Xqh6bp2UnEwpytkidPQiTpOkrRlvqpNjFU9xRgN22lT3B0wp8q3lfcSvIaE4rt4hmgfFYPZOjBqeu5VJ4MrS4C5ycLJO2V30DYaYVXwJk8muVa1Y41UQDNHVocoFwJcsNvOUdqcDpcMZLJsg1/MB5tBBTJDpJ1us28helhTJFssWaPd1tiqCR1cYDc+Rh+99e4qyU7zMqhwpV4KELueKgFrzX1p0UpZuJlDrzKLmxF+s5vviXkSQBQBIsBS5ig/waXLR61DEiquMrPGQJMxUPw1PIdpr0M3QLJhWVUrERqZGmXya67YDxyTEaYZStptU2bShgukI3A3kSqJCR5p6m47Y9kqRX0FmebaBnTFdHkcTZg3uSAv18JcPdcGOqxAkXcZ89hs13MNUUjRyLLbonbYWeFQdiwo/C27Ww8FqLAHZnBOrCPjSH3oR91c884RN4qm+Ik4LI6ue1fWlTwchSwcrFvo8N06hMgSx1dWrgpEnpXzxFLpvLiOUasZpuHaVrRFQyNVEdtMxAIjtkotCjrKIPmR+s3zQwnelqoi8rvB0Wr5TORK7QDjOMdk62tss3dBmqLajC2XL06NPJUzZi+0lJFC1aGK6xp3lynqPDzKWP2SfmfFHY3NUqakNQ9s+zq/TJ3Qy8vbNJsxalfFITNjz9STDlZ2JlkapdBYVJfuOpqDA6HSq6uezXNYiYJba2MzjrTLGv0sCIUKS9m/eKIEdwwLldI2m+6wgX1hO0pJ3KLbGjh8nVvLDBWzQ6iaDFiuyy6B1bm2mpsCkbA0a0BvZQPp+mSDZkCa+LfnSUPWYVhQMKzSYbhLadcbCCu4pAdFccZxzOM653XWsUrESs4S1hmRmUDRjmazh6iiwdQFuNxGyV/uxLG4H2dyJUpryRy6LW/VZUoCFoRFeS1yz1/6pV85xE3gfq/wB/D77PVKr/AKH/y+7/1Vn/OcF33as97qzf7cdrmRgfv19kd//Me/7Gu+9gOf+OR3ecQH/dXHPfGLv/Zv/sS/+hk5pru/u4O4Cz8EPH4vr9t9/7bvfsn7fthT3vfxH/Yt3/53910rCbzVm73ZVzzvM//3D3/8g17+5UH+XuT+vp5Tp3UqK6VM02mdn4tIjzAVdWQ6obUKte6/sa13day+sq1kwpU3ac26t87uJ/zQZHOlLJQ6ZYtQn1iFqIpghSP0BcaIQInyldQQWwaJS1eyujeRXhmx9Kzo6pi4O1J31PlApSNko+rKkXUoc0oVv+4q9uiQ2WbrVLawJmrp0o3bKD9LIXvTQsNaqKtTZa0R1s3afw4rcxpLUU41syis9IXL8pvD+i4kMBslifL6cK1v5BzaWSBPAdNPS+XmXBazHdFiNazGdBndqEVHpVblZWGGFs7oUw+Ohy7mIDaNmHnMvebc1tqEf6quLJ9nDZELNSbb1fQEuO3Rr8c2Z8OkxwEVEi+cB4Sx0J5yoDVrm1vXpYxe5VAXEOauclmLAQig80ibq6V60gxW9XSP8lWQiaSZG0gvqtxcmm2DbFyqRjwWklDcTWVRfQkqQ4qJH8XpKzJORWmp6yjMbLugL1ae06KIkm0tQiBbIAsqqxIHbuEmo1AHVkXYcpTiB62HYIDcmB6r9qqdRqBBVq+SYN1MVFRNQgytYBNoaGqAxlw2yw+2ctf9Q5Rbutq6awBjUA+TIsqOuFRoSSc9aDJfWCiQblZtg93Qe8vuc9TV92h7jcUL3M/lZBxx7LZnm9A+QEa2Woq+dzY/TRUDYmWuWXO3PJyl1DFZx/S5933xylhtRlunN4GalbMkbkv0gomeaR7e04zytkKijgyBJxG6vywnkGoile2UAQ0zQSv15WYAIyNreXBbVJyWLLJVrnmxY12RWnI1uktFNYvGabVDS5hcBTFa6yRbgEQpczYNLCeLhhLrRPPMlAydu5cYGlCq1IKdBiKbL0lM4IDNSQXLwNGGezcJSplXsWZWmFcNHJ27CNeKvSij2znICmEZ/UhhVSu83NlYuubaxx+I5TMxKxJVBqBniciygVit5BTpTNGj9ElQNogkXectps2JWZVJnz5iCAwoe1AAE226Vy8BpgQRNGFH6+KLMOhrcZ2jojRQAjJUvzTSTt2NaEYSqSECoPSaq3bHujh7N3bCz5lciJIJUN5axzC0zBV6ep8hNQA5O85No7M6ilXii3xNWFGfo2xFS3QDYUHoEG6UCcyZ+34c88jjsEhNqKJoFPP8s1gau5KHYIhzuhRIvcLbJY8Y0MFNMYNZgntRkazW5cCQA4WTXac5bqoNaD7L7os+ywMoSwGgk4ROqgoVosdKP3IkO91ahw+yIRRlVbn0NA/vyha9S6K1tHYSoilOEtMqziHWm/cGgLLTVFJhOkdSRwbWyru2UIFrPckJbc0wzZPDsveiNXnCZuYmYTIloeIj2jSt89Z0VZMiDbSyOA3Ps1SVM8/1atRY6JXNQjuWPmDSnBZL+VA4MV5hFi7h7nnOanI4TBcEte6lPC0HQAKJSkApyNZbv7Q2VChkNoqnYOkZNV1+VWocRBPeOG96NKsTVNiELfNgJ/tGbhbe0jrLkVKg2jtm6tth1UoFbZ917BGxWCl0BdWBkpOZS2xGUOiVin7Im9TAo69oSxayKilTsEBb7Gu1WqfnpMIcWCzFkW1aP9iOFCNS0kpQ5+bXgd1XFnoq7p046YyAVOX536K/mgSAstGOpaVHN3bXKJTcMJyItSrNc2BF4CyehupaXK8wW01tVuZUESoBiyZj7TA76CUYciHlVO0Fredt+kUelBxhWsFT3Lft5APKRBUuA2RurLvQlETAD8griUnGcpYpvqouErLI5b4KSxgk9BK0oWFVlVCaQzvyrdUgZF0ABZ0AMFixUuKl7+ihjYrMaRqeRpywJkxvglKOnHaxGt2hPiM2QSFHd4HDmRExc5UgR6kAQQZ1xgA94NKlQ26ygCAidUStKTQKZ0+apuCcV1Vj5kHV1OqpYDkjI+eCUE5rqbg0mpdjwfJEG0qH1acSwFFWMKN1N+syIXwFZv6Tn/wX9/1Nqj/1P+V/oiJXX8aDD3y5l3v7t/zzX/jsT/37X/PiT/+4j3mHt3mbl3v5B0qI8PzFf/uL3/N9L/noZ33au37AX3v7D/yQZz3/hS/5kR+rFJC6/z/k+Ll/+0vP/oIve/v3efijnvzU7/qef3R7e/4ltNd+rdd+wad9yud/2ie90eu+jtD9H6L4/3GhdaYGlBNnfhDCWITiWd4WLXT3hF0UC29zWKrG6anIJWsEu9qcKn2gSPMhVuYpJ06feDFeKjtC+bwzU0ztXN6WHsM2kZppkDIlU7Z2WD/Q99Z2lWJzhyGNapbKUwkHW6vLpqkTElWZUAYHo6BpS+sxUEolPrPdgrvy5zhSz/DnUqFS7pjBo7fYDK28pimzW2BAxb1clxpomcyznprKxkRcA6cruqu2w1g4Siu2vuCVLUMSqOddc6sYTHpphXhgHpV72L62FZdjjtwHlrYgTf5bQ7pFG1TP8HSgB11LtcTVc3fVBlWUyBlhCyYjF1SMdfRSEZpU3/HVu9ZOU88uognbykQud9kAVVO1PQtUIoAJJI1FpiELyzvoyCgkyiwNodIrv+HBhmI79Hwp+jW0o+hHv0kOwiTivniYezet6mkDc1M7UBCKSxItSYVAtXdBhWuHyqIaX2bpjpHGyHZaaUyZonFWc0OcfHNoe3B02dIjz5LuhZMf1dIS3K0OpuRv6WOxFzYcA1fUWsVjcRI5Kkx+s84XSoVzXyP2XlOOBi5LuwutX7xqoUpvEEhH7FMIeupZcnPjfXEtph20XZCJExSGC3aUVA+X8z2GwxTwi0393gLn7nGe9b5RSMu9BaAUXMi+ffYQm3De09WsCubdg1UNy33SI8RqRfRIhZ1hatu1hALPQHGW7fQr29XHMlPE5OvufYKB1CsK0xhUEyDChXSQ6t6dUBLKGLlcZ6+iwTqE32yKB1luyyC0tiO2KTvy/KCeVmkuwl4cnjxNCVTkXJH7anHu4jwDkC3iR3P57agmUW4pHA0gw0GgBeVezoyM2eJoiBNVEoqUPJsSktasm8uyJsXiATNKKWrRtnXy0mq1XHr82BqaQb8ImyuzDqQMFE8oqVvBZ+U1Y3GBR+N0VZgqDTFaF3tTxaeGHd6ubCtNeGsD0Aqd3Jr3dv64mCqLpzAywSh2OGzAxcaaMgwI8/IwzMJKHin8sbZhF1kBW3GXZUgEpeOAVrgzhIQQR181dN1yeSzE1UN6xG9PolltpjUAbQrLUx1wLnvcSqELCdSJztiqRq0tJRS9TFd1pcLuDV7TDwkCLNNd89IAlPCA1NPkc8JKsOWR4pZWEb7kgFao8xpzWeTG7BaouXiEpaSf1iApwqae+PbFMVuLpuo1hHkkFoq9fPSttSGFVZVnXTM3o/Q6xdaUqEQW4y476rSr5CUCupTAtDp6LnZGt6RjFY5VESWOYgXLyeakSOANaPDKsZTPnAPCVSulTu8myQTYQnVLhyBTVisqorbNanvpGcJIKuopNzI6gD8AABAASURBVFkl9vl2YbNq0CfWaNDXyPqcig3WUrzkJM7EcXeahhkzqIvlaDfZL8sZglKbNh5yMMlzdBPsKdZfEGx7eIZv0LbDE7ZK/HPNK6TX1HzBsQxZVRDerrnnmWjMLXuXfXdp2FaMozwNIst9BFBZdHmKYewKuzHLpL/N4UczpUKpeg3XZsW303xDc1A0EMPSMloJCdRiytXAvKu+VCYQC1eo/ABtKegFeX0kdovd9omljtOrO1QJBJ2yt7hcUtbCyrVarGFZraZC2oCep82WppCz3Mz0rQNtwXUwKZhKjii8JACZVnOuyIzCAqe8Mk+YQfdFETi8KQQBK8iApewPMpoti2CkWQw7rB/WTq8Awnqy6V04h0XZCs4j1lElHusjXRxou9vhpvmqHL07Ny9zlsnCU5EnTZm2F2OCh7ZF0UPK67hraBZCUAlelsySRZV5eiArFXjKpThESTCN6MJBgmi1qDSQlqTMFViNRSW4DtLgPbunEt9nIkKHZwo8xjm1pLATN6yLxLKlDbpp1sWkAGKWd16a66GaQnL4PHpR9Jtic2kyAAp8hnlTHvetWSN7zr7S00GbTdG/ljanmik+MAb0wOQw28vuhsAFvUNc7Q1iW2uJijiWSjVh4I35ZeuOcSSXwPR/8mM/niFm9T/zBq+H38ev1vxV/tAffO93fscXfcrH/8i3/M2v+4IXPfaRj3zd13nde+7co3T9jZf+xo//6A8/9wu/6D3+2iP/1Ds95Akf9wnf+O1//xd+6Zf3Y/73+3Tvdf9XP/vzL/qqr3u3Rz/+Td/pXT7zc57/Uz/903Meo4/Xf73Xe+GzP/VbvuiF7/RWb3kZ479f1+9XCVVEKZGUejpSZiqRUMxsWWpEro+V55+FtWPeYl4rl65C3QWtkFHzyLOrqYvPNW/rCHW/C67uV+ooVQy05aP6xc/tTC8Mr97LaHrndWsxOi6tekWTllqqavvWmmELDD3viqahJBHSH7uqkdUcNbdR5t3YcPclwzLlUShrzgRR0o9Im9yrZPbkZOrMVXxV15TCRT/g4VitkqU5lAl260PW65MVLoWNy6CKNJNRbc+2V4bPajM5NWqCC6pVuBommVB6NoEomwgnjfrR1cWtbgZHN1UQqZV5voqy7KVZ1wmR2j0vLW9kkqGsQgYATZV3moqRDEyVrLZVt6UC4llQbMw7tqHOGu5QdYIKgWMFbhduy5Z7dMRNHNsuKEgNc+8NPA1zWIcJx87R3DwPxCqpgTevi6emejRoKXCt0uag6jC7tzU5G3Z23raDt7NuQzeQhAmSrMrO6C2bWSO3u8q0EtFRCAZOPJbqHmBlLblNuxzoq0p+m0O4bbm6ULCihhiBS+BmP8a+IzItxYC1VY0kBJRTLuZYy+PQWInNkVMxyuTdCKnSKg6SiywsiLWpRnvgKFwb7+2+Y0zxXvrGnnfW7DjOEJjY3qpuqi4hvAkqtmXQOViE3GnQO+fyI/tqPTYFEOcriqdHOmWgllPLQHdTBzDvpFkg98wDWGS5sRHei1twNPoNuRGdZMq9XDMVg9ulmENeqdbPMGqdRNHqDjiWySBdIrjBb0CHXoI0ppZ7MxOuA9IDCnMFl8pyGTy8GtoW3eAwL5AODsgcVC27Tl6Du9yxvSEAS/QKAR/ptdwqzSLVRWW9GUaqhS0SevdsTQsbvw3stYcfy6e+EJtQFpVDGRzsk3Yt5AJRbnDKUNbCqLnV2hDCanKE/JKlTErhQqy2QqNNYDanrNorjrgiQtTqUSOx5ckb5Ue/Y3bDcKgxWkzDgZI+kauzjTQetOBga6O31liCKNw1ZCLFfF4zd6xEOk3ZYtCAvR9HO4pl0B8JMmPIsVi0HSPzNH1lzBm5i2isa1Nat9KEIMub0ImS2DXrtGBZpQjQgi5Hzwqr4ZFKjxv07jfe9bBgWb/FSdxgJJHD4v6YNzpmbeXezO9YuxAaFHbNURxWPYuzWV26b8ObD0BLDpe5x9pnRhZkjzk2Za6FQ9ciFCKBwotpMDUopDcP1kRBJl9p17IlOBN0VLOkp4R4l3Du6aVqriVfrVZJ4gydJwTZEvyVgGY5q3yR+mTLcGVesU+s6JWUGkyxq00lqHmOlf2W/cqo0s4xSyEkBURcyQOcAJISggVU4sh2b/mtixSrQxmeXPuoummt3ZR3PUuKO6usUFF1jcrDsLMCon7G7cylqM/0gKURKv080lRgJTzbvHaIOgVgTsyFU1Q4coKnQQaWfEy533tdBjY3VJuBKXRjIVKiZsl5W2n6UlUfoUnOMjvokwghjzM7skAKoSOxpGqqDbjaaqcm2LHsmBW7cFQybIp562hqcThmnlXxVqEhGuGCbJWaYr/CD9nasovSY2tjo6v6KCHdmY4dNqei0MqAC6n1upgu+6EM39s85HtxoyjX5Qn3BaElEnNFYNHPm6UtT/FEQdmFZnCYfiWYMsZmyxj6gAKE4L7FccO8M7qozkweu11XCfFqRjYUY+bKI2MXqNtOHIiYUUF6X0OHVbGFvLRFMJRVO1nyZ6k0iZiKLC2S5DSbsifNb6W+YuzaOqVBfSS8UAPZAVozay5GQE71bBdrN/C2jBKcjTbcvDWZp4yJlDd79Xux6fdKlzVuSiD4tXi1zJMZC5mlZluHFBs7Z9tveUzdthTs8Fqb7Te1FEVTiyeNgECrsrhCADSN8OqNbrFj3VooIZSxUlV2jZAhNJQkqYt0kw44fbHNAmvKe2yV5y66V3jMnks+emPv49JG1xIApFGZ3YhN4YJC2ecUfa3cJFAHIGGQHqLOq9FbtQv7KOpiIRdlSHkmYNEUwK52SJauv+SHfxjAA+5/zxu8zmvr5H+Bg+5je5M/+bof8ZgP/NYvesE3fPGLPuGpT3rLt3irV3rwK5lZVf3Sv/vFr/vmb/mQp3/EW77HQx/6uA/7hOe+4Nu+6x/95E//3K+/9N7fpXdV+I8vvfenfvbffPPf+86P+vTPesijHveW7/XQZ3zap33/D/6A5EvIg1/xFR/yTu/0xZ/1Gd/0BS/4y2/55r2Jobr8/96DJFB1/hbEpvQQe8g0FkofTZy2wFQFtv0oVaATqAIaUpuES+eANZUELZ0yYy2lBsX4Ss8QP5NKUhX5EEOhMKqR9zSGs5xmAjYCFXonrKxrbbg8WSoN5mFUGVCd0aH5qRFRlctK+w2Cm/SmEqHLVBZOR1IJ0Ven+ZQLHE07guIqLUsCuofSeWSYqqzROqlhOoRASkIpSzPKFixpctKZbrv6kNq8s861GJM4ziuhXmMFy/KqYqbqHhKAe2fflMXpDPrSOF1NNdnqC0aAu1F+wo6GXVspyTwWQrP1FESKDakyDxWTQhnYZIoU6GbHIC6oy+XYxt5Mu4MlNJO63E8DW3TMLeY291YTUNOc5nPYbKX4VEINovlQQa9yWypZ7IBvsI2gvIF0FoURs1gRrkjKP8jLrtJ4CkHVYG0WLVe2DEosVsZMzZXTJ5fKVcZpDaUFy9bW5t3iIrcVB0iTdFWOkAhDbal6aSTLSjWv4Atd1ssAyMo0Xyux7oYyaxELtDKf0DdxpnA2U9FdkuQ0CWFAV0lL53m1IJOF+0AzGRYeqVBspYoq63nU6SulU5qqZbVIm1pMhQBMX1orH+6HpOjWVEPQcnjSYKe1sL66AlYBWeTCtsfGqSBZgq1yINrKVBJJIkg0uS9TG3JTDxLpmDizqEMGgyhKcjYPOkqwcXNuOvMwTzaMlppLZOooaWidrZ0uHKZVYegqCkj9kgSAgFcwV4TipQzKc+wZUynBsOYyk74LK0kaDe4SbpolO0ggUNkyR4blAiNaaUuJthv2jJjKGNo8A+eVJyjN0r1QoWDJ3XIpFEa6Zp5LK8k6A+0GunwZi9DICkiPxJ8olAPnDAZFsI7Q4RRQirlr8RTNhLZBl2iBFtKGIkuqGhVPIIWzLxmmS72ZvILIv6iI6+rSDMUHaY7u8qoqqxTVlAM6eNG3IBX0qJ5hsSJzFWQ9NDgSXvSq0pvykiWQQsOsh5uu9xn6DQJWstII80UPWZD6KXlaGWl071v2JsUHJbniVCEUVUHT1tLKsy/ZLNfMm7UG+YistVouW9GmdtBaMi6MYDtNAgHTy/08K0X9NF03T0AEsawkevfWLc2EgspMykiv5m2D32R2ajd6JQ6lXE07J+IEM8t36JvmebYCrtRtSlzIinW3WlnJ/AkU2FqdFmTiWG2toVJjVNRAKwItQDogYnYq+dEWmQKr2hEQvHPGvrSEk1uDGmoSVxrCSezGVbGElPE+wwgpbSsFF9Y6P1Rhyiit22OlL7QkxXUFEz7pAYd7H4Kh0veo44xI6S0ldyanMIkqqXSDWocts9mZfRV3KFJS6Hcpv6Q1Ff5iTxurj+jbkvupdIuyqlYaW7X0BnFNjvCcM3tov7fjmCuEZC+4oLEIFTtIJbxyWAp4QPz2TPl8N/DWaDJXKa09xmrU+jJbiDGaJS/PyWap+ejAlhwSzcqyrC7HvdhWQxAaE+pL5kTpgrdi010iUKFAhT5Zp97SumhLM4doYtZbZ2/wEU2h1t0CoQB6gqzqUzuPMhnjpLEBo4qiUjTk3bxZFcVCccV2WAuYyW6htCpD80iDtmBbd99YreCrW2y5tlKVO7BWrpLMBeVOPwxHalkQg2in0eYsqWyypWW0CqtlUExTHaMys2LKFENfY+g7F0c0XUzR0iDPywGcZ2HaU0CclqmyYqsaWZ7saG7yywseKbpwpcHsbKODKfVAIZGzTfTpXQ4Rgqh0udyFt7msipw5ayXDPZSSTuGSLACKvKHCY94sXBYgzEJu1Bm+thqOqoyykOllB3hdue8Q7c+L2gXvUwU/Srt56XRCf4hkYiUC6VXtoE0Sy9oe2z6JUPM6NcuCgEgP6Hbz7EQ10+PK0/mU5iPEyFbVT3PEq2g1G5dZaCqDKlKCpKrkWtly+XOYqkCIuWz0xl4/9cv/5mf+zc8BeM1Xf80HPuD+Ovlf6iCtvdYf/SMf8F7v/iWf+oyv/4IXfN6zPukhf/kvv8arvwZblyP3vvQ3vu8f/+MXfvEXP+KJT/rLD/9rD/mgD36/p3zMJ33Oi776//iWb/+u7/6hn/jnP/mvfuanfu7ndfzYT/30D/74P/973/N9X/GN3/yMz37BX3vqR737Ix/zTh/wyEc/9elf/JVf+aM/9qOpBwLA5ebOW77pn/3Ej3j6N3zB533Wxz79zd/kjWSBFP3/xyEegjgP/RSZNHELoRxp4jfUNXQqqkWbpYLSs7UFU913O+lmo5pjDGDD/4+9Pw2+7uu2grAx5lxr7fP7X71c0YuAeBUMigSRYEpTpSZaarBsgxqbaMQeFTtEqwwoisamUGODUbHQFHYxNhSiJhGtIJogqIg00qMIAkV/G+7/OXut2WTs56W5pPyQsvyQm/vuZ//O2c1asxlzzLnm3udtbE7RmaoSCJ6b9x7hVldsxuluURil3GOXxOlbAAAQAElEQVQpHxdj8JzmJ/i7r64PqghwhaurKNG+cYftnGqE7u6dSmiUg6vmUGZlTxxyJ3ezAJRU1bV0S17oLYHe/wxoPJQa0vd4U90p9/BkUWJ2X+NgZU5k66Lyt2QmOmjHruN6ye/ZNcDhS32CeZtOXAs8szjar/IFSLyt93rdr49cF7Dsvl6fhl72lIBoBmRq38BbqyuZqaq4UAa0UKR6lder9Qai5cnpurX20cJKFdAQs3J2QbD4Yi/SYfqGD5mQByrRmYh2OEx1GedGyE+F1qlrDStIVpPBogQzmlqUccs0XYA8TIe0dw8BYTqepeqcha6m4dF6sQ15DmMvaMWRtLHVma0Xx6shJDMsMGTo0ITAOvBomfeV+VPW6X5rxaDZoMmJ9q0QKl4gc/ojWIMLcdtRq/TuEXntfN7c5Sdpvue4+6ocVi2KmMJZrqqLJqq9z7C0zwE9stXNxQAroJDVZQB7Hl7vuZ7/rM64tNxe0PX9zH6CkaZBgNwuhokM1edY6EWc6DZZQ+tjVD7IferXm9eBFCHJvEZdC26N5+67PEAXve4z95316dg+PFVBtY6Rka2h1yyfskAsv7L1m42dwzv8XSb8S4YzRHcNe5k/79PXXGMshQSsRiU1bnwiRC5NSDFLP1egnozpVvzkC6kMnQ8K/a6+K1PPxXdksuTt1Wpp7sKuiIfU8l+BKUJ+SIeAQSwzvYcfNrQw3rskfW4oBK252V4By1ZsC9FqZlUgZqqJ+kTLgCSYH9N72hd8xvTwIbBscsFGOafV5HvwPODxablIjJZ7ekEuWg3UA7KuUMvcacGlx+TxoUc2VYyY76JeVEZz2Jw+5xS5+gPxUvA/aNLq7Y8/1Z2lTMLpzsbuUapaNOWgyc6WeLG+A55Bfck9s7AhV9rUenG1ugdNDaH9bj/+EXPUsjYFYhdvMNynYXmC2OChpznG6MlcjOFhzjTbLeqPw2X0seAXIOTRVR2BKBU8RBJBqBHVy4dYDyg660hsYtcKXk6qKAFhVa+SgXQFziBYC5LWdjp37+NHHD5DklUeOkMIGFKzLMh2+vBh4pVHIVNmlICikM+WPbvt8c2UQ20Nnhb1vMurrDUcEYxt4kaoTOdBYmNtpUun9W2IJAR5H2uZUjRtE7K3Zr3nudeJefcMQlNZbcIvmXu0AG2bRd3je/Y9q5/C8rbuJsMJ4+BwX/IiUEd+g42VYXWgWLPAMW2ocj815sK5Kh9SQYEzkSBxNFLRs5DFtJYV0UgazZdhCc2e2WvD7y5UNWCccJNqQsmSQPVw2iSn5hEcJoOG64pJouhUoYnVbEFIlkBZJ114lx2oLL2iB9pkLoQ/QNEEpvDS9vCbK86UzzaNr+HLp4IWKp8qaU9sXTrnoLtB5p1j+1BxNldkNeWiLUjYyJZOeUEbz7mNHiqR8r6L0XiId4c9XWthNAabgt96MBvZUtrXta/X+XDO4ZOXiCngU4mxMMbwlsNevGoYHVnPApZtClhknug7RbqMIQNVdEa9L+yljLEknyRZHsubbVBF5XwNW1rrcG3iHneuTz0DpnQhoU2S+YLBGFahTmJ1Dyi4cMDNoeo5J5zgnp1KAaWl3e4HMMUJq+21p7+12PgZUjowX21XlclnxUIw5iGlFCC72AH2I3v2RFjuk3t3pPBdVwvtPrPOtcur5dRR2g9L00wFrEFzF2wqUqIIukvZI9LRbcggRUHCQWAAoEzQw8eufMd97tO7eGiAPx9SXmlKvWesKKrl0VJB5CnvgnZmBfKgc7TYhvo07r2qzFiibpd8eRzyVgCBx1Q+U7ETd9v76vsS8bI1ssBgaz9iOxIKkug0EJYqM6UcgITKNona8WwsGWYYnnSqYZLVUTOhrJr9y/7rX/nNn//fHv/oP+IH4NvtRtDH+obv9t3+5D/+f/GP/10/6if9+B/3b/6Ef/pH/LU/9I/5o/6or/k9fvvT1/u3ffMv+qW/9Kf9tH//n/zn/tm/+cf8PX/JD/9b/rwf+tf+WX/FX/Vn/qV/hfY/+6/4oX/eD/1rfsjf+Df/rX/3//4n/PM/8af+tP/HL/plv+ybv+kbvwLJ7/Wdf58f9Mf9cX//j/qR/+6/9H/65/+RH/tD/uw/8xu++3cTg75y9zvOpykVnh39JLypEjJhD4fZLcYKkqKZYXa+slQtVFMp8jOAhoZiHPNShbJxPQW03ROzYmauxqgZPd6NJ5E1geZhdpQz259Pk9juWbBS6QYzKVNY1alFJzexgTiqaNGtMtIGQzrPsCbKIlgSC4C6YmxO1QtJGx3eqPI06qZcmG3mBa+EGpZgb+tq5XQdItlWT55pbEnMgibzhCrjPKoqtqaZBeU2tYiPzmElYxyQIdTyNkbZ2PANy4QGNSM9j9U2y/aW2tPxuDOtFrFozmvag9oYY84zng41uqxryhhDOFJzhcijpOwx4LSuQWNai4izEK1KdksrYUxDtoBtvAofWVftmedq9fdtjajegJzVcKNE9W4crQpJ0FRL3JbJJ57jIZlaQqsbzMf+2SpJqJzJa8NkgtlZfi6epYCUIDV3aFVxr2KoG7/jXRC8BdfbUT+VpZFU1cSQwoL+VUd53l4a14I9BRys3BIjwvqzaQawwy3GTF8IIsruYhZZbaf4RqfEUSXRrIaAYbfQKEZJrTiUWegajmE+jQ/+09tV4K9zZt2aYyJMt9MHJPiwdhdKYDlT4HazdF4pdOR+myek29mgNfWHJrKx5ZQmlPTLpWiICu+w+1BPLXhv0wvjImQrXRMRYCYzIGaeFCAszatg3s1tFFpjcA2bFBzyHkTDcq/oz7z1bgBtrVjfQwLKj7zXNTbUv4wx9EUv4FTtnfcnYYf0OhZSyNxWAWUHlDY62J13Ppumo9XUmJC3lWCUtgSSEt2U+sdUdnazqwY2LUMssNkQqWKOAz0U4ImhbNQ0zcp2uQgXSu9pMWaXfOuFck/50RIahmMuD1lkQZnAoHYHB2OZftxMa+DBXIBwjLVeyz8Mqj/W2yXX4NAIMFAiesjjplXNrNdj6LC0gVxQXsSRVllV+uskDiwgJzHa1K5eqWeKQVjK++e6l1/5xE9tQbo4GN2JLiqBR7d5phUF1Ni2to0ctFHGsnKrYXAHFtTbahfujbstyivlfJrl7LAKfXR414NVWlvWaHVIZu2z4Yd6omOw27O9HpgFRpdcFo46ZRUzGe3yb1fsuqNSlyRzFp7YupsRGghUMIUyXDa1S2r10bMOVC004hkFc1ku1OYFm6iBTRfjq9uwh70NIX37zHzbfJcacgGS2VmPkBbIA2NwmEFoFasAoxsMEypc8QEdDCu5CA/oJVkf67Bo27RDKhy6UYo8e0ysoUsuqDeejQT1bcJCzybntXKaND2ja7hQ002dl6CknvdmKhWYdMgbDtB0X382WjY5ZRaokJ9dSJFKNa8xWQo+JI4TGpc9EKtV2cLQBs2cVppMOkDkqDLhr1D4aCPxXJ/WbmxAk8QkaSIhOBol321ALoqNkghjcVS47Hdh523eIqa4UHC0/o6LsfMRwDoItc950MGWrlHyFDIgUldKLmovd+of8lVaq7frtsexlFJNctAasnASaJmgM5a+ZRXHjStUQp8LmhEIBf8xhEZIKN0xGF2RKjiURFTgiMvI1iRX3idVLtqKz0nbME6apeN2HqCEuVcI1inv4vG9K42xBpzTeOWwlpXeHJpszTx2H4uSDolGwJODFOBkDtaovsJGTC2OFMzQ1Nn8nDXlGi8tWgYOPcqElPDkXbWRN7K6gGSE5wZT0wYxHWSL20fRhVoF37KwWvmwWIvdaPEyTdwqnwcdCviEGWiP2RM+GqzPmFFHuyHMKM64Kb/d8gnIV64+eiSwYTQlkJSLFTLExuWvD3wm+ttxX20Xnv/9BZucrdVGDswKg1WPLs+CDOOMPWo3AxTWI02WPOGjtENi3UqJ+7wFe2NsgSLMlIsXehpy4LijnS0JqlIp5zBkWmtS6gJzePqlL4tuF33P7HRmmvzEf/Czfz66APyh3+f7fvO3fMune58InX473snv/HVf9wO/3/f94X/ZX/Iv/qM/9j/9Kf/a/+XH/1M/4q/5q/64P+aP/X2/63d7XZf7IB9w9XvLN33TN/3Wz9u3fPM3vb/8VlGb5Bjjuq7v8T2+4Qf9CX/C3/Y3/PU/5Sf+sz/zJ/8r/8yP/Xv+tz/4T/+DvuEbXKXu2zE6/31NF+Or8RBSn6KOlfiqQ6JRWk3O6WjSsa56eV26pXoUjOJOhmhZic6IUxET9lJiDUMrEHSzNcAnBVr3lGvHdrWqtbg7odVsP1m9wpVTrqT1hmXnp9o7noHsM+q8cl+R+uFzhXIMWRIIKZUKJcPCmTwivk7xuYCDTy7iUuuzcfpElm0bh1QiurLTsK++X3HjZEbu+vzmfvtW7hBzmKv4+iv1gkVZPN854tAOZhv1uILnPwl+x9zwI7j6UO8L56E3ZUM/edey/mAHP8XYNU55F2k5hgzRfnyFxnfZ6HFR3JNegG2ouTjorPmUgMKgIPcwP7AK8gb1SuWc3Dx6KxIv7zW4GnKunsJ8rNPvWnuMg5Ft563CPe+SiUPlRKHTW3W8SY24uTKvOo269ddjtX0QQ2FgnWwIk1KUqoiKwn281JOoQyJUdTBabqARYSdWc07TwtpGBwfZx++34KnnDQ6Ej50HojMyDDosD7d+ha+zEL6BkBUPDiW0S5XbimmIy/Ja4Bfe1xBfZfTqAztnRg+gXILXG9dJqhMSaRbreRjJNpkcZzCJ5qm6D1QjhbXOG9JeLicV5+F1AI1oFq97vo5ex0t2RhUA2c1RxWb74nAqHBKYavJMwe2bGaOehdsDjn4skiKFbBGvKWuMWAXBExM3EeZysppQTUcDpY8uOaU0RM+UglE9UterulOaYZAhWSFa31CArPLFmONwoV6j1+yGcJ8WeqiucZUvQIL5qLOx5vjC5qRnV3Z3HVF7N0LA174y5M5nHre0le6mBiSzPXPK0HLUx2iRbtQW1KNlEqWw54mRSVRaBxEl04bu0LRK6vFmyHTVkp1vFYFcZ4/79khNhbVSKBX1HJ3OEKdiUpfRIqaqSyECrFjYjgOZI7DEFr+DuzCh0pSma2wtwZRam9bjuZDBfPxjQJSvW2YPF1rdU7YLNCaw8Ty5B5xNqzYIQ8AxfM+xW9jH2qX1Fk0NWe3Xma8bK5UA8NQkeed+pu3Je2Lns9Hx9B/SY9EdqJ6NIRq42DrVK8EeO+l89kWObh4g3BJSpMR8r/5k402G9baCtqJyawJrpa/jQ/XlU69TkwVXg4SECVZSw+exmfQJFRU1NDQEcZvd3ucZ9xS75VQ5drSXNSiEP8kHe1l/LH6s5eMyGNHippDc3jkwZ01L1eynWg67nB9WL+YCrtLz5px43tkkRNtKR+BxLJ9Cs6G4yI82lsSibT4vZ6ZvkVerzHwp+LdDppeK8FpwkiW61qmzmbfJdNB7GgvzfQAAEABJREFUWMGy7ZjLew2DMul4h9ABGrznuKkMTBsx7KnJjTRg9VqxfIOx0yMm9WzsRnOIP6Csk3y6lVeqaDB0D81ohGXO05fXuM5xvNuypqu50H0g0MFIXS6lx4ZgRiRbae/2cfE16iFNOMB0a28bnBcvw8CDFjyVL03JAQ+daTxZuW8/MYboA821LgDFOtbbRz/uvyjaWO6ISNGNIJvM7NI6t/sE7s7n40mqDnjQGriir9Mmy8ddo4MqOMPwXBkz7WX4wuLqpwZIZIlyepnG7nx3fYLCIpepKhakrOjpB31EYgjMeUGSVGlKCyJPs51QOsDSJnwKco+0RJmJpaRrPFT0qqHHOWznRr0R2m9EmUzeyrGh/PHTE4SPmtf5GPnFzKk4arlsnuSddaq7syL7LsQ9uT94LtBKk764WphH94nS6y37xPqEO4EHYTuNVGHZidalrFmc6QV7o79kfMl60mBloQWc1YA4BWWBzcZj8139DqXmopkNiKjYnd02zrJ6NdYhYaRMVgJeNmTYqNLuVahENh5kVmnFO2OIl1zllwu1LjwRPgqaRI8x7Zpc8zm1pAPWKuypCLgP01wM0Nr4Gf6hawtiXruVS5sCzhCptX6XpFg7tvGGjL7M3QZBEoD+FCeTMHF4Rso23VadTeqe6BfUo9l1K05Q58Zn1dVkW2azOlMthkCOsFO2hWx94zfvX/wLfgE+b3/lD//h3/eP/1/+4X/qn/2D/6q/7kf9o//Hn/6z/rNv+pZv+Xzn2/HHmOtrv/Y76ceiH/6X/6V6aPnZ//a//p/8O//mv/BP/hP/4N/xI//2v/6H/WV/wV/w5/9Zf9af/Cf9IO1/9p/xZ/yVf9Ff+KP/pr/hH/kxf+e/9E//kz/jp/ykn/mT/uV/9h/4u/+6v/h/8wP/x9/3a774GsXh2zEQ/wOY3pSQxvMPOiZb5+iGDrpKB9FDRUH3TMS2M6ypMplT1YYOt7aG0hCb6lQmblUSemO4spBKG68YWwumje1VE4XGiaeapM6oTAZcEgFTIudkzNbyoWmr8IUE6QaUaPDMoaofKo9M6NBbpUXtJdP5aAL81OukXpW4Mc03qWKTx+RHqaRgI/OUh63SBFYwN+zI0ZTDjaYRi7h0IDebzRy9h/IuDzMb1RpYznyhFoQEJT0lAJJvd+CkDqgriEduJVV5dAmVaFnonKr+U8tF991UWqveETAgtT+lRyWv3VwFVWC+rwrV+Wd6ZXcGoCJXGh9mYeQ0XwaOhOlVX6qHbVNpy0tutiw0hlU+lUIKnJ2GpG4ozmwFL2SWlyrK9G6vchzz+zAOekdXNzXBMiEIqD6j256JAZVP4MJR0E1hlUk5eJzPgl5Wt5y8vNUz2oQUVRfEKKbW/376BhRBc8M1Sou99YRWks9vXMY2T9V0GGMMGbQxd41sd9iwGAzNg8+S7OKsnI8/utpRPNt4pvbqESC6vQ3ye1qIX5qiCPcooBCAWhjFSD6NpM7rHi2rjZoS/ZnUYHuln+YZbmuuL2xqudEC1nJ3ClM5Vx6Q7yQ4m7OG9QD4cJU21YbNqxWqHpPj5RyDOrPFNIFcjwf5RfULuunX1M9fe209qZNPYJGfGrtlcMmIwmPzHnJLzvSV5zpaSR+ZJiwcPUvLkxunA6D2NDM8i93kWms+/5NGuozUNAybc11Q40gFW4MpuLoBzRiyN8a1aSfJ6oUa5OPDpBYnGgu2U/wYpaCjUhBms8zNNLonhZF3jhQHoJBnbWQwRXdokMaEQgSAYpRiguxxpGSr43UC9WSSaB2mTMlGoh+mHh1RNQNePbAHtwbqefHW8KwMdFq0iwr9JNuWnC63Xk4z9mrzhwLcXTurAe8aZu4OI8B1+ArzMlute7qk3MqNkG/DallaJ9UOvavurm2NSVu0SdAqIcs5iGUKJbW2+2cxs6gD083RpDwHGqzA/e77znMfJatR/he7skPnNvu6oO66+bhScAVAae8KHEjIxlJDMmjdYX0eoO5CKLhhH/CLw5adYQfM6tEKii0VjqGPOXCxB3RDTZ3QEL7lntN9chomykn9KXxs6PnPW0othaV8NhGr1+BahqEMGNWX8XkM9C4IClget4BaG5dLaZCJjzh53c5Sd+yQkCpxyKQ8WnbOlpMlZWLeTE5r957dr+Sr+dE9kS2YzI0G2fc4JqgAyC596lqaJn/cNkLu0Eq7+HKMb/ZDFoX97O6W7Nm+kJOieHKAFzh90Ex8Ps/DepfUDPSioKJ5pz1QRwOSXhpoaKKNoETqXYIeybkz7txVb0csGOopZYmU0xDZBSYW4ZecG6bDdIngaLMHqVb8JbGSOG0401tIp+7faHXYFa0teehlDgF2ylT79HZRZvXFWMbZbArV06mbCf1EKmbIGXcu65nA5gND5EKokiwdONYkYAXIHTBDZVUfu+sEulrywWInCkq2ZPaQDb2qVrQfnPYwUQ7woj0k0eq/xRcqYAIY5VXelBTMHh81VsHQ0adUQPo8DuoEFYWGqYk4QSU+VrnlkzCIMzNF/6MI2ywQQKdneMmWcAs4GlAtyMRJPYb1e3VecpwTdO3VzKhInmL15XbBUMWs1gEkY7kSR+vBGMt7Miw1UlQiD+3dEf2Glgy0Qe4N1MTRPdly4FvXhL6skBdHtUP8mBPT00YiGycpRZPaPGkhliuUCo8Geich90G6Y7gN6tWTnmmGp8e2c6PvtC4b8Gl5j3pfeHveXZt47AdCkCDG4Af14OhCH695/IsbH2kuP9bSw9NRHL184NW5zkadRkorvZ0xkS+r18RaPZcSY8LsAdb1KbhcvCz/iPnF4ciDk+huTkkosuShpBXjqWW747C0rp35a3/jb/5Fv+yX49tsX37jb/ovft7P/Yn/4r/4F/6wv/5/9Zf/0J/wr/3kb/30/jb3v70f2nf+uu/0P/8j/vA//8/40/7qH/IX/d0/4q//h37k3/IT/v6/6yf8/X/nP/aj/7a/82/66/6qv+jP/3P+lB/0R/+AP+z3/b2/M8xB+/bu8P9g9pMPnYWHUfUaVkboGqESkqw25UGaiBelQcVLfKxrYCYmlTtNMZ8+7DXo7Kw+bPUG9VkuJQVtMNXfdGDxuaDaVKwkSj3MZbUeJYGKomqMDVORnI8VK0dDObEkoHSAwQZPebDaVUl295dKAQKNr2wJ3OkZi9RE1ww4ui3ygsy6T3fm1bXkwqJxenxYr1lGjULzc+YBxdbOmiuuKxaOV/apzAzVHYOEj3KWwyQeCJUL78yjfsM/5ZW87GqSgRmcbaZyjsrpukEPPI36TpV8OV3oVGXozc55MB+oXlXyMFyv7ULvZ/SSqgB0Ix6c53R5sBAv7gthpDyNV4ZFs0BaGW/iQCZetdZe/OR252CtD6yXaY1S76TIany7wV+NqeoU6EJ1aiKINCGnrxjYw7vHOteHnoJKeEdap5MOAXnBZnGIJZWajkzFAVkk11oXpwgzNGnS3Pm4WxJLVUGbcGLABjCyxi6vgJV66ifGCiF6dfNknI7SvYKczB5OX4bLmpNneUkw0UwEntFVMhCOK22leUszpcllElFsWEBLl0w1e7uWnDLE9PvFmqJKCEoFW3faKTQozWiQVpJg4Cx5nzF7v7AvLzcxXfpy9j20hrZNDjnVAisBmvnzCS70i/HyWkMM6WCfxOm0jAExBM9K0NTPRtZJSqlpfka02FvdEuMwkDUQH8nXdtsdWVq3NkRNPawfrTZEweSntEraoaeAebdI0TmNcxG+WoMAd05ltfOUztMUOQDmtNcyDZyMKdTbTr0+7S/yfNCv5e5KXdzs4+IG6R5OdSPyx4+Lv5J0T7R4eYd4iTn7NfQp2cZJH63+2ihdRJv6m5EJ0XRlfMg39AKpBQ92CLHSsyw0WlfbBpVN6zmD7O0OuRaVd8WdeqMaJT76lcKbrbun6ihZl5v+URy0UeKsifR3W6abAkI3Qaa7bSzSAEXLFMbLwuvp6JiHUb5rfYr6dPLdVF8QinLJkQuYsswuN6fwVlR67PHKS4Umz4pAsfK1+zncdVKQP3OrUidZlmO2bLlFI6XcXVYhy4YJK8eJqN1NqHTmS8axpquPEjqAS7Mp43fphXIrkjuGTPa3qITuUmD2xRxQ93EtXMMVGmUP06i3A2fI2OgtRkhD9uju4g67n+vhPNfEy9wB27FOXB0jn9FKuVGCWfDejSzMtFfUQAni9lnxwv1h8TxtsE2Z12SFgAh5Z0XRNMGNfke9s5rQv8K6W8TOlHwB6svncOE6mpZlijdTj50tcvJE6xV/0r39VY1na0oKJvhF8otQztXF+9W371AO5DbxoSFuqLYJHNPX621f8ylf75jREyqSQ9Z1VCiv0hyDcrNedQjhW9lWkHUsoBUUeohwIrmz2AFs8riQeJCxPJZswdwjsvOZ8bSOH1J1ShMoBYS1IiRFTgjbtMhR4uxyWJlMxG3zhISF774Ohpxh3p2K+AxehAB3TMvlPTnMFh9WJu1tGVcqfqt8hRsGnW4NxJYI2jljn4njiz2mHnn6llYatLUGId+FVMy7OaDJGCYImmLd7Sxz5hzncf2UXsPlZmpxoQg3WKvkJeSVkOlzuDexcQX0SoDKwij0Bqu1qZSdwtFpm3FApVJ08t0ePQpTnO1CzT6rD5sQJxrCVJGgO+eC6bFWIBJ8RuFbm58U+qwQ54TBRX5MvlRFaoWC1HT4kKgnMt3pVpfH9JtTFhKLykBTwaC1EElcKIgaOC3vQ9eUDvd7vPUoFB8M4QfkiTqnKPMakp15bXslp66gqwIngayB7VDeZn4ea1KmfTraOmDZBkMPbtrBalv6B5elj/QTDUFr4j6FT1xHD3DlilEDlRkRMnhQIgCQaUxV15cdI7Q5XNQSN711twoB3uQx5DOffQbeA4deMAc1yzWHEDB8G+/h2+wGq7O/8nHS4x5yT/FHhMJMqZLXo3qcmje8Bn/mL/gle9+yQPua80/4Y/7oH/iH/+Hf6fP/RYmu/LL/6r/+Mf/QP/zD/va/81v+v/4fy9Ksb1e7MNEuk/Wp/SsH+vzq/t+JABtoNMVS3dcZmw9ndU07XNdNN55RGpREO+ZIQ6gwZM/map1PbzMGxHN0RyuPDSpNNRrit7itMqDyNmAafnwcs4SZxDvbuzQnyp900iiD9JlxOpfJnmxoQa4x5hx8Et1grrtQokBvRVkyMQNaQ0xpIwsWNdPX9DV4sY3Ka6Zpnv54VRnlG5yNibqGZoVDRkE5FMaNUTSNkm06LBVKpZ5HZWkU44KyjRW+06skYhFqnO2RNgCbA9cHxzVtvsquxAqY5I2E0O2mihnKJBZ66XZvKeTtTGGGBoFLVZ9akMmkyocm09yrdcskQaBRtpzuRlrBD1nURHe+Lk4JiYqDYNdMewL2+fZ0qOy7gwb3dpdnPUNaAGGhZ5fRuaZuk5AjrtquYBaNg8MxuB7nQz0lE/2sq+koMsJ7Ty+fmjeSFkWUw0xaRrBxzuUAABAASURBVAKFdjrH6/DauVTNICPRhtLSreLYd6lU5yHaj/GMEmByZKCnhVoyt8OOTGYwNMqOJEh7yKoYMwbDkGlj49o1BIpN0qWAYHMmZ/VS95a37ewjWYxaaJuNYaBpwLzS1Eb2jjieW3flNYWTfGl/PKrsOiV/GK4osgaLDtkSrWn32ad0ZF7mCVQ15KgbtR/WQTZIyWqg0lrLkUH077f4W61UsCJN4wFWUxdQnjn0EB7y3Lxn9/NoWRzQE6RrekPLPKQ2BBGy6jQCYhUghnRB39VSvk9nJYfQKpA5BDQ2kSOnCczsAKS/6TTNKhmETl1Cg1mdt8yUTSxjuKlj7qt6tPApJ5yO9mwt4Hk64nEs5AYeKIzkGn4tG6Pb3qF3gjB51zcF2pNasgmglfrai0pdUXXR5CVc42baKh2lcbto+1HUOeROj8fj6iMTQ20RRVdi6NYhQ8iEcuVz2BaErWJmVXJJ+p4AixmeJRlixoxSQTIaOMUciFIdl7dsLovUqoybnR7Go2bcZ3MUbbd86BTctHbxv0M+PcOqRugmM0WGVFU8FnqYbFeH+QxJFJbPtYTNy2Crc3aMQZ8+ZIUs4NkUnkSYlVlry9K0TgrZZuVIXHCDV1WWQSaUKXeAUxVdoOaZyuFyuk4OTva7andkH4xqPh+jBN49cvMkQhFnt5Q0KEbIrhJN1DS5p8A7pcbUAxmhR4x3ehZhDSPg3VcLifkcsm1RD2E+wsfb1TXjMxYNSDhDMwrSk4+1Hqmgmkyu0xmPPMkYbWirlEkxU/RNus4q46Ra9U4Oh+nNhRRC1cFbNYNyVucuNcfkZTehbRgvNzd2eZVpJ4ZswGeiKiS7qsUdDdL6I731WEeW4xBqoDOpJPWSl2jxEboHj8dEN23D4AMK4Gh/NFIgtgIFVIy8vTefjEY6C1YN3TMUqpjVj2onU7s5fA431UEkSwBQ0evR4rfDRotN+WzdYJXC4ldRSMioIKKgGxiAZrjGj/EUaEOk4st2xaWG2DsgYoUKFhXOoTxBQiH2ZPdKTHnmTJlYbfHIPBNHIW47LXI/1g4/XO96heJ+npHSAipeAhxEeVchWldMlnXLb1rqRtSTKEe3ZymNZMoy5b8pQFKMq3JBBM1wL05CygZtuIDw7pmN/PwP4eD06cNqIJun58HKNsKQpoNDk+eilQSyzeXdKCzgEjaBju4sZMx8ex4FRumsxNseekHR2IaYwSvdymp6+BAma2DqccuU3vJB+YF1EYuHGR02Qty3a9CFILm9wqIrrdNx2Lf1durzjTx1HPdiGWSelFASfdfasNv1HqEUGiEnF80kUSNi974VxlXm/cAJBVW3GBIHebs/K0IlcuPo1xMSYm9JYqvSaJ6VV6p+dTXZVKgQXhGVUVWty0PFAKetyoZi0CZiSGAIj2HHunggdBQi6FGqZmp/WFyGpAQM48J8YQ4ZLyM/VfwrP+kn4fP2Xb7+u/xTP/Yf+HF/94/+5//Bv+/f/4n/zL/8T/xjf/IP+lNs6ded+Pd/+n/4N/zoH/O+9+eBX/34Do2AWKtdEBAGNFhiOgZpVGqJaVoWlWwEsrjhQWIAKOVuHqv2tFJXGT07V8ZT5UJ3Jao19eke+5GoFeGiXsS1wcvFblG4WnoW6srsVMOeIcFET7apAumnyEfVkwcdTrzWGB9jLFkAk1CYjy7b6NS80IvBe/l2K5TmaMKYXC9OaYBqw4AvlbeXpmgI5a5yvY3dKoQf9uWFt/MO6/MoQHfHiNAr6yuHPB6oUcpz3O1vuN4ZdB9LqB7KdJTaabumipIKqBzEnLyW1hOy2MNUCVspGmiANI5r9VhJiw2VhFuLr6vXOkIW+iP7wrj0i3/Re3h/OJZ5GUruNfqgBVprlc7xCTPpGnblePUgpwy4iZvPbzKgtkE4VZkp9d1kyZ2rfeViU0Z1u2o5u0iBWNccNqfGNVhd0D0vPB2pobRGqcJBlytYt59dB8I93EBfpZVvekIFlpEaVpTjYMNIHzVUG11d0kEUjp3dd9ThrutWmNA7cMpK9lKbRNI+LwVr3qqKKERfGyazCCGGTVHJWyvEEXEGR/cKzOO2HXw0OqPBxOuUS1MnMuyNTFOp7QGz8kgJvbTW6mUkUB0IrjOve00MmuuJJq4KrYDsLEVM9f+qozfLE90IwXD3iUzNROiSy4a0GVLc3aOkJ5q3YiKlpQu6I6tgjumA9QbvpIDJtlgWF0v06uRAv1zTEzvrVB/38zinJFLYZBsAMmif/9e/JOCmBfW2VXZBWydKnJY+HYTamk1lrKMH9b5MtupyedbIYUrmZilFNY9bTA+8CwVoOFmyLHufjKiAtbmxhR54JKZFPNqLMOHXbUmZUeY5GZZ4nz5lRptPTLXOaeGsSiJHpR9UOE2s0zqPurBHK1poGoz93JKm9HXqldvVm+rHDD9c4Fier7nHQzhRYbpNf55xBqbEy7CCPrpVYzohWaYECZdTMLMLNN0qfip8UoDKT4w2YoCyrblTTCkHh2MgnrhGu9Hn8rHgl48ht0q+5GcCoMHqytNx4iH3W7eqmtkd1YU0pzi10uzsiC1gbfm8xpqcVKhOE+2Tkt+15OtRIrPNu93DCKbVRp7uqkJlBuIMYIAxpjCtVskR7kRF99sFhOZzLs75XIobZyMC+QQXlDrv5XilMqfG43O3lFBkgNSa9LRU+sFsDLbr7kmd8nSlclay/M2hdiZ7Fq6WUI6co1wF+GU5ZBwxSmVUP0XKRjSqBAfdVDbxctLEuRUakW1RPF7qBEVFQH51eAUg8TiL2xjyQKSy2sMCJhUWHIAEw7pNDG/ZQjtsWQldwQP94LR52ViUa2Bbwy9Sv1JdJTzembcME5rGZb6MxiIS3a0QljY8DdtVoOHZSvqdpZspcRxGvQ56jXqZxjn1nrv8obCTkpJklJ1kVTtq2nvUyezngkxFWPkY7CVjjCEDip4Zdsdq+GyuKjeYQ7Y/XgmWfMCZSJU/kJY+bsiNhBADZBLUUItkgPC1nXiHBRojxnVsInxsvt79cVTfg4b0roaVAISXO+YTJ1BQdDDMmgAsOJKjjewtjGgp5hVm5Mrqx1u5XrNLJCBaJz04wFXg7PZM7IwH7xj89PrivH4PzItjrEvjRklgwO8a0ZQWDKZscR/OaRLoj5URSoDKIecI19VGB7Q0pZJOp0YqCuZBT5Ryru6E1vFIt7JFl28jyKACbNFztyAV/HIhWtLrRFXmYL1Urvbk/UXii71mT5hr3YnRYmyOGyuxvASJAOyRyka7tMSsdiGHSs9oAdMmkOQeT0WKVwPB3H28U9iL5JG226vou+0Gb6/tPEK8lFo2LsfHyguHKsbGktsyYlNHlwnteNn9QrzEj/fJL+veeSrJTwNfDhwE9if78viXMe+KjnaUnrCuL7hWrX7beVscFBwkWMQ9+g2cGnkuvl/8NP2xaFGd1zHmQzBRXx6P1fMFLQUUYS822Xgu25G9M+u3feM3/6Jf+PwXVP5H3/N7/ss/7h/9o3/gH2bKlLV+z+/0dX/E9/tD//G/7W/8if/g3/f7fv3XA/j3fvp/+GN//E/obh1/df8OjsBDw1JqNLoJEBxK3Tm40FaZKqKnZrV7Ke+VGEpUum6OamXm573i1oq+ovXwr3qwrVURkFA+6OhulhmjoW7lvRHJjsJJ5GE+oxy3WUL6aU+VmqbMx7vxaY84KswMRKChlCTD8Omy89EcNSUan7d5ON8GCeZGRWbJXqKH0lKpqJlQ3oxCNQLznfO85ZCq0yE2kaXqxqCay/XGegfPSQK0Rb6CPF5lRVq375L3aj2YStP9Op/W7tmjfNyq+uyZZ+2N8+nUOYac61AItaxrQfuadQ2hWx7G9pv27n6rDcW3wj7BlJiPX8KpMa1Vkx3wTDMNPQndhwUFSYS1Fu13zP2eJTca5v3E7rFWhe1Mlwi/evjjTWffWfuRIE8sht0Xz6hCqRL2GY1I5iPbzcdws/bHFhT8qZZM2HviTdmrsGfuOoqv1pkbQwtACR1PGxWsd3J3A4GsqM4CJ2og6LG5d98d0dXRUzN6WQsm/b6lUJwZ5/q0r2/N182hIMJljq0112ULQ78pXDe03CTwhm1ajhGcJ57/CRtA5gqdTFKcScVkEKqZ1+NNpFe63WzUWdiKL473p8dQY6rmXzXHGSwX2QR0t+wcdQYUAWs9AbvuwKop9z+/6rKkYjrti4lLq2iJv4O98NRqPJ6LkpO1Xp5rHIItf4jXwDLYNCyRC8gcN8fGM6QdUnP5vHrNs+Z7re1Sui/GNepi8ux7ny8Hb8lcpoi/wj9wfbhjxVHrBSlqiZ5PsAWByZbGFir06bJQxuOTGJGLTa0XBLqFaXRHldxNyIkPk5ktWWlFTzk3E0/Mzpi3FvYcGSPMz7K6/HFxz3Hk3DQC1kXF3oIig1C/+TR1PloGGIbFNZsvE1PDhXmDiMT93nsnz7lyX3lEodRlRobIWchU/shkPFF5PKPD5xjmuthqN+woY2q5jfVhusyaKBUJCTrocOuFZge4yw9VGPB5M2sVkradqQalk7JedkvFhCaNd9tZ3k7TVaIHIHR1m0E7j/rHCcvBAnYHq0a5tcmvInJOrhfmy9GzotHK4FJAHuX7eOgR7T39duGZbduqFl0S2kFvGFCWZacg7o3oIb1d/TQbhOA7+/XpvO6Wn6zDbmXPfMtRHk0VtkgN66kb3uSwsnXs4nRfRu162bEwnUN3e6iqGSYGz+K9eIyJBvCgclOpDN0TcZTCunEHv8wSvhphbSuWncsUVulsuLPnwOJwW5oDtPzwwIKaJL1GXSxFDgUDh8aaioUiYGhC9GHNwqvpan1GYuSUx7rZmXHiPvjUBsCKc5sd/bq573fU6cfrCakZ8xhudAIDuEQ7wYu3nffN/Vmcho1qK01JQ5rpBNUurflYQ9IG3KGQOm6rYLb8SDBKgkWpptl2pmXzViH0Ki2IEgYQmNFXAI1tEeNN35S2eGhCUcoqxHCoyGtQ6oy9Fa9NS+i21IMWQBiMuJAuxFblGl6uZPS7KYdLSLTPcr2bXue43kvnpoND0yH1ZDggrVQoU39dRbWyIbbCIZOfnYhVMS1r2JlWV9lHrY+Hwj1XKeMSV0kO1BPvhbj4vmxPke847gmU+tklb+U6L/gHW/fNt+j0hn5gX20z6HdIh9DRLr+4nMNnYHzZeNOKY5oS4G3UAniT6Z0t4eZnMkyHh6VI54lIO6KGz07ed81P+Hi76iJTuChSWrDfFMSF06LT6AepgJ8hjfNj9RetBwAMOi+HfJzhlgs+TBkxkwwk9BjRuU/fksS7V+CDGkLRa0uJYQijaWbCZCiCcVdouZvSUoM9aCQ7DSe9lFe/PZxtKlJAnLrvCBGKT84zwfQ5h9MrdOt9507K6zk4Rlt2VSWQL48xSoC1NX3QLRon8lQWxQlhIuASFSRKh62vMmUstgyBElSBts5h6d5mCgZMR+7Rci7uW9xLAAAQAElEQVSOWF5QIlpO7IEzS0lJ71HGWq0iKwsOVF4ky5Wp7M9pIRVsCVrIn/3zfzE+b3/in/AnfY/v+vWQDZ9P9dESTfsjf8Af9g/9HT/qa7/2O+nKv/Fv/19/2a/81Tr46v4dFIH+XX6rOrCbTQMIB42mvcGnz1aO3YW0Uvl6SqTe6iZFQW+g2g9HakJDk50SooxJcVPpaEzP7qrOkgKlR7Zeq0Z41+fZ1S0aVw9T6gIcxKIqxKOpd+nl7UG0KaOBN3AfSxW/vLodQDuhJY+mY9eY0aqQ2zSj9Qpka/XKQGRLNzxdZqJAdA1V7jgmI5g1Oh1BqMTcGHevG2ufmZuy7nPCWUIL34hJEkpMD1UCA6fjmvgA5gEPMuzgkcS8Pe/O+7GiM0eWVXKERhlq2h620dmqsP0gmqhEqxDQy1UKPWVmqrx1GtsEZghEsN10Q3A2ANdezjI7/exVRT0WZD735mUfM/RKZaZBS4mtZ0pHb5XXzoMsUrjrbvou28FMnRzXuumZ3SiiLqgv0OqU1qzkPnH6PjLvSlsHdggODDcPTWzGLoZw6kvVc1mbSQpV1HmyEY68rPWucc42sFiFLlR3qVcf4pQAblJS9VdWhb5b73Ye80BSpVoEydU9zppBISufNEyFFRdssmtwzy5CQiFU1TTps9oh4KHWyT7SX2Z+GeZjIdrc4SRk0bY+KFjjSr4akpTeN323Ghl2GGsMOuEQZY+uRqW+IqdjXj5el7mKMV3FOoSxPPdzKrJqug2jUA6KvAkpogyTLK+h3/4+zMZw0OV2tAxhkUYZASb5BHtqfJjdz1PnMyzrZAQ21FzpNe6ceKyDe9EK2ppijY7ShE7pvreuajeaXc0rsRKvlGHT4DbIQWOLKH3YUmvuDx4Cx1bZS+JkksBtZrOgqI6wlb5EgVBOsYfhQw7p0lH3JEMIagMTFV3ZErB8vnwa2eKIJoCm5LlbzkT7RkdGqxPv08oV8RvdWZ+zOa22qKGpjXhAbPTDyQEDHTJelvUmbvK41Rpj+jKj4SB3dh5ztTwib+hxWKkpDzkG1+CU89atHbv0o0Me5m7PMoHjWKMGny7I9fQg3e/uXSgCQ6q59M3zGEAXO/oCZsn/SbUt051c1cvbDTLGjM/OoKu+xpGsJwVjWJiLUx2HwpfTes0WRt0qIWqsKNeLh8zBdAcmY1DBqFZwC97lyhhRTucmlI0Q7CEVkY2kLG7zrVIjE2b5KjpbKSJpZaOnPJVUa6ciB3dpnIcr1NkVBbdEpGCIUTnM/BLxlwg8mohGgfsx7rS/W5F6lOsGIHrgcQENXfOCSzw7TfhWTcKnG4wlOTCnWF19qjpi6T7c6G5ps9IkbOVYPk3I0HAM2fIOnzeGZ2qaHraDWT7jK/8h3PaqaMlsyEzhQnssjDqnQwXHF5W/LpAOzhknPWlsmqBVQq2C4q8L6SgveqSdTX12m8kZeWYoQrboW6YIVjacumhFzsa19eZAyGYrHzSemufjMy2ao9gyeHcAwbpRu6WafESgqvMBa4JeMl6AQOmK9vGELYmdmpDH6kzAHLfpd655K2UKAiRLZkuW06ZI5CdVshmFPsd4q3S+cK+x9c4Hqq1Mp8JyZMHdfHcV9vQeIoS8LdPCaLv5mMjtHbP2gABLYZNld31GJoWB/JQ1NgRfq6YOdDqO6qQzlXk+9IbsULmUDck9RELGRlfPrA+DElPXsxktRypUTq1oTQhnT12EVts4J/dulX/CUxzYzACLrrCaapex2yHeRqvcwEGTAN2GkWZG5fGADY5pYrS7CKYTd3MODYHuz6DJ1fd1HjM3Mqqt7fFtmGA8ED4iksbPgmXLWg3RN8xLEqCt5GcLFS9/0T6efY18tQTDg0DnjDRvqOheYSt4NSc1J6Njxxt5ZIzjIuw97/Bj8oXyIGbFOrGilGyeuj97DlvGqQq8n5DDOVePKzhPmxIOLRP36bhP76MQLbM55qQt8qK77N6Gu8c7tRjiUOzlAbMGtlhzRejuDijcsF29351vzTB/6+RWqeUr16vm+jn/5c/F5+17frfv0t2fD3+3D137I7//9/0hf96fq6u/+bf85n/pJ/+U/85huvs/wP5VEf8/jgB/h32Nz4diB6rE2c7q6NbRiBwFlMVBVJDH7M7eUaWaUtatUuVtNtay6wPr1abhubSoHZ+YD8GhPNAcTerQgmGaAv2twGvzSXo4TDJUsGqCE1Axitveb2Xqox6I5jg1qgInV5bTivOpmbMXVHmglD9em92tInjBrtFueiMbyrph16W6rPbHT/tT63m4yLXcTIVXduZkkgKihUAR2Z65GkKhmO/upGmwTJinXtkf4LQ1zFU6Ls9Lb2xWNtUejd2jTsmYhVjw7nWfK/V+KehWPm75h1PKai06yvQggHKL16rXRVugJhkaMKq03bDI6FLVMhq0Fdg1c1zQrwt8Cthopve2vqmhArMd1AiT/CoFpTBV186eFU5JqX7G7Uf0UpdXx7TwuwpUXOFL78L7MiMeZ96g2o5n73GyDnatbbMvH1+Ma40XLq/VBE3PiUe10GPQh9B52lRmslIdjXcYG25ma0L7cFpcVf5E6mo9gcpx/R5yBVyGDXu/8tOK99yRe+wzdlsjAK1oKWFCyNSMdoyqZ5rqpd/X+NLsE1kOAVN23rg/jftekewO9CZScZ+c0y76q/0iTDHDeq+5hZYYHqSWmUGD+grMcCgauvWOugXxnmuvjxtra7ndQ/dfGAMl66QhhEWAtwyVOlTVSS3aapE6Gwd47uigYsfZEUdkOE5FbWKsXmzPQOQBtTcfWfk8W/ceYukg3cFJm6RsF3PlZimlwvztuLVr+BYM0Canq6xED/FAybUcmF5r8LOZs7n8tahF/EXhYFBonxju1nq0uq6Hq7t6e53p7QMcd+FOIfveStFl4OVY5p6Wd8urKgxAyjohvFvr3fW8p5eXezWu9uku9V2wDQMxW1F8Z+7ceg/uR7IWfHCWz5CkXNaDPqnmP1/d2l2zUd3CeCgOR1ly7ZpxhlBWg6lDNTltWX0MZS00a69dKwlFyjZ5W+dIG3HB2Ff29QSPOxkbrDPi7Xk0XAR/AgOQNO2dPNs+G3wahXoYN0AbJ4buiA7mbhDaE5PVU26DkwBSjZ9KGtrCrsRqObfNb/hZo536hzN3XmdaXeyFM+pNOyomaDFAjdaJj8rL3c0tKaTH2tNyJYZcuirUqKBOyUnrF9YX4kx2ZKlEnot51b10fKD4jIMV5fvIVUcannERkbp33fjYGGHdluTuuTkero0Yq76YtYxew/AaXK/ZHx4fdhxCJBEb2M13DcnuT4I5JSVHHki2+pjboN9bttmnzE+IMxujBwkXyOLEbU/wcE7e2Nsjrb2kvYR5CnIZNRrTl0/Rj2yqxgMNKGY6XdYPNKBAmUjFR2hmftzb731UmSSSdbNztDS2zOY2vq9z+tGJM7w5UVfpZ/1oE2HWJ0GV0f2evteUzWfGPdBuwzlm8XHAVpjDtSceyHrac12fujQShuRSxpcy7APxMrc5IBxJWekkFvKycuugdQ2XR1E4CdWDmT2pbRBchH6cU2fpure7T6dgPxxxWDszVLDzKY+Mhbv6LeaOlS8B3eNxS8ZUz0V3QgZI5ih7BeahyU6nAo5O/evuY3wb78a7lCJUkcp+B/bp3gOlGckubvecyhHhZPd5vd8r7rb3maI+qZinSkLKDLmpOJd8fpQezrddNy8Ne+f7Cf9LtpiclGQC2u35tBIWPLxufAgLpepTumw4lHO17YSneKDhXVZ4gS/4HMMElbGoJxvpG8L3I1WOelim5WYmata54lydNsiPsqXnqRqtadHYxRzLphmf1eMag9eKa5Qydy5QGmGg5WSuCvvUodVHqexUCZh91n3GfWy/S5y2Mr5UHwZtjMaVZfucqi1H5poXRTH4acV7daNl5MkZSgJxQkqILiDbtFqYLHLCW/zggadqZ17Z8/jq6XNC7pCw5oCJgm64ZiwXcdnPZYLROAdVqDTCXZIl6RHZtaBgtTWkAw4QmlQqvUOhJcSSZJyok5pOGassbEQqcpoR3W/4NtnH/+D/9TPweft7/7F//Ef9wz/uN3/Tt3w++90+GvxLfvCf8ZX/z8T/20//j37TN37z73b7qyffYREQtZ6d2RWJW3RNDrNLHB/mZpZmnVP17SlI3FXvVCZ6LS9lJYoMeigFSKXqLA1neWuH6eapzjOJMZsTYRZtqGck3Tm04OkUUH0JqDdKGcGmD6UAi8Y2wzMD3YoRq9hMJSNNp5AKKcppqewi4TJCX1CiwKMHlSDo6j5hLEyj8m7CYVZlHkozH1AGmxR1zA4VN92FakqhlMjStTmOMTULrtrgGTNwqRDEMCUxHWSrjjDYmmTkMB/Ty/MEMrzg8G7Tze5oQaJeBNdYCx+r5tR8WQx5GHyOJ3o6MRhuDQy0GWAjHekpVbosgymYnNMg9XKOCX2qrrXm6CuqI8OwhY4JGnd7BmrlYTpryGa0oyaPBj3IlhHmMrYZD2ipiwBKbhGwkA+LvJpDYwgtijYzh+eaPTlMeLcZyjp5Qp6SRS+JCNCgSQK8S0cwwkkKE5kgndMxNCu7aqbPXiOXSx6poQUNZFg+w1G6Ai33ofWWIS8RFlCIhhrMzz1b+zC61ezUQi8MxLmGTNl9RVgJWqM5ZdVpF3y0puATGAWiTceCR4uI28BjmPijx4pt49CzXJjK4wvutISd1oQGpOOZTiFl29gwmY+Kp21IgTJdoqlxCoxYkLJdlkGbLpKSq4LuhCVNJNml/l+gVLPNniHeYEE66JR1YwgRKTXGtHA8BKhRCanQwDK0gY1hkCOm1uBuv2UskfLVU5RjdndpL0QxnHbZkyQjw0u9XJQz6QDFAtRhBKIzWCGLWuGdXevUeD8C1DRl4xFnXnNgKANphMAuH6nRxdy9Q2qLvZffporAI3iakKlh3j0Ro4810IJ4mpl7m0tEcWXOouWQWvC5DE1kmGX5V8RALV5n7WjZc8g0GmyI6elZCEpPU3OTJ/LInKf4tTiPAEC6UwVoTOoroKSWJTADYCCrpHDos7MRITmYDAGqS6Hkm0E1soBTAagocTQFcFoKukBWlgRMphP4vNvqSy9YL5RZkMbPgD+1WDwA8PkUUDKnWZnnYWa19LNMk5CgwQdKpQMWGgGGFeyMqRFJGaART0YIm8IzmdaGpvik/G/TiMjI3Oj9nD3GobNQBRmJEkaGRz/M6iFFNNUZoU1GkCPHul2/S1Mud9QDEwmOVhSTtb1LoZZE3ZRguB2YAi8yPKh5qYGGQqcqLYVyx0nrakGMzIw+XYfxwLdLMDtMdWLtpdQXAHg2ouHoF9VfFckQJIpZ8rFR+ma+0XdYnupome5GPHmgR7dG6tWMKSqt+EMzDZEMKctULV7UgyMgsQWRb2RZEaVaOEQmNK2Mk/2KHHKoKa1hEFHDOl0DJQVGggDzeBbTqmeSmklMaQnM0gAAEABJREFUEX1O2jBzG6AXoQHCiilql4ZBjmdrvo/yIUIwATNhqZzpWeIrfEV7FM3NXZbSEgkNfD5MqmHshdLzkKru4lg9xx4WjmA1BS0BQ49HrlFiNAjpsTtiR+1APTbL+QY1IygkCa3KJTfTruSAxCQupUc8pNqZEY+z0j/6vqSNUiUI2wyq5u3diXMr2cqYA1ghQigukDSVXSMcQlk87dHwxrA56hqYbmOq0lB+wWSZ4PRcciCfUw2kY7zormI/xqZnQ6KkUH50skUzih1DxBcxZOlTOBKoh7ABMRwXbXwGs+WZfIOxxngwstSh5KHQCnM+KNYTqD4uyusOEcJDHQLEiwpYPT4HGE9gS6pJPFrKhrZFUgY+mVJNDVMDkqrJdrVfuqehiUxZp0pDzr5Guz3qCzBAZYsQPrPcFRnojtlYervFZ4KsAoelqo1Pu79wRaM0QYoOraiwpMkJdAWVn4GV68qPkZdJ9hj18lvlDwq2X5qcV7S9W2WtRF01NRgVemJfdflnBE9mf+M3f9Ov+3W/Bp+3b/7mb/5J//a/9af/kL/sp/4/f2Y9WH6++js+vuaL1w/+U/80nf3aX/Orf/bP/y918NX9Oy4C4sdnhpBmNKC111MkdOSYD6v1ZH+5L2g5mMPHWErU7NQiiU/OZzfcUfd7Z+y0MId9TqOoiqOW0PRaZHSK8KmE0z1VcVeCqfhSSUSupyR5VQLvxhu4gQ2vdvX/eKHHyDkk04gn8VRH6mmebnAnS2kJMyqvpz0rtIOGZ6CbawdAafc21JDV2VA60yh9ldQptmqEE4v4qPoi90eFqYbnF7teEZqnie3haBXO2d1hcfSecBy9yO2k3ol5vlZ+zIIEpmRrpVkXv8btCl9HhfiGXj0M1XIA7TKlWyC4cQy9EqQEq/Rt4EiApdxseGK08fMyAhS1ooGYhuEwOVX92Bmr8wu5pGqjEqbCrB8Uvtz+ji75XYhEtBasboNm9gV+kbiqhwellv4ZLyGmUlmFczoaAIn0uqHdtrMu9oTro3MqmG0b3J/hlxpdWZoyBoz6hugjKXGgJ4BWbwjTFUARfjiREaitZcGhsH5YXy3ZOjYOi5VhRfWnR6F/ma2xXlwO6eBWX7Du4W9Xc2Ts0S3teIfvnOLF4aP6WYHkgjl9jek+KLQimmlat6boik+fMj7dorHGH/o224t1mU2T42Rrtcmqxjj9yhZofOpzu6foS3Ggd6rlnhgYDOu7/WBpnvcRnVI/SIzjIy/vj4uTGLvUGMFqDDqXUQ2AjZKdHfOk3a0gHelto1229BvEAE/hlp7j3Fi1B49c1lOG9JV0ONNBamGxJnuSbkZRdFo5no2mh+5YwEoyn/hUWwaicA+8x/6EO+ru3AoJ8lPmfVv2ar7cZOWDAIRJmh2fYavLvWiyGyNqRHbr5Xy/UUeEuoqMPsF999vw6eK5/EzCAMvPRnTLgI24kXrZcW9G0IEvyC+gJRQSnQLxKvmtNO6ZpQfD2bygHS9gAvCqKXKdYSRM22A7wYmcIptDah5FhZO4yeOrrhfW1fTnejJFHXepq7rPOftUJINynkmzM1at5dcaLrkooPqJDoA2a7swPgxTaz70XidSt8myeQTPLEPZdhyFsJQqG89/6PzerdJBGNxgzcrMJ+KUtRd6UFdH6TU+yvN99ZEzkikP7KjRlVXVRNcDwERRLePZlSovR2+rY1TLHZI2uCgmyNLOxJZtOqQ802kpYMK3rEpArNJbfekFrNsLVP0Jk1EPFFHz7nXCO6pNaqlfEMrSuSdiQLIzLNMioVLfDYnQ2+h7tIqDAkAL1SQfo1XCYdKgP8qHnMiFt42jo3h5miGjOxVXrC4FMzRFWT/kh0+AXch9OmRKp0fWu+pkoSUu4GcxXsqWZyRGP2Q4Lor3zepQ/KX5EHsodLpLpZHUVUA6xeUFolBn7Jzt5vY0cFdBB70irOWrcq6rFZ4xYc6+Dud72D2qIJHQVnLIDtlDKgVNW1UD8uzdfX/WNfpZpIa0YVftVonU/YzKR4rTCIjF+GCqMi7TbEHsDdKapvNUsua+SzMVPnY9qHENG1+YvcpXmgxcRn81XPAIeSRqX7lXtXVAmMjichIOXsQzumEJJXK/26IHQJ3zdt/rVfODtkypxYS9rd6r34LcJJ9QMo4S7RBXHZMYWtcc+4ur5mU6SfMjbnVF7q7H9GoJmrE/8r7yeNLKpdDEzm0Vo9oFNditoEaVfK6qNA0boNPdpRcwBYKzOHIQs83Xhcvh5IAPfNZe2KEkgjA0r2vyVVDtYu7UHzMieWxsn2cwB9ogje9z7pSp72ZEAzS6uckiGVJ1kLcEQDyGC16cqk0Z+0ZmtR2zI1GPsDMLSF3xnsYprAe1eWLuFgs/8X7bXVdxDblwBUYD8h2iCE6anqnf7eGsD7PLv7Kk+TMmwsOgH4GUEiBcFz1p94D0l8+uFbL0QJsDI8vFVp00KCMhQr1srGnKOsHXrxHXquWKJlNgBXhLxd19yxjzNkGQk/fEm8oeItfcY5RpS6Fnhh6WY7S78sAzpVMex6jb+v75v/yXnwjp/yN/4P/0D/j9v4Hkb/nGb/wRP+bv+Qn/2k9RFHT92+5/+h/3x9JcGf5//49+hnD6tre+evwdCwGlnXVbf/aaBipDJtXwgITKItTZublhAFTpaK0gXOl8ZhyoJ+muRqDvqvvduGsytKu+ZJ0Wf3Oacywls3sMr+UO/eqrotDQkKx3Z2hh67edPXL7UzWmleyqPDu1NF3I8Sq+FtYXoZWPmJXrPNXlNAuQtYbpAzbavKlSrEIiK/WOn55QqbW8WJ5eMeJYVXaG/pS3yqyFHB08xV1MSxWK4ULBCKuwPMYDA5oAtA7tB5y2LJ588k8t2TZG+a12Ob2rBdnU2OCzpqsAWQ3bsBsWxpyea/RLyHRDxbtLpTpm9dh2nSfBy/SlRiV8yIAHnkJ2p5wViBJArSknsvbp99aBrm6t6zKsSkIRrajwjKmGolQQY8TjfofvY7qcU3c8vPIVPRIUkoF+u52FXmmUCYm7cahq2Me8ro+aA/bUnBFPgxkt4wICzESegfCWjIMGG4p4OYqKCOYFDUjLowFZOCUUD71cHUJRtUzPfTKhmyH2KLw8c0tRz7ZBDOGpW/HETMGmuXoyGEl4UCdGN+LK0izPFl1dUJ22wOwxcMEnZ0Kd19gpZXFvQffl3e+jhR6SZSQH8nIsgFncwXdlYnvF1bo7lzbyOurecvBgRn5AeL27H1LdXukQD7BLyATkLnWJNn1OYlZfdXvnkxfwjzkuH0OWo4kN3tXRDz/MGg71o2bQGjA4BEL6ybXPzO0UBmhNUuR6VI7HZf2tysV2EqNbHT0MmG7IqdTajWQqRKe8CiXqnsoqmVlVrkiLoMkuz111yrps0l/sqd6rTkfi9GPYcLqIcA0uVyTIxxJ0wdMGlk8iqzMRrQ4bWsEjFVw9qfeR/NUaw7mKLpiQKW+H1tinh/S2Surd4RlVcrNtQs57ewZj2zm+6TdNpwiBKz51GsQzbij/RspEbCrwCCBFg3GgfWavUqMWK8qPnMsz9naglWLjia97KTFNwiQdu+voppjU3UnIO1CO4zPdg+bFK6BnzErcJVMwGJc9aW1uT1wXlEV9Tu1MibAWA2qYQqvG4DIhMCU7dtQuWfhqinmQpE6haXEWS4aJttUyoLqT1dAmH+3UaNVrDtdnawJ3U3CgbMS8MKabLSo62VVZ8uBYbeRteT7av6Ce3ZyBTjP64KjmTZFkguadH3WvehPxKLpC0aKZQ4PbZ7Q/6O1GNfLhyzm5S0HfyawRPUoBrOoy0FvQAZ7tADFqjDNXmzo5K132EW6ChkXlc2gROJYiOs4gMEbNK+BRlm5m7pzDx3CWyyHAQNPnTOLzluyHv9GdsuMW/QMRuyD6+/269pIgWLVHW2Ccrrt4knTT86npUYnU8HjASXkolDAyZoXBjBfxCk0QkntEzaBU2x68h29DoYxwDqvBAlN/KM0zd6ebVSRuhd7DLLyyH12y3UfDW7TLmywAOz2FnMnXOed4/ot5ojlSEqV/h9zEAYXSGaYVQ3Lu52L2BAcNnAdKXchwOYMueWKncY51uvTPatZJoDjlPtq62XBqsuIqUEylTaX4PKZAQseka111B6leAK9NATpqqU6ECqiXPBj3YLtSZY16UfYvXh94KWzynlpQ7/aSg93IWTXSkVfnbPlv8DG4RpgHrWVID91S5cz3iLeGXewpiNuYicoQNUxUogzX8CKRITgXXi4gmiGNtXur+qBhobhXnu7oeWpmj/IUEQxt4qYcVcZkEmF9Dxm73ZJG0HS1wqGrbxeAWuBSLnfiPtyJZilRPUQCQdELdW3KiyI5i+O41YQpkL5IL0stc/fQSoMsVyZAahrAs+InNLKnx8tjeafZPe2M2U7IByqRme5ttzG6U+rTi/Y54tEgxFnyyCKlaUAiZp7rtCmhoRWC1U1ZrElaOewhj7NUg6426SjhATJtHFlej44Qa+Tv6E1RmB3Db2EDzLSP43qYdeXIqGPQLe05SsBytBvAzv/85/2ciDDzv+TP+cH/8j/+D/+Ff9YPvq6X3hL9I//Mj/8//zs/tbvxbbbv+Q2/3+/73b67Lvys/+w/vffRwVf379gIECJINUTbxiCmtQOisDKX5d0F6+c8m7cNkRJjGc1FnhTVoeJEzzI/c50mlCaSZpMSZhgWSxJVty8LDtSE5EvofdeJjDi74oSr1GNfO75oXGaDiUjGoQhOncxOJbmZ1n+nUXl0m0pEQ5uyyw3lTxnfnbvVHmh1VpL0kD3tDVd69qXCtG+cL3GSW8vIwqJdBfbnLag0Olz3Yr5g4/bcSJlRykV4l3wzffYIvR7GI1szqSsHdVMZWrkytANP8cCT2pOcmM2rq1UO5sYKuWfmVogdd99nBMdZ6yxr/7yI9KQKnSrmOXgfnO6yzuAJVPEpaIa2bDtQe5GfkDcz0BBS0K2F7cwBxdPgyKVm6JO0dZgml2AJATq6UPCEpcRMSxu6S0rDsZK4bnZbByRZ7s6ZS2rDS2/pj6u9HXfJtbGqhtaCii5J1CTQnT6heLcPeXVlzdOg9XNJiMvwd0PMekytO3IrbDJ9cRhnDH9f9R79rr47D+Te9l3cq/PV6mqiZBOFrc+LenX2UtV+L6gU27V97D5x7nNXiRbs6U0qKIZjnracpIztnVpInKa2yY7qq3lofBs92xUc7j3P4Q0qsKKdCwXMahvVV+bMXagkN3Eqdp2stCx2xFVnxuZDRfjw+XIKPDUJR4phRpoPy8XbnwmrNKvEo5PdCgfQ7qZ5bevk6Mff3bajPbHw3HsaIT1lk52uHd2NbM0DliktQNgMg7BFKfqnlU3ho85VW5+dg/0yZSOomJF4nj7aLEp9TWHAF6YzmlteViFhctbAQccc+bK6zClEpuECXm0fgJlEPcorx1E79+YTw6w78S6eGl5KuvnRpl2Z2JwAABAASURBVEbGVmB2jT7eBw+ztBwrtG42ofuzYTf6buESVmekmFOcMLQ4KgqV/sTVbacrrNv6MZSlE40zW16ue9jVu6z6kdhE1cNxw5AV/rHWnJiWV+elIdwNKeyqprBoed0GpUq5Ih6tDBETDlt7UQWmilEmpzl7DOXuHPHiGR0HoziEklpr3ZexJsCsl0Miq7N2qczgNOVqKYZSbmkO6WsjvWDbdK91BYlt6tpawONanIQpAC13Su6hA9rc+1rPPkEG+6b40WaQbyp3s2spci6vKh4LPNvCcAbqwhjjBWe3iHhogNvCeKkuljn0BHyhhJR4mNZVILo8wphp3Fw7PlqNWjFZsvU0ujmqVyvKTk6InjRVB3OW9178tPzAPDCSKPk/IpRGb/TWZJi8/IB9QHPpq6hf33xkeu4RW+vJ1Sqv5vJcgd1iRjkOB3rIsLlv5i0TjxBvn+e6tJS4yQpMWIVVQthyxbxyyZ+wLe+RDYovXiScoqx/pMl6aEhpYiOlajTmtr6tBH3jMQJounS3hHlac0ot6dR10Vf1rOtQKA5VqqAG52uk6GJ4R0d0pSjBDDzkUX1zvROAWZikUdANkomUjHaHIUTSUqWRHyJswm8tMkomZNre46QmbOqql0xABnlPu52CN8Tf52o91n60f8AnzduSPMSbvfUQunmSPalXF9f0D2oMKIPsfkGYaTyuxlKkHemBTFf0CuHdr+ZrmKaNOZBTAe+2ds1nGzG/4kGGBajB/tHmNDKtU0/rY5CKls7ZIXeUDFLRQWT3RsbSO5yj+EIXUKcttFcSBehTbh7L2ndtlYCoLlUTHAp3qD4qxEB366+ihPxJlNmYLmvBA4CPb2JoqhBt87wG1DfMx64q7jO2Sn0NDKi4ujerAc2yeo7ShuDyFiq8Ka/hHIaZsKPEmwczwJSxevg/t59tBbqgMFwqHOOxsq5WTonrqUyyDEayaO02XzF0+BXSmOwRn9uq+dl5uUWXGVoPj8HgS9KgA7E2Y0Jd0A5733YOGgK9+zGlWktKyAZ51aYCtsL8uOpeNM6DcyiUAqBgu/zd1HvVGtYNyMcHNAVxD9OPL8coqJZz/Sf/xfM/TPydvvZr/6Dv+Qf83r/X1/2oH/ZX/+1/0994vVQuzj/2z/z4//bX/0Z8m+33+OLjf/b9v58u/Opf/at+3W/6zTr46g7gOyYIJmo22PJejE5RtTuhusdCNTI6i/A2a3aHBjRpeky5xlopqp6KTzbLjAtrtEqxK3mW2cca10UsBGwrE4f3DBWTpCRJYgdTTUd4NpUMJR4/OWnLbfSYyuSxOBxT3Jdpp/k23kOFTyb7lbw0UfZCG81pOj5ZO2NXbeggM6taeRXAZhweOUSyHTWzZrgX3KQ8FzR/gEPOEY/PrbYnVFm8vLrj6cfvMVQnMI6KXudqlVS74jX2q9K3ZBdUFYssY4AlUG6bt4ABL69BmeKhflejoFoVs7WauF4CCxLSybbeXZ185quQBqcWrVQgUNBteB6LbWowLYXRHP6gPPR2ElFqy/OTbCj3vawHuw+tHqeGEfKDZXgc5h64J7exvT5f8LbRPh8U2FPKJMdh1+DUiApUtGqQgdM4SePnJiB9yFrs8PtolXnX+BLr/mz9ZCmIUwuwJqeqYKY5SJnlqzkiGaKA6qqnzzf9U8aJGFuYmBp6Wmv5yKhP59xxOkPj0B5mqoJ9YPdg6EeeFSqPaHTb5OMCK7uPywqtONkFA0QiERHbW3Fw8+eHgk4r7SLvS9J2Z2DLSapv2hd71ZhnmcKQmjVTboVkSN5mh1BR82G7v8KqdqmXLhGv3LBkCVmYgXGfesc+rFZA+iurQGSr1ssoSlzWbP3WogAYqkJKbssb3KkBZkQ5buPG6F5d1txlB+xhmExDVlIOe9rAsXFjnhiBZyvaMatVCj9kQkuvs3WpcNpCjJPeF+tFKmlf8JfxgiwKz0LJYQYbUMTKxODcWSdxagZH0kIKoFjeWG+f5R1296tqDWm93pwHjlIbYwZqap0d2quKpM2hJzFLeOR46/kzlQyZTKVGLz5E68Kn6lMGqIKo5qgeaHcjxsCgoWem95Y+6ioXOHrId4WEK3w+9BikNIYrHDF02xf1zO0mwSvVmuPD6I8/K55viKmSrUiGPZKsUzWgkKeiMpEgXd4zFE6gXDwK9mGmvds+QY9FZMsQH1w5RvrTjPkBojslaON5iV9PMg36BIgwylmWvL6WT5pn8xQLjtlQF+sP3TZx3HLo0jI4tQUs4LtGbueXZlvONpAi6TGUlQwk2uRWD3OvgTIx4dHqIpOYVinTu5eMGaCP4qp2UZYg5Ucr3Fa8yVssMNBswNEPQx5ZpmCsIQVBxzBb1IciUbDbNJEFyfR6KIs5oeE0C16Jlcnq09p5lCNRo8qoCRR1se1R2UZzUSaFsgr5qYyshiXHvRm7ZUoLY+hbOzX/NfCSy83aypYFe7Uv1BLQSMotM05t15xePoITLony79bBmbzniry4pyw3CZ5skQw6sk6vffX+4Hm5nIrG6Xx33IRuJeIzHYbMFt1mtSyQf6fiVkCDfhZzVoIZK3qam8lDuS7q29vtU0dpVgFU5LRLgnYKXb4eUpu32eCYtCHjRh/F/XHEH+RLYWfvUafDukZr9ErosE3qWqETSq3LNSn7ZdUtozvdehll3OgSiTtWNUOr7rBwb3yWW5AZrSVUspjDUnxqeJlhUuodw6tVT1tPUoRRlYcQNCUFskbnQ5i+Kl6ZqhUDzW7E8axRbaVzEQv98NAj7Q7fY+RlovNm39V3IHWknInltyv1DrEHNdV1VI62sPOmRkF2sfvWHlEd3UXATw8lGNBuqAZAJJ781G22XfQP92lGVdhDix6nLevZofLW8+ppqQV6Jty8p4fhCIv3mBv6ye1KXCFHBShlD1oRze7HsyrPPRnDYYqFWbssis7CFn/u9iPkRW/HmRljM81v028btpNVVmSNztkxbbpm61yWVXLryBvUsAAJv2q+xIdI/aNhDqq6RSlpRoUi650ySmU64TpAQhAxy6lS1IqoLBMebHdSAp4yY6dFeIU1GOmRqIT8IrrJUyPiipAV4tMZI1ws9fnll+9f/av/GwBf93t//e/39b+PDmj8c37QH/9D/+K/WMe/7Vu/9Z/7V/8N6uh37Eb+wO///b9y9jN+3i/6ysFXP78jItAiFtAUr+V+UTU8gyJd5efVrZRbpcRWJRkqXI1sS7wKl8huI/268zrhES+zy4cty2HAGJyuuuYSGBtRVJ6V1rtQbhRyWyfEaqV3i8PDpuPqeoGzB/tzseKUuDncjcrDLtZhHzy9ApVS5Y+A1bqHxywZD55j5zCoCjCSruGujMrkLtzQHqFr1nSHTyvzcI2FSpHV0w2sdleHlKwSCkG204YDU9l3zMsxOlaWxUisXFP2auJTiPgZTEDprIwP9LvGxkhBRUzExdAyAG2kqb4jR9rEWvLUCQO3epxmdgyGKkgjzGIsVRmyzBvGgxE5VNJZwzmGdnP1BA6zrCjdzP0CFqGq9pRK+TXSpxa/YJvXrJQLs1Mr9uhqWpnBgQXFomtFWFSWVjjva/LpuavsHVCgVTZZoP45H2E61jmEe9UjQVVRtDBJ1eKxn/ZYUCbOIy8NHGlWMrV8tV1Fr5L+0CvvNXOMk6X4WljXeAbLWd1ukSWpH5y81nPKqJFnmN5r8QEuu86tmYY5OV0xiqe4F5a5Y8r4bqtAdO/L78v6giY+3bnZpZXEeapzJ7RIhIkTSK+eUHM4L/uixhVaqAohGRmhjkMyw/32EX2d1tLYnj2yTeHjovnoOR8JgHUjK7ZlGDJ1Iq+pYKZ0ZhVR5ukjIXwIhWSWADinasMOrb3lko/yWRjoK4ADYQJ2WUdJvPIGw4QsZelp3NFvfN4UC6c2eLdDZMZw2CpbToVR6lsGoiZxEcqnGcMpBxRPdTkV2ZWPJFKfZVVQT1la3B41xsO6K5WXOQozH/+r2T1odEu1PJpGGOFWE9VRlfuN1INyy5300RKdmuMm2zRJ0a1RbbKgKvLZS6dQpMx7PlZCprdDWkSDybNaIFqrbXMnyAcm0PAcl7f2pkP6aTkNH77WuOArZet9xlNampB1nvCiafOxMBUYJ2w8y3hUBiu9wNZYp8lWfzBts1YUK8edFPSht5ebfRxtjyyVNMUomqk60h5V+9y3HtmQgmCYSqeqg4riQQHy6+nzHYjsI3xNpgz6CKUDkCozVgQpPkWji3ywkuWKgNg2SjnX9UDcoek9TBEdqqS0x5cWOtCfPIkO2VmuzKjWOCz2wLPJ0uCruSwf4dH2Tr9PxftgiwAIWD3K5WSIswPmYA3UsHMxRNWr6TKzZcK77cRjl6KsCWWEEDO10XajA+Kw9IdtMKxsbhvbiqNcw4BQ0uAJZlseGSufRYhO6W/h1ZmPGyqNBm2Exg4+4W8SdTEvi2mTnIRU6+dEdYHUZeiZ3IebjDelV8C7Sjb0itarJVrCzObwIfjMJS3jUZ51YOGjxurpqtW3K1l3KDWmBLR1yh9JsvbZSUZlROSO1iC2XizJFNw8+TSWoLTor3N0mzjuVHDS9MzU3ix037t3MR+3MJJSTi1HmmPUBhp6dK1ZL8dy6uxN37p5YUiEjxanJBrHs7hVfuXrAx4HeuEYQmRIoqi4DpQ+gYG29JHC4rpiLN1XMJjdVUbZFqPv1W9HVmjVmGEmgcPai12sLkgU8BxVZ7X8Yzg3VHI1ScG0kG5HkDCpHxaet2S5mfiTjvfpEx3tqQIwdVvg67Q3OrAAf6IUeHDXSUsOXaIM7EpA4I3uxdHwMhnUPugco2mZLnpCALPjcU44agGhpoKa24Oc0JkMryJzPhI3xu0fZ43b7FiHO+rDzstjItMqhm8fu+ZRTGxCqgZYZYcVdrKPFkyE9KVeN6xB+a3Km1q2xff2loyOekZE93GlTgYtz3x8rmwLWeil3JTCIYVss6Qsbm1KdhssWBakcOomtFrLSEb1bpT+gv34Z1CMuUZfr3j+F8jk5ONqG6pVrKfsXs3nDAEcYaFFzneNCljDKoEIRWsEZtDRz9+FoO3yU9ZGG+1UkM8v+hW/8jd84zcB+J983+8zhuvg2d3+3D/5T/wev9/vp+Of9jP+49bXt9m/z/f8Bko98It/wc/9/7j1bUZ99fD/3xHoZoMA9afcVlIDaSxX6phS/gzVDNzBZyFo1rT9ivd4B9/dyhZlpuoSx5YAU9HHJaaiJSeoyq3q+kn1r/ZsjSxT6eFMvYtVtxwmSrtx+rOpfrVehOzmrirlU2R3hodyug+8bcFmwT+Fv7NOaK0sdwM/0/hU5Tm3ioYyV2k3bTjVADgmoeVW351afFb2Ovxi86OG9Su4zlOQelR34SSSmKYHB8KgIbxsrFIpWZjjaS5HlFLSDoWboJKTnvrdt+3xbxR0c/Qa6b6D8ZgFAk/+dhraGG5N6qIkkKFrwMRTpgpxvFQCWueqE+9wximAAAAQAElEQVSuN04Yx9S/a2CWHuZ6abJsh7aS1Skdux9bBYZdg2qvhlHx0wrpk/SR5Xfa0x+8btOSdHZ/At/k21vFhApUPHCik9bTyGIKirSUoEEzXRUtTFIy2Vp/TMtVZ0fzyPW4kC2zXH2i1PdAOloGHnSo7A7Q5bpWYZX1ZhnqgWTp5yA3AgGkvNOR7qi63vTw183XXQqnX7QpCbJlpimIklAMX72aQ0vH27Za5dzyEVK1NNCRw/JFu3p4ovP5a6+wBrW40Wmgjce5CegYHMG5h8vQidJQyihI3tRQeaNIbOATcbSsHSBIOSpoFIzL6sF8Gvzl9mG4WINaYvxeUHXnhE/ZpMl9fVKw29XS+q3cgH6zsQ+4o6vY5cuWdvSllMmYqvV6ICgHelyl5rpfkNsayKCCqSiU+TFLZ7tsMhZkOUNfmiSOLSg4ozhSYRiUBspgNTSTcwiRHXpVOBSE7n4k3k5YjNzz3AJOuG6wWMpIevWgmr4z+qBQ0dUihE4ogne7InR8wowgq60hUkxl2sfmx60nA3i3acBsQ+fBzlb1KHNesy/1RMbVCkwMbNTOyjDJAQkptNPyO4u7+i6W0WdAVlGDJtvRAGQRWmcLvArreaYdlSt1o9J1V7KMJhIm1TNl50a+EVpxo1MqqWqBNBG8CpESNbkHN0cPs1W1hEpIymjR59XPCIesic9mMmHRHqCQZ3aHbGq2WTmFoTGuVB+Tq87HySug8Gmwku4NHJ2hO0+Uot/qmG3pX80KdmONc41YPDPSsjWS3j7sMte3nGoV2KfxbKvPLojdbnMIYTM0LKAkmnXo76MalcxcbcOt2PvhzmOs9XHrZ87TjUTJC8uQVdnqczp0rD+Bqdr0WbjCFNtSgGY1biA8fN3jOta7d+GpmS0BVR0lPwTVSduHz0wiJyYVdBWMARmUA209eRbUqmXLV+OerUvuPn3OOdzFm+A6vTIgrigvFX4o0w9D2VWxyDFoc4CQxjdMY+QMJa/HrdPMSjHj2Ihq32fc0iukFEmBK8Qm8fIxzE7WLRs6JQl7oHSn5KiG1QBmr4VhmNBUPDn1iS6SNnJ0zBKco11JZzbgTT89Yy+/58g2pULv6feYwqoFY7fdqJNq028Vt2wW7ZmWAX/YUyK0CUkiS946ullluF3+HGdek2tNqmjUNC0kc/VcbENo5I3XHa8TXnzs/fz6q123xPcoSWxqg1kOB6e1osJ38ojZvKl4tC1ob6GJO7HrIUYlcWztHmP36zyARvWm3zZvM44w3ZC207ZFRF5R667rU38ErwceewwUYN40oFHNHaEEbfZw0qb5R/XMs45cvQ2H0v1mJN89oyeK3ua4eF25VqzRy6et1/DXEGkmVOavVolV9FALbSEHX4TJ5UdDE5kpnjJtYLDFGk+shkcJoFlAAwhD+mjxgFIKGNpRjxR/jPfdLEhmP15YY6BXHea7eoeS2WFtq/2iXCvzWLP9I/tCGk4o/0tPJ23s8NzQq8/H1TPz4Q8oiVawrCsxelgbCDifOyKxwuiKh2JzZJa7IV3vv/S6QayB4I3pAQExPX0lhxKkHkshF2RAPGa3PqyC2G66llBZl2+OMbdDTqCUB/lq5aC0QHD47J7V0LSr4pIL8rkzfukv/8Xn/YnkD/g+31vgfWVn8zt/3dd+l9/7O+v0W775G9H6/l37d/0uX/9dv+t31fnP+nn/JfX11f07LAJiOhvUP7CcdOMwTtFJ56Bn+8k+KYDYzmSpqiuFut461MxK1eTnvx+gu41utS7RtTsOtFSgwtCqHU8O6TvGOFqnWUqqdjNXHXAO0jqhspyh17iByj4R6uTUh4vls3gRXzT0G+bIstyNA8loynigdu6QYYQqpzqjMdoGbBDLehjofl96AbLCtEJLlWZpLFRZIDcLyO463XdjU6cOXNJZY5jDC8phr5XpIRd1bU7H8zzSOe6wWxaM41qTVyjjfbVqWslvja91yjPRiCbY1PrN9IaqnIC92VuPcR7Jp3SFjcawoLxkJ1GqDhOLmNBka17tLwg16lwoxa59I9/WtV5rfs3LxljAR+YX2ReNGGoEMiATIbAqBLAEPxdznq6oLTPe5W/MpNP3RHq21USPTmMJxml87gasKdtZVWrYQytNvRJupDnB0XVVCgSvUWRZ6tvcpqt4gVnoErpRSN2GydLZ4lbfZLxWfWESZDm1U0iYgLAcsNUv5qsIoaQASrYLXVHCQtNlbXSKmZ1VCTS9FCwtZPCSEd2djRbjKCJNfO47Q+7NlndsAx8XexhnN9Vhcr2p5zg8g0xsfJdwslRUOYDh4f72fI9M2TA5DBPlNPUBNkbF7Hs+Ov3MGS/3F5+HXTM/bkUZbRXdkaIwHLLHS7TX0tiPENiiTRN9q/vd99Fbei9x8eC6MZo1PGnZFZk9sy8lVs2WBenCZUBRO/i8WQu/9LZMCzwxgkOXlJU1GrOph4hnkUqgonMLJ88c2X7KogqJS759lnNz5JhD/uSa+4r9rMRN+oJppbpwLMuOreIHhPy97cTiMQTwSOaYNvRYdrn8LLkfik2LAGXeg+/BPboGJNClUQ6i2TZqUNSRKR0D9aCW7IAg1hJaZUwMxmQgsgLZLO25sq7Decj0bbwtum4VJmlO6/ATjd5AWLfSs5THN7pLbtyj32a7W5iQB6Nzjpzekz0juFVxMqvFtfAZNiWwrdyhuDm4mmjAmpYzahZcMy8KcdMQDOMF5Uta3q42jJkTRx1Dz4QmMPeW+0UayJZ50fIS3lyaXFNeA0IFipt2Zzuof/X8R2myUZ7upbbBRvvMaU8+U6NRYNJknkDSBOntBfecnVSctKPSI50bI8MrSpDeZHE4cDUmMvs8BZhw40AjI/Pobf1RSluOCmEnOARG67ZFIRWR56GlamecrF048MCTet7D28j27qRtF6JOhHH3OiluNcd7XDlHuIB8fdj1mvN5B2zpznLKvhSRoK3LTnV20cxVwW0SDCjScqSy8dzgKMPp3pWZMZhDZidxrs7L0mYpP8LEXJNFcJGy+UgJOVM1A+tQnfi1t2LVzTnWy1ymSlaDLWceIp2OPsZymdlavtxMQU0g5fhr9pKrDAGydWlBreqxodvAEyMcaWu9FlDAzdtlLOt2SfRwk78Zp7dC1jWp3MlClhhugbV5JV1iFDUzAx6AJ++BT3ikoorRiOzWoPSZwi2sktmPHLmA4/o0O+07PUEi0aXAdXpxYDolmdqG5FdFdXexwzSPXkqJqkI9eNjn77KB0J0adnwER/SIgHL0k3nY6JjVTIVpPwQzpI17WmjQ6/1AII+GgvFSWej2VJLHyHLN2HbKpWoI204fVn61xg4UuymKLdPHZVjWbqDOnsS1HOWuX2X7A/yi/EXdyWAJDOEJ6oG6zmBNTwVX4MUIJSlRsHKDKVgafjoTXrgyXpkv1GosKAQt94XknvPAGaP1RjJ0YBIQh5EE68ERZAlxc3/goRKwQjwyjh4VM5Rg9+pg1Qha0cAlE/RlnORFrf2EfJVBFsZdFMXDzzGEXXDXjezZNTomgwxgN+6q7Dv7HZYKLEwkwp3jvXknb4xYzmE8rG21XSvHuvpaRipUbGPp2epq0qynw1x/Bv1zlZdU7DzK/vNf9EsAjDG+3/f5Pjr4nfs+sffW6ev1Bajv37V/3e/5e3797/V1Ov+Vv/K/et/PGB1/df+OgkB/dvS3f/K3c4NA0568c6fyKQ3azZTKqBN3ikvZ1EpLF63rrj7IXvd85RowgoXa/Aq5E7PU15WDRjYse0RBVNcRoQLJjqepRZv5cJ+GRh0litpn9aSrAIRp8Qasofp42pPjsc2qRfyhJAtja5xXIEkug2utk0qMUTlKaXqkyCxroV9oa2LP3BKlb2wVnvaSo0o2o0zQirszWoZKfgUzlNNAhBI3pbHSR3w4Psgpy61g0bUNMVRkrIEOOvyaw4dxSP2pu/O91LMlLhm8uC332bFLq/2njTtw3MJfqWcLSXAtz5hzXWWziqprQuLQjkm+CY4yLQeSnJOuQdzJvOkNDvbCJm7wDT1B8WCgJzBSVR7puGd+Yr8tP41N1kx3YVPqby7BMyqtknCzgWld1XoiGeCH2aWLK3sex6dZ+yWjBkw+EqrtKyLvTycOiAn12rDBp2EboKnAx8WYEAsG72nbkarDvE3W9l2IanfoWWvZBbXedVmsGUXFsGRCl+8YcXrm/UXFqzAEgPaWiwpeer8pllYKpYRxCyJNwp71xqkIC7qRfqrevs88xZvdJhBjmiyjCndC6+wBakDWHsQ7jsKTSWuHHkF95RzC2F1T+83aU1p6dL5av59sHxuUN8weMKc4IKUysGZTV9jOHC0GaB1m8wpwb+zMUIIMZEEjqDXnwhxJlIJWMgsHdmRtJUthTPqN0QVlhMl8AG/tHJ/GtcerfOpKs4/Cb23PFMqwrkYFOsEkwwxKmp7iASDCaw5Azzn3MgFgo5ZGcKyWL7k6x3PghklHLh4dGgfgBGSsV6mHs/Kih3cyEI7TdTru7pzL/AviFWflhomxRT5mNvbuvgVdd6u+mFCQ0DbLGhXU3B6E1LX6C8g9HwXBVS3qWNlIdEJBfYoU6rMg9WAa05BluLquqNEncN/1Zdy37TINdjGQapRE6zSFx8DZmoOyNiHcdvaonBJCpEUhASngbpys9i20fJS5xhM0khqAfmSYCTvzxWlPps7dFVZqL9ar5pVmLbvXDb3scZYcFnOTQ7FOsKS6oSlbz1AeuFCL21aqsmIRasXMb6+95FiICMulHeyUuCtCXfNlnIDcRBBtj12OGuKKqTK4r9ccc0T72bAbU9kjhd41ZE2HnUP9iFV62/IR9fJlX6x8jcaYegY/0xKpJNGzloIN0kvaXnhMacrOKVNtTipboAhCsfZxRN3OrGpdNx/TJm3sia3fpvTeg2KMsuWyfXHLavBzh9hgNayc8bgB6t9AXZkzW9BbmJTrltyMsWp9mEQ3kdoiNUtsxV7FUZMCykBPzsI0DjcMdy72ippZV++Fe9TNjEYqEuMKymyl7fZdnhO0pEeJRy0K9+j03qf3Vokpa47Nke1GTGFencGj6wmctsRqKHFAOalOshsOTpiPkReg9WqihzVnY1LPyAMQZlU1EC8JFIKbik6YySEjBxoHHSwzJeqjhkmWkuURl7qQ1klU74FbGS6RwAio7Z9d7ALS6Z5dEYl+nGRNhNvbx3k4B11tpZzSpa+WucRyX4IaUwGinv6lYoSKxa2JA261kCv3pWw5fRRaWxxugxhUNTsPM7ALJzfqWMU6xft5eIvZ9sVcr6svB5tVV3FVWT0OzAGKGrbGZxdJJggVjpx2CBWTHjpNZDxr7F1K84dFbHyEQPEB89Is8xwOQAWWN6xGaRGacMiDWze6Z7Rv3fcVfOW54qYYzAiGYKPDPjJe937Fp4lP4XuzDlZNwyTYrmw77mUykOaK51U2lHRbbUDe5e+en+A7xbiJJGCQ3efwsQAAEABJREFUl+XMbjaNBqF/gaKTrlSAmhI5FetRGBoCpxnMZE2tToM/A/nMLhjgkoZpHArvSrfw0vp5CnnbefsJcRJt3j7CCIGW2ZFd1YQuFxTuaEEr0GY9OcdehWu3x4ieGyymUXqeL80CqUm6Or511y/9Fb8CwOv3+Nrv/Qf8/jr4nftv+sZv+nW/6bfo9Ht9r+9FfX2bfc3xff7gP0QXIvOX/6r/Vgdf3b8dIvDf1+TfyYbPNBKTGvoAVO0bKlvWHWf/1t/6zb/hN/yW3/ybftO3fvlNUe+su3aglXeXK9OIjbhRYqUKhjMLd7aKTlspd8tGzJmj1JcuHHW1jhSRTytnZ5lzJO0gK5UNbPJ3eDN6zpprjDWorARM4hIaicqLrVQzlRhHeyvJSvMcGMOJYTAZD1D5JCuqEcSxUWka64XrdtfSXrrIGFbTnvnBhimBsRAOJRu8zILPItzWZnAlW3mpdrRWK0ps6wBA66Kq2CiXDUVW6x+Iaf4x5APhnweF95kdMtLY3Vpk2LSwkW4YeABdKpcqESbbm9N5PUIDGXWKcayjmegDne/IXc3WPFyuwqwbKIGEeIB6RJTLFHSYvC6shnwQOpLcs/qCr6Kp5Z6CeZh8bIWnydbhcuhJTAOqKuR1mteQlWF9GGnytowktLlhuOadygM9SXQ/ZqjoDbbiZCiL7RUTNVkGKSVhVShWQp8dqCRqwIZ1jXN8B5+yZ5J1O/fIzaIcPiJRt9+g5qBz6smlQS0BWt7dU06H656iqNrtKXkO4aT2tEPREx0iOyMaR/iMAmRvVYuFja7U9ZbWx0i1I6XVUtaZcaweJhNQ1UWDXBmd7E7t1l3dWmojENuQ3pLLSAtNERSmeIfskJ3mzWdVLkhNgaeR3QrM8IJnygh063K/idBirmGmxRdoi7TcOqiWGZZlzYUnW9iu2y2EZKMd8+0Lz9Y5Mka2U/mGjhabBGXIXq31mVfXdD3MhJUEWCpaCkQL3jl6utTPEqBdTirgJcPKM0e1qYESKNOVORIvY95n3jbPNNijenC5jVn0FvvM6rMFsnPWPUJ0brV2rZwYvTvuc2/T2ljs9NyeraCaKDQB4vGMjYzPmCqfxfkL6pXkULEgqUZQXqWQLoNEC+gB8aGt2ssm1zLTDmMRKT2KAJ2myqHLl+x7vETLVAWcdFDw4gmz359XZKYrv5BGTUUlpF51wjTShmmbTQ8AnchGa0Qdpd0wDKr9Cer9xO5dM33mtbS3Xq7IRWVkJk7Z6SvVXkmcu88eKgil8lRk+RDR2RDVGnJZKBPiOoNZQ2ZzNGdytUsiW2ZuY8A+76hCtbh49XERwVK5z6mhawRxWhhznhp5dFSqAG6SMUyCIWLYbRpHaTSyl+d1iQOUzEoENVhDlVJynoBOyUpDaTQFPiXMvKhviBEab5ij5wVbKcnmbQLpGUU8nulCKX2shHFXSVFSZiMi9YYnKqsPTTvF4ByULgwABCb7ZVBAurtOZ7TuGp8g03QZMq01Dk4sG8Nf1a8T41SlyxUp0gyFWEx4oOJJVrV3e8m9SusUfE9cJBp0+OMoCoGsutHdD9hX9wIUx5j+YFS3ZUAPACxHkZAKnsl8KVcsac/4lvXmUCPer56rJmjlDshYI5RPijIJmMKoeOpwNLzA50NEZxKoA9zHlGbWMMr4BBNIs6Lg7lE5HNOHmx6JRvqs6WWe02Iq3wRtI7x7EvZwQNOCtptdNsNlriSkrCK8wRroiYJHv1J9shU1He1tU3iVBzwJMR+DbqoIeVU6c45juLXP1o89XwrDlvCh7sEaAsgM15gvTkuXXU10ZtQOHBsp4+0q95jWDpFFlWWoFh16ROOWuNIEVLYqdZ2M7EC2zJkhyADH8FZbsUAFERpcyGR3k0VPme3OQZM5HcbWDnYQWd3RLYIBCRFiQYVfzOmKqJAYE080sAltfNwxPul5kcPKoOeE8kqrw4Ts69pswXGkGvSvDApoufCQftKtq6U4UT2rRAYvRULLTgXEuYCspFeM2jPD9HZQC3lfq210DC2kHm7Vo1XCc0a4qI2ggvdhJebJMDRGYchPgjBd7hoCUoDYU4k01tUTbGTIGN23TmZ2MeQyBJx2aGqDWV6csIlv+vK3/Ybf/FsBfL8/+Huv6Tr4nfuv/9xm6vT7f+/v1fr63fc/9Hv9gboQEb/iv3n+i/g6/ur+HQsByt2HF91ifYl0d+xf/mt+zX/0c37uv/Uf/Mf/5k/7WT/zv/glv/E3/JZv/fQm4VSx7lNiZhtgHDk9lG/XXqb6crPvU1E0XLPnoJ6u16aHsq/C62224XXE+LMiFn3aNLiWnHMf7fHWSplQKXpRxWdKA7FWji/StIw0+kCpqDVA2nrKHvVQTsCgrS885YpdQJiqhgPKj55ILfqys+aH8bLnOUeZHKX7GgEM9DQUurSx9NoVH4Z1yQZvstlQqVrWa3HQVRWIOtRPlbjfwiPSUT7x1HxoVSrvfMyUvYDj8YR6YbnYZsr+V8BDAv1Ma9XeD76GfdAvPOcTvPo2vfJffRyNIk7X3XanLqIJtCK2T9112j/1uFMt46CcH/q12fsdpVc0qtcXa05Fo9Xx6vUpw+0M07IsFfgg9ZuQjHK5OOhDETtOIdxRM+bqRTMV/mB3WCmyiWcdV73yXdiZKeSozYDZmJBXZQKruFKlMwUorDkCOGj94nMDQUJDEkbwgZ8FZElHu4XjUAtLR/ZJZOce+9OV9YJZCbv1lpbZmmNfUq3u1jKckjimX3NeZnLXhuuL5tKgWeagDdDbTmuNrcrIVCMmngi32QqR8Ey9sN674s6UfTi9duvB+uqYFe429DvPGCZLz67M1mxdR6/s18ZHteRHXreYfYOhVQzOFmx6arujZWePdyuyEauH2gJHTu0NRxeJOfqx30zxsNGg1LzPkalyaH6x5uXDOQGh1OX5rAzY59qimx3MN3h71xX9xel1RJzuJ02hrZnxrFql8DyQlFVdp17K6jNYWi8J8T8iSzam81DPQnm8pHC4lrTqQ/Uk2plA3ZH7bo3B55VUi9vVZ8bOipAuPbTPapnaXHJsGRbqcswXPzjGDX7qjngEFAdjjff0d/euqpn8gC2t1CWyJOwsGWA0atOQPHufE4pSGcsQqFCyo0kDRoJ6Hj2QoLYpZSaKT1265cJEX1hTiTzHNf2FOfPluAx+DeVo+oSZJHV6hjJdpOZAUeu62aPi9MPCrg/UIkQyQcwqS2O/iCU7hwf1YEj9mjk37a7T94FohYKsfU6i0QnZ3c5aTueCdoXp7N5fMj45ksPr46P0InnNHOPMxqQPtFqhwS20rU8r5BMCi1IOiaMyrldj2WeJ3ox8gsndq+pDUaozTvRdWdD+GKJ0akVLoqemgwk/bVEgMFx+ekNutzTXq3f6Hark1qCAG9DLC9GuXrnWGS6Z9obyONG39f2qc520cxyhkT6sjQ0o3dMu8HXBZlMpM1Lvqf0S7pgOkFaESkKidsd+skiHZJGZB3FHfEoorljtC/oB8ePY9QCtSEGgsoWQ3k7jnIPmUP6O0YM9YMSAtgoqmQg3eB47d+8dNyJGl3mnas8VEG9kicjHSofwoRaYM7NmZySrFnz55YCf2yOEpkLx1gUS2jRjemF0tVVZSYRbUBY4Wu/6qoC2ZZwWpeBUjm605upGTeS144t3r80JutswXlXeYd2DujKph0bvQmueXYWPKo1AynXGsU4aDrDDdzvMp8Dg8FgCw+ZwM1Vp3ch5vdf40k7y+Wl4AZYzyqtohr4q5u5SYYgx80mQeiz0zqyKc0qkaTMl/7zbbxmllXQajYy2DkxFqKebgzy0ryQG6dO+WP0Brb2aXnZtvhqvZfP6HAYleSmAZ46YIs6efc9QRRLS0UUct2NfiD9mNvAe+/AQpchYIg7et32p9UNmyDRkf64g2QVLQhSwhvr9UQYh6DvHpz3fCiaD8V7nvbrnvAQUNAKnPg+jO0a4NWMo2ft0b/m4Wqrvjb7hWwPQByKwSmrq6e1rQlmUHLqzDVLYWvg5vcYSzPseu1aFtLQ2k1lnjO0zJChlmG3iLmQhZIqAE5JmjxEbLHZDEdj6yUo6S1cAMWXoMXC0ja4R5yqZ0UNwCp+WAUUym02I5st8mcgfk6LHsEEMw+RjUXAcsjxdfj0mWJaM1C1dbvHbRC1MCao80N4iKEK9jOFX/ebf+Jt+y2/5T3/Oz/sNv+HXA/gBf8j3lqk6+J37L//Vv1aydPoH/kHPjyc6+Lb7N/z+38PGqqpf9Et/2be9/tXj7zgINPDNX376pb/q1/6rP/Wn/wP/3L/yN/wDP+7v/Wf/xf/45//CX/3rf2NEqoTtiIZSvqsh7qcpiziiLLKUqmsMp2qZOuF9s48yfcBHLtNrotI85Y+V6oFW09PIPuFNA/WnFcsIEAFl/2ktEc+S0sqNZfRWkUnNnYSRIJmkMvU0w3cN5QBHaRyhrV2aWK1quZ80QWne8BhjT/80tTDQivOM+TaTGZ736FQJkgim1ndmVXDLseeEZi0rHcPCeHQJcJr7k9Kwst6ojOh3IJS7aRQ6uj56GiHTT91Z3W1NoFUKCuU6pcpChfqGnpoRHG1697cMmgZUW7WnBCAevGtYX56GtqwB6ptpptLcxh46FUzFxmxZEcmT1rrkBY3W+zyp7EJn1d0N7+G4vLRCEalxlEQKnBnwVP3yTYYMplRJEDYBkyp5+vQFG5oAh5trDRnh8/AVvU4/v6WVEF5cpB/LqPPQRLqBJMSeREpFXKn6P1Ide72qsGftF/gyFwQZJ1Rocw9scyEl7cZJmvQKBILkbK1XYSfi8EhMz7YpmBdrodfQanh0LTnTXDzp+W75IsNyClqiJFq4ytBBNHNTvMtAsZfHKKCxC6ce9hWu6mFpYkEH5GmAmTIk0qCHdD0Ei+5jlHtSS5CWWXUuwgg06WVpOBUdxb5JFX/KVTd5C/21W00c0a2MCfXt9FBvcWhJpz/boBJx6nOGUuuzyY0wOz2qvCtKAQZa4h8lemLc85yVBYDAQunr0O9mSQsHbBzMrCU+mEBqsBNiT7QSSF5W+F0rMTAtRkuJnHGH2ApvimFRGcxwnMlNPyYeW3uUBYxWyMDerNM8NVJNVcK9lRzOUGeDLXcHmzKugNm4aO5mwwUk5Eujc8hNEter9NvDYLkW5WQXKtBpwGObXoR3TpjpzLwm4AJDMUSTNG0KbMnBLMLpjvH4paBCJ/0wmZ+5ta1jVC/IGSqDKB1gyGObAzZlVB5KO6RYwo0UQoL7LJW1nRpaxQN7HCrOxNR5l9gEpPwRrMzL5VYzrN529IIdxkmsUpzgAxCNhV/TW6SQYdCA9ifXKQeSbKZFS+CGsm7snKnBUwSUbZpjLl99U+kpDngXq23D7oYioT0zUcdT4dJYdDnrMUHuWAOFpCW6pQ8dhmDlDLh8b8LaXA4WZAkcyrQaDS+OGsonkx3jSEaEnzP7OFM3LcBMY0gml1oAABAASURBVE3Nk+xjdUYCB2gr2U+Xn2hFdxMY1hMJl7LBFh6Qhrwsl0g0TJmpdlnTG9WVbcDnMPvRNWg72Rsz2k51tswjJssicKKiAyanWlngWdU777DzyNLQQG1Duh3z0wx2wiq9w7kpgx60nNPbttlb+Bpm1ywlkcVwNZZb/SFX9tWayVVgkShozKvr6lo4rF19t+51EyivsDpVavqbRRdun4CtilipJlg2ZSk4aQ75muW9Na6m45o9WFofIHzb6c82a8p0RabQnTKg0cKWAhPNYT2nvpAKnbQFcFD61G1dgSlngeEE0XdhhzEwIidCKYeq4mkPKpejoGBGtq6oq5bhnkCWQnHsda8lk3TOJ5wBJmno0TfzPPGItjRXCsjusos+zEUISrWXmWaiJBoIOe6kS4BMsf6wWkTJ1iQakuruuShHkakMcZUfGSr0KsnCQmuCUzjUl3hvyyc6ftKUvSbIq5ExMleLj4upRsSLEIiykbpbmrKC1j3nHp6WzrqMAzVVhcLtjBHU8+0HYZJgEl0d1cU6to+dcmVEH8ZmvEflgPwec07jFY8UWdqzJBVy1mRiPg4JQymvVlqapQIzSZFu1GEHDLVmUPRi8MixLTUa1QKzxcK+oImFpqVJhmnKhpV7yd4HZAI+OOe5+lypz1bYzCcuhGBIOXv5M8Scs8uUSDKt7XXstX3cSyPdHVM6IPf6GNqMUrfB//Vf+cP+xL/wL/2RP/b/gM/bH/kDf+Dn79/18Qt+4S/8yskf9r2+4SsH3/bzu3+Xr//ar/lCV37Bf/Wr9PnV/TsOAvc+v+S//m9+8r/77/3v/tF/+s//W3/0n/sjfuQ/9C/86z/j5//ib/ny0zXnb/2W3zbcv/73+to/4Lt/tz/ou33X7/57fs13el0+Jz++gFKqetwbd6hpY1+GF2vuWOdckR+sIRjTcHycp47BPopfZL5wj7oJpZ2qyjOoUGKyT9r15Dqfs3qKYbVqUkI1+1aS4zwpRaOqh3LkFeoGacdP5o1TJmnSqJpZrdqnv1s1X+vBo0KpO3J0vkKtr5ILt0xyQjmZee0z7uSdrXKGrka0rAsrJaJqYysh9+AequA7uSv3M9BgE8pJi6xE4POCFFJSxcaiXe5NLSOhVUZv1PPkvusO131P8iaOqfqE4e2hlyjpJb0ocDcTsC7HJ6sjY0q/40iel2qgSgKu0/OoZfLL5mcrpLJ77Jqf2k4VWz0NzEthKMo6PKJP58FjO85Eqq+xL+mfsmOjn0XwDmbTnNfsKWHjpKfMMRwHSSEGqCBmW5r1Gr58mSqWm6aRQFLLfyJtnNVnRitcOU/aLShAVPup9T7ryzMFRWTUeBfesP1EBIJUKxr8HIutp4XrdZb3xTk5vA3uZX3GaRnqK+d11jzemWX/b/b+BWq/bzsLwp5nzrXWfr//Scg9gVzJhUQsrRIrWMWhaBSEIgoFUaqB1ACawgAEadRSQAErNyMUcURtxaC2qBVBboJ2OKxFrVoVggTIjRCSEE4u55z/9+695qXP/k5AQiTgQNQ4zv7t7333Za15eeYz55p7v+fy7KF1tNSXXHpqeZqE+gl2thZSBHfi9rF7oQ7u8ej9mOexSoJvrE2Wyy32gIMr1iNES0ZQMhloq5zX/iC3WvjEMUzPljPicd1xN2CQ41jmD6+5rjEFM46C092mwgVZblq65GcARUUJ8qmHu7Q6elc86fKhinaV1n8J1V03bmthdFHc7SBKNreArr4tbmuxNVMTXHlyIZ/CcIi298ts5QVB8WTIuVh4viAesCERhvbE3Np7aL7YZltiwC5oLSOG1USjS/GVleOoqXi4XD0wJq0aqWXyiT7tXs368Hj4+cK7OwV2tN6x1pW1I5+xz7i6RKfr6NS7RcE/zC0boznSHlzHXMMgu24j1dSNDt1vGu4B6oFE3fZDKdZrl9ft5MFLPcFjc4TMNONYwhtlpaj2KfMBOjFbXjJDjBGveSehpueANhLiZ7RijXST44ePAwJpQxmeBbSJhEpP6E1x5iC0tWTCBzgjuU952mdlIiFRbcQtapZgZEtC69PSRs93fM1lJdbiyjduCmYANvg4xhzuimwF9waqZux15fEs3tJ7p2CveWFdqk+tAGyuUioaC93ZFq0YQimoWjECo7Cij31OWTiqlLdAe8TR+z2Fl415Wl9yQoVMsMkW2c4yyb52PANRctLbNTf7xXu5jG3uxhl2JTdY3V0w2BpjTDd3tzYUezSkkatLshOZ3r0CCkBlX4mz6iyl8BPvZBzUg9ulG3vwHP4q6nI8zB6H3b+Bcc3iGJgLY3DAvWf5iJGF52S6oVu2oZDbU06Obk86akFlPTquOLcUqCAEFGy0247akad3HrLaFfbu6rsGzE7Bgmt3iWAJc9Y73e9xW6IHrXON+6Kq6GvjFdQkrPYZk/VgcF79EjXYkIUJ96K4o6yyFBTRqUjotyj47uy+hPAAZX2XWSg5ZjyXAneVRZXEi6khWZCfKWp0W3CEznBPmzgnlLPmcBmwnEYLCU9xRstxLeuFJCIsLo0YN1zX1VdUiIf71IcVPadfE5w1rREopfhTn0RCmTeGaqxa+6h1OS6Z1jkL00bM+RxLRSyvmRdL8j3NzgUMmpuBjZs3tXVrEohscSCSzbnHcY217ah61Gl8t+d5q5egqwN9R2ht9DV7u7XSxNbtus4QizhQBxpNk6JhW2/BZvGSQpnw4B6igXm6l0kzsa3LityFOMt32LV5icwmzpGTWucUBJneRASuiztDCPI9jYNhTKN6kqEOg0mI7OVsH4LNTHNaCTV2zSyZaaokKkk8NdCi7Sqem68xdr8A75Av5nPQxXtcq9sBiLs4U1yZ9jxmyLsZ5qG1X4uNVFdUxt59mVjFVqQVb5+9SMMMPsKtVtgUCJFme44YpBcIyq5r5PPOSvLijBBbPSAGavYmLjHMLjRZih2MoaAZ/Dk9BqGjiRtqa4zibDPeA1oxrS7q2opQ71R75/n6vvf+8df3fSuAj/74T/xffPZn6uBP7RH5DX/8/v95fLy88ynf++P/1PU/dfB9Pu5jP+w9Lzr9mq/5qp2pgw/t/9NG4PV5/jv/4X/yC77k1/5tf9/P/DE/5Yt+5i/9Vf/33/Jb/8BXfw2vd9/B/vDH8emf+L3/5h/8A3/c3/TX/rjP+2v+us/9n3/2p37iR37kh7/nPe95We95zJc5/RiqgUQri6DspWq8WWjZIJW1xm5lf2CBInLYY8+V9IE6ai80sapGZ1de3ufgpWTM0aAfrT6IklRE7MqTeVlvlfhUnVY12dcIZRenak2TqJ67hmpZvQWtZ5cK15yYS/kehTPupDHA0egMSQ0QSqija7HZmXml6vDYc8rSGGgN1+cs2aBKYqHJmsOK1AIXpRLTytF7XMGtfFYsu2xGee7u1BpneS+enSOamdVXZ6GgAmEWnqq/NaK4uzVAr+2ewFXdakW70d0qdzmmSvOOjNa14RxmA1RD5wi/a3WsjePyVVzYXhLAvg7fg9spUGGavDW/0vpauqrK66guxKmF0eS+MUZte247C6rQ7Y0RuioNLvtMK8iG1xx1TC5zExQAip4K9SgNHQir9ihvFX7PSwq0jggCjTYi01/33E+3q9EV0qOrPrqEzG6UwyfngHrU4WOMx+C0giylmKCoSiezqjssT3SwbWotwLwVIlNrV6BFkN6Avqo7LRTLDRO621Qyx4AmFSlwJbllieyUK6TVGpjDj2POtXpOVXd6T7Ga6XYtu3SqIw11o3nTylSpp44EopwrVgsA5DE0TqZSbl7cilQRlhjl3gdxyNt2WStXFutBgVrgLjadVJxtGOmrxc9z5TnOVLt6XagtT2tXJzQhbiJ1LMhLVHpdcrUY6rmEL9KoRiM9ATRw9qwWQykgqhkM2DnrqasNA1MWGKvdwnXSImbC6HBdZPQNmGtig0bzIRuxrJdHc6uNuHOGQjQ8Q+kwFOjQwMWx6OaU+5gB3aqqU7TMkrjHFt1ad42YxtEaJkmF7p2K0yAGQFBqpfwqCKaUteLO4ckRGiWpbT4Ksy/Dls3qX9yXrr+WCgAqYDAF1oZXdUUgA4hG3rL1juWlQ49rsYdw1CX5CJgcpbSbLjQ1NFKZL+s4JG34bRfgdNIFBUC22QWKnKHxAjmtWj757fm6sE4eZy/2PMBDFsnz9rJ1ml/deZ12XkRffoTfLRYD1ll2xdgpz0plpkxCLS9SeZujaXCY7HQBFEXs9itn3zI5r/AKruZ0x43RyHQ04e7DpibmbXDhRrhRV9SzRmIMKL1qjQ2msqR00UbMERy5iZOdQGeTmXV2b+82IukFc+DgftheY3LcMqIyJb4pHmGH7ayrpcM7KULvM/f+4BC9hc1VMVrlKAvQDNUJUlLlgXbCiTdzCX0P9mE9G64z5cWUCQ1tjZEcqsB4ssOqNGYUbd87a29eu4oXjg1hWnZJ59EpUKhgNEv/0FZtDWqmN5v6MMBus6Dqxd4z99ru+0CurlQ+TlzNDdxFGKDSo6X07IrUM+VzerkHZMgKLkXYOrE1zpHOXsvUq8oTjR9WfgxbhvvRZ6sGy0u01CtR08fd7J+jApIA1EAPARGpmEkxcFtdtBaIaP0bLVlm0lnIrOwrLLfpkFDel7Vci+YJ0w33a9rpDOmfxeNWrLXWX6uekng411S5plvyhok97Obagoocjx6zvKOqM1v83sr/Uarsreeyaz2bJcBGgmU7VWOInkyzM22H0IvyEl/uVPXq2p1P9LORt61P0zdu0tGnp40z5t5DQRFWQ8oPrNHmEi4CDhGqUIZ97Bpb4o6E1Sh4Otu8y7m31TZ5sshBfUxx9gw9RZ0l4q9AZGaHMQhxhkRaX1aXEDTJ240LGdY1KT6EoS45RpqjtZsbhoW3qmvv+wjloULUJ6sI0LUK5rLU8eYdhteO7VrLOoY3tJs13c3pSQu3HMxRZJq1pshiWNvs4XBSrkFjTaKr0Cd4manrKvFEThsrmVmiqx1bJVO0CP25l8l4IDtL99G4tcXjOldc3bWBPaGK60ZQCr1kn0EbAUKKPRXedCl6VO4PnFmFP7n9tX/lX/HOy+NPnt3fH3g+/9Af+aM6+ku+3/dbSwzS4XfaH2t+yqd+ui6979u/7Ru/+f7v3Ov4Q/v/JBH4yq/7+l/4T/6az/u7/p4v+Jk/+5/9F//FL/8DX/G+978PaBWSz/jkT/4RP/SHfvEX/G9/1U///C/+e37sD/8hP+hzPv3TjpcPq3YtU2lKBhf7VuORWCKiN5UK+ijedGa2YzoOC+PFVvalmHr0TeJuVYVFTYw4sl8wnAduxdEdRJpv1aQ0ULJVdtyBbhUyGGtYjsGWltrVKqHP25qUIXovPhysI5Vt9SbQGnSlpi/ziUJVKq/yLv1H0p+dUUH0w3tJqIY4E7Vnpx6mjlq0AUibQYnPbkRojrLRqA0NbU3m214kJGUMp3oRzjSlt+606u5FSTur4RJ8AAAQAElEQVRkxy3nLsZyTk6x5Wmx75Vlb6hkEe45OjquvLpSdYJZBMlBmndSN2HQ4p+HApGht4fjLpCnIJ5lq+7lwW/fDbZYriXBBEiW5LVGIl1qhqkADcmT4lPIoMsIrT8EUihcqJDm5EjOhhf0pOYRR8WQ4xzOeRhVcROttfiigU0K/U25vEEIgKNaAtgL5rB07K6IGtnL4Y3WpdLjAL3VpEBLQz6oOgm/wCKpd+rHkMOdXVWGktikxEDoaI1RFTSqCmKCNAHgbRRfQNy3oADvsfVrwmm8oA7Ra4GHGf0GRrWcWxhqmIxpou4YTsxp0zhEZDAB8LYf1s02LR2z5BFfeBzjkI2wwkgIvaGXRofWSPVDF/K0OIXx7OjQlrs789YzxY4e9cHYW8hgpBMDZIN9azTqvkWSgNqAsVBTyCkoDd1P5sbePHNevbRC1ao8tpgrsGjVekhQ6y6EHCZIcCdKQ7mEG+gM6pZJFGszAmGlhRZCQIvO9oSBbn2McIasmjBLV4BwKQhdgh8t63TkjZfmO8kXhCw4Im8gsvvKt/H3UDRE4GMMLj1UjhpDNimgeRYuoow31OoiDCBdlaaRvGpEjQp50JQRhqaut1o5VYm52y7j9eg6eLsvOZrdd6d4T4QmyrwWgDLRxJy4idNC3UaPART4DHtuD/WRt9WObgXMS3qsICo+cIOmKbgPHG5qQdOV1ZVqS3TSLstI0Lb+0O4UddiaJ0eMyXsmrmGhXtOGtY+YK1qOmT1rPMPParVu9jiPGcsCXaEru6DfeOvVGbdfrebZsMuQJnXh/TxMYouzLuWl+kdgINe4VKi5e0gH5HYnuGtg03URbYpli+eis6xszfFZw9RRYeSdlWrxPApFDa/7sgqhbmRPmrZWKUUAISclOxMJklYFgYZUjyvQ/TYGuK1t2do0k6URKiK1O9JCqOoSZZaD1kv06ct32rbe0BDIBBiNVcr/7okwvFWmaOEQxm33EDZa6AgZjYZ5W0nE1cx0aCOhOC6dlS5ov51kygOhoGS3XHUhb/sva9k8JUSUuFyewWlwB1j3WXcZYbeSVRwFh5wtlS/gcip7ZINGA7w4LhNKVDxyJuaGSenVlqlo1MgYftKv+7LDnG5GASX2wXPMdj2mmMKEyrKuAxjD/UakD2JQNg3UcPQY1mvEmCctFZhia+I0BbsLLbVMeW0Kk6SCNMjOlhlTZarOQlVRES5MWQCvUZTNQqVL/47OO74EHUvZAzgFvTisJ7Oxx2qbVGnt2fcdzSfTdutJZmYtxmEcjQyEENTDbckkpJLGOmUl314MFKHn6rw1lRyO1XJlF6/h4bjAMFdcCsHnNjGlGkKf3hkhJ1s+OcgIxtlVCdeMFmL01euIZbrbt/F1dFiEiFNCbK56jDKN1yLRJmOhqi31q0tr39uN4v1qsnfOEDiDMpASdQFypTE2eXlJqNgdSh0mVUi6wPK+lFlsiqZ3sJhoeNsIW5eJFyvxkjyaYniW70LBmgM38VwpInlKgQpme0mM500nB2i8XUphbVvnAyoHNnoD5x5nzWjJSlOBYSoCqInLI6ioyjLZtGuchiTde3jOI6fDpDlWbb/KnsBrqx/ZkuQJWdOMLK1mz93PG2e03AGalqOgMWgQ2lqSZvgjWuuRFQZsvP/d18zUzXun/ci//q8l3wbf5/ff+9///j/29ff/nNenfe9PcPP70nf+0/DP+tRP0rVv+8C7X/uN928vOv7Q/j8lBN73gXd/y+/+f/0d//uf/UN/7N/xpV/2G77ma7829mXuH/nO+qF/9V/zD//9P/Xf+nW/6jf98p//87/wJ/7QH/SXf/RHfgT0m7ZIpJoHRFlkl14KKV/sZqa1triscpqvF1/HMpvYg6lbsKZXIRHoZ/F5ruuy6FI6is53mW/R8MW4nE5ClNQaX7SKYj0PbtUVw3LMgTHNlulRfTRnYKQq2jlyz/PO+IbrF5AebCuioe1qLT5lSo7qlsX5VAW3VXho6pmqPbDBBUyDXrzYXc0mx6Pu8jECg/JKqX9ZgzahBmIidUPp/AAfPsZYlAPdHbeX8t1UkdUBOaVfOTlb5QDRqYrgy3ho3Vmm9VbXobqjCjLSTZ2i9kbdyo/Y4zwr9Qv8frV8Lp5Hp0rHHD6HLbZ6EVkUxX7CNkjDlGCN2cPLJziwlr04Hxiu28IkhdfImNDMLEztI2vENXOrKG7vGFQQVtrYs3PtWGfmzs278rGtq8guPzYfgSUkAONUYUVXl6qUszUgrq13otlmzrt1X4Y14ZIOkMHRbXf1S9WuIjksm9prpr3keLRZMZKt+KVu+KmKbEuatQzcpbi9cc+54G3HGtNNTYver50sYhx5WE1CMDQ0UuTzkPlds7d1SH1jgG7WWtxFFomZSVOXcU4ZSC1v8rSxm2cMrUsiAu9KLI0b+uXchl8Pu+yeyyFJNfLyKykPh4FifF+2d0SkrFUXVEfQwipvE6kRN+bqtTpTl1TNIZNy4Vp8ndCvHz2z+7qXgV2yA9ALgUfKueXCYui9lFis1W0lF2zSWB5A0VsX0tCopnzOXIGZmN2TDW3dghS7UzZQUxQ+ZUdfhyDWFOhGSTckc0xF8Wh/tM8ckN2A5HS6AvQMXEJTmXQ+PZ7kBbYLqwhh0XejcAGbElRQFCyhcF5pYi5ZrlBITw2D3LGXhpZpOp5Hplh9iRRoUMWpMedpLyfnZl25ozKL1gZzWVWm2AR927yCfdLOqpZCQsTsyESU8keVzJRcrh8qrsRllsKYmtvtUQt0tktjGnpS5IITuIPM88R+Yl/3Zz9dLcmAoNrAE6kbsuumyvmEIu6M8Wg/yEOCJHaI0gqKWxpBwXgQgBXmFpCb+2z9iCDhws9oEMxEEbLGuyAte69SBlsrSTTRq4a6BNUda5EmpoXgkLfIAM42tR5CovG0cQ6PG7ipOFUYgrjABMIyp1WPSkVmbFsXNZTlIfKF+LaGu93jJFRGBcd9dWB04My6bqwoN1zhZUPFVOwrtBj9dJ5rnO5P4hU8HXo+aWWtoaR0YO2em1KMQTzoyyZtQKV4QakLbfJF/PDuccnIp0mQy5KMnfqrOkMPcxeQKv1j1py1ZlkrpfrZ+xRTRIdNSpT+qEAX2CqidD213GWoWmY8mI8jJcCsgF28rnUCPQR9Z8m+NcdhE5osLytQqpY1JugJzzLUGOGKqzekXyk5izNtqfh3k/RpY4xSELWLacqScYyxzBTu6raz1nOvSK92NxGRo2eLH+AmGsqF9C2rLqwnjpMv4VZpATzBGwMOqeGADyXnvhiBNpqydpylQrJ3bM4aLxwvg3PqLroR4B6WB3u1LJWLRsiBoyCGG0McYwgZWgzsA+cLtbrPwDvJ93Au0lx/ACoFjL74ljtt1RU7eqdtoWjSHlbg66i48y6O60SeG1FmcE1Mi5iQr0xyv159PVmnM8192MPshXwRsC2oaZSsCxQJNXe1Twh67MZVor/fYCeFnBIyrsra8Cd9LwlTJ/54XIe39XFdPBNxu4hVrYAHOobVHA2DEmlfQ5ZKD2CTcpbNKoBu+qHT95V9btU0U/Rlh6FFTY2YJnTGEb1e259meg43hyM7lHrXynNmeiqBhueL72kNZZLoL2ER/SwE4PR3oKCaoXVmTpO/c8fa5+x0AjBAUrqDkUILumoo4A5oJ2tbClEUFQSrLq07VAo1Niq7PcGoCKikaKo7jNrQFoYY1V6NFIC6Iv/LW/w/NahNKm4jL31bU4M6L+trQloj+5KyKhtcDqLQ23gt/0BceSMIbZ/0SZ/8A77zf+5LF7/um75Zn9o/5VM+RY7r4M/Yu/HZ3/fTZOR+vn79133ov6byZ8DzPfhUkf36b/jGX/cv/cYf+ZO+8Kf9vC/+f/+e/09mgPYJn/AJP/5v/dG/+hf+/N/1Zf/Xf+YXfvFP/BF/w6d/0ieWj20j7GYwNdPaKKa/0TWzxe19tT/T90bqMKNLiXgY13T6qPTzpPLGJIlTWc87VSrarlqqolSKzPDZqksSXphJEf+iKvboa81TE1DG4M1/KCEcCdTbKVOJdZcFQw2VTTxnFO+0tsuXZqmKQVtWZ+nGnS3ZShNM0zTXxSuppHENVPZ3JatGKwk5BtxsFTxg2XJFpdGLaA8eOVaapVV5K/ceykAjmxqgb1WQGB41cNpqTsAbfiMjtQmlMbs0WibfN7yNznbMUWukjz4Gh4V1jm4zqI7WyPLQ+pOGkyxrjIqR21V9ooCGSoA0HUJTXfaemWzQ8GZTCypPG5IQZs0elEo7es7C6DAGkKxmOBUMvYcZADEbC9Lao7NlBgXOMg6HmazAiJq1XdJ7QIfXHVA0Qe0FXRcFGurWRrnrmslSyUlD+igezWk0G8OXYmI3LoQPTIfJosStN9uv7EjVz0vujKn7MlSiMw5y2VhYw1yFkC3cXUUfbs65epnM6DCE/DbwgVrd4vTMGr0FRanTWD70D05nPuwegWK0p5r8qsoKfbRaBs5yxop8XD5ywC0dwuxmflVjsxMI+W/lDm9ovVT3SS1hwomDDrrMySE/aah7dEm+bQwVfdBGKbxRCuAOLzO6xoW4dtO4RWIR0q0Xx2gf1aP0OGbDzYy0Ls+0DlGHOqVJT7fkePmu+Tr8dCYBmAJTWDJWVuq8iDCZn2Yt09GCCHcsrTSgt6NdxtzGaQX1gkvgQytlywnU7XmnMOtZdJihwVKZQOst63zkOmpY225BUnoA3x0pfaWIjRLPzSdstpGg5rQeHy4p0pOeqDlKrtLggKWINXfMK4X0lhK/WaRcpWxMRVhSaiFIYDH9juTVCtGFvo7Srw+va1zN1MXbvovW0g+NN8LKLe+SNwqjmD0SJvbX7aKk7F0qdZU1GIfn4UoibgmTqLP7Od/WZ+8AdrEujF0jmlZ2XHrPbRZtSHbJGU7WkTGq2d1CDNDXzaQugQDlirIHEzJOV1vNXMSSX7SL4zThFJyXAHmb5tWraDnFeBFFcvesu15I4xbvOLIa2lvedBUyUKWUh7fimn1PUrbVoUeCTe6mrGwBcsOpoRCqo+u2F1LDmJWjg1a1Ur8hFAZiIBVKH+zbHmzDRiZKzmjIpdJ2h2bQBn1k+61DY02I0hfsuL3AdIxBa6lEV3S/trjjpRxqoU3crBNAAWRnyPaktx/qLC8X9HXPKVh3CTaRzPG2qfK3wIaRB200yVGKd3mWRBbRkLEXO3taPuBWruwUBwblnjkoX4CbYsM13ynPSosHZYG3BvSoN1qq5eW8LetCcRgX3HMG/Rx8UsS0Urkdfh/60+c2QTYjjix0141kVWeFHioRcIlFVutKIpqKma5Ic15CuHZ3ebc7RS2zpGUJBVYQLauV3BWar8sDLT+oDzjlc0GO0DTz8DuAo0rzU1zoARL0copXtraNU5e3bXHrNkEZGGNAYJqM6ejK0rXE7a/dk8sAJEUnCx1KCUa1eDOhrHapukpJ8px+c9vSEy41Kfhny45oSX4YrZfz7gAAEABJREFUHkM/H3FwuA83u6NYLdHWkIw3D2hUcikfSoAYStWcQ37LZmZfrR0tMwf8BXa09B/gqInWy/61hQbhgJIeRUp7Q/5kVmZUVVrZMC1B/vAb4W4Tkt4YHYagCWqItu6wpgWsXdYaVau9Wv7Pu30XKwH08MbcbUpDBEQC83s+QOvZrbtglSKN0gx2L8DgbuBRNlP0baf0yGljUfqUUDOVoGh6cmj0Ybk8FzX4xfudxhAHLj71GJ73fyVQuqRFvso1ic/uyLJLCZ0vKFkZQDWIdBYGRY8SZC0LS23XY+1jYUy3Y/Yxa8n7akaAF/3V66ngvqsTyAVrtwt9JlKObqhgNt62v/IHfu5Hfa8Pfzv8rz++4iu/+oMnf8lnfsYHD77r56d8yqe50Ab+wFf9ke9690NXvsch0N1f/oe+6hd9ya/5UT/5C/+xX/krv/KrvlIu2Dz+yr/8L/ul/4d/8Df+2l/9C3/2F/2NP+SvfnnnHVGsYEWCRnI0nFRu6JxKLsvpqcqkBCHAzuZWIpeKbIju1Wjt98eJCqnd3kon9zG5uN3UkhUGqMtsaJlV5QZUFzX4mq061djZKgpWxypN44A1uIGzcAZis0rlFirN6eh3yHeqVzBOeyafYMrkxTYAxKZ0UmuCaxbpy0yvz7xeUU+vPkw51zuvd3ecF3c6ZE7hCB4ArZ8HXh9Ql7HRJyK8VNg4C4YUAE2Gioq8tlAhV80qlfRr4XQlffmltMftKXtomYBeMW8lb3Tu0VSLPVe590gbnGMtn0taBSBbjvvRaym/qbNqJX/rvdC5UyLU8Y3zOlo/IV22qldx3GG7VzUtg7JWKnBt7M2usLHtfgPvl69dM10hMTcVI5WuI/HwPt7J+TBdZ71gr9tPQUGY9/DEeDvCFKzqXBxBhVeGuUmMu7UJCWm7vaU7y6q8QHi77nTsxHlh1y1U10boBeYYGioNRcPtMOzQLL35K72Hu1dCXt7l6rdO8arMBvQ8Z9wXhMaCr0GOTW7pcRzmc0xM00qIuuZ1jlTDhTCnyQ+suHs/QQxggyHzbDo0h0YNgURrinWaiaMuHuZl2DYSzjYFmUWU7NUXZBOQrNiX7N1meQw1H1ald55CPHNWPZpHcobdM5sGd6MRJkgV49XpmcymPBdqsVskqQL1zwaONR6HxqrtzZP1tHp6BK+m3lWJd1AcJtvf+DFPzk0KneDENE5i4U4gDNQqvLSCi3szXbaerKNxdCvCDHlKA/VnRfXzyC5EQH+89spr1WXXdv3GVEk2HDbGrWP2cZxjvs55Hgt2AB7UXYXDl7d5Acqti/Uq5KuzmlmzMSdwv0ocBXszQGkyQKFK9D1xL39O0cBCq63GXFCyPESFhLoH3jQ1jxfE43RPObPSjcvWHO9gPcQyuWVeowNaJE+xSrY09U8p+JyogyRWQyt7SrFulLvwvbPs3cLZgssGxkjKwoZTdemkQjwTkz0EuVcO5HAOn2MNH+08y6/rEAd4b5q8y+7qpfR8dt91cys3w6x90R+a6q0pJ6/XbOHboXlm83RLa5gw7Ts0OmjcN9FrxppPs4vMcD6XaVdLY6MI7Zen2FIqgR1BhPllI2C1iWfrHJz00URni0wtV5Ftt2FXyQNqmmavtAHv8H4aCu21nYqvbIajrRNyyFuNWEzqqg93l6GQRMX9/s8+eqFi9j58z9W6Zyk5dJ8xxNgqEJTdnpIHVHcByqkCo7nhhMnEtxtlegAeRw8tHy7ni/cdKnQdRZWLdh9zDj/MZN+NHSReJMBIQ0h0l3tzQjtGF3mt2EerKmSOMlNSTMd9VFwBqnztkiXQw1cS030aJTNZ27raqj1yxHb9wCGyAi6k0E39XFHLQKPU1F2avETeZ6s6X4nN5NDSWAOQvLYm6Bd4GXZ1AmncGJ1rqUJ3L1OZkURpKilWL111Np8BJUR3jcTBfPS1+l1czz4zr1GYNUY7ZP5r8GrcrGAZ4OTiXHYMHnL8yXqi06rNaINUzbizo2jnqBxX+Hkir7BuysrmK/F64LnwPKIkUG5IIh7WD/oaD5qLBzO5QJngZdJrDSXccKtlnEUFQ9JGYyGXha+GKgcVZE0z11JMCJQwqmj1gS0flw2ICFomY+DV7VU9xkgZ5pD2eo+dDz+nkhblba64lu4ICzYMxMgmHCqxxlFuYiqWjrCecTyvtZ+duS2vA6eq2LAbEfcmZfGaz6E3P90mMnARoDCbzBfWg6oq/bBYlGVlpOyR6DIrWNW0oRoxAAHdodSZV64QfFruAMLcXWP35NP7qdwN3C6s2ccjHlJhQsBGL5LGmOM5bSvBlf3nYRAqJpps81DlazPtbvSIcV2Slm1lLgikfvVeQsNpbqC/mae8u7xUUKLR4SC7lbkLShoEKf6rXLDKa1iJN8smbRhBtFcDrPt7tObrZJOBqr7LjhIs0Pz2D7xGJN62v/EH/UCSb4d/8qPxVV/9VR88+eSP/9gPHnzXz8/85O8z3HX9937572vp0tGH9u+xCHz5V37Nz/xH//H/zU/5aV/6Zb/hG9/+dxReHi8/+m/5W/6lL/kV/9w/8Uv+9h/2N378x3wUxbA/zUECoqZBjIM+qGRou086aTkOziWCzCpTbXQL1yEh/m1GoKI70IkCwNKMm8E9oeH3fFFLnC52VXUWS+O6wO1UV9LDPFVVOWEH5l0xIzf3ZbEhY5xmPpQQ4LWPiMGQuei2NC2UuWdAI2D3VRvlI12eEOV088nBHNCcrlmYpSu43chrW5Wxia5Wx21RLvPBANXtJru7snSzTUkvUIDKlvUansDZ49R4FYCyEXStBcEEu3DDcWWzyrOglxb6RNuie1s1tQKW3Rusw3et3YfKuk0NsYM8WiVxMgxBbtoFkwR2G2NIhMtEvaxIlCEnS3cKGotqdpiQBVwGl3scFmNYU5aQfLtpZe6YlmZBMMOvWqdqMWZ7U/UL8qMSHWkVzsyjzinJ1qBCGDQJ1VoK7M5GO8qn1boNyxtRv5kg0WVdlDSXHv2ZZLNGxUDpGIDurwwrHdg8Ta99hubjEXxsxUmiW0yCBsi3FHpFPdRkVMD2mlhusuMc52khoFtRaNRgDBMkZJMymV0WXRsiFBGo4H3XWkaYPr05hdfwemEciJYslfitlUzTRxIXr+RVtlGJ1IXb8RluYVE4BdHMu9eBSRa7hZTBjdO5jE4j3PrwWiOHAdYtstTuuqgCfkMQnaGLbhgDopVMj8zStRBV5YWNEgGQ4mTAky6Apmt1iyGBZuY0HSCcNS3eWpenF9620q0RnBtH+qrD8VL1EqbgpChqadajJRFotBDcs0vOBnZBlHUPY9E00HPI7VHQbqWHs1Z+OuVzK9Me2A8JFTXgl4aDmx4tmBQKDPZkFku5szVcu8yTXmobcrJ9g5BzJXNkQ442YurPxjPnpfvyxNUNXobLBSOtOPY0WUXvGwI3BT4t0yGjkzq9TX9T9IS9Ox4xV9m4L1jV3MVollLIIRMggZLmNSCjCFiAKSxYTVr7eNMCa4mYiqo0hLGmQZWqXGThxb7jpHJBEabTGIrAHtgyeTYejZtxpetUy9G7DTW4QWQfYVONRytOIkUVeCpOFIipVOvhl2rPnjtd5pobzDSmACxgyCxrcSSrZJ8fhUNVAtIdEzGKLKKpHxn9jaVNVJbeFsgQu5K1fVzagbhvOW7BpamOFLMVu1LHt0qmSmOR8PahAXf3TQuMC+sK7R5CrbFpF2dau/XS0B4yt7lvnqko57owtkzFmBQqxRaYek5cCxw6cd0sDIcvr4fVA5htltDmwr7gk2PRH8vGg5xw3bnNs1aSpKuGVSrA0Ygi2iDq1mK75DxCDyY+Ap2MxToU7zTI3Ore5B6sl7KVZFfnk6nnGyxKRcuElq2zNVUMd+FikawLAk3DrQQtLvhp8rCV3152yKvbOEJhYlXRxHPz9NFjwvRPOvfUMHKYHO/hIcwMG/JXZptg0l63siw5RHKGqImu3Km1p4yYJpNFQ6A7s2Pzesp6hXGAM+c8h8VAKzTz9LkPhuohZIab4IhuBHxzSu3eqkQb4tRVEhQyXfVhVorODQRxeW7Phrmth41DF2fEEeCN7wxnedv0OY/Bh5cTnKPnDDPb5XHJRciAJESNvCQApTdvp+vBq3Oy7wH3l2YChVKMsGrMcCvGRoccZsPvNwHeGiZZXQUxrXmHAUyAgzzYB88Hwra1kmLLY6RRksYIO4T3ZRGsCiPWcBsj/Tbslq5TCNKMlGiDubyaLjdkuvw04K2MNG7Ji+Xu5BC7lREM3fQ2mWxesMAsZRBdtyfKFDCmcqAdNDDcGtZtugWpKzIHQqeoERgJtqfNoEBsvVcQcWpQYTWFvbvA4ow1c5mWNrlYGFVH50GbQmLpUhDImFWLXM5+wTUtMVBTmsMsTdItk1Gw4mw/QK9AyaRBOIoINGQyJKwhrnZKka41FRNFR4fAx37sx/6ln/P98F22r/5j9/+5ykd/zMd91Ed91He5+R0XPu6jPuKjPvpjdPLN733v+9/VKzAdfmj/HoaASsh/9ZVf/TN+wS/50Z//k/+1f/Pf/PZv/3aSH/MxH/dTftIX/I5/8Z//x3/uz/yBf+nnrLX+G70Sre69xGqUEUoiY9OqRTqWaDiHqFn1TuXQZa7A4p0aKuXVV44nTO8b9pjp3kXVBnQbNPrt2sBj7/n6nHo3c+eO7jVQnna0PXyPx4VHKusaO3bGs/psD0zXAtTDqIPpsqOy8iy9lEAP7UUVFyWISyC0kTjYgwWLHrupUkFuFbc56e0OTO3T6Q9VDtxlgVk4u0NFqDcQbWoaLsgoL+cu7RLmbbahuhmp+n2nXKNACRqUzD2xnRWLKgYx98mIJ3AaXwdevZ8c1/CTvqGCqZmpRaaZUC08L3nqDYzuB88DMaSMhBNDesfs8djTVbngUHAov7p2NtLIceMzdKT7ctAI3AhoZJd8Ypt22ZyZqHxBv5AvEBqAx+aW+whDOl/nOtfLRbv6bvivhry/FKWQa1Itf/tEPxWDzul6LoHqGFIDg942cMd9G3rBhnuPIVBO5wnVuNnqZdoQBREmt43mQLvJfqvpe85s760KSTp85Oi6S6GCNA4tR/pFITq6Lu6z9y5shUrOoiLQ6pLC9XZpzSINxdrE6SXXyrxroLxbszsTkToqoGDkDZpBW4HQVCurbddVmTFKa4j3yJjX6RFRuYudhhpeEqDFMtOadofsJtmQBFLTaDZmLzMHlXYTnLchXI1HYbV8jWxC13t263ENtaNExacuwta2deKI1FlDC9HyIXExcUbvbXq+1ZJjvBl/UDnibYBIyu0rltXKGmfddJOfonhdnYGQT1qm/FH+gDvZrYRSWDdpdaO+SmumvbQ8z8ZetWdmOp4L2y1LORLqh6iuRS+C/QSShJKAMxjq0E+jQuOZq+8+huUjxtyLidaj2Xzm3JLQF5lOkIRiUzdPNbYFrpzsUULSE2NPz6PMfcQLt6QAFjYAABAASURBVHWU7vWEaULfJNKMvjb2HWq/DRmk2s8jeGy8cDxWqc7UGFkPjRXbxfB19pARG9fZd2fXmXNcDzuPLHt2X6ks09O3LSoIcVxYJ/3ZhcCUwyP72GlZFzrAhnjEORVV9OWtd7vxDvKhEve4+qFnMjGdVLS9MDZsE1dTXIS5Hbwpwtl78AQc9gK+CJVdHW/ilU1WvsNjK8m6GTLvltbmDSMAKpM4Dg7viR66oKuDmDQYmgoYQy7rzzqHlIhK1qcuorOtWsayRkp/wYKWQNVoDlqJKMzO8icQgyRE6iOhpxJohiZ3uaBlTqQrJy3P1U9QoQ5F3S4s78ObaBadXfItGqet4CKnTZqSV6og4x62D1zHuHiw5rF5oNzzzgFVBt8N+VHdgz7bIPtuh9PGtgNvW5tskfYTLAMYua/r3OcNIGwMe0w7BgfJZJ/AFjk6vItCm0dA0QVApHUpZnyG6qI1hik3NE+H0OadziBOH3Gs7djX3s+Tz/Jr9E2dQ6sgTN35GEH90GGPNv0aMXKvTooMwuCYeLFpt499rbqmn3aPrkYJsnuf1bPsoXHjgeFFdiPLumWJE6upsOu6t0qwLoo8x9UPwyL86rgyt4gXN/a7bfcRZrW8DwESBjkhYbP7Uduv3VeYPLd2Md4lDDs7pTJQDRIw6KvJ6N4qSriyEuGAllcHK1ltTrrF6E0m4BC6TkXn5q0S00caz6qwChZaoq8LZ7YM0p2QwCSIOYSEcpV5Seqt2gsH7pqosnLFXTplHKHUtHMxloyTvO3cPItXVwotREnLALS8sEOnvY3yodOPXi8uMm52RUO/sW6xzFuwDshfp1nTNbdMUXnK5MvRQxeUA6mLmtVtaPYGVKRFI60K91x0Ji0g3kzJmPQh2PFIjgAkzVONiwGNgZr6pA4RxjiojAs5uWVvhl2lKX7POqvf1qMbWUPKDUhKerZ4VeZKOKWDjzJuR0hit9+/LJuc7rLGDTpTau0uE6ud2gwNMIe1aWHX+JxyegqhCnZTADLvdigRkgsYSwNM39JJvYVRBkumaonIBEO8+63fknFb/Kmf9hmf9PEfhz9jIz74/47yCR/70R/+zsufcfO/Pm38ZX/p99fpN773W977bd+ugw/t34MQUC79nv/iy/93X/wLf8Tf/ZP/9d/ym19fX0W1T/nkT/7ZP/Wn/o5/4Ut/zuf/hI//uI+lEuy7cYn3PZGybgpCLEeZlZjmUdxBMSxE0NK40UPXdaBP7bhrRlUpF1xlem590rOogjbV5djlTNZdplRmLbovlX82kmPTNxHWaaUKszO6wqrGnSa4v6b0KLn0CZc9UwlV1IALK1U8jxVzKPtQibc54x6mxdCsC6irSnW5dZO409AstPj0aM7p2ghk1pm2tUbrmFkjT8s9I+aGp731EykJWmrV5VVBprNqdqkM6gGI5sC8jQVaFRdP5+leN35+C6hhNa1Hh3UICBmQxtIuzO9vKSlX+WgV2OSbEqr4OPrRKlEYqhmwwovW6rK7CAdt58jLr9SrT1N3dQ2ZbjzNkqRqRqmmZ7a67fPqW+9WmZP0lsvoKRyWnhz7fqWmCY2FHlSFTqoz8KugpeI2hXIYxVUYRaZjQ4uSCrObNw9dTlP9k7sjY57JvRtn6RLLgCV52RKSW2jeBdih8nffquR1HvU8oOYMSYtq4XyTpTXIqfjL4AgJTGNwXT2q21mHY7kM6yhcIcfcJJFe6hNN6z2XkEBozbiMl9+rxGgzBeDsHXvHiT7ZF6BLChshiK/sQKbMYx9GTfDbY1JcmgCdKJOfu2+2gNTipc7AtAZY+x3u6TLOiZmKFW5eUmg3bzBC54WO9saiT2hNais5Zn64mmFaQTmjvVtH0q2dzQl3OnqEhhM2YcO8rJPyLw0yEka4lo9r5H50v0Mb9yW0iwKSBm1yLrIqy1uTwTLnmBSOtipGKbRQaAcv5dwwA+elxn16kFr3dLugpGK3IUmKQbhM3ad1Q1aIPYPmGNZ3hpKl/HQ9uczMm1xycq4Nj81Kzro5Cty+CnwanTfDTA5d7c8yteTVKP2TcLQUTjf51bORbadooHue3jEzmN2prIHqCShHYEaD/haExuC8XRpNlY1Hu7wDG7yaz6L6Hp60kDp5ViZh9URcLUPN5rTpc7jd7PPKodomNOKBGIW+EHfZks99dLns0MWSdMkXFrZS88JwURVgzBwCykw+aVhzFA8fj+mcMnRScYbgCpeEi8gC4g79FJqNKSYpC/SrIxX10S14V8DT2gaPWceRdADW1EWRQMXa0yn9oBlJdLUqsz6Jayg9ZwdL+NE1/MDw1j+0THFzmgQwdRGj01zg1+hewtqKMnJEeVx+tnBANfqNXVmS30BphouQM5tb+6hkFjcZPTIsstsCapmyRoWxcDSGcbryo1G1K/awnApkO1QRanRoqmdZ4gp71rhq3vrq6tsxihUUBG97xxHPR14HapoAr32XC2lshWaRA2zrN7iMEEIGlwHdlL0yD3ICIGG3mU+ZjbzNaLOyqiLa2Pao9p28Gg03SczaGbp/T9UYCRiONcnWrN04e5x75mUi4nhTnIL1QXkNNfKgJZEoLZVX5VWd6S28fbTd930PPEed1i0V1aGH5acS1maspeq1Hml+ok+PEOLyzRXMO3ouARu2u7oTGexclQNazbbJrtzdF3CCqpuXxrFoNzCmosr+4EBWtvxt4XcsirgDhiYFHu0sYdfKB3g1tNHvkvfBsyzxmZekNwPW96xUVsCszBJo37C90RXee7K9oIhCiN86HCZyXXpculIhOWLYnhZD+pJoKsOPuF8LyPK6kEkDwdpHtQGbuKwLF7h5awrIEdSbDsf9dDU4qCpRdtOpWhIyLBNC65bEAlPrv5RVj06LoGBGyi7Tewt5n7dXQYZHHmGzZTigGNV1ZZR1L5kCkVVy3MSY8lFOjaHkaM+0YoddgWpvRb4xdo4IU3yyVmCdEKwFBDU6utJL2aY0FeLSokUuGVzlB22xrRNVst7TfZMyMtAhLXpd0pvdbQQX+QBnF8eGHjceo7QIgA34sDHpNN/SKv5kJfpaTKtKCaJ1DbtcP3orsLQJNycOQnfy3fd/awpG4Ad85v0/MSwr//T9vPa3//Gv15WP+6iPfHk8dPDfuMuMT/3en6Bb7/0T3/xN3/SNOvjQ/j0CgR3xH/z/fu9P/fm/+Mf/tC/6Xb/7d17nU2Z/9md+5i/4Of/Av/pP/+qf8hN+zHs+7D1F6uKfYxcD7hFEi/s6EbVURqg0EAcrK67ofWeBSlLXA314chS9x0IduGYH4MqpMNNepa++/wa6kyGxUmCtVzjtbxmDptIv21KVsZ/sd61VefdMH32oN9D0HdVnU3UlbAAP5GF1GB9tR+LYmHuMy3ynhMtsGCZz4HJrk4JK2yEnwNnqI1qNrkmblpSFfhw1XWUP1Ys8HLQ76RnTrqkkr+4gJBIyv86s3XfGDmJ4zpnTe2hKFyrBfBuoExSba/DxTtWjtADdAej2JPc9TFMw8XZokueeD8ZQt967dkR0n0roEx6Etr0trtby0SjzaDuzn8GI0VpiWmVCgelN9U/egvlK75pI68xurXFa6WKndI+mtyTeQe4M5KX4TMLUovjY5oJ540bNO+/iA5ae+ZZDhpR7uMkkY42UNmejfWtRsLYRQ7UJluexw1+RWdHRUHeWA93iUvsNECDSTAUzaFpHNvWoJeMghly87kbbTAV0pN6mvt4oNUSWUFHvIuFdRvY0O4ZPmk4jrXOsWKPcUThKNBkyEtDUqkp0GsygFUPME9vaAx7mLVEy9V4Xr917U+9aWVtkGOMBf8dTvZ9RDhz9eKmXyaPhCRGqZH+2AzTPSf1o3qrsjFukuHaPYnVWZmef1SGelSgSQqIcYUy3nKPUV6ii02kmB4veOlqJkd2CfQ3MAZmQxNWhcJrbMcdctgef1hdbfrq0VEUgLsO20TApO31cY6WslJ3Q1eFj4ljbLdTerVO/4mAAbtBq4laah1TU5ZhJaXGhjhF6FyAUKl6YavdE+hL1iy1St9JyIBUpZxtsteunjJdLGdUSnR6iahEieKl0EEMUKnP4EvNmipcFxXixJCAtz7kvZU3fQRFgciRgF30byL0QYFSnJmK/kz0uxLbsmeMOQiBL4LZYVmPEWqcP0SkBx+3jcvZh1zuWL9Ur2GnQe9c8rKapEiW2zBm4Q2hSAyWWbs2WsMPrgFHdnvu15uVSqwSAWBaoPZ/gu/reXVKHNtSgyWrbUL1hyDkBnxJtEIZxKCjTcUwSq4RRtSAVNL1mWO7cV+dzrku/go9ppWEGLKZ+m6rHyJej16o70AIJavbUUThPsALjwiMklV4pezqAFMwNfUiOZN3hS+I8jj1fuh/GJabRFLQyJfgCl5tPSMR27Dkay6Sxfcqx+ycj4BRYm5CKKqHnxwU/y690QL6bcw4J0c+heDxznHtfeVVtVAjcPTvNxDC7HnE+xJTOIK+5aprckPysq6LFnq4lke5p4wqVe5ftrecTedZnywIFcvMG2bYzrPDBrWGYFkM/TnjYAB1pLZ+kGFlVWyGPAbQy42XI++EieXqVbt94keDwPkQ/HRl60Vf5nY17Ryc8m9XQP7m9RwsdqsBjWDhUK21dx3h9MSUJxIzXarWrF0qdMvCyFG1l+JGY8uMiK8HTpgr6Bdu7r91ZXnBAZtBlRGQJSb1xkSi3ECWgOhM1Lj4uHnQfxCGXWnaFllvZXmY11h1iH92Cz7fq5or058kz6tl5Wp6sJ2TlKAz67cWMHLuXUFx2jDVGrbEPV7RhJx4QjlLWYOFI/e41RlrBduNsY4/Z5mRb1ez0vKDVrbaKQvvVWj6q+6Qmr3U9HtecNhoHc/nFmeWKH8XyNYtQmG22FHYjql65pWp5HblX9ISP4Qdtlil0xfPswiXK0BRe6xiNQmeXOAQ1+uvisbHSJPItyumqLcqeysk7ilZc2fohqPo6kbt7N0I1oZDKY7djmJVEas4uazQZwJZeyB6hA9VGVHajqiM7WtpKk3bVMyMr6Omy2JJKOpPwjMD9kjAz60p/lketLeNDI0ryBuAwh0DDZSOO0V5Wp225fo06GVemIDCy2dVQBik3UudaOAfNPVQ4Q1VJhvUU6ylhPBPVV+vyiGrs1NfIkmkSDZHZFEmDQXd3xSb29PI7MPJ5RM+UJcjwuop7O3pB71+GmNT9ld/wTXjbPvW/6f/M8Y9+0594u4nv830+WbH44PF3/dStz/rM7/i/ifwDX33/bxl/1zEfuvI/KgS6+z/7/V/xM37BL/l7fsbP+p2/87fX9ZR5n/NZn/lLv/gf/Jf+qV/xd/zIH/YRH/5hrQKlq3/eO0vEvhOAeJvZnYWILq3eUSNVF2BlrufviypqOKEi5tY2+xgcqgQqCQ3VA6qGR1IlYCi1Lcq6J7ka0zGUKjU9D8thXaNqXdpbVcGIGiPmyoNkWGhpi41WmbFreKINtRy+T2RUAAAQAElEQVS6OwIroCoJUzuhagBtYVQx2Pss4bGX0VoFcpRWgJ0e6Hrb5RQ2VCpnpWxUPU0/4GQ3MfoYsq2VaCVIPGxmS0lay3RwQkPaVHOQmoBoPNFCpGyPfBsFrQjg0MzAC+qFveBHSdBEeRYvIlRLENbh3W4GWtooFYN0C6isacxpGqZj+mnjnGO7al1lIRUp7be62mSSrOk1KLljb6s2c5u0B7U8Y4ByjorW2rUU3HLVvtrNSBMEMiMf2EdcK68pgYLBt3t6ayHmYBGEoGOwNOZUuwD53Syouk3bx7ymNymAPQ3nvLZdqPKrvEqXzW7faITex86SXt2fIQ1Bsob36l4Cytq8kSjV/BaDfLgZa0aNKO7cEqtbeqV+clZNYTYYaX17Hcl9maYO7la8qcZdqWKpDsp4uiXHxCBUnk06tTeouw1rcruFezkE1bJrsUa3wUEr85idLg3yymKi2bgCz90RKs/oKIvLS94C/ja0U8tbhtfpLYMlQ7MqqGWrnHAHDG8PQn1kyhvLlAbfiX2bBQHRg9vsIuX31VqDkiUODhuztFOBCbcn7dncBZnROGEbcshbmbQAkFCz62sYiIAG5OYZvLSkyD5PTkufOYnZewjLjmbWyDr2TVultw2WkLYuS6TNGDN5IJXUlbxUJtBVZLnR0UMxifuUCmf33nXu7AsL/RgYA6K6NdTZrn05LrfNru52km2WnWerWYdIX3Iuzoja0m3UP7mxWVd3VER2mxCX/zraJ59Xn6fVpu/qnVflhU50ULwihP82+RalgBobk+1WGCgbaZT16zYvesX2PLuuaj1tbqXRgmhjAz2y/TKEJnhBa3cpVpLt6JtR0bv7aevJtVuBlZbiKB9SU3fMfMBXuwOKeafewO+z+6SFeeolBHZFVRpa1hUocYVarOlU7B3DXQQYp/lWAWhxClkdce14BU46QavMzju7VSlWnqtzSeJj5qIgQ99wDh5zHnRU7iujRHfjfStOu27sZFPOjkPwQH7IOH1qarcVXb94nEdfq8MrvVNDWhV6HRiHuABAwijEVm/rXXll3YXHGjOW78UciihQaG22jaexyHq7AGQX0BLTsCBl92APtyFyh7swJO8JGmFKp5rMWfjgNjWwDmsHSA76Y/qcPtL1SBcBtczYzrAkTmDLCJRhj7qDpUld7GvwkmrcF7mcS0ZcKWqW/A2fEavCUISk7PLmYwlS8yEqVHZ7l3EbNMQLnsKt7+hM0+YyHpTnGhiU9kYpDMG7xkOqfJQNMwUIaN3KzK3Amqy2mkgF7qaRvlgCJ25LkiOGiUfTYnHTLlADDbRNV2nMhrOWx8OanVWBBjGddH1NHg+uY2FaHtyDoh1MGesDa8yJQ9FLVNWuiNidJ7UKWgDuOSFn31xJCx/XGDnYHhMxs7RwB/xGu3fmiUx2TDlpwttgh7kdMw/Z3AXCpmlOQOkRsv+Ycx09u0YHnT3AATczl/F3Zrp0ai4BsqiiURlSEOHhwEwbp6OR1lvr0Jjjfq2osPpEjkwPWK+5lr84BprMal6Ju1wmUmHe8MJww5xUvQy9o1qhd1vzAEZ1ApeaoQcF1oweZ+PqRtF6DC+b29jWNnpYmaVZNTJrC0XgSTTbdpqquWZ5NWPz7dwUox4UY/eAMqcOmSgbrEtgmFngTrLyNq+hi2nP3deW1W3WE7Zu9h6XHztXXrSCL4yHJSpr732iZK7WWDldla1MvcyTnazqNkKbjvKOGkxhVRjMhAh6shzeOZRVoSUsRQTOsm6tD3/wa+8fTDTZp5ZffX+n/cu/5jueOj79Uz7xO934Liff/7O+40HlP/n9/9V3ufmhC//jQuAPfPXXfuE/8o/+hJ/29//m3/7bnu++X8Z92qd86i/7R774X/7Vv/LH/LC/6cPe8x5d+fPfSWU0CO1NUFzUrlxpsNrQNKs7o+6zFn+Rra/Qn4gc1WngdB9T3yxWzn16qbRn0sGlnI4eiVmSbbhri9S46uzIWpWrbIXeIuqF7SP8eH3w+VJhQd5GpOuYkB0bHSovyEF1GBgb48JSAuPM2yYC2KhLad1xhrLD3HuuDf3IHuOUXemJQBf1cYENOoepMgwdpgESbZ52xDyUdK07cGA617yz8l6F6rZrJkbAEg0klIqtkjLsPPx8sBdBOfzYeJTmV3sZQAhFx4VObPWCXqHl4drHEyPX5Ithge42tJTdTvSVR2GG3x+Dl/f26kPAAJpyH03dxpAlLdPhy8y1mr1Z5eBBXxhz+GQfCFN3psK5AlYSNCAsrmc9X1XIcYdHNwWT7R7N5fpUdepbGPA2nqPbFeI7DlEnVI8ttGRZAbaF9KDAMTkv8GKJAvcytHav7Fn7toyKpL7FIM0Y8+LatIb59HE3EcOm+UifTb81kyaZslz+H1B4tS7iFOuuwgVmDP02MEWC0mYq7VdE5Hl5xPDw1e5ttCjKik3vacPsAQ62irmxGbdoy4dp9VVTMGVeYWtCzNBST/UVZ/eTfa9zo82aovIoXZDc3qEOUw8sYOlSthRp3nFpjdai1jUI7x7cLrrcVT2vzmoDhPJMKthmV/qFIeAv2DOhFqpT7JPE3EgN6p6tkl+Z8YQeAsMPzAFSZL4NE0VKj3oQaAXbsA4bZS74Gtoa8rTlrprmgWhAS9BZtZMQUimQ0+lGHyjIKV7hsUelFS2HXybzkdy62SHm8NXn2S7oE92zGhH70jI3s5dl29azQkwzvchtOzOf/UxrTFPASTQZC9dLb/EXXQUphivNyhSc2+aWTA2UsbqXV/RuQ/ukvNJwTCkXC5+OMLD7YLoHrKKyIjMbu2qnjlEhwyH8Oro7q1Pns67JV8yr9G6YXDDqcuqenrsftEkoxPus8xToMqfNMVDe14j2mqWGxAjxKoFk6/DmrZ3RZ9UF4eQlp1xu7XWcNmWvvLhasRteHLtko1y7Eh80KgbSSQLUhQ7lsUH+6xRQDF6ANchBIXAZL+vn7ng2XykaUQGw67JQRLPn1ZpBTqWBfoCu41IKG93U50BSi6FMUd8j01OonS0nzFraI+pMnjVP2SiDttxt3QFvY7zTSxJ6jjxmKt5WqXIukxVFOnUD7ihCYN5THKonk3v0Nfp0e05PYYAaGetJvR2xbu+CCk6kuESBQ5p504JVqGrIMGOpzeIIq2Hniz1fZMt9eigE8omSg1vjbSfbXtJeLswodLuZarmi6shEXWRP78n0S8/RWULyGSV2CODZ80gfQb/SY68rdVw5diNK5iSsVTHauS8tMA8IhW6AY9hjah9LHXahWhsQjC5zG344h5cb2A4MAI17GGR/y9ksjxQZyB6Tj1XLYdQdBWhXqDbImq4X0jXESl++VNMsHzxNCYqWqNZycfNzxbCQEdWoWwEiTQZXDErsbAxzGl1ow5Zhol0+p6HFmmUTZpsMsCFUe7Cc0PKg+X1iP/P51PNGVeQb9fKQ5tuCxlnyIgoX/OkNl2+Y5b5fuB/Rj4ShdvNJWdhAE8mSqy5cMDJVXFCEVLNCoMhIo5mU+5Rdt6nFRXtgPGRVM5O5R8SUcULf26r6xqxVi+o4J3IWex+7xtPspJei32P1OJprw24jBjFohHaKNIWW53ZD1YKllD42dFOWlrWY2zNx39UNmnmVI41lEHgL5VmVTqy5xpDZhmxvn/pn4uw4wUteiowKmTkOcSSPrgM6mcCSQKJo2+XByAVNTwCTPvhO8bj/x9nmTgMpA9rgdKVgVp5d10hdufkXY/dIapg5OZaJscr9GphGtS1KnA29Ssq7TAv2LCnRt8VqNVo3jQsix9HtXmYc5Y/tq2lL8kB5ZTpRRZ+Xz0hX/eDeXRX8+m/8jv+kVu5oyf3O+9d/zR/+4IXP/NRP/uDBn+3zkz/+Yz/iI+//tv0f+oqveH2ef7ZhH7r+PyACVfX7v/Jrfso/8o/+zX/n3/3bf8dv+8AHPmBmn/F9v+//+R/+eb/j13/pj/q8v+Gdl3e+Kwf+nAa35miH8hGqfCTB+/zOBcC8bRYW9QD/9DgHntbl2+154BqVsS16AWtpUTivl+tJqA5krla+U7N7QFlm3FbkxT7v2sUwJhqle9JiBbvMkwRVGaJSacZYqJe0u91XojZMyQnpVB90n8jkjkuFwLp1DIAhAeAY1JroV0sm9106ip6ONinsvO5lSkeTkkiZqSX5Sl1QNQTClYi3OC0ORWDAVpE687q69GIlz2nnVJet1yeUDwaaJTIYRFprLOSYa7J1kPIzfG9DVOtE1i6klGtAUoXq0TprGwIRKpyXBnQLBkndwsRHm0MYXwubo9VVtRm1xORUKLo36tm8gEDftZPbMrB3J2BUqxNAPpmXBBvQtOZhcE+jEO+o2HLv6NTqwF1bEb1LTYMed22jscxv3W42asx3Ma72KDNz/ZNQNCAPtVKYXcvKyZHT4dNaU+X3Ru8Ww8ptDxYLUjX3KVRlHpzDsdB228wJDDTRt+kJZs/A7LXMDtbC5dmtHqAqo/si9mLMxLzMX63O3JqjwcuOJUmKAAK8OFOWtRhZU8HaBbEijg1/slSx25GAFubT2YMYCZOkLdAaDBXl4zGOo6gpg86DrYVyqkSDSY+7A5EdkZaSU2mO0g9hZmoMIIPFgEpGQbEJoezVrpE7dYNtCxxSZWghgdk5aq+qG4weK+dKzCK0EWzIGSuBZC1DIEOMBiNgpZ2UKPYHxzfkVVadI59HhSSiR8cc0mTtxrTxhB7+Z5QHKpRllUjJMUkUfk/oMaQKQe7ZMRIKX4s7GuIFXuDOyrx4lYXmTIUUlmNuBda82wEzCLUCIuRAsxMotLx4RL30ufpdR+xeV3uIIaNcbh0C2sYwIT6U2sPNBriCPojBMq+cKDfMpb0FNyDJkk6TnyVUZdEF7BYMdyd1jt4mbLKzRtSoepska+o649nPHOGTy6C3C0Kkp/MhURBvGwFXAdx3kwu8oJXOxgRIM87Co/ulg2wBfZnMAIf2rFI7l1G3cQJBYbaQSlYb3Gzmegis9LIjx+om9biYNSV02rSceSouN3LQo9j1yutihDQrVFYyo4fJCisO9dW7LLkwpwk7sGpVHC0GXI2uBrvXCbuqrkpveUSa3Vp1swtNYCyzqUtnWVULYSoVGVXlWYeqUyxsoecKYktkGVth1VHDiGFKHZXWPOEXFcfxkEw2rUtmIynLe1Ow7GGxFtbMnnWxL7ldSrUxGwpc4QkKy2JnYO97zwu4inpLsGWdS1Xy9JbbpiktC7a3IoxMREnjrUxxsdO4NaQh1wytyTrnaXd0A1lohDNm5JFYo4ffNlRJCq4TcTZvYuKwHBSaJXDHrtFtXmagkKK+fQyVn2nlSg09C6UQmZxD7isAd8pcqOrbOsKLhxhCsR822LgdaJvgvJr7hqg0WkFBdbf6PVVUkb6VBJQLXmmi3Ch4yxO8CZBFCEOMrvsq7ZpQvJQepuEAbjONRod5Oi/D1Zl6G3Ke9oTrB7tUHhduca7hMiGR1hNYE29q0gAAEABJREFUKNlYKEJ96WkrcnV4opNbP/62ypqEdUVTl4S/bC8HDAmTVdfbgQK2cNl8bVdf/axLU6WuqUldW+EQEKC8XNUDuyC55cyp2OpUAU1S7N2wlOdtSgKnNV269CQ9rKFNt6ALAJ3ls3TeY+DeqhNny63NdU2XXRegoBKylVaWlMkTNvkYdj84Dk3r2PWauSn3hLmIVqPiEOSi3Y0Dq6ngH4yBlOk5rTU2CaGnnehCdF9CzEJ6ZM44sCaWc7jdf4oLcnr47b/bYe4YrAUcoi9kWso0K5r8oUYPqkaKYYKVHfKCMHkgTSiFsdpR08oHnJP5wjwAwkFTjqNV3Pug+fIhHAEler9xytkePVoCWraRmkBLm1rklsWDPUGmfOhHx5qhsXcqla6a5/MDeNvytuLt6E/7+IZvfq/OHo+Xj/qIj9DBd7PPOf9nn/3ZGvD7/9Af/NZvf58O/uz7h+78941Ad3/11339P/CLf9mP+vyf/Ft/+2/NfYkmn/NZn/WLf97P/U1f+mt/9N/0eaD9hdtEilIAG639/nCDq5UZaBMDLaeFdwyr1XaUVlNY78ZZ2N1Ms4CSs9h1EAOFu0KwMboam9Arg7TalfeyUVBlaLCVxUddh5oN8VoSLkaoYubFVFqDxYSVUsk4vGyc5EWcg+fAZRTxD7g3AY0tx7Jx8OE+E7yvvqVXKDmH06Gl5K4q6i3TU/VIS4jXHpnSkkB3jtpTmpPyCVp6oDlNvZhgoaqjtd6hlabwTAgq47Xmc2H7rrEpB3dVNojmm12t4wyG5OviC1vL9SSM1nycw5+u3qcNWqIlI+028yDNOgQdR+cj8pBZlHusoR00AJTBTKnaUVqvK2mept5DVmZJ7ZZt5Jm2w1WDJEKRSGRMXi8jH4dEcJd17+HX0K8gQquryFqyXqUJfsITRaR5q3wuHAfUPhhVslTGbLYPHbfi1DGrjuSLyr0i7RNzqLwXOlvWnOitJoSiEEY6TtrF2btt360Gujrvod4Kkd8yqVWubthgdpf3w23OWh6HyejI1uOYZA/b01V8OShzIGjKWdP8jniiuoAEy6gTq5qdrmhkdBjLIVswRIar6oJC14qMHeHzogcaVTixZKt09Bw1RwzCa3jLkAEMY8+Zdr/ATFe4RbxWooxRtjbfiRodLKSgALqLVai2krvMizzn3GvUwR6oWa3mT8vDFCXYS/EHV9rIZXKOWbL+plMKsQrrckibaYnCOGCrMLXEtzqZNmgTT0yqqkRCKaX3GrFWzdU+SfNBP9K8mltabFYa0sY1ZqxqwWDbxW/LCQ4hCaRz006yCJu5vDTQu0U7qXX0Mk5zOjHcD5vTH9TqGoeS5oY5LhNOMGtXRzjqMuxpzVbI/bTjPKwfjjGoAzn18LXc3MtGQ1I52kdrsZX5mVYgQeeYvdY1Hq3lVpo1YRlGJKWg1c7AXWrTUowLjsZQJ2cCRqoNBVwRuRN1Ld/HkEmcfrjMly/SIlBZw2JajcJd8TZmmwItQl4W4SiNHO7Ho/qInKK3CL8HTsZZZ0HVoUscAGA0tjnGNJuOybxJE+ZByxIiWQrF7LlqyZbW5ETfYRfISrbzSJWI0CWg3TVIACxSDEJL/qQNiSdNXroyOXqiQxBzW+egxqAQahGx6INTF1rMbFMtsWV+kKjqKhDOuwW7TztQSdlwjWc9zj42fYu0dLbyLHkXpS4zXT/rPrNWuRAF/OHKDZERUeOJ+eQUiSTOPH0WHwMHtybmqJqm+HRQJYJBnF4nuzJYgIDO0WUKCIrWslOGR18Xrtut+y8TgYQHNC3Q3RQslmQ6tGmCm2pkznIrstJ1s28XMPTgowhgiCRm1QhNhxRXpcToKo++I+a+ldwuPDIKAldsArQuanbLoyMhwdFx2T0RNMlHbqaMLVXflNTWyPaMKeXWkxgNCDOw224XX9OiB6cP/WvTtK7d0cKBpXKSREorqEuqYbcuz4FWGT86R7XLaGmYPAe0SCHLrrQMQGIVfN1Lbty7BEtyJxVM4eElE24lUeL7m7Eig5BAmywBPX02J81oJeC2XmNBKUkPxuVXjaiGJHhqgJcb5sZxqmZP3Fc8YWl2iqW9o/JqmUeUi6gZVvACQxnkpU5DNmt8tYt/b79dKsh9T5WhIjqMzTHaKcK6w5Rbg1DuyGXd54Nu9ybdLpATvKqzZaPYdSlWqECIRqPqUfKLDXGqFGZBJZq07OkSEVs0l6mWFyKwN1FGaPjziadkEMO4jLPrTgxjamZ5NIv9ttdpYoICbAA5MCeGMrbGKAewrUI5NPdascblaO1SoSXHGvonmkVVdxuN9+qhg4alREEsIquEYto617wOQtHLy/BsO/OBWoZpkvcQuoKZlkM22GOO5TbldnYHIKuVikaiLSW9IHJovOmcMFlOtu4ibwUfPKfUu9NsTCwH8cHtW9///g8e/Omff/jr7v9g2Pf68A//cz6oDLcf/Lk/UHOfr6//4e/90H/6S0j8j2X/qq/7o7/oS/4vP/oLvvA3/qZ/Q9GRWd/vMz7jF/3cn/Ov/Jpf+bf/sM/TE6au/AXuIuQtocVDfRfEt2yr0tqhL13SPgur7GjtIiWqlXvAAo9oV2GRDKIPtHKI3besZqd4PbZzH6EF1WjDlIhK6JB4V6IBG1qAsuepxmBh+zNxi1MKvcfyHSVQqSZIIlbXvMtyk2Xcjr2Yx8LxEAh3tgFKKKBn4qVUjpSsNCz2HKCeUg73I+cjxgHYuBI7WhZSVtPdYtnzqPZqMJSNbHhQRTB6XKD6krtlnKpqc19r6wfkQGqJP3dfHqWHm4U2pS+y9xnPUwtR+uRxmEqITAvWXTyMkFANcsBJpbD8fXbkvnAmrslaWvCtjRdvA4x9e6yCAzWJqwmsK+er1gPd1hNGqMBANqonWkVVhx4tcM5j1HKgVWsUpAKYkVJSfaqw2h4jj9na571MXLaSC/ClG8/BUOMajarGidE9TaVV8dKdl4l5hzo2TkMI6DSZuoato8e86Bc8YS0/FO84Vg5L2dH361UCQ3777ZdVTIb3pmzCSbx6nX5tceAqrVxHjiEXVqtCBrSgBMg+mlP9xTZPdQv3uj7vDz3/PdJWjeljzIlhdfXrFc9WmFBwKh7NA7JMlVa7ItGdx8a6PHZfEbXVDtVsmbiJRDtiXa++N6q0NAAojhKxoGfcLQaFz1wHRdgBYe0tPQLRsMT8sZuQzUFmqZczsFy3qEHidfVuDz6MWrQtyitHn6xnXNdVgnTig5t13QjjNqEr+3zievu1paJjX8/udGI4x8wSP60uz/snOD0dSAIVCDqMDYaoimlTa5FYjj7NTp/BF+KwmpBaH3sJyYlmaambiVnz4MvCOjZfNl6SD2XESrUbqLTLxzWUQzlxK/WmNejV+m3kgC3v43iu8eqi4mapfxEV52lHHwOCLdoVAwhEV3ZOiG8w61pKgbzUuGRZ0lDFTKK3CaWdXaC2roGrsYHSbs2jHDhgh+Exc1CtDZ5A0KfzWLSHVGHQZs6jFuCGRTxWKpB8+P3YCPbZLUvNnvKK3QTotCGYa/X2etb1xPNS9zZbuTT2yf2kbNTa/HgRDeXZg+nitboDRSqBBpDEZUgY5NK0XtaSa5y30SJARvaz1Bmj7aaEiIFTFfOaqNHaCiSGccD1SLKqZ4ULmNINSKYbRfIJUF60xWV5ul9zye3mNNflPS24gKNjJkcO28uf4N6FyoO22lRT7IqpEQxDqK09dq5U4yX6bgjXgbqxJEwBnKF5reftt65/oy/lwzBfrigfJd95uWlHaC5UalO2y4Yli1xx440OSzVW5htm3LtklCW6CJnt7euuJypBA6Pa9nQsKPsOedsykVUnBDSrl7itXPeWKVNOFYZCMx6Fh6wDTpSWJTzaTClfB2N63o+/LgGAa+J6pr1efDcJszV8DBl5hzJbUpVw6xCeYjpyZ51Pvp48o1OhiMqN18SFavTsFD4d3JybA1E8G0pcRU7kMUt5d8Myt1tgoK1uFci05zWwyRqTj8NfXnytQdcMZHUi2rrKUsmhWnBe+9Jh2+Rw1XBDCtYmoR3hFCGVH4G6djwjRCwMI2C7rU0umqaac7IWIQ6n9YnQrporeFIgRyDpNEqSSvSTL2nvAI+ptePm2XHYbaKGcIAGb8xTREPQXyeeS2Rodol9L+O0o0FInMJo8wRP6/3wogm40EPfDHhAxfIV43VNHHxYz4qdqsCn8kOT7CYkNKHu4EN6c8pIoVoGiQckIXBvk/FO59Abg3eBV3jCFCU8ma+ewvpJnUZiu+exHG5X9zP7rJbjd9w2RisEEzbCiK4Roa/WhqhIVdmWJmaP6oPpVqGEWwsCtpK7GJpynH1b3h2GPXgZgy2UQ+ObOANP2XLKB+Tsa2ZIaJgKlMZiK2Rle1gsxDIq7e32JUG57G7u02qGCBiSloPykydwVWZsj1yt9CBEwaT4dUCF3pnGup3qxtWVCJYuJYEihXJZ0/KmlXze8rPh6GZL8a7q2oZTHog+Oa1BPaApMLi3D7w+768/7U9i/8jXfrUufK8Pe+ejv9f30sF3v/9Vf9kP+OCA3/nv/rsfPPjQ5/+wCLz32779l/1zv/5v/4Kf8qVf9hv+xHvvH8e+z/f5xC/+GT/jy77kV/y4H/E3vzxe/js0r/FBJlFcRHWj3GAm9lqn2cW7CW4uKEFVznCPIDFIZUaHiq5W1D46pwddhik/R4XE5egcBrhrAarBXp7K7vaokVqr2K2xR3JeXBtauRo3sQmu8lHUQCCg3tFRVjkqvcM6WtW5nZQRk5QKyDi4siRZtJYoPR8ZJEnOaC/y6ju/2tBmgBM02+Zld246xmzZOgAJECCk4NBL0Ljf40jiGDClN3fiKotSxW7oRJaY9bRyYztLDnQHUrh03YvZAB1u+uT9J0RbxkSvPB+pCqW3jlsvbEIulxYHLvYcbWOZGnCaw333vOqI22ZZl+hNJAmb8GkEurTsVkBwt5GjbG1jDE3ppsYibwAbpniEVOej61565wDNUp7nOjBeXDpr8tm2t+9nqy8ui1T90d8Zdp5eT+cmywsmgWiScBujDqh/1i9URlXIZDSt5Xi177QtAV49gSW8tMeE6mz5kgDcgZK0CFxXaRHV7Jxdxkyra6AcWpagyrhRhZJkGxS8M9uzpqyAFWeOiYnSmlLPqjDkg3GIXuJb60x6AJT+WEzjOSR951HCDCtzRjRufyWQOTKE4+p2dBObeXnkKlqYBopQ2+UibxYW/QL27NtFxaBMZihUQTYUrO0jpstkk+GwW00TFP4yjTpNwIb+arPPxtkz5IoMrgtj58ptIlVWFm4Di9UIOYozLbWEZPU9Nau72gpMyk3I8u7skNBZSo7WRaJnU3aBXdDY0bjN1cTbEDMjdVVDyry5UIt0DMICdrqlT/YY3c4E5KoNaod3KkYRDFR5KDRAvxk8SzehP5LI5gYutkws3JsBPOBaXsNc93iBAmFrTJg129zFg9oAABAASURBVLANV/euzPsvJLUhWeapvCiqTEhiXYbLvMpYLdVvzt2ibQVlq0S5YSy36f1Gq3JLm5d7Lj/cX7hG3gyqrpCdwbo6tAc0buzJmF0iVIVJS19tp9BuB1s+i1qm5/cTBNoLDtmOVHGySQr3LYhuv0VioDRFICdNMU8qRU5mWPdomzCNa+hdSGxWykzXMDZk6lgxbCtKTlsQU0q0CHRAmyS2VGZkqHjJAM0oWpF0eYfJAqQDfT+cUpYaGl5pFzq90Tem4tG2xjShMkDP8oBCNG/8WCN61jQsq8GivCfLRqlqN0sRPDHDJydkoJmASD1oyVNiuHhRM2ymuckDtqxW/DrVL3pSCacxcCtVsFNZuQCna2/IVJKgzBx+eV/UQ302HJrmbdySRQIqNIOTYZa4I+b6Qtm4oKMh+JuqhXD3w8TkYSXRmrtb2DE185A9VQ1iGJbTJls4tHVO65vuNKH6aD24001zBV1StmyRF1IhmDnGsCUtmtbdIjwLpiK2rco0QXslarftmg1ygIOuz25VnNsoZfrIHgEvOnuuGrNGXxZhBEzW3A4p+CrYdwLDabeI23GxSBa1OY/iLJvFLmSx0lrlL6TUJRbKBnP54pyTBjPQ5EBnqt5UV0v7SuVRGgKP5Gz38kkOwNHecU+gtWrw8l4uOhjDvCTbHTKBKCIJvO33RzZvv3uQR4+RXntWubvppIRJaYYUENY2cJtPkGwT72JFjJPZM3o0GhFVec9oMxTYUYuYw8xFYBPQGzKzRNG+8GZJJ1BuNSl4nTIEUmD64x2NvjVOmawSqjQsJk3e8tE+JVSCjYTY9gaDtQOLMpRgYpSGtskwIp0pO6Rzy2Vdl75upxSTocIGWZR581dgu/gQYJe86vsTaf7EeFJFsPJKu2ii36StNhclHTXQI6uz9TG7X6oeV8/zsnsRLRS7HWG8LdKf6uuzQ1XCQ167DWqFee0K7SYkGUIwZQCEiFqO3jdio7gCvvdZdWblKXpU1TxNoS7gAvSi5QxVM2ZFadmuxtv2je/9trfv//rj/a/n8zx1/p6P+OiXh9Jch9/d/pmf9qnf/+0//fXv/Z7f87zuid/d6A/d+4uJwAeez1////zNP/Lzv/BLft0/883vfS/Jj/6Yj/15X/T3/5Z/9td+/o/5UR/x4R/2F0M5b6GsZjZFZ6CVvL7H2xseU3KRVYaCWZgScVJZbKIlW4VBbdK7ez732uWGc9iTyiONFqGv6XpbV8oOTVFlvotbrbjGVVR73lNdqN6xXJO57BjqVmhpLBut1/2HVHcA7/Z4Xsf5XOd+nPm4rvEMO6ufqvC3sTLfTCVCJUtGIS6Ld5XOu9itSoXurSy7Ano31NGy8jGo9xCrsXRaBtXcOaAb5egDcnKCQ2iE7EbPltF7VGsDKYHoLQwKDK5WtWmj7DZO57ir+O4+r8pmc9Fd33VbSswRbqVqmqEXJS/cLxC2fHf4u7e7tfpcdo1hsx/H88VeF7JBFYCzVRps9wp/XFLij6IWKS0K8zbm6tyn96VFrhf26Bx5W6XB63EdXqtt1ThCFtzGtsoaj6pHaWyYk3qRdQyoLnZZ1iP7Pa/5OMNVbOIShFaByui85LXt2adha4VSx1LmGyPoQVYDqb2xQ09Y/kSeR+uRYTiPbgeIgRij8NIcjvut2uwaphst7nWprG7yaqZcSBvQy8uxoEXrMg3EWCVfgq21xxHO5+pXr3CXGTjj2iqg7MN7HFhTQU27y3VbyzikBLclR4GB1uMiDrNpaIsL8ZoWm71hDR9u001RvfJ41tDFULpYcW2sk+MJMSosUsK6eKXvUgtvejVol3ko8pKCHL79uLiyVsFB1hrb59XrtPm6LNa08eB6qaVmwkoGPA2SnmF2unh6tIhKSQk+zjrOPgrdR267ro5ndGRnS+gIuUKwgBvNqMhgqwkwq1GpyyGIj+hDPmUbBEg0rhZ4zFnXSv0qpyke+djXcSnfcl2Yka6hhku/qdytWy+eE9dUcuUkjZDdmuRXcktFd56UD9Tzw3nMPNjeuC0Tm2pvYR8mnGP37mYDqkBdSrJkXlYNzmu5tBI9mMatG1uPB3VpXZ0P4MW33uViYLOisHte8GszNnfYJu3AY7VTM/fVqWG6BnMYW5aQnrCNm7WudX+AY1w+X+knbiHolHnXmfEURFRjoyj0wnhAHZk1sOHlD8xHC/yLmfeYS9fVwrUnbfvoZRxbBcRUFCIj8iqVr3mVqAaERkf06/2oMOd6sXWAjytzlwJTFBStYA/ednu2KyRxiXGHm03pEFTP5qsL7Dq7nqDKY/S2kXqVywnlVZLbPYf5RX/t4ynu4Oi7n5KJLJU9btHeOlCXXQrtUz+I+gIWctBd5Qbqk+cjpztypuLdhB7znpzNtwgpGTekTsnVQmfDnpCBnbP2XIbHUXzJ/dLx8LvgNiyR0ZHnmZmaOUSD9ur1bLF+9qXi8QIpWH0+rqel4jzzTuHqzH1hn6hNH9PmJLTJJh4l7YjF1gqgj1J/eCLfbHNApX+kYFlw52MonFVLv5Vf8XJDQ1FWLLj/IzOHTZpld3aYaHI/yR5p1biqIuk9pj98HuBhWgzgik8q3d8BHyagjlpHPEyhW61Sc/XqXileyccROa/L9xM4zZ5aBhWQQ3QuCAvIJuS6ETitFVc5eoE9nIMCuAAzOMtNpdHouupDlAwKxwYrLbpa/J455u00IkuyyYS8d2x0jX571qrgDl6taxsMFc3gaH21k0MBCNr1SEy5rpg92o4FPyBrUajwOn2nVnY7G+m1rB4oNlOH9DnvuoXjSWq5r0fmyxlHtQi4tczskUWXbyjVP0l9VB3i73VrxuHuhQ4FgbssfFJPMuOuPaedlBA51QxkBkx78k4YAaRpNwce215eN6+9UzVpm8XCPk4VitsbcPkyWajA9GPY4QPtykikLVt6fTFJt2fN82a9w4NrC0+DDcVAdrsVpvlas1UdB2porlA0RPkOU0CcIK3RSf09HvvxUraYbkneqS+j96oLcnC8nMfL9gMFYnOdtz5WNK7wff+fAbwYDmLhblfiCuywSu/dWXkBG9bi7SpJEsdEgwT9DkOPR8Df4Angec9ZuAYFoi7i0ly3rV/Z52gYDfmWz9Fz10oKTUftwlMxF95QWQvLOWo5Te4r3xTVLb8b170WS4uSEn/sT/636u+Tt79v/pZv3Xdnhk/6hE/g25Xv/uPD3nn5X33u52rMt37Le3/zv/2hH1WExP8A+wfeff1N//a/88N+4k/+h37xL/kjX/e1KiAf/TEf91N/0k/6rf/cr/uCH/e3vec9/+3+F73+Wzmg3GnRFG3sRH3De7/1v/iDf/i//MNf/Q3v/ZZIka7EXfVz6LZSQk5lYZXxriieyb1LOTLZo+d9MYkS70zjW+v2iHM+L96/hFO0f6v71VXcd9KvgF3s7V0+l82p3OCGJ62VTih09q4oWaaTitHX1MreulC4dt+G3952FbRuKINQ6UzexdiU2anLdqZHa2Ud8k8XNEuZOVQSGgm/aPpEWGy/2tusybu0QCf0TXvCYpgNHm1rm6sOEeaYMrIM2mhGLhtjOMhWUVV3/xQ6bbJ77uh9RV9lbSN86BEuhwZidLu860jrcpVi9L1YhNbBzmwEVMKHDNsXVTpL0LZ1U45wFKdkDAvz4JAbV61oo6qqsWWmKq7MloENbAljapLNuyS29KimM9VfFSNBYDCocDDcCfcqV1nv1Cw7x9wm46lBHEy3nZpVRJjKIqqlAg425Pt+ZkSZmDJ7eQ+jG8wAcE/LN5sIg+aIYeqL7hUb6/K11QoNF7R23ydLgYJD0MpAUhSDGyUpi10SIXS1xiIeHYqGiuvAbae1Fz2h0N22dTWy2T27RqHTqFCkBsw0X9arcwRY4D1Aj2Eojn1M+YoJ6LvbstS/Qt13yZIxsm3vuHLnDYYcBxWW7L3tks0Ys/zuqaqss7RYNKNljRnHqGFtJZpJg90vIBnGbdKuQKydvkVU3W4IJRoV1obrsw7vac5pkM/WAUQpZ1Ao8z3nE757lCYCZPjU52ouh4+woZwBUqkhfzXKFV+RwEuoLS8tRANKx0Chy6oFtnZEjxN3V4VXyewBmCHdTg1hUypIClX5fYDvJI4tl0Xa6lX1kvTnSFhNyMZV23dJK4m0CthuJoYybQevzawxNJBASbMxtaBPBXT4VqfoR92fco1SN7tnCEpos2S1Q3GQOMiHjuyr2KDc6UQG+0zbsapXK1flRXVFVV/ovXDfGN5z5Rgh/WIN0OYYgEyROYUiacJ/rLaRmAlFcyhppMiuXnGDEjbPcWxzsXxarzGMlKuZuW3fyX+nPrIYhWTTWh8jnUK1G7K4yQbx9kfo+JbAeaprWcCj2u8plMMaOErxqma2p82SXmvJwluuhilMZX2qGrvATwiLRraimZco1AWdeUt9mRpeCcwsVYVLBLMuhQaKXQ80EcVoBuryPC3TWuLl5mAJTizWQBJ6Miz9snlKWdx8VAzXaPngaC9xQXDUvluvKlWiflKEQ7jkQ48jVHIidakvYrNlEjQNidyhiKlsu6SVhiRDEtxKoRdwN1YJMcB0MsB7brC7QxotaVCFse7CjUIT3d48yqb8V+dlMT0H3f3BscrGTp7JbK7BqeCnRO2My3BJ+Ggc7UPKumUr5Tph8lNXpLoygctVEmw7FZOGyyv5AVBFwGdQ3b+hQMVJNsmbd65z8fRZubw4E+NKRpjkrZapqh/uKAo/cQppau9f4De8UE3a176byewALkT1PTWgpGewGoAbBW6mbmu27LmNzZaw2HzCNiBQtnO7EJSLEmUFqWloRJbboEHYUSPBTcbwa3B319UacsG34bI7QJFg0bKlsMJzz61PWNaU1yDFEZll7lDRxEhxtUkHF/JR+TjdYvRtexChEGCyR250qc5o78IAjqL0ASouUeUtMikaRUg7r5LlAsOirX1rOkaTstduQDqboYOE5V0Y3NMoJpvIsqZsQUVG1rPVpDOC0l4JuMHKq0bZyont9VznVrysSnd2Qw9E1zZmD0/6BkLrFqeb4g7U6GbS2lyX0ysrLEoLx1E5Gt0KVIcCJQ+k1EVak5EtvSaFKuUViGvFc0HXqzsF7OJ+59jHAZjJ7pYs9BzDKZka128oVUEKuAmdm8YCssXpB+cxle2+t1ouR/K2PcQJOCjqyawTbCsS1Lfq9YHpWkDClZqjOXyfde3A23Z+4NuvyLfD7/h47/vev9+ufOr3/nhp/46r3+3XD/+hf9173nO/rf/XftvvzNv273b096Cb3xNMPa/97/5H/+mP//t++hd98T/81V/z9p/Z+/Dv9RN+9N/6//infvnP+vy/8yM/8iMa/IvqR5l4ZnS+q99z/q3f/U/+y//6v/0f/Wf/8e//A//JV/zB3/u1X/X+87WVJp2sYovQQznZ6Yw5roddj3FODxVSzFBG8ZX2qqpCVWqxWgvaDqps7Q3Ntx4zdCdjAAAQAElEQVQjx1QibZxt50C8IN6pPaPQXu7FrLuqNAzFvrCfrcIn9bzWuCYvjoI1KlllUL4IHNNXcpempI09Xdk0WXbXFVyvphWaffBedRC94+5boTHwloxq6zDcNVULJm5Lo21j5jzC15lDZbWLB30p1/fDYinlbZJD5Q1n+QUVVzJumWYgywXarjobqmnYYftCIqxrpR976GeRd4CFi3PnrKfxarPG7PTeAutqSfAoTwz06ILm6+uALu3Ga9ul+TyM414l2RpA7ruDOTQhZQS9caAWBaUMKVxu17DtitQutSjonBJL/UEK0CYT5fTRY8IIVaEG4XQF+fESx4M2mr1N7wBVcAkUtV+j9mppEbzYLPViAWkdNm2oyrNN4kv23Myx4RC0nKd8fF52hZTE9P0YcbhwtqO06AKDMWWzek3fOG5wiL6/75ClQaRF6xKSLfRKE8x7zXqH+52rxxMMjOgZ5kGKMSKPuoL783ITMEIw3TbXE3NzyjuWu1x3KBQOvRYUggq0LF4Ji97JrfecWkIhtWL3jrTKKcjh1ugMChkzd6MiaqNgpeUoW0tQtc5MS1ZQXPOmU5Lc0zo7EkysqiUMRW1mQ6I8Q8vONAUHitCo48BaPNRvxUIsXj5LMbIyNhWEisAV0GCyH4OLa+BgTQiHkwxLdDFvtrCbd3DGNoeR8sCkV4PCQit2zaXMoeAQ0NGjckHtmahtd8hFXQ4hmPe8dq818TCfmFIt6Ic/13FXiALS0EfNkYrxImfThpmvGp5EoE/TzxmPy9/T1BDOCL/RRt55uqCnVC2e74y1ZoN90jZGyfw7nQNMhUu/UI5uh7AyFFI9TLToKh+t2rb6yx1Kx6GpLbjK957n9kuQSX/sG3m6DHvc+EKHEra8VFNmtXehkUBIubxVEKDk423EkFE4so8LYqvQyoNEr72PUsdhVGQBPQ+1B0doqs8W3gUKeDeTz7PIJ3FZX1NCNX9BQSAbkGJAib+hUas1p6oJ3TNLHzms7jkNwmz4GK7phdpgwQ1Sq0fAixXZ/WSe/tTb/FCUFe96o+NVav4my0EVEr5m7749bogmjSQCqsZXZb7Z6BUTJfFdQ+Gt8E7T30r1a1GlMsMo63bmbYyU6LYAGxRiVdHbES/RL9tmS4QevfUp6fodAdcQUJ0qKR1d+gdhKEuiZDCNtuA3gbrtLEoSXnBvbTI7auOReOnWj8Zg7IWzj/M6Ig5guPCLlqLsquorldmDPngEjndthGuQzO6VsMsYbDNONJnRO+IcJW9jIBjgLq+ra/eOfralGQgI6toVl8U2pGLQGF0TMbqh37f2GD2n+xw1TaCjAlomepeqpcZodXHI0YEalKqT86laxhn+SJ9ZrUCU9FCFVSQYMnaU0NmSkicNBdVOLUL65KZvDt1tfayrDl04dR9JxVhjp46Sda28TKIV7GaKL+LATjREZtcQ7Lwn9A2cvItOVlN3ObKOjqMvv4H13vIKwisjM4iaXa4lvVWbOnSDJlpyZt4Bu8WJSVM2YwNmvao9q/Tih9F+md+wwkjpkj3xFpWWYREVMuGOkAHduOQlANWHW5pCZAG9ipL4ke2SxlIxgqneuyYAe3c89dUiSSvAI80pG0qqMMBpRvfwuhDoGBbmoZuV6ASqm3pnYxeEz66K7N3YRjS5ZUMVNca42dpbdQRC25HNInBQNVacVI0bgFzaCgN5xshQzmzsk/sUjbw6ndejyqXdRKYKS2VTA9sgBEx0glu4N00HotkJbu2jlSLbFNy4siPaElYKXnIWnBK4W16My1bqp2jQKhjhu2fMsccMufksSh6PtoPHLOWLe63QOrAHtnUO0aqW2eI49z4vYQJtH3h9vv8DH9DBn9q/6Rv/2HmeOv3Yj/xz/xdUNEz7X/IZ3/cHfM7n6OA//s/+0//oP/+9OvjQ/t8DAt39//0vv/yL/o+/6At+1s/5z3/f75PGdRw/4vM+79d/ya/4BT/rp3/yJ31i32miy39xdyox0N/4J77l1/7G3/of/OdfHlnDfe+Q1m993/u/7r3vvSrFWR+VE6fZa1mq4u0+oh+qG/aAVgFxtKI9cjGnNclWHq0q77shUC75xr26hTLc0lB+wcsd0/JAeJ15xk6LXmf5rrH3Y6drHHp6PWauUcvUxKnm2F2rY0yQMhO2TQk+RkCzlJD0lmGAOWRJpWWOruWmNmqgDCouADM9dlcHuaEpq6BCF8lI1J3nmRrT2VB5S3qo0ngHLMx5mOm7d6XQiOCZOCVM5tv1ztwHJWrsmrXdkmob1vIFWxsrbCYcACdtyAUVNDNLH6GOcky6yeUYQmHWNexSJfE515gqAEIOVGXuyJbtaR0LcTiOw/yhgmoZ2Dt1j+l2eu3VODgMC005psoiBYV6lj3bFUuajK3S8uTmRgiX8PbJNW04CGi9AATrjRS28DCQ7uTQTS+9qt12WWuJCoa1r1wrTL3blJgLrdBGPRsXKVF3O4TklCLPAnKy4GhLFVSjXsw9zS9pRbAkuOLkK/xpqowU0jOuR6fLXqj4hkGVn8f23uyiLrcm4jk7tTYuzuG3AqhSU2EdrQZxlY2UBjKwLyuBD9yCnNYmUPTLxLgadcpofVWuxLwGanQOO4dfB0P81tI3SuFqG2jtws8xrCnHApIO8d9Ml8qJui9fzKu1tEpPKyZaABBdfvW6zK8JJYZ05Z71dFxUSthWnHpqAd7AUxDY2PJpuk/4i9l01kKu6kyP4M4gAP0RqRS8DSkvuq51lxVmCjOgEPobF9YFu0ptBC8bUXZPi8staTTNEkBVPmhmbEtZvwUU7wtM9z38pIeSo56GPdHTJh4zpkiMqMJoc7AFnuwEhtGNBCftgFfZ2VINc12Raczw2Nqb++6nvdo1f0Hpk1PY6D6zDUE7q7VfLbBvyg1oY5JQR+ZP4gQT9DIrau1VPmtAEeq4rpI1aXpre+AcODsrlVipqZi3aY2RYuzVuu2Kt61mWZxQs5q9hSMOOSGBuiwMBFg7JLzCK2YZr9WvCqMPG74O+UfVFhVSXLebizKZpKFVOdHKBt/1KKpvqRfkHD0dblKAhpzazhgNhoIgNE/MpwrjC8bqY4YfV63zso6mBmWqRdU8kMLNTJkYjPSsGXImnapOGO1MmdrX7MsQMZDm5mu6AoQEtmXZlbz22Nfo6x5POoYLVMA0voUka6N3dL7FcpRbP9xYhjZS2aGRE3mITFczC2w3GWE+pqu4OcgoqxjZlnqqxYItXdXag24UKijXhvBteTAselZPie/iB+MKtCy2Qqfpeel62nVeGad1DioDqgmmdFiHWWrYDu5tu/zqu8s6ydOqpBUyNx1ljnsXDZpnK/8iWXJoDdJ0u4J5mThxXsiWfPG2vIu2Cb0+g8yFxs7AUddU/MDRZntodxwTN9Bmpb2HpHn3shyyTuKJhJOEdcktnYEtyyT1KruozQ7aC7mMYsE6MJWWbPMwkHEH1RNm6IfVC/2oQ7ERxqvOGa8um1D3FF+chuGi9bunaLQyhbIJoOvCa3TJBsOUHXk/kwnYUz8py/tYnSsFn9ll1hxMjm3dQyZEtUiBgJsvcxcPYlUtDcPtmSmA18zNzTylDN3ZbAiFsVtp0mHVvimAhAsO3UpzaEVch7tKHypwPi3PhfQqds9eY6gKF2r7jlmaHRomuYoMHnssQUhqnS4xsjxKpYWdqCuv3VcxnC4lgOqJFU0xWANjgj5AKRbcVq3HLn3IwrITCLTp4kgp1h+ENI6ZrMrw2nOca55jXVQhMgtJrXI5B6ZCZTZsLsVHuGUns5nSd7jZmvtl7rmAh0uclpA2mHbNmLSpgHrl8Jx3RRF1SPlQHWSKFNPaMrETvUmtlDqZsdW62bj2CpH+oqyXSi0GfoI7ycK9Ndk2tmihZzWZlZY2+zExh8JwCQGra8SFujStIdquUJjxvtfXD7yetwhAb8Hf94HXDx5/8PPbvv193aXjj/xeH67PP599zfFj/9c/0t31hPNl/9q/3t1/PrM+NOYvBIE/8g3f+HP/sX/i7/qin/7bf/fv2qd+h/Af/IP/6i/7Vb/il3/xz9Fz41+I5P+2c3PHb/v3/+Mv/ie/9Pd/1ddq7sd8xIf/DX/FD/hb//of9H0+5iN1el3XH/+21/aib6gNQld0VbVSH1vX2/n0cd5Z15zlk0Ppo5lwtuNaHmPClYPKxERkxzLOHlZqNldjNQ2wLZERaM1p9tVvwjlh022pZmCg3HqMurPBLkcNauStiTVU+N2Vlko2PFGS1oSZivNCDJyz94zSqDk4ZsMrKrdas2gUDDRiuAqc8mx0LgJ9r8hxHR3exXt4Vyo5WsPlo6Y0tDUrVecqsAlJg+6X00bzLtPV0+uxek6Hlicm/Oy167grRtecqbXBqeuUThUirTgvYyyD+c6Rl9v2UWOYy6gX9DuS2PcqfNp54sx6ss8xtta797iATehXqEi5tNjyn1Z5oB7mrmal1tVqCEe6AcjdHU4zemd07AWZORNC8mwVN50PqqPnbiuolcUl+WElg8pniQDZerl9Y6xS34eVeXO60wN+Vy5qSoV6hStKEHWjmKX1to21HCIMfOUygdbefaRs1P1oO2HBRlLBy/PYracHKyiykPVEy07tVj1Pg5Sd3veLpy5UraSpuhYXZJlklJzdZMnbAX0PBxXO6jBP3icSCmGdq0/nVThFEVREZxZYrkC2rx4zFMlYFg/Xgdvm3KI7iyzz6T4lqXfhuthJpI/w0UoIQCoLuJviEaiUO9iiTTzHrrnnVIkHnefktTzNGmUDWAhHmycQ2dHcGDknxCw/0KtzVFU7YBp1Zwburb3Co+XCVRYac8nEBGsCM/Cmujc1s0OfKCLoJQNafwX53dC4qmykSevKGpneDcn2efNA3mp0tWt07USfMo8gZGJSbwMT0NLmnr4rU60Hw+/INuWcxqWnVjpZ124cC7eeO4PNDe47+8zWP3IzLu6rd7HAMlWLm1Rl6q+vQYwejsVQo1QEHKoLYgEV+gJazdgwpa6DVpHnM+KUpBuwMTznbj0L2rPrLBmJ0rBmb7ue9Xr/RzeVqj2MNGG8O2XNtkqvVG2a2LSUSjFIfllefp2ozKJwV8xK+n0OPyjToqt2oVwNxkizQDPZtE4y1Hn7XraNKZfAHtkjBGwA6YpUop4ep0exDZBJMZmThVtAZbUyu6q6UbwTozQkXYwRjUajSWgedbmGU5WvnKXrUpKlcG3FqxfrReOa6K6u7KqWxOkt6s9WpvrC4VT8jYIXjLYsYSB6XXOe66WxlLaPiGXdgzG6KczCSnZNl78muysEygHqzYCQYHsDWYZSYwsbAq4KwVQkEtlOyocZTV1W2lgYtR5csuG2wppjUg8yO6u0aNUbvNFdxjZIdpTk08JvAO4pkO8AihFzp2mS8O5Rt8fAMjyYhjqt9JwhCpguHZa0UplzVx1QuNKVp05QeyaVCAC9l5cpPWeHd5o3TVchU7pRsupWaIv2AwAAEABJREFU5mxxSgHcdKiSX2sVH7jrNsl29Jg9Z4IKApLVKiOUVD3KHoxhMRCzUlCZu1g2IAQM03oJwKRnDIdPuso5cZAPDvOhsMvdUoCJNzW1prRuyYJKWtxAWwcqGble81E4xj2rkNWJbASEqNuelisVunZzf7TIpiB7tECRb850wFr3avhpuFh38TNA5UZ5a2K70CiJKy8QsgnV7nmMa1hYy/UMWWLyfaapUk4perg+GStinWrAt7ygnLQxsaaZt7h1DVegvCSmkToeOUzI0Gk6nNeYW+PxOFRSNz2Urjt890hFXepK1DDOMQ4ezkdzAcOMQxxABHYx2eJTjqDE2SlGMgQMOaYgl/VVsZ05XMP0/LULqCHUueKy0mqT2TLeh8g1xv3fu1zYS2p42LCx7pDplmOIDQQLhdt+czPq7mzr8itMa1xalJ9ZerhscMhyeRse21rnioLlZBAKCBzlCiKquHavLMMdUd1NI2l0cxd1YJKDfgSPnLoqWKJ5QWZUdxvhEjxP92erp4zit7zv/R949128bR94nu979zs9qHzTt3yr7ujd/Md/wvfWwZ/n/sN/yA/+rM/4TA3+Xf/ev/+hH1WEw1+kXTz5E9/ybV/yf/sNf92P+fH/ym/6N959910z++zP+qwv/cf/sV//S3/+/+L7f4658vkvkvI/U2xm/hdf8Yd/8i/65b/kS/+Fr/v2p+j2SR//MX/fj/1hn/sDPuNTP/Gjf+D3/4zHmprz7e9/ftOfeF5Kgbz01skKhs7Rd/NvSFd9zLjbyx70F/MDNiE3OCpn3UkIdkuSN0C5R0LJo5R9DtNariVf3YcSo3kMnwemb7fqSb64veTUPFLprVZE7UsmG0mlCFXjcW9N6zr6Wq17UcQJfzdxxe4jx3toLyp00BlOR7OggndVVQc6IfEuixzlLFCpOW2a1qZ2M1labZVZDFO7v/vlwssreXorpe8sfliv1s8WQUS7OoSZXJgcx6VV2lRXwFtA7Kh8Ak8wgL4/3w6IWT2urT7JWmmPYtHK9XCgCthCa1GlXj7ntjsKZipbj0MdGSPV7wQ1v8BEXrjO0qriPZb7xJiXrWePM3lVX14X9lWBSnW4LwOGHdyp7cra0Uz1GOayTkNaWzGvFc8jtXZVl1d5Z097rnnCnlfnu5o/3/WVi3p1sxb8bnKmvABkVPQNIksgklpJpNLSbLsHvYTvPUG2Dwnfc1zLFKPbI8ATVNXdkPe+YDMB43l4jUEf2KgLvFoluaOzRIUbLNR7rl5Xs7WMCCHbF+M0P2s+T+O77e9693GaPWWRKqwN8KX7UVyFYenjSe12Wiume3ttWsYYuUCDxKopaH0DukCtUJPbTNaBgN1Fe5BCYG2pLNvqRyAskdP2QuqRBVwZ7DNwvoJnjVC8Ue0lEaPGS+Kd3O+gDr0rP2wf6zpewl96DMUIvQVnJ/OV+W7rZae/W3i6NPfh9o65icAQ7eiXj9Aq1aMawdjMlpOCVatwG5DmXbo5ymZNmTZBd+d04UDduSBmpGdRvMmd52kZ02p4QelD9BCa3TNLU7LGGXy2HlaamRZyXMxVLErztVT61RpWulOdfaM1NKxTzQGUDlPXEtmdg5g2nN4sqK/BZgcqVS7QxHbEJGlOmdAlXAan5ihk1+xYSrAbpKa1ZHhRbJtlMhyAhKjepSKT9nxjXUO3ONJmt/WZrUwJu+qoHsoWYZYoWN7DpnOMNTCtqyP4LDzLq31qL1Im6w5QYblH7pXpkdWt2bUcRya7akLN2QXb9zCuKw4R8pyVdlmf0JRDEiqovD3HznEZa4mlQTk9HKKYnqwezzquthSc3rIJ0oDZaXUqR4zIgVfDq1pXV3U9NM9ZVGV4xHikWXjT6lH20pBGR3ePCvTd2MSL9cF2QSFFj/LBAVSr/lp7czYPcDQaTJ359GHmBgIJPO1tYJnd/p/yJW2zJIHgtIcEn763pFvASrNMifbqj9cSI/U0vttvbZowMkbWjDnrAR5hxxsHmVUMab6nA7fxZLfLA0PVYB8jj2NPK5rRU87LXBaVRQzJGFyPXH4N5kws+cQ0aE26VF9UH7uaXXdELSH97osmqy5JIHTkNny8zMexXuxYkO9KkcK1vWtO6+lb+JwYKXepBDvqDtDMUIwzcMJOV38nzrXMksmLMRBk9fBeB23ZDTsVGV1O2WPSPMqWUjHYu64nz+dxnavDoOxqv2QHenROVUeErg3UynHTKHnL6ynOtogVjcb23CYea3G7gDs8XMvfWfYyON2p5KBiZ6nMHBjGxbGwdGC6NttoBjfeDg5FLR0XqU/DoDuQXVWpLJpF0ZnnmXVFM3uCL1Yzer3WVMQbgly1uibVpq85R/Vx7vncHec5rj1zAzuZAI6x3A+oMno7r0fHJMRRSIhNmJHQlcdojgaUJtWF2uxwbjOxeBoGNWW1P4CHYblQDKsgruS5/dqWhEaVrmzsV0RoMlOYKkqVNSFCcu6E8kllYx/9lI1wvcaSmexkhzSnxTXyMgDUZt41qKU3XmM/K0KRwLCSXYYidSyvWoZsjij37gecCm/jzMgwceIWLUP8dg9SkvKyhLcEVBx1LU3cd4U+so47TI+2sQ89AU5JvA0BvWwExpWyBiaLI0ycYEmqg4taHUk4zER5UgEI8dplWeo6BnolGEIdu1TT9nW9VohK0Ha++/4PvO9+MtHxB/c/8g1/XAdu/p6Xd3Tw57mvtb7w7/o7NfgD737gV/3z/8Lr89Txh/b/bhH4tve//1/+N3/Lj/hJf+8v+zW/5rouM/vMT/+M/9PP/ln/6j/9T/2QH/S/7Dv8/90q/LNKq+4//LV/9It/1a/9iT/rH/x9X/GHuB6T9cP/qs/9h/7eH/fRH/cxwQU+1nrn+3/ap7tbZkbU+3fc2VjxaFXbzIkY4y2daCPd2gzGm+52lwVT/njG7FRxaFQpmwGqdMes4UqS89BaUaVs89eiMsk4J6W6ZuZi2kTNbqJUySTKS7VCjXFAaWHTVH8vllITevzg/UZi0199Xnw01mx0KWct29nD4LOUS0prlRFoKxWsTuN2maES1NbpuUdxAE5wMH21fGrOkjuuK1peuJqmOtBd7eljTJuTUH+zZTBY1XxCrYa05rALI8L7zcwsO/dQJZNlVvAG0RxqGpMDcvs5stCIrippRluGWwze+qu4Q8WHNaTN3KdqsypDMm2c9LP72rVTsUUZtJnBLBwCMLMyOpvh1Ajcelp2jwfJvqqiHWm2o6HYyqoaJjOvRhSYLli1WBjLBilny7LEh8wUFiaLWOy7vpm1zUwLVSy5GkLE4EMO9qiYGWTv6Zf7M1lNQe1sbSk4aWVmhMqxedVNnUKiwFyt9iQnFOTeppJ9ZFg9aRc0KTWauzUSCp03VDOPhndndshUmMIrnnYAVrRgV+cISeyHg6Ov0ecQyAXdGvKgVOI1stJid1V6NyU9KRny1QsGEuJpj25Ahkp/d+lYYEyLifTsDK9k6W3W5XYNk1hqSRDOuwICeQgxzSEkX1psFIyb4+oX9S/hs3q2FsL75azQ94RuzX1ZXpm5I6/uGIhVd7LdThvEdVkEePkw42xHIFJ82cNyJDqoExDUNt0OCgMbgrZgAvAwHJrM7tYfJU/Poq7HDNlcTeKmh3xAy4f2gLzA/5+6PwHYpcuqwuC19j7n1PPepicauhU1RhEViXEiRkPEKRDA4ZchMs+DTI0IDYKATAFkRhRQQBAHBEEEFeMMCQ7BKaKixoEgCN1NQ0/Q333qnLP3/lfdVqP5E/M3DcTUrfd566k6Zw9rr733qarvfpdWTzhbj1H37WsKAeWeREda5ablgJLIFHK37bU8E8kFQ2tSaFYmhQvQGbbUU2QbWn9ceFUAoYmGXRBWApoSZnA9s+RwXQvRfG5dgOxN38UQAmhW3txpAKtSzi/hkYfljY01clqdFF8OmLOBPbPtwhSomnNYG7SbrmUSwUujt94azWSpllsbCXiY8tCugGYJugQlo/puY7Yj4VJNbuwUuxQrUkOxA7nQdDLr+oxK5gUmu4svJ3MrctLKMKkWYGEomckOGrlcIk3ML0aY7k366oex9z0ie+7B7SwIOpl+I4QDerK2pcrcRkWS0VoJn544aEdzqnCVn6JIKNS7+/3oq3XYKDxkDZRvcEoiUWAqaCtRy2t2T7v406cEQPiIEYRM5lWAVkV11ZASrABBuaCAxN6xguWGg3GzGF7NVqvsAQQ2fRlBEmY0OobH4cmBbSr/uGgrzXj1ViWiSYUc7w2t2RikogXZQZeQUXLAbZcHGLL44v1p1OOT6E0cVfwSkyUhVZFbcDHoiKOypXsIAskxh3nCEp7WvfVuNuBN03bstRjhaMJZAUKIIXH06COMsyd6ccA8mPmkBPdIQ1I8UXjAMy3M3OkufzakuXBJtkxZVHgSNconLzCLeYUV3KZArN2CrB5UqeNOYmvCNrs43etyvAD6xLHQMiV5p6pJbkTJb/ccI/U0zF2RuDBsJnkVhHqN7EIzN+8UDdPUDDyA6oUDbLi2SvNN7rLiBU+VScOOrE1kQzKjsgKLkBBvOMgDvIzr3K3igNYKdkEMXTVJkQvEVl6bZluduc4lETwG+4PpJsPEocyjotUKbZlAObOpyzrasAthsw1E5t4Vou3ehjAUCIibA95Vc6xTOoIaWkjIWMsTcZ65Zsjy2moKlTRYK5f+xsPorlMIlgO8htxXztB8RSka7s6nqOWOafVXJ4SZHAlv4UJQ8K+9Z5aiIT967cFqgnVTEckxdb+xuuQ2sZsCk2vl/a6ZTSCHrJR2d2mCpRs0tE3gXnPjLOyOVChKyA/CFCsb6b3TtF7SWBJbcXJaraE6kisZy+oCoHBtJTsIIw9K+H7YcVslB8p1ZTOyVglSLUT0hTWf+vf/kceXvfLf+zcfX/nkX1bpzZ/59KfhNdne5s1/+c95o+tvqvy1b/+bf/Zbv+01mfof19h/+Xu+7p+97e947fcfQ6/W2n/pr3/727//B3/0p37a933fv5LkZz/r2R/8nu/+R77gc97p17/V6OKJzv0E7S/+oZd9zpd91Xv8thd881/6y7fmqPwlP/tnfuFvfb/3/HVveXudZ53kVV8Bpj/n6U//WW/wvOe+7jOaKXM4Rci+rE1xmtUIUzoM4Cgfu7ToQ4nWTOg3cJUjc9LNwRbK1l1Uzu2mFqaVQRZUcUfuo2Ec2x4kORJ1J0/nVrUnRPjtUUr+iZyVO7vl4X7z0dQ2ICOvHFL11G5Vo2IUO4ZzdLUNGpFAyQjIDBBUCbTd1Zi8UKBOW6lKJbFgJ+WFx2qq7KpKW8uogTTl98FslAh10AN7bOWy+qgyVxp88CqvPVUI5ADXlbelB+nymqlSLLWCStO9etutVVOBm8aEJDO6LHAnLXHOfLzjtCclQ7iM6jqWDzTISqu0SWy7msEAegkFs6vitQWf7FFjw2LXVg3Wjc2JmvAN1uUAermriJXEhSOTVdQAABAASURBVFe5Attlh5v0NHc9mfU5CxPjzttjt9OkcXtbY8BK5anMyAPrgfeDMQbtwe2WTTGbc5011YFpaakNkypdqrvNYhxxLbgtdUe5cF1GbrcliwBckJ3pIU1njROqwuJKaLEjsgjBxb2EY1aLbGeYamLkheoTCPPmfGAeu/A4606TKro1pyDyChrYad1LpM0DKdhqj1yHmDBAMwGs7oJZ2Ip8LeaqtkRjecKSOm41py0cb3frp/CoUs42XA2uFIzUczPOlosr64yYgHSxEcKbI9Bje22rvJScq83JClAYVDWXfWZ0jW02LRem53zIZXogdW7eG556wP2AVmtRl7GyVoEJtNNdK0iiiZ3m6nKPU8/laon1AARRxz4wG8OY8lNLgM4F0UHXrnh2wmh0cyeBXBE7quqaS/MyR4N3OPSbvXpnNlsaq1khWyv0B2kJKrtRuhkNrZFHpbdsD6l74TKby6Zi3QwHKWkSgiVjaNjd13Hsh4NNejCy2qUcAuyuq2hmlPFXXfLtI2UBEygITIBsxoOwzMp9ynjdbE/YAgH5CSQ8WZ5oGT30ZlDlJSk2jNH66Ebf8ndve4p6odfm0bI3WMFDXpVxtwY91T5wmNYOu/dQvTOT8xrVGqt7KtIGQMiJKLIE8oOPyNFDS55qzN7LGgIQtkVx8RYL+3HFXbFWlMiCQdpG20e/nrbcorScdQh770RHlq2lHL1BcmHHxu2kXw9nvKFpvWNKfkdZDzo9OrcjsyMMJfmCjDfk4GITylE98rb2UHzkycDWblZufLC6+W5twjM0kwbtzWDJtftj9HvnNGShKdB751Vr0q410cKyPFtK7gIDjeFtbtvqAbWJbHIIMqCUbk6Zxyta0zRUTKORlDoNsibdO4uncYpT2LKmhXhkLi/FNShdPL2l2ZIeRUB7Romf4S4pfeOAJpRo1RMD1kFPeiO1TuO23NdWclMYIYyCDu3CAu0qXG11S5JQ+GfD3eKOfXrNY+ToBapWrJkqSYIXGYgLFxN/gC3iga3ghBlgQUyqzXCDRWtmJgL5Ds/tXn6YY8QcessxQ8gSG5i1Zym1VQJZ1Xe1nbamn2h3HFWDbN5NztVwwsQwMo0AkShoSx0lM7kTQT/NptAXmTKGRrRI2bMb16DUSogXZWxtXJbqrmHL+jzw2Kmnijkxtg1NgZBexDTV5zMzSiplgCVsgRIq4WVqiHKcDnnnYZkt3XrjgYmMNMwhLxwXv0xuSALlxat9z8vS0rOx0bMdRqWC6rp1+Cgg1l4bKqUHyoFKLSiMW5vaUa0VlbvlXXYKhTK3flCga6h89r3bXpzY99pqTLF8L+UEWKmEE0WUps1XP85+zCvQ13/Nues6C/RoWMqto0H5OkxBlovuhELNy37ErnXmnhLoRqd2XJOfwKoarbfpAbhF62ktyu9mj42RPKY/LB/LIgUSPGdnSABRglX4KrJAssq4AjMsFFBsUtVtD2utHT6aAKkVTJmqtDMPYmNbpWteeAObobHEYw9fLrK7mUAMjSlJJyIdCzVrb87lYS2bxUAqi1iQtfASf1OGZmpk9vJhg/nyl78c/872Q6945b/zDS950ffpqxYdz34N/9kNc/+ED/ug28OjivUZv/eLf+CHXio5/+/ad8SL/8iff/lnf80Pf8v/8trvPya+V9Xf+Hv/4H0++nd84Ed/zD/5Z/9MMo/b7Z3e9u2+9ou/4Pnv+W7PfubTlWw6+ROz38/zq/7Un3/HD/rQr/jqP/qyH/4Renv95z7v8z/mQ7/gt7/vm/ys/5RKzmVW4ueOmitDSX5r/bnPfnj2c57eHj3NxjBViodSBSfcac3SmXa6TVemkKFMUGGZC+dS9WjlRzWt0/R8hEg4lBVgFjJ6xQ1hgHqANfbKyK3M3ku5qbHQ8iGsglRbWfSZtpUWDqVARyk70ndYABK46Zs9zEi1+FSLHyUDB1UaYabR2H6VeF0328o01Un2DrnATsiLDOSuWMG9GLK+EJXpqGYmGWHcWZWbeXpFt6IXqkoVeQ2LS2BT/g7OblN6VvUz2xMHgpKEypCsOjwbwQ09JmYy2MK0nmt1FQVGJHdZCRgHvIAgtlzwVt1URk9HVHmm1bpGWUlHlx360h19WA0LkzO7ljyCVre7sQ7KURJEZzVPNABuqjqpaXRr3dzdpC4CVFUMCFkNXXoGWAPlZlCDgEvKBYvnkE0NiuTYYSuwEFuGyzxNtSyTMwJT0hS0Qdj0nNQjpoVXb0FuynGtAJYjAtygXMsVOhJADpBPMIedZFW22LxAKkHTnGpXzWEXFy/Ti1Hj7pwDSNqysS7+DagL1By+ulrCEyjM0A0NohhWIFjuqcpO1O796gs9MbaIaWYpQM/dc2t8Sw/gtDqbbKgWMU7duiZ1dlWeW70zGCEFo+O4mTfbZlFZquNShIRhNbH3QIyjroB0i6GwpHnoYkbjxNXgxcPIZaUOdBnXQuYZyq266NeOPQ5weF4xDfqu3FtxoMQAgrL62oRs2eZo9E7SdGZa1eULBygvAgXqTyJXrR1x5UGGrlGj6KKo6bJMY5e0i6G7vKRuYp9yFpUGCJqhzEhXKCzQvMTaZmlZ0Iheoo9JoEmXApXgljXmdNKMQexLJUbLwdlraxLyskMDKSFZ1U1Lu4u7iSclQpJNnrKyxJ8tAqSv1c99KD2sDNlS3NoK4dKaJZsV3JYQhMkyu2D1lCWIgii2IWN6N5HjOn0WBFcIM0+yjEVuaQYkkJLkbAflcT6JkqDLzKvxt3I3c9uoSaDM0lvSklVN89JjjVhdTND9QJOIKk+0MqHI3fQspS96NnKkWFhKBUPBMztgrcJ32tmxtbsJwC7BKS/mLkWlECqT2gHkQAzROiOZ+9A4Ot0lsiiqidUSa0pfLlxRIAta4ndlom1rwX5ZAJZciyxkWaRiU/INMRlVJHBNY14Ls5zdVHdU2Pa+1b1hsrRZ6gkHGpugDzJLQrzXaClVvndqDACBBDiIbqXkdp2BsFGJSWgkPZxaHu/YqhMDpVBZXWZBWxFhsicFKQ0JsRRn1kzFjQHf4KZwhIKT8AnoIqnXQekwE5Fjt9h9kxjhFkZAK8mmMzpfl1bJymZBqHJsSTjTzllrbT3mmWkhqlh2WT8yTeWrCtuRzmBGqYzYbi7B4ShuEw0ijqwR5voWNnFNhxOZqRowcyuPQykhYEXo1OSSINlfdNPWLNzv6EvlWOEyi4aCLLw+Lt4pXLtyOk6RQZe2Kmm4YPNiWzTp9R7jxsNM0Gsduq+rsvxCrkxThotFKhWa1M5qxSQCAn57bJf0nXU5GxDAy2NRsxHlIaJYuXnvrZpvd9WsZlerqmVYiaWCJEShcNMA8UBTpSdTMkMgWvRRjT1wTBvpwxQTc2koBTiEa3lSijISO3SU0OaysQK1uMMzb83acaM/sDldudbB8QRIub+Yee1z81x2Rpv0AHGBk57RJEgiN3qV6ihSykUNUr+l4TKV8G5dosOteoNdQ6+ENTSP1stH86N7O5R7MgsyVs5SYozp8qCVxNmTloBygZCwhNKVnehIlEVe0SD1K0p1E/vGOCr6nLHnFi1o1YiGoBefCDSiJCSNW3it1C3u9S9CRjLVtAFVyBh0Fz5WRxmroxQIQyZiQWuiKYhEPZXCqgmkNNNBhkRD31EpZa15Pw4ePv1lL//3XqG85GX/+7/5mFWxJAQa/rSHm6a/Rvsv+nk/9x3e5q015UUveuELPvPzUzbqy2u6/z84/lX3H/qSb4pXPvkLPCSP/trsr6UfyrXv+f4XffDHfdK7fvCHfsu3fdtdL+nM3vyX/7I/9oWf/4nP/4D/5A3eoF5LBa/JdD0n/Qt/7dvf5bd+1O/6gs9/0Q/8gD+8zus+/XV++we//zd/+ef92l/xX0Z/2uPtsauvfFh5PbtCnD6fIh7nOPOh/BitH36YdbKhM7uYKr7us+oM7jTQzcTcilnKGLWG0zAPi044S3uj3dA9byuPux0s86JYvoETdrqtjlU+MebuazLStx1weJvjWMexhuU1PvK8X7dCY0kfgJtSlpijPX64rd4mSo8x7pXTIO3ZENjYZzvXbc7GuJB39j4up0pdvAZlTUllVMKAcZ1NgxOm5xI3V/Kq1t3rfLzuu1bnUgnSk62aNTMuRQPeL1G34sEacs0ZpsOmYbnWedY6BaupcBdwleNIYGvundw4Fh5NDkPvgOfyqQhMZpW8cGjYskxTuQ+PrFM3ITtapazd0UNdT0UDaBzs2hWLreppkuXot6vq3Fl3xyYq1LluG67jRUbHHjzdA/K1d7mNbUiHOl4pBtCHAkTOy2weQpy4sRwRnBtnwxoa1Mu6ybYCZOdDtYfVezgTKKNOjWwugymrfavxFGuu2gFzktCls2M+7HX9t/DVhFQ24iCvOHO6acmj0gqtHm5tPjricHHRuQbVlzX0trwvZ3GDV8ucpjragrZ2xX2P+zy2gofgmC4lD7x0YhdUpnOYNoZB1nrDkIcFd4r5fTQj+r6YcWvRGaiilOpJ79nks8mnXYUe1lPxSwfJIc534Igtcwpra57xZvY0t0fNm1xrZFQGHlMLujH1esqOag0UaAUkGCxBnQ+U5dqpLnuTag/vEKiajgWstuOWpqf4j8Yaei2FJ9vlTl5HxuY+StZZwVbjPoAGFNSE1knpckkDW9C3gxqjiOh0QE/sCo8n7hviS2lO4epwyJ4abBU+m++ju41bHoieoSWa7KYgo/s8+u76qtgso9Ywp6+zr9NSU6FL0euu1jWXHG5oXr2JwaN2OzdnKFN2aC1o8/SdNPlxm9RqCUFZtIiiQn08cHjrbAnxee3b49Kw206vc+8ZVc1wPFTv8iFi7nnfKntOs45ouzgvSQ36erAedjzailyclffMvct3KeWuyasie+EoNgqmFAZ9z5bbi4AFWZ5pa6GWasTJTAg0c5OFg/RCu8Lsw+bRHh+8d19kUrESUDFVLY7IRzKlLIQBSrqBITThWjIx0nlav1tbxpQyZG1uYSpbZ2VhA0z0XYryVSI8dmrZizV6PrT9yO+tn+VXZbqDT3k73WbWXD3mI+xHla8uYzlGKnpYGdPv6Gcv9kW5nMgJbqXv8cBHHYOT3KC86OQjZzewNEo+GNYDy1JGoRpoFtHqdNlOl0SGzJsnqqJwR0zLMriGbzJQTNNC/rIxNWASO1irdIkpHXJMoi/Vrg1GTehcqifFx2GPA4piKRhRZ0CU2PuIOfYSJYY142G0E/IeIlVVTsRTVcIOdInbAzEiLJO7mHFQ97CxA8g2qntCFszdz40UqFVXwxSLdb1wJx573Y13WJqXKwekDDPXmmdIS9C38qxyQyq9QW3A+959Lk3KGLMOrZ25eZt2aWtTqBB5DU/m9P24xVOcj+2+xr3ddEtSqjdn2kQrN0Wg7EwPMdgYg2EF5TmGYEvHurU6hnzd8ASz0qBy+1TfZ5unVTaDmzlxsRwehDmc7OGS36mkpa2Xl5a8AAAQAElEQVSsyJXJM4+1PbPILQ1CiDio/PPu1ZkoxUxBAsaKEfJ6nxTDhBKwJnEmK3jI+EQAYXI1git0Bbq1cdhgf2gkdtWUWjCJuxYQiTRS/qhou4zDbQmSx0fe2VBmSQu5crAfoGnocRTdT7PHvc4jJ2PWvOPxmY9TK5zHVSft7mNqfK9HVk8rHKpi21e9OmP2ed5Td6mJolYAD7BD5h1jiAW4Ne8Yfd/aOnq0wXZrrT0qQadYy/weU+8wn6BRJVNamtrzw+KhJU85kU31s4kjcjNjV6/9SCumY/KwMvnUstqqutK7dmOZAsgUZ+k4sEWdYyk3rLpkISgGNJU5SwY4U4DB6FUug6pHHVFEKURZpd6Zgp9ubiZlqfg+mTDKLlWXuLzCb7dQ6XVFRpaEzSs58G+3F770f3/Bcs61nlwdz3mDfzvg//8Dd/+gd3+XN3qjn60p3/qtf+VLv+brZaaO/1+zy/e5Xm1te+6zft7f/rJf8P1/8jXfv/EXvvBP/sIXfdOr5fwoPqvqX37fCz/+C77kV7ztO/zpv/AX5pwC9mf/rDf6fb/r07/0Mz715/3sn0XzH4XYH92UHfG3vvOfvO9HfdyHfdzH/6N/8o8r45nPevZ7v/1v+uY/+mUf8Jvf+mlPe1pEq9WuEqBOqK5f8O1NPK29W0WzNFPfQSTOfXF6g6ImdqrMFhPczt38GkldSYtVuTQoxWKd4eUuGzkKygOmcsiy8koKu5xaiA0l22HKbr/Bmha3ob7ttlVed88ywjwdpTzahS2xuIyquj7N1Ci2DNHokr1VyMxSTVOlljXqeTqQSRO1vdTXA5BASCyKDEInykOd0Fu2J01YqX9g3RgDqtGTNguRmp/oxQbo61JFC85tO590LyllqbCUAhxGiQZSv5XdpgPU2Dl2CADpB32r2fM6hN4yXPKd1am7G2YwtsUceXa/XC1JCsg5lZcm2MgZ44weQiUNszxLOlpb1GuAlhBWhtn7/eGYHRFVm9i9Uj2HJVFL+rO4w3DSTmgMbKuwGck9ah8pY6mGiLNCbXsjtk3ZQNlMlLadFYqkamaDd2+89WoILxrNXB+9oiedILys19Fq9PAb88YtcmUiKoxLr9e8IG+0ciitbwymrVpj6+1oHEYhCatG72nigvndj7NMs4AFlgKTzg2TwkTGZaNcglZjDHqZrOBaV0NBEvpqqGDJXvimpsXm2kKS0kKp3x0C1+ktTSC6b2Fv8HALcSCsBTyv+SLEUOgaTcZiCDUqBmSJVrINbcA8mu4nYnulJgoMNnJT8OcKq9KEgNwg+4Yc9ID8qsBWx7kbJtJqqcubIpBtVuyexa3olx7oVrqZeqLTlCa4NiFgvjfPBYmBkEGfdSgzYZu1IMW7tnibKeHcbmxoI5oDUhKMLW4qyEDpFrg12cciNoDytrvVKHnpGuxMZUZf/mg3Y1AkriqHurRdeNkMrDPrnlgU2O55raI72ApeQSTsykRleEX2ta6Fmoy03JZhO189KGC7OiDwSCsXIIdPAVzNSvcBhqNth5K1AmSVhKZnyYPgCF4+dpixQiYywpPZx9IsrTHGpHA30ty9zOqWq21YabkHR7lsnxImaXLDV2l0CwiRuuwuQFCU7BJApeNiJTgR9wtvXVVz9yZ+6bqMCIzEiBwltdZnHGt6pOCxkhAZ2NY2rU3K5NO82ToyJJIw9n+dFTEieOZORXqzStAI9nXb0YONXqUsD+5kynrf7iCYRHUKldLMtSuuTOqgjDFl1b0xnAuWl60O0yThboEKi5DLdkGgSdInwZJ8nO22u0tr3napfLSSBtCY4O4tKX5Awy/1YRWUXRUBkNctjTcdwEpgpBIPJfXUe8tN4U3HRTYZlVuYVkO4yb2dTXuhpRkuTMU5yUaDd8B1jpnm0cWJ2habc7MUwbV0NDavG5SboyOpUqbrLBncKrFT97ahmEtP7Qs/8Yulu9Xjcdq5az1OniarZXnrIs6GLt5S7ma1DK80S2gJOTZa5DWdAtR3tpUuI07lXkYh3YpWYpEqhOVRgk8wr62HG7aTQaQCUPLFsY5YByUtNX6dWtcjtxQ5RIrU+fKCrjMvkQqTIZQafIJJNkhSFRkNu/POC2XAxRTlb3AvlGQAgoMBn2yLvEeJXQ2hRLGbvGFSNndUj7jN1qI7vcLjZN3Tgp5QzWvWaENxNQarrExmMLLukY9dBtglh51suCCiXME+TQ6BgCVsKUNrFnZVhiJcKBWFnCXDDc2PgtqJ3CoGrHTZC17eiZ7LUaSGmQiuBIm58h57VbJyZB3Q3Xh61zQUEjpPgCzDFvCloaiJHSUxdD/MW5gShGkUkBprGh4XWqVRWyIAp85pxmWFKhJUD4lGXau9nzhi5ahjy0KzdJb4Nip8l0W6hKq4CSlI6fbK3Iyl5iEe0+Vkli6KuWqntnzcbTw23bW0cHSLpgWPbrWoqMA9295im2oY3PbwkAvgsesItgvfrCoGe1hlbsHEKKWa4kkZAoaIaC1Hx+gKYObaAt/gsnFUPdi+ja0wn70tqWNOrqWB/4f/1utlL/4+KcKTbWkTEMAzD0XpyanX8OM5z3rGJ37Yhzz9mc/OzM/94i/5s9/6V8WM11DG/2PDX/Ktf/f+z66/ASIL9otf9n2f+lWC3Nxfw91oTipIEvMa79/34pd87pd/1du9/wd91R/9wxFB2s96wzf6lBe84Bu++At+5X/5S15jca/dhO994Ys/+ff8vvf5yI/5m3/370hS7+Mtf+Wv/Nrf83t/xwd+4DNe93kTRwZNFXNB3oapmaTyBzks26DSLavRlIgZcd7XmnFG3bffp2o8sqDRNFUKHhGeWojsmZ77YDRx16sjGyWDbkUEYiFKZTDK74FZyFDaivZSlr1fj/qkDjBUv+ZlIjb3lHi9FOjKW0hiwzis9Z6HpUGbwmVxYA2cPWdDHdWOMsuMc+69dubuVKYnVSZSVUljVFokjHI9kFveGNjSH1Q8HFqoKRN5OcV9L1sYFzQqm+i0UVBZNX3LVOGgFjftDi3CTuW0HneQhe2YrVA6qtbseBjH0bqpuiV2orRTuqvLqcise9gyKwEFYXTJFm44Ko96gmelbbd1VQJTGRmjeGB1JKsYybynMO1xouv+ZZXObukfJSVAPdknebUdt42qrNJKq+duqWqu11+nzI7SiAy1eFrTTKMoXAqXYqXHjasiUrJX5Zm5wyCkrlsO50lLNYSCa/TKhjQdL9pqknGpl9ZW7DhcBItmFCsuCICFuPd9f4hpAK/lTHMZIyBCuiXGefTsCEFQoSvye4pYBVvwU74gYfJ8lVZDV7OyqrLLtXjY6xb7AWyjx2hqjqeWgvN8gpxmybHVMvtOnnpAWnpcpNAUgkDbZtO4g8xwn+wL5sGH5Q+n2a7daqpJHlXOsj5vh5a72QMUwIFL2AIXW+gdW3uw4eii/ZqKl+ZuqERkSnZKF/oum3JpWZ1+POZx8iGpMMiZrWHYy+ZqkS44I1fG4krxPJzZ0pjOcgt4ROHaWOWaWfsMbYnMQqLntYQwMCorNsWqFp5nXasDdWG4i2LWUMG4blITSO9tNGsdGl66sAPqljagm5ee6vm7admks1GGffMobcDWBM/BzbKdbW+1XWMdaAcP5+VJseASqzpiI0sWTlEiuMIqbTCV5o4kOLL13XL3XWM1r+svh9MN3lq6b0D+UJt7jJsG7F5PaoIudZDWdlkULklsw4bdDAdtgwsWPkJyLCsn9GG92gEHjVp63qIkVchWIIMs2RxXFOEKmSSEJPuCgJrel7u+m+3uOKSoQWUiuQoFtKi24XezO8YWrZdZCInO7Jkem3nX8dFqoBQjZQXX4Wc7ZvZz4tyYhjIjhb2PVTb3Fd4S5cUvu1mjDOgZt20jTTq3qEgpsWyuR8F6OFXU6brys9uS2fqZQrllyVg4Soa4CFZbFzVYNupeXSoLLVnZlkbQoJT2qsiahRWWRjs2rrurPKl7ayQFf1tpsqPjKu2qPplpEp6BQshClUg509m7Sl6N1Ce1LUqsEJceNoWZnmAUGSZDD8vWThw7R9Uwa9BWCk9UhrRZcmxJ0sQSj6xL4hMDSAVYJqcIA5SBTC3+dZtoki+phm70EjOgh1FVS0Dt8E2qXlOb0mVDfWT4YlvSMY8r2OU00q7ooO1FtUOs1tbw2SUWik6J1C749pJtLVoq4ubRiBvsKLPV6vRcnBtK7kSQ6GGO6r5av1c7mSKCXhbCFsSpHTRYMxcAStoDa3DdCo3N3S+hShiWOc2RMq6KEzYp2WKeutejhSE9s/J+5QKXIVQ+id2qDqRDMZMzJlxbRa/VnswVL+7kglAN07DMylgVIYZU624Pgw8ujiDz4oJGOreLKLWnwLdotof41HrpGVdvIHKX5FXpsn6jynWcRVRcHPRqTKtKqZkQF+T8ci7ahjF76XmKDzbT5Mm9fZYMK4XIrGLhPPe6z/XUWY9P3E+LUwHcI4MI/ajMKO36LW5HPHgeF1wqGyNxIG5punHGyUrA4CYktNfR2hgyzDZjK2Fk7QZCw0oOeRXF8+iosXfOec41Y4YGlrUSLYssqCbjeom5VTlke4fIIkd37Fy1qyY4vbaJDoxrQvc4+ra0KJTMgdLHqRaDLV/cqgspmQWX/ImW20O1BSaQwnZxp6Ua9YVel42JWnvez/OsmZy9Z1fBN4Wxw0SJWtDp8BQKCZa0XjEnQIpGLGYhNs5U0MyTjldvNh508KqnHi/deOtI0OS16VBu6/NHt7/pz3/j57/3e5jZ4/v9Ez7n8//n77j+LcIfnaifyFkx10u/6s+9WqM/+/o3ZF7xjX/1FX/t77/6zE/A5yt++Ee+7Gu+/m3f77d8we///S/+gRdL4+s95/Ve8IHv/4c+9zPe/q1+bdPK8rWJisS9JvuO/Po//1fe4yM/5uu+8RvX/VU0/0W/8Bd/+ed+2u//jI/9z37mT0OZWLfSCkrrfbM8FO/mG1yIYlxUF38LKqHgNbx8F1RDVIm8lvvy4/Qj7SAOW52LM+KeqdmlDMmj6eTdcbe926qDlApXSVVFEIF7RZ3zvs+TV3eT6BJ1d4jKqREIcDdPr4CKS25gp2/0hRbscPerBjtlHJDLZbBVNaRpcemp7jlWG6e3BVvKIGa0GceebgFLjYQp44GKXVqY1IYvdJUp5SJq0i5lwa2KkrnKltu92RwW/VIqzQe0IuBVXlVTSE2FbDO1niKp6TPHmqzLHZUGG9YOa6OEnRCeApMekANL3Za2G7UGFRwzVcN1vqE5jVKthet2FoHMWpc5ScvWovVdrfYQPiG/2jJLJ2GOAlh6YEJDNdKpM8J8bs/dGQejJ5pBQjUSMKe75MssCGjblosQRYKd3tRIusLh0bFcL8JrZ1jBw33LFtX2bFS4dubeQOicbI211VGKRmnPZTNslmeU7d1Wesg9vUlplZXarNaRIXaMHJVjT66IypLdMvs0gUrRU5OzSkV7HsyehWD+BAAAEABJREFUlmECRa2ALJXnkmlLkdP9gsRs5xKJ2bOG5tgelk0wlZ2quzuBMhl9j5iS703enKglI61KHkKqJCw0CCGuRAhXUmHVjWnNgDYyK6/OcyHWYsvAij1LS43CJSVNzsvMwsgkz4CUB5iG6Bt9m2u1VFw79UY4NiE0oxmtHNWr+kS/z35uLW6YYMEiUdjAMoZWKD7glsi4NrM6ZJeUr4qloYauABtCYDEEqUB2KF6aZL35MEO22iY4wCxLyENWZEWQ02w2m7AFLlT03L6XFqOAT50idm1qzuZKuxOrT45TMhFU0Vg5p35DOWuyw9FAN+zGXa4cOZcvjmKzgmO7ZrHMnequV1KOqBswtPBqmiu4bBLyTh9CPgOa41pT5WV6pFWYZ/WmmwS2hmJlEOmeZlvu6GuJlLrrOroos72SgGZPnjN3Ks6QX3e0ux0KavA6twNry9OUXdqxeLF5F1IaCHNLlxM31kHhCtGCLDddsJbWNu3iGKSMW1/dloIoQlQ5wDCmsaJQFfDHrgWp38l7WpSh6BWtLvi3ggoUrjil71BlsywaerMxmjddkLE7qvZlumSjl3gUEgG5MhGTjuq2lNWqhN6NN+RtzbFW29Nz18ZFnYi4yDrPqnsxHa3V0Zr5CFhKq2xLkUVGZ1UQ8H6NcSvKxkQpu0TqFPcszcscEh4yQWRC0XGdwii4ZmswEtBUBYbZTigEflloGNcuhN3lkOXZ9rYUIVRdaGmmkodrY1GGzB3nqr3KGQ89b65oXyMhH6rSNFKMh37rxd3yWVhDQdJtgkwThbTi672LLnvuWWvXTIg7MG+j+7iZP1g/3FT8Y+Z91kmbaKmmKCgh1lkJnzgL9/Qz+32PSGfCBUXhMpdVXugkvOH66X7hgdihroe72QZloTXng/PoSRWQViCZ1+7JxvKjeGy/bT8oe9RWWEt8N811mgS45Mi4VRbyc8874jFT+YcnV+EGvjpaBcm0ILbHGrFUNxuVegpTj+lLak0/xS33EKgMiGQq6/uaObZZGLPk/9FhrxYsmu+4BoNbekiLNmb3u+sgi3NVrmirWoD6AxQAk2K0Hq0n5EaJeXLdZ3VWKxo0cldJeO2K0kbvOcQONFL8VA2oyg1bGkljO9zaSI5dbcvq2qxor+aCEw/GB9hB60x39OaHtd76MdrtVmPE4OS+r6f2VPeuBjqQ4CKzhR+pXKoq9aylLuVhPV147y1+xIZYSafRkbl0R7AZ7CEoKLG+qGvDzGW6EddWhvD1pGsuhxhblMqkKw65FAVE5zJw11WttDapwykNoO3oiNYA05kD9QhiXzGswi+3Z+WZO6XGGFrAKfnSSmCJswrF3lnMPUYeNxujw2tjrZT/sopuNE3N0tZgyt2GAiLSFyxUa5rZv/ZC4573vOfp8+U//COP9ZhFR//O3q9RmvrvnHoNDvluv+Gt3+E3/ibNeMmLX/gRn/Sp//S7v0fH/5HvT/3D/23+9eue6nXe7Oe/3ie/t6ytub7vI79o/9C/9z8b0Pkf8/2px/c/8LV/4te95/t+yud+3ve/8IWSf3t49H7v9q7f8Pt/7/u+4zu87rOeCVInf8L27/q+F33Qx37CJ3/O57zw+6/3S89+9ut+3u/42K/+3M98s1/+i6FkGsqs4EpVwSI5wh8e94fVx1U3I6psioLnbLVNbC1UqhN1j4GkmKg+qIcgbcBVDNqVQcksqgSqZmSqS7LMEk3VQ/XpTKhWPCksQqDESqcP5eWqiB2a14RPoM4dc6+Z82RNQsuoasBRs2GSC+rHeiLRJZUVsqiUvS6RSOmdqhkls2FVfRVVuySidbQD9rDNpzMstVpNKhhODQSCchClQrasLfq22JiVE3lPm+Fn+cSYuC31eauyJzr0e6NRYGKYyv1uUOZ2WDPU9YRzIQxCr5TwZ+Fx+akENqJhO6+H8Wi2dYrbPK6YNHrJQtkjTBLm7A84hs4hS3UPMOtQQZx8MiB288ddfeHIdI2XCYsAS+0cqiKuAqKWASFxfdUiz/26HB4ly9tVnrZsEAKbverBtWxJ1OVfIUHsgHCtgsOHH+y3PIZsjQYpkycsOSQMG7cbRAkFaefKkE6TjOs+Q1RiuIQs26ete4tpczFmYcGEEBQJ5wZTbibbyodZbSJPFUbNo2yKmnvP1FG5TFPxVyCLmF6rg5b9SYwGKVKam7gYOFMiwdqj9kHFXe4EABktSkQr+XdKqt3NJ6VJytlHarpH+bZWJhQABlxdNBa5l4mlTcu5OD3UXWuigrYrz7kfT6xdWgiCK0QeyyTkH1sVs4TLTkc0D8im7rMxTMvreWCNa8DKDJZ0SC8JTNOjdcmHsRT57EUzSGKhYLQwhO8yCYKrR3cL1AyKa9khVyUildBk74dZI6NTr5ICmeI81iW2tOpsIldAadOrTOZSC5IS4AFFdg3LhrDaiF1bsCFAz2zrRKx9KbkyRovKMTtD6KXM8LEuiVVnqPNFZIk72Yc12SHE5JcYoMzLREgRekIRpCO86SFDQxuo5spKpa5uOesoDhmKljki+k7MWOd6fJ5q+4jJS2qmDtZjm7qz0uDD6mabpjV3hWQLSciR3FIZ1SDIbnWBVXXszSX3KrIJY0VCrm226f1OnnJkYZaw2O5z5GXAWDnWbLmUCL3s4OhmrqVQT1o5amDeGDeDHineIo9Z+xRBGLoMkCzWbDhd5S2radcJGajKKyoInGVtHceyI2Bc0e/RZBi0zuKZde4tm9gnDxlKsB/uncJANBCrbUKsO7uv0UqJkdylArdMGpGs4TUowQ3WL76zKnPJU9/btmM5Z1SsxF6lz2UM66DWzfTK2pWxS5JIQDsgy7zv0dfRT29b6VdraUQprfRoXwEsQl6zzMpgXjRSSW6rbBZmUrcXkQBlbIwNeX4U2JDXUapWmSPNlhZx0TLc0AjXkII2SXd0oXVHnchTFgptpEN+DuiuACZTzfa4reMWvVVwL2b2gjevI6MtPajQl8FjZCur3VZ5yfENJSys1WG8NdphOSD/5sKawDY/qTRyoztaS3hGjzUwdYtZdoZsqWZ6/TJvfnauZmx+dD3Fk1mW0LMmqv+cVffWwoanN8DpbuZURjhJXNnQ9NuVbkfbiq8y1jx4aeRWGFm4jlNBVuZVAW1nn5trr9QZ8aC4hSJHiPlLQLKWE5QqGEt/MrXCRyh6Qn+M6pdkKde7Gg0MZRxyljyWdzsU7L5bz3ELwRJiQakSYJeUb+nKzTi9pqFQCsRRfIjWJjgz1lq6FY4tzbgs6lmD+3JdCPMA3E0/mlma78Qw3SdC99YNuaPuUY8V/LRR1A6r3TNvRm9W7KmoVrfW+80PfcAlMr3uh63eWGKD8DUOx9FQZCjO6Cl8Dx5H3gZ6x/YSbCpWPNEXGmAdpeLuMxECBqoKrjvTtUNuhTVDI7GxpxrNygTZupLHC1UE4YK5VlZYQnYPWqPsQRamds3Y1VKnlNQHtQAToarn7pUAMm2X+VZsMe7sj91UKsGkIDnnnJEW4DbKFpZz63swMyomc0FxSQxfB8UZOVHj6MN7q2ZZEl8hj3W/rIopdctDcpGyyJULiJli0qQJbqgcXXXARFkeFHZifJpmaTeBALz0lT/8eJ76+mSntuuAKOFwHf1ofsz94z7ofX7Vr3lLkN/zvd/zbs//8H/63d+L/7i3H/isr6616fbcj3zHN3iXt3zmb3wz2Xt+1/d/z5d8gw7+w/uP+uoPv+pVX/l13/Dm7/DOn/Q5n/vd3/M9VfX0pz/9nd/27f7cH/oDH/V+7/Xc13vOj1ryj27iK3/kVV/5DX/qHZ//Ed/2N//mWmpxx/u88zv9j1/9Vf/dW731o0dPE6NPWgFmotFU/opCoTMEqCaU9GbW4JV6ztvKw8djPe7fSA0YNh7Zw00IpyWVn71EXS1DIj05dtdEFNPd6QfjMDXp88ho98SrCk/B7uWnql+1gEPFOV15aTbs4YhuG1Eqm7cslzrV7H39dqMkG41a4mmNoFzZK+LKEWnDtSW1MoJGOOUJlFw7VRdr34oPSjzVHzxYqB/4A2P0QqtAqrvkrrJSz0T59HHvVcck91U8VmaxslWqRmkIm2lHi6Rm1ixOxFapXo81jB1taN+aEyh4ZMM8qNLZuhzZ0MlVV/2JAuVuqQt1Qspsok91m1bogaM1gdxQlubLxmOOGSp41lrrvTdc5VDLo1idVLRq8CoN9JXHfQnadeDuPq3JVztVWJaUs4YW4Q8Kr3tBjaJdhsbKEnouVTltPTY8bv5YUa8+66h11B4KxyZC2GrKUKTBTnlqlC7b5ru8M5ufVtcDuAPenNCuEnvb8rKdFFYdGLVGhAkFnTAmZRx1W8qRj+xazB2svkMgraRaCG6dY1SzsJWDeZi72NLUs07D9gUPc9MjzguBITAdak/WXT2ToYp+i21YqAi32dtj8hyYnmlVSDmP3AJA1pqc0n3wrUIe9yhFwkOhO9p03x1k3FBCKrCXTR7ZVJBBzNwnqOUsdp/r0dk6ntb7Qxsau7GBaGYPzQ43d0CSbjRRhdlrD5w9cohMZ+95OG5ubNRiMZGORAnhserIMBkd6WoaczKySWrnZmlwBkNEYoaX2BxUCkBb0ebwebQ4mGakC8vWqzFqr73XmnOuFVrSYTWs7vcxYrSyUBvCGQ72W8PR4sZ1E59Lw4h8VDzgXfLCqpitay8Y0Uz+WwO0011Ur0ywGsqFRRpAmZc579lmHrhMG1TBUdabmVfztJFlSCkC9uwxu7QcWMRZfrfb6aJUzFy6MdwlBTtX5FOtnjrm3dYOVFpo9bWYU5Oa7W6yBp0llkhpX8GFK5WbPT7sqQN6gzFH32MMGzeYHRDj1sHl7MAj+MHmV9xGGkPpfdhufVtLtfPcQTnWDd3hl7+oQa0aHifvd66zqJ4eq0SW+xlrWkm0PBYyEdzG2fv2wT7cFVmV5vYY7c5b4tZtPA3NouRW0LcsGQYkIEAmjOyHUQMGq1UVk4ZqPb3Rw1sY0jZ1qWVasraVyhqYTEO5aQkq2izrU4MHoikoxduJNquyhOBug6YKTVuGVXvnqRK8FJ3osuLiHzvsARi7oPOQGRA7cV5FAgbNd7JMQMnbbK25iHI2e9IWSgEkkiirzbFi3O8QPXesrEzMqld5qFJINIFWeS2y5MZgitr6dM0saKPsZ3RFzdGgqyttzYxzyupMFycfjfVoXCZk15osPfeQWUB0ah2dMEK2rJAjZt5G6/pT4uQRfcyW0+4z76eSZ6eKXwhafwgeuOCcEEm5p+3Tiq6ELr3Q8Ya6KFBTsAuIpkA+yS6Mbt0hjbtqKS1Qq4OdPXErf/DR9CUSO5HVNh5O3BZlyYEB+hbdlajXpNlUqmphb6zNKC5LAYs7rkDIAJQmFBqswwe8JzNN3DxTsnURDJOk7WMpFXgwmoQgaIFhFBNvzdw6B2tUlujQW9aVTHIA6MBBe8imm/I+H+P+VF0PM5LYlkuhHagnAx5+JZMAABAASURBVCkuM1j7SNUQMmVpba+pfHua87iZ0mcjlyJvl2GI5BYwqhY14gIHhe1YzarM0nTSYAt9KxpWuZB3NmU0u6ue2fTYXKoqNBBelnVAvx3VI+Braodu42aG7jl1Gzwhr4mFmorMzoo7ffsj6zdF0xAa3U67nU1pl0VkARcokja3KycuIg2Y1CsbZlRkVVrkkaUW0NNqtXWOOW/31e8ptWfgvrUv1YjChiVAfXTiaImhcHXsY0QbZS2l2c0mRIuesGRlq2ilTXwxeeyh04kL/NN4dl/GLZFAi7IduJSEcWlqyXh3HAOSTpblLLW2nKiEOovqVAnAPUy1a8kPORQpC1EFCFUFwsw08xq/6xmPHuHJ9rTbTb9f9kMveepVr9KB9tsYR286ePGLf0Cfr81+O8bnfMQHv9mv+NUSorcE7/jBz//27/jOemKRzvzHtj/19//FK/7ct8uq9ove6Gm/9I3Z/Lmf+n7jp76+zrz8c772lX/nn+jgx3Z/6ctf/oe+6X946/f8gE/4zM9+0YtfJGSe8YxnvMNv/P/8id/3RZ/0Wz/wJ73+612x+7FV+R+Udj/nn/kr/9Nv/pAP/8wv/D0//NKXkPx5P+fnfOUXfM7v/LD3fdazb+nKE5GyWzywhhVVr0XZBLfqzFUsYMhR7FBSmDd4W51s6b4KO8hONBcNR+Ih47ayr9Q16nKBtN7sZn4Dh8EcSpndMtpkzrajUpQOerirGGZZLzW/1llXYjRvfpi+mZlWuUrjVsziMovmyirkgbruMiybCl7VguQlUUKlnNta7Q5lJVjoTBm6y3eojg6X4fTi0OJ9m/wEV6rFqPjsItN7WUPxKpUBSatealq7ISWyh5aJ2SKYtJAH8NLYuAwI2/tYy3YhUaUWvCNVhLG94gC6s6GbUXM2L6O3SwqcekJnADcqq0oVh4ZGGJpx9ISprgViOxZ9wfPylU3oIn2rDGGmay7rMorQn5oWC2tvodNym1WhRWLKpgCsWWt+mRLls2wmVmVGyQiTcVkoXYUKokorZ+WMS5VNySt9CICSWb1S3ajM4/DqmS3p4lEJAaCVbASdSaaRlEcGdTV1PaIjGq7eouiH1p/Eidy6NWlpQNc6aRm2vDC56429D7MhMW6lr+ZOh3FL4T7ifKhQfKCLIyCXtKpRUKxIjmQTdmQaC9Uw3dTe07tOhCQwqgV7yE5AsDYITd9pBVIcoizIahWO1EDsUdFIV+hozuwW1hUNhjlEGbKVunS16r28Q2eB3KFGrbJNbzgcN8FB1XyZ1pFmGdDS6NpzaEY1K5MNHmoSIC1NWcLsGSPmzWe3BYU4xDVQMv2yHKC8Rq1CyD40aTAuQOfrKMoWJhJVSKOgAETfpNpMZQbXxgSyqOOYxRlC38VlDSNNaJigaVXyU1+tDcCwVESa/BSEyp1Gsi4/L3i8ul9qhpBbtZUj5emehioqOeKiL4tgXK55P80nImujUkoty9bmCgRQcJPdum/2HVARKYgj2RWmDWxorBwtSHG0ys5r4X0pa1nUckW+ZbmoZ7gKCWR69gj5J6cAKP6yNR2b2IpoOxqdsgNQLNc1Lnav0mizaEoUAmZSJ5e1p3ShIUOmAZmmh/hx/XM+iS0BUjQrY4Wfu8+NFYI4T/makAKjmbsDXuBGX8oUO4xHD0cKEyI6dHMoX2Us5YqmAAKvnmxhYIIsHKXyvVHKZ6SCM+hQZGCoQkahAF4fiW7Zsa0qSx9WoKJS+rVFsGZXHfVqV/qhXI+OHlWMll2J2LyKCmlIICHFu2yLZDUYbimETacrl83d5q7QECjtbVDF3Qnuhk0nL7+lQllTvoVQVWmxtNkCTdb0hCd0ssIjfbJ27qhKCHlFKxulyVNKqQqZ08VcvHpLZsgpuJu7fIPt5FSZjc0KMMg0krCF2syxVUKFjG036I50QDd5xBNtV8KYNVzjV3k4L+ml9nFiRp2VCRpgZtQvPNn2WXPpyfoOJAk4QkGkhqY8oLJadgdtY1QNVwyt0qOQuc10TfAUSSdcFdtdMO6FmjK3lB0KyaHpold1VFEptpZvtaFVEZaKrriQfsmo8stZ2d9LYMtId+vdb4ePA94L8pzQViWssinxExajhd4I5UAZFAhAcR1jWZPCBkWgdMJkc+mSP0k9yMvZIlrIMpjkas8AJBQ0mlvRJVC492ADOniEj8Q1uC9QFFhG5dNwa5m59lw+t2MjY25utJn9XHZipU4+MdzgpBJfu2wpuPa8kIZwo8TRZL24WDErd8kC8mCZQbYlhfkybIul9ExobFpcX7m3vE3khCyByGdO794b3TIphmx9EuXKmd1S9DAXZL1MfERoAEt8Vkx7dJO1KZ8io5gkRCaUNuhrYquAz5SuvZgyfyu1NIiQ+Y3pUCRYl2lK0brmyoYyi8ysrEYeTopoHsHUtV46nSd1x1aTwgIKUxgTwn50WqvYuZb8BgQakhs2zfZBBf7Vta8otThQfZEh/UwrUW5L4IVc9iyFDcECs6IkHoU0z9d/1vUXMKDt4Rn60P6yf/NvPt6Ork1n9v1H5to6+FHvUvv013n0uR/xgf/1m/8aQfWSH3jxB/6OT/hTf+V/yswftcwfv4nf+2V/6tXCn/XO/01/netG7uk//XnP/ph34eg6/92/9Qvjlf/6Xk5fX8v95a/8kS//mq/7Te/3gb/jUz/lu7/7uyTt9rSn/7q3fpuv+tzP+pQP+8Cf9lN/kqDTyZ+wXYH+1r/9997nBR/zgk/51O/+l98tvc94+jM+8D3e/Y980Rf+sv/iP892LQYfK+cqxuI4genl3Y6WzUKUghHKAjGfLWFlLFERrSsnjV4ip0dyV49iZiihWyzxtIIipJVjO9WAis7SE5Z+VQ6oBkSdXNsW2xqIMeE0O0qVScncgu7mR7aRg0owc9Xt0BPtboS1VA5m9NK6dvNSUWHJVjBzadpZVVstB0A1CetaO+U8Akehm0mW6p1yP5Rgq/fTuSoSoeFV1y8VFj3ynGwBQyOgk0o3obNsmB/Nb44DdVApOxmIhdwoUsCg9FDpVlJWT6pWVUBpLw2qfmEW1qWl5z52s61H8Q3URRRMs7O3JZc2VEZ2QM0ym1mHNZjLSyyqbe+oXbWuNk0hD8ibrQBNUg1S8zKkI6D5kOFFVdLKSM26hkqCJbR4k9N3CiuYlawr6K4jn4I6aWqGZcn2XRbUulP5MpwGhbddj9p8r76SGzlLQmtD90G5SkDISM1hCVAsqFg1UCgSiQrZ4JVWgi4YzXAQTd5Rm2I7mm5B76lnR5JHwIAwbMuttwdtaxDVg7NffQCEAqqHeEYZhmQuu6yppPjqFCKlha3Nuc89UzQxURJ0pHtR4RkXlSRFXwo31nFV8glfFzqdGS4IGZcnLPOtpTPbvXj91yPGbbIttl3kKZoI3Ec2LpN/6Z39sPagTwo4LeRGFBWWSv2p0kfWAlPCFYyAJeXU2qJGF5WqAUf6wXYTr5PCWMwWKQsuEj4a7E6CAJy7bG8K9A4lC2ClhUYf6F3HKQCDUMcR9WyIFpolb6ZSuFps6m5EpEuTVhFJ+JdTsnta2+AmN7DVLuMxtt5iXH4qNSDW20wX4nr3BTalpBSjMhNp7A0HzM62Vz+rb0lHNOpl5jpWXHyByMlKjzpmKFAMEbjEG0sIxb08tu9te8Vee2de11AiPk/FyH02hUTQ3B1nW6U6cmw2GC3T5TDbk830GFamHBdiJhPLkwqnBlTJaRRlCbBn5E7NFvQyopWIhgYQidqsZBZiY93rvnjX0FrOu+Fe+x53FRHAHfKbo/YRS51asgnsHXvO3Cf3mVIzjctV2oxV1TI6YRiNA4Jzp85SMdcI66C0xq41GYqEYN7le7sWGCVTsKSWK29pR3YmZ0LhPI0KJ8TmuU15L/ZAUaTcsjLSy6IwMxenXpaZp7Md1Y4NsRRZd0KORYnarNJUpfW+RPVMVdruw8fILoUAaD7MGFCq26YJ64FqlCbwqlpUrQ/RI8aKplCfObK6ouPemqhSTIF0fSoUofQpWajSoBD4konbeL/hPvpso3rzrgg70Cy6l4GslEOLeQdkhaYdSA1AYl9LycKTrai2k9uAvuq20JUSPgqmWcwZawnljAZYUGQIVLcSJl7IZjlc2V1ejWHIhPqXR2XuEqRJgylcQy5XGcJt+XH2cW+m/NF9w22m9IKRPUMqF3RB30KnCEml3Np2bB57tXhccVZxpU3dvmaIJJffGly5Nd4I84ApyLmxCwuVCmsLXdV5XhlofbMWNABBBDrTlbu9rHnzIV/kf3fcBtTXqh/LPc3ZrDEGxO8kXBbP6pMmyYWgBbTVRbote6SUUVTxhyheS4GH0vx8wGq0tdr95LmhJzM4SaM34QoThmU0b714W3XklRa6krcDarB9+zhbVxw7tl8zV7EUm2zYzKxV+2z7XiZG1URtpBJFcaQuFihEL38pQY4BNJBAkouCaE3khO/SFdPkKhX1e8/tTuiEVWJbD03Lgs6wwbqZhSngyhz4YxwLLeWwnyVqi9RUzdIppiRv1mUwJTJXJ67XTg59rXy8YkYB3pQswpFtm1ywLZRxhLNkdDubnU1FjgprMi6krche2aHfvnCcW3OKXpS5ETu0AOA5sZcmGZlegguKsEBkLiKH0HfRRihZuTFNY5zbjmx+IaQxxZPYcrpMp7rncYlHJBHedP6SjKmO2dBMhQoPCaUdIMPcjK2jNYDggs/Su52QE9I2aJ1Pe9q1EAcwmulT+wtf8kP61C6bn/l6P1kHa8crfvi1XZoX+OxnPevzP/r5b/PfvpVkvuTFL3zBJ37S5/6BP7T21tf/ePZXveiHHv+ZvyF7xk997k96m1+ugyc73+Adfs3tLd5Ux/sfffd3f97XoErHr83+kpe9/Iv/yNf8xvd+/0/6nM/7ru/+lxL18Ohpb/NWb/PHvuCzP+cFz/95b/SG1lS3qfM/MXtm/pW//R3v+3Gf8vyP+4S/+w/+gZSOcfy6t3iLP/HFv/e3f8AHvc4znr5xlRbV6ISSP7y2KcuR26gaVvQs0bE8HWvUY32KgljkUpWqFl2Ez2hxZVsk8koUZlVImhebJOhzsyKz9vZQ8qUhJaUcKeXKA0+lQSdH5pGrezSmEq6uMl7sSsodmFaq2sqCZmVWSlBN9yhGhKkBLtRUrQChVdkY2XRnVHdWyGk0pJmUJjMrSnbkAJUdpamxdixdKcvS5UxDWMnjaL68Kb1ymW8logst2LQ+D8PtSjU24UZNbJRnJaCql8qaN7ijmtEgg3UoiQHJl37I+141uq6rTK6qXTKMCGoKrFFSbQZUflb5BpblbIoAql+TNT4SWyGgUFRpj16LSHkYUCVQpdlucll8bpUtQ6tqP8tZ5umeCFDVefGWezCMubGnpiaM5UCmS1poGKoloQCvXTsLDrKD13LKwqkYVWXZ3G0uu2/cty31+jgXdnILeH1gF2Kjhz2AR8mhufe5auq0DKmtfLe1AAAQAElEQVStMi+fh8FHyMTmpVJZCW5yNikyx6XWojCde5h1GKJyRS0JNIMxIEWaldWW1kBo0BJk51rr8d5iRxUkoIXiIH7aRVKubgptuDGpdnRcC4LyVSZhYUFLaCfKrBRVBU2VnEMXJEaeQzSUYslF6hgU7JALEntrszfFglawXW3D1HjgE5yXCfTQyxWR3bNwypeqVGfQRR2fiLANL4Nmpxf74u0san2sFS8Nea27KbdvVf3yVFIWUgptyPMW22qaZ2dJh8azpZuCHTIScgpQLKsbxzY/HYGdWcUizag/vbejS1jpO6Bcf0BczwlqZzDZtsTtwly+84gapYWzeFjpvq/goPJS4XQCYuEQuGdi63RtBrE0IsmSk9dA5Zw7II6B1x9GM3mwiykgYKVtr1p3crdWo8uGmN1O3dajG1ov9JLbS+u6Hq1hkN2t3axft3mNAsoubFK+SnmIRW3EUNAqvHYhZVHVKt+tb1frLj/RtQdGLcOk7UWNZEQiw0rJ2qycofoRumIVPaqHezSdrFhlqNbM1OjhmZbLagFX4aq4WRxmTVKMZjasNxtNq0XrRZkDRUT4xBMMzm1XXhWK9OZGmlgyV657roQUsYMGTSxsWBndYECFGJjKMgdIuEOeB9uZjEyFsRN9sy32uq5KRj3xCg6/YiFwMvfmuVUTbfcM1SNCOuSbpYk8MlbTrvT27Imh1a1K+ji2CjJdEUNwcc+2TGukAIOXaYNjYDQZuK4H1qgnDsdMZewCt6Ya4TJrs6cZ2cjh6O4+ervAMwg372ZeIUBKBEub1UNKlmKSpBGMKlxbtVKjyWNnj80LOJYbxB5ZmZlTMVMUdUqkTdupgbqzwiUxfRfiOvWEmU4EVMYUS8W1IP/Y5xglug0B2Np2lZhSgd6sjGoT8l9GH4cfQzbDZ8Z9hy52rsOg3ZBXMSO0VUEad/AJA+RHGQuWuymFIFcrJhD0RRYryCU5Hqvua4sTWgiHOawbfOwaO1tBFMhiqqdk8yEo04V68p7trgwSv6ogfkunVDlWx7JAblZCkS8kt2rY3caJvqFBxdCU8ihfUNTE24we0nb5YmAY9VBqy5tIX9QtyUPLo++b5Uh48OKQr8U4mYEyFMHSRAIX1werESoBE1jJOt2EV7YLJCjJ1Al4nU+YJ7tWKCeoZqH0Nh+EjOGJSs5Stb3kHhtH7hKISnI/e7v3fooplV3meocNdX6lR7JVQQ4m3ItNGoQEIxN13eHovZeblezapUcGWxxmuO/uZ6tJLTmANMgRWebVumJRLOVtKHpRJnDkUe2AOGI6IZTz7LVFMKPXNmo+mtzZXdY8WX3AzKARq+U2TV1Z96qdwb1NcoRfofKqfc1lLHdWWMUReSBprJ5ZJnuGtQO9TVpu2mkPs469facpQ9nM2BwYMrNPeC6FIfWVftM6yBSCgAbaUb0lewmoEmJ6mjBsd6ZzWLiFtfBSTAfQqZz/KT/p+i+aFL8Rpz61v/AH//WNSgE/6TnP1pkd8Yp/89+D6euPei/wGa/ztM/6iA99r3d8R+H2+H7/PV/2Ze/zsZ/8Qy9/xY9a5o/5xFf8sb+cL/8RiX3Ou79l/8nP0cGrd97Gz/i0D/DXfQayfuQr/uwPfvs/evX51/Szqr7/B37wM3/fV7zlu7zHZ/zuL/yuf3m9tXh49Ohtf92v//ov/t2f91HPf+M3ekO6CsRrKvhHP17x/bbv+Mfv8oKP/7CP/di/9Tf/xjrvis6b/qJf9GWf/1mf87G/7Q1/2hustSJluGrM6Fs148Hc4Wf6ffvjwEKACa9wJLNyiudX8ZqM02pG26ESxdVralGsvFWSsVQCLy5uKtOqjhThS+Ru5+I6U/NHbQtl8sq2rO8GTe6lpqKZWIZtm4wmhSe1JMiKFmUz5457S1WmOGRmRG6Ect31EZZ0zUqwQma3hWOhbUYoE00gWq0+HvN2sk1TZ+K6t3U2lBYumniWnu/1SHXLKqruSkppvVimAQRswxZb+XigP5CNLJ2BzLLlfd3GOnp0FUN1GWfqahUKvsu22VISM2BBwKQu3KAqCIavdZxZJ+5bpbbSDAS4DYuqIYkKGoBMqAotUEuclPxNC+dVt91LC0RVnAAiGnYXos3du5ujwXrYcbeuuVGyXOXB3BpxlI0qQ1A7Lx1RKpqVss0p4dyNuyOHvl01OKJW7NQY8QGae9RouKV3mSL8D0RVglFYyHtknKqYalQIrztzZmCr1DbVw9Jqs/ZpOmVRTdicmrWR03G2dto4cSRdAYalsbzU9Tu0cHC1udTKY9MS2G0vD/WkkK8Vu2SeQYOhR6RxW5Nr7XnueNKT6WM0dpS4NrsvgnP38xynKHSwOhU1yvy9UatBeK5RQfEA7QLL4WbBMamC7MW2s68lN1jloD6NC0AVZYV5+KEbkJWzZFqGSC2boZ8tC1yy+rEfPdo8MgXF48LJiFKEZc9GaSQ3bQdzVWlxK1JYQXS6xojJjy0FkQXVe5oogkpQPcApY/a2tb3CAC8oxwbyKDyBDTl1WhoYbKDCqchQPq+JVaB6U7dt3PDAsdXbohlas9JquzH75V5lyuNEFqAgXUJ67uhLD/Yx6J4CS1znZXBHNmjLFAsspVWopmxtSoVHnQ/G4WwKz6jhda1KHOUk6Lt5Ht5aJ68TVchw09nVbVKIV/F65nioESaXikWK1CXVw9Q72VgQz1SuDCZc1caTuyAMxNYRGCctgjuvCBmlxsOagNi7YiM3kZBquU1CUKxmq7d9WN3cWj9genk6KCt5Mx8LPCtPX7PPzSnZg5Rz1bysmXf5MrqrWtBqdsgbqpCUcbAeTEnK5q2n8IvIpTv/WJar1ZWQXokCmrPdin2zFk9m2Om3e+uqoZe7lxc0hoI+upQKTLOJMdE30M/o52PU3DqvKNTRtzkUzl7njffeFgl4YzusdYckgUu0FBJH1G3uY4YAOyseW5x9331rFRhVzTgaaX1nKyqo++YLIpxI7FHUoezz1btahwIMODbrLiBCIRHRHyfmLq5tEQtX5oBO79lr31bdUoWvSJmOXoLBCRowDGwliYWxZWFxZjLnLdcjoUozhRGCjnBvJnrCXLJyZ0ZAgC73rYDoiJ2oQo5gj8uNyvQJP6WWT3iRuVI5qUBA7hQBSwXytjGWcQv2Y0ucaYzVPnLdItV8MEflA5q1pn7Rb2mjdmKrh7LfvN0IK/lsNRu296K8Q9UyrNFBHhOqKZqkNaH5qogVWGgllwzVvUy/skLm5b3kXO3N3F1U8D3GdsvKe+bjyqVqA1CwBGdgImfGziBLL9Rs6UvN1LANKGdiBCScg4DORlWiBZvEP5GUiPQoGnV+xrHSE7I9wQDRgZE5Lm0dqVm8O7bcVZeOMdOWQOaGtgJWcQVr6xBbLX/YQB0oSUEgz9Ilp/d262ye0bBMv9CWFB1N93sq6A8Vx4YLRutg7jXPx2udq/bpfGwtxJ8xO8+MjDUgwDeVdrxYsC3X2KFkHnLmVF2DraxaEDieRCq4zYs3lCCh3FxVIcoVZYM+pnnQU7jI4kJzdySwyeq9upYPik9d4BSKNRGnak1vGA/Vbzup23VqiZD5sMRDNks3eg0tuYQzGOa7ZfWAeiamnJjGKQ/E5ophy2WqudOUlYalxAK3SYYZ1O2MghgaHJSlFj3OsRNPrEhVH8vu2SwHyyGaK2yxRHGDoS54LEnIwbmxJvdCVk2u7Pfd92ZJtvYA066yBmdri7hPMb74jNd77tOf8axrgEToF/A9//L6D5CeHOL1n/1MHah//+DLX6mD134vsI3+0b/lfX/nR37E6zzz+vdVvuVb/tJbvfv7/Om//K1rP+Hda6/jtZCwXvTSl37FN6Oq/aTnvO57vfX/QdLtp/+k5372B+pkvPKpl3zUl6xXvGZvmeba/+B//Wcf/Rmf9V//d+/6e778y17ykpdU1TOe/vR3fru3+9N/4Et/1ws+7A1/+k+HP+nQ0vETsv/wU4//zLd829v/lue//2/9sL/3d//WXqu19gve5E2+4vM/4+u+5PN+2S/4z416+Vzb7uCs8FyilEhsHsYGHuVeSg+VLtamqR8sZlCdvqWoHB5KNojY3Ai5a95NxamUd9ilvPQyD0c1ZQCU1bQ1JEC8VT5eGARqa+ayUR29R8/FCcybq+8oGWvphslWD3VntZA8PU+UCF5kqYaUVdReUSu9fJi6IbSKaSw9m4HK0MrcVfJ8JUsaCQIOJ5pDD+hZxUxlD01lqWeY1CwgBneruWtOntVXPOS+YasCQmI2fGt10nw3vzsXZAUktOgJy0XbkDhn8bpSyNIQ5r1tVTxdgpss0FrBd8iqNKB79doq4A0pkU4ljIxnTHnfKO5kdgfMlnrP3FBDKSSwpMNYMIc/KRu1L336QlMVkqtGc/mJBhHQkU3uQecaR+9uo1GWZl7GUoIcIegym3wpD/bNYWP0zubDuwJlboCGlnrZXBqtUHSzVof7uC5Qcx3l1BRqMAGYJYfOVcrqKNmYJpCx5bBVqIewX1YKqcDentVoIGCC9To+gCqcJv8ISiwVC/mt+KGEJyQie86e2XUa6DVGdt3e5o4I+Uc5koL4IZuwoJdcljGSYyJwikcbdZqrGextnNmzbhBP2GVLR5ruNfuqjqXxgskKh7RhjyrbziiDWXnL4dtqhTSvUo+vXVmoKmM5UyrAndghbkZFmtJKgYS69wCoK7tfnC4a5Hay6NktZIBz2i6GAKeusnaGSJvz0i67FvV972RpJp+gbRXG6dwFgSULBCccWZG59tY4jd1ZpXzUyDywfW3qkcAqBlyIp+J8xt4MRU0umOMAH6VrreUwol0ftlMTsc7MBK4oCYswtaSuIvNUYc3dzt0SBmGHFJ52dtdiFcdwP5opatm5vYSI6HMN6xGNYq1RKSKDCkWjyOQjq2WgZLnEmQrLNA3eJdU1oBWH1iEb9OJhCkwgd1ql6LeiFJIypvzLq7Uq8mWmCrW8LQxZbnAWMwScpuQFV7LM2Z2jX5+0bjQxO7dHUpk/aduazlFZJbun4V7Y4QBIpXZoPgzRstytjebaiVYKiqM6tvBoDj1SgJZ9mzO4dTodBDy2CGID0Sv7hlSXpGn8sAREc4aBRIEKVVZD0kWlyGsVY7LXujuBKcmPsYSDFJwIa3O02VgmQGCL2KY9F3bEFJGoacOosNOoZLcWwtWDtos7KjOrQmVjU1lqQS9rcDPbw+8Ny1BZVYA+JbqubyxvxWYCaC2esiMLiknPemThzPIKt2amgoCJWjBAapaIe0bc104kFCZkXEmZ28Q9sjRrt7ad1anoUx3BNxHYipRDW5UMTxq69k6TJbJxFU4ApRuzfhPTFagUCKusLHTem/dOPSRuRIvyCFO5tJ2VqLLtlqP13jqsZ1oElGX61ea+zXXdtcHRHk0f06FduRKzsNMs2pHNhWDeAlJ3mfFEZxDhRyjEDbAYZQwLZfqKkMOFpLiSyTRjB7V54NhUPTgLeQAAEABJREFUiOQJ2BQ7JzVihq+Sr2XXsrqVWm+5IpeJWdwb0C4T4GqDKQssmyDbp1Spia5ISTGHnrzLxGRd8neL3VNAVBg3hWuEVqktVvNl7YKtpDWWU3hvahZAlWI5U0AmV/nda2npUCu35W4lmICOMhXIVanJmMZT1AQBkbRsSwjM0A/2bs5SQLSWvibRwsY9R+UQ80z8215GdlJTw3fKUTkT+takRHGbdCXFvm5WMsHlPh3bsHftqGsDRK5ee6CEnM4oL/QljyQKi5y91girGnX2a67X5YJuBRyyD7K3GmhoMIdmnYbTmuIh0QhAYhrkuZxyoylVB0jsVKCFA6R2SIJ1dC8qFFWPy6YQA51Q2ULL5BXMtlu3rJ7yIsiS7RAAbmYwhJQpOtEOXj3sUNXJZjIAVSEr9LOEZq/sroKcluEVrSIrzyyliCDwW3pbrAlhLHdW4Z56PnRuv4v6YIj42LP6zLZDDKvlwOjepWvnXnMtZfGznvX0Zz39aVL+v73oJcfrXLclL3nZKyJTZ0C8/rOuM3Otl77sX79muc6/1j9mfJdf95Zf9umf/CY/9+eSfOELv//DPv4TP+rTPvN7X/Ta/q3919K0l37tX57fe9nw8Jve7Hju9Tbp3xNI/OS3/VWv82Y/Xycf/4N/8cIv/Dqkckjf/m/2x/fzL/2Nv/UBH/s7f+N7vd8f+4ZvnK+67vqe9/qv/y5v/3Zf83t/9yd92Af+lJ/8PFFKgP/fCPqxu6wo/+Fv+uZ3//AXvOCTP+V//ef/TILd/b9601/8JZ/5Sd/w5Z/7q375L67dfD9pOtf/qIfiu28kKhzBshBDlNRaMyihdH7Dt7FqK2Ngtv04Uzs3MwxJHWC3nb1sWLlyItdScjEKab6NW8VqBHQP7llFnVVOjCiVsQzll5JMOYRiBRjLPNQJVOFSJ/yJ/aVSFRvIw3DoE5ucpPKJuzzRnaNzuFa7OgZdNTCuDL6vNtknNAYob6QeVt2efGr0IZ1ywXA9N7/sQqbmKjvDW7KVxMA2e2qASsoZLYuoSZyH3w9VJxkGK4G1u9I1ijuFlVca01Aph8sIN+Pg6nk/+FgHvJzljJnYBj/06EQZbKQMbaotp+95deY4TBbvxlJ18q6B7FIiO7FRClb5hiWMVocEta1zeanNQgTiugjZTGCMeNTi5iCsyqsRN3jbThVZfaowWhJhlHiNILMgT3oL2RCD6fDB0cZBu4X6gtYJSw+My7bccxLN60YMT+nqD+WHAuOqvUaafunzUt129hAJwzvaoAY1k4Cu+aADBPXVnCKggqBnjrrOQpNDKMCe7JDa5fIXdLobHE8QuX53AwsVnpO8J6a3ujW7lbmoZLn1mcWUD+ihJa91M1FR5TTvAS0psMYR3mDujmGmiv6AGDytzkDNsijJ0XPTETSABMyKjT3LxReLtIq1ed9+nqyEeyMHeevpes54bD0bV+tfeS11FgikHK6G9NV4P7K0muGiOGS23U2bvBppFp3o5c6kz6rg1mSP6hkGqsG1KrV/iFviZRzYXvFqn6NSB8jqBcOrN53KuUorUpN6ueHXgmGvXbFg0UyfWz5sK0HMXXYWllEgd/JmF3UbmxDdy85QUDqzUcIlWapkdWXG3osLqRdAyiP01PXcqhRHVW9FwVUFhWYVMlFbPLRdDAnrcE9WRGpHlRlcDnhuX2mLOpXg0uNJ0zeCNH+AdRRKoWnMCnlwnj53D6bmQMw/7OFGdxTyrIgRbYT3jV7pMMELnumLFzdO4pR1sdz29eZBjDjqGmmyd23Zm4Q68ZmKo1dytb2PEypAFUhrihsIVLbSTdS8oZzccIGrsyqdVV5CoGDVdrVz1T1CYbkC5k5vrbm1SMXj4KlsaArD6SE0U055MzOW+HdkHLkUcqpK+E7PdRWDFAG9UzYHI6oEPlNBIEWvhICusDRCdgEVEDXnDtkQuRNFXWqOTg5rx+rtPpRZpfiwd1hnyUno1YWrdKlstlRYj6RNx93XycgoXN5cn3ZplMO+sRfud60RtQLK2ZjN2eADBSOUfO1o5ULVkpCzlGnnzvvMtXabxYW1TQtkzFKEUlshgqhGNl64+EFhXbFQiWsrUgGVYZqYoKWPCg89otc8c2utAy5JuiW6kgsWZkWdHF6DMqcbegMdC3JBKCOzjLpIZ6/SY3g/i7Mp8xWBqqi8KO0CMB6wH2oytAC7z3kKXjfZ+UA87DCtBfWitBogh8Ws2sGJNk25yLKSSJGOa9OWIh3D0kBoAl0HvHSZmIV++O3mo9XReXjdajO30Asw2Y29m+l5TtneWdCDPYSI73Dd5lzFp0SrFibJCRQrjNoapC4h7QYcnketsZcFZEZGqxSrY7clO9lK6NDDOjgYVLhsUgLKoFBvKubKKygSc1Q2VImILqW34rGtbSXIbDPaLgLwKWGFuk6LyOiuc0gEEJShCas24A7u4Hm95/NanmBplFynmR1mvRQjuryRQoSSgUSNwJj1sOq21TuQgWsWtufsMtfuw8OH+cg6lsBYaCF7XDmeoV6zp2jY0ckR0VI6R3IUj5BCqDs6U6WlID8BFVlFI4veqgsSUNaj0Vq2tq1H9Krh222lc/cDvVNC0KU6exl4zZhyu9goGpyDs9U2Y7Pm5oY0CLcCLapVmeY44SSSjEBqdhjdFR3hKSaU4IJueLyl97Se0lNBbmcxg/JbkWa1JmEKqUOetAoB1LGVtMaWUPdqhBG6rIrsi5algfpBgdAeUpbI83Wfdnvu61//9df9FT/4jKc9DcCLf+hlEakDDX3Oc54Dyon84Vdea+vr5I/RT4K/6E3e+Ms/89Pf/tf/Bolca379n/4zb//+H/iH/sQ3KS115id+n6/4kRf98b/yar3P/S3/H1BAvfrbv/Np/Klf8Hx/+vUXe176Jd/0I9/xz/+da/8nh9//kh/8yq//xnf4LR/8Pr/tBX/pW79FbmrQT/lp/+nz3+e9vvr3fuHvfP4H/cyf/tMUkf8zTRr4Y79X1T/+F9/9GV/8pe/0Ib/107/gd/+Tf/pPpeO43f7rN3vzr/jsT/+Kz/u0X/vmb4Z+07I9Sv6zdbqWgN4IU44TVIlFR6kbLLtoTKNoel0vOZLZUpRlgLrbTe48pjIG2ioTO22jlXLMqnSRJbIzXRWllqZchL04axJvaZljgrNqK4OkqJeSf3QY3SBJ0IJwM6xUBfsImgU8S2uH6iwqcVIFfMu4Vt6gvIQXMqmzZDXN2L00VtWfMBEeQMIBqzJZpL4DOOVwADPlQjbVW2DQTOJMzbL33rRyMb+DsmdqIFPtLQzJV4vU5FB1FWCLWxrnsGVtoUuXwb2M4SjltA9JvQDz8Epdlz2BOGsmQHnCvkdbhwsRIWwhF6g6nALE3er61aDqcKvWNz12QUUGLSBfBI3cCTedUtPZq9S6ZQMhJ7cLFmyBFmYl49PO4Im6ymorEPpoMMpOWkljOZlq4dKheLVQwCY1fCe2IQS4LGqWrZWNTC2YbFUWop6IKlNIoVGWCGAVY7MF5QN7RQut5xTLVrdR48Y+5BjKMozpnX7ZgspEbcijnkN91sqQCtQuX8511OqxRZXSAyx2xTV8xRPYolwTcdEUKWGuRz+3sB7u0TmbinNGBSo0Xk2siAG6nBZGWtJJicJ7MA9KsFd5AKVgYqzyc6m7nHXc83bWyN0J7XbBaOZwsdsUc3pqBhX2PWo3JNGSzWCAscngXkLJzUlnNaaXVSj8nD3VtxONOCpGKrWRGmzQiqRBmyM5kN2rWynnoAAmfPa2bx1DXUoytcxcpYEANV182cm11R33WbUp6EbmAW0aVdyE4My2rK/Rsgm/2mlwvwRKT8NGZp5R9+J0no65mbqpCpNVDX5Z3BqbHFlWG5Yw6EtesdwCIapwnZhNnjIdOjcpuYCuBFILyXvw1B18bEXibLWOTOFUbJkWKQsgTtk2m26rtPyjEugS20LaInuV0SGiNXfCE0qxOlets3LLK6c1oe7t+m3WlFBIq9JIM4dZkSnMWBZlBSOOFMhmOyJL9hbIYiV22Ea7Lnrbjujr2o81RU0WZEgoWSURpYXWSPSCAsZ8MhkhKkVtOSq/MwuxTeVArhMSt7yW8IWsRjI7cBS8yrIywnPantyLc1eU05tpIdOPql6b0uHBkdaLAjcyAyhcP567YxOEOYe3zm6pBUkPrWXIogy2nhTbj7iOo2wG1rqM8UjPQhk4ttiSSLnkQLdqRymmKoZShV04gZOmpaUyRr6hzsAUcBU3pMsummqJjgRl7eKSXZO3VcMQw89h26uoWG+X9x5osx3otBFKZzMkdRKBEIxSetExI3Ziy+cs4VDbE5QOjBItfGsNiAsFpLWZe+XM2IXrzw1okcbql3+pG007C1MqRjmaSrtKRl3KJJ10s0ZYu+Yqf4fib2GWZeKprCA2rdizmaarfVzpuGXMNJ6CtIjLNCB0FOZrcBuKgncFZkiSXwKgTN4xPcPABodTTODQoSh1xOUW2UEggxVQsGdJkKmqWavWyphuhAzhhlI4Z2QF4QM8rEaV7bymqncJfDiCsWMxchBH86vc6NNBSnKEMAO2dVPOl108lc9QgGRNKbVowremY6uWHN2O4e067UlXHEwbyVi2t3Ro4mVft+EmSCs5Vc2o+kHKrPTctqsqE36abc10iTFoYs3Yu/61v61US3uZoQMNQkRzihc1oLJVC6TKwq2b6lTZURyNrUQ38b17NzsSLSTlwsocl5xRsLTUvYqIwR5OWFnsVtUZB6Nhha+715RP5W3TWCV2WVaWOi02pORItlW5ucsT3FlrlkyzQVolQuVgldGvhNwGVcJ5OmMczY4bm6NLM1HQj7fVB1wRfcI/UBKXqoxublIC6tDQYdHHVujQu6JPnTJc2g6PZjuRE7Wcl1w3pKtRZXp05K3qBhFLsYrqkbcs3/ouZuhC3gqo2kHs3sEyTIHQmMOpR5WNbnXc5u3R9QT2mpaQ0dtyI+Q3Q9kI65DB2XL/0l/wnwNYc770B1+sg+974QsjBIwO8ZzXfd0hFcC//Rv219kfu59nP/Ppn/ThH/TFn/Hpb/DT/lNJ/f4XvvDjP/Oz3vn5H/kt3/53MuWlzv3E7S/7m/8ovvP6GyPPeIv/4pk/4w3+rxQ/esOf+uwPeVtdzafu3/vbvjCVFvry7+9V9bf+4T/+iE/9XW/1Lu/xiZ/12d/xnf8ot0oQ3uhnvdGn/faP/oYv+twPfY93/SnPe71/f9KP77en7vdv/ta/+vxP/ox3+7AP/6o//nXf9/3fJyOP43j73/Abvu5Lv/ArP/13/sr/4pcOuy0oc/rOIXYpL5pdpY16iFAjK72iM4+W4jHYuR9Q6uADrZtSL1pFi7CMUkexYEsVfLfTa/VKJiq2c3e/ctwR7hueKrpAErruyE4zb8rWSYu4MlW/ExNKgmlWUvaQeuwtNTtyAdEtDonSXuTYuO2tfChsIqyUH72OLmuBnXmluSyr5N4AABAASURBVPJg2Vq+0kq6fBy8NbVFZZKCsKNWlR7GaVdFEBAlU9tePFUmWOhojuEgDdaskZdD6o5jepcfidyO+w37oaLNLVVXeamzIhhlRDvSH7KGfHbQUkWPu4jS/N5zeKqSSkuRcIABU8so2d2wB6bJQq/TW+F6fTJYDeGqhaqKZO5ruaZa2UfSalstC+gw1CwUQ+NVVCwqq3TGXeW4S2jesdY8ec9jtbEpkxRGnsUpFOWlmanyW49Q6JuA3abnlHkFFlm55V+pCe6Izb0JwOiG0dAvBDPrZG5ZQvUrDZTBUVyVa89YAnqDBfquFukg2KDVBm5bYZU/8NIy6DHjpCR0RAGq+gu2JVXzy0wgcmc/0e7yu0sFShp3+ISlpmhV0e4QIHJ/l9x2ESgbW6fiCYZkeu5jp0ho1PbENYFfTvXra3EGhbiQUi/h7fLRNi7bFByJCval2xVDeQnv9J7o2xleUZZlIE0fPbValNSb8WjtgLKNEEoahayW26uat950tfOg7sezgmLuDF/eZtdDurbN030nzhmxqqLkgjKgHAa26BUeVRbZxJEaCz1bx7hBcSelEfrQ9eLM8oge6YtTKbIbFD36UCghdPqgiGl5cqVacaPyanCZSyZMVj4cPjQnEMqualM2EpK6Q1htICXJ0GDWO7vOXedLfsXI3e/xqGq4jCZlSKZF+Daox28TcZYEXCc3PXLERq4zS+2/BK4Sr4oo8+AFNknTaaEz2xBh6yHquMO0KJYT1llmSRnbL32lcIrAWdoA0AqNZlSGpYhQ3We7/traZtbYMABUgZEHCVGGSaZbqQzqWhGJQqWtkoV7KabWhUxvVLFyxIA1ptmGF2WAi+viM32WqCs4UWUeJpxjC7crrJlR0Def+0jdfijQXCQ64+DunMbEpV2248opkR0h47AdS/gxwlJ6j4FhKS5e8kpRaO6if1GcunhUUrgDGVq/HA81Hsh+iDpoa0C4WwtIuWZLCEYJxD5wOFyIxFRUgNKt43lE9i0nR+k5uFxgVtNukyLhAS3gZC5k9Kq2d9/p0TK0plbdYrM0wsnilma7oVvrs9+2SZViJ8m1HLPbdAmROffe7g/jPHrKH9cDDrsd2ltzNGRDObeclDQ3mV6+RfmodSKeyr1yMQVVNXHjuGIAajPPYM0VWq+lGOjMdsuu964U8+P02C2a1VWkCr2lor7WOiNC6Ge02n55xT1Iy24xKDBjV6yNE76H8agO+HlgHQK3S4adq8dSAzmstX7QWkez6Hlarpb0bVhYcyPK0qq6sIiW6NsZSKKaguqjmdxpohzrgLEYVbq+mZIwEXIOBCsZWRVKmiGTqjixVs29rLZaHEyhyFan4K6eJSSzFMa+ghkK7ulNXaO3m19lJWtnPU67V981Ms1VC5bpRtwrmrJuFDvNoTxJqHSChFlZg4wxsJFiZYeNnaxdbbOgWnfUsBrlFpVxZezGRaviwEUVSKDCOqZzHpZDwrLUX1Qb5w4UaZeKTvNojK7yhj6i+fZj9WMSskZCNI6k0tsSXhoHD7nxQLsNbzdKkEZ5K0lqYc2sNXNKQ6V4cbS74q4bo9o2qy0bIWltmu8DOcCCzyafXOBpfaDveVkuCFAuEfI4S+KsCltzYQ+EbtxZZ3BPVggjpgRlIZJerfvNTeAQWALnLFeRRrUh10Jk0gwUOuTMcs0PPQpsKaCwm21vxOWCJAYxSzKMMQwqDmVjod/JpbBOYHvH6CZ/CxCROUtA345wXyK9ChF1mQYhf5/3+8oNs4Cf2NtPNhE3VK8qWtGCtpQYPMhGwqpUBcepiEMmoXu5W+n8L/0Fb9xaq6qIAPDKV7zsR171lA60v+4zn9l708GLX/pyff547M3br/7lb/pNX/R57/YO/93Dw0Nmfvvf/lvv+fwPe++P/O3f8Y//1/3EpB8Pvf8HmWp3L//ib9RJuv3kj35nHfxf7saf8iFv9/BLfo4G3P/Bd73kS65ZOtY+1/ru7/v+L/yjX/9mb//Ob/8+7/fHv+mbXvqyl8mjR48evcWv/tVf+Tmf+Y2//wvf/q3f4pnPeIYCrPE/frvk3+f8oZe/4n/++9/5+776jz//kz7tV77r+73gkz9Fb3Ve9apX0eyZz3nu+7/Lu/6Pf+wPf/7HPP8X/eyf+aDVUBxdlFD1E021FgrrJWqWLzDsakx5z3h8zLuqHunZ22pXA8xSkaQ9Np4pGoFUZuViSwl8ALTs7r5YmuLdaBDn6CQlR1XMtYpe7Yyxd68J3+jVB5V0Ba2IEPIlZcBcqboUYRN2Ju9TT+jH9F5lFrCtPAcutg/ySjVic7PdOZSW7BMyb81zTu7zONNmMmZRZi/PknkgZBtseeh58JxR5+RZmVqYtHT6gJnqWF/ZIpXzOsZiPd48T4f8tKMpgeX21kNKOEolTIzumNK/ej416j48JEvIpTzlUi7nbCosc82dT0FneuGGuC3J8Obmjuvvlsk4jrI2HaftTZkPOK/C02oRM+tctuZYulYrLRzGcpYVtm6ySmgQNDBS1ZEW5lD3s4YLQJ08gZUoSXVD11VzgV9QE4OqSaFxd7+rcjQzF8g8F3LLiMoep3ZNJqv17JYjkakqNlOTF31LDkoXmqG2/C5dTRWunZ4+cmCZrcLj4p0IL1XD7oD1qxNjB4SSTSIpqLbiKD7WAiAPt+NETHWuqF25LUsXaEmvzmxZa8YWRtJlx64hc21aJ7wZbbCN4gj0hFcBaZjGoimCpi86VydrUbbbjRgCTSAkcT9iq6pC8Kfu9GxCS5jObs7Zp4yCRXnCzo21xUkxlVZ8YHToXKwsVgxWl0AoJxxV5LZ9trVdvU1BhGxR+8KuitoddWukiN5pbJYYU4BFWJ47phCfZdMbjk7RhwyrDSXRJhYuA04lhmEdC0fiQB47LxEE2RQ/jO7uWhI+zNZS3QrQFTS60XUU4ISBw6sfmZx3+a6TiZHWNNtGt25UYkIUIqrdU+2HyzIeZj3sbCT8kCOaMGWe77Q05gF7KDGd3lI8leC1yWJbxYj9pMV54+FPIHYTx9pe/dwmAZXSluwBC/ekFQDKbBnaTEm6HHk8Xsd95tq7TqfOlBGWKmIXO4+h/F2jLxNOJbhbRJP0WKHEKYp+NsklyVncyRMeC6XyVJU2BEjr1cUlnY71OLRkXY/bjr6thcamV3lRmxorZbH0WCmOssJlSO3ca+31VOAOnLbC7tV0s4XRNIQFT4VcGSMhCSZgzaz5sDYuUCwRosBiTz/EfhwVngFGMdIqxVVUFmTJPM7ZtoJRx+EPD4pZ1T5jnjlXK9gV8CLm9nmOFW0NzoHTEZq+K6brZNDghMJ2EM25b9gjid3iPCoPa52di3bOsWebJ8Vh8/QOiPcjys5EbDZ4cx/DDm0crZj7PHEu2xhs/Rh93Jo/sjocRwP1B7g+dCwL9D3N4dXr9FU8ZYvIexyk9PRIr6Cv7nGzugEtjeGKBAwqCdtzFXKT05mGJ1tuAdcgiMKyHAK0bvRmhAPyi8P2wXNcuVnbaneICCKISxAlMfcqWSNGcWuKGWhe5Ts8zRr2IUDv+0oAo/feHvXsfWGsU+vIQyF8yPY0ji7XuO91nrlCBbJJ2mp7+rmbopCphnz0s5ngin1WFWQiYU1UOMkr7m2LgRHJKT+221mYWygXSGWLzNLFLSDkqVk1KATcs2ZOue8U6/qthrOtPe7niDVQLskRouyu3ADlZCMLCspRWt4vUQHSoEvWrDUAmVWzKjM8NvbU7NxpupfYe5cGw61cyQYoQb1Bz3KGmQATAPfJu9Kp+77t82IUig7vazw6ect7E5eaybuLcsRCJLUzVdvEses30MFeMr6z5Cg72Qg36kKNKzpQyWQmlS9bxiI2Ayj5BW1UabN78irfapozbYXPlOx7Dd2gybn1xA9NMZw97rd5P1LOcI8WfjD74aWnkmWM9Mpu2XHveHzU7LH7veQCWvNHx/G0Zg+Wu8d8pLrbWh2YLre8L5XK6soUoSrsH1edAnFu1Ibtwd0LuevcO7bWKiXToUWairbnysI9cJ+51Rl3sMKQUGgWNGdDBUD5aBcwCNSqWqFtVxXpwk1V4PE+73UqxKm8g2dkbFGlCEJHW/JUo4hMWFAZd0v6lHyhZXfBNg0SgwyGuJKOun6syGWXSiQd5uz+S3/hf/Z6r/ucy4N/8/Mvvu+Frz587rOfOXrX8b/6vu/V54/TLp+e/oynf8KHvP8f/JzP/GVv+qat9cz8y9/2bb/hPd/ngz/uk/7Wd/zDudaPk+p/K/ZHvv07X/Ut/4u+vs5/+0sffsHP0sF/YPenP3reZ32gP+NpFfniz/+6V/79f/5Pvvt7/sDXf+M7fehv+2/e6d0/6/M/93u+51/KBXd/o5/5hu//ru/6jV/+pV/0iR/7y9/0F7tS7j8g98fikkj0D//5d33mF3/pu/zWj3rzd3in9/qwD/+CL/2yv/it3/rDP/RiXTput5//xm/8yR/1EX/xD/++j/mQ3/K85zyXIuemqKKkg+VRNbb2cJGEJbZho8/y1KGqT8plnGIjdnKCJ2bktXpAiNjLLPthaAMUbZhKn+aA9TSnRTcV1yCKNLr4p1RZgQlfsBmqWBVaLkute8EKBYCkJ9qmBodIX6lsyERV89K4hsQmtlVSgindYnshyE3fKvgw1e6KWMqcWslEGUopXNAQbGVRlKuWBwBp2ZdAhrPcjNlYV652+tEVQdUryZJG9cEzubf0xJHzVjnKrppBFb0CBIHJeelKo4xsib7kiOQWuDStQgVhbltCEHp+lVkLBbmqu6WoKRs6jmaH0lRwNVrYmDiWalqZV7aIvaH3yedp80Se5Vf0ZL/PGAvtwk5ipk4zDWTIkmtQL8mQsmQVIijXBR6Mva6SGTF29IhKmajCk56rWgpkCKJmUHitvBQCczqbvrBMYry6XzEgsEuTJOJUW1NpPFKdQZfqwijqgl6Sy7I3P5rWdm6q7xnQVMW1c7XaWm3EblMeVZ6anAd3uVgSq+TLRgUSVMQbt/QTpWPzZN8hoB52f/IslqHFIrfnHHVKdDZEc9hAWcnSS2qWpjX6jVLEW1E1U8RM0uQ02tw+C6k7te40a8mHyTE1jGGIARFgHbwfx9Z66li3Ph/qsQveXLvUDEoUhcRVJqSehFCIrBAfQkpIHRQEZrMg1T9iRj3OXMoudeB1yqkGE0AAzSA7t/PsHjbozUwCbCdC9roqvRV7gorzrkux8I72VBznVpbrEUGuznPUfazVgiwNbpWKT7fD5XYK0sSTTZJHXgEECHJfopxlSFG6cF9zz4Uk0K4fX01PFfG4VTQmNYEiQ4oNtsLumMk5PIdNMgI8iTTpQC3fOZa3lAGpmVNrFlHTFY69U4Q0ZlnfWoXrTtOVxhGeulqbGR6XYb1k+ERbJZtvrCtVUtHhEVcxqRVxRq1CyetZObE1s8RrF1tukHYIRIb0gyhiYS+cJdbtCi3B6QvMFDhxQcAGOJCMAAAQAElEQVSddqIE3fB4EE4lNAtyxXWrmY6gUl21Z4Oz9fCRKg/oSSSemEeYViBaRUCyaofufqKgitGPffjsNt3TXXHuXjfQsmR+ggVmqdD6E94gWRS6GZI8fB3j8fXZYzQ2U+otYWFi9UJ56Hl3+Cm3JArkoCl4VRmoCuOS3bWVxpEZ8qXLl9MgbBV3AAXk7EtSdEhJI554ZTmsBqBygJDZjapc4KLdq5+pmHijRFRBZF3ZIj2Fndxwu0oLYXBTPFdoGZV1KXUJ52WQ5e4SK24bC75ynGb7oaSxHHvg7BmYXvMG3UBM1OUNZbYFBFQjHOZlBIpkNbcuhSFP7MSVag7KfF0WhbJtGJV1fkFaBoBhCjWq0448ulRmy6isqoTA7aVhkm4hbmfmkp3SRvA6SFBD27RjYyhKlSvrjl3H9gfiaNVVKq1n3pSWawIG88vQAhJSsc3UnVybcDVMy3TJdqCHzmDvXDU3tqSToQQV/ZbVZbZBVrHMqIIh0atySugFhENIbZ5h24ujtcNa80rkXLUDloKYfhAHpB9JiXKnOVfaXfqWAKCRjYqicXfTmIr0Gce9lFe9GRtSfaupJPqao06HuCgDkyFVGRBee9vaVA7UIhgY4aOMmVfD27kTAU93mbGlKg+mKy47TW5zraPOAxIeDeKMy0R3sOdyIXNFpZcTnTQsKnThSuq92y7LIiAkrt1MCVm1M85lIWFLAUUCBtmcujlTJamqqBICKWnXTU4w5UsVMiSAEspZiDSdGHsdiotTNxuF1s7W0hyAUOkVTTuqp7dtWhdho7yZCKq54M5WPpqzcxJrG08xaLGvcBkA4VJ54jLQocDLM1mdtfUIs8T/2keByMpUmdisENpr6oGarbMUZdurNDxsql3KATlKwHJh3X3PVqspGB53r6nitm1nQVVNYGyQF2S1ESmxUiAzj+G3o8niWqZ4NB44Okc3+StjUingFWRpsrBfYakjAhBCriCzypymSPnrPHrGW/03b6FL/3b/7n/1fa8+fubTn3bcHun4ZS996eO7ENDhj9cuU3/Bm7zxl376J3/2x3/sz3+T/8xV3zL/7F/6S+/8wc9/r4/82L/4V//GU/f7j5du4EWf/cdeLfzp7/YWNvqrj/8Dn6/7S37u8U6/WgP2D778r7/Lx//md3//T/xdn/k3/87fuT9+Sief8ezX+29/za/5wk/9pD/6hZ/3gvd/z//kDZ6XoM7/eO/nXJ/35X/w3T70w//gH/+6f/SP/1Hs/WqNrfc3/tk/+53e/jf/kc/7nD/5pV/0Hu/wG571+s9afe+Ds99OP07iPtbqAbbB4daS4uVcHWoeDbj6KntvnTqjTrF2qL3NyK1c2qfdwxa9VFDQIQHhNmvfEY8H06nNUCo0W9Smsko81ei+YVNnrdAq7VqvR6WKwewqOYbd6+oiYrc33sIetMTUc4Xd4o7x2GyK4ahK39XUrM5eojmL/Ur3MDxRK6q3HVTKl2KgtDCuZulW3s0JKwVHOa+pCSC99qg4YN2Ht+6SEmWblTSDt22+yFDSOlbnPixvKIuMvaeVWuPqVhoKZWBuTanylOpWcoSuDITgrc1VqbcjKpRpshQaIiuhJI+sWXphIFh9Fktm6SIgRC6dju6q2+4bVFGsyzx1/zwyHSC9HWl+tsix86i8FQYk2hTmiZRJaRYSm7nXOs9cUUVkhx+gIFjYZ6lozG3zMrMrGpQdu+2iilJasl1/Gsw6vXXYQeuAM54YWTZRZ5pcH3BxQMHoyT6DOyjk2UwCs2UIvNumbRGLgsyoLpMDpwoj7/MJX67msagJ9LRb6NbDu1pXa1W3M25n3VK90109acAGuknusdfI7QbzZhzWdBQqoiKmyx/IYwagUxEZciwIGFHDcDgHy54s+HS6ukT4VVRXqueIJtnJAYlUUO3JDRVNQW4rPTWPcFL6qpoy5JyI2USZltZ22LznTgnRwDINBexSVklF9LpBVD/wZU21fTNW5J7YkdlCbsBvk0dgQI8Zq1mi0gJHcsjtUa3BzUGXY4mz1O6sYECvGpt1Ok85Rm5FrW3mwsq6KGaVgJiQAJHEdi6rQBquTUPOcDlYWvjUsYtiKsOMbpfvtqKWona2NWvtuWuxhwJrUdhK/xYQeZpuHjZETM66L7/vPDGJapRWuQkZ5JmDV+iKregtXOwySQESYRGSE9OyoDms5mcbS1BcEZjw01xXNLRm2JyIWUHElcomX8wmlRuoBZ/UmFDh2Sn4S64Liie/dJjmbGgO2bYdy0JzfO5jus2OMjTQi4K8Nx6ggnHRHlCrDDR653FQFVUmlkx1pHPbkTWqDFlYbqslfNOi3PZVHGiU1gEbacNaJwhE7HPLSEXSexEFGQ+LwRxPFCOyxOLalVV1VaAKfYG+jGJXrrS+vJ3sE3qEMVIMapLEzNrnWiuXkIXTmml12n0NTONaw07YQooqkL8GssoYQ/xsGx6MymSkUXHcBVY0JJ0YMq3FsoxrRkL872XNXDkKRsz5+LyfuZPs2SVPJbF2lBJyzZw7Qo4WhHRlsa7kW5W5oSJSpdVznWWruMtVJsOxGqYJGxNZHJqYu+Kcceq197zKuvvlyZihktPkBb1KRWiNgFvtZrt5iIthTdPlaI/opbDM3WN3yqAMYAOSEywYYQKDvCse2+J00coQwxaxRH8sjhUPdz3LQi/nk2BxuodBm/DaqFOAVcdpvGftu20V77CqQt1Zpy0tpIU7i2SZ4ume3ne/5WHVC45yKeJM7ECBOpGVZ9bdfOr9TOvCKDd9yzlJgaRBxojZVoEUc3AHZBMBye8cnQeHi6xU4qZjKt+oe9JsM30pWsN34zI/eYQfxjqwDzEhL8eoaN6SjcUxqTzDFsU9LXefeUy1TaVflsAzqdKNrRnYmnYPHGfI56xKq6T80ScCHdGaUuaOTCQQRMkJOetik+qOxQwS1rnJE3bayD5a159sVqhta/kZuRGQp4hK5Q3FgVhdi/FULdxb4hvR2+6+3cSPtA253Vai1up7dYG3G2a1Oh3iAkR7G8auMnuUgr3boi335VZ2AVuSYYpCCX6D6AymGNospIuyTfaNEN4YUNIbFZVaW1V1luwEvah8ezIJst0kJ4BVKzBnbXmcDSpZlyk1AFBR3Yk4yMGLJjlt38e+VhyymbLhkEw2sG/FtGgWmkU5Jw2XdYHcVbFYq3GbQtShgiO3lwCxETQVIavLqU0JbQlHJONsK27RjhqtGnTicsJkt7ztXpedCl2jkqespBYGXBSTWimHNkImIl1jrCujor/3219/6UKXXr3/i+/6F3z1EfCf/Cc/XYc//NTjV/yb/x5MX3/89jHG2/yqX/GVn/Xff+bHfewb/5w3lqL7ef+f/vpf+4CP+pi3/aDf9ge/7k++6AdfqpM/tvvL/+G/+OH/+Tsls//8n/G6b/4LdfAf2F/+yh/+E3/uL37YJ/73H/C//Ll/dguNfO6/esUv/IG7Dsz9v/zFv+STP/IjvuGLPv8LPu6jfu1/9cue8TqP6l+jrus/7vvv/eqv/4o/9jX3+2Npet7zfvJbvvmv+C3v975f+tmf/mf/0Jd87e/+7M96/vv/sjf5eUc7QtyiuBXZwprRLUSulkukeUIXUSbFyxbbI00Zky053HlrGC06s0opftFKqVa1HHsgxkWqBSy79u21Vapr64zsaUzVjoAeLC9LMCEzlouy0o/OkBVQLvtGz/KCcEtrQXU3JQNhVPl1bN9rVKh7iN4qgEofpl1/PKhCIMtChnWiSk1sJC3Mcpi7HYRypKlN4N5rdU9Tpo5S65SWKyugkrKjbcmjWrZMU5ZwM2ZFVHBDBrusLzmHhEeMUMVazHvkTFVA5wVHYsdWxSuZjTZ2Vyf2MK0SbAte+V4mhQHCoZrINhwmZxBlGb7SNyoyYlWoWEVc36quyyEjXKN70XWcqmE0T1dh7fu6IOtDpQcnQV1p1gq+WJMyKYRLwa7g67NQqXiUG/ugirmsSnmKrNLeMkeqGagCqpzJ1MWlogNtTcVWl2UEq5NDLsqzlMATU/vSdOtgN9goqsjBSsqLKMi5JpciSuuMJTwVOZuJTEWnNRX/a+RWG7kGkBhQuask0OBNPyLJYVsLnN1y+t6+StY442jlQC2EFOQWY8DSLDPRlgkmXWwWcIDEJRmGa1NAwnTGCs4i5Eoo8umE12AKKWjpVzt1TYN4CW1lkNsFoaL2tFqeXb1wF2QFLRtyMJrBFIXhTQ3EtSgMLcRqwwM9ZUCrbVksNzSYLSDTPLtyzeTfkuVVhmp25UCVaHSoD6btYaEFeLWeraM0R1gZ5ZAXKCOAAiTVq7EQukNw5fK1K1vKrLRlBZAmG43XtgntzEuMXIDjyVYKnzBg8Un0SiiU2KZ7edc9kl6PwObGOW2t0mKrtpLXX22vyHdlBVrK5StBmJC0lVsrsmUMEU/ZSKuUbrdp3BAiViTgWW1CqCS5ZGaCioFSEhstxNob2khr4VUNbtlLe5iWOPJjISEKb1FIKwkt2o7b8h7W8xrkkNCLqSkwkjKg8kmU1f53kYQwb5ZupJkXR0W/kj5YmULOLGCFMuibujoiEetiboI0N4fdFvsexEj0CnmYnulieBaIouYUoiyKlg3WTUsr5zC5NZwKsJRBxeAeiJ3UEq9CYGYLMkZlz03dbZ0bm7uU7c3KWFWA9qROp9RAEU0q6XAd9eChBef0DFXlHbFDZsuNXgKxUpaBWJ6zc5VibryuVFZcQoyr+fa2G+OyfDrChUOWFsgSCcLks6mALQAdLvfl96hSnHbUnit2aqRiSvdhdcS0fWbeF87ARfBmuuB2eDkSkfqwKsM2JfiO3FFheGKqTsd1kOA0vxum1u08F6dAC4WJwmUI5R05ydNdmQUsu6bTS9iAdVkNowzWrl9+DZguLbpmgu0aJ1xLxlTuVZg1Zj3suLXAw9rHqqOUgbolUKy0vCtvlY6pSkA6cBkv2yoLJSQhBRbDBYLCf51ZkSstJTqkoXJmSiGTVAa2UcOMwUoootIUbsttcgTFeEAq3AT3hXKJkKlEyE4IwqpGNF6e1rYEqJCokPi5UpTdyN2M3eBMRUr2UYYINzHQWWVx+tYL3qkekKuXsIvILDe/qkCnNbOi2CK94umubiXF8lTGN6EX0OmZhsSQ4VDmt3BNdJpZa1SVdLQSx6ombKFt7xpfxLXVaPvwGFzYQcna1zKDnhJIeSqTB1URqZxMRPSE8qlZ7yVJE5iyeYs0192rgN2bCPMQPRRcUloEtNIFIgoX/U7doVV5ulBOFWIiW7CfzVf3cmeqSrcebGm2GczlO6zYE2Mr34XmkXQ5M0udtWSieJY9lrxPk0a/9C0qiTrj2Nm3sxSZFgrIXntr8aEqloUrCUMcLTZAYVTF22BuXkYbUr4frVTmSKu68Akzt+7NpYiausORXZLEJUXGO0gaGr2VX9FDgzu6zkZlnrv0uDHRdGLUHnaq1LM98Ip00SZAFAAAEABJREFUymRF7MCjh2y9ThNUlqYSW7cZfYObdSIfc50uGtdSXqfYwYCBbsMxupOJ2E8C2DMQARSMuk+2m7uZ0Ak5afT6mT/t9d7+N/3v9yrf9a++v3BtJN7g9a7/S+8rn3r8Q6/44evUj/+PVL/O057263/Nm3/NF3zmF37qp77Jz3sTM/W++Z3/4O99wmd99q955/f4yP/+M7/t7/y9fTn0Y2PNw09/3nM//j36G7ze8971LcfrPPr/FRqZL3zJD37Nn/nz7/1RH/df/ca3+22/85O+4Zu/+bte9oNf/br3fDL6Q37g0e98vw/91q/9o3/gsz/tHX/DW//UN/hJdFHpybWfkI+19tf/hW/5I3/8a2Xqw+328R/54f/DV37RF37SJ7zgvd/rLX7Vr/i5b/gznvXoURVKZK9tCNNBpsdqeGrY4wdRUwVAD5Z2Rt6ppCSQR6bl3sVl/SmOk7YlQ9zsN/K2MXK4+7UaPcofpT2s4pl68nfC8+K7EfOMOBcCPo9SYoUKfoEhWKg0veh4bBz3vEXeRioHuvcOparyQMUw+nQR/uJuZtvK3TRs77PhtFDNW/RsrglONC5A2iqz2+6u/jqzb7uSr3UzmihdhK10ZU3u6olhEtDOo9KhzbbNnY8L62oN0otVW4V87TULQYOpkPr9bPdX9ZqtYmdqiZBZ0TZaSL6+730urs1rc6TJyQfZMsGTcVo5eNjR6HUlpWSOxgfgBqpiYJAH2BFh14pJlVW26hmjn3PE3RUQCt+RR4vDWs9h9MG84RyxVBwqrOJm84bHD+NVx+3ePBXRPX2mnyihZ442emuH78HzYAxZAlU7ygEVWFgdaA/oD55tZGmJbSkrcKoyGqqouj0Xz+CKEgaVuwv/FFxb55cErsEgAg14wH7w89Fx94tV2MG4sx6z7rbPdt9q+GHcFq1CfltPeTnqfkAcLZoFam9IwbGrJViboQ5+BB4t2D3WY9zvxxapgbUzzuLj1qYhAc0NuEwmi80wRBjrAVN0rPkYikR1QNGOzCzRXNMnLAxlVViJJZqYL9522VIakJrdWJqkzicsZHfYw736ueq+61SEe9it2sEuBqj9ltT2xhujxYw4p546+W5YQhQbTBhgzOjrVDo82Lj1w1TOW4rLR+VQ631C00SdPDdP83sfOVrR6zTcDbI9t3xmSWsnFLcboXt0g6Aww9Hr0BOWFruqgTKwzQfOG9vTeNxqdPXJszL7thHW0l3SIDrUbW0F1IYCsNISDdpF1HHwNrrJELfS+TKv4XXzeLCtA6F4Nk26d9+sAymWZ2lxJXzE3IfbOvRet8v1Yej0iOMUdVb12rqfwEKtYhYAllFDuN1P8rE+mxx4ZJRIQ9tsujzCHrI97HZssbyU7uBEbjr9oLsdJkDDfLKB5g04HI8Gpd5V42rurQVzbG5UYXutm7VH/vDw6NYfKYCz+rQqGRPBEC3XzjP3pCydzOI553mf+7HxVQceez0RkrYARWgyECnmMMdZfofWLFoVlIUfywe87dbi6NYMdMSgYuYPVkesOE/ZloM8jKaIplizYU/2ZIVD6xDrTmtka5U3nlxzz8rdMkQ830t1tO/SpPDMI/W4pvl037aS96xzt3M+bYuDyzStCNjNWzeLRG0wBE0ul65Bzd8KK8hyL7N7MrYcvKKVlYCuAK3vY6zWZ7W1a+0dYl91g/dER0KZJL/ZohxlDeZwp7dbbzf35pY2zB9GO1rrsuj/y96fQN/2dVVh4Jxr7b3P/f0BQURBwTamyqhJlVIqGi0Lo0Y0MWqwQbFvUEGQxh5EUEAE6RsVUdEgaoEgRmPZG5sRozEWsUnUgFIaFKTn+797zl5NzfM+k3KM1BhVlUoZq8r7zrvv3nP2XmuuueZae59z+X+IMxlqAyZtyVI4YkEaxaF+0F27NabKMN1fhg3pjibzzWw768bB2qVpj1kPw+qB68g8KGXEDRxoWrvrjfPiQ9tmBTMbj2evZ/lzV+6rcanpqGH1YuufueJ4hI06rVKBTB7Ujw5huo1X1LPrAAzEJaJEAHigDyUuXtSziWkleYkQqPM9H3u/PMEt0Neely+4+X2Dc56rnpMFcsdjXwI/j5pTKHzl4Zx0o2dYqohiEL0g2RuWn6tfWV1oKomlQfW4wl+1X4Y60sZp17UltsATlunzHC+vMK5QxE/JSdRWNCtHxNLUcj8W32bViz29zo7XtzOdF6T1F/BtRr3Mq8PjcjSaTTHQbk94bPYpDBBC8YI2uw4/X6bkUnwhbU4Fo0stgWEYuXSoynawN3kpnRIA+milkN2Mlo7YF+1JbvlrIqyv9qhR8FLsWs+fFReZ7iVWD6wHJg/WjNPirN6lZtqIuoE9Ci9V2vQcpNDEa/zDYq7tM4f1CNRukbYThR4OWQtzXfHzga2+YiUuESqiCu9SmupRKbwnqEPTTGo6M/K6PCRIPuCkB3Eht3Y72Uq4DfIFkNqElvZ47Jc3Lj+Iop2YW5xT/SAxQGNP8g3Mh3RTtjlqzOHL+2Cs0G5CsUsYY9kxzWzdqNlihWMrMRJEDJ1rwHEJSgoADVypfcDVpkiN1g4N3gQnhhwyrTUyoZfQtyhMw7nRmTcHSsdgDtyEOEJ5SexdF++RIOmyy213vjpMORzqHBcsPuhn/cRv823+2f8O1d/98i/vFjLo7bt9p+8oX6++5Zu++h//s/9wRV//hRw8Xl5+5A/5AV/0mZ/8uz75k37oD/1hb/s2bytU3/T1X/v7v+RL3uf9ftF7/qT3/ZTf9Xv/my//+//v/9+kHW/3tu/2/u/9b/wXn/sOP+fH/POhveXVq7/5d//eZ/2e3/vev/CDfsCP/Ykf9ut+3Z/4M3/6m77lW0qCfTy+w7t95+/2U370N/2MH6op6l3v+Sf+1ju947ce7vr6L+zIqq/5uq//0j/953/yL/mQD//Yj3315lto/ms/9EN+wU/5sd/67d7WbIZaaHlzqWgKyPbe+ishevWoDRVFZjZyuq+WMIgkW2PsuCtJ0lMx4t7/ugqlVF7QqmKlB2DTVQQ92gx39QckTvh1rdoP3C5HmGehKykxWhQYLSlKtlK4mdppjqxOCELJp0pXlQQBNQ5ydINZKonXPdrvEaYSoLm5k9aCxzJkwhJCQvhr7LiDHjSNuVTbAlClGcekqxyB1h/U/W5MzRc8/dsAhnH4IPwuPOzMa0cFKphVpQbgMCrSbu2yxtNtLwtS7UazW+i8Jt1rqBexaO697sVDDdk8NTD7lLW6Q6MBg+gOYsuEtZbrQQXXgiGUxACt1Hk1kXJAKrAcLQYNYHNmNuoa1D4j2xp2JzlAuMa06VOJaxy0matrRd+ByutEe5WlWKaaLIRKPUSEymixSkveGGXCpvW+xWJekG2ikaKkXrdjDPUebTvirL4vCx6b6Yw3GIcuMjUekUDCAdJM6tIXwszc1Nh2V2TcY2b7w+SX3jkbh6n/z4QVoFj1xW5jEMbb7BWReQIX+6LpwBguRe6Vd7LU5YrD0E5lhKNa/O3uYoMy0daKrtDsmJ2mkFsSvXN0ymJHdwyrMbJlPFNEQKo3yqDIhQVaqPeslvqtXHqSWRepHmlnjTdFuIKuYl6UlFRu3BiXHfDJaRibq9STNa0VpQoxr9Ra511GaxvUS58OpkOn+ub/WevC0WG4a1fjKwDqSNizb9eZZO9kBiQShW+2R+8JSE1611bJ1fMLNyPCO1ZT+jBPodU5bUFSKe6UgqA0CSEkRkjj8c9q1bvnvrcdqIjGlpT44tRyp08YOZf4sNrMbn3i6zzcBRPm4KCN5ff/fo6CK1COuhqW2mmm1tA55dWaNGzHBrLE6uI4yCkuSqicYvCuF7KSpceYoZJFKLu6Sv0zGvO2UaUUigutu9VJEWAGV2qnSZel22I76PMmDUdxFK1ho2BnK4KrUhUmKH6YyjLce5oCMpiTw3EcMUeaFz2j5aFDMSzmlG1ZsUqLbVWlILswdq0Upy1NV4eOROviPYJ7Q3vB0htv4jqpDF+gnK01h9gtFFp/Rvio22nA00TpIo2iEd2OcxqO9HUefho2q1KOHOlC0CPKd0111axprZ2/D5t0hwPGNtCshZE3w0bSyaI0zEK2a4dYV3gqxOpLdwTYu+LqOBOqVr+HPK10J12v8yl0bbvujQjQNsDB8mF8jPmGNkjKCapRMLjnMDRH+uzDbhisQqbKlTSBKNeeZfe4sQ706hwItAyDyDsGb5cJgBtKo1jj5B7e6oJpdPREUcoM1ca0FORbqHbqaYAzkZCiAnqR5gNYLYNR2IG6iqcIhxwSzQjLbbc3bvZpbJN9V9ZGyJeYlwrGOGnRlR2akxowByZxtOrfvTS9TbKwK01KXrUOG9pNDq1Xg0SUZbiltAnztBl6rxAepaATyPKNVS28oFNJki9RFjF3Hs+cZ0EKsExmwhpDxbGwlQVrxYbB5i1vyJOGbooga5Pe27m7usuS7JlrpgjVTYd6pgSMad7uuyPrQp4ehS44OLPnFZ29aVsjJDwxdfUseYMV186FZkHZi5DiUQ1IOlXmwiOaxs3f7pU64qVfTT4VvE+bdhzQ/SWl6p1ZCfE7aTZHtEVUEmu5OO6S6avjVqtl3fppDhezcyZbPjMvlagk4bha3bfrYu+CVpUK7sSGlzF5HxTeFodEi4CpAaLHCZ+YgzYqFl5JR/oyp+SNofxEMbhyrp5mXg7ZavnWgbaRZps8b7MtS8OMbPKCxtmE7kjqMSWt3c0QFg60XeGhJyBPKuABGqzDa0MUYioFJdV0Zd8NZgdFiL2m1WoLpKwq5daGcit6wiXsbTG7PWBbdCJltofnKFFqvDQSKP25Tffd/l8TCK9qLd9WObtGFyVDh97pvRfiYDspCgms2N7P1E3QoGYpxAahYlb4e++u89F5tMgNQc+OFn+goCi0XZ3v9q7v8mHv9/PXWgC+7uu+9s//tS/TBx3f5bt8N73r+Hv/8L/T+7/wgyC////me33OR/3K3/cZn/L+P/Onf+fv+B3NTDC+4h/8/U/8jE//0T/jZ//kX/yBn/p5X/BXvuxvfMM3fwtaV/4nHYbxouhnZH7lV331n/wLf+lXf/Jnv/f7fcCPft+f/bGf+ul/5a/91dpPiObj+N7f43v84p/9sz/3Ez/+j37Op/2GD3n/7/OhP/3xvb6LLn3zn/pr3/CF/6k+/As7/tKX/a0P/vUf9+//nPf75R/90X/rv/7b8vvyePnFP/fn/tQf+yNRCQMdJk0TF/ic8zzmVoODVZjBbaj+fZ99bj0CqWDTOSDRz9C/0Ce1JNhiuoRrqtjaYz+9LskyPEOtiNlUp4Em62PW3iNxgKNpXd5tKgYzDg/1GgzyoOqIcK/hpatAZ+XZavJ+cgagHhGFNNTqdO3xdkwdqdFQb8cD9UAuQsv23DxOKJCy1nqVPr2WnnG0Snp0rMpZxdyNncwSKN5QaQQWZC2mCmsTTwHsWz2DPueyx9FLBXrl3iBa1C0AABAASURBVKlwdfs/QWyvNgVrDnCDMs89Z2g079UsXdbaexgfZgfGbHUAs6dlutbWy/w5POXd5CuBEg6JtjOVhIQ6JCGEqmtXc692DRZTWplsw9rbxa8Xqamax1AdV2qR4HYdiCFg7uoN8SDUA6+X56sj94E6rA7vSfgsHl1ThJ6tzUR1HWgvbsGwUnwIY6HZLoBqDl2pjkHlvSBBdaH2nd6VviSpS5+RTHjDJqgHU3q/qaqEXYmMKqmG4lnrXZeycRjfeDle3rD5Rg0G2kzYODV/zIvjyXmOcZ+iRhtbfgfMc+FSL7RLGDK6lZwNv6gN/+Fjtd9piILohVJVStRoKMmHpaKp1pJlHZaFjejUvhAtGq6uqOjavPXCukZIez5bC0yNsm16mkkRL/4cPpI4EXqEXjzNe9x+DGrl0/djXkMZY7ex4BGIzK5LIui4EzeKwwb90XPcCA30ls/MU3K7tOlWr6bm3ojQJUukUo57shp31Lh4hEbYFdqEeOoK9rBcRw79dDoiKkq1fV3ovsnokc4+2MqVLkVrjmAHTMLBwKAiuqqkdzCDkXXyDFWP6LutF6BtR59JuVSklIixExEZcV59KbJeI/wl/BFijUOVcyc/3csPQMungmjmncuDPubCdMndLto5+zz26XvTaiw+FvzB8A7KybVH1KI5tQVSqnaLkmkSiIhri+Ju7I5toaqMnnH7KBDViNR7qyXd7CngaHWQxZ6A8BStMRuWkCJkR6d92a1DRc4QJdYpmLeRq+sCnq0fpF5gDyjhKBPgw8Y9R583jLCDy+fR09NaYnmxa8XlKaGbNNTz7Jkql27sQNwagCFbL42Jyo7uLLGbVd2lTsMmZNTNPSeSyiplu2g3v5gcg4tAMM8yaP9eK8nLsIVz1aAkoJxog7eONiNDinMlKBTc0eMFoqO9eaRrwGpOmNey0oZwDhiTorIvUsBMCkK1R86AX7DL4hlXpm7y44XxBmLi6rYTj8unKDxCHCibdLM53AemlYt8pRCeu7LONU89H7aXvB58Htgu9W7mM+3VaXFKDbuvs3N7i9sxfVVZXq29alhuQyVn8UGx5LTV8yExdRV6gwHAhxxzZHJrphnHkAgi+4zaxWqFmRaXpQYDbD9yeLhFU/kJpNKiDVPAC1MDBD1WX9MDzGLnKpGo7IBlVLZdJ701JjHlNqsNt/amybtZYCVmwTqAEmPRjtnrhfNws2nEayubs8VaYp70Pbwkq939FPmmbv4Gn69LuIhiqD+EqZVkNAqyW1aREMmtamIN2NiHxYFySkxmNNc4gzh8NbKO7iUOzbuHPqrALn2ouTBe5nEcD66Vw2yEOphGVNWOTBnftxkeNt7AegTsdYPdqLqbblBoLs5tj6iZooh+EXt0sAsJZBUrjmqno+gRMxMl7AqhXPjUsLxmY7UtjbGSxF9Rfe487pOPgj8RG0kfw1aVSZXCZqW9x+stxZ30Y/pcAw+cNU51lxP17FBiz40rO4CmrKtm8EaNoal+0Fc3Iqp3TfFxh4rX6Axw+OAchilhT92u1AKHnEp9aZETqt6D8knorDUNCYIkelZjV2ypFMrF8Du+rera1lHOe/U5VhwqQfNo7uzKDpmuxN4zrwl98Yxj5+gi9GI3M7Cv0kKQu9BGOhnC3Hpx5BghKaZZFSo67EKnnKBucSpiG8BrYw1gLNhqQsXSrVWPNodaflpBCKtbaE8qFYs4HI/Mu+avsmqDmx2TxlQqMliJWTdLd/i300ovtfsm+/WELGaLoZZdxXMBZyPkKew//BHv9cN/2I8UoIj4dZ/4m/+Lv/nf6PP3+M7vpncdf/srvlLv/0sdAvjdv+t3/sCf8zP+wGd+6qf/+o/6D37Uj/pW3+pbCcz5fP61L/uyT/j0T/tJ7/eLf9zP+QW/4CM/9nN//xf+hf/ir3/11319iguN+H/i+Oa3vPll/81/+yV/7I9/zGf+tp/4Cz/gx/2cn/+zfumH/u7P/93/1d/6W5mSLB5v861+wPf9Ph/2i3/RF3zaJ33Bp/7mD/yZ7/Pu3/t7Pl7eaHC+49u/4695Xx5Lfr7+E37fm//gH+vDv4DjPK9P/52/+4//mT/7Dd/w9XI353zPH/KDP+cTPuZDftZPbrSyq5M+OBy31JR8pX1kHVI36i4BVU4ePllTIusLgd4aZuoOFnOG5D4uCdCRDlWHj5imx8dpZ+IZVbuwmzWoq43bJ3ZadneGvEQjOHM+fDwMCxzmPObUY0FzdYFOdoPQYaoL6LMBsJ18deWlZqEzuHv0TFqXFoS6BzqGheOkt9G8MELnVwx/+r0st2kHODgrFe5o0IvyFcGrcSGjojrHpC+WGgSffj3nWSNJCgC1zGdqCzQsbbclR6sg1TvYVqVaScGd2182VPNaWpDyaKri8dC8ILLbnWM5XTWMjUrN2VQhagCm2zRitobKoaw01SG3cJW69tl9yY8GLV9DZgxLrtXOULA8iCXe9z3oFbnNQZ/WctUL4G0ZUH929Q3bdCgAUDwVCWoRNXUX6yHiYZ2UVmQXnC06XtOgUbPuiQFseNWE6QTbZMFdvcnU7xgmhszcTbyYmm7wJizSPMlS0IF7Lh8x52UtyNnPK1+FPXucTuWNVmw6D/qhVc8tqR2Dkp+X8qTZxDDC+xwWkznQTEIPDUKR+KH9/iAPtLuVKR8sdf20blkui2IRIBmG6zi2mErqSTLF9t69T8tim9GsqVovUXvJHctZQ7wPlCAK1vD0pWeAqd1B6wWgpKbm7tm3hcI8U/KVtxTFLFmFwMGVnBu0lVsO3FLoK2oXwjyGSwYNMFxLECrLW9hZ4bujeBWye1kttGlceobVHd+l24IXtPagulKjawB2v9q9QU2u1oL9PHmqFgcFzAoIu9rOyZCuEfJLJKu6+hp1eSkY5LS8KcGQ71LxCR/o7dCsNEea9hILOXA1d3V3iZczGOwEIGeKH8uEpWSFlhMX80nd0EQU6tbGgM3EyjxCG7m40WnyboTwKyjkIQEKpOCRd1pcF7LtMtuLYWyp4GI9sZ/Si9ZIj/R77y/cuF/iJUZxqBeq6gymgNvOmOdWi5iq3EEzamjpSlbpZidrVDvvhNwrMUDtb07mPgNndrTCDL/IhokJKUIbB5LuziE1gY5himsfO1cUceeFlCHmVGOqtoTOh6oQxEQs5dfNR8tv85YveqeKAOE0hwJFNNKNU98kCZR5TeOCFCC9DWlYV3iv9yv3EjYzaGrP7EPVIhOGmhycA8N7lor9KrmxJ1aAoh4ly9xQ37Akm3qBLqOuxFbdQ9LscoHQrs0VLFfPF65jmf6BoXvkfqQYI2+0d8KKpStm0uBAHrkn0kb16vK76K4TolbSNwGw0vDk7sw+K9UVhUn6glaGDqcu96YVae0dlheUgYzuuNGjGhTVB+olOaGiaZnsvnXZQCssyA3uMRvaTT0kasmsi9ku6yrQ6jQArWpJJDNVGdOvObaPVBo2UgNyeGlGAMwxcUwf3sl6zngOGRrmEl6qc3Hs9idxoavkvG60slxyYQTdleGhYOqaWZRfmZQPcXAPGSXZlFxDNxCAQqRQzcBL5LJQcy5TesVyN7L1cVYOBeRVE9es61DT0KWaJ5Xb7oIy3mW692vP+xyrZdR0rgWKm0yL+80wp9lLSslhVWjJappCtWmt8XmZaunsUTTz1o7CKQjA64ETPppEGS5RShkva6z0zimohh7y1FA+02RdzvWPtQPU35GpUrV9mSB5KedI9EVsIkwqf1SrLQ89ytjwksLgRgpAiRiWwnyEFkYOOmTvAHTfPhSsNVzp3GMIkFVbhYhrDKimKHW3xLPMD/MBJXZO0MSS7/QrZwFjqEUkF1USIqa0pmQrm+T9pp4VmVczyhjmMVuo5Mi3SsTMBm1YeqZHjYRBoafkLhyCJ1Db8/QsbBTYGuBEp2ReEUrvxVlKf7rcJfGkJWguZp9EeQ3HFG7v0+uVIXzQh2HMdEsqubYbVyKLGk8PTqFDpQloDnE3EopzllndPcOMvTxMW4/cMm9jvpSNZLLvRwdkIShtVbdseOeo0medaUziJjDAUKB9JxJi6wKgrAcqJrlai8yAor0thCHNe1Dt/oJfRe2P7LLl/h/+uz98jAngv/uqr/pVH/sb/87f/8p3e+d3etu3u28J/sk/+srz2rr0v+DRjbf/Vm/3w//t9/jYX/bBf+i3/9aP/uUf9h7f7/vNt7nh7b3/3ld8xR/9o3/4Iz/hN/+MX/JBP/J9fvoP+Uk//X0/9Fd/xG/+1M/6vM//vC/+w7/3T/7FL/5z/9kf/Qv/+Rf/2f/s877oD/22z/+Cj/qUz/iAj/yYf+/n/qL3fO+f8hN/wft9wId/5Gf/zt/5V/7Lv/7VX/PVb43x8fLyI37o//5jfuWv+COf+1mf+/Ef8/N+0k/4Xv/r/9U8Fu4mh3/2It72h/xv3vixP0hf86u+9ut/0+/LTH3+//Txd7/yH37Zl/11efk27/htfuZP+2n/8ed91ud8/K/5od/3ew/IuxBIPis5mzAppKG2JUVwVDtLor/qUBPTJazR02t0cxfPlNQg2YdLHmym/t629hox7DaHnHbRN6i+jjQLabVZRRKDYZSi9DTn6hPeqnIbww1ibdr9zWYDFyoBFryg8lexm7vpy9TzWrXxyk7BhIrr4TZxI9QktWjv9myrNmsOlIqHNbDvyahSVVMhKmqjCkBF4OiGHuzxide7pFIQOTxUD1ATya0dgJVpVYF6BSKr9jnrPBK+VbqHsDun+tWybkYawqyW99CO5cr1rNl9TBuHuwhOE3ighyds39ZTJDbTq7VfXzFnzZe4t2hspctN2Dc0uquLtVs7lNQcLzta+7t0UzPazHrd0xx3Nz5zR+621P2QHMOcW2y4Qm0PVxrQ8PZVNw5ZtbsBKcAFmjYpGEUvH0o/FkNdYCghN2NJqjN5gd1tFNXmooeku91igj9wDJ2/6e4BTnOf1evqoW7Z6Bl57BgSAC84NeQIeQvFF2WKYpvypHxW619PGR6mYZga3w3oyGBexa4xWjbE5SVylSIJp8PW5ff/ldjLwZejJalSfCINOOHbD0pqsovE1Vmo10bNbT1cMQe3dpuZyLpTqhsdPsjhNHl2uSpBAgW5FrboZo9Rg43M2FkRrc9oh575no7g4ISNqslqYyy/7v9AQ0wQrobq02vOlAU11xkB3STJRpji5dktgwif1PMGu9NhlCfgomV0xdm2e7EfdIvV58HLjOXyaYvmkIxQFKBqss2C+toSPqtuutv33Qt0rZksjB4Gvv5T3bFbS6rCMiQHMEj9HjipIstQSVUAGovZc/T09mT32DaaDuWn6FmdV6ZuWRIS0rhgpRTMYTY8YcLinY5EifmIfV65z6qtYWNrvght2CAXCr0DqVU0YHdMBVZ5lfqPG5ymNzFPXa4WUc0o20Q4UuS3XY0LndBLdjVjGA+OqeBEW+He6ER4hVkqsWaaWVbR18mFZpdNAAAQAElEQVTzPg92e2NKtbLruNibpUX90uYhFC2FUmOsq5UfYndtQ64WOL+50g2TpE1trksJgIrB7CY8DZdg6ujuMnItswkjVpvE6Ys1vMpaJrOAnsYp5aZF6pzmVKNS14fl1OAm9ET6NnqDslbQFu1hVot4qX5de6bTAiG9lj62U9NiMCaSrKyqRJW3cgSoMLKhQ2JOKNQODYGB+lzmOR1L2Rio2f1we9g67Fh8zDFyYVtfClePUMskBZ4IeB4zD8qC7YRuoNlpuEZfCqtYsDATKMD1WRFyb49tpbSwdfb1oUpXF56AGdokrLeiFiyT5puJlgYlseZFC/AkxWIXZGhAakhkiEpIvTbTZqThdNO90/Mo/ZATw4VkJAE0oMBTBvRtUjGXjzuUEnhC7IiTMuvXhw0fNtkCUXoGGG+4Nl5DZPDemfYor/bdrBKGjUxGWqa+6xDdZluxmVwVY9fesa+s3VIfLoxLJQeTIuRb/5TSQG8KlkC0mGN07xsVlTpCYsfymnZjkZ8lguDZ4wruipR82eW4/9PRlc0s43WM/WBpjVCxp11RG01ymek9obSqdsRmT5ZqMZ1XRodC47j/zPa1MU/lJu+QFvtQ4KMVadu4y4P+KNrWzIVQSqRv96GFyFNYXXnyDmupXDH0RM2MUVojLxwiUnyoXVWejWgvDlN5cFAkoKEp02mLVqQeKqgThFsf1gvqk3PQ5c/EUOGMsfdUJ7RJN/1CcI0lvWrgQWXSS6ZXq20/BjSju4FCN9vYUJGaeDqcQ1lARNUV/WwCJBQwttirqE5qrX1hO1JBnTXU+TWGTl+AxcUrblXRNDE7nyUNSyujxYo8MWGZt8UotkBkWaSX8lU1KdYEyATNS6FadSk2+RyH+Rq1PCiiPaeWMJPjkeMRdlR7ZHY2EpCWtQOcyhAaftFCmsRWrKf7HmsPAwirtMv7OmqvfY1sIYbjhkVwwJWRQm/gadSc57Tr6DO5Lw2jKcE2qoeDK59ep9gJZJYsKbBl9gbq6HZgMIf676NzvSDe4KvDXq3eQxw28nt/9+/0ju/wDnj9+sp/9A9/0a/68L/3D/7hv/7d/jWd+K+/8h/902/8Jn34X/xQRO727b/dO/2kH/3vft7H/4a//Ac+7xM+6qN//I/5Md/u237bl8eDZtd1/dOv/dqv/Ptf8Wf/3J/5nV/w+z720z/j13zMx/7yX/XLP/DDPvT9PuSDP/CXfeiv+bjf+NGf/Gmf8x99/pf8kf/4r/+f//o//pqvectb3qK4xhhv97Zv+32/z7u/3/v+1N/z6Z/6n/+hL/z0j/rw//BH/fB3fZd3Hrqh1oj/uwfx7T/iZ/i7vKMuvuVL/9Jb/oX8H4B9yZ/8s5m3wn7lL37/j/5Fv/B7fNf/9eiX3mZp3SPULnxezdKQpG3M3YckSLflvGv5LjZklO2CusGa2i5EZ0bGs7VLKK9ehQWajjZtA/JeiB8QS2M+yo8EWsotmPpltB6OHmZjHMAjQ1rrDIR0XdQXJymCgEJFqai1/epXsKfKfRz+WNPgIOsw+ILhtVVweT7m8xgnR7/qu/m0wJggu6qp28+hRwbZqrir1NCzuBtGmibIXIVHzqtEC9EqFfqikJ/gNtV0dRLqTxgmn7iX1F2Gi0inrW3+dMfdM+Y82h51qWsYlraiR62XeIycVnCPB6/RmtqNYL+JeIXzyesVcbmbTzU9C9PuvTbVihmTNdEH2w3m8Amsug68/vUhtE5p3AU1P9w2ps5nWxi1kNg8617vjdBEr5vV8kzXJsZBV5tW2tGC7la8Gc+maAHFSfOZ83nN7OnutBFlcV5+lbCopYS6nUeBsT2380pmoWfXMVv5ImhaOnUV0d2FkTblkjVG+ywbPUjv++z2nbZpby7bx9jHsmNqSa/K0Dq8k6z20vZ7HwwDrsc81+P5ss6HvjdI0x80gDY7nU8N6smeZg3bEOYY2Zlzl/gHUct86Y8W2zBuYd+1TjxaCxbMx8YMLB3pKC2VbBkyXRAVhqACKlrL4DYJq0bWvdteDLNtykDq/Dbc2D3axLQOc64Jv/m09ubUzWeBSmm+ZLxUWRtc1I3px+Hr4LiXp7P4qrdEpWv+uhDykVoGNNWu4a+o+/5SR8u+FK0dwUPsh4mPk3guiykKLqVH2stkbKUq7jwcJtXBptJgTaA0BVtrvn7hPMgBR927kurOeDKeLSsxjK4tp+AR2vHnWXW+ntm0kobNoVbgVzvyzpr5U7TrIpCDlzSyEmPfab088wg1kvA8R+RxgxkoorSHeKZWL22aOjmrR9i4hsUCJroj9lO9ZRDjMWw6lfE0g242fDruVxtvmi1X5lBm78erq3I1juTqViekeLjWLMiBwRqMer36okklXR6kQ0noiXj6q+sIX+0Di/SSfF0lV8+sV4C4Juajhp7PDnvS3jS8Mj95BBYqLS8xSO5hcZ+OkxkKleGWhUKrCqqrw0zQ6Bv3/6x22BzL1Fgwe9OarqEMIKsmMAYJpsJ109t9Wpgh+PrOBsuq+LpH9DN1qQfwRsdR10DCG24S9Si3sxnRHRAGYINbmvahXw3N/LqGbsGMHJxW024zMtzZnYnYzBzBmWPmpNqPO6bosIDvPc6nnxcvgp7aGSlAmGGwXO16XKdXBvGm8Tk9j9G6pL6SkEii5ZE2o9fuZa/seCUpcHrNNxSJp7tnHRnLIElqZtsBP4CB03AZeho4zWcN9v2Kqr6e8yq1SNvNp574t4cTpP4Cu7rFzYUVtPDao/sof2CudDbd0j0a90sGtzLHVCYwX2ciOWI4B/TWQ9QeFkPLE0JzMf1Ug+GkcA1Q6WQKDwx2jFyHyI3RXGKorMt38YqsK0bmArV9Ueyi5rQKtxpzD/10r+6xOgBthU+0AJNFwM30ptNQmXaEa0wz9+y9oN9/igf4IhQtaSlo17/JSpVB7PlKGrD07pGO5h1wm13w1Og5wnkjrAvXK9tPdQoxn4ftge6wVgO8ihluMY2ms/M2VQM5+3pcT4tTkfeIa5aMdfsgxmwIAdAmMGKD08bDHo8xh3Jd08PEWGSfjROTw+hLUWqr8kScHd056pq9wUgvEBzeWiyO1gXe1L+4LVNWxczlcfF2SYaBltNz6NC3egJbyaSJg6Nav82MHn3H7120wCwMssmT9arjTdQ17HphvKGwG2njtEfUINK1zrQa2eG9hojjtirfTpFpKhmD0RGW4p8Fr5a8yASqVWUREymND5JwFs04FPwaa86JGWna/OwBWZHSxz0rK6HbfAbYBqypeuuQ0xyxvU5Yt0lWimLKDRClf6SP20UP4o4bCiNtl0dbwlYJZRFdKP0Tfr8LpIBmle5vdV4jJriSXiHX8CTSTa9Z8lcjlIeeLOXadNs6Anfk1WgHH2MNJZw+O2fpAFJHe2F0STxseeoqL7MewNhtGyOEehRnwIB6l3d6h+/6Xe7/PWK8fv2jr/qq9/uVv/pt3+YNfXvLN3zt13zN1+jDv1SHKuvlbd723/+h7/HxH/ZBf+4LfvcX/tbP+thf/avf973f+/u++/f/zu/2bu/0bd7pjTfe9p8B7mod6mKtfN3n1jre4R2+9Xd4l3f5Ht/je/777/Wjf+X7/6LP+U0f92e+4Pd8/id+zIf8vJ/9/f7N77mWKoT30P9Hf8c7fqtv9/Hvd4/q/upP+H3XV/6z32TuM/8f+PtNb3n1n/yJP/5Ww9/523773t7aI+911bFTZeJOI5Vn6PY8nrv3vnN9mZXBoMs09QlJ4a7XYDq03JfnLfVhtSzM4o68JHa2d9i+kBopZS7WAdXZSIxk5djpaVb30kWtfGscQ7sJFrrFNbUqVEFHN+55ycZdFxHVOxty5gneI7Q8SI99GOewUT3r/i+jXYO84QMau6nfXbWodVMEuANusm7ViohyUKUuJ0De6ok6oK5zQC0bBg4OaJBmUEZRJP3uLHPgreBQj7GXK35ZxwvqhTVRzRY3Nsz9nh57Vk62nqco6qHrlZGhNS2simj9jYL2luPq4/4pYMgTCaIKJQY0gNZQ7LbX6jVxKOAaGdN4HHSlpO42JNdsDLSzD/X8q7stxhpzLGvKIDq6we12p4zCXQ0w4VHe2g2kMGozxZZvaD9xzbsKugRV5MEGCiY6Kg2vx8pssAqZxvvfagVz9dhheNa4dAWhvRZHqhtauxdFkuskoo1waile0yyBK/VsUL24LHqlHaltxmH10vV6ZbByT2buwN2gcLXFtFT8Btd+uCrUW+WGXq1bBmTfOjR3Kp8pKA29xK8BXuI1qVy2fBu7qaqnAsureW7FB0cOD5OXGYc+VJi2zVdmyoMf1VrwpnhtkDkRbGR5buvANGgJNFMu5CPlDLpcXbuzsX1sXSocm3N7gSXXva+uLKteVzvKbxtjHkMvfbvStgBbTNZBoVttM6kJu5XD0mlTXBhXuhBY92gFp4x0JHRcgAxUd6meIBxEtsJY3aaFnsewh8ii7czr0i7t8j7nPYcn/ClnvgyGdoZxmzWAxLhVmiGr0a0zYN8w2O4mfXShI6xPG6VENGiD/ihpmRTw+1DUz+QZCPUONKy4engt9GCXdaLDO22GPVKrsVsMpFZlzwzosoGz1+Q6mlry5nWjaEDeIfDbZ9MktpfyRz+WGlcNhCOtwVzIBzFeE1GvQUFWgxWepbOLaA2EuNPoAcy2RS3C47W39YS2bdgWZYXVfijWwyRCQ/CmRH9pZsfql5He3SnDQz5KifX9GM9j5hBAZTFUYiGFxbIcU9Nm16oaVSyiqls/oZwpPUOsp4SLCVGWqigqayGIoWLQUDfjW3OhRqLcn1aX7Q2EaPU2rwM5pOdo0Vh74tQ5ZXG/5rarrZJRlvPw4zHnJCm2mS3RulaQcE8YdAeUKEeLKaUZp889Z9LQDXX9rXgkNkT1GfU8K1ILEXLhFoMsdoDAYIlRfYBegg5vhR5ES+5z2HGMfszbp8aUeMquZrth2ZhO5aUP6+kQzgKqBQC3FAD4VIbpZvoItGLAVnqEw/Jg3xzKWtQuJka78igwTWynbvOf0wMuumUK1nIqHju7+z4he546XaAwz8qpkaQuWZMl+bDX4NBVVOooXVwTrhp1xb9xXTy3GvEsBTNHGdMtJZZiUz4CFdSnNus5SzGuksMhH+LQDoc5hArgnY5NuWHTephokSqD2mYaNyxpmrlNt2+8hhelMHGoGhFE4SJN8TVGANGQG8qKyn5Gq11ZKmydBy/YJT8LpekKU+czWPswU6BFEQkgTbELCj18nsN1+9RZvmsGFSfwkjWRGlh+axADtuBmqBFaS6B/i2xrgg60RCXK2K5vHCiKel7mvbzGTXxuLbIaIvxpujgjZms4OdmSpXJcqh1lX5MNIgO2fcTBsMraWqElSJzErgoTH95Y7ElijKResiacLVFf3VJBCa2o0Ooh14UspvjuluLFSFpumVUJpz3alw8fR5qSbwgMtg+7LUJpxcG6F+wBJa0pokup+4sFswAAEABJREFU7EIrLfZUsH3fJiyjmnUNhTd82yO4ihDl3rd6XWrwUYJ8zZX6R/rZ505hvBsf9TUj2K14K0ooxMvcoORf0qEasVymQPXRfcCHM93OHOe+l3eiJ0qNU+5amMzEv3cKbwpvtRJPM/gq6HAFguqRa/ZBtbXBjd6NrcRKwwPT8LDS8033hgKRCCaMkFo9lytQJ30As3NG+tn2RF/MS7nDwIZgjUhQQ6gagiSowfR/5wf/QJJv//bv8K9/t/s/o/+qf/JP/uJf+at4/frLX3b/Pxt5/fFfojdFALAEeozv/p2/40/44f/7j/wlv+APfOKv/0O//bN/76d/yud/6if+zt/8cZ/5Gz7q437lL/+YX/5hv/aDP+jjf9Wv+OSP/Ijf8Qkf+7s/5RP+o0/7pC/6LZ/xpZ/1SZ/0K37pz/6JP/4Hft//7du+3dtKKY3/116S27f64e/++Ak/WNPqH3/d137mF/frnOrr/7xHRP7pv/LXf/aH/Yqv/bqvfavl3/ElX9R3pSAb6V7ulrEitcdeXd5n9XPXVUkkpDh2WxXKWAN+2Jzalk+e5FPVsabPwclaHZ4huXQWuGnhgDWdalCX6pAlj/qCGJlza4lr65QAAR+9LCeoFUGCVXFfsbWSaceWT02Dy0x7b/ilHf8WnEv6vtLqiok4iofwtWtmIwkhVR3UHK32PKJxgaeEvxJHY5pzHrTHpEq9/VJV9g1IeP21thfdzYZRUU2kes3V2obMqMPtMXjYHRS6bkrVe1fnEE0qjhwjxth3vAHEogZ23g6eE68mXrNSArmjr0RHu8q07MLj4hvgi+PFU7+1GM72SxVmhlHtjh6tze/ZiOm9fJrciVJAANznGnaoyYxUfNRFaKIzCG1mOkF/KFUioK+6LrvE+aShO5Wvbn1gv/6n7pRo4ejq3KdA+lV+wXZTYSWZtO4D0A7EBmF3nkmw4ela1MQEre5koJod5IVxUfQrvxIYX7/A6qH2rdUOVr6I5Vs/i4g/Q2mHuU63TWzsvOVkkyIjHQW3dCuqt7aCQeyuGuQLbW1ypyacmNVL8/zSlEur0o0YCyGJtTkn1mynorcrdCD0QpY1DOyW1vQ07toqB1lZlDG4tGnUZa1ivc/aCW3oYgwl30e43zGPc4xX6IywfY6OY/KB+bhsqmLCoxeAVN5D26PQp4LkXKUcz9JXEd61G+fE0/EKtUuvRivwTkt4DjMOUySnIZywmBYGXPqJ69XAthefC7OVMGT55or7t04xFjfyKmYNBOSnVJVOU32+oj/vZ4R6nqofUOjVIjbPzTM1XM4reIbd6WTr7uKYGDQ3tod0cZYy5ZqmI0dBL0niCL5UDzigblJeMTSLmmhmuLeMU8i4Tjm3Fgd+8/5knxiXBLPSHNaAMqeU8X4BSsGAbNL1mVUQVgKrS49dHhlDuhHROiSP3rf4i+gQQ8mtzRAnUXrwpnI5WAf3QDe6JGGUo10ySrx+NXXlqo70pgKRTs3KvF2RLApCG8ug28w4Kg7Ax6j1cq4jzG+oXX7VSPoFK6XrHFaTin21zg2kydFAzUpHkQDVDA/mVHVxpHuZy9T0PPy0fkpb+8I+u66tB+MRLbobZFHpZu3M87oiNHbPxpE2ywldFAFgUGNTQQiOAlevaW0LTMUy++Q+cZ6IqEJiqFxs98owu25xt6NpRszR07flZkVmItKuFG0KXbxMasNcQ2K9wXQ2S3mt6NN5vnAfpeTKy1Un1NCzJtCwNKTL+CDGzXZj6kTtlgA7RuWRcaBHwUPEYOHeJFkntop+X91tyuwc05QdM1TLPM/doe5+4jj7UPqvnlWmwQKvX4liOHqMtBVUSxhcqw6P0cmrkXc2tacUEqhJexqH12IYZYGKDVGQJ0Ivvek8b3gC2SCmQf3QEyUR7daHyXZ5nGiPYvKW0PBUEZayue9yD+KcKUodl0Tl1S0CyS62wu/RWoTu2F12wy97lK+WoaClQl/KAQJdyYtzz/GctqdissuGluaiOdejD88DNUmz0Uux2VYW2lELdpSPcqhHTomoMC+zpnmCUT0v+W10FssgV1CaSikpUakEiolq165R5IQyLzgDPUfOeQ1sRHdqrZin8RVa/aen9D4ezKNryA9NGDHoQpeGbQEd0LQs7MCVteO6KsKCy33dA9HIvi7fPXLA9NbDQDTboMMoeQmabJ3g7q59lQTWO2ps7Q36xWUMD2CGbOTtr3Q1YndhwA9wgqRDQQ7SfIknGbL99FenXbu7y+5abHWJWnWtnbOgEoCp/vtiymdrFg7DTO7tr0LWr+XPw4x8Sd0UJCwvsT1mCz929FNHV4CetpQ1tXevOgoH9NLeAFXACdspZyVZPyYmsVNnJnMebQtwsDe1+oAJSjFFXN1lltXY19hxZFuSVzFj1p5lBEuHjVoLTjNpMIcmNTV9O/TFqgxw2dGh85pAh0F6YlLro7ZFrm+8fOwh1Xpbqwzma4Bi2iG4yluZhimkUJ23RGJpTAOs4UXJaAyzMZwEFHZtlwTsxtwaRRBY6IOcNKM5JYJ2/xH/hx/0i3/Oz/r9v/23/J7P+LT/ww+6/9OLUreTH+C//LIvy6rXH/8lfWuFCmbfCXs8Ht/pO7zzv/Hd/7Uf+O7/u3/n3/5BP+FH/Yj/8L1+5E/7D37Mf/Ajf/h7vef//gd9v+/3v/2e/8Z3/07v9q3f4e1LAgL7ZuR/elxd/W6/8n3xHb6NTLzli/7Tb/5j/7k+/M97/M2/9xUf9nGf8Es/4iP/5t++/ze+3mr8a7/x617tN6EWZuXk69QGWvv4GF73d5CUKLg5W+lXu5OKCqElrm36mLrMlso44bN8GgilWYO70CG97uUqw3GUWci4ju1dakBMeMMNZRmpXrNLHblq8DZm9CYj9Yqr1Ya2nmqo/kxyM6LLulYLR3UDCZXDns9aiVFkse9M1tx2XNQuWIplD4dZdzSjPcrOlqw11tK5pw5TJSWv5Fm+e4Z8TRwPqilh7ITibyrcaqmEaHcFmFqYpRsAQ86zXfBIQRuhtqTCP7M1t1PUdJZ6do2KWU+PwFUdkEnVZYtMzTURlVrZfRjnHa146mDVaIMYk5mtlKGiWzhaslWSOBhOBZTdTVcibI3WhBIuByE6kzvzmVVF5vAkUvGPNCpsu8e2jGZb2r1MXfUaCnszs+Q2TI0l9G2jsrmrLpZ+jitrMwcsYCVb3u5NnRiim25tXlCPIA0UM62W5JYw5bZyF6rdyq01RekZ3au2M6Y05bTtM0xTugUjqtoaGtnNgnJwi2XRbsNz7yPeuoEx39qkOk5Wmujnpd+C9hLTS/qGVKj5TSszoRxyLXO+t13KcpXrKsUcUbOFF1POugFjTb+LYVw1sxUQaBoI9H0JPAIjpDiNJNxzSJupq7Pu8y4TEAUiyHW54aXE3J212oIpLx7EHjvqykpa+0yoDkxEVQZwos6dElThoL0YJ5volgS7vHRXIi5KJJVSOk2rflOn02qvUsnAOZIqCZRejZBTSSdM6wXDmij2Rj8viouWSoi3nvTiUtRnZbYIVOYxEoT55HgJ98vzsr2JGqhpHKD+4n61azdSUr0igpdoKZMG1so5qewOa+WhWsE+yg5Og0HZaiqoi33qsd0qpQkgOKEpNtNUQt57IhfLdakMad0GGaJdhKbelDe6gAx2kZTpslChWMFxX9PZHCOWX5Na36NxC9CrmPfM1gyMug9omtIJupvTIAo4cjqMELSUB6zA2n7UXCxtC6xTIqhWHxPjq2JloHfrpAVM1lQct6+dYmYC1uzuoJx7tZwMs7vVLt4gFZ2I2D2imTLtyk7LSCHFQUsOykTda/6dxmAmA2z9/DCHk8kKVlsa2zgMCzVd33Sa6UafOr+BqI4KKaFoGgEuwgV4dBkTrJ1RqZSICoKCUEBWZIe8u3YxPtxNA3LVNbFfUyGU2XeTUmGYaV25aKnsQ3NNew30kO0bzsHbsJGANWzLRZRfdIlUchLIC3e/Iq8pE6BGFVo45ACVtx7MoJiNQBHhd4gR3tfQ5U3bXZFVSoXmjeKAN2cu4GgnxLr5MtdJoK5OTeEYQ2fSoFripFv67VPooEFlLfXqE0H1AZ1ty+q6zxgPo+tjsROmUsow6XBEMSBK4Wxq627as29vVbOCgcg6lYeofkJ722gLG5d+J1ALn7yTmJqU+1Q6un1jhLhkB0ZjslhlqcCU/R7UNvIyRo/apniaDnfaNAVm1N7zsWAHsLg9toXmSiMT2jDbrGGpjxBKQ3sQN+FdBhihBJBJhq40dWVZ3D9VOm2yOJXsbIrGVOcfrawVkUBH29bdwV4RPMu2j7w35ZTZlZwiSZ9ERWJWjybCkNbykF0q0EbrX+qeRefYN42gPgodL3IP2vCHaRU/3B0gvW2oWFIsaCMARdDNtDihyoQFZsMWOFN2rFKzhpnL9UV9657g6EQpt5rphDBAnAy2NCe2dWEHlVmOuwxkS8BHmSxKNHDTvWEcVAzjWeOCJ2RuK2tqjkoXHG2wbFzbSslHKcjemZFqJDvZLcbLKc9EsQrZ9/jKrNxUUN1SdgC9EjPg4grRFHVSt43pnN66nHnTJ14wyx7Qwye4QkrIItNbZJoRZgIFlKcO6jsxSQNFhsO2ZamnQGjBUhHVEGK2SMY0DTRocuuyQhUGKUXYkeAGr9fkOcm0PKG9ReEyvGp7BrOi7bwHdRJJpWzox/YLiWV8Y/GNoV3myRU2JFxfBRcdJI5VGmGYGlngbJ9iLL7zu7zTL/3p7/Pd3/U7vPM7fevP+Nhf90Pe4wcAxOvXX/ubf/vNV8/XH/+/5+2t2IkWZHUExfLWM/r6P+9BjG//jt/hI34myHpeX/fJ/8fzef3P5eHrv+mbf/2nffZP+4AP/GN/6k9dzzdl1mj/5vf8Xr/1N338f/Txn/B2emiPS9Vg0O4Cfcs/u6+y5vEY48XxRo1DK7Ex1LHafdOvRETrRTeln3PBuy2itIkauSfzUEdmtaSuoHy6g9r7qw4FwGcOuSuMlLIkZ6Zq5YxxNp9OTVcjKK1u1mH0HJqprRsyGjWvexOilPCuTrN0Ix8j1yhvlQGoIAI4adt0leqG25Cm4CAsZlzQ9Cyo9KPrijhr75G9MISzoXXhaVeYakgJ593yzuK2xrDWk6fzwGke6ZUBtYxAlwGY1z4uzPu23qiVRm0vFEFfI0/f4VHq8gvl2OSz1ZQZZK7uh/scw6epkIgN1e4BvJAvzUfgJfBIsu3q1upcinyGNuWX4QILMLPlvjJxPfM6Wybci5XXjr2rwdCqMYQz7XzurqfSuajWuxRdW/S0VooYzm3sUe3Z88lx2dhAM4XMu13+QgR36i5pp517PfMl4tGb6il1Vak/zVHK//B2hmmL4Ver8483FA9xKAkxsInIunJ3bzjqmLRBGDuzdlWa6Ga9UVhS3zgoFWLsxNYA7mrNrsxVuiTejEbtAxSO5lVpgP61WXzZIa6gUxANo6mubbZrSU4AABAASURBVCLxaj21rOhskW3i0Cm4co8BPmBvNI+w5rzr4PDHA4/RblXH9epxXmu3xGVYg+vAnHUoBO6BVEKEfd9J9rTVfCgqiAhhunDFKMnI26ySJvvWc1BAvY1j4gBslzaIVwmvHz3mtfBqcLuWe5Vf965rV2jyoMuW9Cxuem1I+jF4rVKQtrYNaV/JKlahebmf8C5j2cF2JZZP1HbB1EgxoWVsNiZIq7C8soMVk8+XVS+HHnJO59AAoh5S1HUxAtg+Xg076dFAp8/w1TZczKwcuF/MnE2CuT1V00VrmPalEscEhhB1V1QrZjcscPV4lB5vaCkVXsj0ZVUC1ppqUst+2JOQHnqLMI2xVnXOLqm8FfCw4tyxXj0ZcbsGp995Gu0Tt84l8hZloFCj0bwhUZdSdbWhSssdfW7lrLXyz+SiL3PAChSjBvWbKl20fbq4wtYpXW4cjaFBYCv8zX7WzghsQIzmVOX0brxCPUdenlkoXYO1N1yJOzkEulWcYqRkY5CDwohLxblHX7OVapXtpC2zMWsq7eJSSffZspNudkCYeczxePhj6nNaZD4t77lawlWi2pzk0eq5m2qwaNVijk7dhSt1LjgCopHHA+NIeifGheOsvvQI+nqq1QiW43AIxoRbQq9uOpQpECKFsDn6Qe12q7tL3x3iputpPLU1kvFHj5k4diot1oDrzVNpCXtdU4Q46pFY1by/oCKh5iZLS2KsRLE5e5FHm4AXGM1q6LiNWU+KsMC8amb2OPNll/AmlKvlNS0HDD7iwfPYcZwYPRYfB1Z3X5nYlIlxYIhsxx0gYLs7utC3aF98TIdeje4JOWIDDSY8a4LD78NBbl6bGTcPJkq6uDWyS2+Yrfo5cMxyhASMc9uVI2MiJ3zVi/eLiiyrzspXeUmlVs+xw65Wcl5JHkqJTLWs2h5rPzydwFsPFf3ptinF5cGa+qC4MbyM2cTT7LqBvy7X4N3qEqM4KvV48E6BEOsAaTAzUhrxJJ+0p42nFg4gbwqaD/oQrexcsQ/FF9i7n2mpmZDeNlkB31R/6kuFodJrGdJJXLBCNkqp3EW517Gn74WWRCjNTLor1oFmVzCenjGynY3ROtc0w9ShDK9+MRWnMe3pcc1n8yJKxBxe3poEmDc1lw653SkEGHOsYXSKpOZAmlAyT1PVpv52aEbRksSkURUoYV2tYBX7uMM3+MH5wHyBHcZBG1OvbpOHM6AYG/qmQC2D0qdwtAHqDc+evZ0hFlAF5VHttD2Ruy7Kt2RhDmPFphpWXPrt45VErO7ZnCXeLGIF1PKl+nGdcz8dPsZxsI/QKvAshjRnj+kv7mNCmQqX73Kny86Y7Ram1GBLyiC4qVo9GNNjmhmjUNqjZafh8hI7Tc1ILW5UJJ2qk7LK2cHeySzfsFMs5cbd2IRFqLCpi1dDnA4m7cnKUNiA8pojavO2kG1KbZsXGS5ZCIlt2bdWpJVjXK3TAhmaYwQckPHciktpQYnr63gb+y2/+aP+D+/5w2j35W/6xq//4j/+p7tbQ//V8T9mQFy/8V7f/+1+ynvq0vW3/8E3/8bf+/8+V1//jd/0u/7gl/7wn/K+n/8H/+Dz+ZRlFcb3+Tf/zd/5yb/xj3zeZ/zI93yP4+1Nq155ST1KqbQkYd5rc0Ofx60Z6zatFLA9JHILOnNBC+Vpe2NfZjEnjK2sV/dGS1hbTR9OUEa3R1gkXieeufsq9Q/40WWvlY5huSzlbZAuv62ZU1e7Cw57NA6DG2qN65HpKtNePsy8JePFXh3O4t1XMnJXFeWvLcmwurwC0rTRB900OvWxsYAx2qwlf+SJeuW6/SC6R9dQFBftQl88E2/Cn9BQjrCmxWAMRVq4kqHQhE/sokyOdcKi7qK9wCQ0r9zLTAgFwbx67D2ylpUPUP6rPMyGABX5eolAkgUDNNpRg6Cxe+09Q16EUSOGajNUrN6lvjLnNZiWRa2ajK6oaGaOCiuN4nI7Jm/OOl81hNFNbFwzrnnHIZRszJQegk4Ot5GT4ZrTw7NlFLKkAz6GrpdB0ctNKe8X5DoKSQ1oRZo2lOqKSP1t5B0CveDRI0SX4pUHA5nANl5uhdJg7YBOXBdjz8qb7/LG0DhRYA2moTQ/qzOKXc2bq1Ye2qjNAqBZosRNOjkdWt/UOjVUvqa36R+ayUpERrzqvqxD38d1rGsuU1vmhHm46dBUlsP0madz500xG27p2gdOL400KoCT2BCcFN/M1ecbPN9wUUeDIOtKBPepXLkrocfOQxmkObXrmad1GVrBtCHEYkliwsB+SZvwaRTp4kEdHBoZ4qcjN1kLHAmeRRVXdlthlc7umdtUpn3TzeH0ZgWrYDQb2nd4GGsYnARGt7c7Hqv1WFVnB8L9Gp5uhApxDCx9gE2ad6KuqLxYp5d0EBzbdFP2wpzolaLF8fpVjV1epQULVSUi5K2TmVCqGMZEPPt8jusa6iHIIrTLGweH2aApHfqEmh2jWhimrnO3ljodDREGXpCKrIZ0qyBi5jVxaXnto8ORD8tpBVVUzrpUxImuWwQarPBglGR6CLKQclvGyJu7s+y8sLPTYQvmxpipDtiKzYyUAgU/1rWP7FGQPjyEM62FMlvJrLPvvqSRcj21CitJ3aCIid5Ru1pjNb5iXOV7UKCd5ejX1O4KVjwUPbZOKwgoW/CgMLyVD3cM0t2GjVSQyqrDHO7i4zBzGpNa8DOvQmqjujlDJeoshd6jtVd4Ns7dZ/tF5U87E2jeHJgz3E7gWVVbDRXoASUvulCguFMOBdFN9BtG1Yo0+ZGOGe5tkzpo4gtyOFzBiw3HNkt7hB3CN28u1ZekjwbkqlOL1iuzy6CqE3pLjtNXzztLvT1y5Bg9CYNfZWeJ/2Hu0nMB6IC4RV3VFT72PLYCg/fT/ZWvHF5mPVIEDEDymHIkFMqpkqO16sQZ1hw2OB4b/kyX3bY239Y7cyO6g7wSN6/F8i6/Xcv7TYRT1VAz2zbFSdqSNSPRYA4ruwfPO/0tUWxmK6sOO3C8cB6mWMsIB0fDKSxlQ5TLdg8UKWWJq9dnmCoQtCzrtLHFSsOb0+DeQnB0PJSbLH1zYLksKvgUbI2Vjah+Rp1Xxk6EllC3csKqqyK49Qwk7/XeW6DOaY3JOwoFItcjw1OETqMZ6Bf8CYZikt7O9ICR5WgEO70NPIg1SmIy3DZ0tat3ZKPC666yIXY38cw+1a1rNy7HFrtVRGFAEQr5gEpX7e2CJ6x1yUrrx70mDn1rOYVeBlFv2VYJRltAEg0caJ1vNwohBaGj70cUIc9wDGtrSPFdqzCSyM6nxlEmycyMfXVmKUEP8GVgYWqtVYUp9Tm4w3iplulSGdkMjKv1C4BblO0WVRCy2bqJSj+hrvoMsS7PJZ0m51kvUYuT5sSR0F3HOk2TBEdfxOBABFJHekMIMcFpgnFYzt3iLEWM4hCIGm1KFWsYjGhAfxsMH8+pTY6DNPRBrtEKykAzEUFhZUph3ewuZFWg0ypoXsqrVqR2a/e6+2DAdEOpIoUmVRcJ0jgaQ9/UVMfuudNFc0fccvZFf7A7Kvew0A0fX7TaaTrUzDPXVV6vbeuxISbCJNOoajm3O4ZIrUrIpurNYjev7fsCowDxsy/WBSPdUBFx7Qgp2j/1Iz/svX70vy8adHzCb/vcz/+SP5xyoy//6vgfMWDu7/RB7z2+y7voytf93j/5LX/hb+jD/7Tj677pm3/vl/4n7/OBH/IbP+3T3/Lm/SvKnPM93v3dP+PXf9QXf/an/7D3+EEbPLEvpLoAS+JVxvucvDg3R9ndOV2CzA79wVOVVSC04HerIeg2w2YkshJVtTuiOrTJSJMIeU9BGyTmDHue/czbV6bmzJSR6DQ819J6cTU9Ze9wn3HwfDAW0lWaFx5dWoM47wE13V11C/YoTjVZf9nzSDJ0CBvlTYBwZu+SDF3lmXtd4fteyh2Y6MEstOSbPWxPVeV6vli8kXhciWsLZucx8eI8RP3uuhSbJnnrwVC8YdeLb7eiAdSfgop0rwp9B+LofWjbhlfA2bhPmg/4KnW6xV6zfXSLR662w0z1nDdbPAt3rfXVVWyQ3uJPWDer/Op5QR7a0Qe39wY2vMup/bG+ayW/GjutabaGqalASWjVfru+qbWuksN1/+crAwvl0DUq1ogjgyrjaM0egzBrZQ49DcIshheG16oeu/1a3Q9yDfcFLNBJ5NHqmdmYxQFuUJ9B0CiutChduPnIUkBBRHcnjHgsX+MwjezTczPOjqc6RwHsHgKVEkqFut6VjO3qVUSH6bnJBEQq7eq+op4XTg1rRcbuupeEo/yNbY/UOeGMNzweggPhf2Evivth3mQPCE7XFgvwNBk+vI/qtXtc7Ktq74qIUipYqbJgTe6HiUQxdU++Qyo09JKBV7aerUUb1nUnM4kyfdG5VIdOWWWC3n0kVpRtKgUX6hVDj/c8+tE23cQ6mR1ALeuhZDg3rcQc7jzFDVpuA6w2QEZ3jlPrQulL29jO67guf5XSifUhzXVU7ZOWtnrIBXtqPRyCMjCnVkJVXw3TvuyNHgd79B54HlrBkM+2V5wKnFq8lLR0CzdV0e65FUiPgG/FxaMwIzv37if0EgF63+aXW0O0eNfoSvZJVrHEQKGUtvQ60c9oGc9hMVlGDi6Y0Cwv7Wi9wbzYQUsu6RHtG9ioq4kDQ48E6XjWyHYap+ENYGDThDSzrWLEnUhkMxxtBBrX5H6wvMpKvey52O4s6cM6oILpmqHL95fL5Bs0qBXJVxdUQ/403xx5juuVn5dJMjJgur9KiwbkaVQ1GXgJiRKGRoXQ29hOa2mJFWZUVtLZTMPtsJU0dHXOFtyNo+qN0lMb7WjurLwF681Wi+g23ZvGS+RLYzhqFEaQW15aEEVzXSZqRbls007rmMnRrfhb/VHec1/YMWTgBoyjsVLBAfkaQVlulZgtszXMRsl2oPZdAsGu29hg9O6thOg798RzWioxfKPwUvYoU2bQTcLaEN7XrHMypscUz6V6y+u5U8a7fF9+PWdq2+nsR2LuMbNH7QUNn11qO66Ebf32PNPh1m7ATTMVuszUzpSmngIP566XvA6aahI+sRxima0NzbYqovbccYSNZiuKfnUiAzQeK6ceVuStvUKeVz8VOjQBlRQtF3UtLgJQcDXoaHXvTN8Yu8Wg0qcCaXSGMRWHdNnmdLaF4DUUTd9dq0iV3h4Ns5eJN2YdvFMklbjOtUpNakOZXKhVrIfZ7FZ9i8OhtNm8olN1oX1yrxP68aCiznG+WvHK5zPfCB65LFa/Gv1kZey+Lm4dkUO33adZzFaubraf1WdXdBvtped8vSDIn+W58tXMkx1E+iifpVZTR1Nl27Az4rpSOR2KWbe72gcLaj1mrlFjpd/8SzYlBXu7iMC2ve0c1ivcr4Ut47bP3k8VQvbNqV1lreDvHtLY+idng9B078OxCtjIXOjJTsMJCGBR56GXksFEKRHoDdw5s7s7thP+AAAQAElEQVSWScoMu6t3dPSwMYwMxM7oJpozy5/3MCloTgwSYX06s0dfDpVYG0jFZHLz3FpDoquyKkpiuWncRXmVVcM5O1QPpGGBA25tupOup0m5wF5jrykJ9TbxtuZDi3i34s8SGFd7M2uI/c5mt/to6SHr2Nei2G2gveuAiEizEuSXRVEkwNUnbU+jTXk3tKr7RgiFQllZbkenq37QAUt6mWizbNzS0jlejUuycytagrhHiWSd74tV4P2CuLkD3qAqqV9DYnu26RlNulArOaqQx90+jWERdgE1qNqYHDPh1YMKkJlWPNiz3LrQV9ZVIzCK3nQBQN/u5KWse2R7NHE7ipZQrvKuCcnlNWsE3ext3njj177/z/1u3/n+XwPbr77lN33Wb/mtn//7z2tLKf+SHf8SwCHWu77TO/6iHwsj3jy/5jd+/v66/9f+N52r+m/83S//jN/z+37aB37oR3/SJ/39f/AP3hrVD/4B3/+3fcLH/tZP/A3v+UN/kI2FNEoFkbWjlD9lv1CqZgJz0iZK/aD3iSiA1ZYaEu2pznmp/OKwa4KzhsvUttj2vMbeJt2wlX4EcQ2JTdbztiYtM4tS2KNbANo2PGiEhOXErcCAZJFES9twytC4MKts6K8FDs0UjtZoazNoErr0FWi9Uv/okuYPqPnGAqwdPTRSfu6rLIMianWcPHdtSTyzNIy3MSI8tTlqm8xZ+/U8Y2tF1DErBZeuqHEUjqS25A0KcFBDoRdBoz4LDUBY+gxM2WQOqKHBQAqLILkZenYuDTh5XLNP1NV5ASxiM8+Os+uEelYEA13WHE7lQwPjhJ0Y2kZ5Ui/VsVaum567FbX6tCqcVmCkSJJjo7af5QJultp8aP+gprpbedF3Ip3hw8wHIWCqZKWzlTzrjZG2appidsPwNgtNNO/jqDeOUCNxwNsJBTrvj0CCSfcxZtPvHfTZ1050TNuHnQe3sSD29+xX2gA0N4h2q3tx0flbJEOdlkKvJaMJCKnCiQ6y7YYyE9i7MtW/HjE8XO0xry6dR4ufSWoT6DC9qFjRJXqdOYll/qB0MhIWyLCnjaQDwmFKPMsic19XnFWhadYJ36+TTxPvgdKZinHFPGtFT/lEUUvZccWKs/dVkdwKpTu7Wu20BgI06bq1gFA1FlpRxcRtLBBKxI11oFa/VjEYGq70b6BAGpQBiCp4kSzZPbdVDjE0LHxePmUE7uKIGKPmuESFE69Bs+mhzEDFvsyO8ofnmLtcSRW0ApgQSFVtb+Eu9DmwJaVsBtDQktpH+NrL5LYhZh1JBah4lQ6xbNDYbLx+9bBrpB6BawvTD9M7TFaZF0UStoGjJTw2bkXm/RGtP8pjtVcNaO1X/gkiu28XxKBpH2EuqCwBPSt3aZahXdSo8mZBgtR33JK5oFEhz3SIuG6oDZlS6JHYpaki+ZaVCrWdNC6Dbo5Uq0PlF5e23tqQ7ZuduscExEqn3TH2DVD8QvYlHlSJTbUTLnfYAn20P6C7v6GV15vH0+cTXlgDx6Czkd7b7/gqAR2buCBpanTRto28ifBEX3Ix3WcTG71Z6lggEFYXutk2TPjvkZnn2fpR6NzcZC3y6OUqlKoSc15tXS0zzOrWmSoZEl6XbsrQuJV777RkWMR5y3zqtDLm7bqKDGuzMjkQkMQsJbPAgCWk2UqVZDyrr9QJbSpM+PdkaL9xG6isTJdfmk04ynJ3RAkWlR+LVFmg0rxojSxGseSgo2sHzn2nDwZF7ZT6adG4udv0TOtIVFwtuXUqp25uBCUq9SqDkwotIjVOXVV72szXil8OaHKLvq6zufWtqCzct00IKjRNdKjupTOaEmQmkqUHggauqrWDEXWDZtM8KUYUYOnfMGz2nYqJXiMGxSFHmVjqjF1n9BaN0oaN1h2ebiBjZni6OKIIEB9lzjFoRji6oP2tGB7bWgnfde3el7dWmD7ERpjSl9XiJw0tXd+878bZtiHR0d3go6dhVKl1yaRCkpc7G4Aj6bABqNu2UtPUhbYSA5N2BI4Iv8KutotTlGXJ6fY7d7I4ZZSKmSjMs6d6I4x3xFXi5ibAISKmopPpIdIIwZUDclCtb0A0SH9z13FtfyafigfZ1QzLPQNec7YeOIoXeHcXKxxhLBSZzlKMuAtGf0krMyhfuRg+Ox8aaomWAPfwVKwaHiWHzDZa3G1PNoQRwa0TD/bkRZRmnaxT4Et854yWme0WvHHIYrTEEEKgZDkhAQpYIxNO7b/hR+uRIqc3O4U3yZqm2/inOFK6G1rzjr7DEheqiGYlIT7YCmIqd1SCsiyTCaEULRpt3kqsRpjMohXlyrzHyPbL1q/oh7fpVnfcZ9HGnp7aqnmXW5NdEEiTvQYDXd2d3ZDnusPWGIcN+r2k4BaxpyNniVea/tyftqYWtllD1w5cj4gD5Fj0F+Tw3esSnfOOuQQZCsVl141ksMJEuMkC0xQdqqu0AY2VfOzWxmy5nqH0QApvc5ZE3AKN7hLYEt7NJeEg12w/6M7V96D81t/q7X7se/0oxaPj2vuzftfv/uW/8Td/47fc/79HdOZfHf88A02+/Xu/5xvv9QN0Mv7P/+3Xfe4fbbGrL/+Pjq/5+m/8P/6xP/m+v/wjfsYHffBnfO7nfsXf/4q3zvi+/9a/9bs+5ZM+95M/7ge9x/cZx9Ayt6XpwNjjOG2omRXlwbootVNpM1WIVVXGpXOUwHy6Kc8RFkE1Viv1iVjAzDlSl5j3CWmvifZb2H0/EsGVtqWu4Tm9Vf90woCWWNt2PCL1XPHQGcnK7pUs99XVCUJ7tEt+dsmImtvdre7tykxaj80RREL9+jIEsvBEX9JjTxvyZlrtBX4kV9jq4WUMaFzBiryd2+7u2CNOa4FUFD0qDwq/StHus35h7jn2gXjkHnVxZKtDrBi1J8qseli5V7q+AbgdpmnIVFm1lmu4dhGZyHNCg4QZG54t8pyF8YzjqqOnypsCHPf4Ue1q8rXzyo5qrZlaPyu74dCeRhO7K9O7bRTQAEfVoTLNu+a8yzpG27Q1yxCJUIU3W8uT2lfIYbinetc2f3I8bW7TdwqeET66PKPjmaUMXq32pETGLD0E4x0ZICw7+kyetJOz7kdREglkU8mbUgGB19jotg473ijzKEI84vQ+La7WCorS5qC3e+nh2qL0Y+CwYc4WTdywxDCUuhRphRHUjEhW04bfitKkdOQUazVxTW1axFq0Ag7rvTIejWloq4IGV1dHY5vVaPFwVOhJrMWR+ZJaopoigGmd+lHgXnM7RGiZjGOg2NmsDVE+RehOCelyXpbhV94rb7HAG+E8a17tz+aJiopOuFp5w5Jt2WqrDq1jEmb31iyg9aENXI+e83QPRznIVhJawO+QD8f0GKPNqGvCkyWIkeJwDPRivcxrPjr1afaxaiwxpQxXUWtBiQyCQPjIOdqhL0xkMBRMA/ouWLKapsfDJS5TM9NK1WzCQhbFdJYJA1jD+m26X5KmG4E6LzuLMazMrP+ZOa/LcHptI53DempuKV7ct5nKhw8ea0/bmnBylAB45NCSsjfPHbkLAL2bcUO1hk+uBSqS7lWlDFiflbEhcBBDGzNzdFsntJN13U6mEPuej/IltJxb5iJ871lxG6gAhNpn2qo2caPbqjfW9YKnx97ZYRZqRx7KBkPpAqJQggu/yGvY1o3INNoM2kWk0sThOrGKM8fwgWlo7ygP6UhOzGnedDFWtJS5DAra/WPpwDYpggpCYzA7R7QnjBjDtM/pUGysHj3k4fVsdusyFlumIuvaKtcNLxyw5a4rapEX2MnesEhWtmIT2oJvCgycIin6JXpk4YJtlaJNzlEiQGpkm93cCq/Kj7OmEhbX6FzWL53qA82d9tzcu2tXiz/IH1oxtX4QERuUsYvMYteEzXS9w1HDCyZ84mgF5zWGmPQLrhSreUAaTPYT9Yzaz9KnUZhwNwCtyFFoK/qdUEgmmTPiEE8a8mI8umeBm2JM7HVoqfFnzUuWJaZolbfytMTwUAngCmbKVrd1kDoUPpRIE9XL8LL80EZ9yGwDIJQQCKTA3BPQoBgdmbVb2S2EmIGpNaRYd1P2UpozU/DN2pZn7wspjI1qWezBXBUuFhPqUCv7gSZkVqnt1el3xNmitzzlv7P6VKZa0w2qKlvXEHRUwl5RfRiXIYhNhKEoiJw21xq+SItmdgq+odtUUn7RUnNPeZPflFAgcBq1rrViaCEDFFkopjIFN69aaDyYD9uKbfsIfWeIU7XADhRAE77q6NoETE1uOs2qDdZlqOHtxJg+XZo/Spmj1ZBgIy2LsnwJJTrT6uoohnnpmYAtFFH6I2ZQ1rcvsVJI6dWmmY3V00qrXrtI11z1HDWDF8B12gO9hWC1EWv7G+c8QiKlfmVO625Z2vrXoVcDJ6Aw9Jfn1chr4ZW2EGopoagMxigxTdXG/ePtotvrNTtNMaDUu/PF92OIhAU6xI/Ogl2LMXpH1RMmiXYvT9ECh+AJZPM+a+ZKgRuLDIHrVv1dNbfOHK025BJBVu8n9sYTfk9q8HRuLR1UJEdhZLNkt0/kln+DgHc37hbQNKuySoZGolXlw0Dxw5Jj3F/MMQ8ci2MiJvYbZz9OP/YxL/esYeQgiRaF+tamYA6TYIFm1zHsMcci5AV5Yux2JaxLb8fZflWd+SqhKngtUUAqSFYZ4TQzd3opKutrRFuaAJq6yLg491zNI3qEYdpdQWMjNRuzfvyP+hHf9p3eCa9fkfF/+tN/6r1+xs/7vC/+w9/wzW+5w399/l+9vZUBHuOdf8X78O6P+MZP/4Ov/urfeev5//G7qPumt7z5h//Mn/+Aj/yYH/lTf/qv/fjf9Nf+6l9+8/X/oZeZ/ev/2nf/HZ/yiV/4OZ/2Q3/wu3NZuUmmtw6yUHdmvd6AvU3Y0lIOh2qJ8pHZahah0W2PU7uh8XC3YQ0160S3QbqWBW4GeE2GCmCGHyVdznWP9UcNrxk41A3cdP6QcAibbZS87g24OiD9tH4iKonrML5Qsm7oBK6L+xz5lP5unbvLUMFQZmHzhAqmGL02Hs/9eF6eCXWC81gnX7rcAjhZMaoOvd9f0USqwtqt1AMW3MehcqoH8lGtvvTiw93BCb08OLbfnbzpkrmCbYEq21oo9hFbu1CPgVppD3Oa5mhRYRfcW7Wwbg+a2wBZzpN9Ki6cF+5mGCo37OiNolrMyq1dxSo1tZfKI8c094M+SSIY1Y0NUww0eRPSNWwcWriwV1x2Flujp68DHAlZTWEyGcDS5F17P+N81ZdorTEPTpFAULTUdBzETGjbZnIV2DuvU7cpiS6hr2pchYutLQgPluECrqzz1EPms48ndMspa4qtM2DXtjeffr5So3G0WWO2T3CyWKWFIhPPmM9Xo05qu3DA3I90PRSZF8bTHttYaHSpVUdbq8tYEuGJ9BDrnM2eFCCtM6bla07ATqF11DTlWHvPzd6WSpopUaUGDClAlhexusR/PK/nGq9QlwAAEABJREFUdZ0pVzbSJKj5vPh8pRyFcTvhVBFwwg70wzFHDJxeiT1UAlQgtxMJwWLrz7X3yephqmAej2OMl+7VnTcC8I22Q0Zm5KinLS0hJ+y5OryHbnOeeVw57sB990oMFg/kG745SpQXNVmmtZnwGVr6ZzRTSayBGJLJo6/pAUA2Kq/GrtH2cqM3gNFOXz5miVjcrjmuNmw5PY1XyxQAtpo7qYVjOl/Kj+3rydGaCmou6orUJqqCqUFrlQM57Dp4UdunkZdXuhF36gHIbrmQKNKnWWDSqFCUKOaBMpUBI+FhjwQDnXsjQMmGVYLX948CmY3LXDPG0TZyIAupIpfrdArrdIlC4t2BrfNsm0+bG6TyDz/8MWwVh0x392C3xnUkmdPwiFxnCZktYS/P58Tm/TKqQmIA5uQaPUZYXV1Z2bubjNvLvJUAJCbHy1jLIfn184otNg41ChWmKb4s78si+vVLqb9W56o58Ogxb1hsv2ydWNj+uHxmh+E8uF02jaOpKOaGnWYUf7geu954jnm+ZK9oXESmecxRswUbN6ZhhtG5cC7bTYTbBWFAXcrd8Auz6kE8vLXxDfQdSowZqvhDY/rVA/sBFQMJIyjTTsC6IS6yi20CqNNo2z63IVksGzEPjgf5yOuRp11P1KvMLdorH3k3aaDTsoWIz56tqlvThtWt2cuPVuWV3d2pJAPB952iw3oK54nanbRQjwnnmSPqBqbpg4XZeCx7mcfyw27gQoc9LcWmGVVA47L5mitIi00aDgCy8eSRODwFH8t7ob1lsPAYHCpyuvxCRg6j/mx6lPGVJt9HoduDJtbHbXK04VJxMHTXzXQ2yoABDTEncMEuenoRd3TyQZ1u9HPWc+VzUJe3dVnAN0aY5k7hDVPbOXtsPzyVMfrtepEvy9bgzOGn4/bot6/bpSf79DPEFbfxHFTDdM8H94IAgOgJ9VEzTJDtiBm6tg1dai7aLjrWmsd4PPhiqb4KVxJbMJ9rhpYnmtY69Rp2QrlzmXkw3mamakhxX3VsrIIMOimKFmNUeas/LKOsXbOkuNLW4GFr2fBhXMiVMRH0LHb0URjW1OrlOZWuq23f1SU2n23P5Em+WaWR7G6W+nE4YPsY5wuvRUheTFSrmFFFoV52+UvPIavakQs+2pcQoVzGPboh3MpDjLPtmsF1GbNrRh7XVEFePd50fa85crqSJhRpULzUsghEAdlOn/TVK8FXHZdOp+LqUYHUFxlGsTFpd3Pj3lXRGrJ5sEzhPmyYCiw9W/1llFyMoinnwuOVozdtE2EpKzAwqwoneJqKCC0cCmYjX3m98t5jB/IMaAeg1Q2oyR7evZADTerTrkf2A+AYvQ479HYZCt26SpRZm5cgQGwjxCBsw8NHY7B1Pytmne0IRxrRssW9qHz3oJMOyGV3CpribyPlTG+IMS/vyyGZvaIWwKHEIplN3C+FE85yc4NniWMUaMrB9Rj1xuiHl1nk3BwqiPLsDggAyO/yru/8Y37Ej/C5vtO7vuvjuHvA133dP/24T/20H/tz3+/Xf/pn/62/9+URefv4V39fM7C+27u+00f8jPtj9T/8kM/A87o//w9/u7/um77lL/6X/9WHf+pv/fd+/gf8so/66D/55/7sq1evVINvHfIu7/odf82HfNCXft5v+WH/9g8Yyl9cKWnqiQYesySVuIcpqWos0/ox+zGwptu0IDaYgzbhwy3tJWxI20r8MLaP5BFkQ41xq1NoW4Rw1Kw5WwdpLSWazdFHwwC6EZNaijjT2jpml5xojcqRbTe0i5rHw/HocrWALcGDxOrpV3tJqDK00B5QPN0SX7cG3Aos1YKKUba7wrp0aG5xwB16vOUtFQZQLauqCAIJ2VpOFYsJn7d73lj1tycCbLUyeZfrlovyeekGwhCdas5Xq/HZ6fbsziLpcmS4X3mXpdAm2ZhtL82HU1ujfeSGdjZ9wXaNvncmrjl2my9PevcAR2tv0V0AoWK+R6ApRBpKUxxajhNi8TC8eN3dfqoNVVbdlq/l6XI7wurEJXgQCWPZpFuhSpZ4CS2Xw9QaTaRMYBYlDH0fcnFV7gri0pQiAIeyJkjQaBMam+ALyk2pCc9yBRTt25gWOdQAE5XsPDLmvjqzzTAHpsPsbgj6Gz3EXeaNapBaA2ywYaGO3ato3RxMacV3J7CF4ggfMJooh5aDGaF1tIYkvPA4MHyLKo17UELC4sZ8nas69USm22JYyqax1APhQe3RM9XlSmIso0g5Ol3hd6ntPkfnpB3wg8rM9i5nPkasEcOltyGITRND3HOcOm3KQ6rxmsbksfb0nIPD24ROYm4zlPlV4xLDO2fUkVw5lUuFNVb6DOOVUPigtys10rCNdOukqrPlOGbnEkUmFBTXkEh0GdjNxAF9v88mSkGDl4KWwoWtAn4fdyFZFtFV2CVJZjPmzDGVxkgqq9kIsG156yw8xYF7DUJzovSWXqHF66gmLBWaRi6sAYDF1uzRfqNBN2O/XHyJOaq1kfYyM6fNriUfklZ1JAga12xD6suG32VQZsnZCurU2ewR7r3aJYx91rWlzrRxYSZl0aQHVjZk4GqEeBlVjq5hMdEuqOWhda6bDH0ToY0JiFE0CvLaA207K6ouxcsGn8at2G/I/gbdwMjr6jNGaA/SMA9qulBWdYsBOKcBGXUv+oqlDS59l+xeGa8ll3JPoELOtK6zZ8LrNiMLpUgm89H7QE54ikSxHiJrVus5Cc6uLJQZdYASiNSREsvqdDWrptbiWEcfD45JRb+6Zwi6jMkPijH84gAU/2h9UFJoim4yDJfQ47qtuKwWWI3dar5k0+hCbzPNB3LUlbkT1aJuWWOkqFdBphnMfbnbNGLIrqXUqAJ3XSsW1NgkRmUZ7Ks72GGkRr/QVlsZpeA9TcGrUnd4YQbXZfoAKOq79xTkMocgdmdDJqw0u1al580oxliDLuQirZQi0jhM+gsNCHrPZT6GyfeCwa2p3EAWjxKa4fNQFyuM6rvPrrAXcjhdZojRbawEdw7VL/RqIKD5Lrs+UWKGraC7zEyNAROEC5PDUcjdKseqlhWBRo9pgryGtbaOMy9UtCVHKpMazwvMVvksU+hEmXiN+4t7SI4mdLxDPoYfXHa9hG5jc8mpQW8K342WJmcqKS1RcUgvp+kJhJz5nXjjdiu4Js1u5Qwt7b2wjis6zs6g4eD0VPwShmiN0TVI0WgsQwpGCF9TZLimE7rUfjc2tFvCd3ulIRwFtLEVSoV3KaY06egKBcoiZU8j0HARXbo8ylcdo9eg9e1TexA1U42sXfusjt0V0HuXXwo1IERRXiAAwzYrMlDERgZ1qdO4RbQQg8yZ2rmfzNLoG7oJHv3s+azUUmldENQRHGEGmmNKGkc4a+rS9VZLUd7pqNFN9aD2hioUWcjumgkZ6EQ+s/dpvZuZBKtMCLX0QKvzg31M+Tkv27EirKudSTblWXDlgNDMNpVRoszTALZ7FUOreilO5cMd2sn5Cl0jxA/uJyzegax5xUpNqwaazF5lc7dww5QKq/RdXjQdIp1Oad+2j227EdEpxjdFf0eU/gR1xwbFYtBQqtdIog6aYGlbdR6yB7UyO8F0LRDXZKbn2c88rxHPw8JttB97UTTQNPXWo/UYAauabGFvKlT9RXRHAZC4BwfvhuQltg090fJMjsyRwfCLLhc9nxP7YfeU+IU/9cd/wkf86j/82z/ns3/Dx/xb3/N7ypCOr/7qf/J7/+AXv8/7f9D7feTHfumf+nP/9Ou/QSf/1dHob/WT3/P4t7/3TcVX/OOv/rQvuj8Ar57nf/5lf/OjPvt3vM8v+eCf96G/7Iu+5Iu++r/7v7z10lvf3/7t3+Fn/eT3+ZJP/5Sf9eN/nHQQV+BZ9qyho3IQg04d3jarXqJGEOo/x1TvLZPoLX1a6z6Z7XE+Og0IVFn7XO5HtulBekWM+xy6VSxtFau0vmRm4Lw8ctAGdNuxGqPhXjY2KENoN21zx/1/FPnkfLPX2caNdZafCaG9tmVpz7S0TL2s5Njn6OfBS15QV0GPjuy0maa1914DwFU+bEqTFif6aRZjQj9JKRLTqlUdjU4VHkgClDhNtwqXX81t3WvDL3DX3dR6FwUmVJ7T6v5zjSuG+oH5hm0gZ8Wqa2Yfz3mklhoOk11AqwZV7pEVGal4B2wl9MyFT7m3nq5e00s78Zm2erxQTcPK+zIPuJxsWSKVCW/Q1Epi+HiZfYzT8erqfNXcl4vuShZkZmoTr4f4V7c2TkrOpTAS6qJ93U2CaBvDjzXcFzEsp4uHVKwtEzB35kAATg4k18W1XQSaTYwoi6ZtG6EGzKmwKrXBlpi4TIsEDjPSWcvygRzZonXBp93rqfojeMG7p2Limb2beibmh4wmtNHj8zy0hHRHM9L6OWofrW2+HwKNuQzWmbqubqUFaQ40UJ3M07ceM1ZjbtrpuKgroOyT2twN2U9Xvo2oQX1MTmkrL8vrdew9Z482SgE9X+F41ce1p59qitREtGWpANQZo6/9fCKFWjs+v524TLGOiWHqvtM5lq8x5xi+APGgUEuQnj7BeYTpoffM/SruzTVkWCHM5MExsAyHr2GTMKKJiNkxUOJW9uVObdYUNbWFwN3SWbXCZx1ubjpzXdilAT7Qw1XWaed6XEvQsuuqq3feCt8RHSnXinsxFmJEReFyN8MbCojRlc9Le8JIv3JUQYgSWnCqVUgKqc3mGn4MuO0yrU0sjlagPr0NdxqaqWwWoZfeKCvprj4DjgLLSfpKGyfn5Sb98Ur5uAMzHukI8opZ0itNMwfKhei26ELz+tGifLcKxdpwrToXaowe+l1i5DRzUbTXGf1mapmuuorP3frXVHLuhqP8yDEMEmixtmual1ULeyMS+4Ik2noIdM9raoOyAjT6AWoVt1TpYKCliAVbjeM857m1zHbJKs0aXeZQOEvautQ8SlWmw4S8D9qik6yGjLVFDYlZBY7Wcl8Ju9rT6K1tpDrWzn1X0Yl6qlaUeWNN+NqjX99XhHlycS2OqXQ1u6ZI56q+dXed004V8h52aXu5TBeOkQ9ymh0ibCpJ6PbIGYr5fPb5FHHYS9zo7qjPvZ+Wl4Qj+5RWuqxvwlhJfSCq7RzzNJPSvHtwQUehNuIaisMFb04fEzEgCZbYa/hOIHBc1MOdCVjAa07zYTpfOLfhOdQGVzpxq81bwi1Mx5ygKCqv09k0sHG36rvBd20diqkalairkQWNCavghQ4WifuYnDpgkuy2lqLqgR6axdrsktGjauE0PwWvWb0SR/RU6e2Nak1s5fI2cENYl3kdoBd1UT5atls/wB9TgemLwXQw0af21FfNK5ee7XQ+DedqDmnDWmGhC2hpd8F01yluRBUau+dFU840ULYhr3vVc7U8ogJ6uUIrb23zLsNOkVBuqcZwP0YLlxdbhAZtIrb2jCUl1XVkv23iZUNFmFpW3PuYOCxNg93JqTm1oV/pY0e1freTvcYAABAASURBVEU6AWH1XLXHeWqZ6llC627e0AFVzCweG35RlSN2TmJX5rn7TNFXbtsVnHISF1WvYlTpyut67q1+SoV07xai9RTskVvgJ+77OfBgCtykDUlZqcoIXOeR++ikdFar20oXLuNz4FoeBq6wkRiCoOF9+6byTY7OBekZJUY1za7EFp0Z7JPD+mWmDN4zzrWuw2R7YTpnuGvqcLxR/rgmn4dQXP2SnGw2WhuZN8e4lLAqP88dzx2viF5AbmzRYC0PY1qbNW7J6j3KU+wQU24PPM2kOVzt101601xZrKO0nomjPlFXSuC9DHb0NQR+dwaKzLV6jkUeaDlgk30vI3DWQh16T4mUa+i95x7c6l7lO/024eno1X4UD8212kABsAJb3thAFFJnC2jFTLSpmev86+8a2zqPMp3ve171DrWS24JsUBmWKiLzNq0FKC29wiTdbdG29Z2r8aheBSNNdBG8jVc3omULUdRnMAdjuVRbwJ1+amUm03pXRV4d0a2Ocv/fDUCB5bu+8zv8hH/nB768zB/y/b/P7/7E3/CTf9yPE963Huf5/It/8T/9lR/zsT/xF37Ah3/Sp//xv/xfvuXV862X/v/2fbxxvPOvel+8/duIgW/6nD/yX/8nf+mTP+8LftIv/sCf98t+xRf8gd/3D/7B39dmArr2zx3/zg/74V/w6b/po37pz3/Xb//OeXem0dpTBK1d/eKIva5L6YF1jt4r007yUs0dlMpJS3ToaAvzcGvCI5TwVMrNG0R1hgqpqs2hhdBbKe+7ERgkZlByi6rcRDps0qG5oaakKwiqhFg9wYPqt22P8glwxO2zLo1G97ZU9k/z8jG41CpXbCsVZDQjOturh4HyjbnX3AfrsU0tYlSNVFPak8WBcqoLXirZvlcn6HUDh0GRsaMVSCFUi+29ya0IIcTI5j2BWRaVmdGUOx8cs22UucgIsLvdLrOtD7INiEenaXpuZEJ1UTClIrxSc0xbilQrGCLnBiGvY6jg7UjzG09Wqd2IN2XFFHE/VXakiOjViisAQVLtCfRt/xJvqi6jj7aG6rO0XBYo7kFrknefKOfllg+H2HZ0yU81UukxlqFEQgHWPXVo2kTrPoO3BoyNxmjOFOl903O1X5SkJtxsJR5ZEzlcQUB98XbgI9aq6TWpnFV2X9WitHoIE2naT9pL9wFtFkv5pECIsNNS6X+daqeM8TCtGcaKxIaJeR+QMLC924U57kYTrU9FwKK5TdpTNqOsttTGMAvJJXU1rYI4gXNT0sbwcfhcvNeQWWGZ1mlss4ZMJrIge9vlSghT7bNTIT3gWt4ie0tCujYbZqDEbA+zqY9At44bmAyGgoDsQiuL2H+tHOHRARGigKwh4MyDWNRcObIzPENBBEwsEVR+KipL0yFW6SeWZE9R0iuuEa9U1yXJS5jQeP1NoSY7bIe4QaKjIjKT3m4TdkTNTJSIgUJmkWVdVppoojiDlfKmgxQCVrETcPRBOoY8XDqp8HU4YtQ18XQdFkhaCjwaKAtmz4vjiTo7zr5CPqhZQym9jbHarxqFVTcKVKuMag/2chx26DCZ0QLTWd7lRQtnpWm7OTe03IyGG80pexM+ZF1IS7Mu007nrNRS2w4QpME0HBPSYLq0EiNrJasQsHTDLdZGN0M5cj+N27H1tUHqfsicKEXXnM0lSiqPFqHFu2ERmT5iLnMpKjnAIaPtpDRSEzUM1FSf5eoq4ISzTXXUSxev7FukGmiDy8ZQYGWWYA1wtHmZ57CQXItW6ZL1M/2ajMFWdOU7GYWq1GWxVhUchZmx7B4z3I2yNFIFOYzynyOlElYwS7GLk8ttOykyJ1qiSGXnHuid45QL9u2dgMEGehZGKfbBG5hMtKH02tFnd5lxjjHmgm6Uhul7UyGjWGK65SBHFalPQMFKmVZKGjIHFXItSLXrqGnDVTBtsuizBxPoTHaapEqAqbeC3qQBWtidyMbd5YUju7IikTIgZuUxaGWUIfm6vVNuuRNxIbUrzEBdEApPTa/CBUj6MlsKMhtxO294p/acaKBm9Tipq2WNIeEkmmxCPMEcwlYGXdf5PbCHbWDLkKUqmiy2N5RewXKiDT1kMoxpSAU2CmxQjiEAKn2RBVi7d2tQAK8PtbZKwZ4gxRHJGgtK+ayYGmLK4ISYFTRxPEJ50cAuBRpVV3RFWUkpoHKCFHLaaFpndl4qrRRW8WuXYnDWKEqoZrQFLjY1EQNQIGmhQ22+u4GOvhuy6FSRafe5BM4smmoPmXJgLklU1001FZVcG3N4ucsaxBGzSwNMETtjtvY2yrElZ8DrMtvmsnofjiYcRbsKQYrOuXLaHRwEZ5RmiG+NyYHXUwut7FQxQ7CgryDvwyc5ZClEiWlISistNljXBGbJzMQwoW9LJWSsFHABWp3euo3Ateu+BVRDbkZ7mSuiUgw9QLv5nQmrgtjXgYsWPpS3MWBeNeZ2Scy0WfGANSlgaBdLklWabVIfwipW0GMy5AEY7V5uIQbvu5H5khjZUMQ9aB6ybZ6SQ7JQKW6BQgpkIkoUts895hPj2qjrvtiQHmD3dIPN5Nw928yNZtKLF272QgThpj4LCmeePhtsgY+utOSIRidm1I2oqKmtoQmt5XHVVfuVR86gqmnuc+oWTG4OmxwrQFjJhM3U9GBsdOlc2DjxOPvl7Mez5iWKENJalZjDOWofGAfUNkCxTyFjA413eNu3/Y0f8gt/1Qd+wHx5W/z3r6r6qn/yT77wS7/0g3/Nr/l3f9rP/PDf/Gl/4i/95W98y5s6/98P+f+jf8XT49/6bu/wc3+0Yq5X53/7IZ/y237X7/y7X/7l16ltls793w6S3/N7fM/f9km/6dM/+kO/1/f4LjYCvp1U1kW1/uUckqOhWRsqJpe0lPEqcAAHcyi7FuQZ9uqyZ7SGxXCpWQrq6+4nNGPXvb3IDdCG+skBdw7GI5+PyKMNdPWinpuqFNnLVZgn7ezM3p5qIH1L8Nj0lD0aB6hBnkCRPoEpnFmBHVU6C3f69HKZFCVP680g0tVa0Spenj2C3G91MegLti7putR2LsiS4lRxXZATBUEhxFIXUYGgvHp02N1pVPDjjkzIVIxvNd+yI6+WsGRz9lwcPthHaYXcL3YNvq44sDTunn5XTdBFNnWmUapvD440fc12XMxQEHgdCi85Wtfwa3VaFPHm9PMx6rhJ1I4MQLcsMcH2uHsQB01BztpeF1XIIDkMD8vVEGS2DY7lwrrMzUxGtPpBt6V6nKrafZ7Qw1JNu/doWhIZQCuA2uCGl+x5jZlzKkNOyIC1TMcWdpGkSHRUL/WVtGrfZUKDHPDRtqjRdsKU+jQq9hvoTkFDixrytTuCE8t92DKaJUUTC0Ul7MraXVtauNOSCc1ghVCWyFg1xBDzwHbsir4fLl8Lenamx7NCFHtnavFsNeqkxKKVKrfxdDWmEaVODsio6IQsmU0QNoMrarCEaw+e1qed1U+aboYx13QzwRMagSg/q7RA1xVKC8RHe0Hdk5lktqIIAKKuWtq0vtzDmAZTkKv7yLLodraiB5SD6LhY6VVyIxdZXZnyAm5VT1pG8Sp76u5cG1kjADaVkAUuUj9nWYcGnSWKfZQxLMAcctOQALuAgoqap7YTdGg9brLhXQLgai1P5lZAjjlsksLapUnK6BH9ctVx9UjJQtY0Q1NrIPw+BUAspFUSef93tda+YSEvoBUflyn8C5l95vmq9rmhZcbjWJguX8N4MzTLi576IZUpEe09NWr2eKFNZzouRGAL/eJ65Fy7rRPeCiY1tRuygNEDryu214ExxVCHth5CCQzAlSBSim165zx7ZoPchkunZ2D1WHbMZTwSj82Xcx25CIQYjkKlmU/6anejvbYLiBDCbQyS0dgxUEtqrZlbRTvgK32GDbot7wMxoXGuk5gci5RBLMcYVEE/ds7Qw5FJGJ1C5D5RR2LVWhIBBqj0qhkMbABNEkpANyX+fjX7zZknU04kaKfdLkZRMgiw1acMhl61R29HkNm+a0V7w2FEj9GP2eMoDk3nrYrVoreJaqZ52M0jD9gbtAO4t303D04Y8iYh9QvTE+ceF+d5eJpBclQq4+jrpeOl6wBLQ7MvaCOTz9QhPY4LqgBdEzNtI+mpOtIhJwMYasY8akwzf1D9T8tGRrNu++1Vi7FM7zbaVWjl0IvST0VkPdHnqHadviHJ12aWq1ZRXhyi6Iy6rkLUjARUXoySoT3rMik9yCYzW4+wucN6F7slPY0F3my+6fISV42zDyWo+2qerhFSEGL7Dmg2A4jZfZQkUQPtTQOFltY3of5onQPYoYabaOkmp8YByFBFKZ6qjsvfzFXT+n541OGt0VbIystrH+zh1B/RA0aLXdutfpBZ8JS0KCYpwfmyNN65YCnWtIyqOttQdnvEpgBzH3Yd69kSzhaJwoyGhkw35YTLQAFsuVbIwFTWgQ27IPMygL14Tk9bTSmea2KuMsUtK+UJrbVjQimcBaVjQGcwQDuI+wchjb2TaFcoRaNz3Ak9iSQHNHM2cHnzuE2/gRjWEAteIcozhZZCOMEHJVZ9LEVNr27r7tQHEWSbM9pTnc8GvMfc9dKhH7bGXEaTTZSG146UWPIs7UpmqoCpJ1pvlB0SrWyr6PoygN5F4VV3z2Z2sxfjDW7VwYvBJXNmZUOcRqw6Jy7rndxGlc81V/vCHDaNNNawokUKJq+2jEVl2h48Zk9qZqj+URw1BIKEM4kn7czZsVYtEYQQkuqqFicrxL2+BCPr6gwPiS0VN2cNGGrIRBfiynHm41U/Tko5W8oddL3MFKS2dmNw+OI9BbrzWBmjL3mIDf1wiG6Tb5vUTG1nzrp5dEsl/LFsPcwhomDnZBq7qpKiDs5HcL6imvDT+ByOKcXCrRXiuieVUEemab6QwZwtZzt8v8o8n9jpbYcuK8Uz9hFP6hzydJWWm9Ykc0BR+Fg//6f8uN/3qZ/03b/rdyWJf+6Vsf/p133dF/7hP/xLfvWH/6Cf8D4/+QN/2cd8xmf/x3/uL/6dv/+VX/313/gtb746ry2v/9yM/1/7+M1vefMv3f8Jymf+qD/x+f9oqeviX//mfp+vPRTnt3/X76T3/+H4dt/22374B3/gF//2z/xhP+QHj+N4lZWh2uQCH20GO1e/Ovy5VqxZ7tkqOcS+sLft9jCe6NDfJ1AY+ovbgHx2OJJt3Yu11IriNu3GeZenYxCjqW3CzJr+xAjCp61jca6r2BqeOQgovXofRS8sUtV1ZL2kfpY9V6mQqkfUkfGiUl3Jh/TZAUQ1tJj1MXLOzfm0Wc2u7gqUAuUVdgdbbUG7XK3XqPqOC/0qWmtGgSJhdr3R/cJ2Q8NU9Ra1OqeVtQmfTrYaG1+MD7Z7wK6G3nnYmnb/7nOtKrtuL1Yd1rGQs+pxnROvAA3H/dosQYb5CJFAz0RHq43clTVan7u6W6czQn2tQqzu6nuBTYtGy69xEwnrUvH4PMfHlWZOAAAQAElEQVQ4w7du96KtMIDDee++bsavLLVcpzWVz262nHLctKyaYg43lAy/wGAnYpcw7TCtyCA5aFaLqVZQUK16Xsv6oJLrrMlaTkeK86pdFU2thLk2lBJrLTDaNTlbAKAETMRhMRhVO+5bheakFoyB8gZVtymMu/FM5tbOYx459IjDOtXW1DnsUCuc7YU4e59X9CusLZzeMlBReTYcw92clLrcFG3OPv3a47QRo9qSVZ70pnnfXRXYZSEMMW3PmbbKqdSwQumolX5ch10PbIkdwm6RfnXHHVkWDHNxrPCSnxxZE+hVMTIZaOU8nRoXiZTuYiNCF+L0iKF7NKQaY/l0zGk+x/2PmGcqJS6Ubd21pYm+4EIHGzCj0N9m+6reAbaZdrqt1Egc0sFEaf59QZzooEvN6bi892B5kcrFOVzXZjk5oWO6TfnQSaDuDZjFbg1G4/7nRDVIDBWD2bICS78PtEdyb2rpCnYUUQtQDPKXpvS1pHHRT59lq4tKgaHZDYDya5z6h0hjHVMBe8E3ebI1HzAqiwpeabuwTmnoUbk6nBW0Z4lRfaQHZkBJHk4bhBudAO9CBisbYkrxVKecg664p+JomzntMrtqVnuK0YytKLSXGcXSXB9C1IW79sBbX/Kjo717lGBq6IE88rKn1N2ajek+FqbBo7QVR8aweHA/4loR1bydLp09FaSuKYWm5NRzjhrDrKfMBHaPbaSnUxqDgjoGlw/YoV+HZaUy1WSyGOoJ7Mt2uZq3dBhuZ4+ryTnXmnNqtuq1I1BimVJw34XrzdKWZrKdXaPLVIpFxODupgZ3ipUrKZZUlYMONwjVbYHi2AGnDUUrQxdwxdi0okrT73fZkGVRKfeIrrMR6PstNyqZoLJkfeF8lk51FKN9q0DlbLSvbcetxYjuSt0CtTofBN4UlNgYJlVbpOt6JnmpdXB1QB1iYdKYivAybCW4HQQn6W57jIDtXduvfTy3ZaTpyhjulFD9mpZLE8T0RtJf396UlJa6SvMestFvMlPzrgNxeE3nHDdFM+uNyEOYgWxACsL9knuWYd9wt+9z5NMrx2UP0WLIlaEd7T55ZgWUL/UbYyuSgQFRBYMDgz59sOdtDLdDB2mooV2okqhT5EGMLZsu9CKs1C/8WbNlwKkSyMK+/HqKC6wEq6/szQ2rMEFaNyGNm4YRRBmwGDoOM5sqIHkfo4WFcmF1pZ/0y5ZgTspKmaQ5YfPGwsVBjoRU2HmqQ3N1rl0IhRo6bYYFpdOX+SJdysijroFNhd5e4QwXm2F5ERjuq5eloG3gMoZikH3QJmSo6QX2hitBJTzeONIEYUBjnMvamGDBicNVeg4gZTCMZW5r8rZENpHIKsVnez5yvNFq/sv7MCgRc/jo9tYwZx/A4/U3y0pDNS15SNjYPZ69Thvp3moKg2m8P+qbh409UFPanC9zvCjhpqByZc9orRi2n62VpG37zCGWB9htfeOlBY/E44IcKobbcJvRqtnZWR7jiLVo5iHKqw3w3haaXaGCJNou4LwjbdVC14ZVOUgY6FvSGvaKd2dJOmb52uN+NxcFDsvm1SXL4o6NTpRlWOnfBmtwz05LAyaxZruXU+1cpy92RNuOo84JfSvVl0uDYkpdqY2gwrExxoP28HPxNOTETaQSJf4Foh5rrylpNMoBa2c95NiqvbtLYSKFS7apE6zd9QpVzLZLcwBC7D8kbQMoRHVWqBKfbvt+MkOHIhGVY8/xvb/3v/H5n/qp7/3v/Xu+HvjvX+tbv8s7v/O7zLl0Is83/6u/8WW/5wv/4Id95K/7sT/r577Xz/6FP/UDP+zn/vJf/Ut+7Uf/2s/47Z/3xX/kT/3F/+xv/Dd/9yv/8Vd/81u0X9SM/289quobvvlbvuzvfvln/v4v/im/5EN/zgd/yBf+oT/05qs3f/23/5a3hvQjv2n9a6d/1T/6yrd+fZs33vjJP+G9v+S3f+bP/ck/vl26JXEonZcyn8o1VBvuF4bUm62M+ZB8i1NyupOZ8BM4sTP7zLvCuw3GsZyjy3ChtH3uvj8nKkkr7SRdqoPXrY4cOyy6ZX00VT0FT05MuJrMlZ3cOQShjVyppkbvdNtOncka+3I9bPLzfOxXK54SkwE+GtOlO9UYdnNDexk7saJXY5lwtAqLyEEzGdIh+XpzRkAzLMnq7kitIA+PuVoNAdBsFnfbBio10qtGYWgoGmXo23tZFVWC8BwTY9IP4zHK5tUVoqqeyOgCrOQoe0Qc2EZZvC9EZHviKPcUvJa97bIvTHKridurrbPFewWTFl77LvwY2FP4a6eaHnOs/XhD/aZ6Vvgu2+ovVV4F2YNo1uf7Y7fLTLFkVJy/vmgJdRILKLCyhm4dj7OPjTFADWcx1DAsa6QohchMeG/tUmL2NRZssjFbTaPgm66pnaPTgryO2As9SGJ53QvI7JqQp2QU1LNm5djkHtaTLv6UQtGEanYK6wWcPa66VXjx9aRS3m0cPo+j+RLh594nIr0x1E+GU1Rc6Ha23Fm3mDQfNgxFHeQ0jGVzDPcpv3QYnHVHCFHTQaSp5T2muk/olPaqoYoJpbHmlu1OsGgCztTixvsUUWgEFJAHsu0ayAF3weDcbtdiSUfEPRpxIdkcCkv7k3207orNZJOGXiXamg53n5RZ5UGDtbW/b4wuKiHltFmcwBB8YwNSXmfLhps5YJpIhlMuQncRCnARi6qN1T5vTQtC0tsUPdlcFsdK1fZavsbdL/SMtyouq1dT09zpcodVpvsAyiWIcbOpf4Tydd6Eo9C7uVGGczBG3yFdNZ7JE7h6bFsp9aclux9Q6NBLjD5fmKMImy1nc41jYAJSYMaujnYIr+Ciy1Fzal9iS+Hypi0Sz7CoA7ibgxlkioBUuBx2QGvr01oplaU+kSGQDMXBhOcdGlluPQLzEu4brSnZsDK/htUo9z15OdIwWLOb1bvW2evpfHqUbyXcl7y2AMfdEKjIWvnIERd1lDKiRhbaIa0LhziYqvJuNJjVvWOUdiSVRMB01vSPHAGlL32BTwq25llzhq0gcuum/Xpzx/OsOktNnRfwrN3q3xLqzeJMCpcPN2UcIGlSi0ENbC64Mtk6p8YjwnNEqpsxtDy7+ko2IS9WUXWhVLe9QjlUetp4GdQw5SurlKcqdTVXGxUsz224ZodHjTTTuCs7NlLBdInHmhYCku211dsmY2hPdPuNU0bsduxCaWqxfBnU5/SxYdKbttIBxAEMeC/2i7pC0jf26SK/KwzB2i2XopDD5MoUyijMBHUOjtIMgdLdyXlrItsu2jaNXoeaRbvqDhf88tGSljJ1urTdObeP1L7EH95T+ZyZADa5bx90LuKQGikNevCt25+kCs80Tge7JjTOVPHhO+aux1lLqMlWExpoNjNtpz95sxft2UMD7kcmtwCFRuqnN4dC4aDMEQcxKQcd6TocPAxKs3nJXskGFHYneIU1WINi0KHelRa9qhVLxjP6yUfaG17LdjFOqPaIUvZi08oWzBytu7Llg1zddvs1SwB16XuIjsVat5cW2nZgIjSp2c+M+1FTxOBe1A3bGfu6bMcKKkGTy3vpMeARTbuVE8LawKuwfbfNiURkbOgcVtfAM/ebVzyfladyuksVvAaHXCrqS8FjPvnG+TJyJm0rFnPW8BIsZ7s4iQJh414HDVoyKxQKdVJCsZhWrjigrUleehI1LRW4yF81Rjv1ctg0iAnhqm56+hFquYQ4GmtgjFKCbxIh49Uo0UGWzT2Oc61X8K2ULuMxfR5T903hXcXca5fIOJTU0nwGlL471T4w5Ede5RAtyb/q2pW1WymrOfLhtSDZp1qhWVFDK12NgmKBiFl75kbEZqp8cmaPliD27nx6X1ogHgrTC1YirHt1Smc+nToMCqJDMhsuSUgElXIsNWKONktWGSpSN1eRwsfL7CzTKOtw2zayl+dQWlYK3BQ++fJk97gROlwElo0GvC/rpyHd01Qg1bJZTVOqfThdrKijDZuz9bvzhAwEu6Qs7q02JdkOOtrFUF0BnbyiKjDLl685lUFbAv4Aj8NU5LZMi8DFkTFCK+rmDM6NERwCNGZ922/zdh/7we//yR/xq7/9u35HvH5dX/+Pv923e+df8DN/5s9+n/d5j3d/97d7x2/7+vT99pZv+Kd/5+/9nb/61//6n/zzf+EPfOHv/7hP/ZT3/zUf8VPe/wN/yvt/0Pt8wAf9rF/5kR/xqZ/9hX/0//Tn/8pf+4p/9I8z657zL+XfV+f5t7/iK4Xzi/7Yn/jk3/kffeDHfcr7fMAvfZ/3/6Cf9ks++NM/+7P+2y//e/8D6v9u1u9/x6e+vmPYz/zaF2+4jx/yHj/wd3ziJ3z8h/zid/u232ZfO+OZSgmM/iAfpfLBs/Bs3HrUSjmhPKpgJBpjefU0FV2rELYWmazCaf2WMa55kGtKi6PQgS3teaN1mAq/pRA24sJ+tjY54IaXSXevhyAbBSayOvU5w1RxFyUBlqf5tnk2rsqTnUPSNBho1dZVrNCnrlnJ1LeuHhUsFVKqTKRO+KQvs9k9OxdqLsOja6WWvzsO9b9V88AyX/9X4v487LpvqwoDx5hzrbXP+6MRO7BBTGzKXixRiGIvpYnGAIodCtiASh+FEDoFBLxcvNL3XRAlIBjUSFSMWrFiqYlCijIhtqlYqImJYonmfmfvNZsa+6NSVU+l6ql/6nnqfPt9zzl7rzXnmGOOOdfa+/1daHPcek4eLa1NlkVRVSq7OAPnXbS03d6RdtU4J5/W5y5tc7Qsrbut6c9OoZophYiZsfbTe7t7D0sDBpoU6Q4Q9ysyFV9b6ithom42BmkLesaaM5KeGNcap8/sRR/2sLXqoWHFUbBER+yrRZ61mSdXQDVHQw9CQ/wK6G+Kyo1SbQdMp+WsRQrd5RkQOB3sbuCCbe+0bsoD17KXow5tSNtqjyi7IB2IlRTpydJEdJtmotFhpdkOsaiuMAwg0tkLHGEWkgB7g5d+s7R4dETqe07PRbLH1f303I+kbt4OIcTwe+mTOZFBtIyMqiHLgphitLhSK5YiLtSl3mqwIcmaQ0RM40AZboDVK3hcfWQdECbh09pKLI5hE67vpp8wiNb7Z488R22yAuOpEGid8IBlN6oYbDhtuK2JuabZrFpxolKDmHPuaQr2qlsPVnrYxZe2kcZ0C5OgjOWzFDvbbqCEOaX2UXA1w/tK0oIohze15+Mpx/FoO6om9rC7QcKgmaNyZWpnB6VEbqbvQRINbLZkosSNrdDb2LjrjXuObSOaKtHbjyQojuUvNsfmQU7SqurauzTs/qoHS2vQaM07syKclJLoRva0MrM9Z01rFzdFExsVSs5Vvn1KESH28qawQIoB7+ElTJQZCK5EkqkP4a07nE1DT5om21m4spWQxGxO2ehSFaLp3raqdSrdlSY95LQtxU+lxtTGkBgB3hlMKaFFBPXnK1FiWd5oQyCloEaosFZgkdPhhkEfcnBLAUnXpgAAEABJREFU/gb+GqhBWKuRlsXTX8u20OUkVXvJlkEabVIfVFy3B01iAdutX4a5a7Zt98IAfVTfTIUwwqDNfrALHHUr3OUl5PweZNlSDs9GqPC1LHdUJqWny3tLBF0VVn27EkproOVhLBsLeFg9GMMCEgC6oZSKflBxursptcM4zWF+NTYTgsBgdpzMy7ghLi19brOdODPU43NWvywf3i0B3nvOJ3LDLv2xaB+V5oGR1Up7axpHwWRGdoXPTW59ch69BIM6dajpPZfVbO0SfRCyPJx3TpojaQXXb5HtdrXvmBnqBFaGED3gM6W4KN9dqTC7OtqlnVdRT8nZgUEDlEWFXmV5NW4h7NHpRdJcmjL3YebiUvnTUqmje1PktmYq0XWT64Z79KJj9F6loxSILmlBOAe2AtopJgnSZFuHg+1rr5eAXq3cF6rhug7T9VL6uEddo0P+WghpunKn8RiiCuJGSAppFSNKCUogFKu0zJsvopW/fh69Z+DeWfZhsXwbcuwxQ3Y8BtW0o+Vdcippi3OOOUorPOdJNTDsqmhZ2CrDi+OC7guzE5bGVAqk8O1yTAZxyS2roFlZKd1crgBFUQ9xJspclMJLV3QdAl4KM2fVrHCEjYvMuqd2jdZcYUvc45shBDFTBLcK2F8l4w45gMlUJISWTcIAwZKTsgt5codym8Wq6syUDHBVl9KCjYpMXXYYtcecCC3+fvJ4ax/nqfEbAgDrmLwUXuTe0Vk6g2DLDgK6cpPk9RpWXq6YxIwupFCkyWlglz/JC6PbXRUBM1GYVmLodF7er5BvlXlUGG7bEF6ge5SvMhZ32TNH6qC1gzcIhy+qSpKVAdzjrKhxfJV1ismreZqpmrQccUDigkkj32cAtlOPjnB2xyFgN+ox6E44jHeb7ZxXjrOmFrIGdRq1ch/Q8pLpqayJfh7r3jCwjefAaZ6SHEABugdew0sWm6I9hdHlKKsrQj+ltHk27+FosFPxuxbKCZhM8GhO3I3wvs1TdToUyCQVxhl2BmM3OiiC/Ipxao6ZT9nr7hLhj76LDeoR6a3oyUhNoqg0Hn4MdaSorN2ZLQSgev9SRQ+tH1Jhn4nrmsgpAu831PR0q9KEaWH6g43lTitF3XPWY/p7v+fP/qOf/5b3+7feey41XvyNv/GdX/2H//A/+Z5/+nEf9tv/9Fd+0R/54i/4xN/5u379+77Pz//ZP/tH/5j/1csb9//EHP/zKyK+55/+k7/7f/o//dW/+pe/+Y99yyd/9lt+xyd80q/70I/4pR/4Wz/o4z/ts77sq77hT/25v/5df/uffe+/+J9n/P/tvbv/znf/oy/5+m963w/+0A/4yI8Wzk/6rM/+8j/4B//st/1H3/ldunP5P+/nW/+X4P7YO5y6XfG3f5sf+evf6zd/4G/44s/4tC/7vZ/8M376T6ljBq2jLbL32VBZ2zLdendkZqOr7d6clO/TLi09ZZKLwX0RBzHLIfm0ja5H9AOtflMc0vZQUwNpdi9MGCWrTpViV8SOujb0WwZawjlAqUta6OjUIWE0pbwGurWoVKc+KC61h43cDDWAkzPtoOzKdizkYqrAinbdDxUIG/I+xt0dSfVCHNXS9jDBx/IY0rwZqIgiTdVeiqbdaHOQmucEDJovm2ncqMRuu9itwrkk7tBHlrSogrLQemS1M7VA6DHTI9yqY6d2c51iEh2r843GxDDONnUhv9aR2kvFlG1ACcj2kFc5oGopzbrvJ75uvSjIbUe7/pqs39Pog3P4ch+mSmuOhjZlfM2bEhgtuCKl1E7LNRxsUYEuVBX6gu127IEQD24+QdMQXUULvDZxLuZhCiWOikfmajjNNOw+QO0rutR/7gVFZd5tmhn3uqXmqyaBqyvQ2qu6DcKAgiVGq9YPL7X/vGqfqKu5g3VCfTXZhMa6mXVXXcGrxvbV7kVPevta4+EiwPpuHq/zWx6BuIraa6JpVRqnJM+abaSaialh+DSauyIVKvFVZLVHDkXWC9SVrm4tVvNq+acL9OqnthZk9sry1FYjGyzMsGlmo3ps8JQpkk5f04ZzktqaHMghJurKDkU8c2qvgSSoMfCJ15RH3XuoS9H5MvqB8cg1ynURVsYyUAgVAGWkmtA3tluxs1SseZ9aJpWXV0IvcyQscu690N3eHCmIhYzOai2Ce7VGt+xAMFJhN5AU1/Ip0uQ/ORHOXePSxJKn3RY9T3gUCq9hzAEumTFxME4+sg9dsmrVpDduYa7so+ZjPMY66No/tLazLZujbISZyksrAk7HOTuGtZkRh90NAiBkPVkhvFu7khvqhU4PeAId1bq0rSQOaorewrpQCu8OrbQbe6xQpmIgp7UJ1aWUaSoSrKJy1MksDU9aOyCvGa8jtrz0rHIWptWh+cAwqi51xwEVWkotSC+Vx91oNPf+UmnSBVM5ks+VqnO/7fboGvJSBLyMzVR4mJNr+nQuxvBYAyZcMZ8501fOGZLb6fHU6caKEv9y8ay5ZSQ4ghUe8C1yDWqRiaqbNzgHxwDZ4hr2ABaNPnN6DW9AtFnCI3tXXNqg6J5FmEjEzdR0HJNmyB4BK9LRCl9nAIscVQTazaf5S9YjL0am8qUkmgJSnNUJaSkbypSptBoPk5GY3V4yqEoAwaY1XfMO9Unpas5cjocejVyLtyeMQXXNoSfHanZGhVgdssq+qG+zOdzbVZFLUmnRjU7GVmx8hhcUeZncNhjm4eMOK3hXvYXYZStxIsMVVgKyqW3gA/XifMHxyJeJQ3Ks3B2XokGbmEHFfXSCDRebUkqvamulwapGqfMLG6SSkMF69nWJ7d2KAKN5e17kqns2oHmPrYHl+ugL5m1zD79YF/el+nrNWh3HVid98P7DP8osMCW7Sqpma4cSemKHuMmN2uyWv0rb19Gh8nIb0NmTCKbP08WfHdwvK+ZUWWXIBNKcc3Dc4BRSSNrSw0hJDVrvZm/IsWc0t2JR32zoc5aV0pup6uFdYTtxP8rTeUsbJEf1CEMPuNOz9rnj0lbkgodYtqUxFFxsCJ5zto0aTYi2vTeuguIxFZStc66rJ0NMFa2PhstwUMgMZhSH05bRhTCmuMJAUSmsK4nL1IPWdd+hMUJp7dOsNE1y6GT03QZTeksq0/q1X7uqGHt3pMpGo6wbzUJDnnnhXrjuTtncSW16rovSR7YgeTBKW31FpsMK6nntSHi3VJkvVY/OFWD1VUohUbIKjdjpuxWrRYpedT0xGGaA19Ddpg59UAmph7h0jNQuJOsCNCjJLIbCvCZyeM7RTs1sdnnm7Bq0okeNopDufgTeII5pSxlzC7fLLVU1R2K1mXstyxLM9GeKDo/Y43pqDVgGeidGxMKeOA3doqjJdmtCVBVev0S1t7ky5ZvaZbFNohgyMBouBmQUWhlU0FWr1DfyXjg8B2BTP6DRur1S1RhVu+xsNUe1uwP0AgOVVG/SbWpfWh+CWQi0mNhOqUUpmBpK6ptpgnyh5X62T3+4H31TEpqy0Zml21MdHYnMRgO+8Qix0VNxHPquS7uuVoaZRBsxf8gP/GGf8bG/6w9+7lve9Sf/ZDN7Pl/9yW/7s7/mt3/YZ335V/eY7//L3uvTPupDv+zTP+VPftnnf/t/+M1//T/6D//U1/17X/7Zb/7kj/qID/zV7/dzftZ7/uAf+s5v+zZvcxwHlcmIf/69//wf/MN/8J/91b/0td/4Rz7t93/2B33kR/38X/Vrf/av+80f/Amf8mbdunzbf/Kdf/PvfM8/++f/06vnq/M6r63j2gKd90s5Uzbw/4NXZr31ef6j/+F//NP/+7/2KZ//xf/Gh3zU+/7m3/oFX/VV/+13f/e//Jf/MkUZOed6eTze/m3f9oe/87v8wHf8If8Pr+Q7//B3/qLPetNnvunT//mbPuDtvvETf85n/o5P/B0f+K//gvd44+3fcAmOm07zgapRMa9r7FQJSQ3IubE23tg11H26KoHoCjZHzwfGi/MxTPkv6lnpedglXQ6CVcBulK81h2y7hxqDcj/r8A1PpbRq2ulHQO4N0qKJdWgurKjG5eWWrop19mSqm1Gd+QSzjCh23nrbKWNavY61xyOgyp1x7yE5DG9jtjTzwPngNalFg9pn4H41ho4sl0bv+i+xcKn9CejMWLIayd1q7S0U08OkMYPDrVt01CxbPYeQyo7OyPCCOh/M2pGDaSxQP6u1a7jYOxhaY3qY4pxB34YYdxegXXK2WSpagIDTXb+Dul/vvGnv4NymdcO10pmRNoE5MY+2qepgbcsLgOyW/h708sD0JJ/ZUYHWxu8UrzG5D8ujMKkNwp6W3h2RV9z5IobLnNuFvjxSWbPAkO96zZe++JXYu7rSAWvq/cK8nBg5qX3vjfZSRIIC3iuLhgV5dUF3ssU3skdfbU+ocFFWyviuFMZEtVgSXaIUg+k6G4qor5F5HzCCDai+QnTlQK57kixTWKxlsjZQaV1t/Vy8FvrguvkyCkInEKAbKcw3ifo4GlPjKxPn5nnV9dznU6ulqBGmgobCUAdjSHnioofiLctm00GtpWIU3a42xJUmc6zLcRn0OzQGjs7qOIGTIySD0JLvo5dEJGRasfrcSoSiwgE/pB1pxLUvmaWwI/PaWuk6B3XTcRkvArx/5q7jCj1PNCRVGGS4BI9rVio0AYegGQTBAdHQtq8draQbfImAyV4irlO5SZEyukEFPam7rUGCdUc+CByVfoVqI9qSq6fXY/fLE0NKU2aoFFZFWmzH7rzOOFVIUaztfsID9pp8h4GK/SjVHYCSjFupQ4Tf1D212tzjn4bL7+vWz+N5inNIeIfVwkWp7ZJowG4T+ozKCyi6uakfcESZaUHfUFJkxRnL9WBPiNIQ3gkT1pJ9JITXtmy5w32auSB2F1GONtBBLZwcjaaFEeCCrc0ZGrFHBbV7zNQKNYbGeLVWUHjfV8u6zOiTMHm9sIIztewtuVRLa9HgSrXD2tALCqKlACJpkl4vmuWo3Rkpgpmk8j/LlmZDmArVSI7rDtsTZd7mqjAFJYRyfIx8WdfhMQxmABHJePr5yiT83RUlHm95+P6Xff5PXa9SV/y8GCHrJTKqqhuOuTgc7qQ4EbhrmsSAaZw054BN0k3gAyxvrh7LDp2V9ouWNSgcpAQ4p2YIqbHAzO721g1qaASxE0n0wzBW827y2c6ESVMdqCfiLO1us16VndWRqkotGbQ1sNpmrkqJgNe2PvWVw8A1yqy3CeBhfGP5PKaPSSw2Z7ch2za7RmM47EBPpUWxGmllVUMKuBiZnYX7NXQhyewh/YoMUyxdZ6Ye8G9AvGmUtWNNseC5R0R3FyrQ1lLjllZysgE071+QZsQTrYd2ZfvBWjd6ZS83ZcAUpx7Tum9SnmWz7xcxaYfYh1qwCMmq5D5zS55R1hCz12p4zYuuRpJZctqKlVZy3tVimNZKGY6ZbkCCJQ6KrxetQpvZerFxmPpV+uJ8OVzUamCJ6QI9erBsdq6+Rs/5P0wAABAASURBVGsdrNHD5DlFe+OmEUMG1amCTVlvo6Bk3Y901ObiYK3GEEFXmZ5TYfmxRALYeYtXbWzn2XW14PDFlD5BWNYPH2+z7PuKXYlmHXVJB0rwIOwx/GXNMXVwSgzhC+Ph9iLF3CvN84T2uCq/NDGIzDt8eayiqj2pzfeYNTy8tg6gmmIyzFCqYx24X0UX5pTmgDANvQ1xtw5DtxECg2pIG9SXAR/lLjMtJn2AxlJdKS+zXG1drXd2rXTp2hVzBlFQEo/Tjr28Jwag2lf2+lawEHAYpWPUqd8EzN0n7Uj6K4hT4wDdrFtJpkK5pc/qNNYCHGY1qdf9iZheAxJfsQDJw6fMtVMKP2lbqd7izCxb85uVVbULGxaOAKIqtBdS6VEwdOfrEFhdYbjbdAzpk0iD9BIHaGCRJ1xrPq+9rnPWWyfyCKk3WKIThLfGeJ+MroCpvXa51o91qfdgGm44JFvXNHLhHNzE2fnUpnddSk94qzY0dE7c7UFCjvasw1NB+3CbwylcnZea0t6173wGcWWfz+4TK/IBVfAeK9RF0hmm56RW3XEGMpx4gHcy4f0eP+Mn/7Gv/LzP+Ph/58f86B/tSnvEt/7ZP/sbPvwjf/PHffI3f9tf+O5//D8kYGO+7du8/Kvv8s4/791/+ge873t/wof9tq/+fb/nL//7X/0XvvEP/ftf/EVv+aRP+PDf/Jt+0S98rx/zI9/lB/yAH3AcD3RnCt/+nv/+H/ylv/KX/z3durzpM3/t7/iwn/2+7/ev/6bf9qs+9KN//Ud/rI4P/F0f/yEf/8kf9smf9tGf8hkf8xmf9Qmf9ZZPfPNbfs8XfNmnftnXftpXf8Nbvu6bvuQbvuX7jq/94//R1/2JP63j3//Wb/sjf/rP/dFv+wv/z4fOf+Of+o818tO/9Gs+/Pd82q/44A/7Rb/m/X/nJ33iH/ljf/y//bt/MyKE5wf/oB/0b/xvfsmH/6YP+qyP/7iv/py3/OEv/qJP+9iPeacf+AO+53u+B69fb7zxxvu993t//Zd+7i/5ef/ar/p5P+v93/993vmn/Bij5LdyvFgvT1qXdGzzGPYYpa4RlqGKRNNb5Tj1vm2Cq3vq+nYLGhvuRV1csBw8XSdzEQsx8qo+r1QqsWXksZqGYgfRki8KaPigFMnhZz+Sb5Bvo+/SEYyQVNyHBlO1471m6bmbRIQAgwyH9HKMHGt3X8giYGAP5OJTA+2k9Rp9QP632oBT+MFE3r+6NIYojsQ4QbUkC5d+XiTt68hzVAht3cKN6ni9iHh4YdCdC048ahx9/1+VmG2ze8jcaD2gut7QF2/1YI1/jirjovgDSznJ4VrPc3QLccF1VZTEztoiiBoBRbK8H+M6GJqyL6sneSX7CX9iValopuWqVt8FQ0EJ5DOoQAMFCpAIwkPZmajhmQeuZdtGqOC2UjRxDUtzGxiKPOTjKlxgicaRDbW07alupGaHbrGp5MfcOTO1+uST+zn2dpVe4dLI0SH1WBvaQSp85U4WUYyeJ+ceWXjriGtcWg4L2NlnlZDDiFE9dq/ADBCt+STLazMzktuqPEk1E5CQfkoS3cHc6EhUtbdaVmZ1V1MvGTUromRHqJQEGjPkFhpQmiPitWzeVmuweeN6lhphxDPzLJkWuILs9c2sDEM8TJcIhpsLhNLaR10TkiEuQg062wK+m1f0vnTIZ4H3TJtho9mtyyuVP3kvwc66vUVneRUyyM3ZcM2h9NpR3UAoToG+DDEQxqRVe97kFXWOEnurgm0kJ3p1eJ0mbsRdWkMRkj5FQs2+vK/drzLLSho4CMuWQLvK0AOplkCiFhQsBOIqBZYYsPvK0LANhKilkkV2V3Rr3ayuhHyl8OpkckeJhudl+osOL46QMlBVScXqjmU+e9hp2IbssN0erWGyi4Km6PDG973SFXP3mJiTkLLNJYDWjlWbi1WSYDCzNdHpnHMfS9FYx9hlGlndXewcLmFFClyUdbfMGmSa4V7TeNhatxPlTcHwgB0cNL/cYlJ4RYlEkckgwm4OsYzqEyXnVJBaq9QLp9nR2gJNpHegwgiUlVBqoO3pgpfMwgblX51X3LDhyhVUSdEWaZma5jdOvK7eNDQCLSy5POSl1eeUZdGK60CbS/oDmIbR+nxEjCtVo+ZJa0lEDFt3eZ4IrbI7bac/a2zFCVvORRKVvWtKJZF3di9IT6fX1gPDI1M+rE0vhWGzelpM13wOr7XyODhtDIF92rWRdxX6xDjSX7JfNNhuonqUcjWEFN3gSSVqZU8lqjoyLlX6rUnVjhkA6xq9u7eSKRJ3IzN2hpoYtJHfS7C6RWGbNp73IjCugXJ1t0to4cOHuRtFBPXL8nAcHC9uD9Sj9mBx4FaC3DWa+sxreCpGnZFOWj+Kp1PXgL4PlgMOI0AEcBmfIkhhq5PE3r5r1WC6AtRmhp55VGoCKCMyGKzd3OEd5kXDWQCayMMwzLoSGdz0GDQRNHYrp96kkgq9UizHVXujahKTinGCZSbxGbd2l3YltmV7uCdm+LFNC2BknUBR7ljeyQhEIQFlxs3kXoTpWyeKV4/zDs0ZgmUmfMqNukqMgfFiaXu3UCAgmLNh3i4RjS4lz8FHuXPsNU6rsetx9oyo6NKOofumEEhygKPbq7iN52Ao0zQOc4wRpkDYttqOIgPVyQaxdPg1xh7TOK1XQWpbBlXCZXF2ltKDxqh51MMktEYZ2t18HkbJQvjSVnHC3EVIiGipsotZlB/GnDmMdkeG1FXuqtFcw1QBGD1GrlHOLsU7umykzpZWb1h6aY/d3qABhtkywypFOtD0FAU0oaFPlWOKCBxuj+EmBq7KVz33XCX/uvNpKQ2mf3lrFmltdsmJjmaEDlMIa4xSVTrBfqDfaB/0mp5aCx7di1TFoMgSE93qCtflhRvhsK2Oolshu7oyQ8KvBro0Y8AX16S4k+MoURvQeVf+DFwt/WTkGfsUTUhtaawThZ211e/n2GvFUPLCqkdjWHsaN3DB6jYgCrQbiYR8jtnKKPRZCCjIozXvRGQrpwFG4ep98kzPmsjFGqDsSASK2vNBG3w0DsiV2bACz1ZuEcQlM2ftgNYgFVcWIipZd5qwIo5LArq6qbASnpuMO43Ww9uP1gosStE2i3Q8x35r7HMzOX1qBSlT0FVeOJIWBpHtax4f8Ct/2Td/6e//3R/zUT/hx/14khnx1/6L7/ikz3rz+/2OD//gj/ukL/76b/or3/Gd3/PPvxeNVv7Ipmn+27/d2/2kH/Ov/PL3+oUf9UG/4cs+5d/91q/5im/+0i/4us/97M/6xI//oF/363/Oz/rZP+7H/ti3+/4/CCT+59f/+I//u//mv/m73/Vff5eO/8Pf+D/+1b/+1//iX/krf+4//U//1J//C3/sz3zbt/zpb/umb/kPvvEbv/4b/tDXfNXXfOUXfPmXft/xWZ/3eb/vcz9Hx+99y1s+5c1v/uQ3ven/+dD5T/3sz9bIr/8j3/Cf/O//8j/8B/+3//m7fP6wH/rDf+57vMenf9zHftOXfuHnfMLv+qjf9Bvf6+e+5z/5p//0M7/gC3/np37ad3zn/6HVs+f6hT/3537N537W7/2YD/vBP/j70yuUVCXt4fDJcG1ilNC6FuNgD7aVuDVT2dQsDCihnK1U+jDV35AeW3cM7spTUtnE/RDjVfcr8ubce4PPhjafelrb8Sw7lfiKqD5hG1Ya8Ko1pkuVO9MPEEo199aJi/m8t29woxliagOYe5Xqr1ShdEpaNM2QGHzM5cfyMQQQ5qjV6TvHpXYAU9WzLnbod0df5Vom+nS18Sf7nBa+mMvOZUkAPYmp+CWiVD+XPTcci4dRDRZnphCCGgkZx6CCyYqyrT7Ih9NHqf3s7l3YZVLSXjwnAlXM8eh5cAwXqx7JvFDnbHUMHnCW8LpjTlOMAAxk2l2C4HaIAHWu6tpUkZzGa/IcsXFdKWQqzJTBrKFJgu0ku1XVIPgYImr0UnKhFDf9JmslfUf0dWrFP1jKg8svonXr0M/ODRRlgkAnVPvYtGresZ1kyMPQ+SjFG53F6n4qsDN1+3YX5ViuiFoW8WrG07oNrjpDFC/z8DnSpZzns14RYXPycdTLzMFBm9JqDbWB0qwypuMcuORed8CXDPEYQw9Xe+yrk5fZpVRctrc+INpS9PW0fhmiV5E1StHgbnAW4KUQEpUVHbvPyHPXq1OL+FSVwMNZk+vgWKS6fDXP6DM1f1th1BE1r/YtRitX10wZyjy5kxt51Rm1K7NEAHQZWFmP61ylFWE+iJe+JnYxt19PPk/ui3I9bNlxlKLVlQbbmQfOw19xXrUGhgsa60oJi6WqMKvbiZabHkrtNLhNs3ln/DZT536eez+9ng/Gw7SheKCcSkPCqrmZ4Ol49bDTRt6X0O0XlqQNsZfdUor0XzvkVsRiGh/TBmdgvdrrvI6zjj3WXXvhnpw0vfTxbK9aSCXPEXTBO9ATl8WrjicjkGQYA9X6QZfKYoW5gLSzJ3kA1Hno1YDotoHFXoDdhThgi8t7KqzGwCBXmSegSWxS9VwTYHXGFfVUzGZBD+K5haC7NNRSCDR2xtA2SlwtbIMcTnAZ5UN7jLSd8SxseHrv3s/MU/Z4pAbhgZ585f6q64pL15WRMUyQ9lhhq6kSDcSJ65yZh64MyLeAoiJj71d+nn5KvxN9FEdgpJACUTglcVDpKAwcni/N41JJCM0+FySo0IRaV/MaVodzJu2in5hBS752k8aYauuObLML88RjK7ShGN3bDuSD/QJ78JhzHfM4hHEJTveQ7jaM0zSSiMitiusyme4VIdCE7CKAMF6Oa2R2WpVwsqKzeqfFeX/1xtSFymT1aEgbCUboU66xFZFdWy5X7GWX+d370E/rPXboQMZp/RQTRr0kWvEiK6W1bNyZa4LApJJ4g8L9/ag4ak9DT7WKo3s6lKLi3CV32E9cV6W0OMC7oka2Ju+WY9z9XKipkqsNGngARxvRZdUyagk02ipH786d+05mO1iW6mh4Ca5d0ftZ+0w2HO4q4rk2ShMc6cIv0Pp0AwfuQbLuHdbVsl4N5pixeC3B2omUmXIRnWYidR7hQyGI7aaWFfo8yKPMFKpSOUTR854tfjhyYk+hhRSGTKXinEOyufQZBQUMzXkJtTy/O8WNGoBbl2faLj0pFAIp9HpmXXdJzQ7BFysNY3ch7xXiSAmx1a9f0aQNQx/dins3bkEAlQWp2WtMf8z5GJi8gFdUygkbcJKXDCJ1Ybk6C4dN92mqJQ9G4ll14g6DslXZ9lRf8iVWWzSjIp7xvCTdVFOHaewTeXGH3c5TNsKlwVnQ1n7aXQgQpcacAV6W3dfsy3CG1em1DUkWmUQ9ts1ij/sL0g3LVPIaYFfbq+J5C3aoVR4NWG22giFUL7aVCHSmshlGDMBECBhEUJ5roEfp90neJRS6Lk5aqFVAdk2+etS59p5XiUMa5HnYnXVdV5jtPg4pgW3/jJ4xAAAQAElEQVT9TLz1tipO+TJc8BVydgsOJfVTKQ3PbdyE0KNGpciNKFH3vM7i6SOXp1fdYu7e8K3A5gtq8lTZb/VhG2Zc2JRIkF3SgexZSTnWXtfYp7yIVTCYjUtFswzhXebs5Tok6+gSqMBAmkV7y21aqwxEu3iIdImdt+ZEfiN9t6lbJm4My/IozG5TmKRENEQF7u3MEXl0DQdvugAU6sq+ztxR0nWKbnN3qjsNNc5tsXFu3DlS6hCaYlqy1/S1ZFqwuRvsniV3V/Eqy6S3L87pNqY8NUap3QwRJjYosB7tP+j7f7/f+qt++Td+8ed87md+xrv/9J9+HA8Z/97v/d7/7Du+4wu/8is/+OM+/tf8jo/4oI/9+M/9mj/0rX/hL/7t//M/fHUqpxpyH30jB9x/+Du907v+xJ/wvr/kF3/yb/9NX/P7PuUbPu/3/4kv/bxv/qLP+8xP+Pjf9gEf8Mt/0S98j3d7tx/7o3/sPef1z9v/wHd62x/4Q/zlbekS3OtT/z/69aP+1R/1hz/vD3zTF3/uV7zp9/7qX/ZLf+gP/kFSzjd867f9ho/+2I/9vZ/xHd/5nfIjpz/zZ/6sr3jzZ37Z7/uEd/uJP85LqycJppqAeeim1J2VHVdCtaL0CaQRDe5ylFlPy6NeH+2zD/ZqQEMkfMNMMro3606UrJaSvtykiBfE6u0RnljwqcLilbabRs26+aycOm5rjteLWxSrTDWwM6IjuKHVzfNWxUSo6CF3VjBAU7zH6KGgyOohE03X/Eqry2NbxfJ+qIrkMBLM6pD1mdoBVXfs7BCUsNXtqp6xMUG3xIwbBqOHjuQsdcbpXA2dUNgwtmUgTsgM1Q/1aadaFwst/uibetQ092XydsGL1tTFSg/OHLMGqTLrKmAjRKEsOmoUzeQv4NCrgTSEqtJGmLe+MWy2zGE2DF0KXmVFXenQbzTaHHa4ja7uvbuiZMoJ3aT4Kj7aVCZzpa0CG1WZO3E1ytqPNhe6UPi1HcIK19BRMuDCAwUudcQN05QCLLZ1iMAuB63c6DkU6eWmzbUmT1PWMVqwNYB0uYCk5iUcIgLeiZCJhJm7TbPRS3hrLu15thXRg9P8KElpjDKUpSbVdTDHtDJPpCKRpIgbnrIhIwNK9na0pvbLYz+m+vhZIS2wyBKx9GirMu62qN6l1tRockjxNYZIbSft9lHuLSjqf1khq6MtNy/1LHkvjUKxO1L2vBre7ZGCY9VM+dOcPfO0CopJioN06N4gnRldbfKrbLNn45BvPNB+Zl4KFSmH0/tY9zogMfaQgFKKuUe7rDcLFnQtFdWOarOUYe+Y1Z4OECjzsHVzplRkm+KZdFNda9cnP4UCE6zN1h7iHBJBNhvRFvCEJrTvsrMgXTWsMQq9RylVEo7UuUYqR69hJYeSeGkzpZWKCliX7/Hu4AI9gOjuKkW4gXufNFs7tUsgBlR3Rjp8Qv1/UCdb1aZAmuMaLEFCIyAmDD7oI+co00CNYbuhR5S2NJVeqfO7u9Do9m6S+kkq8zpp0aN64T6pqMr1cbamJ4ROZ+G8AcG7OrMqBKVSeoEi38oT9cgmiZ5Kg5crcEuKKoS2VAkIpTEpTCbGRslwA4SsCjwcZoC+N1Hd1aHPUPSScqXOhLi0mRBevxpB0hThYe60Bjty5h4Mfwk+om9yS6CHt66nAm4M5kO0dWVr/Q0llAYjnNKAWd8V9tJz8bCeATzZNVJcrMP4cKwJrfCajkixV9IhXYI/wdM85a0bVzATBbHKFNNy8H0frZKiobUOdMpCn9CwgrAVsK0VOlOgWl+qVL1SQAOBVni1oOg2KaHcHX5esQquKy2uEIPXw9uE0NCNLpYimjepkdCSkpVhV8xUcfHObeasMDDoV80qwb9tswWob8qFYWPstlbYJjAUfoDQSIAVSobeDFBH1y/IoVLiAFqxyFgbITiIG83iMI0sRyojVO2EdxKpRtiPiXVAuQTABnTVMkUuUzYgpxhXWyQzudOzxAujUe3eMkxQTVFtVu5rjBwPG0ua0o3yVTvYtnDvkVSQtZb7HIqCaP2zNll5cj6FR98b3hhw99ki0MXSGZQN7Nu3MHdNJFUL0zF9KujW08rMRrG7DKo0RWZWd7A1bA9Lq84UXlXILCIspdimobylk9laNzDaB2Q5dJocg8eaetvRrxJqPGmjJCRP3f/Jp2sySVdWV7UZbRAjLt+bWwkarVQrBUp+cffYJrIAU3paRcYQjtGFlF6b+pXYWZXJ3GY6Qr1sGZ3tJZxSIQfNlRMz2XIkoCxEe8Jxg3+wpS6NLoYkIA905ZY8oq110KJZSmv1QHhvdOofIC5YftfR7qroGbKqD5UNZqtuMgKVk/mwenipnuVzOk1pMku6DhnNkcFtyIkWA/JGlU0nZKhqGJcNx6hte1ttAeeADTX9s+tZGdXyeE8vE6yW1KysYUFL90t5LVrEfaDkvgNx7tBiVaUrmCaKFnLFeUBHUDG0k0ZriuzYRmmiTdzAk0wLeLjeIZR8vbOfUQdwyJaCsKBgwKtNuVfrNZQyJ1GNfb1UaHHE9O16ojA7HqKIonLFflxxGNTangMnWFCsUncPdo3nOV69lXFaw+1h0NOYY6aB+q4M1DPuFadhcw7T/YXNpIub7hbf4XWhMwa0ObZjjGGAar/R1RJoABcq2KREs1wwoMG12lSghMmPD/hk0TZ4wiIG4/H2b/v9/s33+gVf/wWf8Se/9kv+7Q//sHf9qT/N3Ukq///wv/vv/vNv//Yv/7qv+3c+/ff92t/+ob/wV7//v/XhH/vRn/7mt3z5V3/Nt/zJ/+gv/ed/6b/4L7/zb/+9v/cP/tE//qf/7O//43/yd/7hfw8fP+yHvNNP+Uk/8Vf9kl/8Mb/1A97yez7+qz/r0z/yt3wQXr9+9ru/+5/5qi/6i3/wS/7zP/K1/9kf+8a/8sf/6H/6H3zj//Yb//Cf+YNfo+Obv+yL/siXfOHXff7nfPUf+P1f9ZY3f/mbP+v7ji9+05s+51M/9Q986qd83/Glb/p9X/SmN33Zm9/8h7/w8//4V3/FJ33kh7+2jff6hb/op//Un/w2b/M2/+Xf+/tf/6f//Ju+5Ct+6W/50N/7OZ/zt/7O365KM/uFP/fnfd3nff4f/szP/EXv8a9Ry8ygnqK+/l8rHL3Vnz0lfVZZwq72t+Y80yMh/lPJMLQpk4TK85Tq7zyCRAFPxuknkUOzS0W9GYM5BMzcxxLrlHpm4KFmiak248qf7geOjIfl8Sh7o7HSZ69BPVYeSlGP5OP0GYF6Np7ReW282v0UJoCg9XacpD4+wDdkSC48LyEKVEDtQmCgUmx4E2nM1y28h29dNYORo7pUd+TlHd1Z6LJulUBf0veSFdRVUh8sTuBVULfOr42WpKmUG4eK9ro6okqdKEJFyCqU3tHhNCfn/RWocsuhk7Je13i2aRU3NQ0NLMs549CXuM7SjXE001FITUNDs73VBVRfF7QMm0U5tnuMY3I+qte551t7nMN6qBIkeQVJsz48OVDeGxQ+VRU6ra/B01b0A7Gs4KzBpPzALviFeY7xPB7X0GNFNeqbav3ZeqyA7rvYs+VhFKGmwnE50qE+qDWLDgrBQTvAxyo/mjqnxlcMQ09M0+ZpzvPFrpceA8thy2LI8VH9kHJEkzKQsK6Zl2eZAMJnC6FwwnB/X+zh7lPvGFvUn80QCDPXqYk2FEUT14QfxXmJpfI72CEt1oDA78ouzHA1NXYTOi3OSqC8fJ1vzHib0csrhW1mdeRrpWixPdbpx7Yj3Qp9C6jV1hOZfua9k4Bjch/UdA6fNtrmqTVAuCopmgN9TyzsYijJSrfnPGzQnLamu7q1IBFNwc7ClTsa4EGaEROKcCdT6UxXaDJHAELk3EP5Lk1KqeiyVnlIgj7GnDL9MC6zARjD1EmZNt0OU+9tVELD51Yb1cWsHKV3NdqnPgf8xCDHo/uActPVlA/sjR2IRGMSh2gjYSovnGaXuVYOGMCm72Gn8aLdg1EK/lrAYQYC2dY90OLYxYuKU4vacfANy0frEXbOvififhEtOvWplZcoCLki8WzfPa+1rhfGS5o1olrtCSK4yKfnNm3Q9dHdFIV6kt/ygg3z9UhfG7ONFEW5EH4/TouQFUVXUMid1TuxYfBHgaHAM70fnY+6jio2dBhaJsxgRW1PXCSppwQUJ6xrZdwxu2FOaE8JmSfb5bkNuZrj5uqoy6ur/JVgp67DFsfB2y5qtDLA7fOV274Z0Xm9eWN0437RVKzzor1arWfuyqsuCbY9Kge3UX/vU6txoZrOOVv4Ml7bmJ5eryLPrVpJJadb6J8Dl8SjX60CDRFONeQ9rMYsepJJaU/9M0MxtQSc3vqbSPud3YkmvPUFJp+ja0bOao8CgrbhoXFMUAJTISXlFdKBnJINfcvoqgvcsFSCHfAsFaqGCv6Mx8op10q6dmDmNjUdAnTvolRIXqXVZtwOvMcgNF1QIZdnQtg1FzpJuHHBBqBroa0kDYtSI1+Ae82CGEng4tyUbEGV4l4Lw5XaITO6rv6g8pyFl+435PkGOROjwFbrebCVf7Wy6cM44V4Te7VAH9ar6A0Z39o8AUR7VCs9hbFtPM0DSgyDFFXH4BoGc9HJigHp90bCrqF9XUZdLdKQlvAYLrlihiJmQ8pGNRp2/5SkshXq5FoqdzcqPPbZ+xKBOIFXjZMQJ7MElThcnTNXFU7wqY39vM/cD4U4bY45BDDTBOAujwZIupqJuug8EsPUWVqnVvukjeW0hRhbJrkIIpVvrZwtthmj8Ygy6SD6zDq7m+Fzg6Gh4KDoaEcA1+X2tLnD5WI9g7lvygZ8juHL+w2Pl3FyXFjB1XbIPb3YjZJQR3mpC6omxWxBRuxJu0ZxaLlYs5bdSxv7YE1rTODhre0sh9R3zvOClg4BmvRBJ8Uwm9bD9RLfZb71ZObosA6wbACPzslyFwK7WHd77YyUbHkhM0uRDaNP19isR5aTXIbp0/yweWANCQEAKQWJKV7sLcGyvbACdk72wgBXkG1c6JWAOI6+6mQGe2Iuk80etg/bJArHxcerOfKYttYBH1UVOEGtNJXF6+S+oNwx19oSN42V2rErUTsb7c4hP2G5zQrLIF4c0qONFLQpGbfzHHhlqalk0RPctKuPq1cU9j71Iy+mbAnioFi+0gOj3dyTClkn56yHMjjl1nAa5V9rtJ1qfE+VECgrFmdl5s6SyGCuBF3ONInMqRf08qwBEnithhRsZffQVqmH29CUkdPSjG42qZcyuZPq+XhWxwwOCVCxNilhBTpRXWlnWLVD8yijrT1Ja6EPSaY8MQuP8fgJ7/IjP/L9f+03fv7n/iff/E2f++mf8YHv96t/7P/qJ3y/t/9+7t5dr169+r987/f+7f/qO7/tz/+5r/qGb/zsL/iCj/nkT/rg3/k7f+1v+x2//Dd+0M/7Vb/mvX7db9CH3/gxn/R3vvsfKRRQnvEvqYK0mwAAEABJREFU33r+w3/8P3zRH/oGnRlj/Pbf8P4/4Pu/w9u87du9zdt/v7d/++/3/d/h+/3gH/gDf+gPead/5Uf+iH/lXX7ET/nxP+5df+KPf/d3/Snv+W4/7ef8jJ/+89/j3X7ee7zbL3j3d3uvn/Vuv+wXvOcvec9/7We+609+4403/tJf+2v/9d/5Oz/+R/8rP+FH/6i3f7u3/RN/9s/JuI5v//a/9is/7Hf+r3/pL//VH/LbPv3Nn/UHv+mb/4d/9N1V9cbLy3v9gl/wTV/5pV/6ls98j5/x0x4vxt4sEmY2WAqP1lqGMHd2Sy7VD2DBR5AJJKyovPjoVIY8qP4TSlI3dvNpKhOJU3nd1VKoFkpCG4zr4Hmwh+lzT5amr+rVtjidB8ruRMQ93autamUekDOPy2sTNHNpswu3lZoqDWXUJXphaiIppVGbRuVSQrRywNs77ZSid5fypm9KhDdn213vqFs7oGejyqyWGJBPtGgZrTVhDzXcEdDy0ck4TYUQLClvmxjiLtkVgScjPcBu66DmGEAD3QfU8NRbvRtouMGJe3s/RQbcuVwV47LEE7X1lws7oSZsIfBjmIg2C0jZ0ROm+UMgqYgVnx6Rza10tL6Gucq1yluBmPjobf06Y9Ak96LtHCgu7iEmR8kaTTUOUbavRogFI1xmNK8B0sApP7g/0KG2UcbCLC1kWu1ERSF4p47MYYIyLWdr+1GeYKQ2lIpaEwcUt96RTnKsWtajGxd5CfCQt7TQjkvmWsnm5A0bhNtYfTzyIWB9wfJiRjNzRI+gpXqUml1UZu3qhCJblEGlVYJgoiWGXsQD9KNckx57jVzMw2AdKO21z/AIpXtoghoWtlpOi319NxmtUjg35henTZGZ1jqNe9meCFdZWMs++hADR8Gz+uqSGuCortZ6GPfa6hzE0eGs0ormEsc8aiwxcaLCgRlYu8elyLZAuTLp1/FSKte13HEnU8MM0qkI6jPjfpw4yJcSP3ULLYkyBQcPs62k6Yu4TFHWJjR+MxVp1QDSuU12bYATvRBFCaIbRl/U0iqzSrSlFIDpoA/9U1yWZ+7z2kqAmQKbIsGUkpVYdQdeuCKkrXkz9Yp8ktvRTQVV7Up133Bxb9RvNOyquuIM3ZnvorQW841rHG1HUl9LifeuNeNl5I3EmpE8z7H7SK0ECqcJm2kCnLIGWGFE2RWt1TK99yBdfa1nuejhVly0Ib5SVHkcI4cDJMo6zVpmzVtm75MNF+TtKZfX4FUjzo5zR1yB2GTYXfTm7pCFLAt46SNNUNB+53gP+fSpVKbbfU0cFQqoYWFKjletOideieHQaRimpkvgiqKnNitpmb33FSphnuby0hzwSfMBjL7jjipZzqa+6dykQNWNpjHqZgWZUbw7w9WqR5crH4EjLlZE4vm0vETjKxOFo7qVO+hVsu4m4UR37Z2XRLiBnMVpysthsmRRSgsLDkN5J1h7MmiynGSLydi2Qzuco9x0P+O2p5/TLxsnXFsoXA4lTJ6ypRKeYRqJ1WRCk0MJdVdXy6GFH9frmus8Rg80ASuSKKIXbejjTcwGtx6KiS7OapU41EqNHH14O9JY2p3OXcDeA+FsFX2bAscdRWLGQjNQp+lEh9jyviw1WqKB0dOg7035CriYyJb3VvPEwel9QEksPgbElSBk1a7bgNIeZZtiV/CpzaUmtK7gaoo1Ot1lRoNhkNF4DQu0tvv7CPUBO2CjFSAetAfHQgMJlN4bpAThuC2ZlhWaNHBeLQ0HcyO3frtiUfz3BDtHJZ+wp5nyaLtRpWvRvrnMbND0byfjUtmhL0LhywkJIb/0NGfB3CBAZuEt4vb27imtjBm0i2oIu1hlra9KikPj9Uus98CN0qXQGIkjVd5uW5UEPU7pa6N9yJKx0SHJG2UvdEPGPQgXEETzVd+nCc5KRawwfKcafaottCVXpG4LVus3BodLGGXSz2StmWNIGQNivT1NVqxo1RSlnrSEDoHzRDq04gzrwbKb9GxTXKPVR3xiZh9ZMzlvH7zBdNy4J2NS2x6xYroKzUpkI5KdBpgmlTQH5W8psixdrfTL1GKoaEt/gqqp9MrmUZeaE3AmVLV0tzFBXfTtvGbniPaQTZO4aXfHm6a+wayubBFTkrz2STyi2V3CiJg6mWQJAX2UNijWui9UMEVszWXpKmE3fwsc5fl6c1Rue0FOc4b4CUPW8Jgz3E526dWKJ6altFP6wejhlMNxDa2WpbBS+gB9mh6qWa+4y6pbaEvKc7u/R4kux56jfe0H4yGi1IRmyhc5pV+rQMqZb1vP6U/zq6Ujci9eRz2tdiuSLpkGuvNEhgUZ+qZWuIMjxhs534C/gMtbks2KZ5+nloPeUwN6WWjfMhaPddcHCIg1QAoi5KBarFIS72OdE08VXeTuTo0r5sl8Aq+ko54tB50d+jvgiThpZ/O0XXgefb1YzWOtd/kBP+hX/uyf8xkf/tF//su//Fu/8qu/4vf/vo//yA9/31/x3j/tJ/+kH/nOP+L7ff8fQHP8f3h913/1nR/yuz72T/1v/9Nv/tP/8ce96S2//iN/17/xgb/lb//N/0rD3/Pd3/1df8KP04f/94eC+l9e6P6n3/sv/vrf+K4v/YY/+iEf/8m//Ld86Id+3L/79d/yx774a77mfT/kQ3/j7/zYX/nbP+K//Ft/6/vmfft3fuff/Jvf9X2f9Xuu9U7v9E6/+lf8im/8ki/89978qe/2U3/iayUV/HJ17EzpVF9EJg3K5Wpd2dwlIEWxqSB1JbulWdzy6xlQ+lmt5lEeu6O2IVzDXEmtzBzZL8VHwZRxIieabYUy/enkrPXK7JXjOXHR5KVhdWVdu5Whk3yFPlvLbMo8LGdBbYAwHZO6G18Od5pqy9kA/IYSt4F9Nk6BWQBNMpoXhoC3abjLDYyly5Q6bNMMhj2z5jU9hOTuEXct9MF9byiNHAa9TkmjRk5XBcEJm7BRUzqSjqO1jvQZfaW60vHg8YbPx+F2ECtVUgOYbX7a2G778PPl2I8Dem43yXXLNnFC0Qrtlm8zyjIMqiKhOmLYvdbaHLWsDcCg2nAMqAnQ6KYToknbrhM4iykO9Ey6HuoKr8FFlRhmZKOKg5NDiCZPjOta+7lwzSgLdFELzmtq5tF23LnLyV4ECRQRE6Vua2cpXqQ86xe3EKvzjLj7YPY8e5xlKWj0ujtGbgHLTlg6a+rXXnm5+kwjqPOVqO5W5o5uWmHcb7gzjVmtPU1FpPXlfdm+5L+yg/EcV2kPtPfLGevZdQ7FQABGc2/1Jj9jdj5Y4xEcVyIGOcC1yfv/oMN52fOcV83SoMTc12zdWz8nnxoALXEWfNmH9UPpouRW6AZk4zF6eZlHs0G0mX7rTwur3WqxDmVcC6rKR0B8XMufzlPNsA15DF1nc4bNPRHHiXWZpfYAsa48rrKz0VvVQTp9iH5NXhtvAA/UlENqke9n92bgpflG4WaP0tIGi4A0hKi6kMpr2EDPuX1tzgRbl7Dr9jH73vEZzvQdumaOXuEuGSVZaVXGliYeCwu2hOqtWtj2dezzJWEtAL6NcUg1OhjTUgpHmfLU1aEsa6E81SPcczAe2EduqvBlp6z7LqedpUNw8QSeZjEPrTl7SB3XMFEG3UtwtF5n2ytgq8NHTasp5QCkFOQjfGzM0ud2IeAVXSUuNQAcNnWPCIIM8nK0S6cmfW+rs4b+iHzdejb6xE3gxdYOS/VPkZW7rhIbYtJdRqXKnXGF3Sj8WKb1Q3/sWbJHD3TcMqCZMUxdr54MubHwx1YabJTfKMJRbiIYcKC68kxhyYCIlrhqhOojkgECUt8ZyC7THDelOnLEXlUHao3LliCmbKZoLRWJFA+ocGVb06WJxk6/rpGJsjzH9YpXJCCMTa/uqNz5PK9z6wHgpUQoGFZzY9SylsZfULji1H3aM1AJGtbAY9pBep86hp+znq6jQ8qzwddF0kmI38rCDsv0onGVz7i1QV3V925lx6wX7XVi8+LeLnWmcm4FlNdetymjOTVL5qK9auB+RUHkWXPOHqPhkaxN7znAGfArJ0/9meXlsMfidFWz3GJ0zYg+EcE+LmodOyD7EqdQG7SzuKyr1TXxKHsBvJQI9ZErUAWE49VAwFIRKDXrmnaOOr2KkH9b+/5P9Y+wWVjdtrnPeeYbm2+Ez5QI7Orxqt7QQhmN2H2dwEnFARvpVmzlbeW5Ku5QDUp7PPy6+5unj56OY+DwGpbVLbitPAcBh1Ch9a9Us4prQU0n/Nk8hxrf9axXoSQfGys0qS+v8yX3gOwync+u59XXE9jtORZfFo8VXGn3q6F2vKqGAsvNkNsWFePAmDERfeHSaae+NHHkfQhziXVVVTf0YisZMfyqI3rknb1Ep5/XiFjoQ3Fczeerjk2z+dAD5lVD+T7Nk27o4aeeb6A9z8eOJZ4UjfHSIyotBAeMSakXz1PsYHnNdfW8C6LEja69PhrzBF+BF6xS2pDsilmzsdqM3by6lSRoUsigIaiwSu1tSArECajEPHptRdK1VVYDPFzMDWZ2vrr6GTPyqJwaDEZTtdnhkVRbKU4Ml1HRSOspvxom1yO0gCwXg/fkfrCXhInz4jNGpKD0FWCbpOsoiSAUW1jHQKl/u3SMZXP4AqytaWf2UytFbTv2WEHT05EzfKvj94unxumAsohL2EdzlHtV2c1NmxoCogppM9fS3SRwuqa9Yf5iZqOVYaj0/eLYc+zH3ONxjkOcjb5GbR+EjwtjC6klO6AIVOZh0Z2kiFsv5hiDHCwvDMVfvTzGrLq1wdbZrYnJVCFQD/iGqbLbwNvkq6rn7usyJeWIk9h7ap+kq+2QC8i1xHW2TA47XFOJHGh066eYoL5SVYZmI8NuaQRW9dCARpVrBGtVzi4ZBaQSebqALOXQOVYfOkxqriEVbPXayC6QYXPb2A27wT2b4gJpggUQDSi1WvPuT0aMgfuO8oV72vb5Lu/0w37xz3zPD3n/X//mT/y4b/yKL/2jX/2lf+wrv/hbvupLvuoPfPabPvETPvbDPuK3/oYPeP9f9+vf5/1+za9+n/f+ST/ppwL47//H//F3fdrv/d1v/uw/+Wf/7N/9e39X2dTJ4zje/33f51A/15f/b8fzuv72f/vdf+iP/8nf/qlv/vUf8dEf8NEf8/lf/uV/9du//V/8s3/yf5/6z/7Z9/zXf+tv/V/+2f/t/4SXzts8fugPe+ef8zN/xge+36/9/b/7d3/Tl3zeH/3SL/4Dn/Bx/+sf/+Oq0LtcgSeqxb6i170JVUE1VFHBqiwkYO7TH6sfIxa359WaKMYsaV3dlzqEyVTDvYafk3tVHW2DI+fMMVwHetsZdtW4YuTT8aTqWq5bLKch2AlarZXT2+X9Lj+lqFLFr1zrpATpMCgfS7YnXd/GIF8M0zEqjCm4MJNoDCdee7j1honSQMCCdkmjg9KSlhUqgFQk3NZPlYu7ut2FSotyKx9NEqAakWoWvGoAABAASURBVBnurya2FDY1pNVAPMdShcuUCPQSjjnvkiBRXm3Z4jC7S41hq+vvobKIpEMvmdqJSxnIzmL3AB5mS1cDLNiI4VAB7EArOtUXzFgyLlCSa7esRAClS1q9oNGlWYJupMaQNscx5xzOhMQPzUaO7mw/+zhhhRqM1TxqTRDde8fWbg6nd40ytLexJtsa0g1CXptyoEwtMxsmlmTF0obw1IaWpYaMLROinMkZ03oVhZ523MZCjOzTKr3sXqmC7A0/x9j+uHqdnJdl94k+K3dcVVuFCuHr5HWyopXFY/RYij6VE2/ozJoc7sPhN2K2aKAPtSe5KbyKfZ11tl2NbNip1CjCFTg2HnRXERwWwy4Tv8rlGDLRTbiJjBZagKRAbzDT+pRdOjBYbjonYYe4tsu1gQkcOebwuXivEeLhzs4M18KLm8ubOvW5QXFXsFQ+6Gy3QDWi9D4Mi3VQWVOVAEWezRNQ8WqRsFfTrsmCDwgGLp1nmNIjnCjvvD9iUlZahVZX7sxgC67ZjW3Q/B5Djc4eBWsQjfvQKWR371TVVwkUC83M0jljCqxEX7odX1qCloNEOS9fJ47eq9ua7YO6NKdNM7yWG4QKNbGX9WTbmabtdsiyye+EisC00ceEmvpZcxfvCipkjMoBuvliTwjRufsqqLKju+rK1DcABIaZSSBqQqZvWlgnOMcUFnaqFKrzYl8PSJsQipA54yyORO2MRPRIHkgfu3R4cQLeqLDY2sSpfoNy1IN9DBxuQh7Dgku+zQSj3YTThhRi0ie1UkFIowXz7odZFmFXdog23mAO3tmJa9VpGioSB5Jyz+Sl7A8PnzBRZ3P0GlzuqJF71nZExllX7FcoBdek8Flzbl8S/Va7rpT9LhU6nmpIpadIC2KURpZ1duauLEKThXg6Ob2N0UidatPmnnXVfdtSvlWy1jd10KvNT9qztwq3XCbMRcc17DIdS585wGV+TLiTRGZV3M6sHRJ5m3zcbglqV7YmnWav5agnRvkSmAkrIFCaRphDI5hmZW7+WOp4flCt4SXLsrqEWrY5Z2FUdFZkKffIjXrV9Sq3egtM/2BN1Lzw6Ji9cVVdalF5dzJ3TVfhbI8+1HbMfVjO0sAaVm50LOeYxhFtT/Szu2EmTlIiWDKsFCuPo7ciu0PSTwWzbpnHzL2qHjAZgaK8z3Y6iI7IZ5zP0vaxFbhvu7vLubRTYZcnmdCrwXBFKah7VFFRstuQxLN0cDuh0ZVadm4OWGm7/RLig/0gBt1tuCkQTUMpO11l0R45ULRR/pLSerpWEa+AqDtb5iC6hMvSFDTbRy3fjtpSLJAgYU5X+rUPjb4sw7sHWI0sBmbZwBqilbUy1l1x6N15NZNzkMwMFUqylSdYUeek3KG2mxjFaRhGy+5y2pI/cU7lcWnVV9YAP+3+7+Euucs9go7hk3wxW3PYkD0m1G20M+IF7VsRS6Rl2JnXbkGtRN8dwJsCQIIG430ldLUBtKlnjFKRaFlhW7sBs2EIq5MRt6q2Ru62q0QDUVa4SyiI7k3UnbyNDBPlJIdTW55Rw3VdgQieRTivcfScuqYeOhDQvozikhjeihtLYHxzpdscosYFL+QeT+Jy1CqfQUOTeku/ryuAwToGjrldGfQ970P32FDrPodtI2EPQb/2GefToyl5LL/GALhCQ46qtQ3br221TeceORTCpp7JPFLUNa8eV3siyXQO04E5bFEZ8fGwaT5bUVEq7vZlY9awU6OrrXK0MiHFTDkdlVNp4mV8ugC6YIzwmWzUawFdKsBD6R0MoADt7EaD2OHXOfKkFThJytqcsIfd5LFc4i+dVBtSo5Ve2lkE7j6RhJLauique2xjEhyCPLvUQ7BXxCO3bj90HF0DDR13UW3YtkEI01H0U7icyoRyX6xtCLPy5TCH5mQqjFTFmbHNdOb2Bajlx2vDZaABXrpzzgMqLVWcl3lauyzNd/j+P/DH/Mgf+TN/6k/+xT/757zfv/5v/rb3+zWf9CEf9KZ/+zd/wcf9ts/5+I/7wk/55Hd8x3eUp/+X46e/x8/5yt//5vf86T+V/y8X/hdf/8bf/nuf+JYveO/f+jve/yM++jM/7wv+d3/xz//97/7uUrUAY8yf9lN/2m/9gPf/mi/6nI/6kA9+h3d4h++b/dPf9V0/+rf95s//fZ/2H//BL/8TX/L5X/uZv/8zP+qjf+2/+W/o7z8//B2/f3al/kXyKt9QSJc2aS0ObqJaZKPua60/OF0x2eQUY6f503FZFe9MU6Wlnn01LkS6OE8nQUury6K8qfI0o9kQTWhezFu2HgkkeIu7IevKw/AS8wlDm8ccRp+mJOpyIfsWxi2GBZhyXUcLkU1w0RZIa8y8LwBSsLcNVQSFpUPRsCE/oN+/u4FI6zY4tH2Q8IuvY2JJCdUztbXUACQty3i13jT0PjRpTws6IREp/d2DehQgL8y8a6Vc2yI3n1aOArqQhdB9hmjiM+yMimhselHdoHurP3U9UztB69B090FhTzLKbfvaOXAVst16WfsdArIaFSX7QKZsuM7rB94q/Bv/bB7kMNgq1yGfgrGt82DpdJr6iyUQDP0aKQ4p3DAt2lfW5R13JcthCU/7SJvpdnVechxtrDVUlt2jTGFbqcytDWmQLKQTy7GuY2w5PMo8aVGIZg/43cK504QprGN2jOpsu8zV7mqslhogeLuVg4zeVRcgWnqS6gNmgP4eMOdzmjqBebtTCywFVq3rmJxaeKhIzSWg7N43o/KxBaKstXRs6grTm54KgWvwZWBN8EC+sKdjjBsH2FibU1ppeGJEyFh1pWwffh1Uh1ROePkdflsaczBuZcFod0qtlXH39eCLxwMxkCNdpTWMnCirbC8q8KVCyaaOPcdlY7d1L0IkDhXs7grk6djs3Z3p17Vk7AF/A/d82cr2zmmCZ+wHRMEAhI1X5jZlQpHAxCH9Pn/H37C02ZoY0lOhUydoCaagbD1nuObu2VQgTF0XB9hiRpgxDpF3WB+NsYMt86YIBfJkR1vsCc1VpAZYq1gEWVXQNQmAKtGo1m82SOXA7MXmw7Xl7PWGaBKh0u7FusjiPHhMOI2SlDYvCVnxlbZ2IxBbXwFIC05YAw4a5NrZPjmGD/LmonKjxGOtuFPREidgpIrZzmJtU7imOljc7dttK0iDMlAKq5M1PPzWiEJws/GYGENgi3fS/Dao/SdXLtpBu7FAaE+ZlvzXE1M15UzdK6geu9CvC6iZ4mxcdUSrxdk0uhf9MmU2lOSxDFNBjNFjcY5p6lu1rvCdol/NoSsvmXHLKapGu0FQ8fQ8qQsxCtHS1rrEkGOtEu80VXN6llu1kvmG8zHmA+toLm8/ukWAw7wKBUleAtB8QPIBTCZ8hmotb6FJp70NezIOxP2f+lstQJ6NsEFOB0VcE1hwz4GnXU+kMrZK1IQj1byOB+4iEUEWejar3bwaRrpFCq8Oc1lDa7AMwaavww8Rds1+6wOnySGn0SBlQFv9y/p2KZ1uqVVN2Sr6DrqjcHULwVu9nmOXSW5BJ4ZraWgq6zpnpa0NIarait1zY0SJMYN7Td02TrbOd0Lc2AxX80B4l9y5mHudY7138UJfjZNA1lTMbubs49jDztm620gvJUNzldDse5JMl0yHal9umIOt3ocBoMFUnUXZ1tSn98XORp+FUF2UrhIKR/wXWtaot0j57iDK0aYYqH5kj5hLLaSsGxfrOuJcXVNu6Abz6skaZqR4H8KVlejQcAuTH7Oa1os62RsaPlb7i9pZFywG8miojnn/ryhDGbkwN17Qx22wZWCE6FI81qKgfKTpTPcu3zUVJFQzmqxZ8+qX4q0sa0FQYLLBOw2jzeX7hohVPZrNtZ2XZ7rK9AaxCO1KH25jzjLralw5n6HUVFjWynrslu9EPbuUrexBjG63HqOMRbAaBVmXhRS4GjBwlIBApF3L8o50MICd0hm35pkGqIF36iy7NQMyk77Vai+/7Y6GQ3k1SIKAGQLcr2VTDZYNoVCExufgK7fT5qYrqWArFO2MH1ZvjHqb6fNmIbGvDuWTlsPT0dwwLcQd6BQj5Uh5tjltLvMFtxi8dOjsXay8c0xgVo24Qq8hjjhvYrmKZgVTxnpdtE2JjEJ0rRkH9oi0kpduIrNYMg8M1Oo+EgekGeOQV8lAb1T80n2kcFotiwWazzJ57xKRSri8wWNbnmlXMGW6a9h1pIinm9O4Qmopr6zYWw8fdlv47GFY2bNU+3O3QjgIeimEplBXqgWsHDdzpizoZ1CDs5pxo1bly8f0Xnb52D1I08/RsLDsDo9m3qmArrXmG2IKq6o+V/P+jzGtMDTq2Tx5XHa0UdX0RuINpv4yeniKA+ZMeLtx2So55C04tEKbPS0O7oFIIAWQBFqOIK12vB5pBlMqkFu1OJK3yW4IYif4auMd3/GH6jbm7d/u7d7ubd/WNFrRvj7+i//8L3/wx/w77/3bPuLjP/tz/uC3/rk/91e//b/+b/7+P/on//R7/8W/+Off+y/+/j/677/ju/7WN3zbX/ysL/mKT/28L/yWb/2Tujn5l//yX2jq4/F4p3d8p/d/n/f+ws9603d863/wR778sz/mo37rz3r3d/vI3/qbP/EjP+Jt3v4dPvHf/uiv//zP+Z0f8pv/rf/Ne/6If/WHvf3bv0ONoY61xZK3UGfs2IVo6dgVkSNtnMOeqPQuzAvr2VYTvnqMNvH93GeeWx21nFKTGdnGclcO3FtdfAATkErwpOzcpKC4Mo9zm/bhCZhvzroWL5MqDkBr7WNAW8OHx7QisO+0drvBleuNcfGR7ZBJQwFggCcszaDhwOuTsXFdjOF8mDyAVmO2vbT25Ht4UBZySu+NbUMnpVLN9Fa3vfc7CnLPOCUP24bn6HMijL0z86r5xEilGaS4GFS51K1GN+aYZUaDM0uDfFAstNSJJLJYsqfDrQ/4oxzZW4U+sk399spRqdB02VypscYozd4N/ZOMymIzguhhnE6abUfcebgYupXoFAWAkEH+4BfWRQZVCqJpXC255i3gq/KiOkO1sAr98GKDV/OV8FS1WmU3wpEP1BvgBFtjtAQolTKjOnguVzUC4I6ugFTOKL//PqyOQdYwDRyysFMJrvTd4oWdXorEr+2vTl5n7mrVmpJlcpcddSVewYTkIjaY6hUiLEajBKMVf4uYJeictGV2HD7dNTBbTAceNQ8c1p7b4kREA5BEBuYSMztqn7FpPQbH4X2Qcy/ul0gHRKIuwEhexLXqmjyn3/89fqoopBaOJ/xsFX5oMHpqlbsU0L1Y0QXMNvepSEVq3z9h6LbOW0syahJQgy445i5ggn6UMXXB9Yt45bfqbGE6vKj9XcVLl8beEVIabm3pfWYzcRpfuYuyBnQJTU8FkrSCRhE06U/7Q22P1dkIF4+ZVNKEiq3etnw4Re+dxY5udc7NaiQ6q9QgdiU8JEjo8ThOV17gUAgEDIqCw3G/cvJO5QtjLNctAAAQAElEQVTVMU70W0vN5Tnx1kefQ2FEYm/sYBQkL2WDBSaDJkFJzmXtIh3K9AQGtS4eakfpN5gRY4JzciiP2i48Lr6kDyjQ1TwUuSmuDiTbrX3CpjDi9esWNCut7pKWD+BsPtsuMe81pxbME/3k6D6Q008TG5opGm3WWDmEpxQt0GQurTEZbJVRXJVnA95j+dQNg0i4XKOicWm0Ijwvez77EnHt21lajOzVRB1QkSBhKYkAs1sFd3iP4d1jn4zM7otdaA3jdt1DLY1Ht6YbyzTRxNMAidc/+q1tjE19ceZQU6Kzp+Xy0jfKiTaM066hPjJ0pZoFH+ZDkbc3bftohZM6HXzxXrN8Ov3gXCWjzOYgp5lDX43nmOrjOQwTuWBTtl8cD0uyIfNz0yWagsnwKIXnwgcU5eRsXsNLAbSuc/WQWZRHV0buaxdjj9zk82bU046SUV6p2RfH9qNxeA0GZuZKadrEhqbrzM1b19bQVTxqDVG1dqzzpHYpgmCLJh7gSJ8t0C0s11ZF83l1hbYFQURN9P1f0WA0tHREu/SSLMxCC2bmFbxK+jENxnnlqx1qE+oRSqYGjcvX6Z6d3Gfv/6lwDtsDKvlCmWxySwlVtfsm4JhcrKktD2WVI0UQMQG4lzC7lqpH97ElYvpzzC0eetrTXQ1Kw3R0x466Sro1F8N9K6kbBDzvA80k0jHMhmDo2NbXzOe+rjNSLwgWs+fN0mE2IUje0dVyQINbOZJWNS3W4xaCsqPLu+rkkRgBzZcnOVJ772e32uMrrUXlmws9TLZNA8KuPZ89n4WsVn50M5lQAVQ4ekJ5gpn8cctfu3XNLP1tcJRl44QOKgQGjkulJjP7xlWiyoyacBcMBJk3btGg7DSMRVmYhbZ+nfgLFXayXg1lS2HCUDWoyo5pjUmRUOIvcVULoQ3cNzYralRQJJMDJsHdZ6xstQSuVKM3UneNb3U+zXrE4qujJAIf4/6rAQbABoqzWg9a1OWmbqbzFoay62bMSoQSVlYbEahsqzZLzVUqC6KOGV1GiccOcF3wCxrUkBJdxjXmwvWsM+P2ZTkdt3A3u5ECQAWxC3tQT/e3sY12ELq90zLgBVFh6+xxFtGaKrj60MLe3jCWPkFGZpknpcDIe+RQ6yQAEdRC70r1uR91HX1vxLzNao4WmBrQjubJlnSxwbMhkyYkl2qsX6khsIRzxpzKhlNFFGaSO/smodmKZUPsUCHY06QpoV8OHpSYVLtX4gzTDqGjXCXweiuBqTob3PMIMYaWNTkW18AT2U7FJyFSH1SOzWxeY4YWJnNpZer7aUg4ei7g/q+Y0aLWy7tYQFeZ7BSmzuNWpzUMNt1MASnOm/ewJGtJQlzT/fVktmCWyy1W92LfJYCi9TTNdZnAEPss+cAlS2m2XVHfkl29JwItjrQDE5hbt4oITddRSnxHU4/qCnR7y6d/2nf8mT/11//En/ycT/qkX/q/+aXv/M7v4lNe69r77/29v/vH/9SfftNb3vwRH//x7/tbPvgXvd+ve/df8Svf49/6lb/0/T/g/T/sIz7tTZ/xtd/0zf/Vd93/axZlet7/r05+3h/92q/53/2H3/KZn/Rx7/2L3+Mdvt8L5WzvrmTyfX7xL/oTX/klH/i+72PrZd86XY3FOaDdpvasaqWdLdqcJX7IHhX3fRpsNNTtH7E9DSpllyprwgfFR4VfrSoI2OXjdMIolgHFr08+ORwDsCzgLDFmVUh9yF1RpR9EAMOUWq0tLhm71nzmsL4XFyqbTVR31Fa1hSkcfW7JKo1y1tXILhhaIcjPBgstcbTyUKVrBJwUDBa7Sr/gxtvXa1yhVUuT0MN1UrFrs10nSrgkQxkexIRNwOq+Cp0tlHzAGusEA6XuRIRNicHZbiYRXxLfDNPKeWNj1tp7ReiDRduNm1Wz5VeDnWPFOtTXoxM8qbV+AQSMSon4GF2joLWTrRALVncYnDbG5LJeWdysFhmhAXRorgy0POlRaqotGeC7bWcz8qZSw++Y2rOOFopUbEnPNqQC9rJ7S5mUlFqV52P48nG0qeNaGIQ1b9ttQfRsO5TyLquwO8ZQanq32IEoAJLiZaiy5fwyO8e4Fkp3LuOi6Vy1BLsTZUa5ZyMKVTCkd3jWzL1wWgeClfrL6RvW65huwzFGm/eAzWKzijKEuJOnc2i7dJRiv89LFp1SYhK7pCfSYGtwrl6NI2At3CiWBnhevs8d8bzqVfjes8LqrIy9S3uPQpdrbCONKU/IUX1DbxCt0KtQ4VRYF5WOaqbVTTNSF1rFQbLRabkNJ3AOU6bLZU7NzpuQSopz786MaQrWHKRpWvtVQ+1MkDmQR9Vj52JJFS52VymYCqtq5fFuQzBJCjbJ2eDVFS1FhEJ1n8OUZ0CAu4MIZNVmJLc+akwFW3oPhzqCNuCs8JIwzUA3roPHoVuVg8LSCaQXxi2fEgl9K2NpuMfyLYlQV4jSm8uFbKdYuCkr5VjIEVKy0Y4xhtWkSBEXWk+1W8GuWZgaTRBwK7VwDbihpYLNFqXTkiPFEhb8GNMfKircrxLrOSgb7ZVd0oF2qhfymsGV46gx2J53LimtsBooaZdDLPmsm0pPUAntupjBimrFIuw3l21Wgp595F5K3NnRLAuq4daO0qnoynGhNxqWYhGrWk8WGVR13R5ND3AutxztHXM/0ZEOKaBNEjEBEwFr1LByc8OAspuC1mI2gZsZ6pd7m5M02uG9Zg/vvi80CZO7mnJizZZvppuG+WhaWkjoknlfrZSjHC0ncJRVmBDdt2W7WbdTR8KT0zgnhtbF6tmctDnkXvqWdU8ZhnUR0JvN09YrPOLmuq5beBDTutJUAI0clpOyfGdYZGu7HFJJKcK6QV2wVIXrYbjutpgukZgPQvq4MpWY4g1agLsiW9ttMR7Mpjhsajzxml3L4XQ1mz4gwIfNw45p0zQyGOEXcY2iGvpq10BdWyJS/rPO6BAa36WEirNi1mhamXZWpvJUxkM3WtXC3krt3LcWzAzKIKNLZcE+BlS3UFJo3/fbKUw+2i2cZ+/K02rrnvbRPpLCMQeXS9zuw+FGpo3yFXSkIV2eC3o1KtneMCXwIFdnpxqOlb7oTxAj0cUcZoNOuKDnKj2n29yKrhJMAJGCGua5Bu9/rbFwapMVZko/q6aKqE83yUIo2ox6FV3KaaMM61QAW/oVQJlEFPZrdqBcypqKtlm7tbmo1qmy7i5UhU4RcbObnOnz1kpvoqQBdHfI1sht2pvxTvR89hsndDt/X3SZSm1sZAgMVUuLPNxr8I3o3m+zrZxiYFA7w0QK1o7Usq2m3PR4MA7oktK2HemkCPNFTijc3bWtSy3kDr5CMATNpMni2BhpTVMw7LIqQDkp5VR4DGPVOjD1gaVqgfjVRLKNZqVIbTRuiq3A20z73TEDqS5ObsoYPKjsutZKPf4AoKFn+Blr14GeTkpuHruRdVOHzqq8WlwXifamEACtz9ZO5UImWqx0NTSjNCO6L6vtZ4ndGb2YwtWjLyFRr+oxfI3lwg4h2LgnNmj3fQ5lKEWQGsQL7cGcLLlR4vu0Vix0fx2DpoW1dpWXZ+tCRJ253yq02ZUpoyQMnToTsXEqeANXc5USJYIzVfs9wUFp8GbvdCTBWK02f5uARa4zx1XqOLUYw3ccZ7xcPusQvlxHuVTKihLdCF/Z6gIsvHXnK7Rs8Wh1iYZugjyX9UEcLLfMKKGuat+820dn+2vJgAaHUF52PbsuKKdG1KzyZrdXaUS6rihVorWk7z2n7uS7zXcPhQa9nBCRgO3sM6KRvUY/Vg9zYd3p0WQ2KtpUNx5cpHrMjdgxdsxXZz11r6ckTMDb7iodGpyjMOfiOOwH/MDvX2s83ma+93v9/C/+pH/nW77ii/+9z/2c3/5Bv+lnvce/9i4/4l3MXUj+Px0kf9APfsef9pN+4vu/36/8sjd/+h/4lN/zo374j8zguXmGn02Gqp1TAVnbePyIH/ouZUsslQgNU4kZaz5a69Yd9NUtOm1MOaWWx04Lsz15kpog3gp4TjwP9jLFejRWKJk2YbAFrbHOLYItUURT0qA2jVqIxCJaedHt8RhKXu5XyjL6MsRQPmOWHtaHHs30utLz4rVx7drqEOe262qGxsSSqCNtK/koZa6aKf57gyndQPUTrX5kSLauIPZA3QEqlkru7iu4r4FrWvlI8KkxHZUTQtB6IuvXrBNn6PnR9czby6iHWJo8zFZjoE18HgpPASqogj1vjCVIw0CjqIckcR+l7LE8w64uieGyjAFQ3SKsAmdihzKFw3Ec9NWtdqZZYiUpocggyhjtGy9t87jvfJ5gWbsEesAWsShtnagtlVr26JQd3C9aF+MKxQFCbUp+7loQU5eHymIM6XYszmUYc7tvZqv1HGmz1UEOxmE13DmGHziOmH55lYE9keg4i6/2PHNtOaMGHrOWByGNgSIDVttrW1WHp3YkNgLU3JGu7EO9W5B7mrnqJ1FX6ZXOdmvFxgnOvsGNp82nDNS0K+Z+uprzYGsMlE9Y3LWq3abesy/HdQALw8o0kAUjQUNUnxv72qlG5shl8WAsPh9xEtUN9ZdOIdEPIrCfkfuSUtjiPT1u+JpoUWjoC9QIB4XBLentKnPlQY6k89PGyRkA5pMmfXIr9edarzCroTohHTKTe+/zilDpStBdHN1rm29n+Y09yyIEEL1KgpFaFua6dRxX5dnzPsAT3GvkcGCMOqYal2arTupqbDhIOEVMeJ9AeCkDOqcMl7nauOQH4b2J2D0uWhpsVJuCm6hlOX2bXfANJmQvjdfg+ZhivB16ES0GeEWHTLXTD9oIek7zA3PoFDnMp/TXs9NjT8Q4mhPKkhg5lLTKTgKGUWOlcs3T8xq7mA4zuNYXdQk5mSKLzWS+bv2nqLSgycX9d1MIb90xto8LQHeHeqUQLGJoVosaJSOaz4AWPZ19cK3djAoN9+39qnGphZTktx/WgyXiLu9n2TPGlccVM6E9y/HCx7CRqCfi6XirjYi22CJg5tF1BCiPciius8EKdhVUJfEceffYKYK2jPYlXcSr2nJkXovbwR73f3GF0T2j51bWFriSTNmS4cirO9UN0Nawi7gUfHKWkLjFqmvlNXESz+ap8gvjxnrieFrvuZVXky/qL70WQz34TIbU6I+L8yzR3ldAjVn7m9xtlTP3us6xz6FHG2LeOyUAma//+ZHioEoRHEU7mbIHNrz0u/XVnpvP7kArO1NE6Nt47s6dxAlv2GLr9srLq0bDkV0bV2pESSL0MWAP8QDSkFqCbSdSAgUBWrttL2UY6mF7WtjVyhGNqlw+fL74cDLRIWBHQdKYwjc37bbg7nPU0fuR7ji2PzYPJv1peS39vUvaOO285oXJOf1YXAddf+XwvuB7WB9tKEZZyAvCR2io21q9HslFmxzGA5Tb0bnQLtJEHFPU3FWe6dyT4Xdl5qgcSLDgZYRgeg/TuRKJeP1LhSingfsE3/K2fQAAEABJREFUYD7WWi/DXgwDO3tL6/0k0nQR4KRbkzrCvfWNzW7JyvQ61phDZk0lpEWNvrWxHGkv5S+FA7JzMcv1mEU+oT4ugSlSYeoJda06nmP2stuH0qrFnw4S0O8ZjqfH807ZocFDsSewISG1Rpg45cDR0BZCVY5+NvSX9tM7JJpOzzS19jtqZ01cVqe2E6tk3bpFt6Jsaskx9Y+rrJSAi+oHtTuyqxf6vvvrMY0+eqgxiGr9hliExdA+wG21uuthYLd1uRyjiaSVdojuEJDNuNAB7x7CI67ad6GyYcl5ibmEGuNuobSnzQuPhChSS8P9KjD0c6NTXfct+6OPBx7SlMbQNCgqUj3MbqqFzdSQ1uzpxpbMi6nOc0lFMrsQK89Rm+JCYSr2XUKTodT2E+ceJx7mx8NMT/1nldgSh5zACKKo753H3o/zHM+znq/i+dw7sgCDeGiC9rqPa+1mldPW1PJkUl6puTydEk3KUBLWOKofLfJ6wA6OR43HE4daj4uNpEMGZbiBhHZIV40ndD9hHTXPp7YbJi4d9kjpnRt9IuPGqfHqfL0HtlVR5S6fKo67QdYgrby29Cfj1erKUgwqSxixdYsxJqBRME8TTHUm2cjZ26RE7qaKYWbPzL715RWTxVEt6elyFzVtabJ3mNq6iQ4gaBBBiLSn27VgztVE3C622oe6aWW3p1q0wJGKIVJeu4+2ozDrerTaF7vstA5qOIBqefeE7b4/N0mxawkmuuUX6XU/Z6iMHS0NyjAvWJICpLK7ESRS5nR4yJ7IMvMpIT3cxjK+EKsxG5pS7A288fZv9zP+1+/68R/8QV/3WZ/6H3zxH/gzX/ulX/WW3/fvfvRHfOBv/A2/6pf9kvf9pb/kff71X/pr3+dXfOyHfdhbfvcnfetXfOm3ftkXfPOX/oE3/7sf+ove8z0exxsAWO3CfE7kQ+ZnjlU2B7iAZbY4dTawdB+VlyMtzXo4B20Up7ePHF68w0cOxIzSam0FKxLlGUNUwQtjk3pfbg9tcdpucc3tnrBOGwUfVhOKTIRBGxk/7wSCoUpObaH8AT8GXZvq2U27Yu09O2jVDQmOqgJ0oEpnaHf3cNDZ3jaLrmF3iYzuAYBt1JW0SMGlmLAu19BhpkNWsJsbc9eMpPj2J6Y+SHXUON4drcB7thf1LtfK4PB0D2MxgW3sQTrumWO3R0sQ3a0qYbrBsIorWqAIXaqSwCu68qqS6VITeLBUEpSMdOYqGbeU1DVe1gSAft2/aSp266Wik7Q800x2Ew/aAV/pQ3VfN6y66l6thddElaD5KDPZg9PArq5SLcu9IQd15o7lHr5NBLXPVrLu/usjzEMZuAHgHilk14g4Uimxbl4pMH3dFAzEUH6LyXG5QGci5c8bZG7vmsMnpbDk+aq3ztxw8gYUJZ1AX+FKHWCEghrm7tSlvhrZJgKp1XB0Kygoy+1QD8SQbEgqSxdupZkcdqHOVi326/aDFgOTssa+K9utZNvrqG1xoa6mxGMWYtmhbne8xoEAN1gGGYeY2ORJj+Hty4XPaqnCjq5BmutorTF7ZthIW6CsyOQ1+t51tUCmzZgr3JulCGAss4KqBEdgNb2mpXe06Lu2ZY0qkeIKHtUZVcpf15WIJ1E8OMaQI6IHbPrAYABXiHORVsEIK7EDICG3gklP14o8TzAuw8k+CwESoEyyKFzKhpWUm5CNLtaqOpLkhA0zGBqdjtJ7SEy5dIfnWG5Nv+hnRWaVVthCVeberbvGrRXEASn+sH5Ura5BZXlRypcTiMQ7f8V7spDQhFlNZ2yh82rf96gWYe2ScUnyuDGpYMt22CVPKrJ+jusSC2cqvb1Z8HZxo+jbIRkXAGHT5hUKQKFs8eajKQWpCy9iMQyF7BDjLPOCvqcM8SgKOGQ6VYhsmJQjRlEhSlX4s2gJinETY27G5XS0hgYZXnmcOE5zWK0RWFejjZjlFJDTotUNtNdtw4BbT4MG91Dez2dGX+XFqeFHc1AXCbKdSc8aHeQzuwSs0nWF924ygCJMnq6dV2RGyb/IL2dIWvpepoTxFMBoE9mJLoSx6YM+ZcnrLuqezrWuiefKqy/E1bqNuHTJBhy5KmQJ99g03fvYVdzc2a8CO4k2p+tNlHj1Coyr7WyRDZZC0G85NUOjdwmTTLntBTnbUbx4M6zZCh33z5Sa7FqxPbq1KqUataRIWjezW2+LvQA9APUWgjRvtxxoBzmyTE8Gsi9CXT3EUt8/SqUu6wCmRt384cZkPs0WpxvdrTQfDPmJ9gvSgnOxzBSL8Q5T6+gqn+qLoRwrQb0cY5o7Aq3EpCY7YLM5TSMr7byEO5uVrIuX5I55o+3EFZXCKZPuyqiDLlTMHSG2iin+brF2d5VxUy5Y6KJhaiwAAt5Y8DEMEzGxxUQBUNE/c2TO1hdLVzjoBnsMaoVWUK2IfegqA5K51YQMy3pv67g5Mac4YWe1td0cDLahS2baCad5zuppZg6TU6h2MQyHgQQcVk3pNM/L6tIgn0N0Dy8baKIwaMOIG0/mrmyAwA3IU8zVpV3FHFAYXuyyi1b0HpR/ESkHIEenmotHWGmQtCOX17U7k5XiZOJmE+XdvIeXjLrg5tGwbaN9wGfJLxqtQ0So/WgW3EQvh4bFnVz2UFAC3tA9VdzmYKMUgaZlKaGZY9sMP0qgQ/ZusxMiCYZSQ4iKqCsqL9Pd/UitF1otalCCgpeoDqM2XZebqk6fSw3Nbr5mbhG8E5vQpQLlL3bjLJ6n1fYKj9KK4NlpjjWlrKm4K7ssOM5lp1b+3uiwjsWLHWobHejbztUJtPrnut87XAs8jO5NJlUhWzhMSStpXDchwlsshU954yCsCXWgfZf33gN7euheb1Hnoejr1jIq2BKysWfbS/eqdOlIb0PpAMzHtDloQEsFtYsnkArYsubVfrUMGuuOLQavh8eLXXNdNjaYAh/klpbKZ/DgnnOPWYdMf9+1pAKefTrUbmoE/RoQJXI5GctP2hW4Tu5QXzuqHdl6pZhVykxQSsGnWbUpnMv6OsobL7Lv1pM5dGU3zbBGrdF3jV3VhdIyMM3NovA0vBr7mtEA0rFRG5S0nqGFzUjtC3RfIet5qbVVlmwHR2odH6E5sdkqmjuBomjrDHt4D1MkOhkX7iMYY/QxOc0LKDAgHufZakpF1UJb7o7zaZkv/vY/5B1+zI/7V3/hz/+Zv+U3vPcnfuRvesunfMLnfOonfdanfeJnfNLv+ojf9L6/5pf9/J/0Y3/UO/6Ad3DzvCzDQdjopWeavo++v8GmcVlN6hvRzteF1KPKEZrXQ6odGWoDbmPYWK3WkhD6BfVlKUlcmW1f51jX6D2UEC1TfcOvxGW8lLLRk3H/z3vAKS8hVnsWDF2UYLbjeeA8PAwSvET2BvkYxzEeHGsfEk0Rp/G8MzlZc6ar2tUlb1BstWTefA4ei3NBj13n7OPIyT6IUdOL9OQ83S5vpc0aw2VDpACNxLi4Mk3fJydr5VYFSEwtmM4aILtLu6/2bp04DMeATDQL0CCJIrl1MxzLUjuakeVbvpu12wOmjjjRk9a9Gq63UZva8ijsBrzGKMM9dSpi27NrFVcNv4Za6u5rKx19CYiHjMEnlD5FruCaZLRdiQ3bY8QYu3BdFefZfQWvRlGh0mlsAaG+AfrCHtR03OElKz0UoqUKGHampKNzt+0mCDixas9KK2BTI+YZL3F5ncjNBHQlRoAh65LLHawZbzlBHWf3vsBXe7y6Hns/yJexJkaIpuoeMJMGtpjuzmiIdO84Mt6IMsWQAypVG0zyBJ+GbTtHSN97WNycpXoe7dbA2lxbBasiv4HHxVYp2MqxIESQuzKpQb0BJSh6RE63umZdB9USfdjh1zSFKRNN9YepoJx9Py93zBFmp69YDz6ONf3RfJHDbPURUkwPc2/Y3ugzPUP7cAkg+k5HT9OLd8AkTOVlY4pdwRqwma3+7sVq29bhpZK506272ME79aP0iOAc+2naE+4rr1PplyaXi6PJakQxGzDMpYMKY4fp0ZLkhedQ4NIsed0pU9xawQ67eazaZ3YkBH2hBkv4SmbQPnL6hSW+s7KHilJslXr6ijlMxHQeO1bWbDyCK23AbRqlOAHKej753Pupf1XUApDJvJq7xLKHeUs+G5XI20FkRilNIAPQNi1uGutIDLV0GEWLmmIJ7QCHKilm0PdBPJCmTSw2MWZaK0o10uTZdtUMLPPX9NMAZ7qizOrNkIQhfzIOi6R0zadM2jI7pj1KhtAqrj6JVxxbVqwH7/HZVTp4ll9CQ6umsncUdZdDZkHRaL0JEGizXofZg/3Ca3WpAqV97Ugg1h9Wc6RS6XRPh6ZSEcnT6Hnky0sdB6euj1nD3eU+Lc6Z17IQLcVKgixDeqZH2o28UkGCmI7VkCQUCPQdaIHXPqVlyA7a7Fz7VKLvaVVR6mJoAHbRlTBTZK2J3PBLE1TFR3HuXkHhZUeLmrXt2D5rSUGtuTqIZnaX5HeTBX1B3idMrqUJqHvVMrBwnxc+5aAH1Mn5KK4kMy7GzSGnvQ4kGTLom0PPJdppRqMTg72srVnhcd0cKEp23vqqPtQk5jS3DKi3VkONz6k2snqvvJBdW7XLOqPOZ+xX0Xt79+F8QElqJMIsFnO6/ki5H5YHFPHinhl9abFuBh0S59G2wFk2ABOPso5LIYqWrgKLbIN3GvQOCI5SZ9kqUF5EIToiXuV+1Xl27ox8/cdVgd64TbjzGPGGxRvFtee4nLrRU+/R/XmpcQYRovzals/uLQLzNdH0xsuNSI57d6kAIjvKGrZ4P65l6OyUqpNpvGZvZxJoEOa1PId1kxElGY9SvGZqAQUr0S9rerPyGTa3abaqA1QX5qruZgOw1MK/pN49xE/pEXCX9zCVjgiUlfbCKjHULMGhtase1+K4181q3stPikfBkgV2S7qzMQVsC7zl4JxwV1zMyrSNFZgYhqPb0Q31vxL7RtIMiqH5LC25aJmrWTlCKa8O7qu1yU9rjdXIblbwClSVmYT4hO8n+DRcqslRLROQUeea2y0M6RSy1h3FBqI1Qkj63pqpPeW8o9dk+b0R6s0yG8EF17boAAcS1I3+FbWzTukhgE3bU2Aod6Y0DNvurw8Vau9oLZaNFzNfyEembEBxoq2oxPdmK8LUV5saQx9N6CXOPAUHCjH87HlOXqNCwLs7eU9iikz4TKxWwdIkefcllAm0fIdIcrPhMPauDo3SImq2rBb2HKlWwSlfEzVNbqEl7Ln71atTAU6NJ3FYu2A3GFV1lTCjDyhYc/nBhNTcdMTQypLK1VFj2qANDT5TkzRX36Yx6WUKUOci6xR5fp8wwCU0P9r1Z51FbRtjWkKOi8j0faev4aW83BpKUb5rnRqQ3jla8IcC5ZD6UL0zLzAd0+CNkkwvo0TghgPa812Si6Y1hm3VERJIkyk8yiVbbT3aNKk99ZEGA8S6vLbTXN5k/sx+lSaSQQxdKhS6q44Dka4AABAASURBVFIhh3zKl9jvYjU7cJuzAsoUkw2Y64LHNj7FMvqIUjtHkVKCvIAVDaWtIJWd5GbJOHOzL8HdjQiWggGty5ntrWIoaCSjkQm5pejzHCY1pbGVJ2n9dD4DV7qlGO8pxsfmDHqSCIeaAijHIrqBFIWw4N1Abxe7qb51ptABbVNz2EqhDeMY0tEGAtKpeUwvnTl043tu5g1ePT5N0ghUmlKDy7lXpG8wuxK5q5SValWWyxpM+MOmO9aL4eFydfs77XhK/lxseZHyS6OpUaYq0vCcLAK44aH9DqD02Ug7QJrRquxu5rbLL2QhdEqBl2XdmdWeCxdNdxlczqVZvmPtHEgZUs2LnO72AM/SkwtvdxeCYT6hV5cS4Vd5a+CFcYLKWaeZMnZQAPNYxXtsiy+RiR6RS1RFlVHylD3e4FuZay8g4SAmahGWqGaWF1xJ0lncOWah5IkDHOLGZNuu8CuGzqNEr6pPxRrKYmCQNl3xuIYDr7EiFZbRhq6U26VUVRowzJytIcKf6I3OTrkeAd6LHxXvoMhJwRGfrVrI5JZLv00LFSD5aC5YaiPpbHbh3JFaKeMWiwICjcPN1dkOMavyDO8otOradvEquzjCZLSY4WcaetjwGroRc6SKtP0mx1USrUjaed1qkw3pivc1lVFfzPZyTaaYntLYGB2eF+X26WsPqr3SSjiNFOhi6d3dbOy+HV9pSYMrHeG2X+xOXLHLQVcQq8tSKfCuIWD3dHcpdiMzKwtDBd7bayPEZnCkPUboPjSswliKa3DeWwO0T8TCqdXAUCYA0d49KDzHGBIpnGFKx1Sa1kVuK7IHfA0ulUDKS0kClCQnfXLO8gO9Zh0Dr0NkFZQ3Pfu4gQ+UoGBe0dez8zTERE0ab8259Mc737i9mGLWXIncq1RAFi5uhA2jdCkE6mE+NVOnLWFpA1osSAWRFWekJFkT44AfWaydqCAUgxxJbs/uaslB3/IaeT4qRqtuPEvLpKGDvfWmoDzdymCNh3EQ5oO6ohTKrwRA2uz5OH1oF5SjxNqyWj0Al0VYwOQmWnRUo5Qn6EUzhZq4UvsPD3mg8tw+/AgfOUbR2AM5nKONpTkGenUxCjr0Sdywk7qENNum29qxC9lgtKWMFmbcogYJWliH33hM3MxlepHuy7BGc4RE4OZTyGBQhFxpD4xjDhPnVAiq9QhA6U9F1HJlTOOGh3iCuYB6mnfbHXgKdvVEi5Vehcm4v0DxmHUP7kPVr6DsHgkDKX7K2ITKriKloDZh8aqhUFO6eVgvDLMB4XKbcMBJQpPoydWpDJjf1M0xUZqbfYs1riO11xehCzmvtKruTUtHS1pWraGjnODtsu8GP0bXKu2ainKKaXR36BVkIYzlKEhwymP11rojZVn1aIjHOwvY11XXqfsR0zlY0SQvQOGLEMWr+EzqiERsRokZl+R6o1JBiY3tpq4T8oc9cKm344K8dsM3X3I5GrJAhSNjuOHf790Kuxmvg+uEjtAQD8yChFXWV2Spo1+h8RR7sNCwzg7grgCDLkhPjWVtaGq/dWvWuR9zH5Zgpe8amfRQm4jtqS4UXYY9kQOUQvD6VYVrx/N5JZ5QT3kQVH56dFO6RmciFGYoK9W5t+SW2mjQDWlRlIFoB3SgLczDrFCtCU3oX6kC99Q6XG7mY0pul1c2ZL4ExLGRWT1yzlYBUKqsxT20dwEH/DA4ghKwal3gOAl9smxehWcjAOVvTpoeAaxu3bzy6tuilIJb/9V1WsqtuJy2p/zdh7AnZut+RImnthpznNPT2ZMcy6TKZqKKCvQmBHeGYSkA1i8dC5JaNnZQO9O+ys8aqQOLNmAOEx4FqZqn6YFdbPbpGZ6yWlnIYqrKBdkURVWl++YDVCKcxYZKYnsHI2/GBm2aD0IskwVr0NtG0wUFDriBRFsXKmp3Xpbb2KS18UZlCU88JLnEZGhSidM5Tf1nTrCtVKAX65IBhPWWmkn5pCJvUA4nemZPyPMxeQwfU0+axxAAVUkngCx5QREbli3nkTzh+iOXYmS6Bb1EvzkHXQwokd1MYRXPjT4HTtN3k+B93Kc1vrqFgO2O5bWA+/woaGXEZma7QFKNN1G3i6UzarEK3AfsaG1NWjnpUnSpmzBsjJ1HXEu1QQnLTUyBDm9/2Tgyx7ywTvHSwrVgM5aHjxtDm05TTR4x3V74sJzQBiPPsa7jyDW0NQj2Vt43lfCeG7pXnw0FEtkqa8826b+pvx/lqJkaMK1HgxqkgyU37U6o5aMrDWqkrdw3DaVhyu6IQcmqbfsKjBa/adbo3cCAKsGat/YuWegeZkMv3ga8dd/OI0frRqVMWrOC6R91RWy4Ymxc2c8Itc3us0185emdFE18aXuZeDmoTVGXVaCqQbM038oZhV9AVRliu3rF1AyDs51GjMQIpPQq4a1XegqKkTlO/Ql5X1kXXOIVCX2lZZiKcSUWvGJcu6rOrmun5/XAeTCNG4zGViQDDbv/EeQoIkRM1DwNT911uy7RIA73yX3OCgciU88B6nI8H4gB+LZ12eTCfCj7JIhSe1q8BrIH4EJzyCEveFbzTFNryipB8LgsU4nQESUJhHqjgZTj7righcNCDIHqtt2JFn/KI/ykPWVB52PadZj2T5rkGhiK84XlsolugAX1XkVXYlFjKO5JlUptxOsYxPOshr53LMsXE/dj90N8UNTnviovaZDATDJbEzhZqm6bgJVkPxYgEjNGhwRF291b2i0R27LHA3xAFGR1hpzlqJiKSBGYmwl5RYbcdUkMSg5hoDl9Yjr1wv3q1u+r6zS2LxsGjQoTBYjVtmDTfNAM3kRJ/d0W2XIL9SQ9+4lDHoyQxd6SBexCOfKFfZT6UjZ2l2ZYpe5QR+L2oHDFHTRtElY6V+7wAZ+S/BXWGNS6oNLUveL9v6EFbeTQ09lRaQnGqDhYepg23YfuAdMPxlHpBcEMq2fFVV1eNUR9dicrR9PbrDmMSzFZCxwKKE19o+pogAQcQBbyjFTaDwzH6rTeZRrsKcmg8y6y4OV+YkaNeoV6lXXuki5100UBgeRC7oGyViax0BJDFC94YaL1dhGaUhvchQQG8fBcaq/KTyCbT/Lp9oQEYyKrB3tSeYqapccPa3KZnv2YG30Rq5K9bwFcwLPyLBnC0X1o9jCfNscgjxDmtlmt4N1U9KFdi+R/uaXPkWueVH117xZYF7GtVDb6pggOaasL2vVsPZmTSCbcdckhiuGzh/ejemmHpKNhZoOH/mZ1bBEuZnrNlh9dEOnbAn5pGagpT4ZbaeVUovS9Ohupv7cvgzTEZhF6brhnJEtQhpuzTR/zZe03EK7xlUjV0XI9sjLv0f6I+UaPh5sdl/vT1dJC5Fvp1TdLDhy0RQBNSE3tjjIPV7JxKultIMTfC48pmWM6fQrdJZGzehRHNrMysU+/zqNKmntanIhEiJNpHD2QTPRldWaU3I0Zj5fncajVbRFeNSDZaSTludvhw6xGh6gpN+3lsS74Ex1JlApJPXOlMakznWozJe10tm3ylY+Ts20Nk0JylkLZoLg8LtjG3rj06O9l4XGAwnUi1BXSoVn+uB4rXJ8J8S5mxrZHkKWQcofENlli+9r21ufLq/PlbNEdvvYxah09/G6NCmgiUdFI95zrnCu0kyEOL6fsnFcECjTbC3qixbltnBy7WNWAfHRf+iAFL/PZRaHcMQQBTH0/tq0ayp815efM6xx7db0EHyCV10SLIdkyzb6w49odV3cAZ8ydVq24qDjDjePwx5gP8gE/6FIjBQXqad1W0KzcnjkTs8lWnW3vGttxTfRUOuhU4LY5FFjeOvBOwLCouuvjiqWbkJHzKJtZAtE3Q7Jkba6QuyrQFzzw8LG0+q+DkBe3PWTG5FU4vKcSpBlgFJ4SX2ODMFeTXEvKqY47LN57HgMJegMi416hp+IHlNq83XcIyS4tKQdKUY++nyZrjKc0BXWsADM7dl372rG3GkNhsKfHnKnIYlYyFUbXITLsCdS23gdytaiHtbZg1JSaENXaPqluBKgqM5hbEMechKXYPs0u0cPlHIctzLXd1N06bw1alrBNtOFkp7VD2ZoPs0EBFo7SB/UO9RAprCvxenA1WM0+O5/7GbFVvkKzm6kQ06AvvFUwnGtQ5LZxY1ytfFHbW1qjN0Ss0+YcYxpVgYXLeA2EEN3DjWUie41+wGY5guUdlpFqzhla+Yax21S1u3bIJDAwVk7ZBQte2pmENK/5V+KK/ercl2RbPo916DWOaYJJrOwjJEewEVlV2onjpetR++g7IEsoiD4lAONhY7ZnmYgLHp5TXeXIOpx3PAarnGnU3LCdWiGuetBsapGq/bKvN04+clkvUhW2ypzeS8ckl4rCM6olA/TNJExe0dDB7XWtKN9AAWCDYdpaueIdWx0jJJ8L3hBI9exBKDGwszWVGD1ITYKlOJ3sx4WXp728Gm88/XiSCaCn0UAk8kIHqDS1VitpUqdHWo2LlNb0zcvcOmfEvHSHU5B5wrpG2/Ixp2FCZxvC1EMxxUXZRMPJYfp3YCwHxVl3MMubh9nhw33Q2IY2zYPVtLDe7J0QKgyKGTWsiOhL/uteREZi1ABHGIWyAOqfa1gFVTNDc1htKQVWSxkvZsfgmKSLulBtvdjQgjfDJQ4es2mo1twSI6yK0EKAoit8wWtPtU+a3BjMnEYC5pvUGCcnYWNgjlaNQ26VkIEGVFG7vFzXMeRF75NmkuGgOqGqvuaCv5RrakAao0Sxszb94nSBtukGsJOQNwstCkupfhpheSCmJtzl0gAdPMrUAFgweU2HfrugpuHMfKpANs6L50adBWZLIMMhzWiPU4OmvYmXdGrWIrQ11cpW6eYDi3synHohXJd9JwkNAVt4a2aoiUrll1N/xIpzd1zWcBmuET0DA8kO+Y2dW0pspSlKio7i1odLp6OSTiPaYnM3iHJIk93o8jtb4nLtnmJfyMu6WW41zYwj0OofyucokcAIJV0aNYu0W8Aq/9ep8WqiZfKyuppCc5IJLdF9JK2NJPTe7lVHasHcPM9Wd7k0oxyADCg4SLCQsXa9hjsNxHbb7SiaenHvqixZd0UubeTYcGG2vMHJjKg+Q602z60CeMRQwzDWzdhqGFtiOKmPE2DLrn5DgTaAgoMrMbc6rz7D2chkNjhsiMo7eQPDjeYFpcCtSQrQOVG+Bo/Rh7JzpKKPYJ9EjJaF1z9ymHWHk+7DD638L+RCHuxJCEeewNmzcQDZJWBpw9Zck4ttAgL5g5ETOuDVDLnwabwP0IrKRSp4S3NAxmLsc57hcMcckKFRxstVG7tm3sWndCssqGk4zsp93c4PXC+2B5PY3aflNpNXtg/CTa6VCuW9WBpuo6GGgqjq6tfANmtb5Uhyjw4roOnmJnE7NWgrdTrTQFYEske5pYaW8lxSVN2JFU5Tu47n0G8RW7XiFO4BGGSyvK9Rr8w3FwTMyVUYjOapuhFBo2939x3IdSGlFEjtAAAQAElEQVQEpJQEFYRGbPfLsDFLJXcNCrMI3WBRFL8MDpkDqiL31i7kDi4TwbqIuz0zWkzPGOpt+m0xcXtjC74yMXYPbU1eMU/H5Sg6QZShUzQ8ps+1gvNpojfliO6NUU1BuSfv7RlqF+J8hNnpDHcba0lBi7SSMU0DgmJvcpAjG5HsbfYstaioHVbGVipbO9+6aUpNGpnSO5il8X0P6wsWGK0UmfuNVQwDqXRLX9pq81E4MimZeKTBmotwRzy0ht8RtvA7YAXvligMHa24S+J09ztrDSIhdkxTDaTGdKblabxshhtccB2nY1OigX40KSB5wbOOyJElE6GwcyNDxqAa49Wl3GD1eMFYzY66c6dfpcvZHV1kOG7Xrb5QtpNhY5hUE3Wps4fCy8HUjK1SyM3eysoEBkzQ6G13BkmiptaXW01tfpM2Gi21dLUEoOb0TESE9Z1CR/Mmm8+2J4dyUw3PtlZ8GZqnAbTRaWKjFbpt+lYyWTW7DqYrZY6Ah5IJxh2LYuzKrEhs3aMoKTstgyzAE35euBRwD0Ef1lqlj6OHD5vDnMaEbRhqCN+U/GzAIStRXqK5SXd/mNiEhwbSx2EHa7R67DlijyrRIq6TVvBqdWU7DddALVKKmca7a/ogKGX0wT5c2O/14lX1xWhu03bJ6ZAtvxMFtD6HqTw887JKU5GtlXp0oFwHELSc65yPHkdpm6i075c+pySMsXmcnDF9T1zgs3pbSpzpfYu8hwrFD+JQ5up2XTe47WNDLyqc1ntxIJfAOWpZuSugN8gDXdZ3/Qc964FUiNazc6HcBGtut+QoluSm93E0R2JuSCJGEpvKolVXobOZvbpdH2J0sgPCi40OAgnfiYxkQsUrDINw0GAsVFKboxSgLM9AF23SHD5qsEZBhGztAEJeOFrAkFtPAUTSGpi+PEfrWkOTE1DwVqZHM3Xr/3nGValkhgBOqUJQ4uzerDoA/RVzcVqOCnjWrFuDg69VDcAMouZBX7J4m+8Ny9tFYwTGpYHmNyEmt44xaDah6Hbpboux2Hdjq/ukdWdR/vMOsluaKBGAdC96g9UotHWNfdml0frKmu0eK8pCfpyjpYs147AeLVa1liyQLktdW/qoyXRlRo3l8lQxVV/Mi0geAjcNR+VKWjnTq/u6+1KkRQn5dE53k2fpzpqD3YgNPVmYGfNej/ya8+I478wmBBhyTgCmn1slV8z9RJ7K2FWrQta2egJb/Kw1pswHcutmLsVEQ7ErGlt5reuE1qns7GpGaVWcLdXiMcOnGkSEMCXGlR4muV3gxbI9dzh0vuzBsZaLz11MPQVLF+bcsbuCRjvS7QTPQreBVK9IMS/qhXOr79CNPsUONASdkJsY7OGD/ki8XD1eNV9BZTekURXF7QGlomwHZTdkuvOoXiKlAS+dnam83iZhBmcXENBM1yRYV2WGztUwvjF5DFLcCoPeKOoaJGtMcDVml6Hl5gxXnA0oYLOmaikS0V42NRFqnqZUC0iaFEL6UCDl6HGan5CdEp4xp9NG5+gQOviEKl3m5B9NV2MpEuapanOj8N9Tr+2XKtP8cgOpjvlISavRnYqI0Jty0/B7Nmwk56Uvw2RiSC/NVJB+2+NNw9SAmne9DcA1u6qrUSFyQLMxaYtjDvS8ghuV6nCTTZkx9veZXQGPtFL2++jlfJvm4+RS5muwVBzuBRgvrGeaGgTi6rwCWajF/VL7iGBJNDe7sl6F1gZOxCpVNvO1r2LtatHr7muaKqpa1WIDa5CQ+eP/ytPfqLeu6zwDIEBScrrO/V/qtxtb/Bm478xkuW1iSyQIgpTs7POcJ6zCjFQfcyAcLqJy3stLZ2Eoq6dwjkbS5hCvk3ZNUDfS/ZKVeRFm5a9mLNu7OFg91qC1Wi8+Dvt0XVMYHkbVnueC2MHL6UOv1ydgp02kAtmEZposH+CAZasFcwQA08wVVNf3ikoVP44P5XWADB7Wg34ylWFOecCv1Vu15TKor3U+NjRNVOaAmfcQMLwvjxmKT1WgqrfXMAZUwadTh96ya3UvDXbZxgRHNOlj6c/rYO16qyBwHEOIhekaNCUWE8H7DfxxHltma01YLo1Vp5OR6c43IksqHgrL8W4bcbOoBYrLaDo9OU68inMgIa4NpeT9ORy5ojKnApBXvk0mphUqCAsXSBey0akuvN82NOQWbIag5HXKnq5nzW1z/OTqVOk5qy3HjrwxNHZmoFOlwAcMLm+zlpmyWx1wQfSNH6xnroIXK6OeEB5dMjsdd+5TEbRXkeVdy5ZHmLIAk/DymXPPSfSopeixMtYJT7M0gfEWPmV6oKdAR65HTbdmvoYkvMeyRL2CvhgXJfvFGTwJ2c3kSQmyZnSiSwEayjSTjRw/tXWTxvqhq9u10OA8M6WLLXb4TLdSiGfm7nl0wXotbd5M0rCuV0RjmJDpzD6oehMlnf7rUQlv7cj7oZKylhgI5kglj+miY7kIJUSlgPxlYJqY958iT7cO8EL8qBNbvJQbGoiR8OhNYTuDu+J5H/6tKU0fIA0nshRjp2r3/f5A8Gm3WdnZLBtmx8HuCA+YDUwc4X01ohhj3pfG+u3Tmlhfa7Ecp1wkzPnK9dBHz2MQaMNYuLnDPclaTrdtuLZGLCqGt8pNjTAMUjyJ6hbJRm7n1u/h35W/2DGNUeVynq/XrZwV/G5r3GN5YrQhzZ77jHLS7urFF7QMqkbeeYzTS8Nq5AZ4xIQgctyUKW8zdSQ1dPSp+X+vVE8xFZaVzrJtDV5EbOUnaKsX87PVTwujZrCNazuWtRFQEWJUOveujvSBfw2qndHQdOWi09lhmtvsEY1eM8XH+I4SlLGZJm9Ns2nUeGEBatNmni27qxt9253NTGb74UozDCRuPDqVIL5pD9SUllt8ei24zyzM3ubhMPmQEH/JRiz8RMRWtfzUWqXZmTFfNSia0ExJRZDXgc36nO3qEkdsI0XfWFBS6XrFfognFOML/wOsl7PFkTZm6bogDq7pjz/Wb4HcNS13WJEhOpIiChLSwoW4St6zdrabc8XjuPPk90s96Dqzxrz/eOUreBeCPt3zQFc+8CVFt2ea5hCDdh63fMIfGMjlK/CDUmnuNs7VgE7PtqFpwCWzuK/5jlUaUC41h0bSNFIxWzFsro/v2Gu2m0Fx4bFJItUw+PT6pT9yTCvsxOoJ9sfqUkIIe9B6/IlfW99wbQ6I5sSRR2AkDLWYI+hghH6IBXhj1FYUrf50Pkgl5Jidks/W0oXPYMMkv06kzICAKm1R5Gg0mRPj5Gfsmj25cjga44bYQ4kE1sM6ozLIw6zochnrZI9Menkon3C04SXL6HKSMwen7Hl79t9W05YpcaZUQR4094CtEO0xT0gV2iXYpd4gDcnGheUQP8ZavFcLgzCtTVvIqVbPydSvHri7vl27SANQLRbPcye+xW/WOU8rB1JbBcs1QuGNwU3dUmnwn1QVl1lnoKaqtAW5xx8ztJUq6Ex1nXlKH55W21R9QGUerzJ8IMJu4msKeNpaD2CtapeKtbRiQIsXCjOvgeqWSrNdxBGz7Xn/A7mTftMzDFRiW+BEjVBoRmfepbyijKYif2A3RsWqj9dLWqnO+z4tdM2SJrSXPq0pT6XyjZ+pn9PXPetRu4Kru2hrvZLLYrCqt/y14LFmbNZV63p8NxykBlHfF2wKUjN7pIJzcI59YWXT7Ornebr0IO2dAZpK/tFpvJwr6BorLRputoYmMR9aD6RoN1lFNypdQms3McKiJPGAz1j7tlA5mVoUJRmlHY/PeKt0YND0QS+8ipJxqJlMtAyiBwORIAL91FXjbOcBT/fkN/B6hEyQzWo0pze4g/6iwBDlGFlWQOTMIGuKhOtnNeXGxqHSakKkpEGXFks6hfssI5XzgzgZIz7zfPAN9ntlhYUNuwQA0XwVc3rMuRfXYvk8QMnkstENjVkNM7P9YSRHtzQHQqd+ut0li+T9G+cEnuVnRS28KB+LJ8wCaxAVysC0y50itEVsAm7HtNj3k0ciRyvox5gko+fKI/TaHH5Rj0gwRS9L49TeQ+3pa61ET5R5CwTQ/cd7qGpHDoghGyG8pjZ8zCaGDtlBsSc6rt6uOwG5bekfJl0EU91CDyftWdYkNL5lamH26f2IzvIue2a+xmPAEA1Q0KzCNCMOQwWcZTUe4xvaY4tZcg1HVC70OliPsNFe3pdJQ4LWMtOOpiX8ZUuxYUNXCEW0UZLQDa0Cr2o4HTM+LIFAQ1fLLNc1to1mnMX5hPqYMcQEoahHu3kdXU5l8+YeX7jWyJ/uFhIw+JpYHV7mbrNNCaQKVW0vAJrTFDUoeTY6nz53qtii/Kq4yj1nTunK4GneYqENZUq1FUKh23cpc6feDp5wPGDL5D7uX48HpgsE3cyjPaZXy8INPAp2vMGC3fR86/NDYtCIlBX2iIRuDQjMamhzzz6UQVDYORn1G3kL4MnuPJPVovcN6LROyEqpAS0te0g+pSY+t8qY92ugW6EhCp5VOkqVeRk3YMIgYHNQg3HDkF+WTACCnY+Kw3mb3S+wcVR08Z3v2ubXqw7/tGgG0Iwm2+Tm6eh0JlyeXzmNVKw2sfTzg7lQq4waZOrRmZorcQ4Q6VcrGSZL0yZEA2bn6VOnvJT2cVW5Ij4ulbYNR2fcqOKV1PyVwdmyvto0PgZQsFHyVUsmWe0nWaXzDcXSbWmmsDgpsSGXByzKo/90Lhe95vFR0T4xL+mLSUojj0JYkKz4jlxJ6czLeNA39KyvT6BcYcqXaYedx09eE6AXpkUZyKsqslENs4iKeITG0KYzresmn3EM90zqtCNddrWU1BBwHWynpi7zGNeKaDlWU+hSNrG23M1s9A915+/qUGFYopYGbTXvklYAqMAr4zx8CgmUSfF1os+7XMF0PZm3acDvg1t7soz7WLUPHGN8XybibMyh8UYPi+V66S0UyUAeBxLj6bnFxQSwvNY+V9xhqVwep44UsfogDmAtolicREElIWJeQA02rKJG16iGYXKmPtXetLLFvFjWvG9+y9M/z143+6vloMTouvzzYxrL9OkN99frS82DXWehIuBSt83nhH13nq1yOHQ5/otllif8235AxbtsNKMDaZJWeqlN+s63H22jxlocBKnSdzM08Ax73h26tOozEINhS8zpvcN0RDO+2++tM3VpxZkf+/92SI6hOEWUi8/WB2CNroZxyNslv6ymaJOz8Z4pJdVHyQJhdrwbwv7QChSU6OJz9/fmk0vIgKa1QvtAuqZMNWTDGxSeaWIAokc9yb5HjcbSfcx6PN89RCN7lcyrNV5NH2VbvqT0QmXkM/IIG51vX4nVwjYOizSv7jkzwjGAnJ20pz1HK6vNl3am1QhPPd2pEcISNRfHxv3Zdm9vNeAXHnLW4Spl08jC+caT8YXQAKBh73Yf9OsHU523Voaqp3IEkLW8NkX2/w1hKzXab0QMVRyaJHio6tJc/cVpWYKuWlm39MX2Ha7vqibjfTGFJgAAEABJREFUuU3dTlzC0XCOwoVka5es1TDfieEEhG/WjJfWoScy32gU6+vE4JgRfaezEv4Le97IlI0JmdxkVKN7jFwSBF99yR9Nb5rVhj+1mh2zXAuascxXCKnOFDVtsDDmRl27ACVOER6iZKMXvzFf9O3NaWuJ6ezRrWJ0XTeuR4SSGOCMpKVcNYueFmNgJLQsh2f/EJ8wp7XGlv7IWk83TLNtNB19BHyNaR/mq7lPe92o6mzFrrSoAI7dB1/r22TGSRvOyPWT+MX8GmpPBiXr5GkO5h2E7kQdWNfCN3Bbpuc4KDIP8Dt2wDaAgAwPZAGjEN5dKKsqn6MSgkpBA6Cx6gRynM8cAXj/90IGZzqPI6FxxOp2fRnyfKdKSZRZVCtpXNRu9+j6QTZ62NBrLsN6yVC/rCNpjcy8M57qtlTiPHxRSmy8/03njflS648o3gqFTtNLw7AzVQZmvUqimhmxLKxtJVrtrdNpqAUPRwyrsZcQttgDVWvzbsfLHb20FMpBpy7rsCnZtGtCCnZwgIP5AhlILW2rk3gGX9ij4GwH6FVvbcHAsE3b45d47YfP+Qt7DaJnTcrcPDHqgLneJFiNFIewE7yZ386neL958kmb8xZkj9XuNLF47C22t/EeC9rH7CNG+FrSX2F5+63PCI1kUUI/L1t/zkVwjQCUTJyjxldc7V3IeWmKnFeK+XQeamug/K6GwijbySvhirsZhUvp1a5/dqtlaDE2xwGEbqi80dwLcaeSio7DK13c3eNpgJkr4GplQGAyRmeGjiD0Xqk6Ei/pDJVHNxWBvJEzpmz2SCTl3VY1XUfEj59DZkQ7dfqsowpScJP6V2jBNmubpV8S4XvSOCYoBzxlp7wpVIiAsSM97mXpVuk5oX2iB9abxJ5RPKag4Q4CqnyI0OxOVvsJyJ8G+dDptuQmOlc9S1vCqjro0kCB68Yh6hWu0zawCzstvmJn9LOWutmfkXr7jqGIduBqxGvcQAcDTl2oorRrQiKKhEzdz4gNytD4NLsg79Wp0ygoseqlNXqh+arx17SgWbuIqPBn8Vi0OakgAQzE84qy7iml4jL/RGxzr7En1eLbrJaXhUkgqYoqN5E2MSnv1VHzmloNe9of7f3KS74bLPnp8OIcPGmdonT6JLpj2uc27ZbjnPg+c3c9Qw3xYzFvCirUAlZSn1wneln9Y/9MBx4ovZO3DCu1odirLC0gYKVUEe6uUJyYVkGbQoNBr543UPE1Ym56vGapkVb3JDqbKJqYNe8u9vPhfXn7arHQlZ36mTPWRi0js5xcKBX94DTMybAVEBSPNh3+plLQoS9tjVOOGcH+IC/NEzcmrQqk9giqJ3uVgF4ysGiraa+W0EHoUDQzPeorZgmbGeEdd7m0ct0pZ7H0MTzko+YJKuc+9sk3AoJsc41oNbYwTJ9WkTCFsbfCJgf7SK60BqzoHZgNW0bd4u5da1VEb04gQH3Btr0UJhxlKBfgchaRaTiGtEHQ3Vfo0L2K0fkG8BIKQl6OTSmhcVNKiilTIgZovtcW+8f4U/4jVPdQZTZsSVag4BgIvipNO01xTi0AfAgdY1S67aiJY49fpRbxmfn0xFHYGJVIvQGyfVKy49zoB3NcgLE29mfiHynI5ZHS1EIvn1GqFesaj9qRFoOXftKNpribOGp+T5/KbAnuPXLmTKZ+gfg/HcqQgcMpdSJrq4yufdJLaKbEApVEme81c40o8ykr+KHLhNuYN1+e5GBGoWOUPVMBZeHkqBNouJU8SDJvNjaevRJa6BfKx6vWEQVeiBH6jsBes+m6ZoddrXgmHGEWxCIYUbFkS/GIeP+axkVhXnezbH4+ksmFdSW0JBoZ3N5OJbrxFHoGGIfJBauaDyIlCF3oQQEsWyeuWRedy7BosaI//WzdymlGsK4ofdH8AQI4RL5xr6AOh/SgVdWtEEumsoO93kuxGj4J5lhzAUt5W+2rXO+2xqx5x6gKdpQMgVZlSSplCl13NXx71J35ZNY5qJ7SkNVleawaTcWQ00JpeldFG/mfYeXI1rcjaYAGYuTURfdLJ+wFVbN6nJpga1wjt7qMqH6gzmCjDCsfYvANY7Rb2gqb/g3RZa+QAAe7vCaekYjOeIudR3metUypY5nd7U87pOzrerHUWY+Ox/vLNxlf2j1aUZoSUilnqS+dZDhkwreVx7EgYGiMNi3XGXu12vLNTE/pH3pReTNIpW+HerwemzMjypglroyWsXANL+3gf4Hz5r5t9RILYggoUPyyMGqsPHJQ4bq1kT8VWKdBlUHSMRIsfRQsNJiK+IgL+4xWLWofJzjHvEdSsLdOZBjImr+DrVRhSt0QTtl/dC177pJrL6j0LZ2+ROBINMmRveVuUkqcpi5qv0wNaW25lNcBu6jO54z9s65l2xWVf0QejFIhHVyONcmqUTSCLFlOogsocowtzZne6lyzrEvNcSV04H01eOpVBd5EiNdUIr6zf9uryyy3vg37l7PUXccF834ghnycCww5KB8CBsUojqSxOFzVFJlr97XnZ7iU7DPo9wiMul9PP0w9MW4lf4qj7ool+wlr2BuZ4LWh6NQqk265gEgVrSmhOafnjXIImA5ZoI1ZLbd1lftd8z24Z9oN9CY7HDT9HShn0PtN/Ou+utR+FJdCcSQU+OGkM9cue3u3WvYVh4azDSprcwyAIWHmOqeWBXfakkxdKUg5VRDUKRixkTFHIaflUboMNRpGR/+L/iEDBhmD+Uv/6fu3z7EblsLfVYF723fzxMClIKx1sJ/cXU5whblxhl0qpxp0cwSSzuKCTOu+Ab/OR23PyXJ8fW4ijUM0taboPmoGETImGJrdjCE5D8/jcnWk7YbL2rxEp+ldwZtWQEGTgXTU0iCVVH4Sn7QLc3WGvudPGej5gZsRGq7S6OedmJ/V8f5/4fijmMCBXozedutbEFY/z3rO1ZqpC2IKIw1K7/cX95d382FM7z6yKsKUyZSRbrwl0X0e8+OXQgKoIRr28BVqjbWhfHMv/9D3gX2BHA3jCzL6zUA9C8rQGrOh5r/rxaU17gO73lpTTxoY+RnuR8crZQ9ljP8Rz+l+Gj1Vu+8PHuN3cFfdeY4OJXdEavwgroEUiquGIkiNxBdXWCxyMZG/c76qMrlxGGDvavnFusnUsfvR7pBhiatHeVCLChFaCDSkNvxGnQ1trzBGPdg+3uBQubA0vzGZjXMD+QZa2c+NVHBluxll1jGvY4l41r32s5kS/Bvb2LeX2nbMbKjUjLiC2wAwB53j9wG7ltEhOl0x676xvI9PKaEaCJbzjOkZD77SpD6VvR5nuhLPQ8jGadeYhmjpi+oc0203rVfPVuASwPf07+GDK0lqLkem3Ess/pj2LfqWcPEHvZ/eqTe8SnFV+jlv12uetnZTHieKGLbKJCCL9Wv3lzUDgNU/Nv+2/bN38aTSk6Xc5uzGZwwTX7r6JqYxr51Q8HaiU2vnmoEqxXhmDz7IBWXgJsv2zI/lhUNxtx7o+JzhHAiIt3Tigm13vk9HMi3nSpd+tqt4m3gUxLon8vjcMd/4I7SQpVTzfUXYdnftLohMVI53b7j5kqwWz2JtWOjcs+fswqftU/yhGYMWoBJ5Of+nN0U7nNa5D67Lf3bsFbxIMbA5l58LuQkCrVyvNpx9J2sUD9czW1grsH3+TS71BPb7H/v5+OBV9Xzdj2brUDr8Xsz1Zjnvcx+FJ6zKgGlJs6RgCN7QsqK+nuVnv/+b5roYe/YeJa47jjRevOB73IoUtzlHe+0//QOgOXqN7tdHU+CkDPwbk8TMy1QObiOLtq7xn+EPRysrKsbXuMJd0YBNhfpQ03SuIfi6jnJmet98wbc7VQV2vswv3qRKWXLPF8O94sxMEo/13ewMnIuzMdGW4OOZnPIav/X3Aj5jS1NXQxITRjMpn3xEeOKNxd40ZKk3ypgsMVqi49fU+2RS0T4QtqSP0zjaBl7fjAFFLq7BPspgvKJ+zelnnsE51kdPFQBopLZEfQ9ufdD1eGLlD7FJW7yDN9Hb1xX+DxJI7t2fn+TnHNqpSyy64fD52vdciTgyj+fg6ep3XyYMAElcnB3Z6lT3+a25Hb8XvgEV3CQ4taFtPuvtjPAuf9Fc2EuhvRZqL+5tK2D6eL9xP/KwhlftwNbJIWZZxNCrR0ElOWFidYw9Lw1KqN0bxxut8h4HNfScdx2fu/1AV6xVJKtqi5Rv9H/+PA2cH9eyrDdmsI9yjpl5KwP0rukn+wZVDrm1M0H2TDffv/1N3lwckacW8oxp96QxKoNlWKZSHtZz8jwlDf40f9KsbEaQn7Iv/KuNrbuc7unNNDYvSPS93ooW6mf8TS0mq+1+QlXbdFBgfSg7zImbfsK/Ox6jkqb1aIkwp5M2KPShYB+9/xzfdZkbDWMl/rAiPGJBy28sBkdXmmzwqZUVg9D4oH9yXxN/twzVHuMX3R1O4RAc5bXO/CbuwpmY5dC++mIbI1WfmqlJayk3Q8kkF4XOQPqFz4f6ZgzBU535oG7vVu4/IwVoBj3n+vbnd/b/m/lq1JfXiQAa8x1AShPw06Z+wbom/3V9phfElr3tMBzunRxxpOqZ4twuacw8RAdDx8XPhR/XuACdFivECj/3XCc9NSUVm77Z6/7Af7AutVOIYOXhGXy1TtTPU5++bXIgH+x2Kr0K0iBSR0vIlJmQrVqqYGfCpYnItXrYCYw1/CiRSsGAsLawYUiTystIQSemPsByc4wTzjVpJQg9MPVmGVzDTV3fr1ilYzxlvxnqYNJBhVBznuo57bJE6QHPnuczs4mF2VBuQfE36We8JjBDqE1LUHzX+2q9kjL+2JS3gI80tfrNa5FvFRV41mswGE5HA104Y9nv5V7OvWLZJbalMzfpS6N2Y2noUNFFgJe9cRJii0rgN1jCGCjbp/d0xMzHclkVkZR6hTZORd7Ubs9PXS2EGL3ANuxVP6vV+0KyGXdKYcsy3rx9OV89WvlfqD0ug1Un8B1PSVZ9oGww8IdLk3bz58Fn4vLPZZdLSdlACTg/iB/X8ycFjHvWbUv21Sj8Zy33K/jZKcF97Lq4ROj9O987jv/MklvwQH7GSLq6gX3BX6duQb6XP6KsTT3h4Gne6alxY9Os6ZwUCzLiH7Mfn2tmsdwhxPAwYDeirdNO++NXiz7nwLv9zeKr3DwSXCRcwdoppeylHALm1O8Je929p2WjZG7mTMszatRBTPZiFIAhNyqKYk0fVa6lQVkgDC7cqCTGlHnrzSfwdY0DNWLMYhgFwMX649Ps4Xi3Jo5AAUX9vTGHErpGjlv6SgtNggCg3xHUtJ4FW27Xa60p+Hg92ZQ0J7m4bCHRt83BGoROU4ISGvnUbx1mwb2xKOvTrzC6zLCprUelbnVVt/au/yWVZUsK72WaK9SZ8tY4C3D0YTSm7C2PQzujFUKY2vytKAxG4ISquksfCmoDLNmRHEC110BsFY2NoWVxNZbDFtusxZ5p3LhOFvr4wMcAABAASURBVKDgAUXTQI4VoeSs98zgje90tQLXtRl2R7Ys4EQrGRrxekiUGCNIyUcpRTd69JGc4DM8ZVnOjndqcdoNG6OPwYoXxhgI2KNYZUAESC8K++q+TBmokRAqq1LdX0rKrMzTeJLVczjaSIg0piCKHUD0WBNgyZyjjQ1LYKYl+M9U2DE/dp1ls9QMe9cb2GiIi6gxU9TZT5XiN47jvaSoDGOrxbHSZpQPhe6shWOZVsJydCc5QVZNVbPmxQGasiy3j+GZzqqWMeoSxiUBs7UNYYVqgQQHI0C6hnBg+cM4sEotU4u9F106+zSCkAM1spGHAzVAEyJCEEmz9yw1oh215xgfGcErNZfvUAb/0joKUxy7a0PdBoyVeYW3RdMkugOm+e1MzmPTvuE/Epm5cunWZq2fByFEwR0r2gXYSyFaj08vUPu0VS67ihSgNK4VwwYCmMWsPiIGZ7KRwk4POfKRbi20itF0ZTJ91LB1PzuCB4sxYeiAgkugZc0UgcKjTLHKoMjs9aRUaASsHE/MWfXglrdqHgF3b2DK7I17yDPMxEi/SeFAOsqmlSIqmA43wvNYyfEJZsyMLIgp6f1OKVhg8Boq3LfdX6uBXmzYrQhb4n2spRiNEePKh9so2darVTGBXjOCdNjZiXNbv044ISnpoLsOIUKBPQu6+sBrvIzJFFs8ARi9uRiOQTbzEEcU/Lkqt3v1QWPaB5EjwUbBhyJSzl/DM9KaDOl9y6rAtN4qswRWm57L7PRVqmedrmzxqv3GPC0OVJ04Cx0iFiilaUZ1Mbok2KNG8kJcQxKuYZMarIUE0Ala0dL8OB+2yH0B2eiyyzVW8ueOz7OkNHXmfoIjCg1iTT9vgNTbRlNs9FjDDl2NQy1rMLzhGQuqodUR+XqRIyVIhpRRd61t2rf2EWaVq7U5aNZFjN5wDAKlt4E2YWuT3E4bG+yZkq7sDip0FqRAll9hF9XoE3ehDAwFOqOM4JuZ3TLNIxcr4WkaMvqUEu0DcTlKnJFe4Wcx0e/rNqitDGyUl4bQ/fUj5YEiGIqvacr8LUuyjzE7b6vZ3Z4SCvrI1JKknG19MOeNs8xk02lq64wWyEnNdB6bjnv6W+dMDYa0DVUiZ/wkh2U7r3jWCLRASrHYiprQr4JSSlGHl3r5GyWFjxwsVVK5C4dB+LTp3Rev1cseVlP97zETUtM7mbQAyPYu6yQVYJUQBF3dwtVtoDyghyjXeegtGoOYWBOmaqkYLtj++72I92aHgVqdGqhEPno7UzhpaHNVh+bLVqRdKjX5t1kO3Tx/2n20S9fuaUkiLgrYcglrRp2tADE2pibgJR/wadOgeLibIkY84u+VHP1VNxEdGm0YiZ02YrXVbMBv8qTOYXm5zk/l6O5byYT5aBlx0mcxdYSPywKpAJTUliyrMp/76Vu3OM9eJfxS5DWvZoYKFv+HbDKUaRN3GBjSNBczjjH5VY8I9rUKMQ0FtmI+u8XgjB5tMdUGZhKTowXPDoNSkg0L4OsgaUcW3r3qWsel9bI5Xo9ipFAwlVtjKR3RCQkttd6PSpGj8MyFRLAwVvdk+eE1euBDUdZAGh7qUL3Q8LKHqLxOrTrOYWDEXABap9D+FLI0rcgEmxgFq7dCFTOQyBtdm758KcbH9NyHKFgyEqY3UpINqOCsueSxIVIm5UUM1OvNhET5MFLjGtMEzJyka86Vo3avi/IHyS9rhARrKM33uoo/6BhArPZ8rV/xBk2ZrXr7E3TJXXFViXExiEGTYytcK6lpRs3pLjG0n/lpWeuhcgEIg+lXE6g25axR+dqzdh/2QBNPV1FFoWAEU8uJq3CiFIEtcqMWlN7qqe40Yi+zzXnlNzMy4i96WdWAN0JrhabHSE3JGA4LMy4qiVpS+HpK5SnMQ1TCovFh/3ipbbTZAxPCDKSeA2hXLWs85A3FaGrakz1TxG3xQOR+SretWlHLe3b5hfWJ2esA2uSdrGfmQN30JG5iEHgDs24Uk3PvnlWmBy7M0ok+mDqw4/Z8cP/U82nb0M2SmOjPcCmxav3RWgXtKT2e3ZLSVbUenG/kN+zXPnf94A4J9a48c1c93ulrsAefB5ZS5fOgc7RwfEK2Xcl8VxEPG6lpLfOVthL7zMegMZSfxHxHfEAU14rH7QwGXRDw7kjx6yftF6MtkH/nJyWHyn6qs5UuZVKsQUUzgAa2ykk6SY/7vWvMjW98n/61ea4X45z789xLgpfcItbY22HX2VdLHYLQe+397/L/LfsH6Jk1mRCXSxLb3auLVcQZV4XG05/sn8YFlybhN/eDj82+si7eF1TLvO6IIk2Jy9PnGXwXbt3Z+xFUKMSAnomt6Y0hjCq6LLAZ0ftf+3bwcsZu2+gFGoLyOGf4EEmj+fagqYHZg4FhrMhX6kauSn8eT3VShX781U9/q3/VjjoK03NXnZyn+T2UyqzzwuipgCIUGmAuP5flZfLlcspvT2JoYXGZRQCqV8n0It0pOOM/xQ/odKNvuI0Sa1ORiMfjMW+do42zdbASz8N+N++LY0lIu+IeRjNoHGF1TBnuwQR7szTmRY3yWRdEAPSiNQi16bJjw+i9eI1JeDbgiNQlQl0PoH9ufOps3mEZ9LBtryuiAW/8JP4Ntlz9+L35lahm97ni2abblImUh9vsuT7lgSRv9/qx0QdtJIITz1D9qDv1fuaq2UorRgr3zg1zAZCT78f+C3vGRGokolfAL9dAHnj6YD0L9570kuQFXwCdvIh3UYdQq85VhUIkmeGZztFIFfU5/Tx4Gs9pvw/xqK+MK+muE1/e9/6OPcZBeF7Lrh3uoq16pb6yPagWeUZTjnWGNcc8gUQfQ7kLA7CJmYa12B9fr62Rnuq2jJifLN6dB53mDy4wGHiC//Xc59SUXeSWW1lJgZdYZKof1dEOLB/njX7Qg6fxpBiteA6FrUf9IO+eb/FpZpc9J5INpDNdwyKl6hLcKu8ZqO/mC2X0YWBX+5Vy/sGsD+dfPz9HpVkZq7hmPrN2+XS1WO2WWSvDpl3oN9zhY8jV0NKnU0rYcGIFF9nsL/C7FCy6oNmlcyJ7lDDkTXzpSTRaIl+j7ntR2cb+D9evd/JLu0c7VBx22mjYxMmdebHds3FX35zmsPULvSxXQA/k9OBXig5T/33LgojLxgxNPxNzKm59h4Y5PkZshGFVe2U952TloDbqx861NHeX2byL1P1uhBbAAdRJ/7pipIp6eviN+G/vrwoKKnl8FNF1IppUTfrowXJZHTxtomVGmGHuMPlKeyYOqXMqJsXY37yP3bmnt5tBudMQ9JSpnc/9wyI33W2zRJUavExPCxpm3gCeg2y3uNbGyDsl8jF9Q/KjiKns+tDML/efcekM/bldjUFY+cl1/fr8qiXOr+KU95xpY1xrbQetpr9T7/3u6rXP5Y+vHgnGWtQwFXZIQnOd+VRfBjNimoRHhC8/bXfzGU+rmpG+pLvGwqyX0GyNzun0glrHaip19/BmnLVHTZdhswx0VHgvs+iPff/VuZpRbk2jTrub0wx6DXFCqmq9YC1OgdEhxKk/HDWbO+rb5/uc+5nn5FsKi3nZCRYcs9EugeTvU/ftp2wMe50VN/tAQX0hrs3aPxbrc4065lIiiPPwe8/9ZGYlTMctSrvZFT2fpBaMDXMsa1Kb6AZssHJLR8Su1uHtDVeAVEy0ERv9gX3EKQnx+x2lRivk/y9cpt7dxlQNc1xa8S5UnzO/OapHtbAguZYtv+qxzMTc8wJInMLtfQS+qWZ/jFOF771PXoD6eSJPD0/a/cqN5ibUgDqPKmVl7dMqkoCwqsUEVTnMQt+FgyXtbduBuVbBclDlGhJhtAGsuXJUqbIejSVGanCaxV6eV2QoGGFayODxYc9Syk5XV9kZe9r69Hp632M3cKOOjb458SO+6yfO9rpydsJZs9kbw1y4L/uu/WV8BapfxxA+6RQxYipUC7edXKPs216LKiGqIPFh/Ru7Dq3QgxzvNAimhLw5gfNY6uqDSLWWT5mNa1Wwf2XX05/nWSfT6jfqFzyzj/3UtbVKno2EpCujUkEOhXP3dhiAHuLeeH5M3IufeNRIeZ1YqXEKLY5Frc0lXYakMgNWaysyuerE25VwMDnK9r28HeqULsLeK3fG/agrSXUnPh0fH7sqAxo9qQgy+J+pG3POGq1Aief001k1KPIGv8fqQWe36hxfVXLZM5ZrqeDVByjNFWpVr3vVs+++SpzCCP2jm22IX7ZNGZLRvNDLntXPPnV9x9Q3ds/GmFnFdAAOqesN+aLF+LRi9iqibR1+hm8vp2EAXYObjzrOQZ3pNm+fr+WDhtwtmGsgS8zGleFqT7hP94xANuxgyWzBZkhiAs2saE0Lqn94TR3L8pwBGk3cgWfxbJKDmi6dDwli3CbYZi2NLHKPE6rlc+MkaxztLJ96N+CtwEbypDf0BeDPYE0OT9sxq2VioYhn8J1OdEf55v6YfwQ3FWPLuEOBaVo6vtvOj4ns8O2xdbXsrSggDQV5jUlOvi5bfoAH2sdkY0z5Mj6hNfsMGm+/9TO72psx65po9Jnz25XcxhDlNrJ/rBto2guV/VCeKCbCYWHQYYCoBoekmwfdxIkNu3ieV7qohry+5A6k4wM2je0yGjPE+9IA5jhIQ+AlaBS6q1+kghHQaU0xjJx1C9Mw8nIpvOHsK/AJbIy8t4dOD4STrzmPDgakRMf2XNJ01UMVBOBYqM0TWuZXN2X+Ravxbhexu195NBMzgH6x2kB5Cfdt7lQilDnobCsFfLdEu6SSCtUXUhlSdRmLXlgFq0EldFrWRARcZt973A5TvhUgMe2NqPeqUAviD34+uILXzJ4RZ0vRDzQYaHGqbBeq9CFNWtJbxeFNd7yBC2/3EsSaAo8jxbZofQwIX1jR5AiHNZRfmSy8mpEQFoeNGl1NeLUFxOHwhWEao4tdkK0MpyiZZdgGmiYMWRtno1ZCnpW2GSX/kE1Xpmav16Ccne9MWoxKBHsQNJdkTtWXOKyDHMtmSd+PrML1Cu02wgbiFqM9wVQT0Mbw0l7IhMOnmMXWiHZLK/B38axVK2YRi1TLjS7LN2s+o+AM4m1n61OHH7ouKyEvc0ibKq1iN9hhvl4ypNU9lOMyhQ2nzIKqQrWq50UcDX+vhBkNbUiPWjimQ6oghoORRrfN5Ro7ufK7zs1pizIv65aU7MBRTu09nzWPTZIFn2mlmyPbG6D+gUblwMwjekVTejmcGuC4aUNYJFHrnLiL37fUMH/q8OZqNSEYmEblaQjh6wetvJHuEw7NhtmNUNagPQYT3m/aMC7bwtmaNzrH0htDWKrFzn1HfsEzNsG9PUDwAerZSn72yXlUK+vgZ8ZhLgxQm+D4AM0e65IYxAQeZ69BQMMIBeqr5noyTnXNDKt2Q+l937I7xpYy/xUUlz3rsVIhlDnd3GZ1r8ZSB6EM4bWgVNDkFxDO0Szarvrw+ZMFzDDsRzzMq+/Pw/3IaMr/Wrk6AAAQAElEQVT0mzlTjOmqJoJaTMmRUSb3EViIQoWURErz6Dh4y11aBpTsVLtVWoITNf4lj2dTGOdz5r1x1RLtRyN8vDVTpChmbVfXTKuaOMMhxkjnMl/Uw6RlvQBrJEOy/jonmp9jC262KzfT/R49IFhFJ1O1e70mNk2lot6qZqDz4kReCeoQNWJGI5+PQkh/T2m9D30g3cZwtAioOosqqVY82B2bYsSkryxLUqEZ2kbpfdM0YUpcgBexCKOJF7EFkYkaY4UPjcOgbSx1HR9o/3Kb9v6do3WP6n+NUtVDhKKHOTw9T5VKoc3wKn4Wc6mC9Am52XJsa8J7mdxClZXoNKuoXnfbk6sS1bI32o/r6VUfIuVIf46bWDemjVwc/Z7q7krL4yUooCyrwOal0bBzPJNWsBIIJ6YmdbMyc3B1W6enxfwY/qVGN6cPUuaAMXpMBEVoX8+EbIuRBW2ziq8EZkDtst/HSOsdOT5tTykddmgHPgsvln3q1ZZkITAq0n6Di8b7GqBX6ShVFZ72r1/p21pxGKF9GBbCafYSp1bs5oJEbVfm6JZdOWHly5SMKr1zLJ9XDqrQJhRcvns93VaEwHgl65f59WoWtQVUBT/WN5k7sIMBvIS2mEjjAQdmKmAzS4kSek1j2kizAeXBS9dp1+rrKlvQ5SqIoKYd86LDUPaeGByMU7U8uruxodOseYoi7IbfsGxDE73MPsY17f1MHsWKl8ZDJmTQQHDG9FvQ3FuiYrT4hz+zqhWIwwJKIFfYliuQVNCjt2EQwe4Jw1AGsd5g2GgAi37BfABXkT1U69x83Jo6JD4UX3pLWiogZWHwtuEZBmZTMm0TNFxCCG8LKHPB2+eMeNDNU5a9qeOzPPcFu0SKNQERLwZYV0AZe5FRZ8lkt36NNpjwIuURy3HZ/EzvVI9MV/GdzkaCpUEe7RxmoVUjpV/T05jBy0RDdkfoNPJ1gREX7Q2bcPO9aHuUqVKzUi/EayZnZMBqvFVIKA3fm2vRDE7QAB+5V6NVf6M+L+J6at9jT2qKqWtddGfQlXORlsD4xLa1X5n3SL73156HraW90zh0A4V6qk1qkqbS6pFSHK1LYRDQq/qSGW4wQP3rwKOtJX/R366/R0JU4AjOJ+yf+WVQD5oRR4raQcpJFHEizz6Kzd+E+3hoExXbQxsyaunrBsD/i7SP+I3k9dBLYugxMcgeCLKQjLgJqhtdk/Gix6gOQ1dWqMaVsbZT+3l+6rv6NmRTiSJKbccCQeWrc1q4xIKrVPoZ09VptZtr8Il8vyuSnBpzzzycWjRfdi45nBEUARvlft73WDCTVWsZCOCnuJ6r7jgpEe4fsy1GON2JqoZBn2VQ3nvshN+BY9M8WAKLOUIMrTda1yvmGbUmFtmUu7EZa6v2mkS/8Ap55Nt4XCZeKdsIU1+6HaxbwFRcM2rvYpmy4ae1W6FqGq7YdBQNYyEPXr1m1lswVuAEqfOzIfLGCzFCzQylVLGoCevZgPID6rZmtSshNmdKh7D267W+aKUywxVCilEMgYvQI5zd3E+YJNmjnnWd2TPifl1UqVBiioDvWQtqAzHw9mlgQHClKac9Wf2atYH2Aa47Wrz8DTpn9NukDLteK2KCaPxNLxR0CCu0rIBXmEJ1Rfyq922h1irj0nLU2vrdkiMcSp5is5NbJ9LcAL6tAFPh35B/1ClkFu+ysoDuFZyS9J9fyiTQajouYpfVYnt3KYCsO0cPMxSduc5XUMLQ+O6pk31Kk7f1tdOspogU1nI/vpI+QzujyYBAWsv86OmsEzMKEAWWNlLvEVDPddbCYxQbM1l2vjrmPSOTqqcJ1B5JgVfDulsxzRkfJY7rvch74WYfy3p3oUX/ApLhvTuVqAB0P7me4YHwLsx2+A6qR5hSZdP0qH0N1xCikWgdo9iaE9uuHcpajNISxLBTc/5mFnmG+ijjVIKMfIfu9++Me1iEflVAY2gjeuBjjvX+UhEqYFTYCWudj15Wa+WKjJWIGs/p8YK9SL2e00+inQjSk/tx5y7uM2HjAIrqE4p0UUKqjx5qMSZXpbdG/ADaxXOWCduiQZaUl5qBjbOjR/txE/TFvSxicW1gM5Mq8PjW53tkjJh2zEK62vNn7g+eC+UjqbKEN1JYBL1tynwmRJsSPyMJ9eQZXe/3JKUdo57LZOg2y1lmbd0oSD0s2XKDUITpl88S2WxJrub+4n74yKq1744FOdK0oWtCyrgrPNnDuE2EhXu8imupAK8TO1rptWNszTKoGeJ9/aXGOS8L0M7ylTVWQWmnuB9lfKdWfzVnrm3qCgPoIGWhRaiGFjF/p4j3HzgQx/9XFhLjz1ER99IpvIFqZEmxXT3HeBZAOuD+2H4Q6UaHqtDmwQiXPv05G7H9XmDM26OustW6sOBaMBVQ2ciY+vaoYFBJElrv5mO/tu6JRzJcvhd2tI1IVxg24BC0Hqqjz9TujoHwkD3+NN+B/mKIKnpxT+jhyuqfLpcVQyr7rsTkW6WhWGQwvccO2ISW4ZACudxddol2Ia+lNMrp9IgsMolTM8/sEb5ZF/SEdl1NsYHpfEWEOY4Tb7n6JEUPdN5MWrxDu4i+w9sWwDcivIFJWSMH7W+yduPSNaoDqKt9rZ+olCqEQfH/axP3eIi78ZXhe3AXn6PLjUi7Bhd72bP5UPv1qmzMmAISk+7aWcwmTa5GvhX94y+fZmIjrV7kmHozk1oHGeVkNFXNG3CaCIpRAbo8fZRL3tekO8ZFT0u9itxreeri9aoY72uo0+pe6Fk9SgSkAmmFdUSP9qdHaGosSWF9ZwAssq6E5eBYHL7FKO4+4x/sT1+SmLupa9W85RNmSiwBEEMStGGjgTZdlxHFp+57TFcarsP42dQ3Rx9Vj32ztDT+4pOjLe5FYWUOa2LoZd112zzk1OJxHuNYYGjlL1SM8qiMzAAwQScyTIpaWwxWas+87JgBirup3wCJZhWfkoKbd63nJZ1tkIS5ct5jEDX6qiKylowHxwjr7Pm2Nj/TIjbhX+0ZsJUKvNlufTvQ6rh0P6ZvV66ntfZQVZfyT8WkfG68wbExSjkEalNtsU3cyEgp9i7vpkSkiTbUntkcolTBClT0sVFVfdPPsybfXo1pXacGkO3zXXWvc4dk6OzNdFrxJaI4jPR4QqtUVx7xL80+GyUAbQ0CVMf/tCSbwKMB/XqLOO70XnJSfDc0KewcmA1W0+DDz+BDt5GRHNzGgi5gMAk+5ioZdr5Va09ZtREUKxRprbxy6SPhE4sVlrCWxBkr/GMeE3yZwRmZm0ECZWqIswWoRZ5+KnwW4Mr4q5OBzh2aHp42BzPUn3fmHIyV9i5bv1sk1tTchW/XY13gkIU+OZOO8mqMkVQea049NY+krtUEa9tnm6sdiQAv34iIpV8zY3l25UYPqgQavdcotgfnd+e5yCu4li1fxot1SWtuY6aB2ZGkG0yOiLaDq9aaHQw3c502VYHEUBTR2ec+59x5vqyj7uHUQA1rd7isLOITFXfzbm0L5X5HmwvliEnnLDUV5j35SE0DsEmlW9zqDuYFT2IjP38E9V8+v9TUVDs5aJHXrz0zkaTaDqUHOsFQh3AMuiFGXbmQ2boCyukajW7APPYlFpzcRr00uQY889eDAKXV+GIdnZ3luMwd6hV4jGlmitGmkIeZXrYZAE9QQRiU+Dufb/2XijxpuZUvaeKRBhvRMFGJo3F6+3B97U98bX1WjU844XKvQ91WJeFZ74XjL5dyooSl27O8IOddUWXlgQVCVvV7QtEvZsil4Z71HD/d9efICktHy0m7d9r94Peuu0qzaXTf5kZpUiTOS3ZPKjRJ3F0cZXFaw0DCexzqUxIcWqHj/15RMPSwyluV+GaCDUYR6RqrsrScdyUsLTx6NKvRhM+KNJ7CM1k2smE9nmL56cyjs2Lt9vm6aNdbAE2JvfKpPk9pLzNEu2iYGPEhNo1tK+GVQj0cusDXdHqfNdrF5/VK16yphxKj0gNa9PngDU5ZtVbbOGBBszWpfvFI/fOkaXHzc9y+uG6pG+Oqbp3PEWUPz42/7RTjCEb4fMDVkJ0bEsDgFdOxOMAcEXOWtSs9ZirSN4fKsq4GuB+85NmkVPcoRoZgrTzrflz3Wt9+91ZDmX44X/URzkgDefqAor2YsNv7xqm6p5+ZAh5obOVo7IwDixL7smMSOsQC4dPrjDCVOvtyI4z6ZAhTG7FoylE5hOkZszHdxPpvqx37mCK2VQqE5lJLC54WTNuoS2tG1m5ZXD4i4oZYUPhUohzn8nLJS5MG1szWl+s2S7Uo2Q9DvQUiTHuQVovaGjvCMUACKpq+D+7WB8k39VcyvzgfwZNR66Acj3mr/nHNRHVUxfhle5voXq1QFoDhEVdddsxOQFWcWgV1HmYLm7VtuC7CqyhnUq4cxylXUGjO4z7YU/tWhhTqZ2Gt3pfyp+RS95DUMEt/9TpJlAwGPGDrVXLRsunHrp7FeQmpqLQiJDbFHQZfcB+zwjTrTTtLiYI0x22MTUn0bnvKytEy7dP2ZHQuGyzSIcv9QPKoJzMfyabngUqbnUD1qw4ApPtWCFdXPAelqlKqdblx4HdYLqZp1I4rtoUTOTgFZAsd2EdnjvXZJlBy26kKV4qYsMfRNUw5Mw8uakUYyverQH8VGmZa9hfgdNJAh7VFzh7Rg2iwKGyHfhvR2Om7xXughONwVJ0K9B3jCNdmQm4xN/q8EHv62AjqwMA1l/NjKr2ub+Zt6KVJ5ev2dcYn047KPIUR83I7NZGXabUZV7xeGaBF+PvP4TOrJ2aQxfOofn1MfsoMy+kGmMk1mxxdEd5UcbZ405mTOAcocGCn7aTaDMj3ANoUuq2w7Yy//O4v1vEYdXvcM5oBqrNroLI9FJerlyV1T+bZnG6RVG3TURVnmA2HKtgMBvpQGblWSQC2bOIq81qntxpQJ+Vg+qk+lbRHm/fLl7eL2K4y+9ol1m6zW7LhA29bVJZFo+MLNSLI5QyNbjTnCIhTo8bIV1W6nOnOvaEHJ77KYsapEjncPbvqUowq2KkGYGHhvOoFPqY8vAsY5Q4D1YpE44mulS9BQqry5qToDuLa7f+Oy8oQGSNIZtt40RddGUhpTSHDtMYmNKm6nob6+uNNGeC+VHyuaom4TKHGop7jyVftp9yGV+CaO1pLamvfzFDYyomrHoPaVI1wwgbeqqnrLY67u5STFO0uVF1RA0xPlf7ENUHzntEgndFI4d+cq2u36CAMQ93knLOeWlr77tXfjVyAF9lAVmdmd2koJDONl16Pb0RMlg57wCPa0LqGsjped0BdZcQ2o+HTl0M2NSq/T2ogevC03Wk5NA45qFaXll44g+7OqmS/KHJ3+RN5/5u80BZyIZJQQlbvgtbyLWIelJb85xgqWKbVG8+pW/tl4N4rxSVdskPK47gVPTmJvK2/7C/6ybTK1bSEVrBp+c9jp9A9muShvNF6vM922VTN92lMgif7jN6Mj25SJgAAEABJREFUqD+Y029oaPeC3PQTc1ODRtacHaYM12fyx89nj83q9Homs1QubZv9L553j7yt32UT46UVScbn0O5lE1A0llZf78dQIOTtr0MNrzwhPDtnKW3O21irQ99vL20Gnu63p51p7aH6Sz5p9nIhM1RGDtzHV2+vH5W/HgaGn2C7Qh15qsR95nnQ38Fxg7BYW3PGRUlJOFUnp4fieQg3rgoZtHE05MtrBXxqHiXt3JbPqozs9/6AthYZhpnm87gusMPVp2R4pMTFUNc33M1vSxpgTCyYY9aMz0TR34FyrTPYPQs1U2ekqkJVC9s99thOj5m1cu9RAbqj15wLiAxKKADxoP6r8z35bZR6nss5s6Wuq/HP7ePi5ifH+gFaMRuO+DSmOtSDVYwOqzmPaGMN0JQDgaVwUWmTuHSqp7rEmhScul5Yqag3uYfqxUdY6At7c9O1hfxb41o01ZHeIF3wstgY2bilSEa7C089kc+uuTIu9WHVlND3w9OK0BD+HqJBeck5UsWtrNoCVtuGo2PgbYQkwd1kqbufUn/9kNeMlbmOZqxqdRJKwkN1y/ZZNmaTbtJFjsbJADaUEk7t9E+F/I9LkclKDhv6a51TWdPdmt3VR3xPDUuTqafCdlXvTK/J4jnX5BYQBpeQItF3tqbkjNZvkLRROBVyQAIYdONU96TIeAWZ3Ee7qpCNvOZruHO/3kxh2Wz/jvx4P0FExHa/QIUtSzQaONm65egcL/c3IO83kT7NVENpZOpiFmoE4C2jlHOM5rHVLkZzhLWJHrKs07qFXt0mbcA3KU7ldL37qopL3LOla45coBUUTtTxxMjCnI1H7EtaVT6KX1ueLRXWlPb9J0bMvrOSSodWTlg1JfUD6UKXj3nauqldw1tOBFdZdOCJFjvHqMBS9XuKh7rhjilaqWwvlvdNPhtKjPY+XOKnYE/9nLM91RRLGOdgTgntcpgrtTw1qRo9/V7WJ0IF0l7pVXisinfzpLdmZXVnW9IBD9iVcJUIQHO+L0S1CtQxRrUD5ywyRnJTCjnsVvtq3UmelrKEAZwciaZkkqpz9VKRqMY31hAk5UQrbFuBaVBVpEBPr8pVz8xJThk0u6jfUwJWOkWdTGXiBemjicbx5dzCwgmWSta9x9QfpCBUTTWfMWW8lUkzqgxnuTI1vUeFVQ2xv8Ra+8xnlMJ3RZmymxC2caFM6yrU+ZyJfMOdAs4M6ja74VOS+qjumzVvfHNjGgPMdEInYyaQbm2hf75tdIg8Dcipx9CXuaiXKVO9x6ztl1tIVsomEnjNnoEIobfbwIQ+N/tKiyH+vNEUDPoVz+pLj3qXO1wW1N2R063LzejFs1vJl28OETzDUhE9ufJ4pRBXSlSYNWbNLkXbAqFDrszEdhSurk9WYCRLEhekG5tvlL6rK9dp1xk5qJq5p//U8dBAnVtIw9Pa5OoTdMJq9Gf4R5zNLO8IDLw7NOm+7P54blM8UYqwOWWEoBhmBN8nL/ZyBWQPQ/WWes+2lpVoueEDBZIHo/JQahx9jRoopl3VMvVK8wzuiYNVWA3qGMcrtpnJRwYAqfrNgKO9FJaIkv5LJdM2o0JRFc+bU+Vczfrpq8QN16zVVwCLtajfsDp8nus5nrHqw9kDaRNtx/o4j6HHevAC0y8hAJIQcyMUM/vkrm/NkwphJEkm3LguD/1xvmzsiNWaQ1JzaCOdWwvGv+DejliTGwIvTpDNARVo4e1dhRydUalYpLHx4qnwZ/u5XCEYTEdRo0jJoDpqbM6wSyc80j23V9hwRj/i41TUayhHdSloITuvZXghSpkViVG+e0Ve1Nbt0R3PxuhJzfr6emK1q3ouy8vUUhIzsjdvihZq4/F+ujoHrVBc+I/XvVJCTdPVGm8LxNCzlWg9ANGintqqVHhnqMYMbx8IU7cFIEE97McU4Lb1Ebv8l3nlw6nKxpku/cq7T2kPNg/U+7oB2xb/M92ukAFe8MtkUjXVavLX0cfQENDkdfSISmHUCLWJ1HcoxLpPmk1vy4W0kd7kBd3BpuGdiy3WB8LZ1hXZU1Q6BylTKNBPfbrWlMo6y6odGs72KESKcM/EXcJtWBC9jB7MtOw7aZYgbCS9qQf52DxhB8xXpZbGQ2qsGZeHmQ9XcZ1yFdv9KBHZ6vTqKkfAhCY41+AFYTV1Op9u3YDp0C1NNjzpL5zn6flVKTaszp6U4ReUPiry9rq77sys1AxJ+JFPeU63dOYaneJP5GfVUjxy10qv29H1ab/PzkdNEGS5J1HKWrUcLJxgKXaqaiFC0uw/1G8CD+ILf7rVSm1m0EcpMXbRzqBS1LYt4W+x1Z6566D6GX9GCf/02ljLhJ9Jaqf++H1rNyRpmalS4/LYXAIroCbWBGrUnYc5V85Pq+dOoDmNOfJ/tx+sNns322G3x5HE1ASgF0EVngTdeUprYh29SQX5VwKccY1QWAdd0DK91Cq0MB2LGzvDlSPldQyy9yJUhlIKlPJIBeEjaK9gW+q3ZzhGaBUjxqt0TImpA3ydqp4zzJzWEzAdVAqsJuzxn4oY5fZnsPhgviU+H+AePo277VGLnoCm2EnUsGWYQw6opQumGd/m3ShMDnSMgdrGOtI81+q14C7ZGdH2feK3VA1KPfRqqPdX2VgA6wKi80X57a4ZjSN5OfbVutLQCYMbbXnj+hWa52vPTcmwcrqsh0ML327/89E3cBTlEko9Cs+28LsUpu7xaBmq562mdfyTfT1pc1o2RmWqMSIIKV5zTCZVRKPbT09TedkMBA4sU0SEQZHPGXtKOK+2GJ1SzBZlKxYVPes5mSmSpBiudKZpvpEqDMQdfP/PhNJRKmVzA6GXu1uQq2BK5Sg3Y4aObtfVsnzX+0HROHpypNKJSXUW1VTQEe6klRGE8kjTZBtXz1CQZb/F8xTzfV4ifTQHNo2pyvr7XyFSgQpxQ8w647N8X7E+feJ7K1jQZ3ldlVJBfGfXCFzxzZzbLPZV5+rUFpE7m/1eqYv5k/M5XMqUL8ZuYxswxO2uR3vGMQGxkyjIiYZwc5ZM3LJew6fsSc9aZ3a3YnY8ZL1hmq14UQmYRY4bJloKeUroaokNnUjuU28cPNWSiKBlX0cLWMOtFwUbu+k0M9fN1IKahxkdvV3LFIQX6PNk3i87LDE8xsZqsPmorH62773NV48NS+VmloB0OpwXqn4o25smqpzPKAg0dfodJl8uIqB8SROP+oJ0hQp8V902KLdq1xQ+zjKURFlYxRXO/Wn8IEkdgi328NpvDOJL++pcyO3QHzBRbekK7Yy6j6XRthZb+L/e/55LPfB5ujhHMdBPA8pDguv/e+Cvz4PkwAjL4bf8qxnyh4LpGPluNZ/vM/dUUnpuFsXVuGl9puiGKMjuqXtuDZOOIB/w1kixDc9ed9mRSRyb183bJ04r0f+r/t+p+H0nPwBYmjFt4uNwtXwIHO4wyXH5z0//27UDNG1B4swcTfDHliT4pQvwy546zG31MI9Rt6H18VkGvqFAenpTKSB4PCm7UCQy4d/Lavmhf+ddW1OZaeGsb/LOz9TP23IOrqxF8UWVxFnzdfEsrSjpEgxy2xPiOfup+L6VtCO1PzBDK7IYKZ/Rat5D4uVZP4J9kk/5I5A+tPE2WCOSuCcl185y4mPiO6IlT6kFhtN4CtPlmNVrpfegXJOXVjXXtaeqJ9tZwVZTi40w47ApAvhQqzC12kKv6dGS92g82hYieNnr8/OjTgW+3J1vtejV88rBexJWOt138v8dq7GxwGJ5z2Tc+e82Pbl2iw6mD9yBa0YEKFJReT5qNTkLUkxhHRN1ihdzH/DrcdTLOpEq1gFgXuFFQmSy7GXT4CPuvsx7JAdXAvd7Fa3Caq2B75Nlm6J2KM82+Qj7LtwU5rkfVFoLs0yhqEafI5gGY3f2JOa4Liiaxoz5i/O0eIIrcStvf35xHx7gDXoVxVPSp30NNjKu5tWGhzVz232jKtPzcdx77miVRVevZhgm5uG08GurbeK2kCf7vVeRAQGVdUUfRiyrn5GYBHS1pFj/TOz70z9PaBmVPEy4ca/4StSX0E2HEpBDmX9NUSbglQpd3H7HvuWZUFjy+sTRLQApvlvL+D/2Z7CJ+AzX1BJyhdDDHdflP2s+c/Zz4p64hW56FO4vJ92MvIoby2GGHHH/0jVk24x1S0IocNw7POPAUiimbJJTAejahHjlavOSGvidEi3Vp0blFyp0nCnUu/sZ6xf1OJoyrQndmBaSozYiubRj9FzievC+ptiPLL32QSFEl+EU5rSL+QL4nu6ux8/XTyllM1ROOWCNKlRnuvRpDRWkv2ZIj4nVe1d8SmAwyToyzJZ2zNkMvp5MGT4903IMtPyWfcnnlQMtTEHPg3zOk/ec9CI1c+Gx+Z0WUIki2G2p9eVrV2oV0WPapalGceiSmxjQe67dWre7o78RZ13t0VE5EuXjM+4dK31Vb+TaCLcLKo6EHWrRlqMmSq018375dvgWFZjCG43TY8pHD/DaWjCHlmbpnoOsLvUipTYVvzBURSVzukGaa+on+Nl+EYaDJZf1J2Ola3RqNvlxXD6mMou0VzGBWLd5dq/Ez/gP8PNG0eEWrhnrD19X5wiaVlnF9QOVJA1Iax0Q+AcjwVKpUUJRgj9andaBbiF2yUnAL6GQZtolZYHRGFDTv2Y3OS8cJS2e9q5EMvof50IvDM3h78MKKzP2mJxw2ocYpXk1Wa7UJn2cJj9siujJtDyrRehQ/UwpGiwgDtezglcICgic6bdxKIdDb9tuETKek8UvccyLO3s/ZX7GcjwXa6HeyZTGord4tN+xhzpt/pQr7Gwi/7I/Sw++uS3Lzu338efgW9Za0O+NE6hpjHzydWHKkNuLYtGXbJ1a0fxfc92c7JToavQ6oNwAmMyuh52voN5cAar+DHWkv7GQeZRCXYblXFhrrj17+3K9jGug0KYwg+NZyKms6kPewd8fy810dvNADFOlFROua+KLcTEohVRNKQ5i3o9qDFLul9p8Rblpc7q0mls5sALah7j0W5XIY30AzVRTXTisp5Iy77HdBY3vGL8L7+oqkGwQJtJKF5rZ+mAGuUVp28fRABW7YqGHTSgq2T9OX/aP9hn4AB6k7tM+n6aDk+JSqdBe4s7+OvqSd3zMNgXUBlnx+E6xwJdOKbncenx651wPru6Yx/ho+EJLNeO9wmyZ3r/XNDTaosROOCFc0aP2hDb+QRoqj4f9kDMbBxLKOmWcoxR89msOdCk5hyLj6TyDluwn9lwb2qOwc158ZMptYKxhCrGwe4iet62/f0ZarkNPdRS5R+/ot8dZuR9XR9gEFlWFEswbApDqr3hfMSPG7OVWUI3p58SR6p/po5jaXitamGfl2DvESqeEi+UiXA8mVDQ2ry3MwGA77LN0t0V+qBbqTuVgYioMQ9ZxPNpkhnmu+L38UaZ9GKDoFyWKAiYfO8oEHJaI03WG1WJBx6w+URO1r1wbcmbYpqSgBBAAABAASURBVLBTiUp0gj1NJg1S6Ae2oDzJttpL/Ffr1FWKcV7lKX4OrRXDGi3gdvnHwsfQJDS1i3M4X5sn3uUGSPII4azFcCPdoBixLlw/iJ++rEMV1pxchstMvwkNWrAwH8es9k+uK3fMdnPqxHNVR+qyeUtMUkSfmjqG3OiPRImJOK6H2jgtgfGuUkdfJVynROxYmtl1ltUSawRqIcNbWXblbhl8GiM2tUeolPUGPLi0TAb/8oPBmFqIS5DsXH4DYkCzZtBm6YkodkudD2JA8q+C2vkIqqq6QA1EgF5S3Pq3sT9DrSFBI64WTrKcwkWUrGtPkQ/nHmjujA9Nah9kVaVcQajZ7n9cutlFbFikonM7bY/yM62MjcY/zMdk+8LehuWg/gXhrRESry4KNXAG34N8O09XP4kejaRC8v6/lApos9NEbrbmPD5vY/aJliTpByPaBRNdnm7lL/6ncGuHMz1e0Beao3WrhdgNM+x2KcHIMIpHe5o13r5A6zP59UxhLduj+vHL13b9TNk5SrGioXPw91JCtOTNqZUZ8/IzZihyZLHn5JzSXni6pwrZUPTHayLHdA7lNhwitxoV0NSi4Gk6Bq/CsxfbOCj09O36fEy9BVhwSl3ZBeRApnuyUajBU7i1JLZI8lcoowGq+aEICBKJlgA1MobbRkXklJOBVXKOjRpmy8Ge8FkEoQBy6lAMfGfeCDaprqFgxQPp06OXsLS4ddJAeQp9MK/xF2Upk00NFAtCMIrraCOsdS6fYc8VpRVKLlvSHIwuSI5P4+mZrjF9SkBXTGUgq8qP2j80bvo9b3DCpt/t3CnLtm6nLgGIoXZH4juJih4Tu3G7pRDqkiKUneoWqunVtaevQJjLXTNlWB+XkkUh0TEC7DLjhZ2t8IvzWKVlMJfkayJULMDl3IXjaTtmKh9ejG0OGbsqr9Na7yKhHHW2rDvM3Mt2vt04tIhmPWCZDwNF5HRW00ZG6E9Ntj/Yp+K5CQU+qkp5BZnx4nEjYCKix2tWmrV0j4MepDgSpWeouQ9a9jWQrTeP0iY62T7DsVL5yioHWzRadc551MiQIlc8j0pdrP1BHE0JE74LtgrWnJ6Ehk6XZuYop1+lGEQYwtt9wLf3pmbB33MA56l8ugZOrVG5Z0Km6oyJzoKSugisGqUBWpDKp6VvKkLrx96OljTa9VfBCc+phhp4riVuiHaMJ9ZDU4H0Nap4LploChroFBxSSMVAq6Ak6vU4a8FcC5vAOxlusWnhLj9uP6atU6I7y8BQpg5Td0ly6r4uYteEFNgrZKdNfX6yQmBeq9ZkG2PZGkNhoJdAmnAqfU4aAeId5tYCZHA4X5PbIywGbGBaGnFhUzHPNe06Ga80FJH1qOehDaSmaMWE/0AFDRhP8ImeneEQoAsedLe3EJBSDAloi2QroZ2shQC/b+t1/AZBJVPorwh2lDgr9p94hXNNhZWimeuLz5f7jD+j5xGVfgYdWKSOIlO80QBFMUNrBMZkBz/ZV5dAJCCCY0EHX1JGbBV4d3zhb81gSoITE1JeH+LxmH2503tMzsbJccuLegXIxplMvpNkLMGjwP2uqDSMaJ+GmDDRRJ2Y3fhkRSaseJWpJa1uil6NgE/5pI16zoyXSbxKqKxzVE8e5vYhfqr3UyYHdfE94PW8cUtN5u5jcwRE6TihD5jRRQwLl3y27deVzJcdxGOrczHfGk1S9B6tp3uN7prCzcSjuFAMbd3t8/fAmQRz6jv3zS+h5md6VGDP5HQFPBbXj+1PLHe04gliBSzUQu47n3taVJWWhZQDNcvamEuwXNTKnHRArcyts7vVgPTYBi97fAQEIlLIli0bMXgBl1X48XX0+f7M7+bvrDpbyXj3/P12IzIgrQg35l5EXM4FTEIN0EaRPQvCruVLCZaGzrL7oiCADXRQk3aIEE3pZ7Qg9rQ0xi0ZQBQL8OPzSLGrjMWsOeJDk18Cb8N3zW/0HafWF+twtawtix3+2XADMF0yaW+XWJSVPN1a0F7zaOWxH+SNu/GYzV9yBc4adPgySE62zSUUtZfYMlhK0wuWlm7Kbw/Ua7HHo4nRCIBSdcaUNyCfI0MCkHAd99hBl2ukee1PxfWamHgZa1UFNEnU2GckAXymTCIAYuiEiWHOiyFzxBuVumFCSsHDyUsjHFiTLs+NnYjsOv2kHMoCXfoJtjwwXuEK+Oa1oHyv9V37qDoZazROGjPlrUjpcaJWjd+oR8UW2t3k4ZT8kxxQXEKvwF69lUf5dJ7FUpSmonmNaRzUDyJna7d6Vx+2bmh7oZfnFfdSGiat8y0G4Jjuq9YTPs1AuadNeuo+wVwUg66DoZhML5gDZjwjNt68A9TE1qTHbHRtTVxnrW/gMGmjOGkrilIQtCyRoAheulAeuTk7eT1r//q7dUliUpJKavaqvlpfrA+osKyP142j7whPievvaM+EPkUts6fnJs92Pb8ZHptHwQy6bHxhfbhi2faJFhQ/YWMvEgMVq9VtmVb6d9V2BC7iQ4W+lF6HbfkPvoXGwZQIH5HgFkxVIf7/r5cXiQm6SS4sqedklsb7zKqHmdRQbRV2kz1E6jdQIRIIObZ3JalBJ7pLo5N+sM/bOqdwYx6fdMxgoMPLllg0CjB/MGKTLLSxGWYe7Ubr0YwgoUcoV4gJ2rwuXgf9BrQgH0OJOHk/8dTV0gsb9pg/VEmRlBH33N37YBrJGVR0KiTSfC+XRwUjK9VsszS+2hTpO6Yn79KlRh1C1WW4F289pjKLsag9qeA7/egh/zEkdqmYrVuHDzL438W3uS0uYwyYwsFpU4ziTswCHV3XpBM5L113zTn1vc9TpTZcRgvsvuN0VenXNJyvINVgSjsWa+0+WKMUR4dT9KCIBgy6VGwt4DWGQmSZfOyHOz20hMwTVjTh2WKb/XLMbudEj3/pX0zdGd973+VlOVbTnRn3xOPKT5k1X2eKDCOec2qewgwW30O2XJ6O5MTwsGDHmTkucW7rj9diOVrqBH0salluijw1NxngBiLkxXlE2I34jiUx0dg9KwudukXNE9TaJCFxEmiJ+jFPzf64LxM9lJifs8qWb5gURM7sot9eX5yv1GS9194eRPRDPvAcb/H9kglTaJC/Ybo9VB4P11krjZJI3nj/5233ymchl9SnnKtQUncdUCrAlkO3MiuEcS13KU2+7IBf8LRhTE4UJg22Z667/ZQpGgPJ5VA3U5aaaQYHVtMfzMgEajiQEDBQedFHUO8Sa7qc9O/4t1a+WyNXZRSVLV2E3kwTJ+r55FlP5dSxOl6PpANdV4PcvHREhEesUXP0IfBKWtEsPyb0h/UYCi8un5GPNmBsHocuaIIOgNBGgpcJK7Khrc35Lruvlcqy2vr+jx+lqdMdcdnaZmGvmfJWRPnFk33Q6pjHTkXn7ndfIyJf23hfYgByZl6IBxwrl3m2fSvuXqdX97KKZcuu5dtcPmpJ36jEJAGdcmnkaonlot6GqFfEbJWRaYdlEAdXcj/wW1Wiqr1FQRmCfo3r9hMHddNuBFpK4RrZbXL2stjb49O4OrXwxGrNQTTn3Qq57bZ4pOjVyuUlJXCU/eX1QwqOyZwGuDSY7U97F4XsCjPzp+T7Gu6t4OBKAsPDr6hts6V82xzOPMl8MAd/qK7iDyr8DLraKomqlkL21+IXLv3cue62xl/x6rKblq+xNbWRQB6+LfNoZodjL4+hlRU+D/e3oRKZZ5v/87V17eLxGUwUr9ZdyZXuzxYfokn7JPPqtyr4Wy3NuxyVA1TyyiuR6VCrQivE3H5feNbRqZ/m1sS3Vx7rDhQs4Um3qBV32GlMtqfY9kytc66lShqj9Vje76eZPfQyP93fU/dTsocs6GUB2yYmOURbN0UJhKMHTNoxfD3vqNsieVl+kMFSHoK2mpHQzDLrJWk25yHKvcMYphWxt2l824gbIM108/BgJ1dPsJxtMmjKpq/LbcVobEFBFbSnUMMuyEUDi7jEF30QpLFCHHGA6TGdWHQlwtZITsLtUwu9AGHec1hpNT5jo+YTDuqAWMtj8wgWKJ8xuKZMDI7wjnXo8S1mTSPFV06YXwtrwTVUiNzAGCpadxPIPNoVscqY+saxXfx5c407TJ1PoSgJ9m6ahgfeWPKhg+1Vc/Sz0ndv9yjrFBJinL04EpVtjNRJgeF0mx33ImTYpkdbnZbMR9CXQdK08Tp1xKjViufyjKmuKghpOw5zRhVTCguhGEKNuL0bonf+5FqATMepzyMMHUg5Zg8m1ZIFV7Vn0eZNjs+IRKTzWZXX9AVcNrFgyoOPfgfxwcQ0OqXGSbLe+aYwDqZyui+KFXq0q7T1g4V2J3SM21gMJC0x76CX9vPrbTDAiGET75F79zZCeWOUUxZP5APFDgKkGdBiYeyhtgWdbOANFPPU3KcSQqhTOq2QGAfR6S7ttprrona0IQ0JDRN8NBJCSYoB18BJpXXt8kugZCnFtNag8uG0eMse0fzyZS6UhiuwqFPFmZLsZPSU9zhAvfTbxlfae9nFazM62Bp7DnSIttIndKMwhb+XwwdXYh0DJ4E0lBqvoj/Np8Q2rSvmzIw6YkLujB1QMnJ4au6q1KyMmI/pSQy8zV7c8Xp60Ok9V87VZ40+NhjwDXtzpXFHxmMeL8TQYf3G2CLf+JjotBcVEjbSH8xaozoDvUGDbEd2pCIIS5/xMgrboAcq4xN1dqVjxGuOZQZ1YNAtkedjWYCutfWxfjRuNFNAdoyWH8ia4hVh3eexkbKW21VUac4olIbLFtALIu7ezA1F8upZbatGGhqCowLndl6R+s7PXhUovOmAmS/unviS9/+JjKZMSk93a5sxyHK5Pg+rRY6SfhdS/SFrFQwCMeONSS2Ma+ribBFjpU57W2msUeiE4AV5B2SoBt7hEOul9UjJkU90J07F0WkPIZIDCQkTf4wumx/OCn3GAEOKb5HXhUhe4CKdeSK/8QrDq/dkyBOt3fX9/COn4gLtxTjLcnGcOjMEoLo3ihAuJyirOXjcsYxGEDATg2+Ddz3wfZ7XtVvZSLW0Y9Y2+rAf013LUy+fZUhotxJHi6jo4CjTo/SMV1lLU7JsKSeYZjn6lZewCNC4DNZYQdKvmSRS71JJgJtf8fMveFFb0pwbec9I94xQNn+iF+Fp1EontzETJhbBgTEof+YtAO0DfdBJWT+mfaV8kI+bdvd1uV0R7s70UBuHv/13NNUr+0gDV85StgZATT28wWdfsxfVKfiEirNbouYD1mlXTeconm6i0UtUgGHvUjLLjHBMTMfbBjQRSJ2Bj9tszGKZJVDMivOseUztdATGtSzBitcNqWdkGuS4flEWF074o+u6MJzRLjDgFsvcd5gHeDVCxpUZ9wroxUPLqaMQ5uvsDZM9m9fMPlr/uIiWSlUVasmY47Bd15VrfQ0T7aFAIAuZXV3YmTtL3WIlAzqc7BndYr7X9d73+GWSH4wmCMlKO7DGdsj7hJd4aIUYOtfWE/liAAAQAElEQVQAFeRYpHvi7bG3kjLeJNQELqhvPrWoy6MwScXE23Eq0Tnl9Gu0AInB5j32lsNF2zSjBKhINcxKPBpd8VB9vKF+WepzI+jdRhFpmvA3o4w6ZW3udDUvX3p2VPKNd7op8z1vYe2D66hPCWgRh3MMf68ZRRtbA4marhzmRJZLLe6tA66QfbScrg09gIl5OciZk1XZ3cPSdKN6ZZpsoyCkspB78uqMRD/G32WpncYqqeulcSBS2HtD66Z/bC3KgZTJd09TYI0oS7mwokNVwTcpZI+fuQ69l3hoYEoQ2lReU1atALRgdz497UPhXZOsrsfQ4dY7ckyXO0kLWWWwzY7PzdZTtBLO1pvny+ds/hEbo/WwR9gjTNkyw7AfRajOSxklbKRjFU4rOaTNK15qlEAyTFG9P1QDqb2Oa0FxTfFVBrm9eY4pYEXVbqf2eULhN0abkRveMDvmhVVjVWxAvxICncIztpLW4EBXddQ5zygZ88ZIROsK852UmDMHrex4mEMBfoo+VHdSRU3BnuFtfYsPY7bhgEWOstyoytcu0sWuwmhugRKhd1rqMnu2prTOmiLBHosy1DN6BGFPrZOry9pUHGreOpzpGMMfo9NEyWt532PdAVW9LW5vq2NZJjC6ratE4z2k8XjIFLwpVPoZF1IMONv6cknwiBBLeXnDfaUEc5G/W2HLiALJs+/8+Z6frK1sqqP7PrOe3N0XEeWjgE/ojnUd8hk8YkY+FUeqeYJFIAz6jaEuUKydBylg0DpHiYVdMf6REhYULd4SMfy9nLh8pCbL49lSvD2mkLeLglIlzNP27XhGyZHeBxjFn2PpnDUm2nVGv9/JMJG7LtWSwtwjoUoVkBQkF6Q1uqFSwWg1XmAAevMJhZ6bLUW6raOCxw+en8GltJ64n1WP41i/cvV+wixwsRXdEAUm76aydsubg+wqnvacrQ/aRFzTuk/8rPT4wn/rVQOazBv139OPtv7CpYhJwGg2g5M51QYq4O9z/nuyZLKxMlGnIDG2IB3/Pza9r2WfJcGYHpny1oCGneHIpsHQyOZDyOSMTv/9N3T0i7ygcFyFsn8nvofnIc9gDlFBuxjXqA+h2s7jpfSgI3Au+37s9wdFgVfvxwf2r+ZfcavZbNIoR2zl9Xfqv5rf2/O7fnr/rM/aGuQBxkxQjf4YYcIKvYz4cf6EVOpTC7/gq3yMPimrG2aDwqi0FNP8nP6XE+/GXxvL4xyTJKZE01Nza9aET4gFc3w+XJ959CzCX2+kNy75DFHb1CCE4v8lvuxHXGS3KLS1nKK+SjLlbTG5Ihm6CHH9+0Ue9mE9ptx8X+GWVVNa0LSOM3NUnYbP4f++cX2Fv3GGd/sjkE/2yRJfQiJ9H2Ndu9WvwVe9t4rKO3nuyQJtaGhHL6sfO4vHNGmg2rTbVEA4wRLAxSGYufIxSp1NcYyBf4Gv1eN55uQ8glez5vH5oh5Wr+x3K2O/W+crdtrS7qCyUT2yCeGCyxCGWm4UQQ1E8nIMhPg5fUo6axaicN39v/u57vs6z2V9gfu2T/a2N1V1ot7bpl306YAi85jL6Fqerni2yB7tm/jB3tzDdePKo6oEb9rX5nASnWJTnzpNniX4uf3o68I0Ld6fT1hcJzVp5j44D9CjQSiFU9ruxKp2jodv+KUoMd8198rw1JO5/4X/UItNWd0YFe4ZDcdqWilItqjmE3jgpz9dF25j93QpY1PIZjWgHwnOOBfMbIELsznqyJHibmN+pv9X9/qW/Z747+vfI7yCe8TSIzbiqlZqhy1bUHr/1/L3y/l9+r/7fOvJSZ0mVaS8eCuRkNyUsX8qqtHCRG+Sph8daJuERgwBW8QPfCu0NgU3WrC1z+Yhs9EPZtxit1K9WxfnmiP778F0AlhGUah33RKCGH7AX4tUqfPszn/kJSMaKW6sjTUmzwv54X1JyTbCgtZvC3VYh9acW8xphhnDa/DcE8XLPML1szwuWsBjzNQutoh1NyUmpoO6chnd3snhdr0BH/dj3mxHx9ARjF2xDz9npHkPzg66YPmUJzjyT8qBJgDODdi4Ro0LiPOtBksS0FjXuzaoCgc0MGjmGqwO0r/dvzhSTzdZvEAfEoO3abSJWui77CvEK2jBddENwC1BfONdPCYS29zMS4cuiauanLlFjdR2jiOFZy5TnQ/a+1gfIt15+TZShzcU2YNokt5WsZ51pYoMF68d27aB1f2Uch8DZxlyo35sfgySKhsSxPC84cN9sX6QagiVQmXRI5aEU533aupJ8QfSxwRk6KFVyPfWPg/fUS8EYaskz3aeVB9qlgAvvuRk9zl3qQFKDzv54plY9FD1GBrvKJvRaAujW6G0lMnMCNE0eZH/yvdbovTiD3zzhfcQah8nUjRBj3X8tfqv7WozIGXTODK5x3aZPf/4/fj9WfO5sLVNcT7WsxkfXb46Y56p6qfzfBM3OtF78iIcbgDxaNZP2MfDfTpyOJNWx4pwT1rdWd9qACqD+7jo5PT229bvuKx+gcfq+fRonbx0C7ElocLpOai+0IpQ+r7HddJxe/RlE1SeizOYI4/feX4ttSa32tP9TE0ASsBknzX4jF2AQbHbj9mHZTNocMSKjnrtXqruOlm/M9/L/t/mM9Oyjvv9ummlY3bmdYrvWZNAFa25OVmTyTyYVpz6GDjLG05b28fOIwJLzKoEB1/lsmJK49LujP/Q/5267+pMQ+6YJaoOTuFecZsWnM/zG/P7IL8Wz1pYsDhm6Y5wAvEo7V1aY82rgMJIQxblbO16v8TdVVoN59elEGikqMCMYvZmvoE+wzPW0G3+T/sC9Q+ma3hOK8Jo1U/XEK/UtQsQeROUBpXHw2XEB3XJlpKfqoZRbT+7njUd3m9Lshw+xwTJOgVkRmuBUL1VOXLX3v1anYQfBfMTBNadfteLzUbPsj4xHznVkMdGy9I9vM3ud55CpugwV2BZ9a5rnGNKSsJufAqXSm1Rg4R2reL1LFcAXa1REd/Yz8dxpV/Pjl5wqH/Unlvzxd9bfbOg09bE2XZ2KbQF9aChOSMMTmVAbvlrPMZbyXnrYvViaRKGVFrERMop+H6yh2y9bXFIOF4yDYJEjD6OUFvBWkM0G3o9wCHFsMBo6cKlZemZLdmtx1adEdRu6zI2bc6HZyvRF2DvLDXLbNVpPjiZNdk+QmKGFZqCmA6VQpaEOIU+zHbSFawFxnd63OApt1YdxsLmLCK4+deZMtCRjaahga4pGXtPlHYPNmV4lWvWbFStVKsc05/1m+Kv5+iPJmoUHbNHRwuVVTNzfBiKpKECFUWEjXm3v1U4qIF+mFiSZePVsitwDQTF7ei3YiqNkXknXgyaXDRjBe7gr4/6g2mgapFqinxaEaQ9qWqozKdS8muZG7rmyf2I2KnusRHjihugbI9PL1Oy5C8tW4xFHPPjoVJ/LivOS0GXbGbLQQoQCxg5HqgJlh+R/IZQE4loU5J/nohkU9qEtOEzZqNrctrtt85PniotoS0YRob6nr0SYZ9EZoumMryvNs5AZngEb7vFBBUVxOs7SgYY1KCQUiUGVVuJK7V8h8fQkMYv+u5bdrVfWDMhFEjgGRxl6RSrZ9dEQcHxfaN9ZaleQ5FR7lvct/u4+TInHbZOiB/rFhsOjoxUZx3po1tEFwEJUkPP2Bf2HzVcJ0Waoqmy00KDEaNT2m35AxPxRc8pzLMVsAa+HFh7UBFRDbD2edgqoho+5VneCQFfDVMRcK/mz8mfPPubK3uViPc1tkG9BLZHi4eO05JkKSQsd2c0vOQth4fWNspfEymAlVYJAkqrdqLiNIzLzc0GaJGmP5h3jikgHYbSKL4MKZh7oG7z7HlWwzVsFKOd9HwRRhnLALcRTtjMOHphLgkRMo4/H8cg9ggu/G2MT8RfUnT1vV4HyWoMuxct6IMAnQjHAkkZ7npaWxt5lkLLx5D+pAti55wWrfr7pHTbk0oPlhErbHcwrUWFfK6+JgIulEenPXIsFV86ZpnOsjW2Nju0uVNUUcm8pxNe7hUvzSNbXTz0WbDlFrMCS3VVJAwKwNS6Zk3/qVVQz8xdVQJWM4/oxmDYIowGp7KhsEW7ltuHWZic7se6uiQ0Kw0cHE5F412pW0uY62TbpBJpEU7tqz6TLgjCEsKU5qXishMoG8kcsMctgVFDQYkkc7BZh5nU7hlPoQZuFHlmGs8BxM0FFdW4PnBGirUOzM+pNWNKWkEnlljIsOR0aajIHM1WdaZQjrsverTOyQoIicPUsLQ99DAXkTHDhtFseSs5IzsaZuazvVxuzM8ebfVMPNQoKPHdTgUp2I15rcaMQhxLGFQFj9UjQDRv34MNhGnkvC8XTOrl1BRjTchCjH0ImtmybVrZX+p0qQ22JqIlClnZEKmrtMK0UsL0c9SEXHm4dNVmqL43CaRpN9GJIcdBCWsePj09SgMM76sgqNQMF9Vxjt/a58AYE3hEEr1iuPFZZ0W5yTdEEHHEQhfy/jvGICOq/ZzJt0X9tj3zl3lv69SHkYbupc2DVW2U/O2w5eQQ4is6V2VwNu0auvBDMpFdh/CmtqJ5bORlNTzbcdbKy5UghsFjSn6Rb3RZlkpAG9GgZNA9U9OANhmJntZVWoWV22EcBSgMpSvDoEkU/Zm5emysqQp2L5OAzM7E0++5Bd2yFG3e/RbfRAxA+DJ+2IulGB6NV+YsdF7xnEadsqfiwMUAxxtMg1oJSG9GItTMFdOYzTul8O6a+bzSlgOtCer3+YKlNbxgFIFutGVKsmQsc8F30YNy5KjA+6JOhVyBj5je05sdmu5NpxELbXHTD/FMZSZEmOXweQUi425X1OVnRXoI6XClSeqp5nLm3H2AR3wTQ8giZ1TVvCueiXst3VWdC1J0Z/dzntGEKaDeH0DBTukSsvQXSDWHal07zaQpd5P94pEG8qr6Z6NHFf+o5uM9bMlDc2TNSh+0gF1CsckPeneNZ3p+5fMwjwuaHsrtEV59oB2zm5LDvEylQ0rl5BpxN4JT9j4GRKsptsyLLTrS/MbbjianK1XUnPZsRTxZD45GP4aK0Mgc+wMY/jK56YwR6IPU74aMjcYaGTBrqVxASKKd9+I3hE0thAvv4n+i26D5Y0Z714prYRtAKUIXTOnZr50ofcHJRR+8IEd/xgpKmokvfzru3prlLIlozHtEjUH+9/gFWyiOrvXYKLJq5qycjZILlo/uI8rlObJW1zsfkLSpPNSALbNwGUhrcQn3BVmHKYABRuktawLk+ytLsjuw4oe8FpfTl3ecrjpPqjxVV+ZBfzU9bq2JoCMcNnpfEmd4YablQvappdmPIJidD7BaEz/dDpiGz/AZBTac1q2EZjbrnTvcNT/ndnW8q4OCKlVP+/TohfvB0+YQYy5rhTUCqlmdNonuoQYKRx6eonTEEWbp/y2vmtN57Jg/pswfxLEFylrFh3bRh5lUL05xU408mzTfpf6gLBWu0o6H7uMXJ0YjkGkFG8GutwAAEABJREFU2RA3o3aInhrSTSSN3aUtwORT2sKhYpQyg1BqRjcogQo7c0nhT6ONtJ24GsugTuYjF7qWv9WnDaNm8an+nPqc9g7WtqZkhTlZPQ9w2zyB3vNXoIGIllPZ43K3MIu1TPAgjbeCLSz9M5FSD7svfta1tffsK8uHjC4v0fUUWs+uzzyJN3f6hdbiD3d6DPxwAbGhuUaE5fKjjdTcJ09Onq7GtBLONwxs9VXZPGnavKFKpVsFnEanhPiKa7YVIb+Oe+P7M+eflcBV8iterXJn0X4P7xyWoK6ZF2/0/uT+jAIDxPLgefq5nzxKxDQwlPfmWImwxgNqwdDDY6y9IPErUQKTMzpMAjqhWERqzQa3Vs9/wY8PKNzf09kaI2YNsOy6Z76Yu/v3nt9BsVqlIVeFAXpB65e7gOEeLQrWe/MT63JbNqwR4/JlJvj59rVEje4h5IXe0se7Sl9Y2tZa/wyvIcskh3IF8rdTShf8E6msQExquZVWIHgOUwPx2jOEXmtW4CO5Hbb6e2GmcgrQPnZca/i8WWP7bnkV9Bx1nAu5dQ5wGXcoaPAMRKwAVnyVkCM7Kt/LKaShLVgHkqGvh2tWtzQAklbzyfkp8wkXt/0P83YLXXHQXraG2nBms0RCOZ/NZ0H4/IE2EDY1m7PYURX3eBV1CvLQHf3ogwYTvkqBi3TVc8CcjBlveAu09DQzVKH89LVnd/u38KBfW7QzlMw0PJRzfAyXRXAb3YrWxhZWz3mPk1uE3Z9+rAbjr1dQIldmnmlq6a1EYaZhHZAz2iwl6O2S281InxRXeypsbAn7wK3d3+GjONUtuGg0JRGd9QIE6duXcDlF7gHLdRe0Pj/2c7115ArwAVTI7RwfMdgjwwqbBgEreNoufpQLtzQvEZvbQKe5h5RnajGYlNowOh/0iAVIEPOcyQfTGsq4xj9NH55SV7geGUDvPFpOqIVfczFDMX+UUwgJUWT2mlzQfcuYEDK1D1xP91tP/W0+D/THb93MzQ+wiR40QMz23mtgK7nTF6Vq2eG9+isy2fAxjxU7YIaCpWbBgCkqzzqj3C+zzVwjlnShR/tr57a1aTGJunuep19ZzxN181GljyCgQ+YDj8/tSnqxik+JjkwFAsdMQxcGFCktRN1UGPe7viJbrHIY8IVwLCI4AbGhMixjr7bJKxFC9hoasbO5bEWuzmiBGK3xszLwvZ47WhOsexU3JJLNem8R0ViGWAyxZ9J2P9XPZGrTha/hP58vTt1aOMdGTXDbXMwP0ymO15uyHH9yN1kRoDngfPxzx6dEwtV+9Xurvyd2Duo59TxVT1d519a8IRQxntExOc/B98x9Wi1zoMA2igKFu/XGlsNjxqAU7uxPSR+q/kfGqO1jLSznD/kXUNYce56fc/41dim3kIcFrvcZtxcINXnp71g93efpbHDczWxayX37nih+lmwT9Cv3jwIpiDkURCu7svj19XwwV5o9qO7247vsTyjWY1rtuiEpqM8tHLp0oIAsnxjtJ0pVPmZNYNAHo7BjROV+xu/pzKwnRRM/N3++tk7PPI2ka/Z3t57e96uUuIg93h1aQ+cn7aPS9Hz8ETfT7xWvRJ/smgFoFhakdKIj3pQ4zOmMBb1Ic6eNSFFdRPFa0orz32f2RcWDX7SAVFPWq/thST89ZW0L9rksNhUZprDv+SmsljFFONqLFXmcvZpX28/YP/Nt3mOKr+UebK977qdOdU6lmCgkYCGiKOLPmIZQuaiyTK2UqGC5/T0gGJr+zShJ9k6iqXA4HJCKusin504+5VVK2dycJBoRDLeYrjndg9eEdA1fwxCyNvjGzyd/tgQkOdBgAiwtU7YroMQmbN2uATgQ6eLExFNJhl124KeF+9l5WyrlCk5KOslvXY8aXa09MJZZ+arrAwtgvFqRDyA3kLfFiZlrZE1XMUFcwr7RVMXXZsQ4qwwPXKGNZ++ej4bRSFcgJ6aV7GXGhb44LnI304XqlHVur7VG7lsziD8eNtUITliLkuQ6WPUySlhp5+4XSPqQKqnzndINXTu5Jt4Kqrh6NuqqEyfVYBqozdZGKgYE3LCRumOagyw0AZ+MLO876li3YTk/Y6sGahVH6hgYcCkvVE9X+pS0d3YOao2Q/8y1sa2X8iIv/Evr6JUcea+wVDxGAEZzhwiCuq8tOhXM1myn23CmTtY38Yv4z/ll3VldIAF2Res2sY07n/WkSZRP4FaI5FBJXchZp4OvdV+Znl+8uoOu60fm767n8TysqVE1iArDmBm4qC50rVxqgBrcr11rQ3vPohXiSM3CrsFjIb0OFJqSimWtsrbVbo/PW6d18AwLMrZYlJnbO2XOGPMXTOXINMFoFTCvh2HkVoMl9pWMdGLVirpcbxvidaMu5E8/K6unE1NCGZjQqsX+om+lLYF+/z1drdimFIMkHmWEuQAYDRwKTZy9+Vn7h3EBFhoAYCZBzT05J43Ya/nldHRVn9re8Sqkx2a8jbNLC85ZlahCnpqk7MtSML0PeZs/oaFtnZYCLdjAU3ZOaBHNI6c6dsy135C5yrUamSXyF/XltMhg7+hQ0+Mfh1AQrVmEGRf1ttFZHP2c1NtG0aDkYDcuqDjbTJM4Z87BXfZ0v6Nz5l512NmtmadEHxUeEeVr3G29xywFnKNGXDOzxzbjD476kOaUsBhVYGUOi/eXOeU1A6kJPZjuHpFQEoUemZAP+Q07EZITR9hKPeWndQ/9u/D9Y0tyUkM7b3mpnxPoFs0vI6oZ3xPX+JZfYdOdlg1tNMbYPvUasH7HeVykpBbs2ij9weq3xSlGBFQ+1G1HQC+DKJYZuhqNJb0RnMValhvjsm9/fwzZvGfUNzJqLGFHv8sru18u0eOQGes1aYV6u6mmq25Gk9FneoYSybyTiIHTd4Qr+7DPcA+b6un2AMVkDI2sicr3flRuqKpMxWLwLiVN4RvL+vgpO2fylCh19ehduE57NmYSlFpVqis1F6ZC0gkDGHyj1ZvSD3rxcT5Gue3hU3VD1gMmLZhkJYAijQTpw3V63xUnCY0mlyLB1sVRMlhqDl8gNVqeRBee4CyYhR+Lu5mdrINqhTIVQbsgu45ytAsQF168PhPoMDSsVdetS5aUnvHIiD8dOZ/2V7ZUSCmT9Yg0R8DEBWrSxCeGIiO1ZarsHBz1HcbhunfcH3+WCs80iOWEXJd5m+nte7TQ63OKT9AYbjvs31rmq5SGGj4jGqTxc406M6zsHbj61eYfEa26GEzyeaiSfCZv067kQT7q9o4yVlguKfeK+uwOjBXnzeEMlUbVIXdvE48QAryYRibBJoYtaj1SMMPmY23y0POoa2Fieid3vZ1JuHqWcq6l4RjHN92bNPKy6xL3b5TndPY0bQ1DqpDOJAfiGE74EzxTXTNC+B7L6q1MmtwlzuEpKVTa9iB5LE/cY61Vgq25qkvBcz40NURl1plhojyp5M4IDWgQW3WsEKqCZsk7WjdMJzNn3jYRzak3c8ChMj3MtFSaJBfNp4NayZeCJPS+1Z7WiLL1zHVEgt7KgP/WfoRX3bRAWCxqnf9QtJUffB5XMUIB3326RwY0LNvyjD6KOqzgFVxygaYKogXYVvo15sLhgHTwXVBjwdpqmK7wzuCpvFN3uQdeERY2gsOqsimVnL5A37rUaFjKxry2Ffx37ptPu0qpmWWCQ71Agkh2eY+ftm4j3GnveeVjAz+cS+i63iXwA7pMyv4zcwpTnKYCNJZbSWBgQMwNMDVMoUOhjtLmUAI68sGcg1FdylREY+fx/I6U51M+HDVN9fcz2fNUnfc2pETHkYfeVOGVP/AvpcngKNlnT8bABpRdEZ7qeO08pmwwR/czLrCEOHtiKVZq5Kzp6IQJINbjV3ER5mMGEUhheUvbjaMTMLrF6BZz7RxAxEEzdKYt0lfRIcpVqzlHaUb9GYFE+PX4QjvJ7sxSTO5te1ETQINYE4uYHJ7yU5QwpYxZF1cgaEaMt5IzIfyDN9Ju2jMmdcmouGEwwj2wd2vhM8N50KclGwcsBynOh1DRrFXSNbhk1lSeNeImTajtbtx0xNItkcny3XmjS4BJ9NQZ6M2WM+E2PUhZVIvgMiySci+B7KGJM7FGKieErmnj3qthD6GIr4mr9tUea+aatumm/hkVT2mFOLK03RxUrCqCcMT2FR6anmDj/yzb+NRqrbAVmDb7wjXdblWHU4PcktZKNEAAA3F7gKbeEknCaHxfKuwkblAU5Dk239AdtvpNlj2NbED4e2I0+i3iSUitAjnmhRhtP7HgEQaDoDSijDd5hup70dzANgjMBFp5sJ3wbKVhc6gMO9rdLEL0Fa3NgfBUDw7HviycrwLRRBuLkGXizWsKcAqYKXlmuX1kRPLhqEMo6uSrw2nYLaVewnnR9wsx/RlP2s34rvj1dWT0AWokmKMlCnA3bsdlXAMl+JJVAdNsYhnojtk8H6tdNNViqWEonUX9q3UXMjt69gi16g33VFniyrPkoLW9jG6DPOmjHFvfn3x+8LhXGtNw1pScS1OPCfC6055bCFOjVfYm0hHsK/pjcwlZVqt/DGZQTW3EhmHmRJo9vloMDXTbu597KevXb10za5UaMHwgOAoA6MHTlnooOj+lFFnKHkY4lkhT3IsIwmboqV625AEqgGVpfBQxbS3u68QaG+coyn7LltqERbsmW4wxr04/1Tn19HuncmMeNT+V58jTj+EiDc6R1pAA+B49fOCtQ8rQwfd0Y4D3DaoaWeuBnsgsYbbSzu/gS3y95Vycsn6cP8IoNfguNwjhHOT3PJWHY1BKnd/pL4VRxicylY6Q8yLLZQrTPMYyAVCgQVsu+ySkmxhlxpIOW7P32cg9YkPlcAlngaqv0vB58xPDSAo2ZC5G+u9wzakt26GwqF/ETNc8sLIFcjd3BtUHHtYzLd22TCsYmuk60KQyMqGzZIm2RjWnV9tViBm83N81VSPjhDuXq4pIGo/hUfZ7Sd26ZMrcklevRcVS+kLxMye8hGfkHSOKHzAbp5BPP986J2dUG9p6DqXceSQl4vK4bJuojzfXPfrXIzgwU6dJx7NH2lzmF5eFi9GhEm7X2Z96GbMy9EpDrsKuMbyvIvL9a5bLH2EzGXTP7WeFgkG5tQjSaO/jemaMO0wt4Hbe1k+mNnTfg254mLtu6X7S9uw1Ylx89iDZZ963NmU91ay2LkS35nCv/qsNUxoNB4pNmb6lH5noGAMBB7upqzg0mH6ophCHfnbmT86nYOkpwzKmja5F2RsyOGK4WlZ9tA7czHtODjAOPTvypbeGIg/tTLRIC5fMDruqKErWp6+1XHgL/ZI1o7TgKZzBMzg6OUO3/Yn4oVFjekZj2WMtXIK6j+/czosuuTzq61PUBuexVoxwzRISSyVJ0XFe+WeFopK5GO3lD7qlsJQK99N89OTxPCn7CjjMPpTt4pyuLkUtRUMifWWOLD5OCQCGmapbhyauXHZiDh10X0LeNi9uucUVJa0AABAASURBVDm6zo4eqspznp6Cu382FOL87Nl6gKGOhpVcxEQ2viP+b/YTj+ImpUTTNbmUVTBoYr3Rd9dDnao4xRv5eDem7fSqcl3RjzkpCEqE5DdD9lpnG9wCo4bgItmsTa7HSmiVL0UdYgsD61gi/AgGZrxLi4e0q85kzQBctoeFfu26h7ACxBuJCyT0quaZqZw8RBmpRrQEc9TnF2rbiLFqewZHPkiDLLu/rq4j+4beUPzuuuwz++CnyrUb2pDwNbpbH9ZNYB5HcWo6s9RRpVe2GUBUYVpDBBJQZtHZqrk8mWIqYJTnkvKRU8LHHhReXTaUluFKiWn0FcBSsIZn5dGARFQ4jR7pV0qP3iBi9Gz3s02MCMPTD/FoxY5dFZVQ5Ugy5LSyVqoCex5/sA58zGrh9v7O+SqMGF62Lvv87M+1Xyaeoa5br1hXXIENZSbxmh1orB6h6Yu49z+nmIdxZt8w7bT0BYLS25gRPv3S+CyR0AKov8/kKZ1esfT14Q6EgS9Sm5RDHdazpililk2IMRsGY0WYOewMbkPCU1oGwEMvsFQ1pdlcsDWwwuif65pDKetEfI3HhQLomdGSIE+f7F1pI5Tz/ofpDAx3nSUqLdsyR1sS3M0qabfXpKpAFvlgHV40D5sVWidaIErZcp/edvSpJ7p2pz05355G0XOtUscHFeOMNMtSNCb6HUovfCvHJgH9JW3Qr9k2ifosIiyayiAAzkQWk+yQxVo6gXPq0evcMwlggONRcaU7oLJlGt+2ZnAb+MnIe3fFtOeJfHaKe+lsbVsxQbV0UamAWFODeQPeTMc9VI9pAj5hXB77Y/4ztgSzNVL4hMzJoMubwAAPX/Ttk4u3ReNCh4oLmO4+VW+f5JvxJbxEFpQGtvr+ohTVXY0UASd+BleM/Xj/XLWlBWETFNu9BAjFOaZHuY9Zk2iXjAVBWaQAhjqSRenLdokFBUiRmkEX6B6NX+ERy22W3R87W2/XlsNe0RJeRrVRoUEpaHsgRSq7NJhWaueJeMRdHz6ZVYk1IU/E62vM4NGtQz6L7iPNlkvvrM7Ku/KR7hylzy8vUs/fwyJ2UTqeQ2XAKqLcSkbRbUOjqXsxbAAbxWIQg4DqpZKTDMgPDa1V/QQzAlheHsf2N9bXlZ8a5yzvC9qd/IF2QgY53BMXwofTYM0+iId15uhnpA0N1PU1jHGkC4MF7V2H2dnKIxIz6r2/hhPeLtlICf7cUpKuocQstK01GZse6E+JOWzhNrf39Us/7VXT/c1Xg5oDsxdhom87cz3+b2JHQHu3H/RlrZVvtn252pXI9s7TcsmXEW/zCraIxKpnIc15OHeeyjb7fOLnxz8W9gRuvvrbYwtQ5z16dGTvcz7xKFa0m/n/0PQ3iq3ruq4wCpCUnY5z3/9Nz2ws8efCXefLcNvElkgQBCnZmWtvGR5t13JlIvc5gujc1/RHvJ5JsxS3EtiXXgLdhSoeQOWncezA+JQ0dJUrkwhUMvXA6Fm0Xp9fu/eg0RMDVYU7qKD5a/YbvpX0IlSXz66dyL9VGyzKh5WtcRKHYroXWhyti7iVYJ9Ne+hnIqFVkz1nOASuefnnilqxDZ1JyRozElH6qB8q0Vw5PFXPye+zf1n7Xvsjvn3NdUtkyuX8nnk2Tq9W26PMf0eiVBjg7ff7vzFGqJTX97K9MNYtVc1WlmpuIOP8xtY0iWc5PSgxtiqxLIhrcz0JLWa13dqoHOz7bNaJVXHRpLjQQDdh4ff4b0L7Y/zd+Nat+KuP3RMu0tGlCOGy6/YGzQcLImIUaGwtOV64nstPGBavS9bNm5Y9qYy63GsExMrT2Oxf2PcvItihDfRjpk7bl0p9l8hO4PFK+za/hZOG9NUMQWFW767JvuZc+HWrl1JHLTvK+u15UxQVp2pjH562/3aklJc2A/UB67EM2Xx3UTpRrl/QJjxgQDQGo4J+yp6qdztw/WoPsYKXNGWWK9LRLOkRz4EUtECPZRbw683jAytxcNnINEYh4TDVhFuSctWy6DmuXITI0vVSPZTiGPnhcOqnnh8Jp+aCOCUV+TDfbNCgd40SFl9GDWC3tPC1mUtG+uq+q66ZMCj474Pvd0/95/g6i2bshb12xtOCVp5coHKtP2f5Ds+P942QmH6jvmsfy3GzJTlc8KUCReMKNZOMa647+NH+R119PsBnbM1IMwcGj7vt2m1TGXOIeijN+IA65eB0dPJUO8u9YxAi71TO97fzm3wATK85yzZuiFBL5e4zz8+ej+OzfNVglyithVH6kHztu33cbnOd8XmvGlAQiyIQzr65F76QWdWBf+oSe0S/1CLXi35aq67wxPaamwjg9DzzmtH7q7Upbs4u32cZxuxbajhbFF2Xi69XBKCidNqoCvmZey26P83ffLX8SiNHF0drB84YzD3MQ0AH/7fPf98nd1P89Bo1L+GbNF1eHOTgl0wNb7F8/MkWtZ9xauMpNtwZlHXRugay8dx4lE+FPT3KSC40qXOnDVyERm7gt5CvsAeCxSwBQ3ZXCQH6QsWU97D5QtWcBYU5gwFyY3anxrtMD3hGXVgapcFtyJLRUolrhu6f1+WXj7JSj+xeoDFqoqTRhuRPNzL8rGhzVrxgcBopKzocClZAxpLe0Fj9Ohgxo94mM1fnGgXXVmNIw+Pn4fOgnzpnHuCs1XGDr1CiNejtbR9QX6AZkaOlFVvvFNw8Xudi+8VZPZ28vn2ps05tn/+Mj8aZKCd7wscXlMnrrrhacSkHH/YPePm6aUBvy1970p6AGlRF4Tr+w+sir7UnjvTMUCFrHcD0eXC+amqS3Kk+0i6Z5IBGX1zugl+DngFAiBHoDEOiE4pP7OB2Hjrtdr9M7Wp6ujekcDk15dJnHOMOCvP2/sXziKzS+uEhhq34+cX6wtVQz3oLuzgjt4nQXNX3+krvy2GXeMIQ72sEyGRc11sLJoIWDijM9QuUH3B8uz9rKhCQfrcriVJR2kJFtJFKRf3qt4A01+47K2b0SNSt5HQQ5H2ZFnOyRLCrJDBUj7CNTNkrYJMK7Fnn3L8MtaSEd5p9247eUbhiJflffc/zdcEleE+slz9oWVvsKyb0FjyCUraKpLf5ttiNKm14zqGS1If72CbO/2OBgxmOFqodVvp5nAoW1Jp5AXoYsoLuqq6L7ZKThbUJGVs8jVCvnbVLn4TUFnFB2TfOzFOzFR9AZbIcj/s2lPdwa4EdUyBrlJqrBEFHCUwOUn+pix/UbRU3qSem9+e9jWmi9Urvg1RP5pxr8d8d//z60X1D+vTrWg7vwWJNNfOFb465TC2pZF6DxNCKWTe0VIgoJSh7vsXC2HoWvz7iaFS8WiZ6rC/mD6D6BerEV3dnuQi7waVKjZO+bXCtuNbCzX1xc1oRNUbHdFNPur6YY2Z0zht1I5fraTSU8Tw3sZTZz3BJQChN27v3F5nWkLSup36+dSXvx98qNg0RHeSsUYCmlakeR6sTtkSDz/FPNYXidGUWNyyBUTJwm11KAHJG8vAMy8vqjqSx5Yy3qe3w7omBDaWt/m/2V/5GzpS9kVvDAryIXKirR8I6Of22oKd5VEy/s7QHTGLmsfPY3lBPhnLckBQ8D5XTZWn6BFkYAMU5VC/DvvFcU2K4xjb9oT2Ys3c+6IzxBYsaPlgPPl/cG0TXVa3+FUMltYS+qSKj2Qhz2RRS6lFZ7H00ejCgTkKXUmPEtAFtTS+7Wjtl5UsxajIpa84xS34EWUDbts7BbVScZKtCyteh/7YfUwuIQCyshqdZvSfc4ppriRRoiB2wJYKv4wG/4yrbcQSP9ZFu4BWxfaXQ15a0pDtAOoXmTVPamVL6wDqGNL5HcEgTvEKfNiUjrXsdCISDsu6QcC9lNodbFIwyqWYjrjf7GT/bZewvIZ3SjJUuifIe8RPNOBjRK6busrXlzHiTP8GLDsiw69d6xaaUONpx5CbpKRAzxR4CLnIaOJX5NA6LShV21Anr6zJb5sZVz+Sehhlwu+k7ADs91bJIKnKijOlzuosYqgRCQh6UPhn1siDUFFx+w+deIbOEXqleeq9aaFR3DnOgHn11Y2oGY2V3Q+2I6KP9AAzd3uU1PQaEjUnzXQ9iq2KgWoTL5SJvUNfmvazkSg/vmBpFA6vwCogkh8W49PaGEySCfWH/4Ak5PLtVupk2wkIBDooUM1rTJAFJt5O7QoHgosjtqdNQWfAPfEtIHaCm1MyIkfJVtkawdSlDwsD7akVfAzHZPhO7rmyHZl5jl3qCI1eVyxIFOERUVVbvrPO8ETLa11x8S2ONdgCLuEYBUkiVu0LWX4a6JEvKv8E/pcJJqL+YVtx0Ch9AJGzPn9WRZGS6TefWM3e3uX5yM0/N0VAbQLjGOGYjAUqBBgM5jAGV2lH5DKlcrnKWlvjRg7W4zlpzmblmUHAdipzSdbV4FVlVna8SzsLxHv1iyqzetczNUEgqxYUu6krD0tgglSNt49dKqe0FptHQaPeSR7psOJQuE5N5QVIfcSfhvZecRjoK+MtjdxyoxEo2Fpb3YmJKmWpWGWCjjy2yAMEGBlSSeqaqqPKvms7ddRyzTAUFQ9splt6IIPH9kLpxYEBUQPt7rdC4ymSpIXml40VpMq1DZlzUmmI3ZAi908P9AIc2Ehs8/ZbcW8hOYQ/UTtzmPmWZw9xX9+qwCoFgt6YxaH/+R6h0dNTRAS3jWpp+Rpknm6N/pkZx7OTbBOS0MTLhrb9VnZkn+zuFyTX5MwjlQASqqYEbpZm7d6cqWMnS/Bzl1SviXN4/Y3fpKckfGlxd6zvxN7Z7P/4cLzrDguun1jVOmAzUjPxDWflzjNmWx95u1I6/vang7tZS5cmAdND1alLpu61Wi+E3a6CTot1kqJVFGe+ZM/6dgV6E33RhE/c8CHXqGq0wY9fGOrwSXi8oXjC5iS7pQWkoNlZTzqlSpr1cnbEEepXaiChkh8Rg+kTVkZQgP+vtxWl10NJcN49qrjcwl2tEN1SV1sMca/ORBdlg0tXAtYtKthmo0+5odiuKxl0V2IjuaxT4G3zdbd4xcZ/PesIfQxokg7/pxE9Lj5ohI1VvkSqg7SmKEX4FXdGa6/3Q9NdfcyehLKuIJjsLJfn4tPq1U15Ff9U0KCwVJWA17zmdQpuZK8SK3l7FAhNWUnZ6t3diXj/hEj9HGVJc1JpySxfQFSSRobhE/9/VHO38qryao/RGFyUYkedoL2C0CPt/ZtoeouSsqfog17JYILQ8aX85LhKNJghWY6oOGZBdPK7YlESFpob3erEeLwSNwewpxdAKqiZUTpF9gfRbSEd1G7fb7VwGyQsn+gHOeI6P4jSob2Qjt3ARk11oK16kKx6znAXt1d9is0pm4uDknXM1qWAnfUqjLdSdTURyExxAB99oS0VU5sPLNYYv2hJR3YH/vQYOJyTeacdoqOIARgY0VwrmfJamj5TRrojclo1JK8gGiz5Da/cizEecvO+11DrHul2P8pWl4Z8N3cMQAAAQAElEQVS9Ieqyc7P+AWFQdBNIa4pqdZZObct2z4ucRpM7tQENGb2F9grydiyrccp7WC/9r/ngLAfJFrFt0gjf5qOI/PK1qMWCiiIWTW6b+LYdzgvLG6JZ7/2NqZs1plTYgKoP2Mx0dU7vBbHmPhqIxJzIVJDvKPvf9EhbtYxxma0u7JpTPCq9Ngj0pIwODIJywV04gIF+xv6cS2FQQ44Fk74EVSS8xyinGswGy1nL1QGklH6lpyCg3WkNvER3WUptvi7dYX4O1hdx7DqM1JuJU+ohRhKmlEhchqzpUzhNtQ71H+lfvT0gxwrFcKPvyBDfA41TQg813SHdjHLTYdMUI6BiohumMJvdaJxMdbXZs56KZ7OyodEzRkBjxbQYGBU0bhl6eSr22xKVXbSxx6avgkp1oKFs4Wx5G8iGtkR1QbPbjw5E8s5Ux1GS6va+bNzPmX2KCSvjXpZLvcAVNWpP6vHvOXxTA4BCNaNFVYyof6tlnLzye/XXI/MaPbZIk+AYcGnJUeippDLg7Uui5ihHCixeSY6FuNJ9WSIze4SsI5ennqZKlRA85bnHd+mBncRkjmV10f6+HbhCb7/WX7WNNcVRO/QZ0RAza9qjoLrHdeaz386/WtWRpR4B9lASVD47rZonmcdGNwoElHyE0RWuRh0kui41EkMsIy/OTYSDi22N3qOmFCireR77bj3lxdXHOzfP13M7sDqipBp9Q75eO60e6CJi3jXwwfsSN3JpLuXrpn+xqLPyRk5Wt3KEESyzRRrQEOThBHBne+mzA5+z/k9dAX/WepRi1/Vzaw/W9dvn9zzce31LSpCxgXlhTRmGWinXB/4xmqFqWV6rW0jciT8uD+yRUmf1aC8IDRMD6XPUAtDN6cY8hW9buvnFN+WvBr7ujygHxGjaqPfo7I/jR6suFJvkVaNf0CU2FNtA76c3Zzd3e9LfNeXGcq7wUKREmbR/xvbkRnVQkrdRwLZiuRaau+CzLlw+ppgWc4lyLCUNsw35LjXKGlQZa5lJevaKpiCkqY1C1KbSq0ekVaYQNlkNhy/RBVpX26t6NT9fthwXzezqWCnxYuqUvgD7HjXvPSw67TbemhyLiyWldGXPjPoOoX826o2m20DLM33adzPTNMTEmXcbwIFeNi12TNsk7Vruc12bgWlI0K2yOr1UdqPSntM+opU6W9RW8zqu/MvKwIUoqHbQjnI1jYyTfoiOFgpyoniN+0i1rqX0x1qqol4eNvQ8/H7x/WY/My9vmAv1gXCKqSwBevheGJui7j8lFJN1WuA1KcmcJ7O00hc1BN70EnM5VcS4JqVWIPulfa/1DU/oaphYdNMu7mt2XJP4um8MmKG839MhOIdsTXiGSuAMsThXzD2L+z6/6zzWqfOVSrWdodrP+Brrfs/jJy2aPRLmJF+QQYDSjCZpzhN6djI7UY/NkYR3rt1L+QSsBebMla3vkuIHTt7FT1JbtdSGY/XVO87GlByIlAtjA1TnEXEl6JPmO65ewSWvcgPmPelvPkP12D1WaUdaKd7mn7vuT983zP2plbUUMobZZ+YLblrSe6JlQVGJfg6FZNHhlbNHp7LtKapM65VUSBZlWaJBMcxNGlbbB5/Fz5g/hjQBdy0MfnpKAYVidYQZ3SC0LhvKG7TBm4fUbQfV+Lybk2DDW7/VRVHWSqIKuR2PIw9mXMH7DZdnlZgRIGiaNGN4XwPVBo9C0CcOWNO7Wq23otP4mKep4qSZjXjGk0M/iIYM0EaOjO30wCf830B7A6FhF3aqv3hRBfDc8zU8StLWGil7yzVBKjRTGqzNyzCYBB4dA53lGdRBb8vUNVCvXjy3DbUhnHDGBV5QE5/7qThyNUPo/Bul6c348FEX8K09XsSrBAjEyJnPa1E/7yG/38EXljRzuL8rV7ZKMAkxoXhp4vK6J65ylwzE4CgHhF7Zeg3Vk5XVD4ZTWX8PgdCM7a4VTdmtnuw/vjVnauaMaj+31SbOppaYFPNtHYbbtOip9T2tJFN8Ly3j15qlJi4YfoHoOvPOOJF4M5MUpG5F00kmuNNPKQWirGEPdQNHZYZJCWytRdfaRcMsUzwcBNpsgDHA5IHCAplSxCvh4s6NbCsRncPGXFN22qrNqj59PnMWGuC5PuP6jjWs+QqhQTHz5/4MCYbcf/pd79GDDPveIcnpTYWP+sybYna8zSNH4i6GSIkhTFEdzHGM+hLWmJebqjoh7BiOnLXeNqToKNz9ufhzc0USR0BedfyNVdb5n9s31mBQow506837ocvP2EiV/Lp/55P7rnRZb1noLvHvnSvqutuUkHEluOfb6tZTDoUvyKdxvsbnRc08gVF6jTf9E7zWYTyYfPVQBdWWzQuM1lgh49ulXrytIWfEgsjVaEG8RtyBW+ZPtxpZfs+cY2oEI/1mospA98vDnQVXdJjlFgy01e7c2Dnv/9GirJe+el3DFKEY411QUCSPc4Ml9zZqQOfCcQ3CSHQCokky3mCjdErpoJm9QdSUijLhj4qHZYrHJ8LobDVhbku57y7qTuP4vNBlTYkbFB28eN3r9vtubQIx8lGjsetEPNYbJwtTTk2iZm1IRyVN+hKEAC3FWJYKX/lX6fLKuPf9OfdSSvex1BqrIqrM3qztu5AtMSfuM5Ho9vetrbQrXw6IcozqgYTUeuDYXEeyOP5eCldZyZ9NAN41sXd8H7E7cxXWwdJ0WMDeBEJi0AXRUWUMcuX07lNvmiW1NX0hL7EhQi9ogaxVLUWLxfWxCKEAvpyvoM7IJehUReIoHeu4gJvqLR+fX+fXMAvypCLC0Ea6IA/ycDu+TrligBzJfixVDkVsca68F0w4Ok1vxsx5ed/RYWj4llMQCmx4GeKGXWHy0NB+QNucZ3e1EHIAtPlQ2TFPrTPXgygtIukmFzqvWIGxeMkhOClCSvpLnT36aRIVAkOMxzYvNUiT4RcgtrqRp/AYtQktq7ob9zAGHKOay3LlK6t7hAVDoS77235k+NGqTgfkONR86aUcmwnT6vHSnDx+kvBTsSumtLUyS0AcMbYWC/cX+RpfvQCm41izEIlrJsL5Ll1UsDKtC1Npu6P8Mv/7IQdTQJbzjIJh2oxNey5/1BJf0H1Zu4HU0ud57KT6jpIlXpTCuxHH4lx3LVEU0EArlVEEOiSPGjxcabfPik7v49ovT5s0ZbaNGRiDkqgTmO4ij/tjfKD9TrEZ70zHiUmyQ0P9hvaI0MR6d8fDSdQhSkA92pAs2lYidMjCOtKzvHBgrejG2y4hMLFOYcHr1Njr4GfbrZEDtHcGmgHpS3bg48vU5ECN84YbBVxcUbwnKPIAOsyEzz757lKim1MUyeLSeqKSc0SxjfnlfuvgXEy3HNrBdWbhMn5Y5g/s7eQ0nKC0a2VaUs2MOVOKpLEomrPybcsq0MewHSoeUtRgGZYcT2eqaXN0I5SL70uzlGnopZG+ZLjBNglMBtUletiaJSXGjL82TvSzfOxdL67SbduYNgAaOng5kuQInJEK7hMxl+t8UmHM6++Ax7qvoqSvYaiC1XymL46NhInvc1JrC6YcdJph6O1rzOZ9Qb+EJCA8uloVI3JhMKmXX5GAMSpHbhxYluWe7Cyx5VVSKbyLyEQXNLdIzEQiSqR8fKRieQQaI7zZBlig3yrh6M+a+RkIw3mHSbAXzTVXSI63msxEO3mDqgstbG9Oz18YzuXwHH8ksn1pH9aKEkFa1EAqET32ML6QboUtm+f4Tn6bmyDfsXBrj5IiG8qrqzTXlMzyKM4ntTIK58IBRo2s+RKjxggZRNMR4UbwUCNRGF2QIKU+nTeDLpFGdykQt7ISVV4a66N25fJLdttrGmOK3e7AhYoD7p/JH7bMv3YAIaZMGovz/r2I5fi7jNWz9GAtKTIHNkO8kBzQ/sjHDT4B6I/Iq7buKXTaUWKAUSpYxb/28GrhavNcUZfYdzFzRoBQVh1JhlH6xuqW87mh+2XzEi5MUpbfyFSsdqDNdJWCE3TIja7j/Q8tUjSZa/bQYGJH2JrPvHmpUy/HqfOjTJrAe7ibu37eYsqGmuVUZzLTmeOnTJ+nv6bl0NKUCMg6lB9q4vL4od9NMd/tlE4edlHlNED5aI9wW95UbCpA3fNwGEqOKkVcVkPZUK2lZS/OUg+4XRcIcYOeQatsZnyewOPTNnAqNDj0m9AIDKboWwWhdqhM+CDkZWR8cAqZ6NP2GMUOZ2ScXBTDwbjMaUvDIZPyqq3PAQrGWWx5xEsEOepqWjut8n1+uBO/wy/8aByZb3eZdgu4n0GWQDkhJ8uos6MFzpbFj99LlQAto2iMWV58XBq3Jsf121Oafbqe0yU2ESR7ZO+7+F3yN+lTSh8+QhjCGX04msWtEUax2WMKaDqoY6hMUMQ1DHvNZvId2mGID2yBZtSvv+MdBCoDsEZl75l3F3d1hgaBAuAN6T0CJsXeyegV4m4ciNfnaGBbvK5lAcxZnaJhrYvXEpCpbjhXmJu1qLMcElCRUIGO4lXalb6elk6Wi0EiqDlj6OiSf4B/hAXS4qgxKxOlU7q5unSt5JZf4sgZGuLDrWo627WnFQ9IeHJVr5F3lc6T/fQcYZvyli/aVXOf1rA3M5rvcq63EzM+b155KeXUj4fAyC2MwvmBvhk+lMMYsdilXConkK7epFPpQUA2CjjdT9V+bz0UOxxuUiTK1TpDrJgsJFSWzDLpAGwT9nqNCvJ3t3aqnTCHxYyh0itjNJgFK9oAIld2TFQBoPYMSxggWClCZlTRk0qSq7iAaTZNVDqLPpRHwdIl9rKWFk8+meISZFRJsDmm0ngpr1GVVnXbiZURZm9tCcI0qv2cVaW7hoWhdRll3WyiRv0DdDByIEPIvVKQSryqdCI83FROZdhhtV4SRqmrQAOjl7iVbqpZJZoGGyWlJwqEUIuaZfP3MCvqsJLSVsLLStsY++n+N2nW4+LRpa9L/F+DK/OeYwashdtHh3GP5UzJtuixmWCZbbp2M+09I0C6wDZLBeRxxLyGpHYlc7lCuYZOmQQXeCW1BNxTH0CLAt6mghEEdSW3UU8yjJlE462OkC6HMFJIL8/lTYD96Vx7QiEdWVk+y+q+sPxNZZmcXC3R0T/of6jLj3urppeJ9xYNiddoKAc2Bf/Cvp6PsuCN0SBVLkV3xZ4pNnqmu6PnZoe+9ro3bnnf08eeuX55b1fpeOaqZ3V1yOX7OGMkX81XPESvmSsXT1gufRLYvHbeW5svRe2K3iYiqF0q7rHYWga00M1Woc6M2xvnxdbkM/OdePiT9q8hfaUpxy4hv/kJMQWnqCrMsWltGfwCRPqnEA0IZ32eskc16TvYFDcmxKF8imElAmJHJI5jlGdXWb+FyzTtMW0MPoAePdXna/9SzV4MaOnNOWMHvuVVYtXB6uk0lNyCoJff2hHMD3mpgZj3nfM5rhRiwgAdhIZroqF9evj+1UUSLcCVF+pn4XJIUqi/q4Ai18CRD7RZ0VRw0GgEiwAAEABJREFU05Uz1UQFVOt6PzQzbUqoaB1SvmhUxlKoA7z+2fo3a9XnjLi6vT5GzD3URUzI7AZSoihzytcZPuXVPz0/hc9cISBaVQequ30w221Idqv3SS4yFE5JwKasZ8FjkMAhU9yLqdkiC3/GyfLZq8bkbdwsHCsZcpruivbi/NBumEFm3uL7xnl8g01Jpgvvi+bvenzZdVMVCbUCdeHzqLEdNRFgTCO7WAhBSahTPieeVBmtiLgCJh21Vu9nofw1gFHpw2Q+/RXrwkjfrJFfQEzAdLckzA+uPXdjEbyAu3mbDvd3vJoBZPsFZesBv9iN57b+XIYLEgu0ZyjRLWDvgMfEfY6yqgXsG/jGfFlHueWYUf+udO67FeMqesvIxWVxGbloImwNrsbialsHrO5TOAOxbGbalt0hGWqnAT1f6oKn6RGFl1F5WbOjH+bD/i4pJ97/LYasDNxcDkByU7D6Sy228FEvKE3sUYHiXSHwkp31aF8r9sjAXIR6pSsRIkwSso+tq7XGEZRCf/rz6eXl3lyj2k0/6utnWIxxJmVoAAHk0gPYYzRe3jFaMGejlLK/0lpQxyk81F/ptd12VFpK0DiOzKjfn/69JlmKXH2PUD6z5zvzS7WukVSg10TjUtyZw4eWtOD8rP3z2SrvsdHAARPK3XPNc+GYld/0+xLJaPbuSYS9oe+PzTrmX9oX3GwqPSILYkHE/FCtK6/IcBruqM/dYJ+z61vxRTybJw3qnWsd3ttWYN1cF6Ewjm5eN3bVM30GqP50ffj10ZXdfnB9172vVeaZ6+xQUO3F60ghc2P98HMjfChrKlZ/iEeETH91hq7MxAPbJ/N5Zm97hwGgwqy6i3fZAgP51/0b7X1+mP9qrs3qEZHAW1BabXzghKGVECmpkI3TpsBV/cXrq6Rhao5TrAMtxzV75zwjfWigrWWX33HFutJCPJdcpuQl2vT18YowEzs9nuquo5+CUQoW4kKL/zQMqBlhYPxdsgCsAZzJB/WIBybe38fQNtB1L/AA0vugcyZbmjHvfqW83EwWjGedEzszt6Spop+hteQ6bhk2C/fFf2vdE9zAt/1Rqyih0JbmoyHz9T6ii0Zw9CNY0XantmGLcyVu1s9V9zqyGtYalKe0VXPSr4t6PBmaVoF9WZvMfMjL3FWe76LSo3tB37NK5bWcrhzhKe429TdNi5ALnQaI4/GN60v1Lhwo1gttmC7rJoELJfHrzagcb0lAufM2r2sUoGmMqrNXjaW9YrEb17Xi9r5jr+uxJVJRUMFZl/L8neesR/4WhDCuXNeG1etL0nak+ygU78VUwtWBDA+QaMnG0bqMG3Ob3+QFdzqvUFQZQiTAbEtttQtQut6RJVFMivSQVBZrcbtSM8VpEXhbGeq4xuuQ0i10gWi12c/3LexlV6wVoh+Tgw0K0GCtXv7E7GGfiwLe8EnytO/NXUoz5WI0LENeSpIK0ucGLmDhrybn7U6PGK/EKR8uv67PFXfIo4E358PDKUxJwMeYhGXfis8x1yBUA4am+BMjuCAXjtZpHxpE0TKI7g/ysobE/epvWNnfev8rhU5Oxy7PQr6Ohhl5FJrSVzLVEGGWcrzHepfN+wxTCeczajcBNG61AwhJpyrwsTN/lXI9W3vM3iM5KZhlgnBUKUokT2AIRXU0Z56GxFh2S106uyqvk0p99qTIm9a3CxTxetLbkeV1DI/FN/y/QCrCVwkgbegFb4RR5CzU1H6+T6vHlu4S4zq8ut6avjvsMYWGI55imcuMXT5LHGJij2RL8Dgf6wcnqzNnpnTOMHDMZS/bbLcBiAORyFAoCpvzhdgajVyjQhVCl0ua+VEH/2dHd9kdrXI41TV7cN6Pt7YA/tgcYAKqhCZrRCm+mMfxvbiXfRuPsmnOiKnV/7F+NXx0R/GuOojVzh7V1ioumRiFYuWS7RICGf6YMMNrJBWari7YJaAOLhSzbJOnpw8xfmDf8jwrGJ8xJbG1r+ZQ2p3ehdSeoBSB9YthLDbtoSqi5n1Za6Qe9y8tcAyPy83lCZJGJbTQ+/Pm+OolB2HmZfmL/WutKCmkIsEDAcaswef2z7oWbj7eOZiEeDausEVyyIZeClHcz2FrCenbEQ41W1oZMnpjShkVCNvtv2P6B1N+38ljoFO/m6DlktjuFH/i266aJfTX4JI0u15X8KWjbH1t/QdtHC8b8VrWKSn3Mbze/417IiUhTGobcJ6ntCbJxGnujjroHgNIBd3bfLs3bdksvOHrrDigW+AP3RClCTsjn2hZ51yLHiLpVnau2R8rF0NHFs+Fc1FKw8AE4mXupWJlU74i+6qkBqjvKE7XtiLeJbGuUGKKPLTGalszq/uV1pZaxmwX39KfSESlqVbOHHF7RG6M3KPQE6Vg3NDCQhv59FIjyEG5bPsboSgFy1T3aBgUpZhg4fql6YvANsagz9Tp2q1bn5cALbX1tWdX1leOQa7hp6jAAdmBNGxMdlHkDrMxkvTTd859eDcdQ5ywEQj50Db8vcA2Tf2DYXAfLBvzhuQ1ii7h3TEPJMM1t+63grom9CW3T9iBjzqeaEusQURTJHsyVFmowjyj0l4YX3yl62aQ3BR5KcpVxN+LCqgRIiU2lbkuM1/24dIRMIeKv+XhqrpL/QA2AF+kCTtUXNdVIkVVmhKYjEPtyh6sRACgwlE3rDC8Kjruh55tYomlVMFpFjfv/+P68n9w9UQ1EF/cSQObHPKquGsRrsATOuPyvDzXlLaZkyGuj13iVPCO8Wv2kEc8DAwzrnHoKu6+K/8P6pKTk1mP2DAyMIrJesRLRSpZssNGFK+Bm2lEm3XZFNqK2m3cWtgoRSnMpbTDN2OblzgzHqAo/LEq/Bj6nTcE6aQi7+MqyWKaqxNREHUUAB+zE173WxmjoRIkciAbh3M0+NnvqrphveCu8G60ukbcMebTROut365uOMtyUWWqQ1tw3bEclBy9fmju5mYzSCCdx9StJn8Lu0t1IAZiuAzgTKjTseGgvEVkxznqUYA1r+OXdFNLzqnAIIF2cMJ40WiR7OqnbKflscw5qRKrPNMHlH1HLVcHmVufvN5tiIvGxeNWzjbCGcS6+l4jTHPZXuuBP6pCwRvxO3OOgOuE2ZE2DCPjEpmwAQgyQMFVuEp7bhwoeDNw6fyMtWSZKiUNVsg6uy6LCML6yHiXnstvYMK5Qlccxjec+JalYttKzGFJUlJG562dhNZTVeSl5d/ukR3VNooyocyMM1eVYFbLgaIJ2Bj7Ct5h1IRVxrHBGNoXqHzoY5u33eTlb/4D5WK10gEomigu9dXR/n01JpngE9NAfkeb2CMRgY2LpEHKeqqVkcGRn6GjdVpuNWEnTupTj53GcUoGpqmTwxZhgGNWT8eor21nW8AWo6FFvEb1NopyLdedhCGwHd0UJq0tiMIFmNjUwnItXxfchxy8eTDPitqqkm5NsIJBiAnFY2K50IkuzAF2UB698Gm1q4H+5QAvLDkbFcWKue6+ogynxqp0u3uhFtqhiHymJJ4nrbnos7ziAsXoqxkPkwIfnGaS1ARnXsjbcM1I9UvaPoep3dU2QgNe0VLwqur/SSef3ZKnmAwqtXZNuHw4gbFOjsK0bau43tzeAe1YrurIYQ/1MqPRRbAF2vIglZwcSyOtiC/4nVZfmaj2aVLbGMaYZVG3l0qTjQpguEWimBuscUcQC++LMEOMuOllbPUtuZljVhc1SlO90zfXvlZqDTKO2rX6o+oKQ2xq56OEhHeYR1yKREF8LO/Gm6TDBd5ONypqZajSHskkYQ/epna+puokm0xDuSpIYUUK4LiZ0QmOrgRzfhpO0jEAGhiFuujLzX/Im+Y07oXHKd2BGj0gXwlc39RDkXAFyTelDknDbTSEwYrRMG+13IQ3va2fVV9iat0VCzN8jovWIZVHhEAwUp1usSULtQeXOUpsfU6VCmYkSigwRTXZmfVgtLlWZdLnuqCFgre4bocZPTRfIe2pBOhGOaKhlISXjJgxVZKyXhxUZaMTlv3GMNQUAFzqqo/EXjnSLnC9tpUldageUoorKqPEDyRJuuYFTc0K8m9UIFRlTXRapSvcx1GrzpUVjdndZ1OYNPEKhL0azvLW1kvb5z190HukRFkNmDul+HuZuSE5D1UReBOgfNMYeF/jlhovUrCXVDCfinsDqk8Nn6nRqNeCT0kzqyt4hjMhQtD8ZH/eftgFJMSOkL12ahpooznC6mYKBF5bBBHXuLZ1UWYwikXZmOvgfjpqJlFKn+bLUSkHMMzqDmRN7cYuVOocW7uo4esRNhDeVtVrdpvangaI9bl87p+57hFnd6tYLcdkuCHH6rKe757jOhIUrOQqlgVLDWexlvUiPoSvZesml0I/s0UnxmyUhOACVrVaAESBTS2FORCEBX89dkZ1pe/2niEOm9b3VCSylbVMbJhzPtif+caoLtuhNTo8LeQpgKhZ0wvSid+b94NP4jrjp63KUlTeGrKVYR9StLXw8NCPiVMJ84i4Obl37ex8wSiNxWq2iryn3tIZTMRZ/Mrl3FTLpdJ0j4K/YCuFXDA+9W5HwlhiQAgNfI/jA5mTby5IumoHm6jJ2WXfE/34PKzHK1dqOVAiz35r3jx4hQJJZq4+l+1lx/yMJb0qZnueeCa0+WmImcZzeLQWz336s2vO+eb5v42CuYCi/5d/jF4pzYGOGLC7pqZ99geP2oCBA5tW7uAz1OcoUicUjMOpZ6e4S84eReGHXgoyBYetQSxDUpdnjMe5X1MtsbsPfzg/0xfAodz2GaUgumLSJxf0+7jQDBICTDlHCUnLGDLUTmXPSNkqP3t9M752H95TxjNKEfVH9BZafU19UHvREnk2aeNQ0ngnPxurGc1RN1IrPtCqrE9+tV1HItLOT1YjKb8DvSybO2FJJJkUOIUHQfnT/IoWc665sZUhJTal1wMKU0FLlA6nLB0D50BH9yu01DYwXQSoGVq3J19RIQyeClIqULAQcoVM00edpEvEtgkLZYeA+B+U/jZta+uDKVbhtJg0wEMSbqX0Quv7xvhDhjMzwqICKmO3FmnTZyow1piu+buYaWUxHOqRmHd+oAWXr6ds6+G0WanoYeb1Dr9DSkQPstFC7fXer6tG9L7nyrPOsbMUrijYEAvaC+BAmsT7IkbA4/n7euLa9pNYquZuvj1d+mxpGOi6sqNHjoqQy5u+EjytxiG/skIFWQaldYZB//j6cXUKLrvs7bZx3v569WViMciL2irc8V7iGZRqQom6sP8y5wh01LuW7hargsBK6AsvgedY+DUr4LbNnsZ+DQA6b+J33tAAwgfX4z87fp64N6R7i8I6iEfgi7ozUUM/yDJVBnevpEETu5DH7djVJQZetLhRs1HpxYE/6/pe1/6Juey65xPq13XDnR9q0TMYNG7HbOuHLEjb/jhLSQdkilsFfWzGaQSta2VeJz+Hn2PqeLKgjGtfE59lepQb5pr4HjZxzWfRh+sYzrcAABAASURBVFUir7eVGJjSGfNZRMij8Yv6NZy6WopPZoulQI3PWZb4zAjrQmC6sdHnxiI/0C2zR6vE3M3EhsKdaTAl6fcUbfBeWO1XRbfaoy4e5hfyRPq17Oeis1EHIpnxInYwylfLH4udKuypQLp1jpJr3XQJzokFUM7Edh1WoTEDmM45qKYzstSLbezSSnLmNEdXjcIlyLeeL6pivDQR1bRUIY0DI6Bl56AftRwtrkpYYZSVNtEn7c8xk5YlUl+l39pD/eC9cfE88xw8JRXaKWsNhL1Gicm/Q/GOPwiZyYE6TCZUmdPDyWvKR5b7Yv1PFq+jj/sn7CLYLdv95Xmu+t7n3HtuAdCe/3y7C06axyDEvrT4g3oPMdGKUDU944WoeVs7kxIvclVdz8R+0XWJ5imIhG3c5efxs7NUAS+Re/X+jF9YC6/kp2NqVXrrhRpkQAXl4IW/fck7CQ6XV5SCBTqs7MpxJQVPztOmTlYmSqssCQ3XMZd1BNw485bkVK/pD/eNs6BWrhzpMDNyzYtXaZtnafnJY9Vg00szr0obpTl47i6tH0vdQLEWCzr/iq7dletsmWmiF/hz2eeD66YDQrVJaVGXa1yp6sbojIkm4ZUZaUHr11IW9j7V2t0+k8/0ITRpYMa1GLagsGPKRTJPqu1fCsAaFNo1vXxCpUa759YX2Td0YferDhF8VRvj3HEuiItl7nSdIui83S54EO9JwbGmWc3q4my8RSTPBqKXCK2os0r9/NyYC3G1vSOjhNG4wt2WQIAhTbYdJfdI+n/6FRrBVyPeLXY1MsIvt8tKlEBp6ou8Fi6nj3m7OcN5mRzJnnPAeY1dXZ+Bbs6vVIgzf4JBA5KfUlGQYITYWp1ku22Hxgy1mnCSfN9Z/TVBGyBH0zjgMvny6/VJSD2LToqjCstrPRd7bfYv+hltMKd73KFus/ospWV0A2NbkktaIdpCQGacHZx7sGwYLNXmnC/PceqauUMJ9hihb2F7vUx7CRRbQ4KQp85mOfqibuaFMzqZW3UkvSNomljA8Vafar1kTfozheGQFwhC/iBvpKEgVSdaEwgalkPPwd7Csh7jX9BiLoVa6Uk9wDLTlMERiulxWQPZknfloJSsl7dblgguXS2BVdnXRo1Bt+/X2NoyRDLHN9ZYjI+zzDXFaql8Wz0aWjhf7GbhYe7waXk9jSaAUobHukbvyAInx6pN3FHx9MFMgg+tqAlPUMv9E1CPgHEIbZKSg3HvCxoV7NXUsuFJCvIzODXssdGoxqgjHKWsXT4A/VooajHjMTZan8wmTWdcbSbbW6W44SXk4wjDK1+mktOY1iks2MCUKRZMM3I88a4whfapa87FIwt1VWlVx3mtI9VpdXSkUFhhFa/xZaGXy+RK6IkfSnyySc2nl1AbnGUUt9NKNrULWFOcMihICtKUHLdNl6ZpeJRTJPXLZDuF6TUmi5oRmEiK8lG4oez2TEsIbdtim48ZpQ2FKV7Fowh7XXDOynOdhHRtUwu13uAFYoemevY9ezW4V+0L6SiiC/NiPsHvQgeE5oQdRik2Y6kKru5Pc1Fywawz3Kn+ydiMA40b4NyyakfAK6g1BJDpEfMKMNtOr5mYgfKj6Q6tEOMtD225Wx0s2+u4Ri9Zu0bb//pTm0yHpXPEJ15vglgWpPoarjXLqAndqBYPuIjAUCmyvk37ueN2VEedzGPqiYcKfJSkCYpBaDLkgwXWa2XexxPZodGV03lGXr0YUEQ+sNYJ249n2oDkWBPiWKUMnRmN0/TZHk/70VXVA0scz7Y+mh6UuMDC6E2YKwB2d02VguDTGlUSMCLAtWEPdabBRA8rMQ+0hlXZs7H31Pk7P1aqEsfYNDAAi3eNevhbhF3z4jjm5bHMP7QP1oJ/zPXR9AjAsmRokOjGedlUdOQ46+U0EC5UA4F14MKllmhEvd6UHiuBg2czixoFtGSrflPvTq1mJPfjp6OKeUxxAOWxbW17G9MEUhkLKe3DVNbKc1u2SaQTyreNTdnZcaQyNzhZMK0FUD24TiwOjRQ0lbRGFpW8eVXWyvUdzotzo126V4hI05sqCDhLxqZ6zjDHYeaSalmmJUJuOi49QC3jO34jkyOGBeFm4n1vKMwIQCFaNbpImYHcFzpHzVXuYBW+zVVdXe7KgXNZz+A7/tt8EkfDjalQvVodD6kAKHdjOTIHRktnHKhnxdht+ETf1L4vFxQoLHwu03IumY14HyXiPVAuiYAs9jYeU/YvD31zzlDyJMEEsOL6rOUO1wfIS5IAB1MoReOu3tsLySptPF/5Gdy4AAH2UVkupylrppLSMPFoGJ1yBhbqyhNzwstdBTAH9USrPfiiE3oNmQMV3/tjg3DYwoSZzTIh/2NJgu7NTh+JA/43VTz2KIgEaTCAM4RGzvvB398EKXNmEaRPaQTMKB9crX47BZHOPA7tY2tp/KhvaS3VtCZrlKGq7IIuhcdHZVrKfKcrrauonMjQN+OrG/dS3M317ev5k/zMUIY7r6przoVc5KoVmyacPGUortOu5FdWj1KKQkCHKBjTMO+yHisC6qqrYYpCZ1QYVoDKf1pmQohNY8zG1yhBimYUfQOtGjXFWMmeZ/D2cyX8FdOF4zyteXBrszMYUHaouUk2fVqm4f16zZotIcCKVM2Em/51Yj86PQbAOdE65LZGczHTPnRlLi+Ag2ovPQisN4zXGeQtNGxmSt2NUMViKaFSPo82gHr2eGr37LBXb3RMoqSkthFPFvSQ1OTaZhwnFKUj4WdWVWhrOV0uFkZdItVCXL4htwRAjOl89ii216iyVB9rT/FEaqMjckVAoGVSlaQFN8eUMo13/FnhDKaKNa/lhyIY7+A+Jc8oBZG6AdnD45B+X4JKZiHC6NQ5b60IdapKwkrgGI6z/E0uAJIzTT7TT+dMOkrP8/SwZ4ehqTLR5aSdiHqpk+VWOlp2pRRvicKgVAmvFt9Bh8LGVfNDfC67LlNJvGkq8bUNT1s2BkLroKEGpegIc5O7o3wqYwIBNbDTo9h7oTzGAnRn0BScsTEy1D0tQdhwWS+DqWm2cmNKUtdVL2XDggL1garKS7l248gbt83j9Zhi992r2218lHkPxkW7huqYvadTT8lrYlqk2gR0iMkevpSi+8Aes30xlzgAn8IuAZweSyl9Jqc0WVQt4oZdFRdoRgU0XDlrj4rQBRTswVshuKcWCn8cw1uPpNWlLTHPZn5NwE5VFibNExcrIgcDgCIOv3ThanXwln7Rls3GtBXXwS2utY7WPaOu6AvmRLea8SbFuabBHeV8cWOkMfAs9o0RMYI+S7htlxBBvBmrhRZ/LzdaGET2OIbBi/OpueiXfd7bok9+fO4e63POlqBNLtzkS4cRPxitR58YWREb5xsnvYtUeDMKz6lN2zoLX2cleIZf5XCI69jdveLdtcWbCekoZfnGdb8hDTqRB7vxNeYL+/fk98EDKoVqqBrRM293zt29mzt90iwZIAwS8qGWHIERowagHbXcZlGqA/VAqbUBiXaWoadHajiKUCb8obX1sWwdkbDWgFRXga6aUU6wxnuu3feTn3l+rt/lgjFDIvQafQlLbGVepctn9fjQcTuvZaK4A0AtapveRoe40qkyO7NEImqbpQIpsNPqdHaWjNOglvQhfy6uD+se0SM127cFVKnMFemukkOjynJW2T3LYUJGpw796kGXrg/xBgNCfiS96YtSNdZz1nfH05/SvURHl7GuqJ8r75UqjpEPlQ1acHENb7efsB/zK2lHlqt4iGepd9cxTF08V7aqyDO9Ux9vpYlP+7d9x5S06rtYG3g4Tfj06q/hwMbWfBZc0o4x4IK5f/xe+uVaLZSf1mYiMNoG33jjKeuk6v61xKC2v7fhsrz8rN5Xl3ObidOR/WOCWqmneJ/dWpxr16hpFwWjBAbtxoAgHWCPEnJzLrG41Wjrc9dHhSN0GXj6rr4JB5XfQ1fq8dYEoLWN65q5hMmyWKkspGqe9OBtDFIZ0WA07fB66sovseFV9ldA008+ddQ2JRTrz3Wu+7nWXqH8jzzOrCjz0kKhO6JywF7F0L1sNaQc0kp+wOWCcN6n9rIuProIYcQspbV3VWbx+bq0yCkEqEY3K0fKzOd6UquIdHRYXfXabKvmzn50u3L4fbPcJmCtq8mZ6L56B2QqlMAu1ehezBVma1VHZqQb7sW1xggI0Tam0pvD6QnUjXHh8yQLNTYdrLr2iDwRqLQrJSBIKF3icUp8K1eSE1UtmVDjmP6yHyE3DkEbzNaJqLkOr4I09J5rWvsKPTHUhX38Sd/naDvckA5vXOIfqe0GGrDh1bjeqGomB7v4VQylwiqadrMiFfKKibS7zBpyb5g7dSOT08ITaLKludL5IIJ20T5x8b5Ko6VERbDBpF4GAcnxIzfT6HY7WGfebcydonrP+4KLc8Jjwsy9+bLXEHm+7Ha8/xGICL8Zbh+IRWD4P5sKpLGPCqn6tTJgWc7PgW3Ie+vnPWbK85hYXDXKqdtQHk2ZjPDL7IItnWIT7TS8+4FJH/TgQadlC8I118LyXtJYtSJNIzjAtBjWH2FWs9jK4ahoy6CNISMgZUyees751jTpDOvLuGzx1dOFWdT5F+s7kT7h8MI8fiTCdm2IrqzY205ztyVFAh0UXy33qV1IfwdVFvUyChpNcY7NwEBGdnw3z4P3Nq+MPaOsHnQDmocEzpxGxeA64bUgIx4rbmVYPdSmZteL0df21TJPoT3NeTC/Pr931003iL5xH14JES4B9OgVNjc7XmeyUnoYfv/i80gg3TW5vc8ywFyk97BVgEm5U/sN+4ZkcUnGYZuTZ/qb+J6sb0FqBrF8/E6oBC9C1HQogxwqL+9BGsQHHW0sN4Wn94BOc4bRohJaSzxbKruX6YJg9dEOk0fRKJfb7y/Wozy0xhvD/QZWt02PIW0EOEl9mETWOOaWJ0HOxm/7TuxdEu/WZuJn8Sf6Wtssp9QnRA25Ji6z8Alvd0F80Y0isIag652WLvmocsEyMSII3pgzmdzKZwFlphAPplTfWThS30hg63Id1AKzdsa+Yu4PlNrIoOhGz3ugRhRfPBZ7XLuXJ23rSg0mdYXTSgv+vxeFSUkHRs4Uc5hfayyQb79AwK42e7yOn2JCduexzHjXnyvgOFaCXr3xTO/uJ/UbJNo7V7/9h7mruwOmiq1lX7ffts7w0qp1hcTlUmIWsZ3Phb0goTxiZHZzWDrK8Bi/U6MIDKrpvgotBaUW1V2z5325TdyYz4jp1sg5vZ65e12poGrs0dI3MWI9pi7f151xNUWKLD/tVdHpNXfZz/gyvPkiFsdFmuhXVULCLM6Z0f71218h/VLY1oYsI4Kap9HqmSpdUC+H3bR7bLVn8pejpfblRPet6QlTJBgYKTredAw6pxTeoQieY1YQyLm6P5xY67rf9gN2mgbEnNu+tCcXVL9hcsQYrZKuzyyIxAFhOiXaUoUXu/it+vZ8m+fuDtgZAAAQAElEQVS8ZUxBcAeV9XXhpt+kA6Y7x3Hj8v53YxEvrgHU+YBLco3GGt0+QnpeAIHszjq1n5qdM7WNWLGcqyEdIf/C6xYy64a41C9JxDWigo++CPyRsoeWo6YpfeYq7UqVhcQkWxvtZ6Fn8CwNvBgepGudwI6eNeOk2kWSqiVth587JZuA/E8atzAK5zItJLSYwfac+Dqbctqv+KqKpuYiJ416bP5jfOd285+oJWT2PXze/99OqD2odp61tl8PvMgxVEC/m/NlZkswD9FmNmQ5pcO5fHgl7m3XCWyV+8yTo4Kx0DYg3D8S6CMSdqE3cTSVPa5WdJIDWSMoi2ve1OCq/DkSeRMA8Y6QZF80Y+wWzJLYdBoFq15QK0l3wFLxPt918irck5cnDWp40oZ2Jglxyz+r+lW31Zt5tnq+VC8pPHZ959oqNtpo3klleTLE9mRjVxcgkqIZr1BuDK34gV+vKmyTRxsILYLLzw/PhcdQfzk7y3bIZE2XFzqhliAONb/jlCDL8Il9Ip+g0oDWCdG+w7WFHXPLT3x/LpX73AufuS/62yhu9Aqf4JRSWesb9ix7oHThf/nKlF5T5qTc2xxxwT++4tKHH/Q15SBhf79YF9Oxj502uJQyPrga2onZlBIxo5To5CA1p4mcKejlIltLlWHDj+hMroP1UCt7t4w9F0+w/diqEAzMn0sfu8/6PDorAwLyeiMUtv0Gq9qzlU8bh+5u/MZ1+VoRTkzz66aW28RbSdPHkMejPMTsUC39OhYchPjz84m8BVZLxbhL4pjnf+BlnXSSI30pT4I5LoZZJg0d4ngfL4l2XVQjfGu5J7avZ1FnL39u/Of2K5c5XQBsjTxb32d8vwjISz7Q7JmNt2dAITh7Ee60ZYhIULNHowwDbOPjSPafwADazE8ddRLS7V52mZQxCqO3lZizv+6lHBm5QIG7NZKADZxoiCoRRjAA08cZVvOBSZ/bpmHocHNlOrtSSMbECnpn57f4VfElcS7OZeGIMu4FmZlTOb/voYwTRC1/GCkyjoJ6i1Eq6BooS6Ws+k6WWkorvBmovGbq7v537Hpd6KK/RvoyKUPYOJpcIInVYWfJLKuufvfQJph2Dz4zUcPUIPUujnT8tH8bNd06OZJUzZPrd+4SnyJlxY71gAddYcU1ia39wKPWr+pkbkFRT1b+gu9rxqBXjtRRxyqjjjiBpXMWLOBW5rXCflx3wFdAhBS0Ib1LuhtB2W0bvvGeGMRgzVDnzrXzrjTWiPc4D54Hv9t/0555C7swbnMDNpXl006GwQN9cS9+Mfm20lrX4zH2WprzfPNkATpgLSRc7yVTIG2d5GNTHKJ7UnZHrdVf/SkkhGbotlYsWsrTZREBLGh1SA0/EHGLFYpvAWQDReBuqnPwtfaIyz0JYMiN1Wk9M9eutft1jABvvr0pxEIoVIFZbuZ0g+mDUrqe+exZCkrhf5DG36r/SpuLMPU0M6W+VtKIdNHbFRtXgr1Pt6DO78FzpvRhXpW+m4ahzF+Bez0fe27W1VttePp8VdAf5ofgKMLTyPyrgynRA+ZAdEoMdrQFE9ix6zF7TARYHlT14Ht1ugZum4Ph0ZlUMj9PrT4SgBoulpm9Fj6t/aSLY8qXkmIvsxggU6uiGcSSTIlRnRyqJGzmrfyOdlCt/JOJ2adOPdXn9OTmyenCDMabV1+OsPeAA0JGPum7fCsmU0ADAZBn1fNUrcGiOlfZegx9gZoForl1xvVQgCk6prjbAGOMIrk/ff0zd53O1EhX53+oLeNkoPg3QXgwTSbvwztnKk93DVF+ARKSS3e8mmtb11WjZrcsAErBec++19ES2JeVqTTvXgsG2ZS01oyrb4rybInXiWAt5lLbKkaluK/NThh5Xfh8GLcRQKIVkOF9Qrem1jsaFC6BhnbD69lRv9FPv2N8yyl1HahcT/ljcezG8rCJM7ZbNkd/8Xli1c9PyZsYZR1wh9eFUQms5/BryGjB9eGaddW6yzk6uxTr5KX3VLlXzbQVb5/rfvT078fsNlxeLrQrJ7rcjn967kkKWGPXNWe5iO5a3aESHJAgxGXZu71TexD0j7y62ZqOLFOaDQzkjbPySCPYgq35O/Jhi8Y5j+1feWgxjxCvJspo3sHjPNvP00f9Z3SbUWeCRe9xPVvQYcOYsVYnga80RJ3LJxwDDPFnFPrzGtf2rqWTbPCMZ4G2qf5pAD0YSnIjhtxrfgPSmSXszLsGtMcyEUqp/c2+h+ZYUwSd4rEqB4Xa4uq5qrQezDcy1WXEPvqxd5hbhxx4EQND3RfuNfdlkIj9/9T6Z/Ph98LWviw47fIN9Qmp25Va+QRJw3gPsw52M99kxUN/2rcCXm0/dUXSrLCeffWvJH5pIb/YxubLHuw/o1Zy1UraNaGj2+uITYpc3L/mZ1VDzkFEmJg/zMoHp5zhLtn8YNT2cSzz6ro3fbPPdBJNjoC23G3g2UI6uggbuKYHjVM5Si6eRCkpishnfB+m0VZQq4/CyZrsyqnp6aJktmeee+anZqGOfbf9111evGQamEEXugEVQrSzwDyduzoVzbaYdl3k0Sq0lhkFagnwyFuNvaZATZd+h9BLqMpE6ALCReuIkcnUE7E8iZZBedaD8OVB3jaUrrzn9X/NmPDAylxR/dj4hVyenDkzKcGSgz8/w2m9Xw2jpum333bfCNPOTH7SgCukwxcyZH2WKTODNXIJWI8r7kbn3f2a2Wu2qcHq4921pk2Sla5uaVQlrN1rSkMKeCgnEbAoSeSVvpSkBkcoVNPVZsAvczJkqDPGFMcd4lBOpzWlpYH+jNzQEzgpupvZ0w2J//HKKxWZmEZKqUuZRsr+gIK1utiFSaf0BbMiW07iE6E15+ohrdTx3tHQBKT0oBksB32EVEtIy0DMTA+C72HhuEyoBfdT9f5nOrC5TNqBYcAR5yRuipdAeVK/BkZdHZmNMk+spoeZw+1d98TLTNFS1qnrsx5ev8A3S4TOsRb/aoHCRRCpf3jgxyNNEBSMSueCbLKXQh4hWQeKqoGe9yOddnPFxEqlxVw8O97M2mtRmb3shLk3rVKVwI9idpCaThiEdMk5ZbpNVE7C63/gS3FvWDbF07DArJlz7rPv51z/dfzHlbNYN6E5wl3Xwmoouk707pHyB3qJWmFjTIvhH9NNEuxnGIJlJsKqVN3KRxtmDfXl4M+JS7I8QLsTAXwSimUmimKciuDuY9VxTHWkEH1ZrFgDf703GwJtY9ZaD67Ja8oMfoO3ufVg9uAsHIezFvJqOExpvzGR13vrVWp665qL6tekZCfq/pI+eTbyvR274itJmIhYcS6o8Xz3vN+tPyNd735SRZyJ1hK4YXs4wqCgYX8QOSf6QWsY5ogs0KbFe2UfQ5LThTptdT4810oPDTuSALAUTLi071Jyv6MxYWJWPImSNfvam781/5FK0JEM5pW/eBYXcLegfq3uq/SUrUIUSCdnSQjWKpoXG2djhKMRWGIwy1v3yWoF0uidUPZHv94wN5/n2u3AGCc0wVdJ0OJT78OwRtnrGpkEHMT4qesZf8ryzOREzQJMwajWoRcBpXLYQs5M1zpZW5yoUevubuaCfZKeSPRcxM2+8HBlExKYoK2Ltlq0vdECSgCUDFjRhABR/d5GjD5Yi2nLwxkza5GWBcU77ZV2tp/N1uXad0s60JyUvQKKgjccYq7CdbAKGlqjMsjB0HgZbOZR9Q/aG+9riq2w9XZ5S7ycQY4uKiu+Pn4tV/xAzWpeEGW6/OrK5yymyBS9arAScY55RZQvtssC5RQS2QpRUqsyzrFMYSjnDFbCU11otwkC1MnfCVLPutKtMKDycsafs04qOkTMutzId8b9jD09VmpOL1/QBLx2p7y+rgooyag+lsAZrbjpggf6M74ralZNoBybJmLx7KmXKNUm7oCe3cdrsVlSX03ZVgDl3QSjPuvE9VDbWg0qjuC+Php9EN/1+S7X1ZwoUD6kixqf+RhCwhxyYM63Bu6ZtQUclL8YAK3jpURVtg2HksBBFZ/o7y0au6QMjFkvJcXmGDumIGxO+euUAvxrwgxQbtRBjJUn9y/EON7XAZ+xbFHQqOwapt+bq8p5GBuWJvxq7VgfmhRaAz2IzmrSrhUuMclrDlLVIBqEW/amVQOL8PMGpdMAa7h1dcaEFGq0gtawCmTgMX5HHWj2qLFciOVxvU2F8NEeRD7FTwcn7DSeB1vqjQnv+9SlZXtvzu7ofa2jvYhfMA7qzv45ouh2CzpgB3oNNN+e8cc5t3mIdlSXLo7oX2f3qVaxv8QZF+m+3K+xJaP9jiSbTjV7UzuyNiLc6Re5jFQnNI1dN+OiHJ6ZQ6RN2xSVRyRsC58mcg2WMndAfZIeKQMIg8PcLuiLnqttPZc/lx1f4A1wlAGNxd1z14k5rBTk9164EgW2uOXUnLaWDY9115iWAuZ3sEvqgmaNWs/ycpyeLBHsVFKVLfuGY3y1BJtyuX8kidZL14gGjTRLjOSGQU/VjN6E78/15afy7z8WaTdILaeYR93iaBlzvMB72Vw2EgJ1sYsciCrZeEZ68HoZEHk3oZGEzCshBXXo3P196rfna2gO2WJVZEZzZVyCBp0UBTSZNEhDDRkeQ6xaruG0Y1rFdI1xiRtjT2ado7wrWUWc9FNmJu/9QYZWi2kgZtTVo78WD+7RrclD7pm3Okcz22DuIaXAzNFmWewzVojj1k54mNurJcC6kcz0x2z0DwMVez9h46CkGOz21pAauJskmNq6KuKy0kqQLS0CSuzgQkfmvLcP1WiYKHT15FYKDWJS1GvoGTGXz87n5E5RWdnDnkvQ1IVaPDWt66WPRgYAoeSY9Aux5PjefLQHIMCZYIWPwNeQo/bs7hdm4VxMvjhiPLCcJCq9yqdtdPX8WOk7Mh9EWmy1fBjUOkU+NE++wrGWLU3OxVojfwvz8VnRDQya+LqfvrDNzkido/OQnVGdncFZjTVOuBm1Cq0QQmHL2b+DR9dw3SYJLHfSFeR3xTPXybeEL8VP75eMh/EoVEzn5MN+YvQoUYuOzqyuz4apg+mujxhRtsrtLC0Qa0SigKNMq8yoyoiEiAjsaPNtfIDH4mvxONMI5ujuvSsHM6xbuqEsTEaNGgXAstkxJTrO+38DgK2gYF2TU+QsQsnPa4+l0PB8uI0nG7ui5ULWTLhSE+Cl7vcJPYCCnY0+K/p+v3gR5alUsXwf6JBg8BSkH+EfC7p5a02XSZiJcrxum7JMubJd9y9C1YHqSx++vDJ4XLxpZQnMFfMTeUNn3/iKpql3z2XtDo5R0hffIssJMHqYJW1P9yjYwqChszj9lCqQBosO1cF5I2+sx/0B1TLmm9iDI6VLErUQnHta1UxMKJryNSiROG2QUllAAqsRCRZRErEAqv9QVkbA1/m86VP5oA+lQquCtJKdj0km3mNqAGKkzVrWyV5TMQMdChmwLantz6Rms5qveE9n1TP2ffeLhJXXfXdYKwAAEABJREFUUdxKvg0V3t8J650SiT/KDt5puiY6qrzrsmOuGFxhuFfYKDQku6zGoNwclQp8Lu8bx3POUY5gGPOyyFH+BKIxrUSYWwvFaAgrY+/7jKxSd/XUusBr3LY2CpYLJZ+kaWG+gQtqkH1JaMNJTAsjjQP06Mzod2ZX14SXxAC9snRWGNeIfymeWFghTGF1KU57zw21CAbce2FuXm6CXpVdBR5QRKKIMUDO06wMNa0zC9Rjbv12H6F0IsbuVGGrsKxfA1Os8XEXosvOpR4l128snHX7ddvHuJTvx1hJS18wd0tYTrQMzur8GWiYXS0FHeL1LvL5h+HgOk3B7R6lyCzgBjpJ5FBBoCC6EmernZtaCdd9rs+OT8U1IlA53BlPXaeurxyT82Y62oXApiBnoyjgwAtl91KnlzHFZhBdwg5kDU5Ztqssm9eO+wUEqVASzx6QcS27bim1enZXQslzNXYrDKGZySU9B6QneFs0HQbx0ng2tEgfVscMM6ee7idx0ga3BnlrfTrv2BAjZ7Qindk5aUiJCdGjY2iUNEi9eWaEQkrSMi9kqskx6yK+xNNX9ftwAeBpdXEOEhj0K+kNCL09xX0MfVncpueabkqPrvULmjCJZsIA6LynCKAt2hW2bmJFieYJNUm0UXgk3nqhb23CLsgwVKL4INbcNk6Eftssn7jNlFRs+pgrNPkoqvh4eCq+Q0XRyWmHbj4YobAdvJBqVB+uD67LzDnew/PGBYFAWg4eooIdaE7NDNDd8wpfjm2oROsjze7V972du/2pGM0YMnHlODDhCvPiutf9+dzx+chfyZRicJgL2i4lRB1xBJZwLXMuqTzggz5qUet41PLWKBzk4YtsQVU8Y10CuLpxAnk1Pu3/kgGzCYVgO/1s5rda7VxRQe11nb6mtEg4G5xXTG+EKBKgF+/ymyLmFvOg+SJczVdqmz2e8emINipRKY1s+NTiJlOnZAJsUYaa3ijpchO/1l+g3eE2cOyY74W96kS1m86S4ngGAj1fTJYRimNc51S6a/guY+e91aUuwtykKVmToGqoe5XzTttm3896PmuWEe9Lv9mutIitfE+M8MkXOkc5f3HvGWnllxTGPcyZ+mtOtiCw1XfXxzpMzcxToOsitfG51PeGSMraNm6JWKwkxQPawGWjGla8GtDkRuiayIIf+oEVqOkmgGjiWf3+l4g/Ex9ekiWVb61+6SfXSNs0QAfG+f6lllvXNkyJkAd1WyquQ6YHYzlUGfKWYGv0BPcbnW198lqfjKsYNVBt9iphGU7S+01l5crxhKJ3jaAan+Ho3c6r9v2BvjhyU21YtC/moh5GDnDXe6wWg63E4xp8yMX382S3HPqVC/RxiSMdWi5p7qdN+wl8VUBj1YU6OkxpTupr0VA9GnbMbqjlSnb9P8A9iTzG5+JDS30SyhzUtvr6fgyzHd/gN3JjemI1P20/5srvJfecINnGI5BycyIRX9ki8SK7mrahNojebhkhvgZ67Z5uoahbWqlcajd9bL5WW+/XzGpnxisFHsbX4ixNf65pDgZ/WYfeYKb2tLYPuUeL0xlwXi+RjF8dHdnmdH/zIc1YB3jRTHFm9WzwtM9ZlibT5lAf5ypSuxxNHnvLSFSASBWouB5kIadfvywpMgQoWSIfTPe2mTneeaddqVsRWIgjYAwdM+9/aSoMwLKi8zgeUxJd0BtItyOs7aLJbWKNhWZyoNnU6Jb/FlEGzZxXoL1i1jUeozkGEoR8jYKGtLw40Vhta3z15RWapL7eai6HED/2df/Kr5RP+URTTpoovaOgT9n7R1ZZe+883Wei4APXyTcj00AJoYwnr4aqd6G90icvlitRhkMmuvAOBlrDX7uTzM1KmZsFmNiDIqHRzUhCJI1+ay41V5kzKMIeqrlBtKeh0OMFLbe3qK/WR7lfWXGK02SDx9nq1C0PvTADO+55Law1cpbFThOmPwsJZdi7lUbrRJX6XXePpqkbmix1qZ1SM1QEb8pZNSRCsOEtxZJCSIrMLk0vcZaVVRMDHwVUf+ZGb9tcMt8hHVZaTy7uG3v15iSYxLH3kEFAinJ7THctXQE3rTVcWmq9tfFRW9hm4+95wljmArb2vnJQntSZPwYM21Q6MymjM6Z6HG0F7IzAv6VnljV99uTuPH2qd6Jogp9ePbDyS8GGyR5AqaDXdHiDKh8B3MzKZ+pYDbrFQmZPG6VoszZUi+S/1jLU29pP9jOeWAYGEIHlCgp6HXZhZCjT5whvUhsGBXbbdpFs75iGgOakKuZSTreJwJbqEDIT5maifUbO5bCAHs4InTWZEvDrwVELfXWEspCRUII2lR/oHcREVyunaaenrGclJUkZAEkoKMX6etBTMjnr46VRVPLj6P3htAYKrXX5wa3US11+5NpgV7gvwLtlKKgiC8ub7QRoL2LdNB/2XsxwuBu02ehlgN4aX9IGCl9A1HWC+hscKmJGuWOFR9CRnHRSnxBoJaxGuMSPz6xSjvVJkwPjI27o3WMpuHCjPjnUSA28pjmlEeaNARhmvpZdhqWiMuE7L3nEjLdHh8GguEpasaHDbYwsjwnTM5j8cwkjZNEoHsYghyb40Vw6RpdM0ghbRjVU1ozo62m02OaZbGzgKDt/SRRYE8Yj7pkZpXYENN/LPYT+XTMXeZlj1kzIeV+rwseHa5sX2ACoKzdhjarRIiYJnLI6kSMVWsvi6oxW5Vivy2+7OFdv7KczRQ9geP25QgroPtFFV7tIoHzrZLUimJ7poqceLNm0Pu3OU4mCkU4zcnbJLhqmn8ns2axhWqk+WlZxix/5wfsahTAsWVKSFKRIfonLnfnsUoV3l4mj0YjBocq2RymbGZQOdMtFJuZwlYsrN1HCOWCCeA/90jl5NEAuBFLpJaWDuRxuznFryVCNtgqqJMXgiVW8m0qlG7XcLaWYTbElwBGtb7GXL9rVslFSxAupkYLWECOoUKbxtgTltdygfZMWWThJfbB0m2UwQomTXnSSdFD1MzLnShuwYO/3CZpC1z/ozVUTp0f/dJm4MLrV5Z0dBR+nJIR5qJ3rml5RrzhcrzBZ8F7KBYbSkwBudopOYQJHJ9/f74/Wkben1XsSBpWAIM2rTA5MbyC06KoUXZ1GEVDOUSXoKo0CioZ69PR7w4NpGsxd8egLHH2H+BLZMyXGRKl0TBsziofRQGIZgkm+NTSa3o3cM6+IpHo7g2+VfjNjNQLq/QJAgG9wcj1TRMkANFt9WUoLoyNeS2obpr8jsOo6TmXHgqO5NXVaWVvo8KRuXFDWvurS/kcXii9K44i9vHiCLTqGUROZM51U5q0/xhuiasRwdj+sWgmdgfpY23SIkmzqXKqXLjaxOdsnl4aAYhOYRh8teDZFOVJwx/g4j9RBXSRhFPfbbMO3al7LlhCImGqfUQ6QYAujougRz0fY07qtwUEQYT1WZyoh8EVXAnQAXXWyJkevNzvkDN4rDgOt+WKWEW1TlvEFrMakLg57lwoA3ioi2dZ8jS/34zpFY7snXHRhipqkbhRzTHt0L8MLjDPMxpRgH8O8xto4WD4q8yCtruvYXX2j1wR1+5aOgjgbmdBfCmtfVGItDGMt22YCDhXmHFcO3FwJ9FbTyjJPt4nCHJwWHTiKjlcJoYmcmq7hWIwHbh8bXZ43Vg4hinO4D/duPLiybarVcz9ZLkCcba0Fu82EwTLwXS8Og0oROtUq/Gt61URSAarrO1XCsaGuUmPfUOtXixOzobR5r2gFpmlhQ0ta+bTPeLmVROUE33DvVpcAfQSpOZhhy4Ku6k1Bdym7dDfE/WbRlvNjo6JiqVaM3iZAXuNyAih9quOIcAKVVa39V5ZZa8XHJ4bKreWgUFSYBnRjWqNBUWdw0xvaNX717XUNfLwHwn2bzjArzuM4MzCF5JHXVbCZZ+zsC99rERfV41rEV3j7kkPheKDl5i9akzo1W38o/bRBPPuRoCbRx06xkAf1BM4FmEVftwr67bhO11WoDyRIDIVCb+YmFkm8qxScafbMOh1jvv7N+mcRa+Yuzly62O/UQjXOg++3vyfff9OKi/J/INL795x9qg45N+zHR9+hmQTEx/hI7Z+xwMvq0AQCdOUg8CqEhNS07+nPEX5TiMPc91Of7/7khrZ2t+WtFeRzaRZHXOk5wVn3Duy7U9AJIktSXJ4q3PFQ0KNS5dfwfvVD++Wn+v1foPH7oI6Z+SVUaSP9WGszcS8VJdB5Gj1vss706SlHRWVbnZvf8C89R7AV0HB2z/d0nfYmhV4N0p9h7+i9UOZm657rp64FMxuXvZJde6DkT0GaKQGq+s78x/Vd/iyk72B/Nm89fGkX3AeeLdqzveczI7HM6e/0njnho0NVHBtqog3Uz5wf4h/vNcu2mRoRmkOlOVeINxW9ojjayEl5cGOPRNX+1NJzvzR0AELXh2qfFCcqE35rnVyKoatblzwll5jWWX2xoS/4hj4Izie0JJxZz1lbVX1GIM72b/mGq5jE09WjPFmV6K6hjbOtaxSllHG56YnkPUL//vfqPsGqzAQaPumzwaaEZIIZ3p+fMX0SYg10DbJCaH+fb2CuBMMul7W+03Us0xaRC9pCdqAwbW8OD/jN+G37XrUvjI0PxI3RNEzj9XveHoYnzn/qkyI2eMeYCQxLvj/m/yfW/0/t1UozmcSeVbARUjkaT97blCqdhwJvI+7YrmrpUSCODxUTT6GLChIec9FtLXWMjsnd9RumEqj73/Z/kx+Wf1b+XH3hY1QewitWmneJsAEmBnfhbnX9V5QczEu1ALRGrQtQEKjLa60p+hbFuexIBeRl9lPmX1L9C18EaBgjGILmGaIdCyYTF5QMDw1w1oV9KYythjWPffd88Wzb/Wn+9QSVjz1Akzf5Q/tpu7VcVo5SDWwPmvqmaice9/9ibZqS/DGL64Lf0IAXhwyobX7ROaZqiR1S7Y5RdmlSwCkblaN2Jk/4/rlSvcT95ijFtXwLcoV//Upa+erL5wdvmV0ysTqusZ9rX9cT9jWmhr/NJo+NimLO07uzeV4oz8/al5I2LMzjPCoGL/I4yqVtoj1aZBMww/u9xsI4NpFkO9JwTL9S3erHbKgvN9AHduIqDz+8H90odNQhDgSgNG36Stl3Vxezt6CEc0uXP2RAlL2CEmsT1KxHvEPbuQdn4zvxlYVn/At/tBn5JIxFcKhTyvZj0COTZ31O30rX2rXmC3S/KE1vrhd0qevNdebKcQL3QEuvmoRomInewZwLuHRRbw276kxJmqCW6+tNxtprlZIt94f1/qerb1WsqxhG3B1XWtQEsjH6TmwfLV1LXfI94K0Wpl6a2D0P8ARKnfKDUclAzVPnzhjIZaal327jirmDFxF4WQpTJca1Pq6Xwk8o/Plq1SaPocRJq25avswhW2VdVjsyXaHYHnxbfXla/ua3M/+m0Hfeu+7TtsUSsOK1dMqfXN3XMJqTMR2kCQidJlirfqw/6M/Z3PwIH7MAABAASURBVPp27Uk+w5JsCBuZeH874IaYsUrvdIV2fa5bX+n4ZXL/bOSzSvKvIYcAjE5qhgtllzoBGsjBBh7M16BiK0S2H0zqSuWauaxFl6RXMb0w98y/3T/brlpmSm3kSKF3xjJXEZFJyBeGrNi+nvj0/AOkSWU7INXRqBzAOur5PPX5nZ/vXN9Z2t9Y+druOX1+ux6paRmUrfi43V62XuQJNgwMgwZQ9bD7zFalA1C4n3X+T+z72jQ1ouy5mksRFyVHL7WvGB9YqZDy6dnso0ugjDrMlUhVy1OSFIGL4xpNjOodr+N2/Zmz8Txa3PQeb3Y45dxRqVXhmfVr/kjzF/sT+/NeGH5tcrA3nwffI8kiWyL6NXwDaawk9wjHEEe4n0H6tFK6okyKdZtB7Iqnvde+Yq8EHq9z95iQ37bH9gOF1b/TvzwdecX6MBZckQWAwOj3pLYg2MVvXqcIwVfqL/3BzF8fTz+DDabZ326E3838Tzn9fFK9/PhpFsdvv657lvYxqr9HYS5swYa80Cn5uQx2j3ffbB/2QFU88UhAwgSlXvxTsc4yu+/7X/zc825jwm6bH84/Qr1fxP+133/V/9p/3OhK4qiy7nWM7501YOQ9uo9BJU4pljE+t+e/H/+5eBmQoiyyXX/l2f3EHEcSYh6L/Kn5tBT9tbuxjBajNP6vFJbBVBemag9dWcMFqt1CsuUJlEF23BpsvVGk+/KkqFB+oM7/L9a/tjth8nif9e+o/9mcOFtihvTxYDQ1fMEuDZMKT++ZCQu5VEzPt3d1y8F7J2l4hhuzSWHTiMvnDtEMwDSp3ZKcZbHC/Md7XemrJDEwLJdK7kVdd1Jk8js43dWjxTFXpCimHr3BPdO+Od+GXEPETN6VvnuSVWwrQ61qptVce2KPqbKGM6biG1ZC/Yp30THjKTFtCtud4Olz5lvx7XVGkpBauu0e07Lv5gTeIxo2ReuGz9zClrQdeEwTp+oBKz4en3BpneiT+X2ZwYDOVnoUrq1sy5kjVAZ02j6lTyysCU/Zu5iKV7DI5vvyAC4+V597jx9j+wBwq+VjTpr+lCGDRS8o2Jk6aPnQOA2UnTN9pMrlpkY/L55RA4MyMVCZPahHLfCZleaIkgbvTpeqZNNA91rREewV556hgGNUhfxDY5DmBt4q7xgQcbh+K36f1r3a9HbUXfAcZONNcvdrHmGDO/HTgjSt0ArzcJpa8iuYQ3nyMhkFSbMem9RJF1cLcEJkOFysX+MOmKi3qq7TohfiQH+bxzoNevmbxGQQUqQuQ14N7qQ3uyaTj8BR9SLWB03kQBsH1mu8VUZGR3uUB+39F6TN+xlV6Zl24JsQ5fNyYWibln40btkoadcoClgp3hnOMt59RSrDhGKOREAGtJi7Q4MjZ9Vc1R/2Ut9aU1fnZHHTt3PHbMoSV1ymgqGTEFdhx+XFQACXdjKa615vGVgZMLCmDpoWjm2zLcv3+Mal86RRMR7Job0fY2pGlzVgOZdIGfWIa8soR/+uXmtcXcnVyEYpUS5G2tW8UV8RWiXbCcqzim5a4A5eggcSL57L2g2k+GqdMsi+CFhtPmygRr4bI7BC78YbdiEWpToftYEe9Z7XgM0g0V++1umzWBfSToswjllbwlNa636Hvk4D4Jk8nU/xyXjGqvXDJSfDm++rbCp0Nlnb6lmDCxbv9HymTtVRP0ynVpONkozQ4ce94pUlrF7SaqR0tPGUVWGPziZmMN2JU/4tb3luJnt7btZ+g3bthlV7WzVQYGG5fFezes67BUHuGZTFDjvjmLVEzghN9+S0uC6Ya/dJxQpNHKEYuHestI+4uIBPYAX40tJZrPGiD2RZVjQYvEwLo8Vwck1eNRLWPKUtS9WZ2eQQZm7uWNbc3jtUfTqJS9Mk9yu6G9gQUA3xUFLeIXglQzZYSkL9tbFUTMTo0phkGWxnr4arY2Ip0FILGB/qQud1YO1i294pkO8THT8TN80syV2olAeUiyjoNcRZyM+QNJdvRTYNzBvjbaJj6NVIz/nsvtT3OwYXXTyV2cDQ68a1KL8apHvW2WLiMYVn3ZTfaFjDR8qbOTEVaWw7ZCq3QMoITIYcB0wOyNukbAOcQ5mxTHYD1DapKg5QQKOGdThDaJRFYFZKf0fG2xMAnWYFVts+npUF5WgTX8x3zn+Vp2XKCPeFtcZQNS3NvVUrh/LU1GU5ItNRlDtpeaXEMZP6YvkkvionI6mhZdgrjgRKvP8AaNjU3nN2nnev1EhF60LGhvhpKfiwHL3emBSpYGi1cqM1NAYKvQYB0WFGpwoodeHln6Pfdo20obtSykuxNd8Wb7hjuS+Tqi8S/QaHVBnv4MNouocvXZWBdusRaf3mPzk5VTODSdG8khdoQRFc2mH4gW+yAMGLnAu4iaA+Ese5F0twu9eU4Oriut5S8jNEtseZ67f9dJe/Ff4FHrQ+npSs9WjigmG0qRYgaooMr5Jy9HD81hTsKeEcGR8LwyLc3uOSApxuU0iltoeazQnHMo2QoKCswPMljYZ78u79OaMAQ7ktusIc+m57SpUNb1vHrZSImoXxRTP55MuVwrCRRdPORAkhRvLy/UpXUetjkqe9RqWFK3Bf72/1Ib4FoOcsbxGIZIO50xwWbXI27Mx50lRbExdN2c+GMjMzCgrlq6EKq5a674ErTu2VVQFL749PsoZhQoJ3WjfJZRMNn+IUClZhHeirZw3dxZUy14vhBGdGFl5fMEOrJ/9N65lDtmFB+iKMGXOuaW+iTLNek6GNkh/HaCKEBW3y/3poGc1ZD6NZZke9+aUU63b/oX8gJV4+H8BGI7z6Tii9Fh40Qqdh5pQUj8fjfkgjtV0yKgkElKOuHGsavFafKFB5AGm1AISNG+jTuhF2HIE7LZ2ayVZOpYqon8qv+tgZTMTlRrDy2n9hiovRAqabgxe9TImP6tzaPN1K3yg1Gg68BpUjG3WIliWg10tINXeLxjmQgKmEu5sP2EGBhMCWQKsp3xzBpMmWspmhop06JTloYBvGZtZoJsfwzg41uNRPv9EvmpFpU+xj06Lc62Pn9rOUtpJtgND0QpeVXfYTphREK3XXeOi+caINafgPnn8Txs5j5+vzrGtr5cRPTFyom89ivliBGsnPu2MnqxTsQySG3aoPlBnjDaCN8zIaRUAI8mHn4BmU/nzr6ifuMwqqO7XDyESW7fKds9GttKiolmw1JkPEiGWK01LaWvrbd851wvZaezkC79PEFev+4WeJst3oDJ/PPesSPky/yFUUzhFKn4YJSiqMDduxjnm515pz6xCL1n2Dznvwk72oumI6X4/XPcqtMtHyDJiVW3IgSdV4DTdcVTNOLI4SkKiXKCoupZzjPv8c//xcOGDvwDb5Cz+XLsrajkkJR9EOJxWMgITd5FXBN3MzeD0YKK9JtJaBy1GhiCYqfkqNhDZVGrhUH+xLAHkOc9ukzG5MvaDzFW1Fv3U4cbSa5vgb6rzoL1X8ytvsM+uWFyjmB/ezYl/Wy3XQL1uKld0jUu+ZMEJOevo0zqRx++iyNvwxxnMhb2ht4Vq9FJIWmu78O7TsswLUOACaLqhSzZpjVKx0qZcgLT3gHyrzwulAzPDoypqFXE/F8VX3gmZN0zEGRNp1uPI9STbNKY4sTOxFT6QKt9/zjbBRx+JFaKpMr/JAUVxRGp5uZtZWzc4Jw4dxMcKIy97FgJeViwvfUF4B+KS6tJsxpmI6AEJNdUoE9Zgsl/IBbXqUlwO0sgXpxO6WAxkbBdI2wxSB3pqucK2jqUxQDnQdAMnl6/5Y/cO5XuvTfWa+kiCQ5AzkVJo1NeD7LS2RJ9Tvs5G7KXsreT/mysIM44SYAdXslV7OaR4scTs37Ba08aT9AjXGWYPPM9YJPmc9++qG/CXyUDuqt7jnHScYHBkzIVKgikv9+f1StLcSV1e2dY23nPQPcedZR54nw/By02dnaijwI2Hb0/j94jmaU2ZSwQN+96pfF6EGhaFZ0xtVmg0RAC98DlaJMElKCSr2tkdiTQ6cQRg0QSkWLqjgubh69JxfSVX+m8Ws9e3rqwfL+5zc3cnqyXz3O+a5IgMp/z0qcDE4b3IYA81FmRl4izFr57vpIl4f7MgzlWU4tvasr545pD2lwD5Qh1Oda2JLCeXPvn63uJnSrmZ3tb3Fed3nc9ulxzYhiec1j/plaLeTocXWnVS6TtseV/fyUJJ24tuhHU4XsPfkPrufedXLFIOulKcAG/QaVZshLJf4HSisiYTlSJ+jEaoEW6YL7vBLFTsoqOlIDdWYBo9TaYJ0pUwIztvMXNHj0363yBcXfq11BUlAZE8P0m1TEYbSsYIrSmZ6ddEItSd8Nl23NMemiviu9ahDJyHGBYeyQKY66DXvBI5zOF1v0saL0UtVDA9bNGujKofaueKYdqZI81lkAAoHB91z5nSXFczJcGG6xz+Na0ajAGIYy9aH/GFxzkEdAb4lZT2xfjpTFvy8zMFAcSbF7Wd/ax/smsqRp0EX8hn9fh3BYyJwe0tl6ftr0u6AfblCF7DB2OW97oMQeHtsNkYZbe09tfDauNGg1SnWSG+zdnNPjWd9kJ84n6gfXj9cPn7KjqgbYEBO2T7+ZB/p5rblV3i4TYiHk/N0aYFpkWT04M9c/zIW0CaJ/Vbq1izbgAv45CyRbr4WwrrQBzw9ChVaEiCFU9C9BSJM5MM4s9C6rb9qWf1MhRY8AT/WsDBFC8llHG0yM3gMD6ykIQp5dK/MyFdtNVMiZO969kYeE2xtsHRaPtiDbg1rjAzGlVe0A3DrkDB0VhFpyPTFvjDOzpldIkEo4CtpIqNSRjRmuOai0qH9r6yAxGtiC6SPdetpw/X451n/nrhmET5lPC89ytGS3lf32CNUM/esn7cP/JOuLs7dScsOlM3fdSAvO/cH62I4Vho3KHse5uFc+tPXknaampCsQ2u/cId9lLOBioPot7zehwwr/P3vEiZlRDwfL5gNvXRTQ0FtrDOXZhm0ZUolFs5tTBsABiFbBctocn5m/tksq5A8IGFcoSG1Ji+ewLvx4Ri4xFGzATh4KQYZf9Z8HVsnizZhuGxuSdlz/FGQa/aNuqzkt5pI0Wq7ec5I3FAuRTUPcciWlARIorQ8FhP6mvi+pMYCx8ec1IgeFahhrigVCp3CQw1oVkECU36h6XM6FIByZg2+Y6o6T9Uw3dtHLQ9vuehbt38LS/EpNK0j98Yth7AJyqus9u7cWkUC1O/Zp9WcEPSP9gDb9SXZVzrDbLWdp6AN88zh6MqxzXeZL2La3gALlQPlTX9bP9fkEpKZfLXq1XHMeuHyUUUtmfl/cSVailEoDmhP4NmArI70pbd1vNMpArRMNdWJeLm5ibCGJX3mgnss+kKFq3kGy+y4n1hKnWaCrXqmQxMwKvvdyO/gUdZsgBFFV65IqOyewHchiRYAzRjCXSyaQfp3nYZYIhNr3s4bA6pXNsTbMsWsDacUVhqoqROphsXdAAAQAElEQVSwiGlNvVB322oYuRf19VnN9J7+vipL5eQS8NIJrcg9lLb0QBcKtRkFgP0eqIFUti27IXH5S14lUjMMxdZQOxSsUMBhvaysJ0baAGBiwpRhgro8RQzNRu+hX72mgq2OLzZh8rhQaohrbFpsSl7sdyI1Etd25KpSW9Q8Z49xSHmJlw3Ba+iXtgT1U/SW+i7Ygga9QUxwwolmt0HISNWGfTJ+8hJVCKj5wsdfNoDybuWIm1TMp6e0VlSbwcNoV/N/cRX5tF7etWaEd9SfuwUAEabDnUtlobh8ejrh26+9rpb7TXxjP/48U3XCJlZsN6ld7jDyE6MgtPYOVwWPU00jHJciGE77K9ejPMcQUy39d4+iC3eZo01ZYbDOEolJ5vratdXyCb1apFwxy8WQarqbbpe3eeWiWkmBI9BZT+IBtbyr968Rh2M1selfpUqGBtHbqFVCBBqdHZ7LklYdp1eOdtudlqmFUBwssw/nxmt91yRIhKrKDQprRKM5dJ8XSgHQ8+bdtq0j0WnnUo82jV7L1Hme0bOKgrstRQNKqYVjiiK8XJH5Hs6LMYntemuTzscESkKi2mBX2RvBRd7OBbBnDmRXCTrBNB8uipyA+WOUxSqqBeWgW3UQTQMgtupMqt1URJn1yaqdpscPO9ldBdVgPLyUTIrIsOeO/Cjyy2CoVwZtKhIoddB8m8e6Tr42tUgMyvLvf53Eo3FdhPAJPVGBQ6thjSWu37EjUD3WGFADreeFWbJsBke7FZYkvQdbJPLQO5weoBc15R2lhFw966GAVE0yR2Q4LVohttXff3hiYuFudXzlFNKigWPWEzXS5SG0vm+ocQBoQzubitfE2jIukD7jZ0S7TOKetfwCo9p3K9SYDuW4uVKWu71UI09VtsQOD4ZDbqPcDeNzbCSrzpm2lsXCNAjYhZYauAZiWSCm5dh0YVT/5L4w1soUhOUlTlRggDqYsrJIj5ahnC4kMAoPBdvuZ0UZARncS6lgjyMgVS6XMmNwTp0+x+ss2XuLdbqd4EJb5ck6OllgOoq61pPAjjkXJaacUfk4xmmm7mMtXG+KYMf5LMrbNROdxqZpkJH3sfW9LZfMboFW9BxHGjK6xCkAzvgTvlekQm3IGedyNYci5FVndChW/S5SlqZ3tKIEpWSJY6a6mZpJto2sTJs91E3LpRTO9Ok60wmgX3lxFPZYz2rTPYVaRFc98C/v5MUIX3x7t5xuZFqCsFbzkfVCULSNNywLqilAoCRmG70DDBxlwZ2650FHKwaNVubcNQKJbj70/1ao58rq0DRJaS/5g6JsQvrSb0KlL5w1NZJD+5u71Gb74qhiLzNJT14WeAHhQm4GNTuJtqBX6BN8n7vr1tCgqfJCwd1LyiDAlmq23f1nQMBFVtj48nEDRtdXjWthTRFHSKY2T2Mfns1Tg5FFuPhOCjs0Y3zK4nAdU3sAjLNNBdFTFa0BFJ291DNFXPUuJUa5sBFrPW/6QJ9Wg7SjocHlFhqA7jHrcFkkCbNiNCRfQL2hxRANGinB3YKQ4MHIXysEDEhCSOGUaxETLuE2B2gDDDSaM3QopsSULvmsm/enlp9Bqq652rXA37Lr1vpz14R8kMlq7vJnqGyNpem8od9PulbUP2gU/vciJ5lbRkee1Hv5emiNJybANVLQ+6fvlhwBl5Szq3sLcHGkD+uwerk/gW224QeUeVEZUHAZKznKEdkk8f7RL43Qb8P8oO4eT3RajQ3JoQwXjnws54eXjVQjZmKM7cDr2RqW+jyyH3fR34CpMzOwU1S/Vfa0wPiURradY4ctT6OeyhkhIDDJbmLUEcqpAFLhN6Sqh2/+aMqmXVx3a8ezYAF7Qba0+MTID2RloS5qJU7snCxbjLvCH8Z7S37Ervw1Onu6oQgioJi2L63o7wmDXsoSplBoJTnvUehrcVlbdiV+nUck1H3jvk3nzzv6cWSMMgM02UGpmsH7RQrjSWhrJeqX7nH8KlzTV0hh4LT68YyoU5dQ7XnwGiGCeHuvXsD9HhHb+pGlOY7SXqCZqcmNNfOZDvE00Imc6bG/pFfhbO9W6Oq51u0lMDJv8OZqOuAuJrSCXgv3MjcONGaUw8qqKQzf7qS1+Uhg79b0M+emQLTBSg2jXWUr711esrViLtIJwmkWy32BlkqvDQ3ifEEAdd1ATYmGUS9ApPue2OWniNMqimqAZdzUPJ0bezAJKDfajGFpqjFMHOXq35hvtb4ojm/dTL9SrSeti1Z+n4hag6uVpuG0OdbC+1LdljB52bvNm0lnrRi6ulvlPuxzr75Wgb2n8i+Ky/Oyv7VnhBCxE6ViSdFP9OvARbJNue/WHzli4Trw6bAxBM+FhwpINd8DhINmBpDsqXevP6wWlcsUbCknQk0L7ZSVPNMYenuUaQiwbb6yNijJQK6i6LqPrDhDAZsmMIpAP2g/0JBuw3mPUQbUpa8nrDlZ3a+G/hIoOEMWoWPwvnzkcAklNDgmmtbXzCdznfQzfHmSt31GR8/4H6IJ2CghtOP3w2sTW6hnMnteceF2/Lv9Rw9WdVuyqFZ0rNpi7CYlKgMxyXxr3LoBVwBW7iNOXV1HrQtQdkC2o6xHRjDusAtjUzOdMxquCzW7dEPRebTf8qsZRmEUeEu3dk0ow4FOpOxxMDNFBQbBmOpUnfvl94rPK/HrUIdcoU3XqPZsCUN36SXqxgBlSY/29mjdz4o0uuVCi8OrGCTwFvle31nHFVr3TI7DbZxt85rpTlRZcU2sMm9B0kTJJ+DMZuLl4MP88XRvTTql7hW5PINp4rAZgLR25jy1tyy+2mBNnHeRNPlR+11dC+nR5VPa+5oGTE9jSp1evq9SQ1veVx57so8yym3iT0zc7bcK1FcrgQ31e6Dq71CurrkUlUydPfuB9tFUHkRvx4F6X0TxalEmfywpx/44mrsQrVDNuv1k79NTfE+9bkLxFXdXTnZnIWt4xkXK9Jn+EjlAqm5A8f5pLZgjutqbn4zPYFnPzOtZ9OiDRGnDEitFoGwq0H9i4IhEvxhB5aeXIQK2qJa7mQkZ0Q8nxdVn+LG4xEc2epdUZUbebgsmqS0ihIOyyVau1h7bo84x1RJ6E2/+XfYNixHt2CMj8OKdl3NB1TCeVMqXCDm62ixne4w3PCElgscMErpdZrf5crze440rdfG4Etlq4JrV/DH5ki7EnA0+hpvEaKXxSbwsLelgknsqremaRK5pn0L21PyVTL+vREu042kuveMa3mqouFY4P84AqS4y7NFf8cKWimIOOydOUt2wMs/F51Yjv6qydcpOQ/mckhoixjRXBaeIQrRgvwLxSClQ31IuNMYS2hxJ1h7bbUyj02tbahehtR4XvI0Zmb7TUgOI4Eu9sZWmKADDmZVxlU68OHrECoiiHsEg1TESU20NGvKmlkstTuZH2Z22KuNpOxtdlcS5Zy9R5dEfO/IOrQcCypxu+aNTCSHhBR9Af+3647SnW9jyYil4tZ3RK8t2hkDq5i/s6AzLWVq9xECUxYRMcMy1PxV5Qt+DUVAwWzQfMVJUCC3ZkATZzSmiJbt7GOLEehwatbOVab3DqMsqlX27dlMbS1HCclnefe69Q7gAjYnLwpfZcportBc5RF7U9qp2T1gndGVdkqgtDVsRP+4fQuSV1Ch/NLqDL45ph/xhKY4sJeA92TZS2eimToPfESbvBX+r5g1XbL3IU/JFurfs9cRbNcwjANNKUJ0h6GHORV5AdEM6nM7BBrNxREIbHBrDHmvRZD6mP3KcCXaR1e+14zwLMumYNR3idI5Pe3L2lfkvEQVIPFSdfmquVvoiJ4RCANqtL1dNvn+hT7CFWPLsLdNDgTHOdc1yYeqalM50d3NTFWIiklg+S59eVLX1G+OCURK7kmCM5XJm3dNS53u8ihMm2HAp6jOl8YpDlThnzUONHQU1jLxMxiVS4p3FUuCcRutVGvPSpXvF9IRp7ZzVbc0jbsTaO2VahKYsNggdQafeqB7Ym35EcyFOe6GHjWlZbU4COvRDZcAGgMLHZTpI1YLPoLrHalRBir9dldeix49M9cwrqgOm3MnSU3wEcUb57DcXnn9IyIaXRNdE/bnVjfuxVqW0TBhK7cy1dJmwznD8VTepGS4ZTM4IRUG7gKkQHaqdJEWahYZOVN6ZQkvIK6iAt3nrmmPZXqY0siFxQbz26e/O/+Z54lv2BZ/wvbzv9mlTXPL2As9wNW1ryP8h+oLUIgeYFqoFceWYD22JOnY2d5v5LHULZ409g+4zvbtzDIzhVULAGc9STbisWbQthvvCjRSvR4vJOL3W5PV9omgKwj/mxkp2Smi+VvtKKfUkpwdjU6tLqxNljcugnB+dFUdzcW7aBdcyj9IlqxDHIqSBaMYJbpl5Q+eY+txC3TMIzeIoyB5xwCKTAOEzplTSjGvBjQ0piA7IcdY6eee+8wSEiK6UH9vAN3he4JebommaAsjWjWDarOISqr3q3Pmoov/luksIDsRJ78mjtIh6jCpJOKJH5toHvKBvC9pTIQtSJ1ShGAGECsttj+98yURSadEuopSVKQ2H0649S+khsRZNkQwwrEAGjt4c5paB2T1nWkmHUVrVtiTaG/FlpGles22gBP8cLdLG4y+Sknyeq8qbo5S7yMNLdc45J/N35vE8ds51FPuGNEShqZTYe94/2s8erUlWqX//Vf8OSpIw5acbkDBNsLoEGw1F/VT+35ytC1EjEfYRSeiBsBw1ihBYG+0591S+kl4cZdS1sH4tv2vnlQM3fd+ubrP3oPVXOpMLoYbaxuv/VYLGnazOus7z86vFKpUU/OO652Pp0Cb1fFln5MknnQOVLaMQZ5SOetsCRjc/lgU2L5jjmtBq6+6H2tSa3oeJcacr9S04SpDskGx7tw6Pbncy2J+ZO7HAQBkK4qDSMe22adWmuWmztXtdM03qAii3Ykbc7s6Hs6eROmFl6903BVNNBtKtknC31El724K15x3nmrNOzu7WdgUf9yusQlWZM2a5fGwmcx7j88N9SXn1rSPlY0CVToTwvpRKb6IsjBN2VuaSGB7aMe+WbAEa6WwFqyuFxbpdN5AycHOupgt9YwIqdfio6kq7PM9BpdZr5bHKLEXONW0AevlxlEfxR+pnI06tectWdM3UeUwHCgoiTQ+l+GjW5HRqGp9l4wYlyq+VsdJjuMZkOHs9dHxgLtVUJzV7To9oM42JVZLhp/ADtb2+qitXn2DBG5bDqWZtxvGr0+KxK9c1pgKil3rf8m3SVz5S1zWx4NFOpVunS2nP5wePB/pG2ag9RZVbcImfkbiDifvgUxYdHI3YMfqetVdjJa+OCyTxykPj54aIdn8/N2ZDhRVnFprN1OrcrFslEz0yQ+wEIF+yAlcBoffnPLdECCsPGb4u2F0T5yh4DcZ6FaBN4NAYeuqSAQYjDJcV8f7P8bKukdVR/MNCHyqoNXFrjP68pGoz8Xym/o19eI88KA0hcvCXl/M9OQAAEABJREFUiZ45UK/9MfxEr9BFomYSPeQoPqPeIrO7BU5n94xqtVxOIYseTc+Jbstjf/9VLrmBxLwkZ3iGvbcFFDXq3/PjiEWoOCNfxKdN9kQTjhbYe/EyaubsnCxaU6YBYmJ4DbXznLQk4H0TPxV3LTFjkEMNusw/Ps4938SWcn56Xc5ZdmQMsAHAU55qtNu0un5rjn6ydd4V8W2SVcljmgoQsgsUbfeqY69sv8NtMjdVnEabMC2FgXZTY5EPq7iObundxiGH0aVsann4Bn6Vqb5MgJramUS1ofvTHXXCqgxDBQPv0RcDIh4SjMXIFnqvb6sRSG9qGb+5n5pR2gaL29Ga6oKaxtEOKsLUIlPWZtUs2Zt98OzKXfzt+KU9SGfe9ZhiayW1aWw98rjfD15zz1w3KEtjti3STFJIhQ0V8rY5oDxk+Lls/7vyXqqQzjrf796PYocz19+YYb6D1SfQRKBDRV2y5ynaVU46PePNZVw+WhYmWBP1OFtmpP0xyfA7s4ff498zlalrVjApzp62XRg8Ye+X4jf9Lnqi3cpRndrqlU3f7NvwuoAJB2w/rUiWuORxn7Awl7BZNKhFjC2HeRaPSiynX60YcGmAz7CzmYZxeco955vVnTNjor1/dnz27bM4pgnYb2qnK22+9/W1+E48pNRkX2BDc2sKqUgIXhbxoZxhrPOqstMm+6V8Qjr8Nf5Hft9MmJI17+rOXH0ijVo7/bJL4tmDrSk9kpKEYY6kDd3gAbGtkny70HSbJP93SA7ye5lHRMe0Il1mS9NE3XBkgc81ai9nlRio8zknMFfF5xuRwyOXR0qdVib7cj1OrkiuL7jnwfqdKDUom1iDi+QlOBRve3o3zo79LPEy3cHyTkr5UAYt0lUH4kgKPpjaw632YBfdY5bAnldrqdTqSs1U+cCoLe/9mMvmOtRhVZPTCV0cRiNas/PihDkVrC++dq2ohSVTovdla80dZbFy3cfXKXahhVsKRi+roLpNotP2ia2dVAFDiv3Ghbp5bmkttYhxxTX3J9enlScGsDKi3v+cwtlERsILCvFbz/892iGiMMlK/+7ZIgpmvqA5Zdj0L1CCotL77/9PxJ8oyG0j26LoigiALPnutiVVqWZJluw+//9H15qsmifJrSQRw1tU73sehcokCSCGFSsCINPdlaOCH2JJ7ZynqMI6alk0c4Zyc9IZWCX3wSdDKsuspfgw5MuwES1DhUTOJcLLQxbMRHoQP1sAhl2eCTEXciLgtRLZvUYMNYYuS5foLVWH+Mo9ZJdpkr1emzWEfawQpjVEVWhb8XIVCfFkevtOVrcM0oms5l1ZZvd5sV7Puj1r0oTgFMpqneSHaSigrD2dcOyJPFOfuY2nBMLuIkPDGaOoinTH5vCK8ubSh5FMplmgajpN50QSGlBsk4JkMuegbFLChXROyitfW6ShsijcWM5B7vXoOpqm1pZB4YhFyAmBiFZvVaYI4cRWXiKuWGlR8WbNO+ytStMpJqtcOKJG9DUsNUeNSO7RA2sIozIVmqgWzbYqCtYGmDk69e3JmEH+ZyWQ0JQe6EUDtJuKyjahFyf0mrToMAhLoSAlgDhlSKpls7CAJJtA2E9dBn4rb3kFDapyKO8AKVsaRcqaFplaKUDphKYiGeULVpY8ano28Evy6YpBmmq4b/vAkeLOUUs211aTY2+Qt0Y3YUlBGIK0mQKFyxmEsatWTaACCdUx5U6c0mzRZ7u2BSljVK4ihLcl+eySCm4+mHFAIN3LXOlTusRW0AhOJ32b0MmerUP1p+UcSdNGYDjDQneFAGiFVWgEZNS0VuNSOvgsK71JqK2YQveyz9I1qsZYh0dlSUL5j5AiR60/ZA1zUmOeSucURkoDJiWqEONohNdIr1xqOEYmKtuQOVuV7lC7SJphw7BDJEQaS5eVsMBuY7dRa/CiKLLVnmFPKZtEaJDGeynqXXIWaNecgU23CVBGXIhRFYjSYrI2OAPTBjTUqKlEHBiwMIWhGUwHzZPkbAASiKgRsSQXTkGarGZrdSZtpY2WOS2trfMavVQoQExLcsjq6ShQQK0pa7QK6y4GwXYMVk6YDGlJgtmWmMX7AppF061W4Y57hZZZb3z/mVP5XjBgJZRBS9AC02oqor2kCxrHbqEECR0iwgf62UgOJMRlSpY2bNu7zfNAxQ+CnzJAH8SL5XDe5d66GHbVR+1BWqOFUjAHl6o5xGSWubdnUs9yJIkzkb+/AIRc0YEeLjsCpSMn0p7VzGAYfDydg7OLBYbeC6QYGc1oAzo2wOfa1lblbS9WNN1G5AB2QlFazgkVAEmerMuFWbXPjbttj4hRjJgbhCbEXhvWa9W+q2lZ4e5JU4VpswlhaJgdXVRoF4XqCDJOUKWS2SJIC1palGUiTB8xZ69AN5N4t0TS2mjCuu6aiSLq9NyyjAZGIJEqZVpbzoB51DjHKHlxhEQwIYgjwC5s0fDSonilXRypDBzrKisRd0EJ9pXRNialcRRjNbgh4uK+2pq2pLJEKfWBR20uCSBJ0UVdKW3IFHQBBTg7wpFRXrQI3RhKoV/sSxGyUyJ7rD3daIjRYdusCfaLmDYT5nhrIa2gSnSUvqMwFsbAh22DOvovQ/gUqgEpdnIyNowjmf99JB97jUGHQWYx0D/eKl0LrtgOTuTDfAO6QiQtV81RmiVSvGw5y+Z1CaLFYAiF9GQ5DamULiynKuR8L555R4mKz7Vy6UVUGTtVck/RTE0sKJNDAlPUz/g18QmEvK2hg2iBgMJTBi3opWpG9LblV3JqTgc4OEuzISbwMw1MWXL4p08JcZldnkUptVOZqDIUEiuv2ZtmZdpENtmhVjQLmiCdupAR0zZAIIxpjKwohi+ZMQFFMT2ZUKBf6eGjwqVcwUgk1x8qIqjA0oy5VVo1VhqQnRE0YlWUVi4jM8JZniEKa8Jp2LYsSa2WlObpGRmSYkFCaKZltArBzw1uSI2iyK50QnTK1rxXIaVQ6rAkm3gbPUKpj43BtQZ5ZgzM4Akl/7QZyvmqTdfeuPNam4RKIbNyCwGNKaBgWxi5IjdlXNJLPIzGu+oQizL56cYEYSPFVq2UDOKtWQisLM3cOA3K8rKAjOADTy2ipXti0qZE/0msUUXEWIVWIEnnyGI1GSPHUhrZQI0tV2OMGQ36qDO0M75GV5OGNEPvYIEwKitRMHVEtYt2qHJ+C/CoymA1reowwgX0kknF6JUp1ET5iUpwBcKKttSzXVlKA7tBr8xz2+e0VewHSIFiIeSC0io3p1qIhqb+DF/r0pUCK0sApXZUwCM9CN3gUtTR+2AFNcacNJwA47OA8clU4r87N0QmKaE1miRdm+qnTEM0pAqKfMgmonSkTa6dWDQ0esc+ERiouIHvgHcKVxETM1UxQiQ1wScwPZyIF/looZtApaqftyieNB7iW4FUlM2OifyJTqucAYpB13hbMYnTO/65VTQpIurUKxAwKD0Bl1ygUa0IfhFOK6QL8YSqSTGPttKwLeLeKrWs9a57KjSHV1HBmNASX/oguYQlw710hMVIJHm2pu4WPkkSdeubQOf9jU9Y2B/lI0YlmatEgP0ayVZetbMNCSlpqxJ9D85QmAqgVVLhCnrlWg4v/GjTbjLvDl0nGZMWJss+SfTJWWzRTQzULHS/rCrEXCbXn86hxBXbdkY2GJnaNWD0X0UagSqsBClZ5rhIIsMRPvnaiv5xRZaVkmyO3qQLTQbRSE8ZQTMdzrzrS/WBJLjq2+pIUxClFBsgKUKFrKCFgshcvXbOvr5Yj72g8M1nRdY2aFJMHSqVqKKjdNJMzdV+SFtkkjI+hTxr1YzBb5Em7vSYdE9aoJklQrpxaoNL1AYnhNKFoVr2VqcscDGsXzKeIfhuUUs4shgqAORRm1S7kqycJryhwhwYrFuqTtls2SSId4qtXBBrTnS1bkR+Jb/gEIelTmJ7Jq0JoJINZZkIpCisa5uttWwaaGQwtl8D9qCtsRNB90ECiHD5y1bVxabeJ+UykhakV5RpTFatUS5EBoO8g/tq5WISU1sm9SnFRldmS9tDn0qUggVN52lvmmf6tejquoOGdMhEQUxs1YyqHIqyUsn0/JG1077Mlo0ODPaKdm2dsoLOVTEw85C9HzotMCRnNc2uMnW1qYGelgY5shZpIuxngVlmGWakGEI2SQoJyCKbM5sKdZBW/BTXdNVQ9mBJXysqSzO0CtvBT+ZjKuhAKhkru5bZi163jgQ4IIohxpriIZwvyXuuusyaM3HHM0gPhZSTVY3hwlTQbWo4FmCZy/eKD3LeVa13mZpsXkfrQV8rci0uNRADBzTpRG9EqzSR3kJoFVYjYIwGTVuJboQMBmw1TYAgaHThdC0WpGhZHbAK0WHClWldllqHRtHG0gpURuSyRA4QOBVTzsimIImZQOxlJUDRjqrOwGeimrSphNrYODuzij1YC67Kn5BWQfAfzWjovfrkMHrQZp3miYfyYun1o8Ug8QTiqCGgYkyBxkKzzrVOkUbJpBEFzWD10FadkKwWu1yDSIWAotfgTtfovbahRivWyh17UlOIhZoIlEhJJmvpmtgB0YC9UpaBSXQqMsikDNKgE+j7CA1aVWEt+jORCUI6itAgEtALWuzJNpy6mxNIeAMfdIzLBq9lSXh4JPlaAxUtODJ8V8OjMsVTQ8yEL4wm47N8550YMpwDYZViAlYDxse5BcsEk1M0e1tnqnZLElAk9uCzoNF+kRG0jmSzsEzbCi8AxkRomrhMIiScNgttnh1pU2ivEq6Vq/oqSRWREkWAZGXSyzZf4QY6XAkTTGClCvvBAIzUEvps1NFS1IUugBdCmGifVllUd5miSfTgBriUEkG+RCBDhanRJiEK5P7WJZmaFFGFEtAgG9nXkoWSay0PrFppxHPYshIpU9ATSqIlmsZoT+LSltF9WKYFBU8991ppqobRtKYwSWZAESbwKZZXo3xNB4YIZTXBnrhpldI3Q4qBRimi2a6mIZbQOW1ee3dTRzJKPbznqhjFGxzOW5KiZb3arEqeVwrSWlAw+1Naky2URQYvPi/LtDq5hIEWZdQxWzVyzlSgJtqhvGLofiLWAEM13jMVKVFsei1gBqXlo7gTnKgvg0GI1CHTaL0m2CyCVm5bviZDk0YFIlk6XFYnC0JkdPO95pyRW31SmVtvHCg0TTWt8+cFoQUuuVqsc/hs2P4vAtC4wu7R8ynQy0VKTCFNdBZrzWAM8A/HbtjqOtYJW73SjYiNYwXlGGsm016l+lzdSgApbH6xEKtA1tKFhIuwwZfPlaPHksXH+eGoKKHWXmAcASqk6mzNJ1mbrpOWGXFhnIZUNjGrniFL5q5GwrvIPGGeyJfSkoIJJ2yGd8ltpZURI4uO92fV9sQahEgIQgKIqchzoRAVkBjUZNJMJyUXKtZ0r6A3HdW0tMHKNASpwhtjRloaoiCrTCkzZK8whTTaMmPdAx/XVpkrGlI2ewerpcrWr1VtmL4AABAASURBVDBAzdFXWNA3vj3gt2qxoxoxtCob3pcxLauOXbHmqxfvEgAqRUNNmkI/xEsJxTz0Gao1ckO3u9AEmQSijIoeNVd11VRdsR2V6YEsbHWvcu2FlkBpJrVYMS7GciMFCDCJbXRKvsVug6MITCMkLIkkWYAo8yObY/bqgTLlXgMd1FkhpLUlgkBxxVTZcWche86HBibQqDnkmWgTQkNdYTVasqUa782W07xiGt48dA36Qyth2tRm2J4IywJnVkkOq+gQA8CkaZoMJozfOu+miU+omFZoRPGAAiIUFS7jJ7sMyhyX9GJx3j5k5/wxbHIhkzvjKilk4sqa0CDKf8YpmxxbqwVMpJtKk7UPNxe6TizWAebpWjlIHEOBDsRKvHwoMSaq3UVDt56J7J+m3JuWZ4Kppj6mtlhfiDdKijE3pBboJrVMxqhID9UAEJ58d0bki8NKkYYCLeK6CbXtBoNhyCIyylBQXiq8FecqnehGwsYE+mB7anODssxUuiNpaWk1WTaIM0ng3ENwahXK1dB0qyiyQiLF+NIOmIhRF04qJEgBDkuOg1koZXdRqxRZTdeugxRQZvN45j6PoH/F0uDJXPfsm0chGEq+Lok1mZr8qYmFUsVYYZUlbE5tpVKakITSNdmSoAhdlrvz6RSSNKhjmKypI5TkZI2YxFs4C5z4qFyj2ITxxRZLgyoBYku1ZJ1VhGRWGbhZmqbqkhOKjG9hIlYqqylXHVdlsnIOhLGBOJNXQyyS3LZchMt0CaQCMgioNJU9gZYoaJgSqxBukQgo0i3pmCmsGS0zAREEmkgzg7BRCwMKFP9RqJREtVWbq1CJQYWIOAhcOmX30qbYBBbtbCrGr0QmFGiiSsOVPMaitI6SObx1NSt6AEtToQUVyogQcBHWnTItg1GdIAgTEkAXjpw8hUmnKp2/txGuVilEioOLDpu0qYwLKcercMrW6LEUhDCM7EleYk+otYOGlmeNAoYmBZfSzxLOBN8aoKhWk9nO+NBq+gcYaYifPV6S2Wq0RtA5uzNJi8UilfEw0BxYCAOoUSLCpaDmvrkBYWyCbqTDOTpFqypGRWSVgoEQLthrJz+oozS1VW9cZpNYblgks6AiBySsBa0zKsB20DHQSWZ8NfCiRGgtaEdNTfaamEyVLbIRNYhtoIg5BRVLGTTNti4pg3TnCjAJJsbfq7ts1X5kro5RlmbF2EdwCS2X6kJ1wkTkwJZc93pac7ERQqrYGuKInMZPQTXRsNHhPUcvLtHbso7mjAQxd+ZBKk9SamMhSyp3Dbywgmw3WEVogngQWnM1hjxbVxUexRENoP26AaKDwQ0aXUVw4SVBKGdEIyiUrgyszIl5gMExAskGyMY7V3dj8DJQK8Xkpk4AymDYXMuVXZvYMik+UM+zqmlKcxNvic7IidIU5mytLZ3EkQisgUgUjSyxBp0FTV3aKkppxR5RlHC0SU6tdWGpadzzJMtONlkUHoIUipkkqbIYTzNOAcrLo5JdWCu5pYrY/LRSCzWX7mHYgIxML8ZzMPQOrLBVEFhXMMJI6WxVogIVxcRWnAzGS2yNXlCzahoKhQgvVHizBwtLAz1B8WDAOGpzUmgwoqghQfM06T4JJyCKuyryRGKSEtGg1yQquiajZEXqhNDQKHqzIlalWSkMUTKwyhRE0hovg7QOExAEOuI6+OsYtJT+8l4mk6ycJiWvxFLmspZqSQSZvkZ2rCUDjCpPM2YLVhlmTVcOLNYLSfPgW7caWiuf6PdkaZsDKQQRIz1rAUMpyWsluXWYBPkT1SqbwkyljP7TAuXBFNOeYpDNoY1hQNJeDdEhSrn0sTzIXDSrZ01IhgzSkvgtOi1jSrIttVMYg8DMi46gSIGZaAmCBPCQSmmQVqL0HjyIIng5gSHVFlubSmfoBBZuMs35CKV7VaRLaVAmd1IudCaRRh2W1rrr3i73MkwzJ4w9yW7ITYlAhBZtIYXT/m1Nr7ZFqGHU6s6MiEZJ2Th1VHO1aESaBGgxRJiVFV1prlVNdGGwWPMqxIJU2/w0QDbDu9gk8izwzGUODtfVNLoJHfYuLlW6BYDfJFc3COjBxEKShUQlklyTSeg8PSdIiC1uS8jqymybkDqpKZ+AUodvTHQSR1o28b3KOaWnqCjHUAbDSvt1VLmkmW8k0SQH2KRSM028baolRUStTdp78YGVbgBGDo6MhAomEyu1snTORDKNOmfAJCTX0uJwrd5ceBrgrfIoMJ4pMktyO4UJSyNrxRMa6O4tQh1MjTIXS6PjRutpSlGRAF2IFyNP9kWyWFJBzFNJow1G8aVZ3AE3xybUsxI0MtVSJ6dtHI8pSrkfhksDmqYpZ7VG2aBvIxlKKEErqiW3zYT+BgXShJwqZoluI/fW0aJ4PyUpMCWyfM30AA+1ogPAJgzCvMyBEVg1gh7rUGqIws+xgBpKqLiIg2wIUKaQa75apHHQhowl5ZqITYk9dZ1c2MwnQicQhTDsYIJHYGyixVuPPvnUlR7wjghJQzUbsQg4yQ8mSPRGgTb2+AjR1s2SUiS1tUG/BC4SLJ5dhSSwgbbSJBIlREZyOFEkyLyi8BCllZXkLbKQNKpMwen0r7ZhKqG2ChO7QjMgOaxG/USvggRzaMlkqmYV6bsVfJIO+lTbmtxoV4fQmDIMo/1SnfZRldBsEcIuSlNNmE68G9ldlToWcw/PH4WdWEDZVwmSeQVoPEoqm3MrwMTtXcwaTVCtZtIwgy1sQ2wXZAkJnUYLtFm3BlUANFh7o+RwzZABWZgMnIeGskoqZSnnwBWToxMC27YuRQe0JkmOKpRoqWXrPjXGRlT2FLPwLsHIZMlgPFtNNtX2qMfIbbo5zwUp0JLNFEEV0kFUBVIqJcYBLuq8XzQ9urAbPDhBhHPLgRJ+JjRE0jRVSGnHSE6zQqEN2g3dtFJGthRU1VqMoVYzdFVhr2p2pKhgu+jC+5b0fylneoV232pGtKoWzFM+K0SqNrFWGcOielmZ/Hwp60N9aj710FYl8EREFZuu0RYYSZrkWh/yy8AcAoiLrErDHJIyuxK/iSXnF/heFOshlAysHWIXlMfZob5Y/aeVT+D7sYmhB989zBV7BmtwlcgNPJrgNi+2RyAS1FVUXSC9qo1sARYXlm8+AQEFJe5AQNbqw0kBSbQufTbyw5davRI/B8T2rWbSrBSpWbGWs7l69ixGYN1baWYXvpVxIZf0pwYKTBHQrox1id0PDEH2Xn2PkRAMYFdE3dWkdzSpbRqgEFG27ayX08UEixm9UTGlFRXClwSjRoJc2RSUsIatiy7ZhkwwndTnXOE/xAf3YTOkAyGaE+OOllAmwKJSJoyGhEg0rrRcjFeux2m6U3jw/dVu5HBsfANHQ0pahWHjqmCopDQTagBRKoAxEjEU/6W7Bt/3jSm8OQ1jMdjw1blJ19oGqY42LW1v4eeku+5hBKVCsU1iLTUmH4xxqKwSUFcT0mVu9kuROq7MY/ZiEp3EROCNoR5cOZQGiUN2wI+GdQZhmVSaigiDNWihQjlXMMRpIHR0XTk0uB8LF3o91qpd2qp7Y3rGdzg/TNfJufGfbKOqrc28WnLy0EFnSyk1IKFNFCL0ZQkGC0UecFUtm1i8hK5TLxs9CkBmsGKQA5YGLtDIJWtxvuGdUmftfZ60z1BKBJOeE4x/AIpAMeV8YN3hR9oOLURaKxYsRAykd1TnpJT/uoI1moVMVUznIg6yxLSOeSwGItIkfsmcF+jac2hujNtgp266VoQuJuUaCwqYBbNZm/YwzQxDhKu7uInPsnTd/mvmMBraQaos3AYuk/kk/AGz6zLlWlw8xujiky70eNV1J/nDY1kW8k5cZkwttegGN/oWxbjR5WA9WzA8g8bJQgJNMGN4d6122z3GIyb4TBcX6Z7KqjYILNqcanTJrbJ3NZSMwAow7OimpiVICS3XKLpdrdf8S3WypjNHwP4GmCqEfqlwKOUVy6JVqSgHoLwhlEV02gumNNjJ50BZqgali6BBtvc2xvBJVUFqk7cXMUdYiYSo18w5zD2+AF5DM+n7KKiUTTmz3AJS6BwMHoUEAuEVSy7OygmSQMQMavRSVCFWQR6uWQ4NlbGnS2+M0ugUmi6xs4g2FKutCULprDKmNGZVRk2jV2pUukYWGYOJmVUyGFyi4uzDxhZGOmo279PQPTdjkoRFUr+LrDUWWrFycQpRGrKJoPWVMG+qkxFpqGetNrIPcpbRGKajdW17LBpzc7VFdKBxOqdRchAfmmAsoDJ3ESEkUmiBgq2QirT01lI6oM1MGqnRsSjdFFfhEOGHgFiVclOX24IBMEJGID10XQRLEgHeqrQF8kMkFADzeyBirOtIh0lRiYCUC1IrJWoqm8OExgQkU+hygLCKcAFoJZuQNjaMhMVXrXijlTY3Wa12PbxnaRbnNvrLp7tVpQizE7GaGkyplpZwkIDeBNVDHLoKygoIJDmfrOWMbB/cLk2qk3DWQGMajVxY/sZeWVMaxB4Rki0252pUrZpjKr5wpbQ0lVlnDmTyUHQP6dkVkzSh582kIHz88ShASiWF3zAIcehQSAiXKVKopPInB1ISTCIwcnyR3gFUJcLgKBouIaoBWWUsrId822GhjBn6oAIMQXLs6BKMzyLc7YBbjpFSVbSOlWxexYZA2JqiN5raQPAyJd3BFDNBaxJNhslukhWkYIZ7IEYWF+0fS9GRZto2B4CmPBr6Xk18ilPNoaRTMH9kSiIStKYWqyFJOfxmObLu6C5t7X0867bXphk2BdKHC0LMUSBVyzknfFliLQNBRbEbBDal0jzB4Od2TYd4a3MMAh4iugWGkWhJTxl7KZeKytrQpo4McRHvyj1AAOuwbevTpXoXLk5L2SgjJmtBUytcxmpYuwVaGzrtUoYjRyFTQVCpFQVKB1k6pfANcmuuLTcxMlBkZUqmySq2iJSw1I5WicKafUivrphI6TWjfLC+ZEV4lsO1RpOkOuQq1LSVGpaP4exO3qA4kBJ7a3EB6JDipNqyuss62arNnfKDya6umToEQUqCng9sn1LNXMmwxEjyDd4qaDvNZQ0bbU6dALPCXkKbODf7QkNQJsFlcFJvApuz9WFMHbUUKMhHT03pORljh4UVKhqIksInjMlCFEkPV/4FK5XR+kZakbKie2LNeuFZ1S/gOinicyz/U+ukwkwiohBixIbaKhYT55mJ8UGnC6/p3Y/g4loFabQaVgDzmGtsG5yJVfGjI7q0GdOsbVY0jpnAWiUyGhaDA32pbV0hUOJRnCklXKGUq8MiWAn+lsheWdQ4+MngrVoJ2YruQEqRZWq1Z8vUfsAYVcI2t+qktQha6Zy9o6lKNuFnV5daEYvWwmSsRr7SDZ5OMkwFMolnW13ZQbbLFlLyuwWVgawcLuFazqEqAUVpVeOUMkuVIgzYldJa86WNhWm6GzaS29O5UgsxUItVTSHTyPSxho8U4lFJ15x6CiJhmSYizbSTL7zboyHqAAAQAElEQVSunGFKHV6AlGh0Q58wNeml807mXXV3EqKpSTThUBRr9diIsK61ZHBqODIaItR9a1gNLlkyhCH4aZ1iAdOvFGrEsGkoBW39RhdoQhsyuVj2zYpt2bMQcRvVVjRA1fvesInA5FCySnLShlJH+aY5Ah4MM5Gkq2q8NGFkY6TuvMdqXFpbCUMmmBIE2/lENXGfu3ClRO5GrY4UqhJB1hgZmQ2sZU2JTJUHNXmywm1R0ib0gs0khUEgE9UGiheKydAVHYpUEIrECm53F/Xtx3xJR9F28iEgoWICaVyRXI2WuZQrQnqkZYKHYLj5qiMoDNWIoSl+at3GhCYNFAkmq4vARLvQUxE3qhGl+MqUlQMdUmllnQaKlFYAFMi7BfrWdEzKa6UsByLVNls4ZqBWIfZeklqbW2Ue6ShYcjExqY7Was7RfGAlepbb3CweypmGZMSEGpOLdw0vZCnCLKWFSK4tFiZERluLD8MqnpreZBXCaFmoSIwoRG0vqgtIZGrIdhvlrXQP1mXPtBEcynYhTIKI0pXYalERhHOQkpSvEmJ0RsnY3VZDwi2y+I9uAU3VSnSlzYFc2DUlDFOpRXnxWXot4aMXF66IXH0s5UOY9XspexWUTBK5BLZ93xp0JFZWtL72RkpGo1kGLj9lqALrwFoWqlQgtgqvIWCq7unPoSWCJtay92i/lO0BiVjJ2wWyiAyuA3soE8ZFx5q5OENG0KumzDZqC/DqCKfMaa5O1119dY+RVVINShWusd0ryhZ3RSgUbCWQKq2CinaV3tW2yvAs8Qw1CzSttJtu63krFgvzobF2bEAo1Q/JSFrBwAQEEB5Gga1WicBQrFOWJXVahfA0CJEkAlbWTBkQCLWrMLVasQx3pTAfMdYiMZJR60LHpAxu5eBn4FlK08omjI20ms1bZCN5V9TWQghigZHVSfvUG1VJH+RaxVILcd26IPCIhWitQ5LS0QvNC1EZCle6aFFE5pdOa5PjSd8ghjFoIcuHpXTphknBULVcn2U8Y90b2zRX7klFDS21jWY76eQSWL4oP2sgf6SEGnGYIFNAk/ETBkdUqmV2D+WO3n0XYFpilDMNCcecaABrwVjNh6esaiFCAdhs54cqWW6b0aUJhpna6Gt5CZFspaW0XcktEG7w2DqImEBbcLaqNFR3p18gzsoXOrJmW2Ov6DPhq26poKcrq0OK0YU0eguRyKCtzrQRZ6KYV2H7N4gujUNA6JGMhfIleNVordjamAYm4LLHZErZOcWwOEhJU+KEoklCDEYIWUeVjHzMVmrCvwJJoL5i7ODcgi+gf01CRLysSqRYempC65gnT/hgQo6cY1ubEUlOiibxUIHRclktvVdapgFKYnfNRs3plFjkCYHiDMyqXRm7xlHiQES1CMvwzYMSNCGqDGkIpLlMTgj56LszZs+i5UxDFEDVTt5tzhVKUPzMZKdCtJuJSWb6MHc1nbp1ek40w3PwX3elcVEjPYkqGVY7mj/JZKYisiJXTUhQEy+LfMm1uKmtoC9QFx0bLYoQO++oOqGqjtFkISCmFWybAJVs4pY810IzZK/VctHYCT13QfTU7SetWqbtmd4Jj84mzaQ3GgNk6vA2grSGNFhnCFlSyjeHA/TamtQ85Wwpk2ytt+L+3zZHoqg9KYQMBNVxklFxlnRUly3D1tJFlKFjqeih3Y2EZE2hnTEDIOBI3m6WU2Fatu2RkpdKtKfivOCQcm3DzBWDbFlpLkjTpU2haE4NQ3YlhIXhshKBK2mDQcVVEeJZMIdlREOYeEkxQ7JqAXZio2aUIhClK3RAhkqFyChGGTFG1Votipv8yYY1Vw7PiZb/TOkfufyQ/NE1zERVKyvg1FuoGrYFPBg1WsBS6emI1SJ6SBb3YeaMAspaEbaA0mCWJ9ZJ2pfkn23JMWUBtWNm2QoFiw/rmOYYLAkgoYYJxUFKbYB+pEa2hZjZqvXDih4In9WNi7gA8KKLliZWZgGjemUtoWTpCYnMcPxYcuG7DKyES3jQMUNIoAIcBApiZOe02bUl51bT4IiOUIFwOFKYqltVrSiixaCKBjHjfehkzehgKiAOxi0AE5I4uOP0nSwLVqaL8xGxV/WIKSheKHk0FO1UE2nSZpe2c125ZGv69mOfDAGBZ8GfAEUlilwaapBQrMCQoJLqP2JaGXzVmeZAJljR7FYlQPJrXlRHd5m4tyiTKou0jVdzYa5s4MRG9EbuIkZGUfHaBRM1BgeKtyBZeNcJtbZGhITwbdHxkKTffUlhQrD+mLc5QCrXCJGisSaqa9Ru9bEhIcUkSV2GBw+VFbYUqNnpTl+COciZP5Za3JfJ172x0x+7/PFjXf3HkFUlNRu5WypK2Yx6SHP6KiJQxjDTg5a0ahzIE+7FonioEG96ruFcfWS35sJUBLX/U/W9dGH6EbHyzOC/YklbY6tgaVE20pJV3hfUj6hRYoouIpnO+O4gS/Y1KCS9FvS1WOSkFRCptc62spgAqtH6IkYRnBWthJTAuixkCmdMoW2ZbOkxdKyZG0dnt8YaSJgiPZaJ9GQYqiK8Yq2F25uRsQ6qTcQgvmDoKzkaERamtVfFp782SPioNiqWCnZrRqMbk1ljQTQbvS09Q6uG5jJVTNWB7Y5H0k1GOkBoszzzR+WacM9WPjdRnbLNK/Z2OS2uvgYFURm82w+x3fBcHTvHiByZg3LgCjJVMuGjUiTI3MLqY8Sq6c0Ta2XlsFjMdzZ+yDIwDD5lio6avc9pFLmuI5fd3rJIrQUCg+FeIwYBywxGoejwugbHjV1hEbgtpfx1dnGpoh0CySoOi1yoEs7C1GORoMHOru46RdOVkpGcsJ0w5pJJuK2lMlgbIIMickWUUMky16rc+ueSFr6RIqo4wS3XjhGUw4BtuVzFOUv5CGDVvqUnMwtDezARQ+mM73z4ztSboDu2DvFeSRM9N185BoPIDXiER9EvgKRvMCvQvmwRtkZ4rhWDzNOszHT3THq6QDaGZzk5bFEmNDUy2bI2lODp6zIqU4uCBQUkozhKHEa5q+/WEbLCFpXilFxzOEiFXLQNtKrGPSO7UoBq4FfuYv0xqNO2YRle/NNap2CsSQepADWyzE5ctmSIQZpiNkxakKQ5S9YYkFF0U9MmCobS5JBswgVp8yijZbYs26KXq9bSdUwtuTHhYxb2OoWGptvg1NYgPAcluulAEcyV4M3bWhWRWJqO1Gmp7qUR69jtho9VYiX2taaPJGwb6CWpxMboPQ1MeJU3D1s3+8qSWEwINVL3x1oL6TiCMhjPlUAIslfMhDfDRVZGv3MGMmWLpzcpgoBao3YhHshCoKpVzZkWnqsvawa10REiBnXEDyaZV2zuwj1iCXJrM1lIqJ7NEsnwCPld/2T+k2OXRQqzpZS1eqY2Laa7LQk1iCm9GlU0L5qqbHEGD1q61rxz9YwU9/qv1M0uYYbQWHGqks0FaqRa3VaZJB15n3I9Up3JMhoTLzdT15X2DquVarH2Wvk2jp0Fr7XKVyRL2Dp7zFvsSAAqrYIqirUhkxIzadrWvEorWmaplrQoccfyw3c/IlipyD/WKQTNI43SjAODAipEYJo2yDFEr5hAuv/wHOEystEQoFCZWkknS4UCGMoWo8ZaI0IQfB8xyQyZUtOndUxjmMTWSMJ04XTkSGFc12SwF7VdG4s67RyJTcbQGjYidznc+S3rVLs5vSXN6P8pkoxcch0A7xEA8UIkJGQDcNCTzCnYqtG/0cu58ljslOk0kEncXFqhiujoyslarkpDV6ZgT7TY9bHaLrDTWiT/E/jPGutYM1ZxggVn9nuRSX1ZSbtcvDzZpVWCGppRGGvtotaqcLYcUoMv3rfnQ6SGx+JjRFEiPDFyDF/HghitvPWUTOYz8Sga2iKfRU2SsRdLX2Osug5EZmOYjLuHCOBH4T8luwyaFKvlMq3By/yBjCpaSyVVmaVZfaMGVpl8TLWKLI70MinyoVpQonPxk+LDO02tEbIm1jHSM9zSp0pFFnpVc8EKX+ADBYjxbsICtG+Lp3PmECO7Szb1RpPhs6RqOn2jFATMB9NURbZWpRWtQiPTgx/ZQns2Es5Ri/oPDe4TaYEADGMtu8Qu2xJE00MLNCNtijLW6QC5wVv0kFqKkKsuvYgfvOhEFuNYnpLJaolWpUkzIrF4xLJQoygRWVql5gbij9KVBZjjHLuBfyKDo3fF0tF30ofK9svsRpKhFbXVOeceSrd1bapNg6SPrAWypFUWoiqSzhAiaJZFWiZ3N3O45i71B51jTnGB5Az/j5AoWZmEZhRR0hQLwtFlKAu6MMhVgcjK0irqnJKM+EfqH5fdyLHSH+f2qAw7Y9asrsO7r+A2TteiOV5OtzI1RtVKcjrT00LmKNqlS8bwJKlpw7r6iBw0YGWIpqzaZWUCxCp88TGWQYKoe5EkDcMAuk6+rukUrrGTXBBLVO4w/7C9H21jlWZyJCCjbIt3ZgoJQu6Cnjn9G0hSd/qx9l3oIKhgcRZ2bpVjcQ+K324LKpqsXXYiP8p/JLdWsa5CXkvUPIyhyUKWl0ftpHYtd1UZNaWrhM+5zPJP16Ujp5y1NEbJTmXl5rbnDONKK9qYEGYM2Qi6En3NnrTIdplLEkdCBKLwY/XNgMGQrLNEV4hEro4fg8BWJvfJg2WuNMfki65BqhiJY9E6a9SHq2+fbu4vb29ubu+vLr9dXH//ev3Pl4vvX78+Xdzc/f10z3bxcHd1c311eX15dXtx8/Tl+p8PV98/XN1/fbi5fLi7vPvn8uafq4tvlxf3X6/uPz98/3j//cP141+3tx/v766evl3fPV3c3fL848PT14fvX+8eP949fbx5+Pvy7uvF3eeLpw8X33+2bx+ubv9+eLh+erh6vL24vvl4cfPh8vrDxc1fl48fLv/5ePH94uLh8vL24uLu48XjX9cPHx5uvzzd/P149/X+7vLh+ur+7vqec+8un26/PN58eHj665qK/vlw8/TX/c2Hp5tPTw+fn54+P93+/XRHM75ePH65vP/0cPf56f7i5uHq8v7i4ubr1c3Xy5vLi+vLi5u/Lx8+XX37cHH/1+X9h7vHj0/3H59uP91+/3Lx/dMF79/+fXF/9fXp6uu3rxxwe/v5/v7r/dOX+4cvN3cX97fXd/cXV49fbh8+P9x8vbu+vLr7+/7p4+Pjh/vHT7ePl5ff2b5Qzt3dh4ebv28fr66+Xd785+v9f77cff9y+e3T19tPFzdfLm8u7m7p4Ofru78urtk+Xd1/vfz+9fo/X27/+Xz/7cPt/YfLm4/X139TyMPtB3r6cPfx7unv638uLr5dXTx83YRsEz9e3H2+fvj74dvfT49fHh4vLr9dXzxeXTxd3d19vL/9fH/7982m7vMFkX/4ePn94/X3TzT14fHDzdPHy7uL6/srYvX48Onx+5fHfz7dP3y4u/1w8/Dx5p8vN//5+/bbl6tvf19+/3r59OXq+6erfz7dPv19fB621AAAEABJREFUe/P15ubT5d2Hi4cPXx8/Xd19ur79eHv75eHbl4d/Pt08fL64vbi4vby4o/y/ru8/3Tx+ur77cnt7ef/Pxd2Pz3f//HV///Hb9dfbu8ur73/fEj0G/eGvW3r97fPjP38/fPv7/v7T4/Wnp+svD/dX1w9Xlw+XFw8XF3Th9sP1w4fHbx+enj7d33++u6Hevx7uPjzw/O7i9u7i6eHT09PHb08E/Or6+8XtP5eP36+I4e3Nh7uHj4/fPz19u3x8uLm9vrq/Jjgf7r//ff+fi/v/fL395+Pt90/X/9DHzzy5uycOnx+vL56uvz7dEZkvj//5/Pifj/ff/rq5+3B1/eH27q+Hb59u7/++uLu8fLy6+XF9+/3i+oGefrx7JJifHr99enj89HTz+fGeAj/e/Oev66fPRPjpacua67t/fiwArv55+vT09PXb9+vH71+p937D/8vN9dX17ddbovH94+PDh4ebv+5u/7rb4nv9eHv9cPPl9vrD9cOHy++fL/+5uPrn6vLbFvGLp6ur719uvn14uP/w7fbz083V083F/TWt/evy6vPV7dXNE6n48eL7XzeEYnONyHy5uv7r4ubDzbePj98/3t/T/i9XjxcXd1cX139f3H65fLq4/ufy6unyglF4vLp4uLy4+3hz+9f93afHf74+ELfvH24eGOW/bh4/3ZOH1xdPjMI/nxnli29/Xdx+vrn9ev/ty/3jp6u7LxckxsOX68cv998/P/7z5ek/VPrl9u7q6vrTxc1fF49/fX34cnFPdy6uHj5e3W1mX9CM6y/X15/urz8/XP99d09HPl5ef7q++fv6nqZ+uSAxbi+/Pv598e3L9fevj3dXD/eXd49f724/3d18vrr+fHnz+f7xy+PdxcP11d31xdXNl4unTxffP19++3z9+PH+5uPD7d/XdPn+68XNx6vbD5f3ny54fvv58vrD5fVfV+Q2xf5z/fR0SZpd3v319fbD1c0Gwu3jX7cPXyjz4frr483H+6dPt/98vn36/HD78emacb+6eby5u7t+vL16eri+e7q4vPl0cf3XBcXeXVw+Xl88XX59+nz/z8dv3z5+u/3IBL/99vX2P5ePVPf98/3Dp5u7T7e3n+8ev9w8Xd7cX90/XpBR1w8fLu7+uiLm3/+++ocSLq9YgZ8unp7+frr7+HD98fr6w/U1sbp4vL56uL6+vbm6urv6Sge/fbn8/uH28cPNzde765u7m+vHm+v7m4u7+79v7y7vby+fHi6evv19943If7i+//Dw9OHh4a+7mw+X139d3DI6H66+/333/fr+2xUp/fjwFwd8u/3w7foDnb2/ub4mZ54uLp6+3pP2tx8fr4nM39cPF5ffvlx8+/CVwb39enF3efF0cfn09803+nL9cHN5f/3l/u7jzbfPF4+fL24Z2Q93d8yL1fPr1dOXi7vtzvX1XzfXH65vPt7cfH14+PLw+IEgX3wnT64uKPzxr4unv664drACf7u4ebq5JldvP11zyvWHq+uPN7d/3z1d3fzn4uvT56v7j4/3Hza/nv7fm4fPFzeXFzeMxee7f748PlzRnofri5vbL1ffP94+fbi//evx7q/bb39ffKPNX+4evtw/XN3dXl08fLz89tfVE5H8fHd9yVmPNxcsMjckIdvt1cX114ubT9f3H++ePj9R8j8f7r//df3w6ZJkvvl6efvl/vunh+8frh/+viBo919/Uu7L5cPX26cvN/d/3dwQhA8P/3y8/eev628fLm/+urz+65pLzPePT98/PjLBnz7fPX29e/x688SYfrr858vl49/3Nx+f7j58+/bh6dun24dPVzcfL28+3N58vH/8fPf49er+4uru8u7+4u7hy9XTxws6yOx7+vTt+sP36w9k7N33D7f/fHwki26vnm6vb26urugHM277vLi4/fvy/uvNw+XV4+X108XV/eXV7dfLm0+Xd5/vvl08ff/68PT5+vbDxfWnCyp6urh9+Hx39+Hx4dPTt78fv188klrXl483fz9ef7q5/nx5TYFfr799odK7u78ebv66v/lwc/f3zberu8frb4+Xj49fHlj37j8w4x4ePz8+fXrc/PrA87v7L9ffPl1+++vrw19f7z9fPl3efL+6u/v0ePOJ3Lh9uLy4+3p1+/n+/vMD6/bN56fbi/sbHhcXrNUPF5f3tzePl7f//H3z/e/rm0+313+xXVx9/Mr1aLPw8unm693Np6uHj9f/fLz558vt09+39x+vHz5cPf19x8p2/fn+55TLqw8Xtx+/bkz4fMdq9viRKfx0++meKkiY719uvn+5u/t8d/3x6pqx+HpzR+Q/3d//dXP318Xdh6+PHy6+fbr+59Pd9w8s17c35Pzw/Hz39OHh7tMdSXhzeXdz8Xj95eHu4/XTl4vHy4vbrxd3ny++fbr4/unq2+fH738/EXkC9c+Hm38+fH38fHFzcXt9fXdL7y6vvl/cbAz5dPvt090/X+64zn67vPh29fXp4vrh0/018eGYq4unr18fPlw8fbr5/pVBfPz24Zbt4e+72y8P9OX+09P950cWtH8+3v5DUX/ffr+4f/p6f/vp9vbj1bePl/98vHyktL+/MTGfvl5+/3rz/e/H75+ffo6/+ka0ry7vLq+e/r56+Pv27uLx+uKRI2+/Xj1dXH2/vPx+eXt//XBz9XB7cfv90+W3z1cPF3d3l4Tx4f7D9dNnCvz6cHXJMd8uv337+9vD33fXVzccf//34z9/3T9+eCCZby6vbq7ub/9+evj4+PT5nmsQjX/69PD46fbm68X1F6J9/fTX/e3n27vL66fr+38unr5/urv7eHn98fr64831l+vbi4unq5vvrEhfHq4/3dx+uGBYrz/cXH98vP3w9I2Kvjz+5+Lp6erx7uvDzZfrx49Xjx+vb7/c31zeP369//bh5uGv67u/bm8/ML73P0+uH/+6+v7h+p9Pt9+vrm9pNMP36eLxr4uHT5e3V9c3vHNx+fDx6ttf148fru4/Xz1+uvn28e6Ra9bfdw+X1/98vXj4cHH3183Dp4eny8frSy7lj+TDzfXT3dXj7dXj9ZZKd7efr799unngXuWvm5u/rh4+XH77cvN4dXd/+fT0kZbfPH28JBMeLu+vP99cf7y8/3jz9OXm+9er75+vHz483vz1cP/xnvF9vLi7vr29ubi5JSz/78XNX19vPl7cfr6+/3z79OnuG+V/ub2+vLknZ75cfP9wcfvh8u7vq8evl5ucj/f3H7+xKD18uLn5cvl4dfOfvx+e/n64uby5vrq4+0zzHu7/erj5eH/9N9sT93X3nx4eP9488AEuhwePDD4OlnuG1xjbzdzuZlVlJYf4cH5lJt8hZJazm614mhn/nRg845/HNp1TODFqG+CZWcFLbO8Cigd7ncr4FeGesUkoj+AYz/CI7Y8nHpmUxhbu6ezw/x4ZkZ7B8ZSZFZ5eFVk/veDADKoOyBrJNniZMWJrHOOZHuGebJHJ9vPS6eMYvjX3ikj+8/JID+dcL2oJj3BPjwwPd6d523d4RMZP7bEJjKhgL93lSE93XkV4BC1h40kG71Z4BUVU0qoN2J8SPLeREbl9u0fyrPjn7hnhns7P2HwpPjYHL9njmTwJD6epa6RvFpR7hDsF8GTwNGIbluFsvHResVVwbvKmB+dFuFdGefDIJLjsjXD+i6RG2s/mHM+b7PLkEZHBc95xd37+bHTKfx7kzv/2Un5GVLKLxPPhbPzgvUH7gt3h6eytqpE842DeSR684xleDGisEasHzYjK+DnMM9yp1bcv98GvTI7hgK0rPDJ4PtL/v8v0zDU4PYj8TxalxzaMsxanp0hKjk0q7eEsfnLYJiSC/RHb+P/epwtsUcExbDQ1trmxqavcRjo7eW/jJwdQ4/AtgiM5Zmu+jUrepHa+RfGsyPCIzKjaXo555uBlZVbxrkdE0QX38Eze5Xls3/W/JyOcjVqH+/CM2Jp7sjHBKZU3RvoazmEc899L3uGUTU7WCGpkc4//NobIR8Ya6RkjIviV6TzCI5J2Ji8pa3hEUp8HVXmwB5XFzvTYhnrG/21r5IgI3omgBBKMXbwxhlMM50Tm6rF4Rv3v9IiM/L/n5Z4c6T6K07ZJTl0em4r4eWznHs5zd49tBKlFCRHOKSOcNz0oI2hAZgaPDGfjiVdEefDSeYdWxXbfOSty08s7nMFLNs+garpAIR78CA/3jAh2pCfPPTK9co0cEVE/70d4/FTPq8qfR/h20CTnpXtkbuEgDpTPEGw3fkJKXRHpHB4cw/kZwQvnMA+P2O5wuvvPuv1/ywtncFhQqY/82R/u2wlnVHgNT+qOTHo3nB//2+s8fq4UWclejqYXK+VkOj/DcztxnteWQMXL/w7jHeeQCo5wCmNHZeTWPN3DfbhvZ7xIHuHbPefoqOExwtmiwmm2R3p4bje5aWNKOi89Mmjzz2G5DeP9DZwsj3COjgjnvKDw7U5EVdGx3Aa7ezj7w5mYHFBZHDcygu/9IFvXNoJ/HEYt4ekezs+xqWU0fWzTg3e3v8wR/r/COS7TIz3D09kicvOIf87btDo8wvldWVWeRQPcPdgik0ZyYkR4DPfM7ZM9Wwt+DK8YEevWU8ODM/jHO1vL9Cp64RFsERVe7ukZI30bEMHz5KdvJzwnaOHOK1RlZSRbOb8qnON+Nk7kOT+35vRyuDtlB3s5JbZv3kjO9f89wiMiPYLyk5I5OnPri8isqPT0kcF4DY/BDnZmOCcFP7egRJVncN7WHzwSgqxk48T/fwtC8b+NXe7lnh6RGe65tdimOC8pI5IfHsHL4IiiGeFUvzE+Isp5N9MjtuaeGR45wp08QXFgbHd4nflzsEewJY/iLPeMqBgRHPnfC+cEjmXjAA+PqKqI5EjnjAzecn5kJAcEj+3uGlS6nXjykxiS9VwRyv87LN23FpkcFu4ZbBHubBmRq8fgZQZve2znmTz3n5f5s0AH70dkUETxL53jfTtzjgweyb9wHpvBua1H9VPmNoVjBmfUz/OIbW7ET2EVmVsXPz2GZyZ73bcP+l1ZucbWRgTvEqWxnZXzMn2E805k+s/aRcWezilsvLONCQrcXB4c//81Uoii46eirOCQ7RzgdEqjRN7xjJERlZHbwfMRzn/UzmvPYMukk5VF+U4JHOA/hw+PCHYmmewc6ZtfHMDzwQ6Wi581lmZsA3gnil1svMyfB4c5ZRSdCcpfOcDTnYrC2cE/345NUebglzs7POJnSx603DMikyeUwP7gwcvcZHoQafdNJHvYgsM8w/93TIwISt1WQHbQS+4hq5wQecUmwT3Dg6L+K+LnCe+AP+UIsdp6PSrCfRMZ/HDK89jmcrAPfrD4E4KsrTNYI7NqOx+55SZVj3Dn70OorPTtnFeeURw60v1ni8rI9Awq03f7L94dHZyeHJyc75+ev/jz+OiPg+P/c3jy77OT9+fH706Pjw4OD49enpw/P3/96jPgE+oAABAASURBVPx8/+zwt8NXvxwf/s/x4f9zcvzb78ev/jw9/fP45I/jk5PD48NXx2+PT09Pj09Oj0/Pjs/P9s9Pnx8dPT96dXhwePz6/OjN2avfj47PXx6fHxyfnx6dnx29eXX454sX71/++v7kX7+f//b6eP/P/ZM/D0/+OD384+zg/fmLN2f/enP6r9P95ycvjo4Pjl9T18nRn69evXt+/Pv+yfHpy8PT52/PDv59ePLm8OT8+OTs8JjG/Pn64PXZ86Oz5ydn+2ec9vLF0cvnp69evT87en969MfJ/rujl7+zbfJfnZ6enL08effbCcX+cfzy7auD318cn/12+Gb/6PXxq3cnr/58dfLHbyevfzs+PKRfJ6/PT46Pj49eHlHD6xcv35789vb8+ZvzlyevXh2+2H979ILyf98/Ont1csh/r387O/3t96N//X78P6/Pfzs7++310cv3Ryevj07eHB2e7T///dWL349fvTk5Ojk8PDs5OTs5enNy8Obg5fn+wckR1Z2enp++Pj9+f3T07vD41eHh8cnR6/PDP86P/n189P7ly99fPf/97NXZ+cnx2cnp+fHb85d/nP/65+m/fn9BL47eHe+/P/7X7/v/8+7lb+8Pj/88Onl/cPjnwfHvByevX568eXHy/vnRHy9++/Pgtz9Pjv48Pvr9xfOzg1/fnLx8c/Lq/GD//OD52enzN+fP371++fb1wfnr/ddnDOLJ+fnRT8K85OXvZydE6eTVi5NXr14f0qmj9xR1cvznq5O3+8cnJy+PTl4cH7843X/1+/7++4Pn7ynw7NX52cnvRyfvDo5f7++/3v/17cGL96+PXr8+eH3+6u35wdszfpIqB+8Pj96fHf/79cm706PXL/fPnr84fXHw+vDo9eHh74f7v5+8+uPk6I/Tk38fU93hFr6j/denz89P9in87eGrs7OXh0d05OSP5yfvfzt+/dvxm4PTd5xy9PLt/q9vXr54e3D4+ujw7NWLN0e//XH24t3rA2p/c3Zwtn948uL47cnpu7MTOnXy6ujt+ct3p4dvntPgozfHz98e/vr+8PkftOFo/83Ri9/P938/P2DIzl8cvj1iFA55+f58/8/jV78fvjo5enV6cnBKpecHv1P7y5Pffz168/zl630G6PTt8cn5yfGG5Pnx/zlmgE5OX744Pf7t/PzF2/P9d6cv/7W3x6Xp5PDX31+TLc//eHXw5uj45OD46MXzs/1f35FaRwzZ87fnv52c/nr86gUNfv3q+I/Dwz9Ij5eHf7w6eX98+vbo5Pzw5DVhPz95e3J6un/85uUJM+iP/WMOe8dAHx2cnZ+8Pzt5d0ZnD09Pjs5enLCdc9jBwZ8vf/v9xcuT/aOzg8O3+6/e7zM0z98c/s/5y9/On++/ef7895e//r7/4veTV+9fH/35+uTfr0/OTw+OjvbPzw7/ODt+f/Dy9cvnxy+evz169cfJyZsD0ufkzfGrN+cvz8+O3pxS7/Gf58dvDhmjV3883//j8Pn71wdnr0+Oz07evX7179cv/3366t3R4evjo7Pj47NjEmD/91ev3r46eXN08vrk8N3BwR+/Pf+dbf/gzasjynl3fvj69f7J2cuzVy/eHbx8++qY7m/t5cn5i6PTFy9PD16cnrw8On11RqHH+2dHv/7+6l9vTv71+vjF+fODt7/t/7G/T3dOzo5Oz45+Pz/645Tt+P+8OX5PJpw/f3/26v3x8R/HJ3+8Onz3Yv/P/f0/jg/fne+/PX3x+/H+m/2jc8reP2Ys3p6dvH198O7s4N3x/tuXv75+9du741d/nh2+P3t5evbb8dnB2dnhu5P9P0+fv2fmnr94f77/hoOPXvz58vkfh7++OX3+7vTgz6PjP0+P/3x9QmdPX708O3hxfvj87dmL12f7b89fvT9/8cf581OG6vTF6avnrw+en796cXb86uT08PTk8Oz81cnr54enL042m14Q8/fnr96eH5yd/3Z6/vzk/PlrVsLnz9+9eP7uhHJe/vF6/8/z53+c/3a+/+uf+y/+z/5v/95/8ceLl3++2v/j6ODt2QEB+/3w6M0+Mfzt5NXB+fHxm9NX70+f/3G0f3p4eHp2/JoeHb58z/FnB2/OXp6fvzg/Ozg9eXF69D/vj/7nj63U758xGLT//Nc/Tv/1x+G//n3065/H1P7q9PTg1cvfjp8/f/vi+R+vfn1/+uu/Xz9/d868pt6X789+Oz/57fzgxe8vD/54dQxg7v2P85M35ycnTNXzk/NzcubglCXr7F/vjv91+vLl0cHx6cHJm/3j318e//786PVvR38yI169ev3yX2/2/3Wy/9v5/qufZu+/PX/x7uy3P169+v3F0fsXh38eH56evjo8Pj7aPz55eXx2ePL6+OT8mIR5dcZEPn9OR97uPz852D97fXx2fnx+dnJyuP96/9d3+//6P2cHf7w+OTk4Pt5osE+z3588f71/8PrlCVn5++Hh+f7ByW8vWNDevXrx59HBv0+O37ExUw6PX+8fn74isY/fHx8S8HfHB38cHb8/Ovrj5JB18o/9F+9ZS1+dvH91Qkfe0DW2Fyen+7Th8Oj0+dGrF6cHr94cHrw9eP7uFd3Zf0cLXz4/Pvj1hDE9P3hzfnh+cnLOxDk6ebsZ/OLt0cu3J9vNk5Pjt+cnv58dn75gZpy8PT36/fjV768O3u2/fHPCIL48O3/1+9nJn4cn/+fw+N+H+//neP/f5y9+P39+fvbb69Nf35/8Rq7+fvbq/emLt/u/nR38dnS8/+bnkvTH2b/+fP2CVr15+er8+eH5y8N3Ryd/7p9wv/HHyT7J9vb01zevfv338Yt/Hz3/89Vv7w/+9Xr/19f7L9++Onhz9Nvbk99enzw/Pfzt9eFLmnpG689I7KN35yf/Pjv59/nBT8b++sfr396+ZjiOTl8fvDp6fnz4r9eHv7072n938ooViRn0fv/V24P99+cvfz97+frgxevffmWK/Xn2nBn0+pTevXh7/vzPo8M/X5z88dvhn6+e/37K7Dh48+Lk7Pnh4YtXR4evzo733x/99vbl//z+6teToxfnNOz8t7fH//rj6Lf3R8e06/D41evjX98f/uuPw/95t/98c5Bukn6HJ6+3bdXJu/Ojd4zswas/9/ffPX/+bv+3Pw9/fX/+/Ozo1cnRETddf56+en14cPLy8Oj51siZTcjRq3dnLwgUgG52tP/y+OXR0cHxm/3T9/snf748fvfi+M/9l4Tuj0MqPfjz4PiPg4P3h/t/HB//eXzy7ujVOZl8dHL06vj4+fM3z//1+/Pnb18cnR2fnJydMneOTg7Pjp5zG/bHyfN3p6/OT4+OXx2c7f/2+/GvxPbd6cHrV/tvDl8Spbdnh2/Ojv88P/nz7OD98cvfj/dfn78kbn+8fv7n61dvj49P94+Pj0/Oz47eHR68++3g5NcXZy9fvj0jfw6Pjk9OyO1XJ+TA0f7B0eHh8fHxm+Pjd7Tw5PQ99ycv9t++ODh/dfT26OXb45enr169OThmsr/f3D98/3qfGfrm5Lfzw+evT/71+uzXN6cHZwcnJy+Pj14enVDLi+dvjv91fv7b2fnLs7Ojc4p/dXR0sn94+vz49Oj84OTd8/0/X7788+Do3dHB+dmvv5N1J7++Pnh+Rr0HB28OXr49Yux+/f3wtzcvXr5++er9wcvfj0mMV2fnh2+P9v99cPDHq8Pf6cbx4cnp0dnZ8evjw9f7B78/f/n78wNWmHcnJ29OT5iqbw5+e7P/2+HLf50cPX9z9tu7s3/9ef7ru5P/+ePs19enL45eHR4dMBBHp4csy8/fnL94f3b0/uXJm+cUuH9+/uLk1fOD5/9z8vK30+e/nu5zM7D/diMtc+R/3hz/+vbg4N3+0e+/Hf/5/OTd0Qkz+s35/u8seke//XH48s+zVwzEv7mfP/vt7cm/zg//5/XJb2/OT7iZ4a6e4TsjmIevSI+3B6TQ0dnJ8evzk7fnB8SWW8c/j/71/vRfp2cvj0+OTw5OTg+PTs/Zjo9PXp3uv3jz24s/Xh68Pzh+e3B6+uL4+OD4zdHpH2esTi/+4BPB4fPf9w9+f/Hq9cHh6enB663avDqlgtOjk9MXpyfP3zN99o/evTw5e3lydHh8dn58drpPmr1++eLd8Sta/v704O35/vutzrx68/rl67Pnp6e/KaxKUGWSk0Tjg6fw2WqIxtZsoFdplkgzWHNrYZ3nTbVZIcICDdpVC9PwPnJ7NCuIQHhLmkmfQs2lhZQ0yuHYPYhBKiXBj+Hq1KGoFqk1eJv6pEyj9WwtuwSEb0+6y0xdqVJaQruHiojOUlOWAnyEQ8lENSUm06R9tt6UrfGvq8+6WIaGVk0ufXszGNIgzwqbSSY6QWf0Lr3hF7M92LNqlKCTyizgAEHTnLQ4fhJtQENpVCta6dbK9pp2qK6thiXNMJVmVCigYx7Fg1cWQ5E8ClkG9G1Ua20POqsCSImsVXOBpIiU8K6aoElSwvDmq2GYCrrRtNxTWoeeMQXd43RLbdssl+ZqpZsG3tgmp/hoGdRbJkapZVRpiG3cJK2JNWuqeybEoyfDZxV8mM4smEjXBIZkKGLSbFpUvMVgijb5XN5GqG+GN3AwZYmYlW3GN4KyuRAIaMpP3MJ6aa+g1xS2yrzEVGuX1TqtH6qlAk6cexHdTVAaUNmK7IWK4meTsuwYU/iel3mhAHZRG311IEkZBUMl9nMi+yXAkGkz2avN8G5oKpmc3ZfWogmklF6TqC2zh0/qzSpNlKKzNkQoDLmXOWc2iZIYxTQyZG+1Z+hd20QPttRI/emrlRTf0krTyayjulSzkMbQYeWXTy04eZaaJFq6EiygwK9CS2tjry+zBThrUkzapDph5yeq0zhSgNDP0fcIUtBLQZEO2kGrNdGjZsEW6A7pQ9towmmDxHORZOpKZ8i2wWCYsiV1VTVOz82QJjptAwrI4njqNI7r4j0IiRZEgOZBxGimYmPUM9GmUOMFusnEwEXIQKT8VArVaG0Qf5qRNEOFBwsTmwYDUSJhbRCZ3rT3CW0Kk2BMKdQ4Gio01eeICcRBOkVCBBppg4wrtGSkQXuYByn87qqNhijN3XBCN0zCz2oK6xTnolRC5JolMz3pcP10j8liE3EQ7cRFikFqVQRDQ0gq4QelCqNjusm0YEKjipBBOZxiNWaIyUawJcuhmmzRMKrYqqRgP+8KLMVoC9TNUi09xF0rZqGz0UREZaLJri3KImb4MxnPSGvEHsJKKEOqC0CCt/QmqeAVNZSSt1pFQ5O3m5XQeyQTrAWSlTt1+/xJ95W2Zbolhxf2iEAzqAH0TTwkOIOupE2hzY0JiUEDJkiTZmI/dcJTmGaZPUS3fyqbg0FjMAupahNYV5p4V3ITUrRwo0kYao5sGQ1RBHNTC4VOIvSti7AS7mnOnCGgWToPEdocRZ4VK2ZMJiqyeS6Ui+alaTUEHiSd6dR6mwxNSCx+dCOHEuqnAAAQAElEQVS4PaBJdSkQ1Z7Kss/J2UwnJTmAVdUVrnwl6MWCIyOa1C9d99T2VIxXYwt7NUgjDylEKF2mSC6xANkhWipSJUlvTRIbVGq2mSOqCqIjXVGGAOm0WVRVG9jC9WhPRKBIiDidERHqYQTUeYOqjSImMdMkiQMcJNUpnhkRc5AZzUxMxQQGUVGrYtNqvXo32WsxyeisUFzGIJutjcw38AxUCuHkNk2N4EAyQEIktfWWxIh9UEFt7N1m0ZpWQkWbw3yFWlkJLZMxa2wrjIloSgSR1q59at10MmmzcL0rhls5G0qYlCM33TABRxNgZVdKt/xFaq/QRej2Txt1Cyat4A1hlLnaq4VqUJVunK403fwxCI8qjVViWGyTW4PRAtNG55HKPFOhTW1PoNRqIPpNlIyZRK2LtObzRPLBS3yrus8UJsK5ybpuTSf0Gd3KlPGeS5sqz6SMub0UI6diZqoqoVhN1wa6BogKI9Ka9mIfBFA6BXqiiqZhsjZ1mJgxmxWL5GBd6GnUlYx/ueiwYr1Go4gUCWswg5WoZeNN09Z1NlAlatoa5qpmkXOyxqZWCaw1WpKtaF9BjH8FQISf6YoQRkiF2lisyrBJUxP2aw1NB3Ov/1QnxbsCgs8lumrbLaWOwe0EdTfrCoauqfBT6ECID+au9DCVXmKqAF1onQmOGdUkCUGfqjMNVLRkAyRD4MaQ76V07dabGRdprdBaW267mDkJAoxKOjA1IQroqJ60S6nMDDIJJpPZbIb2DTbaPib1Lj4J76N16Xsmz6zvybZvMTHdLFcTFVhUCzFoa0oDGjh9s886GqWDP2ewHuWkxgFQLgqYvejUpNBZ0U1MLalarEs3zCpbJS7QSnO12OBKFTdBU21UZLOiKwyQgFZmG4xnk5oKKsFqO7H0caiJTGSgAr1olNWE0coVKBpullaqXrZm89Scfi6ppvQnwnKlZFiztme2GQyfszoTSVVM1ap17y17Cps5GlULRRKNmWaqqKfLulougpVrSZikQIfMACeaMiFX3cFHX7JlOMauwheT1aDwris2udanvves9WemU6eutiv54RVjNLCKeRoyUGvILmxFj7anEzO5S0lkBWXWsMy5iKCwSln53EabEYYBWTczB28q4FL/6bpMPfa6Ne0JXRIxNrNtaQiOWcRWOudZPyp/VCzDqwqAaPXZbVpjrrE3yd5Me0XVhxa3ktv/0IoARMgYuqYkhrDxW8zEROkFCimSUkNz7RnzYn1t4oa1t908r1OHFu2QWDMjmzLeXpl0U8S3AAMTdBKh0JRnybKZjaGm3y3Wbiv9zlZrbdpESqtqIKuFGovFLGPCTuCFcoEi9wwzhByLgV3WzkDcU8ygpAW3iWBN2qk76QDvOYqObGyUeQ9m9HEVVEvdW+d51+Z1k6UYWT7SnZ5y4gQewv2rW+P8hiG+VI4Wbl5LYGlCVDHz28VRiQYxIFMILQhKYF0ZKXYMa5iVew+WuKkVgeWQxXPZjdit3Khaam+LTSPF18RaRSt3CJ6kqRncc12TdkuzaZpNyZ1JTQWe20+jhI8NZB2pJ6KhsjAiI2iXAGpp4lY1xTTHrCxVxb+cJCdFZ1bhpyjdORYfK4NXVaZMIqfjK4UWnDSZyqvSmZ7PfDaZamo19SFMmL1wk4BOyduVmT/Kf2DdFaeQ0j0wJ7NljsV8IalcGnQOa0nVgAAIL1+iaoGupiUEKiasjSHJjc9AaROaa2VZFX3rc6Avtbfqs1KjlJag+5UUI3R4W1bmHI0JuoOsXX3KYXSxVi4CoUvaqs2lizdCGfnPwjSKDUkaEK2vMxYqUtrZ8BMtmmEL2g8VmiaTTD3njbXmiuxFFsiQ3CFXr1DJPRlaA5CWe71mCXGo25StlxWp1WUBCLK7s2qYp8lKSGvyMqtsw5O/E5fzy32VCpPRkepkG4qkU2mKajJMnZlfECTgpI3tMK1Nh3om/RUOkaG6dN2RFMw2wBTSgb3smntVzwKNN1SaQEqYFsQ+EunCys6c8+UHdv+U74jxoBZa8UPGUk62OamBPWeCBqXsJh1cQgokJtUP9xiZxZAQlUwfGsNkJ23nmiHWQXTWjp0OVrKFum2HvstpoROqw4LElLW0RLmab5wlgEVfw/vi05Kd4lugZ2t0i35YqngbZTvZ+6F9ge2076Y2LIsPVdayzaOpc5tq5FpOBVGGr3as523kRM6Oqa9dNxQ7gZPFzFXLhLphEFF+dNlOy/rQKUOTgDmqehqGFKGX0Wl5W8XWBtdlCLOgWMuHYlUOpxcoSppEWhN51mmHJHQUSxOj25lBk0fPEIVq1zbF1FlhwaMQJDhZNISPo6OGICmgZF61FceT4cHUWoekd60ZIPd6zhM6W1dTEYCZR/DLyWYpGpUMWW22AZOxTqwWi65MYEIKW+jRLDlZ671ZV1E6JqO1ENRIi2go1Yb5mVhrSNXSLjWrmAHUINp6s0b21Sr1Q2SH2Mlu2OotALqaI0atA8OhSXoPtdVa7FnNnvNu7A1aMRpCC31II29W8WAENVKGSnRkZ81a4YGmstek9UqNTaYEdMBSuE1wycWLrA4OBQUwsNmT5T5QzDphdleCerCG++5H7X4YflhjIu0WkLGo1oRVsedosUMsJFhDbXT5OXnzmfyUsdE3u/tMj3LUGBm70VYyP2SrzcFgdHQ2aJOuEDRhdWVRGDyzMF0lo1zoY3BV8lhGrhmDhlbugEVqDROyg+qFHjPLNxCylsroTvQcI6QEJuQvmkdfowXzUbdrUaG/i2JRQZlyWBOWiWmv5jZmWTWb1F6x1kuTmIQhQeNMejuR9z/oft/5vLN5KBjQIdwlLagdYle5ZJZbBWWQjZ25SIytGGAizJtUClUTm3ya8EyrZTKdkMlFyNYAiInwiXCva7dSBld+eN/VsxXzqAY3bCxWhQqdJDRRa0mVDLG195qmzpVU91qbY9IfJjtV4h6smwsIDSIptpqYKTctz9ZsvG6NilmPO6HOsazrbnhUmmS30StISsuMht1kPyYmC7tTy6x3m1s1qxay1cdiXeGY3JMysgqrxmLkJJieyLm0K/Mqdc7WxZK2MT8rYA5Jg0xbgWONMxKMrI7GoMw+q06Mhe6JNC0Ozgqn12a259MvS59lYF5Hz7WpiwWsahI8Q0wd0kWM/lmvWdAgVr2BHZwupRLCqA1iM42awm3NXFmeuqMVRDUZBovqa+kO3MTAk4AroGZKLY1h20lfMaVPOsRy7RKbCjQbtgVXElO0ucgInqJN9mxv2pvnZ7O2vRodg3LmJsRyAmEu0M7qHsIo8AKltqi4aQKjZKnJMWfs6dhra1ciWCuYmViM66wWSY6mzWQGJhCqyph2YGsLJOilrFwtmuS0y/k/w3xRCxK/5kD3ZM2ZV+tMx2S1GSmcVZOClO0Ul6wV9R9g16ad7K050z/mE9sUXGxTANI6RqFCO6BR4iJlbaCWhXUh06Wi11Y107RUBcJJzH56OGGdwGIfVYUs/SnQBZFqblOKMCyT5dRA6RZRYw0WChpgHcSjhxAhAqjrsISKNtVWLEOFiGSwhYlQhjQp9hYDqRPI22hSYpJiQ/hJiYBDRLVNYrQSoDwXiQmcYQKFbCWLtmZKhnoYc7J4XdwUxCqgZRtfqYmmz5xbuoi6qbfNtawYVRvCCrBYsHaGRyWxQghCkUHJBIxBosFiACpoV9qInmCcw4StWhOhTFq/MiVNis6TRVnbbZuk20Zaa7JpSmGKZihTqhgiJk0aeLa57aIlHKZGL70mSRYAxACSSLI8TeB4CH2HuBbBCmopCjbRpmicX+A0hVn2Vs+srDjG6W7RN45mCYlWTikubU374elDimSIpnQnJEVSTYUxgYpaSEtBJiFpqK6MSM80541I8ZJw8qCIDVKYwUxpDXOZh/SiWVWtygTM69jsL8AVgV7ZNqbxVESMjht4JJIDfkqt1WsNBG+kSBi10zLZTMtal3JPUDRt+kn4EtAmB7wKzaRPaM/S5rTmZqAFCQuemRBYiyFZVEgRuWVGiaoItTuSXW6SP29YqARlkmlDNRuDvbkjxEOzGhSm2bsoRxeFsUWBy9IK5nyJaIaQkk7cawYUPrgHd0MKeMVWLiNahhFOLgkajC63Cptx+KkSIlbCYtBEZLaNG5w9Un6UuVu5atVU2bKYQ8hJREH3ykGpG1MG1FndskWVR4SLONWpFEQTmkM3HiwNP2vQniurUoOAvKpOiZVbDFUSvCtMTNZWxkzokhhRSYkMd88UyZ9UYhgCCOKi9BHF6y1gNJd2ipWag0kpISAvmmaXUGHVlDWIB+FbdaMKILaCaYdwWp9SppikuohZ5gRv5jxjUwzNrDTuGQui1BFNqIbgh2k+A/gSmpETOO83yUnUxFC9ygJaMEBJ2BakuGetqxArz1wko0YxQoWBWLC6oBTVNoUysnHTUwkxKgoO85EjMkBrm7WmNE90FEZOW7BdbJQxXoXMNrYkmwSmKRVIbvsG1wTsqKnS6UHSLN1wZ+KEccCaREUrRSNNqpqNyRY6JiIqUk2IUDYjNAG685MQKMYjzNAhjc1KW5TBAS9jKCw4tbRgoRIN2ZkM7GvAbNarsUs1OMsVLiUgdMJEVlkrR9HUoAtlxdVAO2VKuQTKTbxbqqaao7JR5Ta75oJG/dSaSMgQdW3JRUU42HrqpgUiOknr3iwYfqBQEYkxMCJGrk60i/BGKk1q2veSEwqSPik1uzEmNEToN6uyTmV70Xs2iLhg4V9AaWaGrgamGObYcsqKVKlWZIZoaWWvYFKY0jcCn+hSW/pPvaz/MB2+JGMfFZCQFIRtbRvKqZhU2ERLlMHXZH8QNa0wKaM1xCbEt60uI8ZhiNIYhAhMm2qAJdG3OW1iUi+BAA8xEXpiDt3WhSZJnGQYYxjKGVpQZLGTWI+iXXSFPCKGFZWeHly0PJVgV3Hkf+9G8UAx55USABdzDkFIhWRIW40BTUcOdpqWVRazf7LaohQWAd/UoaU3BLIClMdvODQZm+x0XPpACzQ60VTozM/pSlXsjM1ykRDx3M4bir5GyFhtWfpuNQ8tQOV/G78BEw4j99wjimWBAS5UJT1FiBKGtVhR6FfbAycS7aL6klJVU6EX1nMjQWMwXCRVYd1a10afJTc/qBWVKM+MZJqo+2YMyA0VaZFG5yNW3w5S22CclnRIKa9Al5wE3mjMC7QmJK3O4mZDm6d4kZ1SPMCJ259YVUsILxXO6qNVWZKJqmK2ToguKUk8W23WdN4W9I5pUj6qdFXjEFSvnDmggrNXtBUWuem02PChBWWgp62FUBFsAas11sQaSV4ApmgEmMSJCtOcUHtBknJ85qQgimlCklYw9E1dtGSPTlov6WvKSPKgGCctTuN2gVRQa2HdRZJ+Agqkw7O8wKNRqjBn6L6SGV18smGozFaxJz4jNDaHaA8bYCI9pVUp3Lnh0QQkwa1fusJKrURRYiEtDTzTIiUESKmcbMCedAAAEABJREFUMk2CZ25JEzLS2corIVEG11mDrdGPrZ9ftEVoampxCDAoqJDRKyZwhjg9bz1FVJgQOxFPlRIzZXCIWopKiqSpdFFqTWJdKuga1gpoqFZuQvOJnw8KD8KULbbcSAnX2Mw1OipZxI5NPFAloBzlV7L69oJIecbw4RjBx4iZJBI+SuXoCKUhRQuwt8pUUJMASBZyryvtdbSoNITRXhOyIYR9ukoFUiM3nVIwEQUSGJLeq2hXSYaVduudntG0AQn0qGnloEBzJQlaGr8IZGETA2vVmkEn3ne+JlDNzTwk7Qo6l2hMNuKRtYqMBhYjakrK9iwmgclkE8BsnwrGGcWZAzwS4qAJZINOZBCnlwZFeCub+NJ9hYiKTdZnm21i6PgSwnYxJdnHtlkoIlzMivxJmsZ1aqYTSjU6qi3J9LKNAoQwBxHywgLsCuvgqxRQw0CLZMhdZel9THM2OoHyimpJ6rcoWSIXPnkFy6+LVJY5aEgfYV66BbgBTDr5QfmsReQptNUzk+0XfgUB7KvtLdPeEC3uUBZEtipT4R+LceqcMm2oZsqIkiET94RB0RxVZDJRAgto7FXsofgArZ3xzSxPlPBos3AvMyXIB2EIECFFoKMZyJ2ZJYDjimRsTWSWxfwHxurbQ1uhkCUrQwBdSRxjcIKmlkZpC50dbRUsrI491KKjdZsn47P8JDGDkYUC21+XUmhgWmJe3BYRvuILA2mtYF1rbQfm0Qq+Hm+ShQota43CJnqgdII+9i024agUU8yCWYVe8RpbzyC3tuZIF1+RfC9GEtL33rSnce1Zf3Tnu1qmrjRID+s5z9mpS9Al5pbzdl5gKQRNFiGixXLWbZe1rJI7m7JPfHKvJvRMsKXborWbdTyb8Mss2uAdg498SsvWiojyYiUvGLbeagFIE7EmFN96tHnzXIz3AzKy+cZT4wZVglaOPnbpy9Ax9jL/H2BSybmvbDJFNXqboIrCz8MqhDSABHrWJNZpEidOg7Fdgvu0da7xTGkSX2LUIlGyiKVZVgN0I0+V5jrBDVtCEVWXgXkVg6qQOXvGFOrmrMtaSSA4k16nNMRcYSre+vpMx4zqMMjsspUYdvRqFEEypoX3CNrRBNu4jEZrNk/MSS0ltXIrJBthBdELW7mIJsts3vj+Y9p+axr0U7fYlwjLnZA3Jly4+1bnMlk7GO6+ySYsYQxry9wDfwPaNVu0jWYuzLDVmv+yZ78I385p1MYziCmsVQpGylq1wBYOowO8n6LODiZh7E3j2d5qkw8Rj6pCTlJdIFLlKqw7I+asCW3S6tgpRiWizGGlBaQMr8WpVSF9K8hdWhdeNOHz2NosaQyTjk87kusz8MffnCbjoMqOsCylgc5h9BsuPEoN4Gt128vRPCyUMqRoFauVMjY6mrpZQFJnENCmoRqyGVSWMD73kblEQ9etCyVNvZurZmR3Vt1iVBM6QEGqJiiwUaKLFRpgalVdXJEBujdSF+oTYthUlQGKAKeHOmxtgl7SIslI6Cz9F7VnTYgaJklTV0uVEHHACW0gibEpB/QuuhluQFMYNYuRD9sA/O8hAmVOSv5oTIOlj4Vv6GvwGVLK0Fu2ztLbGlMGQp9kBNbQCEOAh+hCIlOXUpG411isuJSSl5NyjwIZoqU6lUyMYgPLLBNCi0YWJQz40sIbStRa75PuWXbnj/vLD18WcAFa4S4uEvAslzDJibYIi1EIFDVF7XnyfXMfNqdOiK6EoAPNUwk+/8g3bg9zxwKotMOgk4w9+dFt3UTQV206d+NC9Mwof1DKLLoHvkyedLUtIuSJUrswWRgOsiABVe0ioCcgrzM2wmXmap1vfdPSapDTuU2btPdpbs86mGpTjXkU3Z4hmiKOZKNAchH8C5VQVohJUMhFGZxQJob0LUNES1SFvgsdBYR8V2CTuJNMDNNsJkVqcJY36i9UKIVxkjU1dGOvBtoAkW2FzRCx6K1adRsmYxLiK7QpG1OJaxy7qa6AUhJ2l5tdya7EHGpFatMUWlaTSEfSCFM3DN1YQ/8EAZjIXL0tRFVsqx4Qz6gxeEGguZ8oZNGs2fH/YLa5UZZXemBFlYlmmQOLxTLTMakU9uROVj6JhlXrsceVAC6goO7VCwSHYGE7qsCfv5KyHJIGSleKBBxY6Ts9ierhIqnSpCaABJtWmcpUSsRbsaUBVGDbB4ACu3QCW1GUl6+uhABdoBIQpoFX42QbvQebikcWZ5mWh4whSAg/JpQ1C2nraBtoSVNG0Wr0kK4iXbjSTEZ+97XBO7KLFCSKZmxoAFql5tkjFZpQRPku1lVGAlZosW1mhesOOgToQ2wdmcOlggKnNOOV1IK+xrSmFkep0wEtLkAOWbe2uq9W4yfCkVgwbzilBfN0VGYAC1cw1nkVpWmxVg4aI9EJxSgsEhQKFSy8cB2rgcJD1E1KEtQWVdm8phUWSBOmp1gWx2kVlBwAqzJZIJKtq/a9cF13WasW5S9SwzGC46s0QX/FwFgUzNEcGGPUiEb7LFNLnW6AnwQvqJgUCkVoCWdPI2eqseK8ddF1iK4UPTzCi+5WSoWBU8GPMmOVFoYMvRNT1MgKejda44oXVXRSMcW0N6Z5WKORGxIu2I5VNs92JquYs84FOH2g0tgrIrOhK4tIqxXujP+W3ZM0xnS7zZL4bIU67SNwBWsxz/VMWwMFUDPqmcTcA71IhSIXohKUH2Msjh1hW2iy624nwKRzqXmVy5JCrwkH3GJtNcjbzISrRGvS1GZRvqjtri2EFiq9kh2t/GHmU1MaXwUynvJkRLHQy86V90RDJFUcsmwU02G9ZBInNkyLBhELtFG28qc5JK9KMkpplhUsBNERIryV0A3+WdgLGTKKFUboS60dywTmDldvvtbG6pQEYpUYXhkqILimNMXJWM+GmkiAIvAKbdVU1UPpMDWBnrsMWo9qQAPnoZiV0qN+GfksZCq6TN+AkmVm07VbRMWPEKd5UpBN9qABdAy+hagDWiasQalIr9WZUhEgOBawFC2JsFwREbSBO4nwLtWMFlplZVBmIUeTtU8wmwxiTIfcFQaocvTMTRA4xxof7TqfcKtJTgI60JzYppRzrImpyqRG3429yKZZZiW0VrAdpVHm2YspUCpm/JMQ4lORNUo8tTwBZmKJ0n7QgUDmXFAYieS7jKTZpdunC4KWENTUXEUHJFOI4abPQGt0Em2ZGh5FxuhaFE5ztUO6cuNkoDQGNLU17b23PvVoE99hDVckh8NUN/clm8AmaVM8Q02eueO/WLxqp3xMVg9plWCxUCmdwRe5GzkNfLiOKXdTeksWZm5jzLM4kYWQXm1GB9adjmqi1mbhHgBEbguWkJ6TChRgSMgiBngU3XEUSZ/P4I1haOXmLkk0C6DFolNKW6rFBp08ExdN0gir2hDzMk2dtbWpJ0iiPZM+t25c0OaiHQydDAVZW4yP7LTvIK6y6aQgHX07lfI+vK1MrKZQlbVoYiLDKmeTJiYbvowhpWxanbEAmJOplFYW2lfraxnIQt4R4Sm3iP4slFGoUs6liIKgumYT0EQyCylCQVgXLKuvNbwjOIDDJE1SNSB8nOJzSxIKpNAmPhMRXptqEzLH1Ci+Mj2SvHETKqZMQ6hlsXBBx7ByEYaWUEBUukEbpFev3EPM9LDxlk6BRlORy7a1pWlavHSsqQOaqUkyGEEiskOYhpNFU+FZqDi1cgqpolJsDHepqk2CGXhWwhrRYX3mX1rJBBAIptGi5hpmxIUVhS7zJ/saGdgMtkbn3ao55lVbicDJ0JnzTVvSxxAM1oFEFRxtxLQM8zXDE+6W0YVhrD20WU0iBQnJFm5RdAuum1B6kQVWIOf16LpOvXrTJqqKwkbWGAnCUEwgI+10T9ltDX0mCAV0l+bdMW3yArWyVCgaZEpya0VFdpbltra5xEK4vgxxWkLUoICqOQQojv8luKz0XC3BurckOK5Vm61PoppJ6ZUShWJwY5ly6aMmQlEk3F7xDd2qjDnQpBvNdOFwNbIPW8x6mFKVULwlHU66tpDNg0AysyDNWmumSmyQVe7BksrmG5WyasAH69pm8AACyUW/xcI1x6o0BVkaqSAJa0Nd4Z3Qe199HrmX3pCZ6jUhZ4acGKswgpaYNHVbuKyUt5oEypmQKjwsmVsBFZXJhAcgSzVOaoyKViPNfknpiJ5iS2hUQgcf4ExSStPgzUNcGcU0g6J0NzS5GEwy9+gxcq1YNNfU4eJhOVpwRoG6zXSPNSZ7pIlWnyoFkaQlqdKkETByS1gFIzzDRbILGtCL4w1k+6rYKWVpB9NYu5TABSENZWAZk6b2DK1HE+8JxMSOFF3BnRpH1Ezv0JrMTecJ1qqSqgiewgqWhV3FaruydZKhkZWMtdFCMy2TMlSHTG4EdlGqBufkCmwUWHpFV8NG719sbrIXtf33liEmz7JxogiYONIC5LEArJeeikC28CnCMsWBsSEBlZAaMQpDtWZB06pO+ABaXEvmcGwGpnRvbUykhqODlpZtNcA326QUNlnvGn2TvaQ7VtcRmR41Kl24v010kbaNI9o0M2hFJnc38sMIciaCOaNZqkHrWMz63Dv3BK6kYpUkiQdkRC6uThM5tueG4bZnYsI6t9sDsWgx73KlIwxxaHLSXtbspQLvCBTNkyWKQilGtAkxtSLq1Wu7wyKVWZ4ER3fZdiJOXBNS20sjWzxX+pjewAeGSkWCYiHmrHFg1bEmE5Y+Fi5JqJTaEBjgYsm0DtZQ4yRGP9fBf+kpAdpWY4piMMJNvM/eJ0Aa67yPzFxIlqJ9o9kP5XI5J1pXsdJimKqYPD2qgQ7BumnvAC1zKTcFSZmS4bWu5YFSZnCZVav0yjBJVnze2wZtyUGKENOMBGITBA2xLDXVJhuxO8YEf5Yh9EJ8UZoj0F7SBnlGtUHAKqtYoJdw7uUXs1C1xOT1y5ApDMnHu70S3WrGM9WJXCJ5LbnpyL0Y284xV3UnkwIhSud90kVkoaFCc4Y0VKas1emWrdHgE9YejohCxcQGTDTNSBa+1P8BWcSAvQ6W0Soh/TQ1RJySCiLMdEonoCXbZRECkketNcLN7oK4yFApq6JVCEBiKOIZdBKzVMAX8f9YsaCsdHm7p6vycgTNVwcKpIqqiILLJsHUtOYuw2PU8ORnEX5qoHS0cla8ygpvLGtTjVkIs2SCmVaVKnQ9jClslSx+zypnIzo+BD4mW+cuzaaZ3NgUlxZS9gp8m76HXRcOqWcKy4wg19jZTaY9nWeZWR4SzFUkDKPvVZ+kspZgApg8m2qWENrgUyRji6WKJSuaZtvYT7Dc9prScO4t16oSSSOACAgNF2oy8sq6gLEzOAQJYbVLaZHKXFQ+nCenjhRH87IoZytkcqx6SWyuR0hk5SjWzDURFKNACetQDGFZ2jVbe8s2GeZJmagMgP03pnBGaZ5ij12JKk4Y67Qkd5BRyncWAGtCOT0v2gSFZM+ggVJRmzmBHdpueqp7+ngAABAASURBVJa/TEope5JqnnO48l0DaIciTJwzy3LYMtrgnZalyU4R5qGFzKA9vorz4C3WCXYmKqXUtNsMdISKSwMaCizDNAgu6iDgXQLI7Sb1hoEEoB0wjlldloSXRlSsIkO2uldQpXUTimkyC/WAxDfeJRv6SN5xmbjIUSYdLVAnidZgsrYKMWGoh0pEYQ3NBe0/0fhKh7FQFNchcA6IRvO5BaOsrevUQEIAMRpy3rOJLyeB1SNicHwHJmF9GXvOotboQXWaRDshxdpHa6E5cvjghnToyD5qtppmdrfV+tDJsPcs56mmAs3psdLGDXxB0cGOnIQZEyKiYCsQue6t8YQgFQJTyORjzlWwcnGmK+KTrNyECkbJSEYui6xby3bCwsLAZotA1UCmqpkoYWVGljiKCRnQqIx0OKY0pmBH6WYROZTTiL3KKQVO6VhUFtM0UBiBTIAYe0IgBMis2BGZUbzHJW0l/V2FLgwWHUEWD2FqEqpGIUKWMjccNKt1oaH6A1ilqC+z0nWMeanmAKsKwk1im+O58gK5oR+qMcnYE7DNKUb6Fs0nGecc3XfINSla5mBdGI25melR1YIvX22yVBQWeJaXhBiE5WrOEt7qtcxGbnfkjOxRETVKGER7JqbFkD6zRupwgHFPtEbHarqIDWg2qQlJUJKxo0+RfSfTruvOxhqrb6VVfIgMWKa1qknEhJkhVQEbpVUEezMaU7nJbshu13JtfbQpG3Rapl/8F9MZZC1d85VWQxIt0LR+cqdkBXaw1Xq0xhrTRbSsilVxHbVAYNZMZiGlYkMjtbzDzfktqQGJLlADuI+bSih/rDVGFUMEbxwjAdpLOjhG9V0BKAZ8ymQvrEheyV5C95RsKVIvfvTY7QEmPdG8hL7SSKOfsoUHPaJ7MvoyBKUNwhVPVJpthhZlUGJ2spM1SwY1clFw0idEUxCCZaYXBS+NJJbkNWtVn8WaEbuqljALcmJqHK1J5CRT6DBPTaA03IIJO3oNzYGUkq31kJltJFbyy2ptiCaiDQUkzf0xI3guoV2KoY3m2VxboKdbLFhGMotZOrhtbka5ADqd9LBkaeEcKRrLZCeDpJpYz24xaYoFBIhqg1zBpnlEcKO7VBallKC00sSEgoZW0OzMpP3qa69g4WokKJzjs2igTQKLNC9Kl5I+1u7Rwf3c3iaMTrYR0xp7QcQn8BHZJ6oiFJBKauVM5gU2F2gaMjNqmC57ezLvybQnGzRCWVlMIShZMbqsYgtashM1QRpY+JVXs2pvmxwK5xxUDC9fdIS6YLXcPKI0eijSS+eamkxpjR5FRhULgEbQjXkde1Vzx7a3eCb1S0Mn2zYDC4xxiuVsxqTQZ6nzYqR2qNBLMQdYdXLQRRq0ETUKA6j6ybUiWGhFmxX8tFK6L+A/DUVqyfbBIJlwu2+Tb5yjK1YhEmKcWL2wJ0xeaIwai+5SV0iiGATqicp0NWelobdlRcA1KBewoi2+LrVzwtFqG4Z5YFsh2CsiashtjHY0CgiiV6vGwl/cTLNUgjaib0+pmkYy2LrKuqJ2YAqjckguWouCCltxcHdprvSyOlUYQHOyskpN9pT7jL220hOIc4bqoPYoxmO71xgUXjvWKJYOFxYPMijVFq5epFSrWTCjKRUlQYbSbdExydKFwFdMgSlLFDQdcGiVgo2mWqSEGXqHTcXoUfSGcSIjK+gpYCowcF+wMktNm0CGYbfBrVb/laaQKSbDT2vLiSBdy46BCIcz/wMgNc1KjJCvk/jUSycuTSOFvg2UJ2Itdb4A2VmGBNeNZ8N+SXSpEvWynek6R+oAoljMmN61eeUgOEloIKBhRMk5XGGi/IAFqafw2b07mJUa0GqVfDXXFNhDPSsWBE7l/DJxjOFL0giECYmHAFycBosFRtO1wYGkNk1uRCcJyuHD0U64OnDnzJVRh2h0IQzZtFQpmi1ho6ahahATK0a9wF/pQiuFZdBkSC1t0FKl/THXauY2le5BBbamFgtem+0Zo95FhLf5B1CJZbZ02wymvZ0mTjpI3JaBAFHsWJtw5RquCCmtLhQGzdTIRFX9dApgeZgazJRHTzFPWasYl50K0YzY8OUKUAYRqVRJ6clIcWo5x4ATZTJtBdml/fA+KA5GoUQBoDlmNnUxGalLttBZpQE0DO4SSO6r15mBlz1pk3EJsBGSDgnT0JKk3SHcWLBhLR+1/Q8j523vUapW0lFCewTCiCpJsyB/FABTPNOcsBqGAgVxaSOn7ZXDYlo6NbEmkTq4LKaI9dYmyeaDmTHERjNBtFz56aJZShuU08tp6Ka7gAR70DYGdYEZnbMqgtVQW7J0UVMhxMTCaQagLnCyFSmJTBYB+BY5DfBwx1ZnHErne3qVIyuLY6UUFLE0LLZFvYhPYVVf6ARoN69Rlt50i1hztJXQhRqaca5KqJAXxHAjrycpG5OKagMtJAAklWJoMU20CS3QjI7sykFNSlpkj2g0h5gnNtOCrGmAkgJ9bToaNy/YlSzEBjlrzUYRwcEVQLjG0ISi0cTsa+iOaG83eK84yqGlXFuZb73XtFc0pCAoWMjk0um1+RZrccmhjIUGbeuaphBVQROQc9o6LwlcFYq6UySFoxnO1WtUrswGcwrGdphri9ZjMhJeQIPXILaIbsVf4VsLRVmgeRgGWtYMnsHTVurnsFSSCuZjy2ZlXZVYVHfV0hWZHix8cDOOC1Rp5oxhyscMGkYEVbgYEcaqWjOHRumqGD2XHttmILtTkBSJZZ1eIsQ9XfitRC3KU0lkbzsaYSXlzb3TbbFQ9WZ0nwGggc1L2G2cCZR2Ymvi0hNzVcvMsGXRhUnYMU3SYCBhkzKNdobL6rYSgq7FcEh4VoZoEX+4BC9i1XVl9aCGSN3u1crbUgKxzCqOr2Hs7ppzc5nKNQNJos0ivVRHVtA6CHWPkIK3tu7JEMnKrHJaWgKOFBPuk5RLStL3voJEiK4L+jImxrAxZFEyTJYmawNBGgVGz0oT3VMiwQhlraNiwIfE6DUanBlMQmWGr4v4KkWNEAaaVvRarVj1+Ou+ZybjRX9KSXFAQTQsG5KgdjoGolIGYqnd2EQVagK6t4Kg0ZS9ZnODGFje00iLVVg1GeMm3rVaEWERoYzYkhwVauD+iGK9JIeZW8/SLHrj4AokqSl9sBXhRA1BdJFG3d2j79a2rhJeVRT6s0mJhG0gUSIKPHjLdswNWI4Wo698uUyVhNe2EGSVB5bwkEFnxOh4pUm2lm0vtFE8JJrwJdjK4sCiwhrZeCdWrbXSfQS/aXZHKUYimJQ/VVNeEzERJWmarlOShi7pRNfaNHF1zElkG6Nqs/WmE/ngcxS9zyiUNW3GnQ+HGUArJiVQQNc0EqsJTFCOrFQjWCJ7k3JlADI2W9xlI4ezP5MQkKjS6GLjQuJukV42lGWjRYPnzzccDgudq3VRAdTLYywSCwd0S2tVHWLKQeJK7BlaaKiGYIuc6rIVuj0KUIMA8M1CfmoZG6SVNuEynF1cw9fVyPCiI03IK4phSS0MjZRACWNaoDWta9NJYAX2jNCl1MltWZP0sEp26JLgrsidb+7X6kFMZLRc502fJNrA5GiuxmgvKqRUVZakQADFmljXGCvSZZJtteeQcsMQKQOjzwYeNJVkV4FogVivJbvtOzYMRNcObsb5BLybGCahT1RBUXR0hVCljdF2qf8prKhiRhqj3wGTIjuwiTVZjUKBdM3YM9mYkqhIL1aXhGwOB7+LltM4RpLGcK7ULFUWo7m3UUo+CU1lOlOQVDYpromtUoIVGJUA/ZEuoqA3EmnJEUKTmjUFUD4sBmUkREi1oZ3qNY1qku5kCq2sxYq3AK90CIkSYg5anEbXWs+pkyhGJgowST5rutc7gVZqbipkBp0MK5qBsgBWrIGkihBOEVX0DpZ80q+pi1Qog6axlvhGRNt4xTWbVOkcuTGRFgiqajMwO0DBI2xlpeH56sloB3kIrv9tVQosMZo0S1NwA9BThetQSgnH0yheAxuEDWICNYBFj5lgUDEroqRkRXr4qKAaPv+WkQOZqgF6VYyiqINAIBlO11FCH7MYDl5DSbeF1Yaec9QqFAmjTJWfBc+jeKR5tCjalaBgEcKxOjI2wIQQlmRsnjNxBeCgUbpAXVFCZ9ilzW2i+CVlQzBXbN5M4FpDW+nd1qRC01uWikCQogDd3OjCG/Qlsuhr+pDKXphTJpPG3cAMm1tRyWbTCHpMNvaBAoKQWXbhMlwUSZv5STApGsXySlGi4e4xgiiGgJCr6ZTgy5nJRQX53+UCQ6dnPvXRist20IxdFS+U/Krc85wjyHVFpqXPWBsjrdmUZgunMFjatU1VAaZKoyo+bHGZo5ehLcWihbdaG3nJWmkMYhXG0OAztGs5Z6RJqJZ0yAQDKcMhWRVVXqXQPZlnPJvQVXWoMzWLLvSW1ihitHSLbCjRMBmqgHbUpKKauTVYpThtHohQL41mAJPIwnWQ9ugmNoV0r54plGCBWODFLBJWJEdqkn/salSyYCsjlcECCFchw8sGXwUNZY3iHk4kuxUDxR6nONUGNuH8IoPpNjsqpYUqF4jtRw/LBMqBXdkuCblxEzwVwUUf1ZwTCDxEp3DzVTJguplNT4rjA5kkShs2J9PGtXP5L60OPmXlrBCh/EwJBw8aF8bAz6FcOWeQI1KQAKwJ4YNnhaEUWzSyXBFi7AXtx3DxECnKJMwYkk7EBJw+0kfGiFwL9FDTtCg2uVStPcdsY/vZvqGZNAsiAKGVogpaGFpE09pSwsnGkDZLmyipZDCMwvSQhpRySXJS7OdaSeLRtjLqtxq9LeQpWGdS4VIpDJBFTU5eNOWwQgbzbmUEt+jPqS2EOQZikyuwUmAXE2oMRGZVSbXN+f9CJP+9kqJ+KIQe8hNgrySmTMUqhC9KGZJys7DJjRqK0ri7aAI+g6KBypbQUuZna1VtV7JyDCgKVCvlqLQSAUBICKQGAeXy0q0hDGRU46fl0NoJH/IQEokMiC9K1NWraW4hNRailqMIBYsQWg0440SLLSGcVRI+BZ1f0Qu0kf4ETcmQDEWRrc07rWkVUTRz62/CkueWrpWlSEbIpKsYkqYjILVBtF2RMYRqUn6zauXQjO7SozUR6ZXNC8zuJHN1g9zoqsxmrUxBAVFCYozU4pVVaKagsZ+lsnGwuAn5zgf90LEq+Z/aaVNZAjRCjeNp5RZQYd5GzAvoFF3VNozM4Sz6VawXJFmaDv7SqLMN8olEzNpcYuWdyjrKMugh/4Cq5J3+E2WUBq0AvVKhpQyedKkpMTt0dGBCCsdIBOWFhRSFFCnmUg5ax3iZC63N+on0GparYIBw0pWAZPEIUFiXppspEszYgpRK6zkpnzFzKjGT7JGb6pSEBDzHQLoI0pCTiIlI0ujVUHTOZsMeWIuloYjxUJr5M/wwxJRpG+caJoVkOnM9GRhSWLQZhVVLb+7pXBejDBO3KX0PPmEhoNTDarEi4BJ/AAAQAElEQVTS/hWkHJSapQgBO+Sne47Qvlj3TImaMIwmNyPkoiaCVWzX4YybrHMAkCoaoiRAh+wJTYx5Te5XC7oysL1tsGRkUI1qEBFzmqKiDlvQmKulA403XWWkDUhqJvVWJc9FXJKgbWHTHpDKQEUgBk0ubKi1CTbRLedNzyVl3bSJqvBAVaGlNl5PQydC7TRQF4B5UzOUrQNgDbASqBY0XBkmeCPq5TqqwoIBc+LsMg2Y1OiZpUyLctrDUTKsBRtFqDSRDhpgQyTalgKjYvEIl66tdRMQz5TB3eeCafEetGylUcyujiLPhTsNBmEbmFzQVVStYetby1aRDWdl0jq3hVINFNq7aTfwhfDUfNKVZSeHLEM8y3oZr5FVLkx/MAPIoRz4eZiCjZzUjGKpKq5VQ8AqNQqEb1NsRQfGLukIJbZCrxKsA8tSEVkY2dbimwULMCkqAyWDp9kEUGHU1tgeVdghpU3EaLjDhjaGsLUZ2391IuQDYyAJpPgmoQZnbKT2sqIjzLFWKUV2gYKkLIvSCzU2i1dU0lWGTIloCGuBCsy82aBGFUPNst33jUSMGTlvEPDWim2agN050jOJA0TBHSL2aBFHiVk0Bl7XneQyZc5hHT1Lo5DFpJnUzZwAVxb4j66oyCbFBatJiljJBJ2aQEFSDGcRoTr16EUuQkjfRk1oTL3VsHba3lfRCGEZGTm2gZpG+cE/RZM26TTxE9rTptBGWxg3thLbLkVa0pGaBLBkjbTCdojDYtNYXOHadromBjHIJChaLjKCX8UuA5rxUVv3ApMn+agqEEpqaaqTTKJ8oz5hCEnBecHNxzrBRXNLa0KqsM3wobVgBf2SAKpER9edtpFzYWo2dZoIioY2gUmZJMv5HvhcuZNaIoiwafVSJcOi2Zqzrz2qEgmkoLYwUSMG7xih4zMoL8tHrYvussK2y1oaWdsbNzB0tQYTZiWBk0dRRrlWysaiEOa9jFlZDBQ5bZ0aol4cOorRHmIOBsWkmtBuYYYU69/gX6gyCqJqVjIBk6ltnoG4ekMxhFAAAXW+FjVKzqwdakc/XMewUUywKavNLnNwzUgh+KZwa6vqKkyLqWAohRKXWSgVo5QuKEqgWrP6ZFGla+oSRUQCEkpyTwizRfq6xdMbjQVdogIm1IIYrPVIChupDhSiaTYxknkIHB6EyZT1AqtIMXC0kFkQWFxY/qJWL4zNRXqt5Fmz3Ki4GUmRrmFaXWGJJFxFuhikF2fQckmopU4p2kPmtWy4blFcZ8osASEXUSeBxMJEDV0MMTPIND26b+ikRTRPA8DFWqfgoini/GmWAd/gGOE7txq66YvgcBm0poBUFh9FS6lFxw96haqNH9TKal/N1+DbzKVYhbJsh/qBZOqGltow5Xdqxpxu9EQgouAxakQRR0wJC4TCTYAOKDOYXq6VuzWJnWeKsMbQnkF/ReieIF2XtYVP4nu6bknA4uAVpCxIKoM0SlIedAGGElEptoJkAbwG8aB1/LGurDKTCQBmm9JwHSk7gJ9R4lzJtv8MsZQxcY1QWssptFxoiNEbBqHDe27VCYlylWE0Qebme/jP5Dv1VTYuLQS8nIygOTTUespEtlSIuehom1G0a4NMc7SKJmIcm5LkoGOzoayBuz2DecjwKCT97LOpyOq5LLE6UqwpA0RjfIDFNh1GB6qkwB5SWDctWHKzi1P3BNMCXX+yNZh8kFXA7XkVLKAJEVpiRpoLQop2Ei+KK0jQ8gnZNVCeMoL2CikBRMxrTqx47Els0kXcjM5vYTHwU8SK8eY8yrGgr5kexJnip9m3/2SOylOoQ4qfNhWrCAMMCKihc1kYixIGXibza7ZkEWh0wt3ILE2S0bekgpYnKyHRSJqgRIJe1Xa7pbdKBkSzV3VubGCilY2MFGsBrImEt3m1ecWUxeHk0kASJ3IiOuXQgqoiEqVTcYVUS/pTpag5xAIZINNGti5s9Bwqanz9YjMwRbThbYg5oTSt0FqRO4LSElLgv+CHWOcyOglraFZgjVwjiqFlfVEaCSpiU6m08s0mornFyIAypEAVMDonxdXFqMdTnbO5zAkHmU7WZ5m62JTSQqqysFlAOPoENKGQIG6ESJRTxAoipRraY1PMyENFu3Vux2UqRiLEhPrYu7WG6sZ4ulaytPeluEEtZkiA/AkKByEdnRt5/GhcrqLatkQSOJC61MbZUrQKAi7JU9E1qmbqrWaZKqEtzcqyJpA3aqbUh14yuzIck6huMlIHW0nAnVu66hwtkgDpmgrtw56JTA36LNscBG5EeCxd1i7Bf+vQURZqaZIiztydckwy0lims5XNaZxIoSxiDkahDGrYDEK6+gJmSyVCuHnUolZOVYHQxE6KBrKqEE3W6lGq0s00zFZRGupI8jAhDFRbsg2q4IXl1NjQusuEmiuYHWmkTPPRdXQDI2zFUqCjRfJDXRGgJbSqHNRJf7hTUHpYKJdIpBYahHiK0jgFBCQfuw1l6oExVidCBWLfk4PTPLEqfpivtXKvkV18kx4pC2QRxpHR9l4hkCxZgQVjzbELlj5xmVOaKB1zDqUNhRRdhWOxFDdGOSiEojhRs1pBTGIvszkvxRNwwi60Va1vdvMMjDbzht4KChZcLLWD8Ram3Si4GZoRba/42XSNvqRwqpqqcCWdFg7rsL3kr1CwkdhhBIatxdjIKhRrShYzdE0JdykGkjISUbQ+UAEoJlrLENEG3+AkxmE1SujSLjkFBKakLBfkCisA6T2CrBRTA3r6dle5q5v5MmKZZczuujAUuwUj+eCpBCfoTrUSGVWMtdZqycAQ8ioNGsW/kRpi3aRpMoIMjkImU4Wh+G/J3NW6oEoUnfQGq2p6airaTzHcZfXSLn1GTlhIQ1M66cjcJoma0E3HJgI0I1REMJc0iPG8VZdoWpxdYcHq1wc91Ck4sYM2B+PvQ5fk9II4zQJvYZOU9E4k02mXcFcz6AiMRcAYZ4nANsmGyQBQWpVJwyQE0YXoIzJ9FFwKKAtpGzF5XT/3BSrJeTBoT7XklTYyIOTZir6WpLBbIPTDTKVEI6bCBFVRpIEwgTZEFkePmAdfLdCUghIkzWJrYnvaZnSuy6JeHd5pbqzOeLpXMkAJSyhEgI2fW4w5MMCK8NPHrBaYIXNTa017b721PdO5a5eWKW6V3VmKKqUWLaabpkuxvK27USHMIe0qrcHMlfSQNSRNVEViwtrpKAR0Cho+1zrVYh4qaLYtgI32pYiqZQP5HN2dGcNYaoALsrUS6dApTZXntnoNEjOSgbLgNBgC8KKPnQN7y2jhoplbXXFlnrWopjI1aYoGmIoKp4miEXxwRO10UHspKIwZpT8adtv/s7G4wpX+DomSAoTNvJrTSZ+sTaJGbIA0VPJ2T9qLTi2MUmqoOS3k1HThvotA0twoJCvBEKEd1tGMBjekCh8wJk1mjIyumLRZ9qmYxLsU2fJHtxlVSlvdhCe57lUa4039FIuEpAX6TnUx04FiwEKzUIY00j3XZLEK2j1F1+qJBLd283DLNbBL1qfQzvFjYB2+xAhui7JGcnXBim16Vqfxyj+yTnLUvCvufLRMCffOt9LXq02AVmZtuveyZkcr5Z/sjeorARtEU4L4EXqttdPX2qTyb0saKfNso+gRB0ht6byIjtrLhQuUkQMMkCecIppko85Gs/OHBF2nYmUyxFoiqXPotEqNWsOiNjoBvWQOvuzuIVgqooqQmzhY/5QPKMX86tltZxKqFOOTrM9Qz0q4xSO4LCxrFgTQIPQYLUfb3KwplDYXBfWdTKtKMmNGNo89Df6SUlx1IpKzADP+eeSy+I7lJ00Yb6QV1xUuueSTWafNmVtctLhjiSnQU6EtdZPNp+C9ldEsmGMqmZERMQaxYcaMSOhqEhMF++TQ5DwRQ5eaqgTbP6SWd2RvRYuCkUtCvKe1V8xByaigsmBGdcrdbA8Ra7ApZskuRBlV5QHPSowiGURCa9A0rrQVthD7wRzJXyT3SkgYKGrOQc1JDVb0ZgJnepJ4hNYETYreeOSmHtSQKE5TZdGUPkQXcnqX+cOwTiMmz73UmU7RbfdB/0MlFZY5SZoWIDK6pYZhTLQbCNBidqixM9fSnSuJ4kuzHxCWPNTK2AlBQbD4OWdoWVdWmtUw6EWDMwCrkZ8WK2r5gbGTWnvzRu91qCZdr65cLPd0mqUbetENAVBKEILgDpqZZK8zNI1+MlhDGO09E22yQp2jf9KqqClhg9FMKGMhQqfR1jGv0UsxTc6ZIcgSgbatBBTd+ElRWp0m5U29oQhKbPEtQZlCeUMFjZOi90WnkR1kQYC/m4n7MuRHTBmtq9AR5futlqpiwsIK/FdIR6cVEpG5c1tgIVXqIuvPUiIwE0NH7oU0b75yc2oprbW59hqxBJxja9C3aZn6AB2hhJKpRdewSsmkU00ogFCVMNDVMEmbfI9tU9Bd2zppNeoqAJVGx0wFmklXCFrxwhqtTdWinORgERVNIEO0zCDZfX22+jO3id1WJdlUTE1SiWKlpkQyGrWVGFm5q0v2cSVtEJjGVKMno7s2yqcZVQ6TaiQgI0NCoybhm7Rd7z+qDSUkstR/8whNpzTeMc7ogHgI032tWWra4yoRPYvFjhZoZeUQFr6q4ECT6KiW0opOZGEt2yXLL6tgQR3MZ82qNZhho7hDxVKyJJhYtTFU6Wcx+GAuuIpoaRO2bOaJ1RGuVkpJbEzNbLrUPLbVXhHERX3PwCkQSR2QwW8OCujIHtTPYai1sCu6m4KwEEurAo8SxaTZJRlyabQ3c09KJ6UG2lvZGfFGYBDiq6QLZ5iKK5ZeyyQpQiu4NZ9YGcqFi9pQzybIPhX3O5P0nhP9QhWJT/6U8ZSO5cDiOSQ2wCbH5DQMRJgDI2uJvtZexJRCY71kgIVYFwJdm+lKTEfsds7XVawaEqIJRJRE0UdwEv9K6DKjQ+xYVz294AwdwkQb+SXKpWRr4hwYhUCCJJpFJu3K2lkjECGmpnNHa5HmCXJt6el0hcmk0op3V8uxRTukwvCTo+RGbKtLIZV1RZzg7Co2J9fKlIQBYlGywgP+UzmTby06mtLDzM2CFRCcBU7INkyzw7rMKsLZ4CLM30mWJZ0uohjbSbdHZNJC0EpVCD/Tb+2Czgqw/Q9SWoCLGaIl9tZk5LMBM7i08tOn5rPGs6g5gJTIXNTdVGyv6SzQiBEjk8xrTRvxJRkrwQAw84Ug80ElPcWTImFT7IGshLShEr287VbN1BSrJiVaRaeaYBJT5bcUA9FTf0m1AqoEKSbgYLCPd5R0mSHSJFWokKnH3KUTiqlIOE4EuRqUKxIGLsoQ/DwGmmKeQVWFyuJOIFcYZK/J/P8j6l+grtu2qyCw9zHmXGt/JyBBHgpRkCjgC2k2mk18tLJZzarWtEpTohbWQ4rSplYUbSgqLxVE1IokIQQJ8o4KCiQYiZAHYDAhJEAQSAyPAIHk5r7OPfe87uP8e805JanuNgAAEABJREFUHtXXf2nW+va/v73XmnOMPvroY8y51nducgpK3cuafDKtt3XlTX5qjM7deLwLca187lrFJIYiA6xR6vOtyFhCSYKmaipDkptCXZY1hKmFZQ0tqVC6DDBU+aXaBOUs2p6lVqem0VdxXby3zlmi75ZEZwe2fB2G88DwlgWm625yLVsbz1clQciAJdgwYda8YmmtrSojmliMV4rCtxDAOdyGYKh03HFMzpOH2QRdJgyvhSFV7gxo8X216ylZePI2/hh4eE9z2SU0vqV1bHF336iUToGUmyhoG6zbnTrczyEtWo0utx4tJexT2Ard2pbyKTM2pz0OfzH3HjCFCmoT6DKSMMItqfrgZQwQaXR3eTNKUWPwJKUSV8GkGSh/xxykZ0sOt0eliT2G/B+Cx8eFc4EJ1eYFbW4yHHVYuMns6E122r3fiFsRQPusc/gh080ybGXmyYppRTfcvLtSrc/jSKV71DUiAKSppaxG0nKOPLw5UKrPB/mSNosY0O6Rrv7MQzy7TCOXcQuKuQ/n0JvNPhWj3bBLtN89UmSCyEkcxWEipN3jUDnJJs6L40kGG92q0a29nzugHEdoaZXO1ZPVve6YOUTeZbaJYSUceaxyAY+jcYCDHMkZOKMf0bpNYyvL0Y6bUFkN8CpUU6c21eZUImpEeyoW7MGcHmeLO+mlFvWpHNuysIluzGWPpX7knSy9ojs6d10Xr1SikKJZvfCw4zGmXi/zmA7r3ZlpZUMsVCe295ot4nWeDWljONBggIV0ZeHo86xz1Olm41iwC+0n9JKdVhQleCmlNrAWd7Q6z5Hu6vyikrDC3PCsPff1kIGw8vPVg3X2xDBFypc4HkpGltlVvpeNy462s3lkjSspoYhaKH3ad6TcLNQOVdvAmDi9BtuNdKV4hGkNfpyXSFq9JNnSptmPbDLSGDUqPdqicVXvrjuKO2wbHNNpBBPct7Fu4vXR8FIb2lahNc7QtAYR3SEmsxuaLJUpuN6jw3WqNbnHqDkTyp5G1iX6yw10J01gMMtcMcyHWKFOeiIjr64e5ofqkw09ZbmwQh6O8zQ9iGnWgsvK5DhjHoGBILSFilQuIVyjtU3AkeURDW2xAiChHwXXqzR8oXtRO+V5qQRwPKta/df7NBuNTG3OGFndV+NVjlguco6XnqdNkWxHTeU5Q7ec/cr6eeA58xq7rMWOFVdTU/MOuh3h8mWVdKTuzwekaNx9TpuYcfVc+cg+uoGu1ymAjhHqQXYIOCDtvUqtL6t0doJ2WD7GNm1AgFW4AgWDeVtU7lhVgWzE5PLeqMrLhZFb6VlPe/X0q7KOjYn0GTYMdTCE0RuntU/o6cwFW6686CLNho8J9V7FltustJdTQlUucUPYyimFvkmIwyN7YrdWxiUWbex5xosIzjhjW8t3l25mznYtOthhre16iu9Q0mTDBw/Ok5y1vcIBtvrh6zribUD59PVwlXFYSdl0dleuzcrht5prS8tmPTS0yzWIZWJFzitjMdRx6EZamlcPxGF7iowZeFSdyGFtTKhqdh15r9GwvjO8A/lEb3bc71IqyTpG6L4znBt3J0qlh5K/IrprUV3dTQ3pxDn3vP84kBCghvaEqv5cqfVU66G0XJDVyKdOdcKLAxQDLSQdz4wnePG4MBsGK2CLucisZQiF62V+4+xWuJV4zVcC0Wld887uaa7iwdpY6hUVemuP6ThMkjgjTHlrCAgUy+1gAwqt5oAf7NNimjw3bl/qoHUqncNC0DSwaDl8QWGKzZFKQW2oVWfITrnppTYGu16v4QPrVO8cluRG4zWzEVUxhMEhWg/UCTkEyFaMGpRRXmKATp3tO5U0fQJAjMSIjngN+gV8HDgfxOuVr6GGqqFFJLpsGVW/4SX4/qRKu6ntvqq4lqor9c3HtDlJ3hm36Fkwl2J0ZhxlL6qkxbHgW30933B78PR0DT/6OqHqsDzRw61usE4zDm8fm6Nv8T3nuDiX1xq6pZOC5zEOVYGJgWSvWUXOXS5b5/Izz6MdVpD4OjyCG9CJYbM5Lq9S9lUSRDVb/kuMiUBqoVXnOBZnuanOVdSSl8WwJ8YTXOwczaM4l9llll439Ee3UNMhNBICCOsWGyrnBO5BkzFHualQY3DV2JtqDSo0zyBauDDdDki4ff/nhm3Kn+ylsmBwt0lTFnUZaot2tZhBG3rAWAPyCSiGDtIO8aOnO4ZJH0Z3+MK4/59SqmscHGbiI7ZL8lpC3VytsJ5NruPIe5dKDDXLioUd/gRzmo+7hJ9tF4VpVadnH5aOC32V4Y7LmSRURloQ1bRKJB+mNIimI+o0tyHIqPDe09vNqhAS630r4XUzO4RmSE0b8ar6Wbk9MLef1Bb3MY+jp0nbRBruoxudtDbagGRD9K4SP/sJdVnHtuPqcytQ0SPisIsg20mpdvhL4tFpo8qRXZBieB1Z82qPHEg3DcChe4ZsccRS20lXHaboge3WqqgsCYV8qfdFtzxgUCrILl1CVqt2A3KhXLU5BMcnprV6vOKPKEZaRUuTaG9it/bvG7JyunhreDxLexeri9y0RaucCLWbR+LcdE2Wa4fihGzm3nX/py/oZXepJ2aSikiJIA/DMEXtdvVgzZHT6oRIZLT2GmtlePLUpbkNG5ndd2AXuBsNM6fEO9Yer0rPIGaBAu5HHjOnxtQ2XqfORXNv1DOZ+QI86GO0JCkrN2ktUb6kP9KVA1Qpry92nXY560nbefjiUTGsaJa6XmJyE9GAKwhP6+3Xms/dWRIW/TgJPSQHuGDLmqBalaSwzjfyUIG5mY32E36ADMc1LaaRCStXLxre3TuVubKGOWlwY9solemtfEOLaTbT6+l1GZ0qU96For1/aTo0oEvBPtRTngPFnIhJpJZnQaRsa7PZIuwQXu3bDnXMEziTs0wue6jKLrd6JI6Nh8Jhq4hOjrMfB93R9Bpybd6G9I1bHSQnxVUuR2ACxwBOw4Ge7VNC1PBJsC3aTdFZHagHoLqUNmbDtDaGdYoHxQIUkQWJvpZ3jM5ZSmyLAHrX8fp1IzPT8ikCWjIJDKH16qHlVDcCBR2EEWe1775TiqalUX6AYMdrgsds8yKFrQdLl7NjF0oRmw2FJ1P0QEuZ6lZz9oM52ZRVRmbVomPoxRw044F+UZgypSYqxuWqFLvJdThBlmWx4CXzZlbE6pZHhWA0iIMbYteAbHG0zKRYcNDpmgBLHMkTdfqaUh0C3ro4aQ5zQvkpdEI9IZUlnVH61Cg2byClAWLYtkhvRyqVlSGfCRdT6mohlAdw3P1TnwuyWAIOuPUxOYxGNpr32cEcAA08buG7ApTjCuPtoHVpA8LRpA34bGnvOCSUTQZSvN6buFbyXA76FpG3j1sgp2kLC8IG5zG1GPcc6ZLqXTEZcSuwzUGtEepp0oyEOTEPjoly25BibDRvpQRFigkdyzMyQk8SW5njw0QmTPEoz4Ihh3SIMnUq7Qz0d5apoZSQMpnFKCJb8SMj1QjSnzm0iKxKSRFmaBoE6wBGTpYQeUnps8foYyihNNEjgM1RPTPsdbNtJKtEnpWLK2Ubr/sbU6IYlRZqMdu5gIjG1omGsgMrGzU8OWCmKgFKhjxValoud7M2LGQ00dVVaLHr5t5t1HH/amcN5kQa2DUq7qzCUN6lLO67WRw0YmSLz7NxeuHg1nXJGEIp8cLY0zXMq2VmFxNetJKEDa8PogiNA1p2W95a3ryDiJv7Mu+2al3VRO8583CFVzJZYB0eJy7b2shSuTDjwTnggPSkOrU+nMQgDw1vqSBDt1pj8SxOT44oX+C+gR3AzBwR3PJY4VGmLKS+Vinta8mRd0keRpiY65JeteZUu5UoNGAUb9cJJqmCqjBrmsGciqgK0TJ8408tKlpzlRvNJiAuLNgy6plHwsN8maeNGN73qjG7yIY8W5pdQ7NLcDNYjQptDi4ZLaz0uiGaqoWm8cZqC6VL/IhAR2dG1CrkvZfyXqNbZiW1oUlGjTBSoBq3AWtNHxQ/Q8JEJtQyri0+gMpBGmdToFiUGaWiFfJguyaWWTjESIEwcDSU0tRRMlSNMoUuwbCA5kgFfIvYtG4oEFGmYRAt1tGUhAGZqXsO1X/8ZHonu58BPb2+ktEDfMBtiEBcrQmA7KzVajLV6HbU/bThUT2c+pfOchkptCieXCoAwdLINog+Qxr6YFOBwUljK6XCSatiC/qGFFe79opIlGJWSfjDp/lBCBE1L6YpdVVeIkqhznXOOLUA4dbQMJgSUbXEdzniGNu1MntD/pwYJ+epGtAy0ON1WbK1KsLTYbPmsJM+h/7BhoPaVXqWbBp6lp+iV/HRm92im4t8tqX4KtdJt+2IqujYqCxl2tKJA26aH5ItSLM5xzx8THkgKH71z5IzhoZ3ItHlNIPfUNx5Ugt9zbSECTYHKRDlFnYERIbAICUYClZoNkYpqw19xWhBaDGYAzAfONzoTNqWIZyGQdTkHlZAJ6pfg9Kv1mOFGqC1McaMeYodDyA7m6lhBU9NApT/I2zyNWyXIwhL1Nia3A21iADTulCI7MiskLt7vN9DKRO4a38Vc48WWF0GfTj8KLqMUB1SqQsyW8OVjTCEao8OhXsvgFVqKrLcLScVRfXNYJUpCXamWbO04I+uYbK2rXvSThvTCM/w+0aJbQDYUKhV3d6C6OkSpfbnqSEyLsonS+joDmj1cWxwwdNGm5ugEzocNHiXV4LYxsu9/MQ87PQhcmm5LYJdGCiTiQ4FtdxjSOK6X/Elsm/F6yrgwKz0vfiMXu093KY73NscHA47o9SITRyD4k0viaJlQZObneKfxBh2znOOEmD3Vbojt04LG1neCMunZbgyof4vNMzGhVzRWaxG5y12ESTU3TN6rL6704CJBEqvAtrz8PPlGP6wnMp8RsReHcWmEdaUIKSg1O+Wlg70S8HJ7mSFaVdYKyvZ0Jo5hBq6Vt429SNUZgUighHW+lhttU3mSEPPgdPLASvTV1pwlPpLbMi+zsiITl2lgFIVYTPSeimRxSCL4qpCJQ2gm+odUNJKFkNnxDkdHEwbYVyHLd3EzJ6GuxcVOgypsIEqg6qp3ShgJFBKHRVWde8GEsfq2XBdABrW7EQWUlypKAwarGIqCn+LxlbAa7vmKkdpy2N7qtwKNoxgF6GvoI30Gx5ZB31ikLIPBe/ps4dhhnnQah3YfoDSBE9wTHe9aF68j5Y8hMLUwDipzMHog+egbAAaJN+kUfYH9gvVtezgQfgdx10FUzyrq57Z0jAZ7ds7PDOLGyPpMSLP6tM4HE6Ohhtn8diltkDusqt8+bh4c6S551pWHZOiGmy90igWptuD9obZCdHZir8gtu8XOrmSVS5FMO6vAa85VRVObctKSN1ZnBEj99k5STqyd+ppRUkiDdGYBE6IxkfTN/g8culx5lAF5yibxOlQNVkZnaZHaUcPhYpnYefgNBvGBEoZeDx95Iy2qzu0tkY/0O4wc1NSepq+ZhqpAi8AABAASURBVJmYkmmj4a8dAoEDWmBvHw4cgWOrGIsyzdVY1Eua/8D66R4mLljd1dG4iDSYw8Wg2a6zMGXIvUfRgt0o60pYKG1oqsAqWhV35/EW0G0o64ou3MGwMFoLqktg90fRygmdkzXLwlWhFkBIj3UetOlyP9JmgCmtnM1J0aLuKIur9ATtvoROsY8rGTLio+djn+KMTzAMKNwlA5GZN+SgFc5px4v50dR2L9E9IG8Ps2nWACvPjQfG1GmdOkLPLhUVt2PPbksArkGZo6oasDIXm7qSjxVvdAwvUSKvfA1AfI3QWnm6dsk2ccjy7HEoShaP4PkK41X7Uz0seHd7pUVOxuCJ1sIE9mVyFSxdb4h0RmEr5rwsJVqN1iBFQi8DZ87X/zfo3GlJhBQ4txp1dd0POiO505r0mY+Z5/0n7SN8iGtOG+d4GfPRdjYODo7T/OQErQnCXUXXr9l4sE+tl0+7EhtoA7RijFAltg80Omq/8loOhfE6I3EIAo1+xnxjH48S4I5oyUXGp1Mhq7RxBm5OnzaucfZQMnJQEShdkzxQYsZTWFz1Jx2GNtxgzQwlhX0L68SgMMv3eRVfIZ4diZzGx6hjXphhsw8qxCoTs5mGUJ/HuGo+OQLiFn1vPU6WKx6lFABHwUvxEhYxV1gl2eXUrzIUdAE+1W+q73jbHuD0MNvmT0m1X+H21DwuPq4+Fy2l5GjdSB11njwHzC+oQrFPxoM5TJbR9OYJHilGaPrWTO1yri71S3XjlkLVISZIHDA3NmfDzXC6DRfP0F9i85otJlpBSBPQqJN4Oc4XezkxZx3qShPuCgsi+F5RDOJTJwB1BnRxK63bIBs667BzYSwV7Bhwo5tNc9Ps4POyV2s++7zgF2LV2rtDa9/mUXYWnFlEKK+cNIKOMNtN06+yVmuATgvzqeqCDmVj3CMfii06gZ3qyS+2Fbzmy21SstLI6l57rZZL6ivStFyIaSj1QziX19N2AF2H95yKjlLuwt3TA+GsAzGEzgFx2cO9z5kHVQekXDfb1f9iV6HKZVu9pm/Ap4lAH/uw8qIeUPYT0J1eFwAazMtPjV+J3crJ+mD2kwzdlqs/YYqZJELJdFEpoerTkIayKZVZ2qjjwBgC7IUhk8PMIGgTYlTF0OWDeBQkAhJtch2oGF1aVXw1a4258fo/TIjKrlUVC1jlO1FWdmybu22nF0YPKjl6O7QSrXzJOtJNqyNeLr2/LnPaaJesR3SalY+bCl8Y4dNM3e9ECZ+j2Fvynaijc2RYd7tm3zE5R7Kfe+d62tozzPukXjWQDsA6vbcqsOTnqH7txUpphOkpwoVsDeUby99Y81hTDRTQzeS1Xq147ojn8ud6WfusINOOshMUf6JZSS0RvyeDJlCg3qqj0eFc0yR9Ge858mWEJHc02TP73DWvS9Hoj3Gn53wpf6N9NjsJEFakjPc2xtBX3AWycC3TH7mv6KjakdUwjVcUiyyYK8xnM/1O9cOovotDdL8CxV+YKjH04e5fZsTro9UREDe7q2xLlI+Nl2cdEUQJLdxoaIcfnA9zlxz0GiijTOgFMGlaiq7RqaFoyzy6TqtzkANuIv22aF3mhZfqw9rGiONch8O8e2ScoRsqEXSWql0iMP3xdaye5aeaoovxOuzSGmSGKiytaJeLARm3qQ5nFHFk6ZegA8uYAvCYPlSKcjMwxM1c9395JKszwuJSpqKU82O0G5THCmkjqK8nlWkcWSrFUk73RJ4Z8gk2vQil5dawqSz8fuxBxRrTQka7dq9A5uj2pscUIDCP49XkB+yw5BEmtufVsXKF8g0hpIvsNS1VbubycMz5wPQElfPWw/XEBQvjmDjvBSOLCQ9/xHzJfok9V3KrgAF1TDvQeimznUjMMtWXCMxegkmlsXXC8oApDdVJLDGhrVNoKeWBc7qfphChcmrNZJSnOOvquJcjrel9TarMnGtgATLkmCphDjkoSWxv6TWKQSbz7nJKl7WhIZIK0l4cES/cb0AyGBvHq5jX1nK1uld1B2/35JKCt3MMvLCnogwokbLQrDQoPXUAEzqq1GBR7DZKKKvcomkBuzNz9Si8WJ8oRFbldB6DQ/BhzWl+2LA+sqacd6AS2sGLP9Lo0AgzOEOy8PvaxhUVkmaNqZdA7qH4MGKO21evLsFT6hte8nEQAyJRv0bOqWsq5cso3odpWqi1HhkiiaI0U0vTHWrNsJFDu15ZKSFpsxgvOWZ2Z1W0ehrYykUqGkpD5gklSG8XvYdP0KzmaIXbvFP2NHvOsw6t7+5VI3NmqplbKZnY0+o0H5jJofQTuCu8MUoc0NrYA7dplamahJtPyeuV59JakZOp5/e+wX3bFk2VW3fTTjZ9ibK1zh2juwa3Rks8LXTGLoMm6eXZR0i+4n60yRnkvK0KFUDdRLe3+RYZQS0KL5Rs23pXrRY8k5ZhYvImfbj38GCV9Ai9FUJCSbI4SoYEapW65VUapVngPQId1iHVmTnHIH1EGkLTwO7cq9QIG+iyXDdTQVE2ycFI1tWm8VGmEZpApHMX98bSgrGVwWmw3b1C5mgO9yKTIcK47nl0ZvUOyj5LKIjZbpi0w0SOJO4bltZKFWKkRvJOFGePgnhqiLC8z7WWnHuWA0RFZsGc6qb5YM1oE0c9WpXdlF71e7PuUVG3CStMvGZAQ4uWjhv1BtLYlqWj2dRnAAI+naeCChsSFDB4U15ZTWhQZ1MJ0M1TGWQDLSqK+nhf96IpF63WoGnRE9s75FAfSzmz43alripS5N5Ik8tUkh+a9zmvWiZDir2LpwH1m1OTg5C/NsDMoC9SoeyWQjt7WkfZLQ207O9s7XYsXFuh8fpMEsIwLafOVo6rxrMV0kJo8U9z0/ImKfvNwz0DaUNeb59h0dacgzTbYIJcriSO0BjjpmJBe0g8s8q3U2C7mqGwDr3jOkaIVcKQN500oscKbYXSIMqMBFHeVaiypkYIkanWzi7FUDDxDDp0BLDh6z4l36/FIoySzbwTGymxdgK8U3Hs40zOxIyKwtVsM+mcBUu9GohUhqqcPkzaHjqxReEqy3Z04fZge3hqfTsUx3mkzc5pm7PskLiGPlqWgj1Aux+YeJaKaEYrZhz8nCeUojfVUE2puzPaW07NHyaNZlNmjlSDQiMjayWEvfooWdEpA+ygB6FajA0UXDOnkHuZaLcmnYpCAyuV4ay4EAWiaN1yj25gtCuB5aW6MTeDFaDCcRN2HuzJIrvu+4yoSiX8CL+ndhGvzcwUeJDuspXGBjOxm1cPAASZt/3k2ApAqHiz6RT5TkdWaTesgUYbNYZMaH6WiKmCnBiStRlF7S+XOs4pnZa7UJyNF+Blt+9BnAJsWiucSrpePWEnIDGZRjSMVXMDYcs8p1hoeVCDiIptEWSPUXPAzUEF3nvcwQ3wSHjSbsekQrwZN2AmHZNpuktSpSVG8ZG0y+Tptq26Up+1anE+G8xalnpV5yCNECig9TFoJcyl2QGLqmpVTgl1iEByuzSh+Z26IjSNG5tKJ4DUZeMafbUmYSIlQAGOZm6Cd6Q5UCI5YYIhJqRuJcTOGocwGAbDb9uIGt3a/x++Otda0Dq+ux3g7Qg5WMNBdjCbup2/ZO7FcBQrEYlMVyfHpfd7mgHVUCHKw9pdpXlPSpTOMoN4lsKzl1ueVDBeUEfxO0EHMHplx+pIbmuibrgQyRPDzW8b2NW5BXubPEXXVUIKQGWEsUok1N1kanuVZAMtMqtNE3DsW/voLrCVS2ND6ch0hWZovaCwuinv3oqaEhnP5khQKRdloGSRpoMGFHQQKTJHSXu0PqxV3D29fIZOXbR1g1JMvcE7Di25IEG0RXf0zVHpQAofR43kLJ5Rd0u77vUXHY6E77r1bfDhA+fEpFMltHB2nymd20if6cLWsh/s1Agbx/B72RxymawgUzJQ/iDnxTCYUdZOjkldAZJsc2XkwWW25SPHCakHx2w7lBjKSztpXhKfuoLZMGml5mRj9Na3qVhGTysvFKylNZdtiEvlEGwl9VQwaOrhuDpGPi0v6/AJO6eJS2JjN7VzykflXBSvQd6RwYtWg90trUhZDoXLGzoIsMaMmqkQR+o3hwn6WM5FXDEqDrT2ST0ZrllQ3puGOzMu9/CDnqgPEE+3VkwhBTUVc8OgXbDdYw0Y5exZNmJ0qnC9SZYidbPmUbrHEUuH5Zy4MzULCLs7teVqSBV7ZJx6/LPFU7Xd19uutqXPBQXTEb12rasrIboCKE8/1inzsBXIZCsOQMNdmAAhsnIP4WslOhbEhXJjFGP9TJRgCutczYjSRaAbFRXNTbRppKWsc2brR+hk1Q0iDoF9qVqrq0V119pxXRFmMXWHoUzbHRjhTiHqqh2aErVg2mDFSaNYNPqBe1PHeopniBSoTATaiXk0JnIwp7c30CmAjSzBm0+Ma6jvVOBKVCQErl1FN659ZJ7gCyUXMHO1ntu9rPEIcfvcva7q5NF+blcT647M5moPuijoKLGh4jxT+RLCMSAtTjOFWsDtqkFBdGuFN8DZepwLPZl+sI9pfp7DztE2tvnTVciAIn8FiNgaCujiuOSeDitDJpQf6HYOpwzLwW7b4+jzkYbKtffaupF9PmJriyZblfekgT7bBlDa/OWy53PGIns5MHs2vMRyrKNClTzGwrFKf2PqNStP1oPbKT6BARuCDnX2MdfJPO59zhg+YV41Kh+Zo9DCCzPaQXGS5IYysAWn0hFKQoPyaXLdc2+uS+qaO861LEMJdHUFPtm0mMyH3n01lPkzNWHZSpbyZCY3cN5Ch2UjpLTXrpTqNflKi5oUcXU/ixf9SRfDZWo3D48z14lwhwC36KymIFzXWJvCQMuyFppws9lDuafsO3yYznR1XJUXXJp4VbrZP7plC/fRjb2814EaLfYmlDFTkS/Tk09w4Dh9TAftqaLXo7X7CS867FW/fIDHFTMiWrtSXxvRFtCrKlRPQAyUCIQOTwVNpp2FN1IOaJwJj7lxKG91Oua0MHsN8UyVCpWxtONyS5MJS7BuDkqoUa2VanmmXOgsaeBjBG0tZPU4SS322dlLcMGAGczTcel7VVdb15DFxV76ukq1d1ytYuRlmUfhLFJ1BM1y+imNCMFkHYq/uwu9JSerQo3AUDgARVWCBM5uu6p2V82MMK0GeOpBYb2IGIpvHpdepflddu3zVR7PG2QdXeed0Gplk59H0yblplIGl3mrjM4L9ewr8PTavpvl1DOFNw4cfgmvCFMxg6DqEVjhED56+0m8MdZozWmh1ak54mCo6OViehvROVvs0lppiD01DhoMKBRgw1MtbL2u5OxFbAGt0lo0rvO49Od8g0VPqCbuWCY3ENS6xE70ZVhDDlnH2APr7mAZAz2bLI3I9Iuif0I6KQE29ayXBx4vtHOyiLUR16BmW8TLFfPVxrMruI1qBWNKPWkDd8t1qEoimLvvQ3OnPga0GbkWKpqKzbp8J1MjEtkMP6S+ewNW6buVAAAQAElEQVShsbV210XUmPShOKojUElr1TTnpPlWXIjwZx3J080fDu31PBq9xthnQ6C8SUh3qZV1ph8wiumDItevbbHbbu7LgCqItTrAgQEfrdy+cX/ErbuuLDF2TABK8mGPwVHuyz1Jt3hhvZQWt+EmQOPOdvWIu1EruBLLfoFPh5rsK7Wz49qUnBY6ZkqSaJHh4VZmBQU6WnweBx+PMQ+5vGGk9EBnTVus5ZEn8uF7HFIT7dWRT9OzuDVy2x3PaD4ggTom16htTyAARctuLvDq2sia3UMMeLZFe0Now1bcfDSI9ltvfcEXzqDF4r5cFsDSi6VTR9Qb0S/ZJ4oenEUaQZQbp2OyB5IN9YdOVkO8nYXRvavu//mbeDUIRUM9JNBCo78mz1skV1sqjxIrsirxGtgsPxKVraxlwVaOS1kvUTaoDal5e4GLkbkhM9g2L5ggkHH6OrrlTrwjh6vmuiTJwkoQvJUIgL5tXDarvUXbMtuEVb7xjDekH1Zpm3pIGBrvNPC+LX+sOq8LKzbqFTMr+bTxfBzX42UdY9Gi1Ex6IWHFY3NWTl7TbhQn3FsEa5uxWmS2yjiv8Odl6/K1su4/5kWsY+83Ig+vFFUl2oC0DQsZoE2Owcfxud8sizhK5pNMp+jqGtiWDTDaL9Edj/18XPtYnD0HXNmwbkdqjEY1j6Y28eZ4wM4+HNOpR6tO+e7aWYJZNz4WgaEkdLE2bjmEru6K7d1KnC1ZsXk2Zr9y+8wObVCi91KnoS/MtY+qgz2th2jFWK3twMgY1UdACxmSHZ5LvByRL843fD96WasyIMR3b8ESJaSU0L6yC1rgm9M4iA5UArAxXG1OTKyNevZ41eeKWftAaBSzZ9Y57Lj74SlOZu8H0mHk4a2n27Qn+GyPGJa0tqmef3icvVzRC+jB+cZ6vMRwCbZ6rX51+VYzzYfbOWZTSmYhtGx5c6R54A4Cit0KR4i07X6JsdPnAUJYA92klSa3587UTla1AHBay7+PKufFcfciMUJragJbzWDiGrbH0efhx9T59BBZ1Th3H+WuAPOslGcDyjtnt5kLj/TW4aWXDA5G31l05ZXP3Zd1TbY4C6219hwV90agpCIFPzrHphYgMHE8czwX1rPX9ibIoIV+3zAtaxQbDIEb6U74S/CNam2Rz7F8Pnus9EqaYw4Jc7bPhIkVFJmjC1ZK+lOrpVze1rvoQQAJLOVBl7kTWVYQWVwrVFzGmKNtKNdaE0crEVzFqFqoqM7W/FRnMbv//xe+/mZjz2OP8+ojn/Je2uccmmoDdaKNBYVcmymGYiCm5zDh7CTuT07hGpgo221RB6Bn2DyqK1p+l4u7uqHErDxbSUxqFeAGS5lZ7WFS0cl4WI9j2vAaHYIKApwKCEEo5M+dojNhmcakPof3ZqBhNqjZEzRFnt0ialeUp0K2dZhuJEKtUzYxjKfxEVBJajTNEuP1IpLSY+/n0aFa4eickVgdFzovx9O5oiXZ3Vmo4XzDTXwNJX08C9HlxWFmp9VhwaPEVYCtAZSaxFLiaiw4OMDD8t4nZ7/m2hgdUaqR8gCS2KO3s0nQcLSsB7YhhrR+/yfbEdzVQFNRRtoVI/bpSpOMm2KzSDGRgtuE0oQDPaBPbQe0/VH2D+1JhvYetibJmp2HLMpQH91Tpil2u0WkFuVT3c98UhG7LsVteHUFhFuz4IM2U+FmILdVaVEGDvfHUHIleBm0MCZp5VYECwJ5Ws/h+oFwBlSWSpL8hHOHmnRjUyZfW4saGkHAzKLn6q6+dWaCz0mb3aYe01KK9Y6szGanAcMwBnBiz4rp5rjnQDpsKQ1MiE4I1HH0aRqnssgDNQENJgw6FPdEnbbOs4+HaWJzc1yYoeqxKKlJmSWs7+DczYb3sDwIL0AySNkAQFOlM58ZSiI1GnpT6HpXTCB0lFMdtNRsZb8kjxxlw9gmwQpvCdPcYUt3Bupm02xgJEa1UQ4aDDALO0rbNpUR9xhbwT3q0CVkTuIw0veyHbghHhWMJ1biaVx6BHUuGzlg3tOSqs69HRDRkMBRK3pVbaiRX8q+nQld7mYVvdqtyJrRHkQNnUQJY1kUsqxaRClSUlkzCEBOrWSXumRoHi1HphYqXLouu7bZqsWAWJ1jN2+3pUS8MXUqFZToBDfuO8aQORmJ3jtC97ZMc1BOF1Mrf0cxLo+wC7bNCxIQnCp3iIyKRq3RMcumUlhKwO4ssT/gc8xxDlNaRxehuUC7wu3hVaqIMMvppXTkZXt33yJ8gtc6ax1Wwzg1q4S1EkVrGw13s9N5RvW1W31e4ko1laaTwzmwDxHgqeZQSDR6d2UkF2zBN/02QkFJMGLq2xDMA4cbOa0PYKDFphB1U9eTppTImAosynqfY0seZz9npqV3vpTawm7s1andSsqSZ6GCDjNX2hYVh3dNhiZ5yrCl1LoGllq8eqGk2Rpk53PMONAzCAkU5QxFTnf4lBJAcKYdoDCWa3sVVytASa91uV3EuQsxc7fU2GnRllZ0RVxQREyZJlGlGKsL1jSjBiRjW8ZkCnSkL2qjbNUuTiX6Z+6tpj9ks4pLhdMNlvhyAp7mdyEMnvRz+XFBg+966RCUu3N3ll4VeyMXsZ1pDZi1e6su5TwtFXsr9xZk2W0+D+TDcJjiYqV4VUgNAlRXHJKaj6YA9bVLrspZ0+LwprNdXB3UYVuWQxZtcg74oOld2vUyhKONbpjosZMpQunDRIwKOp4TGwocJreHY56mJyWtoAOVoniUjZpILwiZEJj7ETafPC4ZDGYrTpy0F2jHTcqbuoxw3ueX4Sp1iRCdQiD6U35uE5QcnBsoVEe0sq1fUFeLNqHxybNVgFNAJd4YFoZkZD9Zq1X/qwHQXnNxQNlil0fNpFc7YmhJ0OU7fAVSYqmIsgplYtQYQzBkInvck7eKSA9WMAwt6Y/UBAPUuY6r5oURLjgch7sNjps/5D3q8tpnwJfrO7qdwd4VrVZTCBxJLT9FrqS68bUinqGKAm5OjmfzUlRadnWnckzwpftgc/SWYWnUMDzd+g4Fr480hKPhnufUTVpoMG0oeUeMceOQOL01ZnEuHNU4q8ZKW7mzNy2KnRhs67HKJa2NWHo1Lq0ZeSmtMf0adrGjsjO964E+RVV0x4g+L7qoFJ+zWzizIjIuWXid04M1Kb4HUonoDRWXMpOYq0x0pjnbfJm8e+aaeskuHPA71GpkZUVCzZdqcGPbQc5RKJ3nQl3M0MJwcyIXB4xxe1QeqMQ4DIq9XODoRj2JcTOluagLA63UqkDbS3rr1iwFUdGMnqNraGtkOctUR4SDYwFyuh0iWTZRC7mitoTicwmieOkoRBaLPMbw04f1LBHSVE7niGMuJxCznmcsX4UkOYxDD+7GhlSKhDfYUJhrx14VYZVWSLm99+SdLS/GVMHBrKtQe66nxxoQB+FF77E5nwcuZY5+jvNQWMfryy3TZMHC2Y/mpAtEC6Ze1s58ZJ0ZZ6nsMH0eNizB5Xe/GtP1O6rvrwPhWVZl3Sw1A1tQAA3mfXsmjisUpSeSts0Z4lKEA9DHfmoKiGEYuFgSJyJGxuzywugxanrZyLtzUFMxXL/kqaNra2mSGWXVsGDL7m6pVDvAvJknpcFm04va74xBnLNopXPLcmsncYR0ktG5ZVQC8W7rRHdqORrnPs6cQ99BszGH22yqj3bJB4lJKSUU/dUWoFxVYZWiVwY396UvssijD1cB1SGytqpwX8VdE+0TqVV3Mr3TlVipwyHAmi8XVfLUVpIXoCf1HUuxcmSLIuuSoFUKlikz3s07LdnPxAVqr6AeOk/aIWzzFWZC8eMYRh5VM4ISvCuC41GPs4dD3rCTq2f1UZgio1WhR2rtVlbSspSlQoeGUu+K1MEBz2s8o2JoHk3IteYJapKg8YbPvNjLOkGymqtGtANtLLLNugdb3wlg0A88jpy+DepYCs01wvVZEta5ayLEnZTb7BYRwDz8kGSkFzYJiTFTg7vRMBVo2TTI4ByFIUO8YFWD9Zh8PDAf4CyhADqJ0rRGKuiaRh5DU3EfbZpOulMDRYatm6Hs2qYCTXEhpRhtqMUgGiWOK6nIMKGFstS3b0o0Y2XEFS0DgFFlbxCRLeg2agzBA2GGg3A5g4pCAY/h7hrgI10JGqR0NFW3itNzz7ofFx7mc06Hm1J6x7NCjGN13UYMt7wIys6gka51mEFmv5YT7oNdHtlZegM0ZcRAnqoUnYhCKXOaOdrO1WMpjkyWDc7W9NqIe763WldZqWom5eAsaRUYhukU+UwBy156SQapyX7r0GrnrcQurZ4TSMVvRqc1PMJWQs6KWbeW8mS9bHu5XrBeOh5wcy9jt8lIxo7uBZYYUpKk2SJASKxOfTLApkS1W/nUUNQ64zpz6W/VrjYn8dvBQ84JEXHvih97vdR6A+vsV+yNtF5zC5eAn8zhdgfXe+e1RaEA5emaS3G00brJxCyUUi1YtXfHZueB4XcC73ngpiIdKdNpZXP4efapmoCOaq70S4Mv/fX30EiMq3XLZIIy20YfQ536JGZCPKC4dR+mhqDnCJiR4jGlxKPbgwig2pmOKiAO6hawzoC0mQkH1G4GQmSpEnh6389KVqAaRA8zgTNzKbbVHYEID4WZbR6D5unVo+UEYkKCgysZY8msv9jQfBroIG1A2xnZsVbXTUbMLIGEdSEQYVHS1+mSD0zprxnwbvcSPrTUxtBeQcWNWN2bUJpH1bFjxtZIFikfp1N+0zxsJKaEL6Fjl5g+ytR2stRlDWXdBpKu8EQ85ENAkCQsYRe1Dl0Vr2534i41hZpzy1J+7nGBFnOtFmCldyNpcmNyXmwNcrrDpIXkurietsMDSqSqTC/9KWqHdKtGoj17XYfdN2qurxKBFs6jFaKrAdA4TWu9zImjx5XntRnStPJEpVc+oKBKqx1kCeVNT8o0FBYKWRqc6PIRZrtbD8HC0JM+OScOwUY0drCk3i4VmUGXXGtZda3M1RUpTpSNMdoPMxc0ZRg8ds9Ks7B7oyo8RhykUi782d23Iw13VswrH+gXo6u0XXlht4be7ViImcOU8nn7XoNLhOsiykW04CWaEKy24TnGZePZujUwOzhOEdSmjqKqSJOhcbMnLne1MJRF8c5v3CaGmdOKvlUqJoJWZZTy2d3iwWUAmh7HuaeiTcOGZzvQg/qWo2g5ug2wVoL1lGDSlCRrc5jehVgXenZLg51x26XGvkDR24vZg8eRqgoztC40RdMYy8/d59qUMope0zjgY5uJj20CoPjIypLEBCiFh+ill2Uay45SHyud2xRqwE3ple3MqL06F3bXq0gAhApVTrAlJ20rSgGClJzKJJ7ZpTQfU61BxMpkojq3ihMNaCDTJZzZGKjhhI2CaXu6zdVqFGGdvueUEufiDPiWDlVWHdEVm5qsLYxrCpdFjJXSkUCxxQLHDtRSO8SqCosk0rwEspR1aVHSEYPeQ9EVrp3P1g2kEm3sGBpM5xVWQQAAEABJREFUA3XxqLvYxVeNLoOKmZ0KU59qWWul3lfoTwfjUni+8dj3DcaolDMY99Ra0cndSOi7XiqFXb3adnmJCPMxD2q3NR/ZB/bkE3O360eFKHEcA1pmSiSklriuZkoXHFZ9pJp9tXAxt1bHbiW9B6Qhpb9sXs5gZK3OkkMoqwJGZiB2qToVzVS0woNcqRwrnQuz7Q1SVWadsOREHXOJAWozdx0RrwMSQ0LJHunKGTZb+hBGQD7DOlRKot5XIVKEsU22urDLsw/awenpvJTyOmIP7mPEY+QJth9r+HKH+3CfUwrBulOsTPbYOYVsWBwaZlpVqlv5qe3Kup6y5QW9d7rRCa+UgHsbeXDMjTsZgEMkNtvI2f0oDrRgAobS63V9iUvColsgEV2dMe5gijSJMKfsqWHJyn023dctP1UejAUrCCYyb7sSuDyqDK7Aq1VZXaMxBDvDKyjnjvahMpRLXTI0gWHj1FNokRJk4my5Ow8bLg87FFlXdlHce2KmeH8qYI4DD8+Z23o35PVAfZ7SqJwAKnkI3gCU2YZ+8p4jsY/aSO1QUgc62qQugYB8P0e8YjyzO/xopaTpz8Ylc40dzIulAk+RhLtlKfrEnWukeUuVIZ+CAh3thHLLqYBVg6lSVqUcnkZBrSVxRlHsroeVu8FMKRFQNcTHVdwlag1KYRnbfLqdDtGHqHjKqZTBPqr1N3jbzvbT+ZBPTYMwyDbVXlLEN6tM9VA0SPj92H2sms3hh88D08sZ8lctbiW1ndLBMnL6tMP2Cy6D/HJJArkdYcjRMp3gxrz4IrbGVIs3S10znZfOvIa+uL0yPk3tDV3Kk95N5kgxRuWdwrRMRHe2+KjDaG59Fs7MuRd3CnwSPGAThAz4jnOHBH0qKEWLbpl03Fo4W+E46C3p5YTwXm67rWrSKOVqQZ+2H3qM5ErhrPKr8Sr7ueGJE9CyIUZnBItMu5mgThJEHYVxjWfWq16vsEPeiiPhF2qBQQ74SFrQoc+aRPXRp2vBxk5xKEExDtYx9njoBvWV/FcjlqcOKjsbo1s0yx0EWkR3byz5ZNadXYjdSqiPt7eC45C3ZqdHHlnMZrTMHWw6yyC5VmFt8eDQkX2bU9McVDuHRedWJ2NFteRNBa5eMa0lbMCg8RWKqfRgUKniE0wCfXMvhpkHbJbkfei8KSlhtYZCCoFJmaSOJuFK1M3Ki+cbXlNxMkWcfL9cPXZXqUt3Ny1tCIsCEBC5UmqfjeeyWyQYOWzLGNqYGmdh3AYaDm8nX88XelhhVrhf6brfQJfpmmez2jyHa9JFBQ3A5zhM/VrKprTY0LXtlu5qYzUC3MMwyqbsO3IyTmEbXn0SuvNwTpTMo6u9tPmAC4pyoBqarm6/YQzYBdskTCDIXb0qlJ8sT0y49UB5pHWFiFdrGBOc4sRcf9ISAVWB1dzw3b4oZENSXdqErsYSN/36FOwyaAZMm0pHAQnLcER2qaRQNZqH+VHjTmusIZsiRjKHjx6DReGdWcICFlrmubcqUrCtW+bZIk9zTGNAUstWnfIT9wVEKUzZgtvAmC3yiKHek/cSy9BCFaSIZWtxLWEUPWVnuc9BV616wwS0hTmrCyDQah3KRT7Hc9cT9crHrlPrgy41rnDxM7tP8JA3iLmCxWYEIkyYwi2lbVGr3NJ6FxZ5IxkeOHa/JB+tBcYHzEo5DShO9u25sNPB4XTTlc0qeTD6kDeNVlZ4JIbr6Vce3vpA85ZctPNpuHEAhgaqtbAB7dRZFA/M08eLc7oNgxDOVh1FMtM6e6weT/Pg6GYFKOXotAGTd+dCAqyeCRNdVFQBUbbZeyBUmSf22U/GVSuQVP7KIEFswXPoIMqi6toZuqe52348Oi1XaUmINs2g0/Sj7kJCn4tz1VnrRA5ngtdWCmQdQAkOLCFEnve3DSbR1KUhRFjTL1NYN8cDPHpWHis1OIEW9tCGGE9mmKTdUDxr19bnAEMDJNjbrsY6Ymh1xpZfiOTpBI/gUT0acFDvGmC7HkrBg3bSzzn9YM1UOOIU6qVoyX7a63OVaOEteOP2pM8EH4FHjeky41Cgdk0+TSIS93I1GzPIVUjBRbUJzUGVU42jNIOdqUb3KrDucRz08lkcGzISup3soqiZ4YeZu3dNcSK1NKLlcHCMYTbaXnhHS3A6DMpOIdhVrpigiC3bQuhvK+ZH4Li6l7ysLAED2USc2F7Bu9tYAWh0+zbsYWX09tHSW7MbBF4nv7dQead3sBb1VVcgmWOMmkefBx+HHZqrysIrcvudr15XrciNisPqvgqttVNO2FAxBRIsYWpnURGJX8EBFJ0No/Jy3IKFx+w4dx4lFXQhoiX5ZKdF2EqJnyvGs7TTOap8ULV18bzKxD1b1cDXcpVa9ShL0lHaqUE2xpx0OUxR0bc4zwrXLDOF8QxEbdZzYIFRINpbLyMGekCEobd6kZ4iILwg7RPIyhXPXLVxPMG8ZaFOvpFbYKY4Qx+7dU3Pn0wazq5dmtYZjKzuNrFfIqPERsMibSflrq05lbun53WbWNT9pG/Y1bGQZbA5jikKXHpMhPmaw+4gGwVsY3JyvNjLcBWhXACke9mx7KzTOMqd5gYqOahEy2qjR8XIpOZrKzOkeKzt17J9IbTaRnuWl1A2s7hb0SrwXlYht50K2/qRfIk5QjTSGycx2VQFqC4c02sIidlR8wwpkQZDNqKzmzAzQoe3KyInHD74cL4AJ4yqiTy5H7U8F0rxQkHfUyiKdjMSz8TdXw+UI9N2e7SJcFHVi2gEatXtTEqdWhLYWVKqi5JBO+SzrIbmPd0vqk4aoFS3i+0Koc/cQ4lE0hDsaLQ57jrelksqUYhOAadEOUapJ7Sbek9LCl1CoDVX0278mltIWab7MaYp7m60MF68h6e0FqqGO1iFK2XInWTmEQp2og+QY6T51ZbKHjAIwYHLLSbCWUMnIauQ8KJSsi3pJB5H2WyY3BUu+t5Ha4vURiKKK2XQmrMna3Z2VJQo7FZft7pVDDlodasc1B4oHHiozcSo9MyEOOxj8HB1t/B4OmK4j8eYhzsFXP+m9jTLRjSzugAYnS4htKFEc69FrfYp6raY69SuN2ckLYWDQ1I6ZjhhqQjEW7zOho07/5f1ZcqTggeYyRxKkxrZ9DYCCckoRSXTdcWHMKn8y7oy46q6msEyXwOpEHt516QOv9mIrGoSPCNn6R6rWoQqXrqnAFonMcpOujKSUCI3O4cLj0errEVRiVSgh/WJvnXuAnei1Kh0WiMlvzaoLS+04ACaUKLjmngeXJN7BCKxEk/i2bySCeGtquiOQKeI7VbO2CUNdJeNbMETegGlCR5MFKF0tputBAZ3Kvakl89SmEBX0DR3tDBS4+uuLt0fsY/yo8aEz35tuwpC7Q7OasSm4WCxhUeJcKhd9L1GRj8LggtZR3faHVo7VOwEyrsxVostxaQg8bSxbIo8ORCUbvWakT7uEnTiFJI7iVTEKVascoCDSq0b5F+5ZWPuiW2LpYTuR8ZI+b/5sV0WOboGczgH3EKpUXhL8CdrmEEWrRi7c3UH2bzr4HSMgzgKVuYxR+llkK7PTdcY5WRkHcB52DEEUH9XadWN6J3SMdus2sQGodHyPol79Qjlr9vhzrN5kgfJskpvBXSTt03vbRM+2HpiJIJslDg5Q6vJkM3uLtGL6tLvTGWykd2MPGOdEbqrGDBTxtXEDHwpeQLFmVtqq9OeicrQC42RjbhTGYUu2d7Eq3u2IADKskKQEchFVYUF9GjHfQk3bnpmuunzkfHGFregt77KQyxCsutn4xLFidR3r+2xlRqlRRZRmbWzA00z4k6MPzGePC/X2uW6rS+hANUFeBLTaBrqRn1yBS0aSHO6zhchLumbFmWhM8NwoHtF7cpdzQaBSYG0jAxkVZjMWDS3PGmw1CATdi6ou+gJ/Zh8iP9K02iru2RLzJfSeLdsidbiPl1zj5fn+Xjl8w473Q12tI0EK2R82bh4NAQVAiDrh2ZilclV+4s9ppTi4Oxy5Gy9OEi4NeTce1tVAzmotXM9GApO1WXTrYbtEzUwp70cY2B0zMy5yVCD690K0wtWYKnkzWmQNXWcOFpUsfvOUDnd26sr+8LrREV2aV8co2/mrFUXKSDoJLMtC136J7ieUwxDSlpVq16f3hb7ZUsZZVSW0bCco8dIG7s7Y1lveuTEcvmTJDRPgtgaCShdmiZ31sVCQXSO1ikptRTgmTGjrGeJ/xhR2F2Rd7IX98UrSsc0nG5wBqghcijkiNoaGS1WQKMKsveB0kdOo7923opRV/Q5ezyhVHnRBqHdwPmoo+PIHJCRBkaXo4t9DVPXSKIAtDmg89YNSdYkHM5tXmr2M2EKl9xToqnajppomJwYlX03mr6VIQfVuPapMTurKlsHzCDOhecINbM2DdSsplu/biCOh8PaIrhSc1Kfh9kxj9MPFYJBB9kPxOd1vbEwok1ZreL9im6LnjvsntwbTJCLSN7HqDa77r3ly84DTeqqTFrTCkf4TD4gVS7hV6hl0zhhxCzBczgETipgOaSAmBBFpPI0NFZtUkomuj3gHG4qv0gFsjpQfXAYD0m+IbAhiEwf8MMU2Uil1BpdVspN2e1W3ynoi5UWEFtqg7vWE3tBJokC1ILDWvTWhsamyeR0fVJ43kJetMiR7emkhyapt2t2VGeBdZePLnf1E6gujzUq9TGzIvSbgnJnHzrZJWTK7evQpRQfIekpUCk0G2qd2veEHPbOfsVaWpzOaY8YZ8HaC+e+JwgJGgYMmKvcclZbM4r3tquINFBLlcyXzJVwM/w+K9lOlGVy76pXXX003ug+Knt1Pc3rmDxmif7qRJPmkyaWvQQXyEj1Rhg5NWqkqySPaGsriCTrF9gL2zyqd8gBTdAcxS5gqfXCtij3zta5GPqdpQbwdAa7KRb1ninTAyoJoLNa1npEjlYEJiGM5iG227tGt92ZR825XQwNs1LJ0qDDsKYV/QTe8Jxz8QzRI7wPHA9O99GH1cHLKMovWzWu9miBYGHKYQWQZA9ieJkvUNiHj4kDOXX12f1MXNlXVfRi1n2zre23j7RxxXFdMzfREElCjmH+SJsRNfaeuG64h8eQoBRKaSBI3AGE9KnzNrRJgur7aFL1pEhgE5LrcChf5xEHoXmFCuaVsS9tpg/2g6odDTE7rUVUsC94lUuez+wPks+QgkeDl9+JYIu/pgW57M7x9HrMOM+Yn9cUCZCjCbjdWa1bNpjXfOnHtBPGrdXVWx9Et8vkRTHUEJV15PK+hC8HdLIWSuUJ9rS7VQh/QQcZhic7NEsaTKZE18loJgfETFTuzhzZJ0ta1CQkbNdMfQXiiD022i21jyIa9Fa+hiuvndKYmKCn/rK5hvzIF1TYKJoS3sKT0zE1q1LK1gp2Vqk1Zqfam+W0uz8bL8zdJ3pUstpalExziaTlaITKyTotG00CehnShMTMi1aaVPe1PTtna4hYqEPTbVlIKD0AABAASURBVLfgUDcHEr8AKpXNXkDKhpAdrtnYRrUlAFSuLrOrLCS+7TeL3YS6xbIzXaIxmmSDyXPaY7Yi2h67q9AdlsG4gRh8kM52lKPP5klN7Fh7d6eLuI7YW2SNIfdaciOcnK5XzbosnmPtETFvHm304T7mHMILJTG2DVXgBVPJKPTD53kcai9wABx0h7V+xnX4tjsOUJaoa7OO2bcjK1HBLg4dh1FHAFulNi81BcWLVFjP5lUj6zSck8ONHXk9SyA1+LUrylUbdYXRaoDW1Ue1niqSc5epgaHzYE0nJFl5Gn1oK/AoO4SiLMWJtbZVWs3buseKudpTNTOk4qzeIA5RbkebJQtqyaleYmM4p5rQS0uYGtSbJhkgrZnb18ar3UszgqKdNEjDVCnUhSP5YubHqIeXy/E+s09AJhVP2Thk3VjKWglAw28anWY8cunB/2xJzbXS2RIp4tLTpddC16gCMmqFfioXI+xyic17P8aTc9VRW1j7oP5yuh/9iraWrw/mejKr2hZMN7UhhIJdgevp+ZyQzInyMKYRdJakrbRfNmMIw4IMrECrEahON3zbWeZSAFcfqzTM1wNrSuaR5SvHzlE9hh3T722EcFXCrtHXmXWAdOuJYTl6I6XX68p4Vmyluth7QP23TdSaB0aU70So6nsbhBRtpYLaU+PZLWMl8Xdde9+PwcF9wwNgoYCMB/2gujXIgT7ZB5RsdZIRCkn8LjLBm6UOdQbGtHoD9YjUo1I+G+HwB3xScPY+ZFp0yP0oMEkjp92Xy09AL8M0TqQl+ulURArY71NmUo8ggykT3bPaCfPVuF8JoVBWXmY8puYoBKjlCxuZZjXvsYbzweOl+dCUzd0MBfUG83HDr72hfr7qtUGKTuZgO0y2tYgybmuLrYT31OcdO8Wn5UuFerUYhZde4o42NM/TXQmE1X1tOfdpdo6Dh68DMaBSdGMgX+W+6s6SHZynzflwno6D/ag8VusBxBn6Wn4UH7tfEkdppjOJpPU86dM75AO2cKxjyMVluSLqg7oX88yyXm7PkuotC5Z5lmQCxmuSttlFtNrACtmAFt2lxWHOnNzs1SKo9vA1vO76elZkhiZwtD/GcfgYbWhvV/CQRCSAMuUrmXnEPoLTB6lNu1ayRGtyq7Nob9dVz7IttlnDn+aLirmuXheuC0tGWI0CqrNlG9GHEmd9GJShnCGeaqAEhqb1ogQttj+vY++BHoZxhs2iilK1hj4G57Rj+KAC02Pmq1TxksUaYmowTgV+GCbllBXeMJuHVoDpdguDbQW/1aANB2jlKKWzaKJxwwbGKXDQ0VpwmkvvYZ0USXcwq7F1EWVdVruxCstWT9GOk4N5tEwFb2X0GM55krJporlUAUfaBNjKGkTKllKOp/74qn0S79PdIzC3HTWmcHqndhxV44L67pWag2k4eJ0WB0Gz0muWenKNVSz0obMTcWQK65PjeYw8p51DFagEcqMvj+fRyxANDeLyDq1BSvmqc5mFoz3v5mWWPXb7BS9qMNAuBbaJvH3u55HbJaHZ8mxSQJjnNHqYrYy8VKCJFFFX9FZvW9HXws5NW2UriO0eLhkeJ2wqLSPSerNYm50oXIPrjdFv6E9uxxVjhbqu9z66xJCHKnJSFfrQu5lxeGitHJ3HxUMdKY/mCbepPOSY14PXA2kwtFv1TAmiffXo8oFWdsZrlSznVqH2dWScctpQWODRzah+QlfZ3GVRJrSjGyqicfIYypnWzqteXVhLNa9lpE5u0wthKELupUQEkBf0a3rbUZiBQgUAOjiJoYDUQCbFek3UZLocDoYq2nxwHjgeeDx8nl4m66I6E+pMB+MB6oZEzymC0eezLeRMMrYEoUOd6KJMIQ9jD70Mbqb4Z5nM3V6hPmqFro5WWmQ7Uw2nlRpFzZIg0HPARQaz0RKGH/cZOYpyYJ42z2isHfvVrh2tYds6B1JA0TcYaqaCr9qK33SqnDV4E3AnVUOUqR7enMabQnF5hHsOC6Tq1LvpgK7pbVqdqCnrBtXrbXgw7UgfhQGkXirBogwq3JiZStpRx8Ak2Y0t/zlGGguZVdkub8GxMVNQfMoW2mVPk8/qQ/OG1/A2UYqxwA4l5Ca62lMPn2JgH6Z5NjU8zojHpS5YxQyvm2O4xxh3TlIbsNqyKXkbUYmdykTXNEw3xdA9LaYQmmQaZHGwHKuxIi1w9jhgDgWfiogNa9kzkF5SPrT8BiMdu5ltDaSyWiRsmAIJYBeFC3SfegmHodsAszZv2j1LDHgS7V0g2yxp2ezAvTjIZI/U0qeusHgk1dUxKkYLaKjh5z7ZuhMeY9bQxO4o+e2td800p5UnhBCiw4StbNadSlwGoS9vqCR6Qzu0hLeVosLV3O3CejBHrc7cHeKlRkOutKJB5drWyVLScczNKa3ILahB5pCbGfpSgRAoBSUEZV3dLWmahgCgfhROet0sF/TbWrrQW9I3tYSd3ZOJka4YRxpvHB48S51JzbyAAu21pc6tne+11JG4XtNqtoXZyuWrCY09UFPolQAr811HhUHADHRqRKdSmUWFdp1r3wstVRB5Ks3ZEO45zN17lPQXc9HXYLsigel6StLH2me11Kfkdyo06BKtOGA0T86gqKAS00uMKAKwmi0kmi7LFinc/npQGMPQlJpqsZbtEAkEB8dUBrdnnlAehg3BoHcZRGhCcmKdlQ/seSwbGkwGEK2c7MAdJriLUa+tt2VzZ+2I6Aqie2sDxDNvbtK0SDH8lubKGfVialVFmQnE1auRULhaEW0dHcc2i2k5LOiVqC652ab1cIYI2zCdVLOPxjLLg1r6VQhikbzgz7ZoMTbRrEbI/gXsbq1xkIamSdDWpuvmGpBCzC2Z+HT4kLEbTZZV10AN2phmj+aoO1mNFk9kzbHOl+WnRtq2mdo8OFNXo3rr321WBs8YZw6nNw3Q7HudZU5PZUHhFBUPPSmTPXbPhFYl4TTi9Xj9cmG4vW5iDYSpXneuqF1AElvZ0VqoP2QcM82jMiWPlYysytwVcdRN+2mKShAhgw45b1AsmwxcVVdBD1+D8tQwSYG38fQ7N11H+9RttQ3D6GLnTQaMmMqZhWBgQxwHR9gED8M5ONVj7KSKu5AJzaFDdTA4ZIhsddYeA03lGQmTXzfgsHBrvcJ4mXfhHqKFOE+ne7bF7hQMfUjXh1QoBWnAcJdDbezoQDfgBSZEW6VSiEZS9mhUaLqkl7Kc7SuxYEv1wfNozr4rxLOnpmhvo77v0xziQ1gbBnMOM2tjkLAxWpHOQtfIYEp+mOVHH3OcONi6JZmXje1eQglpRDG0gRz6B6QJ4cbckovCnK74tgIH6/bW8jyq9GVo6yTzkre31KDFBNnqxbz6XKSCzdaJKIjz3ZbOpsnc2ULi7kZ266KATzUcu79b0fYxbN6PEoZGa/kerOF6dbGrNckPQmBxB+AG2DC4q7iknxTdSWvNUIXyou85YTbFR0kZiDvkpwnODHoSzftNdbaro+jts01RReFKEz+SqKa3Z1ZJzFmARx8rj+iRfHQdHoOvF/pSouAtH6XMp5eGUt8Vu4G4hwmZ8HSX956sobAx5DcdYZF9dSwxV0YzqASnALa6HIDN0fOoqY9oVFWjlXRIUstq9xZAqGbuqZQ//S7ouTK2ilSfVDu6xT98sCehWw83Ux3IRN92lJ6qUYqelGNCObwcye47tUWxYopeYcWJFE+HUYY4FGZGVcpPdkFrsqIqU/eSdJ00Hwp7unZ/a63al/dy2wOpTmPQYAdo/dq7oDQterSGP8EFlyQrp4a40qlJA3MMDrWPbnZZc7RhjdyDeoV4zeaiWtlR4dxpkMES56LBOY/TJAuwauMmLvraiBSf6VYC3O0RQ8EY15w3hBtcTMRk3my0ZoIdWTIu8QeIwlTlhBkdhzDSCH3ROw5d5sTrFzENkwoQFooU1dw+9rQeJOHwWSpknVbplEDJnovNnLqDu3eiTTOGd8xS/KZKCHGVgzZ9mvKrUyVsgLXLq4um6juTy1hOO9xmhmd0FJjuBmEvUxHpsVNp58BUE27f4SH+NQk1ookkQIGj5uv26MzU40D5gN9KmuhRNuUiB4qpCaST1qYMnfpnha5aiIVMJabXjtwN4S/23VAvYA/Zkac1O15C+xf14nY5duxDXJk4md3HnfoCqipjZ14BbaBiEQ3Owtn2Qj85D6f80yE8myiRfzaOMJRvekMh1rFzriJ2j7x5oz6ngN0cibxQtoV9p1iDruehe1FUt1dr0R5GFTMlD9B0HQpiXOf8wD0LFj5DEHCDBYO4Tj5PaQuhfd8lFmSKJGDyWQ2nnZPHQZ9WZKSWIKphpHdBQxLycox+DCrBg+6lILbsCw1Pc0novjCex3x1+GUG5QAhn85MKQ4zS2vFgOzRN14KU/iofyCMHFBsyVgSmrEHTM1BK9REsLvar1bzO3X+oB7uFI+ImdpaCKKSBZFkJ8xYaWEyoFvdoQppVZo6s203a9MvhFcoHIXvaeceul+wFE3oGlW2SVkN2zVyw5WCaiUzDVFdRXV7MYXjQINL49NaA3bL2dRkDWygE5q2p9dxoud9p5ZFZflg2N0lM4A0N6m00zJKP6mjrfzAUCqkexlaYBRLutitD1ZzNFucyK2XaVkQEZ6JLsHgQUwWoNwIhI0yxWZQfDJU0GRjY7V0u8vSWdP9OFwOjxGDgqLMjoGmbdgqdlZnK/BLX4diLVayQk6UN+go+sJLNPUZZcuPa9wNxhWEDDKl2HkMd4NI5+AYE1qGoO7iQqYoB+g0vQQ/db46tuJdmdEhohpJByiIY7N3af1RHTeAgIXrcgF6WSkP95Nsg7GGUhkr9Uxoq48enrPXiFTVWSf72blE3QFRrdY9e7/GpRo4I7m7g2FKOHAmLA0u7N7UpZDjB/F50HNse0A6aV7VG5XGagnNVNhuDWc2a1F0sgRKcZSj0RZt4hVVIoOS5wRfcpw1QLvYC0qxOlCaMSa0hq3u3Zl9N61CLRuhfmL36uErZf22b7HWf/cH/8jv+APf+pc+9IPEs6B7lFsEld0r8spIdIuto/sM8NXeX/cdf+a/+oPf+Ue/5y90mMe0ZncEIQj60Ah1PwieKWrNdQKuXGYjMctm8XZgNganS4N5sCbLS73kNqCrR4KtJCh7zhoC2w1R4Ck2RIYoyznjGCqTUTxG65t3Kl1HUEsInHLX1SjlQac2ppmdc/h59jCZH1bO0icksV3szHZKND5Ti8W0zT/9fX/ld3zL//x7v+M7rv3Kuq04IH5BzbNw35M9pacLuUqQMU4fbzhfTmoZt0FYYQZGiEAxyjaDDYwDdrLOuX3u6loawRf4G9RzB+i6CbSbTxZ9x7wWryfX1fob8DJs5NXX6oWGK/361bKuMjyxZyxbl8c2L9rdGsEK6vs2Cz33fdgxeSjusmol7RYIFBLPmS+uh5gh5lLihCZsNfUkm9DPUPiiSOuntOauv4b6UYMGHFm0ysZzAAAQAElEQVQPX2MuXbiSyhLpA+ogVi+2J4LoaFsSQAydOJdrK35EdGTpZm3lc9dqSakmcigl3dZqHo3OroZlzWwlVyrQgMrRLclgqnwVpCAOZTpoasvtcpemBoDpmChvmUGjmpFjK71OjLoFJhLWhOLTPxkFDKO3MczM7TB/KS01u9VaGgQVR4ubSkcfwFm0JSRFMW9xIEY2NRhBhhB7FEvpb9wHG3qKcuw6xaVxgmdSwEENkQ2gNTCpXtJQS9B4kXEdWmEHU69Z3/I9/+tX/+Fv+73f9adq9HHGy3mdI4YblAi9dR3pZ58DR5tXdocxHyaOxGehQTOrzv/2D/+x3/GH/vj3/dWP4R4AonomLSha+t7BBD3U587EETZEgr6PWcrP9vIZx/E8xgY3ROtub8DQZpFntQq4E1cpddF2YW4TVSdbMWjhUMtfMPVlLONSqanHt3sE4l7qFuq+K4egMHFr/OypeAwpi0t5KwcoZ+1OWt+HGFyGq+uKfJW5q1KtifdsCJ35HoiDggmIeVGtV9HbR49z+7ioBq+/4twpICjLr+t01DGaxI6hLWd3mofPpqqILDAaYnknnqHPhpLZbMnzQDkuUJR1zwrlWR2qtiHDKLipa6SE/U3f+ed/+x/6jm//s98r9qj1vq9MacijDg23qLPxRtojj4Oyn9/+3X/uq7/5j/ze7/wzctlwK+iQJaCrVfb+Ofd9Ynu+pmzYpawDDhtl0p2JAj98mLELrMObYoFn6O/XdRS7b+pVkHZqGneL0WUSIdQFcHBMSnHuDkHqu/9IaZay7dONIqh6rrZSoBDpWepkVXdSYEGViTzY4BY/2IXd2mioR3QSGI45a4715ttvfc0f+I7f9c3f/t5b7zaEwhe80kBywE8Op4cUFr115TokSq7Hxqmba92DXJ1X4WlQDeU0tDHqUBrAMdgOUPTREgi0OgSAQXtxUwV5lQcdbrdI0rld+cyEMPfNl/k0ExSATfBI8/tmQY1pFVauz1xf/61//L/95m//rr/wfWWa1VffcXL6sHEkfQVis69peRiNsLpTN43DnCKvoJ3NVlGWdYlliagOjCpUqm/u1dfemzutCJ+YAo/O7NxWyQ3XW9smL8KgjjVIEblANA0yu7tiVe9iFVnBDHYNa4NaED3dxkntNDQ7zO//wRFK8SWZPnN4GmRCb7whD3C0oIjas+xRZgGtjp0NGKkUIpVmZqoXI1U6VWDcsjSqO5y9vDYhnd+I9cFkF/rc1hsK1dFie3dUZbNTnoayanHbhUZh6N1cNMMlklFuUSPLbk4MxQ70oj6g4I1JHsdhNliGaCTR03g4nEmy2kuc92h9R1Xv5H6KJGJC2mh2lka2W7r3BEZ2pi3Td4Os6mPnKJsN9X/rIBOqZW33MSKAq7zCcSegrdEFpTTu1hpaU6ZYqI4barjqgqbD3SDqFytYrPLUqHuqr5qRnrh7zO6p3myAV/pqnepu0yMpv6IrCkAbaJCQzZoy1V3oHC08BGYr6raSiHpcqd0MWz+OF6iFKC+UT2PTsyt3BVEj6yahwiR6yrFvYUUZZY7qg0jvchjaW82+C0yZkeG6AZE6uepK/ckpa5bNdFWigZ87zDla5FKpktNAadvTHqbJesmGMId52Rmc4aKghU6XNMJ6dMzayM6LnWT54LzPiqBML32kg6bIhX88NbAB6nxh0skz+4gcYSo3FQrFKro5Um8RptxXJF7BnubpjtN56gOvjhWvMlKmhyDSAQ5RHzDFH12NRAu/wIlP3J/Q+iV6dC2U6TDVbM4I39Gq2jtn2kSLy92+1SPQLQvR7739mV//tX/gy3/n7/v9f/S7yqBVzhkdodrNggpcZcKjP/XZT/36//Gbf/XXfuN3fM9fvNN/54CtCQk57VBC+RxMp4BYtRmlXE00Rd26UV7q4Q3efMEWLXpkWcUIiCH0Y8GXWpPPOhie0ZUpQ4rPSqS2jtrdS1/CsUwlaKpgVSOl+O5BO93XM7/yd33jr/nd3/Cn//IPBOg0HBNDvjrEREUS6nGrWZdx5X5+8F9/w7f8mq/5H//gd/2pBdzaMP/sZ/YX/4qv+kf+7S/5lu/+cwBIwI3DzEDAFBW8xV6VbmvsJKb/8T/7/f+7f+M//dn/yVd98lPvG+9h1W7bj4WRJAADmlCtlvj3alIW8WKc9zUT0Bq3f7Hu5Bw5R0zpLhVwK53Ld3P52Aqy1cWAFAqRbDT1F3PFqow05avbWl2Es/CaX59pKP7P3/PnlcHf+vv+0Geen5HFHK3Rs6gmoEmSmEsm0RlalWyVpuiETx8+hg0pSkmJEoyAkEpwigY0d+fJQZ89xx7e5sYx4E7X5QyvnA2BJFszKd4q9JlcEo84iezuTN4tiSMoou0wjKN4NLmZkoJyU0bKpi6DCPc9TJZVPRrQzOuY+3gkPncUzU0d5jLdn6YOREo0uFoiQrXXXfSPfL2mbKXGdE4y0qSRWgPEfb//wfPPfv/3/64/8B1f/ju/6Vf/zm/6H/7wd/7VD314LyllG8sLClY0QXF5EclqxWQ7oWi6zZ3aerCKka5egNVos2Q30SGFDEmhWHlkOpZ6jlM6md134ZaIhnqJy0Btdtu4gzI2WUA0NEB+RY2zxUll5yI3UBCljtImQP1CYlVOHsOOUTDVv8obGULR4JavZhjz8LIJGKXdHtKfx7R09aCwXuwLfXVpmjgOr/al9hZuzelQHRAs7W/cQmpPyJF1e5fJ1UKlMJg6zBhlDlCU+X//rd/15b/rf/zt3/StqGXS9yz9pc/u+LuYM1ILm3vjjkNW+vs+9uF/4pf/yn/sl/wn3//RjweROWnnWvmlX/sN//nXfPOf+HM/0AWngNj3/uWP/x//7S/7J3/hr/rox95yqL5aCYuBFKYsCF7dWgKgFNkwQJm0zL5CZs1rHnThLHbIfVhVbojeSoUEprPMnD2RU+BxlW7Zjq4pCLLaQFLnIwDVTIfsWCd1QPPsdl45MlzC5Fa0I0WeJI6y2lUN0vRRrzShkNiKCH1yE2bTY3160rpY0KHxIaqVcoym9NANtIF6CdOQeiAqNdnhB3qmdBzSihSTohwn6s23Pvlrv/Ybvuxrf98PvPnxxqvNSzHINhq8EXXeJhljpKSoN0+TM90TjqJVsp8r/ptv+rZf9TXf8PV//E8NU3e7KSIDmqqQtlm5YrcylwmoxO2bvut7fs1///t/x//0Pz9jtRBLufH8st/yNX/fv/hLv+prv0FZawbtKj73kU9vRVulJ2UDCoQ5Vvgz3/zEJ/+5X/al/+gv+M/+1+//IUF78XqYilTVRqcN4V612mJ62WE5xgZVU+PC2FTRxv2IAH7nVPaRo/oAH85zqupwT1M4JqqpWJRbIFjtgHdbhzhXWAN3S5KS6KBHHOs6W3eNZTPA/Csf/cSv/ppv/Irf/fs/8vGPS0pZe3de3corJnDP7llpXarkbsDkICUcsetNOc9kFr/zz//Ff/jn/Yf/93//177z9jvuXS/MKeKGTGjuy/3cPvSXbBZlLNqAw+KhqKsZ7ummVP2R7/6en/nzf/HP+bKveOv9d7dFexxHH1MtF5JMkdln3fFQNjvrWvu3fuO3funX/v5v+l++O11/BssQwTFsjVnGbS3gAumAV7czD6uBm4lWVPQsuPS2N3dYdrVnizON1D0oGdqnIAhRb9NsKKJjhA8pPvXHkaUZ2IRcFWEKF7xsbJsps6VdP1IyLfXJlZ45HOYccN9TKMbR9rLxknyQRtutW3GslN9p5bqfgG79laeuaSiXKwNeBh+nH66SF3Pwy8fGqLvGrA7jOSSOWW2r8ibA+x52Y9neOkEEsLOvHdF6hFQn+4V1zua9zin1IdQuGqIrtL9JJ+d8SHNWfc9W+FKvetFhLyfd0ZLGlbjM40DOta22kIp14fIbRZtP98dRroY0K82LHrQwdY/U8l4bXNqQKXYxFlF7NRdmYYzA8bz9ouXLxdXDYG4pvK7ltfcje7qpYYb1k1WlotoH+zH91PoCi4h15Uqo1gPMko9UeDvqXv9utNrEkFIBdeNRfBnH4+H94OW4iKvg0iuVf0p2rXg1O3fl7noeiAfztDJP65SZNrZDMJ7LdpCjcQDZLZb2ctt+JI7oWe7GSZzyuhDNraomBuIUVi+gQ66h52Cmjj55Dd0TqEP0pduvI/OQEx/lc5nCUsdWdLAGZddOjjdwnpgz2rdEDBO6QmY1bKNX157gqMF1rxndAKoMG3KvUA9eM0KZXTFCYJ3mEy2vzN6dWn6TDhja8vaOg3xJPJQJiNZVLVMNhx5JHQZWMx08jC+Hv2GPsyk2tYhVDdsPxnGb6Y62q00VofPUMxCBxQ7IXoqAY0s9Mh1Q9Ql5DZTNNR45vb0PrpN1tJ2vo1d9QNlG9xhjDjMFCVGB3uoKpksq9IM5THmd157ih95QRudMavv20uOxj3MPKppXIknttfHuu+/9jm/59v/6D33rl33N7/2+v/r9ExiX22KHB5TJEk+y8+nPfvZ3/uHv+Oo/8Ef+5Pf9AKVm2mrsrFoX49mMEhv6w6QkjFLu4iBIQzj3GE/Oq0RbWiwB826Ka/TZPRdEplytbkS18B+P5fOyXkQ0s3zrThKjYmhdj3lueyDfYB5l7NmuPGlLMagEzXnf1ENHQzkZovGku8/hbkqwxM0OU49UdspVm0EN1qvROUKKLef3/+BH/8wPfvwzz+vr/th3i9DEWZjF8nOfx+b0sHODxUuqs9Hw1lr7/qv15z/85h//vh9koQO7WbRRJHE9EPf/FSeDCi+XUnCd5368bL//S4TEGeWqQlt1KEQIoapmKAGWXdhFNd+YgceyuU1EJa7gtXFte0q0DvHQikTexOeVcy1nKWBFBmOabq5Vrfe3+192X9zX0BA502tWzwZFSkCLiElkbKp3mYqjkQPK2/L4gNcHFulm09uzBQwxDYc3Z4LD+DjxGFA2aFVDBfvc49Xy64lO+SaM1mr9eLHxUnbvIDCdrulqM1jtdq+WdrAP5Evqxc8DX6B9FCDdHI0XqBIdEBMbFXhV2sPYSrO2AR1iLZck01qDOCOtesGutHhWXblhS5TQ9+htECACAbFNFLiz6vnK/uB3fu8Xf/mv+7m/6r/8kv/u67/6G7/9t33jt//y/+p/+Od/+a/5N7/iN37bd3+vrPPFygzlVS3pXmIM8j+GtxYWFxntTHimvW6ae5LHo32kZWX47vm0I7RvokqlzixpSWlADxUxr7aLXpN9skd2turBISW7PAgtWG2SzvZKlqpOjCRqlRq65GpO3nEVl4YNg76wTQmgCmBn5CYWj8ttHXh1invVIh89T7MxqEV+mj1urq2PzmM1Pyg8nxavvJ4zL5eNWVCebC5KmkIEbCVV8jgcTmM516wYl/k+TN3sGJhaOp3bu9RvlC5kVT6Du+8vhhKjxP21k6PweDaujCe4vuP7/vwPvfOptz712W/64/8rOExClOjxeqImxBJCUAAAEABJREFUdxEXz4uT3/rdf/Fj73/mI2+//0f+9J97oCYv+WMj1WmeFJl7osxBT8/9WHn/b9BTbhk2SkkxJdJ4Rj+iVTx+X5IL66kRvPqIeIM4DuY0wdTHGfOIgWRrG7c3SeCsfFQfNNKKjK680xSloGf3o6Xr6Pus1g63aFQ3dYF5dk8f084xjkOhsquxyy+nne0OS66QtFjCRSjLoYozkTGL3o4YCep3d1rIraJvdZkZnGPYozU/ZbFaw1aP1X/g2/7Ub/193/bV3/Qd/80f+GNCXzu6qp2aGHXJfRk31Ssm3Ux69tawyXrM57TL61l5tcYJEDDEx55TknVM27NjyBbf6HnykNnXrcaO12Nfv3WPG8r19vvv/uHv+YtZ/Xv+6J989fyU94UMCoXFfTfIUp42HdL8cq3Osa4//X1/6fs+9vZ7n331DX/iTx0Ge23PydEcCj0hjhZK3F1G1DnrfqZhSogtGYduGfSUC2u7bTw2z5yjphdthzrIvQQ0IXPti8iZPHsOGxgI65BJMx9uh1EHtOiejz2O0owVjKtHhUsar3HpraHWw2SXodqyWLu6stlZyGx0Taq2zOiFUe42h9NMZr7pf/nue7n5oTf/5Pf+4GxBaGFb8K2POVzYho9z0Ebc1Y+l8AssEhZSQEtD/vV//E9/dsX3/MBH/uwP/ZBVZmr7dbm6uiJyFAGYS7k92KRP+/9nqrs3TQXsY53372d1ED2huI8R6gUlNc2wR9oQv5dUhibMFVFZMO9FcHIPr0FZ1xUX3q4T8/DDzVqjGKij+/6PU+ueLUQ3ZVZ7CC06oL6u3qtxnUOJujC2UZRmZ8rnWfU4YEe2o8EuZFj2aCmnWv3UUhk7x/3sBFVAi+FBmMQ22Ubop+pYNa7qFZUNUFQoE86Wts/DjuluDo6qIfMV3bEzA5A4U/nx+4xONNbgGgrMAuqah2+zpaU3DlOyWylPFhg+qg9cA6/QO4UzUZaYIr26s6q60jXHuilNr/TC9Ibi9VYcDFrXGOHHLWjbhVVVm62ktbc1IBsNEjhMa5JGQ5eZGFmz25Kd1g20eKN6l+mjldK8bBanAEQqDsU0e0gjxptf7F561L5163uNWgKl8/LixnGgBhRyl9vSdhclEGzlmqbTGzoBYKpvjD3wdIFGQy9lGhuaB4x2iVGhBkcR4QgYaNLqmDG8XKBLTX4rWR52g4MCztaPoi14bMvdhnZ5M9WIarx8rUfvAzmc5aMpN6poiCorMjXValoc1Ew3RaKzSlmwl3VN55jzRmYjqcFQymRsFTeo7GKePYcbBoPoToCiOVKBlfKnMy0pI7sTDeBBe71Z8LFIVXBUK/MySwiihLsuv5aF1pndo5UvQDOrYbSTkv0sl2dISiCZ7nm0M5RgK7j3YekBLEMoIOViL2TO6GPDtyAg/baGYcAYaZQshHftijtGn376HCCaloWo6hV4opbW+7knetJvLlQB8lFhqLJ8eoVDGExg0SnxDHfz0yQFeNKatFmqERG65oxpr2plpjB95rl+3e/5pk+//4GQMnQfMNRMuhLsoq/dEalhAEtEACDT5TzKdb2G9QSPFGaVHi/d6myOi4yMbv1emPF0bR9RW4PHaJNUJ/TgxB3SjMZJPShFYFIjrW6S0OiKO3sl2+VypKpeg3c/oiSzXWq4RxIW0c/X/MoeUTadNopaZO7Ix+xz9Lh50/VbVIfBDK8PEucZPpXI/pv+xh/7hT/m8885/oG/+6ekjyvxqnABeoj7//k1v+1f+fLf9Ee/989pkrXq9ELoVu36mX/7T3pjjr/pR33+3/4FfzP3NKlG+uw7D6kABssJNYShjllhgDyZkTQ0lDzvoFoSeNEvmFZUTe0wXsfYdwwKoJsNAnmYVgd98RKADO6Ur0y7ptW5JbRiMpZdT9uLlUj3HCZJCvX9svChE7q2GCUq1Tbman0OkajBNuCDkmc75JKWTvW6bqVs6J1zSqSiVtpIq+6EQDyVJi8OTcmUrlq5SEh4pdWRRUAPKOfB6TVdIqKsN6GPg/ShiFfWU1Uz0KiNvFQZPsaUhEE3iKnWlUAWS3NOjNHQ+v7U8O4us+CpYU0sE7YwxDAOwpHo0JDVqdsTNQ5zLYG+jJfttE3UiPC9Yscz/Df/vj/0H3717/m+D795uP20v+UL/qG/+yf/wz/tp/ydP+HHHcP+xF/6wV/4W77mt/+hP1JdQayy2n6/m21rLTs9tOTOcr/uq4Vuh+mKoZ3unKMUmODpu66cxElOSg9wFGeS7HJI2mYwiXuiDir5BYJGUqcVGZd7mEqsV0Vqza6tbK+NBbZBNE4hNFs+BUWaHKWTGF03i61tqMnJaBkMU/vYy1KjRC6Igm3cQm5PIUczvNORxt38mm/99n/ly3/jz//Kr371wftivb38yHnAzKrZNP2AdhuR1uhsoFSfyhxg6mqWr6mHzuM+uiz3TDGZY0Pxg4KSLH1kZ8Mujsif/oV/04/+YS8//HH+zL/jJx3AZA+TJuI2IcPV3mmeGP33/tS/5fPfOP/6H/bGz/hbfiKu4jO1XEvi3/Inv/df+srf+K9+xW94+523m4DCy9EhB4GX5sOnaX9pTgdntN+WCVdOXcddxFPrQnYhQzVL63uM+biz+HpfRoRJ4eo2/hgixEIpoHO0OsBpaUXVZUgENDqsJKQqdXlAxOn8JgI3WdHWduIYJQKJ167CS0xywS9DMFXe3dBB2IT4Atgj+nxFrJl1bJsER8D3zWWhW9SO/Jxz7Z8Pe85eXp/+9Ke/+X/5Xrw+vu17/+JnPrNn2+w+5QarUZmCKdOzIIGgOrd4aMh9d8MDQ62m8L8dd2uRmhUVBUKiv2XVTbdw266saQnS+1+bMKjGvmjxw374Gz/9J30BiX/47/5bX6Zx17yawc9+9vkrftPv/nm/5jf919/4h64WJsd0c7Wq+VN+4o/7gh/5w9845z/0d/0UJ1gDWiih9MlnQl0Cna0YIEBb8PtgHWKqzHuUVNCWCinAPT0O3ouZr/C+HGnocXJM+iDUzW2QPq1PxghlgO2GqcBuRZXyGwyYTYN3I5C7a8XoNnzuMM0/XDYoGezAvvZe1yr1PBMEJEaI/mv0Gl0ORWTgHGZSQv19P/Vebr7gR33+F/64HxdqPZku1hO97cZa1so3x3ZW0Ta6dgqFqX8rDA7nGPgH/q6frOb2k/6GH/WTf8KPk9Wqgu5Lo9RJ9Orag2EkTbYP9Gh+Dvr9rpXztBqzxCJUcw2IWyDY0m+0K4YiBKq6mVWZSwFZpwMSpW8NMZqZRsENwjPOPl7MTsrYzloZgsF2a3X5Ocz1iaN9BFQscgOJrjtbBaQEYuyclQdt2NHkSt0qDcAGYTJ5z9JgVGaxbnnYeaVuSWeqhdjN+qa4UrSkULqhRofOKJWdM3NEqNwU+mEq+aSC6TJz3A19QlrTvPISrFzAB1YxykbOkQP3aJX47oxO6S9MOhtL19qVvoFx9Byq0GeX9hrC7MWBm9Xcasu1Y2fWUNvsJzoQPbaN7Vq4515HptiEVXuUQlzVT1erkQXrrWaCK/FMFa8bBDNHX7qPGCgfbtpiFxVbi5pUAVAhQ5+r0Hohe2Sfnced0VZ6dYFkm+6EzuDsNnmWaAMpZnGwcHMcyogNTt4+MM/hj64XtDjLkfA91Hc6Use2Wkr9oaE8llPda+PmtIimqSvo5HYrTkh0+/Byh7PSds9NSdm0Zx5oMZx7d5erCgADc1J/J7gsK7pEnS2zNWwP4CiY7t3uJTO3X3nElslCcmg561KtO0arjLPF4qgt1DiyaJYuK9rbXtvisg7CSpdwALeiHJY2A5/Xok6SPG4iyHTkGaVUs/Rr1SyU5W4o3jRridUUcVlR6/XLbdK8nAl5veTD5vTTa0pJSwGJ8V23ZCfvFqTVFyJFusc0tzrlIbIVUiuMOh71YKpQxMYQvmWIgTihKkYkrsaiKvRE2KGYOyBVOPgy8YbWuGrBqFqtfROViMnD5WiPuqj2ofsyRUgF5cquyG28Plo1ByM4utU1qJM+DKLFVdLgAVdhzUGwJbI7MwY/iTe6HzLX4+E6NE2vP/2XP/R13/rHqsDbZM6Oyer2krPhYzjuo5V4Yw+2GWlmzsN4Zh3PtGCXKkX6MzwLSehrjrjOiKmZDnXfrTtfP6BaqZk2l1TMeGA9SpK5z+rOQtmTdWlN7BILtU3C7uEFSBPjfh/dVtW7FJnpH/Ql88aoq7SD90dmMJVettMNU+Qi3vB6zPTpDcPrQ2On4UCNrh/7o374r/+l//rv/JW/4Iv+0b+/7irSv07Ypz5Y3/UXfuC7vu/7P/n+e5RuurUx7CsR8Y/9vX/n1/7yf/O3/oKf94V/4xeIAZGtQqLvm48C8ibiVpBDJBBdtdn7YOo1uHVJS+OWHnOYanKLdtm97mSzjAQpa1S4B+OwGp73WYgOXV5qZN2+6a+MRei+YCzVXIwrbImRansdZ+P1wVYBjSN6ZorCfbRsbl2slrLn7GHhFnyECjkpfrW5KtUBAR+qzxIcmLnLkmKBstOFwIrxqn2hVyMU4ZqeYwRHgiX6ze0cmEc3erOuWdoIFIogJKtW2Ald1Jko7v36D07HaBdHJxTcJdasGiWSs2fYWSZZNu/ZI/uMkjRk63UimdYxA49ySVSEBahep6Jak3VY6kn7qKwVuEJZqLR7Qfc/8ef//G/9g9+mP9f/HV/wY77yX/t//rp/8//15T/vX/iKf+Pn/JZf8C//hn/z5/7kH/9jnmt/5df9wT/+vX+hlPvg2no4refmJvCXZ2KU/gjIKc1uhlobcQ7aidTfYD6v/ZTvtjXwfDCm8A+vwbqZOjeGrFAnpqmGyfaOoRdg+oF2BCUG2J2GPCOn9AV1ib57sySJhoMs01AtkH2Qbh6sZMBTCS5GMHcZw8TW2TgL6k2O1Dwok7IVe9feiOrKLIi2skkc3lqSj/7QO59UIfypv/RXQ/EhhQp0FfXdJJROQJast65kenK2oStKu6oqNeQLjO4M/G9HcGzMrYhCq4GxwHb0USklafQxcFrz7/lb/5av/vd//n/7H/1bP+Pv+NuUw2nRvHauz9khOJodmpQ/86f95N/9y/6t3/1Lfv7f8xN+kpbDWiY4A/bmu5/6E3/p+//kX/zLe+mmODstdRf3tHGZPNoxx+HuBF6zjIZi5+IBnAfn0BULTbFcygEK3UMrCmgkj96e2yDea6p9t0m7Ez07NaofxjEKiLwZLU9zmIFmVyGjZj0HFizClMiOfQtTuTOhqBbJAEFXHktMilu2QDZKNu9LOOWKGpjZt0hGUSeWHbsnG7dRR5hty4YCm1ZTfB0VM9bcf+6jH/v+j711uBn5yU999tv/1+/3PmaryZSctkpojDZvWAsHKqP2BS3TO+euETo5FYvhcwcRdwPZGkgMaoc8DMordEbYLekGzObnhuvdCLf2tnO8/Hv/7//b1/7Kf+eX/Nx/atC4bcbj5XrY0zRe+PoAABAASURBVP7sX/6h/+Uvfv8PfPzj6k8RqFH2Uucb/rf+pL/xv/kP/uXf88u++P/w03/qreBCrvu/LFhdrYpCCzY4oQ/s5foqbo5d5/1E24ADJoxmbgaHHSrIMJQyUXYHLOzlZ9QbUNsWr0O/cXlfrO7ycstRjcwsde+9W/3ksD7O5OyiNGlw9v8WrLGn5znpg9mdubPvettmlMxuIipjZSSvEr0Sesfr2aPwj/99f8/X/se/4Df/uz/vJ/z4H7eQFUuDxtUm1+oe8l28VDQSS9XM7flsuyQJ7eMO8hx9jPwn/5Gf8bv/05//G37xF/+EH/839JRbYLP1go6wTr+7SgR50cKpD7rw+kVvV+coxnVkOpR1qHX6lTuEpTaM7a4ZyyPPUENsmhirfqTS0Wj5GsuUGWcOi2l5KNPKz+ibiFihwILi9NE4b5Z6eM0Zw6Or9eTU9nXs6xF5lFJHD3Qhm2VO84NTIAnBVKJ7Z2nvjkqrUNZNNKdgV7tIT3V/kWuJGXtGIiGAoPIZze2g6JnuLngq+oV6Yi90AEz6LgXJ6A4yZwPFWfOwg9KDVx2SL09XL9DwRqV3HQoN0Hcg4NUj+gic6Z6+nGvafhxhvkqDc/Ty1Egu7z2yrKO91138stfm3UJuGiLcFdzP7pUj1shlQmZUKB05i4Nglfo6kpYT2/iB3MQw9KAWIa4zL8VkNSnFkhrbT/ZW+xigqcr31p4USNECDA+65LVrdJsBY5ja5KmhGdA0K5kpWCs1Tk5rGbmdYyrAKDyD1zXj4siYINs6qqHbsVeGpY6AaTg9p0Vjp0WYtpA5htThvCRTzxqwYcUOVHW1cqaIQHRWrh53V5U46pX1hhsOHlWP2JKv/KV6rsayOp+5n9faV1dRqeFuW9Su1ajmNyKGCuQ6rjoWKHcgmEjJK1dhl74nR2AiKQoOE6e+xrhtREXubcqKbT8adqS2QhqQPlQDWoV5GuxkH5WjL0VOyCDd4ANTwXitWTW7oRdK6t5zLBw3IZd6uVPjezGzhdtuIBXW17ANDU+iNOJoV66UkVB56btBAsneVyHkWOkZwXmpygsQ6lWyd48bxBiij9Yz97G2x73buY655iEjMEHKOXhwaAjMAPT9r7djT7ab95yhiNDmMXzfQ2jWZS39huiUrL1h7dDqIFlSJ6tx/8gUger+zd/4P33/Rz8MpLIA7wOqB0KpH4HboQbqNNjJSquYOc5wVZSMR3ElCj7TZ7N1eUS5gro3YTMxp7m6jl5DDhCiE9fCneRuCqxysa2WTj291OpbSmisbt3dwB0m8TdImWx1d+6k+E/L1dx1tqRhdkOEhsLYwAJ2z9ojNlfmK0cck7KFaLsupgx8bga6LbK6Luf1+T/y/PF/w+cN1FhqICZlUNf/2kD9qlaKHSXy8ubYp/+NP+ZH/sgf8dfJXFjdBDckNLDAzdqouAf3UEx+lcf21i2+XtrvCK7RfEyaGDdsxO50pjmdQvEABlUQQ8WQikpfC4qNIQGPA8fRBPqiOsoOQx3Oo/thGLMxLOq4toxoEHRobgLUQnG+8uMJr24Qw3saT+pjSz9qcd2sLZUWkoOAZ52ZDmEwlo+gp5BjjjILnVK3BjSf4oA1KseGQOpWWwkrNdtGl6jXxOsWgRxH6cQtY5vigN4Y2aNNVloxdXJfrb2oxvmxx2P5ed3OOiFDamdji27MCfn/XGloKk5KnJprnSZEbHd7QNGpRqjq6iegK+L7yJJGVueCJe716Gu/7Y+tSD22/2X/ws/+mX/7T/3hx+cNHmXH4/F5P/1v+0lf8i//7B/91/2wa8dv+vo/hET2nWOHjduFd49okwMiiW6RVVabCBjKTLSDXmADKEC/PJpX4pJOFscFhXX3NWuxU1r5o3Z3dovcSjW6y+PybNOrU7Xlkg5lT9b0S7dB43VBsu5vw2lDHiG+alksrWizWjjgnnZPO0SVp+5P1WGGUSF4LeaquqqeXfLbVVyH1RuON3wcIpuCf79CBXpj0+eWP4KWE2UkZ8VR2sm0G8WruUkmjBIrZZGUEhS9Jr5+UYlimTkkgLBOAaF596gUS4mBntpWzx/7w3/kj/kRPypqNCSo2lmR/doEYE0tzMvy/m8m+sd9/g//MT/irxPCDW6oUT1iPNrsrw1GtQyrc3VmiWfPmKFszuyRMnVfvMhVnbErNLqcbX3PKqj8chfRt9qUzvbosRu60jrZilmhlqBtsz1PusvwY+2BnVqGTI/rB5ITRcOtE2lJ3FSzYX1jbIXsTDJYJaEteNjEfXN+ndDaXqMmSxbu0WBKTl5AVjF6+F1QyM5KQjT23MClYa5i7tmqx7T7moKOqvi6P/qnqvvv/dt+4j/y036yPnzDn/wzoZ7pts23puMY89CPnBBpXZaosAqvGujJVmdX9gTghqMILvdLbEm9T430fVofwpjY4a0szMhZed6jP/dP52Dss7f/iPn4wh/z1z/O26aLnSYLLjY/NxI4UNa9qhrLuCScz//rf+SP/dGfLwoDrUQsMCWyZKkGXob+uuUHaa3E9ME8wJERiKdC79uqjWGn/gZ2DAxKed41q0UdZsaIZ/YlZpXbWQHbYZBK2KpA3ZBNmbAMkd0gzGwavHIIAvyqx64DzTtAfO5oxbLJGIRyCme5VQOguSrscErdSMWShu1UOKGqLJIY5n/Tj/3RP/rH/gh7gXvRSpnqTpT0lqvrio7QiSbF0nKHybAKjpLsBVuwfXj9hB/1+X/9Gz9MqFEcIqbcW2BiHmkeneyoLMSBLfR+o8PrI4hcjKyMqNEUhqN8pGxksQlnT8a0dCohmDA3+DSY+FRBdQdiG5S6JOUJr2XTLmXBVWevJaWC1gzXJJGVZdso5UCHoFjVyXTP9vubArI1es/MAbophTe1GmtKUxT3BrOUEMM2KIJqa23lwySi7IazhhctkNlZV6Wip6W6zmF9mE03892+OlKJiUhwtc7wTlxHYytYugaOadQUed/sdQi5NctQZIstc03VJBMmMSwy+m75Dpr+iWtVE1Qej73OKhKiI630Qwmfg7RRNbMcGNTWR3EUsmANg/gw9Qx1o2PVuXiKpfLR7qVppoxoLEaLbQmnojM0c9uhc8anKytIwhRJo1NHZCs01G3aGXUHbN1TO1Koq4wsl3NxSB2i3qcg6lZgIKBQkeju7DLegYClTykpEGVWbZWz40jZjepqNrzqhJJdio9juGoSbZlYFYWbrMPjYFuTSQvBvPW4ol6lB6e1t+cF3YPKpDUnQJerBvRsUHK8fWSXThsUusZno1rJB6Kt0rI8w3zZy7ZHHcRLtO5yp0y0Us0rubWPgxOalVsYOn3V8VTdd7P7rDlwJualBrKwIpamWJawxBxFlR31J6YHQaHzSdOfLbRpo1mSixTABtP06nKxqMnUdrIszfsYqcEYtmm78YQFb1X0ztu/ZyuUOygWgVI0K1glM8BWiKBInc1TgLplIWEYjokeAMjuSuDWRyo5pdEsddJ6PLZLn9ix0ZtOk+AdNMnsuLN1Gg4Z28TrY9JHHp5TNnm0T5lCyzSaE2EqnowdjEYy9/0CG44yJWVopEquSrZ+2k/8cXLw2Wt/1e/9/Ve9KqV+thXn0sXM3nEj1mdxoVORLoTlabaPljTM3r32h95+74c+9om33n1n1Y43cr+UNstz+AHOFgG0cbbua+6YE6XMShg3lRDes3jis9erj3zinR/46FsfefO969XT4JYPh43GNAxQ/QfpSJHX18q33n/vQ+9+8kPvvPfZSyi87a8RI3vWqv9cU81ISZWwcuV65zOf0uAPffK9z3zms3Vz33h9NHiV/gARkSk+Pv7mOx/52Dufff8Vy0aOd95692Mf+8Qn3n6v0Rr+zmc++4PvvvuD77+bZcCD9vKpZ37o7bd/6J1PXHXlUeoAEJIirfTBqz/7qc/+0Mfe+cGPvfeJt97PvRxyVKU7rasshvdJO0XHh9/7xIfef/NVKHAU7b1PvfrQxz/1Qx9591MfXBqSlh26iy2lMPdce159qC0Ms7U++KG3P/lXPvHJT7z9ybpiQBUALx6m69TCX+VC/rlXDtx3bF5wIc0D21mzCTsaR+njs7AahYqO4IIuPtRMPNDPNu0+1GC3UVPlxzGO/cYRk/D0itnqRj3GdqZ3dbE5QolkF/qSyW7YnUlrBOti7JHqepw+BsyKrnedaAWWvfSSdMOM5ho4sqxKyslSoaBUJw1VlzQmfxcgideo5RsIT2BZx7C0Mcdxmhk0eZeGAUqNhnG3cpVuhDv4vT/wEQBf+AV/w0/9iV/grScpbhoNFE2l+BN//I/9B/72n6QB3/WXPvTpdz8LVnvD+gX2RusJqj1frY++9ckPv/nmW598dylxu/eKDM0fdOLoG2j3R99852Mf/+QHH7yqHXFdn3r/Ux96580ffOcTn/zMZ5ZWt6dA3xWyMj/9mQ8++pFPfPjjb378vXefkUUkEROiH0NEOmyKmompEo71wcc/8fEfevPjn3znfYl+tAIApwZCUfRipZlGv4YBM+RAu/c4cs4aTH7q09cPvvneD7359iffeTv2E21JJppVueOjb73/0bfe/swHTzHQ3R95+70ffPP9j7z1vrpg47Ylw6PV6ayYn/rMpz761sc/8uZbn/7UZ6x8wr1k6/4H7yRaVl6/rDcsP7g++PDbb3747U+8/f77oeqgjlECgCqv9PFB1YfffvujH/vkekbF3QRErAa8tgGF1TarZkS//9lXf/Xjn/yBt978TFyL6mj5g+986gfeev/dz7z63OCPvP3+D7753sfeec+5P/Pq1Q9+4pN/9ePvfvq6VsvxgrwlmN3Z77736q9++K0f+Phbn/5gpbvIFmXI8F0mn+W9BF8ds7ej0AqK8tGF2p1amzqnl5Qlg9FDp6ftOaKNgZHmztJEnfBs6Y9m1sM5tAcZctNpDJriRdw6tuE1mZPUPBOZcoiCbkIs2FCXuJeZs+2UMtOWOvUu/Y2Vr4LRSDO235oVgoohNXW9//5nvv17vk+o//d/z9/1T/6Df78+/Jm/8qG/+PFPqNyf6Sl/NQ+O0+mEVHKjacL8/c+8+ugn3v7YJ9559UqFrJ3iDQb3QdQj61Tw0sXNBFoP1d6PVz+gvLz59juf+kDPUnf6Pfb1vxqMeSR9Jt5+660Pf+zNj777mWef4vztz77zQ+9+9CPvvBeZGvvZ5/rYJz/xkTc/+d6nXq1n82q9/+DHP/0Db77/mefVlSnnTDm0VFNqjr5qffK9dz78iU9+5K13rvXk3O0LvXrnfrKepB5kFj7xyfff+sTbl9yumcs++5lXb775iTfffPOd9z6V+2rtCCDuwu8Pd/bnnihP2gd7ffLddz/y1tsffvudz25twE19BggoJtiQIKyOYwi8XkKoWqqsvAxbqfbzoEAa2e1sfvqDz3743bc/9N4neUCKeffTn/rQxz/x7vvvA7Un37uuD735iY9/8i3vfBw0PRiRLeiBAAAQAElEQVRHPW/hUWr4TF4fe/edD3/0rbfeen+pEg5E5cffeu/DH3/3s69EeW6mSHz3/edHP/LWx1VKn80SEylsk4Z7iZ6lpv3upz/7Q29+4iOf/Njz+gwsDJqE10dHV2ijI0ONUIkMhyvIdoNprRhw1swWLwQbDar/cEg7UmaNURK4FZhBLFhnAasQ60jdyEv/B8eE+iROCWTdVYQKY4jqMKdWirbjwKTaiKCu8sjJGKYKvNAZqKvwvBj6Jk7ahCO9e5omV8+o2buRwscaZ/FReCPxkjgS3JGhbNfuZlNIAx22BWjC/M5Z566bRYV2x3XmHGERdvHol8Ne3CbZmGVDwaPLe7ESmXVHvKBThZL5lnnEmSqWfGRPTPRj93FXXvTe0SMwG25De3d/2ONxPOwceQqYeyhspqn+FSOGYQ7OOdpnSEk1PYZvZzjakYMizImj49z73M9HPQfCp/A5N6oa6uqhlJ2mc1sEdmVXaY20C8Lq5idwZzcQ1S1rTmvz1ZZpdjvZNJTP1rdSpMyJPaV4sSpOlclqNRGextNxDJ5qJRhPzA86s5cPHto01pTJi8LBg3yxfGHP5NgyqXiRdsbrHbPClzwYyWAnWT7aGn71seyIx8BJO+2Y/gDPageshy8/n5xLeoIdOUTvA+cb83Gcw7wMYd7u5jerToeQGAk/ehI3Wa9858g5NJyNylY6/fV2o0RcI5A3UUfrZuQ86l41BL1bjBKb1bHP9Zx6Guiv6rg0u2DJR5tRqaiM3oHnxrXVqNACdZigR8p+1Wi6kEwrWetao1KuDrE32yi178GlOoyJPiAZNiuAK9EXZsBbRO3HXCfhNmmzc3SoNjkNZ9XB7Y/EiTr5KvHqEhAoKlMPul0Rs0qAcXmuuYf1meqiTRqgNICruwC4H3avZhAS3ZeduHtd9gyhW5prazMBopBZkYjo3F0N2OgyyCSN0PHTvvBv/qJ/8Kfrw7f9ue//H/7Yn1xltbvvJgRJzjiNcg40bgMemF3HvVJfxQ+/896v/bpv/uIv+/U/+z/6si/6ZV/6s/7jL/viX/ubvu4P/rFXr4pu4n2OPbsZra5zSf4r/Znz1ZzrweuwrcaxPv7uu7/5a/+nf/1LfuvP/o+//J/5FV/+s375r/kXv+Srf9v/+Efe++yrprm1mtDsW4jQ+9rf95f/yi/9jb/9//Erv+pn/dKv/Nm//Cv+tV/zX/733/qdr65LIdyv6r3rib7osj43P/nme//pV3/9z/lPfv3/6Rd+yT/xi77k5375b/4N3/it719/bdfSkBJupVXaZ96PL/r3vuJn/eKv+Prv+NPt5wfr+uKv+M1f9Iu/9N/9Db9TCpHxX/d7v+Wf+UVf+U//ol/7oU+8jUZU/J5v+67/yy/+ip/7n/0Xn3zn48OZNqPHavHOdz71/lf+nm/+F7/kN37RL/1V/+df8qX/3K/8qn/jq/7b3/ftf+rOeswVgmfVVuXf/Zd+6B//9770n/tlv/qvvPmm9lj//m/+mn/+l33VP/ELf9U//h98yc/5z3/9V37dt2gDK4kf13o8a6yBPar4kY9/8qt+19f/q1/6W/6v/8mv+2d/xVf+s7/i1/1Lv+q3/dff8J3vf3aXSgoHsljP2RKCsL9+Uaixr7Ofzs3Z9eDiETL5qrCWKV1zGy/rdlWpjTv3bdxg1U3/CFoJ+Sj4rYmW/u9PlnY97VouKg3pmd1oOofZTMxXKRvWskk9RHiBtEEKHrDn7BfDmbALnbVHrykoYrxFY7oL5D57WwbWZaX+qtvIi31172CVDZWMKQABIhJoA5kYqUJVq97WlBTuYNNN19NYgBwgeR5jvDgGsqtDQQImZHryOruwMzd2w8pOn+f8+/+un/Iz/rYv/Bl/6xd+8OqVU4vKwkyaax/wNd/yR774y7/qn/3lX/ZP/aJf+7N+8X/xr33Jb/2NX//73v70e6sryL6JFKzx7nuf/uJf9V/807/4//vN3/adf+mjH/v3f/vv+Re+9Dd80S/5yi/6hV/5L/xnv/5Xf+3XvfWpT0XUW+9++rf8/m/54l/92/65/+jX/jP/wZf9nF/x6/6dr/hv/vT3/pVRon2U+SDHZgS3ne9dz9/8jX/4X/yS3/ZFv+S/+Cd+0Vf8/1j7D3hLiqp9GF2hqnufyTMMM0POSVCyIklQQRCQnEREEFFQkCgSVV5FMSAKggpiRFFeE+acBcSARCXnDMMQ5uzuqrXWffoMvv//97vfd8N3b1Nnn70rrPCsZ62q7j0zHPD+C44+/9Jr/3GzVZgXPLKUu9T0acLTSASuMRUCytS3gJ+c6533Pvi+S69667mX7HvWx/f7wCcPO++SYz79lV/87UaAHolN44Y77kTuvP7Uj3/7T38Hk/C10ps//rm9z/nkey69atxVIQewjXj0/se/3XbMBVcceN7ndnv/+XuAwB/+1KmXfu3mex4AvORBCHNIAsSQMtWeff6Fy3/w83ecd/Gep1+w+xmfOuTDl5z55W/e9vAD1HDkUaW2VkFYbrr97n3OPm+fD5x3x533aO/JjAXAA41BSji75bbVNCF/vO32N7zvk/v916fvfuB+inrPQw8e+r5P7HvqR77wk98PU4mOOf/Lb3jPJ97+0S9M9t2td9+79znn73XWR67+zTXm4TXgwsCFxk34ku/+Yp+zPnnIBy94/PGnc0GhbQgxZDDMyYldw1DEVItDkWdPTYxGCHUG37jvtZtEMQpmaprQNnzUWM4gA9YFR88aSTNRiprNR0rTHMmSKTJ5cDFUeyf3Fntux16t06htyMhQVhuqGrBmcAlcH0woqpYZZpsRXCF1StW41OKMSluVIlUSuBgR7CRBiemPf7/1maXj5WZO32bDDV++0XrzZ0wsWTr+/Q23YuOgGjAh9UQluMaIBpFR4/Fnlnzim98/8qMXT1W2jx527kUXfvtnTz49HmwhYjhUKWGvhMtZTen555+//Pu/PeK8y3d/3yf2fO9Hjjjn0+d9+Vv3PYIv8GnZFRoFSQyfxI793Nf2eN+nzvvCt92l4/TRb//49e+74ODzLnnkmecw+Xc3/fv1Z316z7Mv+MU1NwAy8+au+x/d+70f3fesT/35pn8z9Rr9iGJaipHUF55desXP/nTkRz6//2nn73fqxw46/aPv/PAlF1/1k2eeeV6ZWZtKo1InoqcnFz/95g9euOt7PvKD3133yFPPnv/N7x75wQv3OfNju73/goPO+/z7L//+E08vFg2m4OBE1JBrLc8+++w3f/3boy/43L7v+/TeZ1544AcueuuHP3fuV7730MOLoyAQUnLilChXJ4PxaCzBYiUcd0k4FOMAy5JGTcotCeyw8oXv//INp11w0Icueb57/oKvf/stH/jUvu/96Ee/fhVNRG3yf//xb7uf9rFD3v/pRx55SkmZOBxck97pj7f864SLrjj0A5864OyPHvzBj7/jgst+f+Md9zz+1AHnfna3Mz95/S338LBzp65rvvbLv+z+vgsP+/jFTz79OM7OZC2p4IkGsT76+LPvu+zqgz/4ub3P+tR+p3/y6HM/+8Xv/vzZKeRhfOCHoglLWACmqoyJem+CUsq1bXqUFjGJota1XUmdBEgK1BLxBOl0b0aS2yZyHouM2TopoZApWgIusOH0wlqVK1shtIq7yd65q3mypOos6k0bEgB01HMek/bBRhEkiAugjVrNHGQ3IuYhUpk4oVIg6dgZUQiD9AQTesFQizkaEICMgAxSiEIj0qQJaxyTaw2LFMiVxE0OiDX4RzgkJoomMSnEknsKFyam4WJAheWZqOlZiphLJSkmXpgr8dQszKeoiTokCVZ6IN/Cid2TVwiFqpJSyZiMSsM5NEtKKST1kO09We8Ef+BNGozHWYmzirIoOWxjlsrw2klImZkSQ2QEk2MosbbCUwWNwl2sSnWCIlccaMfGFpmoRcBiWAHfQpOQqBE59aFjBuCNgA+BDkOtEXaMcUCfCJNBkcEUzLMwK5VQjZhhBswhYRN13JVCKBnwd2LTjE6JUDcIh4ypmSGwl4lR0HDKziFKQpxqkj7IjDA5QRJnCbEgOB2QK5mTlJajEVaQgKTvo69RAgERF44g8mgsJixlawXuwRZ0B8wNYNcYZwugE4MFSiywzhS6iZb1QLIgfawhyBIEthBqpbnXMpwbcKjJypxHGUC7kAmCb85FvA9zNODu1GE+wOpJxsgAEmZIqxThZfCNmSkpkxDp4K2HsrMSsYi3QTjjqqVgIkVgjUzcslESdzingMMYFxZhiQcQQ2A4OwnWBF4ypYTAYjFTIks8TlwbJpFMkiOzCfwLggGCUOM9AQDMzLW2fc2VtSTvuBTzAv1GHoMLmOUEPjMVp2IMllUignswDVGGGUIsyoy8E4e/sJwQAJiaiJoYpDgT0fAjqm/d67VrLFyOiD7/k98/+MQzYRDiIcYGFcMcDKHh04tNArl00313v+tTl13+o9/c/vDjsBATnh/3/7jz/o9d+dP3X/KNpxe/wAJl7orTnlfEHjZJ1ahS4Zi2BU7bTXfe+7bzLv/sD39/y/2PTPYFQnrzW+575MLv/uKUz37xsclnqPUBGMMZwku13/zz1ndd9OVf33DbU8+9MEyu9cZ7HvjgFd8794qr8RENvAo3FDlsFVz0jvufeuenr/jhtTc+9NQSjKL968HHPvOj35z4+W8+u3QSH4fGlRNlEQOew2ciJTzjMCl9HZBd1vd/eIUaRqRtCsJhRChweJcCmAPZ/o+77nzn+Zd95Rd/vOORJ6oHZjw32f319ns/9I2ffPCyq55+DrUgMxEjVu5kiCd11f948+0nfPYrP/vHrY8teZ6mrjsefuKyH/7+/Ct/go2N3LhQMiyim++486iPfu4LP/3jrQ88glMj5o6L3Xzvwx+/6mcnXPT1xxEJVHAD102kw+iyhuQkJhhdrA2HfmBbGfhCv4G3yAAiFzZMVCBC+I15bJEVm25llggdZDEZ0yABMQ4aCK+gptQQZ2ZXKUqhoaKJVIkxhYNVwEpUEyzScHWDSy7mzJgxlPxgMYxIGCxz0kCegMVEiKl4Ea6JRCU3KNgYhGFCqBzVkxmEUgSmEhQHggG1EqykyHzo91ACm7xB+lOpQjULjolZSAiLrRJttNqKcO7+x5684/6HPSEB1EPc4R6UqnLsve0WXzrxqC+e8I6Vlp/L4plgjz361FMnXPilj3zjBzfe+9DSHmKor3bL/Q9f9rM/nPCZr9z32BPBUiJq4IcZuEEH0TW33XXUBZf/6Pob73ticcBsogefXHzFb68980tfv+/RJ9/72Ss//+Pf/+vBR80RG3piyfN/uuXOEy/+6i133KVECAsNRKPwdM/Dj7/rgi9efPWv//XAo8gdyMbz/2tvu+v4z379m7/6o9kkcYd7GkJhSjZsDc5BsAKBoqrUZ/vDLbe+86KvfPeaf4KrWI721POTf7/z/vdedtUF3/iRFQuOyR7HVYz8a0VrYwAAEABJREFUnzVXQxEifsG6T3zze6dd8o2//OvuRxY/u2zqA08t+eUNtxx90Ve+d81NVlLUhBLULhsjWvLC0rO+dNXnfvjrfz342LI+5OkP/nLDOy/64o333IeABVtFAJD+NuCAOUHkgZAEqDHKAAN9wIEMIU6cs6q82EmCsktWcW7BIvp/vsTyKzd8yexpIwz99h+31q54pepRgS0N4v7273swtOHqK6664nwLrgJ2JmFRUD7ChUQZzIWNTKbqGXvjMJrAO9jEkFTG5qUT77BphnKXEmxPpaKqV2ePHMFDQBQkCY3IDrHm6gYdJZO1GBB1cacq2BWwz6KIDvskTFuGCQuKurSILjGrkxWpoC+2HxGIVBpmZxVhCkKsgqAS+w8SwPin19wEQVusverC+TNzom1fuj4+/u6ft1l5rnU86HYOr2ElKjlc8bsfeerYC7981R+uv//JxZiJhhp16U9+e8Lnr3j6+aEmE0WKPoeRWCRaMl56+iVXXfL93/37wcciAvPB86v//Lejzr/s3seexEe0RHCyoLY4VuEzERNlCCAe/18QD64oDbSj/3UxagcLJ0kq/NjiZ0689MrzrvzJjff8H7Ly8z/9w9EXfuW+Rx9rGgnkODFuTaPaMjH/euixkz//xa/9+pq7H3tqylh6+Kkl2D7ee/E3XihLkQiEQ2Ihrv74kidPvvQrn/zOj2+4637c2mE5mHPXo09+4zfXHHn+pTff87Dj0SMlZ4WGgg0JM9CCyXJAgCBK3vMQqcBuSjDeA+4HJlGtdtalV33jN3+Bdnxe8vwLTqniNILl+LysIaoFRxUir1f8+nfv/cK3/n7HfSj8GMRXVn+7496TLrnys1f/tsORCV3E8JbhpS/jzNCl7IkdBcmELfHtDz721o9/8bt/ugE5OAwTISsvufrXJ1165Yt7JRMopClalkQKz6p7X7ir2YQQEUGqmVWKYqhbEpp8MBBGI7igJ2tKnF1SdUJZcRogZiYEzWUQPsio1UE0cCfCQT6Yh19UQVwSZ3GVEngiVseNl0QOvAK6RUgzR2gFSj7KnhIhXJ6d2qShOg4a94RHN2EsNTfDA2VswSxYPSaebFAKYYjmyK1KzgL3KDxgCxEUcKs8ktQ0LVPDfZYx5YrQomAkk8aTwpoCLpGT54pSVxiBbkLgsqXUj7Q0CQf8Yc/AsxdwdWgU2IkbslDrOhpPJusSMwu05BSU2SVwLMFTOeqCehdzdptKSAvwx7pGemQ5CVUiQ1QGpdVbq5nChUJ7AVAlqLR9TBRuTbNrqvgGyJtUGmJG/Jis9ZgQTsqWo+YwVW0aTapMahU5rVJIC0mVcB7KOA86pGatI3ZI5J5gIlNltZasYUASIiiZiIxbmBBlEiG4VHDVEjlEaRSW3ZmCyIUGrqSgxkMZZacUl7G0nSqWN0ZNN7QMn7QA6q4qNOEbkQkCZtIz8OWc4Ae5Uw2uBIJRRK1Rve+lHwsh7+BBhHhPUZFARJg62O4dxTj4BeKOwBkZG/cwi4MC35tFiaK5jEY+/FVndWLWSA0YbQNKAIUTPDCLgJxhYa/cgWY0asdtM8ZsJjyBUwaJ8zjagppv8BvnAyNjSDdjD9diOibtG6bsrVAbxDjjpAqTPTiMPDAzqiiUizApsXsEUo/cmA1JJg11RAXimThYgD2livg5AsDZIzkBcAfTM6kQPliQs2CcCVHmTNKwsjbWZuQQgK59QkDMEJzAfRAP6cORgQNM6mst4+ir9jyUGgg0L7WU6Nl7rn24lyBDPzGTELfUNJKVspFW5QA0I2EdcYwiBDlkFD10wU6CGfNnzD96752bpI8vee4z3/+ZU3AOkW7AA9N8mMbEwm0NNndsmo8tfuqsL1151yOPE9E7d9/xO2cd/cuPnfDV04989abrY8Jv/vnvj371O+POOrJJ7nsfO2zlQjmoAeTGFEH14SefOv3Sb9/3+FMzR/mkfXf57geP/dknT/nqWUfvOPVHpW+4+4Gv/+zXvVgAvY5tTE8uWXrOV7/7zAuTM0ftUbvv8J1z3v3zj7znS8e/ZYeN1r3mtrtgCRoD5eyIadtZ/8ILH7ny+/c9/nRS2Xubzb929rt+8In3XnHaUQdtv+UdDz761NStDpZo+IR6gxyggo9oTIhW107TL7/n6F9+9NQL33UIJKD/+L1e+/Nzj//lB09abfnlJYW2REzLLhdA6tJbJn7wiSffd/l/3/3ok1j17r1f+70PnvjLj5355VPfsc1L1q7mv7zhtku++wMkITMOzV2qk+QVQsCyL/3k9yDb+9+yz08+8Z6ffuzU849646rz56L/R9feeNP9D2FXssG98tDjD7//i1c+8vQzc6eNTj1otx9+5ORfnX/mV059x7YbrInJ199+77d++vsSSHKNABJBhAYNxB6JHAwxFPkmpnIpqI+2pwmi1ESd5tFiQhE1EHTIbZQJ94ycHCqPmww0yw7CowJCXoPK19OocmrQScTQwApWJa0ItfaZS+M1qzfZGqlChakjKC1BKBlLJcaOY0Id6qNGhgQha9lHWttUJVvNneVCGk1IS4zDILVKI5EJaXJqGdpM2FAkCJ4RLF2WCoTMCm0CDvVKlZy5aEL5KebVnTXl3KQg7rnUalzF995+SyICMc74/DfuuPsBwhMFTYI9KyiqU+9uHXPHuYtGs2iuUV7o3nPJ1/98610WsedWm3zl5Lf/8sOnfvPsow/e8RVIqJsfePR9l131zLNLa+WuJ+sjpmINLb/85+2zpk2c99aDfvDhE39w3vEffeu+a68wH/0I35Ef/wLuc/baepOrTj/mF/916n+f8c5DX/PKrPLCuD//Oz+W2kuMjcdVC04wZ192xc33PTxn2sRJ++3y3fedePUHTz//mEPWXWnB0r5e8N1f/unGf3tx+F4j4DdZZSO1JJGYiLk++/yzH/ra1Q89/ez8WTPe96Y3/PT8M3/xiTMvfuebN1x1RY/479//9erf/1WIt9hwrZ994uRfnXfSAdtuRkRtTleeedxPzzv1ouMOGbWpesJXZpf+8BdX/eEvxf1VG6172fGH//T8k37wkXefuv/OuBPASe78//7J4ud6ZVUJ5oFFkPP4M89e8+97D9huy2+ddvQvz3vvt84+9oBXbYGseeyZ58754neeX7w4eBxNMZoyHguIasSYAt+pUThTTPWRkwVPBlUlbTQt65TE2uQN11zzRx9676/OO/Xo12+/rP/y97z1Fx8/7csnvnNmMyPliV023xD9N9/z4FNPLAYoQ+0Q0+AH7n3o3sefxtB2L1uXJ1LX8FjBIMnQwFBh1tQYwfWuUuWIRErB4YCXHG8ES0kQ8L50pQB8FJdxMDZW0MhoXGUpUR1s9CyFEZpwr8FFuJfsKmCz5JDkTeDwxeyMYZyEeql9a55cOKCDKRoKZcQTWWlaghxJkDklDcklZ5U8YmmDBfj0isIuxJGpb+6/9+mb739EhV+z6QbY2cht1602gcxbH3js9oceaUeWMlGSgucUKRDZpX33iW/9EHcmwrz7K172jTPf9tPzT/jGme9482u2vu+xJx77z90p557bjmBusS9+/+fIDnBpi3VW+8K7j/jZh07//tnHnrLvTrWWW+9/BLrQhAIFKfWTyER8XNZGTo35mYft++MPn/TfZx674txZ6H/1xuv/4MNn/vi8U/fa/mVtGkv04QMIGDLhihuPLH2i50q899Lv/Pm2u20qK6849ZgfffjMK85698E7vnJZVp75hW8+t/RZGY4AQiygDySgXX3t3+946LG37779lecc+6OPnvLFE4/aer010Y98vPr311aivkhBAAq997Kr/n7XA0n4TTtu9a0z3/6zT5z4jfe/c+9tNwd7739y8UXf/bEbwsCybK+DiKlmxmCMiFDD3oKvXENLYZSlSU098pKHedjxr73tzje9euurzj7m55848UNvf0sZCwoVv+gr5tRAJbHhKHHb3Xdd9uNfTfZl5qg589A3fP8jJ/7iY6de+I6D11w4/+d/v21cHbPRpFAuMUqg2otSQClNzrmUREu67twrvn3f1F6519abfuW9R/78Yyd8631HHbzDlnc+9BhKIiTANFEiUTaFqOQlcRdRUdyKT/TW9KY9uTVGWpQ8kWZmIeRAMeqMLWRKNSjLURkPLtiDubgimShGgYhDsqZJ1k41NHBQSyRBCQyJYEtURXCQ8IqF4rgNEiFhYmbCG/VWQgU/7lGoGDurY09gbIXQRSZRk6N54sC8CCFiBa4aeO5EEEWZkRuK1KzOLuZK2CllklOHrBdtNGcRhkVEZB6mShm7hFH0NdhdsEpwSCLYyQPaqqnBjzCyVofErz2ImtQsWTWywh5uuQoKKdWpI6/iagUQhIWXGl1F8hPqDIHwToUiEqPADXtTSAlMqU5mUSuJpzQYrWYSRQa/KlfqjSrcYQng6c5EALTiFW4QaUg2TZwzwTViqdF0FZVHGB5FjejC/cnFz37qa1894N3v2uWNhx76jhPO/9IVt917T089DQiyBzCwwdIkDlkSwWTA1FWEQo20Fw1ScuCEcuI1pKdkKtjPUyNAXiFDKRINK7lgYq0CLyRScIL8ipQPKKyUyDiqEQVUiIqyNBx50Jm5tG6K5dXN+lqqORVVkxQuQ9TYTXAOkeJSxs3QwC3AkDil0AQUIDeIgwGd1misogJCJbRgGrMCtkpinGigUCV3nHcyiEZsjF4auRO5ATsyIkiiCKaqaRhOOBJVye4TpFkV+NcEyiUsoVKojhl3ecIqmUy9IleIevARciIglcnwq7p7DXEId3ACRuAdRowAnFWB9jBnEQPbRJhwGEKOcUZXclaPQIMCgBHG0QmBGAm+BffB4xpRmXzYOoKxt4Wbi7myI2pYFsAeaBBrNCpCUObFi8FnpqnLYS3cYJI0MDlqjUmncYDnEpGYJYQD1xCdzNYwZMFQ9DsjgIx4KPAhgkAYs+MmL91p0w2I6Lc3/vtXN9wKdwcPOKRR7HfoH5plwg14zdzplb/44wPY11U++fYD377LDqsvXHH+rHkvW2ftj77rLYfs+Apm+snfbvnJdX/tI2Ae8BPkZjFyBQm4YWJsp/b7m26/9/HFwvzJt7/piNdvs+ZKyy0/d/r6a634/ncc+JKVF0Ljr/9689LSAQ14yyxf+NHvnn5h3CY94017Hrnvrquuumj+vJmbrLva+e9448HbDecnLCF4JoENKEv9xd9v+Oud96PzrTtve8ah+26wxiorzJv5ktVWfO9+bzh1/9dDDoY4KIP2PnAS1EcPGjQawiExf/7sBcsvN2/WTFiM/hkTE/Nmzlowe3bOmRshJUbvVDOSEkpggccVP/v9I08vyaqfOe5Nb93rVauuPG/+vOkvXWfN8044cp9tNwc437/uhmtvulE8cu/ZEJMXxaw0f85lpx21x45brDB/5srLzdp5403+65B9IX5c6k2338+BTOBg+90/b3ro6SXA7aJ3H3bIa9KxQeIAABAASURBVHdYfflFC2fP3HSdVT541MFrLxoOuz+95u8k7kqYk/+XjeQD47vcOLfsqsFCSAxzYhNBB6hBlC0yotN76aMaW0Al7MtMmijYkW4ig9Bg8BH5gcRTJkYZiQRZ5AP/MqsgmQcdlcVD3AhrwfogrwMtSCna8FxAj9qHQWfAEg6CdJjYQGRgPiMDDaIoMQmyayCUo/CEgEsq2FdKki4lp6kL69lhWTJLQ6WoRcwY8cWoBREcZcI2Wjg5QiFGVCWc4alstclLj95jx6R61yNPHHTuRad8/vJrbr5xyQvPVB8j+yrEMkXjglIWUJ+E00+uufEfd94PnI/ZY4ez37TPS1dfdeH8eeutudoJh+1+6sGvzyq3PPDYF6/+lZeAMSgHSEMYgrbCnJlfPuWY179i81XnL7fC3Nk7br7RR95+8HIzp2PohXF35Ou2+eDbD3jJmiuusPzMdVdZePy+O++02UswdOsDjzzwxGNwPzyC/Ru/+jXOlNOb/ImjDzp811etsWj+KnOm7bjx+p8/5W0rLTd7aV+u/PVfuhLmGUAzEHJWNISRjHHwif6OO+/DA1QA8YEj9tvztdutOG/2onlzttn4ZRcf97Y1Fi7nHj++9oYyWVttF8yeM3/evGmjFmYw84LZ0xfOnT57zgRymsOfWfzsT677J+Zv/5K1zz/6sE3XW2/BvLmrLpr7pp23Pm7PVzPTM0vH19x6C3ZDVMnh/MEQg2Dwew54/RmH7v2SNVZZNHfWumus8J7D93vbbq/CGM6Lv//HrcAbOxTMR+TQiYY3NdRwlgh+MaxEgzAjHgINftKyiyW0pXZanj9v9vy5c2aMcDM+jMyfNWuFebPmzZlZAQHFDltuAvOA1Z9vvF2EREO0KvV/vvlWzF5u5rRtNnuJhwZXAm+kDjQOAQudYUgPrW6BI4S4wAxnlLmCTc+yqKQcWYMaKlk7BtWVHKyrYJCrgENOSBY0aEIewH5GzSZnhJcCpsAzxgJEjoaZhp2NsPdWxh4MJLAMMyKXHlxPxmoMyyRMsa1DsVBkZyGklDl5F9J57iEKWvA5fvCXf4xLXTBn1iteshYsAy/WW2nlNRfOM/er//D3YgIMOZM2LJol5E833v7HW4dHQgdtv8U5h+214RorLj9n5tqrrHD0Pq8/8417/s8XXICRxAHUQ48/+ZVfXQMr8QzlwmOP2GLDtRcsN331FRe8eedtPnf8W2ZPNBiaaiwcypbJpz4OL4Fi5d3cdtr8WXPnzZqhAjdo1OQFc2YvmL3cRNsyAAgjtGE6BWukBniXxCiA/7jzAWF+xx47gF0brrrKCnNmr7v6Kscfts+pB++WdcjKy67+JZGDR7WRkDQlgxJ2tHe86fA9d1pt5fnz5s/ceIN1PnDEQSvMnYnRv9xyl0eqCXGmW+6+9693PsBEx+z+muMP2GPt1VecN2/WGqsvfM8RB+606Usw+db7HnrgycdECdRCANGzrKH4gQItbMUAgqBBTF6JCodmEwoOzITl73rDa088YO91V1h10fR5y01ME6egGMYwTBTQLYQK37Nd/OPfgL24S/nM8W/a77Vbrrpo+eVnz9puow0+d+Jbt15vtanphIWo1GQqjpXL+sgR3RQCSlv88tob/nrHsFcesdM2Jx+0/zqrrDZr5oy1V13h1Dfve/ob91i2VxJslexZLcFMy2ZNGO5hRT1YjLUwVyV4o0rYLxoSQlAQ2ELSU1QzcyNCjF04kvYqRsyO0yMHMzHpAFliaRK1rTYNWCfaZGobb7BTshGRpG7U9hNSR26jwCnKlQPrCE6SioqA5dGVvg63ZnhUJQwDUe1FiBJS0fq2H7elaiWByQWc6bP2TCWEKBJF7jzGXF377CKWjFKNwbGAfs6eRk4thXjFgZ6Bgbdcs+MmlrzXIJdcobSPwPONfhSSeIJ1ghghLEQ1Uks8jT0zuZNZYcf7pNxgKUBiEKdFqJoylDoTLhFYB+0e2tdslKTl0YTmnKwqvifw6LmO1XBIlIpTI6ECBGml1jg5NJbisJZYQ6JILVwxNSpbhKXCUjIFmjBRw564R1f10iFs8Nk0+ocfeGC/o4751GVf+McN/3jg3ruu/9tfP/3Zz+x+2BHvfO85f/v7rYHHLh1bDSc3IRkioqwyRMUzkcAMAUOoOLo41ClXZ7glPcjC2ihsdRg/LA0SSIFtFKBWIe6l6STjCN/3jErpGGWogT/gBKKmRCPmHMLBqNTRi4+Td6l21Ffrsf8Sp+RNUzGJEFrDeaNS8tpSFasEsxwlu21G7TSAS4QtU6BCBnPABA8GWXEuIctOqQT0BsELrcxERkPkXIZ+FhJFXzIn+Kc9URByxrVCM3EoKaEFEZNmbpOIVPCmy6kq6GdgWzJOmENagrpqDvugg4nZ0Zy4cupqmFfBi0ct5IHRAQEoF2N2CvwkI3Fn8ANxDxaADTpqUKpEWExkCQTQ3gcM2KA1k6pFCUBYu1GNIWw9gRLJYDuRylCseGKsuUc9hP9MkiWljHBXqMrFgQwRCeYifScSN7AOQTN8TVK1uuOOZeAe5qAeIJIlUd9GaepA1qjBUTPygsdjw4VpxVDrMctOOuB1C2fPgIhPfvvnDz35nCGIIC5UY9JUkyBBTlrzzLMv/ODaG9C3+5YbbbP5upFSUNtbU0yaFG/f49U44mD0O7+53k3JhbHQXbugDh60pUl1pEb0ktVWef+b9zv3yIM3fdl6ocEcFuG1zhB99cbrQcLDzzw/HheDzykma/e7G29D56Zrr7rNyzcJZBxpzYJvx4z80J22x9CyxsyIdmX/xd9vQc/yM6ftv8trKFIu1AZ+sTC/brNNFs2bjVE0qck6cgu8X9ZgdhiwTZ6GSva/Bogho+PWGjEFU+H4shUAm3pNvfDDTy/5+V+HI84B22++1UvXZ2JChY7ewnLWI/fZZaXl5mDNN393DbN5iKLGUUIP2uG7brfqwjmZaopKhBYbr73uzHYYve+RJ3MIUVCmLV+y2rmH733eEfustdZqnZDVQDK6lznTp2230bqQ88DTz9aug4HKYCgTbKDhcuhzzgR94Fqbatv0qI4WuQ/tGRgFAT2SMELR7ZL1YB4JEFMllnAYAArBCojAFzEmpfrUwmTcDuegGu6uFOLIcBWCciRQoMKgvNSqjtTomZ20VQF/s5LUHAX1NqwCEFiRoyYGvgRfi3KfM855kUKhfhw2tq6ggOImypmqkfeKMitl8HBYTRTslg2vgB5vOdBFSBQKUo4k6JDiVr2w9NL0DZE4nM7eHP761/3XW/ZZZ8UFTPSrG25798VX7HvG+ad85mu//ufNz0x2FZsNdgAOQBHES52++Ye/esTLVlvhLTu/EkcnUKsyVeKI/IZXb73VBmvCql/fcOvixUvE3VGA4DC6iHbcbKMZs2YGAV0WQRevuPxya6ywPN61OR2y6/YUFmrU1miNhLZYfy0MuccTi5+1QozqWuyqP1yPzt232nSTNdaljsVMQR6vc2eMDt15OwzdePeDjz/7vEsKT1baoFFkYYilzrwIxRNPDX+AR5mnzZjuRGiVopeYNmf6kbvvcMTrttlpsw3wDIstu2dD7ZyKKCQLTCcqQQZE2WbmfMKer/vgYXsft//rIqllKZFtmBzbvmyd5aZPw5K7Hn+SUOGloi7hI9pqC+bv/sqXR4ViJzXj4Rxz6G6vRc5i9MfX3Qh6RQAJwgt6lrVBYZ+jz6WHvUMfE6uj0gkV72s/dBGWwEZGAsCMHnxgXtaPXoy5lMIGBq231qrrrLAAQ7+68XZQIpHnwIryyxtuRef6Ky9aaf4CkL8hG1FJhHJU++QWhFgEjGOwFFQTr+gqxMMEZIDgKasmFtClwamkFcMmKSMi5ly5Fc4NhWutQjpUAWIUvyAiYfR2As+8qTUNacOBhKiag7C+YWg2KB88xHyK8FqkR9FgCTgZCl1UlQ2CUNtMo0B6JetF+0TIaYOD4+f6xb/+x/C/T3nl+qvPmzUNW0pQHo1m4KEVxP7x5jsWP9cZRVLs5XikwF7oZ38d/pzYvOkTh+26g0YKY/FE0gg1223y0lUXzMdCNJ76sZR+fP2wTUw06fDddpyYnfumINf6nCKljdZa5eXrD5TG3CBGRERYFJ9ebMYgVzA5/Kn/W7+iy7mrTe/BSJxB2dQShhSFqL7v/vu31yEr115xwUE7vzK0OgLHkRjSeO9Xb/3K9Yes/M0/bnt68TNC4ZlMIWgQ8prNNtxqg3XZU4SyCIbmLjd3vVVWwNi9jzzOkqJRHvHy8+d85Mg3nnvEG1+/zStNtWpjSI1IKrTb1pti8nPj/olnltAghoLR8WKLRNGYCLBjUnjFKCVwPEdqnEmCpq4mp92332LZJDFpuCo7DdOnhomYNJgt84NPL77xruEG4/VbbbzhemswxVBnTJF9M6aN9t1hK5q6wKaAGSFkKHZ4N/T2jC2RRIz67ld/uxFdyLtDd32NNtlT655rx2H+2pdvsvB/9srIvQi+WihNJTVNlDWyEKjJQpKJOUdMULRADxrNqeL43E9wxXaTrBZshB0JJR0aXK6JfTj5IWlxtgrwF9pHGTI4pZabCU7KJDgiNU6AWU04iQydHIW9YzVqgzBBKuWh1Qyqoi4RezIwXyHVsIkmB7+EOTlzsPdiPpQeI8GWFYYuYwtFcmC7Vg9iZRU8bGi0oNxUrZMU4+I2dp10QExBJINwKDNL1dQhGjWsBI+J4FKYeGWHHo7QKpklVEMpBHYITndIVO+zw1ZgLTg8NsWoRg1MiZTxiIiUFYoqh+Okq6QiygwRiYOFDeK9J4cmBCfSmLQHJlBQU9tnmE7OlcnbiMYHRJAaQTDER17aElTISkQtrmPTQtAdRIoWTAYfffjpy5kf++QDDz9MRMsvv2DN1deYMWc5vC999+vf/e7Q405+7/kXPvL44qycBGoiwYaiAJ0T1PsgggM7b6IiZBKcOWVmSCA2IoNG/GCgiBcggREBBIxB1MjklShMqW+5qDkimJgiJ2qSkkjvOAggmk6JOMH1UDEGExthYYZwDWh3QO8iHWQRQUNiJHRIwDq0wlgj6BdyRC255SBgjOOtpkJSDMV4mCwRWJw5csADzBAi4mpWrdYSZuaQ5D3XShGUHQTvicYwuyIyvRaTkhw0DcgNEzImk2pURZxhc5NTI+B77qlW7ST1adhMAWUabOYQYugWATNQT4qSk3oAFhwSW9dskvCxL23vjRH8CfcU4+z4EtqZGO4GusAaIrgvnmEAXMcnCgILo1BwUGZoSLVKHw5XMK+lpMxGXgCDphJth83HuAhHAlysEqJYSExEkrJChlQKoF4Al+TMiigJ1OJeCIWce1ijlIMAsZt3QmMmq4hpaLRtowoyEuKYBJcvN2/Gsfu+NovgqH3Jj35dhTVpuFcACJUUImAKidLN997z7CT00g6veCk3GY/nVQiFQgsKWz97zrQdXrYBEd1038PjpX3i3DDBaoc8SWnpAAAQAElEQVQ0eC0EQpRhr2o3XG2NN7x801032ySHGmLmRDW0CEUatdiSCVetXjkZyf2PPvnMC8h+2mbjDSYabQnYgu1NSgk+ahZMnmpQltn1uRf6ux8Z/mTali9Ze2Y7LVXmnjmUgXODCITwsiVcCdmpXUHUYkoCjCXxRiOCcU4woLysn5hZgJBICqOhmlFA3TAIWeik7Dfecdfz4wGc12yxsQpTdZ+MrsOvmigWzp29zdS9xI33PPT8ZMdJVFXoRSFzZ85g3DmjwRAO8A9Wj9IQpsWTxQFMZujfeI1Vd9/yZTtttiEi62QWZKRGTXBe9sAbBnGPhLZAwmAJvyg/kQxeu0ZQEEMvBkCGmhyJY+bcNTzGmSpn0kapEctchQYOMtK2x0GbDdUyVcTbCUIIuZ+0CrY8DSJlXOQhbkwOv5QwCxyvHkVQrwx9JEQpCbXq2hi3gc3TVBx+eAkrFGHMnsMS1ywwF+6plGx9U0syj6lRPBdDihBV4ipsPmgiqdJaUsdnDhIhRfYCVMOq6gpojcmTmVhR70gsMEHgYUPeOE+Q7r7tFp9775EffNu+W6y3+syJ0bNLx7+98d8nfPbrx3zysp9e87dx7zWiuFvwg48/9cDjT0HvVhuu22iOgMqo4V4hl9hj9202w+gji5fc+/hjHJWlwkf0oKnCmL6CRJAUbDBBZLnpLYZYZM6MmVGBCFcmeEuaZk+fjSE01GDRTJLvuv/RJUvHQHyLl6xVo7oh+yJIQxDf2HDV4XS1eOn4/seWdC5wmACkK/GwK4LahJ/K6622WlZBdL7wvZ/fdOe9tetL6at0lvpdttvsuP13PWzXV82YnpVJSbMjxGiEyw2VtamUDVnIPKvR12260W6veNmaKy+QRiRxBPcdaCiJkKOKJeZYH4Qqh4bPhJqnIxWyahUmBLgj7Hhq/vIN1sb4k4ufnXyhSq9amQMd/1sTIUL7X73hzIaCLVnzi/OCKxFaIIFBPX+xGwxkM7YKyqF6zZ022majdTD2jzvvf/S554gZyNz/8JP3P/403r56kw1HwU213Ds2XwqtYBeIAiUIJ2uoqiSsKbiD7ntHfYVbhC7mFCQSDCKPOHIibT2yIGXQrYxaVJirCVWR0pOPiZ1EyXP1pmfpJJyN4RastyEf8ORIKNUQ90LUBROyi7iiQgI4nLWEWRhZo1a5g49OZPgVHgP2+AB6hOAODafHv95zD0p9Utll600cJBARNcr8qs02nNHmx59deu3N/xIqFB3VMXX1uXF3xwPDH9bCl9hz5k2rCbvsRPVRRMLO2DSugogQBSxTczL1f941/CWfRfPmrrvmShbFgpzdMQ30UMopwX60KBQOaEmziPDQA63sFa62wihDDKHoHlqrgUJorr1kbL4sLw4hBnim2lA89thjD0xl5Ws2e8mobVzVGwZUMLfpA1zfY5vhXgJZ+cBDjw/PrB1bDoAahKMCmwIwFsvqKbnDxBE2VqLF48LYKKQyx0qL5u++9Wa7bLXJvDkzM4Ofiaxl4obLjBYrBlEVpz82Eufh04s/QYG6XImS5eSJMSzBSRNrUyS9aAUJ84xRK6mPKfsc5nMvIAj9ZwYpYbnH3Q881ANrom033sgJxZIQeg0S1L2myU1DU5fgNRNhBwKVXgQMAqJOGbu0e+GeR4a/JIa8mzYBW5x4iIeZhtXEriwQQMjoKsWR2RaaQlqWzJSpBspYdlcxgkeVKZRVQqx69A7gxURAYHJCea+FKjNxSCW0ICYVFnz24CBiZhoGwGsCmdHR9d4XMFgpGpJxy312YWwFPffjqWJlsEv70N4VOhmgiwjyL8EVck5VqBI2mX6iqSOtjUf2oEoMvmr0YuBAJGcuDZ6AwAp4ntgUkMHBHDa9+ghbsFWzYjW8RuUBCPjq6Ox6YgcaUFFco1T0gxIOc/HOKciNDDY1PJJINIRBGsoTIrita/Akbjwy3MUHg9wcgclFC4zUlFpKSRmI9PAt44Mkgq2YFEArxDkhaxi/yRi3wU11rpUIrgdxsAXjNwFVjZAI0C7nvslV0ecwD5Ic9nEU9t69BBnloCY8UcE4bPrj32/62w03ENEG663/9U994psXfeJHn7/wv059z4brr4/Objz5zau//8bjT/7Z765voNb6sAjHCBEUSjG8FcULE7FBjalmTRPkPMyk6l49cLmHo04YIEjBmSg0lZyrEAKLxyWiMtI8YZpDWJk1S06YHHg+VaOP1KPCKVkKkJZUpFF895owhdSLmAGLCiQYVZlH5KNcVDumXgP8MBjDBSwKMSoEuBNVlV5QOEbF2t4aCzUVaokUhB7+uGIiDU5OWpxrmMFtBoB4R4CbPUunUVBKmNQpRc/s7G30jXdsXkr0TuZhrj2ES2ra1I64zahuVeFPQaloG2pQHFELQ9vwhg25njOlxEgcZhMNSUSszEwNcQpqwjQsYAUJs2BE1bOj/geBoBVY40ZDBwaQsDSM9cRUw7tweJFFk7Yk6kLGUqcUgZnwrHohLlAnoQyuumNPgVcs3DiPqrQmgzTI5Yiw3quBDzCRFVxuBZt0iFdxwOWUiDMTwdJItTY4+kQJfFYRSQlv0MDxHHBPkKk7vGKjHTYeHsn/6Lob/njjrcrc4IyRBoJhJmfPsCL3t07dV8+eaFdZtFBYhGtCLBBReDfgktZcaRHmF/Nnnn0uN5py0iYRrBtRFpLBHiLYisZU++73f73pvK98/y0fvOyAsy7e/wMX7XfOpy//xZ8gAQ3yidtCowefHvcFiniNRXMa7hJCD3owZU4pK73ozbCCHOHRF16oTz37Aj6vu8Ly0yKUEALmqkiNAhbnoKkLvxB1Ti1RWzE61SlCOI0QBw9xLu5Myy70iIG7TP2Q1WMirJ8aUkdR6TJ1tzzwKDrmTh+tsmgecQV1EVoxCYe02nJdZ+rxbVdtMcyTKGQWL0oBp8wAiFqRAKtTxH/sNKalmUwpeVZr2KXr7dfX3fLJr3znzR/+1F4fOH/f912wz9kf//pvr4N2NHgaRVEg+hQ5vYiOcmiSGgwtDupwB1yq4Kyp2MihHbuBFaW+TUC1EWmIUsjgJY72SKNUGehaSM9WpeLhSVYVGgWmEakSNAmhHIDtFQQNgE4CohIS0Su5JTZSYha1UK9wifBWRj2OPTCPARYF51AN4ZBQcWVTKgIsqTIbGDTKKkmcAxIhXOEOGrxGM0LNwDonoBqsrJJTSTRmKdH0oT1Xl+BWIuGXY5aYaMCNIbmZyENmzpr7mq03/9Txb/7K6W//4GH7vXqTDYT5lvseft8Xv/3xr/1gyRj3BBRijzy55Llxp8Lrr7rIKRkPeQ9PIUKJhHytFRcOJnk8ufhpAvDhHuhY1iJFH9H3AzYINidmJl42BneK5z6ykyTWHI2QYgiYFiuEfTb5vx4aTo3w8/yrfnLABz+15wcvfMMHL9zzg59+w/sv2vvsC9972VWYj7Z4yXNUCbAzcTBXw9sRU5OApKfVV15h9602xjQ8QT/mY5cefM6nL/7vH//2+puWPj+ppImBNFGqlPFM0bgGOeYOrQpVBuubiMYtU6Shl/j+h5/CPc/pF37poDM+fuD7Ltz3Axcdfv6Xnlz298GCwC63CKtEMTWfhIe3ASL2uTFuqWp0ay8Yvll65vnn++qNpVxZls3GK3Mo41Is5BeFeLiR9wTgpdqLJrpTNUamkllDPQiE1UMLGtK6cqNDOcqsO22+yUSTkee/ufH2jslF/njz3S+MuxkTo603WIeGLKpkFJ4tmsA6bdukCqNCiRsaIUnCIxA160mM2YGGeApra4laTIa9xZvknKkOhwBOVCcY6VAdISFnMWEULgpxScRa8YoABEsEPIkIrlWxpbk4YQ6MLDF4Q+BMm2pDhc3c8Al20eAjC4QECeEKgo0tQ5op10Z65Z//7TZUoQVzZr5s3TUNBgdGTRpbe/WVln2H8MNr/kYgiwVVr3Vysr7w+DPPQtaaC+dOgI9tWBJ3VuOUKg71GFrWapWoWFoffPJp9CyaO2M0beREzDAN6WJOFvyi9YTLORweBpCD5egg4hhpTDQ+bDkICdN/Ls5jaboQSABg9D9y4GbiIUkeevTpZVm5wQorJpsmDFpJhXojra5ma09VYPN46qklWixbpTBadjGF1OCCc0XuKHWRSkUiYxAjCkAZbBjy1sRNxjfd8e+Lrrr6nR+/5KAzP3bg6Z/c57QLTrv8O5g8tAgvlSpHCMeL9nNQ2BQtjVvPHELsLoAiyFi7xD7MHD67J8zW2rcxmblH/ItbhRuDbBrkBPeEb9rxGVm64vJzGbsGcqBgM2Sw00SdBmmYAA3gUzRm2mMpeqaahaMq8wv1xb1yrQXLOdwdF+mQB0yphdqoZWrylE6v4UGsQRAOkBryPMxBNEppzJviqVIg2TJXQAYDhE1oDH5mQhmLAJxsDCHRuEMKk6PaZ8Xui82wwkDcelCFx4XcySvXChN6x16ZSBopXsyKVGvJJwBgRDUkZwRMc/XIEvCfKDlBfeCXwgMH56S61E6oV/Ek3CakIYsUZoeNYC0cS+ppWMdV3bgn76iWIfGVEtaoEKsXVXhHFaPedVGgB3maAQajHiC0TYJcyBlVarokY3aEToka7EgkIFHUatKTVpBg2M6FBPIDE7hV1gQ1jKrGtQgFp9RzrpW1Z5QXEmYGsUoKfGuRMr6/QipGUsmtDIopCKUgeuJOpBdCJSpifaY6ohDNxqk6U9XGRZWoicD9/4Ti+JjZshgsykFAmsdOjndfvurqUkrTNCe89fBVVlw0c8bMhQuW33/Xnb7xqY9/4uyzV15pFYq47/77jj/9rOPP/eRjzz3dZQeZC2KLqIBSnhglytpw2OacPBLVrEUnqjYUObErdRpIlYZQZysZJIownBI8M84Jd3FgdpGRs5LyMMSKZcImgmxEplIqiCRbQcWvbiU4BEipy8hCawc2ddF2kjCduZfhVCGmEyQN1yRFPNhISLGKGEgVouqVomOpojylLyHNqDJPzQqacogphSZlUW9BE1RZCW5qzl2Ty5SzHoL/OPeYiU2OW1YINKMe9LYU1BK3KEJBiKwE3rE7bn0yh8KcJJYoiKRjcu5IDXdWPZSQUKfeMUo/wRVEqsDWYGHOpGwaVThUvGlrk1kaZFBrLoaNKqoxoQQEQyqiHjyNA2dOr25C3KhkuMk6llEH+3rlzlH6hnIZg+qq7k14yyVLJ9w7tgGFDsTAhQjmEuMFMSlKoKCkEPQ6sw1yE1cFObMrM/YvtszKwtZE1+gYh4FIHNmLG2AiXGBSHz2qpU6MJt6x987zZk7rq1387Z++sHQSgEE0JhGTKCgyTtwvHSOANNE2M3MjbtEbeQRx9Vy7HOYzpk/Q1LX4mWcjwpQpC8BSxJhtMNAB7SSx/ePeB/b90Cff/fmvff03191090P3PPrkPY8+gfb0spMNEU7LWUiyLHWziJzSvGnTUKfMai3YPqmGCCeB51MayZ2K5xRrBwAAEABJREFUh1NPPq6D+YtmzxosbwhFCQN9B24p48PUfKZIKdo0FC3VqS4iZk+5CKfESGoSyKT/XG7Ve+uL4HGH+X96B7VSnEss7Ss6Z4zwXLghUmhtGPfCIyRccKlss2bNwAT3WLJkKWDAudNisBOdjJ+GhZE2EzEG8WsLZqNziLp5jAMsNdSvfO3t9+37wYvOuvSqK3/7l5vve/CeRx+/55GhLX5+6dR0qsTIOqXMlrs6xAv9EiQSSBwn7AGlWoxFO+wXlDFKCGAqhWqVYhKFY1K1T00AfQ6T6hohGu5ePVAFCoQpuESBOg4JTBzMRuEOQwEb6KklUoUAcdWStAYQEcUUZAhXjl6ta7wok7AwEQvlEFbQiQ252YS2LljdRzMWxqbhqG6E514gdw5sl8zsMqQLESGaGc56uGXqTUpIMIlq20qTCaernIwbZkme2poypHalGTOhipIQ9xXxLayVtEgjedX5y+2x9ZYffudbvv6Bd2+21iq12rd+/9crf/bHJJTVSq3gvbDMnj27pFGv2RG+lFIWFYiTmRPTaepa8vzz0hAsCdapjuGl5QH3hPUpt1lHnJh4GMCPCrMYfEW4K0sFKuglZlLYbu61f/KZ54gIBjzy1DP3InEee/Kex4b/6ce9AxOeeOipZzCK1o27XKl1ziJA3oUDBdwyKyQxHrmeeOjupx+8y/xZM/Ag4I6HHvvSz/90xue+tfvJH/vIl7/z+OLF1XyyGlLNIwJ1DxKnGisQoJYMpUqUXOXZ3j/w1e8f9MFLPvP9X/78b7fAjPsefeLex564/4mnIGRqEbmlWnEyRMFi9DAuTZFahXWwrCfBjuKxwnJzMLpksvfKSYdBVUUPGvQi4RtjKUyGjqFxImksgScg3n+yNYKG6mCl4U4bkHWYOfxYUCTWLCkYGVt9nRVXWHPq8Prnf95OrO7xl9vuxOqXrbbSvPkL+8hIAcJshQLCIyns9plTdsldSO9wnidSGiXF3hBRO+t7n3QZBpEO3OfoGVYBeXUhEyPzZMEEegbXGl7gQdOGtJUwhamVpKkxTa6KGSkKewkkdc3qOC4kaSkalsEdDmc3/PQKWhB7NI2NRpRhpspAnmCxJtdR7hOeRU5wTqNnnu7+MvW3TWZPm/aVH/3hsqt/+/kf/O6yH/zuC1f/6ms/+50hgYn+fucD9zy82BWiNIEvVpcWg8ZF8+ZiUwBBE4MDnMm0MseUMRhm5iYJ5VTo+XElohXmzXFCuHHnMWqcs7l7dNUxtKwZc0/YD5sxvB/CNnTnyClGgTQygWND139+JJlCJRFApUEy/ecq6FraV8hAVs2fPTOLJM8MqkwdfKGbsk7MnLFs/jPPPc+cjFOVtKwHEWZhybCyF9QuK+FwIZaNgrWwBKajxjz8+JNHnvv5o87/4uU//8O1/74bbEe7++EnHvnPPy9JxrUnsIM46X/kC6VEKRB5JxxTJwohH8LGNSo2Aa9KGKPBpYy9lRIK3zDiHMjZ4CQvgmyYXtzMn3mhENFE0uktqqD1NXm0AE1SEGPIMTrV8LGigA/7nCAWQ18urr3UrumLjOsQWeRdBq/IMYMVOJCT+jAyzEcnIj4y1aJRaeivrMSNcspG2CCsiHmLuUyF2AZfhtSJhsEOzCDJuFISaGnZtckqwmFKHVPXea0A3CqEsAmj9LlFT4pNyLXlnEPRL2KdVKBLzAQjWfD8icZhRWvNAvJpzkBOCAdAFhcPhkORiMl1aMQwG7VLFZY5SiPKh2KQaVBJxKQC7IOrVYsKA4UhuSI9bSJ5GrVl1PQNY6tD7gVxsFBWyuQN4XVYLsLCzEEU5lEIKR/aE7uQsxPWGmJYKTBJGbjmACqWtDozQwInFyvcleijsIA4kJfN2LGaIFi8RhirKzRXTaXJNhJruQihihbmyoOmwDgFPMBObApJgMPJe84d4STNlkkQVNFAutWGEFwA501wogggzPc8+Mgt/x7+X0vrrLX2tltsQv/b1bTt7q/e7juf/dRer9+tbdta69U/vHqfo4798XXXjQPVAueEgl+O8usiFE5B4pGspuiECyPJWg9BSYbVuVhTSCycCclTiuMNMdxQCEqdI9LSRwREBC9bk6jQcNyJBINrZOz5NbRWhU6ctwo0unDPo6LJOZjAXxOmqMJAyoUIoWoDWwSjA7YYxDNmyBApI1DNXJ0SqJ6dxIiQER7EoXipcCrwyT0RTUsy0UqTGNkrWGEyuKKUhBsmHpRhlhIwIDXmyERNMOiWRTlE4HDAnzF5705YrQSC4lQ9pgiE1OA8OWHbQoydpvImocgzu1KFeai6uNUnCk4lpYEQE2TDIzF1HrkjE5VJKagwBbgjoWSUC9YGMeBx6ZFIrJ4YM6mL6BL3WSrm1yAXlcxZAoWmqSZYrMNNTg4JFm9TqGKhccC6wQHCb29FRppwR0jmcK8qwQYKoSoJnCcirtyW4bBJ2QtoLhGsYpw5qWI6daU4irFH9IwcXG/lFY/cdXsVueexpy666kdd31dHWOA4sQUt085MRFhB1UE176Uz7kkq7DKGYeOpP/uEOSigxYAvQGIJCzOPGt5TLeR2+4MPnfz5r9079S+NbLX+Gkfsss1Zh+1x1iF7nn7QXq/deEMsRzOvxJ1qSYmg1d2fn+xrxb0dTqFeIsATGzwG3zB9MItKL+w5uUzZ+cLk0iJuLeF2vsLqYctvwgffhwVMmIl0Ee1VgenQRwRP0ZhdkyGDl3WiWyTEGaAiTKxpSsHUYES4w3dlhplQ4wW2mtTU8EQzmpaanDCxC3phssMbzGobJeJQ4f/YTsyqoiMRDLtqDQGoNHUxsUBidaGbH7zv1MuvfPCpJU1K27xkLcTrrMP2POOwPU578+7bT/0hFiyA9Uopx1DcKdAxNCcmNshNaSpOpCACKTOReDCFaa3TSj+yCvZyCmpqACsiR8EuzD1TBSZcNZwRfHKsZTLgq5UglJ0oBnUKhgVZjR6D0WhOTQ5GWmoKQWiShWAIz9/CiCqrRQKyqsEcQS49kUUEB2skpsYAhWTYNTDHjLuqIKxlQvGiERFEDy5ieQ2GXZUD+TbWwS9mUbgZg1MsnFNptRPtpSkJzBLH9iLEHlJ6HTiqhZoCxUI5UPqaRGussujjJx627koL4Nw3f30dmwkTM+EKCg/XhuAes6TgFJS4JipdN8YEtIlRaoR0qEtTa9CFNhiqwq0IioIywo7OqSYkA5DM7hwOUAuxYSSCJsF/0KiDQ4qeaU0+erfXnnnI3u99896nvnmf0w7Z54yD9jrjoD3ee+hepx+211mH7bX5Oms1PohTDtGhMTtYbMzQz0zTsuz52q1++JHjP3PCIUft8aptXrLmnGkTk11/5W/+cvA5F954531RvfS8lGVpxpIX7W8Sj5Jn9xRV1HqtF3z3B9+75h/jvqw8f+6+22z6nv12OevQPc94897v3HvnWROIEIwlZiWUuNrE8IkQYEesEDuMozTBoJrZ9clnnsE4MydiEkZwVQEIDRfDcUghjsAgTV3MkkZNah0lFcGf6iPHuzChnkFz7LkvGo7BIAkauYmbG+pfbprdXv4yZrr+9vueePq5h55acst9wx/Gfs3mLwuWKtJxKpLAOizAkxgGgBFeGLLROBxGIow8Uk5i7m5BcIzUWSgQuIrXiFLBeVQOZ640jGQYHkikcFbOQixm5EZCuLPEuSZiPJxGcNS2VsoAEBJgqPoJXItsg0tBgLGgyIar4oQRqK3Rs6AIRiA+cNWCCxNmVxJkh0vwtTfc+tiS4d9Av+2BRy7+/i8v/v7PL/nBLz/3g19/7ru/vPg7P7/h7gcA07jU7/35hkoJ5Gac/ViFB40vTGITzUB3ilEUqLYDnxRLplpwDhcNz1iAnucnx1TJPQwMZIDiUYkqDKcXr4Y9JR+SDZ4PfVADxLWSFkoVHHuxX1OmgRWUNRQY4zgVmDssCQfw1QZWDD1B4KVLRqyIK6pslYSUJpO0tKvDAqLRqImG+gQgX5RPQWqiEsTkas7Ynn3Z5CAMciqhxSeXPHfCBV+84e4HoWWDVVY46FVbnvHG3c584+6nH7Tnoa/eZtl8pmDElnmMvNa0rFOYRNkZAAT2Qy6WYTFbn/qKd8zQQlMXw4KasPU1FXAWYUnaMPPUIAVEGJHrjDYv61EOpKGzojAgwMI43PQCFi4bpurRQyRLboHhVKc4SVHpkzrLlOSlZYw4NEJIJmxclCIGwvwnslDOpIW5A7gDzyrY6sQpAKHkAEOBF7TAMsIthzskUC4iMBd9gw64n5I0JAzoqaFEyiBH6afkFbLAbaN64BfjHREWWU6pSY1GQKoLbngTwXsKUwEobYpGqwzxlxw6YgCGeGZnOMbByBsS4+ySiSlJbXjccCdDXePaS8GQKoYAq3NxJM1IrU0mzMMpreSMLcjFKjsoTCI6jAj8ZadGpW1yTokFSgnH6EpV2JWCiExBQ6ocsH3QQDUQO4QT40ZslCq1RHjinx3eWXgvZFNAE8F15uSEzZlEo8HEiUo4rKs5Sak4s3uP9Gigz4YT+jio80BUzJWZET4FK2qCEvUQgErBgFhrSV4lYJ+YNb0LCkTCftwZgSgUxCq4cqOZhG66446nFw/fje60/bbopf/jFUSzZs788MnHffTM09daY/jrX4888vDJ7z3tI5+6+MmnHsd2WoOZob0oTsfMxIS0qWQeRuThDqr0RgUD7uLIUgsBd8I7Q7OYKm4E5CyIjaUj671WNwuHiAqBQ65hEBKxlkKZGqaM0eJUgomyRErBzFABreqkqLUgHQNu1CslEDkqVeM+rAbUSaFcYFAlGjtOnAmKivJYaZKpp4CyCAquJfoOBx1CBAOqXfD0QUtJZlI999H0sBmatQerLWGt4WlW1FDV3DBqGazLIXgskxyooEXBNkkJPuBm1Xvz6IJKVTZkOSslJeWiMJjIQingHCJlNFRFJWPco3lkpiQMVwCVYy7wcCdidHMko9SnpjRNTRICVyicvGK3bEMa1urRBzQTnBQnQCakyN2GRyOcUoMo4AMVSEIddrANwguUosb2DDwJl8M4/BLmnEKIkDEQKcSNx8g4wRa44w2XzIVAdoZ5bBHIvgrNBVZALJEq4h7doBTdlbQe+NpXvHzqXwv58Z9vuPGWf+OGhpZdWIKlhVZbbvjj8vjS45nnxmxtH21BIZAqMk6M13js6cXLVswY/h8F4ULVKXrxjioa/GZkbvr6b699fMnzE03+ryP2/8wpR5x48OsO3HHzA7ff9KBtN990jZWXSYDFnYOQdaW509qcerPHnl3qkjgRKwQBfpgFgAHbshUUDjB8+kS7YNbwPPu+p58bu3cVxwRoRYCFjLy8OJmJGJ8DjgV+09QFi61PeBLQRcXWT2mqlzCTtElpooncDvEdUYI8Gi5mwk5iTcIpjYgee+a5xYtfKF30Tt3ALlLRZFl7ffiJIeWR77PnTOcmmpHqf4Tk4R2LGOKOe4HepcVnM4cAABAASURBVJpAGpoGj5zBxl7rF3/5+6efXzqtbT5x1P6fOfGw4w7cff8dtz7g1Vvv9+qtN1x9JUxGU/GkMVTmXAe70UXkWSsrkXG4gDiCQkTKpARNQZWIKRgfxClntKq5C/jgpVKtXIw9kiiB3rj/JcKaFJaBfXCBDJNhMQkcSgxJgciViAp0gFQSZE0K16HwjEl756msYWbLgidUBcdVZjLC4bHiMFSR+hE07MEUwcxC0iiUFBAqKIUKegViCeOEFyKUkZItctXGcsIOUcF6r6iGUStBNnVEjunsTgYLGEYMDnz7muuPu/QbH/jyVc8+9+zgmHMl6OIqNWIs1k1MtDttOdw/A/xHhiDynJnT2qRmfv9jT2Tus5QmUNtIeh88Z3/86Rf/TxHzZs1kj4xKMxgBS9G4t9RTYzxUgM65JzAX/cRE4oZfEsZEVT3giMB6AgajdrrjqaXnhVP0NreXr7f2nttstff2r9wHHHjNKw561WYH77D5wTtsduAOrzhwu1euvvyinmtP1agyk1AE0KOhABTSGlmtmejb6c1om5eu8659X33hKW+54syjj93jtRM5PfbsCxdf/Wsfm4Y4E2gZAqwIFxOHSYQSRLLd8dD9V1/3T/S/drOXfPWso3HnfNhrtjpo2y0O3G6L3bd52bQ2Y0iEm8wNsSBq+DzVOPqgySJdB3SYKXAG0cefn8Tg7IlGJcFBYccIeoYWxETWUBlB/9CBHyZJlFiD2gpzaOoKhqdDJAIfsQavL7bAUJXwiCjkPux9r3jpOrMmRl2p19x457/vefTp54b82nmzl8JuJXYHXlSMBmIQnt3gPm48Wb1jrSqeSCAjLBzjSTJOLZSjKkG0GBI3cIhL3kcJL+KucMfUnKQIMnLIFJwrqZOBA8ZGqVB49O5jB7uMJYe2gecOIQIrMMbhJKiq8CgIJyiuhioVJBaMjb/2tRSqA84yuO5Ry7A3YucvtX/h+aW/+NtNWDpnxvTN1l5t03VX23S91Tddd43N1l1jk3VW33Sd1TZbd7UFc4Z/6uo3f79lcumYSCrqhk6sOPXvXz20+Dkg6IaNPApHl7gT6lEWIHGqQZnhYlpj+XnoeOTp57hHAUHNLYUL0k7DkwWGljWlaEgaMD3qsh68slcvYy1LM3YORsfQDHdhBEdzdsmiofj9YoU0i74aMJs7c/qyrLz38afAUBeOnDhTCCAdZjz6xIvb03JwJ5HTwCiauswHwxIe97qg8g3eESYsUw8OshAk+c/+8vd/PfxEVjlq9x0ufs9R7z5o7/222+aAbbc9aNttdtx4gylJxMLaMDMZcW//45fj5BMZeePBhdgiBi+iEcOdMzxbtpjIka+AKobpTYWzzG1ikWXjKpRYlfJK8weEl5aKL69YhYWczbGf4xQDMtJ/LgbaEeRwFu+W9WJrY4kkPqNNy/bK+/F1EPaLVrTRpJG4YAIzL5uPhUYEDgd1BGqGlaDeKcyIHZaJZkNGgHd1TNZLOAIdUYEliSsPxQIz3a1Oqo0brA+OaDwokCeBMSSbVOJKSQprz4x84dYpOeoATqddNcGJJFqtYA3cJVahULfwGoANiUOQB4sQVVbGK6xXqY16FpSIRDV5ZezufQFdamEPFiALbCFJwhTqwgV56BDI0knTCzwjmMnkSk54ejeoZBdB6JKyUgzwYj6CZvCWKCB4yHgYxwoN2BEhP4bNoWI2KSHPWYwIEApkGxXjEozohWEGqzTatNJkkZqQaqjBmUi9MGqDVsetWCIhEtgJ7ByRJ7IQCCSKmshEUeMhkjJzUo9ca+YqsDZzz1QrUx+M5DZhmhLhBY4lqPdw5qr4IiL/8R9DsSCi7V/+Crz+nzYW2WXbrS7/6If233NPESmlfO3KK4847tTr//kvbUiV1dGrGpmQQazsRt55nWTs9AbKJLfMFCoVX45z5pDEjioqxl4ElvZB7qw1q0W1iqv3MMEgXkEDaABxgrpgo5ZSloSQBshGVLAOnxQ4iFYoz4LXQubkYpGc8Eg2mcawllydoBbFyIxIHMGLzFY5PCgIlMK84W0EDbxzL+SGiEUgcF6doiIWqXhrYG6osVB44tCmC3xjQLi3qRBAGpKrJOwjXRIEjBgiqRC5Q0gwFLIVYkwolCBDa5ALuUJmqszQyD21wbgN4BQUlsygAasAG8ML1B+wxUF1KzCKHR4FIpRDUrAoE3ZZZTVi93BhyCbJTpgs1XnMXBmqFcVpBCcihKCHDSmrXpQwWsBxF/WqMJwY2YKFNFxmbCikKPYBvrec2sjJWhSWAXONLCSMVSmiBojvw77MMCOwsNqAJcQwflJKMiJFSW3JGXB7bvWUN+4+c9S80PWf//HvXhj3mBaEu0rpe+pLs/7qq6Onelz/77uANCtHSzyylFxhesR1/74bExbNnr7ygjkiWErBWkLcmCqsYFKUQ75j6g/Zb772attvsbHwKAJwBIIjHOACTV1O5IAsaP6cOQ2CEXTbfQ+KqggzJHtwFWAG5Kam4wX9w/qmGc2dPey1N99zP2Je+wo2YVHKiAUYWjAVLYh6IB7sFaTAJ/ShcUW0EQokqzAxowsNdGsaUiVi8iF+jIDT1CWJpCUEdMM1V0YHwLnlrnsKksn66rg7A6FIa8KX/NffPvwF0xXnzFhu3ixK8EPlP/IxKWpx60rq+7Z2ImMXSEOTkIwnO07PTS6956HhLz6+coM1t33p2gJ8jQilqyhTpv+IAijUuDVsPGQQJKAxoTt5L1xVQ5VFibAbiVc2jspahEzEtAnOlZuetI8K5AwAERkP6S1p2DIzBTkZ9hGIm8pw+AwMyaEFua02hA37Ry3hYB8MHeo5I1G8BLaoWgZUmRlmtDEkM2LPDjOAQRiISg7H1J0Hk1oOrG+5ST5ig31S82ArMxAg+D5IC5KIloVTCm0YaQCZkCUoMh6pRNNRroHqNpl8TGWMODMMJWd94rmlv73h37+4/qb7H380NKqYZxs8JphguVhTaquZpi4cifB7lYXLz5o+DaS5/ta7DH5yH8CLzM2wQ4XTH6b+dM3MUbPiwnleCRsD4ZYPK6dapcZUKlP16APFSGJwYhhjD2VPFEJew4Gya2AgIiZrEU2i/JIppnXVb7jrLrIKjWJYxdFEYGExnazUDe6FiKuhhDkXZ8gL9ApmEf3qr7f9/C+33Ywn6KJIE0OswuYvnH34rq/ZefOXQuPdDz/+3NIOFSZJMMd/DCTvvRTqQepI4XLnvY/ilI/5b9tjx7lzpocCiGTgpLmSox8ta0rqaAN38HmqAaVavC8oSj7FQczmm+65D4OLlpubWynIHgfeQxVCJ8oHJFBT+oTNCx1Dg1XwBsCE4e3Qg5/BWlH4HlgD3+jFoSBgPgBBVbkKBpl8jZUWbrTaCoD3N3+9+WfX32IRW62/5uwJSclYCaUBUU2OELA529itgBu1NNyPtAqRO5WInp0FtbhpStZ+FK7SeG6KNk6CLMDaCksEn2pIz+KilJOSMthRmHqgnMjViSsCDw+turipBOjcKvZLEibD9meYJHATLQrBCxl+ebAQcxjC09eg0JQgvXPQD7F3Zsy/99HH/46IE+Fm8ssnv/XSU4+89LS3f/H0o79w2jGXnPL2S95z5GWnHX7M3jsK84NPLv7HLXeE1Uom02j+3OER1W33PlA7LzUMF3loGKrqlGTCFcSdM1hGseKC5dDx2OIlTz29JA0em7njIb4IwWn6z6UALaLpPf1v2QGrBymMGzC4ikL14uxCVEmCVRjgBceL/QRYApjEyovmLMvK626906MKmwDSpmHJbsF9/dONt2ENsnI1ZCV2hWo8NvT8pzG7SBUL6TV3OQdEE36CKAxpmO2W+4Z/aGuFeXP2f83WM2ZMz012zog2Ewtm0dTFMag10hJm/9PrMFhIgAALYbpLtsA9dzMsxA8TLszu+76iSjreDtmbIDih5GJwaKyuShq01krDXwetHjfc/oASpLpEhZluBA4NU//zowSSSEQUBJOmriS1QfxioknL9sqb7rwffpI6M0QJRagxtEzNHl6Mak3FmwpzItjdS6ASwG5hFF3G3UAY+sRYiJjCgwAvBXMIk7JxdBWnWItaA8ur9JXDg8lT4raRJA6FUtWNHfzFWTWA7mALeYlqJpXaynmcuFMoBheKEjYuYdfacTdJXZ9xMo0M7AGCY0VwhS2ZTKK4dtaMCwMGJ2AS0kdTSFHb2xav2YULYAK5vUJBwqxC7KSJBLVVuHcd+6jzaWa4wSSvPg7rww2QaEiDR5NFYUoZY6K1lRpKjNWIWQ2tpgSfsWX3Qr0BQuKeyRn/iTrIyjz8GZXKJMpZJAl+E2T3ToNBjRvOqxyaNDUNteItW8ZsJqaUqWlc1CC478V64ghvk48yh0vpuXIiBdhM0fV4JqgxpFjmcZJCrUerEIPtrI5rPyn20LMv/PbavxLRjJmz119jFbz5v2rBsnD+/Pcf+/bzzj57/nLzMe3ft9/+jhNP+ezXv1sRSs5UG0bJjJYoN67TOp/oHPBoTkmaxMwicDgpq+SmyVmbJDlEvGHCK3vFIVU5AaTqFMbWqxfsP4qVBAG5ulqXbaxiKVHOvaZeyU3CE6qo4eAwQdqyNCQcYagOii3ImQFCyYCJpGEsJeaqUhjRz6pNQznVRGOUkFxj1JdcOk5jl8EzLTyqWBZCToY7vaINS1ZrBAU7aYA1mQj6EKWsFWoSBYgLtT2Mi0pgHL4wGDYbVKIgcmXY5sIloQ2sEBZBbplb5VqoOFUEKlPG6T1jEfXKfWPWFJTR0HAmTByTgwHuzEVlzNSxxACURlMklwYhqcFdRK3mOISMNXVt7ibEGmIcQPqR+8SYJnqkWgScc8L9QD8ZjjV9Cs5Vdcw0phgzShuxEYc5uRMuR5JX7O+TvY3dK1xQabKJ1DAMJ2YEh91yIdwp4oQFWVHgUSPZvUGNKLX3GGSVWlCKR61nJYXaSL3T6ouWP/S1w1fYTz0/OX5x82AErY/USVp5lZXWn/r/nFz52z8/8fziZtRJY6xCNFDm7/+6+y//uhdW7Ljx+q0IE0WoR7KkzMAIIYB5EokKSgXmMTXWS4/njW6UqugLIfc+8RRGhhaq4A3n5ZdffoNVhqL8q7/e/Nijj4cHohUV8VQIuOWe4Y9qDPOJWVok4rTputX6ww3VDXc/dONtdyIrwRNS86aktj769FOLn38B8wH+uM9LawsQARp6hiYkLWdNmXAuTg1oBzeIUpOI+4iOaCxRtFJfyjAfPgoHk7BtuMbCtab+ZyZf+eWfnu8nGQWs793KcFW75tZ/33zvg0T06o03FJvoi/Y4SlZ0DA17Ux17dOCgI9yIBskQT4wFDA2Hciqo/C92wpPimcHEMeOunCbrg48/jcloJRwPpcdBXeVijh40MWfc+5cmSuNVCsyqhtxiKGQwDIkmE0ajUrXrohRC7WUqQc4AIo00KSogE4DKQpkMEyAEMGAWgqg0juihrvYEbiCoAAAQAElEQVQaFVuOkAsV4S6wW5MTO4VJLalY42Wk/cSotBMIORipvUuPUSpI64mQEUoTFrj10TF3KfWtRmIatFPDPCLOPSUjToH6NTmNCNO5HQuyCmUhiKC2I3KNnHQkgucOjXum6ipFqYBrPJnzmFuzll6+0bpZta921e+uWeoveBoHG3OiaFRVoHjsf77lTpq65s+azZUWzpi51XproOP3N93+zzse7oN6KaXpqkSh9pEnu59dOzyKWm+VFVZYuGDM0ZH/J9RYRKkVbjhQoAQhJFboGPqHn4xYeA5XFBvnygyb0c/ME9NaHUWesNVXmbfWwuF56n//8frHn1k8YkLspJCxoHYX8mtuv3tp/7zkmhsajSQj9an2fdQ+QxwO2A3Xy7/74/dc9rUP//f3n62TYHY/VRSMcOSuc2fNgMZSzZgEEiSy2wiyaLgsvPrASkJYa+o7G3rxw2EWtTZAoMP3IaqPP/0sHnlgpK+FeRwyFh4TIT70xDPP3v7Y0z1PRGkVNPDqVG66755/TqXzluuuNpomzNCD5pCAltRTGqfotRi9mHzoJljZ9zzZJX9xIkqNNDlLbipJb9HkNMwjYhaPxg0zBVYIeWLLKjtstiEzXfevu/9807+IaLeXbxjJS7KqASOawDS8Da6ZLKOiKWoQflFUMKw00g1TSECwXqgSZLEqc8qkI5OJnrOHt1EzFyaAJBRJOY9IJyQyqrVAdBLS1ggNqUYpofxBUmXviIsyOJJTJKJU0qhqCzsJlQHYZcttL7lEEtYhUzElhgRhdk3YkUqmvoG8NsmPrv17V+rMUfvazV4KS6VHnJISZhJViT6QBTts9pI509pq/ou/3UjY+KlOn+BtX7ImNN5y3yPX3XIrF5NSGy8wnZkee3rJ40uGv2qPCe7gmqbwHab+aZYnnn3uJ3/5e0I0asM0cmk48WTYvx8c/t0RzI+KzUAAZx1IgQ4iJm+ZMykzyh5TQgrT1FW9H1M/mcwRBtjgL65JQi3nLLxozvRXrDdU/t/d+K9b77g7eZdQk0x7PLmR0WPPPPNLeES0/sqLVp4/m2qJztheFKIipEnEc6pJg1JiTSwMzZhRIzqhTqNDbUQXwTxtmTK4iEKuhi3grql/PguDsMvMpNa2w76IjqEJMqnqqFJ2ZUnRkIsjZ5uxCGo4/IeaYSKV8C4qEiwsszTYWsC8IJ8apGBU5ULcrzhv+sp44EX0rV9f2y3tKQxLHHJtwin/6/7/IOxsXeIiYQUsXCZESClwRFDQ5ZXrroZO7JXX33pvIOHNHM5Qq9w+8OSzy/ZKBu20InWQWklzEwg7wd6eEzR6KJspF0VSKLtmh+U9NzWSRSJCKGlwF1sCEUqrdKkZa4TVZD5KNJqwpikt1VEXTe/O3DfICIKKDF+Um8wJBBKO4EH3lEDwl0DByAmIJgZCAQ4zJQmc+qn2VDuz6kLOgZLhvWiVHNFSStSwKGWrqDEMaGUIikDzsAsHwh9ACB4Ct4JxDzHiGm4VEEEFCwU0Vim9mim5RAoSdJljmlsMWDiJkKEFwzQlFSImCyoRnUEYfiOcNTCimpUwIVy4d4b5MC6cBjk1uHZk46CxwAqCchKDTjQd3IO1xE64lE3wLjELs7pAPYQEWSVsTeEgNSdkjDCkEJxwOAXX8BlxGhQZtirncP/9n6599OHhT4Lu9upXJVhG/y8vJk1591dt8/WLLth+u1epyHhy6Scv+tRhJ59y6wN3V6kwgYgYFZWAE0xTZSwJyhVVtCoZJarauLWwUrCXwXVJltizczahgEMDFXImSu5kxhID7M5ITepzVLhLwEjGeSjs1dtxTXhUgxAyQXcEIsQBaFQ0hMBPV+xIjtEYTPJIHhomEDUEWtFLyCmYh+zKgUgXwjEC3Atgq5laqa0sVSmuTpKcdWCXiksqlCwHaSGpXIpgvEkpjVrRZCGlF0PDfTPJpFIBwROxGKpqAwdFlGFhQ+Crt5SaSGrMCBe7Z+MW2lIiZa6EmsA2JG6P4Dl30MkpVDirNMyJSMVCzVkKGuwJMfKIylRMvbJRAFAmx6TGEAXVxJEwC8SpHkXLmKILBuAGQDiTeIBRDmydQpyphkXtMpQRQZy4pxLaW4wLzre9hxET6yChEIrRoNM4qiBjApewq4QSlOaQ3LStqkJUginDUhLMpSBPfUldH4fstM2ma6yECS82CB9ub3PWmBjlw3fZAeX8gScXn/GFKx59erEDHqNxF/+4896zv/ydarZg9oxDdnxltRgi6XCMHfZllsxJPaiK6PorD/+C6t9uv+d3f7/FcGAPrZYef76/4L+vvuqPwz08VONZxag2DWf4dvjurxHmx5Y8/7Erfvjow89Yb+S11nL9zbd/8Mvfw2Q08G44JCO2JHvvuOWCWdM94qPf+NH9jz1qqVTBvlTvfOjhc7767WdemMR8IgYi5BqCk6/Q1BVEgg5RsgwWjXLLzBi596FHKiLWG0/2Olmjo8BUDKCZeAmMzpiW37zzK1Xk7kefPPdL33p6ybPwKvfcv2DX3fbvc6+8upqvOHfWgTtsV40LYuq9k0PAsgaBUTN3WSrCY0lfVBAcJCSSpuVpaywa/k2ka26549qb7ugsYJqTPb7kmQ9/7b9/MPXHbyBq6WQxlGXgHwiZogfNqYIyzCW8esEExC2iS3CTSbQRblyGPKjB1SVsSFZncWXKKWdsaBkU8qiV3JQRRyoUvYPD+BgphyB5pJfwYOLEGWEmBJ7cGGltlXpwmVUYdUYTASemsEg9eIc6UMBgalBCkZHUkLNXw9Zaao2ozn1NBUdHSt5Auok4uVcLOASxg6JQr2JVJ4XHgohAAgLMbOI1qA+kLwUrvIUNmokSsUj1pm6w7mo7TP3fe35y/c3nfum7Dz35VLUSKIPVaydPPzv5kW/98Jp/DV8Vvm7zDefNnE5GVPmwnXecPmqfn+ze/4X/vuX2B0tf3AF6vf/Rp977+a8/OvUnGw9//atmjKYTNYXYlF68BnxCOYQIcDU8ZgeWLw4i2MALhUN8SNwAlDB6GAQRjAXIqKR05BuGveChp5455fNfu/uhh7uuuuGKJZPdZT/4/bEXfeXICz7/zNLnhLE+ZUoMGCw5Cd4yu1DZcfOXENG/73/kC9/77ZKlXQSskWpyx6OPDCdUopUWLDd3xgzAiCrUkMycGGG+uT/4xJMisJyYKJjXWWXFUVYMffbbv1jy9FIuLhw9Tf7plttOvPibz012GMK9OnggWBwVH9GeeWHpyZd87d/3P2TqKsPI3Q8+dOaXrvKIWROjV2+1CScSXJ6gA/Onmg+EgVpWZiif6qNQdnQ48hbeTfUhsi0LS3JNETx9NFiOkXsefdyBLPhgoARBEplH8R0333B62+KM/nxX5s+avulL17QsCAQmMAUgJzIQCaYklaxNK6jkRuTAzCo7AGICqlyEYTALMazCyQvJPGbtSXGnEey5GnZlWGXOAEIiCWduw6aJNRTBZKS9NyaZkQoN6oA6UyjVwRFFSciYwRyJCRdzwoPVRknJs1k45EJioykpBxvsUvGGo5XIMtScn14//O9TNlt71eXnz65CMJ1K1MLwgh0f0XjmzNErNhj+Pyd/vvWORxY/DaRF+PWv2nzFebMQnQ9/84d3PPiwW6+1j/HkAw889KHPX7HsX1mERaJJhvzOr9h4g3VWXBBBX/rFn39zw03mnkPBuyeeGr//0u/c9cgTmIxmxcwH5SSCj8uaSiRtWdvQnEbUNhn9Dz3xdHluLJju7ihMBp+nQMAYJwLwZMF0+Ou2nj6VlR+4/Du33nd/9UkC+arf9+STp3/pW48teWGiyYfttENOjas3Qggo/efqQ2Aw0AVWA/T6PyaxuXjgo264+sqY/vBTz3znV3/qu3GQA/Znly75+u9+89Hv/ARDaFBYBCUYm0RPjo6pFiKBYFYjLxpokcCyStSRd8Qg2LJp1JuBV048/Kd4Fa4V7JgapjC1Kd5Nnxjtt/2WiM5tDz5+/lU/enby+UjVJZb25Xu/uPbyH/1u2XwyqEV+GUHoi12kjLSrHAV9e22zxbK98r++8qN/3//4ZG8A2M1vf/jh933lymde3CtJNbOKuwYLkiqTiziz94ba6oU70R6Rj9RU3JMb7uByKrkpKaNoBXiFDS0r52GzneYMXsPLokZiiZkwgaswzi1DYnTOfWUfui24kormnCdESkF+qKuEoBNRx9qR6EipCRJhaoiQPJxqUCFzbixGPk7UQTalcKVok+eM5wORwzlAPxvgxdww9nCSSIIqqZDYEpLYEIfOtStce1fcm/U5RTNibZiFU5+pa6xmJ5QdTLGKkqLDMTERURXDQcyI3IUCxMokWiXGSibERqkwm4RyKBGTUGJ0O1gQfaleTKtCR+o4HN6bYRiz8KvEsNAa61sbS+1hnXeoMgTuJiUeiTSRqWLjZKDIhOfOfaHqiVgFB5thO+gL95NUQWKuHGHEdQrc6J5/7kvf+CYRpZQPfMMeHoH3/28bM626wsKLzz711He9c86cOZh/zfV/OeRdx/73b3/ZxfPBsK9WIcM8onARAixILoCEPSNgB3UmY6REiagIhxbIEPCFmAcLmIUbBVZOxGSCX0F9SMcDldARQxDJCNCzBlwFFcxBhaDomXuQKvAAIitJ65QhPjhcjSmiJwM+sI8Yw6SBDcc5akgFKzhCDPwfegYxsCQrZ8IQemtyScHZoaBQtmAvzp1XDk99pDGnLrQOBQe8aQkSS6LaWp0wb5EjvXIvAUiYRFiEknAGK7wqFHNSbVvU+7DQwV8iZs8OI6OoG1f1KtDGk6Qdpz41aK6JYZQEE0WoD/cajm8cpKSe0mTikuA3k6l7FLz1QEBgsNYmd4l7KiF9oDcGUolyI9FkxmOvFKEGfHqiQlyJqlKNZJ5p0Eaccm0aA8iqMKy3vqfeUsHuZghY3/lkF70bASMtsL40YljrAI6FKWvlYmaEK9gqQMA7Yu+lBEOZq7Tyrv13mjWBnB+GKKglGjXcZh9Rv/PGGx6641ZJ5M//vu+N53z+vRd+8/2Xfvv4S7582Kcuf+TpJTNH7Yn77rLy/PnmQpVzHwiSFqFINCQPFHlQ2Wv7LedOH+FEfdJl33rnJVec9oVvnXzRVw85+5Pf/N3182YMD8iheMmSZ3nKTCze+CXr7zv1v9P6w813vPFDnznz8ivf/5VvveuiS9950Vc6oIXZhB0hxlR7BDLSGsut/LZddsiqdz/65OEf+dzJF375zMuuevcFV77lI1+6+/HFTU5Ygdg1CUWNEjvjA7qI8DuIwh3lpgQtmD9n4ezh6fI3fvOXd1/wjTMu/96d9z0qzvG/pW0UKh31HXWVXr/VFgdst4UK//qmO4/4yGWnX3bV2V+86t2f/eI7LvrS4888N3f6xBkH77VgzjwXp6ZX7VUqTV1MKA8THE1ynagyzROqCE1dLMQTqIwpNXn/HbeZNdEuLXb8hV89+VOXnfalm9KSkAAAEABJREFUK4+/7Mv7nfvJ71zz97kzJqam0+Ilz6bAA0zLuqxjeDWkQK5DPBQhMSVQk8NBAAlsz1MveCLRt1pyMuWiBB6m8CaUGHkTmtBXqVIY1kVkZhEGU4FFwnuSxJpCcg+GMtaIimZL6CIjJDtWObfcTAwtteQSXfIxZIhGaoqmKqRsDYd48IAw7uDrJE1aIN87KUXHJD1GUNxzRX2AagiGeISMoioiZg778NvBv0LUowa71yKll4qEzh4TEdOoyTQthi83VeASjd/7ljdsu9G6yvzD625849mfOfkTX/vA57/1/s9fefJnvrb3By666k//oOFB7MKTD9wVux2qyiTTKiuv+J6Ddmtzuu+Jxcd98qunfOrK91/63RMv+fqbzrvwH3ffr8Jved22222yfhZOjJqTZCp/IYeCCvwjSi5AOHttDF75MARHCgeamzhrTCgPyYsh4FHMLaR4LtG+6hWbHrbzVlnl1gcefdN5nzn2ki+c8cUrT7vkW296/+cu/tHvjGiL9dccNRN9ZTO4mHh49igB4usgjCkOet0rcWDtzS//8e8PP/visz73nQ987junXvCVt37o8w89tWRGmw979SsnJJdqFkykG62x2ignfDzt8m+dcfnXz//mdyaXdihB66y7wvYvXYeZfn3THW8577NnfPGK933pinec97l3XvDVyQ5P8qGSFj/7PKEIVJTVhoiJCDQej8fvOO/iUy/6yhlfvuqEz33jzedddvejT4nwm3beZv21V2/ySLkhB1EGi7EEcY6aKqpRajX9p5OC1NscGTsGxzANP0FRgiqMYk607mqLZo6gl97/te+eedkVH7/iu08tWYLEc1EQrUadOaN55QZrYh3athuuPW36NPKUTZOTiMWgGKKI1WQ4vYDZ3NZA4JicOJxLaG/QjiqNzHPYhk+Fa6E+AjQMTOuIPUgjMpKvhlXGRuYqnhWBZiGg7OFmha1m2K2qgtqU2o5HhbQ6eefc1+woFrCUmLnRhjmHyhTva+0NadBEA9CDKiRZ46k1pFeQ/e76W5989gVhxo1ZnkgyUmrcuKuBU3VQYk1gG7vHHttvSkRPPPvCdbfdH8SF8vz5y799jx1GOYEbb/nopSdc9I3TL//uCRd9/cgPf/a2h54YIZ+wAB7Cn5Zrk0Lzew/Za96MiWcnx8d/9oq3f+rzZ17+jfde/LVDPnjxj/5266zhbzAOC6r31QsrJdRkHnrww2Tu2lMeK8kMWXflhej8570PHXvh18++9Lu/+dONztgWJUTQP9Wikg8BJ15ntZVPm8rKOx596sjzv3rcxVeefflVp1z0xUM+9Ol/3DWVlTtvt9WGL6mSXVptRdopATSoBHjFFYUpD0GCPLSp0SCZbNPkqOnbPbfZdpXlZlX3C773y8M//LnTPnfVWZ//1hEf/uxHvvXjGaMXZT3z/AuSk00QaSG2KRFEoEeyqn0nOBlaj02GSJqqTa0tRXJiGi4mZiJ4xs4phoR1kk6QhMMoUR26WmLKTHtuv/kr1xu+Qbrit9cd9uHPn3HZf5/5lW8d+fFLzrniB5pAgWUrxHmoJiSc9T+dMcVUMiFfffnl37bLdnlqr3zzeZ874TNfOevSb554weVHnPe5Ya/8jxyOTJ4cNDLnXNpRadsuSUW9LfhhtiSUEjMa5WANZFwWY5hO4FfH1KOndcnGFDYAI2Q115LDkLCEmJdIBZUdB6TasfUOdTVoYD6ObA2LBOITysSDI8y1ZWuZkzKZhg+osTH3gdNpgg2ahHHB9MBS4hqN4XTnTBWsTuFtpdzFkLzeEzqD4CQaS6o8tQsGI3DKhngxORFxWKKepUttaZqakkUEUrl3GggkXFVZW/VWaqOeKKRWrpPElYTA2cw+imgUyZsSsXKRpjcf17sffOQPf/nLb/78hz/+9W+33PPgZF9hZcdugZhzNuOKeos1SqQeIihgATSMGFtDLYZfwjaYLibByYbHjx4xmB0AAasEVmilxiNzKGyzwATqoimBAOfEojA/++Xf+8ntd9xORK/edlskFd78f95Szofts8cXzvvQyzYe/m9iTy1efOp/nXfSuZ967KmnfeBC7dWxHQTihdyvSYuyB25ZYUKEVlNsWnDVFLgWpwg25j5zYYQRfnsjtdUqjaMjyEmxOkxSb21HuQYyra2WnZrgxITRQk1HbQke3FBMLYpXGT4Owis5Lq5QJiyimVLbg76JnBnyzUWrp+i5GddERRhshvLEIe5irhhue8vj4cjB6pwspLca8DRXbSC4miFGUTmhoJpkEUk6aBtsZA43QhULE2bizJJS5iSuNRg3DbUh0EBZ1B2TEzT0gtO1RBUyIqW+iZLZc4qkoVJEqjHEjSCKRAOhdkfmxWBUoQL7MCVrZMF0IR6AKJwqkxOxtdxPyDhFpcASnFXyhOkoeCSURbFXsLQE/jpDUXZvXMUlgqauFJo5J6Rgk7QV7KcsHdUBHhgMQy0h6BIcgaQcuSFguOFHCCHBk9aJxKqg+lCaGaAsk2uerTTGAtncvmztNfZ/1Yt/e0pEFE6IMAOgkht/1x6vOWHv1y2cMwvb3u9u+NfPrv3ntbfdDembrLny+ccc8rqtNquaQhokYWO1NW8iNJhseJUBUd9wzRXPOGSPVebPBXDX3HbXD6/7x29uuHXc1ze+ZqtT3vj6KT/pn3ff61apB6ktCb3rwL0O3uHlbU7Y+X79j1t+cv0Nf73j3rVXmP+Jdxw8e4RDCgWFo+4OSWulxn7bbHvC3jtj81ja9X+4+fYfX/PPP950x6zpo7PetOeL3xcxJbWWOgV4CPQyrUFcwVkEpzhPitRj99tpettM9uV3N/77h9fe+MJkr4gR3Fk2n6BXxLPUhkN1ojnxgD3f9Yad58+a8diS5351w60//Ms/rv3X3Sqyxdqrfvzog7bfZKPAkhiC1CqLAI9BEKgRZCThiUkQvcymtOxiIVVXIvHN113ttAN3X2Hu7GL251vv/OG1f//NP28z98N33ub4vXdaNv3f9z2IiiMgAYNuy/pIBH2MG1RnVzVkQA4oIwsphsOo9kGVpAY4n8SdpFByGfKoYcCK6GKuRq/YRFAFRbnJPEo0UlhsxMZYT0qop8K9cGEoDxVoVpOpzPQh0wLaVVFwECIvFjDACOHDRObGlQ0JFC5MmVOmnFk5yIiQYHijqZJ2MDAbJVfi7Nwa4WIKFic11ppgt6RwtihWscsHvG/E1bkaclunbAvSapmqcPVpuXn/2w44Yb9d1ly4XG9+3b/u+el1//zJ9f/83U3/fvr5pbh53mebTT997JsWLT8DxuAxBBgZKntst9WH33rgWisseH7cXXPrXT/6y02/u+l2vF9twbwz37jH2/fYUYWZGPtoZpgLK19s0hPgghvIEcUlinkYA8ylUOcCozhRw6RGHE64IIiBAABicpmI0TH77Hz8fjutuNycpX0FGX70l3/+4u83P/TUMwvnzjp+v53fvs+rRaQEpEWBdDHNdZQiA/IAnDR3xujct+2/3zab4fT5wFNLfnH9zT+55p9/uvmOrtTVF8z7wJv32nnLlxLgdcJOys6brrnGXlttyswPP73kZ9ff8Id/3lKKmVPKfNwbd9tliw2F+f4nnv7Z327+0XX/vOm+h1+6+koffceBa604fAd4630P4jgc4IckuIK25dqrnnnIGxbMnfWnW+780bU3/P7GfyFPka1v223HQ3fbsWEBP4lQAMFjTF/WFHkjPgyh9i3rCsATwsTiNng11RtuVgpirUOfrLJo4UE7bg1OPPXcCz/7y42/+fstY7BPTJJRawFQiXZ5xcuwVIV33GLDhNtoS2Q11T6xEyq/qmMaAibkCJIj/2s2S12v1on4EGNNkZykEkDpiTthTxIpQtHBEpIr5Gso10RjiHcPgzB2JUcKqAZhNaHPmSkEocZ5V5EtjCNL7cJqDciKShgiYiJ1FmzG0QACSCSMOjGb1CrjCuGkAoWwubP4wXW3EBG+8d5qo3WytKKZc5BWBh1zkYZVGo0mLNZbe9VFUw9ofnztjUuXhuOxbZWdttr83fu8bs70CYQdRftH1930+5vumGib0/ff7ZXrrA7JQwvrrC9RYP2G66951uH7g0vo/+sd9/3oz3//1V9ven7cH/baVx70qi3RiQbDguCDMlIAn6dawPzihbxqRPAbd9pmjYXLYeRvd9z302tvuvOBhylKbQKwoRPNCTWDPdqgkWt6ww6v+K8jD1xzheWRiX++9a4f/+Wfv7/p33iPrDz9oN3fsvP2QLdULdagDAXwg4ip5pA7pCkqHmJU2ev/MIrCkoWazp4x7cPHHPrS1VfEipvufQgp88u/3oRS/9rNNvjUcW9EJ9otd9/P5KJE7f8SnxIHuztCFFHdepApMSDXJIJwE0dgLVrOyRvtJ7S0HuGwwkGk/9iJ34iUtKQNz5o+/b2H7v+al63HU6n3y7/e8tNr/nnzfQ9vt+FaR+++I0ShoYKyAGEJFBR8XtZYiSnYgtm02WeHbY7/z175x5tv/9E1//jDTbdjrzz9kN02XnP4sw9MYIo2pJkEK6EOOCWNVnDgECaFny7JSZUIZV6JWEhUMA0aqDrCWYBoYepSdG3tcDIRHnLQwZaOukI9RUUaZMFrUES1iotKFSuMGobJkdiRsY0hg4pLLUI9EjVV/BYLR1dQ9VI9CMobIkqMnUQosptGhUYmJ8K4O+p+4zJC8jruiYrVEtVzT6OSpePh9guV2IYayJwYwWidSKiod4Zzidg4yzg1HafqkB1eOSwzZ0SVwBX2qXfYYhhsU+6YjSklUEPxC/AAT2+4piWLn3vPxz61/1FvO/zEE9526qmHvueU/Y599y5vP+7Tl3/9iSeeqUhkEw2ZEBqRZB4JDw81uTIchEUhYiyIqLMieaKIdVQGRBU9zJiGnyRZoRMzcX71ogacSZrEgq8XJVfKZDgRNcr6nV/85tIvXg7hs2bOfMchB+WU8P7/qxbEG663zhc+ePab9tu3aVuv/fd/+rMD3n7ib/74NwQ2yPtcuhyVEvUiRVOFeleJSLlyW3CLlXLfet8WyxW+NQRz+6Aedd2DKRqyCcGpqyqR4nysCBCKQo5oS7Q4NlKnFIkiKwdmcCqSAs++YJpjX3COYE/kWCGReLgI7JZMrMShVJP2SYLVQ9hZLZLjF9Eo+4Qa0iqMauRCePjZRA1G5TN3k6AswlgoCHjRgJeJWMIdLBu7dNQYQUsM4aTMlAPNxcMowEUHMATFRA1R7pn78E6iKnuw9AoF7JVraBkO1c4ccE1FWJmUBa/wzmo1IJ2V8ohxBOo9kGZIw2KpEsTU2liXdZza0gg3log01FytZEITkmQqhQJuJK5AAapqpELimbWRVgW3Qc7kEpwJ0LU9iRERayShkcQ05glpW5lQSV7JxoEMYmqEEsMRJmYR4E2B/MPz/ijE3mPrXXXunK+e8tarznnXW16/raQgRAQvHMS1VS/nBxkAABAASURBVMRe2tq0dXTk67f/9geP+e657zpijx2ChyqCQoKJ1EQz0R786ld98ZTjzj18/4N2ePk+2215+OtfdckJh3/mpLe9fKP1TdRy1gbshgku6ppgjTEyB7e1JE2jOtIdX77xl99zzMePPGivbV/+hm22eM9+u37l5KNPOuANO7x8429++N3f+NBx2738pRWPELuerTRky82cOPWNe37t1Lcdt8eO+26z2SE7vuLCYw/51AmHb7r+al89/ejvvO/dr8EpipIomdcqnY74zbtu/833H3X6QbvstfVme71yi4+99cAvnfbO3bd9+Tlvf+N3Pnzi0Qe8vg5xL+Exf9ro6rNOuPqsU/beeivywMViQgizbfvSDS4+6S2H7brdXttvfuzer8FzdM5AmPbYcrOrzn7n5WcctcLKC1gQs+FPP5FQM605ctfXXvGe4z701v3333Grvbff8qhdd/j8cYddeOybXr7RGjEE0cQ89SzRbLjWOt8895Svf+D4TdddRXIv04q0AYZ0Ki7pC6cc9f33vfPsNw3fuxbC1WeJ3bfY4oqTjv3w4fvvs90W+2y/2VmH7Pa1977j2De8ZtctNvr+Oe/63n8du8NmLyE8cOABhjfutN23zjnxc6cds3DOXMG+V1QikvJAZEwAN8idyCsqiUYRHifpnLA3gFKJQhXpQtA9UE8qqye2hpkkOWdPWbLgk7sX8sKBc3EQhQswrEwGdSTDukLZSNmIwSJzCzyALjzotRSG8kjYbxA6cgpI8ESaddRmHKKbJBhINB6lkomEsiOZyAMRajw3yA8iYqLkmRzkbRyPz5uGSc2i0whRDGXUIuWQLgLNjakkTGGumbvMrjOmjw7e5ZVffc9bv3TiEacesOs+2798z203O2ynbT5+5P7fOvO4sw87cPkFs7tw5JmzIQfbIJTUnTbd5LKTjrngmEMP22nrfbbf8pDXbvPJdx5y6WnH7PPqrVObnbRGBMEfW37WrMtOOOq7Hzj58F1fw0GoMkhvUiLNnJpTDtnrex9659dOO8oCh1IYRT4RQBmx2WSN1b5z1tFXnXHsequvBj8V6c/IQp7gicNet/WXznzrx44a0nDvrTc/aLutPnT4fl8486j9X/cKVnFyYQJirtVzTVoy9+JOiEEldV5p7uxTD9r90pOOPOfNe+617Wb7bLvFW3fZ4ZJ3veWrJx31us021Dak5cw5dUJdKPNxB+z6iaMP3uOVW75hm5e/Y89dUs7eefS0/NwZZxy+52ePP+ztb9hxr+23PHSn7S9851s+eewRG6+3zrnvOOTrHzj5I+8+qkchyMQNn3HoAd886+STD95v5802+tp7jvrE2/bfa/vN0E47YNevnnLM0Xvsgi9zGnCu4hGhd2Qbr7Xmd08/6ftnnLLR6muLSvaaupjTTP/+Oad8+9yTdnnVKydJ8EWqF9p2g7W+91/HfvucY9dYeYUe5x8qHDh1q8u0w3ff+TPvOnS/7bfYZ9stj91zlzmz5uBx9RA/8cGxaFpORLTivNkbr7WquFSqgEmkaAYIEslNqRccVtmxFYFUmQN0rlXIlCVFI4H/euc+zKjXMMA3imZkPFGkNUhnUNAH0nlIVe7UnB1FvgqDujbCtitVhIUEA8god4sK/jhxT2IUA/HViEEfyCOP6HoyDGvSYdNQJGMeiouHYV9pUNGpjZpKpUJxzEG7fu99J3/xpHeuOG952CwUGpIoGOqkhPSmYg7HmhkT7ZfOeNd333fSew/ciwkFnYbxyvtss9VXTn33aW98wxu2f/ke223+X4fv85VT3rXPK7c5/eADrj7nlFMP3atGXzvcURXm0ihts+lGnz3jnR95+4H77vCKPbfb8tSDdrv0pLe+fZ+d3vj6bb977vEfffshLWopD36I6HlvP/jKs9/1nsP2xz6KzMVuJeRkseaiFT957JtP3H+Xvbfb8k2v3WanLV5GUVW71Vdd9M1zT7rqQ8dttcna8CAHJ2YsqNS/+hUv+eJ7j/7MO9/8ltduA1MPfO2QlSin+7xqMxnB7Qpa0DjKWGZMm/W5U4/89rnvOnjXVyK5QhFOqcxSgLm/6+C9rzjn+Mvf8zZCzrAVJRxrNlx7xUvec9hFxx164Gu3fsN2Wx79hp0uPeltH3j7fmuvs/KVHzrpyved8tbX75YDZ7tgpZdvuNbV555y9YdP3mCNlWO4FAoQGoGsIt5nrq30bfR82E7bf//MY7952jHT2gnWbELGRFbJw4l232Yz7MVXvO/o1VZaPlJQw0ilLL7y3JkffsubvnDckUfuvN3e22x25K7bf/bEI8855vAFy82hqWv6zDYGWiQ4tduOr7zinBMuO+2dK8xZwCySyaZJaZPJ6E077HDlKe88ff/X7731lntuvcVH3rrfF854++tftdn7377/dz543LF7vl5qNEHTWEecFCt5yBeGFZHayEptgP+Ah0rLPaPgaA0ZswZJMIN+1HN4RKqkBWPJEmLuTQ0puVJUNzVtPEkTPIqcsdA9OhT+1voE7K1IUWeEjwoRUj46oqVEk47qxMjNNuPgZRq9Sp/dJ0qMOm/6cKEiqVBbZRTYqcF5GGUEbjFzCEjjxbwUC4c+z87qqiU4ahBV5ZqpIHCYGyZGUYfjNU5UEaRwr2XKgWRyiBMSFmUiOMoGh3viIsPKCGR79ESGOpjIcUxPVqW/54lHDjrx5O/94HtPP/10uBMRXp979tm777zj05d+Ye+3vvv3f/xbj51HVBrB3SGkkJH35Mj8CJIcQ5NQaBkUTwnAbcpkwCUc86kpIl1yTwRDwDiBRVDEFA1LmxqwFVqdikt1veWOhz94wcXj8WTTtMce+bYN1l4Dg/+3Gs+cOf3MY4761PvOXH6FlSni7nvve8fp53zi81+vkyWrKExJNFYqUc0dJSZXY2yMLdGISVlIaQiQcqvMrZEYmWtvqQ9G/AiVouemZOmnI0ZDKJIHI80IMScC02jUW3YgQhUBisSEmkLVo/NStVYGlCTE4klSpoZNra/c97kv2Us2wwwVQ6phFoKm3Yin4qyRqIPtihiHgyecimfLiqN/hUiCuCROjHElSkmTqrInUCDMicCECn7hgXhFMLBEmjYrLPHalTHuaCCThpAGwXvFJ68Ee9zZ3QtjPRKAGWcBsJBcNbA6ATUa7OlUKrKIlRjrSdiVwATcTDScWgbAbbg6YT5TEMjRN9KnKUx6B0Q4TY0I6wTuulFU1p7C2HqqhqoUlCIkCedGRsqZgrkEGTTScClBCLZhc+yJQAlkT2ZIKyR4xuDIAJdzEBM+aVSJSQ2kuGUJzB9Js/YKC9ZZZf7suTN6ocKMmawcLVlDg9lGXGMipbVXWn6dFRcsN2sWDZc7SU95TG1BiWlshfntzltsdPKBe5x52F7v3H/nzTdaazpwKcugFPAoIKtN3ArsCPHK2H1qhYdwkFGR0vzl5uzyyi3+64h9PnDEnoe8eut1V1o+M09PeY0VVlhjxUXzlptZxQqwKKEVpQ7VgV6y8spH7rrD+9/4hlMP3mW7zdadNXM6RVp1heXXWGXhrFmzVAlHiBZ5px5CLrzyovkHvHar9x2y79lvOuDVr9h0uRkzpZSVZk5fZ4XZ8+eOqtR+IIZopLVWWLTWqgtmzZ7mCFWiIBVH1GB9u97Kax6z525nvnnvI/d8zYoL5oNq0cbMme1aC+evuXDBCA83JkrVHsHUisCGNLzSonm4IzrtsH3ed9hex+y506YbrJ9nzh5T7lOtTRdR3MJJddSstsLCtVZcYfqMGZIbFlEhYXJCbNNK8xeusfyChTOnO0AlhDQkMCjLz5v9uq23OPuIfT/w1v0OfO32q6+4EPOT6urLLbfWgkWz582zBP8JoV04bdbaK81fdeE8xMIoSUyoj7gyV5foRcZElgj7gkxUFILAfiBh5uGIHQsj5a26FSRUcbGBBsEMS4wLPpkz6kYMFlMyasd1eodNNxDGxrKaklH05j0EeiWkpwUedXRdrn2TijTOCVooOCxZRVUQIJ/JUw1xphBojFQ9dQStUFcIXhFuLAo7VZNKiSlhFgX2HtA5kiu6wGmnZVmIftbQoZorwbQep+umFuhuQ5tgNUCbokncoJZMmz7aZO1VDt1523MO2++cN+13/N6vf90mmy2asZwGdLGxwKTs1vZjATLVcM2Z1m634QbvPmC3971171PehPPYJnPnzqkytRkHOdAr/UDjLCuvOH/1VefNmtuwgJuwMTsJ1JPFglnLrb3Cqmuusiix5ahJlFlci3FtUrvSgoUrLVh+urZt76PSqxeCHeBKNItmzn3tFhufevAeZ75pnxMO2PO1W758wbz5JApyaeNN7kfcJ7fkIpGiqKGUKBGqFm5bTLBLrbf6yrtsvcWZb9nz7CMgYbftX7bhvFmgXMRQdZRDscIDx88e9WirzdY784g93/+WvXfZZtM0MdTZ1EvbNTNo4uXrrX703ju+74j93r3fHlttsOGcdqaWtGjmvDXmL7/6guUlAAib9sstP23VRXOWmzs9Upo/d/nXbbX52Yfvdcbhb9hvl63WWHURam0JrsM9RDLELcm0ac1aKyy/5qL5eaKJ5Ih2gKOUVl64YLUV5s6cqewVBFLR2TOmr7lg+VXnz5c2W6veDG5DoGZqJ/KWG7/k9Lfs9/63HLDvq145ZyIhGYi4RvY6stCrr/sHEW234drzRjOpI7fwINIIdgbJs5cUVZEXpKlGjl6R8EjGIMYlDGJOUu3YuvDKhbUkRu1n4YTjgeRANCngfuiYuWQKRhkMNWVw0pVN2EU0MScjKV7NO0fmUF+p1hTeEOek3GiQVCQfzKUhMmTGBBvgkeYUkmovwxGug5UE/TBDKamiri5csOrKCxctnBtUg/BFScdV2BqFgAgnr+xFWA13V81K85ZbfdUFK608LydqCY/+Ig0J6SssnHnATlud9da9znjrPju/avNFC6dRa/MWzFl95YUL586hxkJdrY56aorl0i8/Mdplq5d94PA9PvTmvfZ71VarrbJQW11u1mjtRQtWWg6PeJpq4n1474uWm7vKypAx14kTRw5GYiNvvbEVVpr3pt23/6+37H3yfruvsfIq8Kohm9XQuisuXGfFFeZOm9ZSGWnJil1/SEshmzcx2n6jDY494PVnHLHXSW/aa4dNNllu5nKVccwWzYV5ksMFSAcvmj93lYXzFs6eORLKQxQQdEINb0NWmjV93RWXW2vl5ZBOBOYps8C7Mjs122+07ikH73XWYQe8bbedN11jtelNmzitvsKiNRcsWHHuXIump4wH8lnzqovmr7ni8q0m1D/O3kxIyg0JQzsThbtZ7d2nzZy98iorYCPLDVggSNhUTKJImtTcLTd7+jqLFq6x/DwwoRRzHHaJWEnZRqP0ivXWPX7f3c55yz7H7Pm6TdZbo23j3/c/QkTTsi5csHxoMraIOmvaNFi48oLlEylGuSVQLgTCatG62sIFb9yQBbCOAAAQAElEQVRhm3PetPcH3rTPLptvvsLsmYl54bz5qyxaCenaO4NYFTdXhFXsNJjvQWSkaO5Dkekq9R4V3dVojMqPPKJIIVolTMwU58pJzgY2ex7sb4yyEXOmtuXUCOdQvFEdaZtRkbGUCe+SIGEkYDUFIsc9tlt3JhcyfKaSdNj1MC1HQL4GiSO9ikZfmbAzDFSnVCVXaV3agMGYAlwGw7GdSXVVrBWDVI4heRNOYeBgE3AhoLvit+NcEJQN/DSingxGcCPaCNyZIr+AnhDLfUnj3oGHa2MyjQiKyN27pxc/MUZ/4j7KT37/h33e8a7bbr+dIhYtWnTc4W95/wnHnXzUka/aZps5c+YQ8xMPP3j8+//r59de1yeNDMyM6pjw0LdGX2zcA9TBysFUaGl4KncaZdhfUQqoWE+M269eARQx4aBap3FptUh2zmHqLDUDWuFO4nkvF3zh0meGv5rGB+23/6F7vI4Ziwbc/2/+iLx6m62+e/EFr952G02pW/r8Z7/8pUNOOuP+Bx7NJAIeSlcV7AoykQIyobRWSY4S0FSWIkldBMoxtanEzo6K4NP6volepAqDSQiOMUcVPIiU4myRek7j1iZx4IqgqhDUYjUqH1d0GKdgSGSEOzkzQ4Ok0LAUlcn6xsvIanKUigIt2hZuoAUoajPGPFcKFVYhBf6ICwhnKaN2UkrVyYKUKKOsIr+EIrEgKjnVkGKpIiKVwmoxC7wpFHCSwXTJ5OinrvYYpWo1uBCTQLuEOUGuICKuPtyDQnJtomTcQLRMoxHnxoN7I5jATIlcqI8wClhACYuVUxYS5DYDOgkIpACmgxlEqAYByYn6IQsSUWYmFmchIzYa8AU+JapZgW2AhJtEQ+bE2EplZpq6ONwN3w5Fj8pjRH1Rh5nRUFImzj1nc2FYJrUq94k7ZRMYgFHiSJkS5AoLjBVYD6ZGHeYjHF2SojwwgXuHv/gQbeAN4V0l4koIdlMlidSkRXUArlYYVKR21PXIC9C/gbUW5tEz9wwuReHog0qBvSCRMqlAmgaqBSs2mKoJ9jGZcY3sqqyqzkQBmz1RqVQRTIthpHVhkmCG+eQphWRWgcTENiKc8ygnjSyVZewoQRqcPZNpJavUk/dRixv+CzIadTRRKZmSA7o0Jq2aRAG/j9poNRrYwyZeyMzJnQiiwpKEJAzlIFfrsvfsMeQIAlNJKTFnEKDiZM2YDB4GiQOZwXJgDApHMAkVYZyClCgRUVBRqhwwjiqsCgWXS+3RAa0cw39V3QMT1GoO0hAglSwSOfIieySDtTVoGEISBXGP8NQU1GByrpbdhSWapjajItlAiiZL1orTeEfASIwar0oFQfTqoDpb6OBwONdwF2N42zug1IKTFk0o5YwpBhu6huAtah+SnUtn0UHOOPK4tCXEyCsPKEa0XFt1Se7iBLKosXjk6AXBruBPABCWKg1JYnKEiCLgFmuBfnfgBAgZuGEikRPuYaqPKiVn4qRZVUWEnDkYActOCSnRq0UC5pZ40I6UQQxTiSHKmvAoolOAjxjmhsBliSAz6M8kSiw8GFHYe49qZhhTIhjuVs37sHAMOHUYKUHFMQGFuJdEiYhLcJXcZ4WgZJRLEKRgCZMmTVkBp6dCgiB6cBGNFJRCRNlR4RBMZBFpIXVW1Whwl8XEoUESSpyN1TIFxki1Ema6SG24DrqWMk9Srp5jiEIMVEFuRIJfpE5KhOipmPG46thSUYtUHWZTOIHyjDinXFMuA/sT2CviqFVt0hb4aq1BAd9QNyfIWi4ZJcgACtfKXZBR4baHlZQSNUxJEDgPmEMlOeIKFo5dKjM3og2nCfGRWWsGqwE9Y2igi0UhRyogiBUocBaTppiCOkqqjdTMNbGKtMSNkgsUM7KLQAIg5xJEcIiDHn78qetuu6tJuvVLNyRPXKBKFJgDmSp4KIQlTMQeEgaJweo+gFVytlFDWYQtrHrJ1fD9SQO3cRTDEmAGjioBJWQ6kQZun2KCLSeCg8HEZDlKisJRhaxhcE7VKAak2I0Dkwy26ATJBEvjA5c96MUrwnA57GInHhZ4JekBBSOfCUY7lKZWqU3BYGmNikNeD5KSO+zqWulG4oM1JAYikDB4BBclmTYEJJVNxVNLOiJSAAlLnRFlctZCbR8oOeEUrsq51ZySWoLT2eCuJRvowwZHKXOTpAnn8AhiqEqEMmdqiLwzB2vHVIkINghlSHQluBuog2YVSqIxCKcQMsErJ5GcBGEx2A25HETjCGMTqWAkWOBjVCkuREXFOXGkBKn9hEcmJyURzugkoA1SuQmHimZqjCY4mgYx0ppUciSGYSyYaEHFp7gYHDWFYXIbnrSqFAqcMpKNG6rAyT2iEvdBziapNq23aTADNGI2MBFuGs43ml0lsY+cWsDQc3UCAT0N7IpOqCMkotXoDKLy4ufH533zxzfec18NIyVOCqEkcce9D3xv6t+k2XK9NWfNmB2WogYXmNJrohAxigihAGwMiaTF22IjA82ESyJLpAJTKqEqVxv8qYhf1CqG2jhm6jxXSg5lhKNuJA92WCsIo5kyuQhCKdWzWbJgGjkjM9rKrUfbUQoSZRUIaMRR9TilUIZCCy2ByIxcFVI8NaapDuYEoGk1lAcUjLxjd2JwqXfuCdU4ggR6G62tu9RM3BCDX0gApWAKJ+u5cxqHdHiMo8xaJXckIF5O0ahOEPYt6QlegQEqCKdQp9JrxYKUhRMokMECIaJq3gGYwHJu+tz2bY6cgTAVImwFYewUValm9lASjReee+Fdp571kQsvenrJcxdc9s2TPvDhJ598gpg3funLvnDeuce8+ZCD9tzjyIMPvPTcD1z12c/sveuu0PL8Cy+c++nPPPnM4mAysgijoaE2CB5kdTWCYDUippSIW+KEnE0J2CJxps5dLsTOUjhV4oC2EDUZwscgQg2qQq5ESrf8619/uPZaKF1/3fVOOuwgEsH7/x8b7Js/d/ZF55z9nmPeMWvOPEi74cZ/Hvyu93z9R7+O0jfhLTkNWwqipoq3MRbuIoqUmCiOp8CZOh58YeHBfBfGLm6IQjLmwmKB2FqEkwY8IamEbQtQORjikaNP+GxUx9r1yWqbUDIY2SKcsTS0BvQSOALOqErSrEh/UCGTibqmUKFcWYkxiSqBblgEuDMlnJ1SZpQ+LAFeEkHmXktU1DSbChasphDGutorjxNHAzYXScVTqerO6kgiJ2JF9DiYi3s3ps6sz5UmSjSYJB2PiraRRTHTs4eY9+Rj8Q62CCdm7LsDjiV7TT14TuBiFHKnyk3VDErCrwnmCU8TPZqNooLXxm3nrbknLZzdM3cAG4oycw4GKEDXREzZE7uH9cbFkhEbuXlnXj2oOjsTrlK9r/hUEVIhZqcgxxmzVWoIucCcCDtCn8KC2AyfkljbWIM61Lhky1LUAz66NpxyYKb0lYBqGCQ4oY4I1onUBpSpY0fKobAwCdFgQ0BwMDkjnIJ9NTXCpEbDK5xQatRVq9vw6Acwot6aJ7NRjRFFwwQTI3kFhB1yHTJBAJUQQDk4Cppl9wxbQBgBdRi5WJPVhGAaNSyghgjBDgI0zAORuGHLVJB45IL9gogNknuNktQpsamiOcHSyL1BA4kxhwhzJdy/oJgbDysjUYJWcIEIIggXO9AEaGiBj0FsIhZKjrggTAKCIXwGnV2lEoysgfoaLCxJklIb3pbS9NiVRQhiHYuFYUGhAFY91UrAxI1KH5Og3lC+BVBiErzohQo5BGvASjXlHlymDosZyGvmhvCSOTulCtmpIoF5wIjCiWukUpvaJxprKjlHFkmcNKih0pClZOBJNk+DzZhOvQxZRGQEazUx8OMpfWKcMd9VRXzwNMLFJJcmUyMk0GbE4eyVq1MV6lupTcIyCpCUwDUaVxozVzjHhIgxARQKvI0SVgfXCkQTgYiUpck8YhQc0BYVh6FBhXFCTwIJlWhYaR7EaAWLtRASFwY2SpGoz4xQRK9Uc+AzCQRZsUCvaLTiDcAdIw00mkht5eEAXhJZU60pXdt3EuKp8Za47YWHoQzqwGiGMeQCV2hMMSaca3GOMBTaLlBEU5A4wEjVtQzRGBBIEqwBx9nFXUEFykYKawbrhv7q3JFONqk2TbSNtHmk2pTQWqP2yG3ANPCskBqLYQlEZoFvJRUww8QZYAShRmnP2JOGTyawQMgzYUW4xVBGyZEIlCscRK8T40omowIchEpyz3V4yok0pwlGMAjYayBgLNoi5AxQLTOxYm+RPlINhVVth6qaG8/ZmmyiBEFWWvLUCjcagIyoMvKzkOCYUc1hSuCcQuwYIADWMDwiS15TxeQgrgQYCRPhDwHCUEHiqRAje8TwPlAyLAWEwU1wRAfTBnl9eKXi5BSW2IeTUeLy2NOPffwb310y2W202oqbvmQdYlQneMLwwPqm61LplPvc9Nr2xJUrvK3Y5ENZCHVfkykBvjSED14lzkkV9CMGL8mNS8DHGhkec67SdCjCSYQo+eCpEwX+I9g19YuRa4ikMHzPkUaMTZVYMc0JDOkBFqMWDMKJOMyldmydk8UQ1qpRhPAawVaIOiKKEEAnfUj15BKaCqcxDSfIIOfgZNCYBf4ws3LRgKNC1pAqqXGNVFQtEQRFkEqImKJEAEkxLMezhY4K6o7AXNOJAidQX6SVhEeBuXpj0TCDC4z4uVEVo7Zk9UQEoFIQEnyCrAXBDYev6hEMedGQN1pIaqC441BCDjhlOKE5lUDdJiNFzIOyE8KaUydcmurqwg0PhRd5LAREKDkr0qpSDmmVWceqNQkJQilOXqMUeB6hlQVziXSwjhnGcesMtxEiRhBhEbuFmwVgxacagkhETbnXtqj3ubdklEiVsSYq0s41ECmcQKhLedzkXpw4sg7VTRhT8bGnqEbWs5F4UwKOZKupOmIfgJ9hKqKTaufytZ//5eu/vf4dF37xnK9f9eu/3/T32+/5y613fvUHvzzxU1c89dwLE20+bLdXJ0mpUi4ihVLg2ZA1EkWlSK61ZQBHBIjhYp+9tI7TDzGRE4NFPSXgaOBCD48SbrUUD0g7bFDekVijcK5xkopCF55NM/yHx5Ka1KBgISIZVdCEG+UkAiiECKcP7lhKQmQJuvHIAygAJY4KFbXn4rVowYGBWg9YjbEBbORMZm8lhIkSG2tVMDe5ulfErpKpEAkBeeHB/hwlMTrgUjAxAAS2lYAwBWMuJyMl1szSCGdG19BTKwgmJoTza2HCTlTVVWz4zo2FB8YID367WE1hJEHsiSMLCTPUOAAUJmITL7nAG1EMifzij9f94+abr/z2tw9661GfvfyLk5OTRHTAnnt+7kMfWGu1lQlL8JnJI1ZZtOCDJx2362teg44HH3rwuz/+aUQhh97s2EBaYhEMuREMYGeFCU4FprQuDWpQ45yClGAbcoki1eCevWhnip2EXaSy96nCBXiFiWHf/MnPJseDSW896IC2bSH//19NRA7b+w2XZ6bCtwAAEABJREFUf/TczV/2Msh87LFHz/rQh0459+OPPflkYgnsuJpiaAMGHlbcCpAcnHQu1XCY7I165To45CH4j6SkmMo0VALgADwyHGZxxiSiCkUSxmIR5AaSEFfJTolZUBU4SnA4OoEdVgApYdUs2kZq+oRSEkhIc5BCUHtq9kCZTz0h7uQ9h2Ehiwtuq3Erno2pZoIlVmv1agODJJQJ/4UThEEMqhDsqtgCU0YQLMQTYWZ0QZgED2CeggJgtTEIBjmwopABjxTCKkgCalgEARaUwKgacIRo8FMNO1tiCpNaGUREQ/EYRItrQjkX6hhEqZGMkZ+KPZHEcEiFaII1PXNPDN8wjysnNDJvw5oAAsQFVZMkLNQHelNE71xdqSSt09gU7hE8oYCJoeSZQhGbIcqBhG8QEY5IFPCaPZBpsIxRT2pWjEWwUzVUAAJkIlWWLQ5LbuyOHOhg4uB8IwIQmd2sL6WW4mD2oLonqsJGJCIqkjlpCJJilLA9Y7OTxExD03BYiKMQAuYG1cLIYgRBSAhHUOkdd6URbnA5Bk8MchM7M/RwQcJFM8jMwsMSJ62UChEmSMpEmSpThc2hRhCojiUeFaTGHqEUodELmUJ3EKpdNuAH0NVYnRqikUjGL9VC2A4GXyGGmIJqqeFVzHIFsJbIGoKNpEwu4qyEOGKygDxupiWHN6RSE6GumYs5EXLAOCnQYQ0Tw7ZUsrtGE9oyZBUsDNhZyUoUNDeQIKI4m6WwNjMmOpI3mJACIqRwH5xSMWaogKV4NUpOSVxa/CJlBjoIjudaEw5JIoHjIsjrEpW4F4hObNqQJ8ZSliLDxtFL9pw8KbMCplyHMBJpYJ5b4iCGToHBRGEqFU9GUeGNozoDfOlyGqcEFZUw3whVksPYRRmPmbJQZuWEexztlIdpVdxYDCowIIngy5AjorUJbJgiwdALmkOUJrxQBv86qIdZBPJFwp4WIAURAhKpQFoTAeEeYiKe4aByBFvH1FW2cSiizaosKAMOqJ1FGJxH0CCYg4UTxBtFJ9Zp7VOtWlx6YVBAJBSeVUGOtyopC5GwJQEgTuGMHcYdUsw4SKAZvxKsABjkPWMjFo/BWIHuzquxK4uG1hJdMS+Ri6gDaTcNQIdR0swZVkp4ipLAMZCVyaCwhtTQgBESwkRBtVjFiwLaSlHEBzDZmIJNnNS1CeAWUzYoURIeCKOwPYLI0FyCErEAC9AZ7AhmAZNbkexJIntki8ZoFNQaQp5RgQ2JEMExRIyEPIBQJojWTMMTPlWFyMJSmySiBD191C5qz5UyeOAcbA0azKtOA1c9GAb0YUZ4HlzM3IdgCwmDDfBJiFuHYCFiCphPoqyJSCCgF+olYQEetFgXpQqOt5UhrCMpnM+/8sdvPOezR5x36TW33Q3vDtttx4lRy7ggg4NCAsEEYIRsYTXXgH4tJXlRMU4UxOKYiDdKqeWkrgLbgjGAwZ68Ro+aZCRFM77NCa2AjkCGyBFqQZjTGxWTMCKqQ1QooWRzwzlFkyklFhQg4GEWYwur4U6KyXDZGkZ4YFYYLIHfma2RgmSPRiq2Da7BzogkFwvDzloH2ziF4eFZO0acOIhCSVrS4QsbAg+8l9o77GbIQMOaqhQMraFO0gMJEECDs2P/QpbBEasVwTHEkMNRHKig5jZC/P9g7j8AdquKNGH0qaq19n6/AwLmCObWNts5aNt297Sttq3dhja2OSG2iiiIObS2WVGUKCgYQAkGkCDRnCNiQhRQJOfzvXuvVVX32Qd7Zv5//rl3Zv7577377O/9dlirwlNPVa2933OgRFqDTmZLRmZw3BygTLNe+PyiQZPAwLlVZ3lQ5RjtqW2bdSLQ0p0MTpgQDJ6797X4DPfo4c07CJ1Y95JNZSlrIDimWSUZJkrMOqeQCg3BoBRHpTFQ/omaHMuL8HBw99/aE8QF4s3cNWmvzhJTODWUaaw+AMWhnYsYN4Qk5weDowFDgrBHSVERgigMM6+AZaLoTKGxjGZqZnYXKDcObRLuS7P1NdoiVsW1RvLZfkxYNzpTYxjYuwTRM/7pAX/yt79/96n1o774zX9934f/5U0HPPsdH9j3U6fzKeXG2295+eMedt877lykFyHhSoFIughpFqnkhTp/NR2ajj1Hp8FwURRDUWhEdHAjmugVXdVhrsQh2cjWFk6BikpnvQZR75wDU5jBRAq0qhURopKdwruMrQyg9G1jAunoUWf6iTRdYsbIAcRyFm3p6Y2TS2liDSwGhEwzZ2QTUauWG8IXwLBWNQeTgHBEgpZKM0kB6ME6OtuwLHUUFK9EWUylIKs7MUBoSgk1L0LnUp0CGO6uGWC8u87JVJinoMGiAk4NE/pD+jgfrVfVdSNQCe0K0whHZnIwlqGiGin0fhIQIed3wxunf/kbhDYyf3Heeel9Y8t2r3zRC1/1/OfutMP24CT8HzYz+9enPJFjePVjRx/Lb2MirMsw2RAVw5ZpS+01xCdtfJNGLbQ2piZTWqYMGcRow6IoIVGWQ6rF1G3y0vlM6hpO0DSzmpBUfu3Vl5166qnUdbNb7fJXf/IHPPjfvIvc43fuuP8bX/vUxz+Okltrxxz3mUc9b48vnvVT7YQoncjWTCNp0FFmq+tx6MJYobfe59S1jOtxXA8WRmTpuvTgc0NtUlh+h6q1kIHu6qEquUX6qBGJKaNp2tBXQ24UIWci+9SzzbU7uw/CIoP8ohmhHOtdOpiGiMk5KjN7kgzaRjAhTSjVpob1lOsJU2CGdkgmFt1ZIiBBMiXGmsPgtIWza8sN7WVo7JzdoBmjRKXxA/UvEteRm9KnIVLpXtaJ/TNphjuNld42so0t2McsMAq20GtsabZl0tVM7qGBGCYGp9CabjO4b3uCUukMchXOSASbAV/BuQMZ6M15boKVRWUGJIVE8gaIXPRe0IuGy/LPPviCwaVLdA3RNHFopx9MKiiFjwgDDabJhKjKqsaqIpVASvNQX2gH6gdmalHJKlFKmKqhUmY6upvzTQ/DFcqPNjSXKWUyiVpkMF0YEvCuXUUHjJarzEr3ugdTqmvMo3TFxDHB3slFtEBYUfjD6lRGL6tuxgjnLHOYS8FSqmgN/WDSSq486txBXKz1QbaVSKU4mk0nQgUQRg9wFlWVUAi9V6V9Gquch74u7pLM6KzotbRiZHggSYwAmrqz6hpbTrZ16TNv0y+1FFfhaw3+NhHhCzId1FRUS5YaNqBUFUJBQFqu27yOzRnrNOczka4go2lhvhstROQQ2Fjcyy7SMkqAy3pkDRmnMkyF6370pFUunRV38mkzekNBDulVui3dIb0or4WYKS821c5HVh8ki5ZSaB394uxcWJsqWs1qrRwOLNwY6D97BJOLke+i3GcVR4UMqkPCAshkod8sZSvNsrHphmPsHT2nJlOr3gayQ4CAJCFNknr0KJplkG3Etb6NfDMwO9lahOaPMVu4Q2ahI1VciIlmkiWYJwHXBCmQojqOQvZv8LgIj4RcLOLLi6kUtcixETRIQJOOswh4Uadqq6UI1BDWm3jTJhzQRHrtQU8TpaPMYqQIEsvGqGpKmdl0dIE+O3KrYetgk9QgaZKR8pis9yHExMjsFk2WRBRTlVGdBUJ4AzW7YaJeLL8rD7WFZBdFlVC3XI/SigjIDd3SfeXz4H1Zt4fUBo4rZhRlYM1qJXPoqJA0mYuslaRpJGj2zBgaKpEN7T3axA/zWn2lucVlA9hOcoPiUueQNoqOOlYbivABuhst6i7drdNq07SFgc5bKcwT86SxdRZiJcqTJZtXEUOZtXaYBpGutlCq5mIeNFMjsnMxpdOQbdVjXCdrSo9wul180DAFt+RgaIeGSHpGa1uzXWdYF1GGlpmTY8eYOopsGGpVXQmIUHGyhTvZkqDcnh5wEWhqhugsNg/EPYVny83m1uhk+hxtbsGrJqVkAWgrTx0IQCzHEaOgUL+UBHddbnTH1LnCjBDHwE8AWdEHnH/RZWed+6uLr7xmVWz3Rz34/ne/S209kgyOWYlZFMzVvI4LC2e0yTKNNKDxgly8Vp8MpCAhMIhUjYogp6aGaYJ7c++0BFhpMCA60dxsBLQ4E6ojEIt5IWRXy3QPGsAKHgCX8HXs9CqJB0gSax3O4dlbiaksXog0lV4cBvqvWCinKTxR7cbZKxVbkY91c6rzOkvvK/ehR7TYtmUlbyOr0vgiIUF73b3zZm86rwe2TXYuqDIqk2gaCmPlSeJRS6hMySJQ5qHOgzXhlNDNXjaXRjPr5pw9WybHGwdO4XwXPy8pZ1DJkglFV2xCGgYsC78BZaUDKS4G1yC8cyHLyE+R4jJIVGMKNS7CRmgVyNyFyKBLZ+iio/vYsaVX9iJKTce2LSy9dAG8WlvpJDF7dmcrSaIac/YJEmoiNrhQNZX3nMN7l5yGCi3ZuSBpa8YKiSyW7Hi6WtKqK+G2Tmxa9I51YCo6m7hIcla6LmneV+GrKpWeNW3QzcQ6+zrWzacEWUFRPYXAezIYbL5bEBVQGUVWVotVeh4KEUEG+vrGOw7/9ox/2u9FT3rYn977djfdaZujuOdtb/mUv7nfQS/e9e/+4N7ATAylJK23QvfQwKnBBVMR0F4EaGtujrKuYxtKDoExq8YYOXgUT0sxKXxm5tJHeUNWjpX2cZzVEtCE8bmgUyL4zUHUnkQd5DCDknPKBLTIxuTFpEZFLG02ljHdsG7Z1hWNUVW6VITrNlqlXsxHmGDcTJshXaKPOW/R7HNEQ0kbtBjoCiJUtJY6FAIliJw12+iqyaJvHqwRIGIKbuIokkRzQ5U9m85nU3QtAHdRgEztJWiGGgOc2nqWbAUhiTnReovuzA4RgZgOVlZWag9tzTF16w6yykKqylj7ahVDRRVU5Egr1u6XXHk1Lbl+v+FON/z3l7307ne+02fO+NJbDzpst1e/4Ukvftkjn7f78171hqNOOnWTJQS47a1v9YgH/S3Hn/+rC4767GnB9O0aDUgXpCkKEz4QQTQwJLQh2rZ7IiVYP6AgmMKKKQPY2bQ6wbYiqgHtDHDWhd1m85e/8e2rrrwCwMP/+gHbrVY8+O/t7v6byy4/8Ytfe/N+B+3xb2/e681v3+eDHz7jG9+5/Kqr+UDx35t1/fXtt99+r2c9bb+3vW2X29yGV86/4Px/ed4L3vPBw9u8Sd53QU+YaskChp67DVkG9pFSuqlxJ5qlh0wMZim8ZSkFHO4iS+CldlnNUl2qoqjUXoc2mg8WpHfxEA+PcCjpu0QdAKtBaxK8RShjymyZCCltgcs41L3Oriw2VAwIokjjruniHCqu2S1myb4cZBSCyyVNkiddZRZ1WLBOiCZqpIENmQXJTMBU1KC/slCOZLIulpXfRLPzQDhy1XNg2WxUFAGSlCwLVg/JCZHKMRJFmUZLrnTX7jWUMry6b3SqA88E5lk8pYuHTC5N+jTOObBsQ8LVu+RaKLcjl7pLe13RoWHQwtmQpjJXLJRbbC6gxcleSKsAABAASURBVCjGRLAxxxX4WFKNGgKLkchACWf+mNCoRqNVYUhiRW+II8GpBeNA431QYhLaAn2ZDmhG9cU64aq2FzjV1VSTUJFlV0CkWybxAmtWKdXUQiRdbVN1U3ur67AOMHGtt4JIpqdE2FJtPTIaz1UgWsIsisEUzKKkF5kZ3twbK3i4TVGnBTaWAPcEYWE0IC3HOYy4N40+gqXQVeH0n0o90sMiyUFhvhlbQhOiTyMHaCGolUux7EKuXB+iohATPrJYYFgP4zzQe+90iXwRTcJekIWDOUZLEgGECckH44kvenmGlnOw1zaPJsmiXmpUoYc1TDlGoqumcbYYyI8w9x6+2WcHS3eYubCGChYzkCaA1MUyqKnUZFyRKrKSuopKDzN6qrrYEscC8C5vw1LGLqVDe0qGyKIWIm5woZFks4uKJaONkNzmKBrUy/V75VTndY3QdEikdQ4icVQX6eogw1oOcxlCh6CoJTYs9Ea5hdMqq3J40qRi7P3U1Lr4JO7wTixNUgRCA7R23Wi5EjLN3fnNkdPHodaqhTC5CjgO3WzWoVnls5KNMUgO9GEqwjyiiSHaxTJZrMYBRTwJ6CyYhoAClBBKdoXOfBIhqLxMhkaybkirhZ2tomyjgUaKs4YzdiuJwjWlc8kQShhnkRkO9Ir1Cn1QF/rC3zFa0sxIrIFpJfOA1MCymYsQCHqmCSkqQzUuoFJFVbh1E2gOxVclxiLFWP+ZuyU2uUjU6GIB5Wev5LRKEE5jTJMPJmi2dusMtMo65o6mmYPIKDZgIZoyfEQ7Oq1TUVAj1xK6wbusEBqZUBRZGpQJRMgfT/Su4dqccImjOPgQNYaUIlkwKav0wm4CEprd0AfprG+FjA+SiVPIZbgF96h9rus2ztREIJbWsCjNhujUDNRcsDTOULgMriVQZzKFKCPhWSbdmMdVZ1AH2shrkswF01Xkkv+TZCpqAYmh1Whmm6p0q6JWtVdplpFdWABaUdiGcMXbUtusCISimTokRUMF6ipNIwrPIbs+8u9e8E9/9+on/OOxr9v9cQ/8s8oRDhClZZXWsrTQpkPXkh0xC71y01xJ8nFLwjIhThQnhtM8KBhLNnZWh55oJdeslHBIN1UxI9nF5mRti86JYKFzGEHWYBSW0NOwHjonk4jmN1BDs2h0W2uKCVLTzXqtQSuR4CY2pI6utUFSBcZZSlvERcFE4LSl0BDF8MIIJ8xVWwWhoIDo5lspFZItMHt3apfkENoMj0hvNLQilw46czRtYFUKx7YtHT3oZHVbuY3IASFGWTl7SnO6QkXdIpWlMEmUBG3xZoCIslK1NMeSd665kLVmKUxVJoFm0CwXiRhodI00hhRDaOUAENEK5VjhRncijLhrZeVEyTDvIpnUg075Wr0OMiT3tLASql1TgWxJl5FRNVdGmySAQEN4dsxEuqgZFlKKFaHGOVbNBycQmSphppzA4IiDVlMSXdTiNqyF8zVdsPyBqHuRqIDBE6HWB8oT7ankb1dGjObroBwfHEz7KjscaqB0FICygzJogOeQIkAZhj++553e+OzHfvxNL/vy+/792/u++SMvedEej3j4zje9GfNhEjRFgAHiJ6FQQy2hhRmOlgS8ImgJ5YLKFzYC6lApiUGjGN0PsjpNuerTFXQkEqoEVpwQzSlgCKAqJWAUxYW8+EKQGTMHSF9SXBc6S9J+7gJoGlypNbkhFsOQCY8kwYL1iFTuKRNyM2J2uCXlizJ3hBYJKIFwcB7NllgO+eiihNoXJeQaQ2+QUcUshH4GSs+ajZXMMmkuzDJshpKhZGe1ZFykW/TRc4CRhgL6PzICQ6Uczcx5anyXJE50ampJjsIQjB6zJGj9Ni6piOYgZPikgqHUlZF5BRdedfkLXvuGr31t+Rcg2LZdee21L3zN6x+76257ve61Bx/+oc+dccbXv/mNs876/ilnnvHyN77p5W9919b1pKqP/Ye/32GHHTnjwI98bOsmv+dHab0ssGikCFJ5T4XMZi0oECWgKfSRDyeqjXEk1EjpJX1wPnBuKX2UruiQBmtmZN86s3/6pNMpyUp5yF/cbxHMk/9m53PIV79/9kvf/M4HP/W5L3j5yw/52BGfOflzx372hPcdcuiz93jJ3z7xqc9++Wv3PeKYs889b279v5n92wsM0gN//96Hvv3ND/mbvy6l9N7fc9D+z9prr+///LzGKIsU5ABUF0YIXYpqMSmj6pjgk3LtnDM6VjkQ21wV+hUsRNLnQISogHOCywQRyoANUupyFbIJbHadJjOvpZayIaW0Ibu4d/RFriQrSUThI2qS8IOYQqSH9GWctpR5uVI1RmsbOm+IWwQknIlsHfws3QcsuZ5Q6YLokIkVtI2yTQYEffDQQGJw5ndwROdvddM2AivDWFGVukneylGhQ9fKiuISkASyR84t4aSYsO2NzdALU26ybV8TkfsYhJHtWDZNNc5y5Cytld4sXUS7DGHqpfdorc3RJ6CpRVn1pRSZiLBeVDKqZIw9hy4aQ+agWQzCPowRQjOzmpfatHZd9ADdo/XoTucFPUnVKqH0xuCKWYIrSCdMfQA7ugugWIpAEEZqEEa6hiI11aACA81HiCiKSVUzschwTEnwAgKGc1jSTVHTq/LxPBkLzOkRDCbRcJW5szOxm3MaesdSbIaUAq/oNbvCUwEUQRUGA5HowYshOcPmGILxbGxsvJo0h1aRfF0avRV3imCfKlGI4jKxp6QVEKe0nAufAHUNbbR2wFA7SaQiixg3EU0DRmHl1QqxnoX11ZNIzs7yEkDwcrhGLEHRcRy4Wy1alDBnTJC16ESB9G2e+V6XIchQYpsmKPyTBNUwOvgdweBCJmdBFJ0Sc1hCQscZGxOKA5AOmsH1zhgwL4ABCsrqUA/qUekCLsUjYYGN6GOwQAVho0MYgNJJ2GXFB/IGWUpIzTS4gFzlopK0jWKGEJkXjOcmXB3QSUtRoTUhjDcE5HG0qHPf6H0D28pASKBN0kgBvp8KkpK4LRaC8YPSWHIuVvO0wrpQHp9mbBWx0X0jckNykCTlsnlbCofQrtYlNgnkPPscTNKUIdVo3RbPjQQnuOSy4hsQTEtle+3a4YFZpJtFjRAM0BGMYSIY3syAuJTOpT+NE2ykiFsXDaBAWNCWSCRM1Kya8NF9oO2qhFHcKwDh+q420oohbpEtIzqiARSFamkMExKLiGpwOMF1KhjGqVpf7kAEKr1sC1g1N2ombayICYWQp1EEQwYrlxVgQ2QcJYcSTIRVwJJiI6VJYV6oFNaqdPK+z31O7zkFFiLIknPMOHhoQmCEYPktidQwhRbVglqkGKCLOaHGPzl4joBpKohOU+fKszlXE07V3fuSYsmNfDUsIMD70Mhlq5MY7UqtKiWTdaEE1AGiJN1t7tZbCda8VlS0mKQgAHqk3SSMoyfEBNYGz3XaJlabMrSATKBXQ68DLY5SYMtYEIZggiyndIKUqsmMHEJHHeswDivRjR7WGGMnD9WrNMhMuFrMkTOJ6+QE61IG45Jakpaty0YbNqSqspAOU8qM5mXtd7z5TZ/xoD971J/d91Y73ahw6d4tU8SkFobHU7PTE+ePRNYk3i7SQfQh/BO8EikEVWROaSEZIewc0EllVj7nFDoEDS88HURHGREUVGgcg4FCKWniBVHAtlCUPJFiyeAu1TommVppnYte2cjke+ws6ZDk0mImyhIYQjYcK2lGIHQC7aNUo96k8EByWKi0Msx1DKsmwyAyCB2kMUsshRHP6BzaUhKMYpZiyqIfdZiha6f3YDprBouOtiAHXJmsntZYvbgTNQ6QDLMsojaPwWaXvWgW2PXpKyWMBlvmENzFQkBexnYaG0gC2Xw15TAnZgdPeX/VZYxgGyEXhlRSCh0xq/gg4Ld0RcN0yVK+c1AVYbvRErUyW4EAmrIj0GzAkV5y230yXHpnYxo1RBwQFJpZRcYEU7DGUt8AJgSboKQMXUdQthC2oWpNiyTSuS6sp55ZUUqOxnWfhAwoIPoDlsuSVfri6EAtvMUuNousofxqDpQsbqXUWgYMYxtWbSwoVnXb5MUALOaFSma0jLX4psVmmaeyzuIyFBFVn4uvVeAiUDUxU143WBHQI9tg/q4dk6O5ImmqSSLd2b2VICn6KOQru0tqE4awwwJK+2Capj3Ee/QUFxNEqVPZbtYt7mNvvAM4DBgj6zIl2ujrYZ7J2lB3c6iZVIQhxTqPFs6hgZsAIsl9EJa0hYMtGldRTv2emEPbbJ1fBTadU8OUVqUkNYolqqIM9Geqsaam6II0KQGjD8LSQwJIEgXmE2m6zupSkqm2TQTgmtPgM+hFMHZJWxDCX1QNhNJYdvMBkFJNKUSDuEVIOAJ0bQrZCqzh7Ky5NgBizlRF6fyUaJgCU2Iu8DG++dMfPm73PY87+WQA42olQu+RvaV3Xrl+H7fb4ea3uOUtb3krDuCV4z/3uU+fdiYP7rTLrf/6/vfnwa8u/PXRJ59QDFUIEk1JsJRIY5UbkQxXWI/qaWl0RzJr2gghVJDu7G5zF8Y4jQz2lt27B3rXNmHt1109feGrX6OWu97pTrvc5lY8+G/3c847f6+3vOvpe+x53Eknbl59+fUDtuxwQx229WDg2muu/tJXv/Ke97/vsbu96Gkve83HP3vypVdcdf2w/9NnCm5185u9ec8X7/n859/ohsu/sP/KN77xtH/d4/CjTozJtSfZqeaJtcTEqsNMykBDBJF1h3ih45oiQgSZH8Ii0hkYjvKqfLvjquHSG2aCrI3E08wartETvpijhEnBhCOzjDHnOOeUTFWwKFSShMXPxK24AYsGWlXQuTKoIOM5D6ZKG5TMMTD3jLVpUBQRwFwKA0EBKlQp2QrfoA8B1exiU5pHgoc+Z5v5DpxkxpBWg4KQVUBpbJFSxUwKzaLZji4a9B/oKksr4NrD1MP6bM7S4rpAEIKqMgZqn8XbAqcw9UV8WXgMNWuheESmBNuPk0npKH0hzECrS0oP7SgJUxOlc910Nm0UnwAExpQv4EFCQoQHalguCABLgD4zXCEOs6ya6kL+wYN3XaWjMTiBSag5A+Gg7SBWmcsVCcuoALnNmigB0teDXVIL0rKnRLKMRWhERvfkFVEV4V5gKtVQGHj0MB6ZidACbwy1SYEMs+ks9ImiRWntIiB7RS8iJa2KDUWGUaiP1hGpxS+hq0E3aWoqR7qJQ2iCRtI/VvimYygTokgEENBM5RhtIk6b3bwXBmQSilKDla4lOILEtEgFREDRrcyNyUyHVKDu6t14I+lD8iSNEJpZGZR8M6pyZgAyRdSKKDcehiS61zmWAXAoRKpwJQ0DaJkvE8KTRCAilla0DuFAT0oLhSu6eWhbBkIyKgVQNqp0LmTce2cB5IoMYH9z9OiecwqSmpGe3EH5Eiis0aJukgupWkjjTTQydFmeSEiXIIVj9BwcfCQuhJpqaSdoP8LVXeiAiAkqMMTyafQQXNPCgm36AAAQAElEQVTJhAzqhSprIcBAelGhGeLiXSk7ib1o6JBqqbIExptPc3eGiaMyiwdiplMhziW7sNEuOKRzsMAV28zGLNFspresF5zMgtXSoheZbRssGcaqPA3g13iiSluNnBdCnjRMNapXnRdlqUElo4BPIhL0INFEu9YUS2SDNCltgbZIGM0Wm1Fb1J4gGKmUuW1voR51eV8MIsWC0qIHhQT4EJpdFtxHClbX0ivcGBrBPKCrUYCKa9C5RSzhmhORBTESRkER1KS5no4IiWWwhEhP6S65bJHZ3VpaigTcKX3ZF1uQ3QRmUU1kyTOQX8lYsDJxviG0ACZGwU7dCWAZV5CaAoVAeE27W+vaaIJHZO/LVd6mbXScmplfyUEyk8/klvW0JRtcOWgRCArJQdtA/2tYAeMVA4dMwhkUi/To/KFSyqali9ocu9ZU2hdGQ2C1D6WNujA1GxtVdS3C4mK000NamLty8uJO9h4tfdYkTNRP8JPEYannu45oCxFqjAMfPsgoX2BZUsuMJCk6V+lovU8xz/BUZecvIkJLOyEUokMBmglC4T2loaiKsPRsS2F2lZgQTXLbSHoP0DUaszYjdyxilVRNZpDdTM+w7KkBEQsbMyuhH1TpPdlmcwgQwg+RlCJqsNoNi+V0r3fjJ4S1wlMJRLgulQGAQKxXY7ntSJqrmZZgzCm5pFJotB7RgK4KqaUX64SP2VsLWBxt1ZZnBg2VLuQyGVMNq4KqNMijRK5okgbdA1RJZVFfTiqiyrw8NiXtXFefSnjxRXmadMrGYF6HhPFNtvUFiDDFUAVCC8laNzBQXcaUIiB5VJwBnKN3D3flVHAY9zTt/KUqtCok6bkFSsDodPKqqdMuOqyQCJ2CtzkoGEGOdtBwcxPP5krmYE4ehzLY3YUCtNTKrOSSsvGZJ2mOWkERwrloaIJM/lhKRmVAOjFgPIiYhnCncF3EqIEr2mH0QkqrQk2MR1Bwz4VrLWlagRtCBXxEWTheOCqUNyQ5sii0ZJYWxmUHJtjUl0bjKaEZRo+E2wBjIlt6YA6ZopIXnEZGseLVuVoOxmGEKDvYERoi6UUHUSe3EEwTb4QLBlmpsTtoRzThio5UdhEE4AsICOIVhIznKSmSyrvJJroob0IxlKPSUd1rpGUvfOXvSLMYLOisRNLp9KGlzli8wKgwgxSeRHJuOsWHRgdrRWc9UqRXm9VmmHP6NrXEuXapLYv3waZiLdC5yHP2iuimKYPkmCEZYFpZSTHycegoyYQhvpqeEb3V1gdP7jUa0BRMAUhTWQ9gxCVgCasiCk22mrBMA1ODkgSh9J0Yuns2o6GqxrQt1lNayJw9lsyYwVLRv332D5/10r1+/rMfrTa23Oo2t9339a/98Hve+bTHPubhD/rbf3zQ3z7hUY9688v3PmLfdx9/4Hs/se+7P77vu9/zuteMK740xPGnnEYvzOzJj3z4DjvskJmHHPHRy6+7bFsVEXqrNE1ZA1rFpvvWdfZGQIdIQYKIa5YhaJTZEluPuaE1Y8B6i6nD52ozF89ECl/9wbnTmt834I9+7/e322Cxo+b/srM1fPyzJz39JXvzEaVPy7A73+GOL3zWs4583z6fOeA9J3zg/Qe8+Y3P/Zcn3ffe99FxsXy67upvfOOrr3rLWx/3/Be9/eAP/vLXv/kvsv6ro1rrkx/+4IPf+u+/e5e78vLll178hre+87mveNP5F16e2p1sZoBydpbrtbU1Y4a5g1UNApQO657eewhr9IQyC5q7OFc5dHrQHqSJdokmc5dJMBmfFCxIX2ojr7GgxBQeRAySCHdHYEiuZZacTcmuaIO0UpqWRR4KOF0oeJjWq8mHWVlfUoNT6ihkjNakAVmXNBkMpZL3nXzLElKAaqSURJTe+UjPA89ckigjumBdlQ2qBTpLNRvKLCPzs8AGgYQwOZLUdrHgmyeWmmpNZA5rVNAIlgGZ3AuYDT6UuaJlsLJy3KoxO+jlKLJRnd9DVVgE357XRpsUVocYR4yDKkFrIR6pieLKJA7NGBsGwtxoAguEG8izCO/dW3BNlsHsH5aL9FJFBIR3HcpsLTnUqAjtSDHnO7CRYqmg5dxs3WXt2QXMMyFK9DLWwKTiQ8oQor4ktDOtKRRJ4nrzueVkErxNgeieXJqR37QsZGy2XV8NfUO6gqbVWKJSwGcyohQrOmtEQlsUWheLk636lBm9SFtlHyNZyxnNOhQM41TqWjUMVoTNQkqGuVivtY3WS0Kz1Ia6jnFOo508s9K95KRBsa3lPEubkd00AOfypfMXxm6rGC2HHgwjC3AmUfemMVd+MzbxmaJWsJBSlIsk/SHZPbpLz+QpSmAIBXQOhHAr0FLGYVjJuHSxIj72qFMsghVJ20UNWUARXaYk8EnPoFq0VAAWqeEWIax4rLTOukGN2V2DDOZ3YFFTFRr0TWJNh8GWl7U7O5XyfVqvbtsIQFdpk4mVXMRGZkikeGrzcfbSK3JcS5lMc7E+BnGNKrPpJKWn5pyYAVZfU17sC4O5egGE3BIY6IoqCSpK2S3Uo6YLOEXBxHXyUQEOJsBT095rQAtsSGy0qGtGRQLUqhy6tlxnzD2FfJHBshKE7l2yWWREtERnENvIUDaeypw+W0tbp7TkzURT75q9a291aqWXTK6Giyw2LIZwCgOadegC52SikFAiDSpos3NHs+JEVNIDjTJygFVhJTGjywSTu2aUNm/kZDkB4Vk2UabUJpmMEw2YPXsiJQXkWgIqEIMQHF6f4T0aZEIhbIhWouk60Jvn3DG7Z84GBgMlyIK5xGzhCGQ657DgdEZPquSodGCBdtJhqkOjghYIUUR6hyeNUFWUDIGDFzzmKedGE3JZDlMNrU6XPiF6ku519MFE6bUOENaFXKpqSPGk0JZKJwVVzLrKbEsyUmMXd+uzdKpHwlJrH8yrtTJ0cBCZ4WNOA2YxaeNIgzM5kExYbAslWHTIwLkoGkNKpS5IM5lopXJs1+qkTwBTY3Q0YkRSvDabm87Np9k1+sCJ5qxJfZ55g5lhDAWHsm/l1J02igiTu1iUbGXu2kEjCJPqmLJqdSDgm32ePGfaI5WhC+tupExD79gUX4/ZRyw3MrOJZRnVygJZlybqULYh06jSNBs2nZokqprpYkpkJWLhEqmMTQfLaG/gIDizBFojx9ZXrfEgLZgGTRIBxcJYQUWQqN1KQ6H3bM1OaZZSImrOkti2JVOCJaGpuQoni0BCxAtPtkWe32n2HhJ8Ig5rjinQi8SArupZ1Aa1UURADBVQAwZF1UyQNirF1EQyIxySA18EUBYHS6oSyE2WR0G3nIdoQ7KizJk9ovC+pXNHCieieBRNGUg5W6RUghgzwVlUrqpVaokMT6ZIY3r01JJaU2hUKpdZhZCmL7AkGWJO0FySkU2aCk+lEWFNYk2WMkSSBQzucpdasi75AY/mDjrWXVleNwch/wj5QNZX6RUNvKuadDytqkSJzkdJtJ6sUTSPUPYxZ3KZoRZdmmTzCIQYGDmC5FlDSqJIjhZjdXLWm7SJwJWiIPVLqwwBLTRnzJZ5KBBtKb3DQgaJqk3KnGN0VY8KViqOURFV02FMljnyoAeSJkc476SW1ss8C1sSCdJGSQoyhRlzmaAtWDTEnNEFmmlzjyUHhAkw2CIBTj89OZaJ3mciqRCYJlsuZveWkQTRpNswydBN0CLXjSWHlrekIiSpmQoMfSg5ppmP4bUnpXqgJcsMo5WoIuyjIt69BRFU2q+cIFzAhEgXIgAphpUFu7jSK8VqpZX1SzxBCzObaE+hU6Fr0bmIigAa6TPrQGYWXqocoB0FUdAUc4S3uWQnqgqa5OhdOqdJmJDs7DKD98GDSwBxZMc0l5mVxG0QG6sWs9Tay9AYidFo4yD0V0vn7Ixoc58RzAe3xMUXX/GSf3v3RZdcyht/84AHHPb2f7vfH/7e793j7ns+55lv2WuPf99rj1ft9uyH/6cH3vvud7v1rW550xvf8CY32umP7nPP373znQB86+wfL3ACd739bR/813/DK+ddcMF7PnwECzqYEBrCtGVqF1pOcJcEInQpDI51gQIuQc98ieII0jKsZ52lTjrws0sB+LBQzOyLX/8qlk3+7L73XH7/Vz/Xbt184Zv3ec3b3vGbi34DkVvc4pbvfPUrjtj3Xc993KPuebe73urmN9/lNrf5iz/+wxc87V8Of9sbT/vQgXvv/uJ73+MetRQG4PwLzj/wsMMf8bRnvm6/Q3998WW88l8JXg4D8rt3uv2H3/nmZz7pSRsbW9L7yaef8Q/P3PWIk06L3lZLxgh7P4cxQsUKqroVF+10E4VhT1qPBYmAp6aAHGQgS1h1LrxK6fwcoNaNhHFXCR3Wblv71Psa3qWJUCC68UYUdaW2Tm7UyMFBZehSmw4clOjNpglrDyZEukgjxTTGTCYwa/yUiPQlaYPlSplbjFTpU0k3CVR4TSvCSRZMwtw2fig2GF2VsQc7FvN6hiximHUydekhpQ+1Vw2X4FNTDkMOBrqZKjPB0Kpa1ApscB0mUrwvBtUU1dapnDIw9+IsCjRCSgiBYrMqlrWkQtDhrLzUlDI6SBFbV+lGEmVMxjozIbokMiRY83pn2manOTnPfSYa3pUAyxIBhAbl04ZEteQqEkb+iYoIAyEFNLjS6RhBSLM04Tuxkbm/khgsISHuaeGj0CgWHpK7GkrSHO9r5lgmM7jUqIyPdogWEA4NxZwIRrBRSIS5Dw6bOI8zTJ1F0b27J21habDo6gIwlEQ5l3xzoRt91t4kZ6Gs1Galce6oORRNBlGoIdQ6imQZQmsX8UJmoRNwy8YWpcGJnt7Fm3UKm3KYWeKS7ZmKeliEKmh2FFAoZFZfY54R3iEtabvFpHBTMZRQrip9XWcfutiCsEAASdOujOyqTtW6gYGkdRZmUQr3FKhGMa8tSRafkcFraaAg1+yKuaIZsW08o8y55lS9jxOGOWymLeJR3VegNQw7IkQ4fsig7wRSqap2kjpgDjS05NB0pmvJWkMg3m0ibXIqusnEqk3NTSQ53EUDAqjw2WSIBK/xQ3Qq0lm0BZmwRYUrtXbEjB7wAX3DW3VJBr9UtRFmXqJnn7rMbrSiFw1TrcLOmsjuGdEF09C6NRhKaEkbMGavzbV7EwlUxQBoA19+VE2tk9u6lfVU0cdBRDS5B5zwiWQNIbbZKDyBpLlL2LM1a5voMxoLTWsN3AKMqKSIJeMWHO6kOtOIk3KxjE8NwdsCKsja5qGFJcSE6LN90TbDxoZvjN2sOaY2dKoJYmRBJNYs6aPUoZQiZZihc0EMnksUN50GLzthsMgCkYZs6JNwfR1BSrGA0gHCjWRwiAH9UsaNcKtlTepkKvDAoZ7aZQwptG/MMkCLKM8WRKGVt4NhS0CEQVksIDEy0MzXGo5QxKitkj8MjqMxzZGioZJUxtV6raJpmqqLaAX/LF27ZLBCbaQiyZZVMn0ojAqNyioYOla82e+EBwAAEABJREFUFCCo2SgMlAkSoaDbQjiyh1BHZ6IRhMVLM7EV6oYVMwQxX2311USfSFLMw+xYC8cTmFRByawzpAvLCMj1YLg1g6RASkN04SizZD1WNRMqkQ6hoZKjyGCi5KZN3fqsOVn2IkKYes7JvNF1KZMuyA5j3Sis1wmlP64+28RXXERFEBmpLlXKIENK2ZQluVFaKa2uUjaML1HaWPtYUGjxmPz+LMboA6Oko8pKaBgElBMKLoU7kHzK6n1O53prPVNFT2Hp0iWmQiHSu5PmHBmuMhMrFREshZG/pG+WeT3Ah1JGsDiXAsoEAtmYlHAqAhRMmxYU7MJqJXzx2Z2KZpVu2sHUjTa3YMZwIswxaI7mQ+9DI5Ugmd650JVJpZm6aRRFrvpUvJXeDSnVmdUYQB/FAKkdpTuPwFMxiiFtZqtz1jZRZedruNUsA99udNacHDsdlBCEJ7ibcSLdkaAvgYhRXM16Kj2hS1QFJAkYRdJUUqx7mZrSCydZmlhQsxoITGc1Fkgki6qROJIjLeRtW4K0LkPawLQXmYdoNQunqjTIukmbMia0Temz9Q6nUwz2EKBG3sPaGSOTqIPYwPXyhgxWzWuQGXQGGlmEOAR9mJkzNMNFXUlnz2nxi7k7GOe3spT0BT9VAQiCkIH0KfmgjZhJdUloJsv1DHOYFjF6z6HCkyjSBdlNUxmeprq1DmsrLiwCOkFbj7lPPTYNsYKNOYqZEA9Edgpd8AodpiyzpzOsLuIEhAuZ3hYbmhPClkkYtLQydox0S93Hzko9u/gyoM5lXDPQbsFAryXn7rlGzgWi1VBWoJNuGgNsyMVA5NRsK5NtTTApXjJLcr6ryIg6mhViomIMXPogLSswUlSBGckQwryi79zBOlmQJULTWRaScQH1QTJazg76JSKkrwwtNrqvkkMY/E7VhDGIixS10m01qSFRJCv1UKhJT1ZS8wbvEUn6zZQkWSrqQFM0cuiy6guh6iC66lHIRxAH78KjMLC8WJU44CMf/9mPzwLwl3/+569/4fNuffOb85h7ktUiyZ0n/8d9NQx32OW2vNauu/q8Cy/mAUSe/+Qn3PiGN6QpH/34J076wtc5HSnRSfTCdzGzMiZRQga2M/4GApwU0TvLP7wUqbUMVTSJlkVuUazK0otGjXE1l3Lat74HYMcdd6x1mObG4+v3cy+48F9euMfJJx7n7uMwPP7R//zZQ/Z/8AMfsGVjReOvj8f1I/lptd785jd7yj886BPvefsnP3DQ4//pn2640/KvazbX64987MMPftKTX7fP+39+/q8iaB2H/5d9y5YtL3nak/Z74+vucOflq5VLL7vsJa983dP3fMMvfnFxsYoNSxrLLCCsfDe2KmtZTfPQWOUZetWkF8y50qskOeFZWX4mGIOqNijDVGpaysLNXrSLMEy0gjTkeAGkufbOdzSjaEkheokIDcYxKH4JviYJSFx6khQsu1zr6MRCVcQrmpJy3udGlNIFWZYI1EayJXNJqYIy0bv21E5SrlQ2yHO1bjbZ0FZsHGNS3dSCfQITE09YXLDEK3PGklmdZxC2yIG+BZ0Q8CG/N2STEnWVw8qHOmk0m12oCaDTSgNkDp5z3dD41qNrJhtGFhHiSUoAEKfDTnOXUrXoYSZoDtmWmh2LV5N0yvDSg2YRu5nk8siku9kUsyftVqqQADcTp3Th9/PkJBAd4by7SpibhHkU7guNmXhWRJnP9HnbVdUiVZ3ISmgsgIcAqlj8pyRPY58wDNUlnW0tHVpUjQEUAVFskTQ6MaE2YpBIqudHCrFPmXv0nsR+Y4nUzGRvuixbk/ZkpKeHLqgiECwAlJ4qqiHcWWcNzoHhbulDdppKc6F0gasLqw7Ao9LTQrMYAQupYlWUVR3FhwHVdIDXXLpXFPolFTmYp/SM5ooYBh/GJrruTqSXOmicTQhnRBPeoGYBU124PEEzTRKXC8jJGIbW3eEioayGqjCqqNKLzHCP1jo503vpGEgnbLs9BKE0+u0LFHSUx+z61anHim2BbNCAKlDhoObgjgDrfB2jFkkZkyUFlb8KoYkgdrQALLASEuZuXZOLVGS3aFx0jFQVmUI209Ma1XNwKU2W/gYNqjLIIJKQlugCbvxIEUIs6JYzxSpDjhJmHKomAe/kq1uX2lS7JQEW7bXOVXmBEEzo63Sn2MRQStUK0WmQqQqBFlsYqYQ6IkGCpJI5MxnD88xcqmyviBrJGAtEzKKUzsIkJZwdSThKGf6G0sW6d+4to9P8ZQ8E7e4EpmSBIiSnYCarwNhMaAQAei2gNWAExgyho9I6Ipg9U4ksqTWJTIJMHQIb2ldlXsGHkAqzWhn0ypB0HZieCQ52xWZmCySFADWF8dfFSV6hTOOAIF2UsAvHJ3EW7+ozBylqiUp1aaJFMTBySEj6SnwEtGoMEiVTVdQUSReQaZHmqdTaunhPzPQcgpI2OgnohubwyTMSqhDL1EyAY0xYgsJ4QllmWTQ5II24UUeugNpDaB3XsG5NiWYVek/pDATFUUimOZQiisbKZrMWpF+R7rRJgjwwsNQEFXNPGOOhMxRUTjNyEa0Oc8Ms0kTpk3rjzJ4aJlFAkvBicg7L20Cc0ng9xEMlS1ETwkRZPSw5PEbJjWRsicfkaK2kDbkyxrjwG71tzwG1g8uuDUbIrJjwlpdkMXZhTHJRaEWW6xTDHfQci5eJ7MohRLBoK9boqHFigibJUJK7FdA5AXtisU7PtHgpvcgS/sxggQADJYutnY4J0jILY6OQCG1dafaE9BC4afJOag3RDkLoBLoOKCvRoQgAoYCKVjGV7AWREq7Z4QSxozHpvXKcJigk1F2jo/foLbwxfQbppcwqPKbyKN4tgjKYUpMwVJgzGsDpknwOq7yZkGI5CIOWQGDQMiYVNekpMliS/EAVL8IkdSbI1HXdSuN17Vl7E88ui5UazYRtKD0lNbGs+oIkg5imgYC3zthSewBgyB1UrSXUAnQL4cQwrMUwJ9cWBiSrXgrUyBiJVE5qFkkLt5ELWUqsRiuDmoYKzahAaaGzu7emczePxXJHsC27OERLVHUp3SXgsGa1D1UKTLzClbuEoQHOXEIuW0inJUAAjHMgJJmFkh30w/lTFyeMTlAfS0C0WJwBL4A9JLuDmxjdWEhXeEDliyYgI517EyOmYrqquZ1mCerKrHQRKGspa0XXEOmSNJR0oRtCtovQGPquKcUxpkpW6kaPMidFKs1rZZqN7qjSACshRnqpj0Wj1hCFI9f0KgPK8k+MJFHSiy58VkI0trHEsGgaPPlWZ/QYoEQcIg7qm3uSCkgqSMUiIagFxiFE1kxZ9aAo0Yt2Ro2MG0luouNcC2YXaaW4kt50nVqNwcgYkUZsfCLLQlMpGSE+SzYBL0jpWhtNcdaFIFiDJt8qDAlLIfhC85IC2V60MBWbBzdoECj23zKKSGZCQNuQ9KEzHAKuUhs8wytHhpKMgGThx3d/9JNjPnk0gJ122OG1L3rBxmrk8f/Ifvvb7nL9sO//9KfXH9xkpx323G03KcM8rV/x9vefc95F7vRWJZAu3lCgNcxmJHlGv6UlM9gzm0q3mjKAALvKLNKZooOhDMLG8qNfnv/Kd+z7y5+eTUVXXXXlU164+xv22fe6zTVPv/fjn+32ytf88Cc/4fFtbnXrt7/qFa989lO3bGzw9L+/0yJx0TvucutXP/+5R+3/vuc/8xm3us0uEJmm6aPHHPOY5+726nft+4Of/tw9/mshZOuf3PfeR77zjY971GM2tqn4wplnPPrZux38kaM2N2e++0/y0BH8s+yunrVlyRCkQ4LkgVgWYWqoMuKkF+NFFSLMG4uGiOShhpEuZLqAk9oghI54aHi3IDuskA/OsC7JxtaS5sJ84EJn4FO7hvQBrXC+SrC485YmmdK6TC6sIhxfIKpQmTOnXMTKEIomHNc1eLh4oYLKsFBEWVpgIko01kdkhkSIR00MKkUlYT21A8HZNUM6siVPOqPc+UoiY/aeraNFT9K8BAZvhgbpuYE2xsw8X0oOhNnA55gKdr6Ee5LIPHZUIkm90by7eowaKzTTteumx6QtRtcsNpuSUWp0D5HpCZFUo4mISBD4IOZNhzZsyGjVgG2mLZZFIBgtiz70qdLcWRAiWkQ1IM09mEiSWVDMi3Wx3ulI7YGO3sBkBsZSx3GspYgFrBEuutBTJUTZN8M8iVgS1EyaqSgIa55TyMw+g8oZjJqpS5HgCk+0MaBRRgnli5pcVnNF2McyhV2ZqofAGKpeeqehYNEUAkk1IZlIiSQHNZPyKpUxy9IyB0WtKCsZRuFjZGHecNoC4FCWFATcc47o2rv1lCzNMJPSJnUoNCdrd2nBGKpj2VjiEYAHry5qW06ztwyUrBXG0qOhwn5qiRKpPaP1vg4+vCyRsqalidBoRCYFihKgkmq9ll4yufJPrm0QCysFFmIZVYiqWtaSpYJ+O6jRXaItXqpmV5C6pVUjAkwWQcKJDK0W32YwB/DitpEzg9HVeUMbUZtni1Zc0CuyUDGKFIUwNr2qF3SjqS28aXQDBxoYGlMsYRUoUiIlQQB7IsKTl4oF3wVUiEkoadeCWNOB0TBKCsC8nwdpdQFg5bkSZrUMoSM2Sl0t3FO61UM603byGpParGw6iVg4IGFsaSz2I4LryRVsKKJVPNSbUoOWwAqxkizKsQAYONoKFCH4U0fLDFoiGZSp6UN6VcZNuCibEsRm6Dos48GTgDtDWmeEC29vDpjHbfOjWlWlolUKN3KoC8E2W9RaZnWtHUL1FaE0w4Q1rgpzoY6b4zCPha4MKTUH0ZIFUhLZ+2aTzVbWUYNLnhFZoYIhl0ZLOjBCCXNYIDmeojtibfNUFhUMGzxMnDqtJ+MmQqM8ya/OxUEZ+kp9AFmY7sRCnUQylRBbwxpoOE9cMRdzRilVlmrWA3Pyds0gkR3wpLqZIUYMknRlG0chSU+LR51ntDkkUy1RVOpvB2mYEcb13HvrZBhndEfvglCOC/4GJTuSlygMhGcefLLsgEeDN7O51qnyE2wuvILMUjAwFMiYUmYv7hHCnVIVKelgyDspMRSX0qOChBTapRCNpcSQYwbhKNad5PI+R9FikkoX+FODIciaajIWZRSRQuFAAclrBqYxcwEtluu8JcaUn7MGKcI4rgCumGVCn2VqtcdoOtbCrYKzlQERDlFdDYUptJIY1MXCR/eatHEJKAnctM/kYzeZBptV6Kd2s1ZKXyZIL+B4YoVlo3GmIrWnzWYzY5al2KAYCSNDP0rdUvjkZF34JSaNUJUO9inBDKUUBxYYww2ZJs10qSCYMxaEmZF83KPirCLLCMlm7mPHMGGcUflHNCIhooSuM2imUF4mMAx3Jk1LNqA50yFEjEmc6pW4sqGTtcDEW1xyCwaRAiMpGVley2jNe8SCeGLhyyICqQwR3eCVzC65RmsyBXnRHJNpMwu45WaJNbx5U2cJgHogZwhMRkMowZoAABAASURBVI0V1HoN+lTc6lyQYLLlIKaMtmpGeLaQVixqepVeli8tNtVbzJjZHzLW0qfSFoMFoljsKRBmVdUwns4FrXhHibBQrDH3adJ5GrpTXxJsyOzYDJoVqTFoVJkhmz0mZAwsHL0WBlukE+IFQdpFQviUMYvyLSpobhFWQvI5VoENeqHJcylSaFJGT76izYDQQrWkKBcQYkkbE1WlVPUis6KJcoYuvliQsXXQsfL3KLKMTIggTVSMShCZHcIVy3JpBAbN0WzUalEju/M1IJcGjqbJPUNKkwFpJbJmKgQ8QawsNphzmsHEYmICmmqwolKr65hu0cHIpRCi7MkGnQ2JVFmcyjB3iR6o3chnmbuz3CRouFJP75Gti/ciTSwgmuASRDV4JiV4MFhUlSSe0FkQGmmdn8FP8QxBr+Zl4AwqVwbUIyXTHByhk0fT7nUSYw1zlsGBtmsX8Yj3fvjwrVu3kgsvfs5zbnbjnXjwP7jf/Y63u37kOT8/5/oDfj70gfd71EMfzIOLfn3Bk1/yul9deIUEAYOIWhRG3IqIQR0yN4KSgIlVNROkBEhiNAagNK89RsWll136rv0Pftyzdv3wkUdyPaOq47Z/G/Pxzxz34n978xEnnrb76994zrk/p8Z73ONeB735jf/pfn8qRgR54f/zTu257V/M7/aExxz57re+8NnPudktb81p11577cc/9al/ecHuL3zDv5/8hS/PrfPi9TunbL/9DV6969P2fcNr73bX5auVyy6/7I37vO/pL3z5t374404KdBDrAb6SubKIhw+zG4J8IBMgNax40VDGU5hkTF3LPqTbHNFKz+qipJNxgeIDfanWi1CCCJR77XzYQ0GYBKBASdVeM0ZqUteB3ICg1LaqzFfKorDmaCQJ462gBwCiBpsJ+CkZ6FlaDtzJ3gjNFGc0OLKkmRahwMDC8ja0NiILChOUacqAMUPDGFBL0U7xplVAxkIa0M1hHEeqZZ+8TyyXBMk0Bsti3aW3RLhKsI6YpCZ1IXkt0j15wPOgjclIcZVGoqgoIAH1wsSlVc75mTVYZqjaeJdHqmQVT8NljpKlWjVRQhYRiQSYzRkiUkVrZvga2USCs0PQS8wmHaDbYNySU0W6IPjTYemJcEhKwjrlexGO9sViSRVRLaiahRIZoc7aowRpCbKykNFUUWYxxSeksdAXZ4omYaQtJWA0bJubtJPoC00OJ8IDinZzGkElZkEhHNFFUpe5rLdrRgGiWYz6iRCtilRyo/eyBARLv5pTXDlmKafLYK2uNap51RC4R2ajk2HgyLlL6w29WYdwSR7WGKiiKjlIjIPXFawmRACiJ4REwVMXdc+g0jl1Ou/i35z8zbOvvIo9YkgRzqZRlAgi2TN6zLM2Lw6FJj8CWIqiJCUJLHIMGWif8zCjQ11Lp7ms2IUjeQqx0BI0QaOILxCmUrwGLaE/GRFCizTZsHzIxQRANIR2QkIhxarSfm1hEzBpzEWbVriyrifHmbkYZ4zJKpZQD7bP6i5gJ8YSpQZvGelgFCtZEaPrRsC6Ryf5PRyeolK1FuLLbPLRwbznpCDVNEXEaImUNJureIHmAoCkJQRZaSitdNVuwhVJDpFG1xQ+KJnGoCflCcSgloMmMjNEabkZkN4TbnA1pKkLiVg0S4FxBhERKRVCIrKZ8GsEQsb8zG09UJJTI7RH6cEcTFQvg4+2cDhgnoN7QcvsrpQuaQU6iFRRDVt0tqxOkOYAh/zw3POf9vp9nv769373h+c3qTSsqPLL6WHwwWKIKBOYH+gqqbxVrIQo8cuU3nPqfUoqFBYcFTDq0OAAp6p5iZkl+3+n60zt2RjRPhH+CASD4EkAVGlmCJzKjVxNgQJ8HK+z6cIJzaRMqBPJvizdhb4xR7RncTGWMKc8aWIOyXRxV3SmMt9AN8vYJl6wkC95LL0gRMlMXgM4JSy6RPfFJmgW9SqhUlUYuopQD2EGtdRAJCsPmUKjB+kraYbkaReZTX528YVnfO8H162vhbaEI6UEpRWil7P0Lp3aCJMACSqMhYzRDdvsT6ZlEVEst5UVMGMQDMrNo9JEKuK9lJpgLo5oo//q6kuf++5DnvjG/Y/6wreVUxMIEVSF0QOQbVTHImwSvCW+ONSkzIYsoFtM+O4jYoAUE7Gk/ws24uneW3dGi65kmsoAFhkheNsmS4WYKUhccjyIM511NoTFejHAIBBliANi0rVMmo0IADVq7bylkUjqpGX8TQtAZwcoQ0NytuBtlLQaNjiKh4kaxaa1XuclHcRqEg0XZgjojZA+k5pbZslUEaGBKoCgR778oCOe9Ib3veOjx7ViXiyh0Ws2Ch56DJyOxcpOGDQlwcqr5FQKYUURHTRWpQ1oNneEiwRKFw0NlgjJoA2dcYSK6gJOQW6LZkoIBYPR87kFM1NSLSSdDEFWoCLNAPjCGfTsiSbZLKIEyP5A0kWGoXdkW7ilTsp3SlQIi5g67XM6Lmx6MgoL5iAsIeNyI3hvLsOsq0hQkqpLlSzVZSkhfIJBRw/QF0IWNQkKLQbRi4p5zG7OQWW9LAu1KB9lr1tvPfO73/3Z+b92h7sJDW80KGisFvpSpVB/KeL0wVtMjlZLHzUlCVR4epau0pWOdcEsXFVTSiY/Rs+xaeG15pZei66KWeL9nzjlia/ff4/9D536nAjdFlYpplIKUAS1aiq6eodvQ3QYsBrIKZRQRpyByaJeVKyK2BKfdNVuyhgujqQ31nGO4UMNH4iLVO6MSl/CxkckDwOoK1PJFncNiEDNpFTO6ktUFYnmOUdOSSsZxJnR513OFJbCKXTu2h0K8GcoqRrC2CZ4zRc+GVeSs+Wa5ZkiUjNKprTm3rqwLNhsthbhY2wUquMRrW68A0kF41qSKwSwZC1MipQOCcJFVaHuy9NIF9LIPVtPD1GR0XILY5S0MIOUihpMidS06kNtg3z9nB+d/oXPA/idO//OQx54Px78j+83v9nNd9xxebA5++fnyX9MM9U9nvHUP/mDP+SF837+00c/b9ev//j7uiF1OysbQ5bSGdZCuF1b5CTibD0oy389r4X1TrgYZEDcfvLzi17+tvc87ElP2ffgD/BhAMDNbnqzt+y917+/bM/tt9+ep6d/4QuvefObL7jgfB7/0Z/e/6A3vOp2u9wyefI/vyfkJje+0a6PfcQx+77jhc/d9Za3XP6TYlu3Xnfyaae96DWve9zzX3TkZz83z6xD/yFa7c9+/74ffNub/vUZTzdjOsTXv/3tp+36on9/74F963rk6xFP0ywkQZo44+8msKV+DW0h1AIYuUK21hbjNAvfDM4BFFf2yGBGIUXJcuFaMBIzpDG/zSpzBLMgGqTzCvMjzdIWBhjRTEOuSOnGZBAMXes6ZG7h5ABWkiNyaCD4kU5OpPP7uo1FLMlIlcAAqS1LpIhEgGRTvuDaTju/u6gemihCo1jTx4CKAGAdwSS21bQPIMfqyFd4m8bXOxFJlmH5fzNxIlxalBlaRAcToRJts3eH5Dj0cUsvlTZYkNj00TjSqaDWHLgcsi5MbJiY2mCk9tBnaxMTaq6qG7oqvsEBWSRGDdrHGqIhcLpB0PnmbRyDZbSzU+VitvBVCqYCh3ofgpd1kCzWYeuUBGqyD3ZZTVlm6X1INtRIPgJmSTZ3nWJoUQ1SUCBjk3Etq1mrSEp3YX3kYimWdpKD1RobfapteYUBqGWU6BvSK2OpvqCQoqWYjsIC3YVGsRkuXSSrd3UA4gyHatoQOvqo3RhEpJdMTUKNTCLJcg0THYBBoeACus21zRqpgWg6r3WerQdSO3IGke4OmuUs08JLkVO2lrPIJEsPjVlj6YEhoB2hYYzP7NHcA760jjpoHQPcOSSXEIpYSVNKp54S183XvuygD+/+/g+96kNHUq+qiQRoG9ddhhAhbYPM95W3DQ9Wp+ir1ofokr2He1LMNueIfJnUplCHqahKSSm+BBnUnDRphkxaOgFnq7CRt3mnQRowJxiVRt6gOmcn42AgXGnSajJDBx/GVpWiyFVR15XUQQB4ZqgQ07nk1pzWc/NNyc2CzaIEk81ohA2iHNn61NYN67QJFpIj5tGJYF/XzlAw8FRLu0G5LjlbLNWhV1tX3VRpdCq1kmhYxMEyB3glB8c1NlzMs2Wn8llhIPnMsmrWyGXdQ1ZU5CBc5assITEn1DmrhCbCvXlkOEXHIGnUndEIrTjTYxjpqSBZl4qhFBRSr82EwvpQp1Vlk1raFQ2u60HXzDUpHD+A5cFXBcqdthNBUixL08FXiAGgDdHJ1WTpkS48TOF69ZppfdbPz+N+zVXX+ZwdyISaDdoqNqVvkmaTJl+4zmgIujv2YnyvyFSITsIqh6eYOmeQ1PPa1m6TSWpkxty9Z1ek8YM6PYNBbKV4MrZRM8VyrtKHRG2wBm2qQJGscAXQLddF52Tyl0qpZS0xO+e5GkPbCY3ySwlmWXKF0lNcQJklhORnnGZYisBQijAeDHkHYzfP4l00aIVZFk3NnN1pb6OJESLEdrYSGCHFDDp0KQ3M9E21KS1SASUZiAWklbj42mue/54PvvCAj73j2BPJMAo0EaNOY4FboO2kxWhQEebS1Jl8RAi6mGZZBzBZhhIFnanRR3CVptKGMlfj+n7urQWLh3oFAVLp2ubSr0U7+7xff+/cX110xTXai/CpchJNN3UjPJIAHDkT5IJWGAoChXTRLkPIBn3yYlFKEFxjLDkaM5L9SiZneCUG7sx4ITXCwkvj5xKcYnPaetKp6ezhpE44YZRcnm4RmuatOtE3DCIDFgzDEjwqIGl7yc0hpyGT7Q0CiKQYikKYJmx7RmSDkLMbEkLRCAQp1LtgFubgEhlK5kRwS+0oM0PEplabloQQN5WqG2PdslGK/uyCi35wzvm/vPCSnpghfYkhXMWpbgpMXVszMNDGYkQSZPCGaDIlkByofRAs/x+JHDKUl4SZV1Ojs/6ka5dxYr1WTVmiuMqolJelE1crJgVkdSFIGpDICIpw180+zCgqsqGhmWDWQBmOHIfgTGlZJgqnRhGC50MwGQNMxVDhaAuUlMGTin3IeYUcdTAbMyS7UI8OXgYxq95rX5tPBVHpKAbNMcHUQ6roEMVaausWzlIcQKdjxCloj0kf4IPkinL2P+70lxx05Ivff+jlV19hTLOIaJJtpbnSsmLt6xVZ3FYQZEdvZEiZC0hpCImZxv7jWeYQR9BuSe0eaybglGgjTzOyTfAmRK30UWY97zeX/eDnF/z0ggs5KT3do4rwe+oRsCJCh0xSPPoaPluo5YCoFjYA9GWmCppD4GukIZckk5wJLm8M3WQtnjENsblRZ0K1UIrJKdYLb2R0wUwcwBCxmcvc2BbnrMzO5R+MkHTqgERw4cGlCcMI9IxG5xFN3YtDg6CEthTyVNksoK4R1oKl1ZuGmZZiRWsCLuAIHZIrTmRAsrED2diV+SVYNg2pEYQ1MfflAv3rprPq5MSdkCmOAAAQAElEQVQHvescNqkwysRHUKqLLdZ0c6TWLvAe6U0sRYqCcBU1k0GldAS/HI4BqhjzQ0d93NnkRZ/22Mdsv+3vMuF/eLv5jW94ox134PAfn/uLSPrGw2Xf8QbbveOVL/39+/6eiPz6N795wote8Nr37nvh5ZcnJLZ5JqHbqOpMHIiSzMTLNRXSZj//wisP+cwZj9l9z7994hM+9LGPXnb55SJykxvf5LlPfvJxh+z/93/zwAc/4M8/+O533f3u9+T1DIZnUfrn97zrTjvtiJTl5H/1J6A3vtGNdn3Mw084ZP/X7/nSu975d0qtvfezfvSjV7/lLX/xhKe98+APXXjJZf/Z2Rtsv/2uT3zsMQcdcO/7/L6ZXbf1ug8e/sGHPfcZJ37pK/MMqEktQvrS8YayjnHS0VXIoggkClJEc9u5apO6FCkZLcdoY+N71qwQLd3A5tW8wVxHXsHCIjcgCZpLZmo6BKFgpcDoJHjpapwADyM9JRl9qOhgqrLo1AjzJtrBeSZSdMlX5CIVlJNkWkawgM6JRicYM2eEglqVlNYcGmm/nMERJGZP0BaHRgZoT2Rab2Um46m+5oYWzhNRrTDk2Jg7M2SOiqjM1DFoF9GQJjEXulo7LyRjKQIbnIukFdZ17iwuAEQUAFOQqw3mMlePmbOwLCXcqZWQBEIkqihHxlJDzGI0NVOBcPIoGLxnrrkod6V61HD1DFpRcpmwVGknurRClaVHbQlSdcViBbEyaUWnofbFYKRAxK2w8Efyj3AYsoiIqRdbiB4SXiRKuvQsblWKoGYIsqpLsgSmdxBFG1lpM0oyQbNLCXYNTdHgnDqMmtUnaywvSK0yVDOypboMSWoEqHQ0fhTOnXsGwQAS6osbEUk+ebTMgLfobY5Yd2Ep7JxjHMQe11VTJYpklbDUQEleYqEqkRYsnDkHmmd3MpQElrh2c33FtdddNV2nQ5ZlSnWnNL1mvb7wimsy8bPfXLLpa6KUcDopoyvpMJQCOqC0mhU5OwMUMIAZUjPFaaDMs4Yb71hN8FEc0cIbWi9zL1CpVJfJ6uxOpyBQdSNqFK3VopgzPOHCjkDoswc4Wjo5lOqqodoNgchFb8kgjgyNleRnFVUALhFKNIIwtg5S28NAsVqLlSWSYiBHrNJw44iWQUIFn2itpXIkXVHpol4KiqAAlgHhTxGW59AadLdHsCj3LLOMSfBTlbsw0ALlaE1H79bpaPTI3pM/y0WN5K9k1sv141JmYycXhGjjKNrjhDe5lpCipl1tjpJO7FZJB4oAhI7YLwKqCu0kxwlVdMlZpVVD0WIsRATKlXgbQHcXxZqEg16C5hbncmqYQCv50wSTS5uyTxESPgqzwJOoSOD6TYT+JTtrSAJKJKEAk917bPbSXOm1EXC64qI9eLciV8CG5qq2glZZrtyjmVvpZGDW4KxCLwBISnQqVK+elc6qwKhd0BV0RJEF9A21eB1dNem1z0Iw07qYQ53DCSKogIekTaSHOw23SJl5BgJAJNARMwUoJApDMMyEzGwQufqarZddfs3VV1w3tT6Lz2BxlXUd5jqkwryLrxFr97VH954ySaFry1Q6a4gEAezBhEvy1S27pCOhl1917SVXXxeZP77gks05XA2ElNSKJouSoAc1omoaHe8SLtGFJPUQjlQwz5naSCZV59KuSF9JlhIYe8zXTFddtvXaKza10z1xYOEeraU9+O3WOk2mlm6914ClZRoto0S31odwyuJYeiAaYjQxsXy2WblMwjrIYvIpOqWbiJihGMNBKeKOeSYwDAeDZqBsaRl0o3kn8nPQH6ZRcp4ukUVDUqAlI2KCAVhFrc6iFGJJcEII3dy0rQuNWnYShJqXW0b7q/ZCugTQFxamKkkyZ7oTAQyig0BERbbFBbyYPKqUXXqWOZnqkc49kctIW1QsPyKk30x4xYXWBbRJmar3aPC0kITNoutBmULCm+FJGROQUSVqKUW1QgWqRmtNCFMuiWpSjMGMACg1VBTdci7opcLMOGbUZCwifDZxJn5qb7nthAyNIguVoKDBJXXoIBDFEkyxsaCoIXS5SA90WHQkIpGLvto11jp1m4hj2Rr98vV1V2y9pscEEVU2MPBVAA3zYPESEJRIy6bhNXUVxbqiRaZDMjS7aYPSZzGIWAft1j7RgvGnv7o4Mi++6hq+vy7RPdkKSriBSwohdlhnrCFdRTZkGKBLqAwQXsIEdVjALZlxBUZShC9/64Vk2ky/bOvWy7deN3lnkckcjFFnXGgjDcayFRdOKV4tV0SEOALMBDjrQvSaueI0TRFkIjr/zIpUp2WAhygBcWLUIlrnBxWEWJFKoyJZE0hJ6Q2L81UIuhYNNqTC2ASQMGFGdIGkMI8QFIop+TszXSWrYrA+WDNDV85QCV0yaQQGhRWRqrpWnasTpeI+EOksYJ8cay1Dpi3k6CV4MapIKUmYslIMcTOPjd5Gd2nWvM6pjBc6tSOC4ehIVsgZdDm8NBnWom4QhqOXMg0li9GOSsaw4ER6h3sgpcOcwixr9dFEu0UT2n/BhRedfMYZxP6ud77zX//JH5JuPP4f37ffsnG7296O46+98rLf/nt6nmzbb7TjTu99zd4PeuADeTat1wcd/uGHPPnZL/33d3zxq9/45S9+fcWV1657jxIN81Vbr7ngkivP/vmvTj3za+884LCnPH/Pv3z0E17z5jd+7VvfpNOcvvOtb/3cpzzl6AP2feFTn8AHA17hfvc73na3Jz/eGAieAH9w73v/44P+07bD/w0fITquVo958H/6xPv32eff3vDXf/GALVu2ZOaVl/xm/8MOe/jTn/Xm/Q4+79e/4ZXrlf3O7Xf5yFtft/e/Pp9fMfHKOef+/LmveuXub3zHT8+7NIuyjoHJFclwiGf13JhzWPflPwuay9YjXCJLyhg5gj6xYyjBGVqwUhRmbJlSJ9fuQQKJMCWk5zALy2FvPvs0YwppERkiDHHw24PCL/BcPSGWNpCmRVCiVGwpWCVqhCxFpy8EA0kCPu7qgDBIZs0+9I6GaNI1J+NeSDqWduZ3GKVO5q1OIl6hJLhYpMxeN6320duWyTd8c0Bn2Yg20GM3tQ2xcaE8ULpgPfQ2hg5a3KwBpORK21gmHVhMWF59wFwxt0hPBDRMUZA6JdbZJp2kNGYVizbE3VsmOAw6S/ZUFgj6ps1qswJR3hKoFRkGUaUymBajiM7ldgbl8oMJO4el19Wk200+zMQ1+mjGOTUZIXYoJFMwPTlcxaA6I6csHauQVUpx3tN5rLkBKUAyR7uh8yANU7G5RkMPSEjpogAMuqFRMnLufXNa5+xDDCxd4CVMPTh0bLrhgy0EGjKqzkqRmm0F4ZdJJhuoYwrXtavWStJsXyi1SrVMDnUuSHkkRVBRzUzEgy+H+QJ/pgWtmzdaiOSIOkop3crkNWIwmIqpVTEREgdSpShQNG2xf+6xptndQaa+7kNH/NWer3nyv71r6/pqkNWpzfmV/5Zb3OTmT/qrP77bLrfc7e//cgvXP5EL/wahwUOVDdFVFpKvlKYCDWgTwqEAc6EYlvo1x7g5bfG+3YCNKkMWiveUqTMvsJBHPCPTNZEokIHWJraRYh50GspUa1QrvF4RXD4KuJposSww57CmYwcrM0PViqUMNcqIyWRWmbe1Bupooi6VqVJplKagF+SosuTXMqandhCeZSnBmFFBzDJ5NgFsGafKWUz2yYWwB2lRtNMX5XtFVVGGauaVLCHJvYeFW040VRGr5E6LElY4Fp0Z12VznZuzbE3bFCqn7wgwc3tPVpZgCycmNfoQRGYBHQpmvwwgWXObPTXLqBCuUkwdyyYUkygudE11o/ogs2KpGAbWF7qownsQDhaPFIkB84BpiQBGkCqVooVZ6NJb8TZm5xTi4wmyaIHPXJJZkZTB3RSDcp46S59FoIhsRF15AYgZPVJJk6JgzTBeMn4IsRYIzVV1Mh2y4VnDF1MpSwaTLYotKaMXmUiwFesN31fOnKNgNpUkOmjBaNTEmFjBB9YMYcQ6WkpX16pGRUyGdWJyEFf+AEuByc5Q9WJhRrRqA1w623WGNuisxUuJAGWvRCkoX/TWfR+6+xtf+t6PtdkjXKQLkFw4WaVPHDywzPXOepbhhj5iGtJphLM+zBbd+HjTOaOLNKW28JnNQNrqjrfY+QkP/ON73WHn5zzsr+qwXfc6d21BknioDjauolbO1BDLQYumdcYn4OCAdCBSgp/Ggps516T8RGJSmd9z9Kf/9mWve8Tr33L11uuMOeU+t8WA0mg+owfiOGfrZKC2miosYF6bw2eXeVJiAPqntWtJVRBarNUmHsS247mtOys912et52KQ9rGIlJpQWrLqa/MJnZAwRGX5X+95kKA5OgYXiyzCnA4eWMMQqA6Wu67OfI5MkHOYURgqkEeLWyaivJwe8zYHoJ7p2rKk1lpt+e/4MKbau6BLSatiBRCkF+lWXIlZQEMNWgSlQAaRMaRmirv2yXLT+3o99Xl5CMO2TYChVz5Xc7pmQxCDriVyTJEhsqaEIBRZC2oRYWA6uYZoNrk555sUGoORK0tNQC1MSdCyIaWkebCSezOKoDchnmEhKQb+KVFCk9QSvsBYa5FEDSlMAKd1AXplXpLRg83devJMsaoyqlXoQG2sE2ZCEEEkIhAFXjHVMqdGEtkm5NMnv/zVv9jjNfd/6et+/OsLmxeCqD4PMlchXlTXuyemyM2wKWsz7QJajpxygTV4JotxEGJgxUB0ukh3+ISnPOQv73X7W//LA//kLre8SUS4StTFoghSsJFKMbfeyoxRrG4MZbs6FNZ2DJ1Bi8VswCleIWRELg8XKijcf3rer/7THm+4/4te88mvfM1MKqw4QmQWAbiDWyUOOUiugsGPxpWIt9ZBK1B5tchQhK1GCrrQxe5sUVMfN3miuQ6E00uAT2i0lznXlEaYQscc6qRokJ7SkRQwYmnwow78IqmoSEpTaSZpUoXqRCyBObnRMtFiukKpnQ8qlXEu0sswcWGihfjwI0cJfuW1SmhWCdM0gqDLTQtVKg0ssiSDEPVl0bp4kcax4KUOpsmoXjOscwdSAszj6mks8Z698T0FOD2FNykZQgjHGTrbEvMMmqIoodItQkMRms7iQ1w8r9cRxllSFBWgmE999nPTvGTpPzzob26w3Rb8z2/3uutdOOm69fTj8y7gwX+933DHHd+290tesutzb3WLW/L6ZRf9+hNHH/Xk3f71H5/5jEft9txH7/avj93tpY957kv/+Vkv+Oen7/rIJz/r2bu/9D0HHPjVb36rd6KB1fY73O9P/uRNL9vzyPe/5wVPecLNbnyjXEymJLqUnzztC3v/+9uvH/kXf/LH+7z2VTe/8Q2Xe//7fhIoxf76j+77nle/7LB3v/1xj3j4jW96c4q/+uqrP3jEEY/b7QVv3v/g//y/XrE6POEfHnro2970kP+0PC/11j51wgmP3fUF+3300rHsRAAAEABJREFU45t8taYkLYTRMwaFMki4UG8yT+JdSFdJ6BJyprmlVBI8IRKApzB4FlxntIHVx3sgnFTqiQZ0D8w9uShxXoNQhqYoSkquQZbCNAYDVLvqXL2TNAYTkEQsKggRB2bRhEqqtQCVc77JNuIpRGm3JNOqa2nCxqDB9s4JSSqaksMcxNRU3Ygle4QyKUwWrQIzuNLDTFCUSJGkRFeQiyYyEOTlBMttSSVR+WPiYkzhiObzFFP3dQRZwR5CgSLKngK4QyCDJl9FlOJVjIfiQ8YoMJEFKecmjX4JoeQuxCmA5ExQwhBSA6MHE1PhVK1ItW2WCq+lSGiBFUgKAxZdXI29j8fmYQlVREGyOxYuS3qhkPDINuuCddYZLDnUF+JSZgoq1B0hksISwfTWTinsPwEwoFwjBDKJqNA3pY3LmSFMwqyz1Gbt7FghzZAGmlo4QajTvSVcktolks4lHSCMrNY5RLFWk4YnIzVgrKZsarCMGk5JqSpSk3eJZprRApaPDq6RhyYb0EpNSj+QGQIvRazQQhqYYoBYet1GHVy/SZPoXKwJ5UIUVp/6sL8+8mXPfsgf3RMec9e5VxeODQ2IQ6HGoiVcJ3iIoA8619JRekoX3tU0SRiF6rKUr65D0KqFcZSQAWdoiWCHphQGTKNnn5iKToWI1JCkDNOhcvlTzYk/NVF+QrhBAsph5lwspJloBS8g1oK5BMlfrY/UD0ixYVgVvgS3Vcfg+R9oUwfvMgQ9siO6LnYZ0lSsiqwGWElN1+AKhbmWnuJZRAYRFU51NWIRnCIg3AZPp2sMLMAsnSCdD7mToVUh7sVbiV4yFKgoGzmsgELA0DxiMSKKu4Wz+7NgpHTeNtgoWXsspM3UlZLiAqpp4RPAEKe3oGHCUqRD11WWQrK30vpqTvYbZKd7XgBR0Ed0WNIONxfvtfsAeqXCu8GwgkdWYZU9HFrcahML0O6yUArLpmAetVpyaaCFjOAzABrUpSJVSUugShvUiwbPhXc0LVJoap9QJox9wbmUwvUwLZmhnToQki7d4EvtyeulEWtAhIap+uAzk0qm5CpVMoVw0ieWpU5UGC5RgRAWS1lSxEAmci4vmfKSxqDBploYkCV8EhTQoWGZGoAoUAC1hDr+YysMdUt1ep1EoZL4QRBhqlVsQCmslTR2cNiyjqucGtKYEFpAc0QAMAghtBJDynZqu//zIz766hfc//fu2Sm4aYY4shX0FfpIe2oajcxcSiRBHWiXaCmmpp7ozXt6qhh3upCLRqR5Y0ogqW7Zc1ue9JTM7BVelovbfqgTQ7DcSFVR7SJNOp9e3DXpJYqgIjTcxRsJRMLxclZ2hwUILhe5e+0JPuD0znFUqk4/swmBFDjj3bVPyW/6swFJNVk0R9tW0bQQ4Q44I8B4mIgS88VQJKndkQGBaujiHQGsy4kop4CbNF0WpA4OSt2WghKt+KTuiypR1aJhyiC0MKeD6GFTK2u3llBEka6lLWGUJMO3qXYhk5mBlIvrNy4hWYGkRK3sVlmCDI007qYqIgUU3cjWwsCAlpAqUSWZr32dU1+KB6FUxo8axBkLCK1anvhaLKr4QwCTsYJXCWNDNczmLs3YBRjZkn3IXiUxJvs5sfGAMESjMOOLdknGj68JjdXDpKMGzG0JoRiqoHhXdrmclaV4FDOEGIHPxccghMtv/tDtOSW6gOGrbnzzsBIxdMe603aFK7UJBwjc1IV4mDSvjbamgSAp5TKgBdBERtzvbnf66N7P2eOf/m7LMIgKTHhXM5SVFV6VftAqMT5ssxIpL2AQEZB9Cye7hCttFZ0liT1SSuMiYsxiVIjrtyR3uCNBFUxIcP71d4TxQaeNQlY12hPpSxCyiJCCNShck2w0hfC3w5pneAQiKSWBFMYiOmNsplZNaDKdS1qllKrdFhrNjsmTU8zooRhR1xCWCkMY4zLIvNKpIohBuGWaUCPBb0QpQXStN2k9J49GOR7SIzNSOlhFhPKc7Uvrwlhw0txjbkGvXLIDreRk3oRxYXQ5CdnVZhjtB5ETLGYDGkPpxvdoGnAf0dkulCVJdRAOFkklPrM6ke/CYCayKb/HS5phkjVcuFBrYF2IHrpogVmxKOLR5hNPPgmZN7rhDf/2fn+G/6Xtzre7rRKx3r77/e//twLYMp7+mH86/F1vf+5Tn3rTm97s+gFXXXXlT8855ztnnfW173z329///tk/+cmvL/zVenP51/wcsNrY8ke///u7P/8FR+/7rv1e/6p/fNDf3HCHG2Qmb12/M9of/eRxr/73f7/i8ktF5MEP/vu3v2KvG+20w38Zcf24/02fCaGDd7/znV79wt2OfM/b//UZT7/ZzZbHlcsuv/zQjx3x+Oe94D0fPvLKa65btAluv8vOb9lr9/e84fW33vm2vHLJxb9547v3efIee339rB+qhlSfS99qObFIFDTWNuFqbuZyyFJIYjKDs9TF+LUC0w2qJMPcMXXpyWBLGIdxqIBEmwmLBEnCdAAkmUiy7Tej7yEZvKZCyhRtLlibTIiGPitmLgc0OLijBFIWnlAa917AyibKLBNLjkhWMTdMKrMP0ZX1yWImwyMGhJUU5TCqUyMlVZEqy2piULEiPK2h1kUcUF5ClgyJpuqlgDzmwiEg5Cy6IKhwGae1a52idkI1A3OgBeZE5iBcilVLC2inQk4pk2zMugGuG0ffKD6mFhlMqgHpHQ66N6XwwY2yJoA9JRLceL2HhrL7rHRt0WtD6XREaYpJKyVtEKZxmMzI5ouNvWYrComx+0DEqnRm6NBTmlvv5g535nT2nizkc4Mz7yL4rdIIM4JWgqFzJNUSkvS6zGIzyy6aZskyUHpj8oKWq7iqtlpChWNsnus8UYLQBFMu4iAe8KW2sCb5FJhpW4f0jqTJdc4xy0AnrDqx6aNwKWFVaq1ZaxdGRkU4QsYiDDsTbCYjoAxGT+uN7NFIU6CmW7Z0eq1K/CroQw5mKy2rlLKgug1Y3pOoGcazJcyaDTKbeUkkokt0dfBuICK57mCNTKil2jLPFS4WvhKusGbLSTOK6qjKZTjA5VQjAbTA+F5rFB8otDPUGk0yVcA/6TI1XTe0HkHuZHbOBESd9W9lfDQw49DiqICBPGBISCeNUHHQvwKU4lk9hyUGtVd1Tdov1CeqxSrrtEvO6S2bSJcBUjPdOzvDpGhcyFlXaSMIkyygKWUVroh6ZuMijqsOBiqckEpAsGzUUTqGJrUZSB+nEwGHL6oghLLRU4JPcyxqkVplqKR7UejA3ULRkQ4399FJpa6YnRllkWkFVbUL2dRSCLQSlGU0JxAsWkBjOm8tVnJCDdBwsdJ9ZDHiSmiOefYp04mbApIIB6GTFFCio7tGYglwMbcxqNCzmJTBVAdZrIFmM+vbsMeyRdK9bpIcwrkhLWQKRi0AGVLob5ZggMlvqDCVvGgYqHBprw4XbVyNb0BHXp4z5l4nrwu2zF6lhaxVIFwqJBizbLmSask1QDcEoQC9noCkftnmQaZ0kyRiws2AQVGpXJdQUb0YRHlSXEsYTyiHnoEi6WfJ0GRWahAoutFSO/5jG7Ex9rFMWXofWxvauoQLKK/qsGE6lKRg9Er1VNNFmljCCPGYIplowrKcfBaqGiOxDySNJ7HoAY8BWlYHEZLP4OpTwVSlKYH2Tt4XqVoHXQ1iRULBmRQJXqR2F3Fh6YrZYi4CEYAfHDrQK6ovQlhSINi28f5qSKsJGswwDEDJlObWfbF7EOElRXJ8y2gaayMuNaW4mS9POIOF8giuc1uSo88kkmSgIbxmDJI69l6mjs10R0rQSBqQBKWIWZq6JDheG40ygwkLRdeWPqPPEYQ/EiRR90CAtNoIG5WDkUgPbVE5JokFeMbixDbtNaLQP6iEFZDHaukJynMnQ5dZi8KeLfgGvQSXakmhpAS943hVso42Lmq2/RRnO/AVvUYJjORFZOQSFwbWGLfYkDZKIA2dsJQBMhQz+uc5zX0iMJwoSaORdCVpjnu2lqwmSdJmdhA5r+bFaLWHOkGdsrt0Roh+uIG7iBYvhUiyHoApWxOlk8PMTZurzioUKQ01lgCK835lYc/qXjwkOweYsI1L5WGraEaZQGzzdPktXSGqAmE8ULJoFinJ8TLMKAEFEhJ0NgeVYsr7JPvUV21mb1UP+kvaVkVRiKAn3ClvFTY6wUWpArNu0kdgw+qWsiybq3cQlETEjJhYChQeHDSUKDS0RJc5sa7OdxxFvYRKCP7zliLBCRD1wXjy2xtumIdsOnvOQQIEQguMEyWjUoGnhmhqmmIQ4a5A8LW08qVXSRgSYBJECtQwAEMgXRt6syk31rYx+dCDfjqnga0TXjS4i0kUm8ecLbtKegUfYSFuwcgEuDHs5G1GS+1OxmQ66+iM6NIg65TN4Ce00q8Qd527zXycWUACyc/g9+R14TYUHStDpNkKGGMrXSQnwcRso4QURXKvElXVjGky0CC6PaJsqIySyvJvdE4IQY0ikI5oQaai9KFMw8KyLo4eRu1WolqWJcoC+hPxg7N+dN6vfkXH/uC+v3/rm//2KYKn/1P7fe5+tx1ucANO+doPzu5OuTz8P++3usVNX/gvjz35Qwcd+q53PONfnvynf/THd7zTXW5wo5uuVhvb3fCmu9zhd+5xj3v944P/bvddd/3AO99+xpGHHfLm1z/nnx5yh9vuXGr9P8lyj3/f/5A3vue962kSkSc/9rFvedFzdth+u//TsP+HTm91i5vt+vjHfObA9730ebve6MY3oQGXXXH5vgcd/KjnPv/Iz568nhv1Fit/8+d/ctR73/akRz+aDmbml776lcft9rwXvXWfC6+8wkt3mVkzWac7M4/8HVIqCTe6D+GjQo1BKoz9ADJvUmUqozGa4zANA4NYkndLqnHRblVWQsoUjo3OVAmNpq33tcxSOoTRZ+1dmlJXoX1cEHlk9iGm5Z/G8wtoroeiR/M5nRwMreSbCD3x9InX5pDWB3CfFQuBgTS6FRnGq8qYSw/zMCafKKeKdLdNw+Y4eCWDy9BrCTPWgSWtBLIs1BvrXGSFiVWthRWyy5ImBmZtMtkyuoqvDKuinEMq8xncS6a2Isu/shpDh+TMcQgZglLTvQWnypL56mVwGbta4y2wDziEe2T3PiUVABH0U0TBqgKoYzFgrn1mLk1pjdaL8Qtk2UTMBr6IZHlOXZISo0oNtdmUdUv10muvOuMHPzj4xJM/cPwp3/jOT666ais1ZjqInq3n1Uy0PvfV7x943GlHf/Fbk5TIRE+dYHMxl59fcMEHTjzl8JNPu/yKazUU0WZdf+WnP33/cWccdca3KkJy6/nn//Kjx59xyGdOPfnr37ryyivGCNYEj0i64VDtF1z66wNOOP0jp3x569VbPdqvL/71MZ//+gHHf/7jp3/9vIsvdmGlYqi4AMw5JVREa7plSwL36wt/c8wXv/T+k0458HOnfu+XP1/PkzCeRMacK9zmlmoAABAASURBVKmUTveb96M//6UPfPaMM394zhz1FxdedcSp337fZz5/3sVX/PyCCw8+7nP7HXfy+ZddSUyv3ZwOO+XzB5146knf+QbGKdQJ+hGnfmW/479wyrfPzjTG/YKLfn3wp8884NOnn3fRpaCuJRajyCCkhOCKa646+KRTDjjx1LPPuwhpdC9XgtEgtr66fetHPzv05FMPPP7UM7/+vSuvviL5rKNIMS+FDk1DbpY2Z6SCmgpvdEY3kB4gYLAZFlIkBJmqLuYKcpjUgvWwWN5/TSmNCFWuEMA+JBmkvTXhc7G0BDETb0P3QsNTZu0+9iwJgorKiVqLQcRDO5j1UxJ+mpFiumE6QNSlNXYHz5ZU2FzSYZFDuFKD9lgixtmR4N7BHjigLc/JACVHDzCfwzSH2qU0wCPT07oO1A5jQdAQicoeo0CRXrEQVmwbLkAhB5FNkZZS5qyUAYDR6BTW1WblUmJoMUQ3Su6eHtG7tl5zWulckJnqXbxTkav1giiOIagD4tM5vzj/sBM+f8CnT/vsF75+6cWXae+CFAuTNuTy98EIBDVyNzXDAB+EK9g5avSSvXZccc01X/7+95lWh33mjO//+LzrriNo2kFwibIyk6aYjjr96/t/5vPf/fF5hHvrNVu/8f0fH3biaYeccMrp3/vh5nXNmslU516uafGD8355yOlnHHTyKSd85esXXnJpzE2CHjCIJanTh2yCSZRlz+dzf33ex077/EGfPfO4z3/7ksuuC5hL1VB6V+k34gc/O/eQT53y0RNPy85GK5ddds2nTv/GoZ8588Mnff68X//ahMg3zC02+zd+8JMDPnXagZ86/ZKrl7dav7ni6oNPPuPAE8780g/PkRBWCamUHVHzyvX6mz87l8w/8DOnHHf613/56ys2YR2C0mX0YZXD6AArBx/jsKCppFIyaFPrHzjx9P0+efJXv3s2K2SpM9/p/Oi8Cw79zOlHnHBG+tpjky4ffdpXaMlhnz39/F+dr9FKuiCYX/SsLMtEV7jw6iDN4ueXXXjwZz9/yPFfPPuCiwCwxx1y8uf3/+wXjv/Kd6u0qtQfvM7dTME3Yuv5G9/70UGfOeXgT53ytW//oG9uZZxY5VPTAtaEdVc0lHhXzNnP/ul5h3/28x/47Glnfv27V199XabBB/JgHLqSjJbgZl1kFrhmKb1SEdfVgzBPiqS24qGZgg4KJJ/ZathNhFoYonQLpzPCIVXSSk+Ze1/P3GI9SZ9VQiuVYGEy6R+WITMwsVykKB/bhFaopzRo0D7YwBZdtBf3jc4IG/HHxZdddcpXv3Xwp0/5yHGnnXMueeiD9DL0lcZoVnWoVkUE2zbPqaVT2SwJoe3Qlm3Tz/rJzz584qkf+MwprPCXXXd1LGg5ozCVtuYIiW//8JwPHv+Fo8/4Chkwt/zZLy752ClfPuCE0z/5+a8wuSIjbKkyLn7JVVcd/9Vvf+Czp37k1DN/esH56xlMKTrrQ6vDPPKJH7JOXfuSFxddfM2ZX/3OB4877SPHnfmzcy7YbM0JpKQK6Hxov3zrdYee9IX3f/r0n533a4k+b2791lk/Ovik0w8+4dQvn/X9q67b2ifxeZh947Kt7YNnfGW/kz739R+fu81XfOqL3zzsuDOOPPWr660is2IGIkSSbv/qkos//aWvsvEdevJp3z7nnOvSVaQWhSl/CIxwoCQLQcucY3Y4S1aqff4H5xxw/JkfOeNL164zfJTJrrly80OnfOmAE8445/yLZNat101f+dFZB5/4uYM++7kvfOeH11y9NeZZ5gnRIIBZgHG3UCVBuvbZ+xFnfvXAEz/36a9+0zNo+ed/8JP9Pnva+0845dKrL4uYAPIreZ27SEDahRddfORnv3DgcZ//xBe/c+FlV5lH6bM0BhYJA5SGJzbBNDW/4MpLP/uNbxGuj37u8+eef5lvKhikpKDS3DiPpVyih6WJCGgj7Wy9yGwsNjQSsyUrE8tvbX3w4JIfWcN51RA11WAOctTb7D6Brs5zOsWZDVXG0krpsBStYBdoStczEdG9R7ANIZmmILVXanzhIF01+Hy8GnSsSG1guyq9FPpF/8gyYQsIpYpITNScjATPu5WmA7JoFmEYFQpfaR+FthThtdHVnBB1q021W86FuUCyc06YQRZ9Qmwke9Lvr3zju5ubm6r6kL+8P/5Xt5vudIO73e1enH3eL35+7q9+zYP/y534rDY2/uQ+99zzaU847G1vOPGgfb599IfPOvFT3z/m8FMPfs8x733bv++5+7Mf84g/u++9drzBDlZqsEL8N4IuveLK57/23w474mPuPg7Di5/zrJc846l1GJNh/W8G/z9xQahI9QY77vDUR/3j8Qfv9+ynPOVGN74pMi644PxXvfktT/jX3b/4ze/07iTaTjvu9MrnPfPD79vnj37v962UaZ6P+uQn//axT//Yxz+9detWbSGTkF7CIAYPMrNkG9EtQAIvTOoCdwGzA6jGOoehJl8EGMk5agxmUgaUapaF44VBTtEkh0nP1lSmMrQ0FmVnuKUw29EsnRXXMhfFMiRMVMKYvG3yeW6ZWDbRgDgzGUi6LA3kXgnTRorPVjMUYeZK6gdZnqwB0ww+JVCa2mxcrnFmh8w5wAd1W7nWrtJNhUrd0fg0BQpHESzPWka/NALcSFH0zFmM5xKquSHYsBypVZLtdorG+xIYUoqlV8Ln0C4zV/cULRn0RGBpG1K2U1RAiJC4wSW7JD1dVInQ+RiyGycDiiICmEf2cCaJS1VWA7qLLGWBvyAk3aBptZsmml566da3Hn7Mg/d+8/Pf/+F9jjnl/cec9vz3HfEPr91nn+NOuW5aZxGsQJg545NnfPO9x5545Oe/xEBMxKh3TleehP70vAv2//SJHzj+5MuvupoRVdor8a0f/+R9nzzpA8efeu55F+/+jsMf+cr3vuvjn37/p094+aEf+8fXvPW9Rx+3dXNNf1pyGTlkyC8v/c2+nzlpf44//6J3HnLsI/Z852sOO2afT33utR/+5MNe8c5XHfTRy6+6nF6RoU6aKT2VRP74vN+89N0fe/je7371ocfue+xJ7z725Ce+db/Hvv4tn//Wt0MQrHJFkdoz1m0+8sQz9jv2xNO+edZhJ57+qFe/4/WHHb3vsSd875yfn33uue875oT3ffqk7/1i+Sug12yuDzzh1Pd/+oTPfOkbKdmR3v2jJ5z5/mNOOembZylZIbjJDjvs/8kz9jn61OO++g3iCQNclZAHn0fi5G99a99PnXDoZ0+TUXuJvgQpJ/Rjv/qth732PU95xyFvP/az+3zqxF3f/9G/2fsdBx1/Spu3DhLGVcsW9AHUSA5UlXEoY8FQeK0J+RSRDkgW6SW7ogdyVobUumhmQ7QeLNzZmneCFXR+JUkqpuhatz2lmHCiU/5SX2f1Lg5RTRGAUA0a46CVJX4YnKQtkeLefJ7DPRVqGHQs/KUZlt1yjpg1JotWSIfiGL3bQkB4krHcI4buBXOps7HQD2EKUr9GDi5M9NpEuM/0hNRBrSgbRVbAmDRJzKwyFcZljnmIMGkWO0anZ+4gJWi9SDEp+I8tQJxg1BuzyCTS0aSvF1tVmAxeJTO8tehNSqjS8KFriZLSI7989o8e94b3P/I1+7/5Yye/96jP7X3QUX+911tec+hRV15xrXjXbGKzwiOoB9xEFMMgrDgJ9cWRa66Z333U8f/4yrfsvv+HDzj2c/scdcoT/v0Dj3jFu0/9+ve5kpJmxF2tc2nwsZO+8v5jTvva98896pSvPvTlb33aPh9496dP2Pczn/vX/T72T696z5e+fTbfU373R+c/8w0HPPZ1+77j458haV9+4BH/uNdb3/LhT27SozIEIRNDaqYwrF/+wc+f+Mb9HvuGfd/5iRP2PebEPQ/+xIP3fMdrDzz6squuTYEWrYoS8YMf/3y/o0846LiTrrzu2rd+/JMPeenbXnPoZ9531Of2OfK4x7xqnz3e9eGrr7pmWMKXX/reT/Y56tR9jvrcxVddC+DCy69832dO3ufTJ5z5g7NDLQoBxSR+7Jnf+ce93vEvb9rvPcd8dt9PnvCqQ4955Mvetee7Dj/v4is7k0Sg0qoSugws1Xmp1JxblYzczPb+E05+z6dO+uL3zuLz1qhNzb/7k1++96hTDz72tPVV17zvI5955F5vf92hn9r3qJPfecQJ//T697zogA+xJiAypHAB62ONio65yexjSPVzf3Xh+446db+jPnfWL5bmvjnNBx53yns+feKxX/iWIBSushhCWzzypK/+8J/23vcZbz/s3Z848T3HnLDbuz/0iL3e85Xv/FQEpiIOcUa30w+ucc747o+f9Mr3PfmNB77rEye9/9hT99z/qIe86K3v//Dx11yz5uCykmG1LUlIVleWcqpJGMSqlKHaOLAgF8YrBPMYc+G7iuXhooulFsLEnZGCQCG6/IRWHqRKD+1p/tu6lEr+4/pNU0yEH0zp8ORF1QohEhaCBkaSs0I0WMHaaK1Ax0vX0+s//qm/e/Vbdj/oY+879oR9jj7+iW9479Nf9+5v/uCn1GtjGiGKTBRiQJFg0kQGeFVnp+BwjdO/8+MnvmG/p7z1oHd84oT3HnvSiw/62INf/qZ3HPnJq6/bygyL6AxKpJNF+x172kdP+uLPLvzVbu/c/xGv2ef1Hz1un2NPfO0Hj33k3vvs++ETpvV81dXXve3Dxz3iVfu84pCj33fsiW//+Kf/+Q37PPEN+5xz/sVZRhSpxelVDONcVxddfe2bPnjsQ/d624sP+Pj7Pnnyu47+7ONet//jX/X+r/7w515nPojl0FhUrrhufchnznjvUSd+/2e/PPM7P3zEK9/5rHd98P2f/Nz7P/253fb90MNf85Yv/OD7JICJXn7F5gc+dQr1nvDN7y3OAkee8bX9jj3psJNOv+7alvMKc/U5fvyLC1749gMf/dp3/dtHjtn30ye+7ZjPPvGtBzx677d/9kvfyWCkIqsKNpAMcUIyBu/w7pN7IxnO/O5Z7/3USYd97oyt6ylbdscV11530PGn7XPM575/7vlnfPesR7z67c9494f2OfZz7zvmc//67sMf9sr3fvH7P53CWZdMtAiMMimX+VPCNObNdviJX9znkyceeebXIhLAad/90fs+feL7PnUCX5QACyvwH9uvf3PZnu/60KP2fvc7jvzc+z512us+8sm/f8Vb3v2xT85bp96DKxFREXiyjUScf+UVLznoqAe/4j17ffDY/T5z0juPPu4Rr97nGe846Pu/vKAUc7WOZGNAdIoXZKhKFRnQhz4PCeNrkdScHM3TtbmSC2hpAmGzsXDpqSkhRklTznzC7nQgIQiN5PdTdQBPuvRmsW2xJJ3pruhdYtLleZUFhaiUQYwtY8OwWFbrpDajODOKZbkyBqkDd2BAFplDpz7O87Bu5EjSCcw9mOWDhwpl55pZhgoSn6Yu9iAEKqWgDjpKkT44NyhxAAAQAElEQVTCNYOBTQAi7GiqLXKK3hZ36X13/8q3vwXqHIY/uvc9efC/tlPBX//RfTn3qquuPO1LX+HB//vdIT3QoR3C15IzD0SdjNm2//fmUsvZ5/ziuXu/6tQzz+SYG974pm/Ya8+nPfqRZsLT/y/vVCmCnXba8UVPftyR+7z1sQ//h42NLbThrB/9aNe9X/nKd+zzi99cwtOA3OMOt/vAm1/36t1ftPMuy39y4Lprrn7d2/d5xu4v/+LXv917Suf6cgN94Dou3A1YiIc+sU84M4rwdB/cBw0yzfg+uYJCkcswEUgAXcTVkoskAEJeuCQZm1kFRZwP0cWCXYy0WvCWGdwRPoYOvVg3rim4lBJ0Z6IkZwMaLt4ZlBobq9wYUeogbcBmtcbWxY6uMW6ZsRHBK8DsGR7LHrNiU8eWGx6rRHVYZpaWZSLfGStLZF+GiivY6FOdObZYrqwYyMicpM/SGjI9dO6lr21ak8xcQlkVN+/WM2BdyixGjAI9bZ3UAgk+uoSApUuomxokK2RowzBtqX3D+iBs8wtAKgkgOQzNso8C9sVhtNWWwkd/Aq0UJtGXYWww1Zhw1AxMhqaVLnVp3TavnK9++aEf/tApX549dr7JDe99x9ve6w673Hj7Ldet54NOOPNVhx/LdDZZVZhldBZbagWkphfMA4AezhSXrjxe9mJqpY5lGJVxFl66euvmc/Y57Mtnn3v7W9z03ne83V1uc4uNoW6d2kEnnPGuj3/KAFEGdfn7EBHgtm7tNR86+hNf+NZtbnLDe9/5dne73W123LKKzOO/9r3XHHLkNdddI41qUym74ju/OO8F+33s1O/9eCh2x1ve7L53uu3ddrnVdqvh/Muu2vuQj3385FPZ+OdYxTQwuL70amrAF39w9j7HnrT9lo173n7ne9/ptnWU7bYM97rzLve68+1uuP2SC5R2j9vf5j53vt2dbn0L5GhRyKdl5rafFLjZxg473u9ud+SFM8768drWQjTCMbXCzqj5+e+fzVv3uP3Ot7nFjfoYhH3qcchnTnn1oUdfeMXVN93xBve4wy6E+qY7bLee236fPeNdR346+7VKNfRLRL0M61rWAw8wdlnNpcgQ0HU6n0FsTukCltUuPqlC+AcFrmwuEiQjq9NSMblgy8lj8uxdJEgHSc4zYUBrGmkYXefUrqHSKaCCEhkHh/ReVerIaEpVNxK/yyZ0kykjyBploMZiacvcCLIfXgNDV+rIQXIkS9wlg0t/L9qUWaHwIplkEAbVYSW1ICFzMAGqa9Uc1Zn9GlKbjM6mooNQlYqBGFgZm+nECWhBGzFMvfSmrQMGqfwANxGoGpKQFXYxTwsnTMFcUfbxFM1iCPM5s7GqNKtUCLZPRrabnvSN7+y+3+E//OWvd9iyusdtb3XvO+7M7FCRY77y3ZcdcMSVV15DcIMouIF4YtloANciWbMMpqVccXV/2QGfOOTEzzO+t7/FTcilu9/+NtuNldF/9QeOPPEL33BHRs8kQMt0/hzz5W+9+sPHqug977gLabnLTW/Ii7++4pq9Dz3yiDPP2GPfQ3/6q4vvsvMt7nOn2959l1tvvzE2jyPO+NoRp55JAFvVeewo9EdP+PYPdz/wiLPOu5DG3/12t2FS03gm+NFf/M5e7zvs8vV1fSjBfttUghpw5bq98rAjDj/1SzfbaYf73JG03JkVgDdO/95P3/rRE6TkUOSmO+147zvejvuWsfLWdqvxXnfa+T533uVWN70R+beZ41YMHzr+y6869JMXX33dDbfbuNcddia9d77pDQnamd/98b++/UM/u+ASnyUn0G3lg2jFILGRXsky1vplebDNGkpPERfpCffkKRYLX/2BT330tK9vs/C2FP5bC7//kzcfcRwfNedIRrFDGyw4FQHhAlVuMG533zve7r53ut1Ndlj+fwCsTve43W3udafb3XHnW4oYWQKO3KbilK9/7zUfOPbazTUR4/jb3fwmpnLZ1dcxiL++6PIE/6QwwaWH4OjTv/XyA48+9zeX3mj7Lfe4/a2J8C1vuENEHH7Kl950+MfXW5ukFCrwgmnE5thznMa63ki+fHVhqimgi0T+QF3SeSYQXlRP6eRhVItBTbNaY69YeM28cONMZgNLVi11QDGhNBZEpKAVczNwKV+FaQWvzlU1oOZDpiQ67Qogc3ZEsNnkFZddtse+Hz7y9K9n5p1vdbP73vl2v7vLrVbVfnDeRXvsd+SXv/fzZbSxu+Y8Z7BxYdmC5qZEL8ncSz36zG/ufcgx16PBikqqE42tc//gyV967cGfmK5da9fSDdtUc/4l12y+8B2Hfuec8+66yy3ue6edf3fnW65qIT8/fMpX33/kKa858OMfP/PrN95h+3vf6bak0K1vvPwPJH7664tf+4GPUdTg5EpVVAbv0isue/n+h3/izK9eb/x97ni7u912Mf6cCy/dc/+Pf+Gsn2Rxesy1iXfBtu2TX/rOSw44klG+3s7/EuWDj7jo8ouq+XaDsWhT1M43vdG2GbjLLre6N2G57c7MGDGVMvzsV5c8/12HfelH5/Lsrjvf4r53vu3db3vrjaGcf8kVr/nA0R87+QuZk7DHKmNCr9VEi5aqIkLuTCprHl0vfAm+TqFcx/frrxz75e+89KCP0kIS9b53vO1/tvClBx91/hVXuZXQqholW4mQxSAZVDT19re4Gan+O7e+xfXCaT+n88p2qy1mFVqABYQrr7nuJQd8+PM/+MkdbnnT+955l7vd7lbbrwYAh5/6lQ9+7ks9BgeZwlA5Q//ziy554fs+cuI3zqpmv3Obm9/nTruw/pjK1392/gv3P+Inv7pwKMp31FwKmRRLCzdXzaFmZQZjW/ImOU5SM5clUtKQXLexWKFvB9kIVO/wEBqXcFJf1MqwcHslsTzpBWkXzTFP2do8y9xGD85TiVB4sSglxVw8NMMQXKkUqoEGxJO7wei+i5KDzJFmQ6dW+kdmzFEDTKKU7hIiJqAUdHBd04M9RnoXbymLiUkPRVtFFmGCZ6t9GuamM6jLSkJpvwd78NxjDUTBpddede755wO43W1vf+Odlr+7xeP/tf3BD3zAxpbtOPf4U0+7divbMA//d+6Z+dkzvvisl7/m+2cva5edb32b977uVf/wwPupyv9ONf/zshJym1vf+jUvev6h73zbX/zZn1HANK2POf74J7/oJYd/6vi5sVGAhPnnh/ynw9/2xif/82PG7W6AjO997zu7vfxle7zh9T/5xXmZ4exjaVyCFCUHZpXGBwUuXcm5QPYq8yDrwjVHCefaFtJiGSDkQUaSA704aljZRhFX0loMFCgJpJCzWgqXA2THpN21oSRzgcxpEikQskw4uYjWFAG6oJnPFlGECyytvCHOJwOyOkQCEppQSCkizFCubkj4SEyca+RWilK/idTMol1qh7lLW7gvTCl4V4MWLQGam+DcTiKLksv0maeWZK0wh1Fn3okm4lKCV1QW6TQJVE/UgkuVRhFLpRl4DSpp7uoO55I0Ix3YZvACMuf6YqKpQAC4pRsPeG5WrYxihipYrumChKd6DsIwNMuk4eliTUsHv/eXJnn6D8768o/OVZVnPOj+h7/k2Qe/6NkHvWjXA1/83L+8110BnPTtH57x3R8WwBh48H1l5UVAVCRFOq9qT5kFDkls26xEVSCqJQ1ZLnEZZJC3PevxH3jZ8w7c8zkf3PMZ797tcbe/2Q157+gvfvPLP/phGXpaTxAO4cXN5pdec93LH/8Ph71it4P33vXglz17/xc/6w/vtDNvff3H5x7z+S9DvNAPj6uuu/b1H/v0b664+kbbb7zyCf/wwZc887AXP/ODuz/9/bs96e673IKdb79Pn/q17/24Ne1NcxYuWSmE+yVXXfOg+/7u4S/d9YN77XbQy575wPvc7f73ussBez/94Jc/509/9w4cwDb57l2feNCez9z9if9Am2piCF7+7Z6KMPTMB/3JfXnpJ7+66NzfXIrSXdY95kC//Oqrv/mTX/DWX937bqONkiD6X/3OWQcfdzp7/j/86b322/MZB+/13ANf8syD93j6g/7g7j3yk1/81tfO+ikyBcnxMpuS8r0gItFDECBAaim8y3YQvEDIHRrBK9vogGAcukoWkpPsE0ZYSGuX5FANlCUHKIPxhliKWag1QyiVcLYFXwRF0oPMtfhE5aImpdQqI6PK8+ArimjZaQLGLLWomFISL1BHoCVThiktAhgwigxFVYunRkAbBheaQWFpEpzK2t/E+dDRTLBIo/uJjt5iPdvUxojqSHpGN6vYIGLa09m2InsCCWkhBN9TQUsIuwBFZFFa1qi9azQx11qEq2L2GLgQTjaWZZSVWNWZ2ezg27Dao5z/m8ve+tFjr9mc/vDOu+z3/H859MVPO+RlTzvoFc942kPuT+p/86e/+ORpX2d6crXnC7BUuOyeMdOgTIFZ1oM/c8oZ3//JlqHu/ugHHfLyZ39o7+cettfz9t/96be/+U2uXc/7f+qki6+4LHSxbYkolu28S698zP3/4IN7PvuQlz/rwL2fud+Ln/Wkv/pT3rhq6+ZbPn6cCv7tqQ8/9GVP+9Bezzhsj+ft85wncjnIuwee8PnpujkjWo2+gfOvvOStR36axv/RnW/3/hc848CXPu+Alzz7gy9++jP/7n40/hs/OffoU77QRTwLwdJMSuD+xbPPeeZDHnjgS5536B7PPHyPZxzyomfe67a34vUTv/XD31x9tazysX/75wftsevBL9719je7Ea//zq1vvt8eTz5kj2f+81/dn5HqXS6/bOsBxy2v4f78bnc6dI+nf3D3px60+9MOeslzn/OwBxazX1502SGfOn2avfMVFpGyQHWzXoQssiImCuhvjQF/N4nlb6X/50v4wg9//uyH/NWhe+x22Iuf96GXPmv/lz7lnrf7rYUXXHo5lylA0Btd1k0UlhKekn98t989bK9dP/TSXR94r7vQ7O03Vu/a7Sn77/G83R//yMSqQ1yojHfw0wsvucvOt9z/hU879CXPO+Qlux6217Of/Xd/YSpXb07Hfv7ryY0jl3zJH5534buPOnk9t7+851323eNpB+759ANf+uxDX/r8R//FH9GC07599klf/YHESmSVWbNbhqVZVGG4ZxI1mF8aXTxSaEEkrQhxBMdxJ5Oba7ia6lBVuCRNMlwTagJ2LFUVMYOKAgVCAi8OsEQwjxYpIcxMplJYb9l6eglUauE9YPZsjaodGbl+36eO/9bPfnmD1fjqf3nYoS9/xmF7P/vwlzz3fbs+hc91V1y7uc+Rn73qOn7pLS1pLZ1b9Fz/kyEkkWmcdc6v3n3UKQsa97rzwXs++7CXPe9DL3vOYa/c9TEP+ENOIBqnfu0HfCwddWC+XT93s/Udt1u9618fd9hC5qce9pKnve1Z/3yTHZY12MdO++rXfvKLR/35731o72ce+rJnfeAlzztw9+c+9A/vyYln/+ri07/5OmxYcAAAEABJREFU/QxNgtQk5/mgY47/9jbj9378ww948fPYXz748mfv+8InX2/8vp846dprJ00l7pZKCdy/8dNfMsoHvPTph75i1w+8/HkH7bnrMx702ygf/fmvF4k73OrGB7z4GYfu8Zyn/81v/57Oa578yP322vUNz3rydjvcwEfw+eCQE7940ZXX7Ljdxpue+ejDX/6cw1/63MP2eN6+z3varW6009z7wced+ZuLL9WcwuZUwkTdZlpKYfERVUj2yKAxy24pXGCwM1kup8A3r7eQvWwv8vB5B+3xnGf83QNMFx5+6gvfVhk1K2sZC2p1vgcmyaRCbrCxZZ/nPPVDL3ruq//5EYU6gKf+7f0OfcmzD33Jrre++c2gSP5sU3Dterroqmte/aR/OuwVz/3gy5/2wb2e9t7dHnuLnbaPzKO//C3eZWpKgPX2mja/46gTfvKrixmaNz7zsR/a8zkfetnTPrDH09/0lMfQdyLw9o99RtiL+TapuroN0zj2MVM72ZwmIDGZ2lAVTS2pAg2tKZxgDT5rm6t35kVJZq4tU6po1YUuUqpVVXNhUfZkuvArbDKWnI5lKSmpKKkV1cx43KK13qc51p6TdjeqrCHcDTCoiWkkvElMBZNxUaoNkaLMJQyWg5mM47IEA4p0s4lfJqIgWmcjyo6gatFJZIZMWB42N8GFrs6dD04rGq8K1jExpm5PCmcOj37xtRf95qLfAPjje96dTvLgf3m/0Q43+Ks/W/rBT352zglf+vr/spz/y4keccARx7zs3954yW+Wf05zl7vd80PvfNvv3/2uBOj/cvz/Ty7e+653fu9rXv7u1776NrdZloMXXfirN717n+e9+g3nX3gR7UnIzW9205c952kfe/db//D3fp9Xpmk66YzTn/ivu75hv/0uve4q9nkdRJeF1TykVyTpIa7Jh1XuZBHcyf3kRyyFMzoyGngY6a6BAbXwwzhjwhhig2KDa5pEYfAFSaUKrIAti/Bl4Slz8vUUOSmdnMY4ipaFdMV1yfoO8knXsBQNaX1j6oNDKUh8FpnJYBraZWCdUKhNYp1r6+Jm01Amy24BJkJxqm6JltklycYQc1olWslc95xarmfyFQ3WlV47U7FooT81hQmgMK4XbU2NzAepK5SxVKslVmjF50UG2wAKrdNhprmYVOZKga373Lt3gQ9gt3FjzhEHy99SPotFKZ2i0whSIgQLtrR52VOSK1PaTayZoehEQ1AFRFTSl5LHr8I94oZbVs/9xwfcZMcV1axS73zTW+z1qIdfvww683tnZ25mNhdsnSaKVciAwYQAZYeL9lon1eAt7iq0YUJvfEIFlFe4v+pJD/vr+9xzhzpkzrql/PE9b/+6pz5ipy0rvpY+6swvi64pwQYoU52jgaf/7V/80wPut/1OO/JspXaXW97q357+L7e9yU6eefxXvzW3rZZr5PSpz3/tnF9fbKovevSDH/IX993pBhuMiRa99x1u/ZZnP/o2N96BHXH/Y09oM8ejhg7dKJA7Rb3iiY+4xU1vkgI+sZU5hyZbnI90M6HhAO6e3mLuOUH4+J28cv3OKX0FK22Q6d532uXWN9ph7v6F7xKlmE3WNdcSp3zj+3SNDv7xXe4saebWrt48+LjTts7td29zs5c/9RG3v9XNaikqcoub7/TSJz30zre6GXvXx8/8Fs3RyMzmSWpjKjFZmxo2m62ZVTYU48uq6qpOixLCDSgdhF84URHFIGppVVh5M0tgBIYh6jjruCmriSRl1MLB5xAypCJLqkSBFyZVdz5t1bVvzDAnnJglvVCCFAGXar1nTMm56NCUChggECGB4ZCFBp0sLN0tiLea2apk1anopAZUg1Fjo9HhGUlukqHArNnS5qANyYRKWVpCSJ8jZu9z73N6UpWwGYDI8TjTDfxaoC9yHMmnUaeV4G0MtDmd2dNaBrShJpkvSvabQJKak4QbahkG2rhcmGdtawqsB3/qc5deu3nzHbd/1eMefo/b7rzaskFS3Xin7Z/zT3/1gHveJRInf/PsrVdOStMwsy9j2ybhSUjnKrP84lcXfvSMpZX88wP/+JEP+vPtbrBiq6We+9z59m961qM5/FeXX/mFs3/Yhpx5kvxZdnLjZY9/6M43337UXtFvvOPqyQ/7i7vufMvlHvCyf37og//gXht1TNVho/7R7/zO4x7wx7x1zeb65+edT0FKB7UccPxpNP4WN9zhtU9/zD1+57a6qlFl+5vs+Ny/f9D1xp/0te9dfcW1SGHUU3+r+4kP/NMn/93f3miHHempjbjDbW/05L9bFmo98gfn/BzStEzjsNUIJP5ja4xYZaGUFmWefvqzczbnRk4/6aEP3Pk2t/BRcqw73WTHpz30r+5/9ztyzg/OPf/qrVfP4lwodIKlnVWN6wORqijGCiakB7ZtS3ScWoWLW9l2BU970F8842F/c/Mdd0SXEnKnW93kKQ/5c96ihWf/4ryKueRUW66aDjKQlkD20ttqzjKJrgW/9RQQsOS1nL3MY+3Db5Xe8RY3fcdznnSPO91htUW1tB23H/75r//s1jdZ3qecdc4vEi0L+G1fr7LvJ0+5anO63U1vuPfTHn2nnW8l45CD3vjGO7z4kQ++7x138cxPfvVb681AlkzlLyrUChHJtHDtm8Iakc0isbBi21MJco2YzTtAmscMT4RJkW7RMM9YJxdYTpEQc7EZOcfs0RavZMCypSKClXf5TGJpyhhHdEQiRsEKSrEs5DPTJmSQ7517/nHfOItTn/fwBzz0AfcZth/X0kqVP77rXV77L4/k9R//6pJvnf1LSIXoooiXln05GYP9i9m2PuC4U69H49X/8k+33/kmSo9nv/ENtuz2+Idcj8ZnvvK9JqEjzFS2SammL3vMQ/7ozndQNTo8bjfc/w/u8aQHLXyj+Pv97h32fOw/3uzGO6lqFb3FDW/8rIf93Q23X/7/Ct/9+ble5tAeaN875+fHf+MHHP/sh/3lQ//897bfUou11Sh/cLfbv+JJ/8jrNP57P/zlEGWwNHNe4X7HW97k7bs97m632wVWGrDdjhuP+ps/uT7KP/jZLzNoPZGfVNbQ307x7uiwAWUEOXBd+PfP+SVF8d3WA//o9zbKFvbVIvXed/6dFz/6obx+6dXX/ujcC71l09ZKSy7RQbEatWA0MQh95rjrdyaMFRZrIdO3XaGFb93tcXe6w21jVVqV7bff4ZEPvN+ttn2t9MNzfjm4lKAEvvPlckUsw0KRfI22bT5LHfdtcvgRiFbXzAp3x9yQC/psIXs//u8ffv8/4LONim2Mw31+984P/bP7cPwV11x3ySUX8YtarGWOevp3fvbFs5d/qLPHYx/2l39y9y3bC8dvt2XL/e57j90e8Tcc/81zzv/xL/ltgVBw9tREMVGTubmvURv7vRVVK1ZQylwjhl7UK1uFSriH97QGOiGczoKvGHTUsDbrFDW1YkhYmPDJe1jpaqi1iKkUSUvt6cnVY5rH2GLgd+q9oXW+CshOooszYasn42zBii/8YMrxl2YOubxHZebWIYYxbVQZJHlvDgabvxm1HKKbBqgNcKjCBMrbmIGeuk72Sd5I4czkHY6VEFUbpFgY+x3iwosuiwgAN7r5Lfj5f2sXPPUxjzKzCN/vAwdfec3yd3D/bwn8j8kXXnLZc/d+9bv232+a52L2qIf9w8ff8W+32vZG6j+G/P/L72EY/u4B9/vkfvs84/GPH8cVmf35L33xkc/Z7YjjTiChFivFfvdOd/zg2974/je98XY77ywi11xz9SEf+cjfPOrx+x31yavnSSAMmgKiicGZgAUqMJUCSFkiiCS/cpRW0IwXQrVbwpAGMi5IuwyNLuIBICFz5w6mIihkhJgYhIdMQR8zKgLpLDjb5ohCyC/RZTJSeVNyypjgIW6VcqWFuptGDj0kEhEooQOMHOT4hCKN3WQOaTBPCgv0Wdps3cPNhfmjoKmurjQx0XsmC0HSD6ZGpIQLoJ0JJKtetjhzQSU51sWaWggB4luBlj4HCc8byabNXQLizAFU7+KUbK1ZcErSrIB360xfKBOU8HBPzpWkpxmCCcK21ZdrYFGiIELVxSKFYzjSpXpZgW2HCHRey1632Yrr5n7JdZu9Qi21sLX7TW684767PfnIlz3nWX//F1BaH7RnYxyplIGpmUPyt2So0B6zFFy/dXCw8jh+awMP8ft3u6ua1KQxCmF85O53vO2fb/t7U9/42QWXXLnVREaq/g8h9/id2zci0efivWZhhG66w06P+YtlWXbOby776fkXRObV12we++Vv8+C+t7/1w/7oPsP/i73/ALflKs5F0b+qxuieayftrZyRhCQQOSeTwWAwBpNMzjmLDAIEImdQIOecDDZO2ASTbcAm5xyEQAIklNCe3WNU1ft7beHj77vvnnvueT7H977Pvceaq3uEin/VqNFTe8sMFWlwyKx66MH73P4G1yDvH/7i1z844+dYvtJLZa5hF3CZoy6xZcs2Vwq0ALFwIM1aMZfN8eWjillELnpHLIpS4aWfmnKzhEw12q6tq6sdcyR7P/bFr++eo+uQdQyxj37x6+y8yjFHHHXIgaquxX/4y1997cc/Z+dDb3njbVmGntwHk+ms2LYdW/74Olfk0Oe//cPf/W5thFoQnN4qiHERkz7qtJL1EK0GnaGSWbpo65SOZhpK8KDlNYOKSDUrpsZJJdX6olNZDjaqMHGiM1My4N29RScjgHbjSlu2QKpMSJKUGsxArNAiSeH4qOTNJh45eWtOKUOJIWA5mBdRWfSRuRBUjI4Yuo6uY6qA+IjQDo2EYSESRVogWBI4Y9ZMohQXeGYAxJBpmoAZo0dMHjxAzkrJkpGm03KHFINU6YP2UmatyQULB3CVIGg9oE38SZ2jzjAGdHIPWY06cmHPpDUSJbjHcbxZbwjPCy644KNfXr76vu11rnLofgeojVJqgY09uLvd46YLqH561tmnn3V2oQQWlJUc2VQoc0W3aPFXX/hScz9w547b3uC6KqMnIgNYtLjc0Ydc7ejDM/HZb36vw0IW63M522WOvERNkZ40wyJWyZ07txxx0D4cYrvBla/UfAxfBGkpkXLM4Yewn+2iuQ0ZZLP7gt0fuVj4Kx+2/w7TVjTVSpSRRfndN7+f+elZv/n5mb8SVR2LiHE525/d+PpVrRbREkyAJH/YgbsGUw794pzzwNQIiCaYonDxZTZCzVQHwSpCgwqiR57123PnInRILO9pZFXLU+7xp+9+ykNf8MC7DdvG4GwRIiTmnNPpVE/6WUgdC7eFeNJSSXdtwOryvPlzuxtdu1ZTo9GpuorI4Qfvu3UoHDzzt+eWHmPr1dckFkt0DKEUN4mc7i2T3iZVzgVkaemJJVWZKp/B66ZXu/x+O/bSgFB2m8Vix46NS+y7k0O/+O35VJQ3YXbmb3/3mW/+gPd3/6Pr77trR9iyPKVDfWvVO1/vqhz6xk/O+MU55wHFSfwAABAASURBVMbU1WlM6YSboCBp7uoEnPYwh6YMbC4lqblZFk9N43qlFEBSMmEAZKB3GjZ6TgHuGjRfWPclDhu8KdqyCPSNlRzSS0ZYuoqiVIUs9/BQQHVpIjlarwM++C9f5qZ2yQP3/aPrXslFaZIW2qJkyjUuffRxhx4QmZ/+yndlzpWG0V64+LKs1q32PPu3F+6xxj1uer1d23bSppJdGtDLYMPtb7hk7G/+5Izf8Es5TVGDUAqUUo474jBt6q12r54FYccedmihxsA1L3cZG8aJKiaS+ua83967tm1skPdF6zWkq84sd//q81/dI/wtrnM1EhXrkCb0q8QVL3vscZvCf+JrDOSuEN+0Kync+KqX22/XVstegpNnoO3cvrrEPjs49ItzzkcIeQb3EAvigJ1sSY2sDYpqMBGkZCaAX517gc/du0wOvt/omte43LHvfNKD3vWkB17piMOFAeE6u7oniQoXKUAEkEYKl+9pwkur6BL3e3puerUr7r1rJ2HNZgYodmzZdsS+y4GZEpJ19NaCidPmAhe0rr3VVCOaQJfU2EOHcmLzNtJ77z43LFJj/1173eRqV0RKm7XPQ8sNt+FSRxykInzRcO7vfgf0BKfn33/+6x5xxSMOue4Vj5VpohFFi5QhV/U6V7rMYfvuisxPfOXb6bEMSU9zSqEL+w4nGaND1CFYJmjkcq/Qilqx4Vknl9nAtOo1m9DV0REULGLZXnpDcG3CohdIkbEKD6PMWI7wdKVIU8Mc2kXCqtgqmWphzRbawjoG6Y6coA0U0U2aDcAgvnyaWa1aCtSW4OjhMU3Bba1xGTJ0Tm0iaUV5wFRXYesLYNdEz3pAXw3Mq5RPFpWXn0BKRgFWYqIa5n7Wr85ZRoBjDl++BNhz/z/9edzRR/zxLZbT8M9/8Yvnvep1rfv/NKk9C0nhHz79z3c//nGf+tznMnPbjr0e9/BHPPP4hw6rjaTx90z6f9hnAlu3bXvsA+/zppe99IqXu5yonX/euSe95GXPeeXrzz3/gj3CquqNrn31D7z2lY958IMOPOAAdp573rnPf9ELb//gR/z5Rz993gXzFLJO0JFa+1hiUBHoAOKjCjOy1c6MHkXSNCpZiAqB1jEFekRoI9qD6UNlruvGQGfEoEvmCM4XUlJigDS7aFvyG9NTZNdAyajhmlQDcMbwEEQV0QdHYXwxFSQpg5Ud06K4lZjNZ4cz1fchnUdypmSfy7QevGt2AcPG1s7CayzdGC+CdbHGlV2IaKiJmIGIpJYSNnSzpjFH64R9ZgufsfRzuJDw3KJjdmrafZ58HcnKhdXAgNSe3qTH0HVsRfm6PkaGdKLQmNoVEDgwdWnNMosIu6DplpOkJ+OxI1siqWDhBNcqzF4JhpmMoqUiRplF3Q2zsthTl5BrXubYsdh6bg9/6dvf84kv/ug3v9odF/Vh9uqHHbjfpQ479PADDwimCph2aoHNK7Oto3dlomJqlNWko2ORh6Nz6m6tcxmo2mJudgFGaUWslFGGla+M1YrpFY49ioM01Tm/5fvOFRYKyR62oGSYbZ5KC3JVlQpc5dijOcR25q/PiYbfnHPRL89eksAVjz6CQNAWtGKw1OLebHApN7zCcUMprOG+/4uzsOrYMmHg6qWp0CAyllatS0HWNObOMjLDAILNa1TbInUII67WmWv0zW5+ZCBY+4RA1G52jSsV02+efuaPf/EbkSoYfnv+7755+llF9WZXv4zWptrpkq//6EfUbSxaSv3uz3753Z/8/Hs/+/n3z/jV9884+/s//7VvWuqi5r/97dkCRo2zLoaGMv/GMEJXwaNISgsnf4MRALO1eZixwqAozPeNdQ4z+WBmKlC42qSrCeOU1l080jvTaOdhdymHCuf0jC6NWb9AampJFAkTJFYNW2Zdkc1StpSgJN1EzARVOstQxBr8nn0OWKGV0ui/lcgQyNk9JHXQWtVEaLfmOqUSouJJzBBIudgutYbWQeuGYINKGZAgKzB6i2FDpJoITSpFpRbq1ANtztgNWZvQGBGdIhUdxyxj9wFzRcPm1QXzslEYUrjJDH2uRKwAJmoFVjihecxrIcomBr0rBCr4zs9+OrdOG+7Ya9v3fv2bb//izG+ffub3Tj/z+z/51Q9+fPbvLgqAMRxn/fa3hOWyhs+bzVSNAptD5q/94Efs27V9y3nz+oe/+NWPTj/n+z8/67tn/OK7p5/1vZ/8erV54P/lOefPu+mRIRgdnM0mcGawXmXekKy1UHHbWgtH2KRuTKDW1me0FjN1L78fsqgWpbXv/PAn86bw27dufGfB2C9++vNf/OD0M9m+ffoZF0wT6cwev/rtb3RgSSCgzliuVdEywAZitUdiTmJsqKYALmxMMUMDv/+0Dl1mA1rKMNi4RXSVMoaWvOIxR+67bakmT37P373qvR/63k/OXF+wu3aiKfbfd8slDz/w8MMPLnWb2haTDZlHaaaNcOFPEBRAAL6HOMmnDVFMAEnsubauKB9yFBk1q01p8Xvhd8/Et2QHAT0rkSk9B8SKpTAmxJy94/dzscRjbcSEZY7OPelijURkEYHwaWQLKmpCHC/sGZiSNI+I46vf57cr4LVltfGDn5/x/Z///Ps//+UPTv/Fj376i++dcSaP1Bxadz/9V2cG/dS51SFUmKNLxkb4aIlK1cWFHMiluPK4shLmplJloGrVdLCsHMuEGOEbiqRK6NG5Z0ydyg9NVnMprTKycnIAySmoiYqkYUVmYSeGYuPA8CnRizeUSEZ5hdRuEl//3oLSfXduP/3X537/9LN+cPqvfvjTs793+q++87NfMpsNw0AKZ5z929g9q0/czfl4ccsiMWSrX/v2Yg1KvNrY8t0zzvwhw+TnZ33vF7/68em//tnPf92acz6t8etfn530Tdi/eUESS0ZQbiuDzEXWSRyZLjLXcZhEmtsmLCbhnosusgxRSb6PEJYDNfeE2H47d/zy1xf84IzffPf0M7/1s19+j1r87MzTz/r1xcL/5mzH5Fm9XxwpJopgFurmMwG9CitaRRbiF/UOo1tSiTumR4q+p2nWBUnrRFdgi5XrXu5SHPnCt3/4uNPe+ulvfuuMC85t2qz2raNd6hIHXOqwg/bZsVN4ePdVzNLWvbdZMgQ9l0tCSYYElqakSVPQK2nLM3+WecUkLd1655OUBgmOXNS9FXbNwU9WLLYs7tCg/CqEDBkyX3PmnlaCCVn4skgDkhczVZFCDVMYbbPrOtDB9csKj5xSYsN8letp9w9+dgZ7D9pnr9N/ftYPf/LL7/34rO/+9Jff+/kv+dLtl+ecV8si8Om//jWiDe5WAoUkJcRsZcKjLSSaOBE7N2LTqfeS2WHkZmKmGygbHQMHXZURjAUjM3WdU3gC7/COhHiJYFnFwyDfOHibItdNpolLgmdczexWpzpkKZJFo2YwpiRD0CRm9A4SS02hFTKyuyStmgIV8I+n9hDMZNSTLmL2y+iybpXWCYABxT2IsEmWWT2jaVKslrxogAH0E4UESUGxXDQ00ZXVoS3iq99c3lmy/9AD9uPn/4/N1B52lzscePBy5vnrv//7U9/8ttb6/zTNb3z/h094/osf/6znnMFXucAlLnHkq5590n1ue0stezT5nyb8v2OhAFe+zNGve94z73/3uzPaI+Kd73/fI5/+rLN+c/a/sd+ysXrgnW7/ntNefuc7/tm2rVvZ/53vffcxz3rm3R77pL/96GeniSWsMOkY3+8I/Zxg+UGIiBAusAabTbym1GbaAO6MmcgsWMZTIswDCZYOyVAEDyQQ2YQVOEW6ojMevJdsVVhCa1qJQXlHCAXXRQweNucSd5Nar8YoxpJ8FrSBgEyQ3lKmVu4l3m1hn1wFzET/EnQMJ4iDtAeoGn8L8R2LuIooliaWKAy7UTEmhjmUkosms0NWKg1eArCpqKJ0Ue6BVLilRwapWIEZu2dR95K9iJcCxsWSbhK6yEDDRScRAYUMrnSyqFQWQMmoycDj+aUzKpfQlugivZhXc9MAnElCECYiVDJ6n9vcoqcKuBFd7zKXv/tNrqsq3/v5mc9921/f9bmvu8+L3/Kit//tF77+/R5BtT2xvDZymJSL1hOZ0lfLL5caWWisGiQlSTU5yCYdsmjI3VkutgI5Zu9QqIlBFCKq+++9zZTnLj/73PO765TqXL3ZVNIQEknl+Jt96dh76/Yt1Xj/y9+cq1nP+d10/u6Zj5c9fD8RQWT06BlSREuF6K6de1EvTjj3vAtCssNdL5YHFNZmJFXkWVHnWnIsWYWI4/yLm5ksFcPozfrsSbUvHuDqVJFMUyuXueRhxxy0pKCPffnrFII1/Je+9cMLd6/5DvCqlz2GmSoW3uXHZ53L1VOPh7zybbd7zqtu/+xX/tmzTr7b0192jxNPufvTXnHqBz7CUbZzfndh0joKuk7FF1PSKCWldtVuml7o0qTjjBYSZU4Gi8shQj2Q/KOUioQoigjShJTAK7UzKteWLiLOo4tQ30L54Yyiwv2JuwMzcw8E14mA3Gmb5jonqz10pYdpW44Gt4NBwySLQ+mrak1oTvUldmE9eUfOEIUyYqGRkmBDOFvLmIIQASmacg6NyXncnKRbTiuekpnpnUSHlIHeLKas1lkO1xk89tECIqTLWEgG3cxCGcwdASEcyIFDoPgobiJjYgXhlYaJGccrGGZ8JsAYgULRaGGuXUIvBpMzzj7fIzzyhe/+uzs98+V3POmld3jaS2/3tNNu9+xX3e45pz3iVW/H5nXOhRdKAa0ri47Yc20S7J742W8Wd/OEc49nvPzuT3vJXU982Z8947TbP+OVt3v6abd7+smf+eb3Of93u9cTj0qUVPh0caNrprA5zGMEmAxoo9wztghF1y2HiJb0SumbJl0G3fnqISPxk1+f67Ep/Hv//g7PfDXFvv1TX3a3p73k7ie+9F4nnXL8K9+5zAaWF0++7rQeYk8PP8NaA+sA7O7wiXXQki+Xfmj2GnPxXvz3oqZ7RoBQ0XQ4Mb731i1Pucdtd27dcs4Fv3vLhz5z32e/4kEvfsUTXvvWv/7nL5517u+6JVSU3oYFzIoW1UHpI5fOBmuodASWizovPjLii4Rj6QIyexe++PHFNtYXnNKLm2MUpIc4VCDhjfVM0I5dqleNoXmZvSxg25zMIBpqLxuNGbtTfdfN7uWDeCFKJCvoFJIS/LsrTbpq/OzXv93TecLr3v1nTz357k99xT2e9oq7P43hfBrj+sS3fXDP6EW7L0JG0jaUXJH8ZEXRKWPSsTKQWtqCxJnyVNAuLA5qqooQy1ZhzDdTJt9z5GhlQ2oV5Z7ZuYg3S0OIJLuibGKePNKFiUDopO7RJ2RPkrMQNnWYi0Qr0gh9YfWXp5/N78rw+e/8+B7PfO3dT3wVE9HdT3rZXZ/30js8/6V/9tyTv/qj06nLuRf+jojq0UMudgQ7SahDOuyHv15wThQ/5Y3vvtOzTr7D0151u5Ok3k2nAAAQAElEQVRecfvnnXy7577sjs94+Ulv/QAns53z29+1dXYGBg3BZywKW0Ed3WpaqhC72TdHEJZQMLvUTBqHMVArGS4rW3eVMTC00D3Cf+47P7r7c0+523NOuduzXnnnEwn4V9/hxFfd86RT/k34FgZNK8vyPfSdtpmDL/VMrQoTSxXieHPMJSGcqjQZKMRmp0GsBVqLnAJeRO95yxte69LLW7ZPfeO7Dzv1Tfd/wSkPf/Fr3/a3H//B6b80q7pRsbKoIupYlrU2R8ySoQE4q3lTbFLmR8moLcsscOHj0hLRE63n7FzFNVVSl4HNn2I2WClKuARFDRUGZeWmn3PS81QgN+cBBMfgqanWVQ1a2AUgMkkXNlvptK1RPTBvXrwqTIIsxKbd81mb/77f33/xW3d53mvu9LzX3v45r77d00+549NeyoR2/+e9+kdn/obUfnv+helwCt8yu3vAlYp6Kz4bGpARPWPNTWaUsLqk6Dk906uiUi5hmttCo5caVsJCxTkAHV0L319OGUF66sbfzTu805BpkaYYikipGWP6qFPlYktuECkMf0kvIdLMgycOoflTO5+zN5+6O1tyWi4xMoU2zg2vQ6sbs9Xd4GsvS6f/XAQy0nySSacI8dRk6KpOA/elyGI08TYtgyLxLsLDc8lP+jtvPzp9Oe1t23v/ww7cH/8R1yUOOeiEhz1wtVpFxBvf9e5nnfaac867+GuE/0HyXPjN7//oyS99xX0e84QPffSj8zxpHW/3J7d+x0tfcPUrXDYhSPkfJPWfO00ge23f/uh73+XU55x04IEHUpgvfPnLx5/0nHPOO5/3e1oCB+y37zMecr93nPLSW93sD0spVP9r3/jGE57x3Ps/8oRPfvpLDB1ARAD4sncEvc6w6akzo8B0Xhw70/ccF/FaopZuBFoYuEs5nS6D2+CrIVSXSZpamTyWQF9gkIQsQi1lCawRsfKkEECDrZFsTDsu0qXMhBUxXWVzB1JWROGaiaIylNBxzjIT3amDmIowWDo3tqGlOn1G7iIuAUlJeCbvpSqTheYSo9BEjTRuF9I5hXgVNQUTXnFubeRcslCMZhqLPZAoglKrFTKk7gglKe4ggzSVjs0jfgYXJeMM2YOVX0kTnqTIR8SNNACYJPVgmg+dJlmvtffI0IVeN6UNXSkSmqMJwhAavfdkiU4jqRaTbbU+8ja3fP3j7nerq192r60bF+6evvGTM971j59/9Cvf8dCXv+mrP/ppMM84lUaKbFmtyDQhLVdRKD6kRClTjSjBkaUVSVY0aJMpdbm4l1yxmIcqIcSZUVNpAFEImMs41jH34uzlM2CBMWjs2rVGbnYBEXSYbj6Y6ipxMfEyjj4MqUMnBCkqksOWZkMVEc5nRSjLMU4JQT6yLacO732ONvM7m1Xk0IV1xEx3/54bEhLc2129UWExTy5kE4jGIH1MjFJs586t19/8e7qf/NI3a85Dtk999dseeZ3LHbtl63ZuN9lE5nrhunHt/2XrSZMuRqGeFnSXJ5UqLTccG+GrOeuURAicfl88XInwxQ4LE6TnYkGV1CCMMTiGTIKewLN02rdoJHGmOsGaVpHRckgQcMFqY4a3zBZ8LOgKR6S7A/Q9TJQq08/iARh0I20QFclM+pYrfe2drixBkiHZE5veKJydRdXETMtSjszZp/QplhI1G5dI0jLigqmCBg9MsRTNqQ2D5jhKGcJLi9LTMhl8lFtUxMmCH0q+LCJ06uquC6mETTk6V1tu1LYaOzUvIXVmY1BxKSmaZC25Mqw0S2lmUUR+t14v0pDKf7e1TB9YatG6C8c9c2O5ZHasFxvt6fs//XSihHGpnPrfsEH/dREG6JwSHVisH3tIZO0mTUsTm6Q2HaFF9gwZJMF4G89f0yR7+v57n51kW/jcMy/WNRLdo4dzx85WMuvFpAFx2czZqa5m2HMtC2diE70HqTlISG5y5Su++YSH3vvm1z3igH0i83tnnPUPX/z6k1///ns867V/9+l/FaQqpxFWyVxGF6KYgjATggDMFpu+I/0kb/NSvBBYwY6lNZ+zz/xDoJJxUTHYMgB45m5m+bJCVn7JOEx93O2rNUlrr8xQzFpjLlG1TCftEdwJMkr0wgzOjqWfTKl3JwCLdr4OZ+yLLHa9eHAKNJHGw+XS8X/1E4uyIFrdFs7qIiE9Zc4aMJrRrAn45fg86DyIj1iUUWhFGGOPQdelR06wpsy0VrldDFWFfpFkdDH9ZReEahhZ7JGHoesojsgecLpAhDYNSCQDH0wKmVgOP1OWRoN67Fn33/nszmq5TpBGa+yZJ3DrDLWmfsH036C7Z/D/62cjRNK7z2R/8QRiQcQsbOwyuFRqcbEwklnQBzIRqJqWUn+fyUW1oa5zuHDy+X9M+M6vaxDVLpYzBVNiDdbmlkIEaUATF18dBJwGpUl2XtytdE2I++bBoTHC/dD9tr/04X920j3/9JrHHsGVZ517wZe//5OTP/Dh+77o9Se+6f1nn/c71+w2Z1ljaMJgCnSGWyNlIR46+XLZZqNbJNycqU02O4BEWfeYu3v21JoyhIBybw6riFSGjVRO84VQUXp9ZqroWKb9ngr4FFW6utOgVbRU9mDzmpO261pbLcx4XaOLX6xsKAyLQ/q6EX6b0/97H/RsWnFbXpUhKGgXeGYSgx3pkkHzakZBmEISTPcTfEILMOKD/lQ3ep8BsKHgd/jFazUbqo6aI/FPKaTQ8VwslFtCC5bqazMWRIX4l1mFRsi1Lc7NDM05I5Yso7UwTfcVY6VHwHvpwbiKybn/LIUQ+9jPBNR7CdrD+OZ6zFQkJL3IbJJQQR1chjkHdtmoWsdAnWFhxkKQ3+doyUzhQqGLZfeA2eAXXHTh9368/C2fyx91iVq5Ef73TPk/PnbT61zzpMc+Zqi1u7/vrz54m/s/+JXveO8Pf3ZG64zWRe/M/0aM9xHBmXzN/KVvf/8V7/7AXY5/4l0f/si/+Ku/vOCC8wmno4486hXPPPFZj3rwvvss/33hf1v5/4Y7KqpqN7j61d74wudf4rDDKPKXv/71+z/hyef8u7MKO6F6qUse9cInPe4drzz12te4JhPw3OZ/+fKXH/jkJ93+4Y/8+D9/kZarqeZI7z2mCFemgIR7B4sV7oyJJW6taDDihowBMJLlvLYlsCWYqTxtnjUYZ5uzXKsXk1KV+FGREmCz1rV161lmtWDcjB6mMNKrkZhAPllUt2QZvRDILTNSBNwa0qQLN5ACIeyQzJMrZ/YgxwTRUJxY1p5DBydySSvKDSZzNiagPrU5okOxeYVgEZXxo2kREpak7yKN2NdcZEVn6CqTkNBiIICtiIERnV2VFUrvGWuqIwUblQHrtUwMCzHurdyYMhn+DB9AZoEvSzd5UjoRrcmKxtlBH6aC5u6ZpNa98kGUwmQpUihcs2gkYnKNy17iOQ+6w98963Fvf8JDH3yrm13m0AMi48s//OnDTnnzd04/gzuFgceAQooARKTWhcxEWQDMqTMkeLc0xuvYueO7UVdZevgTkGRT7zmvva3dm/cLL5o92U3DZaGPKT5+vyAt+8ps9EF6kRRQycCSXkhNCIOVrLaw/uATfree6OpOhk6xBKTpWVJ3X7CbEcoZe23dgl7KPFYS4vNmW5JUoi/8hUb01vp6CucWlpvjCO+9dZYndGjWolb29POTyqNXd11sL3LTa1xu+2rkG6Yf/+yMc87d/Y0f/4Jzbnq1y5YIc6UDcojV5n9HccCOrR964RM+/vKTPvyiZ3zkxU/+5MlP+OxpT/jMy5/wqZc98bMnP+WfTnnqlY+9VMSWhg3QR0iLHpup3OGzRdOg6zvaxDyfTdOpUaGTmznnKTJCJ8/1jPmiCn5/0oLQbClOm5WhEGoFqQHuKXAqxkizrBWDQUpwJFkgjOZmnVFrqtV4ekdBqDTh3jt4H8RVFhzLAPrGXaYJ63XOFwW3IUMdQEnQvM996phM6Bct3F1XkFFQbLahFwviOWSeZXbqVSxzRPAXLBfxomcs+FCIUEWgEz3RRcRG1aIVZXBloBd0hEtzm325pfGRiyoCDIqqkiIElxsgSWAVWfCpIak9uP+ol0KNi8M8Y2OoALYM9Q2Puu8nTj7pI6c94x9OfdJHXvGkT5266amXPOkjL3vGh0975m1udqMOTSgn72kZcNVmdSiDydJ/4yse+7GTn/zpk0/47MlP++wrnk7/fvbFJ3zyRSd++CXP+MjLnvqOZz1k+04W0LP0vocCP32oPhphHxLMOuGZ7N1soxVTaDqNXooWGTJtcwSmBahpNmyM7FmEf/R9PvXykz7+0md+8sUnfvpFT/vsi57+mZNP+uQrTvrEK0761CtOuvPNbmAYSq/i2HORDSNNuy1WAAm60CmbY/xNaBRBhdVNT2x2Yx1Yz8s6slUZkYOEHHHAgY+4863efeKD/+oZD332Pf/kxle61KqWM8857/lv/9u/+PCn4IEpcu6xeEwaxknHptZEOjEjv9dVoKYDQjIEgs2Ls2W3E1Y+WY+aGAy2ZwGFoCMCqqiWtEur2QFvJpylI2npxVQAZME8ah8KtVTAgn1sZK7ooevJJhZYglF0BegyxF+EI3ctdOU+AezcsnrH4x/ykRc85SOnPONjrzjxE6c+5VOnPPXTJ5/0Ty896Z9OeeY/veKZf3idK88bWI99XSdxH8hEi5casviwikBoe4UV8kV0ndvyd4+oMjl2l2mt1DNbEtQSSdtLxeJ15cIU6SLOWBlFVgWrCkAUtehKwNjNmtyi+mDgE3Mh6c3R3CM7uxSg/RkJguW603Wv/qkXPeNTL3jyP77gKR974dM+/dIT/+mlT//sy57+iVNP+ugrTnrtCY+QoXZbRXDdMn/5KU2HlqWtaMVNa7ztaY/8+1NO/IeXP+Hjpz3xk6c99eOnnvix0576qVc+9VOveOrHTzvhRle9vMGrdSgNsRBIpnzf0nzVwqIGhhB6bxmBRBdvtLd368wJshJdFOSgqc2JhNSNjd8Lf+V/fMEJH3/RSZ98yTM/+dIT//ElJ37kJc/48EtP+sRLn/mZF530hsc80lB1CmkX80VKS76aofGzEyESTQVgw3IFEdCbt6RXft+39NviKEHpTWJ2QnH7xsYtr3eVFz383n/znMee+tC73e0m1z5w57bfTdNf/fNXnvSKd5xz7oUx90LpxYaxMm6LtiJrSzd6bnHCQpU/SbwaQf5vEvBm8R6RrxFU28LQC8XmZEqkNI3H7Cnho8815/TeemeuRRdOVhLgVDYRSEmxaYYzbAvNxt6lMTbmCHcWYRxCzODaZQDLYlKG962DYvN66K1v+I+vePI/vuLET5/29M+e+qRPnvLEj77syZ94yUmffdFJn33hM05+5P2ootSKOmrRYr16LIpOmmgCMARNmZvCaLXYnVgjFiNQqbnTMz34Ttkm1ebaE85lZjYYo5bYDTFLK6IDam1qWZSgHgYro6ootyRi2w0OTQAAEABJREFU2iJoI6W8Zm6AdMaCFHgVmBiYm6TwIdRQVEuwSQr9nz3gUGjGMleFhFIdttiB/U5ZEy2RXa2XIYuQjSmniUjpRWZN9izfeXGZQqXARGkNi42M733/ZxdesLzdv9LlL1vM8B90qchtbnqjF5/41H0PODgzf/3rX53yutf98T3vfeO73vuej33yI0581gkveunTX3rKs04+7aSXnfykF77kgSc8/U8e8Ihr3eYOd33IQ0999au++rWvTNNkZvvvu++Tj3/0+1/58hte++pW6n+QdP85ZI467JDXPP85Rx+5/I3hb333e3d5xGM++9Vv0Tj/XhpVveIxR7/5+c9800tedJ1rXHMYRwbOv37ly/d45KPu+OBHfuQzn5unrs5dAIhCB0ofYxaGSkhGqVKLgqnE05mkiIJagkiLBXCQJYM1tL40z2SM96Iig6UavKBBMiUaomeLoTOtmYjNxZoOrKQZ2ZBAJHHpglAwcQfDnaE9BZKKELi+hHQtqCZVUDLQO9eIAHV2bt6apUldS2WekYwBTbHs0j0kunBbSIwmViAJmS1JFpEZEZ4RljF4DhmjQ1tk95bSWGakqRSTQbNaqgUlnEwmh8+OmU/JYV00FaGmHWkNldkyMsiik7IvEVdczWvNwYRbOHVUdYpNYZnSZnAtGNFAWMUw2lioqPksbS3topwi3eqw184dlz7qyHv/8U3fdNKjn3v/O24d63m7p7d96LNVq4H0sHs9kakIqCqQPZlaTHJASmJTZUAyjRNUixQ+YfPqWBzSaf4UF2minvLTX54dmWOtB+y7t2BxgGxO3vwQTYtUKCETUZPDp5/9q6kvWl/igANEcp8dW/bdvoWTv/mTs7J5d9D8pVYK4ptm//kvf82tmTT327lT3TRUIJy/p0VkCIrlRs51mmOd0lFC/m1GUr9gVp1QYYMMY9XNQU5QQRZq46BzMw4/eJ8jDti7uf/zN370le///Kxzz9u1ZXX1yx6zoVxnSOvmRx+8N/mee9HuzNi5c2OvHVu2j1u2jVt2bN3gdzJ779q617ZxxzgmDeUWjRnQKrEiKbJIGgCVIm7gQid2pGssdy1ksphK9j0Ykpw9GVqcHb3n3FrMM5xoNWOsZa6kF9D6EszOorMQ0ZV2KMy3UkOGYrWKqYt1rdwboJTDo+Rk3M40F6TyiADOlZ4IJ3WvnS+taChNHbIs9BxoPYlzOmaRlMBgUNOCy6dVKKVQElCahW6JWloVnr1EGRMpVC8NKIQZ80ITxsJsflFta+tNkmATMNpKjSwxYUEVOViGkzEhgBlJtgLYlGyevS2rABB8QQqhQsVaeCO0UyOKd4vIS+6/VzGdej/nd+uNcXHTznHHvsNq73Fj17hl18a2XdtXe23bGGtRGEng91cKXDTGzOJHH7CL3b8678K6Zdi6145te+3YuW37jq079tqyZedq2LV9Y+euLVu3jAbQicWpDngxLFWk0AIWygoVTvxdLDUwpNUYNceCYYiVdku6lcuA3fRxDdU46oC9Lxb+gt0b48a2rXXndtu1re7ca8te27bu2rpl322rvbeNq3EQG0qOAsPmlQrVUjCOwFC78Vvm2jdHoOkiAeUNzGVPJz+7dnqCBIyIiSGdEguNrC7bZHXEvvv/6XWu+tJH3OvdJz7yyP13NY93/uPnLvjt+aWnzSG0O2g8zi1ZJIZwczqBZNnIQykQgWrkyif2IQI1yxhCi4VrSgkabxmhCGqhmmAfm1oyITUNlwUJy6wai/Sbk9VVuslc61RrS8J0s5sfaW3K7BE9wEkLWZAbRwCGS09E5rH7L4H8u2ne3eddO2jVLdu3bt++fdu2Ldu20eCrLXutNvbaukF4U1I2RC8epUOpSxFI0pbp4lJnqxNDLol7IClKRwCOPmeLJvCqUSKD2I3kQuVKS5qhQVyBylw+uNFUBoCkhwGlcsRhGhu2DIFmMV3+DYa67jqhiGJALNAJv+QBiy5nnnf+uLGxZeuO7dt37Nprx147tu/YunXrauu2YWP7xpYtW0ZhbuBPFvz+koRJZwAcfchCgdZYt/Ve2+tee23ZubF1n2EvQn3btr22buy1a+tq323DsMoiLA9V8mIS/N1YmU5GJ4U4pU9cTF9ptW7dyxrWaQXhEiq4/LpoWkN5/ExV+73wF27fOu7YtmVjy7gat2wdt+0Yt+zaWO3cqDu2rVYbY8CZC9NJ5OImOohUBb3JSj2WXLDQXkZLmNDAy0/IJnaWXo4KlEtiLGnCoqEti0E4bpX9du243uWPe/KdbvW3z33C3Tb/KYV/+cHPPv+V7yZjwzmZocp36UmHGKlH2mSFrsbFVwvtRZg/vcbFXQBZi6RZmHaxTPoMe64E0dmyzRE+qzWYRwouEp1yCBQxhe2ZKpCB2XgadF6FWxrp0+rLYAZzXZm6zqm0s/iocrHxBS6YUOctG3Lo3js4+xdnn79ly44dW7fs3DbstX3ca8eWnbt2Lp/b2LN1y2pjMIwKG/hDnoRvSh+lVYlYtjFLTZb4MmYIukuTslYNbcCcwTTtGRGNirH5Zv7mFsaJ3o3OWXS3njpBegFtUrUPIJzAQ7mruVt0qItRBGgVEzUU49ssbuezgFyR7EApFeOQQ81hkKEq99eyVFrEZFUOVoo4V7lIy+zsA9KrOyafp6UUc2iAsEyPWYWuTaV4FLgHXc0Sxcw0B4BhqXxtU1Lsn77wr9i8rnXFK2z+/o/7ENz8etd+58tecMdb33qvvZZ/8YOkf/2rs7745S997NOf/ou/+9B7/uqv3/EXH3zXB//mgx/6+8987nM//tH3ZwYPJwFbt2272pWv9MzHP+5v3vS6e97mFquNDYFsjvy/+UNwxMEHvvwZT7vspY6lGj/92U8f+eQnv/fvPtx4dODzvzUBk8c1rnT51zz7xFNOevq1r36NYeCWBx5X7vuYx97p0Y/+wEc/ft55LHVMRUqyqNoIXbVxpWOtEJs7fIbMUFG6u8ly26wwGTqhx+Q7x4IJYU5bSr/E4LFqvXrCtYOgA9dVQlKKyyqzqI86rRSEDnOLOKS5xOYvnUJ6empjTDlxFm20XjnTJHRxmkRaUrnITAagYaCUJZP0ISJKdYFOuQ1kUIe0klxLKgxbXXZK4jxJpUgzykclvFqYa3Sblp0nKJF6QtESDZlGAjp0MwbAoNxkGPIs0KbRpw1pihnh3mTqkrNoiAJguTKVVY+N6AQb6x6FIACxbpU8War2zrkmmtSflJtYA7/CLCQTZ539m6e84R1PfO07P/7F7wRXKgO5D6vJTG5w9Ssffejyvyn4wem/bBS4zei7t29Z/rLsPLdpWkvXpGyxwcIpx2nKCZuXQFIqzGAK0sSeKz2zw6nuwJzA7bTjC99e/irnlnHYe5+9l/8kfqBvcs/syAhDz/TeVNZiu8PWn/z6tzlaVA7ee4eu+15btl5i8z/7/MRXvnXRdJHzlUyB0wnMkAXMHf/4tW/1iO1bVpc/7ACh6wQzJSKJPS21qg5FzJ3JU7JAV+zAv8lsnCCjtZXtLjr3mIJEsFwGJj4ZjAgMmWg5vfHVLkdo/PXnv/bRL369R97imleqZZBahGAP67McfehBpnRefPTzXw10Nb7hGbRv0IWguUQD6NXcIArtQW2QBqHgwnrQG0rXYR5X07DyYZRiA7Qgwr112kghq6aEPoQSKlIS6bIpsZBDEtdUtLtIl9p9RNuwZqzzVZzcaPhUFTaoSE2xLj5h2o1p6h5dtVOwpDxgwubGQ90S3HKDAUiZzUoZRozoZe51Qu3KTL3IIXPXabY2S3gTxCCghygkCZEVy+WqWXrI2rxvrH3LGqtJxyzFqoV696n3OSDLCWnIrO7iDfSwOBQJ/hKazMbZCk2m4CVw5UZHGyA7ByMyKHx38/UiSs/orI+zhGsHt0t3hksIGR554KEbtXrkJ7769dDJJFbUZRrTV2Erer1ajjKxcXHMlEDIkG0zVUxVWInMlz3yUPb89Mzf/OinvzCoglFOQYTiK+pKKWeGqItAJIvg9xeL1iGmGrMwYg10KB25Z1Cpbqc4I7gPikrkkpA2xzZWLGjXbtMRh+x3sfBf+Ybz9MKoI50xojRG37zUbBNlCQ9acK4ISrZJgeHmAzqpqqhmarqlbA5RVhcKFI6OiM2+zY8aUqYUp1OJs2e9+/2Pe91bX/OXH2o9wqnVILKiQy55wAG3utbyb2Gde8GFv/3tBRVStJTw2nuhE7JXZfTvrn0tnpt06WpIy94VrCd/L6HARItUtdIGYx20TN4jIYcA6pk5dGxI26jrobjyKQaGkvCHSRYXX5R1E5pMlDGTy8Xd/EWNjYZYTE/iQb0A3nBEYCvXmp7HHnxwUWken/7at8QwdjD/lg4jWYD+WRtT4VJqFeSIXG3mfQjoLyWqkj4gGeFaBkn3Pk3uKGGVIrYW2ZaZKTSrBfXvZlOi0ZHdpGVB469ID1gsLGdgFsfmZVzlSd5006Zv2R/wWAxXR7Ut6YN3oxsHUnLfg9Jv/Phnp5/769hiNtK60IYeydMVBhCYRguQj9O+sslkMYk2JjBU5KUuccgea3zqi1/NdAX7KSKyZzi4pfMlAiJVmPpL95HAupgIesl5iOXvMugiY+35e/oQSCmqpQZ0hsygDJvLuFnU5FscR+ZljjyMfd/48c/PPPdcLU2kJyPEaDJmoYnI9GHycZ3KBE0MVU7e04aMMask/TbAZfnZQ58CZKlaTSAZuWc2EKISFU3Vo2T+41e+efzr3/WE177vl7+5QHWoZSwpNPJY9AF/ess9i350xplFB8HoXX3u6IxzA3QZzdR/H0TBp2zWmvZlFCCxdZGp6roq+6cyrWsP2TMIS1OvGiqJZJzWInWsdRiRxfvmLDLanJyBWE7rI6yIJK26tGXIGJ6qLJH4UAuG0YwC8mFp9ENw9xg36pEH78+Oz3/z++dfwBcmkp1wWnQwB6cv2cdAjgpCclpw7TZn6aWwV9Ktu1pPOhFFkpXDmMp8xjQYomuV2dKLqGttGJd5NIVpFOKmYT333W2eUmchIAkBmcN6mHMSUw5NSn9kFgnUTqZhQJj4AldR/qlCnzlinntn8FBgFA2zmem7SAHYE5Dsam2gLUR6SIOllLHbqgttJgVRMwHKnDQu9XY+eQhmUOoiqaC4QWRohiwhTuCYo85MCzn8/WeWg8oB+x9wycOX/QD/oVdCDj/4wGc9+mHvfeXJJz76+Jvd9GbHXepSBx50yI599l+U+z0vHTa2btt+5JFHXefqV7vTHf7sxU976vtfdepbXvz82//RTXds38ZZdCE///+hiRx9icNe9/zn/PHN/4jq/O6ii571spOf/5o37Hm/zp5/3+ow3PBaV3/Ns5928kknXv/a1yllSRBf/frXnvjsZ//ZIx/96ne/+6xzz1ksk0zsBbyLSL4eaMxm4GMMbbY+h/hcjMDjBMAYZQpigpMES6hIgpcgCZ/m2qMIpAqH2HE0EB0AABAASURBVFedwIFGaoSl1xTGCt8QrmZBAJpiIeYmrB+YHXJW6SgKsezIliSaLNMkFuCFOgqKqaEIsRqSDMSVwMIzpHOtsEtEAHRhZhAmEZAuSQyISl7Ev2iipEkMKcoJneojsGgMuEd03gq5WAzahxrL64KCVtKXgeTiRPNwbaHdzZLdy/IUJIMnE6ZihdmLCqxzM2cJ+RnErPAPtc2Z6oGRJdST0jOhy7d+9LNPfunb7/rov6zXHTMttEStSkytTfMMYOf2ra2zWmQy6ofsvw97fnXueT/62Rk2eelkR6Xzgmn3331h+ff4OYoIFKAqPcwIX3qAj3zhi8qqRDI0VdXC/ulfv/Wdny7/idT1Ln/M9u1booQbd2rfM/+j//qV3ykTfEtv5i4S3/v5GR/5169x9LKXOOSIfffuPhfRm13tcipyxtnnvvXDn4pgWuPO6LSIS37lBz/8uy98hfMvf/hBRxy0r5TGmgUS7NnTDKZ7pHSVFEOh/VJl2+ZhjKhkwQDlhSph8KCJcfGl4QVuQXqdu7qHXf+qlxlq+f7Pz/rwF79VTf/oalcgQZoelJJE+3Dpgw++3OEHcf3rPvTp7//4F1Dy05IaU+mz/Oyss1/4zr/5/um/TAN7zVwJQ6eTTY0urNKHwhZG+5XAILysFaHD1hSFXBTMjxyVoWAcZRizlqzUCSQI60A4fR6pQMUSEgZiqIB4dI/mOSftkEKuPVsK9e98IgacegrHOF8YcqSUSgGFz6DhLMvgY0Ex4WNKBMUvWSq4QSLN03poBpeSXqBTIDERFMioKBxr6jM5dNVQdcaWlqjKypnlrGvT5G0OAlN6qit6Jti7Nud3SVkspTq0QyPpfvBKkO8YvfI+iLsajI0yZ9ntZd1tLaUbkttgdnqTaiHp7VRt+27feqMrLC9lPvqVb3/iX76mLuIcXziT04Wtvfzdf/P5r3xbepMeggBXksnyK8SZblw0/+RaVxyLXrCeT3nPR84773fEXWY28dkgQ37puz94+Tv/5rxzL2IgFClpZZMAZmdgdprKFmr8XWgPIZPNYQ+4UuZMi2QUR5Pfs3YslSHCd+3accMrLf8k0Ue/+p1/+OJXOJ6mBBuke58uPP/CF7/j7/7py9+be05ICkMEbNKm8wg2EkWnO0JFiizLwCvF3OA8kecaEts3A8QjJJWaLpQJHbRfn3f2x7/y9b/57L/+4le/4irRChZBJpL9vOXfPMU41K3DaCplFBuzyjwwiqOrBzIBaiPYvHinMad6591mDz9kEcqSpWJN0TBP4SIOADQHf8JjDu4dwZVdTGB0OSvysTvT6LYtK4B8li9fo0QOWBgqRC5mCl6iBeMIln2qTGxBu7J3aY16sGjpeui+u6596eW/L3jfZ774r9/+vnjXRigt8HPtZ1943ove8f4vfvsHrZEpizQeyUbyoEUhWcAeBe80GeMD7Uq/EcyxSBvJ5C7cDIMeKyKF82h6rQwgbxZdqHEKbQJNZTy1YL1ExZ1yYo9uLWZvPTxdonP/sJhFOsylaimqUTxqjxFMJml/+gdXrKZnX7j7Ve//O34pJ0K7kk6TnNPap//1K6/7wIcuvHANB8OIi8lkT6P5g9rAjth//2sft1jj/Z/68le//qNVr2RBMUmFM8+74MKXvPdvv/zNn6ajgYcJ6s7updGxxFTVWZV1JDz+mxeCUtD8BtMg0yQkaaVlEREplZ5obul//AdX3SP8aX/x4eZ8l9RU3Kwr+cA//C/feMNf/+PuaXdZZVmZDGWTAD/S5t3qi2fDVbtap7zJATaBiBdpxuyydWPFHrbWsqO6MBBbqo+DfvQr3/rQv3zzn770HXJTqqoMBvTUcy78Heez7dprKxEewzK+ju4e6SZhqRZFYZxycUtZHghgwcUyJKTVsZfapfRAi95lSonNBeJalj+QDGkpzi1Ma1EzyXTPnls3RvqRk/n+KpxJvDE3ksmicbJ7aaZpg6JUBZHntrDaQ59awAEIarVbXP1yReWX517whg98qLeZBb/P8Inapmf7wGc+9+aP/OO6rdEaekNzSYMOqPzog3SjdBJundl9EmlSQ2hSW2QvLrUzb9ODNpgLnLKliUJM+dgScy5dIrAQ65yOQk9RzKCXkkS0U0xkRTDIhDHRgNll1mi1z0tURrdlJ5vmmBxJI8/IjhySEzqaxDzCN+iaYN7wHq0NXQYTq9atUD4Zi45GjYIBwPADkEJ+BGIWz1XEKGCoSkCdJphj7osJSRry4zN+9fOfLu9ij73UpXft3Itr/5c00UsccvDdbvPHp57w2He+/EV/+ZpT/uH1p33qvW//+Hve8bF3v+Pj73n7J97xho+85fXvPfWlr33uM5/9sPv9yU1ucMRhh5rZ/xJh/h9AdO9dO5/32Ec98B53oyy9t3f8+Z8/7tnPu/Ci3Xz8P7ZhXN3o2tc47aSnvPHFz7/+H1yXE9z9hz/47vNOO/Xm977XU1512vd/+XPCRNq69XntskYJGZlNfOBrXO+pQSBYpnUUBo4ot3cVaICZF3MMEYN2LRN0DiC8gOABxBxCJHNeV+/DAsKe6EzVhBLHFcTeSpdv/kxEbNbC5JrcQiyJcxCtpDdrZiRzIpNegzUBWRcw2aBASgpfm0pnTZcsPvoSoy2le25uGd2jS8MQacuWCtJzSVYFXmFqw7DaasOYWZhrgB5lbuNETXtqR2WEZBWHcUCR1LLanDXXpTbVIGuECDWhuLA5qrvREtYKIy9nsHb1ZrPHklDKoGUlY82VCWm1NKbMxKS+lqnnvjt33uRKxwH4wnd+9Pw3v//bP/5ZhATKj08/8wVvef/3zziLQ9e+wqUDXAVLudaljmDP+RetX/Duv/nxmWeWZdOfPve17zz21Pd84XvL/wOLo0wIoi20d1oQi5DsfPlffPj1f/GRM886xxHnX3jhuz708ee+44MXzW2j2n1vcX1FLGGTTLWcu7T3/9OXnvuW9/34F2cUaGv9I1/49qNOeddvLtjNvHbPm/4BalmD+bT90bWvcOXlf0mRb/vIZ1/zlx/+1a9/nS6/O3/9tx//1xNe/e5zLtw9lHL8HW5mRXr1XmaVvlAHqhWMg+QgM100io4moTHlfBETPeecf9Huf/3WD7rb7osCMKXbN2UEr0y05k6IUPBMmkzk0AMOvMaxR3rE3P2Yg/e/9BGHFvazkiDECrghbBu33+sPr7eq5YLd64e/7M1/9fF/vrAt/2zUhRdd9KF/+vLDX/q2t3/sC8950/tBBQjnojDIguUUk1KNimiRGNzHOYeZU1K0m03LljYIhxQQCK/BMCBHs2G1GuryXbc0oZSRMJTNJqbC1DsPPAkjRLJp2y2t5yzpnMpqj7se4TqNtQ20EMsPMQBUnT/JSZhAUMvKZSNtLF2JXU/GagFtZaqDqEEVWJpAalmYI0qs2ZQyMY68dKZ3zwbpwZeCdMkyHeCCzPBMDQxp/C7DklYcJVfwlQZxASo19D5KGwaopTg9CGoDLMu5EcNiqu7WCJbqtWJMs80JdM7olExh2odYso8KmTmsydDuf6sb7ty6MbX+rLf+5Wv+8u/OuvDcLNFy+sK3v/OIl7/pLR/+p2e+5a/PPPP8zJSBSwJ7Lm60MKHojiP3P+i211m+RvjS93/y0Je9/vNf+2Zb786cTz/nzFd/6GMPPfWtb/vIP7//w/8kc5QslTvhJgUS7J5RqlglOIPKYgNCkyzDrUqMQY4uLA245wYklgHgd7s7ApYQz3vc8iZ7hH/uuz74ur/5h9+ccyEg0+72xW/94HGvevvbP/b5k976wV+ec64DoGEuJoAqZOPZozfLPlqnvzQ5h7YjG4uwLpaSsX3rVnb//Nfn/OCHP+d3Tf2iCdnT2p1vcC32n3Xe+U963bs/9Y1vMOIYImefd/4b/+ETH/jslzh0zCEHHrj33rQ6jHCJRVYCqcfspeXoZYARPJzIlqU0rfMs3X+fQ4aBCbPBmYzMs8qc2nOPhEBIukbklH0KWVMcEXIqIhUWPO7GnvPV7mn+9De/vTvyAs4bYVXLxVpiuTjfpIhaUmdHxNIJ0C8+NZorQXLl3re8/rbVeNE0P/ZV73rHRz/5m9+dH9p/N+/+1Fe/cfwpb3jvx7/wwrf+9QUXtO4r6ApFmoGBFQSG0rU6QAypwkO0E9MFRTukq0QNRRvpZVjJCrdoGrNpmFEeisEznaQlw8pSEI651wnmmwhJqj9ld9qiizN4+AH01CmWNguycLWWtBJSIMMVL32Jm1750tTx41/59uNf/oZvfO8HcyNK+1m/PefUd/3dCa9732v/5uMf/dyX1NUkRZMzl7YQ4u9CQYsM97/VjfZY4/Gves87/+FTv7ng/DbkRX7hP3/1a8ef+vq3fuyfn/XOvzn//BnqzEsQLlya0AZCaZJ6dHcCS34/FEhaomkyoAFBZM4NuXC/aD1LDDKJzu1Kxxx20ysvm9dHvvStR7z87d/64c+0BXF61tnnvOj9//DUN/3lKz/4yY9//puDrcpQyQ17LpLJDJk8OynDxX0PbZAVmTCzJ7f/WbestmDz+viXvrk743cSu9Fo26tf+tijD9yXI6e8/8Nv+asP//rcc3arX5jt89/+/lNe9y72bxmHqx53hBTHGMGm9FN4MEjQRBtja1WM0capbIkFVKnUmk9sNIMsF1NV1V4xq860FEc4kxQkKnpJ1vG9JWi5ThNJX5QIdN86bqiSBj73rR/+9vzdTfwCv6gRFp2aL0SWnxJQZb7rjIK+pn1TfOkHA0kmLVNseNu43pUuc/Vjlx3//Z/6l6e86h3f/ekvdq+th/3kF7987ts+8Jx3/tVL3//3n/3at1rLIHFLVKkiAwWsc6k+FA0l8mNivpJoSS3UYNzIQOW4B4mKKZEdmMNbzm7zYExwo7WVYTWUVa1DqqQRfWElC3JMwojxYKHrmR5XNTXGsUinGLRFV+ugaxkOoRKiCCUToW8XFWm50hoDybpomgiEsxBBmIV0RS5t9hJzKWtTZ+htLjcJyzDlXlc3kYkUca2ilIqhRhf3HgRq7+at9P73n/xs0vUiN/+Da5nqwvx/2U+CcuvGxsbOHTt27dq1/377H3TA/occyM8D9t9vv7332ZtZu9TqVPl/mQz/zyHMreIx9733ac977n77LoH6sc989pFPf9Zvf/+/WPk/yjkMwzWudIXXPufED77pDbe8yU22btkiIr85++y3vOedf3Svu9zjiU/420989rzf8RWnyFhsw6LWTkw6JBWytEQQQuhiIcaNCdKdePZZYg2ZkskgRKearRAhTWa2jt6iw7MkaoSGSyQDOqK2GIUbYiR/iTk0IQSiKzREPIJbHOcPrH0ySi7SdCQ5gikqeyZ1TEgIEiquPbRJmav0AiyIR0oCiqlosyX2ZSaHzXUwUIxc9DIpRWsKB1swfwdzBjNQnzGFBojpNHRLUk3TLlwfFjz1DKvNldWYaLBcmqkCFGGOQFCmMrTqAAAQAElEQVTQQMsBoGLZJCgDCRCdE9C4pnRThLEQQR+hYwzDQ257yxte4VKZ+YF//uqdX/DaqzzoGVe579P+7MRTP/T5rybyRpc7+k43uA5fzRdaw4brX+5K1znuKBF88Yen3/E5p13j+Kdf/xFPf9jL3vz57/7oykcesggEdJWW6ZE9aAvZ07nvjq2v/utP3P7Jp/zBfZ9x40c9+8V//nfnXrSuZsff/hZH7LePRFRHXWuhZTcXHLz3Xn/zua/e43mvvNbDn3HNhz7vMa9638/POV9FbvsHV73Rla8g1aBBe+61UV744Dscd9iBu1t/y0f+6XZPedn1Hvi0mx///Ge/5a/O/O353DBe9OC7HHvEEWDWNNpZDLlJfvnQunw6aHjtVbUqHaaZf3SlS28Mdffcnvz69139kSfe7+Wvb/OYbnaxKswI4N42B9ZGW0qtNlrdgN36mldaKAJ/dI0rjquKNmGe0ZtY6JA+2vWuesUn3emWW8fhN+df+Oy3/9UNHvOsKz38STd44kknvPl9Pz7zN0cesM8J977NiNRIItOtQIoQwwqVVKIVXaiEhSs9LUkDsGTXwYqouukkPLazkBWICPVM3mQxUHBFSkjkIomjguhtLktSnUH9LZYNpmQuy2gk0a5skpY2AIVqcW4HXBHGkEIPsEenViiGaEImjSaEXBSYCk9KJZNaG1mlplmvYP62YFCJRI9sXb1n9DnblD6bopqpKFAzNwQLFgikBmIijJINQYJiAiwPnMh7PilzQA2lhRixLskBLJd45clUZ8rKJdAS1aKK6KIOEEAOBqkmXOyZAYEIipL/kYfv8/KH3eWAndsvmubXfuiTf/SUF1zl4U+95sOf8cAXv/FfvvvjvbZuPO6Ot9pvr709AtZICL+/VClKhY0iw/G3u9Vtr30lU/3GT854wMlvuc7Dn3mdB550+6eefNpffuSiqd30Ssf92Q2uZQ5xSiy/JwBSDGE8W0nVUBfJfxuzpMrQlM0rzBPkvgyvxpXkIF2s51GHHPiSR9yTwk+tLcI/4cVXut8zr/WI5z3klLf96/d+sgh/l1vvf+DeJWPVob+nrkqIeWHFQJf0kCnKjN+LlTAMRY3mzvjjq13OVM658KL7veT1V3vQ05/3pg9mJsKvc9ljH3Wbm3DWd07/5SNf+56rPeQpV7nfk27y2Oe+7AMfuXA9Hb7vzsfc4Za1FrCAaJ7d3ZkolgqaN0l1axnG+nuOCEmH9xaZv+8TWXpnxFR8ZoaJ9IvHEmqStXTqUBGDTVWbc9YQSw7NQWe7yWWP3bFlNXd/xts+eJ0HPfV+zzp17bNqWmDPZWZh1omXaHBKKCGxYG1zWDzhAm4qOl7pmEs+895/umvrBvV64fv/4SZPfNFVHvCs6z/8+Y995bu++dNfHLBrx+PvfOsdO7Z2IwC5M80AET51CaezMwrRH+gorkUGtSFEL35WLdhkqkbjp9BQESGZtHwxCmLqROygVVEd6Ut1liysAQQwQ8RQBlGrPZcXYOkGATJknjUmbIazL9GNKVJzfOrdb3XTK16Ky7/wnR/f6wWvvfqjTrryI0+6xdNOfutHPkdb3fa6V7nlta+pAg0SUoIZvGiJTBeJNKRc5Zijnn2f2+7auoXWeMGff+gmT3ruNR50wo0f/pzHnvr2b/70jIP23uuEu/zJjh0bMoYOfRGGFIBQI8BTAbqVn9qgjs1LmfR4dE3ySJ20zCKtSlINbGyMyzkaFm7Fhifc67Y32Txoff47P77HC990reOfdfWHPf2WT3nZWz/6Ofe4zXWucpNrXANRxGWxziZxUhFwT+VHGChPc0uwF0Bi9nkmNK2ryaUPP/Tog/Zj91s++pnrPeypt33CC848+9wQ2VJXz3vgXQ/bd9dF03zaX3/sj5704us8hN5/zkNOfvO3Tv/lRi33+6M/OOaIA5MqSlMqtRrnUiexGdRKF/+WMDVSZhMWv1okqzXlIxtl2QhseBbPIeoKdfi9gJujTgy4RavBol01JKI7TZciAcm9995xzWOP4MyPf+17N3nii6/+0Od+/Qc/ntMpOTv3NJWgRTQb7Vho56WXbJdf/PE+0Cg9bcdq67Pvf7urH3sJj/zYV75ztxe8/rqPPunaD3/an5106vs/9a8ecY8//INrXuEK5painYEnqQFrSZGUSDNQSemLbJmLeRnNAcpYJVeaq6iVziGalBhvoQ1lolDqhERNq8yDIpLQ6FwtElo6hrmXRiy7KloJr8GErTaLrRn05CpREGPOVdawhpG8RmViUSbbyrS2O7hPpUCHkBG6wXGY8DVWRdnoxh0jKDGlDEUHmGEzuvWpRKPThGwlJ8dyZktZrJY1siQxZFE0TDIQXdv0dx/7OIBde+9z9StegTf/yW2R9D9ZhP+t7AU3vfbVT37GiYcetPx3LP/0L//ylBe+7MLd6/+uDHKpIw9/+YlP+uAbX//ge97zkIMP5mRuU5/7l3951IlP/9MHPfLkN77puz/6Xs9ONEsbtI3MYSNB0DSa9UYTxxLOcO4W6da7zS19XhIZ422QXiR4EskGIigao5aUGDNZGHC8JejAbYGhvSZ+upNkdkE27c2mWDZn37M3MflvZKliqiBFIfJABMoCvu5J/tqZzkCikSEKA/ePMqpU5VxTNWJ3CYGBUdKDKjUwqaovYcKYy8gMZNE+aggQM29X1gocfObikn019y3eVhlMKNkLY8QntZDBrAwrciT7TZ2Qpt0slNubgRozwpowsnXUNHRGGMkmVYE2kT66bplsdLKhCFKgZdyx7bkPuNujb3vzI/bfZ9tq7O5sLPEveeB+j771zZ5zv3vutdo6ig2loBRRedH97nGXG1zzwF07gOTMsdqRB+77mNvd4mG3v/nRhx5wyUMOsNVGxyBeAgMlweb1ykfd947Xv8aBu/ai5boH6R93+EHPuc8d7nrD60IWQjFL6WURenP+E+56m4fd+qaH7Lv8zWDOH4odvv/ej7r9zR59l1sZ37KwpmFOFNBJB+y9601PfOj9b3GDQ/bdVU0pUkTuv9f2W1/zCm894QHXueLlMs0xRqzQjDvWEQfte+xhB+yzc0vGOovTxMye64SXAlNNucwRhz32djfff+d2eorUti420SSQ1I/k2kOXt8KeQxf1IfpG0TIMXJl67Stc9gpHHHKZSxx89Stdmk5f3BwBwhFT0zlqxlhudeOrnvbIu93g8sfss30b5SR9YuCgXTvufINrvPpx97v0JQ6ViS+WZnoOYYBmWIJpus19Qu82Z53NvGSIzDbMaoLNtLyu0aAtdeZ8whRrn9cxzxFdlZkcFsjUCaQnHM8gjzIlY6aFZJHVkBuWNVVMmNwVUpDGGbqGTu5J+tIK+oY4wRUjCCjvjDhQMNRu8No7m2BeRA40xUwiKjqHrLPNOS3A475bimvMsXbLbpJZ1aVoCr8t2fDdY9ttQjCAEEKo57L+opDdi/yL4amJwA2kNmo3tIygwChUJCHAMqF50cnKbIyrWZBLALRBWLRimbLMEsNKbNXNJsvMTnqwocm4RrncUUe++pH3ue0fXIWVFld394ggrm5ylcue9tj7/8FVL9clkinCCeZ6zGEHHnPoQVtXG5GLNrPVXn3LljzhPrd5xr1ud9zhB2/fuDiyNoZy+P77PPlOt3jO/W6/fWODXOGU1o4+5KBjDjnwgJ07zFQpYKBkWkmX3GfXXkcfQvoHVrCqFvggURWVAClbytGH7s/RakWkZqpJG3W+5nFHvf7xD7j9da/674RfguKmV7nsax/zgBtf9fKISXNm5th757ZjDjvo6EMPcoCPdEyxDmIjgnXCJQ864JhDDth3r40qUYS+SALqWpc59l43vQ5f0UWw/ImN1QC1pFzR73qL677wwXe68tGX2HfHNo8lk6jIwfvs/NNrXfGVj7/XMUfvn6tOHbRF241p0tmZ5tWsF7AhkdTl2EMO2nfHXk7orCEhu7ZvOeawA4897ECh23IpSzRcmbOtWbWjDj7wmEMO2m/nFt0QWWkdtK6kDGmrWcrcpa3FibopNg4/4MjH3/6WB+7aEbkkLqagJcw6KoajDtz/6IMP3Gvr1tbRvPe5z5PMvEcevN/exx564CUPPMBiYJJljhi8DPPqxle48mmPuc/Nr375/fbajk2CmXnQrr1uda0rv/JR97/mZY4aF9zxCD4xbsHjUgQBNMc8+brHOgJTMrjGphJDT5mSEA4wQddFTwQkQ5KlLcqsgyuZp2hX6yZM7aXQ1cWYgmYjNBMAh9uwktUw1lppAl31Xud5jNmko3hqRhM0UgYcXBVTt41xB/Pwk+/0x0zjW1dDd2fbOtajDzngqfe43RPucfttWyu/laV23uWw/fehI2hujx6OZBALo1j/4ApXecXx97vFNa7w76wR++/cfqvrXPkVj7vfFS9/TBAjxHXowfvuS39d8uADoghPkz0WbUbllwBaR73koQfS1/ts2zq4MJ+AjvZEF7heYv/9jj7koG1bxllar6xPq1vdvn37sx981xPudqsjD9zv3wt/qUMPfPrd/+T4O/JgrOt19t0s6StxdewhB+6zY3vWMcYNGeoobmii9PK+xxx64BEH7hPo0JBBzGLXtm1P+LPbHHvw/iJCm3BzadxQXfhxzOH7vvYJ977D9a926L4XbzqcwIi40lGHvuDBd77LrW8gpswo1VHFioqyLK8ypyGtICqmXds3jj3kgCMP3LeKBJ3XbdQVzXLsoQfts31bjc7lkhICAzmvDt13v2MOPnCZP3drrplCuiZaKD55wRI0GUZA9dF3ue2Vjjps4MHdYywmrtqD8w/ad9exhx3EXcxINttG+KiEDRK2ddx2zCEHUqRtpRIamUABie67Y+vJD7vbI29388P223tjIbjAY8fGeIUjD3v2A+78wNv/yUooBzd663RUdp/CZ+EbKBJAAVWonWcxMlQsfLxruotMEgBjfonNWWXSwW0DA1I6ARpdOalFdEdvDK+0Nlush2Bu8o4+L4GkNbtkZxe1o8QhzO6mG1FrM7IdIZRNq1VxE0cGNZNlDcBc4tV6zWZc3EVopnFQBhWSY0azqmYQ9mg8dMwjucKDwg9B/wUsQhgCIFkKGCpCT4kqNcai6Td+9NMff/+7vD3u0pc59IB9efNf7X+/Ba56ueNe+dxnHXTAAWT98X/+59e99y88CDw+/Z+2SBx64H7H3+duf/7KU0567KOvfpWrWCmc/fOf/PDVb3zjnz3gIXc9/okf+Nt/+M2ZZw+QwbrWJsSJK+FHmEmFmZelP6FLwHOtJmNAJPhYpBQrWkoM2gcony1Tm5fZVyFLT2Hg+ZzBb12IQGXgBHJWdRj3gVBy4grx0NaH5mMPXVICSwUggqsr467De+9zy24AYzpLtMFbmWWBrHYMM5gzwKmIhBeNoiohiWReD6R3De4XLrYEBMNGJhOvosHAGZQPEzzFTZW7ctkt4DfLDh8QTKwszIq0lGY/bgAAEABJREFUql2wXD3TafkMqiElhcGy7OXqNWMlwaklGYqMc1gr2gtlK/wipwemJdSAEHAjuteNr/3uJz3oTY+/36mPe8DJj33gqx5z3zc//iH3vtlNd2zfkpoIBWnR7KPstWvL4+/8J29+0gNPe/R9Tj3+fq8+/sFveNwjbnfD6x139GFvf/qD3nTiQ4445AB6aLaanI+Lr7227nj8ne7w5ic/5FWPvc+rHn/vVz/+3q96wv3/8FpXlSqk78E0gywWevH8sQz3u/nN3v2kR73x0Q963eMe8IYn3O8dJz7ozn90nay2psyqNH0tdL2w4to6bBx/p1u95xmPePPjH/iaRz7gNcc/8J1PedCzHvCnxx5+oHFKqHf+YoEUW8ROedhd33/Sgx9zxxsLEnAR11zuJpWprpqMjo073Pj6b338/V/x8Luf+oj7POVedy4KzRwVpx5/1/eddPzxt781SJcbVkLc0pXpdM7cunXj7U9+8Duf9uBLHnZgWkUZoNpNG31PmTsiRVWudtzhpzz0Lu884cGve/wDaIrXPP5eb3vCAx5/p9vsu+9+XYaG2lkAJhFCrFBA55fzMWcPvjMqmYN0o/+8Q0KI8FVgXIQIBJiIW/TZ52SWJzNfThcqueRQgymEmnpjmpdsao7iWVqghyC0hBkFBsQix85TJo2DFB49Sk9hyBikLmRKkmgh+rMIMp27BXvEyKgpaAkHd5u5gTUUpCVFC7BcSi+xeJhsRrGiUgAVWWypSA0qzm+h0BcTlAnVa1GLKuvaL7LlqAjRpNiVGsY8LRAHVqmV9u/dl1gQGtgACOgiNIMrHygaxFUIAywok8FTuQdlgE7REE5K0Qk2dYlOHlkb8tD9d51w1z992wLa+7/6cfd73RMe8JanPvw5D7/70Uce4CVdhIbrLpe95EF//sxHvPfER1356COdO+iswcXC2E8byh/f4BpvfcrD3nbCg1/5uPuc+tj7vemxD3nfCY+62x9ed8uqVoVkdueRZuPFD7vP+0981ENvdVMr3DJT0EATlEZZ7nPrW7zrxEe//1mPUvUFiAAht+yeUo854tC3P/Vh73zKI6967LEC2lTUuNYFftQhBz/jXn/29ic+7LTj73/aY+732ifc761Pe9ALHnrX4w45WFnoeKZlH/of3+Cq737WI972jIdv2RhDpJS60lqlSGG63v/tT3j4e0986B1vdDWDSgzuPDJCij74djd+4wn3P/XR9zvl0fd/wG1vqcrCcpi1ig1/cKXLvvJx93v9kx/y8kc/8BWPeMAbHvGg9zzxkc+67x3323sH/UscoSYzpBMVfDCVkbxMyHvGIPb2Jz34PU99zF1vfFPptcQwiv7x9a/y7mc+5L3PeNjO1VbOMrVazApI5OB993nL4x71/qc96i43vYYWyazuNd0Qo6WxQ4KmaKl0FLzIzW947Tc+9cGnPea+rzj+/k99wJ1rKWk4cO+drz/+vu99yiPvdN3riINO1U3Iei5CPfE+t/nAs45/3eMeVCpqWWTSgmQzu9SRBz33wXdkyL/2Mfd59ePv8frH3+PtJz7g6Q+441EHH0C1DF3RVFMBFE0xC0oYXd1LoEAD6UBkuvfsTt5Ia1Jng1jnuFQBwTCoKUSCYOEvqIqIgRQwhpfsxZcRgDwGcpMiIL9SIGwaSjenVwSNbNaDgAsWf5JJAwvDadgybr/LDa/+rqc84I1PuN8rHnu/0x7/gDee8NA3nvDwW177appLgEuFEpZaX/KAu/35Mx7+pLv9sUWIe3gPVqZ9Ds9LH3r4Cx9893eeyLR271c95n6vevQD3nLCw556/7scfNCBTTSkigvfl9/putd731Me8/YnPXQ10jQ9KHYX3knWY4884u3PeOifP/thf3i146TBXCzJhkZKMjv5Ifd65zOOf8w9b0te8CAcbTGDbBtXd77BdRkCr3v8g0971H1f88h7v/VxD3jnkx5yqxtcs27jntkj5in6jr22v/rR9/+LZzzsLje8Bh2SkfSDAiVFmz7hrnd879Me/rrH3GMsGKxqDB2V9rr25Y568xPv/7rH3PO1j73HaY+95yUO3A9Cj9FXefCuvZ9yjzu85UmPfMWjH3TyI+7/8kfdhzO5wd34KpfdoOBMgWuVSUbnPszvgyZFLE7tRIRQgIfe+nrvf/JDT3nIPbYNo7Umy0uQfd702Id+4GmPvvP1rpHiKUgsGMIAHXHCXW/3vic//NWPuqe2blMMkw/uokonEwxCN1EuEmaPysH770uEv/5xD3zV4x/wmic84FKHHahkCX/onf/kPc962GmPuWupqpEiyoBNqa3rlS9zqfec+NA/P/FB1zryEnWWgb2aasyTsrFt633+5CbvPelRb3rSg17zmLu/9rF3f/sTH/Cmxz7g5le83AZcrfvQO4Svp2QOCG8TUbmJUYFM2lnGmQfFeZBWrbNKy5TZe/fm2Tpdy1OsDxVmDApGFsS5h0xBajIxQKAGqeA7DScYFQxAGFIonjbTEG6OPcXFAJNlmCVQG7KrSJbCaMFyBW2aHTklJbZAbcLIQWYinTNVSCMHx6atVLWYc9Od0KbBuRhIUC0vyApRtuRC8s5ZdJaaNkJ4NVHNMnzo05+9aPdFvL/Zda5pqosE//Xzv90CDKFjj7zEKSc9fWPL1vT+5ne844vf/M7/iBQJ2bVzrzv9yS1f+9xnvv1lL77Dn95uy9btXLh79+7Pf/6fH/usZ97+4Q864ZSXf+FbXwtpxBBRBFMmE2Ysk7QhUPiEYigFWsDwzzZm1CwludGNMhQdq6yKmQiQ6GmzDBA+x0p7McYOj9sDeNtMGGU2JDHtQbTyjNPZ57VF8YRGLLVVaFJGIcMaUMYXv8kPgRUp5BmDt4zokQw+gRPgwpgpkcyTY8FAjsKs4iTDPcV7hDuK68CiiRHHOi4DSnrahTGDDHNmEWR1Ns0oLVeN8ZVwyXnIefRuHgA8QWqld+tdSmo1s8K1nXuXOiVO9YXaOGNkMPGs0op1YacwZn0zflIQKrJ1GI4+6IBrXu4YtksedfC2bSODL4vznDNneKctQKpivaofsG37NS95yete+lLHHX741nGjJMsPUdEivspeJFI1SRgXXw5kxq7tw9WPO/Q6lzniuMMusTFsob1BKy1TBMosANYN2HMJVLBt3LjCkZe4zuWOvdKxh+7aNpol3didGUdgBi4BaMrOBOaxc8t4uSMOueZxx1zp0kfvte9OKarKD6GZ0r36uiRNRu9JZ0kbyeUQytWrtIGWl2zQ3caToXjoATv3vualjr7OsUcdvGsfU8DQXVsvLYxCliEIv0KTz/AZk2MNTOAxwW2OVfNCS43WN4ZWigjrg1K6sgBKF0KyiB2yc79rHnf0dS93zFWOOWKv7dtbqDsaZNIya3HtYVPSt8zAc5ZuRYdW67pIF/fwbI6gk0PpZRraRbBc3WPuvWlHCSJdLdI6KoeD6DB+Vb5G6zMoepWwEtSr0rekSQJU1EoK3/AOwe/4mpkbRLIklJwkjb8E/JOaoQh6pCt1lx41tYaKa4J+p3l71zl1XjaDmcndGFptpGhJuRFFxxHLvwlFN0t1DD3QZu5dNEOnJtxSdC1jVLoxWDmIeeecEapeO5ViPPDdYqednWfxGjO9xqcoklzO2qcMniN8A1LCoNmHnMaInNVRBCrp3GioeJdgoOgCWNqUlpVGa+pQ0gSJXVt3XuNSx1zncpe+8nHH7NxnJ8y5oRlStCZG78Ac1qjibuUm6J27LCZoo5W0bzIZhuHIQ/a91uWOuM5lL3HMoQdvGTe4mIoQFynSQZWTyUDGzEFUhMShcwjrl5YUfqAsoqAjyDVpeu89GtOQpW5JbNCadCk8qZNQZskkcjwjddeuva9+mWOvfblLX/FSx+7cuXdGj768BwENUtCtTzn1mBBrRAtGQwyClWqRUjCIrNRqpUjoBVMJfidt5JY6DkceePAfXPrS1zrm2L2376VZYDVsCFSgVCsH7Lf3NS97zHUveywPb7u2bXc3dE3XSIE4sQgDlBflpbHNF22jEwARXSMAgRjKIEUjaWFfJyHvJjmKDaq1UFsqaklNXBAZ6ERcq2vWFfTGvPhvEForQN7Fc0CY79p72zUue8k/uNQxl9hnn0omUFhFMVUBySU9s5TkRZMeCUXAggOaoEezic1txLSSPoRJlPD9dmy7yjFHXfsyR1/luKN27dzK+EEVpGEy1mc1UcTShixVqTBEVKJSNYyeY3PbHdmzFZ2ruwRCMI0+rxqGpgrTUoRJncJFWmTJKNQ1kd2iGSiWCMwaAHIdehbuRTQyQH6DeIF32LqM67rRdSjc16aJSmiaku6gpgoaCXW1ZePIIw+52uWPveZljz36sIP32jJWRkjnek3RYFxZMlqwTgaQ0Vx8TGeXwTWbBBXBgfvuvMZlLnnd44652iWP2WfL3tZFFmEswqxTgUVKZAgoWKOExRAq2VIaalBXkKr3aAF0jijn8Y1EL03KPFIK1vwz00E3X1s0BZBkoRt146hDDr3GZY651nFHH33wAeOKihU149hCqHgfo9PixSE9+CKhryNmLgYB0GgP8EGrUIJBhaZmeunC5bHX9vGalyZgjj3qoIOHYZC64D+jRLc2Yctqy2UucTjzwzUue/RBB+8qtdM7Og9Y16TuHfCQ7NaZvLx4SqfBKDTZWrMyYciO0pt6INE7WmTjhzrYpXRAdunEHmQKnQPoy+rFYcRH9QHLLUiXCOgpNK0BormxdbzcpQ6/2mWPOfaSh2/ba3sR6j3N/aKpTXNzbRBn1q2hQ7A4d52ZfbU7oKIDtCJN6JUIky7pItT0uEMPuOaxR12dFA/cdzRYzNpbSO8l6IQalWg3cxlSy6BYOZQidSFW3DBXmQaREqWD7uwuu5OORybHVcUMJVGh/CTaXGgDgkFgogVFAFMpZsnvw5a2QVFHiKWklJ609xBkE5zXlaZKb6KTjmtstFxllkyN0MgKhnGPQbtZqDbVKTO7w1tEm+mLuZISTZQh2WqmEOB8A5IjEZnSFTEOqIV5oYPv4zL4hypJ0yAVlzjnogv/9mMfA7Bj+/YbXevqvPmv9p9ogctd6pjHPexhIjLN0/Nf+yZC7n9cmI3VeOXLX/bZj3rQP77rzc9+4hOufPkrmBnx8stf/uI9H3jv3R/5mD+894Pf9rd/c96FF1oSuqLO5IwQCUUSYphVJskJAQlpoRNkFuG+AohCUFVWXGrMzJ3Q8lSEqhrLXVuC0KKZTaXCjFGpXnrUmQGSrO8MSVk0Qy2KapFI8c7ALkVRmJfF54o194Gx8hzipfRClAq6SAfIfiUYhdvswDjSnBmmbn2WbOGRXZih2BgmlL538UW5MKCkWIjmmLGRbpDMGlELVJhJnZva3EOmxMzwTACLNQRCvkzaYcINtooY04SJBBiC5Ofp0FB1UQ9DcNyMRSL14kOmKZckFMp7SicpWlmx9DI1+hbznL6O7p6gRBSIWT0AABAASURBVMHMRDqMeFGNqqwcMA461sLDYQVUe02vNHgEkkuw51JFYUYlEZfspjSZcDSE/L1aiFJgwcUXTT6EktySoTrCmQsUwkQlnMMqsI3C4liqaFFHZx0zAckZcMsGTKJOysEZYTSZkytaZXofaI09crmiG7xkLV5l1uzcZlKgBtNaQXTQW2uP1iW6kr5Y0jhBXJBxFcrlHT0iMpvQgEJyqnOoN3qb9BchuCNBB2EChzYkmah4YVr0cEiayUBvqSxaLQ5Niy5z9tmIDRGpRZTBISRa2nqISTyXq0HWITP3k5bD4j8BZ4eYU8wa3BNAS4tPyJbMoSazhS/SzRAXUdqGji4llU6DtQV/SkJ0BHoKNfWqbaizWkYKGHMOWIAcYjMeTWjMcEqrNDjnrXOYZQjjtDSEOg0irpJjJKsERHIvAEXqHQjQejmjNBIN8y6Uyo0m6ZB1epO11ca3Cyvx0Smj0H5C2c26DNkLaIIeQixpDDXLGD5qYPOKhaqD00HcVWTJRc5oyc40lQ2HtZatpwvhPvognhmhbIkiVlZDGUtVJTfKMtHTPYXqdR2IDStQnvaUZQ8cmVhilTrlZNktvLSsFK7PrXVvWRqBGVmSVpxceZh1K85qkJwQxSmpiZgKJV6aezLmEpFUmkZazzkJRXMBshE+NqdBmGBANye807bYczHiG8sdiUK7FBSFQMI5h6WlhhlMQFU9IhPCQQ5HTt5nzI6FyyYboYr0nQ9tsuZS1IyvPofiUt2VBh46+CWgdRgfoYSUk670KtMWbWZJedzRXROjSRF49ugpUeryx2VYQxh5QNPW0MMVkqydeskGzLNiHoTVQlDdWEuyOm8inRSYXjRUyFDh4mxcahyZ5z73llyFLJLQAPWgZUFloRmp0evcbHZ3rBmUuSgxg4gqQbeOZXGwkTjlpx8a+lqm1pszfzeZQmYuMiEdgpHcDQvuBgurGZTOU7NvyDwOrWpyoikgylZiAe6Ys5hnlc5KHVDX5W42bYJOkimZNgfTPCkBkEij1Zg60AWz9iazq1NA0cGUEYzligzm6Smii7hYi9XcVnNnpAjIgtOp/aDOcBoyB2AwmIZHb2yUGVahBhShq8SsqFimtswuoGJJwfjLJUyiKEyrSqkiK+8st2Vu4X0xjVE36jx2rCbUGSzgRUiXge5hPaTTDctangyUvps7j8wUGzS4EKiMQ6YvsxTwp8QAHbtZG1QquSayd0zdPWgxqqbImpzK3wZhDLnkvMoYxKpWs0GVnZgFE5zu7PR8oiU6m/YULi5iI4RStmDoGqhgiLmb09pR5yV/mRZjToixeDFmCgHVDbWsUKR0ZHN0CiRkaihUpU8tkskrbUyRjDazZ/F/Ww1OOgNXRkEopNFtJYKaaIJ/shNpi8AZ3cINDg0BJCim0EBuNPcsrQ+eK6QqptQZJVXoVwElikTUKhQRVNXTKWLJvpI2MGbV0wIGk6B9jMvMGJjKk3Wgca/uLWLOnNfBtzCZSiF6pUudbrEMRERJrArVHVIUcGKVs8SzSqPGG67kQCFSaFy3ThoizLVkNght7BxwEQIiDVkcFlaEAasIioy0Tqqp9GtJ45c3FrsZKEVCu4lDzMVAubxEpz49yyxjhGULSiEBcRRvNKCk1AS5DiTs1RkfWaek3ZORHb33nHvINA9Ts5Ykl77opLQPiqhS1JQQIfzZJepYUkp0hgIEnBzcYUP901/4wo9/uvzjQje/4Y0O3GcX/uv6T7WACO7whze8yhWXv0P8ra9/5WP/svxrsP/3JBLduWPHHW7xh+897SV/+YbX3vcudzno0Euo2TzP3/vBD572oudf7273ePKLXvL17/5wbmssMCHQii2QIRwiAdGAJQwdDKwyd22uoSDoGSF9rK0MHRqZnFuw5MXKYMkOCWKf5xapJc06p2gvg2c1X1kvcDZRgYlJKnosTcQGLoCBaC1ITuC3u3XkfhSkQCbLssookcJ1UdGVOwGzBkUiTw/JDJUEW6T2GBs1SeQS4c4EWR1WVIs2k6kw1iVXsmjOgO6uvdcIRUiEgFcVGSyLeBTvS12UIgYdo67SysyocwZPz3HGMPs4dfWuJkVroRKLLBEqItWWGC45SGejODNk0qTduI2AEao9GfC5sMxcBVMBo3MIKQGFKp2mylhVzc1fZUJdM6g5f2lm4O7PlCJOUQMinEXyyEAWw7IlKdVLx+aVkmJNuNFRXfYwhU8iTRVJdbmQG5Rj1GEoTPSZEo4ArIipclKIZ2EfzVTAfEaoLHUPs3KMSWAEyICvdmqYSQGdqYgCZtZ5zG41dBF3KV96+tTmGdywUi3KYoYExKiBlS5Tz1l8GoBqNcsgtEykzHNwf+u9zktuFqhVVBXx4sFNTibrE6+1o9WqZTnpaQzRqtNACc+gaUrmqCA+R5jOxgpF9qDAMk1paEIO0dPmqA01qsCqsg5Z1IvUCNXZS+vWm7LMLaEpo8uK8+dGJCxGEErGrc2hDdpBbLrRFjSKMg0LXNGJB4DkhP7JEkmbps7BPZGm4xPxlJBoGl1skuoyQqvQFUu3kFCQrsEj+pwRiIip46LU3aKzEvspgBRLnljpYOLKHOjca7BbNyaeQGgBejg7VegLwUFDVAKAwERWoOlrlYUHsMgbwv6sAFTDODtDonfiJAEfNQcF99a6yRaQbFUarafc50oDt9+iVocqozbBPEVOTafObDL4UKKQKbmFDkjLFBIFtJgP2ot1KVBX1mcW5Mjtz1N4ZWoLC86PrByPxfo29JA5eYJJWtqRruklm4jTUs5KYZobwza4C3ICZbWkTgNilRiLFEUItfNIJMmitmWXnYrP2llkdTI0TVGH2MxvA6vxcXEKDaXEkbFjEFE6pi8MMuHdaSunxyDRKHQ0uFSMdSg6aINMLpGaYMsZ0umYFskTccvslEi5yCgBD6WsphYraylIdVe4WeGFgc+dXoAwvSzGSg1UlyLsnefa50UkqTEaakASQfQQPN6JRLRCOy06JOPPxczr0Gn0Ln0tQRhD6aVqOVryfJBF+EjPJgeTFyuxlu5oQJds0nt0FzQD31sEDZK0Z7ID7pnQ2FwXQTlKqopmLVEqdZMFA0PGkB49pjm9JcCMMictY51eESGlEO0msyWtSeJmqQWl1DLQBVzRZZFEC33cE3PLaUoaEHNITyN7603Dg3I2flRJNpA5eAnEAINK12jCoou/lmid6W7imbz5vkBHmAk9YY4Sgugtpzk6Xc39owbPNS2jRQCpVVCKEw6OcKCjkGOmpBappZjV1JpGBcCXBBZO/wZcaUBkSchEEei5InRFpLZWWqvhfDb+FJGasDl674ywqUU6itLy6jFEFDFgFK1KIxuWK2kzUQh/gOiO3iJcHQNbQKEULqNLa2VuBlEdiKihmYb2APO1dxImLV1b2a1oQ8iGUBlBzTSP0OS7paYgmqV7tsju0jq6LxCs5K3qJlqCL77GOg9DlGDVUmMuM2daJoXeEFlFK5ilNlmJFXAjLwiakajjL8GiHpOeUR8TocUia9bKZ9sEJRJJFzVYp9ooGbRpcauxMfZxSAioUoYPmQUM5XSRSZY1XSzUEspQSkCWjbq4yvKCb0PA2YJwnafS28AtIfg+akNlS5IsD7LaCWCnx5yoCeIPPUgMZG3U1uaxxEiio2CoQTQA1qABoZHpUFos0GmmhTdjGrGCryIJpqQwgwxjsVFspbxok+jILhIitEktwq9keLN0BEN+sYPCVlTKgRk5cTYIrQG9ZGYoV1ZE0dkshFOr1WLa1Xcb1pXGjB7RxLpVDSZcG2bla9zInuIpSsqNWCSYg0ajQBZavWqOrsUHYAixWdAl0slzUl2rNUsVGaDDbinrZb1xV0F8/LOfxeZ1hz++RW7e/NfHf6IFMvNTX/jXn/7il3tk+Kt/+LDsufu//+mJY448/MkPuvffvubkF5zwpOtd9/pbtmwlmQt/++t3feADt37Qg+59wgkf/ORnfnvueUMSD5tBL7wtRJPxa+3KCk+4wzCbNO3NfALWUO71DCuWFZrEGItGpknCG5GMOoiKbuZ1Uc4ykquQAPFL3KmLlCXlcuuBZQ6Y1FswmLgbxshSnYl0gFs66TKNCWYxsmEHYkZfWCRVUKSYkJNKSib3oAFzxe4CvqWIbiYb3OyaSCen5GROhQqzpTB0uowSospYX+fYUesgqw3UEWIkniSp7iXaoLtN1une5iVVuJS51qYc5KYzN21ufU6bIT2cZU/nNG4Za4s502GKKkzUQo28ZZ+jgXuWZmEO3ygYV1lLZLio8zhXBj4uvCO1JeaONnFzpt4y5TAlt+aMcCwGAC/uw0L6LBgqGaBor0gB/yg9AIUI4JF+8QJNUNTkjhmYYGvUqZd5zugzZB1l92y7HV24jqlvy8Q0LCZIpaHGTmSUGrSjZ1AsymnULuhga4KWFsxSLcaCMgI1eDFhFWitikFmjYlqLgU/aQrF53aRIG0tyUfuDXPSK6C3SML6qL4RMbj2qvNKZDAVKY1NOT1SZsoagEnRUm00NaGqEqRADag+8x2UL0bH2oUvAjcUGyJUIUWnmnSSE2YeQ3MEV4lIgVW1FYRaLGLRcHOiiVJcfrshc0pGGEIEswwTWGFLggJxirjmJHNPrhKq5AQFtxVJyxZt8nZR0IMGRyTmoOQmUik/KiSLZE1qUBapS5U6KCuOBTnSIIRD8nSUzO5eLayyz1V0CFPPZVtA04wBVgIjpBSjJ+ahrMs4V40hYG5pKTUoGtVJXuEyuzWa2Hs3+k96H3IuaNXmYaPpUrNbrzyNLxZAAlGQRkyAJ7XV7nnLgqCx5dh8I3uVnoPkaDqk1Q6bpqCyQ11eCwzwjuRm2DoIhZxKOo8v6IpeJChfIljSE61Bj2/UuYw9t3AnHqwMK8NGycpmYkVNUUADtBCHqPlQWi0wTXUZmtau0jU85hl9Anr2hvVc0euWXlZeSoweqzXKmuyVms1ekaNKVaNnFMpktGoYxa3OOazbgGYL0unfHqASC93dhXVKxWKdmSWPLi4rBTnQCvAN5JaKoUBqptLgYg22dqYBJoc5be7W5sWcupptmKLELLZbrEUQM30dc9NJrVWLAVV69dCmLILmNrQgE9EWNHohEqyaWQfbkNzmKdNgviG5yqRmXFSiVaHmTeosdS0jJYaIlE4YzwNmYsa0ooxelb6gsyQmgteQRnJFUFxsQUBVLUVk6RXG1BZRUiaIQdPVwjRR6P2y6Fx6X7WprOfWdR0EEk8dqpynIrS2qYlaaJ3rsNvqWhFwjRgDBSn0YU7iu9NnZtdoTYj7JWHJ7q6Tc7JSm4YhvKBDMkSaWlalFIMYLTGijzIPqzaU9YC5aqcWy/EpJVKQmiYYXcbJylS0FwclaCH8TTGTBPmSfqiSgk6v0IuJREVf+ZptkK60ltWolXhOTPDMJfaFv6VHmenDKeb1kgLWyVE6VG0qMhvIRhQUGyhWKTBkCOHydIqaYkn2AAAQAElEQVQpiwoL1hcOFYvls2Bx1RCTZnClMuVFeghzAqdHzWbNsRvWtXZK6spU45I6UEA+UwlLMxOpVLIbFlA5b10HGaoOGNRLc5tpTUBziSiZgSZd4NqTNWmq9pJhTnWku0roIGwYCmCatciSkIVPlKF1ZttSdsfGxDdWhPfkg+fog61Z0GN3w0VTricJV/eFB6ALFgu1QeaS88PTWrepS2yaYYSuckCMucwZtNRhZYUwqHPXBQi111WvZWZ2xGwSBs4e0FQywXtZQVdFN6qujDZN82qTWidrbyW6qUANQCSEwFIKQmCITTJOOi6xO5nTIwOJlDISIBilj+glkAF3RSuShQsNDTlntszwKAQP8SmDMN20EaCopY8BI1/hpsfBWjItPDuRlIUCpSdincvBIKeWc4STUvPaw1JkKNzgclx0ryKjghtIQsiWnhcLtRBVsOxZBhfn+xCyJGmRLEJDTWXY3Yd590ClK2BNdFbMBc1KqIrQZjmUImOZB6WknZkrvRPAUdxLLAFo5sw1Xt0ZeGwGMa1KtoW3gIiAj9Vq2tC4IQyNaMrmwWMSdepOu4GKm1DuyAA6w6AMGM67aPdXvvs9APsceOjljl7+/TXe/1f7z7KAu7/qvR984nOf/5tfnUkZjjv22Ifd/U7Ju//plnDolq1bb32TG77+mSe867SX3+fu9zz4oOWfCGPq/MwXvvDIpz7ttvd98Ite+brv//j0JJjFBhEjoDREmi55CSkaFolZiB9AkCW6ZpNEMo3xM5AoxqgMYXgyn3d2MAS6FE7MdDDgGbKrDEaGozdWtss9+8ybOMMaioKsAeaihVXzMs98RewknpIeQkC38GBuNgvlZP7oAGMyULGUwWOYozilH6AmI6RCAMgS5sE0kbk8MwuodK5wraHLLGPmDakGW6Yr1y5KRd8MPCgfWvY100YKRJUJSMThjsmXsIosNJCjzxIuzIqW0IhYFnCxACyfaBlR7rOTShtER6vVGLFp6AEapE5S18GdDZkSYdnQebLpGeGLE4N2pzzqRx6y/x9e9bI3u+plhZaQmEphWlWgpJP0oiAsVaKiK9xsn107//Cql//Dq15hny07Yi6+jj53T+9FG6txJsLJMGPpMXoqu0dLesQxBFWFZHqaY9UxRkf0nuBe2CilqpgAUP4IUoRzpUPWmhNIJSEhhoEblrAmZqURQV8YNrssyU88le9QaC8EsIbSRKoxQAYSBFkv5qDFx4pxHFENKsXCpEVQDw8HyVOSNFoZoNNTDbyyA90kmCu1MiuPUcSsj57iaE5FF/iK0fMwCGACoeGKcmJCk1AThJFDCv9AnMtNlbRNlHMpWm/CXT6KaglVkYXI5nrNXOhVUiEUImaakadGkDDlSq6nmCJcFkZwZ8BM0+Cq3FkTMFoGwXFsiNeYzSMyk3w5J1FmS+4J3LCJI8gY9L6IFFKpNFXxUtzEFU0wSQbHqNZc4YXsOsMpkbr4OTQSVFSg7J4JBc5vwOzD1It3E1aD2Ly0KTqJgWTBxeKNItKAoZoIdJcGC0gq+5PBMIEb9AJpPhs8srfmrWdP7vm1C5YtiXoJSwrKYcEbqU5DtGKzqqukSdInol2s2dDLss9RUzbkKDEo1KSrzZAmzuKEGomoJGVOp1gZQGSQr7iYDTGsplpQQqUPECUHmGpwS7SCQLh7BnuKFohlqHYYhUpJXhBA0wmjQEYxLzETBZsV0UpyRJbsEjxSpMGqDoaSUBdKRFFcgNQhyY3u6qRJ5wSULtAIS6dUoNwZtJnEKNz9TRgP3P/ngi5YnogcqrjMI5y6qRjXL+EJieS9uHSWAVCV4Ag0nMczpXyrQis6izwdpRZlwi+UznIwGbRWUS6RIUJ7RmOUiJthGHKsHNmUS6mA9U6LeFIezaRqNP2kspYamYZpML5eaDzqFtfqoGOzac6GRstLEUkabmX0ngknhHXJDpkpaNDcWlyVvpujt5hVWiHTUl2UbWGX2YNegqfQdqlDt2FiLsmkfWkMFc6CcFmpUUqjSyp1oqgKyijiHFIjtaDN0+goyuIlPXuCyAgFIHQ/iYmEi8/eWkCWYo8IVSbBBC/OAfWzJB0njB2gxbSqrGBDqjiIPYlARDJ+AOoZ7CvJ6EUFTZF91BSABJM35skmNBrnkKR0ByXIhXsUi2pek2mbsc0V1ljOUUQGtjJ8Z56VSN5UbUODDueG5wbmR06zSAvCkTD2ZG7g70zK5T2tl5I1Ka9ShQSxz1yRpE6bUCpshKxSq4BzJtF1ykyhNCqFHASVSB9kLGmFQi2WkIAAtKNE1TDpFDIr1VWxZD817oLeo4d7cLtPXziLKQajWYRXEgZDpWu6esfsaKWgqqpJbsqwFoSZmhVFajAnZg2IMGSZDoDsmo4ka6VYbKqghBWMUi3BAHFdvtMP0hEnALppFtm0RRq6CiWt5IEeSYhmM50Juk5EhBqMCoO77WJXofiLgbN6lDmsibowqJEN4IL09AAEqAK14FgqFUuZNFqZXSfhIPHp9GR0p2+QbpQStBn1Y4iFIkVFAespXcytZikU1CmiRzrc0IcUg2Ugu+gsaOxOhmbSEkk0cSn47Np55OpawJRkg/IGhJcVV+7NHpaEGqSZkEa6WC4G6eEJ2jYMzbzJohsoOlsWzQF8xYiaGDNXwIrhTrcLEBggAx2slA3sAKRXm2mnJqxCBr4UaqmNwdK6eCT/SDp+csaZP/3ZzwBc5wqXMVPe/Ff7z7LA7mk66eWnnfba11x00UWU4S5/+qdvfdkLjz3yP+b0SFSl2qWPOuLJ97/7X7zm1Bc99YSrX+WqwjiJ+Onpp7/izW++7YMeeP+nP/NTX/qiRyMOuDkhMzSZ02BG/NkSjPNAWFG41hE9NxOuwRlxOpgYzyErn8WZ0CM6sx8cIIRdwcgXhgL4IrOD+T59SjACS5QSFr16H+c2NsYnvEgUSUhkunS+IgqBuGQnQ59rbzVnrTM0MorGaBhNlEtMXXhQqdiUV02XKxkdGR7hjghT6BBZ4epMSlmQDJ+ZuqQ4lwGbocQA7NTfmQ5KWYK3aTTMk3QMWUbUMcXcNCpDn5OWlVV8aG2IqIv8lG2OadIpxrXwzXvVWi1tJEmHiwXGjpEnnTZrLi9bogs1jNm7iGudUJvTNgpaXj1MuCFw340/vNplXvygO7z0oXcctDtf03VE3yiNL08h8OUnbSnZlCkErnrskUe++EF3f/kD7nHswQe7WmMa6LP2tahzyxUthRl7HsU3hHkFpVHPdXDjAk2eMaHP6bkQjhBKyleZZc6Y4SnCFosudH8dRFSnpFCdG4qmSwlmzOiwsDGZvVrk1DVzlLoqGxVEE79RIqTCF3sGMuliEg1WFrV0+jMX8BggokIEkceoGAxMgjKH724+dwkoKC1CtUCrZglhX4fMQCJHYEgSgNtmg0hIpkJIWKE0Lou2rCns7ZmdeOE6yq212+YuNi9zKTCTbh+tRAzZV97L7NIIFHGQGpFSq/CCZBGCuFdZL8BxiiGWvWhSPkYiAO29tqY5h5KIJ8KAEaxGiXjnSa9N4hOr9VXtZSN8kN2g7ceWQwhEIkBMw1GE9VbNVrwhqUIUuGgAtJhSFyqT0nrJbtqL5Kqp9sUQEjJKDokBZUyiGiYuyIg2x7rl5N5oQWFpQGpUAXQu1siJzCUTy06SxVEWcYA5c0YPbGpLDkGHhUZLfjMozpiDlIXe7o42reBbgBXlRenBqiLFQmszWhy9SCA9uMoYBQSjpziF6MCU2mEkp86AYtViItaTyGwRNFkT6YRTrwt0KLQlVBfNivYofS4eCQSFdquNtS+VLikVLCqrOrxjnpNSiyvDXZqgg4WHASHBk968BbkSZsXsWihXD03mzDkd6GaT6hzos0fzTC+IQXgcapQDnRExJoGWVaRqMZCqdAWDI1Y2rYbd47A2a5BgZELHhQUg6qZh/KXW6d5BMKrI0DHGXGSCUmS4CyYIM2sP9xkeAJLvI1bCr35mHdNH8WIiDkkYA6CWREk3wyLRUgKUBTdAhdfsAu+SXIIypA5dlpXs6b2jTzFPvfU5MpAqbugQDkxRPKS4156SFHVQHQdKG2JBigI6gKCkiJmTGReCS3XNT5oUU2FdpRkiscQZdfQyyrhR6jAW2RhiNcwjV0WW5YuVqWQvDHhTDZXJhL4ISuQkJ2Ac0lMQGs1YCHGAdEUwah91HnQ2uKd2+oSJowiEXg/SqjnQemwCEO7RqK82lEjGVtIOJDWzFF2mWaJH9hBJVd4lCKeh6IBa2jj2suos8aUYI9d6EtmOCZqNBoFWYBWNsQ5vidl0varTyppKAIut3HtP2plWpTBDWhVlI5cZ6qFUvnQNx5wUItPTHHw7tDHIBqxyRiQpoYu5FBdOiFSoysAxFzQltDklyS1JhHtEUGkagM0EIgDXSZqopmmqZQ6R/My0bgM2rKy0DqRZlSCfdW5YT5jdIFaAqkFRM5CiFM0H66ueK3BzHUW5k4kQVi7uiw1pEUFCG+UhtEKqDxpDj9IFAQEzVtYA5hBuV30dfT2Hd60xaBl6YRpZPJIkkiniRFt04Uu9mLlM1wWTEQ2MUVrNphSaf66zDw2SJA/yoGbSamSBiRiKIoxeA30U9L+gV45zZRJpMx+pQe/WeljOZEDEoadMgrWha0/0AllFFm9Adh1DRkkjx16RlYKmR06Z655zhC/SezIR8VYEtYgUTakZY6KY+eZ/3TF7egdYdHSyiWgTE9Hasg9hEsahnlSfwIqYQNZz6ASZQrhxtlgyvccUMZeSI32BxQcIKRGRPmVbAySSyV8ptDlFoY3UCaZaUKvy8m5zh4OXSqpI0oohnATGFkUW5mmREPL3Tvyol6Rc0lCIiAHc9VwIzJEDKIMsOMvS0miFBs8vfut7tAjJX+/a106Kwrv/O+2/5v5HWeCc885/9DOf/76//pvwvmP79uc+9YQTH/Hg7Vu3/kfR30OHHg7IXjt2/MlNb/TWFz3nL97wutv/ya137NpHRC644IIPf+Tv7/XI4//o3g965Tvf94tf//bMX134re/+uM+tGOpcbII0t1wzzjyckRRwIYYAMYmCpuIhSGYi0STKMwg9gjO9yjyC5eIyRPAl0rQVnYloy8r8zRt2sVxx9d68e2e6nAGkJSxUGJhSGAGRGQT43KP3ZrNYKtRiYZvL3AIVdi2bDXcXZHLRIvxcZDZ37qDcfouxPlwbI4Y6RXbm06XapFjkCPITvnOsZCy9yGQaEKYYSTToLNp0eXVUV4OONfm6LlloWVYL0wj41JiU2IRplrmpt9LJF8qI3CChZPKV5evuLuuOlqztZGZR4DRWZrr3gEeFDWpjUTMUpo1o1CXNiowloDQMLRwhzZkJ61qsm2CEakewUWKPhDPGW6T3luEJAQZxErB0BNOaFZjRhmShRcpK6iDCfSuIwkkkkJItmvvk2rJ4L+qwIHcx0NChhICmGlhY1hRuAuE2OVpn6IgWIQAAEABJREFU+nUrnWI1RzZTWBUUDZI1JQlVCaKAUqQK/yCN1o8k015IpXdtPeEwSgEajdZP+o3TxKTUtKGneafxOFGzQaIMLGbIShc8cA24PEnRZlDwhTw7swZ1jyKwRNBKQgbMrD146igEV6P42acIsmceLZW/e6dVYZRTh5BCujLP2nh6sFGlqktCQyygWO5SQlPMMyhgK00qdCVqtYhWy7G4laZwAbN7UpRQXjABSEDB475bh3bSUESJ1G7Zht6q99HdumNOb5meCF9cFaXH0t2yNUpWPaw5dYNkpmYoDWiRoh0iAKxYlupqtL4VFasqwyBlDJ7O1BpKb2YeNVnPORewSbpkyOKWAoiisLVaWx37ZhWWVLwr5tpde9KjYimSCummM4TQHiRsmClccKjoFpFhGaeYWMylvQ9dhknGmRvyBCzlPiHo0URmBZQ6LWyokxBJAogr0iRyUU9cBmF14WMwqpKTx6DLpWBQ0URHNgtCtY7zamgrmYaIxRezZZdl2BPZEZnOq3nMKl2rLBckFgkySqICqU4LOwKZYMB1iVYxVb1ItfFIkbXN5hPJFdVBrfDM4oYwsmhY/EHhmSdYNhiNLaED6aZFWCREMGQvPpfu1kWce7u5L4HD2JE6o3ormAfWQ9l7aqPl2Bw9pll6lyT5bhaFyksvbIYcs4/BQ0RQfqcQbhSaBljySsBDYukV60N1s4zRrWRVpo5EWWq9iQDzqXsP9O6TR0d0BaqQHUKED10zJaWIViXmxxRztSTY6Os258zwWndya+hr956uJYSYQkEvDptpQVFIrShVtGjyQVFIZW1lLuYVVC8YzCVbSo8SrRQpRWkgmi5SmtDEoUE75TBjWMfYCYWwmkrvGXEjDoG2Iq1mDs3QhsyKwUST6EVGUlbxGQwicC5qCEIXXNE4lfeJTgeDkRF0U7dutGwXeNmMgCY2aZnVkn+GoBlFrHVru+lZUnfhIWPZOIKGbNlbySCs1UIlqHRAHeBguHRqIpUrWpKS0cIxOOcY1DyTCPSEs39IVuy+1Ot1FouQZYzTEynRCKKUbAP3NuHsFC2JQtDNPemTxi0FdKBkN6SqBmtqtRyAyr2vqebCz3Su6cpEKjaI0KJ8eZPknCxapfXm9AtNliJCk85a51w554RJWqA00YAUhoWOYnuO/jkQRBZUtWPuQsfS2BYC5mUSypSgvoHlCskkgjLDszm94HDCTIyhxZibEI6QwNC5BK2rU2XUhAZVU3QRFwhnqKe1MGcPEjRt6Z0ipMDSpLP00d1VZ41kV6fPRa2a0RAJcWSCIz3MQwHtKLOWhV93nz0jyIKipUhITVmhVwnabS1lUusDw3sO6UneXD1La+kAeQ8ctF6MfoByKc8SlmKwCi3UQlw1IjuZTFlcS4rCVTqMI2kMpGg0HVIADoVF2sJGIrUvgmdQaPQuPBohqYVyW0EA1EiR7GdemruEOiMH5J0bEltbbGliWZShyfywch7AULgcPTBFzurESoyt2bRUhZ6DMh4m0KnkFrm4BvDijBaljdkQtYauio4rHTcGs1GiSKfROvo/f/5zlKmUetXjjuXNf7X/FAucdfZvH3risz/x2c8ksO/ee7/4xKfe9iY3ElsS5f86eUj/0kcd9tzHPvxDb3jVUx/5iGOPOqrWISK+/4PvPffUV1zjNne45m3vcteHPOqMH/18Y81YJ2SKNiF+CUPGt0OI+CbiaXPoHG3KiyadsmYddFQrBLJUkToiNTtTCsHPiGEi0FpWI4aB0OUWkWNa7aVMsIAlY3R9UbQZHkXErAxaVpBRUNm66BrBt72tRRPtAxiCRmF6dlCWEs5KjnEIPnZhZ2+D6+BVWVSyLW9EhTxcNLk2PSP4yN9cEMjF4MudGDAAmj06Y11LrKqOFQqukiIoBWqRIqE8CW1MtprN1pYzslOVeagYCyetOjTIkUliEpm1LgTQMtcZLUInHyY3CrMxQIcQc3hU8VqdFqLakrwQPSgj7w2+kq5oUkILyuBCu4FX1awGSjvv5k7IHWMI7vkNbZ7Zq8giy7Y/AGVTiERJbvfrwJpZzgbUKnzDuqpe6DKBs9jQQDC9AtWTvVSfcwYW2vTGIEmX6AB6oSmok3VUrWkbCbNZrCeFdsWmVUeRJf0MgqWCcBKGpAqwsEnuXp4pDu0Ok7ky2Q/OGDCqzR2ogxlU5gltiqSRpZci9IMaipJFpEGET1JggJBe8LfSt73RiZkQdQEhKqWPQA2QDBJIqkFBBLKYgxgR0G50bUcRqChSQDgGQcx5EOcXYXV3t87+UupQB0hhNmfFNWVGh8wuk+hc4J5s/D3QpOPA/UWSdpHSimNoUiOKdhU3cZF5FIqWwoCpaGMEnQjbHcJwqDO/gpOSZC8aqkF1AsIXBj0iECFwmAPcelzXa7lo1jWlUVTTQYiMIuSyTptplCqoi7kylGEMArlKUsZSUNSqWKFKVVaDD5ZkRroQERu1rEJqpmWKqVkZNEdAWfzxrgQrxz54lGZo3JIggVGzcpEhZgvSMxsLq4FZ5tR5JXWLjIXhaZglab+1YXeReSgpQ8eCuxYCpZgqWcQ1uswk322cgm9B1WOyMpXSIQBlF11u6Ewe3eYxZxPaE2DHaH0L7ZqkNuRiF6hmiEdO0idamXgFo5rau/GAwYqaPo6Fa9XYMmTR0sNmGi2FXMbUYtDiWWaQFO3bSu2lgq4ehqJigTmxZjxz5y2DLTaWCELJW3ggXWin5MHe2ZdzVnR+s1Kxlmx0cZc6d/jcJcOVskerLeDDTJYMhEbVRCBCkNJExiVu2cfoLHJo1GJBxRNqokXS4CKN2FcuYmaY+B6qJ6s870DPaOktpxlB8olVzS3VipAvcqJPTOe0tRDbiYD1kkIToNPJoDmlmFawiZnSFtOg0yr7ailXJsm5ig9Jgd0xr0EEmxDIhGDMpGkclV6C1STL4Q6bYfRqbrHY6r02b9F301jA8tE6T4HEom5gwWL3TvuVzFGWtC2Al5yL82VCSJMeMmtpLPS8ShTpFWGZEqSViQBt5+lz8iGxdIOzyCmBIKsU1bqCbrgMTRVjlg2s6GEUdCwZlB5b+brSSVJmOg26pvhKPzIvpsOalEari1qUQiEroz47ZE0RFDlCTMM4DV0RKtQKKhRBRWSAjpCabtw4pgkzveR1jTItnveRekepsirK0+VovRqJkHjPsUXJpnweXGuzHphAi6S3pVlgsZiQOI2SUuaqGMTIt+UCNEAKKLNSGEoVi4UW040k3UGyyFXLkjRUJ6BmcQaoY7mEbOiqOfgh9JK6KMe7dfrGRyTr0giqQIMVGrbbRrOVqyXhN3e0qD0X3Nc1Ix4yVe1DiVy8Sce5adYSRWEUsdbUGm7R6TFyF0qUnIVU01I0U5EiBPMKjFkRyhiDaLGSgzrzZ/aSTBdrY6UtJKMRKBIVTT3Cs88GR0IW6lbMCmzoGHus+qycAO2lzrbR+yqnEc1IQEBOXFcimbihIUNqQdEu0tK8VeFntZk9FhmUIRQCaFblK4ikiCvV0UR6pmDeYMsYnfpRJ5AsMimXd6K4mPJLkWrLvjEk5UyUTX9E1RhrVINqKagrrzTDmBVSBfzMsNawblBpVmh2TeXF+YAkm1oYZtEedI9aUVPUdJ0zabEYSbaqiEs08Jcp3//MFl1ywVFQum6qXOiMQ07QaLXTRA0R0RQ5lCxDwDIGCVPhn0SGAJi7f+P7P+TNMZe85K6de/Hmv9r/fgucdc65j3jGc77y1S+T9cEHH/q6Fz3vBle/iiz+Ycf/6rZgcJ+9d93ttn/yvledctqzT7r5jW60ba/Nf/ktM+bdF5x/3vEnPf0lb3nTN396+kVMkkYkFRBtSyNUq4i5aoQwTrnCmQetQ2boFNy+CV0bJU0Bj7l5DxftZkv4GQSZ3LPCl1/BX8vW1MRCbdGf02ywHC0LI0CDeUAIXIZm8t6DFTpTv4YRzJ1bUu7ZZxzGOHNGKBcgXVMstMAGYb6yIq7C9J6AqFAIpalL+hCdqQCbl3BQQbYO6TyELWqRAKsUSI0cI43Vc8w9m0swtk2ooSxcmFe4oxSqxsgmvyq1LlsBU4Sbe+29RnAykPxUUJwNbbVOjHqup6nMKJaQKEwkVZppCi1QqifPHPyBNQYzhzkpWW9rCJj/ooQjaGhtXoPZY5bipVKNjsK00MK6gCu1yCB9xSKidJXiQqnowwSvTZY6llqGEoPSVsZUXkepxaHoohk8G9WBu5kWipuWoLpmDXSfOh+sFrOV5oqJzZiM0DRTmNGaJAZQnypBxcSodZOhUejZadGEtVz1HEIKqmlZuCH7LN4kvZIF89rs67a8kKU5ailjGYxSUjnWVrNYK6DLub9lUt5m6ErK6JDOQWq0gheOScwkKVhQIHUTHZY1UtyEewR5DwVKpYfgPTcsgbtkcCmSpSbtXuYQX+y9bBQL6JovZnSmZndkBgVgXY5SUEIHF1Jjt4c0jqhUWZoZoNGat9k730PaPK1ajKkKSdEZNfrQ+KUMe6rYgGVfUatZN8IGV3MRloDcXkKqpRVkSWFDl+StFa1cp5I0AQhXsVyqnhW5qzD2ZgYOvGlMgXmSNpcMVbNarC7mWXTuAV4CLYUkFCwmxViRamVpQLoK1+7iJNd66V6dicFKYrFXEm5RrMfgGAgqWaVW6VUmla4JusAVy66mtH+XnFAmJJPFwksZXIPImKhI9rpP6S681JAiU9PJyZlmBGGT/J09o3kES291AJIhmeGgeFxTEUZ50QWpXJPJGwTggWX6gByBglBBmgr5KCeGBjsrrPaqUw03oRqlhJVeBhgfqUt4UqVi4EApXWyNoXkJphxKzfqkBDgNm7NABwuJEhlh0nLsPJ0SwVEtqjjtr6PIogNjZKY0gCFc+qR9DZndrGmdsvZkrQoBDQDyBlEABndtQnY01bKUP0YRaByKLJzBiYQgazhka6wNcy0yZQmyZuUQttggR4/SgCRbRvAsSr3NdVAdVmqrIjrsMR1zYUo6JKT0rM5tQa0JnQOnGYKSL1Qi0SG6smrQgQBFKE1tkiE+9x6dRAyMHvFOctHJUSKFE9QCxVE7bLZMilEEpjBRjUL8dc85e4enhHFIUlkGLV/+dP5OQMyyKn9JE5sxTGFtFjLtjEC6jpo2ELycymAHoAKtpjSXMAcrO9Ir0iAqQouqdE1m3tBMtWQ8UMKEhQ9C0Il7ybnQS8XVLKEOdF3kG4QmELCPmtPmyI2MlTN1z0XmCi6EeEbQeulVpBjZZzoZzqC13SOK9EKL0BhK+sEplZ7hu7FKTWhE7YwaqZZj5Dh54XGxmbtyyMpswyRlVqVVBrPFLqo6mNZSLGpxI+IXCwpggIYqMd9rUoLdIFqimyhYpM/W2JcCEacrKKoY0w5g08yUFimSdYAOXSgtHAJQGVNBXQzoDM1ZoiloU34YpAAoi5jSXX1d02Fi7E5rSM+EQFSW1GZZYRVMTaVCjf1cy0bjUAPv3MkAABAASURBVJhBpSRvCzFjlE7UUJJeCEZCTgovkCJWhcrRagn+dtOEAkrfdD5PgiZJxJlSDCyTJSJdY4FJCV0xxpNpPWeuCrGeVXKoqqabdEUpf4DQZVmkugREkmAqugnzo/CABnq9FRHTmnQNIxdr87mG1AVbQsxSCnMtKQOVVyecQiUqNaC0TitLgDk3hKK0AV67YIbP0WkwVRM193ROUk2zSCsu3KrHwpQnDJrUolIFI6KqlXFVllcuKgZS5isSdTovQaxU7dY7Q/Yix1o1RhWFzqqpYg4mjeIhZLyGMygzYSgmm2VFapYOBomRJ+BRwX1BJSXARBSN6aZzSIHFA2f+6jdnnbn861KXOebojXGka/+r/W+2wK9/e96Dnvbsr339q+R73HGXef0LnnPcJY9KPvxvb+M43uCaV3vZU5/4/le+/GH3uc9Bh138d2O+88MfnvyG193xEQ+68xMe/+YP/u2PfnlOSqmbjbECgSo/iEsryayTGb7ufYoeINySSU36iGSILIUDMWke1lV8xekOhlFb63rWqWPursBQbDWUVa3jkna50gPuyJ4CchKBCkMOZhCN5AE+eKm3IZjh14M0S4ZBlhnjZLVV68zSrJEqWM1jSK9gdslkQOVgUAMYJ8JIXOIEvEiWKTUHmccyM5wNkkG5lQuN6gsKM2XrfXZ4lJQKMFeYZFEZq9VVwSBJOWJJtVksmbYwgK9I5hJZQlSERjSsQphMTFyk8Q1L1zFHNdLjYk03oTiSWjXLktLmpvOS0WE9Vsixi3VjPvIYWLhMnYEuUIh4Lk2VGY1nBqvCK7IH7U06Iea1NEWIF3VuZF0xMcUBzCGdXE2KhZRAtVLpj0XlyVjLWqcvIca8k0JOSn1qBjdp1NaGtYtDOK6wIq4l09Clt6WCmMDfDmtS1lJnqY1rRFlE9Ba6G9IWxcMsiTAZNQYS7uKdR4Laab8cEpJo1ASkqkXGkitpo8wqHdpTpsVqEQInNrpb9sotRaJb90LSUiUVPfhITJgsFcaQNHpyo6O9EKBNCmGoppwo2UxkIOIKXHwO8NCQTs2TwtJpPF9QnlRVbppFMtMpYnZLFrNkXnQYsTIQhKyIsiOSdjOTAaAsphrQ8OzeZ21Tkp62qMlBhWgsEFKotiFbCTfEInQpNhjdSjqVoCva+TVbNaiMta0K612W9lp8UEIsSW1hgQ7ASJt1Cz9JqHvp3WIGWIU2FkVNeGTpjuQfhPCTkiXRzpWA0DiguLSrQ9J6gntVRpPeMYfNaRlB8c1NMKYMokYV+O5MZc6SYbRPyoShaQVRFHBvJIDsIiosYGWjBk3NDu2dN4W0eEYv2auvaSAPojgNWjKr98FdKe6syQgRE0mJ7j7NuTvKrGPH0FO6QMi8dZu9UAOwqMjdmVN4opvSZSWhwBIHlp0+gUjQnVX5SWWDinWCJaxmVGqca1k+yVsI9SWi3NCg614b43ziuwFJl9JFenKx59TCp4Z16lw0ahGzkjbSRz5KqCDoXIIMScPQdFKtV01WCtWocC/SN8QGZ2AFSssaLDVnX81t9EZpMUQsaUf5dmI52glqQGjhZJUgyUCVuWtbUKQiyQ5yKIX9pbnMEV3LXOqMIfsojbav2kBnsf7f7bGmVXotPhh9WhR1+TVIMaXAEbQjaE0395owKVjo1OzpAWoLpTQatHTYKDKKQixkhGyoUEdNSBftTDoyMENMaXOXPnX0LkiQW3KBYam+hm7KTKWSggItHFRNI6eWOUf33kvmitZlsHvO7mtE5yuN1RAjHSjZIZ4WjKoORw8jhtO6jJQhaTRcfEnqAKlYDEn/JB9UAPOkXkRiKPWQBNgEYISyRBPKVi0gjLjiuhIbQYEtyAFoGVLcNkJXCu5AKcyZiNRerLGFRE/QovSRpHRXJwZRS1bp1tsg01AngWeX0lRFwCsTHqIuhcyhfMzO2X2QqDVFZ0QjUY1SmhHspbqW2ZXO1YxqkCq6sJAVrPIJfSC1zN7Uu5ID+fSabcRU0VObG8XMAkgLdPx/2PsPcE2qql0UHWHOqm91k6MggpIkCGaSGUFMgBEEyYgZFQlmxZwl5ygiCgKSc5SoZCTn0OTUTYf1Vc05xrhvrYb/7Oeeve85d2//f5/nORZzfau+GcYc4x1xVnU3VdRy6kVckqacQARsYIpVhuRZLGNLhZeFUCjhRoAKJQFOREALEA6eRpRy5MHUncnYKCgcTKNTWAaXZjyrcMcKFy2N4Owl1CUqnIyagXXS8GiVprfWJGfnAA41tUYTQaNI6uzVKx4PUbVcA9aYgTZMXhPXnE3bKd6oDGYCZFVNxXPIyFPLJAx7B/7GBbIRDXEaQU7JoA5YMhQ1GllqEudRcCKHsw7emJyInJJZS6XR4q0XQdfgKiOtsBmsr6odtmdK1XKp0gdVmAj2gLJLksHJmyB1IsWMcMa+SozvlYO4YL4PiBMNj/yCw6FBmDvuQATQ41TlwDZqZR8jDkTbho4yNY0LNyRSsyOyBr4KbAJgYjUbC8SDMpgc38ktoAQD8SL9mIvBzb1CWOgAgplyL95RFK4Mv2ZizkZtoSHxUghFdmwOY0wZAcOEeraxl9qNSz8uaUyjPmfn4Z/BHTAieuPrXsc8//bfn/91CDzz/Kxdvvvju26/FVuusfoaX9l+m4cef6JWw9f/XU1UV3jlsrtuv/UhP/refB6YGQY2e9bMm274+49+/ZsPbb3dZp/b44A/nHD3ozNKQRYZuFWilnQimtZh04RAXFiCBa4Er/FeAjZOkcVS6rWpcKwwCdg0cUxdrKHMkbhgUpsiMysH8jbGwceUczAmIIxoksya8dMYEhmOC8hM5MSIkG3Y4CcEFsJr5aAUooWpIFJxda6BjSscmQjPbWkepRKCcJ1LVTclZ+xGXAlxHWE4EhkCTds1abLB0+ae2BOzcIA9S1oVIZPGgU+p2HWoqNWEWJEASx6zdTy2UqlTtoQ1kJPgoTRin5DaEkIshfGwa1gE2GPnFAMTZERV3KQiHAwjQSgmA08r2TRIPcJBywzhDIeyMEYjr5lDmDx531DfeiSTxCTYqk4SMnYfIGNMUclLILoQcdLgZMRGxlyIesQgq2IBYRIN7GEI4OFTQpkEUHZBVcFAMDkRsWAPr1G9A5UCTSYeAnsTKTHmmFXHT9UeSLsa2HWKTIGo1fRJenFXCK5EzUAMeiqD7BRgD40FsBIrbgF+S4xCr1HCd6ZgkCpSTSsNUDITC9aZC/fCoZTCmlpFYyo2VkIBJuBYmy438zT3YBnz2SU4HIW2VCwCzBbI/oEhaTkLiZlbNe6gI8x1lgCf4QTQM/JodoT2RNpTqkTOPUmnqapEE4QsmSzEOUA7QCwCOyHlM8I/54FGcstGEurEw4XfWg17eBuUqIKZzrwSkRAT9OVaOZekNgA6UE5EWin1zJSDRkaAphJPWpTKRKqD4aTeGEYwWXCyyOhEqZCgSoJJWB8eJACNZGw+Wa3rwgvWEQXF4AABwGXqW4laCHQ74gCcSb1JJWtFda3qWUor6MdUN7AebI6rFq4eEQNk5BzVrDcas0yyQkOKagm7NdlTQ9CfQUQTrkTFazE3VlbonoWtV+pZnISJGw+BLJlMgRNZALsUklzUKjPKAdcsPHLOPSv4MCd4plVzeDnil3BNhN2gTeIaTQfHEhKCoVpwdOA0HCoV6TkMSIDtcS3Fo1bhgrjlYQalsBLsywiUwXVlrpgvFOHS9TIAgWJ4olIrztlJmRuNpIWkugsNhWBL3tIgYSUITCkpNxw5IJvklJucpGFPMuBYXPrAM/QR5mTiFMQsTtqRFlIzUE1eW/DEbBDeE8M6aqlUCtXqoYoNiCScyChoSmAJoNETiu5cDTPNwpkosyoU10QwcSRiJg0Sb8RGWhrxTKTEIJmZEw+GDh5AxnOlDHlFtRFKXBuqo2yc2VUIdCllyZzRiDP2HuDyIqV3w388MJZLtOOqvTFgwsrK3BMXArC9CDQQGYZLkIKldx4PojA1IsqUSGmcFHEYJidR8C20WiJsj3HOEo2kzA2LWDIojhyUploYseF4V8Sgv8hEjA6BHxY2OCzDzcRtMLuOByMnwibEwUwWCDXaC0TSOkjhVdQoO9DsjGvvVEiCElvLyDXWuGUqElC1My7RBLZUlViqBp6vu0BKYYJJMdgnSkTCDD1ojShgjZWIIobQ4oW8uteebZ56z4T/MnESAT0Ee1h/D2Fx0iiGNUQwHbDJRaUXr1ZssHAqoYUFfjCQBXGwIMIqoRQU4FmjwklAyRnDGCFJlAR4co4QwuoCqQWcwWgNfssF4MNci8JBUMBzDLSD3KXWBEesIq5M4WIqklSdGC2ESd1L13cxWagHZyKlBsKDBRiAGORFvY5CG1IhgrfCmoiZRBjCCQJAaVIkDUqM/VmZxVs1WAGJBicwAaUQF+jMhAcxE8HeNZxhHEAV8MDVInvflj7D8TGU2TgTrLBkrSTYGt+UhKkhyaQBXggi9Dl1KhDR4J2w7Opkpp40Gq2Nda3VAUVVAJy4CkYBi4eaNJ2kbvjeAxgiNXILc6taQIuIxeBfI0jSiUH3FKKakkAyCMDDBIJmGAYY5JWi9/AgJiVNVakTV/JEMGAQpYrf5LDTirlGjWMeOcK+d13g8E89AOYgCONd7cdgStlBjihVNAAnBExEGc2YCxFaNQ5sgrRMyVUHl5LALiX14Gfgu0KTY+U+cZ/5n/fdR0SpnVh9lRVx8/+u9r9b2mdfmLnr3j+563a8S4k111hz/x989w9/PeOL3/jm1l/b66Jrrps7Of7fy+AKyyy90NSfAVt5pZW/8ZWvvnGttRdacMEInzdv3q03Xf+rww7f6NPbvHPrnff8/k/+cMrp995/73OznkewCx5M2Qn+iWeEDSO9UXGuHgEfgZFzW6PpEYfQxbWkQEKGmWJZTpxF1BFJmFwRctzcBydlJxn8JSgzNZwV8UcQ8LAOkbtimhGbDsGGQyKkwhmsFNRijBF4DZJFiAWVYjYuXnGMJ6HA7gFvrOhvq42Y4E2Ey8Wc4Z0RzKRSWq1JsHyQIgguRlGEKyqG4Iyc0iNUEFmAS8LluHXnEmAqDG7ufe37WqRKNjw+YPVIjlBhyY0I35rHnp/9+LMz582ZS1YjBBs7UUw1MEVKUakvbqJhwAo+3hGVoN64RioWjkKKiiZsL9K3asAoUlT1kj1YQ5JJVEYdiphaCR/mhFRetAZCSg4aIFXyoM5Qv4aZO0KKsVQmB4jInMbE4qCZ+qp9T8iyYj5sIz0OJaUFz4F1BL6sb8yaINWh4UYcy33AXaMPnsQGQWKcu6YtKQWzC75SCKKvVHIQQpBOAasIyc6A0ws2YlJJmfCAUSligMHJ2cqodq1bQ5CW1VirqCs6PLIldUlBZCI1tJgCl+yKtxBNqkL9MFS5VowKUkAlc4lIjKLBsAlMEKITKkOFD1AYiYMvEgZqos/PevGJZ1588YVxEwqoGZfRPr+PAAAQAElEQVQ6FgD/AEY9KEt4LpGchIGIEKQlSFi49uAKwuTcZmyQE7U8HEs4KJQq9MbYC/M5WAezY67E4S5QwLhnsN5n6kZSsKE7FQMaLmCQaJiHYyzs1mBoEVmclLrKpfcy9loxQrnxPCLOHC3XNtXhiKCgRRXEqgU5liUhgnKpuKPk8MQBxXvRUnJNGTUWFNIyZ1JLTc0J2bLCW5UoC2dojsEaFeYxswVEGTZQZSbHNsCoVECfHKhiI800/GHDnBLY7Rk1t1TSYYwbTU0rTRMwfHADYpk4AXehJ56b89izc2fNqQ5boshKwwZJAEVEgRZhSg1p4spp0rVMhhcXr3A5tywBEJoiUFxIWzSbNEKcycSHy4ICY+xqLh1xT5AK7tJTha0RsQ6kVaTRkOQ9jAxzonqOijdcWF1DK0ZEBEakYeIW8F+C5pCNqSalXnQyCBBjrwCWlQw+6EAyYYcgsgjGHmnEqR303HXqdSIID48hr2SGEk2oBmcLGBw+BabixhTOKEOkUDJnd6vel9rVig1IGhbsQAM0FZJgKkgMI4RoVJrqEsSpqlYVGF9Ghw8rK8EfWKomFwUVhn8J98pVo7YeEyatEKVSqBhzpKSN5giuzmWghl2FYBAhTALtolkbFUxzgRxKIo7f2NOBfM/REfUwxQH8mggdNdwBsxplUIzMOYWKMzZxYudEeMENZEQphUNC7xMAEkMxOxZ1aRI3jbDC2lIjBBfgcKoQjXA5BUoyC6OoTcX5lbQQJCFyMAwgxWrqve00d6QQy0QII0KuElkkaiAIxBC+zXGPZUV5CDJWoQH3olQouWkN6lh7BHhL8GuGdQF2VYVNkYIX4wAYs1+c89wzL7w4azYg45q4NAzbpRwk4MyMqVPrtXeAGBSdeWeCCOsTDDtJigzKzClCyME8wQu8Cxo4osqEtDJwmbymrqfOe9N5Hk/MfP6x556ZU+dGY54C8BqsUkjNUx9iElCPYzsNbiiIjLQC0aySExNVp37ImeTsgG5KZwxxw0ICR5bCiYKxntyk9mSdW7hhKiS2NCKwXpkLiVNLMEKAUzvgWZgANBFrgUKRjQtGHByBjYFR4GBdFGucczAFgSymmIo3jUs2ys5SG6oDn40yC7JQBAGEyoHsGjmoiUEBahTGZTA3UoPdtGDE4MdiPWXzhktKFlktkhRP3gFOj4Fll1FISywAAJFO3RXImvU1ao0eoqZeRyVlYBFk7FBjCkmBWJrZlVwCjMC8mSuOj5PFe6AFQwkmZ6taalNJgkMbElG3NLbBEixT20gDuWi4WJSVKbiY9JE7T1QYvINvdyhAugA7qWNwEn0uHVNfUlcS2MSGhJ/eo9RAlHLQTk1ukUe56WB+MGGt6EQ6song7G3LmBA5AS+FdZlTEevBbIRHsDNE6ye4KIeTEcizikgibnrmEkScmn/ePRxUllt6yWWWWIL+ff0XIjBn3uR3f7v/zbfejD1f+9rVDvvx92fNnn3Lbbfh66233brrd7637V7f++tFl82cPQc9/1taTvrKZZbB1mDsoxu964/7/eaPB+6/2+d2efNb1llo0cXRjzbj0YdPO/f8n/7iF5tuu/0mO+283Tf33Ot3+/zhjHOvv/Gf994749lnZvZUBgNOHZFYzYW5ExlLwk1whTNGIdg2ygIxhclKjypHEhdi+MdYiZSTaxQ1E8G3ILhYyFTUQCgQrUk5B17ppuwBn6sjhjsjMopZuEeJqKQh2VQ65U6RAw3u0HDbQERBFmQaHJeTmcJ3MUgF0anmvircp7QsSggVYLKdbKjXWt1L5b7k0qdQMfDLVIyJJavmUCfqwW3mllLDSRmM9IXGHVkvUkiYKBHcNEJ7ybOsbrLn7z6wx74nXnCVUhWUmmP2MbFVAbvgoOown3KMm2Rto0kzc+uUgEfhWiOkcBuWtCKkBooux4FQAzqSStkQ3gIRNMH9TQO8GTHXJJEJokYyq6O+TlQgi0BMhMjaOPIlIi6YJw/oCbpJNQ+wclfFCyOctx0hr7KTExd2E2JmcYDMPCbuncGDMiYk44kq2A4tCSlGqkSP5Q27gs8mqPWpXF2DhRhZtFcuEDVUnaKWWoczppNXZyRfr5VrIHP05A55wAI56aU33bzHIcdddv3NlCuis1JOPfDgrKHCwQS+OmbAwDBLRGXrxGurVdy8OPfWRBkJoAzOHE2KprUkPeQzg42B3YSQCtbBtCjMyN12+/Vhm+/5858cf0pwQC0NlkYYo57NQRCnBPeVjNyiOtIA1UrUQ0yYjBPYJiZgklqipnI2xdzewGrUXCgx3KADDzmFtA3l1oBbjd4FthbRx2Dr5JwMUkTp22ItZYKAQSiyPBO1OEykkgkO0FP0BouslqOOcidp+FeP2XEs07ZJsFhhTpVHwx8/mHAcApKKMBFBbi9mVEu2zi0cuVpHbcoTQmouY8vFGmOFhXNgCZMo4YuIqInAr4hSEk6JoRuGuDkIdLkErJJ7yk4wlqhSKnQVSVyS1xTwAW9abSeyjoZSl1m4FvFeqahGJCrF3r/7LzbZ4+d/uPjynhomlZR4gnhUawbXYTXg2LmHgiJjkzZCGyexyg4VUlhi0GnEEUKEwWHyDL4BnTTKzWA8ggmqnhtTjAtpSpTakNa5FW0ltTm1KWHAPQdzaQnPCVhIEhFMDBskgQQM/oBwFRo3ZVJQ+cCAlSUFk0GBZB6V2UmVBu/k2iMcybxeuz6gbBiVh9fe3AtWSBPUwGRq4YIarCeCi5eWQ4RMmQgeGahCCkkJZ+9zxWeG91Uyo0mGe5PkyA3nRjhxceo8YLVRFNVFYkJcxlmEpMIRSmLOogqHKs5h5IhrA2nEnGwgFjW6MU12ASLBDPkxwxlhowZXDc89p3ms48IBLcrYR2Wyid7d+4LHWAjTdTJ7FU2amonQBSo1iHPRVMpYTQSZakSR4hKCrw6WA4ZdyZkjJeDhNXuvXNgjgE0EY1WXoms8IGxR1Ghu1SG79pTGrgZskicIizhIblyJKIi6GmUeIV7CDxBGglAh1kicVTCdqprBWJ1SJFWFYQPDRoXw03AWlggqyBuhBtmZokpUMFNTZqwgiD12xwG1KFvyYHSwgK5OCrAhKzVbRUz2wNxCv/vj2R/85i8+v++hfT+PqXIJAG4IiwHlGFZwb8AGySJcKMM2mbUlGUG7rQhq/oYItjDP8tgSmKPhhzybp0JclEggdiFyErBLfs+jT23+rf3et/tvzrnmJtGOZZ5jVyYLqWZhPWYGaeHhJEfYUhNiJqg2nePxEd7JyUAJpgAteLLAVJ3UmFfZimpVMRN3BmhpmORugQ2yQZUso0QsEo6JLJUqUXBqGhkJteQtMd4lTmQeJUDvGsA3OSWGZB6ANSadAE4zDu3cur7WeUpj4UrYkqhKFGxAlOCOoVhpJNjee0BB0B612rVRck+5YykaVT2AahaKLJZzDTAKq4LwAp8x9pxM24qqpYdaOnG4I6ghLBlpZStp3MP4KTyiFPiKUq9NMDHD0fqkUGsXxfpx1EqWRVLSSFqI4c/Rt9XZqBZ3IwyOMA7oGBNNGVTJCwfU6IhoEAVhL4Jo6LUKy3OHZIWtYrk4A8aU4EjV0TfZindUx+JVQ2DeUWUw74CGGa4egdXuGE5UW7Cr0IVL9tRKajIL6hrOVBP1IkTMLhqNCfgtQQWsUEdD6jMMw82MQyjgr22EgkkDCFEpKSfxpDXV/rknn3/o4QeJ6FXLLrvIQgvg5t/tvwYBlFw/3PeAy668AtutuMpq+3//W0suvtgqr17+gB/94J3rry+DT9od/7z5Wz/56VZf+urv/3rmjKefxcz/4pZSWmX5V2LTp5968rkXZorwqiss94Wttzju5z846YDf/eo739p0o41WWnElcIs5aM8++/SV/7juxNNO+8lvfrvDl7+21ec++/FdPvvRz3xxqz1/tOevDzr0hL+cdv7lV137zzvvmzHzhXE4iyB9OjtJVXyG1I5R7BvDZYrnGpkEwSZJEoY3UQmug03HUP72IX2kGhTkwpGEhI3Z4EbBsPGWqEUNATcBZ4VSRyhQGPkF+5LCdRihRdk1F8nwila99RLWwZuxguBq0Sgmc5DCnbo87nLvagyPr2aIEQjObI6IKKHDzC4I9a8HljvxEJBAK4Vkhxg5VEzd1cEl6EmYhCNoOcoViBBYhcbBLXFDwy49kmjppOtBlvBunRIZHJdbkwESRJdWSiPMEICzUiiXEJ/MMplyRXetE100vWhJMYRkEdKkiohmGq4DaAgHJlQzQbLEPeRxG8ISO6sSJ8YoIIU0HEyCTOyNdVKqOXvkQAcx+pFHXaEd5tCMSAP+NSxHBQUNMiiXuaqKUhPUUEXYzWxNWOukIMDYDeV5Ar4UHqYWwNSHOUbJQdIdUY6LiEsKZitWK9RtTOCAqEKMFDRv7tyDT7vgvOv+uffvz5icV0MTS0OizD610giiS4LSS87oCqI2fBTeGrb2am4wNDHsMZiPOiuTCrAKMa/iJUvkrNwwCwsl0Af+RlOXYBslINcYZVNG/uKoUIt6JBKBTEE9WYFqDVeAiBKwSBwEAc28iHmOwHcKQqVY0Y1ZAoZYHFgxNknE2A+iw/ZJmYKLR09hyWGKWXzKH+AeuRYpppUY9hciFmwcBsoUAw1R4hQgFkQYICO1CrURqFUOx3AObYgykxKREKFezFjKNGw/dAknAb8l+uLVqfCQ80rEFCaJhnVKzCIu7DC4nChLNORoKpFURqwRebAPJ3DOicw9Ksj17ji0Rw1UXigBBhhjADHAL4AgFUvZ4TMwcGwPdtCC2YQrjYJygE+tqOVLaO/hVF08DL0spClj95Sw65i4stnARwpWDmLDY3dUGiCmoJfdIbYyRxaSYUZDjFewOeF+kCyEQgV2ITyYZUBhkpigXCWgngJqRU0FOQVo4l0DAkBUw2RyxgzhGJwFoAfgII+oTFWYCHsaWVAE7twr+CeDHYWYSprQDDYac+5rlBpWnW2YhAcjXZGAlJQaMBlu3Bf1jnWMyoEzZ0mZsgg5AZdg95xqboKFmYRUqlCxAHxaBsFSAi81vBp5ZQkmDkQcZ8eSSslcC8H0ISsIRkCLVgLUA/Ny9OJ1MJKxeSfVskc2J/BKYFjCmeowt5JJXzPoYTfVEOFKAMiJq4t7ozYcDUWCcBmMpQWr0jinPiCHB5jjnrLXFADNnQsclwahRhSwCnaBrmzY0MVCIIB7FMwTYGzYUKFKMDRsTNimunooeRLKzDJsYmOmotBZEGCwAE0JVpMcbUNNywA2BKyS66DPxiNhKqxCYfTRhDdAc8SpVUGHk1uVqFKZO2WgC2ocjgsAFe+rmxHGyTmcXr4YX4FaVOBcCj4rdTBiFoHbqYJnBP5ExJxdxROxOIFfF7YMOhIgR8I5wDdQZGFlWJWFQrvOFIwfMB3JaeqKIDxCqBAYc6owGCQzBHqyLCyCPSUooDDnQRMUY/VCU5YJEgFysBpM/qZy9gAAEABJREFU9BGBH8/sIA9+sYQjZW6gTSXlaupVxFSZYHiElZWbErAyGCBIqeCRSh4cOEw8RBkdkI16LWUwXVNygWKg4KSe1TnC4SBFa1VzyK+IBMA4yNgKeZhR597TICGRE5gVhkhDbArLFtkYvNHAMVsA+ULUNankROjEF2M2IgtOxCzMTmER5lSBm1uN6gkGUZIZLEJVKFEgTGTYjFhDDJy1KsZSdSnuJYA1WGMOGK3RlGsYrDCRcAT3ziZMeMag1BIYhMVTR9FxmLIkx0Inq4z0AIhBCcsAl6KfIaJ7BJkQMRF8Aa6rMphfJe24qchtEMNYLKXhc4qjgHoEe3JkBNimVuphnkKiOA03TSSlYSuLFFWpAz1yJwe74TRcwRQt0YgYXquYmwnuwjJhmRGrgRJF0YbTRMREtX/ec39fKha+fs01BQaJu3+3/3wEmOjXx/zxrAsvwlZLLLbYb7+x26uXXQbKanLa4M1vOOhH3z9uv33e9ta3YBTtwUce+cX+B2zxha/svf+hdz/4KKah87+mwZjwqmf+Xnc+OPwvQbG7B2luXr3csptttOGvv7PnSfv/9pzfH/Wjb+z5iQ99cK0110oJFjd/Bc2dM3vG44/98867r7ryyr+cdtovDjxot733/uzXv7nNZ3bddKttN956h89890ff2efwQ0/869lX3zDjuWfH1Pe5dKkrXFHtSNXEKSVhmGYQmxhJgTtVBC84gNDwdDgTCjFnPB2ZTLiFc+KhB6U+CTVNBqfCxHASQhiq1bkQhbC3XDTGxl2vXlE1JRGiBNeAU1lAAIiBWCAizCFk7OZwxbDCnWlnCGsOeMiFo3VEQ6xxUi9mfS3mvVKfGPEhKhzMjIIjNaFNiA4UEX6jE5t0QZxhKknAGLYlDhbK2khMhMFpDSGAIgbu1UjDczizIaRAEKjCOLOOcm6aRJoqGK2UojZUM5GSuDU1MuYjCCNeKINGSmAYg57xarctMeHRIK2TCUI5V5MoKeNLkFHqKZWA8L1buLpoFfyGQL1En3jM2SIRESOpuJNjS6GaHOEmIvFwJuEhbjM4dUpSWavr3+986I+X/OO0a27qOQoeX6PSmkY24TWzyXB0HCO06sBDCKJ1qpJCU4aoJI2knGU4DqlgCxdCtCM158gU05RHDWSniSYP5UkVTAgJk1poHFwR7VWUJDnlSg1TBtzZM5mGk6VaYBKYFPjB986R+SEe5qne//jTf7z073+87OqZ3RzFE7yUOQQYp5fVZwFVgRMjRn+TDDs5ESEfYqayJhuKeg/qw6tRkItQYmKuwbCe6Dx1rOOWIxGmAl0gEshgpUl9A2Gc4B5IQzYJkKdzmqBmgtp2yioYSgI9bRpKDQGTCltoqWfvvS/hRoJtKqlqK5pY1QLPkpsxt2PKY+KOIsBZcTeDvVeiygHmQMtDiYiJkFqyJY00iJ7ExB2iVLPezYIhvdewWoG3GeECxkqDDVBwihi0Bk+RmEdR4BfcApam4VYkD5gP9WsvuVdw5gXuE1WiMFrtvPS1Gn5XSOPUJJ5QapklUCET2KXhYiZmJkLDxsmIARqBf5Ye5XL2QmqlTda0whMtN8Kj4HbKQkGJSMOip9pF9agB1qk6PBWUXBJCRuSec6dSmGHzFJRKSFepLxRDB5wZlYHUlFRAzIXMxLit8LhGilajsUsFX2xERcVVcOqvEhV8I82LMEw0QpyjkFsQCXFWatVHXMi8VpiBcG5STipMXr32Bg2bCnRWKXpDczdLTCJEYDfUPbGN1BsOYuFGmxHBflihRQn38M4RlqBsaRpRFCehhu1BhWCEYIE1B8MnrVYP7IuIwGKwYewBbQJp55GkEcvghTDwwpgIplIi0TBy8wKQtIo6CbvgQEGABKbBlNkbrY0OrgxVKCmJ13Av2KfBPpWtZKwT5pQ45WhaJlainKjJQdl7mLFhRYRjvnRgliMIvDXEmMTcMCVoJJc61EGgNvKxeoGjsSmcNagyNmduyYmIiZQha7iSSbJow6H44L6wd5JwMq+kGEM/jEh6cjeGegjuNA4qFkqeOaShIcCxuGZgkYQariIe8CbEsAANYsNqdVJoPhpEg94CACbvksGbLYdnZfqPS4WFgn2Q2NgqACZj7COcJDNnEQY+RtIZDcOB/NoXgFOUxhNacmYXkaBRpbbqlTfd88cL/37W1beOrQJJC1GWNpNixtSmHlJqE0ihkRE0W6mSnNVT9A1BeTFgRcWtj74L7xngZ3IEK5pyWuSeUEL0IGaCgprwNHDg6W83337i364+76Zb65B+2gF68mCiwSGC2RldQUJgxtUrYHHlmqmKmfmwWYSVQUp3MiKHeWbiVpEkGaFneA/RQGAoGeIkjpSCFHw43JZBr9AAjJagwOIIDW5liHUk4JWIWCKYgnMIZKKuOuZiJ6ig8Yr8m/Cgc8xcQBF8NljB2rN2krvS9F0ew/lFAYAISUqkbfCEetuwDG7K5FiGJYo+4Qkg0SUea+05HPtUCqtwyAROQGb2nHmnXHH9CRdde9sjj3dJxjn1IubBBtadge6Ihr2mRHTSKgLVhVZoTpsUo9xnwnu8caFalVgjsSvMARAHgftElAjoUWbjMEM06YExkwMatBj0qFOzoQIwCXiEWTOTkHONiheYoEwkHhRcifHQtApZ4oGxyrlPWrEjRgIMsLSexYWxS8gQphCF6K57b3OrzLzqq5enf1//JQhExEnnXfynv5yEm0UWXni/H+69+sorBvN/bJ5zestaaxz1q5+dfsxRm37gQwsssABmPv/cM38+9ZSPfWaX7b7+rdMvvPT5WS+a+38s+dfelGoznnrmrxdc8pUf/vyw4/4wn/jDjw4Hlfn3L32CZZbpC0x/9auW2+ID7/vpnrudtP+vbzz3tL8efdhvvv+9L263zebv/8Daa79xkYUWWmD69NFopAqDplL6OXNmP/3ssw8/eO9Fl158/Cmn/PTAQz7/zW++4xNbvmmTj2z/le/86HcHnXzJpQ8/88y80k8ZO9UIiIpshdqBu1Aj2DhzE8EohMKFqjjg8DAPCRFjhkMjYiOIobZIZJk6+A0y4cgUwbgxkkoCskML+BATvpun5EjiA5/KIBTYzLGJWzhIU+Hos5taCJwvnFNpqOQSrWOLEDXG6xF1lUiiAoDAErybzCRo2FBzkRQOlpwcCayCNDlCUGD8JVyDqZDhU5J6ampI4X74QNBA/CHMLFF7KmAtBFQFHwryQlWTIaA4ALBEg5vLVNwCSEwK9ghkNQUK7OFTkOMEaPUAJ4J7T3ihNYg17BRgGZAGOZjsvVbrwsYcNSmLa3ZNHpkC+/BANmDDzAEENEcKaswygz9i/EcgDzSDhh6IfsHfb/jVX04/8PRzx6UDty5BYsS4nYqLgWIVGnKk5VAncQKamaUJSYTGI6U2gUs3dlxUTaUKSmhdYFr74x0+8vWPvm+fL26n7fQ+2I0gQS1eKnshrTwyniDP5GRE2JvVkkAxnlShuKwExVexSrCsOuiiCKSQfPtDj4Dtn5942hPPPx8wEcTjIJgOh9PUhUheiqO2CA7SSEKZGDYZJmzKJkKUmFmDlQQoqYfCDsKZTXXgwcWYLJU6cnS0XBWDw9oklQXzCHxHdYJhVKaKPZTVuUHoz1EGrh2FlWgQEZQSLPis5jg4eIEi1CR5k1KbUlKlZJ32xauBRyrMnYkhXRcTSM+O1ZBQybMxCKIFEgvIFAg/ASF7sh5FgkHcoSKgGPyqsA+kXJwZXkyMbgdhgvET18qlRO9WK7atzpWEVbNQglKkH549NBAZJJWRLzWrCjFE9sHqS89WsNY8QFYplKqThdggMuFiSi3jwYAkYhCmLDWwnIgJDiEC0JyVA2MsTXAKg1YUFRErQEO90kepYbVnUK7u1bAf1x7emCtpIQ9ElmEw+qqlwO3QwBxZ8XC4psMyqxSWkrhmZRHB5tB4Bg7ekVQhUghXm55zl6NXD64S4FmzcMOEhvnoYfDtwpFFoWRlEyniJZcp/5MBgeiJO8o1krvIYHUKjVSC7k0GgwmzcIdwqpyUlQVUXQxPMZJS5iTaOjddUGfwKLiKM0eiMOiTQULd1UKCwCCYqSHVpWIOMycCDwCRB0TcGBxmz42ruNJAR1hUEypnZRxCUCIpE1PWaEehkgwAjgXwsQgLQgvGik5YglaMod0EwYJUk0BXhQIAW01sqSXBFdhbPTRUnYhKqNXMPtS7GiREypTYFUbiEYapBBqKniSSJZLHNLORc64ihcgGc2qqpk5BDg0zfMK9MQjjBK2MAp5mHtWAUOAC6oHAB4l5AKEr8AqMEzAfRqPCRym5cRTSKmrNFBfVjbxi02jYG6JE4GhqU1inEyQkYGPMREKVzSU4h4Cllxo3Af1xKJwoJKgBypwN9jEpUSE3UaqihZhiYMZgZGR4SAa9MqAT7JcjY44YkdNfLr36Z38+69AzL5vXmcG0RJmhJ03/4VtB6kqRgDysWLNNJCiVOJHjEMeBfaIPGbtW7MkRgn0wCo9x6gN7sFTxytUQRoirUOGK/r9ccsUvTjj9qLMvmYcSRBMrsTpMvGI+diTn3FsKzFemXJ1wpi4VpIxg+1xAfbBcpBcScBskxMJCYF2jBBQSlZVTzrlVFwQ0CcXFKoLoIEAwU0pYAeEGExcljCloOCdjjVxDw5vwlgasI2pQV7X0jXcCXSGMWIU+3aBz7K5MAjvM3lN4paiJqlZmb3SADAfrJBTMxJkJtk64QNjaoJGLGEZaFmWOoFJ5iMnMIiQpFJaSnp4163ennvvLk07/x133lNBqWl2DFASd1IhMSsC4GSrjVMkJUJszWIgkmjTjh5mnQpIi+Yu4NiaVwqDiUVB2YgKA4oylMeUPbBxQJqMYin5AmjIzuA9E5X7M1CcilhZgl1KIHawSpIMcDbMaJkxQaaVThso8mCyojglGQyQEu0oU2bB1EJFqSXL9HffgdsEFF1rx1Svg5t/tvwCBa2665dcHHVT6fjSa+PZXv/KWtVb/72/K/NrXLP/bb3z1lMMP+cJ227xymWVU1cyuu/H6b/z0Z5vs8Lmv/fQ3Z1921eNPPdPX+t+n8P9nL1zryednnnTOhZ/71vc+uO2O3/rZz8+/9NIXZ82cT+a+Bx+af/M/+oRRsWqTm9VXXPHDG77zqztt/4s9v3rKfr+49oyTzz/+2D8fdMCBP/nhj/fY7bPbbPOBjd/3utVeu/wrX7nkEkvmdgIEA15c67zJedfdcN0Jp/x1rx/88J1bfOqjX/rajw495ppbbps1d54NpkypCy1EHpEDJWwH/4fzcSiJ9Bq9I6zBxonJNZycEK6tFwl4kU1QjFDMaWqxtiKOo6CA+6fwpiD6R81UEVNEmXVgiRBsUE3VvhjqjqrioxR4cE2tIbqxNCyCjYLMnaWTpo8R0YRIq3g6PKGciYLh36oIBio1EMuKKQJHBWvUU1MiuyE/gT0n4IdtiSSq1g4xmAa1Sg8d54wAABAASURBVEqRc2UFiwgHkhQ0u4i+WunM+xBEYOQd9PggvSqejxZqg8Gfc0KKsoEJJq1q0QQnEjCeEafwDbtRMMUQdqvWIUKRqGtDKIoYgvYupTBkJIOOqBiRV+FQcOKjxgY0sSA8EVIHId8EGSdW/OehpbTkjRu54UEKnvkYK1UEK+KX5JWwhAjIELaqI6cGJFRiBRFmQ6zONSFWEeSOSL01nSU39uBg4GiFut57j4okiTIrF4uVll5qp/e/Y+VXvzIarkLjSqUjxHHUFtQz99jfM9eGulTH3PdmPob5IIpmHWkeWcpVo3LnAoujAPZ9kPUEBmn+ha9OY5ICJsiFPM/vHz57qr0UICU94VwqotQwFOXOESEUKTg7NxQZAqLCpHHEPJdJgnVjWBgMATDqejLiISlmJlx9DFadCARUS5aSvOTetbiGNRqtSisAFA/2uVSqBbtBhyagwdEOxZ7DbyKg9hjgg6E2EqkadzUM0EABUnVUucVwQzBOADNJQI0sVbBAWNe1dTLV4kSFGaRgEhKSGOceltZJjEgUqTQbcpKFWxAuBhAM72J3D8DTOc+rMS4+2eEr+Cx5IrQNZSyIClCtVW4ncho1UAqlhqRVlGENUVudrfY45njP1Dl8f+h4WT9MpAItdqSVIthRF0pQDcH+bQp4Z5LkwJGEnHiy1clMYyUTdPZOVh3raJCpKPepVsYWWjFemAiz2DwqVKDJa47heQUThzdeOMIZH0JQwZi404zNhuAhVbkIGJHGqQnwmC03SNlFeocSqgQ8L7eE4tNhMso8wJpZeLhCqChXJQqWotI31jV9H1gK6zK8gZ1g0lCDF0lDbc54WSKkbhaIEhUKpr5J1rYmIwu4JUISDnwYJomUSOCdFi0QofCgjsyNiFgJ58dGSYnQjSYcifFVmESwXVimKmpFohD8UKFQ6isXTSWpK1RQJUVOmmEkmlqlCUbhH003yEjKDtI9uYeFkyDCK1uKoGB8x74aIj6qMXJEONDzGrUEdmOiUIKj9FEpKnNIK67JuHEfAUBughvPbWlyIaq1lHE1w0wJGoJWZgUPziCUldFJxeD5WolLig4xnYIpGmEhHtB3ZbALYpnAX0TAAseJO1VndLVAoI+w3oFAAxsh2BhmUvEwM3npK1ei6lzGueu1WIA2UTMlYBqiBhXuSMcNTJKEWfAfQ1RYH/ZCvUsvXQDOYNVNNAFzZuU2qSS2KF68AlL22vQ1YQNEIQQdg8USC/ZPyD/hSfGKoSoTB5ENun+ZcqgSJwwg8dThr+zNH2CiDMGFu2S1cWLwR5R43MikIuRRRCDVMJZHQ4F3ViPyRpnb5LmxUJ9agDBQincMv6JxkS6ko6kLplcchlcpm7V9n2HiAC/RYAn46vCDVJNMySRmWqoRBIa1Bjl0peAwBU9zQm0B/gka9UmPzlIhJSi9IcjFVmEwwZYFsCSx5NQSiYqMJDfRsOugoVBoPyz1Rj2bN8WgVXhVJPLGcUTpR9YrOaExMa7knhEHfEgaxFRTQgbpkmMhFMQRNOSvPKjWsEwqydjJggIAcDAbEYT0DKMHp5bbKjmKaS04H0+o5UwsUob0LliCxgFzhaCiQUlchu5k1Hh14p64YFepIbUCd6cwrKFBslZoJIgWGh4BJVRvgoTAQ44+IYsUgq+4KuckOalia3Bn0Yc4C2EmExZImn/ocYleCNhQYk9qIFSZgHHAFgh5aqTeCDc+aK4WjRpkZOCvh8kG5nM7WI5kpwRrnyxl32P/dM0//k5EKtKo4ubf7T8bgceffvYHv9v/xdmzsdEun95q8w3fGbj7HzcnXmHZV3xt5+1PPvTAn33rm29761tTC++j2S88e+HFF+6+996f+PyXd9rruz87/PfnX37l/Y8+Xm3KAv/HBP+7I+Z+270P7L3fwZ/84le//6tfXX3ddX0/RA1tJ9Z87Ws32XiTPb74hR0+8bH/7tr/USfkYoWdDqa8+KKLrLbSqzdcf91PfviDu39m+32/s8ephx142hEHn3TwAcft8+uDfvyDz++w/Qc3eu/rVl990UUXm0/Qze64844jjjl6qy/suvUXdj3u5DNmzpzXkLB4lb4ogk5vilZqpipKAY9glc6aikeYY9HC8FkQQ7FlKiRYSgQvhef2nI1a+HtbtekyArZaUa4BUkouNFxRhXoxBCKXghg2qtKSaILb9Ym8lTRSzg16PRoOFWYmgncRIkVFInAEHse2cEqwIq5cElhixItUhhczKINazlkC3cxYPGxLBBEjI3AhzEWwMGUyaTrCg9co6s7sSn2mIgQnJwQY6i1KqR6TWTuViAS2kkWgOTFxKJmqs6AEIKqE0CDISWgu4TjSJHx4wkTKRXLPLBYAcqRg2RHrWbJwJk5ekQSzBkgxeRrIGqOaMIXCEXxQC3RBpURUcqNa3SaZx5GKg9FKWI+1oWCKpi6tnroiqDqIgpQHgUnY2+hzGDaTIKlJevGee0dmqx2PDTonS2qNkEIGU/IUzpDY2UkIvSqemfAtAvEvxKuySSA1ILEZBhiMAxTMqMGFsFFyTGkDNsTqAjLM0GQfXKaWOTnxFNcEmmTgoqMYB+FG5/dPfVY3/Ad6ShySKzG2CyYLHVftSwOZoQlgon3g9I2MAjWEAyOsT8wMNqDgDDBYQMPVYExBjMgNzUfrPFFzg0Lemam3KFCBBONNUcMZ/znXnqoHOZIFtw2nNlITmUIgQ64WvTOsiytrl6kfUmuh7DSSyC1r0sQSQMuhMQvYVhfFwBxBDE8cKN1TlYBRokWuQ3dVqgw0NQcPNIQ9YJde+uJuTKyUFbvUmqPC7KFsjZBC2nPqGi1AiB24wwyEB5UKQ2YfeBYX5axZeSTUBnNgc/zCDjBVHfZXatpMUxc7YC1sbv24x9WFu4o2rMKkRNA1CECKAEIlolbyQtFXi9qz96kCC83I34xyV7E4OIdIYAn1rReugiPzmJrIrAJyxlaZ+8RdqE26dJUBcTaSlKNJzMJm3te+QFcJ1EjJxM1Cg0ZOWjQM4VJDhs9KVKNSMmkCXECsJoiJ4POWYJcuBLQNisCCqAqBlSxrn/CdNTy7MXpAWVBudKiylNBLNYN1FOGQUKuL1xibYbcgyIZNiYySK9QnimlQOhSaqEkQFBpIEg3UxjBKYU5aE1gqVHsBhFVYEEN0sEmmgLm4NJiUGG4FaOogAUN1Rtgaswu7W8C0PadoAJWRIno4rNmqZbPsLozYKzWUe5LiUg17q4hQYshtNXAksD5AiJxCiDSAvXk4oI1cSqohWK6sCcFxDP2AhAMn1M1qEkE1cTCR1Ia6lrosABYsVyY8/iIiJs4GCDSbNsZwRoAFIlXcOIxYKoaIPXrz4n2EgT9v3bV0yRAjYoiqsBZrcuGmBCQhcO4R0FLJDA4gZnJ3mAn0xqnWxo0dOGb2lALFs2su3pgJ5tDLV03kipnhKSnB9RyAmqFLpIrAJKUIO7CBGFWoZCqtU1MtVeAPO63GgZCtiAK4kZcJU4gHVyYz8Qom5OUhzIJXJOwj4S1RQ5QDtT780tVsWMEInwm2zdEQJ+ZSyUMQUxqhJEaMo1mF+RGYkkzQZQx6Zpp/ae10PowcsLAUY+ARRJUgyWCvVqRyYyicRdVEirEVLQhLU8RIwIRidiEKAogS8DcYASemhvHJoh7UVRuet7hIL1pFAgwgChUlb0iSEblXJLGoAmEqQ43FcjUhY6nS9GlkWYiDA/tQgEWRgboOOrSevDqRETsrh6AFlMUE4J2KW+kdk7hSy6SV4EtsBIMyAB9ErCEaJETEjMbsykXYhZhCyCQAD82/gs3hJ6mQMI4wEIMxUUiFLWkRwWzCAgksJnPvosBgB0KCLasHuDKFpopBUxToMK8RThGilFJq82g0/DJFUDESb2TQ2jCpAgDJ2mhCyGkqgqkF+FICiqw1kjk7zN1NuG8aUwQKhoFls9yX7EUIHPfDHBhTTEiZUIOtx8HH/engo49FDAc1c0RQw82/238qAsXs27/8zaMzHsUum26yyWc/9clBl/jyf9XgAIsuvNDm7333YT/70V8O2vczW39q+Ve99Ef1nn/h+etvvOG4E47/6g9+uPWXv/qJz335l4cfc/alf3v6hVkwwf8rwtSXet7frt55r+/s+PU9TjzttGeefBxLcm7euPba39x115MP2u8P+/56/2/vvssWH3v96qti6H+lzRcWn0HsQdOmTVtmqcXfuMZr3/uOt++2wzb7fPebx+/zq78eesBx+/3uq1/88tvXW3c0MQ3beeluv/POn+y7z5Zf/fLxF55tWvFcIsQZXg8PIqoapoM3EQVn8gSjDziKw5EEMQlBkCaCGowOhu7F1KLx2gy10XjEJccQbSMx4hNikDk59iV8MmUGSc6R1JSdxTr1DgVmhUcjQmVVT00vTVGFB1aSnrggA3U9+K7u5sgtTzz5zBGnn7/bPkdv+6NDPvuro44669KHHnsygZ+cObOoikvmNLUpPuDX2pR2oiQ4MnHHXD34khvu/NnRp271w/0+/bN9fnn8qRddd3snYYksnKaa1Prj4/780R/v/7sTzyHiW+594Dd/+uvWPz34oz865OsH/fH0y/7Rz62MeOL++JPPHnXqBZ/7xRGf/N4+n//VIceeed5jLzwtI2Kkx9AxJA/SgMzAhJ97dtbR51642wFHfvpHB33x50f88cxLHn38aSKR6moFz9Qo1dsefuiT391n+70PevKhJ3hef/4VN373wD9/6oeHbfXDg/c54a+33Pkg+EecEinX3X3XJ763/9bf3ffCG+8gotnzxjv95siP//DA7x11alfIGerDxsZsN91x/0F/On/nHx/2ye/uv93PDtrv1LPufugxKwEdGlNEVajNIgclzQolR4gV4Q4TvnHMXz+690F7H3Fi2xviJoL83Y/M2OpnB27/q0MfevaxeT7vjGuu//oBf/rYdw/5+A8O/cWfz/rn/Q82Ru04eEwxRpgXlCx4sIOUmmrPhR6d8fxOPzl0m+/97oizLgXbaN84/C+f/P4hX9zn+OfnzUKCIXN0zm+R6K5HHjjgL2d8au+DPvbdA79y0PEnXXXt3NIHeaD+ih4cGiw4tAKUiEeffOros8/d6VcHbPvjfXb/3aF/Pv+S5597AUfoCWhAmmCiIHKkGafBIGzKJIwbxisGvBoYVUOhE211lLWBKM/KGUvYIwJ5QlPmVqnxEBJGvuZgN9gwwayG1A/WnUk0pPEpg2dSCSOv5H1oRCY4WNUKToiAvXhAAYKUqH0ybmrKMP5AigPFAHl1YY8UhCuiK14rXIHYK0clVJ9PPfvcHy644muHnLDFzw7b5qeH/eoPZ1x5823mFZm9Q/5N0GoziFAC6rDJuOa2+3/xp9M+9eODPv7dfT/360N/f+FFTz7zbEpZU4JArhyCPdWx3/yGu0J1jPjmXe2H2pHk+dlzjjnr4l1+cfCW3/3dzj/Z/6jTL7zrkSdrQHVxVFbYAAAQAElEQVShnae+jmrP1ZC9HfQy4CDNwMUHM1DiJCTSJe2klqiojzUqK/Go4czAqYy4jqRXKcHwDqTgxN4GNeYCVUQpJfoqVpWNs7MUDvgwOBDCGw7Fw4Vgghd0EV0QZkaqoeZWyYpaFexHIeJJe+ZepM9tpFZQioBWrjVZQSgwDAtX4+hNnMIbx7mFUmhSwUCEA56AOgLFixBDV1bZKhE2S9UTdJgaT0M9a6EiwkIkxDR8sCtBt7CKyt6pddp11qNYyUyNcG5ZJsgmJBolgXp4CLYAg8EakUVYderIxjhD8KT7GAIa1NdknEEBuAtqtzCLVItEkKfw1iy5D3x6eESwqrCyO1kfVsOZWESIB5Zhl23t2+gUhWh4NbIAIQ1gyRl6DIKlQHTtiLvATo7hUU0JtoeCrqTc5wYWbYJzQQeDYqJsadS1MHXuKSqFFp6oAXVn7qcoJyGSqGKIF1VKtMyJu3586bU3/fJPZ3785wd//KcH73ngH35/7iUzu1nU9s6wIiDZUBJBVdwI9PPkrBcOO+eCL+171FY/OGT7Hx6670nnXnn7vW7O4vDeTlPHCgMJNnA1vzlDeLTkDCogaH3Xn/ePm395/Mnb/Xz/bX5y4E+OP+2Cm+4oAEnIM9VReNOT9omqmpF3YyrzNC645aYtf/LbLX/82+vuG/6M92PPzdz5Z4d/au+Df33KOR3juFXnuz82DaJJqpfdePOPj/rjFt/fDyHuh0eddtXN97uLW6SeYIyhYMdjmsWIbp/x0H6nXbDjL45FvP00hDrhrFvvfhTWFi7MWVnPufqWLb536Ce/f/BtDz8B+g88+cx2Pz/iUz84+KhTLkkWTSDI1+yVA6Km4TOiBj09Z/bn9z3yYz8+8Ma7749aLr7uxh8de8KnfrLv1r84+Lenn/PPRx80aBauVCNYkuYJab9/+J+2+P4+P/3DX7tufPIlV37toOM+9dMDLrjhRmOqzGHMY587c85x51729QOO+cR39t9578MOPfGCm++8n/pgU5hYZDv/2ps+8f0DEIgeeOJpo8HzcwWiIVVB4bIb7vzkjw/c4if7Pvzkk04UhN0Z+1vohTfc+tM//nmbH++7zd4H/vzY0y/8x23Wd4ydYQAqEphuex7wh499f5+DTz9bjO6//6GD/nze1nsftOX3991j/2P/9Ldr5tQOZkDBfU+7HXDsJ3+0zzeO+NO4wHHpuIuuQmGw9c9+98BjM5gcSVm1JibSBGfBQVeTkzITImZWR83D8NkCXyJydUtGiCNCgIGgmDbHtIi2F65SqYXZVSYSZw1JGgj4qp6gDCWP3mPMgzMydB/kHEZG/ZijqHhOpCSGUgGVRR8aoUxFRyU1ruAwmkoZqiWqNCi5EPZnxCCY0PU333bwcSeUUmAWaJOTk8+/8AJu/t3+UxE45pQzrr3hBmyx6sqrfOuLn8tQIb7832+wspxWX3nFPT678xlHHnzaUUd8fqedX7/W61OCpQvIzJo186777jvmhBP2/PFPP7jN9h/Y8fPf+c1+fzz3kpvuuu/ZmS+WWku1YlZqfeTJZy645oZfHH7sRz+/6+4//NG1118/e/ZsZl500cV32OITpxx+8B9+8/MdPr7p6iu9ZtpoIpgD1P8zG+ijjUajVyy91DqvX+tLn9z08J/sffmff//L7377HetvAAHd/YGHHvjer3657R7fnjVrbuIM82dVEhG2rIjhNWfktMQOP6AUgRjnDNfKxJhC1rEZmzArMUV4LdEH+TA6dMJlVMiI4RQGWcGPwzUzc6vSDuecthQc8ZXcqQYTnLhXqgISVG34LnVqJ5xUnKtzUXhwPeWqv3/q5wcedMbF19z54P2PPXX9vY/sd8ZF2/32iNOuvSkiwE8BwVCRhE3RAoGDcK/KDOhHpnNmzvnSbw/57lF/OvnKm+5+9Il7H3vy1Ktv/sHRp+617+9nzn4RoQCso6xryB979jmMPvzkM7/645mf+c1Rf7jk7wj998546vyb7vre8X/d48jjX5ycPPeq67b9yQGHnnHJDfc8fN+MJ6+764FDTr9op58d9o+7H6xiXRT3AoULwOB86uU3bfXzAw8+6+Jr7nrgvseevOG+Rw46/cJdfnXQ+Vdeh1yZyBh1DRUEkPtmPAHeHnpq5p6HnPjDo0+78Ma775vx9H2PPfWnS//+1f2PuuCaazMycROz+/G9M57EvrPmTkJYc7//yWfuffzpGc+8UEkChiZ1bN0+fzl7l32OP+r8q26+71HMv+mBRw8/7/Ltfn3IkadfYiUolCJxVTIKA2AsCuRdpWeEe5HHn51174ynZzz9PARBgG7FSj953+NP3/3YU/c//dw3Dv3z93//1wtuvOueGU+j/fHSf3zhgKPPuuZagVkYgSZuslKWIjrpyZ19spYHHn/6/seeeHrmi2Ab7ZGnn7/vsacffOrZHkpkqBDGgm6KiJMuvPLz+xyDzHH7w4/fM+Opi2+666d/Puu7x540e3KSK0khLSTVclQ83Dz+zEu3/9EBR5x96Z2PPHb/Y09dc+cD+51y7nY/2++a229F6cNJa2oM5sBFpJIKDiW91yoVtSJnSI2iSzngCAk9Vce9dBUs9yi7chMZRIQrrBo8gj+WxEopu2YnDAR4SYZ8oeyNm5YSZsgXZFjTSxQUP6KtN42atD0oUJD2wzuARM7qdUCq5QYe6Rp97iv8jiOIgkEym45IYZqKeo6KwlHsnOv+ucOvj9r3rxdeftu99z7+1J0znjzpiut3O+r4Lxx45PNzn6uoihqu4Bv1We1fnP3ij487YdcDjz7h0n9M4fnkNXfev89p52//u8POvPI694AzkxApEVYWHzgc9qaecpHcQ1RER5WLb7gBJQKs94a7H7xvxhM33//oQaddtPPPDj75wivCCoqhFoWihETVPtLYqK9AosIUVFiBM7MoaWsOmYG5K3QupYoFJrRJJpTywIw2Lk1wkpx4gq31nktPpWcwZCyusFBAbIWgTzY8HklUAFZYqn2OHqoKgljOkIkF9gQngdFHdekNmyfGEcUIQpIQKyITJbgOpGfv20qpOAd2gjPBO5PCSigxteJNrgDFtS80aTEOoZTyaMRwkaCoTIhnnimwsnCUYGKojrN7E+CbMKlUQpFUBssIKl69jvGBGi6yipJqYS2kEcI1NyVzSSDUEw0MqwcqPhpzjEX64BowoVK51kSWGXrKnpMlYy/wbbgegQVjsmANxouI7FW1sHSDfVYKDxbH85yEtYlUAoRSZ8nDxV0d+0nguNMxuKCeubOoxagrgkNp2HBYYzMlV/glmVXHT0lUFUpJbuJIBwQ2aGrb8J7EKRMliCssQIhdgyaM2yKZNKu2IiOYY37yyVnf3u8EPII56W833Pv4U2gX33LPPidf9NHvHnT5zfcWUCR4ktpQ+HEonXfdDR/de7/9z7j4b7fff9+Mp+555MnjL/77Vw7547eOOfm5Fya7nksIYLIUfQS9fDG5kGgkdWLmJ2bN/Mw+h33/2JP+es1N98x48m4415U37XnUKbsd9vsXu0lJnnmygcTG4ppClElSb0334pwX73tsSC5zxx0R9bU+8NgQwB9/flaNwRWgLPSjzZ2c/P6hf/zWEX8++7pb733siXtmPPGXK67/+gG/P+nsC+HfCaBalr6JSHPGk3sf8aftf3HY4eddcd29D98z4+lbH3z82Auu+tJvjzjkpLPJC874OuEz5865d8ZT9854el5fQL8r9f7Hn773saeefmF2ppYpsyOoKAUYZoKUKawZzroPPfH0PY89cfODD+70uyO+//u/nnPdrfc9/sSdjz527EVX77Tv0UdfdDH0SR0ycQQnT/ro08/eO+OJBx578rtHnPTTP5595W333vfYU08997wqDeEw/IY7797y5/v95pSzz7/pjntnPHn7Q48fc95Vnzvg9weceTaCauLUclprleWfem7mfTOeuPLmO+BXI4NPiRj0oG7ptCtvunfGU2x1meWWCHUSmHXzzLOzP/fTw75/9EmnXn3jPTOeuOdRRLwb9zjq5L0O+dN4sm8tWjDZa5T8yFPP3jPjyQeeeu7MK6757G+OPua8K6cy+JOX33rXL088d8/DTpo1uxArPPThpwDRE48885xHENGzs2bf/9iT9z/+5Nh7S4JARqlKg3BlFQ4kA4qSKUlKlrUi+UhAR5SIIVaWpiFVZgmE9WxCTVH1JlIqGQZWnT3YnAKNQwSTm/C2WNtVqcyIFebmpVfrEltQ7WopbqEhSRVOU7mHIozVoiFiosKpB0csoEekQWoqphV2NtDHFlx/deDB/bzZIvrWN7yBsKT09z78MG7+3f7zELj6hpsPPuoo0F98scV/ssfXFlt4Qdz/T7dR2752pRW+vu2Wfzng19ef+dfDfvObXT+z89pvfOviiy2mGU/QfM7cuQ88+MApZ53141/+fKvPf+HtH/n42pts+sbNtnjzJ3dc+30fft+ntv7Kt7557Al/vP/++8ysadrXrfWGH++1x2V/+v23vvS5VV+zQoLhwpqY8EH/5ZezSEoLL7zI5hu956if733qkYd9cOONp02bBlavvfbqD2/3uX/e+UANdlZJkkWboFY9wx9IEylrqNmQB62xaJHrw4wsBp9pUgPaXoV6S9UQAHMMicnFLbspofaB1ERj43muczXVJkXDgwu7CbNKiFZH0eBW2Etbx21UwSbEEZqcBa7XUOAXnXblNT/+09kvzB2vtMySn//Qu7+19Qe332T9pRdZYOa88Y//ePrlN91q1BcdQon7Syjj95ilV6mZiOmF52Z/ZZ/jbr7/kSalD7x1ra9vuekeW226wWqvEZar73roOwefgECcjNQVuZqmrov/ed8fL7l2jRVeuetH3r/Hlptuu/EGSy2yIMLZpbfe/fnfHf2D406rZtu/d/1vbPnBr3xi43estQoWPjd77k+PP/P5MWJC36BKMKDLJ1547Q+PP/O/Yf4DYP4Viywwa974J38844pb7iAJhCjzcEQmomK+9wmnX3v3g+9ea+U9tvzAnlt88ENvXWtak+d1/b6nnPvYM89Qk5dbZrE9tnr/np/80Bte8yowO9E2X/vIxt/Y8kNbbPQ2UYR9oYijz7jk6AuuruZrLv+KL23+/r0+8ZHPbPKuZRddcNyXw8/52x/OvopK69bWmkqRIYkieiLYpUjI16pBOqVAkB+UQKkkLYmHaA4Of3rc2X+/+6F3vW7lPT/5gW9u/eFN110bHM7t+t/+9dyHnn3KhEwpSzTkTBU/JUk/LS286KJf/cgHvv2pzT/wlrUGukTbvu8du2+52S4f3qRpF5pkVE51fv+1t9+z/6kXLb/EIp/b9N3f2GbTXT747uUXX8QjLrrlrpMu+zuowp7UNJkr2zGnX3rgqRf1pa6x/DJf3Oy939j6Q596z1sXX3Dac3PmfeuoP994/31gukrum4znipyIEn64iBbCudsLh7lYJPfGWVzDiodxcgAAEABJREFUxJAtqO/ZoJksrAy2wgPKkeJEwRStxgifTijEkM2wVlrkNiwv5FMt+uAS4jlQmTdKjWBna6QnUAjWyqlyA6pElqg0hDGXYW5TVd2x1AlCU1KeyNKqsHKwefKbHnj4e7//61MzZ8Mmt93kbXt++oNf3Py9a6+wLGZfe+f93zroxHE36VThmEx19tw53zrqD6dfezMer7x55eW/uNmGe279we02Wn/h6ROw2O8dd9qJl1xVsZEGnI2d2cDgVBO2CfCfJTUpN+ddc933jzvl2TnzVlhq8V0+9M69Pv3Bnd7/zmUXXejFye7XJ559yTW3CHECYCmIGR6cq6BuZdSDcOuGUdzVFDUJhBPnple8fdQssKqe3LxSkJKOAhmZGPeJZSTaqIR6AI5qw7GHlZos4JQiPLDISVzIG6qCVcFsU+YXQJWJJZiM3UFCQ9gtoNwpk/cwkCBzhkpLuOcaoONNCRQ9gg4swwxnNU6mLC0FPEDFmMlpcBtH0MjCOaVE03JNCfqOSGAvp4qJ5lYw21k1A5OOtRIhhFot1WAlw1dxS+AgyAV1TVMR/6SwdEGICVqDCuN8UMEtdZwLabGw3p1RxgoQdZQ45ADcUR0pBVPjlNBA0ZLD7E2wRwkcb7RyIgF8cJ3IVmpEYciGekZUNREa0HbCf6KhiUlrhYULQAQHFYx6j0g9bJUpmurSE+IcbJKyRYZ5GpehS92pCykGvBtiIoJGKo7xEC4KYVmCuwQ2EcrKGRAFh4MIuzDn4a9UjeZNlu8fftJVdzygqhuuvepeW39wjy0/+OF11l5g1D4za843Djv5xrsfjiCAZCWbpRMvuvo7R588Z9wtvchCW717vT233OzLH9l47dcshznn3HD77oecMJ6s7KRMAsuQoJcvIfJAWFDFS4YXnv/Cvkf/86EZoyZtut7rd9/yQyCyzsrLM9Nlt9+358HHeu1hK6lGMsDeJBgkM6JLTrbqq5fZ81MfgmussuySoL34gtO//pEPfnPLj3z47eswMUMJ/tKmh51x0eW33r3OKivs9rEPfmPLTT++wRsXmmjhoYeeecl9Dz8I8LE8ajOeZz8+8uTTr7oRBvPGFeG87/vGlh/Z+f3vfNXiC8/ry/GXXHvy367RTDCstVZ51be22fQbn950hSUXxdpXLLbI1z/14W9u85EPr/9WliZk1OWJTlq4AlcVJyaoPNLLIBx2zuX3Pf7029dceY9PYov3v+9Na0xrm3Ep+51x4cl/v5q8R67uQ/oEqAgX4syFN96+zmtf/bWPbvTNrT689sqvEaNMdP0dd+111J+feH7W4gstsMPGb/vGFh/+4offu8oyS4xLPfz8K44972JlydQst/Dib11ledC56NqbRlSEgwj+lijkhZlz7nn4MSJ615vX0px0xNzIk8+/8IXfHH3bg1NKWff1e3ziQ7t/8v3rrDqllNvu/87Bf4JbMDzF8PslDq+8496fnnjGkgstgPSx16c3++yH37Pi0otFxBW333fcRVfAaJTt05u845uf3nyn978T+2DHDV+/+je32OzbW2++9NJLFNISwI0CThgekZ2bYFyOH2gTRhUhDJPvG3MEh2YkMUEBRI01ssKHqzWBVUqsXTR94DM6rp0XH7N0TRjcojc2a7niKU1KVJkrjFEdPgd3T5gRmFQdppnBUraeCd+4wvgwyQcWJDnnoKyp4SwRrlwAgzNfet2Nt9x2K2R727rr7r3nHm3T4P6iK66GPeHm3+0/A4FnZ77480OPGHcdwtaXdtxu7dVW/V/fBQYHbcJtRxOjd7xprS9vs+Wp+/z0rKMPO/TnP/vijju89a3rvXLZZXMelDt/r7Ba573YP/9EuM3vYU1LLLHkBzd676G/+OmJv/3pJz+wcTtqYcTzR/91n/9LlCDgqq9e/nff3uPQn/901ZVWAq0nn3z8C9/+wS133+cYi1ajQTgTWDkxSYnck0JCMaQpnO4jAmGAe6WAvwhiHNKqp0Y4j4imUW3NCOmTSi/WC1ViRB4ihMWojIFJwFbdQEeVacjaIiQY85LCMoUKnJbd4OJYm8QbtTQKfvyxJ44652/uvuEbVj36Gzt+6ePv/tT71tt1q/cfuPuOKy+75LjaAaefj7c6jmoim3Ol+Rd8W7jLNEn9uJajzr0ET8UWnGh/tuNHf7bzJ7bd5O1bb/KOg/bY8fMffldS+cd9j15y/V1sE6gWnVrigYQyf+5DGx642xe23/i9W7z77V/YYvOD99h5iYWG/0vSrQ/OWGyBiSO+uvPuH//w1hu9fZtN3vGLL3168w3ehGX3P/HMnQ/NaHO0HMhk9z38+OFnXQrm37P2aw/+6o5f+NB7Pv3e9Xb/1PsO32uHl5m/CJVMRDIbWSgooHXVfvDpzff90nbbbbTBpzd8xw+3327XzTZG//Nz5l13131Musryr952w3d++l1vX2XZpYiozelj73jzNu9ad5M3vq5lVkrPzuxOv/ZWBNd3vn7VQ7/1ue0+/N4tN3zbVzb70NF7fG71V70CS0646Ora9VbxYDb1qTFFdVmDoHTinIXBvhJj4tCYEJ+VNP6jBxzuvfVm+33h09u+722fet/bvv/5rXf9yEaYCg7/cfe9lmqVsXMXVKDhzpWkYWqXWnSxLd+xwdbvePu6q66IyWjve/PaW7zz7Zuu89aRTIsgR6mAXqKu+kZvXPPo7+26y8ffu8V73rrrZhsd+bWdl1hw+EOMl9567zwPFlUOEfvnvY8ed8E1wrT5em84YNedd9hko0+/963f3WGzg3fbcdnFFn5x3B96xoU4w4D1oEwpI5YLs3jOrow6rnJfUVBRYa0ShVC6EeBAvQhbDOlq0xX2UrgvqaM0Fp5UHwf1lcYBa4P3GHACN8qSosm1VUO4UI/k0ZK2IgngKuaqwbLJBwkBqeC3M7rFjMuY+3HUWrE5UHAh5sjDhziJw1dUiJqobeBTT77kH+Nqyy6+yMG7fXbXT2728fesv+0H3nPAVz/7kfXfzMy33P/oP+9+ULiYRN/waX+/4dq7H8RuO2z0toO+tOOOm7zjE+9e7wtbfOCo3Xdebbml0X/gqRc+8PiTJAFchkocXVMtmOb3jEyefOy5w864tK/29jVXPOrbO3/x4xtu8+63fv2TGx/zzc+ssfwyXfWDTr/Y3dzxYHnUUWsNe8NJ8C4GBzhi9so+hmaZEB4oiEmcUwhCQUPm1PfRwWQaqSIlUJN7HxKFORjZ27l6dMFjanqsYk3siBs6BBonxRxhmeAy0opMzA2hD1tUxmmgVtBnyaptK61EE6HSV+mcLNCiFrzQwAPw6lmqKhMpAeWQ6LEpDhXWmXlUmn8BVQsOztBskxnaZWEi5tqmrm1qkkYioVrIXBlFvjt4x3ohAFpIzfEwdrASZgSf3ISMQhoApo2RViJnh31kPDsdV4L5WadWp54UqI4jcMQRd3UWYgBNbc0TPmogGg3+w1GhSTbMESNM08DOCbcwPyJidtiRSnUEneQEBQwlWIep6qEwmtKz9LklyanXXEhKI9x4CN4gzTPuRWiizTg7K+QWYoLGEL2lIx7nIYZ6YlT9vcQY3SyYn2m42FjRcBtchTocz5UN8looBc5XbY2EeAh3A69OctuDj15///BnvL/+sU1+89WdPvmut33inet/Z9stfrbzFsOTkXF32uXXMBOCC/Xx0KPPHXbWZUAHVn3Y13ba69Obfur962/3kQ0P+uaOn95oXUB9wwOP/eH8SyciJsxboZyFXr64KnaEusdWDznzvIeeehYR/hc7f+IHn/nIpzZed5uN37Hfl3feceO3qQge0Fx1w91h2UUoMyUmoICjKiuK1Dev+MptN1r/Uxut/8rFFyGihaZNbPGOdbfZ8G3vfNPqkjMLcAj0oznxVz/6/t996XNbb7zh1hu+84c7fPw7n/pAVpnsyxW33SVthwNkRNx49/2X3HQn5m/7nvUO++oXdtpk4y3eu8Gun9j40K9vv/TCQzI686ob5052QPFNq75q643fuuX71l1qkQUxf7GFpm/1ng223fjt6629CtxgTFSFkWihToMHdqLVWy8NHASzh8Y/2f4TB35xp+03fNtW717vR1/Y5uef/9T8kHv4uZfM6TsPxyyHuvGLSIW/tNm7Dtttm+02ftsn377eG1+9EhWa+eKcA/563ovzxqstt9Tv9/z8bh/ddJsN3/6lzd53/He+ssHqQ9Xxp4uvefzp56hSUn3Pm18nzP98+IkZTz1NEhRZPEvV2+9/6KlZc5Lwhm9+PfBSiZ76g08/76EnoZRpv/vcp364w0eh0G0+sMEBe+283SZTSrnnoSv+eS/lXBu1Zoo/IpxXN1hjlaO+uesXP/bhrd/z9l02e//Bu31h6YWmY/hvd9wzdzxX3T769tdv+d71NnnL6zO0SfSmlVf49LvfttWG6y+yyPQelt6nEpk8KcJCUfEU4TUwYkWpH5hzqNPr4KWF4AdVaSxevYj1LY7kqRKWE2W4o+VqsHGFA6pGkHokcyUHQ8yaXVv4fXIa0gUAEWWRPPKmMUnhHG7aOGvYgAg2cWSh1INshFHNZK3qSFVFwAoxRSCbnXTOOaCfUv7M1luuveqKq66yKr7edMeddzzw75cqQOJf35AqDv3TX+67/36Q3mDddT/5/o1x8y9vQVyJF11kkXe85fVf3X7r43/5wxMP3PdPB/zuV9/99me32/6D737nu9dfb4M3vxntXeuvt9kHP7THF794xM9/cvIh++/z3W9u8KbXa9OAwr+cq38ZQZZ1Xv+6Y3798/e+e0PQfPTRR77/8989//QsmL8wioZUWXtheJmTswcbDykZNxRCRogi1CkVuGsYS5UEHw5kJXXkdmEWMyrmfRj8E+5CSSQxEmch5PpxIPFaUFCmaJkaxV21tvNURDsVi+F5J0EHTMaYh5+TL73u6Vlzllh4ga9v9aFFFp7GEkZwQH3VMsvu8uH3ZtUHnnr+jvseFnPxwH80dQkNeyQFKX/osSfO+sctHrTVu9d9FxJGA9miVsSH2Gbj9V63wiux4sxrb6oklZOJ4ivaa5ZZ8mMbrQ/XH8JGMFLSq5ZZZqOXXwV85oMbrrr8CpYyYFDKIx1t9d63YxXaY08/q4klhoBy4sVXI+aCebzAWXih6ZUIIUwsrbjsUp/f9D15ivlbH5jRR64qMQQYECCcTz607huhgiCnFCy02dvAiWDsnocfkahS0J0TJUCEzqEF8jUPGunxLZ6dOXPm7Dm4e9uaK06fUJnCG1u8Yoklt3/f21FZLrfkoi/MfCFJECOQciQtLE7Yy0kZOwFKc0BEwwSiGmrc/rccbrreG2AyNSHRcZhu/vYNWsU6uveRRwn1vaLwNJSItUjgwVHNDLqIzjFcoEnzryAKwsbZfRAm8fzuFZdZ8pvbfDyPplXL0DezLfuKRd619hBjZzz9zIs2hHiYqqkfc8GVc7qy0iuW/Nzmm0xMn+4KcsKNsiMAABAASURBVKARr1tp+R02eYcw//Ohxx987EkIINiZE7ZO4aOgIfr3HB1ZkEHnCaucqsNalUZMDaOGSlaldtQXr7BN5xwJggR74VLDQBGrBriUSdTFQw0lLhQJwSrjI1QdD9qyuQDSIbhUwsXEkoQ4sSXqCeWhFmc8UI2YJNhIeFJSAbeEbZzNBHwG6tVaUh/3PTI8cVzxFUuu9Iql2yrqGpzyxLTt3v+etV/9ylVeudTMF2aDGrLbzLnjv1z6D+y43mtf87nN3jeRR4kxGfvz8su+4ltbbT69bWaPuz+f/7fwQlE1GfwQ89GYUDtQy54qnf63fzz2/KyFJkZ7bfvxRRddRCVYgkpdbtFFvrTZe5ukjz734s0PPNKHlDrFreQK0VlFE4sQLBkeaO4eQEUzRI8iqVADM07iNSp+ojIZdqYIJmeKEgFcsDwzDWuqco9uh83gHGFClbm3prcJiyYoEg/1BLOSJAKTBKJuwMGECeW2IkBnxRfHA1JqLCmOU8Ee5mZhUGMiVspOGpGkElemXsCXExOh4m+gGjFuKDWwBdXKAJqsMHkwhwp0gcAJXlwlksJnKuTgQSIhZzhVFaEs2AZrFNspc5ulUWeC4CQcCTuQBVW8KB5eFUC0xFORiTFHPXM/YXVUHbaswiKKDyWwiG3MtNYcMcwXEgpW5szBxObUSUwKTmDicC1VGIMo+GCI39Ww2pvVYPEskVJiJW9Nsmmqmdwp4DPeEkKP0mDpqULLuXFJFXMNHkmNq0Zgs6BKaoxjW6OEK0hLk2wU0WCP4sZRJYBxkDFXbCHRp9qHWVcRPCLmP1lvVNZ9/Zp43mEOWElY119rrc3XfwPiGHIPdZOJKkD765XX4jVLq7L3Np9YeemlITFFVLfc5s9+fMO3r7EiWDjliutfeGGWOmQWVUbP/CYlqGDLcucjj15w4x3MvO2G66/zupVdhJVT0lHb7PyhjVZ95VLmfs4/biop9RkhBJAU2CIpcGq4V3bGV5Epeenly4JDCOaQCcjO7/3wum/Y8p0baGLTALSRmw3XfwMyBUbve/SxECPtma3r6jvfsMYH3rLW9htvOCEiakOYEH/lMkts/rbh0dgDTz07c868ICGCjRmFzZcqzKO3avAf72ox4ExggZm4ehi5RIGd4AtNXRu+cY13vfkNWDVgCe2rvu0Nq+2wyduE+dkX51x9xz0kuEKnJuNjlWWX3vJ9G2BiGVJABU0o59Kbbrv90ScnmrzHpz6w7CsWo6yU1JI006bttvUQavCW+6pb7qgReErwxtVWWnrRhUDq/OtvM4H+wRu4qpfffBs611zhlSssuRTygoTf+8hjF9wwKAWHore9fo1GE9gIU5Vmhw9suPKySxqUct0tXVZPpBxYjvaqJRfbY9uPLLTwNOhGoReThRdc6F1vWBNDTz77/Kw5s6uZY2XgaPzSksH/hIqwsWFfcRacSIpqBfBYB9JogLJWDvBKqU/UMTqMzKmnwaApWC0N9YyUxPB4sqoGj3SEleyCoilFZmWcdGrKZKMWRwxGRBj8ED85rBEbIgcAlKQtKd7WUmTYRCTowSkIL3THFu69xqR4NR9iaYGhEXOImDE6X5w38/qbhr8m8ZpXLbfeG9Zi5u233gpyjOfOPuL4P5ZquP93+9cicOu9D5x81tnhtsCCC/1s96/lnP+19P/P1AKeL7LEYou+brXXbrbRe3bfaZt99v7eIT/94aG/+Mnhv/jJYT/d+7d7fWWXT37kHeu8+RVLLhH/5/X/T+1ZfLFFf73nV1Zfc20weOvttx/955NQVbIbLLwqF9aOmmqtdHCpmqKiHnWG/whVTDGPvsIHkIogs3OMqU4y94hiAp3kpm8ET+AcgRP0G7WJRDkz/BJfrZSu1M77HgSjEVeaCpo0BIIshihQIsbhiGPWJXqx62+87xEsfMsqr150ocX6foReHcNv8cBP3/LaVy+ywLRidtO9M3Ts0Zs75g6NkQyptuzIW3c9PGPm1N/l+MDb31JSLtwY5KkkNUZtWm/1FbHg9keemFPmWVNR/uAr2rRRO32kOImkltoUo7CJ4isuvhiG0FZbZnkJXD3SUk7SJl526UXQj1aLB+iTzBt3N009tlhntZWWWmKx1ORgAY8FsW/cvuk1K85n/oZ7H6oalInTS9yv/ppXchOEGKcRTEClyc1CowziT704zw2vKcYUFUuI0Tc0FwnJjNMRqs1i06elaS3KCbrkxrv7WXXkpBrWREf87te/4civ7nT013ZYfvFpTfQjrMFOQZC8J+BvwkXYho4YKOPH2QtUElxovlZp9dcsy62DAY+wvmpvC+roJQ5nz2VGzuI+pHNBLBXrddxR15coeETVazV2kB2aUkxETJVCbaOqaegkWn7pJaYvKBwBqSTlaDOLLr3gdIw+Pxe52wujfvUXZs+97aHH0fm2tVZdYpERzEIyShkl9/Dx21+3ck46e3J858OPSw3tjKGYiGQizgxkyYjHBOMTxqmhqdr0iqGCwmdC+zxwCUpRqxEMtgw5JWA9nAhpJNpKiZIMBTJmmkVnMrZcKHWZe7aCBKYWxAO2k9GMvSkOolM6E+JGuW1I4QWirowMK00MJWQ3lmIURLkyQdnmxcV70THBfsytX2rRhYno7hlP3vfYo6mxdjhAGTfxiqUWO2y3HY/dfccPvmkt7hQw3X7vwzOem4nJW2y4zmiBTCONpBKszE2TXr/KiutNPem86rZ7ujG2qkEdiWH+0ACRK3Eal/76ex9Az5tXffWSSy4ZA28MEqQURdZc7tVLLTIUHFfd83CfKaJwhUkEMBjMlzNBg0ivFqnzbNGQp1RUh0rVK+hYyj4YL4OwU5gk55ZoAGZgpRrETySo5Um1x54J2CDAABQPwqpsRn1PvUdAgJ7DOEABTRQyMGy6F+ojSPBf5CrOqVfovB1RbnLOiETmyjWGuwhoLbB1g/IxpCFuKKUASyODBpRIVDkxsVHtCQ7gHYh3TKQew3eEIoKJ1IYqq/QN6hl2Lsn63PfJqlAIe0Cv0XfKnpRBUK0hNOXa2DjD6CixNiyJLMAxayspk4AitUxiqBDbqqkf+bjtS5rn3AWRO+wU5GqTDYefFEYOEGComopI5XCIVzEzuBk0wdEJFUyCMkoFYpSoQkmZg0AA76kmuOKlWlubNhrNLTgeU99Zx5034wQeGtgqWYpOw9JwX8UMjKtkRUwC3jRcjGJKDIJTb9pjSxJ0K5e2lonOdZJoCKiU3FM/jvHkEgsPdt6bX3TdTZQ6PHKeaGP6RORRfGWbTY/45i7f3+Zj07IS07x+fNXt94DaOq99zeorLcMCkXKMg4pJsYm22exdb00qL8ydd/39DxowGHZmzJ/fRKAcg93ecO+9eBKPuPH+9dbOnJQzGgyEODXNxFtfOyQLPPuY280TpjAupYxLV8lNtNR2XsFDGgrgPZ8uQXV9tVpLwDBYivBLm66+wnKjBMJF0jjEnXnUTiw6ASTpyVmTBBYhFeuGr3/jrz+73d67bLXg4hM8rcvtOCVLNaWuXXbRRbFJ9ZhEdGUG3MQJVTA657feuTMaF3dYXOlbL3jnkweTHHPqIpur0+DLw/TFFlko0mRHMDtlwnmeUsSGa6+xyPQJ87jpoRnWZnjkCElgmE6LLLgAtYNBEaa3FMmJ6Yo77sLgisssteqrly3hVahP1As506uWWmLVqfe3/7j7wXkVj7F8semLvXHF4U9/XXrjXUPQaUu04+pzEYtA5H1vfd0C04S9wMj+ec8j85Wy4TprV2M4CXvLNadxWkgm1n3tSph/60OPzZ436UQCPvCdaNklFl1s4QWYBnTJanCNtrxi8eE11KzJflYfBdOEBsdgeukCjDB74YBAFC27GrRM5BQwbQlieH7DgY4+qCgcI/v0XFsNhnajKdLCp5hZhWRknD2IkAqoqlhSV4BZYGfwQ3It5jCQ7Ki7LKQCLxcJUaZggmYDO3EV+AwFRgssC+OtWsMGPmsig7wRWj2TCzv49CA3NGjdb7nnvhdmDX8fdP11150+MQEhP/rut635utfj5uIrrrj02utx8+/2L0Sg7/tfHnLYeO5sEfnmFz635OKDi/4L6f/fIQWDQ2ORJqeUkTgVRhIMU/q/s/r/WXOmTZ9+wHf3WmrJpcDWMSeccNeDD1NxRv4igo8xNY5MRzREGEbyqQrrrwpXIKKgYEfkcRr8Fp425Rw1qCLHpSycG0o5WDCX5tNsWDRlzxJIymqVUdsW3EdqPGmvcFQDUfggooGSCUcIwxUfmzV7Bt4UE70we96hp1100F8vOOCUSw889eJDTrng4FPPP+78qya7Hts8/cLMIIJ3S+A3DVdAL8wSnOOm+4a3cBNZT7v6poP/euGBp2LteYecfv6Bp19ywF8vuWnqjxaMS5314uxEpPQyBUIJJEO1kSMSsQzUVJjmXzLcIQQSV4akGKSXhqqFjyMsnp01a8bTz2L6c7NnH/TX87H1wWdcePAZl+x3+oX7nHbesRdeOZ/5Z16YOUXh5fVEKop9LYkDIydnROshqIKUDeiTqxteSjcvgYx+RDBXQbSqTKXK0kss/p43rMrM19z5wMf23ucnx5187c23vzjnhco4puWJ6aMFJ1oVwIy9hpCfApg7E+RUaJgHivh4GQrGEBhAFhgG8MPEQ7MslrSGmEVU9KPBEDhpVknByINqKOQd9GEJDrtRNgVemDg0FlgGS0PaSMB8KA+9Uz8aphQKDcIgSItm2CRGPIK5AvSO+cHHn5k5ew5YeeSpZw8+7SJYyKGnXnTgKRfv/5dL9jvpohMuucbcIQMQhrYUJjUYOTE5CQOumiSScyqJK7gVA+6C02dVqkKcMnvSAilgRyLGUklsCh6wwJwYPCEQgFjBKdnJavKhyWC+TUgbkTU0UKWgbOMCW0ExV16SMdRdzUA03FlCm5oTI0kxwa5qRFe9K1JdzM0KSnBQaMaaaorN3/6mpPLMrNnb//LQvQ494ayrbnjqmWdq6VV4+sS0aaPpbW6VsTn/84HhT86Msq766qVUnLKRMvTJRnARSfGW174GqL4wd/zczFlT2AIcdEw1D+vcjWfOnfvg40+h68XJ7pBTzj3o5Av2P+Xi/U65ZL+/XrzPGRcedeHf5qDCIQLOJCG5TEjXVkgWYVqEe9FxoOyRxprU56gUbqAdEJRIPYQIZT8YC1wV6LmIsXJYE13iog4QkgI8FkSFSSYjNvADXWIVAMMXM64GwiiQxjRkd4d6B/KcKjFKAqM+omdYBHwqMgEflgFvDRcmMCPOuVIOwk4FxVdmyiw5RRJoMZVCqJkLhkmwG4zeYuCAhEPZMZuphJQaVoXwKrFyAWUmBDbBzDI1aUAC/qsRqDZSGDHDADvsSjgemETA+pxa08az1sQ9c0dg0QAVgw5FQFB8UjhsLgf8D78pDNIT5BMGdsJJXUgCFI1wDAGPLtkASqpCsN4UUwEZ6FXyTqTnMOYc3LCUJzWWAAAQAElEQVQQXICoBlvV+RSCiFkYNGUgR0Kk7MSGOU5gibzHL4J4kgx4iWAPUqVQuAcRBqM4DvNdQYmMxXWadBOE2FCdqQKjEKxmaSUJ9A/S9sbVXr38ksOzoUNOu+hzex909OmX3fXoY3P6eRFFGs5DpZ8ovHp5eu6cR6aC7RorLRvJXdBpZNEUayppl9ZYboXpbQuze+ipJ11KoGwNJkKj4cquGkn1rgdn4Gub5NQrbzrkTCSaiw4+5cIDTz3/wNPO2/+082576DGMTvb9nLlzubIZ7ICKWzcIT4hIFlKMir1MliCfW4IZFCE47oAiKKAxM2xPIWk40xAbYPvoRzOnvuTKCsMvVGtAA3WuD3/u97jzrjrklIsOOu2i/U+/4OKbhz8Shvkg6pWiJqpZcTLAd/Ria6gOn8IwKfUhabD3oT2UCDtSDk5k+vJsCpKorfdp0H5yTp0ts8iiC04b6ttHnnrGVAwKJ5+ijdkBZ6pVQpMmlsY4+Y1334fRUvtjz7ny4L9edOCp5xx48nkHnnLeQaece/gZFz774jyMIi2WQV5WkQ+89U04E973+NMPPfI4s0vymx94cNZkt/gCE+950xqiMK6BvZvuGp6SQCknX3H9fqeet+/p5+135gUHnnn+gWecd9AZ59/28KCU8ZRSwslh9dhmqnENJi/uDvckOBwJ7BaYRDhnp8H8pLC8JNOwhpWVTCqhL9Q5FR7+zK+hy6w3w0r0KjZhBBkiiMEpsTIxWGVOTJktU+Fhr56pcngYucFSBYxU2EsPNsWpVK5wZR/nWmIyUDaA4UaqYh5Rj4ZYQggpuK2OWNwDdkZUIGQKZVJ4V8K2MCW4EQkxEwQWIvYSEeWJGQ/X0jPzW9dag6autm2/8fmdmtxg+Ie/2+f5F4c/dzE18u+PfwECf730qptvuRmE1l9nnc02ejdu/t3+FxF45TJL7/SpLVgUXnz06WdFNeprqnA2QlyD78aomsLdCPGY+4ragODbPMKnBLN7iFETeMqFioyEECsND0/wsC8LJWYZSBESUC9cGjiQZEoTrC3jRrnm5BkzG/VEJVtpXbK0KaHGZTyFBUWieZPdXGxNdO3dD/z+vMuOPe+SIy+45IgLLj3yvIuPOOfi319wBR60ENHMufNkQvIQLqY2JcKvGkO06cmffBFP6miy2O/PvvTYMy879sxLjz370qPPvfSIc/925DlX/n0qCJrZeF7XuCQGvZeaUyohPXEvYqI16/8R1hMFpqoGhqKO++g7xIdhYSmVQin8hXlzXmL+jgeOPvuyo8/Gpn87+vzLjz3v0qPOu/S4i6+cz/yLc+aOxFuCwDysJ6oA29UorCDshqTKIwg0f1BCc5/xYhnR1P+PXo5ItWtiMiMyCkf7pa023+o9605rm8dfmH3iZX//wr7Hvu9rP99rv2PPvOYfM8EiAjbyIyn0lNhRWU4nb4jBeXgCLaJE+ErDlShaGh7iyIDr0ENY27Vc2uwpC4igjPapAXzgezuSPBHUuOEAwJCMFGIITc2vSJFCU5c6qXGQkighT768IwYbl1Y4qwuhkPGOIC+jHw21VGhjMf3ZF2tXPYIuueXuo8694pizLwfIh5992eHn/O2Ic684/uK/V3PMB8KsxMJsAkBJGESLcE1KzQCEIK30EImQa0ORVYJJGs+jmlsbjWxaU0cBUSpZjT64kzQorGENSm7ihchB07mpMepRZFJWxuNSzeCVXKoRYR3hV6GGpi5YZhd44gn3CHBUchTmHE2ro4SCslode1S4Zw7nKBY9MVgHxUbft/7rvrP1h1652MJzu3L2P2799pGnbvqNfT/704MPO/Xcx597gVLDo1GaUOGYOXv4n00tudCCC0+MmIB1YYowBaY91Y77BaYPhYhHzJo3LzKFUJMyTV0RYcXMaE7pX+wgI91wz4O/P/uSY868/MgzrzgC7Zwrjzr/8j9c/Dc4IFbMmj13Qmhay9oysFYiwO4ahaAGqBoDIwVaYy6TUcELWGGCGrgwO6SD6XIf4j2AxTrnLmmPxA0Qo5fi4hSu7Kwdax0U0gbDwuCK1nhNTqZcs5sOhgrq4CAMa4LIOy4d3gNE6THITlKYhPCKp0pvqCUwPULChEXwxB4sBFZR6zwaUBkU0Pel9gFEfNAHylGB+ZPmRNIENq1EFiCtMXxSz+TqIEgkhFNKECIfdMNBARg8EzcByc0YC2CfggVmQIZgCNKoJwRkLu5G5mzslWB+1Jn3bsACDEqQ1hCvEW6BaW6gqByaAkwBJlecD6JUJ65trq1Qq6lRzswt9oSKCLoJjy6LSZbS5F4lqnMpHB1TLxRGWkJLkWK1wIyEmgZazgl6pqhGFfqpDv9Q60ZkrRLih0JbDrWVyoSLHSV7l/COw5IkxHuu7FU6H5DCwbBMsI2IEosmWIMTLbZA3veLW71ttRWZ+daHnzjk9Eu2/NFhH9zzd3sccMKN/7y/K9aTjynGTrPmzuuwP9GKSy/h7mPvLWpD1oSKiVWeaCZUBVy8MGe2SIFRGEuDjdCFhsCfM1B/cs5QTL842R9xzuVHnH35kWddcsSZFx9x1sVHnXfxsedecuN9wx/s76t1s6tUZYDXMql5mXR2anCf2RGaho1o6hq03PoIwFMwIbS+NAToA3MN+DK0LOHQIBQ6tYiq6aTTOPfW1hfHsw4/+fwtvnHAjr8+7tcnXnj4WX87/JxLDz/v4ium3iBhfjBTjZhkntSoFT3z22ACirDm3AhDIwqwSyV3lgQNOOyQXHX+ZCIe7mFPiGECW4EtJki45PSWiObOmwR7Bd0k+Dq/jTrK45atJYbkPlnmPju3w9A9jz1z9DlXHHvuZcecfekxZ1187JkXH33WxYjMjz7zHEZfnDuZ3CU5LPDNK6+0PILYuL/y5jsVYKqe84/hz329caXll1lyESOyRDjwPzFn7rBwsj/unMuPORfZ/5Ijz734qHMuPhJlwPmX/odSxt284LBBLEwfmg4WSR1ePSpGCG9RJV7iX5NkT1yaQPsPzJjgQ8mqmoV5YXEIN0HewLkcflSLIxDaQGSYqsQELCUXGHjEiKPlYODMtVOTLlun1JMS5WSSCjCmULWEBwbDXErhYMwVFMMZuawUsnDi4Ihwg0O5UxgakNAA1dAqcBzpuaXISYnb8MwmRFBsCDqUE+ckqXno8SeJqGnbV75y+Fd3cI/2jre8adMPfxg3zz3/3L5HHQO7w/2/2/86AnPmjQ8+6kjQmTYx8cVttm6blzI9ev7d/qcRgGdtttGGyy+3HCicf8lFjz8/K6qL10TeirXcqZScSCOJJUbM0PCWS4uzC55SaR9UhUxJ4BXJFBk3hzGO/9yxdMJYQSDN7CTwxsa9EVehJJLnL5Fx4n6I85IYviyJcpamYW0ttDfiUhxVBQ3Xa5dbet01V1xvzZXXf90q66+x8garr7T+aiuvh7bGSuuuudIqr3oFi4gObZiNHyFx57FF7+6BjgVGzbqrrbLuGquss+aqaOutscr6a6603ppYvso6a66yzhqrTJ8+EuYww+T5zZnxrSJKEAUzvg6EpsY0hTaSVIWQI6wUhLWpAaKJZiTI4ZzNgfHQudryr8Be66+x4vqrr7zeGquuu+aq6625KgRZb81V1l1zlZVe9QoqNXUmZZiMH/AbfdYQVmOtmhxRB/1oYMK7xqAlIgRhopc4Eg4oxKljLZKgB5mYmL7rpz9+yFc/8+VN3/P6FV+15MILmsdVt9/7k9+f8uXfHHH7I09aggxcISNDJ0lAmswrg5eAtC8RHnaIgkmahhgIFuY3IddhcwlqLVrGOWb+AGQekoElGqgQp4Syj5tWNGmFNCgGYGYvUWc37qtWY6A4kHupH6SCsgyEUFKAcacAYXQPTU04lJEkUJoRMfOar37lAOmaq2yw+kpo66++MoBd93UrQ79or3rF4tWoGhsgq1IgI0gGqJCwoH6H5RLgZKJMmkiCgQshTYFpEdhVSkOu12BxGLBrUyUFwca1A13DB4yMsT4RKQaQafA01LTgRIByRBWZKcTGHn2PVcNmVMytVAqSJAMocCkuStKYNJWTB9wmk+dMqNyyc1s8kTG5sZrq5u948zG77/yjbT76zte99jVLLw4adz76xJHnXr7Nzw7+69+uLtXJByL08mUutSPvmHsNpkjsg83Qf+SpIAqIwBBM5i9iYmVSga+/ZJorL7vUequttP7qK623xsrrrbHKujDm1VddZ7VV1lltpXVXW3n15ZdLosxYRwCRUqWmIG0nAseuoKpEPJWcQxzb0fxLwA1VcZY6GBJhjpQYClpEFAkBFlqJjFwVCz2MDDVAjycpOepghkkoIaxQZsJeVeEEyPYIOxCPqBKXVB3VBKPqcYXcSgzM+1wL9VFwWvFUNJUsXaKqAdvVCHd2h5ZZi2sdbgNWnciSOYYFW0NhNaKSm5hl6xtoF7WDNOyNWusVDJIEqnAUtkSFqkfHBEMogVDD+ErWq2NblxCr6DQfiLAPgjniF4IAnKiB/3EluKjUYMfKQQwGskqQDhYrbqlWrC2wvSoMnZk4UwmvVt1xSzx0WOAC80bQATOpslBDxKAsFW5SeVhM5BGVHS3I2HoB9QqGaNLTeMCFRGtm9HfCoY1wo3VEwIqlH7TG0YeNqQJPImImzBGRRCkrpSYG8xA4uJYYjUkKe4QFE4twIwFlE6/6iiX3+9J2+35p60+86y1rrrDs9FHz/Jx5F994x677HPerY059cXJOSIgEM710BQe2SuDHGCZHxEOPOFHQcIUPkyPU538f+sBiDlPQADLoWGjaCPaM/LLB6kM8WW+NIZ6sM4TrIXSvs9rK0ydGmA1za4QaYhhEgpKVcsOQLWEMVOY3FoXM4KXXNIlz4BRPRDmpwzCYBm1UpU68m48TlrEzOwknmt1N7n3Uyceef9VTs2YvPG202qtese6a4GGV9V+38krLLImpaE3GDk4BJOGqg1bRiRYBG+cJFvAKiCqTAQwSGo5SjflEJehAMfOlFg2DksKSvU8cKSQPWGGUoaueUp+AEr7Ob8yaEhNsyrUiV9c6v3+5JRZbb/VV1p8fhNdcZf01VtlgjVXXX3OV9ddcdYPXrfqGlV/dNmAHeijtqH3HWqtj1RX/vGdeb7N7vvqOB4T5PW9aS7lhsBpuXD2CiKCUdYZ8vfI6a6yyzuqrrrP6ylNfV1lvzVXXX2PVdVZdaVrK5LB6NEwfmgpJckpR1S0cUvyH0oUdFqgCL4LNDZOHnyCGFB5Jhibu5EKRWJKGsinBz5koOFVpKHAHuxpAL1jI6qRGqXKKGKZWlUJSBhIC1gQEBEvhNLnAcFRyElV0RZZAaBEnhj2I8yB5YibWHiSLsjO+ToSNrLboGkdbkSBBDCpl8ZRdhNgpYFSsJJoU408+P5OIRk3zyiUXx838llPac6ftXrXC8KcYTzv3/Mv/ceP8/n9//i8icOJZ5zz9zDMg8vEPb/rGNVYL3P274YMj4AAAEABJREFU/SsQWGzhBd+zwfqgNGvWzL1+++tZ47lVUJOWRKZhSkMKIMrETOKUK8mQRYyl59wjV7FW5iAX9iymmJgjkM2mgj3TcIXCOQMlBoclRApiUKGAK2EUblWEoqkJEVAqoz9ELAue4ZkV78cTSkkYUz/1zjcdtNu2B++57WF7bnfkbtsfvut2h+26w8Ff3eWg3XY4ZI9td978PQkBmScU8QCziRhcWae9j4ou1LZEtGCTDt11hwO/stP+u++y39d32f9ruxz2tR0P23OHA/fa6YA9P7f/bjsuv8xSiGQejskvN9haFXDulILFhGxgBqMs4NQTQRAm3KowvzQ0rr0oxEoLNdPmM7/1e958xJ7bHbb79ofsttNBX9vl4N13OXT3XQ7f/TOH7vGZ/ffYeZsPvata4RgzKiqQHlpwZS0NCw/ZggCRDN1E4M57Rm2AMk4rMtPQzcSh2V3YI1PNUigZBpTiDSsss8v73nXYV3Y49ltfOPTrO23y5jWzyu0PP7H3MX+ZjN7VzByFEUQMEIkwJzemHi1AYX4Ll6gCRHn+dyJgZIoOqzI2Ms9BeGI3NcrkiPUod8asHeZkctQu0IAQ7CLIELmJB/YwHQQZZ4ZaDer2TlDyo5coq0bOgW186gNzrGL91CCFQdIhxy7cTigDJPrMh991yJ6fOXyPnY/YbQfYxkFf2WH/3T9z0B47Hbrntofuuc3m735LRcUXPA7unPuerKtUC1MoCXigmE+bmVU5ErtZDOkHX5BQ0Fqjtmd2dkJOaZPJwEoQG2rWUkdRYf3M4qBJSIlWa3R94Pl7NQmRQT05Kod59qml4X3AkQSPfdPIm8wcQsbwsBrqtSEZMbeNtU0PoxaGe0njcJJqTtUyfG7ZRZf42PrrH/z5nf+w+xf/uNeXd3rf2xedPvHMi3N+85dzb773gVqQ5GPhEZ5R0/Nz5s6ZFxX6KMi4POQ0SRxtivbFF4enlcK80LQJF41oHEIMLBIxqSgYa1rOOkj80fXfcPhuOx6x205HfGWXQ7/y2UO/usshX/nswV/e+YAvbX/wrjt86eMfNOJi2axxGVkrkfB6ThpjPGtk7ok6ghdJaOucgEPBFqiOSBICB2oizpzaaLIrmCgmWJJgpYWoKFHjkisijZSIwu6EWgexhbA2KTUJkFEkCexeFLoB7mBaJbJak1S8CcNTSAwRSbVEPYwAFl9IqyQKCSHLVMSAHY059QRDxbfslcRBhlIWJsbchgetqMHWoVPDvOiz91itIZWyDzgmJw6r2pXcB1erZr0ZzhIuVKB9I5xUyV0Dfusc5CFOEmHBxWkcMRxgRp4mAty7g+ns3jIr5M0JPheSUAd5uPBwGGoosHZeoa5iO/ZgJzAhHCwByoW0J7y0soBD2AAgYRKUxJBJ1WF+haPjGPgywTJ0Ui6mvZMxYxsPZy9eOzy7D0zEQQWvfrI0odNqngYVxaATTBq0E0SsJmJExCQN5xZ1U0Wh6tGMI0/V5qERYkYBdUufSlVzkdDEZMAjj6bnDd686h7bf/igb+58/N5f+8mOH1v7Na+s5mf//bbjz7lSBbYWCy7UZoW26fFnZok3SaBvZiiNIwj9Oq+fZ9iDaJGFFnDYm4C6WjhNXWYciJokC44yOpacPjpw120P/fL2h31p+8O/vN1hu2138J47H7TnZw/Y47MH7L7Lr768w5JLLUECzKA3WAw1wC9COUbKE4lHIPFykxCuTIWtF8cNUJ8acsfWbsqWMnYuU4fMqRFiDs3OTOR6zpW3XHnbvejfdJ21/vCtzxzxrV3233OXg/ba6Yg9ttl2o3XRj8bkNHJuKyH0MMiib2jQL5lpEcFRGPCCsGSlVlQtibGyJ0BDL19MOByIsFT2Tss4Bx7gz39JMoGzRZXcC5a9PJ0q/KAlxF+PqIa1OkqK0fVXfc3BX9rloK/tfOAeOx+0xy6H7PbZw3b9DFLeIXvucvien/3BzlumNIJxeMWJun//usPfmHjoiWdmPP38TXc9+PzsuYstNH3dNVZlRB7ioMGhF2wTyC65wOiAvXbYf8/tDvjajgd/aaeDvrzTgbshd3/mkN13QTj63We2XWrhxQReOpxVMJ0S5GyTgiPYQJhzhVHyMDL141UEwFcSaAJ2OtWJ3zUIOlNpNLKb9EPsEZIcmiILKwgquHIXBzHIUTmqMBYxV6FOubAGwYwkkZIJVRIkG2i5KVhisOmAoigEJIlHTm1RKSPxCSG86lTGEmzdRCSQ4VRFKVpGPWBKYA/8EntmmB1Ch0dlGJFE5lDy6lYG8lJd7PGnnyYiYLDg9Gm4+Y+23DJLffPzn8k59333y4MOfvaFmf8x9O+b/zkEnn9x9p9OOz0iFl100a0331SE/+fo/HvVfxeBd6371oSKMOKKa64++owzjA0BrZI4qoI+uTdEygA9BTEirmeKBo4sHI2SUISDLMN54PLmaYhdSKSSJDPrMOTCVatLJ1IYE91rWM/VcM9daN+rdUwFuXXSyxhvJpAma1Mi1fBmoemLLj31D4Pc+shTYKRGIO9HtTBhb4REQxE9tc+IDki+tRg2RfMITCIJIV/zVUuj56nZk0+98HxjNffGhaJwRaA1eDeCK+JHcS9dHzaUMpg+NJBAvmgpUEZFiYCsPPTjJ7A63KpTScnzKEmb0T20ZngWaMS06ILT5zN/0/1PVAYriUXFmTuWLmgc0VUqndTKqFCy0xCNBwrCrtoF9vCReyuepdowQAhu5ExqkhANKyECEzoprEIXqqRZkooCoWJWzI2xRHM7Wmrxhd+yysq/2PHTO2/8Diy55/FnHnzoCbJgQmSrvZkX5dDEnCOwXQ7Meql5cFSiAVtAMHQChvCo0hVsgbgJXullDj28WlEHtKbU0/DnZRwJh6zqeJzH45GFgMBABzrVRlhRnOQCTGAGU91MXISocoAQytRq1HcC25waRVEhONpxXXbpRRecNjKPOx56nLh3hhDSp1RJaViF2UKhDGRZHFVcM6DnUSwsgHmNcK0kBgGJEN+LKeROAQ2NK1cc2QEQJLUAwMha0DcPwLNgGgWwgw2DUSZPMJKEtOROPeq4AqQigpiZ8OH4hRtRIqQvwsWMI7CzOYp3LqTOOUXmYZmxuMBYRok1gCZaSdy13FMKlDt9RG9exUljUBgtutCCay6//Bc33+yXn9kyq86aN77mtruqwQrqmisM/xr13K488czzGFI1TkiLkozbItrLjfc8BG4WGOXFFls0wBRLBU/oGhpDgr7KxGiB5RZfBB23Pvy4ZAslFyEmqBFi58SNMirVXg2gWaVq2RmwZ+1Ve0gDqaFLm29GopZhpolTMkq1CGRUnrJzeEBiYzYoZ3A35N2IMbJxCLBlgYqJfIKjIQBKxAS10eBQxkACJEmkhhsFE6tmTaOkCYInScINCxglFFykZsWjy4GHITYcoyQVw+PKRDXEHYPQtXN2UtCCTYPL7ELUVGkKE0UPpCiHZM+pJgVVcjymFnJLPljfwEREwtcabGH4aEjbaIRa5szgncSDvYmCFUyVucJC8TYGhowQBZ8zFmZOXLWad+6VsUAEEnEjPriqCRnOnI2yCgMQaCxogA8Bi6ggKmXKKo2oGMg6VJoI6k+Cp8xwGiYEXAgWxGGcSuhQMw+xvzAExWm50aiZe8ShFgBrJlVOqbJ2RGPRmhpFzMtCzBCpZ+qTmCtTIk5EDYWGYYQiMIFDWIgECHlUhugBwKWwdEI+CoBnVBGdA1ZQTMaGd17zLY5HE+1ySy6x+frrHr37Z163/Cs88Ir4vnnjMdhfYHpebonBRO+Y8aSCgaoRVNzhKx0JKd3z6ONzx50wL7PU4mABkCViHVgZWNOOCHCHrrH8svj++Ky5z8yZaxlEUrhIT7l3dRAiGshS59Fjew4OkRBFpOtg9gFBuTh3HQXITDUozr0kKY0OMW2qb+pjmCEwSE2VeIAjTXUPH9FEtKzY+tp/3o2ONV61zDc/udlrllxigYmRUFTyit6X90A4C2ZvtUxLZdQED2P4CVgx1xpl3EXBMbomiSy4BgMgFuO+apmiNMym1ruGKAMsIivVSjzx9OxZU/8OzXJLL+lRphoiFGYPDRgXQdTuJTwFEmBaffllMHDvU0/PKwBeEhuzVaN5wT0RdkIv8l4BkBLwfdNYYeVlV11miZlzJ2+64/6rb7qjur/xNcshW2GbcShs0Yjmk4VSnntuFoVzqpwHSdqqbefQDlhF9A5uRLKogAc0BiYsMCyKYBhZkAZDYxhCU3J4iUc4BROj5+WmTJgVJdCQVMiwmAJEcS5jjAk13CcsrtJXvJNl9r6RLqeeoH4nd8x2SkVSgE9OQYgCnjyUa0wNQ6YBDsQED7KQyu7spKzKSS0FEMKIBEhoNk19JHOhykRQfe0xQ7ijwdb6qR7iCpZ0CDJhzr1F5fAyOfwVFBEZNdDsywIOv3nTjd698TvfiduHHnlkr1/89pGpv4OIr/9u/3MInH/5lTMefxxrN9tkk9csNyRd3P+7/asQWGn5Vy260MKgFhHH/uXUBx572gjBnSZxtOCmCy7ZvIErCXmKofakzD7SyGrCJMzsYl2Lx0U2Nu5MgzI1GZUE3H/wKmEkX2rCBkclBIVwcy11Ak5uWBsCj4RPUbj3tSthQYSokJVYF1lwkZWXHY4Zl954Ox6VubtVuDrD81lYJcZdqUWtSIW/wjut0tQVgSxFLMGtr73K8niwgpB04qVXsXuufS5jyBOcDBEkaNbsFwnFa0FIRaQh7D9FgxCi2FNCFLOKhRIhYGxqDFGsK971UjsZzgwxoDE1QsJCAsKx8IILrLLM8M8VXHojsufsqFmdEpXwWisCic+bPY/n9bkGaixSJub5FFhCWi/sJaTYADvFS0w5GMiMyMOcEH1ZXloC9WEtI7dRcpIfH/Gnt+38jY2+9J3JcJvIMpqSooaHvPm1q2Em2ownnwlnYkaoBDihwkmQWwigIWiiG5PQmAziBBEi5AAPusAOTou9VwM7YsxVovIwQMTBVdmVQAslEhUOJFJojZCwahUvkEGUpi7Bs/qGSaV6MpYe6ZqGq4cqLHqT4s0Qo4WCYTZgYhgN6E1AypZcetHlllgUXRf8/eZnn52JVb0oCgLR0FKkl5kz3caNWpMbTuAGWSwLS0I6wy9ys57MGGLUbEUJ+1dgQtaIJRgsgVsqHfWdoi4BmJPJYDqT1kwW7ipOKaOibBxJakvcELQyYGlMTC6CQi0E+CIZtSqjVtu2mV+hxgAUOK9RehQFAZ0nJeXAQOE2eFpiPH0OpY4ABlRnzB4iVa+49ta3fen763zl25ffdltNVlqzhkviKrzWqitmkCF67OmnJXXS1DVXW2HJhYZ/Le2Y8y/DZJ6waJAF++yUKz302OO3PvQo2Fh/9ZXbCbz5aiywCzqGBhEivDfNo4VeO1XA/e2WOx95+tmiFXBBxc6BeZpp7NZVxxU1OKpIMZBB7VCVjAmzghlm6mQAABAASURBVAKNCHICJO6RgSnBtZLzVNk1mKITDDSAFTtpAKDera8QP5FlCfQigZcC20mNaBIicmckbczxgbxgDScjNg9yDEMBmaWVyBQR4uA5GuPGkPMJmi48SBCJhbzxXvo+R01U8RW0sSIIKmGiwHoKZzJlEVNMcRps3hGnErZhR7UauURbCV4IIh2nKuqNwLxhymycXEQYjCdxbOyqlpKQDHVXgYZ7Eyc1EnT3w4EQ+EiTTLmDmEW8ZHMAoYotwDOHkFsTMY1T1oZQ0rDGMOgO/AT4l4BlCCVlhiDkXFmswWRNiijs1AUC0nAAdCDoAVamGU+YsKhLIlISVqEMDQCiJG3bajMtpMWQG8i5KXkCNokjAS60igXgMbBSo2FwBNyJoCEvQUBc8YWtUulTtZYMFoQy22B/yolVI5gqR6X3fuMn63z5O3sdeDx7YMeGcsPMXkfSvHGVlYnouVkvjkGIedr0iTetOvyzEBfeePsjTz9FgJPCwJcIsbjVC669EVax0LTRG1dbgagS98wGCvObhhP+c1lrlRWzyriU06+8rqg6bJRAhcSrkCn5C3PmRHEIgsVVJGTEhPcD0ILCVaw4RqO6MM+nHEJwAkAzIIGHXpB9akBEmTkRwfZCwhjW5jT/CvRLFgJTz84c/tGmhaZPtGkanvSg7s4E5xBznjeJcDUswH3x7NGEZtdGQHHopgwWyXsuxjWM2LMG1EkBCkxKAaGi0vxr7rx5WozgUK4wXAHfRlffeu+seZOQ5XUrviqUEcuRZufPx6fAZmphRwrzNlRT+9bXvhr9dzz02M33P6DOqbjWzgitWvTjbhLPUEqwBbNgvmoe7jZ8y9pg6ZTLr7v8ljtV5CMbvEmwCtqqVCoqh+YNr11hUEpfzrj0OoYrMlFDmkSB2QB4eW7ui0BcmkHCIIAKLigCwoFF98pkghXi8N1hCD9CCbICwcKkysQ0dTFHDtO+UldwEvBxtl5LUM9UMGvwdrAPNM0McRCiBw8yaGUEJykVAicqiCFJoUBiJsnECrfW3obEJcJZCZhSSMeM7cPF4HnU9KSGGFA0oPkoSWHALIzlHoADekbMqogywhU2ZpNWeyTiVCKZUThYEXXlSljFGim1CxJBpw6Dxs1/24R5792/tvSyw99dufrv12715a/88tAj737wYRxQ/9tp/+v3/2+ggIclp5x/ESQdTV9gq00/iJt/t38tAkstvuiSywz/IxGQfeG5Z8654DIKGbwC0U360hS0LntHzbgfTQ4uy4Ho7WXkPVouSuNRNYUrI2JUc4LXIqoTIfSAZh9RODSodWqcUrDAf5GohUVIJfApClfMFG3BLGYwkDhNU2k1cps+9p712pxmzut+dszpTz79IsiGEoIuRf/Ys0/tccgf9jriD0+9OGvsjoxljnEaLpbIjY0yNc1aq77mzSsP/wzin/52/enX3VytZLG2KZq9eDn38qu33/vAs/52M7mCbPCwev4PB82PcUQg26tWeXnUg6tLNWS8MDbynmqdv2peV0jUIepEfOxdb51ifvyTY05+/MlnQY4wOaEg4BkvvLDn0X/69tEnP/ci4o2OS4uoPp+CplyZTSwIoas4Xi61YGAYBDp4ioucR7kVbRZeYKhBzXze5JxkhaqDiRLxxlVWEObJYidffFW44T5YTXRM8fd7hkd0oLXSCst4Q6aMZOw5cSPUSM1kCS0GnWHSlORO1iMdEVL5VNdw432YBQtp5hFm+8vAuVBVIhZ8JNI2WgXHRBGixtRLFF1wNLBNRC+8+KJzJeklATTXCfS91MRLRdJnZvCmiRKI8fwxFVxpICh5i3etJ8IPPzPzNyec++wLs52NuKr2WePhx5/8yr6H//S4E+fMnt3E8EIiUSTilCUrEp9qIL13OUgBa6qUO2jNSYPwELltWJjBwmDUjhq/jiJJNBUm7q7meGU/IaKSnUa9KAwiSR2qNbDK4HTqR7lhJE4WApfaiKpMycAsLHAINTIrHRugYR5IVwrg6lOzcJuL5eqJDWVll8o4ha31muUmmmwep1759xdmv2CMwzJBccD61nsfLGZYu9KrloF4TUtLLbbQxm95HXquvuO+v1x8xaT5ZHAkk2TPvTjrV6ec9dzseaMmf+I9bxWs68m7iMD0oUEI5Fp8Ksnm71p3WpNhUd854rRHn3yeOXiAEnPt8Wef+94Rf97jgOOefXom4BdlGsTqqnVOJdAFggMyAiQGcIK5KLACKMmZIEkUTwX5GafBsUdHNG6kb9RYq2AWKaninF8i2GtTUbQyq8DGSoZR9gL77Et0YSGo+ECbGN+tRg3sTQELNqBUxk3UROScQgTRp/FQozSmBphTpWF+UfLUhgsZtGDCrnARcXCATbUm7RPiHWAyoSpREImmHKTjwaC4tloHY1UoLcOR2xqNeG4joDQdZIdZEdnArNeGLaPCgNb64jXCEnEmBlSZYIKNhgQmExGMlttUWumEzQQQaoHF4SCQSZqQDA5NiEBQ+sxjHbB3gFpShfwkpok0oA+AGKDBUcdOYD/AJY5Y4oPQFEmjaR2TB2awGQI4kTF7kDkzZ+aJkGmVoQjGe9DS4axNPdFYZdxkb1tqNQEro1QslX5kNvxhKCZmywRp2d20VhwZFTAnllGVtg8l2EruVQ0sYCeSt6w6FL433fvI9bc/jKmZRATeaXPLnFsfGN4EvmKJxRaevnCWNqf2gxu8eYFRQ0Q/+ONJz7w4M1RJG9ZEXE752xUX3zL8y7kbvWn1pZZazKA8wD/wjOnzW+BXBL1pjVVet8KyuPnjhVdf+vcbKxXYCsMUgajNO+2SK3b68X6XXXddcMeCWVxCiimFqINp5QiTMNWFFhj+rE1f6ySUKi1Tk8A4E2SkqYuJmErmrqUxB95q4RQqNP8SglGI2ijZq5ZaHH0PPvHUo88+w5HECcWu9nLdzQ8ecd6VGELz6t4rVUYMQ25ZeGrrrhTrSGERORhINwOoAvVZUGfRmVerMBrOoIB24Q233XDXQz4mgjdUGYnc89CMw8+9DN65xMILbPTGtUSSk0QF45g+NIZFVJfeG3NmCuX3b/DGRaePevNfn3jaXY8+wjWkwOBQjHfzXpi9//Gnf+VXh977wMNmLIWbYFiJsL7/rW+E/u6e8eSM52Yts8gCb1plBcJOFa5OoECW3rraqvOVcvyF1156zR3JNAeRQI3el3rqVddu95sDL735BmWkqYK1A3OEZGjRj70LqiQIPfAkBALCSsLFOUVO1oQ3MX2BVkXROXtyHnEAF4NOCjoomBwm55Uhn3JSF641rDNiU8FstsGfSoLWQbomcjQIB6+nKCIuIM3JSq5FnYdtYJKpBhMFiKBxLtT0A1bRg7Yj7lRWG9YyE5ZEgq+nEinwYjOyZGYoWhEGQEHEOHWkJciI3CSClaPRSNMWGP7Z+Iio//HibBDqpZ9ll1z897/68SqvHf6S0HPPP3/Mn0/cetev7bjHN4886dTr77pvzrzh3yB6aeq/f/3/RODO+x686647MGW9N7/5Na/89+sUIPGvb6u9ZoX/IHrOxX+bN29wEIa7D15lcCM4o3M4zJ3YCXcMh4B7SjCFwE0xp1ItgkcMFCwUhAnDdMKtM4eKJ/Y8RDRtuclJB4dXZwROJcYluUpjeNosKQtNsGQSbaqkss5aq3/s7W/GxGvvfOCrv/v9kWf+7YJb7r7gln/uf9Z5O/32yCvvuO+mB2fMHk9izwghYpq6hFi5rdG6NxM6bdePfHCZRRcal/rjE8789rF/Oe26my+59bbTrrh2jwOO/enxZ854buY9Dz8lNWWmkEIv0cBvVghcmR30Broc2Ge4IUokghmOsJAttFIgVQ9DE20LTFw4Eq+/1qof2+BNA/N3PfCF/Y845KyLLrzp9otvvv2QM879zL5HXH3nA7c89NjcyUrWUp/JXtoYD7aKp+CqSIfUBfUVew20sWGAG05TbERaZvFFmWnuuDvo9Isuuu62v914e6lQGW28ztqvX3H4ZxIOOP2ivQ74/ZlXXHfpzbefc90N3zvm+D9cOKS3t6y8/GuWW8KQd4h8IDaIiBoPt6FBCZyg4Rt2JMnsiVwHVU1xMXwwiVPLOhIcJLKyCk1dU/wJE5CyIGifMBTCHHjCJyOTbPqKRRZNAvp02GkXnn/NLZfdcNvk5Kym8UH3U0TwIVTBiYM9U6KG04jBAAYIGUqccygOI/yBN7/5fW8Y/unF82+4/Su/O/aEMy+//PrbLrj+9t+efM5nDzjy1ocev/mBR7vJTqwyqlkvzISnsk1KYIAR1L3CMpVJKUQAeHWSMqSvpCZNrSkqOA38YEWaL6uJoZEaN+4jLq2aKgMjq8PZlQDBlHEkbEZsyJI0Ze+EXYYvRBQUtSHPwIlbCNP32hdzM3cwRUUI30otjts87EOqXgc9pLL8qxbf4t1vBZFLb73nS7899g9nXXHJDbdc+I+b9j/htG8eeSIMYKmFpm/whtUFDFNqOO/w4Q3XevXwMGKfUy787iEnn33lzZfccMfxF1y2/T6HXnXHfcBh6/es+6aVX5P7SOZMcHQD8aEFw8eBDJx97ZVW2uJd66jITfc/+sVfH3vEKRdcfN2tF99w2yGnn7/Tr4+46Ja7bn1wxtzZ89ScOFASOFdXw/PFghuDGAnAR4KOEykTC9Uclt2Yo1DqvamuAJ+8Uq1SvOmksaZNjTZJsEIcZUXORMxdFRynSKqoC0tE7iuXUiseMVqF4XEwvLH2gZONKfK+lUH9FJUYd7VyaGraDHRkwrA11DGYm4MZpuFCEMtcWYtxRUsJ+CurqETL1gjrcPpWDzPUAAUfFODFU4ZoXEMQElSZxZ2pVzCPb8KO72ERFevYq4SRwSISD5YgGEweIMrBxOyALIGoJfcckhgMwOasUPUYihLHBxah2oQteriZUmSKlmprBttM8NFGK1NldyKBrtUJj53GY4DoPXq5B1fYL1WFqYVxEJMPxo8bmCGADZqiC+rFUZUO8CSLVCk76BO5TclDw+IqUYTRnYESBPRCVLEz4QoaplAovkfFM5JEqDdbYwo2iMuMU5rzME2DcuKkO2/6roWnTTw3Z97XDz3xp8eedt61N190w60nXHTtTr879pYHZ6jw+9+y1rTRdPGsnl+/yso7bPJOFbnx/ke+uP+xx51zxWU33X3utTd968g/7XPKBX21FZZa7HOf2HgAnblzquE4G4IxNFc3qSS2YJO/+skPLr7g9Dnj7ofHnvKDo044/fobLv7nnSdffsPX9jn+Zyec8/hzsx564skMAbyidKQuzI24EgMlMkEpyWWkyy45/KvKT8188cjTLrrkujuvvvEukFcjYqapC34eUbkWPB5pDXVurwZbmRojYnEiHGPqh962NrT21Ky5Xzj46D9edsVFN99+2lXXf/eIP+5x+J9emPoHyogIJWW4C9xibE1nyy4+bD3j2RcOO+eiS2+4++bbH5poZJRI4ca4W5zLAAAQAElEQVRmgsciZUx9F5VIcijUARo0e9x/9fCTfnz8Gef848aLbrh5nxPP/Pqhxz/74vAX2L606XunT0xoQLFK8tJ8rGGjZCk7ejCIIx2/apklPr/Ze0Y5PfzM81844OjfnHzOOdfdftnNd55w/hW7/Pbok6644b7Hn37+hZkwQg1V18alDV9h0SXWXXV4Gwaa71zrtQuNWvDGxcEvxWAzEzx9t49+aL5SfnDsqd8/7M9nXX3zxdfdfvKV//jy4cf9/C9nQykPzniS+vAeLgkyLzUpoRUmpRFQj4tWYgA7NcoMRATmJrbQQhPT20xEZ//jlhP/dvWFN9754GNPI65AQB0WJbIUopGwRsi4uhRJVTgxMrdrH1yYCkJYNuMIAiKJRFLvKUiwp6E+imE14wsPJ8hKqWiOLHACpYATkDuEzqVX3BCcBYQI0yMXzqiHJthGjeeJ0BaFUjuKdqRNAxwN/oaN4MklrMAZwbeYcI2F2kQvXfHS7//2F/PrVlv1Lwf8dtfPfW6BRZfAyNw5c66/+ebfHHzIzrvt/uEdP7vtN/Y+4Jg/nPG3v9/7yGPFYLyY8u/2/40A1HT+FVfVWjHwife/L4hx8+/2L0fgNcv+HyfABx64/7knnp4gbTjEXIgQJRuKRrum6ZDzmiAtStxgCmk2Zc9F23Ezrcg0pzYF8tDYEwovc7Dacp91rHhclrreO+8CLp2NEgc+amRDSGYuU2FeEyeJhp2doP3KbGw56Ve3+vA2710P1B5+5oVDzrxs90NO2P3IPx9+3hWPPz/rlUss+ptdt3/FskspWcuNDiECE4mDkElpzF7g8bTWa169zy7bLbPYwn2t59101/ePPe1rB/x572NPveKfd6Nnh/e97fMf/iA7HjxJizg0EBh+WqdWCCVxgAbkdLWQYYAIcSslDCNeOamCb8Sb+UNzJyfdK4y1iTSKid03//A2736J+YPOuvTrh/5pr0P+eOS5lz/+/Ewwv88Xd3zN0ssmlgmmhDVTJJD53UOyZI52SKdIhTY1gg9G0BWAV41K2fj1q6+49BIecc51t+1x5EkHnX6BmrWki05b4Oc7fGKdVZav5pfccs93j/rL7gce98Nj/3LJjbd3tb5l5Vd9f6dP4HkLNvHSe+kQRvsSvcPHInNNYeJB+EbDxZpzyy24RNAdOkgDumpVskN9RMo0Ep4aoTAOvCevQ6HdRdenrubKSqQkiRJay6u88pXvXms1zL/hvkf2OvQvPzr2jDlz5jYB02F0zm8wAs3IBYR4GwMBlvkDRAY8nEg4JZ6YaPbe+lObrfN6DN77+DP7n3LBXgf8+RuH/OW4i695ZtacVV+59H6f3+4VCMJFUZ8Nf12GsZKYgoaADmbJCYQzVNgyt4whKkgrIOcwdUoWeBaqmViRCKgJaSuNDJVgTdW4NxQrDZGKepZB7tpF3+VO2g7ftXitbl4DBQ31RcjlpWokiAA/VkKIUCPua1d8UprKo+hGPnYbj016auB+4qo1S7REGYVf/cxH3vvpDddrVO589Kl9/3LBNw44/tuH/PEPF12N2mXphRf4xjabr/Cq5au3XBqapFeMFvnRjlu9aeUVumrnX3/7D486bc8DTvzVSec99NSzKCm2e/+7PrPpxlSzkjZSc+6Q9iA9WhAbkQRUERKy06bv3fF9b0P/Y8/NOuTMS/c46A+7H/iHQ8+6FCXRKxZdeL8vb7/SskszDLd3t4A1gz2EAwvrvQ5doYVShWsmhjEQc1QxXFJhRYKEqykLN7WRccNjGBaPUppIqU2AlzihSVKCV2pfoyK+wAod+tDGs7K6V68FdWN4QG2pd+kVj0bLJFPxJIRTBBirKE8dT7CDqSUdAVC4rjViE6PIDUciokJwAnFitIKvSUrWSLBzxk/DhAfODSWuTVekhFdKPbcUKbNnHI+KlR5SGYCwwjEOqezCJQ1SN+Q46WRSjuEHB4qWUwb4eMLRpm4UfbLKU40MeBVRh3BDkJx0qjV5VWyae89umYfG5nUoPhWScxaTQXHOqrkJ+FVjkZwBXGYJCjMr1iOUjRJPa6gRosZjRP8f9v4DXr+qOheFnzHGnGu9+4+CvUaNJcZeYo0VC4oNEXtDQSwIgtiwgIq9gIWmWMCOBXuNLWosiTUaFRtIEaQISP2/a805xvietUly830395zk3JPz++49rv/ae693rTnHeMYz2pzrNSQ2MiyTxM5rGp0gnQWNAgQMA6kzGNCb6RHSRXxArgy1qqlWoPiMnBNr9BnzZDmDc1fax9pIKs85M5yBrwgIdICZBL/xceupTMosvQ/TXFu3UspquPn1r/nK3R9yjctf9vyLtx77jR88963v3++w93G38LOTf19MH3332z/innebZ1mzdjnC5AkPuPteD9o+E7887aw3Hvel5xzxvhe/67gv/+jnU++3vv6fvfmZj9tu2w1FKiBiU6exic2j17BVK9ast1v/+bUP3uMRV7ncZdetf/b7P3vROz+672Hve/l7PvH3Pz+Rq7U9H7D9U3bcfiS5vck0WZsKZtSWpXXpTcONS5Z+/zvd9gqX2dI9PvL17+1/5Hve9fmvEODsOSvtX1S26CTCE9mVymuSkvbPaABygXT+vust/2LXe9+xqJxx3gWv++jn9nvr+w84+iOf/d5PrrjtNnvvdA9sHr8943TVKQmD7Mf8yLvd7nLbbMzd3/e17zzr7e/74Je/IxgtK+iExs0pYz0L43yR72abIoC73PQvttlYfeRbP3jeMR965tvf/56vfPvSXcpj7nmHne5+SxG6zpG5cPfPM9BZuoaiw4BSmqErrNSH3/O2z334fVZDZS1611f+/vlHH/e8t3/4sE9+5YTfn73tltWrnrTzPW79l6vCkZZ0oSbn1THucYsbXyr1AX99K9YblufIOYLv/lh8QOS3vs4NXrvHY//ZKd/7pxe+40PPfOv7DvrAJ79z/AnN/ekP2P7J978H477TkWGXiuLvTK7nV6NWMb4u6EysYALwAZARJmKi4jlofehdbsvbv/vDH1/2gU885x3vOfOc86pIRYyuw2yStZu0kp0MLN/B1ygl2LMNpUmho8KdcLtl19KiemOd1vSCiZrBTTEKq/k6vaV3jdDAkDJ21i5Ud2XmAaZgoY1R56KLJodnNmEliV5SaIVKRuFnBBMySjDTl4xKutYXqd2FC5gyp81rhG/tTqsik9HAi3/vlCtd4fLP22PXf/jo+16x/3NudctbbdnYEJFp6yVnnHnG9/7hW0e8+z37v+TFD9l9j9s+cJf7P3mfp7zooFce9pYjP/ixT3z1mz/8+S9POf2M8y+88KKt60vW09za3HoEtf17ev6b95ixEQuFrTsF8aTAi7eueV5w0cVUwfO88y8485xzT/7d73n+7Dcn/uRXv/neT4//0fG/Ioxv/uifvvtPx/Mjr3mHj3510ikc9vuzz/njBRdSAhFS1Hrp/wuv/00s/+mHXPB963vf57TtLnf5O/3VLXjxp/O/goErXfkqInqp5Az/6vf/0cDXWQpBMiUmtXlZBJh1tbXEnME0w2ZSJZi3BcygwXIlMLC15czy7F0kAD5K5mGWzjRiF5vC2SPRu3ZKL8kBYAWgjjYULzrZZkFc4jYlJAUi0UezfR91/+MO2Ovhd73dDa52xW23bFz+MlvucpPrPe9R9//Agc+84TWvrdnrADbMYbWx5y477PnQHe58i5uwI5hndLghB7nJ9a/ziQOf88JH7nS3m16f0ynkule5wi53vc0HXvK0pz/8XijRpQkrhPkud7/N0x90j13ufJtSZixbrE4sXMRPITe8/p8//SH322fnHa9+hW1WMm9oG8TFWXWKjrbXTvfc84H3/KsbXE8uCZuh/BZaoqzwrEfe/4MHPP2hd73t9Ql+m43LbYJ/9iPu94ED973Jn19bKmRIrPyaV73S0x98vz0ftOO1r3oVEykkT1RJUdceuetO2+/90B0edIe/UqJR7zZF9G23bHnj0x5//9vdfNttNnje5FpXrwZleZO42pWvcOTeT3rV7o944O1vwR3atttsufxlt3nQHW75yt0e8qbn7PZn17iy0IVztWa1J61Hzi280beR1tiPcpe73HHPne+38/Z/zZEi5mrXuOpV99nlfvs8eMfrX+2qZUipdHFHhgIm8qSddtjrITvueMfb0KW2uYMDa6eFSCekpQjXdPPQVrboCx6z8xPu/ddX2u4yC+xrX72Isu/rvPHY7e+6zwPv+8Db3rwAag6LxZo5ZO53uNn19n7oPfd5yL23bIwUyK5h6QJss+344t0fffRznrbznf7qWle6HD17hctus/3N/+KAJ+z83hc//VrXvaIPzVOj1+yWbfLgR25GUgxRMQtalDZX59lTZ7cE5fZCw1LSEctaE23W1tVjsSDZSdngGL9OdyCVN1wC7F8jqoYhaGpvaIz4mEW8wbt4oAk6AEmMjoo6lrGWUVU8M5yThAtPuhBZNKU6s4PdOhNBL3UdGld3KduY7vuYBx71/Kc9Yvvb/+WfXZUmMxLufJPr77PzDu89aO873/4mQtwLXtITxHLtK171iGc87bVPeuS9b3WjK227DTm/3tWu9Lh73ek9z3/Gkx68YxkKmIsldUhLUiJPe9AOT9tpx7+62V/GymzIaku6Vhme/MB7f+TAfR9xt9vf8BpXpsbLbbPlzje67rMedu+Pv3KvW9/0WsnlrjCWisUgugJNiIXjHL2P2USTYhwZAVlipjg0yBpE1WQYUytUtADLUkPJj4uKQIAiUIDXrppanNUoNJEDiDxFBGI6QFOlo3QpXY3qOSPpQgCpxiHG4YjNu92FC5BQQHRwvlKQIiKDdOtBGRKR/Ov0ixEEVYQTEbqml0x1E7cEsx9AAaWnuQghIV1yibdZuDiiyUTABQghAMEBxsFFSsGy9REQEvUmeTJTCAnRuUgb1JWDEyBhpItzm3jX0q2GGCnUBl3L8tWAp1CpZgBJEQUxSlQRy2oioqD6mrNlQwrrcUJFVWnGKrX2YbOKUbssnBG6e3OdqaK2tDnQeUnTqsooJqnBe0Fxk2mUQYdaCtUyS7yNcxSGevM+9548LIVKLEBsAi/kEyB/o1SDOfM7oG1dsqlkgZDQJQ0Iky5dQN7lVjd8z4v22mune21/8xtut1nlrn/1Kz/srrc9+tmPf87j7jeshs4VYuP3A2vJVgt2e8DdP/SivR5x19v/5TWuzPEs9ff9q5u84SmPfOszd7/2VS9PHxdh6Rp1Kjrn9re58VN3vtcj7nOnYTSQD0OGSditr3/dT710r+c9/L53ucn1WK6327Jxg6te8aF3vt1HDtj3aTvee6g1gZaeCNPkWt8HzKwkik6ewpBy1Stc4bC99rjnLW+85OY2Gze/7jVTWrf5Cpff5ik73WOfnXa4+bWvkT1dhW0PKgIE8qHb33GvB9/rEXe7Q5st6PjEWG3vR+x46D67PfAOt9x2ywbP29zgnuzqGQAAEABJREFU2s962H3ee8CTH3XvO+yzy32e8dAdttvuMsQRYgvJgWtd6fKH7/3Yu9/8hhzM80bXunowykSFCppGgp+sJGxJxOUjpwE3uNbV33vg3vs9bMfb3fDPWR9YQne8zU0Pe8YT93vsgyvtNQlztV5jevg977Tnzvd94J1uj0LjwXRoBb2AVkAUxdhAP3PQM55w77vc8s+vQVE8b3O9P3vSjnf+5Kv2vvftb6KFQwQmqRowMI1Lv9cdb7rvw+/3rIffj++wGrNLLEsxLZZLbwRll7ztza7zkVft+5xH7nipUyj2Ble70sPuctvjXvqMPR5yz3GbqmaWYLN81L3vsveDd3zgHW6XUlLUROh0NVbnevOb3HDPne/1tJ3vsd02oyT3H1Ibs0d2fcB9nvXQ+zK0KPaG17zqZS6zEbXAjJwq1YuEyIwIuMLHhkEoF1GNf0Spx6NMYbEkGOV1Vvwkkhq9sARAgkUCwlCdEM00WKygkA6dmDF9QJoIUIVhCKY2hQ3rqOusVAmsne1JLOaxr22acu4On1KmVHeBp7Ww3kr4qF35Jxmdub765bcl+PU0//Z3p/Hi/+qkLdttu90TH/7Qz7zjiC996H2vfdHzH/KgB13/Ote5whWuIKqZ0Xvn1uXEXx//jW99870f/dihb33L81920GOevvd9HvP4O+z00Hs/4ak7PXnvxz/r+U98zgv2fekr9n/161742oMPfPNbDn7Ph4889mM8j/7Yp9/zyc8f9zdf5fmBz/zNuz7xWd485L0fefW7PvSSI4/mYE7Z96BX7n3gQU9+/gGP2fe5j973OQ9+yt73f9KePP/6oY+5w4N24fnXD37o3R/6yPs+bleeD93jqY94yp6P33ufR++5F2Hssd+zdn3GPvzIa97ho52e+CQOu8fDH3XHnXb564c9lggp6pF7P3uP/Q943qte9/K3HnPsZ7/49e/98IRTT+NWxj3+r5j5j9z//Vlnn3DiCRx5j7++48a44sWfzv8KBq50hcuzIfyr5BOP/1lmT9aPYCHL7LEUoVj1ZN4xZqeQyW3qyYxbMleEJWq11BMW1inXjlli0mRiU2ZYCfZkqZCBdQeF4ualk88miSLM0MlsrsO62lY20+gzm1yKpzG5TZF898OS3ftw/Wtd6yW7PuxjL3nm1173gr89+PlHPvcpj7nv9ls2Npb0DmXBSMsylifutMOTduIy9ZpgHQJSoqtzpcmVhdWyy11v9+a9nvC3r3/BNw558XGvfOELn/SoG1z3OsHMRszSclwq5wPudIsn3++uD7rDLaQ4AS8FXeBL4vsNr3ed3Xa65x473ePKl9uiYEcCkaKJh8Dq43e852473uc2N7iBpuaEbMlC2Pj6fYgbXOuqL3zszse+6tlfPuIlf3v4QYc9b8/H7HiPy27ZMkgmF7/D1Gy63JUv84Qdt3/i/e91ratfWdNsWazQuhFq4fKI+95p9/vfbYdb38w9e6IXtJpd9JpXucZLn7LrFw9/0d8cuv8BT9rFiDPmyA7AhuGBd7rta5666xdfc8DX33TQF9/4koN2e+QDb3/rbVWLR+mpHcZ6LFxp2HIrvTka12IZJO7+d7jNE+9/z/ve6TaMA+85p1zuild8wv3u8eT7bX+dq11RK0nvGbNzg8O/icftuP2TdrrnDre7haaYlzHrCtxRcD2mI6DQnkMjM73R2G2uOO75iB2+cPCzvnXY847c77FXuuwV2jx62IPu9NdPvs/2O976pkVJTcIcRk8G+vqmf3HtJz7wbrvvdM/LXIbL52ab75CDb2BYq4vc9MbXPWCPR376Vc/+5sEv+OprXnTwM57y0O3vvFoNDi6D2qwSBJMqhDpNvc1wT4UWCRUG7eS6btpm1v9eE8rxJmHkMebe+hxcg2BOIUEikhRrM/vBgBiCtnlyVzIxXsehrjYG7lpnxeyZHpqR2jtbljDwe+aarhEmTC8r2KiKOoiVYEcwl0SqcL0MEQC8Wz0tJos+uEgntAG5vNofTUjIfrs+6D0v2fvrb3jxN95w0KH7PW23B9/nqpe/3CghOffwWbJXybEb4rJW6NCD933859/83C8ftv9HX/HM5z105+tf9eqlh6FpbVTmqhHMZ3vSfe+12wPudbMb30CK6kpso4/jzC/NBt247rX/7HmPf8QHD9j/m6896JuvP+htz3vy7g+862W3VENHTSGhOZReGQAZhDygKgpfRhauRKWJzkjyElOyTkgWqEACoN10jXqGhW9kVrEUtIyWjnTLEGTTDM4Yqg5DjDUrYE1KUAy/HSmV2406dPp80KwiBlFlHKmkxjI3RMOKjGpidCKb/ZyILuGaUCpgtfASURJAOLhJsG7KLIZ1upHOn4CGCHTx0AanoM2ZnNCbRoOGFqw0ByLTpuYVwvJY0jKts47YLEPToqq0IVEmy5l0NCpwgfBUKVKNAyyVy36+H+WCxroqeI9PuD1zRaeTe8mmSDBmuPKRMcUASijBLW2trh7gMLLE020iYKCabaxspVabsOoFgJJSIEUsUOcss5u0wj1RFPWSzjAqiRFVc5T1yEloYXSJKKSKVIroSRjkaiw+pNNInYNVZhAXTRAWFmA1kiRIECfV8a80gFkCxkVxEq1Yq25FaSmcIKJS6nZXuMxuO937sH13//ZhB/7dm1/8gQOf/dxH7nKLG1w3rTAcoM2i1WgEpFrFVte/xrWf/9iHf+Tlz/3WkS/55qEvesNej97hr25WbURf0flGQ9IkUxHc/Dzxvnfe+U63rUSYBTk4VSvjd1iNq8ftcKfDn7vH3x56wN+9/oXHHfjsFz3mETe4zp/JaoCQVCfxbWXgd1aMOR26SagheRbBgJQbXvvPXvOUx3/lTS/6+hHPf9Zj7lcs1eJKl93y+B3u/OQd73WTa1yT5isyVh58yceAAB5yl1s/8f53u9+db0dPtWUXCpmsxup2N7rRi57w6K++7sXfeP2L3vms3Z94v7++/GVXl6uXecoO93zq/e5579vdXFFDRpcaFBh+ixtc49C9H/u11x34xde99IkPekCkJyOPeYChxYZHZXqAhRnaA5ce7nHljY0nPeCe73zhU79+2Au/+Jrnv+yJj7nTjW86RkXWvnAiUJdoD7rDzffY8U4PuP1NRglIMogY+CmpggQihb678mWv8JyHPuidL9j7S4e+6OuHv/TdL9x3n4fc5/IbGxFMI4lMkRRwdgEKFNtdbssTHnDXx+xwJxGZ0uZxkLFubNgGTEg3pZZWtF1+wx5/nzseut8Tvvjm51Pyh162/4GPeeT1r3ENlrBuIlVM3LI95Pa3ecqO2+94m5sn4SIzGOBaGJcYbv4X19vtwXfb7UF3u8zlxiTeWYprUePLml0feO+PHvScrx1ywAdevM9Nb3oDHywK0zYYsWHdJcDxDGf0Ejm2HBJES6jMdcYOrAsm5mAMMkvtfdBJWSyoHbAUpbrgByfGIbNOKD0L5aIxs4vIKOAFwJonGs6ooke9RFSIqYSIsFbNSQg5m7sAPAtkGGAMOxa5uabXDDpCDF7k9n91MzNbr7e++W3vmFvHf/cQvc41rvGYnXc67CUv+NIH3/Pxd7z1XW86+IBn7fvgnXa+461vdf3rXm+7K1wJov9fYjL/eNbpvzv1pB//009++ON//NI3/u5TX/zSxz7/Nx/5+MfecfTbDz3qLTxfd+ihr3rjGw549at5vuzgg1/zpjfx5tvf+bZ3v+sdH/rwsRzMKV/6+jf+9tvf/vvvf/9nx//s58f//NRTTjrz9N/x9OmS/y91//kPvr6YCCnql7/6xT/84Aef/tKX3//BDxz0+tc/9bn77/SkPR+19zOf9Nznv/yIt33iS1/9x+N/xa9f/rMa/uEnP710yh3+6q+EHrn0w59+/89m4LLbbKP6f/B7xnnndS5xmJOmlenD0LelLQW/PGiaDgRTxbNz+dYSjS1IUrMP7mMPhSe0yGhLEoGfSgc3OYNYrUMpo5glUxFM8ujJAuq8LMkpqkwACSRf4C25hmSyZZFUTbYQ7bAZyVVCorujdy4WJKoOpgWmPcOnllwAEQFYWaT0opAKSIBv6bI7y0LtuWDuXDsRtS8oi/ETLyxduVQcunJpNlah3AopgIkVDJQltF0dyvKDTrmpBquuknNijcwSXKhYFRYlluto6rSqiFqRasFhLVuDE2uEhlSua9YR66n5FNQe7AjgzcIiJDlpNhNaCDMoV0XUGZHRIV1pkyUtSMs+RIyw0UoxEQ9WeiGeAIlOl+zpAQeCwkyRgZ7amninPUBK8m4ZdKgmmUEksyGGQO21zIXF1+dsoT0smsgU1lDCDFU5l7sE47J/7n3OCTJVm6sSN0EaWsl5wFx0In6qJjOr5EwkTLLWztVQcqRq1oQSciM+FdBzaunsTTYKViIamg1BuzieA8UhvHI37wrRVGK3ntYjufLsSx8gu6me4p5prQhtCZJGisgIF5v0zubiQkUlClykk65Yoo3eNiSCDs9OjkLMCUigKmJp9ICGGtjJBJvuDJm6ZnCEMACGGuxjJRm+1BiaUsCTIgkGPARiFAYU4ZEqWXgXINSWjZirwUwkLXylfWU5iAm0BTqss8lBgvSzP7cKUBPlL2IEEZkZAH+lsAm6hVqTsmBI8pGED5GeNqd2UTGjbuv84BohC2ZEFVlIEMopzl+aBj4SI+ACVZD+hZrF1UNKBcQ4bChZ1IyAgnkxc73sY3Q2T1oI3meowejJ7PSgLpPERhLXkxHUoxNqH8s82iTovaPT6R2OaKXz7CkRDB1GuoqYZxEf6jxYEhFMw6gfXAsULGALUhHLmOIkpocko06HYaW1iIXb3Kiih3d6UUKRTFZ6OpS01KK1EjeJEQ3hQ5BciVQNCDdRAdRihYM0gTmzRULA3fmGyAqyIhi6rSCYFUVJY/CHSDiINklXPun0gws3b30WPq6QwVAJNL231MbyiZWb5TCbMSDhQQgmLuQoJ9EmRtQiBk2RbjGZr4s0Uh3Qea5t6yDr0bYWxo2o6IZY1SJCRs2ZsxTQhAtBa1Ubl28DqD9kCFQNLrv6SqIQ+JQxSTpEoSKCwnH0IDIW07w4Yz4t+bCY1Q2pI3EtRlEvAAFKdqC5zQ4PXkHo0CXNY8woU+jcS1t06eQk05HFUgalSGYtz+SMWqKYRnLVWxcUpRNmCa3OqOATA9/0M851BhaPz/Ct4c0TbAhZlr9CKKgFQjdn1SyMkwZZi0xVYgQGC64S5yKkZ+0sfp6SnGWIklkzGQlSHUMfrNsSh8C4KU2Eghyh2csaq6YDZ9blC6hZOFuYXWWzRMrYmRdNSc5SN1iMVExNSwmjdvIcCIEP6Lr0FUEfg6tjlkaq6FszuqQnidSe6pIBX9yfUVNNStEy9qGwIG+lGQxgLzLrMsZzUaEcoDAahX85Wk/ukILOHZTdGothOdNh2hJdIpLbDCDTaFz02rysVVhTkYasyJIiWlXpF9qJghg0itjk+WwAABAASURBVHZYQmu4OqUFGhUGQzsZxJ51loGPTRg+HgnKCIXyNOgGpIToFOGzs0dURREGRZFQmQMsp8kfRAdpoBohPEIkhePoMkxeyNSkvSvFuGqnYIIkhTGjg3wkxkxdRAmWMZwNqhcRK0KIroyirqDPlcE5Q5zWlggOTEgqrKJARZXX0tIiSRAfZOlBzMYHphAkB5ibdNDOnpjBSBpLG4Z50G4APKPVNtV0G0AatSEngxfGalX6l0SHDVlKzbJCHYWNu6gIinplJthk1qUoSo2ycc+7bX+zW94awI9+9vNfbv7XJ3j9HztlqMP1r32te93pr5/2mEceceBzP/iWQz/+jiM/+663fen9xxx7xKGvPuCF+z71aY995CN32uFed7797W5zy1td73o3uMrVr6njlv+Y/P/fUZy4sd0Vtrvy1a969T+78V/e+GY3uemtbn6L2//VX93zzne+3z3vsfOOOzzsAfd72APv/6hHPOKxj99t9yc+6Sm777HvU5+6nE972r577rnvnk/flxebd/hoj92e9KiHP/wROz94l/vd9/73uuf2d/rrSxFS/r9V7PPWU3936t//8Efv/8hHnv/KVz/hmc9+2FP32uslr3z7B4/7x1+e8B/Z2gnw81/+kjK1jje/4fV58afzv4iBy2ysRMj3P4u/6JJ1Sw+wvGY1hmtqRSRzSBDsbqMKC55lY/nhDQ6cdA7WiQCTSUfxLdAxlldKlJicmSwK4mDZk8psNqZVoXRnF/Heo0TjDmfVMYQMwlHBQi7p8KXxWqbacpc1hBOwiYwVxFAoU8BHlZcCz948uO4IVoAMQdbQIUyTdcHWTeeW7CwVtlKMwAqleMlcOevUAqygBsY5h8Z+atV0Q5wvuwZWHS0yDlZFZU72UmkhTrklZOwsmhUqRI9VgEP4eqxvcVS2/7Qog49DLm9/NQuRyLpzYwKCQXee5t2jzeI+ComtnNiJOMIdAH+aAGnQwVcaI0xVqdWGebHM1BVT6agzC9NoQii0qYizR6Rk0HMUFTKldpGUmlIoT+gvT486i24VuHRVLyWN0zjMi2DQYfCiTbNzIIUu//sdaUobLEDeWZSTskouSnpEaz5TVNVex0BhC4E3eIe7ZKOKMIeJVhuKGWgGhsw6h3YY77Hipgkd14u1RcRgsxJgdeWjqqWyWTRFX5QDCAX5iKGILmaNERJoqkED6JBMsK0G0geJUtkixtnMi0gV+tOQ1t1kooOWiFiCjNYkBIoKaJRM4wAYqGCwMtRSt6QuLkjLFIerLt2F08Dlz7BpZgvvmYgc1DaqjVWqSUWSWEWiCb+wApACH2LRwcHpqS4aprLKLB19JunhQ4YSBIyyRqWUxTJBC9l85WwINX5jwlWA0ZpMDw9O9SYCFTMldLiBgrUnT0dOUGcehAli6GD0DwaMzYd5sk7oFWSz6rLFZBe1kKATfXkU6IKsHK2EhCjL5yZ032J9YsUQMkIc0msLncPcAe/KVlsmJpVISYyiQwWdWWxt0qq4WapxEbzOac5pkeUxtm4xdXMElme9z/yoXYR26LLchbhIECyXOR3FQUYzUxrEVYJWV66xcqsnGUilN2tC1JZZCqFbmHF8Azkrc6/Mc3YHocpAnstG1y0F42C0lFb64q6qUVMs0gDBElxJTUpToCbGW1w8j56kLFMBM0gNK00WcMwS61ldhWlJH1AA0hNc9nRLGZuQM6FUcCY4Zg6fvfXeiqQWKLT0LJMnd3kgHiSSeGOQVGojs8aZQmt7M7I3haS1KJ2D3MrMAgJ+n6HqLsG5Qe2yeM/gBKXWYbNp1xplEKO1llNFmKnwTNc20ZmN0cbNKO8gqjSTFvAZc0o3CVriSYtrjCMY+2HRjDCl0yxJPnINb+5TXzwzW3SRCNMUISMBDkfQHAT48kc8gRASh9476ZgaZobMPDA0xTLNyLGwpFhRHoYEJ1KUZVd0ZO/iTQjeKYgfea8HpgCjV5hTQ4GNIiNd2IWDWd+Dqzx2L6BEluwhZAqZIEIJCAWhoBjdYdYlw5l4mdQJ1awdRrWOoJ/VS3oJl7X2eYiunCwKN2XWbjU2k0HaStcDBwjj0tIqqlkpgsHJWAYygqKX6IWDucEvWGv0jNm1MyiQoO4FZBc06IyayenGdSxlpY1OrlSIXGMTJHngSKVocSuMrqAinnTKxZ1+aXPvwfitaoNQYCfjOYN8Bm2FB0uVLC5KwMW8iBs9Udtsjb7WAdzijHyQEsVYeFVApBMjygp5VW4rHOlUMgemZKPvS80jRBKXdChINaMqezKsqktpWMbr5HWd49rGTn/xq72shMGGAKB4yBzzTHkWalkKBsgQtL1aCGmRCULa0maUdQGE8GdGrjqUSENbcmfczIMIKKIpNVcOEo6gCxgKRKmkoaG0waYi64CvKY0kQcMkNqlPGJoVL0PqKoy/K3IIihvEqlQM4oSbsyUTCGy6WpohhIhM2iB9UNSSg0RxN3cyi3mKmLt6H2BVN8K4B4NrBxmx2RfHaDIBODOzMmR4y4Tt08Z73v52AC6++KLv/vgnvPgfOgWipdQrbLfdn1/j6je+4Q3veofbPX7nBz33ybu+9rn7HvGql733zYd86Ig3fP5db/3ascf84+c++uMvff5HX/jU333iw1869j0ffedRHzrqiPcdceg733jwUQe//vDXvIYX73zTG95/5OF89Pn3HfO1jx77/c99glM48bsfe9+3P3TM337w3Z98+xEff9uhx7310GMPf9PRh7zmra95+WEvP+hNB734TS898ODn7ffafZ/8sr13f/GeuxHAc5/8hOfusetzn8Tz8c/d4/HLxyfvykcv3Wv3g/ff7w0HPP/Ql7/0ra9+2TGHvOZShJT/j3/z6S984N1HvuZVz9prr10ecP8b3OCGxUxVAZmm9SmnnfaVr3/tkLce9YR9nnmvRz3+GQe9+n2f/+rvzjybeYh/72CEnPGHc/nkKle60nab/48+eP2n87+CgdU4isi/lUyvscXxlDnRVRrzMJfDdKkPrEvMNoxmfG9oLuGx5uqpOKqgZIh3acISSpnLZPdocAcrD+9nalaPFYthAmJBoSIz+D5xbq5ulZkeyhpl4ZAe1nKYSmm1dNUWlp3L52IsPwWE4yW7ZFCcZDHYEDKkIWtPaVQcbt7B7hgdGWk2czW+NKdqDcaq3OdOO7WbwFKIKag7p6RZMyg7wIMleDaf1VuLmRs57onck0YWgH2hDMHpitBEwptiGoXf6aPOYmthpQY0tLh4RAkxAollapBrZVniytDgRTNLpgj/xNKGgNRIFVcjb2qiJUtNraIigWwa85CoEMVyR+u61MlqN/ViXiRIWmmLF7It0MCGbspOkDpnoegiarro6pEZbKDJbWvNwTZLKEthzukBlnOkIEE7p9CtXGeErDNpz4A2yqToQvacKAYpVarSCQt1Alou1YG1YS05O5pmGIjZVcMkUdMr6uAb41aViOQzoSDjuFyKdZYug1ctQw4QSzICgUpnW+Q8mESPcJkl57GBy8zqA6L0jrYg50ZHDaIwEx3EtkFdeWGxJwOGJsWlBr2fg6pVhgLt7SGOWq1u0Y3lv65ShoqiUkAwvUwJekZ04pqzClaS3P4W4g2dm0yLl9nQQLY3rAyFqpGESDSOEUAKWVAuAAgws3WNMF1GDlorivUijbxNg0zVSBB5x7KImwpmaOYCVpWKq4yFZpH8HpCW5igZ1AArICASX9NyyZdg7AYd1WrPDddRlRWaJ2mUOeB9Ci4fAllrq+ilO9bwsFnqVquzbfpKBOCphtxg7LXZZ+dSEKGBnIXjxaawTld079JdEoy3oiG67tI6R4CAi0pJ+s0oUCTpCDqBg0DWhTHtSmZLpfUunDqFNBd3y47MdNDGLtmF4vmh8VZbgiBDUtKsa53hs0zBVq69qFVb2bL0AnmUNrnHGktpyT6K1xKqcwcmkUZnaUAQqU4+UxOAuOY0hDN5I7Knd0JWdOGZWr0OOQw0VDr6lHOLzmXRQkAwMJeCYT0okvNbDQYwOqIHp9M1KqRTSyluOjliymTlyQyGvUCoXhhBBhAQquvgubzaKRhXUkuqBB8B4AhqgYP8oAvTwRwKqEL4/Y7CtAldkr6efe25LtKVJBbxohhAwabMMAymynJrQvCaXuDqcDcRsUJ+gw+E7AYzuRup6EiWr4EsCsfxJCKKbpRAlclLwkMmsXlD0HMQximdxFpVrYtNYmuNNoZsAx0HNbKE2BoyWwmrDHVGfkloEDHExGoWpHVIVnAFPFgWRslWl3XQfi/Sh4AFRAU2qGwEavd5ykt6TOGpIXxaMgVAQhGB7KVP3LXqNKc7KeymPmIYowjjkJvH5KBlxgCMPdB16mVqZfbS+L6tDXBLcoEaGDjO4TOXpbyHjEQnIDPYck2VKRpAIL1KkF3QGJVWkMqIYLgQlzhjsBfGa6frFMuz0BoycoauUqoSXPKlRkHIosX4G9rTugxdyL80lN6ZOiLiWmYZfYmR7M7iTq6wHJmY3DNTnEAXUSyhwu+/+aWQcHIUzyJC9CpEW4gjrPBrO0nUybW7eAer+4yYpQnN6Iv/SezCWQfRm3YuHiT50DOaw5kk7EuYwZTx7JJBCyHSM6cIz5b8lZmUEmqeAg/xBqwF0YQb9yFdDDO7OWnvobrWgVWZNghSODNDeGWJYYa2QFqUIei/mslVxej8WsKqSQx1Nptj8G4IEtSAmWgKM5M3eA6ZI8RUIrU39cnD58w1QGvMSQ27rWgVHSTY0XllTCzK0A5pJm7QFA3LjHnonapEZtSW7MaRZMFNRbUIBkmx7mNOKrNG98igU6qqSjpAcJ7e3aPP3maSoSPKhpSCYikK+mPO0079/Yc+9enFvcBPTjj50ov/eb+ZOSKiQ62r1cY2W7bZ9jKXvcJ2l7vi5S935Std+bp/dq0b/8Vf3OGWt7jzbf5q+zvc7r53vfMDtr/Lzve+Oy/ue5e/vvvtb8NHN7/RjW5wnT+/2lWuesXLX44TL7/d5bnW33abLVu2bGystnBhOg6VZdHUqObfwKZeBW2EArzmic3j0o/6bx4tt1Wt1mG1WhBS/pWueKWb3fCGO937Hs/a7XGHHnTA1z54zK//7qsfO+Ydr3rRC3Z54AOvf93rXvaylxVZNi1nn/OHL/3tV1/x2lff+5GP2WWvZ73ure/4wU+PP//CizJzkbv5Q90n//5MXm53mW1WY+XFn87/NQxcYdvLFGC1WeF8ViZKMCm6wxwDK567sLx4sjpIUYzh2pLVec0XH7VxEItfb7ms5AjYNCSbeGICq1d3rg1YwkBpJqk8ClShjpjSZzj/sR3xeYIlyDhHOjNT2QDEWQqUElXSlJVyWTfME1rrTGTGlrHBjVUEYCjp3Nk+xNFTPSxQgtWIsjVLJGdbOhc/OatPVCHCITyVoCUUfakFkd7gsVRMzK7eVRoV98rFm3Ry4mtDY1mBgC8yukYYRBOJFrE6ykEJAAAQAElEQVQVZK719DmnpcwIqpYNs2ChM6tqA4aCWgYbhlJEtAkJ1FRQCrsrC2vtUjoKS3GEsDBRTC7P2cVHRwUX717GXjQLQUcQLMCG1tg3zEOdYmiYKSQXHIkAKShBqD00ll40olqMwdewa2TPEIEBcItuQWo6y4Gy+vlgXMN1F2/OL6/muYc0lF5KmGEAu0CNvvKsvBQNHURplSBKeplziIzep94mcsoK7BXUpRyTmkkXgKvwMJk9OdKVVNcuJTp6zw4JVaHb+VdcittCVJqA9kGRgEiqNkNqsMyvEhYSsTwScS7CCDB0NiKWruomUY0EZmZ4s2gr6eMIGyQkJyytsBfEAJZJo4Sk4AT3ApihoZYktSe6k3T1tMUxydspSQHdHHAhCCwgq0OccSmLQ3Dp0YVG00oGWE/pbu7LUy1eyiRUFAB5ZB/z2pf3lZTQi7RCtggmQbfTLC2zeZOevB3QJBLKCVVwAGMEqho2zAwuT/AIXZIbBRgqCr3lYCr0LClkMEukRkHTmOEMiaSiTD5U4RSgb+oGTLUQSvZos/RLMtZOv/Ueyz90QLojmiB1lORXRebJfc26902ohJV85imemiJaIBLQCKHVbkOvBQUgcwbGkIYaEpKZbdMLBEh+o5u4oWHTC6Zh5lYIEd4oe1ZtVbpRKJQPoJQQ6N6beyyuk6YVMlJDzpKTk3dLhGIWj95kDu5pNcHDNVvxgHfPIBhRpZmsW64OkyJGHidn1exdOtMjKGNO6fRfL53ToVWEq+5Nfp3CLdcsc0VAX5YCBpHHROkOCrNSrGgwt3KEDwIV4QsYaCOxriWrWU2mcRfAgNITns0jEpEVXa1jkClL72Rys6YDLWcOolc5Z5mUXXPSmNTpZIzJiUTaxFpW7l+RXK25Z6KDNBAOmkgnQUxT79GZsMaAwuA6NC0OJC1rXGJm0BEDqZkTm4dkKlAkitKQQpNYVLzWrqVJktvZ6MwBwyDc96t7JOaUWUwHLSsY5RUabHSnCnTxAXpkd6ZKQMLTHTS/h6P10nzVMSinmg2lajEncswWHc6RCijl0DZ0KYGxx0DXtZ5c0EaQWR8NQ8EAGafUtQjJgKCCuouhLvZIw7i5SymSzIihCitbhVcpJgz9TbRIguVVcR2QNZMJMTDMdBbW/QJX7h4sPZFLaBVRkYKURLgGq1qYknvSa2Gmg1XV5TmfepCTQjAVMZZedWKRECRNrJ4bSc/MEtxLtF6zW0VTdEhLNEgKLj3MpDIOAcrX2Lwn1BSqaZYiBFRK0bqEpEluzLqaxsI6jnSJJNJUuitjgrcgu16yC7UQcgUVEQ89JQswTSqnizCLJ0ubz2h8Fl2ADejQDYJufZbW2D2zU4e52aw6iYYkSrQNn1egtRFiTpiQallLiCIFvshjYKSHdCjMAmIMVJmZRCXoKFrLm6WX4kWkupoz24zyh+6EuCnBkAU8NsnqvVrb0D50MfotROm7VcvSQ5N1F0NqijW1ngpXEIpFFueFdSyhygB0c2xkoW4wv6aUBtI3ETNodg4ZK3frXdNUyHkRq4TH7BSjjAq+vlobIt3mucxNeFlsLuRG2IDhjDnlFTB9+3vfPvPM32PzOP/M/9b/Pf3mkH/99b/PBSNO6zDc9qY32vUhDzr0pS/86rHv/uQxbz/slS971EMfduMb3OCy2y7/NQLScfzxPzv6gx967N77PGzPfQ54w2Ff+c53zzr3j8kDi1c5YKylGv3Dyz+d/yUMeATy/5B81atcUTRBysWYA2uROdUBjagZ6l07U27OnMFpqYZB1VIVggwm3TBVbUxPWYSqQApLh1jyOZh6SzGAizfLrJLGPyZM5IBmWJfszOvqKF2KJ+ApcFYP5dhQVoKxVPaCXljlWsOc3mMOVkqWBkCla3R0Z+0LXwSmOOWPOozGVk/BdWva1DO6i3c1B8EPrIWIYNkUpKQRGUqENd7BWtjF+eKMpJhZHcZSBiE/CMzBZQBmyCSE2ofIwVkHlbI7cspplvWss9cZmXCnKVq8DuzPgRRxnrSbtZAvMimT6GVdMbPlqJjL0KT0Ik5jC63svc22bhTYM12FQ0zmSrmT0OpZ+lymeSEAbHCEDzYKRIVVyJCiTjaIZECUgGQoVVkUiHSYiGgkmjea3eA0zqFG71axAd20EzJrcodzAaqNZRhF3Wi7oKmG0KiJrgWr70zkMFqN6MtLpwm5FmmxAKbdmtiMGMbFCm45t3Q6jOMDMSdmGsjpVlTJzsz9CpcBVZWzkOxqLdB7SWrz6IAUMYOpEJM0oImkihSAmx1tIF+MlAmdyzR2fTonOUYDVYP7XW2uU9YQk0UBDUrrvayDvuXwtUSEI9IT3Zcos7Fo4dKi9glcfbcWDKkeTkJLbcWglAYNzYYgAQipopw0rAgRoIbiITSXl3RvF0RyXJ8yZ3b56CG9q3ipCenogaY0hfOAgPYkdkUIJoAq3DyLL/AFaVbgRbso3WlIVcfySQQGRgPFdSTnpiCov0gMkmMtq0qERVKEP5kQ4aAOJByaUgGujUjhRI0EIEu/V6WWaCA3Lo0452lgGEbntIR1jAx/0gsJLQ3GRfbsPT2wmG9BopuwmLDMCKcE5UaNPmgvY+fqzYyIpZSiKylDM520d2mi3LCFunTRtRVqqs3Ua5NhrbIufa69154L5i7WEPQeqJg6LKVGClpHzMbcXejtUR3Cf+DRxYJ1aCI83upYzaAlkgyCbEbyu4kbYhnq8MnVo3g3px5BmlixIRi/oJcLQkX4C64KKcKjJCGMJZS4nFuuXsKFUtykW3Yn8gGFT6W2rDELnLqqUHQmzeZayS3gPdbN13Rosl4aiaQMlVkpmZZD1InZe/KtAutCYwX3lEC1GCoYiiWSQnr3uSO7GBFOFZMyw8UYSAxW5X0CiJBUFzSu4lg8WPSrqGUIJaioYIkbD/qD9qckTYgqriX5h/gJH1AtxugsXipr3KxIEahhBEaHUhTmQPKfqRCsLYRmQkQqSo1R5kGnUuYlnj3gDANEI34nfjpbRoGZ9465RSycLbhWmquSNU17rSLcKriQxkxVZ64KkM4YUWYQDeLZQQLFLEo6wSQ/VsMotbghZS4yjXxYrAoFGkUGHzTTKCb0sUSoUp0VkWGxrTNoAAENCRVG9zwwXoeeS5T6tMI8iIvkDDpPyCBQQUkF0GUSxY4mZUgfwJ13wKgiY+7JYuEZkmKCamqyYjYji6UMkWMTY2HjFiVYx7okyCaZbsyetrJYbXeZy7/wcQ9/3VMf95A73WpDxYDAMkZ7FNbYRvXLGVK7FrGhWNHMntGholIEkhG8ocntHAqCeIFCEMLg51CZfGgxEnZtfOnCGINtuoGCCtyyG0O8M1AIX6vGWGmIuFgmb1KPz0Vn8hk5ZF+B+6NWB95AnaU0FYQP4VXMtIqPOhe01Oyq3SqTt/lqZriFGcJKh22GDzVIY/xrz6Y+GR0tmnRzgUjY7DaHsGvN0h2sBE3TqcthXsENvaxSKzc5prNSVyzznUUgO18EJD8xXeqcZb2pj58h4JFQhfGkI9hM2MH68r8CWC2vzAop7DVdwpPxzNiM1SD8JpjMg6Zy+qIm1KOSHLA8VZfKf0rQCHoXQfeJZdIfyIfd94FPeMQjOY3nr046+fQzz+LFn85/j4FN50Cs1Bv++XV2vs+9D37Bsz91zFEff9sRL3/+/ve+612vfvVrXDrr1N+d+tFPf/oZL3rxo/d+5j6vesNnvv7trZdcfOmjJW/++epPf/7nM3DeBRdGMrz/WfKfXfNa3tOZVWqsszkGV3IwK0wwllIXlbTlJxvgCYjosGSYVG8sPCw/zBqOwHIkKqTGAFfheFEKNYGyASU7CWucdCmdeSeGNLigsfRKQjXYuUIwW0ya/B0s9hqp/FNjBlFmzqJTwhf8wfLCGZTQw1k6S/aSKb1EVOigxvYq6hCWgJbiPaSnhRqNgQoDNT05UTK5TDBWUAACfuBYYwfyjbQRRG9s5wSITBcWuVwjWkYD60SiaQ9W0WE27YUtSXxDyKDmrK0jUlmjJCU6WiSXpe7IpkiTUmBEE1JYDDsK/xGaJQdIhoDQPTLds0dESnaaI5I5I6k/w7V79TT2o1B0Ay+o0i1kMb9AhHNZPyEmYoVdqLvEvIjWkrZM5AahhXvL5MyAiFnRWoQMQSDkLU2DvYQ+r1SOupaNdR96FGRC58DcaVzS210tkwyGpQut7U617EZjwQqsrigGCZbUpjrD1xGeCiiBh8yQJANmJlRYhU4kACx3w2xZSgEZgc4DlKxii7jB2atK0mZalhSGhbckDFKS2vmRsyKIMXk3JcPczTvpy0gVpChhzCkNOdGgrtmVHzSTXSBDGQrTYGlSIsfO+ODQSOdpLtYqnKsc7SawBOVjQaBZTaqqioCHZBKkFLDjqRnXUBVS6J5sPYmjdNISFTAqUoGSF/CxzS5EiszikCAFiwqoKcoQ2Oi9ZrdIXSYIrcvqROzA0rxqBGUklmbtpATJ+8EhGIQZUooWFUn6Oq0qPaRL0lQnDFFFDVRPZIR6gAYnRAhQRjiXEFqqZJWghOSPpdIsTXMIY1ZICz8YILwUIulkYAy3JOtR1OlmMQjT1FJob+jcbZ2lFyzKUTJXXYdWNMbEwJsyODQlUhitpSsDUpkKOYcEqpSRkJKL8qoCYwmKyC6O4qWkgH4x9YHzSsNmvGnQmpKQNATnJBCZ8JCW45yjpyJcA9pX2TcIdihcYTJQuL42as0k2ai11LFqER5FjF+ZSjXAhGZRlmdKcOVGj9RQ+pGzpow2N8Yxo8fMO4uD94U4KBkPwjAs5Bi5gCyDZFZv0T3DPXxONEpFwjqpYdWmtoosc9cIj2hUqd3EiyQxDVwRKj8v+6ikoplD0kHTZPGPQDM40ugGUCjDIWXTxTNqJ/NACSu9IkoXZqNwFJPL+jJ7oVw1E9pJs6QuNgDLI/IwWh2CFggCyWkiaRKj5iCCDprdmwcYLiVXiA3PMVNSwofJVw0WnEimsrPUZHSRrvBgnDTG97JyrJKL/FzzKRL0eW6VnMWZGUy1MgoqlHIAVsmYNWkuNISHigKFoWtFBwtNuluC4zGYqSHVGQ0crBVSkMLYqoWmufBb4zpDHHRIX8wIDRgKYB6WVALIkjrZU9ONy+SclWtgYfgFqFZSrdOKTslRzIR7Ixv4gTKqVKNOr9mNciWbe+s9aJ05pwnTtBRRhq+FaBP+NhBxZUyiM3AhKVYyJTuRSUkmkpkNd7zZje9961ve+BrXoAItoFM9YQ3aNC89mU4RKRAi0uyaTs6FOR81W0kOTwgCiAKGSVFUteJS0xe9QGeHm4koB43qZnMtUupGWO0u0bsyLgtsoIZMS4QuLkIRQhpUISy1K28mM8x74daAtJuBlScEyCAIUwIQohNK76JIzmcugFfpnhnBS5IJMoWO6NrA1wDm2SO4UXcfpI3qY00tS3g3cprwPsU8L7uVgIdkakjhnjQ3rMcxdAAAEABJREFUVYyZNYX1IWOxsQRF2ZKOHkW7NCeMJDBwtJqVopUcFe+S3ilwhk5ZHNQoq0UpL0GgDdwnSjZBCu2EpkRGJ26ZVNl/gtSoDIphpcOG6IZj1V1tayq/Fu6RNLaMw7j37k/A5nHK7373+qPfH2Rh8+Offv13GdjY2HKjG9xgt4ft/M6DX/O5d73tPYe+cdeHP/SqV70aJ0b4aaef9qUvfeG5L3v5BeeezTsXXrL14mnmxZ/O/yIGzj7/Qv830Xurv/yLeZ1tVslalNUSZfBaE8b6X6SYbNSyGsFbylx3lmBlFtWYxqmvHFVK5XqejacQcGYJJplK02Az9nQTqGpPnR0xB6JnTmJbbWNttYERMGM9W2PN6yoTgtU4u6dzeIZnsGxMmDNSpmHg25XcYJbDWBUKpYakCkFQR2E77GydOtfsZYKwm69Z1UREC6dbemGnkGZsOIZm4bzNgtbRt8pMFYQqAh6sTXUu5qouvcs8Kd82uSgrlhgxRqqJsuRJT1uHBD+NVlarUjYsRnMzd3MKlu60TVvToAhWfGusdOBLvqwQFOgq6hCjwHqRXAWXR11nT4woQ9rQzTodkzrOZZxMGi3jYkE667GlgG1dNzqlYAgQW599jljWAlusDxkyD7JeQG2W5Bm69kDvhUO5zF0BlKNNyVvNqKKE1pdKKoblDM00jGVc6UbBiv70ENcyW6GBKWwAHBLg4ZtcD2Kj1j7KxHhwkSxsR1zSLjU2S3Zd3gcjswqKuBUMg4wSY+/D2q1nekWOIkrTKBCpEgav5sUilrNHzn0zTCS4fSqrWtXUHel0aBs8+AZW0goXrjoozFOWBgDGUGZvcPcA0fqyDqFFuab5fUu0VRdapQmzGDSUtqcoOWsSkyCk8BFSihYrlbDNha1v6F3FRUIttKQqO20FDGx0aEZNZEeQBgmUrsyvlW0MNqxCN1qsILVWGwcVQLpKVtDWQoiNJqWrSwbF6KzBZzpIHQkRQfqD5g6WBNwLepGU7Jhd1onJLRuKg6uRMoQp+NlyUgkxLYSLgKqQPMgIKSWjYi6l8Y5gcV9qy4GUGGN3jba2aRb1YNqMJhtWxjqIrXqpEKniVR1D5pD0IRAm1GKDqQl4wjR5WcBSUldiXCAMQxBXppTuhd9R0d6eESIBdNiUvFB2+kpGCojUMfRWWwMLA6dlr97H1rieHTCMqGR05IohyYjoAqxrd1/L3GvTAbUQzIi0xUNrK+tR1pU8S51lnDlBaZ07lyrOCDFJSeSiJ4Vul6SIHI1r2FBk8CG0woZUpcBl9SDYqKlVRLW4SpPsknMjCHURLoS0uqnrslbyRDgEkgHv4c4RCgZQJvcbdfKRVKqUWrSQZuLoxuAJq+QmOhIMN5oSAyha4MPcx/ARZoyprGGgNrI3KMSy27pLY7JmGkxBRyak5zDHKudqPSw9QdlD1+V/qlK3tGHwAaVQ0pzzJHOWVpHmgEfXmMectyDpS5o2p/Tlh9JRKngIgylDOZpiAZJJsreScsZIiaGKKXEkbecCPDqYccWKCGsR5uwTcq0s2KCMnOloXOLsBpmoG8r8bOBU18UkM5D01Oyst+gsBZNn98zgIN4DI5bANbklak0yTGhGkWqyGmSsZEgzab5DyIEFK5Vpr5hoQ6bwcxemX87wBogUJu0Qni3mdU4ko7fok3R6yEmPgoSTt1IDDC+ss67DeE4y9Nwisw4udBQoNDtBkRxREyMq8pdVcoFLnUllgCIsvKRbUS2lD2XJQ1AJKG+Y0rjwpoPQDG0U2u8IMUGVUJVE1aZ6ScfW1md0KI1g9dPUFXKIVOhCnroPc0NMXuZpYNqgBcmSMPOlDGgT91SnSNIwO3o2rv1lI0OtJ0K7lclsLTaLrNXcCkw1XRDGq1gJS0NL8zSyLT3R1Gd1Rkdxq6E6WKwYfVvUBlm1Zo2LwVBSEFqmKuxmQW/HEMGIaXQNRD0H8WFY12GWMaJKI68d3sF5CU0BymzWq6R20eDFPGRLgJr5IZdUK6JF+uIgEho0zuFrw1rEdYYubb539K0kZkvOo/QCzjZrapOMW7Mq5REtXcOLhNNEA/3H/A7nIR4imZIhyGFhpDNI6f1mChOVRR5B5zKGbIYKW44rV1lDuomIEWMFvETTqUkPcUFm74FQkFPJU848Hf9yHPexj5x82j//L8H+5d6f/v73GTDTK1/xive80x1f+bxnf+cTH/7IO962x2Meda1rXnNg56J/NgWcdPJJD9ztyc97zSF//+OfXbJ1TZ9t3v7Tr/9pDJx2ym+ZNpeKu+y22/3lNa9RGOLQpaBYTWaMuGlLYZVgXjPHEwKNDEfv0lMgKqLJAsQLlQKYMveYtUAXihgokPlUEeaRsyS7ZlE3NGElQECZq7H8EbH06sqGEgLW9TpADRHFc5EZS4cJoBv64Alwy1O1rExG47MQ1r8y6jjIIEW0epVl7aK9J0sqVIhssBw1rWRH9aWVVwFgLHwJsyY2syhI09qqxgos9LAssRSBcIv00LZpqyUG8jILWik+VGNbRZpaltFXKhvQjWZlhnSosqGIwmlpSjUzFUAcJWC8ImmIFujdetaulZwIOK+Z+VCSGNmTuPbSoXRQjEKlpGgmBK2C5nLU2FaaI6rREqVfioRkl6nZNEk20LVQdFY8NeOnGLsXp32i4tRUeJMLIeNDU4Bjw6JLOELgtBpVpRTlVPpulhJSOaqqjWKmi68jOARAqjUs06pILXXgLBMduSVK3mb4BAehm8z0idXCZxIVLhqbpND2rcGm7dmCEEfJQYiHwWPhwjo/a/QBoFohC9F740hJ1aJWIHCAmszY7dI6ytZiQfvAVQjxFgy10KLGkSlquSiYYWv6INUChqjiFokGdmCh3z0Z8E630wXqS+iJVRk0x66rPlRfwUculJpKI3tczyDBKE2oiRg2O0p2bB5CDztd3hoymUQJS1cKt6QvB0MtWdQhHRpqKPwtlKpQ5OAuYJ9L51vkYe42dXp3dnU3UJiLUKxHOGGnUwOQRpVaqAeABHzKqSWXF7EAZSA7cVCoZagkEB3ZM/gUnFQ8pScFmRCFZlcqlEhN+kUG3qRKPhNSXIqsRIqiWhYsJ/1UBuFnEap2sBv7Et1uRqjZ6NLFYqGdogJaTMxd2sxk1CkT4TIJ1nzNiL5Ew5w1LbnMKOElo2YP6S3cQRpVeG82mcW6cFGBKaWHgqJFJJd1pGARKhCoihZgFNRw7Z2q+NNVnUDIebrMbr1bdqmqg9lojC9TQCS6YiGUmSHSmQ5WQzUCmEOd4iDUY4CBpJAapOeiunf1rrwk1TWydjFoFqP7GsMZNakpbXBbYq3TpS2tdfOmNHsw19qTwakmS+oxBguCZ6WBMJEqdBphQBRW3ZRvvil0GBlHPX2KacpIuqCgDsNQqkmBZ3SP3JpLVGR3iVmkiTURUC1LJirCWNHKHNK7MzyUUVqgEg6OTidtpZfSSbDMyowMcVfwoLFuPdCdwZXwCd5Ld66plUFIF3HdyX2QjKlCFxEKZlFuI/iB8dITAqkhQ8YgSRYyoi8hq9ABpqmU3bKHyIbKaEqbpFbW5rUMjhKaoc1pYwgMYi42pYWadFIkI9yY8IIqImBtEFd6y6Xx7NTVtbXSu0QyQBwE1AJzT0lUrQpb4ESYs14xCEQIJSCiKGAmj9JruqAlAtpdlWqjriArxYBUUBK56cS5XAoj3tfKLYCAt+dG5G6IAUE5plJk4WLxyNSkNwYahP8g2Tym1jw9wBtCexd6g7QKYxYKKZklopICDuEN8EfMRFhmmKhYXLbYQFCDdRNPdBcE59eKakn0pHJtEZRmogTTEQwZqrf0gkli8TA5FzrHo/asAWGMGhILhcHPqtVKLQzsrpsKWmWIo8+GtUlH1lx0erHO7AnOC6VIwLpkt3lxa9HBdalPiNDSY/QouVW0qUQKg3MBLJLqUDKfQNNMUaYqfZNK6lUAlQ4WC8oUNxEVU7rFKkoxWmfo3LeQwznClWKtNGGERpeeBisqSwxIMXrFldWRQgXMHq+NI2O21hh/EemeS/QQAwdrsVbqBFaM6GvPuYPBxrSSmglvLO5bfTUFP6mFlNB0mmFh5BWbFk0Ry39MNDxzGrObRjVCp0XRf/DDH+Nfjsx8y/s/ePZ5f/zi17953Of+5pKtW//lyZ/+/ocYEJGh1r++1c1fut8+3zju2GPfcvheu+12lStdiZPJ7XnnnvOpL3zhifs+8wG7P/WlbzryOz/6yf/A/1cWivrT+e8y8MOfL/8Z6Esf3faWt7zcZbcbi9QhpDKBJVARCJ8Ts2vriGSWzewHPSM4glmTUQSj5gpgKctI5uXahQUB0sMm1OBjqVVZ1GJ5nuzDK2hlui/v2rkbUPXkyYaco7H6JAEYTEQBM2gJPuZnY9FgW8IgrEmNxcotWXOQZj01nWUyiWazAGgOqiN0YKGdh56r2casRoMMoSqsKVATyjaJEUTSjOV0FWJmuqxnzVlXa4iEzHpppXEDqsRgUksqlCVvUB/MjRVRq4kMFqMEi6REwsmUeyczVatqNZ61LBekBCPSgBRk8Mzw6N1Jq2wa68hER3ExNvA2ohX1Et06H3BxlzH2WpqVOetieHWBWyOVG4ZxhbqN2pahUidltmgzwcAVk4WzXWYaXRTC32qUBSepGxKjYsBiMgRIV3cu9PhWrc10XTEt9EcIZiSxIVdVViXJB/tNyVK7iINHY01PMFo4TAEdiq4GK1wThvBV69yaexOVGrb8b+qyDMYPHdLEzWb6w9LZcjtRLyISFW0gUVztzHXq3CNqSlUZq4wmgATp4xRf/sxcmqoIkqYs/2PiZjprsl8QkqVVjCVHPs3QyKKVp4i2lJbY5F0hEAtKpfOFpnf2DGnkH4oxbWxaEs1kLujKSOBmcSqYKX9xalTeRcvFUg9HOJF32LzIxHJwIFteW+e8Zl/O5FajI6ACEfqdJ6+0K+EgwiSrKSnWUcnTsj7wThEBBlef55krE+utJj3TUrmcygaw9UeqKFBUjIE9htQ0YiH5U1vWP15617UTOGVSMT9NDJApM+YEx0wzukSUnkJyM5AaqB18rzcKsRjTWIeEIrr3KSIgIN5eh6YVLhLJO1CaUMTVu69j5lCyo2sRxkl35gkxF4lSOD0ymZ6mKmqBwqcGX/4zDd2i6+SsRnPkTLaM5i3DFCHeiN2d7y82XMoUlC/JCM5OjnJKrLk6Y44si2hP6kxxj2R4W2JBhoRTTJJC14wi00qmUTojRdK4BJKa0EKSijaQN8mtPkx9RB+sS/EgeAuToMOyJwN3oVmDTpQUaVy0yNAxIKtyhznXBgv6QzGaDtUWlUKYksOqGr+2HihQXKIzP0V89mj88cYkCWuis4CIZJkrVVrRGEK3cPHLspKyNpshCbDGMLQ39yqF5WEKn6cWM6NQeQymK60jAS6BOnfp3DdSTXdBGAubcCBNai5TWuvgQspyWXRrtAw+rcKioE+yrP0AABAASURBVHRyd9IpmIUcV86iN0WytyUWiRRJVpBNZJqzdW3Ck9FFgRwUDjTFZqgKUsO1BwhkzVrBErTK3JBLw5+iKU6M+JKuolShGOmRQSHC2UaHjCKqvE8SC6UaC/mCWCYpW3kxEmaOYmkbHWObhVsnrLGs7zORLuLGtiMe/DeD8cZWVtaIdTiBJeOiL35h0CQBMWBABzJOVjZU2SJg/Be+KQPDTiVTIiQYE0kGQ4omk2OUrAXKEaXlopIJQK0uylFz05mupq6YW/TWrM/8tramq0KqskWZQHo3n21raM9G3EbqOBmtSldWWyNNVABpJDMCGYOl5MDTRGTM3EDfcIwOK8kv4DqBwksGl+jJ3l2ELIWFcyoVq4kyorpkUwspsdxLExSyzQLUSbekRihzUlwlOVoKL3oSnmIhC0SYa4VvMPgKHTVkYx1FFaklab2rhyQnxBKMRttdzFntFeZZIxXECOqNIfk7SQRAN49JVNMyMy1izCgQVqk041VfNaxmGZpwJhbgAC9hyZK+UMILRKanCAUmnyp0lLpRMNZg/vcl0GIuGarFh2EuunWQqZZeVlkG4SAmHV0hDq1to05jmYsEeARFt5AJ2rKmVNSqNooSJGtVsNx6KsKonHWJoSwZWcKt89QSVdyyq3dFA890kcUoMGpjUv7GnNo7t+kDRpOBY/D5r/0dVQ/DMI4jLz74sY/ttPtTn/qCFz3zxS/d/XkHfPuHP/bYBMdn/48+/xeDZ5zWeodb3eIFez3lza84iMprHa5y1auJCK9///vTP/TJT+y237Mfu8+zD37bMd/7yU+3rpdqy0d/Ov8HGGA2/+LEk37wox/+69w73e6OOow5gNVAInUGG1uJQXks9yJD5rR1ClOddUGFpQUS4rF4qEhW5iFYVv85+pM5H7NzZRFhEAtlRl06mUVUuVS0YDnQPpZurHCDJtNbDcGRkSkptpyhmYiU7kW8mlCWeFF2+RAgJfhUQ6WbsK3k5OgBddbHWrMqapqyCIg0QmcVXa5ZqAS8LcjFBAmweipQLMcxi9E0L8jinWUJFl0717qugFpKdVOwG7ICFxCEd6BDoVLEJI0f2JVCfBGepcGaLk8KCAWbULWJZTeWkwwWMCJGQmlJwhHdWnDtoxQkQQVNMhAurQ99hjcnnAIYzeuWjnSWLRTOy0lkUplMe5j1MvpCraRHNC4PetS5Fc8uMZHNQUI5mbN7GUIGIXwJNiAIwJ8uHNBdG7V31F6F81W2CjGzkKYgVObNU0RGwSCz0OHujQSgU9HcvDkdSMlQiKZ7b7GslFKWe5Y0mxes3V4IWKIkaiRFU4PTWHA2pEOnZpTWJdmUpJhoFe5brUhV1Qz1ToLCPWhSZghtMEUxFR5B+MG1B283E3bAKClFS5GyTLaI7AkK70m9iSydDQxanDSmBKOwa4RxvthiiFzaMwJyqXxCDSFRCQE7FZmJ1FDMaDy7Z0Q1AQ/BzFWBIty0A63NPeegP6uiKEDjI6irOJSYXAIG2juCfc1qRhIs0JOk0NhZtbMVDwRChJOiZXFZcDuTqQygawsRCacJOSIiD5lE1yaT9oBHRy67ubTWl6ClJKSJD9qG6DIH1p5TpDMHuGrrBq+GwWgpoIKs3hZnR8YSZ6EzzKm8SWle+yy9xyS+tXTyrsJVRKHYZHcOXkN6ogFTEhRCRdUGq8VUaCCSdIhppaOoqWGaYyJlxKNtyS3tErNw88FMZTtWGBg/vcRcfclh4SzJJvO0wIpiMI4AvU1MfbLWamhxnqb0s4I55qtIvhgwS7WSxvpkjkDv6I0shMxcOLpk9Iok3mTkKjJDImTR6sQOcAqakJOGuWNyYbDQqqpiRCUSYqgVVqGGkHRRCuM9wOgwMJrIXGl97CLCpO3cZ3QPzVBEkbnQEYAkWDQoIEwbYYvQEbkkOL+n6JoGJ/MRTuwMQwloAKkCjkSSvIZFsxWBwhnYLpgHuBHWUkEor3tM3eHcYwWJLixfbt5qegU5da1dCDR7ZFrSLhNVFIRGgkdCswqKz6VzonJnlb1KLIu7EBFfIm0IRqETSlFyAsaSewqY78IaCleNqKCJqMavaavRk3Rtzj0m6mUF07TOGZHCAONkh3RRwEqoQCF86BaugKrICDeu8N1ZkVqk9+bTFDErcaslv9VXRmgPdUNK0gh6qam7Cq1Op/nqLaOrellkRYEOthR7A6f1vgQ6wS8557XJ2LTQSJC+IJsdSf9MGpQZuuQ4BWVnwvWkpFShPs1u6ameFi7iZNWUFoG/FUGrTB1glV13n4Xxa72WWbQBzhCuIjXFXEMkLVG62Kws1JoUk17aHN44D7Iwzji1yUsnUwlGtAQEkAyLNF67SBfJVAPjpVJFLhjoQ3KkIE0AVFIojaAkOzmosvATUrEI46+eCGF9SzGVIskNVDGBIJXiAkC6ZjO0gg4PbdQnqt2VvGrWAVo4CO7OXoNA7SCwAJ2BWWozC3qJFqpWVSO+AE0oKqIakAQVZRPleweB1dTCejVLYYpkdqBLMtRRtZpWFSvdrC829i5B/J6Fc4RjZpMmBhOv0cbwJSyUwhq/X4GqxLI9Ko5CmIPLSmwDGDxZypd01QSEp0G5ekDQHdyiJ8x4pwRQtFe6rHd0h4NSiN2WsJhEvRSv1ZVuhZP/ajIAq/zyP3z7Jz/9JwC3vdUtX/G856zGMSJOPfWUNs+8+c3vfPsJ++6330Gv+vmvTwAIgPf+dP6nGbj1jf/yyY9//DsOfu2n3/mW9x/+pkfvsvM1rnHNS6X8+sQT3vGBDzzpOfs/9Cl7vvHo93zvn3623mT+0qd/+v0fYeCcCy58/dHv2/3ZzzvnvPMuHb9ly5bb3vrma1MmKOM2uRSeUOeUMMggKdoVvXSU2TSYQRVDQWEKBuBQcIkadSkU4ZBYbkAUwSIorXn3BuGrh1YtiigLVqK41+bWKQ3M3aKsLKa+4Ong+A508DNVZ3RWD7AItdoxNlQH898GDk4ecAs1rgO6T72xbrUID2nJ8lbEKoEKpyApli8epIelMDsjHeD2JdgO0jKtYZiF9oHlspc21d5NU2qypUnpEdqd+V4yaJ2UQv1IrmxnyAwsZSJFpgFcE2Vvo0QdFuoSDSpuBeqZWzO2QnqTPuc8t6m1rY1Fp1gZSqmymBnwZjnz6xohA9oAF3gVgQhXEBMrNwe4G2YDl0IhXTMrtfSFJGRLik6ZxaZaQ8zSZA6JGSVSM5pkqx41oQnxlJ4GVkYXltzoWHzIaa0Iv97IIblfbRUTW47MJdqKC7Wt3T2JLALZ+ItTu9UmNWbB3NXnyqVozN3nxSeZQaHC8IBnSnJcKH0AOKQjaR1WksXYkSYty2pEVF2Vo9gV0ElAELUUlyEHpUdAyCBq2BCjMaDyUktoDCIy6Rlz0SRvK2hhkCqbWvaZgDMjw/hMU4AgM6KT2qyYdEEEFIiWrCWtRIEJybLK5630ZDAZNSDYfnNEDkjrJech+kZL83TiYuLUKEPP7J16hSFLoQAV9hoodaXDCmHgtYSVzGLsSVnEtXfpwZZJb4lTQoRl0tyaqEnTyTl7aXYNHYNShiLmpmSthXoU4jGDVgqsjBnwnjlJJmICqElY49AHCaWknuG5RAxkzQTwIfs4tnGL1zFVu/VpSVSfLOdEgPLCKvX0DvFIMinhK5q7sCRBYUwqsjREriJqtM4Q8GipmaOWsWwR3eIM116WcCAvfNLROUSki6AwF2zQGNDMI7o1kxzFKu8ZVyEd0QigOCTT6ZNO0qSowxIVGC24UnONifdITeUqBEQbITKYjHQsR5q4kUF3XUf2QsogBgktQavp/rL4tUZyYNZkyvRwjzmZPUFGZTMKSAdK2tBRWy98T1C7mIChrWnKQA9qdjIc6ZkJPtGSKkx/hJdujJE681ED9fKr0BGkkZ+n0Mm1u7DuqosyAMYILvMDSj5CleAx6xzZIAmw/JYMRcDgjBoErZJ5VFRQiMxNIovq8vWmMCkomcbk3NEonsxIHX20Di1ealNeFY4rMMviLtHp7iwUGnNFatTuZZqkM2+yMFTMpc/qrH+UPkAoZpGF5RDRVG3DktgJFlulF4akHn75VgevYkqpfAvjqlZqMcauqtLpxY2aFD1BKpllY/ooxm+huFkx6KXcSvKK75kkFPRrZss25URdaWjIDkhVDNZVUkFPCMnKCcsGObpYDyYbGISsnGX20bXQfyvBsMAAVBQKDMIYZmSiKES7Y6KiLukFTaUJi4VxBLe6od7n9DX9lBkRhK/mGHuTbLO1WXoLbhLoPptEyJmS78w5estQqwONhIiJVEgYJiFc6yx/uRJR2uWltHHsG1SKbO5TtG6hQ2p1SAM6ZHF5lWpMKBHTLgiPHknMTSLYnjFTo3c3F1vmhCbnBRhFwt9CO0V41TP5mnIKBAgKBZpM4lhFUzjU3EoaVPiLXLqB6dY9PHOpNAwZUjBL7UAX7SZBjB0SndWoEDZ9whcckORFdGQIhSLHFPPE4tdM1pI+Q8IMA1J6LBR7arhFeMiUZa06K0cIvZS6sJ6MWrcMWusYvJhrRs+YEX3hBCbQyMpxknS6CrBka88kxBkkc0wwRAuKgEqxfNVMq4NChcUuPcAHQ3DHUKUNaBoGYQ0ZEKM6vzvaMuqqZilRTSgEzDt+tdkbFCWkZtjYCYw1gKM8Vi3GjpXGUBkKSFgm66mWOetMtS1linCLKtiiZZSVcjnEQOlCX/R/+tUvX3bwIfM81zrs/qhHPvJB93/fkYc95AH3v9nNbvHg+9735je5iYhs3br1Y5/97M67P/kZB778VyedsrCOPx3/OQYus802L95nz3vd+Y7XvNrV7naH27/uBc/78geOOfrNb3zw/e43bLMdSaYLTjzllKPe894nPft5933c7s959SHHfvrzZ557XkQwyv9zyv53Gj21/qHPffHhT9vn6Pe979x/2aWQgJve/JbXv951MkXbiGabaRo9MDmraUFbia+KG4tBQRTrfNdaZIKs4WtrbnOAI8EhKiYsopTJKqNmbLBgtWjB4ueiXiCrklVZoZje3Xwachqt2YbLYMlVRB+E5Uhmt87a4RsxsxybtiwhA4ixJ5ORra0NBoojWoW3JfXhmaxiyq7l5o4WPb0T8pCytMhW4CwF4sIk9/j4N//xHZ//5lf/6ccYgGGdNi2lz6m6zJN3b2nOIqa6+dqTbSRG36qxNWmvJdkoLCYEljxUxTRggIBVhyo61Il3zG6RoSzNRruIKSM9tQdLdAibKB9rxmZdE0WX8JlEQRZBI/qAPkqOaXUedd4QX5mKahAgkUo0dLY9H8ILe8YqZ6GwZG3ziBb88cqKJgBLrBuSrGmC7SYhyTqnqQtv3cUjxXzIVtZeGjhFhsToMtD1XtKFzTAxi4TVNPAzhJy2wrKL7kk5UZDDEDIGyVORlbGKDnBnWZ7WazZmiKHUMFXEEFGIsXOydE4HuUtrQV1lGZU6uEKko3bh0qDiJHbmAAAQAElEQVSKrBwDG1GXltkw0xIWe08SrwJAogxRV12Wek6DEl3Da7MylQVrCJ3Rk1V+luDohcpMC5qXBCZDQZHN71KMerk6CsaRZNFWZavIzKCUWTwXkkK31r7WCABFbRWlzpJbMc0xeSZ5DVjKIBgLDakyF45MIKD0YeGPVjUOiOK9zpB15gx0MICJguQPS5LlvKYnO2MGKlKsrLyUmWRnSpBGQah0sVQtxes4YdW4oU5TMFWoUuBVZ1rO3B7mYchaTWxEGYijpDInaqAsV6JhmijiKpyshTK1Shu0C90KAY3YKmhT5jRpn7Cs9NKWYxlLTT6tYs0oou2dOOnrghi8F+RKpQphoUpnDQgII7Jz7BhLEC3iogsTL5sK4zbG2dHanDGpBKzksukYJAfRAXXsVh1RIEUAVSAhDWW2mqhoyhwJ0LUYF62SDC/PbCZ9CS+VNKQSQshaoluXyqiNbLOzug3dBoLnEJILpopWl6HrCGWkkCJQMRyYnWmWteUYnYYBDGlOYYjMqWtNphRESsJ8yU3PLslfPZwRjJnuI7CUqlIURn+hAUHJEppGk0IATZ4iqcLxA3LoMKqV1sEVX134cz5JMCj4tKSYIIXjY9AUOrSUqMrF/Vhp/MC1VUQnOKHKVIZcIlIiQf+LZQ7uxWchUpMl39UQIKMlsnYrvZJYuCIBkohqSoWLgQKhOH4ommWo3HkReZbeanS6biS6CKUwelRDxQdpG2Ua0SOXnSCw5IBQCOnQmKMd97Xv7/f6d9x3n4N2ePZBx3zlK2uhd9QxJIawwU1iWGwjcuEalBxrd1XxofRSk3YtfPJ2CTPWerekmdkzgMVSDhmgjHKMChIl4m5oRaMKVBivo0o1DMVqqQU1RBngk81NWyqd3LpQKMYO5qV2/Py3J7/83R9+4AFv2v7Zhzzl1cdcdOEczMxhgChbUnaPTu0kJIwKqFvEBI7sgiYIZRQIfyl/5Vh6HcNrT4vkKN1kXYCkaWTfBQI3pyxbsitVACTcs3X0HICNIgOsLjk2dGIVzg2qYeRQwDKBtiZX2DSzStYUbQbqE+aSpQgjXzzR+a7KLVqGZwcFLLXKGSphol7UVRiV4NQilY4ZgtA6Kk774/nv+Juvv/NzXzv1zDNkdFmJljz+Nye957N/e9xXv3nxxRdlzFPpW0f3FcLSu9iaZWxJXOTQozTJqA05p68Xw7yHM/RDa5pkjWAdlnBX3gV6SgM/QsPVmiiDYBHaXehaxzIiiTaXFyxC9N2EdYJDS26tfnENWtrFu6OvpffCGcZgNVFV2pRlwmqSgexODG3KBFJy4bqoWOXQNayRqyiZBgaSKRhc06CM9NlY0tpigisYWRsaW7QN1YcSChWxgHVus0JYSOCZDkZJcMRKXdRpaJPexjm2NIwAl05liEVFl/Mv2PqCg484+w9/ALDLgx+8413vpKp3vOXND3vpgV9411uPeMWLP/uutx9y0Euvd93rqdklW7d+/POfv9fDH/3YZ+7/sa9844xzzptb48Q/nf9BBkTkX0eKyLaXuex97nzHI15+wD99/rg3v/rV97vvjle68pXFyjzPZ5515mf+5gsHHfKGu+/yiO0f9YTnvPK1H/jM3/z4l78+69w/ruc5/1XK/94X51908Yc//6UH7v7Ul7zu9aef/jtkXP6KV778la5CVhjGj33gfTYGqQplngRzOS+psWaSd6aIJCTFoCgZQ/RV70PMwnIrcInoc7bmE9N5yCwcp1gO4QSwTbEudfWW2aGZo7Ik9KwZBVHUNZIP0nmxtIWxQqpFwB0ZCU+DssxuWeUwhmkaZ0VHempEBcvdAClLQbCemqPJwJswtnRWJ47JWbnVSZSo6iNaTTdW3vCM937pH9583Jc+863vgcWZ1U4RQavUZ21ZJ80uxCBoY8mNASvwc6Y5m0IKiRKpAA2WghzFTdcpcy76gwJr6ypBvWUxmcNpUU+2iXTxECRn1iJSKrLCIRwMX9gQEi6NRZO8J+1JduTSVecyzmWjy4YIbVnEsKmBNHvEBDTTrnV2rlIpiqDc5lmmOVnWqmjVijKmmojawkAAkVDA0IG5RZsjg0+lEACr1axIwSComiKZrJlGm7wiRqUnOoRuKp0eRmZstT4pwrSoUl8Jq2ps7MtyuNCmHs7eSZEVQhGMKAzoRtMzO3glTUh8dv4Dh6QNYiZQujtUcyxB/GIsB4Ti3vrsfaZmiAQYAwNonCkjBF2LS2lpgYYgBYlmQrcYgm+rBhdxhFA3LaBlSTEpoj7UWA0igyw+EZBkLFGYTkpIVoeR5jRklAjLJIVzhENJVMXKpLBjNmuRXXiXs5w2VFWuM5ZFJcA5YKTSVEWySVXjXEYlSI6jO2aqVQi8RCshFnxYufPhazrwvhoMFGdwhjzZmCBbHZeIrCtQgJqztbXOMzHQ+A5JFA3TEI1R6D4XDMEclFFKlVoKo0y8qNfSTd10Rp1InxbICBty0Uni1XMjO1eUROhpKcLIITUZmqSE6kchda7iSU9KTqpdlumlNBvojszo7tJ96GAs1kg6qTSUzJW1UeahrWWeQbqTsBHgImLWnDvdwEBEXcmwklpRza0wI4IUFvJhyk6tiWjwCbEO7W4e2oiNtOVQmEau7oQAoRN7GcFNR9F5mHxoEJfmuTVwMRYBPSRSRcK8ldYkM0eLjYIKVSlkMI28JbgwYgVMV4WKCiDMN4YmqNlbdUdKlCJ8Cz4sJiETHGUCCe/eibdpBStXCWRD5zowO2XF8r2hlNTiLJcShMA5JoRkHRZeko60QO38gqljRgaobYZ4rUaoS4h2j4y5aGeppBtBh6hDKKwgCNcsjQA915lbwZ2J5hLX4vRS+By9c7BYEaWBYR7qyViu1C4bU91oVh28J90JzSDaoAH+pGVsEoLlEPB+lyHNpGqyjzCA3YVr6QW3NV1NZu6KOZPzg/RIXrC+6Jlvfe/LPvCpv/vZb8674KLzLrzot2ef6559Qgtukc3BurahZmVYEAZ95bR10V/AV/UpLdK90R7wK68IsinaIU4gfVAfwUQwJJETf7VSrAg/K5HQgqLrQeZawhiuIxaZKIXT1dsYuepl1a30tJABVgHJ47727ce98vBPfvtHv/vDeedeePHPTv59qZVp3Gusq84p3o3UF5hKVPUVMDgzCiFUSjVbNtEwroIV2Lllb7VkL7URaZimAvRzOOPHMwJMM0yD+apEHXyjxAa0CtATrCWTSRaB0uOS9HxXS4ONWizMwD9my3Cla3spvVZqZLpJxNjKqrPaAAqGCiCR0kH6GKO9Rcisqy5G8XQpBAFABJrG1KioBbr8DHnaeee86RNffOMnv/Sb359BUaIKad/7p18c/pHPvv1zX/rjxee7Lq5HtV/97qwHPe9N937ua3/9u9OVhcEXjW2JNjBUx1XXwYkgZEnJYjoUo9FmcwUKxS7DhUEV3T2yhUzOoiq9kYwUmuYilsmzhhQ1wNIzJ+RaZDEpuD6IAa4kluFJYFiKiEdBjiojdJAwFY1gBKv0rFMyAhKZfACttJ5sdWaPtEmjF3o7m/WGxSVMcSEGjhgkCx0JHlmkDywUjW+shlbUR80apCQXd3VdwmYtICS6ceC2l8qT/vUufS78srSz6Ai6qOPjX/jqj3/0Awq90+1u/8r99oIIr5dz+SsQ5fGI++/w2aPfcuB+z7zRDW/Ij5nxjW/93T7773+vxzzx8fs860WHvu2oY4/7+j9877e/O/2Sretl7p9+/nMM6DZbLrPLve/+9lcc8OX3H3PoK1/2kAc+8Pp/fp1hY8ulYs4664zPfvnLLzv44Ec+9en32fVJuz3nBS943Rve9fHPfPfHPz3htDMuuPiSzLx05P8mv2nvGef88ZiPffrRz3jWi1/7ulNPPYWGb9lmm8fs8pDDX/FSDefHa1/rWne/8+2ZcWpbY5gaFwrpYZFVRVCCjEWqdzDfCouBhOQMYa5j6FaapjNnYhaP6nXsxVwoltMQipSlukmv1nWUVLhjURssO4O6ydxLY0mUYZbKj/yFuiQXdcQk1lk0coCwEJvOzqQcPVS0KV9eIWzRxGzGXDAUJmGKMq9Rku9eaYCJC9s+FjSemFmTOh8WDuXMfz4tAErWoekqXfhRpaaMzes0V1oesZSY2DTL4EpxJcUSdT2OW4dxzpD1Gn2dfaFhAJsBe4v0ScSXMh7hJVxlZOmdOiQwFlmNVocxq7lUgbAF5JQIEUmuGEXdeYvaeY/FNqmUeJUDa2YpIcVRwhSjkIYJMSc0u0nzIaZSA/yy2ehD3kZyrTzooGQzKlaZNUIzGloXBzIi+H0PyznZW02Vb4skesItwZ2IzVImvrscqo7Qgb2rQQCRFpbOaktP8reERlOWyyJcdyZxKZQgXZdvpqXWGIwlObWnTiz/jkxhgNXIkguyEVE715dNaeYowlIu4g7WaU3AFJWdgjLJjU2OCb5pHBvIsEgaCgGzN1A4J3GChCDZgtF5YZqF1FX3MXklqT3AxWmgE0mP9J4U6JKzZQM4uRKHgeEZG3OWWWuwtVkYoWVlh+b3LCFujUnQZHCC06KqXJRa1Hku68l6N/LLk9AolKRBhWqyWW4tPsOXDEuVJSGkV2FjmyVazBEOi4UKSEa2vmBNZAhVgglTaqd5yY7sfI4AIixaZgRNmbv7nDIhp2WyBUkWdQHTh8aRT5rP30QDyS4t1dUYuSrBG3SUF4LsrjOG5L6ib0y9bKXrRBJVi3E5ouggV30B4M7VgzljGUSy3AbjmfrXkDkG70NjcZG+Fv7N0BTtVeeajQlfWFmsplA3MbUyR22oXiSMN8lJK4AqICV95AKgr8NJEAi4mghJBudGp/ycL2k+O3m2Uo0Vx+clM42OFEZASVVKF91aZG2ypFBRL8gI9JQoxQqGaLXTNgEPPiBByQTuQGeuSIhNpuSJLmCmUQqIQCuykuFFs4FSUkgJuI9Ex5AywGRxqNnCH62mw5J1QZ3R5Py8uI1fIVjL0Y1bWFRjWJTsxVXncYkNyOa+BXD6GUyIHNQ1E01jGtaNfgwgQTgr77VPiClspkBQW/SYnSQJWDtizBgA04zsgTlKpA4xD7HWYGDOJlkUSLMYLZhdIkAoUh29DUHSZrV1YGqUQKLGFOtok0/kP5eYgvM2IJTEbfe6JFeUoZIkZPJkerTVLCt6VarlRvU6EuoUYMhK9o988Zt/f/wJInL7v7jOy56482v2fMTD7n4H0k4luTW5vNRZNBkDQmCamUq5nCsipSqVIhoaSrOIjcjRpwGToTNHlxZQrYl2sjfTSggzesyyCtQIKiFqN7hJF8yQ1FQ2MZl5gSUuJahiCLGoJUtRQZUTzvj9wcd9DsBVtrvMng+458FPefwr9njEZbbQQKQ43ehF04qUjSiFlYHu8nTxEn30XEnWkdUyRJFAthSS2wL8jAoMEpwOYhNkIhYDZ/VZyPLo2FzFM7aZKatUnjVkcCmRyMVhpUf1sqTh2QAAEABJREFUTaLUoKYYlSKL6UAAvZXFidnhjPxJMSdiTV3hkuSnA57kXVNYBt2C2dHdxKnIqkhUsMCQJjpEzEU4DZom5ApjJSayAn5e5G0K84zl1vJTEGaJkvY9rtTOu/CcCy/5wo9+HPRNn8gC1ZLhQWUoZVVHE17WYswQXhTUZOQCWvpQ0yRiypx6rHu2LtGRPdgUk49SaR+pixpzlWbZmziNc/IjS8QL0RGQa2lSmzNAljPdZpWuIH54zRwDFjU7CVZV5tqMtpTnHiHpJTWyesC7y8y5DXODO4INoIen+lKGCrQgCS6DIdi4tNGm6YPTSxZeIowKC3/VdI6Unq46mcy0NIfIoVGSd1oJui6UQZMxf/Azn6UF47h60T57rTb/z+j58f90ymUvu+2TH/XQjxx56GsOPOB2t7qV8p0ycP45Z33re99/93uOefkhhzx2r3122u3Juzx5zwPfeMQnvviV35xyGhvK/0nOn278NxkQu9IVrviQe29/2Ete+Kmj3/axow5/1Qv2v++9d7jB9a5f63DpzPVFF/zoJz/5xOc+/5o3v3nXZz7rUXvt++i9n7nrcw846Ii3f/izf/PNH/3TSaefuZ4YOZcO/3/b79b7D3/+ywMOOfRRe+/72sMOO/G3J9JCRu/9dtjhw289/LUveO5Pf/Wbc/94HkR2uPcOl912G6QngrkUCfQ078Z8M2HiwZylPjVDJXRkRWBTxFbTXoQ5qpZqolmW0hRIpjN4hKJDWOI0xZZkM1BaSkYIwqC8LyHaReconYkNae5M6DQmP+s+l9WQmRJTkEAPTM56ABcwTcN4j1fM5aQe9gxdajdCWA48oZKsfaYLXAgIp7EqJB+L9Gq9CkFeehKdpkXUjsLqsRFL5zRhDyqLSAnWjyZIFgsaZRk1gsrEIS4ShRW8RfTkXy6wtGX2DNBODxaXMgvX+gCrkCXNjqTUZMOxQYStR4QNlsyz4rADKWtSmcm9WKoFxEGx6eDCKwGRGqBCLVEGotWSwqJmrLK1zZlbg5UxNBpdkzb4OGrRyvdkiiJFQ+hLArRQVXaSjj6j07aefGDZXaewJhrCx7SAVZXbAb5wXkWOYlZKrHTewLwKFleJlAkIAcEODnMVkPEiQh9SoszNOx1mMqzGYrWCbbNbNInOpUxkdBOYsMRWOHtCGaNwh1IZFqz0Q7SCmQrS0xkJVAcYTIGUIKNmzYqgmBs5dpdWpJfoJdfFIozzsotHbSgdwhsk3yQroPRQjx7sg55K1GKgGT2z8xZapFtG0SggYOhsdS61FaVbJMz6ANnow9CrCyZJkr8mexRdTUYplnDzVmNazF3QBhWAR6qww0SoB51FJhJBzex/Jdl0UREMG3YlIKhk5aPNg7vN2XtrwaUneipURZLiNIXgSmxaxN471yU9e6KHUwJP2uSiogqLbn0qnZqjaV97dKJPYWZouBEWr8GPwKwxiU/RGx/yTpU0BPMhKatzbaODCUzd0ETIVoR7IqMMacxeNtgcBHxCc9I5cFk0VUZIkhpk9gAW2QatsCGVtqqURWboAtSTRpUQs5Axu2rPAv6ZQmePKaY5PNOql7FL9S7dbUqZ3JpbpAnXM1rFlestbxlCFWGD0PmsNwRLh6SpoQi5UUuFQNRqHQdOTWuIZQ9lWt3Q0fuylgiSih4lQqGJGlpg1AUK4ichT6KaaipiosIO4xkeQXaN0EmWowpYXcFfEEkxEudOdiPhIIUOKk/Aa+dDc6mN8mpgmBelsUxCFEFsqlo4g44RQ/YSNHVOaaGZqgF1L/AiTVlomztzj+Y4TU3iF/AIpa+pURXCsZo9pUPDShPGpLKWFkmjP5IYqit6TQrOyPSkdZlzSHIIDY/wztxOD1BIMH0BxoWwiIxcNE5iTSp55l1JxgbJVM+SS1WtKUbUGeTC1+tL/v7nvyG8G1ztioftvdvOd7jtPW51s+tf5xqMrJp0wFpZvIJaIWBk0OEpmhAJtWJ0AgurzGldRaosCakZmhzKj1kVZRmuTIlo6A5W+TDaBkihA7z/+sSTd3vVO5/wynd+5ye/NnfzBdbi/w7LGISSMjvJoEpCoPb49s9+cck0DcVe+MgH7Hn/HXe87S12uN0tq5BVSIbQHYPIWIQFXpd15tyFZbt1czLazWZZ1qnpJbsJp/BPzko3IxO53IGEMUgELHxVeVTX0iUzXZxhhiCxIRShhBgDovQAn3B6kmMxcqASDE4Y0iAIbmfH7qUnRy7xWTQK73uVqaRTXmp41RhqlKLUDBUQUE94moZU0KGb5HK4pAj9l0FpnQ9s+YxLD4Lrae7qKJfe4W+GPFKLxxB+i7+47uW32dh2Y7zjza7bbN0bXTOXwBKGICZSXSSK0n8sGCndbaa0EDJlXiorBYFWelTQTFwUdBavwpQOkAhkl4zlukdys9yaddclXD2ImzrAsLcOQNTCLoXvtCZ6BneNS8gtFpMaU37b1kU9wnuL3jqimwZvD9MwzGaegAeWf5kKklZnDE0lV4KBZs+zTxPfpGB29PTImeiWcRKlQgZ01jCUobH1qifTt0uhWZY6MFq2pjVKQQ6M8+qn/OH3v/jF8QDucue73PQvrs+L/+Ypl7/cdo950P0+cPibPv2uo/Z+ylNve+u/MrN/nXLeeef+9Be/OOb979vngJc8eLcn3f8JT37pGw/77N9+89Tfn8V4+9dhf7r47zEgENlu221vdZMb77rLg9/+qpd84h1HfvnY9xz26lfu/rjH3fWOd7zila/6zxIyL/zjuSf89rff+/53j/3Ih1/8+oOf+rznP3LPve6/626Pee6LX3TIYW//4Ec+/c3v/vSEk8678OJ/nvL/zD+R+etTTnv7scc97OnPfNKzn/fRz3zmjN+fzrAfx/FhD9rpuLe/5ciXH3irG9/o4q3rz/zNFzNiHIaHPWDHvtQVINTbkD5UwYbEhqzTcjJlirBVFaQJQswx8hIuZeqlBbulFJVaklNrn2sPjgOYod7FfcyoMFY4a8npAeVz1lcT0ca8y3AoKJ2ycuu0nn3uY5WNjWKjMHExTTk7l6k5dcxTyhzGoiRNmPF1FoFKTSsssEunQUs+Yj3IyAiP5bkmsFZ3jh48a3LRoTMq/uUwpRVEmSy/HL98jD5kX6mvVs3GKTAHM9Nk4WULZsMUPrs4ZJGxPKMKSsySTdosU+N7Rpupy1WastiYW1adR9B6dJHOlQSloDlLpqfMGUgZ53G1tZaOpb6W4CJslCiuWI8xrfq8AQxJVcHnREOhnNpC3Gg0z96kp24aLSmQtEKtalkIdRJ6M+a0mWvZeVgFWe/0gK9F2shlYh3giEsc6xYWsUIfvGvkYqPwkS10OhC8sTgsXfnOq85TncLm6rJBMTFacJ2ArMRj86QymXIGyfURbTAXNTYHsoS+yZy7YJ1l6iV7UdQiVpzdowl1VvhGzkOjYd66dzSTzoIdTvughWIXPFPGhN4Yx2JVlb0qbMpsDjpJGcfBHZhPOaGsy7gu7IqJiU7IDEbPgNUgowqDWUvDMAEJLrEmkVnYJtnVaY9YdjDAAE9CMweHse17TUZ1OhvV7DEVhUkpsTIRTk7tXQsXL9PATT4UqNJHBoB0A+FnliTATF7LIOxePgrvlSJV1YWLBbRRUBJcDra5S3B4YZ4Gkyu5LaqD6YZpTVlCA8MY4xYp5BErzPSiWWDwYdWGjUswrr0ssUawEZP7TGMsSaRJFEe6edaW6j08vLtFZz8tBUY8JovJYl0kyAaKYpkqlQTpQCo9CqNuXCUhDV7LZEP6WOdVzWHIWm0s4yi1kkeP1rMtyVGS8sA9QWxkGcGplgWwBnW1btqrdgGmrJMvWkK46t3wHCKUSNJaV6Yc49p1S27ZwGVWujLRJRz6ksmmTbS5Ll4TGawglGQKf8vY64bbaL3ULGqDQUwhpE8H6RBIqQI6ACm9mDMHNzqLj6GpAHyGsvArFiKa9E0qbUt0ERhWhVzk8iC5zPDsPTqy0zofu1RUQom2KczSqsgoRim9+1bvs7L2dOm8o0LQlfFQ0mXIzuJUNDWE8wuGQUYT5rCIUNgsWKOuMbiOKlxfLuqsB/0ErQ5GAPMiZmXBjxz6YEHCRRUmPApKsQWLNjANJGBI6hwmqVs3yrSFCk1bxzxHayn0R5aShfaGo3tkBmg+z8Ry8DevXSgFPoCZjtGGUod51LX5Wqhnq2Et2onNmSBcOV58ySV/uOAiTr/9zW64DRF2ZKPxxSvKyofBCYG7rk6pHCRd0RQoJrVAC7Jql2WQlVjVWL6tSCittah1dutNklbT1yCJBDC5TMk5zCMGFlvZRZds/dGvTvnhr04594+XYOro3AO7qzQnyUrfg1bSO0mD1wF+xbM+eVnC4TJbNu5007+QnFsE55HnMhEQUSbtMAswh1MymZxDdLbM3jzQIO7AlNYTuiBxMerRDFdlWjqKQyZkV0BpoIxSh74yNoZpnNd1PRXWQWEV8hbWsdFUCZATaivWNySNSR7MHkVSFMMx4F08iS3McuBrraFiM0dJc+3c3zHmHCpFbKXVxCAiVWTJfctJ4awWE4TJRpE8KSoLmskcEc5ocO5YFgXLA2jT7Aw2LdUq7/BMsiE9c6Hg1n/5Zx9/5X6feNWzb3fTPzewgIelDIEQj+TRo81tgnfEghw+o08SsyQ5I2ulyLiBYTQdRwwboqsxx1UMq7DR07ILsheda3anQkp0pizvuNTIDe+VbkjMruiV77dWvsQc0yxcwoGmpZWFFi+iGUPPOhdtI9SYjAsL7JizQkTpukGCaUPG6C6OQB2EJEKUVoNBEJppPYJgamIFExQy5eJS0iJBuA105GhhktFp5fKLtZL6xYpKGZNzNF2YidN3/+lnucDEfe7816UY/mPHxmq85V/+5fOfvOtxb3njL7/+pS9/+AOHv/71z3zqk+919+23u+xla6304/nnn//zXxz/jvd/YM/997/7Lg+71Y47PeBJT3/wk/c+6M1HnHTa7wmaqjLzkvV0xtl/OPHU0847/wJeB73EB386/5UBEVW73LbbXv/Pr/OQHe75smfu9b43v/67nzj225/62BEHH7zfHk+87z3vebVrXfcy22ypwwARbzOZP/3MM3/4ve989NOfOuStb3veiw981FP3vPtDH3GnRz7xoXs9+5kvf83BR73zvZ/83Dd+8ONfnXzqBRdfsnU9tc6gin/V+f8PF4yNaW7nnn/+3373hy978xEP2mOvXXbf45Cjjvrlr365deslqnq1q17t6U/Y9W8/cuwbDtj/1je5US1Ldfjb7/zDT4//OfHf7c53ud41r2ZIAaQX67XkYFKUn5f6MuUQLFEiouxLPHOpWSIUQs0tfQ5PSeFwoRAJLDPBY0md6LyHWlBT1ZEZvSf/cEZk14QG4MLfA1DFLcM6YracYQ6WAoOms1z5kqLJpEb1wjNL9NJiKSqBEPZFVpaoyEHEahZEsrDxXjJZhSMIsJiwm4sojkEAABAASURBVNJMNikInxHl5inEomGBMi8ju3lqN8ylNq7JVNzcM9bALMpX1ogmbWtMWzFvHTkViaH6AK54m7DmImiqZA5pPAuUSlyQCTRl6zcqKTNVRPKfQshOjWa6Vcuk/Lzsb8YZg4slDAu7bA80KkumqIiWpTgFv3PYrHLIRSPr5FKliw1VlyUxV09ARLYMmjWnd34KsK1kNw0RgalndQrmlY01Sw0r5JJ10uiKwkEJNsUZmRJJttGIZ6DBGbU0Mo3Fe6SuQqpBi3djiHB80dmGKIXRpADt94QIfWpVrMgSFPSv+uIopw5SnKR4UqILuvvSdWQRIdYAZXJgZkQXnrxwCVdr9Cskk7Rioq3qPvasoaHphKpZ01fRB4UoP6fCabUikVW0qvIOx2YmOKcm6UUyGkfLNA/nAytsfFIKr+eYpz63RlozpM3j1AoAdnjS0ADuo/ss6ECACnUQao0i1EYzzMAjs0yps9N0mmCKWtNIX5IlRquYQKBQBf8mPwh4CKpSDZhGjPvaoF1ooWWtxWpVKPESU3JSsVLqMA4FhlDhyiCMY6VXjQE5JghGK3uuE1hw7TVL8UGFUdFdupNvsPEStwWHioUOoRZcZa2kS8505wxxKQYUEUJTgYTopnoPWUMngAkKMTMxlVTAGLYh3IArCkNjttpzFa0s67NwCWZ/1o7aqdHUalksERGQUenL6qSJNRv6UJMbgDqmW5tL60JDAjQLBGQpJvRkTbrJuYQJNcnFJemAKOj0irpYPFVJmqFhvB+sGlwcZHjv2rOEkstOVC4lRQEN2rhki9I/s2FytF7gXI4oF1o8KT45JkK8aedwWLUylCocwACi98RhW7ttTZ+MBkPEtGef25IApcggCvLfMxAJSsLyG+GaLrxWgYpQiaDzFqANOqeESRm4W8GgIFzBMgVNrdeBrPUu6jakMrYN0umfYD1jVctZFUMdtZpyKmIsulF1VB06KnFlm8UnDSKARfBUkoYN1y2dRapbeBEbSladivsYOQSBhi8cJHkDJOFrlrQ6Sw0RgYgaU6pIsRwl1BOdj1B7jMt2JLhoU05jWgKX2WYVIkQ9LuQLo4rDlRIKskpAaCs4P10g1F3RU9pafBIBlARDzLNGK8r1e84m3eGsak4q6R6YIgHWoq2wqZTGAdohzGgsR1Jc5Ow90jfJjaUwtOLzKoNfRHjazNKHdI/gBFPZsu2YFVNEbxlcWNPZvcJBHEN4QZNw6WG8kxSvmRHauk29eM/s5MNDuG6OXhkyUBTV5SIiwyGz8oQz1iMtk4+W5q1JOuCpsyiNSJpVkmQ52xaHieiAamSC8gQiJIx4M0lPLX1VdLABdAK0m6ihmLIA8HpSRIo0K92oU0wwiLO80B4GU3TQyN6Tq2iQgvSiwfnCEhB9mvn+EZuHKoyRVaCguM1b/LXUJYlqjXEVuMK221zlcpfVEAXGgiIiSUDLHo0O4de8dFwX8uXkW9y5q1RLr4xReAqgqrUOahWobuo0pchiNytHcCOicLB8bKhU4TBCCUdHdqUeiYSr+TCAZPGamKBYSpEaQ1YMYiGAumpkeriml8ySo+aQYAgLZ3LvwjMg3guF04mrYeY7JKNSxlDKOuhl6WVErVrUCuHUYlUSGS6MUp+SW10aaiSrQtQAk+wS89CXd54rw5aKsVakzo2D/Yw//BGbx9WvfMXNv/+ZX6KlDKvVxo2ud92d73GX5zx5t3cf/Mof/s1n3nv4YS94xl53v/Od/+wa1yi1RgQ74Dnn/OHHP/7RD370g7e/930777bHJ778tYsuueQN7z72Ho/Z7XYP2PluD3nYLXa4P6+f+vwDDjn6fV/7znd/eeJJp519znkXXNha/89g+n//WLMyjhvXvsbVH7z9nZ/9tKe887Uv//7H3/ftT338g0e99fUHvXjfJ++xw712uNENrn+Nq151m8tsK6qMh977PM/nnnnaz372ky985SvvOPaDr3zjG5/y7Ofs9ITd7/Swxz5gj712f+6L9j3olS963SFHffgTn/zKN77xvR8e/5sTf3vG2Wecc+4fL7x46zQ7o/y/mNrene4+9Ywzf/KLXx3z8c++4JDDH7XPs+/20Eft+bz9P/DxT5xwwq9bXyLhile44j3verc3v/ylX/vw+1/4jKeRBzW9FBoj6qj3vi+X8lT2ftxjRzDVU1mzO8sScpC+ql2ZYMzFmaWdl8nkYUrmxPxJCxTVmjk4m9Sc3pr3qTHzhlmHprKkMTSXBBXmsgY0DLGRS0PqzSdv68yOMJEtJYbKAQhW+mGIApGINufU6BErKHUphGddeOFJ55x50tlnn3fJRe7sRlbnyEtaTuuM1gXL8qWW5LIApV+4PuOUc048/Q8n/eGci/s6C6IbdUixMJuhcAx+KRlAYimwAUkg4D2WpU2NOftZ515wwulnn3TaHy6+4KK6btb5virKbFziwQPUG+K+oTrytcYwrpXrEI0+lBhXxcwEyGTDAGqLcauq6GTWSRAkwpJLL7ZODKY8Asq2IRBLt7lLd3gkkQrbGluLLauP6JnEyAHzuJ4HN5c6s3noYuNKl5o11GFMHWhleMd6yinaZGsO5UxhA+tqWGxN5OLHUWSMHHvX0rHK3FBsDBi0CGp4sRZ1WTnwNZ6nI5uwngfvdUMr5nX0UTGk1G42G2svvCG2srz3AU6PJqnPKXNrlsn5CtEs6AhTKyVhCMZWEP5Uylavl0xlvQ6GBw2vgCmkIgr/JCAO7SSZrAhSpC9YKCCAmljRgrYob6SZQZCleq3rIlNFL9WKDSDpAZqBbhXDyFtF2Wq9M9zQPDGndREM3co0xqRzq2sMMRg2mBnu1lq22aNxqIRbZxKYyEjqPEfLOnDtQBJsnQy0pLSxrinLpTeAJ0MCSZhd4KIpFothtADBPAPZcCVc6RzljJCSMmbdQB1JxqBlAGdpwA2dfquxKcGa60xwOizf3EhxEVERJhMSKi1z9pwTrgM56aq8zYdDcS2Rmh5UaBxK/aTPuXKoSi9poRZvy3+fYU6Z0iYb2G0nKRc3dt9Mk2owIW5BEhlVB+ZI75i7rVuJGSR44MhwDUbR1immNol12yLDBgoiPLn2DAAKTZCoqZMqYYQtFrD9j8GVgIsBvDGXbCWplMUhQbNlbhoxqFaikV4wDb4eWiutKV0xTLlqWX0x05OmNrBaiBrlpYPMRYp4lmaM1x7kibVnmsFLwgyLuWtzuLU+Zoyu2paqyPT0mGZiL0mXZW/es4dSUYOk08EqfIKFSaVhKm20tpK2ijZEF8mINI8hYqTmcBYAySKqvTqTR4V1Q7x5yIxY2zytwgeQZkyGKYXWizF8ADq4d89IerawiJY5NNZiDYM3adxlTKYzmFFSBaVEaIoZiAFOtAGWgaFAgUyBF4AUl866Q4MY851SvE5DbeazTk3mEFErUikoFFkVFQQjUtxKQBwZIPdbwINQbakI9IJoSg2T1CA9g2nhQg10XYFpjrWrxbkXXXLi6X88+fQ/XkI3AOdesP7NGX888axz/3DxeeNAcJ5d4YTMOR3o519w4cmnn/2bM84+/Y/nzn1tfcq+7n0rnS0u2ay7TT52L+QyExdefMkpp5190qlnnnrm2eutc9FVwUBgbr1hnv2SU88+8zdnnHv6eRcSO88z/njB8Wed8+szz2nNi2Q1rvvD+ctNYKbWkSeefvYJvz/3zPMuAOAev/n9Ob86+5wTz/hDnxk75FTOO+vi3558/qm/u0AZ/Ze0P5597im/O+28i8/TsW3udGUJsCEviTj7jxeecNJZvzr99NPPP7vTeZzNWmYROidaV12Ln3zW7399+hnnnn8xd2O+Nc+78OKTzj7npHPP/eNF6yW6XWswt4RgLtw6nXDm2b85+5Tfn38O1wYGxpW5Up1kwWgySA65WAKRhdhpUfKH88/5zZmn/+a8s+aNYW32+/Mv+OUpZ5z5h/PVwc7ArDUJHaKpn3PBRSecdtYJp5555h/O7ZfMudT61jNaGWZbdpg9JZBEwpMTVxIGOmGee+MdnkXVRlvu9uGiC/3XJ5/1m5NP33pxV7PKdjTk784797ennvOHsyaXDbfVJb4++ezTTjzj1DPPPQP9olpbGd2H3ktMkZGMPnYtn0e/sF1y4u/P+PWpZ//+rEt8q5cMt60nXHj2Ceee1RNb6riFEVxnR+8pcxmnsdDBCh2kKPdalUglU0XBtOLXMjI6U0q1I703aaxEs8gsCFMeWWc2ZAwqPaKH90hn3Ac9CFKQDb7cmyRnhYtDUqSalAJZEm7xANSTAcuR6M7cNV9bst9AFhWsNQoeEpksqJwgqoRX0UbMq6te4Sp8yPOnv/x1RPDi/8YpEB2H4S63ueVeT3jc+9/0+i+895j3HfbGFz1zn/vscJ9b3PjG1/vzP7/iFa9E+X8499znHvTyJ+9/4BsPP+y0U36b4bzJ37z+/Ff/9o1HHvG4Z+x770c97v67Pukhe+z56L323e3Z+7/4zW99x4c++tmvfv07P/7Z8SeefPZ558f/XbTU+T9yZuZFF18yt3+Oxf8REf8z5yycA3qF7ba9w81v/Oj77/jcp+5+zGsP+tKx7/nce4/5yNsOP+aNB7/mhfs/6XGPu+89tr/VTW9yjWtcc5vtrvBv9fetF53+u5O/96Mffunr3/jo575A8vd/+cuf8tz9H7LHU3fa4+kPe9o+j9nnWU941vP2eN4LnvqCA1/45rcd+t4PHn3cJz/02S988e++850f/Oi7x//6x7856fjfnnLS6Wf+7qw/nHHOeWed+8dzz7/wjxdedMl6+rcnNyHnnH8Bx/D81cm/+8lvTub0L33rO8d++vNvfNcH9n/jW3Z/7vMftfd+O+/x9Ec8ba/XvvlNn/jMp47/xfG9d6JlYl/7z651r7vd7fUHvOCT73r7e974mofcdwd+iQQI/uVI5OEf+Og//vRnvHGnO9zx5te/VjBJu2RnIZRgbmRvmlMZmg6axeaeHh2Ya1Urpszi7EP0McDN/8g2U5i1LEzBlOzMMnawpSo588nEGOsQgQmYiwJFqLvMGWv1YHEy9ij11CVOeF3rqFWCGcYbgcj89k9/9ewj3vWk1xz66Be/+TEvOWKPg4563mHHfv/4ExDQDJkDXCAAjbW25rkXXPD2T33lia9728Ne8YZHvPjgR73kDU963VsO+9hnLjj/EnRwkT2beFHJZJ3A/3EIkmbSiJRN1d/+p988/U0fePRBb3/wC4/c+cAjdz/4XW/4yBf+ePaFNhlYyUSMfWB54ehgreHLlCRVYFvPIlKGanwBw5VTQpzsKQuTi1v46FLTTDKNq6il+pDamMm+0NbaMDQUrjpYyApvewuJbohqbCSzZ5+da+Toki60Xi2tpBt5IN1evY+8FDRk59yL55w7veJqAREqNUFVLhAWHyUdYlZVq6JoprgnWEqpyoxuNdDXVlNrz9rd3MN7hINU6exKGdAuYMnttsQQkWVyYPYmAAAQAElEQVTT4BoImQj6mNq9T72xVzePrm1CX8NZrikE4kE3tFJnK67CNZdI8mmGC327rO0ykkZrdtVmFsWWct9Mk3tSW2YkWl/qvaiYpZqXMnNiKpdEQ+uWmYZelRKRojFmcqWmEiI9FF2KG8NTtGuZEC0jmmIyrttRnatS7lLEGp8Q7QCyJwXFsqhwZWOii9KWSRAMAd6ErZqVdUr3zh9yZyni0O6FhhO/SDMLU5JctQKlcyE1KSul92xoM+gzz5DwEqQZSkMXQqSm0FeDp7mCdKkGJZMhjg24gUsuh1DKnBTjvUaOntXN0jF7+lSE67u04BkkR7G5uGScQgJO47G4EFQHFA+VDNUuTE+hZKQC7MdIerhPXEPDTbJ2YFJfzuxTepB1hQj4jFZgBrMda+IDIqJz5d+cEkQoj0kwR5JgayKzhjO8gocvRw+4aidqao2hZIVLMPy7olUnpSFJ7V6SjhqqrxKrnqU5ZQn4UEy0WHAxZgHM5GXtbd0JIqhTgzUhIqMnFWPBk5bBa/rCyTJoR6HJvOQpFkWzSgzJmMpN+0BLQiK1p1BtJnSArCSLNIoHkgM2gUJdrKt00aCkLHNqQ84ivRrdEjU6aL04VYCJHbWlMY0megaZnUxwo0LrCVAzS8BURIpDW+TkMTkJiwQsuKKKDZsHmap0k2C4USqvunmWxsArWooUpU+p0yOTzhYlXoZqbsoRgVrwFvgUygDwTOu9zKERyRursEHMFG5BpbYIdhtgJvxV02qCRwp8kK7esolzQ8hccfEuCx5SNbhKq+HVYcLJH/zqD3Y68I1PfeMxF2ydAHzkq9/e5aUHP/jlhxz0/o9RVS2iqqLmpiedcvqrj/7IU15x5MNeeNiDX3TYw1/5pqce+s7PfusfY+2so0MG21RpwGTaREP+cN75h3/0S0941TGPPPCwRx54yGNf8oanHPKWd33xKxfPnXpDCwvd2eetn/Km9+z88je/6N0fo3aeb/rYFx5z0KG7vvqoM/7wB3pIJAQQVwc6S9JYL7i4PfiAIx/8wsO+8bMTwJ3VhRfv/MI3PeyANz7mZYecfe45CpLYjvnc5x/98jfu9oa3cx/ysvd8YtfXvOWRLz/0yI9+jqYUJNfQ3GD8/Je/O/Coj+7+6qMffdDhj37pYU989VH7veW93/qnXyQdkNHdeZLRCy655Jmve+ejXnjIZ7/6nZNPP+eAd33ica9454MPOGKnF735ca848uXvPu535/6BSUVFb//Yl5/86mMe/tK38umjX3roMw95+9/94Gc9ZLNo0/V0MYpA0yIYROgRGTDksZ/5+kNf+MZHv+zQ8y64+I3v+dQeB73lYS9+w6s/eBwKltHi7Du//PXvDnrbxx7/ineyRT74ZYc++rVH7nPEe771vZ9FtETyV3ZSPkQZREgTliNIGb2PaIyl5QZ/hBSAMcKM7N//5YkPPejNO7/0sJ+e/DuRKlYw2kve9/6dX3HwG479ZLswPvyFr+312rc86kWHPeRFhz/iFUc99Yj3/uCEk5NSQnrQsiZYK/q6zZ/5u+89+XXvfPiLj9jlgDc/8mWH73noe77Jl7snnLjri9/w6Je84cSTTzXCEtESUsFTLVMjqmYlbHRKUQb2SqSk9MS6Mcckg3mCJSOCEc+W7yvHCrwpjF5GBOeKzi7M3Z6mrsqAJx2lB5tbb+s5Gy1VAZOQVcuZ7wJKBMhJUFGQC55U5JZeOhOnI0JkYUNMYMq6kQwKFqrc9KJIFTWtd77FzTe2bAPg8KOPPvL9H44g3/z0P+MUudx2297ltrfZ87GPOvpVLznuqCM+/o63fOqYox73mMeJlfXWS/7uO9+mmsttt92D7n3PRz921/vfc/sb3fBGpVbe5Jnh53D3f+IJf//DZQ199Hvf/dLXb/4fYDzt6bvs8dQHPWH3XZ669+vedsyvTjo1Mzn+f835q9+e9Mh997//rrvt+NgnPnjP/Q445LBPf/lvTzjltP+VGP57ljI26Hm90hUuf4sb/sW97/zXj9vlwQftu+c7X/fK44468lNHH/W5d7/tk8e87S2veeVznvGMxzzikfe7x/a3udWt/uxa1/lX5v9Vfrvoj384+8wTf3viT372s+98/wdf/87ff+zjHznyne983eGHv+T1h+xz4It3e/bznrjvs3fdZ7/H7bPfo/fa59FP3+eRez7jEXvuzZ3Gw5+290Of8vR/ez78aby/N8fwfOwznrnrvvtx+jNe9OKDDnnDUe865pOf/Nh3f/SPJ59y8sUXLW9uLsVwlate7d53ufMzn77Xh4449BPvfMsxB7/60Tvv9OfXuPq/6/Cjj/vUUUe/gxMZUc9+wmMisrfOVIhgv5NZpC8FxiGKwo1K1VmwlQWSN9TKqGDlt4ndSgJqg9VhKHXFJEEX5nbAONcBuFauu5knyZwD1/Wlq0RNYdPMKPAaqWKhAzNQ2cCkFWG6FbNByphm0eWoj3/9OUd9+FvHn3DGH//55dZp517wtX88/umHvuvD3/gWEizxQjmaEJz6+zP2e/N7D/vYl48/9Yx1d2KYWv/ZKacf9flvPOPwY076/ZnReyZvL60XIDRegxOlsGpIiAc8ox/z+a/se9ixf//zE888/yKAtTt/dfpZH/jad5/11mNP+v15LbVV0ORxwFhCIzGJO3lSrqRQB5OlsElaloB2hFOE0UCdo0IKH8tSfppFA2fCHdlAtsaWwwx1m6vMQ3Al1pdHbLfsCiKsbhEd3vk7sUZphcB1AApEDPPgk8x0Fl9IsxhOMc8OzzGF/qlRJFiUe9lYly1r3WhcRIgAqQa+UYsSbi2tAWROQ2CCKrl4NqzE5rqLT6WLNqFY626eyHTQwkjeizL3ge12aXqxjlj3nObmHi10hkwdvc3hU2JONPSJxGiWIXV0cgYtSKEkV5FidJB2+mNyjblYlMqYgAFQAq4yFv7VIBskJQr4WtrGqIV/waEZQm/3zTi0OqXN/JgpqKkryJjZ01t6iJqUDSmFrnHJOdRnId7SEyq8Q8JznoQ7IpFaV2MZCtnms1KxKk1JWM9wQ6qSONJTWgwTIbU0n0loAtqldiP4FMwrk0GGAl2Cf2y9dA+yNAWXmh4ZVEuXWkJimZFJnkWYLmRJa5i5SQ6CgvROgluQF+pL3siM5q3N65wmbb0maHC6ZktZZ5lBD2IKaW2IiXgh/DcyhjzTs0nQkQqS0MGyAJioYDMyGb2Le0e4KQjNc27BCWkd0krvXMBSgDvZkVHHESvrRchNbyG0Cl7TaRWF+iy+LjyTyLD4PHWWXCeDQiYEG/GUjaahZ0kMtmm0diPPraNN0qbSZ6aGaqZ66V7pL0ErWJPYXGaJSWc8lBKsM1LIZaR7dkoXMi7ZK7pqD20upMcVMqAOXKs0y8hAxqCiRWUhP9TSKsxgCqui/GHMwJoKQzOzSjPxolpNNMmQZlMG1BI6XYDFjQ5zMPjG0OLCRFBP+mgQNRUkwlNSmAlmTmgQB3gii4AnP3TtNFjUhcQIFGSU3+iZu7dO+oKqqIzS6EbiGeBMr1FVDHPNqeSsKaI1bZW6EbLypuQcPQWSLm3O2Z3Gk0Oqpfb0gmCd4auZNKde9bS59DCaA1BwIllrglTYpKWzfldVA7+n6ACEKGvlFwddenToTEWU4EhB8ESTnK3N8IachRGn+PeOTOpREzKvDPev/eCne77uXZ/65g9OOutcRi9n/PHird/71UkHvu/Tr3r/Z/rsQ4oABpSIsedvTz5lvyOPecfnv/Gr085qHhx/ydx+etJpb/zkF/c76t2nnXsx+irb6L1MjXWQz/+dM9Mj6BIFnFHtI7Lwuvw7Q//llmY3aYLkDX5HtP/RH/7Y3//4zM2OduElawHnBxviJ7/2nf0Ofd9Xfnj87845fxkKnHX+Rd/6+QnPfvsHjvrU59ramTTdjSQYQ5OygB/85uTd3nT0p7/7k1POPi/4ADj1D3/86Ld/uP87P/DL3530rMOPfuunvvqr087svhjL76n+4Rcn7v/WD/7DP/6C1PfFZZnRs9NHEqKshk08mT4M4E35vftL33bscd/4/u83vym68JKtqBCltPjs13/w9De8+2++91NqzE24f7jg4u8cf+IL3v7Roz74hbh40pk1QRRVahn+Zb1K4jYXCrlc4F+PZAzz5afQ+aDwzfvCvKZcEmubn3HaeRe89v0fedNHP/+LU3/f6APg/Iu3fv9XJz/7LR/5yS9OYzwW2iATS2N2P/KDX3z5uz9J5665BQXOu+iSvz/+xH2P/OAHv/QPS1W5VGKQBpamgqFYyUHnURpTj2WBZaSRm4Cq8ofFoM/81+dknEsyFSWjxJIXSuwWqcxDJgcslgCNzahj5VAtYgoWMJYh5n7SvpY1KheSOiCZzDlXn4aYx5w05yXTTDfK/4e9/wC3parWRdEWeq8ac2VylKSAgCiKiCACKhkkKDnnHERyMhElCohkAQUFRFFUDGBCtxnFgAFRlIwgea05qnpvrb2/5lLPPmfvfe47++xzv3u/94o+x6zqoYW/hd56jbUW3MDPMaFxEgvIW2kIrZ4hVq/wUciOWAWQ8Mdg0kztRFl2yQX232VnZp43OYkCdNcjj/nNQ49EDOvpv+piJpZp0yYWmjNn2SWX/NARB2y7yUb/pP2+o4+87OzTzzv64CvOOfOOaz76g8/f+qnLLj3pvcfstcO711tn3RVeuVJKSJCCi5mxykv/0osvPPr44z/9+c8vueqqd+65997vPeG+3/4eQ/83tE9/6evf//53//SXhx/405/u/ckPr//0pw47+bQt9tjrLTvuddyZ5376C196+PEn3f9L0fsv0IqJBW00ahdfeKFXLr3UmquvjsPhe/ba9dzjj7ryw2fe8tEL77rxmnvv/MK/fP62z1971QWnf/C09x598B677rzN1vgSY8WVVllk0cVVYYGh8dRFzPPl8n6ym/fy3JdefO65Z59+5umn/vrUk0899egTTzzy2GMPPfzwv24w2WNPPIE5aLDgeO7fC3SeukC6bdtXrfjqLTba6ODdd7n0nLPv+ewt37rlE9decM6x++62zhvfsMiCC/7hL4+e9dEr1nv3Lm9657tuuONr8wXAJwC/5tbbz7roI6Xvm3Z09OEHrbbKCsjXAS8PTdjHlDwJtdjTkkztHwSnUrzHlFxLRpASIaUFe4rKLghTGbL0UAxwIz6RuhavahBA4EZsUt3H1lvpvQur5MRIIiOlUXB2IqWK2MTujnCviEawLMl1CNEmR9P85A8Pf/KuH8zrymuXX+qyo/f59iWnfPuSkz601zaLzpk52fXn33bnA089qUmbLA27dfNOuOzT9/3xkTbp7m9b+9aTD7r73BNu/cDhO7/9TVn1F39+/IPXf+bl5+dpH7milmBGYA9iDj8Mu6ugcpQJ+tKPfnjlnd/ua33Lqq+66YRDv3Pu+778wWMO3Gz9pPKrh58499bPTfZjYmYRxBthXbpovgAAEABJREFUp6pIXU5GxMlpZJywxVVn5L8qHG1QixHGVsnAlJsqeOWv5soOAILYQ0hyzkmZohY3I8CkRMlFAhgJprBjtWQRGCO5a2fZvAF7TEnZ24bblJVBwXH1KUpKlBpVzcwh2re5i6b0iv2SHRSZ3QvXXgw1QhfUk2BfN+MgQVZ2GyrOogGKbXATCtahrRFI11Rr8tAqUdRRB0P0VjSlJG2KhqztawMJyTOjcWqTtok12YC9Gikp2BBAINdMMJ9MrRu2ytbzBDcjCqFCAIMxhVUpcwUEBXk5sEJZWtUJVZA1lt7ZQ1iF4LgqvbQdZWy/VizIoRETVtbARqgR7BJJSAf/jEIEIgwBNFWgAkLgRBTEJUdpammHkxKeI6gqwyJ479v0oV3Vcc/VyNmaXLAViXvFf1GJWYxbTCqOTlOJlJPmhqauQPUZTkUsAvw5UUOMKjTEIQ1TwJDaeEJNoBRkYDRMrMNizJME2YEg6kEYoBb4EGl1Akk0YqnKPdPYvA4Yl9DeBwclJlUsZq0wnYVQAFiCTqIOIhKW4JnJINhYAL+mSE2SBEfDJO7E+0F0sZRFQa2q9QRXISUZLpXMCVJp4Ik5MZhhM2fqRWugFFO1wZcyaY4RV+QMDbBujHQsXmof1R0TubVRwl4eLsEluCeYGDrUQUdwVmNDwSDOQBYOaG2xXDlCOiGcU8bwAR4VzzCFFhdThiNCulGKUaK2CZwQVVyBVBECZQaSXBjRMGCjEQ3hdUQxHCYEmlJYLqhiPbGzD7mwjSqtRJuR8wBzANuQZDRkSM4qmEjQxTRKEzUxQ3THxT03vQx5IpM0wvC5DhaLbAEzSeepA3BMCB1h2BQfCnFZwEtqw1AlEeFbiIlcspuPo7PBnVRIgix5B+FFmsxtqhrmQb1o3zBUJTIKYwcr6hJ3DJMChBBh8QDEXDGc+9RaVh+FJepqa/hOZsxIa5k4s0wxSsxaCcdXLZC8Ji/E45L6PrtTJoOhaaLWpg6yES6ETSRVCYnAMdk7aKtacq1aCleACuIo/Wql6rbDpm/+yodP/eQphyw6ewYW7/GODb5x1vvvOvt9Z+y7ezH4J5US9z7w0Puuu/1vL81ddNb0U3d559fOPf4rF55w+ZF7rfmqZUu1z//wl5d/6e464MTUBiv9bfLF46+/6YHHnpzIeshWG3zxzMO+c9Gxt55y4BZrrR4RP/j9Q2d+8rZu3Dckiyww5+bTj737ktMuOnQXcEd7327v/M7FJ3713GOWWnjBWnxcvaOo6t4WkrFzv8jsGfd8+NR7zjt16ze9FvMXmj3jWxef+K0LTr3rw+9faqHFiK0SMhFGaO64w3cF+26x3q0fOuIrF5166n67V9Mu6V2/uv+Cz37lhXnj5Rdd8Jy9d/jWGad986yTPnLQLisvvdi41Gu++t3bv/UTs4nq2ZwU7jYQo2//+sGZE+05B+94x7knf/mc0z52xD6rLrMERn7158f2u+iaXz306HbrrPGZ9x361Y+cfNuZxx641YZtksm+XPSZryBsnaoByNpVK1ULtS5NRIqS2UiJBHSwDwLnPTd9y+dOO/o7Z77/woP2YS9C9v1fPHj6jV9+ebJbZqHZp++x3d3nnHrXeaddcsSer1l2SbwcvOkbP/rsXT8APWaG3ypMShXU0CpJYXbQRsPz/GYsVSkUHoIl8/sqRU8O08x/xOevH37sjp/ct82bXnfLyUfe9eGTPvfBI/bcaG0VxgHp6ju/4wKqIRWpJL79g9/c+i0cSHzx2TM+vN/2d3746G+cf9y5+2+/7CJzvvKz31XEBFGY1b7UUuG7g+aqTCFmakgYVFOw97kzAMPBTMKh0ic2oQhORI0gR2CXiVwBplQmVm415wAhCXwGCywFNFsozOJK1FhgHHFVEUzCwuAqWGlVuyqO0EnYDbkxaxx50EU5kzawuCFEomftJUxKiAukUkkcRM7Dcx4r96LlmAP2Oumoo2bMmu1m3/vhD7fcbfdTzvvII48/8a+hpP+6q8nNmScc+5rVXgOSK67wyk03XF8YtmURmTZt+hKLLb7+2m88bNftzzzhmJsvPf+eW6778w/v+dXdX/nqpz758YsvOv24Y96z/77v3Gzz1V+z+kILLMDMc+fN+8Z3v7vDQYdd/slP/x8SGHL+s71h1RUhKB7BetbMWU3TutvcuXMffugPn7799uNOP3Pdrbd76677nHD2eV/+9vceffKpvhRM/n9wgzYyGk3MnD4DeC679FJvfN3qO22+yUG77njqUUecf8oJ159/1rduuubnd372Lz/6l/u/gaRy65c+cd21l3zk4nPOOf2kk0476vD3HLDf/rvusuu737XVpptt/LZ3rLXWm9daa+03rLnWq1ddfdmVVnvF8q9CW27VNV654kqvWeONaG9+87qYufN22+67y84nH37I6SefdOk5Z95xw8d/+OXPP/i9u7/96WuvOueDp77nyO022nCFZZeZPWOmakLQ/eC+X+1z4vs3222Pj11/w5//8uen53a3fOnOZ18Yjjpz501+4PLrPnjhRV03Hk1MnHDU4Ttvuym2DhSXCB84FTNJ4jQSxUWiYYqUhHAaJUGsMHJGKTYPQcnBOUQQVyVKCa+VzRMx81Am9SHmcFQUPkQIdo7Ooi+BkkYrtUSYqZhg4gi5hCzdB0K/MAor6rBR9iDD7M72w1//fm7XT2ubc4/Y842vW3HWrFkLz57z7g3ffNFBu8yeNipmX/7hvY4aXZKEfOGbP/79o0+pyJHbbfqeHbdeaZlXLLjg7Fcus8RJe2x5wo4bC/N9f3785ru/i3f+ueuJnP55BZ6iMFfhp5574ZIv3GXum6256nkH7b3qcsssOHP0ikUWPejd73zP9pthxY8feOg3Dz0kmOpcq2IPL2TGJQQJjBUz2I1qwWtUFHoDzWS5KaNhWA1ZRMix07BKmSBDJVrbpuTh4JYoK5J1zwbRGpdpFAmFSqqROpfqTMSiQ2mDFNYQZhMnppBq6p4Imk+UNkOPVJDoYqRpAluy4J4mwgFRNFCRghhipmK5YylMNbiYIF2HM5OPsFVRlRrRBaERegvhVXljOKOwOFI1zO8t+8gJFQDsboqCFmV4zpIUO52xh4QKExyqaSbaPEo50yhHaklHlNTaZHjEAmfsjC1bw8QmtQrEgCropRDvkwM0gbKV1FxrZOwvYqUxF+AEiVUiyCmKYyVgoWgolNkyVw4LUAmUE7lQdm8YsnVkmOrYLQQihlTLHpTK1DQCOiFCLEQuXBO5ukLyFAR4yAO27giARF8NFVGF1aZ10ppyCHPR6LTHzkEpR23xBQYZM6txU0np75cFDlPYdnCqcdzlItNKGtWmCVENzxRMIpw54C/sTIH/CFcQSTDjF3QG5YiGtclEqHxHfWT3luEjrXqTamp6TkW8Ri+EVSo5g1YfNk5eREyEQIs1yDFBmUSEBa6SyUTJoYIkIgA85t7YoD2QMxhQdJpmGDMZFtXIwS01EAUe2TA3HlgDoCqkTZQSazbO1RUGSVwxMYsocBFWyIATYhqsSa6GmYpeBd1hGXHyaHujQHnRI9IcICZmTSwwsY1cMN3ViauhRXQVzi2p8HzsIbATOUFBTlUa10apTdQKZ1HSYaUFEzi4MVciCqCQwEZTbzruER9DJwdYCBciMyKkENwVGtzCERbK3hCcyyoF+LHz4KIWoBYQN7si+IBKaERrkcXxgpWTKmsyqFXhlFREIXZFKiEaoUU0btNIoCs3QnggpBolZqRkLCZScDCkIJiV0CmRxFTNgx2USjQ9/LODiwtV4nCFjMQcYrCluVF0rL2og4Bhj3Z2OHtWyhpC6IzK5FmCspVstSksoZyIG8ZvWLTiYIQ0YuAYHDWnroXyRKDYV+6BE8BDA/OWedQkBTBqCJHeoK0Yqi7v1U1DmCSC2WlaahecMWNWO4NpuGY27aIzZiw0Y/qMdmTVJs1e6rqPf/nuF+ZOzpxoP3rYPjut/1YcIRabM2eNVV99wRH7b/KG1QD1p77zk5/98Q949YVI98Y/cfc3//LXZ5PqB/d59yHv3njZxReZM2PaSsstfvqB2++/OYorvuf+B7/8ox80zXjahC0ya+ZC02fPnJg+sCeaPq1daPa0BWfNAMYWQCuACgzOg/+bWCfaLbhoXnCRNifBEmFeaM70BReatvBC01NmEiH4Gg3a4PXWsTtufsgOm79iyUUXmDYxR1qq8VLXX/HFb1Tz5RZb6PL3HrDV2m9aeNbMRWbMeftrVrniyN3XWGGpYo7XZ4889VciMgWWAxfcLzh9dMPJh228zusXXXTWQgvPWvs1q1146N6LLzALQ/O6fs93rH36/juusuTii0wbLb3YQvtvsdG2666JoQefePqxR/9K4eYVocowdIQSKyvsZnAE+C3mEQnz4dtueNj2m71i8cXnzJwxrW29s5fnzvvY576O49MSC87+6FF7brv2GxedNmuhmbPevOZrP3LsgWuvvDyIXvP17z2I2jiTK2i5mU3RA8WoEoPDkP69hyiMqovryNIEZszvN/ISUeCD85+JmOnIrTc6ea+dl1/6FQtMn/XKxRZ5z87vXGvFZYnowceeen7ey5SUq7z4Ur3qznuA2KKzpl/xnn03Xes1iy0wG+bbdO3VLn/vPq9achHMRxvCFJFarDgYIfCnIMVOUoMNvmgiRbAF9gT3LEg0PJElI4kld+RORMggEPwVuSA6cuMQpsSkiXDHBBcMwzh6EWCGIAzwwxO7AI5qxM7KWQThNHQLjog0Qdpi2+vEOiKm3FjOxHCgiuSuTATrJyURzZJACW3gCZ+EnB5dz0F60K47XHfhuWu8fjA2Xkt/4tZbN9t9nw9d/LHHnniK/g9cs2ZMv+RD7589a/a2W2w+e8bwauHfY8IEfUjwOXvWrFVftcLG6669z07bH3vwfpefcdpXrrv8G7fe9OFTTlxl1deklCfnzT3z4kvu+enP6f/wtd5ab1xlpZXARFX33mXnT1564YlT/2zA8sssk0fT0I/2lwd/f9NnP3fQscdtuMOuux/53g9fcc33773v6eeeh30x+v+mJsKicCYiWIFmzpy57FJLvW6VlTdZ503vfsdb93n31gftsduxB+33gfcefu5Jx15x5vuuO+9Dt19+0e2XX3jH5RfefcPl/3LTVT/4zA1o37vho9/51PVfveqir15z8W0fveCKM087/+RjP3TMUYfuvcc+79p623e87fWrvnrJRRZhTlOMBl7zURp3/Tf+5Yf7n/yhHfY/8O5v3AXPRD8Oh/Wl5379y/v++szTf3j4sYNOfv/Hr7sab+zRf8qRR+z7rq0SQiSlyA0New4VZnPSnsSDiUQja1G2EKpZEDrYSYyxl9VhS3XkW2VXpOhunPqxRmd5TNJR9CmwBtkHYmJrGsIsoweUhmRgSg4qhPxIJdRqEzUYyYIMhjdnqzyE6JjC//rsC4MiSWZMQ+lTC9VamDpdcZDb3PwAABAASURBVMklDt76bQdsuf7qyy6J14V9n59/MW759k8x+U0rLbv9Buu2qEVSJhQclch8p43XXvNVr8DoXT//9Utz5xIbeyUP9AyNCQIE1aj9137ws+fmTi44Y9o+W7w9N4qUw5ldagnb/K1vXnqhOUh/3/vVbxULUL9AIMhMUaVUlPaEzINtvaLyztHrkOagdNOF9OQclCveJlXqK1c8Vs4lGjVpK2AshNVh2aiFXEHFtcYE4biBgqYYd0adRAySIruFJ/KGSLUWCkDVY7kTuMCeNOjVAz6RrKNGGhYdNxQTESMnZqradU1fBYJ6JsrhrZeG6kAwULYwlBr2dsGNGhBEzeEl9ePcByMPWjIWSkIjZnXCHuS1kBsMSUQcnEhHfdP07aiMGs9wKDK4BAveVzQjo4wiCkWZsBJ4sFkM5RDuJZgru6HKHGkIKhp4nuYKP3EIBZGdKkc1w5d0vftQ3wgPeRqAUVe8L1YjBq1GSSYy66A8VGYCeAJ8wJKQ0yeLjV1LNNkS6nsu2BKjkkIkHs5a8BAhSgzrA78WCqWEMKm5TKYyH2wmgCBCjTCWEUHv7DRy1hhFP8JxRTrOvaVwVKIZAFSnjggTKXcihptQJYw7WU/VE0wdbUC+xNaSQ+QgWDMAUsOsRISZxcxNHE7JIBgqkkSEmViNm15TiKq0ObWCAwxW9eKVRaJpKeWIWt0KJbMJqgnQjqzPBOmoNFGnJWozJ2IJEFUG1tF3pe/MqNaG6kj6EfUNBCGlnFkyZnIQ/LcwORYY++CpPAgLxlwYPpK5Jg4QjmQuLomkIc4E7XBAbEqog2fiPFLkIxaohxIXp/1cLU+RHdRiGgbcmZiS8oipMRLEVU7WBJNJ6RIWeIAX4cLSVhyvPpVDXIhy0dRRGpem+Khw44lZGEBrFXEYnBJFAnFpJLf4TcxG5qAnptq5d9U6j7FwFaIwLhUxC+Zg5dr1NC7eVy7knvCOulRBACFuHUZkJtHI2TgHKQujJfiPaQSxRYqUmXXQDpVc4C170ZZYtPJw0q5AOhWVOmQKCiJLTBk0jGEblPuNywTJCMPeO9VgEZiIBHPhMpV6oh5Gn87RilFC2hz32XqlClx6qZPKHTPRkFkkyIJBKollNUnMHFSNCgfCSznnvuUxQ09jDWs5GNtITiGRuBtxSSCr5iBEREHsnFywDisjiwviuOfawfVFYJIeMR5gIympJQVvDawzdywnDU6URTJDmp5S98Djf/7x74f/M9j+m6736qUXxbbkAUABYJo+kQ9790YLzJiGSLnjnp9Y7YvbC/Pmfe5794LUpmuu+vY3rEE24NVH6z4h3u652TtWXnpRjN7+vXtrQCbmLqQLnmKO/iCuEJAS2LtqDh15TEiMiEdELZMwpgSRFQQX4WKzHPBX+BPskkkV4qGfZk2f2PTNazJnpoarMtXE5fv33f/QU3/LKodsu/Eiiy3sUonHBHMQzZk9/bhdh3dkz74877u/+CUrhRDAG2gRrb/GqnNGM6OqEDcpNMXis2evvPSS80d323J9UycRJdLSudU3r7ri/KG/PTc3evYaA240St5wl7zXqEx956WLCMxsctryzW8AP8SUMcJU3PiHv3zwd488JcIHb/P2JZZarJAZ95AKcC0wbcYx22/VJH1xcvyVn//C2MLYA0ZtQA0NzhIUFUcAYIbnqVYTlYStRSqDxlQXBB4kwHqJ+T5AtMISi777res4qcfgH0IK2NddY6gzn33p5RfnTuKZk/720cf+/OTToLLTBmsvu9hSFV1pKkk6LTB9Yru3vBFDaOwu5syCcGRsO+FBBOeFF+aw7B0JcEWv1UoQuLIgmUMaNqeg5NFUpHEZ0pMYAQGgWUmCmFlQgQhbRCUjcdY+vFYQ40AKDCnuyPnUY4AZsGqqkkwy1CbI4ewBrV2ccrDE4DsM6RQfWTllgv+YYIA4jBiNKkkJ8MldoWLxhteudv1FHzzpPYcvvdTS0PaFF1+4+sYbt9nvwA995KMPPfoYev5r20rLLn3Z2advv/nG/+tkhYiJdeEFFth1m3d+/vKL3nPgAQkaEn36jjvp//C1wKyZxx52aNOOaq2fuOVWCz58r91vuvj8O66/8nNXf+xDJxy/yQbrv2LpoXaEIOPJed//yU8vvebaXQ87Ypt9DzrklPff8sU7//CXR/+epDDj/8WN/yE7bqYsAqMQbtAtJAkGIsbjVGMl5qHznxPQQ7iwFp//TnvuhRdv//o393zPsfsdc9w3v3X3/Bmrr/LqD5968gePP+Ydb1ln2eVWuPDaT+54yBHf/t53MTpn9uxzTjhp73durdISihxCCgslR0zB2x2poQQVCAFBOJIOwYI8FOIqlJSCGQFHtTLCkRIj7RA2o36IPGwwqHix+WMq4ZKEvOlTBYkQlmaw8b5IhyqBGiMmN2wGKTyYepVeEO4xvKY2Q9RpxGtWWEaYX5zXffSWrz7y+DNm0RfUlKypfdcm6+y/w8abv3ktwi5U40+P/fXJqT9E+5bXrNI24oLodgWPymaDFlu/ZQ2I9PAzz/3l2ecGOaoPeqBrqomGBubZvb/9IzqWWHjBJRZdCLtSJEEpEK2R9vjm5pVLDpvZ7x9/0sIYkkVIVCbjhoKoDBQt3EAqsSuHUCApO8ZCkgMrD+S46oT/YkiTuIlgdNRKpY8aQEBrkiALcmenbJwc08yHpIquYX6WkqWqRAwMPCI8hgWAlMAUIkEZQBoulFgkB5rlIGUH1EalEDtBgyyeJCSp5ByJ8c6Wh1OIE6ERkVE4Gy53gz4B3SpEqa4uQvAFABBhtfpgl2pRnIy1sBTRymhTRCCcEXBgNVG4l2FNOGSBuIPW4cLBg4NBDFHMDOiWaBCRQzFxUB9ChZJhaiQHOFGNUXxFdmnNcTxkq+HDStAiCU3UKBxcyVPnqR+YEEylGPaoNcgAz0DSCX4HmqrMSdiF+ggoWbkZt6nPbEyEiSjqnFGFavUcOIHRSLFhICbEohnOJFVSqOAjAyofsKAUrAPFElTrFKrBAvRkyrhEyZUrmxNsEhRCUA7YKcYZYeA1jN3U0SeGWBw8N4SJ2TEdMHmEkxuMBboQ0kSAmZIwZgH+gseGU1aAwV7dzC1xTWLgjK3NCuiCCHTEatcmqKVBLRUmq1aKeZHgiFwcZ8fEQdCjqFvj1LARw/AgazEFabAbyFNtYFGoQ4xyvaAkQ4w7JUFJ5MgFicAQKrALXtOjMYkkycyAkOoUEUhFERJwXHQny4mViMITS0oJ4AfsPBA1BH12CAZxUM23lVIAZyKlAEEW4EWCJ256Z9THpYdCPPRnBw0iqJUxVzDLhw9IGuBAzENgDE8NVYUFamEctGXMqTKR91JrDkzjKuSQQatAEFJWTUp5AMNhcKxx0spi6oBkiIfBUCHiWZ0lmD2zt2zK1aGjMVtmmCRHJHKBvJDctffwMCOCBhY8iA//0BKZSLOrQI5a3TGmRFkI5FPqoumcYCCXiMSVxELc4KMB9yEHEhRMBoGpIdOo1odXUrU0UaStljGJLLQiIAn6Wg7PVJXwOUzj1kujPQnoSVAD7VQCItDU1VdIjmqOnURDc2U1ItjZFXS4h0wsxAmGdhUjTQRnmFoKvYIbkoZSiszUKv/6Dw+NS8Xo21+/qgXwdg9mIhFPUpdZYqE1V1oGo/f98ZGu6yrZ/Q/+5YV5Y/Ssu/qrRZVDiBKBKGfIOHM0sfGaq2P094899fSzk4H3bj1xJQ30Dc1JEN61wvUFF9MQ0qlwrgJvU6AhRAy7a5MaIoogK1rRnE2pIglkIiZcWN62CbfKklQ1sSa793d/gClmTIzeuNrKlbkAEoZpwBYxyqssv8xKSw7fA/z0wT8TTxIjhAOk0EAAQkpAH4YIcLjEscj0FkNoCy04K8BVeWqOU1Db/P01sZC0niF8gn9IYhYLMchKIphHFUqAAjOrTgvKIsxoSQDYvb//Ew6BTUrrvO41hq1cOXJYGuyAhLzy4sus+orhj5/d++DDxcYUxjFIDGpoEFXByQmQ4HF+cw6HQANsFIBmqldZ2/l8w6Y6aKJt22YC45BEoS1WcbQJd4Q3T9UQhy7sf3z8ieqBJW997eqIJA8EEjNBA+gJAylNXUEeAdIJcZcG/u5AlhLDKgLYI5gsc0mFyCAa1IMmuCNQLxalpoJsknJJ5OxSDREDn3TDOslaU+OaCNzSEJGJIjF+KBolxhNbcCUvRkifpCUr3rKUGHPnTT9kewlTHjMiO8G81OIlizNB3RSUqnMX5kMB1REkKmHRhky4qJdSx+OYnJw90Ry069Y3X3n+UQcdMGPOQtD6yb/+9aobb9p67/2P+MBZ9/z8foiMzv+qtuHaay212FAh/ecJMk+fPmP37bZeYrHFQOQb3/n2V77zPdzMbx6BU8G3fnLfrd/6wTd+9LM/PfI4Ymb+0P/O50Zrr7nfbruAwvMvPH/86Wf94c8P436BWXNev8rK++6w3TXnn/2l66/6wg3XnnD4oeutvXaeNhOjtdaHH3n4S1+/69gPnbHtvvtvtfcB53zsyu/f9+sOhRWG///tXyHw2FPPnP2xqzfeeff3nPb+7//kJ4COmFd/3ZqXn3/uZ664bLdttlpw1sxnnn/xmScfvfPrX336qSewdPnllr/6jLO3f/vGLIJ4c3L2mqNms+TRIEITIxIiQpwoECDY1CWKp57aOsr9RK4tdiEsrDIkAibPYvhWgFRro6VlabgdhQjWU6JeuTfEU9PpqGjTowQrJI6NTbMqIo4piHti45AmmsYyNi8ttQluhGWztV67+nJLwT9v/taPDjjr6kPPvurqO778wwd+P0ZwE6Oyzo4E5ML1L888Pe5Lk3SlpRcz5jBXq+LVXKyOaj/xysWWAgLm8czwB+EyBdKdoQcNd0ycmNTpgUcHoPA98q4funS7ky/c6oQPb37ceVuf8JHtT/rIzqde9OPf/5mIXpo7rzJSkLQcmSq2GSwOBleqpIWhAhMJMxrEsFSjKdJQDKojY1O4NsapouLoA7NFo2pfnQxWYXCAFbINFKzhmriXYAlomdklFOYRxzRz9l5Tl8QTmHuqLmSVBQmt6Kh66kx6Tvg+uWauQl4IxsALw8o8fO01KjiCWRu1JcnUJsKbwTCUH1IDqZlBCr7gFS/XJllMM2UaJJdKMFjvFmGhFNgGDYwJk6NI6ZR6IgYzM/Yx8yTkRKFEBWVKxjGFnZpSRj3coxLEIqikQeTAUHLiNlnigoldyuOmLdy4aUvUIOFrZU0qMlJCOTUyyA4RRI0zmkZuFLiX8DGBuyTKmdiCa7S1JrcqjPkqoVxd6yBCNAEY3SUXyYEtlyPUgnGw6ZmcolKUILPAeuwCyXHSjraT1jkrMPdapC8K/VItrZkMSgncuI2SpQ4OwlUrvCCgBcF0zB6NUcaL94i+0fEolWlkiDMuHMSRKIA4e9Fi2gNuLtn6tpgAVrgbNyGNJao4BITVIsNIDS5EJYKtkwspAAAQAElEQVQcjUAGUlcPSKQAhiAWkwvMRYj3FC6DoVDheW/cFTFsf4RwtD4HQjAS3NwY5bBzqJowaHulGho1EfrHDGAc5EvfTJp07oaREo2hdmBPhByAVWZYE+HETuKSoiEW6ITZHETYyiMpU2PV+xpjzx2nmgQgaVhDpeEiGqEUDWYRwCGe0lSoZhMvbVebQiHSt9o3o4hWO2l7CIm5gAJ+CpvkSIkyh3hP3nG13BFOr0QWLUcroa0HXqPQuHrnVOHPkNM0mWbjZCyWmqJwNXMaq0fuVEpyTLVivUMAliytJjU4XUI+Q0ZCsmMONrCRPqRX6tU6qYCvuoQzUbByiHplGjSHjGaKL2fcmUkb1wloU0jhjUHOBPwJuHn0FPMIazRI8SNRI/c0wdI04Y0HZsESLuqciJVNAFsOAk8iwoM2XWgJpUgNwZ+lrYl7oRoES5iZy9RqsKyInnFK81JMCo0tBSzDENUzF9FKTSW2CLcu22RLlgh2pYZDHbzCyToq4yi9dCW7weAsPAhFWK2pkpqTWGQnjurkoQKpRQTL3aODdkrESSznvvn9o8Mr88VnT58zc6EAm4yJULhS6khcI71yicWJaO64f2k8SSl++9iQ2Ge0zfJLLcwSwZKJkpJDEWRPpvkvoRAtTz/zMkAOCmfY1EBkqkUNh4RaDZYP1h7BUjn6IBMKIZIgwJDn9QXzmYKjONYUMpPK1AnECwxFRCljp7Go4b2xjqSI/+7xQZ1lF56z4PQZoFWT9q0AcZfg2qQyWmGJoXJ77qWXrY7ZYZ8KUmiNIl+6KiSAWsx9OOQDCYwR9AYhRiInZmok0GSQAYMqgFJbwreuQ1T0Q5w5tfA3uHDL3EIDTEMbDJElTVCexjzBWPbAE8+g/xULzVpg1ixi1jbriLDpQ7Ua4UIrLDlI+8LL87yMB1+old2xBI2NtUpLlITx+PcWgC+Sk9B/62SCbqLhGkH/uLDrZ+WsxMlCYBD/xwhWGkeX3Od/nYKv1JdacEEOC1QLBrUzAlzzSFjnL0EfTlEVCckkVSXYJWB5x+aAKcHEjJ/g1qjBJjtWeITBS1MJKhUnNZiYG9aGcnjqkbgxwRHXcIAqjG2DfMAORJywbXWM1CGSOGnWzNxoce4qWRhxDXLCfIKwlZKltuKcg8Q1hAIBFgESrA5iZBzYfPtxVKQpG5BzdwhVAwInSNQiaELg2wZF8isWX/zwvXf75nXXHbX/gYsuuhgzP/f887d/6Yt7HHzwBjvuftpFH/vF7x6c7Lr5oMz/tBfm/tv28l+f/fIdX3vpyb/926H5PVFt/vL/zc+F58w66oB9U0qT8+Yeeer7j3z/6Rff/Pn3X3DxW7bdcZOdd9378MOPOeHYfY88cqMdd15/l32uu/W2rgfi/3meInLiQftuttkWIPHoY4/u+97jHnrkv33jJCwLzZmz5mqrHrH3Hvim5edf/MzVF5y7x/bvWmqZ5Ruk2YgXXnjhV7/5zUc/fv0uBx3yuo232Oe4ky+5/sZ7fnrfM889P3cM0SqMA8r/v9nu+fG9W+6x12Uf//iTTz9tVoHYum9a+5OXfuSOKy5854ZvzTkfe/5HDz3xlF/cf/9LL7+MgJsxffohe+z2hY9etNZqqwRTh+ILWbeUcLgWQoSpqPQ0hGjGJ0lFdpMhIhBu0gkiLIgJsaKNMQJMSg0XVqGR57aihMSyIbpQCKRC4kTENpVJGOVJhMxvjhAa0k9DMWo8MdHAHSU2tg0WoqzcJGpTKIPIggu0Hz1sj102XGehGRMvzRvf9+DDV3/l24dd9vGt3/fhT37pnrkv4/WFhFZurK9dEHKfTJs2KixGKYJJyZUdHFyn/+MPHL7w0ssUQrjAAvwHCbAxK3EaG72IxEHUlfLo08+iPfLXv021Zx/969Am+x7r4HwxbD6QkZuGqcmFxMEmmAMU8d55YmxtjWShGtwaN1VAfxgcEiaxsknqPCFFCVXVPlJ4ACRTLilBZvEQ/EqBStwSB9YrM3b9on3liEjeoywOg+BITYCNsbxUiwJjtSXBhLWSleAOOVOKaSWuEiYQUXIRLqg7o+NBFEqZRQJZWKsHEiFLcCbO+EWBNBwVBYSAmTcmbWQmPEQdFjAlIlIwKkQ9ZA6vRJbZRoEqBvk3WIzZUWOXymbJhRPD2lUCVqsMBuGQxoblloe5qQ/IZzwI3bFUkHNtwkep5jZSy5ynyp0iAjisNwpLQSPiVIEbR2WUCZM5+kyVKYhxJVX4ZHFyUVFNGlCe2BQqSCIenp2pZOkHnBw7vhJQDYow52pKNXPVARSFyi5RpPYUtSSIrj0wrgTXgEIeFggCVpYkCBXHoxgJoAqOaK1qwFHHrn1FH6uxV+ndC/YscaBDDJcQNhIX/DLuKCrotQLbk9ZAMQ5BK2zuBkzhY82YtWcuYYZmbtW5t6aPiQpUaojVpAlFRwM5GCqRMcIeizkcS2uerE3vtdC4aFcI3hRsgKcNBfBUKCppUBpUMNOoHO5sw0xzgZWsy6lyNs/VtXiqxiQ15Uo5PBGeBlNQEHUW4YwQZRXKzAlWCi49Kn5ggVOztiKNkIgxQAgPG5aFOECsTJXgIOSOkCA4HSo0UC+sNQMvIg/M9xhcsVYuBQ6VilKfun6iWsvwno5KgQYCH4IIsBKGHYVoWKl9tbEFUMtVW7dEA2UKpmg1RiYTVfOkBwRWKYktiOCxzk1KQJhJavCkwBgN5qCUV3Qk+FMKeCJJwBSAQNlcqbRUGjXIrWSEiqPvIlAH2RiSBNcgwrTiw6qAK4gSJmo1DAVJgXTAwwildOegAMy0GZHqOKJnNyFisYgAGQwD3YYY6iZRUYYr+qgOlZrC1zkgk7J7SAl11+S1IVOqiFC4A6ZQYCKiBv3UBDNoE+Ybc4hWoSIQtSvwQoKlKDLkJ6Jg78NAxc3FElpwtjSqqHG9EbzdsAgKI8IRICyxQz2uwozF1bw3FP5kBDYpIianipNZ06cJKjN4C2zAtThSuIOrhE6bmMDCUms/rpnVSuCxbdLMBA07gRZOYlXBUXrifvrUfMx5/sUXiceMcIPPiKFnqjFrYmhu5lGHPyU5ol68BNYrwbV5gNlJR00zzGeSZOJEHgE3Dw9zCsI1fDguM9hDqTIsGAVZiWjOjOm5ULbIHPjxKceoCh+UBWbNxNqXJ8eAojFJNeERDaDB+JWZIekk1cKdC3hiCA1Jd5ADioYQJBQJeCAGiCbHOHCacGEek3aeikNfhjiEPJkY9Afkh7lwrDCFFkNWrIiZvoDZIC1Xz/2wUSiHWk/YhaGZ0swZ07Fw3hhbpQG2VIUABbrQglVJM5EwERrhYkTz4K3GIPKPqR4Ar9JgHUz5e3OpTLBaRFit1THpH0SImFlc5IVxJaJpTUKxIuqq2SUXU2gYYCqDBTABylvrSGZCzI5UpcUcVpqCVBDwRECBkRukdZ/wQccqOdRFiqZxkh7n8IZIuQ4voZKHCkRwYqAIi0dojVxZPbkLmbK1ZMhpqZU0XdKE+7TaTxOjxJWabsjU2bNwQjBHEOq02nHfJysaDhGdEMcQsg/C/m1qaEAWpUZrwanvGyuwG7y8nT7KowmmCRpnGefGdOE5Cx+26553fPya9x560PLLLqfos/qXv/z5ups+ueXue2y6x/5nX37193/+y2dfeAnQ/mLZ7f9te2Cl3ZbY8yN/ePXu/3Zofs+f9/9w97fhD+vT/+bFvP3mm+yy7dasaXJy3ue+fOd553/42k/f/Mhjj5ZSzCzc8dmX8pc/PnDauRcc/oGzxlOV2X+arWq64IT3rPvGNUHhoYcf3vM9xzzw57/g/n9omtKc2bM33+CtZ59wzA8+c8Nt11x96lGHr7POeosusgiLmNWXX375rm9969yPXrbrwYe8ftMt19txzx0OOfKQk0475vQzTr348o984uabv/y1n/ziVy++PPd/oPz/6kd3h0Zo8d+r8eDDj773jHOeefZZdC+5xBJ777Lz56654tOXnv+2N78pD+5nH/rY1bd85mZzb0cTb1zrzace8967P33dSYfttcACM0TCcm9p7DaWUsXEmhypjVAvLIadllR52BA6BF+fueNsNdd+VEvroZQso0bMvQrYI0oR9kw5akI6Q4QaI5MhVDEYJVM/RCbRyKIhl1Sp1WjbaJRIYtzmouQciTCjtsYomZrMU5V/T0MOp2kzph+5/bY3f+C4M/ffef/N1nv1Kxaf1uS/vfjyRz931+HnX/70C8/WJDUxySBOEBnIuViViMYzaGGI0D857mjqmtY2hCyUTVtRHlZhuOIVJ7aFhNAeJr1p5eXet+8279t769N22/Z9u273/t22PW2vd52y7/bv33e7D+279RHbbzwSxtbNlL0ZmbZW24AcHbSQxplNapWuJGy5lBJDPMYuJZaytyzZE3fQVGWCkMWR95gEkEVVKiMuDVVGji4UvWFzFgJceDVr2ljJBaUHhUhg/2+7aLs+15Ak1LCJQw9HCqsEUHQCBq0EzkSQVqiX1POo6IhEkTOz90LFtYBUIAkz9zR8DzGmAF6ZeJQIdmIQZx0RpWrDfyRNYp0gyrCcEidVSXjAToYdknMlOEvmseaaE8qhiRrJNDiXYEc5Cf9jaik3nFOEZ3Mlq1E76x1fxtSe2QJANG2Pyg6SE+QR4gjFJqAE1Yy0sFVyoZqjY2SImpDMCaU8yuygeSMfYKHCXR+11IbLRBMTDasoMj45sRC35GCiE46jJkdWZwGXTFW5pGSiHJq8EUvRZ++yVYZzRYYu0Dpy6pTGhLqKJXjklkCX3JkMxQtDNamSAZeQwgLD7gagCBdmKPUsXSSc3AC2hxvhkAJh8QRNK6Uw7HHeejTMngdFIxPlcCHnSoRmBFYEqSTDRRI3jeVUFL5EqKDcAjPTIC2LhVPFkqCGtIEcJuZkHgx7hDXKDaEmVUK5UAmYYcvugnpSyA1ckIddtY5EMqvC4YxTFakpB7VcGy0EDiHujZGgfqeYlDJJfWVT1mypra4B7VKEVKfq2cqoRhs+EaUNQ7+GMQWWS0h2TqYiw0fHDs/zYoa6E3uUm+MiyGkCOKFSNOHch8NNlKqkMngrR8UzgSIZcS3e12qOvOLQX7ESBPogyNMWOG2wgzUJhUrlQDC4V0aEEIwjNbRwa9yYZJU2Bb5jtnayClxC4f8CR246Z4NAgg8QdsR8EelThuoQKRPiIVdqQYISkQh5KH4EZUfuEUMcEj1DN43aMIQXUByLdVrxfsJ0MEcKUTge8q1lTGGu0lQfUS886dQbuQsF7GbJ2KrWaM2ThWAr52ClEGKGKEqc3UeVpGrH3An3efAeiBQKl1NpOCunyjpuuEdeloY6MVNjiYCm4dAK+WOKZd/QuMH6RGDvxjWoZObEDETAMdqmywqvd00FKhB14T0cRimyAAwOMuaegGhWTQkcinlf6rCeNAqyRYAaXMaaXnBHZGbNiCUx/LsPIyb0CyupoEomwm/YKmVY561X8wAAEABJREFUXBWPgau6FKeCRFEBF6q7ZF1weXk8iQloE9MzN5VbpyElMHrmNwFpFU9RAFvCKEkb1HjJMJ/A6zhCyTAL84MIQZ0jGpEmw9e56eBqGBkaZGdmDxkbFXLH7Ck+Zs7hiSrifYJcRR3fiGDnTPTi3KG2aXLKMpEltTq1AAi4dZNSIXuxoKhBVph84IKf4NonnrJLJmDmBrnQjzYtp8HJpUQucFusGShKISTUGgCLh2dMJC+m3ZjwZqGGRfEaYIQBgD8ErTtq6SiGtyyNWQYYAHMevumjJqWkEzxkMCWYF2vQAFByOGHAT/E41QRgmXVutQxxN9VHtbfJvlq4CQCa3wftrC9jqyjgnZ0AIsHbpwajZo+25nbaqJnqIM8uSFmjFIkLFCvW1S7in9SEctI2OBPkcYe5hq/o+wqfzoZcy1kYPgz2HEzM3AaJsGLzajNnhZ3GDQNhjBJhDpw2QdwKLyZK4soG6aJKsJQEeSrDyaujU5glEzchOqAd5tEHMpY4tS5NMFdMdddq2vfcF4fTOnUekJUFgEIepgJ/HOKpV7VgqqFBTPD7BDGdGEnTaiVA1RTTfvYCMw7bfYcvXnnJVWeftd1mmy+26PDNFxH96U8PXnbtx3c6+LBt9j/40FM/iJ7/RHvu89994ODz+g4p9j+x+r9bklRPP/Y9Hzz2va9afoWUMsag7VJLLb35ppuffNSRZ5xw/ElHHvH2t7515uwFMPS1u79+4dXX/Su7ou9/uc2eOfOjZ35wnTWnzip/+cu+7z3hJ7/6zX9IhUU0v2HVlQ7efddbLz77S9dfc/W5Z+27226rv3rlaTPnzF8V7k8//vC99/38y9/45i1f+PL1n/zE+ZdcfOz7P7DdfgfudsR7//SXR+ZP+9efL82d98TTf3v62efnjcf/uv//afdW7fG/PvOz+397x13ffP+lV+15zEnz//8qR3/o7Du//d1f/uFP9z3wp8/e+bV9jj72yccfhfDbbrbpHdddfcZ7j1xj1VUUuyW6iL7305/dettngNKcOQuc8/5TbrzojL222XLRBecwJW4aHmWEh7Kj2uBELCSEvGhd431DFtgQmMLxVt5yFbg3wgeNGBHmTDZ0SYqkLNitnLzysCVFxW4ZiJkIKxWpdxDFGPknparqSpW98zDjqMxGVIJ651pzCk2MhKAoD0h6JSMOTLOBBHNKwirTZ818x5tfc+T2G3/2lIOvO2bvd71lDRX+xV+e+NQ3vkuYLs0iC83KKmN8E/LX57SQYHUwk6gysoooPfHccK4jotmzZpRWHLkJiiJhoIuQk0I4NOsiM4c/wju9TVuvt8a2G6z5rnXf9K511txmnTW2fcsbtl1/7e3WX+dd66298ZqvTS2wSy6CFAACSjIFEGgMAEoYRTDSOWoyokglmnEI9gMtjPIB4lDLlFRkSnHcCmVlTUh0yLHBhnTaVyQvhnb4EVOuFB6ubJKDsZMKKIs4CROxRqg5bgi/Rl6bQF4sjaJ6jlRDjMUZl0tYskE8FxIhTgKaVgJZcspsmOCgYzlXQr4GbVHRzDk7C+HNjjETmKK06xkTFIyVBcQgezJNEaKe2CEtgWURQpXvHOJQZfjAXhoMYgV04GJoIAzYDKZPoDPkWWRZmD2aYK3U9MEUpAC7KdhWTWohH6ZnJ4iZiggLK3NUKm4VyrBXscGLYAQClcLcEXm15A7TOZBuhJIO+hkBAycqxAWMkPwbHzelT32RgvNJwSmtSc7ZoQQ0iUTUcCg77gIAMNYzG+UKz+ph4T6Ri6pQIw6KTilsagWBDeEK4E6cOGBKhlwgpabujROSsnKgGiSlEDZoFQgeAx94UjhmOXRDeeQNwRaMFZ77xA40Bj8JiKOKSIU+bqSsUDOrwC6VuQftIRitsZpoysYJkwhYBW5gxKbnxlmqRyEDXeZGHW4ilJI2rMIEsuytcEPSOPTUpCIEPZy4djgzpgoRK/yVSIizD55i7lUCsxhLAsozMxWki9oZeQ/wk0abYtAKVuuBIftgSi2U4JbE46h94HuMLgJJpvZiqK6JElOTICTOEC0UqhykPWcTOCNYsAprkACXMeRI8K0kqpZSaVJNHlS573Pft33fsI0ytSlGubZaVAxR1xn3TpA81Ake2JakVdixzjx3olWZeBCDmacGMIYOLBHjNPQkgsJRuZaoMdQNBOzQCLai3EuquBFgowQPjSScGs2Al0EKeAsZ2MEcQMTRJ9BGCRu4tAzP6pJMxQi3NVrcFQNQvXMh4Zq4ZLzSyIyMAIdCHiEAyATeHMyGrFzN8ESA3kZcURjjqcCjhCkglQRlaQaBWEaMqDS4bQ3uYU9LLA0xkME+wCYOR1PAyzCwNwMxAgViJmkUh50kkB+eUbmieRSDBGqRJXDcIINATD4EcyZpVZqciEiIhUSUHUhKAI0lFxj+baGHn3m+VgCMKZo8z99ImMDDn5p6kZdzGo1wGPYl5wzzX5g7fhKvjImGKCBIC+UDVy/0xNR8IpozZ1ZtxSAoZQpFz9Dch82LgxNScAxW6UkAcWBGj8G+wocjuU3OrzGCag0PuBBePEVTiZGw4EMDLWjCiXEJc8AfJOvis4evIP741DOVOmZDN1BAKsiGDBhU6uPP/A1LZ06byJpdbIgHPE81R/qudTikpI6bntSJpwaIilQjC9ASwqcIafj8sa7UEEZC752QToFwA116s3k1CnZnSPx3cZW8CQGmzMKkDeUlZ88iFLdPPRviKsWseA9lwSFgHDZ66plht50YtYhLAsPB6fALi4iUmYKsr/C8qQ58wLLUJId8RMxMUxfYR88QjsmmOoaPINiAKRGqkGGVDJ3zfypcqQgAXWbhBdDzUlfHc+HYKkIqoG3kpbdBToxONThTI2DHFhTC0mhS6Ag96pAMGWI6TN0YZ+EEN4cuwiQiJEoMs3cADylZkzUKLuys2PGQVYQELi05iCgGVASB3bNVQfAVdouoBFqJSWCUKl6Fag7LRsmQNUhqEGVWoGVESMcOwALRA8opj6QdhbYUKQbZKRSpByGreHfAgQtw+wAIG5zbVODskYpwUZc50xbcdO11Ljnt1DuuuvyKM87Y/B3vmDlzsGhYfeihP33xa1/79sz+n+17izYvvXXV37968fk9v15+9g8XSfPv53/eN63SP678hhWbJv/j6X/rd0p53x22vf3qj955/VWfvfryr37yujs+fuWVp5966B677L3Ddoftuev1F5zz0bNOnzFzlrtff8ut37v3vv8tfkSLLrQQziprr/kG0Pnzw385+MRTvvovPwGUePwPGzNLWnKxRTfbcIMPvefQ2666/M4brrzhovMPPvCgrTZ6+5ve8IYll16mmT5g+68p/PxXvzzwhJN+/6eH/tn54F8ePvqDZ2255z5b773/O/feb4s99tli30P2O+mDZ370ymtvvu32u75114/v+/59v/7dQw/jJPM//Dm9fxL5P3rz/Isv/fS3D954+x3vPev8LfY7ZJt99t/hoEMPPemUa2+47tvfvee3DzyAdtsddxx0/InvPuDg7Q84+Kj3ffChvwzfSq3xujU+eOzRiy+8EPE/Ajvi3t/8/sxLLpufK48/+sgt3r5uyIhCNAiTwoVI4agInyyUlIRDqhOVmvuSbZzISngtrsUbt6xmYggrzxwNExnFWAn7OY2IxcKtDyqULUZqqS3WGoKKwjCXAiE6sGDpifG2xMy8OhKFYY1xqYh0Zg1tLGWEqZOQsVe2ggwfz78470vf/+VXfnzf4888KXglqGRCnmLlFRY/bpfNX7XkokT08wf+xDQsWW6xRSdaZFq69/d/YIpBX6jlhHsokRr73u8ewPwFpo2WWnyRHtGsatDUA51oAw5ROLpVll0Kjw88hlPtmLwRSUQcg6DDPqMoZPE+jVNJ2Vs25XBkGc9Rk2CiuJFzkdQ32mfIC4QC+FYSZ0ESBb8mqCFSkUAiQxZCmpIQPHOrSNYEwQh1rYS5+5CECInI3atx1VSbkbcZu5d78hAoGKBsociAyOoa0cggDgQTikQ8qtx2ol2SceMFhocuyJwuGoRqPTUkmdgiemxOkDywCOJUSjCaYyYLeaKCfZuxWoflVLGCqECoCAc+hIzLSNJABKoxsxEDEgiEqTigaSEMBUgRJeNcwiJAHzoFiRBDf6BN0ThlCmHBL2qYibSG9i7hNVOR3DtOENrXbN4G5QIpVKFIkpw95U64h1xUmC0BuEGBwQcIDK3WiA65PFFi0MUU+BsRkKW+4k0hjBNEhn7mgaNW194cPDCCXo3hL/aoiziMDuGYdcA4BLsFDTVilJQt2qhNKkAKo1wxLxwPjViCeWjqEopM1LphrnJEZhLAy6PgJgAmMbmwKxawY6/0gGflyslcarUqpYc1W6DFiaGoVKMOB9K20sgkOQQTBROQSWQNuAnRlNxGMIlHkBKOGt4OYADdAECFNVrHF02pCc2kXBJVZSfI3TIiHpszjOVE4YG0kYuqg24IiOScmVPHTQn1Xrlrom/JFcIRNnp1h8Wdi9PgMmKcGRKqw8s7vC908uzceAIpeI9FdSZnhtpGwTr8lyK548GNuRDKZOotrGIVIxuBHrNWEVNzhZBg1ISPqDZSGq24b0mavs3dKJd2VJEuBmCqIz1RJcM6aysnYk2imVP2lOvg+4KiUyyAJzlSm3eZuonoJYiYLalJMPeMQx68SzCDmRsd4rkVAARFMhn4R3ipgXf5yISEONAeiAYkpqaP3Af3BCdJkDLwnpdaLe0IydUriDGG6mBmpE2CLSmQcYCIhiYgGQ4vggFEUCsLkm0vtcAWjYeSJY40IpkQbygAoVEgqQh7QC3cIfUjvLhhUogvHiZSJRUWMgxDHdyRCIuy4jeyeHWyahjPiQC9OovlWvM42pJSNKLQg8KhPBM8hiRixJRFHSCSsTonQApTQXEs9DIs8BywVDCWJaIGv7GYSIPbxCSExRaIgbTasq8gomJ+728f5ODMAoslagSMKOaOX773weHvxC67yALttFGxsvKyw1/vtoif/uFhCAVIWA0SMFAgYBY//s3w9w8XnTVtkYUXRRR13MLxGRKCDZqHFtR4yOqeKLQG7G9VoYtGZdjVxYyj92ltg+loWOtcxTGpEgt2F/gL+tHYpQkeRU1aGKoGr7L8oM5zcyd/98hjFOEEcxJbpI60j5dfmPurPz+OhSsvsVgDC9di1ONxfmNwoSpqpNVTyWLMPH/I3YJLRAkQzZTI0z+GgBesPGz80MaVyLNRU1gC8efgPZ8CPltVAfTK+BDCL151uUHaueP+Vw897AlI4HQVosyqxPTyvPEvHhr+kP+rFl9kIrdhTlLQT/Ov4PDwvtYKFWJ+37A2EzdKuWXmv3eypkipJog0v2fqUyRJaoQnBCpgLtPfiZhFFE+1rrTU4pgJ3/jFHx91Z3P4e99ET8BVOP5OngRaAv4iZEMvkMlCIwmNyNXVTAwLqLhEDAhICDYtroEAABAASURBVAuREDoYDODECA8uRH0Wa5MrRzD2gCgNplaCYPAKQqokA72e60DQST3IvVQcmwizCJtz32TDBhDICrkSFw4b9kJPkIhDs1EmyBqSGBt8CxEEga2ZMrqCFZ8SCEWP6DueF4PRqVBC4wBDOKFBu5Z4hOrHMEyVhZdYdNGNNlz/0jM/8J3P3XLVBRdut/nmiy+xFDNfuNi8f7bz5vzt4Jd+eYo+NL9nrU+dsdGXL5bT9/vK21e8aInx1QtPzrApNAZEqN79s77Uqdv/kg9eYM4Cq7761Wu/fo3VXr3yYgstKAIL/J0y7t/+pjeceNghqjpv3rwLrv3E3Hn4ZvHvo//2VwQcL7ARAaSIfzs+9Cy28ELXX3Deuuu+FQ9PPfXkYccf/9FPfMrd8fh/3VimT5t41TLLvOOtbzn1wL0/dtaHPn3phXffdO1PvnDzvXd+/muf+uSnr/jYpeectfoqq4DU7x78455HHXvFTbf87DcPXPqJT21/0OGf+eIX//SXh5946slHH3/8wYf+/Mtf3ve1u756+fXXv//8C444+dT9jzpq90MP32avfd6x4y5rb7XtGpts+ebtd99q/8P3POZkfI9xyrkXXnDVtZffcONNX/nmF7/9/W//5D6cBH7/0MOPP/X0cy++VAtKNYfW4PsftYgYd90DDz38le/96NrPfemSa69//4WXHPmBM3c56vgt9z98za13eOt2O+x8wIEnnnnOrZ/77P33/+qJp57qpl7JwFtgiNmLLMGiIA58J19+aTz3JXg/ht623npXnPnBhebMxtD89vyLLx1/wWU7H3TIb37/e/S86lUr77TB+tpjq0U4J0EBQBHe1dJ5j9BphRFYUiP1JSmyoRkx9n2ftLBK2MKY4OFZBN+bTtPaCDbirhI2ikyGsw/SQ8/WIwZCJVIWdqUiqXA2xVrIwFwF2696laiBVMo27JjMjoWMdzDtOEZIA9Vq7+M6/EGRTj0QPyRdjademHvOjXeeeNWtt37ju0LItqlyy5SYpUmjaaMRWPQFeyxL6ReePX2tFZdFzzfuu/+hJx+DzqbI5UiSgz5PPvPE3T/5JUZf98pXLLrgAhwifeL639yPFTqbWN3wtStj2uPPvnDbN37sppo5TQg3UymTumefe/bXf3oIWb8Qj6GHI++PJcaifWZSYyvFagf9UuuaqljlsVF1QlxU1spN4JaRchy5BQBXikLIlKwGWqY5uBFRTvMhxEjmsZbqUIOkzyl0QBfWGjP1LXsGUelL9BbgImwoRYgYzVgZ+bIXCiHkVscCRQ4lQVGJl0faUApWnJdYNYircS1qqDEiRdhwi1QuQqwUXqnihysLKncO4xhn7hNezhISkxdxEybhSrladmvdEiZ5hgti0KkOo54osBiBU8NDgiLIQ5zBJQ1CGrljZ3Ebbrx4qYNzeBfcV3iRSOVhrrKpGLSUGAoICQuNSHBp1OlC6pQdPjAR7Qhl05RQlMgkBca4Vu6DencvAK6nWoowUrpVHcQkZdg9Ky5h5SAK6agZS1OzkoZ6AVJMkcmZg/CjTDkbZ6LMJjkIvKIjK1CHQhpSJalkMBoNlxIwCoYfqPsEeaNYBaARog6iIElQ0zSGoxFom6SeqY8oU8AoVwgM3EIlDc7JEYShwh5iYOac3Rup8JfEwQ6iThjoOePUCt6wuyQiUWyFntw4KkMM95bgfqgCkrZMTFEhDpdBfKhp1BuXkJKEBoWMJwvDNqDsWcu07Cg1BLRR5ck004kxJwMYFA1xKwwTO07EXgsRMyeQzDk4pZ7afpAqiIvW3mDPxJ4F0NM4Uietp1aaRkG/FZngBOwhXFh1aB6EF9oGB6xJTKaUgS0drNEKWR8I11RUAh5qBuuZOWOlBbsrhFHmRJHJkBZai9alCVYRkramxlMS7Rua19bJkZWGASxBIOjAQkxDHYLVYcnDqjgMpwxXCLwh4QbBwb2nQorY8gwTefRMY2ZjHlbDM7iXOk4ltGSOlmrmQoNoWpqRSxs1iRPQwGnKsSaiYQdnF65ttVGBw7beCM4mroXV4LuscEepQQMLEsI6ckldI71CHq6OuKDKDtsrd0Idec+1S6VqieSoEgNO27GPgbRUCofoJooSituqGmoUCAUfOCSJJH2wVTVuSZScoh/MyTRcQQWWnZoqoglZDraUJnPDwjFMpqBUSTsGLO5IAhSl1L5ULAdOiYnZ6xC85iRrrLjckgsO29+VX7x7PJ4nbCADc5bQGuXrP7zvib89j1Vve/2qwl6oLLno7NctP7yE+sL3f/7Icy8VpV7dc1ALAdrfPfjkj383/HuP671m5RmjkZEGSShNjIAh+JMStTAvVyHiGlw4WKpmJ3YjMuPqVMU8zxsXIojKjWSQEIwCuVZsAoDR/MurlC6i79tScB4STxusvtr0tpns+hvu+n7fUe1ljIPwmAhhyfLpb9wDP08i66++KlVgYwiQ+aRUhlfro2kIeglj7wOeJzx/kKgngtXIIgy/lUOhwNQgJzFM84asZRcKCuEYLJMlKcnQMzWRWBVvO721nvo6pExfZ9WV50yfKGbXfvlbc3uqhKw7itzKRI5pfNv3/mVu16vw2163aiagUSxADcym6DH3TkPyxbqpjuGjBoczV04Vu9PQQwQpRgLezPBe+vsFPxQH6pWIKVoNZLG/U5Zw6K4sKyy+2CKzphPRp+++x8Ux1XuXEtOSZ6m/+ctw5MMokZEVH/BkIU4cgpztvXhhDKnDlRDP0RdHM+sFYUhVa+UaELiDnw+5ExtCpMoRZJgOUsisLKBbETEkQcQcEKIOx1KCB6kSrt6j84KcPrhzSCPRiDcEUti8SA0CNYmZEUkYYCTSrIHWwG7Dwc8gMhDgaAzAEVXNFti5IY4Jdh4Kx/K2ySrKBoL41bho79Z7Ie+NSqdkTcKERWbP3mSttS88/oTv3njt56++8qj99lr9tW9YaIEFMvKz20svv9x32Dh0t513XXmppV61+HKHv2vnL3300jOPOvGsx2a8ClSIuIElqL/3gb99+fv0f9cFePZ819ZvXON1YPjTn/zwtq/chZt/25559vlrbr5t1yOPWXu7XVfZcocNdt7roBNPvfJTt/z01799ce68gIX+1ZqZM6bdeN4H995ll5xS140/fOlH9z/htL89/7/8d29EtG3bWdNnLDRngcUWXWy1lV711je+fruN3vbpj13yhte+Fgwfe+LxMy76yNZ77vXhSy595pmnWWSxpZZZ+03rrLfuessts8wiCy00a+asiYlpIgJTWun7vps7b96LL7307HPPP/Pss4/++Y/33XfvN7/zrc/ccccNt37moquuOfPSy0447ZRDjj1m90MO2WbPvd+xw05v2nLr1d++yXLrrL/ceu/YYOe9T77wsl/+9ve1GrjPb8+98OKXv/Mvx55xzjt222/ld2zx9h12OuA973n/WWeee/mV137q05/70pe++y/f/cV99z71+KPPvfDCeIxzYEybNh2yvfENa+69806nn3zSLVdfecMlF2fsrUMinE91+Nxm8y2+dOMnP/mR85ZefPhKAV34IujOb92zye77fPrmmyYnJ1X1tausct5xx+VI+H6jZUuNcG6QwBA3FQojhIwJb9f7tqsZW2itI7x1T5Xg8CFEIsmSjpV7VkntCCSJ3BBVUicT14QwrhHDfpdz1VZI1EiDgtyZYXeGXCRSE7KEGJIJyDLIh9IQiJid2ZEpE1cXq1TcwwuCu+m1EZURS15kgQVfs9xSIPTlH/78a9//xbjDlqkeo5cn4ys/+tUDjwz/isvrVlxBJJJGctl38w3bnF6YO3nS1Tfd//jDk0goyp357x9+/D2X3vzCZD972mivTTeYrmlajWQSMUQW6BOTJ3amEF3n9a9ZfdklIuiGr33n6i989cXJea6sjUat9//xkd3Pu3KP86/83DfuKX1Xx+a1CpmIEzGTJgJ2ZM6iyiLCUM25Ym0OUw6V0MzRgBHSBjJYMICU3rjvKTqjWsnBOhG3OVCACqah9LDGAF8iZKTQAqI9GeB3cEtVk8eQI4kJTYmDBWyMm6i59jI1CkZEQaBNoS12FM1ZJFBWWFR3h1l8QixzHQzJmC5U1DvQFnDRQlILRe+NgasFwTEsMlnDyIlGZhTuuQx/kYlr1BRFmbg21UclGncNhw5GuRFKZsIWoRy5IR54AEBT6CKVUexIhajYQboSXk3dWioiXDn14Ok5Oyo3bykaEiUhJ/c+kL4TqQJ3pUzUEAlLFa1e1WsTlCQbbEIBqlAleqq1Ful7CVfGhRMWWwlIFjnHKPkEU6vcKLQtocGZ4OvYhSrFpFEZZ+oyAxsvrB01htlG0ygQc8APYkVUaEpV+0hjmImAHk0Zolqg1iEtrbgE+oLTJDMKrjFTT1gF0A0GUiahVFD5wwcolHPilIIb8hTEjB7P7OwklksMITuoUFQ6aUxaITivAMcurIej5AJq4vhgMWKyDOtQCAdnY+6lIsycGYZRZB9yJiCmVtXcza06ekKUBHOIQBitEvU4Gomq0rA5BucgRWgEYAoyZsmSk6ow3K7mCueKIIKjqYbiTXsD15MuyMMjPJFRoqnSMIQCdk9FxLJwI03ikVCT4ciVgALBlp3bOGys0k3lFA3Y24xgzGrmZlHCS1NtwmxEte1rQtVsHTEEhxBKo4YREZoo4Cva1zxZmkm4vzGwHUlt4JlOYUkhmzdOTYjIyHKqKYydnNCcDAQL1569utWoTtVRYUiphnvhrLlNoiDKQlOXSTHt4YbB8NNgmBE1CcLYiQuTIXIonI3h9ZmVEuzvOXCo6aUnL9SnfizV1bgy4UXPZIKGqhPUphCAHASDIUl1mmp2kqLU5yiO5kY12AGxcfTRm3dcC6QBR4IkCXgwfLTwuNSu62opmETgIzJA37IzhwuhUSBZCeSsER12CiqFopC6ihJhFKxQlfXGpClxmyAfNYmEidiJIBkzU8M+CtIqwM48WJsMR6Bg0PKgoR9zI8vCiy7y7g3eJMK//stjp19/6xPPPzce+NG8cff1H/zywlvvKuavWmLRrd70hhSUNKVRPmibt+WkTz3/4geuufnhJ5+BJJ64RL3/oYdPvvqWuV1ZaOb03TZaP4cIEYRy6SemDdyJ6M9PPh3CqkA6qkVAImWC+XWihiCjERbAcAB+1NLUlSVlwfdYgAewMGnDNBAmKGBSeoF7cqWm8nSXlZddbpM1X4Ohr9/76499/mvPvzjpFRmKX6zjT33r29fd/S8YestqK75+pZVCArYBauhBgzclxGkCkim8jdoSvppyjAxNDRaA1xDMpOYszFPKYeylrgehkcoo4Bnw4lRZLAlhgqYgJNC/MzGWOmwNcOKuc+TLsvQSS27xpjVgru/96oELb/7Ciy+PU85Jmnld+ew3f3jlnfd4xBtXXG79160s6qZw5MYHKcCWjMjAkNXl7/Sneo26nqJSGFxt6CHCCpkQRm2OwKSH2WSbAAAQAElEQVThAtmgwkMecsLZowehEbsOY0TCzI2Y5oUXXHSrtddgInx7du1nv/LCS3OLN8X15bndDXd850vf//n8+equXKtESRxC5jgWV7z6dIpQgoBKpGwMS1QbfJcIRZMlaFCCaoZoIZlZ4J2UQ5NLJqwrmnoVY2Qe8crkGAcnIRxDoBOoZvgMdMGdO/hmoO2oRpgDcgzvQTCSYGiQbtiUrFa2ksgI90xBwUZciGHSAAcCBlyZiyWaTDpXA7ltoEiDeDAfyVhSRUZoHbsEEo0awwDwgLGVSeo66sZGY9fSNPKGlV55xF77ff6SC7/+iRuuO/fcEw8/9K3rb7ji8stt+87tTt57d6cM7wpG0Oobv/PbZXvISvMWmfHC/pvQ1PXIBTff/LkvffHub/3gZ/f96vcP/PGRx//67HMvzZ1nhnVTM/7VRzXDK/9/t2HJv5r4H94iD3/gmPfmpsGMqz/16WdfwLt83P69RcQ9P/rJNvsf/IHzL/juD37wxKN/nvv0Yw/98Q93fuMbp1/4ke32PeCd+x1y/jXX//6h//ansLCyaSfed+TBpx515OzZw7uQr3/rm8d9+KJ5k2MM/e+3OTNn3HjJhXvsuON84vMJzp495+C99/7mjdd89mMX3vyRs7/3uZu/d/utX/7EtbdccenV551z8QdPO+WoIw7ZZ593b7PdOzbccM0137j6q1dacYUVXrHUUjgCzZo9p502cz6d/+jT+/FDf3zgE5+6cdt9DzzujHPwzckDDz925ac+8859DzromGNv/vwXHnjgtzYe/mLZPymkdmLOAguC/rJLL73Kiq9a+w2v32X77T980vG3XXXZdz936+evuvSMY4/c511br7TsK8667IpnnnwMC2fOmo1EgBu0b37ve3fd890nnnn2sb8+85s//unG27+482HvOeikUx9/fJi5xGKLn3HMez976WVrvebV0hojP8Ct4NoIFGxvnAJ7wyio6Z3NmInQBlevnnWsEx1PZEoTIo1oINhI3JGHh0TRhLVMXBsb576wBRFCCRMaQniI2agvLZXEvVrI4JNwEu6jmZRcSIjFlSr3fZQIbquOnDMiDrHdIss1miUFCw2rSLK000fTj9h+i6UXXuClcXn/tZ899MyPnXzFjcdfddMB51196nW3ze36pRaYufvb1sYS0lbyaJXllzt2h83bnP745DMHnXvl0Rd9/NTLbzrmouv2POfq3z361CinAzbfcM0VXkWVREgS46J/XMFaWUx4Ytr0k/fZaYXFF+6qX/nlb+5+xsXvveymEz522yHn37TbmVc/8vSzr1hkwVVfuaz01RHagdQBHbJaDk02EmpTyk2yLJbJU5AEp0Kt4a1sZsKb4wgNbkKQWtyJgX30SDkyL3SyxLgPfG3VGzaNFIHDFGqVIGlSy4068yTVsfcYYG5JEzZsFlYehnO0LTODurqIc6KKekg4VxToTI5MjARHMBtMV6k6gXah2tcyVbJJUsnqGpGI8YCPAnMNmzIlpizcEDP0AQd25MS2xKgiE5rkntogEchVrK3ERaMI9KKC5YWoJy9UULo1Fo0mzcwMYaqKUsoGt2Tw8JHCC5VViaGHJmpEW4UwoQPJmhJaD/KVqzGZkjDnEO7F0Yis9Yriykgc2GGj8MleK7EyKVWXPtqOqIfrmwVxMGGmDscpOAUZcQ9UnKILmjSg4iVbUa7CxDR1cYRSQA0phEKqElURg03rVJmEG2q7GPVpYqz4DkbgBsHVfGxWk3s7RQQ9XdPX6KADMygPLKijVqNpA9QhKbkFB6aLQ9CoTJ5aSq2qCAr7kUQS+FAYKkLtYprBEjDThEUybMB910SnWiX1yn1jPuHUEmMJSfWIsKCoQb2aQRXs1WSiRdTY4GPhxRgOMZqW8gSsAhXQm6RLarkOu77AU5LShMYEMDcAEfCNZvD1olK4DW7VG0AegxOYM4nQYDDJIi0HQoi6iJ484P9aPdeiNGbuQ6RAaCrVgL+PHCsoiIyIzbWyeOZoRppHmXNLkQneRgqNiCIVSZ4EvkMpPALmhH+hHMM0lhAKFmYRFAJgyn3PtcIglHvKhVqPhhmMekAQfQFNxgquuCkkY8K3HvCXVHqxwpXZxJwsW5+lUxwmGBAK1dKXyfG4HxepBtC98XFTulQ9seTEORGhZYqAt4l2bVNGqY68y7WDOomNtU/NmJquiOMgHCqNctt4SkEcXt0hd1RAIkBwbCjcoKyrezZnaE3aJeokqjHVJJEFZHoS6AQ3t95s0qVG5lCNYPJgdkBFEkRGjB/c5eTUUiRnqhylsIEcDXLjJuBlY8V3amIIsPDMvca8qH1BsJNLMskVAUO4gqXAeK6QGe7iPds4IAX0CCQOWEYz5RRZAKlJRxlCD7o4Fhf2jkoEUlakYIWLmO78jg03G/7nJ/yFH/16z9OvOObST5x8xY2HnXPlSVd+9oV548Vmzzhp121mz5qWRBtpM0+8ZbVVDtpig6z6s4ce3/esa47+yKdPuPwzh5x/7V7nXPH4sy9MHzVHbLfZK5dcyksgT4sFQnCxRecsOmsaBLjhm98/4rKbjr/isw8+8leCAxE1ge8R8KlCLTFyowIyEipmmE/AEKDmIGUgq4YZwsPA/B8mwcY8rQ4ECPpki6O32+b1y7+ielz99e/td+4Vp1z3meM+fuve511+3mfu7KutuvRiR+601cSMlpQMOU6d/nExIfA1OHnWaDLI0SAKDdeIk6SGtcUkhWWxEn43jEzkCYpGEg3PLuFKIVAZkV+QrGHhf4hbjTob+ImwQl28HWjjoHdusc7KK5jHbff85IBzLz35Y5845mM37n3m5Wfc+MXJviy76ILH7LTlaDTRSdMxfIApBqb4wW/B1bbUjpgFPUOTiliY6GNUBt8deoig4SBUUvvH4oiQCGWRqtGL2xCWzH8X1DMVJWdilp3e8bbVl13SI6768ncOPvvyE6++6fjrbtvj/Ks/+sVvt4BoioGwaChcqaoXLZWsGkJaTICEiQMOhEQgsYQoe05VGvh5aGZuEgIR8dJqN2q6li2X1FS80kA4gWsfwgrhLVnlagacqkoICSOrRwUvJ+ZIEk3xtndApOhxY++lFiSK7KwMq0QRglxUMB3YcQSxuxpxFcdF5up4wq8YfIBHHiP3FBWJfJiHOHRnZMVOpGYxELZQmzI5aBGHOSQsHlYVLUwAIealhReZs+F6bzxsr12uv+C0W6+59PRDD5jWzuSkkbkmeu4zXxt/9jtA8kWNM5on9/vO557Ijsf06z9/+tRzDznx5B0OPOSde+239T4HbLPvQdvtd9A+x574+NRfXcKcf7a7v/+jbfc76N9t7zn9nPjnvP/pzaqvWn7zd2yEKX/+y59vv+sbuPln+9lv/3Dwiaf85eHhL0uMJqatsvLKa62xxnLLLZ8nZmBOuP3xwQcuvvLKd+9/yFlXXv+35/7b1yZNbvbbZftrLzgPX25g5je++Y0bb78DN/8lbdaM6Wcee9TtV19x9ikn7b/X3mefcvLt115+0sH7zp45dd5AUBJPnzZt+Vcs/YbVVt10g7e+e8stDtljl1MOO/Di046/4fyzvnDFxV+8/tovXn/VF667+gsfv+L2ay7/3FUf/ezVl3/6souvOOfMD5968olHHXXIfvvvu8ce27x7py0222yTDd6y/tpvWnLpZYi5L/1nvvSldx1w8Dv3PuD0Cy8EYtBIcrvcsstvuM7au26//cnvOeryc8687YpLP3f1x26/+mNfuPbKL1x/zRevv/qzV1123onv3e3d27721SvPmI6cyIR8QvSpO77829/eDyLrv+Utn/3YRy75wPtev/rqeHzppRcvvvrqbfY9EG3rvQ848cyz7r3vvqgFSWGnbba99dLzd95yk2a6UGORwyAYiYeT13APVpFGURpKDCGEDIqYZHKiSnggwQxlqGOIB3FEVbjV4u4Uwp4IMSpBmEGJGIErzo6ISR5iSrXx2sawk0xlFrxNcEPykNZomjJeogm5kYMehSFJRKaqICGi0jSahQMRjVRVKAUEldVeufQlR+yx9qtf6RG/eeTJr//kF1/9yX2/+vNjyrzuq1c494BdkBCDLJiDhTnt+PZ1z9jnXSssvshLk90P7//D3T++7we/eRB581VLLHLG3tvv8vb13aVggUZA7ikhASlaDPciEJ142SWXPPvQPd/x+lVRqP3pqWfv/umvvvqjX/749w9R0BZrvua8g/dYYbElAFlWZqWOtKtaI7kQ1NEMhKbe6pqyJFZ2jsBQCENOrQTOxhoOKSpkaXrs5sgV5EM6R52H7dgLUqZ7JBIh0G6NR8KKU0FrJtVAUhpSJeVhRsqOIikJagRXwQohGMgBNQdBqBKjnkbGjYGEpIhggxWgMhMxe1iPzWmojAw1eyOlka6BVaBBz9FbuGXWViShRMD2nNWZYCgUORJQVxpG0xwqBn7uzCXLgIeCPIOwUMIzbNhRrYmQ8jkpKxEMzsQwyqATFiJFggop8TCSVDSpwhGsoZKkijhBZwL7IEwHN+xbA5SkHkJESi4Ct8dEYSfGbPxGuq7J4VrD5ubqwqhGAS8TNhduwjNbNGEKj248stUI77gWNlIgpNTDa8MsBgUlRDzhM4KGfZM5lB02Zq40lNmVUNYpoxJpRr3w2H3shQN7ByeGkBR4yIP8kCeicWpKbcyUoVliTuSCDQMdjr4woj5Z31ppwwVLg6tpVFGD2oMlg6omyzoIyx6M19YOKxBpEBlZ5WBiFocI2PZMmJnI8R8bG0lVMRCQbJSDkAVCeuLCxEkyq2B1TxjgFG22VmqqRl2FLws0TWYJlZSHOxAppD3ljtoORqtNHvZvMKlMHsmpjZhIpDpgWWrngeqTCQ7WK2EOO7cejULEStYNjkoQQ4KESAbVCIbxkggWFFQcWZlFE+Y7xHGUiRmzCOVXEhYKDVJXHHhzFhAAsM6eOJIKJQgn8MfgMA+4pgxHCUuRiLITwwkoPIRrjpoCl5i3PeyKMSMzd2hGoMRKkcQSqLmlElyjmliR2gksyUTY3SENF5QgoObSMspbGoi6xgByO4jTWMllkFG5V3yPpL1LOAHhlgMKQJdKUcgqoxtmIWKUIIwvwYCVIGd6zhizIYsYU0BgthIWYK7JGdTVAj4SEB1gA7jEIimZtqaQhJwDllZz2BiGMRl8C26niZVxAXkNMuGS3NmIK25dy4B0KBwkubKxRmTnppIaQX38BAe6mcOJjLxH49q794F+yIlTolQhl8pewV8kROjv16AqYyEpEcQYhpAUtT151x3223SDOdOn4aTxnZ/ff/ePf4HdoUm6/uornnfYbmustLSTE4vgo7JKu/dWbzt+1y2WWmjOc3Mnv/erP3ztR7/+0e8eqmavW36ps/fbcZu3rKU6CBjVqa8QjUWP2nGzaW2D7wru+eXv7/rJ/S+PMaYsxtQxI8pcs2gS3BNXRpgM+EHsgG9D7YFcMeksB7n9kwAAEABJREFU9YAW/UMLZqJBFdIGrkFigHmBdtoFB+69/VvXAruH/op95/5v3nv/Hx7/Kx63fNNrzj9yjxWWXlwTkSirSMLygZQNngufECsYktQQNaDOwxgRC3wA9yoKGRN7w6Y0deEQDWmNyIWYMXFoKUjc3Kx4qP59JtZLsJJkloSpLKIxffbEufvtscuG68yYaB9++rm7fvrrr/34F799+ImJJm/8hlUvPHzPlZZaqpIWwjbBws7sU2xBnyQQLZRFk8j8TgerYPUMRwS7+Z1BYQH9EHjzO4iZJIUO/Nsgwemi4EzJcFjCZQLlAK2JxEILzX7/Abuss/Ly6P/Tk89882e/vuveX/z5r89uvuZqe7x9XXQSU9KkJl64+qCzEzJDCk4DuUo+5eBOxJlSpkY5D35ZtSJsm4ZV4NAUUVV61p6NqEKxXESLahUVeEZlqWQWvSNqGTyJIW30RtU1mTTWYMMRxDMNo8xmSIgYLsQOyJ2oEIELspqEgT4A8wzZ8VtBW+qggPeZrE0Eb0JeHE9ol7TzQnVMvZMRB2M1YsFIJBo1IsdT34qPOI1AsDAPkVLFgisFmmMfETF0sxvz9Inp05pRAtvWraXnf/GbZ866gQwC0rULT/56onbd+OYFxxAWbYfn2ikUqdb6wvPPPvLoI7978MFvfu9fbv3K3Rj91+2Or939x4ce+nfbt3/0U/5XUz38meeex1nib8+/MNw8/+Jw/9wLf3v+xedeeHHXbbZuRxOYfvn1nyy14gZt3HXHnnH2iy8N37G8c+NNvnLttZ/72KWf+shZd1x54TdvuvLis8/ecuONZ039KwLPv/DC5ddctesxJ93/h+EPgGLtVOM3r7E6qnbcm9UbPveFv/33X9eg/3+p/eGRxy+45oYTzrngmptvfeHluSutsNwe2239gSMO2mO7d6603HIi+j+l9k8wmBgZO82YNn3RBRdYevHFV15+ude9eqW1X7/GW9dea6uN3rbbtu88fI+dTzlkvw8ddchlJx111Rnv+/gF533i4vPu/Pjll5z+oVcsuSS4PP/8c3NffA43OTc7brPNF6752B0fv/yGj5x/7onHHLr7Tu/c6G1vfuMb37Dqq1dcbtmll1hs4TmzR+2I4KF/b/TPa3JyfN0tn8HjnNlzPnTkoSsuveQm673pmgvPO/moo2bPWSDc8f3Jk08+MZ4cvqsBr/XWWefqC84969ijllxy8dqw49tJJopcrRm+qYVfcg2qYYbuoZ+bnikY24tzEm6DW7xHsi5FT1SCSglji8aHoO3EJokLCRajQGu15qak7COn1AVKDSO2hMzmTKzcpKwkED4ihSOzEgtJotQaQ93GArmrKhUNQXPC1mVETKDPEY60UWn4NGwJ/UrLLnr50XvedMohJ+261fZvW+vdb1/rvTttdvWJB595+D6rvXIZbMDFzUpxNwpKljZfc41PnnDg5UfutddGb333em/aa+P1PnbEXtccc+hb11yzOvdRxuwdEZgg9Vx+1B5fPveYU/Z7l7urpeSqULPS8osuce7Bu9140oEn7rzZuzdc611vW+f4nba48bQjPrjvrissvFgT2oqOMi5CAuoJe0Rljsyc8IvIIAv25Sw8CkE1FDVVAIkGLR26meFd7bigpEECagg4CSAiVhJxABQ9mzErJ2UmrYWjCylVo8vc49SSAJYYRlgosQvSoPVUayhFlmAKIDKVoioj6xByKzXDNgD6LuSZNA1Wb0gEardW09h47FRDoIoJoTKj7JL7ACSDG0DCzKTiSTvVsdBYpLCYNxwNdo8ckcgVFLAM8zjDV5Rrdoa0miB14xVfLztOVAEBFEIFhRXBSckcv/vB7swmQKo2VNXxwM5u2IIc6TxwmwNMjKW6ViJnIwcRZichyKcOPBKFCrWtSquRo0hfUPlxHUXFK7sBoDYIBjMUnZKrYLII+1BNZhbcErFlsVGKnJhbqhN9x12hPjiI1Kkp4MCcojZsrISdolfvuBY8EjU9TQusY3aF0lBImJkEPkrD1QTnIHhFKsYwdQ12G1WbZj7RRdvzqDD1yYrAM4lKTl1WeEElq5WQiasVp57YyIGNwn49SddGabvS1HBOXWo6T30nped+EFA78bA6Cm+gIfTKkoQUqIlmlkZCFbrhhT+aJ+mwtxUETD+UbZSJGhqEFoI20Yd1XntUNRomZCQWToY6u2THzCB3QpHBHA1Ty8HRwwZ9Kn2iXqga9DBCNwuFt71N9JYqkWWORCwSGFPuWwjP8D8mUobNOJz7yr0zhbIDQ2JTisZc+qK1EDoz9C8iVQKmguQi4mjElWCNnuFQ1S24Dq9xAy8gkSoduYw74+pQMBC7DYKvkcqD+tVzQbLL7IO4opQgY5hHBCUCfA0WKFcIBe/FeRDcMlGD+WxGiXnEBKPD9n0vpROvgUghIxLOKhhNRBpO7uFug6RkTlzgzn1KVQBaDD5B3FeKniWEkV5HRsTJ4LmtJGU8ZjJhqwOF1vNowBN0IRmWwVc8S99o33DO0mRNiSUHN2A+cGv7MlGsISZKpRAcDZZGFaaURFtNjaqIsqqmSAmm7AjwBEFswhISlTalUdJGRBhCond+wxwLgiEJWWvAOldUgKiTPMGxYb0E0/W1eCnaedtpNrHFZs+8/vjDPnfGCTtt8tZGuIGIBotIpGDgRTTRjg5552Y3HX/YOfvgPdS6737bOke8e9NrT9zv7EN2eM1yiwcs7Nl6rp0Rvspnl6zv3PAN177voAsO23X3TdbZccM3HrjlelcfvdfH3rvPhmuskpMzB8QKKgYLMRPxBmu99rKj9tlj4w223/DNR26/yRILLRyuU9MGSWsqkktue809RxfWH7bjNp8754SrTz2wiHu1qBHGXquX8RHbbXznOcd8+tQjRy2CgkBewZGEIygqwnT6grOO33v7G0879EN7bLfjW9+84wZrnbTLZp88+aDT99thmcVmKfcUwabJ2gVnzLryxANuP/3ogzZ/O3XuY67IBoUaWCHxETtt/ZUPnfCVM46RYAdlArYckchl9eWWufPs47/84fe86bXLSXS1loq8nzphVyMVWI4TF+Zu563W++xZR95y8kGz22i0ZgbfUe4n1BJJJbU5C848ccdtP3XiEafv/a4d3r7uu9Zf55jtt7z2hIPP2G+3FRdfMjgVxGJF5GBtXeOVS9155vF3fvCEN79qZSSx4JIidnnHel8555gbTz0QtVC4YF9inljn1St//szjP3/OsautuFRQB/G5+NkH7vSlc4646NAdRlkZZDOVCSothPbN1l7zi+8/4XMfOGH5pZYIQoBWCAojLrvUIh/B1n/UPvttuv5Ob1vrPe/e6Jrj9zn1wB1mTf3DnvDOtsmVaiARjesQ9qqccpOlJRbY1VIfSkTg10BnNZk/GRlhSDjZQgrDth3RmA10StA4AF1j0hCUEQoBKYBOrszBiuAjhU8Yh3tONqGRLHBo7wv11DgnVSF3DOOXBEEAzxw5U8OZa8KxRYwV2KZEWU0VhiBi4lBolNklBWXyRpA9YNsqnYmDUlttwqECMlROmGwUbD5MZoZY8A1EeGLsAk0NQWqDXOZWvBYvgddbtRVMh7BSXv7jw08eeG595gViXuSYnc797u3nnvK+t711/V8unB4eIdBp1cl02DpbHnXAvrtv/+7NN9porTfiBYBS+MVXXb3p3gfvfexJJ3/4gguuuf6Kz975ne8Pf6hx2rTpq6z6mn+2WbOGP3DVvfTcs8+/SP+4/vq359beats3bbXNm7bcBjdrb7n1cL/VNrjB415HHtl3Y8x98snH9zvulPedf9H5V1x14kWX//HBB9C5xUYbnXfSCa9catmZ7YyJZuacGTOWWHLJjTbc4LzTPvDNT91y7IEHzZ41KyLu/+XP937PMb/47fCXvLFqflt9pVftvONOuP/LXx76yS9/jZv/RPOIT37uC9vstd9FV1xx0223feD8i3Y8/L1/fPTx/wSp/+kSJkKjf1y4/3tLqUm5eeLpZ2bOnjN/dNVVVjvxPUd/9/abzz/l+NevtuqCs2erpqkhBtRXfOrWD110yannXXTlbXc88ddnAM7U0P/48b1773v6r0+hd9N3vH2ppZfx4a9m51kzZuy/045fuuqqbTbbDDZd983r7rjN1mcef+ydn/r4lRedse5ab8gs8POYyo9enB07FzqQqhAK3AnB0QkBQGSR4LQc2KJtQmIiFRzsGT6WsZP0hvxFtSPHph+hPESY6xjVlzjlnlBHwcHFyCo+uJD3brVahCHx5hyJWSC8BAKEKlOHzKfhoCMkKiknSZjThomHG3fBHYdjGwpVUsG+F9ZZ7at5CZdGV3nVMjtust4pe21z6m5b7bLJW1ddcYXpMyZqq8OrVXOpNbkrBVUnjzkzJtZ73apHbr/NSTu/66jtNl13tZVmzp7uVCMXbw2VI7CpRgjQJRaZtfRiCyw4fYaMRU2UkVdK8j5V5EB5zfKv2H6TdY7fc6sT9tp6x83fttoKS4+aSNRnqplDkyZGRjKNjqVnRuhHCeqDIHMFO3YSU4GSji6oCEwCeYpq35sZUipkEBYmJWc8BhEP9y1gLlmrMgAlwprCFWaJIJEkWMAhEcj0QYGNkM0FW1E18wgo5iAjgSrUGeMiMEFxwoWMZWpMwCqpoQFrUVJl8K/h8EfxRCZcOYnlRIOtwIoMOgWmCkw6NYsVxKpYb971UcyreqSamn6CAzCJNz7oA95BUFJFRUBcioCWFcZNcmKGfQk8Yb+oyLui1SmcBymhl4U5WQAVkyFLRxUOJYkB2jBxaAWqYRkbbgUzVHsh0ETYRUMVs52Laa2N9k3UZKQOGAurGZFRdlayHN6ocxMG60pjrPAFZypoQg4NoJQ1XtUx1kETjPel9drihmAF76Mz7pmqRE0cwpHJ2xgEJYLDZDJlsITOhNpSiTQYc+E5hh+oWWuUSkGoYpqgtkI8rQEtiBsiHnAohMudw4FSYC5yi2INNhSPYf9TZmhXRwoZe2heckUVDsbJTOAlzD3wMZJE0opk0oa1EWVRCkgJn2bNgguR671RZ1AOkgf2sCkROpdJ5kmOClxg84BxQK4SB+xG4IKSyaAZBYercm6EmRweDG+pPXWF+6p4T+Fgljk1puCF1pSYCE/JGebIFMgRjONi4gpNK4uJSjJOTmQ1hhDCB4yOZmScq2RXEgb2NviA4zIjByKMJUxOwYIJEgyTWuBeRRWYSURGE0qMBWRQhjHs0rsXGAFe0gKVVj1Dci6c+ozXoVRbDLtRcETj1CBYSSK1nCdSHqWcCVGWxENahhEVchBRQP5qPi7cuToEzoRBSo4IIsURi5gFLhgqJUkhqdWL1y6FJSKR5CDJnmoo1JYmNRM5j0SHrkRVMYcSsRhqkiYTFIQAlYkJnclz4w1KomgIQVFJyBgigZypTVEkgv8FAXe8HaGwAQ0eoGOkppbAPVKSzKoFqZ0EfDEbmc8Z0lI2OMoAABAASURBVLEKVJTBSolCACjhYqltU3OuRMglldzEWaEzJXZmjzDGjgM3IPgFQ2apyEFLLTxz6SUWXGBG2wSLoazLhXJRjmwAK9Q50dKLL7jVW95w4h5bnbTX1rtvvf6rV1hyZpMyk8JVQqpZtWK1krmyMPHsWdPf8qZVj951m1P22PrIrTda99UrzRnNBt4QgRLIWwzbBPippokRt6uv8Mojd9zq5L2223WT9RaYNYPImdw9HBIbkQ3GhwKOq9ZZE9OXWmzhJRdZzFKuDPCYphZ0EjPmtMssPnPphWdACECqadg4KtVOuM8O/AW2UXnV0ku/c723nLDTu0/badvd3rHuq1+xRDNqgCZYMI4qRh6arFlq1kLLLLjorGnToyoizwNeBbQLc7fg7GnLLLbwKxZaWAqr1Yjea8WvUBq1aRhabKGJkVYanArQFMSkFzNm7GwsGd4QPnP69CUWXWyJRRcUJeS7gL16Rk3ejKkpnAFCMp3mK7xiwXdu8Objdt/uuN3ftdNGG66y9DLTc4MFVRiJJnGfoieHw7VLL7LQUgstnBIcqARXZ58xbWLphRdcaoFZCK1K7HCXVqbNnPaqJRZ+5WILTjQC5IyMaiw2Y/byiyy8xMKzJUskMThqIkoqJjNT+4qFF3zFwgs3zFQr4KnqMQX6tDzx5pVfffQ2W5y6y9b7brbu61+1NHbP3z7yBAZnjdpFF1wQcpAAWmOJrAExGk2D6yWORKZap9AQRI4yPrCQqGAyISM5vNZ5gNhJusBJuDM3uDHDPkIxmNRspD4tUavJUx5nmpeiBlEQq1ACSONEY/GSOwKcTU4yUlIiNyYj6Mgi3nAISYKgU28PCA5HEkwOR3JYmFqCkRh4942PJ8Ka4ARjC/davTFLlJxTxe43aAwXYSFNsI10GZ8glIkmNLVNikZRWIXmgMHNa3XU/0XFmKfYlqef/+tBF8TDfyWidss3L3T8XovPmbPLxptd/8Gzv/iZzyxzwt6cFUMb//KJg3fe6+Sj33PBGSfedNnZO223DTrL5Mv3//oXd3/725/4zG0XXXHlGWef/sKLw1Fk1+3f9ZUbPnbX9ZffdcNlX7/+I/vssiMmo5187vkXf+JmtBu/cOcXv3FP968ufFsy/+nvN0AfAYA1RN/87j0fv/nWj1xz3W233YKQnDljxqF77ZYmZB73UKUj6QGlp8SURjJnkdmH77XXlz5+3bprrYXVTzz11L7vPe4PDw1/VAyP89vOW2w63ER87Z5/mTs59invGnr+v/75xvd/fMoFF7/44vMsOmPmLKz77W9+/YELPtJ1Pe7/V9v/6vyI+M5Pf771vgefffHFv/ntb+Yv/81v7581Y/rSiy0uIvN7/vl56XWfPOPCi6666dPX33Lr6eecvdEue976pTtB5J8T5t+8NHfeWRdfgvuU0nabvCM1GrlRzYq38F2/2CKLn3/KCV/8+GWfvOTDF5x0zJ7v3GSFJRdNQj74Uy9DsAuipRaHW6WGmuysVCiVGJlkI44gCnh309ZRsqSVxIbQaERGQg2VVmoottLcdxzESV2lH2h0LXetBW4jJt0rIbJ9oF+I+vAahg8OlO7MkJ9RIfOwe/UUHXExoklrC7ZkkcTBCEatwV0CJYvCEg3TiBvlppA6V4lu5DbqWDCILqop9W07TqNKKAmE3aEJBXJIoyLYvdhpEMOdjVvXRpJkxYd4yiU11mRDJcI1HMJlHqaZp5LFlBCUYpFcUwXW7DgpRa2pQh2qysNuEtk8G/Y+yIrFUTk7TTC3YQl53rDRBhIlRXGvhiciAUNXH+RqCPapgh2lekN1gociNwXnSrl4ChdCzZCNs00k5MlegKqiG7IBrGAsyD5qmJk6onliaGMrpQzBx4O7QQEIAPVCg1uC8uSJkZyqU6eGNFYJaZ+iWqCrOMALRKwSaxBHbZhajhFbw+hpAk+cRThg4s7EKtJVYS+Ynciz94RyK8Zj7nodR9vD17SK9grlQb0Ew0vM3KkMajU0IZJdpJIbiw8WSqrMyVWEG6VEpBiI7ICezbKhS4WUFENCznD1IA8xCsdH47B9cg2jvgbEsGgwGhAggKCpERoYWtWhH+rWlEgom02jMpG7ZqKnNqrADm3vatWFS9P2IaWr3JkOorc559SwC41JCuQwZji0exC8PqIWNoNtlQgDphaQXBzYjrCpU1MjVwMHwsWE+tZzQAgCkICILYIqU6dUUtTslHoRTOCKMM3TjKZVGVluGAUiZ+UUWWAmzsKWos82ZnMJbbxtoZOrEwT1EkQsmEQkvShHarxwTCJDoEn11rmJzIMkThCiEpuRVTZOTpAgEKCkXYqqHcVYYMtOrGTL00oeVdATmIa0FZnmCt7ct1EmIuBAwVo1FVUQIYgyad45lepaJVMjMq3ntkpwjgQZlRlSF00VjkSjnkZlEA7dJOxOESSN5CaUgXR1HHuGQn9MPpncVETaxHnAI2DpHMzClQMs4exCfWM4WLJoNqS0xoSisTRkiZDBH6SNNI2jTZXIioVXik4Cn44FBTV/q0ktUS1Uqzg1jvoxReV+7OPO4AMMZZtIQurI2m32dmSpNcI1FupaqtO4maAMaKnCeX1QqzcyKEfsKpxJhFgtWvY2lVZLiZgsBPKIwGBJBPKs7OIcMD38QLgVagAeR691SFcUArAowJdYiJgZz1gwdJmigxrD7ygMNyDkbeEqWgnMabgsy7hhRFgyzn1oMEh6RCFBKjZh3JgoGOcJzeCtoFydqhG5uZW+H2LHCDQIeIImJ8q5zyDoQYW492bMOo+5g1xdEgALPuosNMGEvC8d3EFqTi6gXAz2IAICEQwKQpa4yzwOsV61F4G2ghWDoGDLEkkzPBuTpYZACK5QkbMmCWbkeADgbfHcwZtKWMGrBimhTkoq+JKLNQG4JutoFG3DKYhqkNcYMomkinNXk6pwT9Rld3ybBbdzr2PkjOwMT+tHqaaAKCDleaLT1EcE5I0uacfiOCiMqUJRfMtFbWqCEzhQlIYiU3imsfpkHXe192SdmkcR6oTMiKo6J0+ZWKE0IB609ALx2LvI80IRxpEKT9RQMZNatY9U3bxUhkcBTiJnDyzqh87B56lKGIkTUyZCsE13WKOmcSUsAQZi0vQJtCM6QpKRXsMFA4lJKBlxgAaZhEC6xjkj9rkPNy4m+IwQRVC7eM/ucD5mpHzKXBX2AyFj6ZlBqGVqGUZlt+CoI/ZprNM8caXK5KOam4IX/ZmFREKrcw1MrR6PP/XMmdd++ncPP+JkTlHx9dY4c9/+9Jd//NbPhlLtrauv3E6fFrn1hqMF7qQRUJiDQUsSNckgTsbuw/DHCYefcNulZI2FdAR2JrkIM0Vr3gbkpsLcS3iGewu8iEmTYybmqA8qoy+AeA+dKaBgBHuIEEsEOioRfuHHa64GX2GvrAZUMQkLCVd17p2LD1MZvBPAq0JdMuMSlagnhB9xnxtJjbaZBYpJqlAOVMAUGDGFwSHICOQK9lqHPaC4YSAoB7UhjUgm2IgYLuYEv8ToOPzRU68f/+wPRDRtndWWPuMwiBUapEWUFll4wVUP2m76usNfUfD7//zc3T9iUg/sMaPD9tpjo/Xe+rrVVl1hueXnLLAQRKN/XG3bvn3dt1jNXtVc+5ClFl9k/uCXvn7XeZdcjHbi6ad/8Lxz53cutsRSb99ky7dv/N/aq1d77fyhf/dz2VeuuOrKy8I7LNc+9b0CqYBYExWaGqVStCy19CKXnf3+jddfDxSeevrpfY857l/uvc/c8Yi2wtJLzl5gQdx89a6v73jw4Tff8WXcT4673/350c/e+fUzrvrE355/ET3/UXvx5bnvO/dc7yZTyvvuvvvNl1+64oorYvL3fvSj793793/YAY//59oPfvmbw05+358eGv5U20ILLrjcssuqJrA785KP/vTXv8XNv25d19/93e+hh0V5atoLLzz3gQsu/sXvhu+m0D+/AZzzr/74H//8Zzxu9I6N13jtanVwKnIlBDnis3pnhmBMzhrwVIELRVspF4bvBlkg1DRNsQhELdweHYkis8E6VAP+3HTeGCcSITGi3rgg+iMLqgfCJyOXEw9BI0E8Iprulq0ys0dGFA67RonAauWMWE6cGWNEyHkG8cKDcKkxVyE0kAqbSiKKjOvGbpjj0HbQCkkwd2WYqCAh1VqjBvldyTiQsiq2ATEjqxpoIeEchfAWQ7GxCOfELOAHlKQKtiWjWqVOCcrmxLlqY6IQ2BUK53CBuJHNchSNYEjTWV8qBFIGKoEyHvtWkAO2RMXYK5MR3J3zQIPFENQuYS0hoZlGRXIwtZoDz5gepRg2NeVgxW6g1TX6URQRS8ghLaVU1UwBvmHPpda4KYzZmIdMDMo8hWRJ4kklKdSMrD02W0oOzk7kHtZ7X7AvEbjkLCxaHFYKDcs+fKojSXtEVHYbVMO+IcVqT10Xw8IejoTXrlibfSAOEYbm0I+CrR+wDDBjdx5g7RmmIzLsVZmReikiBQgzoRqIXmtBVsQDRBFWNgV6FRaxwExiFhYqyj2sSywh2BDIWBxFGEcjrmzqFUe3TKzCrlQbMhR+jTLD9piZe0cjUHVXjTZLQ6AakIywCSgsX4tHFzxmbCaZwTVqEJYRAoEaikQgTlMyCjMTw/WE3KWvqbemUmLM8bDiNibpsAMHsxsEqKwdkniQaFXs2VGhAocobJ2iaFQCbsHwUMccySm3GsolaFwJVxAjP0Jxz1QaiZSYlEkCnwC5L2HYCQvEblkTO0tPbAybM4dS1YAcaniGQWErJSI8FK7BPRPmQ5+GqVE4GRmFlGiLpGAh2KbpLNUOqtVK/Vhqid6jxLDaqtQ+SgEDOE/1JjxRlTDrwwbSAkuQZE5qKsCCoIskPFJiEaQ2BghKplQoKgf0RSOGpgHUCc7dg2JKoTkYcTeIx/h6oi1t9pxMqARVQgKpiQpODdyQZ0wErAJNamr6nLqGcaCDZkbsrIP1GtVWKWNKGByBSBLooVk2ZwjhHC4AB4SSFa2WHJYHIiABYb2q9KI9i8kAFDMBa86KHR/GCa6ViptRDa2AISuKqITkBztTRaQjoqRHKMA9vIIQwWqAhwSiEMHkgQ+uUFakDO4Xgs+MOOior8UCMoYm0oFbRLGBsDdsrcNqnEvSwnAi5+JIwtlT4Lejk5yYjbWGeFQVB8sKb+gYta2XZJW9khUuFUAGppNzYZ0U6qTSIJ9wUQooztRmRhebgmMi0UYzcgNRmCfQg5sQwTzQmF3guUlJpar32YpyodThwTDFnOCQA30acIDwxmDXSM6aBOygCKJyIGQhHbYwUqgWBd7JHYJAKmdTDdJg6shRqJODfuQxcQkRJDSzMOSBUpUKqQFtMSR7bQJZljgLkSqhhxHTVimcsnjS3rxzN47EodZLrUGBO9IKaUPI1RFT4CMW7AqflGpsEZBFaMgiSaUOCrtD2QykABxKXgkuRQoCh5TcRhEZImh5a0R/AAAQAElEQVQCTchqgdnMEcS9ae2zVIHE7E2KpFyYxsE9sUcLy8NddGxUcUQYV7ZChBTWhTr62AE/ScueCWoPgksF5oxamaAUVQ1IETYSaZIkUvCRMjiye0/k8HAJaONcO4J+4RywPMGZoo0YxfBvfwgxHHo8OB5M0vcMzLmSWnXrbVyjUAoWRgyy4Ra2orDoeyp91B6CWKbBJz0gsLIzs0jDlMRdrI8wSF+TUmbWEDx1AbkHDetgAnEiq/AnYw4WqkI9p+JSSCuzMQUz+Lc81NbEXMz7ct0dd93+/fv2v+jaM2++/Ru/+NVPfv+ne+7/w0W3ffHEa295aXI8a9po383f3gg3I2kytTqQso7LOPDSvYefGSssQAO04Qp79ka9cmQlCkI0FfgCkSfixjRZklZlQlIjiZlgYmDtnDwJMfAg+Hk2wmsLcXHiwI8FhBfKlHM0TYhGUBTgUVMgN8Fd4JBm0lkqoMkD1cBKRaIwr5X7SA64sYKxZYxL4IzC7ngnRsRVcmjKSbKwhHBHUjwTc1JjBWf4PaVSc7UcQJDCGYQDgnGQMKlyTojUtpEEnYAHoH726s+Pbx3+Ar0stsCi5x4hiywQTFAEklcAD+4TaaFDtuQmk/nzl90GkrApCp1XLL7YdeedfevFH7nlYxfdfv2lX/rkZbdcefFFp7/vxPcccdDe+6250qrYrEvvZubMyy69VEJZSf/+td7aa33ig8de//7jr3v/cTd88Jgb3v/e0w4+4N+fSrT2mmsessv2wkAAOJOKTKExIMdwIENElkql537mrOaMU45+09T/7fGhhx/Z973HHnLKB7/+g3snuy43eeEFh4PKSy+/9Iv77//6d+459pyLttrnwHftf+BR73v/lddceePtX4j/QIJidv5V1z76+BMY33TjTU/ab9/XrLziCYcdjEe8zbnyUzeXiqjG0/91e3nuvJ/99g9oL7z08v/17H/MePb5F049+8PPP/sMOrbceNPPXnHx7VdddMR++4jo3Befv/KmmyP+O9nv/f0fn3pmmPy29de//eMf22/33UUEin/yjq9i5gsvzX3w4cd+8Mvfnn/1dZ+45Vb0LLr4UiccuJc0ZPAxLvDQmsMaYvhfeO4rnBSJkBIhNNSxwbUk2LS4MkdSyTmYqyFnErtg/06MzblHeA8G8mGXQ8xxIhQjnXDvbE6ExTULIo+k0YCPR8OmBCj7FN4G2DQRKWpQdVdCNuUR5SSah+JFgqUEpvugu3MM0go4YAcbSzJiIQsaV++iOjmFELIVSAthRRATgsV6IkR+NJiOdGDah1pwdeQ0U6qNm1it7r1IVSHBuoDL1TAilAAlCVWswtrqXMmR4kNqE/1EKS2TtqnifYmAOaodlyq1U7grIl7YExlCnQuTM0siEGucBOg4YjJHYDQZE+jCwshi1fFaDlmGJRxHH6QuDWXKbmrOPtwGKQ3mQDxbFkqoBXIm4uiNq4UEYxUemaApay/Uj2oVh9bFk1lWbhIqNuOAnh3BJgwH8MYAIbmLVTVmBhdNHmno1hpK7MIWMlTP4M941F7xnriEdeI1R4W1QgsN6ZIYM8dEnZDnIIHwNeBC1SKYoIJwOFtRCqJkeCL8pjYBUMhHGeBVo865KFwOvhFNGEsN6R1TDXeKXRQmchx+W6bBh83D3SOCBGRZAHxHSKR4ABpKSOBO2CRQQrJzBLtzG9QYwMQyB2LK1nJqeJRYhSGrs5tz7RMjYrwhVQgH5YiEhohRYhFn8cjmOM/lgJkqKZECu1xDHKNMQuQuUN/dotToHPUSd5SC4BaSpTSpUwXgTaMtUrqqF6FOBOwZYhuFsHBqKcEuVKIQETNnZ47cc1OsgXEGZo1m/BcksLBF8eRC0D0HJ3eiLqgQV4lgMo2qCIAQH7qICaJHWK1ugA1SNyEjhzxZAAieA3WuDF8cOZnkKk04ixcS5FG8Qik9KgbqGCUA9wFA2NkMIAjVDAsaFfiqgHgIcYrMwB8R1vPwx7gI7kiBCxNQ3LY1JXhwUfAgIqSCSuwADG6fkhJnbO0NAOAYAMYMxjBUyzXhOJUrepwooL8RGWnA1YqrGe4IpAKiS3IeVJMKB1cibKVK6hi1cKMIDdZQjUaoCabgygzhlaBZxRQ09oC3ELCOPhzgDdapRFVASgii5pZVMyvOSuS1lL4AghLaE2oHIvBInlLlVCOTO0Rkri7hlqPglrkIhUQIlEF/OFkdQipYGkFtY42UTIUN1CE2DRfk9KDh+NinWhqnltBUiNlgZKtkfbKAduhxyOWgGR4IBMpFwhk6OBXyXhE33qXo1KfirpZcYZpKauSZi4qxAGowSRIDTMIkmQhtoMejkia8Saw0IOykUL6YRg02J0KW9apU2CsbwroqCGOuEudIAm5wokiEi8khh1BP6pJTk5LUFKZO4IVdCDhxBR8WM0IIBKEcrSIEJQc3YU9ieApPlZJREDyZKwwIzSG6EVWVntWEWfsENxJEg1pt1LkRnwiI0zOimNwZGgA2ClTIwa3XtkbiTJqIKEIKpUnlQla9t9p7hXdjANyIgBhmZUJk4kuU7AZppnAR4JQ0Zc4QoVYplmB8Rgix8+AcqQtkDoEiaERYSda0fZ7Wp2m9ZPgWWHKlKMo9PDXgGiN1zdAPWEdUyVVTJFALEynDDcOJKVIheKWbw52GjTMHbCzaaS4KSJr5MIcrVYBIIA5UHEDmaa4TObVKkItge26JNcgjjb2BtYIq2DBANdJSuJ/M2J0I4V2MDJiWBoBpwCRIIGO4YESB46IITsUClJgoV1FQFQ1OQs4C8VOJ1gyYkYqDhTAmciVoD+rVuDqFBX5pYAYMjmeiPrhj7RJ0C4PywerY6ULIk3DShlE0xT5bbbDBa1fGmeSWe35y9JWfOvCSa4+84hPXf/MHL80bLzFn5hl7bb/sAotKUCvDLpWdtXD0UkutYFzAlNkV1DnYeurrkCQg39AVTHCHYuxECpMo9OBoUpPTKCFbQGh2k+JhbJyRKyByICwNfiUaLUnL8MhKClQiq7aS8dqBoCHDZ4xE2DMZJAKhCDgLnpKJuOqgoyNsCoh56kmCq4IPeXhEEAQWSgzJmJiZopFoht9MEBkYMQvnkGzC1LgzfJtIZYgxgg1dYU0idh6IEYOhiCTc0Itf/NbfLryZiGT6aPFLj55YaenUVkzrep506jlqgFgdvX2NiTVXwrT6iwdf+PoPGsemVZiZRHRiNGvOnKWXXOLVyy2/9mqrbLvpBofsusPxu+3WCnN0wmPhkpnXfPWqP/nMZ797y61fuen6m6++7OqPXHD5h8898+STTj7u2GMOP2yjdd4EEREKRRIM44VWXHIJ+sfFzAstvMjGG2zwwWOP+cZN13/qwnPe+Y71iFUJ2nIqaPAPFXUCOCEBhR0gmBMtsuCsj55+6gbrvBnE5s6de+ddXz/wve99yw67n3beR1584QV0zm93f/e7N9926+//8MCLzw9/Hz3cr7/l1kemjiLzJ/zzs5qdd80nPvGZz8IyC8yZc/guO2YOtljnDWu+8XVrYNoPf/yTH9z3K9z8z5u7X/OZ29fd5t07HnAQ2nrbbn/oaac/9P/FX3EJorMvv+aBB4dvwDbfaOOPnHTCK5dedqEFFjxgt52WXWYZMP2XH/3wV78fRnE/v/36/l/3Xceim26wwWtWXOX4A/edMWchDN1+x+1v2Gyr9bbbfqs99trtkEMvvebaUkpSPeWgg165xFIeHhRMCFHoKoFwT5SJUU5NOamQcAj3WSdhZkqVGYATqcEvcS8EUwyrKyFnwCaUAm5sDLvUoMpRhxhFjsRM97DO+tIZOTgl0hGhlu0ikDIGPqnn7KKBaBRkFnApDPmIAlybyFmyZuFk6IFqQk2gfGxEtQq5JpamqZDWdGyCGKNEKVNqh5XwHkogBmg5SODZVb3i5W87ptS1tZ8wapkClm48cifaCSGPjcwm8EymZCZeRGLAhBOEAjoZP1KDxizQinkIzyRJBXGPpMi9SbHGYqidkLakZUbU1pxrUmo8pzrKNWsI6HfIyEzKAxReU+3YCpEb+1DsDgSdkVFJiRVqJW8jcuFkqtKStA70OFfOAekoRc3IXkRIREh3MlZCPQGlnaqQgwm0Fa7EY9ESyUIpgqJnrmFaaxN9ozHRAHAIJtXZmRxGZ3HkbibI6nCWSCiME0yRQtyBrAR5dQJkDClAXMLFgilcazSlwg7SKTIHGQmmacccHMKizClgA26TELH32WuWOmilAdIJsDmygXYJda97qp4NgoUGkIeAKCyd2RrCNwlQsxQqlbuQcXixcKsUNcGK2KoY41WQ/CJgOscd9dgiKJA18yA5CfUiBUISlhFB69qmkhuTxJHVsxoL4UiYkrCkzFNmIBI4uoUV8MSdABQQAHGxwXYSBNTVLEF+qlqcC0WJyoRDXg1hIvXALgy0iIKGHniUigBhjmAXMMYi6oOKUSlhVakMegUNV7C6AkCC7GEUrlThajkbrGAAC8UhZSWphE1SapfBHTAwuwaIC8MBInXkxkUGQ2dSdvJ+zN61ZLkIV8xmjLESJw0g4QG558PRiDSsLcYdSAB4gYhCDk8aHIpEjMHcIlwoIBVTTZWkZ6mJSQiicxSBwMVp7ISjjkUnXJiZJLmp9YEDKsIUfUBJwpVTy6Mcg8eoRYqAHRVKGFuIT3HhIAmCSYC5MxOBe0S4A0mAF4pFIjmazBkx0XrkCnagFWYGMwF6LK/BSFYINAEPh9dBNeKWAAZIQS0wsxTOlQkRhQ4OJLN5Tp1HhPGU0BAmBA9kLL2QARSKBEGrRT+O0kU1C4Gcgfw6aKhwWiNIOQSeQnZxOI2Rc1+4Fqpeqwy1SxJ4gLH2wi7Q2eA4XoJ6ZZMBciFHuYfUlEMTNUFNodxRrmjMkJwr0EaYJa+wnzHECg4IAomzsCRl4RBG+LkMikuKwBg4E2ycGfZgNk2V4cuiVQf3ZXIKygV5wBFyHlxIjWSYyfDDPgVxQDhxaFOoVOwZY4Po6pyJEkmbdCjZGojK/UAPRoTEmmvAyDAuwRsZR92UvVVtcyvDm5vETdJGJSXMm6RUWjhuIM22XBr4qjQYC7WCr2yasWcy5KIBZsDllCOyEEkuJZfBUMASsUdRG+pH2ufklNwIkvaO39BBSROpSOIsaFk1szK8olOob6jCiDCTvBqXXtxSljSRVIVLZuS94OTCfZauDUvFmcIbzSMdQVIWd62wR4LdqsCnOnhOMiFuKElkoFxVSExgIiViuAFHOCm416oAnlyUQC1LhmoDbEoNcyMCoLhxvHxMxdgQdD0HDA23lYABQSbgrAK/qULVkIPcuyJdUUNAhAqThwf0VpDLSWGInImzI54IuxUx/D5QDVgxDyJJOaeWaDibGUKUlSExS1b8Mktd19TamHFUc0I8QRt16ELslIBHEuyKLBLq1Q1CCjMrOvFJwhTkLmREuAtS/CbpiI05caiSiLtbRf0/zPMsWj1rQQAAEABJREFUkbACKSbaDpatBMdFyLDlHCkvtthCpx+8/UeP2HXLtVd/xUKzhBlt7ZWW22+LDa88+eC1XvNqpOYoRFYHGNjhV0LBFAwRci/ZKRGzYmculcw8gsRrroVhLyg/5J5JbsbBHdduEJMTxHSRiPDepRbQrkj6DospYovma0eiKY3wYSqVmQSCBREaRwhxUpZGSuZxQmZqHN5iWAkBSSFCIxVfdSWCYF7EHOKgnxrTkStMwsEq8OcWRu9rWICyE9VgIyZz2KQRzMicMJHUmBGjFGBDHNEbFMvGgN6Q3SqMCecK4ed/8dtHT7nCX54kkcVP2WvOW9ZQmsxSZDBCVNhLPROilth54aO3hx7g9uyFt/ZzX3avVswsOCAavChna6U2WuD64ehlWK1oCw0CDtKk0QJzFn7FkkutsuIKa6+x6jvWXXOTDd+863Zb7rf9Nkfsuv1W667NWJKCYTaLCJ8zY+KNb1xrh222Pv3k4+68+RM/+OLNV57xvr222GLFpV7RtE2EUohA7hpSKEFjJx5wKH1S1wxKiYVAr9KSs2bfeOE5l555+qorr5Sappb+r48/etsXv/j0M0/Tv7ratl1koYXevv4GG234NnQ//cwzF11zwwsvz503OR53fbVBrL7Uc6687orrbwCRiYmJU484cuWlF2c37m0i8mG7766q7nbhVR9/7oUXX5o3OXcSS/uuB1LwNvgDCBPcbt64u/C6mz543gXPPv/8eDyJ9twLL9zxlTu32v/Q7/zoJ8Ok/+AHBE/48EWf/uxtGF9i8SVOOeKgiemIYC1do6ndf7ed0f/iSy9dcNXHx12He3x39OTTz3zlX35MFDOnT3vzqq9tTScSr7X6ahjF6eXpvz373PPPvzx3bj/8uwU8ffqMkw4/arsNN5JK2YywsUZiS6nXhnSUWknw5SCu4RVe4ZSLSFHqkclCYREnGITBThTLYCWult0RwEqtW+NjpeKEhEGDnyOStBVRInbkvL4anEcDbq/VCFwiVUkOW9agjrIzBEgNkZhRuEX0UfE6ghokb2o5JcJasI/MCIccoMsejEhNTWnaThVBM/inmmI/ymU0cmRJZ6qYUxvk/ijuYZRYGg6pHCaBlIvhYHQIaROQuzIbYd2QBZklkWUjpUS4VZGUSVtCVDeAsmetRATdPJSciHvXUtUQGykP24FmZQUZYVVNmtmBk7BxEkSYhwy8tM/iUCAqRCoqRVqyxCVRUWxMOiiaSDQaBhv0iBNDZm2K4tCFl1S5IPArk2OdILqBRAy01VWZKGM2p4AwkCJgQmeDHxWIHjUIePskc0+YyrnCJ3JigoQgaEyGGSzBAfUidaJj1HMtS6OAAbs5kUNzJhUfchNygxpzIXEQFCYGNFqK1YgSYZ60JtTPqHEUo1kryGUNptZ8Wme5N4BRtXYVL4UH/KPJNmJYzKli2wJbVicqDAwYXkSUCZYjQfonr2TEVZrKo96k1BLes/Q5ekKuJDyaG9XBF/CbPNRKgw2DqRFgAzY9+EBd9gGbKlxyHpwJ/ikj7qdRp2yDLZpMWWDSxgP+ZD1bMbFe8DUJ1z4IywNImGiVVIPNG7GRO944NjUJvGFCOYsmaxgi9NJFGUvfK1EKYMtkULMSNircVqHi5EGOfb630oXh9QNPYDahs0K5VABlIrDGTK6kUHwsBMeIHE0CppVKcesHiFyDUiEtiWOCSYQcHIIsYIoKy5AYZIZxo4o5QxSwoVCuqgWr+1ZssGAktdx4blgGXWQUwiBBiS1JL0jaTTQ5Rh4Z20gPSyZmiSSOhE6jPpoSUcQs96CKlRFK+FWtojlFkFo0ncM/0uDgnAgXusPgY2IpV2kMkwgDECaE8OSeLDKVBEeJwiBTlXt1YwsCJNAWzhOMW86UIL0GAwSmmqlCY+pqYMd1JsMa82Cz1suI+hRuplZyYAFDFpVoU52eDfLVEsURKxBm+OXEBdEADRg6URg8r8D/IIxzCpXEBKTa6EDQDbaF8ImClGvKSBJiCdMUId701lqfpNYUPUsfEV6AAFsOT5WjJ1CJ2lidKJpYmKOqQ9xCORg2kranPKY0VqrwyNxTC6t4oiQBGrkwEOLakmcJAGQNwhUhJ8lT4nZIZsIqLAk/mRDZTBgjMbDybN70kcZMoO/GYtEEBShRIXHO4EWFo/eozkY0tCoV5qaAqyhcGN2mpSZ4QxE1RRGQrOaoIwN8EoRgw/GEiJgia58VmauyBYZNxEeJRyNpG8qKI0vFexdNnlMZtTYtcwLaIX3Rvm+Mm2ga0lDzkY2TwTnZG7VGRVOqMgAUpvDNkGJs7oQIDIPU7jB0QzGiaKpLqVSIcqI2uHEKlpoaS8rMEsKeYnCMQWCiCZGkKkFeHNGsQnhMSpjUExDLQdNcYG2CNWsRKlkDazLjK+MEDCIiBUE4IZaBBbcRwx4ViRla5ULig1MKOfjAEwtDKSnK1jshOSDRcdskTrmk5CJChKWSMwEPJy4dEWYGCympBgUVi9ITxoy1ilR87ylj41IYIgYsFdAgHCsYurTKjaaU8YtJsYogaiSIrV1E5UrwS6YJghRNo7BVBZgZvCSPMmxQOeDdniECUAsuqiYNnG6Qs5CWgC+BWQjVFDVVdJlUG9wYaDF7oAUROEEyThCAaHBF82y1zYzGyhEBszJ5YoDiQs7B4UwRyN4OMQoTVNbQhoDZtGlvesPK5xy8y1cvOPlX1531i6vPuOyYAw/cdvNFF1iYGEL01TuvFemsglsmSaQpy4QwXn5mbA7IG1wHl4JoA85axjAzFWifIjslA1wZAiCD4DvpavCR6syVtQZgd4hrJFVTCNApTcUZ2rlkRpADCRJiRJBX81ocyjOTMKWoag5EoLJmZ+TS1IchDCwb1KuUxaU1RCiWRzXMGWluWfNAT3gwvwX2tFq9BjkFwyZtoYYcXAbHxUw8NEYQ1k0Ks8NznBD9XCuiNplR1GIACbad+/gzTx51KT33EhFN32PjRfbYPFmFF3Ooq0TLOUebY0J8VFNT2znrv3HaG1emJqXFFvDxuFgd185ql7g07MJKgMWllKjgkyu3DP9mFddUqtRClNzb3tlgBK2oB8bCPbx7cFhBCOHWENU5OaeYmJVvv/zs8087btd3bbXyK5dqEnCA+0Lj1sApeiqlTHoPUHJXcVp3cK0BvhLAHIk7JRJvyZCMqfR1kw3Wu+Xyiz921plbbrTRcsss0zQtFEebPXvOa1Ze6djDDv34RRff/elbrzz7jDNOOm75ZZfF0Gfu+MI2+x387oMP3+Xw9+zz3hMOPvHUfY874apP3uilwynl9GOO2+Ftb0ukkJ9RTHhZ97WrvWmN12HhvT//2ca774u12x1w6K6HH737kUfvd9yJB594yqEnn3b0+Zcf/sGzdjj4iI9eczVwmDlz1u477LDnTju+aoUVmPmFZ5469vSz/vTIYyDyb9ujTzx10Mnv//RtwyllxpwF33fc0csstXBIIQcKQT1t/Y63Lbfsclj4jXu+c/xZ55107oU7HXrUBtvv/JMfDn9BZeaMmcsuuShzcI0TDthnnTe+caUVVlh1pRVf//o3bLnp5vvvtts5J5/w2as/uteOWyKeEZdpDDjha8OKlmQ4S7Nz5miob0ohD2yF40w9E/qlJEtN1yaDK3iyPsEJ4frKeE6UGxYli8B7MPVINKwho4axOSBtIKCarKRoUrGNCMQUR9QEtgxJsCtXQrpm91QimTLB44y5Mhw2qGP0ViHKg/mJKIYGLXCrlaIoHBCO7UlCmSEt95mcPagy5AjQ6SkVShaEF2LBiXPjbVNHxZFprbOeaq8WiVS4wbCkLuVech8tcZbwhMKEulSRLiV6RdmhvYyGXMGQxiES0kIHSatARSiN/RMgcT+kB7eBrXs0ptkyu7AZ1vScxknxtY7HWPvK2JACAggzCdIbpwhI2GLHji40euLeU49AVLeR/X+4+POw676tKgwcY8611j7v78Ll0qpcGiEKREuNAZskNokpE59oLGOM0bIjDaY0FTEgedDEiqaiPpFoBFFAERDUYId09tLZYRcVQZFGEQQEES5c4H5n7zWbGvvD/FE57/7Oe87ea8055phjzrX2fu/9xdCqEeeOqmIHhKm7CxRcrz066Zbz6ONoP9gOAuJnThwzD3uOPg0XJDLP6tZT3zQoa0aNrDi7wozD2rJ4wS8qVZ4MlDpB8GomAYo0px8Q6JaA9lEcWnN0yDYRPSZ9Wjk7Uc+T5+59XXbF41T4jl2wwJEwVNppXhSQst5matbZWagyg9G3jXPOvZzjZfXyYN2rpJ49boj7Uoos0d208rvdeLXtXNHqfX5vvcJ3m0RmpJtUcTz90IPWs3idhVbLC3NUuxW9ZYqX8aIMky0hbOdVqIutBabfMSAig1cju4kehqmpZVfyiXEWd9pVkslkq9fNSSnKD71TzIFmQ1sDH21dkLtaWY/cj4JjyOiV2iCFK5aJAm+WTJZyz3pVeHL1eHBBU4ELSOTorQBG35hdVCgIoJamVm6J/dy89rQY6p/HvYVtee4T47THxdmdjO9nvirf+2G9Jm0Vp4KQ1rKSuNxO712UankuxrRsBK4tcl1EGNp8tA9IyEjDOSTvhpVPOG3U6zYu8FjdoqBd4jKVRWnLgfOwPAa0q3HrBk6Iy7pQGxGDsbpm9ixQF3m/a+mxCkfqmBXcwZJdGh1U/s9YAeRBLE+vZ51Raa0nuXIM44adul7dGTFyH7lNKpc/icB6oGfSYtU5MiAKSqnSh3tMXwGRI9XMaaTSNHd34Cxupya2UCtB9x3kMeOomGpnIqWgpGtjm6s7qQJTpS5eEzETnovaGc0FqhEMWVJ/4MV8IjZ1j0SoJnoMcx7NR6pukqGrr5ReQMyYotpmBXXuru7OPU0C1wIu2sv6Xm9h1toCmUxb0qCy7y5juwyOIVchXTWOpHCE17kQVNJyPOKx+o1hL42JvmeGtghikFG6kbCLFdYl62vcN6HYgqWlYLJ9O56zL24XqFRrpDuwah+9J9LWGAcHbeaYgsVI6HnbfgdSlnwokBClaCLgJXVfw9ILCIvwi8xuCAel9q0/Z+1AMczjsJoQZ/dT7OuMeF6994FSSPfpGOOiWpjpVZi1B0ShhSlns4a1SW8EyHKqn2BReQ19doPi5e68MlS0eLY9gd0a3Gq/tdrQ8+KjXStfq64w7Mq61DyKsuxiL7XLfSXFqzSUAG+flNcCL3TKlqLNGn3D7RZnvozm+nimRGioAUIFfqleu3ujrr7gwtgcaL+TUhaFDijfuolrp6Qv2K08o1CyJl67rcVUucu+qiisnpoUyGBGo9WdbLGPZrWMtU6xRZ3Nbgc6WYnsmytINjGfOS9DrxNrm6c65VC6Wn7WrENK66vypCetqi3OKWpd0qJ0MIyH24u39ANm3fYtyKjXXBdtwGf7KFMMSkCcz96nUQ9ywB4VtstERyM9JRJUrMKRRWHUvLYDuXL7Vfmnf0kAABAASURBVKjOBS2hotku6xg3g34UnURL2rOoCKuv7IrF68FrVsqzLrPJwMq2BLY3590eXTaOM+35jB2nIljA6ls3CnXrY7mYxw+8hLBfYq4w1nnhaouWNfk0I4wG3IehVReMQDfAlmO5bH0uCSbKIpVDAElusyQE33E7M8VIS/eQWyNLl4eXjVEPlrM0K2CbplpXEFqF+to6ZIFQD6S6f8MmbChg+dytYCNV6Cav8B6QDOQSEkZTQKxzGnSaJqxxnv/kv/vE5z/8JgDv/O992L/yGz/czA0smchZoCOXoKCEoQZvrZu/9//4S3/o5/2GD/j9v8be451qeBaCG9xdmfddGVsnzRROu1PwoMcyL7fi8vbpfsHqXxJUQOGe2FGpCFqkFlt4SdiCoG4Obb2qvVtDRWDS2fTK7B28MsO22o5OzC67wYu6Uv51GLTRHOG9PeqWFXe9+PxpH/ajPuF/+DV/4lM+4Y998id+xm//3z7zd3z8H/nkT/icT/td/9Uv/QU//kf/397lWHPjPd70Lr/il/0y+gTw9f/o67/y7//9v/l3/s6X/JW/8ie/6Iu/9C//1drnG2+88Rs/5qN+3r/305rAHDzsXu3tOtb4z/+T/3jO2d3f9s++5Wu/7mv/wT/86r/+t//2l/+t/+Mv/MW/9Ce/6Es+/8/9hT/62Z/5eV/4BV/xVV8Ze8v+L/l5//Fv/pj/6jd99Ed8zu/9hP/wZ9z/IbJ/9m3f9uG/+qN/2Ud/7Ef+zx/3fzl+yX/7a770L/3F7nq3d3v33/4//Nqf8ZM+FN3sbgSgUhGW43/6NR/5Tm96J1n+nD/5Jz/rj/zRv/0VX/H93//9+vp4eeMX/tz/aBze1l39I97vvT/7d/ymz/+M3/nHP+13/u+f+Jt/26//Vf/9r/zwX/wzf7ruCbnalrKkBJm2Vi5ZqWpnQp2+1VMsyrIqUSWNbSrHHhjVI3s0FPyMVGLUpzQMLi2Ue0nqjmZICrSmuUtU0kBpVQ1UkzaGaSDsVjN5wU96thlhA3RIPXHLpTpJYBBtSGm6i5l1qfGUXEDXoACyCylZOForpHfeJ2mAC0AjWwg7ILHgCe1ARtSUhK2m+0gfal4qvjBBCMiWFgHdUkiOztRhc0B7BOmxGGFXgqWLxdtRlpaTkn+ohh8bbBTEKIS/dwuSniUdBkWMElvBayNTn1pI7DJcutvw3QzNZPdtgH0xasEO+qAarijQviRh4gGGZmmHWFf01SU9pGcOVcquUmeNVXoqshFXdxaRrB8w7YVxtaKXgYYVJmjWPTQqR2x1yiX0VCSE6IOi1O/Ojo2u0tqvSKbJPfQyzW5rsV1hDOuA2pVG6vokHjA3v8G7QLRV8cZHoeFgTqaiybadnQFsFbgmMvPOE8gmrh/gdgILMcQNrAu95Rw1kLPLt4YN3+6l26IcvJSQ3TvEfyayrF/jZr2wHpZuFOzC3JyXjh5bNjR7ONu6rdoUUSLSNm6s3oPKvXcOFQIRGFvT73cKiKEHwC4JM6ohdQS6mbSkw0ylMgoaJoHnWBctoIlmGA4OaCdgXsOkSarFgAsD9BMUsjSnCbNrRpaiEjbNdvR9ZrKVDe8y3mskrJUx6h33S1UjnaCy0cInMMPuaa2Gd19LKQQSHgySdw8PTZVxEShXSZHJh+OwYTf/8y7U+6m8jwO2LhNERZtENVg0AI2txLXgJSWMXXszwxX8GDbv+7FDYG9IWV1sjlqGh8JVnK6QNBUwS4z7uHMOt7JxDtteYQRet4jurrjcU2zVQHi3p+IHW6+yUP7DMkZuxqVIiwI3APEtaOfoU+IhYUoU0zLFGmXeVRDwDey6y/ROAjXEk5AV2SgzPRXTXlZlY9iGKyiKWyxDaRefqcBCsUEvBlF0aeCQfkJX5MPcQSHWBs1yVFtX32N1dhY8IVsFto7hNnSHV8VQdGbTjFZ1S/NihqXMKOSsCjNIPxqxjG5NRKNqVB0dS59lFC1HunVqVuh2ry+IM7QBXrDybA9n3Ns4wmSwJQehuNiKA9V+0Z6D5mYpvaAkIcEYV6qXmzhqkxlDd1W3SrBCnkpBap8XPpKz7oNdaXUSsldQkJCUrbrTUx+Fn4ReA+at8puQ06HTrthT5inr+qVUhipgSnSvxzeQRnKgh6VZNAQg6lLnyexW+J0lSNBMUYyNUqoU5YIdUXXqmckO9aMJn9fBywqy1T7MaQxztP7ZHHRnE9lsqULeleMphlonjdM0+s5Nnb6fvFq4dGVDrTQrwypGinWvpEBK9rwtDnDcgLajRGTSLl1P6XGKvT6I5f560c7GdWGnAi7eYVUpUUUBMfGPLii4W58FGsI7xIY+R0uil3Won2qGNouhtkClEmgz8T2zuW+udtel0RVoxehNXcVoY4+8EYvtQqdiUOrtB1JkRox5mXqkEgCFyJY40lKjxTplLJqR3q1onbOPhZfBxzZuKNwzIMF0jsgUW4xiZakY6Xl3Hi0co3Nq8qRQSOArcX8DHS3205AKpWrITqVnybBMNBOzMBsip+qG5KXAZXC+Dj9rVnuLB2q+JJ8ntGvpbBWlluM0R80uRVsbGbhntQHjgsi9z6ikxKB5urRi4dYcZuNWl+XUWp9RVV2FFLCzSxyHau45jjwmx8RePBdzALx5iHPFqb88D86hNmKyUjlupyLQDOoONgwQ8rEty/U85NLM1Vxol59QKgkWZnPehSI2ePss7V/9KIVzrjoP1KBWpIVyv1qc0sRvYj6nX7hZrGjdVOWlHzzR10Ae7oqvk3l6x/SaI8DWH3VDQQb0SXLrkfAghca6D/bqcmg9eWx/g/M4vL/7t37m9Wfv/4mRv/8Pep//7f/Vb3kjHMGlNXqbTD15nvZsabpYug18DugJxjv9xB/xrh/2QaPphBpYD5a5HF6ZsTd0btKPZWMYvUthY7dFsRl+5yrsTqKTi2O0jyuOeFqrtOoM/f0c0r9XzwYCqoqK6F2y4Fd0KyAL2q6+5ZVowtumMMD0mkOPnfZgSEBdGMwpFtCmz2669xHLPvCoYS/v9qYP/pAP+okf+hN+/I/5sPd/678Cf6PuUJ5C5MyV/A9+8k/5Rf/xz/lJP+En/Kgf+aN+yFvf7z1+yPuON70Fr18vLy+/8WP+25//M/8dztC6dB2vnSDamtb/7r/x437hz/nZmvih/9qP1dz3fb8PeMsPeuub3+MH++NNr2f/y7c3v/t7/bSf8pN+2//063/Vh/98BQvzt7zLO33M//sj3u31f4jsH3/jN37Rl33ZH//cz/m/HF/39V+v+W/9IT/k9/5vH/fTfvK/7mIgbmWXw16KL40x/s0f+6H/66/7de/5g9+qkTreePO7/eSf+BN/1S//FX/8d/2u/+YX/UJYJyrMwmlrvrw8eMwew+YbZo+yCZvG6dDlCaqx+4jL++yx0+rJeupXDt0BelmplU0M3PejS5lh0rXFloJS2VI/2ZkpCbjIObuDXaMxW85LuZJGJLrKS8n9gWUdVSb1qc1JTZtq94VqhqFWLV6aWt0XfZsnFR7Q6lwxr0Zwbz5LVdM3oxJHaH3lDClEhU6srHm2Je6JHJUWeWNS2SAxa09ZtuQw1e1QOqs2M8ZWXdugW/txtyYxzqvuzsoDeCRGdERKllcLLWAgwXnl3DUEXxhVjGyjFMKelFgHfKYfxamUDARQiJGntW4jClmVrBZbrlmGsBQxs6ReWpEcrqxFoXbN0Mq5x+o8uOGVd4wG9YWBHN2E/hUFfQ8/aTuq6hLGpgl861JmnVfuq/JsKoqLlb7brhYsanER4beBibX4Al+XbHYo3wpGIaTpo3WPpA53YHSi5cMNNLHTWjujdC4dWlbGohGDJdpZFWqa+ZrS8la78sHVOAIVQr8oseRAhLR47PJiKgll2W45ZvRyoxoKBzsGLrOdLC0kSkq+jsHUO+qE7lZal3PfPOhvgelCrgbMfkwcYtsYbhJXK2G4MV+vY5Innq0Tjae0T3jykVwGfXrFkLglK0PrBNJn+AIkulabd4vXE7dXKIFFlciooF21sNfClBlnW6nU5kjVFNV3u9DAdWdVDMKVCFBGK48rNTZ9tf5IIioyFaxNk0RHY5QslL60ue5RkbTG1AIQIXXgtQ0YYOKxsWiPvvXOYZi8j3uzcd8mHM6pHLmFWieVZ0AdWepw2Rv0Y9gaA2MUjlTP7KNuu6AlkWDc9XXnxsAHJNHnQA2hKiWLWxKUyMxN9aAKkpuj98oQ1b1H7VmKytETmAqnjM1uZRVoU5wOvfSmeomro71sts/iUaWQ1o1lFHhPnBmuWq4zlNKu2qVZpQ+GWEqlMsSLLWuraJqTvGDVcE0fF9b9yFVhC4BS0qw2gGm216xpgmY7IBjU9JZiDcWqbs0Ld0LVqqBF+n02n8gopbeX8jgUjm3d4I1H+wP9II2UZFKB4poUwsa0EhHyf+Y890vWY2AKUSn53T1YBuFiJxuRHoLnSFKBjGevLKFotyeRfeMlaLCRGhqdZQTEmBmp6w3B75mQfF9QorEviEAQEAw3wEvzGzqfcasVVpM5SVkoUgyNy0Vd6cGwYGvGxtwUuttxDjYnZayXLImeOxjs4hWuFSbidQCrDoLBOxw65tE+Oh0xRh6L2sxKY1DHSWgjhh0iIh/jWgMm842BkmeA0AkvZ+tM9639Mu7ZFzMKavGKVEiAhuo3NvdZuZugD9ooWmXwuma+OsZFxRdEGEg4pmF2O2V9chgE1y4oLcaC33yqP7ySoGf6bHHsoEs3mMWRmnqUOr361L6zJ6QGsGL3PhnFGoQeDLz4zW3SlD8RV+qHgnBzL1oTJpwHzLpuJZb+/hKNSpMIxKui7CKain8qOQmt1wMEZR49TjC6oR+5vFDVQxV0sNwvTF2V2yHMoyFwELjr3CWqFCBHaYiPNGNP1MrWimhbJjWWanotnY1KavwMpWpAjwO2leUene6N0SfwhJaU1qZ40i2X7eGnU9Gd6LMjO2XEd1Uk9GrVUCkASUmg5OaapYLbSi870Yp1zp5qRybSbaDBvKA/YZoMihkNsKnmNwkdMlPJSpNZksNNgvHLfFM9SfGDjYzWTkWLgvJtCUtpKmWnXCRCZBHayGrN0SrcjQI2TGS2+M6p5KULRZWBWpmlOJtp3nfcQe1foteG5DBZOstROvpg6+9qi3ZsvoRc7VjX1ubiDnM2hulf26HBTIphLSglucl5+Lktm20hBGPknYNI2ykM0Rq0qnW8IBckwmoNEDJTqJapJs4dt0zpnhxynnUHdF8vvc2WbTIoMmg9F8YsNxgxulAarep3XrTdrqlU6GXqJTpYXjrvKvaQbke2ROnPEU+LrRP+nZ/2p9/2e75AqR7v/ub3+dSPWu/9lpvSVbWQLyYnDZROSdk17g/cNztgYmDPzvuyEmwTnLMEILpS+TzH7OUctEJCCQ/c1W4ntdcZ0XLZ7bDhGjS2za1vKUjI10ah4iZ1/upH5ZBKxlX27L4meqjCSVG/AAAQAElEQVRDtYeZHFlofZzTXihqPIfVNHoKYU/k9Bg66mQ2FASnreVjkmY6akksKE8zWRMm5UJgE169EitVb295443f9NEf+Zm/47d87u/6uC/59N/9ZZ/xSX/rj3323/0zX/TXPv8LvvSPfPYv+Fn/gStxjhgMYDf2ReTEePgbb/z6j/nIT/64/+XTPv7j/9Dv+oQv+cxP+Zt/6FO//LM/4ys//49/zZ//wq/8s5//lz7/j3/Z5/3xv/o5n/Wpv/XX/yf//k8SsN22e1413vNd3/0PfuLH/4Kf+/Pe873f9w3dQzz+/1/H8TgeP/tn/Aef+6m/70d/8AfdOobCQ0kqPjjMVJWzzMbP/Ck/9Ut//+/7vE/7lC/8/b/3b//hP/SZ/9/f+qt/wS/+oA/8YKlCOdHw9HxCTldD7W86j5XDwis896xrQMIx1rEgniEXGbCTM5vp4aype+bQnpF7AAPDqIzKxjm7hiiXMFBSiuddhLkts5RdjR3mtotXQXoIZROILD2wyuu6xRtuJmsA2/RT3tI10HbR0xrU56GsspxSD1CMOBIzHXUj3UQDaP3S91C59Pam7ZH7KIUHM4DeeJit24pMEkZzCUAWqqzb2GkaHfsWabPM7RYQ7uLbV1xRrRFZisxuWsxSorNKJryHgSoMzQ7JQuZQpj6ytS+9SVWRFXXCmwvUqo1ZGFVD8qOoBBsFKxfZnrwddHo3qNNoQ1dp5bwQZVdrxIqcupNEyVVLDUYc0lW2awpBPapYgsDMkYlKwM0wVw/vmYUrO6/WeVHWihF9WZ/3XDNRRRbIHgfXnMqvdRcQFGLoY+tNYWtoTaTA61928f4swFQKO3vv3M+NkGRUoj2oEtxTf0iqgtltH9G6bJ7zwXHI03jUmjHQEqftu/Iv6XQOsxt3mbeomOih1Dyc6kcWwFW+a1bblfnU4h91NwhphHeXnOWjre9MdTc1YYBuBjUvzrxjEZahamoJVSFkC5FrunKkreXUpGK1dw3ULTqk2qq2ZTUVhaKHbArK8FI/2WvUPMjZVMfDLvat+ryL4/bpFDgxJUMTNagEnV7Ko+P+UxaAwtiF2hAN8ndaBXJsP7Z7oLXkVEty7rfoGhVszRqBpZmT9jBMazLw+tXQtmTm6LV6GRfv4NmLsTpX9CxzwKgXuMPVfLknyzOnKq1ODXOQ0JviejAGTzIjgdJ5A4Zm39XZJytKFSKiqOVIXVIjaiCkdeRuDS9SM6O9aLoifcQ9MrEroxS1FpZsrQMjwlk9u5yALfHZ2dgtFyFWd/kVa+dM+p3w0SqMHridkIPKos5ECk6zRaj2AGuFzcvwZG96Q09sbjFsMa4l51pu9vr0TkRYXTNrhqBig9Dw4KRPg1NbnK2zFdb5mgfhQraKZg7OsdzJ0UrTveqQ6UYks3wLiyCPugU3u3U6j760FA/p5Jp8mobBBbS9l0zU7cs0TVXYXTpbQLQsmcqvTbkz8Gh5KI7KVadpo9faB2VaosNSU9VrIKhC/zBOde0aTYlIWzcrevTcJeF5tokZNguUaQrOsB4j75XegE6PGFtMo47KiXSJX//KDU50qPE6mBjBA6+1UTXaJ21ovsw6NTYJdYjoUgM/JRGp19oMDvGU3QBGl7UyV8JiDRPBCIXkCqguTT+7nu57DEE2ebz1onktwkUvHlBbSC+Yu27C4KFi1OZGw6zSZHV0EPIgunETKIw6lrjybvkDpHNB6O6qjrp9yHo7hbCuxtlZ2GWFweAK3PfeEnQqaWFgKXIdOKwf7KmAUC5iwdA8E5kQLa7ZnaqQzLNkMGykr54DyvZ2XNPSZ0i7qKKad+y2Ug1TjCp3r89pjfeLS1LNyiz1EMAUDWeLScaa1/HIAc+9ch9t4zYv6M9Re5o5xWvf1je65sbLHhMLgKxFR3Q3l5mRaB2Sgk3jGMpMieWcrfJo2t63QZkoKGsEaT30pjlUEintW7AUxpl3KxHRhmu0Ok14VxsxgMECT5i6RjskCo8xE25Zx9mmKoYFJDfUJtswoId3Ii4gdlLT1FFSlSxBN4wsuvAM59CX1iyFhISYdWpPJJeCWkq6Pvk9yCUBEgaJuAdoahPA1c3SzYRNncNEsSUMu8q0tL1mqs3LVc3z5Ayoh/BmEDcUJWcPPJmBi7UfmQd7+WOI2DFKUVSLARSYxdxAC5R0yw60MlBEC74NqtvBoxZyDoOG7c6rWhFRs50cKGWkU4s9BHIQwoWROTK0YCqgxeWSaO+WB2szlOMS61IJhm3Yq7YnpKaw3hPnbVLzjhwvz/ECaEnRcpPDrkuN/HnoL96HtvMgAb3R6ZPWqy5tSLqZSg98Z4UQMQavh6kRJxh42xf9zW/5Tb+/d3CO9/7N/8VbfswPU15Vu8PyOM5lYXSfE49Va0KbBfOl1Gp8dQIXj4svMQbJw1yRe9NtPIZxRNez9Hgxrm4diqbtNZvmbYtKcQGqaWMVEq0cpU2349GHRYNXj4ARdKfcx1zKXOrPT9rZHFTde0sKPlZNbWHIcQ1XZ6JQ96U4WnvsUX68ctdzzuwnTlE9D9hkH91HiqSpecaFOua55ju8X6FOJTbBBuDoR5XQ0ujLj5c3vTzeNF/e+Xinl+PlXd/53d7rLe8JVTNm2gKsM+NS6oYAW47SGUqWtRZf3jiON73TeLzZ1pvXm975jTe/+S3v/pb3/iE/6L3f+we96Y2XoRDFQqHaN7y22zk/6K3v81s/+lf+nc/5rL/++X/iL/yBP/Bnf/9n/unP/Kw//Qc/60/975/5BZ/9GV/w2Z/+cR/7UT/ond804ua20RjbRhQHe8wOl+a1viy++S0vP+r/9oEf9CE/dL2z9nwjqJWs9oUuuMNJZO3UN8z2o92DrSq4oCdJ1zMkMwJzigB1AH+VemB1dCkF04aqUkO7kncWbffj2S+7D41cVx9xNyiDCxtBsoYnoAag3OurUwmu7uuKjGqN0H2jA51X1tNHH2NM2gCOzodmdwPYul6SuNCTi+fx2No1U2fSOw1Uht0mIEy8wwLlPNsz7e5Rp8LtDqHxQaWZxTFtrrHMgJIRkdK+ruC1q7JaowSG090NRQ2qo/txwV8hL8tmM9pUuVcPjKE99VhpLsXTq5ff4F1mSkhM1gfoOW0Ppa2kdFECtPESo+RkL0tRNa3foNYxldZM7UeNewCmSHxe059LD151Irv2Lm6RHcvUv1PJ2E+Z8cN6agM2AlDziyYol+kRc8cswow0kE6z2T5KCLDYw7VDSzwgUYzGo6iEu3exoDSrJKDiMBINpBRlKCUNkf6MeSVNgCPtFNPySQ2ryNgd0BJyy7QjeaotP1tFp9OKAFjOOZxjSULxyOvY6TROOV+TPgvJPscl7ElitECruuYQa0IZKmmFqCtY1UPhWpMc28eeLnGMrJfOOz6Ww13/Ro2W0Ua5iu5+3kPdzBZSGuZ1AC/eD/ZLQVsUSyXUJiU4bc/QZ0ojWojqpmAAD4N4zDY9rt4qRHE0AEbjib7qluNBHJZ6BGBn9QWoFAyQn1Gk+qrh9JYWiA7llKdDhLadxKaWjRN8Fp6nX8+HXYpA6K9A33OMGGEsGTRtVO/ILuyn1qzpw4+jh/ws61uRwJ0XBr05J0xXVDMW5JN1opIJssEwvySoVCqvtLpURBbtVsPC46pX2cphCoEg37Sf7duqcb/ElekhAHtUrPPilXe2FbYK3rKMpX34O4afibzKkCLkBM6ZYToV3VeqcXe1vm1GQli29TX8wtgKtmta0lko3MkmO1ChSs/uHNgmUwjIhT0bbWOs6YdhKFXlXibF3e17GQi0cDdW4yU4JL5q555dR9VK49m4Wu88B1RhO9BpBv2IeCt3LvBAjdwVEjyxQWJaSanauq0BX5BnDdE5XRtuhvbanTtty0uvLqey06suZAIQ25PlVXV1FkM1MjE1xFqiqMgujOnL3MEqVLG95wvWweU+ymerYDc8y/Ks2HlK7oeHTiiHz6g9gOWT8jOr0IJmgxy4y0iTENVt8OEvPo9aVX2a1tn5xCPwRpcKlDTkytKJmRl+xUjeJ43ShsGsvZQDfWmK7tF7jnOuwjFgTmFPZNdVVdTMVHpq2ea44BtIu+m8AuJl7h6CaVAmTptBF0/oFqsUoYxLjjShRwMZhEpSI6qxw0t6aulqnLFQtB7Vwu0ddxogUqFqFV1hq3wuTFeO5S442u7nIBN2u1nJcR55ubZjp1ChKQe7Is683tHnybBxrsdWIQAmSEZQ7i5uibvKBvxl29ozwqXPJ9SpjaS3oGkPAzM3E/IT+2olQdHALvrTebX4pmdrOZrdHoVoVkqUYsLZD89DkNhhK6HuNKFIXaJGAfIyTGVcNDn1y7xvisMIGERok9pJrL68k8yGTAsFuzSEWM3jdXvSciF5L+AlzeM4c24Z19WSJHHXw2GxTBKrO0ukig98DuxjSz1cQuUqL2GTV8W9HbvRVa/zVEjaK9p1XP3GdRz1GFg6F22KFkyf4TN78jRl96b5AB4KGsIRBl9lvrs3MiSuPLt2QaPntpfK0YlS04U8tuVG7zIBmJGPFui0aqsQOSDa2XRzX441c6zneLn8YL6eKbtSk5nELEm7Ayr0yvv/OaqdU4Z5+YEeULZyjxtio67o/ezbA/rwNJq1KMEwmCUOobzfC7f5K8ezXOq+gICX1qhtuL9vWJSp/SGztIx4zylTgxgjjJexde9zQOBYt868xJlWA7rL5+gaWdpPEkEq9+Wp0PV5qpW0TShxGaztlLlSGxa9WStzXOkZqGQALfWUm5A0M8VmgCqiXC4uL4Em886RN9iQFHp2r+KqWVLBlGx7rJwyqwlZX/+N3/Hf/W5831Pu3/yrfs67/vyf0i54U12wQj9FpO4JHUrKgLYCfgfsBY/tpUQCU4jMSpTezE8DHTZaia/uZ8R1RpxizFBIJPU4p3TPBuZgDY0FFN0Viu8Urk3Wnna6Bz00PHsAg6RT/nTVR4pyfY27QNE0N3MlvdQWS05B4aFVS2uwtFsnkN4vKr2No2ikTgIxCsZR40htYQwtKSprqus76qHgs/dVz3smZEHdgNWAufOOG5B1qV39sIoinJF4VkUmdo6tJAvYgh/DHyOXJSI6LsYF2aEi8+auPjuS17BrjT1G1opLWpkdg6020QNveZc33v893/MD3vOHfMAPfu8PeJ8f9H7v+57v+9a3vs97v3WpYd7dtS7xo6BMAd3cm4R0kaIVO+zqJlo8UcWt3UvfYEFxXkWrOTBAXF0nqPgoZljUqZjdNsEVraLNXdlJTz5wmV+wbZaipKBfBLob2QjxmPdX9oWU2URjkOLd4LowLKiBt1HQes0edlN2+w8tKu648abstAGjxUDMCpZqS3nuK/r+JrQKwkuD8g5J6k0ZalXs1jlMDmvIOUCmYRvDIQJupMUSWU7ZpnvRu4ECG5lKkvX2ARdswq0dTiXMRh+jRIlwAxjMab56apjK9GKWB2aZApAqCTc6zcyNmQD23AAAEABJREFUkthiamDzCD/SvIAgbi1kcW90bF5bN4ECbexBFdmlcHd7+3KbZn6bc9xrJq14pI+LI3AjdAitzy7Lar/iyHRHjnnpUdNdmSh2eFyrpTWpE7fYOLxMicvuG40m+7zWKpuz7ag9xKaiERU2KHPWsh6Cx5vXQgXz3lOujaPmsDVunGKyOiE+NF46wdFi2rsEb7TOA4QohbnLsZpvXlUiBKSRsk1u+uWDYhD3Kwyg6ju1up/VhWapJYQz70/dEmcHlT9NT9vVYSzFLVZuT2rG+Zj3+4GylC/jXZOHyVRlSUm8RW9ofb5acUum3ubo0U2nua3iSqlVIZQwVKOtS4qv3TI3EXNgreHCUKltUJ53dlJqVwHtKyt2l3Z34VebUq6kU+3eKqYSk9GBuPgK1Mag5JndHUE9VAjvverezNy8bkdA1zCSI7uGnVNpbTX8MWDyrcbUVMomYb1hZ40WubLpAGGkCAUI3hGm1aZaACLR0dA7FKQTBhul6El9nnlbsW5lyJJQPoMhhPKgWXt1uqEn2lFByGYRwCgcNLaVuyo8PYthVjNgxYbDbpPKnkwzySiFF1NXeqkUjEOcwl3zc3iPQdfghGpVgpSHcDsHtwr+hfbw1h2cpIvqrp0yB8v0SGYIWHPrPGlweZ6ge9MFwVCGdnAEXeAzqwuKwEwsqe62jRDgwNgYEthWZNZDHLnxqFyIoeI5WtXCojgya83DYb5UwKPF9Nl4huflpkJUfOVdRfVUKk9D/uOa1/WoPKjSr1Y6CttQI+BBGaz2SiHiAXlt3rKy8FUiRgeH4ksplAVlEXozeKldHR2HsmLKsadymWbQv2C1QVAHjaQhpBmTZOXU3EJ8tG+w0dY2NVZn4CnjSnsT1gXXLcdzTr2rqTjU011yxt52JU7bl5VsuYxtbm8pXEXKwFRAyrTdZF3o7MqkwmsVAHHzqrcEd7redRIzzEM1TOFJy8TNEXq1yKhV6U4bdjgmlCaV5p3KqfnyBr061W5wKoAE8rLrHVpvB8uslNg89rWkP1k3Djg1w7I1t6pT3yii11DX73kYl/to4SEzTVUdkM5cquutFIBNkxmqKGplHa5J7IX0rl32hGoacAPknNm4SemaLFoVQg4bRpE0UOyygNqR5itDzawViVKC1YuIXjLNMgJUDVdUV6hy6Dcl9OIUvK6eBVYBoX8NhaY7nr4q29K8hqUKyRS5cDkpe7v5LLu09JmjR3u2A9mMSTsEDy6LxGa3Aml4ALoKS59j4vGarwJ1EjRMD61VXSLWtkVTcihe7HC04vXhWkjaKogNTQtDT+vZ5EYqEYAETYePGkONKEGm0UZba8t28Zke4bwLhwWk3uSK0WiYjenjmMNXl0cqFXFBnSLvqxoAls+thp+mzlh3MEjrMDEWkAOpTuzqY1oXS0nQ567yTN5HcdMuTIMdhuPGKbfopl/wc/RW/7cmhIBie9pltuWeUMKW1cG7PpGXUoguUqDhDlOcMIeJ6Nb53R5ltcSBJt5ycSja3SUCrlwBs5lYzTHT1zk8FyFdV43IkXa0nmpzCorcDGlBMLhPaL+HMOajWpPpFocKj+1NkapQwbzTrItW9ISyEsEK10q0O6uexKntaLuBLpkJZlFkpwiqGJm3wlnw7gO9oJhuXT0Xw7fhuXasa+/v+Scf+zuvf/rtiupd/9N/5/1/9c8xxHD36dX9vHAK6iVUPQCCxA+8bgGGRavRoMWbglzFEVCGi12jrs7dvUvA76oT+ZXrynnJSEPEjctxPnAeLX2JHA1u/SJL5LMaaEOxCiU6dlrBdXHVdVSqxFQ3DaI1ySIhNSJd+DReljV/tCs5zZlJLaF+pV8CRpWx5kKiKr3ZHRAR1tfIy7LAvh0d894WejdVERuh/cTVVm2SqTC9HqIimkf10Nai9UwtdylopspUtVI56rxNXkI9MIYqDENNBXt7asCWaYHI1kshZmWWHivG4TVHtjBxnD021VZECTo7wKDlcIVWq8OTo9zcakhwkFE5i+zKuht+N1IMaWapt0oU3AdxSLM1UYoTZRrBCwrahq9hpO1g7DbJt2omVSmTS0vsTIyrrvMKZVkSVy/DEa2nZnZ5Xd4Vzv1gzZJjZCewV7ZucFjXxj5L/KlmXTKq6S3RtHeytIKUqnQMrnaPRre5wpr6Z7So1qY0+uYOAm8yHwpRZetoMlCKT1EC0Fzr8gZTh2zfIlJYuqSpnab+7ppjk0Ppi4MlBgpuZa78nbtDu+XsKlQYdNjgPBzLmmbZFkLEQQ5Pn6EIjMPUh/S8bbDxOkcEK5iB3XJwpyndSqU6yuam7zCVuelTtPJ1dobtFsNdmpRAUKFAEx/bpdy+zkaVGdbAYVwYUmla64wBCNxoR1NAJ+kmQFevhCjEUF2qYAnv4ZrYoF9m11TLmujDwGXplXlKgiEiGeY5jm5p0ac8lSBlTeYxtk8l8iyem1fp+eQ++9ya2mq9ILmGL8yhliP5pJzFpNLqumK6ux2rpxnbZDLc6I/JY6V7VeXeAQ91TZ9QSNXShvxCfOu7zojOlqxqZ20R0rmQL70XwoGWcKJwpe1iR6mKRmmRHAazVvrEuV8KaBHHlW+cvfbyXKXRV1kEKvVxiygZ68JG66ZAiKgv1d3iT+ENFxPk2S64GXfM0F2YjXCmLeaBGEJjhLpWsrbvJ7K1MbuRml09zgvnRtZowpWELFcfWaHTUU9tFyysrvsJ1ZZUHHFjK+UILowm0vsVbLdT2Ayl35xeYFKA2g0uQhVStsOdE8NOz7N1f9/GBqtQ9wcAFABu5rnPuLsYqj0wn+ZbDQYPjKXp5vdP20hbW+IKVbbJ2VMsQS/Rr3fpftcK1e+AsObsazFMY1oPvVuFbj1ZjwuPa7yh2+E7QdTK2VxuL8vGGNiDF4eiUycwyu2wcXBOd5+malvwpa/m0u/oy+S0mK2okq4blXTXMLq1a11iAaqVppJb1qVMlnb1UOp0w9skBtuzkUr7Td9FpF8YJ9R8RJRx08vUOV+IdbODtBl4qdYfXHSllepVUMSACULa2FrQl9dxA9dMHB62ALeibeAJwVCudB8Wely78uqQmjrmgbscolFdKSDrJsAVoXqopP0cHW80X3oIQ9zPhFtpdyMdrTiJdrYR5Spx7qpUmSo4FmHGablG+KOxjDYaograi6ncp2NiFhkucaBiaumsc9sVJHsJ+sQ1sAdCwAD5gzK7shau1TkTLg7TmM4yXTKEHPqImpEaoxWuwHvdGKn+qj1INWk9IYXKd/NqC2NqbpilJo/Rx8SwUoVec58jcgatXOFcC1vetfZk2a4RqjupL3XDHg4MH1N/93nJtbYhZdi17bwE8TX2Vh7Shyw1uqs7W4ZvkgxYjWO0H94Sf8x5ee7nV3zN133x3/qq6x1iRq22/aVNTwltDAVrQ9KI1fqjG8fVjE3Jhu2r/aA72nn5jrnbChqtC4pb/Gc9mIOFCTw2jyfsyayuu4F3AZWhdga09EwOr8VarSYSfiZ6tzdmpsZBmUl1hEnpf1R7VObuzkW8jLlUYgOdVdkNJDIpEi7rU2CDslYBKW/MHn/kz/7FX/I//o6P/m2f/nz7972gF5K7Ixh9bH8DyhapHZPGI/RluM0CdmdXqW4MXTpYItcI+ij303mXEgYxPea6pt8dlgJRyY6WexEBpQPWvcwPw5Q1haCU+AvsKGNVUyP1a99Eqi/QV0o5HqHVbjhx2MnarUBLkVKoEJpEDC+3HtPm4evwY3HiNU8dabuoL+bupMM8h7ezlTz8wKvboA3e7q3u/Zq+03PDKo0nPHrcoVqZ0swQB0CrRshpufx8eKxZw9IKGXbVvIqNntaPR6uq6xBr7HLamG6DTeUXlWm9V4Wz6DK1ak3Y9HuMkg/t7PIgjmU2RmrSbaAKAmtnKrHmPI/as2BtDeoSeLk9F8Ji8Jqm5e5S+k6qvtyKc6tkVBEpHgyl7FvCHLz/kmWQlTZiVome0korrxgJ10G9q4ucFOlm4tzMTbEQlw7g8igLWJCBuw63tIKaIq9b+hG6vaMiqkpg8R3//aftL/8HAMaHvN97/YYP55rdBAhjjazu20aA9ycRrgu4X3G7LJO10Oqm1JI0TOSowKmEq7A6pVlKVLqi3eFkTtzfYAOcRQpBIUq/WXeIMjF7LfgSk2vzsccjhkP8RHRUKKKSL9y5GilzJ+bVRwpI1ZWZqqQFe3GNiH0LefTg9HBN35aYPMYyLO9sAU3hJ4DmPTUrI2XGTmMvYqCxgi9lL8Y5lUu7DJJki87LfJvJOhzsREsSkm515jA8Dh7HNDUisAQ5UnHWhh4nxZNaiygoqtvZm1fUTdgqGWvIPkpk0ka5qw1fs8qcLQ3ONisvzMbQGKBcqlhJ0UX4IIfQYOycl27mWk4a6x450sceSle1hSaDsLJFN4PnSW2MVGRMN1lxGiXQVEt56q5B1sEG6z66habzdkjDmrDVXDEnxp2Y6koHB/VF/zAQc+Sxeq6CV/Kyak2iWizHOVUPC9spKk2mzI9JXzC3NMW1ikeZ4vGrzqhToZQ99lzauyu60ZpaA4akNsmvyxnMRtjoaTYEgbiqNzSCgCBY+9xTslr3VwKGgFAH+PqQbp+RZ1SKo9Hw1hCtb1bUhOrQU+0rLMru62ob++SpbaiuEi61xgEK+tjN0q4/C9YaCiZwgZutZX/z1rJUE13ViR1+tXJaZPosTNQcl6Ap8Wi/fSmE7gJAyjK0nHPrRLy2qvdI5pZLqF0fnUfVlLXypmyYFWdK1LfG1CY71JyWm40BuExqkCKSD6u7YQhZNRom7I57UlldihpS7wYR7DZB35GZFVvfc7QRAxyAGEVekv7Q15aCSPWVl8KMaXnYNdc55mWmZN3QisO8XalUl2h7tuhQQcy+RBdQ2a0KZcLYQ9oANNR6ui+bh5ng3yxJN0Mfq7U5O70wOOuw0FIIKAlejaxSlISCUKBpCz6ct34oHewHwzQGeaoKEa5QNLUs0pRNQJOh0JUqK33QR6c7p9latGU9tY/LpAhgKfeo10GVZu5sCbihbfgxzFiWMkqHIDxAac2annJvFV236VvFaapMPxd7BXB6qM9X92vdpNn2I5eBBHToTepoZV3Z8yp0Fq+QWBpTjakcu664YksqG9gbpV/QK4htraXlmbx9POlPjX7sOK5yJUD6bKs0bFrCYnsACUUqL6jSySUY8ph6zFEtHZijIRANXVcWk3tXlAHWYDpzCK4RbR5aXdVkh405anjIFQFO0uCtglbuHQbAaJLVTHqx4YCqRRr3kpxG12hfNXT4yfWcYwuViARX0ZigYm7h0iEW2e09mTO1cu5M8YoMBRFZ1y2WbLZzGA5etzo0gVCs9/MjlNLWE/erZfNqvjKcI8PZgCFMGN0VhtCNgNaM6hvA5pXjejX4quf1XKVHqxeu6ug2wsxKwSDBJlr/wlbh0fmoWGVH6iyrvXAze/W+1CTvWyhRcUTaTi3CueT1HBMAABAASURBVOnhnq5E2V1Jaa0XyijhRDhUQHM8lvmCTbkavnQX7j00Oq7ROdEkvIjb0yAGzEu3na7azw7rc5qsODmixyVw7Qa6lWVwq0MF9RX6S9i0Mc2t9V1ZQMviaQY3W4OuC3XsPc9Lph2YLRhTu2u+cAwa+3VOLGsUOse9QJWBZrZtaJ0S3GIyt0mW8KYBUBJYZA+pSFIa0oYVIQyATvuU98HWrqCPwWmYTjvIl64HSvvGYfTbQim70d/0jd/+kR//Wf/t7/msP/hFf9EbzvsFgFFqOer4l890I2BotZPKWXiDPBQeRbeMdKJS6QtQymLLAd17eek6Zcp6HMRhsJZTpuqWeu/I3GillIRZm12sqHCdlb1WrApS7ma04+ZdcdKQZMCzQVOUNn0YhC6Gzs0+TU9C39j3gsXX6himO7ynBGwk8c/f9vZ/+E3f8vXf8m330ps9ImyX9tYKAgngBhjkOepieseEuMRledlWjRit4FrGalUPSHtZemUgZKx6EqYJldpo+m5xZiJVJGm2ygsFQMFwEM18jueeT/NriQ5OTS/ePNzjuwfLEKNVxeeIk3nqOyROTMC9YnXabUdT+vU5WfFKpQEQSRftyRk+bU1tEefUXFlIiC46zSAPY2jjAauFnnm3pFsbo24pwVja0IHR06jbHxxeI4kwwtuoQEBA45TzWt7sbNR2K45WPrqdezIVVDUiHZyc845eBCh9LgpHbN+q7gxNFSk5OxZsksN8mt1/9kBAl/fFZ0jBb3IsQq8w033U1bPqUOWyukvxC0Nbd49M3DLuzNoaOtfTiKaXAp41ZkOa3n2llOODAr4xGupL04FHK/s12Bg1j9RfzsguIoV9DZem7Y32N22saIcwlwzqcNRtTwt2dgzqKBfl8rlxXXVqrargd/6WP/q2P/TFCmK+z3u+36f/Dy8v72ZpqIquUs+0cFeSONxohWy7ugJ9dl9ATXP37o6o3EkXRijeO6DqUj82CdrKjqnUuz1M+uBqM3JYjkqv0zKcUEy6BsU0qGbyoGe7diuvzPe43YgAorJtt0cx0WnYOigtSClI7IJ6f2OamuIABMvOVAoUdvUWcK92LWC+3kG+g311jha11YosY+w9nuUKhJd5qEzlEHnplA2scaztekziFQJFC/SmPveNSmyILjTEhrChbUg164Vr9cso1xJU167dO/pZmmtUWI/DfXSnXmLJWqAZ7PRTYorpF46Cgeg2XM4WcYxle3ZYb2DratMSVDUoOLSeoFClutdu7ZRRLHjROdWkiuwyfUWVLDIe85qHEOHMsWtmeajRLKldjmS9rROi2xDYWgzzTupsHk5fxFS6VLFrw08f51xZaA1BgzX6/tLCLuVwCoHPAc/0iLlvzNUyazVduZ0IGnqxp35LAcJV47qsFEH7ttzsMIDWdneTOhxWyof5yW6lZm5zIdmFsq5RCvg4xmGaQxZdB4B2a1pyiLPulM8ptG1ZFcIO7/QO+I2Q5vohUZ2nMofUx6rShvMV+5LVoiduMe6OwmXQN7vm3A8r7ShaRa2cogdJUZkV1bs1waO7ukQBRusEk6OsiXSBLG91xIO5enumeXPSDjWj24NmaZxUtplJtOa1Zc9L1LZKI5CnGt+kzCl6uysmMLUhelX2vMAsh7Udl73ovMEmKBacxtH6lJYhrxdmtnnV7ODIWLxYih27lS0Kpw5DQFFbwlJ/UV6P6zFq7IuX9FK6RPgQuApY9Oj2gieZJv1qFrqa0SgUdTh9wv3+Homdng2j7rC1jGR3awhjoFQXq/lwn3Y454QdgCt6h5sPM0AzN73H5Cx6ggUJsyi1yKnqxJ7e58g9SouIKEkxNlwzR5ay3dqtdh3s4WgZTKY2/Lr5omzd5gerZuUamIsP8qXuLGEgfIdHShKxKbiuhUJm5b6KaLcx57EOt1mUcC2XYrSBsRDTn4fpCZeqURrSM8sHVYXirEEb9bBerVRjAIoM1Ez34hu0aQWDQtWVloYttvqDsIOdylsUqyfcqQ+lUwx0Vygs6NXoTVHHY5WtuHN8ae++ErIpQor2etRtVXlAUv6Apod3ew7giKloG0wUUhqipLfdOxtx+2ErqZernu7JktwBU/NXFIqnnKI54aVExQ8EjTwW+IJ62VsPjivr3spXRTJa1uVFoODGsWizyRqZ4+LY7ieUhLF7KgT5fCSmImw52fSNqm42bYxxYFqKP2GxNp03hmkxEWw5Kk32Sfqo9mcgo7Cduv0RWOwZ5yoVB2QxL/TuDIhbtFg7S1RgTawBUmT3Kb6ZoSHZ1j1Gqp/FOPd69toTHNrU5brrzK410++aQaKUNQUKG+1GKhkt02bpUyWN+9GU6i7tTgZO4pT3uvoOVzumntoHempliG5s2uliaWYpKth9l8zKiTqMo1eW7jgEskJC4lEm2fmQblSwo0qwxbpaWXA3wivdGhPqXcUb2YF+pO78ouvsPMl06jUxZusWa0o7FAyFRKOJ2iHaB0De3TuIcuMc0sPYpiJx3hdbaKKHlTRhnXxFHf4UP4R8M4+uuZuSh6uLK7eQk6vy2VUwKslQLSPRCU0BaWMeEGW7WC2yj0fbi/Uqtd2L/SQv79R03qlTFP/ibd/3vc8tar/2W/8ZZUVSLdSujCtxyk3UKlEIRFV1dfss125R7ulNPhvn4Om85PIJpqtZjeHUBvOQk5IwSqJ4zJpGB6BJ4TNspFPpDGpNQC/Uyk5lKC/6a5dOUHezPBICnYbs3tWRTLGG9QjcNJLSnczKdJqFOruQiDXqHFFmpY6qrZqnqlrnNO4+oimdid4uyo++bsUfbdVaTKA+eJZfjLDbFturpVHOvjey3uYwa7blNfJpiYQH0eM21w3qQcylEl2wEiSBlEq5GwjDHl75qBxQ2VrpPqjQtjVuyqI+V2UyMIrelGd0dkVSW6RptqwkPJBIBdxohBpADWRbBFNGC5o+wxd9TgnyBbcxXIaz1UXcuif0RMhEomnWHNfBODqnc3IYXdEW0aTG0noQJjfYRHiGnd2XSZUtfgqAwxq24WEU/0tJ1R6jKlz3jtJXVJZls50CKrN9U+k1pd197Z3KrKo7byM9pFc1AinE01oJab2kytopf4YXgSzKFXJBaIeMXQuvFoRGkx/XfkiL1+L1oJ4i5nIQUCaomAojb+pHN2PkiWelupgA8Nnz6VSxr2ObbbkOhc1lPIamFc2M0vEcytdNh6JyxFg9JnSFNEVoQPP21JYihuamC8tcfx7QNtP9u/7oX/6OT/gTYs1ejvf9uP/mnd/6Pg1CnZA44zxzO9ZxrPnGxNKVXL1dVa4HPxunPqSKUH2DEp1AMABxZKHwD/SSb/FMK3eYL/eDtsDDcagolzrMfIcxfasE6UR6PidegaWc78Jz4wrWaX0u53qZc41bci31VgJMUjw3qjS93GSoTAjO5FaSa9w1kNSaG4FdnuUdTT1DDKLLRZg6W0jRlTsydW6ZOoBZj2vYK+NTatWnndX7Qm8DEQZVaF2YGeo4id2ZevhysObYanZaa5BDT5hjN7MWw8UaGpmOJ5XmGZfrLyt7WiqBQ93Tzdx3zWuvjT5HPI+8pMGAFw+nQmlcqJMQy5aKqiWUyJYcu7J104XsqJCCgZsJFdbJPhVXOHq0Jfwk7y45sZ2ibltv1zSwISCQNIf6G3Aaoh6dI8ATOG2jApEK6Gzpzl7Q2kC5oGpAoFQv6Dtt83g15pNbSDpGb8uwvEYkAmu3z+1aIHESr/9TBeonRPZMjg0JnRV30pQEZS+hS/WMyuCBefjSTw7xNEpZqH5gH9i1M57Ku3g8ICXIV19me6DIvHPieo/Dm4pwGzX3Dr+qLvY1VJxTWYJnjeoDfgx7MajTSDKXK+HVsdlqeluZJAljNcW2ZQzL5X2wJ6KHnmtWGkdrPe4ZMDTW4EP0r97OcwqUdiPvOMYrrUaQLaUGNse9XIsGBVd5Pv2UUOoyPRcw9S9/YrZ6PfVWm5JGRgkdtCbNEcc6D6g61WDnmesZI8pKzgVEVRV17YjMRKcYTZaavs1bADihuErEmBFH+wJM2azrZjUC2OYpLsK8AJRSujoUBVb1AKYPX49eL3Y0j5ZPlG9E4ZLgUWYYoGzrW5Y6mWD3teLS9u7AOQZsmRJylVxGd6EbzngAS/K9OvqqcYnil7DjCTuhPFzDhQuir2+PFMsIvc7njlfXuLAwTQZUnKr22gdz4H71cH/geKPnUctrIo5IPqt0X+CLy+cyPI5tQ8S8Yj4Nmrpa0alKCbCjNV4hTsAvsVeZQv36/NnrCYS3xCFa7eQIUKyBJ/wUX1Jvie1rt15j1VytAVlg9LFjnUVZu/3lXJjOmrdcAbQ1PYY+qVWu4sNsHYNvMC0gpWTe6+vDg16G1uLKa/MyaI6C63HR9NGPXnecNczStQ0UgJs9OYBK8MA4+rGE2feWlg4p/lLlKE+qLNA0jt1SkZWEG4c63rJjjWPSVeV+Xus8H1ViXK73aRUODBMn3llszMExJ8xstFspIMBS6o2H9IFr43nt7FecFx+Q7q4esa2TKNX6VfvMuGJHbuTJThSYY4bZJWgpNgK900Omj+LxlG7aczfUqUuDvKRK8a7M93Zr05LoY3AurGnrdZPyc3oN+Nb8mppI8RF7luamO9pG1CPvPYjoCOlCLBrbPBSYc4wqgTh3P6++si/v8+Cr2RfDo1eYZzmU3lriByRYNnwea6hi9MeBc7E45zVWpNUzxUxJA9O1xL8wJtKAgOv5Q/SjWjVpyNIppGdOzQoxE0BCvMzei5cScAk51bXYaa9y6dnFGR3X1fHKxjvMT7IDjHvO7pd9vkAd47h6Zir7O+sZOJthKzhKw6BzHTGbE3PE6OAGFXGaDCU1q1CN6DwzI9p5p2cPNfxR6feVu11cEriLt1nt6gFqeZBHtUw1TIymgm6dV8YWYDF5HV2WFLPYkspA01DeiqVPHE/OZ5oWbgSR1rIB3u7mdt9SBwFDR9Wz+ryufUVxBO1C7Ni8sq4+A5sJ1fKjOP1f/xE//Of+mz/mJ/yrH/iLf/pPbnTvqluIfQ5gYrB8t72akAlFTK6hStXKBtaFfqaYMXkujZh4NvIVNHyWGbMoZxrVlGVlSHXnzBaJrrDHMTAFl4ic2AMhuqYIeR2XSUC98o6uTPzlIFAWjBJ0L6pdK4yKVurOnifmVV7KckrSWX3BpIwesmOGZT1cs12sC+59NCqs87jb5msWXWQr6mHQHDaBrmblzJSzhEKdKkxJfW3efBfA9pGKy6wrILqLsAkVFLv7lmr32PM4e13bNYZ+5U0dhW9qaNMqW6kZOwyRuJNUWyNn9CwVUx3Vq2AhWK3fJyrVzZFT8e22vLLv1UZ2nNKPUugZsk0f5jZQ6L5aNlPKaL2qm+w2XC4KASZ5zdzrzBVCkZjeuhPyttp3WZdoYG47g6863lHnM88doa3Zs+LsuCAcW7duU12nHlFzi715SnzRuForeIUyX9s/HQkkAAAQAElEQVQi8uwr6gnphw2T7btsJMpR/Rhhj1TQWoM0qrVnx0XhWJiWQ0mV5HyHh+KGtamwJFIMBVHS0iIPGHskRjpidk/SlfrRyhWqEbpskFu7St8TUHeV5lGhhUiwaXS7ldFZu9Qjus1IM2C0JCmp00QIWlNbuAnK/4Lp2d4DXDeWofTIpdvusQEBXWYPn2/Mx4y//4++9zd8Bk6JyN/11/3iN/+7H9YD0j6IjlFlWa0ZBpfTohXI0uKkHhrym+iSCx5GhTs0zKuJ9AyNc2JonutpyLE5NdjQEh4L1CyvUhTa62hUY3YpJMsakRbJKDmmVlCXrdy6PFP6VoPm6Bzi00PrXJfhAk9hbkfIl8ul6Ei2biWAHpI1xK5fGOVTlCo03T4kkDCNdO5mZFVXM8vJMZQlZ7d1ehcMWLgmis0rpculXZBk3KnLMYZAWGmKWBi11l4rhwuO2Kkq5fZK7nvoHDAjeiGHXZwb40LdRA/WYMOqG9EoQPNTTNlpfcqlR3ewslRTmuoyYCRbA2Gitu4pKqikALPKPO4bgimpsU7v05iGWwM3aHnQ3IIHFVyjkyyCDuoRzdzyVS0xNhNH6rQU1vrSKgoLdSHLEoEn61XjzI5L4T2t951ZG2pNDGvZRYJqNper6CLv3V3koznbTXAKGAGtJCu2WfVEmnSufKgdqDqIZe3VbJI9bRx+jDSeu57bL/VqUKsavS7RJlftQz+KqQsIHYUNobJYR0yt5CZQuEHpurfCydytIhYVgm5QGPJWFHRa8ihYcVvpL+FB8tSuJQ3lKjEflPg2LMQPKctsQpWC12IMopgxUsnp3XWZtRkweBl1j9qebnVotjJDNJ2tXudebmysVqfdiCuQIlB4TPHJYrIC1NFV1tFdKFCeC/fHRtoUPoN5BDPFXolDccSxy7JwT4kQPYEZLoB2nTLWEoFnu647YlHuuoiwnZ0C514g5CxkQNEOCrLbdIcOVcE0NYJhPWBKrmWb990TREk8gAOKPc0j5c9P5a9mKmSoUcOtWdXlURI7VZ99U58jlSN1J1XKjcwiPRRVlSNZ8BZZIxUZkNHq51trwGakKGeTNFRrVdp6ZpB9NLR8oqJdx0VZaFgrKsXF1l6oBmF00AitxsPTKxhdZvthuWYq7TCERiO04dwuIEa9Eh1QgeLW21YOzanRrfCGA125EyHBUz66cTYvEGLLy5B8tp9NFdE1W/oArilFhI2idz9aNzE5LnkpCICCM2UH4GgftRjQ7k8IvEWreGxP03xQDBkfrnB0kHBgkOpxXLKTs9pksQlhhHS2VXALtTrY2YZwhvKpbVGHWh1gBGdhVs2+pl9j1DBzH04Zl+7PRDZgbpDouwWsqvPOlrtzWd1V7E2nXgcF9AKC6ikaZl2MG0eMiKmmkVWZAQ11M5lEsaVidBbO674V01Wd0OlU37WhKGYLCiTnQQxgyVeBEAzoJSwIqVNHCwbKbjfd7dFDOSs3DgUp+DQbjincMHEuNzmyZt3kOGBJ29KRiuWChaHZbQxShpQ6J6yhEvJ93XhTJ9T/jjLXIuaz50w7Mg87jxmqllnUI2XHnRVh86nUyrNci5IumOIqb0danUQXR6SEAtKgjKoVNEsvJbRY3VEQWIa6lbYDylXdSWTRBVSk+3bb/Zr7S1m4A0AHsMthB+ei2432Zj6J7b0NT+/QxPFq6V1p1P68XE6TF9TclNGSyQYIDLayMULRJEqZzB3ZFTdF3aZAUMideUXpQsKoH0/a6bgM0MwolrNG3KuKoIvuFkOEGVVfTnB7PUfVxBhNKuiq10t0z/LpPZa29an84mpVs5JbOArSvL5GhqZYdic389TSAt2l6NdMqXZUEynN26KkwHd6441f/8t//id/zC/94Pf/QakpKOq6o304xoAfiRWYe3oZCJloI1iuVp4hSTYbDiu8VnoJRnGUH8WJtm5JzLK808mZg5ftS+F0kE2vlC+0BY4Tj/OeiUlzs0FIWBq0C1FSyX0SIKm86p9pVomrQgaYeCDvDI6GZ1mlRWt35Ux6CqMAygnxf75aMbSnZs62I4a3AubyhqFaQ7XCuCpjm1Uv27rAHtj0J/wE990XwiHF6JgKJagdkNyZwWVTTNe5sSF5dIuGONDTmx5SwO6xaXWXyxWj0aaLEJHdIUFlVXQLCKu99NRiLsxB2izO646tFQCN2VI8LrT0jKIn1+Zjc3brBmfuGlcz7q71LO498RxeZn67qgIvzQv93uB2lsulQd8TiPtzcbTPpgfkMSUdLQtndAGarjfolCCrf4Ht3maSx6ye1GtAjMKtl8ulpclodyarTNBH4mH7caQ/Koadd8KW7zHSh1Qv6rJjd1c77UE9jLN1Aw2RU4Y0i6lJ0QZQDteNAJBsbUAyEgI2vCBrBNCvf4Qp3WU8FHjXsHJIIG3S6wtLSu12YYyAfkp1S5EEmEKgeLDNask5QhIxmsGQpTSIWAedLZ9MocDorNLDjNQqiNNZrO98+7f86t8Z3/E2wXnPX/Gz3/eX/4c1kI/AcTJ1edDeEJKdO7Y2Kx0YG6LTyLQRNp8KuWaUjN2aeIA04e2iQq07RrrV4SoGGtF2g42+oiMaqRhA2jL1Q+oS9h51TruGVxkTA2ONh4+XnEuIT9OzX16NCiFWCU5J2NEAU9UtVgq07NkqwJ66UJZ52FYfb3fMQxvdJeV6KcOXbtnXI3yGVdgWPzAXhQ44YeZeg6e7RoDDOR/6J0/wS4s4NKRabcbYOBqOYTzu96IcQ9S7Y1pN9Ryoy9RZftpo1zLqo+yleBinuzzBeE9iWyW0zenrHmg0dLHCd6Dj8itHxOp9UEGBNAMIvRp8/bJZ5u1bDHDyBm2gF6wHa3QB7DS76GeNTKv21HBoXQWGwk1tj6fKrWERI9IJHaMwpYOajseoez2dgkU1tY6dFbuhwwKTmKC6eS2SNxplUj2jvMqQ8g4lj1QEz8c+jzPZo7F0ZZd4FHWbiNoeYSjAzqFuf9BeFtdyTtxD6vW9jByLex8UV4Ll3oqhIZCuwFh9vyq79AmEXoW8F6n7U+vMzYycVWeI5HtOqr9Kfo4n8qx7cEooW1l0ONon020Lv76ZmwiM0kcFHZ5hF6zoDg6k9wVtJPqiNALuXbfqYSTY3TIiHadoC/iASaH+GmkbnGpUcw5pYywFHGfnhuGWgybroypOHjzBykS/Mp4x9tNry3CFp49e0JGsjQ4M+LyLqfFAOQvIqA6jEnswjuh5ljUBXWLG7K3x62APBCPW3lKGT/SUbFC3UKlcTowXzFGUyVaKAQ5q2E2tRKXGJqUcOlmEmIwlEOJI4irhLlnw9qM5FQmhLUKOtHnhuErqU+JO2FOUT2uxAo23gMbxphWmq+cY6i4tT3hWPgW/NdhImgBI3TFc3FmGxxO6GdBfCMZ1Rl0afkVF2j0IDrj6qVZNRKPI1wSPo0BlT+Kw1rk2Vw1nKWV9dLrUfymVYpfmJmR2nWNfDja99IsABntYcp0Qee2RK8PCccvGmO3omnmuOq3EivrD2nVcsW6FYIOhU9S1lenC5hVDksWFPtmSg3EaPM23UDeQxGVRtjFhx/Q1zakfpWYAMobO1iqjs6LbZORElSYKbKfJ7bPjROQsW6m/zzxCOzy5zI1MKAscSsdiWAuwKJI10xXpz5OjFJVGmfd6uPxSg65AFEHMe+ObyKtlzeSRbDz6esHTqC6wc1ylrby6rU8lAlJqneeExNNSpPkqHqks5wJG0+U/s1AhbnSGPaYKSBBzulp/svTUboHTReQ1a1tpdHcLzhxLeAS6I6/cWuIbV+KEPJssoZ2wYVDAMA8zBUeASSgbFQd7tUm/dKoRUUN1Jaqu6i0Ly4+HPV7MTCF7XA/0C205qFSQjcX2bnGzMbX21cjmKSUVrCnU5i4cFejO4aWbPFo2MlICqDQ1e5BqoW8UZPKivzJ/DlOUoBQVGk3xhGPb3DHr0g6uHlBXSx/goLRvBrOQJwHiZbycG5M5xq2RibFYGrM5lJ66TNpeGG7DhhhvWovFYSZ9npQK1HSKCkVSV20bZvUhoUovzeyB8iFuHIpIw0aIMtNORs8uYp7hFTZSk9Khs4SyeBqVY9/BurmIzN6XlC6J4Zg2HdxtsTVjeug+GFSGgVT+Ds6jh4uRUljYYIoyX3ChB8DKAXdMN9KhNJMn+byJmesxjuUc3Pef5WebswuFVqxlKNVR+1BAlAXQ9mDcVnzWVGqVkxLtKiqlr7wh9cpjD8ok23S2Y3TDIMKncdLmNXiBuLQXGHqcedkIoBjKpc49C/eP5Y217wveXGzX3fgQArP2NArh1E4lfHEdUNn1xHNge4mnwRhol09NBRZs1Xycx0sIdwFqjq8qbjcCJh/4gdc5xtPH0/CEDHmPhr2+WsSGbkX8MihbPZv3pWkpT3w9We/lmsVNv6QYUENmlATvBTQFeY1wfddaWZ3R3bxfk21r52NfczxjvArqjww9w6QjuNnSD4eckGkmkaEKPZoHbGgXMteraYobLGIQqsEBQsa7OLOOzEW4K7NiTpotaY3aKuxWDVeoEI6V0wyKpoVaD8FKkOVEXw5CYbKfmc8tzrx9+gGuO06fWENvgMsDOM05ln5LN/SuIQTsmH4pHabtF154L/yYzjlJNzYB2Ki5YjgItxzsgVIkLQrUsJSEWDg5N8UVaMncKkOhH73WlDGIS9EyO1/EEG59QJAOxMJmN8RXP5JW0LdqBKlcqlBSOG22EXTs0UFrTX2ddukH0PcNZGhVuo4qWZW2tHpdliXw6QyzewQ7XJ7VJSQPD9TVEZGIdEmNFRrQKfcqi66IvZFnx9vf8S2/8uPjq74B8vbjP+Stv/YXcUrpgPYp6LQmfZqJ5QKurNRithlbkYiuwdFC7yNsbLML1khNo6AplU1AotgGOh1jNg36gvDKlpl7JVQYWUep14NGOe7qZnJ1P6iQ7uKEu88BH6lyL98l7kvXyjR+wAyHPshjCWXCUWZ7hfJoDrp+wnDRrrIb02Gco22Moofb/Q7L7vvqIN2pl7UquEGIc2ki6LUX0qlzkoAWeSSxp5UNXD6vNtnwCHECHy2lt8s0p5sYdQ1jNitHJgtOc7SsubiFntn3EPBuWKEK8Bgrp/ewYa0g9GZ6unXV0FyDfpRpqhybwmhWrccQ7t33M9duKwXobXwd6YxL2jQZdgS9MFNpGwu6XulZVpQYw0z+4NlVSqOM3Cc8OMv9NfMnLQ5TRxDqcZv2qYkrJBT97VprBDPvtYBI2NWjUVrO5MumEgThEmOtdaQVfVBbgqq+hodpBjO6shg1r2Bu4ASe6JIx36J0uNmti87rvPK6gOh1cmSUdS0RYUdRUXuRaSZ2qpOEQR2stO8emb3PzBTD0Glo4eTFQodXufLT8KHB5mmoZgD6EAOyS10Lc7jZgdIpY/eqWo1hyo9HU2pWatWhW7OjoqqQrQ2ElhBj3R3QEmyBkn9lp3pmSapelmWXRpes1oTTKgAAEABJREFUskqEaHQNb+nUldhGFVBjlZk+JNFsIW+aUd24XZah2ggkLDTFxcMdkwYVWZLq8jKQMbzcgB7EhJEDSpBbt9hmNYBCyQPQGo4huKYktRLyquYz1dTnCDi2ecJbE+5xXb2zdstk0bJRAjiA5RgO0EL4sDES06lzp3U6KDjlyjmvmpXGjMZNs6Y2K++iulKR3JiVczTBKfC3UORGg0vk6qCMm5aGFkd0mxPDyqngtY05lDupo9BC1RcpHXQWsyUAD1k0NyMaV0D71Og0aE9ZhynbhturKNMHWehMhpnq/5p20dQByg4lyxot7ta2Jdn0uEybQuGBcHQ5ci74mmUjwGE9lAfiujK05WKqx5mB4oC4lw4xqEJAO0qKQyHCKpdOWGvsOGIeCV1NVqEM+kz9U0UkunK3wmvRYNNcTDRYLYxQ4DssC2odU7007sCL0KsNMWUtqiMUNfQXpOkqDwlVDjQBiTsf6jGLdDN0Q34CbHlNVDlxjF5H2iou9zkPckHIoydVrD00eKO2quSq6L7u/BnAAWEiZKLhvvye5moHvIM+zTetemEPDR4DPkqfaJoAIFgKveDmLj1bVXZcvTNP03129pADr0N5qBrZXsPph/s0B6jOEAmIByrGyjO4pUksmrnxLgQyVXc0um5P1FNMNMHzmD1h2qsKJVwRetO7Xr8AcCzjmjFtu0JNbXDpcA1u3L9IVndkRRT6abfVqxpsJ47QCnmRuky1S5ZBHIAVQAlhLNnTuWkwwgzp3HOme5s5e8pQo9x6EpoGvzi6mi0FbE1xx3IMD/OmStMOxBt1HpIuw+zutAbA4G5WFDiiZLvF/6EUEPBtMijH8B5o7K6L+l1K3biukRiaDnGiEKCxPnp4g7CEZw1KFDZsDg42IBqu9gvzKmZ1NxJMYFdnWd0WDJ13xqo05tQZCy0GtXZ4hVwP8OGvdV8HazatAaqcpG3ouvC2YsKEXi3rdOG3hfloTA2ewVWPaS++1j2wGuHauPTGjuu73v49v/tP/IVP+rwv++pv/NakX/TN8S/e9r2f8QVf/Mmf/xe+4du/XUG/7fq+L/57X/UpX/iln/y5X/LXvvKrv+fVqxDXPqEnwfn403/zaz/x8/7SH/4rfyfRqupROWIjdrP+5P/xtz/pC/7s3/2ar+sMRndUmZHj1RW/70996Sd/4Z/7in/0j6KSShd7ODTibd/z3X/tq77ud33Bn//EL/hzX/hX/vo//RffpoCHGN80SaHMi+94+zs+/U/9pd/9uX/+7/zjb4zm13zjP/+0P/mXP/lzv/S7v+cdBprGkPITtb/6H3/DZ/2ZL/s9X/ilX/TX/u53f/d3++2rxdN9DFJ/KrLIZqSFswRgw6JGAXYTTYImZEhrpxShBhk5sSevibCKzV1+3dpPo4o/aAHc88fw42HHVHC8yjuxKkbeUi+Zoxz04B6lKaix6HZ7M1+2vF0oq6JLhVazWkK3JK6BcG+NxD0YTS/Td4HUOTpstORXw5XFju7Khtpj4ypJ7w4bZiBbGCspD4q+bJvvteK2oRxGR9SVtYdzuNkArMB2G+OwuweYk/BZNuXO20Ws/NyRJ0eMuXW9FIRpNqgBLBmB7FsZYlo+rKZQa1ajx2hfiliEol5BtdHEXp7TN9BR0bXBxCjpXFbIVB9wWvULWlMVkMLMSCifV2vhuxpbgioyCrEdT2qTNAs3z0NS3/MoqpVwuLbhd3NtuVF+ovJC9Zx2HO6zG9H9LJ1Sl9nLQqVtQHtiZpiO6iNrXlfXOwKXUpZtLepH1iAg9lRr31+vvvl//NS3/7m/oRPHB73v+33Kx6BfGHBE7dwXNyGNHA6BzB6hxCbHCd8MORt6EjcTBiMrvS/fCrQz7m6ynTHVHAks7OXVxj2RU79TXWCMsbki5S8u16FYp+3pp8+g/kQs8bakIMUUukKMDTwPe3XbZQ7xff8v+Z43GMjew55e0lSf06qmbpyQuG5SFF3fr5SRCvmT/khaL8KZF1qZ1ACgCtbmHOrGxsu6gDaDGF9XtDYTz64r0LtdhXeqnT+qJJXGqO6pm33d/OVVu3vPul6yHhdnU62QIDkFm9wVUu8ymwPaxOZMdSaASbtEwRuwN1orwJDMj/H6/6hF3cfFg32Q5iiu7KNjhvRUF+v+78Vobhc7ZZJ3GGUd2tRiYtuQ+njmG7F17yfelZF26WABM2PFc+mJcArng3xU8+qdLBu0USV0Cy2zbdWAgw4ZuHvATInRpjffYL9xXPKX0MOwjn2iw0QeiW4UNF+PMAS6LbX5O4mArlUdF1Y3X5Dv3NcbfsplHsqvekEkkY0dstfXnR0AlWfFlsQTx+g3vKx2PDPv/0PTeMyyl3OvZ+BpCO+2zXGtVcr1ij33k3E19SNT0u64U38HZZMKd7VLywq2iDT2mj7H4fXCWuXK8q6BorN4izTLUDqjVZ5STmqEYa4c+nOb740KtkBAL3Wt5TbHNNpt21ItZnEuHtSjYSDrqs6kAXJsajDXrvOqfZrtY/VaQk1WmxR1DguzJDVULMIG6FP4kuN0jznUjdjgs3mBKaca0+IqRl1O4aomexrGlBGwbD7JzaoulDFMQFSqTYROSnCPjEMWdiITaJj3LVJqLmNbhKrETfIk2lqvOx6xM8KPbIEzWKCeFicqW5VwPPLl2IfOdrza9aoslBj5yGvgHYvPY7YaXmvEwT1m4qXwyPaQF61P5paSGSv0lyCe6HOx1MYfM+axtUM2p+GAL0ywdUJcD2v4zoWYXvRh/sA6zKdjzrB1lt3xnZKPpUczygJa7YhspVvTZ6dlENJnKQ6ucKEaW8Rl6fqAnIrbnKwRkoCU99K1lCMATh7iiUylAbn0u3ZFZiryvpFqRwfoauNk5VCptrOhrly7MwbSNOSYtUzDbVqNEcHeUm3DvOT4HW4K4WKCakaa2wigSqV98Wy08p8aH7ZS5wGoY9r1WPqkQxLX6UvsjpFz1E3crXK3Cbsn7wtX2gWCyrtV4czeLemgOERsmgvY8nZvsIkePQ6MB3pi3y45TTV3w75lHpt1SWArqVod6zTtYuTJeB3jelF/ocs3ATl9R/s7sC56ic15061JPaohuvbo1LDdvsNiYwd39BU7tkpqq8/1HaUzBknQksye3cwSSUpDDMT0a/DSwr/rNR+mCDqyMqNL3KDgbSPpYTNUjLNN7SNlVkaEQKVwwBVitBW6LS7vmIrBIOm0+nZKZLdvcaBPWR6XRiln1eu530gtZclHxsyLeXWpo5RgtuxYncBZSygfHcNCruL+g8WAGTjpA8Qxebz0Y2qG2BBIgwN0qGkpSCRe5wyS08PU1O80icEcaNNF01KfQVwd184tzdz5TS+ZbgAjeISkebAOlEsErwjNKRnsZbwhJFVOlUROjCVEl+u0AKVD18zCbVu1qafBVY9jYLFGPod6uHgs4iTgstZDXsMDC9QwUyyQprur9hWnUo2jalpDvU01k2J7HGOZ4kHuzIpWj+M25aMbXco5W5FA7+pxppNkLDwf/ZR12qB+RspFBC7pNpW0M3Y+97/4zu/9lM/7sk/6/C/7h//k25rqXR3o73zb93zGn/riT/kzf/7r/9m3fOFf/Bs/59f+9o/8lM/+5M//ot/7BX/hv/mdn/6LfsP/+lVf9092zeTIHN/6bd/1yZ/7Rb/3877ou77vbYu7X53Xq32e+3uf3/db/tDnfurn/YU/8Ge+tPKJesU8vdLn/Opv+Nbf+3lf8pl/+ste7We3dQ43vu363o/73M//2b/x4z/i4z/zkz7viz7p8//8r/t9f+zn/Nr/9bd+1mf/i+95O+lQXIqy8D1vf37mn/2rn/x5X/ZXv/rrfssf/pz/9Lf8zt/+x/7MJ3/+n//Gb/4W6zx6P67zG//xN/3Xv/WTfslv+ZRP/Pwv/n1/+ks/9vd89n/0a3/bZ33hl5w7ZEOHYa+ZL8qIiNzIZIDYbRVaFnuVlgK8ZL+R+2HbTVOAhp2K+35iek/YmlCBjX4CNaKHGu8pEwCJOXzNNw4ej+KDl6YGzk1ljzBdfJk2OWSbzdcqaDzvqmySR+MIeo4y357hV1OakdRvFtoJGK4S3k4H2FJmbylksKfU0Vey1FduHGxCTYZ88rjspSXbe95uNWxZO2K+7PUIH1XFkJc+Cl5T3m2ryiHDvBZzEg4OLTVv1HzoIULJcjezLNL23Tj7LgtBM8lXlVFXd1ZFBG71zQKzo6EDNGTdHUFRcz9Wjal2VF2NE1HqkLgMRcHX7rRnaxri1WQajb24X2DHRXVYZlSfW5VuSqa2/FFn5/baHLtnNuW0rJtFtay970j9DvK+6C2WatslaLVHb4GvqYSvEY8Vw7KGQBU2cQ3kYBiC3f2KfaKVfhq0YimX99kypN2+zNuPYXoB8/qOz/pT3/UH/iwAe/Ob3v1/+RX+bu8a4kYFExev8sRQsJaN7iTCqAPWUG+oapWlXepV6hrSiht1qXVR8bYYgrHcr7GenEo9L6xQyi6xB3mkq9msxnA48zbpKFUUSbPEsXEwpg4rU6zGknkzNz9A12fqgoEqgBvLTqt0Kdl2ul3uQpGGvfo58xKfACXwQUNnKpO1LQtX10VcUTsrStNnUPcD2hPZ2OYp18OqJ1oZsCrN3Zrd3lNIzCDmlzYMimU47G5WaClUTbEilL1Ah+WtQZdGVtEQbVuSPqcFmdl5lRBlQAqp3V2mke4Ps+NOc9JDdPAIm+Y4rB9RQ0/6zury4AjzgMhgm6WZ4tlTpd/TekiZCOpwJbBJoxA3gQZEN8vYjjLFXgn0/dNIE5fdwsHuRgX6aX6pOR3kkoMLcRbO3WdjD2KSboKHiR4X/ZTjAzZh7RkWl2xonHpWa0i5w3VLNGnLVGITOazkiEWa2XQ83OdY5dyI3periUULR0rdFj4w1lArS/cEM5VUmAGOpiIqKNoicSui0EK870tJq3Lr5aDiBzVvAANcWMMO1zVdqOat7eIgh7ubyd90mtYry7xzdPe+AC7rcNM485oGQ7XF3eYg3m4A1DlY2Lowc5q2zwdzhNiveESsxjC7N3MsDU8fcJ1wJkpmZATZsplk2yLNlEpLeONo9cQeQOMOqrqN6bPntHE0XUlWGEbzLErk1BOza1jbkA/j9hk2xDLdLkiyV1i2o1Ndk/v2Oj2Osce6+MhaVk444NreL49BiYKdAy0P9OJ91Y6ehzwYCOpN7vS7DBe1bab2nlseoMI4I9WJFZYasGVCZgxC5jFsT8uJEqe4MzCHT4OxYE1rt3zRehqpTcuE3e9V7JYFDks3qeyqyUs3a0YUGKQgmGYjDcHYls/JNKPZLHeaKcNrm2W5iVeOa+LyVC69e8nWDP1KxQ7IQu+VpZmDq1Wlop3orN7luj4GjlGLiVuyjNFlqWTI1Aj6fg1nMFcr7so2iQi4g6emzgFjdxQueNUycNltzeo2B9J8cBqNTnc4SgaqIjvCSlQgS3WjmhYnkBVVqc0hwiQAABAASURBVCJpXIWzVcmZLEiqsMLopKf0H8vzeFQdCoANT1Bj3JIKgCLbnMs54JUz98xAZgR73xKw6Mp7JmrCj6YBnSp+eMgAcjPD/bKZ7syyq1htmAvTZbyofr0zsU8XYtGEVmCecW9A1ZttmQ0f7FFyXNVypISa6RMAYUUraN9JpJRTbMlaHgDvkq8SDNaFvaOuGoHZGDm8D2IqUdmXEq6/mbe3MnldDImFw+A3W+rL2k0rihO6aUB3MzqyL6pErAh5rR6JcaHCd+gd6OEwdLA2om4hsgF1pI06WddNyk6c2Zkw62nQ6vNIrByz72Ntl6OyuFw2WnWRh5UgyaxTcVMQb1r2WfHcFjEq5VS022XMA+Y3iofxEfRL2iOMQmFb+zmFLul7wTekjF2+LYtydq6+Vuo8xxM8EdAznuuUiLL8at83F20hCJWWwV2Sf3fo1Ip+0Neds2myXWp+NFo5EibVWivYbkSjhXSVSGd1R4FFT+dWD+DKfeSFrmQ3hBVHqWChnjG7VT5GvVQwk7nu60OxlSwolN6D8qJYYTvt1GGQyACdysnzsOeiNk9Fuk7eh20oRwiz4jzrJc8De0C63HujQmuEFnufGrDRmLR7FsCmXabQ2SALQFT/gS/+a7/pD34hiR/7w9/vx33ID/2h7/XuQvttb3v7r//kP/Rtb3ubHNP73/oRH6TB3/bd3/t13/jtaEovJcYNf+trvv7tr05d+ppv+mff+Y7vV9VNljGI/Ltf/41R9YPf8uYf/X7ve5gtx3d9z9s/9vf+75/1xV/+3e843/c93vKhH/SB/9oH/dD3efe3yPUf+yt/+2N+3x98+45clh5dN5kyq+NzvuRv/OEv+xvv9a7v8qE/7P0/7Ie//5sOX2xnfct3fNev+J2f8be+/p9qjDD/uA/+wA/9oPd/zPHbP+fP/7n/46t0UgcpRMNczDvaDHQCqhtX+8loXG1PUUJPDKN1QomvMvHkG3NTm+SX6qG0RgbzLjvtIW4zJvtdBLSuStpqUFhwuem7LMsNKgs44dbwBhqbtZFVlVJ8keC4j1EYG16gERoOSJpae7XoAlA1mjLWZWZU3ITYlf46IHkLRYKXuZBpM3gc6sMNVuuyFJlpbRzUAS0zAxfU1NLQ0+04XPfHrbJlCJ2Vj3S7QVZUwC7R1mobqqE0nWgZLSgEE2CwB84jT63BG3WabKCgGAxaHNygXiDsEGqNDWU1rCUdliC0MAIVZProOZteiFOtrc+2xAw/QJqiho1ug6o2ripJo2wPph4jiSIL1R4eyhDCFAOsCfX80+NKNZhFTCVeNAS0OddSl0BoeaSt28eOWduQa+RUti0QE/voOCJXpu9k5g02zbMAYvh98ybaizNtldQwQfo0nF/+97/v4/4wsrjmu/7mj3jjw37U6/6wX+F8VrNNS4MT3alAtHMZJWcaXUkx1zKf1dVoRVGr+0i1Efd2aLJ70lIXk60Gf0GMKBejQ2nZTamqKhwlMtdo7TYQrbWz7x5Ud1pIG8kXaXrbKJNZX9m6p329sIhy6PZnVI7eXiICVVJi2diEbrIqvWtmMSQGIXa6SmZJW7BIrQtxKTBFx7CWItSMoq1s5WvVjvCxD54PfwpGlVct66OV2uKgTffZ0Fbz0ftAUkl33AGJ/UqkXrr9EZDoDikI6Olhhx7n8aJvmEbJL3dXBvpCKIa86Slop9WTpikbOKm5JeIGYloPawW0KWmFlLfShtRoAkZ9ZNuU7XDWNNe6XoZqwx4a9eKhAuI0qiWxocX0NTlHQDQ/uoFWaq7ZqdVyNIQGl/PJiPvjpsk9RjYl70tPJyMrQqdzPHq+FF9Cmhs5Zqql32442i0mQuCNWRO9Ju+N9CRs6bCBpUXN+qJdPkSuwnb44Xhx4YjBSwEo/YBQXLnB1lo3h9nsGKl54HjADsAMXI2pTzIPiaRXwqIUbDWhBGLSHaRBr/Les8vD2EOT2qizyK4OzcwhCRsSnMEVcFTOK4QjVSoVhtAVG+liru/wSkq7F4MNeGG13IiuETUKA2JUA0pLmoZpTxDBTNksmepW6DUGppUpsXf9wKjsSvbyTBefhmUF3hjvd4Ur4CQCWosFOS1FyotJmDZCMekwshqhQiAE1NzE3OE9BqHKM5dI9SSvBMbgiz6TXXrdyhy1zVO+wUkqCLF1MJc2F1NNwDuH1iWZdyutHzLNdt6hOm6EDluthKGtBTJ7BrSTw8W6euc7ovRXlE2TbN31Z41eK92gl6J6/T7YYmSu8CNTCZd1Qe62W70gh7lDRGi8SmNIEVdM8d3lRuW37TVdfWfxgqmawhHeMg7IElDRlfoFdCpY+PBY8zr6slsZx8kjawGcULQbXbtvC+vYx7xcoV04nsq3bHaKJNNuylUkwlKl9aOpiVRZVpMn/AQVvwudTmyo77HnwLHsxfylcdya0HhkO2mTU0ZQqS6nDAYOsdVmJhGeaadBmYrQJ3iiW4ITqLIqfbJmEKfFyQzsqu5QxxRDCjnAkjqAe2gOUWiroBfF2jWrCSE0gN0ahFaioHNp+qjcz74mz9bfkQf21CBCF+TSUsvSzIiG7uhVTgIvTQWylDdHlHqwRVsOC2391C1Wc4XpXJ5+nawNt6BHsTQfGomc3Rl27uO85JpQghIXUFRyrJeFlcRLlRIypfi0wbFsmpFuav5GZahDQW6OU1JfvAvfNfgKPSzc9MIqAUsrCYWmtuKuUsrqbA8tODcmuIipXcwYyIFWtzShY76mX6DLrhSye1zrQp36Mp0Tojxad2x2WQau1LiRm5YYY9x3+Y4RvXYeXlKFLGZCsy8IqmHQRtvgMk6WW6N0yvWW3SqqelpJXVBqOt9gmWBXwloEkNTwvLu3xMTMIRZSFEgOuKoT0dzk6Rl9icZj28v2R0CbShMzrnaAKJGk7hiGkIU9s1kC19bQS1Fvzxp1a7dBzl7oRag8dU1Htz5qmfKnhkPW0hzTmq30dj4bG6NFvbLWhotQ7CUXEpfGDKbZ0/jUJSsFZOZDvUMLiz51uYZbGlXk9yNetG6yTou4HVBuxtEwdHord6Xxd10JCbsF1IgqS00WaRemyMxLd4DXCfSYPibc9xiYwwyueTpUIktGi8i2bp3R8be+9hv/rR/5w3//x37EZ3zUh3/aR/2yT/+Yj/h//ts/zshv/c7v/tK//RU1ts3zA9/zLW991zdr8F//qq+vEA2D2leM48v/wTekQgP++fd839d887djzh7tXd3n3/gHX6vxP1p3Ii9vWcPN8Af+3Jf+5b//ddPtV/6sn/qZH/0Rn/KR//nv/tX/5Sf9mv/yZ37oj9TIv/cN3/zFf+8rpeV0RZRKiE7q+M63f/9/9u/95M/4Nb/yUz/yP/uUX/1LfsQH/BCJqZu/7Y//uX/+3d97zPFf/6yf9ukf8ys+9WP/q0/72A//jI/98J/1YT/yu7//lSbq6Jglnq04OYwHMCSBZe2obDXUPGdcK+7aEq3WYO2x8yH9CIW2tiqZ2XwBRykXos2rJcRFGuUgunbvpMS2xItyMmdq13tITnfydKKK0bf29r5qNwKgKcw9oRHLqN+q2+0RM2rCtK7ZFeO8fDt7aTiYQue0genyhZIlXnu1NLfTeE3vwfH68mizwI2r5arNjW6ym7OvBfHal1lSXfQWBjtRUan7296Tl9fJfSEv6bUJb6zsiaI4vGVdZJae3niVKeixhQ1bLXA45EqaS2t6adtvM+jJ2/1YbaZn7SqFXBhL8sZSLHqrhxiYwyBuVc1nKQHNSTOQhY62mHkNrYgzelVOJbUJOtaU0C+Xm1V8OGVpEYs5it6+4M47f9Uj4Dq1PI+qWcYaVjO2KXWqWTGYw4IqiW0VhgI2sYfcLJGKttwjrlXhAdF2e5GVy5SYaIoEfu8//c5v+qhPybd/P9ze42N+4bv+vH+3lvOFtSrBqNU+bbLY51nXq6vlHWWetlS9od7PAVp0XjhDSwYuz6RcXA+W+tohJorQkTaenNqXWak6o699Xn2qL4Z63B6p5aZX58yz+1X2E5ZKHSR6uVAnYClzzPakB/Es7L5NFRBixGWVbYN0cdw+ctk1pZqhAQUVtwIq9QB4ZUQm0z27C3JTTUM/ZIjAsL1G3WHpLAmyk2hV2aXILsQ1uo5h7q5+gmZ3b3ZVt46WJq+wDFpJxEevpfKll8lCG8IhQQtoCkjfOWX67HEMDu+B0GFDFxNZONGhMeUV9sw+G7pCMhhyKUoM7mDdKb25ubMfbjl1QC2pLSCEUHhgq+/vUTUbmgMUcJHR8lGdNzwO2CifiVG57tBxv5JXzWcuMY4dva+bEI4eI8jqbiFtayuOgF/NiuUxh/LJlVDG2GbmPlUg7D36NC/tKNJwtQhVQ0rOwqBA9m5Kx92BSAY014dR2xONsEJDkbLjQp2eQVS5tZuCkjDpCkta9Hyu8XRosxPWEPueQGh2JXlBMK1obQqwGIGrKnrL9qBgEaDGeqVlenR2F4AKXpv3KHEPenK4mVO+S6O4KzTGWwlrj7Ro2zSlEF28/Us6cwuj3Co1XWZyY8jMyLuhtQi4rwFWBgWnAYfjRYL0VCmLu0QRNVGjcynKes4KuTTkQDtb8Le+yMwqaNElKDlrEvVJvqGbLbtjkLKc4oAWNa5Ucd4Mt1Q91Ots3ExrTOHqFtEBK1jCG5KHZwNsBb8mxLBG9A6/0nShkBJX966OQqPLRBWkr9d53G2cmLMOpu7WEZZppE+zhen0tntSlp3XiFy9raMZEOuUtT4T17YrvV6PLKs4ek9PsyoEmtRH1fElMYvTSoU4sYckXFu5cAjLJAYEyOQtOp8dV9SVVj3EixMmwoRcFdNWpNLRIG/j2bJSItun3OFKCFIhsS5aecJPSGURvQO1oQcdroe3ecjiBWwhRJtZ24RN+YFFDsNycUlEdwut6elTTx4+H4ODUX5bixYyByOJC32GXhmlwOFFReloCNpYsKWFYGJBV4RzZM5LW4UY2evendIqneG1u7UR446FOEyFG4CsIH1tvIFao3NSSVWIQp9bSEYXTHiFeSCIbOq1zMQrCZ3Q+02+hl0YJ2aOQXNNKTBFqL7cTjpTX81Id7PlNmsOXY+yPA/JXnqYZo8hdQC9s7KY5YhZ23IjqhPEQR4cevebvfQItw0JiBrhHljUDcccpTWYPsoX/+UxOFkDTxPHbVQVSZq21LGGufdAWbQVzE1lQTVIDQILaAMmenYKi3ikmrScEm5THZKwOxsqAKJfD7ZN2zbOITb0aGY6m0pdo+DtA+5QbnUQ0Dkl+QrfieiQpG9zgy7RDMIIGh1mYl9Yahin643dLcBEpTYWE2Wt5pKvb8GgDZOeD1+Gm3RkQ+xZ1v3dAnamP9MzTbzZJvI2kqQOMd4PA6jOcBQfcDmXLkl5UnXYhpWNEpcH7ldTjbOgVv3c4zQ9P/fkRpYkpEMfbU/EgXNlH0kZXaAYaWZCrpvwg8pXUBZpLZgKFPKDuXvHh+cTAAAQAElEQVQlR6lbtJTIBC/3a+Kyq+oa3lPKNL9b8pWdoRhaC9QdF6pNzVq7I4vwq1aEXUEARZxDZUle1s/CM0f3tNRmUFOaYotaoBqFzpuQYbDWxB84bIJTxpEtou5zP+6Dfuj//J///B/6Xj94iKmc7/7Gm/+Lf/+nvfMbjwa++h//E2B37uNh/8aP/GEa/TXf/G3RTxuXZJHn/jtf80+G24d94Fur+0/+jb93ZtwJIb7nbd/3T7/je6i/0nzwv3KNG09Wf/Hfuf/Q8RM/5AM+4mf+2+/5lnc96C823vou7/bf/Sf/4ct0Wfjyr/76pn4CC5B85A/4cT/8/X7lz/0Z7/nub+KyphJ7B/D1//Tb/8o/+Hpd/7//mA/5L3/6T3/3d34XJUQye58f8p4f+0t+3of9sPfVJR12U17UM4Wx54iJTaZQ0dyIkbf0rapT6CJZl/HpdkJS6ViM1e1gu9ectog3SgWd2vwwu9WApPrn1hIgGybgaEg514A6kRLR3bWV14ZcKm15124PgKDdR6tQTB/R3Wf12b2tNsVhdhQryHZCUKFGPAqrXyeT0t5ZuIShRsYRLWHfBhdq6KNtARH0wiPVoLerV7kUaEIigcobhL7P4lW+wQsVFa21MKEhJS83XQN1RzGJybk4H1iLN5iuwKm45bxvPm/BU0EdVm5iIKtTM40tjx3sUHrFeAZD0YzJ440cqyj+zaCErtaTP7Mi3TEcVDevjl21YWUMG8UF7ciUE3hAX2VdGo/yCGM50y0N25qwwTmt3cpRq3EURnMYe7BdMch+SNlAV3uUHnF41Sh0+pXayJpwVtEMfmDOHrO8cl3xRmhdAoFKCm3QEuoD3/P8tl/+CfUN36bPLz/jx7/Xr/6PbGFMuCk4DBFaLPg1GM2o1gsdEzUsVbrTY5liK1Dr7kmEuo03suQFacIsMFW4X6vv1f51HJCDZO/OuhUsu9AgJa3LcHtG9ZWZ0WiiWMHc3tnYxWffZRmqjsr7zqi4q1MwbRthnN6PQ0GwzQrSwkADwuZ3FQVM7mp3nVmhj9yOnFTECzUay8qmc3gMJroTaL/nl6hDRmqT3hteNBkeHcS23FYJVqOeZVd7hoX6lFqv8uBjuC2W6Ryz+jWcXFR2kitFyzVMVyEBmQk2aHC43hsBvGo8STEcz1IvTRgFalRqoZ18GBdd1rP5+jC9N9jpvT22KUoBaxLWurdRvKXiVIkCuyFjNbxdc3FzrKY82kb7oykZwaxAdRa1mOINc8X2DL+dzLIj7SEmnN6mvqQW07gDlN0WnYoNIhVFHcZ2a3doMCr5vBhP8Eqe2aFs91me23FfvL9mIQJSQcq69VwYBI3KsQ26xdzac+1CYrYtXYJ00IWbK8WcPaSJlFkofO1EYNAnCcmr26s8ida5+yxRFsa0LBYEARqjpOK2GWJKYxVaRWfWLoZP6zXNhng137B9C+YiQsMas2ruUAcDIB8tArzc1IXLCxbybTUMhgGOwsjsKjnVbFIw0dDr/oi7aiD/IbHrsWCWXPZiPuK69SMlMApN3Bmjo+xOTyFFRLLh+sfMO1KpWl1FnVaBqB82qtq6b5YvahFfiQOiitoxaTe3bp7LvHpEswOld23KwlQZLSZH2AyzGJ0qRMSFVloTAhAtu+gSl/rUaDgUCsTVuGpJNsttmi/cberQHs24vAcrNab9okWrXGeGi3RWyAQo7B1ZqSR00NMsrW+3xjywB7UyVLS4nKilGhH4rrh47REq1IDog5gaZpSW4C5B0jeh4gy0QiSgFAEKwK46sjVQAWS3sM3NkWVR2SAx9a7hCbmQLkBQKlcGGxqgo1ovQsnfNl5RcVUDTq0i7ugygw3eSMrZtNduK4mU+PUwDWMs+qJuWIJKlBt6QFYheEqBBBrop+cla5NSUg9efq8FQjInROocgFlpOh2l2aM5ilZGve+0OlmXbFa6jk4ZVVBgtwMkoOqzQQFXdeGEHldkyoLLrOm6RoAgbfZ6KB6gSiuUiILf151YBfWH2fYwvFiu2zNSbde6q1pp8fbGsXthNB3TLdHRmVrwTVUvvSgWL7yuFZmlCIR6UbYKam1qPNrAMcB544spjEuoW9HhTjFNwxQzhpFj9hytQp7AAhy0xmR5s0nQIAeurOAWX8IKQ0dgti3DAIKdcogWp15CVnfBmXjlaF/tL/ChhDV1sVs+NQetjpqcZnaYzTJrQ01ySgfOanbLO8T9xXplfYoDK4Dr5BvFQ95BF2usopgmda0lOOG3aZRZjJYdWLsp30pwdlQlwmwPew7c3V6zoHPcVArQ0UrQEZjR2AqNWN2W1MeIedEurHBlgref7tE2YTB5F97O+6zDnG6yFK40IZCvKnaljjINE2GokAc9E8gNwNssi9kWOopyea9ORNd9FU0WPCCZ2Op0XRBbpvMJfdawe5x+NSwM28VbAjkATsecsryTITHPxqoa2XLS3dV6wPl49ouem6TYS01Dg8nBO/PKKfRS/Uscg6S3virMSBfK1si2hMmyzv/AQX3zK+/uxB8489M/7Ee/cYjIFj70YPE93vTOb33zm3T1W9/2diFxUQj+1B/9wTrzFf/4m97+/d+rhXYw/tE3ffM3/vPves93ftN/9v/46SS+8uu+4Xu+9x1JB/kPv+Fb/sXbv/dlzZ/4Y/7V03ChzuYf/I2/9st/z2/+bR/9S+c86ENZEF41wHd553f5wB/8HjL+nd/zfRXXEC6Zc9MZHT/mgz7wZkoa9Jm8GQL8S77ya99x7jfW/PCf9m8PG4bgvVtQktZjvdOHvP+/vFG5u34r2LQ+h21oGcpq3IWxJEKvhwq8S3fwVOPq122Zut7Su+bs1XHL3aRicgyaKzmFS850NEJpVFPSaB0Mnditd6FHkbqSpbyVG9nGWE4jTD9Am8PYUKolKQ1tDe1AbyDqVgOqTd94iW0QirBQUPDdBCQ09LRcbNuEGAZzMhdkTlJXjHNIY21RzQqjlqmIgTZFActmFqK91BCqrDP1XX5obsOVnBxsGNIADtMBd82FCkJDI1WCfk5KapCHIIsOoruvqgtMfQWFuQutD6R6uKIDTXarPWUJDqhzMEwta5itSXcCYLeChEg0XpyB1Tgcc4VZZHPvR+xVOc5zXhfzVeJ51VURnkKesCu1+BQkMNrE3UQ316mb1ZW9EhQrm/0c/hy4HwBNT9VVdpcWjzMrUEJQRC3joWdQC2OUCVFHxjnzuWofadz2j/8/n/79f/2rAbz86x/01k/8KIKPax+vruOUtvrhOEyN8toXAqQ0PAtT3NJyjD3WniPIFNMuCNqpajWjUiUqEmv7Oo2Kqy3o5FI0K32mkheTdtgwNzPXRdebtzvMKMH2wrX6NGWoulT0GVmKAdZQYN0igeLfd+qgdk51VD16j0p0EOfKOHY9Nh9b2+523NyjL22Ar3uvsGoqx5d0IiQLujsbLYzmzoM1EUqw+ECgnymZYlsXQE6FOpJDniW8unnHCHNtZXFxbgMNcE+br0y7cMk8j6jRSahAznR9AR0KfAy9A4owArs8rhXnS55HS/DlPSiyFXFVp4YJm1afRCbZ2n6IvtFmZZHzUoXfI2NkLcSI5GbEOnWeYi2zr7B7Ik1tOQABrsJAz1mcSEMEt3agZXiNzylGahVdQTUJwqf5kqSsR4Dbo490r0kBaj2K023kvIQZ7XH5FX2x7u3TEPqUPnds+cBQCp5VZ22gaMQ8tguWqmBnicUaS8GXSJstzFkMMoTwUX0UZ46hbdsE19YXs7YSAnnRSmXb7AJAc+DoPCxHyxSO1B/7h8EVNCdtug4oWOiULyze/3m1USpEOzev+wGMSlFZlXdF22Mbe0XPJ2WwhhPeLWWlFNKecmLmsmpyjja0hGI9G4pmcMGGlrph1+iYCWtvedbvUglDLN6zScPlSAdYQMtBt1JS99cZUng3dRFApOTuKBvZelZ5PPEoqbd9qAjqWX2l2Iy8X8QWjkrl4za0wHmlP5+IQDRljUXog6xnVGV1OTiMc4zp03G3zDsHigui2fOmjlZhV2jfyrht0NrRM5ipB2fz1evSKPqGBW9W0NDAU0T6nrYP3A+uj8daalaWu/OZfQYq4d62XJxFz7PXFo/tyuy6KNFRFo7msWPGJWGfhkSzW2sfOzZqSw5DEejoYnZWnYVUkbhyU8ksYVI3sgSJ6fZyuD2slm3WudUrKXeSmw+TOPYbOCcuvKavJ2ylhNewp5QFGjDv0C5ZNZfzWKyJOaEqr9nX2kmtMAGN2C3hDEGpZqRyoOnDlMfimSzF5kVeHmXnMMktAzf0Zpsrv6aXQKdRjeKG/vCeo7rFINOlMpX9Neu5kOzXXRSh66jRKXnYvHpefEk+AL/bSwlMqlp03bhbxWjQq3uG2hH1NMLEgWBLkneqW68KjX5NZo0TY/dA+2haWwkuoUeNwIM9DuSgOGmm4mUrqz6WYUhtCsnKLJdpDAoKmpf2BaFotJj4I5uxkdGBLk1ouQ2vhDicaQ/Mx3AtVnNjP/u84lmhbm1Dr0UbhMvHqBv5ripLIBqhkPskTyjLLCSsOAE6qDXVFE9dEWdsbbNLHI6oleVbn6tZ7phOghGozQ7fHK/GvHwBQ04SOywkDh4maqChLTMoNFGuoFcqhsu5Iac2nJi9rc47dNFILtYSx9NCn6ZBIaAlQbnsCtrVdqUoKZJmcqAvUXJiDYcQmhtcofsJr2l+wJC1s/cd7YJ0N+ueeJt2vemUhrU0IZyg+7phedGyQdwVKM+5awd1oq11JeUS4OoeDaT1xb66C5t1WRUVsX4JK6vB0VTZdYLakaqyeVx2hPFq7E6FIFosDE/YiWydF42rtEcMZ1mRNakdhFpHedTaasqgUmIp7R0UkDVsFfQK4GoFZmbOdFHtsecozhu5oyfh0LQxTp8nXr8Ezdg+eo2x1qSLfWZUyVqLsjtVpLRyaH6no+z1vNfpUQARdvt6DQBwB2b0CHmheotp9QtxqSmv1BPuvKlw1oe89a1vfjnOqL//9d+KmF3+t776699x7Z/6Y3/Ev/qvvP8HvNe7f/N3vf3r/tl3DBy09Q++6duz+oPf+73e653fhSn9yHhQTsFR67ryO77zbX/vG77pK77pn37FN3/rV3/Tt726Qu50NO4fhuFfnoCRdH01XqZyGmAlvvYbv0WD9Wef93/v96BdKhEFQazkSOrK/3l47snL1Vqm8r7pp2u0UritQ4ZKg83W8AWILOVEQx1hGTNyvfKO8SRPh8hd7DVrjEQLXNN0bBulHurWIpeU/sDWC0S39eu6ai+l0eZ8GF9QUxXQ0EKpBtgJaD8gzHeUKG9Yr2zd7b9Qf1xForYsAZa1znKxOsU+QpRQwAfv9Ml3y2HoHzG6BtoHbDpMa+/MGhaNncidlqmla0z4wblsuNkArfWJN22EmphgaWCzCyqCunxfUxWlupL1RRz22s2Vs9qpzqJSdwasMrjb0lDIetX9CvbKhPzlT31UPgAAEABJREFUKtVfVWuEhsFZ1RnI1Ei5CXEuGeqS6ZuSNWyuwWOWjY4ZvRKrYvCZriA6zbNn69lfy5cEvqlz3eK3qbGobC2oEX3vk3hfYIVt2ukVHu2yEHOVWyKaGq3W7iMMyYQ4rO6wCgghSAW8OGYvlgMkaFoPcgLf8XF/+Pt///1/oPf3fJcf9AkfxXd5pxSbjIE9MlkafQ/uChXCaHvMsdageYj1BJJMV8xR+jAHfEpN2n95m1HjZ8BSqhvI0WrxrTuDZe0gYcJlr5maPmFH2WpzyZBwaLpP40MJAqu4FUspUjBNV9lkG2mTYwpEtcYUxkWqz1SVSGJ03dto3TeMBt3KDCTarV9/GkPf1LupF5QI8yr98kkzV3YypcX7GlsirRryoa+H42WomqAwdmlnbqVqkV0KiQaCRpiCfyRmJW8lzkxroKwL3UpDy1PiNSsTupLRSi6jWiMrBd8rG9mhnB7JUTYMi3yUexJxbckjMdJMA+ueaN3jPoreGNEm3ykfyvbBdNFoVaxu3CPZiQrULSYGTRlU3IagqlawmWTKYCwoxc2GTJV+kRBFLm7QFfeqwvz/cfWvMfd923kQ9jxjzDnXfv/H8XHsxHfiGyTHNrbBISTQQBrahvKhRSBA3FIqBMEphKRNuRRRkqilBEFFUYAAKooaSqBRUT+AlEoVFBoEUVVKIi4BDFYUDMS5OKlDzm+vOcelz/qdfKi6f+t9373XmnOMZzzjGWPOtfb/6MAwifnZvkLo1OiCHYxTOrg1l2hK1UfAq0IhUAW4eErJtU5ztGm9mfE1JG7wQQ5isCjUp3IjoB7I8G6p4Qjhla46GKU8PscAJhR/QlwOhF2HV8NpekFrW08lwg0ESzyYm0EfAHhTiTlDVOmdoo+eW0IC0k0bap00wiGJzKCYVp41qgBS0PmgIcorlrLMfnCPUb6SdreCbzdNW5ZLfIULnOpeoWiK1NGAFLrapuLuYUFsQzrJpmbLlUhiYIRCNA0KEy25jfe4jukpiGTuhDFpx6z8iUTGFUPgZBypxFgKolopVWLj4jZJQFIwyNHsZ5rJe9MkH9PAptCWLkL5Uks2r9k5lMEoTaVE1p546u4owQ+NQwbCLAzdCgoMVpo6F48Dyjxab0TpljuIGskSGusBnt6xH40wz0xcnNP1SrlJDbEu4hDpYnYYpY9ytLOr1MfX818tpYmvx3dXRwtCKzg7ZLFhWbNsFJ9zqprdqW/k4AVa2Ststj+XZ5dlZGUoAqrmX1T5aZryAKClIgOnyXaoeDNHl/aTNhw+hWBDDHU5xuIYtmwMdyj5H6CjqpkyDFmCQOIuy5Zu2/swn6JCPddbVNoqeGukEqiChZLr+RiR99V9SdbenG5T/cFJFGUKEMSnbCjaxF6op5DGufJa97K3uTbVZ3iblXqbhsuHKcIyzS+lJQUPIGCVk2EmCtFHl3tCraEHtLl83nTZybFzyZIRcI1uTe/OMiS8a3r21feFUGoOoVYdGqo44OgHtdYJ9zTN1tSjO46CqiMv9odB5LG7dlTuzKMlrzQpYW/6lhjWFMGjemjEvvc7dqVKfEz6BCaUCRk2xfB0gB6J1ZpmnaYUl6Rc4kk/GaRCNHA2xTyC5yFeoTz8NFSZmgQtDqFIhrm1AlBKH33Ohg5n+TiSUbF39i41I9EvyXb5064rlE5yeALn5D67SiTRzUR4lJ4w1K2lHaWP2q5wBVcKPWKZaofVj7hhG3y2RtkpU6/dr8BIXauS6xaO4YdspeAGw2b5JSm+zEfXiLqqpyp90acC0bvKUdreiXQNlvi8x0XnvHBN+Q1rxXL6yDtDwY1UD2NJIabVoqssQKnt8WgPR83ypJtssPUU8cobqalXLYNRL4Fq8TxKezljTxx/bktSARxlsBo6rJ7qm6MFHJMYgzUZXu+Z6hM1SuuRTDhknXsUZprnPC1+A5keGiGHM6kd3TrH63Sb9XSsgWk1eVur/UMvQjTShIECz4ZiGKddwvR4Jok11yCybOgqoU+Gr70afWjbrUvX//Q5vdPnrs5EJydSpSsLz2Uqq1WvqPmNX//1f/b3fIfO/Rv/4Y9n4U/d+3f/B/+JPv73fuEPfMMXr1/0le/V+9/9e39/t+9jv+c/fv5vJH7hz//eK/arY6kHdK08n37mv/m//Ov/zt/6j/1zf8M/+k/99b/5n/yV/9A/+St/8z/+1/7mf/wnfuqPaboOU09MEZWo0kcdU3UiMNmqdGugoL72h37mT+nSt33562x14VDR7GnplqAnoHG6/ox8zqkT4Mqap/yU7bJAFxIKXOyJHtg0m+RqUWYia7Q2xvEkptFDfaWyqiudEm5Pqa/DedzO5RIkTDuaMokL0J/mu8fWMI0XjNLkpijINi0iFvp7t9+w6KYCsiGR+4NhpBKJqZjHMBVXqiSp+4Q+M8JPsgxCuVZdV06LYXtxL9RoXQEguCT1ia2TM8fKJcWVXl0RprgLrCCb7lDzWUXptX32cKwuz7ITfscIsdgCIQKeaXfKgGJRKOZjzLHcFvuRp9Xy8CUpQ6pOeC9Ps45i0Ytya0bOWdN1i5ge6btUap0E0nFWn9HVGoiSW6XG4DSHuXGVsiGGO1PlTVTNxFUqu2vxNXGtMh2v9suGnlkNtNkBKjvuXff7xBv9JkXgaTWRk4KnOY0hgNrzTJ3Z2lNx5FL9iECMHiofsVi8C1vWuonWP9IwBp4E4U/8rn/7p3/LvwzAPq5v/kf+zo+f/12kGl6XMFzdi7BneJmVgtGwNo+1tMHA3MsVelcmz63eYo1D38OC1RvcjuPZUIIMHDIMiLUnMOWWare3Hl/0LLVRlM8DO8VM64TaCjtaG7KFvkzL23lCNSHQ6DftGFTpPmGKRLY1jN2xEeFPzWMcjq0+aQgBc2DgLO4vVn68/GNdk6vBQKXU66k2LHuddvLZgqYp8BzYiqtJcDjHLKNW61JPH0UE0Z2rzqvTzVuHgi2grsbCY45sFe/1sb949XQXlcUZtRBiaSARwJGRbpkaaZRoaIXSCcCTdkM0VD93p6/kkg0ht2vF0PN8fH7gbzugVVtbG49eaLqsMWWj9KG1lRA/M+lnICiVuhzlpRzXZ/e4qU1SH6Swy4AvHzRlrqrSMlTDT0o8a+ZtUCu/LQ8jRyWy7sDtPNpL0MbxmXXVPfoe0sUG0gADox8HrZ1q12xVGNHAcLs4P3K87nN9eq+vvtXehJg+h9bGXhWr7tVvPS73umcfWbWNAqIZydSJIkq2wIbMn1JehypA6Sfeoz8Boapm+SJe8h5kmQZARJ/d9516oWUTPMjdcaL6qWw3bSsxHdM4q4k2dxMyE6nWrcfQIwi4DhrXtCnn0vLTGyM79oV6jb5Ek+V55F1mbE0l6klQS8cP9oB+5yybZ9iBNCLDbBcqikeZJCecjRZ6QD3N56H6rVWBsMYoMQYs9Dpp741qyFfPFiM9y1nnaM9ke+k2G2vZgF5Nh1aXfNVRiGg7Nm+bnx8JO6hKE4DerdJNGUAd8+J0+ijlVruryGL1RD9fc7VaYRNIUmKz2/zw+ZBgpd81b4yWNsU9DRwaqpSOu6ldZb5PvxNv0xPups69bai0bJi5hCQvBe4DHSVsEyDEG9gt3ocbXuNMHnZa4FkvEWbirzb7boRTLlsrsvy+HsBahHK1i0pVGk95aslqZCEewyWA8XoSotY2E0IhItRbhpYdGwWGcUPqXVuco25xOrQASjdKNHeqI50SSJZTHS+uyTXHxetpIq2ieGr29AxopimNFHMb/NT46u6vRmnuGr7m5eNqWhuaIUoTMt0qTpdFewRQQQUxqt2eEDmse5oq8vgS68E+jjNHXS83VxKP3IM1XWoWRj4qE6eqsou3mfL1/Dn4/JLrVsByr6GiNI+hFMQFSe6QR7nINB7Opg/EiFDVV5YUk+WNrlbmkSL2cByME8gIRFF1BbpcGBNVPC1RXMzRpoyVWy6GX/TLTN8WQryGRH6aySLTWw6aUb7R9zicG4ZudoxNBAp5eELnuEitsRdtEIN22VzJ+W5/17ULWyIvfYsaVcAxO1gFF0WXuT9zWA8mApIj4kycqyUMI600zu2Z8ArOKq09rHNFXRCUqgxkDaasVj/1D6AF/mhUamvQOqnLUmmnIaUYMHUed877LFHOG/MuPcu52GuVXQeesnJKcMuSHhB7RnB5+LW1XimV2anSl5sGaJiabIGrSxy7ZorE5xxFTo7cfJ9631pdXHZeSzEW4EeNHR/smekZxW4DpkPR9EgbT+SIFn+nA8gJSWsLg82mOagNjp5gvxJUpkzJC4f2O1p6bVbKHn1ivvSb7dLazGDziamRgMDxcl4zL0G6lLQiIrGhw3V9es1XQ60ALa7v7jdwMtuiZ6s6To7T0m48iUdDd7J2n3FoLT5ffLpGaqlVwDLXtEIH1PzOXffNT8Hd5j1ftj44UBaKFi1sTUoO3fjTL9NfM7Ih/K0POqaPA1ZVKvlpXVa2dF4H5StGJG9HXvYXf/6vv/6Dn/gDe5yf/GN/6Md/8g9965e/9JXv/Q4/+Rf94FeG8d/4vf9RxP5jf/RP/Fd/+Lnx+Et++Ptt1Jw9yz350z/zM//T3/rP/4P/4r/y7/74H/gv/9j/R/b//w5TOop5CiLB9fu5LmBmEEZ+LYnWyuApBYcvf92XlO9PhTijtYu4YUqN1uFn3vMjolZgoBV3tbXa7dZK4adt29hzxnQQRRE6yAu+zKWXAfWqSar9+Zt+0xpU6TPFhRIvttmDW+wqOTzI5MZ6h2XCdq9o35tbhrsbwZ1+p51UV61kFI7SowhJw+gx4lmNCVX1lqUx6vXZOWoUVuXsNDacwkX65Fww7w4dhliqdR6raqRM6wD7MAPNph8oQR27x6khK62VcR9GC7M9f+Ug57zVXSdCI0ZytrGowZAE/cIcMZ6KFNXO88GW0PRd8eIY5JjhS4EPmD6kPpJAmfKmSf3quEa/pkofy2KaglfOFtWhwlCaWWNMn0vw9mvv11E+31D5mDq4tXd7wj9HVzQdOmPaEBy5WiNdpxxwcI1hIFLN2gjqJVJKzgpd8Op5am5i95OyLgao6T0czmw8VKmC1mVr6AzZzKL2J9JkPSbEaWVGlyftj/5Hf/AP/Pp/tj7dXPPb/1e/6pv/279E+R2gKZHm6TIxYFMpCFlfjaV5xjCpz1NBQUXVk4XUBqPUUVmmodprbb05FhIU0ehBHYM2k36EqanTBsnhQHoTsmTEiPIomcPzytJLTUCbdHFlYmM0LTWwEZEaos86vDHKxulrg2kHvuUOZjm6rqPKmxYDbYDJL82UK3c+DfdVOQ1UWjlBKjw21QKtuuoBjoo0LRCVpkYnG+3gYA7RY7rUZV4+TXlL56b62hYbgMKUDFf3BKbTBmDP1AYhxURbBdGmMErkPjlXVYolCTi/RkEz4fEUoGojLMDiMAxre6yYPjSFhX2kF3QBGaztUsUU6fbQ2W2mzYlo0PwyuuQAABAASURBVDUp0kv9sVeaFvCWOrOe9aQfX5CHbVTG5+DCGdTy9yQ1ja2LdS8GV+Gl+K1g3dAftFcLWisGqxJo6/JK7wKU59k+YcZxqrdGciz4JGwQgrmsfAgkFIBV+dkDqfT4oIxW2E7JPaFQy6ZCSYe2pWQu5NUPuTQ3o4bLBOQ0mc3TtmmJaAk+o7lz3jHexef/BIntCTulPCcDnWzo1YVn7m5GQwHJKEWxe3vrkD2dQVvlOibUNRB6fjvfOgWQErROOwT3aL4d6cBAmAyg5mkVr7WTbB2o5nYcozVcBKwomYZiKHFeZKXH9ghSKTC0RXtpfqufGatQYmtPT4EMXVcQBbTAC0E1nA+Nj39xBKEYxWvrrxQ7Q155iDTZ3wbtW5OtSIiSbamjwzqALCu9K13sLOkok5Btc2mxGUecFb38ijFqeiuepDjPEpQUG7w1rAXSl0IpRtg5V8XylqLZilQJBdByLfQow5HUuLb7LSGadVIPz/wNvqvzLujdrUrKHCjH0/HVoQj0QDNZtxZ+K+pipkXiSbaalpSSq3oESrEfKIuToyHBMIREALS3ib373mBN7U8XvZHlu4Qgmu09nC5fUCb1R8Rop6XRspYup0P11W5GuPZJKRY6gSpmfW1VJWETNeTg5AwZM7usdSrZuy3qmck6iQyHEqSiUitxYz0fuxGWCs7CKkf1bEAwkP3QLYlggIbnolpZ56mIpBUmUmwPSi4Po2PmRRj7lFVfeFbu1cP1wCJkT5ALQBPnsmPc6HjiOAVlYdiZvK1RYtCGon/S4OhmZJ/IqJIf1xbAmFDo3dX9jGdMP8DWoHyqTvCN1FSIqTiNgDt9tDKPGQqabs5n4zAVlwbao1Q9XDQWBj+/ElQYWRCRvtptfjZpQp1ZfsK1dZ0bo9L6gKU2x2V0eb89VQNpJpLM6pjubHsXzvHuVXNiaHXmuKBYY5aewYW2nOAN1Y/y4g57iCoF3yPLNPWhoZQpS0e54DX8mOdEjDuRCr28aWQ7PguJyqWFEjsAGT1m2UzNFMcWkGy9iFThUSuXfOrSp2bRoJjYhyppfYRMKsDWX13QZMByMomWNW2mxHEgq1VCyj2epzUt5rKRylB1QinQwioDUkbiZm3mYKnEKX2oi69pL4orl5cGxozhSd5HvZY5UKYUuTWJklR0aDDmyOmKofPovCJ4IlWuQAMIoKURBEvZgpiRhtVTTZke3WJYn5MnP6fHKsauPpr1DBUlyrw2X8UWualQT2wqFpNhzTW9N31EN6q1RivkokJtlohBamI5lS1Z1BQX01Q2ujtCbeE8TcRpZg4zCOVjTG9G0J5xmvYcYkNQRdYUBZL3cw7ilYQNc/1pr3P1GWi5Qcv5EwtKzLB+4Lu/44tr/mc/9dN/+Kf/+O/78Z/46j4/+pXvffnAiR/+ed/+7d/0s3/yj/2JP/CTP/lf/KGf+q//+M985zd+/fd9+7enRCzJIQXmf/0v/Sv/7//8DwL4ZT/08//Jv+Nv/F3/4K//Pb/1N/2ef+Y3/du/9Td8/3d8s867magMUM0D9gDQychkPwrjrPQMeyf312kfCfzhP/4npM8eqNlKajGKB4riT0+Fznpqs6UOIQDh9dxLrwy0uBzps9zT+6FFIGvAAedDRI/TvoFjSfUExmCTLX0UxZ2Dpcy0U12wWv0EBTZeWmdkxSmbVawuK7oM93ym6qyusKKg/GeR3jKHhnc+iUgiPTmiNfeBZYRqyjzaESYwMhFAjs5RzyATJgFzPvMr4lQaS4arVcr+9Jawlt1GcfSatTyHfJTW1UBIZLQHg9CXGpIajDxOt0kDS67kwdqmrTkGJ2PmJp7gBdE2uYVZhzHNs8eQm5J+EdUIhYLR5R7mQTDLMlVIGtyHepMqBh55GqQkqMba4XGr/hMdIZ5sdydaxjClXsiBJnSXrqO3XGUiWvhdcjGURTKC8kobNjVpQp5dJiQUW+iHIY3bbdF2erUWOWjTfeW+Dq5SeVzd3mhF2k2NErF9Anlaq0f3Pvsn//gf+bF/ov/wnwBw/dW/7Bv/ml9x5Lswi1MW6+PTvt73VMSfkfTwUltX1gJV1Q4MwqiYFyS6iKpPbe/Ha5EBNRLcaA1lhbXEMGCrpZjT4pUYUIYEMSMqtfAlJVgvGTiOACqJd1dkBivVF4fAPO04u4PQLMihjmxKBccYWteg7U1dDWsr817ExUcepTG6c7iACTxS3Ue2B3JRjLpTAHXB0E5HGHZX51c936i4oU1Nla6Vm3zJv90+1QKtJcZ+JK43XVZNhRjsFIQb84yJSf1tLWhImcjqlh5u2DHtUEyVZSMucsTie+5PEhhYBf2CXpRBudzWt65UR9ce935ugYbUoavKgsg0x1AMlfJ8RLRWg9AVMZe32V3jqIFL61vFcyFnpJ3TWRQ9JtKVWQ1IV2wt1/6sLxue5fhc0lssZ1Ub0/3oMC/7sHEt1zqSFmGqCD6JUwUUraQPRxua0GYoXrytsrONJrDWI2rmoR/MEHYBaShvPeqeuAHN08ei3627qlHS2avwyjF1c8VX+eT0QR94GZRoI7pBrbm9s3Sj0K0zQG1Agmn9VsCJIhqFXZY1BvyiMv6cAwgI3eTUYkpKoQ46SxsbhzqK2gfYmTjF6pd8697jiq3g+85QvXegtcwpBrxK1dqSLmzjapvakkvBCklATf1aXwapkGfDhEd2W6k+4ClxJCgMXtrN9+1qF5VS1gkLeHF2WeiMaXTSMUa3lEyqwBGtnqIragAjxYDXGjH9flmNSxiUFQXWpTiQpw9ie92O7VAGg1DO1mm57trB1D5NObXu2Yr3yjmOHJkgQENBsjgfIPP02nqWAaIpsg/GPjo8pd09GHP1y3HhiTcfn3Esk9KT1cX6MEpM/fx/0bENbteoyzRxW4elWc0ybzltkySRyIBuWG50vLCfDUtKJ6+Nq2D09JkmXeXVe9V78C0pPAfVuBOne1cfaVIRkhDkhCigvnD6BP4pfZO9yxT2Qrnm8lNLMsIdM1LV52gQEKLhZyJG+SWNtM5mE4I5IaJNUI/k2LowRJNksvf77pTwwatlOyHfk+UWbnda3G6p+pENf8K+2W/loqL7hmfNzll0DD5lm4qBd0B1ZVKcRqE3bVN7JwA9qzxDCrU6zprVU8xZnw/kGjW9CRRcggiYqJrva7b7aFy7oXOPFdptfneF/nmmIBARVm87oabPPa0v6DZC1ajN1tPh5PQUmrriqzh3e9TsGEYOe4ijoR7DUCMNdnd5q9QLd3WQzVHTjiUzJWXBVp94DmCMuV6uR/HDMdlODPQVXlvKb1obbeB1pTY0xkYJ7OPAu4gTUHdgUnTytMeZchDxOvmRGO5ui/lCTVZ1nTii0+dxa3kbMWfK3unTFpjniQlws0Grwo7I2uU6n1utzFjpeC+W5LGOz+Da7hjda9fcNQ5mjpcNvajRhyKdRbPElfTbGMY2w0UsNM3NCb0kxr7O5wzbXktMWMZVe3Xq6hFv0XZqFS+sxcuMylzj1D51p2SZ2/qQO/ucjKMLafqc3ZSA64UYqErbJTsWEJG1TByZXZ6XqTNyCF0ZkGC2fE30K2nbIzxaBKO6C9xEID0w95AE1DR13IjqA432NOtibNEX4KElQbcxhw/NwlFHj26ht73r3ue8d98xw664+kw+jTdiIV6QFjG74umkVS07YkydT+/ChKTfhj27X9Sq0qZhGX23C7e4FRAAje6RpWRfFHMvKhvHfe/RakqKmHzGOjQflu2tSX/6yEZ0AKVNrmL92lkRqaTDqCwqMDR9a44OgP3owI95zMT3fcvP+bZv/AbF+u//+B/8N3/fjwP45X/OV1C0Ht/wpa//C3/B819//Vu/7/f/+z/+E5r8w9/3XV/Mj8hrHxM7f/zTn/y//77nPxX7K3/pj/5vf81f+4t/4Hu++ctf+jrjx8XrZQpE1nSUsZzJ0WX6qCMyd5w29GqsgpUq42v/4/uf+pOf3tn2cl6prqAn8TFYUKapiTpko6n3CdzNmExp8HmeiVb2S9ecpKOdwT6JAg2a0IU8fksW/UBTSgbCNHAwHLv7aAAYZI86M9rPxKPkMZatwcW4tFewG6PMueTADZdvQXC5UFPAFeB5tADkVLvaOGm6Zi4c2/ITsV1BwBsDqftpVURpGX4Ehzqe+9X56rmeCChZZCcQxVS3cwWVxZNMUek9n9S+Eh9pH58RyvsWDTQbyzhCAaXhKHyjj5oQj2zg6CjiTOfLx/Old1l0Zp46B6FG5Pse2h/yT7U0Yt7J6soqlVAfcVyPcLMF2ebOtVO09C1Or90j5CKTvdMPVmiHSEaW7Eaxwnksm7IGJccLQ8aNt33tLgzVupjoU75rRulEmiW90pgwmNlwDNu0W0VVq0tbm0GUe7t+N0e6DOuAVxhV9jj+qEX0zBxToec0DANAlo9Y+afun/p1v+X+j/4AgK/7ZT/yfb/5V2EUBuBaFJCHdTd2h8J4OJSjvkyqENoOD/gxxIS55Cy4c9C5XLO7pfzBFCsFA9Ljnvt+ikBlBg65yJJCVMPUXk5QtphRwGlDGmkp9wTO7uqq0akDaDpsFPXE2REc7U5vVqHkqeLxZI9pmk8NZSdxiLT2od1led9Zd1VmEIH6pNA8a1S6GBdnLoI9aaQzZqnqc31KS3RbkTLUUxBseGOcI5bqgVoK3oeqLsXZ2WY1J3w0WsRiUHjwUGAnPE2VpuwRZsvdzZudiLKmDcK1VASqq4nZmCXPZjCpwg6yWwb0B++S+ZoZq0IG2iA2dD9kThnKkTCwxyCnl9igOdpazqx9THMbls49S4E+n7DkPNuDSpzhdN+n3tWq3h7lsjYqRsl9tXnYo/kDSa3LZdxVbNRA5ue80qunsFJLTUxo5xAqerC4ykRktN1mx0ujyRQ20ddQY1tUBRWWBbmDeZMhf2OA04SjCHwmluIVmgFteoU9VUIx+ja8XV1Hhd21QcGvARWEQUQ8dIhLYMAeMgpOXRiY1l4iB59fo23Ch7tNqiVu682zq2BGH+0rweiqbmMCpuRFZdbO3K3FPvfJxuY6kL5Md6dDTSJhXq46JVQpACHvWMvw/Oc8g7ABGKE0ZxeQeIwXvElvvCJHvKsigKIBdN1/nvdIRdeh22TXqE5k9V0nmhRdUK5VUTc9U7l2Sct8f4xcSLUrDcvK7NiPnK51xrrh6tsKmOMxerUYD3iAxxDO9jG4vBdVliovneKE3FuR2k7fkAbYKZa0L9IyYk7CoIggkmGfsYPs6el+yu/NSJLmDyGDNnJafZhf5svaxK9BL7P2WUp5uiwNsznDx90ji5kn61lsLGCHSuZEDbcBdaTlJQOlREmhGM+IpoVb0kpQwZ2IG9ighJ9pCF3RdEHSG4tU34We+0PXWtYNRFpKwKBB+SxnaJalKxUPTceYZGqc2fOLmX60vQl3uInUyD6JJkjF9oAq0k28IFudqcFaV7+mMghHQwdJeUBYNZuskrzjU3XBAAAQAElEQVRGlH8GXfkoB5AuUQ+DE6JGxh4+jkab0JTphnbOFMdKiBCW3FgplUEewgRA3mDBKRrR5m3T8GAE0KWf9qcUABtdg6EyO2GMOUU4vGyWj8cVH9FqA/l0AiDaMlGBkZyOSR0tSc7UmcHyrLpPvyP2CQ3LmpEDB7yjQ/Js4MTRMlK3upAQGZSehzEXFMTklo28PVKFd8U0DgMN5cCl2G5kePRkG5OjxKXQi6Co1uVmDKtpjUE6TELVD1Xvo8csu5oqUNGr5oiNJxgqDVgx/MxWo8gpEw6iI+tWUTGb5jQbRSoER8o8Hw5RnaVz0zBm06q6QkuZGWB+YFt80WKMcBwWEPT0BRc6cwFs7bfmDlSp6j5N3teEDQ97ErcfH4U+j8sqykIJxASXwYeqWRetMKEKHSYAzWF0fzydHFv9Zn1prysvKc4IHa6rKvUzahsPnEcQU8DaqkX0Tm6F7ibBvC63hXItzeQ2C6Jmt4LLZ7oWT6BgMBM4xb0hgZQ6ETMtD7zMMJ3X4jW5nCaxTW0Kos5BJ4g2dLMDaDe7NGQem28wjqlEnrOhYaHK1GzYy0LC9eRIGwr2pXdvesKs54zL20hautWAl/IKpYJwU2qPSSKuhqq+Z55swfA6gz54gRPFR5bvmFtR4fOrO3dp2TCUjv58TmDV2O1U99BB6F+D/Vzk8yvRXUnQv+5L6y/4/u/RuX/1//l7/73//A98wxevr/y87xy1aI6x/vwf/Mpw+9d/7+//d3/i+T9h/GU/9P0l7FDgrZX1J/7wH9VEHb/o53+f1bNjylZdMMMo47oAZKVQ02RwUIn5fNJIQdO+782lvSgLUs33fce3kfhvPr1/33/6E8Zp1VSBGmFu4GMHzyu9jvexccyLzYbpZ2AgvUXZIaDxJFGNSIB4Ak/TAOSgRprceWruc6FULbpHOftk7iwtamK9ZGRR32nqPjQGesiM6L0S46BKBByYGuLbIoRtNMecw4YuZKYwfDLbZo2mn6WlRgaDEX7CKihS1PeOu073zWdNuU05oaz7JoV6sGeXM+QZ7hhP3TSsD+1dr8Y17CWfCqdlZqQqFUDBUmCHKFjgR2mBHKKyQygFx/HQ0XFau9Q3435OQ8m5cEZsq6QQp3FX6xAOwo3Weo3kVa3ElOdt/bWVDDAtHTa8XSOlDn229PkI3KP6yLPlfPV4Ra3TSpulECU6lZ4SQU0LWJfNtFlmrdR9DpcKylXvVXQnp8ewm62p5chLyRyF2XHxaKMBsJwY5MylboC7xOasCb4yXhEfWSPMgJSDYRxmlu2Bucdq/vT/5rf9qX/z9wKYf8Y3f+tv+XX8ug98YDzPk/LZbekGJUOgCbUfwZY3gaZjjYYMYSYmUVRYIjG1hxh/OqIi3gNpoP6ZhkWKmqrMlsnyzgW4tNJQtz7p1ZcgrlXqGHNouqqlslvAe3l9mH6DxtbI0XkRHwMv6+kqJYUH0Qf3vjzMKuHbpxwQ6bqqbGI+Rutgv6VIaDsJqH3Ak/5u2xHZW2gwCn4KO+x2k15byPSAptm6MgCjYSIGd68tOAbyoUVedaNaWmwIHz7dBgyDEEWOblRSf+it0srnhm9ivlpPJ5pAtAWcbPMsxNEwNwW30j1H06Cf87jznorIoletq8yh3c4JSDGQN9roWhnLj62TE40xGpdjvJjLtk9JtbmUwYoRNYMLZtbw7BEuUpi0bkc86l2BIciP9IDt9naGdHugJ496dP6sC29YJDRLjgQ/tQpaGGJmIRO4Ydoi3LFLvCr3GFILIF9a3sHnHxtC5AAecihhrdYNVIkwiWCTbebOlZpr4JO04OMOQirmA3nkSH9U0Mcsra2p9d+bHj7quRXQo6BBVcNaeK32ESKvLvRwCy2eY5eFEDxHt7qwP157W0vKb1pq+SoHhX7stsQe9knl2awz7HwwRaRyXkQwNiqcZVWIzlK/EqNa4tN7smanqZcKcxoNXHdOcWitCaFT4XqHrm50k6YcYeBcnQ6dUqoWajJ0XoWhu5D38XgrhRoFWLVvzMKYtEG0lXrwMSbESildhlsEe4y9bbeEkEIHkIDGU7yfec4rhXWSly48ULIoCcpIfa2XsPt0bijHZaOk1EeGQZkcitFrLvjFscRcs0GwnQU0UF2k1byC40EXLMUugcvifarkAtr5ct6xEJL8i5QdU4xue624Jl3KnKvHy4S6ou+jGyU/Az2ePuylZM8eU891fKaNe8572T37OLbbWVbXk2nReFM5bZ3121xfZx4rkeHmL46ZVAYjlZjsISViasZhB0/xGQcosoIiy8IdvAPqXOonvZkJJU0dq65Z0mVhAl+Yf9H+0oq6dVl0NKtNb6GJE7eNbVaj58R19VIZExUG0efiAW2u4k75DqW6mF1y3yI1ZKCLQ+KDqyOGI+oQOcpkT8c0TPaQQUN261qM1gbjDURfo69Xi1QQlbB0w0W+9BGKsWfkODGqTFBKMlBCDubm64yrlXUgUNtKgTcUgVRKdc3qVjvHDkZYn4u5uj3D7xhhi64Qoa1ItB4InNKXcI016X5YIYFVOx5FMxA3cIwpOMp5ybABKmW6NoV9765qtpSrTf10OuT/TNswVRysOGkLNRFPsE4Au7nNys0XOdhOdJG3q+G8+JAyXvABBWI6n11ZOIBYMLbR5CNabVewOHsMEZda6gTFy6/2C5Cc0KIEn2duUBVbV/FFfxqTbGULCPRb9kAFDeUmMeopQG/rFu8D6einIXasvlfcfmcf7Rsu0CZgYbLVzaAJK519tTondXsVJau6PARYMhDpY1FbvjlyjDMG55rLxxDGAmvYWdxX1yKNAyTQkAmJpiRHHb1LbOwpl+mh1VXpEpDXxNSX3V5G8b/iY91fjPvizXX3lOb7LnYY0YPlOjbmO1Zs812rarJs5lg57Uzk49ijV/aU0BtZg/A1qJRJzZiPmQkN83zuV8fZL+5XB5vVV9kXYwxHmrJyo97La1xWH6PUOwTiUcwD1d0uSOSoqNQ6hucl1m0nT4sSDmOtuR0FmLpmd+G59OhuPxS8qscz6/lhtWRVV58vkOs5o5+2PC793NHKKRfa0NQFsOEBFJGeLoj+5//Z3zfdfs9//BNfvc8Pfs93fvOXv9FFqKpnnh/8M3/eN33dF7//J//wv/ef/Rff8MX1le/5ni0bY7eWXfrXffElfdLxX/2Rn1Zyl26vVKWYhfFv/97/5Cd+6qd1SaUjdzNcbVSx6IwOJZocqVSEhDeZy9J++Y98/8/5WV/akb/j//r/+Oqf3NyDxz1Muf2v/9BP6WZJE3UUEMZykeOZ1MJQkFKH2TDIQ1k/mURHaivQ1WjFCpR5zVmvq9YQJRCnXRXB2FTLsFAdFECvVj+wdIMyTw3VkIKSDC/TZ4N1BXRP887Ujg7y17TpuFxbfghyNEtgWjTyBW0VtrGc3W4bdp8RNZLE6DEkNvYWGVKW9wWwFNNuVDrfbmVjqJtQ+Svnvmy/pNmJHsZRy7TSvE0Pru8z9TiuZLW9j0gCSTOB7adzK1w+Eg/gPnZuOUQhokKlRVnrcOQILLj3IlZragJN1y+ghMAuH2vJbEaeIxY0hryGXReUEB5b4rjVyEcrnCEBpr4f2zU4rmVjgRShvYy63nLoTG8RO0qSyumtasQLtTzGVOhtmsB25nB4G2IwpylEb46uq7AIeIWfg0jJjVbk41nrIFQlMipsZgrSWeA27WspnplANO9k7k//4X/6M//ivwbAftYX3/Jbf/0X3/ktDiMdNDfI4ax+UbWc1wLOrE8jP8349OI9POdwt2kwnkyE1o63/CjSxDi1jhLtsjeGmYFeT/G2Oo5D2gsknSaou/MuPQpto33Mxyg03k3Kln3nME7HXNTSQkHHaauitGB+4I1hObSFuQhT4GzqVDegsC0Egnoj4fdGb3au1C4enqM5aRM+s5Uz9SHJOqra5Z+WQI7jo+Yyv9x8WvlJezcO+G6eHt1mgA5g1dMBnORcmDLsViplVTmrd6NgNHMVodMhVK3k05C0/SCFGb1dlc14gjI3m/SXcehyEzfn7YRj6IraiuIfUoVkq1Wm5+yen9cqoDk2RlXNqFlEWL+f0C3RhfbCasVHVdw+iEAKelsjEifp1jYjpyDTfK6JMYNzt9/ZuyIYx9BQELTLSc30PDihGQoDaiByQG3sOqVIEyeGmq02TVZGa2vXim84NVbvpHjkZNDlJ3vN9HX1Q+KQzL27s6gyz/AO8zAD+Ujz3Ne+tZtm3MUogVJSKgfh5kOhoMywnK/FpafSSnhMiPmyfLKzZI6RjNah9sF8eoE/sQFgtiyquA7RbTph2Z4BdvERwW2eLhkpQmByDr/G9Rqva62x6FPncqYihwUUNeoMeVS7GacrtVDr0Yk2Vank8qkJVQLik/ZoQbrpll1B4ImcNWfzg7x02j9Mtkce+3TmVlVMp7WMV7wjSw5HruuM1WMUFdlJHGJPkZMGxx74PK5BQZwDV7nHQrk43IUQHWozPY2AZXrk4hkyOY91IqUcQa6eYmAnlPmT0zC9lEJ0eJWVsx73YqzZ1tTJmfIfHLIq1z1zex4HJ02/rVBRO1o3KfvZzp75bJ4DrUSE7Ekt0q0xtBG4oE+ekMnBHpcPt4EHeKnL14jG23AbVV3doADMVI9tfyZFr82pTEqYw25+ZF8mgwAW7NKv57+kuTauJimPE7HsKN90iLNypGK5mzda32e9SS0O3DLbHnZK++U6QKAVSb6rgpjOl2NMKAUw0wmDP0GnNNUCXAAbKIZWp5re10oRU6YKLVgaT0OWs5k2jolelfnKsqTSQo05ZKIKKboVRZk+omW1u49lj1T8l9A854HuIWIV0edmW40WBvUE2I2xYSHgdTTVtdTIxWeAJj3tVrwlRzJulQ8MhZQLMSAADYWzm3fnjbssuoXdsxlNkaeU1j517nff0eGaC9FieenoXpKOtyQI1f9ADctlXI/UDRd9OWgdBNKExvMM1rxAinGTXGb3oBsd3u49mYs0wDRx0eaH4XJOr4Ez0HCi2Z2zJIJ2wNTh8ansxFTtmEYADatg3/AbIybb29BMyrBbPJ1ggs95ar5BJfv0MHM7zudxxsUWsFLchjCUSwz+ci66TDQgFBg6TzlrbYRSw0enTsnwcB8Od82MLKHqm77bSzGa0MewkrhoVpQyRIMuIWXLaQPm1CVDQn0HUpxyPcZ2XbEJGxQgibAPJJrxkBNmu+0NLbhIRy79DqhmQ/GcOY9POrWY5IywqgPcLtiWMijr2Mf2VoG6iwN5h72Aj8YlAZUKq3PdvCBnL8/LN8s6JCUKM6ER2m3tnXftm1qpSrGMweHDde3RXsn9BXwBLhSqdncNGQah/NfDt9cc6rpKtJsUBJ9Bv80FNg54DzsueUqderrg77aEgQ15EMpn/cLzonrmdk+nKB7aDJne2xnecETG2VvOm0Yze0B0P9MAmvu1WjHqrVKH5yV4q5RjxmOGrQRLkUKNZyiQggAAEABJREFUx/VHYaLMCq1//md+13d88bqiHoN/yY98/5fWkpeyCj8/68vrh77n+X9+vCO+99u/9Ru/8et7VI8w1vD6M77l5/6Z3/pNAH7b/+13/59+97+1c3PO//KP/rF/+l/+XX/vP/d//iRGgXMEXhpWKWcjNfjzQeL5wn21DeXOjeC3/uyv/8v/wh/R1f/Xj//Bf+Cf/R3/yR/8Q90jM/6df/c/+J//E//8f/3Tf/p/qc9SSDZTzR1eSGrPVV3ePYHZkuvdvSP2joobR78TWXIIheVm+lOaKUGLoMjuFifm4DROV/Z9mA2mqYSix13jhMVWx6q51fWahWLKpPrVI/VHNgpMnY2lSNxfHsM3PEusUpciJQRt4ltbcNFeLtpVTIv3tAM1nzm213v0DdNykiJBIyIl+4/SJDpQ1pJZKEi3YbCH2hS7oQ7atpPZVpPllRFZlY3M4N5+cgAOmCnUE4zDbH72oTOVu/Kgw1sF+BQWtOaJBgxtBz4Uikw9AKzdcg3xJFsiGt0lHYu9AJK2a22MT+7BIRgo0iypYWrOm8RFW6lyQG/xy2EDPtCq+uVwuyj602eqnYXXcaZOtM0WVC+3rYhEi+ktjV6xBJo9TGNO2ykBKg2Q6PsMyZdbVTswIK/wRdFGigMKGrIQ1eeZ0Igvfvg7vum3/frxbd/0Hf/4r/u5f+4PjYQhBb2lNtg0dV3TdBtthsI49dr9eWEMIB01i+PI0F14dpG5unBUuBbolJXu5Q93isU0NHrZY/SzDdTTtkelZTxk4xqUkABLaJFkj+cx1uW2MKaCzbB4dyUyDXv46dV78ohjKIE+9KZFhPKkdxpz5CHKd1677UakhFaN8SDAkB8b4PN9Rtjr9lfpo6txaXVIhabyaFNWYjm0mAwnypAsFbwak9LpDLWsMQbNQaYxqVHKzDQQHbCbeJ+SNLvEGEgNdJGhALkOrLtRmdEn+VyE+Ou2nkNUQBefA3JkDOVe/XhSLUi1w5X5ypzSonxei8ToFrrmLkbJ37NBkNHOCe0v30Khxs+jjNGq0cq0jm286W/yyVtnKfYwv3vetVpkYQ0tJjKJlhWxXU+9QWQt75cJEj0hj2JbZRhOYvqjVuYSQ0YS5j0Hppm14q2t0BZ9mZuuuAY2XZ27ZAO6C6oxPX1JIMCaXG78TH1SwhohghBut/E8xIk7iZOdBr0cvGy8clg5ZG+GjTJC+3axD+U0fRvu5h1PjXSImHftPbI+2gfRlBmlE2In1GdxD+yhcw6CYi5zWz1PGUeTSmHqxwxPhKSJjLKngGa7U/asNWloubd3GxpkKlV2lCx7B+9q2dR5b1jJqQQO00gbXLPXq+0VavPUKcwJ0zbOh+o+FD7eY7zXVdNdFL7htw7abV5Ob3izENF3RzHSK8yyiGd7qJU1xIxaERaX0qPBhoaI0HfIl3gsrP2glJJSKmzzdO7RgpwHkaySDIfsylM1AXhB7d9JKVN8d2tgvRGbWdV++yywmT1L8ngSzjtHtZ5DXSOM4qJTlhRsFLQGZTLBYj+vEjzIkTZiC2mdkbGrw620fj//NS9lZN3Oo149NEARoyJaN1Ym/aZFWpEcLXokpJai0wElSjJ/LQxFAMCUJIltBjRSn6xsps92g1k9XKr1omWrQ8R2HYUnCbYtKG51Y4/2HV63PS6ttL70lJxsN/ORjCa3ZctZlm9NctBaZyXzJEtKHNCurjx3n1RrUHZajKLq8zSCXrqKkW09hg51h2SFJlPIT4w6rA4wZsdV9LPqHr1hYl3LABS/Kn12wT2x8mbp9E7ZiNA+s3fkjpPyrzHCChEy2IyWVBtEGeEKD3oZ0M3K+Rwx5FgyORT/Aqh9DR1u1lKJulDkveOrGWUGcz7M0sWf6X7Or2nDSdl8DisfrvryUe4xvS/CITMPVZ8xdLokclm6pQ0rDZ4WFlHRz/bZ2lBTnm1MqmU3W1f3iGISNrEMZJEGFZ8qP0W0VkowsZ5T8nfPFj1RKIVJ9ivdzXuI4bagh949GVTcjqDR1LmmT4Boorzh7UqTzJeBVy1FSUE1nUmZ1VVeMtqNUoolinvwXniLUBZBuSuXlvvehbu8VL3q08flDataVadiyZXn1e0kMKpmiX39XXFGP0CyKxXF3cxiZ8/oKyCxCeTnK21q8lidugo76jiTSZ7SWlUW9+X3mFq/qjlQT6KU3CMXsDJTUqS6jR3IRGe3kuBJ8biU393jUPQt2qBbj1VjbucRaSjolTDBjOq470+f6kjPqphIQvNkRbfbLDF42G1wilZFDUTXNjsNvaxNwXqlKPERIqndO0WnrzM9n01pZ5gGIOZIXtJ7nu4orU+4cU6dc2QJ3Yh01Ec990TP92qcOKJrwKAj0Jor/AOUAj8oJoT1maofPqCFhk2mPj8HtX/bYg5qePoswA4QenUTKV6E/rDa7vmtX/dz/9zv+U5d+oYvXr/4B7+Pgzn0+KUbrdXgV/yiH9IlHb/4K9/78Zq2WraZTzK/+Fh/91/zP/zyx/Uzn+5/8F/6V3/Jr/uNf96P/V1/xT/wj/zvf9e/+c1f/llf+9+c/PTP/Ml3HPGnaZ+plyUMqYwcAXU/0TQ8wRAxf9tf/t/9pd///K9i/o1//z/+q/6h3/JD/5O/7xf9qt/wa37Lv/AH/8gf/+HPCDWZrcmwTE+J0YYpY53IgCssSJNpeVeqKaiLVjdKYbJJXWqixehDBPWraG2GVs9QkYxW+53DlinM0VC28+mt44a6qFRZWhT9UFc0UVmfTbFhRTQRkRUJuTEzQiWx+iizalEV1iquuWlJpXRQLWJaShpljIvxirgiJWRhNa/2iipZtNMuk4I8ohminXkhLmjvmpmhg6kiHRZ63n+19LHSlDzdJB61DHSlJqEG7WoIhD6jP9tga+tAczZbZMVErSptGnlb6YtC3SWYspweJo9Ff+6l2BuDNEW/iCl5H2S0ZO1RpmUkGjvHTu+yMBkwCD13YqN7Zr9ijGNjqFgA84BV6Uc78OE6gQBCzLyJIy4JMpTZ7CrxvAGUfki6YrXWTBVClVtx7nntOULTomTFvczEWvOYbeot+ylQM3e3LFRCgfhwd134OX/Zj37Pv/YP/6z//i9Ga7KubkWmXIg0mNHx8NSiCjUL49nmal3UeQHb+XnRDTkZVsOgJNroC663MdXPzjYHXwalvs3TZ3Oy3LahovkGbrd2G1PwuqA18R2fdC9QCkZxi8zyhlX2Sb3AbCtxaxHs06MwQedpkd33bu3AbtHneXHP3DjIg4juLo+cpyhZPqBko9CnEABobr5sGBy6u9zsYtI/mT0ZAYnLHuDdqUsj71Ff5XOrpAkmLS20UDIhf9ijVC6Bfj6OynkLsvIpkN2saqnp0U6foWpQx4tU0T3xeoSfWFpC9cBlCHGfJ/P0lpMiC14wJKkYT/lJLTHjCbeMpKRmcXRWATXtEV5KL1oQdImRnQHxNxNS96inKN3t+WNJpFJdPe8a4r2I4y75FEpSncDTGgymjoH5wfUFS2tXV2Nj3Ofi+1qfbEYYt8I192k+wVXhVSbr18C1QIcIALZDLKOqdmXU3dzGY9rOtwohtafUPvpW1kDYNONDR+EzRpxStj3LkU9ZeZRKzFrB+WhOJUP4P3+0Eg2IhqKPtueAZTfAbqJFGTrrtB9XV5vkQycgYBqDcNkSA1DzST3BWAZNki3hRtg4k3uYFHMUJIR47zxHv+Kw0mS9qKh8Y3Zb01qrP1UElqMx0kG1BRcZcJqTA+ajxjrIRxQsWz7dBFy0nL5v7rfvO48kXQWjJiVKMmH5wpzlLHRBVMZ1+uPmSkXTdeKOOrlPbeJ08SSO/tZtAgNo5mXr+f9Z9OWcElUj++Etq41pFF+kTfqsAXmUOOfmhbl0kkjrtPM5jzn1SZHU/Oxnb+zTyTwfrYYL8ZAqhIZNiAy9VRHy0NvM/Vp8XaVIwJIS3z7O9STETlbIJ2JET9F+LNBnCGWJLJjx41pfWq7vWzzaS2BdcWU6bkkD1S0UUaJ2ibg56hppqqAHhb6S/YAa06f2r8b1zo8wVzaUMSNg5CJWUH1WdsI7Bqh/5ehXc7Da+5jBFAO0nM6hzdDV5yoMDcLqdAVwztl35xut/lUCrwtDlKKN8qjf8kijijNAMY4hmroSUVbmQ1y7sY1JkaJMt0YRnGbzuoYNh7q8Un4aqfp9tytiaep6yaxBWCSbblOTUerFiGoshUb5iVxvzLtL/p5OeTyL0R3VJ+3UfDwqaLwmdFMoxXSieYwhSY+yQaUzZ6SnEJIcePB7UYWqj3w+1jV1pLOVD8kYhcZEv9gaXE+dtyb2tRRJtbzjtGnv12pMffc+OCmD5mEI/WO2C5lgf8bGfo3w2VobqkN873xaDNSIRvmHCSWiceK8Iz8F71bU6+3j+dKjLUvGSpq/T9XpxTO1Abqw1wg83bxbKSpCTaiUbIe3E57SBctE+xt11zuxMdiXQSP4BLiAi1oMpQwL+F3qlApd15XG7Fa2bo7bhD67j3LeMSunaigrd0Zq5/8UPdo9yWYpqG69LaVrBE0grKB3M20e0zNFYoffB+9W77gLT926C/GtukllOA1Q1PSzRko9xlLYuoge2a+tRxGlIdAoIfVRz5vqbuidENg2kTdjUkQowTqVGSUNdHtpIwLegX2P+x6f9vx0+9HquU0GpKfR1IQGSn/lmlap5jL6tOGZh1ZrthlOaaCFrAWNWVbVuiaw7Zsz8JJ/1aijg/zE8QC4C2/1BtE4Y/m7+8bTQBI0aatXtYQq52phVO2szwA21S5CCRh8HEAvgqMESG+zcWREy9pHYoryUPv0q+0aPVZy1iEDY4yvfPd3fP93f8c3/Kwv5cmId+HTxxf2le/6th/47m//2V/3wcZwM5InLGN0f++3f8v3f/d3fve3f0v07BRx9ZBwbBz+sh/54e//ru/8i3/k+7/1m35OUD0vBX+aGu/4pT/0lR/4ed/+Q9/9Hb/4h7+i/A/Q1X3j1VIu/Jf88A/8o7/6b/zRP+u7vuFLHwLvZt/xTd/wV/9Ff94/8Wv/5r/sF//Cr3zXd379133pj376kyXi4K8xfsF3fusPfPe3/Zxv+KJth0mCBxXgaeWpnhvrf/hv+5v+pr/0l37nz/nZMiWDMvujf9Z3/2N/29/wq//SXy4Yf9a3f8t0pdLCvDXC7OIcY7VPaaibTnpJRsPaF9XMpKfpZRalxCuhKEgPsowS+2uIJE1yQxv2YrhOByukSCaUlJGHtnNFeTSsemYMEnoE8PI5VXcxcUyDTZqNrGwVTNG3PX1KgrKUX3vcawN/SStLAioVHTFDAhEYFlPVYLPGzOkNlM7K7HibxxNSN4Ldjh4Siy4KQkOzrLkcy+TABH7wvLy+QH6EFlG4ywnhM0npRtjcuOguQZyJXjaWu5JK4smDISG8HeDOUjQN0OSRDUo0yAQLV5mj20ofZDbcwvQBAN+du3dSBUjyhSVvgAzuLmQwpzIAABAASURBVD2FaWoMNBumyyb0j9+EqbV4led2PXYUUatvf2Z536SaCjRU3ZF191NigmM9/Da+ixUm2ha0Is5xuwRNozwJqVwXO1h1ou9jN8Z5kp1ybcRojIXxok8n17d92acaQye61B6ZstKmQBPSaMveo4gyUIReiVe21l1knpMn6H15uximjI1rYl0YQ0OU5WjstExjC5mOyFGxFLIUkxnZ5eVTLV0ppKydfe4+t96iA+y2gslKFRqKz4lpbc6A7eNaiyurNVS/q1tvdKRE7t0T8fSNyCdP5KR2BX2FSF/ZHhVRO9VU5WZOSkwzDWGdipiGYTWpY0OrNdlz9st0A5RX12vnOHpwm4r5yZGHeUI9NKlVVEnPOh5wTtPN6FICUNmI6hacE+io8RTXZLqFMnK89+i9ztZ5KIGra0Z0VXKY0Z3mKtPW7XFtS+VXg5jNPk0lWMNEazbE2KIe1hFm5Whl+wO2ilSgTehIQ9GUJVsX56DsG6nKUDKTFuQAjBtPVwZzMF/WL3AIhw236UoBOqJwb889vDAL1Q+jVSBoZrLW/ZxjhXKGeY0xF4YrpUBkq2h0pQRNlLswVcnIYR1dVninI1UpVSNtthu7EToAuTDMldfIMYTLY6gAuijSBf94qdZrOuiyrkB25VZ+Rpi2HhEePQwCSdIBjeoBOEGyAah5SQmy0WxSueu31b06l8ZMP2WZ3jFRs8FOnMiInXE/24nM7qwhLAkkrAjQqkdwdE/21WPkghI0WhdW9fPFCV/Xs2C6a07G2Z1tDXM1QJmJ7ojakXc+9seQuEgxAQmAsGm4BiFlb4uAmHyQc4avYGtJ4Lip3HYp7DXJpQVIUxtPjAZ5KnM3e9SDsu7UJQWgC3yoEU8GmLXYKpZmuElDCuU5yus9e89ur6zKyNistFY17uZpb3NrM3i0b5HWNQGXzzOUZCnFm4M25hx+8dgzB/WkiU/abj47iIptu54ib9ceMtRpYLLIAuj6N2dwSD/FxrNe202vJoAJcWQGEVA52Qui15BDM3SnO3Zzl0o0UQ09twpq6ttX+mKr4iQA61N+0pPggpvYVSJtDn0c6UO5biOvaa5TZiQk+GvCXZrqPFYPI20hHabJ8UVd67KoCi2yS5FRANtO4p3IJJveo9Oq5dCgWpPbnt3D1LXgEiCI1DQjvDBYtHxImJUDyqDiKZqEB2snDYqQ+pmNK2S9cJQwFEhXHgFpP2c1m6RRcfnrxgLwnFg6MdTYrOinr5vz7fi0WndlIU5dGFrhzvYLa8GmjoIryN5kuMEGH/owL2sKuBaFcqbZgW3mLpFRLLuq1+7rU667640OLc83tTYC5BBKdEpXHJq662SHPZDFlCwV5K5KRd/KKKDo0Mq+PW/rjnwHDnWvPXSlaTUs0XWXdIdi10D6zBwCNkEnxCwdGn0O77vPRqUN+AIVB7sLAZw4GZovYiORERAy09uDIX/hLkpNsNCHKJlVYhVGQPJhZGc0ofzaaOVa3ZKjG6V8Qec13tzn0k9g7eeYZYahWdmKjRAJrkzB8zPrcsVEBZtSBnuiOFqjsoZkJX/YwTspM9ZQW2DIoaOWn2f3La8BS0FyuKwoSkvxj2d6RcttE8NEg0EQQjnph5eaIwaDDwJRlUKR1Vr+jpBAvgx4JjRGpYJsXodX1UWb/urxMl8OSCWLGlLUo5Wd5Ugv+eEe/DT7Xd5HWxjXCuSSTiPKdJejUurwSD32GdmKqQI7xZM9+IxyfkWPMjdew14uTeqKEHUqZChWuRJCdF/ZFxRkHUIB5x7Ql13bFH23j74GJsFu04iOb/nGb/o//oZf8zt+04/98h/9foutTGPkd3/Xz/ntf//f8jt/44/9ij/3+41uNkaJHessVv7Df8f/6Hf8xl/7m/6Wv/52NemheJuuILv5V/xFf8H/4Tf8nX//3/rXDa08sSUwK2VxDby+/HVf/zv/nr/9t/9dv/oHv+87jUp3rxJTQ6nNegD/6Fd+/j/2a//mf/rv/rF/9u/+sd/2d/3Yv/C/+Nv/3l/5V37j13/jr/wV/53f/vf92t/+D/ztP+87f24owMS3fPnL/8z/7Ff+zt/4q/6qX/4j8HP8LVFIvwBoSvtizS9fX/wdf83/4J/7X/7qf+rv+Zv/qb/nf/zP/Pq/5X/3a/7WP/+H/+z/1i/8wd/59/2q3/Jr/rqv/4YvzjDd9t9glnVLvg8xTDGThpueCn2aWuYjU3LWMzK2Ci+jxIUU8gAaGMP0yyAWOl3UZrR2BOdRk6WNUirULy9s97tWCyUku9ZCqJQChgYZ5mkmK9YuyaW90z7F2DFxPlwNS7vp09bH5nPQAYRTutqjlHEWkNowPo3yuHqeoQxH0Di9lt0KTUJEzu4VprlIQJMM7r4mnlS6Nlect2jlkyU69WrADZTuXX6qLblKtltousLzeBcdz8fuQlWjNanZu3ScitS+3mpAp6rkNvCMsK6rw0OrDcRAuRcN/z8vPlbUbUb5xARUE3hHif4GbVorjuM4qOaxPpk6+oTt9gPPhgOWKZfVrsOgiK6hVTXEeIGg7hKuA0kZRX00s6Z+0k0dtYhuROmGqXRf1SqDg1B76DpDW+Db1ttwt1rpftYMaqWSrWJWII9ptpGy1LflkXQq9Hn6bBsKu3MN3eVJWHhbfVoZq2p5+QoKLgPc9HsO7Zg+3LWxnSrBU5nZSgBYHSmGbUO7+HnHdd4rbs8SHQjveFynxLdSzcfK7HAIaMNsLP+4eE26g2YupdR4R79P33vkufw9r9t9K+C6IY61h/QRL+PVc6rPDOtXKR6/R7793ri3eDGHQ5GjQFjr/kIAFOVQmXgBEgeyGrtm2JfolxFgcCgL53T1Ad6mznWrB94DsdjzYcKZl5nWYuidtqZ7IZmeat15vCWAlKWuGs3VY+iD+gu0brRolJaMYRWMAxLjar46/Gm1n+55Pl29hSSFuBP5Vqe/YGt5Lt6DMd2XOaBV4e6xPxYv56yc9fwXDMJrebxiNERlT+ewQVvnqtAj6Y9+5gKPmMOsnto3jR1MBd5MFWs32bdxQ15MIa99GjsUobeKi2ImhbT8dFdnkhjD5xij3d6loZVlWIvjQnseReusiRzo1s9yMPMd9TaeoRO0ROmf9wMYhBvHoNAJG3P08UdhvSkZrFTCBx0tbkwZf7Y6R9iPtLxOeKFVk/Myuuwq093KFD6/jmWqyJRnQGlwrXuxo/I4YWZpdqqDUIrj8vMkLlDHBuayF03a95Hx4lkz+iVEpb1WnYTETmWth2bDDlUMLNlLSGrqmLv7oUo427TTv7WHJGYr1yvKS6RD6sRSab2yZFnfzi8oiUMpC1dc7/YjAvu+IEpE1yxzLpsLY+Z8NgBDUb9Wr7XtFb3QbGGr+1ZSNBW9BbSbUNmYUS+AZuLnDNzLt2bGVfuqR+u+yQN3zpXXOq4KjBP5rgzSe42as4ePgWsRyXMsQp13NxK0xmgO14kOyDtZZEAZGI6r7doTMVGu/tFZas+RgE6mDWANBS87WfqlYEXmykbILGK1dAQHSNgcc63rYr/iLov76vO699y31YZWgeCOSwuYVGTH+s4+ko7Nmmyyb1rZSHoaNF2UElfhSqxyN9Dg3VfWerdvPJ98tg74KEhpPok1VYd1URVFuhfAaN1A+vbafrb1duwad/CwN9WspRG12JluGU/0jeN2azdA32I1F1NrXSV3UIS3XK3LLnC0dVvq6infMtc0LaUzynGPsfESQYpNBtSz/GHq9cwGUY0i+mP6h9tLvE03hQiAVeP07BIXd88TxiaS5+Y7GIAF/c2h3DbFC6Zo92vEwDY7NQVRVobNNT/4GvxoGzICtCxJv9vjzi39INLBi5wQsWA23uDjG5dEZbQaFUuiHOzBkjRSqYxVMkhWUQvnqsttuQ0xdRqpUfIkPYmasupJOi6Uvs/58J4alsHb/EyDQT6TLHNykRdaelLO8nRG292zAmtzNp0yK/tQ0ZDSnnfZ2bx3vvf9aX/6dLTanMPagBY19Mvq6h4iEnuviC9ISbkYu7PZa4wxJ5fLrS/3KUDT0h4sr+aU9K+BF6CvEsTBejAg+HT0Gn1GvE2rUzfMACV5TrhRIj3i0HUKc7iYNXlTRT2PBz9V3MhUc5fE2TN7lTAeQ3V0F8o7dWoAD4oP1hyHcx+/377VDuwLzKvmK0W3iF3gKp+QAmyYcIzZc5aPMqQ32himhEIxX6lesiW5cvZlcyrmDzFQQ70GMcEEhMC6KL21lVeO8C79qvC7e6OTUmJFtZbGIOHkKDoIneR9W4RLiSNJMQO0zX6tugxeZdnuwEJ/YF744oUvnIoVehWRpryB0f6px6eau2a0PtbuPk9ExhI6R+h43kOTTWvbh/uaxGTQTl+Bh5Eew1KxebcX/JGNCitzoH1C2e+xSAvmxv6Ed5FubbY9zthbLbQhRkQiAZs2riclBR5iK/9Wkadis8Je9sX3ffO3/5Lv/b5f+L3f800/6xuqpgxh5FiPFhGR77NPbBWHaGmFCwKcSM/oZA3iBcFuxber9pe//MWf8wu+9y/4yi/4+d/2zV9ciBHaU2iBbltlq1U46FC1Zr8TOwSqh1Syn/+jSAkIH/BLEmpTDr2O91ldriktCIqqup9FZLSeCVP5RTiF4px1ksJH/eoYj+RqDKXoeqZ3S1CEQ0R5tjKe95N8AS+dbR8OceohNWDjKDqMJ/cf2p6mh6NkauEs5Oz0uhXqacQwTG/QKAfdhbTRKrjlBn3q2/Keu7St7H2lSuET8hNn+hwYZtSrezyHOvbo9/No55YcdEosnbas0UJpqKkAte/uTjSlqno+7yXybqj4s3HnuOtJnJJhtRnAuXBedqaJAIBMjrf3/cq63pyhjOSF1qGNnbKznrV2yUPt7HNDZE6HTmgZUiayljFNDOGoLHogvLMDFewmiwM2Wm0kUr5h3Q4QkxQhg26PeHbbCSIK3Zh9LuYaNJCg6iDopeI5iu9rjkqhWVE5EBxAVHd3FG90dGZCoaoUzqHKTVMZF3JWR8ZbQs+0lHVVk9XzRtKCi9+n4OvBNwTLeMyiDYouGyjiCH4siyVTF8J0Oq2ULCmB7WkTAKtGtoemNDxg4fTp17jGXHAvVCMVKK05dIlwNOUgkXsy6aCsKJB2vQGN5YjRQqA8QFfFtKrBbQ4sYhWV7QJgGlgTPapnyA6Q5C5U2me6MECvwdPc4LEMnEAmOwzFi5BHTUo9mTvFRKfmspRBjjQ+3Uc+SOo9mX5gQShaelN50MewOMw2NNiYOeY9eC9Nv9qGsKkJY5cpmTxCCoOr2gQbslRK+BR/lTu3YLrcadJiu0NekaAga78o7I8HXbRmuaJBBfPOVobRXeit4BVAGPU45PZHHptIB1gmEWxwm1YVa0eNrPm+e+/OkjLc+rrCv7bfdXmZMlg8KQMmNC2d9iBLwgxYa2/wRELRsIfTpKchYE0GmdNy+nHfIkhJMEVS7Mi121tw0MPcDSwlAAAQAElEQVSeTZqJkg6l/sm1y5jLgPA28zBu670sLkvvAsqi/cGzQupIYSTZRdUABAnQBsUtraqzoQmPI7Pq8TiuEZjF8ciD0ba75BdC1lIJmfQtU6UFPbUUa/vgZdK4a4h1LZLqcx/Dpqh/+jwiwTTrbi2j87EuLOrXARWnfso7x+DlbULyfHKKKDvXdZbjZTbL8EReTF2GKyZS1nI29FeGsy0Z98ywTEE8WjN9Lv9cSCZ7Q6RBo3hohUFlu6WBRzNvtmoRFIbBmiZPCYQhJ3K1RiYW/Ep51MLYJdJyVQwVFuHuYwz3yYGG0oKJcfn0uXSmUMKl85VdO3Ub2m9CAINDtu6nv52DaHVDTnJ1Q6tWGR8u5JCmj+9xjgfRCz4UPjJVJo+iG29w+xAtyaPMcwKL0E7UqrqgSaag8DbeVUGdTP1yCTZPO3FNfGghanGKzwlgA6xhe3BPU0BPvMbPTozek2Uymx4KTua9VSwNeXH1ft4SVclwTurGpgeqLWpkeXIlRlGh1hjiV5EFDBrc5nSqDTgh34CDQokMJjzNYKUPXR32tA79DjJATE1znfooqc5g3RM1u9ntCa++NvT1yBWqpENZazHryhiHKbMx01oedJI+OIfMzRieyxt6dWfF0a9RZqV+p86FV2P0c0VSixNHQ54Roh/ggJnGuWBoOR2jdaYXod3V4lrUBVeJtlJxgFIIpCGRUgGsHgUOmiapk48Z2vStzya7vbvsITvcQyUHCyAMtWjuVr4gNh4FgdmmaKPyFG4Z1+7j5aZJqkrfbzufUFreY8AHXo6x0GAJUZ6BA9GnPM/PjZhucJvmMAJPWqJN9DdEQ4kYXW8HZiuBZJq8s9oqrA9VZdkQ62nqLLhFppAlvahYF0M1bBoMSq1o6zRrH7BBWJdnSj4djBCaubUH5OffaVu8p+SFgqZq4ERSAoKTAtveZS3fiiam4NFFk0+OpqvFB4VTtJm3zzYGcbN2qTQe0ZPbolGfbU2CCheAQw/hJAKzmDxKL6bJDREOsi1Lh7y9Fl/qgdM4KY/F0xXp2SPKAiwjuhuESFSgsrva1hCW00w3VQ5bVCw8TIDKPxSWUxXUTaF9008Me89xTE54IXU3dYEeAi+NoSRkUVkU87OlLTlV0DhaRCvezNtsi6rPh3GoPhUiJCw6Qnbw8Fem7OogqwZU7Fu0qpfy6O+oUlEpUXj+qKgUgy1ragCiW1NOzdRCdOxxTZajsluqvHUmDKKYT6gl3yfHLelWRHfwCYCrpvVklwiM2MqJZuMh7ylM9cW6jg2tSZtxi2AFcckUFQCU+mq459IYHgiUBKE0UNmdGx9H90FlJIa7brDKRrUB5ARn2sjFfHKScx6tdmMk8HBCXe5eXRoMJ2YFO6TGQBU3VUkCjIbrn1TUWbhbl26TQTeTDp0ss9KEZgX6oPdD/Wzo2dOTL8lDGbwspvZOWqpiO9JHlxvMhIGywbbRPUKSWWaDNkqMBke79C4JiCItV++LUpW1WV8ODmC45tz0bXojKMe1pmWPNgyrBRUQnvEmOloi55PEWWk3sclYani7FZkEq81Hq0MIi9KmVoYCDXBDP9G3oraH/0rqoPASFyWXpKf0wUGahQ4+M6SflrpisSexho0pY5oqxTm1erSrY2EB4G4EvOk0arAR5Q92J83hrqEahSLejECQvQrTYdNUw0HLQCa6YH1enRdtKva2LGvdY1L6UcF54xX2kTZAMdxvW2UfJhDtkV19D9wG3Z7eaQg5HqjnDUZjNlri68M4OJpe5aW4McEh9HvIABw+YWAk9qh8CBZ9LOnDe1M3wi6uk6FVjXZ6A2+fxQkSnhhhihyUCSSwa6j2+l4UrjeQqrs6xDF661Hp47PQNYBhNgbGOj6KXRK5dECrFTbTrVeNjxpXYwj7qac6rRvGBjtaIcV5yE0xOQhBMI5hbSN6bPkM8WRmFMKN3kSthg55Ci91lAa95sA13HzU4GHvtmzrVC0smlRLyGmSZYqPUS2sulgVJ1vh9vXikv/emfFulpM2jNKFCxRoMRDMqMjj51whWSDVrXrN1o6tBpKdQLkH1SHsDRHrTabP1IKMkaXA3FIZ1fQb48zVNFXyOmbKKPS2BZztBvG8ApoNn71EdeKkekHv0fuj94z0JyKwWv1KN+Xzpn1KPXaAdKCqP9GA7rxj1K2z/ZSf1MFkikmoCz6ASZTVcZGt3+NcKwQL0Hmfam1mbpXQoZwNKiBxsyaEOCqzVW6pVI/7Y7W4kCajIFdBpTmfkNjsG707E60sTX8wn84C7InOvLskjlCErdm6HenJWkVFWGWBKX0hUYe5mSE5KDNDRVoXcwlszzyzFLCi+pwWmgCEHbANkPxstI/TXjm6x+fqsfAORB9Y2XRfRobk72gq/lKgD4Of6ZRy2o1wpq8cU/TMwow7Q5LpqrRum5RcIcKIpG6Bxl0r2rQYfAF1xgnNlRUxgpjPmV4tDiROzcd27O7UfcUGUK1ubmL85SUkeXju6qObtkPFJG3y1fwCuMDRYdqXnTf6SIcl2DlnvxSUeZTnozOof18Qd4RI7sPSBfIBzgatOfLJ0RZzu2yfte8rYnZdNl5zfnGN+SXYFwdfJPTgl6ioU32carhQE4ndqua0TEdJ/xxysqkxKh9VrVujAUo8RodNyaEvXWjdAeB2xAJAHqiPPYIJJTydLWG5uqGuHczkojKWWtP33qFcLbo3Jfl348Z123hbv19R4xg53YZPM5YCt9MWjwtbe0phg2UO1kDoL+aAdvCsVmiVbWx1hdk9xRVSFW4KQIEn9QRvHxfbt94XekPqWgV3jzXjsp6759mrY4zyCZ+8ZP2zLSsQYkOd/sLnjWFLA+pL2uYGmhf0Zdac55oxTQEietz9xeGiAnoStaaiEVGWeCJbSPP7XPvTF3U+pjIPui6rbJOQRLD6M4NtSoFO3ZM39GrUUaDC3c/9iQK4QMn48hx+gKOuezqPRfM4AjDIhltdyDELH+gXw5Cy5MGhe78YTWpQlERMrGmX4drtu+rTxP3B/aG0YLkvm3LXkPlMFtBMrYjAGSYYbEN7if7maEwRsGm7cgeqkDDxFi1H5tAm+INnxdZn1aU2q43ky/nhGFSeWQy3rbcbtSNTDMQo4ZjjWQBe5AfOK2LdOTYltKciZhatU0IZZmu8Xmu9eL3q0jcN18l1f9XiLcEogbwLJwMlHbdkWw0qfqeRqU5zuk6jgYSweMAiEXflfU7cKpQ9s0elV2hI1xPjZ70q9hehdVQwpLc8rYsy1E08pGk047E5YdOmvt3ydZFr5VxxzTOpnVyvk8zdUICsmlmdVnbVNQUWCMb2Ez5KAdoFJfVBm7Ir/UHbTGHXRkJpVEoMYMNS7Wz18mbB98QxqcZRbmGjlM8OatXCDTWwFC/5QixuKd9JyggNV2MaXZKx0U54Ou6FPWw/F9bVNhw9GXLsG0hNwlMCS4kRDxxVq1ud3cTIxj59sgN1kAk67IvmF/ecAb1aSGv0GfNd461Rkkdf9snkrgHvWH00lIcW5j0oGSZw2tIMRkArOVrJ2wenMp7ES5XkbTPUi0zpSWR0pYlu32G7R7VZcUgTdF2LFJqeZms5xZz3Xef03Uddr0riaXF+JmO0ALiH7IsYfdLHsLpZhw0rXTIU0ULfkrcL5MV5pY+DFfqCI16jPpwfzUt10xWYwMv6gptfKq7olrMwr7Fi2AGLJXMJgwo8RfYcPVrnskdA752DLioolRwphAFa92qsmro03B2K2AeV1hZYVDzV7PotKQqI5osK6OFE61PtzlarGbcw9CgoIkIuADzAHBfr1e3P4JaOmWCsrFW82FQDfmV+AcEY8KEr90qtX7kQy+IadJoYOzK9ylkrYTQKpQeZHUrROtPzUszRqFY53Pmxcd0tQcC6rJXYo9ppIevZvB45P6hkZz6zjwkXgHbmnLSXcdJnj8Ea456+nfXYcUufznUpLnuVOEWg3ob3AD4wFs1RRVX8m4iJnoJKvZ5Mm8EXbBJleE//NCwfr2kCfWp6zVkaG0KNEzzqL8mi08cwgRlWw26M3Zro1Y4mWqbFbFRlwrJHhCslLSs4w8/U2JZ+HiX0yLhOzOwJd9C7mVWP+30hFX4Mzc3M4MHsNdyHm7mnrRqE4qOvbaJ2gF0ShlI5pof36V2t5NVq8aVk7ERlj23r7bYr9QDtWZhYB8j0DoemAfUc3dSHFKjUlP7cMwpHkbkrhtt4r3Ev32ZbgWtl2813atw4yitVYAe69LjEKdyR945ovQre+VTsgosNr62DKONnSQVHwAMP982dyh6OQmMr8lbmJ+oS0Sa0FTjcpfBnWxefElQV6g0hFO1nWA8DJltkgWgrjBQnyerMuofXC7hamUdRv43RedeJDjLZO6sD8MI4wxOGSi/FJ7noNmJROLjnSA5u8DRKVob54nCZKPkMqp70aPg5oQS5bCxIy6wPkag50koSsm2TzkanACkBh0gSimI1vBN9PzGohFP7uLYumT9V2Q0bLcGaS/Gz96jQ1UY1ToUaosxM49DPpHyMMqGLzu0ZYzTmbAiYGM5lsTwVmQpoQH7LEGyZ0to0NRHW3iUiukP138IEkb6hhLT4QuqjTmpOysO23tPKZslQ04veTywSXkP/GOik7LZZizxQFOhvl9vxkao1d8JJw+48uGE3eQfOG73bKq37a3Pq81wNdAQQBVYPtE6ETdrLfTqMOsdpXHb5WBPLesSxXWqBvh8FCkiX7dTDT28UUQu3PUtGtZnMqtWfZilShQil3jesWgMBWMCOofUXiojdlqYGEjkSozRCGY9s0dDPJI6wecT1eHjmkedodl0WRo0rudqNu/tuu5WaGv7GjCNoTSoUn8ZhSQF5nCLVnNO0HLbGKMd7nT1jLztKtjhSmnBTmBUg42wtmn0nT1im2WPQ3DGUsUidKkNeKhV/Oh+kYwymUqNwP0fTFBEtyjUXMu/N0eYt8Ugq/UQNqhLHqCEqD2zLuPsa5a5gCgSXGwdxASO7gF2qPsW4I3bmZh8TP5aiTVeLPHRZKqR6FNSHmdKBWDSe9DtYJeKbTjdQ29H5vG+DIMmF0iUzUQ9fjQJDqwslAqVHqA41HM5HvZWZnW1bDs2agKON1ZVnO+LiHvbOOneol1qXl3Ez7j46AR+Yjs/AJAclFLHLT3sRZnAjEAF1yB26IZOX1Hr8Mu2jQj2tjueTvNEh5unW8AKt+OQnE0/JiTB2CxJk9IhkjUKhdBxAbtEaGBFPsxjoRcUYvlKsz1SqbLG11Fp74Ak6qHnbhKu6Ya3QwdN9R+3dFYSWhzC9eZqV2TXGaw4uQYgTLTwJZazPBQkWl1LAlPEwO349lXA6I4xcmhwc7yfBwUw5FgpgVroAb6+ybm9OKHAjAY/meRB0TWjlKtGYUCatxfYYl7usyh+UlGRxhg+ZKBMfKBGCZos/NSXzVE5RtPJJM1YoHXIznojmnFaGnTJmnVqeP0uBqeEDdlh+fAAAEABJREFUYa0FJ6rY6WY21EmhqB7VliVFBvTjSP2pZGzuqujADHO5pKbAr+KAPLrRpz8bFwUZGJnuYrlGtaxjQkiQTTWIbHsGc66cekA2kpZFbODGPNCGag1QcdMOmFPmzUFAmamIzuOW03MyB4NqozzwTYvizi6IkCqnMIqNQpR3T47hY6LHCCnaOZOXlsXW4N5McZEBKll+aAnL7G4kVnA1KcjzuN8uHm4yOZw2CIeiadIHJKBkNkPs+DELdY0erhQlwNAFSiEhj92C3hjhrvQX7fTMpxMokGp87WVQBIPW16insaBLqlPSbIvmOcxbzuXLegwMUzdGy716IdIZAz3dOEYWWnnWn8PaMzFTihJysI2aViXmMpSFAlrWer7MX2bDRMa8tasnqGtqKGd2A1ZE2EkPFw1G9HOwlKfJGp0rj+0Qo1FesIFyYdMBmZEacIHTSDobbOlLafSAS3htwtPnzvzqRmohy9bIpOtv8VGbKQKpMNnhHQaTDaQGjFnuiswNg8M4nHMAjXgqmiDgtR17UAnKkoFnS+SigRomnQz9QCKHtgSaA53taKqeKZxia3rPpgW4G7u6UErrVL9gQzREoR8FubU1WR2BE5P0MmGMU6Fxw2IWqN1n25ljy21O1BxJjWrbOtxT1cdIy1IZWQHV1DJE6YFTeDR0zBijRKNo2E5Nb/LxPNTFeQpypjkcx3LL5bDJS6wcK0UYulymeKaZmE1XXCojtHyJ1tmQPGhB0wJGQg8kp8jJ2ArS7fDKuPJmbzAM2/rdJadqot1VrZdD+UAA25HKhT4+2/4Jt+egWG0rkz7EyBlPIQaYKq0PmB4WaRYqyHDXWDsYSMdNZaTahDS7dguU4Klzihd3zKLd7dGaRz99RTz/AWnbHO0mNmtkue1cuVfJguyonXiLhAFIbubDiGX58Qpts8YIrJseOml6DV0XqHJgtgoqEc+ThwMLafCD9apEQfe6uDHfMd5N5UIJu6BJSkrH5gmP2VLGqOQuLbr7REmWOQxf8rqYCqBH3LOkHpVPVsttmpr1aFmpDSnVuo2FqoBtOPmIaI50VSLwhnZKVuRjwgzDakINEasxgrGx025jjH4kx6qnXAoEaU0lrs5EjVGaXC2qgKaMlaib1dyLWgl5XT5Ebua7W3DJmebqiEltKMsCls9cBdBH4BWbV49WbG9kEtZgETmU47JZPkAaRbEHX8c/KldvE0vaj2boAl3dxqUJ2QKUtjYEsvWRWbN5cYiMaOxg4bGvYSjzeFktnWjTNMc0rBj4tEq9NfHcVDmh1uFDMQ+pXyLyxrCew15u65mH1EUYveuK9G0odYfXdV6XVNC6mMw370+p5/fVrTOUiVq2yVDGxJY9XQOWJSPKKomW/MUAwhU2g2oadswOW4IpvWVKHTLuzS5jJnHgGRPB+TkTYxBippV+l0qAyiyVZbMeVgZ6lERr3qZ0QvtZXOQX5EvOuo6WolDQ63WE7CjBObXp7QJL/OWquE6OkmBoFeRx70FBWuaD00rAnnDbBQf+RY51nLdXTmqoefTDZ7alIHV2yxEgLqTMELzAxydc7/Szgd1M0NJm9PT3eG0ugRKUVDya+sSwtvvb6ozo61HOlHQCD+kozc4yT4FFmh/RhyJ7TJ+myl5+A3fiPAc/5Ui4G1z684YOILqShNAnfZfFAQ4UxJWB3H3a3xh3jcxpx+WG3gZ4IvGQp+2JzKv3113YW+0C3dSQkpG6ZcPlcg7hi4ahrouvYZeQyGufdfR42kYtX0Ncg5JWeECckGIxu+RcmsaLUueynvJMtBg3WgM9jdol63HolLTO8pwdde473oepYAdi4J28a4qAVIuxpgIwECQdObMUacTomFKmrJeA2Mh+Qd+DhwzOqBX1wvMe2qwpURKpNydItZue56buBX3nZXmttsEmxYWUJTWTPnIhXuhFysm5p5bPd62jPhidJzOEW9uFzCE8Xbt0pp9klfXtrXRbiHlZlrGGZVulRfDsk/vmvlFhBbaDSEYU3uBmnbFzAtNbPX+pgSxJ4vS7KtjonBleBYTU7RWoatTu/CR+ZYpu5np7Jz4Z74GbMqwNlAi1AS1Paw17wcdZeaaGvb2U+5MyrybZgtoKps4DMFWrkDvuakkKZiaONGQeuKKibXWNbOECtLaXNkhoKNptHraKowuKW2mb2avEffvuQLzXuVeXwx1PpbSCnzGW2Zi9muNQONBF9OW4Ji8VRe9T97s7e1ADF8yVotnw1rg2lnWKqnNH3oG7rIqtIUA3Hp7EnpnNIp8HAuchVnf4LZbZ0Chx+IRmEsI2C60gSlFLhsoSyJooc1N+yNk9knbLeRiPefkFVbTbWOlzL49hrcFr9fXqa9pF13T5cU0FdMlNbs2eADDSLJnD8gO9lADJqkp84j1LX0mclHx4b763kgOAE75wDTFsvVe1IsXQeeWjpRCWSKmsDkLpO654XH0iDPfsfGiZxKxnbJ5+ItzOWKgLy21BL9nUbxHj1VdzAMagH7VCrs5Ftbyb71TSeyi/cpUa1OzIkthTUyf7kh+HZphie8lToXqVf/4ea8wtVUhr6ovV9IwR+cTPrKLHHP2y+crr47brpGmH0vsTUn1M2Ihydi/EF+DVGtyNOzwShnwxPiDZjbqvyFf6UJY6BrZl+2FVoo1i0T7MF1eJwXbW6HJhL6AegX64+DC5g9LXnB3GxBrnmrl8zvbVeMC99x3n3RUA1/DXhWGSocJYFz5muTIL9Q276vqoD89XqbgYWjfw2Bdmahm1aKTI4EMAmUQupLZjbnq/u96OsgQ1OXLv3HfuYyc7oD6V0Hd2V0P3ji5GmVb1+QYVmgMqhnSoeJRmV3LqA7yKIA64gXLq8DhrH9dq3Dvi3i0j00AjTVHRJ6g65dHS2Gj1sIpmcIYKbDnGKBvZllmZCeEuV2gMsHTDaTu0RaYCNI6mbaq3Hc73tT5d822MgHhe2l52UWMWXRzo8yMwKUyrRIOp1lH25ueNmdVhd1n3aMCsBkoCYY95puvLKLfz6nxFj2MIlzRgF2E8wbM1t2GK5SDCu5blshAu7pQi1e2yl0kekhg3ayNTQGBYOvwk3jeOOMTDjSvk8cZ1b6tPqE9HNNKpsaYILM/Zke/CAbJLeAuEbD3+aWegRyoXLFspIguia9x23GqNHgLGd/CuR2t40vIEzBHT01BKFlNBdstnz6uVZwx9KCwNHsPHGDZm+xxwtYMj98Z+3maO4hrjWva0ZEOzPUGCa8KNwMN05qOknsBKn63L6G5BYWAFhwy6n7EOlhCp3GyFz7RZJTg+zKZh0Iz6eLGofOgJiTqpwu6mMv+wrKaoGO1J62iTupCoPqCSnuq6LHeUUaBjCsHzbnIsCJnCgX7O7K0xpUjmKO9nt2nh1DrSkykp4kR2B1sgltHpg4MTHIGhplMEnHqhDaf7RHXKO61J1KNDTw3KcpzBcMiw3oPHdLqG0dVrQlXUmCjP1uJcu2Wuh9K5zvDo7C5Z6waU1mrzHkrEguJCXdbXcAHphiSJTlVIm/IQEOEr9BEQlsnW8iRLJQ7P7fur43xybogAHWk4xiQSWdCngHTkaqjq375cABsQyUrwhpdNmIBFW4GIyjit+Ib30qo1WmZFzhNmC3zFrO3ibZbJFOkKV+fl2CHBTZY9wT0FZPm2VOHhdgpHqcreB6dkkhgt3cERV9cQeaqX8xRcFlCg8qMhkNiUB9UqjKx0cW+9en3ketVQWxugWuL2ibleU2qfCNWQWxtlJlIzPAbadFi7xstvTN5j7rpujC3DIqRa48WEiHuTu5f3GOmzaJzLL+JqiQEww+e/NLWjkFRKrcHknv7BGkOEdrjfUEVrD0XdHJ5oaju2j0Loyi42Hjy9DSm3KncV0rgUtdYot+ciraRljc6MJ5Oj+UHoGGwtUTm5p5cz7S5tnY73veqemY5pZm0aJpy0IWtQQFp33dJc8A1oKLhh/po+l2JqbyrrSOcBbgnUWVrVlg1TGYsLuh/Tnmq/MzdGzAl3c9iiDTepsxEK0EQmKA8N6jp7zbw8ruhVptS5LxGKSSV2n73fJ97M1K3VSECLR0xRpit57w6No/Ks8n9X3QUrG31NWzbsMZF3V5TOd0G6MbagSCvOngV1VLuBQIkj67v7tGpHB7PXzpnwmh0jAqoBPMwucHR3MjglPHcsgwIy5IK4nU7SFCD0Gm0zh2xoTWfcdh+lrcjo6lS97ab03kgztQDQWcPbR/Cz6/xc3mW4c93nqh6T6+WvUYNHCdnsEA67p2nJ6vmctk6F6EMV0uhsbC4d0VMfPnvNBEQH1Ytfqz6uskdRJrjJ0kqAUJ8xjOqV71nSTUyD6yQ9wMiRNaOkuOa6fX2y3pHMsugOKy21dWVJNkMSPQOSmim2ogMQN+Oaw310W2lGworiqzEg/now5UTpQpsnoWoQQTLSsBQqaa+ylCld53B7Guo+8122TaFs65Nd2vSmEMrJPHnFMTGusFUOeps0kpOtdWLA4a5e63WzmwYTDgs6ysadvIvgNDfxpZSHUSdgiuSxbyT00u8JnyB7Z34KvkW8ZyDisAuyNoZ+Idp2j1PM7rSOkTdP1YaKRjdfWokS2USXoRxJnJYws/PY3etwVNG2D4xpF3yJRUctezuPojDiSZTg1ZzaK8gnpe4bW1hEojvdptkavnyih6jZoi75tFfhqTYJpe+DT5qkDeHpcTCfGLpkStwLr9S3a+iSpodKb9+NG7i9dC8oGalNt75tucsPl0kD0SX6rcFSFp+4is+DuCsUkUMutX2zGGJx2RCzVhCzRk9bgT5nv/cJPKHCGwhU9uYWfUf7Lolajal9Ooe2GXIEzTeZyNF5VUq0J30rgzon12xvsuaIYfVqKXrYfvH98nvYp+KNKL7NtlIdPnQ/XV5yO6L80cCoAU1KqyxGspUy9Ublbis/Pe6eOz1C3qCLCgPHqQASDVmXd7sML49lp1DnMZCWz3pjk3qtzBXv0Z84bghHV0kOMLMeI0wAyFVwUb/7c7TGxbVah5SgbbC9sAzWjewohBiBYk5UBc/xg6v8I1WKNOzkbuWedpbFy9MdUo87OXGW8sail3JZNCut/sOSFS15XtvEr4uJg9H2Ys+RT7l2OLvaso2lRu2zBCZKEXfLaQ/I4HmE3qPCQz03Zla1neSJbm9bpFOyz9OoNCatbSjXosIVeDdYjW4+gMFmxhV1RX7EuTpWq4nreg32GjFGYTLdHyuFCux7yjGlO5panFdOEUwyZ3PgMSsSCCClvZx8u8vbaOjmadoTyCi6YuToZa0+INVtjUYJDSGUEL5mQfWLKD9YUVqh4Xe4HjPhdmosJDEWjALjGAR1snHM9qXf7AYO7IBvq7vvfG8o+TnPmXrq8a4ItawNK6/RB1kKUq1rTL+aV/SIQstJQLXAo8Y+SJinP66rJNpx+NHrBT0BdCo6TVBTbVkRLEEzcLEW44KoKFjfyHdUpqnhX2UzzJ4oxLMAABAASURBVIS9JZ3lcOVL1anCQBn7cTezZynpuyVeVrcRi20eXI3hFIcjtyfJD/ILm/PRoPcdVm+KAQYrwbBVQ8fVc/AyeQQ+nqLCgtteeVuLnm76fvRAVqgcRovVRCsnq9NYx7x98YFxcB2skj8f01SJeWXMoJVmLZyXWsFM9rQYPFTY4Glt3x9rUAMN5cxKmaNyrTVj2pkKPkgIVV3AvFR1iBc+XZ66TBW3NcWU2CmNS3l77FtqjbIcHkO1qVVi96dQ0/M2M21z8sWYDbafxI5WceCarecliidL2w2e25jKixItVUbxsc9uhLggVPA1tDSc1OfzyrhOj8ikkimmHv/uQo8S9v7wFrrQ5MIIjt2UbHMss9UmJAMATSvdUrCtR4f3WUU3S7OAiv5uZlVDXe5pk9b5gljvdDWS8qpV6exUZqAXiYliFzSndsZd9x67RlphEi7+sROa0EcubVBTK3iOMrKas+vKfulbzpItUFwJo8EUuTUQsLu5BWoVFtUTNMJxfCi2d8SnuN95dkPsdsZADzdfwlS7UxvGaAWmBwoz6Z/FpjWq6jEiTBlWNjAMgGetkOxporazjRz6hznMhEYdMYvdY8zJtXJo+/lSCTxWeJfFYFkNpCU6NcVNhOvEbtvZJztPnajIEs2pj4VslBv8sumtfWVP1FSWqNPIYf1QAq9mtjSMerBO9lUtbhv2XCkgaanfVRmIfN6KSNl1T/NGgwfKqBXtDNtjygsdExiEQZm6bxXRKC7jtTBGi8gXSr58kE5oKNqQJyVaw8HcxhzjYYjBzA4NcVUUHt0+XgflYVpdVq8xh8sYaNHjxLAzVxpL2KCm695qLK0ZLic9BAqqSShAA1+N6cCyuq70qYPmJeKMMHZPNND9GaXm2+2aYPOa17h0o/mlPbX8cWe8gdBQPA2MbHUNqV+483iGnNWIgy42LKb10JjybrqySmWvWhYOKW9yrT9aYGyzpdDTrboI70KUBETRozzjtJ0SQZYPL1jAleL/siP88jVLuyfzlq9RzRSphpoaBkjuBicHoHTQZULWLfSrM6XdKA/BRjcyiNv9cHYupPjqAVXJwcWezBaV3dUqvVAN28t9TnOv1QpeFzRETJRNTqXfX298vDkx/DUw7Zjdmswei0P0tecj9IzRWmhaU6b74OTnF7IiTGH4+rAvLXuZC+r7UO3eYXMZZpaYm2/ZlSiBYlZVnCyocV1hIyla4MQcuRjWaWjjPOMVKnDo1S6hrucZDFFlJ+dupXpoWE9s2Pv0nSng41EJntmtZfVJLRbIs5ScNpswiWzseJKl+mitk64IAhDBYYrrMqm5RL6eCYtLRNfm2WClz4P1xheNl9OdLd1YxsyjjmMxoEpeTFbgwPslisufnnYikN1Jwr1csutY0LKtrDz9gFc+BZVdJ/M+8YyHrKdbOEKNobtpNRwjWyQei7PuXLq/uVFYkU9M3Wr/Q/B7RlEUKzuMPXlf1ss48AQ3eyjqbsnQT0Vb2KpGVaJIU9ezecKO0LCUGh1wkoNcpHQsLNGIQN6JXaxqZVVSqGwlo0vI2zqQ9byQrchjW2zKy7TmKFF6TF56G1OcrCu51C/6yHn5Y7ghpiyhWy2v6qqS42RCAZjoLbbuW2IQ03T05QqvJz/XUBzegdhV1TCCjS5kVFSG7d26dSr5jxpNs9m4WuwhyENJcpDDleJh2cx2VCPfytlr6MKF46UcRB+2Fn4ptrqllGYQ0RrbK3iF0SKUmpEevs7LPjPZkMmO6ESosnMBpnJx6t8sGPRqIK2AXWAJ9H3MYq0x/MlhR6E27N0eEE/7ybh59YC1qbA823fH3UfxQr1TcZmfMSEoirLuxhtoLYtdK6Ewu7Nho/1DDOyI2OoEUaxaluYZs+KKHJ+AAjVReBje0UzlP5BP7PIFl+qPOalOcaYX0cGhcnsv13FTZFhpHYTmiEJ+7pEAATxxe9fsdjNVvA+1RBcMgYuWoZ2fsRvSFQs+dr2ypamGZ83KUZBH6UgS2NGns1p2J3oQPkwvllDDim7u3q1Pjxbv4+/9cGY7GSLYyDmxxKl1dUUZwqRIBDoJzGVLZbVKwkuf5yHPhpn2fcde7/ZPGkWoJSzU674/GmZuy6BUScbFw7PtzoeDopa4qwviwNceDE+1En1OV3zpG340RMG0aZGyO1bcZJt9TTfD47JwPFALrY2ShLMstAo9E8B2yklUdSDSgjPTOnNkzlAqekO0kRirXkuV5FV+xHnSQKWBqQH65e0iZQySUJ4DV5VpHAwwW053xMp7dQ3ic1ofxl+RfkspUN6K++RR+t2grjumjdPX7pW93NbiVFTdVJbA2qw3+za9zVlBJNCy20KNYrfa/tC5ZIRC61aEPAZ14Qyv05XR1Ike2VRJiR+FDmWWQ0Z0bEttJ2Ksxqga2AZRL+GNU6vyGjGp/qXME/p7U7VD+mNCO9RxcwQm21zoDiK4I+UXCoEWi3HRrrhWPP7O5LkY+npQ7UqOotWsslE0TnIQZFVLuWw4TcvWKAOQom/P3hNSf6mCxDQNQrW7tZvgAq+eJligTtdOqzNTXzXHC3vghtBHjCet3eLR5Jb9eGz5tK7VMZ+RZHuXkCBcwCQrRprsPWGd6FJ32MGsIQA+iywUgKGEqs+9TBeSFcymyqDY1mLiGjbGojnQj0cnFuAmuK20ZQsTzDlBb00+dsJOajXrip5RH8Dlr+uLNS++rh6XZlNSwlExpcnXgMIZ6gI5qP5cOFa3MtkHW3uggLWY2M5SwcrxrOIBSlNm00s6udGJmuAqUarlQTdzEqUSoQjZGl/9Vlekako1ye7jvdn1vESTUKAx+wy2j6VOUUNdVro4Wt4V5HhMQwWiOa0Ut7YOzrSMhzI05UefJ8qTOo8DE0yAnORVYXeew7cYaRd2pRVQUiOxA4eJ61DRIDpO4FZbrMvM1kx/giW4JSfo+mnuyqOZUWPDgJ6dq2VkY2ffVvHq56kHrFUpALRi5fR2MQ1NiLLznue+Ou0JjE8lDIY3FUgvQEwO2rKhRxmqMAv0RiR3iWcZfBaR04oe45NfAWNJiPax5nrZw7qppgpvRQ7HE7HqbJQ75Ss0EQWPHpIqnula3yOTMF02mkahQTRHKplDIKX7k6lKFsJl+WF72XvwvepecdRsd6wdCsq0WbjVYObk0vKpABGoU7m3dOS+/WrlZNL17PKle5L5qcc7dD3tbnEtmx1L4IDxACEATkk+L7YDhlW4ErPEpw2zMX0YvTc1e2qwBqmJ2zqwczp3dmvmAAwZFmGlz2hSwN5iNom7xRjl0YzwNih6/UIibk9tZNQRzjiSOLRNvV5K57mqrrILQ9jK0aWs9LaskcNv4245YFo3pBFdB6Nw9swY3XL26GOWYns86cfbreR/uPkANST0GwotX7j11GXiVlLAIZ9w6j3ZRn2GElrZdZplGJfapRRYllYq6ePq0eywjuG1Ll6zV7eH7DhhIkQdclJlXuiGRlqqyeWYScNoW4nVcuaSbAHtpc+wkaUAVD0wU0ICjzwVyUVeRtB3axVbfGBlynRnP3+LGgTZaQSt2/2Qd9cGS0z6KbsDkVXtjVFNwAeW9do1j80zVlEj6Z16uHOtdt7Rp7pUOI6p2iUBtYlnxTIf5a+D52ZmGseHUfDQlXIZb+abfQDRDafoX6zVe0hSyIKXaen56LfXSSoEE3RTHHeoq0ewUaCqgPcwkVxASmtzjGu8Fl6mRVg8lRere6dU+BD1wCMWIH7nns/dW3OljZo6Fs2NbE89IO3r6Pr1iqHgxcZGHVXLKPNWKNjeX3V8muP9GmJSMqvSkMKtrvRO3IKEtKcwtJ6uYcOdYEarKXY/QhzYphT4ppXXwPHS32fKGmfOW/dSPSgbpQGFSqnGJwQRt3FroIknKR49VIl1xVi93Be50GbWNrLLT44EymAebvmgEKWijWcU9ATGeppU3AJH5aQE1IZy1npVabxjDk6qjSm6A6aTwyaH00mlouKJzFLW6GE4U6qVkIQJV/iFNSk1W3ZkblcoZCkA8FFAnrY7EYEouVSqBV9JglgCC42W6uANvUkyBgqkyaTyfNyOZ/hWQYzn0uy6gNc11hqPLSIH9wupknIoMlNbMZlduGfuWSnDSsFUg5WlXbopGdvn8SvVT+kooxyWAoKYTEKIAIa/Qjd/AnZGHrz7a0eXBge6T1BHl+LQ87/3J+xPUqG/zLQ0gQo7NqNXjsWpZJUcJOsgKyI2pBTt+CNX1Ixe1Q+McbrjswuMgUmM1Gk+VKHEbsjlCd3RvT7l2KJzNPUt8PIN0cuGEcJWatKxMxJVqG5JHHzUXeb6RBsw3bTbPh0aypOuJCt8ho1WRltqwmL6TF46DHM2Lbp1fDZ5qm7ElhVdcKNbQ0FM5aq3iIBZU+hMBBZxTFJ2FFlC+fxVeRx2AGejBHRC4MnWy3d8oK5OIvXRIru08ux372jJE+YjJnW5m12evO764oRWTiuZ0tlHFXFZzqdj2cmpcpyKaqIvbGK3DqbIAaijRZ1QAaBwL9rFNWMVV/kTlrKgCIeaHbqUxMKEerKq6Za8Wzils26xO7sf2IiGlDTy2X8AYgEiSnL0ujvkvmBtpit4yDdNFxr9fkixiKknRkw4qDnUO6uWTq5nCjvMzhgxL2hdUJ23EBDqrY8ejzOF4XkbUlyJQv09N5/ctClCtJTZp5RMuTsyr6BHuZeVEqKCstZWPUTmAVIUo0xmsjNK4oeMENPSOxz6tB6Y5a0VsoYGQpUkKbcevx9ZaKrO0qMs0uJYqsd3qc66ZTw1oYUL+qv3R+dBKBvj5ngLHASZ9MdpdYsmZ/mg00dNgoVUXxZyykp12ONWX0ZXOTSqpkktEpmL092fVxVNG4Bneaqf+rIxPW1utmiSB4E+6DSW8ixV6pTgyZWtHvJq0jdcLssyhtZXQDQopzrFPo63xRuKHyl537aTByLZ2TpINED4grGVLfskLOaU5SGnOlyIeiKGuMpUFNRlNrRQiWXqNaAODfd21OgapS+oygW8oPuHe7H1sGj6sOU9aUPuwSzex2J7ixuMNj9iR18fvfyoYtHe4VXFPlbHo2dieslLG7YgtzvV4WROfDzZq87K01HSiTIhFUmmRoheaPxhZRWCkqiObDwMgU5dtdZflQG0lGhg08yf1S26Tqb0Wc2ksuLaykpeqgXzIopsIIxBenPCR5NWPRomxpToKts9PsEeQpRaD2a2clot7iNaSi6km1gcQput1bqDioqDNTRlGiCUaAt+pgAoIFrpXiXEYYq8UV3VUkCYxxgtjEkRAQ0NezrPZ4E1IQlUJStGx2DT8bTY6dvH22doWVXSSnmbgCu+akXEelKh+jW5OK0Ehlym5l/Mi20aaK5/opwC0nGsD/HOdd6mm4fTSvHiHANch0wCXkqIZaOzpF80pnNOmN7pbGlYU9E7AcWo7Oo34N0MHWUQQXHZYXa2WJNkuuJp0neNpLCYxhh0SocQeapnAAAQAElEQVSuQPIhSFWx6ZKNEpKQcEKLyNcgoJTPRbXqFs3Z1sp4LvpyV1KVePN0xqiehyvHdPOhoVFdPYzXZUO8wHbh3rW1RDVHD1dcov0k8zgVwyNx0eYpK2rwWqPFBJsGGew50+yAar6F+dhnpB8OxVUyReVmKp1O9YZQiqpFAwVsvFfHEvudluATRjdg3RaaWA0ROsSBPy8O50UxD3ilwpOzkiUxSuERedRWUOSyzNvmeNRinaUnV1kBFAblLhneOal9kZnA+cghiEWIwOxpxUeGzOkhvgjpW3pBS4lyl9tbLT1lTvoZ1u4GeEADDn2bRPOE3qC1GknNWUvbJ7/JPZiEJ0ZYgxJymuIDddpCtCBbTdzf0TtPY7c3vvYSOeym5wPJkJbFoB5HRsqx4nAzK1c9OeU5NY9lAxido489wSlwbjGCEsEcpVCVf7azzUK269Gl+BfJz3IpO03db2rlpPZvWQpcIFqcslKTSsVL+RD/47iwyY5u9mKof1e9W6wMNNRqrZSC1izhEnvHKK9JlqLjU0prwV1JhyWnQe/Ue+5uxe2AUq/D4Qj3UGqnS87upI/hl0RrQJkSWGSXIWhVjrPqvngPxlSTeDEhbfZnptEgjJoGREkkqrqMiNItGSTnJoZL4JA9dFTnUQw4xBaK8cDkMHPxWBZguVstPy9TlhuDAmuQB04fy8cQTyrFwTR1w9VFQ38g1P11w6I8m4UySAVhWkfGgckqWhIotnjqJtSrypPSF7zJQJw6p9S2slBFZSV1stDLr2tc7ku5SW0ttMQdE8U2p1rbg8ZHKQuiAZpX8ifSsmWdYZ2CwBJkB03ESq5WKigoJKkAgtYl7GfeUtQ2hKicbdjjHDuS36mxscKRFuRNRQIW/GiJNEdPj8EabYojvbQ4JETXoBDqTYM5TZGC4VaTyqYmJNqqr1D9qno22SoZVLDUW/WbXj4ao4U9FyTkB39B0msXQd2WLkH44bxJkQUwc1ZOaIreh6SAMh/Tp/dEMEvsp3mbdUsJ2ME72e3umJazw1sxZrF1Wg2+WiXiJ1eWYsQqiEkRJAg2aj6m7WHaYG5pdfNsysTVFHgNVKS9lAX27RDB/Sjdi5ZPApwwmoYZRSa+dt9VjRgCPOk6fQraToUnPBUmeTBbVvSh2x4dkNaA6WAprGfZa5fuiwiI7a6r+gXR1/UgwRHiNFSzhmEwrVWiu9WJZsV4ZqG1moWnEiHKoK5MPqlTgbqBaKPYZJWMQVu86infZRocXfKs6639i6vMisOaXuYJBTRdeYMDNbu8a1rBKXTQQEXOILJRhQqcQ73nSj3FFVDIq43UZ06nTXKk2ft5qiD9+XTFI60ofeG8zYtomXoqXuu3HMiYIIJSQDpqIH0c2m626rCNOT2MBc2ilQ3SJdPu0KNJlWfdm7t8K1XUDZJyzNFi0ZT/RBcE3RTA5zoKsQNsym8oGCkpawWHWEj9Vb9AIcuhrDFjRPlpJBy1uieOVe0a7143PhKXJl7TfKFIZWyevnIv3XSK99vwVY67R54JrqZn6xCZhF5GDoeiqehdDKw2VGJmzcxKlmBG0D+FvaNaC1afebDeYx05fsq/p7hUU5hH26KxtemGy8iyPe3uKzAU2DSbGLO00rygDEMJGgpw7O1vEXjy8XRca1m0ZQ9CwJIqzbCMmW36J7SFS0r//9L0L4qt8zqvKAqQlJ2O//0fdX2NJV4O3LlPhpsmtkSCIEjJ7rxsBeq7F674fPzHqIxBqhhVbhUFCWYx4X31iPgD374mbvADhkEknNZakMr5cE3ffW7Xc/UWxS8jJtG4diO2C0+NOqx/YU+0ZsKYpmeOM0+v8oWwdVVwww7XVmxNPLTv8tRelzdmdYsPyWjFXFc5562QFnBfa10WHiAPqyBIX6AGOOXS1dmUSMWAQ4T1nOav23+3BCoGb4OOH8alVcfU8vypKD1N28q2Xc679UCzvEdhbtEAgxC79onTWfXd9T11Ttd0Y0MasAGLUCt42LqW1SBsmd3gj0hcH/2h57ZcA6eP+bfXfnfrr1WONZTH5P7a2yHNJ9DR7XkU1oyMAQ6xBk14D5W+6yudq9r0JxnFv2I+t8dnlUlchi/XF1cmRk1Jttb6/uO5ymwuLmOoYFLaVaAwoyvrr34tsC7E531XAQ20jmgtKN5PmMoRUEXNz5WXHkGuTQ7e13wLqomQiQ3R2XkNAqjk09xD7VzKUnF3/3aeU/NMT2sh3qM0QYacbOKZdepn6jPluip/l0m2oFklzm+Lec04c0nPz1hVq6tU7tOZBTG3ELeN2SFHkidbOOL0Kuo0CX9PTFZWafLM+LQhoWGjlE7xaIRwndHYuWSFc2H877iGGitnTz6/lequLEepJnxg1Ccdo4g5JdXofChwVzgC8FOSHW64Q9/wxvKknWfls05+zujhtPGUykhs23bp+kxPjieGlM5EuPql7nM4e/IMBzSOGcCJ8vu4a6RinaAyHVQ2fU3ZnOneLckyxiQUOx6pHpax9CubXVAfxPv3lZvnol+1+A09NPyyU9Fc5BXK0gCiw97Lh0I3DRusMz/Tlx9fB1i02zywCipktlnWOk/UlpMp5WsyrTs0Ge8C1zby2J9InSrdgD15TtbIPuQdx8dmbgUH6MGs9rKzJkDvl5a5gPc/2iQFkFCUz9sg7A3Kvse+bdWtguLBtLnR72LgfZ1S4c7a5TNnxbOijOqnC2b9Py2Gcs4Td0dQgPI8ykcvT7c9diZkcsRpWWV3JezV02ucFKkZL19S5jpuWpBGAEcwCfE2YlXdgrQWBa3Nwc81t4PDLP+W6VkGuoxcyXice40QD4SpaiTW3XK7el9ZyoGdZuWYSFob6G32zL0zvo1fQmu6IFkPQ2DGK+KxeSBfKO8pNbrZ+CDuFVqGdC6t3eBmJuDNHkw2qhozEIm8SRVIsMZVbr4Zb7ERoJH3yo/vH2QY7OorvjdKZRQd3oozINPH8bX+2t5awK+Ze8366fXv2E+6q7CGKNQ3dl9QmVj8BP8N72kqITgHJ1U2LmCYHFaRov0Yd/JsgW1YDUagAPmEvlZVSsBnsUnswO+axy1591Kt3uBFD3484zU8lYt1X6WY1Um3qnxza+I15emd14EBYyjrojpQgVOdXnUfVdnN0kfMiTzevct+Hz7aCGi3sKVPM/cfj39cPxyPGpNWX2OtXM/QQB+ZJGZhQg724IhzXRmgZ1JuD7tHie2Lw6DYHVZg28y8k02cFQWFqb9P/cS+IN9CM+pm1nalUSJtPj36x932e9SVshy26FocyiQ0RTAqReA/4DvIMQAE1syfEo+/DwfqhsA2xfECnKtNvS4eZWvsXHHuu9SE5kLbpOQsM1Q8KLaCcK/L8hIgDua2/lB/TZVRH4naf85ttrRFSgK1nGEft5/wyw0+fefcDQxlkq1/mWj9VoTyLla4rvAPTH9DXiU3Mb82DyHhJFb3Ba7ja9sqKpJJbqVO/dZqjXrkfIw37eLyujXjmG2KaFpr/e3Fg2gsCFjoZWEIwmku7prdLeWCc5taa91WSgAymEowMSMyrYBWCOhgUV7XKO1P2ckLdV25ftz/75r4aDh6md9OvXqD1X49dj3Ow+zIQTMBKedYjR3wJQ4FbulJ14RqBdcNENvn97LfW9NDYRwFrlMTDN0zfK68p26FUFNvwC2UPYJtHgrPZ4yOWHN/cN9UaLJYZ88QuPAuUyMo27k/c5YUruKO06DotL78Matt/qWdM/MtbEq5OjCxEEr8RRAGj/aXe7XFdSqygZcuI15JQWUbtu+rrwXVir6/18tPfk5fpC3GT9tds9guEm9JrevUPjL1lbxuX5/wK1pJNleGuvnAvjn7sZmWpLrYSqOM23nQR3EULrO4SdWhQ6mL8muWSZdmf2MHSgpLKXioXNhYg7XQ5u0xYQIackD9OORdtT5iVTCli7ta9Dun0GJPGZ5BDVS02ke9X9W4pWHMumpdguFrbsfdZiUUStY8N56gkqsgxIS8loX2PH7t4MM6KA0tWBNR+Dx9n16KZXJVCoEDErQvXT9ex6Hho87AU8gUNzWcN5JwDQ3WvJpHPW0pIRQc21BVolL5l9ZdOMYNzuXKL19xHnUUSG4X2wUlYABbMn63twXE8BqoJ8mSdhapGozjlnRVqpOrDXRaeLz/cxoOifhQnkf6na4+o8Gh9ikDBU4EwpSlplAmS6UDPrV37WypgiWa8e4lNJh/QSmZfTgZIxbNF22Zsn/fwXvVzaMkdk+iNrT96fxSVoIVr7C/8lSqGcJfPXOVSgf8sK4uq55qErOslreo1LEab1xthSmxaqOZZm5GF32VMu+yZ8CZ/CI3JBBdb+uvZc8JnGsUrqsn9zJGg9rQrIefiqUTn661W+Uw11qX06EvjTkiBHAzI2l0VVGoZU0pW2WTfKNNFEetrO6YUKw62SY87iucr6Rq+ylP4dgqYGohcMNAPckEZy6fy9Lr1HeeR413CsNXnz4uv/Juvdm6lzjix4oEESbhjA8CXMOXSIWlyAE3qfMASsOgEuSERgFpfaq2TmrJaQkdsDEN0ODDGTJbX6+MhVnQVXREMtKccO/wXD3WlGn0dE4+ommwq7uPQjeNe/MC4yG+gp0upXCRBhb99HX6rnccmDOlf9lZOOjs0sKV+tPvW2HL5ib5ArwesEnAJZe6VjqGABhe6tA/nDXGWqktx3gjPFYY9JrB+O7rOfZ77Hlr+Q6/rsVL2h+PFFtE9eu4NNjYgGZprkhWJttU41X6DpjDwGkp1QSAprMj2c2zZ0t3yYXrcsuBWCbbonlZBSUXIqnxIDnswmjUu52oCLjRQZDmN+eeHZLtyMW8ic2OnJWlyIwvOE6PwWLg5fJS8ITXdBV3qkEYpA5V5hrz41leahtK9utaPM+TtRtifYqqy9YymdEaNibLgrFi/B6LLOaMcoyRefb53xmWmxnDXszTyO/gv2E6zHpxFDbGB5a01FSRVuiiKiqfVOSk0w2YNJUHpDuOZkkzCmOddvUOoNiarjYn/VdyCnwNGqoFKdAfh052GhMwlsXmXRMGXlAylsPlpDX+j3rAWyxBmIB2lI0aaGnlcGuNMGCxg9mdCpgza42LZDsmF6s78NB+W8Uj2x/OOsMHSMMYFOY4ijyKXJxusmGvVguVw4HEYPehknlPm3mOnVHyZJWJt1QA8bmLv+bbFWKNyqKxER3mHBtb31j7usZ/bFaJWAlr96hnz7zxrhrvBtpXtvwsFGzoIPHCidLzusePVr5nsKeOP+mP2C7xeY3AuyZ3U9bGOpmDNKq3Vw2OBCqaEDKWWE9YXrRghNQWpV/LGeNkuJurqT9djxa/bB9leNPghmW8pzxzUtx7UofUJy04Sg2IEGFjDJi+eafX4eve6kTpqduGRtBMpPjgPVUlS1qP3J1avn0cuGqtxziaP8/CCW7zbV1RaciDNwGGUBv2qPUGefytl5mYYQ+6MYqTx3++vI4I2WAqWENdFAAAEABJREFUmVKILg9N/9SmWtAZbfcb3TQ1adJhXhZn1rdN2yWtxnUgMROa5vGq1C5YwDUUrwISWSJZ1iUPr1rVwmlyZZ3KjSaWAj2BdIJQbhENtp/BJt7hNbnxvgbcUk9OIaXq6hmd1jdRj5EJyxo1/xZttiLMZMqPbKVKMLt6sqjv0MTYFseo7zKljTLGgiYJ+kN8Y0SocJzW6ionEg+tgGPeSiEXUkuxAXIVCDfQUnnByTi9enxs9RWzaCYpQenBC1Pb5pKBkmcn36sA37Ko6srRGpQSri/9A2Nsxo+P1Eq0EqjvGi99+UB863CGud5GEGesK7psMO+rzWooXLRt915RN3g17uEPsCQ5KGylEdpSPq8bCSRHOBpWvjouxEVJMA6vX7tyRhzwxVop9ZzU8+drSKaZpJa+xGlDZhF8RYdxVrBMCs1cxG06847oN6JqyxZCWNNbQy/QqQdK13Fr9T7506++1bpMZns4ARkxE3eX08xhmNiztmkGlk84RABqJGJ71KBoY2iMiNG7nCsjR+KQw5T8m9VdPBLKEKAcWLOzE0PO+92PWVmA+pkrbIVrYyeH4BjNxRGwmmt0dLR2N0+yVYN0M0BSLZCXm5krSV5jDYcTl+eFNGLgORBsqqQE3oRwv9Ae63P3XId61rHby9lXt9sISl3WHG+VG0kYQ8Gy0T0HfYhU9Fx5zejZzL/Du+A9ml6y1wBoag6vjsNHWgLf9ucq4mm+zcUa05pS+KvkfeqcSbJJ+IB2mf8z3cuG09Ui8L6MWrIn/Kxu7XFiKGgvuZqQIy4JIbqmROi5lKWoDiup81xvFduX3FerrboBG/1AtaqKSLcjgSiVZQQ7ropbPM5UoUdtRC6VfpHoncDBLr3ZfovRRkOegrI5HqTySMkcS5C6OBBMtOQK5LwC+Dvvc6brlADYKMOyMvTNKCwQpdlRVBksExcTMf4qkIcp6qAhrbo5zRwG3p2g+lK1fyEjXpDbkiCUausTJ1FFtOu0fg0KWVrZ/NDbSA0HUA64W8TVcWlBzZIYJYMT8zXmrMpbBH4k8ww1upZixDhDY+fEesZOjXTKTvrX47iEEVOilD7aKvBNxwtrds1uJFbYHfahL/s4f8h/zZ8D7q49UyJ0mfZ8y6g2w7au3rFPfBvfyayevGo4Trsd7sNovI8t+466ovouYa1lsJGmSbPQ9j0+zbXfDqKTqoWncVoUDFkSw3Vn3Ffav+4f00JSzdox6Soqm7qgR5i2Kq/eqrOgZloFz4XqtpOex7Y6GpMeJjP2U7y7Pt33zs+WLZEqLepZuWkvaYEizijlK+kPOK/JwKXI0KaVoeln1bn6u7pRUEr/y/hanB/rDxni6QNt3CUfo75L0YA4j7v8suHVUKtcF9c/RJBNpL3v8qSjp+Q/vwYdGhq8rtBtq7t4ycEulOQgoKE0T3zrUnOUI9P1g9l492jaZnU2WZao48oV09nhOjBLFfPz1QN03Dh6xGJAizui33Cdfvvnss/y4Oq6Nz5SFS7lrtHHeeBQg3mYj/eY4HgkTHz8YRPx8zvaa/cwrF23r3cuRxAFe9x+L3wD+wYNokkOIVcm+ymRSrg6VKQLo8eHLnSFyTYmQ43BkGFy/2KtxDlW+c/zX/RNwfkIXRmojFHWIanDRWmT8HLTQ5b9gy8nT3LP6stGi2bgTTogLeW/XZ8vsL2O8czKltE1sf6MDTJ9H7UypvKP/r73Kdo59obuz6/Ta7QYqyhHM5nmfTlu9yXEswmRRnPnj1nQi/H4nZTxF28FfkUSWKNAKVJGag4bEE2q6e8xXbxEfE9rM13gCfQi3NialIIkIZRYK8OI22OmSCbVRMpHeRKb4k257O25P6mtyxCATJMSe1jDEiM0wx5YzTpzg7eF2ZrWWB3UB82bOSYsjZr3sNT29Mb56fkkQ9PnoGs00tUYVo9V5cxjcWRwLLq1PBJag8Mrpv8Ou0bPK82GNgOBLzQ0eVmZEiqLZYgVbpYj3XA0dlzKdBVuVvaDeu7fc/02Va1b99+XW5p9NbyA0kRNhnOkDAb+Yuv6V2ogOcDTyv5Mz5voHjClq3jxYDSHjRFlOnkkE43duUuPRk7rqsjLKRHSbrC/COydnYjfsqfVNu1b8XythPZAbcPiLlN64s3yfzb/BZ7Pmn93fG610h/E7XO9/rOmpN6Tq+tac8d8LD702+riRp7Ta89nFJll2xQlkFtj1YxUHj2KK3Zi164+InfEMNJNF2uCXGjNcGKW88bnw8s94ABdAwBT4NfkmjSdMNCa2FRVOWrZCluMW/U/XNFX5LUSlmf0B5bv4bOJ7FsueWAs8f9y9iaEfTE+ecWB+tFQL5O61Mex2zqlc5vQalJnJrt6Wrl64F+PNkL6pH7cvN9SfIZPv4bgynIjTvnRNqJCVtxh6B69XWzvYo+bejT6Y+UxkNTNoUJW5HhfokQanylMFxLYA4WgDwUWsSO1OOCeuE0Hbmo+up6sp3jGT7cWkOeqZ02xhjNEy8g+7BLrbxErJBZiN6qmu8tHJdZJPmJxrKwxtIFYulqbJPWmZeMYKdG6mWNf+uOilz3OoX5NQ//o8A9MsgSIajI9Nl2tJLsBdJu0MeOmVkACmf0IBGAiOC/QD0lIKO2ewdGcQenQh9fF40/GHnVrM4waiWCLqBmCn/a7QuqA0mpoF+R0irqT+3Qdz2IOm8qEfaQxzZce/R2LjPpKtiWuUkAbp6gQ/p/xd7lEwzvsokvPzf5LOTg6FAxgRVOiWEqfjRSOgMaUzTYe/1sAeiFRzWO+7+l/6VfJNdW9I7hYQBYOMu21gu/SDnKUL/QIMd46ZlAkw8ZQ5BFwmxJTi1+5EnyIK0AUruqXodvz/W/+IH0fe9qmoTKmpCI++N3XrqsgPgAl3mhwlcG6jD8hjzFzjW5H5+pap+8zl2J2HtbTSuv08X54FLcdLenzg7mlS1GcF/sSyr1NNblLeESSd93Ta6WH4Y+uqlHErfzDYRScN7uRiqEhUm0ETfbxSlX3PwFt3kWqXE+PccJJc7QJC9TmmnoleIY4FVvrKXRdbcNfxv7/+7mw49jsSYZSkpiu0TNMKbWu5n14qdkVHrA8O9QiRgoUueNEcC6L6ch8VdX6/ib5MZsQKPrrUV3DZtL2+Dacj4aDRsGdif7fMq7oZFUwvPOKUmd7ZSEbGAMwbmauQaoLNUTZlSd2SUrocGiVigt2NdbLYKN2b6UWwuFhy5Rao5Iu0U53Y/SvVGYwlf2xbBTz2GkSFoZV43ViMmy3KS0Kf+I5kds49IBqgGzvWcDlxnBaRx4/mNJpJoEm5UjCFUqre6l2om5VuVQu3hXvwjjk4XBgFUiJRSc0NdE5OZKyrK0M9FLHFkbzJ15OTUYKo/yOX3KDEJ97+ABjRh1TqATQYyVs8BnzBo5UI43FlXSxsapXlT4cpup/Isw/67qWa2lRkJpUi0WY1SUYgbVM+g6Fl20ocvs8SHUXdNvbWwSbTGiIm7IHosEeFzc8HxMvwIu88I46vXqTSWsYde5FUxpNMwzwtH1hckW5LIlJm4/WeacFlC4X12Ns1/zAueqrXM0LR+deDMqXYMx4uwPyBD9Darr56BQVX2NjnkK1ScSBst5SxHrwU3IOwMrD3aPcSrh4ZrZtjRvq39xaPKusGKKwQ++wGDcEITdhGaaHEXWNaI+c99iw0VUpWvhnLD1G9F/gRaxBjJCphyyHEQ4oGpa8gQZQdM68p3ow/VIa0GqlVh1WWl75nsOw3tHuNCcBKNfQmQXShNdG17fquxy5uBdUFZYdqfr8FC+ZyjlZNSWfao0ComwaQR+uN8ymclJaYLQBeVe3EiCO+2jHqZTOyKY9FtvWkQ2xn7la3EWlaauRVQIxvsilSOagtoCax7pWfGKsOjefYw9HK4c67huF3GiUSg5S36g9jL2Ei/2kFWZ6qj175fAl2moo//r2RvJyTs2f6PGBGdrZgXEMVfaArgqtma3X7guh9ZzREjFu6sNtp6CSl9bmwJIS3MiOl1HWaVg6CDeUM2Xdp3l6psbPeLkxeJmvFlfENKipabOpnVS1kNHcSV32OLb23CXvwBu0rEioAd30Dfl6VPsWEKpN5ZuhNmxC5WTa4KBoJnW3fqHX5ILCHFsP46sbEIVX07CBt3TXy4f6pGloiMlJaEMra+gUVKXf8RI003kqS5gMq8zzhQIcssegb69RUyJqMPj/CG6r7efrb+Ja9jX9Nepdcs9rQusAn+WPwna8RfQ/DJx2JFjwhDVooV1JLXV/id5UHW8ug3Q21UEzR/Hro7iUZTH4h1HijyaVCCiaMZtVCGlDB4sxCg/CWPoyZSIVD6nCkE+rkkjeOL2EPGueZ766lmtgUDGeyQohHDe+TqyeyLY0HHgJGkWcft6sHFWlwGN3ZUPnjXCpblLJbME/6KKRavMyJwFVneo6OsUOtvUQZECTqEjUrSVBGyGxEvQa7r7O3ClmpgyA8c1D75vfFYfWPZiH3T4CF9aBXeg3g8Yo943JAQarzWrNRqlwLa/7OI9hqFSfRun3gIUwmLFdEelaQBbbFXGwCZHW6ILKpvMg5QqDaSGjpk2wPtY3VT0GRsMyY++pDWT7qGBFZlrPS2anwLXOQkrc1joPUFDYRnPCLdFdirFeY67l891aafQKXp+IdUcvGxsyF1vYfU9sMN85gspXfyG/TPcCoJG1okMy8o7XoFI0I6DH3jj0MUSq+Zg12SJx9OQwtvQEZax0v/ZiUDZ69Yk9Mqt61DvGnrnaPvBl5mBdL6o3S4/MwKhWKVxCUS+b1OZB8ob6ZmBUtOZsgINoGtCW5dWYHkKp2uLLOm5eMn/JvkMjWzlLeGmG2YUVWDI6VqMsythwhqZMYNIqvcfSWGQrqaZXDLsm03JElHWXspu6POaHPOhurckj6ZjusuSoRbVNCd82pCxJAjMq70yfDCoOIbTxyBuCQQX6JOaZdaDuaVUcQbsdNDzXlA/UNRQE3Xh5KzHoyfas6CSokmwsNzczvK+SEX+Kj3GH79Dw8XQfhwjrYs5KetvQxoz6F+Bq6HpanQFeStEnO3dW5mtVsKSQbkL0iSlk2dPQNkk1ID4No1wo9zsaNpBt3kXZl1YRfyaAtm4XqyKv/bTLXm3yST+PYOe8IpUDyhxdH0Sip2yJx0Q9rN2DVL2SstbTKtCDGBiMAgCBRAfnUkhoVeTsyt2VU7J5kcvdw2ypnEVKO2A9ahSZ0byCFNZo2ZnRe0mCq3jkaM6hsnxgo56o2KLlci0iKNtIWA7VaxtKBixQ5t++HnWDoXLjOfP0aC+xgdaoXp++AoO1zbUu/AecXG3BBaxMezL3zjl7tESnFqTuFiz5zztKopTepml7OMBtelJtvqPsU3rIo0IwQGKslBgv284WMTT3tVeMKWU6RC3UW+DXEeTL+p16l61CSzPjNZQuZEP8eH3ugk9D3uuf4Q63W2QOMuu8P4ONPibHLlBQeN6zMOAEmtAAABAASURBVGxY6Wyn0rOKIXWMNbgVVIuhWW6uGs2frDvLm4nZ9DQCzckxBeONkJXBmaCFXzQbAUQJlbSiLJ20GYWOFOX+iOGlgffFRcdcxBpCBqAwjOOUzSsrdK/TO3G2YhlVpmp4l2R6JccmLeGCydF4Rd9YPf/GPh6LkpzAJv5iuqbVL9hD9cNLwzUl5nwqL3XfRgE1PqOgugU5hlV1zj5PjhRpi6TIn5PDhz+1roB7gdLmDVOo39FfEKzN1mCdxS03ZiEdNqWT84zQXggn2zTTNlgDNs2wWES1stQK1na9WqK6C0cdPsbuvnwEcB7gt4U3F8qBst7VL1N2zeXu5q9NmcZVIzqUsrTz6F6lvzMIrpt2OXWHuKZ6+ki+adyX93VroTL/cFxQHmiGPROpjelwncEmmphR9gGs8aiFXqCCQpegoAdXXHdcOsVUUBKryw1Z7wGhK2OGK4BD6iuz4zxat2zL6JnrGSufCqacrqirucZE0WVQiBjXWl45WzxIBW8hLwpU9RQ98XPyyq9NOl9il6polYWMB9HO9BWIe9bdcgAXA8w3ZEFlNuXXdA+cceoaPTtgHNbht/cj7ueLfrCfPrtr0IBZLbY55hP5ie+6VNenDnZSIYktB94GR0SANp0i62Wix56O6rYpRwnazGimqvZ0bdN06+O6A5y81EOudesve3eVne7nzlptQBTgtVc+USUg3b5n7baa/8/oOs2a0spxUltAbFBW6+q5SgfxxHm3BzESY6id0h5OKrAxGzqiI/qyE9SXpVhiid3dKhEhHtccW16r9zram2SwVTG3SnF463M09CD5TreU6rwbRAIPhKmk6VHCYan0IK0RMpBXHnWnHJEwAg82xd6teCFKUwnr65gsG3Qd5boyXrTUSdOz36uyK7fneX1yOJI/VTcUfWv2uxnTVxWOjZJygdoNXY61ingZYvdcX/yckuMyvnodeXt7WDfV/vtbpbLFwDgeA2BGQQEabuau1p0Ro1mF8/AZ69Ud2QOmGrXTNKeplCbf2MvyoBPIFJWeiSx4uzSGE7M5CfSIgZ4W99ZQ1tdoklDTw+DXYUytOMpS06qDs6yvl+U2Njichn7h0vQmfE0EIE4hNVm3xYPrqevsUZ3ZE5FKWfkbV4HqQmoHki1R1+hh+ePx5f3Qa7cUPLq65fXtdgY6lTq/cC169KxTax8/wDHWEHlZhQkm/tGWYHgnzrbTaO9jrVIoWgJ1RLt1ygpq9pnfU0/WeaHcYXcsu++8rqFSiDlQAFULocPkwiV/BcRsaxlviLQ00ztZcRVvmPbqNI7x62uvC2ZeDRH8ON+nAsS7ZyhUQjA7WSWYOffX/Nzkz7H7sThGsYAO5lopK1n29upuplaIDs0cRXWc+nvmr2gUYUg7j8+INHLg/UrWjW8VVi/2vc51nbHaiq6lOB8bYDKh3HgG87b8iTFvshWgLs7fCxQrbSnyNRbyMbfhZ7UMKi3yfoF+uaRQvnLGsd3TbcKgCKZxMIKsaDk+GDYkF+g15WaXr+uz4of4Cf6skmheOWueD4WIJTwp3NIU7NBnBZxvpmSoR6QLrPSh0bm6YtrY4slSAfvCJ6jnevoj2sWSZG7VwqiPTGnazZeeuBJWhSkRMoccLtlOei5hnK3kAVta9wR3U2GSdV/pn4YbtKqpfbZkIhvOua8V64qI6xq/NaeouKevnM8EIzpQ3pz2gczay0nDoOpkDQ1m7TiUFrYzKfEqNGojUU29cJc6DD41nnJ7lElmlzIGNNHedqmQTziUnrvCi2xyV51z1N/M0uQb0eLYTXzeyle49CeD0hDswjjLBdxBISt5AMRSHCxfepkQV00/VXugjVwvKEZEQu6eOEVtvxqwmdUjUw6ZgvJba0sMSSkm5EVdxlotoHfm6dZ4UZOanMCBHPNNwta7+tbn8XVC1bowwr8e3E/f3W4HXtBr1MfqinJT0Cgtlleby7WhfLbvYztNxfD2eT85faY2z2EVRaMgD2dMJ84ZnVkX/ENePhOyR5MGTSoW6OB+vbKgCUMHdD5SH7yxiOVjMVwYAxbgwhSdXm1zDG1Q1rzzwobVY0wLca6wR55pTZE5+LPkakaSAk8KLb7u37baSoq9dh3anCxTvc5uoZoDJadHiXmg4BlqpHH5JdL+GFYG1IgxyrwJhlN+BX4emWMG9kH+R/4/swKuPZ9n7vJ7liujeIUmnRrcpVEp16ErcnEV1KypNlrRRSgz7At9qWaOPYPW/H8Nq2/NPhpzj3vpKmB7cPh2blWgHbeiT0jDBpOt2sU6UtzGydZrEqxVvJ5l35Vb3+VAtpxUWLGITza+5PZ4K/ZGr5cx0Np86FQzAUlTZR0VDnXCXO90KN9yvTK8b9TFdJa9/TFL0g+H8KHVSSgi8Cl8diG5OS09SEXtFLOr6ceRyByZkIRgOMYDTA6qoUg4aV7gmKzCW4fwuQnLmjeMEvNil/fl6pcY4MHZlbubY5FaIbHaODQ3kV0cSQyhzIg6Y05oIdxnbM7HzurNvc+TO0fz0RS6JzN74NdlEuFATlDAVoFQy/u52BLyG1RcRRWvNPpgHkprAFvquR/Er/kZyNskjfCw+2hTNNrKlM5THU3Bv4hs7C7XjbPcPNDJ6PkkxJjJdxUa0coQ6Rz0eEWctZ7wR2X9vTCENZHMmkPQ846tMkmjxNB7r9/92emzzWoZXcPh4iDZ+iijkWaPONHuJ7AVngFo845oaiWDEhs/+VmpC6oVXiu4Qq6Ame70PJFAWiqNCjxKOIYhJBLwyNrJPo/1YUI9ShuhLuUm0TkKcdiAF+9c12iFUjp/V7V0gGVvp29Fd9iibsocc7NW9zSy3BptnSGCLBCfl29L+E57RojQVdlZNX8MCHb0muIp7ZN+KU7gDnJqxAWJaYIF3Yb8Jr/IAwlixqKxvrjTrgq38Gvs03FjLQ/l2EDXiKQXZ1mpwSjwKdSKvpw6bvePhtsyW50QrBoZBlSTwm9jQ1EKvkknLWwcNA+4ASL39GwIpVRQqwSsC3N24zdDcsiZ1jDUCLJRqQJhM2uM6aej0EvqoWKUdFc6Nkv5kXVdq+1P+eYMx20Fb3+Dtk1/tIFStbONx4GFK+zjfvnS28Q6tILQm5KixU2KeCObdH5DVFiRo0PM7nmBjJxq6Cmc9lMjp2e6Lg043M+7VSyQZnDoQkos3WshDCaGfLiM4aLFrNwn4gP713aVEF/WIZ27RizG0hSTi35G7OB9SdfXyuDgqG5n5ti11WMtQh5mtRoFoikLUONZpL1sUiqFgRrk1jdyFVBWIldf7tKtQksC3SasNq4G4syWktAJ2yNNE+1BM1WZS8+KW916SyyU02dDKwJSvxrUq8erpQxKFn7GnvYBEYAPSZkaq5GOJGXI6MdxW/9EfQJXO49NS4F54k288TIEPRpaH3sklak6POllgPYQWuKUfYgYKQQs8eFiV3RI0yAjzPH38j6rvsbCTRfLgOGAYsUDVInKYyvDEgycY22GkFhC7O1SA1hnQlre7G0twQ0c2oOXHsV9SnBOorQuTFsjxm/NVmx+TazH4hefr+4cjldhF06vZDTJBqVD2UO3TVlXGMzp5kuieQ/9ALfXsm2lF0tMMY078rjSgIW3lBMQDio8t7deZU4gMnyLfbNRu3R2KBHH7Cwc+pyI7dQpcTVKgepG63J6lwxPYXAv7cfVH6aph1vf0520Dds1EqhKTUyJwosTSChdUBcwUVA+eQ3v0cZPAd3X6KmrmMYBt2CUyYUVxUjqdPhaL3R4kBKqDUTzKdP9NKq1lF8mFzOQqmL3XXR5kkKurpVtZXWu0XIAvTTykQUbkxEOVprBxTBk6vS/5JrIWntb7uYZiHSzO/j+t/9WurBlzDGKWpiPIJ2fmc9t9gPMwZN4ZNgNWMPbGRZjkllzP+v7+PtIETSYkoYSXfJ1nj5dc41dZX6UMMxEW9SrBJA6w9entb6wAJVWoexv2A5uUx4McKPbopSMxfPT36vKVN+jKroM6uCL8ILsyB+0R+A7ISjIfNPtAubdzN2jq6u1ZnJtxgOBuVSqXV55odwKvnM9tZ7Rxlxbq1lZdsj0Vly1ZeE5vb8yN+u2+PBe1Iph5wjAMnv/M8rWny7tKu4aRaO3xKil3ZaLNNxJO+aK8YWXPfvx/kUkQgJVPuwCl9PbqOLW0crDEFCYgqxKUqQ1pyfHhmOgkh8YTbpI1YhY/HJSqvGm7cZpdgKSdE9P7UEJktbHj7ciFy74TGT7YUs8ahD1jl82t6OlY+EzxXf/a//XsWBcBh3CrKaNdfKtVhtja03XrkyUj60d8YhXarp4ko7djJpjgksABnjDxs0E2mZDOzar+/b7c92xLoad6qNbLwELWJz4wf2htoGXIWasS0KkPQuqigwcsQ4KoS96wJmcGkUE8O/UGE6fR8S35kdXVI5Q413TxaFyhBeWplGN4Tv19fpiquRCD5N/SFuoz9564HiHrVCRnRn5pZVcLIThdZ63hgp/JPxF2j2T4OOxTVtyVxqA/suZ86h9jVikU6jL4338qYfH8QV2KgLhBsE8mgf7iNNWajNRT2gHVR2NtwBGDDQ6REKbgy2So/1yxWYoxXFOPpapTi/J03HHXIwom9IAcqDY/U2OrtsEwuseeJql6+i9GiHjA7M2jvkwSgWO0AzR8mkSt45sa+QwAZk3U4TzW/Ob3eesOv/In4lIq8moinEq5SthZ2u9LWRzex3X03412lqjjsJrFkY+LokWYyGxoTmEIPTyfZnaoxuvt6hDV8QHNbb0OMmBggkfmdwpDkdr3SgRDmN7C0JEfcSFTtJGpEMkQR+6RZtiGCDjqnv1O+FBpYhXXeEI66+w53o1hRGXBahUlXj5VktBQPcGRw9bLvUJX8en5QoTJqvQlEqVOPTSCTvz119V5r7a7sKNpuINtez+Yt5WRJW/okO9TdMgDx6N2MM96Bo2FxVbdVbru5p9ysw9TeQs+MUfcBVRLJVi2cpeM645TkN6Y7QGKAwkqnoDmyNexKW87QuPpuPukdLFzAz06pgWTx2ZTArQrCWpmTTc8iyp0ELmJzolLFQrpkp0DsqhTdtcm3ju3vaMbv23muxZrBGZ8yJ4AtuFp7unDXCBkUoXx/1gKQ44w5Rt9OEk2T4QN07aMouWp+Z+hTWcVoHoeZypWJsK9kBmx2YIyOd0W7UXamzzOuG6oPNQMKQKPlqbjGD+a20AIsI7LNV7+PLl3V0t6jVa5DBcFXkv/TYYx0ypwPZ+q955tyL4wf6cukk664rnWmf5hMd9x7oCcakXAASllDSqeSYmgUPPy+cnfR3D6KqCtkhKvW9ShJure+VIeZmM7FcMhYEpGmtAh9rIprVSRBdhMeC0c0wyWZ3BUtsHD+voTMuviMGBpUJlihAth7yWACb6MT53nB+IdTdyHLBenIvwAfjmAjgkutCMI02Yl9n4Gm2QGYZgm2frgfHdUJuBqzP08Rrfw69vIvQtAAAQAElEQVQuo0frxZ5RsCVbboofDTVFqhKsbbX8jfPEyvvCknX81EuQ9Wo9fCgHabK+WkShDAcqnMPdr4nR0HGJaGHWYMoraU0VzAWGphjHlPw78HNxLSUsYMu5CMf7oumrOTFsDtA24sKhUz6OJFquiwGnsY05gywhly4uhvsFD5oYHJqGY1KpSX5yAgXdpXkV3ajOrhOcGFsTnb6/9hTGWr4h4soIifs59V9SO39yLWNYKTOB9/5RsiEoMAc30/X1aiomJe7cPEuF6Luvhyhvj1EfCyIGLlqOsbRCWpG6y8r5+NjtJvwwQT7nvDjDY1m/UaNqCnfeP3rCRZl5E2ZzWRoV1yj7w5Qyp21BirBKFBRND09bhTgrjYKpsdsubChZa12XMwCXyQZVtdXY2knalPzOiPAmYdf4P+9/thcflip2sSioioejgvl8K/bB1Fzr0RYa1lkYzaXs016Qa3jTrot2zyjg6Fhw8xIl5bkB5RxsE9aLvnquJI8ppg4l5AQ7WuZMKW52UxNajlByQGXef5z3e1LOC0LJOWsnn3NShaggL/j/pf20ph2+APp25TCpuooDiUwoTC8YelqnFixcFLzYoNpbn6P58xFdbOrq38XeWSe7eo+3E9cMs+pAG8AOt8uky2izrsEJvJah1zvsTHUXOs+crZiGlm++RDoNQ33t6+SVqSYlJAviALQov3MVPd2OwvgJ/HiqMNpibLnEVrZPfZ88p2YGrlw57otjLAOzXYtWkelI1xBMdseUt6QDXQBc86ikiHsbM4PqwKgPjuH0mIbNQUmgU8M3rwblrFGK9R1QTi0hMKJpsstOm/3OswRq8oScYkwKRgkKwtA2MtnpsSKudyIxEqX3vdSintEWUA8ArGToXCyF6wGwB6l3hoVLnNpjYNvZIZJrtHhJ0CaU4Usrxb3gV8FzTsapZbOujn9cEZeC15ysRwWkmiKDXISXRumvOLG7TzWefCljRbwuOezjrFuW4EDPEieDoT5ODkqfm12puYmxvyuFORAHN+f+xK0FUAQWSEVPQnFPtZjMFokKIMGtiwujGPiBeqMNrKZFX8jFkQe5EuNqv/D2KaBb3iTAiPL3/0GKNUq5FHaWNhHzENu6zJ7iV7GhZ5SE/sxcshXWJicaDouetYtPIeWarhOicYUklSUqIStxKYzrR/04s3+f8/xWbeK1YaNiuIzC77MDukV50GpYAOiNNUmUX7TLrI0b1G1Eg5zFcUL5WlnGGfe2pbJSB6JCFFNRfR24CAcQo5XUljW9WzewfJp75gyyShMCkEXtbdRZJiu1cTzh5AVqIV1joDIxPsfxdcsc6P7oZHWOYIW0oVkz72bkkjiCx0cJ6gNVhX7Ma6EMm/rr2UqsqlvYRKRyAUybjZLarUyI+NnDnOxqHrMtAa02HO8dM9Guw6FgjuTpCw5xfruUeRsu9rJx0JUOPRvU4GJTVMrOWWz3dpaL51TNZYXPCrcxim/RcmddcnuV2i48gUYnUqWtD42EHdoQejVw2u24yup6QCqH/G9M99NmvBaU3cBAafFnauOc6QdIs2Ut0bobw1s7N7/63IL1UxOOuqZppp8ECTP38dlsVe6UrksJUPqqN7JOilVJzEPrJCwyrlxr67hwLFsTf8d+BVS20sQxZblfM3BAwa/P7e//MwG7z+TTXTUGl5LX7ur3bvhJMWFqGXOFFtymEo8v8DWmFAlOzHFrI93ETiOOss1r5gZ/MKaspqyPEPIilxK+gWcwOnomUeqA2gjWROy4n4k6htM6ZsPKbOCiU577iy5NHI727iN2I+FiammuymqFUr19lN20Wx7djGqzk6PYo0bi3zJhDCr9ama6SS4XHlHvq53V71gFgiKqi6c159kt4aAuq+jGtxSUxtw6MxmoEak+1qO7i9YN7OPzfetlXthCWDn6KCTDtXHrqAovqs0u0dh4WRgr2EDJUln6hbDxGU7S05ELuvruCs6EpV+S3yzOxY6l8vZjKkHqB4C6c4kE4mrdhHuBYrH64tdeVvuUpWbdpDc6u6ZhrVu1+ijkMvLCddO9bFUE8PZKzxGchPfbHny4nSeQqNafAOZZfNZ6URA2kDmcsK3Ns2dOaydwDpRQNBSUoZb10mr28e3+0J9yDeikOgjT6rGjklHlUw5M4CdeRAOamOC7c11w1RHjTfJV13VMwyUf3Pn6L7TCNrHUVIC9hAo+8AZm0I3SXt29mY0NcXSR4/O9Oq+Rc8/tZyhZyCcVllZmXKAS7RDEQvaUOtyUMLEw1DRlD+Ztb3I5b5qtlz6ACLaDVLUU3GGhJrswQUOLkeCvhivvF1Q9pncni7EhW/aGoGmqTzMqUGtQHmO7yp1Tg/TBKq4DSzRGZfR0bu2mm6S5/JgBgXmnWNrI9rX68gWqMDkYM9osfJW6cY4vAUClYcK48PeaQbUXo1dTknf16LkmrnEzpvcW32dsjw9prYHw14A0bR6jlYlOt2vZvZRVfQf1xohFQlxMuc6APtr1pbGbSdsmvwqNaWJezWoOZyjM7dL82SgIg2wMWr5xO66wEH1YCRNFKEkZyvMSzctqrVkuRqpNqQtyud1s7W9D0DGQaAhITsfxpVi11VckPZFyrcyNkY6l/ipd2BA0YpTxWyKlXpBLMQZkssROwyEnem5j9sRf8lRb0WI4y3eaynPgFA9ZnB1U4OpA81enc1BHS0JSF9kQlvXweqQcf4FA7qyXH7u0RRGRFLOYmSOpzuhyI6urAFIshOsDkDQdMnJbSpIca6CJ9inF4IjoJc9n/T7+/VrXook6+EAWOngkFQMwL/4cG7DZCbQ712JYg2M4UY1xcl0W0vGw4Mkoeiv8x59H7eIgM7p/mtqC3UqzeyEeYl9Vdx7OYTbbDDGwoWw2MSHTWsP1xKBUAzlZOAPduB31G6fFoZ6dTrYc9KjFdCe5GS8IWAJEW1GWNtcX6wEwgAJquRGRXaqHkvpAt8vDGU1ySncO1FBz46UuoWmJGsjgLB/1AwR10JrQ2/IO6JXD79jXBELrBEp0xANTfgaOjsnVddm+rm2f3750wazimliqF1tByso4MqCC7KvRwyMo/rq+CHOMiXSFssEDb6cuyQf1DYWkdDn8X/YG4ugqCtxEzTrHKru6yyDyCksHxIXmAbpYgfLQYS8ZHHlWtS00gQ1+q35H3q1xoy+mMlnO7ZHral+Yv0Oj+w85lCVf1/VZ9y1+ZU7bpy+PlupZdIPEAPHM4BJGTO3M08164V41ljo3QCbPmdTVZyMP5hhnDI1xIeahSF5ig6qhHqHgOvvO58oz1PiUOk3D+cphnYnn8PnaiAiALizKQmurEw0qS0/YbpWyIXvkyyFqWqUCqVBnppA557Q+oHzOVecSp77fPYERY3HwPp+oCsJIHM8HWQopsiOL/Xbzfmbq5UDraae8rzHPcPliy3AZRkreeAk4KQMiRzYcAVyAQgWqmQ0NSzGDzrGagdJcegRiAZVSvithtQ3KmDCk8XRUa3Zd59znhA3VcCEawRYpsAHnf/654MtNdLNXpJY7DzS1knGrDRzreoeGEBH3Rf+ZXvWWpUALSY9KEc4O5m0TF0Sr9qrQDiyVyp7IQbo+Gkyr/merfGqHHfFBjT6wVGyakHvOQKGDPsLwZGUJp2IxcQ2dhjmrr+fcitmoKsnpbClzfHFd1PzEaTxWqnbm2LZRIG2byMY5s1v11IMZusQ9druty7hcemUgqKeq9dOtlBldvUfZ5aFXWb1Mrxbxw4Yft71CkQzIGhMn6ltCRGuy2QU5AmrQcqqFQnprvJ1s2gAoTBYJqppXpr9So9yknfH2GA+DNC7jJbOaIE9D11FmLwYMjEqqqNhAHXQpBoAYqc5nNFtjNCgNTdNpKmWDrQOVIxnWliRk1PTcC/0/2G0+68VWo3/VpUPWfFCw1lFohogQdNy0K8JupRhq1lnvIPp9LY+uSXVy+eHUcN4pS6Dho2Fn6ow+RFPrfPAjKqCYtY8xsDt2xRfRZgFXLuzJ7oRAMwFUjeaLtVFMaqhZ15OfKmfUUov7ybyQDiWqVAdrgaos1ylL9+8rdYl5GtPDk12V3gxtrnEVbIwjrM4pw4wYh4BeaUtCTZE9+oMQP+964jV4OPqbSgUbqJ7kGZZQoje2IlUKB2l7+3O0ZWQp8ppv4a8mXGPdD9brGHKohX1GYzJf5c4Xs8GBtDWr34oFrrYQSAIxvGgOHNOa6Ql76dCKPCP2PMk93OQTvpfD4z2VAJuaXWZpmCFnXPVYoybSQ3tTYKJOE0xlDpqJQqOviXtrFjFMjTPcktYZlET6dgNaEmS9zkioMXzKbiwDhWh84DCNmFYOxfsz9T317FL3MCGB6d9gLEdKG1iTWbWn0pQwDFD50oyGzSzq5Np8bzHOGwaE3TlEUZpuEJrSnBrSPGwt/cKMinEJL9e8cg5zNRJboL7V1WoCE9ZY2VFDRy6VsFzNgAXKbjQ8aUW6I14nOq3ZYfQy7qjiebG2NCE+VyttWgII877tXL09n8qzzzldAp+INo4MRwuf8tAlB68kP2JFbHZBGGigKBzlxJeTaqr0dgWgIoMYCZM8XCtWZEOFY24RbnRApM/0i1HfDQqnhS1QNj02r4rU+62BZMH0ACyFv8qq3wlyy0FPZ0n51mQHZNdVPLNkGDAf2BAKdbN3VVeOfLCTo/MQVZOVfdSml+hra7AcE2MLWFOGnhnopbdWPbRpEqnZElBHn6CYMVFPWPPvYk8pFCWjrjstmmldJhIn5HnvFmZS9FA4iaYi9ml5QPgSZeynSz3RD+PwZQqq1jvdhnCD6xxbhdnTAGGBhXZBFwUGWcQIo+kjW57006OQpxMvfdjEX/5AhXTUS7S7uXIMVYE2DpSBxID1V0+prCpxnGVpCtNAK+UIPUHlVMWVS+1mZH6tvhdCn9rV1h/3tqBgB15zc/J8jw4RP95YW7HZyrnR1zquructUjDNAqDPE4MoWwI3WlNnlEhxDBIQszhHWDKs/RIaKRaT4wV98aHNdOcbhg0wSgbTV9KIjDor80rGuJVX8TQbAQXatqZWt01Vnz4yscnt18iNL4fJ+WFno4XQQiTAu6GvEonZODqkhPOeU+OEDVYPmNvx9Not1tW6lIrdcmxN0S6Eo8xCdhQf76Eh6QV/+7e2FbN1wpQHBT+x2hQ4fJfIljENL7MCduH0W2O2J860COFKEQpwRs1QaNp1vl9+RLWX8s5ypLFJAo7xpShdmCVC5JRWjVw1JmnpigYNSne95ZEmgLPTSzvCCVlAy11KZyOHhPtauhu59LTobZHPsN9BWBJv0JdFmH7TfOoS7Q3R8S6sJXAmUzMlhxjsmUT3pGEIVKGOoi01UrUCnesxDeUAPUwsYRtZQ1OMWImAN8MH1/agmeKE8tt7NOLNQmt0hakadVWXwXo5UZ+ox+aUA6vdKvhCkHQ6bBxtcumcYF3QtIDEZzAFh7XgNtNn9/NukaznsqHhYMVbRgAAEABJREFUYNIxMeMH/e2pTlq5AuNIXoUR7PmDRYmwKhWdMX0lFa6SUepDOcgCzMaLKLNGQIy0nVJv16rFJIdj0mvFkKI1M/eRQ4yBzkW67Gymmm8LNxQE3S0AXRTGZvVsYG/oDvw8wtqEsKpqJW1nIQ70PkAvaqk6zt2r80Ya2ViKs0ZMLOvlgtEzHAZoHLgKoa3kCXqJhBaDaXNaSHNAzkCalRK34rU2H7NxF6yTfRqFrtcApCeuqehj9cG+5xvnyH78mj+X17rAa9r3CFEDwxw2Sx6u9lXvfxrTwmrV1+bMmOO9maYe++iBvjfsJhzewxcxJYyvT0pYOj1mftG1K40ij9ejEoxaQ21Z7toLv4LAJeyQS6+RMHPn+a/5a/p71f953bPvenzC8Qm+/zngGj1pYL8e95wHXRh0v4LKB7Opr+ZZP5U/huV0WPS71l8qgfXk/p1v3nvddptdMzbQcVFdRhRaV/dUAl/wqySrNdpWcWEa0guo1G4Xp6WkoQags50byrG4I14ESJ0J5X6OQbtR/fEM4XP94OdTvGS/UvNHRgeyoQVODH8JmR5Prm0Bp8Xo1l8pgqXy9xP7c+e6GzFDLw8aRqgRZ5tK6kjZUPIKEC1jB9fgMxG9Srfku7XuqoN5z/JT/vxObiW7XElxnF7fuQoOHPD4AvXg2ZZ0uaTk05Zl3na97i1H/J95ej++07N9yJdKAlNhffmsRUj3b19PcoredZVGSr6WZSd9K3ql137CLlb4kWBHXUJZA5xVXscI0GS+VeDMpLoLb2DZmHIr5azE2rSnFdCx3/1JLQo/1jd7CQ6lkpI0tqksbu57nn9d/0otKoFH8cgy3mahRVAzhEHRL9PLEUtxGFZIjc+15zrL69IWfv+ynjFpwJRH6UG8OR/r38zfr/7+Z2p66vbTLEmzFVb7z8Q/n7/peT1Yk4X9xD78gwC9VCZW2TimgTZODGssaQUVSeMZVIxfmyJ3dS1Rw3Xs/oZWlYthFuRS7D6KQxaUzNLmYhYH96hVdkTH0syOaqBgjd48T9X5tlYXL/iMKQVSWrcu41X2eB7Wb8/fICh5fNC/0//Ni1d+9fxBakl9heqi3wQtw3Xz0qt/sP+dXCfEttDAlbXD6LEy65jyU61u3V3Tg1EB326Xrxmfwzz+DL/W256a0bxPnY99/+FRc33lYs+hNP1WAOv4wLgQP43I3f09/Epxu6RaPdbo92rwhxGgaja23X+HwQo384rn48+HWDRzg3olNAnH7TGvdoqC2LjPBDZ9Y8EuxNX0bJ4Euiyy2FXDYdNGeySq8QIGBo2KDCyJ+YJoQD9u57azak99t3+/8dv8Xah7qC0gu6MT4imRI+Yeix1UoRgfziOxzZb6FMB4QJizRo+4N2xjZtoArX0BqerFpRZxr/zx/WEuaIik+/3m8/Wp6+LlAtrtKv8Cx2FsK/lFvo3P8cKfXufcqU6XF8t0MgxqPn1bXcjufc5z8hf8xbUv73twt6JXLJvx2Ptcdyj2+JaofU+f850+Xfr97GdDgQA9zZQFZQsJblqFYzWQdvo68zZ0tYT1YDT9i1IS3A9c/Fs23hBs0ip356/ld51CzhwZc39+6sTj873QLtUiqnCs45p7mS2MbhOE+2fsRrjdvD72uexWecx8iV/rfWWFsD2AjAqoKEshx5iy9BhqHuf56EB+hlK2UGXNHFLLtiG5p7+dUo8rvZgDBYOCihF+j33ol3OkaGtlPN3KSwHgZH3r91e0Ja8eYhDB5XYRYdMxxz3tFQWtW3m4lq69gcneLFPDTKMq3vxm3Kf8t7Frt5o9jPiQOm+vKJaBA2RP5sy3kL/UvkJtCAvtcSjw74+x8R4Ys4dRExIPkWCBhOST+tR6a0HM4ulGNVniJsViaZCZeLrU7uFjfsgDoVK7ehIqLFpf7Ltm/Xb95nN2ojkDkx1rJWqIUq2VmHqY3zkCcSVlqsq4Yi2/yMvOtZ64RBxk1/axo85ch3gW9hoQaAnMW0OmLcGkGmens0Lh4K6Wim58zTbUt1E9mFHBG0xs374uB9/+5ga7QghncnpbJjC9ULJBoZLmAVEYloOWCtJm2E2Nk7r/G8UvZO5qVefmY6yWjMa7fIr1RB5safj6Q3L20989ErzPKHZdcAxtInLuShMnqmPzcjpt0Zc5iVaezqxp7b/UEGA2M5CgcNmsJLdJokeeta857cO/FoveU03ZrbEh5UlUq5LPfqY2hTAv7BsVaPTwOPLOioMkfi/7/lylbdoVt0kJyEEhZkWGb2IMUP+/UimSI1Eyy3kZFtWGfgOqsgKOyKxrBB9FJlf3VekjZ99l5x4EHLF6UT2U6DP4AtLIM1tKVGIaI7GMgsMhv7q+OzdbgBoMr1gji5ujFbtEyrk8HTDNcxu/cF3izEekIx7zX4qYggiATlor6xDjXJ688rHc1sKMSCjZunTFz3q5mnrkusT/qgnPvh5lTRK9e+urX73kzC7vywgCsk1JorsUQovnyxmX+U37wC8otEfbD/XbdUSD8pSO53jtKxD34u3uvIjbLEY2w33dl1+XU8aVqUPxOjCXuNHLEtN5YvIOXPdcfjwTW8jVc6/BP7ypUJM5onuhb84d/HyWzGLpoa7KoFCqz2pMQMKUQhVJwY7ZWdkuGTACdANoafe8H5tyw9rDXX4Qo2tMt9JvqaUHwPE4K+JGeDvyjR/jkgVaxiT3NfQxI3VcNMwk/hJV90sIbodFjL1N/qSncpEXR1Vsq3gtwwr4KmFXgjMsfY0CaOycmQcsb8Kuw/8rWZuJ2jYaWIFePX+6gQjQaKlAUQpJBc8VQm7htysfTvs07yYH09btPMva3tnEG6lQw3o4BRRnO7Qa1YTqM6tb6wY1G0ZvWFVOvtXBLU+EGKMoXQ534d0mJDZiohKtJtQXpFxSU1MBjDJi5RptRFMsMxVILydv9Efaihm30iWrY7sEoCEb2pv5alsFHkCT9ujq7vn2nAPWiOJ4JzY4bEMrsBrWRh915qMzYwBJ42V2Yy2YGVsSXHf3PxGDUh2Veqwf3iel9hFtbwQLERPOpfFqf16zrD9vPkeTTsrH6Wf7VgFhMxQSojDaWHa4WZAOiN+h8AkW0CYoJkjW2pCIW+tj4rer2FBWfRjvmCKL9qgjaQnmdZWJ5Z7qaUVp8CvsMne8XXYBQjY2ZQAp7CVXh/g6030izANc0PW3ObYjje1jfEOKaaUENhlTnPkbNlNHP5DFyyxcdGMarTCU34BejlZzMxbRXhpAh8eL33RVP+Z0X7zC74hr1trxOXbnKKUYmn5aW4TT0zPDhH9tBDBv4u0p6iqx2nVcFtctGjCGPuhdc9SkD61V677gsgXx4HBP15BT9do1IDBepFwg1SG0irRnoZstnSg3aFriHixv861oOOxSB+0DxewiShumkOBWnavbTbQqsQWcyFINKxFKVd8ub7AKzX7GvoXdOaPaeAGqrSiiFtKCjajn3G5XRKwwldoYpY8Kjd+oRinn0fLOAXTdeThPZVZqyCh+BeUQCgAMkg44wQbl4QgliaDLK1k2Tfy91ox1GtLmFUn5d+s7oNwbocC//psmsVjAqJeBrmo7o/aP87Tom3f0xQkU4bBFkOI0McALaXgNJa2urH6rlTAo84+rCWuwM0ytYsT5tM9gmCXa2cI5xV3IM61yPz3ZzW4kmNFbXRGDJFuedy39tW7GR2l5GURRV3X0KCCaJf2Bn/Esm5zqzDqTadXeJAScoAEgNCfbMx1Nd4uPX+rbfrV5EZqomaWeSjvAkdH1Lesjq985/SeohkwtjDIC55TbL+RN6J/G+2JRmoQiVFf58xwECnu6NI3j7iZo+XJeipIsG8nKmyZqG8PUXqHfBwOi5Y/HdDnxTk7SWsSbj8dcF3ljhH6MCk668ix1KQ7a1piW+UAH44o7XFI2jA5wpnv24Dsre/H7Rm/9aLArCI2pEpWjxGUwl43Q4nSl8qcBoJz4MSmsJsbXJV+n4sks7cpaJoVQJSG0pZobMfCqTb+ppExYlj+bonkifbVakEvcDgvAJ6eLVdG52IaGYPc4KcoFXhw5PCzWLOvFpAz/TZGGhqBeLmWYgFjDhqWsQiLPWI27S4HqV1/TAe0tADpMyQA1nGOGklm9E5puF+0aeFl98W4Np1Vjsj5ZJpetlrfezFgS2cwZQRxlVLNzdCqBHkcZi695ok1jIKapl4bpnQpW0lpkd1dbnhjQrIJ6d4YJiNiRiYMrX51VZWtd7zZMy4VixNla/NQFXAAvnBvtlKNW7s6xHCXzhXBG645CGpoew2zFIB1q5AJdrpwT7DB3N9HTD/TEq7Pa4cY1ETDD+6JIazkmBEKeiDdaAqODKBF4yONW1f19pDlsKC3OMQ5ayK3ONZLTu5HWDPG3gZKE1NzSBvL10cY226sVlUMza6a8RMcsZPRe3WtgcmOaUS+I78xjGqWQ5273NjbNQYqqmvPgt1VUxqVIJDzNs5K0qZei7DdGappHU9emp0abiCOiIOH0LXOebzkark3dvjSqOfIjMrzN6EjW2zsyX+7eUGTwmnT2uuciXNoXgzRYgNo/rlaCMd4NfZrtPEtUvQ4vO6HbCBZG/+TKhtfrhpyBGtXpmek+TRF4XhyKjborMD8u5mLKAThKqN1xhRkWYDNuFjQzvwzaqt92iPkOj9iLgRxs43bxuFxj1F7oiRBKpgCPua1rQV5qlGm9CYkpQa0+cPkobLtb/cqWubtOmsSIqV4qc9Gkjm0uBmJIS3qDbMe5W43i6ZN5ZrZTRdR851mlKsNJ82aksS/GXUu6NiiFE2hBNDWgIgFjhcm4DAPauIgXMwTfsarSahOpHGX30GSR5TyGFjaK6ynotCDaYPs5n73u1OPFu9wzhP4z+X+te8xA/Tz+ySV2fWFkEqjUUXo90Bp5n7l3STCQfdkD+kBtuI8Y49iCSL6VDENBNMkEKWCIbVe+d+MUEAyxL9202vO7JPUofML8x/hZfgXu1TbyW2V4bu5/xEUX4fN1aURs6o/faiMSgIsFxQm1Bx/dJPpnLnPKAfRS+rFB3SKk+1DaGZpSRXdzGApIvb+9dXx9w0RbWC/wGl4wI1evz1l3fy4sOk1WNXGcDZNFca9zA90suR4U6pnnE4L9mCdX4jonTvEAxmnWyW5JAR/iJTnueht76HbjZ8/94H7C80ZR9ZDWfyKFt9K0IxqY1tOjb65nPoOPpdk+q74fPHoUelF3qLKeJHxW8L74that8I8SWKoD6RLjvg2ZovHN5LW4ljSkvankT9TUtB7Vdo0ENpDPscaIjJv+7+/G8c1stQR/37H0D/gAF4Q3q7QfnGvkv8xnauYAtaLVNGYZ1iuMpUW8+kgbDkP/dH92U0DVYFB3wK6B1exduvtSDv6SAVHt7gYXnuf0b5WKqzM7p2fmVb059YeZG3O1dDdja7sdcAqS7S0UZi+uhQlgATdxN1YP5p75POLzydEi//TUDOiurdUAABAASURBVADqXyl5iUnOW5pqZAmey0VdWLizHNKEm9kLLpoknH55KODxpg2DDBSpFPYzcbikWLML/HTdcy57d0YmbwC0GMj8bYiV5u3kojvUfpb+FmHPRd2WKO7P9IJQioC3CDb9YOWr1PE0SBr7E33dJj1LGIrr+4sjPajXYkwHKZcGsF037Pe57wnxMlYZk1fhProtVGupidLSVVG2yj7kD2tZIjCCGu8korp2jULUs2D5G3MDkXy7CM7hfiRZee3XflK7LO51PXHnFXjbeXY881Nm/PuuvbbiwmRjz7XNoZd3XWTYzbg7PsOfzH/7qIaikjzRrX0GEFSC6NEF9Y3OAjGUrqLTp27cYT/dsdO05g5aTEOcwA6qqg3phM5oXpWd4cwrG0WM0TldGcNJ3ztmr0jR4YI7Z8/+Xr1vvmFRZk+gJIN+De9GwqSJ7zVKvEwoqkAuKVnLVhFabt4W5DXqBGvKXTZmIPcqJ2npaqzRg4M7+EGovqpRmGQfZkHoYnj1+KncMyXtrHWP3clVEEyFoQYzmjlvwwnHLL7euqW35+fq623GhoZaqG/KvMqhRrvp07KJHrWBSPBNyJBhMj/t/cV8wT2wgWMpNsqKURLUELaU1nYK3c/Uo050pov20OR7ZiTs/CE+4fc/WlBqIwHpARhCOLVXKfFwZhWuMYKZ6h5s1YKvUQ8dnM1Rjkyl0O6FRbE4WFF2PcVKGbsXXBqZG/ix1h847skPGPTxC+YGuuAvC9G1vTK/vR/h1vk0ihS1rDyDb1umra6fkRmlxuKQFezLetYMbY4jbYC+2KGWMPZAmu7D4xoHva2SHbXArtVQ5KVYMDctLu8ffiO/dc7D78TvdakHzEyc8Tw31Hfc8oJd9sH1M0sC35n6o+Me9HTvnEpwoAucJ6Fv1sBMJ7+JtGkFTHGp8brizUVcFHWfNZ971IqUc9kwQrzUQtHt/LP9Q16Ki0ETECjdeBzf0OLnCtCPeUd3ZNn2KS+YRtYHqhs2Pj2X8hCs6UktIDkLqpg99VeFY5PCKYPzrDNzrPfiLFuXhXy+tNZpHm266gfxiQj/cD5Ug+qewXFslmK7xfY6fhmVQ2Xn3DZLXWJEnqGQrSfplcLxRZ/pbrN94RhI2JtxZdMfE11Nto2Zh4eF6Xqdqadt13pL/5j4gHZa7/+icttCm4IolNpe/k00E7vkHE4OxQsmzOZPwnCYwQDMzMYc1Zf0en16eelx/QI+VJBLGmGq3gAtB8xEPWAJnMIGMc40ZfO1AM2VTf7Vj2UvCU1b09nqFJQnHRz3bVANfrnrjbsEoGnHqegpkD2sabQiAbW06vRID71u+1yhdSG6J6k32eTwnrX4z0KJwTgk0PsT70in2whPwBwai07r41MAt2sV4wCK4px5GolIGlaP9bgyy767tbwLbxxoMxRFUzEADbpT9FEy8w6WARhYuafhuAaLs4VRA3aIrC59Zy51AYd24vx8+XniU6FN67947vl1/6oPKPA5nzxi7DK//VprQVaDT/B7mXzIXgu4XN7wm7fZCyytStz8UAn7IZaTphQQGyE5NTgHc5hiZH143UY1CudQhhS9tAibe0mzPvAaJrVB0VW/7HKhuFZ7PC9LhLdJ7lNd2Gm7w2cJzSWvl86xDvEAlZgJUH9acUxK9pvz3Pu75szMrvme3pNdJf5dHs0O+QuV5VFxmDRepmB1iHXLsdLHMS6Duh8s36K/Rl+0k7iqMV3T0uuxSaCr3xcy+3fXOeWjqb3LhXkqZhwk9D72fuCoTnp0xqyVYGmlBy+CFiae4mAMwtSkGvMDvVNORvjVN7jfE4MqvC/a+NCLkUKegsEBoVzIQ41y09nsVONau6LaK1eeSw40SNy/5X3VMBVFNnpcAjnQmlB/uGzZjB7QRqcD6hJmQThUwWMUxMB2tHqKsqAZcusUWXCDLYbOxgiDeFLosg91c7GYCqAPlEOZT/+0Sy8eAGb01j7jCX/JsJLOVDal1UXnTC40ToIIEqqNDNNIVHBfrDXt6qRaLLYy9HXo/WXJ2zRLiHyu9TbWO79qNjMy4qGmMU5RCM9BgcqDbgmPZfozsoFc0scbBcbg0Teboll4WeWFxblNPJRiLPLYGns7uNYRBUFriheRMvFaj+2mm2JMW1UkosWpcM9RInbKvdHCCeVLxble3OG26raHTCxQPLzOBVoeUXvmv56j7L1XFma12guM7q4VRhOukRlKa3K412+uGjfEEgW7p6rPWIbNBUS9RaVpAERmTnHKWY6xSSIVYHWr9a4Ob8P0dGvVGbUAjeLyWLfbbQK0VXqyPyq8lXBIB/roqVlMG/NelkLLdxwGpFbiuUF1m0ARNUCbl8I3h6V76XzQR6pQnFxB3kQImprF4yZQC81TVdlSkkGXPwFzZXq11vrGjLiSYQfMjC2dK6PZzBnCnOpzpkZ5f2sdyczC7gXBWo01eKfNqJPL0nt0Trdg9nv/LMhNG9ISV6enupKYEJaWGArz/np92BUWBBvvD/OlwEvhcVEdaIX5RX3icL1xU4OvqX/5pmiUtJJfzhItC0MMYEf8jITb6kLz2xJMHBNcgdHpYb3xSdnf6hoZVkjqdCG4Sul5WWmloK2Fc5Segk33X96/hb2pmih1xQ61uJLPSFjSiOv1zrMjlVLnvXj5otj2H/KOHldZ6tYiBy9zn6kFZbXFR51zHj1vOtMak89ISRQpisWoqN7PzWgKixnoqr/bdvj2OBzps4dpSiMVU1t03GWmEGB7rJgtLaI5Tbl/MKIaJmKqwaKTDj3iWXdfRoID7/JTb3GBhvCB+byis5kROS1LmI7BZbaM7vMCpcVl2kWkduTjSLfv4Iy3HJCt0E/jadvQVhIVzbtdV7XEV3DCYG++/jgdala2a5beWnCsxozyOHZzXOXR3SdTQt9ztDfNy0qVd/nnM37nfAqs122VghnFJY9XUsAeXKM+tXkPGOa23C69gwCMUBzwHE0HSVf89dLmaH0m4WXv5a6elBP3dnLaUgkVZK05po0fIKdK20cTyEtw5M2cbmCQep8yLagKRdN1VmfYhZ5XWiBgkmae0Y3L881vnTzdZVQ2LjYqrTJmdMIorWGVQGTiHJ5UmLU/lf8OP/VmwCCcERWRE7UdSSiWMJJo+LyxQa8pNGt0fRqJaS9NgDKlo5u7JZCwXKuWaWyXBh1g22l0j+xf1aaPRihhJYyy0jNlI31ZjyWyJl9nXMevWoE1wXYk1ecsSR1HKZChGZJeZmnSnLJ/21nGxPpmnG0tvyJRS8vsKC1708nR5LeUDmVScHKscajKiaHWKPirqC5rk/mBGvFmH8kB7mIYDjELjkHFOloYrob3EFv5HydiSTTraqlWRTvndGJggHKDUsONxZeBsqo/HUI7mi6k4TgTrqNLY5zSnw1EEt1MXIjyurKlVnURBV0hg6S57NkoO83Wx8vJ15/ionrBUDXX8i5C5lVABsZHgb8nYzzogQA12eR8auzsgBxLAVpElsa6r+WXBlcpkdG2ECJkBtRVMy5ipPRrE+nZIdZGFJ1ZrQyWW2uG/niyqlyb1cnhvMnCAEo95U6mMJOCnOz2TGbpctObVjolouQwg+PQNK0qkBxoZu4MMWO7xXaP+7x3zKuDEBMBRgMFo5lFyyAS0qXLQTW3+Q6rBRtJIDwXymS9pBjdWDMV6wULgD2QFIsiqYEOm3VBJSYjpRmj6wN5daNqvC3LRh4S9mYm0LrYbup/75Pk1JRuxSYFSX/i0GGAi5kmZKvT8W5blkLKn67VTRY9zeDl3hQmKHOus5PVe+M0aRxCXOpnBtK8juxK9nfhRIxGAIJmaBv8lWLCuwzlCkvHKL4cdM38Mo8lfS6HA69RFtahIGCU0XhylZbAZHWxJo4pRAXQBBlhl5nbQOFnK6sxZyEv5GfmylkA34vmKscpn5aLFp/VLznTAtF7WsWL3FbZGtDkcI3WKPfHeYADG9kaNZE6lb+9H3vG2yVObYUfk0lMckbO0pEGpWZgaS4JPLMsUoABa2Lb/rWdQmLK/QU/E99e28REZZ2sqQJq2jJ1Q6QK0eJRYJMN5rQcPmwBm6ZCn7UP9ZfpLIXSGEptpJdBz3TO1gjopzC19QOiSKnFBQ9Pz9mu49G4+pOHj18TamzYu/Pb/S3Po9Dmhdxm7PaTV5+Yb7AZBg+aQdH3tCPv2fcLArCRP/ZYvglWBbjuh63PIIcDBSVIHCXA9qAaGjgFNSxo6Zhz6YHp+cu+Jq0+0Q1460n8vuuJeYLjNkvRGh0BvU1xOuB0tYXrfOb5aLNowiLoBYn1NDbs1G3wteg3Zg0EIrNAzgoqzg5yvRk76EF7dLhOSwEzj4+eyT4/9jWl+H7Welz1ysDYTAIvTOiUSgKDSaEJEzgQtlp2zfRdS6jxdU6dJhcoRIfrwTqkGlbNu00736kjm7TChbr9CQkD+kZVVd3YWvRvd2UB8sbG9EtkMovZ7jNX5b0rMkZeViwPkyneGSo9SSW93ymQhifJEuaGEqff4xyhdLzYh6GG0waLuR0Kj9ZU1+2nzs7ttT/Q7t3xQt6NlMUoXxk3hI8H80zu1q5BEtZFjAxPo1QJvMTM58/yIrrmeUOBmKEt0ujhvHxkPXO2ZKVzoDi5MD70XkA44ChTVU6zh2lm3n+PwnaNyh1MHepk8qwJ94ypDaiDCAo4ek+MhhzTmEEeZgZqxQ4etQn0jDuuEKj3fhStXYJ22XdtZlXZoxHa8yJb32r247J2mcd1meC1ze7KUQOdp/AkKxWSDaLUMw2u7CH13QECh6HG16835yLIJoUZBgkO1gJYVGgjFo3+N8hcSh8hmDL7jXWkfjnIWmo4c4Hia86LAbv4HH9af1DByOY1/wIf9Mru3XM6cAwoiS09qoE+3brBeI4uttiAi35bpXXr02NnKrtbLVwbUYNUZ/H3z1df6E9hFVQolFFRytrKU9NLNzB+vVT3t0/m7r3fPKLzhYFBw3KshDLdz71O3FWL+sLgus/7HzaMabFbfzsknNvz9r4WpFW76iUOYpBURGYH1TzwdkVwlh+HwuPMZWKg7h6mgQGlbYn16lbWBMAHylAft4I2k7jyNfiodVsxyhecFBJOa9qY04wgUd5pMmNWPwv/rjLu89L8mKVGI30eU4NqYlbaor9Pr1epDD1osqyBC2dNeRZSFdW+yIvD1ZJAT+2q53Seiqfu6QhGeHS8VppTpiDBGjYIhbxSvEbBR4Pr9Hz3VVrb2l8e1BzxvoTADDByRdvamBzQ9Q9UmD2KXmMCzYJuSSYvpaym5hQfWA+F0WZiMOVz7kxP6eS0J72BSYw63UbK+Lcn21QCN8StQATG30+WgTYZxdtF9kxOa+K0lsK6npbTppWAAmEipoJVs0tWK83KID3XgoA0k3a0Mi3tP20XU2rdQeXujRXv5SE5YUaQEFG95hhTQU+pquQOEvDOfrQ070BeDmd0/Uh+KodGDUp0GyhQI0OfvFooAAAQAElEQVQwUX4fGRutaSYT1Ah2Y7Q3eCe8jbv/4n15maGlaYKql+LmYBrB0TrgLKX5Wf27WB5WH0+jKD2IsYEdBTgi34vK+xnL4uyBcsQpr+PVV/EuJZu06ZlefFuEyD4+57b+OD+urfqNtdJRGtRFdDT1x+ClDtVNTIHqVLkcyxSl0pLqBGgNnwEwxuEyGQGQplWtu9FKHlCBWTbuLbA0s4VYecmdaOOi3+N30TY0IrxtKZe9KbUxfC0TdGPZ0h3efOQdcbBEVKEcqStdMWXi2lr1OqxSXpWoxQv6JJ0KbHY9g90mhCCURrP3y0W7zdxqXu6e0Ys+DNDo6/K4Z+7ZUULvzb/fCd+CUNecC7XGRevyJE8mD01Livhv9gGrbbCejkcDYeDMC5UQZfF+RZe2nZNr02TuxQRN00AXBkCiKWalPkAF704V9ku8ynmrl6duz6+agbSBp05CC883a59TjUI47FpzDaevcz5PXtlTPEe/l2AgpuWgZ7cyJt+ku6mzGCmz52A/EtbNo0OVT6ea1G/P90wOStNvYIn/UBqs3tDXaStJTsUXp6xTc26qhjbsOaExSoFGxL4MDtJALyVF9iDplMIHCVE10zn8k9Qa3ilG5kjgoJI/++TWK9+cl9mIIWHWdKcbFrGA5inuA40ZfYPpMkVGVuQRExgjOSB7BM0U1UwpgM33AftjlRStc8YOxeOSDrw7kB6aYTmhvuVseOeNvSDDFoi7fZm2EzamoFxebry9XMTI1egcsvKIfJfcl6s2AQR72Ql/7EpemRfVF8/kKW3X98hH6cvMtF3Ta0YTcZnSON2zhzlmdI/btVOl2r9opDxrBxeYsYJeFZ1eovvCvjksUxd9nt34DraqZbfKaVbUugYf1AezFOForpeensn70eazPRXLOsPDSpNSEgnl0s2UxO73JsZPzdPTiMayiKWshBSQPkfyIVCNI/UNnT9Qra/plWVPoev16FaN3iXZluwQhKLqxveosqQKdQRrq2JpPFNFKO8x8BTCwiB7MktyqdPNGC6nWxiDJvYu2XP94CCetqf7QW52W7uZG4mxbPsiKkrVP2JDidScaPdarABB3/SaNbnW2EUG8Hrxddu1ZGOmp6YqM4+Sn1RvQUeKv9GF+Xoe6wM+fwVNGeoNaY903mE+Zidbl2dGItP2YJU5Q644LhQ2rmCW0cetY5kIN17osXpN+wBr4b7bbusrcnGCsSZCdAleS2yF0nAOiG70cBc2Mj2HUMwABtaSaIY9HhtX0Zs6r+N2amOCFcbwDLndgxOm6yoyoC1pD+zLLuaMamkWKXcK7SHEpv+0fTpXDUXGeeml27q4fK112SLoI6634bcp+7QXexafWlvgYyJavAB8pS6fEIbRPdYlSuB2gJJPeeXxt9Kr6edjfWPssN4FlPoIApiEP+bFj+HjLVxd5/ieOGYHdlopkUDCym8YFe+gOZqyrE1nAPMX87vBfuYciErB3oIHhzMwLkeFnpmWBTrhVivytsdkfk6wP6L6V4SzNjN5LMuUrTDNnuGcqu5aMkZv57ZO9tjczYsw6KWsusOdFoaFdIrlSuzDvVFtM1TEzl7hy2A9Z+sqZHpmc77QaIGpoFJwyPPOMBMzXDRI9MaRod+cM62QzLV7KjXwC+sneFtbnLLMLm2uJ/eqjJV2bV/jZNBkQay35m9nuZjwO+eeVA3kwnGFoA6IT0vNl/ECw9+YrVc+9nwn+42tr3vCxwzq9blNxTXdCvWsRpjRDT2dByOWv6tL25FoMwAuk3V73UsfiBbc7orJkP9G10qGlLEpGXsdfqE8cfuMdUBE98TYC/5mufT4F0rj2ra2o+RbTTBpM0s+giAJRFposjA9u3bP4O8g6s1XaPZ12jHd2O18Za74Z11igKJ5C4sS74oYeXi0hnW8IBXpKHF7eX+M1yjKULKTfmydgF/HogRgGMrbq1Lh4xJ/ur377Fl1qMySf0ClHw5kFNPR+yfzZ/IyzPJaq+MmFtNJwgEQsEQc+ugzsT4nPgpkZ+2piU4PPc7YaWf394huKkNSC/SakQRbFu5bEwc2r9+DKraj7c1GnbtLnCoCjOHVJViiwSW2LV3w/V+TK2rNaC0FON9q3VHxtOX05v7aSRFCWrcy2b9jqXzrvgBipKj3Ifsu3H1bXG9TX3SjRu/WlovopS9a8L54vv3fxLaP2TII/xmeKUf9WN7UU65UajM5SQwmUDYF0y9RYZJUAIwL94c/qy+d8ZoYIbBeqAWazkU1y+RXIc/peb1wwbXA2Vr+8fvO+3Ncd71jIl5sIR6bZ+36ea5babfWgwKV+CBBnHPyKTuMitGRyN3i4xgkrEdEP6OOq2/LzwV5x+Rk1YzE4SYVEf+QFx7aA03i91r7vlqQCB6Yeu9YLatLUpAVWIHu5XedD/eybcAxbnUTc3NxTHoispmNHOb4gzlgagjcmt71wkI3DLwMbzFwTasCVanfZ51HmEc5wEmbQ3RUYQNpxJIDAl64kjzs4w2BqotzAQFGNE3uHliq9Orp/J39VfByaBPU5JFpvF2m12b8htplaud25s0abdowbiZDNQo92BfT+fKbx/ax0w2WXAGyijcMKKNaKY7h/HRHVn5rDiAGee3+ZDlf8G0cqMyli/gjTbOHfA+gJgulSYETfESmXbMcf/s6RT/Q3UHkiVbhOOxKi4YL6112fxlnomb1u4XAJuvGxLhPAE75EOwea1uIn+NXIp5tj5Q050gjcK2DbrvnSPSlRkKbwTRUAN9a31zVi6CjRaLSXGiMG6QSaFCdo91+ltKWdwvMVfhs3iLCCrpdNbv6Mzt0u/4trx6izYr6BcsJjhEQR6ogHQbK/KsOSby4v/PNkziHqackkoBpLJYY62G1YuW6+o5zveLc06c28Xzs/DA/kGBrVBohla7MsFEztner4TPRxWEuPUEyCMYY4IfW+lW4aUoXFkdc6tKeo0TV9Xpzg9swyNW4k6vGC1M0fVAystgyqYve02PS7XdK1erkteir0mcPWzQVwGNe6ijmRBC6uZW6b859jb5XXVNvBweXZvcaZwWGJpxopvq99qAfuz6MOzquA59ujJazjFDdKjso8w693DFT6tDqcAZzW+5htkKiUjbIlkBxUZ4JyId+NBXyGKOuTST6+wpmA1+Pba4IkN0jAliMaXLH6fu8Ahh6B+kj4zHDMeeKiShcp68tvt01aaEv1Ux2jjb6ys2y8xdoefbsmkmL9nDahZFP8Vwbd/XP9PU9dmzmOmZ1ly4bXJK1M5e5eRg1OakVzSbvC+t292HLnVbivhqWq1NuZwClG3o1UKkdZWdN4uXsKxTggTVHvJC84Mvc6VDnsB8BTHY1oeY/tHaDMeCeztTSTp3RdzRE2J45ysaUTWNkk8OrZkHamrkUlxpA+2fe5OBFFa0b9ac7h0/P9/X0qKY9tsfjbOP4ae+20cvqnWOmgu3bePM2XDOXLosQixW9/PF+QkzK5qRkoyRJi9CzZSk7IG3IgNhAd2SvAhW+W8v0/xJkMeoihDZgJgBtKD/qRQpDuP3yFrA9uQ2pjT5+MBRDCn0dfqovnJgv55c4Jlww63euiT91CrktScXjmPrlTI0mO+gcorfz6/NcPJx+BhInl3LyKt2kHDxQlZWWjfJSuX6uuVwOjMqovQ0isWAXLo8feZvMUfmOjF8iBw6IkDXC7MIztiKuj6xDsAnFaexAuwYQRRWdOpNQjMPZxPTkvIvFadO2sHo11VERUEM7q4HmKVaOEBkwYbVC5Pag/Upt2nS2fWZB1OvInoKi6iqIRE0LCM0QwAi4Oo9N8h0UdGnuDasG+mc+95pA8jzYBzlVlqXB50apZrsEs+Rk6QdWbud2Ccdf20jFSin+M74IBnG5rYHnTOMFbMp9v0wLOz2otrKY1tnS+sZ0DUXSzI56/FSPFF4fU7xONyzrdm6j5nvThFn1iNGl938hSZUbLm2nxp/wJ5ZqsXbEofqJOrnJGQdBXrbMV0sDGHSzgC9wJFaaSNGq4R/Oj9fyMop1/Az+jYrDGyvpNV78nLX2HU9oYal96tR0j4jPZqqtwF34tGvt1AqaxdqGXOrT68fuJdGuu67brtup0FoZ81EAqBNne+qmRPBG55nawO7ryAQBcugHoU0Gx3gCv0tbq+u0RwqeTrkAaKBIprsxrtY9Ltxq/tYslaFUmKjS/PJXqe2y/I7QbKC6qZSJHHoL6mf5bWHmmqEbnLPJBgevfPfIa+C0NlI2sxzmfI2Z4/OZf/f5sfzsvL4h9cIwinL6iBXxZZyLHey2rpE9eUhpQd9iCpaNmg2kMHNcGJb7emuMgTE00YoUTT6X9ZLclUH7QS97YNofFHoHnlmZSyqFlUT2mKUFbEGvGWzp0IBoTkPl2er2q9ugQBwtFtRDRgBA9UXQmiDPmlojBMTQEAG7qqDem6XOYkccbWOirFrNFe1tn1zX2FK0qQua91pKF8LZ2X3kwC9ea92X/TO4stRQ6k8TDa9BnRR7okW7N1iM+nbpmsKgxPBcb+c6wm4wb/BAr6GNecQ4NS4HQqw5TkXK6mnj74ygrrcFmWv0CJ4WTikjhnF6SZGpVMnsPXMPF3TL6CEXyIl8bm7togqm7YvoEm93s8uO1l5caPmbmnx4UrZxPr1vZuCoR9ihlRlp7JlqpATRq9WQHvIXaEBs/zD/jbZ/muGHa79yX+VaZW5cgQk7KniNrXGTAFfVnWmuOUoCmYRCR4mNz/jVwRp2y7rmbMORPsELuA7j63xM3MkOlmxTqbYqmRjXae30Pz7qELVXa49x+pzT2kbMyIlhrsZ9uOQeGs9pEwb5rmO7r+JtI9rStHvA43IXWjISbRRhZXkghSRg19z2XO86eBJnxIqIvffybbrcMWawcN7AzbqQPocC2j0FbFShJropImRJ2DmIA9/t+0FuxT+tmma/hEoTQzPlA5JAZooiBz4J+Yz+Gd5jFCtmA1R76t2mHOM+4WY+MqZGe/tajvYRj2cMNddoIyV7odA70ArAf9d63KkzBVkcwq8KbijW7h6FCclEtuWw1/6uo5jm7RStRKpgQPHy2uK8PLibu6xY26SkEgUOMSDe1wiZU8UaSd+nn6xim+GiSsnZXmNakyY7vvokmA4PKHUqzzkvSJLQARhG0RrojWjJ14YsKDJgNV7s2py8f3152O//iAcCK+jLZLINR53OcuzrsWNlrUwTmlZB/LHm3U5eDuGr5k40GL7C/qfJsgjzt6X86/nH83l3GQrVRyPQnZytQpmfrruLKIwxZGBsjS+44aex1CS6dVWpwYg2coAZa1XIZRZGgs1pNgKv/hWvAicAQe0dfdaeK/lpfrQ6PeYFBwi8dvVxe++LeuDDGJfGR/InFGSb2eKLhKTDdY0UL0/sETAXtqxpgKBxUWxcN39MSi+Y04LADXsfLKoYNFDYTdEZlHyetFNGgxQ9lImln2rMuyTnYtM5V+FOX+0+9tb/o5J+SgAAEABJREFUcrPwmcijnnyoKhIOUyiAtNlOIBzmg5cIAGNGI4xpU8yGMpXO0XN7XzJqoo04J7Z21anpqgYxLdPCN/ZFpAI3S/Nn+sy5re/rin83V6BNx5q4tTI0IwUMiiP+HnSaKeGloFHGDlmVxBHEO48ZPJya8ebCMlOjtLvatYxkpabqYGvsjf70O5U+5gmRf+8XUZZnT7MUbC19AFkvc9jUalfa+rzeRxg5LlrM8VamKbqtK5qoJqT5agXihdsoB2CouDkCWGYjShYVlDsXedu5Xx8Aoi243K+hKpbp8IrPXj+9fgafAU8jd/c5yG/Ps+dsVmoqlA+lBuy2UojmSqpDvkqlMdM4yidKsm4tTZuWusf+udaHl/l6Hbv7RRMWaXeNihM0iOfU7Cxr8UChK5qCmQpkKAGHECgt292WZkHaGBT+GIsjAApYAd01rtxgmqxWdRrMCLE/1mavr5lRdKZ1s77N3drr6VBp96q+ckhpb8bwR5EbLp1pAYuZ1VTzW5xwOPVV2VyZUa1JAKIrsk0uNEbuIAK6kcnnaO4qvxPxHMyuVel2xltMawwt9DhVcbWWlnGksd8sur6/1LN1ct4ZRBIvU07ljuaxrvsebVoXvreWwofr1DrJoTDBtZTXepOkDvwmqKnzf00ZHEAi5BostOkL3aF9DVkamnkpOutcI7Ajv4+gwwmZmD+W8/Z5/7sxNq2U17xX3GdFhb00qdgCFJIKbn8dAPTF63rHuDLpUcZN5Zun6i0hG1NdjRh+ral3atU67WmX3fd10x1E++lIJ4UUvy2SYYiPco23ENauD/QsNRfQQwEbBG3FgivUiczp3sN8jMdUm3Gs0h8ZTcMD7oFQfQ3bJS4tvIrUvIEGB3qxSnavowemow8wjvKG6vxFZxf3lvURfrvblHl9OjHwlzi8v3GvkVVvUb9jDrvApNWO/r58XdLE/dM/F36GPyd/6nuV/kZzBOAUoGbMaimeCMAg/rLQtuAByWYfU5yj0T6m8P0Cb0Qgpha060wjIE/m315H8UB6KxqbXnSI8dAq3iGrZTOxnBHFyDFxSFdOtVLx2kpgzQjTUK7M+HJl0xiIHShjMscLWrHKW4gMcc116bYv4ZM12u8lcnwmphP8cm0tIxSQ0floZXuP1mwlrjFD3Owf+EWaQY32kOfNTXj5NS/GqyT2Bm3kWSuaQkmbM3IPkmFcPcw+lZ2Pf0d/J5BEdAEELeWjNbiUM3cEsOblCLpqt9l67PqaC7LSq3UgYu6XeXNiFOSttAw8aV/nWQFNERuoIWntKEM21IvU8pMtaUlzeayeK/K+yxThksp1oXzq8jb1DJ7pZHUIl9C9nzpjnjU7xsiL97KraQUBlw3e3oREswfanv1+8/vNlxrrelOt4A11MT8cNU0VsmRAFcGwLCrWUFu1vec5tTsT2s4d7LLdmmdQcztxgPTWtijvSTML+qJdMXGBprDFZClYgXp768fNtEKuwQX/hJvbU/H7jed5uVmr3QBM9ryowW2h1bMXxERyqt8kgu13CqBw5lxzxLPK1WVCdELMp6bNnSlyPDCXafqpyvwyj88xFu0oSe4iboWZ7B5Yctn4BdNBYGPr6EgBda6Wxi7XTCu35swfYWZGqvvsqx8fgRwHiFEMPRyZNrROtFirOk+dHhhbJtwYxHXbMuu0qaBxfCZ2r6dDYmnxeCNCXOmPl+T68v7FtSe84Ydo8F2mj270avVIrhQ/DNg0ciA2ITLZY5ZOla4ejOFu/1gYXVjGfDxAJvo0UK/gTXN1AgNV8Pb+DuoE2wKpeGTK2syEWCLkjBXJNUYIvlJ8Ydr5jD8brdvijTc1NojhZ9alnclzlxL7ilUYa5/U3d/Y0VPeuqFOVeI90gTROUuktp/j3x2PLo4tn0/oBm50xdkeHSvvtS/rFlgQ0M9EtBrAkDVevODalFxr3R7X9MpkK2QFMDONGR0Uyitdo4HX7EVoNPieS52CZmRbNrc09PP4v+/67ND4Ucit+HUNe6jZhnG+xz12j7ugqSJF6kg/aZAu86rhBg6YSkOMkVdBK52AFXqYgxTMqxikSRAKbTfOGb2uONdHPP+/w++u3OlV1vJACsnymtU0cPeUHOkK9A0YQ4f8S/OWqLSCdbgt9Q2/zS7lU0OQNTmjLI3H9D1tCpQJLaO7+B3hliGKOEBRyIXLkuxLdamKG2vV1/t/pyCFowlvulBMdZ1SE2YW1JEANHLzlMFM0K8Svd9EIRhL3ZVs9rCLUOQF5aDbIdWnH9gxpB1p6JhLzda0mugxYzKOxElTTOJuoBT7RX9v+RztPrbG2FYdInBrmpgWTzopby1sgyESLJFn0DZnVXjeNu9EnYQYEGiHmaCaCnnCdF68/h0Kngtqe2iiQp8oYxALwxkIwWVreYwrLkyTxnXZLe508ZhtDY+hFzkxkk5Tn95IHYB1B+tWB1FKUQ0Cy00tVgYY8pDd6hP60EDxRaEPOvAGo13ThZRZYTk5Wps6m6bKYRpLSUWJH7LGcjR/9uoTgmmQq4Ey+uZGzHaN3HfjTdU7Xr8VFGg2At4mZCTbKYtSwuUZYfo5ajQ6Czc3mrqgsi0zbHBSo6mJ1dYDjF5NBS1qtTjl2TUty+oLvYpTr98ac1hAA6d1Lqd0PhVUn85SJcpgwEh302vcBnSOPrc6nSbbqHkACgVg03MWIZN16zGdq+Q1Hg0UXlHKifRi4II7XcaMf5FrVMLPhN7HxhQxtsRiLXN340qzBF6UA4XUS3cK2eKdIkAtWWApAVw9FpyfVbfB20m7ou+PnqCLIEND1a4A6AMbTcex1I2cwm3I3p51ml3p2DeHU83KOVqRVM1WmORM/4UKLXM1rD22qdZq5HKF3z7lUBfHql5i2nDGtCXtkVIUrmCsGTOY83Z+wuK63O7m28tLRq5ur3lfgHwZ9mK2wEwddYSkYONhHx4NU8JXzhooKJ8wqEYkJc0ccTmqU4tVdhWMxz3FrlkXmvQXw7jVQAzO+wLgoL1v7h4r9EZz9OhfTco5ZsVcK+536ERU3H1dKUfURNKh9VEGkED/HQXUyMlr3MZswGnJUMaSLec5mAk4jSrYMQ0UldBKtWy8MM2Z15gujZFSIEJ90thB6EzSDye7xD5fx6NJB1WNadPYuQit/i5M7zZtPCuoV8BEiWkMvI0D9Iu2CUzZ1kZD+YpujVdJaHuXrwwKarADg4UQj1INGYJ0NcJRyUwFqECXfI97r5Z5BVEBxWkoPx3SxRzIoZXO63tJZdk9rPLqzpI4dXCPt4IOc3NX7KLYTWd02NOhHVs3lSITr1AR+jRQbYM2yxBxxamFQ4XlQhr0QSQDjvBxQeFpWZDOKABQlUGPXZodo91jU6bDGZoY5TZwXcGzPJe/F3sgnxKSKBywCy+r6XY0Frq1f0us1YXmaYrITmBblaWzLki+w7Fp10f6sgBUOzI0Q7zRx0R4GKi4euPvRZ1wR1c9R7b9jXiNgrnResak+vOWWVKJehsqJgrx+tAWITRQF5viCApvsT/AEse8ZsSgZqXfx7ghtNlMRoPzRkoIiAMvNBn3sjl8Ws3gdDdmaL2ClwVNtWJY0G78+szHEdMEYIt2qfZ9wSL1bFDbnL2ylNfs6LGcUt3v24/QJdYpJTpLPlpORlbM3WOkMs2x49ZoUsS35x9O4ZhSelBRUu45J7/Z3+kDrWHPoAGSa+Dlll42tfJ4J2Pot7sjgrGwRBgxNVlsXIzL1oJnXQ+u80rIfArKP2tYC31DXR6ag/rzM0Wm2TaWoZ0HTF3GcHJxnGzYA+uBUXt6rR1NULRKkjEVXri6xPmQp7CTeTRah7hqsA10GGgNNKotK6CWW9NICe1PzqJSQSkwn5DXa+ymXxYe5EreQnAmU3UIbSAFvSyOherVe/kQNnqbP+SWEzl348bo0syikrqudHtYqmDFZINXAiF07aXxddWYcYQ0aaKgU6y2hmJGdvUm0nwxll2u50Hh5q876DXAGLFE/QVcaJk3ms4NM1HZGiB9+RxRwCHRIuEJJBV5OY5VyQJfmwsd3abv4yacoxZVxDRz/KHuK5mqrj8Aq3shXYVtua0lya+7JJdaWlzs5HG5bHGOrM7uNsAEL14bspoYNei9MDZLwHOmxzDLeQdoKE7quzPNckVe2DbbZKjANBYL037oUg+NovUyX6ZIwk/wkNInxSi8wsqbVirYq0Xi5TDTVw6NvWRFyRuN7/Q979wmiJcZiOQyV2B9LV6QQKC9wOLMQNYb5m4WXc0np3QcMa/pNjp5pTCOvzpfMQvWtpqeA2nZRpXYcyxfGThQhhyR9efdHS5mWEoiQLZPczQqDM7RqanWViS7EzUitdEQZyDMekHsnpCpcqoEIXM+4jdCn5jCX/OKOYW1y48eDEAWEm9TyUI3QPlUQV8FU8Be7W/z/p4+s7HSo71bm6Q1atna5EePqRtl57REryct7UOm1XNVvo1UTJmNq+Hozr5Zyn+za6p4DvKIwP2s2jKNqI0qpAiQqokxqduVFR9dY+j7BCzmanweXrsl9tt400Nz0LIM34vPu18vj73wOPeysWV1+ZbNEthMG79Hu5bFDHCJpwkPt9twR5syucQ+vHhPLGG4NEwSOh/Pe/Vn+bV5J96zc+FclZ+Zm4F2HhOTEA2YQmHtuVODalgSXYoqtWK+9jETueXKyOZ7GZWcIs3hJKF/DStbNTfO7bhXLDGqdQBHk6mgJDLrwbw59FYjw6q27gIOkFgjnKMc7W/kCflaF3GRqqVq72K/OCepLpKoYBsmETXmNHPL4FZoAmPgGISx+zV9q/tT1q20brxxC4QqvWXVbVtTCejvqse6GrqojjS7esOPWTvcZnXcGYHiKziNsz4+KhWGtCSfik0tf3oUo3nRtPvSF0eD+NI2/U0/j7ARetPpQZMKblopWiK0Rv5PjBHSQ7zjTLxk/1R97CxDKu39KslVT3BHG9IBH1czvMqiXIVwnkmFF9dcpqziSvdzTYbQEPLZu8+21zfkTBw4lvcrSYU4zrowpnhpBk6z4DVr+OY7NNagaUgoYbMfPZXsnQpX8ddWrUq77aP3zT5KXH8vUytWgcYD6pZgszb5LlskTTIC9EaKOZECgpF25SsLU1LNTqmOHdFtuyonJVSD6ZmALMZ2azFm9uab25ZqdS7OTf2pFT5yMfLwwmv5H9NOUpU9EpAweSKSVQLGObSjlAJSvFPsixi4AaQCl7gvx6IW5HuFuq96haD09s6Fnka+jmgNWVVqqIlaCBdKxogXiHkKwNP1bJ5fcONVBaqZvXxcdm7ysnB3jReGGZM6lOdZsKRurOpbumE+2qkeOW1ESQiyIPUO9K+FRWvI7eotaSJgQXblPifOyFdOvb8HwhSL17VuXndDh+CKWSf99E+WRGFMG/VU3pKx1dx7f05qZ8EX1lvJ2vdc0zGSxUgnKhrIsJGXPATLvcB+tPCI9keQEzMNPNXPOXue0nC+rQUrU3UAABAASURBVMnQbhYZyuGVbaZ1u1+p/vFkSu4MoBTghhqfPrR6QcxosMsOni+fR3xAqRlROqk+1KrWdBzRh8a8ZL3UwjTimJ5XaYktds8eTwEgXU6alUwBZWlcNY6Ks8Oo+QcjEY3GOZtSjPRmi4CGaaXaOdqjtYph52vBx6+6/Ph65qpRiLyNtzIjUPVlHZWZXDptYd3aWNkSCMUqKRGv/b3qiVY25U/iVMhyq7wfwSzFhCbKR0/xJ9wmrmP3xid55YTKzc6rydcjDPQe7mPPI6cD0RrqL4rNBi6GlAbDM5NAra6LHaSR0L8Zlcn8iTtM5Qnoy6LW0hRR2M56g5FQj9yoLvT0YfriCW9Xoh+TZWaK75w4pa6Etk4RuZZBo14Z7+BjLFFd6lwqDC0ILgjzY/g46a6ejCts+X3ZfZHXQizjsqY1iEd94tVWLsn2RB2rB6d7X6OGUXlF6emhG035KFV3LNqCwmG+KR1cxU9J34hpUY5uphKs8N7DWnJc3AF15VB6r77uiWgbfRO+bCj33rgJ1RdUDAItf3Gf+HnmZ88qOqlEckYFcRo1M8oPQCIw7KTPWgwL5l3Pp44IBlAaWb70IMnvhG9B3rSkdKNN4dvMQ8kqjxKU8YERel+Du8p7AApsyoJR34p5rvpGnnWArYszXpAeGjjFozUlq9V5h3BLiBBXfFF9Cauzl2VcsM8tGk6ogfuGbcx/Z7awgsrs0h96/3k70b0yzVJhrfWmcWxNsK0O8kvBeJ2zy3ZJLxpe+ivxusy7kY/OqXBzdp+0TTw8he+D+k5saPnyxblMsY9fttylDJ0VIyQBIaYHaMa1IKJiFju69Fd+PkVVbc9DO7He7cynXUtE+yg1QC+cCxKquYIi2eiE1jmbegOLblEq6tQ290EJkWltXrBlTl7Dq5VUU5sbV85klCGNaX1OjkjuTMzjJgZecfp4jN9FYWBG5bXfjg/L8mwApEM8GoTEe974AJBjM1OA+PlN/j7rd6v/TPrRPqhmoZb3pZGNGZw5Xz5qceMYBXKk5Cs/iz8WS/WfiqoEfe6Ro0/Oz2nPrJoz0Pix1iIx9xPr/c9f3alNczr0ClBm/v1wfQJuXZaDjf5e2YrJE57pe7OyR8dfCBXz3KMdxMzX8ITDec18Gl7etN81J7Iy+aR6mzx9Fx8zGYMIoyg9fyVua8mrjotq0u1Xiug9cqZUmrsgEonOg+6/LwuqEfO38q/B/4+GP1GPG8mVQOEAkMmSe7pta1+897nv/0L/9W5L1i6754yLTCx/UHNuiS6xkplAIBBAkqX+Whlt8DL9Q4U3dqwCDTU3ogt2GApFZqmQpdVv5e0ex1kZJLQPkgzBCMXv1mfeRZYySClHZS5g2KXThmBTUqyaCtXTV1pUsSaHbEsvMcoJUmilVuqV2xgzFhcyGVEBDC1vI6fIJ0V3EoUlUVXddGdHaW4BgjaS0QppEDNBtiRzLLwinR1u9GpkoYoIt8blq2OLIDcBBharbS5xRdJaZrOQtijmChIjIrCWoqOMmTLRzaPcUQjVaFZdsZZaibKORdnkXSHC7hzNZwnGEarCo6SWShYE9aNNSokpE8uisWUJCshZafCdGFVXp7AI06IH4gDvcdjvnfri12pGAyuwJWQbwtYEtdaUt1ttzDLWzkd5raU1pw7eMPChLM2ABpLSAEHzXsmwhIESWCVHN6WbNH4hzrypkCKJkvKqEeHhkm4pWQad1Ey5YurSTDhAiGSaYq9Rus0eFIuqtnWcOao280YRvH3xtbqykNAElRXLcB9ZTHuSN1cYbaraym0T4zf2bJRKjmqdQ2Vpti6qaxDuWKKc4UBDLZtAWIXQ0uy2gI/wWKMMJBhJ1Bhj8Ugn/ZsKS2RgEKPwxQnrAa4oAoMzIG/EEyZlVtXwiD1GelQlnRhBFAKrjUVtkZ7azcDev9GcZFiSXkrcQpReh5vTN1LNu6VZkTu6BowzMmUR55QS0hxwclIuiAm0FaSb/orwwslyumT0EiJcu+OQHtgR2bCj9CHgOjOZWivrSVwdQQRtFUMwHlGR1viPKSqtXIMIkkowomUsdkJXrjEof0pzPIYvEipDJL3Ug7+yiAblZSPbUj5sJt7t1GZRJhijVUwFbY0Zd1MImFWVJo3hs4mgM0Zz7kIuiix+nCQMQKGK0JrNworMUGtt6tRFiyHDoZFWnOkq1ao139QyxSKRXCorTnCdZ7FARlimkVH3gVhAyQMBroRmCNdYZaf2UmOJEZ7KnEP4HFBEaK26sGGlpvfg7WXIIz+5BsW+k1K5lrLwHaXMn2aZs+M7tJI4tBYVypDjZC9orTfGo1awiCl5w1Ks7E4sFRbeV0ewVBUuU3dSjGQ/aFJaxU+SZmBhVKfNPmCDhKGylTZmVnpDM37ui+8QRbPYTMvUC5CsdOYNyZ9GysENigFaS0oD2tCfSCNEd523pJoYwZsxb30rbUmmJrsQS5cypixHxPBMmcymFS7tCphNZfuyKCQ9SijhNjeZCxFemSgZRd6lmIkSpTEICDCrojwwSF1LXW3RkoQnR7nOmQqpbtEshfMXzaGMi+fJwBau1mRvmkxIdqqHLV5VqaB22YjdZxqjfdCzRntSvRmb40TiiUlrKUT2zAaQDRNTwtMNZEeFxZpqK1XMhUQ1Tw3m3Mp0CMi1OzMokRVDJJpqy04KlE+ao5QxYo0ylMRANTcq1jR6LTbIZTmvqmMauRPxJPnu5oU0chRaQ5fBLEg2ER7GqLJjnuBcVNE8SGIR23qRmJxdXSaQHEnWnKlV2QhZqGahHroQkrDwpIWKgG9rxZRkgLc0O27dpQZirvxN96Zqqw3FCqm6gtRlqzUVCd2KFIFKZx8vYE4ZQ8mI1DRpm2zatJ1N08Y2YkHhpUrwNQd8SAAlkkQETRJVqIBRNElvq6PoIpO0Jl0ZokPn6jN6a9NkaBroXuKqrLbZZA5GiQImtal6KynqI2IVn8KEfuitFmZKOnRS3UhZcJpUNfSOZnRszEGGVjI1uYg6n0iZ0E0sjbqvhdwlG75ZGGuY7ngIwiynjo3yaclVlsKABLB66qWdWYgqCH1gjTOplkwt2DqGYlmTCX5M05hkbMT7EjRSKGlAk1LNVXgumiZcV4YSyzLaYfawWtdmK1mm2SsUjNX5S0SmdQV5GlzhEu613n1Gpy3NzCJNgIpJyQp+SfeMdBR7emJQHibFb+WQUR6mpU05NRMVUoQPHZwMOCcL1heBrUeiVr2sTWsujSESRSvwyAKKpiYjS7BKAyGsJ61cSrTrGjr3Rj6IhUsEldwqoKmNewBEnAfhQYlNnrSyFR4QpdCeNRUasshfR2p492zB1MTsyRRJ0XvaGhBc1bFJZtdKp9CpSEYo7YiAkS6bmjUG5b2NnbFaZjKU1xiFh6Q3oYmsJmpsryG1JK/SCBlXIWof6RmOtWgSQoroiR2vNdUGJdmL+my0J1nq4i4jhW9VDsrRjGtXEQWVJeXqqUu2FUtqrUlgjOq0mykVGYiwMRiTahoVYeiWwhJ/EtohhBdiS7Fe1+ZEpzslfRF4X1hZyoFFaCXLLR1rHs2jScJikZgDxUf67E2amqyZonUpkoXVXVNQddJhXUQAYipKN3oOgUOEk8jrKpelZFRxpQ3uv6nGCJ0RV4hUqvDSCARjbJvc7ESXEAxkVTCdfam1K6MMYqqiVuBV8v+bBK3PXqZeWBZPFxEzQBDK8FoRHjPUVDtTyXWkW/hFEPiK1S2D9jX9tAgiWWPjsFXsCLqo0C7TJDxjbdQQgl7/BGxbSJiIFfXInafBmzlDZCaFuzdjMhsb950t+n82sn0SY7MkIWciw6t4DHorba13SnS4B48ALQlTHxhRc0mqohsJBzdF8eSDMOp3lQuTP4tsky+HtoVtbMh2hOcwK7Ocxrz5z7D/1eRflGJqatZqTD5aMI7mmLI2ojtqG5UNsqUgUYO8h/TADrLLnPmfrG2SuCaBzl0v1zbp2w22nXZKK9RGwnkfJ1ZNU5PTCTyKW/CTXHZyGFpis0YrOTlZbBAD22xbfXKBVpnkxvVJ0SKW4BjjFi1KaVssDYM8vtAYrfCrtilolKPNmX/QjhQAKxU+JdfEzPQ18qVlYo7mxespwu8vZonUolJE2Gi4pHrSdh+l/KZmaVWbaWoTv1ywGoUZxS4ZTYwbSluZGMnmYxngqpwElkmLjFKyWswu/zvs95CRDJXOSOI8xGEk3ssjHmMUCAsACyGVjJH1O+ie0wSVVkW6eYIKsJf9ztiWL1pQtqDB24sOZ2PlFxc7opZT1E6VAWAmQhe037oT2VEmReFXca+L35Zz82XzuzbbmijnmlwptxjsvBIVBeLIkWxO5tuW67ZLOIbiu0J62QboYDfYmVrXJgsfUjZtEuHz9B/en+Q01SSYRKZm1lY5lLeaQzEXk2ANYOtg/Th435+5smA1NIvnGjUlp1fwK5InG6E4AVZWgF1gJ5apZsFMPvK3JHMVtaDIt880mDo5lBQgFGSpGXHtNGkbxcZy07NPMW2q9eK95soVmZGWqsNkVvCPtXxeQBUqhYnPRWohqgLzrCKAJ5UklWUa1qMMnrpk51awLMUM5W/yzI0gHVWsBHNMY90ugg/zO8uWH1jLzM4CBLKqkkbpJYbMXiUJJisLSeWDX07tKHaqTaDydio23aWt1zSwASsjoyFMWuGPgScOLKxD8lqpC7pXC62uskM63EfEQEGZTS7gzcNY6JSNoCaBCSVsv0m2VdPthN9QFrlsN5oTKRytzUF85JVs9NCddY4ROqLYQ50WZLNj1qfiTYqqWG/WN0XON2o7+kStsy8GMxRqVH1g5WCCtAabwOnCQKUtkgtiZhSaNQFWKUvwYM0JmRea5j8RRPAS2YlpJ9bvFtRMe2Fn5M4YfRaPHtlLNYRlpRZMewM13zc+7QjvMrM1J4OTiBaYzuJJbpo/6bnzBPIH/ElLscwckluTaIL1p4SFgiLxYkGWx1SxozJZh3CSw7aQpajWpDDYyN19tqxNCCtuE7WRReGrAObCCMbCru3RYkhToe+2Ye42kpPBpCOahTYIVasJC1LXy5XM15YQwMZTyPUQidCxRt6AcpBMpK7BkW9AqmjNNIXUrl2fgccmB8uOMY7FYrYIGyVeMtMNLcPLRxvLlKMbFwoaiLIpZBKfmofpULb5qW9l41SsPfHN5BurJ4INUdskQurKehXntWTxswAkNoGNaw8RxoKiLObkr2rb3KzbO/fFMWeMRJQ75ggfROM11uW1QW20m+3k9MT7Bt30D9QfkpNmWdvaZoDykh2RjZiCKhcuXLvdlkllYOqsVAYsQ2sYd1ldqIMGnfJJtz8x/YFNmbmhdkQbOskcmV6ZDb7B9gkFq1hqZ1vTFqDqFu4AS7REFwCa2FmEhEayQegsEzZSnYKUjBJhPaCG2mC9BlGjGmyqRtpEWMcoaIWA0WPBtMCSs2rhwAhe2WBQggxKnL+5h3VfjQrWD3MZAAAQAElEQVQAz4pY3xd13gP4VJjQpmw7PvWxsWWjcwdBc18pJsB0TG1MfNbrrdvEvy5sFqkR5HoxdbMVLobEVrdbYV6SrV18ktE0DbYpe5Jq9IxOwUnx0tw1SjEryyEHu2rNVnOvpYGTdsDgRsvsmFrtePalsNji8ECBJLFoVAst0coEO6p/9NY2HSqMuZyQsqYY6lvnESPmUdsh22jufYVSSF/S5xjbxWPJHABjGTvdTRd2UyazCkWUlaxlaSHlnaclKLqhEYEiqypTuzddNKNCqSaTUk1MGTsxP6l4UgQDbGPM4z9zjWUH240515f0ajYEvJ0Iem0z1EskfIrUmjxsmSu2FE4KIGS7TaKTcVX2xgGIoLMS/jP891Ieaby7n4Y3gLT/K6d/LawCnbgYaMIbmKn1JiJYIAkkGw0WgcMgplJh4kaRcnsQ5iHWhrqAu0IsLH8GOUu6x8po/McQbJ9qNibZPsHWamvDJ+5+HiQGYDDcB2NxWRnOGbKoxBpKsaQryhaQVWLqE4v2j6abFptpZdgqQMpHbqrYIZS7ClsCeS3R37XzWzRsOz3ZNqO9NQnICakYw2teJCLoYc0P1jxWecrwPsZUJSbC2hfjpSxLGExVBckbIXMUx2Rn5n2gj1oGYbrmsEzOaQaIeqibSVfWRgsmsJgUmaamvRFE8bLKPPm8cUw0ng3ebVCpPdsY9rs0TaoVX3BPMhMzT3SYsPNiZ9TkQFlOO96sKs2FmaVImZ5JlffnfcNMkrzmWrVJ+WNs+pg4o8jvVMYbK1IYs42tKWNGrOnKxcpJmZYJaqw+w4XdQ0ZGshYpC2306cIouEZozhrWaEkXMoQ1hjbLFOxYlV3dhKaoGStVcgnqKoBMJjAFUaIBi3Why+JZWeQos3F0qIX25HqOyly6ePfVdUKLbU2q6LU04tEelkbiTCB01MVETNd3hSYES9gYTEBlS7fO7bMkiniKETKwTJ6zNrsMKy9fMt0bqkuaBYRMuiqjYKozx6BBEGyLmDQYW/G1EI/3aTbj6uHU9Rg9SqV09aDhwVkEI8RGnLFQBs1n1CJYY5bQoKLRVLUrzCBTWqWQGEc4nHFrBZ9lpgENI+il0mXk2hxgK16fEBtBl6mFaQpFOxgLXYcJCx0d3DNSZYAlJgEoj47olq1VKr0UhK9s5YZE9cFbF4WtY0VlRFoq79d1kuyVMJIoAkuSHOqItSv18oplrK3HBqZZdIuYwzMqg/XYdZAZZsckmiQxVBjbAs9KlVvyEAwUECbeMLSsknpopVYUfu/QLizNgCwp4VU+EIusbNIaUCsWW7Pe0rSEcmWvjhruY8kIiWHO1m0NKkKSizNKpVSduloUruAQyWBQunCGplhODVPlk/JNSXcV9mvKK8I0wQWBIke1JsZrZNEO3a5aE4qh1qwZtZQEVAS5hmCKVqEZWWwgBQHdJCNB8zLh1iHgq0ipMyiT4t2USQg8kKN0fiQLKTTsbS0fuIgzd0vCQQIboIKiFa5jZPTlXMzsCVKFXkA3FJWsgS5axZsQRpMVhOBhQU9MiKV009bVWhWvSxUC4RLJuAHhSpYYx7yE5DAWVCbpDpTTeJPQIuXGuWCyszCoJ/Bzo+S5t2O9N26mNqFrTQKOV1impq9FR4/EZunqbk6yy7FaAtlKi0JxBhiq6LBp1h45kQEJQuR+s5SUlXQVNRUTEVgRptaq0jWboKXA5NqgqhScQoxHFyFw7etsgdKmLdbGpJiybKRUGkIASAGQx9lKanhE0allgulaFl8gv20aNJuwSLLLZaJ9wtRzMpYezCFCaJR7R0pmJWqtLKPiXRiOgf64SMwUKzg6bKpNYewbvCv0YlAQcEj44q3MwmyLdHCX4GBoDmFX4oXskhOKV7q0EN5krZk0gfUku4VgveQal4gq8UgA7oVksXblpkkEZSDEWmZ2yFizohLF8LSi1HytWoWSfwo9MkrcQOV6MmOMr2iyUixJu1XRcRcSnLLTZdpIb5XhM7vZSLhMoewqGzy+GpfIisqNd9iks9amyKzQHg/GGC7gRWc3w0omJys7LTqTy5VeERQOEs1l42rokFZlpKAhhQjrMVZmhCysW9yKgorslTzaygrpTg0IxIppk6ZMTWuQClLGX6LClNf6bhQWyjNYG5rkpHvoGOtHoUBboYuLEnZ6OS1sNFvLJiHFW490R4A0rI1GwshGL23Su/GlxRzRi5q1VmZhQpAc1GrC5yAxGNC9mGrUFNmDTxrWUlBwyiGEurWEyRoMoBhMYNdgq6AeHBhotCDQ0giKCAkRukTLktTiUTxVpITrMmi2sJQxppLsqKY0zhIQGgOyMwgSKyNqxJI+azJrmbSGITUK5YlKF1v6lGjcTzCPiEVZ5k1alzVOXkjGDjrQ1SID6hHdU8ksJAFpoMzLiJCfgpOFhFOudBpRFSIzEF7irTm/EZqMceHxVSTIIRFaxQFF0Ky4yCM8wEXBGqZwaNNhAUshC5JVOcJHYMmpyibBZLylSySLhpMBNGlN2PEEiSoV/iikghJZpQZxIMrIJidXVRCqjpKISOcTUjAwLdJkxWWUVcYSuWSUVtNs/M3MDqVJTM67CIJ3iRQKpHf0HaxVrCj6oDNByiqCYEZFVotgZqREHxMX6CMk56Ju2+D+kIZFNbQzEV2jGVKbJ+8RWEaVK6kLSZ+GNU5lCIgm2RujhqzNhtDLlDnJkmDE7NWZhjJjAjrZzJVQlWTHEWNQFAO7weyITEFO0A4zSDfeUoeuLjJ98OUjC6Ysg4W5QiUpA+8VIo2RiRRowE2L7SAgs4VTYhuXHsoYtEKU4ayzCksgTKSL8lBY0QhDLaZzYcNiKBAJYeCAQh+PJhwDpApqK5tomr3Xk2ZNqFVeSuEkUmUuaRVTiginGkAr1RK6Zi/cvRDowseH6hoqzutQ8sJUeA/e23p0hHLdqnIUPGqOnJPrSrpTfdqasjsJ7UrPJxO/cOvdNiJTprLnaKApCDWV+qElbDaJTaYRYdNs5VbFh0cjvJFKm9q5Z6tgVakW+0yX2ABTkV5JIASPIZX14i1UE2uwTZrC6KiES8k/i4W6aouXz4uiBBowF9E5+4BlUhxOpWXx9lXZGQojeZc5YpvLHL+jfrtsXTlU06jmgqpswvvAkhIvDYmqBRVDx+zj9yxjGPkS8DWkVq0UN1rXeaF+lgwPrxyUWUSA33rx/i6Dk10kLMM4XPyGiEaRmcFvqkfkkurZMiPhGXPyoeF3YKkqGSULEFHB6ZkjwpGENVfOLuFSnu7r76KXDJQ3unH3mJcgbubBXZYlY8yrA0m1LB2RXD/Ast1wfgz4Nmor47exmLhaqAEvT/HIdM8Yvz3+d6GkJUMrUeGBcKOoLNOiKpk6B0Y5aeYxR8Qywn1FzzAkhcuyUB425vIlGPFYl/FRam6Vm4Kgoub/xNgWUmC2JhKkKAIJJiW8tnNsA/PqPrkPLp6eyCyuQNBJeZWDhxB+YQQpI9THUHIRfnYNWLBmXPI/8P/4iHSroqykUkdKMF30T8c+ryFkEmwm46FHjxCK1CtG8WcuDLcYraKhDHw5XWbUnD4QAffuW4w5aiyQOSafu0cLZKlDWa4cdlHWQomAWR9smmVcmbRdHhQVR5dqCxlGJGNk/VQxA4vbcPk9av4NPqeD2EgGAvCR7jkytsiZJHL2MsXcw2UJd4//eFHmVQQoi8MrHw+bSwbLYonwrMwMmhJfspbA4u7FjlgZle6Zc+TsyStzlTMiMsj+koyiPMPLl8JSCYRoJsPnLxdmkzzV1vO3k1XaceSooviXJf4zB/U7jxwRwWqRhZaGR/L5xQs0k6puSFuybzHNaLPHdjuWmbJSZ7mhIisilf/cmfRZGneUASS9CG9TQIoCpeENwbo3ySFYzPitIvEMH2MZ6SmeNqcu5dEHQ2P/qYpKT6RzDco5pVgx/1mozJxTtou4r94X1KLCFquZTGc6otQhfEqt5mUjCovrNmzxjCVijkGfUWO4j+3whaz+p5IEetUSNoc6b2xUR1SSLB7itYmycM/hSeQgwlBKeJt8McrUqF653vfBZOVvxR+SLIRCZPAfAXafcyxBkLzAWSExkpcsg+HkyBqZo8J9zINKLmZWoT7GWJYZFfrYGyRIeUR5VDHfte4Cwif0OUlNLiVVDvZfyi+XWMuDKyRH1gh2Iq7kOnUC4+9iBL7+LipGgpz/Z9S28nEoeJFsgVVA0MiwEi4UH5nzXMusztwaQmh0KXhZgbMxkvxGci15QyvpkVbbYG0KCivESsn1IHdbxLKMYHGxjJAZNbyCYJfImefmJGWM7eJLrZqP5Nky/x7+28NrhEU2zxZZA+laqWJro1B6WjznxQnYrUYHs8W6eNxhuDSW5BZFJgWeiEp65xSazN9JKnUUv7rvQ2xU8sfSzE2HiHv5GLOPZXgsUUuoB+MaLRcI69fnHIxprI10jtpi/JblP7lwPoJYYZESRapkbYyJGkqCpQRGoDFLbF2XwcCmJIX1+Piw4iSUoaATYcCLDJJksZDh0FhAvzncPeZZtksfrsRR8wjfzuP34luf18tLpotGb8V7iybc9KuQHKkKOgEieFZRwXtFr0AGloylYgaluYCX+E8R5THIVUR4wKscybw6N9wGqIaXs06YuYArXYCTmMCxnbdLehRXFNKLInOKwdtYhN/Ex++xXRuOs7PXKFSWhUghEYsOx1Iawl2trx5HFIfmQc2PJWOZPX9vx7IskZ5WoS28+zBCTHUnqjbcWBsZObxmsC/10dUJzkctlcGNIRanhZj5SZmz1OLhlWOkz6HzAO0nr6ZHLJmjUiptlNJyZdL3GGSb9zchS1REyVqxZKiY00iC0JwzKxrbrQ0owQ9+RpUHr2AGwVQKW04Vi4uj2/RtBH1lRLKMq1o6CMFRkNBVG4n1xQVe5qXFT4UKSRdOysqES82dDwO5XUhB5motf48gibOHR5ErjwhGFV5Zc4Lj69zyTNpCSCX4QdicuGpxpk2cwH5nDV/CZ2dfsqE9jSsqc5DYqgpytY3VosMcyGLjECHcQgpHGkpWBY4Z81Y4e5EMyZGxEMCS5Y5g0hS0HN290nktyt3XN/XQGqmDMxdCGfRIy1IesczktVjCS5nTmbVgA67yRdPB8pmX7TIGIWW0FGUyS+ka7NUDlZEVoQm3mBslFamimZq8YI62ZUhcQLhZJQDDKoSHpzA35N89x5BlwbKke8RALMUTF49WTFUKz5Tzc5QPjaTQyXdmGRIa0CwCqUKGBLfCoSUlRGpcSV9GgLziyQrwYDfnDucgPqfHSOIaUqNqDJ0X24ZGlqW3YkyRqN/lFNkIijdlBMgmgK6cFJkedN74QSTSh495LHNplQrDH5xUxXz91vyPxX8YQ8VUybsxjSrqdYSMFLoxqU7HxBKRGMHMhRAdYmQso8jSLBYmYUBVi7CRcIQ1JweZWQuiJFCOefZ5qYXnK5JgbvXL+dWHq8sPD5dfbR71gQAAEABJREFUHq4vbq+/fb/7fPHw+e7nx4efH25/ffzxz/ebX195fvfz/dXDh4tfny5/fbz8+en23+9vf324uf58ffn9x9U5R27++fDw8/P9zbery/Or2283Dx8uHz7/uOf59x83X84v6eXjw9Xnh+tvt3ffz2++nV99vrl6f3v38fLX98t/Lm5+nV/enl/cfjv/9e3qny9XD18ubr/++HV+9b/ff/5z/vPhx8+fny//eX/BydeXV1eXl/ffzh8+Xl9/vr07v/331yuiuv18c//55u7Tj6sP55efzm/+e3y+vv9+/+8fN//77fLnp8uf7y8f3v/45+P1P59v//3l6v7b+fXFxc3F3f3F/cPXm/tPl7T58PHu1/fbf77/+OfbxcPXy+tv15ffb66+Xt99vbz5enn79fLh/Ornxc39j7ub89vrz3fEcP316oHzv1zffby/Jg9fH/79/eH+4vrq4uLq/Pz66/ndt8u786v7c3J7fvfx4urz1Y+Lu6vz+6tv95efbi8/Xl9+Pr/7yoh+/Hz/4+HTj+uvN5dfHy4//Lz88HD79ebnjx+/Lr4/fPv+8PHHzw939/Ty6fbq4prIf17c/fp28+vT9b8Z1Merf388//nx+92Hi8v3N5cfb6++33PCz4tbGrn89OPy4/nVp/Orr7eX3++Z8csPtz/eX16+pwDubt7f376/u/9w/fD+5v49M3t7eXF/c3Hz8Pny54fL2483V99v737c/fpx88/Xi58fz28/X612vhL5xfWnHz8/Xf77/PLXt8vVwgcyfP7P96v//fHz19Wve7L0/eLh+9XD+f3D94ebzzeXn66uPt1erxp7+Pnh4ZraeM/wL24vLm/PLwn+J5X25fzX14u7L9dX7++uPtzefv7x8/v5w/fz209X9++vOOfqE3V183Dx8PP8/ubLww1pf3//8/3N/cfLh690d/PPp/uHjw+Xn++uvl1en3+//3L+88Plry+3Dxc/H37c/vx2+8+n238+3P16f/XzI3V19e/vF7++fL/7+oORXn19uP1wd/3++vb9xcPn85+fL/5Z518+fPhx8+GKMr789nBFfs5vbs6v7r7fXH67ufxCqVzef7+5/3J//fnh+vP93aeHh09399+Ypoub8x/XX24uP99efr2+5sjXO4Z/+f6e5N98OP/n4/k/X+7vvv28Ob//9fXHvz+f//P15v77w/W3qxsy8+s/C3vL3T//+/3hjpY/0P7d9SdauLv5fPNwcUXm/035fTv/+fWcaO+/nt+fn99eXF+f319e/Lz+fn/16cf9x/P7R43dfL26/XZ3//Xu/svVw5fL+y+3Pz9f33+6uOTx/ubqw/3d5/t7+uXx4ebXh+t/Pv3458vlw9eH668Plx/vrz5eXX68vPl8/evi+t8XP4j214fbh88Pd9/u7znt44+Hj7d3n29uvl1cf/1x8+n6/iOv3vz8fHv3ibDvLz9fXX76fvuFyb38328//v3tx68ft7ffb68/315+vrz6dnF7fn799eJqBXNxteK5uf7y4+b7xe33q3sWwuern18vbs5/XJ7fXv14uKEgv189fGRSLu4+0ub5P9+v//fzza/3N78+/bg5P788v7j6cnv18ef1x/uHb1e/Li5+/fhx++366uP1jw8XP76c3329+PXx6hdJ/vr9ivN/0DIL5+7y/d3lx6vrL1dXXy6uP1zcsQS+Xl99ubz+cP7w4fzn+/Pr97dXHx6uPt9dnf+4vKCjy6uPd9fvr+/fn/96/+PuA+1fXn7+fvPt/OH75c+vd7++/Pz5+eHnOY+7nxc/bi5+XP24ujxndPcP7+9+vb99eH+9SuvT7c3Xu5uv15dfyfPVjw+Xl98uby5uf32///fnh38+3N1+pN/bm/c3D58u775cXH04v3l/cf/h4vrL+eWn80uevF95u/xwf/Xt4eH77T9fftx9Pr+8OL+6OL+lKj5fXn9kllllP/5hD/90d/v15vLz9SXNfrq5/nR5+/ni4cv5A1vxj+urr/dX9Pj++uEDy+r+9uv11fmPH58vrj7/uDu/ZiwPn35ef2JOLy7p4uMFGbv7dvfz4v7n5c39t+u7D9e/Pt78+/vtvy/uHj7dXr2/+vHx/PLzj9svtz+/3f3z+cc/n8//oa+vt7cU9vfbX98ufn47v/lyfvvh+937i9uPl1ffr6++sz3+vPzwcPf+7v7b/b8vblbj5yyilb1L1gVvRD6Sh++3n89vvp/fnF/cfLv6+fn+nw/3P9/fPHy5uv9x/4vbx5fbh48MjS6u7i/urs/J8MXN5/O7j5d3H+7uPtzcfbq4+XR59eH28sPd1aebK1br16urT5d37y9/sit+ur7/9OP26/nVt/Orr+f3365+fb/59e2GreOfD+e/Pn5/+MT3y39/umOFXn65I8nXlOXHC1J68/Xm9uvt7eer608/fny4vvp0e0tpfby6+3L98PX6/suP208XPz9e/cNe9Pn27uslJXf1fu17PymMj3f3X++uvt5efrm++nJ9c/7ArfDX5c2v8x+/uOr99S9W35drGr/6QBJ+XH/4cf/p+uHTzT3Z+3J19fn6jln++PDw5eHq2y219OP8/PL8/OoLJ9z9+/0t/V5/X0cuv5/ffD7/yePb+e2Pi6vLq7vz2/uPd1fvby9Z8t8vb9jBvlGTtzf/7/XP/9/V/furX+8vbz9dcRu6Jh5u1itRt7ff7i8vrq++/7hmnX5ZO9Kv84t/zi9/XtzesGbPr24/X/zz4eIfbrXf7rg9XX27vvl4/bA24et/vl7/8/2Wnfn+693t1/uHb3c/P988fL2///7w8P3q55eLn5+u7j/d3TAdHy8o7H+ThB8/7r+eX386v/56fvvtx8/PPC5+fr+4uyDg2+vPt7fsP2Tgw90/ny/vWS9fLy6/XFx/O7/jbvL1+/0HHucPX37cf7v/9e3n//6/Nz8/XP9DL99uri+uri447fv99/O78/Orix8/ztkQ6P325uPVzZeL229Xv77dPny5u1293Pz6cvVADB/ZGX7cf7q5+3p79fWSPFz+uLwlPxTht/s79kDm/eP5A3X15fbh8uHX1c395Y/rb+c/3p//+Hx+/f3i5vz65uvN5afLHx8u779c/7q4fzh/uP7+cHNxf//j+u7H7Q01PzzPL/9NJj9d/vP99v78/urz7eWnq9vzm58/7v/5/vDvH3e/Lu9/Xt38PGcSqYrL6zWia9q//sIOsLaR+883959vHz7fUjCU3OW3H9fnP26/Xd19XnlbDX69vfn28PPTNeO9+nx9/+X+35/vfn6+uLxYj9sft78+3/7z4e7Xx/ufHx4e+P6Rc66vKbOvd7dM3LeL+4vz2x8/7r5d//x4/8/Hh/svN5TZ5Zeryw8Plx8frj9d333+cfflx9357cPFw/X5w/WXm7uPl/98uPrn6+3D9x/c7u8/nd9+vLj8cnv57f7ynKq44Mivzxc/v9z8PL/5dXn98/L61/drYr56z+JlCVxff7i7Zjm/p8xu/vn28M/Fw/2Py5sfFzcX3+/OL2j25/c7jv/78vafH5f/XPz45+sqmJvzy+vPN3cfSNcDb414A3D19ebq2yWr5urb3fXXy5tPPy4/3F59frj5Qsaubt9f3ny4uv9w9fPrFQO8/HR/+eHh5sP9rw9Xvz6e332/uDonjQ+3H27uP5zffDp/WOO6/fn+7vL9zeUH3tL85JKrL5c/Pl1dfry7/Hp1+ZVzuPbu7juFd3H96fzXhwseNx9+XL4/v/tw/s/Xy3//uP3n4u7nJ2rsnJbvPtzd0OCHm8uPN5crPz9vvj/cfGYIF/cfv3Ph7ce7n9/IwM9fP25ZLP98/HH//r8V/ePq4/nNl/Or8/Or7+fXny9u35/fvr+4+XR1//3+5/mPG45/XXVy9YE4by+/XVydX199v7/+dHP38eqfz2wdV7/+f+c/P/749f3u+vv9JRva+4vbDz9uv10/fF+V+e/35z/fn199vLj6eHX7+er269X11+vbD9c/PzAv5w8feOlypeL9j8tPF5d0d3F+850kX99+vb65uL0/v//55YHq+vnl5vrbjyvtquufbEzsv4eo8tD/e3WzrtaUc4R/2mlqxvPe++OZqTQVjvD4vxPjTN00o01e7aYmsp4Lpwk/8rzRwuMxPU421anxZWbalIv4bz3+O3NDU6q92dSs22q2qZqsM9ePypP1UOUcbbb64kx+5LRuysNUpta6GU9oc726YqFb/tKuHNdNt077bT1XmjKd1gXy6GidwQtcwKPb+rE9Tm48V2m0b3S9HmbSH/+mtuntv3NUaES4qokSEiebrn5VlcubCQ+uon2e8CrfacFEOMH4JjqZcaGprofoetVogQuVq/ixN2ucw8vKEzU1M+38p2q6BvLfq6vxxxEOmsp/j2ZqxLBaUI7Q0aO1FVtvShtTI+3rRy7nOePqHOGA6P/NNzNV/qOptvqnZX40rl6PZmSyPQI2E/K8TlOCR+dlFV3PuZ6rpJua8IQfja+u1vmLn5T+eK7kkBO4cDViSjxNtSuvqnGaKf982Tsv8qPwt6kYB239yPNHazKZcYDnnKDrhHUO7UzWmvKSNhUej/N5zkMIv5vSbl+Tq6brwY+cbZws2nQdWT+aTpzDyarrBF79rwtOMOWE/5up2o0v0fWqdl4hCiaCC5vRHSPt67kaP5hMzf7LPAnkCIE2U3s8uhmX85jMTFZTPKeXptI5YbVP0zLRmqiJNFutTa1140s70SoHlUv+O2KqNM6D5oyrzHjJVDi+Tuav9Vw4mQc/cRrH1xNVvvd1yWqQJ92MIzyacT4HxXjShPhNaNY6Oef8daHYalYZOJHzfOI/ARfy92pBGZr+95wfaZcIN6vgjG8813U5Q2ub1vr64lI1eTxUu5nq/7dceKJcssbFc1mv8iNnmIoKkdCv8sWRbutMM6GBbnypmfBSIy2MxbhWOY1jPJrxXFp7nKbKQPr6zhObOP/xfCI8Tli9iOn/Hb2pcb3Smpkq/zHjzdTWc1LHceVHHtNjvpQLbR3hIjPhfF7ie298W8cbDXKOSm/KVzdVUXs8eN74QdVMra0LzJgaFRFT5ZW+QjUOmsr6bmqmnRN5otp58FwfB82MH834bqITM2HspZTWet5N+cNjsy5eUXVatvWkmU4cfDTfta3jjWLQbkr7hMyjczK98KBpIlHZTOuiTW+dRk0fpaKb1ja9rSOcKfQotgYijas4TVd30xonQ9CmhMpDp2bEwLOmytTYOm2d9Dgi3ZTHOuH/bGqz1WBTGn80oesERtDMmhrfVURV+N5NOY1HZ6bMTIX/Jp6rTHSqasoJ63LOMVt9NVMT5cv00YsJQ5toV9XWY52jj3ZMtTV2VMZLuqyrtXXCOm2iXwEndLPOc6UL48fHQ0xXI3w3kfXQdYnqOt5UViPCEV7i2kbv/KtMV53MTNfXOkHVhDNXOyyvzWbDS6brx6lba9qbrYGomO1WEPQAABAASURBVEk3vhGAMi4e/NjVHucrlzQTnnPytC5ZbTYVVW1mqyPjyf8l6L82m+nqQllEPP0/C5xv+v/ZpMH/7+g0oUI8ZqtlU13XqPDjeg414SGM0Uyb0uN6hZc2nYt0M3U1Na41elC+Pc7Xx3cx2lGlQXpZL9kaY29mpo/H6rHT6PpRmqlyvkjTFT/NP87RdYL+d/7juGlTMaHx1RpPuqnpfz8yHbzKj+uldeEjsE1rPJrxKk2qmfBoplNbR/g+PV7qZlNrzfiSqdEOoxajcSMG/lZOM11XPbpbr3Z7nKaynphyvDW11b7y3NYTMdWVPUhXa6Y0tF5aAYvxktKgcryp8qOJmogq/wkHjYOynvC8qUzGOY8fOa7SzVQ4Yqo8U/7quka0mjIhEq4yNZPHQdM1Zfb/qcK0mfSm9t93GlLtJKRZaxxTE1kPFdrkLLaS9d146TEKeYTxaF3Xd1pbD7N13FbjSmvrCS2YdM5RMbK6nqjxXEVNVMVMObPZOsj3Tjdcomrra72kpsppuv4mA03pYj16M3scbLq+Oj/ZOoe/eU7bE2OhcdWpcXxd0owzYQqerIfqhhnX9cUrvakap9GpTt2ara9m/Cgm68HrukJW2uf4f4+prcP2OKFziDZ0pYKnjQYeP/Jt6hNdma5DttpULmHfaOu5PY4zNXQqptJNaZQnDLY3XuUyMQNdMa7/O1R5gQej4/xuzLLaula4Sl8e7L44fnp29vTFi+evzw7/Pj5+fXJy9uLk9MXJy5PjF7v7L5/9+ebo6dsXz1/s7x7t7R4e752cPX95+te74z/fHfz5cu/52fPjF8+P3zw/en1w+Pbo6N3J4Zvj529O/nqzt/v62d7/c7T35uivs72/jp7vHu3vvTrmhJO/z07+fnH8+vDg5f7e2dHB8cHBydHh8fH+8en+y5PDl/vHZ3vHR7tHZ7tPX+7/+e50/+8X+29fPHt1+ufb02d/7x+93T8+2Ts+2D14vXv47unhq+f7L48PXp7tvXux+/bF/unpwYsXB2+Pnr1+9uzF0703e4dvDg9enx69Ot1/dbr3av/g793j/+HCvf03ewdnT49ePDt4sbv78oxGDw4PDs72dt+cPHtz9vTNi913L49enx2dvtg7O9l/d3j8/xwfv3lx9PLs8PTkmIBfH+6/29t9t/v09cGzNyd7b04PXp3tvtj/8++9Zy/3Dk7OTg5PDs7Onr09ev52//B/9o7/Pjz+++Tpq7O/To//enV68PbF3ruzp2/P/jx58ezk7OD12f7fh09f7v3r6PBfZy/2XhwfkYTXu8dvDg5eHz5/ubt7+nyN+mh37+XBX2+Pnr45Iua9s7Pd1wz27Pjt2fG7l4dvXjDS49OTk4P9o+PDAx4Hu4enu0dvd/fePd99u3fw9nD/9dney5f7Zy/2X57unhzunZwdnZwevjjdf3t8+O7w6M3eIU9enu2fnJJSknb05ozGj96d7DPGV8f7L/aPz54evnl++OrZ/vGz/aPdvVenh29Oj18fHZ/uHtLx6cH+2d7h62fHb46O350dvD1+9nr/r9O9P1+cPH999vzd8bO3lM3Rv2jl9MXBi7Pjk5P9k7PnL872Xx/t/8/REU29OKO1w7e7B6uXg2O+To6P3h4d/n249/bo2dHpU+bs9dnR28Ojvw947L07fspkvTzbPzrdP3nx/OXp7unRs73Dp4dHz08OmdLDk6O9V2dP353++T/Huy+fH7x6dvz6+fHbg6P/58XR35TNMbX614v9w1cnJ6/Pjt4dP3+z/9eL3Wdnz3ZfP9t98/Tp6dOnhwdPX549e3H67Oxw7/XB0aujo5Pj3ZOTp69Pn7473iWMlwdHp0eHL0/3GePfDPN0983J7t9nu38fPX/FmWf7L18c/M+LffLPemEqjw8Pj0+fn5389eL5X6d/7Z3sHh6dUPHPmLo3+4dvnz97ffCvlyd/np3tHp0cHhw+f3Hw/O3x3suzvVcv9s6On77e//PlyS6JOt07fv30+M3e7qvDP19x2v7hmwMq7fDv/acsz7Ojfx2fPj05e/567+nbp89fPX/++mj3zeH+q+dHJ8+Pjo72Tk7++vtw9+/DQ2b55enBm7P9F0cHJ3v7R4fPD9cZe8f7z14e/vXm+C8WxYuz3bOz/dP9gxdPD14/P3x3fMC28Pbs6avDp6cHR0e7h0fPDk53n71l+C8O35yeHO8dHT0/PD58/vL4r7cnf749/vPd2V8vz56fnu0e0/jh3uHx0fHp0dnx05cHf70+3Ht3dPTu4Pnr4+cvz54en/51dvLn4eEfR0dPXx4ds+TfPtt7+fzp0cnu8enxydnJi8PDl/t7L4+Pjg6ODp8dnu3uvTl8vjJwyhQcvTo+erN39D/7h/9zdvj6xfHh3tHJ84NXuwck583Ryat9trVnL54/PzncPzg6JPaT4+OXZ4fk9tXL3Zcv905P918eHrw+2HtzxHCevnqxe3p2wIo4O9s/Od09Ot4/Ojng4OsXT1+c7b05Ofz7YP9/9p+/3N17sb9/unvw8vDwzeHum72/Xj/7693Tg7dPj0+e7b8+2P/7eP/VwR6RvHi2+/bZ07fPn/598PTV6fNjxntGqo+Onx0x2Hcvjt+eHr0+Pj49Pjg+ev7iiOT8+fbozzekkRwe/vlm99nRs/3j/d0Xp89enOwenz07ooUXz0+PDt6cHL073X17+Nebg79ODp8f7P91vPfn8d6/zhjd8Qm70Nvd3b+Pnr8+2DvdO2YcxydHJ2fPD4//OqGdvSP21ZOjoxcvdl+d/vXi8OmLw9XgqxcHx6eHx6cnLw+O3+0fvz0+fnF2fHJ2fHZ88ObogErmklfP918/f/7u5OD1MUnbfbv/jG3k78Pjv/ePXx0cvzk9fndy/OZo7+3Js1enu69ODl4cH788OHm5f/Jy7+jN0dHfJ3tvj56+Pfjr3eHzvw9Zgycvj4/PTk9enB2enj07OfzXyf7Tk/39d6cnb8+OX58dvzw6ert//Pfu/t+7z9/uPyeMl0eHZwf7zPTZ0dMXJ3+9PP3z5cnTl8eH9HK8f3By8Pzg5OnR2d6qHFo4Pnp7evzq6PjF7v7Z0903h0dnZ8fHp8cvXhy8O3v27vDZ28P910fHZ0cnx7tHZ7uHb4/2/+fo+buD3ZdHBycnx2eHx6+eH7zbO3x3ePh27/jk+f7x3vOj/eenz/Ze/bXHTvv38fO3Z7uvT1Yvb073Xu4/e7N78PrpITG/PTh+/ezw5V/cI7gLPH3NTD376+XTpy/2nr8+/uvN0Z+vjzj4/NXB81fHh2cnR68PD188f3qy++zwYO/04PjV4cnZwdGLw8PX9L53RHpfUCP7h4fPn73e3/37aP/dwdHr3f0Xz569ePb87fOjV7sHJ7t/nR78+Wr/X+8Od9+Qw+ODv08O3xwevHq+++Y5Cd99d3pwdrB3/GzF8Gr/4A23id29d/v7r4mZFp4dv9k9/vvg+N3hPpXz7uA5XZ/9dfTq2e6r5/863XtyfPivs5Pnp4cHZ4f7Z0fPTg+fne0e/b17/G7v+NXR7tnBn2fP/njzbPfl7vEL0v7y2dnZ09MXuyw3bih/nz5j1KeHT1/skr3d491nJ/vPXxHe6REnHJ8w6r+ODv91ePSv10d7b54dvX5+dLz/9OCQH5/9z97h2/2jo6O9o5PnL8/235wdvHmx//Js/9WL/ZNHJt8cPXt7fHh8cnLyghX97C1T9vT43bNd8vD3ycHb44Oz/WdHe0+Pnu2zFk5o7fjZ25N/vT7765g97ohvx2/29s+Oj05Pj14eHrzZ3X39/Pmrvb0Xh/tHB3sHq98jyubN6dGrs5OT05MXp/tvjp6+Pt47Ozt+cXZwckIqds9I4+Hzl6f7rJeTU0rs8PTk+csXe69e7J+9IPJnb54/e/d0983u4SkdHe79fbT7+nj31cnBq/3D18/33u3vvjnaJY0nxytvr8+e/318+Pfe8bu9wxcnz9kkX7Dwz56ekcaTvTdHh6yytwztZP9vbo57x2d7R0eHeyenvOX419ujv2jqzdH+i+PjM1bl3snL50fvdo8Z0bvdv97u/fl6l43lz3cHe38fH7w+2Dt6fnB4+Pz4+M+XZ89fne6dnT4/Pf1r3V5P9on27+eHb/cO3xydvDk+otlX+38d7ZLM5y8O9t4c7P59zCgOTw+Pz/aPXhw+Pz376+Ts2cHRwenR0auX+29f7L452eMO9fr5UxbL2729d8fPXx89P3y+f7Z79Hrv+O+9ozcswJPDl8eHbw+O3h0dvjrZPT1+dna0z6vv9o7fcIs82nu195wUHuwdMkiCfHf29M3B89eHeycHz86On72mzdP9l2dHZ6f7pwe7L3afvtt9/vfh87PD/ZODw9Nnh293j97tH/0/L47/Pj16cXBw/Iy6/fPVGdEevjg7eXty8vbs6C0bwou91y8OTl/sH508f3m8y+bwZvfP090/Dg/+OjzePzw6Pj44PNl//uLwz7Pjv05P9s9Oj18eH70+PXx9tvuSUj98erb39ITFtfvs7OTw8OTwhKhODt4dHf3PIVvBwZv9v7gzvmEejw6OD45PDw9fnu29fHFAhfH83cnzv4+ev3j+/HD3+cGzlSXeVLw+2n11svfy9Pnx/tPD3adH+38envx5dPL07HDv1V9HTOvp8SEBn57unR3svtj/6+XRXy/Pnr0+JY3HL48PTvafvtj/8/Xe0xcHK9qz46OTw2dvjp7/fXb49vTk9OD4ZJ+l8debw79eHz09O35+fLp7RDEfPHvNDnN4/PfJyZujk6P9g+PDo9Oj41dHJy9Pd4+P/ni1/y828L9PDl+eHB2dnLDuXpweEfzr3b9ePH3+8un+271nfx89e0lsZ8fHzPTxX/u7xH9wvHv0cm//9dHh6cnJ2dnR2fHTd0d/ceG7o73/Od57c/CMu4mK6ON/6Jh8oVRy/Y/ypMBXWooOQWbVUpYlLTBVqhRfSGhwRHT9T87KCzJUgva0zNDUWuNDUfF6pUiqutBGhZSjIgs0HWHdbePSPFD8sYL1AI+WaZKmoKEqzUTwKCGwFSQ4LqEaKoqcsnrRiUWKYx0huCIyqKfFohGVo3JOeIoWdABLYcBWQyldo3Vos0QbASdCHh7hyXNXXURHVVTwBazuslUK4GajCZReS0A35ZyVQg5FwB9XFD8FakbMWlmT8VgUHEr3QhGw0QSZRUECSTs20Oi3xNB62ORGuhPIpqKqKTTnrpW0Q5s+hNRaVHPvS5g4V02NzMQUySjpgzNNQotTh8BDMnqUzPzN4AIyjJZKFtFoQCMWQYqQta0LGYPCwuDrf88IBth0aJ/BS5LaYJs0q03WTgoXr//JIorTip+UnkAlKR2GglJxAa2JlFoViZeWpBwRGOuhcGh5U2ZFGC9yFh+WzCpDMZBaHkZpRFKODD5LIjihW069eiOkyj5kxWEoARhQlqV3UkAhKElbtbG1HKYJBdFGiYvMXNibAAAQAElEQVQkYY8UJoc8TIYnWhNCirlxzTGtwFINqpAmBLAumkuWoihKMoykZzJ54DqFD8SAM6SkvvtEeGCJgAYKDbGhOh1cIElinUiw/i8I0Ldqi6E2w6gGGmYlYAoj2Qw9mS5OpWg3Lh3aTFbOgFSRaTUOlRKGEz2keWP0bhArpeNsGJ0pqULSbDEOkZCqSs00hoi51v9IfwF9NfNOu7wEczOywIhkWWSEpJcyDO2dShvUNP+xWlDgK2lPgBJLHoRb7llOEYRqMIeijVRMAo0gQ8PF0cFomvZmmwYqioBclhS+LfSLQEGyizRpzLXVVLUmXDFJtQod3lBYS9T5qvAirAzNaMN7zqqj2dI0jVmkyrUKfOlKTKSORZiv4qpREUwOoqooUNcSVRFh5xBOFoAxC31VkZ8G5eBU2kqNB0SsmDDZeLUla/HMLCl0rU4uKJ0Wzs/FbFQRxRSwEKQwR2gMsCDFsrIOM8CEHoqoGUuMBnIILRJZGlEqKJGl87CYmI0N2o4qSQrHGIynhEZYVAFx6EDPoEB3muxYm1R65U7UplqjCxd1IAglTXySmDQlcuUyGXZyfVFXVkKvlmIVIilKCfF38VevasW8MkApEctSjxpgFlYvbFYcgIiscaGyUqLEU4P6KwbuJYmGqenESmMqigqpRWQI0sEesS7RdCPebE2wkkqLxbSiCRdZ8pTezUnXaOKdeg8fy7LwHVXrtTCEICLLyeXURmsLP1cpqhvVEqsfKtOVYCdjsYiE1qwxGFis+QY66F5E6B9ZtOwmQQLKbGhbIJXgR9LPGVyX49E9tNY89PAeYQzQPRhawmOpWBLJZRCSTPLYjIe00E63WtUevVoxgBnlabQZ4YVR5iTfVLuJSalCwiybVKUkhzD16EwbxGVDXTBUZI0gxXQYak4JlwinR0iEKg1IQHQIZaN0wRpTgqm+zc2o7orsORpxSDJaGEyhDWKISUg+rVehkGQ7mR2ogFNkcpuGMIzCkrkdPlKTtWw7vbe+ESUzklGRTpFBlTsBqZLyXtGkWXV61Chi2HjbIZIqbakWm4wW1qLBQvqwKcUsYBC1STs1whCjKCUBILxQ4iWjNIaqNwtVEXZASxY/h4LpDG1eWhmAA6h1eQ2lzyyoV4UiFQIh6kAkBKVSXcdGqwOZ5qFZ5QaIWVlzsZASHq2Y1xQtF0Ys6S3DqFMwpbXQQ0RbiDKLl1Pdp/CmrrYuZusQZqklQYECI6rUVCH7KcWXEE2AWLmvmBQNW2drEUYJGKmWbBqspWyJCToVJp7UGpEErQYkUURHzxiEkzWlsBcZstF9YPVbxZCrQpjKYi2bqqAFiivGiEEhV0BFCJvva+lqJi3LLFgUWUaSKhVASSQPuHM5Miul0hI8YQUW73CmdERIkFP2sc3UWrMhuUiSghA4bStEkrknsVCE2oI+Vy0j+TKgm3Z12uWOG+skIB97HKFooof3HCSQEUM4e81mWzVWKjBIk96s/TcQgUsGoiokqvE34N1HYywiZYKmAlhbTGsNmp7IqdGwkFbO3CC7UjejqEO6jlUkqTI0lkalt1qkVbIn06aN6GRE0juGooIlphLWQiyL04yUIehZUianekG9yBCumvq804O2eIl2p0wJD1ooCt13mu+obzQ3AHuXBLMLTmZD1klaQ2c0aVLcf1CL1oxk4BiG7ANYsXKKAsxAc1hR7igOdclWYZE9oidp94rBtlxLYKV5lMGtp7I5bdQp0VKquoHdg12ikuTQGHOEWcqV1JVFSTRoxyRmPo3aYZJhwpHGW1XdZi1MIQpeqFJDk6b0RKhjLVDpG+5/3n2WMVfOo7bOpkPaM4z0ZkmVCL2tb4WVVE8hFtokipw3yAnS0tYJU8veKyb4RI00TWCOseRYb5tSS9R2atNrR9EkmB+nXtKCDkw6GEY1BW9md1DMgBJ7CZJj5EOS/gFUuOfsLapRcrksviwrg4AIiDdyyFg0sVrkbcfjUlkzNcKtinBJl6IxkNUiyYkpl01lE3LHvIeVoy860ZnABUgDB1NEvDc3QlmfbXa0dSNO9oyNRONy0dDGwIKaDEZezO5vyd/iy8j6X1sLgHzlRtzUy0p2gA1chRukcMATMIi2SovFRy0hC8AOMcuaxIzKYJgp5cKdb/CzQEUMTCsvUAdtiqmV1ZAkdleLrvzISZAdKY1YRjFyoQImjObBqRayMX/St5t1a4IAFK+QLtY0kh0swd4iGRNRFoT5qYasikRJsbR6q24BlENGtBzWR+24t9pWbVPTmmw2sukqJJAVuUbC2JyrC9yr2hbTGNPyG/F78Om1Jbq5yDZ9GUthcOVGzETQC83aprUuooCC98UC+LARlhTWVGWjacFbBkYNbzP7NlhPDQTODknXc+K386pENuVmyVsfbiCQ3JROZJQipUrKJ4fFRumWzmuYDd1wyPFYJcbktTm1QOmGtRKRVrqT0CpAlOwztwgnOVa20TZN1ae0Ulk7B2e7sf5cEEZyQF6RjhgSkVWjUOmWue7OteLqmIwiggLQhLFmbeZdCSnaoi0BLgsVYmmT6BTG/WrJGBEFNDGoJimaShsxQ7v4pPPUfbOJ1jiHDrUV10emDwJP6Yo+gTfPfZLqhPTI3CAtBsb6xzafLDQ6VKjhYor7ZoEEMsuNR1DBoeyFQUVIei5rjMXJWDWDRQQCEGGiVHkKJCLp3YOj6y0XayfaVNHUA7q4jgU1yoX11EPYNhSBCqwYks+dQrAbswnTJtd65mNC20ndgak2CsS10iuLotlo25j0ph1MdBmq6EY6zdRY8zfX+l4pWKyyZYo9ghQpNCqCnxs4XzJUXCYOLa0tUy0gzGT5hJHDXn0jU7edphtJrQrXrbcqsD9nr3mjy6TRoT07m40Fu+kWayWoMnmQSPekKLwhJxli25QtayTn4laRlcEpFtIFhjAMKTqn2FIqMYoVZb9dluFRXihhGJpMk/MdJsQBBVnhskhorAGyoCpKPW1J8KRss5I1pdE95jn9v02lu0xkjgJpNCvMhEitvpsynCK/2iRb88lq03SnZFM1oZT+3Cl2UYdqNF2E+LbwGUstwZuXPh6tocH6Yo0KcCbOxaSkhZgFOrNCQaCEOsam9x0DyVQr2UwpvcpYLzUyFyawO7eqDdyy6AEz46cp9Z58hJ2mXNuLoaaB5oVQ2hdlnnLRcgXMTFUFPB9YI0aZh7L7DZPs9GRw6ywDr0gkFPS4ZneDNoH7WlSPIhKJyMgxrLyL8zacKnMYSW69gQyuZcU+uxSiIAqjLQQws4iCvW51j9LICehkeNODVkRiIFPEuEIlqkhaqWlrk9oGPFvDCo5mdBndeMqsVzVvDY2mMBXflXGHcEeqgklTaes7O2rGjG0gOVzQMpUuNq2ZbaIpKE+dHduiTrW4SsCf9Oa+CfYUsU1iZ4mphulikkpauDw20yyILEnfAOxJCqgEZB3KjtwIupEbZl6qlRhEsGYyGPAyZM5apoXUqHtbMzajb5lX2akyqVS+MUY00qZSQEVhXUTla9HnxCnVEvzFvYyeUeBK+q4ppAlfRlOeuVVW3rYti7kAPMhSL1mlwRAyhteyCG+Chtb6f5vQ4rBET1CT09KmYZZcSFDIEiRowEI3Y9rMfZp7m8kfsyc+tdyZMEElWjkpa5tZp620gTBlDkUgawa5ImCYzCZVTWMYFTyRDtnAurSJNGMSNeGrKqtYwRSYq2VTaqOCyuR6kSeDYEOgQaBCgGta6KbYhtKRnoNd2AvMdkqKjc5AI4R1MMyZPmLggZbefamIkmyr6zWDKmJm0dk6dgQbBhAx1qdiFqbwXtFM0iaP7kMAB/dbGzzxypkgJMDrPdsODRL4qg0sTJzUJnQnpVMddKwsFslEd45jA+F3BWoJNrRpBpOhKAQP5pmIuBFMol21hANF8QtxbyR7iSwUVdF/R01lBT6htigLIJhAxdrZTJ+UTgvBSUzITpzWFut8kkGaFvn0XFnXWqyRWRoyBTmCSbRKoRhQnsTkYovyov4h7QmRi6wCbDIpS6oSxRbJZ+VSwpMx6SwrHE3bSTwJbEYiwwXETaFURVMCBxmnpKxMVddC6Gg7tvOk7fC2tCO1k1hGNMb/bmNkSBGZqww2pKbWGQQnhbBZTAuJGrQ9wNRWQ/QIYzCmzK0IpJVOqmKWa1P06qO33ETxhnJy0aFgG6GzXDzcdbgsJeFaSVdGvIyNhOQq1OBkwgC8yLw6VE1bw0oy25QbqgSlOdLHWIABFemEUeopg2u7linQUCYVGosshD+GjtnYBZhLNafXXmr8koeNQIw/HdUFhKObsJYizdB7mGyRQ5Ltppkybg20EA7AZrHBFPEyskqsGi+KL4iF7apVAJoii/AOjKZTiSwQgxdBsaVBMpqTKzor8jZP/rvnTElPWnzgMgu4l4eVbrrs0MgjLkXyQsaiXE8Q0rcJD1itG+5k2VikY1EmzBDcHSLBe8eEiNQjrkyBK4ZpKMWkLOc1KdGa8xAtKCAqWAnPSBlCQzzhFxWj+dClWHRYgJGVCaAJW1Kiuba0xnUwJSUJZppL2YakRsPcagssrLBkVMU2j+LzdHfyP/2XIWmDXCwtlg2GljDhUam5tdhWLfOUvzeymKxexVNpNhezdJOBzBCKp4hXIEyzrs5ZJA4a4DLzvinlVrHx4v6fw9ylCDyzI3pb1FxpCBVmbvRPm/DQQcpErdlmst5FNlamXIcyU96KNp4vJCOUJU9HPoS28wm0SWOgi3LQ0lqu+zcCWiIFKMGlJg8XPvjVyOGRQh59R2PDBQqsE4f2ZepFctEhgBKncHvg7qoO5GquDPl4zRRKB54VCRIi5SjSs3gu6kkZyIpKma014RIZCEeUqZo1EQ1IZNZwX3LMiuR8nzI2UptmDYpq6wHUan8RloUFTMcKijwim5ChDUpVQ6QK5aXC9kCeGYNKkDQrUUrNSndC13CrdRfLEERqLaK/pXwVlUyACUMsJSPEC8xGG9pN2YSkOJ4mXBSVRckgBZajyEFFoNiHVUHUQgISBNV8UukKt3AtzpfQCjGnAmPirELQEnkgi/RszKWKqakaQSeM1xFJm4w81URQEPouN7ikV2mOxmiqhmP5LfF70u0krsRTXBqeGQUtaa7ko2dNhqbQqVyQYRraOMGLMEgmnCXcnUDX4IFJwnwR1n9NolNDY5bWQeM66ZM0oiqJAS8pAKoQBThq0oGuvMddqQ5ZE0b9M4GlUrGpbeOyzHQIsXitwakKK6PIETJJrQMDAj46uvpAcEgzU1Eti5NLQyRU6xEId8ugwrkU8JJkC0kIcRmhMVPqNSV2SAI7HzenICtiDMqIuZypQnNpoUa4Iaun8qi1M9CYyrxY/qfXdsqxEZa0cE0OoBJ0bRJoAqGXCma6UQmi4VZDOUOkTBmIcL80MdWsTMyRQ8n3WklSnK4svQlQicK24EpSIaNyYAjTkzYlkyAmaw2k0ixvzdDUYelSo2Q0BynYRgAAEABJREFU8XVYeJFNUNKTX/l4GllqRqip2U13ZNpIN4oo0iIb4U9qfaPWhHLUFJQFt/vWo7cqY70wA8TkPbat3MxEjdO8SVqlIUKjkihKrfoUxtumBKWWwvVenFARdEZCEhzK3tBVhDVJ+EKPDTHBdyo3gHJKIgQQ7xKbVlPTtR9O0jbULVeVzSC2J5JTjDYPPpKthhvQQcQ8D2W2apnaaBQjNU8EGayc9WZDQNTVip5RQBH++pt5MNZKa2EtkmcTN+gnE7+D1IbWVLtCiwu8IooRpwKm0oSI1wMiWik+KriJhWhCs+B0Ab5YAckbAG4hJB1sEDVBaBJl4ExxU0YdEtJTGAjnu0plN/IMGg5vPjc2feHLHCWIltKytTJibQYr7znYPARGjB4BehI0o/aAPrIvtCaqktQMtxd3I/mtVAuEq925Ulx0CBEVPTG+CcW9tMEAFHSOWrwoL94ERZS3bbRtdc+A5IqiQ/liAOQ2aN9kUWPLHyqD5Nq6ycyYl9hGhZoqhd0wRBfV4NqsRmWt1iRV0NOEGSVqk97NNlI9EkvqLGvyWlAOYoxy4eckSKURVYgwgDLlO88qHztJkFteQWgGJ7jZVsgKglVPUgzI0lHkjxxVoUpQpLSrS2Q5Iq2qEVclhot7c7b26IQM5hIVIAbWf3Aew0t2iGzkNAlaIxuf75hmcgOUMEWVQldYWO0eIwiLLtexbMyNBAOCFMBCSxmusVT+dviAe2V6UQxWIl0pqdQSmqW79a5oXmrrGQNczvKUxshNvVl0rjAGa4qSITUmT95+KhBmdEw+u0H4GWQSJtK0uvCkRDA0Hf99VWk5efdJnB1MRDy0Rqn7euc4AU01dKkVUkkO/V3xG8uiIcI9iRdNktMMYAS9SDaqsio8ainMrRaUIyohAIEAmmKppgaFZJBy5ksDzFEL7a4cRnLAghd69MZbNONDRI95nVP9SRPTgkS1CAUgq3l+Fedb5kjWTj81ztkkJkkBXBhUka2pi7WNsAOrCkgVyHvVFHwl8XoVTbcdtQZkxjKnL2Prs681A1+f1qfqCoVJdfimRzOebgwb9p2WywQWMZm0AtMcGGXRukzVCH4JPn8suaGXskwyWcEdIBkEUXVBCfmQWNlkzlKGpyfzJSWMQ0lJZ07okYEE2OwKGcoaZV0a04nSCtUA1zhLbyPxpPAkMKFtsJmQUgVQOV3EpGUaFtiCqUQ5sB4Vxb+k1GKb2rGJ2w7d+Dz7snBpapuxM1dfuq1f2tCVO8aCEKsd9hmtgeT9eVqy0Lr06ja6eKtKyMjIiOISYeoo7oXduIk0pNWwIhMKnYI8DJGSpugTqQJmia1ixjS8DT531NqgyZfTb7a25PQfmp1Cp95107EBXT9eK++Ye7lmg+8IKwh0BK9qqK5phqIamVOxVROJinJW9CjedGxrjXuEZdKkyORoWQJoE8BYd2yg8jtrFoypFmWYZSEtukCy0a9KYUo0hyyVoyLpdeqLdaohxiL8SoriSNiQHtkytQBpCcsBOM8LTI/olFPLnmizd1/YmFSZkNBNyR+WO5vgjHqCNTEUqUw6dtpirWBdeAOhxqixjVwACsfWd3X8C/3JqoaiIEdVlIpsmhqNZVuGbFP4oDZXMaBJ7A9rO0bbBUUyjcXdIJYA+59haX1eb0Csc2MCwotfKy1r3dh2p/inRNHaFDr9g+aEMLKGSXKEeJSiHarENwfTmcXy8C3mke7lFK9oMzSBuMSQaNV2UjYiVlPZk6HTYhjwHFBvyUHyhqXVdtoM/SNz4tYTAOWKocITHsXIGEpr1BGKWYAOpq5Du4ntSG5kNF1sGqZpUZgrVxuplm0qoS46Uyt9Gz3mlOTtZuwon1HM6IRzPWtdUCLUIiYxTIBBKHzFFNwzwZL9Tzifb+bNOjqlT+FK42WbELiVT2BByr8m3WiqDkpyaewVT5D/arnTRzWfSwati1J83URr6jFN0cgup5eaiJUKTADJ6DwAfuLofwvKBxjCWJaBZca8zXnwhmGpyhSZhULPkaSPh4CRYSmZQcqTo6ZoViU1isFRJE/w2CtmpsuyqKygIWP6rBbReZGxTc3cKHrrJRa5pqurbKozk30py2jB2/D1xNLBGTVN6JvWzQwMpRgF/7L7u4Ffz8wu/KaaCIO0yrQ6qYXC1Kze26aJVPGvcL9nCxarEzAaUKWFXkLhSqPKgqCkVbA/QPhq5h2LpFcDhS3Vwi2ghK3VzVUcPuuyyHZgpAxMKr1L02hSstoXFaE5uvU5fv9Ht7PRfhvTzqLqVeLJVHryCw9NhGvMZqmGTK2FTPtSMi8YS7IOlupzbEaQMNCXTjahc//hLeCUmATJChMB0LJ2lvwjlh1xQVtkZzuoFEmSJ+DWPPg1biqxtU1SWChbBuZl/aqqRKMSDNtdigGsFqesPwo7tKyMLFNEumnvJtAilYCQyDJzFo52KzZD1k2p9UnbRjiPaoGzfUrvqhIiQ3r6xqjmwUykQRVrPrRV7VDrRoZkmcvDk/47bLG+NaVd6WYaUmwaoQJJSdKHJBiDseatukxTTSvdMposkybBlvUo5hpW6NKLGzjFIcLn7Z0O7pmbShuFiknDcsTw2X3GsjISUAMmsNQCBorqUR4JZ/bHbPOYtjUNXooJoS1DMmrIttpSVESfwI7bdhrvUpqgE2xJMvRS8DWALJQsXbdc33jKuIAGbJQ94bf7dsnHfaPZEu0R08JetzRjBxRMEyW58BY9f0MKulnqiQx+2TUst8x5OqNWMiBkgHQXK07p1Hk73JjRnWE7Pk3C2yWdyEAUUx1dGazOim36kKWYw3DzcrFBjxuxDUwEHFsaJV3deepJ8gQJi9JcggxJiTRuV43SpR66rPHbbOYboQVtnht3lmwJuU0WYpXTS6yfrGViTSOWTSazmpociYS3iO4lBQjWF0U7CCcyq1TQ+c5gh2GZMiehK4hoQtlXhJRIz42tN2e9aLRY8b5gMJIMBEYhK+E+fN4igj6szKCtWaPEUjACvrgEExjcc+gJIIjkQuLy4u8SYsZUfcre1HonB9npoKRMxEosBanFnickHEWfEZYmSnegrTWcVU4Rj3iG+sBIsjCJbEz5ogXPGmwbs2x+o225ahQft6cIjQA4AZQ110g5ojBaeJ9rh24K/LjkvA3GEYmSSPUEqrpUJ81azDIg1H0WwivLUSlFRO7EUsMqu9bKy6a1SSqjeNOxrnsiupFJtI8msTNyZ2AjmDYmqrFFLdwON4OhWE4xtxFaBXN0p8VBzZqpwAgH8Co+RrJzYOdxKEHPQhXJVMI1MiolozPiGbGVbXYvY4BEM1TKsjq7ImcFEphNKMI5eVvWsO3TPE3YtD7pBq0tG/89zfMfszwZm80ywTWHjpRBp0FxcJNbTIfEQlwomPbNZpr+QE7bLafRd3BPXeatSlRwVZPsjFonVbMknQhe0UXKbVWlpTa2uJWBlBbWeANb5mL0E71BC30wNyQoioy3PromqRHGpqJohZ2Qzdw3o5ULnDlTsaYrgxVSC9HGGIzCe7NJp0Zmm9YmgzSFyKJkcFHh4kxQpoSINR0WkvRg2no3LpsIjlazeU2jeqmpSmsiQFZFRFEDPHfzpc3BZIy5hyuKUcpoWG8xRlloF5lMWZ9O2WT1xJTShqo3RtRUuLdOhZbkiulf3CLNNmYd1LMFt4FkNTjC4WmVArGEkVSqQyFaCUkUza9qLghAHK2C7y5s2BUyFG6Qhs4YWJ/qQZaihOs4TddST4qrNqPZMjU2egP/HhGaAJqbDqZHStaISiRAFpyGUytazDpYoUbWY9OCexxv0IQ3MoP4ZVHhF+PQTVVbq4uBVImKsAOkYjEMoezgRbN8hFhCogiqTEiX9DJDF2gDOhgCOKJrYDVrji7JHUmaj4m9aohGk9oAjTO1izVRJT1Boyufgm40omF0KSlaMPpCsTuwYhip2IRJIZlJ1rZlo7j/mFNHqsW8Fe1MCH7J0GRWcW2GJ9QqA0WKYWrCjFsGgjRFVnlmpWcsC+gmqsqYcYEJvcfQoDRbtFamqtKhFllj8YWszkWGhFyqJkMpwufRlEskoYkpxmaMcpqeZNgmfLOORNtKpbBSUoQ9sWHYNLSzwrCIzmTYxUaYMx4BTetkaJoycUPpA1PlRpmo8gBf5AOR4sXnPoqGCxqE+MHQkN28y+jQnlNHI7x1BJQGdGAlWQhjQNb7vcjVnqDraHye60HnklNSRQvL1xmWTZP1FtAFjGxFoRyNwkL1DLc5N7/BuxIdVQNUOm/7SqrYDmBgmSySM1s3Z3uJtwRj1xT++GaptkWwhRY/awBD+RSQJSXC5qWEQ2cOiYpiGgnXBMxsGlw5qbRJTsqPpkIVi3AK7YgKC9qaclQYFY9MXVM+JMXEtJd0Fx2prFiyQDzFtq1hVh3oyyo3EYDpaslVBJBF05FK35aNHIsnk7JULAovZSTF2yYUaZeuqQIFDZSsIYVSoUxy4wcwccTFi4kWKWCJBdIjaC9KeLizDjV046qFzBDMJcvQhRIT6pv3sBPzYcKu7aI0WUWa2sxAHRG6WPNWqaNA9unVGzCBU3lwrihBCm0wKRByLzOp5FkSui2TbadKBHwgyyfJHc+dANkprSFRha6yaVBy6C7D1ctKdK1DUYQkoaLmDYZhFoRKYN0bHtWhPclRVzdDa2g9SyOs3ABFMz5YmVqDCFAovhCJChfSraNbKcgDKoVLClb8CIiMSatL60TZ4TtwSkG0GehICLWFTC5dQpsQpoATg0XrZA+2DiVCZJb+W+Dp/GdExKkEM6VgpdQoKTRtXSi7MCW9KP6AanAMHmuxcee0javNS21JUAntd7qtxxgNZUFuUQRH2/Ce5Dl6j9rJNHExx+pPuBKZWAZFYBEdRZ0W+wQpXDvkKtDu6KvhlmuU4Et4Yhuo0pTJ2jBYwzZIWQFFXslIM66dfZn9d7J7ZRWU6Ca4ZaxzenIHFGKtTF5N9aT7RqLEkhTAGtZfbUKpVomqik6aYpHEql6a9JcliFERwyUxmUwbLqdLn0c6OzLvfrfLIJLKAevJrxh6pSzBbMAUCkpBIiMrDLl+VI0m2YrEFT/7qmZeKvGohJRUlNCLWRcjonAsWUsN6DI155ZUoe7JukPqFGohj8SoGGmoiYROEiYJ0h85PDxdsSJJlKPKREgHhLs4nYYBVim0mHyhq260OpjmUbItVkK5AAZpk4gKQdIIsFjFFFFskkHfwh2TG24oB7RSm4ptUtYl9G60V+wUFJUuyQaiyXpkWItEWVVnRcga4MaYmbKWxbDKWDX6u8zJHYrxbzYxccYwXUBqKjgLJfw9VMgOVwigGSVz2ly2QODanLrhVsnMbNOGThCSuyZAixMgjIM5KupZsENBLDrCRloyVcZ0lE7Zp5qmNI4pzU6RqklXQgEVtITJYzJ4onS49CmmjUxtrRSSlg1hMqvyS8CASg3mZQkJmLRmOmmpcl5Xbmy8FeK88HUAABAASURBVPFaov4TzdGtNdJYvRIMq6qbT+omUKgopIPPTs3C+nq+XpZpOBkn9jHJMCtYE+vaG1ppa7rpMSHV1/z7cB9C00UJSCYeCcmlfJGkWKXL1M1Mq6L4pcsYGOvuIRKFqtRkrlKUEYoxFbre/G1brcxnUYvrbVamBiNwJqmU9RQeAB0VKbQqFQJcd3/GotJ0QGfAB6XLLRp8OW3zpsInzU6ioVnC8GNKXonQHEA6AYkIBSGVK74+qmcKUcJDlqTmE1FtwDh/QUYs4rzrCYOaYdqQIzUtY7oEi5Q7uK5rYKXcG90hQb1jquRXVBmSqqEYiIDTLULAz1WFJN0yqYn0tD56H0/EpyB4eqtJRZvV1MZkxZnZwaCqdFRLVHlm5JJkiIltSqcFo9J9rhhpNGGNgxD+cL3Ypim3qGCBYS2FbqJAxWrIpaA8+IsK2Ij0TU07OWlJUpJGtheJUhjoQ8gRv1ZhSEgIxcFbwRnJZqJj7ETtVNPoQU8VPs258Wp0xEiotQBpkBSUSnULHq27qisiU31sYrtJWtQq3ityAtXF50tRQpWgQ5gkaK/Bu5XxG7YuAY4lxLsmpctGXU6i+Zw0vI9F1LlKpYmYtobehBaozKJBKGtrNjjYY8nEFPYkenNhSKWZkwg7AjbqG+ZCYSQutVVJMP08UDD+Q1Ym/7nIIhJijxHUZpvskz639MlUJ4HAqc6CSArtK29PMUslNbG+S0UZoHxNU9EvTEFvIEshbH22CLZaW6tUWkkxlynGTrp5gDortSQ1U1NZN5Ad5Aa+qRQnZxtZ+A0mNlZTVxWBKB2EYlVC8e586S2pHEikLMxJgOoVOlwY8wQlGlMXyZIMW+9BF1AIRNMyzWFFfmH8Bk8ZUiTtRlAckoNOEOmZC2mrmhBKrUbSO5fxYWhaIMsaa3QdsvLkqARzk4kolFSBzlqwexG2NusCg7SIPgfbgi3V3Lt4U5+QRAkC9ZyiejlYk82jlSuviDqU7sZgJovp2nT0HTE1Q9+ksRCEkEXXxsxYFmexEEMTa41XwAAxnPmqBIp4qOhiRXfRXmUSJgkphscHgeyOHlDO/e/B8mtYsRiS54KeRfiyIFOiGIBNi7BJUc5SlAtYMwlNryTqeX339QaKl6rDp4pOGCL0DL4qohat+Uny+QlQDC2n9Qhuj8UNmsgHBNCNsCBHYgl1qeyrJqQkMiPclthwr6djwKKK7o1nBNkzqK3uacn0GQtubUwJlAg7UDGoYu5Kl6G0I+iKiWnpi+y49Fp9M6vlTPQ0ZFMq0mDTaFOIUMEbJfKBla6idnVCMzPtKozGQfl49cWn2ZmGUK5p3VSokyAImLC+OEskQK1tmIgqQCAEWF2LWWK8PAhBRjEkuso1RhGTMu5Kte5UHiRdauGWKEuBTBVXJBW6QgagVaqhnUJZuo2mMSUYErtzVeXg0YGNKtGXToGdmDc1OsME3zhToNIAEYRhsVx0iVoqZi2XVtYcLR0km49y4gYXDZgr+J1QTMCOtieijLH52pzTs0Jqbis3WE8FasWe8GRj7ZENfyK16do3ajtJy0UdNtUuvaepYHU4FSRbkQqYmWJqKU2qWkUb4VgLQqsU62uduAHFb75hAExtE9EOJgLec7T0VtnExZzF38iRekSM/2QNqGKz3kBpKveciHQB89TSxHsECbBAEk5JU2I0bVpUTjoiimeoQTh0LEOIc0Oauwzk4hnJ5Gp1QaNRYDB/6boWaBnBC1Ad0SVTbas2N3FF8Uoqa0MqQQXLkpqM3YLbg1lkiIdSFsbLSCpArLimASql6kLEJTWmjH95TpEWkahZJIRTwmostSxj8VEZBUYxOsbUFu2zk+BWwr2KSRHhAhUDuVSTblCt4tFE1FrCVDkVO8yeyAbZaWZlq7j3ohUk6cHVvXFEvfoYQs8xz4ilygmE+2Qt1qs17tMlwyUCYZITiqw5vUlsIBN/tIXweaNH1+gJW6FXVRRCIDwU6y+gaZEUMgUnx5WNbMiilgXFIrIdbMky1t41yhybLBpb7ZCOEGEzjFp77KIS1tCmapIIWhfpkp3KWTy8kM7K6+YaSYVVstaq2SidUx2WKtpTFQB1+CS8rU6YT34NK7NgzvQqk9oYvxh3OiHuUjXyEbapmjRJdqKNtDkyeZa1hqepUbpkm0FNGELBcbQdZWMtq0C5SzlkjhYM21K0DNIDEkCWFibnu1emVKqIWhfdAYUWrPUUKQGmrtOkqgyqvCRIZtGl9xFTJTPDlcn4AMVqQ5J65Z8YooFhGH0Kx7HaqpIsJmtYhvVkfI1rkIlCVRvVFtFoAhOIBAF2FGUR7N+NXPZhTQyqLqwvzxkIJXQxN4bHS2WEwgQyASM6kqLqrQtCYlSFqTZRAgODm1mnXVbSNpu+mazzi16aqVbZJMSSxeggBTrJTgebkiXxinXf2LzRELAZixeSIciYi19QRHpVjqzZsDyB7GCaqExkFe9W2SOLE0ZEJpeHFBlh4ix6Bf/WYqVk0w18KZJkMCCmQVMmxGQzlE9RNSScKWTVLkTVQnmthH8WnaSUI9NwnYfMtUQOTiSfRdrKWtAY9UbzyQFVJc9J3pizig1oqTIQa0BFZrssmwrNwqAlT9KYLSnlqZj/nJx8da1Nq74DNXLvRdwmtB4p7mrRNuo2lXVOEElBMtdMCKK0IM1WCMUifpQBE19QGik4kiR6t2L4SHKlQuAgZm5NUp3WS137tk1LGReULuhL6Chxy22vWX2rYBGXSVe2MLpnekyqoxoCweZayySY1utMu4KYoK2BdW2yrfqNJcxhTkPF7wqIXghEoRIYXsuoWBwLaK5ITWlRBOH8KNxvalPVkwitVZYOQZBYAacRVPLcUaMKyKYpPdmPTHwi6qJU8LiAQnF2UeNFITv8yCaJkKkIXJQcth6Nj0BPSltaRauVt6kxtStnItVA248dAJoKaKiklghETasLj4SQukWQmkZJ8D4twDbVgwlUaS68ExAnu6XspgsZVLNY02+qauA9g4Bq4ymnp/aIKVntHBLrgpaey2CP97AiikRmcS+eFTMjsEm08Yc4ohBZ9KQQi2rb6ltto6tNsBbFW4gmymxNi/eZ/TpTaClzDm5s7lUkmHBQocHtOESLlT1IDAD2rdRG35hH+RC2bl7mFBdhxFVokY0iao6JURf1IGUKQ8pU0iEIMtScTCdYZ7HQtVRtdL0OJHP0mFgsaduBmdUArZIsJB6JLZTTBpSVQgCWk2ECsRUxOX5XLjzjHHpugOao4T7gnNOUXR5qEHl8c3gUfSwlS+JxiqoIO6zx/syUyioStEgtpZmmYgo+GuhcOaiCBmLrXEclhgW5mSypmqpE8jUCTr9JHL1Ka0griBhP2RhVPDIHikuIx1jywhvG2jIVgxZTe4hhXZxg/0AUXxQZcbVczF3CQRp41WwzyaRhkhAIg2irw1Rxpr5RIVSrsCB1oXsosKbMnK6DoLRzmWYpDQ4nJmQr2kGJBZqwj6phfYWIJgGAhpWXqmwZ6UlkwkuoKBmu3KnUujXUJrwhudTLkmuZgw7QqkVtBjYYpi5WDKQCCBnBG40aiYJqq+hjFlpHVg7N0cFpusbOsIs2iy6HB3lQY4KErniYp4VW2JI7GeiR3IMLErJOkABvAqYaG+aCKHuLZtJYr5s+pr6YMIRqnJUMinaZaIQnA+XSBrEEhJZEaDN5VbrIJmiKkZQDKdZCjVShVFgEYiw7gNlOpBfXFzSFeaW4agQYsqSLOA17I4wKWhA09J6qEJi4SbYEHEkJOlcNKqP1ZkxPIauwuhLWLGcDoiFWwuFY1ycLsQcwqOEdWFNeE6hARLSGxCzhguJaxoPJhMJRZthlm77EnDkowgQkR4+5Jwt5k9bZFcvYLBkemB6UiDaCDimQ/fWWOTGK1RDqTuWqkrmNZp9ENkIVcFdMfsurlWKjNukqAPiPkI32lHenGSSuBpAMdIHMgcG8c745RaAz2PDYgsxqjbRhEZZYM3/yJJ+06pXNk7o2JYvcKWKouq27YhqBgilTUrJ6ibkhmB4wxq22uWRJjWAldJVW2LD+MIsE6BrOyFbmhg41T4sZ8lt0gK+kpCNpXlldtpn1D68NkuFI7yzv1aGsrc05GdWVz4X0UB7bRd17QUt4C4mAQbIyZBSHoDZLn3UKmVoxSIlRkQXmUxRlrCqEFEfIqYZNownAoLKnTMCTlQTjxIQIfyFbBPhs4QNJFUQ3FoU3K6ZKNlxT3bSJadqKSV3LQfnBKN7gi87UktdLqLsp2qSq1G+KJ9FMo0/BCfwIGSZuAMHS6vqo25QoCCXcGIPoSFYoA07qvQGbkEfY3G8drJHiTVV0EE/L1kIRUbUovCM2hUYr1QZ2FmWTUQd7Gm8vI3JlMAR8eJpbT8KhD0W2YqcTDLDVlEBW5K6yKCUzdW+d22GNSi8GTUIrCqrWpP2XB96mumluREwkNb07Jc0Qs1LESYhq8lZFpRiHOQVH0sFfjRwW36TAXCFak27WTXhCJOFC28NY8FSBB98QpQ64eQh/V2WwJkzCxDVKSoa2/5jNlt5idZolPGDWocxkR5kFDKkN6CJdTUXFUFpVudpZa5t6AGuvQ1aGbGnCtEGqISfJblCzlBak+1EElUkVSqZCxJTBPYncLGhzS16BEKdm9Fy0HrPBylHAinEWPRnPJadiBWYWN6cqTeQo7igjjCWIIeRbAKPFQkAh2YUq5d/+c7XB+u9zteKlWkRcTFQ73yg2+hGuFFGRJJMzHTmJBBk1h/pjCqSYIttEZzJ3Wm4UjLd5ZPjWayGyxATpJbI6RIXksHL2m6a/q9LBPAi4NKVvRYcImaF1olFv1GYLkcRS1JJQgyoigBY3S3LLkNdemSFaIM4sNrpZZNuFlgTSwT2teJNF+ubELDKsucIt07gkTEMsDCvMTTedTP8wWo6KKC9UGSmwZGCkp1ezIIf0T6mmAKqhfQj7hXU0Lf5VwLbI/9Q8sNCNidBNljRrXVTb+tOlDA64l4y0ISD4ptSWGj1mVrENoJqAWfXaLrUdwhQFMfdRLdBAI71qTWr07ayzy6obOqVekAhvFTuFnlNH70kVVwYvSJaF7jTsbDBtnNzP9XvJhfFSeUVtEYyoroxMqI2w5aRqVlGrbl5kU0fFGOG0RzammotiRg5JJzUAJEoD66tJ1Zov6sqEsNFCpxnmCUTQndCTwsLWlDmVkslIwBFtYRiWbtwlMoolSukUCzelBBMJY0RjgNiMLNp6OztSi2pQZZa33bcbHxPPNbz4kkJBQyQTSQkYdNK0lhAS31DMsKwUVPA5opYZCFNRCncb4C5V/KBTN94Gic2LbcdmCx0GGGTN3lTYiGnRfCUx5NRMJ6jVFPWvWZ/ETpNJuqQVN0H2yYIIFJRX2yHf4ZrbYjNqojToRZprIZkiot7NnzSa8MmsyabJ1GCyIk4dw8bSYitY/VpEn0NnYbhaJIvFUlkhqTqTkl6B4ErR1dyfAAAQAElEQVQOG2tgBDO6+AhYWAvVpXTLA4wkWhQpalUmQS1NGmQQJUmPJbRqurTu0Wu0mHln3oMJIrBSpCCh1LIpwA1z6zKnOSnqGZpBU9UFk3C6qjVIgwplIpsUNl3pWVKVIsP6YKoUlVE+dAktktK1TV0af7Uy5pVb2yJUTJHbQEG0WJg2usUfU+5Y9hEUMFFOKzIiTS1gIFw0sWloOruNdW2aBht3mWNKbcU0VqagtBKoIeKUTJlKretsdaZgbfKQDcykgAjPcGR0GdoXdMZfZEYYRnroiI2ju3BG1oCEAq3YT9bcqgP/Aby36g3olSpLm5J/td3YypuDoAMyT4iNpEmKRIqHoEhk7Ij/AaECpSYI7WpOEfxLBQMSiEOEMJus2GkqNbeQAa3kSC8usq5mwQrSLMEkNMLYq4rgqUyJdaJ1mHHYYrGi6EoMK4KoQiyVY8laoCkmVpMxfFat5HC4C3PA37CGZL+QtQliRC0WOSUUlfwREZ0G2sJVhjFhSPnwIoOFx5fBN5raId0cGqXF+mjTBOsONsONaZ8ULRMjgai2uCxBeTWr3jm7ovVg5EMUnNctduA2SgIWeAL8YZTQaKrCTW9j0gOkCgKWbE0lO4KO0nTLpVVUylyTjz/Sd8KnjJ3MTapR8tUmB/cLsqoDbWba2gC5I7RYKUGmFLqgiehkoFyfEAwWIKtEh7YgZZtSTpA+si8qpQWBYZqKkhGyGFQCQ5l6UKIbWQPjcjD3ADQzHRWNsTdpSJoRUsOkIWEpO1hHBdTqXPrbpi10SfcaGUv4EuGFkYKB5qlwGgYmmdkL1Gm8KvEIjldp2ejUpAQq4NRZ7T+FJWpsq7ZzBn8QIq5CY1jXrqUzSl0ZGZFSSlJVimLnfpIsPxrSVHLtnjXKhgrjYXOIlacUyaaxRto0J6o3+yZ2NFgvhMuWgY1iYzG12HBtxTCyS16K3l1smPxuYxtLOW2TkyRNJrQrYlqTBs1LL+3W26bZRsh2gOriFHLrMsJK2T3ZSqklWmgSgkTRDyPhsDAOhxZxsteawNZatSL2AAy6odyyWzVJoNSlhRqYbYjMLX4D1E+qaKPulV2kMsPcuy8tqaplYUFhNlsImd5oJUnjpvBExCatjTptp0ihgJDajuR9xxiYrDYdzVpSUH1qPFCUNnKRGqKhqoB4Ra6qrx5N+VZKtwxSHQzdcpZcmFznPPBlhb7WhEwCaCGH+OhVm+omO9mUXwwvxiyIeac9iAlfEBURqEYITdVvT/YAZclOWEPZmVQ6YwhqCrwXyIzytZWL8gKkoVJLlTWlvDE3dg8Mby5tS+Igop3nS5UTbHVnoJBNamNhYBpqRJYmJeSeOAARTqkNAWRl2qJMbSrSCpZQOtgUCy+6ZbOaaIdpi/QEN+GxKQcRRjHxlbI4v2gmGBonbN4G1FYxJCV947ZJndaQpIBksU0Ok8RUNXGSAWw8TbDSPVWQdDIhOxar0LNJGZiiGuBLpxSua0HjHKAD43VCBlZNZnqAeFKYmRIwmoUyAC1IaFVjrC4VykuKpcfCPdVoffUAFJuQRxtJATbxRrtFYULBTLvkkLEoGQixrJ6x5qw9MWwaBcpspTvKNSFQYZ+ppdqSjXZ5VHlExsK8gwrUWHM6NWkkvbAwlChQtky6c2ou4QyHuRIx7c2eUMmPnZQ1ryo9s8iJKMwovRTG/zswA8He2jZoO9KnwmSjV3IwvVdNkYSOQkgmFTMNs2ChtYge0Naoo8nLykWztSS4xljWD0o5z8hRy5LbobGdiiVaKmbkSJS7aSCSjQwOHczLBOsQZaqgWa3KtKR3UWoM7jWiUkRSbbHNrFOoqWhHM6HJaBqTYAexU2losMlbG4LMwFxYKulkMBellqpVEGZLtWm2Glos4wBvKMivVU6s+GoyNe10JNx/Zc1Tuoy5Yek6oIESuGFrskjJ+p97bCGRBUQbPi3ZB8wlvEdtBN2093U73aB3Y7Rs4vxTF7+qJDrNZjVJ1JhjDudWIyFskrL1JDR2EbXqlZXwbFldRGGSvVw5lsQML8yCRVCTdfav3nJqPmEoCyeBWCtnUwxgGxWzb/4zb7YhXhWF//6KQRsm3HM9TBOC9PWN/cIsNABfuxB1MIzsm6EQgSyR5NW2FQoDtCd8cBrzsmCOyceUtJteyblSYfzoc/zexjyPGYvr71bzTnEeRFoxNOYni5Hrws/ReZfhS6/RBGShjLlcrAhb11wqtJhSISQiAhCjRViCo2HioKFlkdnFQxXYwHdigUQoFpVFKMfWTHnzsKNLo7Eu9gRtR5gURki4yxLpaR4tBiRDynmhlIpEMXr0iE3Ghv15ElFRg07Gs0ra60bb0sC/CmOT0nU9K0oqo4/4Q4XlKqXsxqQbAlJfxtMIxoDWYpr45DFCc0tGt9AZzas736FjI+MPWTaZucjyH9Xf0hdywOpRPJlsZ2oGqVpJoQ4ZU2/WN9bNFGJz6FiMZKlG9ZJWKUAqYGlaUwNEoaId/YlMXfmqAqUI/kKx5U5QCHcRW0rcqlpFls/JkLrQJnpCM+GL+byptWAoWDaxuTSoZkzpTT0MS+pCRUplK1UhdZaju/dYMRARxKVgYmWCXtIDRtEms7HCJiQHkaN3U+lC7kR3VDYdSr3A2URYklYKPupxcVAaOVYnNQxUKWMqJG2FTiE73rtPQh+GBTmkKPaU1eNWZDYdj4CK9mCr32pRBNQZACAkE82ZomJ0Db7KySbl3dOkvclAn6W7NFk9M3PbqTt3iEknl38tsVMLNp69irIuqUo0g+2kbJKaq6GekoS3EbHoSrEk5TmWKjHhYMvV8lxVIF/021S1q3BJt4JWmWMSmVpXE9SsYOaIqw1QEdyL8UdiUlnRmkmlLJlzsRhGY54lmWvaQU4mE2TKsi19KWSn9A8xswYxUPlS4lmpQyS6rOA20jdqlA98qWVbzuQXg036WQnvy7RetG3oNkB1VFFHNgJVAhSPRSRFEEAEZCttu8GyCa4u5pd3WWVTyr9+tydzU8CnGpsaPUS8oQi4USNmDELaIkroI8F2mdLLek0KKoXxSPjq15EMpneZjMs368yFnikHrFeqo9qaIW5BPLTchWQVEYcbAUvLekJqyBWzOWQ4RgXtG3WKqbVJVS3UFuup3cR6NZVyrpuAJtDmtcOmEXP6vE0qN5JbNBNDSznIb1UT28gk6J5tIeQlsa1aBCETuLrKl0AsjMi2pXMJ2yAmZNOoKQHyPcjngk6ftoPNjj/psVHC6wpJhkxvLdFr0WSZD+8ZVoGK7myD1XIgSKWLiigtSiXY/h0+JJLS/6NVV4dm8hGuNUZmaeaMPpAl1Ae7NES5fEgjlBRZa62RaJRWbRK23cjYeGk0KlimYshW6/bWuuvUYBNEtFAFx+S8zJ11jOFZrkr5td6sS0kmzQpaCMALti5q1MEyYdtz7klxEEMReao4AJHOVFnC0oV7QVuEhWxAgi8GEAWBT5r8NYr3d55wxtmaofUqIRMeBinD0jCgoPfgbECEwyyw6BUcDpm3GmxPvPcqMrjKe1KuQwTGUBttmssoxQXuRQiZLVx88ZxnXeWnmUTjGnQ1d912SaalFmVLXnMZIVw5gpJlBXvfbi29JFrARhXRMQ6lT3Uz71idSxBna+CfTKdaLIJga+xgkHmZhLGTvsHbcgnS7sugqCqZcuHdqMELI234TgX5kM6wtaRhVZmR3ZzKpygblrMuIxMzJKozYHdzCAOg7HaQExgcKeN2QGFUhRO5NOLUWTC7jzFjng1DNtUoHGFZ+BDqDOvWs2EKkQB5b126aB8oyjf4NOkbFiIMacs2PZIJit68kQVtS+mKzhbmqrLJ0ldcZKsX1koRlmYnFsgsOTyQNGZEJlUJz0pViMmkYjBpSmZENUoJx3KY/0b+TlRY8vHXKSaI8Ora1TpNSKSBDaIzepE54Ntevo7YFDIlOjsZUqjnxn+mScfMfFprqibDamk+NgAD0t5rWv+7N6BXWR+9Mf8RnotGTPGoqUwyUVOQN7QC/TKq2edlTncNNRhlohIm3oU+s1Dkt/gd6thsfWeoAkGGd5hLTrBMNioLGKad9kffNGklKt1NYmIshMwwyQBh5saatalZV4goi6N6GoNUqpt1IK21qmnxxuZTTbQ3nTq4jVDFGhvPnYVJQW1QjLRPap10ctMpZPPoY+74z9KX309k+2STTza9tZ6lA31UD3SYacdGsWlglhbE7BELxJmmLL7Pw+v30sMnsA0Y75OdqoQ3FEk3M0ArZQb41OvmJQ6We2vLBj5VEiQ74/RkFCtjM21l83uoMdqUdIRT3klGrZKmSIeCjIpVNyYRJJtl5pXsket4F+tgGsxjCko+w1vElNFcJGm2mUm1IiZ+VnGjmQ46NCZzaMs0DFEvtgySK0oXMkLDFUFBWWO2LL3lwkBYBShNFs+YyYzM2VKn6LaYjCmIqGnT4mBShpjAXU+NI1OO5i7cDhmjNIgIQoerB9iAHn97rpS3zBaukZ58LUiXHAw7JEmjaXSaHsK7+nKpFBAQqRc11YrItX/kxGGrarktLI5By5E2Z5uD3/spZQc0pHF9RGVh1OO8ggQsi8sLcICeSTZZxVz6G7YQBtNjjR1QjV1Ap+qPGUiTglboGnNNrXYkuJVTqZIIKRZvpFcl+AlDMpCoUPFJo1mREF7LVdWtCa1xjWZ255MCmJYuVLJnBI+RjF1IYi+1FEnSJZwr6/5eo/vCGGitGIRIa0DLMmQaqq35EBM6yt4Gq9AcMqjRrKmqF8wytDhCy+jWGeuUDJb0J1RUxUBvNOdZC3xgMBCU0IbGSh9KJRuDSycQq5IO3Ug3KMh2aT5KS0PEjfuDmRhj7qv6owqyzqMTzheaLuY1EGgFER6iAlOQdqSESy4Gb4KuZs1UpIKUY33zTO6PVBhBN9EGVTfWRoMXg7Y1xMxwH8E0GZVprMzuWagUl65sRpNzFTKSv2PSaqUAGWVKeq0dRpMhlXnqAh1gwNmEeaUOiQYhHjTm1EJjCKvlhKN7b9hB4x9T6EngNIOEeDWaJ06lzElRMCoFi4ikpkRyToWoFAMBOVR/7F4Dkly7SjnKqxaYa0tqklnXVE2jXW7cXVzJIgxNhIxIoRCkfB1EVjlrTmaVWUrL1QcjEy6fVFpT/gjVUFLCw0REqxp1mQXIKiwIsvsQ1jZxQFEdyVlrLPxFAWmbwD5Lem1ixujFC25CRYoqdwjrS5vCIEImI8QWUd6wZc/ec2ppSVIdXhJ0poBLLZS4m3m37AIe5ShaQDUGwI0DoFI1Sv6rKSa4shd6PZZYRRULK2hRpZl04auQpNUkJpKIYtAp7MwSrbzTeoE81zbYylBZlRQS25UEGkR5SCqSZ2SImdAmpYpgMKAahEisOJVfS04iHQqIFLgchUQpxMh3E5ahiqmWrJ25Miu88bqiaAP/fXFQipvJUCkh7lDA1oNyWVKSyYgl2Og00VSh/C0ZpHGsfQM9RgAAEABJREFU73QoxayYmqFvVHeURKbQhk6pLSNt8U7Oi/88NLMhFRbVlmgrrwxAFQJhUNZ12oD36PyMFSUlMsAtQGfmQ9VFCUiX4sH8yFLcYipbLZvyjVTnkUpboW3e7JRN2tC6dkzinGLwEKSsMKRapvERXhlnMJoVYaUWNHvKJoV/sSJY5i5dkjDcwTQYhK8aSvFUjtyMfOKxAWyNguvTDOiS5rUacz7A87voP0C7U2WHcM9kiFKCUClb53cFO0wubX1uyiYqAFNaNhgOiRolIRXCvY061o31aYcPgsIbCyfHSoCcToMgu8lTHj19Whad5yiWY4QVaIUfFGglEqio9QUmI+hfUUX5R4bT2dpRRBhwZRI6O9QKuhFWVnGh00W1CggxFoWHcM6kD5SKw5KrJRXJnYoZVp7LkHUBMUqkE7mgmnECx13KLcMsVaMJVxfpiXUnUgAaIQnQHm1JkSHlzBZrH9uJJN8NQ9XdPDsZ87IliJMoYW4N0oxm1wrsBDekhUjSP6MnWviEMmi3Eg3X4eLBDbyGJcElCilqalI8AKnUSE1whDaHWTCTEIrATES00CqNHhQs3rbkzuwWDJJCegyyl6qiGFLm2oqqJT+aM0cpEFErsNIyqc2x0N/qtDhSLEouQNFNSnNTzq8GNOgkwmVZ8JV/gwhQPMJ8QaS4GLqooKX0MBmaIQ5kFX8psXGhV88Sy5wS9shQ+rZGkH4u5rqg7lr6DtDLwC9N+DtMA62K+UrYUjqSnksreWPdK2zEoyJMzMqkgICRNu/C7bBnNi8MUqEjlAlIGLSVlHpqMRpDWGVBqykLRDOpIXKiE/omJ0ajEMIl9aVejXWS/I0EFFNPo1MGBV104sTGpUQbjBwZlesy4QkHUpJ6mcxaUpOdda6UQ4Ws2RSoECA0CjUc8yJzKjUrZr1JF87ShK7oWoauN0WlRTzrEwabNhoFFRQX5R5jYvF4cT6kWzdM5awpQ1sfvNAazQkzY4M5UtbDaCPATAsj5RBSJKZiq5JplCxrGPw3hBmuvgG/5OxZcIcLq0DKgCK4R1zFAlkbCOi/aEc1aDll/eKiVMD2rBSfDhJfpuw3nFDLWAYD7yNJB8lgPDTYhBqwRhIga/GahCl9DtG1p7iwBc1DCYR3241dLlLZrtEKjy9VkD0kYtHISfCkCxs4CUjOSTM+a7knMKvOKgx/02XiohLu+T1aFcGB2SxGwwpJTw+P1bx2bXzkI/ebQPdgWjvaZlLeRU+1EGYrMwgnL6GzIyKKu54us8RWSD8gUCMnTc2iyXaKbashkShkInIE++wIeIvkQ5pIMjMOpnOyISC91TU24jvJjmG1yflJui4Rg0chBdACWQODLNCkxHi0DSlrKirrIHPFRNiOszgTzO0k0ksa39OF9wedoyoGA1S1G4OUPok1KdKlTL+A97YstsqGNbNMYzFinXNaqrtsXMwgU7XG7z42Zk+kNa7TJJJSydYGmeuZmipespBqmuRVpqqmiDZmKVfDhLaJqRcbCe9YbSk4S0EQqKCGbFn4dEVfsoGYqkjRJnXlXcJ6criamy/Tdp4GqYUkgArMC+YZniQ1Jz7AT2MklVlsYwwRJepKX5HF9pY2S9sKZjDDDNosO7uWwKQMzWoCmCROmVzWR7ANCbcKFqqGS7ph2cj6vwnq6excItJTn4xph2IM2hygFrKM5FjLpoUSbka1JBzAmgRh06kiRtEBCepJBMYUsU+4xJJsVfwKRbySkYygfmhGohUDYYUTsUixX4P0VAUs0cVKxFWKe1frWX9obIxXVFOt1EgoA6XNKlhhs743yY36+vcPaCc0IllpoSZsgWTxs4aJT8WsqTFfDDAH0uAi3AqJvRXhQAqZiMQc/nv47GsOIJ26CYIxL4VaagO0lTSv8pIQS0YCCSjlNoIFQ5AMy/l9UPWhO6U7TTdaDaPLwi8Kmo1WRX+11NiulbayboqOMK4sgTW0DlUguDNwAAW+iAAEmk7RlBDVTvBRl5jLibyo2qoiJdz26VG7sAU3ArSI9UBJW3WARm5YqEwONMllaRXorFAu+N36aBulEmeeSaBKlkn8ScmTKE2i5rd2j84AiG5IUbZCHyKZlOewrccsI6YQ5QSAiUhjxCisH5hwVEJmTK4dIvxE39nFlfXXlgVewxm68ZrzCzxZIiMGnHtbJOGqVGmxpDCZNIusOSg6BSjcZYOhQZaIyGUiNGgtbQzh+hY1edigsbkYr6apmyRXYvVIiAppTLMACadSOs2lM9sm3tQVIK4J2HEm2RPFNaR2p1GUzdBFlXI1QgTLFAspHHhcVR3oQfUAAt6MhQh9IBXoqhsTE5ToFvpbMbORYSRpKsdwmyFDcoUBWhM6ngtjxcJlIgTotCrFi2UZVcuyBlz0oEVNddsxsJ33YFBF+gxSTWEqXAKh76lVbyVgKXhSn1TmmCz+MMITGqe+ir8Sue5G25IBRrQjxr2yFdAQKlvwr36bQfUnSHPM5PGRUDJdxo1xs4FR7FJMSKrbNCi0ZtKVLJbGsFgsXF2Twk0yEgzCgQAkYmJcv4NvMz8ZxBQwBGFHZ5rL2day9Zhadq0pCUd3BrXSsIq0RVD1koWCYH1JgXKhiRTmq2bYrN0hmcEcqjbZ7GDnj+obgXB0LPE7ahHwsoK/txHLCPoVqBQTL8wG6zKL2UqZA4E0gBQxE/Q8iokTl6gpVKu5YqsYVutWA0gIWJei3rGULnwGGJoFQAY/Cs2t0VbpiDYPKmRQ8ws3ZBXp0lvbmSZrU00SO1mNcZVxf4Ut2pzlYl3QJFWB3nTiXjrxTLrJtG6xU/KmGRPYTB4j6C7TQibLpCf/wKAMM02tbyaZlEmXBMB4uClmsD95ySJWZSgwfxRFFoXCvscOUfAqqjeYTgrNRDrKnBl0t+HTki1c4CapXexJ150ubYN6AmwSJpLGgl9UltT0krGo8+8+vCUwI2Tep/Bva7Kp3Mii9EKGp8ppMPZefTOEzhAjhueiNprBkFUZMM+dxB+wBnWDK1I0l+TBvLeSSZJsprcCpwj1CuGcCqFSDPRLKfNPNk+yukKmFnQgpKcE6AXGo+iGydYceliuf7ZpTK4lgymAhCd9oMnoGCJbX0OUmoqpXDqlQd8AFqmtrt/1EoBwtjD2UoCmlFyraU9tUVrbPvGrOCF1SAs6cO4+Ik0JTyzYaYt3ICz14hZP1VYUwUiKQ6KLWDGTMlbbtZHsyIbZmOC1LWUNLVhCi1kuAmMKsshqi96w02FkEIbStcttIDujWsCKjGhF94HhI5lxoarE0nRMeDwqkKi5RVA4ZYS2ZMwRkVn88fQI6itFXVFMijuBk8YxlZu4qjcNEllgZLNXshAkUCOT5SrBZUrumCneeCp9oyeEYcELlWs0IoBE9mBxcgxrYQdGZRqNVYNY6SRFG6y2hV7BW3ea26F1eim2MupUguhHiDOgKngShgiEZbfTqlEZLbPTDMfW8XAsRCBRLFk4yp2hLmghpoX1gJAWbw4MpTUTfpWdWF8CsCfWqPDyEpcMzlAkqaFECvzhaYDG6FsbsDoa0h+FrxynTTD+HmguOiwZXHVTFYUwQLiWSloFyKRXFOhMYqj7EGytj9aEL5gw3aNjQB6p7r0JlPe/mVOD0PMMFiJN8XoVndJmVYUgNIdVqSbxO8Q5JJVcVEJ5AsyAs6eBGsxoOtba3nEmxRhWMC7VshZ8l2Si1Zrw66qcSGgyC4wmAYzGd55zRtNq6lYCNwy0gIVWWKQIiaA4ralNxuKhWFXBYLhGYGZdm4aUi+jEDjxt2jpkrEHljSeWAgKsXVa/bUoNJeqdZWREGtKC6qxS8NVhAp4xyuBdjTmQyUSyMa7DWekuI/pIdUHxrq7nUAqSWPoEPh6aCIA1+WVBawoOWKGSfayGyKzq2YANARq0QENDkJxWLYWHDvKOsXBFm3JDlO7Bx+VcvAZ/ykkddcxfQDGpnTQCQQum0kp3ijefIM+DmzG/72a7qRJ4tFEyimAG72E3WZNXQymT06VbSVh5H77he46+TSbDm1CBAkM2SVU4yscC3l/QkCnCdAvMzhGKh26so03KNmhhWGijZEUcAE3o6EZDKxtuHmBTWXJE8WKHqmg2MsCZ5klZsC1kz7SKpmii1mlcQmVpcJXkki7SxWuKwdxyFruv8I6YTdcKwh+ukyY5magy2qkeVUWgWZFSAC+YYEBnEZayatrj0hSkQ1k/vC8aGkMCCtFirdS2artSQZ2aEoaUwmUpda5lDkoyLN0Q0hbv7soJUP5eMmZUSjCEUl4uSlCMQVEGBRWhQViVyAIZKekpvui6FcK9PKhOqli1rDtkCffgHSFlnBW01VqJCKL0UU6d5FG1XrEySkxNRtcEwMoIIfaQCs9iu+KqBtHQyFoi0gNkAUp06lXUTvJzqDmznJOESY4c/1nGqNXiSqe4r3cjS4ylPLGi6HQjmRaYgoG1hAFVMcB2R1YlhcSWaEGd0TEClVFcnlnJKtuBdSpbelYj+/A0JzE0AgeIbNZayt2XyiG00dG5YKJsmMGVCtYO3Xko+JIoes3CXEinq43OO+U7AzxYTUIgBClFYFk6ii542hkvIisWqaVRJiSoMCBUDbP7iI58VbBEkGUlwnRmVRFhdFuMyKB0WI2XTKnaFWx5FhcJCYC0lL72CGilaCDAe6V0ITWoSXPnSdgU1TOVSeRRwraSCzx10b5MCGUD4VH81m0waYLMSYaYCyIyI4gYLapxa3NkiGWoDmHq2yRmYVap6RPG1MQaU48liDLLDWEd6BXg6sgKpigKmBqMQUFUSsRV55IlbP2Ga+5VG5kEO6WUQo5IjOTDMAHT4lLm3aSp6rp1wXgu3msQbZSiizInAEpAzAJf0QxkJRCi2QrKRBILcUgzNgRMCXghVpBrI/OiUIUPc5UMl9SxBS4m0QUdpYxgUWKBCMgp6wqMqzKjqF2CiU2m+dZBIVWWMFEqmyouqmYkpLu2pH8AKQ5JtTRtIjJQS+qje/bNxSpJlKGZFFdMdCBkg5ue0AyKyCUNolU0VylRip7dQKxTCJMvRNCAolUKYwhTo5m83CFdQTIiu3MgRovfjRkOZS4Wl0wrqHQTqw7KQiZtanQAw+B225hIXg4R3uKWt6n6jvIF13IRMNikIExg2SQ16D0TFaE+bBlCvRbpU2Jfp4uaTVPKRhlgVs+YtmJDK3qlIAKxEr2GCtE0SwgHR9CQslmILV1HEyflC4xPk4PdQQjEUcUnpa5igi7Me7WW6FZMhGMQCDwtOM0yNJM+VaJVUkgFQzFJC3zNuyprM9UTnIhW4MNI53UXLNSKbqK457eRXqxGWvXWYqewGehRmqIE1L02C+0PIFgWzJGgBOKwBWwiDE8oZEtNU9ZEbHKanHWjSPhgw80caC6b1I0bS1m5niHz0BQt0iUSzKFE8tm0hWgXJnTOWCi9slab9v9n6V20XNdxXVGApOTU3P//qWdVbPFx4XpxX0YAABAASURBVOqb5ZlKbIkEQZCSnTG6W48eF6Aob/DGSy51kRwzb//c8/m91ndB6fTb14ll0mJqVfCGRNfMYwesl4BWm1hocGow72WN0Cf1gbPZL+kIMxh1UV+tI6A0GA+6JuVftdU35l1sEvu23ebh8eGKWpKNwjIAioatNub3eI8S7PBP789xP9bHqusMFHjrTKmPgPb+VH+2BkIT5s0BG9pyfAcQJkNXn5OPmtc0pfyNNgjqqwfNqQ65OpiDTgiLss0yp/qsyVfPlDSYEuqsGAa0z/ne/chm1z32ADVjRjO5RA9yqnGA1FMjq9Wzvrbzuozx070q1f2yUISUMip+qMz77616ZKRjtPfuN8/ZDlWpgFOqDTQwSj9GarGSCeJv3KCbKYFYTNhQcwFiHBlQB1AtsYUNafFoDMVB0Jq/sK9GXNDtvRM7sY7jqFe8cWAem2QKnJlJBbtyzh9XPKD4niCMBBfXZ/wHKsVpEalk1VuySaoD83lkC1R462AryLC67CGf09Onqllw4MPzw6bEFJ0+nrmzmbL2yvHqWFA7UWA015XPcy+tJV7PBxJkPeDX8YRBwRhId27pbGNEV1Q51qrNx3Do5WvetHEIkXMTGFxj16ibAJv+kRIu5L57apX9fD+f389+4vpgX2ifP6nFEefbFFU1oLl+GUgOGsKhKvnI6dJnEwEa06VhMh1GOscCz8YXOA/rdjvv1FBlrY3Q5Zkle07TrCYb76GOz+c7eU/Tyv1BZgvnl3hU/XZDEhuF7d7Wad+8nv4kLrMlVrAx+8y7Rh+FbWAIS4yD3svmY2r8HlmwdsemKUH923lyJoFXTSKPJA764EE87ur7Kx7i0Yl7MhtNcDVk2DMkGkmJwEX/N/vjl19DiUbHwmjUE/31SXu8f/185anUs2DQMRMA4enr7KtIy7YHyFGsN3hsHdPIETqulDVM6M9XeZiHc1NS7Dh5df1gPtHhaXyVbTClo406BuoyKVJK2jDfZfEgywo9PRjKscbv7tVpXfNUDf4Giy751XV3E88iTeEnqtW/1fRWrp5QL2seojfCpTBzmGzqrZbMcHKoxDDbUzQ8jL63/yruVHRJe2tQFQHAndvappDpr5c1z+c8pgDGwtcVa5mMK0hoEMSMVKxlhedzyh9QeozuleM6nlR32jOLFqNrTys6wg5CHH6nqh218fB57H6urgsMLDzX1IfqPNG4nvE6mtlF3mOZ4g3i4UDPptVQTkE/Yjwq6RaX2KD0QQ1AOHt3RmE6etwAivPKlMHKV9KNNNmQ+fEHfjvGmt4i0KFikcYe4BkmxWW2SafqH8QQEAVVniN0vzNf1ww9qFZ8dp+5dWY/tWpCDKBlOa5zXednZqtvtN1tvyPBeYsR/1VT5wKVgknve7TGnIeWse36+HIuUu9cVvaZihfSVEbnJR7La3C391vby6JLFYr3/5oCL+SZaQFGzRyFZ7rR2ryJOnbqzS9h0myHlcdIIr7usHs0t0Bk+JGHu9SgmCVGKLPE26h+9OTYo4S0a/rukzUjvhlnzVBCPtBg4Ga/+rM5jXrWfD/zKMtGB/660EHpCgureek95R7fwn+FfAa31pYd50d1JvJpqGBG9vw2DmkN65nRgvp7Mxsj0gU2IDkpXeyUa0FxFv3Y5+bP/QbcXi122oAAiREYBbSixvHgrccnz1RCbl+nx4O23cb8qfXVfGOEuRkFSQG3Stziy30rN2mleaEl+Bv3775/Te0Np/lFZFzQxHhz2x2ZnMlW5qQq2mJ8gM1c6jXfxJf4uilrKnlpemJy+YB8qNZ7Ybn9cP+j2c+0J0T0uc+TD1rcp4ombHwdOQC/4s+BgSNpd+k9Y//u/UTkTB015i+n1xhB0Z95v7OonAM7jNK10qvkPuL6kanWPzf/x2uvD+UKz8XczvA15fnUMzUrsP65/3NeNKflnvuaR6TDHNt4jV+EQWNHMg76v9ifa/myMoCUm+6+q6vZZqOqcGrihx1a35Q7muwaobEctOGWCoiZtjrTj6gBU+RdCz+4V93yRhk3UwruPEeflRjAa6j4Hs26gS/8wHQqTu6sa/Kffy/cZBf81mi0GonZDR08SRzFjmuVX+nRm9jLLKgmkpYirqfAxOJsMcYZ76Yk7uNWMXd0Dq2vjWUe4Oq3RiV44O1mlXP6BQkDStN7pjp7Hkjm22KFmYEAmry7tbVXiCeYjkQ/7X4WD9k9Wt3WcAnMDu4xGyov5zP0WmhLiMIapKm2rTcZXAynXgq8qhNJ3QDM76ovZR5S4mVtusquaDWo4DinnRrZcMGVsu7+3jw3xZoOfzwyVvJCCyjXo1KiFxSoiK7kkU9xajOtHcvcFvf+5NoD4xgUr52I/2YpXNbjKBhysxVGyl8KqY/v2RoMHIlEcyDudypof4aFoc+1eS27DD97XbYGnr3KQjEYoViUkkGcMVI3j4zXdxVPK4ebW2bqoBLH5lzP6Emq+EXvLmrNuo1t9Bq14c48WA9eupe1q/+sNozl2JN2n6UgQlGoee2pawOf3dc16jxFLTQccMye7f1ZvhVZV6UK7wUGLGBTNEgLA0v5v6ksjbTaYnr+8fxbGZdahPu4SSaQOK1moXyU3M7fnN/D5+n9/P576moDP8mfLnSNekHttkth014w/kYnfwoBh/3ErR7YLNAIARR/AiSH4Fpw0SyxKESh85MmDd+UHuxozjyjNkXBRQ9GB+YYbmFd1/7w/3RvxxdwDnJmTlZSnwewM5wAfor/V/yXVuaHUcayprdTNeIwoFtQmZqCljRfQzkibZoje3UO5By6rqv6+WvpImNoaFb6A2uzcXGnJrrRy+EpnKjSfM5sJ4Wyhe5bc9MfAdN0H0dbz9yr55NKvlWpHkXnVl7hLsMFsxk7Eg0N0tpc/U5q48u3DcRsCgtqpoY+6hgRB2JcE/8IhwxMLhuVOMVPi0nAR52tqF1tcTmVlykZ83SO1ztu0gavHxK60noFZq2xGLKbZ5T/aYyA+3NcYp4RsjI/LvkDNu5JMSEy34E3WsuqgLmwDuoRS6erTAY9GBCzUHcJg/adhKEIwdIGufr9r0vOLABv8dEOIkPLvPoqoQJs9lS7oFuPZiv2Sd6pBJNCHyyHDMpM5jUyYFA4CGE4wKM55hbmjoiv+ffE+UYrnAjx1+rYCrfBDvRl9uOx6aNcyA6GU85qU3RYnCBcCOm0gPr4xAPPEXE8Oefuecoau2JVuD7GzEXtKFpxqPibw6K/rRpS3yquWT3eglmQ9pSIHud4yIe/RhQ9qopdeyCtSjQywgNFDb3s3aGLn0EWn6nb9IxV4eWG0t0G5ayZ7/BpGyNEZNc7+sgqdSIwqyCl6noqPhc2jZxeM+424Tck/WqlgqoSE6u3ak9zSUJATFltvWbkqKE0w4aOkTVxZlOTo25wVBq6IZP61Y2S2TigXMPc5ZWaYlYOsSzRbLOP14b4G1hRdR1NDki3d4KGKyvQn9aOR0tHhIcF+Rmor3mZYrj/9wTyHuvXMt+fwQKPsiDqMcQTdvQ08mfrEo223MRIdHVW4t3AtrruvBuAKEmxas6ZPNkqBPemCAWm6CJYMW01ycnVCIXaKhc/QyRF8H6zWFGSm3Hsjx7584vcCkP4VHQ1wAkeDZb405hBM/8ZuM28JJRt7n1tTR3qnMwPV7Ebp1WWwJAANRpWXF+sG35M5ytGm4Fnz739segOe3wdi2d25Ye52ISI09Z0DLJiBqd5vfXiJO3v9EyPlm2RADvGYyiMmsBuaImM50EXqYmh6e6DNd/R0SkBEZfpElsTvIAR0AfvCtxgPzprqbNoTM5pcSechd0zUjK/ns9+zsphvbUxLVgX8SGXtakgMZBo0mxa9A2sLNrDqJZBgjI+GArfilrKE5XsnpKDVA98pqWPoubjwii/c0syYmvR0PZWGu2Bci6MS5abSuo7bmZazCvMAy/ftEUsA0QyPQvavUF9VWOIdugGK2VSma9xDnW+bQK9qveM25bpsQvUN6t62u7wswEHDCRfIUFzGMfZSFQiW3FjeV28nSemo8pO82kUXJ6onpXfqixZ0ZI2i4PDqX7l56BvlRhdmXvDImDmQ+8yybvvle05AmAELw4N8KrQ3VT+9Y1qWqf5d9Z9VguGZH1ZLagtDZIEoNwLqFh/K0kEmFuYc9A95/g8UCjO3EbZr448r9HGpK7Am5/CfqS1bLTD1mzOsjEO4eIPTUUkgzSVt+s8SoVSkH/T/GmAA9YIV09TmFSoTaajFC5FZ+kCYKbZitHFGUZffMKS+uqyQRsP+ALluQwYU5OxX06D4hikMB9USiE1rcrRHfHJmFwyWKLe6jHohj3nHvXbYZHDhlwT4tmkCh5llR48P3604RpYvjb7PPPtvpN9bN1xpStChdFL011WGt2u+vYnPGkG93ZXPkAVrwD78GXMYEYjMVCG3SwCbjn99JP5viknujha+1aaxqWA6ZB97fvWnIVHirMWllbFwomANK3FTSUR0iWBd0/4BRIaNXY0TXNoCDc55JZTNN60RM+ViImtmR6weBa0IhWmAoqPAHvUPtY4JYE3MNLMBxq94C4WC0+NpjjHhkyfR8ypCpAsLS1LlgarRgKoYin3EwlqCthr5eVnW3epCFepPEX8TING6BZfK7MwNO7032eet4yo5y2nvW90Qa8OlioloBmLCmcYzXiVW+2el897g/xWk8ONDhJE2ltr3ezhDObVzowUpqrIfZ1QB0NFDmunAkiPu/y0PJYPvM2O+Qkcm+PyRIypgM/jc8LUSlJnuuWKMbBBF1QRNLtgn2xTFdakRZpnOZ+ev3gI0SGDBDy019Nb+ZX8GBmCba2epQzJ79um48noJIu6TiFjFuXWrLeOeqLxYqBBrgfK/t8hjvFGromTXu/HgX2xbkoIHBN6jEwpqpFZGTGdHKKm1MmhgDSJ874FoH68wRcvbZlu5T1YokaFqDuXtTXfp0ARihGYMZnqhVJxrFYLktLot6NWg3JwPypmVpuZVjlFRk3TN+g73WYWuHpf/VnnY32NhYrBooyHBmjph7ckd6pbXkdRKAPVGLEAwFijwMUPmzjjt0ICwrjM9R9UIR+fy9zVeo+QZ7OHC+dnvhuPaf17D4iCVD4T2fNAi+uATcnc4VTHH8rJqvIFOHSR1X5kNha4mG49Wn0iNEnJEYwinsaTgq+dLpTJNblr9tjlvtr8ofRnh5pNfSU48twtiL1Y7gVxRMJsvRHfEC0skTDieOx+WL9kkUpN2DtOmcXz1DnsQ3XkyfuZ89Bun+9Ae0pIrPZt7VAUJMRk69YEhxBrQS0ZbX1k+4WtiOrzUlWf1deHsTZbvzrFKLLW3ARyRpluH1thYjo3dSMRfD78ddxEllQyFJvyMS/A7DqlajVgGZYPrPQ9j+UjHV0jjD5HJWUNCQ73WNuOSwDGdvZuecmAxLLCw13ZO4YmDXAnP+ACjG+Xu8ocVQBdOtT58VKXCPTG2DSY79+wGTTFAAAQAElEQVS0OrBTSx1KOrbNK65dyhkfjnT1FPP251HPMcHt6kHSlXnZvyK9u9QXl85QIQ98NtQdYzWA0hakuA07aBQrb2CSkaqzK7UrtDMyWJi2mfb531pRui5kHsqODNKpoDnLJjhNtLu50R1cifWauB91uZeLgXU6J0LxQbIasZ7M9kP1CxHriSj4Cxtz+3kij/Gokuu1LhRjRROR5gwZP9UHq+ZDyTiRZzJZ5WYWjgv2U7HPnrQ78QwAQ3Sr3MyfsjyYQ/ZixaJs0oymBJqFtbMtOvaE8kiftVrVRtiLYQY9UwlQE3a99pjPsB+awsQJL/hAOEJ91vaiDGCpFM5/Pl+qTjzon8QHuVWIIXKrkdDxFtZdndWY6NGlq87Ox9MMG7E7jELSLuZtsJqXQi1b5DJT29JVQTO601CDes2KXWLK0FF0WEQYdUYjFhFBOOhqI4cBJbOU69bwh04O+IBfMAeB/kxfLZI4Kx5+boXGdeDf1vt2rh9ya3JLRmILow81PGMzgmTLbC3YBi6bDWEy3E09/H0qNch9JM8AIg2aMioLADsyruaHWA74a5yQssgmsqcKlmpVDiO99Em4p0jYFbZBsZVUdo3yaxrf0wWcti6x6TSmne47Oj9mHlbKaiFGKEcUURYcJLST2BADSlLL8QghB5j3v0FbM5qUcto81clm37iOlHpgqQ5Upau05aNOjdXgS7ORW+cV+wdw2XxTp38COeDZXp89O7R+SBZjBc60xAjJded8rNZ197985PR8SgvLXLPaVgVzgRJEOZOAGBozNv2B3yZXg1mTu9PZvs/aD+MBpZ8oRBvevHDibQiBVN/lOT2pJYBygYJG31LntRT2S4g605R4uNb88/nBtefD3oTgakjAPnesX0ap3zxQRSrN74VrZIZ7sFAhvyjiKWSZZfhjdqZG2WfDwAHTWgsV2e8XMY5jTB+l020FF9xbOmpJsPXkJZo2M1Dg+9juwXQORCQLo8LmbfHVlk3m5lnMK7CNL3HZxUcH+LjiUDFA84iSAzUva9bqXqrNcgxnXoOAPGi6swMIglR2ES8MfU6g2sQ6FQX6cH/x0/0hXZVJXRX5bFmxcas1I1IGrVkyEUk106froabKPkRUtx5kC+qi7xiJNzUFUIRIePsHvglXzMP20ACuNG+KGIFFjYyaPbRM51l+uysJQFGvF0RYcK9cKnge2e2WTDaJAA1mypXGttBjRIxIoc7u7ftD1zqoNPSNzKy5O1O9GLVakoxadsRGobPaW5Ksa1JN+fU+WB6f7Z8dyzikUmuxoND64D51qjQvKnUufBZ7axTXWddxT97gN+bZ99jxmSvtSn7GXi6g6NcuKWbGtPx0Qz5Uo3tNkFSIvdMm13PsFIdr6feyvQe7zj5lz+AxO8A8Y9rgZ2qJdn1/l7kYMU5rUxCtXxxdNtvPWfftT/ZAbI2z/RWaskCQillDvpHnp/AjA8bxLK0Lpkves0vCInRFILp1xiW8xaND6VAqdA1Su87u7p9HsgMIxVY9XbpPuJO/5d/RDenqbVBLwH/o0r3arqa/ibk372ImWnYum2Xjf59NtqSw1UnpwDL41xpwR2mZoIY11Hm1W5srxmwmcQ67Zlo0CZk+DfRxPBv9TCmD5nC5sD2zCWcfnkdjenm6af+iz5AtHzF0OZfkrWCqe0ZFByvReUapGsq/tTbDIUNJ67JKU8mwnkE1qAWv9G6aAtlxoczVymd7EYo/aJaU9htCz4SwJ5hyXjbFAzUMy2XvbV6wR4QY8DrkxvJjUAzbndtoeA9IPN6SwqmuI89dXmrqNdk6Q8zy5qq5HtelM5Yjw9Ds0RLvAXo09wMTKMFyGRxqnnA2WgFVQW2StJcdEblelt8Bo0SSh0ixdRKpm7oncBYq1MiGEqoN7DX3StKK+tk75gVRysoVbrzwxgsRLsB3P0cdtCVhVMqGTbhKRt+PyzB7b7iXhYpmkJYTvQx76IR5uWE1Vj4abaZBbbodTmXNF2ToHUB3SD+NyTTkq2PxwzNvrRWNHhyftkBr+VxNO+uUcgXQIB34l9fYNfg3oxE/nB9Tyo6xpmJyo7W98MppdZI7cZTpKUNTdFV2TimeF3uU+59VJdijbTXIFsP80nQkTSnA34tSjjIx/awoPR/hO7gmrc+lAh5aWp0oWFOuINaGUaPbNHvPDobTulJu+dJRbfUIUvnAVZAgmGCpIGhWwUJPPpMpAf9g7dZOlA5wDBgyRZG+DuxAQQIFP8T5ND9Uqi63ILXi+RujwaaqznPmuYHf8WckVvfElbMg6wJxVp5rUjnQEl6Y0501neCgvfPC+z/WR4UxJ9iyHwKkg1DBYbdKEwDhYMhhhzqQlZh1VjTsMTUHy96dxkx0ATdEhOZzbQ+ZdNoQOXNbaTuzLl6XaXlwl92gvu9tyxEO5zhFStgxCKg6wSmIhW7gW11T8COmgeH7bY7zCWuXKbnWHuQR0ShVhDuKJXq8Mm7tvOrCaFnYW3GOjUwswdMwtDKDNM8d4s3IpqzKrwISX+e2VszFXaDsn5kWzraEtYITtkZqlkIwg1Jgh7rImUGpnhuC17bTtVos9kCL62Qb0ltyRg+psCatiidbQc8QR341dRDS3rR126ml1qAYScAKYaLOPLpjmpRLMBWJdl1T4U2Ro0jHFLBT8lF0RhjdPMyXmXFo7VH0EuA+IwPEbl+p4puk5CFfBCAqrcqraKMizbWSVtOtvHcrDD+DZ6rm/dvxrc9Te+BQ/7KXXNoFLirH7rLSLVUtxY0SQLbAbLOLDEyP9gPPeEH/HVNTfJqyxXFLEqAUY96L8GQ8xc7j+XymrpcCg02zVcR6Ulea0LDneKdz9mXbfI0ZlIdMJakcNFsqtX418c4di9xX61bRr4VlNAZ5Aaop8aD0PGJow4Gd7q18dI6WRSWpomBaHx5YOaCdj6bXSNi8A6qbFLTE9+AoRGG/rR/miVSN2ELsUY1wE29JtvHQvzJaRlJrcES7AjdpeHkI3meVnpF9YBfcMwyEyEK6Qa8xP1IGQ0JuvBIcndcS/XJ4FKeNCHdR1BTingSLNvZ6pznpGLlWlg/qiYH409yebk/NGWWd9Wy/ryg3abbUFSZk4jqjrbOmAOBMjG6AuMahmezmU5LLkL28wwQhh+J2YMZWLM6HSEsJjBxBgYEE3+XJb+ezWlCPtnXPbrEnzjRItthXzcqJzKrOUqJ0WeTizZRcJN8rds6+W3u515/5tJaw3YjXFn6o+1JqZVJMszABNSFWhw7A6N37YKXA6JzaMLwp0jyHNTP8+ybAlvi7YZrJQSd6OHSNXNMCaAPZbsUfNpvuuTgb6WzDa0fOmiaPSu0SSUN4tA5AxKTVWfMsy9VtSRHIE9QguREBturNoDNRZQfx2GKtz9mfWmGdyiXJUT2mNrbWimEF47LYxMUKJfq2WxvjsXIftwYUBfBEPIySH8Dpi/bZpgLj9lnvKEBTYHhfAzrXO8rV8mGZevAv6TTBaFOHVR5WHWphE0+QjjDRvYE3btcryM1YLTLmDaafeCaKRipK292BNC+L7KXJxUhDOdrrtcG9bClv6c/T+Ywl1slQ5WKqBdhexpXG5hSHmjcyyXDaKG1z0HcWbpiGx+PXd310O9YXuHq9KcJMK7LS6lTGUXAYZUid8e4WazLps9w8PFQIygeRYv+PoYkpg+bUsvFQVAZimGbpPdaMeVl2QotL1TSeZ9V38zE8SXnFWRumlNlo8CcY4bZD3MxP5n7ebL4u2PJY2iYeAWZTjtiDovwBB0KRw2ck3XtNuhn9DbKXLloXxqbW3Ksf6xlHfVghjvSlW+wI7jCK1u1dJvtuWHy5FCI3J2IAsLGGa8K3t2zsEZZt6+IOGFwj3mFBBkp3UfCpbV9E02fU12kjlPmdzEd8jIkLITF7/bJroESfALWduoxbm/RdvXywoZjaWQRq1GVcZpXiwq1U1UyViX1awH+yf9jb34Amq+/hke5XwW6v73VqC5AF7ZWnEMu/8Y/tntbgMNKisZ5iPvNSX3VO5ZFjOdTq2zl6gcMXkEPbFqnfRVfsjqu4k7rBj+SbKmpdWIlBH30dheI7tpuSILJH3Q+RymPXZOrMAZ+FCZiNTwl+1yvjHgOG1qP3SYW4WVfrdtGuN1tWw6ehrXt5ObXuyV+qBnjWnJX6dqOPIxdlG1BVZq2yGK1Utu40Kf+1pMf00BNWmwJRyWrtdu7DX/Br0/bMfB3KxIEljAKqFMUFCI7XAXKULKVSHKXNWPTs513YdWI2TN1zxv+WVcPhHBUfDSTRwGlOmgQneidnDsUE3VsaOOEMqoR/xH5Hzs+Zj6xjNBGvjWX0RBx4FxS+PeYZlGcnds9Pz3uF57FXGZoiFfjQW8eAoqeW2A/SqKHp8NfmlmeALYgd1dF4sUKvJPTjTO62NZ/Ny8dXj+TTyBIL7hpkOHrQGIspNwrAVfGHUQgCFKUpDAvGhoRSx8/IhdSCmCHBF43Vn4wPvt0iRvHBwAv4aWq6homH0T7swMo0WTDbBXoUq6rvHh6Ni1LNRpjZO1wgcdc8J1ManCqqnmzMckfb1djTG2XSMZ7gseGoCstSLbmWl8S8sAJ7uaRgKEcRQLsXKS1QXMFYSvQ0WBhAA6hVzdZDPDKlrE/pWpmyD2AdXjc5L/xAmPpWK3vaqaxj09YpwXuvNR4Dql646KpBqnjsBfDCfP/yEjnv5kK1LNcUs6hz/NYmdP5BM8VCaTlbnR/8/fZRaYItZOmk4Q0LGGmxEvWmXWnyd2MzlxA8n32HdiSrej/uJyAMWjVoZDez+HDE6vBMtb6IQy9E5YhySZQzr3n0zOhfjj6Jp6SP9nGjIsPT3ZPWj+ctAlF6LKRdr720op7inWu+9mLrhmuhWQ+up69noh6x1Xny4EjQUomzzYvxDkmFrbNvipe7xYIHmxLfRXd8QMc0rTzaDTrloAbMA3VHg0QAA1f3xf5wxBjMhNHygRzOsZSWdIoaZ1eZt5FQzLqkm1Ompj0X0/up55lnkHIwREvjT81MmZX5OXIiCKDy7VGmKtWuXnqwKnvbxD2rECAH8oVVsyjEOf5ovyK7JmhFlDzPUY3U6pIq27J16Shq18yWMKeDyqSQzFCY+nfNfVleaLXrftg1XUAtx/Uz16WaOMdL7L8TyodGp4IVWHOpVCJuAK3vvrA5C4rJWSa4y2aD+6z1WA3UEIWO6jMzp02bn5PoLqrjmViRY1HiUJwcRZNz3oHqvfMiY75I1Dh86O0xpkdovEjtleWGpMqnO2LFuqACGdrMJQzLXlbZWp12TaSUbFfg5zMfg9toX3H73MVDHmMSosBGlFtTX5Kn1jNaodFKUA1Hr+6RpjkM0DA9KP0bZS2PtpK+VKKfgUP9DTXvAEEwOqzgv7THz5hcsidOq4Wr/a8xYhnXwvv7FnJOd1NmRiKWH7HDIpqr4RzrkdcDybJlKhEjPEO9lPxyL/qxfcc66aUqy4H4HZh9KAAAEABJREFU+xy7nvBf5zfmsNENHM69V8sPzFDN0pk3Q0e7GVl7/QEzhpdJY45VBrltay+/N0168bTqdeCHPhKBgMj8Sl46ChdNZxpjUzvbBuJXRuW/x9MVGNjqJwVpeF4b795mRmjKqjTNPF8OZ8/3w3v/8UrukiIkiAVOx9Ov2eUt8UNO7uK3ozMuQZg+/vy3nvIxpxOUAK+azzzgk3XmQIqVwMLgC3YF95WmYz87jgPryOGsMWBj6FX7FY5jOhsp/byH0jTicZi55lZxy4V9XpP4OD/L7G3rB9ZQnt5pU+SEURoe7GfW91AbvgEX4QMYSESW5WGVXPM9ZYgphzYpj3G09zF+/Frm7lDf+8FziRRx6IZtFsu4gWs8dA9+i/deOSQGIx8O2Nipeu7zPPozqpofYAFlcWJyTfVk6r1bCqQSuNRnd5gBlJk8VgmpSnYkBc3a7w6YcNO/GGw1g+kT/eypd/OVymPPdLEYxgVKZoC0Y94a7pyf9J/nE7XHNEjHIKAqWZHGRznggOr6EzVrWp8z4uh4Azq6IMo6C4+zAoDa0wf2z6AuEJ5hZ1lyCm9k6dMmE8CTfG72o22ADEHO/7CWAKNmMJrzcFqAlf9rep+2tFH4siGYgOaROatVtRF4Zfof4m6vFhcqIZCtbZMnvdOAi7xkb/yMNcekrnVsDXR3jqAUh4no7dB8OXozD1j054PQhn0u3MLLhY63/gk5rS0DXXm0tLae+7zttNqeXrfadttqd67wHRP7Pcw4ECGiu31i2hutjPoqxswAWcxymDMMdMO2N8Ue/n5RLJhpWJop0IaiKSJYAiKJYZwKRbshXBoFa2K2KurdQMUZZk91J+bp556qOGVSBKeGx9Am2iaqXfHTsdz3mf1Ik1A3tHvZ90KuB1betQef5T871hLiac6J/2WwqZVIWZMApzEt0ntb76CbF/yZAVVtI1bjhmBEu/yhfY4aOex+RjsfSHRiLmAyYl0XfSm1QGV2fzG/QLJm3ebqrc0MjM9AMK5HNz4wJRtEr8kr+wf+Wcv8X+KT8emgIhEnd+aXvD92VtR4vWWTY1MaAChLUeQBM61unwdT/QrVmAxtCCZm1mnkN/s5yETdY0WP1cvTzJP+O4I7j843uuaUSLI+xjb5oNn7JyfkhNpo0aHTkmU1nvp+eb6AQlZybP06Hvaocj/YH6Vtd/ttNfumFoY1/3xfxMajICQkvLVZHbf8yjcbM5TWN7GapuahX3f5DAZavM9lT6iHOw524pKUt1Cgmnh8j7YDP21XRjj4k9j3/4IwAv7yrxjmPC0FiDSxSVEW31nDlumPgLVNQqvszK1FapZxR1/8bj6rZ/1yHVx12Ec6fQiNUBE4EHfab/I+8bQk7QcCiTTFbhKa6M+3iLDnuH2f63l+norEKKBYnNcw3bFD4UNcqMOn3AfVKkC4ao8OW0e50z7umsF0NxJrZkEMWWlqO7vAe3gMFZoI07uuzxRb4nFL97ujnzD0J0YPcLyIs95Z9xP5cGTChz5N6hh0V5bE/xzeD+5f5H+jIsHDfFC6TnMP4yTrP5/fyztWgGuUpLZPr+uEvHQrmzOjdjNOo69Zu/RzFQMPrqd3ErZSW0n6V/HdcjO6dQuBzoI6dArhXOk7ldIjcjHj+Pqk/vZsz0urupBPkOGB/9VSKddCl1L3urWKdJ/pp7XtKqVPt+FLcShgRZFQVKf+09izo9YPLeBis0Soc1/HfpTGyFHAJHzFXr4VfttbjsI87PW/p58btpGMvC9IPvfxzjd8zaMzL35FDbctU2Klg74VwXp549PQsnzufjWbUIDSHfsOGYcoeMt7YIrjl/NryNBZXPu57OYge9oHu7Edy+XQuicb1TG9xT0WwKGIwrX4UQVhrFJ94swi6DmqrbsE420eEpYWKu+0+bbdYSMB43Y5JpYBlqfrFP62OmBjZKrfFoaoNhTUXaGyEQP5Xl8uZnVtopLTo4JSvlT94346cMdK+qAlAdgX+3c+raAUvPmXlf3wDJMcA0lAQ5UCfLt+p75SFFyF0Ao0XLKIko8eP4OvqJgjBB9fP2Vxeorl1mL0YG7ZGtuqfy7MKqxHC2k2hFFe9D5OjzeAQneVTo4hDcrwvA8ADy/jP7dtDK7G/po/jvECtYc/ydQ6ISSy6eV8+5DB2+MEqw8qoRyOFkYItSiK55z7OdJFAik/usP+0rRajvymj/yOvXO+akjjPaM27wIPQbTJ2X0+NrgswyQ/D86e+jz4tGLU+HpwfiPbhhoQ0BrKJUykRFe1nkGu0tkPeMletuNYKwdo8KF9ub5j1V0cckR/mSaVCUsuy3XclJGE8s85K++oNvNWqsOOj2LqRdDs3Y/uQ6vNvkQPDBAuzSzaQyrQIh6z0lgAk0S+RbEgewNRe0MrWTFa9O7CNL9mzw7Q0cTYWXiWQurjZOz9rHWXOrnfjz23seCP1bOq91O8j+7Tph1h0HpgN49KRIsnH8i3CABvTEEJkzTkpDjdcpzAe9bBJU6qvMtHAdkgRuNgLGlSsagjm0v28ZXr8nqizzyFro5H2RqxUGK4rnj2TuyZnrzP+X3uU9/k96ySKooWlPGxB53I7f3ZzydyD1dajC/x1o1T8z3ze9v3jvtYlJxUjKoOzzDnOOr1iTmKC4AzlqlplPMIHMVzvTUCzZkuEjD0TL3VqY4wLXIIOrUlcMbg5yDu6qwcycq1V2i/CjJClk4BtGf7d/EYe4C2qTjt38pzplSfbHPo0WH00LTPusFzgNswPnshFhjbLMK1oIyxuycxd3xvvw+SwGrux/Qxne0Od9qivpQ8ervouubaO65l4vJ0PlMKH5efNfnm/bZo+CZ/jB/6Ne5PTLpKeCJG5l6iBneqJDL2xGV7/Hrg2Rx624LkqkDRQrCMi2CyE8ZRjoB7zslH+YeIHI4FZqPpSO80sRx59mneqAIgIQPXwTqwpzNb2XwuPIHWf6/idJlmxsfnv9XfK9OGC9xUxkuUzAoEe07iUd6lhSET8ZRnlZJja4jhPdLIm3b08Uw2MNCrILb1UYMU81o0jQaKq+LCxffkpq+2awxJPsZb+ckDnL1qSaaGxUVe/b5jIS87y98BPaKuWLWqPgY9t94WlmuKAHEhFgO9Wlbf2f7j/PlRm98dnuC4xU7fpYpKiGlNm7mtHgh/XH1ddC2HtpiXl8XQFmp7X2HLg240gdfI7SMezaNwMS06F7QXKbGgg9XI5CNJPI6s8Wd20izOttInf5bB6Jy1crsS5fGsebxpHWYVprQpjCIoqbSygllQTU7OJAbR+EwaiTHxbwWoBks6ac2RdXsBWptlUNB/IJWKBoeuA6bsgQaHbE1LZTPjRFhj5ePTqOm/ZfZE4Adxyf+YeYmT7WY2sqE3H3e+gnYV4XdwZnqSaNEXCUufchswR1AlR1ORFPgCgETVR2MnkAZp+eQ6FV2zONt8YpVSvJmCDg0UIdnRsoXXxwxap0ngjUNhQcue6/T7J5Fvt2nL8Xa3JSC3l4KmYTn3MnqMW3rpmKAtW6F0EnIknAIFQjBwJDqTup0MLBstDa7O9IxlA2d0qeGN/WhCq1ONKy2k4lROhhgYaATZ+gS9+nC+xhEeczHYCknIz2g5w9joaGeZtxEoF0XTSkzWaPdHpeJ6nwfAUFAdllZRuZ/1jJWa8siWAZr60qJ0zhHWVbbnPV1CJoRSgxJd1X4sxpYPdk2g7PXRrwzDEuxm048vRalYfIg2lnMQ0iLlxoTfijy2U+GQbBLUS/mfhIxl+bR3Vw8wZhNWhqd4Wk6cMKNCrDrd2TYyMC60tea90zV0jSJxZcybLy20Y+rI2n9qQZPZ6a6u5ygi/ct6epQMuTUrF2rK7ZRmRtA3l71KC9DLrdgG/f2+MXDehqJUmwtrzai3ySUus43Le0+jT3UeXRBEoJ+ubCVPi9MaiL9KluKetxTAkZPZR9+ao9i0WnldrR9kpBWN99FZsMQP8fLWYL+njCzbTZnRh2nWMK2T55F9o6YzOq7jV7rUSXWWduXB1H6waO7RH68PBd2ceomMOeEnLi246lMYKbqsn8rvI+4AiCQW55Q/tbvho8xgStoIYWhUocun/KSL+Ry9jYIccIFRw0I1e7zIvrp+SrlqeS+DTsJzFGhjKQDXIFOXl+5G08dnHEYs6H3cGeF2mRbgx5oGWrXVgzrZU4I2IydKZLwfyjzVypQIJDIFxMxtW0c+9i00h2gFPa3IULRWxBhJQDhwzIAQaySVV0BsQ4ILwPTv0h+OKQhoHfR7LEu26jUhyNwCv2aERJbFkhDpUOCKtzLmDPr9N/RWZtxVVtmBA/+yC1/wTSwefSHFQYxHCQAMUOAgmhA/DFJ/hC4FaRQFqRGE+fjiDjHZ3q30GFsQhVPHM4JgPRHl+9DQI9VaQ0qfer9VzSk+pSl4z8pNTR42/UUMSN0L93qeJeAzNrOmY26b00VFO1KkaBDskl3Mu2BrrgLQ9+Q7iG7ueAmVuQFmWu+KwOF0RWCChlGuBiOThPuRY5gifX3ghU0Zayu8l9KpOcFwe1Pf1VmWY2WkQ0FQ/xzY98QZO0NZgCDjaUzDWmXmV1HBEOmehvPHn3nuwW6uCteq7dcafTCmzwuwVRstG2G2TY//L4xCF5kvhTN4L7MK7xjBVgADxSSADTl5zcZxNhqTRoVZb9ZYRDtSNWe6PXPW6E+MogJLER3fuQVFVT3T/aTqAQUzczU8U3bs5HSJhGNLAR9Ulb4DTf71+ekZU+wFPKv0jOpch+vQxZC614x4F39d7Lx4O1OgbuEoR1FXIBmOK1bPijw+2tKhZrIHPOQ33j7U4riy0TX2MFRiJkavog6bl4Q7Qgks1QeZtNOCemvaQIY6pw6nCInWsy3v9jyYGR+NAAFj+apPwIy68kXddj/xaHMmOtzUn1JkDIT8mKwOoKi6yISmlxs+4H7Mbu0xhErcH4M22N+0VPN64k04cYKzTPTJP7Mgq6715Fr7aqf8vD74pk3iG1NKOx3jRkJ89rASI8b9wnLbvGZ0o2o+vfpYjnzO25IMcAOjXZr23RPVLknQKdbt74U2S48kldqp4oxS+/qh/LyBOdxnvJMq/bE0T/dWnciGQD4YZaaeUTQO6ko8fj0mk4At5+t4nVZNxcjNaWZzch9cZZ+mNkM8EvcbeJl4284lDA+PgmGD5TgL+jlIVxfGhRBKwxShDLbEWOWNOOH3tvkH+3GjEgpmCh7N9U05k35Edhs5o6/93iGd7tEOTPJQ1tuFuRYfuXkpLJs3vp6WHV1XeM0WWJ1pA143SyiuOUvJBQYUOlJ/4K2PwgGEEBRZ0H/DgRamszRYn6t5UhSsfg3XzOmZLqMAjrUyWoExqUXNYlqWO2zksjTr3sr3lFCOkuy6ncBqp/iaV8NpmgC5k6vqoy3NPX5017Bksre8TCJGuSzamJlwLs623sQeN9gbyCt/WmLdWAUHGoL4/vNKR4mrPYIrUtW2trIWkXEAABAASURBVIpJ/Db8VJzvjJTRKHNwNEfNguJDzNmEQUAUy2tR1ABjJYql0QFTseq6glI8Pfib2YzhNjMPSCWFfGvh3jmfdFls18Oi4Kg0dNDHlK1316S4xIYAKCInzTjp52ulrGZp1L4F98WiHnS82kCuiysCL0draIInnnQrRJNYPSv6LXWqFEUdVYaB4pT7xGKE4tvaMhwNZsnOeqveeozH+l5Pfqa3uW86w7kDV9CFTNSri95aFkUJMicFE01hD0onz/RDiBM2TGfliBBDYlNxLgFX5MTfq1b0utz0owQjz8pki2Ayj92/6/6uvAMiD3tTN2pns0zRqqBwk9o5p+uq1IdKO8lU+qF/Jcnu0mIzKQgTqhT7PnhUTDyM5xW3xr7F0vqMgECNQbganvRyjh/a15EhSNiYhRmwatwQa3bYqyeFOk9Ci8A0uBomq4Z5OY2WMBNdxdHDn3oNyUl0uPJTWqr5yGIOimY2Wwdd6l7T7mfZvaqgy7KpPvcO1vgbskiL0QzViSmpSho6j8AhiXIVIa6nP6feNCo4acoGqgLSB9HnqtpZn9ZThmOlRnLeCoAu2p16MLv6GTwy9sgeRIGEEBmcy+ABMRaCAL/uy59gotqK1r5yr1xxFnPmTnxH3Vb1oTC8uC2WHsvQU2RSg57Qx7hhaYANTCv7T+dnnotPQIJafUJ2Jo/NrYcSC07b5LX7uo4oVZX8KWFG8zkT7A/qAy3qzelsPs2MGGlfQFovTRsHxHuBbSGG1oJkePkbKwJ6Sfm2Q4Kdm/wm62vxtSUNWbcPX1m3pdJzzzpYQ++NdHtaTTOgAVG1nox7pMNncMyaYVCXVGjWQzQmpTV1DhEi078aVcrl28WAGrsp5tR5TzYOvOmDIW/6PSKUE8AyQFGWq93GKL3DllbFZ0/NyOmCAqnBqXm0yj12dV3z3fkf84iCFO2um6p6F8UwZwt9CpKNpP4P89NlkrrU6va2vZ2j4KKT+NKe8TaHuTvhrzBhaLMyl9Mn0UcnzMNjqybKpqXGoixOC7tAjmDqMKPu5n74+dgnXLCRhi/0nPNNqYcFjAUQ3CIjqlU7T+5v7SM5Y9usNbHvH8/LeYWZJdkab8KgXVN/ex/dtXEJZ80GlxvJKk/gEN2CRy26aY39wO5EFW1sDVZP8I517AKXrKIy6qxDnovzM7YJNQvdFuxVZqlUyPbSTAG1ToNuJLpuR+7mSmiIcvJSjbfsC+worKQpzXqTTokpZtrT+YVMUMlc3lTFIf1+ru9Z4nm1Xe+6ELq2PrWu+Wz4UmwUa2auZQJROfg++9wfk38V51W5DbFgeq+JzBANr22gBCfh97AMs89Eb5+P4mYjR7/dPJnZp6U/AoEKwBEc/UUhSyYmiqH5SjsGW6UictuzYuBGmvxMv+eitVJRQEGMjh7IGlgmzzfWs9Rbdls4TUX0JruMt/eXYlXl02+wvrkgo9LeW3DrtIqQT2nf/gCPKjmB17VpNKA46maXi/A1avMtLRT44E9oSQ0Lc2WoEk+/8jB5jqt7AQw3X4vrk9eVi+lqprLfbF+SY8UrmRkYjyuAn8fV91E9+XDusOM0PJ85GtfNU15a5sdg3ThPVaYffbna/j3w0zOndCiaREzG1rZJgK7VoViSB8wFQcDjrU1A74lrPosXtd46Z9x6rVRMxGWjhtQiU6EMalSZJRD7lqRyukpIAWgY66ffnip5H/1g/MrH0EF5EtIaCfLC/PDtrOK0BfRF0Yzhblw9co1WLgvq2eeD+Qls7yV2RLUp3HHFjusgjpFhb5bHniefyu98uw5z1nGveHO3GpFCfQO37QntrT8fXLskxKkWLNFoSKsv52um5qmbW4WjX75+774LUhncdUZsKwGy6uabDCWlpUIorFwJoTT4UAC+LYOYSMN3TUZNAwUzhKa3t5ab56zs1eziUxSQmafzfp6/3dLwFTAcUBDd6u41fJblnvRqG52HsyFKJLvDHh4R5Z5d2Sl27e79W57J15jtmDXFVES3GKwQp65h7EbTOMuUg1iqlkJ5P7u/Ybli4oXiNaaFXhZWOWvn44IB9Ga7pPj4nHcBXeLLBjpGORN8qGfhhThd8/Zi7NYkKLQudFfw8VWzKp0P4+Y+HSUTsq8/irUJgOBxe8zOQelmYKrY5VMmtFNuE++htMib571VIw1AXJzTd+qeXOZxYo6u2vyaFMc9KhAoPefBFB1iwrKlcuXu1/ym5xnTzvD+Ps9/9xyE4oea/vrtyLTVs6loCKVXSe7nPHWeHKTJYk8P/gJQ9b7LzyUdjAg/ZhVDFeJMPKRIxzwU3DRTEY1K9U0u3tBgHFkczEU6MaOBnLZ0vzWts57GAyvgDWi1xf//d1yANY63lpbp7uQ5hJRDuNF8bKB2LvLfcBoEltKK3A5Tzx8/Z87JfiYOqVjYzapy1pLtqFFltKQl98vwIQNlksPUgkqdM8rYOzMKmjjIjpOrtXs7rfSZM2IF4+VLe7ih+g8U/4+/kqdPo8/UCFu7yd9dc6rF+A6sRYRDVLYZFFM7TbFhonVAEG6zMujk5bouK6OR5hAaBeHeo2tKV4oW4Y6ySzSpjOS0dDFnyZnbOEwjYUNTbIgWZLnGzCi0VwVm3avKWtrWQjLP0yKuntFDG33wQdD03zjn5VHzkGRiXt+dyDIxq5oi6z2O8dBa5BzZVxhHqNkD/X0Exke4dreW6ZUusNVMxTPU0rP8DXY8Z2Wtd5uDLnsecf8O5SJ9rR0SwlQ+z13P7UqNSMs455NH4QyIiZHZFOs1rmPoFByybB5DOlWnwy4q247aaG0xfI3+1LsXijc+OT92en/fnmCAUrkUH7xlS5bq9YTGqKX+uclZ3QZR5cfW/aKQp952VkC91CKMojuObIGaqb+a2V7Jvm1U7r0mQ7p0n1pzPkbS0T8QzTA3UVtYfLY9nMJpjrdL/EsROtZl8dF3HeV+E202BtCpydtt67O0OZndX9iNSDqkZhdFMzDNGJVLqf0dYWy2WPMkUv+pG01u8Wt6RWuyEnMlt0DaKBQR6nQLgwtxp88s0pZV9IOWNYg+Mas+leWYTUVqHCP0GrN2hwhwwdF4e7LrlHjC6lk6u4kNVYOxQy4wlGNGkWX58Nzd7/hDr0bl0TKlbprqJC1p8viUFDcHOAslugC1UBGlv3BLOZUunl6lSobJqlzosHTXpq3e03MJonS4qAnONKtR5C+G5vQERhtkK5XfRbWYipQ0M13SM82RQrr9JLuH0LA3P77gbjayhbTyoL7SJjufqWMzqy3KcQxZ08ts2yyChIshANYaT2CQrxmfdumTBVh4+BbJZlg+scckNIfcvHLK6UZ66Jp7LwwlxbNH3lrcgiAF9dRT/iBAV4VYERLH+DN+4LA3btNI/dUXwok1s7M1op/BiBxZpKh8aU28lX631xnrWBmuATYVI2+GnurWxkrmbAlZLs1SouVeePqwMFBT27CArXC9DVZBH2YsZxocgEUJO7urnzi16g1QVCjkLsjONE1D32ScXtm724HURFHZIrNGfAsP9F0TOXtGNpuoIY+S9seJue9SEQzFpWObBylsY9WQ/+xRvZbmVvrcst+ayuBQfzPqtkpUIbNSq+sIVDT9aStVBuySQiBTed/nHN5pZ0YkIUcoajyj2x5DcpIsuMLveQaPaPXTWrpdNNqLi/06phLSThOCDrTihnGiZcs53kSZvjyzDnYyBGnAQPhsXaoBpAyJc8STPg34d/i0y5WyYQq+IVwKDKfwJ3tHmPqtCkeXQ1ZY2A/XGSoNPmMDCEkbKyhZnGaq82Ss8l3m1qTgmVRjj++J5etFe1IbC2rlHxLjgrz7TQ7pFpeFrB7tx8Qz6kJGihviGb4ox8qQzjGDPAyntB8JI9tRlm01DCE9FbhdOjaYsLiNWzsrbPS7hD7DtAKktqPLqRawKZtxRJ9gEXBBGwIubL3Yxsd4ojpmHG0gW38JLM62Ng5sGkzTXdGyHcvc6fUK8HDS0iw3M1imlcHV/lp1My4SNDFoDCLqsI+MpZ7eLw16TYoRYEBR/0KST8g0PeAmcRRbij01eb9U0SAZzkdYenSlR65qOxXoCqd0Ac1wiMWFDivE9IwgChpRQGtOd5s8EaV3DVOquXkt/1DJJLs65zlI+TEo0Ols4eg9jekewWl0E60QI+iXwZE6b11RFuX+ltO7GZQtSHxPSsOm3q3QpLJ87OSu/vO45o4sas+u3ZdKBzCIkoA+E1AJMNPVK4xDyvnBtyWzMPdFes/kMT+jGqoBVGQwi9aO9I9H65IBlMKhLrQPF1Zw0VxHiDezMIiokyK7MK1itVx2v01h4B4xy9JFyeP4Ekq5CDcZ7vX0GoevY9epfbc/TiWZNCa14mFMlwn0KBOV+is6cUTNoERoVmmdfd0OUUohS3wySDd6wIxsdBrkqWy0UTw1mlhQFrLtntUVnm5YFruuSxvmmhx+LfScAy5ubmhnXipolXGjss4RbzKrRIIlDetd9Ll4u17AYlZ1J1JDErXOeECVLyvpgxih82Q8ZPXg0dga6d0JlWr0RPbMNKPo3h2QXKSkfoTduaJdH8SG22zNSetnZ0aXU3oyUXwb0tTc8AfgDf/MekxV9Ohe8KIWykL5XXEaOL2aoR7wZ214BiWwfy1cSG56jkGH0Faf1vYm2QidGXHX91IrUrM+Wl5yJotiV6HRPOITE/MKWidNK+sCnVLgBM+2juaCO5lRWhwbwiMurRuvm36DMGCVxbttWJgozLwYR6R9LZ+oh2I4jaAuJ1frTpxUCMynHfe172vVlniv6aW2BelFeVin9/OIYigxbaGYiB4+HeLVLmqyanB6zAq6LM+iYtp2zofY0GhJ/7FnTOTeljdTYdQUUC8cQ2NQ7Kenyo9SDM+8BFsDc8DxmBoctFxBSMzXtNfXVbl6vrHnX/D/RFNF96fwQdmFo17+h8hWeigGQlvDviYuxI/7Mhu1hgfnwCsuhkielYj03Zf1ApSpybRva8Fx7ol/DZ2G5P5VxpRj0RBDSbGSc1+jW/40ZVahFWvsF/aNyLWNYk9RpuKZhXZ2Gh4QU7t0M1SGgHzAfHgBq43VaAAqm9ctaUXXQybgmJTorgVLxOhvxK13DZCNNgiPqJmnipI9a3nCBkK6Rx8G8g+7StRYCAZyeBeUWOD9H3rZLN32ftaAQMUkUuy3fFVHVUiISR7xvxN73J+FcmANN8KxzULyncvs3yu1iyVDaJsTSf6u8+vnebsI5MHoZqvCAAHBt+a3Sk/8n/3MKtJo1spetbYBhazo8+lRORqYprctnQnM4Ga0niKqszlfCEJRrTGjx3i625p/zZ+n1tHzimo4TJngabWTGg5IN1eCDUdbhymbB55KQg0wvqG7g3+Ibdu328fCiZmpiTO7ykd9LydvF2mEUdlroIDn9Hn6nH4wMoe51+SGGXbPyj/fK7B+SPUCyqYFduDdECxTqLiyrxudkbhgF906mG4xsDEsAAAQAElEQVTlPaYUtjObR0+ID7SzCIZCW90alvgAb5fR4MFmK7EmwqkHmO/l2enSgjeMLUcW7Zjd9mmuu3nfdX+V7uWjMWp6EsKseU2uK+e663qKEhyTxgKfnANwSfVCMc7R5VrR6joxs2dvckXFJR1PY2X7qVP2gKJrzqkbk0H/MEhvZvGxfCKPikvkupghzAsGvWTjgZK/s65Ra38K3QLQtu5gmTBNN+tYP2MpzuqZPjgHWcGz9u9a3w/zX+HiT9j/ERdotDHUIlwoTC+08OWUZjdHLYJX++qlJ4YKmZ2Ge/O/jbO7rnNEBZ7xGrLS++VPAclcXrgv+3+2H4gd7VKeOKUYxZvwC2eXxJJvSlecjYrEpHYimrBLRbuuE/sb+zFT9GiytSp6F5nYB0vacBX8VD18gTxFZOAGbuPD1jIxTLJIYNq6bQaDZKad0ioWMx8/QX2VlUiXTnToe40f+YWcDp5W7+hkjinvxmMcLFfmPNd8Y7Tqncbdk2hcww95BV197nv5d6/vK7gQiFHipyS+Rf8pqvYBcWfK8sCGaNaYoi8uIX+IYUlK/+fcRM1z7gczBIykQR6gxVCxTr+hNUY5v5HH0abatz1jUznowhTlQHb8DaHFz+tuxOPRpjocYysRTWino1lCm5XZ9LHAx/DT5kqnuP0ukS6f9s55WjV+Yd6wzU1R8a45OSguHNfQU3a4H9oXSGFfy5cD3jCA7zb85/TPycuPX50fitJpMKH0TRnKYmyBa+JT4aQTgVlAPOSzFKaGOkrK+NQoVk2ZXCwQGjelRXP5E/aVpHwx7MP+v+qrRDhEzosMPiPjvsP2dguNn6+fR6w+ikQNJ2DYhqv8J9emO5j03OHLl7irzzlbw6HGioSZaujFD2giRf8r0VHOURxiLvAi3dBQvKq6sdGW1zS6gHtGp9APFdhgT+zZcS72NfYz6/86/nmYoaozR6kST619XofU2FPg2HnDFkoWkSu+V3ydt8pPpt9u43LM09YpQoNRo19x8SvFLN35lQmljqBINaT1/dqZS544EvMBZy0LEXy8FKcP8MydmX7GFYjRMerQQZbhqN40ynr+cdwT+sF443ExRQjrwFpgOA9wtx0EXBddEV+0ZV2WrVqI6P15PFrVuJ6Ww0o+88kRPc4aPMqNLXCPIRY27HNv+3883+fUd6ZhL1Z0tVJ1tf1k/CRhz3N9z89RZuJNsJ2xx/azltSCdcNPi77ma6FLUb1Jo3YwRUtazcgzuybPzN3Msj7CQ2tyQm4hLpa5WgidOkf0UG+bKXWZsJbrTAdS3IiIQWrgliSJF1M0lx7PPtcoYeKb1KXlY6yengnTcQQNHrYuXmJYSW6h8KVIxtYufJ7j39Pf8dr/Vly2Lp1sZY2q0tve+lTf2lCA0WoYNbxLTYJj5SYC1nM+Zy7EpYb/a3iy6k39SSlXjWN+Svmayfi+C8ajODhl2mP9q9qeqqT53IZXOjHtTCLBlBpAgAPobRSWyxvUYZkQ72TphA00gmzvG88zqUzKiHZRL0o3W4woL+IJpG6/JfMpdmXnfVrjNJXjowMG+goLg9mU+XExFBgPxAWok8Y7wnSTaEtWtNJQER+biUbobyGB9D6GMiMXbYPXMVPXlgCmzWQcWknm9rnDHvcDLepr0qzoberbehPfovftapnKKJOCYzJ0RPw4g5BPe2xyVDDv98URZqoeFveq5e1u4/qDY3zMDht4Xt+D9+U0ElMl2qTZGDgwmKel7HH4XrN/Uqp/+ZBwWm0rOoSzjpaZZ1ddHcGP2abmAK1j3LCMq43qBtN0SoI0BwO2lqtbHK5bNVEU6EsJ8hxjmqdvGOBlaGUJ7cCmOUG3LpOWKo1tU2xB6HxWzbI2qVOxJGbe4Abyve/5PPyknJCMmd39lksYbQyzwDVQ5NPZR4p4Cg03OsfZmgHBc8x18lO1x9wHwsjSVIyXGmgricd0vltdIbocbWifx2cUcC8vxDORQwgdMud8s56D5JSarfUAb8DrYImYI3zOWd5iXkX80Boa84yaXo/6QspTHnzTvyJIsZd9phdBvN55xI3WbjzHZdgqVu513EoG4tQ6x241JTY4oiU6r64NN1zdnxoHDmV+tI0QDYyGxINHOEqBWPQas5u4Z74jbWvmCg2jNX3cZtCJfnq0LepJsbGgUtF4ncZu4RVUmnpIaffnMQS7gazpmpmi0ImFN+bRaSeUrUepkC4GIg/eHtpmKUd1za0dNjRPlwZGu9wua1UJqWhkPsxC/MNeeJy2HqbyJrJXSVFMyAjFFXx7fDw2f4AryQeTi20ruXOkF6RSl8FijAq1wGobebMFkdNoECICzmS92XLxKQfSDOF/LrkeRGKZ9lXmykbjFxr+Biu2i3PMBmRX56k8nF6FPaq3HVwAoWDNM97uTLgr7NEoQJfcyyQmlhNRwV6J0HooblEvendu4/tfy4dsEXgT52mUbFoMnZpuBaYzoIFhWHAPWS5TX1R2v3m+9xR8fPETdfkdk4NJ9EE+M1LJeFNLKFDmsePn47q7a/cex7T7Mb9hYlFMvx1tCHIs9LRmLqo7xjp7J4OD1dyDT/Z1Hzt55q3BKrIUelK6ttSnVlqUbeuCOh7Omlb7NkOH1VqzAUKAEsx0qJ4RbKJzBJ9n5sQ5q9RkJfiOaq+MPOoySzNtEnWja1ldCmo+rEsQ1kmvR6V+yeh4L4yPyBn0iM0UJTWgQ3uX69O6Y7UWB4SZhTjEjIgwO0sQInufYZkNHNC/irg9ZADzYoVTs5Ytoxu86WPtNgIlaE8d7VLcEYtclh4d78XI9xwUOHhccSKjoKlOiiBLiDF1w1N6Hd0L7xujInhjUQp6M5fKhABsEAd8DA9rinYrqeYyRyTY5cqeSSvVxseosUrVCLyxXLXjfJrvKY12zoKKqtOoG4y0JI7pIke4OMZDVVYv2uZumPrh0yVrGKCY/8OqJk/AXkbRri7M1KkyS1iyML/Gm4KAAENTDT2GcVGnMw54U0cgTTikpihbDXaKdYUc7IGw+MBtSB0a5AeV83Q/nPRuzoiRfsVU3merC/RwAGKWR2zHVps4B6kpgo8pwWuQGoHWTPaDeikDjRSVbjJ7MJOtQLwV0FAEYDStxkqwdM7mlZvo4aP6NeGTK+y0lzQFoPRLLitMj16AroSatLYH1QPkeM6VrToEts5+DY+E0DO6KubX3pdt4zrNfoGMmApLs2NoRVi33Qf343evog9bHA0Ddvm+qGUNp7L69tGdc71rC/UCCKUNpGDkoJQqoK0TCpGE9ELLIp5t2EY5Kz1ra0WFRgdOKAMziZR23jB2jXeJABFTMAnxp+zCcqzCSqVU7B9WC6QH1TI5+bLYCxDfAv/SaQ9NRkJeMtGlGMkw6mWgAaGv89cSxN7g4nzAnZBDS6yqNYfe71CRWOCMqGpoGW5pBUjqJgH1KNjdZXdMebmPCptgGnI3V0uG6ZS2vOA1oory/fYKTqcI6wwb1yyFkguPK04bK5F808ucpI9LPImWLOXAzVwE8x6leezUZvuC0gUqm6n0tToTtxIAnaiSK1kQOuW13AANN2+3XtYXYO/cZeBnakm/U9BJrUEShNV4jt/kmzFtiv5I4QopI7b+xFBc1W/ko86yD3wy5VFgoH/D9GhGYFlKd2rP4kXRS7dMgqjAY6YuebdY7BGAzlF9Gd0taBh2oWre0tMHfaSctbhYDNPQU9VljTVwlKtb+BnHgRjqzrPvUnK9Hb1Yy1zbtnt2Kqkx+KRuOGgqyTnv6O4unbbeoHHDLvHRypRUOkNIIfb4Zy5f2718btY37YhaVvgpPg/mTHBEcZggdzWG9OWmjgXhMnzBKpogRWuQTQVhWmaWlWrVmJiE00ObBzbwUAvYHLDpqor2a96IWQ7hNe8IXtMcBT6HPIind7U8+pqIc/Wz7qa0i5k1EzlzT8vihFkMbRqSwbjWA+EtpV4AzCzYrnlQzfiihRMrMpRYc43AAjpmdql6V3EZY7lRmdKNuVeGNQlNfzliaB29+rrG39sQVfzj90Mhk95hQh/fWr9yTnOz5SQLI05zxscWdtPP1NiztDH9sbmE+4mqSwX0dY0Ge4wP58ssm/ZZQZDi9LRMaYgjA/iD70G/EuvdRCjPnYIM40Rg/9i+5NTaBpFmqXqUxVFXVPyaP1H0xz0tNCJAkT0XclFnutXZc/Tmq2KNmSDYA+8xSBXWUBGeaz3758C0bn81T/FBeWwEatt9zaNtCp+ZVtCypSi+8uYXeS2qxSs6KJon53mODpVS2XrbZIsZtKSmTrxiPp/4udxXH/Hc3u0jNpDvf6nFYz2rm2fV9+r74h1bwZlTSTACqOm31qoymfJ4No/jQctCzzPKomdh4KP4SE26UFe9q1OpQYhZegvOzMDc6e6AP4WckT1zi7ZIolhlDQ7NFjcR8j1GgtB6cfJ5sloNSUJ5Gl4TTzEHUA/6ueczmrYWXTsyO9Mtiyi55QxrWgHg0QphUlM7jIhs/y/7rvpG1obZ2f249FKJyWbp5IvR7Iq1PYBduIDL9pYBOnTun/GfCkjth4ZVs/LGLaScCTeN1dqikwxtUWGK+sEkFKmPzoifWg1T3INSoMDTcXqLFAXeMw/YHlQlKqiD0qBqDIpS17+BxOpRYFLjDFD1NKbBBEByUDPJ0ScfOUht+dUXrH62Hs7BTcjT+iGxqRCg17RZy6ciDptYZQsMWQiKuoMsSLlSxKDUTrI0AYpJfkq2cqGNDdc5JUrjqtvMoGYJO10zKhMatwk+tq6kHWW4Tt0nDte97Fn9P2YWRuoYsdbeGZLjEQENtZPla4W0+hYnNmqxhRQfnI9/9QwZccsR8CLZaT675pL5DjjLKleWSmOxmJV39dGfRA5zWpkAv9S3EQ9/Sw0HkR3j9uo2g/e6Ws0SxBvS3M7HOcsgb9aGUZ7todVsno/VZ3ipoSiJnY7aUctnFFv7Yyt9jyl/Bgz8zLrh90wpgJ38+fLzBY/2ZnXjSJ7GXgsuLTptxFMMfbQXWH6c4lwaWIf7xLqVZyUwQiPlUQzbNWvJN2+V6XFwrevyvb4Xbt2I+cBE54JiQylpkq7N0JoEFPP6IGRCERhiIM3yO3XbqSC48ZI2kK5GVTdjYvqpUGH127kqwChTx1ZA1qbEKc5Bj5hE97RufHLKs1cNvPrqE/MFvuP/SURjU/KjyjFjBO0iP6YSydOvwMzKNCTmXJnrNKqmC80ZgdDfM3YsMO4H1zPe6Fk6pAuNZT1WsGeUupDzaT111u+98lQ/fV/53SetMQnlXstyh8OdsGiItJBCsLyVZK2T6k16OqUmE0fdJ6OP8IwiHYNerX+aPMttbdoaCMtTJj6/2gRRns5jfsxTq7rhaMOxMyWmPU6aMiK+Ce2k6hAo4Bn8xxFjYI2a/ZyxfAsRuq6adtYy+WFa3FjfoSrlTmRRym8bgTTrT+GfthUn58l6H8UOZS8KMqEno5Y9Gqt1LhYa/qR/0clcQAAAEABJREFU22/azRy8E0Glo1UV0G5E/W7w1INzBF/FbyrfHTtmzZ+sX0YXuGj2Bw7WMjvJ0cZbXE43zig+RWy+rpnV90C4n+YJrwuc8ZY4lAbHiAjObTiqcqiVRs3WRBN8UQyS4nszpQ0BlgeYSRJ341v4nlQ/UIzKYACf4q6SgDAKmLLOMcdFyJcIZ8keIt6WdqF+6vw8j5TF3sOLs/mY/WUE1H/jJY7JoDYPorWN8Yn1b3lEJfK0EBlMezp/YClhzZqWrik5i1aUCuhTtgENGxBK5vn6c1Dfo1zwmalUf1ZEblvVvWKMJoE2L6PbUGKp6Cfayq522dI35af6FfSHsxVwep0d33/X7z+7Y97CCr0k92LdhawFLF/xYnHVpTW96DTurT9A1+TTkv38LQu6gKlO2V4zexDVR2ARFlo0pUAkwzftkoimJZxEHTyNggKtz6l/d31uyYt1OBh4jjVMLsMvGqcKz4l6NyM/6VpS5p6eY8A234irNAewY/ZsPIvSBkekJFvexCYnR1mVtfWqYrqnR4JEu4vIxik+bT2DdXwJA3wmzkQvi5iYZGqSwwymJik+L0XmmaO9kx1ZLzu0R8vdheeyVWPdpfOjoDDbRYmsd4bO1iRSyZiF+aHas5sFZ+T009hkGCGAw2wtzKIxMWSFw5FWEy3h2xYYmQVdcprDfEerPM9WvJMvKngpJ4pGbLMJlV/uHt00e0CiVw4MWKw1FfJzlEvp0Ueu/ggCDQMWL/CnV6gAcT2IkzMHmag0QbKxkTookmoU4YHC1/Ey193ThLI1yzo8LTR28Ww7QgHT1JDY6sgYpt6vPZIBNa9GlvVFajfNlB/O7l7Sjz2t3dU2N1oWnhINm27L3kPLt7GcT9hv4BnU5EsqKCaVFGcbWi2GOl1e5sdNa0VR4pweZFP5S9gzlm0mgnCbqUdxxDo9pK5Uxyl1xipHao2KKutReG2sqNm6fmoemhwQVbi7b/JYuMvKBBvRs40e1noi8MrtKvuX3F9oTRVeeB91rJ25FME7WRgtD5izyL16ObiHTtrIi0jE219vU6kkLLeX1jyoZnS5ocyN3hfnMriqTQlRUFL0SHLArRHWxQc2HoxAENKqvSoCfKRv4RoJaBxDUyOlWcqps0ImM/xBHH0iF7Bax5j9zTQrUpYGYqoTrAkbheADq1I/6Ta4R2i/83Yknada4OPU0gBzWQE0tHcrHHK8erIrgfGN+Vi72DCjwdbsq/Wb4g65kDpag3IwTanDZkTn0u0h4eB7dAfKu2xS79G9oTNNaF5o+RJJ4gfZ0DktJNSPCzw42TkGMlgmObLkRAXZGj8VwJuo1wNmRlU5+oVgukGFKpbXy7J4dpddTMkbnbGkkxT5lC9VtX7/UkCEE1SielVFtoszPmp25UWT9lRHhQTz/TbWgJxoZziml7uywHWbNqVK8LTpUDQ1XZjTdtdoC9/95PTjOMtK1emVPfgav+on8h+6tTnHp0xsASqPhjX9MZzQIifnY2t2UIexZWOMEO7WxuH3VKZrTEgC8aSsUSHqOMl61wGm0nBRtEGzMHIuwU/ChBIzvA0Pp3SR3q7IXwMn8oj8YgtE2xIuTT+J76PCo0aL1SFaLwx6ajjdpyenCknWFbUX1vuAQ8KVk+S5u46JJmtSW5sxGTIkplH22tEnGxq7r5kldDrEabayUG8BmLOs0k97qnvDYCS7Xt1My7gkckQgWGDK/uTKXAU8xocOifeo8KBZo/adkmL33B3naAMbNyLVSnwGd+BIbKZ1BLvp9VIGiWCGrv8U1dVYpSs2UPGZrhEQsJ1Sfmd1yXEJRiJRB+lAQOviay+Hp1/kyFsXxYGiBwrzpJj8qqCsbfpKfKB1j4MhwcDfa5o9aEnI9F0XVN04xu96dy/H/I5L+dzo1cUZ2R2AcNA0g0SJEfdDjP6rnk52dj+wA5PIJLvwRsS4A9SZjknvx/JAyJVpRaDd7Tzrfla17I7zgA8828HW36O0j/Y6wJi3qvpDXL5ir3AXCpjAxWgf2Fx+4lXjnyBH4mh66o5uedsrvrbH1KtlxF2XlAhZhf5RGdjdXgplcqAtxBsohmgizR5FYSZ3IRg9o/SCX+L75n4kSerJoQKmo8NqWa7rrChB5t9rAu4eHjAr9ZbiyvE+kM+F2AxbRLwEexvhg6x5MIrPPN5gN93H5DrtfqtrujSIfE31ElB2m2QWRRfkMQsVva+15dllWf1JzOOx1esC6Sc9j4vcTDywr11fX0eFk4sFGgSoZc4SyJG/tuS6w25NEk8JvRWrdvUahSgFGlVCbODIlIjfpfuapTZhU+4JAYU3MFU2acxapWgab8KxApfNlbaeHY8YcbMXOupkfu98phJZ1nOh9IAifPbGNlByTjJHnUdbGsVQisFNmazgyIiKUPOSSEi0fqKPn0HHUHciH26fYC+vhXSmB30Z3wNtsuMQI02KB+kDSuJuqDZslVa5a+lX0VLisMSbh/qsiEGO59C6CcvvVTfmjEPSOGHI5le7KLnm0lcRXNMa270amlSjRqvE+mL40DvxPH2qadoXbnuW/CyIT7VPcS9y1DJGEsFI/jV2DDJE426PbJ1W9DoF6joAaRWFk3sUtsrJCfPmX6wOESKvGm762AQNS57l+68z2pQLCUz8WnO1c0hAHmW/RZXbI77FwdsTtLhYaca4mwaJGs33Lle3kr0Y7Ok/IAiArVCyQdRrJSaVbitg2HqbGrXMsHYbDiaHcliYeiNju48Z3cyp98fty/WMP+ZQXJPxjOF9iZOeVgg7ZtkBj5yeJ7Ik1nDbe/l2yE2rodzo++TT9fV5ttW6uNa1/LNeKqdT+yPi3flc1MuTaxRv+fupuIH1OgPxv+ON5/0oWAOD+jXQZLk1lkJYY6EskI27R08VpJ7BRbgHbXuaGz6Bj090ny/uo+wfh7BazTW1vRYn8OrMWxmUdZoKr2r6ztHmoe5Vj8mvQ/1IxKiuZSUa0JhupeLFpg79vz3W3NSBUc8fXcQ+5olWDjrnaYq6KUPBWo4BcerX4T6Pn++lbr9esaRBeftLT5vNWJaNkEOnNEUXNWBRrejoMtpHO6+6GGPXzK7afVs9OKOEKe2EMD/jd/HvNlYzmy4piVtKFTftVMBWXAGfFmAbKVZaE3XwRhRG6M00bziE2GqBwNjo0gBiVX1Q7zpDk4mek1WJ/GqIhmvSHx5Rg0zrmmFjJ3WnasOq/mowRSeJ1HcWNGCM6Wpo3XZOfytPP2XKtXZ2D9DFmQVz7pyVZ9WzrMzQDoWkKGiU68Kk7h79KFTabe9ypySOlpGnlS5ZGpWBnjRe/xibFSUvDYGwnLSRvA+b2vDvG5bAzBigQG877WG2gEW6zr50QP6VLoP0PZNTCf2GoCuwaUt6+2c+HxXQ9nDpcA0oqt01UyEED1Ezb6sUWY6xC/gBr7uvYgyFpp6r5hoJngB52oTYAHkFOqsPMKHGFOyYr3fugysVhftREV3OpRkH3drH716EixKFnsszAm5sgdPPgeZlVGytFeUKdxfcOZ3dBV05tgqXa2V7jIUGD9SFMD1QQByG0ay5JFDoZeBSTfVD3Wzifsn5nfXUynyzR6fba/PzZPQzGroOnFGi6srR/ZDPisv9A0QOvzVZ7BX9oRBvTZZk6unOHoJmo2pECBZwYo69TQAt2mdcX0fplR3aNfZ/D3cNlWLVoZxLiRNThlFago8OTZcb00JH5fVpaiSsyKLyW+9YSuFKoQqxXwMicV1YCqxnWo1Sf6jC7qtnVXnfe2rNG7njUuK7JX2/PZrtrOWa7G4Og9IKAdZmp9KR9CjRnsBplSppPdYNpP5g0qf5TnHaD8yTfTB6QWfBFpCmhgxdASoQOa8T7yGTaDclcd7ko5vVXvquvnl1W2pmyOyl3mHuM35GHBCOMiS8EXvWosbb3a2TwkOxxHqVIwhD8dMSk35a+0mEoVb1jzrDufjoEfg+xwrWa+B/uZSR8jyWpajDwblIU3Rv4ULVrVEAFaP3cIqa/WUci/I9afZU3Ln69so5pn2fkEJsvSWkltDi5aP1Yf61ftoJf1udD1SAhEIfM4bicbRZ8w2xYYkaPvB7bMpQ1lJU2RngRa/V5wgT3WNZyECCt8/jc89T80xquMy/9M9M362pPKb00HLNu0rFoIAHu1X16dMBkpIDRw2mKtniAkWqO2bgLJ5r21Jhn5rnrv6dOXi9LGpydk0OmxzD6DQ6qU7c+jccN1zBf7FiLX3qpWAblfEt+yK/fPPS0Eil/EWjWp2Ws/z4TeXQCWmiOVl6sRoCbd7hvIzb1BSgMWhgcLjDbK38+Ty+n2Fr/AyovF66QNpA6WwxAcEFm4YwutmmLep1RvLXVmYeO63nF9YK4pgE6+hg+Rq1kPF3Jjih6FGn8RwrnVNlfH7VII9nGnTC/qj+C2n5uGbI4xp4Fkr9qFwyKKbCEomU4fFaQijFC1rQXJJ1gzDCyGjbo5284n151oaqamo4yxge74CWFyM0Gpqs88v2Z9ZFBDVwKjs1XTiCDprS+UqMoGpzSu56M7flm84J5Bq1iIICOqxBxR+HNtB/IxTIqj7yZyssdB5kwWbIpFW4s1U0G+3mJiUsCt9UZz2PKklEtTFDzQCUGfezXi8wuaywjsEahd4oFFHTLZ8zb2qX7WtpHymbMoDUpS4t5ZIM1VsXZHr6DQ0GvQRKMUIkNZjTT5+C2NddjcWEY4lDjTMtitAvXFuGF2ZDA4Qf415g39HfGCGx57a6aY/FCf9ybih4QtSrtgA/KDlg01WDzfE8P3026K6+jFQ5dbvMm7h+pqsBIzfDy+02fF/NFo3YH9iukhgBOGLBLo5+SXRrYRJNhGFoaOt0wdPX9mpF/GBgrW5jLdRDcCDnzqZNasRp/uqyu31oaymBRc2ricytD0oNg1pBevbwdeSkzyaiwXaded/zs+pjGsb3rBKY4aMaE3z3wzhlSACkmUz3yll0vAuimCDGbQxmgI42FUYmiNyidz+963tpvpFyyDe3BM2cRvHsOW+nGDkZQkqLi8tnF7yBZe8jDEAfRXM8ZdmYEVe3qBp9lCXZ1dSuiupVeSW2Zl7GT6jazcoqqbU3O0HYrG2fxY/bx9bGx4sUv90kw0w6Bu7D53bc8BonlDkDlDEjA4P3RZTCVtI9VMnaXfzGfBVLK7RtlyrbzUfHm1T1+7kLR56s4GkyJ39QLkPJLX1d8uzRAyaZckSu4utzxhQ284lRi58NN52AaRZN5cl2QBo8Eq2IGHphH+jpG7UCfFPvTR50I/00GmjhkpfJp/I5/SSVhgf92HmH7MSC69df4bnWE9cIQ3cQ4bZ0MWNr57dezAKMEikB2QwTmadYRflgi6jOadVI9luDoI+b3paydRL3mtwPr9zoz914Zs571bHrCm0F2sAxhzsBoy3zfY1FDbN61AifCR2ji3ReQSH76/2HoX5AK4TAKFd4iy0858raZbjj+/CIK/mZ5xv9b2oAABAASURBVFOP90khn9esktvwHtFWiXoIoYhtqgwBOjYy6JgLZ1nSUgQN5qE99DvQ14RmH04KgGUsPeN4XNbeFf8xlnLgBo+ZVX2dWvdZZz6Jn7HdfJsmQGDYle3fsQSNiCZv5DM1HQ3FJCxCp3wtmLDBRMqUjzY6D0rAVtFlqpWW78L3BSYke+VajMtdcXSlbuS/Tz6/p++hBt3uAlzLZpPeb9HfUHPjGesSGjmYc51cWQrBpCCUkeixOszH82FOjvT6Pk7a0Zu1SAWlrcuREXKMUmzPefqcb7HMsKZ0TZEDkh9PQplW41bqaOVaTiU1BfpLfC2ylrd2myzOsWlZb4CK4RRaH220SHi0L50emZXBJ/kk0tjbzk/UP7/X7vrBd63f+lT7tfP6B/vIYVYpDE7Kxh4tLusyNYp2oR3VCZZPrLKdtgbsQ5QGqAcoIQpRPSGnmqPPNsKkkKi/E9V2Sh1mUQ31TV4C5y3P10yitA4wmotCWlylUWYh64pSCYaf6Ceehh5cZGMAfxFoIlL+oO6Xa760p0em5V/Xfdvesfe1fa2Ile7HrdR9+I/8IdaL7/f2XzUDlGyM2qkA58wT3T89xh6JVe1x2lUueJLKHb1c/2hTUFMbZdnkQfS/0Uo3MhKuFqI46KLIgdWOspS88Gmzs95V2fL4KTuR8LcL2zleFa/UHg1HGKXoEGtoG4wCF+mjrcz7f3a7XhiH1rYt5H9dEdf1WddPexyWYOnQgnPuyieFFBWdLjGzLw5xy7c6khPKDDCiVnvFNC+dSETmKmmUI1uy2W7u+qtxkOhmxLJtTM187xI3ivn//HPh6lKbCU/q009ijQH+l/DS8maTFJvSC8437mdB94EKhOtZSw+AyyXZacP8NC+/4B/Unt+eb8XXrntft32+y/5bz52QYMkYZ1XhJJ9ucTjXb2iT7DOX94+0gIW50vaoG7h9cq4HVgGugJguk+yJIA14xxI5u90Q81fKK+39ze68HM7gGT7KL66ga4RLd6/40rtW2zK9lKdQMZpoHfSGej28G9WDhN6G3cK4746udqZTea7dJzrNqgNlMECdLURgpF3fWN9a515HU/uSTuesyQWo/1zlXhDFOT0MxUaLvRSu8ZOKMIxjK7me2CpVdHrePjdH06RzFFi+BkxDL4xYKJ6S+uQh2i7l5+wWIlukUxTqOkbheWBbTUz/TyVAa6E5z6NdhqVoU4TTUEie8/IvsCJbpi2H2WsmWFDGNFMA6En+MfEFf23uJaVBQLuQ+NvKvAknc7HWpNfDutVi8fT8gl+zWe6h/EgeZt3IxCnFfchZs3QbrHjsLKXE7Wue+4KIoTClY7QdUKjUy5nG7/Rf16J4UfExGptn/xyJaujNVXG15KazVz+7Hpr44CFS27sS//Cu8AktSM5NfM7aZ6giVfs6vXs2OiBraCWnSiF78c1Yhqls1CsyZkLtsS0Pn8N+8bnIJQbRDE4ITI12Derog0Ila/BehkkO19K+cHut+N37Vz9ZhbjfNR9FclbOOm8WJThVItrRVNoOULMaH8d2IFSj9rideHIpQRlFZQtam0b0LZiIX4iLRudcEKE31sHq45hpLae7aEr9k5XZk6hT5/zWufPcd+uR6EwtjJMANQdFkXUveyS+J67Da9o9+9PPZi+CbWpygDv9Z3GTM54r0nAwVTM56rFnKxnmEWW8A7VeDSXwHXv8g6Vlwza1mdk7P0a7o9Nu8VhvXWlS+djB562+XJU4I11NNeVlxnpY8BMce5E3WGl5T8r74Mv4bfy+lfGdq9eCs/u1zKF4Q5s/VL1umi/0VkInDEqSDtflli4ao0843o81rJ1mXC9VEE9+LARBjYW7LQDwHS37/aJxrytgVBq1R+wrZZzReiIg15OF25UmxXHNufLwPvncXX9bEdyKbjTj5dW7KquOIthy5BjKwUGcWRwTHroNvORfnHR1Vg8MbnHZR0zX3kfwH9gzUFwqNd8dYVahnZk1YWzkOU89+gs+QErOieu2fS8UIZvPZM55h4Ozqj7P7O+4FgSIG+0Y8BcgTT2Bb4uoc6ZmXnRTXqenO2eaVBGaNzwGn+ZnZkG8Lh9l4jNUSiChWVmXgR4Id/Mo9wwV52oQ00AZjxAfWMnW0jj2oA9OIjXALEU9oyacW3agE2J2BhgxSn1d2quF9qjNDkrlzyuqZfa6JWkY2nneJUPnBJ0DpbsXBmNJ7+GoSZQewL63GUrO74Nvzj19Rt/kChGxltHkt0Slep6Z3FmCNcoU1JiHnCpxlIa3YVpXIQELU/C6xrw9z2tj8Cc9U+LAwBYPOOKS5kCYAIsYMa6W0YKZEJmujjOB0Zyb6iNyD5VBjSpvtJCAh9YzLwZmEZfHFb6NQURi5XhzBJI0wlkR51q9vKHv2iJhOTxHgT/NL/y8aNyn2IfzWnc06LXXMT6KSN0vNYkOEDiGXIAKpfXTNgVFXxTVdUoYTWuonYmHcdzqmvkkrWLa3vjI/rH+Yal70rQVDkngSSVgBou4YN5evnKv8hAOKbOmn6waKbOq56BLTOgiFOn4pE1OPSdnkRdF9HTxdVei7IAKXYyAhA0sybTuKHKc8BBHKEUGGOnLIsJBNcr+Uthu9rPqfFg7K26w3oF1oZanq6UQSacSIMDqu0/jPJpuNkQL7JzDp/iA91DxdtlANsTR016lYXLHonUCOV4MadGkcHb6vC5bFyrq3hxsPz9xVpSZcT7T11QYYesNgD1K75JFc0lsiG5Y+irBI8cwXP0WG7u821pcTSgokdh1twxA44gSeyV6Nw7ZSZl18S9yR9fc8grlMVe8nlX0jyl3g5/Dq6U8A1Q2smY9LwqF400r6JAdjdnCJypeSA536jCjMoLQbk1dMfe+hbQ1KNpWwde01ajPPo9X2SouSF21z/CRJEyV96zEejROfufUKIddY/14H0DJ2lA/G8khl+XysS3yWyL0HjPESDFI77pKMlEUugaIFMlCHl3hs+rTGWjhHZGiRjxF46JrsZVBSC8ilSm1uZu508lpoh2SurTaLwRSJsYrhJyPo2P07TC/nd881bVi1iVu5LWyT/ewROMMRhHPR2lCv7Wv8A+Yo29EeO/Vi4q7UTPT89JSw2x7qgrCwgr2WqMla3nHSm5RxzKbcYcbOGPPcS0p4o9zpAqD+azV5q3rVgZVnK0ZEbIkI/MMP6TcWtUq7XBe10oXMrv+uCy3E/bCGqx27UGD+q9HNgdbtsflBma1B0F9EaqyOXvyMh06ucmt/7iUmsBRxB7qwuoAH5ok9CzpDygyId7ubN2ttfA2WjRgkml23Imw84ncV9tHcQAEk1Tjc9QaKcV0brpVnTwQyN0Mmkh6XgnZbHA5zTWz2WMJm6YVTKXQmB4kougNY7sfg6TFuJfnMsUbIxZ7GbaPDnsdBiZGsS/iOrgeuqpwTkM7LZym/kxrWRndgtkBRoh0zE679LnA96BKh+mVeyagBORdedhNzBDw4WewzDxc547MF3GozOo7ByhYjwn3mgywGQU5CGcsKBANET3KTto5uI8/74RRXQgpT4/EJl+ebo8QQLZ6eTlSimCKMNjU6rkes9YT9NWGrpzMOmeeh6fsoRhLufaFF6njOKTlLgOMUL3e1jmcdtTSpe7smgZHKXafWdML7WZUwW9/lmtrmmuePSc66YKiVUQey2uiIxDW3rDkDCtUjLepl5ZAR7+6ecUAwEd6ZBjLZSo7HuC2OiE++gaeiWpbiatpSqOMg2w4BCoIt1ZTHnTXd/LGXZqukGy4YJebgjZPt3KXhcb0nHrTO26yqCjn26wiHqrkdANlc4wpHkw6Fw8ajz7W9e750+RdGvhbS2fkWEdgFmlifuZglM9e3WKg+bfnbEDnyvVHQIyahmK3aLOq6VM6M/EmY1FKBtGcjppdcaUtoWA9zmZwggXMEKUT2r98+IZKyrwJhJl2pU9nZpeGxcCshwkcnztGm/0HcwY53rPbg9tU/p8OeVuY8UJIzN3V+sYZK+8S2MicfjfZkFHKmbUgqEzaJ63benyGL7J5pOKpnjyjtOh9+Mgs7Ta8HFFirpZxvCKxMUXf0ROjluKWXrXPcERUHdNIUOLDkv1x8T0a2dCnC63bQLS3aS7UA53KkeyTaJPZBeyRmGPKvECABr6jAAOWxBo6g7L6sr6Qgih1vUSvl7RmevtSuShCDfQNtQlAT6yg+prW7Fl7tsMG/3u9jXNSi5D8qr9dxss77MZ8n6xHCReYqWC7vSBmxCnG0YtqDxJHye34y7aJ0gorVqtZ6ZiaHnsedBLtGFXl8qQ/pfrqmiIQhStV6bKgQ4lYljvUDF5UKgGlle6X22UBo6b0erYf2aKPrQmfpfq3xbY4tbN9IGbh8ullpn12KuLBQofywVbshlmmdZHvUm5o8hv83X7LhdaVe0dbKGRTUEKJZdjQOwPuWF1250i9PnWxtKiIOg3X+T5jqkgdXd2V2ae7UXj3J8LyzAyHSDKpxD+uSSVFunBPasofPHNfNCIOeb8M3uOyTIM5RYRX7btEZaapm4gt/e6TB33LdNsUPBXxXCNKzjrfnXIH9KTEvp/eN+JeSBsrhCwan84nc5ReRYiYjVEmnoFkoq+xS1HqchLlbxR34zu6cbP6bIGyrBlRKXNh6dAroI0S1tBTzxi2y4zI+2Ss+ZhiNAgRDgiVliiWaH2nUJ+r7vUKB62UXBRnM+BpxXHxOA8wuQ07oFRWX3UuTKgYO4P3Zbmmmv1QEaWL8ens+a39resZt1I3jPDPeIj0eY44O7OTe8pX4VNg48F8DU9Exedo8Npku070vfEsVAhkFnSwAFlR5tnB+JTHY+uGn1jH/R67hV53MPHB3ojFZbE6vDSzxVeLBnE20N9ae649y2FJlpHEtop4V55NxSoxEzSGjfaie2ZAHZz2nC7ciipNVK0cP4enjxwV46ji8JdxawWwKb4e9cG77FE4SrA/sasXe9yhyoLYMqYMZ8j8QG/12Ch0CUgYfI/q0M2613OvFEihxoK9GrrIfzg/eERkkeMbofrqVHOHnKwD5ZxJsc2aZce0xShq/2tlOpOZz3xv+574tq6m3tL7cG72X+AgbUnEUggm30PJqD9NWQfocLq398EZnr3mEgMgZnO2d+iaaZEz9EauTDx1Krunk/bS2i27ojEfh9poUw+uQvXW0vwXdnOVfaBGA5SdXHfHq9Kr6pL6g/I9qOkn/KFlEcft6/gCc8aLmwzrGT0AOqOHjV3WAvCIJcXCHuibMqgt1hm41AW59JwqmL0hwnCaZ6bRkveV89PqWVDRjY9FjyzO4KSd28UdVURpuGee55T+eb0bcDflvfW87roQrobROdmTEsOMjI/wfEF1wOfJPmejNmhFJjCtitiQ5Ra9PpDbPny+9rytiRKEq089wyS9r4W1jFJF9dR4zCcgsFAMq/I6t2u2zRrTndBCakHVjenZn4kLpiayPiHmSnnTOnXcZbaUgrreDCaPAAAQAElEQVTtvu3Up+3H1w9tF/DUZLZGZLirFHLMu32EZGCSEHuZgunLWi1MwUCzyhTXwRwbtaGtjaPrA37xLlwtdb9YdBF9lNW5VATwsv2IyGgEnQvaGk5Ud6KeOXd1ziSnmt090yqIXVxz6OL2JON2PwqtVa65mL71CcvHJSPxy1afEPBYCJvd9NIJRcMJZ8fLbyUlZXkqqGjHXOFpf6WBUCQDmXjYktM0ZHj5Wuqnq841Z7EM7ys3jsqe3i9TnkDbedN6YQKDEj0xb62NzXPJEt/Xhl9YIeigBPf4b9oXVXwGX/apphw/qIPqoRvjon1cAvDt16J70ae1bNu03DSfxyrDHsRTftpGKWOsCo4PkRyJf7prxK48HWarp/jdzKp+Ay1Q7Wft9pW0lvKW0w1jILErfmYZrMhHK5NJIKMpScvtvabYWZU5UBqkZVuaRlHyDMUJ4D4zahz5SAouCiknnq5jpquEghIPR4tdxQzUJhQO0T31glWkd/j/MzznWB61p2uZ6wkBrko7mXdKPAeWwKjKjskEYMDDN92zx2MtW67gchrZYpz6JM539pW26eFwk/TQMy2YyuOrw7Iy0nzfkg/e4Bdw4Rh/6d8WHV2iWRrQ9t1SjfKJ0S7iNh4bQ/tfNk9KWSkCYmGuT14/96ynoHZxnyfnKa1J6gvQLZOvG/YftMp28Y3K1MMqp25VCe57vomv2b1XilvhLSW+elMd3K4dy31hXf7x9bFL2Ytc1mr5hEOv1VBwOvHoNIJti7dTv7IeObpn32N6bvdwTkC40itLlTye/GjuNKmcZHHi1drGE3m6WtwrYUpaZT+YZCPLsrzEoJbjmPfLDVdBBNWiq12dN0EnXGeUfRpSKPWl3SQ89Z287DgF7SRvekZIK1rgYz3mX/BPeq2azMI8yl6/rwf5i+dLNbjBAM0EdHFYHQMjin7I8jIF1kqVtO65qu2xfOI5Uk+LBETBp4E3OMK8TV/HUbHGfvhccwM3mTa1D1ga6jwLtchQzftihj8+7VgLe3MFt32064kX2jleCF7/uHQ3t5TKcsu2mrccoBcpqCdaYoXyV+BxOxcyZjhWJbh+yp7pb+H+znuTQcAkD6VOq03KmmMWfa/Y4ctGQt2ee7nxA660OHaVbcXnhHaShSqDLerwrSE+gDJ7s57J7kSmtfpMQXHY6+G0J155jLJvmkLTC9mWM9KRl4qyRWaapXrIWOY66UopO7taIQ+UKMnoNYPBarMyFpkT+Y38tRIbI0RYnE3pp6ebZ5j6opE+CmT2CJbNhEYDcqbisij3JtqgV5dloQWn5Rf6C1KfejK7bv15uk+2/pNreJoX/jczBcccJn446rhKmPBWswZVRMrH2J9shOLtlb7QTuk4paxRbb8+KWxFmu5YTo7uVXhruIeN9hlulIHWUB5TPdWwTeykI03e5MX8GFQetXrEv8zHAWHcL7ThjJqEfrqnmXu7wd3E57hibRa0VET6O3NaiQRLmfRyGx9xdICRuXa0lRpBWztVluaqAX2Kzn1Sy96ST6BLcTXZSlWpLsXvyGpjEtl8q1fRwfla0T5BchyvqLOeZAKkCZ+Za0a3eIwsCa0MXzfBHPpMeaaLF5qsiMri66b4ehgBPme067Ps/vNtU9K6hXNJwj/M6y3NjlcpGtJPz9MiHq/9JT78UFgfKF4ONezN4p+1GfFCI9/1Jy76lR2pDvN9tiQpyQIJGw6EoJ+s6rEfW1dcATNOkO4yKfjiShOmAAULlbbNoZViVZpXjjZ3G2OuI6WchQg3ENYmqDO7Rh0m0I6e4nksjwOi6FDTDVVeY5rAppU1TLv82qtkRLFVihmTh0Mo6a3ubKlymHkHQOuFw3QEPVDeR9rSW791NoOHLYlsezVlyjhMMMxzeQmTwQEVruegW30k3UtXvRhN73I+ZjcoFGIFQE2mqThEhnVZAUdvq1fI/ft0rzoVZ6rG4U3BMwMpzmSJCWeOP7r6Cnt6urJP5YNzM/ULtu5D79I47WUug+ZaqoLBTqLNOnzIak1jvxEQo1FjNOOCoC/FwhlwChJtLX021lpQWobTdUZdI0cvjvXEIZsFbbIkMjwvVn2wVpxj6RgFbbfvs3fGOouiYbqPlPHA1UklH6OQUSQr65rQZSO9N1pJI6T0b+U5fRo5FAnKWIBe4ri3VZg01Zi2qmWpMx/DnqZAjuxpCkZryMaJFyqrIP/5BoO7+SQya0p8ds6kLldpsjtjX/FuwrZOpMRX1o2e0jDVCZKVXsc4CFcMw3iJ0kTtgGlzAk9waKsYI+fWVXOGD2VF0NoGYwZ3rKTU7v3eOP6ojJebGdte9BJBlPE2TaRaiLWHclM7k50Pzzem2Dy0r7JoAkPHKFjUcKQxF25PdSHNvku891NapFIVgwFCXgjj9tZ8RJUD44Yy6Of0d9cOXSY5PSoQ3GMJfwLpTSIMWkotphWuw0y6AXPl2Y8S5RydmGodXm9MsLFpHPSd/RyIxFHTmu3YkWYtec5KWyL3dt1sZpkYd+lVs2qeVkADB32cUNPzMXjpV8JndRl5bKUEJwqnKGEqzKF1uAQyEUMnzUWadY9VXnVWjfpiD5IsKQxtfZTspuE9Gj2KoEoGSyfkEApjTNh6npnDLnRxtFE/Fq3V0FyYvdqJWWKktcHRfpMrqZSOeQbkqyXtzhK7I28Unxf4doTB8BXINa4tvtLD5ghxiQZylCLZqFiQXpzuSneXEM0CHNQ/KEhnkg2zkVGsgrAJukyNNQJq+Y7Byf4mHjWltClFMuOj4JIt8ackwVrr+NXgfSvgei/HjNFeGLBKVMkSvSca/ZqhRreY4TkrdYbkEo5hFANpeA0LzR9YEjBxHwNrcizMaFTHePKuTsL4hlXYaY6FTdGXXVL6SBU5pzzzyuczavdjReYoIfXY3GbZHol1pBrKftNpsJmotOoZmYkcl7gFrAO25o/UATggCb3+3iDcR2+NcWv33Eu23YumPcBXiYOZbfrud1GLi+aKZyXsIFuOKGR8EDfRPGPJDbhNW2fPcygRm1pkDk7h5OjsIDVa+HB0tgm11DIfCyoF65g9Qw3BCKfe3m4juVBG0cJ/uG7a0ZWjU1Sdjyg6ImWZTip2ZUURjW0sunFhpoGXGplN99Q4JkRWU4z6GcrVYmuDyBe+v+89VDaO4I6JWA3P7hZfR/4evLAGxgH1psl6b/joq70vQGXpM1Tm7aTlQU7LKKG0VbJaOqtGiQclNJixD+OBPYXuot5H5FmJME2RfTejAimM5md5O+Yig4QsWDMAh9gtFUVR87w0zaDY+1Fn8NrOj9XiSEfox/AYT8sebMRMxBMc0TUKUC1ZygFZgwfCM81qKpKuVjSjm2g1AzHmE8SacJqtqMuf5SKamtQxGoAwA6Aye49F7Sp7r1mbeimXt51ndIddQ2AhQhMg7NorpSJwGYkClXE5fq2uehxaN3IqWw/bdNep69lU+f+23cayGvT7bzCNqf+ZntfOE6Xq0vOKPvYGcnRnnMlY9vH4Gf0qqNo/b9q92oCnvWBljX3qc+tIU4oi65rZb2iXyMC3Jx8RN0iYCHH6Hu4zUhvaZKmFq8xb4T3X9YgzNtVVvkc0859d//z6OdqUyL+YYE0lVDct/LR8WW5fz17iUjHYnNbrGSvtcrfxwl7mZiMfHKmv87Kz1K30dC/kOWMd7M6FDjAm37EamYabflA3UMuVAzN9PJwDWFPPt/5pPB00EWkYWfA1uIx7cy2qpf5Ue1c5HhhaloWSGjmaBvYglZ/yb8Vv6r1EoaQJ2aMyLMnsSXHq2dCl6ZudM6Ln1Ko756s7kHEsmRv201ZneZl2Smtac+5d95oE9e+1KnyfCxY8th5eB3pmQPR9WW/ShLo/cWPufKWXjpeNs/HoVsl7oTjakGpv8jTVeTuybQ7wDUw0VrvBnGBTFCqMs6Q8046scrwNbqrCR4rAzMTke7cgG2jHsS6UFkwMVLTuYtkn93m8KlFf1y8Hk1/wqc6SQHXlHDf6pQL5EB9JD1iaDLunnqOB6rLZwxEgm5ppBdURjlg1VEoIJeyK2Fpw+cO5pgBFpYNMQv+Fkoc+pqcrMkdJhIeC1jjtNZf3JyoIL48jOXl4Yn3Pe3PV9c3+fZQbqnyjhGRQTXEzS7ZFxwReXv4wH6SG2EsBZzzfhwEbvZjKmThtc9oKeKDCn1i93Jqfns1e6imM6ZdINs2nN3L1Gfl/+GCekdWW8EIyGpx47cqbKYcVrVrk0d5tng5lM4WUYiNpOUqQLAVo2ELQPvf09+SkKr+2CMaMvBLyGzi7+6X4M89CmhdMreuJM5a7+Hk+8VzyeKF3ANuL8sCd2NInLOnjy1eEEUA9Ivv97SCaCnBkmwxr60S/7ayRY/MOpWJ52+I8iGfHw7jV3UZJ0QxMUWG9gjyKP1/TqnuxRzNn+ZTjjbGDEv2z3xbTMhwTn1kfJXsMyTqvIKxCZbNMSh7Wgvr8LCz2/xk/FrGi3NPsEVtvKq62XYpnUjX8/vOnlwiuae3HH2Xp0Gt+MD/EP0rIHMTpK8uloDeR9YZXBV2AaJHh04bynxKHGVuNjm+PGik1NYiqLGupkdcj4g9xZvmJf1+LL+bxR8hRs1s+OyxDCCGMrsmBcW3GvIuZnffgi+ux/azrDt6TXxFdy8rWiLdWsUDISNCpBnqg7p8JFV+beOck+W3chXPO1DPQCSc34Sqg/9uMy3ttaSiUTiCn9R8A93J/TCIm5EalLBO6LVi0dpsxdlPJNVLtT6NV3pY9Z0apkiGaZiuCMxK7SJbq2cAp3dF8Oh3OXpig1aAR/X407Kk4J+qpuVt5g+WJ5/FsYf5E82L/RC3KReCO0dGLrPFDa4Jos9tX7cBmW5ZSY1PCPIU5xiFdhPUa7g7CJ2BbUX0hMwRUV5X93HmerCwIrJ685kDsjd41ooX5tU1Vw+JIeIqAyDlMoe//j6W3UYxc53FEAZKS02f2/d/0TscSfy7cu9VOUmVLJAiClOw6M98SyT+4eg7+MH74n/NRvKad6z7YJSdosaG4Bw800ftFVY7NOApRqWddrlXh8vf6KYVmWi5c65yLtg11X/yELYumK25g5CHsctc8KLIzrLdZ2Hyy9folMwMVyp1XsQvVPMn38A71z9GG1O5/ft4VGT6jYYpGXWGgYNGtyAtmuaJ+5B5r9j8iEX9eNRmHdIE9orXRiklTpOvl81PcmlkDGMj4euEh39lnnsJy+FYiinanK+/MmFy666QYptfC2dLCaTaBr6e+U9fQipGiNX/u+z/5Pnk4pYTYT4h94MP0daIuVb9faCx9++YO0lqXnCU/ckSL5XQbABxz+PcJ4xgQtghfGBo/8v9YiHqNZScJ9Oq0+gooCaUSeltzU91n+mLkOccSxoZDf1jBwmfYZBQ2jVs4A428aucW1kpL63RzBGVxLXOwR+dyIf+twUtsgiOrvEquMSaiVDC6SePtunWAv1j/O+uFXhpUAuFzVgAAEABJREFUAR9s8V8UFl+K7Z+2B5XQPSzf198b6IBM1ycDqCT9V8sPNGOHPbBttgQWsnO+rqL+baL+2k6YHRVl2Yxy3eETNmtsY1nKt81urHR9AJvUuiV3ZkMA5ZOGg1YjoOry2Gl8/1soS5fiG4A5pjEi6nVcmxZw1HiOd0TrHObkLdXUpDPDeqHXmT7zHiTonOXAiHHdahYxQCM/O4TXsrMxAYNMd7b/Tf69k5Ma9ow/GZGxoOB8Q0vVj/MPtGoZ6v9ayWr7jqFEmi9zbrIKHDWz/ilYOpqjHMehSTNHD8pDql81kQkZ6CtIhK5Buw2nuRVWtbkOdy5msJ39yX2PGevFfSdP18kafjOfip30rOZN/XEktBCpzvRZ3g1qG9NRiBnxn2NDG+s0wa1pM3gwHOZdLRnEnV1qPe4dAaqORECvhCI/ztozMc24pOJbzTVUXgt11W3sLYlEtCprXiNnnIjalgGoJJ8Vy23r94avngFgAFVeiwWMjFEnJNXuUziEmByDQI9PL2t/hEABrYZeFzhgefSPl5b7R07CoHvRMCe+8MjNGbSgncvM4DyCboPpxOmRhxstBhvlgPYVN0a7/evWY12EXjLBFlaGqATFx4BkUG7M3ewrGHMGyhVIwkqAL1VDIm+RZrKjH3rRO5P3mljTAI2UaWgATTm0ybF7O++blW1zY/deih1aEezCZillMSYWjHeic4lgis6UEqY7MFK4EsAcVnK0kSmVNsP3WotuijOGD/BInUvx+ajdXuOE+zbfgqME7Thu1YINc7M9+UyajKqHd80gTc36mQ5T5hJzJI5lEu/QZ0yDO2tS0ColgTWwTu0DdvYqsFr1Nw00RuZkgzQbx8v5vPTHVJ23SvAsF+xBPC2pj2bVULGdyHIVU+VMmZPbQ1iRNv2xZ1NdLIb1WoeWkOcFb1pxiAri41KUCaKyPTDokKWRxWnKlPqpAiVHPcO7vgO6IAMvOlk6046hpNrWI4pQVZlJfejxMlMru6Pdaebo1i+BJLptXlOGASFS7Pis0mi+JpguQwu59Hncyj+WBa+xOte0j7rCY8fjfN8yP2SQ1azsyfRTo8yaUYc8labgQ5j6XZcoc4AsQEG8owJ5QZ2cJTAxS7iltuueNLEJa2BEHDTNIc6SnyJ5TPu1uiF3Ej+9neIcd0rGH1M3EfduZnQKRwu13JLQndK7VWqWXMmntO9IW8qWbjkkbHxpkEcrIjmKK+auoXSvguGXC+iTWPCB+GVrGxIDCSFHNr1DvI76wVrc0SKUVhKN+a9bSezsNPwajsWMTxtmFX1iWYQOhBU657JbLkSnL9sRD9w/gZZ4njPKgKeF+XLRZjf6YG4zSxwuPKHY19gCHdQbh5E9k5qvItFVmlyjpVg0OGFSY8eUc8yoeWrpaySJwVQRKhZY01uoT+B1XrCv4a/pnfVpCla6xogDDruRiZ7RvdGZbwfe+sQeyXq0L1F4JGGmzEx+KXdU+G9YdoEC9Xn75shvP227HQidL6pesQc66W2Kfo6j77DKtGpvtP/AHqONCWKlec+CAB95xCJCogUKfhEwZxDo5G1Fba0P6HVzn2vdZVp7Fhjy4SybMThs08UvrMcq8UV0xZ2DmL5I6VEFH5oWWAtuVGA+Yg+mrLLZ/cxsMaB+aqFSS58a6kepHxR72E3WrCvTV2MQcOBJRkpW/grkLIaU4y61Cu7pbjtgNkYt8/VWleg9IBn/IdWMK57jmx5quT86A86M8iR6Ss4MkoaZiaJdpc7pg+Fq28LuhMd4NNdQ4Y4UZWhdKYo1g1GnzV2/Ifp7oms5fIV952zGcnCbKcLvdNZk31bG5f7Dbde+8k4x+NVkmpcKtGNSoaBmwE8HaM3J6ZxM68v72qsz/jIOrC75QiVswwCEwVqzQjG7m2HQPZgy1eSGL2wbpSWDjVTUP8kfumO1kkYRmQfacM1YNLQTjsPVtn0tCxHBMgr29Kcn4W3YCx6X8D4GVeEgpOnEvcImHxJMfnMs6VpPC5tLzxxm4VfuPBA/pvxIDJ1tXT94FcEkOvcwwHAyiqolLyopQjs7GH+c3HV/9FBST0pew6+OhhA5fYeF7iNDfAlOGPGw/5jv5fyynJD+MpCOsdkrN9uV56IBroOk6YdoYCgevTCqkcKV5AyiS3HAZOfc+j3qHIMUswbBV52IX8Rjsbfrsd081lD+8RYOZrx8l5tChhasotOfUDP7XHXOvBm/2Ff7/+fQ3sERiWrp2Dkr3+Av/CRlcW5PT31F5i2CfkfKpg29R1AKXbqstxDz2D7G+lKjEEC2humU3YHiFCCuGXWUXduv+yncGuv5zzyCBDi2aq9ZegNlgGayXUDJrYOOiN6Zoilb5Yn2aicsyvfr+i0AHSVDTGFgp6lxjX6nRi5RLdPrm1ZmpWZtU3vutjOSwJi6nrey0l/DON5vXLq2Tu/Gb1BKUJZGajjOZF+IGsnqRIv2hImXcFm5sOIMWw1Wb7KpllJHJAyN/w5L4xvWaqIL5nszpEhqDtdbVu1G7NVBdFlelrxIIjm6ieijE/m7tAK3AhvBR06XyOqnETfnaK9ivzrHIurjve1x2yFfKosHbR/C0rfn82fsP2NQJ6qy6iZUIRpgQefaJmHHbN7FsyxXXI/kao/+oi1RKXOGUCKA3WU1oxyMKbkL15/CH3Ab6VNzWvmbJuDOP4s/RsvI6yT0ftPMbdZ8zxJtyyCgXJZ4gMwm2Z8h9+8aW0J0DZjhlUs3wXiEORY9qvCJDGjShMhAYPTDaTm0t/Wdf18KSP9sPo95QJfmfaAyULfibz15/7N6jAswQE6wRXQMlGslzX7gal5+JrO0fxOOXdiGiSYSlhhUs653g6Cp8Sf9Gl7ni58LfQ0RUu7MpbQqF9RsYMaVOoj5BlR+Lf+LUHsa7NNWWv4l3AP5W6V8towT2jCtlTJBOdMPaiSNwZgsV7QeTJ+QZLpO87r7j/2x+SEe+ON4vH8wT/uD+IH91+3f9A/br+nZpQ7cmIoLdZu+Cu0kSuITXoceXa5wcJ/av1erPxswmftpSWZ8VN3czjCQXtKr7upWrB88G5voVraU4fvMsRt4N7VKFITP2eyCJ36G/5HLYDxW15T9CWPIPNUL5VFx6oOK2TRDLON3JqsFtm1d6lY/XH8yt11ZTIjhtX39idgeiiQvtK/ubp7kPSoK0TiB+GPrCZNSZFik7J7oZM1ctzfirPhF/K24LbB3+o5QLLONCJC4C/9apDU3qMcAQu/n8XerBT2oP6avTY6nkvM235aPm0ufTBE7Pcgt9+Zov23ZpdRL9vrM0Vem+bdP1sVVxj8FErlx9qTBP/9R41ddr4OG8RHE/ISmXEzlqN9jiFnvbPmtlgSz/xtVwGta0108Gi5ox37e+LlP0N3L7C4CtfAulBKiId+NxIxRWOboefc9PeWESnz1srbRBCEgC9+hj/QJqh7JhVmREVfClo2ECdpccVcKo1fedcaTHKV8BpOC5k0MijpYvUutPS169PXQT/o6xRKjRxaqF7A01ad/WubKdNv12tRib0CTmCMt0mJhPbXXLFOo7IZyUm3VAqfD2x6aVNHPyrWKVpyOSfErNKA4mKGiA5BSxTf6XyJDOCWQbyEhTG5sFE/Sxp3C4fXyHqVIDnV8HJFuGpqz7iGOjI7/8fU/z/7Rv/p57n5eD2iQgaS1oZqjLqTI/2Jeau0KqnvXPNNLovxZjMcr4uVz4D1g5dO9AFKW2mfcjNtHWxmzEQPTH9XzgeRQp9iaBRxj2dTkuvfJ2tMzJ1trClpv76BKLrjGf5jPunx+a9dfUiwAHT4jxIaGVPn512gNqNUIg4f5UgOu5x7195GWbV9f/mA9thS4uqEA9HRnoQdHMge11eD9pWxu//PQqQs5dQiOjWDmU70v9h1rtq1EYKTGGxASG7gk4f79NwLP/C67dBi51XTo2VAaWXDGirXMxGzoLFpSSzeGmabMUZV9jCLgFqKd5FWsUowLCRK4rTaVqAZmVMDsABx6xa2oMYcHv4tqaCZq27ojvWHtLWCG/MIWXMkyHAPNkiqhxhePYkIOXyIXoJbnPzt+PtCOn8nnkvqOEjs109xaXxM4d85XbJUGOFWVli4GscmHQVFWhhdeqZL1J9ef3j/Y+r5Qab4H5+3zxSWdQPkf3FTj8oa7UHmTCrrVcju/sGlgJzLr8jSzr+Vhihp+G60WTdOjASeYa270q3PDHhc6U8MwlHe7uYfbgtQ2xJfEjz5JLFaa355Cz8cO1hf92rWj4EMTn9f95X75bTTfjIMY8a6V4z/Ww/aByCjsMoeVtDT9Km3UbDBsRgK8k78oKXv81sq2GuGmWoSZ7wW3hojDEZ2LjcK9M6Sxnywq2pL+fgLb2qC2UjGzxyOVFwvSdXb1oYgrvd3oEC5jLp4NAsJtDRLuTsqK8LXi0wwh6ZyuOvNZgJ3tekwz0Mnkoo4hOEX8hsrvwUcXyj5rSoeuyeJ27RH45JgKO7UW90NuLVG0r3VdYKje6IrWYlRbCVzExSrht/JSkifTjlaR9VG8uq1zplpizG+BfTn6nimVMeVQPD0toX5kdpSWNu3IXQlhabVwZdv34M+dNZ1rXp9DZE7fxoDLuE2/O6T1P2nPcHRBtKxae9bakI1hFHkij+Xv9BlANG6HW4H8l3NK+uo7o7OqkHoyUXOKdzTGWls8jBI67NOj3HMWxql6l6inhpBi49KVVypB8KHn8kZMBY5ie0oJ6YmBrjEtujbeUCjCXDI2rqb8E/XjLd2giR4N0urlGM71ETk5Y2/tv71uAZPh9Tgksmy+KWPV1uWR9afzD1sm7bH+w9lQ4FOYoma2iZr7E/ePY7d6JP/ot226T2g9hxohNcN6dYVa01uSc6ZVIrNbEFsxw3KMAjqf28/z5CrRIS83RYipq9gDC2h/om+hFMI7duhJmZZqAjQNLhiw3UKJmG8TLDQO+xlbokcj9mJocdxcgApSv6BCd1/uZiYsGqbAXIYaqK68ldkDwnR+Tv/2/CXaiyxlC9LQcYEwSiaC+JO51KdWa52YEgEKQ3OlwHjgj5m+g5LChJwDvYZMuqIUO6VCg36BqWsFFlo5fs766XgIZ4bl4yPFty6pF3ql8e/iDYJDkVb9/VuyA31qTO++uy7fyvyEN16B1763rTnOcu/htJvAND+5JTY9LB6BaNOn435tzDywA0uXZlmGnyf+Uv2iBkPDOLU6CQcBp3zXed/ze6eSytES54tlHyKo50l7YCcP8Q35+k8F3FSN7nTMR4Ff2iXL2q252sxtHu/HSLOkdEANFCZlvotZJLh1kbvhF12YZA+q0fopRVST6ItR+x2NLC2FsCob5bOyb923+xjaEPhCAm4JqWrqC7M5qtpatHCD6bj0WorZXGtOEZK2YZ7BMrir9ZhfdyUrCQigGw3c5c/449hoKX4ZQ1EpGSnMQuw5ah1InWmBRcfMtiEIM3oo6D+bP1//N1xveQUvgxQAABAASURBVCOCbVSQgOpKcX5BiNY0se9otoDCZ1y/LIaaJicESGM31eK+6PuFHyg+X5sT0bBb0ykDLaA7YksicIi7iY4yJgvI/u6rOO3RvnrriF7e3qoa1YH/uI5/jseAoX38xsSVUjMwFuY7/Fl6IwlAKtU4GZtaF6GsCKvZHa8eJWWUmqkX72htE4uhmKvtzMq26YH2GvUejWwTF24ThrXad3IBJlO2D9cZ9igCchSFBHkVsY4vrna7y1OsTUO5HRvnXXn9TNwNZbr/DPaMKJQNDKJntSnVXDrbg4JUzleYv6sXz4HOdSOlxSp1C01OhYqJaU2UxK9bYmpmxOtk9aGdb9P2x/pRqts5ZqA5GNR7HQnNsU8e850zKE1bKbDRgMuv2sBp9YTvnvwUbxWYRrNedmAXxBDurcSvLsscIZ38FiC8LnFhxM8HXMDEYK3n1/ALrWt/g+WMbL9t6A8LL1CArMJYFjl6pLWm/RMP3QnqNEQH2wjAYQrGe9Th5i3lVzVspT3TaM/r2as1GQPOhJMP+g+1w5F6MCQhC8oPxv2LQrYkdaxr8S6379wI2ijdruFOOM1k6T6O7SviQSzYViJFkV1t434nz5cFh2i0a3ZpY+1dT+VPfYvg8+0hs0zYilCdl09BIAlo87Sb63KnKm5Ugc0BZiX2gVYTN4Qr7zVQ/c2vtVLfMPFbStA+9lOSLbtHtHSH3HMVdADiTx81W0aZZe/xo7acnVCZ8FvSpS9rPdHRCm7E92BE2Me7MRwBgAH6yY9yJE7y13jUsLcxANX06b7ANHEC90H9tKjcMC8ILSo9y0dpnN+Ze+jHHx1j4DIloGQk06ocSo6yNPJIAbcoPEk2qyBhxW9JPNAUAYWtOzuxRqm80p6DwfBQxzCtSQWYUXRwxE1O0lJMelMMZiEFDAXKpQ+f7ichJpohYMX3zbwaw0VfxuGUpsqHzKGV8gPPUe4CYVzDgJrlt9mB6ZR89we8c5RMYEF+qMal8HLstH0XoEhH+jwuTtbY2qBJGyoL2QNF/ffLd/qfY+vbveOMtepYt+A/l0s5VyxasJQ1TEkRpbJc69vnPbiKtbJllBJt8GPOzUYrAWuQM7j4FEv11O1c+IglKvD1LnUbVWOMxsfiCtMSA6QCe8Nfs5erhJOGGeWw5modwuo2Oa18Z9KmfRYly1YNidv5grqsv9a/vClCNz2SuoXSEwdFoFMzNtq4vL5yOYO+sDxXXX+VNxLLlutJ+GeqE/VO/Xa/n97OmtfOb+e9LeHFrOBWmMKW6BTS6LCzmcXzUm1ToJm01/GaIEufecCC2NrEEwragoGwu7TOlbwp/aW0qmfRbqmS0JRE1tPrj3SFv6k1Q6Li5+W2y3wPWzqMabtZNYV1fdeSWrE1W+YA0D4h+Rd2tpuE92x7XI1oGc1GgQjCXSdd0QD99P3hGNnO3GLqsfL5rb7TANbgSTzFsKIpccf4ueVCw6vjXuT1e0wF1qLTDHQ0V9mar29YdgvtnSuHFe6LD3ufcgEZBYXoVrMaWNKblBG2Y0we3SzWcmwqmweTmC70lfIMtYnH64nRe+Zr944CsU8ja+jAAIKF5DdWBPXbrdQA/mMlQbgaA9oGMSA4s0c6y6rM35S6ondYeKgbMaBgZ+Ws/iSyoNJolLFYsCu0Zgb7VwCLtTAP5zG4fR4UpwF6srxshBbSDBJxIvvnjurwJ8e8EG2rNbLJ6z7+BVAlplrAVGNw5S1auVpoVndqdJqkEbtjzU/Mj8VTtkpJ34qsx2zgnIiPc1ntvbQWKYNu5a0cGRFDGw/AgWFcoXDKPn/fe3/vPR+H7uIqo3RU/x0l9FzWGGg+ULo3GRhXN+83IVGoAZa9/d1klO54VcDm0ZTfpBoWf3FOKZstAG0ipfKUnlJI3cawYYOJ1orvkQgUUc3h4/bDUIyPqKjBTPW1LpvWh0vAAMfSz2GpV97cN21yplM2KFJa79VlOjVL4wB6MQpe44prUlxzPZSnXKgYe2CuP390Cr7NYsN+NE36hsjIHArJyn5KyKcMSSWoqyZnCjoJim/8lG+0SfDKf9ymim2U5ZtaXAWVFrZ8kU6Gl1lDpqY1G1CGwi3CncTUCOgkRy/l0Q06q8dNBM1IxTojfAXAjcvNvRUf8NKvYi0O7PvRoLzIO9Wj1lYQqLnzwVYgDGBq6qiO/w1pEX1HYhQ8vZ0UBHi69RrD2EBUxhfkKOz5sIPRct24Od0w+A69en1rgNjKFPUc0mFToUQpLiqZrplQtCNLStbAp7e1xmG5rxUUnaNwtJG6Bba0LABtfjXMYkDI5Php/B2+ZRqVuVARdymRJu2KKjjGZ74PCY7OaGYPndjCsFl7DRcUpFKiMybPi4YaNPmPrBbRM1JqQAtgrfJ1l6kYy7yse2lNeh+/S9mI8CUTkIUMCcPoZsDyCdNr0xzqSKbVovWickGlwTpNUvsgKqpWUWRH6R7tQjeWNQtn+9m4dtt+8fPbrqLCqUm+7VeVilikcdqrrI1qF9gXFC/voCQ/h1A/luTohJz4h8ZKRjK7S6Ru9eRcepuwFG+7bc8sFxUDwPSjX+1A1GI9JlvIGglM5wiiZ5Qshda8yavYkIqOyZC2PiM1XsLL0GTvMZbZFZPc8CCcjc+qNwKrVqRHN9lqSO4wDwiCEqbLDNMM0x+L4DKY3Au3WLudB+d61oPzME2whoUvMyFjUr2aSCcHW/HO5fyeKi3kHJoRJKZZjByFl5FpcA0mhaB+BiulPsVH2LTqpF1GQjv9/kojp5K30bz011ZHCPp2/mmK2NagGbdyeYIGj8JWG42PtFZKVC2OFcNtI7L9eZdnYCCTcrRVXNAP39VHC5SLzQfj06184A6xuF1fzLa+ho1ZRsljD2NsjexOjHyLljRrjYWxU7eoVmeJ5eWFQEaUiX3ghcKZAT7/zVHCa6AgjBOGsF7ff6xDB1bhUd7VG5FtIMOS0vs13hXl+mTKNwKfdSYr/ejGqH/MXKqNdRdEtvgXMivTIYeK9jLTsiUAyr/6Wp7/K57AMpHVqnP1liyB4hp8FkY4REpXZiMhfDCUAOfm6/ztwr2Ya6NXI8dgy+mfRjSed/TOveCloNKogftj7g8lgvYZQTkDNV6upcNNJFSwQA6lgKMh4pBKOYpoh+guWMEJsTD0bqccfwfEcEGomj4ROioaJse1NbfauvPyXO35dfJs+7bXpRGFGkE8lBepSQmGmcmXWAFneppVTI/annuTD+nm8N2xRmLZpTZq6M8my2usKPlyaMAed6FlN1URPZ9VA9tYJniuqN17WZnys0OplhoHrfENbxWxLONbna9h5vuJueuopV0J6usI9gb19ZG5BFC7FWVmak9iNau5iqtFRu25mjmj7qdVUVRxYMs9MAoP88vRQjA/xMY8jefYgkf4wrNmXK6R/TA3+pFRuc9bmWqt2bvr+786zc7qIx3FF/szUoF+kJpvPQojs24JrcnVRdUrA28KzGC4rv3k1vMD3J/WDpT2b1JdZq/WLtJMuRfsT3kNqd0lqdnldpb4nBpOYaUY3U07XgfnoEQUFeZiUgt5VmLSSx80o08prBYEIXD441oRVOzW8Pfz0RPV0+fjFYWB5vUa+9xSgKhQO76MKtsEZgZdym3AdWk09vfB+7RWRR/dQcT2ZVZYJTe+WlU4sYDYg+0Vgv5wls48PbsllRkir/eRQWtHfztPuQqOx4lomSOrQ1kbulAPFdaXZkm7FvvH+o+2wyuo+rijTemkRMZvSrRgyhpEqZhqlaqZxFozZ1jCZBE+yoW9Y9lM2B2bNjeBkx2ghX21mezblWF73cVToWeqazDshAqNR/tFLhudyrFOoykIXfeenzTthfG76v2p4zhjb+M02iXDzW34cBJ6XODzvW+wpHK14GbZpLO4Anvj2bk4bJtL9ROftCrd89SxTLzTV5+RNrNAwR6EMt3lmhTqS/MLb93v+sO1uGBWKkZ3hEZeRtH08ks70VITOq3bWpGYvj96kb+nrwh7lUNs0BzjJz2TKA6+wR1K1uNUzr5w6XeIy35HKuOYm6JsTAMygIFP/oiN5qemFzg6Z8Jm/mnKjaoyjefk7pSNLENIWbX/dQecqVLyNieY41elpUMn9ViXWbfvsfkVBkfLd40GjsbNZJd5i+7O03nHIMMjGngxLepKcl+z/kt7CoYeTBW/O3R5WiKiw+LHV2jP4XaT7+v5wlR5T+fP+vt8X/Nqg2RpTJ/PSIYXnUM2/t2KLWXKmabAJYv6RE8q/ha8W3U4h9o3uKrgoT2wTXVooYFGfXb4rvldmaGe1Ks/w2NMd70VJU7NH/i2tcw4ULB9nFhuxFak0+fa0anWVV1nfcEqdm4jjYouNF4nUuVpapX/u+saHdoTMs50MmU5CuwG5C9VPdw0A4g7H3HSoui32sE/j0khq/37VLZx/8SVCxYrBShFpkdSs0enJ+aayCuVz4KUqDxW960X84qLny21KzM43e3ppl+r5+fvsr9/6nefY+fv6PhVzldCqxuzqKZ+w9oCXEaoyY3OzkghBdSAU+pa/61eLs2lQiE1EJiWFgxm5qMQNbK/FuYEtQ8ol42UhqiU90xJ/UKsqxhDxeTqi+rRTgEJXllZVvHNHTYIGcmn70OoR5j7dwoqD82hjS/4DuNWQo5RJDQKG1AZW8/HIf9h1UA10vADdM6kKQ/N1jTCkKScS88cRUJ3jAZ88VjQAyMsgxzoJXvfN5TvPWdOyl06Pr3MWAmsRXMPhz4e5JrSaRvBnpnPo0wkPmnqWnx/1WTWgRe65kOGC84N5TxyKK+Vn5IY8f2nnE5zRbvNljtkftXlncI67t/jDAg5rQTaRjx+2xaHZLsdKi/7DGrxEA+CFG1+t4RT/qMVJJ+fI/bVMV3zizNtVmzytpqAUPVoB2Bju/f6nqc9sAAW2zCNAdQyYJLMVGelSD2x/tpqjcHj6uVjh3zNpSIxQZGEZfjxfFy3ckX5KVg6a9f73F87OrN0qjAJpUm/WypKqcaJtVQVoru7WgnRPPwk48VN/4VfZ6pMhBwmLk0zuKzdX5vW2sISxf+iHG2Nd9+NX+Av7lUurs+70N7EKxp/4Ju29E+agLJHwXqyVymt0z5cpHke/3v7+5/eW7xbAdWq8SKk85vn9+aLSisFqXMd565UxhsgTWwKIi7zlNLaCsQlV03Qbe06XE3z0Y3LuBk9HLSENxZnr2Rkt6MeQmgVkQ3xSRmsWrefU5EHo+je8+0SBAu7/Y/SGcv0vGxt7ScC/eTfqBf2t9bJuJDj54u57SohQ7MIpVVPRDUIHSYoP61d07b5/J7mr8IzJEdUgoQBsbBC7wy0Do5jIvAE/1v+xz3YGZkBmiqaTwnGhOr67Mnd5uqk8ygj1PdJnrY0zlnxL8hM+x18k9e4paioB6NlArZe29e2r3D/cvQpqQ/RDO/Hamf/T/ef5FLwWoFzUWWH/606XfgI9OjtbTZt85ZyTDscUV0VAAAQAElEQVQCo555oP7v9Qu8ZWc8eymoUXwV0D12BQn/VFp0NpF7RCL5Os7wHqVLZpG3f49ssOE7vn+w6IsUAk1oW0fxGxBYP26hOdLNGj2bmG+9x+1vfU8euV5mRkwNU3WiJgw1GbtfJNIZJOw7Sr146hHnq3Mw31Glrong3f3X3l9V8FHELXUtc+ce43X/+xNnc2zAVECYO3a5RpHzc4prCo+iDoWcflnl9/qb/tLKaWlRIHP0rFYBZiPBa3hbM6CXhB3UCNVW6Qf9iSKs5dbHY7gPRHc3hzZOoSF94GZBKq1h0HCqNoLH5eMSqu58DCEKI+05uerYzDRR0sOkj9JV9Gr0HZxEntZtpb2wG6JT/WD8k9tk8ZZzwiO4FsIhkLyhDE2YB8RODxoBKtI0tRog/ee61gcTUUYuxpP8+b1PzU9jU5qGAoIae6iC6rff385f93bVPXilh6zPrBe8TQH3D6VZgWJC/WZ1UAJWJHhDXHGEclHZcpjNxixtQlJoXfmHEaW7DLdWAVWK86awW48cWAN9Wb+Ydgot1Ar8gsUBatj9gSVR7JpUnCv6ca1ekldSisy60p1cjAss4G7888wT7VaVpXg+cwYsm0Ua9Bp8+kmaPlsxRjYrBqA1OTq9BFxejWo9Ez26qJnTpoxsWEDCuH1lX7PKoWyO5rVrXq7phTYF8qdbbk3xGNiybjLkHHrKqRo3MdiIPfun10/6U4bl87g9M1+3nUoRcU0iMxHjmI9rwcyGdmq/9q3jQMgFqg1poqnKfhsvgBneirrM0ieAr2kXgyMBDhWuhPA1Qa1l9mLf+KmfH9/rwbI2TXmlkgVT1XXZQSRCKDJHO6qvCbaHNoLq5NDpHjE6T9XT7TkcGRjx07rAcUtnz0wJuShr46XAMKmswed+yZJlTeHAOEZ5xlxJpws1Qac73ACDmYWWBXPvcfmrMe3IpX0HJC1ldnx9lyplGTlibsQqyAYkKG2knlHjEuNCkODNeS9KmTVKr4bRqFEQNiRnHGPCgNBF0ePhbUtt/nZNiueo9oYaTxg3OvpAOzhv7aJLwIFoTS8tazKf2A25oLAEVd0Ls4VRMBhpS97QxCgUTXMUWNLy7dbiWoWBLo+GalH7nouOi25ROwk2XWgr/Y4SxlECo+VnlBKWeRGtcULjHs+aJaXiTF3P/OdpFCg4sG67hNTPUGGZk5xGfUJ2gouI76ojvcerlSbNLfJLRWDr0LBObTAaDb03jCjtvJZC4/5vaG8vizaDg0YODW5SjDnUH/DCpgP147NNvLs7FIbOor11ZQIR6XZn2ieXbnFWihKRkRxx+h3ToiSlz4kWVslZGEbpzl13ox6QHkCMQhZd7V9RqXBGXDtqhKXV/bnM3B/YH/QzJQY0IIW6e1CwUsHVnMkrsjgknOKpbf5dxNRQRImlcnMzeXNoJ9kAoavICzE5Vvkz75+bWogt1eCPieensU0/VZFfFX4qm4LLlvwY+dnQ9IZWzTMr5XzXitygaRF1cfixVvxMdr8zB1/QE18sJQTTzZJqj3mpO6lA6polRK8mZR+JXSwBcmVV01+BVZfleBvaur6AAP1VdsY3TJi3vdQQETmaCIw5jG7utsL3YmNSsOVPtwn3sTSZgW7+1XOmqzAYNyoFRizTxs61V4nlaHbPOzX2Bo5yCKXDrR2lEOw4XoPMlXupbMPK7FWC7le7hJKplmdRsTtM85ZNLFrIClgK6Hr9X9AAsnVQNbNvuzAr6JEjbQckoqX6TEH93Gw1xcnRNkShNdixyjY96D6aBStC1/V73NQB3YZFXmMSWBT5WHAlTuHlt1FQZvR2YK212L7k3ge6U6rNUrGntkFmYr+t2qu2hKEC7of0JUtPKYmQXHsk7o15ytYl30ElOh/gj9e2qyizu1ptAae8M+yDxMQcoTObtepP8McZJoQZc4Pl1p+MbAZOHYPPMKWD6k+umn5nUhzZpSnO4yyaAKVm2pt5b0JnlFtSOppz63SfNb2G0zgyhtY9wFig9gL1RGv3mEJv/o7sRsORkU0Ju5zqq5rcHiM9aRJTC+dVCMqCC+pU53TKHczQi3fzV6nkrJs70+eqvOCvVGfmFjH+kZbGYcyyXlWump1wc3kXS4pb2ga9qTT4oa4PcYBCkAtOTYW3+hy0jMV1EyNJDhSY1oMt3MpWOr79hAEOCSe0VtD0lu1oTCoMKVb5lUmbmNJOz8vsLL/EaAxrvDvS/DrE4WO9DeYlKo+SQfD7aMvtwZiS3j3CWhodAgTKwMBJN5OjzXbL7KnfUetgFr999b1WFRj90/yLymjzIc/gy+mAzGXXkHJH93Hn6mUn5g1pAnah9qBqkfBKsY1D1YIoUButPpev9viqmO/UE9rKutLHUfKutdk4YS0vEnJN90f0zx6RPS7JsHIkoALKsqPM5aM7E3nMyjbateVHVTFf72NT1dSnvp1Xt9X311ttUdDD2kkREVdFiFBsOjhjo4hGQpGX6jxC7uoc05aCdCFJqrw7UcS4ucmKc1ESOjh5rn7ZKeoyIDrrZ0YPNqMatxXAvaykonn6/OC7KjUxu3oAvX3MV8OGpmhWrWVhinpUF2l1nldPL9sGuPQK7Tn/QDX0DUllbgx0WTElkYLo1Tx3sjLzniqJEWv24oYVWOict6sQUKviGt9XBbCuea1olQNN+5MFfOzQsRa5qr1V7T7YhifAx7CCTlgLNyDTdzbgC62DGGWp6w5kOD18llb3VRDcgYb7MLrDGpxWVl/D3/CzrMhJ8ppmYoITFgtBbb5Umg+/LmFvTzo//8POQh/UfVurXVgFqxVhF6Yme+6QXNsfh8vd5w1tkJ6aEKKaKFFZbdbGAn6Ht6EmNuJpVMu1pqz1+ofMn1aLn8dow5GNhN4Q36squ46n9tOjXdqaZ0uiBTn9K0GRcKPT5FgCdslx4ARJ6FfRAFiN12UjW8JY/ALyMHsYTy27gRd8jWetMZr9vxiv6ZJ2UdGLYykZECnelku04aCJNINu02tPr9TbEW/ZXmU6Pq6fuH/s1+a0bnRr0PBqT5mbGaSLNAjYUsRpFC20DJhi6FyVzMzTpbGEh61ljoF6cSucMZjp598Nv4zYZ85hNXXY5SaJGB13+o5O2y6P+SxoxJ37y/pLjab3h0eZayusos9o/soO9a3qZim1Tj2MGU8Aa+gKgwopW+pS6nXWhk5oMpTUJivQpreY5Xz2nvVz/EewgJqsHF5bRpMCF4+6wgvJ+/+FKpEKf7TLBvjTmh7TA530Z4q46ZU2A9ro+vMT7jtpb9zzB8Wd9VyteuOAUPWsAps4UCZtFXUQko1PI9V1fvPW2ChM53jgex2ijLQ9zt8YfSSGfvG8d/2+XuI1s6sSAtz7x/2PSusZF4KjUAxwAYiZ9W3Jfqna6WENvkb/i1P2i7jPlDQxqRnVej9UO1u6adWi8PuT6lcXhfMRkGH358kVL+t/5/d33l8T1XvXs64rV2m+bT10GZV85i1NBWZUyzxhui7a1jt6LITqcvRe/qxYbpgaJSZ7WjAw79R7J6VM2HKpLV1JGxVxNRSEqBPuyIo1FspFUKJsNgZDcMlrt+Sbsq37OZO+NHMmVgnijmVwVFarPTRIl/iTUkavX7O7ko5wrO3xmP/Y2hLMKnhzhcpB3bMwwovid4xZIzSJ0yYoa2H9SD9UeoabHa2BwqdCbom5cdpOar9iRM7MpylBkpd9zdtNwJ2klQ2etsfM3QlqrM9QMcvCb3ldoGri1+1EXeWvP4HWQb/H7lUxNCBaqVFzT+etYuHRcdp/Gykj8PLPay+6Q4vez+Gf135qw/zVvpqdn5vWw8P3B2crnZGca5mDC0u4ktL3QJXHduH7jbee33l0SUYgZRu7V+fTN6TpOLqPUYKkxRI5RRuaKqOxrp6Z9foi21M/HDHbtIaH4/GBMvuX8y4b0dciBQCb0vBHmc6hp0/XbzNlkRRzGpHAa/uYV1xzWYWjyR5JSKViM1V1s660OJcarKhbmWPtUL/4YRC64fcezt1U/YzVSrHP+RtqDa0Y9n38/bH6o7FsFcnb/fv+IwoFRaEU6nHHFW4iiuto5d5r/LHcLJ1xA7WtdnkZZxqOJPI2j3XHNbwiuYblq80GGq4dwIgnJZWYwpt8RyM3QgUZUnt4C0FrQ8/Wpk83LqNlX7ED4v7srv2L9cZKW34Wr+l+sUBP7jSqaykrbQ2AMX/2/XF1JX3R0NcY7tGmArvx3ZU5oS7tqbYwUiw4Pc0ZzV6NRawYjRgRrwTwj/kfcKtT0Ms919z1niX+hfBn0ZWaf4qon13+qLn2Yb2JurAyHzSU71H69bBfexZbmEdiGQwdFqSlT+7T8ebk1EDBXSVzTDrskgGRZsOALX6UUXPzcnpt8mfheXrj+jnzW/Xy98Xft09115cLjqEfwD/9Lu7AH8tniu1rBH9RHGCAJNQfVCxQd6t7UW1gMCxATXB2B8+PWq3j0XkRd1uOSvOILM7bI+/5wpLKq8JPzKePpoglNmNzO9jgUeA0k+lWGU6oC43dYouqqFndPaksHUcNeYCj61jZuoGznlt15F2dCj7jPhL//Ln+vIF3Z3rSa0LU4mhj+rN+fvbPjyr0BpSOKVVS5GxgCgJz1Ie5QLVQiSi9lesG0LyDFJjRuHU/4ErKkV/f4z9usUeB+pn9ItSqZ3MeowBNMV/La1PNEaI7nTdlrEV6GLSFAr57oDvq36LZlIcqlxBhRmtGerQSbH8EddqJhvWXUq0uqq3Vs+7wCqdtruduP587jVgfbwmbgRH+gcnKqgb+JUAN8MhVymren+7/2GuprR5z4YZeVFXa1Xbniliq9q1tokU0NF4WQYMy+sUad3SDKZeKLn2Wm6lhvM9c3sIvXJZv7LIWc9KWClSI/GATD3t7l39pEFFkIVVA/gtPcPSBAt1p+kJnRruG+jPz9SCFbVBKVQLp6g5Qb3fSVXj5DftZI5YLWu/7KNDcqB8h/uSrkqAsTw0bsqpymTN5JGL8Blou1kANKPHxFyZJ+urlvsw8jpYgPI0N/mA7R0nI6I88UEnSBgHTIXiAHOzeP3xoRsqfSe2PQGvfTnP5mERLjE/yKaUdM7g9WakPtlQRuG/xJESSvqPz/YemwZZxtv998ojhuRKDpdf5vPp0DwZy6DOsy37NZS8H2QTJH+tvqTS4jAGrD4Qj5q45Wq2tizKAA+QUWw2lFQvRzd+D/xVTv/mmZqH2+kJZERjPFO5qDYOmlDaG3tCmObJ8Ohwe0+Cd+LtcyxFNnQ4xsMaHaxY7TL5v9am+8IqNZwmvyPNSs+vNfkDXAhOjurQDzle8UpL1vW/WVYw6krx7+mlbtaI+15v5QMVdrK5TdcQ0AIsK61VjL3FN5NEY7eLYVWFqzcpKQ3I5PYdMijjjdn+898qvHhdpIcp7K7p/lX+FriVJrRYRTkTPrvozvz/4ffCacObFWBBryjut3ODaC2Iv20GRAVH61PtAGx6QtBdgwwAAEABJREFUnbzHK3cXlR8PrQM+6JtqtWogVJGWsfAAD0FvPaPsuKFIOD3Oerz0W7N1QJlSfvXbyG3Q+kdTogyXrPkMmOuEfREPQi4FgvQxEzrTbzkXJ1UzpLF9VPPiSLp9zF0AuU/91xWsWtUGKDPz9Q/1PrWXpOLcYb5kvr2lBxGh1ql9duHQUlOiLWZZEyBThazjoShIxQTMUiwiBzbVtIpIrP4YHb6MFClpcftnMtCFuZG6VyntRTLxOo/bwEgohPriDXgwfointbiWFZQXGP6lVBCAzq7p4vQe9XKMRKx3u5eX1kMR8hZuxwxbMgXUlQVeTjZF3YxIV4sZo4UUwKXQOrpEnGTXzEH1yItyW+pfpzHtcaRlFc0QCQ5NmEDUQINnxAZvqYtNFmpYtbst2U2y1QIglGx8pIpr7JbsbSBz3zfBUlLN1IgvGRfy0aDaWdEaoklRNHn1sQjGvgg9ic03eWmjK0FzMy5qm1N/mhvhLcHpWCAkcGmvP/lwgM6pi2+zAaZBr5kkW5SQY6DJbULku64ZmpNmxaVP0EjImO02ZZa3u6zvZruNbM8Q0hyiI+BOpWWWVIJp3lHPCCBUZumuJWpEpgFiZj6/g9CYrL7THzkjvY7SimYLuKKFqLbbfhvduIOE3E7rQ47lsMf6WLUETbLlaxzt9UVR5AToWMM1U+Dt7FuyK3lISAL+gQ1ZSeZdVQ8c1A3Yum7QPyvIi/c11JcSYIpoc4h+hkP2AW+41H2NbaPm+Vz60ZWFZ8ZqOPJa2aMAakzpLJWhm2+jhKu4FNGoRKqmqzGuxJI+Dox+5ZqzK2X5OxeKp1Vs65TupMUOL3jlwzhUYIqa/5y7Y6Duflh3nbveFlUtAmlGk2Pr8ZaPmO1f0TKT1SIYaB3WbRoRUPJJlnltn606ondIIz1AAe/0L+7r5/W8o4cpvzhXYTeZ5BGeMSfkcGZgsvNr69svLhmTIYHQ7+QoWyJ0lDt0t0jTpwhKaWxaO0e9NpJicKbgaFeCqGSXD1mrZzUNnzdrC1KUlN3sSQmqDTLYgQKPkv6MPvIDeAZZYmmORNaf3hUZjE53giXbEAP/DqG1JeNYIkIrZC2MCzsJGBStbi20OxBVsMUOQrYqTWQqLXv1l3pQDDf0C0j2UTi8Y5UQuNFV46y5/ObKrJHKgcWBn0Jnazkr2hd4ME0ZW7jhGawYixYz1qVo6mZ2Z6pVQbZ7N1fZtOpqjEl7gQtdYkEnrn2IrELp8cR9763qEgNeR2Lzg6X1YTx7vaOGJ52FTXCIYvf3J5haRb/C/oViGkE9w9IlbSC07J8oeMmRCgtJvT4ePgE8HJ/zoRlAKItZlmYdVp6Ft+ua1ZJTA2GUwMCRVgDZMAga/Xv/ufsk2J5u6d2hhE1EfYZzo7dLI2ZGKUDRiGv3cVnMMAAOhE+ILVPxkmYN5tzfm6eroKRuupQoFaYAULoWBA0crRu2wBVcyyirBrOzcXe1YHVJyBLFULzRy0X/vPw6QwOmiCZM3cpd0V/UlEXv8A2Vj7LWDprbWJzxW2gB3LZcYl/upj9UGN+b1hME2/Pv46LZd/QmfyJ8mUknBkJSQHH4Ja+grcKvSJZbAhZFP4IbDEUrs5okqYME5HbaLvWCOB+vmuHI1PAm+wjuasqyOymBhKk0fgfKY0gnTgoH7VvbkPgw5EI/0yH+yRIglUxYQ3taOyXuMKJpjqGD5iI5SJhLKaN3eg/n58psJFNd8TGj9DCtFxXA3maLLfS9rZaldVrNSPdZI/T9hTuj2lFe1DvRMwNFYTQAhuwPLVzeKJCQcka4YYVvP6pT1vgYGqVO44ca6nTTCBWcQtftrfxa74Vtd1wZlrG1LbYmYnJ3cpRgV7cb0kosNkynDDTb5qIzW5V5BYcDeuO5ShdMn0zh03LYsH9q9oGb/G9R6U2rnkoWPJdJTtM1WiF6l6+PkocWdCh/XlqXz2BkdV+szECKfRrgm8+ekAKk+wHrH5NaAJKpMFzX/+lflzJlJ1/eJmwEysT3LStpT/N5o4ty4tRCXijayI9PekLGOucfYkxKZR3oRbNWyWCqDMc+ZWnQfFG1SqKmj4suNyrZTK9XwxwjqF3VaBNRYRnTLqKqOFf1kBVv47ZwKWl/3JSTQRxaq9RVSjEK3UD9JUcv2cI428Sxkeag7n8IHyOVJQIC+s1vQYYBgfl0oi53CMWn0+6ivaN7vsXTk9Xe7LLTRlexhhL+lfyG6cb6+SJTGvUNyi95uAo/QzW6D0nI70wTNfjM93dSqZX5gIfajeoKpX/rCIgPWM7ZtAAnUQnhqOwWC180cF1X0gvVSoCNcgOwAR3iDvEf42ciVCZr5ieqHElxzeXzWOGc/NUzMzG3w7YDiT7GXx2FPAu/8XPtJ+e5p+7vO3qR0G3ZY2tPLl5h6up/gi9lnBWRy8sJR42fsXfN4bxgxuqf5etZDLuo02/Ha8959mvMqu6z7P7P8/tI0mqilmMgaNxHBOm9ViwURGI278Q1l6kFfQ8As+nJVFCICSEYjel39dH9LwZ/03411cafWc/wgT3oZfXYL6XlGQ3iorRhmAX+EN9T4jE9aP8ZW2hTRJbYolTeplKrsAgjBzT9XHRm1iutNJJQk6DTSFRUatlVh+36xagKvl/TV2IaQFy1E5acBHKic4uuJ+cn/dvvts8StVS2u6Hyi1nOptaAEWsKvY1ayVTkAjF3TNIf/xjQWzKX1VrdhtS93G9MqcEhtuHHx1VxpK7JNXHY1dqW3WGO4MgfODSd/60r+GyVR6H1wpefGEz3PXfut1hSbhbBr3RcfKrMw16pIsIc7gqwZhqRRj2qvJpcZVUqvadtUYPKlbdR8sVEybn5/IT9mJhP81uO2TAvTTUnP6pwGa68NBRP4yk4FbLPMxaq6JI5ZW/QMBEoBJweNRRcdJXQzLrgZTa0P74hQNPLRyt1K3NWlMEA9rnr/PZULbhQGAzNyFilNuGyUYYBpqgHKjs3xruNB1FYAV/B5VAp2AJ1VWW3d2+BnbFjnQaBH7hgHjJJCUOxtVLHusoReBg3ZD74BB0TimVGE6DBI1pkqnzT9KWnRB4juapbtJ0JEaVsAg7EMJr6vY5pz7gu13h8ujo179zPoBaYZCf6n/2LSYpH1+vPg6WWSRXi6tmoTXXq3OSAbeYULqmtrcLDxHvi9KQpnqEPgDG014f3lFb0rpyP/2GNOJya6tAwKixqNGj8rFa3vui8gYrRqVg6a5PVdaezL+36T5sjlaTZnAVwSMX363HCe0+qMTy8yw++/tZjl9KmVIhaVwPwA31r09GFVrOqYcOJ/8ckJQ4agjpnM2oxIc5b4U2WCLJBAW1Iea49eL4PrjEJlZjyVdLDAz1xko0P39FeCnpwcZSlXHlltWktivUV0XJ+OgOGm64noP/+36ge87+X5+/0XzF1RzlTn+0cLYmPbuJ69+L5U+dPiyeUPKH/ZMe5+PtOXnZ/VP9SWVnRurXgHPsMOfoZ7ZeXKYPSXNWrUzASQbXQi3jRv7Jx8tat1qfk7U61L7SyJQGIm45MzzsAY0IX7pm3Rz3w/qB8lJTAXYQBd3jGapabsh6JdfikVlBsTM3cbxESPS+jjELeuFXnnpMJby7VxGv9G2/Z7/T50plcTdkGoufJ+7SI1QmP2ZtrsD4HHR9gUg1xPT7aftAbM2igGrxQNjQkMMEy9YvEXFQyR4eP4JOOWPl9/5qy+ozrizN9ss7UOK72DU2t+91SAnwmfugi9FKKUCZ+za+hhkddxn5y/WAtrSHwHGQf8A1/HyGnIqD6mIthUx+Y63UMMtQDH9fEbTROoUphfCe1rzKS3abvnbNdnh5cV0l6jWnVC+MP/Kd+Qn/XZoAc/bQ8qANj1Dr+lD90szXx9H6w3b9PHAVSas8CUZrTl7wdejf8ELCwRM78Z/g/XNu/WpEEkkjHyziwHuvuml/pAF1VWYplYcV+6tnlWKMHLX/XiCO2Hgjdh6nd1VNrlSste5/159pTn0+orXgrMauNUJpv22cQBR6KgTd3csN38M/ItgDCYNGk/GoYhgi4j33gol/vQ9ZscBkftz/L46vqKWrte2+9Z1TckqYZ6EDbyFo4gxbY/Kb+gWhpaKCmSQRQ3m+LLPRoRiXPL35fyhDReyeW6vYn8H/+SLDFRt+th43/ykyRUpovdwlVN9ALr/GdUctSrv5r+4l2L5uKA+1i9nKPxiJjxbcQGaaqdat6ByP/wjSKszceo74eCaaznQpI8H8UelhRLlDd+pmGZsE0t9V2+qo3xATaQSDGrTmtTCpno8ZF/znx50KtVRNHnKzd23pBAzwU4TymefKRFbfWwD72QFD1lt1DjkvisDPPi5Vmas4iZeH8xyu0gxF9r6XICqjrjPfLPjnVMgfJd6hmINAaWoCU8fa61+47V62k6wMtuRyt++hVtkOptv7p3Afr17RhDAFGpElm4qAWJRM0lAlJyHgxR28UzCZXcTVhVua//XNmzZhYguC506h4ljZ6T3+hQ6QWDLVspIhky5i0oJrx2j7asD131i3eQurA/Cpsw/cSllkqK+15vy0Mv+xauY+ZjI1eVzZhtEWLeVYt3NC5UgQP7KEZvbTv0XhZxcreNYsFHkheUxgKfpm1wXR02oWue8FzlBYv2zCV3tw61e8gwZw7fUxfOdzi4TT0QyuhuubtmnBhpzgpbjrUUWyiVM00zFDrCkjswDbnbRWigaaASnmlBE31r9VuIlva+W0J9BKir4RjlJE/KeLNKC3pGHKgIDEm1ZTsTVbJGIFPH/wpW3AFOAJbL6oVMbVkJXpk5XFsVZI7YLg7zs9qLNOPTHKshbFcfuNIj8UJV++0oLyWSaraUdsLyLVNe9lXNfgwme4D9EANKX3KkX0gXFG720+PuqqJr3kU62TPe3iun/aLgC/VtetyKnIOx6asCmdqqByLQ2EOUeedq9MzOegds11Eo41JXWLr8WKfF0xf2iSVwMu7m22He413AxgDDUYdSuf0XOb4Mci9/Cm541BJ98+U3/sxrGT6jBLPPi3Z6Ji0+z5vxTfxo2FUHG4TVrTEfAV5zUoNAj43U7ripEPzR2gZwiJGF69az8PR2tdOjacb9U92hpjB5EwqyylNFtEiXWZIBBf4c+DV3kkplMco6gyXdRUVSlU0zus4sG5S5oanUAmXBpyjSh9vnTcnfB3+/O/dv1dK2gxx52vWwnqMLmBYNksEKidJOxIzpXit5NfmmtZ6XkIUgmiX03mb/S6/PzbqpQ31v7gjbFPml6H1p8+lklBUaNXWaWqG3uP+L2uYFGf9GTAY0GP/8q33g1DHermzt2m8C7GHxx7RW6XiJS4F/VtE1B/W5ELZoLOV2ValD2mKEn9GGfC1zKQ1iYMh5+OADo0hRWVpX+sb3PJ7gWvWoWtYGjXOz6mqNQ39gZxw/rMHEC2mKqyhspE03tv3tCLT13yukGcw44OV2MUYMe3SE/jFGty5Oi0AABAASURBVMigzBvoMHbsMieUWoGkd6j3S2B1006uWyG9OF81SYPdkk0TpLAJzR36GKgrTjOW7A1qQEL/HkgOgvh1x0OUTk1Fd98pkfV7/H0Dr9ux5YsrZuOfuRuRbg1WWYuqpgPyJFewaAuMd6uCtKijveg9Vt2FfM3alltE9GLZwGw0G1T9i0gCoVPQtExVBSUUdrFmlEfqsYF8Cr5zFtuHvOD9JGOj8Jh7jrzNWedG3tUwstGv96/GakZ9OueSkxFJkSHvVDACaKdGx62PkQp+wAzKon0xVI19rJ/pm88pPy9/e1Sw5XY9jvPOtIoTRX7I1ry6S8cZpGEWHjfJ8z9Xgr1Da+yaIgbqDlGw35jX0QSm0I3yHp+P8ZFOPcJikZ+yP/AO6uJIDhg6SUM7yqamS6KYJNtm0FOnq9Ps2nr5vFzvRM43J8BIKPQ7+Sp2plFA1yphmLfm32eXApUmMxAW1CFPOqiCmBFeGAbfG5oZoPj7+2QQgIR2Y5Q9a60AbQdZXgJ3iXrIDXpD1EFds/PFSMylqROdu64WmaP8N0a35je9K6aip91KxWLix0yrgjhjJr72UaGrUPVLpSPMgMmJlfkrEVUJ4cJ8/0fVQ2mVH/k6y065ZPzDdtPk25przKG/vO5pIca2wrJumXA1jGE3hBcLLkqNgwQuq/ht6qz1maMIAeXZ/5FzrNrTmSaajtl1ju4K/ihJ1FLZCfPZIXKCsZf5QuuG2zM5wpBh32/zq2FpVIh648v472609MCwudkkBtKe/d32Pnu4vORJGEzXaDm8xvvpRFJR3UFvY8DxghPGhNSrD49B64KqpmMyKtmXKqhub+BC+yZrUyUxKlwGZyBOJsUCMstSbDYUrHFi6LreF9V6+9EyOqGd3RJEJSHVL7VOluKJkhTXokfE9uX401hl+tUUNS6IIK6o1nZadKc0xLf8PewXaLCBgUrZFJprTxz/YqJKovPXKpWaNHyvMjYdNA1PGUV+gcGmg9r51ddCXOKc66XG+a0Nq9oUbduC2YxXobNGN1s9x1hm8hbtSnywTVDnB7VHL7vdR+6hrMQsd7a3nGGc6VRON7FkIg9/j/2a1onv8pMZUHI/W2Z321k8rlljAh9mTmNpOugkyZkEEh/jHsf0uGLr8oegU7THYB0VgfcAPVYkgKj5NqMKZ9YZbb9jzIKM1bOndNlbk+Sq6e2ykTSpGHtsl+/pT5BGqx1Y9nWYrJQ56hVqIaA1VbI+ihwO00cMLMeT0bYu0QrLRtnoxV/Di9vavBuaCxMCy9b9tsDAS0w5K0QNlHPXYxrFjPI7lNfi21A3q66cSlTaXMNMsH2KCjutxYDOqkfY/53b+NxQdeHCosEwGa+ZGq0Aky/zahbEzZ/hPsva5JzrjhjxcZBnzfvHcofZNo2cEOstjzO4owilBqx50DrCWP2P73EprsN7u3Sv5edzOol+2QcqnJ5Jx5H09fkDaj1EE8I2U8DtuQq6fBF/Un1hLFEXv5v9P0Y911Hv1YREvLMvfhIb6Uwucgkq+gfc8NUQHb85f78v9C9SyD19T0S4MsfOKC20FTc8BZlSld2y034nBgMUmPYRyIZdHSNZUhE0EdbawuFhP3HtTynkizgVMzRds1zIh7WXNCNtyqCa0CheG4hwk8AwK1NEpJb/MXyvATQmnGI3/RzVW3y85Hj/Cyvk9njcZ0qT5ZB9a35Pv692PyXQj83mB/9g3khYK632DqqVSf/57q4eZzzgg9GqoLOS6l3+Try5TtvEHRULkOWTT/T6r/Yf5cSD2H52nMf7WVgblLON+bTWoq7ksdPz7vNifoe/8La2KFMFkjU+RXzZGXapoPW+W29ZyRyJ7B0eC/qP8b+BF1Ldqc7kayMe6nbev8W3n6uUf62DYUBUrs5tHY8LbDzmes2zuXa4UVucZJEDjikjiUn09+vLRw9Si8NrXx++wwP8Wl2/bWe0VjARtM2Nr4OMSvIFTqOKJWJdklrPG+tINZgyjAmUKuM2Dr0XnOPXRo3INJrKS7lmN78ZWZZq0GXK9uZr/rZfn1wDQbeQVKp4UjxhN36gdYcItA2sYErzi9ZlQs0lWdoNT4GgjHgehXeaWbwXuKQy8mfyD3pivdv/bk0De1bN0lWpmyJeQOEajlP1Vqo4W4k2Zyy3FTQoVKa+HPvr9drn3zDL/fnzrPVjEeZqMYmdZS9RLokAwkRKlWMklz4LaKFd7qcxYHHns/IJ1YLOND196fasH9Ah5sbKUS41KBaJplUpv69fDllqIsgw2743I5p8R3RUXK5DYcZT9fMi3s3entitTmbKkFPVnZYvqW+aNeK0Ncr6hAkBWuF0/DB+zH6gJ7VrUY+T/5yfPydUjsA7c+doDtAAiA8vjOlTkyP2awbTWcjyGbULjaRZLP9o8aw1N3ycRng1j/Hu9fpz+5kOGw9FZms4pdEBCLQg1pS4azfutRT3l0mp3QK00eALZA8tg/lMhvd6sGRuRiY+LxoGoeMlT6ANQewZF1I7v4YDVMKyXTM2agPuDo96UFQV3FvdrROIUNplq2UM90tp02H8Vtu4+zn//eR+xtU+v0WxZNm7XMvcZELJmsp1D0q94LrHz8TTpjxR5M0cx+/P3DVSPmHy0Kosbv957HH/mfWUKR+83Ucq4TyKnMsoRVgozSRBg1IAB2ue1/7091/S++KooFJlys0noFLbtTa+/GiOWJ46XYfaREmbzMZfzK+fVyJ/0Ktnl233/Rj+ZO3bSFRZGUgsg9n0mt+4uRubHhZjyvC+Qo6N1jA8oQtDTs90TYv8HC/GjHMGt27Va5XW1RqDiZZjrBwDevRDwBFhxgVjrvfY3/Otho6/f0YVe5tiAOfw/J03cYMMterEZBUaD7k/vEqvcMBep7regN5UNalJ+undvQAMNEezwKbDSalE1AeSdsamMjr31MJs1KPWOhc8vY6SUFeDtremIWPewRn5hu8EU+GSwrZCnitV3yqx0oJXX+h9BjebNSFkhNDrdFWJOFRBf0vmcGvOSf2MdgUtIS2L2M9aK8wh1zoEDa6sQj4P1M9klzSTKuwZGHTOWjvGhusDZj5iA2sVIoszFEQwAHHiJgtd3zQadNHZG9f8qMhzy46y2iixR6iyl49SrNp7Yw4EwyyUu5FlVGtUk+/wDmowifl/26qZzEndC+t0O0z6iy7OKSTVVJm2Wqu8rM3CF7oJMcM/xh6lkk44PmFpOF6xpuWORWLb/ZlDnfFJ19ymqi2UtoRi6uUwYMaUVcd2TKvk8h4VyGltIzQI4kGNRO2rtl/bXdq/Dlpomfy58WAGb+G32ZnfEtWd1EbCbvEt5nc9xp9iKK9967S3qVKpQaOdgXpuSRjo4ZToOMr1O/0mR/0y1mOi3u9UiQvOQ/w0re2kvy/uae1QE5DJJ/gHJeuChNFafxZTbarVFBY0cxysnAsvPDlrhtHwtFZLuG1JtqfZG3ZX6oJQPy/tl3VFcdkz3FOB40ttoE0GevQ74DFm7nQ3XROxYqpbFQ+zY/uawaE6iNnBzWX8n4+UrUkV1Yr8NMQqCPPrOGvu09hjrOhe3WyqjdsLE6dmV8+nfx6EgsBNSEIsxPeczAgUbShTjZHxJNRjszCnp+XlQElK+jFq6zEz1uNj0NW5rVF8O4qr3N/Hfx8ep9IjrTLbNRxyhvQsiXMbfUPK7Sfveq86RyNeWy+9mYMjv1MjWAI2qk9kV5XibSiuwSgVWGAqgEZKIpa0Bk2VoujYKg1aBW+s9iBVkaaY8GoIlruv8GWb9pTvUbMxhuRSitbTvLQSxHzvRuxw6jP8Ctxy+zFf9cU8IijJDrlrnFQ6pjBSdb178kEaSkkorqvEfB1WGfKEnc+0pDPX5wrnp6rg4EvZGGxVeNG9/LkRtQV7zonf9yllQD1wRooxc+bqsyUv9PBzRi3oN1UNMKm2a1TQqfZAszBbLkW3HMGD2+n/MoiQ58/i6KqYgnTaJgEU4Td/7vsw9881g/YQD+vJN+5v571dWaUClk3SfZRdS6vxv9Axqlalsq0UzFAJo6TTDY54VFJQhmN8/QObFiNU5Bpum09AMA2FpisQ2Wg4bUVIyQvtOoSWFMVHQM2xdVmn4kIU36o05VvP6sZ6n3mOBJGU3CK7h7nYj8f6kUV/yCfbq0dhyIGb+vozN1Qo9x0tJrddHFNdyAX7S9MH/34hATYIjFt+cCimFJ2csK5TO8wzpDFskRsjXwse482SYColsEU3gD3z29DCeyvveW9mNZmP5V45637kTkIwk4Wv0NFEAlrx6lXWjp9rt7v40axfEOx/7Pyv4a2pg29pzwVBndTnqpoXM2iVmYqpTimxVy7mlpZ6ofygiw8zIzmM8jikxk9fl+Z70rrthY/CFFY7EZedPdNy7tJFWK8Y3bA+VJob80o8U4nM6NrZMqm2mmOiFtb0Ep0+6olYM3vadJID1u/kK2zZ1uV9s97f/jaQ7aodx6zpNXyIxdlGm4Dmc32ygFjKPN23RyLQr1+0+n6tZcSkGHl1wslvUvcnleLpufLctA+Of4UdnzdXyr41fCMf0gM0H9sTTr3jsGXh5nPuT57I1GnfFo+SXVpntXTmHRKqjTFlZIRHyWUSVjC5FCZSl0X/Y4xlMm2hdzAHKQ3NLjONDfQDBW6sxXaWdDEYwp10jPUdtPKaEKihmjUP6gri2Pjih1vIfE3tqztLjZZY/xR+SskwFGSN5TyMzrAR5CsFvMOrSToh/3sKqdBGeWVC6W+gTCEi+tv1mzoSmqJqOO2eHpCl1KqF3Wsx1I2DrXMlHLSMSu+U/xmhnSRmKfoxA5NVw3ecO+Kxb600Nc93swOfYFDQDDlqqaCHpRVncFH8RswT2OoBEfhYIsBKu9fOr9/f1aqdBRiAoJxK3QaF6pvroTk4tZo/HQ7vng/eb2mVxG90ekGVArJ2a5g141oPDu9dZ8SqNpMOWw0fkeAbDIPMdvJM5ACUulpRTlP2RaoDut8QS95m4zah2O6YFtx249dgKIuxsUcpVgC/iL+9c6Tvj9lNbo678tgg2qnAko3z6kHEt0ARYyzymL2M3/GbHI318X3/WK07c+eqW+xqpSWVbhoQyzyiuG7Hb37XNMtH1mCP6xoBA5YSdonX5vgUFNO3D9gjd2dU5mHGr0ValyGXpS1O1Hj1TCd6Nn08TlgraW7b8MwsFbHkrJY5eUWZxCrbbZAcxn/a/iitWpGmztgrvstC+SRoQcAxYYgpa/WG6V/igBdAV1b+Zt1BOdqbgKd5rmmR6QQpRmS2f63PkzJPJLQDPVaKlGXoZXIke/Y7qrlvBrM6y+6sFOMo499A+miw97A4FYPoHQoTb+Tvvu/6DDZ264FkeLsl9y+klmqF16V+aapPGzpJl+VEjuqNSiBgKAvtJ6Sa49nWSFrZ94rgtrSS54/oDChaMWg4pnoBGLKoTQx+aD/uyzjDbMWpN938CPptvK1JE26itU0KGBZMP110AAAQAElEQVR4jBUx65+NNh+ESawjA5OQQyVDT4FUZQ464EXLBLUCqW3xyHzl5C9O4s170eMjSp1OLIdJIyvGVGmbZ/WZucekqW+5hqj5/sv1j9bZwAJsZjInewYiZeyKpffqZod3C0Etyx+CKkFMy0XXT2Xk1UDtYueVHqSM+YKFRInbq3qN4Pikvtm195SUY1LNWben2Y0a/Ug35YpvNdcYRR6GFArMFy8VJ2TWhtD4QQfHHe1zeVPyhF4h5cW4hJcn+xQ0d6229Zr/mrcvp9FQ1kcHpaVd8bWcGbOe1UARPXW76o6fsmmDKYD56bvr3Ur8TFfMcWnIgNFf7QABe4W0b9WrPpCdc+/oAYUsjlyyoSIJm1gTMtlGVcHFVLf8rLfiXgqHq+rKR4t/rVtWGlhhLehuev8GddVRTyOGPX6xZ9ygj6ZKPdLwO3MKBcXT/ziGir8wTeRab/yoftQH3QWBoEJnpY82zQof5OzAj32FrIsZplbHHJxlx6NsDUx1FirMTiXeyE/X1EOGfkvbkNOjft7y/9nu7qnxBFVHeWzeEE7INNN5vXSz8WWuSQG/RWHGaBu3T0ZfU7548OVTnScdysZ2/PgsMBDAhgE+bVlUSE+7+5pn9GFbPdZkDzhc6Tr0WVTAyqxENCoTLUliNM6ml4w1vU1nwYvV95nfLbBFS7cOdz4hIZG517HomZZUVH0VF/6Gny13wsQ1A3R3QeBuxrnRAupoM6pIRbuI0eyqynveuQfMMZ3BAW7JdMvIotPBleajN8LQS3qQaR2O67wiBh7Gx/TbyI9alVmtEYdSSWG7bSCy5ep2/079zvtWVSpsJRa1SuSx/3i6FwWyhlUD5Q8iW750bhgUBSJAo7sJRVBAcTywxbwKZhyl1ISiNFEjcCLkIfSdhpsQRJYrthI1mg69Atem7sxL3VtkQ8VhKyUO2RhQnIxpfftd61pcRqs7x88yPUdVl/NRHoKxawmEG0ccP0jJQHWyGlGuqKh+MZaLiuBJk5R9YRbNaZATNKaESs4lvn6mZWZQkIKGaTyso8pHh2l3rEKhWYkQjOYbi+uUq81qVCX66PZy/wx35tP3p8Sw/D3z4/0jnTFhB15Jq/gM2BiNWIq52SqW0y5J+iwrGDKYj3/Znk51OUmmP2RdhdSX55XArT6YdLi2Vp7DEsJH86haoBJg3uBvi4oaG7cBstQicNyw2vVEfx+uTJ/XrJosrIzFbbGhO026IM7IlyDp2F82GqXu1yXeFQP0GlhvaNuSHgeWKoXTnZzrPdHS765Z2XXr3swpCI5aK5zLfH3b+nEaMFRaZP8K55nvZkWl5UFaNzo1ws0BrUT6pHjJecB95d8E8h1xJVYuRkYMkCBKiWhHDd/UM5YohhArrh/XXlvqdnUWGY0ZK3mbp87KEnDBfnzW9DTFuGpLdhriSAOtxwSXKRTKwdQlc20+z9JDuPal1mqjh2/zX/ZOOuVVk1qp1Z9p75Y+HKOoAUW/HFLpeH1CUYe98mquzu99V5dhK2HY2x/zh/zRJS1Snr84Pa9PykbaUnFqDmV3BE3m2kZ8V09lDZqRoHpxPWm80Fp0L9hSytTcybc1XEXAKm03vmBBbrZ9oXanhIwCashhYAg0e6YxMkP9OjMX4qNHRpc04L9jmWPdmzBbYJTxRt01Qz4zqpC1ylx3o4kRVkHNgzvdc1QJYYywWPZw1gFfqnPcbA2bdGtJiOa0McfjO/gf7TFN6OgMaV7u3Tz2klKS51cbmXjhSYK6cbxW3b9+rv1KW3FmAfxaCUxQ/2v/72Jrpe2hbXf/YSr0Vi7nW8qyE+/fblE2RePIqhiyGgUzvOWvfqMB1U9xjQcXZ5nbCvsxZQ1zxJGg7i6Jkw7qS0EtaZ8EAviZes71I0LQpA5ivMQ6BZVK2FxxZfjEZrJV/rqlwZI29C6r1GjlRzE7pd6aPYimQZpbHQsroO3Wg/mZ+dKRVuV1fZq+XOq2Xb2nIMM/8zy9fWIY3Y8X11tLi2jRveYnJzQOrEHSxr1DSGHfOESOQGFN2lwoOYTqZTTCoL3kOdAuIOsIAGCwB/E0/0vsYqTH4X7vVoXMCOEomPaCHGn51SmbnJkev3q3a+zOZDJHNIGsZYwW6dpP7TRXu3ibej4GhA6Ty54BZtRZNCCuKWBvhJluttrtX0HlvZ3v4PDLiGgUF3f1ic8VAaONmcIeR8NGl09Hn8d/9/prnl/iere4anUDSaVLOSzMDQ3+aPxz8uceGww4NP6YPzR3fRqvjFuhGQOVhChBqzyEcYeFw75/0S7d8r0oBeEMn1ZJCJADAbP6uBPvWwkx/MuAmTkslDq30p2WOTtxjiUkAvNdsOzRLwrndDGL1XOivm8GBbTa8juvvrBt9v+FPE7YQ9WnboRM4m9igCycHqJDnUXFFrrKJaHZT+buV5brF/VOCtDk7a7+Sf6fQjSa8+s8i7IFZ/mG23y01HVlopqiZhj4V87EGI1UBMsG+x/RmnceK84nEVb9h1p0W16S0L3f+llOvUQRBHRkS4BH/iO4BHx64NfVWNU8viqGStwMehHyLbfKbP6+fdsRq50pdzIPH1fCslUQC/Xj7zNlYVr634hWQr5xt+cqBZ23MnGEoR3Vq3SzdtwlglLX1ESqnJqJdWiaTNTIZeFe073K23mRmg32CKw3iChfoPm/l9JfLtRQiU8p6bfYUq57hekvJNP97erTv1EoBT6t+c9oO2bx0tWU5xz5VQslGcTGtT4LqQ+l9jKwHukO0Gyl6zvCZxGh1jFbjJFQWLOyNBSFzwewDT/O0C3BctvLZtusqQdy+ws7ZtKCbpvSXW4fm8CIuDwGBdQtW6v5IIwxQSqoVepBBYiXQ3VguxQr6oTUdUZqjHGgQNnJuhpZ6rM4hf56yi/wSm6qRV1hH+/X8qommd8/bcrTS/M5FAg4AFIJRq79rvUbfgVyfyFrafoT/jMWdyCxB0yCe6x3XNETBikbbPjAQzzIVFUgw0tXVtuT6/l25RaE/v3ziGkK9gCSzF0jsJQ9S0ef8TxL0rEjOdiax3yNmbTZ0/CEn+I9Xpe8XPqsa174U4z6LDcW5CYORMpUGtOsiv2rE4IgpK0G4qFC+XFs6lJnjqxzAKF03sWr7cVSjOZtGDbZ6JqUBF0Gc6PUN/xn938/+d9SLagPoHvGCREDcjRxNR6NxFVLVWLtiwMVNYGS1de+Njkes6mJRc1pz+zKYXmogEuOcb3eJ/unW9/O4fetc6da4cJBHYb1xbVJc84z+YOUa7/B38/FNxINEKKc8W8Gmla+Gdt+dqx46Itu5gYFTcXdkNCW97PazVFbX4I+mtLwEhU5k2EVi7SYsTp9f6tLJKio34O3qYmUcfd/wdtcjexFAMyt514xokefxQAIWTwDdV3fwvNofaeDBsPEtCLba8JtYaunaq/87AV9aqJ08SE39hN7cSuKiZ4Pp6yuS1OwtBQHBe/lM6rU7aXKtfGpUGpRvYoxosFnKU2ogQITRVhLbZVfwQCho2FjisNFS5W/U39HymzZZYn9H5sf09ISAf+8lLrMY9RvBwTpJc7P9BqaPitCRkPyRl/0+ecCFmId1D999j22E37YX2MFi9ZUkeS+Y0OoDv2kq/MqWvFhq3Xbpi35Q+FosTFSDEjXYfQgCVnoh8qIxWejbteZOcyJ+4Eq1uHRM7baXaqjD+IqaLFbDl9WyqdvwAJGhfJ9zNffy0xKze57gcZSfeVHp3IydMliAJTNwRdOQktIHNFfj58fk2hTYvh78V4TvU2bYX2qKyU7PFU/4AVfmdj98QNwROdPKnCz5iitbWz4GIdkQLrAQyjydLsbtEK9OJelTzp3qPFatFrLrVrR/aW/UF9xn47slMKv+uBwFV3bUItr4kL1ro89/enrLn9NgpgzfW3apvCllL/j3yndb5482T2Q8Kk+X6XbZlZTPa1grSNpVyP6fjNx/wUDA3tGo+sMz6AIcsJaoaGtTr9Ha53iKAGWX0KAhNeqfJQC/chotpJR6CbGRiPVy/4wH1dCiQ0lcnGRnlO/0LHux4GyOBKBQ1vVkayXiDTIhjhUFlulJYsXigJqZ/YMHOSHUvg7x+48jU16q7iW1KHJ7Oy+JTL+jqcFTY2r+Mrv5Nunz0Ul75UkFa5DzQss4PW5a8Zan1q8mKIaENBnxiiK0h9b5raodPYE29Vmg3I0Vm0O6Cbv0Qy/+eS3s7PREnpawUg9WL0/V9pL8R17uyrfvPfKleSvluB7uAtxFQZb6XuHNQoaEt26YUcNcpx0kepdcfuboTwK91zoNWaFGbRO6oexGQswlJ93n7bMgz6cNI3SBNsRK0hpHDKh+rzH8/3e44yyHruFPK1eSQQD7171FYxRPgLtNr19fPnsp/wZdd8VpY8MDCdhRb3gVBXP7+BUX+Mso/qlfAOkQlqCOt8cS+MrrpaXSL/O+brM426Pz89gdzAePLHcvGkvtDjgNL7Ed1kpJTRBRYVqOy4W6NUQpyKCFuAOhLLpoyVjQggCvVjA2Kcx7aiuVy6ciFd7XZ0dUBFT72DQiwCHnOhUMfvllF+dAoT4/VmzN913U4JHZ+u2j12Sp+QQjhWGjzb5/qmx3+z3dheX2398HvX7fqA+iymOprCdjZnbduEj6nxDrxp1PfVigeLCAJYTZTYOigNqyIjppaipH2DzhKZQ2e0v8qXa0d3+4uF+K95W4Yzo9XDahoJvWRPlba0w1pk/d+RE5ktAlqzmmr+4/9tzT/jfvf53/3f10KMZOTqEoWXoD5S82GaPrXBVUi3coPYvVJbIZba26zY4olxJyFGVPl5bm315FeCukWpHTYYjw2pDiVCkj9XP/ht/XjmwiICLkFvogeEj+HIdMGlFT9gUWO1DMSpO/w4uHCDR+PCODz7Q1vMk9niH/YZpftNFrN6cD8oQ06Za0rw2Ku8XNXYYSoBbPXg3vlPTK7mwzR/s4GpVFmRY8EY4MkqmMCLDIQvR7gux2iB97xN+H4pkgTJNcaojt9DCTNs+0Z5ihoPg0KjnDPIkoOhWp8FbSMLDgttb15thK/3nHZcQLOFD10RIF15H0woKXSPX7rVGbz4LB6Kve7Sb1KoxMsdwIIgf7Z5+yG9TgFbol17+3NjacbaBlbsyMlY9gdAPrQNc4yG3jtGiuMrDbBk+PgalbI3e+gdFcdNNBEJZqVHn9EmbthrMDHqWMlwfFNNUEXIrb3brhj0/VNWF/nJgY6U6csUr5kbBzh1eICcLNSKQYwaNCeTWR1G8AHdSZIQLKuXWxF23KpCL83W7ajWYQGmsNW0UawiU1AQImBCiDXOFbaBT0Wfn7FpLuzYLKM8+/bTmckSrEIKBD9AYmh9Jdmm3omp5/mzuP//yqYtZPM0DNLK1+pcWb72H57hqABiTWtI+eAou0CHLy2jW5WhihM6aBmzCF1bw39WFiZl/JPtwg9ab/GO2Teejurvu5KH8fvFZgT0qULmhj9JYjQAAEABJREFUMTGpX3bHa6SDY0LmvypsCBTR/k1gyhiKas+VViZPbJYOYlw1nF+mZVfMg8MvGSpKAZwx5V++tjFMHE7qlwJRQDKopFePbh9yFJmmlt1GTpOju7UlCwJJl3KJ1sxAsJkzOcKnScMpIKFIN6Bpia4pskCN8IIC4yUkIZ3htApnqgF9UlKdIwmIBHR4C+Ge3m2f8s3le4hCJapmhMMeWjRnRsTKjII1urmb8RutotF7xOJycxoMjLatuUIertanyoZK2nRlaQi/VjSkDx7ysZBgTXLzUeMEpmcw/1a76TJkAGvWY4vr4VruZgxTq+pVQ0g3RgiQY+lvjovwv/h6TB0ySd2TwRWKhDSOcjvWqbn7mnVwPvpzZccI/JcmYljVRwyI9Tb5dq1JaivmxrVtLdteFOPtoDBnXXa5nNWSlUa1/WK//jPxtSFvsDoaO2OnCOri9OOzFjzkZxIog0Uo9UEzG0N/Uixe+PWZad5ed9bR+fpnryqmvl64Go+YU7jmY66y+FYiyeLWKIxus1K2nwVTuGeyFVhB0BWUU9ICmAbx0xpQg/662WAS3cJ2dVYHKcYhzohSsOJXxdcbsyb/dDELL61cdMNMXsfNZthlRv2b0C9p9Sg/No2ZZqS2RJKyN+wGrqMW4ZvQYI5LpFrbvdN3Ryy31V2nWpAA0pTbGSC9K4TPbKA/I5C2uzZ1VgDMtMFTTIWq4RwXB32rtCZngqMg8gHczKHnVQgQLD1WusjWZSEdvRNXAscWP8jPXEDTrnzP6KxrbF57W3u/mdFisp3LRFp3z2271zLlutQ00zRElkhrX3Qvm6RaDkt8ChNgPrY+4GiVsUuuaAz1VTfniaDa3k9F3IWiyBwt9HZXwOAjYmHjHa47thsyPQrk+ry7fq0kWGBssFRfhP51Q7XXhnK0EWgKqxYtvGNi+0MrL5G1XnjB1WReavSFSgr1TaILrWqT/EYrE9JZK01gmJAU1Wq27/qJ+Xnuen7xVIYVBcStfJVgt00hbouHLusx6MqUa6lqF6siUAWXlQJQbrPVGoJfIBxW8bemquY0rjQ0y5Xc3tXboAS3r5yooxm6L3CW0sBblgoFEp950qH8BxdpLfgJKRAnb+YFSmbY8uthbqFPBjPMQGjPJxm6AjWEuuCDciYIEt9LOfBBjAJRTbRMPKNxE6ywMaNeHoBGy57y0y40Orq/qAYv1b6313p62egw0PC0/RmJyDRXgxGksA0t4f0RaAGGPKZcG2JslUpjVfPL+eT4oUsl0kcRAnJMG8QgNO8z+CU3l/VmL8omwO+lveOEfx9GI+VINEwC8kzAejzb23VIEI2afxKrbBVe53w0oum/UP4FRErKWTW6//i6opJYaZOOg0kJ7Mvx4kT3yv6+nsNx3JzGHes1WGWhm4lrLb6QOt/2XU/cYnIUmAUJQV5VGlASlYzPPxYp2XIkP4yg+tie2O3GEVK5nxrvgpRlR613GFNr8sfe7ep7R9EB7lwfJdEp7vpzJXGIguVpdjvkAJrqCmNU3JXrtr8lZmA2FHmDN+ftOWBKwYrs4Q46zf7Zk/ZsNUxT2CUvkqpJzOv7BViLDDglKaRNN9BmA3w5q/hOVyhRpgoOcaSrPubLGAsmciHPLSX0wO4893tyEKX1UwuLGc27505dFMWberdVIQcjVCJsoiZUUTOOaXlx9aVVXG473A06bYLTQC0pBTJIB2lag5T9hZmwi33hfdRqqMiFzS3CVyiBzjZFIxtqY2pNnVTf/No51DZmCF2i8tfrqYgWbVJXr56VbpdqLqtrVbrGGsN1Z5sEXqgGvGLcPoashER+mlHOMhOu6mj44JneSDeNI8Vg3p3XT/axPKubDkRT0kljc8BEaeX9mlPbtCzrXHMVdsvaNx6wypl2s126wYNUm929fNZiPI+eZONZEg9K4zU/ZblBhfCD9d3zNCOHNlJA1eDMlKNcBPCjPZjPThnyvWq5ngrOnvWT+4+WDS2979LDlpMowVttNqslI0EqeJocpbVcshst+1of3jtHD2d+3RLkTeRtzGGcCa/5D/2jIuU78dbc/sWoEvtg3mEqi22KJIUxqDAW5G9mBCCrun81bXRyjcYxyfOpLOlpDt+qNAlZFRXGWZ4rNZ4cKylxfsRr2Z7HZ1Pr0lvxe1z0MBC7TI9ppmaa946O1kOohitZ1qr3a1afa29Uj6B2R6elNDL+J37+W3/2BCsV0azyR3eXr2C0ekQ/F08yzLBdPJARjEUzp9Jd+xxX40EPEver9CKgwu116s+L+Bv4C0WPEiIoCxVCapNrzqNuiCMGUaa1KkYmoyLatJT/8NvaTPHOOjYlf66iex7fj21tpHld18DD9dfWrxFi7lr8avCI3YIjN35n/77rTTGZN95T/9v39ezFAfPO78mjoq/xF3xtxA2tVx/XA2c/kAoFT481r+rhitCw9kiocdokUaPVfZbND3sbzJ9+nmN8+5x7bsu6LO8T29k/yD+mXcwBLwlXUIE36nIawfEZUVm36719fvv33OoU0JJBTFdJeUhBB9g22GmPlMFSOvle0QWN3q/WPHMzFdvjKuwWOa4lDoZslqyJvFYgq+2Hgi6uIPvMX42AoPChP+aruJs/mJ+5+/t+svpL4Bqag1sDw+lQrwzmnL9T2cJ44v5uNCDskpUyPjk1ysFP4c+VcmbIuygC1daP5+9KZfkUX/4kFklfvXdtg8SgNUOyUKWHSQitLClMEHSnInllaMSG9xgBR63zO/f34rfwmqVD59WQvaSD375vVl0FLK4URe1QAtWXVq24yzqA5pPmA70bFvkvY5jVs4qQOZp4VI7fmRM/GU/Gele8jkShLyYB9b7acm4zdadyqEKZCeJB/4x+z8ZsnoW/xl/ogeiuRtxc/fU/v2fVN9AEiA25HhMmH498xJ6cKxC+z/p9cPfHrAdD0dKLo/6gtuGa8SLfvr/UiNHVn7P+O382/wNWoYy9bBl/nPuHwBbGDyydpFvTWzZ61kTgqYl3+Krjfyu/LY7T1pjL4S4+vR77MTWm6InUX1nJURotXNls6y5xoSBWmC9CWXQudb99dQt/4SWChcuS3iYEJMZhm+qU20YjTc25soa3hefBPBCQADcpxvVGwlBdi3F40arHiis9lJD8WVjLrIk7ly2S8ys9h140Ny5G0ARpLTgGFxxdWGQ0Z7xVr8jB+ODnrt17Yt2HZ9cLphbeazzW7w/vE9cscQ9ugaDpMLr3dg1qpXZ3WwuIvzH3qfpD2w8UWA+pkQopsX/hh2gbQ+6ZaHUihbrcg9IldfFyqoljrC+4Wjg/fCPKJLkTOF8AS+yeZCoL1hpNaOnxaUoczrHnZMz/kn+5b/iY4h2IDGy5okNU9QzgxAad/fnyu1yutfRITa5rLRpardFknWbgAp/BY6T5zJMjTgA5b/+36HAVVSJlp/edLWox+IfPaAy7Zq9sznQP2CqPFaetxhbs+3/XYuEyjv6su6mtBTCha3Iw1un3Pi2Yeo+5k4VUaBA0LUkxhF4sTAdty+cqrM5YLQFd9gs/+Cm6rMqa+FupQoF64WrGzLN6R7mG/pq2DyXwqO7KrvOqoc8L++Vz2j+WPmljAU9Dp+yThigpKlEfqxKhQeF4gsU7uG71YP5oeJl3gHIsLaz6TP68+bSa9Ow63f36+pUMznvP+/37ve97tRrdmr4B3VgMy3HVeXb+Xe9fy1/0ufiiTBFKl2l6QHvDY/PiYytxyrMMX3b2YF+OzvW9N1Ot+8ooWVxZ6xzLEie0P+VxRgayiw1riPjE/9a8tOOeu3r9lmkMmsqnC8Rkzudt7gqJUArrqaq+9ZViY+jSSLq30pZAzswFFQDYNhXez8ZyeFshrq9DKhMaBprv2f9V/Hf5J+vJd52DzDtTA3Ms9SKsHjuU0S4zNy1F2C/813FWlqToFwv9TOuryT/Hf94df52yorr3erCifijvdqGmuEhjdZ9Wy7E0IAD/Uiw8VCHJmJb8N2+lAnkXVbzN0Y3CXhN2cK8oUjg/EZoKZVk0yICpXHyf4JBTnMP7ytPI31Bfsv/Mz9JSLknjn09AkXXtKSlG5EyjWcO0qU8adS3bkhhnIP5k/Dez+ZvsF8LP6u9fKx06JivPnFJifsUwLSbW8ZDSlMILLYSiXek5FeeKsO2IEM01SEyplg+YAX/4s2wxaupSt40SVMMSkQNrceXAqnkuKfoqQCoUys6ZTqt/gEeDAlAo69qq6Imi4qm8v+f8/p78e+bbvyd4fF+tVCxgxqYFDtMKn2W04eqPqVzeD5uKZEZ7VQVvrbX3WqgEMeY6clN/Q5G+NzuF6cPh0+yhgNIW8IiaTY9FGBsaPJlA04J8ONpEWD6jSEMmYeBQJgY3p2baNGvxrClTpeVQpwifxfaZSV5lX6NksA2Fuf0BLgJTqKnUL/HEpKcFRlFSmS1oayDOIZcmpqdb8IIRMEvrG/d8Sch4sU+I1fQ4vmRDUOrIL5RuoN1HIBfm20PF+O7xVogHNul4bdopEaoS+AH21pJg4aZplXvu7vIrAt/0bCkByKPKHLvtJWwhPFBYSFdbiDSO52i9uxNJ4a9lMFrPuuAXhm4c8SJfS3WycYpSh5N6I8aykfrHtDVCsVqNq5R3k2OLwxiEgZ8sMANzc+mX/CyEbWJVW9bnIflCdAioPLtyGh37hr1eL+q+3Tks9SVDUjKbqTV/V9197nPKCwyjC+HyDK9CKzGwMrGWarA50/QvSXJ4Lk9xqLooKja79U/H3niK0YqUbmsvi6dnv2l1xsu9/zmxWUCkm1aVnLytDfGRPk6vlPhP+3ut6i7jGsYJf2299DuG9in1Cfi9qPLRkoPApRhfSs2oHfz+f5QsZghRhWmForc+Ssko7u5SWm8ThvUgfgx7pa9xSPCmcdG6OD3KNY99Bw1K/vLfn3WDx9mNKeLCxqhSVEJPf1HiHaby2jUXfSEn+Y0x7RH2bG82v4o5BniC1/OdxIxB9lqOQKDNb2iPjQlgOAWVkyI+teqsOPvPqCujgSMTGqQ0fTqRT/v1ukqgUFByKEgM44YBSggDXMndxRbln9oP8+p28puiJGoBIqeNLZ/ZVARJlonOItOpiDdty/3d3+fu+C5/o33aoUgsF8rQGitvSKN60nwKgHLFB9vqT+fWRmSyBEtH01s5CA/qaDOwiGss7/DZjuXSn4gfyeCLihRDa73h+ST3XyjcZF/vRohX5qCqRuKx+4WDbqaCkVHAxPTmGPtbeCF2KOLHAEUPU8/5zbgSgaWtY5ZjpZjqYxE9aMP9mdd0LyLRLs0XZB9+VjgCncEb+ETFYbhzmf8w1mjfPCDh5qaPbqMGHPBvJvQyrE1/tjvN5ekbazZQQ3KpSjO3OXtR84aw4lP/7GBgjS8JVJTV6JdfdKkxEOCx8BW+N9yvTAv0KoWLMau1enl7YAGb83jurQot60Sp+Gtskp+6r+5BoS4FJZvGCEHXMFEC0bAlk0wAABAASURBVAWM2kt1S5jHeKlp4ICYQCLyK1gVq6mK00WUELQNRnHxk4xSQMA1p1EKwm5sMSLk5qGD4/wmBsz6Z/jTEfcPR9EQYEP5I7jYexAtnvA4lzPcp62yW4WkdxpM8VmdxdIsGvRZPqd7aAYPdMx3EfnpfZbikMWwWSEHJOcSaTZ0cYjlXGFPrGePRaILOR8V1uUIs03MglZZ3eJ8QCcIEsaYMZ0ATPLIVWodPctusGiqcSEqiiKN1RQN647LqEVEmnGNauTZKixzSklI9qG6YJds4W67a9v6Wb5+iN2QN41cAU2GTbtaxQtPgcg7c9LqbcvbdVoEWeGrgIsURwpbSZp/76xbi9BPq3RtClUaMSkR1ppS/EcGRkVX81263wBdVCI5eq+wdIjDnkGmjul3SotW7+KfkRIxCo4P9dsxWokn1QBaj3heV2snhB5Kg8Jkh0N0NlE2zTGmIzkyTknLk56jPmd+3HRp0OoI6KxJ7SFTq1hCc3V6BBpI6EmoHgiJMK+ZWxA31SJvMIoO6U/un/JdxXOnG7rgEiF620QIUSmNSkbX/3adlBF4KeMUujbrgbbOD8/mMdzO02+xpJLeDoN9htOK+qBphT0MDjEKsD+HRfboqhjy9q7JuiWKohBoJclsvveJTnmvpQAdbdH4tAyauemTe8UMkpkjw2WgmsGm71GK0/nSshcsbDH+iVlVhQ9IymVj4JKngvOitVHmnWVzGqKmzpDEw9atjbIiqWIHafhGj41iKDe4EgqFh1tx+ufgkWWRm7iKx5e7LHvl/Coeij25ValAO0KMZ0YJ+bgABsERfUrLWIl1BjusFCjRc127/CyMGpXKDdZUY9xNtTYtj+ujoQX6LZyZ88wx/5WH1/gKT2MEoqSyoega2NAVAxXPcMFVWovX5GfUC9y0haREqpZm6BgDFqm//Cxpu5OcGrvjVUbhbEdLYrc7rWalPjERpQ18BJRou6dLihnUeBuG1+N9vEWroQfS+tV+AQ++OmJohE6T99NQyokBIikmNrQo6Cmqojm0Kof6dpJlU6xwPVoyEbvz98n/T79/6mrWzIYZFECXXewrFYESL3OAEI40CHdOtpjMovjRhtAV6cXRzv1YX7aVqj2bYoel8UfBRRJNXNZvvy/fXIdPPYvbnEAF+MfC1feVK9Q37SJVoK0RK35++Cwz10oWwkIbtZzBzl6/jUKW6SHnqBPfXak7zJ+gchG5WAYkec3LZzgt3gKuJMdJuzdKH2w22xsN3vDfH/xuvHEr3laS0nEW3j+uJYj/t9GAikbLcQ4HP+BWemW/nfZPBxYmAHfyTLeDis8BxHANnlapmAK78JNh15mdfbuoruFXmtPY7+iac/AJW1GO18QYYRhaGyUTb8YVPDXCjwvYwSTekbF7Z3R1IuTJxqLWBt3RVE9SDjXfhd0WZ323cAed6GCHV1Pp/cW60PoSbqZRsKEoRq2Z6v67sp5S+rwX9G6Ij8C5mLcn9fN7kCkMg4VZc63V4y+dCv81PaCuQgZaMvh5TM9GKSkA0IBlHVCnqfLw7cvcyzFI9RGkikHRi4Zk1czQEYa9TAFqW/VVvLki7tmn1xR/Tv+5R9KYgoJUzoiPSZAznlB/ldHuLtR4za7YDNJuTK01/iNTullmxoiiSFXtCtc3XY+EvE7He0H2pnZaptUbKBllX1ch6IOZ5ACrntIFOEhFMIAUR7MV7Vb572IPXGp9hYMoa/3VRClDBY3tCfudOvUrbsWI8NjpyQv8ajiqS7LUitE9kkhBvvq5ajCfR4YJ41FQRFdbk/Ugf/x17T+cOvEUflJ61kiXNWrT8S+F4is0QMcMznzLgFxYEXfui/dOA/yw/F4ceG2FRZ/hBQomzrYaWtGUA/VFBRZS07xP/1XND1i2hLipWgmRE8zgsZGEkMpLY0YhycmIWp+L6avieajFttR8t5PwhOxL4u0tTlEKzFmy+bTpieeyGcVFK7XSd83x6WHQbFPU5DIZaGkE5Dh/F/4+1TstylC69UtSA003C/2ARmVwUiSpl9q7VBM+V+6zvK57mVQ0g3+v6d1pTNlttyzyqpbaRu7/cP+PS8Eo6uHp2/htRXjTf/PjVB1x8nyd4EXnutS9zLCKbZAtjmoWzi7iANXGMYdvdRwnjKThqnrmBGuzKCqpMbomhSgnAdjXXX5oKiOnL8I/3KL//Z1T6Jp947/2P0PrZoElPZlBEKRsZjXusfOyskbst8b5djf7fDjoYw1xO2qVFVQrmLRIPl8I3mY6qVxQCS5UYcAvEACikBf2Yh/EAY9RbfImz0EO+z/U1mpxajRNWxtPu5gWkq0ruBwtKZPuAitrWvzizD7cr+2aB/OD2POwXfz+4u/v6P7z1XAucjMCQcVr3y+EavPeLbQ6WTGlZeuH52d+/5MWZ7wVQGu47/JH1LPKT9pvKuEDtIk5tdCPQafuRY7l7ZtfIAjMv4yN213Tcfkc/NyJPlgU9Rx10+Zc/fkZ3SSsoNmCNtWgG0j03JN/3zylEp1FYUyXT9ne8j6ThnzwL8eYO8iKvJ7dh/cf+BDnnLZv+67L4XDS4FY7LIYh6tIW19JXlWaCo12FGLHhv4B6/iaUG2+I1MdggMxlfsqRgeXtHwNdWVB3mHJor9T9DSsrBUcTALUZnhm9JV7iayVl0lYNmxgCltvfpckoaVrL2vf2qIo0at7b5y2RITvI8eoAQbvD4zM+3yfaMoYhrAgBUFIfUkQFKpBhLYJhLiIH2QIsJEJd34dEY5XvXP6X9/cejhpFSwAnvY97WkDRT8klRPFqxvwpe0qaU3kO9CKxN1UqbEUXLC2XxQL6OXyuf6dpdDC6kK9i5WjRmKe0kB7Yce9YFbu5MH8G/108v7KcsiRBla2a0NFjpoARW2qq1RnThmm0Xn4lMyrZYSN7XvQsNjkId26OD6izPa6La2AbSrCZuRIyUHfpp/u59Ui3udcxvqB6WHUo5c+0M/WjBNwE0giXe2/lHAoXYgJGtWeBzs/9ui3Klq/Yzwr/FuKW3KZRt2/hmukjRqzDGkreD1UKdGBQGpO/3vcP8keqnzHpVwNWOl6dd/OOSDDlXXO+D6a+oO6BZObkrWyFLAlv5ZkM2Po0QYA8Rhk6tB426JtLCcf5lDp3BtDZ0SOstMmFdCGEDXwUpOZrWk0Jp6Q58Qv/Lf92EtKvHmpcWLlxwtKWHPioLH53p7O+933G36MlJ5z2Y/Ws/vYlEq/350WAsJw/AiU2W2IrYwmrD8PdYMiyv+6/23uR7lCCO6rsnimFdSZyhHeUqmZWX3zHV3eADCyOy0sbkjzh58fvjzqrGjXfDrFN25Ra7QpxK3rmU+en8s+4qwDmQRuRJgH4WMT87PPE2foANtA2UoAlMJw2lPNSiTO+ZgfitzRuvGSiZ1bPBukc5x1IYzq0P5oLL6wZBzAhs8jGqepq3GvaL/P38m/zJa4pg9m70rvZjFLZebQoC7pxqQUyY3RTkCNCNFsHbfDAly0sRe5W9NTvwJipsKYgZ4YM/I791reII8g1vsseKxWvx1AAxcc62GeYmGuauXJceyFxEVs8KzQloyDubSQqypkm6tCpMS8L3rDju5+w7WbqHl+ONEXWtMhpXVngXrPWdW8jWcEUa1C6Pqa+BigdOWEjIkqio3RT8GkfKtyh5Xpmi1MqVTV4qcJZw48VH6W6fwobRhdxIXjqw/4er5rW21KC1JR2CsePcwvFbhHTARCsUgH7r+93RQVca/Siy9tAWpiZnLrzJpSFgc4vmJXNVdfqrHwnD5kiNYwSfDCU2iQslYTjSnSBx3Q2QFtqwlB9Qw1JBHDPcumTaqgs9LBoum4wmfPACmx3a7mwXdyNBQTJH2tHNxThKE7NY6451K59GiQw6Byt+aUGNvqozw3UNPvYTUsgIWzlY8+I48fNzEFv2GkBlH/5ESBJPbzHCT4IQRolCBQC48Rgke4qCGKUl16ALVroVIZi/+ibA56iVv47zJbcHxmxEbBvUt6pkzw3StWolIyqZpRjWQXNGoJamBQaEG1KYeEpcXhMNTZ1yZY5oXES7rWQiqGpl7KIKeVMuizi/bHzH1tRoee2/YoahBqbgucQ34utCp7vhEOnpoE7MbNM9mCKDw+FBFk8iVQmREjOrc5mw8aMLeD+2ZUtQCawQI7Ckzpd081DUDueMRqkAnBYLXZMIOiJ1cKZvbOtJku7E1F4mCrNLsFXRJi+2WpAbTIhaPrdMucf0tHFvkfsGYyCHJp2Ru1hvb6brrMmrS2r+HTdrJR0ySTVSvIz3HrQqbjuTPYgzRn0UJhyW/2qYcxWJ7gbYyJr3BxUf7ByA0WDYk/OgLqiGjF8wiYn0XW/drjLPYb70qCXgaZ/FI9QAA1lUWGrS4235OkX4uJfyE0M8DVK76IoJaB4wiTYUMaTkf5k/Pya66YVKOOkwlMwqtBRw0vNe0edEKKXUnPtJ22jaAnT7w7XLKEQ3oTy4wMfUvNaHwWY49HmOSGoipapwFhvdNoXtw3D7P+n7M8D7emSqkB0rYi9M8/9inlQRNRuhW6lxeG1LQgOLQooQsPTdmAQlVYRsKWYREVtGQQUpBgUFBBo5kkZpGQeZRYQi0lapJkpKKqo8bsnc8fwVv4KSt775713bv7uOSdz74gVK1bE3pn3K3AbtS7esq3LGK4lQ3w7l8+EpE9Z5RV9Na+C6bEoMUgH6u4edgmwezTaUZMBKP9d6G7WYrVAd5rl9NisTNAAAmYD+aB4z86UR/RRCLRthIbymtzpCXG0nToz7TD9KWgdiFXAwihM6zGU3Oqq01uPLbQONqpQq7lUcmBREa/wIBIZqEiIJ09atYAq3ZXBDq+yAAqtH0Est2VerUvIYhM52MbEhVOhFap1patNU1Eylz6yHUHPGJAGMNPGcu3ZDu3HY1nFkEbPWlXVF2ZDZUHZkUuppTo6JAvNzdlAUuFqNa/gVe6ZhpDkqnm9NUihytFFw7lR6TbQyHCG+iJ9GEwoLQrZot7gg5vDJnqrGlapCLux5PgklaIr1tYZHaXYCKANT9iebUMb7/bp5KQsZslqcQ/Olawqpmohm3TZqA54+SZihZuRHsEGaS4gSs4qX2opIDIE5Xo3C14sz9nbTh02jE5TtneLfeRuNtwbOLuXwLsJzjT1ADpqtBrtGqIOVxbqVAXMsBHdWSf6lKsq9AICFrTky18K84LWPUSJyeUG/Z1pTswxdxsTnIaro9igebY0Fx3cej6cmCFvwuRhctPKaWS03NgsuwVup9uVUxdfJYKWdxs5sI28qfm4OQn5fwL/nOMuGpB8eTVBl1zKUgF1QwzqmYqpUhSumBIpHsNgKmoFkUQgU+6VCWkLdPce8NnDFyWjaI0XLtPorkY3uw2JTqZyKFdb1tZr6kQzYQ23po/i6EtHJ0vRKT40mN1rZHmyW5uhc+EokZDWNtsUaHGd41yT5ZCt4bUZplKFsqF6SYv0ziG1V6JCAAAQAElEQVRjxZJBfdHnNg6H61OUuj30dafCNiNKkLPFcWa2gMvzDD6V2rqY+a25XaaxFTS9gTKnGWgp2TjFEcgEV/u1CZCNQJipR+Wwod3jpMonIW6qIi7tWHWZQpyMOc65pYOeZqRNmKaM3B1DHKG9cqwUs9EWNsKdBtPBgkq51fUuZK7Mm6mQKLuRVDDMssTQAYymOHRM495+9XPA2QqlJ6VywSvBXohgR1mEV2sWJIZhyQFMDWeIrBMoUBZHK4jsNGpbKEyUOQOsSqPKRCartUlkEdcru6tkIW26hA+dJkizBBZMsue4nGis5tHEyBloEbtu83yovB05M+kt0tugMGb3LMoKhkSQ6XIACJFo2i7is/fEBSAdABHZlYZwDUc+NJ8BnzbMrFwiLKXRlpcwTbSp+KRlF5BUUbUTsqKIL31OKAAqnAv7gb5WW0WPy72LWekI4qEN3IFb47Z0r7DRyCmDbS1jwrYS51H3rDDlcMAbKmsEXe3saHWZDN2+6Kb7AdibyogMliHkv9fWsXFN7zkwdveJMgn5UCQqhuUKyAHl2kf6Fdjo3lRldXZJjYU2O3tmK1dVxlM48VSoOLbNTM/LNdhawWiuADzVOao9S2U9JDXLQyXJO/xQhRkmlEgLPCwFEjlyqV0i/cRcYy7ugRv6KeYm2jIiEQ2E1OSEZXekyK1p2AyDYtALjEuYEglRQEsREX3yVIBNERfAYZ43DD1+3aw4hA892721UdoQEsnE1Xj3seZ22iaWpAZ5oO/b2nRqV8x11VxXJrQzqNTkuo16arbWQ6kMlPZ4laOWOLRL/pu6g7NoJRnWbnIaUAzB4AAIWavzLqqZChR6tYaqvqJY+rSUKKLkYJZ7bhbix2jtiuU2sU+5uVilvLDaW8HkLG1RjlnhFCFil4nUp14uVtY1fZsP23ZzrTPi6gy95FjT00X6JY+2LCd1GBVyYIXxKF/CvpAd1aIARY2pIUONoc1GquUwyjO2tSxV+m0T3KtnlbLZCYDYvG5z7dYP1sOWQ7zRVve2jqeOmKtRhrpVmtqbFpN0+ByDW8Crki1RFJssJWt2jUi5pUg0yp+0cYltI0Yw7651QQ8URHJqcnvl3thopJtjs96clpC8kUZpFFXjiFF5Xdefz3QSFtv2KDlMqyKO9Gy6xtuZ3iL1BC/OR6Jg2hjEaHajcY2/mKvB8m3N/Zweg21y1MaSPqtCRhPXFPJsMYWGiliGdtpOH8PczYS5ZQ8qUIiZPqOyQuOzVzyerQfDh2OpHfuwHrIQq/PlKBrnyHMrTt98jnYImpiaQzzcFvZVe5s5bW/VyqXmRD3W0lM6A27TpTqJQgtuy+USI02AbEFDWbeTw68fDCzvu7duBDBTlUZZMLHVZaS5aWclVe+pjGNWz052JJamLXYtIPR+GiQhwWw1Cj8LJ6NbOeIVhsyJBKjcUzi0IjBMvN5uuW9lUsWeNc7TqswukG1dEk271krN9VLk0zEImkkQJajomJG2EKnV4JzqDqjGVu648ENiPbfuGUZBljTFp46Q/G2cHBmuzcm2asN94BgIFFi8Fme4gUCnosNeyqtIlkay98SNBVEQFinc1BtDaNGKPVXbJ3BvWRMMKUm0xJMKzNA8NNq6zaADyNVr1ZlWDbIc4YRcEULZaocR4KJnIbMDok4F4Q1ExalbJIYsOSfaoxk9GlO1Uk5ZCnkDoX2e+VF9rHXkIf0BxSW1YAWLOVTXXSi/MHkB+mLFJmXg7HXKVWBd6cnygHRD2xKdqsMnOYZIg52ZVSupXnhvO002Qt97zWwlglzC3xemlgdH+nVGE7IIuRvBju2seWochTGg9HKxdR2k0ySCa/+/TfgAEzgaeSrvpRcEqNuhaqrR5jVQuAzv6M2T3jkZY7YPJllwoMxPn+ccS5+rWokYDRIpjEI1AE+dONWffMe8WtswujnHwDZqI3iNXQpPLikOddU27YyGheRot9bDk23o2UOokLzZZXlWnmw52tof1ri1LE/2oMDJ/ITEc3YeXSea3oSCRDROSGEDbWC30A0nB6Go1XG3kPiHSwIcsJtx68nartFuuDkfWHudvk7xAGySS29PQ+LvFrW756y76aolN4xZQ27SMsdZrb3NkWJ0ALc2tSEt1KJD5YCdNIyK0QoqyjNhpe17KYquzWNiua1SOnpuPjaYJ5SD66xbmEMhzW6TgChmpG0dpRMuKvsKMFovSAOCBDjkryV7sxCoDiKs1dpocGnYV11YUoyPYeVq1S5/Jfx97l06ttZpFeU6IV8rqktFcbAxNpuDQ4MXVlqpxkrQNbaXZDtpY/rYHMrB5VVTpFzx5mvNwiB1aSicUafXHUiYSTu9eal/zHMbrbZuyiBylcpGyi9DbxcTStuu+QQ4TtXmlHlQ/nWCLbPblVDV1BBensFD4Hptng+Dcx82TRDaq51deWHqpBM+zbYdvruLv5WjW3eat526AEpmAk69I9skZ1wWTuNSv1MsvVparEW7m913LKPCEjZyqahLeBslEtuaw2iTpFIiuXooMm5po33q0gQgPTEOS5lpbDTRafvUDVMMxdoFouBaZXRk2pNUc4FXJXQNnYqi6FAozSRPQRzZHZBomq6hZ5a+kOmjemapxregSS6AwhgGJ83FrwjVyO4MtCKvhiaxu4h0gr1HTVlOLRjapPTarMfuc/OWbr0ls9wtZ3JAWRt8amLbC6MTDhiGt3sOy8GeJmnNcd6wJEEyiEdfhz0depZ1Z+dedHeaXujGChyoJTPUC2k4iVZ7P7Z5bqYa37ZsuYFsYUeq2Eqz4pCy2DCtSQstosOEEJFKZJ0nMvdR++DEZE0NlM0EUT5ghEO7QW6gPmbe+tzOY6q3N65T1G8ATbRARlmeJvJTWemkwUxdqxmBI3iHHz50urNxbQ4qolZ2OyWTcHFg8jf1Vbu1Nc6YZ6t7Qgk4DXcdkENcVlvuhJUkrMskjxVGI21zVVffgmO1celaU8xlq2cu7V2KwrJl6E/xYAGE4PC+TAgFG1ZKGGg1Rk32phFsdYGhAsVjQbWsajLjFbahSFlQAXfEOZib04ZKaGuIP8M1IO0KLYt1SatEXZgWHCSv16TtafMe1M4tM5danyljZPd54HHdTsyFjLgffX8E9LWx+3C7Zc4Ww064tZTrpRM121z3p6kbSV0tMx2g0zHG0nFZBokwaAfUIKK8L+j7ZtsccDuYj8jH1Y9BEdesiqPzhLLrSlVqcq5LCMv6dLWsG3ipaLqygD6rH8vv58bFXRKrpryqAkcKgKmpCO2aUQM670YCndULwa7NxH5Pw0jzGOpDqO7LdTk02frwPKapMbZp4hL3pxCi5AeySPOxXWLSAqAcA6YqlgJwnHbdLhl905LVuiezKXogvkcZ2rKtW17C84rcpMAKjkffl4xoIdmabsbpSkF7olYv0TOMY/hwAnHUY0Ohq6Ygm6hAPPa6006zJXmlly7YUEdQ9vS5JUzesrZMZi14iJBW4iavAJOQ4Dlg5uiZxfLF67/XCd9bT0SngHQdgcfAXWRktNik9WY5rIcAJ1qHEsNIK11iK+qW6XMyFNhU1L21iTez1pdSMMNKfBP18vaRopE1Noxt0P2iUhmLXhY9Qq6OcXveS4+ff/5LXvDil77wZS978ePLXvjSp1/yspe+9OmnX/zil7zwRU8//8Uve8FLX/rSOFvNfE0c3pnW7SbT6nNHh4SnDd3SqdLqJB6PHWpx6sGjqQcve6vGtwUrEfiQtml2B9FwSLqD4r2NCFiGlsdZ4Wf6HRalLQxTMx3nwN3nHWMlGWVq55Vc3U+f9xe/9KUvfsnTL37hS174/Be+8PkveumLn37ZSx9f+rKnX/j0y17wspfpkt3V7MumghjFWYBcNR5THWu0XymxOctvOcRop4VILifAFlGxnWu0KgFpXNiWj4B3gIuaPqkGDqeC6KHf0AfpJfY85zrsFN9Zat33YwjHam3p5gCZ2kZUV3ejCe1ZzhSsiwi/akoFpQXFTSMbQFSfIMyv7q4lcYSAK5KGhPZykfZY9JqAgCdwGNak8qB2Gymge4+JdKgn3ifXbJjGc57h69TpMU4bZ/k9/QhtNCCZ7U4fVJUtav31cAvvZhpyIGe10tUVeqmuOj3zFjmjkR1VQG6+biOm4pibTQHATM0uuo0xNgpBVlYnSN0i7LP2CSclDhWj6spRnmGX3hhex2DOCS1BYqYa0QwWVbRZZexJ8SF5UGzeW0nSnHPKvKGaMpTRVUvu0qRiG0ISJsvIVtOuM121mj26oQAkQ0k7iOrF81BrqXkCuuaF0eZdFgu5YkbsoWXMLadEPYAnDd2q3ZNiZau0S7gzagi1QjMFugyChzlEh5h2b20O6KOlUVjAs6/PzuGuMU6DkCJCncekgUVrtnMJUhBXxF1C23ppbSoPjNAkqozaSh71aYscGdVZkMI3Xgj7LMRiw4ZRnsCZHNVz5V61G8wIzQdvZVsSYeuk1hhbY1s3zw1taadZudOwaWwAZS2WMG622fTSIJREWDG6htcYx0Y1jTR9rdyqtys5cRAHLcu7N3CCnJ0jG2dDJgk6zNqEosMy9atrsoYBk+lT30wgwbwIUjYU6ERPtpokkHG00nnW3hiQRiAUHvIl0ZsicdAA0V/zqO0oL0PN7Jk5eHJkawQG0xJWJv5duji3wPY45hqjp8MvLY7ykVBmxKbuPue4MApyKU/ePgmTjU4lDWx6mQ9yxxiXLnNjbO7XdMUoz13Jks/etBGcufFOlABE2zXAyrfQPtaV+JbdEaJA3jOWOpFCbvU29YWDnrnLa2N1pzc35pzpcxj3MhvsrY9Zd49A67rUzi4obEXgRkqkphjBG6A7md2EtwDBQ6thLhHGhn7ubvfZInc5oslVPKTXJ+mxwFi4dhiDypRTJglCjGVZKaGaYAWN85RzKQi7udbILlaPw0ZZg8uwLh5nSbfmvZngExBz2+B4KJuRUvwpm9l9v3CK1M5lddrefBh2I25c4Bl+XJkcy1jjaLlny5StnUsK3uWDvhVvbuYiZVxXwqNdYUmhaRolzRkHfeo3pJ8wizQEZSuG9hfq0J60NEWyQs1n4QxrNWHtBaF5pgJxxrQcghllqoKdduneygZMjXBsbX4OjUJOQFA9hiWf/N4jXXuqzFbINcqU34GWzbGf3Feq2Kz0eCkWxuKOGPOI/b6UebfwgdlDHZKovtqQZ411+uNp904lUxnI5AkVWYeU2xM5+hx5qslN2I4xc3jsrbrR4HJwgkMQoudZ8wCaaUw3fZIXXVKR2dG9Ep3my+eyufhQ6kGhUdmsBtmSe9XqOidiG9cOGsbA7PQMVQk8fU9OJXTJuFvTSmSf5fdF8S+RStbVMmXtLqmoqTgxokeUpdqnlC9odZu9b+aq1auqLxu9zg5ZWWyZVZi1TcmzTuvEFWLalurkTd0yrULCoBfRRtlx1+4T7pGGpSnMgrhIYjmblPrV4hPnyfOA5ulRVgAAEABJREFUBKkx3Yjaorc0Yi+lelHx2mo7UaH+XYlIx5pKAYrGMumBpQVPU1GSP7kQ2nxXnhY1UYQ3mA3pj0uBCyip4tdidqu5mUkF8EJlBUXVE0VPTmsxvQG7KZSHVutQQYwFC4DZtpSsQLdwCCf4chAdGUeUGCkhLWcBdw1WL5nKlyW6kpkDwuAwUz57a9GIOhFnK5CtdX7ZWJquSinzdjvN7zYOsVJpXuKXpg45DANt0ou5+C4FRgnBFSUsTGClP133nlvexiXdRmHfIUSFFKtRCI6IDbWj9KRHDBTENIrIy8KaEQ+RD+1jTps3uAoRWdVd6d1biSoH0S1hokufzUbUSN03t3IJ6Juf2WbYBm4aMJgGmCuCaaF9byYWa81SOZV3tSr7rOik97bFrnXKI5Etttg0ubNq7WaoLQvn6Z0IHzl38ElZ9iSdcEe1YlHSWPrl2ZuPof7TxsO0UKoaTwm+5O3EKGgXf8s5BU2mNrlSxmouuzUcajU4V0Uua5Vmj+X7OSSlvbUFsF2CkvV9bPIg/z2yRrQVMM3mmK7Cidv9fOqoLcnwkqBW29t/4Me91fv/kz/yzA/9I8/8kLd43w/9Ex/wYW/9fh/2Vs/8kLd83w//I+/z5OR7f8gfed9/8HYf+KEf9Jn/6uu+6/tf+HiPEoVNnuULrjzKjD4rSLdylnlQcJvRE9hgU/DZhTZvLb9w9YA6Gy2GGNhyjBrIyZh+aLbrniKHHmhvlYNNXMWIMETs674LPFDsU00gv/xbvuuPvveHvtX7f9gf+9sf9VZ/66Pe8v0/6o8+8yPe8pkf9see+SE63uKZH/JH3vuD3/hvfOA7fNBH/uPP+eLv/b9+XPXDNf3ezLRRPiURzDv37Cn0Hi0IDHVXqWVUR9cace7xaLZyMm4et5kOLY8ObM2tXcnzSu/SaX2ZpmdENZkXDYkzdOSoNft6wEzLMctH+yW8U12IR9npLgPNaKZ7O93aVGOb9dBSgNUsWo2pihKtax46ajQJc+wPtrnLbZ5bdDcOYg0vHym7Td2bWnO4Eby/9Hzu837p8UBzx2TNEtTgiOIVrfpRqvpczUlTN7mkASx490xswZnmgG0xtgN+L7vXxVlu4Cjzxa1ta8wndFB+DZcJGLFZJ+pelY2BOf1h5xhEowk3zo0a25zQ+1Zk6RYgqwM8OVc+QLEbuGFukvtFWWhQs9YQm75Df2CQna79vI/z7DyXRXg1DN1tCQ+Z1SfpymGjpbXeAREfjxUvFbajR1AhUoCLer6yL98STISeuITapjXKqr2beSIDHTkYkwEpM5UIIRGwdW7rUI7sCsirWE3VJD0utJk0w7CYup/fMKhWMipGgeJO+YBSiYL1sBJz++A0Ipn36jNdIBwL1Pts0d1oXN8cNf0UHmatwIrKqIixYltNIK5Q9sTMaeeDxzYUyF7l7IJ63QCmuYubdvXOa6WV5YLIk7s6/cJ4xdIJZWYqjDUhagox9prDvQ1PMrxuVQ+NnYoOkzacu+vD1XF9mcduJZGzqcObFgmBLvmxgJ/GJR/SZxqzBYBUHkfRYUaDW5gtqj+I2gIU3zqsQ5fL9tVjFTury1qr1uYcRr0smFqYV1LtRg9W9AfcYhqCrAIDGsAwhTEhYvXVVCdwLkOF/sl1jbGgrjEErFu5rMsdRhnhC/PkaCWseyt3DKVaEGKIs4u0Omt2TO/haKpZW2UOnLINJFsE0o0Sh3kqzFY2HGBXK59VDadfNolCHxmrrjAhY7Quy+b14yoLbCcVJxfPqoVcqOWlq+J7gMUCU44GAmQLUdtTgr2tfbRbaeOoeEbDluarCqw34w1KAnM6zKSZvU7a2R5A1Wmy2S0sOlpnWO0VKo/bSJfbNgrF0TLGMiSsSbWVXas8be92pqDAZtnUjHSCbaI+QggJEY4KRnhidIMVQETXYqcLEQdMqbCYDdEPR1muQp5CYd1F5n51oEyZLbkbxBwa16ZRZu1N9FBS+g7cyVWjUoccwFtSuo05BgytTWYVB9uR1WgNtKWo0YLZQNu2/Hbunt6uBaQxw7tuqao5wV7ei0rp6mQrm4Un0RQQKnzfIKmIWVmfml+UD7EHK0F+Mr5Hym2nxNHnDMiRIEK8hmXJ+Va722amui5HFI7gai9tinLO1ZQolDrqg9j0pWTZQotPxUCYLhAYgDnSPDngE1vSErWwok95Ex6btk9emzmFfTJPlEYUWGPkkGClsOtbsg7gxFU7JcHaXHM7H3DsxxpHx2IoGomEYqoHuNHnpOrFMrFWV7RB2eJUsqGXrAasYVa9NZQNswAPlg5lI9LRE5xpI3FlBR5z1Jwips3h5o09WrGepceLyIazxxApVVFFhRvDUh7EYJZsQ/x2prrAhuHOaHUg5aVzR++g2VAXMLGTd69FVLC6RVTT5EDKLRPjt9KyHbbCQuWm28cn6StnYUAJTBzrPJd+hIRmGErmtoHCuzcpNy3jzRXa+xq7WwQKjp/Dl49SbAPiMXS6u2iLHtr1uB4ywXeamLGOilzLMtyKXODp7BvF0uizofZxGpZzUfV2657m1MeL9klOQsWqeCEKi+K4HRi22o44c90rV6N8tG4INr/vJQWO9ln7FttePgWwLiBgiR1qx7SH9HzKc6+OeCKLaRKSNaiChN66TBdWaZlCp42w7TRbW2ImyvwYM8QPbBRwoQL0VvQ2G/ABScU1zo1O+aTiOk16FJxwKBBSwKQdKJ6QBbfh4wEX+dmzcGvf4bOoMrwEvYxBi5P9dOF8UkScSS5UjJAtGTSLMcpUP353q01JOXodEENNh49Bm8SEZNRb5FTSOIai6LOgG21tAVYvMcjRY7iZX+VQjTNxTz+p4HrPHBHGmFNXZ/Vk25YC9ySw2dzEQhCah1ZblGT0zpZfRwPduubdnhR9pSGQFRcrQzWGHq1u4J4+ZL+vQnYWZWUuFS7YMqJ/J8a5hEQWa5hvsjULgmxTFal0tEwOXOoq+dS3yxOmKn5TEeICEpn/X48V+dwXP/1l3/F97/Mvv+Cv/KNP/vbv/0+KrLvloQwQZkt58pFixx1X0qzP4D3UFB6691YMWSNOPbEankVoByXd6VMAQdagmyP36ziGNQ0qArRP1I2KeqHOrqXyRl9FV8YcytUhdPlmb/ib3/nN3/id//Abv9MffuN3fPM3eac/8ibv9OZv8o5/+Pe+4x9+43f4w2/yJ37vG9kY/+bb/8O7fdSn/O2P/dSffe7zDM2mCPCGV6kH8b6Nu24Jkl21lU1jmSqkCmleCphxN54ceDLvtD7AExJf8whqrdFKkTJsBB0KuGUf3VbunHCdo86cXXf1A6L3wQfmhILr6T4255gJdYDbIKlIO0GUzaQUQRkGx4l5z3EEGoEBTGKjSqX8IblldhxLTCk7oomDTlO+IQvlUvaIr/ju7367v/ePv/EHfqgv2cqPfNA0YPWMQ43F5tmqOeE14W0P2OlqAA6lFwYJWmrdkINJKzMFWJMqfWcoXQM1B2hNWwbtDA79hsKpotqJbrfPxNkXeWEaK7uAythVjynQIC7qHOl1b7Xne8VpldvpPGf3tjDiyaDJ2qEZOb2mc3efPWfNrSkJJY9LMCV+lcqLQ6o8hrPdFYlZJ+skdCtxOkLfzV2BdhaWrPYGeJeomO3N7b5ti1u1IAxDyQ71AmVPHNsYvV34ZDFWZkmrleyr04icoSi90WAPL9+i9gprdz6Y1KGQCatUT9f6ahjDOTpmpGfDujaURIFoHGWxpq55GKFXlvdhPMw0dPByuKM2YSmNHuvueUCp6GJVB1yzZnNabjJh3eRJX3RILlcilBw0QVWAZbIOnEcoDxnQ1xV+1jwxxdgyl/fsBuoKgVTL3VTY3bG1OsMulZzBtdBrVE7lzVpT6YGRcinOYkNOs50+FPcpHmVFOmM14wTPpU+ybzX65S+2W5txtI++dGfCKg+xjjglFuYavbxFnhrGCVnJlCSvyM1SSeHZWhPSGN4LKvmKihWrQs+XK9AaDgfoBTFxoYG++0y/YsCFw07XdPqFKyUaUdY5K4Y007z8L9GC6yVix4Z2yeLsOgry2VgzT63jXGaL8wh7XHYIcBYKSFjRQCqMPnTToHUqodz0Y9cjcCjGw6VbXPxHi9EtYLla5Q9lBEf6agI9vHejHnhJ/7pwwu5je3RTeTaVBAtIF7LQ+4rhR87zdLSyMcc+ho1unuiTQiRr0p1mlqETDEB56pRdqBOpHbRRfLXIMWYrd9NbFaCTl+j2OdRbHWBWVWsYBgtp0aaU2hChl5+Wl/BRbjK01dqO4IrodZS2PR1peU3cEF6FsEqrsFC5ckpJYg6Sxh2+LjIL1FeTgniLsS+fGKbGuA8NTKUiWzwPKBXRA7mNnnv7loqwgIZ1jYB2cvfIg4wJXPdqEXFPrtxKFTb63FjT2k2lEsUQMNYa1XvWRIkYmamaLThW8MoBqAxiUC81Tmsi5aakEkSIJHGxtsotXdcKefj1/KXbL3NtELIWFV5Ah0fOpQ8y1pDlsoytxsabKeLpxQQkkIruhDc2o0/CK6oZta+eXbAVcxUVbxpa6VezACZ7sMuMQu9s05RSbJT+QshSY320Cads+Ao7mgcRLG0yvGTAYBJw1FpCOhIWGkxFzLpCZcg+ipcDA7yNitPHmn73GT4Io/K5qlOB+JhzUmPIBix7hBDS7foRmIsAdW8rvUKsmmG6bR7bEJsEaDlF7IMsyxyyVAhUulK5oQAM40Y4M7XRuq6lx7KsdqvNsFfbgTzVpcjkRAp+t2A7ARg4HLPV7iA9rDYFI3Y0TuCuIYovXcMHsRW3ZXNxwKaJco3G4JNySGJVxkpmoxRqERAznttWNgHfCruAc8l5jS6vppqIV+wSnLJmZsQs88MY15cbz1kSlqprGoYIcQBdHa1ANRjdZ0B9oKgei9ZPVeLU89Gw7EDWrB7dFM3tSW9S0bkBykc7i1fsLCBOzUOnhpu7TfV5tgdHaIZraFPuLtIhz5N62dm8L0aUemb12QhrqMHdOoafNbwuktykhRXH07nUoKBeFjbCoSw1ssMQsKINq4twXvRlNYLCNZvD29gkrFtJGaVE5KX9k8wb/IFTibYnxGee55kRBN0VgjXdBvTkzVTbk1e5lIprsQKodE/Vfe5RI33VEPW6y2tCL9JaIlL1K/cttAd6ZV1Lw2mWLEEcVaJCdJpSDvPpk74t+BlRK1Z5zNEPbhRphhqjzGW+r9wDDr1HV7J7c25uBpdpW7CzqDZRrVleKfvK3hPdwJAKRvqxrK6iGW/DNoJKU3lrTkOYlK+ugpClfhUsB8sLDEeJ0crbNbSzlIYG9E7Sh0ngpa+6IJtwasWEEmE1oAxfOg9IGrtUwi3mzOEA5QP//71+5Gd+/m/988/9im/7bmt2ykDLyjS/Mu5pM+zWiktVG2zdjeRiw4q0qhH6c1AMscBaDSlLBpQSGXK02N6a+yl4Q3wTkAsvc7r1tPBMq3aN1BaX+DgAABAASURBVD8f6hQ9i5WtAP7I//RGf+PPvu17v/3b/e9v//bv+Q5v9zfe8e3e98++zd/4k2/9Hn/qj7/nn/tf/u5f+dOf9oF/9VPe/y/9mT/0e7/1h3/sr33Mp/3QTz0XZlY6CmiK+nYswyLboOahd4Ic2n16aRNsF5qJtSMdSlDUVSLadazU8lk8U99tyZRi7dFa4yRvWI0n5JiPy5yixFHVB+0cVlrTtPsQdxhb+0Q5ywgopMtFd6n1WMpiUTU0IO1E9EVjGZom5gz6CDgUy6VvWHaDya1sgwJTPdsETUypgJg6BVyT0vWuKUS0Z2ytMiobZbNdquuRZZcWhUlMNdXARnJGsyry0jGVIaNdfKmcHYM2SCfQgJUjbly7Njh1ZB0IrTESBkeXV6Ar9U8jaTa6LXTquGhlETqAYWmeSHIpoii53xNiQvYbIGEDHBAMl0NWb42tp5svh46iwHIWzVrXLs04zeHOdouqVp8LRrBhPm03m/RKX8IhRZybr2sFkDNBlCHMhvJPdEre7JRadMKVRl2lhWi2EOQTKBhoaOJKtbfr4HW6ywErEkUETUfJiOJhy2RWVtEo1WmZ2aKUs4ZY6OYCKt2kqsBYT5pAV7KWl8iNquoWbng3SgGqkLq3UKW0e8EX/KStbYay3EYQaENoyN4Ue87wPgHpr8WZ6N2qvKP6SBzQfpWq6HRwz7mf7vfGOkcIgPJ6hVQj0pBplvtoUnPrXLlWHoVgsUJCFkDPGhWz67o1bwy4qUnCHaYtFWbK5mFPhHIhNwtlbhJMw2q1NdT0jdwbnt7Jk8pHWclssqORjeqqyr7S3C00ZS2AGC1Dt9q1OfBsZnWiShO68gpSLKEBqCXkKFqBi3a4HdwiN5zGld0rJU/Ckl4+YK45LZLt3tQuTRAAUczBJiNb+0EVTjhiXlHIV61SjY82eRGCFE2pgVlCHQpHSVMOKCkFEJJXsEtoo8XyqWiWd7H1UhyzJKOGGDDJAbLTUUAPQuG6dhdcGnzxTytOJdaVIoPydg3LMCXeqlFJSe3KicMJUbNg7QYhqYQsAShm4AjeS0qbwKCimHLXPZMerGCyMJakMRzT0kaZUykmmg0PeGqpEseoGmlUdpGL0Vw9omYV2cJZIsKqsjIqsjT9xrplbishpApR6GQ7BcuX6bGho4EuiVZ7mDNJo49hihp7uIAFOmF6sYRFgwtKinXOEWM7x1zN1GlFStUUUYjqqFqCYAzPpb+4oJKhhqHfGm5ImdbKfj6FvJ0cp+Lvc+A+WxAYYhFhfm7b2VufW7c7zOHD7OZqQBc5bdJrCrtcJvTL2wfpovsU9LXNpAmWAyKMNOEz7t17NZGC1Og28XZd0yiNq+tMgNUclBykqo0w3VnttoZCYGk3uM69Th+NbcTcmvQW2uQGTgeu5KLB7oqqioS4d4ag0aQoWzhPeyzdoiCJllk/DlP5C1Sbdc9UmiHyGFNRbRvbo9eJ435NXWbY4FtDq4+wNp2j0yMsDyCu82pmCVxaahMmA5/8BmXZppBLaKIESxM3w6QBXZKONRTARA5P9bqalSbf6KP5GHlHBog0W7SEoU1f4Rj6lK00iPUjSzKx4ZvPoe+pJeTsSDsVxzCNFhpv7PfW874WV2zR6dZafEp/v5kcM8Wue1tDqkc5aljboA9KBluRLCpBvPZpe+CiQ6EOakpTmUwVYKZlbSe0IRzUlyiEWHGh5eycHjaWvoxSoNizN9Q2akz9Dr+QQUGlEhNdp0qKvtncoRoRlnuOY/UZunbIK24G6WurHL7mvJs/Lq+DM3oys/sxeTrP2eGVpBgzigxYj+HTB+3qcdmeawtc/5ElypRgCHSdFXpwlJIMxbkOZwJn9WrEwLHh3NA7toGBalRJl6bc0iqxsiAiG1SVYlBfmK3GCpYgsITL9ju3pUvRdZTuN7KFsptVV6IbK8gTm5SwVglQtiCwlZdN8laIFUeG1FJ9Zx1XOuE37GYTNNpKHnqEcm/dmlPKwzS629zyZrWFuW1peyklkGHLHtm36Af5LGR52AjsABnorJC7pScSdWmhzeEsoS/HGgz91rjptc1CA6vvibvhvtl9+uM+DiUFlUE7zBaJxGgOo0HWVb0nkWqyunnyY0ydHlDiIYZpxghbh0Y1qcq3W6lu0tnlduSEbOqp2lposajCVMqDiLarcZ/eyw3cCZHEXB2HfmPWuGFsqLa1WrltKtEaA0tveAGN1ovigJu1niGxwDNdBvqOX/X6La/zmn/gt7/Brz7e7A1f/3f8N6/7G17zVY18xcCXHeeHfc6/+b9//Bd4PKxDf8G+OfbZQ2YLyxhkA95W0HEFC59wT0lMY2BU5aTyYqXBg/eRh6kqVhjiYt+SVd0lFe6Mp0babeR2i3nTBDc9B1g39TsOH76NievFND+HhRMp2Z1YJ53c53CMUKmP3/p6r/d+7/i//P0///a/8MIXf8QXPPu+ltyhMx21w7am+FITXA9aP1KdwMvm3GwoTw8hoh2CrjFeUHmcAijHGV3Z1WmWoNSfB+0UzuCOpT9mT6RUWqryW/Kpcz4Fe/J/OyK8j1nHjTnUs+gr7V5KYK2+6vMUMq1y6oUzY6+78yCiizgBZbjdWjyHoXXSkSNjVliV9XDbjVv1lnxoDFDTmkJ78V36oo9UMbOW8dGYQXXsKSj74l6QTKat/f64PwZPdg00UGfmceI86/4Yx3HWkZqybz0lSioNw+noifRYqFWeV482hXJHHHnKhh6Bbdtt59iLanIWMY/aDhupkuZQxfbd6qUTjzeoHibnPrbbRjfamnYfJhGNll0ZU0Q6PYQ3z1sfxlbOF67LM9xLeGxsMCfhA7BAL4MIERnBKizrHMQY7RvmDfbUVvut0/PI9ZgI35NCpYosGGho0Qb1GWSluvsd42nzA5cC2Esg6UqfoWwvKNHbtoyF1NxB9YAFRoxec8QYiY4I2UE2Smj8Pv3R4kw1S8ImKUlXizmpQiA0SJmyUT0VaycWqYTLvD61qv++ZpzTDzqbZM8BRaYnKmtC6LfOPWN7Qk7E0OgjN8lZzF6OnzzQE/xqLgAEzEXiZvose0+l7VJKwSH7MHZ7pXVACb8d57y3pdSl1Lb00IVx2DygZtPnwShZFUisVSuu3vrYCJQxBtPlAAOSLR/OfnjM+XSNo6Y0RDO36wUB0hEFMXJMHgM1aGWeQ+cvVBzqRF7SgD/WCNtoM32e5vdgCpFxThvTt5uPrW0rw1l1uuE6aXYD9uoRTWGskwijWpneVZdUnsG1bee4RQ7tbWJpd8/adsxb+/Aq/VFiVtlQSaLNFBFhyKpAFXtTUgw9PcbtxMyy7qQ1uLPVhLgse1U+qsGPFRswNyiHpdnhrQOjy7Q5gKhW1G3t5Ex4QlPVZ/ft3EYSs7Rdjqcu95pcJ9ahoKZaB7bgLXv3HiPcaxIbQb2NIVsGd5Qhqiv0G/LahDCq2OZJXt89l/fJUmSF1YM9mywV2XzkeBx990Muu8CSIje/U1s3P5OrLcY4pz0J4Cwe5aUWbZ7JOJmJMz1Oq0fHwSwEndwMu9eE2tMYY46N3hLfSMw1hpndLDeJMXKdPMdcD6P3DutKKJAWjbN2KPxOrPuKPAuxmT24byYPg5jw2SI42YfyXUDVIKeZ6200gQEl9dI9Tlpzn2O64l7NBcuLpLrybShrmLzBlDvJ2yHFlCms2kLhcksthCo0DSvvVlUP0NpQLhYhU1mX0QZQo0Idq9s41BP50OrupZkJNibpqQwIHgrioJaLZTWKfdZmYCCBJcKUXx+0nbbRJhTN5ZK7oGSjmRo79M5BVT4HTphkgIrLLEo7xmOrc2J5EMoGHZu6Z7oUTTtTjkQvYGOD1pLWbEVBAbRiZ0ZGK++aJtSKpmA5eo0IrzR9w4SUVM0MqW3rdqo9CL14zAE4ABbZOoCkVQ/kJtEA4QzzZneW6jK1JEo23HTKIy8ln82Tfko4S6lIz2BUVdcFVKxnV1JfWmWRRDe8aPpgLf9yKpJ0drCEo0BhYQzc2ae1ynR3J0e2xxPEGZDR/bBtucqWnpBerxTx8pNoHbI+0ZPu2G6hYLstewY8rvHLPac5wbYojxZ/IcRaEhTDShVLRK5MTRsNTwlFHx2Tw8St4imkfOYVAfHE5wJP5xWhJq5UoZ5K++a+8wn8rNmtEFEjSpBNtvxqEFu2FmF1D5iV1s9Tv3XHKkALHTqET85Yptk6wAETK6oSUKzYYsYlzpqZPCsXosUoXeVTQjclSiK3KrcULwZYOstpAkHQqkxZEnHpVr5t2OdwvRq6SgAitYBU4sIkoIEoVu6derIxD3k/PZbkk10re1Xl2WvxKC6qOJpr4dCV0RyCdD5ZsZwidPYlmY6S8VaHBBwYFCXy2Um5xfTeZ19VNt1kz02fp8OFtivEdyjmC1GyV+uuyBrQurVr12JBhgyn8FsGlFXCvN0MIMQUATNOdbzhxU1Z0lYsMhVFV1V3SWk+oweXe/pWUO+APEnXwbqPc6lNbmjzCqJcvNBu5FB1DciP8u3VIsi82yHuPa8UtSIwpHIdClQDRHWPLo1sy1XyD4Qh9oICkdzK1Zq1L18PncYGlD79o1NBpLlOahRa6qw21rbRpv5tbNqpe/3ox1ySjPi4ZuPJ613f6vd/9N/4cx95He/8T97znT76Pd7xY9/zL3zqB7znF37Ie33aB/xvv+s3v96TUdevFz99/+Rnf8NSufc0lZiiK2P0qDRoC5+dZQqLlo4lJlReVxbEyn7aaKd7bFwTkmIpTtFcZ5RUO1Pkrxr3R573rs6eRZ0cJcp9iCC1yNPUdsRL2+N5CpB1G6qv1qg0illLKmJJw71gCe8JaBsw/sibvfEf/R9/+/f/+M98+w/+qCknSohnj9X7AT2bcCsQh/tdEr3kZ544o+9g2CRFoDWQpR3GrFTq64p7C/MS+ezra2JBvdDzckASVvpe80yvS7juboZmocSS5kWhY1VFNwFNaOh94MqnI43ZftTIVvhmNPcxNxP7iFPK79YfAk0K7MOr2JM1TJaaGRdRvZZVmKIVEFEFvWgCoImFLFGj0MymhB1wtI+wLpQhORvDVKIar1OpgojoSqAk9amsjBiWA9C9BpbOWdpIg8yzNUmQvcgAl1PNMJ2KLstSg0qMJApGCvBAeZ6m7+j0iD1qJMFRY/Ntjn0IqVQG7eM6NAKXO5bAqklehrvlyKJXd6jNXRkbRuVBLoCBHgkLo2KrPgBp8Tp8KgmCg74umVdbScUqSPCaLXNVNQreBnZTpFmsWQqsZD87IlP5wwzsLaDSyWD4WJwV1HoSEgGpnJMKCbLknoPnZaozVt4zjg5d6xbE7COqcEU2Dd2dgN5gTSe09XAxPjvpccxekyopXRNEy0JfnNmGOXNvbFkzwnJxsXOUgsuk1qZeOaI2o09wophRByFXqV+6wZzVDhGUksDk5sQcLV4hQAGkEbChXFU2hPc0bYV1jYge3dZQIYQ+Yht6dv4BAAAQAElEQVS9E7c2R1BOtJXMk7UgrAaJkSy2jh5ZGqM8ZAsfUWaA3iZyakDAruE1M3uVIOircF1DNMc7Z8F1nLQ26INWABuTCGSgRB+G+8Z5JdHOZU/Xpm19+aSwyYQqx0waVq5SuUpPTMFu6yohUugb4RgCvFWbIghHtglPiZMGk1Ykt5j7I3UfnqfSq8uEuCK7LUooadabxegckSN1KunKvaUCiE6Bvexksc7yQyeNNB82oKaZZgUqxGXd7jC/xkEPP2yZVq/rOd4mhzCDSmAWLWfnFvJu4ACG2aARaRG+SrZaRCrcago/mIAEUwVTNAOX8MoUcna3ogChQN3FUneZnWKtrcqrAZEFiaIlirJOfzIhma0rGtwxPOx2xFZRnQUF40jkur436IIi//oWobpYQjONigXOdiiFpDldEhtH60BqbGvcvesyIhWlj2NnbuooS228BgTPufZKX/JmKxFCKYtEU3QGPNsvLNf0EwxNogPKhZBPH/u2qfXK8boEP8GbmfRw1R1IbG1aCkTF6R2aJrON4hZjK2zCbT1O+CPmCTEqt4EWYIiD9pD4QehMWzQOg3B3N5KUvwyvpULqob4w1NPjrFal7Uc/eRqgSbBreBcrd2AXdM6FKzQoeGomnAO4CltDnWQZykpIRqvLQwnElWA0KSy67hkdUWe2inYph6FlYSZ2+Ow5MAzmLGjFK/LcuR6AB1D5GhxMV8P1cjPZKsXB0xIPue3L9QFBps12PbJ3zzmqCEGOK+ahsWLRRm16Rmmlol7nrWOXeLwhhE6MbK2shvRRAiLaqw2oQkbhUHyxTU25anWdyFoUr6aezCwv7YhzCKztTgFoXiI6MA8fd3Cp5utqYMMNwmG7DTOpLhRKGhJG0mZSj8cNzn4w0LL7YkQRLtvO9rQrJwpF6cK1TT0amYRibSCE8djm474/Toa0kCtVPAl9sazes/fV9KqxMO7G8iZ01fQwIO8d60oLCfmxRKOjK0pvw86ty9ZYd65YFeeIGKeEFDaWP9V9bY7VgniJ71yzayMm0D44t91vkq6XIj8pU3a6YHcLOacKX1qei6hR7m1EVzWznyScV2KEV52Ly9FsW1D72WuN1P3scfp1y1Ahk9OGc+vp5+71pAGnacptMx9Gs6jtXtuhdxG9tSvUYCocuo2xje0pm4MsBZ2KHgWeMDRuh0pDROBOu8NjdKsQd/QOxZpIBQyFU+VQbAFctm2gGKH5aH00acaAeVX+HOYvp0oDunFcUc2G68LDVj7rsrBxM+0DlKSOLS966FTBlPRBnhbnxH2Mw/xpIJrTbBf+rUOPZ0ejfGs9SJtTebxX6YkZYjgcVjFy6ePk2Gvsd4ynyTBSKY+7HwfP6tXQqZBFo5a+MVhA6eYoM7SP3CN3b78W4yCWaANZ4nQ578Kjz17DyzyxJaSGsoy9KfbK5N+fLGY94e6bmWDNTrNajvs2zqkTgV44WyyTptpI6Pn2eWSnO26bpqk1FD1tS47QXOTaqiY5JIBB9BMlnQsIFULi//3Vm9tTczwD/tTm8ynLZ6Bv46mnnvG7f+sbfPx7/5Xf9Zt/wyvG//sf/fEXvORFhMjDz/3iC3/meb/4c8/7JX142eO9paEVLkwlGNAN6cnzhWv9zEue/skXvvinf/GFz3/RywZ9rPIzX/LSx5974Qt/+gW/9BO/+KKfe6H+asxye8lj/eCP/dxnfdW/++Qv+/rP/up/9w3f/f0vfNEvzTq2TqYFmdpxqipCQe+CxMKMHAhY062mYxtuWrFTcXbrk3PYWuSBP/mmv1eN4Jv+4w/z0tOwUl6jUTkqnip6S7rnY7/kl+IlL3s89Ae9wtGey1hamQ3qTxjT+UB/6Ol5S+v0vlToM+N2P/2FL3r6RZq8jtygTLO7g13oWaBjbZHXY+MGVA2t+pKHy4ahEOdxnE/X45prbOmzbVu5d26Www1jUwdRLE6lGYiZktoZFRHZ+qfQurTLfuw+uqsKZd0qrtIX4uo6BYCXZ+hPmHAMG8NFI5GkMEaPEGVCbhttiw1ad4sqDd+6hcFm6w8p/lA9xgkLnDtqg3dK0UjAXBNn1DiUYhXKPnbpyfqsvGc8ht2XH8u5oBV7D5XEjfGwPc7bPZ86Hzc8bSLM6jAtLRHhJqL3xnZCcjpW6Hno6Vlqkx6ztZK0wYlpkL7DUo9SqUrU90KvVNUoemPsnYqt1YcUrObo0WDsCQGBFpcnTlM5u8YZaqFPLbUTnFXMUvODUQoYXRu1Stdt1Dbl1rWwnHKV5qRWWx8H1DNKTcPTRPgdV1IgFpfvZbPC+5zMB8ekzCl1q2JZmjS1tRQeqae+quCBmkZUZ0fbAYdSxLzNnFLejB4lk+nPwHiKYza31KZXCZ6H7wu7UFHtiFrM9Oat3FsoMz0FMWSsWyZdjaHDMgNCSwCqsP3EzLQuFWs95f1KwH7hRmhp7AyONm/9buHFuL7nKIXaqrQ4DCf7pKesuRS22U7t1i6NsUH5XqAuhteyOJGRLXcLc9WN62HE2Bbs6tMZWKed5wzFv7lSdHccolDGsNFugi+tt/iWxlZH9YK0cJ3GQD90PWV6shNXg4B+WZ58XH1PJDjGVc/shkjul1f6cJ8KOyZrqzl7wsTpaX5wVOvvLm7mY7fNu1lyKlVmKrOYN/pOXe6BonrAcVRWy5wNQwpmtgX3pg32Q8W+lh13j0dbqdymyDSz2kdOa6w8jvs6E/DNxwOxJxyqCJiXsQdh5tTfY5SAjC453U4tKJPcQbkwV51qKdymQDHVCLIHMEYrvSfrWHUGtJChdTObtSq7NUIAU+rc3HZzu5hmeKbymadFzGo5VYNj1UWBa6JpaRprJxDAYp+b9eTG2CMe1tWGgKQtuWdZrz1036APxQrEiTo0zUfNYXbFeZLhKM+qUXXDFZFfotGzA63HbXSasbLOlY9dj1aC2GyhTVihMXLcAhN3zFNBZfnKrTeTKRu51WnXLu6EGD67TzRRkzEeYjyj7FYO7azUokphkD5FnGVz9dnUgSJ6nL2nTbrNPfbbMsxok7a6q1J4aYRSAa02gMfVtZ1hAwZlWvPapj5RcyxTa1Zyz751cSS3bnUbN7i1IZp51QpD4gpfIRqfqAq4gJ/TcmxKuEanznR0rq5ClWUpbaijcBx9aa0UbSMbSyQaBWHWZpVdHddvzdeiSulSTohC4wyPGinFGfVySEY0TcizpeO64thAC4u2xSHd+4WamaZo4NPGMKLJqqwjIZS++9g5d2glALpBJ73S1RGjNHh4qwaNlYo+RILOaVDbRcr1j/qlfsEygS/FCjOV8xV7JNjubr1pIdMxNdNQ4OU62bYRt4GbjYE5wAvEzLASC52VBzu0yBAa7+N6yRllXQsKDO2Ew0nJfhCKHCqxsXwrGwqlIEuFShbQ3VzDevZVlSKk6M0NUrkSW9F9hFKztCRMAOWtPhYN+XFes9iERjMn0+WIlaPh6MuLl4IaI2Ar4owIMoZunV0WogId1ulVlMGtN5b4K3OAMooqWQabgz0MNK/eMne00Qg5TiLNluoxXMwIJNDQeGwaDjOnXWNJo16QASrLKdOeVP67oV4RksJqA3xs+mdzcA6YgrAWBmQzQZhGTE/bly7pM54YcIgxDXVoCGqgd3JyWGqyQo7CmqMUAGTrlJDXyQ7FpIpsKRc9AS2xIvcCb7AeNJ3YTE2S2/W1J+VzeBnj6ibw0aLDgaGUqGuIlTBeyTlhSYJmnHChKC1m2cCC61pc8Ra6qTGT2Fwjq7yEXOa2cs1iKyB0FbVoBjSWdFd+A7HW1RcXInBSoTFn9sWqImPgCnJnT3MojNilCllfLV7KGbPlp8wWdLSAmtlwDG96to+ycaQdvc6OjKrUZkknToVYMjsMVMfpTMqQ042leLMNeX2EmWSUw2iDMCFXFtoX/U4/pZPcsl0KDV9JgTgdi5QKZqRsa067KdlBlk5f38HE5TMY4ZliykrhCAh++bVWxkKLK10VdXSneWlCIfqpp/Z3fIs3/eWhwEuefnzuL75AX4X5nT/0o9/67/yTP/H3nvXH/9bHPvvffW9Gs6AkO1mJyrbsL/76733Lv/mP/8T7fdjbvd8//ICP/dTjzCzvhU/64m98y/f72Lf+ux/3tn//Y/72p36+qPwP/9ePvf8//ZS/8qxP/pgv/bpPePY3fsTnfeVf//jPfocP+hef+1XfkaqRSyrb8hnSaz9hRyBAhkOnAGXf7HoTkUZ0QxCa0A+r0fXrXuvVNePnX/yy5pM0pnIqSc2AFvHze3/iRz/qi7/0r37Cp7zjR3z8u3zov3jPZ33mx3/Jv/0vP/uzXZ26SdQBhI/0eT/ti7/xu7/sm78dSGoq+mee/6LP+KqvfZ+P/5fv9hEf/1c//GOf+VGf+OGf9jnf/oM/+BjHBXZSri4wEsSda/kZm2xitZ/53F/8+c/7hm9633/+2X/xI/75O3/wx/zlZ33CB37q5/+77/1Px6NAKyBukBaNikT115K9ufEFL37R137f933kF3zZ+33c577PR33m+3zs5/2tT/yif/qFX/OdP/Bfnq7zcDuHpzto95cdn/PV3/xPv/DZ3/ScH1b43/o9z/nkL/iyT/rSr/jkr/zGj//ir/64L/zyr//e70djK06SItBate+e7MrQHvLKoi5rEdxunEMIpKxA6IZhDcDM9QM0PDBbpWDlQ5WaZdY2DINqtG0wlbPertldgLy5mXnTlM4eUFvK2sgJ1ZFkKt36KqrRJqrKWmMKnVBqWw6JX35VjWiXXbniGAJJE2Go6AxWmpiEtSZmF2m4Fgxt/e2UaMogUDbK9a7gQHRZqbOHAyjqUJpjsGgFSxG7te7EbcjOaFNFbhFDWx3kaqEkzzZ9RMLKmIb0upoECWPwCQaj9shGd/1Tt93dfVOmtyu2EF6KVOnTMsYZXFcUgSjd2KoPjG6HtdmVJIZ5Ti3WttwOz8UMVNPhBv0Q12cKLrakp3bYfi0BS2YBkJYOMQjdwNhdoxWCFRWt+swQLywrlQhhDaCzO4RsT9/DRvtuOlRwulQBXa3ROZjVUWPVntcg4SGFw43aipqj2zJNpEVUVIVMK3SCkzYxPYFVfc/uy31OtEkqvjU8e5zkNWPBz8FWfnskGDAt9FICqnplL/XIKhdR22rtMNV1VojEPg25W+6ek0vC0FdS4CucLe+mXiOmQHqYln9QTqAWcO91ipJ90+Zkzhh+NV9JjSVyjJDsSO+aCVfcIL1bNs1AfeRosxbF7EY10tBgYmSgV2lJmXHBzRkxQsiimD1Kk6whpZR0hy0g+8T1kl1zFaymnaNOr2RlX6bRRfCa5NyNk/r68ksNpKX4ty60gChzQWlMJ93UYdfcT/djoC5WVytyDVO2cPEaVOKsfUTPLJ5ZoUMRkU2aRpaYYLvXsKK8QQ500JPzjNsq1zcVhJDL6HLBcF63BuOCyzGUA2704X5xu8IPKYmiorR4L91PFUBshkk0LSA+HgAAEABJREFUkURxtAbPKyxcKc6O0hjlodsCXkoD4WMNL/llmZKd97nuWEddjIEGjqSlU9sRkeabuavhqYqRUP+4Sk2UQWG3nMo1Jb9BUlnTJyEYqbh1Iru7Uh1mdg0em3iFOG7XTVNcXY5CgWG9OabDpM3zZMnkYPk8tFNcmxxPE4aWQVRndp7scO+cfkCPg6SS7rLwLW8Pvc8hCtEGVDDOleeCUKzgcdZSPWRknhklRwqkkVMHWXOmoqXxjhQdnT2jRFVLepwIs7AtbK9hNS5iIU6jKeaiL2vQvgW6wRKgXEzoLneMBGOlfnNuc7+50mVBhlQR7JPIAUxXS7jGABs1TxLKVkEIdAKp4RLJkX1G6+WK7pKUKsdRNtqGzHjfuVatVr7H5EVcZlbJPrfqrjbEuDJBBwH9a01jww3mUGk55iQBBSYrYiZNyj8tlhcMELGm4Cd7Kmf94OU76qavHBXbedq9KttodJJCxVL4rTKxJ8jpGEPQpn6xt8ZD1Sb5kkfPu25hBdJytJ7K1qawVa+KH0Z3p2C6xhaJCTdlx2dtPjcOMZESO3N6buyqPPoO7ZQst84RaSgojyViqCgUImo2vNkC2xAT0BCVl4u+7mhxbbWUCNCrZyYsw9UhZhYdWmce6UGnYbIGaG3tVsNls/UPppvie3Uo1aHQehRhRjNxeNGwj3YtnFofuIzaR7R8Y4GtUihDNPToQpOccOrl+hwTFHnWu0ZC+imZpjfQgT7MUkzdzG9RpidmlWW2OB9rPGLc93HuLTJaFtuU633DPmIbuSsdPM1Ous1t6kGnHY2VTtCYCgOVEIArTp2j/qGrEKmrSvDMmJEqq/Ps0lZa8O0EFqNSBViw9mluk2IpyhkPY6nkJENZ6VZ+wnA6T1CVqRJZbQtW3amXLFZNa3WMlsxdYlk1CE3HaN2rmFIw42brZjm9tDyEEoYKszBfvvXgmL1tNcYBD8zOMcq4qrKiQ6Gc6hX6vNxj7IlxVhx91q+EDbLhSW8ApehTKSoqob25ouuavWZg1AQVaow+97NcC0lPH5AoOgN3cSKNKzG4DMFbnIOOZqS6u0KNY4tzr3aYlyKTv185Esz0XiNDup5Vc2brcT5PrQnZWL/pdV99ur18eESd6xRL2fHUbX/5Sf0usAQcPoZxSLi4GFqm+HT1FUfLl3pHCforzl0fvv0//vD7/NPP/q7//JMr6/r+K/9++vkv/Mdf8NX/7Iu+dvhoH4v7sl1EHOf9GiJz7VQ2AqPLkGLxOicwA8HOlWoTbqYTpQYEdF8HarC31rOrHi988Us/5FP+1bs/69O+5Du+h+Qb/qbf+Bt+za952bE+6xu/690/9pM+4Uv/zdOPd6E6GMeMw8bLzvqsr//Wz/+mb1cTj8h/9bXf+lc+6p994pd/zS++6MWv81qv8Rtf59dW46u/6zn/+8d9xnv+08/8jz/5k9LYorpWdqVVMRAx6tzyGLq7e7eP+pRnfclX/fwLX/S6r/3av+M3/8Zn3G7f9kM/8sxP/Oz3/vjP+InnPT8F8txwWie6ugO/8PwX/d1P+uz/7aP+2Qf8y8/719/2vT/988976dOPunv8kZ/42U/5qm//a8/6rPf9+M/5yef94qL6rDfsZUd89td+y+d9w3d+1w//mBj75u/7oS/6hu/4wm/4zs/46m/53G/4js//hu/4puf8UBMDxUviTSQj9Fv5K0m4Y7GTahFQn75JJVgtDauRdWyaSGV4wNp006elyeFuopEdVuof0ZbcOXa7VtHeoK1rKleTtaFdahSqjp7nJbk5229r7LLMmW6JVApjIXsCMlzW0jo8sZ3OItBIUFV9Nzu3Sv0BarqiYPS4yt1MX+wa1YikgdLn7N6qxhm+ynGhBcboMdueWDSUuhSoHxdIbWyXM82T87T9cC4iYAWjZhT1u8Li7CxPm8lWVVoI4E0kpaqBCjU9Tmo1VaFMcKjt7Zvd5tzHHO4Ytm7j9BHSedmtcdPT6FL1Fw+0DlWcslGh9oeYvsa8soxSf8kqSWtUsI6us3HK59i4TWwbbKDh1U5CuwXfCfoSH80Cm+pzWhp9sVaeyx6VerSI1DgDVsp5EjlVgaoeqokp+oTHVKh6NrRjyolYwGUP3dZpDaTXkgw12HWOLarNr87kDKKgrObVoZ7MxGW6U03NpjVNfYCZpgGYJV/GUgWFpLMlRui5YZ7FnJ5PbbzNmjpGK/eTEOoUKyEattpGei8cVYfU5BiDG8uxZsWItcU5Y1mHRt0lIC95ciiAggogx/JNa1m5tlyRGWbpzpG2FaVq343ToWACACHorEk+2P4wxhyuAFaBSbE3whgA0ELo0jPaCUij5hoghmOOLPYSPkwb3EuhAykHyl+iFz16Sny6C4vslvLX9N4pRZwcd5iWRgkxW1NwvVqoemvqJZG0bqN1QKky9mgiWkNl0veij+CU8zJlsgPHEm9C03nBSkA9rAt6FyyTYtDU2YyW21mg0dx6TkhUcsx2F6/6YjA7zcRfPW7XSogyyXICgxwT5houJKjynltueipyM3MlITuI09R38uxYdaaWvNYM7G7DzTEHb+P6KP1f/yMfnGGr6hwNF9gqSZBq+dT+bT5qSfVlfgwLw+nr4L0R3s1mAa3DWhp2jkEbzOhTPld2Kl2lOESiBhZhaM0T071mx25mrfaiY/NJ6BRwFtQ/G6O4JWelq0rZitokBrIufwHRqJM+JrUsjQQRZWuhV7Opr5SBvcYuCBjiu0lNQM6zxoIXKENoxREjTQF2dKgVXAph2DWMMW4x5ul9F4mRY9Xs5AC8ZU2w9ee+qbCi6qy+kLc825Zj5ObYYbrKqM5VVLuzuy6z9ombXWbsHKVFMjw41cXQMq9eO1ppULsjoXpWHGKBHaiVOrhilp4whNjUAAvaKdoCeSbizF6pQFo5x6kyMauln1qqCholHxmvbI2OsDNFqLsIOGmrxyJDmfASSdHXzQwth8tVi+DSZrplQWhhYUWkFw0qVRORyewuau4TGtD6SV00bb22MVUjhhOKYSkG68Vml2NNLLXiLWsrd52oYixEo4SXU7Rk436dW564RW4t0bcqB2D5OmeFakOhH0nJkjnymH2o11GRiNSNcdvafdA3UM5SUlluoWtM8GRBMaYiZVvpjLDRHRsPw9F21CZqcMux02/IGaVF/cxclVaF7kazWqmwxCSor05zN9ncgD1LgSQtRrYojdDddZx2Lo+zmbZrWob0C7G4s26z99k+SZfa5cIibJ3oCp8nGbhefZWXeRCaJiAWJfsuMBgBX5ZlAr74hE6SZiQ0cEYaGq6J01p/gL652q/E1ClxaHzuwAZ2yfAAydFlXeolEccZp3S7boZp1sATAmQQSFeRHDXahk3a1qJK8ryTR5T+mJgQrE5TxA0K92E6nVGVd8TBSnRbS1e5heHY+tgVMYRf4ypPjruPcwCQ5fZQEDkND+BQcEYba8OpJWqTfRs9JCqhcTag3pDtlmPCXAC1sJlWDQV6ph9FgeiR9KwncUsUCr8zkDmCo5y1de2VNzUWec0xag4YQQmhWi5KypiJcX2+auOATIQIR1OBVSdxTKoaO3t2SaP7Vrthl/X1wL55bnbsfffjFFepopIJdmlicjZnmh8q1rCOVqKbEAK6rGGjeHnFse9K6QZdwpMgFrRqK8ZoFn1gSk4r6+XjqamTksa0ePp+4Fe/zEAvRxs0Td8gx796AGA6DGVSA19x5aee+7wP+vR//fyXvOwVZ371h6j+P7/uO773P/8oxVwowYjRDw+bxtBK3X9MKUHNK5aqrVSW1okm2wGxWufGpU3bj/z0z2jKb3u9X6srRXFGdYGff97znvlRn/bs7/yPb/Mmv/PT/t67f9wz3/VD/7d3fNa7v9O/eO93/eT3fdff9hte5zO+9ls+4rO/sOOu5sFarXn+K1SU/Yt/9TXP+qKv+q2v9zqf8D5/+RPf792f9e5//uPe7c99ynv9xc/5wPf8U3/w93zfj/7E3/i4z/227/tR0dASsUpuLOXcBnTmn3/51/zDz/3y13q1V37We/z5T33me37MX/0LH/yu7/Cx7/5On/m33uMvvdUf+O7//ON/7aM+5cd+6rkr9XcXZTLLTnA9/fT9G//jf3qDX/9rP/Ld/sy//uD3/uy/8zc+/QP/+qd+4Lt9xv/x1z71ff/SW/+e/+FbfvC/PPNZn/GTP/3cSvHM13jNZ3zW//GeX/zh7/0eb/+HFP57/qm3/dwP+oAv+KAP+MIP+ptf+GHv8/kf/r7PfIe3kbhgp5pS806crhipue6bitNap8AzZqcTEn4iKmXLSSYycoVWABS9OYw2oSWN7qqj1iO9VGs8Xdcm6UxaNrVnubQdSul1ATX6SXdusmB52a3MbLDn6Onozc8nW+e5Y+/eD43S2R2lQrKVzcRQgzxwZJ0VshzeOa04MQbGE6NsOObkFEomce17UmK3hpZPb+dwKWWTD7bgDrhLz1HKl8CPQWOmgi/1TFRIibWEYZj1ZjVUcwPmw903qozP5N2QVMw6tOqtJQ2ZFcmmF+1udlZlL1UoUsIdTvMRSvZhvRS/iEarrKu1q+BoGWtA/WKZJhhrVFqWh+kYYerjrRdiVWdtiWtFUhmIWDbkVBUS5qVMecNR8oYR3Qmh6JUrYi0I7Ms9S7eZvXSVoGFaDluA7ttPywY6UMWWpEfboOBRu8Ac7NnmCQTFBEsbMGxVYOmszaKmKiJsbbPBjo4Qo9kWxrU5DLTarAa96WLj8JIIMbxsaGU7A3nqonOrqQw71HTcMOzcsQZR+jT9bio7s5LrjN38NrZpkxSSg7bESRWvhaUUZnaXlSG0y0BAvf70qpncI6caypBENAOt+g2Th67RLeLrROc1sUcK72zRDoUYlmDzCqV6hUrCo7eIreA5cm110UPTk7y579oAaamwGW1RE32bw5xANfQVqpouU9jt54izjvPxPJ7OVDoIow9uJjCLca9cydL6fPYV1gjfU8SxQqJCaYHQiuYOpHAH0W7QfBQiLEJ6mKUgNVi6gMss0hXjMZBsDROBPtoFsIfenQ56YRq3zbWdvq6v0gO82e4SzT5KC0L1TCJU++Jf/MixWVPUbkjRrvWREQxHz4CaJapNiZ0csuF1WZ1Z+3mY1p7r+ZzX2FN3Pra3baVVTxNjhrY3Zie1ebrs5RkRKYpM93TObu9z97VbG0VAR9UdoV1W+RKqAO51rK4FnFaSendYJS24LcyMAS2SlUqTJh2doVl0MwlDFqdJtMBgOoJSgKwiuww6rxPVTYmprMPFcHXpUo+pn+1JpBDhsw8VZNaKzKpskByY3g+uGzQMgLMV0tCFRjO7UxIrNTU76G3tSFcOC+6578EH8uZ9GzWZFlbq0qaKLt2v2GlYalna8QwKMeskxFgr5rMtOX1S2DYUV0lEZ4uBkRhbP6WW1zP7drS6jWOWnkUFMzhsaJYy1MCJKAjnJHSa88HMq8+oO0kihn8AABAASURBVPNucWp8RVYmmlteRyZjUTkoTReX2yD01Jg8s+J+ni0EjCTOjefOdheNmSKt5+oH1i7Gr/gd/dDXzroyjhSbw3uzss7KM7U8dW0nXerGqKHlwt0w22dMrnmRiiiVk9JtwpK9als9hAqowHG2uNe3MspMNymWOqe1qSG6kaVpsVqkdnd1eeaIohqenUEkJHNnaUcZwlTliDEsfZwavRKKeMmKGBssQChuq10LNQiHKgg6GznEfNUGKkcWa1be1O98g1tID+UKZxw7DqlOPttBYKLUh1wtvyvVcQW5fC8VU/l5pYx0g7neFJh1y8polUV0aYKqNbPLqkZdIlQSI3DVgcFb2UFTn9izg10y2N2o5VpXqpFVJSkutiZTTGQjVJWOzc8NUkYyC6XcPrEWkb2yu2GEBFF6F1ZooQj37KETEpjfNp+bG01ptUdg7WkPtXurrfACr6Wo0aGfPASnEMUCE2zoFTABL7Qig0sVIg8W8Hvay+MDekBnqz16psYznV3qWuV3XC1Ov3wss6PjEeonq00uupr3HtdmTs66vTBza3ftOGDs2LqGuam18qq6wc0lxxgpDrW1UDY1TBlni1qaW5mX4KTMEQ0P9zBfxSNF8JOYJLqsaBXleaf+ANGoG/gUsDG3geFzYJ+lPzHRq6XP7eIj5eJqA6uQgKJzuZWps5nDRFOtkogXj9iO4bqZXrMPcpm3kNxCsQ+s0eGVF6FQ1u1sbdJyraYiL9iiuiSvAaUOqqTqIe0Y8I3ziktufvmY5mOzVmkr+TrXUZ1lvYYHvNu+8/v/L51++fFKD7fXfPVXSwZQJF9+8vpN2ma1mQAVqOm0CHRf1/7rP0W6NcwMv2ruL7zoJT//wpe85is9/OU//gc/+t3+13/4rm/3Z/7Q//TUvr1iWnZ/8pd97Thzu+cQNhGJuq5a1XZefSmWdsuZqByplXZZc7ii2owM9pG1/tU3fOdtm2/6Rq+PFUqegD0+Pv2PPuNLfvinf/79//Rb/813evvf9DqvtUsZnW7r4WZv+Jte76P/+p//X37vG33lv3/Opz/7a/dS/xWTy8Zyc3n/9K/8pi/6lu/5O+/8Nv/wPd/hd/zW3/gqr/wwfU7bnrE99Zte57Xf553e9h+/xzs17f0/8XN+4id/njUgLJttuxTR//a7/v3/+bXf+vvf8Df/0w94jzf7f7zRq7yS7qB5R+uvt6/xWk+965/8ox/2bn/ueS966f/x6Z/3S0+/TJlMs96r/Xzd13r1L/nQ9/8n7/UX3uJN3+jXv9arvcrD9tSct9vDq7/Ka/zu13/9D36XP/2+f+qtfuoXX/DPvuDZa92rEqNe49We+jWv8crPeHglYX7lZzz1Oq/yqr/+Ga/6657xjNd95Vd63dd4xmvut6Ge03F2HMgQvbTsrTmfMOgGnfXzGMfSbmKySUoY+72nusa54oi8N88YXu4sjuTW2NQSRp1td5VnEmrzOP0iXhZcKjlZK6LjxLj7w6Ntp0mt4NVsZoRKo8ubY1Bst/XV+bc5Nt/HJYJGNa0wEignrB3S2xGx7ln6BKhoZ9JavBs1QH3HunWWV/queSHBZF8/frQdGHI5MaGDW2JfrupDm1BhgAZsmfvi6OEyiLOp5MTlw43aATvd+tq59VC/vaO11GiNjlBDZkn9JFuEQjAEp8T6cdahlahDgbeWv1mq4C61cI1U9Lcbpin9aH2/NW+Xida6t04N1ZrsCwqoh5Uh3EKPTeGFUv9YPOWt0yKt9En1mIFek7WNnhCtcEufJ00LtQyoYfs9uXKVBWw5SpPENXj0lnZzQG5Ro3Jbp1/mSr8Iv3lt3q0sXgQ4N/QNUH91kp5kGdspjKnE1uTpfvU7VTQvngQsZiwphTWtHtCbtJaz27WQnVgmP22Nq5cqAl6pV35xdaFBPMB2THKoz2NzG27tXavlaMQcT0Km0eaZM0RImJiRdRgoGw2723j0qgtWDbI50BMo3RSuG/sBPgqeDfWUk8rBUR0sOQwTFdZafqoppyIZEWKxO+FKy4LnYXmwZaxMNtaI1OGGcbOxbZymdUMOeWxjmYXoAtFEzUvijaFzI2+oW9qIRqYMpamfVWmbuZfvMWf2SJvpHoXzyDhPgTRrG0Ws7nv36V0TRu8sfatx2kO2N4oKoe4q3K1zp4ZYzYkN5j5yt9yvIlaIlMptGAed0jzN0NZtVaqVlmGQClyfrhNSlR21133zYzwVCqnTRQxar2pb8gtkMXor2EpmRUfI2p3UXWcDbJpuecSTjQ7PsyKPxWplz+WkjNiNw+BO6GeUzwMPZ29rWaVspSjHLJKWInA+3G+38tEM8yoiYqtzxwLkG3EvqD4zGGSZR8+TXj2MjiJRZRWtMFRlEsVoo14oqC+YZAdM6P520RTfikzBvAHDkb4KCb2alX1Oq33oRYmBUv2DFTdF1rKflaWk5DLmuNQBcga3Q6FTWRyDe5eJYuCAnSUofU0ya9d4twE1KNywBlK8yKuZPOny7MG2vAB1l7JhASzCwHllYtwdh4e+T2J2U9ScMGVDSPaN4+Zh2yqrU5YxVCsDIqDPqzg3YI5rdGYsrEt3MSoGOXbnvpkqlK0XL/jWUcFSifFh77mXHKYPTvet54YxOaCrjSFfy0797bhDslB2Ylg/6b6zc9QyXOmBSmRGuS0owZAq5sjNl0P66gXLsjBbiDsuVGZjmqtrkV3DSiG7gCVSe4tVHUq+e2Cq3o479PhW6dc4wWpvk64ekq51JHrQPSZDiVFd3ao8ui1S6TwXhdF81G7LuzKwDuZpA6r505BZhYu5vu0YUEWJ6zGjPRMUiMN5IqPOjmNEKORtYNvW5udg0+qx7bErUmKaY+w2fQb8bK+2oh568nHP8ITJOnJ15bKAKZCLb/hWmrsaSHhdGHrOc8OdkXXnudBZEB/d0FEk2lwtd4xWV599trGGVK6z7pQOiLxzHbiHHRgJbn0q/rHJAutQ1K2XuIi7QUikzVG25dhAG5nsg34/ZwQDa3kcDsFo3Yf4wqDJSSqocTaV2XYWUV0yLAlK+pv4T9qxuapa1DGhMcN6mmnPZvYQth2tOdJH3ghpzjDgzVnmKpFQ/vhYfUBmR+jvBnOLaXcd1Co8DKfxrGHSmBpme7ck6+3QXqbPqGWlLZOPxT3XDcfuBZ+lOisPN0A1U1uG2WnjdLbkbqSPci+fC15Jve0KpZEcYkcrrrqhdXSsPE95OQHJhwSBbLtjnrWj3FbGOu8404QE58R5o44m5irBcOqldHZCBeLIncWZ6VozFAINMhJTH69BgAtwi+IoaAqvJgDLYmm5uQdW4YRyusIujQqOUnBLUVqFjjIq5mID/eRnUpccdG+71dh6mIsA/KrX43kHM617a9vFHYb4gbyiqr7tB37407/6W18x/Lf/N6/3a1/9VTNHlO/bfMV5gyvjVJqIRPWVzzRFJ4SvGARxeF7fBO96+6//fttveJ1P/zvv+V7/61u+xZv87rf5Q2/8ge/6dv/ifd/l1Z+htv7LY37wp37+ZS956UAJvJNriU4o5FV13asq7oazrOvMTqmwsJGgL+cLnn76E77g3373j/7EW/yPb/jb3uD1lFTk8jqf/e3f880/8KPv9Id+75/5w2/qKITCzWK0tSQ2x7iNZ7zXn3u73/hrXuOLvuW7f+y5v2juCiiV77V0Z/XJ//ab3udPv8Xb/MHf/eA2kyMIWElXu7dfwnvTN/qt7/en//gR9eGf8UV5l5p21+Xun/uF53/cl33Nr3uNV/2Av/RnX/kZNw2VUE6vomlP2t5qQ2/6O17/L77lm/zgTz73y779O+V2TtIsvBXcKz21VWelOlqcWHcsrRKVDk3exp95izd5szd8/W/7of/yc8993mBaNmJkSSUGgMDw4ijYOHquNSsMKZcjoB4AsZixZ6v8mQSZU1+6VTVFXRUOHSxBjLovS86y2X5L2dX1qo6LwSVda9vK0RJjdPXl2xhmWrclYDYRTNmRVnT1ifFm0BpTGEn1OsrPhaINJIBBpZ7lfe5n7Ou0WEiOsr3HjT57szUvh0JqoDdVLLiewASs27GY5+W0EmY1zDgsLpaqCyj0UsAyqZ0GUhWoahPEQamuO9ChXNlmvjcGSPMac3G2YJ+X3tyTHqavpUmNeGQ+VuhxVeVw+DbpClBBJuVrLZ4NJbwu1LRS6BqXkM58Q99om7iwm2+KUClBtuzLizYH+5l+X+OIPWpc8IUvFfJsNxslNgaJrmLLWaGyYau3KiGpVj/XJJ3yVCsfEvyuBc1U/0MDTpO6Tu81YDpdwMnVjKEUU4aUEcgJbivHnXWMNoMQj7jyr5CuSGZGp2mGbG2JW/sUvoFhYmcZc+uYsnoUVvoQjkrrcl3AILYU51ms3ttmwc7h2tKFb43ptk0loI/OJx1F1kzdV1d6br05jDCopuYqJQU2URuWCMiDWR6Yq4c+aBjp2gQ9mDYGD0q+KmHC3QenzV3LzvmQV+BNLh8HPPUJPWLpSRLvYpTcOkWsEtjwo+1oLKqfW4l3fQqO1obSfK5S0TkXRpBl5PRtuFMyqITC6SBKa8RD1n4s+h3b0v1rJ1j7sIcx5lR/voSh25u5De6+vO9Z2j814Vvtm28iyLrz6CN0/uyRQLNG20haQk5g4ZZehppRPOUAJvbD6uRZOE1XVSNDPG7AJk4NNI2Xvp98v82QnS5bJZMqKa3T92Ss6pZCnBjyKksd0RlevbM3FnXXiD3XrbRFkWsUxAjDmD6anmefj9oQjSM9kauOygXAOQaVSY4GLWJGGgOQ6DoKV3bGLecN13/dOAmQsCv1vjWUkSO1cOM+Os3kBhgc08dtctvXGBImJX6UKZ+64TP2QgXbOSER0pFQlOYcu2PMwqweJfoP4gCM5QoluwJFiFvgCashNrJSxmx0j9TlEonpDP1wDWjLkwRKg1TkQkc34UmRX9JQQYRWO8Jb4vVIJquYLjylQTS0dzxpZ2EI5c7Y0+FyHUMxWdcURR0A2KOpNcuToK51aXq0SMzCkhav0zY4LhG3nGiqBrbwXYEB7jS77FyJTmmf1rK3hUteEAuak8YaoBVcZITCylAYcpmQOzfaNjD9wjJoU0CLishan7XnNXPDVA3O4dqwmrRrYKdbDO8pEOovUBX2JXs69i2nh1mJDHmQ2+I6proTaqNvfuPVuUWE3LPlyturulMKzlljYDgkgfbAyCt+czSBhkARTBW/ICgbVewaFQPdrs40JL66BlZyhTZrPC9iVzN7VM6ZLkNhreQkjJiNTayjk50XHsXHWbSDFvSGKs98uosmhzZu3ErLV6RW1xHwMwGtHwrOw3Zx3AZc8+E+CRLCh7Im2y5Kluwa2lkPzK3rFjZTHC1Xg1hOwYSSm0gwiGCJAzHRWiYS3s3qikvFgsyl5Jb5dUhEjCuTpuKd1kM1NDtHwxWotDkl+ZLVLMpWQVc2pl2jB5yWyXVevbjaNKlL2DXVeVmZU1jTAAAQAElEQVSyZQpXNMcUbCo2eUY/AZJyklq+Obo3kzM0UoRDe+WVESnS5a0ps3bCStdHqTRaaYU4MlIIlHZJffSAthMLl3c3eeeTX/SCdJ8JUV6k7ohzn3QnILJKwOVebBcujcvMkKg5d21j5iUT9ZBCCfXmEuZgmyQkOxg664t+YKj5ghxFJ6FIJKxco3Ooaka2/oC2Rbn8WUJHgwVPG+kejkKzshR2nahklcsAzyFrNIr+hIvcbCTR5hLV6LJUzcKVbNgSEa1r8mFANxru7Q64pRCdtO7hYcOooTIARd6+DFqzzc0kvnD1r8bR6tYJJFb5WfPMmZgctHkNtFAGOKwpUSlAombHxiXzcs8Fvw+LKUKuA694veAlL/vxn3ruT/30z/zEc3/mp37h5376ub/4Uz//gh//6ed963N++B99xhd8wD//nJfc1YSv4dP9nd78zZiKFefY7sd5nX3yr5qdXn2RVrpZCHS7CZyIfzLg5b8k1cUVT6h4+Rn93sd4rz/9x3/j676W0NPM2735u17/9d7+9/0u/Mrr1L3BC14gDgclqH6Y23WlmcurdHrO9tvVcM6OKDSfvF5yP//dc/7LMz/h8z7r67/zD/z213+vP/dWrp1loaxedrz0c7/h23/9a77an3+LPzgHbDjc03tZBauVjsl2e6WnXuWd/ujvf8FLH7/s33+fzU2nodSTL3r6/gff6L97+//596ouRAiWmTYkYE7U4FX22aPqj7/J7/p9b/hbvv8nfvbf//CPVI4s6+wv/ZbvesFLn/6rb/vmr/vqr7YrjK5VGS1au9og7FJJ9v/zf37j13rlp77iO7/3PB+nGeEJX5IO6c0R7Mim0Ha2BRQUeti8bX/sTX7HyvrPP/GTE6lpjVEATP/gOoOl9qw79rM8wruI60dAZptrMJMZSKmNMlOVmkVRNCeu2KHwhFGhZAkqfG6uqxRvFlmldsbLH65Grz2TS+l+te2VXstadSRQIGWNqQgypWOtaKO5rRpLRWj7GFMkmqwdo+4byuTdW/E20KJ2XeBXRYW+O1XStYFGTXtoeBM0HaWrOjSjaVaNSDUcrtgy94GxUcOuQc4wnmVn8Gwql4dpTU+M6A0xWq5WZJMbzcGWB+8JTpGWQBUqkyrOLnRaYlpQgXnSArOUWKfRYNQAkaQojD6pl0PQHEWT4Z4F2R2DKgPlr6A5EBk4PXRnWJC8pvkoV6dpKBFBD6L141CBt1KUmwPTBa7YzZHCqt1GDUKNt4LnUkNBaFLS07cxx65jzH33ucGMWeqfsxTodm7zWohpFEi5AZhthSfpCH321RIkVkfpaWpIQQ7tQ2OPhRXSBQ3DYbtxeI9utgRDvVp0TEHAk8I+XCtvxZTvZbUuPQRg7K255ZihtYsOTtgk3CCBhG5Ow+S7VPvLGoR+mU7lFXbLCZ7wyHX2euSK2S0sxJXgiEYMw2Y+bVI4mdhODnnd2AMYIjGsy4CUQ6WIcKeY3IoPZaMVDIJViFkx9C4UGgqrSa+6widkf8MwnVfT1+oLqkA4KSgJLKFYXXprNJUXO8srYYIa+u56L4CXKxqmabaPMX3QiOw+GEdHmiZPF3YWL5YvtD2SXTpZkMi2lhSrx0qrdIOZs6wVYQMGKhyP7jMyEwTQqGs+jRBxGpnX3sWkX3pzskAtkqlxIzEKkLhFSchFy5JadOsaLionQR1P3OA6kzece59CKUe6ZE/M4jLikGRVXoqjolSRhgGOgGxKK7a1DRfeTK7EuaqiSxMVHWFGvxzuoKgaNHmrCihNxPAcVmTSBBg2zEfLuFKXJt1sGmLT3Z4M0lCVGsOUFAGjmsauAUwbgLNN8bKUvqZFjuugC4G4sYBFWZLKCqCBWdpN4FQfsdh53Lq8/QRSOl5LHWQh7ivjEMdbupfYlRLH5oojnWfhKERWaIKcdlvBWrZphA3B0TGg0TASSmwrO5bykwOLoZaekYIbFB9XJnFNBzqzzu5ofb/gNpTLh9OfWkpCX2cVpXV6rg3ualNpEW2RU6FuRg1XlvLR8bTNp6Fb+g5G6fRI28qILijGgtyUthSajNwMKhKDBuijFqBEr6ZqBOWjaEqM0mzWZBCo9oKb+/CBtDxat7M39oNy4skd2GZtT4Vdf/hYCx0lISj2lKnWk8+dPa3NrQfrMtuAOsiaS7GAuqfX3qqXeoFJGByb+zbHDRyZyJM8bett5z5iAJaGRFD3qb5qP2voj3DrPDuU0lrFA5UzhwmBz3YFdZSdJ7rFW26T5nDH9DVm38bYuW9Br9ZYAYW506dxL3jHkpN5DK69IfRNU2I6ob8TyPHGY9sVvzbJN+y+LE7GYZmp0ZiNG/mQY+QGOBJDW8gV48oRRa4Qq+q1MCwlWatpejcX9Kcj7QeuteAwZTlve+hR6ZhUVIcz2fqlTtsSlbEHxY8y5oKu1MKgxBu5UzeK26Zgu8oObEfqUZQSIP0K4hDxPadtpZiybaUewOlvHGORpRJwclrdJAdOj2EHsVCEPiasyLRmh4eykRFn9qH+KR3VQjTERev50Tw5TRRtSe9chQrTL1q2FqWZzJynFGbdsxRZq232qLaUjaT5xrm3DXjBjrDQ6eVTtAdgiUHURO+0wQmNClxD2odvW7qvYbH50uexRWp6BJa8dqFV8qZOs0OL3aIdp+lPUB26UIIpiBS7dJQBaIAFi9ZtDvR3T/2FxL13y7nOsY7Ow1jDcf2XnpaG0zoH7AHzJvFiTmJjSkjcsHaeYC/eVDykt9hsWhvN4MbRY5aglQCEZu06YZBAq/bQrf8Gnyn6iDZPZbCnLjaMYbsMXWnqgXZgeMmgOd2vd7voVcAzCjjJO/tU1q7eFoROWuO/vj7p2d/yFz78k971Iz7pL3/EJ/2lf/iJf+Ef/vN3/vBPeZeP/rRn/ovP+uJv+56X/spdiib8xbd4szd5g//uYWhFN+x2k+x09smRiVNVoYAl6mpitG89Bvjk8q/8KrWVmaevX33+9V771X/7f/vryJWoEuJVPIuB3/87ftuvzBMtvSqgzCg/wP08delrv/cH/9kXfsUnftG//Wdf8uUf+6Vf8dH/+qs//l9/xT//0i//hC/50o/6rC96v4/5l+/4Dz7mfT/xs376F57/19/+j/7Dd3uH13p4VUlFdZ/O5/z4T//4zz//9/+2//Z1Xu2VILxXThwS2bwea55Vq1eqXwz8vt/5hq/+1O3f/8iP3deasM3HdJf3d33LP+RLgmKdFRdwFhuUKpZqf+a0djXeP/+Wb6zy/6r/+Jy1QZvfl9XxNd/9A7/u1V/1zX7rb9MSZVnQBjgaq1Sc4zQPCUEO+Fqv9Izf8wb/7Y/+7PN+5hd+kfIHCW23beM2XT1UbaIGazimCY53eJRDX3/zr30dDf+FX3zBFdbQWJ9WhqWTJtmnqSSK+qpKPzkWZpvRsBk30xuWa9Oju01qm4kzZ6dN1rQnSVvJ1Uqvygl722x1vRtj59lXPTGVwRKQnuSc1DNfv1gJNTzrIpANGZpUPLAFLWhbqoXmjaLnjjgvYfu03Vp1j/ZOV4/xDFPPvC87ayvOy8jFey4XnnvjsSrRtBxK0oVqk6OBq9RsgIPuV1LWFjrCKzDWYM7rvWDdqsHJKBfA1veB81ZqphoG1KrS+hrVUAE21VaUao6kEpCTup1SYxGxgkWCfvie122Qk5PDOGQD+vtD1FiVZXdsh2/cue+1CQMb4Vxu4QbLZkEveTvBFLFWp9m9xmNtaZuNB2quTU7k1lpBFhgtVOyBGkhQlvw+UWgLG2uj1p4hqtWJRZRYDiUsE/1kW22ix21wI29lGzbr3dbWJxfQ04WTnFlezQtYtQRSmQIn0lu+z/QQ7ESubgVAal6Lm7IJ7sADw+eCRbCK4KAImlv7luN2XTLKdqnQ+zha1ZdSo5djXX5bFwcu8uEC4MZpijVmp3VVl3CG/Gbq88leU1TYkBv1xrA8sBazwnA6dfEc1OISkR3LVpkWxOOSxiXjPiA5axd3jaBpoZJHA0ZDkBpE08c2t823IXJONa8qyxhddKf463n6tuZ0+izFC6R5cSs44Tq9kXv2OM+qoxHKGAQh01rIToMSN9E+26ZROX6c9XQex3mPXLp8Sp5eSzCR3jbYaFwKbRMZilMMttP36ZtfquoFBlHGY5uPNHnygyPVqLXQ6uQ6XLHLbu2oed1ePlprRmAFogAoYzGsFf5RKp9ujJb8Tatcuup60Ckk6iJFk7urkYc4n7DJOdtHrlFhVPq9bYoK997GUv8YaKWVBr2MnDo/zc0I1yqi5ZDapBCIRkXb+WQkUQlkFRaxKk7LY6ZqsczJnb2hNwm2+nopDT3aNli7ol/VC11VPB1a658W1DstegrEbqUNnQ/A2yzIRVsKr28XSqWLJc3zpA6B0W8eqjMNKtSCp++hAGFNNFmipwzheVZFsmD6WmDZaIoQYAXP5LLWr4U8gVOX0XBMmst41nmmFoslkQkY2cpAn53iYCa3xNa9d3sLtCb11tRJBJmwhsaMutTcRDqKqMJYuJYhZSvBJMOdgHq6xAdpSREpNRndBUikYVwd0WF16qMo0QExBkGtVHswK2iyfkeWsFVZ6ZkpRCsp2hg6qV2CTHSTfYHtRrIpgnqNuGdqEyg2ugAKjl3XO5QtiCyqZezLvfQZpRz1rPAODS43uFH5jS0XKsLtdD/RoRAyZUu9aCPGML+Z78BOTKZUtaK1Sc+4UHhqe9ZmBncMo4FCWJKaWc8hbUDXEIYYLFW9ZXv0yNoTWxjv7HCouEShEsLBbcIncrOLcPlbSU/fbNetUJucYp58qKtMqivPstWjWulXzF2sk1VEQ+1kL6f8XX6hV6+Ra8bpiGm5bfTdXCYRVUdGdtJisuSetcZKD2d6pkFXahiMfoXYIkIuRVqT1OWAlbFQi5VXgBjmlxyNNlJX3FpQwrEkkIpRObtl6lIXWq/geZdmw6ym+sMAGDnORVvFWKLVnWoijlZtalgYloufw867R1E2Wr8GY877uAi0jXNLv3a3LXMGdCFKyfGmX4JpS/Mr2crKmb4Kqyp4Lj/X7HQBsTZZtZRiD3bKDjXZ2xCKvEMtppi1AB1pXbMEdBtUvsWFeDVtO7RuVKhdbqO2qfiGABKongh3DRB6M84xfA71zhRov6ttCl+UtebHueGcdbihB0+b997O2KI8IVNXhLmQWv1DNCk+K6UwtCRaQjgzVJCM3XIq+a6yc7m38tE+5NuBbqZ+m7dTjVX5lmVFmkOXvFMhOTxb64EdpoA9Y0MMKzo5hsxcjJFscSJFk64s18XwE9Imc4xwT5gEaz3sxMWjMsUBg+YBIItgTf2Chrp69lX0npKq5nUVvJbGqKZOq6po/OqXdPRLL3v8/zj0F4Pq/zruYY6//Wfe+j3f9o+P3edeuqPe2EPEvcIQ0bzyjbKtBdApaYr7Vwx48sFKgUC1Bv5X46/2jGe8irYsES3e0Ui9Vwde41Ve9cmkV2fP1wAAEABJREFU65dGF63Mg7Y45q5Wim/+gR/9zK/7js/8+m/X8Rlf/x06PvPrv/Oz9PXrvu2zv+E7vue//OQb//e/+cP+wv/6ef/gfd7lbd/i4eGVM6ct20KU+ff92E/L7pu84X8v4JfG9aXdmpbie+JEHVF5tuO1X+2VfsPrvNZPPfd5L9OdSjrSlLFf/+qv/Aav+zpc6EsEKj9vQpowxV8qv4IpQxPg73z93/jU9B/8yZ87+2mO80Uveul/+YXn/+bXee3XeqVXadXA0uYghUE6HaiR9J4bHga0zN5+52/5jdX9w//3T0o8BuWcw3wYX/Syl33Zd3zPP/jcL3mvj/v0d/7Aj3z3D/6Yd/+Qj33/j/m0j/28r/iKb/veX3rxS6bbL91PgShMSqSrFRr0kixdEjKbPTwwz7ruASSZMqDSO6xRm62pSsusHIXdNFZDzoU4u7vaVARmbp7Nugg4i4f7kg1LY6BL5CmPsunIqbxKdNf3hrwDBh/YXBHZBa8TjLJI/UbrKq5UzFKiNFZF4ZF1HKG/qwUqnEv3+NcezjEovKvraCXr6ULAu4bpAA25I52As9VAp4UmYdhldxQFVMUh2Z3NUP44nrQUQ3VVw4sIbwly1sWDkOkLrrOSiT5WG5589UqPqkplXgxJweojbBrdbdi0HIhGFb0us1UabpFtvSbvbtLZKqtuzsQoEIziiorMqNW+fNY2lIdqHu1t5M24O/ZJNagaajdlHsSFMikIPMHS9+IoCiiEwXEOFlyXDX1dvH63M2FXeliq2kFOc+cUzPPsOE+Am9l0yA7h5EZsutoBJCAvPXRN03b3qamuHEjNINtImMm53LXGlous65qmyFSZAxRSbat0JnpUjxYiDbm6f1y12Motg1C5Qb8y5TLLQo6rmZ19pC0ZcXe0rYjMorSsvO9cdl1L1dbA9PZSJtq6bn1J2y3NAhJOrw7dbepQMVR1LhmKPJUE4nqVQtF7NlYhabhoAKzZ3Qn5K0W6Gx88bn6YPI2LD5vSz9I0xDA9v1IDCwGQsEK5xdG8g6fzpLZUW9em2CG6TDGPFHWkzxCn8hIZcUTHtTdRoBAX6f5kDJGQkqZwtJ4oZitDbmPMBjNTAAgRxyRzoNAa0LJHgclSFJ708NFu3LxvQsGSLLQiZ/jZt0duofYDGjWvQjsemRbKWy/TsqXSF5ak3C3XDppHuHZsaXezc8sY0UwciJNRXZLMDtWcks0mVdnJhVm2O+eDUXInHNUA3UBTKafeWY5U/sYFuY0w+kjJyGxUjWqihEFvATSiGOml1U+OZmDcXY1XsQuyxaJW+hScVSoMYFD/Oj3DsgVrNDxZ7cvGuTOvAlaqEyUohYnY/dyHHvgETCayldPop4vRlwCm4Cg2hVmD1xRD01CqNxi6ZF2P69Pt1Aa3hmHsD7SHSzBVC9B1uLBk3fN8OuNE5xw9HWTrsixVK+Nna587Upw4qyqvLEzZy8k2KARQjJPDNpu37jkXtwrzhQcdkh6k3SHLGzRiO6c/PeyRnZYpe1TGFftQhsRTdObWUAuaNIVVghUGwpwmdchZKHNArsLT7Y+w5XDjltRwBoW5+Rh6+uuKrO6KTk+aDlzZoIKSA9RpOKt1z3w/kUd6hDegRNAXtLdDzX1wd1hCADnvRf1hqo8aQSs345i182oiHeeZEmC3W4mEupY1MWMsm6dsdkVdnRbbgqaLm+qmmDPRfyBOivE5ShBaHfFeY/VAqzHP/aqXi2cMwzax3a7frl8tfEMTCe7BW+R+TaLMIqoSBjOt9HMFjo6FO7xS2ka23UVQIRqdSKAIc2xq/DADL4ExTLZvszYPTFEDIqjhBdnYO25sh07sWde2ZxFqM/dZd4/73vdtnP6wONVM9ERu3sbc5tzoG1oH2tQQejAdx73jQMv+IPbo/WjTNzHSGAWrQldQsFmqjZKwLZt9rzxOxOOyx5C16itx2QF7pGQVHc56yufkXo1rZk2sre4z0xK9quroXkmLGFLwEkF+45zTa4yCd07qiHYZNzW2VMkOd9uuztfd0MtLKdUIqD/mslwwKFdFliG2lkBi4Nwrt2jeAWEOrlAwW2JmO9ljK9GtyNYhqbB7EmKXBbDUF/02lI0KqMV5qv1Ac7qlMw1UFhFqJPcdurM5pTpAQ3xP34z7Lbf9pB2FU6064Iu38zq2g4xuZo/UHgZ+eq8RSmfvaBGgguyUvHD2ds+t1kP35pvbUAL8rLF6hAJBqSKwQZEfxVP11Uoa2GkM93IDLsKh5jhKz4LT0MJoYhexddM6hgrDa4nVg9sxhoiX9XRcBLolqRkTgrZGRk07tWlpbxkCs+yIqTUhYLKBhUpE6lrKEm5ZHe69d90yxxm8OMP/r69Xe6Wn/tIffdMv+ZD3fue3fvOhZ/2j24tYG1eWUvTLdtx63zGnhOMT+gMmyGupEopfHvHkbVY9wLSbuSJ6cka/TNGlTIpCtpiTDd2/tnO4rr78qL4KRfizruRAY4H3+5N/7Ks+7G99/Uf//a//mL/35Pi7X/eRH/C1H/EBn/Ae7/Jqz3jo6j/7+3/PW/2e3/HaDw/jRJ4IsOShlkc895deJMvP/q4f+LAv/IqP/Pxnf9zn/5uP/Mwv+YTP/cqP+5yvfNbnfuVHf+5XfsK/+rp/8jlf8azP+tJP+tKvef6LX/ZLj+dLXrriPs7HjszXfa3XoLMJHy6BYtcnEAINuq0b7nvo5r1ibHjqdV7tlV96f3x8fMmOev4vPV9p+4UXv/TDvvDLPuZLvuzDP+/LP/Lznv3PvuTZH//Fz/7Ef/NVH/mvv/wjv/DLPu5Lnv2Pv+Df/pMv+rff+P0/AuBnn/cCtKhexlyPT/+fX/LVb/W+H/YPPvtLv/kH/vPzX/SS13vt13zNV3nGMP7oz/7il37Ld//df/mF7/Hxn6HcdHqcey32Au6jTgkRYcpcctrQz5TtBhrqG/oYTbWEktqmue3A1Nrcig8kL+5ioVThinv3HDM0dyQzkKuxwkWuZNDDTuI4Vjwecc8I7XP87jgmSvqZG6hguq3VWm2v9JJcT+BwP/dxPliY1ll0ztlPDf4yynuFtoyKxaIdMbiGQLgbzQZoKbNsLYm0RWY28xpEhsqhs54wGNqn+LH5/SlepQc72u+wU4OzfUl4PgtPnXGL4rLmDnHVG2vfOG82KEdRl3UzeeYp2CqvFUyb2ERHR3WVUrwKGW7qU2s/Y6xyGhyih46UU+5H+dNam1IG5S63M6cysOYqoeg2Pf65n6rsWCxsNsY+eyrSzPVyqU0Y5n7st/s2w7vYYGcP9aaT1npJM3LLavV5qQcQDIfvsY3jWjPby7e7b2eZMrYuirONAFo5pbogFK1nOuBWk9qsaYevhxX7VGq0FPUgJt2xzXZxsM/b5ruA1sk+zKS7a1QCygFgpvRB753WEko0k5S6m1Zjb9vDRpjW3q33MlNYy49UGpcjTMI+cS7J7UqD5tkqnKfVsVVusI0DWyl/R6WSftm+vPc5zfb9VvvDfbs9zfnYytXktvnDFQdJwBgDwQv23o/e51r2uMYhCLEs7hvTG1hdod3QGZkd6BPMredo7UJu93JlMKxFsxUkKeRMavXz7qCFmVKvWQsdIUvKMmuippKHhiig7nFEJHHrw3MhGmW6NDA3XmXp1ZbHoFYw6CN89O61IydwcTsx5qwxHofKzk6d7HSc5oue00Ibj4P2yNuRWy6uylOKne3z0qbBN3vQoXdO5bUH2gQhgCyrwhzcZnHesx8znu6S+hGsqAPaaGFVwI6+nfFU1Nba2GneWXY6VLNd5LF0WAB5rbitSIVpnKc/RpwDNvaNHESxrv9VsJxXDQ1fkAVxVRw1LPcWYPUxc8C5DWrjsj20aRt+q2QeKHWNe0fb+eDYDeJCIlzkfcedTKk/tl575kOpOcV4SGq9HOuOMzqsl3rF3I4bcouap5KGzCu7kH3y3GdoJwzKslJkleClFldJn3n0Okve2VeB6EH3tJzs0ZOSB5O2mtKQUJRazOA1EJjYprlMXJ3uzP1sUVw424rDfLMxc2OMljDUD9mUa8EpRWdMFwKTE8ASo3oqe2BDPxqo0nad1ih0srDaREerKjfHGJKIxg1oeReajXnFL5tlnUCJyRS+5dZUHk/yNKa1XlhlSnKLiR6ICepw8yoKZC0jcs6SesAHlM2IUanIVAnlJSwh0kqWlibZRgyG9am+hdPr3LrdWIbAis7VXLRywptC04q+BB1csqkvrT3Z3DCcOnwr26u0wOTQ5MaJq5F0RnXA0EaW6khvBOyyg7UYq5BhdQoHCiJcDC7kURErMsMz3cyfoLWwXAgxhWE2YTPhMs5J3sAhvqvvWKdz7bPGQJZKJyNLsYJaT+3ePNL0WVKmGwSsV6nftCljbbOxtfBJCZ6CPct2jR7gzSkuNCGy81zd/XI2lA9uMf3ueXClRDeaZrooyr0wEhbRHWkp4jFtjBxWMBQVslIjBTUbWYtKxxIjWJegTPhmk1BbVn6VaF4v6LxyMsJ4WhVAIbskTohOiS3Ms5T7ias3LWq65sGKfYxMWhQ1T6Q0ZuDiuJf1GkJ0ut6PtrMvKRmo2he11GSSlxZQ1m3LWyqEgqHZwJgw3bPtwDCFIvxoJmZjLxhMKCFFBfUhNb9FoDyjN5dwT+IAo4dQZfaJCorEtvYBhV/dKYYaDZHKQBdKWBSnyEjzkFih+7zUn0LoIXrdK200Jjl3CoLMYA0eow/xBJkC2BY6OGIMepvUxDAIUbeh2plK1qQp8m7QOs3EZ3OgHbAWYJXlgVGkZojxgTsRKoB8QkKmkmDKg5yKWnbYag/NDppmFzq6ktLoWS7onv4Eltxbq8RsK/PUN5aJD/YwzFHTavMejqYt0zMRnLXybBGJVOpGCQ4zc2UswTTIVk4LQ0BOy4pRus3Cr3r97t/069729/z2t/mf/oc3/x1v8Cd+7xv94d/++sP4iutv8Ote+93f5o++zq959WSsEXdbRytXbPTjr/qvwq7vWZ4YXcZuKmeIQPcrLD35YO09J8Z/dQABUzyi1RjOUNDdbrZt7f5kzvXLybFNhZAZsns/HnX2Gc94eOVXfZVnvPLtVV5le9VX2V79VR5e69Vf7bVf7dXe9Lf9d3/9bd78Jffj737mF7/g8WVFVrZVO5ubmijFR6ZEief83z/xzc/5kW/6vh/5uu/+4W97zg9/3X/4wW96zg998/f/0Nc/54e+5j/80Ld9/w9/6/f94Fd8+3+ozF//mq9W/SRLyn/3NofakZkLpAwFY+FoJMrBQTNzue2LkvSH2y60XPDQZCUFP/0Lz/933/+Dz/7e//hNP/Aj3/h9/+lrv/uHvuF7f/irv+cHv/X7f/ibnvOcZ3/3f/iG5zzn237gh37yF37x173Gq7zSU3uzCvHil730g//l53/8l37t73vD3/Ks93iHL/zg9/6Mv//ez/rr7/zR//s7fNrffLcv/6C/+QV/95nPerewmDsAABAASURBVK93efe3e/ObNALzdcmhLq7ZT95a+kvlKL16ZG0FplI1qmk6XzRY2yjJJoanWQWka1fkaIOS10qN26SPclynFE7hGtp9LS1egBzqGjR3FQ9JdfPcDVtr7uBGcYPOju6QTeGw1Bya7p5U+V3ITlhcB4Wx5uMlZihtIzYLtXgKmIV6SlHlrub90NzKnRguHCG3vbJVtUFKhrUqlq3wCIur7Dq5de2dalbWyqUsSolClXLeWLkt7UxE4d49ZRVzz7k1kb5ypBIJih5otTkgItwHh+k6mg29SH1J6+uLPkl/ELeY1Tu1EbtRfJCA/EXibMmjonNBhwxquje1f3Wdja6LjZyZO9ppIn5R585SowHYMIhcbxe+okGtqjuruxZ4N/UiVh2G+9axAdZb5y5yNZJGOQMqQsvvEeKqo/raQBCcZhqNKMm77swly6LZ0J5q9lvAg12WVRWt8mXTNMChIaaACgGdVpXXk25QZR648kdlP9Uy7FTSJtr4hKsizk2Y1aaUS0J67FaWncTNclhekS+oA/oa25W8kVOFF6PSEgJT3vAuxdQWmOs6thrOodNu4jEsuTTaQMISXBrMqzI2xgxdJJdvbBenVIAje7ZCUvh7H7MfGdlLUbUcwkizFgkmvhFI67WXDGEFjpMpatJQhh6owSTCWNNqM3MHjNaTxcuVVRm73TQki13V3pzVG6drv91T2wvlb0LDt8aWmjXgTm/MTkeZj4nd0+pERw7Ch0lw8gFz0k0jWQMxrtHjLD2k0DPj89r9T+i+idqz71DZypzpRBQDTGt61BDR1qKGLIgDldqpd1u6CBP4aaf74iiV9NTrgmYUvRxpVkiU1BCNbKpIyyB2MaXcaCvKf85al3FzM4k1/CqRSLTIAEaTTWiAl7QOxbdaguxSv/ETXN2dYREQ7lZ/kwe7at2oPIyrTraUYKhe7W6kuWiBZ1imZHigtcIchUrRYWpLlOdDOdeGo+7Wd6j8NDi8ghXGmjRGU3xhBE2eVy2BaJT2f9hmz4e0DQ6t5ANSC3BaHUxpRLcISqNC136pDEp1jnH2djqS6Owr1XN76mGfYzJHhO61bfnqPS/gurM4N1z7Oe8YAmPlrfYfU0JiETIKUidhrW9SkCt1IuYW/VD9VNt+327HkPY1WhWTNAMm26/gBxSa+mc1JaRhySgsUglJmQP1RbsQO5Q6aSolK+fNagxpMcdMbpqjfadKXTwlp/K1wL5WUWwDm/dWLSsM3TLddrOb5/TTle+gQ731wtHKcC6rRWFDjw6ey44cK2hVzuqB5a0UShNWt8pbik2M4fTZ5WpJC7G6zuCxKivY2qKXO2BD/y7CVdhxCaj7COhWQfHB0KKsxNpSgmN6cFToMJBNO6F9UdQQn+xN9OlEVKUgu7AAzWicqRlzpDnD8HTVWSC4UUzvGfsqB226vMkjrlnKvopCd1rz8pGZT4qAooRbD2qYin+XjSvqolwmqws42eWplXpDbattGddmvVNPg/yWssgtasvyaw5WYz0pSL1rBahiM9FxYRjcdqESrRlnx5L1GqC3Qk+y1BLKjOGjofPV2722U1cN+xhj3wU0uzxiPy/ve2JQE1PBA9VsWYTak/6acqIgFQA7HgafKm5Vl2mLh8RM1Ionq5T8mFk9IVJCpfwLRo6jFbmmNN1901MbDjej15yllZc35Oy2wAVbZx8aA2XRnk+y0GC7ztHNIGTBSlBivctwVeicwcq2HOwh0lvU1b3iKBU8EoopnCF5lLymtNnaNK+J8hqMwcNbViSM6TnVgR7YE43udmBvTLsAWD7hHw6fvJais9UEbY2Zw9UCm+q3bdOm8m+5+bK9Y2oMIj0VBsIrZpZsgzw5V7Kgb8XWW19FfQKHYudpPLdMCWJPPWI/zB9NVDJS68+V3/ROEJ7QGKOGVg8YYU2iS4cDKqOWJp1uvXUTVzKTLP1AmNCCULZ21DyTK7EK1QvKx0RZdxXTLXe0R+FXvd7hzf/Ah/6Vd/kH7/ZO/+h9/8IHvcc7POt9/+pffIs3e8X17/nRn/zUr/13qX1e6jFiReAq3bZ726/+f/j49Nl1mlVfCeJKOWxaYq14hSl9aFdrZV34FYJOXEcbY2cOY7kwp7hR5ZDi8rr85F91rwpezP+/yPoTsOu6rCoMHWPOtdY+718FRVVRBdJjNGCTEKKSoAQVUWxAMahcNKIB6a+GqIBgk+iNGo2JCTGJDQjYkMYuahQN94lRiZpEjSY2sY19iyAq/3f2XrO5Y39lMM9zz7u/856z91pzjjnmmHOtvd/i5+nxfFnjPm0dQ/u6+3+foqtRHaV09B74lI//Pp/2cd/r//wbf/uX/+ZveDP7NU3XtPBpuWYsvvWNhyx86af/0K/+is/61V/x+V/9ZV/w6770877uyz7ra7783/yqr/isX/OzPv9Xf9kX/pqf9YW/5su/8Ku/7Iu++mf8v7/6Z3zRB7zP2+YR68E1xre/OgUVEoxQowrR3KGupA6a02EiXdCUgU1+6z/6J27u0lbNY77I78d8xIf9qi//wq/6WV/wtT/jp3zNz/isr/qSL/jaL/2iX/elX/A1X/E5X/Xln/tVX/r5v/ZLvvCrf9bnfc2Xf8Gv/bIv+NQf+K/Ct3TwO//A//p7/tif/cR/+bv/ks/5jO/33b/rW4Yz08aQsNUwy/D29377x3y3f/77f6/vLi4dvQrezVXqm7CWX6TZ2RbBvDzgYRZ3F7sFQczG5F0VxRFYCRYjeYVLlZ2LpYIepXd5bHfJc6AntQi1rR1W0fdLF47BefsO6zqSRwyVEe7E5zQ4u0s/Khn2baJNBJnWiXYEe7tGJpCIzVd6WH499OfpF3D16Bxb6/uTdm8jIlmQzqyxQD1XOmYP7Uw9S5M1Myji0LvvBKn871UyW7V7LUnOCTMObwyook+/LkbAQgitZrZMi8FyXpPh2NKuVY0sg5JeVmFZqlMaSQBtICVvQnbdQRO8oEXpjiSRYdFiwnsMXVD88jF1qmiZHqesdXZfRJKEflqGCOzk3vdRdF3QCeTr/7HATj9zFYdybQZrWRx5+0VtYvdQkcirqRnMKqVPXBWYN2IbFMhFTis3djADV/Sl5oQw2Zxmk60rtUM9aze3iYTm1UyCbXZZpp6WXDvP2onoRpsuCKYOU/EhqDmGbllKevao9A7vmoF10bQhaxi3cwOd7GyEpNnr9sF264dj+cx1nNNC8JEP5nJ72Drq8JbMeN3e0wk3z/KtNW+3knQgrWTTuoxhM2XIqvwqi0Jm9SXSYRKj3ze8ASaa0tIL56BAPEiqQLiCN8HyIDHtmWkJBEoVaioayaxbdHhcs7ejpMPMWzYs65iowR6ds0J00JqWamVHq0T4Rs6ji7ikb1lV0DqvomzgTF4yi5Ku2sZNio8erkJMgwYbJ3lULe18GT3MqJdMm6XG23L6KCCpD4dTGxZvm+aHuT5TPWEHroIy2KLxsusSTlKZVIV6TuTqWNFIZMAL7qQTUOgY23k9WinpMXj52jgaL8QbZoecS+6DuRKqYIMn7Qlpsyoj+0qCEqKZy8sQncC9squWfEwfbtSEyLlD28DMu+RBT6halGYqtxxXrfPkvnb16UiKvZoDZk5qPuZVR2FM6M/9NLHMqhlc5TPWusY4XXHniY4aVgub2nYInQIE0bPvdRyvECo7pbVLMs2LdbZni/mj5Ep/ENISOxbXWNKNCqFpLRNbDEP5GjcBTXUP13hzK9Z1YT/JZ9mb136+elVXKOBZ0K4MjS54r2XrziEpY5pDQbR4lIqvxWrarAnwrk8NMVhrIgC/IYjfLLLUqxUKKxks1fWawzmLlh1dxdb4TIm6YYWhr5QJmTpUDaW8WLq1znVkSZLPouLauOnICrClje6+DQEFXDbCHrA1TICzo0JvoC45hM29D+eDyrKAEC/ZD6oPgD2N7j56orxGa4ET7pHwmgZfUBXJk9bcvZHn4Ml51grCvXRvIqEghevacSFlknPQZ1Llubu1fIQ7nKrKSmEtiRJHNh175Z4XjsspHCYjsQ3hEOqSkTF8jXEMaoUzj93xKq+rktQq0quBpCgJ1Cteb9b55pXXiWpECTE9jFtd4MXwAI9Ay+sIKdHuotUnu9SV/Jq4aCmbVJYMyN7FZ9fFgFaoXqS2yez7VXj2lBKjSk4mwkL+LPS9t3I+eVNmXakvdwPUbrfMrprdcw4u1oqEeowC0SyFQIbSm8weto7jcbysNVrLPJ+MJ68tzywbe860acG6WvaecBF+scRZ3gCn7TGr18Bo2uW+HfQajnFMf0On2eL7Wmdqp+IaYerwWyvSAqam4JZTdzGNNStWqulGZXaTaCXPOw2qcrSS5FXenXKl5LiItpE16jnEWensdEyjdAIO4FFuGhspzo7cR7c1hw3OBa0TGBGGMusy0RH6rvkDnDA3w1x7jj1MGiz9oXin8AjY1TjJUMvzvnDbNPcx9KYi28IeIdgCJp0wN3A5toduEK7DagmCwpJkLNph8mWKPmor6NHmPBojQdXtUAEqgEeqYwWHpF1RXVJQIjRW1T2sH8crO9RVbMfic6rXeSlIMzG+Nhg3bhS9ZdcsdUa14h7ir2xEinnBEatV0VZyepWrsjoq6Re5iZQBiPttccpGD8TFUN3kND7oy92MpMawbasQfKXcJFJxMfo237I0xPdkK3UTNw94z+vN89z3aXjHETmqP/NTPvGjPvwD33O1ur/uv/+m//lP/QVum9u8BrFacOD/z/+Hj3/v2/5RC4IKZ0YrPuwpeox//x98y3vsvOc9YVf0Faqif4aALaSILqgPjsAUL/rKjPdMut9Jgnr5cJp1deuswVaD+laFYF/M0gaA0g5eHp/3Y37Ed/ug9/+df+RP/tb/8Q+0bM6QDlKz4RPzO7/77bLw97/t297+jvd+xzve+q53vO1d7/M+7/e2d7777e985zvf/u53v/Vd7/t4/3e+9f3f+l7f6b3e+/3f9tb3e9tbHg9pRA0/o/Iv/e2/++3nEya6a7gNA72UAgFBE8IahQptz7/t1T/4u//ozXe+9Y23vTwy813vfudj+N//J2++z3s9PuDdb3nHu9/69ne88x1vffs73+u93vW293r/t7/9A9/1vu//fu/zrvd/69vf973f8a53vvPd73zjvV7QeV7XN/6xPzedP+3HfvLLW95KKI+STDVK6jz17eDyfoSvmAAkdkLvp/XJccJLJ+8TNgs3aywYJmsx9AE1sQ/WZHuXQcX3nEirAb0sFJ4pSH2O8FQJNMmr1DtRpjkWrs+11bbh7VxHz9Eze25qsfLXhXZtSRIu6QMO0p1j+rRxlI8y3AJoTTIKYqPriq3G2cSYOAxOoDUoV8Wja7V5jNq4S4QScR2KpI36GdIGyHT2bYzUaS5Mw9q0p2lZTWI4HraXuoqfbdn3MKuZ2mykmJhFC0TkTmEp9d5KbzGvcVm6MC0OT0dURFTRTSLYA2EjaGhH5ppjAAAQAElEQVSNM6lCKcJVWmEQzYjOeF44rxWtbjEP1/60h8h2vZcXrtQohWoc5sd8fcAYok5hoMirb7ahU7ghjpM8p7pQaBjC8m5qlj5rjLYB0gkdpUZOtDjcPi7l17M1HDHZy+bkWjnsFu6lXaXWThrbvZPY0E3jmfP+H0NtbHaMrLF7RJks0yp1tLreiQ7cFarU+hxl0JrVrREyLSDC4yMHdYS9zpBTdVkEQCRVxxHsnqavs+1xmSe3Z3rqDAND2bOzecFPUCyb2bgTEzctTQV8eKkfJ69iw+S7cV07nqfFlpDvRWOoZjD2TbhcNwp8znkOk7zUhDjVf8yuNZ5iBYuJ+/GMst3H5trusLvfZWlmEHE0HjGUit6jcwKT1vCLc8+VKquNTv3gSpxRWa+Va5fb6bg8L/VGdd+BxsjXIiIHRer9uCd3aCT2TO0oZKuVGWgDwNEwu3dtLrdTdS4GCTeJHehCNMraLcyf4Rf0uYy30GxA6TFRQbNuM6CcqS1BbJy7Q3uRuqbVGgLixXXRXzfY4J2JlvyoXcMDtVYd2iSqtNZur0T3s/rb4a+o2qpkVHFvyxQ0qv8qSG2qc1PnuwUzVbul1ewoY7D1EndmqqdKaFjeXyGInrWe19qpqYmMskwD3YxdfQ+9yByTfjgO5xRDOa49MhQwvKHdqv5SwV0ar5TUTVjRu6xaNjTCltnjGL4I9w0viD6mU2IqhvZJvdpnLqvRHDAapEw7jnosncV9U30MlRScYAFZ2Yj2bczZmrWV/qY7l4PdVdDRiKpdcQmTWb5oweVTvjVM8dwuRCzQpAnaNH2IzkQPeTGxGGFXW9ILszRG2ASt0SjpIGSKlLJEpptNuk3TGzMLHY5Q7J6OeqNLUSWEbQAIcSx+bjfONsilJrQ1VCOixpJBCI3oSm2BKTqi+ymvEGVAWYU8FOqqCkXKe42WC69RdaMFZZnD2j2U9YSVqomPa879SEXT1ETbtsofxuEtTH2V+krRSq3KmBQWXXADV0idu+V2t3ov9XWkRpXtlmgAV+zjQfrtvcqqcyNPZKi94Eg1wzkVvHWLAbTrXY56Fg7vsaqnZo7Ypb6iZIowjMNJyHEXsSckADGTqWqv6IGlcG1zVqv7+bQxp2FYdHfiqH5YTF6GixGMafkW9oGaJuKqo+92qdyKvyxjAVBiE1fUuYV7Q38SG31jSAmlNKWykWSsUlqmtUnwSRiN3jlU3jyHZKZoORJsRSv6RUqrvesT7iT5cDXLAR89ula06NzZZwkpw0WHWunQJ92178QT3GDeQKh+l8kug1zzFSuVmSOrh+VabRNirLeUMhNLe8yC35pp6CQvo+TW1DONPhOpK8Rs6g5vEKwWbzp7o8b9Gg1XYDSFoUUIW64T8+rxmohd2HveQQM1K1bDGqDBW7WpxEATU5P6xqVZMbNUr+4wb3qN1crZNCcAQw7sZeca6UMjWLLTmh7MsN5qPWV+UaG2HzVnr44Z0kQZFRlLOLSQI6MFtXJxTysDKN/SpLNW9rpgZ2v7EG3ZA2kIDUG68gBl4SSv0ZBzNOWU3rAUMRpiNf0alkNyUv5yehyrHsqZqLLVlDd0150jKQMTrbbGEWFafIYN0wy3gDLPBC5Xj72TJm+tW1PpX8G6qirr6nzaudFBP2tdV1c80ZoC49CtvUvNL+wFpdepCoKYRVWoJ9yJkOnVoMpukUf1jQ3/9+txTIEdmibqq+pKbcl/xk/41He89Y33DHl17V/y9b/r277lzVHDJXj5ZHvi/d5x7/XfM+aP/tk/133BSxTrjEG7yP7H/+Qf/cE//Rf09TsOmbdS50j0d5wDmp12ocuz1TCM0RUZyP0dg6p7a31XCOPY099z3gq31IlZNs7FMNFNhxlpeHnL+gWf9xnvfK83fsV/+41/5E//2fROUvgW8Sh8v+/2kW/M8U3/x1/Q7oWI6ledUefEqzVrLmqBkx60g9jT9ljneFwYJb9a3zLrHz/3N/zRPwFtNWpr0X2D48WG2wCZAt+5+35JIP/zn/rz2f19v8d3BQUgpJd/8Tt/8P/1t/7e3/ibf9MVoSGnJpp1oi4EZvoB1W81qsQMTVrS7zfffP79b/m2D3+/9337+7yXyGtKmey7yYmlYXDlhoQlv/Xb3vwnlzQNCnBXR0Z3myah5HJa0EvBzUkdDujKTRxiIPVVJwwly57GVnSzcKekXL8L3Xf9F7090Imru2oyltZInsWrLYlCudU9efc6MZ6pzULnjsyqci2EaeBs9W6t6q95A7M6hUQy8mrfwbq/AdSPk+72GsL9FdY+tDsqqpt5CGYsbKpyHTUH5tKER5V68YCsmBFiSaRZY7RxUHPkEhU6WEkB7gdt1CwNNpsKkX4atkf41ruO9nDfHFGKQSRQQqmOOpU77SVzrxuPPvU1qbs9RRA3l13KIVy+K4d2gWfGxes8tBzbdHtQOxafcw3tjoTwPdu75GjX+saeRiGu4VJbWDPPEXpfQ2cpyat09ogcYr5NBG9Ej5gH7EjoSfeLxfLe6KtFT8+VR5f+Jo0rXzdKVMuqcwyzwzjlVyxYgECbXYp5giu0WTVcxQtHH8uOdsr7NB6UANEjUfJ9ZTBbc3Ueb4E6E/US6w2TxtlNz3KRc4sPRFPn0PqhQYvjfri2KAWyJGkxaa9lfLYEh2Q14pIn77TWX3PmFrsEsaWasduDHqopehpCKj4qRA92lqZS3jlwOCccPLAmX4zeyLTd8yJSLeIlpPW9bSc0rbbES31uZf/CzEkoe22l7V31aK2Hx7js2FhlwaN7kFyPnnrAcRBLuHNz1+hApRKkE8iGvED1xavVBFOIRKzxiJwRuPFKO2DaLIGeSl+lLNtQXRAQod5kipSrZZQMHAmWeo9lyRdbLlDsNLGJcLQVzGwJFNvtNNMGWkm3eZTILw2GVLbFmfTW5Z6HxV1NLXcy/LAyr7TQCMVDaQODvXKP2loauhBlIe4CG6H9ySUavQrl7OnDNbwcpSVrJXFiv+p9eYjH9vKo2XAdO6DupWa6FUMr+Hu5FHvjCg0qWpmVTIqHEWMVadwHrumbriCdBd8YaaaP9+QqeZM+Rg4ru8UauJNwW0AOD2jzzcnx8OM43JXPxxMvES8ShUQCs76fP+ujDXC0kmKCSsCauuYSlFhCSmxdLdxprefFGxckH/mq9J2uykuiBmtZtfWeHnOq/If5wDJhqX7jwFsGJEZK4OCF/vaKV5YkDpY16gzaiRVdkVvcn85TahkD83UFa8gNgUUtQ6+dtwXuHFGVWNk7KoguX6gXXgdybFaNu+YXSiPt6pFwOtUKimP6y3GY7sqmhX7QEA3qxg7MYeIDVKKNydW6fVefu/JVZno714zDL/2RRfwRjESc2urnFbadG+xKa4lH4FQUqG6leC+/DsZqJHlyvsLcG909DQ+KLECMUFzabJtwl4A4ajrmBKyUjb5CoYqhekv5S3AkW9NGj9nDVVGsrOqg1Vg8Zg7pgwN2gEe9/nNcd6cs3XplAJXGy6xbkTKGF4GrtYqnuH70eKGtB326mWjhdB445rXsOZGqZqsHlUY2cfEWIXerXPqSnAQr9qpWEGVx4YL1eMAnfcSwsLQd85nKjWCHhCVATY6XWsdlfhnSoBNKrVjbWlh3Sz+jL5uFiR6j4JGMRFtDUY9UZY6+77UfRcF2N4J3gAjppxhE3cvDNA59zIrqFImEtbHBgNLj5xz5ODjuMPt0RFqf0KHhT9hFKwOKHeJQBsRgoyUyX8ER8LP9hKri6n5V2KHBTixd0Xara5JTFTrK+DRR042S+BQFFExVWd5putSgamQTDj3hUe/NG3Bs6q6H9aTaOeUeWaNT+nJqLAqIwHVaJrvNvV32DHAZot6WtaO647KSaAGjdD2PwYN7smYN24NyQ+zHiJcRt5YCPDHTHu3L6HfTGODI8n3OyAl/fUBeTIqM4eENO6uu66rowQlwl8AVmSay7dpzP1F9bnVEXbZ5cDlM87Jta21kb8yNR/cxbKxF0zDKEOWIpEEfjsyV6pEN1w+O5sPjQBwGHxQHtomEWTmG77VyTLG/slVaWnakNok2kjskNDZoTc+amiMa1Lakk+I5/LrpFAZO2AFbQz7Co1yEpcpBqS2XTqGcGiBw+L9fZljcA9INMnCrIvgvfNgH/7RP/URp9T2j/srf+5Zf+pt/+6uOeh3lLK6ND3r3O99zVe9/7m9981f/7m88X12tjnkiX/k/+NZ/8sv+m9/xt7/l/r9Z1wAdEhDymmOvCVIn/ukhidedo1UcW7VTJiimSrV/NsipJd/Um+7/jWcfdQsKBSS1Whyjxk0ORaLSYAe14exh+JAP/E5f8RM/1d1//q/9HX/rb/2DFbqlgYmDiu/0Pm/93h/x4f/Ln/urf/T/+MuqWG71sboQqdIpS93F9riU6pnXUTEgad5w+86V2Q3s1/7e/+mbv+1NnIVnqVjsGgMu9zUqUEWD+auor/u9/9P7vOXlkz72e8U6lFenffLHffSb5/7ab/gmXW1zE74FDGgiqnGBT/iz16uwUPrLQYivphm3+v2Z9dTepepoXU74yHHADFF2PVH/w//2JwS1PXEEl9Uam6t63CcJsa3SzW19TYC92FOVCmxKVktA5A1FU+Uo27dj15NmSo4asgo05OQ5bc9DmC1o2yj80+bCXG13nuJW1MNcX5G9WefonIpBPQV5RT5jvzrjuXE2kwOYMItReTybr+a+HhuPXtOnWRsuslSBb9h65Hi56o2KBxKj+4GeaTMm07Lj2VtQI1Y/j302etu6pJAj2lXzcs3UhDHa0HePTxZdNibdHSVvQT85Lio0AD0U3HDTenaXXxdlwMVn17ExTk0gocztTWx66SPvyzksqeGCrx7zAu3jkiXFtqyZrCokNR65qJrdS+4PQMWqS12z9soyFf7Z6qlxdXq0ys5TP3bfaDwupez1lDyonLBjZKKb8lDmDAGGVduN2VBdu3XSyzBQ2kWwMrKeZz7P64xzI2PcAQ/YbBAcXWPDMV0Naw4+WpTDsMpWzsOOY8zRxnL2HOaOyr4Bm55eJXVLEwZMg1hqJvLkPpH7sn25ajlxK845iRsa3NXyeRAjA1UASANnHfOc9vQsZXmAYyTHznWVWp9i3oZrQGHizniqQQMpSJqfPi5YE1Am3VtIlRWIJ2WvuIzGpXgVlfvF9SyX6+bmuobusdXRusCAqw8I9d46k6kHciLHullz4LB+kS/xLVhSovShSYANDOdovt4ZiFKz7cP8sRQnnN5Yu9QYJpaCFnilSVZ8ljEYVWU9Vz0Gh1DK0gL0hHm14LS6QLDDBWKjd6tK7PRxmRBeeQUuoTOiFbvmHZbumexzMFzgSALeBcpCAncNCulp2iyM7CnTCHhBMGWGGEZlGQs5hK+md1MSRavb8HHi5eJsaIi9ZdXLsW1e6Vu5H+m4TF+iVWKIQVmXxjgAhZuVFTtSARmaHdsu3XgHCy316hRyZAhgSQAAEABJREFU7T2V7tPlYRnuX/Lvo90VRAGRXrXYa2QhdtUVte+GXtP6QboY7T7FS4aswhyaaRVr656wxfgj7Njh8coi1LM6p6e03X1K1gLmg48Dh+eA+IuhkMhqyNedC0uoac09es991iXWScmqRaeXwixYpYlVtd+sO2bBNs7V4wX3n5QOUfc4THj9tZ7HPd/LOxmlt9xiBMFISoB2/xebGkN+R2G2gZNwa+jVYAVwtbTQ+j6CA8kI26lTkRW5Vf6iKjr7da0VpSCtPkiQF+00PNmp2TBr45085cIwph/6RzekWbQiHiw5H3Sat3KWMHI4JntuZiop1NzR6h2CZm2o2cmMiH1VmCKarbmbatlW1sWSfk/lY7fC9pYnIiqqQidrNYcNjNVjQH51qDlZFUNvXmY0wZ6oFWUKGris+gZto5VvcSBmykfOCZWX1R0O9VKETKtMSOAwlyXi5qHZJVq79RaBFk9yLtRsu5PCbPFbtQUec/RcUoxJp43qbAenZKex97duNKzlmsJeTq0eu4+wleYpAky46x5hRU2lDKrRLM0onRa6uHGUULpDTBidYmpGj22o0WNO00snS8lWCCLLp4Z4twEgs5jKfpvHHDV543ODDMGN5qOogFpFA+0MmxDjtsby5XC191cSY0BxKAUKhfpaI4bfhx9Xzy4T921V1rR46Vh195NqCndnoNQb4F5T3dd2r8ajMCNwIZVtqUZA7hWNyT69pMoiHbcBFKqQRI7dtiUi7xqu+Vo780Jf4oVGqjw2ujZcg5XCHddOmVdSSr9b1kZziFpA1sRrdpyvT2iu6lAycuv7IiCv2N07kdsRi1jixSd8JkfBvHJc2y+4skoRCQ2Tj9ZUs+FjGZ0w7cY6Tcyy9AvemEWH4OqSA5REJPaKJDnGdLGflG+rHh2NLBDiceuTlsPu+xGS/sAwQpOtxaq8hmpvdc3Z6g9to/fsSzstEyMxYZYTOuBOA9TwYBcYYAINeaaXmNUZ3LsXKhdhHiy2wm82hNyG9SqOcGvz5jA4h/fQxQ2e2Vdmtx7NPh9xiSdVIPqA2l4u0jdt3wDDIDLY2WWKAf/s9ep5UqrpgnoaWUYnJvjJ3+97/8CP+sjvGPcNf+xP/dY/+IeAsqo72YGP/+7//Hdcre5f87t//xf/p7/+63/v//w7/tD//l/8jm/87P/oV/6OP3LvmL9jzHkpdripXL/j3P2BhCiijYSOdcc4TF31lgX+Hy9R6zs7SxTVfZ4CSlq5wzXbBvVB1FZld2hEk9/no77nZ37Sx/3Db3/z5/3q3/Lq1ZvC31VK3hz+k3/wx72s+cu+/nf9zb/5reyZUGKusgwpIa1FX1jX+Ct/51t+4df+d3/1b/59dKFKqAh+0DveJp3/h//1N3z7q4ASliajKLCLWhzkIlVl9mt+++//K3/3m3/8J3zsh37g+/WcnHMCH/8vfc/v/V0/9Bv/6J/+nb//f2O6V9OyF17LKquuyujg+Sq+7rf9nv/hj/zxUsBpbz1ePuB93/7X/t63/Jm/+JcVRfmWOuQw9jIZgZSdXdcf+FN/8rf+kT+u2CEivDAkvmlmb3/LQye/5Vv+AZrCecMt6IPmXUPlH8LrjVHNUjzFFEkKFBoAllsO0wRUQ3PFDrBFuLlqm+mX9ls+XfcqDjHU9wtQQqwG0pJqo8OnjwkXjrDcjI2tpbLUcLvkp6U6ZyGSmfrdZhizl0GeEziFQ1pR0/Ak1SVw5zkNIoKWN/TT8mKkmsjFCs8a27vHtnn/NrnqqqnTd213ZukrEeVImhIg6KwM5+VqfEwUGQ4O2KCZGyyau2SJWvUsFF3rpA6yaQEvdZBWu5kNAqU3s3nAHe3Q30FUbOWcy6arZRCdBRVyDDaTSLvaYpKzbehCX7n3ueNMCsjldTN2pTJrHLQiSm2Erh/1hEDtTvaNiRid2szrYChS6bqzUVmFzREUcYNo6Gxo1sW9bZdHoVmGcO7GRXWkQXK4mZxwOo5Ro0pAGYrYWnE29KJGHOBKWENmdF/Ydx8G5aRKdGtWmZWjKIVEqlVqy3AgVFvVOmsLirzIaoPOKC+dqzH0IyHMNM/Dex2OobTyWtjTI6BcH5XDC0o4gQYTVKgqLd4IQZCYlF25Fgmti3WjbqHtHlHH5VaragqOVpk4QoMHWjyKK3fCraFkXbRgZ0vBCae53a0Wikn3wutqvwwtCpWJ2kCO7HvmAIYraQpZ6pgwU9XH6CBKFIyU2jlxkhdMBLTCUCpgitV0ze5xllCr8kTf/tjoRgaq0LrulKeojqtuz6VT3QZ6WcWE9tCKXL7sqs6zum6SvBqqD4VkRSN72r61OVlgpbw2o5VRfZH7jBE5AOHCPTB8BmfdNIoBzWkjTA1WO2gtivcJzG4F3kCiE5nyVS46sbxm12gyRm3PHJke3YkL2mWwZYruw3sotz0qV+ZgiRfSDNpdrZkD1TpTLJm3LK/siArsDenAhJWyhOh+3Ryqt4Ao7qNuvVTNDWV7wswc7K2b7dg8i1ePrG4dso+ROmxdpmejyhNsKqOYVo1Ks50qGSXvdT8dJoC66Vj58H0ErK1snj6enGd7Co+phyetJ+lQsm+ooeBmFVNHLGIKTo87GOULW6O8XZbkUSaPhx3uBiPE0XyYH8S6uQjFuHHuvJDSIcflx4mXKx57r4g+63n1qWsmCaesiZg8FQay7JRc5ElRRw9R0+Eu16VrSAomNECkUW0ELFhgVI8utnWMTtxnZypp3Xpu+DJbfaNMygvv59plwey1KUbNxSOdA/okAQ+1HrvwWnLpktqVs/eQRBE3Kdz33OKIuVJNQXD6qpZuBHNnPSPzkiuZ6sYTpZYbJGtZHhaPR83B0eiQva4AdRQohC/ejwoTFa0FZDMi0Zs82We3GFCM1M2oZmi+JI2HjQNLm1OgTOOUUgmjQw2goRdBMbbPDqVi62FK61Z6KrkbdfkzbuEqtKGAPWuYCiKnJdHZpr23ZDsurnP49WAepINWakW3g1Z8TdL4cOiC2EcjUdtkQLfya/DRx6GtR9/cFgYxJERkIoMIWxiTIlHkmyJQI2LVYCzk9MakotDgQJVMS5si8cViDdXN5F5VvPmWpq48tvu1uAe7TTC6ktSqF45tds3mS81jD9uk/kmOB2S/LqIW6AXRvZtNg7OIy3xzgKadL7wSSr3vraW5r46rW4whDx3ZHmCwU87n6PtOYHr1qji65cbEVXZfg1uNhJArKZU67b29Tu+Y3vN2TBM8yripZW4tCLA7kQJExZNQZGoJJYPWrXzkQky0qKuSzQnZC7RiuVKcUshmwHuJUu6Dp/ECyoqekP5v8WyrwqaM7lZvakSpmjT7aiRtcB5Urjj00de29Qr2bKosY91F0F6W1zgD3SXfq8WoTx/aifhOPLVciz0dG3FGn9d4Js/CxpV+whruakNqpNXXxdYpLQ/RuzKUzHATv5VJFJfoQJ2AjuxeppuNPgzH5DpyzqAjB66FXj6Wa4DXZlwCBl5Cc/Wra+9UQSXRyoJmNGSrWZiVzrx9+P32Hf+OefSp3iKdgBNaSd0I9jjWl33mp737be/1HSP/89/+jf/XX/lrI4NVYvpf+54f8aHvesd3XI2s/+nP/MVf/Jt+z8/5ut/2K7/hf/yLf+vvTbeP/KB3f8eAFz1GlEi0BGr2d5yFSKqJS9yofKvvTExpRQ54C+Q9AxXDzoBH6Ta+tY18HQNbUK0Ua9syn4Cijb52nrui64YJ/rhP+n4/6KM+4s/89b/zS/+r3/XURYiF8ez53T/sQz7rh3zc3/mH//iLf8Wv/xN/6a87XPVfHs0mMLPVhX/f//KnvviX/7rf+0f/1N/6O99G5YUCoqPfeKyf/2982jf9mb/0JV/9X/2Vb/7WmHYdFYzW8rbLq7/9Hz//g6//7V/3jX/o+37kh3/2p3yi4E6K217k+6w3vvhH/4j3fe+3/rL/6vf8tt/zTfmm/r6wu1WP4w7HVMb5d7/t23721/2mX/l7vukP/ak/v6/dpSdYb/n07/8x5v4Lv/63//m//TdsQK8baco2ULie+dv/4B/7+b/ut32f7/rBw5iFvJcM15iJ/b0+8kMew/6H//VP/sN/+G1maVatO/JCCHJn8dKC1MJd4JO8/+OsK3u1DBlISWnIzVAHPDPOquzbJZQ73FcFBcpOmeN10th2HykZcZCPhfs5oQQuezispkCp/M00zHO7NIx+rVFHedq45rzUUauZ7q3EKstVak+pugrdWl2Fc/O+r1SyNExtQaUPV0MmG8QNpA+XVPaN4sLxqh+BR1PqBiMVdbY1p0ZHVyBOx9NZYqeruwoqHRV+q0OesKCBNNLVj0+sLIepZZxj9mP4g6oceQ8bNm1ISqNrbE5xTbShb5ty2lvaLPZcHA/WqLSw3uzsxC61OfSgmBm8ve32qJkla8pb+HVhR4FaQ2pgm1TJVDa1zKG6ynZD1tu5F7fo1LXKUkO6ZpUamEXa1TgPbt27qplhTVkDJM3V7LDaaRkSQ7jqEoUy0DlUm3c8+kCqm+Z5XteVIehF3IQ5c9IXTNEDXeWdRmUHoX3TmfOsR/iKcbAHw3j2rtbzC2WjlUc4IHcp3QRXuAo1WTF2zbhtuWkBVdQ+MSYNgqguTzcJ6hwWrUD8JfxFi1EyxdRV1Upcyu6dA+EEWj8piiA96mBDeW4pYkZqVbufJdyazLMrQKMAP17O45FrQStmO9smg52aJ25oUpsqIee1Z6iRBSplbkT4rqjS3/VeAyDLukaiCxqzvV+H6BATtydp9Cwr8X3V3p2jHfAk9ReQy+zK8Tr5kKCD1Pn2u4KYqGLpC0SMyGAG86bpWGu+YK6TDKDkl0DD5L2vvNMXz+J1DD7CRihhG7rGaepTbWWMMU+ME0zJs/Qvi1JfFrCbl8ZwlM0ygGGp9XajtSBll3WtypmpgHUVzvZVnLLiVDiqIqnFHKNxNIcN2jpj6NiwtJaxZJXDnK5/Y2F6Puyy41W+xDm5lQ5Ag7VPGBhITzUmJW5kDSbI7lXlLDHUpRPb1EiUGyVGtYuaXQfaUq8AczDmONfYk9e40pXM9jBztG9wyyYaLXv3ItvCo+KBWbeJG5NbU+RKhgzzGPYyWrfV1mwpr0REhUFR97HGWkuGW7GeWMFJDjSiuQMSzbaOYdUzaa/z11pbvKBx5p4suSSGYbU2/IB2e7fhNbe9Tlnbxig3sqnICx1Mp8BaO9pbdvWNcNIN5orLo+wq3WyrumMQ02im2YKiyClDciqKxVbFhdqsmo2h7gYk8iqFg2BD58/yCOKsAVWRXe6bFoCwyxdFOj3GKlsNgmmj2klxTRKR2ImmTgyaVYkTRXD7163EqjbKrKaJluzI4L64tysvpZ4SEsMEWV600u/hGN4+2GbhwlCi4x5Az1YXqmrSXVd348x6xq3uVnDoF6RYaDSzraQzLAMVyB25w4aN4UuIWpEn2d40mWsDuiBWssXNVkocl2e+Zukg/Y8AABAASURBVGJWMUKBnj02R6TIkQNvKD2EXFvdv6qh5HnpKdKG1rzdnhit9NzjNKzGyNesVXfuJ5/b4p6DalaKAE93pXg2tI6gtBpkxN47s8SHZKq+ZlshQtMaGX2p+zQUozWszBUUJN9ttR0XcLUkJc3IqqaxsrfemLB2iU5d1Blu21URk22jOPUOEuJkFNylLuegN0aAwXodYYiS0XcfH1TKdJkGGqVA5C5ueMCi5Epca0u80kxmVAdGqoAWVHGcTh+cMm8JSYt7UNWB2bfsRtT0GocWbxQY5Y3hRW+K9ibh1mtcy/bt7XLfXjEqWYUEikJgmSbyNdLu32rYO6SjAfWx1XhkLo3cpVRkewNGwLuJaj+3PS9E6fSdvXkkKI1kRXVVZFWGdxqaZnU/8xlOILvDOh2wGohDrXtijXFgLvUBqkDqsgsI0U/pyRRe3/Zi575ai4rUE5iEMQlaK92FOEeeVlmorUPeu+VwjKL4i/bEHOaTZnJeE1IfaZAJnzaGj5a9G6lG0GW0LljYaM1x3uuDhymRKCIhBrqapU/JlVOKadWW3z0CRsc9QR9e04bveGWrJH23DBOuH1oQu9H1jvd56y/8nB/3mGIGev2jN58//2t/87f/kzM7yut93uutP/sn/Kj3ejl06f//WG6f/yN+wA/5mI/+Z5ca5LGvdV6s7v/H+fbc3skOj7tpHyK6xXp8xxgnX8Z0h6hqw5uvTl2ybq/sTPh90NoaEHC2aKACq3L0McfP/MxP/S4f8K7f9Uf+5G/5vd9k0kFamIP+Ez/h+/30H/WJf+2b/+EX/Yqv+fKv+Q2/+4/+8b/6t//+t3zzt/71v/7Xf8cf/EOf95Vf/eVf/Zv2zl/4Uz79e3/EP49WaofNQaO8f5/v9s/9os/6Mbr/+fRf/JW/4Ov+mz/4x//UX//mb/47/+Bb//if/r9+1W/9fT/pl/zK3/RNf/SHfcz3/Pe/6Mc/FtGCVQboGMnv+YEf9st+yk/8gHe8z3/wm7/hJ/37X/lf/vd/6M/85b/2d7752/7S3/57f/j//Iu/6Df/rh/z7/8Xf+BP/6XP+mH/2hf/hB8ujwHfsI//lz/qJ3/ix/7tb/3Hn/3LvuqXfN3v+GN/8i/89b/9zX/j7/6t/+Mv/qVf+d9+46f//P/iF/zG3/2pH/svfP6P+UQjUZbqWOqEFY3SDeeP/fjv86f/+t/94v/wV//6b/h9/9tf/ct/4q/8xd/yTd/0237/HxyZK1TGLYnAqTxUvX7vUXCAUJHuYdvmxQ7xZxt66TzmxkPnNYymLKSL9AZvHWbxqtrW8NvwDBtl4EhnPTBesFYv07fdfQHaVnI32vVTo6yvFU+VhKbXXYYy37WDp3zE3LdeLu/TeJEBtJUKRaIbnG42rP0BmWpAC14kLtHhqUtO9dEj2uUDJvMtkWyiu9SzZrT1TFqw0GCwM6qieuN1bXOyBlIHAUv65evUwyujmiWoUrS8vbd50NRGW34U34aCh4pcXoS4Ohs1uvSIpxHScJQ41b9q6QxD81WTl4J6gR0+DlM4SKtQIpJj+9gCgUxqUT2LKbjrgrCjblxoTh2Ee73uW91CwHao+lxN/9qOmo572V0qsOE0N6o9gc3GiGagoZcgKZTuEVAL0goXhvIEttW94VTdBiKQRvmCTYKw6hUQBqKpsVVqzZX61CLOPIfMq7p25K7YAqa5IQvQZImJWus0I9VpmfegqzOrhd4qZRNW8piMHt3orlI8XMwH9+A1UkNTp3fyShOGKb/QS6OrR2pFMTFSXZ1dqbOKyWPOtOPGXyn73DhQWsaEFwPK/rQhjRmGmKh0edfmqHVX8xQX0Mv7pibSrvYQC45wK9GpKehUa5XLtM55p0kK6CTT9V4Zlq88X1HC1hTzdia7pL/oCuZNXMVQ6q5gG0UTBMHSXYnrOVreZun+AHQby3zCUECiZUEOwN26S+lCtZKMRhMwjlbn1e1sKeEyOzFferzIbrch5clIaEIH9VXv1dbQKdoQk2oR50JMKpBuMXJtBL2ny0tVdteiLw6Om/mAn2UyJR1mUsQvx4OtoFtQZde8aJcjZ4CqdJ0aslbOdmtd1QloKAGhB7VJEUAvqUrpShzsl1G+PGw9Wzu8xFlDNm+SNN08NRNqei3OtCB074bUrOgEB+nokfS00TZk3zBZrsDlFRCMVZpy11/c8ma2PXC97OtxwrbgzJshsT2eQjDsiStSd6ACE7bsscbsqcuSvpTzXNbThsdSnkRWcFTMfmU47yp6Y/ej6G7hQ3Dv2MVVsyJ4XVKaxHhe2Hp+E0QZ7yjXYfPlJotSzgkNjT7uLJjyMvEYetQ8Zj68lykvR+tO/I3mS+HRz5rx6tHnW7qOghKtZtGHwUTKvax4pl/gZeNVH5mHB+wKxK4qPecIeBZzR2VX6P70jcBL9aPjCPXf4ZgYbJK1PBcxtuEa9JJvrqcsR10SBoRpHWVj49Z5qWCHZpKiwKJdOUtk9yW6vFLXZVnxFfTicOlfpbHMRx6MB3KiqnDtkkYBFv2ERaICG4Kn44z7kcPVxmOswWE0kxrAKFUt9ICDTsoPLGt0TO/1mOPl4FG1QmP9YfXA9n3FtRPRK8cLXwYMamRqFnMPwpt5CkdIAl3RUS1Yailw8byGeOn0up9+HPscpbHX7r2Nbc6wo2309DSP3ZVSTeMCXhdQtpoe4gltPbuxB3JG+vO082n7iRSsfLYlVQfcEIepHipCqiqyr9NyC6NxkNOoxdQomiIiQ0+nlGvUseQepiK1Ph31MBs2FSXNJBnkpMqoyEI1t8e53sz5Zh8Nf1A8pipOgNHoQrR3DlxlJ2b5MPdHS+7CVpEpeDJ7Z0O6KedYnG6ammw9peqDKqdc6Nm9qIm0w/pwUZktJdW0shml/ts2J9ZMX3uPLho1aghI51VbEm5gDPgw0OskdEgcKXaoU1DeXUutHgB1QGOb0ZWaq1x5jrugDqVoQwpVXiYUKphhVQLnILbFOZ+vZsWUwbk4Ho3VMJHVtNouwlqXyOX2MAU0JI46z+6rWxdZ9/LucfASlcdY5S9nH2cwLrT+BinNbbuefl62m3nhjAzFCPMxZHANLNRLcoZ6FzbilPpO8lyz1wvHAybLZoqgR9nDx2NO2l1yZe0vNd/qxxwDNgtrq7L1N5R9JBkTIHkdPCfUrvT2kITopF/lUUfX8tJOQyFevp6Px5Z2XmKNc00YCbL1U+/9xkM3GG95LL1v7PBKooAikjfsDo6txefV9/7uH/RZP+L7a/zb3nh575fHX/3mb/21//3vO71j3AXzr3z3j/zP/63P+piP+PCXOfB/v+TnA975Pv/e53zGj/+RP/SNx6FZ9/GGOrMzPC+LPQ5f7/EuAC/HsRWDw/uaEXZB5cbIMes9EzXmrS+Hi1K4TQnyeNsbb9GlSbI25NlunuN1Cg/WG96PYS/kS+bqHG5ve9t7/8LP/fHveu+3/ob/7x/5E//nn7dSlDZ8TrfP+MEf+9Vf9rnf57t+6B/+s3/p533db/8x/85/+sO+5Bf/mH/3K3/B1//OP/+3/u6n/4CP+Y2/4Kf9K//id9lDoBy9hq9hrljrkf/qR32XX/dln/tDPvq7/f4/+Wf/7f/sv/6Un/Mf/+Cf/R/9m//xr/v13/iH33LMX/RTfuzP/Zz/1xtvHHVGPHedEFhV5C0z9L/4IR/+X/6sn/pTPunj//Gr51f+1t/7E/69X/VDfuYv+ZH/zld+zlf+ht/5R/7k9/iQ9/+qL/ucz/rUT3k53go/Yro47+6f+qk/5Cu/8DM++H3f/t/94f/9i/6Tr/+x/+6v+NG/4Jf/hF/6K7/m9/6Bx3F85ef/uC/9CZ/yhr+87S0vb52HBc++nohLos3x037UD/3Jn/h99Rekr/xtv+8n/tKv/cz/8Nf+0t/yDX//W/+hdpMrxtwuIql/WmOhXFDnh3ZDF7aeVQXrGshpvdreCLzlxBvdtMAI2KVdof4aycu0/XjT8CYj8Eyq5nmmveo+1RoRljqqHaHlaaykJbKPZ1mqSLDPmQEOPXCITVwxr+dsNibGFDRjmb7VYNHMeniMo6huGXM8dcn3fJSvBXtDF0/nyUi+Mr5CCkGndTkHx91PVWJkZ1v4ymPWnNSknMMJ7SFHp/rrhThFIOppCOj8MBsmflCPgppUEiyYDm8sAkQ0o8Njr7gxhUrWuefxfMP3C/rF9qr0HX1dVR2tjf/VIW8OH5JzHnHNq7AXr5cj7MW5lvl0UExgsOt1coyBfnbdnSRBMyE/nE51OqCURwfRi3aMw0VWM23kmnua6NW62JcAO4CDWA2qyWNAC1AeI0fromLD6fXsONuvHpr0aldoAfD1OAbfcKVSKb4ugQmtRSlrMHByOi3tdZOfqlv64zSXGk8+xZzRrQzZpQT0GKAyy77MYwjKzMv1J+qrApK+XUrUxn7yPO3NC+fdrMsK3F2xk/sc8cr7KtrlvulJEtYyGtxRl/XzMbYbxJ0ol69qtuSHJ+JVnnHh2GuUC/G1vCS7sdFaHRExz7JX5KUZMbzdTFMJd2LkxnO3DHRuuZzeUxK5p4sEHY+2B4ZprtpsIiMzhJtTcluz1flpCzygYlJLhWo18g0Ztyq/MRLtmXqa4IBPWbvuRIgoqiYbOxFl3QSNRYAYDWND3IpcySAPpi4AwTvvl+RhJvRztAxKKS0L9+isI4obZ8uSolhGB7QWIB029LLBOnBKP27yMijs0zBKNm8AzrlMduU/ocKaWKZVLrb3uSyV8+7ZPdBOIbygK1c/lDB0zjzlfyDc08M8VymXVm2J3H2lfi+KKohlvuw8MrxlxgSSwkhtb3wOV/0b5X1cY1ZOEUTVCPo5LJcw+2IPTeT97xJDPY1e5kpAP8GsrX9+moYX53b0MIOPa/Jy7Ol1LC1T+ptUvPnE3rPjJh2ypx6Te1xJbXhwFQKQ2qWnlqqZXjGvnBG2s1X65c/yqFE62ktMttkWAeY3Dc1IQU93QfSBKfHFzPLyDZ4znmNnBQSmu7Czr6gWDCnCm7l9p6PSuA27Ie21E9MgjkDS5a5vIXOgh75PM9Xiat4ONBzP9OBIXwUzis2ajvsjQJRteUVibnvs+RYRLiZcKQzYphKDGTj0BUl7pXMCmzE0twTIexOyDggUraAJSrJVyzJfy7XIchf3bHpRA2NRRxsKqEqFq8Da73HjmtxaWjguiBhj9QN4NE1csNvKGVBFG9sIC7c0Gb/1wN0a3lKy+4Ri1PbRODTqTita2BoetgTcoExglnxCuO6LpJm7L70vcIZJA3n/xVlmMY/jxWWK1t5CNrCPquOia61Q5Y1MocobWJ0Wp2XIbKlUtvVpYqlzAYNsBaKwGzAvHsUJsWUbFhjNOcag5E11FlM1S7zEVp7TO3yX/FmIuQUbZjZxFs7d2gOrwuhny4y1+aCNTk1VeKDpZwz4aPg2XppT0lJb1gMlYaBGtq7STJnrlCjUQy4ya1qMhh38AAAQAElEQVRMpgi2ZG6qwqoJwrsQ186SF0hQim0Of4F05nfsBjbGlvWGIRzagbXWbVPfPHRKoAt6GRSayjXvJqx+8kSenZnR+1K+awBeOaS5wKjhcBNIKiIIUWtyTei0qTMrXUKYY7fvDT+x9nAxDLTAsMnsuyezyy3dUbOi1DA9e1YbCZowaXi1HE+0HgRoHLybz8SbDRktTCpiiberlEnNmibKp93vitZ5X9eU9Ll9JkxWhdYkOGHBCI60dfWxL4ugaou4jt52ZZ4WXSK1yGj5IAZqJm4WtARVrLwV0KN7FlaZVgEKtNLdezRHzbEPP10TPMOjfd+iEwS3khbr2pFRgJtPQhQgFAYBVy5Q7MnrYB5Frapn4ClfAgGsiBWmZZjhnV1x1n5yl2/zHGwIsgIBNZPYFvk1//Znf83P/Lxf+6Wf+3Vf8VN+4Pf+yKmTlUKikTnwlCq89JmgG3/SD/u4X//ln/8bf9bn/fov+5yv/dLP/eTv/33aG6aqDqXpoz/0w3/1F3/ur/qZP/nn/Bs/8qd/2g/5kh/7w3/5F/7E3/gL/u2P/5iPmsCP+j7f6+u//At+w8/9vN/48z73F37Op49W7rrRn/YDvu9X//TP/tov+YKv/ZIv+tIf/69vumI1a86r9dFD5j/ofd77a37WZ33tV3ze133FF33Vz/icD3v3+3moNO1l8BM++l/8DV/yhd/3e3xkk3TD/YPozJRUQ5myqra2acN9cGoh/S4f8AG//md//lf99M/8sHe/r9X1ANZ0LlUxvuuHf9Av+7c+69f97C/8RZ//47/gR33CT/3RP+hLf9wP/RVf9Bn/7f/ni77s3/hhb3vRBjbKSz36tWJaWb2P5ma/691v+zn/5o/7DV/xhb/wsz/tp/6oT/j8H/HxP+czPuWrvvizv/7n/dRP/riPknnBKki5LjLFcAkr0dC3fnmsL/oxn/Rf/9wv/s++8Cd/6ad/8hd8yg/46T/mk37x537Gb/iKz/tPfvpnfcR3/uBW/lPhwZTZ2payxI//qO/2dV/xBb/yp3/uz/3MT/vCH/mDvuBTfuAv+Mmf9mt/+p2a7/tRH5kZH/K2t/+6n/EF/8YP/HglvJiVramM8TL8p/2oT/yNP/sLfun/+zP+nX/zR/97n/tpv/pLfvJnf/IPZg+WqfhQG50cmIbpvaay0FAjBKpbjSAtbObyeJ1FXkYVORwFnml7jwoEM6zcxVY69d7N3B1X5UX145YKZ6uoQUOKkb7T/gg8rvLQ2HstGXJtMsyWuUsWQvXDSTrcciGnqsvWmHP2nBvjWaZGU/26QkUXCEM/Io/dunUZ/SKL/mRFG9S8aKbIcCtlFxWf7u6H4z4wFv3osQoz05UD514Wd+Oh3blbKSjAEAFLnTR71X1CDcmnouqKyvuvjs12HUbzBTvgfKBfmgMyg2zVAaT3FYSYjurMHvBFgVFBUD1RTA0IqmvsoM3heqvBEP72Nj3yGJ2DvE/jkWPmcJk3Y1mdo9II+hACX5O3iTamV+r2qPfdbsoAVEPjht4KqS8CSc5hc5n7bJhWs2fc+x6sFDUm3w69WZsDA20AqTcr2DPv+ZhIU+vE3tqSsYaV/ohmU5/QhMNdHsSd1z1RIACY5U37zMsyK7rlBE4FMEUMjs5GX85r3MZvt20VDNVnSU9um6Y5ooNzQQdnkwQNhr06RIkWoinL3YFuVki02ibC0mZ3A+U6zJV/b05c0wMzJZOdd8kH62ZIwDRWDEokWUdwhnua4MiTDyza4Giaxt9lBYG1HMJRoCLVwQHZx2TbvXWt3YTqvU3X9E8wRqcWgtnQjfo6NCwT1KFVXvxwP9EhduOWkKwKEPWLTs5uPy/Wk9RdQh9ls8y8pEG5xTxqaAPnnQUZu8MWR0UC4dpNnDrr1Stj5Oap9XuzUPdU3inecXRMy3sCdMYRj9JYqa7ZQu5UJDfBvc1O+tbs6z1g7dq2Q1mArlenhs47b1qxFJrw26g5Q10JTbZxG5RcsHW5Wpn3voAMlMQgrgJowghPeqR0N0z7+OjKVEdpjdNFCrenKylT9r1ZgBhXzN3Wt4zZmKjV6RHYs0ptCRAhGdwlkSHFVRXrlmb03ncuVIrT65hh96P4tpQfXqgTFff4HtYTTW8b+oBxJV/tvssps9h2GKbJZ44rjoiDmnEDmQKCpCVKKcRsaceywyLGxWq/mH13j1rbX/ZYKYp0dY9WS4LaXWzsHCU9mJyLC8+Nutqaaa/ZLxlVznunHqiKLwzrh4cKUxKqhjKV61Udb5o/jc06iMO4JG+ulr5VUE/fp3AZ3Xu5kMOAMu6pqBxssPQYAC9Vyy7apcQVASShUt7ONh6tAJUpzUR3aYa1e1oHQuNyuFghQ6n3LINkAOdGR5UkM1r69jsd1V7qhH1EeUVRBpgyXIDyNtRYNGq4zeVEC8NI06dhYdTz5FyEHicf7gYX8r6D6WaWdU5KrF2w6PuPLrmHB7zuq8gAQ2U65q21TdEcJQfFhrdOvhje6NQyYJQchrKQpoZs95eR89CWtASxUOoYIRd2QYpvwNTOaSRt0TWcREFE0cpEMfNQwgl3+hpjSeI6g6VcisGZ5ulBXhOhBWiRVPi+bMh9serKOtOryxIjKSoHxmi/wb9ORSPTUGuWTsNqwgYI6z15+gwfNViu6mBERuwza7fCUBaVA3WK0aWciUozuS9ZVWnqTuLaEG2Y3gs40qzGhLlD0ZUyt5tKvo1eo4ezx6zXn+QaO256XExIls27GON+9rdT1Oe+40p17BSwHh1DelBwfpNpaGtaKqcNY3le0FPDjKK+jAKZ6ZochpvKR9uj3ehJqhFUG9MM1rCL3He+nFjd6qem3mR7u6Q9qw/LcSexAXh370Q1jsVpRutiAkS4Sp1FpCMf1sukCipfQ63T1MqyXgNLn92iGm27tDsZYdjOIJC0qyVl3GAGlXv1GgkWrjwr2f7i9bC9Sgvr1RGdBUDJGOQYUoTwj8t4gjetEK5diMIF3C5u7PpCMaW93JXqkChaFM9dmSHGOCj8reGdq1UBXWgZ2UKLgftbmaDaChyRylgg1WBiXDW2eCTk9R6GSrZquz7gfd/vwz7og7/zh37Qd/7gD3jHGy+80ae1E5bMWqFVJw+qE5BjHW/5gA/6wA94v/f74He974e93zs+8N1vO2y4CFBIVQrAV3+Pf/6DPvkTPvrHfdLHfsYP+vh/7aP/hbe8RX92j2Of7/UyP/yD3/0hH/ju7/Tu933H296nNQV69dve+vKh3+n9PugD3u/DZPid76iSVyiKViK0Oqxt3gP8sHe9+wPf9QEf8IHf6f0/+P3tjdkVFiK/3vn2t37YB777LW+8kfRSxpX2NivCRI2i76rWr5CKJa+S0G0y3u8db/mg93/7O9/7rWYyni4Hy916hloaP+QDP+ATPuajf9KP/oSf8CP/tU//wf/q9/vu3/V9jjdKwspGGpOAuK7KyLpz3LkSK5zb813v+7ZP+le+52f+0I/7nB/+CZ/2/T72e3zYPyd+EOFn+VZcC4PukroBvs2e1k/LraAr3/bGIV8/6RM/7gv/9U/6zE/+AT/sYz/6n/vgD56CfOtbQTQjLC9lfjKhfjpnHY/v+qEf+iM+9l/5ST/yh3z2v/6JP/z7/cvf5UM/0A45QJQKGh/0jne9/a3vVUPeSNhMmsgRBFvf6Z3v/gH/0vf84d//oz/xYz/qIz78Q9TXQMNQcaqIdoWEGlryhrIwsgdquK6TUar0I23G8Fjykr0lponQ4VIW/DKWrth2qA+YimXcts3Y8N06qkxSBiTDwn2GL6e2THcmVJZ79eWtHCpZeEweDhdwIJQBkW+wYcvVOfajbfa0XixzabxVTmGtcWUl62pwSir3GLnMluIzGyz2xS4OYqBqd1/QbY4HUEBDryZILG8tjBzooa/TOIzaP0xNukvrypm3t01tGFb0oXFsCDuEl8wU5Kj7LEzarIP7QK7gOI3PwWgDJAmTfLUw+IKZSuDehKCG19BV6av2I/dCECVrAriaC4I4sk0RJxTIcl9c1itMq0Zvr6ZeUCEH6yQusGS/bfXSICsgT/RutIIIWIJntX6syyH6bsG1Q+uFuWGoA7qa8aWMt9Bx6ZKcqDT0LghKC+houHqUXz0EoIAapo/6oIE6lBi6uWarDzfx2h+m+XRbAiiURByWxtfkHpWPwkT3sDymHcdYS4S1+TV8A4lOkccJTSHbzNrtvmbGwTHpPowH56HacAeoAGf0angqO61/qdKH6CYaTFqKFejVY9h4OMZsGJokJCsJSHvUCmh6DvZkmYdmCURLIIKezBh5m2wBG/sa165uODhw+Jg+Zas7IKtGqMsO5ad3eUKk27qRmwFkmgkSaIMigt7U2fS+Fq+DkjLurBU8au7QolEleN2DNccFz/BU0ogyK5sYR9Od/XCuUUMYFEcIC0fp6w16smhSXKnLdXbdR+EWdJb6HsVZdcfkZmmTIchdjbLRmKnAr47Iq/Ac83KX9ISywKFa6A2JC3ky9khJT9GChM3UHjV9njYvQzkdMFM+c95Wh2ORLi+BlPXUja9usatb0RTZSFA+zDhaLKrDpFlpRoPZ3OIDyqNrL2SmWepHZBUUG6xdVBvEkbc6ipFy+gagaCgXkgzt1kipk6sJnwPbajOVZ4PNfjzqMZVF97Zq9bhxWdehcEpBM6wuT23oJMdjKAcY4tAEuVXwwug6ezgpADKZbDKdMbiHbaq5OIq+iaAEUwpUfCsX3Seg4SV5+ugxQk9sjCWvuRTcWUp9y4lBTLYDDrnxBlqttyXKqNY+QfVe0FumNneRkpS84rBr2iV8bOtu1b4ilvHBmy8nKLbQR9uRCilhynXcbswcKihr0Ge6b1PHu//iqSpNcUnAiFL05WSp25l1tEUYEt6pnwrV/RvjGFPCEqmJvO45o9NQKnvEANkUNWUUpUqafkkwQYUg+VBANeAeregQQJAB5QhMNzMOK01MVIxWT2iurMe15xVqeHat2nYGdqqujsKC2t4qjIsRuAJ6rwtaRGTA+sl9IaPkHe1K0e7mLjZRA1Lp1TiTO5Xca9k26JIN7+NoNbeB6TnUneWBylDTsu11pApL2jH2LViWKQOTKpFqi6Qkan1LZViRoF5WyatZLrmNnrev01ErtTvEARykDBgNait05Sm9q8pCoocBOlcuZSCEPqw2+g5O1N8ODDROVeQAypAHZVE5F8C4IXYmOqPRNmLcLvS1mKWPh88Rzldd5xUVLRwH/aVdZ1H6gTAp6WxYlinpqUhSJ+1BHurdWq5HEWpiVtWChuyWQ+s2oWuODdtJWQCidqpZt7mONC9z+iApROOq1VgTuqndjJAVkWpLqMtMgWffEGI7i8KpaQPywqQFV9RjSyJ5gAPyXyBMceyCxDySK3tUIXftnTtQfDhuJUTq88DxyPm418on7ISfib0RlyaY5XTO4gyOArjR92JNg/CX3SqW8RTcC8KrCQAAEABJREFUhrEWrskL8rYql3ENnw73NMEYqSJcC8swzFRxIc3ratOKSsV14DombU3qOm6fftKz5UWhpO0WEqtWtuJZGYXRJvE0ldJGZJZeok9D8j3JKEXYNxHsW5+X1JApzg2OmKPnFMld3pouDbHV+JSC0vAgwoMWbDRYKk3FZ+7OQToTIg8Y1QuK/T7lIiFxI9YEIyBNKjbWhFo2bU/34ct46HuSGDp8lHkZZvFQQjphOkz2azMFe6TMrJYRH9Nt3HxpCAvdM2nkMMpVQ6+2Vso2NaBgsbBZKQYykjtmpEoSDE1NN9FdD0HqiaYmQ9zpWWNSAm2zJMSosUzDRRq1obINcjrXTBsoIFM6YYe8cOfa268QNIBmPcWevEt1LYbvwVY1S1KFKehpbfdUNT0xQZqzHDqXk7o+YN5WCk40Kf7QA7swwBJCMi5pUS79grzIfJkpTUTcJiXzS9x0HxWuzkjFZLhtd1ZdxeSgm7w5spmqxQFlsif9TpGNuiETq9csd+ac5zhC502h5WCQN8199yuqtK3adGk2BpzuNZkyOSExT8MEoeCFTRnRVrgaa+ss3I02W4HKPRxFAsZhfatESS6kTpNHb/3Z8rFTheDXmDGrRlCHF+jEJMAB/R2ih2cyCgEMaLbLIDXGqCLZkVuzpMM7WFsud9FaQfa9EvdMzMtsC/xwtsyYOh+7NfdeRwADFT42xEBv8mJfLfHc/Brn8HmUaTq6A30G9ok6bcfc6ZKKaYEv/XWzT9x5agUoul0etNGwbsFubb+ClRY1s6VbddvZNshBrEWfZlPRhrNKKcAuO0tAK7lDj1ntClyVV9Utmz7C7fK61P/yRnwwZjfSKrGVwybTrMiuKOZGn61LQKmeIYGJY0inE+mukdkKrrET166qHAK8d1+JLkgNiquyqUhY5WIREEEtu0WUQeqgwyxMHELnumV6soYU1tZg0QT0FessFZmXUIm6khOmMiqa2w1wbkcCFDOyh2EqnnSCEJNs81peSuQ1eNIKJjEu2nIuw4J6HzotIIxllTozp3F5m8AkKHYUFOVqGNMg25zT56RNtwEyrLMBKc0G6TGwxa3rBI3e4ULRPdQdomdP76XKEGftSW0FhpbNalSbEsmtpxHaI2hBu4jLlTTKIwYNlJMbECQdboOiSj/MqVoZuqzrpq3bLg83xQJlQL0/vEsswCIrGBwxV80H3NLVGfLMK+tsBBS+tV7EXtA0xYTppedqh8ZCKeaz51mE370Regx/jn7lVBYBWAKys9PEhwnQfaQM97hwXL2iBHqbvQleEKnlGkps5NkRnWTe4mDp7kgUk6t6aFxbi7NezbVbhubVpYrKm9jVtXZZdjv2vcMabcPd1H1m77G3ootzd4ZZcrUf4PDCirZS9j2Hg8ZuSQkgTD8+zJ3DSF8wR0O1P2u78mljYawe615cbhdKndulyLBci+TlyitGYORAzRbfx9wP78Ns+Jj6Y/sQfUJcUWUEKbPyZWP44SYBj758F59zvlp2PozEAK15v2ygB7eNLBeJMH218pn52CK0GuG2pysSqkNVkU1Pt5arIE/4JXIVIbqQEj6MrqzrU5dfdlxrbV8FstCTsW63UDcKq2rTLDnJLloSjZawbKdutqD7rR6YZt526bgsi02Udeq10comTJxOCo21somjerDbqWVRO4wGuK2D0PbxqlE17KauoRnBmdo9gEJ3Ve6r1BO86nUfGaP50jahE9zZXe0hkjTT2AvaYG2tGdYKV0lXhm9ma0bNZ0lTswCn+v4GqgJQeGA7XmMVLIF2m12jNeJKZtnY5gGGBmcJ0QltHPdJPWygMs5JU+40NLsEs1uR6sMd3IjqnTsTJUu+VY2XitIfPRdWBwShdkbk5tYDOS3l945NmO96H0qacC3WGr2ME3SzvnMyYEcvKc+us+JKFh3i2qE+oAKKXjVQehoylH4oJ9oaX2iR0yPNcnQYEqbYMA2L4Upz6c1vwR5WjyOWF+myFeIId4SIkLG2bFNIkcarcCU2q52EW4+Go0W+9Dlxg2pNDfo2a07uF8YQhgGIk4bBXG1WBrMRiUSTVLijeCdJEfG1XQ2kEq5Y90GlMFsvsOPu31GA/Jrypw/6UqbvbTSvOeIYOVdO7wf9GPfJKkoxx9rH2FPJlWnIfadyZHqBJQclTfVBzDS7dJ3D4N3KK57oAuXMxS5kjkkPzQ5H2SwO0DRmtp4QRHe0xgqUdZafl1+niBN4IxZwBEyRt1ko9Q2GBjW2DEhDKcbhKXeWInKyZdlCF0UXGtaluqXuxmbdcIfyyKquILcNH2NMW3SZ8AyvZKPTOlwZnuWr59qPYx8rCWm8n0IzTBQ1xkXXEh214RdXgivqsTnDFX2LhL7RuPcakxgVxotT4xSvtyS1G2rluPFGsxUcSAXS6amUj9K8aT7wcC7lZo56uNrTJs5ZWtSr7l3Vkx2DzRqVKmXlDIUGlCbRBVxqjaBTlcFD0BOZvACyRDC97GA/lFoqUyj2nCqscspAVZs828LQBE3pUvlIvkPUkk9l1mwMycspa/7wtdagkoWY3TOgMrEypeKeDzTlWmTBOteumdLCUJvb5Nn9ZAUEvpkQgcpwleGGMKhqnGCLyKIaSou9qyQHgUpjHGjZQcxsTZYfF+fIUZ09ntAjb8vVdsBmY0mJUMSTw4zyiA3DP311e6lo8Tr/GkS72V9FmkNf2gRJn1pMkN33fDKprz6Mqtk5XdcHbkMqAu1X9muktwP68MfB5U39iGUShqjCDmVvtjLVYOlniBxpAVnN0PNJZWHajQxw8UC6U5AwUYPpLJ3c0GZrIm1WmmYKnxPaPjCjdtqVvvs+UrlQVojRrlS0tj010Q62RzAT2UMbF2JO2ICzRQjVjOvAdsZCHLMkKlM6121qt/WdUmVmKzYox8plqr254VilKipVkZIHmTOdpCs2VZ56gW0BGmnQApitXdF+xXjTZ46FSU4IWEXH1nSZvAaSlahmOYbBwAyocuD6nCP6EH0o3KUVbBVwdEegCxTHg6P6OHNklMxWo6E2IKuRBy7HkwJxRl+1ddeU7Jrsh5r3GAFZTciYFLi7+zJe6qYSkxhspQ/bbc+51zwfs4aj2U/sV71V8dKafMmEvWe67ssQNNm3KaGcEWdenWG5G6Vo1FFE3D2h2pSIB3L2BQh4v2JfrhpHB/r1OokxbRKwafrk3aZkRolAaEp2KpwrI+4JUg1H2yywoflM71steh/cLlXm6Jj6Hbo8aGKhis3UsnGs1voxMnnuqks7zvRq/ezWplPORoMY2SyktIHZ0TdueJmaAnH3vyf0QEf9TOMPrdBqy+RQtalGFWqT3qZeK33IWlZ2BkPxp7DoS6I4KhfGCv25dw00LOjm4+BjYTqoIbIT1ppzX4V69rnXM4571zb3mCV9rTImrEpiHi3sQ5IUvdsUr7IAl2l0SjAlm2Jsym8lES0PdUelEW0BXKiz+0Sfa26hsDFu2pS+kkSLMZBuqjykQrKriVF+bNdeyKQUD59j+nFgquRxj+pUgkteaEZoc6paDZRoE7vbrle+T3aFybjlmnHQx3ucYSRMM8TIKFo42wkf5uRhdAerPXvmzSvJMi+dQ1q038Xu3dCZNsu6mbFST+Q8oNpsH3Tr6Tk9Fnulz03biLInZ+SLvXpMNabc0HRhN1KxW8wU7gu6XU/ceCZU7Hh958MG5T26NMZieU1B905NF3xOps5pOFdqIUkks3Ga52HQMUhvl9GGBTP76R3eQFsZA2ocvYQePm7ki6BC4eHj5bDpBWRW5DPqCuuX5CNMvNfM0IrWzSDKmwJesAKJnp1H75fUtp5+IfZZO9gweqnPXI5rIXUwTpXf9lHLluOFWkFXL8ZqYZ4wN9Ms6W51aDBfp8wUfJRvP2JCZalb0KbGgHeDKvWKrkGYSbucZdgeutebL77UH0vsxDxDacTrQev2Omdj9jny7H1d597P7c/rJc4H47FiMeWKxwk7gZA7dbrnjNf/18Jwh8rthekoLf4lLzsZCaSM8gBJ7LZdcl42wIO9xMddGlv7f0lyOg644aA/cExNUTvzczhN9MxZdELRDB9eQ6yFAhURug3hpSt+Tg+NhnpYKzNKCHLyGlrwx6se11BybjQv15wXHZ16Jv56V1mdSkkK5gum24IUdvg1bUv9ctkTTQ1HC1y31yauZfUy+nCfwykCTubTQ0o+7HrwOUQ5S5WqDoDoFhdNQKZq0QwP3ipz44C4aTnQVTDoWw70dcQY7W5i8CxqLdwdt5EAc4yahtFm1SqL7oBMQ8vv0arY7FnqK2qgmuDIYe2HKffLejCJVl7kv3rSoPY6cP90I/Luad05M7VD2E/uMA2HrnUVLG2UL+j+bBzV3Cq16AUOJUx9Q+VCzmnHpJ7g+xi5/DR5U1Anj6ev51Ad9HteCaOLDRq39yvPLOYeFS6qbD3nevqMogfnpfa+r9Y30OGrTB5F/WE+Zed2ougoyhXfpnqk2s6F7AAl9Ri9D9uqP33t3i1Lw/sQZUt/vIVuN8STYAHJLs82Fu1ynMtzrb6fZs4n+TTdJ49zP/I8RrEdWOSkk8o0o1HZ8ii2qiKqhThjxLn6zeM4H+vOhjwomSJisIwXmbC+TRBOW26TRgEtv0Y91/OVn0/kZQgpcQXsDF4SoZoRapgizupOsz3x5iyhvuC7UC3bG3JUwL7Xo7mpVoynWoiPGNzQIE3tRs7Aca6RauLbIKNKg+1heBnrGGPQzGEmZKvAUgpWrRc7joeuThjLPdxrOy5azkfdXbld2u9anS8RejpjWdWVGgBH0eVZmxbdUp0Xdtlt2esCLy1CGGwiTRb3mDXkHhQEp023QUqJxnUP2uCrbO2rQkztFTQx+3pynAPnWKdpAYqzIjqJ1mS3acPv3JUaRvaNei+rR2KIYO2Ouk6OJ1ayCnV51kwtSm9s6f+AKdoCbEDPGdZQAtEtna29+WrjedXY+VL5sJabO1rL7uhEF6zN5ezNNV/ZeAW/GmqGz0ZWcMeSXFvr0XJ3TEzjYThYi/BZNpNstyYJ4AXXi7/5wmuICSX1XJUjb+I6rLrUsXfPugnV15ZI7SS3mySucPRs7c4tjwJPYttoDKVGvSAUN1TfY9qca4y5TDuyuteEeU0L3/KCGLUPCi1Nyk8tH+IOnQPJO1qV9RnzVfiluKEmqisFgY9S4fWZ1SoHCxF8WUry5aawpOBj69wJtZmr8kw7lRlO7dUiI3ZdcVdydo+a6xrHpZ6YQEUjW4RzMly3kwj0bNFzsQEDl8i7cOtOY/2yWyylQnLUKB97WB2mgpStjqvPyrKz/YlxwaIsAqdgRWY2UU6lRDpVvHn2dfEqXsbda+Px5AjZur/k4+y5DW3dIscVOs6aMM7m3GCDHCJOqbwhUakeCgho8RgJbpMCNUZ5vxX8Omxxjq4+ug+4GdESVjwML2aPQaWsbKW6ncl8JFO/5O9FZ8HpiXXCX0Fr1avGyaVePcIAABAASURBVCi2fmSV6vBRsm1uxhs0bhzy5cDQvE3fwqbygdg26cYgZWPCh822pSzqsBZDUpHUG0YW8mnnaZEGu4fSh0bJAgzHsBfOl22jOu3KlWP0NLXjhiJ+GeMNaLfpM62V+OaePA8+jyN7dBWk8AFxMDEOiCbpOU5IYjQzN6Vq4RpZGtJuY/hBW2UvyMGqG9ROZF+mfpu7KqLz4jPU/PWYQlsYZF3oC0ifr3xp82Y+BScSG5u8pJMS1RiDMHsaX02EU1L1b39lr56WkdPzmHFM1bQZXAMbKGqaQ9ft3Ep40Z/mzxKMp4zvEhyBY9y6OFwZh9x7+eu0ACYGtK2qeQvg1YuJGvGmXHoRIpv3aw1O9nq2n5RC42SeqN04wWdnUhkFeomL1r+9Z59zAos0yMDE9FgWA2FZ3nYME+G9K069Wb0GZ8da7rP1KlmoAzwSalsOXuDW6Gf2qbvq53OmVg3ChXLWsGPmG4yjOaiUESY0Co3T1mPKLuWulavZry95y2xprA/UrC36oBlb+JaUM8Zsn889n89HXIO65rvsVgTDuhbadU41eaBnzRHDYnCPUjUa0yLGTqsQ6MClv9Fc2Q1OzrWmjyWLEqJS1FoznpdfYUlGd0TrbD3FbEeK4Q45qmHXxK4r4xWf5ywcYixwz+ggtdQpCozLXvM7rranpKNueidDDYUz7kREe6aVEstsRGbYpSDTS6yon+nRBOn0kS5DEZajOPfwVi4dnCW+3BjOHDfdGJSNHdYgXLqREaHS4SyVjWhqNlQhWAkHYEEhrsZQ0rQRXl1D86+c2SM1XWoR1LYGxIjxHC5quZoj2Y1Nu9yDs+fqMcsYjDapi6biuedq6nZPd7pmNedwm1qtGiJdrpWl6lE5pG8RRPjRpn39iI1KYUwzxV6VvqENyQvskTbDvcOhOi51BpVVanK29DpM1gUPWvrKsMe6nFHRWgaouLtdgiQgmnl/2o2rlAx/pB9otw1tb8qEFW5DQGVpZ5cVdZJFmKUuUCGVd0agwrNGN1lu+XqY8nh3Ei0WRIKnwHqfOoht7C5X0GGe02sonuOJlQHstqvHhUHewzrLUgSJY1kegfG08cTIVgaptzTKPASwNyD2G0xDKzTvFnwD6dZmjYLMQyjdy7wDmqJvcIeDkBx0D7WdCrEyZTbt4Jx0zQmBSot7Ja1wkc8sz1Zy2Bp/gWd6bdX1kNima0y93pp61bw/vA7uQB4GXzC6EQMx84mt9Cgov7Ky7gYpRmCHubsZeQfTr+EphOGwibJOpDGW68kcpCq0HOFkXQJ2+ysygc57unFZjwd4/zeRoghckzHtXimlMgVQPRI3NPpZfqr6ZUZol7oddiDTo+8VPsEewGre5reYsOjaWldYvI6516iavA9ryoOVEaKwFpUuVDIuzXJv8zQvm45jwmUmr6aUrWYoJcfsmIwCwi39dSY1iqSleLpCbVCJKXaG11590s4YcR3Wa9Y42h+VvUu72QyNKgGTlWHQK5thI10QENy7Lw11lCu5mYYUdffsqJCz1gyadw9dqzBs2DZ7ApvZSHK0vTQOwVXPbIGezoemVK2n+KQmZauudEjWvLPoo3tE1J2yDsBoTvVlGx7Tcxm83EVboj1orVY96CRa+MrlpXs3q2iFm+T3CMbLXFdVPkhLKWJTK8vh1/CrKnZLbZoC86ZnmUxI65UQ6sLCDaElnO6EO5Zru9wLk/CqytLt5qmYXRAru7pxYw/0ZuMGDS1iO5iCBpTBWnmcSFbLXVg7dTtFJbLbErxEQeqjDofRiytK72zKYDlVxoLaPYUZZjrLTMrFrls1ER3n3Ta0b3jxLWC8I6fdWWeXGoiDmlgHY2Z1tGKVInB0Dds+NtRQ2Gpq2pAIANBg6T3v1YpjjE/6lz/iB/9LHznGjUM88I7cumaX0tHpceJ+DlRSi3jwSKDaoqhw0+SPHjlKFHXmiDyqVU3StbzIguKprID8CpqJdxkQtxB6b3ez8E5eI1UWHNNVX0pkquU9YbmJK3BthExkQTyX7ctTS2+jXcyWss5E34B3emwtOoY0KA8Sr8tzF0IKUSvdme2XeTSrrtZug7ugKvLkaOUIxP2KytbZ7GoFscKWgmxl4CJPIL2yjUncWBOuvhZzholwKsO6S1mAyTUqA7lN3GUVNN6UYXZAjGZ3ok/D0z1oZnhYLCt249KYlgfrHoQhS5mz1pjW1Jb7pjQGAUmNxRhlLLbqV0sKHHd9HQ3dQlgJS6btgJRjmsdBFQAW7QV+lB+XqeABmnMKB4dCjcFwXma6q5NoVNSScJulI6zVy+DAcJ0pVxxZiAJtt0ZUeuosDDjoB8ewYfKnc4Ui0oVdFkfXkSbo9+FWNnfN0ACrMTHdDBQbir1IEGJaxcJBN3TLh6Z4PHhPGqFTopyCOcy0FjlaAb92NhwwazfYNHdzGmF3gG3wm+MyJthkOZxQSR5FvxzyPIrzvmYAHSirPtCTXAvDOOlLEc7BIalH9oaEFpUli6HcmpXaxIptV2IL5sBk3X8jYvHYd7kCUkOLt8iX0Ca/AY2TI52/gxqFoRMlLddN0AXFa0kJZwAUkfLdpRejlYmWRLoVVHPwVoB0Lp1cFkJgWaNyKHzlCi5eKAvwuFPhNHPFSUhQR+ajahJQpns1X8CBW2ETcHW+y7TGRFSrUeVdkIxCqUGdZgEvJUm9IWNiH1XCXNtTXLqgbyXBKGlgzjyYK0Ii7dyERO/LDjs8rTLkyh2je725hx5LVGdbp0NC8sQRIS3gZowNk+bGS/sMBQBx0DagkpxGJd6oIWJH6R6zXcFQOKo1sKl0UKEGRjJvpwtrcIkgBbKp+zDse2gwM6/UItTh7OWiaBti3C7MUJ65UosBQEM7tgJjR76mJoG04YPiGa2MHDum2oJQETVa9A4ZZQ8lkr0tt2tqdnVnX9AZNWHcUPSRT/g2yqiUpfeyJtJvKckarbXGhVd36yzDok2Vkg7tS7lSnTAr1RuuFEZD+50SGN7zoqKB35+tgFqBdU2ZDMwNb9hwOuEGMXkAh29nwKgrqlzPc17nEJe2jnWMh/jSRqL2jbXQu7lFvSAPcozoPrMDkBzqEI1sU1KEOKiAup05cE1g6bCb3PDWYJXQTUHLZAIKtXWtqUizId01R4sDhhBDW9W7Z/itizXmnAPTklVpLc7NNCWjox017eLrRwUu2+QFaYBXe/SoZnZnde3spCWy2Lg78ePGrbPnRqRnH9WT7UNFb+JnSwe7NZvClhunvifAghWUjIyzzuKmq08MONvkKWTicHtxPkrBKEqW6ZMs1Sb2xL323VFCyZC1gmLo0uy+SUSAQbR+JOK99xVnSg6AK5Ot2FHdtl/rDTEgKbYEwRa206E1N7hRWqnroLmt5CFiAQ1q3gJTDLw/AZlzp54qj5tXyC0g70J0owWHXFIuLqmGGBDAbERbwGKObSKYMuUg0DIHFITQouxZ/Wy1UdilwSINMp8JqIquOELaVxvM0Axsqxy746pnxt7KFM6BfTjmA8fBY7VwyTt4v67qK+OCXGqf24s5bQ9uEyKp/m5pXV2ONmPI9i2ILapZNhOraWTkOFUjUmlCqEpVLmfjAD11GPoucdok4N2sJIJMxbpCNbAaowZ5pNoxiFRHzOuZJXRdRZQbRb98gU1AliWS7gW4WXMw1jx9XGPAdL8gaCW13rdFEqJGEag2Xj7SjLTh7gdV0VdHpbB439N01atX0CWJmBt6UrSgCSOsQSlGLTKVeMIFqnpktgEEqZxitCm2rEr0pmXewLxpHazTkEBSKsjUta6k7bHqeITGObRabVgYdf+zFZgQoxSe2CuNH6meqToM3zWbPlb7pKF4FZ45o7W3WdAi6xkoGGe6FQxiZosZ0T/7zq/mlALR9bZqFBoI9NMih0A8fW9u2cDZHawslQBxLjx9vsJ4tp24l2enFos4Fl4GDyobpTyPopZa80u/6v7UaEr0E1BM6Nkx8ERsPAvbBe+2Ra3WUUwCTrLEMWCaunld991gcfpcE4fF2Jep/rPHheNVrX2Nu8MmCFD/RGGfo5+9z9hZNO2M7GgOmYOus/VjofAxi4s67AgfgVSKnOHoViLaI9i7R9fokztKRDhgdIOIE80GtZi+Frc10Y4yb/VFhOT3SDsuHmcNFbAaUCsbyzi4LJeI3cWzuVvsem27H96c2l1cXrWONT/vU3/QF37aD+Fxm8kWfYReRpttHgIA76SK5e5DBb6HM6ltANITVamIwhUV1wWEuxJhERY5WT6ybEdeJfVE51Z8jCj9m5Fr9ygzKNZy2e6mKW0xUTBBOWRO4EdIiX2McNuWF3aILqA1prjaBmeNx7WOG25BFLHZKhlCz9l8FCgFmtgDqr01QId3uafNolHXRe05+1xQiwDasDszg9UNB5Sdhb16r0oFhN6YZ69ditPElLl+qllJllk5NvvqHQwMq2l6wBu6jbGrkeLwwggF323WsM06PfeBfsNsufWEojIZJpwap7yF0GKpb7TY7nrOvHkq1hPQEgJgsR/BQ2b2Q5wq452RlWXnfuzr0ef0ndoqCOKNkQb5hhZpwiwM1E9DQUCgKJLzLLWuC1JoAEWIGK9hmI177NR3Vmkosa3aTlpPztG56jo81+TwOXm4psSMnCXbwxLqiLeiqAk8go+thmiIGZdrpZRkioWBdqiHgNnRuFKEqnKgMB+lhzZzWEvH40olw89Wvu5mnuGlRMUUVVBKXHOBknlVTXnjQS4ntZ14QU+dH9iz5KsxCNcI8yYCsaVUG/ADGFDZFxgtWMoNh6DhPS/HmLGGGg2qfLcGUYitZGepoRxaFSemGMxuoMe0e3sEez2dWdJKb3r3GqcvrSBFndOGpVtGuZqinKARfVB6CZkBNb1BoYY+dyLy1pdOm/Io+Jk9Yct8yDvztZboQQuqQDTBEzNb5yGij6z7D3bivTkLR9fqC1LPEj0D5hqnkCTtEjuD72EUbewwKdKutWJ6286ObCZMMKp34ezayMDGftY+L9bzwXqUNjvuEIDSbnFxiFcNClU9RLUVPUlUWG7BkTkl5cqxG2Zc03xpitxznHX7o2Kn2sq++dhVZ3eB003wC3Kv+V2tuthMJdKUgQZK/7JU80oahJl2iQ9Tftik/MEKNbrXmaYm5NwDJV25nPqG7dnxuHCE2JwB3l5l06AONFbcXECvBUxIeUfbKM/GFXXtIm3MQQ7C9G/hRjb13WhGOqVYHERbV6G3t87puql68Mi7evwJBaTT6hFQhqln5xcZ5GyfPR261gJa+hMLi5Az82mcnrJSMMUPNkDqfdjs9jYJRd+808URXxPEq2KLxvsocQViEoJNN3ebsJHaK17VsWtEmbioZuuiolEqd/JEh45SIlfAC6LibEYyi1vc2BCQINV79swaKQDQq7sxJInLhkRz24O3YIsbmFSSFt7hu8dVdt4ZO2BmU0FBqTLpYheQocgejsfBx+FrtQ3tbc5r5tZIplVyleYMAAAQAElEQVQUGA65JYVv5hBPVallQZWjeIQfiO5SYLiflatttCELfrldNIi0Su/k6BafQjHojgQUvHYV4wlLBY2xcz3VMbOBMkUjsxTMTcBa3Ho9TGUfs3yme/o9CBGWWmjEykgJJhw6yhPCazrLlik0WT6gdE5dAcZojLqq9lZvaaWUpZEjOIpKF7olM9mrvmnC1RT/MoOC+k7KvkRh6dZyO597xtM6qEIUAyLANFE+a0VZshK7siqpIoiuRHWJJ7a1epPwJyGgOqeiPrsCbczbFE2hQ2m+WmUpa5uji4KjI7uhebJmnYWQUGS6qkVcX7G1VtSeKkoNDERQnbFa1XNTutnKUbsp2V7maTCrgRz3EJcRRi/R0cqQIt7C2g6JuLOtwqE0tSdkMEWToIRGipCcBWhvFStyKkEV1uHdMmCl4dKUgqMGw7bitK5OCGMO9BDVHBePxpil/duoGBFrP1lXV0pu1TBFAL0YpgakaWbE1MmKZtHbVIvSQKsPDQxpbhpomuwXLNQ8NnzT6k5UIG9UbDj6gM2kRVtidJLyAhtQqgGyid0elDvJCQPDzUyjxOUe8uuseVzu2QbFI6zZKGsKkN3ZotKH6I64oxGfIAIsOSc9fe2pY+axtiNGw6LAdNuvjcoGDSaDXjTFidd3f1CpSAcusrZ6s8i0xLjujz41nKp6VYqIGAMQUMc2+e3CKBv3yuOKpleXafIlsq0FPEttLpH6IqVXtz4XglXVdulLtAVWgt24IW5oKnzTw5jGcnCC7uUtj5fL0pIrm8BqziJoI+CK8kq9qtI6LYJB+QVc3GS1fiSe6N5tSb6OaR70R/jIo2JuQEV4tWjglgGZcW+fPRVxCZYor8ydqIIRZV2UhjELjvaWKxSg5AgsTKR4LpXJK2tNUkal53GPCIzYI6qyo3xrE6DNGDWfduqSHJMGTJo3R9MVIWEWg0nvsgpPTnHxSmdGmYc6q5NdLl63AI6EFgXT3rqSvcHTLAFDuWtt2DUrZ2IKtpmqVpVS9wZTRmTSuVuyGS0chIsCS/dLtdnWWdhKqoyRSwM6E9qMXBsosA3dhU6I802r1r/ivjzyVjsloTIqlYUE0cPWHItjwt1I03jo/d45abW2RrUlxaOH02mH4XFTq33SeSd+ZjqLZPnc83GNQ/GBsjXMh05iWGkiBlvd2cuZo8F7AlMqOjRGVF6CfYUhhIlsxRGwU7HDOigK7VJGhPG1L0VYV2Gn2M17ZdXjNH1PyumcTaqlpyOnpY32VWvZGndQih0QOW7b5mXKvMnzHSNlFKIw1YLMAerosCph6lOdFfC+Q8JG68fQlCd6QeLVLvMZeLVxgUKuCHEbDAleVfxUD6SUG468VxmbjQW3JmT/mVD9K8wdlO3kLk0z9KBJEUb4/bkkDWrdmN0rSWnBV0p8JALztFVmvB0nkbxRcJDWnYzQaiAjC3SzNgXvgpOVEU/d2V5SVWNm6wbMW2g61FjaLdo2NPSlbYb5NXAZJImGfqJ6R9XV7DRrcYbOjsy6qmvDEkz5n0znNnUdY+S4nqNOpUVkNKaJhmhlRIlVvGPTt6jqYjVCX267w5qs7O5KIgxtiMXLeY7umYMlkHewwzjSx3PeJyn2UELnJRwldBAsmfaSRQiR6hsaVJD1VDQOQW1zwEDtWIZ+adie+zn76ZR3DTcmWomLYnE4xgqa4HlgYI7l46BpJkxtZtTlUCyVlUglOspKwW5D2W6ehAxfZIrHCEVoivAO5/V4ZGOPviy7pISaiHFPUzsIbYQWTTWl8JQQEKDyclV2XsBJXolQV9aEud0vV/tNyPyoOaWF8Logn2dm7Ivd5rJRmTtcSz+8ubYtiYpQPLqrW9HG4nHW3FngTmrjklnNkmuChMO6et/PVONEBvQUO0RBYpxmqruSWgBssPTL2mbBKnxXP9H7wZye9IaSh+F81FC1NAhrnaowBZzwrCGgKTLlcrWPc/J8eN7JpW0q5sm7s4Vl1LPq3o5Om660Disx17ZL5cY0WTAITjesxkuPSesBPTsPWT7cxiIH29UTcNGibLeehbhCyhWiA3qZJDflU2lIvUrKLoO4g5NqfGVW+tiuAYQIP+jLRtasbZFQyju8lQqI6GfZKztPe4apoja6M02dvq624Euvl3zYq7HPjNBWWIaXYZQyr6ORRLrHPE5qQTLaND0Ltc6OwAXfKhLdwi6VGUN5WlVubcvF5NYUMhz9SDWGUjBdxbb2+2yWo0bM3i9nzrOohHhPYhGC2RKZ5KA0rdwHlCArjKYuoUISnlXI7Ozq6Rg3A6gMPZ88ADcnlQcJ63rOZ7pik0FOy6Ghc3ENakQFQqQkFHEbaYO1mCYEPkCXCQDEKkWgcHV9TIxRxVOTBMfc6Z4aPTrsCguwfNTBPnZSNVQaVSU/ludCjls78zZqxVu1LOL2R4jddg+fLZBqHW+sOF6YvsRfl7HFWONKO23o0Y5qWidNf2fIxcvqTIs4Zpryxy2VHQ1yoPV31QUmlIFnXyerbUze/2GGjC40h4iRnoito1FZI9u0jQ819Go9QKCmM5oVrQKjWke60jpiU9LKMlEOGqTTUhZyd27Rd39PWKf1M1hVagpPizfbnufBnKvabwwSJYRhujnAShOotjC+6XiFfvWM55s4s3KEe3mbnpzs/WifJq3ZUIe4Op8jTncpv5mJKt7E3cYU8lbrsudbI944e5wWgG6zXvneSsEAXufAbNMu5AmtaLsux1OL9kxYDCGnClYM2dWeoGB2X+SrxXgpzCftVfLaMjaolIcAjFMQRApC7nK8quP0o93NYkgaXUHirodGA/pkVLMRETnUemsDUX21MPmG3HcEdAuXodgc8xjDlsH0XYnrg9RN/LQWBFVjlaxWIUPixPIyb+FiQd6lnwP+GHnME1Zn3Pm54H5QCM0JrWtlnVly2MUW5zUZ1krxnP4yTCLuAhpNh3nOGQ/fSl0L9/CizsmKLmKCB4ZCnzW40efufe3cUbWck6Xg7Aw72/fsvDWcKqzhLob23Zsui+3ZC2yb7aNJ7x6s4TG0F3JDVHapStbQMNFJkVxCqXpIuy48S/l4RL4R+4j2S8z0mc+NV51nZCbJITqtSAMmbQ7rA21gCaFEoFuzEmATYNGbiWoQZV4uNKaYzvNV5JmdAl7TeNicfOnyExKZLA26SEyhYSBb85Neeor3snwqvJWbQhd2QCg6S1SdjK0ohCUPbi2odVwc2+pEy24oPUZZqmxmWcUa92Qoa9SKmGhIQC6RpBdWmxEw3z5SfySiC5Yz7wP6ou2v32NfWYpyEXnTe4crX9wYDh5sL1pO5XPF8nhQTVpUdK4SLkKrFNVSMANecqYWILLSCrN4wBdN4i1/yfFGcniXcjkJN7FfnGhDlsYOqIMtFL2dha6oECtOH2MZrYEU/oP2UGDCRjrARUy56nFWPKE2HA+zsdy1CNsL7LC7WOzYsVKuIdfaob6cGNHtyNml7aokBLT7NY7gaMhdIVuRGGL49rXniz18PqiywaDR1VhUs92ay1g71XCOrUqv7WUDa+RtpUfngXPZq1pvxsuFYWZusGwldvU0WwqzqwTn6uMq087Lw0yLXwvDrFrKgkKH51ITMYeN0WuSx4K/EXxJvhCisjEkg0tSqrtNdlyyFXKOyczuVrexyxhEsySPMR+Px5Q1Seg03wt4I3pgQxOrpSsvGuzs48zHdU2cQKBScjv2tmfuJ/Iclg8O4yzwAl/BowRlzv3i/ZC9PCLHFbJKl1sSMMI6PXMyX0bOAdcrnNrXiQwqCYU+73hEMeDUOlBv7HlsrX20vl6u8+XsoaCpq4SZPnq0RRf22XnKgMtsG409HWuCyKh8hdeBjGLX6NIfRgbtldsTlpv+JMVAWdNrQCMusXcVi0unEtJZvxp46hCOSzqBZqS/GZYBWg9GEk8BnYaO3lmXdaCB7mpYg3B9queufbWEdJxKvo3QGvMCiXfRjeoQ8ex4VpXUP9489Jc/C/Wf64zzsvMaca20xTFvUQHZLQSt1anhUCMp3UFocrQ3j8KA02fMl2et61rnRVWZMKUGi6M66jpCJV6HY1p71d5Iki+53hrHoZYGC/fk1HqTunY1zrzXjgueLQEV9+Yz8GaO6xx1sxrY3e0asVFoEdiR6lYyXC8TjwdfZ0a60mVOSDgHzpHZRZrM+k6xl772eEu2FFMIsVYEUNukrCf6NARm4kjw3PF8tetZU+zY6J4h1OAbD4yllCjON/rVjCtP+uUGaLFPXpZiRGHFRks0NOXPWEmEsZkVUbqm79vOZ9UlB/7GfhzXi+fQHMUJA49apo5zMUIh3c8J4yXsZfOhMdZasXq5q2gG9sjTzpjnsQSkFfIddudRVHfuTUmU9VCkjTAU1fPHYNIuKX5QewH91rSuvPp8hXi29NQX88l4psTX4ZEe6mzDzNt7W24K5nhNoKSwH7HXM+15QWQ2Q+r0I/ot+1pqkkY7Mo/zEvQaFdJFo4K7ke0aPZXbageGEgbQVMsmkd/OO9q6zDbZs9QaB2skFjgJVtxjSrefQo+r1GtGmdOHfLb4wgMUaRF9RZT1HAqgKksItCMYqmtaAsicoUZT6HLUkTVbFTtKC9mQBKantqEqYIOj2YF+baNx4yRjdg4FKl7AhNgV/WyU5ek7WASWkKkEelRby0K1Gt2WPbiZESSUlwShyxTIUP/aMjLTh7Tki4LfylQDwiAdpRqYJmWpRoSHyGlpWpMOM21BlOlNvxRdR6srjW43eRfndoGCMUgnhgXsTKUDmvWicxfy7FCGqm/CKy1KkKIzqs+s7Ds29GjDHbAQ5SiOgnq4rpG9+ialgM1ZOFhzbBtJSwekdqvqbr1F2S6PKgNEN+WQtwAllMl+YT0obgXB93jsOS5a0LoaXWCJCQFXs6PyC/nwyyrsGqmnc7o+VQo16IrTWj3nXmbPa17Ky6u8zootxzad6wF/VM0bT/LsVpNRXHVbL4y0Y/ox7ei2SPWkFHVpysCiClZE+TnGHsQSywbM9nvRX1PGWzfeR+aUr8guzQ8xyRKqAdneVlqfeQxOb9rekLRnQWKbNztdyh2N2VL5Pb0Z0moSzRKTiBKXmbTkTA5xfHMGVKpC9IfTpzpeQxxgak7aTm5WzuDrrg4Za42tlgpNpxwE0HrXN7GIDiuIFUR3aRAkzEbntB4KwTVzczwVS+tf+pUa32blVtYOGMWXU+KorXf53gpCxorXLVI2AUZ7N8dMP+CzCri2MXzFnJ2m6NFOrra1KToVA5mcUFzypqOgStBENzGqdt8k27zmXaQwWKZfp8Wr7Eh0YiJuXQ5wtvayad1DHj01aQBo8cu6UZBlynPpPUpZYNQoGkb5iFaJeEN86oCCSNyL2pSZIDLQxZ7d2ucvsR2oDXnuErPcDh3aUHLErJyR0nz0gmb8U7bdmoQ6RY+ZPgpeNboVLSro7rfMUJ3bOqkLXWCFopM+D8gQOv3cjLiv58hw3/ZILeltPgAAEABJREFUOwzOLkdRuYKxWGfvU928KrOzIVNuOLpHRbXuwC5Q8Ymrq/W73L3XKN1S9GPmUBHYqHbFFAfzZbTqy9gsDW6ypA0kdnjsUUlvNcAp3krZoTyUG92Tdg4vfarW9li0YaojuL1wOUzRj4Y+TJp3kzehbTdfAqUDLcLQ4rZvjT19pnA4l6VbGTRXPoenzxZ7YWospeAHQbstC1pJrMK5d9Wl6G8Aw1sh3Hbb0aYwxKm1mY0iCmkM4IS2B7vQ0ItD4odUakMo+QSChh4sEWfLeNAcBKzGuDTGrPoOyrqMIg1mNJsNtlJ4lZ2wy/+pcZNhEWMeGAFLlClCtqcarYKELO/APo1bkjoGhul0d1VqsMDKsSJhCtNMzifnSdsDte65aHmiS9uGBLRru9QrDA2SQoaDEGf6fpdeZvTeiMYdZZVsE0VTNhk+uJxOIyapHYVYQ3qEc/MBO8hJDK854tHbYr92Z17urTcrjtKtxOUDogRxq+XZUV5udATOV7yegv2GLa/XpdJO91zcs8Bt2R6WaVKLgkFTzE/Ho21V3hZapepWbhiKPAxn16kyiIICbkR1KivKOOgFNx9zcMq6d7kAoSq6IlX1Eo12J1cz0jqmeADlJO/GIjf3ESUyVWYRtntetl5zobNlN7iujq07KmYuxNHhebf4WSLKzMlWWd1Hm0Lp4T26WKkLJi/M7h1SzUAonGFDrFiAISvlUJK1S8ORNnpA2x6b7YCIRFVbBfxUfnK4CkGIAiiIuopCJkucpDhN6ZXUzCIkkpUyZqr6nP4cnVoxPdo3PDCrhGu2mrioc43PsNjYiVeJ3eK16Lv6ymhNCs6wQSo8BbaVD8nzVY03q5VKwxgDU9Y1vq4+Y1TOgXGkWw9C5iijZ2AHSt89KK0JeLthLsxRzguhSjA2bViv1BehboEZEGjrgoJoyKUiFU9V7EQXLe1RFHUCGEZSqvG0GWOEO6Db7rmSs8AaxjWqVm8veTIaqM207ht82xw+HxxTaIh2AxpKXHvDIacjOHJYOaifUfEStbJMVuao1+th3TEbndeLXdpyeVLEsDa4S3XlCQq0DIMWr7PH4oK5nHXpNfI6fC/6wDTTAXbeiUaZp8/QgqFgZUbrx4iGXp6m3MC6Zfza9mw/9bkkpmqNNM1sRwspFUobGBjCUTyrzww9dxMLrb53M0YDzOAk9LKk1V0Im7dMbqHTb9Na0GrEbcIynNncoU8dpeRBMr0UUCRuodaF24F5DbLQwp2nljcZGiCFOzuyqy2VON2ugJkytDMzZKuLVQqypuuXZqThIo01kEpWp/c58KRlq6dN81sIenPxOJYkmi4z0fIsIPkETq70F/Mx72J8Bq+bNajPWOjS8jy054iLdTp17G4r+jSOIm+nUBothvVRyv8woizTxVdMqle02JPgjandgjkbIlbq0XSkjTBNZumRiD0uXYqAip0Wduw+QHdr8cPOXfuCKLQ9j3wcGJOyIfNGcmZZZUgklSYXJuE4oS42XeWCmxoIeyR2nrkvjzvPHAyhxmptGrpod/i69c9qKwwh0SSM4COwAi5Pi5hsaam40XRQAqlCKqGQ+IYMX6kpnvIaenXiflGCqCy9h1U1R/Q84SnCGl33etLa3mWDvd0uuS12t67Dbwe3vVZw3RkscRFyX60uMaK8YjB8mA83h+a7nVaXK3LqnxAMC5/SaGA3o+RYz9EuoBQCqXzOMJUcvZWlQZiCpFWnV4/sFTVFnwC5wqfc8Bh00WtSlMAFOoxtNM8pCOOAWs7o1nWr4o32MWt52SzKqUb14et1x+9ClcncnK29m+7+pkg4ZcptWtN1tWAYhna7Bi6LkpmH1zSggbqJ0rbBRPfV7NJIOPSJ3Asx5VHf0ymYndm7GFprF1xjc7duG+6Hm+XMWei0tFGTvYAja21V4yC152+TmIt+0c62Te9jQIdZNwKAIkSNLjRK7xbmOTwo7Vld+uSP6pfUH9pDTzuNWzqVdUYyAyP8gDKhPuEeMeOaxjmptMqVOVncJyKu8lD0PWgGLW/A3tx5S0enjqQLjBLTUE/pbvW7R9UL+0Fw8TqgxwmgkW49oTwrkV3suBtI012Gh/Kn6VmRndKt1Fzw6qNj1V7Zo8xBSAfD6IOt+W07NKbbs10r6t6pQhAUMzdXKAt8kEelG9zv7jFC++GBVRwbmsNdLFm7k3+rRwkBUrFbpTZj4oy618csJd72MBztM4iGaKDpV7GFRfFNH4dR3IkOlK4rVHIMM4G+5aJJVbUl1Fy9D1yDpUujkaWtkOUgp0HG29Jm4zBMfQPaTAarU80A7U2E94ZVeAV356YWoUshPZ70NxsXo2hzLF9vxFzbqcG0auVKUKxcHkZox6tPZobXGGWSr0wF7cmZmFui5KBpmYZkWK9WXIeVrVYfSxxXv4at2DX/9UDKEvaRWq45wkbqKJTkmY2gJxRDX43TLIZMZTfGPo58Y/ZbYDNxRwfU6BgSREmtiLKEtgODD+OSCZcJXtnSZijpaLFkVY5q0WZAi+cKcRISTs3eA6GTOnSlM5CGMnQ7r4HTq6C5Osqo2OBSztE3lqJf5Dn6mhEqxAyor90RXd1F6zXsKB9qIxdltjcrcL8ahJCxzCunCvIMf6Kfo4QGmnrZDSqRGRap4tcaUV7XrWWPWq2SAwYxRFKidQQs6emPdgnb6MVxzn4eqUYVjdhe5+Sew1wXxV533jBAXc1IRDCbpR7ClO7YTdJWraMfmgINNkz0YsvoiMtKoZV7mKVEJHu7rquvCyckOGETUvUdaWL2lDyOeZdIowfqUbbuPI5UkaW5rug5kfRkENJAiEPl/CXc9AArB9CGBFllhoeKuNcRa4W7TsLHNR8xlkTAUVC4qMtRohmtuSxzHBM+1VaqHWJoYHq5F1hRHdF1RdVZ2Omd6JuTDU02tpq3iQ7m1TutKrOv5E4pxWS/AHHnKgi4pBO71a40M4aFmVxOG2OZi7uh4WeXdjTBZoEbfpkwG3H3XdvaBIyzXjofVmOwzbtcH5Fin0ZpKtbsFxeny+C7UeyiYB2xjwjF3T7S1qXQShzIj/KqUTPbkNVy75iDSsosRaRtl4AK/jXEh5DUEo6hNSlqRD5OHE937e1a24cOGLZzkzkypsptlwzflPpwJcFmNxsiKRQtMFb6KM2woc9YU7KSI89SokyqH0FCQ1lU73jZ18wN8d/e2oTVIQm9Jx2jcxjbZF9V1ioBLZAK/bJ1xlEq2bIxhjqu+wDlRDcHOe4JfrUVRbizhqVnm5hKk3ykqAV4a4Zh9rCLDMGBOJVoR6VFoayMWVX9bNvjEY8XxzwCRylz5F0lCyVFM5T0CRzgJBT6dCiTZsvHQwxxNkbq3SYLqJQUrrs4He44vFTv7qkrOsgSXNptK2yeHGej1Ep4ldJ+94BleHHpa00eM22lCu/Vgo7ThJ2QLYrOe1KOgeF0KO77X6nX6ZYwq1uHMpKKs70o0RNsoNNRpv6pbF8yw9S1ljDvP8QIyhyY3jJTYA3HfTpDvevSP3GOqZykORQsUVIgndKa1VDP2YlrVBxRcydPKCqVllCdc5Yb4CDLMsfu+aTa6YwxdF11WlI9qSS7BRiKMLfdKNq1eiPskf5omCKTX1icRMzuQSttIGrgVpNzwiYUqRoMKOVKMGP0YZjpHlI7Z9Wxy1TV3Hvu8NqQJhwxbgDGYQKq0ooQtEDdT9R7GyhfJgQE0drWmIgVddTQjO6tc7raBabprWyBx0g3Qn5bYtOoDmct0m90wGtlyR27WBul4hn00XSlplODqinLZ/uJoZhzXFgnVtUNUwMBLwPv2M5UIRdmD7cX0XVxXnD5rU6Z13Lb0tPELqCrsXGf10fo1TdGto3UFBNEKDPcYEDhstt00SCqgtWkTEwL7ZlNn1tfjZjF4fqlHbUlRRwu5bWulhIcKTslrAlJgNrMWKiwtYgpAJVZabFIFWXUVdDTMF2shizTzYepMMYdRF/yzB4idhA+L3qD9GlUZjUjy7rZlmoXcO/RxWyJRl1GqYxbWhqKY0+vB/u4Y0MaWj9GTvrBsVTb02xOu7uGJ1kKjP1G13Q6NUwLFIRgcwRG35EPg4ZReHShve9lS6dw+zjSNM6gKzD9smwGlfBOpVlKe+2hhwZPeznkVq3XFOVtESRvbd0pU2PstQSt2Ge1nA1CVZP2fJUiz9Yej1brHtb3IiUaNLJzHde619Uw8cRKbwmC1G87ZHC6vyHKLslidqpmBDolVMICLEXf9OGmyhfiiERhDi65MCuCo9eoIbs7LNKjZvC+gJL8OkPZZyNTBxMos3YVuoqQF+xyu++s/O4zmdZ32txT/VvvNFr16jTVAgNWuK2JDbvANJi3Kmk0y2WmLl4pFWXI99DcaZiESAegtOiLL6h00dzA9R5ECoHuRburlMCw8hE9bptBtubfTpvdDi0UPLLnfp0hmWXnPXl2eacGD3XJaFd+ebmn05xz2TT3HqakuTxAg4rONivy/0fD36g5rus4wyhAUnL1eu//Us/ussSfA/c8X5Y7ldgSCYIgJTt7pr7Co4zLVaZrJykH8JiAApmu0YWW1CCRxPRm+PxBScOBXbO/uu7EvQqb3vgacV6o+OLOF6kSUjQUVRKYD0XDIGvKb99W0z6kEuPDP+7P/rILm4bSLXwehfWtLlOgDA0WetEexLrwk8pl1dSshpI/SqP45r9U4wsR2iqdLgHS+Dhtffnd9GoEaW3M8LQ1FKQ1YgJYrPA2Qpj95gNu07I3bCDvp2FuWoyucxRM0g7tnfmf5fEeBNMwU0QDKLcOtjcLfn0dWGIKvB6vxTGdBxJx6Xd1IdXBzghYJP1NO9fEpVntdV0bK9ma8ZYGdbrarRrTnBwTRIWpTdCWINV0lyiOe56W4eE8kGYHWqCUr0r0gWpqeGPOw/ugf8p0D6DIxrJnkJqlPyhemNrABVpf+nhdhx478Bc8e8TgQ4iqAIGBwpgZ8hrFhQlvlbduiAp3+tT8JqoME8Kf4zerdV+F831lNIxNlCpdZo2mMKuiO2pMPKWTppQhSnF71qpSK7CQS4j//j914Lza+HFmOSM2l6VJre2wpcwk5ypEOdMX2MCAmc4+F1fundJcjx7cnV97jym7Wu9OsTVKmsMv+dJ+YS/WtU9EbXa9KxKOVh35An3Hir24XWuh6ncchVVJ/I4fLITRZxXtKL3hqtwcDcnFXnCDRdi2sA9NpzKxm0Ez1z+6WIfTFfK7tjQV7g8ERpojpqAlPJqia6A3umzpTavdr68DdHW/wxey6wnKXGkf+gP1RuV6eqrrOJRkrH9IaD/AT8/uYiq75XG17HO9e52l0AGte6q7tluuuXM+K4jSronLkUonKgaRm+9SgGO4y87ydktJBn4Y4waH/moD6+yhOAmO5lnJGb/7ilQhrPn74H323QsG48iFmTSs5sYFhBNA53TmjFpaA0mmdohyGMiQTG1MowpfMymxpiz2hkf/MYkz8+gAABAASURBVD1wEstTqoFxzFoROyYszcpsnLF6rft8VRAWZm4mwtMw5KLORCmxnsMzsIufPDFijkUNNB/lBQrV4xC/KNV/q2SQmAsUR+gNYxaFpTP3Yl5H7/l5ZjvYkE8qsyNj7YI/aEUhlz4jE9kDnZV9LzXhY3Jjp8wOOUyj6FbuORX/BDa5cV0023DGVAzKYDQeMw+FZFAVvz05BgTZptuKOMFvd/Nn9eMTLsgOiFajQWhuFQRGnxd6wX0eYA9k/S/m9khG6j+Prpi1KuCdqiTTXVVSpYlNClbHRKicFGk3FNcpKc7aWEoJxTrMCcnhKz8TBn1v51nMdrQcpFl6QTukp+k3+gRUEhhwxtgCH/6V3XNFEtAoYxqK6MEX93d+W1plvdDRrQCqtSTNAd/F89W7bX7V6Fa25MjWaNlUiaUKDTQh6eWpO8HtGugmceEbrK1q5Lu/2tU+fBSDcEYtaSVmtEfwostItbCRgwCkPEEr5TeVSBIiyWzSvBkSFKLYyeq+uJlFzaf5ivVDZXbMxXQpuKrWk9jBBZViqe4J+2OUixqkFAFICxLWqK6q6+ZNn2XYJhEW9qWuHI0xuUv8djUvJCd2AUkbFQXdAaoBi1AQ8kvRrGthn6t/CuCMsRXN59jmLuSigMKoMa53oGeEQS7q8pw4iGPy75iH2O1iSmNO5y/6lwaKFpU+V2K1U+StmW0Iw9ewRLsy2QI61rACi3I5sQqRrahzorEwgHTAk2t+H+GKMkKl+w7GRsrz/a3K2hqks5YVFZJbiGpVnQJC13Qa71cq4Xexd0uSpeFkzR5uwAzpKmRVCLXOSIGpjKxFX4S51xN3Mxc443XNygOq0RDAmSwIMXs0GFa59KkRo5njZg22WHe0K7HoKnvn+YUnRUBBNmEKp6HaVQ2HrbX+2ApVH5c7lynWRCmNN3neH+ZP9OYEKaqHGBLlHxMYsO07HYxnTNFw4FDf/stVNdOwAkqQJTIdwlMyPc3mT/tuk2Swio8ceHVUfQgxwhgchaEPcrt+sna/wcM/LYISfntqLsXyMx6BhgKuVNtBD6z54/YjCfiU10iWkVe7fx+L2ct+nCJt7pSacoR+7a+VFPOTP2+zVBurVxTyE6FnEkLFBND/aNqr9EBO+2Zjw3uU0U92lPO6ln/tXq2nT5creqOqZb6iOawziTlWupHsGTFplq1GBppyU5yKrsU0H1A5uuTtLzpWLcDcbce4AexCexWyFT8LQiANjHdT5+KKULcOUK8alZI6B9QvRsaP89IG4sGDzuepn+d74Fe9c5xyNL7a1r88aBew7zCprB6bpNyHpNBKEGRlk/Ax1/5Qi1lVQ2OiQ4CJ0iy8CseFpdnooqj9I5RAt6CWztKokM0r5lusR8PG3o5XCeyWfbMZRfiMsm2D4SRE6tR19ascFqizDUo/EFBdQLtXlHtaiDtZwXNz66l0GtW3l/MZi1zekmPv81cJ6rkprZllKW7lHSrcWNoluBRlBtVT4RbRQ4MrMrbCHkAOhsAM8mSf051KAjQCvCPVquXsLJeQJKnS7NE1+yCLaz1x+XP7p9Orp2zKR94EHMoYjnm6HXhp9/+amtXs98Z7R/3/rDlhDRlnTn9szAMZH/ukVLVZwSFtSq/E9zX3HcWQI7Z6+rfPV52652knRseajlY8Muyj2HwUyiy0OaxN8U4rVvaYdcumwHGtVnrbkWOXop8n+G7UurTX7FBZsy5NN+sRI2vwzUFhZa6bTBGL0YDxxpPjJU6RymnK+XMNgwROuVwtWwt7+s/Jp4+jS0CasjG3WSNDTU9pMSZR2rb0VULp38orAr7/KybWXTWL4tow08K1ZsRAMl7oNv2riXdGw2VvyYMs9ZTGKIceUJ5apIkRyGdVC98ig3uF2WO2jO76N7YLzmqtEBYSrijPmax78naKwLWVZhBsOeiZwQut3X4pkqx1mnntCgCUN11Zv6PDbl+5jnD6ApS3RTGzfrB/jvkvSmbakFSAYh1Kh08MTHIkjylN+3dC0lMiKIxUVYcbP5+Xwqmw2tHL8Oi8wjOQ5dErlCyNWI1QWait9lHStJZMKRlYpp61GYHlksfCNe/RP5ca4rcpBpThWOPLSGPTGw0UR7LtEIjtQ1dmKAe2igvy/FtxS8nqYh/RMrdQxSE1VgVslUwQpslKN6SwNqfMoY3FMUhD15k+45Cv2p7m93N9IXRSMfldgwmSPmo1TK0MTuEYldxerbbg8tg2JizWcHR4jZ3U87VMv4zCWPW6iJy4crXRa2rPO0IoIgdUwKqnbh4s6cTdZQmGGaBp5ggT5pwvjjEfuVYxCJdKexH7H4CeL4Wkm+/WbtTELhpCFle+Ztqaauu6RJuGFrzvf+wyk4I3+yDUyw6nifn+o7cx2Ivlo3ZxBy9lsL1ang4oDxKBpJOrr48GyIEB+ggNKXSDpT8yXORc0fZpFNNtaIo8rNJwoAy2fOZPjuV0ZasmNAQgBEJjulV1xQfcbL2uIY3K3ATSXZkbfeuSQUHAsAsqEtyZX37GUIocXhiwLFK80nZLX8Fpq2KWyDIDdd0OVpMpfaHng2FY0Yzz2q+6TZnVx0qNjRkVSzqvt2IbH/x46SfrHQph1I3EA4bZnrnqteqqwJiTIhEfEt/6bylbYmRWciesgMTohcI66vGaACj8Lb5YE8zR8lIHfdY6j6oxfNxTmmHBEt5DYmLdtUsYDRam7yilwgyuPRG3KJ4RxOsr4+OvHFpF8PBf5cKtHWJC8RoUx7Q35MzAr3hMi4wGyALOV8qb/gBxev5a1256dc78HbzWMHlTDF8p+03rE36WDc3Nnnb91rtHVBjJm/2rm5JaM8sM3GOPhzBNHv2uL8Q+nx2bXWCPXh3JhYw48WRQVSdGgCXt4U7/WwTeua0VaqXvMisR2c0X9Tff3/cKK/yvb+1lZ4CrQ4gFb4iN2Y7F2afW9wxp1B0xuSlpS1OFla2FAzXQv0GllarHTUz6WsIznFah7X8wlUcE01w3h60EH/uTtluDWzvFM3fOzFvX7vvT7zPvDK/I7/Dr1AfyZXULwDwDg6wf8tegSTfnKgvYh85xttbRL3s2y8HwjH3NRmYohuHSUqJB/Ydmggf6+w2lKRkY82GMSq+/VHegF0TKDMS/ruaN/PX8/YaQKhdB+pO17lB+/gVvitaZD/yPr3isH6/pe7t+36lEtfvAT9V9M09DPMpJeE91Zb5VOnmIxBzXst79feE4xAYO/HfxPPfa3ysTNvmQD/cud4jYP3/zjxRHueI78Xf46+9db3Rqm4wG1uHzN37+F9LAvN//reUrtUEPD+clz6AULwF4038dv8b+5tUP+kGRM4YOWRLExpSGQmV9/PfvvKd6hpBYF6T6XsPI71SDLLfcU1LHpOVar8Z8671WihkU34zfD98E3lWp/FTbDHWowc69vCc+hzGunQBoDQ/4SsNxKSrTrG1pfKXSvdQQ3Sb6Whf/r9Fo+ivLM2JUW9/gj+6HWMNKaf4nVefyo6qIURTI6azsKwbiUHp6eimvUJ5HNdZic5/1pNIcrJ2v9JbFc7LfY9RHXCVOlNgsH9OjYcxtaBlKYLyCsxPPdW8obgm9YzjNGZjcuEorYb/gN97wtmV66bqm0z+M1hM5cUvaVWomc37PeftvSWeO6/iE9ttLgihqQ3CtXuUWqi3MHgRhyhnQ8nEwKvLF6ypNba509B1LQtQ0KQCntStdq6l1efl9eKROCAwjOn7Kn46Y3QyEc/UOLdcTxmGwnFXWubL12cyxEqbC/71zlKjx+YZIUaNkejTV7v1+hRmh+5m7PiAix0xzbUPdJhXJzaj5fzb/ef34b+vYVzjpYViK6cM/9SVtNJCW7achJJxenX9QMSBAq2V3u5KBGs1IXyd+0jdyzd/Ge+amVz2/4tD8BHOBrqoox6FyHrzmaSL1+reC4YV6ph2ajI5nIu9MijSnSlxtq8CZn+ZTFfcYDiwF4UEqp6qYnA3bM6sS/aFClJYJi5mOunsOpfJAUhGs3XsZRirZo5rVUWqO883NZxjlNq6sR3SgBMbqerP7p7Bo1GoVUmC9y373z7vXfdDr1hTGFK3P2PoL/9/w9T5rhDjFm/XZJ39GoIVERw/aymz0WOI/N63fW30dr7moGinWKxFe20tZsnlW7TgyIx3+7VER1S2iVSRuxmE38MtW9QwI8w5P94Ndswb672UK0jx8/kQ81htZzDThNnXKn4Zq2CrEJveeiEv/Lfzm+evZShlciouM55LFuTrGX/95Y521AF+jC660efHpZN35beGaMaS2iTHQMH2eOtMFvQg0XbkZQimLhynRiE0Fd3rU6+6jb5DDoD3o3X9dphVjgelpdpcSkvhM9hnPMTbWdxVDjtJNdmnP8HvmVk0f+B1v8o6+ltYyE2PY4GoYzdaa9XD9uOtCvf77u39/f8BHt1X078YODFqQCmcAuVQUtBTT5d7tljBFBkHv7kFJc/CpdeZ5+fO7ohzR13/P+q1dsPPgbow9DSa09msJn6bOIJmyOGt1+BH8TBHbX7E/VG0Z+A249aeOfvJ54i7rzdlje7DnYpSw0SuHZYO4Y28jryxT7ceogYyz7Abw1eVi/Zh4fTn9O/F3r3yCizEKktWefGSHpDnc8Zmc36yswecLBSRQY32jBCAhEqTt11n4Cru7RfxQAcxmxWpyu2IcmIZeq7en5P8meMInHqcHND7cnGyoE2hT5NN/fPYqPr/3+d+138xrL/zX8DtVl8hNrL3m2b1+aKYpy+QSZi1sr/XfVcc+k1MWx9nEOPQaDEdASwignNN0i2WNfjy3KYfZOEcrTx/eWsb4MW04P8ZH3e/S7vB3+i/PXw1Au4Jjn6gUEQUTAzo/J2+dv3r0LPpHJPLgyVneD/Mhto9wiZBWhmvRoAP/BDX/i9DOIO+uC9lz71A66+lSXxxbIq+KPG7tMxDv3dKsQvy1uTCpatdEgm+M2tOI2RdQbG2mLmhqMdGm+9f9PiZs6JKObjh8LVhzKqr8uDrtvLCLZfFYmIPP+OKz4MCI93GKpFaLs1z7nJ3/4/17/mYJG2LU7P771ijo14xQuHOPd0fDDIKezDQpLFbtR6EZscg/OqJ+1vE4GVrYMjblVs2h/FBllfMcpeVW3Is6nfMbCisAUq2nKbB0M5g3qeiyMTM9VVPdJZcCLnz9YycsCQ6DRuqTxg8AtqRpTgWve8XYa4sA9ujaZwQ9bGtjEYo1nJyZLPUgH1pBFH4dUiUkU4qVQtYZFC7+GzuoRhW6qVwd9SquqVCn6JzKyWx1t+4eIOBbzIMiUaPVLzutj3malVEZ7rl2X0fru2uZSh8QD2ejRTm70YTAQmGOGQerodZMllDNMtj8I6cMVOjDKsztyIrWOTNxAvKg1d9az0KoGcrXmAOit6uzW04ATgfKuo143IxmY0APAbDa8LqljLaxMY2c0S5BedHUxXEv4VaZTkDlcjXnl/0LvtQL0o7IvwjEAAAQAElEQVSOQiW+jrQkWZmR95I6CgOoSO9YaTDluKjUwBpO+9nxLKslxZy/CoYvZpqWoAgXsWL+n0rwTh3oPbsOJcQexVnOQzuJ7mnwwlNmu0Mzp9j3y/mUCtOLsFHATIfa38x45zOpQvZBjwQiTso+mO58SNfowNicxpUqLjsNRMU/lfrq5dgYOZv+vchBARh2oUqB5wJ3oVOXZuiNlXrnpR9TdcxA/bE1xUZMFydJGMEc5vcNDTaVOxQ5sM89pxY6FJAVTJRDk6nQ2H373LpkP2EhYFDfsaTJndR5qebBYpiFSypmCg6CJX5KZPknF6OxgrUS69BHo2XP5KBHngBy8N2ZVL3FujYaIgRKLFpwfMRTT0LYp8nKuLDr1mLVi7g6RurI7rTUVTGnDV6myZlJvTqGIpH39lyNkjvKS88aiAJhAYzNzwMUjyddQKBmVutypcsSNVhVMY1SlOQsagVus+E/FjXZhHc+4wVZ0/m0SQ0XkqsCF4P4Ih5iPHzt/U2sVl2MjxmXFAX58B4XbQElBC/8qsQTaqs+HPOSTEyAwxnBFaCY6DVNncRyjYkylpz1TBdPeU3UeDWk/wZHniQJUJWUEoiao8CESYr6moRepkH6Q9ko9NVBysaYmpzAVNzS/mKCs/2joFq33EqIAMRi+Td3yvApAXqf79SiLcB92sXwXH1rnaAGNOemKZGFk965ecLflnYhCPA2fKhXlTUUo2GbSJNevCm+Samr1c80IhLfPDFgU9HXR+Z0J5TLhuPdIit9p5BH2FinyXelZKE5qgiTqoblLMGAuFT9YAxcVIkyHJ8zMWv/WBAO7KdsQ2XxqYcou9f/Hl1vosWuR7RHSzDZdhOpOEJgnLaitEbCC8oXviiFyd/Yx/5f2ePLwjpmvrIApOCj22JOwjC62xCYbn0dXSIQ5m762VNQZaaHHGGG8etghRjbUoz9YMzgYZpi01bdTUVELbp55EwTXdfnu4S+xSuR2cPYjeeefe/KYYk1bZDQwVhY6l/GUsQy0LIMWhtaDpw0YTAHSAxmhM5mL/Ibp5TK2z5rlzrhPMQX8rcWKZyMnR6kr9J8CWZ1+ddDyVJKH465Jghqfm09hUvMsOULlVZqF8eemysvzty2IjOqHzBoO3xbNO3ceS9uNaTM0KT+oKPoaZCIFJBw610Hvhh4VBencGoKLPMc7SCh/WKvTGsoSvCMfrR5/uqSmWgaC4HVUuH3zmG/JuiCsfAFEYaw+TpyNnVLJSyO2byPX1vI4F2G1QjqGHeaz1ptLgSNxVgRszzdtLAKJTiUrm3a+pZEgwFdIBLRklTBr4kNlcSw4WVraKobxxF2jELEntFZeYnj6+31xa25ptM9ptFD6VkSmk7ohGpTmGkwwMepe6X0eX2OZa2rCTbmqgesEBoHTSwPBXbM6BG+whHQvuE2euBLmT6GqzAvrIaTNd/lUUAwQq+aKhwfFQt11hvL8ScmFiX5QV/tgepmCX+OJ39o4pFNH9JIR1E5QQ27Da/Nr05ZwFBxFLd5it5YX+P8wGoYW8sbZSYC9Yf947bMFTNYjQtPrJkfb+gXSkUbilWcJntaFd+CgNhtgmBiHrZqdIODPK1o7vA2a0Q1R0ygylO31UVdrewutXVWV09ZT2RHHpv+Pk7UJ3SICmlTcjhTgQktD9p7M684VBAwo/Kk94dYSPQ71Gj5iylcSom/EBl5TSlxb6PEpAPyKBxWtU7aEdL1TmS5JdeB45Ap4980lZuPTinW732BEtuh/1p9NTfJEVjQ58MyE/o2Hz/FGute3S7KxPFiLZ9F365HI671pxUSliSg6viSsVCGIw4mF2CO+ZD69Iwkcls6mN/xC3KUnHrs2kY+diKOES1t6HKtSddaItyGET+mCc1MCHJfmxuyazehznEbnW2SarOFfn5qBfQYQo2WznlMY0Vt18G93udzYw0xjV+T6LLz+hQxLbmByyOcoiGkoR7RQiqW2LGcju7WSBe9vTHRijPKvcxaTfD2i7xLq1NSHnqsGCnads++40d+AyMSv/QEsGirn7ibfx8KhyrlHJzjlXJzkEoDRZPJHuVfw6niLeyboXvRfVe0NVF6yeMuPkXX5EKPsNZ8f125kwn/EjTKtcOI71/B3nkuHwRpaZjw0NrHJ3tn7nxlrIOyKYYGYLWP4iaP41+zWfP1KXzJ9rGhjfIXTbj1Dvsv4j/GFgzgDBLlKMkJwFtxOm50hjQhq+wIhccJr3lwfvw1f0dx8Jt2hcM0pGDZIz0lMJqjmYnmvKwz83kZ/Skw2cpfw7NVB9YKufkMn+pnoIBg0NavJmvUggQJqo/KugeFb4QBnotn8V1LE6l8lnzKMqfkXgWi6/+X0SB9ZHXaLuryTWUuodx7dy3L9XP555hWVapQl/I1n8F09tP4A+6q3fBC/8tYkw2SFevdUT9ULzO8yOyLIw0ssWdX0RZ/SpGJGODyjJJt8zge488ssb86vVIeWUo9uXCdL5YOLVtcNXitfp+qp9TnhgBMJkIjJYHNK7k7nPIyu8NGTao/HMSm0hYUIXFGD6qRmNZ8aMjuhgXiD3wV+ZouYgF/WAuHi/PzzPOHe2OxIisTNdHzLbd+l9obTrNymOC1f4T4NnvarFfBk1P5+14VMmbgo6yMLqF+lDDQqDMNOBSCugeY3mkKWNmfoDrMYHfuPOrhk3m7CikT5jpfu5r679+RbQd2lZMG1UVvDNZ40FU/7Q3NPI03/YgkPKCWcMqF03cs2w6nRAkzOZelQVubV3j/CTWC3MpsNeGuwcrdl44YN4RGLqkIvWXscaNENXPaXqr9e5dVq/xIilndN+lQcE4YPkbYepdhZXamu3ilo7cV7Z14+VysA5bkg4H2Dh1SivgEKLQGj7DaqgRopU+HJP+NamZ55iCv4+9ax1YirG2RgTJ0+fQuGU58gSkQc85MCcTXm8zS+jWOFm9b7hvONNTAuveUdAfjYb9162hsG6QFPwqmCD2e+V1favuB0kW/XxEYHdDMhy1qoSysFM7WVb/0VNn2QkbkeCsOqEb2HgvTBDb1R0eMCkwmFIuE8wutN/8btR76SKKgCi3S3QF3TVkjm6VCoK3rnl+QSTSkdw4uVWUPjo41WiRatfE0TNxhlSyQ/rFVFD3EFEYpC3SYrFx9zrGTvJf8HWjFnG34s7BC1uPNJ8s0alo7gpppwNqf65bi+aurpkKl+Wpfl/3ee07JsBTYsKppMx1rxJAA7G84CHFKH5Ky2Dtr/9ZKaQ3Tsd61/1Jfx3I0yMQDmiZXTamsm6OZAYjnnCO3TS9RQvikTY8RhkF3Tn9PV7dsSAyIbUt6aJeMEpZyLyK3zbN6lu5+eISsxR/C6csaikHO9CXuY++fyj/D7TTHw/nPzla91Nw7nd9DJe0nVTTTZXUX0qKvIUfARoAhRfe3NwvGQpjCZV/MAUV0gI+cMqJEmjLwoJ+8cV87Emv23BZiCvy3l2mC/YVYPbAZx3BKx6CgwwU+x1KjIMo//wpqEFfgGgBlgEqvj+BRBrri++jkHmoTgLvt0Ji63d2Lj7mDAMzUZbcpcddLZNO2a4VvrWcKyc6wNKpQ7a+pfgNj3iaNuNyxSpkZ2phBoKVxxaqIi10jPsyuhjPkiCLsW6NlTqjnxXMRZty7fedMzyXaTOvBs4UL+tJa+9vqY2Rmbs07TJXQsrBlkDtKyIR5uNGRZI5xoC2CDugTSEOsdCo5LQ9u7XFFH2Ey7f2xrfcvFswAVCqQrcgAlUxtK/cGwWyeRmey8kE6SyOYYsehBKuAG9NCS0zq9Q3Gov3nW+usD0mP8dC5i74zv+2/rS7DQD8oFwXsRRljAy/xQrmUQEf0Vq9Uuq2+ZSCQQPW081qkrbLPPmCoRbUZ0Ei0/5+sasCCqyy0r/W/9N9RgIoAPlanq9pGfNbOVkDVPQgF9dD1upu/hi5acfUXY/dVa5AgHQ4uLdXdnLROZIogXdZHEOP/VtPbdtO+Jb3bkGNllDsZLGS5lJcr9ewiA8f0Y4zuMXuqd4uK2czlelcasUJLBFQbulHSrFZUeFvDhyyXg1I6ctRe9yGGHXwf7wiKAOXtNf9V2XBElMf6A9sFXQRHr25AOvL44XooQQE6c50ZupQlUTdaKpIkL0jDE1gbXCMDrcCEpC/umby3PwpAmrmb6IVla0PNFxooYurHpkdGzOUqYBiRrMNmXGQf5xiN7lzhDB9ApFDJoRmxE2rUW3mRboRKoSsr9UHGWhMBypIknbBSdEDLgqkx2Knvizc9VQRuQy9jm4jKxbO6TOob0QREYZezFjtsNsYU2VEsDk1cyaDbfyZ3WD2rWj+DO0jBjSHo7ZgvT9PkgITF13QC+BdS6dyYw3VBVGmGoi4RRke2Ii4pSxc1ye7zr3Qr2GtSMQAiXlw8gF/YlfXFtS10wqZtgNN+Zx/IfeWq++SrXZu1SOnVHZX/eCE/UoWDgllag8PPWlc3J750iboi6sjg7MEaJxQlKupKbdZ0g/noz8RPz7ewLNAGA710udU9hMEgB+pHkaB2iNcBzq75c2urTJoYMsev+3HXoJQLHXCGrjSYxVv2O9SQ/rNt65c8rW/a474pZ+YeIbhpdeXEe23G84PnD3w1eW3SVo07XEEQn8egr1wrd+RPzFrkJjlWrZRVe09ISMar/AMD3TFtNdHeMlN7iFxnQiWnJCS90fy4aBv4jE85CtKg4VoDaSqlTL2V+WzHMpg5LWD8d7RbAXfEGjx8h4U7jblU5GqceMs5H34BRFJnlYZAjY7okUFXZOkoxcGivANtCnpmV2zu5a76oebKo5uMy5RaaqMlCQzxRtN+Y45bqj6OYsFHQM0F00m34D9B6OowwG+9v2JqJDp40aiiSSCzpseGZsZxdDhsDygyyjGPlKEBWZW6iu/qU4sZDSk+Sn+1GCgdy8bXUAJUKbaju1iQSYPcO4KzwDVG+As7IeBXUmmcKj3mmDu6D+G/AAADvYQzyE1oFSIBa8rkVbcj02LMFd379N3UYqhRYXqDqw+NEszly2ON5oHQ8VFvumi3XN4BNy5To6Xf4CEnLV4sIemquC0SRiH1eB1Wsgulummq+DW4oRPd0r2sYnoMndA5tqp6lDRraSHm33vQg9FihtMYeZtPDqf31VMDXaMr5JWKmrLnvMF3rMtQoZSKPZjcN6RpmEGqkShyFM1tBeR+4bCnG3nuKT1oHo2TWydoSg+dVJll+inTFmoA07+MUakU2d2uVsRxMf7Yocr/Yf+w3drAGVpu167z7tZ7OsYNSkVpAnuyI2fJSXi6QrEUSAgB1zKnWYPARA8TIRV2B2tpp7UKKzm9RpJj0CGOigI0a2GbrKYYvWK59eEi8opY0lrm0vv2lYR8pABbLWfibLvyq5ljis22tnzqTGPRFFyj6MrRew7aSncHGVVWdyop4hRXgt0kS41i1LP2bVWmoKFL+NB6Kw7YhP64jCCN2dBdn3R6G6nrxW+K8ZWw+QAAEABJREFUolM2V9WMJ1BsIJknbq6WEx8GFajYeC6lpLqc2VGLwiNR/hOh6GPTS1x+3XTCLCBtsPQE4Kb4GRkJK/NuES1+KF915isc3OPT7gzT8yTo1sH97fik0kNyzEciusRBFXL8O+suLlAajb4X2q2NCDO/rhwGRLZSo2lc7bglX+faHS/HLIh94MOM8GWmFoD2GRcRujXV1nnEUqPmi1mfh2oiCHjYlmOizXLFXR9rt6G76XOVEANjws0txijPFZPRkiIDUePZahfD6dCjzUNXniCU3zUOZhsXYIAWgNT8GDWw0HcOKatuxEBJ0NHARN0fHl9ot3aVXMEGzqWFXYbY/L28JZNtKxEX0DCI38kElBZzKG2g6JADuVHDAAy2dSjtS3lONRLqpQndLPdUazJx1VckjjxSkVDQhK3gig7qXk1w9E0p/MpiRj6SErdqUmZOq39DQTcmzQt7ZqtT1b2sl14yCIWT/bWOzKPsK6ZNEzrxIElBLmRVljttHTzF7e3bFK34TYm1CHXRI3/do/0NrqIBQECRU6yMKz8y+bPy3+8it1JlMhpBb6KsRyS4xnvW6humvc48bivMGW1PzQ/abIp9BkXNTz+51CqvKeNXXKeqmAaWWZoL8R576Epwt1e7fMGnDa/1tSuvyRisnm5qBexluk2m6b9vqEBVd1Hqyl4tabavcbPote+yMqWlvMUve6ydMI43l7Sh/frPN4IRdOX4X0lezF/gwPtLhHKquW+VultyQw5UmekJTE8wDUOld2zaNSVslDzEAPgHeVpQhIezun80/7tSZVSP/g5nchUXLIykrJZeQw59Zu5YE84Zq+q8+arTzCcqjQbjAwmpwUx2oAairZIajXxzEOWRxu6e/M3vf4sy59up7bXCHYOuaT19UI9ZH6TSDscUY7dS51WUeRdlknm6jCurfWc6Rx2EOQGshYhW1BqOam10AuUwlw5Yf8z+7J/tPx7q6tJtD4qV+O37TgohsaYtxtZdhLWCjgXXCy4kC61b6a095kgMWZVHFmZqPLUYtfYgksoveGYucIb5LT3PpKwpC7MHq22uneSl4QfUKUNF1zLdBhcMop1o8aFPZvqr0LVzmuvIx/I/U3O5mkOWWW2FvD90koO5pg/Ehf9OvC1qxjhKJTiiSbYGLWSlJW8t89CEVfP8nl0qQOVXi77jW2YfeaemTeAsJNjT7F6a8czERzsQwcelX2WhjwYM50uQ+11qHpmy+tzZqa1G7KRpzDv9kmmBMHLWqwzUsEuKXwodein4GSENSIZBZ21g08wNLhgk8TieR6HZxdPzWEOmfk7/ka7tXT+vr7+2LZ61YP8CGe1Wn6hH/lO6GfXD1etpX1xU+u53BZ2uNMbGKNtU5UHrKXv00wbCfdmXR6m7zmb/98z6M/PI+XghSgA37QfxX7u6j7Ov9xnnuCvfBnw5ChnhXXWo53Wr5QnUb8aKQySpWClizKBI59pbSEytOz4z0W1ZfZB/7f5v7v8giN2yKzOpSjD8XfM3mNABsd1TQA61O1ipdmhgmNmO/vnh88Of/7h+1B4av9XvOv2kCugBFNhSZ/r2HKohWGx3Uw+cfBO3ovpnGsluZ/vm/YPf0CKdF2+v6pUTRygISW3RkEbQ96xoqkCqbxWGksLdSO2iVccm3FPEADIdjTVvTFIyFE2UzW5eR+r6b0W2SAFhA4lF7zgyCU0fNyxrvcNldBqUdx3f3wDNKgQbtt2WPrAMTnweC3Hpd6DXQi03+pKlmttatVvWRc+P8QmaYnuWP+bMmt+sY3PX1Lodyljq9JPzXJJTe2ZfnVHNR1mSJyjoevsCueWv5pUncaPzCxud8BovWg1HVtxoAoAUg0ApbF8iJzlaQq+IdQzRwwMrEd7V9fY5h9nQKaW3nrmuFvToBvSoFpWMavuma0n8Pa3jtJ9acwTWdE1L6dj/e+a/n3hc9eEiCib5/OGfEARl47brKNbdp9cLe8dG1arSfX/w++AEpqAY9M9W6/HO41J9PrLfaOd9hDxEYL3ZmYgZRsdT9CxFNMXubfPHfdXe2uh9IXZDe1lxp3z1tOf4lYdRrMG9x9jsNfMQK1xuT0QufiT2sSPDpE89I8ov5gJtEsJswqU78Wyplu3kj6QwH9iM1bY4Br0kHfG8rPznwP4qKwY27JgQfpYYnA05KFeOKKc+xgrLpUzz9637e+YWfyGVD5Y082oY8mUWBrn9bjV6rhDb/SxCN01juBo7T8PdafycQhp837naLddZJ3dPOEfr31gPMIMaHupdKPEJySi3EgypFaDe8leNqUODzUo2+5fvKxi/qj2u1tbIVvLjpAs24zJ429TTywHZ/35zk7bXNWmc44YI82XSbo4CknDh2/gf87FfdJ7psu59/Ccj+DwRywC2QhKMwUBzCyE13sdbbbwlk90dSgw4mp+jr55T1/J1v8skC9mWbDBCCZcPEWZKM+9Yq0aEdTzbjp52Clj8Yp1gP6QKkqj5N/7D345kTR9WS1ELa8MXOqwX6FQOlu7jdbwzb84587d+a37h6bv4zJfr+ZPzU9LC+FcMZ436o63ffmbWP2HNKl9jj7W6c5joVYizqSRT1MtYswWL96g8f1IW8Yd4anj5pr1u9d8s9ZjKOmoOXvGgJVVQLJr0pZj4BZxf9IZLy7v6HSUZeSEL58drQ6zFRNRUaq92KPo6R0YT+CrTCm12/3jqtt8JuL5javzFlhLaBRQ9LKDkm/Q92KlIe5QFOTv15kplFM+mLx9luPxNvFJENtj0NC/psLonRy8JJeO4/a4W7Gtq62YadwyzGnZyui6sBmh5puzc2/ecvDUEfMMeRQDiLNw/c8W+KSZ8prr2tCpf5ZSc6eZryChF0IrEp/mbTMWHRW7SrW3DfaktPj6BlFPJxbTCADPWiGrR+djAyQf+M4xPXEkY4Z3rnF3vz5x/qNNWbzur5Gchf86s38mbFyfW73/B/4ftbhOXOyHSRGeNGdzRBEgT9GI30HBMDz5C1s/EH3wNUer2GLomRH+sRSneMH+wg0S0ADoixsRYi1YJaHEF/6VaEX18BVNFvHbMUkLi61LzaCwmzf5uvo6Tdn/Z5zGLP2v+I/4MVpOzhksdcBj8QpB7nePYh1qbAsU++i0TW+3md+/f2L9UV0fTxOH6FxkGzjYo21W6KLelvO+wP/bzX/63JXt5YlVbKZWstByHeWCCBzggsGJ+/qT/vIhXNPdvnUt5e4n6TE9N/+vGXyZJcXaJ99vlvOvjaUP5CjNlVVq2o7EXM0gyZ3Xq0AaSysK2VtrG0YyxdT2u4rVc+9KrH8yfzj9qpaYgNdJFCdo4alEvcGxUhe8I3r2hI4/dF520SwPoCDc3aQqquBqF+XwCMCuVRPbv4AtKA5weQ3CAxhegBsDdsQb/Kmrk9sLf7HNuzxQBB/Apaf6HPqkZ5aVqORtXRDhk9BlaJVLiK++ybmuuC20I/hSCTmMzGxphVzlwRdWjpdLVWSFDeUIAgqVKQzeqZ9Au1RAf75TVy2gKCoxwA4jC9/UDKAHA8PFUrCrjifr1M9BXIAkJYY1Fut6XmcmVWr7jfh9s4oEO/yE3k34/1wjgvxE3ZEI7AqksYeJPttRtxNNglIeZL0v6xHD17/P4G1rWRcav8XXegN22qRoqY5QPR4TDn+YzAqN+oh95VNUPO7xoXd7pMMwqxgQn2iwV8hdLtTIA+ZZ8qOhjetWJHJlO+Lu9tMn6Dqbxgt3zmUzORUo6L+IXfgWrLA8kJTntYiUpl8TnENfvGWX8QD1I3jDUkvblQGw6sFtZ3he68fqjbIjrXFcpuMdHNz+NaquKmnXWj3oGTN8R132Ms1imYVPsDlpsxtMrMiYntYpR8E6YgrmSBsmaud3vmZs4wyNijVfF4uYugQXxQzg4xF/OwQDhx/evx6+t1/YLTbRDS7ciXprOFPSxnGB5lAVsfZ97/t77v2wpLgRWWpQ9FnARCXNU4MuLe4ePQq2jgDFN1ZXfu9U8BLd0gvMYNhhDoscFa1Sxi6aJbVawTHZa9epZkOzEs0h7Wn4wVtfql/VaqSoEMLBg2wRaq16dItP1TSO7unB7GpAvN5nLjgLAXjpK+xLOqG15bDyRy4/7Nbuus6+J1UqTvkkVi1KtaCi8y7eb9NcXVaMQRiQZ0GaHfHOmYMMAoBlCwMs503dmejBw1prD+p16j0C+3ac9Z1evaROBXwRB7oDMlI/4ZRCBshmjyfogGg6Lwa6So0EO1EoeRpibeZuxY5d5tZSxHTLBFtC8nVL+5eetRyKbr8vXQPnTexbU8a62Bv0EFw1D3clNhpDzblfPnus/f12bLDMh6FnatC5ylaFZwEDTfMH1xwiv74CKTzANbAPCsvg2fsd/ES++acbrVpoz442RRlTQoRNHN73x8ifxDHxmIDK/P/KVAfzU86QvUcHSNA4/r7iudUylq4gVA5kNvL4OVzfBlBAnANPpaanYkTvLD+od5WZsPP1j3kh5dQWrIYbfp1sxDZCpoIZpa8LDqYwkkYU5rVbnrXQZZshLz9FS6HM3x0dneUcbsxzdMnkRs8QjnwA3rjTiKCo9vG+ceoBlNCEyQVEh/lAmXPltskVFjbB8Ad7xxhpfNhu6NfcV7mtRm0/NBOilTrDs19FjHBrcZdhckrguGJxo05LBMxzphsvcXTEUOzFdUTrS0PbNWwrtthe8PQbrYH2G5YnG773Ha1yOR3lD3NYDHTv9rya+Aa3IlGD8dte0zixqCaKWYugMdRIcRikfcDnljSlN0ErxhQ7JN997+x1T39w/Zv6puquNFGcFbbr9HvTpm5nTytgoTeDvQMMgcTd3P85tVFx9T908M3+9L7/hCr0c+cSR8Y81JJmfKMocM7ytS6iN2UPOMoG8jRoqIrGmzlVSgKITRYnI/KaJPwhOiQzoVWRV5dG/LCXU7vXUfBALttGWXXdGS9WkG+hoCD1QTn62NQk6Zykmu6UApXbrD6LA1+1LcAKv9ZEtCDfUuNSpR5XjpX16OxtOVyOZhdytdPrG8gnJ7JRVq9TF0gV0J3CbpanVduHXvMcmh1MwILygWT4qnY15kmgtGqv+xfpBYmA7NyVNznHLCLUtQ6z5Dg1XgM0xBRV0BwY87SrPxDWmmcJ2EQyKdtXysLoDYFNHD2pMYGp8pFDbTe8c/AskTE1qu87zkCe+SXHGRbF76xrWdLa61rTseEnJAmA1m73cl4/ZyNEoMMP3xeJo6mwr/0l7ICjaCfS330iRUqVh8F2xEyzlngpINie7jvevCPzqHl4dZbzetd22eyB8gvwh/zMuxe3WJjdUpTDGV7ekMBiaSSuhq13Vk9ip7+VsBbhgy+35yJpBKnR/sB9wf/QAPQ/sjxCaTsGCcDnWVWFRVo2tEwBM8BNVysulXbNfs+6ZZOJHHRamvoOvNqKULijS1r977dLTqleWl7CqOznNOFzwZbHWCqNDmtF5J0w4NeK+MpcAABAASURBVLBCp2iwNa4IvE0bi/zF3CKPxS/WhYJYuFqpAtCENIC25OW4XVEou2izDM/FD8UoObVK686NqLXlb1GjPOgclUhcsiFWz3RKA08RAsS+hr9GiUqcQURQjSQKPg0Nh6ksKB1dalNcKQnhQmPMU1kUIjo+EVmyZbpALLhH8OMRKXznh/dnlZl9sXTkv8O4HIsjVC1g0hTFzm35m2a2SJUEUd6jYyA65u+es/pGp9q6rhpiTHiUkC85SsQbuHvlWtfFy4ySNkjBVtLwup5i2xUdIKJ0ZKNzmPPTtSD1k+XAXHTzODSUtKllGq0i8cqZf1BJRztHuQUfzmJ13fNmvpStqpBdjEi08MfjD56f+on6Qynf1RX8rvU++12hLPZyGEWQO1QG/UzKm+Fh/8z9qX5SqNDs7Ok7SqDSmK3NfLcufcllzIg03O63em5azspBSl83n2pdlzb541yf8ZYYplMhjriwmql/poK1lStJFIA6EpMz8QrWbBS7ZVFXABKGDM4+s4QJXdZ3OcUHvLtTwmHvxqMOcJh3RB/U47L63sm0yUWZRDU1JANpKN5cv6V7VkuJFN/LhAStwhRMuYrL0tyQ/1j5Y1jHuqQJDlMPMf7bqUdfotJUiv01YJciFRSIdoxKoeVTXd52/+x5rDmlJiMJ13s7s0f7bslgUEmx+fbk1MxooDT6TvRnDru5tMlJXCmhew0Wc692I0dafLr2ELnnPpU/mGewNXiyBqjNK8WNKXiNttXB01UJTKtNPo6gz4SZ8afmuansVOClnw6NkfhxGrc1QeeIO3jb6tfUEVAYxcsJ8aJ/HlTTxHr01d920Ts1XvngXx7Y+nawik5ziKyPkrJS7k7nfK/FWmz3an4r+H1JbXpfn1ap1TAJVVBFX1eKz02lmhACi9m79sKzPhzglGoaO+GX05PM363t+khWG9SrYQxo6xfiE9eqV1p8XdH+6eGY30K+jlHj95D+IKjVVNXJ4oc4BXO8W7zOnFvfBO+z8++uv49VPObP43zWxC7lrGfE+H6+JMrNcLA0fdrNXHWz12W8ortErRCmbdIXaAp84uRP3g1dR1/MsUnrZoIaURHvtgMcdhZ6iP7Ds95XUaSliHO5GZq41r2NRuyk3aVUtHdwDG1w46LHkjZS9r2xRgGYlhvo9U/gmBalyR4bEfNolqZJMSt7V3+3B0qfgY4TlR92CDVZM9pgZpDLtfV2T9+wbzqgSLq0PeV1H+2rvgHBRok3j6aUbjOdKp8Xc4XNZJB2tqXqZTe+RkM5HXiBUlL53Omq+Q4Iej+d+nlGpjZ80d1pMebH5KaEUaM1jA+Ebo8oFEv9O3mQL3CLVdFF/xdk5PXS1L8qmbYpbigUmy3JYB7aE7Ye+ZmnBHFArQVKWc0aodNM+TJD6ChzBS3SObI+AMcs9a6s6BOoM0wHQ7zI5DdpgXoBkAbVA3rP9vnDln9NVDiS/olsP6AeCt/4KTytDqGRtaUTFVe8Fod2KQIgt0UFdYDWOhtgkleJcWB9EfFpruE/XowyNlaGY2ytYzpfWF+ixmhAtPjqsgVs9Zgh/+lGGcUb/vvznLUVmsEcYs9SD1R7clR1hXvrVI4zROCCwp/knITuOkc96UiC1RqRukRSN27rD7jaCKObMfQBerUk8It8q/XoY44q6EmKjx71QNF1fvy4COhc1ZGA4nD1lJznnZ3wT02u88X7L86tJWzKqm7XsJbRlqXNmUHT4IIg5kbp6mNqClVnOrtd9A+eMhtRETnr3lWvy57PDsSHFjn8ZLEs3BQFwnLb8cme3Vret0voH8AZUcIaoA31cNQ6UZNqpje96zE6ZRIDuZMXm56K6cdrrQtt4DPnNBsisCjiqRCgb1CzKEgh6IAyPJBPuvl3XmdIS8aB58ywhFcrc8Yf1dPad+80lnU497NiP4rhkr9AstPQQmUQZkmJEA8/ycVKnwMRRQ1YIOV0LoR+QrNGXW+od96eUkG3WYkbg4iZLI2Xd/uE36g0yf6OMpYlm8VJpz5hvld9/H9WFxCOeML9Iay//vX6+l28SobYW7v3IzcA1O1ikloDRwh6RKadMfEnBmy96+e4dt02BuEThp5qy9feqw9CcIDrwsxRU2XNzKcDBSjLkp5rIdm3xGp3XFFLzZHichVi+G9OD1MwG53o1+r+zPwE1a3pQ+nuidlmy9U/lVNBLJb2DQzNEvQ9d1PjrJ0qXZQoEV4ASmz+UbZlf9UNZbW/pV87k2vAT8PMoCfQ8+QNCUbjpboRnynbDlaiDqEkjh/ZC/LZtpfTNritLIqyayyg/xG5pAe0Ccn6MHfwrpbahG6XYvJl4KoJ5StbIB0KzXQFsiLfxY9qnIk23fFRklkvIkVGy5XKxUjgKwODxJOCS7VngZe9maLqfl3hsZkYg7Cx9NnU37uXtYhDANFr81kIv9RJoFQEUszjE6vKujkwXR4zZuG+Mxm02O7mnwGmGwpW0Miy/IWee9dX0erWnC/PFDoMhLPUmba8PW1LV1Sp9jPQmbWD+iMq20WhdUkJ7Faiu/2F//pKDW+F6zCH2wy7iKInHkXp0ClR0r1r1pDjyUjzWsqFAWFYiyHuV4AasaaVXyMYmIb9Fn9rfoG7YjRa1iWJdhpMWZ/u/Bp1E3AQ11LPDEpzhYeuKOXTKLoa+KqADc/mKbw1h7yO3DKOJHqYomQg9oKld/m5e3oVNU8JBRQgkGLWUl9KxkTC5+ASsqsuxiPWu12UVf4TfekEZWHmnXontZPklSPlTs0aCcxImApJn1SHaTqh63KsqNzMdH4AoWlqBy+g36ln3ge/1mdOsopaAJVagQsLXyZzhpYlTM+tuTlfLzGFN4byLH9NFPRXdpdMswUupDvEJGbKOq2vprGTU9Oi7XLGR9zHIizSd/oSY5THH7ftXCHXys0yd5fgfua7nSf0MipYTHPSplHRR0KAWu4+uXMCcOFrhtifD05y9QdXg9Ula225Us50Z/piXviJndpB8asKza958N2YLMyKE/EGT3gJkKcGY+dXYcDRl5TWIkC3Vrz9OV8GpzIVdn+sVE/6UjM3RUB1fgm7YVrsWnE97NDUZ8Rlt1n/+P2jmJLScGKP4h+zUu8zImZ5u1hWxwiYzhRRn13cYXmIHh0qWBqslblqOwfnzXtOI2dP/+l5SvqbAjW9xY1Pl+G6UFSPpqGAwcgmHE2+w8vWGyS/OV/sdeJc3m9gs66XIM9hXG0qNQlCJ9G5KXu+YLvHVCpqMyLKMFpEA3I/FhhSJ0S138D1o5G6nivLFXjaYCnwUUvmVLZwjIe7mQPrd9CYodibMpzgXTzGS3vT+/j3qSYq/0w+yAAGn2dJ+SsS/5c3ZPOL4A4uKA/KSyDUceQd6iPP4z+qCUGdMXxnECUpaOWrzyAW/af5cxFHubMRLP6O1sety56z5ijr30TzXP1rqF6fF9HLGSrYe+M0j4oE57G7oMxrId0SNM0+nPCLJVm3SyPxVvwmzpeWvugvdWmt8tNhcD/PT66fsR+V4DmTeRJvLfTahIps2Md6HJHmV0TFmKtB1kLCFKS1bZFF1tYkzuN8yGf6QT6em61RHyWetEP7rThlOhEbfLr8cA2dRgi/cXwSftN1x3itJdGZwZ2uSc5hN3oBy0hRopZTaaeW478dP3u5h66AVPoMDHLpD4Y2rfyal3NC38WEDdx0wUZwUiK/D4XZ1lrbYqNU17MO7OJ7JShhNRuyamgM8goSIMCvdtVcCX4/NEyVceho4nTXjDxqUjOT72X+u6y0qf34F42ugc2v6XYOdRRQ2j7uunqqM6+u2EuXZkd5/Rj5rA5ELpdicBsz6fg1ltqiulc4sTodOsYtzF2kDYFpTut9tXKkjYN2bPPZl0WkMq2Sme4W5RdD8jGtsYJTJ++r5iQ+fck46VSYrd+sbVH+VdK0euxoxbA8yBQzlEV1WJBQb+KGmXWvPAqNvAkFPCDIJmYgrDSYvo3iqdWIO3Gv9zVv9xU6lDjlr64UUXtmlUos2f052lBY7VO4zexBtnWFURqxzXbmdGmvbdPLKxyftyIa/AA0rEhFpPu73cO2YcwySgZ6phf1lPBoqM7jU5PDlz0T0TGjrmtyba1Avm8YNZ9fBUr0ui2p8lajfmlHfGRohf7SEbAYBbgXlsjlgOK+9aq0UT4mQaEZKVEt6QZobjqnn0MqNbTMaiTAlmAEGnSR9ZHjdybvh+NrAM0blCXeyzN9JUwD1zKVfE+di+7wXtELZfjHiRtMgzCUeIan/j/MAyedcJtAi+WSQqq00NdYwzo9D6uE8yNP9tzxsFeTA0n8inCjyk7L8wy6oltS7KS+0WhBsyj5TOjm+UulZkHu9mjR4C6jxpJYNnsjPsvKCaAanRhMuVTwktcEvk2C7GnRljO35ooBqBohV15kWo6dqSuBDGp2qTkBBlq7IgQH6O/KjEC13ZKYZXqBe9EcmOY47MH6uVre7chf57pXqT9tEh4xy/RaJ/2onc9Kf9J8lINZlCRyMx/cLf66WLAEKzFHAY1ZGmkdAKErKvsptlAnXuujeDS205CAvvIiqqUzrGwZXprjpdr6BbRS4w4UlG5mekf7M9xjAdtRz3PWFnc5ijK/DDvqceH61nXrQrds4MPhs/QYhZK0dkbyVvCTu+rB2JCitU3mTUkVQd88DXIyyrylilh2dXZS0FadQDEoNlomMc/qtdq0/K/f9eR6sJcg+nDdjHyZwgJXM6OEW7kzlXzgcV+7+dxe4ghCW5grLqeKiUiaZs4VvQrkQ8OY2Zy1sbawwsqtYWoAj3yuP99vAQ/T7Xf413ExrCH+b8QEy3zKZkBHLzsxUpqGLUqi3PyMW8vLAp77dfRi3LA2cBSBGbbhWe7/7oqoVGBjAiPpCOyHvW9Lfpoh77dRF7icGhEWYcs/X+m7t7VYGEms5ZIk9NVlSaC7Jj8tKvxujiTdnZlo7fVfR2IacoIRAxJ8D62wu3U4a2HW6Dpaddbb03iTarb6Xto6zpZqaS/ihcuWT3sT+jzszzK+NymjTT5rSmWqTZ1qJZvdtHFqJKrWffdNa2SjIKoFlQ1+1mSj26Zo0EsTrPf0Oh15WL+i3ueTrdvIZBkq7vy8s3TkfEFaChIWNW98ZEQecOcLfQMqM9ClO+geYKYnzSosPQreNTm46F98bI1MBRBOepE1/44I/Cz8NNaI4yDHorhKbVcNSTrBhxr/91KsZ7dWPPIjQK76E/KedNB9MxbsIQIfssG+2L/uufmlWevAQd7Ix+Y/1C5VUssr5foBJLeFfr4j18wqrP4Oh8scek7XP0hG95KLaNdHUIzMpP4YJ2xIyvuYsuSn7dSIFwIWBtl9JtUg7CmPDnrIO/xMHbvDhmFGdddXad8Dl7opytT4VBHURRkrTrkrvwOATfoDyrprsnVV3u83Q5nTfgABtSvshGD2DEahdZs3fEQv9TI3d6nXeegFTCMP3pxsVvJeVAPeDOXPX313AAAQAElEQVTz+wgZOalB1ofWY8oR+uMjVYmLRg84IVbm3//IzgTt0ysGlnxUDBW993mihJlJLfWW8KJp3gp8rcXlZWrm2IDwwUp0C5QoaEFUl5rBXBvl6w6USzR1Sl5QlL8dd63DVSWswCdaa7g2GVmEgkn0Z4Wt8KJ8H3zHfGy7+oSZiaMWHzrUbcItEp4EpIrFpTlIQWAl2aNL+VOpR3P4VwFJYKzSvtJXFcrPB1CfBFFIxee+ZorJ6gsANGUHkIE/hmfqZ3o7SLHq02YXcyr/5UOGoCin/I64Mflv5nCgEC1V3V3v1IvJ3vjUjTEFm1QJ9XOCvdMstce10YcjSd+xbvrkVwii3MeiInI9erjbj5tCNpqXrWmigGwWhkysW39uecM0wjTSQitMGm6jCx2oHygpVAfQMKUL5XNd7/HBG9TgfrYtxhZMxTGN+bSmgFOXhS0XVYPd5rn8hFIq80gQs52ki4HOq7i9tQQhfnb8hPY9FXkty1pYFaEsXmmpYDMSik7P2XMXpIsZ7XlM0DrtndS+uh2YZKfNN9/YJlyv4mJB0ZdWDciUCsS93Ib6rzhn7kG/g28v4SOlZHb+zvyaKMf08l7y3fh4+wjHQ3/GZY+QT0BBaWPtwYHVsEFQ1WRbgxLyWaZky4YsLOVKC2ZSndWm6LDtrmrCF/wtjM1SdXq29Vv+qtthPxrVYfxya/o7aqc/fRQEjOVIK80UL+AsRyy3x3qb9IkfwCFP50IP8UbjMkY9gEohkyrUwKfO1eNNVJRYzscmFt3hhjD3hqjIOd8EJdIAXv0baNmJkeeP9TI3J9bQbfyH9mCi+uTcO8oGOTKW41nomaF4yqkSO4FlgqkBxT1UEKMJGnpg7U0KAAD5Deqvw3z0aU0vlI3Q+Qh6Iuce5v25MuNMRdPnL28zsZPoyuliDxWg9fc/ITB5NukNq9sCEqbGoGSSoyD7SuFEUNe8a/r+yr5cJuqMdA1JT1iUKLW5M/1256A8koLa0jluTnWa6VBsBFU8qsZJ2fhm02tcRTMicFxNWiMUa1mXl8KvxoTlw5Ia3ek0EfhmndN1iTSF41QZJFtBuqfbQJzqL4OkcpsmuC/mGGrNuPmQncykQgSNUnY7b33xZcmkHg62Ts8y/BD/sRealGHSHVQioD9m5CKfjJXKnX2jolRoDLFY0IZI+Zeajxa9GaGYprX4Hit0oT51OFxGZFKXpE1ES8xy1dOTNb/df/O8XccHwqP36k8jmjHa3nyHFcVU10TW0sUqlXazuJaHhKq4kKmuUVhw88CP6SFkLo6WibXuM7/au+wakdKZYn0EhSa2cK3PKm1Dh78qOlp/l2/hvVOZKo9pIZ2v9Q9mRE85mmqKzIkZTSriVrfn7OYemPdo/fjjteOqidPul/NWL1ij7RXEvpy+0YJEDTd8aU0FKDU+oylLJ0KPVuZ2nFpy29UzX5JgOXOpFBcxD6Vh9Sj1g5+hdtW/kYczmgFRr1/IXqHq9SU5iX+vsTY/P5abShXRios1k2fUTjRqL+49IdA37kjflI5wXZvmrXpk+nTW3OHR1dsjojA0YEx/YRK4Qr7NCzZDQO+aVu6tWeUDbZt+BhskvGZluO6H1orH4HIGNP30NDhOvdepL2KxIi7QlvXnt54eaYuU4y813Zqy31QKFVETA5DSBBZNFfBGnfhtVKn28DS0effzphSIvyWyFepVrrDW+GO+H9oW9MN8G3mgISOaeDt+M/7+2rlLMoy55e/fx35/9n122Srhn1HGbr+vmn2kUvqn7zrTr//S698mza4pqaXtJ1IBTy8969a+wS+ew/2/hTc6EWgRtUr07d+9//ez3r0OXKH8ah/z0/rNd/vDeMhQBhQ/950/yZ8L/PvBO+e7E//tubdxE0d61bIhC9okFkn4T0m6XC2+lRhnhKl8Jjae4Jqvdt68f6ty9Mq0m7t9x48Wevv+hwBvWANr7tPvH766R6f05Y8pWLmsm/N7KjXGIvafbZtKkbQAmH3tpw/6F/wtzxEFefrezMlRA4AYRV5UjgIYKIwCldetbCECC/Yf+F+OnrdxZE4mYot3fB4Or54+5gS4w9wWJDNJw/GwFw6q5kPu9Sh48sGY3fEzeGNy44a/Y5mCLNgLXEFX137WXXvK7W/G/+/1e+WZUqlQ1sZsU4fw39t/T+qng/IuRZG+5tl8AuNR5oSvmj+/87zXZ5ro4Dd9sY2pZJofdFbnndb6oPe2rF3X5h0/97l3i4/UST/SKmmGoClM3KFQAfPfxp8//vwXz1LMGf/Kwy0e/Py/8u9/xvKjXfjvoOTWuWAEW0ThZ8/+oVjht4iVWmSC36jCF//98ffPOorHY9uS5ymWtH4tuVQtV3m/Z96L357q431X94ZiPPTUGnu0616OAdDDKT/5c3KPm+ltTTt/Y79L/ZT4mVnIYX+7EX7/s+OySRZ00rUOoOFNzjP5k7+rBRYyQMeyjhjpEtqzfkA/ptUfPd4FUdhCkJhCE0p3MdMnV6T9vIgUNpSrhfvvxJt+ys74Gb6/N/9XWqkJm1EE7+BXoTTj7jjhyF4vnzc8Oar2lVTNGKzHLudYJfQb73sTIkpdmIW1sLdJ3ENFsrtMC0u7aiLr49GMe4X9UE8IZFrlOybzK9XbzLmWSZqmTmKvJCZDM6Gk/PYrNzbUXD1Nbagl2WmFBQEbTLXKwbtJ4ItFIwgCVtSAQh2vX++/6HPz/paeQGgpJIYxkOh8zfLc/H3skCX65/g9i+kBqmYXnuB2AF+UFbaUU0xQWXqu75aVmp2lvFs38kX9mr/9c7Su1k9ci9OWYuKKCHH9krfuPfeONgeODqNtIPobU8XWNzN1olHWTbLs7BHhhYEOs1EHyWcqxkqLam67G3fd3+gjmFq76utmpoZhUaGq/Jq+SzemBUdelBK/XKUURF1hzkmRuEczsDbcypgn7l35kv8zaIPyxPrjwWuC8/ooCRRj0VKXaY0fSdhX/sT8t/Dj/DNqb8UZOvbTa1+EGmoqCrWgsq02kL/Wgja/my9tOE/Xn+rnupeaeQfSJuFpupTlNWHPfoRwMC/sQiz8CYNH7z1GqAOkTyk7yBHP6GQLsjJQLWYxpgD57F7W/JTi/pr/xfydKrt6KOr7ikRRfkYvYvQjI/4+9S51zMRwpUmoSmyP9CsdSUSMAlLpESeB6caV1sZvaPSe/nhjCvHwBb7S2rtiafTAW1E2xcoNe9Vd/7WhiZu7m8R1+w2e9uqpvKg77IoqT1rJf9XKscScjVyg0wDYpxbWaXU0lKLd35sJZLcNXZv9W4OjoQ7/g7V6Nko5ZOAv+Pfgt+X17T/ZXwNQEKXYHtgPn5+tDS3L5AT6RyXEYOzxX/iV955ozfDM53+/+vnaR9/MbdmCeJ+JPLsk3FHvWIsPy9HH7e/z8+6HHvLVxWxr2x0bcdG/LAWm+7ofLVhEfL1RYtNQyqqTEcEV046/K69IEgrBEka3cKp+HasaahDb5nEj95l10qowlESohMhhMiVP1MMdsW1V4CzcNYIJoDn9xRCSnT8DNQKbmHTdxbvMi+CSOeKZ++Rh6Z6ngTX8A/yB/SAIA0aDBiHATdOKnj8uEZbwf9cyO9/6uI15Yn6IjVgwF4ONbk0b4xiSNSMZmF2T9Lzyqe5miT2Nw5G2hThy7HZjH/5c6aTlGeLBFgUEvxaSw/gsOar2W5wT+3f/mb3kEnrJWZgiJUa9g8BQheFaCTDwYv9avqsPURpCzvqWld9nhOTTs9aLunR4gNYisQW7nkovhSAnOq8Uuq3sfY9nq266KSX+r+xX4NrXjPqTdfIFpxjpHFfkJ+x3Pa/v5oaiUAjNpFDGGtGdl/KIwcdJf9H3UntQRY0CT4pH9fXK3cAYhJ68MHaJSU2TtLC8ZmWuKRcMmZoaJHjIUsSvyhCKS1StlpTQrT5kBXP4jjF+O6LOofKDaZtr9n41owtFpnuaJg965kNhCMBaDiAy7O3JmaZ1iGGWQB+JUal6h6pe0An39lVEM6eu3doCzIaJLNOMhixPl2aVOsghfkGFoYjtC1yodBpyU0IPK1arU7S6YiW+gO9YurVUoUSqs3t81RFoEynVUdmeKllh4lDu0K2DDZTiap3TcRqlaL4zpY8lB3KqTQ6VYhmDnzZFFPYlQYHzmMDg2uQuM0kQ68YMRyQtkq2oMuWmadflK336p/DY0KFRxRmDzCkfZqRR4GY0KiYNOVCq2iyoluNOVnCkOKGJpqezzD9QoPaFa/LTg8mR62qB05QRiU6txXy4SyRdqnmtkggf54/RCcozNXqQYoalU4tm/Yl50WO7x/J8mFqWgcjx0ur2yjohw2uN9HBdiSgG+LMYQThs06RT/qEidIlA61il2RUhMZQ1KoUDvcRbAvqWkyJu7LgfhgLkl9p8OldK7JRhajyICj/aGPpAHFPIe2w+OF7Lil5CqmEN6Djp3xZCzYxiJwqqCA5MIoGQKnG/nN9BCWU/nB9g0QZhouMMdBtW0zAsU4LI0jushvmJBVNNHqwUwxiRtVeZQYBAmtaGoQ94ir+vjTKnMbZ62LJk2pi1+QelVAhQTYkIwZ4qqC5uQn2qhAJpWuw1iRiiQKEtfC95Ac2Ny2c90BbWfVxBjKmRWkYgYlk88AehoErhZEGigYZIQaCTbh6DaJXchDljtUzFH4vo5fdPtBZbhL8UDpN7A8hJ4jZHEWIwOffgvsgy2UJZJHzSDO0ULTlU65s8lilv6EAu2V/pcZdGCBpwx5IOC+PT4NF4dBH2M7VrtFSsoBtMLg0QI6abUZ1vrSD6JjC6hzpcA5cd79Gs1XrVt+4MMGDhnxPQGvFOKH/exjuV2hefVEfgjFVbFeON59hT4x/AqszSByd2y71JRxl2yBaa+cgP2AxToLPAJqF28MrWO0iRAIEPwEsxLLltYTV9gXBlIwHNoaJEa214jrYo16N1hu7UH6OmR3247QzV93693s63r8wH6Uriz/SjNSW1FZI9S0dLcXa2vmFqqryVH2Xd+kL3All3XHHctBbS87XCS//FaMeHHgP1giqgvMXAzBkc4IZEHxDjxoaIhihuseV23Y6biDAo203r3j0L096F0i0R5riTEaO6aHbmjILoEhcfvmv6KD+yQc0S9iUnqxhtRrXI8ajPXlmIf2WSI/qh955RRNH5Nc75JKMrhDTCSryHrUhHYnX2g/l5VEH9WQGdQrMsJYEsrSRCzG9SZqeNKTUcWzXe9Gm7bR8PM1nAFTRfyx7jAj9AdwKznQ4oDO2vf3cWkziutISsKIeTPZc90TCbdDWA+j8pgDbWZfftOh8a1YVDLNgv4sxz2qv6X5cyWLT8t90SCxMLS2kb9HHZ1Me1wrd5yCa6DY3BdZaJPA3jXMzIuN5Mk8UyO2bk7QOYiRby6UQZe0crce2QMx2HLIf8kq1kocarrVu9TnprGDmCI1foEVelkGkYG8oDSUjlrgmm/KggrEB64x+Iz0haviBwTAAAEABJREFUFZTT7hmtMcvMJ7xozf6sWAP8omCVjWbrAEDABFLRFqTNWqgHLa89zPlKMnO+2h+lka9DR2rKxNDG9Im7TJn0kQ3/Zt/HKSZ1bbU2Cx/LoKl9vpFXKKe7ReQ0JRiRvDEbbngqHvvXwMLowHSmTT3eOtmE6uPwT2nhf3RdJiCEWhi+HCynUgeJykSlKYlQ+9wur81JjH7C125UEI3jU6uh1rru8qtFAePexoLGim8Z+/DrRH8tpIqt/2YA93Dbi9uIgl3+i1AmtW5LeWu+08xUgE05slluo17ThhAJWO57wtsVwUBDGkpeTH/rD0rOW35qcG/PW1B1q53E+e6VdDHNFLcsiowcVuBuKh60+r+Eon2MlzX3iWhzYEtQBjF+63RrCsw/XfqXdCVlPBGVnNPq9f4OijJoZCgJeiDkkgLwLbcSBAShKfH07SytaGJVdXkIMWhwM/XX+EYlcpAujr0J5U7MtFP4h4rWinK370RCUfdXuWHgFjhHG3UQmoewVm5C5Cm3DhP+DELrlv2WnDCM2zpEiE19EzVNrts7F4cP5nFh/sjBYLKRCd3vUVuT5h/yjwtYl8LKngYTnqa+6dn21ij0vvSCfVb05o0WI8Tok+iRTOgdMXrXEKAMUBCs1LqDY5NU107Y8uNCBwWjg+pGPjM+uefbOA+mh7J+eVuZby0lqu9Zr8e7PAPpUoa4pj5JYr9mL/tqEWsSKvIbfKPaFLwYsj8X0TNUv/t2cs/cr7fi80lynMrYmNNo6FXFY/VO3rlXoH5QP5wAqvLV87C334OTxmt/ev6MwuwewZCpMh5n6RMw3z5yVzNL1xUUTB9EoPrEU1ia2N/6MnOAQ/6a1Lpyef6wfRWeHiOgLJv+Uh/FmugNjq1MT81+u1Ug2bqyjK40gCUwUwovGqE29XLknZq25v6oUqDVVQR2Lqh9ja6ItxkOUYuysswMNjVKvDWgLIbO7KhtAw/4aglyDX7ufnJtUJ7NRlfIXal77TVvoNDizbpUXlJmW/5Kj1xszWnlmJeEDpf9pbAgMLBl0jK+yhMc0wCHskeON/ylH2jZRZJJVTpSPsaKTo/lEYxt6wf7sR9w1bAJZVktK0As3oiuPS+VD9TZ2Vuu3R7FVVorBE9z7swLtIXtx0nWOACnOOhwsWZ7+WO9bMZaE0S7Je21TpNw/m4cj+zoKyppVXNPnqyxL0KTzZDBHEiXo0+luWAjbb5n519o+51JqD30YDjqNS0vSZnsPrTf77EHM6q8MpACb3P7XryJd5DTYBmz2FJZN2IYY5Ko8qpvL/xCidCazB9yeZklzVz/CdKIhNMfgybQ7nRA4aq0bm/MI3W2xR0fGTBqPyO6RDZ5wTF5fmMuUKLgWh6vUiU6YV+Z1W/x712nLNALDdXBMDmDAqY27qOEgacgmj4ODLMd2/TST4DzWqlFMOFFDoW0DSqGmHkufvJLjEAYICVsqJamLUWwNR58K6U/5NMRWAzXZJlZXmEzU3USZ5BmULaEcHootPMr5rnuYASTQqDN79cwIgahUz1dhNwNvIXLQBrO6l9cLWZZxsRGrzDumEUY/r0GX/Fi1ER0w/nblZ0FlmEW5meoB4rmLRLbNFK0mBVEaSAXJj47Mb07n0kvdZVSqSzcP4PgCriU71VCxUE3Wsl5PwMPa/n1f8BZTj3JP51fIAVc2LE96xmjzb86KLbxUr3rSGP4pPA4vpR9tbpdr9VKpcJQFZVYcfFVnDadlUdbZdEDXNoNfwMal1Bh7zJPayCd99n5/JSLJTMlmHXjr1aC5doiS8kK+hHTNmo5tRuf8xqcmasgc7PDF36i/7N+SgbQM6kaAYYL3DOCVKbiBKkcjNy22jveX55Siz1qE/+qYJ0Wcwq24AmORn5GNE1RXEzT3GLxa15+TSv6Y7niupL84/1w2AYtSWStHD+IFzQTjDBo1tKXctkVwrY223v+X9Qfa0VVnVl3SJq7UXoZGROB/7CoqahTCA7KpTMlBoACX/651EeY+uFmkFGwmv+DL0b/Gfc8c99mAjS6fZMcyTt9mHRtD/0/x1qAeqPQ6G5uPYXds4Sq6rQ2limIIyTKb85Uol7olLd+v24lqmy+vhaBWZbkEWm/Fr+Mg7aRRsQhD2Qukc0uISHgIAaVrF+3o4ZezhzOpbXKoh+ME+muKYZcczaP24Xw26QP1lDQIWqrS/3517TPM+h+59hcRRqTCmzFCv/j+BPQ06/dyve0Wl7YbGsf+VTipIMWMS9GHnJMpz/6JLlRSmxxnvkwhZceHb3eObSBa6CVlPbaranGdBPd8OIurjGtqQtaurCyd1U4enNiYPJde1LE0X30Z+S6hv455BGH4+pVffoj9bYlvIyzpDhLWnq0Lx1H2nOy/SdNLnrO6X5nf928HqtFhq3Y6hVEpKyOXS0C9nfmzXzf39s3CYSAMOCBZ/nPij9Qw71eSco3RHcI+w8FVrB7GllMVSX4wi+cBlBh4BU55j+w3bRCYUqlYbc5pf+QMaPmrcXbY1SuX7ZlDSmhyKb9mn5jX+XsGIrWNImpI36NL0dRK5vP50xfoz46kY7XJQ4RaSIZkzfrdAEgQQsoMYNWOYy+uqiYZiv93pwxNWlTUgVWbFTVKMVoZPVA/6nF8mbXHH+uPbejT/FXjf+9OM0D/PX6VbCY7/9twOUXZCnP191oa+Af91NFEVoWrVPWk1kJ/V2VVreQZ6H01JhaDzRrx/Jn2Z+Z9Q9ISStiFTP/p/nuLvZt3Kvo1ap/H/5ygFnTmsLKXlkPa++2fSf6TimsuL0kuKTazc9gH4q/0r2jh5ZP3V9ybLjLn5Fo/SPd1bD5R3Tf+hg1LJqQa1t9LzXAwkmlSs/0Jggoc0z5OOOvres/sxyuSxMf9c152U2NaJMleeqZdyhilayQTxeNb/ZVHVQPPT9/BJOa1ezzM3+f0S88rxzDHXvj+a/FG8gpUSBHV2I/VZetPMJU+EveEKOvOd792Fnrt+xkrcTTy9Pj0L9TbdXEDLs9hRv2ydygE3eyRldEoGmUtzJGE/Nguc3XeJZKqi9Uwz3q2sVJnuMt/PUh3OHOgIoEE2F0T9siEzXVfXDTeqi424+ai7zNAEKURQJuLcby0vKrcJexVnj2cYx1ja/zjGW5dg3VsJllJT+g2ocN0V+zmoZQK8Ko6VY5xBiorVgUFnpzaD1wcrvSPKP3hwhXNjTLpmF0D6V/UR8BYoAyE8qSczEKxkrzVti/UC94sp+SXDWOPQ+S0yN7znBo69t5rzUCLvmV5LgV3FrCnwaN5pX1GK4xAopiuqDhuSbVaiNz91HkUzOphQfTqR4ty6u4K6jr1w4kpYGNApSn3taP2iQMCkCIlBpHW2LKZ+JVmUgDbVNdX8+spF/EwXNaES2vr1I32imLFuL8VlYpnVO1CyHOS3BCOZEKItsr0TNt3Ta5RxoZ6eWcSeEekK74JNXqHOnOIdY6MA6L8T0RQGEkbxFQbmlz3WqA/KJzyIABVuThqDdc0cSgDfwtf9ual/viqUZegVXL6Ox8s7p6klLJQ1kmISMlq+Bnvw3NrmnNM/GwMgNJ3sE7dftkZp1RqtBauNxhNDdAnGrSZFmJXJ3c6aEhmJFFZlgXqvCB6eGkJ0tZClKtSldpQtFkU1dbSfVWlFyltnxHed7AalgJ4Xi30k2FOUNxKcispHqmeDfLL2vSYKIvJmcKuqrPPu7yipYRhXglp6Pg4A2VeX+WylhhOmTsvtb/a+ipSt2FjLreX09ujY/Rcgw1F5tyHB9l0rAsA/oOQpFREQAQRHKcIgkDjXSWS7C7bAmaFiH7uFcz/9NaCM04xVISs3Teh8/QTAbqiyt6YJdLlA6F5tDyGgBN45eKmKbGuAL9rrGgVcGSlNHq/vwXvTjtba7uQrNls8JG2PWuL4bR+NIeKlScxRxLURRYrrsxXxXrare+nSS+Q29GMgyuZPQV43Sd+TP2ACFXrhZhp1zjF7iBbbHW4/Lmim9CHoGuTsygHRXe8bG4G+sb4aV6HZRiHcPHCnu+CzJh+vDpWumbwHiJg5aOLyCBc3l8L+3epcYsv+0fRTOPElo5rf9MEpJj2Mid5fgLHiCNgFPHQDqUqn1ThFmE+VhLS5qM0khSZhlu46ItSveuttCKSq2q89bcdFGg3Gq8s2yyrQTwCxkFKmxOtNrlOBzADuydvpILSvYlIVG57l8o+56GViLlexYgpA2CH2m00cAYtxPzolsZtTDbYui1TJO+TSbn21J8ZAAcePq+9JGpHC00mO75WmNhSicJncmBaB2s4I9TmQyawJo15VTS6rgJdTxLXxYaFg6DRHfoOXNlKqXyTQ8jrcBEzargMOmqMrURqARaaqBdi1pTQpwpLCakqhUfBgriU3uCx2bv4eqvk7bZGVPTGDNQYc2/N/9nYtpbmI39QUllmOqt3+CjELOh6CC7GCEOtFCwpsr6ft8sGH9S7liKkOfXXt099JqxBs3q8Ru85Esehb3CwhcUgc/vuje60ZL6kkSgtVfSDSWqVg+7Fd5hax1MczEqOjE9bVSBp0IZ01dRcAdpbMXvGt1XFhGWFj2rqVXZsNu/XtTQFPtcNAGXXKQ5G6i0WtYk/eyp0HF7DzicQZd6ZaUrMTojX99J6iOJNmhB/mig6RRQhnJdGFMIWdndH2jqjxt/go9jORVQa5QAUHa9a801v1ACpKmCX6JMypBOXdjLInUfUXTAlHF5oxl0SnhP3d+pAwMX67GrJyl2OInRPx+ogHqfu9+zUzsmDUnVT1St1G7Q/NMl4aKwAE3U4pY1dq97jWu3+rbgtWBNKZL9Szb1ScEmLTtawKZd3mZGfNXFPZ4pjZiNBJfNhDddIDGd9ZU/xme8vCouOT0aQ83WhGnqnW3enwFkdl7U3dPmCUvVq2zqG3PZ0fInDHMlCrRVrRrgwvRHfWG/WFkP7lrd/uj55nBMOvZbUACCNYq2TakChgqx9RqRbAEwtdGadF2wNW5Yo0GdmCSqerogsEpMOdIA/wb5ByTnZl6eXq8icdrga2fWpse3q0p6qWFDRuVM6IVqs5Zcdrc6j0kkZXf6WmqqcYB2QqPb5woJ4R0rV1Q8dyZbfBs9qMCRwrcPl4CqpfAMWgZG9t11bgzLxhXjlwiVUbW6Uf3f9AAdyrK2yZYW1avKuggF7633apZE0Pi/cxij/MnAR4ELwqcoKpGQdYNgt6MggE3Nh7m2owiduhevtibzhl+jhnSLR+3LtS947IbhPPi7/Ze8iHf8g6r41utR7d9g7UjqYene3WMXNyH8wqq/4Q/5dHsie5qg2bKm0gzcliZ+RvX4U7lUROongxp12OVWz/4b+38KOxsH+F2lZ5Tc4/7fD589tJL6WxGKDNnXass/63ni50f5eH0fNUGOixxAH1u/6vxJjYLwIXJNbraLD6ucyU8gA3uh9cLz3XglhxiwDE3MpalUOmC6g5bqxjD7oSK0PcpDglYYeyEAABAASURBVD1Oc9uOEO+hFI89mv7JpX/3OvFn+Gca6AvPWvj3+nwIiuxJpWMNa5+M82bWhWnP8GP2QLJSUW8Si2rfv5PVJb6eEgf/9AwktbXuBKFhbDJFWloPP09yLKCjcNTs1stIZ66GUjvN1Gh4j7KybWyBxk/WnBs4ZIF9jRrOtvWO2pq3zmvL2TFiyrJ5r3UapzEqoyzt0Ss5pydP97llv7N+J5pCon43HPqM5ajwEif9BV722+83uXB0DaPIjL8z7xd0AhcstYU7ciPFzbbyns/TXV1rtPqigWrc3KxlI0cJy7XyxzTgt61KjkdPVErVa23GbaqbI0OT0y/mLbspnWIMIsNgSsT/29hL4wbpDmWqmDOHrWT4cdG6qzs44UB+YZ/TffmZgMwAelaosoiZedGa0esn3DRausB8lhTdxf2t1q8y5Cyv1WMK+v4b5dM/yBU6cdRmFCmlGX40Nawy1ByUAp8PA2Pc6cvDV3B9dIPy/dHj7dQTjiPrA9mvwmvTnm2np5vgYCnsGquZ1ErAPKOmoQQbpE0j+Ky74hf229QVFLpewfKrla/0yEFx0s7gKBtjvxJOyZr1gNLPDCRkawCEwkBrmCDQaTQ9FXRviaPY+XEUlhbvWVd9aVxNaHRz/JitRUJu6ks6bPz7EYBj+6htJfH9WZs7lLx/+qQZtKk1Vndl4a14tV+cXfLL/up//j7MRdqXbgphTxvaSTkO9z228O81HtBpXb533Ra/zcxISSqeteSrWhT4oQ3VfcGzmRGuAZpnVcr1y3eUH6h1Qd1OhkX+dOzhXrN/aH/IJ2B/ZGSBa91BQgSqlj6JAtYBo950plpXbbRw63n6qD5BgPpnosXG+C8fUAm53ZjXVavZPKNF+6f4s35+1iMiKu1czDt2sKacZZHJe6MGhfydOeb5rHwsmYNeVAxGbYTx39xlLx9AvxbqJ4V/6wRh5fulaeovAxE7/Cl7ftVKEYTS5TiuChPONR/RGzBhhVrRQrF//P1//vf/Md0U734jSLrBCFCKiWq9A8hkdbBdw7L3r/v503cXutU5H5biOjzdic4Rg1g3/EQf66VkN+b2rXOYZrOhYtyj6hzZFiC2ki0pyanRWidjYOpEXTX54lxjNvsE9eOkZrvAwNy4rH6A6JHZNhQw7SU6nDQWOJfR2iXScYk3Wr1Nkxt7xgsfXEt7zn4Ey/+78QfQbXt++nKwJ7JDzdIhmYZhNfS65JEnEBZhum8K2W8/YAUqrGgDLoRboPVfpddsM4t1baVOzTU7/vVstQSeTzPIzyGaFNFXlE3Libecus0CHg3tv3dOx5iNlhyDKWFcpLtlj/hq8CjZvlHO6907S4lsdGHGXYbM2NCb/Qz84j14C0d8LlcXkF4Xm5WTlyMmNMs0cBLTBvzUdzzN7bTtFXz5LUcF6qq1tbCXZvzfwUJmVV9jeoxHZ2v/KznwzqryVCLkbZ2PI8PC2v1j+AM17AJG6kJzgV+mWYaz5mqRvdrvPD0YSwAEqvuX+Gt8w67jBboV6YN8oMWslfeyVSJnhR5p3o/O26WWkXhT3MlEwBYf4xrK85FzGNq0sexzStVtV+SsBQEqhVJX0fmMOjHfmbeRWr2/e/Klk5w9+byzMuAL2+BsR66anYa7RjawnEI/KHRDkR39usKfV2KotIunxuU6z9tZDS9ZapscJfSQaZl+ZLXtaZjVu7RDOOjjv6kOk/zfib8p7YuHkhTQmLJps9+yVwZC4H5g/49wa7cblr0v/nQ+/KrptkLzKyKCszhqnV/d/WQ4gBj39GnA5n2y1iA+6zhh9Zht0W6ufHW334rkN2sWtCpkc1LJUAU5BpLFfVvKaByQHPPREOWmrcdnuHoeullw0Z9hsOA5No2qlO7yay0mSJIFkhRAzDSzp1QLKl+p6MFsqpoipJSj64BhuqukIfZoUhMJ+5XZ+/kOwveNffbO7bOsKDSQRjRTVaJEMddPrT897Nv5Cq0ua9hRXJpt9DAq1mXNvIrzHdxn2Y82Lr5y+MKSoG3yj4JjOF0Yug/y19/0sYQLGCitjp5W1JCNrZX82+j1C5Fp8+z5Q/uZkSNFXiqBhfkomszSxkOhjVWZXbrDvnR8f0UBvtBR8wmR5abgrNNwj2cvs21r2ZJcPj68iqV/mEDv+RLPZdyhxmCBWP3jHrY+KyPEJX6txmuEP+dN3SRkAY0QOYBPRsq1iO2JMdCwwVBKMxRB85ZfBTLhJ+IKHI2kSBgQFh3e9NYILNoK23I9M93yclnaiL9tv1h/5zl3d6kXOT1EwxGt1Q7F3P/ypcy6/zNgwiz9z51OgRUu6E/j5JyZbIjjmVHVX5WzC1AF25gmBgF+lpArMmJmsfcDLL5mx0w5ZRfflzIbUdIY7ISGUx2jqydruiXJndg5BA7jrmiXCK5XTdqdB/6jgOVVHP5Qzjo4QlkCiAGTUZrEEPUC9NEmW6Wak0Xo7EaEOcygcDrBw33J63Oem1uZiXy8FhDVWlCWyOucKhF3W1lRL+eUoSA6klNkr9VLMEcIBgKv5jXH+zx595mAf0WMlsUo1fe6bopaM8y4fHnHZ8soo5Fp9RK/tFS71PVr/o5lB2YVXV1Xj2OZOgU/tFfUsVTmlJvxqWk0asRbwGjQebcVrhNjpLs9Ect8mS2ORYlvI2gGeNIvd/pu02yHa0Rj5t+lp32X9q6MDzp694l5dUJHmNgFlEMbhplzI55+rA2DjyPepU75wXOFYeQ2+oKoketunpqr+bjQ60Z/yVioZTmuvMqEILmb7eEzvumxzR/EKndoLyw1KkCM+2iMaakYrWT3oigz98Zp1Yw3vLlSkbGcV9WEgIZok4HPt2xW/NxYp9EzCagaq+L2RgXSvgwfWMGkA/vyomzMC507hOZIVvn94O9IR7dKJ0vnbDwozn3rQ1FJE6+Zdq53Q59nLNW7b8hb16ryAfeah+W6U6++8Glt5M0uZLV1hyw/Y/AwVyCMGlP0AQjavUSTrXfrskte90tPeEOZJWG38Tt15NcE4APR4AShWB/eoPp6W0ugnOtzjAcuhYVOtdXwTf6mzHQy77YS87OHPfr15ik82WtaohQDJ/tIC67QRyBMy0BhqBgHeo017ZqKLckkjPqio4jJQXldP9fuFfM+ks0YCyBsQRlEC0ah6GamyaA11ByeuD/791nnI8ZCF0kW57RswqHZoVL7aHqK/699p4Lbh+sax6C936N3TItK7dZ+sCJ1RSfOCKxoOxDPgDClnMaIPxm/QEY1p/rbBlaRtppYTilfnYwlcej60OHe9LaRD5mAN5zl4fY4jA0m/vkgKCBzLc2v+f95jrBYCyommEzi6/+ZXTdv9R01dFQNRuqly1TrZGEkzpJfqHTduAxGCLycvcMyjJYmgVKZ82k+Bb2vEsV1kjVmvjgP1Pom4D1xJl7qowya4XzZVKIkCnHaNfV+yLqUccDQz9QSOW/hJUrU7LtDkksqRECReBU7CZrsNe3v7Hcewrel+zlL3QeFEDvqzGqdytzqtsZoOpwe2307otNlyM1dPJnsNdEhzLRFNcdYBKlsob46IrtxC3cw3wVQ42GaHq1g3NVonSrUlGtNYatkbNz6u01YoLsmsigkBdlKzCXGZAOxxN1IITpkXxfyRR5LMPWk5kcrGI9J82NFr5DxYbRtVtihZ5vDt5xQ9gXAECGPLhEYaK8ykXwrS6QHBHZpnGXGlCwyLd9RMSIn0vfr+5ryqmsdw5mFjz0DTLFqugMhL35ouYZLF4bb+Ticl63d2CGPqzWEyPf0aH9cQfK6J10k5IhNeSjnWAAbw6SWKVrNBiRhWFQrUZhXOxje4ptQwc/vsr8xSmCJLbH5tmulvv1tL4Im8+jxojyyvUWYjQCvKlMNNu/Ydep++prEUrQkc2oO1I/uxcnJRoAxy8wpca2V9TO1XcuKvM2kZoqa3nd+XvejXKiwhxJoKk3tbr4mIrVdC7HoyPI+y+oxi1z960pHr47VpryoMGs+j77jk9W4UoFmuWgRQANUkTSo4wQwInb5UDvmIBmWu3UWrXF/YEYOV+nDD0IwdHY2D+uMWgCY31iHXn49QC0hESem8OLONMrWKO/H1uFP9Y8dUSXWJJjL/rXz21k5VS0dm5UAySF8EW7XOreyysiZv6hfOYzHYi+X9qR8zQIgvjzTuzeVnmnr6r6j9LBLquZuLrqQ9Ep57bS4yzsIXozc++1Qyy28yaSLP6/hfMER1+2X/B0cjWdZjtWIK/drzy0rkXAoRU6KHrCVByb46/OGbFiMR2OoLHhXpyLW9YBtCYHtRoho6AGbKnG+cqqjc7pTI6rQw4x82Qp4IOM1vzgV+dUmTTIVdoqra2lt+PJHDIp2EZ7UXeifaW1vAYkZrWh/XjVJfHABaI4bHpufht/6fBrC9oa2CC02Gqb1IPva/D7MR1kdFcDH1Fq2pT1LchRmcd32vtY68a2hbFOsilqr4v8qjr4C0cK3ZOHGcCE4rumYhs61AZRkynXNxUrNvs1J1ngOS1cVIq0WT+iA1hqYE44BwB5F2RfZTNtJv985OseNvZguW2l21PPMs0jwqbaeFgq3GS3h9/bbKHyvwXTp0ZuamyRYd7Ja52iy51AomtflKaFMP1RxH9+XPxh3r3gu9oHKwHEJtQr3pdWD3rDLSZsbN+1kd5nNCie8Kuo2/MRul59xdlOp8vaY9XwBGwT2WxIJvdpkqtUQsF8+b0R5KTwTCvgCTJMx6C9cZ1h8lxV8e7Xf4Cs1LvkwcH/WQ7PFb/pTuesFbrOly3csOwqroboKfkEKCWQiHY2ayTFrmJJ11CRtbOPZ/PrRNixz2O71pHlBMlFDsW3LfdPNAoGRlubLR1nNGoKr7Km1j/28pcemj2ZilOIzmXnnzTj5jKYaPhcDAeg12GxX0uGY5aZLX837cvG/3B3KnvjJ5lG9oKehjqEQ7LMQtrY/S4tfc532ObRbrtQIkCtPm0GJOTEHUrm9zZvspMurk1HzjxZtZUyuBOI5I6n50Ai9JLU2lK67XcWgiO6dud5yw+5V/eijgxFNdexnD/eMTc9EmsoOVB6V2klvET6zA2tjr+UkRsq1AiYO/DYUoHyX/yuk4kwF0tDc0c7RwBIlo4Ixpk+GSFZwdtWM5gdYGMfomj3pf3Q0lqqJY0VrzUOH4oIpc1gKtt+w38Fbo4484hCLa+if2HPsY1t/8syVVOYIcRRGZfG6AECIZ0hTEq2So+408i/Mc2xeGS3eYsuno1znLWH6BgFoKyhBbrNqnqynBjfe+kkJeqwF2BOTqM7kW6ZbntLK1gcjLyEuG8oVpo3lJjDodIAV9Tf0/Olmn8izq1YOmyruMigCxaxWkRrsTu1YiKrMup3yZdHPrqX6QN9Cji4CctroZb3m2gwruz4iLqrrw4rs1HL1zveB4lvAISPsbisLqfNInCZ2zRcQWXglM5oIsPRVfMhFSjx9cyTV697P0oCYXsieZV4QAAAQAElEQVSwxkZ/y0CZ62hCWoa6NOkDfZNwPeTsY/y0n+TR2qw77EGP4lCgCFEgFEQjD+paXSiELolg3Pyh0tnxte4Ut8kZA0aPngItJFKQDnnT6XGR383WXEzqpLI8QqThZgJVNscaAT2w+4OI0VRJAvU23hkZl5gxpZ5ehYKNu87cLu0QWgY2FboCXW2eT+TSjogjs03CNdxI2YyR8inyaDk2xn9RUmLsgiJvzLR3EaDOygdbHhJBWx/vmqW9mUYN4J38vkN5m+A3D0zn58IT0GVcUZEODFiw5oelvL8Ur7GgLjET5xiOxeT211bO03dnyrdk1dC4H7WxgPETSfmkFTDetFrIpaHfQO1qwwawTN0o7JXSwRgUma4esqGo+uOB6h2+7opZsG9pvW01VAIV5A/WnkdgFYTXDL8g4CP+K/P8VpUGaqpXBNr8wETSabs1t1jprTpy7y1TDjf7WgHuwYtozYngUPakiHHSLYRs8I+8H8fDsVZ6Mec7zmtvMqHACFM8z9znTfttlfl0j/FzovJuM/dZnKG3RZrG6yN9fXqxBgSMBX0at44tVglg8ClTlFYjg/VACd9t/ynAmcPO8DEbJOvae/3WtnL10S/kt0b8KpMbpcCGrJH8VINR8GOe9KpAGoRgegS5qWSalbHMGXutx7ihnvE7/hf83azFaZPLuT0jgtPirnXi6xEJTRTKbvSpPqOyTAjc/efcA24wCEquyeW9yH1KLVTxOhBoU9gaAoY8t68UpobDHneV4U9pfaeBzSlvFayaPqOcFab/HoSVtfzOtKS7l5YrQAQw2lzGhfqoKBBi5CNPnjX7jFUF73Zzj3a1BjJXQXJ03YilcWA68/OLRxuWmYyWeGcg8bOVCeyxDzMXykxhdwuktT3FR6lAT4wH5XBQYLoJ4GKtmSnc6psJAbnmivDZWBqs0UjKkijULebbMyzs3psCamlSL6pNI2awM3e2mDRZMIA6h+7x7JUjJTxoirlBNu2Kq4AKthzlARph8JzQ1en2m8/bT+IBt3+QManm4umCddcnLGpK0kb/iVwZ0pOXwZeAi6p9yVKunNoSBaC4K79eKadYzVW15ARnyTzCezncY0KPEwA5GUOvDus/nF09Kc5bndh4AXQMnNQ49ujA3F5l13iCZwN/4NuCLiLDGGbCONOA2Lzsb/4erIENoXqMDfOxgirIMm3cSpY4v9VdszTq49IqIUvy203JG/I4OpFUv+zfwKEnluYaRajLhbVaiM8z11PM4LpCNCdXub2BdjrIxv2Hj6A72oemQf9w2x286GNa8YaHOJblA9q0n5y3KmPmARZjZB6uyeHhkAkGhyP8yoE58FVyX+teSRX52s2onD4t8P+8Uy+U4QZmmW1+gJTTKz8jjV77bj16escJr97zOjvhuv+dIDQtpiiqsiGvy76bd7uw68+4h1FYTdcU8sygLSa2efzAva1oL1UR6jPlGCcG3r7lQUEDjZJWqaqLhlXbMHy7L2t6k4TS+xFVv6YVm3PD+ivjSWhBqJ50a5crPu+4AAAz5P13WwQ6QMijAktnKwdmko6n6faShRYA+XCf+GnXTyZ+b/bv2e9sUWDOZ/gzXDOf3QSwsXTIkq+S1AizBiSsWwom3CzI6G+K2HFmIVPggVPq3cvhzoEOc+iZiT2DnjrQ7wQYg9BXLpaotzCdgA++8UPZR4KNSLNLJaYVwuH9bbVqufGRmE5Pls3d6N0QoyA/Vw5G4ymsY6MN9RXmL8ebFooCxiEHLLkr/YHkby12RjS3IIxykerLVmXZrlVnNVYRwwu+4AFEQpbn61A5vdXqiDbiDE7oJRpjJBEGaNA32FcOYEIyNn556f2mdikd+P3P7g/n0d2H73HHA1u1AG96+hoKX44dSMszE2inkwob9B5kNTuXDquBkg3lxcwXzSLhr8sAjKO/kifqHuvaI4eUgtY+/jS2aPOa0VhZtobN8WxeTFtTwjqO3oW4Haf8CNZj6gepc3vwY3xsALWFeLGSNiyPlOTwTC4cyrY6O0IKprgQ4vjknRepejZwVSjuCA0xmlGfQGnAj3nDMRQqbPZCQveIJ+t0/3vGyKlgbabBfJaL2AFkE2CZlMs605nuo7ozXyBbud8HfjpP60ZA0Uau/caTi4gyOVrYTjfyqdwSb0uRt6ih4hpshWlPZ4inbqRyBDuDvvAyBYoZDewcJvkhGC0McyBQUQygSP2doUSefcqGCvSb9n9e0F/RdKVO42muBBvpULzH/Xdw8ElVzj7E6lCpDK9+nzhbFa2Fp1U8r9/cPRQjipuH/Xfsf3rmMrv5DJVEq49dI03tV/7qxhxSwLzKu4C2IBc7NICetM/w7le0B+uabl/zF3MUM9SAuu1O9MejrwVbMqWbYLxncmrijLNnfTwwLa6SIhUFrY3XeRZzW6/5QvOhD/jRtI4rUn25H9U7uWcWoepnUCunWTQjx+pjgtPe5mn2lcnluXinX3E2Zd1LOAAp656VUnNt4wqdMa+9eodt2eSPnodfOIGtOPTBbBSyT0Y3m5POmtWDilKhiKyYhksZcjMjg5whChpGXJrI/iA7RL2WCuX1C3CaEOUUM5DqPsOt9XK6Zzq71dHe/H3rXMU20pd0YDBzqnEYsudm10vKg0AJSyjsz6RDQ8r22X5+pny+88csDbApqDd3CvDo4lbe5XAUmBgX6sbMGNpdQxRqbopyB0Kk/I7MKYwgKMCtIAUE1Q6V86NlKl/eahmnyd58JZAHeSdbll9nLquQuEMx3jrM2YktLmpBAsiw8gfzh3ejNmZbPtKAtzm+wJX+3RbqSXc+lm2uzwSUEa6mkxIVkbzzVed5OQ0S6gLrA0mob3JAjHnzOdy/Y9esv6D4lcupLkU+/slh27+hPSzSzQNBBowWokBlsf4X/lJ89A/sB27RWL8tyYeCXUmK0G9ot2UjBxgsO86rhPkd8aLt9r/mo9zCCZnOhfz0BrTypUQfjhJdUBqix5NWJq8zjq/aF2AGRYnnzp+BQ3YcJjytT/RFM7I+ewBlFReSgj463VqF9UCDbcLPtrP63f9+T5x0yIIgbfXaS1U134QS6q2e4lYEqnSCQmAU+4qr7Eu3nBF3zS/nb01+h8aiE4I99kDH/NR4Ky7kYhoAYiAkvRjb11KA1Sf7FUm9pQgpABG9nBZ0jqaJken5NGYX60Pkg8fwsxSOSK8q6d4ueY2tcZAsioOamdIamQvl/8LIaYPtiLUInTJP+MG8PL3e9dxNmMa+DpBok0kIY5j/cf+htD24XRdTNv0Qm0sBnZ6rPze83aQCvq+9Helk0F3phzrgY+mCzlI1HPdioGKl7UqbI9IzYePbYvkQgKLRm41zbBKoiVHkjMX+ufqyoJGr5VLBOMVyvH9+8PwAQZUdBGokjQgLSWRE0qRiCwhVQNJgzKwYUW4FXkU/kGtOs/HvxQWs0ZceOihfmNP9dpVaGUfyyKsLMY7k3NZrWjZMJnpqBiMRKB28rSKh6ast/+S8KByGZabaWtL8ldEsP3yae+vfVfNyWVawlPMGCWVk2mVXn9BK7gYMoysGApBfIrU3ciYVViX7OmuB0WYVgBj9N6+eaRUqGinYGEMrgRPI6Stj5vpPU3jfuTUn5j6kK3Ma5jD7eGXojwGspuK1lStqhYyi2rw7ZLih1x28zrvneEmkwoUZhfpTsO6K71Ih0ccSnhIA0AXiM7PWDCdn/iH9rDfVYeeKCh8CPuaF6r63uhKmnnJaGUGBFFgGJVfBpzsD6gtlc4h31vQPRcYwb5/qO61sEBL06PecURaLlupsclGmkjmDGXwvOsJg0NiPX4Cjb7OYz2h9au+SVZYmJaHz9sdsu+Y8dhfUK8c+S5z1ZTN6h35VFjjNbPQ3rUb13ESTgJh1BbK7eWrekzgaCcjrcPU8GgIaSWttp3tnPKWKoWxI/OJnyv7/LP2BYuS6jisAAqRkp8/b///TnY4tkli471Y7lSpbIkEQpGTnnhlz2xEKjNQDE9SMTguGdvjQK/Q05Lg1K/qiR/es1N7tNmXeW3gBT4QXr+AnEp7oGh3/GqeqjUmdAVhu/hVUQvnFijpfNbzgSDLMKp6Od6JmUVjTelEHU5pfyNfAHbchokJSL8jN6pZlIjrrQdj6LCNJ0XERTu5QHaBnrDCk/spAsGkdkRlr/9BiTQ0P13ut90sxexHL5PQebYK78KXIMfgwZaWRPy1yJSi6oiLwCf86zt5e7bNCtKIMt0m25Xstt0wXh/b6NzElozZaWg99cdTlUOFo+Z2KtJPYzRt7515Y6QCLIbePteDasmVHePo6eU/sgEJeJc5cwPb0f6PsxXMQAwhTVe3kuVwyT0ZlKtxXt08EJjmx2NxAfsad7EuxwYBmQCvRv6oc4o8QH0nEb4T5TNFHrMyVwchQJnWZvpS/Q7fkIMnqOM1jzkGX3+HuyLjHffvnuoLbhgv5uirD/fRT7CvrcIJzkYk9WL1daS/zxRoPHu+z8lZukIwJWO0BxLghmBFHJez2TC28icphWA0G4SVSEwc4Vm7/NT9miaZkk2llvTgNv8wA2OKjbC6LDeGwan2r54m3d3v9fUa/Os/u90o/rwEyILeXk9G5FTkTGjZydA2/g77hjIRhT+5hdmk90InvVGhZ0qbRANr16Hrg027ZkmrGjW7KoFZhAcyZtO01pjuQiQp8NdRd9TvtVsGOSyuTsPnvCJrwTFljbWZ2HNJhIQ094Zj9pek9CAtyN32AsqvEvxc/wiP8OXrw62SViAlnbqOMUUuKmvgYJBZhz/RPODF39U+fxRdXmyKHG+eKuYBlpdPMMQRVohMRCq8k1csx+iMtxLDgYJ7cb9reQGOdxUnIaBO4oPQnR996mZW2s2D/EWsxVnvcqCywWXF29ndqaeRCdJmr4fuTR/jteTAt21Uv/UO3du/lOQ++GsdkyJZ7OKN/L8Ai0+pILa9tZ1QzFhrpteN8QaViOTZWwNiTDpmiYEfNguMVhhjAasxr0ryMT2O11a5omJ3hO57uMYNl0Nf01eUGXeWZA9agWj4l+dujcMm9zlDuMAmr2q7aip3UkG558NM1/9nQW6sI5/rEvPFWOLGLBJ25oD3au4tGUPoGA5GeCqdxLEfUQflD+1Ie2Ed44Yfj1Dc1iSWbi38F4L8838WUzxpBkGFuHDvt0tlMOvU2Hi0eB0+ThQjwagPlFug8bo/0kollZL4WQaugwxWNaaKShoupxEE+E2q6AyT/SHft0Pb3N3hyzYYCRhNJ86MrYLCG2orXwjOrB9FNm6CdK/QPhOjayuC93AmeHLPcywOSNhQcYiA1TAmfwAEXkAPY4NyFLSJUruTUwnXpXudyb+q/+LYOH8/S4nLaEIAJ6p6n9NjIp2gCExQTwbCr5qusiObgn6Jm2jNei10a2R5Av2KtpCkuKKxe76a8y3szJ/brGtmRkYtcrV29iuZmoi1tAFtX4s/ENVyplb0UQtIHgjnRHAAAEABJREFUPbLIcZTu7OE1RM4nM669/x94/ysHS+sEKVkzPuEHNbNVNDxTFYi0C0zS04Rdk/VJ+Y1V1i8d5tm/zx4zt4UwAS4AY155wQQ5YeXSwzVI0NcNJG3CXdLXTZQHnEb/K0uEVnTQIGu5MeH9/pZOd5ufY1Div9R9NFCyNWyGl9sEUjJ9sYn1aYptnv30otzMr1uZduzNRwaABd60KpqAM8XYGw6mVGfQ6YCDkYEIDzDRAU9ZOBu/450e6klarUaxuvNN37NdD91Adfb0zzvxFN/pieLdsU/zqbfZugT3LbpWlb2m8X8lP+YaSyGiYj0OITNXbvY9k6+zgnBV7FJoCFPjdy+UWXcyIvGoB2OUabAER1AAwSBW87sAzwzvpS26U4GyQdzMwIUr8RN5f8DcIVgxmWECCQpNPoiDVcoaL4X1rPN3nzdfsO6wE8/gISRGTXQjuva8ziFQHvU7+22IE5e3ZN6KawEr4uZHMKJwD7//Ax7BhYSf/rYLHxGtfEvnlJczcymTF47U9baJ8NJ6HV2/WIdz8Byc46oayz4G4HC/SJ/C+7L+ndlr2a/L8Dg764BGtTrgQiz/OANsUCk77qnTcSZ9fQzLbMV1Ly4mFDKVfweH2fSDpdUlVttQQS+kLr6djlqYGb6vmcjSFI74KI+1tv8ATiCvBe99447jZyKMlxxrzWWVSznkWegI9QVtkLh3X/t3hWPqw/If1QxRHXTOSYbXjWe/voLSskyblkEuv5DppFL4EvI9M31gMPsoDPU/4E/0nc5OxGyeC//rCKvGC/Or/7Fl+3pcAugWIKw9125eo2vlusgL3ERkZDIXmGQmF0U5m7/TtjTYwNVtk4gL60cV57dR756xYtODCazf2K+DnuAZvMzikt9BHIaZFA5guVsqhY8onzVb3kG4xsL8+lnJyo0MxGTMgmJ1eJOTTq/0rOjcjRsk0tdeXMdwgSt65fG5SXZFP84hn5cl0NVjv17A6s5jC+SXH+7lmWTIiph9nnafIm36QmUE7BmrkhOe917ZYh/nmuZrL+wm/8JPMzC9gsEd2gMPQIP+7pOz9sNr/BhpfsCfCC+JmFNV3YOEuWQAYlA7Yatp43DYqQniu7RIBTuzIpZ0j/dsz+FbS55w5Voex+Nyhz/FGiWbbj4eHIqefDv8/JWv2fc2tK23r9va7xA+bH6BF+gyjFqcK43Tb9sQtKb4/OL9+mwphSvW5r1x/0Te30Rjc27bS49r72sZ70aEZeldhTu2Rf0L/gpyOPRkcI9BPXxOtpY4/DbjZmlquWHzOjt+f7oCrP2jfTnAPR5p98m5b/ysta0zf1/RF98Vv7Pm98LZPddwy7kyIyu/AkFU0dH7GJe3Zo6vB+7FFVLBYu024HZBXXl2uqHNtvYyekcbhxBCUwf1nN/f83tk/p1afP91pNzPVLqI2wHmNbNO55kEuJJXKF3vbWsrZxML3G2OfUwsX7JpoIVyK1pwZLPhFX7Ydq6b8qPb/fHnrx0sbZZzOM15g2DabTmS91UNsGlxm4Wa20MlEL35hbLDCR85Gi8f8Wccgetp02WE/Ev4kezDc3xAR8D4sOETqLTb8ctuaAh0tfFE5Eo/E71u5H24PIfjJDzLHpU3fXns/n35/N5/f9fz8j3GOJGw4h2s01HlZcKbAxFlT4aTuU9ezRWKDJqWgVPk+su+rrnT1emA0Gu5CFxLdaQphVfx50vSxnvFBG0R/c55xjUX4yq6OHfE8l/uXW4LuOEc4gdxcy3nLu5y3zmZhujYfub6kb8wQrm0Ni4yA+SZciUjn1jDgK9W+A/q91txpsGOyDs2J7uhlo2tvZDJznYRF5psxugj+ltxlopWTpzY753ProkH71h1s1I/xH/SNXV5GOGXp6N7huXGhzBNGbiWLmtlmxsOKcJtIfX1pnxG/3f0SzxrdTryi/AU0+utT7E6B56baNBF20ZmbKmgdvSCAmhRjB47NufPpc7U2hP7NPXC1SR/Ku3KW3ubAceIUdpoGQ4NaiiFuIwW1qQpOjK4ryFsJStmvuupYHl44PuQ0ftqp+pJPOD8gw4j+gSlsOQuPNc53kfcmr0KP4Uk5ifjytwRNPKX/Tfxd7m3DCHvztN6JLBcN67WJYQs4u4qt8770X6N4gfv7to6e55wN3GavdA60/lGmBM0jLJVnqbfzmPLwMDAM5ho1lgypq3Hkb4+Y2dfUzMVUBaXJuwb/jW49lyrbs2qNVivv+zQ4vfvi0lmyNY78mH0yItQxGE0gxGz3EivWhvf2Euzhzmp0pzR4FW8B/ql9ScHK9oAULM6NkKka+idv+pDE3yt9vLnvIEbzFzjM3/yqwRvGH9/eX6RHY4nbuknxpVNLZzMwwRyBW1goWjv48zZxcCBu3GO62Ds38PANKBjjiyiwZal0Nyqq86FTCdIRvdKjiTeWF+0qG1HfZxFYvXe8lL757ngpeTcwgozOk5ECz0qlHeehSNCi1Z8xi/4NHTgVYYr3vtb9hcM0BUwvkAP8c4mRrIxU/gW/+19AgRDOyYxCDz0XUW8PnuSY7L2gXWf5cdcj1VD738gXafvoFm38aIeoT9Ap6v7Yz7pNHLGSkZd6BUdPAnPdtpGH1/Re53r8rX5CH25iz9OjuEM1IYVjMG/mLvOhC+74iLhOkjF8ghZcUf5FurDkEAO5vB37l5GyJvj7sF0WggiiJJRaGTu5nLtc2wOmUOjmKnGqygk7Se16b4TUSY2AO8d4rJMEJLKP6aC6O9bTzVbrvbsjDZ5HlTRbcPpSeFxpbYcm0vjaryqwiL+AH2lFod6YzpGqXUrk8zsXo8L6P2+WS4Dx7rmSb4MV+Exm0XaVaLSU5mW3QHfUK+WvxFm22NeY5NAfaPaJzXwVwZiKfIjVD7rPudwMidYIoAEEfDX+X65Ji4W/g1svp8JE4lwL1436bU4djvW+ByxE/7tHd22q9QJt9cWnuFUfIKRKxtWuB1c/a5pMx9wHSY3kd9q882ecSBGo7AnraV/y1xjGWKXrfU3OgClDOkZvP3pxuVCx+xTleM6TMjmskmPJRvTFBCptdvN3mbGDM7x81LnU/H09dbus3Ly05vJdPIwIKMzvH4AnkHsUISc7EbRAmpcxp9pgc7kzvhJXmFPdA/gIoNKdLKIhnGUxh/Uosas0CPyu7vJHDhjUrRJaF/qj7gSjuc73rPad/WeFgbSyzbBfxJ0qgCdmYM3eX6Ct1O18tsw3IotLuTa18oL43khaJkMDFBDlVjeCXm1JnAVdoECyFlZsY+9+OsYlQ97qsY8fBvvMjckJPMzWLOzb+ruiRlGB8dScvhnQRs5XC/ytYkYazgEPNDLctJ6SUkMHfQc8bW5dgGEDiH4J6C1JrMVq+ePbLQi5js4AROdSLqsGLXwXeU3tonJ4vpNvqmCWaeSXB2r6GzmkDNt+hSvYRFex/KssUDsdzIFJRSlkD1FJ0ZUjU51y9FCpBNMG8Ep/2rm8zH3MyaA1CK4kk5BeuhLvDxCGWDuSCYlz4J/IZMRTRzKPLc5sL8OG+Dti/IHYYN3n3W8wODAS3h4eVOLzoK8tPGnhw52nXDKZTr2d+diLzl7GYoztdcT6SwOJmTLAAg4cwledFONxRrfxvtdGHPr9Hso0/MdRjrIFfRoQ5iB7BsdELSOqfqGHMbAQxQOIK7xrynDvsYVmAZyR2wo5zXYPBNFV5LMrYlVJWHWvgH6WgrZtHGM+QoZTYXP56tv4ckeZ8qYHW0HZV+T4YCky2agSzDkECM/Idnwd3mCI4OOhobtrBzzrWtx33Kaq5PjgIWoMUG9UEuOVp/z6+sEMNzvDGOUUh7EsdWUyeSnsZN4UmdQxU8eQsM0fQ4T3AjfGzjt/m5hpZgr986LsatXGRVn6NmTHRtuX76gLJsZHnTBuX3mdTObKcx0j8+8g5E9je2mkmd7RkFKbWsO9ERZTumO+NVladkjeH0JQ4BkeNxkVR/frzlKygno/K6Jo+wJC4NRkb6lQsT+wboS+9vdjNV+Za6YoMV8WtHg94+i8Sjob+FZtaN/XHsoYFozmjjlAJOKS98asJqjz5dyvvJQA0G5r1z/ZFucY3UEFvDtxl7Er2UYObLBd81YfD5MiUOwmyXasnlRO6o06YJfzn8vIldmfPbDsvdvQ3UY3wWQNJBMeBTONWV3nInu9W3LQEuUB/9rB+iMSrcap0/o1i/n67MZwe0oHbEb6TqeThb4nGXBAfQRgBrzVHnL0w/6tQUsM4JIz+1393vLzT62sY8can12Nxmgyer5auNd9/FNFEI9KkFIyBbyPvGnZlNjKXSfR8fBjG87ndWJaQ8Md5qMWIl9t/496hjbZuKTp9F8j6SoMyw5R6Eo8XEscjVwY1Z+FKx4Eq3wkqE3a9jQYMIVI7wTT2e9gd+M3xUVxXgCM6REttXjbQ74Lry27wh6Ly2Bx6SFEwgoC+nKe+M6ucUIpb5gv6BNvOQwtsNA+woixjcbd+VnJ5xeG7Ex+0uEHejFPDboYst/j492eFRU8s3whd/O9hFqA9K7LYNpoBzUQIQcrV5jTyA3sEZxhjXzLwfjATnyYTjQ59F8gRMOqqWedqW6n+fs+ABZCRhycEkLXBGbuZzuQbQV3zcq9XHqmO1jHE+NPMGlfb3af6GDw4M/tRZTGBOvj9In652qp+bpNuc9yz6c5XFPRtvErqDoVvT8vG3vdOlwvTR2IR1Le+ocGECi9j68xsdMvO91fjc6P+azVGOjo2Y3Sp6C10FZ/b3muH3nsh8aJ9Qe5JEGcA7fkf8Fag1cMz3STDa2pXYSig62c7ohoY7UVh7nXu378I6uPHYeYhzMGTvOSdMwoXa0d3Bn0hnmP2rXuoIxYJHBa80FYwzmz/p/1/oP4jk9dWJIgEZEZ8k3frGMJORl+VEVnHEDe9JRmsuyLvLKFXvLO9pZzijC4B0LuuxMcmLCMJd2GgmtG5fL70x9ypIsIrlYNGia+CsQhE1woMnXIIuO/TsdGdYVQdfzxZZY4wzwrDi391GXBZ1jQ6+tk0rEbvuTM8erJ8bn0a0aQRPR18/sa+zb96VdcPDbnL9zfp0fb8nCKFa5McjPqLZsji4S8AWcDvINPhd4ay0ZITpPwyfLMTTwIsl98QoS6/XhFMgmcp+VMi3ItldZJ3tleiDsyIc/70u5HfvB78xfwBIIeeHvH3ppiRth5dDY9zH17h3hbO0gIB/hcEvfxyAztvb2Zssd77qL5hl0EY/Pi+ut6/cJPdqFC26ElfKf07A7o3YcxqvVL03GplPAL3zRWREQO9sqsHKew/PSpy7oDuZFW5ugVfMWD4DslRVWtFcX88+/sX77cjF8xs/kvIHZZMBsdhTxbhOXESsNzJFMyrBmH+GN9zhF7h6UtwwPNTJutnurE1ztbDemJGfWYulVsmAMAzy0aKgV7jS+c8vV9KJhNoqs/heZquW9PJ8ONq9S/qI6noxnIdfc6M1ZnNEK0oIAABAASURBVLBjWFJUeCM6kfSvd+PJPvpQmbMVGaBT2E6o+9/m2j3rPfOOR9VvzLN4kmYPZupF1lDjMH+5K7dW8k/qJ8+qmdcSinB2NUAh3o7f1qH5q9ELp+zne2w04WidtX+V+u2jHIFDHeTAgX4evSS8bi/LqcYt/ExvvCffY9bGu7A4iCbsKzqyruiFL4YYhD2Pebv2vnLHdNcz9Zv6Bd+H50E3jveT2627hbIs4bRrkiZVPIzXTOaGOs+JYyRqXeL+VMgcO/aewpfxNaCC4porTppup9VhuNU9MYfTU5rWzJzx2g2bQ8eus35P9NcKmAnKC2Dczf8GtylgOu4n6u337+idPE6iNKHKfJ1q4RFG6DHVs878ad7eBvpmrf2HQe9hnhf2m9H3VT9h8JmLuQQOsYHt2YNGnH9nL2AtWmlz9SP9bXiVMvb8zORBNhZ57dqX/Z/km7t5nY+tOUt9LdMTmIizr3Pf/f3/fIlGvp3Fwv3e93HpmV1v+1/e7/w59TPDhbNNb7R/zY174b+d6879/0m5p/xX7z1PxGvIYSnzXD1xxpGbPmTS+ZPJfJ1ShxEy7PPyvPCoeN9uO5y+ZeXK+HRZ9gNPS8xOhLh8vgFXsWT9hP7VSfzCfaDLCskEkhx37PwDmtCdM4ne4eeJs50eexO+TFfIkRSnDzUrJNo0VdGeAZDITT/dEePAxl+F91ImQtrTl+ravC4jo/tI6hCjNLyCJUA5jDGh18FdVsTbVh3EC/2Hz0+48VtDi4xCP/M+7zlnxjnPPZmHZvI7jA5tBbx0LpfobddasBnc9Mu+GuWIduCfc0bu6/Ifdf7sc+8ns+AOvQwJsDxfrmE6mSs8dZ2HZWSNYET8sH70+6PnolKmLvc73/57ZoiOBh9xXlz1AQhanPW1lBtxxcXgierqPKCbR+wFXOM2jpR7x7y+PDvIvayCN86ro/FDCCXoY1u3fw7/077hUHMmLVq8F8YLpD7oq3W7d/MFykZd6k7Dk3YspKsu1peydU/4arHPT73/Rd174hIQUXENthcf+7sybTqcW04TFe3oCKc3fyaih+0O0TEyTs/Ghgv+7fh1c3oqT2WbGnlyNvMEbIWL8HqOUMM9gb/AL+vlAcaWbNXIMLmZV/DyqH6esa/6ckQwsDZvd6rshCnId61X19v30YedZ/G9WPeINPbz4Pfl355abVG4BuJNnAWv2PePm35f3dTrFGDgzRJfI4va67l+3rnx3Px1n7HR+lInUQSc7kfzF/MG3dUz6K3OpXHWk9iMNbEc8gvBFVTRRp4M1sIbon8T3Ff9uc4fU+4/N/2Gqcocx50eLTjvrsfAMY9RFFaCP+jLFmbcjk3ag1X4E7LrFaR1sdFmIKIB9eD1QpGV2f577t3NGtTG8Q2RWymsKyjKbd4Jm3dBA74RJUbHriWt0npHpdRn3u+Ge/ADhPTkeW319pXI05jTMeME8yK3XEU8R7/NMzLD6lPoJ00Kv3T81no1J6ejnAQ7H9LkhCK+6K1nbw6ueA1K6wlXVsol1M/bJmGNFQ6e9ro0GPyUCSXfM65C9Orac4y+fCO6OL3kuI/VfruxGAMaaYpQX9gAwZif1H/EnS7Eu7Ub+yDmWOs0o04gXbdpRD351pZM4WwW2ZFm7gHccSwiqhWekx9tMhNonVPPZyoC8Gjki7BCxuzOjBNGGUMiN1d+Ur2gK8aJ9WmSoKXz5cQlv8VLw2q1Q49eaPa74nx9Yrn2FG66oqJhEn/Y926HNczuxDGGNSvrZu+0h9QikqxE70FAfhkSIsJgrjSMFgQSyh76lZEZIeuF+S9Mx4viuCKUDGQAXOPhoEjPeUYP9RnxgCbOhPxLoWKV/nLKUWV4eAG1NDGMs+Cu+5vrfKKK2Fjr3Hqv6hwcWbKaJbjquAxnr/7CMuOylFfNjlwrkW7X8fZoOsIJbN+SIWFvhm8WoUiDnG/ijGJ6RS7nwtLBlyw41X3RZA/QHxVYVFp8TZ9dyGTEgn19bXT3s+ooVZcdhFXinFCQuTDHrJyCObMj2n0GDBGRNuxE/yEv0Nelw3KPdKjTrVMsL8JaQU/QwKStKGR3yhEsrNtA/AUXHQatPsB47Ba1YjxxNb6GeWahTIU6u2KsllGexWfbpWDpF8JX/Ek1g9dqqwbLOxELpWxWTh8UEWTYUcMhFVtsxjGzHj0TTq960AEEyP6xVFXhbyGvKCR8FpPhWTbGFcbbnOkDgGBGZtgBmRAdgrbpw8vPvxbLu7Cr7V0GdTKfxntwyqn8whKq+ZVaij+drK9pyJYuMpwKAScxBtSIsg94H+EyjLk1W0qTc8/s0MbwEFg0oB0ZYCQytdbiuvyr0pgsMrVo9OY1ojJb4Nhl9TwqNz2Q+Xkzp6aWwxhcLT+Lv4zej2efcggUdviaSNelj3dZ0xGzb62IZXbsBDOjnpma9s9MY1oU11TW+WaclFjRZ4072pKWe0AtzL8ibzPExp5M8+wfOEYN9Sz9ct4wxGI3BWQcH0DBpbwGibiQ+4ufiupo+VVEkUWDTycIC1ShZC0ZHcRMRvj3gANpjcEqnLLTYWjnwiwg8pu57atFTLRD9dvP8VZFK8O1duCQ31I7FUYUXoj6y6/rfNZMyocX0UNftIlB9Fxs5hR1PKr840Pd6AK+LPnHBlvwnPiYdUqqU9grjbNX6Y7ItUB6SoMhb+eYFfSkVKxwfFgGOAjP9Oq1706mG0I7lBB3c9XmCT346ksCCIGm5CPPMXbco4udPj1EoRqvNGa0Mj7eVCtkN/RcHl6PHfnz6uAbnoCheZaDCEdm80vOP53a+ux9IrmYmVbvRl4NGE8HlJ7HolvWnGhPtPRNp8k2BgfBWHevZSujI5ST4O7Uhc+A7/feCbOY6gue7XaB8M7Ih4Ij8z+RyAGc+q+sFNMuNXsVOc4RxIi14nI4Q5kdTq4mnajrlRtyOpHdKxr5nn0sbVcqyC9AR68JTyU5e4/3jdiOCbtpCpgMIdvdB8Fghl3aCaRpNUto+YoWP4qkpdrT00+ryiZjg+6zt/at5e2QSxcmWK0wAveO+woyuuGFrQZJLx7WY+UUnLlrKbcRru6ll/Bi9BE3HKi8ULkeLiflAreuhR1t2kao9nXl6IoxbruT03NcWyoKPgzviDMQxhF8xRrs+IdLzPZusbMUDmShbmgLmAr8ZrzmAWx/AbnJP4Tr8GYncuZ7hKEBjJGSDw+ls2iKRb6b5ydxhTxXQV/xkBmnOuwgqiBfu7D+xOLMeU+/L3vS8zPH3TNoN87KkOj4l2s0zuErfl3jQE1nezo1AXmpDJeHb0RzI7Jdl49854QZw9oZaxCt4RdnO/O7MhieN5iy1S/sIenhHTnKqtRzc25yr9g7/RYZ5si5O6vdRkuABHGtheXJjXl72hDByjm24ziD3z9ojpxfnrmGO5hiuEO5QUka7MaCoxb/Q//gpI4JViTD+Q5HTwxn4pTemvnHfaFehwnZlmbxbD8+iR4eB355l9Trc0MRX3/raMEsw4nunCfP71IHNnOvjJ1YJlIHWbpaYeduTDDRbHmF8DgwO8OuDCXt6FMh6YEfFTnt2588gScx/MrLpUL8ls7zER3sz+AWL0+CjhX/M+ePgEPLokdEZ86+2ilbZCoIctID1GfqVPvFHO5x+sLsd0eBQBNjlEi9me+K44CMl7P8BJnPLWd11BjjXaJjar4nj+uRsQNXZIaT4O2kNw0D1Pp5+ef1wm9NUDZsyXxufgkXWrvGvyYB15RrKaB0in1yHLnShRqF1Yfj+N3XYxDIf4gUB6xo7PrXgEgRFIxiLcVWXNL6sgw4/9F0SmFFUH3JSJWMkDfCnxeuMFtXztomPQUnR/NO1Lmnbs7e2240+B9cLWrFNwx2c6ndOelsLoP2tQp4jeyYg7eqq0gr5lMSRFlB7cx9YIZRscgrettJeBwR/FIGR+QY4nY2mxgQ4AIsc4w9EySv4ppeeTLLHSiq5Ts6VUfPnk6PJBq73gu/wYeBVbHf5cYEL39pji7eP9j+ZAE1C3ZlCIlI2N2YbzeFCJfjLK0LdyIT5UPj4LoMRI4DJpLmbzMMCZObjlUuzyrHzOBa9PIqyfp+Pcpn2/ugl+noBpDZTc2aWeVzAeeolZIXrtkod2werpf00KscRMLR4TEH15jeZGry4f3un1nuuJ87J5S8iSSG9d71/ow7nZh5/uwn7mN3vUYxsfvn0voRb4zdF2CAnhaAD4chfMFvzDr1aWQxkhEI3KtXlHcztEi+9OqtmErRV+mG/3mR5wNWF9bRmoI1lNZ44MgPcfnU6t+w4FW3uGfmTNeUTYpG1FFyFjlEQpv4DAyisXBf+GFtiOTLJQ8Zeapt/uoubOTYLdnyvzlmHrSI7+/p2++bj+8CbddPgPZ7rvm9afA1qqNf4f8yHqQAAqFwZnQj8utyl8N4VOoH3+Dzi3lMHgO8rFjP8cEoc4D7s7GHEbS419UWDLZ1wSC2DXl7OTyJ8WIZwvjjIb7XmDCsSGTwOzMrPOhLeCHLmfJRknooO2gyR5SJurIZb1uJDzjObUbUE/PCJQtWLSgy1wqjyTvjvrQdAeL4qapxRoQzpwmCkRQR8DO961q574d//iKriufRMQBfI9aAR+gxpNaM6ug0fJYEwOZWXGGG7OqkH9Cs50F79IThuYMbqoz/IxWAiwfXGusyG5QZnUkc02pGbXG/SbP+rdhP5znrHdY2gjf6wBU4Aadroa/pC1hJOGycVHv1+BNYjvryAogWhHDTPW3Mb5R1YH8aUFg+nH3AY4yiGGdlDPfr/TU/w/iXp7ECv1ujV/P7HK/qcCLyrkw/l3juPn/UG+bDRhgao9oHKkCiHfGa+GfNnqhGd+gsTDA6Xaye2kHnwYO3AQWlbBt+nB9fIBUsusdhnJX1i/iNVr7g0858yY30ApMMOrLhaBrwEJRLIq/f43Pzk/Pj9EMv9ct11h38uZDp3F6q+DYrNfDYhqFEmtV/5OGqvXRHbi394n3wwtQQmyu47RVAzn1iTbITpS60z3PnybtNz8k8MYoDWhkecmUpz8P+q3GpYwdjOazrmXtmYArzd/P8QH+AP6ImByvkJ7RBPoPXKMpkvlqV7IvtvsmAS7rDQxvh+ourPWt69cM5X22XOb+he3lziWBSPzPLERARq5hvR71FK03V0kO0YGZzjNjW4cExWBXbh9y8vpMY0mbMuor+8gY7g9/g76vLJEzNAC7rUQD3MJZ5xOycNYX6eM+tDMnx/pK/YOsgei8yM/1DQ0GHyiAkHHyi+L7SYhL2mADrG7OUV9/7+QMvFNoI4lKja5qo3L9xWYXY5bVs57UzdtJdYyVuptOxHMRMHCXFFXtZBHteWiAOEDS9vl9wRRUMGHPgImszDBP4ni9MrgI0Woer2YGvij9SDfuXOKTa7NPpTQEzQqFcY4pfZAOI9E9neK7Hf9wZ1p+IZdceHEW+V/jP9U+vx7Qc3qPNwLXw8RhVtLPxnX5GAAAQAElEQVTO7jWxkbdjdKZgDuEULFPv6ByyDGI5PWywkDUxXrFkIi9gnTrv35pDGKO7QcqRNTXjEBK4uBauTA5P11MeFabIESQhQ6d+x8q6ZjZlwnzFZdS/kGJFmHpBU8Fp/4ZHaPHk+nX2iYj+Y/uMdM6ksRvzte03vFQqoiKftf5y/38njlHaxVp0ed935HZcxrzkv9+1+yaMc0cmrOj5rT5ljItx6Z5ZZ5wEN5p5Vd9HTm17zBxvRwWfrF/8/t2/Z5skcva1lptJ/ikj3qdSNn1j0loby5fHwQeflX8vPt4Xo2RtOSPgoryDqYWD0zIOk8h9OzArPHrfE3fzp8hhxxRPqI2v9O0MXPS8+XPz/on1B3Whc7wA/TTuNnHtys3qcAKQQJKu+r3OxBPAddxwQvKculvZduLqaVNQl8e0enTIAEn4DROf68bhIGpHWyYaG8BcZCxh+0jHn4SJnq7j17yTtZdPu7h+om7ryASXShjnDqzwh83Jy2KdEY9BYdHM/3XVUtPxDidEPTWjAt1SDS021r7utbboYcjOSxGygGDQK+israjdsx+tcRodjM0AAuAh9taTGnehK02vqlmuBmvakcfrRucs7VRs5/ZB/6WY66fv/871Z25vpbGZP8gf9nYAV2sPXKfkF40moAv7p+6fL0Nrgr83fhcUb6A3d8Yld1gFiuu387fiBHHN8t9v1t/pGWQgcgCMC0Q39EMtNaJt3Bfpz+dAx0aSjZSSsdbiFdgKTzWBhGtAETOuBjP7bZsq+IoeoPF5H/gY2vT5J66jrc3Yw6yA7/Znx4FnQK6ajQ7V8O0P12hKfdq4FocC1HbkmivqrSkPoFzYjAA1c/BWvrbGenEeuNbuS/lHsxoato1Y91mOAExMtqJXjCPg+BmPR0k+294l5Ql2Ut8wDBnANZ19MNXVqFr9ugGlUshB9IwByYBEzuYsmJoGO0Zh5SPhCsUO5UavjxE5mfYx0O9YgstXmBEBHxRj1ky6koqyIUhGzV0ZZ2XRxDALHvidpm1do2wAGelVKJiBIZDclxHOAEf09fXFw+9tGB1hNCd9tUOGBA9JxDf2eDD0tfbVLsE0BFFnTy27hYenJlns4vky8rejvFfvzFqrYHjWW1kwbYnsKhv9OvVhD2g/l7RqMP1x/qO9vpblzVUuzVJJ/hPwAz1xzvKS1CPZVTkLLKzBpjxyH/s8oWZMfyEoDC/P2uXgvs/hYdp2GcS6ZXg63pfU8FzrcQGwLEAoBAglDnfS/H0gNZI3LxgsMeEeAI7QE0UP/Djii3ms+q0dmU7Nxw0bXeEtXlnF/TWhLbq3LSAxi3DuEWOTsjJ8IEFzaszc0JW9clYzDnI6aInCa1i+UpxaZqc8PbYsTVz9sc1WfBgBohrjcJJrf/O/snIU+kCflBnjsiw9gkJw5ur2MxnjmUDDrwkNKkqpL/GaWt3EMbvglXXt414z+Y2u3Iw0fh9YhO+yfxDpoA1IDgzbZzOHNC6+vE/cwtoNttvK5p9Lzkxt5/f0FOd1mXxDCtMshROzxylwzKBo2z9+LjcO+dPDIOicOWX2E+wMUzYvG13RHbPWXNkrQFuKc+isqMWgNwzu8z5isV1Bs2U+PH8KeLPM40hDBibhevFlZCadZ6tUyZWxhusB3kIVDvR52gthPjy8/NSMlCHp26BfGTucFvnylbnWdr16PdVy9mIsiY8yj+cRXjp+kLPGN+AmwCHsHmv7wnVHXuFz4ITTtaMcfhIRyfQrVkqqo3rH1WOW5DNB+J9b3CznO2qiizoYV/IAZloVVGzWnY8wXdOnXJBLvKBd8j3bBiJt0XZaQBtn4hPLVnxZnlqQt0LB8GuRSfif8bTHnp3nZ8+dnonxgwa3EKjDzoTHyizrc8jHSzwNeH7eVr1d77Er6MJsNGS8vEs/3bvfXXMNwlYafUY+PGuISIOw0cdECTtxB+Ormx/ix1Lan2D+JekMBp2yEOgqqd+wilTf2QwF7Ebe8uTpn9DN3OQOl4p6tmX9wIVZgotVMIErzaPemNpjpfzjKhV71oW6pvitJuajg4OcvrpX/88cTyzzt4kLcsaz5yosSORLAVxcd1o75jb81cAPXbHC6kp3TQfBINxMrI18GQ1TMnqVw53ebMYFD544VEtdUWc/uipTC7mUu7Cf4ls8pGkBlNKyrj7LmGrzPJaOBFlE3+L9KJ6Xjo2edB38uyd8l1406tj12tz3zvgZxavxg5I3L99uCAG/MuiLTiT8eOa0cy51qHdgJZKRDqwq5118l5MVohOYNu1VqCxUeUPXSbOGGHzzFhMwN572Cq+pWm9c5QMuMvWHXu9vz+PE52JMQPRZZsUauFDWxVjENE/hECdZuV+ko0iEXwBlf+vJeJ1LCGMT4BZ2Mm7wnnN3s6rIWlPZkexN2ZsU5fSWf5nAoWVLIgRMoD74oGPf3Muh/esGVo20ObZR0sNGd5Ysj6bNwopbWHTKwidsENT8y5k4Pv9jS3eMHxUPHwf1wr1gEp8MAzCiZ3zqK9mZHDediqP2gGXgdWMyCYuESwy6SU3reCWsRebai3t3rLpidkZuwycRhAHP7gkFdPXaWEbmWJWW/NLtiT7jEZm51oIvAQd0MzKiIw1w0ReSIzqp86YJ9Q4bIz92tF7bKbKHGLv75q8x72BHwxUG+Ry+lKScjfaEmXB+t3XPwFnhGD4NOzt1WVA65oxJmrfAjEVogmTKlUaWkGES5vko5MudGXvH2pNXM1gMcSPD/dreEZlf+VpRsmbff4teoUtwEjyUQswDnAlwvt9qD6pxBh44GhhwL4L5M7iCudgJU9QYcjyJEbjMKGNHGqF9efsQxx+3CZyg5EkXZpMOzBb8mT1yj1k7760MfGF6hln7IMlhxCZcePE5gdySNttpOKrOcXKTgcOvW7udHi2BhM/3mlGhmk+gyCRj8KHN7/KEozo93t7PoDHCeHO33jfOSTX9E1/mTHBe5OYEjjzW8pKtnfCE4HXHyiDcxUwLtVkZ1sK08BmtwVgdpk9f5VdrjHNehMZhcbW3IldO+kJ952wCvfEuvLuRLrsQeMjf74jXF/7GKpgWAnSq4b+tPLvdaH7LPYWMj6aQOWlz4EpPs3X0To+00vI9coQNNSyBByzw1SWs4Nd9sqlhySuG5pzu+v7vLEDOjtmOZJiRIE62Hf9CDqnoVqC52nd4THHU7fb4njwMS6e35kf4k/NzvYynxlxevP5gXba81RcQEEG7gfPym79Y7w/jB3c62e+8VaVRfIr/BvV8W61b1pujdLuAAxq7TjlFHVenHwvj4B0WL6z/4rK9C97gaB+4TsEe6otq5XJUSQU6nUUMOs6sh9vZ3QwPw4W6sn+GaWG7Vs00T91v3eN5gdl6VniVOZBpYf1uFU1s6rrf+358f2RDOcru3eIByqjjIPgllW40wYZn/7Jqv/sur/6XL5OWtfYSXR44Mwd19ltRM9IA0Vxj+uifa/jnxJ+X4Qh/Nn6CN/njSCM9RqQ9Ke6ZXDx3HMZ7qt63uptDTgTdUTJuwYsK4p+czT40pldm7eR+cpX14HxjnJpv8xF6U7ZyTn3KAbH2zgVPaE/MAKVWnevRnzd+KpbgGSaC5sqR+1dG/xMinJNMmjJP27Pc383CJ6cVgb21MSYTPYohmvD2YnQtB4zlmuppyYi78y2YqH/JcEIRkpdjF0zVOIY3nOwpFeDctd39KBZ81PrS4ZwY98cxv7csgwLNG8sAEvRpdOAlfkON7wT2FZePLaUOqYzY8XXJ/eLPwMx16F/9cMsB3wzrM2zr4wRMZALeisUb+N2o3WvLj1B2/3OtwJKummsEr0LV0deam4KD+h2XEU1MWGk/rk0Ae53wntJMjbGv1/ZpBqcna5ZXN5Ol/ESxPBiebLurepdWc5oP8f63xhgW2ml8w7Y+pPsWfSziB9Za7LnoaRVv1VP90rrYjY0RceKTzm/Y3DRO4tzc97oWV34aQy6vCV9+U5vKnLXfdZXVA0c+SWfbFC8ohdzhIyLS1CVFb4zvE/fk5StrgR63seL6gW8ENqrHmOa39b6DOpoyKimJ3RFjf8o5WYff0ZSpiwNLdqvTETMAmRrOb1oBb+9X6/hI62qhN1rsd/PLms+BI09wuRvuxbgTawPWvpIdvpouzIiXnqE2UWwZyhPxa7d+aHts4Hf225wuDBrRWVi/4nnHn34AJw5P59Nhf7ma2UVUQH5otmpw4hx3vSQjTQuNEi9Ucu7nOtx9sRY9IxbjCv1Z514nU5gdZVVOR80etbo5Yz/Mt/WeM11fSV33+GHYhug8x/Jz/pOq6T4zEwiCNN1wPZwNb+xexjtx6PEGSg6EU3rGeWlJmYq76SKyu9LSYPGb6nHT0EyMQAwd8OYsdv7z4zc0eWBgbxTy5JSrnK6qiLgE+wPgBk19WQ0KCbAVDYyq1M/hqQ0ueH3KIX13cWJst1wc86GdHOCK2vfErojDfG2EtKkYZLMLT3bb6jReZrmK172N8wgujaiONWt5qYlV7MZkznWJS8L30jC1wrqqrjHhJQcNMHvT2SXrOu6vNBE+bLmdzETPPO/0M5HtxXKfuX77z6OtANzXk9L4xuftt49QsdtaMgQNZthj+uzFWtmAFrXWFflH+PGyp7oyro1NrLFzockT7ruGwdzLW6O2SOIoj7O85eWwPyNfLJOr3dv0U5mzwibSHo9l3c5NbExw/M1H+sOohEcOWwAoohdmh3nJAMK+96+X4vYilxu8INq3GTgmTONcorUQYR6FFWWrFJwR30b0b35mdwR26oa+mGzTzhnas653LQMVDOPp01ViIIN+H+hgCgNnToUl3PIeyQB6ZF7ViMS6uC+3tbUikrg6fybcMrNBwJ6dbePSNWt3Wkb0bLWtkJ7AQKTPa1t4adZ+Cm6WbUDWVxdRwov0X5TetKAnXly/dhPjkqXtlEfINqtWP4GXPHRuSkHoYztk/cdVXwoR3ZfZ+Mnc7vZXnrllJ20vE8qLs3CWrP4MVycTtgKW9F5PhaWwkgsNHOPETXjcCtM2jAp2MvCtFNfezJRizFxeybzM2fset5jxwuPHMe5dMUNPMFFqqylnkbbcj8YCP/PtXo5F7PzeNuEnLvOteZ+mYAxG7Rzprrj63twbK+Gx5Jn4wtqw98W54Iia6Ag6ylnZe2a1Y47ZS3FRS24l7T8yeDKtLCMPhmBBWgqSQKzoUFsYLqt3L0eLgbvz+au3VCbmByIg+DydREdAMxAw+1xTxuc2QVZXPUfH5oDgyB47Tu82bHfO98n5m+ogA57kMIaFnOvHkvsgmEiIeWIXVrzrrhXp9Gk641k/v70sXKcO6yAyjJWLgTLS8bYicC3sgKBXafX4BI0NdOCJFL2lv2xe+c2ieszEusbZm8T3mhzQrCKGxPJbfafT52QYViFkSm5+OrmDuyzY3fMVgi7C08cDx2/w4Cxlw1Q0ltVaMC2+/G3+rjqrnqiZ2mOn26F6xgAAEABJREFUnCQjwm87M24gP50C/OYbqvPXrbJlgsEVh//oo6ydu/oeWxD5v144HqKgl4BfltfeN/PfZVGG5GYkt8w5L39PvFIFjuVwIh+uR+s5OEdm1hLa84KP4ogTiJg1nV4N+gzackPAr8GHk4B7sL96HEk7mTFAxIV1aedZKNPF72/B/w13uNV0LODT6q6D94Xgs9BlU4aEORz3rLFpXHvyGu/f8sdpkKchPZJM4B9P7uBPXHWvSNe6Ul6A3wtMRU7vflMPcKizWOlBV+4dGw5jIBLJyMo5u7VjbcYl7cJ11tVco7QlGx8ueDTDb4J3VPeLPxM73C33a94xCk3YVnQU98QdvALO6x/GDzOEM97afYtatjeq3HmZW2IhiE35ndadD40H53oyf5m/4BG6ybfC5RQX9hUR69X4hTErNu7GNsfD/I0OJh7hGSeUjjET3Dgr3o+Yxb21blx/6Ls478CsWtVCroZaXVNf2wqf+2hzkzjhArMUEaDX1cw2+VhrbG7AxpL5gcgBaIHXubpiLfq09gKXPshgwERsTnqF82HOiiJyt+Uynk/ZANH/01yobRs7CMrRtFsm5+q568An5gM1E5Gwycn1MmoSLVpY9UI9hjQwI37UuqBFePBtI9ZRf6nOYpwFK+XY/At40IqLkXbuNbq/Uyfj2TAWn92rvibqJcO14dEqzFH0LDIdYtARVcK92CDd4MVohhCOe09anis2uKRmVb6xfnM9jAeDUC4g3Ecdwer+0pPDtCICw1X6mEENZzTB2gYfbRgDjr1EyAZG/aoPH+wn9owBJyOtyikrYaM3M+PCvcylGTX1F+cHvdTt5jqBWhthLfXltlGDDE96kzUxHVD4x/ye327fw/r2MizYZ/KT6igxweRaketTgZkhHIFzU4C2SLjBTJe+tbKPSSSOrzz8+UW8jSnzg6URT33Z2OpltSGEhR6c98z3HwObbKyWplsYU1M/gWvhcwEKW2BJNv5wXEvUb+mpmZqUCW+ZZnkgM3Qtm5PexMt1cBW+2RlzBTcyEOAX0WU6YAHbYwWKLTbHEc7XbpwJ3NRFEoQRm22mz6atOb9kWGkebhA8Z9dE/Q4e5Uvb+GalKVKH2bJBK5VxUpMDWdfniygLvkyfk6uhYo5TzDXOgUv2L/V/1EkiNiM2fFPTa8hOjtVSnf3u+l2/tZ/ZpzMfsdzm39/zHLOFdARSypij00W63jXfzWeMReDBxkKz4SFMfP8syiz8dCyFFj6+5WR57hXuzc8nnTeu2Rk/48RYzuCYv9CxyvGz8BOevIgYZm0HCBN7IX9ibXLE1qK7NTa8Mox3U464rPWW3Y1vVG5jJbchAd3T7y+eR2+7BeZccf3pfSOX49o8i4cYlLvj/fPuP230cIsZvSwjCNYax+a2IuHfwSgClN/CWpabR/X1rn0sj+2wCvsYKoaRyBWfK8YryySXN/PxzU424DMhQ8UFXeN3LGAHaLcMZ/mgRrYDefwC8bviMCTr9Yxagdz0nurKi7rgyuTUsn4txi0tDWpf77UriGq9c2YEIYN+KOPzDucA7SWgjcJYF/kBx2USYa5vGVtwsm3wFpwZvzWuelf/dj3H5r7/tfw1sarjHQyssv6f/et6zRRmC2zH5QqzGv2VV13X/LngLiE5Jsqejevd8XvxV3h+6Q55y6vOklwNB9Gg+UkL8I3HaS84FWkiu/gqh7kUbEzrmXlb358QvA5IZZhAoE2ErRmj89S3xrpymOaNtjHNoDb7plZwLVw7rkv3diGhpZ6O7m+bTDPpevlYCW60V9DrF3vSDGbE4qLB0q8i34UXLvdGSf82VaGIcSINy7lJEn4R3z9A/nWTf2iFvxFVtGt5Lp0st2Pb0O38euBW7/mN/Is4njbIcfjGuU4md0b+VDgJijVXxDasMbJrcd+cZW7Ky+bV4duBGAGVYbEe2Gh73RuY1lov+JJYbsl+c4pxSv06EVogI4BPwi/vh/uhgblsijiOcUCazHSqQPekE0ZYOaaseeZLU7d0fPh7ff+N6sF6m929Pzyn7vP7U22RR5omaILvNX/ZXrdtLTXD+US8AoJq9Hh2FuMsKBCZaZBGIuNSytugzIopWiomINBarRxFi9/LDMUyqPqdUygbhVE2Ki7bcGV9JGR0xIHLDbLs4g/mD3SbR+EjqANnsZeKtu2KOXt4YV/ETzhxoOX+rlW6WNtdJ0pBGm8U+deXPehElCkEI5P7mus6mw9c0NGxrLRPr93bsBgrI3coWAwGXNGRkDQPvS/UsXrGGhSGcNlZsHFDucmVtSyaKRueMx+J4sinrt4LNwy8L5SDAd6aOb1MgyQvOwT2v2NhooftcDO892K2BXlaB6hYiB+dfd7Sq1TewR/i8hxKYwpla/4BTthTIvZ3rQWNseSpNPAN7DSjXyxwAj4JblN0UH7o91uv6wV+STCupko8Q02MtTfSzPjKN+2aDLoevgbUOhO/fdfZzhcgRCmO92G1IwarEIczfMPmVA1pmTZ3iTu+8E1pU52t25woABoC2kpzem/mhWDHeaO95bTePs9XnHS0slTa8oNfQbP9VZo6iT9EkkWIQ7p1g7t1mb838s3wMaaSsqM+UI0mS7sn34kXmDgWUlbvF1vKn88AG3nWalATqjWyfpZkgRe8ASgzsoAfMiNA6nKw8IdI5ffM7pwrZF34ppvRQhed94/ZbxQRGSEHFMfnA+Cq4TlqH2WnabcDr93WGsbSUCTHnDqwMphMbo9ZJ2IWyuEE8Vzxrjhia7x7LFRFcckL9EMdNMcNHdtph20WOIGUtkmgJznzQDHEXMJC4A7+oS2I/hdwmH1pIjzNZbQ5/IwyGznwkIP4qygYVVxP/Xh/gWOynB/EkPXq9a3+m3C/LKhejPGawJXE4hvzjqbkUIFxYjqqgBYTChk+GRNTK78bp12yLPU0q6mgr8zBvIS4LFyYjU9kyjJ2M6UFh+XLADptqw0K1e5zPpNrMr+yGEYPTfSrVR4VHqsYRxk7GRdxJRGsyIOc3tGLh3JIjiUv2tuxh5d9vp60RfdF9nq8qCgBilX7dWPDugMrajhvoZsBJE2BH431uJH9hs5ua9BQQdCh7GpLdSsvufZYS4XXeMME8oS/9UG9OaZ0uUmALXNolobSSMZtv/yQhGmTmW3DNYF+FyGy8GVX/z7ncDnO10kYeO/gRSWGg5BgeyhNJc9OJ3aEj4lDOZoSxxqrCY1zMbmGXgk3FOiQU1XtYbC0YIsonnf5cKGRuFM/5gxy/SUY5NEqq7+ZXVP9WyboSuzLkjRSB8bdNP0L1cfGLNH9GR40UrZoNhiYpRW6oHtw13yicDga/ztjRK+jlNt4tmEp5qMBrtGMIdIzd3n5yyARHm4Jy/FfHEZbsbbjBEJiQyemQg7RY+MLus6MI4ChxB9HKWep0yPOeOjmazs7KKauNZsm8RiDz+xZP9ZahDPe9g2G2kzmdDTnQD2BN/qZfup9dJ6Uq58WGO7vfbycWF50RG/F01NshQHJBiD/CyBcBnH8YYlbmZS3L0bjEJMWQIJXODWz1au9sRgm5df00J/BTIT/OnXX/jnrfiKLuTPXXswl5+gsjfOA7djDJbU6cs58963vwBDiI8yJSrlkJ52VljpxNuaypXuvyxLG3j3Lqa7iJLzy2bHhai68myctuzY4w/qMsgIiIGRYgv7ghGkNltwtbmXWp7jwBbCWv6X+6LoQAYsw2gyZgMbgGxLT23VlbTWyMjoLUdYfknC+PLrcn0oqY/sz+V9v8iaNYlg2cgbuE7WXdrrDb7hxyMwtM3lg2dAqCjAycCcvfbkxtM7TxPzzsuikd6tfGdSGSQ1e4PKRmo1ZuU7u3z85aZ0WxjFMQ4V8ZW7dUmhO/LOyI9/Rec6UH7t0ALEUnu/g3Z5/L+myDHX9hH5We7lc9e1cdvYKp/9jQTNORy1zY3lRxmVGlA0UGiI65RCkecovjeTThdfLYZsqJuTeHgUScryW6Rsv0DM5Wm2bcchOH5Adrd1p9lHDnjXmQOu72zW2Ae099kJkd04lWq1COFaYIbvBrB17g3vOalDUumffBDYm8MlrfJYnWCMbqJlf+Wkwx3YEOCjIGXN7QsKwnBXKc2rFr2+ACefni5JSVPOlKt0GxugGKmul1YMpWx4HFrlu11lfmJCpDXKMPCYTGcGAxr/yy/QXtWpcRT7PlXQEMiIvbOqqOI/vGzhb/VMTUzCtdmw5PyO/5yDseqKtDQ5sGpJm3MZqcP6lxzpIy5B0JZbKl/k78bf1+jIlfONe9UwzHICowMDkkUUbbh6FEJHYgWugdnWgYoZ9w0z7quXIGRk8h1X4HrGJ7hEW3N6Taz4ciOQdsRaWXRi1PsYfnt+a6qvnpqV8z9mscFqp6frVjMMy+UfOZX/oCIHGtDhX1EI79vAPq/H099WenLFX9X/4Pev/Io/H8lMgM/+XmHHExY3FyGfh+aPjHo4b5SDv6f3WmjYghOOCQf9g3xDnRT8a+NtKF1S2SvmAXoCcp0+CVwX/Xnhuj+7rGFKR56fO14+GYQRIBDtjdmTetyJsJbwJcol9TLYVvYtRATNV0BN4cvmMzMGZMPafZZpg5lGEloTTvi+x4rQ2HGirp6XxVTtlnAziTfRydV2TVy0Yok3Y5txa19wX80ou94grEAqdJePM+EyaLIBGbuNObMSvBT68qsOO6ozMzDszRTG2xhuDkAn7660Nn+YT3kB4B9k9jqxuvv8ZOflwfuft98VzHIV0Opsr170iIDiVnQ9/mrd08/yhby59Yc2kKtFEeV50G7OjC5MBdixhwYiHABo++wC/zWK4Aa6fsL25+nf33+Sv/DpggXS8AK2z+aeagjdTXxcxo8JBlJkBciagxpzwRtBF2z5NIuCXFejJ9soOymFYgCynS0AFj4eYtK5n8Ez41PZWabCoBAjgH3b3BRHHTQ43ZnmR2TVf7Wf4BZqB3ULzzd2exLJ1jDCR7VjcYZfPyUkxy3yXyujmiyefQns10xfPgcn/ZRqW1OyDKl+gI8rSKkQPRVpyP0uBss8KvVtt5xoQJ9Uamts2kNYaj4O770OdGr3tgmre8EqH+CZnWyHgO+rueK39lyx4r5475GX9xx7L1ZiG4hCE6+hnPm0rfInOg44cg7uD/c4YJUK6PdJfCvm4zKzyZT4KeE8/LpaQdyL35WG6WCknjFkWwjuvpqFgbmD7tt5nysjWDFPacogGOynvOsrFHne4YAIRJht4oGe9ul4zgN6wLQe5o7G6MD34+Herqc1Z4SPuivXAMbwL9V/3n3fyxOd+1eW/+K6RJWV+lTwb40ggCzuMBj6NMOuaQw39ld/kM7v6Z/hHe+mCKL84SE2gzZJUPn0PnVmn5fV3aoAW3uBvyIGUEw67bqjHtd3+IocJR9+IcaOhg1f+1Arh7PN7vy4XaJE/vDL/YP/s+F8zOSblfP/5XlIr5YYnYjguWRxEfcsSqptWYN78Q3oAABAASURBVAXhusX3anGuMM10tXox+c7RkfSsD2qJMdoRsYmbvbK8iypcxYtXOI1JA+3Q1yacv01cEOzKGd+ljFlbuRha8O34OKFLIoUQkyv3yog2U3IFg1KYiRnGk9sPFA+6/O1FPJedHIeEIfAZ8VgkkXAQbQ2bQfuRa8aa/zv1f5h32ANL4xsEf0i8uAHv/EiM/0DnG44/uXYYhno9lW6grhJfpAGZ+7qpa5RP48iFxH3t+77jWrGbrIXKTr9RmcP9XH/qx5d9/eLMfDfjz05dYYHQ3M+l2Rthej4FtxC+e33S8sj/pWDcIl60rV+MP8zlWWZstfVG166Dj3F7WHMsTf956pkZmIgISwjmX9TKvpdzrcGX//GVuLSv2ph4p8s8BQ2513nz9zef3zinWbasEDgfXpMt0XyH1ppchMwq8Vysn0TeXNdiXHHY5QdB+nK2jz14//uTcd3XxHbr+52PjO2JHmIh/1/g762/MD3tUG/MH8xPw3X1g3VFUGZm3JKvkqCibCHwLH7nqfJnZ9Xc7E0X9BLm5Phm8biZjM/Qb89/NdvYVkyOnieeNxwkEEmz3q7/GIvsHfSodApG+gR+WcCvrzmJjprMrbWJFRVpJAkz4dUXR/7U+nJG9mS/i+VzxXwyKwOevnZtP1n/RLZ5AnZ3+utdcN7miI1ABL4sH1wHkCOuA1bcPpkE6YzwVTzzz74yJvfEfibOCYnWR57lun2rf/XWP6NkJBaYFTy5FI4hE1nIh/pll83+82zkrENYAMcz9AXusMYrgARKKbv5C/ft83YDfLF+W6fbkjF6KdKHL2izr4zl2tf99KV3OYurm4o31uHVNI4Nl06Pb63O0MlT7Q+u4+2Kcj5gLyIYg9WOPz5PZqye+JB2KiGHCfoXxSUtmvXX4XLEx+Cb62+sBm0l071jpobtJJx0E9Iq7JHH9vW40D1C/xfv325zKF8D9lZeb65exWgGNKZPkOagFJOpQdvZr8FYJXsnXYqt9g5Ar3ZZNg7hBCthCBjkCEJopqtsRVwmLg6uMh46KFfolPU4r/eLP8pddtNdbxsuOAaUpCcV55nD0xac6p5etL76PNFPTnkEaYLUM6cxoJM0S72cFgNxD4A1Ugn3Mi0hwU+yAc6F5+azeQLnw2tYDtUxv/B+OgQ3hXBfwMJkFPLMamLl5JpRHl3Qpy7nfP9d/x4pI7cyzcLHQM7c3W6kPXq9ydyWaXDyKR1jvvfcWSHxDYi1z3O1u0k5F2DnvEml004ceMxUTns4yNm73QMZT4XvmY4dqLRsZ+KrT7bCJ3ne6PfCLJPAHBh3hxxzme7wlGp//xeHjdrJsl+SgtevWMw0MfU1ABOs6TnKF9Fmjz2osc1Qdl+te8z7LLqJc4VnGnna0HyDHbW+cempXHT9UMGxRnzVZvu8mEkGTOjShC8B20W2uOAe12zRrL3AUZU6jMVbfzhScqUBTw1dnLjkrFlq+lrNIStgDMpwQFqjPF93Ge0OJuYKU4nMDA+xBdBhFcyEGd4xCrtbjWscnOsCD+vggIbANTt0xURCIyjSRomJPjXHmnS+rCQPaDjp50G/NdUW+kEcr1hrOZ/RQ8M3Ow7Ei+m1O/6hPpXnWac2tFzsjWD9i6gvjPu+I/UFqGCQFidDohpTHF1rbmtiv736BJse1r+wIw0MFjTmD+Oq9aO1DubpMrf2gkuz2Ru1ZmgteVYZyHW+AOXc3THbvs5tPTkdjmKYrkcgBQIyjB79Np7m244Pu9K3w8Fd5sks2T85QL90Ve+ahQlSxja9zlyDa32Hw4dsIVsh944603YQ1Lp6hzOhGDYwe5sW+yetG7Pjqn3VT3e/Wo9WPdDb+j1qu0mag4A3x4o23tW8LPqNuGgqORPnOzZcOmFolf2JUdI4VhHmfJQcGuKOd+9a4YCSPcQ7YZxRzrS/IGpEDehGIutOmYZpR0FpoSdPez1fRQ/6pn6zMZaGZtBCuDoCGbAPHzFA0Yn5KFFNOO4AQwYTuVy+/lnfwPQ4VcRDPRpvTDotSac9Klkp38JzwSIOQoBJYRtEYK5UBm2VpqffwczquMWQPUFLvrZnMl7kK4M5gKNkyHBJJnyAHI30moboN8/JUsjw0s539xrEuJoIj2rBk91Jfoj9D8yQFVm5hjuQQBfb7HMiOnMCzk+GlecM94zGV6/5xLF6Ryk9YeZARySQXqU/ayAKLNEO4R8WsvmtcGkg/9rnse+zHTNTbnPlpufk92q35rWvXLGSKxNx+dSXYDW/TtVEkPjM2hYc32R3+LTCgmXWDu4F7JjLq8Yfzp8863pq44sJGeVBDlAmz5QoA0p2sGh+4HT98/4lmGAbYOaSEznuWmrIkxi+FKB53PhYNGWLsXx3dUfsMMsJh/zixyx5SlTZnR3k4GLvbKRi0owevEfeB3cP4Q3GUCHfledND601naMUdrpzpy9aLqVyYc1XbdWck05EH6MdvhnnSidex70n704vIRuTLGg4QKTW1uX3Xv17neP65JD50b31vUPOOvXRTDlefTKzpgID5+gCEwl1TVeHs48oOP2f39C4xeeqsMEY5DvX2zf99eJaMbJcPHMEU2LL4CsYvCKGOcG6OMl2jvVPQtSgo8ATXdsdfowhHC1mEnBiw0ibPXxA78Rfx2nrltCN88MKN6cPKaQlZAsGMWtSldXok0REYgIF2fEYJt2irSPn3mzRZJSBt2sA8rcMakX/ZGUUpscPJo7exhm1bGMmaubt9zf7ya9Gj9BmLUIJLegSN16oB4oZf6Xd4yFfG98r8xOStsYtNu2915T5maEhQqUuR1SkG5Cpyan79QFngVZj0kl1TTx5/qp+vUK+x8V31/w58TX0YlmWeBcEV/p4goXW9vheeDnTo/qmjU4Yo1vC2r8rT3MKfAKzyOWAgkjmMEx1O5yO9jG+wqXWOnLGJLyYR3M+8bYcx9htbtN+BuzyLPx7OasXldfEPo5WIS6cG7MHM9ntw7lZYHgypfXKIrMvsJhv+kaDfQB5BIbOSs4b+Rc/z/wElmtr641y7xqksF7mY/gKMr7rWulphUP7RmQn27EgCzwZnZtYKVlCi2/aRxMjfh4V4WOod6q76lutDEXhVnaocvF6F1ODAcokks4hEctZoAHzXd+lrIkzmoS7Af+ol+XBgUGfln3NkONb0mFwvERHhJOxl4djd2jy7Tyl9oz2H4WPBev40LJjLMSP9uXYRSdX3pjIwSoDxIzUxtq2SEe3NAbqCxFczfBxIoVvM5OKb4LNAvVZEMfAHIgnEgX094HlYnO4AkC/HdDnc7sTcZN2+Zqqc6YUudbPxeUQwAorSNlaY+P2z5R7y2Bl7EwT7qorSxKO6F4740Lc8rUweNDhZAYzu/erPBbJDXoT3JnTK991f0eGnCfNxLgUcWt54gGMGI4H5hMxRCzuz2g6U/reYKYwBwdsUh7yiYrjaxEJTwI9jbOoC0Mn7qksZ5NGmreDolHlrIYKNVQEOnAI14tVYiOXouoq3r65ia1Fs1/4af4cLvvrdbgnnNcXbgpvxpzg7+y3iLNuWRMG8A77GJKM3OX7yhsXbxx0bjYdR2Miwm1n+5dXcLVPH8H/LsidVHpfza/F9PrbIgxlYnOuHN9zphb64ldRqYCpkerj9Yl4NgaBSDpHvEK75eey1ztx6JLNWnfSa/ZmA2gYkPeNbpPWcem3u316Qznt9fJ95mnjMiG6lkE96l6C0fefqWtkItiS7wud5s17Oam9rbGZN/vEvMGP6iCcK64Suw33NOxqRhLtDRXR+T12vPNCXr0cxGSfnEnkneFukjfwAy0GuUZh752/g/fpfjh/Lwej2zTBu2L/va80UC3Lpkrf8CXRp+CX2Qy+g19r6jWkVOy2g7jjA/qxOt8H29xRshv9HVdNpbSjbzfK0MVa+IVeh6DjK/wX0JRPWaIdXrXwPUZpkc6G9SF8DMwW7g1XNkIJeVf5aWbauRMuMgPY6MV2/6OXemax3zid18J/N/74YYazZiDlonGQ/1w+z8K7fuam1gv+gu6aduijCxQSRTmhC777c2rOkVOwPnDOqb3DcE7itQT5t6+jP6M/wkLYJCf8IXMBlwung9iRiybpp842/L2/Iug3319WxcB5w/7pfTcWlfd7/cxlK7rqeySyA7HdC6LDRq7NiNVEpZUTV+dek/eT61kxZmXH9SdvP4tcgw94Nw/zZETQ+NSpyj2OdNDsZ3f9vLpPspJc1vD4afxd2o3G/L3m94p3ls6lk1M5uOK788w0fRbNmYnufKAn3he/LQedXP9pXe7WXXiJs8zKTozJV3ijCWLCV749gx3Hz+KfW3kZ5YqiVcIUdshCjKYRbNN5qN9RPZHnup2NO2afik5oq522AU5Pn8kRknAaFmfL25GJdzkIfBb1vQ9QDJiY6AW3ob+9/35kLHSvp/BkmS0xUJtfG7GfWYu4Qxtkb5wf+jFT0W4FA4isvdouVZhH8hVuePZP4a51D9fD6ei5MH9W32774b8xutvGYJEZNHcBq+87gpj1UXi6qoXwCJAKiWd8YpKeEdwERYtvMhP21cvCrRkKS7wyVt5B75j3rAvXCv9jLm16Sb9+GD/7XNfLhBLHLvoHvbyD8NeiNTMZxxnDHLuFXy13h310CRCms5QfQ5bHXpL9+3rUvzyiVijigE07MM6a9QW8/TcJV8QPftNtIN4328F1xamsWPPteWOaAjOxbn+S+eiGCJiGka8SEUh7SLh0zq22gB3rdWXei0Et8BYStiPYd/c1jSiLZ1jri29G3wb3ede8jP6nd2rSQqVnEnZVB94FmPNty9uTLfjjdWh0TNVu7m+/6i8YEmfH3/CUF4YLYg/XYwLnnXGjjN/J84Z+FSYmc+/lPeGOQpzmGUvt9qm501FXWwL9Cqdq6leyHKgJToSDIhdir9j3DoMvnn5bv4HfmDfeCBdrT6Q+CQRcrAf15Lzrmww7+ABTYdZuyN0xNt7Nylvk8BkneJROzDGEgV/2jSEqKYY/8W085RPXxo+xaHtNfT1qhcWJt1gOGNu95lVVt2qinRqncZ+T59d5CEf3t98zT+pZ/F31xlj3GfH1xDXSm+VyX//qClD7B14DrrTUqAfvoVP7uUQtvmvqbuwn+F7nXadLEnEFNiOYNfnaXmx6r3etT2Y9ODqdL+4KdsbEJeu1n/NWv7R0WiM7PtA7/CCd/TbKIcFUCJbu1XslxzmAjuRJp/Hpfx/tU03LHPNJazVAgQ5gZYTBdv59+fvA4TieMq/Xw+V6vmrWE6iYgWeAJGgFWJazMTu1w6V14CFULL/u5azKsCaPo/B4TOrgvDoP+Kv94j55iS6VJKmEszJYFBP1QUuca2Y1l6FqEIXUXvuKa1/33jvN5zXaE9Z5W9GBhkOOPus0f4EnbsmtcGBwvqasgEmwdkAgEmPX4TH28nK97KOuYZu7fDtd9PBcLzXrTMgpXHGnKb7liA06Z4WJ0QwuFl1ZAAAQAElEQVQ1/IwSkF9lM3qFPm2h7ecLxDhz73X9XNeP0eeh/Ojs5TufjUbDbE78cN0rLI6+0dtUKIpjCUXb6SwOrraij/KZfCg3lnvBGxR+zgtdpuzP4NYKrfSH9t/5zyK9MLPpUdzwhIzIuPjFY5Fr/v2PR9rI4ZykfO3qL0nAEiNa2S0cr27y/G1B7z+x/gRvaw/anfvs66ylFHb1n6ifFdSCaW2nMFbsDNafwH/piZtP1D8Ovt1I/eS76qlfvCchN+K5dXYHXAvXheWMDeEIrifTaog5ZmJVeRGoWDM7mN6qYmXkfcVtTi7WXe/1t2kNmKuk8zU9k5hL3LXHUK/0n1Gv0D45/dP2l65S+Zuj2iaxfyZ/zs91Ivvdca7UFame40Lhb+L3Qkzha1PJlcxKBzcwznWtvCcW6WozhNA7GEuGsz2QBg2s5gXdMIft7CT1XbvhXjuWoF7Bbel142BRypluvAUn7pubOV7Udth1rLJihq8HaZNiNOKTVntDgscib4PGO4eo/0r/PchWrI+2TeQEGATYHNjbikznIJTE92YF3nE2Py4OeDKeTbvogzlKIDdiUbM0+XkPHZZSEZzQBEzFhozJg+YYqqtbVs3iXgfRDASpoBHYwwAoY50cn0fEEvI4jy24alabq5MJB0D3B7eGcH7MnCj3Y+fZxnv5OhAKCCSx/V3NZ/B7oicidywbR8AwmFYuWHbhbL3ICgqFM+HGcI6mutWwMet9bc8hdybTZ1pOTEWUU/N5pPMwsk+naaXythu66HGgwRQZsWPCuwG1CdEwtAzYfBmZMQgeVDs6zI6zxdfG3UFWhXp1p8JHYF1jNw6wh60RhqnZXi6cHc0k5UAR+8xdtdc4aA3lGVDGZDCYGTEOUF0+3/Cc3WmHdNijjuwrsez0cXPjKULfMe1pRNsl7Rhm4ATLjBDBDod5LftnAOiIWYv5Zco9fDNX5rqwLTp4EDzFWpFR9Ypa61n7JN2KFt90PbRc9t58YSPDNZzRyzAmu7cqWGZ4PNzdMtghrURc+Jy7Hb1eVksDeSaXSZZhY4L2MWFGBqi2rexAXz1XIQ13kyjohNN3jxeYuJl76WLfxhVNmsxT83um3OFdGr7Dqfg2XGNURsHYyw4MwuqaN74N/ZvxYlmtGAtkE+xGnYQ7jcMLYAOJSCapNeC7u/ZTc07Pc/jaYiLXviL5beSntsOZ6OIQNmYtlFKy/pn4UKoYjQjOrFNe4oR4uJ6L7/J1wekWvpupGbQ0LEfOceIbsE7HL59bY7vMhj2F1OgOYgfXGFUNZgCaajQ9ZNaqveeOXvMPDRvZ4+gzngw5DAvA/trTWnm46pKr2ASssIkvIptYDI8mjil4/47R2oVDW0IoXkXRepDfjOwwBtc1GQW06/l14mOBm55CYPfs5nbZxc7t8wdfIwXWtp+vTk2X8boibMDGe7X/qumCU0wrLLkuVLV7Vuc0+z39vP212Y7FvdaVgSVfnyM8zDddvrvkI9paOQv6WGzhDEAYsx2ZXH2fQTorPU6ErzoumDiL0ZO+O0CslVwxxn7Nnop3xjq0pRq9GrM51rcSCgQF52aS2PF9o7nDR7XjyzEl/i4bZzk0f3a+AffmsmmFs5hjliXOB3zQ/iQPFbEwoVHLm5ZzXAVlQfRnYHlEOMp2iqhlaTkTRmq2hmsYDq7LUwrmsV9/MrPBZEZE+jnbRXKccZ05dhFOakSlThz7W9Ai7M1AcvNi3k1MIFbkXgwyylFR+s5apf7N9hsQ4bAarECZunQJuL945ohVKk9Z4z3Juosf/Kv6OpPDwXoRH5OwrzUndcihAx6aB/szHekMFAZUBhdX6+fd19+Mx81pQh8rWw35lR1XXz91WR6Eh/5J3csTIYvntbi2bAjoT7T4qMPP+AFT5026xfnUke8ObNAKNyUCygedFHwvhrRnMuzZRkT3acfiQv0X91V5nb06YDQqaCCtkKsWOdiMKzOWsYFXvAFXVzvYI8IhmsBufAcNmBDb5WALsjhnxbvy5XXqdgBhaPaAF/wVTNJgnGPQ/NGcD76PLvRkJhe8u0nO1kR/MzVjsk+ET8UyDJMRkU73umlSdBcC8FAYbtqiY/lkNRpbNMHZteqhdREh1/8ruG+f9xtK2jkjwQ0F/C8J+PMycpyAi8pHDhjDOMmTcCGER9ndRZMWsUmTtSMsT/M5QpsRyR4S/SnlLJyvBRqLUxCykZu6Q8vj19vr5Z7YiFSywn3mNb/Ahby4lhEVfHKgCUjpzO027Hgh96JcdXHsveWqceV0DH4a17u8ARgYzz8epyGQROAr6wG1pukOIp9v+17hR9URFGTc64nr5D4g+C7UnbiZaxAm2RNnKF05YQZonlqYjxoTLDdtvRWwL//Q27f+0uReV9vLhzXg0M22qTBwW/651098CWzVWeYaFnOOFYU1Tp2/shpOXL+fcLmi7ng3/KKY2BnLv+51drwZE4l1WdKOkiymwK+cyujhE8A7MY1sZU/2M25H7/TDHG5ixWSZbHcGPs1HfEFP7omv/tuBifC/GkdS9Mn8lhSzirpibu6Ni9hJc+akva+8jlORDOvXDS3sUT0e48PuODRhi0HEIJqZ9tLi6ex3cUbrMSqHuyv/dG4P3LOW7owEhTnykhAy1RJFdrJ+qvf3JKSvwL33lVfI892SXnWj7afyR/Bp80clOhhYt/LCdwbBXGF+g0du+k1vPqZ3cd4uv7oaL4FMmniiFnAHt7/B2SVmSxtKR5gFSr2qXA3Wa023oVa06+2b6HEXtGWeZ1pthN5Gt6yUO/BHvD+ahTCnnTl3Kjjq8ahv0CJ759eA+oe41sq8g//h2xCjyu1kPIQwQc0YA84++c43pb1LEfxvhl4NsiMwtif/avpvHg6/p7TAa3HHDuxhUg6zPc72QEMBmqnZ0wTE9fzk7+7f9BP60yj6slc4W+LutPjtxkYcIRk054FU7atcjfQTvGBAoYksRgFSsQev8w2/PAXytnK5GMLGyxhsYGwPJE+4T3h/uid+FH/ivrDktJjlSEY63YZWv6vN1raWnp85rMduqFgeoRUwZ0Fnzh2/O8BlzB5h2ffjTLo38yyDXuBe7IwnbFDjnlRlsO4irWWijCiGOEShJt5GS7GCP9f8+//hQl7ULdyd0n2Y78yRiNiIG7gKwRpaNxnIO+eypx7z0Q+mfZW35m5s8gqsnqhxwGaQXoFOhHW2iRu1JY1fTtjS/L/GD+K6gAujNeFt6a1a/C15J0OL6tdPG/G9XI6RY47f5LEAiYBZtWDhB1PTWUmL2loCLHvPhTDuIHLEL/Lt7DPTtgy5TP007CCcgsECHG1R/p3wrQdDMfIwxDNUk2VNEU+iPUohbEZem1dEfI7ts+HBOD2WSa92NcK1vze4xwIo0IlHfVTqDEqfquglQ7E5/2X9Sd4Ms32dSX3+CYQoXcqtvGBcYzRvXw//vKOZl4buXcr1h7YCVKENMehqdjg58WesMgs6pGxAwrArwKWIx1zCJxyvTHTc514GqoJG5TvaezzLE0Yig6QC7RllJyO2+8bZNbf1MgbUku3mLaYHmRTN5b4HQN/s3Lx+tLYnH86TOIsvApgtfMpBK8/EOWiH8a2/DiKQJp0DHydRO5jeu8YyKLkVTk/QHFlfe3/1ww+NTWHOP7RuOpT1R5BjZtMhzMH4M/yxGmM3vuhjaA55XpdYp2rT0T7FMyYvZNFXW4NYdOrhZZLmRctqPe5WT6+HjmiqT/cxiQztyESmbLkTul58N4eyyu2/RGEN1/yEK0hRMnAPtcY67NP1kQwIxhWYkL3bpcko4Bd4HbrXTt47rx1AnGafV/WiuvvRPB1nzGFYB86ez54+yslUwmZNq67GbuSIUITfhuMb4mcbjJxU9KKvQcne3THNsdHjCAAvVLfVZ9HgZRaXRZABK7xc/QCcrmVp92a5SyMCduLA5+NEtL87tNIsnNrP8fax1bWgzORg2fO++v7xBnfUp+Q1LKQ75kZ7ejAc/F572XSDRolrvJ5+tiM9R1+6tc/O+W/451zbT3CRnptmoIH2rDHuEDy1/4TcHb1YFJ4ZE6j9aHWIPDltVlJMB8OrfOwfccOIH6CG7QCJAYJGPy4sZUM2PbQ7qSDqJf6u9az7SVsmorkQG4wOCuF5tMhM3HaDd4ym48Wu2PRhC/Mq/lY+zpKCFio/cRzhDJ10+dMMvvP4rhMCndchgW3AUoIxw39aDF8sb9iykr0xnG65dT7CnIy6/8A4woi0iJSu6f/H7zMvuQIGY6YXzkbbVSt7zEMGh2tqyw13fEExY4VcxEbAL1moOhbqmcimExuo1JgJ+JsDh8jBHZERsZIrQP8iWywzqehPwYv66t923VaePW4qsRY9OMhPXdWxn8iXtE/hEXsYhFcvayGNttML1nrxg/nhufz0Jr1IgIyL/BPw0/jZ80S8MkilEDQAw61d+L7OyPpQOxcI4MJnuUh9OuQIJj0Sn9dwPvDd9RQwthcAx8PW8odooKRZ4B8f+nwZsYThMBU3cxsyyKJ6Ez+k99CwbuCNwTjD4qqIzzmg1swpPmOylgLzxzbhH074SCO0W3uUfuv8um3o68LoBFfGNgP4f9LPCf1b9A/QUaAbUG8wsD5j8yy9oNRBfYGEozKsRHwPdPz0Zl5EMAyTsB68DvzSlwCfnHPJLWTCKyYhoIJmYEem6DAVzOQO4Dq8Xy7nkfiV3BPaPW9yAFg0cIVavsIXj1mnCYC3B/EyXlytLW6Dd5pkEYgvssPqozsF+836zX4D/uGz4v/GlWD+bHvrWl5LlAZemsSEJmDtvrWOISvQC+4F0yd+jWjB4ZDNriwARg9RDUm2UOg2YiPal3PocMewyxdn0iJQvFQlBhoQnp7ZXjav/Ot3KUb3W6De6y3rEqTvEXX5tz+tiCt2ftLMF2E4M3ICvxqNGk43+hdmfZySnFBn64P/Lp1M+5y256bkhsfbYjA8xhM6XmuiOokvJSsMIhgBwqTgHzWjOISiLYFwMF6Px0rDDxBk7la6WUnO5ND5QS20U6EchtXtBMuV1WvKHyGMwamCBZZxVa/33c+TY4AOD6ga/Z79zh1yfZja52SdC94d4OZXFvEmn0jFdUVatZX9ZlvsR7INq7pZZA+6aPDmcK8AdN7z1Pvas/2pes4YT4cBuVym35CjIyOIMJfhd5MWN7BVqTNdrwEa+fRovAGqb+j/clsDm+vxiJpuA2MCf8hrh3eRGSe8kMs+6KxQptPdN7gufjto44OYuSLJ6Fzj+9TvD6PrmuKpc6pLPG0iLcnFRYtK+NzanoXkq+PAowKmaS44tdTFWSlL3NrhVtzcm4HMJsd8tTWxrgRhgS/83lk75bp1LQxQCOuundNccodePQW8XicuG23J3eKMt3Jp3CsROPA5L5GgmPrmxIp1m4PkCsGQm66tfpzXaLVHYYE5sb99DWxowmzRPci4mY75HExvW6va7biMH/ua5EZDJQAAEABJREFU708gUUTb6MQLG2vEs/qsd5aL2s9b+/kd/c085MdTja44V07uURT0OGWNL4d+c1wbxWSvVcvjm+YBUn8jR63jUSRnm14nfEkphZnyeTUeK9rheYRpvmPuGjZoQZm+pQjKHPAQnTHmNNikFT2JryDedpY/FjdOooznZn8eamJKRhv/Vy66rMW8gmu7zhawGJnusj3dqoFVvUK3s2rwHzqr6pJXSjeqwW7clVdFVkW5qv+SfwE3kgnYEsAZHwu6UlcMJZzmFD70YWx84Cpy/uDf5vJaO697rR2wDcdXH0kiz6rZnVE2FtGb2sDSF69YBmtfF+9F32wbEeCRYZVTjlg23qz69xwnovTx3xqt4e0ccSVhjxn/JDmdv/54w36JzqkcOCBrCrkQGw56RScijAHpaITh9Bqvio/0skdvyieGJBgjV80GHRithffLYO5YrkAFjkeffMX2xQE7fCzBozZnmS9TcTiGPJjGKXbPTPlMC+rWe1D2fGZU77iJ2cUTqPT8xSt5hXIDV7ZVjGgHNXYwO05Oc4RGzzqdvqRaRH6RRX/ly9+8nll6VzyfRSBNjj3DEWvES7EnCIogwEmGFXK4mkFj0BAYfD8eQwaIwqgPWouG0YEeOJyD1euGO1eEmj4z6WjKqaeCxwMPquvkqSXt7FilzbYQwZz186xPpcELk6NlFq7JC5ZV7IXv9Jq3dTqfvSuzLJLhdIJ33lfuTSzKhnFc4FelSVozl3QhqaW8ev1MXI5FcGiH1Di8lnXE5Fnp8DPoKEBBmGdxEjZxCzsiM5lmZR4XhYcsuqt2eOvpwmzNieH2hExlGA2vf5iIkYx8KoDMWImM9GstIFym/Vd6arpUnSI/QE7myuAnVBIb+FwTDMFJKKjERpxM7PluR7qXy/gB0JPnZP2FF66vI8J85x3xUZTOuJVT7tbRH5LYiN03cYVLfA2vwj29MQHj5lSPYdnXXb4HLnRVSzWsp9qoa+IgHsWnbIgJ+EdrdJ+4f+Nu33For9ZydD05hFuhzbfw9vQYzFEblWsn5Qh+P5PfadO/vv8ZEAh2G08Fa2WSd0U2JKBCLh2DNKrzXXYph6863+R2RV5H+1UWp51vbz4L6Zj5mUkMquDy07GhubYzCFzklTtyD2Y+IC7kZpnXiZLk0qP5P4u6mzdmowMwn9+abwmHfsE3op1GePlKgbQEs3zOmE9Uh0oo+x8TWqKw4PS/JlceHmSGaXTGKU6xPZbVdLjoFx935ZkoDx5cwvZ4B5RWaJK36MjTj9JlkxPEIjdyAThdZ8waFlxjXBNL4ZKhFuEwrMGmqQHl0Z65tZZnB5Us0sLzUEQMdVqn8nU74eXMD6YT7dHBmB0d+QWSFtsJb9e/JLRkAM3f2e/Kzo2dgpm1ssFCHsRvhf+EkQDpnwEN9+uUEF12mYM0CVbVhUwnaiWTAbv3cEXgHlyFdE28/vrGZ1SJWM/lFuJaPaNCqwkkvsnM6N3OpRFvkz1tHiye03FK3cEwe26aE/S570x/qMzmM/UbczYqXVm266S8TG/HHBasyG5hFCZ8DJ21etwAIetzgxdzVcSLZcQyZcYjRA4ztBPbHmc3o92jguyFcdLQq/vu2GIQX3mMXhgA3po6X3U+du4rXx+93JRO9Ai2u3FzBV0YfIwVCpm66jnl7VPCFj1tOwIo34zZVACv3vJA0v45u8RKkrm5VqRLZnhm2A1Zl5dVcfAlfVrTLhkMYajLlyFPtJAO8y18e4xJIJPYHqUWvwMww05R7PnwLiJJwTf45uRaO67VixPm1plkAa8nvFqP/uCfrCxWmRrPQwxjrP8F5mJckcvkZ2e+P6wto3ZjPZBRIiQCKPawSn6PZjTQOlOt4gLu0FrTH2UFU5Q5mfJMOe4jD/kyM6CrnX8arB5/m8gWq7JrF4kfeccmRh2875TgOKoL3RQlL1mbU/96xmE3ZqIYJ8PkOZ+2wMXe6K+w5No2YDzgwKELTJh1+ZdL8x9XEzAMgBRSGZG0yHq5zfiC6XLs9Y2RFmIh+eFwLF+mN2NTe819yWUHW5jocdStMVE4Ee0WGkzNQi+Vda1Dd9rTgPQdXww2ruCCAs32adv9E9st/wfLFDlQA58VZwUiU8rp8M4mTxi8Qvi3zsA6thhTjph9fPRCXZxbkSb4wA0qB5+x+DgZeYxxZc4QDf94QxKcl/MLv8sZMGzaDgFPJb7JibFwiISowfHvn4F3FYaDQJL3cRFTR2MFjDWr7TnmdcoLjEPEI2uaw9pOImUzNewyBJi8NofTOfMjP1TpJYOC0UxFa0sLxg4hFAjKfSSmRv3rQdMzjfJsuCtanhd0r8mrnPT/5ZW7J9u6sLWbcBKNGR0aEj6CO2hO2fxEOI6LxurLPuzWhxrzNbHLGPv8Ubtw4oxmgJKPHklczJ9cd9qgkuY4i86CS8V9/4vS/ewa4/lXeHjcK2tdz7p+eb2KX6w3vCS5h3sPo66ebnwHlhy537UGCdj4FfuCcRbqxZh7yALsdMyGUr5CWP3h0f5s2uDwWUkPKbjIKoVP1bAAZX6iZ7Vz73xJTctgz0rXQZTTGeN6gEUcnrZnIt7J6T31IxcrAuY+DYiw1X8/lmoTw7fCq/GT3lqWNSiOGQ3SCaPrKhe5DWWCDnAt40HayZKt91VvODppkA/iBUsYEAwm1xVcrlvBNIX8y1JfaKCsR7/jU5mrw8iDvFE5nu9vQo+tWZbwFrZZ3aetyOYyQUP10/3O1/PihIrmBEva8W6eBZFw0LM8/KZuTWRZrFzhDh2Xr0+0+PgAXBdyRUV4UzWQU4GE0Su35ymIyOZ2a3sdg97F380iMMInslAs96Q1rlh3GLovTKYC/3vJr+PBmJBZxfKFBeaK2SETPBEjR+ExDEvCw9SYiWdSGqkwp6orUHEdi9CG9WUq4CrRv8iBmH9f5wNvK7k6qBm0YnwVnUTUwgsbpFPjFZLLRWBh5RzKQ7O9R1nRCasLyyVzw9MoY87A2pmG7tSYKwxKeA/H9cVZFy4X18+HnjP71NWAt/2dBxxJR37q9Tv90OHFlkVbWN9FfiMNgLYJy1KkgylSmEEdVo/FoVQ6JgO6aAIhq36mOlQ7ewdI/2QzBDgE184CVsKLRAStm/8dpmqi4a46vhVIdhTg74J2z/32PW/qRFaEqJWzOEZiycmxQhOBfwCMPlw7zlxoCAfpSAAEaAjOUfV93tXmvFt7Jtuujlg1ZnEdpSd8EQeQxm0bnmpvcZYtOzsSZkv3ICr1rPd81YQTfGABEpZV/TNrIqp1xq/2yM0n94Osdv95EC+zTd1uXgrHxZnueR3gELIu0upYZXk5xwkQxnZ1byhoV/pXbh007SAF1nibhBon15XkqKRI5Mq4F7YDeqDyVtVNxjrRee3N06AYCnGwX+d/As53ZlzhEG0y+d7r+I+7HwAAXKG9NWQP9BIv+Xpco2x9WGbIV0f/pD6bnoFetFVAavjCNzeccP7bJwvVT/lvE6qGOxwStfSG5LlyI/H2YB+TWJmTa6yiS8q/fH/1fJ1IltbFLy8eHGaPseDma35hn2aqJGxGJLA9dVlp3VZXDmO5+D2sxu49+ZL/wpiX7IstS4qGTJrEfcX9LYrd6D5GGeF1VLCR1b6hMjSyiEK8cDczsXV3/em2NfKnlV6GX83rMRFw6zFMyX6dNMLMBCJireTlK0RRJzFL5Oxujgfvh9dvchKhznfc8csqNgzPB2XDCaQaMxWXc/oXrGzGIV7EsZ7cORjg9Oq5ooJfKmc45SNP7XlvVI773GCwh4lo+KNA2INlFzfiA2ZItNc9K52Hldj5OWrwYLlqTWks7dBOXp4ScOBWUIdXsqAGxKfhwKRxwn82GhiHaEG34NrsOl2Qd2xc3/oX73ghL3cuhZe1tFm09zn0cMOxymzBJiqidBmGXfxM/TntkVQIDsjEdsBXcKI7fmVy/JXr4r3D/YQxvAZ32eXEKAq7fCYomooJBWld+5Jldw/vWasSD/kLHAx4djwXdMkM8GIvPclJOTko5Ivdfcu9sqwdy+Hfteitd6Ea/S55QR02c5TWHOZzelE7Tv/0uZzihm2RNL++X8lFxkIG1qathCazcXGWUXP8L8VL34i69vNnvUkBOYZeK0/ATehQs20IEDQ96vEgwrO0Yl17JSP4vaV3FbDJJBQjHrYFE9es3bng4tpw7EaQiRvyvnBNmZ6n9Rw7mEy15rfoO3p/WgO6x3yIA57fINjB10wupq15S3FRjjeSnhyGOP1MvXWsGfkc6UwnV4ePLG8XtHytztE/jetTRx7J4U6yiarxzQe19s7YDGM2TYXoGZxfzt/Nd1nwaUom8IZ8KA/yTLo4Lo3bivGbMafLS7kLlAQixXBfXn9fGoOEjIVwKvWMb3tcrUFqcS6H60PD8YFgmmxHok+xA8Bze1DW3xBggw8xMyYwAoqB7TnI6fJZ2nyPyfrJjxKPb5uwkZTF+8VVtFTXg11gUFzApjOFBS4WvKBEFf4JLzIinNAMWJD2FZzE3K09X14jM9dqV3/GYZ5MsjafiEPTw2YkVlTsYTal9XY8+l4p2JZq9RiES8cnzAohjlcROlSm//3D2BJpW7afKo2TWGDCwlK2uuvMhOTusr+zLq9+Yh7GV/FeStgAgnC/WtGd4/PPwuOAQ0HdiZ8d+4pxDiNCs+dNtLF86Cxxx60EA/SJpiVvOfCADuecV3/PKvdQoXwSLz2lSAKXDLHzt/Yv8o14//FjDKtNYd3s9KjPrBnDr/yYa8259sl1Ugd1et7Wr87fL8UIE4fRdI9CROALYMypNfKR/5PPHUXMS9qC8VrBPGFWwZ51zlJHTJsJAyxFdQLpXpSJ3uNFAJo8gEmmJuTK+cVMDc+obSRLi59fJ1HwvmPMj/8ye3y2qL/4dw4wDtvZHXf4Xonjjv2Ek9FNHwLSZXc39ik4j4hjgzMaUeYahF+CMwyHO5DPJwWnzHIj3sVxWn12MDE0IVwMy3b5DRGjrHdNOae4thcrpXIJ5Iw1To15eOvH3TdPLHhpCedDCi8cM9ZFXoGP9ITcpfKMOfAkC0QBR11XNHfj6rHQT61f8oUm3CJeTGMg4AFODCaiNycgSyw8w+OWMWLEt/EUy+Hcyj+8tqUzqzpqnJq9dKFx+nn7e54Ff9WCyycdXQo+KkMr3Mzuy0Iwi/buCtPv9gprwZzV74IjcMwqz9vGeObYYp/HaTGyiD0Lk2U6HTHsxvU+dKfsqdIUozN1MbIXz17lRZAfpaA3AQ+8xWyjiR/9cO7RavClzhi+RIjFEd5kQ2iTA2Y7zFL1tGZCCng1iTykdY4nMJtYcQXCc6Z/oUd8wEdwBbbK5n0pLAXYkc0O/VvFwXccKIMr3M7IlYM9j4/UOLsWCqwIa41IuPtihxcEy+WNeOqqTvjFZjh336f4ppjua7/7v7N+DvPVPN3PUcERsjGFbm8TaEBSTwkVUJCeTbkbfGrXBpkBU6jg+WQAABAASURBVLj2ODxFys5zO2RPZ2ZqwVyPNWBsuk2bLIz7d3wvsnRore0qZ2MFl+GJ43I5mMHYmV0ZAlm5nqVZE4u5g3sEw/3+C//2SBFUelSdVSfH0bpTGsIA5aQ0KbpeyW2Mhkr3qHITw4Wz2vqulXE5R14QLJjRi36BZpIrsGFZFcpKgM3VRjlFDD+Q0rUns7mGOfQLgYYEBAEzqefFv/9LtOBpvgXbNfroMDR8urQnvtplhD6UxDUmBjDhip0ZIg7HmDojk8uWu/tY0hhHTjquRX09AS2Ode/I4FMuUZ5KHnjxoFfHsW992JxJ/8bKTPc42ubAvdq5nn+p1DBfug0CTgQvYIczPrt6vczHXvLNGIJ0zt5TbZDu/L7k2rZjDGtcQ0s24eMjJIRsOOvSKGK8aPkGJjJtvt1gwASX6Nck3ms6OsqFJ8msCvaHSL9iGXIKLBMFICJJmOQZOKOstQbBY/3i1XeSYUV6sAnBe9hKzILdCMBLOQB6AwBHqSWldzgRdHi28VmgIdCaxYhwGLSSw06+ZiGtmjgeGYiIjPCI6Q9iUOvqLeZod++a1RnPjb460BS0khdXyiR7nsvEbd27gDPF7hvlxgsMIQ8fj4+ZgH15TVPJYjQyLxfcFfDvmIYbeEUxfMaNpP7/XREp2dg9X75XujmUrNbvzRMUZmEkO132ldB2vS2nAx0J+rVifFX/yv+SkkaUL+cdGN5lxd0OzaxjCp9p0yq1bfpAOMCdupYH01e3cWTaR4aMLCrV0Qwsa0D+BKa2QaQN26K+aPWNKdhje1QsIc2NJsMT0RONBTtoWRpdqxRfjhNJfapkAwcmSDU4r3cVYSpHTkoxT4cOXY+UNwevpeGJF3nFmDgnahHw2Ska/T294fB9xjGKIiOxs5Nv1Om/bN8I+G/4vWz/o8DmaS1Ew8UxxtaZxObaMgmeboItvjtmXYg14Zx8dIrmAr7T31ftu+6f/on8qWtbLdqHDtm7A97oVLXi2OkbLMnFiWz/CXFcwFy3jezEz1Jy5ge6iB9k0g7+B96xRgS/l+nsfy+UhzNWwolydCF7s7JdRaZxzNNLuBKne05jILMuTE3VA5T1dEXsxXv3jiPUM6j6hNyVJ4hLm9omubIHD+hm6+lbzxWvSQVAE6iCBtYxNuDTs6ZT70QVFYj1zq4T6F7sH5ilZ+dze/nAk5aWrs3r2h5YGd70/TNjwhONaC1gNXaX8xVqcc1smcV+yCZmOLX4awdKwIz2ngrYoznwGDcK39mxO8yA8EkPCQUbOFnnHhP+LVEySUYuMJCW9w/bzuUbA289bJEsRi3/7chw5yfejeOlKCevRo7hshoxfbPvH60fC6lzxJo5Vc46EYytWCuutS5sLytO4B6MBf+OutEH86weFmYiaHBOQa9RDG1gfTaYMSsnGnVgy4gvXNPDaftTmHqs4W64tFdaq+Ec79LdJtLRMmyVV9573Q5AsQHB95fPaKgGJaBXGuzeuRl8rz6rEAMPFUZSAa9HKeZYFv+vnv9m/pBmD3/A/xjXTLwzj6ogktfLdSioQuDOvrYrv9GV1aS0m5ucnOJv19tH/uSJvIR8J53TSTk2tHjkJGabfPPkQUXYrrs4ruI07KmjZhzYFxICHf4e/ZIn1qscpnx2W1Nu3a/Kkwbf/yzwTFZrHGS6al/IgX+DE0bZ7f3IPKbdqvI+yNM+yn6JI0DMXtdZ18QSOGjQHewQBZV8vVec5DO+zz6OYejTmgEoLc9ZzR6oiU52gFTSQa08N3wUfMXA1UsMXMwrLCQTS/sqr1288lx3Kq3Na+QBE1/uP4BzyGhrv5ClP8SPx20YxQFf+sun0uV6geOv0PFo51phpf6wLryUcU8tTIgf7H9vQ+v8H2xxorXceDCTOKadN5SQRP0LilG9+o0xI+hYAbjo0t9Zv+P7EjijERCtAPVX8Z5b53qRtVGbsziWxQWuvXG5j/nJZnaD3Wvch0RCV4+1if5FtClUTjsqaJpqd5ItIdEra6EthNspoDG/8pT3t9t3UlNhNAjlqou1cTIeo1d82bQBm7RoulP44Yow49OZbYlPJf8P+TSkjkZjv/rvmZ9pxnw3uldqA8HIjH8/H4Wyl9+LfWU6PJGGizbD3VP+XKkh/YNIxLIeUoZ8TGc/3fYjeSZw78BNrR48UcfSYgdBZwLCwMcntSizYEMy+Pm+/KV3lfgbPQYhmWggBmuw4d5RO2qpMMeZcX4nw/cU34Y9TBUb8LU3ujmSHRF+idtR46371I+XuLUX7uWlz1NgfrJxY/y8aW9nM2qz0+Yg8w/avUKT4xzi4jgZ/Om58yu1DF1lfgXn7qy0K0eZPBsWA7au28tz2qMfLLpzmEsnzRjCDEcsBQ/Ewn30p/IqN+d9CxEdoRj5ucI9xw+B1xSi6v6O3uqkKUncORnFfqd8f3HQWsDeig+Aujm90JSAAJIZecH3u1rShbiROxeCFShWewW3H1bYQoRM5vNJduIeS8x+D97H49aLeB3mWpkXsaBwGILNJy58eVcddyRdXnXyCvNoNPIfupw/GYcitIrrBLuBViZsLIb79XEG3pPpJMYqdL1P0qFxMW4jTjt0l/5b/YITqbVcynSUlej4kjoT/YWdKhdjWG2uSCDHIfyq7dQSScxNh3+Gv33KiTAvC/OFQMV2IhgfVC3bg/Dl6ktZzVnD5TTYVD4zFSejL3VMT3uggt/BBAzt3tr3JzYc2SBg05ev9/jR2/PaeGPTg+UgauQ/EVnhcuwhswPZYenMql4kg47zcKo7nomPwD32lCA0NV8eMfIigjdOo4YZuKhNc968arIOGwllJhkeF04YwB3jcrhnfk7f3bewe8HhVnQX2MyIdfFaDAKgAVZkG5N65gk89LhpX1wA6BmO2pVvWG9X+WJPnXi+/1oTA9dPodC4a90n3CsCSIzTatftjMSKtT4VfesAR9PloI6juSKismqOE0x4y7Bv63m/g+fLG5fMF4bKYCC1Fn4Ofj79Ak7gz+dOna34XXrWZ0blQJ+q1x2udsrTeS4LPjx97VDoxDygvXi8pke2vTuzghO0T3/wvXHhgE/0y/fMe9Ry8hZ4MROhVKRP0EDgD404Qvfq9jpzwdvy90IRPfl2/LUshv0RQji7/BjWgIPlJKmWujXHPy6aaY8ZRGOPEgBV2V8O2/1cwU7YuptqgL44Uc5HhyjvzDf/3vG7swMzEMzc98vRVF7z8y1ID/H5qMaUp5Wu4w4rU6C2PS9stmviLdN3ZLUJPLcpNkAPWBnX/gS/+IG0Kfd0tJ37ZHwuywnDF1oYbOy69rjXBPD/I+EPFCTXVZ5RVAI76Vnfff83PXs6MaCrzF+dqa5KbBBCYCe99rZdO3BMofH7LvrI8XnhZ3QFrphADex1ChIqfejfuheyeHEJ3qH4+HYwuWpFRQb2YgpzUO12nWNXwvdaE+F8wemBt2FV+9B7bYNXzIxkLbzBQ8y/WnCu4SrsZsEiwOM8Usv5DmccX8hom7cYVoz2eW55h9w+KZa/ZzgEkBkLl8ZLzjXfLnUtVygZ0cvZCXXqLM2/Hdc/siZY2zVw5bPuij/z3fPdz+s+YCIA1nzUjPOmHrX9CLvGS81Sk1/765OPd1VzgFec3Bk3rXymIQ/gmD86HP1eJ9nx1QMpq76D4BD2pTmMJ7+1DLDcjqvso4Fw1tQ+1/z6zqTQiOJWflV+k1fsvDIzNi0HCCr1e+p9rO/5pm582Rz6sZby5vWnl5fJq989xyHsFZ4qQzhv5QlvOK8t5iN4EZJrNWDiI+vrdlcyv286+J5/qpXj1jGGxzQB4AP8v4bcwoS+4k1FztrYO7hXRb4rC4RfTsuXrUqUt6NEGAt8MdwTegF3rAvLXFtcPaqHPIsKi1ursR5+zyuCuGFSkFlrHa7BNXnDc7GldJfAhW3ogsln2ALmw/limo1AKw4/+UhWvIEATPqCNUepA5XxYP06/coQ/SrEDKaAU/7FEcZjXcL9dnUY3LevHPl287/ct/1CQF8oq/RrrSWYBmoQjy/5M76X7fyDKNFn2yXybX5gZO893kgpquylxtDvAx8xXzTN8XSCJojoxd6YjVgBX8kiHUcn07qUUwhDdwo5EUlvc6yymqj56pGBCP+7EeuzOy/5MoTcnbdsA5425nc+n9L1GEisYbTFwvt1DugaMj9d9hSvc5W2yMXZbMFr+oPnTb0RlWGYmeQKAgYsWhnBqazvVuyMu4OSyPwgGEl4WAKfHFfr6loH/M1+l8dWOGE88cn+1NWxczF2cKc4M416Vx8zNbqKy3Ydr7BsEBlMShvnJ85aEf6beERGObAC3rDncvP3xv1n5U/mjl7xylsasIdwPkPuxMpXeSpUhE+ud66qayYaXwd8gepBnwjdvjNJuxQ0bW5inxUHccgP9oW+4RVtHJKXGzfB/nRaY7fJCKwOTibTDTjf4HPF3EH7xUC/QMOl8XPPdSWuXenCIaQMG6rsVP43+z/ySnP3zpTlFR2OY3o1A2nizjq1qncInu/1F2wTgtYcnfL2+riebDeKoMjhgp8+lPksrLd+zrmj62fOT/fu9lYi6TBBhIGGPe7xQ7eV/EyUEX5WwvoaZRnCN3I6U9+r2vvNn3CFcQ+Yg/9+XVyvpe9xzm4MaeqlB+dIL2SRJ5F2lKG85EdGdJl4lEXyxvXE0mayE21Mgc8/or74nXjnB+Aifri3o0p4E8PMK+9cP4Y+uGh9p2qPW+ZebmJ0S4XDdqqVroPhAE+UM8PPz5afwg1qaiQDn/Z0Q+1RDV+xJmJgVHLLRkfVbjfwugrXQZb28BvgaOlRkf76Cn9VJSm4EtdP8AYucAtpABZtrkZOJR+msQC9gYSjs0CuzoUN8qteHgJwSL1SK9eKi+FTE98VW6iAfWJtWfeQ1fCgK6zMxITrXyLMuI17IXQqug/4YnuMXROV4z5D9m78aN/YN+k+EfkO57TeOo1y+mxIB/IEl5N3+u+aYyyRTlw6QLtAUvokYtNA5zDezWdxeuFd6EWu2TXXYR50y2+/Ff1e1fm6BG0QSs6dSM8/8TUV2NDa4P6CwWjc+at4Zrc164FSQdakrl93QwaIj5DOGHbPEZqR182VbMVbLNIivP7Q4XoK8neK897F6GiEK/Fv99NOURiriphRCBFJLsjs+uF1f5nbWHnt3DHo6Yppp5pgk6/l4V8VS9yLYeMe40XHyWjnud1gKL+28QxzkOJlBvBVoDx8y08QIlOLQIcqZWHt4DIWC7Un1PJnC8EEXxLhnzQHiRX4WVhrICMDTkoriMjvsGrBsifIzNqr38cJea0bWxd4Qu7sv5ruDw9CC56O2IPdjs0ztkgCiefmb+Rrcdhq9FnV1Hyv13MK4y9Zk6MbuVbs9CTr11QokjC79jcEIyBMa8pHzGzVjUrY4l21ykmleTIjBIfUYtyRP+wrKjew5GV1lpr2Gphk73iufHLXijJPNNWLSoBpz98QAT6i7nHbAAAQAElEQVRqF7JCb/jOtx/q/brHU3jLjWO72qwgHizXP1jYpR8YIVF3z3VEGyHb++AvkTdmWXzorqliT4xH1EAdNC5T6lCPa6xmhjCZ5TKOGZmPoa3ll1J/8oSDKMykiJZeJ2WBP/xqw4GAWxq8He2g9uQ1sY+WHSLQiRHkV3jgyNHbinEQFXO+zJQ/CvXph7HXvWndOfynLYsvEHu2iSC+IwIRk594wjAuIm0z2OCAMQirxinEGVajbJkcJz64DMGp02TLNZIv7tY/noheWVxCAMORDdQzfvc3QzN8pLDH3hPBCJ+JcgSOM4lL5Js42SedCwvKgI3HZCt6vFK+hW7Jh9U1RDsFHQb5D59sI4FwFGFTxGQo3RKvyIiiNcBXvkrCGE9DRCQ2zUFAObOnl4PX4vm3Hva4U7FJeU6slSuDSbABQ2yLd9m4rY0cOtvYuuDbzZ55e96ac+DEUnK4kR27cw2dhDFQzkxl9SfDMHFenEx+hxXWfp8V8lN3ctzzt9byudLMxwsZ2GFox2kMuG6wIdEPD+aNlu14AF1RqME5odqJa72x3k4HBvaGncs8+Km1FuSFSvvtfTqfsR+vOb81p+AxoCbiKDvm2e0dCv2RPVHILxFEjEs3bsYPXT8/iWvW9dzLSR28Wb7K1HbaOs7snku2ChXabbWShdXfIAB9Td9eY0+YLPzEXBykHTQuB10L5n07ryu0yRya5fHYZvi6BBCuoYp9witU/BbfpkPIzMuT4atjIvqzakxbh/GWBcRYhG8+DQYsv/rFGG6u2ijWO3BysvcuUrT3nbwuxvVlodke06uYhwwoNYtvqAHom+C87/zoZo8e4oQZIZBhW+5ygVfdUCb2xubwyKu4Qxp5Lk04OUaqpMMMDIwdT3z8FdKHDU/7GsM/SS54bEmGfmzZMesKpDF9w/6pJaJ3InacdE1j7DatCNY6E6U1SC5bykm36OTXGU7hLXYYaCo3wBCswjWdZgwn48FuOey5rd9P+0z9S5lDcWeawaiAiZjlLy/Vyx83eBNfU1qs5FCLvTAgv39Y7lpeyFYjm7YcQXkLN8tCACwFLwJx2hGGzxeBUh67EIRIfN0uGffK2KtiNWMhHF8w9kSarrh82QNLbj3UI/W459TXi6KbrrJg7sxtMOan/FzDaYJTfUI+XqjGr4YjMSvmu3zBsPMs+L70LNc5SQUKsEWhN8c730yQEBPjiXQpwh6lkZnVpEB3i3DyfAp8kcAK5UrEZgQRImBBVPNwLMdY2L4gJ+8T0MhZsUYdqGuQoyyZNrR44ExkD3vQ46hNu9EJ8jWx8zsGC1p24X9hxomGR8/Qw6I761lvufC8q4lA+JdJxqFIJ9Lq+QJyydEpcDa2QUk+hwra1Bx7Vn3WSnxpkya+86GXRQu263MoGNt4eON7t6O1rexhSp/R5L6AnYccwcx/okoEPcB97KX7UHqTeJ1cY0apah2gVsR2G7su3LZkSC7CiCesbB/21SJIM5qZE26a7P5iD7OygYuTHnCJV3GVEzsDB24QIoeYDAJQqTAn6WPG1DjlWr46rZn2HGBThh38lwBNzMxRTTQuPy6dO8d6dVEhkwaYbj2r7L6Z+Gdpv3J2kYOfsQzSICoc9QzsgxpTlY11wAHRDFcEMhhIihYnOyR/T59cpTleFCjkJkzqQuQ/prcp/EBLglPpd2t4voGdqTX8539/gN0e1KbBORde5htu/T+Qt6QgO6d2VRgh6aw6sC8ga/eiLPIVMrLdtOuIaOdtUIc4EZX/nNLxyngYYztH88oashpSINbEfn0psPxF9MTv5krDEARMdR+Hwor0OfrcR4GLEaRPFHPwJcpnOBh4ANbmtfLKvSOjkWX6Ajvgv0dfX9BN59yzggg3iGPN4N9cJrASrhL4EwPOdqInRsh2R3InDF/3dJT6jXiu1QCn1tv5lyjrdDf21M3vjmyVdeoTARRtaaCCekE+uQWXbRYtAuNaDL/wgQjZBd2CtTaDlEYhc7BDG/i2xDmZ31KxAgH52OSm0XPUj8FRL5yQMHwlIpU2ZAx5kDbvLw5yLs/t6Iq3dTjT1Yb5eU+N52uixiTf8Oid2tmC24YHRynZDFRtVdIvYws+WYUn8Hvh9z4V1Wyz9g9oEos7Z2P8EOtq3y7O1dKr15u0mXANxC5H2PfC2rG2MzKWvVp9HI+5cDrcslefRMNUOobLm/RfZIHwy3hjVulW3sk/aRPaK1aSPnp5R3M/ciR10Si9PlaobHyfl2U9lE1NmierZC6OsXh1m+cH5+dbMp58sJ5r//7Z+rPWFX5E4dh9ls/obb4VD/031FWvi8r4nfg12N5oxPvASdnRyIBZIVt4sZ5xSAG7xOZCRCXDvvl6OE6nfAz+vWqin/jT/GnswTR9xlcixv46uuVud+g04QCDVnc84N8YPwoDRQjypvh1qzbJcVGh0dgYiMjO1bHlTiA7iNNsn6rccp7lzlrOIejqfH3Fu4sffUh0+jx/Tz3IX7jP/N+Onx1XxD0uK4a64vdv1lxx3bGs7gCwNOudaJb2a9oDcA7Ty8GTfI3WJzQn+0nvAmtYuAJx8/zRr5Nmmu/un67FaX1aev92/U49Or9znvep3wen2N10XUDyrleX5KTYQT6K6ln9e+m5r44/oO+Ft33AamMLJgaxcF3LCd/w6UYY7rggO5QwMz+3bpfJ33v+d+HJNTvxg9ltUurtKKwgbrxbnrKs9kFa2HXi91un4aRJZ9gAObgw/+VcG0r6j+XvbzggZCOHO3Nl6JtyuhycZfsCfxF/aSJnry+Q68a6+NG8d/i+iVwBrGXJ/wX+F++LchHN0fvm+zmWlzPDbNzepx3zBKAtq9g9aDcH9vszjsnTKGjTMdeeXn7SVW/N33c9WF4BhsZnV7t7V2V3cKzLc97yrqKkTnAjjEvXVflH2HaGNSFM7fe5Sj+MO/eAD84v64maUWh89+EPjRE9vH8eRhkOvaXzZc7QdGufz2SBxxkPq6NpYhlw+1brGfRXn4ygnGzleuIuoeqZ+ttudCu4Ps5yNB4M7ygRBrSvE7STtJ+EpYhYRm+kKDZ8Fti1Vm90mLYUVmhd3pb+bVZv9h2twheSU2eXroCrbQa6XPLr5T38g4/DX+XfzfNH+dP3dX5Wb58fX+KK2Ol3p8goLvAWbppGTUU/PoSZwCHkswnFEf42TgeaJsa5QRyrqjlCBJEJpgnjdAaQOPd+cj+IRx6jLSszxxe+cSfXi/00PZ+fNjWS3QBBE3rWKi+KVw1NZ6pXdC5iu8+de363tZeb209OL0Z64peW7z+YeCypWeNzKMscByhCRh2qS29YRueLY9oyYiDAVV7oyD1aTVx7fqJt9sZ9596MRSZhdVS5pp0f5O3CIEPa71z/0/U3UNExB7/N35fj0Um3A5cynU3NE/rNmH1dvC9zn+J+6Okxxekxrqj+EOKVwR36d7y5e//h9d/m2jpOOBoaNzdNurYkRdV6q34tdQ/OHVi7sapTL0x+nFNleq73cd+7f07+AZZ/5p75k7WuFus9+Pur54FLGs8V/jv/G1bk+XkfWxjNkMpyysE4vCqWT5Bv0bfnY7hGBVRC1j7zQkTPSGImr8SXJK64Fn540nfip51FvEKNLwKuujBnwMByS+FjPUKZGZlaV1lE78rjb52XP5mKPuq/yIM/K7bbJj+bVXxOvDJBL9hBRv7gZ7kBwFnqYnWjmXXx9+7/hX4LPkubbsgA+MDl1ML3G+4Sm1gO+EYnxYhYK4KleWBDZF+oG8/N3vTV2CvxJSEMUDVrlFGZrrimE1hVz8Ew7tDNN0vwwlOv9JLSsP7S+xuiMYtnk2DOTl0994M/Z136oTFsXHYpr4TI4splmOHSwAYXrowf8Jbdvu81b1hpth+GqGXLrge9qbpq7uafxv0qH8xLN1ssxhXXjYWH56/OL+CQf1iXPyhNojPM1zfhO3q5VddE/w7fcSkFnP2po98Th+u1aXjfcQVMlj1ojnlYO3EDq4GRI3Cd38F77eUAaRM9Qexd905eW9uuQQF+cwS6cG66VeA6V8wGdizFj5RC1zyjVqcZX1n70rWxApGMRVNFDEeLuCLdM8JB9yu2EhG4Un9C98bKYNDJ1nO8KDfaRccZntRzoZcUtOdv0wd8iPu+8lqfaOfVNJgZy34wfuQ8M96wpXtJuaK3szbCUm30TfvdMdvwU1IM/GrKh0HBCwuFhKuMcjy328IkG9BADOSlvDavCwsFq7ysfkd4MX+aG1zXunL9p7g+Q/OiOL4em7RDyHP0joqrbh7/qYmH0/vgPvnnxR67iuOE8UKsr0KzFvqDgHL3e3R+OZVAOEumvuUIf8oiYbelgTLBjHaeUoiG6cE4pr31s9wbHAUR4vXePuIIb+O38zeu4d5Y2arpigK1VeyypHHvWfY0pY+NpXROd2hFWYkLWHTC4oGOIhuXCWvJYGzGG5vnT7RXkyhXuHPmUTwuNlhvQUzuXPdP/ATMkgMYjEwdHvSHJoYLxJHnqccfP0iyCseRLIlkpBgB8+eCgIcb4M1IvuL7WhSCf7rxdr/z5WEexkk+qXM5Xm85d5koOdlGD1SEdkJ/ODdqpRcCMynM5NSmkkLYrbKDx+Mn05AMv7Pdhgpm1MoIg8curfdd6m8eJIHjHHl/IqjlGL5OYiKkEK1lgmsh8wQMd8RSfvBCgQExtNuNDHokNIihzRLw5FyK1elBv9QDa4DTiqrEkziJzAyT7jvHRKshD/ZEd9ezeBw4ZryueeEbveiTx4JoeSxkjgHH7mkuDiAtEk2aki8gThA2jtiGOEb2XbaCxX99KtXEqz29xk4L0DKNxdY+ckWAVRqXznjR0950D/8S4879OhJ7aq+99uLAzQMYosOFLDsnxg4R6OQxbJkXAyQuOlF25avhSSZn5Qc+M8N5XHwvnau17NMkjVm5+Fm2BUf+b+ZnYcgvP8p4NyokjJxrAjQ34aYzRat44bpwt8sjPWV8Bg6TabQMMbqHZ/JVHKqi9V1JRgAZyj25GCR9F/I+8sI1jiQhEibZiG2E+YVAkDN3nJ+YjK35eWbjn0Lka2FuiMIc7HDRhjbEZjUOp9V4I1/gW0cPfAHEGJIdJdeKi/qk7g4NMxZrHD4WHEyTh3aNAAnBZU0rayIDh/XI5t0Vdu7Lp9Y4mPlMRxRR3V2TAJGMTQTt1GnJ75n6p0dzGwu2jG6JPUuVqsiH+u31KN/m6wAKVMpj0+bgkC8YUTTwfWmwnTglBftwHBeXmdLZp0wFyEn3poSnCz1lrj6y20vuif7Kgaz5mDFRK14ONY5gVr0wj1IHaKj5geXYUNNaZOdiJjPoV5TXEdNUwkFUbHB75ISBxjTQQxv4WevCHVxyIKklrB7fDaR6EcAM5FQ5mwYUE7Rjhxe+puTsNQhAl3QDC1ZMoAkTGOOhTBiOBwJmNN0P1GFvpBTtOeNiu7wrsVZ8Cg349Fc8mzZ3ws3g7ajDatE/Gpq8rQAAEABJREFUeXEvRMLsqhsljX++r6SrTtvxjQzJ3hEemBdiD1z8A5aVhFm2ZMvBSiAN1/R26ABn0KQyPGfTveAnzcAUarLSv7tKcI7AADv4+pEvHK/Rnf5c20D16R45M7D/lWeHHSBewvGpoUd+NQaAJbEj7FKQEx0xkdU5nfDhHqM8htQZsxIRN7AZ+UX3ORiMbH2TKQaSDK7hVoRdjD5uHG0KNhl4IZXWc9bvo986flppNmib0jUTwHAsBaX0WXBOVPAiHEdpE59tU5iONBLxz0c5Na2dE1ttvXprMT/9SQkn5gTbmcRK5mf0gBUeGwgND8NHdVQtcuUV7ohyf+jKwuULU4HCAi9OhkCZNCOM5ip3cCRmaI3T3/BpnGPUA4hOI4MW3XGrtQ7WRcMAYsaMgx5kBBbArTvXZpIc09m2Z+txF+9EIiqW79wKi80o5RGPvGKEbUXaqQh085xsBdiIMQAwRH9dAddlbzdwf7qSKzXOhKuqnOYAuKLIzwpiEZEyf4eYoGV45Uo67hbGpr2tLfXILdXJdE7jadaRz9FRM2C310R69tgSBDmJGJnJaCyREQQNOf/+xtv/qP22RiMOo6CX3mzPO84DZsF6aObpeGHGDL7YbTULfkPPILiYiY3kt6tcneo1szW5Jrxe0ihk846bGktn0vd282ral5bgxuMcZsUu/sC1PIo57I6RXsfuGjRsJ9WAfmMVKJpqVoz3W07agKO0g4nBQtqQ5WWO+OGDeejm/AaaXKA5/WLNtq7SV/XZQ4qYcN3IIxwWCY+E86E4lVXwUHPY4XT2sMO1qmRsOt0mpU2P+SZMhWzXVu15hUjCZZqr7zxrPzQrQWTY35R62vXix2SeF27lXx6Jhk6Pj3lO/D3xOxwq1ClTogq4UeeScxRjijNireU3yGwC6KT5ms2GWSHZjAmXU2RuYpW/Zi57DHD50ldLZnz37Glk8rJNG0IbiU2AWRvlhsr3Vb8Hal7IO/bGguln63tl5E4susJ7DARfRJGpSJMX8M+usFcrfzzF3FE+Owq3DfQEvilQVF+FHNABBlbYnTPwffu0hok2RdWFw3QBarIMfnm3LU+GFeBidZ+qhvuPl/IpLzOe98lOs7rMJmmHxj2CVRNFdE3b6ptzbudqXe2WZMdAMLij1mpX5o4g/LJgsBaXOuYN6eNur713/CT+XHKSOEuIAPN7t7M+JoZJxDoKd3ondcThwrVg+iIY2AzegMZtx4qIWAFW/xNNjDUFqV/xzegrZ6e7rhURd/65uL0eqFlAqcjfn1W+axdceHa4nDuFo0ONk+BhUSj64ZbOj1l8LY8BD9arVWYabGJ62MFDjozL4x8vZECQ7pF+5H8/a7lG39nybXReuXbGHpt44d0lrFss8Ke4CA7QC3Fxb3MxqUGr/+K8Ohrft63/eP/8rPuPlu/q9uRy8qkT+PXd15DbBEUlfod/wV/sNxxkbvDO4TWf9jHZEV6NK2p0vFrdmiWGqHfjvdAX4Gl+BHOz/kzfp/ECLktFGxkDBhuGd2Z+228VqmvqQiUZXndxVy81AjNqK9ITrIHgf+Afto8UZHHgTfhB0Bi5z7DO9Z5LkyxrmIEVYTv2ZWFkI6E/UTuOVdnPJ2kM/W1alRjjTbWAKoQR4CQUXMyV/2REKY92+6SOSSqYdDIuUxlerreYdtJ4R+9d+G/yh+lT0mYo1/gJzrpsKzaTcfsUP9O6I8h7VhqdI/Mjp+tXLjljiej8WbHucg35zlBtdVxC+Pf8kG5UpBk+B89gBDrbmOYHruM9WW9Y8/EJLZj0IQ+p5jH5E/p3tIf7RmpVGC/FznzvfZLwD3Eth7KiN86PnsXfQZ8crTHc1eHF5t4fU0O+aJjQI/Rublf4K/UT3Xfv7NWHdZZtggmquk0Xw4y+x0BpVuB4if8f8X/AxUsRvg23vsYVknOiD9Q79EMnO8o+t879xOXH4IVXp2Rtrv/4n59x3pPODK8wzTJ5GIrmqT78BzwESJc5fu7Z9yB1UO9Tz9/+nXk28aO1XShNRxbLKPBJqB9NgdT66fVfxM+fyLiCP/le+xd4BxCWcs+1+7K2emb0/1497BN6E05VzvxkXzHBhf6BObNVVdqAUjkrz8/gD3iZtAX3rmiEfHkcjm8kd13ZmnkN/l6y0MSSXjhVmm4RdBe93Frn+uX1/4UFkffQf57689s/r/zIxHnF/NL678mJ1RnB+en579X9ZNSeCbN8FA5MWXOV7tKWkSR0S6sph3gK5822gfZQilNxOpncrpaN5dFLkxhvsXixXXx/2nJKtQmtj4WYvVZeETcQMyOz96pfJ4G5Yl1wMqfQc399D2nIRvCnGi8cw7tw/k91RTmInM5CxAKMlsNqvoIv6VrXvXJvk6kp5z47aGHq7fHyNQ51qJMoXOS1iIzf5O8qxxRm6mjhunhv3Wt+Zv68O7XoidJ3O2T5H3gbI0+PkKLrPfG4WT6BbsAtsqod2mHXtbQzFjNlRDf0fzF/4Hmw6MCRz/tP8Zd9eGgiNIR6xImlvBBEv0+78VvasjeN/1aIli+sm7bUlnjEWvoJWbd/wiJ9wMeybxv8QbiuaUfagUzm1rpxXeOUrdYCFasQ7wQq96w/vS6FtKH/I/4v4DxkE2fJW8Xf3t+j9vEfsOOi9s+zf9pZtdDfcK5IMADF9/ml7dBd6FsT8i3ZD2NfPpIe1qZLakMAAbMcgC+Q6XLQjAmNngiu2+vdWX8O/u/oR4y52qnBLse06sbvPXZfiMEX1Gi5xB30hIohCgHTzY72+n/zXDkEgduX3QPNCWWuqEX6SPj1cD2xuumxGfm154ygM2yjHvQfabpjx+/F+m/xwg7rR7rBDU9ILXxOXujpefsZvgN8eazoRyrhPe5wXrX6GyjS8a66V5kNONmYmjH+XJbDcFPK9qxE/5n+IVbmsrsI7NFPn7t7CtVuhu/N8wOgrfbpOdamfiWH5aDtqphfLASTkZ+O5qjr07qrjH80/8e2UnylTxs/vujGbKjiHPjJWPubs7Ci1kSeyNfupmK9Jjis5jn5nlVOTMDv0TTMkFwPbO0JBCaBBDsTV8ZKjr8YxcDG5JG1+v15ar2KlugpyMZ1YJhajuFB9xrB9IEzNrAPPdYKglv3F+bxStMEQEx7uHTKacHXPDCfJaTiyvQfEJik7EL3zIqGVUVaL/B77MZP6T/hhkN+a//vpP/sUTA6QZu6OaKtwzaE0Mw4KQiERXPBGqx4vP1YMWGL0CF/PcNulxkwF99W6sVXj1xrZQTGA8Ojr+h79W7GCR7KntIFPDKJDq9Ed/If4b/I7ZXce8VXVYczYXAFNKy6wbxuUmrw5H5tDDjDdseYgCFvlymCHt6q3/i2CTLahFWevLEgr2EX/HGH7oi7cP1PP78K7Yqr7sTVyAPUVbXn0wmBpY8/e6osRO/Cn4fxl3P+nPf/V89/M7dwt4sE/17mDo+sXIxFE8f2YyGstuu1IArPye6FCjmoURs8fkA/vdFpnbGVSKX1wIG146iWSeqAGfOXZK9wQ09puXKmXmLWF34GM2MRG0gBDCBimDBHJ6vUGocSCXlNOIId+LMtZkhtZXnk5yjKZbVat9zIfPOzpuPQy5mBLUxm6Trzc2KfnXMRJnbNmsrzRBftPVWsxzwluIAVyQgZnItmR2Sk6yZThveQtR1xkYbkt7V5eVez36U2jc7JSfW23QEdDi84Ri1FaxXHx8w3IoNxYd2de0TLrR0I/MmzTFFM7kK6nIDKq+OeCt8Dj3nENE9MuSFcsfYi95nr9c7nvbOwPgf6qK9tBGONsEdlQwGleUbkrHAy/lKPWYc2mETYN63tO7lh2MFMEyRqDiz0GkqBQde4t8qM2qrG50zRHXkFb80ahhZW4pLXCU9HRCyBp5wZRU8I9rtAkG1gNlg1frHAZsAvO6IbRLYxC2ZO7QTdXDeIwPlydK1lp4Nly/UM5vX0zumQXAKimaO/D9+X5/WAIbEzNhzaxcgPnTWyzXBxvzIAGFzSXofoGZd462970Q0wiQSnGceLHSPoU65UeLX68de1mgseB9gBLnuErMdZ0vV0qiyUIBAr4NIa/rbebvM7DpZBEtLUW6dq2L17tk1G2nElyia6+3nwe+Isr1NH17cDOzXKb/rFWFDQWizA1VORdB2iRy/7cRlFYFYWo+gXuMaT/77zPCOPjtgLjnT6KgtbwCKvzICLIIxTH8NwBt5YHeZKmO4wABtsVyPo6rX3gk/7HFZwk4AmiFj+h8PozInc+B53eIZtVM/RdFbAbSf8O05o/BMzBkDLyN+DukIMaIGgJUOF1N+R0E7EbTfBHbg4q2vPUDPqbkh0TLrhUwHQnew4zeWnAP96ghVpmI5rD/8MogkfpbFM/UA9fN1ukzGKY+OKtCJwml02bZn6GF+mxUvRAK7ZO9fKyVVpbRoI/1Gzwnu2JKmaeWXznymFuQ7TDn+Sa3CIx+6riNmOu9crkwM6WuTMFu/JHDIEDYEOTH6H4+3p+ILX6SjvDLDkpFHKxnoqT8HGJ8rYHm9wvCzhd2EWYH5cIx8BPWdU3+xg8+vAjmCb2XLGz37b60XBPV7FQWgR98am6zQJkFhg8P+9InNnGp/6S4mQ4KJ/mZQ2bmxpV8+3h6upCjOrngLfxbY9laXFiV2Rb/os+tJ8/69aHStm+XHPZXP2FsxZVyVFXHCt0jjgYL3OvFljc5ChhE+HropbP9eHznaQASFmnOnnX8XoLHcCttBmEQFAodVIK6FkG4mL2iy4fiU5cBvZ3T5Dhwm/3p3nT75XIW2HLgU+NtCYej8G6/0Gj7AG0fZVSDevPj7RhmEK9ri5xJo9ecsRYYUiyUVa81SY8pa5XzsiVxzEKTeuSKzMcC5myRHGtlg6Bi5nnph2IiK4Ipx/I0MULHiWMx4s5FhCrXJ79VfCiGYZaYGjwCwNxjYu+7UbTb9dbeW8DuH8nLrOSUXyuiJvOAiJXTEvISoCxkskPGXGvx2v2yZCCG+mOukD2OyLnjVqtXnrTjScluh3zxPR8KsYR6vO8maFU9vwQOZiLO0o6sB+9nxbqvvKWGnaAry4LmVgXEpVOvZwOnpWGOHqXJPkOkaQ9hJonx7c7e5EEdZaxUJsJAVPLs3oneoqdNkoBBJ5OReAv0+6iF4sc3HX/DTuCjLKNLhxrVEQSnVAGgsLTtSnMmHBTgMfOV69fKO6PDonk4EUOQyCO3VN7ffNPnTd03j6ZLvM3H4jzHQbaUhZyxl+FWdM14aMDw4baabCBWi4oMGEuFiYM+eX73Of8yNFGtE3f7aXJcyfqR/1xszEEaxh4Yslgzv0vXv/4rbowC8HGZHpBp/mU/565LKYg5HwuRxrjBq3gUnrrcMn9VkuvPW133qDnQlGlJzzkltq+FoXMTYdiRAw4ROCbZjzHvK0zXa2diSXDwZoHyH7NH9shFeJpgcfc4M92FRg2ox1zteOimzGePiXFMBEd3LWdosn21YAABAASURBVFCbFgTokxGkEZqTpdcKzJkt3g6mYGSyR5uoFJyRGhLW0b7kvM53yvAZxxMpqaEDtmKOsczmE07/J8iEkjIidPPU1lzbZzwBHOPIwZgez0qANH6Z6IrELJ7FyrEDYMG9DGHPHKGIkioCPmVJENhg0r8D54JTTdTuWaXlPXdd91xR1glOyqiFGc6Eoxi97dTIZv7xlWZvyheJJjs+uK8dw6qqQGTnajqQFR352EUnOmBTjT5Q8+taxXyUx1bp+7ttT/oXNP0KV6C3VVXUOAFpBtrkHfiGy7wJfOEmaPGE6/3bE+CYvy/sZim7QjWY6s8mg9Xx+9IhGZmbGRVBmAZwGvPOvKf6deNaE2A4nCc1aWRKyHf+XSt17YjIad+KhyoRNBJIcAMyV/ZqBB/PjarR10Dbmrlca5CT1xbLR+HSV8x/2puWueF14bor88iesMi97XeoQrX1Pg5zH+SXO9N6LVz067MHnogT+hZ7X8Zud9G4OlykSw/ljQoC962f//paL/utv1N/OX/J00DZagCyj5mjHrwxk/+QtHVugb2LCo9Jf65UfKkco2saLh0wRPgfZaqs2JroD2yadq/6O+EFBL0pL+nyNu2nIHfqMUEdXik7rRPz1VBFerq+YONe90/+XFj4inhVUDAOmRR2LdkH5WkzAX/WqL7ch4Bx4t+a7zlP4NDZgUJMfIcXFUJOe/cUpuiQuC2zc/Osa/I2+7w4d48caH99OyuqVmvjVv5RWEYQjvwqwBQ7J/sP1h9yo8DChVgMd9d8FQMs+rBBTQkPq7IdUYyphohiPk7JDRB+GWEWQzmI1j8RD+izPgAesgi6CVzgwhDNcBwRCJbq6ad9x9i9sSITyOBJu4VaKSxsxvYy6ZY2nh9fD4Ymmug8J57uIxCZuZcNBMDve0uDf5jTlZJVFoIF8ovVRfb4tmq5AJwzSur27RFK2YgvOe3Nw4gMM4wrYjk2VkOtBYXz7k9IrZ9herZDybJppmd5XI/jRMm/HYLnNPXlAFrsTYviDUyk62NT6ZUga4DBKjoHJAFFwiR6IVsv+MqWX+SLOIt1E2Qo4hvZrZIIi8upas/DDbNVgXfF7OQKeuhAA7uRGbXEJ8QWjqdPr/Hi7UKbB6hv0Nvx2/eBI/csy69Yh1OJDtviFb1IhBm8HOlV/cVOKWz0Prq+WXAGPe6afUekhuNRE91WrIDl+R7yfTE7bc/yWfpls3lpJXzO6n9VbQL2hXvFjxiP6oXBmYGELS/vfc1seGWz1SIQZuMfGnAEg69PjNq+4PKXwRrp+CKGA6MgLJJ+Kl4L8BeOYolZ9IfQt/JM9nyDct6Vz+xT8PGWTnFO05qR0wNxMzav78DKcvJgjX1OCNLpcUZ2pDXchfpusz1Z8Q9HmYBiHM3T9Y4NzkjRE/84G0xw0uJo8oVxqa/Q5WBt47emjNxPBZ6h6d45DvIdazZdoAvYonvHvN64xWnTgrmzd+LizrgSEUQ68AuOAiEH53g3n7h/scbBadbp5fgH/OLJRIaTg471lVU4kO+QoGY1XvsBenmAumY9wc7afNcZvoL3cG4CR1/+aY00zVbh+cV5pbJY3ctOsuTKufdsE2ANDjvy+Htc0rKWU0qPNnBoJ6/kvQzNnSvf2lUB4WOF3RiGUoY6/KCHPr3e0m4fc7N+8KZeqvXham4TH+ObCwRZN54f/joEpwVOUMtMwHnpsFET6HqTnNt+3nMezZGm9hw22haQu63amOCbOGgHwwAQ80a/qhlndJy/SIOVJ42HyDgJcLHvdZZPPTOFkcWO3uiIgpt/UvDQCEzg4Xo7+3fFEwm6PMYXeuTYbGIH6PP4NJVM+LoHQSq6vdYJFZazIgAsbq3MmOiy+MvpVDMmCUqab9z3JYgZ6TnekPyrjvAStCRq4FjQK+dObYZf4hoiOItIkU6M8Q4bzSwmR98dV7/swbSi4jpra4cHT2lesbA9j7gw/0zH1TBzhhUMh3aN7kIMHunvqMp4I3MtzuCMPturFnof5mE0wt7xvTgI2UoYGe29YcLgBhUhL846iCejLg/DGJZvPsDPzLt17oA3SSJGR3zhgmSQ3Oy1Ji8uS3TEEYAk2OOKGYh7yCfPyTGUpJ9O+UKV6YY/y8l28tDmXMUaTkStmJDT6k4tm/IxLGeTpmA5teRUsP0LWlrL/I5n2AilAAwMAsoH2UHjFlwtJdXq2i6jd7ad6Wl1kSc+shAnohe+zH/zbQjsa9pNZIXvgn38BFaEMsdYxF2KhsbrXfbac8Vgqs6pmsoEYwPO4XUye/5hcJ4AZwueGPYNvzhjRv1BjLrwm+GMRM2G99WPzmuEFOgiUeFIk0KeoIslqVtaTktbAAdf9Pa8cDhHzjKASKYzBHrqkQMvEyUSCQYIIOyZOViWgSqjwyfpq4x1ch2qs5SN8HyhfYlfzo7c3E2bcm+EQ6JxfsbktF6PhyfmFv/IiUusQAa0vIY552xR3Ip7cL21+mQ0f0Y/jk6O9xupPXL7HVVf3anmedu3R7/tB3T3ebOeMJ6gIR9MT4fVPYqpOeWvL6vifLTX47n+YKF43Bd/0IA8s+cj1KvKf0z/hfUisi0Nm3Bd6FFQO9e6jdX01y9cxavEg/Pt3bIQ+Fr7x+OC8nBetJNR/uKwN3aSh1lBxNA9JL7oZ2Bk9QpvfVNc1/HSRfA9S3EL+0YzevjbjqQpWpi0WmD5WM2S3Xgy9cY71ytrbb0Lz4WJUKRIeEoXX1qkYeppgMst3P2mhgdq2LCVUQ/myWlYETtwF/cknHcjUD6HZWjvXlipjMo1X4ujRdW/QmV8MrNu0pkjUIG/mqdUZwsh2PLprAnNyPLc3fExPKejPNqIGXPxWXEiX/hvR6tM+bWEXZWe2ZEeJSaY48ElrzKvbODM9gLsfMup/D5fY4QVuxXdR8+j93fGdd+zJxI/2Dv2It2h4k2zlk/GA5Xhjc47r8VtZiLsaFx7kcqVGRnn2v/fWgc7ZnOsWTn5Lv4krPCVZYbY0GAC5h14QmUo2QmD43ojZkevGISIjLYcPl2sdCksnPRZxDAGDfwbtQb8nsec3v33kqGqOBNClPki9Hn3LsL15l1DLCcBesaTDAyXLyupHK1WNBDJKxGxs7dlt9QZCDBacIoiAA+GX4QhOmnsCcnlcUGBKcH1CywfjIQsrfTIWAiarnUpeOJ58lf7ODRmeuvbV1TyFKpadXqeEzgRhbAS4Bu4Nu6l17bAa3SZA6d/uTNl+CcxzHZneHcXZ2pcaz1q4gF+wQZTHr6qQgN8OKdhvE0S6wv8uvIyH3Ud3N5SvJuKdccdtPV5v43bc7Lb6kNtncvebQe20LkiIiXb9hr8vnpPn9e/69yaiM2fm2tvw3raeyPlWpf/BWQV7EkDiHH2xWpPrjaxrXB5xFVWjK7g3gZUQJHCyvazrr0mCSeFFRP5LBx3/vRl0WeX2+GcBEGeiyc5lXHS7pDqqi6FuAOx6F9cQY/1/K25vhbxYNXkwG3gRl3jKG8kA+X9rbPy9+7fP1U22Nc+99X3NSthTJoMkFm6SuTS4qQizoWz1WpjuantC2nvn+Z8YYb8Mu8kHm+k3x/Wn3v8lx8bOODvQbm3HOQkQJeTZs2EwIhYsZIrQSJgZ5+0a7xdO+/zGjHJbyIr8eLL46XJrn5/n5G53drXo3/2armE5zUM038/6+rtZPCN+GVWhGG6z8HZcGxM/4j4lZ7Xx8fqCN06vnU5w8RsHuCUyuylibEGPQTMpdzyOhvp6u/QmfPq9+TjTE4ccvQNBG21fmdapr1MA55nxu3pZu05GAeiavgVtMdoxFvzqDut1H51fqdeffqcdSbr9fkAhiFdlB97WmHWFXVqTrXfOr1MKLgx9zkLrxjQFd7lns3hdMxDPMxHP0dLA39bPHvesNitDOFbKb/B/bKecT/VSNHMDmK3GdiJOzJC83WB57Cdnf6xsd98n+yx4DFI0Z1lw1XHCR8G8id/f9gww6mme8cxx579yY3205ijf3kCxC+Bn3gckta49uwXSh6Ni3Uslh6M4ohtjGjBSzD/J/46cIR7zw/PH2gb+so3WTN5sGftyLUZywxV4NnhbVLnFTuQjagxrcDh4Cr33rlmdnVirFURSPjphu5TfNu+27DUjjWFCKQB+FiHl/nM7SF/3/NrWWvyBt2CV8hbo9WNqTN42lsxywNbptbXb8519do1PisN1flxM1hn5e8VxVzYG3sY757y0w4TsaaEqVF7DfKKWB16o4yUf7qvnmkjjpeFNQ7VdfcG3tWKVz39G9XfsrpYYRF2s3tNk+3E/2MR4gymhE46UmkdS6Dq1u+f1y5kiD42cgMWmAPida1rrztnxdmrI7P5c3h3xlh/0N+ax8mUrpXXRmz7aD+RHQAznBflZiK8G94wPgUXBocELEttZnDBxu9X94P9uEcl7ci1VHZ2LWZOJTuWvgzJdgN1sa/QWsn8yhqz1YnJBFPmpmOK4mJs2P569q7MaPJR//apt3QaIrkCg3P0uhLzrO1JduUBz+/b518FuQ7nUi8m40Kuks2gJqCg3D4qMLGXueK/yh75pqC6zbW/53LAEcxYX7RC4WCdWDvzJixdg5YNSV94A1Ols6R7eIkXuDNcvAkXyzhT5IkaKqVlnY0K+XaeU+d36tX0gWN1GpOjNtJ1wOI0gq7FSISkfj4OCmyPnuv0z/Ou34kXnoYF6wpmQI9Y8Mrg5O7oP6WflhNILLkqz/UcaA5Va4wHMVDOuaaMTygr8TLwUN981/U3v0bvLudZ5XhlTMH4ij7PG+eZeV62jR0HDAScA2mm9cXasQ7XM7vnPrNeX2zif5PejRJX5seVVX4K3ZwJAroAQzCXcMu0NiSOOc9I+kitrJQ05UoCqFjjpCE8jchkJ8wAFDHxQeYnko2Vn5GcKwJcccX1w1iykPSMSuOS5jov+td6h5GQE1BqnD8n6h029+RPxEr4S4fzrbX1zwv87vL57Xm75//FA79kGxeVlpXXeYZzQ68O0iRdUxGEaBNGUE6i8dQ7JvYcNFChXrYSlDcLC+P0dnSvbmoGgQj/M4fPO15Z23O+0+zFwxzQ0aa8jOdszC7tZ8Hd6AzUme8dv5u/4jTTE2p7c9S2n24Spjvys+B/ZccjyLnBDz8e0CyDHjMV7Vg+TpwVzWmVYSgikz/MC+kIJpDBO2bF8UIzHj5qTetY3Q2d0LD81ZreFVdzjVOQodvFsaIs1ozMkd7qAzaI2GFD0bo77l7RCyZV6ZTZDUbUZzrrNWkaO6TIETx53JhuuQEBmDBRkbSnq8laXWZcfS/tHyghex2+A2hQnW+sWi7tflf3rbyG68CIsBfJf37TUGy+jbgij78BW7UtuP3ox03FVnlE07pWxX5NMj3cudUQZakH2uLQgvF5mN4nyxmbO/Mn98ayRHxjXKB2cl8Is0zLJbNThiobfUwl/jB/dopE9mcbZy32qpOnLNhjhJHATd6UlSM4cdMoAAAQAElEQVTxixZBR/Rzy3cqM+zzM2163ahkHeWsjb2QjJGXtXglJzjzSf7GlGcU6ebQWVh1pdZSrGnMmRkDTAc6oAJ+M7AWP++rGKUe0xP6whngVR5sh1oYdOeMDdHZTAqB8VKAFXXhvdV/ELeVwzTHI6fbQxw7AHbt86YOcnoPmZFJv6qyXsibTd/nW/Bv+v76z3Btcqm7rTunpsNjk59D0wX+MP4seTsBEvygzRyiF5BiKIQ12JgNUoYCVfJbxYdT1pZH+j1G+3UyOrtR8tqrt7MqIFOilOMNImwlNV3ndAHY1M3eHDv30Diz3481S1ycb4neKzIx4oMvJ3I6WDbWjDdRP8ENX8+eHCdvIibgZATGQfr+N3JHbEMcqlDl7JWDsZ4tmJXtrF6RXgB/pD9ysgERHTn2u/Yib4wbC9ACoB3zb8qsnJ10J57r58tVGHFJL8IqxzrkGb1vv33kFBuNp48ji5ndprHxzpSlwk0tgf7Yfrd7+07voG8w4X8xMPM9jm2BrUATRfSA0wEh2j7a4fi6Pz3fuRMYz7bLR5jQFbgdziLC413x2sfWoKH/YWbe6TfmYOyPCzsDBvPqevp+n9sPV95vZlf+b2D6J4O0a6vRU4YruMgYnsNpybPN+hHmVZzmqxT84m46XQafK8K1ua4Kg4n28DfZWNFXeBvb332a9Wv2CFx8L5XtV63nif611zpRtT9nFtSZrOaUenBqoXOJl6H0O3h7l8usjDiIpb1tfLyV+KB8VPV8sSOmbZLl9jKuxYxBaBasB9fvqlzD7eXGuYJxwVWYJ/vEFEityETaQShh0IBvqZy5FOAstQxxd292ZlXCp3VC3yq0CK4ZsMvq8fBBNDfzjiQhuqtKNkTaYAD/+pHoiwyzXNEHehcnIzO2kat1RjX1zQ0EMzx+KuOXUXZVyVmWgOu2q8/4G2OFX/tKeMX0gIIJvoCtq/PuMFqMGAuIA7MMZYzwd+qV8XeQi5FrcTHdM2N58BAvnDN4i7Ggq7jqkivBQslhzNdl3/A7jAL91oy/8jPmyw7f8SyPf+2f9Kzr9D0Vq+eaCQtLHsFp9fHPdKcVByl0Lj0XtPBPd/0AFXRZk2eZ+naHMFvG0OGqXL+d51nHvkKgDaf/ecKiSMKW7omrcp2IacYL/qV+hdNZZenIKlMik7nsJz1tPsSSfx0YwMeToGNSZOMyVU4ZIi3K3FioeectW1TEumNZCVVwIZMb8k3ylfMBcCS2KvP/ZegKOJbeaBuJW0xrDk1OpHHzUjiXSXmt9CYWhuCF4dTvmf/1/Ns9ZjjEmIrjQ6tW8ts2frT2ifnd6YhDDBtJDIzJnGMOTIW9I7dSx6JzcuGPUMv3Pd6eOhWZsH31lzR9iCIIBWeCVGDI0b+EHdYLWQGzEh7yWrAGfrUuU4pKhiL5vaR50WemT/LYGB1X09XAUhRMQMOPgF255Nv1ngOL5ug+c2lyYa3xtPkMqUquly/e02au4568xYjzZdp9BP2LOmTP+sqZk+lVW9ZmF6oPdUyMiENoDXI0fBXzkRGAPYUR0md9a9SWoiEeqEYYDq62DNZQX8fKGaOayFrLCyaTIsu6S8LaHMeX6nvafx6IkyyPYPhKYfziynuvZQoZJXyCZY44HqpFfLrz0qAMxNfjMuKHe+Vy7/M9zAS77O/zTCS4PHAbW3oX0gdpfl/EARBr53XFCucFhgsDBVbsOz27CAeJz4XbFBwlN7y93pe3cr+ZT4T30qe8XP4ORjbog+WRQX+CIQd607QM7Y/uKWmtfOIC5et4WVL5862Vaw/5BrAWvAn/P2JJcM6y/XtGB+exaEIvzLotimsckI0lzMw+yq9RYDogVxUWZgjXwBvTP7TZ/HN5f+GAjaLfhNM0ynEApUnDRQVOf5ktdyXWpBBYaUZ6As9iyw6cYXTAe8zxX0RyaX9jfhL/0X+hrC/je4MLjMjonU/ineMWCdvb0oX+gVOGH7Q/c2muQbycv+yn365X/fbnTcvZgedRK7CN+TsiQYa1J7f5tRQLjhdAMr5LA1ry74QTiTQ+ffZlO58r6U/MHdr8ePRZR+VLEyqYGnru6nPNWJzeVC3ucLlopp6pf05o7/K9dH04szuolIyIUeBL/ia9rRm07eLTjreuV+KHuhngsuOn7efis/eLqHrRnqGsYCWLK3Ilb5qZYVsBPT57Bmeq+2hEBABK0Yea3Lxi3Yy7bX/0QG/r6fXUzzs3EBtap/V1Hgx2OS7cV+AHb1Rr9sGyfKR3Wp/ZXBVfHc6Bqvlpr76I+EXA65er9NHNa2TG9Dx9xhPBS/cP3FopZBKTHWEFNbYP+hxoyqb2zL60bzKiNWfa0cEa/wjcfjZ4NQLX7K1r5+B6z5pj7TlomqgYwhAQ1q/yuDzYp+e8/Z7n1d+6n9zl3VniEf9inaCu1k+v6+yY7ahHBCY4scaakNNlRZocrtTKd+WvC+zC9dN/9uwB+5uBaPINa0g90gM+TEW452TGxK74A/4s7HSeOBmdcJVdeJPPhP/G7DXLnPIU3ydeh456kq81rbTEFgJKt0HHckzNCxfrwtF6z12/l35zdW4hI0wDs2A0xmSj8+jfEs9uVDu7hkyE+aIpQxJrvc7oVKlEAchB+iMSZF/d+RR+C0/Eudjb+q6sCTOdOXdwg9PpxempnmxeGHPQ+AuNrRF36nMSBwvKDR9MzZITAleDzSz65I7zo797/rfn9xr39/Sr9ldBTqXeBzAtdcuZiz0RTtfb5x2Bm7w4lyqkdhDNqVecXAM/k7usXKovJOcm9lLmt8itmc1y7/np/jlnlevnnvTum12uRS+2uv6Ln+tnrytX+O8T98XFjzfMXtTlYxQDtvcoP2MkcHDqnhkDMYYxho0V62fmmkmQkcgdYXvhyrPG+tJ7j2c6LTmImWXyw8HG1Ts9W3AXiQavf0dSLjtevTZ2pPjRsRL3QmKcjl11mUTIPyVr1efXj37+zH839k7hGo+YH9h9JOFxM3wYv4qeVP3o7GMoLv01Z6MSAWfuG6lpOOXdkYg0YrTGuvi3TCKt/swIa/a4mMPopKtmOSW/chwAg8yEETvoTpyFvLgR67We/govvkQ6SWvrJ6w72hMXubAzdua9uCz1MDFsrkK2TqHxD5+RldWICA8zRWT4ggtpawjVpiOCnX4G/2H7faNeL5LPQkVG749szldLGIaEB/GETvo6uLXyLLqRGD2c4rSDy6r81hhLYXVZg8CYV3ncEoU4Wevn75+f90qJsCwFZZyo5jCTYbz81gerGqiYZ1ua6j9hfxI2dAt3IEHoA7eO2xivJNeI7kTtz14AeB+6be4pyTXSeBJ/73n/xG/w7aDVkfVz1VrHORhvOTa5x9lX4BhgUM/SE3nyR7zoPvgDbUmDcuxt+4wM2aEpjv5RUdWt30aVAU6E/2JD9KYuRXIrP0bGW1+1pGH113iGcDQj6KgP+1hSQno+D+jV5OhVHNzy3xgCrCO3woHS6XWwcUvhfveJ1yIzb57shKujTLKTH4iGU8izOi5L9S/eg2NbiRtf8t7uM93Z7Wwa8N6TUAAG9xuoZXfT4oPdTIBfAhzBgJ1EiPEirPJxNQ8xrrp2miPhXydU7hcrmMYcF+H6Wu9k5ffRAYSQxf1gn47J8aQR5gwtzWdW418G7rMvc9tv6xFL68g6kfYV3HfPpRO7jFE9cBbkGAx1me4xF/iFt/V8iTbEyHFWLdusqOKUdJ7T7/GXM38K/wEOVeNqn98Tp6+Bu6fiD6/7uuO/FX/mWrX0j2NDUXUDb2CuiB2ZV8Kbwp8V11qMffhFbZDxS55clj1FqsWBIeHju3DGWCooQ8z1kL+aQ5uthWW24Zi+eQALn5AI++KVnXkm9CUOLnS9r/rVjAexiMqeVRgt7VzpCfhjqOv5s+q/kMPbFberFHsHjRAmPsLQmoHrh/Tq/+P7Mp7t+nO5rFl3RYrbx1l4WV4K6wWV283K6d5bptkfaAl4KMBV1/3GFZ2rzNeRCYPF5n8P49GaciYXbcat8AGMIJysb3zgyFUDeFKQN7UNbeed+5pcTWdjgSn4JSlb1/nev3JwRRTV1CzBYwPRQLleZrFv1cduJtadeXsCjofaVXf8Hrg4Ae720NXN/oKka4Y/ThTJaIcXyGDCnSK7Um/wRJcjopeJCGWWW/cVkx2sncd797wQwQApEl9irrbQ913MI6IyTvr67DbHf3/C81R4X6s5i3tmoYCgB0WkIk5YUEk42PkwtOXHlaGeKXxmFyvhkbJdAAHYUSd76QtaDlcDDD1okB+2gU7gWfHk+v2WiGk08lVYYbaBoH+CElgwgC63L5Qt8CyeL+i19sW1HWZw1rwG+Dn+JngWNOzhqat6jczWrXVHXMDCv3IZUeP89zt4PzytfXod43QRQbBaw9x82RkKu5z7NpiTvma3zsyEHKsB29vgG3nQLyQ5Xp9jhpl0NFJ4asGZ+38XP2kQ23LoCjKWUW06d/i0rU68O9+1mrbqTQGMlHPoPDkcVmn1bFXYOahJTQzj8+uqZHt4dFvQsWEUlgtQRA2Mc5RjXXs4Adq5/8kcIRSJTwH6SDnqalTP0bjxTn5WnHc1oZW9ly32SDP2i49PewhDWjmzOB7v2LY5SPiNvkcQumbmU9FOBZJw2AjTMz1jzIlewTvSrWXPsoaxTL27+dueLP4qXgRgh/hSD6Txf1+npo4HtcVCUzO+OVmtwWGcDV3CDoPCDFx2Y5YjcwUdNZccIIIf7hTuhZ+fvjd35jWxGhoZ+Zcaebu3GA2cf9yvzvSXMQ2uhEXPsZkMLIkGllJSZtw6ap8Zks6W5+xGDGC3sSJDiXJk7K3f1COUksyvzdrSt484y5K0AhKWm6AJaC+FiRt1dVWrn2+dUbRr/tqdWxaC1whxOycmJs7kQEFk9AQPs0mgibPgXjMLHSMCoJSafJ01PInvEbNnvMqO7DtmTfXUO+0SRYWYWtnCKx30uDt+rFu4lfbADJoaO8ukoQNf9CIJuszV5rjHYf/7HjHmLTKw9+TiN24QLRNwq31iEkXAttxnN5rTbGzKGcXVeYU7RPg2Ew4EvsSZgAKT3iOh7JXfcCssatqRfON2YOfkOOtpIf63dW/7WBM/kRnDLr09D7stDYblfJyYsdZWAWYMuZk7cindVp3Sj5kLjAxGENrS+sJJCEN2wIQzyYyQtUjwQzfgltciS5DuNRgLPtNn4en7Rt5AjpwgWTOfCTTVzr2Zt6dApLQ5CQm2EPIEdkRpPXJPW04XIhJkLU5A889z+9eCufEFTTid/Q/qZjiuZMBuqSb9+/8BMLq2d3lGOFvranNI2Dc21jeFtugIAh7WHxukjcLQBm0jnIbxLe7snNZhI8rYdu4Fl2RmwIyMgYc1BadSXJVZtkSZu5ux7MYpNk0WPyyTpu2snphxCTd4bDX0sMo7tphMc8NDeGc3JY3hiYBM6PcR35RrWQV07ItaKLqIojpnYKaabwQn/gAAD7BJREFUU24p1VUx7w6vxVgItuAw+IjPcN7E+ZA3POvgGv5kWLjEms4esRSHOY4BvB1cFjzHeVMbHLvHYaTza1ajVpalumKFs7CmrrdoCbNfFQerczunJM2LSabP4mRUcin2OIVpIHx4dyQ2DWsajscqucANSOqYWIgl0advIrmDTqcloOX5LT8feWcskSIqnX8gMvKi23hnPMsuQ8sgTb2qpsaxIUiH1S1VTw/QXqo74HuLWv1uKGl2VkAOqUdmx6dAfJncA0ubRqe5qBsfXtqYgYJOEp11cZLtaJPZyIJGE/Xmo7BBzoDWJOkYbS9EoIm56om36ZtzSKFZq8NSGXEo5AhwEovV6CO84+woT/yR/gSvnbqjEx1qeEIEbdmzHv/zJ+zLZWcSeqamKUSLryN0Z2Fz+9f4zLyBYtI8F9pL20FaApqNXMxlQYeTyvrMVPVb9ZEELfeAHq/iVSjp3zK1jJ10ZtIzEW5jHt2n5vj5z/Eij0IGd6bZUNNQmzkwewp0RCBS61/DyPBVc5WxaUsh7JGjjrDXRkwuXGGZWTYLYrYvGlQOdml5IxNfmhRR4cXatbQ0C85DGrFGBmre7RcJ495f3qNM8ayeIPxjpJ+b2HLyy3dP3mhbpmMNeM5KLIaZ0XOmm4vuH2vv8AVRdvE4aY5pkY5ql1PQ4pTNXaGLc8PVNSs0jjCjv5263CRexqwLuXP9s5Ztaw5bNlEGG/r0IWOwf2xFOpit8R/u7rQc1cI5MvkYQmijcVq4A9Z0FDDtQ8LMGuTEDDTWUtE+AMTFdCSxKcY6sdqQwnzNPr3PjKfsa5yAjHHqiE4qhkZCQMQrL38Z8qjt/NomA7EzXKrEGZ9wIc86cviMIg3rTKm8tyh1QsYQRjB7uLF+9s+OPQdRFVH5c/A9ECnQ4NHI4hX7WgH18G3X05+LP3f/XOfeXrvAEl/wjV02JIJCNkzcBWupFo/Y0Hgv8HPj3pOgxW11MLDUOzxRFeeXv+WRoNsWp6f88Pp59Lo1I/wzAReEH1TMV76SKF5ae4L4VgC9pt5mbc9mI9uteK5kbCA82I7rYq0/ff1Xtx/qOKZ3xo1s2MOn3X9fqBY6CTix+fNjiix1Lznv/jq/bWWaXEyj6tU8gfeK+rPOt/YlM3Alfjp/Oni+Ie/v6metQ6fdtBT0hXDKIdqJFpzeFOiAlnWS6HvX3rAfyPrE2J+Cvg6XOjWjLpZpfWrXcUiRvmsuAmh2aX57Te8cXuwbs7EW9opcZuybDzVQEeNWf8VWrl4Xsa+2Ja2/56drhTKRn025yfRL1c73h88VZ4m2HlJAHmGs3Nv+oyvMSpcxNxYy7g3zwQzyoksqaOdEtabFGlZzrNknMammT9WLxijL2nIF1QfDMxkaqca2i6u14xf3ixu4ooNHq8d/bHAMqzwUR1Ns83s7Nl5V6TDm0bxb5VysAEiBMCbqwKUV9hfeLFfhHQmWs5K+b9W+Mu/LC79+Rml8HWdcnYuThl1T8mYrX2VNTPvrA8wC9yCBwDBI64rEbJ4fniQH14Pr+BzbvPTMEElvmgww/p//292y0ncVcjzUsr3z6WLIjt1aZUdpcRZ/gGt7D1O2oVgRl9/ywrqDP9JlA6qX6Hat763YxkSADtRs7BE1VEXaCcGJ1hYzluI2wVWJR/xVm59c575f5q/naAk7oIgvnPhxmec91/698btG6wSPnfRHzSVXbeyj/X7YlwkWYMLqbeGBno+Xvea6xfXlvJvtZygvu2HOLcBn68W8+N4bh12rjUH8YnHZY3sm1uAiLuT9ZjzsOe/1t2+D8VUviHHLqwXcr564nOHYP/fyMxLD8JrdgP6h3ti3rhsMJ5oGPVwMIpF5bV7Zats+MSdRXLD3pG8xHF728MBZM4GNu08/Tz3tvZvr73NC7qbrneYg7ILpbMAPmvph1Xd2IuWnYX17vgkeQf5nRfR1+n5nvRxZPvRohevK8ssn1vMD3uOdzX9lICh9/dHCjHnoSpDkCEfcwB/hsnT3qZwxECHJb4GFhLHxRK/pHLsYjC0s/EY+dZ1zTwdVOypdu9ODk/P89PPjZOTyvjWCf3qt+kndpucouqI+VwEXhj2BObjUFv3ndq3Ziyvp10gHM9Ou/BTTP01He82K2OPSX7EQa+CTLgjIiel2gXhaEc4xl/ory91vvIDTYb+GjKrC+94vrorwPMZauW0vx5r2saC9Zu8nvVQvQek2iPoyBHm70HAldaj2OdsP4/S+8p/kjt2bxdG2M9PFr7fBNjGeJlNy4LZwxXcTcdnHN7sDgf3D64fpKKt5rO+DU7RlW5XRdvd3BgKzM5G95405U31ePrr+J2+RZ/ClizxuQG4HJxLqxbpllt0zwmK+lavcvUhzt8qtx//amr+aDrWbNRtjFVAXPrxxnIsZnOEIN/w4Bbd77d2dlC4+UDn2IsfFzkE/mudk++mDALbBNaqI72UBxju7RcNbP/zKNuAcW7f+3SZE/y+1gkZjv18WPhWmJeCRFtzq98762fNn5cWLZkTTFmGNk2dhXIgPbcmtYz66+mf6dkjpWtJs1QqKOfjH0b8yKW+XvL4PfrwheuIuieMk3oNV5Jv8/McXBgN9gXddl1dYRmC6+umo2f5m/EiCTvKFccJkgj1HU6MSawD4u4e007hmdu10Wb1xzrSVxXV0PQ/7KNE7PqqxwJ0bmQ/qF3i4GtExdanseeyZXKsZr3PW1AS0pqCulBUhUy7T9Fv9GofGANIn6cQ659HL5kSedbdLQ6iu3/LK7usDtaI+RRDTel+0NyEdDq01D1AZ8E+MpON/niR74UO0pZvUXmDi3ytFf0HrffyXM72Dcd3ACQHI2BE/q37Wc68TcQblZeCXjuWLZgkbMNO3u0X8FO+/vF4ti2e92jq5HtwPrpNkMoNmIerJEzZ6nR/0eoPPlefyQiTl6RBI6rp6ZzIuuouGk45nXVprWWZ3uE1gfySyNK/rmgRCWJ48w7f0W3hUzS8aOtYZGX37V5YzvpABmqv29BIRaRFSNRrxe61g8gstrp1hktpXNYPyARYh2u6CP83EHHhiM3u7JlX1dtkhAPIwK83yZu9veMGJ+DcaAhD2b7vCq3h8L/KbToc9zKpClnY72YyB68Ar7dTyJCRyK+iT+meF4bMeMZOY8CkB4gauKU41hHBISUYDhajwjAgL5rUmpxIedEZl76ucVaQHjG8gvMQmg7PR6yA+65u6bFE4gNdRNMPD04uCxnYUBylFTq7J0DfUGGR6SSxOYCRTGY0gBSKpHZOXc5w7wt2ZyQf7t/eZTX8Bh3jTXeNEtEngQZn2aOV8l+BVUZNFmx5Os1+4Quz8n5KsMChqunwWbUH4b4q15nWIK+Uxi3vxaguYxpH6zlIUHKYEpsyZ4dIvML4LpigcUdAR9IwphGrTC4B3b+HsRDr97+iVLQwTlpISutg+NgaYiCF9bWQzE/zYBOHZKOn9zIBcduGLsxT0DNd3nsB8UkROwhdwRV7BpFJIpJpVX8AeZAcIydM81n8/fJBn8u/D083V64bzbXhhxw4UnWoIA3xertGOzDA/G4GZef3ww8b+iD+O5KWFA7OekA3IAIEmTwI7a31X7QfvBi9HyQAMZ+wkh6FVpqw+CLdd0NQ6U/jXXvX91xteLLfNfVNaZvl/4scSgdHpeRH+qkijZ7bsbrQmkgy3o9WzZdcvcQ4lu1hePI2MH2afOtD0uKNnWdh/Wv/1LBieqFDbgAdKe8ZMBMfZ2OKXgCJsxArc3fFYICu4Uzt7Xb5f8xbhG5adYTelru45VeW6MRJB7R8hGnfVyrLxwbzCX+IdtrvlX749Ey++bnkyOr70uiF1o16gFXCvZcYWdo1Rijby9Puet12g0w5ijtymonLnrKs7/Y3H6BIV+pcUObeH5lSwVfQsuFwzGGSYn24OvIiZfcMwHuRT8SjtzcA/iXas8lilxluOUUi09epzrEZ7gb/BQoTxpjIPbPXAdwnL7pQO/X/Md11nxTiRZiZAZvpfRGQ2+OsLb7ThRjBSXJU/E5sT8mXJDjKCayPTCQpvY8/ApdQBgxe995lZdNbx0dDcL5fJ6PFPZc3nJmOSZXLZZQsYEBF/ci0szXonerRaN2hgsberD1a8eiDM2DwwMgXmj3B3NbhifrqKg9W5Zy8jDI3nvOqCXzTJ8CPGfz0DMt8tzdimdS4l6DeTVtPtbNBYGQ4s0vUZdFR+G7txFseC06/4S3RYGbwsw7YBqdvUEqHclV9EETOfnzlT7zxtwDTHPpfRhC2eb3R2XI1LptYlPqfrUblXkE4YUSifsl+wEd8RLLiXW2ukM+5uvg0AOIpS1pLug30wEe3tZ8YeuBA+h3YSZ9Z4kGGQHEOJqxQsj15RX/bN8bxl0OqPoeF1dL/z82o91LlO/CB/luv+tF4D9Hy1O334MefTXwWm8P2PozKGczii2wccAMcywgqlMbwn38lm5g4XUU7fVf4rWFbGm+yAc0Q76JosFzTvmS9UifDLI77ZlQP6xqGvFylnk4yr5qfP5vHI4p64QDT8Zt8rPC5QM2XkpURl1t4dPMMpQpp4K/yRTXcwfl0PtQH7sjdP/tH5GY+2VWc1BoBLlEZc4/yJB6uYQGRHHvLtfM865SHjhN0B3+9FjKQF2ckl81Rmrzz820mcwz6hsjnbbGQw1zg2XjbWzcUx8FyPXTk4IaUkMOxeeIFfscSrw7fNd89g3q4yR+CVnu46+mxgBB3h1Tkuw6/vXAq7Cn2fyYBRoL4IhTX8N+Gbj8iTlDX4w9n5dhg0yrZ/+ueuL19Fp0tpcNfgZ3T1WBDVU2X52wjA8etRPTNljBNLkTOhqaMuuX/4viVfr3s+DVbki/VkOq33pTtmAdgWUhKLtaa/SOP4kyNIrDhXP9fp7QS/1gMTCMLP/txFpo1wvOzWn0HmVt5ybU9mxW6sPPhznh/VXr3yQ88evXYRay6vfhFBK8A20x+SkEPyvzh717UmkaFLvSgPq2a1AszAxfC952VyPNwRd1M44SLd5f7V6lATAzhD9homXFeMSfz5jV24FUZZB76tekr1jV+pleHWYOaXHifPHyjEUljC/gQDStecMm0UliBUlDtvrAYRXwq4DNZI5rzF81x14vPgwea1zq4ip4MdoYQ/f46K6w2KTt53pRSdjHCsQ/qe6UmehINyg9rPZH/RVYQLpNx+4CsZwzsU0fhEOcLWtZ0GR+ADjWvoeg7p4Dt6Ts/jqa6iurr64XPy6HIJduTYPaxaYOCXMCU/gsDhPnlVxoDqrEpZHb7N12vPjY+tJFeVRV1u4lov8h298mpSPd3mrJSnEmVmlUZPNw0NdWX98F045sb36HODhpb+vtCbs/7/AAAA//9pC8XuAAAABklEQVQDAM6RvSSxSRS4AAAAAElFTkSuQmCC"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "6a1f943e",
+   "metadata": {},
+   "source": [
+    "Diketahui terdapat 3 jenis arch support pada dataset, yaitu\n",
+    "- neutral : sepatunya dirancang untuk tipe kaki normal hingga high arch (kecenderungan lengkungan tinggi)\n",
+    "- stability : sepatunya dirancang untuk tipe kaki normal hingga low arch (kecenderungan lengkungan rendah)\n",
+    "- motion control : khusus untuk pemilik flat feet (kaki rata) atau yang mengalami severe overpronation (kondisi kaki yang miring ke dalam dengan sangat tajam).\n",
+    "\n",
+    "Diambil data sebagai berikut dari website RunRepeat;\n",
+    "![image.png](attachment:image.png)\n",
+    "\n",
+    "Yang perlu diperhatikan adalah bahwa hanya terdapat 1 buah sepatu dengan kategori motion control sehingga ke depannya sepatu jenis ini akan dimasukkan ke dalam kategori stability terlebih dahulu. Jika di masa yang akan mendatang ditemukan semakin banyak sepatu jenis ini, maka akan dilakukan pemisahan antara stability dan motion control.\n",
+    "\n",
+    "Feature ini dipengaruhi oleh lengkungan pada kaki user sehingga diperlukan user input langsung berupa \"Arch Type\" untuk mengetahui sepatu yang cocok untuk mereka."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "eb80883e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 30 columns):\n",
+      " #   Column                       Non-Null Count  Dtype \n",
+      "---  ------                       --------------  ----- \n",
+      " 0   brand                        428 non-null    object\n",
+      " 1   name                         428 non-null    object\n",
+      " 2   pace                         428 non-null    object\n",
+      " 3   arch support                 428 non-null    object\n",
+      " 4   weight lab weight brand      428 non-null    object\n",
+      " 5   lightweight                  428 non-null    int64 \n",
+      " 6   drop lab drop brand          428 non-null    object\n",
+      " 7   strike pattern               428 non-null    object\n",
+      " 8   size                         428 non-null    object\n",
+      " 9   midsole softness             428 non-null    object\n",
+      " 10  toebox durability            428 non-null    object\n",
+      " 11  heel padding durability      428 non-null    object\n",
+      " 12  outsole durability           428 non-null    object\n",
+      " 13  breathability                428 non-null    object\n",
+      " 14  width / fit                  428 non-null    object\n",
+      " 15  toebox width                 428 non-null    object\n",
+      " 16  stiffness                    428 non-null    object\n",
+      " 17  torsional rigidity           428 non-null    object\n",
+      " 18  heel counter stiffness       428 non-null    object\n",
+      " 19  plate                        428 non-null    object\n",
+      " 20  rocker                       428 non-null    int64 \n",
+      " 21  heel lab heel brand          428 non-null    object\n",
+      " 22  forefoot lab forefoot brand  428 non-null    object\n",
+      " 23  widths available             428 non-null    object\n",
+      " 24  orthotic friendly            428 non-null    int64 \n",
+      " 25  season                       428 non-null    object\n",
+      " 26  removable insole             428 non-null    int64 \n",
+      " 27  for_daily                    428 non-null    int64 \n",
+      " 28  for_tempo                    428 non-null    int64 \n",
+      " 29  for_competition              428 non-null    int64 \n",
+      "dtypes: int64(7), object(23)\n",
+      "memory usage: 103.7+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "63786960",
+   "metadata": {},
+   "source": [
+    "## Weight lab weight brand"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "984853e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "count                             428\n",
+      "unique                            393\n",
+      "top       9.7 oz / 275g 9.7 oz / 275g\n",
+      "freq                                4\n",
+      "Name: weight lab weight brand, dtype: object\n",
+      "0      7.9 oz / 225g 8.1 oz / 230g\n",
+      "1    10.7 oz / 304g 10.9 oz / 309g\n",
+      "2    11.9 oz / 336g 11.5 oz / 327g\n",
+      "3    12.6 oz / 356g 12.4 oz / 352g\n",
+      "4    12.3 oz / 348g 12.2 oz / 345g\n",
+      "Name: weight lab weight brand, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['weight lab weight brand'].describe())\n",
+    "print(df['weight lab weight brand'].head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d51e216",
+   "metadata": {},
+   "source": [
+    "Karena RunRepeat sendiri biasanya melakukan penimbangan berat sepatu menggunakan satuan oz, maka untuk menyamaratakan berat di semua sepatu, ke depannya akan digunakan weight lab dengan satuan oz."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "8fe6a1a2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         weight lab weight brand  weight_lab_oz\n",
+      "0    7.9 oz / 225g 8.1 oz / 230g            7.9\n",
+      "1  10.7 oz / 304g 10.9 oz / 309g           10.7\n",
+      "2  11.9 oz / 336g 11.5 oz / 327g           11.9\n",
+      "3  12.6 oz / 356g 12.4 oz / 352g           12.6\n",
+      "4  12.3 oz / 348g 12.2 oz / 345g           12.3\n"
+     ]
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import re\n",
+    "\n",
+    "# extract ounces from the 'weight lab weight brand' column \n",
+    "def extract_oz(text):\n",
+    "    if pd.isna(text): return None\n",
+    "    match = re.search(r'(\\d+\\.?\\d*)\\s*oz', str(text))\n",
+    "    return float(match.group(1)) if match else None\n",
+    "\n",
+    "# extract and typecast to float\n",
+    "df['weight_lab_oz'] = df['weight lab weight brand'].apply(extract_oz).astype(float)\n",
+    "\n",
+    "print(df[['weight lab weight brand', 'weight_lab_oz']].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "4bb7a16a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "count    428.000000\n",
+      "mean       9.359112\n",
+      "std        1.370511\n",
+      "min        4.500000\n",
+      "25%        8.600000\n",
+      "50%        9.600000\n",
+      "75%       10.300000\n",
+      "max       12.600000\n",
+      "Name: weight_lab_oz, dtype: float64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(df['weight_lab_oz'].describe())\n",
+    "\n",
+    "# Distribution Visualization\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "\n",
+    "# Subplot 1: Histogram\n",
+    "plt.subplot(1, 2, 1)\n",
+    "sns.histplot(df['weight_lab_oz'], kde=True, color='teal', bins=20)\n",
+    "plt.axvline(df['weight_lab_oz'].mean(), color='red', linestyle='--', label=f\"Mean: {df['weight_lab_oz'].mean():.2f}\")\n",
+    "plt.axvline(df['weight_lab_oz'].median(), color='green', linestyle='-', label=f\"Median: {df['weight_lab_oz'].median():.2f}\")\n",
+    "plt.title('Distribusi Berat Sepatu (Histogram & KDE)')\n",
+    "plt.xlabel('Weight (oz)')\n",
+    "plt.ylabel('Frekuensi')\n",
+    "plt.legend()\n",
+    "\n",
+    "# Subplot 2: Boxplot \n",
+    "plt.subplot(1, 2, 2)\n",
+    "sns.boxplot(y=df['weight_lab_oz'], color='lightblue')\n",
+    "plt.title('Boxplot Berat Sepatu')\n",
+    "plt.ylabel('Weight (oz)')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "517fcbf3",
+   "metadata": {},
+   "source": [
+    "Nantinya, weight akan menjadi filter yang disesuaikan oleh keinginan user. Misalnya, setelah hasil top 10 rekomendasi sepatu sudah keluar, user bisa sort sepatu tersebut berdasarkan rekomendasi dari 1-10 atau dari sepatu terberat/teringan (dengan tetap memedulikan similarity recommendation)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a48e460",
+   "metadata": {},
+   "source": [
+    "## Lightweight"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "fb583cbb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "lightweight\n",
+      "0    301\n",
+      "1    127\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['lightweight'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "cff4bbed",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Perbandingan Statistik Berat (weight_lab_oz):\n",
+      "             min   max       mean\n",
+      "lightweight                      \n",
+      "0            8.9  12.6  10.070764\n",
+      "1            4.5   8.8   7.672441\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Comparative Statistics Based on 'lightweight' Category\n",
+    "weight_stats = df.groupby('lightweight')['weight_lab_oz'].agg(['min', 'max', 'mean'])\n",
+    "\n",
+    "print(\"Perbandingan Statistik Berat (weight_lab_oz):\")\n",
+    "print(weight_stats)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee8bb9ea",
+   "metadata": {},
+   "source": [
+    "Dari sini didapatkan informasi bahwa RunRepeat mengkategorikan sepatu dengan berat <=8.8oz ke dalam jenis sepatu yang ringan (lightweight = 1) dan sisanya termasuk ke sepatu berkategori agak berat."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "aac417b8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 31 columns):\n",
+      " #   Column                       Non-Null Count  Dtype  \n",
+      "---  ------                       --------------  -----  \n",
+      " 0   brand                        428 non-null    object \n",
+      " 1   name                         428 non-null    object \n",
+      " 2   pace                         428 non-null    object \n",
+      " 3   arch support                 428 non-null    object \n",
+      " 4   weight lab weight brand      428 non-null    object \n",
+      " 5   lightweight                  428 non-null    int64  \n",
+      " 6   drop lab drop brand          428 non-null    object \n",
+      " 7   strike pattern               428 non-null    object \n",
+      " 8   size                         428 non-null    object \n",
+      " 9   midsole softness             428 non-null    object \n",
+      " 10  toebox durability            428 non-null    object \n",
+      " 11  heel padding durability      428 non-null    object \n",
+      " 12  outsole durability           428 non-null    object \n",
+      " 13  breathability                428 non-null    object \n",
+      " 14  width / fit                  428 non-null    object \n",
+      " 15  toebox width                 428 non-null    object \n",
+      " 16  stiffness                    428 non-null    object \n",
+      " 17  torsional rigidity           428 non-null    object \n",
+      " 18  heel counter stiffness       428 non-null    object \n",
+      " 19  plate                        428 non-null    object \n",
+      " 20  rocker                       428 non-null    int64  \n",
+      " 21  heel lab heel brand          428 non-null    object \n",
+      " 22  forefoot lab forefoot brand  428 non-null    object \n",
+      " 23  widths available             428 non-null    object \n",
+      " 24  orthotic friendly            428 non-null    int64  \n",
+      " 25  season                       428 non-null    object \n",
+      " 26  removable insole             428 non-null    int64  \n",
+      " 27  for_daily                    428 non-null    int64  \n",
+      " 28  for_tempo                    428 non-null    int64  \n",
+      " 29  for_competition              428 non-null    int64  \n",
+      " 30  weight_lab_oz                428 non-null    float64\n",
+      "dtypes: float64(1), int64(7), object(23)\n",
+      "memory usage: 107.0+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9968dd5d",
+   "metadata": {},
+   "source": [
+    "## Drop Heel Forefoot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "cc69c025",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Heel Lab Heel Brand Statistics:\n",
+      "count                 428\n",
+      "unique                380\n",
+      "top       39.9 mm 40.0 mm\n",
+      "freq                    4\n",
+      "Name: heel lab heel brand, dtype: object\n",
+      "0    32.4 mm 36.0 mm\n",
+      "1    34.3 mm 32.5 mm\n",
+      "2    33.3 mm 32.5 mm\n",
+      "3    31.8 mm 32.0 mm\n",
+      "4    32.6 mm 34.0 mm\n",
+      "Name: heel lab heel brand, dtype: object\n",
+      "\n",
+      "Forefoot Lab Forefoot Brand Statistics:\n",
+      "count                 428\n",
+      "unique                377\n",
+      "top       22.7 mm 24.0 mm\n",
+      "freq                    3\n",
+      "Name: forefoot lab forefoot brand, dtype: object\n",
+      "0    23.0 mm 26.0 mm\n",
+      "1    26.6 mm 24.5 mm\n",
+      "2    24.4 mm 22.5 mm\n",
+      "3    21.2 mm 21.0 mm\n",
+      "4    22.7 mm 24.0 mm\n",
+      "Name: forefoot lab forefoot brand, dtype: object\n",
+      "\n",
+      "Drop Lab Drop Brand Statistics:\n",
+      "count               428\n",
+      "unique              266\n",
+      "top       9.6 mm 8.0 mm\n",
+      "freq                  7\n",
+      "Name: drop lab drop brand, dtype: object\n",
+      "0     9.4 mm 10.0 mm\n",
+      "1      7.7 mm 8.0 mm\n",
+      "2     8.9 mm 10.0 mm\n",
+      "3    10.6 mm 11.0 mm\n",
+      "4     9.9 mm 10.0 mm\n",
+      "Name: drop lab drop brand, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Heel Lab Heel Brand Statistics:\")\n",
+    "print(df['heel lab heel brand'].describe())\n",
+    "print(df['heel lab heel brand'].head())\n",
+    "\n",
+    "print(\"\\nForefoot Lab Forefoot Brand Statistics:\")\n",
+    "print(df['forefoot lab forefoot brand'].describe())\n",
+    "print(df['forefoot lab forefoot brand'].head())\n",
+    "\n",
+    "print(\"\\nDrop Lab Drop Brand Statistics:\")\n",
+    "print(df['drop lab drop brand'].describe())\n",
+    "print(df['drop lab drop brand'].head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7864c436",
+   "metadata": {},
+   "source": [
+    "For convention we will use lab measured value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "11fe3ce8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  heel lab heel brand forefoot lab forefoot brand drop lab drop brand  \\\n",
+      "0     32.4 mm 36.0 mm             23.0 mm 26.0 mm      9.4 mm 10.0 mm   \n",
+      "1     34.3 mm 32.5 mm             26.6 mm 24.5 mm       7.7 mm 8.0 mm   \n",
+      "2     33.3 mm 32.5 mm             24.4 mm 22.5 mm      8.9 mm 10.0 mm   \n",
+      "3     31.8 mm 32.0 mm             21.2 mm 21.0 mm     10.6 mm 11.0 mm   \n",
+      "4     32.6 mm 34.0 mm             22.7 mm 24.0 mm      9.9 mm 10.0 mm   \n",
+      "\n",
+      "   heel_lab_mm  forefoot_lab_mm  drop_lab_mm  \n",
+      "0         32.4             23.0          9.4  \n",
+      "1         34.3             26.6          7.7  \n",
+      "2         33.3             24.4          8.9  \n",
+      "3         31.8             21.2         10.6  \n",
+      "4         32.6             22.7          9.9  \n"
+     ]
+    }
+   ],
+   "source": [
+    "cols = ['heel lab heel brand', 'forefoot lab forefoot brand', 'drop lab drop brand']\n",
+    "\n",
+    "for col in cols:\n",
+    "    new_col_name = col.split(' ')[0] + '_lab_mm' \n",
+    "    df[new_col_name] = (df[col].str.split(' ')\n",
+    "                        .str[0]\n",
+    "                        .str.replace('mm', '', case=False)\n",
+    "                        .replace('-', None)\n",
+    "                        .astype(float))\n",
+    "\n",
+    "# Verify\n",
+    "new_cols = [c.split(' ')[0] + '_lab_mm' for c in cols]\n",
+    "print(df[cols + new_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "4bbebb8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   heel_lab_mm  forefoot_lab_mm  drop_lab_mm  drop_meas_mm     drop_diff\n",
+      "0         32.4             23.0          9.4           9.4  1.776357e-15\n",
+      "1         34.3             26.6          7.7           7.7  4.440892e-15\n",
+      "2         33.3             24.4          8.9           8.9  1.776357e-15\n",
+      "3         31.8             21.2         10.6          10.6 -1.776357e-15\n",
+      "4         32.6             22.7          9.9           9.9 -1.776357e-15\n",
+      "\n",
+      "5.329070518200751e-15\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Selisih drop\n",
+    "df['drop_meas_mm'] = df['heel_lab_mm'] - df['forefoot_lab_mm']\n",
+    "df['drop_diff'] = df['drop_lab_mm'] - (df['heel_lab_mm'] - df['forefoot_lab_mm'])\n",
+    "print(df[['heel_lab_mm','forefoot_lab_mm', 'drop_lab_mm', 'drop_meas_mm', 'drop_diff']].head())\n",
+    "\n",
+    "\n",
+    "for index, row in df.iterrows():\n",
+    "    if row['drop_diff'] > 0.9:\n",
+    "        print(f\"Index: {index} | Shoe: {row['name']}\")\n",
+    "        print(f\"  - Heel/Forefoot: {row['heel_lab_mm']}/{row['forefoot_lab_mm']} (Calc: {row['drop_meas_mm']:.1f}mm)\")\n",
+    "        print(f\"  - Drop Lab:      {row['drop_lab_mm']}mm\")\n",
+    "        print(f\"  - Diff:          {row['drop_diff']:.2f}mm\")\n",
+    "        print(\"-\" * 40)\n",
+    "\n",
+    "# print max drop_diff\n",
+    "print()\n",
+    "print(df['drop_diff'].max())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c81bac7",
+   "metadata": {},
+   "source": [
+    "Perbedaan antara measured drop dengan drop_lab biasanya disebabkan oleh drop yang diukur oleh lab menyertakan ketebalan insole, sementara perhitungan manual mungkin tidak konsisten atau perbedaan pada midsole yang ada. Dikarenakan perbedaan yang tidak terlalu jauh, maka ke depannya akan digunakan drop_lab_mm saja."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "9b9dce2c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 34 columns):\n",
+      " #   Column                       Non-Null Count  Dtype  \n",
+      "---  ------                       --------------  -----  \n",
+      " 0   brand                        428 non-null    object \n",
+      " 1   name                         428 non-null    object \n",
+      " 2   pace                         428 non-null    object \n",
+      " 3   arch support                 428 non-null    object \n",
+      " 4   weight lab weight brand      428 non-null    object \n",
+      " 5   lightweight                  428 non-null    int64  \n",
+      " 6   drop lab drop brand          428 non-null    object \n",
+      " 7   strike pattern               428 non-null    object \n",
+      " 8   size                         428 non-null    object \n",
+      " 9   midsole softness             428 non-null    object \n",
+      " 10  toebox durability            428 non-null    object \n",
+      " 11  heel padding durability      428 non-null    object \n",
+      " 12  outsole durability           428 non-null    object \n",
+      " 13  breathability                428 non-null    object \n",
+      " 14  width / fit                  428 non-null    object \n",
+      " 15  toebox width                 428 non-null    object \n",
+      " 16  stiffness                    428 non-null    object \n",
+      " 17  torsional rigidity           428 non-null    object \n",
+      " 18  heel counter stiffness       428 non-null    object \n",
+      " 19  plate                        428 non-null    object \n",
+      " 20  rocker                       428 non-null    int64  \n",
+      " 21  heel lab heel brand          428 non-null    object \n",
+      " 22  forefoot lab forefoot brand  428 non-null    object \n",
+      " 23  widths available             428 non-null    object \n",
+      " 24  orthotic friendly            428 non-null    int64  \n",
+      " 25  season                       428 non-null    object \n",
+      " 26  removable insole             428 non-null    int64  \n",
+      " 27  for_daily                    428 non-null    int64  \n",
+      " 28  for_tempo                    428 non-null    int64  \n",
+      " 29  for_competition              428 non-null    int64  \n",
+      " 30  weight_lab_oz                428 non-null    float64\n",
+      " 31  heel_lab_mm                  428 non-null    float64\n",
+      " 32  forefoot_lab_mm              428 non-null    float64\n",
+      " 33  drop_lab_mm                  428 non-null    float64\n",
+      "dtypes: float64(4), int64(7), object(23)\n",
+      "memory usage: 117.0+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=['drop_diff', 'drop_meas_mm'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "3f25d4bc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Statistik Deskriptif Geometri Midsole:\n",
+      "       heel_lab_mm  forefoot_lab_mm  drop_lab_mm\n",
+      "count   428.000000       428.000000   428.000000\n",
+      "mean     34.672196        26.082477     8.589486\n",
+      "std       5.139009         4.691054     2.959573\n",
+      "min       7.600000         7.600000    -0.800000\n",
+      "25%      31.800000        23.075000     7.100000\n",
+      "50%      34.600000        25.850000     8.900000\n",
+      "75%      38.100000        29.200000    10.425000\n",
+      "max      50.100000        41.300000    16.100000\n"
+     ]
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 1500x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Checking heel forefoot drop statistics and distributions\n",
+    "stats = df[['heel_lab_mm', 'forefoot_lab_mm', 'drop_lab_mm']].describe(percentiles=[.25, .5, .75])\n",
+    "print(\"Statistik Deskriptif Geometri Midsole:\")\n",
+    "print(stats)\n",
+    "\n",
+    "# Cheking distributions\n",
+    "plt.figure(figsize=(15, 5))\n",
+    "\n",
+    "# Plot Heel\n",
+    "plt.subplot(1, 3, 1)\n",
+    "sns.histplot(df['heel_lab_mm'], kde=True, color='skyblue')\n",
+    "plt.title('Distribusi Heel Stack')\n",
+    "\n",
+    "# Plot Forefoot\n",
+    "plt.subplot(1, 3, 2)\n",
+    "sns.histplot(df['forefoot_lab_mm'], kde=True, color='salmon')\n",
+    "plt.title('Distribusi Forefoot Stack')\n",
+    "\n",
+    "# Plot Drop\n",
+    "plt.subplot(1, 3, 3)\n",
+    "sns.histplot(df['drop_lab_mm'], kde=True, color='gold')\n",
+    "plt.title('Distribusi Drop')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Checking outliers\n",
+    "plt.figure(figsize=(15, 5))\n",
+    "\n",
+    "# Plot Heel Boxplot\n",
+    "plt.subplot(1, 3, 1)\n",
+    "sns.boxplot(y=df['heel_lab_mm'], color='lightgreen')\n",
+    "plt.title('Boxplot Heel Stack')\n",
+    "plt.ylabel('Heel Stack (mm)')\n",
+    "\n",
+    "# Plot Forefoot Boxplot\n",
+    "plt.subplot(1, 3, 2)\n",
+    "sns.boxplot(y=df['forefoot_lab_mm'], color='lightcoral')\n",
+    "plt.title('Boxplot Forefoot Stack')\n",
+    "plt.ylabel('Forefoot Stack (mm)')\n",
+    "\n",
+    "# Plot Drop Boxplot\n",
+    "plt.subplot(1, 3, 3)\n",
+    "sns.boxplot(y=df['drop_lab_mm'], color='lightgoldenrodyellow')\n",
+    "plt.title('Boxplot Drop')\n",
+    "plt.ylabel('Drop (mm)')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37ac147a",
+   "metadata": {},
+   "source": [
+    "terdapat beberapa outliers tapi untuk saat ini kita ignore dulu, mungkin nanti di preprocessing bakal ngelakuin scaling tapi untuk saat ini kita ubah ke kategorikal based on data yg ada aja."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "7fb63a73",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hasil Binning Kategori:\n",
+      "  heel_category forefoot_category drop_category\n",
+      "0           low               low        medium\n",
+      "1        medium            medium           low\n",
+      "2        medium            medium        medium\n",
+      "3           low               low          high\n",
+      "4           low               low          high\n",
+      "\n",
+      "Rentang nilai untuk heel_category:\n",
+      "                min   max\n",
+      "heel_category            \n",
+      "low             7.6  32.9\n",
+      "medium         33.0  36.8\n",
+      "high           36.9  50.1\n",
+      "\n",
+      "Rentang nilai untuk forefoot_category:\n",
+      "                    min   max\n",
+      "forefoot_category            \n",
+      "low                 7.6  24.1\n",
+      "medium             24.2  27.8\n",
+      "high               27.9  41.3\n",
+      "\n",
+      "Rentang nilai untuk drop_category:\n",
+      "               min   max\n",
+      "drop_category           \n",
+      "low           -0.8   7.9\n",
+      "medium         8.0   9.8\n",
+      "high           9.9  16.1\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\caxyl\\AppData\\Local\\Temp\\ipykernel_9068\\1102539456.py:18: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
+      "  print(df.groupby(col)[col.replace('_category', '_lab_mm')].agg(['min', 'max']))\n",
+      "C:\\Users\\caxyl\\AppData\\Local\\Temp\\ipykernel_9068\\1102539456.py:18: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
+      "  print(df.groupby(col)[col.replace('_category', '_lab_mm')].agg(['min', 'max']))\n",
+      "C:\\Users\\caxyl\\AppData\\Local\\Temp\\ipykernel_9068\\1102539456.py:18: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
+      "  print(df.groupby(col)[col.replace('_category', '_lab_mm')].agg(['min', 'max']))\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Binning into categories based on tertiles\n",
+    "cols_to_bin = ['heel_lab_mm', 'forefoot_lab_mm', 'drop_lab_mm']\n",
+    "\n",
+    "for col in cols_to_bin:\n",
+    "    q1 = df[col].quantile(0.33)\n",
+    "    q2 = df[col].quantile(0.66)\n",
+    "    \n",
+    "    new_cat_col = col.replace('_lab_mm', '_category')\n",
+    "    df[new_cat_col] = pd.qcut(df[col], q=3, labels=['low', 'medium', 'high'])\n",
+    "\n",
+    "# Verify\n",
+    "print(\"Hasil Binning Kategori:\")\n",
+    "print(df[['heel_category', 'forefoot_category', 'drop_category']].head())\n",
+    "\n",
+    "# Checking ranges for each category\n",
+    "for col in ['heel_category', 'forefoot_category', 'drop_category']:\n",
+    "    print(f\"\\nRentang nilai untuk {col}:\")\n",
+    "    print(df.groupby(col)[col.replace('_category', '_lab_mm')].agg(['min', 'max']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "928fa02e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 37 columns):\n",
+      " #   Column                       Non-Null Count  Dtype   \n",
+      "---  ------                       --------------  -----   \n",
+      " 0   brand                        428 non-null    object  \n",
+      " 1   name                         428 non-null    object  \n",
+      " 2   pace                         428 non-null    object  \n",
+      " 3   arch support                 428 non-null    object  \n",
+      " 4   weight lab weight brand      428 non-null    object  \n",
+      " 5   lightweight                  428 non-null    int64   \n",
+      " 6   drop lab drop brand          428 non-null    object  \n",
+      " 7   strike pattern               428 non-null    object  \n",
+      " 8   size                         428 non-null    object  \n",
+      " 9   midsole softness             428 non-null    object  \n",
+      " 10  toebox durability            428 non-null    object  \n",
+      " 11  heel padding durability      428 non-null    object  \n",
+      " 12  outsole durability           428 non-null    object  \n",
+      " 13  breathability                428 non-null    object  \n",
+      " 14  width / fit                  428 non-null    object  \n",
+      " 15  toebox width                 428 non-null    object  \n",
+      " 16  stiffness                    428 non-null    object  \n",
+      " 17  torsional rigidity           428 non-null    object  \n",
+      " 18  heel counter stiffness       428 non-null    object  \n",
+      " 19  plate                        428 non-null    object  \n",
+      " 20  rocker                       428 non-null    int64   \n",
+      " 21  heel lab heel brand          428 non-null    object  \n",
+      " 22  forefoot lab forefoot brand  428 non-null    object  \n",
+      " 23  widths available             428 non-null    object  \n",
+      " 24  orthotic friendly            428 non-null    int64   \n",
+      " 25  season                       428 non-null    object  \n",
+      " 26  removable insole             428 non-null    int64   \n",
+      " 27  for_daily                    428 non-null    int64   \n",
+      " 28  for_tempo                    428 non-null    int64   \n",
+      " 29  for_competition              428 non-null    int64   \n",
+      " 30  weight_lab_oz                428 non-null    float64 \n",
+      " 31  heel_lab_mm                  428 non-null    float64 \n",
+      " 32  forefoot_lab_mm              428 non-null    float64 \n",
+      " 33  drop_lab_mm                  428 non-null    float64 \n",
+      " 34  heel_category                428 non-null    category\n",
+      " 35  forefoot_category            428 non-null    category\n",
+      " 36  drop_category                428 non-null    category\n",
+      "dtypes: category(3), float64(4), int64(7), object(23)\n",
+      "memory usage: 118.7+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5597014b",
+   "metadata": {},
+   "source": [
+    "## Strike pattern"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "0fa13a37",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "strike pattern\n",
+      "heelmid/forefoot     162\n",
+      "mid/forefoot         142\n",
+      "heel                 122\n",
+      "-                      1\n",
+      "heel mid/forefoot      1\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['strike pattern'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43c5f3b7",
+   "metadata": {},
+   "source": [
+    "Strike pattern merupakan fitur yang mendeskripsikan cara pelari untuk mendarat. Ada 3 jenis;\n",
+    "- heel : Pelari mendarat menggunakan tumit terlebih dahulu. Biasanya butuh sepatu dengan bantalan belakang yang tebal (heel_lab_mm) dan drop tinggi untuk mengurangi beban pada tendon Achilles.\n",
+    "- mid : Mendarat di bagian tengah kaki sehingga biasanya butuh drop yang stabil agar distribusi merata.\n",
+    "- forefoot : Pelari mendarat di bagian depan sehingga biasanya lebih suka sepatu dengan drop rendah (forefoot < heel) agar posisi kaki lebih natural."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "a7a17f62",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     strike pattern    strike_pattern  strike_heel  strike_mid  strike_fore\n",
+      "0  heelmid/forefoot  heelmid/forefoot            1           1            1\n",
+      "1      mid/forefoot      mid/forefoot            0           1            1\n",
+      "2  heelmid/forefoot  heelmid/forefoot            1           1            1\n",
+      "3              heel              heel            1           0            0\n",
+      "4  heelmid/forefoot  heelmid/forefoot            1           1            1\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['strike_pattern'] = df['strike pattern'].str.replace(' ', '').str.replace('-', 'unknown')\n",
+    "\n",
+    "# encode its base value\n",
+    "df['strike_heel'] = df['strike_pattern'].str.contains('heel').astype(int)\n",
+    "df['strike_mid'] = df['strike_pattern'].str.contains('mid').astype(int)\n",
+    "df['strike_fore'] = df['strike_pattern'].str.contains('forefoot').astype(int)\n",
+    "\n",
+    "print(df[['strike pattern', 'strike_pattern', 'strike_heel', 'strike_mid', 'strike_fore']].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "aa39f164",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "---  Strike vs Forefoot Category (%) ---\n",
+      "forefoot_category    low  medium   high\n",
+      "strike_fore                            \n",
+      "0                  43.09   39.84  17.07\n",
+      "1                  29.51   31.15  39.34\n",
+      "\n",
+      "--- Strike vs Heel Category (%) ---\n",
+      "heel_category    low  medium   high\n",
+      "strike_heel                        \n",
+      "0              49.65   27.27  23.08\n",
+      "1              25.61   36.49  37.89\n"
+     ]
+    }
+   ],
+   "source": [
+    "ct_forefoot = pd.crosstab(df['strike_fore'], df['forefoot_category'], normalize='index') * 100\n",
+    "ct_heel = pd.crosstab(df['strike_heel'], df['heel_category'], normalize='index') * 100\n",
+    "\n",
+    "print(\"---  Strike vs Forefoot Category (%) ---\")\n",
+    "print(ct_forefoot.round(2))\n",
+    "print(\"\\n--- Strike vs Heel Category (%) ---\")\n",
+    "print(ct_heel.round(2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "0d78d635",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rata-rata Nilai Asli Berdasarkan Strike Pattern:\n",
+      "                  forefoot_lab_mm  drop_lab_mm\n",
+      "strike_pattern                                \n",
+      "heel                    24.535246    11.726230\n",
+      "heelmid/forefoot        26.639877     8.988957\n",
+      "mid/forefoot            26.859155     5.423239\n",
+      "unknown                 13.700000    10.400000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Perbandingan rata-rata nilai asli \n",
+    "strike_analysis = df.groupby('strike_pattern')[['forefoot_lab_mm', 'drop_lab_mm']].mean()\n",
+    "\n",
+    "print(\"Rata-rata Nilai Asli Berdasarkan Strike Pattern:\")\n",
+    "print(strike_analysis)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ba54b77",
+   "metadata": {},
+   "source": [
+    "Diketahui semenjak era super shoes, banyak brand yang lebih suka masukin busa (foam) ke are forefoot makanya banyak forefoot yang tinggi. Hal ini dilakukan dengan harapan agar pantulan  energinya (energy return) maksimal. Seperti contohnya, sepatu forefoot striker punya forefoot yang tebal dan drop rendah agar feel sepatunya tetap empuk."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "01654410",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\caxyl\\AppData\\Local\\Temp\\ipykernel_9068\\3679098273.py:23: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  sns.barplot(x=['Heel Support', 'Midfoot Support', 'Forefoot Support'], y=base_counts, ax=axes[1, 1], palette='viridis')\n"
+     ]
+    },
+    {
+     "data": {
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9Nuo77bTTimRNR1X+XgGA1paNBerLckMmNhqvb0/y//1+/foVjR4aN7KptjJOS+X7b/wZZcOm7Kl9//33FzeJF1Q2ksoyZLagryaffPJJxRqYza3Xcd6oz0Tcr3/96wbbsvfwm2++GR3BgtTnsvdK/Ws6y/6rrbZacX6qObFRrX9HQMfh24d2qzxvQ1PdPHN93thsLOfY2GuvvaJv377FDe9syZ3/2bbkmOV5O/71r3/Vtd7PQvZBBx1UFGrq+/DDD4tWKtlyI7vd7rLLLjFhwoTZjvnf//43vvrVr8YKK6wQPXr0KGLcc889Z5tPoDwkUBbcjznmmBgyZEhx4y8LQM0pGL700kux7rrrznbDNw0dOrTuXORxU7aaKHePLceb77l3797FsbIleb6vbH3T3DFnszVKdhnPGK699tq69f/7v/9btEbP95/dlPfee+8F6laeLV1StnxJOc/HRhttVJzbfI18rcZjz+b7zJupl112Wd37zveU7/3YY4+tqzyVt9X/fJoTf3nIpsceeyw222yzogD8ne98p8FcJFno/8xnPhPdunUrPqusvM5NttzP52bMjWXrtdx20003FY/fe++9opV+fkZ5/PzMswL4+OOPR1to7mf60EMPxXbbbVf8HeU52XzzzYtrvCVa4xr/xz/+UXzu2Yosh3nIFpEHH3xwvP3227MdM/+eDznkkKLreJ7TvD4OP/zwohXhnLz77rux3nrrFa0Os8XanPzqV78qjp8JusZJjTRs2LA48cQT6x5nEm/HHXesiyWvo1NPPTVmzpzZ4BrMFmD5nVN+3/X/ZqdPnx7f//73Y9llly2OMWrUqDjuuOOK9S35bkuZlMmKWX7/5nfH1ltvPdvQA+Xvtnvuuaf4LszPKs/PXXfdVay/7rrrmux5ktsefPDBOZ5DAGipLBN+85vfLP4vzP8Ts5yeZbUsy86r7Dg/5fv5KeOsssoqTfYcLpdx6pvfsmmWk3PfLMtkL4j6slxz0kknFcfL8lrWPzbddNPi/+myeZWv8rWa6sWxoHNWZDktZU+A+rJckuW3LC/l55fn7je/+U2T81FcffXVRZkqh/bKsujUqVOL7TnEWJ6fLA/mz6bKI82tZ6RMnm2yySbFZ5hlorw2si4wP+c51a875A3wct0he7A0JctxO+20U3HM7PmT/va3vxXXY/YAKpf5jj766KKMV1+57pfnM3sl5e/5OecwqfXLmE3Jelj+vWy88cazbcv461+35Tp2c+bZy+sl30/2Ws7W/fn5ZMOm+nXL+s/NHs5f/vKXi88ny6PZQyLL441lEiavkzwfWZ4+4ogjZhv2dU71uYzp2WefLcqy5Wu/Jb2WRo8eXZSvy3XY9M9//rPoFZZ/x/leszHfn/70pxbfJ8jPJIe0zbJ2xp89wTL2xt55553ic86Y8nPPc5dl+qeeeqrZf0fNOUY+P+tvKe+plM9f+d7MwrhWgY5Hjw0WuilTpjQ5yfeMGTMW+NiZ1MjCRnZdzhtq5513XlGYyW7nC3LMLPjnMfPG8P/8z/8UhbMzzzyzwX+uv//97+OLX/xi0c02Czp547GxvHmdhcyscGQBIwtuOZRSFoaygNq4BUh2Nc8hdvIGZO6bBdpsqfS73/1urjHneLx33HFHvPrqq8XrNCX/88/XzpuzWRDafffdi/XZcqR+i6CxY8cWBfMsUDe3hUoWKLKCkXFmxaB8LrIF2ve+973inGZviyx85VjBWVjMm6ItGfopK38pC7Apu+jnzddMwmSlIQteWUDKm/7lOHL4gXz9vOmcyZeUFYUsFL7wwgtx1VVXxTnnnFMUNsvnan7jz5vjWZjLzzpb52Rlq/6N2kw+ZME7C3NnnXVWcf7//e9/z7E3TBZs8wZ8XmcHHHBAg215nvM6yc8qfeUrXykqWXmtZOE/Y8lhu7Lr/lprrdWqf6PNPSd33nlncT6yApfXc7bqueSSS4rEVBZi87OYH61xjWelM895FqyzspwF/Uw45c/8/ihXurLLdMaXFZ68XjL5kAXpPMeZ5GwquZLnL5NJWcjP74O8vuYkKyxZOc6KTHNk4T8L8FmZyZ95brNynJWKH//4x8U+3/3ud4vPMc9PXssp903Zyi//RvKayPeTw2/kWN65X17/9ccPb+53W56zrJBnJSYTJHkdZ8Imv9vyOTnecn15Ayg/n4w7bxTlfll5ueKKK4rPqr5cl+dvww03bNb5AYDmyht/+X9i3lDOBgx58zQbjGRDl/y/vvx/6JzKji0p3zenjJPJ/BxOcl5zm81P2TTrRNlYKffNXs75/3uWkbIck+X2lGWJrOvk9mx9n+XViy++uChjPvzww8X5aU4dYkFlXaJcFs3yZ5Zhyw0y6t88z54CWT7JMluWezO2HPooP8t8L42H48yGIPl+8wZoJgHy97xpvsceexRl5qzvZbk5y4ZNlS+bU8/IMlHejM/zkUPo5g3abChXvzFPc85zfVlmzsZ6ee3l8fLGd+Mb8XnzN4eBysZQf/3rX+tuIOfwrVlezc8r60t5/LxGsoyY2xqf94why21Z98vjnH322cW1ns+f2zVbfq08H63ZS/3FF18shp/N+k3WgfJc5Gvccssts/XcyWsgr/lMnmSjorxOM/FYviGfclsm5MaMGVO8p/J++Xecn1H9ulhT9bn8u846epars7yd6tfzmiv/HnPJa7p83eS1ncmCb3/720W9NP9G88b9H//4x9nKx825T5Dl7Exs5N99LnkvI+f2aNwwK+tDWf7P85r3PfLvKsvx2Qgtv8My+TOvv6Pcb17HyDpH/k1kXHktZ90hZbJwYV2rQAdUgoXkkksuyWZPc11WWWWVuv1ffvnlYl0+r7Fc//3vf7/ucf6e63bZZZcG+331q18t1j/11FMtPubBBx/cYL/PfvazpUGDBtU9fuyxx4r9jjrqqAb7HXjggbMd84MPPpjtdR988MFiv8svv3y2czVmzJjSrFmz6tYfffTRpU6dOpUmT55cmpuLL764eH7Xrl1LW265Zel73/te6W9/+1tp5syZDfZ78803Z4ux7IADDii2ffvb325y25JLLln3uHxef/zjH5dmzJhR+vznP1/q0aNH6dZbb63b5z//+U8R+2mnndbgWE8//XSpc+fOs61vrHxO/vrXvxZxjx8/vnT11VcXn0W+1quvvtrkOf74449Lq666ammrrbZqsL5Xr17F+2gs30O+Tr6n+uYn/s0337w4xi9/+csG+5bPU8b8zjvv1K2/4YYbivU33njjXM/BCSecUOrSpUuD506fPr3Uv3//Btdpv379SkcccUSprf9Gm3tO8hpebrnlSmPHjm1wPedntfTSS5e22Wab2WJofP7b4hpv6u/xqquuKva/995769btv//+pdra2tIjjzwy2/7l91OOO/d5/fXXi/O0zDLLFOdoXgYMGFBaffXV57nf3OL+8pe/XOrZs2fpo48+qlu34447Nvg7Lfvtb39bvJ88X/Xl9Zrv4f7775/v77bddtut+CxeeumlunWvvfZaqU+fPqXNNtusbl35PG2yySalTz75ZLbru1u3bg2+3yZNmlRcS019fgAwv7J8VL8KfP311xePf/jDHzbY73Of+1yppqam9K9//WueZcfmlu/vuuuuYl3+nJvbbrutKF/lsuGGG5aOO+64okydZdoFLZueffbZDcqQa6yxRmno0KF1x87/m3N9fe+++25p2LBhDcqacytf5WvlMq/6w5yUY228rLTSSqV///vfDfY95JBDSiNGjCi99dZbDdbvvffeRXm4/NmUz32WzRp/XnkO8hj1yx/5GeT+jeNtTj3jnHPOKZ6b52hOmnuey3WHvn37FmWi+srv6Zprrim99957xXkbPHhw6YknnphrzOmMM84oru///ve/s9X9TjnllAb7rrnmmqW11167NC9ZXs7nZ7k268s/+clPSs8///xs+5Xr2I01VQfI85/r/vjHP9atmzJlSvF5ZVyNn5tx1v87Oeuss4r1WddKeQ6zvLrttts2qDOcf/75xX6/+c1v5lmfS1nOb+oan5M8Tl6reU1kDA899FBp6623bvA3mY9Hjx7doCyf9YyNNtqoqEfN732C8nvN+kD9/b7zne8Uz6//XZav2bgOlZ9DlsvrXw9z+ztq7jGyrjSn+zEL61oFOhZDUbHQ5aRi2VK68dIarXyyG2l95cn1cnLplsrWIfVly4JsvVHuupytRcotkJt67fqyVXZZtj7K42QrjWxZ0tQwQdmSoX5X3XztbJ2QLU/mJltdZVzZoiRbZWerinzucsstV9ctubnmp8VDtv4ot1rKc54tQsqyy3C2FM9WYtkCq7xkS/mMq3HX6znJ1jXZGitbd2frmWwtk71CsnVL43OcrWCy1Xq+9wUdhml+48/WVNnaqynZ6ihb2JSVW6tka5m5yefldVO/+3W2MssWW7mtLK+nHPYpexq05d9oc8/Jk08+WbS2ygm985ov75ct9XO4ouw2Pr9jBbfGNV7/WslWcBlTeWLD8vWScWXro5133rlubpf6GnelzxZF2TIpP6d8X+UWbHOT3yU5zFNz1Y87W/hl3Pnes4VTdmGfl2zxlC2msudJ/c+tPKxb+XNr7ndbfifldZgtyrJXUdmIESOKzzw/n/L3ZVm2Tsw5RerL4QKy1Vf9IR2y1Vn2HGvPY6EDUL2yvJr/H+Wwi/Xl0FR5PzJb/8/L/Jbv5yVboWePjewZkMO4ZM/ebJmcZd36w9LMb9k0h3DK3sJl2co6H+e8HTncTspzUe6JmsfOnqf5/3CWgdpqSNOmZA/8cvkzP4NsjZ5l+mw9Xx5uJz+fbMmeZbT8vf45yPOV+zeOOVv81/+8Xn/99aKcmutz+Kb6n0H24GisOfWMci+ZHDp0TuXb+T3P2aOk3IO8sYwh61xZBsyeCY17e9SPOcveeX6yhXyes+zV05y677zqKCl7Upx//vlFa/2sm2Vr/ixvZlk/ez+1VLb0r99boTzEVMY+ceLE2erO9XtcZD02r/vyvYBs1Z/11ezJU39OiCyX5nFzGNfm1ufmV/bIyc8wR37IXgbloaQylvz8swd2/i2Xy/a55HdJXstZj2p8Dud1n6D8XrPcXn+/xr2Yyu+zfD7yGPm65SHUmroeG/8dteQYTVlY1yrQsRiKioUuu3A3dYMwb/Q2NfzN/MhCfH3ZFTH/g23pGLcpx3isr3xDOguzWQDKwkO+Rhbi6it3K23cRTi7OGfBLwsn9cfuzULp/Lz2vGQhKJe82ZmVlbxBmOPoZtfoLPg2NUZvY1kQnNMwP03J9/b+++8XFZDGY41mgSzfb+PPqKy5E5LnTfecfC1jy26/WViqXzDNpEp2uc1KSv35Apoay3V+zG/8WflsaoiiBflcV1999eJmdH6W2cU+5e85ZFb5pnTKCnAWODP5k0M/ZdfjrADUv+ncGn+jzT0nuV9qPIRWfXn910/2NMeCXuNZiciu6DmMQOPJOMt/j1l5zpvy8xoKoiyHbMprM4dMKI8FPS/5PZKVmObKruo5rm1WgBonDJr6HmksP4+Mb04V5PK5aO53W56j/Azyb7GxrNBmhT3H+s6xjMsaHzPltZ3DJuTQU+XrO3/PZFNT36cAsKDy/7q8cdq4gUH+/1XePi/zW75vjvz/MBMXeWMykxt5oziHxcphK7OMmzfd57dsmu8zh7epL8vUKetK5cYdOY9IDumSZan6w5A29X93W8k4szFTWc7RlkPjZvn0Rz/6URFflj+ycU8OI9p4wuqyxuW7xu+h/Pk2dQ6buiHbnHpGNjbKYaZyaLAcUihv7OdwXfnZ1a+zzM95ntu5zxvV2UAnb/zWL2uVvfLKK8XQP5kUa1zXaHx95rwOjcuHWT5vTt0z31s2Mswlb2rnjfssl2e9MBuj5dCzLZFlwMb1uPrXbf3yduPPMW+sZ0Ob8r2A8ufduMyadbasJzX+e59bfW5+5TBhOVRUvpf8vsnPqvz3mEOV5d9yDiuXy5yu5XJDvubUJ+d0befn27jOlWX1HGYt5x7JOT/qz1NRHu55Xtfj/B6jKQvrWgU6FokN2q053YienwmhGh+jJcds3Kq4rH6lpbmyxURWerIAmuPFZ8ugjCkLe0216GmN184xTrP1Qi55Azxv5GYBc243mZtqedEceZM5W3nnjfVMbGSBoyzfX77XfO2m3ld5/P+W3nRPWWDO1m05rnAWqrIgm5W6POc5r8WCmN/4G7diaa3PNStLOZ5yJhiyUJwFvxyft/5EitnaJz/vrARnS/qcdyHnhMkKcrZ0ay3NPSflazvjaNyKrPG+LdHSazzPU/buyHG0M66MIWPNyvP89iApy4przumTBfu8ydEceUM/K8h582JelaeswGePkEyG5Bi1mbzNv7OseB9//PHNijv3yYn9crLypmRCrK3N6e8jE3Df+MY3ip4vecMg5zrJ1n8A0F7Nb/l+fmS5IJMcueTN3Gw9nj0vc1z91ipbN56IPOfXyl6YWT7KRiJ57CzTlOe1m5eMqaky7YJO6lueaDt7xKbyuc1enXMq8zXubTy38vm8NLeeka+RMWaPmewBkHWjbHyTjZCyXJ7nc37P89zizhvm2UgnEz5ZBq1fd8tzXp7zLcuJWebMm+mZgMvXb3x9zqmOMr/yRnaeq1zK863ljfbsydwadfyFZUGul8aysWD9ZF195c8he7mU50xsrHEjn9a8R3H66acXCZXsEZ+94HMOl7yO8jutqe+wps7L/B6jsUpdq0D1k9ig3Sq3JGg8MdrcWk5ly6X6LQiy9UP+J5jdmVt6zHnJAlq+RrZMqN8iIl+7sRxiJQve2TqnLFvYNI6nrZQTAtntujV6MTSWLb2yS2i2mM8hqfLGevmGe96AzYJWfj7lVjatLbuj503enPQxkzJlWeFobE7vfU7rF0b8zZGJjbxxn+81e6xki/2sODeWla0cQiiXbOGTk4ZnQqQ1ExvNPSflyTXzZvycCvQL+xrP1jw5+Xiey2wZVFbuXVKWLYEy7pzAs7k3N7LikcfMyne21JuXHEIhh5zIzzSTVHOTQwxkK7hMUmXFuiy/f+bnWs7Wn9mCcG7fAc39bstzlMmlnHyxsWyFmJWa5iZL8lrObvlXXXVV0QI2bxjUH2YNAFpT/l+XQ7Zkz8n6vTbKQzvWH1JyTv9nLqzyfeMyzvyWTXOI0hzepX6vjRdeeKH4Wa4r5XvJlutZzqj/fjORUt/cyg9Z32pqOJgFqW/Vv/mZvcPL5Y/8zHJdS8uX5c+3cfkvNS7XzE89I8s+Wc7KJRuS5E3fnGg6kx0Za3PPc3NkciSHosqbv3k+ciLssqeffrr4jLN3SDYeKcshvhaWvG4zsZHXbZ7v+vXx+pPbz+n6KPdmqH+eGl+3Zfk5brnllnWP81rJ183e6/U/7/xs6/dkz8ZFWd5t7nXU2nXocixZ7m2tulL9a7v+e82eTo17NeT1mOcth8uqLz+jbDTWHM09xpzOXXu4VoHqZI4N2q28mZj/CZZb5ZRlC5m5DVNU389//vPiZ/lmbkuOOS/lVhWNj1F+7cYtCxq3osj9WruFSt6wbUp5fNFy99u8GZlas+KVhbFsNZStk3JYnnLrimzJnu8/byQ3Pgf5OG/WLqg8fhaW6p/P7HqccyQ0lpW6pt53ubLXeNvCiL85cmiEbG2fLb9yyQRG/Rvc+d4bd9XNVmA5/ED9LvOtobnnJFvYZeX7Jz/5SV1ltL7yWMkL8xovt/JpHHeO4dy4YpoVxhtvvDEeffTRZrWKytZK2eLqhBNOaFC5nJNMBubnmON5lytq9WViKoc9mFPcWRlr6jssr+WmhsDInirZ+umiiy6abVsmE/Kmx/x8t2VMWaHOsaTrD/v3xhtvFC0Yc+iI/O5tjvx+zu/rbMmYw1Bl75nmVqgAYH7lDc8sOzXuHZjDPmWZsn6DkDmVHVu7fJ83v5sqXzQu48xv2TTncPjVr37VoPyQjzNBkGW18nspP78s523LBhj1za0OkWW+TAzVL99lg4ocmmhB5HnJcmQOzVqONeeeyIRDUw1QmlO+zPJX9trNm6n1y0x5M/W5555rUT0jW5w3Vu6xXC6LN/c8N1feCD7vvPOKoZ+ytXv9mBu/Tv6ePYtbU8510fh8la+xLLNnebrc46Dc4Kl+fTzLnvkZzCkhl43lyrJRV/ZMyXPaeNjXHJKs/rBeWQ7P6778d5z11OwFleeq/jnJm/H5+e+4447Ner9z+i5oqayrZc+W/HssJy4XtK6U7zUTJfldVP+9Nq7rzOk7LHuGzc/cKM09xpzq2gvrWgU6Hj02aNdybNLsVps/s7VHFoCauvFXli0tsstr3gzLgmHeHMvJa8sF4JYcc16yIpCF6iwkZAUiey1kq5TyMeu3SsieDL/97W+Lltw5Nm7GmK3EmjvuZHNll+RsvZUtwbPwmIXFfJ28OZtd2XN9uRtpxpE3yLOlV3YZzbkEmjufwJzkjeBsvZSF7LyhmYW0jCNvzubN3qwE5D7Zqig/syys5gRoeTN4QWRhNFtF5eefn3veEM5kVxak//GPf8z2ueU5yf3zpn+er5zIrVyxy1ZV2Xo8C4Tl89jW8TdXtmDPHgHZaiznIqjf5TxbHGZX5xzHN6/7HIYg3+cjjzzSoCVha2juOcn4cqzhrFTkeLI5jEKOEZsF3ayk5jWS1+bCvsYzIZTDpmUFKOPJ4QGa6vmQrexyWw4Ble8pk0tZ6cjCek6MXb+1WVkOu5UVpBxnOM/J3Ca/zpZreb7y5kpW0nLf8nWYQ0xl74Uc2iLlBHq5f7YMzYlO8/slv1OaugGSx8j3nT0g8pzktZDnJROOv//974uESp7/jTfeuKik502IXJ8tEfO7cX6+2/I6yJsAmcTIXkLZUyv/7rMCn+d4fuT3Rl6/KbuyA0Bbyf8Xs5VxlvuyLJNlp/w/P5P1OYRK+Sbs3MqOrV2+z96fOXdVTpicw7HkzeEcOjP/T88W6uXJjOe3bJox59CkuW+WifJ4ORRm3gwuz8eR7yV7EeRrZ7k6j5U3y/N91W+cMrfyVQ5Fk+coG0hkOTXL43mMLAM2nhtsTrIMlfW4lDems4V93qTO163fGzbrdFmWyc8hJ4DOmDKxkOWn/AyaSjI0lsM/5XvNMkzGns/Jm8EZb/333Nx6Rg4VmvXL3D9bzed+2Ugky+f5GvNznudHzt+Q5zev5bwWv/Od7xTXT14neR1kuTvL3JkIau15CHII0RwuOIfbyl4qmXDI951l2Exq5d9SuaFKNobJ+SHy2shhuPKG9m9+85siwZZzLDSW11fum3WZ7K2e+2bjmaZ6yuTfSr5+NuLJaybPe57zvD+Q8jXy7yWTgfk55vryfllWnlt5vb78LsjrMf/+8vPPxET9+Q5bIq+ljDUbsOW1nL0s8n3m90me3zyP8yPfa37ueX3n9Zb1jJyHJYeua9xoKLfndZvfLVnXyN4T2cCoufMzzs8x8nrMulNe7/l9lYmO/PtdWNcq0AGVYCG55JJL8s5b6ZFHHmly++abb15aZZVVGqz74IMPSoccckipX79+pT59+pT22muv0qRJk4rjfP/736/bL3/Pdc8991zpc5/7XLHvgAEDSl/72tdKH3744QId880332zyfbz88st166ZNm1Y64ogjSgMHDiz17t27tNtuu5XGjRtX7PejH/2obr933323dNBBB5UGDx5c7Dd27NjSP//5z9KSSy5ZOuCAA+Z5ru66665iff6cm6uuuqq09957lz7zmc+UevToUerevXtp5ZVXLn33u98tTZ06tcG+DzzwQGnttdcude3atcE5yHh69erV5PFzW8Zclucin/vjH/+4wX4XXHBBsf5b3/pW3bo//vGPpU022aQ4di4rrrhice7yfC3I9VN28cUXl5ZbbrlSt27dimPn88qfZX153jfbbLPi/OS2+uf/1FNPLS222GKl2tra2T7r5sTf1LU8t/OUGl9/c/Piiy8W++dy3333Ndg2ffr00rHHHltaffXVi+s7Y8zf87Noi7/R+flMn3jiidLuu+9eGjRoUPH55DWUf3933HHHbDHUP+dtdY2/+uqrpc9+9rOl/v37F98He+65Z+m1115r8rP473//W9p///1LQ4YMKWJfZplliveY53tO527mzJmlffbZp9S5c+fS9ddfX5qXfO2jjz66tPzyyxfvp2fPnkXcp512WmnKlCl1+91///2lDTbYoHjfI0eOLB133HGlW2+9dbbvhvfff7+07777Fu8vt9X/m/34449LZ555ZvF55vvJ78t8rR/84AcNXqu5323p8ccfL77Tcr+MfcsttyzO/fz+Hec5zXjyM2n8/Q0ACyL/T2tcJnzvvfeK/3/z/9QuXboU5cgsq82aNatZZcfmlu+bW46/+eabSwcffHBRnsrjZfll2WWXLR155JGlN954Y7b956ds+uijj5Y23HDDopyR8Z1//vkNjpXv+fTTTy+2ZflgzTXXLN10002zlf3nVr5K//u//1uUlXLbGmusUZRTmjpGUzLWcjk3l5qamqIcsssuu5Qee+yx2fbPc5Lvd9SoUcXnN3z48NLWW29d+vWvfz3bub/mmmuafM08hyuttFLxnrM8ee211zYZb3PqGVmu3XXXXYvrKd9//szy4AsvvDDf53ludYc5vacsF+b68meb9eMxY8YU11Jeo4ceemjpqaeeKvbJ+MvmVPdrqh7VWJa9f/aznxXX/uKLL158DlkPyWvtoosumu1vKT/H9ddfvzg/SyyxROmnP/1pk3WAPBc77rhjcf2sttpqdee98XsuP/eee+4pHXbYYUU5Mt/vfvvtV3r77bdnizfPTR4n4xw2bFjp8MMPL/6Om1PvSRMnTiziyveYr5v7zk3uk9fovLz00ktFfSOv4Ywt66M77bRT6Q9/+EOL7hNkXSTL9iNGjCi+t7bYYovSM888M9t300cffVT65je/WbffxhtvXHrwwQeL91X/vc3t76i5x0g33HBD8XeWdaT61+HCuFaBjqcm/6l0cgU6omwBteaaaxatjfbbb79KhwNQFd9t2TIzW5VmK9rG4/QCAPMvh7l56623mj1nGLQH2UMpewHddNNNc93v0ksvLXoKZK+O8lw0ACwazLEBrSDHpW8sh2/JIXjqz38AUE0q8d2WY1XnWML1Jw4EAAAAqM8cG9AKcgz5xx57rBijN8eWz7Erc8mxbUeNGlXp8ADa/XdbTpqZY1TnvBrZIyTnNAEAAABoisQGtIKcICsnzs0bcjnhW06IdvLJJxeTtwFUq4X53ZaTMObwVjmBeg4pAAAAADAn5tgAAAAAAACqhjk2AAAAAACAqiGxAQAAAAAAVA1zbETErFmz4rXXXos+ffpETU1NpcMBAFiocmTS9957L0aOHBm1tdq9wIJQtwAAFmXqFiwsEhsRRcVj1KhRlQ4DAKCixo8fH4svvnilw4Cqpm4BAKBuQduT2IgoWlOV/+D69u1b6XAAABaqqVOnFjdiy2UioOXULQCARZm6BQuLxEZEXRfxrHiofAAAiyrD5sCCU7cAAFC3oO0Z6AwAAAAAAKgaEhsAAAAAAEDVkNgAAAAAAACqhjk2AABolpkzZ8aMGTMqHQYdTJcuXaJTp06VDgMAAKgiEhsAAMxVqVSKiRMnxuTJkysdCh1U//79Y/jw4SaZBAAAmkViAwCAuSonNYYOHRo9e/Z085lWTZp98MEHMWnSpOLxiBEjKh0SAABQBSQ2AACY6/BT5aTGoEGDKh0OHVCPHj2Kn5ncyOvMsFQAAMC8mDwcAIA5Ks+pkT01oK2Ury9zuAAAAM0hsQEAwDwZfoq25PoCAADmh8QGAAAAAABQNSQ2AACoCpdeemn0798/2qMDDzwwdtttt0qHAQAAsEiQ2AAAoNVv6N99993F8EI58fjCdM8998RWW20VAwcOLOZtWG655eKAAw6Ijz/+uF0mR+66667YYYcdionZM96VV145vvnNb8aECROafYwtttgijjrqqDaNEwAAoD2R2AAAoEN47rnnYrvttot11lkn7r333nj66afj5z//eXTt2jVmzpwZ7c2vfvWrGDNmTAwfPjz++Mc/FvH/8pe/jClTpsTZZ58d1SjP86xZsyodBgAA0MFJbAAAsFDcd999semmm0aPHj1i1KhR8fWvfz2mTZtWt3369OnxrW99KxZbbLHo1atXrL/++kXPj+a67bbbiiTBWWedFauuump85jOfKRIdF110UfGaeayDDjqoSBxkb5JcTj755OK5v/3tb4uESJ8+fYpj7LvvvjFp0qQGx3/22Wdjp512ir59+xb75Xt56aWXmozlkUceiSFDhsSZZ57Z5PZXX321eP+5/OY3vyl6XSy11FKx2Wabxf/8z//ESSedVOz39ttvxz777FOck+zRMXr06Ljqqqsa9JjJXio/+9nP6t7Tf/7zn2LbM888E9tvv3307t07hg0bFl/84hfjrbfeqnvue++9F/vtt19xrkeMGBHnnHPObL0/3n333dh///1jwIABxevn8V588cW67eUeMH/605+K3ibdunUrPucuXbrExIkTG7znPG6eMwAAgAUlsQEAQJvLBEAmGfbYY4/4xz/+Eb/73e+KG+Bf+9rX6vbJ3x988MG4+uqri3323HPP4jn1b6TPTSYkXn/99aK3RlM22mijOPfcc4vERO6XSyZS0owZM+LUU0+Np556Kq6//voiOZBJg7IcGiqTDnnj/s4774zHHnssDj744Pjkk09me53cvs0228Rpp50Wxx9/fJOxXHPNNcXwWMcdd1yT28vDZX300Uex9tprx5///OciUXHYYYcVCYqHH3642J4JjQ033DAOPfTQuveUSaMcAiyH5FpzzTXj0UcfjVtuuSXeeOON2Guvvepe45hjjon777+/SErcfvvt8be//S0ef/zxBnHkOcjn5z752ZRKpWLorDxfZR988EGRwMmETCZ/MkG0zDLLFMmistz/iiuuKM4ZAADAguq8wEcAAGCRdtNNNxW9AuprPPTTGWecUfQOKPcGyLkvzjvvvNh8883jwgsvLHpHXHLJJfHKK6/EyJEji30y6ZA35HP96aefPs84MhFy6623FsfMJMcGG2wQW2+9ddHjIJMZOSRVv379il4Nub2++jfc86Z8xrbuuuvG+++/X7y3X/ziF8VzM+mSvRHS8ssvP1sM1113XfF6eZP/85///BxjzWRNxpQ9JeYme2qUky/pyCOPLN7j73//+1hvvfWKmPJ9ZW+K+u/p/PPPL5Ia9c9b9gzJpMcLL7xQvO5ll10WV155ZXGOUp7n8rkvx5gJjUx+ZFIoZXIij5HJnzzf5aTFBRdcEKuvvnrdcw855JDieMcee2zx+MYbbyySNPUTKwAAAC2lxwYAAAtkyy23jCeffLLBkjf268ueEDlsUSYJysvYsWOL+RhefvnlYj6MTIZksqD+PjnM0pyGe2qsU6dOxc30HOYph6PKpEDe2F9llVWKngxzkz0wdt5551hiiSWKYaYyOZIy0ZLyPeUwSuWkRlMeeuih4mZ/9lSYW1IjZc+HTLDMS56T7EmSQ1DlhOh5TjKxUY5rTvJ858Tk9c/liiuuWGzL8/nvf/+7SEhkcqQskyQrrLBC3ePnn38+OnfuXAwJVpaTnOc+ua0sEyurrbbabD09/vWvf8Xf//734nF+9pnUyGGvAAAAFpQeGwAALJC8Wb3ssss2WJfJhfqy58OXv/zlYk6JxjKZkENPZWIiEwz5s77GvUHmJRMaOVxTLpkUyGRJTsr9gx/8oMn9c56PTLLkkj0Scm6MTBzk4xwuKuUcHfOSc3rkjf/sGbHjjjvONQmSMeVcH5lwmVuvjR//+MfFcFM5hFYmN/JcZ6+Xclxzkuc7EzVNzfGRr5dJh9aS56Zxkmbo0KHF62eiaemll46bb755vuZLAQAAaLc9NnL846zwZJf3rAxll/bGLdly4sSsfGWFacyYMbONsfzOO+8UwxpkV/4cizi7vWdFDgCA9mOttdaK5557rkiANF6yxX8Om5S9E3JIqsbbGw8bNT9y0ussS5YnKc/XajxM1j//+c9iku4f/ehHRa+M7NnQeOLw7JGQc1DUn1uiscGDBxfza2TSIHsnzG3fz33uc0Us2bOkKTlHRsphoHbdddf4whe+UAz1lMNk5VBS9TX1nvJ853wXOSF54/OZyZE8TiZecpLzsky01D/2SiutVMwhkj1RyvI8jRs3rpgofF6+9KUvFXOp/PrXvy6SPhtvvPE8nwMAANDuExtZwcwKWo5Z3JSs6OX4xtnCLitUWQnLlnM5Pm9ZJjWy0pYTHub4zpksyUkVAQBoP3IS7QceeKCYIDyHdcrGKjfccEPd5OHZgyHLdTk/xbXXXlsMT5UTZOfcHDlxdnP86le/isMPPzxuu+22YrilLCPm6+bPbEyT8kZ/NoK544474q233iomvs4eI5kc+PnPf14M0ZTzSmRPj/oyzqlTp8bee+9dTKad8eeQU3mTv3FPhUxuZLJkn332aXJy8ZTzVJxzzjlFb4xsmJNDbv33v/8tEhnZs6X8+jkXSZZz89zl8E+5LScBry/fU5aVc8LzfE85vNcRRxxRNADKGDJ5kecjh7A66KCDiiRIDrd1wAEHFHNg5JBVeY4yjtra2rreF/namVTJiclzovcc3ioTLNkjJtfPS5bbs/HRD3/4w+J1AQAAOkRiY/vtty8qOp/97Gdn25a9NbLL/YknnlhUnLKV3OWXXx6vvfZaXc+OrNzlhJI5hnOO/bvJJpsUFdKc1DH3m5Pp06cXFdP6CwAAbSfLcnnzPnsEZK+I7KGRPXPrT1adwxZlYuOb3/xmMY/DbrvtVtyUz8RDc+R8EZm0+MpXvlLMq5HzZOQcD1l2LM+ZkZNg5/acAyOHnMqGNPkz54C45pprip4I2XPjJz/5SYNj5xBTmbDI4+ex1l577bjooouaHG4qe5jkvjlvSCZrGvemKPvqV79aJGEmTJhQlIezp0j2cshkQHnC8Cw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",
+      "text/plain": [
+       "<Figure size 1600x1200 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plotting Diagram\n",
+    "fig, axes = plt.subplots(2, 2, figsize=(16, 12))\n",
+    "\n",
+    "# running style distribution\n",
+    "strike_counts = df['strike_pattern'].value_counts()\n",
+    "axes[0, 0].pie(strike_counts, labels=strike_counts.index, autopct='%1.1f%%', colors=sns.color_palette('pastel'))\n",
+    "axes[0, 0].set_title('Proporsi Strike Pattern di Dataset')\n",
+    "\n",
+    "# B. Stacked Bar - Strike vs Forefoot (Korelasi Penting)\n",
+    "ct_forefoot.plot(kind='bar', stacked=True, ax=axes[0, 1], colormap='RdYlGn')\n",
+    "axes[0, 1].set_title('Hubungan Strike Pattern vs Forefoot Category')\n",
+    "axes[0, 1].set_ylabel('Persentase (%)')\n",
+    "axes[0, 1].legend(title='Forefoot Category', bbox_to_anchor=(1, 1))\n",
+    "\n",
+    "# C. Stacked Bar - Strike vs Heel Stack\n",
+    "ct_heel.plot(kind='bar', stacked=True, ax=axes[1, 0], colormap='Blues')\n",
+    "axes[1, 0].set_title('Hubungan Strike Pattern vs Heel Stack Category')\n",
+    "axes[1, 0].set_ylabel('Persentase (%)')\n",
+    "axes[1, 0].legend(title='Heel Stack Category', bbox_to_anchor=(1, 1))\n",
+    "\n",
+    "# D. Bar Chart - Base Value Count\n",
+    "base_counts = [df['strike_heel'].sum(), df['strike_mid'].sum(), df['strike_fore'].sum()]\n",
+    "sns.barplot(x=['Heel Support', 'Midfoot Support', 'Forefoot Support'], y=base_counts, ax=axes[1, 1], palette='viridis')\n",
+    "axes[1, 1].set_title('Total Sepatu Berdasarkan Support Pendaratan')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca0305a6",
+   "metadata": {},
+   "source": [
+    "Untuk menjangkau fitur ini secara eksplisit diperlukan user input mengenai strike pattern mereka, tetapi untuk pelari awam biasanya tidak mengerti mengenai hal tersebut. Maka dari itu, akan dibuat optional input untuk advanced user (pelari senior) yang berisikan opsi-opsi untuk strike pattern mereka. \n",
+    "\n",
+    "Skenario 1 - user tidak mengisi input strike pattern : \n",
+    "- masuk ke default strike pattern yang aman untuk semua pelari yaitu sepatu heel/mid/forefoot\n",
+    "- pilih kategori drop medium\n",
+    "- menggunakan heel dan forefoot medium\n",
+    "\n",
+    "Skenario 2 - user memilih salah satu strike pattern:\n",
+    "- heel striker membutuhkan proteksi lebih pada tumit mereka sehingga mappingnya akan menjadi Drop: high, Heel : high, Forefoot : low\n",
+    "- midfoot striker membutuhkan transisi yang lancar dan bantalan di tengah ke depan sehingga kita pilih Drop: medium, Heel : medium, Forefoot: high\n",
+    "- Forefoot striker membutuhkan posisi natural kaki dengan energy return yang maksimal sehingga mappingnya akan menjadi Drop: low, Heel : medium, Forefoot: high"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "a0b6c53d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 41 columns):\n",
+      " #   Column                       Non-Null Count  Dtype   \n",
+      "---  ------                       --------------  -----   \n",
+      " 0   brand                        428 non-null    object  \n",
+      " 1   name                         428 non-null    object  \n",
+      " 2   pace                         428 non-null    object  \n",
+      " 3   arch support                 428 non-null    object  \n",
+      " 4   weight lab weight brand      428 non-null    object  \n",
+      " 5   lightweight                  428 non-null    int64   \n",
+      " 6   drop lab drop brand          428 non-null    object  \n",
+      " 7   strike pattern               428 non-null    object  \n",
+      " 8   size                         428 non-null    object  \n",
+      " 9   midsole softness             428 non-null    object  \n",
+      " 10  toebox durability            428 non-null    object  \n",
+      " 11  heel padding durability      428 non-null    object  \n",
+      " 12  outsole durability           428 non-null    object  \n",
+      " 13  breathability                428 non-null    object  \n",
+      " 14  width / fit                  428 non-null    object  \n",
+      " 15  toebox width                 428 non-null    object  \n",
+      " 16  stiffness                    428 non-null    object  \n",
+      " 17  torsional rigidity           428 non-null    object  \n",
+      " 18  heel counter stiffness       428 non-null    object  \n",
+      " 19  plate                        428 non-null    object  \n",
+      " 20  rocker                       428 non-null    int64   \n",
+      " 21  heel lab heel brand          428 non-null    object  \n",
+      " 22  forefoot lab forefoot brand  428 non-null    object  \n",
+      " 23  widths available             428 non-null    object  \n",
+      " 24  orthotic friendly            428 non-null    int64   \n",
+      " 25  season                       428 non-null    object  \n",
+      " 26  removable insole             428 non-null    int64   \n",
+      " 27  for_daily                    428 non-null    int64   \n",
+      " 28  for_tempo                    428 non-null    int64   \n",
+      " 29  for_competition              428 non-null    int64   \n",
+      " 30  weight_lab_oz                428 non-null    float64 \n",
+      " 31  heel_lab_mm                  428 non-null    float64 \n",
+      " 32  forefoot_lab_mm              428 non-null    float64 \n",
+      " 33  drop_lab_mm                  428 non-null    float64 \n",
+      " 34  heel_category                428 non-null    category\n",
+      " 35  forefoot_category            428 non-null    category\n",
+      " 36  drop_category                428 non-null    category\n",
+      " 37  strike_pattern               428 non-null    object  \n",
+      " 38  strike_heel                  428 non-null    int64   \n",
+      " 39  strike_mid                   428 non-null    int64   \n",
+      " 40  strike_fore                  428 non-null    int64   \n",
+      "dtypes: category(3), float64(4), int64(10), object(24)\n",
+      "memory usage: 132.0+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cadee62c",
+   "metadata": {},
+   "source": [
+    "## Size, width / fit, width available, toebox width"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "a3dc5218",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "             size width / fit widths available toebox width\n",
+      "0    true to size      narrow       normalwide            -\n",
+      "1    true to size      narrow           normal       medium\n",
+      "2    true to size      narrow           normal            -\n",
+      "3  slightly small      narrow           normal            -\n",
+      "4    true to size      narrow           normal       medium\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[['size', 'width / fit', 'widths available', 'toebox width']].head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79104384",
+   "metadata": {},
+   "source": [
+    "Size diambil dari voting user RunRepeat dan widths available merupakan pilihan yang ditawarkan oleh brand sesuai availability di tokonya. Ini merupakan informasi yang tidak berdasar sehingga untuk ke depannya akan digunakan feature width / fit sebagai acuan ukuran sepatu saat dipakai pada ukuran standar (D untuk pria dan B untuk wanita)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "6e8a5907",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 39 columns):\n",
+      " #   Column                       Non-Null Count  Dtype   \n",
+      "---  ------                       --------------  -----   \n",
+      " 0   brand                        428 non-null    object  \n",
+      " 1   name                         428 non-null    object  \n",
+      " 2   pace                         428 non-null    object  \n",
+      " 3   arch support                 428 non-null    object  \n",
+      " 4   weight lab weight brand      428 non-null    object  \n",
+      " 5   lightweight                  428 non-null    int64   \n",
+      " 6   drop lab drop brand          428 non-null    object  \n",
+      " 7   strike pattern               428 non-null    object  \n",
+      " 8   midsole softness             428 non-null    object  \n",
+      " 9   toebox durability            428 non-null    object  \n",
+      " 10  heel padding durability      428 non-null    object  \n",
+      " 11  outsole durability           428 non-null    object  \n",
+      " 12  breathability                428 non-null    object  \n",
+      " 13  width / fit                  428 non-null    object  \n",
+      " 14  toebox width                 428 non-null    object  \n",
+      " 15  stiffness                    428 non-null    object  \n",
+      " 16  torsional rigidity           428 non-null    object  \n",
+      " 17  heel counter stiffness       428 non-null    object  \n",
+      " 18  plate                        428 non-null    object  \n",
+      " 19  rocker                       428 non-null    int64   \n",
+      " 20  heel lab heel brand          428 non-null    object  \n",
+      " 21  forefoot lab forefoot brand  428 non-null    object  \n",
+      " 22  orthotic friendly            428 non-null    int64   \n",
+      " 23  season                       428 non-null    object  \n",
+      " 24  removable insole             428 non-null    int64   \n",
+      " 25  for_daily                    428 non-null    int64   \n",
+      " 26  for_tempo                    428 non-null    int64   \n",
+      " 27  for_competition              428 non-null    int64   \n",
+      " 28  weight_lab_oz                428 non-null    float64 \n",
+      " 29  heel_lab_mm                  428 non-null    float64 \n",
+      " 30  forefoot_lab_mm              428 non-null    float64 \n",
+      " 31  drop_lab_mm                  428 non-null    float64 \n",
+      " 32  heel_category                428 non-null    category\n",
+      " 33  forefoot_category            428 non-null    category\n",
+      " 34  drop_category                428 non-null    category\n",
+      " 35  strike_pattern               428 non-null    object  \n",
+      " 36  strike_heel                  428 non-null    int64   \n",
+      " 37  strike_mid                   428 non-null    int64   \n",
+      " 38  strike_fore                  428 non-null    int64   \n",
+      "dtypes: category(3), float64(4), int64(10), object(22)\n",
+      "memory usage: 125.4+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=['size','widths available'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "2770bb83",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "width / fit\n",
+      "medium    256\n",
+      "narrow    140\n",
+      "wide       32\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['width / fit'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79e49592",
+   "metadata": {},
+   "source": [
+    "Terdapat 3 kategori sepatu:\n",
+    "- medium : standar sepatu yang diciptakan untuk mayoritas daily runner\n",
+    "- narrow : sepatu yang terasa agak sempit\n",
+    "- wide : sepatu untuk kaki yang lebar\n",
+    "\n",
+    "Feature ini membutuhkan input user langsung yang berisikan jenis ukuran kaki mereka."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "03a5fedd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Crossing Table Width / Fit vs Toebox Width (%):\n",
+      "toebox width         -    medium    narrow      wide\n",
+      "width / fit                                         \n",
+      "medium        17.96875  57.81250  10.15625  14.06250\n",
+      "narrow        42.14286  32.85714  20.71429   4.28571\n",
+      "wide           9.37500  31.25000   0.00000  59.37500\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Crossing table width / fit vs toebox width\n",
+    "ct_width_toebox = pd.crosstab(df['width / fit'], df['toebox width'], normalize='index') * 100\n",
+    "print(\"Crossing Table Width / Fit vs Toebox Width (%):\")\n",
+    "print(ct_width_toebox.round(5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5077b376",
+   "metadata": {},
+   "source": [
+    "Rendahnya korelasi antara width / fit dan toebox width menandakan bahwa width / fit tidak bisa ditentukan oleh toebox-nya saja melainkan dari overall kesuluruhan sepatu. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a8688e0a",
+   "metadata": {},
+   "source": [
+    "## Midsole softness"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "520a0116",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "midsole softness\n",
+      "soft        177\n",
+      "balanced    172\n",
+      "-            61\n",
+      "firm         18\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['midsole softness'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "2e58c6c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Heel Stack (mm) vs Midsole Softness Analysis:\n",
+      "                   min       mean   max\n",
+      "midsole softness                       \n",
+      "soft              24.6  36.490960  50.1\n",
+      "balanced          22.5  34.153488  45.7\n",
+      "-                  7.6  32.019672  42.3\n",
+      "firm              13.1  30.733333  38.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "ms_analysis = df.groupby('midsole softness')['heel_lab_mm'].agg(['min', 'mean', 'max'])\n",
+    "ms_analysis = ms_analysis.sort_values('mean', ascending=False)\n",
+    "\n",
+    "print(\"Heel Stack (mm) vs Midsole Softness Analysis:\")\n",
+    "print(ms_analysis)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "85f4dad6",
+   "metadata": {},
+   "source": [
+    "Sepatu dengan midsole berkategori soft memiliki minimum heel tertinggi di antara kategori lainnya, akan tetapi terdapat sepatu berkategori firm yang mempunyai heel setara sepatu berkategori soft. Hal ini menunjukan bahwa beberapa sepatu yang memiliki heel tinggi tidak selalu berartikan bahwa sepatu itu empuk (mempunyai midsole yang soft)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "319bde26",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 39 columns):\n",
+      " #   Column                       Non-Null Count  Dtype   \n",
+      "---  ------                       --------------  -----   \n",
+      " 0   brand                        428 non-null    object  \n",
+      " 1   name                         428 non-null    object  \n",
+      " 2   pace                         428 non-null    object  \n",
+      " 3   arch support                 428 non-null    object  \n",
+      " 4   weight lab weight brand      428 non-null    object  \n",
+      " 5   lightweight                  428 non-null    int64   \n",
+      " 6   drop lab drop brand          428 non-null    object  \n",
+      " 7   strike pattern               428 non-null    object  \n",
+      " 8   midsole softness             428 non-null    object  \n",
+      " 9   toebox durability            428 non-null    object  \n",
+      " 10  heel padding durability      428 non-null    object  \n",
+      " 11  outsole durability           428 non-null    object  \n",
+      " 12  breathability                428 non-null    object  \n",
+      " 13  width / fit                  428 non-null    object  \n",
+      " 14  toebox width                 428 non-null    object  \n",
+      " 15  stiffness                    428 non-null    object  \n",
+      " 16  torsional rigidity           428 non-null    object  \n",
+      " 17  heel counter stiffness       428 non-null    object  \n",
+      " 18  plate                        428 non-null    object  \n",
+      " 19  rocker                       428 non-null    int64   \n",
+      " 20  heel lab heel brand          428 non-null    object  \n",
+      " 21  forefoot lab forefoot brand  428 non-null    object  \n",
+      " 22  orthotic friendly            428 non-null    int64   \n",
+      " 23  season                       428 non-null    object  \n",
+      " 24  removable insole             428 non-null    int64   \n",
+      " 25  for_daily                    428 non-null    int64   \n",
+      " 26  for_tempo                    428 non-null    int64   \n",
+      " 27  for_competition              428 non-null    int64   \n",
+      " 28  weight_lab_oz                428 non-null    float64 \n",
+      " 29  heel_lab_mm                  428 non-null    float64 \n",
+      " 30  forefoot_lab_mm              428 non-null    float64 \n",
+      " 31  drop_lab_mm                  428 non-null    float64 \n",
+      " 32  heel_category                428 non-null    category\n",
+      " 33  forefoot_category            428 non-null    category\n",
+      " 34  drop_category                428 non-null    category\n",
+      " 35  strike_pattern               428 non-null    object  \n",
+      " 36  strike_heel                  428 non-null    int64   \n",
+      " 37  strike_mid                   428 non-null    int64   \n",
+      " 38  strike_fore                  428 non-null    int64   \n",
+      "dtypes: category(3), float64(4), int64(10), object(22)\n",
+      "memory usage: 125.4+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "776aa33e",
+   "metadata": {},
+   "source": [
+    "## Toebox, Heel padding, Outsole Durability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "4092c00e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  toebox durability heel padding durability outsole durability\n",
+      "0                 -                       -                  -\n",
+      "1              good                    good               good\n",
+      "2                 -                    good                  -\n",
+      "3                 -                       -                  -\n",
+      "4              good                    good               good\n",
+      "\n",
+      "Toebox Durability Value Counts:\n",
+      "toebox durability\n",
+      "decent    150\n",
+      "-         117\n",
+      "bad        88\n",
+      "good       73\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Heel Padding Durability Value Counts:\n",
+      "heel padding durability\n",
+      "good      179\n",
+      "-         122\n",
+      "decent     77\n",
+      "bad        50\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Outsole Durability Value Counts:\n",
+      "outsole durability\n",
+      "good      206\n",
+      "-         134\n",
+      "decent     67\n",
+      "bad        21\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[['toebox durability', 'heel padding durability', 'outsole durability']].head())\n",
+    "\n",
+    "print(\"\\nToebox Durability Value Counts:\")\n",
+    "print(df['toebox durability'].value_counts())\n",
+    "\n",
+    "print(\"\\nHeel Padding Durability Value Counts:\")\n",
+    "print(df['heel padding durability'].value_counts())\n",
+    "\n",
+    "print(\"\\nOutsole Durability Value Counts:\")\n",
+    "print(df['outsole durability'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b06fec21",
+   "metadata": {},
+   "source": [
+    "- Toebox durability merupakan ketahanan bagian atas (mesh) di area jari kaki terhadap gesekan atau robekan. Ini penting buat pelari yang jempolnya suka \"nembus\" ke atas atau sering lari di medan yang banyak kerikil, sehingga diperlukan korelasi antara feature ini dan rocker serta plate sepatunya.\n",
+    "\n",
+    "- Heel padding durability menandakan seberapa kuat kain dan busa di bagian tumit belakang. Perlu dilihat korelasi antara feature ini dengan heel striker.\n",
+    "\n",
+    "- Outsole durability adalah ketahanan karet bagian bawah terhadap pengikisan aspal. Ini yang menentukan seberapa cepat terjadinya kebotakan pada grip sepat."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "cf364d10",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\caxyl\\AppData\\Roaming\\Python\\Python313\\site-packages\\seaborn\\matrix.py:202: RuntimeWarning: All-NaN slice encountered\n",
+      "  vmin = np.nanmin(calc_data)\n",
+      "C:\\Users\\caxyl\\AppData\\Roaming\\Python\\Python313\\site-packages\\seaborn\\matrix.py:207: RuntimeWarning: All-NaN slice encountered\n",
+      "  vmax = np.nanmax(calc_data)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rata-rata Skor Durabilitas:\n",
+      "toebox durability         NaN\n",
+      "heel padding durability   NaN\n",
+      "outsole durability        NaN\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Correlation between toebox durability, heel padding durability, and outsole durability\n",
+    "durability_cols = ['toebox durability', 'heel padding durability', 'outsole durability']\n",
+    "df_durability = df[durability_cols].apply(pd.to_numeric, errors='coerce')\n",
+    "corr_matrix = df_durability.corr()\n",
+    "\n",
+    "# Visualize\n",
+    "plt.figure(figsize=(10, 8))\n",
+    "sns.heatmap(corr_matrix, annot=True, cmap='YlGnBu', fmt=\".2f\", linewidths=0.5)\n",
+    "plt.title('Korelasi Antar Fitur Durabilitas')\n",
+    "plt.show()\n",
+    "\n",
+    "print(\"Rata-rata Skor Durabilitas:\")\n",
+    "print(df_durability.mean().sort_values())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "563bf4a5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Detail Crosstab: Outsole vs Heel Padding (Normalized by Outsole)\n",
+      "heel padding durability     -   bad  decent  good\n",
+      "outsole durability                               \n",
+      "-                        91.0   4.0     1.0   3.0\n",
+      "bad                       0.0  14.0    24.0  62.0\n",
+      "decent                    0.0  21.0    28.0  51.0\n",
+      "good                      0.0  13.0    25.0  62.0\n",
+      "\n",
+      "Detail Crosstab: Outsole vs Toebox (Normalized by Outsole)\n",
+      "toebox durability      -   bad  decent  good\n",
+      "outsole durability                          \n",
+      "-                   87.0   6.0     4.0   2.0\n",
+      "bad                  0.0  29.0    33.0  38.0\n",
+      "decent               0.0  36.0    49.0  15.0\n",
+      "good                 0.0  24.0    50.0  25.0\n",
+      "\n",
+      "Detail Crosstab: Toebox vs Heel Padding (Normalized by Toebox)\n",
+      "heel padding durability     -   bad  decent  good\n",
+      "toebox durability                                \n",
+      "-                        97.0   2.0     0.0   2.0\n",
+      "bad                       6.0  23.0    22.0  50.0\n",
+      "decent                    2.0  16.0    27.0  55.0\n",
+      "good                      1.0   5.0    23.0  70.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Crossing tables between durability features\n",
+    "pairs = [\n",
+    "    ('outsole durability', 'heel padding durability'),\n",
+    "    ('outsole durability', 'toebox durability'),\n",
+    "    ('toebox durability', 'heel padding durability')\n",
+    "]\n",
+    "\n",
+    "plt.figure(figsize=(18, 5))\n",
+    "\n",
+    "for i, (feat_x, feat_y) in enumerate(pairs):\n",
+    "    ct = pd.crosstab(df[feat_x], df[feat_y], normalize='index') * 100\n",
+    "    \n",
+    "    # Plotting Heatmap\n",
+    "    plt.subplot(1, 3, i+1)\n",
+    "    sns.heatmap(ct, annot=True, fmt=\".1f\", cmap=\"YlOrRd\", cbar=False)\n",
+    "    plt.title(f'{feat_x} vs {feat_y} (%)')\n",
+    "    plt.xlabel(feat_y)\n",
+    "    plt.ylabel(feat_x)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Outsole vs Heel Padding Durability Crosstab\n",
+    "print(\"Detail Crosstab: Outsole vs Heel Padding (Normalized by Outsole)\")\n",
+    "print(pd.crosstab(df['outsole durability'], df['heel padding durability'], normalize='index').round(2) * 100)\n",
+    "\n",
+    "# Outsole vs Toebox Durability Crosstab\n",
+    "print(\"\\nDetail Crosstab: Outsole vs Toebox (Normalized by Outsole)\")\n",
+    "print(pd.crosstab(df['outsole durability'], df['toebox durability'], normalize='index').round(2) * 100)\n",
+    "\n",
+    "# Toebox vs Heel Padding Durability Crosstab\n",
+    "print(\"\\nDetail Crosstab: Toebox vs Heel Padding (Normalized by Toebox)\")\n",
+    "print(pd.crosstab(df['toebox durability'], df['heel padding durability'], normalize='index').round(2) * 100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d666f5d",
+   "metadata": {},
+   "source": [
+    "- Pada analisis mengenai outsole dan heel padding, kebanyakan sepatu memiliki heel padding yang bagus walaupun outsole sepatunya dikategorikan buruk.\n",
+    "\n",
+    "- Pada analisis mengenai outsole dan toebox, hanya terdapat 25% sepatu yang mempunyai outsole dan toebox berkategori baik, kebanyakannya hanya decent atau biasa saja. Bahkan, banyak sepatu yang memiliki sol kuat tetapi kain bagian depannya lebih cepat jebol.\n",
+    "\n",
+    "- Pada analisis mengenai toebox dan heel padding, datanya banyak yang ksosong sehingga tidak bisa diinferensikan\n",
+    "\n",
+    "\n",
+    "\n",
+    "Dikarenakan sedikitnya data yang menunjukan sepatu berkategori \"good\" di semua durability cols, diperlukan penyesuaian lebih lanjut mengenai hal ini."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "dc15a43a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Komposisi Heel Padding Durability: Heel Striker vs Others (%) ---\n",
+      "heel padding durability         -       bad    decent      good\n",
+      "strike_heel                                                    \n",
+      "0                        31.46853  10.48951  19.58042  38.46154\n",
+      "1                        27.01754  12.28070  17.19298  43.50877\n",
+      "\n",
+      "--- Komposisi Heel Padding Durability: Plate vs Others (%) ---\n",
+      "heel padding durability          -       bad    decent      good\n",
+      "plate                                                           \n",
+      "0                         29.26829  13.00813  17.88618  39.83740\n",
+      "carbon plate              22.41379   3.44828  18.96552  55.17241\n",
+      "carbon platerock plate   100.00000   0.00000   0.00000   0.00000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Checking heel padding durability with heel striker\n",
+    "ct_heel_strike = pd.crosstab(df['strike_heel'], df['heel padding durability'], normalize='index') * 100\n",
+    "print(\"--- Komposisi Heel Padding Durability: Heel Striker vs Others (%) ---\")\n",
+    "print(ct_heel_strike.round(5))\n",
+    "\n",
+    "print()\n",
+    "\n",
+    "# Cheking heel padding durability with plate\n",
+    "ct_heel_plate = pd.crosstab(df['plate'], df['heel padding durability'], normalize='index') * 100\n",
+    "print(\"--- Komposisi Heel Padding Durability: Plate vs Others (%) ---\")\n",
+    "print(ct_heel_plate.round(5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "045e4f6f",
+   "metadata": {},
+   "source": [
+    "Terdapat sekitar 43% sepatu yang memiliki heel padding berkategori \"good\" dengan kategori sepatu heel striker. Akan tetapi, semua sepatu yang memiliki carbon dan rocker plate kekurangan informasi terhadap heel padding durability. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "a1a29318",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Komposisi Outsole Durability: Heel Striker vs Others (%) ---\n",
+      "outsole durability         -      bad    decent      good\n",
+      "strike_heel                                              \n",
+      "0                   33.56643  3.49650  16.08392  46.85315\n",
+      "1                   30.17544  5.61404  15.43860  48.77193\n",
+      "--- Komposisi Outsole Durability: Midfoot Striker vs Others (%) ---\n",
+      "outsole durability         -      bad    decent      good\n",
+      "strike_mid                                               \n",
+      "0                   31.70732  5.69106  15.44715  47.15447\n",
+      "1                   31.14754  4.59016  15.73770  48.52459\n",
+      "--- Komposisi Outsole Durability: Forefoot Striker vs Others (%) ---\n",
+      "outsole durability         -      bad    decent      good\n",
+      "strike_fore                                              \n",
+      "0                   31.70732  5.69106  15.44715  47.15447\n",
+      "1                   31.14754  4.59016  15.73770  48.52459\n",
+      "--- Komposisi Outsole Durability: Plate vs Others (%) ---\n",
+      "outsole durability              -       bad    decent      good\n",
+      "plate                                                          \n",
+      "0                        32.24932   4.06504  15.17615  48.50949\n",
+      "carbon plate             24.13793  10.34483  18.96552  46.55172\n",
+      "carbon platerock plate  100.00000   0.00000   0.00000   0.00000\n",
+      "--- Komposisi Outsole Durability: Rocker vs Others (%) ---\n",
+      "outsole durability         -      bad    decent     good\n",
+      "rocker                                                  \n",
+      "0                   31.81818  3.84615  17.13287  47.2028\n",
+      "1                   30.28169  7.04225  12.67606  50.0000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Checking outsole durability with striker\n",
+    "ct_heel_strike = pd.crosstab(df['strike_heel'], df['outsole durability'], normalize='index') * 100\n",
+    "print(\"--- Komposisi Outsole Durability: Heel Striker vs Others (%) ---\")\n",
+    "print(ct_heel_strike.round(5))\n",
+    "\n",
+    "ct_heel_strike = pd.crosstab(df['strike_mid'], df['outsole durability'], normalize='index') * 100\n",
+    "print(\"--- Komposisi Outsole Durability: Midfoot Striker vs Others (%) ---\")\n",
+    "print(ct_heel_strike.round(5))\n",
+    "\n",
+    "ct_heel_strike = pd.crosstab(df['strike_fore'], df['outsole durability'], normalize='index') * 100\n",
+    "print(\"--- Komposisi Outsole Durability: Forefoot Striker vs Others (%) ---\")\n",
+    "print(ct_heel_strike.round(5))\n",
+    "\n",
+    "# Checking outsole durability with plate\n",
+    "ct_heel_plate = pd.crosstab(df['plate'], df['outsole durability'], normalize='index') * 100\n",
+    "print(\"--- Komposisi Outsole Durability: Plate vs Others (%) ---\")\n",
+    "print(ct_heel_plate.round(5))\n",
+    "\n",
+    "# Checking outsole durability with rocker\n",
+    "ct_heel_rocker = pd.crosstab(df['rocker'], df['outsole durability'], normalize='index') * 100\n",
+    "print(\"--- Komposisi Outsole Durability: Rocker vs Others (%) ---\")\n",
+    "print(ct_heel_rocker.round(5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65a086a6",
+   "metadata": {},
+   "source": [
+    "Rata rata untuk semua striker baik itu heel, midfoot, dan forefoot striker memiliki outsole durability yang tergolong baik. Adapun dapat dilihat juga bahwa durabilitas pada outsole tidak terdefinisikan dengan teknologi untuk plate ataupun rocker yang digunakan.\n",
+    "\n",
+    "Sepatu yang direkomendasikan nantinya tentunya akan disorting berdasarkan durability terbaik, tetapi akan ada trade off masing - masing, sepertinya misalnya jika user adalah heel striker maka akan dipilih heel padding durability terbaik, dsb."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "b9716e67",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 39 columns):\n",
+      " #   Column                       Non-Null Count  Dtype   \n",
+      "---  ------                       --------------  -----   \n",
+      " 0   brand                        428 non-null    object  \n",
+      " 1   name                         428 non-null    object  \n",
+      " 2   pace                         428 non-null    object  \n",
+      " 3   arch support                 428 non-null    object  \n",
+      " 4   weight lab weight brand      428 non-null    object  \n",
+      " 5   lightweight                  428 non-null    int64   \n",
+      " 6   drop lab drop brand          428 non-null    object  \n",
+      " 7   strike pattern               428 non-null    object  \n",
+      " 8   midsole softness             428 non-null    object  \n",
+      " 9   toebox durability            428 non-null    object  \n",
+      " 10  heel padding durability      428 non-null    object  \n",
+      " 11  outsole durability           428 non-null    object  \n",
+      " 12  breathability                428 non-null    object  \n",
+      " 13  width / fit                  428 non-null    object  \n",
+      " 14  toebox width                 428 non-null    object  \n",
+      " 15  stiffness                    428 non-null    object  \n",
+      " 16  torsional rigidity           428 non-null    object  \n",
+      " 17  heel counter stiffness       428 non-null    object  \n",
+      " 18  plate                        428 non-null    object  \n",
+      " 19  rocker                       428 non-null    int64   \n",
+      " 20  heel lab heel brand          428 non-null    object  \n",
+      " 21  forefoot lab forefoot brand  428 non-null    object  \n",
+      " 22  orthotic friendly            428 non-null    int64   \n",
+      " 23  season                       428 non-null    object  \n",
+      " 24  removable insole             428 non-null    int64   \n",
+      " 25  for_daily                    428 non-null    int64   \n",
+      " 26  for_tempo                    428 non-null    int64   \n",
+      " 27  for_competition              428 non-null    int64   \n",
+      " 28  weight_lab_oz                428 non-null    float64 \n",
+      " 29  heel_lab_mm                  428 non-null    float64 \n",
+      " 30  forefoot_lab_mm              428 non-null    float64 \n",
+      " 31  drop_lab_mm                  428 non-null    float64 \n",
+      " 32  heel_category                428 non-null    category\n",
+      " 33  forefoot_category            428 non-null    category\n",
+      " 34  drop_category                428 non-null    category\n",
+      " 35  strike_pattern               428 non-null    object  \n",
+      " 36  strike_heel                  428 non-null    int64   \n",
+      " 37  strike_mid                   428 non-null    int64   \n",
+      " 38  strike_fore                  428 non-null    int64   \n",
+      "dtypes: category(3), float64(4), int64(10), object(22)\n",
+      "memory usage: 125.4+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0796660",
+   "metadata": {},
+   "source": [
+    "## Breathability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "5ab1e237",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "breathability\n",
+      "moderate      208\n",
+      "breathable    109\n",
+      "-              58\n",
+      "warm           53\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['breathability'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57ff414d",
+   "metadata": {},
+   "source": [
+    "RunRepeat claims bahwa sepatu untuk summer season adalah sepatu yang memiliki breathability tertinggi dan winter season shoes adalah sepatu yang memiliki breathability low/mid. Lalu, ada beberapa feature lain yang berinteraksi dengan feature ini seperti shoes durability dan weight."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "46e27c1b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Komposisi Breathability berdasarkan Musim (%) ---\n",
+      "season             -  all seasons  summerall seasons    winter\n",
+      "breathability                                                 \n",
+      "-              100.0      0.00000                0.0   0.00000\n",
+      "breathable       0.0      0.00000              100.0   0.00000\n",
+      "moderate         0.0    100.00000                0.0   0.00000\n",
+      "warm             0.0     77.35849                0.0  22.64151\n",
+      "\n",
+      "--- Komposisi Breathability berdasarkan Toebox Durability (%) ---\n",
+      "toebox durability          -       bad    decent      good\n",
+      "breathability                                             \n",
+      "-                  100.00000   0.00000   0.00000   0.00000\n",
+      "breathable          18.34862  34.86239  31.19266  15.59633\n",
+      "moderate            13.94231  21.63462  46.63462  17.78846\n",
+      "warm                18.86792   9.43396  35.84906  35.84906\n",
+      "\n",
+      "--- Komposisi Breathability berdasarkan Outsole Durability (%) ---\n",
+      "outsole durability          -      bad    decent      good\n",
+      "breathability                                             \n",
+      "-                   100.00000  0.00000   0.00000   0.00000\n",
+      "breathable           22.93578  6.42202  21.10092  49.54128\n",
+      "moderate             18.75000  4.32692  18.75000  58.17308\n",
+      "warm                 22.64151  9.43396   9.43396  58.49057\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\caxyl\\AppData\\Local\\Temp\\ipykernel_9068\\668151946.py:18: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  sns.boxplot(x='breathability', y='weight_lab_oz', data=df, palette='Set2')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Checking breathability correlation with seasons\n",
+    "ct_breathability_seasons = pd.crosstab(df['breathability'], df['season'], normalize='index') * 100\n",
+    "print(\"--- Komposisi Breathability berdasarkan Musim (%) ---\")\n",
+    "print(ct_breathability_seasons.round(5))\n",
+    "\n",
+    "# Checking breathability correlation with toebox durability\n",
+    "ct_breathability_toebox_dur = pd.crosstab(df['breathability'], df['toebox durability'], normalize='index') * 100\n",
+    "print(\"\\n--- Komposisi Breathability berdasarkan Toebox Durability (%) ---\")\n",
+    "print(ct_breathability_toebox_dur.round(5))\n",
+    "\n",
+    "# Checking breathability correlation with outsole durability\n",
+    "ct_breathability_outsole_dur = pd.crosstab(df['breathability'], df['outsole durability'], normalize='index') * 100\n",
+    "print(\"\\n--- Komposisi Breathability berdasarkan Outsole Durability (%) ---\")\n",
+    "print(ct_breathability_outsole_dur.round(5))\n",
+    "\n",
+    "# Cheking breathability distribution with weight_lab_oz\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.boxplot(x='breathability', y='weight_lab_oz', data=df, palette='Set2')\n",
+    "plt.title('Distribusi Berat Sepatu berdasarkan Kategori Breathability')\n",
+    "plt.xlabel('Breathability')\n",
+    "plt.ylabel('Weight (oz)')\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "54ff5dcf",
+   "metadata": {},
+   "source": [
+    "Bisa dilihat bahwa:\n",
+    "- Seluruh sepatu dengan kategori breathability breathable adalah sepatu untuk summer all season. Lalu, seluruh sepatu dengan kategori breathability moderate adalah sepatu untuk all season atau daily basics. Sedangkan, sepatu dengan kategori warm adalah sepatu yang cocok untuk winter dan juga all seasons.\n",
+    "- Antara breathability dan durabilitas, tidak ada korelasi signifikan kecuali 58% sepatu berkategori warm yang memiliki good outsole durability.\n",
+    "- Sepatu yang breatahble cenderung memiliki weight yang lebih ringan dibanding sepatu lainnya dan sepatu berkategori warm cenderung menjadi yang terberat di antara kategori lainnya.\n",
+    "\n",
+    "\n",
+    "Untuk requirement dasar saat ini, dikarenakan main market target kita adalah orang orang Indonesia maka dari itu secara default akan dipasangkan untuk sepatu dengan breathability breathable-moderate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "10219d7b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 39 columns):\n",
+      " #   Column                       Non-Null Count  Dtype   \n",
+      "---  ------                       --------------  -----   \n",
+      " 0   brand                        428 non-null    object  \n",
+      " 1   name                         428 non-null    object  \n",
+      " 2   pace                         428 non-null    object  \n",
+      " 3   arch support                 428 non-null    object  \n",
+      " 4   weight lab weight brand      428 non-null    object  \n",
+      " 5   lightweight                  428 non-null    int64   \n",
+      " 6   drop lab drop brand          428 non-null    object  \n",
+      " 7   strike pattern               428 non-null    object  \n",
+      " 8   midsole softness             428 non-null    object  \n",
+      " 9   toebox durability            428 non-null    object  \n",
+      " 10  heel padding durability      428 non-null    object  \n",
+      " 11  outsole durability           428 non-null    object  \n",
+      " 12  breathability                428 non-null    object  \n",
+      " 13  width / fit                  428 non-null    object  \n",
+      " 14  toebox width                 428 non-null    object  \n",
+      " 15  stiffness                    428 non-null    object  \n",
+      " 16  torsional rigidity           428 non-null    object  \n",
+      " 17  heel counter stiffness       428 non-null    object  \n",
+      " 18  plate                        428 non-null    object  \n",
+      " 19  rocker                       428 non-null    int64   \n",
+      " 20  heel lab heel brand          428 non-null    object  \n",
+      " 21  forefoot lab forefoot brand  428 non-null    object  \n",
+      " 22  orthotic friendly            428 non-null    int64   \n",
+      " 23  season                       428 non-null    object  \n",
+      " 24  removable insole             428 non-null    int64   \n",
+      " 25  for_daily                    428 non-null    int64   \n",
+      " 26  for_tempo                    428 non-null    int64   \n",
+      " 27  for_competition              428 non-null    int64   \n",
+      " 28  weight_lab_oz                428 non-null    float64 \n",
+      " 29  heel_lab_mm                  428 non-null    float64 \n",
+      " 30  forefoot_lab_mm              428 non-null    float64 \n",
+      " 31  drop_lab_mm                  428 non-null    float64 \n",
+      " 32  heel_category                428 non-null    category\n",
+      " 33  forefoot_category            428 non-null    category\n",
+      " 34  drop_category                428 non-null    category\n",
+      " 35  strike_pattern               428 non-null    object  \n",
+      " 36  strike_heel                  428 non-null    int64   \n",
+      " 37  strike_mid                   428 non-null    int64   \n",
+      " 38  strike_fore                  428 non-null    int64   \n",
+      "dtypes: category(3), float64(4), int64(10), object(22)\n",
+      "memory usage: 125.4+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b895dcf8",
+   "metadata": {},
+   "source": [
+    "## Stiffness, torsional rigidity, and heel counter stiffness"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "a9f52e8c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  stiffness torsional rigidity heel counter stiffness\n",
+      "0     stiff              stiff               flexible\n",
+      "1     stiff           moderate               moderate\n",
+      "2     stiff           flexible               flexible\n",
+      "3     stiff           flexible               moderate\n",
+      "4  moderate           flexible               flexible\n",
+      "\n",
+      "stiffness\n",
+      "stiff       217\n",
+      "moderate    163\n",
+      "flexible     35\n",
+      "-            13\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "torsional rigidity\n",
+      "stiff       213\n",
+      "moderate    123\n",
+      "flexible     74\n",
+      "-            18\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "heel counter stiffness\n",
+      "moderate    146\n",
+      "flexible    132\n",
+      "stiff       121\n",
+      "-            29\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[['stiffness', 'torsional rigidity', 'heel counter stiffness']].head())\n",
+    "print()\n",
+    "print(df['stiffness'].value_counts())\n",
+    "print()\n",
+    "print(df['torsional rigidity'].value_counts())\n",
+    "print()\n",
+    "print(df['heel counter stiffness'].value_counts())"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "12d1d0bb",
+   "metadata": {},
+   "source": [
+    "- Stiffness menandakan kesulitan untuk menekuk sepatu dari bagian depan hingga belakang. Sepatu dengan plate tambahan atau busa yang sangat tebal akan membuat sepatunya masuk ke kategori stiff.\n",
+    "- Torsional rigidity menandakan sebara sulit sepatu tersebut dipuntir atau diputar. Torsional rigidity yang stiff menjaga kaki tetap sejajar (lurus) dan biasanya digunakan oleh orang yang memiliki kaki overpronasi.\n",
+    "- Heel counter stiffness menandakan seberapa kaku mangkuk pelindung tumit di bagian belakang. Di sini, kategori stiff mengunci tumit agar tidak goyang ke kanan atau ke kiri.\n",
+    "\n",
+    "![image.png](attachment:image.png)\n",
+    "\n",
+    "Untuk menjangkau feature ini, ditambahkan satu optional input stability need yang berisikan pilihan antara neutral atau guided. Sepatu guided adalah sepatu yang memiliki stiffness stiff sedangkan neutral lebih cocok untuk moderate shoes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "1ac4720d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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cOnlSXqpfx6QDKVOuvHzwSUfJmSt3vF676NeFUq8+578r8M/kKUOfLyah9yxy9PIt+Xn3xQdWXsXH6xVzye7zN2T/xZs0/LmAW7dumP8zZvaJe52bN8wovrhGu2hqdRU1S4Kbm5uk9fCQg//tpOEvhdL3XWXKHBFnHz24z8TZZSs9HrlOnvwFJJt/Tjm4b1esDX8Xz5+Rq5eDpFzF+41WOlKwSIkyprGQhr+YKlSoIGPGjJERI0bIb7/9JjNmzJAaNWpIsWLFpHXr1vLWW29J9uwP/u4llkocpzWWalDHjI7U3xIPi6WmT5kovr5+8mLjl03DH1xDHp/0MrtlRQm5Fy77z9+Q6ZtOyaUbIeY5z7Ru0q1eERm/5vgjx1dIuXRkn/LKlDlyWUCx0rJz3UopWamapM+YSXatX2lSZBcuXeGB2/rl29FSolI1KVquCg1/qcDNmzfMCP1MUc6d+LxGZfaOO3ZPDdwcXQDYp2SZctK9z0D5asw30qlbbzl39rS0e6+l3LoZUcH/IPv+2y3HjhySFxq9nCxlRdIpmCebtH21phw+eUle+mi8TPlprYz47BV588X7PzQ7Df1J9h09L0f+HCTX/h0jv43/SDp+OU/WbTvygO36SZN6FcXdPY00aTdRvpyyVDq8VVe6v/tsMu0ZEkt4eLgsmPq1FCpZVnIHFIrMo542rYdNwKUyZ8lqnovNtasRIx4yZ7Gd3D6zj69cu8Jo0JSKawlSs9Jly0mvLwbJyHGTpEuP3nL2zBn5sE1LuRmP83/vnl1y9PAheakJ539Kd+zybZmx+Yx8vfaEzNl+VrJlTCdd6xQwlVT2qpLXW/L7ppefd19I1LLCcbHU7G9GSrFS5c1Iv9hcD74qv/ww7YGNd7nyFRA//5zy4/TxcvP6NdMQuGjeTLkceFGuXo4Y2YCUd27MmDhCipcuL/kLFjHLrmqc7eFheqxH5eOb1TTuxca63CfaHIDmNXHE5oiQNm1aadq0qfz0008ydOhQOXz4sHTp0kXy5csnLVu2lHPnzjm6iC6tVJly0rPf/2Op7vpb4ox89G7csZQ29C3+daF06/VFspcVSefAxRsyYsVR6bV4v4xbfVxyeHvKV41LSQaPiFjqver5Ze+F67LxAXP6wfWvl79NHycFSpSVnPnvp25t0bmfhN8Lk37vvCifv15PFk4eIa26DpRsuWznjIxqx9q/5cyxg9LwzbbJVHo4UsjduzJ94hipXe/ZB86jHf18m/T1V1KqbAUpUCgiPkutHDLiL2vWrHLw4EHJli2b+Pr62uRVj+7y5QdXKN+9e9fcbJe5md5GruyJ6hGp9lThosWlZJmy0vyl+rJy+VJ5/iGVsDpCo1CRolKydMzehkhZ3NzSyLa9J6XvuEXm8c4Dp6V0kVzS9pUa8v2iTWbZR6/VlqplC8jLHb4x6TprVCoio7tHzPG3ctOBOLbrJpcuX5ePB/xgRvht33dKcvtnkY4t68rgyUuSdR/xaH6aPFLOnTgqHYfYDnsHFNeSlCvJY6kwd5ePpao9ef/8L1KsuGkIbPr8M7Lir6WmF/qDLPploRQuUsxUeCFl07n7rM4E35Vjl0/IkOeKmca7dcevJnh7vhnSSvMKuWT0muMSRpYElzBz/DA5ffyo9B4xOc4RX8P7fGrm+mva4r0HNlB07D1UpowaKO+/Wk/c3NyldMXHpPxj1U2qR6Q8U8cOlVPHj0j/UffnN0Py2rJli0ybNk3mzp0rGTNmNI1+mvLz9OnT8sUXX0ijRo3k33//dUwsFZrKYqmixaVU2XLychyxlDYGDujTwzT6ZfG17UyKlG3LyeDI+8cv35YDF27IzBYVpGZhPwm+Eyrl8/jIJz/FPs0MUodfvh0lF04dkw8H2qbLXjZ3qpmKpm2fkWaOv//+XWvm+PtwwNexzt13NfCi/DZ9rLTtPcImgwJcU1hYqAzp+5mJkz/p3DPer5swcoicOHZYho+fIamdQxr+Ro0aJZkzZ468/6AA62GGDBliArqoOnfrJV169JHUJHNmb8mbP0DOnD75wPVu374lK/5cIq3f/zjZyoakcz7wmhnNF9X+Y+elcd2IYfHpPT3ki3YvSvNOU2Tp2v/Msj2Hzkq54nml41t142z4Ox8YLKFh92zSeup2c2X3EY+07uY5OL95k0fKns3rpcPgceKbzT9yeWZfP3MBvXXjus2ov+tXL5vnYuOdJev/17kiPlnvzwd3PfiK5Pl/D2ekfFxLUo6kjqW69ugt3XqmvlgqX/4Ak/72Yef/8j+XyLsffJJsZUPyuR0aLheuh0j2TPbNKZPfN4N4p08rPever6xwd0sjRbN5SZ3CWeXjhXuFJp6UY+b4r2T7prXSa/gk8cueI8bzt2/dlK96dZD0GbykY59hpnHvQQoWLSmDJ3xvGgt1xJ93Fl/p2+Edsxwpr9Fv26a18sWIyTbnRhaNs0ND5eaN6zaj/oKvXJYsWWOPs63Lg68Eia9fNpvXFCgc9xxHqdnIkSPNHH8HDhyQ5557Tr777jvzv3ZgVQULFjTpPwsUKODQWOqzz1NhLBUQeyylvy90RGC3Tz+2GZWhalUtJ3MWLJa8+fIna3mRNG6G3JMzwXckt096KeiXQXL5eMr8NvenGVE9GxSV/85dl26/7XNYOZE8NC3nvq0b5MP+YyWL3/16qaDzZ2T9kp+l06gZkjNfQbMsd4EicmzfLlm/9Bd5+f3OMbZ1+ugBuRF8RcZ8dn+0X3j4PTm2b6fZ1uAf/hI3d/dk2jMkeaNfn8/k4vlzMmTM5HiP9pswaoj8u2G1DBs7TbL5x4zdUxuHNPy1atUq8v7bb7/9SNvq0aOHmdA5qit3Ul8G01u3bsnZM6cka7YXH7jeP3//afIlP/Psg9dDyrBhx1EpFnD/wqmK5vc3I/uUNtKl80gr4dF6Ed+7F25GCz5ou80bVjE/fqw9kHW7OkqQRj/np+/ZT1NGya6Nq6X9wLGSLYftHAv5CxcX97Rp5eCurVKheh2z7MKZk3Ll0gUpWLx0rNv0y5FbvH395MCuLZK3UNHIyq7jB/dKjWeZk8ZVcC1JOZI6lroRlvp+MN26dVPOnD4lzz7/0gPXW/HXMgkNCZFnn+P8d0We7m6SPZOHbDwZZtfrdU6/L/48bLOsVZU8cv76XVl2IJBGvxQUS303YbhsWf+P9Bw2Ufxz5omxjjbeDevZXtJ6pJNO/UaYuXLjy1p5cf7MSTl6aJ+80vL9RC0/kvbcmDZumPy77h/pN3yS+OeyPTcKFStp4uzd2/+VJ2rWNcvOnjougRfPS7GSsY8S1/NLG/92b98sBYoUjzy/Du/fI/VfJKV0bCZOnGjm8tMYKFeu2Oem9vf3l6lTpzoslroemopjqedixlIBBQrJrB9/sVk2ecLX5jUdu/SQHDlzJmNJkZTSp3WTXN7p5e9bgbLm8GVZuu+SzfPfNC8nk9efkE12ZFZAyrpe/jp1jOz5d428/8UYyZrD9rs65O4d83/0ThfagcNiiegUEF2RspWl08jpNsvmjf9S/PPklzqN36DRz8Ua/c6ePilfjpki3j5Z4nW+TRz9pWxYvUK+/PpbyZk7ZuyeGjm8hUx7YcUmLCzMBE8Po6kTvL29bW6unk5BTRjzlezYttn0mNqza7v0+qy9SRdTr/5z5vlBfXvI5PGjYrzu918XSo3aT4tPlod/aOD8xs5eIVXLFpSuretLoXzZpPmzVaT1y0/KpB9Xm+ev37wjq7ccksEdG0vNykUlILeftHjxcXnzhary28qdkdv5dsBb0r/d/eB8yk9rxNfby8wXWCS/vzxbo7R0bVNfvvn/duHc5k0aIVv++VNadepreqBfuxJkbpobW2XImEmq1XtBFk4fKwd3b5OTh/fL918PloLFy5ib1YCP35CdG1dFBmN1XnxVlv00U3b/u1bOHj8is0YPFJ+sflLu8fspXpCycC1xDcRS9hk76ivZvjXi/N+9c7v06NxB3N3c5ZlnI87//r17yMSxMc//xb8slJp16nL+u4iXy+Uwo/H8vDykkF8G+aB6PtGEB5v/n7bK2zOt5PVJL9kzRowAzOOT3jz28rhfsfBprQAzmk/dDQuXs9fu2tzu3gs3vd/1PlKGGeOHyboVS+SjbgNMLKVz8OnNWkmljTJDe7aXu3fuSNtPe8ntWzci1wm/d7+TXNd3X5XN61ZGPt60erns3blVLp47I1s3rJIve7STKtVqS9nKTzhkP2HfSL81fy+RDj0GSgavmOeGNuo+/Wwj+e6bUbJnxxY5enCfTBjeX4qVKifFSt1Pj96x9cvy79qVkXH2c01el4VzpsqW9avk5LHDMm5YX/H1yy6PPRnRSQ+2/vrrL+nWrVuMRj+t+Dt5MmK0Wbp06Wwa9x6EWMo+46LHUl0iYql6/4+lNLWnNZbS46HTBES9ZcqcWby8Mpr7Hh72jbSH471bLb+UzZVZ/DOnk5I5MknvZ4uZzuerDgXJlduhcuLybZubunQ9RC5cvx8XTX6tnFQveD8FbCZPdynk5yUBvhnM47xZ0pvHvhk8HLCHsDe957bVf8nrHXpL+vQZ5PqVIHML/X+9lH+eAPHLmUcWThohJw/tMyMAV/32oxzatUVKV71fxzS536eybslCc19jMp0jMOotnWcG8crsYzN3IJzb7Vu35Mih/eamLpw7Y+5fvHDONPoN7t1VDh3YK137DJZ74eFyOSjQ3EJDQyO30aPDe7JowdzIxxNGDpaVf/4un/UZIhm8Mka+5u7/47PUyiEj/qJq3769/P777zJ58mSTV11puoY33nhDgoKCTMoExHTp4gXp3+szuRZ8VbL4ZpWy5SvKxGnfm/tKPyzWNBdWJ08ck907t8nwsbHPT4GUZ+vek9K88xTTaPf5ew3l+Jkg6frVApm7ZEvkOi27T5P+7RrJjMGtTGOejgbsN36xTPlpbeQ6+XJmtUnrefrCVXnp4wkyrHNT2Tyvh5y9eFXGz/lHRsz4K9n3EQm3dmlET8qve7WzWf5mu8/liboRP8Katm5nKhmmDu1p0hGVqFhVmkdLpXDxzEm5HWVi9npN3pSQO3fkhwnDTB72QiXLykd9yK2eknEtcQ3EUva5eOGC9O3RVYL/f/6Xq1BJJs+cI77/P/8vnNfz37YH6onjx2Tnjm0yesIUB5UaiU0rkN59PK9kTOcuN+7ek8NBt+TLFUflRkhE402twr7yYqn72RW61olIRTRj8xnZcCKip3q2jOlMBRVcx9+LF5j/B332gc3y9zr1kVr1X5Djhw/Ikf17zLLOrZvarDNqxi+SPWdEtoVzp0/YxFJXLwfJ95NHS/BVTfuYTWrUfU6avNEmGfYIieXPRfPN//262I7S/KhLX6nTIGIkeKsPO0maNG4yov9nEhYaIuUrV5N323ezWf/sqROmAdmqUfNWpiF50ujBJh1/iTIV5PMhXydoJGlqUrhwYTl37pwZ1Rd9Lj5N83kvSgN8fBBL2efixQvS9/Oukb8lNJaaNMM2lnqUFKpIGTQO6vZMEZPqPPh2mEnh+enC/yT4TvyzJ+TzzSBe6e7HUk8U8JXOT99Pm96jfkTWodmbT8v3W84k8h4gKWxY9qv5f1LfDjbLm33cXao81dCMjm/dc5gsmT1JZnzZQ+7euS3ZcuaRZp/0kJKV7neICrpwVm5euz+PJFK+Qwf+k+7t76drnTJuhPm/3rMvyputP5CNa/8xjz95p7nN6778eoqUq/iYuX/u7CkJDr4S+dzvv/xk/u/W/l2b13za4wt55rlGklqlsTh4JvEjR45IixYt5NSpUyZHu06u/Nlnn0njxo1lwoQJ4uPjk+Btng++3wIMWBWs86mjiwAn9eucvo4uApxQudyM5kFMOX2cr5dpUsRSQTftS3MI1/b5ktjnBgberZzX0UWAE0rn7vAEQ3BC5fPfn/vQXtox7fz58zEa/k6cOCGlSpWSm1Ea3B0VSwXeIJZCTG/N2uroIsBJfVAjwNFFgBMqkyPh1x+4vsL+EaOhnX7En/bUWrdunXTs2FGeffZZcXd3l5kzZ8rrr7/u6KIBAAA4PWIpAACQGljn0dNRZH369BEvL6/I53SU36ZNm6RChQoJ3i6xFAAAcDVO0QVPUyrMnTtXqlWrJlmyZDETMJ89e9bRxQIAAEgRiKUAAICr2759u7lp4qrdu3dHPtbb/v37pXz58nHO1/cwxFIAAMCVOLzh7/3335dXX33VTMy8Zs0a2bVrl5mEuWzZsjJv3jxHFw8AAMCpEUsBAIDUYOXKlebWqlUrWbJkSeRjvS1btkwmTZokRYtGzAWWEMRSAADA1Tg81aemU9B0DNozS+XMmVP++OMPGT9+vLRu3VqaNWvm6CICAAA4LWIpAACQmug8fImJWAoAALgahzf8bd26VTw9PWMs//jjj6VevXoOKRMAAEBKQSwFAABcXdOmTU0aT29vb3P/QRYuXJigbRNLAQAAV+PwVJ8aXB05ckR69eplJk6+ePGiWa5pG8LCwhxdPAAAAKdGLAUAAFydj4+PpEmTJvL+g24JRSwFAABcjcNH/K1atUoaNmwoTz75pKxevVoGDRok/v7+snPnTjOZ8vz58x1dRAAAAKdFLAUAAFJTes/ETvVJLAUAAFyNw0f8de/eXQYOHCh//fWXmTzZ6umnn5aNGzc6tGwAAADOjlgKAADAfsRSAADA1Th8xN/u3btlzpw5MZZr76rAwECHlAkAACClIJYCAACurmLFipGpPh9m27ZtCdo2sRQAAHA1Dh/xlyVLFjl37lyM5du3b5c8efI4pEwAAAApBbEUAABwdY0bN5ZGjRqZW4MGDcycfDo3X506dcwtffr0Zpk+l1DEUgAAwNU4fMTfa6+9Jt26dZOffvrJ9N4KDw+XdevWSZcuXaRly5aOLh4AAIBTI5YCAACurm/fvpH33333XWnfvr0MGDAgxjqnTp1K8LaJpQAAgKtx+Ii/wYMHS4kSJSRfvnxy48YNKVWqlNSqVUuqV68uvXr1cnTxAAAAnBqxFAAASE20gS62BrkWLVrIggULErw9YikAAOBqHD7iTydOnjJlivTu3Vv27NljgizN3V60aFFHFw0AAMDpEUsBAIDUJEOGDGZEXvRYR5dpys+EIpYCAACuxuENf1b58+c3NwAAACQcsRQAAEgNOnbsKB9++KFs27ZNqlatapZt2rRJpk2bZhrv7EUsBQAAXIVDGv46deoU73VHjhyZpGUBAABIaYilAABAatW9e3cpVKiQjBkzRmbPnm2WlSxZUqZPny7NmjWL1zaIpQAAgCtzSMOfBmNlypSRtGnTmomTLRZLrOvpcwAAALBFLAUAAFIzbeCLbyNfbIilAACAK3NIw19wcLCZcNnf39/00tq8ebP4+fk5oigAAAApDrEUAACA/YilAACAK3NIw5+vr68cO3bMBFjHjx+X8PBwRxQDAAAgRSKWAgAAqUnWrFnl4MGDki1bNhMHPWgk3uXLlx+6PWIpAADgyhzS8Pfyyy9LrVq1JHfu3CZYq1Kliri7u8e67tGjR5O9fAAAAM6MWAoAAKQmo0aNksyZM5v7o0ePfuTtEUsBAABXligNf9euXZMVK1ZI8eLFzYTKDzN58mRp2rSpHD58WNq3by9t27aNDOAAAABSG2IpAACAuLVq1SrW+/YilgIAAK7MroY/nUBZe0Z98skncvv2bdMzSlMj6GTIc+fONT2nHubZZ581/2/dulU6dOhAgAUAAFINYikAAAD7O0zFRkfueXp6Srp06eK1HWIpAADgqtzsedHq1aulZs2a5v7PP/9sKqmuXr0qX3/9tQwcODBB25o+fTrBFQAASFWIpQAAAOyTJUsWM0df9Jsuz5AhgwQEBEjfvn3jPW8fsRQAAHA1djX8BQcHm4mV1dKlS02vdC8vL3n++efl0KFDiV1GAAAAl0IsBQAAYJ8ZM2aYufk+//xz+eWXX8xN7+fJk0cmTpwo7733nulM9eWXXzq6qAAAACkn1We+fPlkw4YNpsJKK6s0JZW6cuWKpE+fPrHLCAAA4FKIpQAAAOwzc+ZMGTFihEmdbvXiiy9K2bJlZdKkSfL3339L/vz5ZdCgQaZBEAAAILWxa8Rfx44d5c0335S8efOaXlZ16tSJTFulgRYAAADiRiwFAABgn/Xr10vFihVjLNdl2rFK1ahRQ06ePOmA0gEAAKTQEX8fffSRVK1aVU6dOiXPPPOMuLlFtB8WKlQowfPSAAAApDbEUgAAAPZnTpg6dWqMVJ66TJ9TQUFBZt4/AACA1Miuhj9VpUoVc1P37t2T3bt3S/Xq1QmsAAAA4oFYCgAAIOGGDx8ur776qixZskQee+wxs2zLli2yf/9+mT9/vnm8efNmad68uYNLCgAAkMJSfWpPKmtFVe3ataVSpUqmZ9U///yT2GUEAABwKcRSAAAA9nnppZdMI99zzz0nly9fNreGDRuaZS+88IJZ58MPP5SRI0c6uqgAAAApZ8Sf9qBq0aKFub9o0SI5duyYCbBmzZolPXv2lHXr1iV2OQEAAFwGsRQAAID9ChYsKEOGDHF0MQAAAFyn4S8wMFBy5sxp7v/xxx8mxUKxYsWkdevWMmbMmMQuIwAAgEshlgIAAIi/Xbt2SZkyZcy8yHr/QcqVK5ds5QIAAHCZhr8cOXLI3r17JVeuXLJ06VKZOHGiWX7r1i1xd3dP7DICAAC4FGIpAACA+KtQoYKcP39e/P39zf00adKIxWKJsZ4u1zTqAAAAqZldDX/vvPOONGvWzFRWaVBVr149s3zTpk1SokSJxC4jAACASyGWAgAAiD9Ni549e/bI+wAAAEjkhr9+/fqZFAunTp0yqak8PT3Ncu2h3r17d3s2CQAAkGoQSwEAAMRfQECA+T80NFS++OIL6d27t5nnDwAAAInU8KdeeeUV8/+dO3cil7Vq1crezQEAAKQqxFIAAAAJ4+HhIQsWLDANfwAAAIidm9hB86UPGDBA8uTJI5kyZZKjR4+a5Rp4TZ061Z5NAgAApBrEUgAAAPZp3Lix/PLLL44uBgAAgGuN+Bs0aJDMnDlThg0bJm3bto1crimrRo8eLW3atEnMMgIAALgUYikAAAD7FC1aVPr37y/r1q2TypUrS8aMGW2eb9++vcPKBgAAkGIb/r777juZPHmy1K1bVz744IPI5eXLl5f9+/cnZvkAAABcDrEUAACAfTQ7QpYsWWTr1q3mFlWaNGlo+AMAAKmeXQ1/Z86ckSJFisRYHh4ebiZaBgAAQNyIpQAAAOxz7NgxRxcBAADA9eb4K1WqlKxZsybG8vnz50vFihUTo1wAAAAui1gKAAAAAAAATjPir0+fPtKqVSvTW117pi9cuFAOHDhg0lYtXrw48UsJAADgQoilAAAAAAAA4DQj/ho1aiSLFi2S5cuXm0mUtfJq3759ZtkzzzyT+KUEAABwIcRSAAAAAAAAcJoRf6pmzZry119/JW5pAAAAUgliKQAAAAAAADhNw58KCQmRixcvmhRVUeXPn/9RywUAAODyiKUAAAAAAADg8Ia/Q4cOSevWrWX9+vU2yy0Wi6RJk0bu3buXWOUDAABwOcRSAAAA8bdr1654r1uuXLkkLQsAAIBLNvy9/fbbkjZtWlm8eLHkypXLVFABAAAgfoilAAAA4q9ChQomXtJOUrGxPkcHKgAAADsb/nbs2CFbt26VEiVKJH6JAAAAXByxFAAAQPwdO3bM0UUAAABw7Ya/UqVKSWBgoDirLBk9HF0EOKE1Cwc7ughwUhM2nXB0EeCEuvyw09FFgBPa1b9eomzH2WOpjJ6PNA00XNT7j+VzdBHgpNadDnJ0EeCEvDzcHV0EOKHy+TPb9bqAgABJScLjGJmI1K3/s3T6Q+x+3nfB0UWAE7oREuboIsAJFfbPG6/17KrVGTp0qHz22WcyePBgKVu2rHh42Da0eXt727NZAACAVIFYCgAA4NHs3btXTp48KSEhITbLX3rpJYeVCQAAwBnY1fBXr15Eb/e6devaLCefOgAAwMMRSwEAANjn6NGj0qRJE9m9e7fNvH/WOZOJowAAQGpnV8PfypUrE78kAAAAqQSxFAAAgH06dOggBQsWlL///tv8/++//0pQUJB07txZhg8f7ujiAQAApJyGv6ZNm8qMGTNM6qkTJ05I8+bNxdPTM2lLBwAA4CKIpQAAAB7dhg0bZMWKFZItWzZxc3Mztxo1asiQIUOkffv2sn37dkcXEQAAwKHc4rvi4sWL5ebNm+b+O++8I8HBwUlZLgAAAJdCLAUAAPDoNJVn5syZzX1t/Dt79qy5HxAQIAcOHHBw6QAAAFLQiL8SJUpIjx495KmnnjL50+fNm2d6rMemZcuWiVlGAACAFI9YCgAA4NGVKVNGdu7cadJ8Pv744zJs2DBJly6dTJ48WQoVKuTo4gEAADhcGot1FuSHWLduncmXfuTIEbl8+bLpXWWdONlmg2nSmOcd6U6YQ/88nNSeU9ccXQQ4qQmbTji6CHBCW/ZecHQR4IR29a9n92uJpZDS7T1DLIXYrTsd5OgiwAl5ebg7ughwQm2q5n/kbSxbtsxkUdA06ocPH5YXXnhBDh48KH5+fvLjjz/K008/LY528Xqoo4sAJ3Qi8JajiwAn9fM+6h8QU+kcXo4uApzQm5XzJu6IvyeffFI2btxo7mv+dA2q/P397S8hAABAKkIsBQAA8OgaNGgQeb9IkSKyf/9+02nK19c31k5VAAAAqU285/jTnlTXrkX08p0+fXpkPnUAAAA8HLEUAABA0siaNSuNfgAAAAlt+Fu8eLFJpaBat24t169fj+9LAQAAUj1iKQAAgEen8VTv3r2levXqZsSfzusX9QYAAJDaxTvVZ4kSJaRHjx7y1FNPiU4LOG/ePPH29o513ZYtWyZmGQEAAFI8YikAAIBH9+6778qqVavkrbfekly5cjHSDwAAIJo0Fq15iof169dLp06d5MiRIyZ3uqanii240mX6vCPdCXPon4eT2nMqIr0aEN2ETSccXQQ4oS17mVwbMe3qX8/u1xJLIaXbe4ZYCrFbdzrI0UWAE/LycHd0EeCE2lTN/8jbyJIli/z+++9m/mRndfF6qKOLACd0IvCWo4sAJ/XzPuofEFPpHF6OLgKc0JuV8ybuiD9NobBx40Zz383NTQ4ePCj+/v72lxAAACAVIZYCAAB4dL6+vmZOPwAAADziHH9RHTt2TLJnz27PSwEAAFI9YikAAAD7DBgwQPr06SO3bjF6CgAA4JFG/O3atUvKlCljeqgHBwfL7t2741y3XLly8d0sAABAqkAsBQAA8OhGjBhhUqfnyJFDChQoIB4eHjbPb9u2zWFlAwAASFENfxUqVJDz58+blFR6X+efiTo9oPWx/n/v3r2kKi8AAECKRCwFAADw6Bo3buzoIgAAALhGw1/UlFR6HwAAAPFHLAUAAPDo+vbt6+giAAAAuEbDX0BAQOT9EydOSPXq1SVtWtuXh4WFyfr1623WBQAAALEUAABAYtq6davs27fP3C9durRUrFjR0UUCAABIWQ1/UT311FNy7tw5k6oqKp2vRp97lPRUd+7ckfTp09v9egAAAGdHLAUAAGCfixcvymuvvSb//POPZMmSxSy7evWqiaHmzp0bmWHBXsRSAAAgpXOz50XW+WeiCwoKkowZMyZ4e+Hh4TJgwADJkyePZMqUSY4ePWqW9+7dW6ZOnWpPEQEAAJwWsRQAAIB92rVrJ9evX5f//vtPLl++bG579uyRa9euSfv27e3aJrEUAABItSP+mjZtav7Xiqq3335bPD09I5/Tnum7du0yaasSauDAgTJz5kwZNmyYtG3bNnJ5mTJlZPTo0dKmTZsEbxMAAMDZEEsBAAA8mqVLl8ry5culZMmSkctKlSol48ePl/r169u1TWIpAACQakf8+fj4mJv2Us+cOXPkY73lzJlT3nvvPZk9e3aCC/Hdd9/J5MmT5c033xR3d/fI5eXLl5f9+/cneHsAAADOiFgKAADg0ejoPA8PjxjLdZk+Zw9iKQAAkGpH/E2fPt38r/nS+/XrJ15eXubx8ePH5ZdffjG9rbJly5bgQpw5c0aKFCkSY7kGbKGhoQneHgAAgDMilgIAAHg0Tz/9tHTo0EF++OEHyZ07d2Qs9Omnn0rdunXt2iaxFAAAkNQ+x9/27dtNbyjrBMpPPPGEjBgxQho3biwTJ05M8PY0JcOaNWtiLJ8/f75UrFjRniICAAA4LWIpAAAA+4wbN87M51egQAEpXLiwuRUsWNAsGzt2rF3bJJYCAACpdsRf1MoqzXFuDYJy5Mhhli1YsED69OkjH374YYK2p69p1aqV6WGlvakWLlwoBw4cMBViixcvtqeIAAAATotYCgAAwD758uWTbdu2mXn+rGk4NWtCvXr17N4msRQAAJDU3vB369YtMy+N+vPPP6Vp06bi5uZmequfOHEiwdtr1KiRLFq0SPr37y8ZM2Y0AVelSpXMsmeeecaeIgIAADgtYikAAAD7pUmTxsQ4iRXnEEsBAABJ7Q1/mvdc56Fp0qSJLFu2zORRVxcvXhRvb2+7ClKzZk3566+/7HotAABASkIsBQAAEH9ff/21vPfee5I+fXpz/0Hat29v198glgIAAKm64U97Pr3xxhuREydXq1Ytsse6PbnPCxUqJJs3bxY/Pz+b5TrnjfawOnr0qD3FBAAAcErEUgAAAPE3atQoefPNN03Dn95/0EhAexr+iKUAAICk9oa/V155RWrUqCHnzp2T8uXLRy7XiivtuZ5Qx48fl3v37sVYfvfuXZNfHQAAwJUQSwEAAMTfsWPHYr2fWIilAACApPaGP5UzZ05zi6pq1aoJ2sZvv/0WeV/TXPn4+EQ+1oDr77//lgIFCthbRAAAAKdFLAUAAPDoNObZvXu3BAQEiK+vb4JeSywFAABckd0Nf4mhcePGkakYWrVqZfOch4eHCa5GjBjhoNIBAAA4N2IpAACQ2nTs2FHKli0rbdq0MY1ztWrVkg0bNoiXl5csXrxY6tSpE+9tEUsBAABX5NCGv/DwcPN/wYIFTS71bNmyObI4AAAAKQqxFAAASG3mz58vLVq0MPcXLVpk0nTu379fZs2aJT179pR169bFe1vEUgAAwBW5iRPQ/OwEVwAAAPYhlgIAAKlFYGBgZLr0P/74Q1599VUpVqyYtG7d2qT8tAexFAAAcCUOHfEX1c2bN2XVqlVy8uRJCQkJsXmuffv2DisXAABASkAsBQAAUoMcOXLI3r17JVeuXLJ06VKZOHGiWX7r1i1xd3e3e7vEUgAAwFU4RcPf9u3b5bnnnjNBmgZaWbNmNT24ND+7v78/ARYAAMADEEsBAIDU4p133pFmzZqZhj+dm69evXpm+aZNm6REiRJ2bZNYCgAAuBKnSPX56aefyosvvihXrlyRDBkyyMaNG+XEiRNSuXJlGT58uKOLBwAA4NSIpQAAQGrRr18/mTp1qrz33ntmPj9PT0+zXEf7de/e3a5tEksBAABX4hQNfzt27JDOnTuLm5ubCdTu3r0r+fLlk2HDhsnnn3/u6OIBAAA4NWIpAACQGoSGhkrdunWlXLlyprEub968kc+1atVKGjVqZNd2iaUAAIArcYqGPw8PDxNcKU2hoPnUlY+Pj5w6dcrBpQMAAHBuxFIAACC1xDy7du1Kku0SSwEAAFfhFHP8VaxYUTZv3ixFixaV2rVrS58+fUwu9VmzZkmZMmUcXTwAAACnRiwFAABSixYtWphUn19++WWibZNYCgAAuBKnaPgbPHiwXL9+3dwfNGiQtGzZUj788EMTcE2bNs3RxQMAAHBqxFIAACC1CAsLM/HN8uXLzRx8GTNmtHl+5MiRCd4msRQAAHAlDm/4s1gsJo2CtQeV3l+6dKmjiwUAAJAiEEsBAIDUZM+ePVKpUiVz/+DBgzbPpUmTJsHbI5YCAACuxika/ooUKSL//fef6UkFAACA+COWAgAAqcnKlSsTdXvEUgAAwNW4ObwAbm4msAoKCnJ0UQAAAFIcYikAAJBanT592tweBbEUAABwNQ4f8ad0QuauXbvKxIkTmTQ5gebO+V5mTp8qgYGXpFjxEtL9895Stly5ONf/c9kSGT92jJw9c0byBxSQjp26SM1atZO1zEhc+3Zvk9/nz5Jjh/bL1cuB8mmfr6RK9TqRz38zvJ+sWf67zWvKVX5Cug0aG+c2ly+eL8sXL5BLF8+Zx3nzF5Imb7aRCo89mYR7gsRSp0hWeapIVsmWMZ15fCb4riz674LsPncjcp3Cfl7StFwOKeTnJeEWi5y8ckdGrjomofcssW7zuZLZpXJeb8nl7Skh9yxyOPCmzN95Xs5fD0m2/ULial0zQDo+U1Rmbzgpw5ZEpEjyy5ROOtUvKtUKZ5WMnmnleOBNmbL6uCzfezHO7Xz4VCFzi+rYpZvSaOyGJN8H3EcsZT9iqdTtlx+my+Z1K+XsqROSLp2nFCtVTl5/9xPJna9A5Dp//75Q1q1cJscPH5Dbt27KtwtXSMZMmR+43fnfTZYFs6fYLMudN0BGTJufZPuCpLP19x9lw4LpUr5eY6n5xgdmWVhoiKybO1kO/rtKwsNCJV+ZylKnxSfi5eMb53aWTx0u+9ctt1mWv0xleanToCTfBySO7csXyY4ViyT40gXzOFveAKneuIUUKl/VPL5x9bL8M3eynNizTUJu3xbfXHmlWqM3pPhjNR+43W1//Sr//vGT3Ay+LP75Cku9lh9LrsIlkmWfUqLw8HAZOHCgjBgxQm7ciPiNkzlzZuncubP07NnTNOQlFLGUfS5dvCATx46UTevXyp07dyRv3vzSo+8AKVEq9mO4fcu/0v6D1jGW/7L0H/HLli0ZSozE9tvcGSaWOnc6IpYqWqqsNG/dTnLnC4hcJyTkrsyZPEY2rvpTQkNDTZ3U2598Jj6+fnFud8GsybJx1V9y+dIFcffwkIJFSsirb38oRUrw+UyJDiz/Sfb8/p0UqfWSlG/S1iw7un6pnNq2Sq6ePiJhd2/Li4N/kHQZMj1wO3uXzpF9y36wWZbJP4806PFNkpYfiWfLX7/JluW/ydXAiFgqe54AqdX0LSla4XHzeOvfi2XP+hVy7vghCbl9Sz6b8qukz/jg82JM+zck+P/bi6rKMy/Jc+90kNTKKRr+dNLkW7duSfny5SVdunSSIUMGm+cvX77ssLI5s6VL/pDhw4ZIr75fSNmy5eX7WTPlw/fbyK+Ll4qfX8yL547t26R7187SvmMnqVX7Kfnj90XSsd3HMnf+QilatJhD9gGP7u6d25K/YDGpXf8lGT3gs1jXKVelmrzfqU/kYw+PiAahuGTN5i+vtf5EcubJZ9KeaMPhyC+6yOBxsyVvgcKJvg9IXFduhcr8nRfkwvW7olNcPFnAV9rVCJB+yw7L2Wt3TaPfp7ULyB/7Lsn3W8+ahr98WTKIJfY2P6O4f0ZZcThIjgXdFne3NKbRsFOdgtLrj4OmIRApS+nc3vJqlbxy4Px1m+WDmpaWzOnTSvs5O8159Fy5nPJVs7Ly+jf/yv5o60Z1+MINaTtzW+Tje+GcE8mNWMo+xFLQDlT1X3pVChUrJeH37snc6RNkSI928tWUeZL+/5+ju3fvSPkq1cxt7rTx8d523oBC0nPo/fXd3J3ipxcS6MKxA7Jn1R/il7egzfK1P0yS47v+lYYf9ZR0GTLKqu/Hyx/jB8grn4984Pbyl6kiddt0inzsntYjycqOxJc5azap1ayN+ObMI2IR2bP2T1k4qq+8PXCiZMtbQP6YNFTu3LopTT/tLxky+8je9Svkt7EDpWX/8ZKjQJFYt7lv4z+ycs4kqf9Oe8lVuKRsWbpQ5g3rIe8OmyYZH9CQnJpp497UqVNNY92TT0Z0Tl27dq3069fPND4NGpTwxnRiqYS7fi1YPmrzllSsUlW+GvONZPH1ldOnTkhmb++Hvvb7BYslY5SKXN+sWZO4tEjKWOqZFzWWKin3wu/JvOkTZWjPdjJ08o+SPn3E5+j7SaNkx7/rpF3PIeKVMZPMHP+VjB7QTfqO/DbO7ebKm19afdRV/HPlkZC7d2TJzz/I0M/byYhpC8U7C9+NKcnlkwfl6Ial4pP7fsc6dS/0ruQsUcnctFEwvrxz5peaHw6MfJzGjs4ecGwsVfe1tpJVYymxyM7Vf8qPI/rIe0MmiX/eAhIaclcKl3/M3FbMjfs7Iqp3B04QS3h45OOLp47J7CGfSanHU3cHXaf49Tl69GhHFyFFmjVzujR9pZk0bvKyeayVVqtX/yO/LFwgbdq+F2P972d/J9Vr1JS3W79rHn/SvqNs3LBe5s6ZLb379k/28iNx6Ci8h43E04a+LFnj33uu0hO1bB43e/sjMwLw8P49NPylADvP2jbQLNx9wYwCLJzNyzT8vVYxl/x9KMg0/Fk9bOTeqFXHbR5P23RaxjQpJQWyZpCDl24l8h4gKWVI5y5DXikt/X7dJ+/Vtq3ErJDPRwYu3i97zlwzj6esOiZvVcsnpXJnfmDDX1i4RYJuMPrTkYil7EMshR6DbTMgfNilr7zfrL4cO7RPSparZJY91/QN8//enVsTtG13d/cExV9wPiF3bsufk4fJ0606yObF93uX3711U/auWSb13+8meUtWMMvqte4s3/dsK+eP7JOchUvGuU0dtZDRhwrulKpIpWo2j2u92lp2/L1Yzh7eZxr+zhzaK/Xfbh85Wq964zdly7IFcv74wTgb/rYsWSDl6jSUsrWeNY8bvNNBju7cJLtXL5MnXnwtGfYq5Zk5c6Z8++238tJLL0UuK1eunOTJk0c++ugjuxr+iKUS7vuZ08Q/R075vO/9CvjcefLG67Xa0Jc588MbCOH8ug362ubx+537yEevNZDjh/ZJibKV5NbNG/LPst/k424DpHSFx8w673XuI5+1bSaH9+2WIiXLxrrd6k9FfCdavfleR1m17Dc5eeyQlKkYMcoazk9H8m2ePUIqNWsn+//60ea5orUbmf8vHd6doG2mcXOX9N40/qZUxStXt3n8dPM2smX5IhNDacPfEw0jfpsf37sj3tvM6J3F5vG6334Q3xy5JaBkeUnNnKLhr1WrVo4uQooTGhIi+/b+J23avh+5TNNZPPFEddm1c3usr9m1Y4e81eptm2XVn6whK/+2TTcD17Nv11b5sHl9k5aqVIXH5NVWH0jmaF+KcdHe75vW/C13796OMyCD89IRf4/l8xHPtG5yJPCWZPZ0Nw2AG09clc/rFZLsmdLJ+Wt3ZeGuC3IoMP4NeBk83M3/N0PuJWHpkRR6Pl9c1hwMkk1HL8do+NtxKlgalMkhqw8GyvU7YdKgdA7xTOsum49feeA2A/y8ZHmXmhISdk92ngqWMcsPy/ngu0m8J4iKWCrhiKUQG62cUpkSoTLy/JlT8uFrDc3IkaIly8prbT6RbP45E6GUSC6rZo+XAuWqSr7SlWwa/i6dOCTh98IkX6mKkct8c+WTzH7+D234O7N/l0zt0Fw8vTKZRsPHm7aSDJmo/E6JwsPvyYFNqyX07h3JXbSUWZanaCnZt2mVFKrwuKT3yiT7N62SeyGhkj+Oiqd7YaGmUTBqA5+OXAgoXUnOHt6bbPuS0ujouxIlYqZC1WX2jswjlkq4tatXStUnnpTe3TrJjm1bJHt2f2n86mvyUpNXHvra1m+8IiEhIVKocBF5572PpFyFiM42SPlu3YqIpTJm9jH/a2eqe2FhUjpKY52mVPfzzymHHtDwF1VYaKisXPKLGS0YUIgsGynJ9vnfSM6SVSRH8QoxGv7sdSPwrPzet5W4pfUQvwIlpMwLLcXL1z9Rto3kj6X2blxlYqm8/4+lHpXGVrvWLpcnnntF0milaCrmFA1/6siRIzJ9+nTz/5gxY8Tf31+WLFki+fPnl9KlSzu6eE7nytUrcu/evRhpqPTxsWNHY31NYGCg+Plli7F+YFBgkpYVjlW+SnV57MmnJHvOPHLx3Gn5ccYEGdarg3wxapq4uUc03sTm5LHD0u/T1qZiVFNdfdr7K5OyCilDHh9P6VmvsHi4u8ndsHAZt/akGe1XyC8i1UajMv4yb8d5OXnltlQv6CtdnioovZcckovxGLWll83XK+aSQ5dumvkDkXI8WyaHlMztLa9P+jfW57vO2y3DmpWVtT3qSOi9cLkTGi4df9gppy7fjnObu08HS6+f/5Pjgbcke+Z08kGdQjKjTRVpOm6j3KJhOFkRSyUMsRRimzPqu29GSvHS5SVfwdhH5sRXkRKl5YOufSVX3gAzB7PO9/dFp7YybPJcyeCVMdHKjKRzcNM/cunEYWnWx3Ykg7oZfMVUNmnjXVQZvLPIreArD0zzWbjSk5I5e065dvGcbFgwQxaN6iWv9Bwlbm5xx+VwLpc0fdQX7c08j+nSZ5DGHfpKtjwRc1m99Elv+W38QBn74cvmt1badJ7SuGNf8c2h6axiunU92KSmij43ZEZvX7l89lSy7E9KpOk4x40bJ19/bfv51GX6nL2IpRLm3JnT8uuCH6XZmy3lrXfayv69e2TM8CHi4eEhDV+IGMkTnV+27NKlRx8pUaq0afhb/MsCaf9+a5k0c44UL5E4lb5wbCw1+5uRUqxUecn3/2xRwVeCJK2Odo82P7JPlqzmuQfZvmmNjBvSy6T61CwK3QaPk8w+8evEDsc7tW21XD1zRJ7+9MFp0BMia0AxqfJ6R8nsn0duX7ti5vtbNba71PtsnHik90q0v4OkdeHkUZnWt11kLNXs0y8ke17bVLD22r9lndy5dUMq1G4gqZ1TNPytWrVKGjZsaHKzr1692qRl0ABr586dJm/7/Pnz43zt3bt3zS0qi7uneHp6JkPJAedXrU79yPv5CxYxt0/faSJ7d219YHqE3HkDZPCE7+X2zRtmxN83I/pJr2GTaPxLITR1p87pl8HDTark85F3H88rQ1cclTSm2U7knyOXZe2xiIqpk9vPSckcGaVmIV9ZsCvmZLjRtaicW/JkSS9Dlh9J8v1A4snh7Sndnism783cLiFh93OfR/Xx04XFO31aaTtjq1y5GSpPl8xu5vh7Z+oWOXTxZqyvWXvo/o+1Qxe0IfCaLO1Uw4wc/Hnb2STbH9gilgIe3fRxw+TU8SPSb+SUR95Whar307AHFCoqRUqUkXYtXpSNq5bLUw1jrwyF87h++ZKs+eEbadR5sKR9yNzYCVHs8TqR97PlLWjmDZzV/R0zCjDq6EE4t6y58srbg74xKV8P/LtG/pj8lbzec4Rp/Fu7YIbcvXlTmncfKhky+cihrevlt3ED5Y1eoyR7PttMC7DfsGHD5Pnnn5fly5dLtWoR6Vc3bNggp06dkj/++MMpYqm7IW4uH0tpI4824L3/cUfzuFiJknL0yCH5dcG8OBv+8hcoaG5WZctXlDNnTsu877+T3gO+TLayI2nMHD9MTh8/Kr1HTE6U7ZUsX0UGTZgtN4KvmhF/4wb3kH5jpptGQzi3W1cuyc6fp0jND/uLeyLGUjp60Mond0HTELikfxs5vWOtFHzifv0nnFu23Pnk/SGTzbzI+/5dLb9+M1Ra9R6ZKI1/21cukSLlq0pmX6ZccIrZL7t37y4DBw6Uv/76y6TCsXr66adl48aND3ztkCFDxMfHx+b21dAh4up8s/iaeUOCgmx7x+jjbNliP7F1eVC0Hulm/Wg91+Ha/HPlNT2kLpw9/cD1tEdWztz5pGDRkvJa608kf8GisuyXuclWTjyae+EWM3rvxJU7pjHv1NU7Uq+YnwTfCTXPn402Uu/ctbuS1evhwdiblXJL+TyZZdiKo3LldliSlR+Jr1Rub/HL5Ck/flBVtvV92tweK+grbzyez9zP65tB3ngin/T5ea9sOnpFDl64Id/8c0z2nr0mzR/PF++/oylCTwTdlHxZI0aXInkQSyUcsRSiN/pt27hGeg+bKH7ZcyT69rWXe668+eU8I3hShEvHD8nta1flxy8+kfHvPmduZw/slp1//2rue3lnkfCwULn7/3RmVvqa6CO3HsTHP5ekz+QjwRfpKJOSuKf1MCP4chYsJrWbtxH//IVk67Kf5cqFs7Ltr1+lYdvOJlWnf0BhebLpW2a9bct/jXVbXpl9TGrP6CNFb167IhmzMH9RXGrXri0HDx6UJk2ayNWrV82tadOmcuDAAalZs6ZTxFJfjxgqrk5H7wUUjBjVZRVQsJBcOH8uQdspWbqMnDnN9TGlmzn+K9m+aa18PmyCTSzl4+tnUnXevGE7Z3zw1cvmuQdJnz6DqZfSdKBtO/UWN/e0smrpb0m2D0g8V04flrs3rsrfIzrKws6NzC3wyB45vGaRuW8JT5zsQOkyZJLM2XPLzcCEfe/A8bFU1px5JHehYlL3tXclR/7Csmnpwkfe7tVLF+TYnm1S6annEqWcKZ1TjPjbvXu3zJkzJ8Zy7V2lKZUepEePHtKpU6cYvdRdnUe6dFKyVGnZtHGDPF23XmRvq02bNshrr7eI9TXlKlSQTRs3SouW9+em2bhhvVmO1CPo0gW5cS1YsmR9cIAVncVikdDQh6eBhHPStNZp3d0k8GaoXLkVKrm8bb8nc2T2lN3nbAPx2Br9KuX1NiMHdTtIWXROv6bjNtgs69+klBy7dEumrz1uRoeqcIvFZp17FhG3BKRFz5DOXfL5esni6+cTp+CIF2KphCOWgjW+mTH+K9m87h/pPfwb8c8Vezq+R3Xn9i25cO6M1KxLI3FKoHPvvd7/G5tlf08bYebxq9SwmWTKmt1UPp7au0OKVKlhnr9y7pRcD7r4wPn9ortx+ZLcuXlNMvowciEls4Rb5F5oiISFRHSsiz6fjDbs6TpxVXzlLFBMTuzdLkWrRIwU1tSfJ/7bLpWeYXRwXE6ePCn58uUzo/Jie05Tczo6lgoOcYp+9klKR+udOnHcZtmpEyckZ65cCdrO4QP7xS+OTldIGbHUdxOGy5b1/0jPYRPFP6dtLKWdyd3TppX/dmyWqjWeNsvOnjohQRfPmzmQE/a3wqmXSiH8i5Y36Tej2vrDaMnsn1eK1X1F0iRSivOwu7flRtB5ye9NZ5mUTD/bOjffo9qxaqlk9MkiRSs+kSjlSumcIhLJkiWLnDsXs2V++/btkifPg398a+oEb29vm5urp1OweqvVO7Jw/jz57Zef5eiRIzKwfz+5ffu2NG7S1Dzfs8dnMmbUiMj132zRUtavWyMzZ0yTY0ePyMTxY+W/PXvktTdir9xCyqAVScePHDA3den8WXM/8OJ589ycKWPMhMm6fM/2f2XkF10kR+58Uq5yREoUNbj7h/Lnb/MiH8+dNk727d5mXqNz/ZnHu7bKk083dMg+ImFeLpdDimX3Er+MHmauP31c3D+jbDx+1Ty/dP8lqVvUTyrn9Rb/TOmkSdkckiuzp6w5er+nsc7593RRP5v0ntUKZJFJG07JnbBwkw5Sbx7uqXui3JRE59s7fPGmze12SLgE3w41948F3pITQbekz0slpUwebzMCsGX1/FKtUFZZse9S5HamvF1JXquaN/Jx5wZFpXKBLJI7S3opn89HRr9eTu5ZLLJkNw1/yYlYyj7EUpg2dqis/XuJfNJjgGTI4GXm49ObziVjpY81trKO1jt17LB5rB2prAZ+9qEs+/V+LDV78miTVl1jqYP/7ZQR/bqKm5ubVH+KuSZSgnQZvMQvbwGbW1rP9JI+o7e57+mVUUrVbCDrfpwsp/ftlIvHD8nf00aaRr+oDX+zP39XjmxdZ+6H3Lkt6+ZNkfNH9sm1wPNyau92+X3sF+Ljn1vyl6nswL1FQqz6caqc2r9Lgi+dN3P96eOT+3dKqep1JWuufJIlR25ZNn2MnDuy34wA/PePn+T4nm1StHL1yG3MHdJVtv31S+TjKg1flp3//CF71vwpQWdOyJ8zvpbQu3ekbC2+L+JSsGBBuXTpfnwadRS+PmcPYqmEa/bGW/Lf7l3y3bTJcvrUSflr6e+y6Of50uTV1yPX+WbcKBnYp0fk43lzZsmaf1aY9Y8ePiRfj/hStm351+Y1SFlmjB8m61YskY+6DZD0scRSXhkzSZ0GL8n3Ghvt3CLHDu2TKSP7m0Y/Hcln1fXdV2XzupXm/p07t+XH6RPk8L7dEnjhnHnN5JED5ErgJXm8Zl2H7SviT+fb88kVYHNzT5de0mX0NvfVnWtX5OqZo3IjMCLzwbWzJ8zjkJv3O6WvntBTDq9ZHPl4169T5dLh3XLz8gUJOrZPNkwbLGnSuEm+SrUdsJewx99zv5UT+3bJ1UvnzVx/+vj4vp1S5smIz/aNq5fl/PHDcvnCGfP4wqmj5vHtG9cit/HdoC7y77L7sZS149TO1UulXM36Zp5lOMmIv9dee026desmP/30k+kdp72t161bJ126dJGWLVs6unhO69mGz8mVy5dlwrivJTDwkhQvUVImTPo2sqfU+XPnxC3N/bbdChUryZBhw2Xc16Nl7OiRkj+ggIweO16KFi3mwL3Aozp6cJ8M6vZB5OPZk0eZ/2vWe15at+tuGu7WLP9dbt68Lr5Zs0vZyo/Lqy0/MCMdrC6cPSPXgyMahdS1q1fkm6/6ydUrgeLllUnyFSwi3QaNlbKVHk/mvYM9tEHu3SfyiU/6tHI7NFxOX70jI/85LnsvRKSj+utgkHi4u8nrlXJJxnRp5dTV2zLin2Ny6cb9nnPaIJjZ8/6F0toI2L2u7RyPUzedknXH7p87SLnCwi3y8azt0vGZojL2zfLilS6tnLx8S3r9/J/NPH7aIOib8f73h7+3pwx9paxk8fKQKzdDZNvJq9Ji8mYzshTJh1jKPsRSWL54gfl/QJf7sZT6oEsfqV3/xf+vs1AWzL4/798Xnd+LsY6O5osaS12+dFHGDu4lN64Hi7ePrxQvXV4GjJku3qTucxk1Xn/ffN8umTBA7oWGmsa72m99YrPO1fOnJeR2xBy52vAbeOqY7F+33MwNlzFLVslXurI80aRlos59g6R169pV+X3SMLl59bJ4Zsgo2fMXlGZdh0iBshGNt690GSSrf5wqC0b2ltA7d0xD4PPvdZXCFe7/jrp68Zzcun6/8qrkE3Xk9vWrsnbBTLkZfEX88xeWV7sOlowJSBubGkcYRR9ZqW7cuCHp06e3a5vEUglXsnRZGTR8tEweN0ZmfvuN5MqdR9p17ib1G74QuU5QYKBN6s/Q0FAZP/oruXTponmvChcpJqMmfCuVqlR10F7gUf39/1hq0Ge2sdR7nfpIrfoR58Kb739qGmfGDOguYaEhUrbyE/L2J5/ZrH/u9Am5ffP+NfPcqeMyZvnvcv3aVcmU2UcKFSslvYZPlrwFbNPLIuU6un6J7Fv2Q+TjVeO6m/8rv95BClSNyMhyM/C8hNy8f828HRwk/84abpZ5ZvIRv0Kl5KmOw819pAyazvyXiV+aBj7tTJcjXyF5s/uXUrhsxPyNW5YvktULv4tcf2b/T83/L73fVSrUftbc185Vt67f74Spju7ZJsGBF6VinYh1IJLGohGTg4WEhMjHH38sM2bMkHv37knatGnN/2+88YZZpvOvJMQdpp1CLPacun+hAKKasOmEo4sAJ7Rl7wVHFwFOaFf/iB8gzoZYCslh7xliKcRu3WnbuTIB5eVBb2vE1KZqwtNwWlnTaY4ZM0batm0rXl5ekc9p3LNp0yYT82iDnaNjqYvX6QSHmE4E3nJ0EeCkft5H/QNiKp3j/nUOsHqz8v0sXE4/4k8nTp4yZYr07t1b9uzZY3ppVaxYUYoWLeroogEAADg9YikAAODqNO2m0v7rOiefxj9Wer98+fJmhJ49iKUAAIArcYqGPyudgNmeSZgBAABALAUAAFzXypUR83+98847ZtSfzqWX2IilAACAK3BYw581RUN8jBw5MknLAgAAkNIQSwEAgNRo+vTpibIdYikAAOCq0jo6RYPVtm3bJCwsTIoXL24eHzx40ORQr1w5YpJsAAAA3EcsBQAAYD9iKQAA4KrSOjpFg7XnVObMmWXmzJni6+trll25csWkb6hZs6ajiggAAOC0iKUAAADsRywFAABcVRqLzorsYHny5JE///xTSpcubbNcJ1SuX7++nD17NkHbuxOWyAWES9hz6pqjiwAnNWHTCUcXAU5oy94Lji4CnNCu/vXEGRFLITnsPUMshditOx3k6CLACXl5uDu6CHBCbarmTxWx1MXroYlcQriCE4G3HF0EOKmf91H/gJhK5/BydBHghN6snDde67mJE7h27ZpcunQpxnJddv36dYeUCQAAIKUglgIAALAfsRQAAHAlTtHw16RJE5M+YeHChXL69GlzW7BggbRp00aaNm3q6OIBAAA4NWIpAAAA+xFLAQAAV+KwOf6i+uabb6RLly7yxhtvSGhoRDqEtGnTmgDrq6++cnTxAAAAnBqxFAAAgP2IpQAAgCtxijn+rG7evClHjhwx9wsXLiwZM2a0azvMS4PYMMcf4sIcf4gNc/whJc3xZ0UshaTEHH+IC3P8ITbM8YeUNMdfYsdSzPGH2DDHH+LCHH+IDXP84VHm+HOKEX9WGlBlzZo18j4AAADij1gKAADAfsRSAADAFTjFHH/h4eHSv39/8fHxkYCAAHPLkiWLDBgwwDwHAACAuBFLAQAA2I9YCgAAuBKnGPHXs2dPmTp1qnz55Zfy5JNPmmVr166Vfv36yZ07d2TQoEGOLiIAAIDTIpYCAACwH7EUAABwJU7R8Ddz5kz59ttv5aWXXopcVq5cOcmTJ4989NFHBFgAAAAPQCwFAABgP2IpAADgSpwi1efly5elRIkSMZbrMn0OAAAAcSOWAgAAsB+xFAAAcCVO0fBXvnx5GTduXIzlukyfAwAAQNyIpQAAAOxHLAUAAFyJU6T6HDZsmDz//POyfPlyqVatmlm2YcMGOXnypCxZssTRxQMAAHBqxFIAAAD2I5YCAACuxClG/NWuXVsOHDggTZs2latXr5qb3j948KDUrFnT0cUDAABwasRSAAAA9iOWAgAArsQpRvwpPz8/M4nyE088IeHh4WbZli1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",
+      "text/plain": [
+       "<Figure size 1800x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "stiff = [\n",
+    "    ('stiffness', 'torsional rigidity'),\n",
+    "    ('stiffness', 'heel counter stiffness'),\n",
+    "    ('torsional rigidity', 'heel counter stiffness')\n",
+    "]\n",
+    "\n",
+    "plt.figure(figsize=(18, 5))\n",
+    "\n",
+    "for i, (feat_x, feat_y) in enumerate(stiff):\n",
+    "    ct = pd.crosstab(df[feat_x], df[feat_y], normalize='index') * 100\n",
+    "    plt.subplot(1, 3, i+1)\n",
+    "    sns.heatmap(ct, annot=True, fmt=\".1f\", cmap=\"Blues\", cbar=False)\n",
+    "    plt.title(f'{feat_x} vs {feat_y} (%)')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "0872440f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Correlation between Stiffness and Plate (%):\n",
+      "plate             0  carbon plate  carbon platerock plate\n",
+      "stiffness                                                \n",
+      "-          92.30769       7.69231                 0.00000\n",
+      "flexible   97.14286       2.85714                 0.00000\n",
+      "moderate   96.93252       3.06748                 0.00000\n",
+      "stiff      76.03687      23.50230                 0.46083\n",
+      "\n",
+      "Correlation between Stiffness and Midsole Softness (%):\n",
+      "midsole softness          -  balanced     firm      soft\n",
+      "stiffness                                               \n",
+      "-                 100.00000   0.00000  0.00000   0.00000\n",
+      "flexible           17.14286  42.85714  5.71429  34.28571\n",
+      "moderate            0.61350  44.17178  3.68098  51.53374\n",
+      "stiff              18.89401  39.17051  4.60829  37.32719\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Correlation details between stiffness and plates\n",
+    "ct_stiff_plate = pd.crosstab(df['stiffness'], df['plate'], normalize='index') * 100\n",
+    "print(\"Correlation between Stiffness and Plate (%):\")\n",
+    "print(ct_stiff_plate.round(5))\n",
+    "\n",
+    "# Correlation details between stiffness and midsole softness\n",
+    "ct_stiff_midsole = pd.crosstab(df['stiffness'], df['midsole softness'], normalize='index') * 100\n",
+    "print(\"\\nCorrelation between Stiffness and Midsole Softness (%):\")\n",
+    "print(ct_stiff_midsole.round(5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "857a0fe8",
+   "metadata": {},
+   "source": [
+    "Tidak ada korelasi signifikan antara stiffness dan plate ataupun midsole softness, kecuali pada 51% sepatu stiffness berkategori moderate yang memiliki midsole soft."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "ad19be4d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 39 columns):\n",
+      " #   Column                       Non-Null Count  Dtype   \n",
+      "---  ------                       --------------  -----   \n",
+      " 0   brand                        428 non-null    object  \n",
+      " 1   name                         428 non-null    object  \n",
+      " 2   pace                         428 non-null    object  \n",
+      " 3   arch support                 428 non-null    object  \n",
+      " 4   weight lab weight brand      428 non-null    object  \n",
+      " 5   lightweight                  428 non-null    int64   \n",
+      " 6   drop lab drop brand          428 non-null    object  \n",
+      " 7   strike pattern               428 non-null    object  \n",
+      " 8   midsole softness             428 non-null    object  \n",
+      " 9   toebox durability            428 non-null    object  \n",
+      " 10  heel padding durability      428 non-null    object  \n",
+      " 11  outsole durability           428 non-null    object  \n",
+      " 12  breathability                428 non-null    object  \n",
+      " 13  width / fit                  428 non-null    object  \n",
+      " 14  toebox width                 428 non-null    object  \n",
+      " 15  stiffness                    428 non-null    object  \n",
+      " 16  torsional rigidity           428 non-null    object  \n",
+      " 17  heel counter stiffness       428 non-null    object  \n",
+      " 18  plate                        428 non-null    object  \n",
+      " 19  rocker                       428 non-null    int64   \n",
+      " 20  heel lab heel brand          428 non-null    object  \n",
+      " 21  forefoot lab forefoot brand  428 non-null    object  \n",
+      " 22  orthotic friendly            428 non-null    int64   \n",
+      " 23  season                       428 non-null    object  \n",
+      " 24  removable insole             428 non-null    int64   \n",
+      " 25  for_daily                    428 non-null    int64   \n",
+      " 26  for_tempo                    428 non-null    int64   \n",
+      " 27  for_competition              428 non-null    int64   \n",
+      " 28  weight_lab_oz                428 non-null    float64 \n",
+      " 29  heel_lab_mm                  428 non-null    float64 \n",
+      " 30  forefoot_lab_mm              428 non-null    float64 \n",
+      " 31  drop_lab_mm                  428 non-null    float64 \n",
+      " 32  heel_category                428 non-null    category\n",
+      " 33  forefoot_category            428 non-null    category\n",
+      " 34  drop_category                428 non-null    category\n",
+      " 35  strike_pattern               428 non-null    object  \n",
+      " 36  strike_heel                  428 non-null    int64   \n",
+      " 37  strike_mid                   428 non-null    int64   \n",
+      " 38  strike_fore                  428 non-null    int64   \n",
+      "dtypes: category(3), float64(4), int64(10), object(22)\n",
+      "memory usage: 125.4+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5daabccd",
+   "metadata": {},
+   "source": [
+    "## Plate and rocker"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "5ae27201",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "plate\n",
+      "0                         369\n",
+      "carbon plate               58\n",
+      "carbon platerock plate      1\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "rocker\n",
+      "0    286\n",
+      "1    142\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "correlation between plate and rocker\n",
+      "rocker                         0          1\n",
+      "plate                                      \n",
+      "0                       75.33875   24.66125\n",
+      "carbon plate            13.79310   86.20690\n",
+      "carbon platerock plate   0.00000  100.00000\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['plate'].value_counts())\n",
+    "print()\n",
+    "print(df['rocker'].value_counts())\n",
+    "\n",
+    "print(\"\\ncorrelation between plate and rocker\")\n",
+    "ct_plate_rocker = pd.crosstab(df['plate'], df['rocker'], normalize='index') * 100\n",
+    "print(ct_plate_rocker.round(5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "227237f3",
+   "metadata": {},
+   "source": [
+    "Kebanyakan sepatu pada dataset adalah sepatu dtanpa tambahan plate. Akan tetapi, mayoritas sepatu yang memiliki tambahan carbon plate adalah sepatu yang bagian bawahnya punya lengkungan (rocker shoes). Sepatu dengan feature rocker adalah sepatu yang memiliki lengkungan dengan tujuan untuk mengembalikan energi kepada pengunanya. Untuk rekomendasi yang maksimal, disarankan agar setiap user yang ingin mengikuti kompetisi untuk mendapatkan sepatu dengan plate carbon dan rocker = 1."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "1ead91b1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 39 columns):\n",
+      " #   Column                       Non-Null Count  Dtype   \n",
+      "---  ------                       --------------  -----   \n",
+      " 0   brand                        428 non-null    object  \n",
+      " 1   name                         428 non-null    object  \n",
+      " 2   pace                         428 non-null    object  \n",
+      " 3   arch support                 428 non-null    object  \n",
+      " 4   weight lab weight brand      428 non-null    object  \n",
+      " 5   lightweight                  428 non-null    int64   \n",
+      " 6   drop lab drop brand          428 non-null    object  \n",
+      " 7   strike pattern               428 non-null    object  \n",
+      " 8   midsole softness             428 non-null    object  \n",
+      " 9   toebox durability            428 non-null    object  \n",
+      " 10  heel padding durability      428 non-null    object  \n",
+      " 11  outsole durability           428 non-null    object  \n",
+      " 12  breathability                428 non-null    object  \n",
+      " 13  width / fit                  428 non-null    object  \n",
+      " 14  toebox width                 428 non-null    object  \n",
+      " 15  stiffness                    428 non-null    object  \n",
+      " 16  torsional rigidity           428 non-null    object  \n",
+      " 17  heel counter stiffness       428 non-null    object  \n",
+      " 18  plate                        428 non-null    object  \n",
+      " 19  rocker                       428 non-null    int64   \n",
+      " 20  heel lab heel brand          428 non-null    object  \n",
+      " 21  forefoot lab forefoot brand  428 non-null    object  \n",
+      " 22  orthotic friendly            428 non-null    int64   \n",
+      " 23  season                       428 non-null    object  \n",
+      " 24  removable insole             428 non-null    int64   \n",
+      " 25  for_daily                    428 non-null    int64   \n",
+      " 26  for_tempo                    428 non-null    int64   \n",
+      " 27  for_competition              428 non-null    int64   \n",
+      " 28  weight_lab_oz                428 non-null    float64 \n",
+      " 29  heel_lab_mm                  428 non-null    float64 \n",
+      " 30  forefoot_lab_mm              428 non-null    float64 \n",
+      " 31  drop_lab_mm                  428 non-null    float64 \n",
+      " 32  heel_category                428 non-null    category\n",
+      " 33  forefoot_category            428 non-null    category\n",
+      " 34  drop_category                428 non-null    category\n",
+      " 35  strike_pattern               428 non-null    object  \n",
+      " 36  strike_heel                  428 non-null    int64   \n",
+      " 37  strike_mid                   428 non-null    int64   \n",
+      " 38  strike_fore                  428 non-null    int64   \n",
+      "dtypes: category(3), float64(4), int64(10), object(22)\n",
+      "memory usage: 125.4+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8ca8cbbe",
+   "metadata": {},
+   "source": [
+    "## Orthotic friendly and removable insole"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "8f3cc5ab",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   orthotic friendly  removable insole\n",
+      "0                  1                 1\n",
+      "1                  1                 1\n",
+      "2                  1                 1\n",
+      "3                  1                 1\n",
+      "4                  1                 1\n",
+      "\n",
+      "orthotic friendly\n",
+      "1    388\n",
+      "0     40\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "removable insole\n",
+      "1    388\n",
+      "0     40\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "correlation between orthotic friendly and removable insole\n",
+      "removable insole       0      1\n",
+      "orthotic friendly              \n",
+      "0                  100.0    0.0\n",
+      "1                    0.0  100.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[['orthotic friendly', 'removable insole']].head())\n",
+    "\n",
+    "print()\n",
+    "print(df['orthotic friendly'].value_counts())\n",
+    "\n",
+    "print()\n",
+    "print(df['removable insole'].value_counts())\n",
+    "\n",
+    "print(\"\\ncorrelation between orthotic friendly and removable insole\")\n",
+    "ct_ortho_removable = pd.crosstab(df['orthotic friendly'], df['removable insole'], normalize='index') * 100\n",
+    "print(ct_ortho_removable.round(5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9bd2f116",
+   "metadata": {},
+   "source": [
+    "Sepatu orthotic friendly adalah sepatu yang dirancang khusus untuk pelari yang memiliki masalah kaki seperti plantar fasciitis, flat feet yang ekstrem, atau perbedaan panjang kaki sehingga mereka membutuhkan desain khusus terhadap insole sepatunya. Hal ini sudah sesuai karena sepatu yang orthotic friendly pasti removable insole, sehingga ke depannya hanya akan digunakan salah satu feature dari sini."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d0317b3b",
+   "metadata": {},
+   "source": [
+    "## Season"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "bb32b6f3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "season\n",
+      "all seasons          249\n",
+      "summerall seasons    109\n",
+      "-                     58\n",
+      "winter                12\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['season'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "7c5492df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Jumlah Sepatu Per Kategori Musim:\n",
+      "Summer: 109\n",
+      "Winter: 12\n",
+      "All seasons: 358\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['season'] = df['season'].replace('-', 'Unknown')\n",
+    "\n",
+    "# encode base value\n",
+    "df['is_summer'] = df['season'].str.contains('summer').astype(int)\n",
+    "df['is_winter'] = df['season'].str.contains('winter').astype(int)\n",
+    "df['is_all_season'] = df['season'].str.contains('all season').astype(int)\n",
+    "\n",
+    "print(\"Jumlah Sepatu Per Kategori Musim:\")\n",
+    "print(f\"Summer: {df['is_summer'].sum()}\")\n",
+    "print(f\"Winter: {df['is_winter'].sum()}\")\n",
+    "print(f\"All seasons: {df['is_all_season'].sum()}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "ebc3e7ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total Sepatu: 428\n",
+      "Irisan Summer & All Season: 109\n",
+      "Winter Only: 12\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Subset counts\n",
+    "'''\n",
+    "\n",
+    "- huruf A pada subset menunjukan kehadiran all seasons dalam subset tersebut; A = all seasons == 1, a = all seasons == 0\n",
+    "- huruf B pada subset menunjukan kehadiran summer dalam subset tersebut; B = summer == 1, b = summer == 0\n",
+    "- huruf C pada subset menunjukan kehadiran winter dalam subset tersebut; C = winter == 1, c = winter == 0\n",
+    "\n",
+    "'''\n",
+    "Abc = len(df[(df['is_all_season'] == 1) & (df['is_summer'] == 0) & (df['is_winter'] == 0)]) \n",
+    "aBc = len(df[(df['is_all_season'] == 0) & (df['is_summer'] == 1) & (df['is_winter'] == 0)]) \n",
+    "ABc = len(df[(df['is_all_season'] == 1) & (df['is_summer'] == 1) & (df['is_winter'] == 0)]) \n",
+    "abC = len(df[(df['is_all_season'] == 0) & (df['is_summer'] == 0) & (df['is_winter'] == 1)]) \n",
+    "AbC = len(df[(df['is_all_season'] == 1) & (df['is_summer'] == 0) & (df['is_winter'] == 1)]) \n",
+    "aBC = len(df[(df['is_all_season'] == 0) & (df['is_summer'] == 1) & (df['is_winter'] == 1)]) \n",
+    "ABC = len(df[(df['is_all_season'] == 1) & (df['is_summer'] == 1) & (df['is_winter'] == 1)]) \n",
+    "\n",
+    "# Plotting\n",
+    "plt.figure(figsize=(10, 8))\n",
+    "v = venn3(subsets = (Abc, aBc, ABc, abC, AbC, aBC, ABC), \n",
+    "          set_labels = ('All Seasons', 'Summer', 'Winter'),\n",
+    "          alpha = 0.6)\n",
+    "\n",
+    "if v.get_patch_by_id('100'): v.get_patch_by_id('100').set_color('skyblue')\n",
+    "if v.get_patch_by_id('010'): v.get_patch_by_id('010').set_color('orange')\n",
+    "if v.get_patch_by_id('001'): v.get_patch_by_id('001').set_color('lightgrey')\n",
+    "if v.get_patch_by_id('110'): v.get_patch_by_id('110').set_color('green')\n",
+    "\n",
+    "plt.title(\"Venn Diagram: Season Intersection - Project Rush\", fontsize=15)\n",
+    "plt.show()\n",
+    "\n",
+    "# Verify\n",
+    "print(f\"Total Sepatu: {len(df)}\")\n",
+    "print(f\"Irisan Summer & All Season: {ABc}\")\n",
+    "print(f\"Winter Only: {abC}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba27cda8",
+   "metadata": {},
+   "source": [
+    "- Summer shoes adalah sepatu yang berfokus pada feature breathable pada sepatu, hal ini membuat sepatu yang cocok untuk summer (karena terasa sejuk di kaki) secara otomatis nyaman dipakai di semua musim. \n",
+    "- Winter shoes adalah sepatu yang berfokus pada insulasi untuk menjaga panas sehingga membutuh breathability berkategori warm. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "7701b7ad",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Korelasi Sepatu Summer vs Kategori Breathability (%) ---\n",
+      "breathability         -  breathable  moderate      warm\n",
+      "is_summer                                              \n",
+      "0              18.18182         0.0  65.20376  16.61442\n",
+      "1               0.00000       100.0   0.00000   0.00000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Correlation between summer shoes with breathability category\n",
+    "ct_summer_breathability = pd.crosstab(df['is_summer'], df['breathability'], normalize='index') * 100\n",
+    "print(\"--- Korelasi Sepatu Summer vs Kategori Breathability (%) ---\")\n",
+    "print(ct_summer_breathability.round(5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "1cfc5944",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Korelasi Sepatu Winter vs Kategori Breathability (%) ---\n",
+      "breathability         -  breathable  moderate       warm\n",
+      "is_winter                                               \n",
+      "0              13.94231    26.20192      50.0    9.85577\n",
+      "1               0.00000     0.00000       0.0  100.00000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Correlation between winter shoes with breathability category\n",
+    "ct_winter_breathability = pd.crosstab(df['is_winter'], df['breathability'], normalize='index') * 100\n",
+    "print(\"--- Korelasi Sepatu Winter vs Kategori Breathability (%) ---\")\n",
+    "print(ct_winter_breathability.round(5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3acc8467",
+   "metadata": {},
+   "source": [
+    "Untuk requirement dasar saat ini, dikarenakan main market target kita adalah orang orang Indonesia maka dari itu secara default akan dipasangkan untuk sepatu dengan kategori summer atau all seasons."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "40c278e8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x1500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# Final Correlation Check\n",
+    "ordinal_map = {\n",
+    "    'midsole softness': {'soft': 1, 'balanced': 2, 'firm': 3},\n",
+    "    'stiffness': {'flexible': 1, 'moderate': 2, 'stiff': 3},\n",
+    "    'torsional rigidity': {'flexible': 1, 'moderate': 2, 'stiff': 3},\n",
+    "    'heel counter stiffness': {'flexible': 1, 'moderate': 2, 'stiff': 3},\n",
+    "    'toebox durability': {'bad': 1, 'decent': 2, 'good': 3},\n",
+    "    'heel padding durability': {'bad': 1, 'decent': 2, 'good': 3},\n",
+    "    'outsole durability': {'bad': 1, 'decent': 2, 'good': 3}\n",
+    "}\n",
+    "\n",
+    "df_encoded = df.copy()\n",
+    "for col, mapping in ordinal_map.items():\n",
+    "    if col in df_encoded.columns:\n",
+    "        df_encoded[col] = df_encoded[col].map(mapping)\n",
+    "\n",
+    "df_numeric = df_encoded.select_dtypes(include=[np.number])\n",
+    "corr = df_numeric.corr()\n",
+    "\n",
+    "# Plot heatmap\n",
+    "plt.figure(figsize=(20, 15))\n",
+    "mask = np.triu(np.ones_like(corr, dtype=bool))\n",
+    "\n",
+    "sns.heatmap(corr, mask=mask, annot=True, fmt=\".2f\", cmap='RdYlGn', center=0,\n",
+    "            square=True, linewidths=.5, cbar_kws={\"shrink\": .7})\n",
+    "\n",
+    "plt.title('Correlation Matrix All Features - Project Rush', fontsize=18)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6f6bc07",
+   "metadata": {},
+   "source": [
+    "# Preprocessing Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa704713",
+   "metadata": {},
+   "source": [
+    "Di tahap ini, akan dilakukan encoding (One-Hot dan Ordinal), feature selection, dan pembersihan data jika diperlukan"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "e4507e3c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pre_eda_df.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "4a14cc47",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     brand                   name                pace    arch support  \\\n",
+      "0   brooks               launch 9  daily runningtempo         neutral   \n",
+      "1   brooks             levitate 6       daily running         neutral   \n",
+      "2   adidas                  4dfwd       daily running         neutral   \n",
+      "3   adidas                4dfwd 2       daily running         neutral   \n",
+      "4   adidas                4dfwd 3       daily running         neutral   \n",
+      "5   brooks       addiction gts 15       daily running  motion control   \n",
+      "6   adidas     adidas adizero sl2  daily runningtempo         neutral   \n",
+      "7   adidas                adistar       daily running         neutral   \n",
+      "8   adidas            adistar 2.0       daily running         neutral   \n",
+      "9   adidas              adistar 3       daily running         neutral   \n",
+      "10  adidas        adizero adios 7               tempo         neutral   \n",
+      "11  adidas        adizero adios 8               tempo         neutral   \n",
+      "12  adidas        adizero adios 9    competitiontempo         neutral   \n",
+      "13  adidas  adizero adios pro 2.0         competition         neutral   \n",
+      "14  adidas    adizero adios pro 3         competition         neutral   \n",
+      "15  adidas    adizero adios pro 4         competition         neutral   \n",
+      "16  adidas      adizero boston 11               tempo         neutral   \n",
+      "17  adidas      adizero boston 12               tempo         neutral   \n",
+      "18  adidas      adizero boston 13    competitiontempo         neutral   \n",
+      "19  adidas         adizero evo sl  daily runningtempo         neutral   \n",
+      "\n",
+      "          weight lab weight brand  lightweight drop lab drop brand  \\\n",
+      "0     7.9 oz / 225g 8.1 oz / 230g            1      9.4 mm 10.0 mm   \n",
+      "1   10.7 oz / 304g 10.9 oz / 309g            0       7.7 mm 8.0 mm   \n",
+      "2   11.9 oz / 336g 11.5 oz / 327g            0      8.9 mm 10.0 mm   \n",
+      "3   12.6 oz / 356g 12.4 oz / 352g            0     10.6 mm 11.0 mm   \n",
+      "4   12.3 oz / 348g 12.2 oz / 345g            0      9.9 mm 10.0 mm   \n",
+      "5   12.5 oz / 353g 12.2 oz / 346g            0     12.1 mm 12.0 mm   \n",
+      "6     8.6 oz / 245g 8.4 oz / 238g            1       8.2 mm 9.0 mm   \n",
+      "7   11.5 oz / 325g 11.2 oz / 318g            0       9.6 mm 6.0 mm   \n",
+      "8   11.6 oz / 328g 11.6 oz / 328g            0       8.0 mm 6.0 mm   \n",
+      "9     9.7 oz / 274g 9.5 oz / 270g            0      10.5 mm 5.0 mm   \n",
+      "10    7.5 oz / 212g 7.5 oz / 212g            1       8.7 mm 8.0 mm   \n",
+      "11      7.4 oz / 210g 7 oz / 198g            1       7.6 mm 8.0 mm   \n",
+      "12    6.2 oz / 176g 6.2 oz / 176g            1       6.2 mm 7.0 mm   \n",
+      "13    7.9 oz / 223g 7.6 oz / 215g            1     10.3 mm 10.0 mm   \n",
+      "14    7.7 oz / 218g 7.9 oz / 223g            1       8.0 mm 6.5 mm   \n",
+      "15    7.1 oz / 200g 7.1 oz / 201g            1       8.1 mm 6.0 mm   \n",
+      "16   10.2 oz / 290g 9.6 oz / 272g            0       9.8 mm 8.5 mm   \n",
+      "17    9.2 oz / 261g 9.2 oz / 260g            0       6.1 mm 6.5 mm   \n",
+      "18        9 oz / 254g 9 oz / 255g            0       6.0 mm 6.0 mm   \n",
+      "19    7.9 oz / 223g 7.9 oz / 224g            1       8.0 mm 6.5 mm   \n",
+      "\n",
+      "      strike pattern             size midsole softness  ...  \\\n",
+      "0   heelmid/forefoot     true to size         balanced  ...   \n",
+      "1       mid/forefoot     true to size             soft  ...   \n",
+      "2   heelmid/forefoot     true to size             firm  ...   \n",
+      "3               heel   slightly small             firm  ...   \n",
+      "4   heelmid/forefoot     true to size             firm  ...   \n",
+      "5               heel   slightly small             firm  ...   \n",
+      "6   heelmid/forefoot  half size small         balanced  ...   \n",
+      "7   heelmid/forefoot     true to size         balanced  ...   \n",
+      "8   heelmid/forefoot  half size small         balanced  ...   \n",
+      "9               heel     true to size             soft  ...   \n",
+      "10  heelmid/forefoot   slightly small         balanced  ...   \n",
+      "11      mid/forefoot     true to size             soft  ...   \n",
+      "12      mid/forefoot   slightly small             soft  ...   \n",
+      "13              heel     true to size                -  ...   \n",
+      "14  heelmid/forefoot     true to size         balanced  ...   \n",
+      "15  heelmid/forefoot   slightly small             soft  ...   \n",
+      "16  heelmid/forefoot     true to size         balanced  ...   \n",
+      "17      mid/forefoot   slightly large         balanced  ...   \n",
+      "18      mid/forefoot     true to size         balanced  ...   \n",
+      "19  heelmid/forefoot     true to size         balanced  ...   \n",
+      "\n",
+      "   torsional rigidity heel counter stiffness         plate rocker  \\\n",
+      "0               stiff               flexible             0      0   \n",
+      "1            moderate               moderate             0      0   \n",
+      "2            flexible               flexible             0      0   \n",
+      "3            flexible               moderate             0      0   \n",
+      "4            flexible               flexible             0      0   \n",
+      "5               stiff               moderate             0      0   \n",
+      "6            moderate               flexible             0      0   \n",
+      "7               stiff               flexible             0      1   \n",
+      "8               stiff                  stiff             0      1   \n",
+      "9               stiff                  stiff             0      0   \n",
+      "10              stiff               flexible             0      0   \n",
+      "11           flexible               flexible             0      0   \n",
+      "12           flexible               flexible             0      0   \n",
+      "13              stiff               flexible  carbon plate      1   \n",
+      "14              stiff               flexible  carbon plate      1   \n",
+      "15              stiff               flexible  carbon plate      1   \n",
+      "16              stiff               moderate  carbon plate      1   \n",
+      "17              stiff               flexible             0      1   \n",
+      "18              stiff                  stiff  carbon plate      1   \n",
+      "19              stiff               moderate             0      1   \n",
+      "\n",
+      "   heel lab heel brand forefoot lab forefoot brand        widths available  \\\n",
+      "0      32.4 mm 36.0 mm             23.0 mm 26.0 mm              normalwide   \n",
+      "1      34.3 mm 32.5 mm             26.6 mm 24.5 mm                  normal   \n",
+      "2      33.3 mm 32.5 mm             24.4 mm 22.5 mm                  normal   \n",
+      "3      31.8 mm 32.0 mm             21.2 mm 21.0 mm                  normal   \n",
+      "4      32.6 mm 34.0 mm             22.7 mm 24.0 mm                  normal   \n",
+      "5      36.5 mm 36.0 mm             24.4 mm 24.0 mm  narrownormalwidex-wide   \n",
+      "6      34.9 mm 35.0 mm             26.7 mm 26.0 mm              normalwide   \n",
+      "7      34.4 mm 37.5 mm             24.8 mm 31.5 mm                  normal   \n",
+      "8      33.8 mm 33.0 mm             25.8 mm 27.0 mm                  normal   \n",
+      "9      40.7 mm 40.0 mm             30.2 mm 35.0 mm              normalwide   \n",
+      "10     31.6 mm 27.0 mm             22.9 mm 19.0 mm              normalwide   \n",
+      "11     28.0 mm 28.0 mm             20.4 mm 20.0 mm                  normal   \n",
+      "12     25.0 mm 28.0 mm             18.8 mm 21.0 mm                  normal   \n",
+      "13     40.0 mm 39.5 mm             29.7 mm 29.5 mm                  normal   \n",
+      "14     37.8 mm 39.5 mm             29.8 mm 33.0 mm                  normal   \n",
+      "15     36.6 mm 39.0 mm             28.5 mm 33.0 mm              normalwide   \n",
+      "16     39.1 mm 39.5 mm             29.3 mm 31.0 mm              normalwide   \n",
+      "17     34.5 mm 37.0 mm             28.4 mm 30.5 mm              normalwide   \n",
+      "18     34.3 mm 36.0 mm             28.3 mm 30.0 mm              normalwide   \n",
+      "19     36.1 mm 38.5 mm             28.1 mm 32.0 mm              normalwide   \n",
+      "\n",
+      "   orthotic friendly             season removable insole  \n",
+      "0                  1                  -                1  \n",
+      "1                  1  summerall seasons                1  \n",
+      "2                  1        all seasons                1  \n",
+      "3                  1        all seasons                1  \n",
+      "4                  1        all seasons                1  \n",
+      "5                  1        all seasons                1  \n",
+      "6                  1  summerall seasons                1  \n",
+      "7                  1        all seasons                1  \n",
+      "8                  1  summerall seasons                1  \n",
+      "9                  1  summerall seasons                1  \n",
+      "10                 1  summerall seasons                1  \n",
+      "11                 1  summerall seasons                1  \n",
+      "12                 1        all seasons                1  \n",
+      "13                 0                  -                0  \n",
+      "14                 1  summerall seasons                1  \n",
+      "15                 1        all seasons                1  \n",
+      "16                 1        all seasons                1  \n",
+      "17                 1  summerall seasons                1  \n",
+      "18                 1        all seasons                1  \n",
+      "19                 1  summerall seasons                1  \n",
+      "\n",
+      "[20 rows x 27 columns]\n",
+      "428\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.head(20))\n",
+    "print(len(df))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "6d045565",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# print(df.at[281, 'name'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "f53198ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# rename column that is named \"brand name\" for naming convention\n",
+    "df.at[6, 'name'] = 'adizero sl2'\n",
+    "df.at[10, 'name'] = 'wave horizon 7'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "85e933c0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 27 columns):\n",
+      " #   Column                       Non-Null Count  Dtype \n",
+      "---  ------                       --------------  ----- \n",
+      " 0   brand                        428 non-null    object\n",
+      " 1   name                         428 non-null    object\n",
+      " 2   pace                         428 non-null    object\n",
+      " 3   arch support                 428 non-null    object\n",
+      " 4   weight lab weight brand      428 non-null    object\n",
+      " 5   lightweight                  428 non-null    int64 \n",
+      " 6   drop lab drop brand          428 non-null    object\n",
+      " 7   strike pattern               428 non-null    object\n",
+      " 8   size                         428 non-null    object\n",
+      " 9   midsole softness             428 non-null    object\n",
+      " 10  toebox durability            428 non-null    object\n",
+      " 11  heel padding durability      428 non-null    object\n",
+      " 12  outsole durability           428 non-null    object\n",
+      " 13  breathability                428 non-null    object\n",
+      " 14  width / fit                  428 non-null    object\n",
+      " 15  toebox width                 428 non-null    object\n",
+      " 16  stiffness                    428 non-null    object\n",
+      " 17  torsional rigidity           428 non-null    object\n",
+      " 18  heel counter stiffness       428 non-null    object\n",
+      " 19  plate                        428 non-null    object\n",
+      " 20  rocker                       428 non-null    int64 \n",
+      " 21  heel lab heel brand          428 non-null    object\n",
+      " 22  forefoot lab forefoot brand  428 non-null    object\n",
+      " 23  widths available             428 non-null    object\n",
+      " 24  orthotic friendly            428 non-null    int64 \n",
+      " 25  season                       428 non-null    object\n",
+      " 26  removable insole             428 non-null    int64 \n",
+      "dtypes: int64(4), object(23)\n",
+      "memory usage: 109.8+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1226d524",
+   "metadata": {},
+   "source": [
+    "## Pace"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "e52bbe68",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pace\n",
+      "daily running         294\n",
+      "daily runningtempo     53\n",
+      "tempo                  31\n",
+      "competition            30\n",
+      "competitiontempo       20\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"pace\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "cf0eebcf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 428\n",
+      "NULL Value: 0\n",
+      "\n",
+      "Sample Comparison:\n",
+      "                  pace  pace_daily_running  pace_tempo  pace_competition\n",
+      "0   daily runningtempo                   1           1                 0\n",
+      "6   daily runningtempo                   1           1                 0\n",
+      "12    competitiontempo                   0           1                 1\n",
+      "18    competitiontempo                   0           1                 1\n",
+      "19  daily runningtempo                   1           1                 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['pace'] = df['pace'].astype(str).str.lower()\n",
+    "base_pace = ['daily running', 'tempo', 'competition']\n",
+    "\n",
+    "for level in base_pace:\n",
+    "    column_name = f\"pace_{level.replace(' ', '_')}\"\n",
+    "    df[column_name] = df['pace'].str.contains(level, na=False).astype(int)\n",
+    "\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "pace_cols = [f\"pace_{l.replace(' ', '_')}\" for l in base_pace]\n",
+    "zero_vector_count = (df[pace_cols].sum(axis=1) == 0).sum()\n",
+    "print(f\"NULL Value: {zero_vector_count}\")\n",
+    "\n",
+    "print(\"\\nSample Comparison:\")\n",
+    "print(df[df[pace_cols].sum(axis=1) > 1][['pace'] + pace_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "652f4dcd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Value Counts Kolom Asli:\n",
+      "pace\n",
+      "daily running         294\n",
+      "daily runningtempo     53\n",
+      "tempo                  31\n",
+      "competition            30\n",
+      "competitiontempo       20\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "pace_daily_running sum: 347\n",
+      "pace_tempo sum: 104\n",
+      "pace_competition sum: 50\n",
+      "\n",
+      "                 pace  pace_daily_running  pace_tempo  pace_competition\n",
+      "0  daily runningtempo                   1           1                 0\n",
+      "1       daily running                   1           0                 0\n",
+      "2       daily running                   1           0                 0\n",
+      "3       daily running                   1           0                 0\n",
+      "4       daily running                   1           0                 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"\\nValue Counts Kolom Asli:\")\n",
+    "print(df[\"pace\"].value_counts())\n",
+    "\n",
+    "print()\n",
+    "for col in pace_cols:\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"pace\"] + pace_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "7e5412f6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 29 columns):\n",
+      " #   Column                       Non-Null Count  Dtype \n",
+      "---  ------                       --------------  ----- \n",
+      " 0   brand                        428 non-null    object\n",
+      " 1   name                         428 non-null    object\n",
+      " 2   arch support                 428 non-null    object\n",
+      " 3   weight lab weight brand      428 non-null    object\n",
+      " 4   lightweight                  428 non-null    int64 \n",
+      " 5   drop lab drop brand          428 non-null    object\n",
+      " 6   strike pattern               428 non-null    object\n",
+      " 7   size                         428 non-null    object\n",
+      " 8   midsole softness             428 non-null    object\n",
+      " 9   toebox durability            428 non-null    object\n",
+      " 10  heel padding durability      428 non-null    object\n",
+      " 11  outsole durability           428 non-null    object\n",
+      " 12  breathability                428 non-null    object\n",
+      " 13  width / fit                  428 non-null    object\n",
+      " 14  toebox width                 428 non-null    object\n",
+      " 15  stiffness                    428 non-null    object\n",
+      " 16  torsional rigidity           428 non-null    object\n",
+      " 17  heel counter stiffness       428 non-null    object\n",
+      " 18  plate                        428 non-null    object\n",
+      " 19  rocker                       428 non-null    int64 \n",
+      " 20  heel lab heel brand          428 non-null    object\n",
+      " 21  forefoot lab forefoot brand  428 non-null    object\n",
+      " 22  widths available             428 non-null    object\n",
+      " 23  orthotic friendly            428 non-null    int64 \n",
+      " 24  season                       428 non-null    object\n",
+      " 25  removable insole             428 non-null    int64 \n",
+      " 26  pace_daily_running           428 non-null    int64 \n",
+      " 27  pace_tempo                   428 non-null    int64 \n",
+      " 28  pace_competition             428 non-null    int64 \n",
+      "dtypes: int64(7), object(22)\n",
+      "memory usage: 116.5+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"pace\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "91e01de8",
+   "metadata": {},
+   "source": [
+    "## Arch support"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "b2218f8c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "arch support\n",
+      "neutral           366\n",
+      "stability          61\n",
+      "motion control      1\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"arch support\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "047bde7c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 428\n",
+      "NULL Value: 0\n",
+      "\n",
+      "Sample Comparison:\n",
+      "  arch support  arch_neutral  arch_stability\n",
+      "0      neutral             1               0\n",
+      "1      neutral             1               0\n",
+      "2      neutral             1               0\n",
+      "3      neutral             1               0\n",
+      "4      neutral             1               0\n",
+      "5    stability             0               1\n",
+      "6      neutral             1               0\n",
+      "7      neutral             1               0\n",
+      "8      neutral             1               0\n",
+      "9      neutral             1               0\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['arch support'] = df['arch support'].astype(str).str.lower().str.replace('motion control', 'stability', regex=False)\n",
+    "base_arch = ['neutral', 'stability']\n",
+    "\n",
+    "for level in base_arch:\n",
+    "    column_name = f\"arch_{level}\"\n",
+    "    df[column_name] = df['arch support'].str.contains(level, na=False).astype(int)\n",
+    "\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "arch_cols = [f\"arch_{l}\" for l in base_arch]\n",
+    "zero_vector_count = (df[arch_cols].sum(axis=1) == 0).sum()\n",
+    "print(f\"NULL Value: {zero_vector_count}\")\n",
+    "\n",
+    "print(\"\\nSample Comparison:\")\n",
+    "print(df[[\"arch support\"] + arch_cols].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "4b265105",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "arch support\n",
+      "neutral      366\n",
+      "stability     62\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "arch_neutral sum: 366\n",
+      "arch_stability sum: 62\n",
+      "\n",
+      "  arch support  arch_neutral  arch_stability\n",
+      "0      neutral             1               0\n",
+      "1      neutral             1               0\n",
+      "2      neutral             1               0\n",
+      "3      neutral             1               0\n",
+      "4      neutral             1               0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"arch support\"].value_counts())\n",
+    "\n",
+    "print()\n",
+    "for level in base_arch:\n",
+    "    col = f\"arch_{level}\"\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"arch support\"] + arch_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "a653f286",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 30 columns):\n",
+      " #   Column                       Non-Null Count  Dtype \n",
+      "---  ------                       --------------  ----- \n",
+      " 0   brand                        428 non-null    object\n",
+      " 1   name                         428 non-null    object\n",
+      " 2   weight lab weight brand      428 non-null    object\n",
+      " 3   lightweight                  428 non-null    int64 \n",
+      " 4   drop lab drop brand          428 non-null    object\n",
+      " 5   strike pattern               428 non-null    object\n",
+      " 6   size                         428 non-null    object\n",
+      " 7   midsole softness             428 non-null    object\n",
+      " 8   toebox durability            428 non-null    object\n",
+      " 9   heel padding durability      428 non-null    object\n",
+      " 10  outsole durability           428 non-null    object\n",
+      " 11  breathability                428 non-null    object\n",
+      " 12  width / fit                  428 non-null    object\n",
+      " 13  toebox width                 428 non-null    object\n",
+      " 14  stiffness                    428 non-null    object\n",
+      " 15  torsional rigidity           428 non-null    object\n",
+      " 16  heel counter stiffness       428 non-null    object\n",
+      " 17  plate                        428 non-null    object\n",
+      " 18  rocker                       428 non-null    int64 \n",
+      " 19  heel lab heel brand          428 non-null    object\n",
+      " 20  forefoot lab forefoot brand  428 non-null    object\n",
+      " 21  widths available             428 non-null    object\n",
+      " 22  orthotic friendly            428 non-null    int64 \n",
+      " 23  season                       428 non-null    object\n",
+      " 24  removable insole             428 non-null    int64 \n",
+      " 25  pace_daily_running           428 non-null    int64 \n",
+      " 26  pace_tempo                   428 non-null    int64 \n",
+      " 27  pace_competition             428 non-null    int64 \n",
+      " 28  arch_neutral                 428 non-null    int64 \n",
+      " 29  arch_stability               428 non-null    int64 \n",
+      "dtypes: int64(9), object(21)\n",
+      "memory usage: 119.8+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"arch support\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d544c85c",
+   "metadata": {},
+   "source": [
+    "## Weight lab Weight brand"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "e7922e8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(0)"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mask_missing = (\n",
+    "    df[\"weight lab weight brand\"].isna() |\n",
+    "    (df[\"weight lab weight brand\"].astype(str).str.strip() == \"-\")\n",
+    ")\n",
+    "mask_missing.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "0690849e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         weight lab weight brand  weight_lab_oz  weight_lab_g  \\\n",
+      "0    7.9 oz / 225g 8.1 oz / 230g            7.9           225   \n",
+      "1  10.7 oz / 304g 10.9 oz / 309g           10.7           304   \n",
+      "2  11.9 oz / 336g 11.5 oz / 327g           11.9           336   \n",
+      "3  12.6 oz / 356g 12.4 oz / 352g           12.6           356   \n",
+      "4  12.3 oz / 348g 12.2 oz / 345g           12.3           348   \n",
+      "\n",
+      "   weight_brand_oz  weight_brand_g  \n",
+      "0              8.1           230.0  \n",
+      "1             10.9           309.0  \n",
+      "2             11.5           327.0  \n",
+      "3             12.4           352.0  \n",
+      "4             12.2           345.0  \n"
+     ]
+    }
+   ],
+   "source": [
+    "weight = df[\"weight lab weight brand\"].str.findall(r\"[\\d.]+\")\n",
+    "df[[\"weight_lab_oz\", \"weight_lab_g\", \"weight_brand_oz\", \"weight_brand_g\"]] = (\n",
+    "    pd.DataFrame(weight.tolist(), index=df.index)\n",
+    ")\n",
+    "\n",
+    "for col in [\"weight_lab_oz\", \"weight_lab_g\", \"weight_brand_oz\", \"weight_brand_g\"]:\n",
+    "    df[col] = pd.to_numeric(df[col], errors=\"coerce\")\n",
+    "\n",
+    "print(df[[\"weight lab weight brand\", \"weight_lab_oz\", \"weight_lab_g\", \"weight_brand_oz\", \"weight_brand_g\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "bf12bbcc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 33 columns):\n",
+      " #   Column                       Non-Null Count  Dtype  \n",
+      "---  ------                       --------------  -----  \n",
+      " 0   brand                        428 non-null    object \n",
+      " 1   name                         428 non-null    object \n",
+      " 2   lightweight                  428 non-null    int64  \n",
+      " 3   drop lab drop brand          428 non-null    object \n",
+      " 4   strike pattern               428 non-null    object \n",
+      " 5   size                         428 non-null    object \n",
+      " 6   midsole softness             428 non-null    object \n",
+      " 7   toebox durability            428 non-null    object \n",
+      " 8   heel padding durability      428 non-null    object \n",
+      " 9   outsole durability           428 non-null    object \n",
+      " 10  breathability                428 non-null    object \n",
+      " 11  width / fit                  428 non-null    object \n",
+      " 12  toebox width                 428 non-null    object \n",
+      " 13  stiffness                    428 non-null    object \n",
+      " 14  torsional rigidity           428 non-null    object \n",
+      " 15  heel counter stiffness       428 non-null    object \n",
+      " 16  plate                        428 non-null    object \n",
+      " 17  rocker                       428 non-null    int64  \n",
+      " 18  heel lab heel brand          428 non-null    object \n",
+      " 19  forefoot lab forefoot brand  428 non-null    object \n",
+      " 20  widths available             428 non-null    object \n",
+      " 21  orthotic friendly            428 non-null    int64  \n",
+      " 22  season                       428 non-null    object \n",
+      " 23  removable insole             428 non-null    int64  \n",
+      " 24  pace_daily_running           428 non-null    int64  \n",
+      " 25  pace_tempo                   428 non-null    int64  \n",
+      " 26  pace_competition             428 non-null    int64  \n",
+      " 27  arch_neutral                 428 non-null    int64  \n",
+      " 28  arch_stability               428 non-null    int64  \n",
+      " 29  weight_lab_oz                428 non-null    float64\n",
+      " 30  weight_lab_g                 428 non-null    int64  \n",
+      " 31  weight_brand_oz              421 non-null    float64\n",
+      " 32  weight_brand_g               421 non-null    float64\n",
+      "dtypes: float64(3), int64(10), object(20)\n",
+      "memory usage: 129.9+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"weight lab weight brand\",], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a917d986",
+   "metadata": {},
+   "source": [
+    "## Drop lab Drop brand"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "7c25e004",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(0)"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mask_missing = (\n",
+    "    df[\"drop lab drop brand\"].isna() |\n",
+    "    (df[\"drop lab drop brand\"].astype(str).str.strip() == \"-\")\n",
+    ")\n",
+    "mask_missing.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "20e44ff1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  drop lab drop brand  drop_lab_mm  drop_brand_mm\n",
+      "0      9.4 mm 10.0 mm          9.4           10.0\n",
+      "1       7.7 mm 8.0 mm          7.7            8.0\n",
+      "2      8.9 mm 10.0 mm          8.9           10.0\n",
+      "3     10.6 mm 11.0 mm         10.6           11.0\n",
+      "4      9.9 mm 10.0 mm          9.9           10.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "drop = df[\"drop lab drop brand\"].str.findall(r\"[\\d.]+\")\n",
+    "\n",
+    "df[[\"drop_lab_mm\", \"drop_brand_mm\"]] = (\n",
+    "    pd.DataFrame(drop.tolist(), index=df.index)\n",
+    ")\n",
+    "\n",
+    "for col in [\"drop_lab_mm\", \"drop_brand_mm\"]:\n",
+    "    df[col] = pd.to_numeric(df[col], errors=\"coerce\")\n",
+    "\n",
+    "print(df[[\"drop lab drop brand\", \"drop_lab_mm\", \"drop_brand_mm\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "fda35fa3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 34 columns):\n",
+      " #   Column                       Non-Null Count  Dtype  \n",
+      "---  ------                       --------------  -----  \n",
+      " 0   brand                        428 non-null    object \n",
+      " 1   name                         428 non-null    object \n",
+      " 2   lightweight                  428 non-null    int64  \n",
+      " 3   strike pattern               428 non-null    object \n",
+      " 4   size                         428 non-null    object \n",
+      " 5   midsole softness             428 non-null    object \n",
+      " 6   toebox durability            428 non-null    object \n",
+      " 7   heel padding durability      428 non-null    object \n",
+      " 8   outsole durability           428 non-null    object \n",
+      " 9   breathability                428 non-null    object \n",
+      " 10  width / fit                  428 non-null    object \n",
+      " 11  toebox width                 428 non-null    object \n",
+      " 12  stiffness                    428 non-null    object \n",
+      " 13  torsional rigidity           428 non-null    object \n",
+      " 14  heel counter stiffness       428 non-null    object \n",
+      " 15  plate                        428 non-null    object \n",
+      " 16  rocker                       428 non-null    int64  \n",
+      " 17  heel lab heel brand          428 non-null    object \n",
+      " 18  forefoot lab forefoot brand  428 non-null    object \n",
+      " 19  widths available             428 non-null    object \n",
+      " 20  orthotic friendly            428 non-null    int64  \n",
+      " 21  season                       428 non-null    object \n",
+      " 22  removable insole             428 non-null    int64  \n",
+      " 23  pace_daily_running           428 non-null    int64  \n",
+      " 24  pace_tempo                   428 non-null    int64  \n",
+      " 25  pace_competition             428 non-null    int64  \n",
+      " 26  arch_neutral                 428 non-null    int64  \n",
+      " 27  arch_stability               428 non-null    int64  \n",
+      " 28  weight_lab_oz                428 non-null    float64\n",
+      " 29  weight_lab_g                 428 non-null    int64  \n",
+      " 30  weight_brand_oz              421 non-null    float64\n",
+      " 31  weight_brand_g               421 non-null    float64\n",
+      " 32  drop_lab_mm                  428 non-null    float64\n",
+      " 33  drop_brand_mm                412 non-null    float64\n",
+      "dtypes: float64(5), int64(10), object(19)\n",
+      "memory usage: 133.2+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"drop lab drop brand\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9172932f",
+   "metadata": {},
+   "source": [
+    "## Strike pattern"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "324c90e8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "strike pattern\n",
+      "heelmid/forefoot     162\n",
+      "mid/forefoot         142\n",
+      "heel                 122\n",
+      "-                      1\n",
+      "heel mid/forefoot      1\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"strike pattern\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "91b3eec5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 428\n",
+      "\n",
+      "Unique Values in original column:\n",
+      "['heelmid/forefoot' 'mid/forefoot' 'heel' '-' 'heel mid/forefoot']\n",
+      "\n",
+      "Sample Comparison (Multi-label Mapping):\n",
+      "     strike pattern  strike_heel  strike_mid  strike_forefoot\n",
+      "0  heelmid/forefoot            1           1                1\n",
+      "1      mid/forefoot            0           1                1\n",
+      "2  heelmid/forefoot            1           1                1\n",
+      "3              heel            1           0                0\n",
+      "4  heelmid/forefoot            1           1                1\n",
+      "5              heel            1           0                0\n",
+      "6  heelmid/forefoot            1           1                1\n",
+      "7  heelmid/forefoot            1           1                1\n",
+      "8  heelmid/forefoot            1           1                1\n",
+      "9              heel            1           0                0\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['strike pattern'] = df['strike pattern'].astype(str).str.lower()\n",
+    "base_strikes = ['heel', 'mid', 'forefoot']\n",
+    "\n",
+    "for strike in base_strikes:\n",
+    "    column_name = f\"strike_{strike}\"\n",
+    "   \n",
+    "    df[column_name] = df['strike pattern'].str.contains(strike, na=False).astype(int)\n",
+    "\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "print(\"\\nUnique Values in original column:\")\n",
+    "print(df[\"strike pattern\"].unique())\n",
+    "\n",
+    "print(\"\\nSample Comparison (Multi-label Mapping):\")\n",
+    "strike_cols = [f\"strike_{s}\" for s in base_strikes]\n",
+    "print(df[[\"strike pattern\"] + strike_cols].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "6a45ea26",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "strike pattern\n",
+      "heelmid/forefoot     162\n",
+      "mid/forefoot         142\n",
+      "heel                 122\n",
+      "-                      1\n",
+      "heel mid/forefoot      1\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "strike_heel sum: 285\n",
+      "strike_mid sum: 305\n",
+      "strike_forefoot sum: 305\n",
+      "\n",
+      "     strike pattern  strike_heel  strike_mid  strike_forefoot\n",
+      "0  heelmid/forefoot            1           1                1\n",
+      "1      mid/forefoot            0           1                1\n",
+      "2  heelmid/forefoot            1           1                1\n",
+      "3              heel            1           0                0\n",
+      "4  heelmid/forefoot            1           1                1\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"strike pattern\"].value_counts())\n",
+    "\n",
+    "print()\n",
+    "for strike in base_strikes:\n",
+    "    col = f\"strike_{strike}\"\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"strike pattern\"] + strike_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "c03b1634",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 36 columns):\n",
+      " #   Column                       Non-Null Count  Dtype  \n",
+      "---  ------                       --------------  -----  \n",
+      " 0   brand                        428 non-null    object \n",
+      " 1   name                         428 non-null    object \n",
+      " 2   lightweight                  428 non-null    int64  \n",
+      " 3   size                         428 non-null    object \n",
+      " 4   midsole softness             428 non-null    object \n",
+      " 5   toebox durability            428 non-null    object \n",
+      " 6   heel padding durability      428 non-null    object \n",
+      " 7   outsole durability           428 non-null    object \n",
+      " 8   breathability                428 non-null    object \n",
+      " 9   width / fit                  428 non-null    object \n",
+      " 10  toebox width                 428 non-null    object \n",
+      " 11  stiffness                    428 non-null    object \n",
+      " 12  torsional rigidity           428 non-null    object \n",
+      " 13  heel counter stiffness       428 non-null    object \n",
+      " 14  plate                        428 non-null    object \n",
+      " 15  rocker                       428 non-null    int64  \n",
+      " 16  heel lab heel brand          428 non-null    object \n",
+      " 17  forefoot lab forefoot brand  428 non-null    object \n",
+      " 18  widths available             428 non-null    object \n",
+      " 19  orthotic friendly            428 non-null    int64  \n",
+      " 20  season                       428 non-null    object \n",
+      " 21  removable insole             428 non-null    int64  \n",
+      " 22  pace_daily_running           428 non-null    int64  \n",
+      " 23  pace_tempo                   428 non-null    int64  \n",
+      " 24  pace_competition             428 non-null    int64  \n",
+      " 25  arch_neutral                 428 non-null    int64  \n",
+      " 26  arch_stability               428 non-null    int64  \n",
+      " 27  weight_lab_oz                428 non-null    float64\n",
+      " 28  weight_lab_g                 428 non-null    int64  \n",
+      " 29  weight_brand_oz              421 non-null    float64\n",
+      " 30  weight_brand_g               421 non-null    float64\n",
+      " 31  drop_lab_mm                  428 non-null    float64\n",
+      " 32  drop_brand_mm                412 non-null    float64\n",
+      " 33  strike_heel                  428 non-null    int64  \n",
+      " 34  strike_mid                   428 non-null    int64  \n",
+      " 35  strike_forefoot              428 non-null    int64  \n",
+      "dtypes: float64(5), int64(13), object(18)\n",
+      "memory usage: 139.9+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=['strike pattern'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "680f279c",
+   "metadata": {},
+   "source": [
+    "## Midsole softness"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "3864acbb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "midsole softness\n",
+      "soft        177\n",
+      "balanced    172\n",
+      "-            61\n",
+      "firm         18\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"midsole softness\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "bf425c29",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Value Counts Kolom Asli ---\n",
+      "midsole softness\n",
+      "soft        177\n",
+      "balanced    172\n",
+      "-            61\n",
+      "firm         18\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "--- Sebaran Nilai Hasil Ordinal ---\n",
+      "Index 0: 61 baris\n",
+      "Index 1: 18 baris\n",
+      "Index 2: 0 baris\n",
+      "Index 3: 172 baris\n",
+      "Index 4: 0 baris\n",
+      "Index 5: 177 baris\n",
+      "\n",
+      "--- Perbandingan Data (Head) ---\n",
+      "  midsole softness  midsole_softness\n",
+      "0         balanced                 3\n",
+      "1             soft                 5\n",
+      "2             firm                 1\n",
+      "3             firm                 1\n",
+      "4             firm                 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "softness_scaled = {\n",
+    "    \"firm\": 1,\n",
+    "    \"balanced\": 3,\n",
+    "    \"soft\": 5,\n",
+    "    \"-\": 0,\n",
+    "    \"0\": 0\n",
+    "}\n",
+    "\n",
+    "df['midsole_softness'] = df['midsole softness'].map(softness_scaled)\n",
+    "\n",
+    "print(\"--- Value Counts Kolom Asli ---\")\n",
+    "print(df[\"midsole softness\"].value_counts())\n",
+    "\n",
+    "print(\"\\n--- Sebaran Nilai Hasil Ordinal ---\")\n",
+    "counts = df[\"midsole_softness\"].value_counts().sort_index()\n",
+    "for i in range(6):\n",
+    "    print(f\"Index {i}: {counts.get(i, 0)} baris\")\n",
+    "\n",
+    "print(\"\\n--- Perbandingan Data (Head) ---\")\n",
+    "print(df[[\"midsole softness\", \"midsole_softness\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "2070c508",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 36 columns):\n",
+      " #   Column                       Non-Null Count  Dtype  \n",
+      "---  ------                       --------------  -----  \n",
+      " 0   brand                        428 non-null    object \n",
+      " 1   name                         428 non-null    object \n",
+      " 2   lightweight                  428 non-null    int64  \n",
+      " 3   size                         428 non-null    object \n",
+      " 4   toebox durability            428 non-null    object \n",
+      " 5   heel padding durability      428 non-null    object \n",
+      " 6   outsole durability           428 non-null    object \n",
+      " 7   breathability                428 non-null    object \n",
+      " 8   width / fit                  428 non-null    object \n",
+      " 9   toebox width                 428 non-null    object \n",
+      " 10  stiffness                    428 non-null    object \n",
+      " 11  torsional rigidity           428 non-null    object \n",
+      " 12  heel counter stiffness       428 non-null    object \n",
+      " 13  plate                        428 non-null    object \n",
+      " 14  rocker                       428 non-null    int64  \n",
+      " 15  heel lab heel brand          428 non-null    object \n",
+      " 16  forefoot lab forefoot brand  428 non-null    object \n",
+      " 17  widths available             428 non-null    object \n",
+      " 18  orthotic friendly            428 non-null    int64  \n",
+      " 19  season                       428 non-null    object \n",
+      " 20  removable insole             428 non-null    int64  \n",
+      " 21  pace_daily_running           428 non-null    int64  \n",
+      " 22  pace_tempo                   428 non-null    int64  \n",
+      " 23  pace_competition             428 non-null    int64  \n",
+      " 24  arch_neutral                 428 non-null    int64  \n",
+      " 25  arch_stability               428 non-null    int64  \n",
+      " 26  weight_lab_oz                428 non-null    float64\n",
+      " 27  weight_lab_g                 428 non-null    int64  \n",
+      " 28  weight_brand_oz              421 non-null    float64\n",
+      " 29  weight_brand_g               421 non-null    float64\n",
+      " 30  drop_lab_mm                  428 non-null    float64\n",
+      " 31  drop_brand_mm                412 non-null    float64\n",
+      " 32  strike_heel                  428 non-null    int64  \n",
+      " 33  strike_mid                   428 non-null    int64  \n",
+      " 34  strike_forefoot              428 non-null    int64  \n",
+      " 35  midsole_softness             428 non-null    int64  \n",
+      "dtypes: float64(5), int64(14), object(17)\n",
+      "memory usage: 139.9+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"midsole softness\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ce3baab9",
+   "metadata": {},
+   "source": [
+    "## Toebox durability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "5e43a24c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "toebox durability\n",
+      "decent    150\n",
+      "-         117\n",
+      "bad        88\n",
+      "good       73\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['toebox durability'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "442a302f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 428\n",
+      "\n",
+      "Unique Values mapping check:\n",
+      "'-' di-encode menjadi 0 (Total: 117)\n",
+      "'very bad' di-encode menjadi 1 (Total: 0)\n",
+      "'bad' di-encode menjadi 2 (Total: 88)\n",
+      "'decent' di-encode menjadi 3 (Total: 150)\n",
+      "'good' di-encode menjadi 4 (Total: 73)\n",
+      "'very good' di-encode menjadi 5 (Total: 0)\n",
+      "\n",
+      "Sample Data:\n",
+      "  toebox durability  toebox_durability\n",
+      "0                 -                  0\n",
+      "1              good                  4\n",
+      "2                 -                  0\n",
+      "3                 -                  0\n",
+      "4              good                  4\n",
+      "5            decent                  3\n",
+      "6               bad                  2\n",
+      "7            decent                  3\n",
+      "8               bad                  2\n",
+      "9               bad                  2\n"
+     ]
+    }
+   ],
+   "source": [
+    "durability_map = {\n",
+    "    \"-\": 0,\n",
+    "    \"very bad\": 1,\n",
+    "    \"bad\": 2,\n",
+    "    \"decent\": 3,\n",
+    "    \"good\": 4,\n",
+    "    \"very good\": 5\n",
+    "}\n",
+    "\n",
+    "df['toebox_durability'] = df['toebox durability'].map(durability_map)\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "print(\"\\nUnique Values mapping check:\")\n",
+    "for label, value in durability_map.items():\n",
+    "    count = (df['toebox durability'] == label).sum()\n",
+    "    print(f\"'{label}' di-encode menjadi {value} (Total: {count})\")\n",
+    "\n",
+    "print(\"\\nSample Data:\")\n",
+    "print(df[[\"toebox durability\", \"toebox_durability\"]].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "00213dd6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 36 columns):\n",
+      " #   Column                       Non-Null Count  Dtype  \n",
+      "---  ------                       --------------  -----  \n",
+      " 0   brand                        428 non-null    object \n",
+      " 1   name                         428 non-null    object \n",
+      " 2   lightweight                  428 non-null    int64  \n",
+      " 3   size                         428 non-null    object \n",
+      " 4   heel padding durability      428 non-null    object \n",
+      " 5   outsole durability           428 non-null    object \n",
+      " 6   breathability                428 non-null    object \n",
+      " 7   width / fit                  428 non-null    object \n",
+      " 8   toebox width                 428 non-null    object \n",
+      " 9   stiffness                    428 non-null    object \n",
+      " 10  torsional rigidity           428 non-null    object \n",
+      " 11  heel counter stiffness       428 non-null    object \n",
+      " 12  plate                        428 non-null    object \n",
+      " 13  rocker                       428 non-null    int64  \n",
+      " 14  heel lab heel brand          428 non-null    object \n",
+      " 15  forefoot lab forefoot brand  428 non-null    object \n",
+      " 16  widths available             428 non-null    object \n",
+      " 17  orthotic friendly            428 non-null    int64  \n",
+      " 18  season                       428 non-null    object \n",
+      " 19  removable insole             428 non-null    int64  \n",
+      " 20  pace_daily_running           428 non-null    int64  \n",
+      " 21  pace_tempo                   428 non-null    int64  \n",
+      " 22  pace_competition             428 non-null    int64  \n",
+      " 23  arch_neutral                 428 non-null    int64  \n",
+      " 24  arch_stability               428 non-null    int64  \n",
+      " 25  weight_lab_oz                428 non-null    float64\n",
+      " 26  weight_lab_g                 428 non-null    int64  \n",
+      " 27  weight_brand_oz              421 non-null    float64\n",
+      " 28  weight_brand_g               421 non-null    float64\n",
+      " 29  drop_lab_mm                  428 non-null    float64\n",
+      " 30  drop_brand_mm                412 non-null    float64\n",
+      " 31  strike_heel                  428 non-null    int64  \n",
+      " 32  strike_mid                   428 non-null    int64  \n",
+      " 33  strike_forefoot              428 non-null    int64  \n",
+      " 34  midsole_softness             428 non-null    int64  \n",
+      " 35  toebox_durability            428 non-null    int64  \n",
+      "dtypes: float64(5), int64(15), object(16)\n",
+      "memory usage: 139.9+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=['toebox durability'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbbbb0c3",
+   "metadata": {},
+   "source": [
+    "## Heel padding durability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "29a06502",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "heel padding durability\n",
+      "good      179\n",
+      "-         122\n",
+      "decent     77\n",
+      "bad        50\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"heel padding durability\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "id": "a9ee9e0a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Value Counts Kolom Asli ---\n",
+      "heel padding durability\n",
+      "good      179\n",
+      "-         122\n",
+      "decent     77\n",
+      "bad        50\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "--- Sebaran Nilai Hasil Ordinal (Scale 1-5) ---\n",
+      "Index 0: 122 baris\n",
+      "Index 1: 0 baris\n",
+      "Index 2: 50 baris\n",
+      "Index 3: 77 baris\n",
+      "Index 4: 179 baris\n",
+      "Index 5: 0 baris\n",
+      "\n",
+      "--- Perbandingan Data (Head) ---\n",
+      "  heel padding durability  heel_durability\n",
+      "0                       -                0\n",
+      "1                    good                4\n",
+      "2                    good                4\n",
+      "3                       -                0\n",
+      "4                    good                4\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['heel padding durability'] = df['heel padding durability'].astype(str).str.lower()\n",
+    "\n",
+    "durability_scale_5 = {\n",
+    "    \"-\": 0,\n",
+    "    \"very bad\": 1,\n",
+    "    \"bad\": 2,\n",
+    "    \"decent\": 3,\n",
+    "    \"good\": 4,\n",
+    "    \"very good\": 5\n",
+    "}\n",
+    "\n",
+    "\n",
+    "df['heel_durability'] = df['heel padding durability'].map(durability_scale_5)\n",
+    "\n",
+    "\n",
+    "print(\"--- Value Counts Kolom Asli ---\")\n",
+    "print(df[\"heel padding durability\"].value_counts())\n",
+    "\n",
+    "print(\"\\n--- Sebaran Nilai Hasil Ordinal (Scale 1-5) ---\")\n",
+    "counts = df[\"heel_durability\"].value_counts().sort_index()\n",
+    "for i in range(6):\n",
+    "    print(f\"Index {i}: {counts.get(i, 0)} baris\")\n",
+    "\n",
+    "print(\"\\n--- Perbandingan Data (Head) ---\")\n",
+    "print(df[[\"heel padding durability\", \"heel_durability\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "5be88f73",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 36 columns):\n",
+      " #   Column                       Non-Null Count  Dtype  \n",
+      "---  ------                       --------------  -----  \n",
+      " 0   brand                        428 non-null    object \n",
+      " 1   name                         428 non-null    object \n",
+      " 2   lightweight                  428 non-null    int64  \n",
+      " 3   size                         428 non-null    object \n",
+      " 4   outsole durability           428 non-null    object \n",
+      " 5   breathability                428 non-null    object \n",
+      " 6   width / fit                  428 non-null    object \n",
+      " 7   toebox width                 428 non-null    object \n",
+      " 8   stiffness                    428 non-null    object \n",
+      " 9   torsional rigidity           428 non-null    object \n",
+      " 10  heel counter stiffness       428 non-null    object \n",
+      " 11  plate                        428 non-null    object \n",
+      " 12  rocker                       428 non-null    int64  \n",
+      " 13  heel lab heel brand          428 non-null    object \n",
+      " 14  forefoot lab forefoot brand  428 non-null    object \n",
+      " 15  widths available             428 non-null    object \n",
+      " 16  orthotic friendly            428 non-null    int64  \n",
+      " 17  season                       428 non-null    object \n",
+      " 18  removable insole             428 non-null    int64  \n",
+      " 19  pace_daily_running           428 non-null    int64  \n",
+      " 20  pace_tempo                   428 non-null    int64  \n",
+      " 21  pace_competition             428 non-null    int64  \n",
+      " 22  arch_neutral                 428 non-null    int64  \n",
+      " 23  arch_stability               428 non-null    int64  \n",
+      " 24  weight_lab_oz                428 non-null    float64\n",
+      " 25  weight_lab_g                 428 non-null    int64  \n",
+      " 26  weight_brand_oz              421 non-null    float64\n",
+      " 27  weight_brand_g               421 non-null    float64\n",
+      " 28  drop_lab_mm                  428 non-null    float64\n",
+      " 29  drop_brand_mm                412 non-null    float64\n",
+      " 30  strike_heel                  428 non-null    int64  \n",
+      " 31  strike_mid                   428 non-null    int64  \n",
+      " 32  strike_forefoot              428 non-null    int64  \n",
+      " 33  midsole_softness             428 non-null    int64  \n",
+      " 34  toebox_durability            428 non-null    int64  \n",
+      " 35  heel_durability              428 non-null    int64  \n",
+      "dtypes: float64(5), int64(16), object(15)\n",
+      "memory usage: 139.9+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop('heel padding durability', axis=1, inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ba41268",
+   "metadata": {},
+   "source": [
+    "## Outsole durability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "id": "0b43dddd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "outsole durability\n",
+      "good      206\n",
+      "-         134\n",
+      "decent     67\n",
+      "bad        21\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"outsole durability\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "5cd7f05c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Value Counts Kolom Asli ---\n",
+      "outsole durability\n",
+      "good      206\n",
+      "-         134\n",
+      "decent     67\n",
+      "bad        21\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "--- Sebaran Nilai Hasil Ordinal (Scale 1-5) ---\n",
+      "Index 0: 134 baris\n",
+      "Index 1: 0 baris\n",
+      "Index 2: 21 baris\n",
+      "Index 3: 67 baris\n",
+      "Index 4: 206 baris\n",
+      "Index 5: 0 baris\n",
+      "\n",
+      "--- Perbandingan Data (Head) ---\n",
+      "  outsole durability  outsole_durability\n",
+      "0                  -                   0\n",
+      "1               good                   4\n",
+      "2                  -                   0\n",
+      "3                  -                   0\n",
+      "4               good                   4\n"
+     ]
+    }
+   ],
+   "source": [
+    "durability_scaled = {\n",
+    "    \"-\": 0,\n",
+    "    \"very bad\": 1,\n",
+    "    \"bad\": 2,\n",
+    "    \"decent\": 3,\n",
+    "    \"good\": 4,\n",
+    "    \"very good\": 5\n",
+    "}\n",
+    "\n",
+    "\n",
+    "df['outsole_durability'] = df['outsole durability'].map(durability_scaled)\n",
+    "print(\"--- Value Counts Kolom Asli ---\")\n",
+    "print(df[\"outsole durability\"].value_counts())\n",
+    "\n",
+    "print(\"\\n--- Sebaran Nilai Hasil Ordinal (Scale 1-5) ---\")\n",
+    "counts = df[\"outsole_durability\"].value_counts().sort_index()\n",
+    "for i in range(6):\n",
+    "    print(f\"Index {i}: {counts.get(i, 0)} baris\")\n",
+    "\n",
+    "print(\"\\n--- Perbandingan Data (Head) ---\")\n",
+    "print(df[[\"outsole durability\", \"outsole_durability\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "f5756190",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 36 columns):\n",
+      " #   Column                       Non-Null Count  Dtype  \n",
+      "---  ------                       --------------  -----  \n",
+      " 0   brand                        428 non-null    object \n",
+      " 1   name                         428 non-null    object \n",
+      " 2   lightweight                  428 non-null    int64  \n",
+      " 3   size                         428 non-null    object \n",
+      " 4   breathability                428 non-null    object \n",
+      " 5   width / fit                  428 non-null    object \n",
+      " 6   toebox width                 428 non-null    object \n",
+      " 7   stiffness                    428 non-null    object \n",
+      " 8   torsional rigidity           428 non-null    object \n",
+      " 9   heel counter stiffness       428 non-null    object \n",
+      " 10  plate                        428 non-null    object \n",
+      " 11  rocker                       428 non-null    int64  \n",
+      " 12  heel lab heel brand          428 non-null    object \n",
+      " 13  forefoot lab forefoot brand  428 non-null    object \n",
+      " 14  widths available             428 non-null    object \n",
+      " 15  orthotic friendly            428 non-null    int64  \n",
+      " 16  season                       428 non-null    object \n",
+      " 17  removable insole             428 non-null    int64  \n",
+      " 18  pace_daily_running           428 non-null    int64  \n",
+      " 19  pace_tempo                   428 non-null    int64  \n",
+      " 20  pace_competition             428 non-null    int64  \n",
+      " 21  arch_neutral                 428 non-null    int64  \n",
+      " 22  arch_stability               428 non-null    int64  \n",
+      " 23  weight_lab_oz                428 non-null    float64\n",
+      " 24  weight_lab_g                 428 non-null    int64  \n",
+      " 25  weight_brand_oz              421 non-null    float64\n",
+      " 26  weight_brand_g               421 non-null    float64\n",
+      " 27  drop_lab_mm                  428 non-null    float64\n",
+      " 28  drop_brand_mm                412 non-null    float64\n",
+      " 29  strike_heel                  428 non-null    int64  \n",
+      " 30  strike_mid                   428 non-null    int64  \n",
+      " 31  strike_forefoot              428 non-null    int64  \n",
+      " 32  midsole_softness             428 non-null    int64  \n",
+      " 33  toebox_durability            428 non-null    int64  \n",
+      " 34  heel_durability              428 non-null    int64  \n",
+      " 35  outsole_durability           428 non-null    int64  \n",
+      "dtypes: float64(5), int64(17), object(14)\n",
+      "memory usage: 139.9+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop('outsole durability', axis=1, inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d50c8fe",
+   "metadata": {},
+   "source": [
+    "## Breathability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "4a55edac",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "breathability\n",
+      "moderate      208\n",
+      "breathable    109\n",
+      "-              58\n",
+      "warm           53\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"breathability\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "5d004d4e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Value Counts Kolom Asli ---\n",
+      "breathability\n",
+      "moderate      208\n",
+      "breathable    109\n",
+      "-              58\n",
+      "warm           53\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "--- Sebaran Nilai Hasil Ordinal ---\n",
+      "Index 0: 58 baris\n",
+      "Index 1: 53 baris\n",
+      "Index 2: 0 baris\n",
+      "Index 3: 208 baris\n",
+      "Index 4: 0 baris\n",
+      "Index 5: 109 baris\n",
+      "\n",
+      "--- Perbandingan Data (Head) ---\n",
+      "  breathability  breathability_scaled\n",
+      "0             -                     0\n",
+      "1    breathable                     5\n",
+      "2          warm                     1\n",
+      "3          warm                     1\n",
+      "4          warm                     1\n"
+     ]
+    }
+   ],
+   "source": [
+    "breathability_scaled = {\n",
+    "    \"-\": 0,\n",
+    "    # \"suffocating\": 1,\n",
+    "    \"warm\": 1,\n",
+    "    \"moderate\": 3,\n",
+    "    \"good\": 4,\n",
+    "    \"breathable\": 5\n",
+    "}\n",
+    "\n",
+    "df['breathability_scaled'] = df['breathability'].map(breathability_scaled)\n",
+    "\n",
+    "print(\"--- Value Counts Kolom Asli ---\")\n",
+    "print(df[\"breathability\"].value_counts())\n",
+    "\n",
+    "print(\"\\n--- Sebaran Nilai Hasil Ordinal ---\")\n",
+    "counts = df[\"breathability_scaled\"].value_counts().sort_index()\n",
+    "for i in range(6):\n",
+    "    print(f\"Index {i}: {counts.get(i, 0)} baris\")\n",
+    "\n",
+    "print(\"\\n--- Perbandingan Data (Head) ---\")\n",
+    "print(df[[\"breathability\", \"breathability_scaled\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "111e43ee",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 36 columns):\n",
+      " #   Column                       Non-Null Count  Dtype  \n",
+      "---  ------                       --------------  -----  \n",
+      " 0   brand                        428 non-null    object \n",
+      " 1   name                         428 non-null    object \n",
+      " 2   lightweight                  428 non-null    int64  \n",
+      " 3   size                         428 non-null    object \n",
+      " 4   width / fit                  428 non-null    object \n",
+      " 5   toebox width                 428 non-null    object \n",
+      " 6   stiffness                    428 non-null    object \n",
+      " 7   torsional rigidity           428 non-null    object \n",
+      " 8   heel counter stiffness       428 non-null    object \n",
+      " 9   plate                        428 non-null    object \n",
+      " 10  rocker                       428 non-null    int64  \n",
+      " 11  heel lab heel brand          428 non-null    object \n",
+      " 12  forefoot lab forefoot brand  428 non-null    object \n",
+      " 13  widths available             428 non-null    object \n",
+      " 14  orthotic friendly            428 non-null    int64  \n",
+      " 15  season                       428 non-null    object \n",
+      " 16  removable insole             428 non-null    int64  \n",
+      " 17  pace_daily_running           428 non-null    int64  \n",
+      " 18  pace_tempo                   428 non-null    int64  \n",
+      " 19  pace_competition             428 non-null    int64  \n",
+      " 20  arch_neutral                 428 non-null    int64  \n",
+      " 21  arch_stability               428 non-null    int64  \n",
+      " 22  weight_lab_oz                428 non-null    float64\n",
+      " 23  weight_lab_g                 428 non-null    int64  \n",
+      " 24  weight_brand_oz              421 non-null    float64\n",
+      " 25  weight_brand_g               421 non-null    float64\n",
+      " 26  drop_lab_mm                  428 non-null    float64\n",
+      " 27  drop_brand_mm                412 non-null    float64\n",
+      " 28  strike_heel                  428 non-null    int64  \n",
+      " 29  strike_mid                   428 non-null    int64  \n",
+      " 30  strike_forefoot              428 non-null    int64  \n",
+      " 31  midsole_softness             428 non-null    int64  \n",
+      " 32  toebox_durability            428 non-null    int64  \n",
+      " 33  heel_durability              428 non-null    int64  \n",
+      " 34  outsole_durability           428 non-null    int64  \n",
+      " 35  breathability_scaled         428 non-null    int64  \n",
+      "dtypes: float64(5), int64(18), object(13)\n",
+      "memory usage: 139.9+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop('breathability', axis=1, inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e912d7f",
+   "metadata": {},
+   "source": [
+    "## Width / fit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "15a7cd5d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "width / fit\n",
+      "medium    256\n",
+      "narrow    140\n",
+      "wide       32\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['width / fit'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "id": "084eb61c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Value Counts Kolom Asli ---\n",
+      "width / fit\n",
+      "medium    256\n",
+      "narrow    140\n",
+      "wide       32\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "--- Sebaran Nilai Hasil Ordinal ---\n",
+      "Index 0: 0 baris\n",
+      "Index 1: 140 baris\n",
+      "Index 2: 0 baris\n",
+      "Index 3: 256 baris\n",
+      "Index 4: 0 baris\n",
+      "Index 5: 32 baris\n",
+      "\n",
+      "--- Perbandingan Data (Head) ---\n",
+      "  width / fit  width_fit\n",
+      "0      narrow          1\n",
+      "1      narrow          1\n",
+      "2      narrow          1\n",
+      "3      narrow          1\n",
+      "4      narrow          1\n"
+     ]
+    }
+   ],
+   "source": [
+    "width_scaled = {\n",
+    "    \"narrow\": 1,\n",
+    "    \"medium\": 3,\n",
+    "    \"wide\": 5,\n",
+    "    \"-\": 0,\n",
+    "    \"0\": 0\n",
+    "}\n",
+    "\n",
+    "df['width_fit'] = df['width / fit'].map(width_scaled)\n",
+    "\n",
+    "print(\"--- Value Counts Kolom Asli ---\")\n",
+    "print(df[\"width / fit\"].value_counts())\n",
+    "\n",
+    "print(\"\\n--- Sebaran Nilai Hasil Ordinal ---\")\n",
+    "counts = df[\"width_fit\"].value_counts().sort_index()\n",
+    "for i in range(6):\n",
+    "    print(f\"Index {i}: {counts.get(i, 0)} baris\")\n",
+    "\n",
+    "print(\"\\n--- Perbandingan Data (Head) ---\")\n",
+    "print(df[[\"width / fit\", \"width_fit\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "id": "732bd894",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 34 columns):\n",
+      " #   Column                       Non-Null Count  Dtype  \n",
+      "---  ------                       --------------  -----  \n",
+      " 0   brand                        428 non-null    object \n",
+      " 1   name                         428 non-null    object \n",
+      " 2   lightweight                  428 non-null    int64  \n",
+      " 3   toebox width                 428 non-null    object \n",
+      " 4   stiffness                    428 non-null    object \n",
+      " 5   torsional rigidity           428 non-null    object \n",
+      " 6   heel counter stiffness       428 non-null    object \n",
+      " 7   plate                        428 non-null    object \n",
+      " 8   rocker                       428 non-null    int64  \n",
+      " 9   heel lab heel brand          428 non-null    object \n",
+      " 10  forefoot lab forefoot brand  428 non-null    object \n",
+      " 11  orthotic friendly            428 non-null    int64  \n",
+      " 12  season                       428 non-null    object \n",
+      " 13  removable insole             428 non-null    int64  \n",
+      " 14  pace_daily_running           428 non-null    int64  \n",
+      " 15  pace_tempo                   428 non-null    int64  \n",
+      " 16  pace_competition             428 non-null    int64  \n",
+      " 17  arch_neutral                 428 non-null    int64  \n",
+      " 18  arch_stability               428 non-null    int64  \n",
+      " 19  weight_lab_oz                428 non-null    float64\n",
+      " 20  weight_lab_g                 428 non-null    int64  \n",
+      " 21  weight_brand_oz              421 non-null    float64\n",
+      " 22  weight_brand_g               421 non-null    float64\n",
+      " 23  drop_lab_mm                  428 non-null    float64\n",
+      " 24  drop_brand_mm                412 non-null    float64\n",
+      " 25  strike_heel                  428 non-null    int64  \n",
+      " 26  strike_mid                   428 non-null    int64  \n",
+      " 27  strike_forefoot              428 non-null    int64  \n",
+      " 28  midsole_softness             428 non-null    int64  \n",
+      " 29  toebox_durability            428 non-null    int64  \n",
+      " 30  heel_durability              428 non-null    int64  \n",
+      " 31  outsole_durability           428 non-null    int64  \n",
+      " 32  breathability_scaled         428 non-null    int64  \n",
+      " 33  width_fit                    428 non-null    int64  \n",
+      "dtypes: float64(5), int64(19), object(10)\n",
+      "memory usage: 133.2+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"size\", \"widths available\", \"width / fit\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "51d21f32",
+   "metadata": {},
+   "source": [
+    "## Toebox width"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "id": "d6afe0a2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "toebox width\n",
+      "medium    204\n",
+      "-         108\n",
+      "wide       61\n",
+      "narrow     55\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['toebox width'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "id": "5683f55f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Value Counts Kolom Asli ---\n",
+      "toebox width\n",
+      "medium    204\n",
+      "-         108\n",
+      "wide       61\n",
+      "narrow     55\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "--- Sebaran Nilai Hasil Ordinal ---\n",
+      "Index 0: 108 baris\n",
+      "Index 1: 55 baris\n",
+      "Index 2: 0 baris\n",
+      "Index 3: 204 baris\n",
+      "Index 4: 0 baris\n",
+      "Index 5: 61 baris\n",
+      "\n",
+      "--- Perbandingan Data (Head) ---\n",
+      "  toebox width  toebox_width\n",
+      "0            -             0\n",
+      "1       medium             3\n",
+      "2            -             0\n",
+      "3            -             0\n",
+      "4       medium             3\n"
+     ]
+    }
+   ],
+   "source": [
+    "toebox_scaled = {\n",
+    "    \"narrow\": 1,\n",
+    "    \"medium\": 3,\n",
+    "    \"wide\": 5,\n",
+    "    \"-\": 0,\n",
+    "    \"0\": 0\n",
+    "}\n",
+    "\n",
+    "df['toebox_width'] = df['toebox width'].map(toebox_scaled)\n",
+    "\n",
+    "print(\"--- Value Counts Kolom Asli ---\")\n",
+    "print(df[\"toebox width\"].value_counts())\n",
+    "\n",
+    "print(\"\\n--- Sebaran Nilai Hasil Ordinal ---\")\n",
+    "counts = df[\"toebox_width\"].value_counts().sort_index()\n",
+    "for i in range(6):\n",
+    "    print(f\"Index {i}: {counts.get(i, 0)} baris\")\n",
+    "\n",
+    "print(\"\\n--- Perbandingan Data (Head) ---\")\n",
+    "print(df[[\"toebox width\", \"toebox_width\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "id": "05dab87b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 34 columns):\n",
+      " #   Column                       Non-Null Count  Dtype  \n",
+      "---  ------                       --------------  -----  \n",
+      " 0   brand                        428 non-null    object \n",
+      " 1   name                         428 non-null    object \n",
+      " 2   lightweight                  428 non-null    int64  \n",
+      " 3   stiffness                    428 non-null    object \n",
+      " 4   torsional rigidity           428 non-null    object \n",
+      " 5   heel counter stiffness       428 non-null    object \n",
+      " 6   plate                        428 non-null    object \n",
+      " 7   rocker                       428 non-null    int64  \n",
+      " 8   heel lab heel brand          428 non-null    object \n",
+      " 9   forefoot lab forefoot brand  428 non-null    object \n",
+      " 10  orthotic friendly            428 non-null    int64  \n",
+      " 11  season                       428 non-null    object \n",
+      " 12  removable insole             428 non-null    int64  \n",
+      " 13  pace_daily_running           428 non-null    int64  \n",
+      " 14  pace_tempo                   428 non-null    int64  \n",
+      " 15  pace_competition             428 non-null    int64  \n",
+      " 16  arch_neutral                 428 non-null    int64  \n",
+      " 17  arch_stability               428 non-null    int64  \n",
+      " 18  weight_lab_oz                428 non-null    float64\n",
+      " 19  weight_lab_g                 428 non-null    int64  \n",
+      " 20  weight_brand_oz              421 non-null    float64\n",
+      " 21  weight_brand_g               421 non-null    float64\n",
+      " 22  drop_lab_mm                  428 non-null    float64\n",
+      " 23  drop_brand_mm                412 non-null    float64\n",
+      " 24  strike_heel                  428 non-null    int64  \n",
+      " 25  strike_mid                   428 non-null    int64  \n",
+      " 26  strike_forefoot              428 non-null    int64  \n",
+      " 27  midsole_softness             428 non-null    int64  \n",
+      " 28  toebox_durability            428 non-null    int64  \n",
+      " 29  heel_durability              428 non-null    int64  \n",
+      " 30  outsole_durability           428 non-null    int64  \n",
+      " 31  breathability_scaled         428 non-null    int64  \n",
+      " 32  width_fit                    428 non-null    int64  \n",
+      " 33  toebox_width                 428 non-null    int64  \n",
+      "dtypes: float64(5), int64(20), object(9)\n",
+      "memory usage: 133.2+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"toebox width\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a9a3078",
+   "metadata": {},
+   "source": [
+    "## Stiffness"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "id": "c50e96a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "stiffness\n",
+      "stiff       217\n",
+      "moderate    163\n",
+      "flexible     35\n",
+      "-            13\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['stiffness'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "id": "1dc0cf0f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Value Counts Kolom Asli ---\n",
+      "stiffness\n",
+      "stiff       217\n",
+      "moderate    163\n",
+      "flexible     35\n",
+      "-            13\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "--- Sebaran Nilai Hasil Ordinal  ---\n",
+      "Index 0: 13 baris\n",
+      "Index 1: 35 baris\n",
+      "Index 2: 0 baris\n",
+      "Index 3: 163 baris\n",
+      "Index 4: 0 baris\n",
+      "Index 5: 217 baris\n",
+      "\n",
+      "--- Perbandingan Data ---\n",
+      "  stiffness  stiffness_scaled\n",
+      "0     stiff                 5\n",
+      "1     stiff                 5\n",
+      "2     stiff                 5\n",
+      "3     stiff                 5\n",
+      "4  moderate                 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "stiffness_scaled = {\n",
+    "    \"flexible\": 1,\n",
+    "    \"moderate\": 3,\n",
+    "    \"stiff\": 5,\n",
+    "    \"-\": 0,\n",
+    "    \"0\": 0\n",
+    "}\n",
+    "\n",
+    "df['stiffness_scaled'] = df['stiffness'].map(stiffness_scaled)\n",
+    "\n",
+    "print(\"--- Value Counts Kolom Asli ---\")\n",
+    "print(df[\"stiffness\"].value_counts())\n",
+    "\n",
+    "print(\"\\n--- Sebaran Nilai Hasil Ordinal  ---\")\n",
+    "counts = df[\"stiffness_scaled\"].value_counts().sort_index()\n",
+    "for i in range(6):\n",
+    "    print(f\"Index {i}: {counts.get(i, 0)} baris\")\n",
+    "\n",
+    "print(\"\\n--- Perbandingan Data ---\")\n",
+    "print(df[[\"stiffness\", \"stiffness_scaled\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "id": "45a7c4fd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 34 columns):\n",
+      " #   Column                       Non-Null Count  Dtype  \n",
+      "---  ------                       --------------  -----  \n",
+      " 0   brand                        428 non-null    object \n",
+      " 1   name                         428 non-null    object \n",
+      " 2   lightweight                  428 non-null    int64  \n",
+      " 3   torsional rigidity           428 non-null    object \n",
+      " 4   heel counter stiffness       428 non-null    object \n",
+      " 5   plate                        428 non-null    object \n",
+      " 6   rocker                       428 non-null    int64  \n",
+      " 7   heel lab heel brand          428 non-null    object \n",
+      " 8   forefoot lab forefoot brand  428 non-null    object \n",
+      " 9   orthotic friendly            428 non-null    int64  \n",
+      " 10  season                       428 non-null    object \n",
+      " 11  removable insole             428 non-null    int64  \n",
+      " 12  pace_daily_running           428 non-null    int64  \n",
+      " 13  pace_tempo                   428 non-null    int64  \n",
+      " 14  pace_competition             428 non-null    int64  \n",
+      " 15  arch_neutral                 428 non-null    int64  \n",
+      " 16  arch_stability               428 non-null    int64  \n",
+      " 17  weight_lab_oz                428 non-null    float64\n",
+      " 18  weight_lab_g                 428 non-null    int64  \n",
+      " 19  weight_brand_oz              421 non-null    float64\n",
+      " 20  weight_brand_g               421 non-null    float64\n",
+      " 21  drop_lab_mm                  428 non-null    float64\n",
+      " 22  drop_brand_mm                412 non-null    float64\n",
+      " 23  strike_heel                  428 non-null    int64  \n",
+      " 24  strike_mid                   428 non-null    int64  \n",
+      " 25  strike_forefoot              428 non-null    int64  \n",
+      " 26  midsole_softness             428 non-null    int64  \n",
+      " 27  toebox_durability            428 non-null    int64  \n",
+      " 28  heel_durability              428 non-null    int64  \n",
+      " 29  outsole_durability           428 non-null    int64  \n",
+      " 30  breathability_scaled         428 non-null    int64  \n",
+      " 31  width_fit                    428 non-null    int64  \n",
+      " 32  toebox_width                 428 non-null    int64  \n",
+      " 33  stiffness_scaled             428 non-null    int64  \n",
+      "dtypes: float64(5), int64(21), object(8)\n",
+      "memory usage: 133.2+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"stiffness\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5386a6f",
+   "metadata": {},
+   "source": [
+    "## Torsional rigidity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "id": "b86d7dea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torsional rigidity\n",
+      "stiff       213\n",
+      "moderate    123\n",
+      "flexible     74\n",
+      "-            18\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['torsional rigidity'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "id": "1bb82d8a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Value Counts Kolom Asli ---\n",
+      "torsional rigidity\n",
+      "stiff       213\n",
+      "moderate    123\n",
+      "flexible     74\n",
+      "-            18\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "--- Sebaran Nilai Hasil Ordinal (Scale 0-5) ---\n",
+      "Index 0: 18 baris\n",
+      "Index 1: 74 baris\n",
+      "Index 2: 0 baris\n",
+      "Index 3: 123 baris\n",
+      "Index 4: 0 baris\n",
+      "Index 5: 213 baris\n",
+      "\n",
+      "--- Perbandingan Data (Head) ---\n",
+      "  torsional rigidity  torsional_rigidity\n",
+      "0              stiff                   5\n",
+      "1           moderate                   3\n",
+      "2           flexible                   1\n",
+      "3           flexible                   1\n",
+      "4           flexible                   1\n"
+     ]
+    }
+   ],
+   "source": [
+    "torsional_scaled = {\n",
+    "    \"flexible\": 1,\n",
+    "    \"moderate\": 3,\n",
+    "    \"stiff\": 5,\n",
+    "    \"-\": 0,\n",
+    "    \"0\": 0,\n",
+    "    \"nan\": 0 \n",
+    "}\n",
+    "\n",
+    "df['torsional_rigidity'] = df['torsional rigidity'].map(torsional_scaled).fillna(0).astype(int)\n",
+    "\n",
+    "print(\"--- Value Counts Kolom Asli ---\")\n",
+    "print(df[\"torsional rigidity\"].value_counts())\n",
+    "\n",
+    "print(\"\\n--- Sebaran Nilai Hasil Ordinal (Scale 0-5) ---\")\n",
+    "counts = df[\"torsional_rigidity\"].value_counts().sort_index()\n",
+    "for i in range(6):\n",
+    "    print(f\"Index {i}: {counts.get(i, 0)} baris\")\n",
+    "\n",
+    "print(\"\\n--- Perbandingan Data (Head) ---\")\n",
+    "print(df[[\"torsional rigidity\", \"torsional_rigidity\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "id": "64381272",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 34 columns):\n",
+      " #   Column                       Non-Null Count  Dtype  \n",
+      "---  ------                       --------------  -----  \n",
+      " 0   brand                        428 non-null    object \n",
+      " 1   name                         428 non-null    object \n",
+      " 2   lightweight                  428 non-null    int64  \n",
+      " 3   heel counter stiffness       428 non-null    object \n",
+      " 4   plate                        428 non-null    object \n",
+      " 5   rocker                       428 non-null    int64  \n",
+      " 6   heel lab heel brand          428 non-null    object \n",
+      " 7   forefoot lab forefoot brand  428 non-null    object \n",
+      " 8   orthotic friendly            428 non-null    int64  \n",
+      " 9   season                       428 non-null    object \n",
+      " 10  removable insole             428 non-null    int64  \n",
+      " 11  pace_daily_running           428 non-null    int64  \n",
+      " 12  pace_tempo                   428 non-null    int64  \n",
+      " 13  pace_competition             428 non-null    int64  \n",
+      " 14  arch_neutral                 428 non-null    int64  \n",
+      " 15  arch_stability               428 non-null    int64  \n",
+      " 16  weight_lab_oz                428 non-null    float64\n",
+      " 17  weight_lab_g                 428 non-null    int64  \n",
+      " 18  weight_brand_oz              421 non-null    float64\n",
+      " 19  weight_brand_g               421 non-null    float64\n",
+      " 20  drop_lab_mm                  428 non-null    float64\n",
+      " 21  drop_brand_mm                412 non-null    float64\n",
+      " 22  strike_heel                  428 non-null    int64  \n",
+      " 23  strike_mid                   428 non-null    int64  \n",
+      " 24  strike_forefoot              428 non-null    int64  \n",
+      " 25  midsole_softness             428 non-null    int64  \n",
+      " 26  toebox_durability            428 non-null    int64  \n",
+      " 27  heel_durability              428 non-null    int64  \n",
+      " 28  outsole_durability           428 non-null    int64  \n",
+      " 29  breathability_scaled         428 non-null    int64  \n",
+      " 30  width_fit                    428 non-null    int64  \n",
+      " 31  toebox_width                 428 non-null    int64  \n",
+      " 32  stiffness_scaled             428 non-null    int64  \n",
+      " 33  torsional_rigidity           428 non-null    int64  \n",
+      "dtypes: float64(5), int64(22), object(7)\n",
+      "memory usage: 133.2+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"torsional rigidity\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c54ab8b",
+   "metadata": {},
+   "source": [
+    "## Heel counter stiffness"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "id": "87764106",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "heel counter stiffness\n",
+      "moderate    146\n",
+      "flexible    132\n",
+      "stiff       121\n",
+      "-            29\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['heel counter stiffness'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "id": "74f2b7ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 428\n",
+      "NULL Value (tidak cocok dengan kategori): 0\n",
+      "\n",
+      "Sample Comparison:\n",
+      "  heel counter stiffness  heel_stiff\n",
+      "0               flexible           1\n",
+      "1               moderate           3\n",
+      "2               flexible           1\n",
+      "3               moderate           3\n",
+      "4               flexible           1\n",
+      "5               moderate           3\n",
+      "6               flexible           1\n",
+      "7               flexible           1\n",
+      "8                  stiff           5\n",
+      "9                  stiff           5\n"
+     ]
+    }
+   ],
+   "source": [
+    "heel_stiff_map = {\n",
+    "    'flexible': 1,\n",
+    "    'moderate': 3,\n",
+    "    'stiff': 5\n",
+    "}\n",
+    "\n",
+    "df['heel_stiff'] = df['heel counter stiffness'].map(heel_stiff_map).fillna(0).astype(int)\n",
+    "\n",
+    "# Cek hasil\n",
+    "print(f\"Rows: {len(df)}\")\n",
+    "print(f\"NULL Value (tidak cocok dengan kategori): {df['heel_stiff'].isna().sum()}\")\n",
+    "print(\"\\nSample Comparison:\")\n",
+    "print(df[['heel counter stiffness', 'heel_stiff']].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "id": "07498d0e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 34 columns):\n",
+      " #   Column                       Non-Null Count  Dtype  \n",
+      "---  ------                       --------------  -----  \n",
+      " 0   brand                        428 non-null    object \n",
+      " 1   name                         428 non-null    object \n",
+      " 2   lightweight                  428 non-null    int64  \n",
+      " 3   plate                        428 non-null    object \n",
+      " 4   rocker                       428 non-null    int64  \n",
+      " 5   heel lab heel brand          428 non-null    object \n",
+      " 6   forefoot lab forefoot brand  428 non-null    object \n",
+      " 7   orthotic friendly            428 non-null    int64  \n",
+      " 8   season                       428 non-null    object \n",
+      " 9   removable insole             428 non-null    int64  \n",
+      " 10  pace_daily_running           428 non-null    int64  \n",
+      " 11  pace_tempo                   428 non-null    int64  \n",
+      " 12  pace_competition             428 non-null    int64  \n",
+      " 13  arch_neutral                 428 non-null    int64  \n",
+      " 14  arch_stability               428 non-null    int64  \n",
+      " 15  weight_lab_oz                428 non-null    float64\n",
+      " 16  weight_lab_g                 428 non-null    int64  \n",
+      " 17  weight_brand_oz              421 non-null    float64\n",
+      " 18  weight_brand_g               421 non-null    float64\n",
+      " 19  drop_lab_mm                  428 non-null    float64\n",
+      " 20  drop_brand_mm                412 non-null    float64\n",
+      " 21  strike_heel                  428 non-null    int64  \n",
+      " 22  strike_mid                   428 non-null    int64  \n",
+      " 23  strike_forefoot              428 non-null    int64  \n",
+      " 24  midsole_softness             428 non-null    int64  \n",
+      " 25  toebox_durability            428 non-null    int64  \n",
+      " 26  heel_durability              428 non-null    int64  \n",
+      " 27  outsole_durability           428 non-null    int64  \n",
+      " 28  breathability_scaled         428 non-null    int64  \n",
+      " 29  width_fit                    428 non-null    int64  \n",
+      " 30  toebox_width                 428 non-null    int64  \n",
+      " 31  stiffness_scaled             428 non-null    int64  \n",
+      " 32  torsional_rigidity           428 non-null    int64  \n",
+      " 33  heel_stiff                   428 non-null    int64  \n",
+      "dtypes: float64(5), int64(23), object(6)\n",
+      "memory usage: 133.2+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"heel counter stiffness\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70d9aec6",
+   "metadata": {},
+   "source": [
+    "## Plate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "id": "7566b264",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "plate\n",
+      "0                         369\n",
+      "carbon plate               58\n",
+      "carbon platerock plate      1\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"plate\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "id": "6d239b0f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 428\n",
+      "NULL Value: 369\n",
+      "\n",
+      "Sample Comparison:\n",
+      "  plate  plate_rock_plate  plate_carbon_plate\n",
+      "0     0                 0                   0\n",
+      "1     0                 0                   0\n",
+      "2     0                 0                   0\n",
+      "3     0                 0                   0\n",
+      "4     0                 0                   0\n",
+      "5     0                 0                   0\n",
+      "6     0                 0                   0\n",
+      "7     0                 0                   0\n",
+      "8     0                 0                   0\n",
+      "9     0                 0                   0\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['plate'] = df['plate'].astype(str).str.lower()\n",
+    "base_plate = ['rock plate', 'carbon plate']\n",
+    "\n",
+    "for level in base_plate:\n",
+    "    column_name = f\"plate_{level.replace(' ', '_')}\"\n",
+    "    df[column_name] = df['plate'].str.contains(level, na=False).astype(int)\n",
+    "\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "plate_cols = [f\"plate_{l.replace(' ', '_')}\" for l in base_plate]\n",
+    "zero_vector_count = (df[plate_cols].sum(axis=1) == 0).sum()\n",
+    "print(f\"NULL Value: {zero_vector_count}\")\n",
+    "\n",
+    "print(\"\\nSample Comparison:\")\n",
+    "print(df[[\"plate\"] + plate_cols].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "id": "d65f79b1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "plate\n",
+      "0                         369\n",
+      "carbon plate               58\n",
+      "carbon platerock plate      1\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "plate_rock_plate sum: 1\n",
+      "plate_carbon_plate sum: 59\n",
+      "\n",
+      "  plate  plate_rock_plate  plate_carbon_plate\n",
+      "0     0                 0                   0\n",
+      "1     0                 0                   0\n",
+      "2     0                 0                   0\n",
+      "3     0                 0                   0\n",
+      "4     0                 0                   0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"plate\"].value_counts())\n",
+    "\n",
+    "print()\n",
+    "for level in base_plate:\n",
+    "    col = f\"plate_{level.replace(' ', '_')}\"\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"plate\"] + plate_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "id": "a3c23d18",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 35 columns):\n",
+      " #   Column                       Non-Null Count  Dtype  \n",
+      "---  ------                       --------------  -----  \n",
+      " 0   brand                        428 non-null    object \n",
+      " 1   name                         428 non-null    object \n",
+      " 2   lightweight                  428 non-null    int64  \n",
+      " 3   rocker                       428 non-null    int64  \n",
+      " 4   heel lab heel brand          428 non-null    object \n",
+      " 5   forefoot lab forefoot brand  428 non-null    object \n",
+      " 6   orthotic friendly            428 non-null    int64  \n",
+      " 7   season                       428 non-null    object \n",
+      " 8   removable insole             428 non-null    int64  \n",
+      " 9   pace_daily_running           428 non-null    int64  \n",
+      " 10  pace_tempo                   428 non-null    int64  \n",
+      " 11  pace_competition             428 non-null    int64  \n",
+      " 12  arch_neutral                 428 non-null    int64  \n",
+      " 13  arch_stability               428 non-null    int64  \n",
+      " 14  weight_lab_oz                428 non-null    float64\n",
+      " 15  weight_lab_g                 428 non-null    int64  \n",
+      " 16  weight_brand_oz              421 non-null    float64\n",
+      " 17  weight_brand_g               421 non-null    float64\n",
+      " 18  drop_lab_mm                  428 non-null    float64\n",
+      " 19  drop_brand_mm                412 non-null    float64\n",
+      " 20  strike_heel                  428 non-null    int64  \n",
+      " 21  strike_mid                   428 non-null    int64  \n",
+      " 22  strike_forefoot              428 non-null    int64  \n",
+      " 23  midsole_softness             428 non-null    int64  \n",
+      " 24  toebox_durability            428 non-null    int64  \n",
+      " 25  heel_durability              428 non-null    int64  \n",
+      " 26  outsole_durability           428 non-null    int64  \n",
+      " 27  breathability_scaled         428 non-null    int64  \n",
+      " 28  width_fit                    428 non-null    int64  \n",
+      " 29  toebox_width                 428 non-null    int64  \n",
+      " 30  stiffness_scaled             428 non-null    int64  \n",
+      " 31  torsional_rigidity           428 non-null    int64  \n",
+      " 32  heel_stiff                   428 non-null    int64  \n",
+      " 33  plate_rock_plate             428 non-null    int64  \n",
+      " 34  plate_carbon_plate           428 non-null    int64  \n",
+      "dtypes: float64(5), int64(25), object(5)\n",
+      "memory usage: 136.5+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"plate\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d509c91",
+   "metadata": {},
+   "source": [
+    "## Rocker"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "id": "73aa3a65",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "rocker\n",
+      "0    286\n",
+      "1    142\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"rocker\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42c82900",
+   "metadata": {},
+   "source": [
+    "## Heel lab heel brand"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "id": "79e970cd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0    32.4 mm 36.0 mm\n",
+      "1    34.3 mm 32.5 mm\n",
+      "2    33.3 mm 32.5 mm\n",
+      "3    31.8 mm 32.0 mm\n",
+      "4    32.6 mm 34.0 mm\n",
+      "Name: heel lab heel brand, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"heel lab heel brand\"].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "id": "483d9cef",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(0)"
+      ]
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mask_missing = (\n",
+    "    df[\"heel lab heel brand\"].isna() |\n",
+    "    (df[\"heel lab heel brand\"].astype(str).str.strip() == \"-\")\n",
+    ")\n",
+    "mask_missing.sum()  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "id": "9927dca3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  heel lab heel brand  heel_lab_mm  heel_brand_mm\n",
+      "0     32.4 mm 36.0 mm         32.4           36.0\n",
+      "1     34.3 mm 32.5 mm         34.3           32.5\n",
+      "2     33.3 mm 32.5 mm         33.3           32.5\n",
+      "3     31.8 mm 32.0 mm         31.8           32.0\n",
+      "4     32.6 mm 34.0 mm         32.6           34.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "heel = df[\"heel lab heel brand\"].str.findall(r\"[\\d.]+\")\n",
+    "\n",
+    "df[[\"heel_lab_mm\", \"heel_brand_mm\"]] = (\n",
+    "    pd.DataFrame(heel.tolist(), index=df.index)\n",
+    ")\n",
+    "\n",
+    "for col in [\"heel_lab_mm\", \"heel_brand_mm\"]:\n",
+    "    df[col] = pd.to_numeric(df[col], errors=\"coerce\")\n",
+    "\n",
+    "print(df[[\"heel lab heel brand\", \"heel_lab_mm\", \"heel_brand_mm\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "id": "1fa2e622",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 36 columns):\n",
+      " #   Column                       Non-Null Count  Dtype  \n",
+      "---  ------                       --------------  -----  \n",
+      " 0   brand                        428 non-null    object \n",
+      " 1   name                         428 non-null    object \n",
+      " 2   lightweight                  428 non-null    int64  \n",
+      " 3   rocker                       428 non-null    int64  \n",
+      " 4   forefoot lab forefoot brand  428 non-null    object \n",
+      " 5   orthotic friendly            428 non-null    int64  \n",
+      " 6   season                       428 non-null    object \n",
+      " 7   removable insole             428 non-null    int64  \n",
+      " 8   pace_daily_running           428 non-null    int64  \n",
+      " 9   pace_tempo                   428 non-null    int64  \n",
+      " 10  pace_competition             428 non-null    int64  \n",
+      " 11  arch_neutral                 428 non-null    int64  \n",
+      " 12  arch_stability               428 non-null    int64  \n",
+      " 13  weight_lab_oz                428 non-null    float64\n",
+      " 14  weight_lab_g                 428 non-null    int64  \n",
+      " 15  weight_brand_oz              421 non-null    float64\n",
+      " 16  weight_brand_g               421 non-null    float64\n",
+      " 17  drop_lab_mm                  428 non-null    float64\n",
+      " 18  drop_brand_mm                412 non-null    float64\n",
+      " 19  strike_heel                  428 non-null    int64  \n",
+      " 20  strike_mid                   428 non-null    int64  \n",
+      " 21  strike_forefoot              428 non-null    int64  \n",
+      " 22  midsole_softness             428 non-null    int64  \n",
+      " 23  toebox_durability            428 non-null    int64  \n",
+      " 24  heel_durability              428 non-null    int64  \n",
+      " 25  outsole_durability           428 non-null    int64  \n",
+      " 26  breathability_scaled         428 non-null    int64  \n",
+      " 27  width_fit                    428 non-null    int64  \n",
+      " 28  toebox_width                 428 non-null    int64  \n",
+      " 29  stiffness_scaled             428 non-null    int64  \n",
+      " 30  torsional_rigidity           428 non-null    int64  \n",
+      " 31  heel_stiff                   428 non-null    int64  \n",
+      " 32  plate_rock_plate             428 non-null    int64  \n",
+      " 33  plate_carbon_plate           428 non-null    int64  \n",
+      " 34  heel_lab_mm                  428 non-null    float64\n",
+      " 35  heel_brand_mm                380 non-null    float64\n",
+      "dtypes: float64(7), int64(25), object(4)\n",
+      "memory usage: 139.9+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"heel lab heel brand\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ffd5fca",
+   "metadata": {},
+   "source": [
+    "## Forefoot lab Forefoot brand"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "id": "b368e9c0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0    23.0 mm 26.0 mm\n",
+      "1    26.6 mm 24.5 mm\n",
+      "2    24.4 mm 22.5 mm\n",
+      "3    21.2 mm 21.0 mm\n",
+      "4    22.7 mm 24.0 mm\n",
+      "Name: forefoot lab forefoot brand, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['forefoot lab forefoot brand'].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "id": "ec35d9a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(0)"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mask_missing = (\n",
+    "    df[\"forefoot lab forefoot brand\"].isna() |\n",
+    "    (df[\"forefoot lab forefoot brand\"].astype(str).str.strip() == \"-\")\n",
+    ")\n",
+    "mask_missing.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "id": "45bf1625",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  forefoot lab forefoot brand  forefoot_lab_mm  forefoot_brand_mm\n",
+      "0             23.0 mm 26.0 mm             23.0               26.0\n",
+      "1             26.6 mm 24.5 mm             26.6               24.5\n",
+      "2             24.4 mm 22.5 mm             24.4               22.5\n",
+      "3             21.2 mm 21.0 mm             21.2               21.0\n",
+      "4             22.7 mm 24.0 mm             22.7               24.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "forefoot = df[\"forefoot lab forefoot brand\"].str.findall(r\"[\\d.]+\")\n",
+    "\n",
+    "df[[\"forefoot_lab_mm\", \"forefoot_brand_mm\"]] = (\n",
+    "    pd.DataFrame(forefoot.tolist(), index=df.index)\n",
+    ")\n",
+    "\n",
+    "for col in [\"forefoot_lab_mm\", \"forefoot_brand_mm\"]:\n",
+    "    df[col] = pd.to_numeric(df[col], errors=\"coerce\")\n",
+    "\n",
+    "print(df[[\"forefoot lab forefoot brand\", \"forefoot_lab_mm\", \"forefoot_brand_mm\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "id": "497271f8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 37 columns):\n",
+      " #   Column                Non-Null Count  Dtype  \n",
+      "---  ------                --------------  -----  \n",
+      " 0   brand                 428 non-null    object \n",
+      " 1   name                  428 non-null    object \n",
+      " 2   lightweight           428 non-null    int64  \n",
+      " 3   rocker                428 non-null    int64  \n",
+      " 4   orthotic friendly     428 non-null    int64  \n",
+      " 5   season                428 non-null    object \n",
+      " 6   removable insole      428 non-null    int64  \n",
+      " 7   pace_daily_running    428 non-null    int64  \n",
+      " 8   pace_tempo            428 non-null    int64  \n",
+      " 9   pace_competition      428 non-null    int64  \n",
+      " 10  arch_neutral          428 non-null    int64  \n",
+      " 11  arch_stability        428 non-null    int64  \n",
+      " 12  weight_lab_oz         428 non-null    float64\n",
+      " 13  weight_lab_g          428 non-null    int64  \n",
+      " 14  weight_brand_oz       421 non-null    float64\n",
+      " 15  weight_brand_g        421 non-null    float64\n",
+      " 16  drop_lab_mm           428 non-null    float64\n",
+      " 17  drop_brand_mm         412 non-null    float64\n",
+      " 18  strike_heel           428 non-null    int64  \n",
+      " 19  strike_mid            428 non-null    int64  \n",
+      " 20  strike_forefoot       428 non-null    int64  \n",
+      " 21  midsole_softness      428 non-null    int64  \n",
+      " 22  toebox_durability     428 non-null    int64  \n",
+      " 23  heel_durability       428 non-null    int64  \n",
+      " 24  outsole_durability    428 non-null    int64  \n",
+      " 25  breathability_scaled  428 non-null    int64  \n",
+      " 26  width_fit             428 non-null    int64  \n",
+      " 27  toebox_width          428 non-null    int64  \n",
+      " 28  stiffness_scaled      428 non-null    int64  \n",
+      " 29  torsional_rigidity    428 non-null    int64  \n",
+      " 30  heel_stiff            428 non-null    int64  \n",
+      " 31  plate_rock_plate      428 non-null    int64  \n",
+      " 32  plate_carbon_plate    428 non-null    int64  \n",
+      " 33  heel_lab_mm           428 non-null    float64\n",
+      " 34  heel_brand_mm         380 non-null    float64\n",
+      " 35  forefoot_lab_mm       428 non-null    float64\n",
+      " 36  forefoot_brand_mm     379 non-null    float64\n",
+      "dtypes: float64(9), int64(25), object(3)\n",
+      "memory usage: 143.2+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"forefoot lab forefoot brand\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "274dfe56",
+   "metadata": {},
+   "source": [
+    "## Season"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "id": "2a6c6995",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "season\n",
+      "all seasons          249\n",
+      "summerall seasons    109\n",
+      "-                     58\n",
+      "winter                12\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"season\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "id": "1a3faec8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Jumlah baris dengan '-' atau '0': 58\n",
+      "\n",
+      "--- Detail Baris (season = '-' atau '0') ---\n",
+      "           brand                         name season\n",
+      "0         brooks                     launch 9      -\n",
+      "13        adidas        adizero adios pro 2.0      -\n",
+      "25        brooks            adrenaline gts 22      -\n",
+      "34          nike  air zoom pegasus 38 flyease      -\n",
+      "47       saucony                         axon      -\n",
+      "82          nike               downshifter 11      -\n",
+      "85        adidas                    duramo 10      -\n",
+      "93       saucony              endorphin pro 3      -\n",
+      "97       saucony            endorphin shift 2      -\n",
+      "111         nike       flex experience run 10      -\n",
+      "114         nike                flex run 2021      -\n",
+      "115       reebok           floatride energy 3      -\n",
+      "121         nike                 free run 5.0      -\n",
+      "122      saucony                    freedom 4      -\n",
+      "123  new balance          fresh foam 1080 v11      -\n",
+      "125  new balance           fresh foam 860 v11      -\n",
+      "126  new balance           fresh foam 860 v12      -\n",
+      "130  new balance        fresh foam x 1080 v12      -\n",
+      "162        asics                gel contend 7      -\n",
+      "166        asics               gel cumulus 24      -\n",
+      "170        asics                 gel excite 8      -\n",
+      "171        asics                gel kayano 28      -\n",
+      "175        asics            gel kayano lite 2      -\n",
+      "178        asics                gel nimbus 24      -\n",
+      "182        asics            gel nimbus lite 3      -\n",
+      "183        asics                 gel pulse 11      -\n",
+      "196       brooks                  glycerin 19      -\n",
+      "208     skechers           gorun razor excess      -\n",
+      "209        asics                   gt 1000 10      -\n",
+      "210        asics                   gt 1000 11      -\n",
+      "214        asics                    gt 1000 9      -\n",
+      "215        asics                   gt 2000 10      -\n",
+      "220      saucony                     guide 14      -\n",
+      "221      saucony                     guide 15      -\n",
+      "247      saucony                   kinvara 12      -\n",
+      "248      saucony                   kinvara 13      -\n",
+      "254       brooks                     launch 8      -\n",
+      "257       brooks                   levitate 5      -\n",
+      "261         hoka                       mach 4      -\n",
+      "270     skechers         max cushioning elite      -\n",
+      "273        asics               metaspeed edge      -\n",
+      "289        asics                  novablast 2      -\n",
+      "305        altra                  provision 6      -\n",
+      "310         nike                      quest 4      -\n",
+      "315       jordan                  react havoc      -\n",
+      "317         nike                react miler 3      -\n",
+      "318         nike                 renew ride 2      -\n",
+      "323         nike                 revolution 6      -\n",
+      "327       brooks                   ricochet 3      -\n",
+      "329      saucony                      ride 15      -\n",
+      "334        altra                     rivera 2      -\n",
+      "357          apl                   streamline      -\n",
+      "368       adidas                   supernova+      -\n",
+      "387       adidas                ultraboost 21      -\n",
+      "388       adidas                ultraboost 22      -\n",
+      "405       mizuno               wave horizon 6      -\n",
+      "414       mizuno                wave rider 25      -\n",
+      "427         nike                   zoom fly 4      -\n",
+      "\n",
+      "Frekuensi spesifik:\n",
+      "season\n",
+      "-    58\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check weird values\n",
+    "filter_condition = df['season'].astype(str).isin(['-', '0'])\n",
+    "rows_to_check = df[filter_condition]\n",
+    "\n",
+    "print(f\"Jumlah baris dengan '-' atau '0': {len(rows_to_check)}\")\n",
+    "print(\"\\n--- Detail Baris (season = '-' atau '0') ---\")\n",
+    "print(rows_to_check[['brand', 'name', 'season']])\n",
+    "\n",
+    "\n",
+    "print(\"\\nFrekuensi spesifik:\")\n",
+    "print(df[df['season'].astype(str).isin(['-', '0'])]['season'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "id": "797c2c77",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 428\n",
+      "NULL/Unknown Value (0 dan -): 58\n",
+      "\n",
+      "Sample Comparison (Multi-label):\n",
+      "               season  season_summer  season_winter  season_all\n",
+      "1   summerall seasons              1              0           1\n",
+      "6   summerall seasons              1              0           1\n",
+      "8   summerall seasons              1              0           1\n",
+      "9   summerall seasons              1              0           1\n",
+      "10  summerall seasons              1              0           1\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['season'] = df['season'].astype(str).str.lower()\n",
+    "base_seasons = ['summer', 'winter', 'all seasons']\n",
+    "\n",
+    "for level in base_seasons:\n",
+    "    clean_name = level.replace(' seasons', '').replace(' ', '_')\n",
+    "    column_name = f\"season_{clean_name}\"\n",
+    "    df[column_name] = df['season'].str.contains(level, na=False).astype(int)\n",
+    "\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "season_cols = [col for col in df.columns if col.startswith('season_')]\n",
+    "zero_vector_count = (df[season_cols].sum(axis=1) == 0).sum()\n",
+    "print(f\"NULL/Unknown Value (0 dan -): {zero_vector_count}\")\n",
+    "\n",
+    "print(\"\\nSample Comparison (Multi-label):\")\n",
+    "print(df[df[season_cols].sum(axis=1) > 1][['season'] + season_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "id": "c7b929a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "season\n",
+      "all seasons          249\n",
+      "summerall seasons    109\n",
+      "-                     58\n",
+      "winter                12\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "season_summer sum: 109\n",
+      "season_winter sum: 12\n",
+      "season_all sum: 358\n",
+      "\n",
+      "              season  season_summer  season_winter  season_all\n",
+      "0                  -              0              0           0\n",
+      "1  summerall seasons              1              0           1\n",
+      "2        all seasons              0              0           1\n",
+      "3        all seasons              0              0           1\n",
+      "4        all seasons              0              0           1\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"season\"].value_counts())\n",
+    "\n",
+    "print()\n",
+    "for col in season_cols:\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"season\"] + season_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "id": "7a1c1c0a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 39 columns):\n",
+      " #   Column                Non-Null Count  Dtype  \n",
+      "---  ------                --------------  -----  \n",
+      " 0   brand                 428 non-null    object \n",
+      " 1   name                  428 non-null    object \n",
+      " 2   lightweight           428 non-null    int64  \n",
+      " 3   rocker                428 non-null    int64  \n",
+      " 4   orthotic friendly     428 non-null    int64  \n",
+      " 5   removable insole      428 non-null    int64  \n",
+      " 6   pace_daily_running    428 non-null    int64  \n",
+      " 7   pace_tempo            428 non-null    int64  \n",
+      " 8   pace_competition      428 non-null    int64  \n",
+      " 9   arch_neutral          428 non-null    int64  \n",
+      " 10  arch_stability        428 non-null    int64  \n",
+      " 11  weight_lab_oz         428 non-null    float64\n",
+      " 12  weight_lab_g          428 non-null    int64  \n",
+      " 13  weight_brand_oz       421 non-null    float64\n",
+      " 14  weight_brand_g        421 non-null    float64\n",
+      " 15  drop_lab_mm           428 non-null    float64\n",
+      " 16  drop_brand_mm         412 non-null    float64\n",
+      " 17  strike_heel           428 non-null    int64  \n",
+      " 18  strike_mid            428 non-null    int64  \n",
+      " 19  strike_forefoot       428 non-null    int64  \n",
+      " 20  midsole_softness      428 non-null    int64  \n",
+      " 21  toebox_durability     428 non-null    int64  \n",
+      " 22  heel_durability       428 non-null    int64  \n",
+      " 23  outsole_durability    428 non-null    int64  \n",
+      " 24  breathability_scaled  428 non-null    int64  \n",
+      " 25  width_fit             428 non-null    int64  \n",
+      " 26  toebox_width          428 non-null    int64  \n",
+      " 27  stiffness_scaled      428 non-null    int64  \n",
+      " 28  torsional_rigidity    428 non-null    int64  \n",
+      " 29  heel_stiff            428 non-null    int64  \n",
+      " 30  plate_rock_plate      428 non-null    int64  \n",
+      " 31  plate_carbon_plate    428 non-null    int64  \n",
+      " 32  heel_lab_mm           428 non-null    float64\n",
+      " 33  heel_brand_mm         380 non-null    float64\n",
+      " 34  forefoot_lab_mm       428 non-null    float64\n",
+      " 35  forefoot_brand_mm     379 non-null    float64\n",
+      " 36  season_summer         428 non-null    int64  \n",
+      " 37  season_winter         428 non-null    int64  \n",
+      " 38  season_all            428 non-null    int64  \n",
+      "dtypes: float64(9), int64(28), object(2)\n",
+      "memory usage: 149.9+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"season\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "id": "9a4f8049",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    brand        name  removable insole  orthotic friendly\n",
+      "0  brooks    launch 9                 1                  1\n",
+      "1  brooks  levitate 6                 1                  1\n",
+      "2  adidas       4dfwd                 1                  1\n",
+      "3  adidas     4dfwd 2                 1                  1\n",
+      "4  adidas     4dfwd 3                 1                  1\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[[\"brand\", \"name\", \"removable insole\", \"orthotic friendly\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "153772df",
+   "metadata": {},
+   "source": [
+    "## Finishing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "id": "cea8f27b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 36 columns):\n",
+      " #   Column                Non-Null Count  Dtype  \n",
+      "---  ------                --------------  -----  \n",
+      " 0   brand                 428 non-null    object \n",
+      " 1   name                  428 non-null    object \n",
+      " 2   lightweight           428 non-null    int64  \n",
+      " 3   rocker                428 non-null    int64  \n",
+      " 4   orthotic friendly     428 non-null    int64  \n",
+      " 5   removable insole      428 non-null    int64  \n",
+      " 6   pace_daily_running    428 non-null    int64  \n",
+      " 7   pace_tempo            428 non-null    int64  \n",
+      " 8   pace_competition      428 non-null    int64  \n",
+      " 9   arch_neutral          428 non-null    int64  \n",
+      " 10  arch_stability        428 non-null    int64  \n",
+      " 11  weight_lab_oz         428 non-null    float64\n",
+      " 12  drop_lab_mm           428 non-null    float64\n",
+      " 13  drop_brand_mm         412 non-null    float64\n",
+      " 14  strike_heel           428 non-null    int64  \n",
+      " 15  strike_mid            428 non-null    int64  \n",
+      " 16  strike_forefoot       428 non-null    int64  \n",
+      " 17  midsole_softness      428 non-null    int64  \n",
+      " 18  toebox_durability     428 non-null    int64  \n",
+      " 19  heel_durability       428 non-null    int64  \n",
+      " 20  outsole_durability    428 non-null    int64  \n",
+      " 21  breathability_scaled  428 non-null    int64  \n",
+      " 22  width_fit             428 non-null    int64  \n",
+      " 23  toebox_width          428 non-null    int64  \n",
+      " 24  stiffness_scaled      428 non-null    int64  \n",
+      " 25  torsional_rigidity    428 non-null    int64  \n",
+      " 26  heel_stiff            428 non-null    int64  \n",
+      " 27  plate_rock_plate      428 non-null    int64  \n",
+      " 28  plate_carbon_plate    428 non-null    int64  \n",
+      " 29  heel_lab_mm           428 non-null    float64\n",
+      " 30  heel_brand_mm         380 non-null    float64\n",
+      " 31  forefoot_lab_mm       428 non-null    float64\n",
+      " 32  forefoot_brand_mm     379 non-null    float64\n",
+      " 33  season_summer         428 non-null    int64  \n",
+      " 34  season_winter         428 non-null    int64  \n",
+      " 35  season_all            428 non-null    int64  \n",
+      "dtypes: float64(7), int64(27), object(2)\n",
+      "memory usage: 139.9+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Weight cuma pakai yg lab_oz\n",
+    "df.drop(columns=['weight_brand_oz', 'weight_lab_g', 'weight_brand_g'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "id": "c102f43f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 35 columns):\n",
+      " #   Column                Non-Null Count  Dtype  \n",
+      "---  ------                --------------  -----  \n",
+      " 0   brand                 428 non-null    object \n",
+      " 1   name                  428 non-null    object \n",
+      " 2   lightweight           428 non-null    int64  \n",
+      " 3   rocker                428 non-null    int64  \n",
+      " 4   orthotic friendly     428 non-null    int64  \n",
+      " 5   removable insole      428 non-null    int64  \n",
+      " 6   pace_daily_running    428 non-null    int64  \n",
+      " 7   pace_tempo            428 non-null    int64  \n",
+      " 8   pace_competition      428 non-null    int64  \n",
+      " 9   arch_neutral          428 non-null    int64  \n",
+      " 10  arch_stability        428 non-null    int64  \n",
+      " 11  weight_lab_oz         428 non-null    float64\n",
+      " 12  drop_lab_mm           428 non-null    float64\n",
+      " 13  strike_heel           428 non-null    int64  \n",
+      " 14  strike_mid            428 non-null    int64  \n",
+      " 15  strike_forefoot       428 non-null    int64  \n",
+      " 16  midsole_softness      428 non-null    int64  \n",
+      " 17  toebox_durability     428 non-null    int64  \n",
+      " 18  heel_durability       428 non-null    int64  \n",
+      " 19  outsole_durability    428 non-null    int64  \n",
+      " 20  breathability_scaled  428 non-null    int64  \n",
+      " 21  width_fit             428 non-null    int64  \n",
+      " 22  toebox_width          428 non-null    int64  \n",
+      " 23  stiffness_scaled      428 non-null    int64  \n",
+      " 24  torsional_rigidity    428 non-null    int64  \n",
+      " 25  heel_stiff            428 non-null    int64  \n",
+      " 26  plate_rock_plate      428 non-null    int64  \n",
+      " 27  plate_carbon_plate    428 non-null    int64  \n",
+      " 28  heel_lab_mm           428 non-null    float64\n",
+      " 29  heel_brand_mm         380 non-null    float64\n",
+      " 30  forefoot_lab_mm       428 non-null    float64\n",
+      " 31  forefoot_brand_mm     379 non-null    float64\n",
+      " 32  season_summer         428 non-null    int64  \n",
+      " 33  season_winter         428 non-null    int64  \n",
+      " 34  season_all            428 non-null    int64  \n",
+      "dtypes: float64(6), int64(27), object(2)\n",
+      "memory usage: 136.5+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# drop cuma pakai yg lab_mm\n",
+    "df.drop(columns=['drop_brand_mm'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "id": "471b165b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 34 columns):\n",
+      " #   Column                Non-Null Count  Dtype  \n",
+      "---  ------                --------------  -----  \n",
+      " 0   brand                 428 non-null    object \n",
+      " 1   name                  428 non-null    object \n",
+      " 2   lightweight           428 non-null    int64  \n",
+      " 3   rocker                428 non-null    int64  \n",
+      " 4   orthotic friendly     428 non-null    int64  \n",
+      " 5   removable insole      428 non-null    int64  \n",
+      " 6   pace_daily_running    428 non-null    int64  \n",
+      " 7   pace_tempo            428 non-null    int64  \n",
+      " 8   pace_competition      428 non-null    int64  \n",
+      " 9   arch_neutral          428 non-null    int64  \n",
+      " 10  arch_stability        428 non-null    int64  \n",
+      " 11  weight_lab_oz         428 non-null    float64\n",
+      " 12  drop_lab_mm           428 non-null    float64\n",
+      " 13  strike_heel           428 non-null    int64  \n",
+      " 14  strike_mid            428 non-null    int64  \n",
+      " 15  strike_forefoot       428 non-null    int64  \n",
+      " 16  midsole_softness      428 non-null    int64  \n",
+      " 17  toebox_durability     428 non-null    int64  \n",
+      " 18  heel_durability       428 non-null    int64  \n",
+      " 19  outsole_durability    428 non-null    int64  \n",
+      " 20  breathability_scaled  428 non-null    int64  \n",
+      " 21  width_fit             428 non-null    int64  \n",
+      " 22  toebox_width          428 non-null    int64  \n",
+      " 23  stiffness_scaled      428 non-null    int64  \n",
+      " 24  torsional_rigidity    428 non-null    int64  \n",
+      " 25  heel_stiff            428 non-null    int64  \n",
+      " 26  plate_rock_plate      428 non-null    int64  \n",
+      " 27  plate_carbon_plate    428 non-null    int64  \n",
+      " 28  heel_lab_mm           428 non-null    float64\n",
+      " 29  forefoot_lab_mm       428 non-null    float64\n",
+      " 30  forefoot_brand_mm     379 non-null    float64\n",
+      " 31  season_summer         428 non-null    int64  \n",
+      " 32  season_winter         428 non-null    int64  \n",
+      " 33  season_all            428 non-null    int64  \n",
+      "dtypes: float64(5), int64(27), object(2)\n",
+      "memory usage: 133.2+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# heel pakai yang heel_lab_mm\n",
+    "df.drop(columns=['heel_brand_mm'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "id": "e435fcf8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 33 columns):\n",
+      " #   Column                Non-Null Count  Dtype  \n",
+      "---  ------                --------------  -----  \n",
+      " 0   brand                 428 non-null    object \n",
+      " 1   name                  428 non-null    object \n",
+      " 2   lightweight           428 non-null    int64  \n",
+      " 3   rocker                428 non-null    int64  \n",
+      " 4   orthotic friendly     428 non-null    int64  \n",
+      " 5   removable insole      428 non-null    int64  \n",
+      " 6   pace_daily_running    428 non-null    int64  \n",
+      " 7   pace_tempo            428 non-null    int64  \n",
+      " 8   pace_competition      428 non-null    int64  \n",
+      " 9   arch_neutral          428 non-null    int64  \n",
+      " 10  arch_stability        428 non-null    int64  \n",
+      " 11  weight_lab_oz         428 non-null    float64\n",
+      " 12  drop_lab_mm           428 non-null    float64\n",
+      " 13  strike_heel           428 non-null    int64  \n",
+      " 14  strike_mid            428 non-null    int64  \n",
+      " 15  strike_forefoot       428 non-null    int64  \n",
+      " 16  midsole_softness      428 non-null    int64  \n",
+      " 17  toebox_durability     428 non-null    int64  \n",
+      " 18  heel_durability       428 non-null    int64  \n",
+      " 19  outsole_durability    428 non-null    int64  \n",
+      " 20  breathability_scaled  428 non-null    int64  \n",
+      " 21  width_fit             428 non-null    int64  \n",
+      " 22  toebox_width          428 non-null    int64  \n",
+      " 23  stiffness_scaled      428 non-null    int64  \n",
+      " 24  torsional_rigidity    428 non-null    int64  \n",
+      " 25  heel_stiff            428 non-null    int64  \n",
+      " 26  plate_rock_plate      428 non-null    int64  \n",
+      " 27  plate_carbon_plate    428 non-null    int64  \n",
+      " 28  heel_lab_mm           428 non-null    float64\n",
+      " 29  forefoot_lab_mm       428 non-null    float64\n",
+      " 30  season_summer         428 non-null    int64  \n",
+      " 31  season_winter         428 non-null    int64  \n",
+      " 32  season_all            428 non-null    int64  \n",
+      "dtypes: float64(4), int64(27), object(2)\n",
+      "memory usage: 129.9+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Forefoot pakai yang forefoot_lab_mm\n",
+    "df.drop(columns=['forefoot_brand_mm'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "id": "ead3bb57",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 32 columns):\n",
+      " #   Column                Non-Null Count  Dtype  \n",
+      "---  ------                --------------  -----  \n",
+      " 0   brand                 428 non-null    object \n",
+      " 1   name                  428 non-null    object \n",
+      " 2   lightweight           428 non-null    int64  \n",
+      " 3   rocker                428 non-null    int64  \n",
+      " 4   removable_insole      428 non-null    int64  \n",
+      " 5   pace_daily_running    428 non-null    int64  \n",
+      " 6   pace_tempo            428 non-null    int64  \n",
+      " 7   pace_competition      428 non-null    int64  \n",
+      " 8   arch_neutral          428 non-null    int64  \n",
+      " 9   arch_stability        428 non-null    int64  \n",
+      " 10  weight_lab_oz         428 non-null    float64\n",
+      " 11  drop_lab_mm           428 non-null    float64\n",
+      " 12  strike_heel           428 non-null    int64  \n",
+      " 13  strike_mid            428 non-null    int64  \n",
+      " 14  strike_forefoot       428 non-null    int64  \n",
+      " 15  midsole_softness      428 non-null    int64  \n",
+      " 16  toebox_durability     428 non-null    int64  \n",
+      " 17  heel_durability       428 non-null    int64  \n",
+      " 18  outsole_durability    428 non-null    int64  \n",
+      " 19  breathability_scaled  428 non-null    int64  \n",
+      " 20  width_fit             428 non-null    int64  \n",
+      " 21  toebox_width          428 non-null    int64  \n",
+      " 22  stiffness_scaled      428 non-null    int64  \n",
+      " 23  torsional_rigidity    428 non-null    int64  \n",
+      " 24  heel_stiff            428 non-null    int64  \n",
+      " 25  plate_rock_plate      428 non-null    int64  \n",
+      " 26  plate_carbon_plate    428 non-null    int64  \n",
+      " 27  heel_lab_mm           428 non-null    float64\n",
+      " 28  forefoot_lab_mm       428 non-null    float64\n",
+      " 29  season_summer         428 non-null    int64  \n",
+      " 30  season_winter         428 non-null    int64  \n",
+      " 31  season_all            428 non-null    int64  \n",
+      "dtypes: float64(4), int64(26), object(2)\n",
+      "memory usage: 126.5+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Only take removable insole feature \n",
+    "df.drop(columns='orthotic friendly', inplace=True)\n",
+    "\n",
+    "# Rename removable insole to removable_insole deature\n",
+    "df = df.rename(columns={'removable insole': 'removable_insole'})\n",
+    "\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "id": "ba30e2dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv('../../data/road_dataset.csv', index=False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/data-preparation-v3/final-trail.ipynb b/notebooks/data-preparation-v3/final-trail.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..15ec29617d9acab86bd66d53c97bb9b848711d83
--- /dev/null
+++ b/notebooks/data-preparation-v3/final-trail.ipynb
@@ -0,0 +1,6651 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f5cc0f6b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand-Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Trail terrain</th>\n",
+       "      <th>Shock absorption</th>\n",
+       "      <th>Energy return</th>\n",
+       "      <th>Traction</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Heel stack lab Heel stack brand</th>\n",
+       "      <th>Forefoot lab Forefoot brand</th>\n",
+       "      <th>Widths available</th>\n",
+       "      <th>For heavy runners</th>\n",
+       "      <th>Season</th>\n",
+       "      <th>Removable insole</th>\n",
+       "      <th>Orthotic friendly</th>\n",
+       "      <th>Waterproofing</th>\n",
+       "      <th>Ranking</th>\n",
+       "      <th>Popularity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Adidas Terrex Agravic Speed Ultra</td>\n",
+       "      <td>90 Great!</td>\n",
+       "      <td>$220</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>High</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.1 oz / 259g 9.5 oz / 270g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>30.6 mm 38.0 mm</td>\n",
+       "      <td>30.3 mm 30.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#76 Top 21%</td>\n",
+       "      <td>#177 Top 47%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Adidas Terrex Speed Ultra</td>\n",
+       "      <td>90 Great!</td>\n",
+       "      <td>3559500 Rp</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.1 oz / 258g 9 oz / 255g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>32.8 mm 26.0 mm</td>\n",
+       "      <td>24.6 mm 18.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#49 Top 13%</td>\n",
+       "      <td>#298 Bottom 21%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Altra Experience Wild</td>\n",
+       "      <td>88 Great!</td>\n",
+       "      <td>2966250 Rp</td>\n",
+       "      <td>Light Moderate</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.1 oz / 285g 9.6 oz / 273g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>34.5 mm 34.0 mm</td>\n",
+       "      <td>30.2 mm 30.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#263 Top 40%</td>\n",
+       "      <td>#326 Top 49%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Altra Experience Wild 2</td>\n",
+       "      <td>84 Good!</td>\n",
+       "      <td>2966250 Rp</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>High</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.4 oz / 266g 10.3 oz / 293g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>32.3 mm 32.0 mm</td>\n",
+       "      <td>26.2 mm 28.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#245 Bottom 35%</td>\n",
+       "      <td>#154 Top 41%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Altra Lone Peak 5.0</td>\n",
+       "      <td>91 Superb!</td>\n",
+       "      <td>$130</td>\n",
+       "      <td>Light Moderate</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 302g 10.6 oz / 301g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>24.5 mm 25.0 mm</td>\n",
+       "      <td>24.3 mm 25.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#68 Top 11%</td>\n",
+       "      <td>#55 Top 9%</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 36 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                          Brand-Name Audience score       Price  \\\n",
+       "0  Adidas Terrex Agravic Speed Ultra      90 Great!        $220   \n",
+       "1          Adidas Terrex Speed Ultra      90 Great!  3559500 Rp   \n",
+       "2              Altra Experience Wild      88 Great!  2966250 Rp   \n",
+       "3            Altra Experience Wild 2       84 Good!  2966250 Rp   \n",
+       "4                Altra Lone Peak 5.0     91 Superb!        $130   \n",
+       "\n",
+       "    Trail terrain Shock absorption Energy return Traction Arch support  \\\n",
+       "0           Light         Moderate          High        -      Neutral   \n",
+       "1           Light                -             -        -      Neutral   \n",
+       "2  Light Moderate         Moderate           Low        -      Neutral   \n",
+       "3           Light         Moderate           Low     High      Neutral   \n",
+       "4  Light Moderate                -             -        -      Neutral   \n",
+       "\n",
+       "         Weight lab Weight brand  Lightweight  ...  \\\n",
+       "0    9.1 oz / 259g 9.5 oz / 270g          0.0  ...   \n",
+       "1      9.1 oz / 258g 9 oz / 255g          0.0  ...   \n",
+       "2   10.1 oz / 285g 9.6 oz / 273g          0.0  ...   \n",
+       "3   9.4 oz / 266g 10.3 oz / 293g          0.0  ...   \n",
+       "4  10.7 oz / 302g 10.6 oz / 301g          0.0  ...   \n",
+       "\n",
+       "  Heel stack lab Heel stack brand Forefoot lab Forefoot brand  \\\n",
+       "0                 30.6 mm 38.0 mm             30.3 mm 30.0 mm   \n",
+       "1                 32.8 mm 26.0 mm             24.6 mm 18.0 mm   \n",
+       "2                 34.5 mm 34.0 mm             30.2 mm 30.0 mm   \n",
+       "3                 32.3 mm 32.0 mm             26.2 mm 28.0 mm   \n",
+       "4                 24.5 mm 25.0 mm             24.3 mm 25.0 mm   \n",
+       "\n",
+       "  Widths available For heavy runners       Season Removable insole  \\\n",
+       "0           Normal               0.0  All seasons                1   \n",
+       "1           Normal               0.0            -                1   \n",
+       "2           Normal               0.0  All seasons                1   \n",
+       "3           Normal               0.0  All seasons                1   \n",
+       "4           Normal               0.0            -                1   \n",
+       "\n",
+       "  Orthotic friendly Waterproofing          Ranking       Popularity  \n",
+       "0                 1             -      #76 Top 21%     #177 Top 47%  \n",
+       "1                 1             -      #49 Top 13%  #298 Bottom 21%  \n",
+       "2                 1             -     #263 Top 40%     #326 Top 49%  \n",
+       "3                 1             -  #245 Bottom 35%     #154 Top 41%  \n",
+       "4                 1             -      #68 Top 11%       #55 Top 9%  \n",
+       "\n",
+       "[5 rows x 36 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df_ori = pd.read_csv('../../data/SONIX utilities - Trail.csv')\n",
+    "df_ori.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "eda2f776",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 183 entries, 0 to 182\n",
+      "Data columns (total 36 columns):\n",
+      " #   Column                                  Non-Null Count  Dtype  \n",
+      "---  ------                                  --------------  -----  \n",
+      " 0   Brand-Name                              183 non-null    str    \n",
+      " 1   Audience score                          181 non-null    str    \n",
+      " 2   Price                                   183 non-null    str    \n",
+      " 3   Trail terrain                           183 non-null    str    \n",
+      " 4   Shock absorption                        183 non-null    str    \n",
+      " 5   Energy return                           183 non-null    str    \n",
+      " 6   Traction                                178 non-null    str    \n",
+      " 7   Arch support                            183 non-null    str    \n",
+      " 8   Weight lab Weight brand                 183 non-null    str    \n",
+      " 9   Lightweight                             176 non-null    float64\n",
+      " 10  Drop lab Drop brand                     183 non-null    str    \n",
+      " 11  Strike pattern                          183 non-null    str    \n",
+      " 12  Size                                    183 non-null    str    \n",
+      " 13  Midsole softness                        183 non-null    str    \n",
+      " 14  Difference in midsole softness in cold  183 non-null    str    \n",
+      " 15  Plate                                   183 non-null    str    \n",
+      " 16  Toebox durability                       183 non-null    str    \n",
+      " 17  Heel padding durability                 183 non-null    str    \n",
+      " 18  Outsole durability                      183 non-null    str    \n",
+      " 19  Breathability                           183 non-null    str    \n",
+      " 20  Width / fit                             183 non-null    str    \n",
+      " 21  Toebox width                            183 non-null    str    \n",
+      " 22  Stiffness                               183 non-null    str    \n",
+      " 23  Torsional rigidity                      183 non-null    str    \n",
+      " 24  Heel counter stiffness                  183 non-null    str    \n",
+      " 25  Lug depth                               183 non-null    str    \n",
+      " 26  Heel stack lab Heel stack brand         183 non-null    str    \n",
+      " 27  Forefoot lab Forefoot brand             183 non-null    str    \n",
+      " 28  Widths available                        183 non-null    str    \n",
+      " 29  For heavy runners                       179 non-null    float64\n",
+      " 30  Season                                  183 non-null    str    \n",
+      " 31  Removable insole                        183 non-null    int64  \n",
+      " 32  Orthotic friendly                       183 non-null    int64  \n",
+      " 33  Waterproofing                           175 non-null    str    \n",
+      " 34  Ranking                                 183 non-null    str    \n",
+      " 35  Popularity                              183 non-null    str    \n",
+      "dtypes: float64(2), int64(2), str(32)\n",
+      "memory usage: 51.6 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_ori.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e53b33d",
+   "metadata": {},
+   "source": [
+    "# Pre-EDA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6791d8a5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 183 entries, 0 to 182\n",
+      "Data columns (total 28 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   Brand-Name                       183 non-null    str    \n",
+      " 1   Trail terrain                    183 non-null    str    \n",
+      " 2   Shock absorption                 183 non-null    str    \n",
+      " 3   Energy return                    183 non-null    str    \n",
+      " 4   Traction                         178 non-null    str    \n",
+      " 5   Arch support                     183 non-null    str    \n",
+      " 6   Weight lab Weight brand          183 non-null    str    \n",
+      " 7   Lightweight                      176 non-null    float64\n",
+      " 8   Drop lab Drop brand              183 non-null    str    \n",
+      " 9   Strike pattern                   183 non-null    str    \n",
+      " 10  Midsole softness                 183 non-null    str    \n",
+      " 11  Plate                            183 non-null    str    \n",
+      " 12  Toebox durability                183 non-null    str    \n",
+      " 13  Heel padding durability          183 non-null    str    \n",
+      " 14  Outsole durability               183 non-null    str    \n",
+      " 15  Breathability                    183 non-null    str    \n",
+      " 16  Width / fit                      183 non-null    str    \n",
+      " 17  Toebox width                     183 non-null    str    \n",
+      " 18  Stiffness                        183 non-null    str    \n",
+      " 19  Torsional rigidity               183 non-null    str    \n",
+      " 20  Heel counter stiffness           183 non-null    str    \n",
+      " 21  Lug depth                        183 non-null    str    \n",
+      " 22  Heel stack lab Heel stack brand  183 non-null    str    \n",
+      " 23  Forefoot lab Forefoot brand      183 non-null    str    \n",
+      " 24  Season                           183 non-null    str    \n",
+      " 25  Removable insole                 183 non-null    int64  \n",
+      " 26  Orthotic friendly                183 non-null    int64  \n",
+      " 27  Waterproofing                    175 non-null    str    \n",
+      "dtypes: float64(1), int64(2), str(25)\n",
+      "memory usage: 40.2 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_ori.drop(columns=['Audience score', 'Size', 'Price', 'Widths available', 'Difference in midsole softness in cold', 'For heavy runners', 'Ranking', 'Popularity'], inplace=True)\n",
+    "df_ori.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a240381d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>brand-name</th>\n",
+       "      <th>trail terrain</th>\n",
+       "      <th>shock absorption</th>\n",
+       "      <th>energy return</th>\n",
+       "      <th>traction</th>\n",
+       "      <th>arch support</th>\n",
+       "      <th>weight lab weight brand</th>\n",
+       "      <th>lightweight</th>\n",
+       "      <th>drop lab drop brand</th>\n",
+       "      <th>strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>stiffness</th>\n",
+       "      <th>torsional rigidity</th>\n",
+       "      <th>heel counter stiffness</th>\n",
+       "      <th>lug depth</th>\n",
+       "      <th>heel stack lab heel stack brand</th>\n",
+       "      <th>forefoot lab forefoot brand</th>\n",
+       "      <th>season</th>\n",
+       "      <th>removable insole</th>\n",
+       "      <th>orthotic friendly</th>\n",
+       "      <th>waterproofing</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>adidas terrex agravic speed ultra</td>\n",
+       "      <td>light</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>high</td>\n",
+       "      <td>-</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>9.1 oz / 259g 9.5 oz / 270g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.3 mm 8.0 mm</td>\n",
+       "      <td>mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>2.5 mm</td>\n",
+       "      <td>30.6 mm 38.0 mm</td>\n",
+       "      <td>30.3 mm 30.0 mm</td>\n",
+       "      <td>all seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>adidas terrex speed ultra</td>\n",
+       "      <td>light</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>9.1 oz / 258g 9 oz / 255g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8.2 mm 8.0 mm</td>\n",
+       "      <td>heel mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>2.6 mm</td>\n",
+       "      <td>32.8 mm 26.0 mm</td>\n",
+       "      <td>24.6 mm 18.0 mm</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>altra experience wild</td>\n",
+       "      <td>light moderate</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>low</td>\n",
+       "      <td>-</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>10.1 oz / 285g 9.6 oz / 273g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.3 mm 4.0 mm</td>\n",
+       "      <td>mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>3.6 mm</td>\n",
+       "      <td>34.5 mm 34.0 mm</td>\n",
+       "      <td>30.2 mm 30.0 mm</td>\n",
+       "      <td>all seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>altra experience wild 2</td>\n",
+       "      <td>light</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>low</td>\n",
+       "      <td>high</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>9.4 oz / 266g 10.3 oz / 293g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.1 mm 4.0 mm</td>\n",
+       "      <td>mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>3.5 mm</td>\n",
+       "      <td>32.3 mm 32.0 mm</td>\n",
+       "      <td>26.2 mm 28.0 mm</td>\n",
+       "      <td>all seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>altra lone peak 5.0</td>\n",
+       "      <td>light moderate</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>10.7 oz / 302g 10.6 oz / 301g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.2 mm 0.0 mm</td>\n",
+       "      <td>mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>-</td>\n",
+       "      <td>3.7 mm</td>\n",
+       "      <td>24.5 mm 25.0 mm</td>\n",
+       "      <td>24.3 mm 25.0 mm</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 28 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                          brand-name   trail terrain shock absorption  \\\n",
+       "0  adidas terrex agravic speed ultra           light         moderate   \n",
+       "1          adidas terrex speed ultra           light                -   \n",
+       "2              altra experience wild  light moderate         moderate   \n",
+       "3            altra experience wild 2           light         moderate   \n",
+       "4                altra lone peak 5.0  light moderate                -   \n",
+       "\n",
+       "  energy return traction arch support        weight lab weight brand  \\\n",
+       "0          high        -      neutral    9.1 oz / 259g 9.5 oz / 270g   \n",
+       "1             -        -      neutral      9.1 oz / 258g 9 oz / 255g   \n",
+       "2           low        -      neutral   10.1 oz / 285g 9.6 oz / 273g   \n",
+       "3           low     high      neutral   9.4 oz / 266g 10.3 oz / 293g   \n",
+       "4             -        -      neutral  10.7 oz / 302g 10.6 oz / 301g   \n",
+       "\n",
+       "   lightweight drop lab drop brand     strike pattern  ... stiffness  \\\n",
+       "0          0.0       0.3 mm 8.0 mm       mid/forefoot  ...  moderate   \n",
+       "1          0.0       8.2 mm 8.0 mm  heel mid/forefoot  ...     stiff   \n",
+       "2          0.0       4.3 mm 4.0 mm       mid/forefoot  ...  moderate   \n",
+       "3          0.0       6.1 mm 4.0 mm       mid/forefoot  ...  moderate   \n",
+       "4          0.0       0.2 mm 0.0 mm       mid/forefoot  ...     stiff   \n",
+       "\n",
+       "  torsional rigidity heel counter stiffness lug depth  \\\n",
+       "0              stiff               flexible    2.5 mm   \n",
+       "1           flexible               flexible    2.6 mm   \n",
+       "2              stiff               moderate    3.6 mm   \n",
+       "3           moderate               flexible    3.5 mm   \n",
+       "4           flexible                      -    3.7 mm   \n",
+       "\n",
+       "  heel stack lab heel stack brand forefoot lab forefoot brand       season  \\\n",
+       "0                 30.6 mm 38.0 mm             30.3 mm 30.0 mm  all seasons   \n",
+       "1                 32.8 mm 26.0 mm             24.6 mm 18.0 mm            -   \n",
+       "2                 34.5 mm 34.0 mm             30.2 mm 30.0 mm  all seasons   \n",
+       "3                 32.3 mm 32.0 mm             26.2 mm 28.0 mm  all seasons   \n",
+       "4                 24.5 mm 25.0 mm             24.3 mm 25.0 mm            -   \n",
+       "\n",
+       "  removable insole orthotic friendly waterproofing  \n",
+       "0                1                 1             -  \n",
+       "1                1                 1             -  \n",
+       "2                1                 1             -  \n",
+       "3                1                 1             -  \n",
+       "4                1                 1             -  \n",
+       "\n",
+       "[5 rows x 28 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# convert all to lowercase\n",
+    "\n",
+    "df_ori.columns = df_ori.columns.str.strip().str.lower()\n",
+    "df_ori = df_ori.map(lambda x: x.strip().lower() if isinstance(x, str) else x)\n",
+    "\n",
+    "df_ori.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ccddc8ad",
+   "metadata": {},
+   "source": [
+    "## Separate Brand-Name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "0bebd201",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "''' \n",
+    "Running shoes for trail brand in our dataset include:\n",
+    "    Adidas\n",
+    "    Altra\n",
+    "    ASICS\n",
+    "    Brooks\n",
+    "    HOKA\n",
+    "    Icebug\n",
+    "    Inov8\n",
+    "    Kailas\n",
+    "    KEEN\n",
+    "    La Sportiva\n",
+    "    Merrell\n",
+    "    New Balance\n",
+    "    Nike\n",
+    "    NNormal\n",
+    "    On\n",
+    "    Salomon\n",
+    "    Saucony\n",
+    "    Topo\n",
+    "    Xero\n",
+    "    Scarpa\n",
+    "    The North Face\n",
+    "'''\n",
+    "\n",
+    "brands_list = [\n",
+    "    \"Adidas\", \"Altra\", \"ASICS\", \"Brooks\", \"HOKA\", \"Icebug\", \"Inov8\", \n",
+    "    \"Kailas\", \"KEEN\", \"La Sportiva\", \"Merrell\", \"New Balance\", \"Nike\", \n",
+    "    \"NNormal\", \"On\", \"Salomon\", \"Saucony\", \"Topo\", \"Xero\", \"Scarpa\", \n",
+    "    \"The North Face\"\n",
+    "]\n",
+    "brands = [b.lower() for b in brands_list]\n",
+    "brands.sort(key=len, reverse=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "3a304c3d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>brand</th>\n",
+       "      <th>name</th>\n",
+       "      <th>trail terrain</th>\n",
+       "      <th>shock absorption</th>\n",
+       "      <th>energy return</th>\n",
+       "      <th>traction</th>\n",
+       "      <th>arch support</th>\n",
+       "      <th>weight lab weight brand</th>\n",
+       "      <th>lightweight</th>\n",
+       "      <th>drop lab drop brand</th>\n",
+       "      <th>...</th>\n",
+       "      <th>stiffness</th>\n",
+       "      <th>torsional rigidity</th>\n",
+       "      <th>heel counter stiffness</th>\n",
+       "      <th>lug depth</th>\n",
+       "      <th>heel stack lab heel stack brand</th>\n",
+       "      <th>forefoot lab forefoot brand</th>\n",
+       "      <th>season</th>\n",
+       "      <th>removable insole</th>\n",
+       "      <th>orthotic friendly</th>\n",
+       "      <th>waterproofing</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>terrex agravic speed ultra</td>\n",
+       "      <td>light</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>high</td>\n",
+       "      <td>-</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>9.1 oz / 259g 9.5 oz / 270g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.3 mm 8.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>2.5 mm</td>\n",
+       "      <td>30.6 mm 38.0 mm</td>\n",
+       "      <td>30.3 mm 30.0 mm</td>\n",
+       "      <td>all seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>terrex speed ultra</td>\n",
+       "      <td>light</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>9.1 oz / 258g 9 oz / 255g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8.2 mm 8.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>2.6 mm</td>\n",
+       "      <td>32.8 mm 26.0 mm</td>\n",
+       "      <td>24.6 mm 18.0 mm</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>experience wild</td>\n",
+       "      <td>light moderate</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>low</td>\n",
+       "      <td>-</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>10.1 oz / 285g 9.6 oz / 273g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.3 mm 4.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>3.6 mm</td>\n",
+       "      <td>34.5 mm 34.0 mm</td>\n",
+       "      <td>30.2 mm 30.0 mm</td>\n",
+       "      <td>all seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>experience wild 2</td>\n",
+       "      <td>light</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>low</td>\n",
+       "      <td>high</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>9.4 oz / 266g 10.3 oz / 293g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.1 mm 4.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>3.5 mm</td>\n",
+       "      <td>32.3 mm 32.0 mm</td>\n",
+       "      <td>26.2 mm 28.0 mm</td>\n",
+       "      <td>all seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>lone peak 5.0</td>\n",
+       "      <td>light moderate</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>10.7 oz / 302g 10.6 oz / 301g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.2 mm 0.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>-</td>\n",
+       "      <td>3.7 mm</td>\n",
+       "      <td>24.5 mm 25.0 mm</td>\n",
+       "      <td>24.3 mm 25.0 mm</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 29 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    brand                        name   trail terrain shock absorption  \\\n",
+       "0  adidas  terrex agravic speed ultra           light         moderate   \n",
+       "1  adidas          terrex speed ultra           light                -   \n",
+       "2   altra             experience wild  light moderate         moderate   \n",
+       "3   altra           experience wild 2           light         moderate   \n",
+       "4   altra               lone peak 5.0  light moderate                -   \n",
+       "\n",
+       "  energy return traction arch support        weight lab weight brand  \\\n",
+       "0          high        -      neutral    9.1 oz / 259g 9.5 oz / 270g   \n",
+       "1             -        -      neutral      9.1 oz / 258g 9 oz / 255g   \n",
+       "2           low        -      neutral   10.1 oz / 285g 9.6 oz / 273g   \n",
+       "3           low     high      neutral   9.4 oz / 266g 10.3 oz / 293g   \n",
+       "4             -        -      neutral  10.7 oz / 302g 10.6 oz / 301g   \n",
+       "\n",
+       "   lightweight drop lab drop brand  ... stiffness torsional rigidity  \\\n",
+       "0          0.0       0.3 mm 8.0 mm  ...  moderate              stiff   \n",
+       "1          0.0       8.2 mm 8.0 mm  ...     stiff           flexible   \n",
+       "2          0.0       4.3 mm 4.0 mm  ...  moderate              stiff   \n",
+       "3          0.0       6.1 mm 4.0 mm  ...  moderate           moderate   \n",
+       "4          0.0       0.2 mm 0.0 mm  ...     stiff           flexible   \n",
+       "\n",
+       "  heel counter stiffness lug depth heel stack lab heel stack brand  \\\n",
+       "0               flexible    2.5 mm                 30.6 mm 38.0 mm   \n",
+       "1               flexible    2.6 mm                 32.8 mm 26.0 mm   \n",
+       "2               moderate    3.6 mm                 34.5 mm 34.0 mm   \n",
+       "3               flexible    3.5 mm                 32.3 mm 32.0 mm   \n",
+       "4                      -    3.7 mm                 24.5 mm 25.0 mm   \n",
+       "\n",
+       "  forefoot lab forefoot brand       season removable insole orthotic friendly  \\\n",
+       "0             30.3 mm 30.0 mm  all seasons                1                 1   \n",
+       "1             24.6 mm 18.0 mm            -                1                 1   \n",
+       "2             30.2 mm 30.0 mm  all seasons                1                 1   \n",
+       "3             26.2 mm 28.0 mm  all seasons                1                 1   \n",
+       "4             24.3 mm 25.0 mm            -                1                 1   \n",
+       "\n",
+       "  waterproofing  \n",
+       "0             -  \n",
+       "1             -  \n",
+       "2             -  \n",
+       "3             -  \n",
+       "4             -  \n",
+       "\n",
+       "[5 rows x 29 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def split_brand_name(full_text):\n",
+    "    for brand in brands:\n",
+    "        if full_text.startswith(brand):\n",
+    "            # Sisa dari brand dijadiin name semua\n",
+    "            name = full_text[len(brand):].strip()\n",
+    "            return brand, name\n",
+    "    return \"Unknown\", full_text \n",
+    "\n",
+    "\n",
+    "df_ori[['brand', 'name']] = df_ori['brand-name'].apply(lambda x: pd.Series(split_brand_name(x)))\n",
+    "\n",
+    "# Atur urutan kolom agar brand dan name tetap ada di depan\n",
+    "cols = ['brand', 'name'] + [c for c in df_ori.columns if c not in ['brand', 'name', 'brand-name']]\n",
+    "df_ori = df_ori[cols]\n",
+    "\n",
+    "df_ori.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d7d61ef1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# df_ori.head(40)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "209d339d",
+   "metadata": {},
+   "source": [
+    "## Remove Duplicates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "498021b3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "183\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>brand</th>\n",
+       "      <th>name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>hoka</td>\n",
+       "      <td>mafate x</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66</th>\n",
+       "      <td>inov8</td>\n",
+       "      <td>trailfly</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>83</th>\n",
+       "      <td>la sportiva</td>\n",
+       "      <td>prodigio</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>84</th>\n",
+       "      <td>la sportiva</td>\n",
+       "      <td>prodigio</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>124</th>\n",
+       "      <td>nike</td>\n",
+       "      <td>terra kiger 9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>137</th>\n",
+       "      <td>on</td>\n",
+       "      <td>cloudsurfer trail 2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>141</th>\n",
+       "      <td>on</td>\n",
+       "      <td>cloudvista 2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>180</th>\n",
+       "      <td>topo</td>\n",
+       "      <td>ultraventure 4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           brand                 name\n",
+       "52          hoka             mafate x\n",
+       "66         inov8             trailfly\n",
+       "83   la sportiva             prodigio\n",
+       "84   la sportiva             prodigio\n",
+       "124         nike        terra kiger 9\n",
+       "137           on  cloudsurfer trail 2\n",
+       "141           on         cloudvista 2\n",
+       "180         topo       ultraventure 4"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dup_mask = df_ori.duplicated(subset=[\"brand\", \"name\"], keep=\"first\")\n",
+    "print(len(dup_mask))\n",
+    "df_ori.loc[dup_mask, [\"brand\", \"name\"]].head(100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "0f107030",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Before: 183\n",
+      "After : 175\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>brand</th>\n",
+       "      <th>name</th>\n",
+       "      <th>trail terrain</th>\n",
+       "      <th>shock absorption</th>\n",
+       "      <th>energy return</th>\n",
+       "      <th>traction</th>\n",
+       "      <th>arch support</th>\n",
+       "      <th>weight lab weight brand</th>\n",
+       "      <th>lightweight</th>\n",
+       "      <th>drop lab drop brand</th>\n",
+       "      <th>...</th>\n",
+       "      <th>stiffness</th>\n",
+       "      <th>torsional rigidity</th>\n",
+       "      <th>heel counter stiffness</th>\n",
+       "      <th>lug depth</th>\n",
+       "      <th>heel stack lab heel stack brand</th>\n",
+       "      <th>forefoot lab forefoot brand</th>\n",
+       "      <th>season</th>\n",
+       "      <th>removable insole</th>\n",
+       "      <th>orthotic friendly</th>\n",
+       "      <th>waterproofing</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>terrex agravic speed ultra</td>\n",
+       "      <td>light</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>high</td>\n",
+       "      <td>-</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>9.1 oz / 259g 9.5 oz / 270g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.3 mm 8.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>2.5 mm</td>\n",
+       "      <td>30.6 mm 38.0 mm</td>\n",
+       "      <td>30.3 mm 30.0 mm</td>\n",
+       "      <td>all seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>terrex speed ultra</td>\n",
+       "      <td>light</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>9.1 oz / 258g 9 oz / 255g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8.2 mm 8.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>2.6 mm</td>\n",
+       "      <td>32.8 mm 26.0 mm</td>\n",
+       "      <td>24.6 mm 18.0 mm</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>experience wild</td>\n",
+       "      <td>light moderate</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>low</td>\n",
+       "      <td>-</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>10.1 oz / 285g 9.6 oz / 273g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.3 mm 4.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>3.6 mm</td>\n",
+       "      <td>34.5 mm 34.0 mm</td>\n",
+       "      <td>30.2 mm 30.0 mm</td>\n",
+       "      <td>all seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>experience wild 2</td>\n",
+       "      <td>light</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>low</td>\n",
+       "      <td>high</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>9.4 oz / 266g 10.3 oz / 293g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.1 mm 4.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>3.5 mm</td>\n",
+       "      <td>32.3 mm 32.0 mm</td>\n",
+       "      <td>26.2 mm 28.0 mm</td>\n",
+       "      <td>all seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>lone peak 5.0</td>\n",
+       "      <td>light moderate</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>10.7 oz / 302g 10.6 oz / 301g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.2 mm 0.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>-</td>\n",
+       "      <td>3.7 mm</td>\n",
+       "      <td>24.5 mm 25.0 mm</td>\n",
+       "      <td>24.3 mm 25.0 mm</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 29 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    brand                        name   trail terrain shock absorption  \\\n",
+       "0  adidas  terrex agravic speed ultra           light         moderate   \n",
+       "1  adidas          terrex speed ultra           light                -   \n",
+       "2   altra             experience wild  light moderate         moderate   \n",
+       "3   altra           experience wild 2           light         moderate   \n",
+       "4   altra               lone peak 5.0  light moderate                -   \n",
+       "\n",
+       "  energy return traction arch support        weight lab weight brand  \\\n",
+       "0          high        -      neutral    9.1 oz / 259g 9.5 oz / 270g   \n",
+       "1             -        -      neutral      9.1 oz / 258g 9 oz / 255g   \n",
+       "2           low        -      neutral   10.1 oz / 285g 9.6 oz / 273g   \n",
+       "3           low     high      neutral   9.4 oz / 266g 10.3 oz / 293g   \n",
+       "4             -        -      neutral  10.7 oz / 302g 10.6 oz / 301g   \n",
+       "\n",
+       "   lightweight drop lab drop brand  ... stiffness torsional rigidity  \\\n",
+       "0          0.0       0.3 mm 8.0 mm  ...  moderate              stiff   \n",
+       "1          0.0       8.2 mm 8.0 mm  ...     stiff           flexible   \n",
+       "2          0.0       4.3 mm 4.0 mm  ...  moderate              stiff   \n",
+       "3          0.0       6.1 mm 4.0 mm  ...  moderate           moderate   \n",
+       "4          0.0       0.2 mm 0.0 mm  ...     stiff           flexible   \n",
+       "\n",
+       "  heel counter stiffness lug depth heel stack lab heel stack brand  \\\n",
+       "0               flexible    2.5 mm                 30.6 mm 38.0 mm   \n",
+       "1               flexible    2.6 mm                 32.8 mm 26.0 mm   \n",
+       "2               moderate    3.6 mm                 34.5 mm 34.0 mm   \n",
+       "3               flexible    3.5 mm                 32.3 mm 32.0 mm   \n",
+       "4                      -    3.7 mm                 24.5 mm 25.0 mm   \n",
+       "\n",
+       "  forefoot lab forefoot brand       season removable insole orthotic friendly  \\\n",
+       "0             30.3 mm 30.0 mm  all seasons                1                 1   \n",
+       "1             24.6 mm 18.0 mm            -                1                 1   \n",
+       "2             30.2 mm 30.0 mm  all seasons                1                 1   \n",
+       "3             26.2 mm 28.0 mm  all seasons                1                 1   \n",
+       "4             24.3 mm 25.0 mm            -                1                 1   \n",
+       "\n",
+       "  waterproofing  \n",
+       "0             -  \n",
+       "1             -  \n",
+       "2             -  \n",
+       "3             -  \n",
+       "4             -  \n",
+       "\n",
+       "[5 rows x 29 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# sebelum hapus\n",
+    "print(\"Before:\", len(df_ori))\n",
+    "\n",
+    "#hapus\n",
+    "df_ori = df_ori.drop_duplicates(subset=[\"brand\", \"name\"], keep=\"first\").reset_index(drop=True)\n",
+    "\n",
+    "print(\"After :\", len(df_ori))\n",
+    "df_ori.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "3e3d8e69",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ditemukan 0 baris yang memiliki spesifikasi identik.\n",
+      "\n",
+      "Empty DataFrame\n",
+      "Columns: [brand, name, trail terrain, shock absorption, energy return]\n",
+      "Index: []\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Searching for duplicate technical specifications\n",
+    "tech_columns = df_ori.columns[2:].tolist()\n",
+    "duplicates = df_ori[df_ori.duplicated(subset=tech_columns, keep=False)]\n",
+    "\n",
+    "duplicates_sorted = duplicates.sort_values(by=tech_columns[:3])\n",
+    "\n",
+    "print(f\"Ditemukan {len(duplicates_sorted)} baris yang memiliki spesifikasi identik.\\n\")\n",
+    "print(duplicates_sorted[['brand', 'name'] + tech_columns[:3]].head(30))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "8ec3d162",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Before: 175\n",
+      "After : 175\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>brand</th>\n",
+       "      <th>name</th>\n",
+       "      <th>trail terrain</th>\n",
+       "      <th>shock absorption</th>\n",
+       "      <th>energy return</th>\n",
+       "      <th>traction</th>\n",
+       "      <th>arch support</th>\n",
+       "      <th>weight lab weight brand</th>\n",
+       "      <th>lightweight</th>\n",
+       "      <th>drop lab drop brand</th>\n",
+       "      <th>...</th>\n",
+       "      <th>stiffness</th>\n",
+       "      <th>torsional rigidity</th>\n",
+       "      <th>heel counter stiffness</th>\n",
+       "      <th>lug depth</th>\n",
+       "      <th>heel stack lab heel stack brand</th>\n",
+       "      <th>forefoot lab forefoot brand</th>\n",
+       "      <th>season</th>\n",
+       "      <th>removable insole</th>\n",
+       "      <th>orthotic friendly</th>\n",
+       "      <th>waterproofing</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>terrex agravic speed ultra</td>\n",
+       "      <td>light</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>high</td>\n",
+       "      <td>-</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>9.1 oz / 259g 9.5 oz / 270g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.3 mm 8.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>2.5 mm</td>\n",
+       "      <td>30.6 mm 38.0 mm</td>\n",
+       "      <td>30.3 mm 30.0 mm</td>\n",
+       "      <td>all seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>terrex speed ultra</td>\n",
+       "      <td>light</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>9.1 oz / 258g 9 oz / 255g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8.2 mm 8.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>2.6 mm</td>\n",
+       "      <td>32.8 mm 26.0 mm</td>\n",
+       "      <td>24.6 mm 18.0 mm</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>experience wild</td>\n",
+       "      <td>light moderate</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>low</td>\n",
+       "      <td>-</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>10.1 oz / 285g 9.6 oz / 273g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.3 mm 4.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>3.6 mm</td>\n",
+       "      <td>34.5 mm 34.0 mm</td>\n",
+       "      <td>30.2 mm 30.0 mm</td>\n",
+       "      <td>all seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>experience wild 2</td>\n",
+       "      <td>light</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>low</td>\n",
+       "      <td>high</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>9.4 oz / 266g 10.3 oz / 293g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.1 mm 4.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>3.5 mm</td>\n",
+       "      <td>32.3 mm 32.0 mm</td>\n",
+       "      <td>26.2 mm 28.0 mm</td>\n",
+       "      <td>all seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>lone peak 5.0</td>\n",
+       "      <td>light moderate</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>10.7 oz / 302g 10.6 oz / 301g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.2 mm 0.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>-</td>\n",
+       "      <td>3.7 mm</td>\n",
+       "      <td>24.5 mm 25.0 mm</td>\n",
+       "      <td>24.3 mm 25.0 mm</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 29 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    brand                        name   trail terrain shock absorption  \\\n",
+       "0  adidas  terrex agravic speed ultra           light         moderate   \n",
+       "1  adidas          terrex speed ultra           light                -   \n",
+       "2   altra             experience wild  light moderate         moderate   \n",
+       "3   altra           experience wild 2           light         moderate   \n",
+       "4   altra               lone peak 5.0  light moderate                -   \n",
+       "\n",
+       "  energy return traction arch support        weight lab weight brand  \\\n",
+       "0          high        -      neutral    9.1 oz / 259g 9.5 oz / 270g   \n",
+       "1             -        -      neutral      9.1 oz / 258g 9 oz / 255g   \n",
+       "2           low        -      neutral   10.1 oz / 285g 9.6 oz / 273g   \n",
+       "3           low     high      neutral   9.4 oz / 266g 10.3 oz / 293g   \n",
+       "4             -        -      neutral  10.7 oz / 302g 10.6 oz / 301g   \n",
+       "\n",
+       "   lightweight drop lab drop brand  ... stiffness torsional rigidity  \\\n",
+       "0          0.0       0.3 mm 8.0 mm  ...  moderate              stiff   \n",
+       "1          0.0       8.2 mm 8.0 mm  ...     stiff           flexible   \n",
+       "2          0.0       4.3 mm 4.0 mm  ...  moderate              stiff   \n",
+       "3          0.0       6.1 mm 4.0 mm  ...  moderate           moderate   \n",
+       "4          0.0       0.2 mm 0.0 mm  ...     stiff           flexible   \n",
+       "\n",
+       "  heel counter stiffness lug depth heel stack lab heel stack brand  \\\n",
+       "0               flexible    2.5 mm                 30.6 mm 38.0 mm   \n",
+       "1               flexible    2.6 mm                 32.8 mm 26.0 mm   \n",
+       "2               moderate    3.6 mm                 34.5 mm 34.0 mm   \n",
+       "3               flexible    3.5 mm                 32.3 mm 32.0 mm   \n",
+       "4                      -    3.7 mm                 24.5 mm 25.0 mm   \n",
+       "\n",
+       "  forefoot lab forefoot brand       season removable insole orthotic friendly  \\\n",
+       "0             30.3 mm 30.0 mm  all seasons                1                 1   \n",
+       "1             24.6 mm 18.0 mm            -                1                 1   \n",
+       "2             30.2 mm 30.0 mm  all seasons                1                 1   \n",
+       "3             26.2 mm 28.0 mm  all seasons                1                 1   \n",
+       "4             24.3 mm 25.0 mm            -                1                 1   \n",
+       "\n",
+       "  waterproofing  \n",
+       "0             -  \n",
+       "1             -  \n",
+       "2             -  \n",
+       "3             -  \n",
+       "4             -  \n",
+       "\n",
+       "[5 rows x 29 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# sebelum hapus\n",
+    "print(\"Before:\", len(df_ori))\n",
+    "\n",
+    "#hapus\n",
+    "df_ori = df_ori.drop_duplicates(subset=tech_columns, keep='first').copy()\n",
+    "\n",
+    "print(\"After :\", len(df_ori))\n",
+    "df_ori.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "8f770d4b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 175 entries, 0 to 174\n",
+      "Data columns (total 29 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   brand                            175 non-null    str    \n",
+      " 1   name                             175 non-null    str    \n",
+      " 2   trail terrain                    175 non-null    str    \n",
+      " 3   shock absorption                 175 non-null    str    \n",
+      " 4   energy return                    175 non-null    str    \n",
+      " 5   traction                         170 non-null    str    \n",
+      " 6   arch support                     175 non-null    str    \n",
+      " 7   weight lab weight brand          175 non-null    str    \n",
+      " 8   lightweight                      168 non-null    float64\n",
+      " 9   drop lab drop brand              175 non-null    str    \n",
+      " 10  strike pattern                   175 non-null    str    \n",
+      " 11  midsole softness                 175 non-null    str    \n",
+      " 12  plate                            175 non-null    str    \n",
+      " 13  toebox durability                175 non-null    str    \n",
+      " 14  heel padding durability          175 non-null    str    \n",
+      " 15  outsole durability               175 non-null    str    \n",
+      " 16  breathability                    175 non-null    str    \n",
+      " 17  width / fit                      175 non-null    str    \n",
+      " 18  toebox width                     175 non-null    str    \n",
+      " 19  stiffness                        175 non-null    str    \n",
+      " 20  torsional rigidity               175 non-null    str    \n",
+      " 21  heel counter stiffness           175 non-null    str    \n",
+      " 22  lug depth                        175 non-null    str    \n",
+      " 23  heel stack lab heel stack brand  175 non-null    str    \n",
+      " 24  forefoot lab forefoot brand      175 non-null    str    \n",
+      " 25  season                           175 non-null    str    \n",
+      " 26  removable insole                 175 non-null    int64  \n",
+      " 27  orthotic friendly                175 non-null    int64  \n",
+      " 28  waterproofing                    169 non-null    str    \n",
+      "dtypes: float64(1), int64(2), str(26)\n",
+      "memory usage: 39.8 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_ori.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "983f5eed",
+   "metadata": {},
+   "source": [
+    "# EDA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1023bc2a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd2cfc21",
+   "metadata": {},
+   "source": [
+    "# Preprocessing\n",
+    "\n",
+    "In this stage we will encode, scale, and bin the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "9f966e10",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>brand</th>\n",
+       "      <th>name</th>\n",
+       "      <th>trail terrain</th>\n",
+       "      <th>shock absorption</th>\n",
+       "      <th>energy return</th>\n",
+       "      <th>traction</th>\n",
+       "      <th>arch support</th>\n",
+       "      <th>weight lab weight brand</th>\n",
+       "      <th>lightweight</th>\n",
+       "      <th>drop lab drop brand</th>\n",
+       "      <th>...</th>\n",
+       "      <th>stiffness</th>\n",
+       "      <th>torsional rigidity</th>\n",
+       "      <th>heel counter stiffness</th>\n",
+       "      <th>lug depth</th>\n",
+       "      <th>heel stack lab heel stack brand</th>\n",
+       "      <th>forefoot lab forefoot brand</th>\n",
+       "      <th>season</th>\n",
+       "      <th>removable insole</th>\n",
+       "      <th>orthotic friendly</th>\n",
+       "      <th>waterproofing</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>terrex agravic speed ultra</td>\n",
+       "      <td>light</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>high</td>\n",
+       "      <td>-</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>9.1 oz / 259g 9.5 oz / 270g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.3 mm 8.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>2.5 mm</td>\n",
+       "      <td>30.6 mm 38.0 mm</td>\n",
+       "      <td>30.3 mm 30.0 mm</td>\n",
+       "      <td>all seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>terrex speed ultra</td>\n",
+       "      <td>light</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>9.1 oz / 258g 9 oz / 255g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8.2 mm 8.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>2.6 mm</td>\n",
+       "      <td>32.8 mm 26.0 mm</td>\n",
+       "      <td>24.6 mm 18.0 mm</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>experience wild</td>\n",
+       "      <td>light moderate</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>low</td>\n",
+       "      <td>-</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>10.1 oz / 285g 9.6 oz / 273g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.3 mm 4.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>3.6 mm</td>\n",
+       "      <td>34.5 mm 34.0 mm</td>\n",
+       "      <td>30.2 mm 30.0 mm</td>\n",
+       "      <td>all seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>experience wild 2</td>\n",
+       "      <td>light</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>low</td>\n",
+       "      <td>high</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>9.4 oz / 266g 10.3 oz / 293g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.1 mm 4.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>3.5 mm</td>\n",
+       "      <td>32.3 mm 32.0 mm</td>\n",
+       "      <td>26.2 mm 28.0 mm</td>\n",
+       "      <td>all seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>lone peak 5.0</td>\n",
+       "      <td>light moderate</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>10.7 oz / 302g 10.6 oz / 301g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.2 mm 0.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>-</td>\n",
+       "      <td>3.7 mm</td>\n",
+       "      <td>24.5 mm 25.0 mm</td>\n",
+       "      <td>24.3 mm 25.0 mm</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 29 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    brand                        name   trail terrain shock absorption  \\\n",
+       "0  adidas  terrex agravic speed ultra           light         moderate   \n",
+       "1  adidas          terrex speed ultra           light                -   \n",
+       "2   altra             experience wild  light moderate         moderate   \n",
+       "3   altra           experience wild 2           light         moderate   \n",
+       "4   altra               lone peak 5.0  light moderate                -   \n",
+       "\n",
+       "  energy return traction arch support        weight lab weight brand  \\\n",
+       "0          high        -      neutral    9.1 oz / 259g 9.5 oz / 270g   \n",
+       "1             -        -      neutral      9.1 oz / 258g 9 oz / 255g   \n",
+       "2           low        -      neutral   10.1 oz / 285g 9.6 oz / 273g   \n",
+       "3           low     high      neutral   9.4 oz / 266g 10.3 oz / 293g   \n",
+       "4             -        -      neutral  10.7 oz / 302g 10.6 oz / 301g   \n",
+       "\n",
+       "   lightweight drop lab drop brand  ... stiffness torsional rigidity  \\\n",
+       "0          0.0       0.3 mm 8.0 mm  ...  moderate              stiff   \n",
+       "1          0.0       8.2 mm 8.0 mm  ...     stiff           flexible   \n",
+       "2          0.0       4.3 mm 4.0 mm  ...  moderate              stiff   \n",
+       "3          0.0       6.1 mm 4.0 mm  ...  moderate           moderate   \n",
+       "4          0.0       0.2 mm 0.0 mm  ...     stiff           flexible   \n",
+       "\n",
+       "  heel counter stiffness lug depth heel stack lab heel stack brand  \\\n",
+       "0               flexible    2.5 mm                 30.6 mm 38.0 mm   \n",
+       "1               flexible    2.6 mm                 32.8 mm 26.0 mm   \n",
+       "2               moderate    3.6 mm                 34.5 mm 34.0 mm   \n",
+       "3               flexible    3.5 mm                 32.3 mm 32.0 mm   \n",
+       "4                      -    3.7 mm                 24.5 mm 25.0 mm   \n",
+       "\n",
+       "  forefoot lab forefoot brand       season removable insole orthotic friendly  \\\n",
+       "0             30.3 mm 30.0 mm  all seasons                1                 1   \n",
+       "1             24.6 mm 18.0 mm            -                1                 1   \n",
+       "2             30.2 mm 30.0 mm  all seasons                1                 1   \n",
+       "3             26.2 mm 28.0 mm  all seasons                1                 1   \n",
+       "4             24.3 mm 25.0 mm            -                1                 1   \n",
+       "\n",
+       "  waterproofing  \n",
+       "0             -  \n",
+       "1             -  \n",
+       "2             -  \n",
+       "3             -  \n",
+       "4             -  \n",
+       "\n",
+       "[5 rows x 29 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = df_ori.copy()\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5729690",
+   "metadata": {},
+   "source": [
+    "## Trail terrain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "351d019a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trail terrain\n",
+      "light moderate        54\n",
+      "light                 48\n",
+      "moderate technical    21\n",
+      "moderate              19\n",
+      "-                     17\n",
+      "technical             11\n",
+      "lightmoderate          3\n",
+      "moderatetechnical      2\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['trail terrain'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "1370c40c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trail terrain\n",
+      "light moderate        54\n",
+      "light                 48\n",
+      "moderate technical    21\n",
+      "moderate              19\n",
+      "technical             11\n",
+      "lightmoderate          3\n",
+      "moderatetechnical      2\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = df[df['trail terrain'] != \"-\"].reset_index(drop=True)\n",
+    "print(df['trail terrain'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "d67f4175",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows after cleaning: 158\n",
+      "\n",
+      "Unique Values in original column (before drop):\n",
+      "<StringArray>\n",
+      "[             'light',     'light moderate', 'moderate technical',\n",
+      "           'moderate',  'moderatetechnical',          'technical',\n",
+      "      'lightmoderate']\n",
+      "Length: 7, dtype: str\n",
+      "\n",
+      "Sample Comparison (Multi-value Mapping):\n",
+      "        trail terrain  terrain_light  terrain_moderate  terrain_technical\n",
+      "0               light              1                 0                  0\n",
+      "1               light              1                 0                  0\n",
+      "2      light moderate              1                 1                  0\n",
+      "3               light              1                 0                  0\n",
+      "4      light moderate              1                 1                  0\n",
+      "5  moderate technical              0                 1                  1\n",
+      "6            moderate              0                 1                  0\n",
+      "7      light moderate              1                 1                  0\n",
+      "8      light moderate              1                 1                  0\n",
+      "9      light moderate              1                 1                  0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Naming convention: all lowercase\n",
+    "df['trail terrain'] = df['trail terrain'].astype(str).str.lower()\n",
+    "base_terrains = ['light', 'moderate', 'technical']\n",
+    "\n",
+    "for terrain in base_terrains:\n",
+    "    column_name = f\"terrain_{terrain}\"\n",
+    "    df[column_name] = df['trail terrain'].str.contains(terrain).astype(int)\n",
+    "\n",
+    "print(\"Rows after cleaning:\", len(df))\n",
+    "\n",
+    "print(\"\\nUnique Values in original column (before drop):\")\n",
+    "print(df[\"trail terrain\"].unique())\n",
+    "\n",
+    "print(\"\\nSample Comparison (Multi-value Mapping):\")\n",
+    "check_cols = [\"trail terrain\"] + [f\"terrain_{t}\" for t in base_terrains]\n",
+    "print(df[check_cols].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "66775876",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trail terrain\n",
+      "light moderate        54\n",
+      "light                 48\n",
+      "moderate technical    21\n",
+      "moderate              19\n",
+      "technical             11\n",
+      "lightmoderate          3\n",
+      "moderatetechnical      2\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Sum of each terrain type:\n",
+      "terrain_light sum: 105\n",
+      "terrain_moderate sum: 99\n",
+      "terrain_technical sum: 34\n",
+      "\n",
+      "    trail terrain  terrain_light  terrain_moderate  terrain_technical\n",
+      "0           light              1                 0                  0\n",
+      "1           light              1                 0                  0\n",
+      "2  light moderate              1                 1                  0\n",
+      "3           light              1                 0                  0\n",
+      "4  light moderate              1                 1                  0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['trail terrain'].value_counts())\n",
+    "\n",
+    "print(\"\\nSum of each terrain type:\")\n",
+    "for terrain in base_terrains:\n",
+    "    col = f\"terrain_{terrain}\"\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "print()\n",
+    "print(df[['trail terrain', 'terrain_light', 'terrain_moderate', 'terrain_technical']].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "8b78d616",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 31 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   brand                            158 non-null    str    \n",
+      " 1   name                             158 non-null    str    \n",
+      " 2   shock absorption                 158 non-null    str    \n",
+      " 3   energy return                    158 non-null    str    \n",
+      " 4   traction                         153 non-null    str    \n",
+      " 5   arch support                     158 non-null    str    \n",
+      " 6   weight lab weight brand          158 non-null    str    \n",
+      " 7   lightweight                      152 non-null    float64\n",
+      " 8   drop lab drop brand              158 non-null    str    \n",
+      " 9   strike pattern                   158 non-null    str    \n",
+      " 10  midsole softness                 158 non-null    str    \n",
+      " 11  plate                            158 non-null    str    \n",
+      " 12  toebox durability                158 non-null    str    \n",
+      " 13  heel padding durability          158 non-null    str    \n",
+      " 14  outsole durability               158 non-null    str    \n",
+      " 15  breathability                    158 non-null    str    \n",
+      " 16  width / fit                      158 non-null    str    \n",
+      " 17  toebox width                     158 non-null    str    \n",
+      " 18  stiffness                        158 non-null    str    \n",
+      " 19  torsional rigidity               158 non-null    str    \n",
+      " 20  heel counter stiffness           158 non-null    str    \n",
+      " 21  lug depth                        158 non-null    str    \n",
+      " 22  heel stack lab heel stack brand  158 non-null    str    \n",
+      " 23  forefoot lab forefoot brand      158 non-null    str    \n",
+      " 24  season                           158 non-null    str    \n",
+      " 25  removable insole                 158 non-null    int64  \n",
+      " 26  orthotic friendly                158 non-null    int64  \n",
+      " 27  waterproofing                    156 non-null    str    \n",
+      " 28  terrain_light                    158 non-null    int64  \n",
+      " 29  terrain_moderate                 158 non-null    int64  \n",
+      " 30  terrain_technical                158 non-null    int64  \n",
+      "dtypes: float64(1), int64(5), str(25)\n",
+      "memory usage: 38.4 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop('trail terrain', axis=1, inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee89a11f",
+   "metadata": {},
+   "source": [
+    "## Shock absorption"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "abd73756",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "shock absorption\n",
+      "-           81\n",
+      "moderate    45\n",
+      "high        20\n",
+      "low         12\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Checking null values first\n",
+    "print(df['shock absorption'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "8ec1e95b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Value Counts Kolom Asli ---\n",
+      "shock absorption\n",
+      "-           81\n",
+      "moderate    45\n",
+      "high        20\n",
+      "low         12\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "--- Ordinal encoding ---\n",
+      "Index 0: 81 baris\n",
+      "Index 1: 12 baris\n",
+      "Index 2: 0 baris\n",
+      "Index 3: 45 baris\n",
+      "Index 4: 0 baris\n",
+      "Index 5: 20 baris\n",
+      "\n",
+      "--- Perbandingan Data (Head) ---\n",
+      "  shock absorption  shock_absorption\n",
+      "0         moderate                 3\n",
+      "1                -                 0\n",
+      "2         moderate                 3\n",
+      "3         moderate                 3\n",
+      "4                -                 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "shock_scaled = {\n",
+    "    \"-\": 0,\n",
+    "    \"low\": 1,\n",
+    "    \"moderate\": 3,\n",
+    "    \"high\": 5\n",
+    "}\n",
+    "\n",
+    "df['shock_absorption'] = df['shock absorption'].map(shock_scaled)\n",
+    "\n",
+    "print(\"--- Value Counts Kolom Asli ---\")\n",
+    "print(df[\"shock absorption\"].value_counts())\n",
+    "\n",
+    "print(\"\\n--- Ordinal encoding ---\")\n",
+    "counts = df[\"shock_absorption\"].value_counts().sort_index()\n",
+    "for i in range(6):\n",
+    "    print(f\"Index {i}: {counts.get(i, 0)} baris\")\n",
+    "\n",
+    "print(\"\\n--- Perbandingan Data (Head) ---\")\n",
+    "print(df[[\"shock absorption\", \"shock_absorption\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "857a652d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 31 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   brand                            158 non-null    str    \n",
+      " 1   name                             158 non-null    str    \n",
+      " 2   energy return                    158 non-null    str    \n",
+      " 3   traction                         153 non-null    str    \n",
+      " 4   arch support                     158 non-null    str    \n",
+      " 5   weight lab weight brand          158 non-null    str    \n",
+      " 6   lightweight                      152 non-null    float64\n",
+      " 7   drop lab drop brand              158 non-null    str    \n",
+      " 8   strike pattern                   158 non-null    str    \n",
+      " 9   midsole softness                 158 non-null    str    \n",
+      " 10  plate                            158 non-null    str    \n",
+      " 11  toebox durability                158 non-null    str    \n",
+      " 12  heel padding durability          158 non-null    str    \n",
+      " 13  outsole durability               158 non-null    str    \n",
+      " 14  breathability                    158 non-null    str    \n",
+      " 15  width / fit                      158 non-null    str    \n",
+      " 16  toebox width                     158 non-null    str    \n",
+      " 17  stiffness                        158 non-null    str    \n",
+      " 18  torsional rigidity               158 non-null    str    \n",
+      " 19  heel counter stiffness           158 non-null    str    \n",
+      " 20  lug depth                        158 non-null    str    \n",
+      " 21  heel stack lab heel stack brand  158 non-null    str    \n",
+      " 22  forefoot lab forefoot brand      158 non-null    str    \n",
+      " 23  season                           158 non-null    str    \n",
+      " 24  removable insole                 158 non-null    int64  \n",
+      " 25  orthotic friendly                158 non-null    int64  \n",
+      " 26  waterproofing                    156 non-null    str    \n",
+      " 27  terrain_light                    158 non-null    int64  \n",
+      " 28  terrain_moderate                 158 non-null    int64  \n",
+      " 29  terrain_technical                158 non-null    int64  \n",
+      " 30  shock_absorption                 158 non-null    int64  \n",
+      "dtypes: float64(1), int64(6), str(24)\n",
+      "memory usage: 38.4 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop('shock absorption', axis=1, inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0dfe6ab4",
+   "metadata": {},
+   "source": [
+    "## Energy return"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "e1583547",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "energy return\n",
+      "-           81\n",
+      "low         36\n",
+      "moderate    36\n",
+      "high         5\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"energy return\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "74393e16",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Value Counts Kolom Asli ---\n",
+      "energy return\n",
+      "-           81\n",
+      "low         36\n",
+      "moderate    36\n",
+      "high         5\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "--- Sebaran Nilai Hasil Ordinal (Scale 1-5) ---\n",
+      "Index 0: 81 baris\n",
+      "Index 1: 36 baris\n",
+      "Index 2: 0 baris\n",
+      "Index 3: 36 baris\n",
+      "Index 4: 0 baris\n",
+      "Index 5: 5 baris\n",
+      "\n",
+      "--- Perbandingan Data (Head) ---\n",
+      "  energy return  energy_return\n",
+      "0          high              5\n",
+      "1             -              0\n",
+      "2           low              1\n",
+      "3           low              1\n",
+      "4             -              0\n"
+     ]
+    }
+   ],
+   "source": [
+    "energy_scaled = {\n",
+    "    \"-\": 0,\n",
+    "    \"low\": 1,\n",
+    "    \"moderate\": 3,\n",
+    "    \"high\": 5\n",
+    "}\n",
+    "\n",
+    "df['energy_return'] = df['energy return'].map(energy_scaled)\n",
+    "\n",
+    "print(\"--- Value Counts Kolom Asli ---\")\n",
+    "print(df[\"energy return\"].value_counts())\n",
+    "\n",
+    "print(\"\\n--- Sebaran Nilai Hasil Ordinal (Scale 1-5) ---\")\n",
+    "counts = df[\"energy_return\"].value_counts().sort_index()\n",
+    "for i in range(6):\n",
+    "    print(f\"Index {i}: {counts.get(i, 0)} baris\")\n",
+    "\n",
+    "print(\"\\n--- Perbandingan Data (Head) ---\")\n",
+    "print(df[[\"energy return\", \"energy_return\"]].head())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "8b60e596",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 31 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   brand                            158 non-null    str    \n",
+      " 1   name                             158 non-null    str    \n",
+      " 2   traction                         153 non-null    str    \n",
+      " 3   arch support                     158 non-null    str    \n",
+      " 4   weight lab weight brand          158 non-null    str    \n",
+      " 5   lightweight                      152 non-null    float64\n",
+      " 6   drop lab drop brand              158 non-null    str    \n",
+      " 7   strike pattern                   158 non-null    str    \n",
+      " 8   midsole softness                 158 non-null    str    \n",
+      " 9   plate                            158 non-null    str    \n",
+      " 10  toebox durability                158 non-null    str    \n",
+      " 11  heel padding durability          158 non-null    str    \n",
+      " 12  outsole durability               158 non-null    str    \n",
+      " 13  breathability                    158 non-null    str    \n",
+      " 14  width / fit                      158 non-null    str    \n",
+      " 15  toebox width                     158 non-null    str    \n",
+      " 16  stiffness                        158 non-null    str    \n",
+      " 17  torsional rigidity               158 non-null    str    \n",
+      " 18  heel counter stiffness           158 non-null    str    \n",
+      " 19  lug depth                        158 non-null    str    \n",
+      " 20  heel stack lab heel stack brand  158 non-null    str    \n",
+      " 21  forefoot lab forefoot brand      158 non-null    str    \n",
+      " 22  season                           158 non-null    str    \n",
+      " 23  removable insole                 158 non-null    int64  \n",
+      " 24  orthotic friendly                158 non-null    int64  \n",
+      " 25  waterproofing                    156 non-null    str    \n",
+      " 26  terrain_light                    158 non-null    int64  \n",
+      " 27  terrain_moderate                 158 non-null    int64  \n",
+      " 28  terrain_technical                158 non-null    int64  \n",
+      " 29  shock_absorption                 158 non-null    int64  \n",
+      " 30  energy_return                    158 non-null    int64  \n",
+      "dtypes: float64(1), int64(7), str(23)\n",
+      "memory usage: 38.4 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop('energy return', axis=1, inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "050f4527",
+   "metadata": {},
+   "source": [
+    "## Traction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "6a938fe9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "traction\n",
+      "-           127\n",
+      "high         25\n",
+      "moderate      1\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"traction\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "7fd8d46f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Value Counts Kolom Asli ---\n",
+      "traction\n",
+      "-           127\n",
+      "high         25\n",
+      "moderate      1\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "--- Sebaran Nilai Hasil Ordinal  ---\n",
+      "Index 0: 132 baris\n",
+      "Index 1: 0 baris\n",
+      "Index 2: 0 baris\n",
+      "Index 3: 1 baris\n",
+      "Index 4: 0 baris\n",
+      "Index 5: 25 baris\n",
+      "\n",
+      "--- Perbandingan Data (Head) ---\n",
+      "  traction  traction_scaled\n",
+      "0        -                0\n",
+      "1        -                0\n",
+      "2        -                0\n",
+      "3     high                5\n",
+      "4        -                0\n"
+     ]
+    }
+   ],
+   "source": [
+    "traction_scaled = {\n",
+    "    \"-\": 0,\n",
+    "    \"low\": 1,\n",
+    "    \"moderate\": 3,\n",
+    "    \"high\": 5\n",
+    "}\n",
+    "\n",
+    "df['traction_scaled'] = df['traction'].map(traction_scaled).fillna(0).astype(int)\n",
+    "\n",
+    "print(\"--- Value Counts Kolom Asli ---\")\n",
+    "print(df[\"traction\"].value_counts())\n",
+    "\n",
+    "print(\"\\n--- Sebaran Nilai Hasil Ordinal  ---\")\n",
+    "counts = df[\"traction_scaled\"].value_counts().sort_index()\n",
+    "for i in range(6):\n",
+    "    print(f\"Index {i}: {counts.get(i, 0)} baris\")\n",
+    "\n",
+    "print(\"\\n--- Perbandingan Data (Head) ---\")\n",
+    "print(df[[\"traction\", \"traction_scaled\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "3a83c57d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 31 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   brand                            158 non-null    str    \n",
+      " 1   name                             158 non-null    str    \n",
+      " 2   arch support                     158 non-null    str    \n",
+      " 3   weight lab weight brand          158 non-null    str    \n",
+      " 4   lightweight                      152 non-null    float64\n",
+      " 5   drop lab drop brand              158 non-null    str    \n",
+      " 6   strike pattern                   158 non-null    str    \n",
+      " 7   midsole softness                 158 non-null    str    \n",
+      " 8   plate                            158 non-null    str    \n",
+      " 9   toebox durability                158 non-null    str    \n",
+      " 10  heel padding durability          158 non-null    str    \n",
+      " 11  outsole durability               158 non-null    str    \n",
+      " 12  breathability                    158 non-null    str    \n",
+      " 13  width / fit                      158 non-null    str    \n",
+      " 14  toebox width                     158 non-null    str    \n",
+      " 15  stiffness                        158 non-null    str    \n",
+      " 16  torsional rigidity               158 non-null    str    \n",
+      " 17  heel counter stiffness           158 non-null    str    \n",
+      " 18  lug depth                        158 non-null    str    \n",
+      " 19  heel stack lab heel stack brand  158 non-null    str    \n",
+      " 20  forefoot lab forefoot brand      158 non-null    str    \n",
+      " 21  season                           158 non-null    str    \n",
+      " 22  removable insole                 158 non-null    int64  \n",
+      " 23  orthotic friendly                158 non-null    int64  \n",
+      " 24  waterproofing                    156 non-null    str    \n",
+      " 25  terrain_light                    158 non-null    int64  \n",
+      " 26  terrain_moderate                 158 non-null    int64  \n",
+      " 27  terrain_technical                158 non-null    int64  \n",
+      " 28  shock_absorption                 158 non-null    int64  \n",
+      " 29  energy_return                    158 non-null    int64  \n",
+      " 30  traction_scaled                  158 non-null    int64  \n",
+      "dtypes: float64(1), int64(8), str(22)\n",
+      "memory usage: 38.4 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop('traction', axis=1, inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d283f13a",
+   "metadata": {},
+   "source": [
+    "## Arch support"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "1d055a43",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "arch support\n",
+      "neutral      154\n",
+      "stability      4\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"arch support\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "8ee15f86",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 158\n",
+      "NULL Value: 0\n",
+      "\n",
+      "Sample Comparison:\n",
+      "  arch support  arch_neutral  arch_stability\n",
+      "0      neutral             1               0\n",
+      "1      neutral             1               0\n",
+      "2      neutral             1               0\n",
+      "3      neutral             1               0\n",
+      "4      neutral             1               0\n",
+      "5      neutral             1               0\n",
+      "6      neutral             1               0\n",
+      "7      neutral             1               0\n",
+      "8      neutral             1               0\n",
+      "9      neutral             1               0\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['arch support'] = df['arch support'].astype(str).str.lower()\n",
+    "base_arch = ['neutral', 'stability']\n",
+    "\n",
+    "\n",
+    "for level in base_arch:\n",
+    "    column_name = f\"arch_{level}\"\n",
+    "    df[column_name] = df['arch support'].str.contains(level, na=False).astype(int)\n",
+    "\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "\n",
+    "arch_cols = [f\"arch_{l}\" for l in base_arch]\n",
+    "zero_vector_count = (df[arch_cols].sum(axis=1) == 0).sum()\n",
+    "print(f\"NULL Value: {zero_vector_count}\")\n",
+    "\n",
+    "print(\"\\nSample Comparison:\")\n",
+    "print(df[[\"arch support\"] + arch_cols].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "bddc5889",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "arch support\n",
+      "neutral      154\n",
+      "stability      4\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "arch_neutral sum: 154\n",
+      "arch_stability sum: 4\n",
+      "\n",
+      "  arch support  arch_neutral  arch_stability\n",
+      "0      neutral             1               0\n",
+      "1      neutral             1               0\n",
+      "2      neutral             1               0\n",
+      "3      neutral             1               0\n",
+      "4      neutral             1               0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"arch support\"].value_counts())\n",
+    "\n",
+    "print()\n",
+    "for level in base_arch:\n",
+    "    col = f\"arch_{level}\"\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"arch support\"] + arch_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "d8b2e073",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 32 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   brand                            158 non-null    str    \n",
+      " 1   name                             158 non-null    str    \n",
+      " 2   weight lab weight brand          158 non-null    str    \n",
+      " 3   lightweight                      152 non-null    float64\n",
+      " 4   drop lab drop brand              158 non-null    str    \n",
+      " 5   strike pattern                   158 non-null    str    \n",
+      " 6   midsole softness                 158 non-null    str    \n",
+      " 7   plate                            158 non-null    str    \n",
+      " 8   toebox durability                158 non-null    str    \n",
+      " 9   heel padding durability          158 non-null    str    \n",
+      " 10  outsole durability               158 non-null    str    \n",
+      " 11  breathability                    158 non-null    str    \n",
+      " 12  width / fit                      158 non-null    str    \n",
+      " 13  toebox width                     158 non-null    str    \n",
+      " 14  stiffness                        158 non-null    str    \n",
+      " 15  torsional rigidity               158 non-null    str    \n",
+      " 16  heel counter stiffness           158 non-null    str    \n",
+      " 17  lug depth                        158 non-null    str    \n",
+      " 18  heel stack lab heel stack brand  158 non-null    str    \n",
+      " 19  forefoot lab forefoot brand      158 non-null    str    \n",
+      " 20  season                           158 non-null    str    \n",
+      " 21  removable insole                 158 non-null    int64  \n",
+      " 22  orthotic friendly                158 non-null    int64  \n",
+      " 23  waterproofing                    156 non-null    str    \n",
+      " 24  terrain_light                    158 non-null    int64  \n",
+      " 25  terrain_moderate                 158 non-null    int64  \n",
+      " 26  terrain_technical                158 non-null    int64  \n",
+      " 27  shock_absorption                 158 non-null    int64  \n",
+      " 28  energy_return                    158 non-null    int64  \n",
+      " 29  traction_scaled                  158 non-null    int64  \n",
+      " 30  arch_neutral                     158 non-null    int64  \n",
+      " 31  arch_stability                   158 non-null    int64  \n",
+      "dtypes: float64(1), int64(10), str(21)\n",
+      "memory usage: 39.6 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=['arch support'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eba72c42",
+   "metadata": {},
+   "source": [
+    "## Split Weight lab Weight brand"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "4027e6f8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(0)"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mask_missing = (\n",
+    "    df[\"weight lab weight brand\"].isna() |\n",
+    "    (df[\"weight lab weight brand\"].astype(str).str.strip() == \"-\")\n",
+    ")\n",
+    "mask_missing.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "dd34c04a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         weight lab weight brand  weight_lab_oz  weight_lab_g  \\\n",
+      "0    9.1 oz / 259g 9.5 oz / 270g            9.1           259   \n",
+      "1      9.1 oz / 258g 9 oz / 255g            9.1           258   \n",
+      "2   10.1 oz / 285g 9.6 oz / 273g           10.1           285   \n",
+      "3   9.4 oz / 266g 10.3 oz / 293g            9.4           266   \n",
+      "4  10.7 oz / 302g 10.6 oz / 301g           10.7           302   \n",
+      "\n",
+      "   weight_brand_oz  weight_brand_g  \n",
+      "0              9.5           270.0  \n",
+      "1              9.0           255.0  \n",
+      "2              9.6           273.0  \n",
+      "3             10.3           293.0  \n",
+      "4             10.6           301.0  \n"
+     ]
+    }
+   ],
+   "source": [
+    "weight = df[\"weight lab weight brand\"].str.findall(r\"[\\d.]+\")\n",
+    "df[[\"weight_lab_oz\", \"weight_lab_g\", \"weight_brand_oz\", \"weight_brand_g\"]] = (\n",
+    "    pd.DataFrame(weight.tolist(), index=df.index)\n",
+    ")\n",
+    "\n",
+    "for col in [\"weight_lab_oz\", \"weight_lab_g\", \"weight_brand_oz\", \"weight_brand_g\"]:\n",
+    "    df[col] = pd.to_numeric(df[col], errors=\"coerce\")\n",
+    "\n",
+    "print(df[[\"weight lab weight brand\", \"weight_lab_oz\", \"weight_lab_g\", \"weight_brand_oz\", \"weight_brand_g\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "4ad81586",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 35 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   brand                            158 non-null    str    \n",
+      " 1   name                             158 non-null    str    \n",
+      " 2   lightweight                      152 non-null    float64\n",
+      " 3   drop lab drop brand              158 non-null    str    \n",
+      " 4   strike pattern                   158 non-null    str    \n",
+      " 5   midsole softness                 158 non-null    str    \n",
+      " 6   plate                            158 non-null    str    \n",
+      " 7   toebox durability                158 non-null    str    \n",
+      " 8   heel padding durability          158 non-null    str    \n",
+      " 9   outsole durability               158 non-null    str    \n",
+      " 10  breathability                    158 non-null    str    \n",
+      " 11  width / fit                      158 non-null    str    \n",
+      " 12  toebox width                     158 non-null    str    \n",
+      " 13  stiffness                        158 non-null    str    \n",
+      " 14  torsional rigidity               158 non-null    str    \n",
+      " 15  heel counter stiffness           158 non-null    str    \n",
+      " 16  lug depth                        158 non-null    str    \n",
+      " 17  heel stack lab heel stack brand  158 non-null    str    \n",
+      " 18  forefoot lab forefoot brand      158 non-null    str    \n",
+      " 19  season                           158 non-null    str    \n",
+      " 20  removable insole                 158 non-null    int64  \n",
+      " 21  orthotic friendly                158 non-null    int64  \n",
+      " 22  waterproofing                    156 non-null    str    \n",
+      " 23  terrain_light                    158 non-null    int64  \n",
+      " 24  terrain_moderate                 158 non-null    int64  \n",
+      " 25  terrain_technical                158 non-null    int64  \n",
+      " 26  shock_absorption                 158 non-null    int64  \n",
+      " 27  energy_return                    158 non-null    int64  \n",
+      " 28  traction_scaled                  158 non-null    int64  \n",
+      " 29  arch_neutral                     158 non-null    int64  \n",
+      " 30  arch_stability                   158 non-null    int64  \n",
+      " 31  weight_lab_oz                    158 non-null    float64\n",
+      " 32  weight_lab_g                     158 non-null    int64  \n",
+      " 33  weight_brand_oz                  155 non-null    float64\n",
+      " 34  weight_brand_g                   155 non-null    float64\n",
+      "dtypes: float64(4), int64(11), str(20)\n",
+      "memory usage: 43.3 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"weight lab weight brand\",], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f8f3114",
+   "metadata": {},
+   "source": [
+    "## Split Drop lab Drop brand"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "d96d69ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(0)"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mask_missing = (\n",
+    "    df[\"drop lab drop brand\"].isna() |\n",
+    "    (df[\"drop lab drop brand\"].astype(str).str.strip() == \"-\")\n",
+    ")\n",
+    "mask_missing.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "33f141ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  drop lab drop brand  drop_lab_mm  drop_brand_mm\n",
+      "0       0.3 mm 8.0 mm          0.3            8.0\n",
+      "1       8.2 mm 8.0 mm          8.2            8.0\n",
+      "2       4.3 mm 4.0 mm          4.3            4.0\n",
+      "3       6.1 mm 4.0 mm          6.1            4.0\n",
+      "4       0.2 mm 0.0 mm          0.2            0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "drop = df[\"drop lab drop brand\"].str.findall(r\"[\\d.]+\")\n",
+    "\n",
+    "df[[\"drop_lab_mm\", \"drop_brand_mm\"]] = (\n",
+    "    pd.DataFrame(drop.tolist(), index=df.index)\n",
+    ")\n",
+    "\n",
+    "for col in [\"drop_lab_mm\", \"drop_brand_mm\"]:\n",
+    "    df[col] = pd.to_numeric(df[col], errors=\"coerce\")\n",
+    "\n",
+    "print(df[[\"drop lab drop brand\", \"drop_lab_mm\", \"drop_brand_mm\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "1d1c4eac",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 36 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   brand                            158 non-null    str    \n",
+      " 1   name                             158 non-null    str    \n",
+      " 2   lightweight                      152 non-null    float64\n",
+      " 3   strike pattern                   158 non-null    str    \n",
+      " 4   midsole softness                 158 non-null    str    \n",
+      " 5   plate                            158 non-null    str    \n",
+      " 6   toebox durability                158 non-null    str    \n",
+      " 7   heel padding durability          158 non-null    str    \n",
+      " 8   outsole durability               158 non-null    str    \n",
+      " 9   breathability                    158 non-null    str    \n",
+      " 10  width / fit                      158 non-null    str    \n",
+      " 11  toebox width                     158 non-null    str    \n",
+      " 12  stiffness                        158 non-null    str    \n",
+      " 13  torsional rigidity               158 non-null    str    \n",
+      " 14  heel counter stiffness           158 non-null    str    \n",
+      " 15  lug depth                        158 non-null    str    \n",
+      " 16  heel stack lab heel stack brand  158 non-null    str    \n",
+      " 17  forefoot lab forefoot brand      158 non-null    str    \n",
+      " 18  season                           158 non-null    str    \n",
+      " 19  removable insole                 158 non-null    int64  \n",
+      " 20  orthotic friendly                158 non-null    int64  \n",
+      " 21  waterproofing                    156 non-null    str    \n",
+      " 22  terrain_light                    158 non-null    int64  \n",
+      " 23  terrain_moderate                 158 non-null    int64  \n",
+      " 24  terrain_technical                158 non-null    int64  \n",
+      " 25  shock_absorption                 158 non-null    int64  \n",
+      " 26  energy_return                    158 non-null    int64  \n",
+      " 27  traction_scaled                  158 non-null    int64  \n",
+      " 28  arch_neutral                     158 non-null    int64  \n",
+      " 29  arch_stability                   158 non-null    int64  \n",
+      " 30  weight_lab_oz                    158 non-null    float64\n",
+      " 31  weight_lab_g                     158 non-null    int64  \n",
+      " 32  weight_brand_oz                  155 non-null    float64\n",
+      " 33  weight_brand_g                   155 non-null    float64\n",
+      " 34  drop_lab_mm                      158 non-null    float64\n",
+      " 35  drop_brand_mm                    152 non-null    float64\n",
+      "dtypes: float64(6), int64(11), str(19)\n",
+      "memory usage: 44.6 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"drop lab drop brand\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a60c8a59",
+   "metadata": {},
+   "source": [
+    "## Strike pattern"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "53e91a22",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "strike pattern\n",
+      "mid/forefoot         83\n",
+      "heel                 48\n",
+      "heel mid/forefoot    25\n",
+      "heelmid/forefoot      2\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"strike pattern\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "8f141a55",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 158\n",
+      "\n",
+      "Unique Values in original column:\n",
+      "<StringArray>\n",
+      "['mid/forefoot', 'heel mid/forefoot', 'heel', 'heelmid/forefoot']\n",
+      "Length: 4, dtype: str\n",
+      "\n",
+      "Sample Comparison (Multi-label Mapping):\n",
+      "      strike pattern  strike_heel  strike_mid  strike_forefoot\n",
+      "0       mid/forefoot            0           1                1\n",
+      "1  heel mid/forefoot            1           1                1\n",
+      "2       mid/forefoot            0           1                1\n",
+      "3       mid/forefoot            0           1                1\n",
+      "4       mid/forefoot            0           1                1\n",
+      "5       mid/forefoot            0           1                1\n",
+      "6       mid/forefoot            0           1                1\n",
+      "7       mid/forefoot            0           1                1\n",
+      "8       mid/forefoot            0           1                1\n",
+      "9       mid/forefoot            0           1                1\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['strike pattern'] = df['strike pattern'].astype(str).str.lower()\n",
+    "base_strikes = ['heel', 'mid', 'forefoot']\n",
+    "\n",
+    "for strike in base_strikes:\n",
+    "    column_name = f\"strike_{strike}\"\n",
+    "    df[column_name] = df['strike pattern'].str.contains(strike, na=False).astype(int)\n",
+    "\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "print(\"\\nUnique Values in original column:\")\n",
+    "print(df[\"strike pattern\"].unique())\n",
+    "\n",
+    "print(\"\\nSample Comparison (Multi-label Mapping):\")\n",
+    "strike_cols = [f\"strike_{s}\" for s in base_strikes]\n",
+    "print(df[[\"strike pattern\"] + strike_cols].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "1b1de021",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "strike pattern\n",
+      "mid/forefoot         83\n",
+      "heel                 48\n",
+      "heel mid/forefoot    25\n",
+      "heelmid/forefoot      2\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "strike_heel sum: 75\n",
+      "strike_mid sum: 110\n",
+      "strike_forefoot sum: 110\n",
+      "\n",
+      "      strike pattern  strike_heel  strike_mid  strike_forefoot\n",
+      "0       mid/forefoot            0           1                1\n",
+      "1  heel mid/forefoot            1           1                1\n",
+      "2       mid/forefoot            0           1                1\n",
+      "3       mid/forefoot            0           1                1\n",
+      "4       mid/forefoot            0           1                1\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"strike pattern\"].value_counts())\n",
+    "\n",
+    "print()\n",
+    "for strike in base_strikes:\n",
+    "    col = f\"strike_{strike}\"\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"strike pattern\"] + strike_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "767fe00e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 38 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   brand                            158 non-null    str    \n",
+      " 1   name                             158 non-null    str    \n",
+      " 2   lightweight                      152 non-null    float64\n",
+      " 3   midsole softness                 158 non-null    str    \n",
+      " 4   plate                            158 non-null    str    \n",
+      " 5   toebox durability                158 non-null    str    \n",
+      " 6   heel padding durability          158 non-null    str    \n",
+      " 7   outsole durability               158 non-null    str    \n",
+      " 8   breathability                    158 non-null    str    \n",
+      " 9   width / fit                      158 non-null    str    \n",
+      " 10  toebox width                     158 non-null    str    \n",
+      " 11  stiffness                        158 non-null    str    \n",
+      " 12  torsional rigidity               158 non-null    str    \n",
+      " 13  heel counter stiffness           158 non-null    str    \n",
+      " 14  lug depth                        158 non-null    str    \n",
+      " 15  heel stack lab heel stack brand  158 non-null    str    \n",
+      " 16  forefoot lab forefoot brand      158 non-null    str    \n",
+      " 17  season                           158 non-null    str    \n",
+      " 18  removable insole                 158 non-null    int64  \n",
+      " 19  orthotic friendly                158 non-null    int64  \n",
+      " 20  waterproofing                    156 non-null    str    \n",
+      " 21  terrain_light                    158 non-null    int64  \n",
+      " 22  terrain_moderate                 158 non-null    int64  \n",
+      " 23  terrain_technical                158 non-null    int64  \n",
+      " 24  shock_absorption                 158 non-null    int64  \n",
+      " 25  energy_return                    158 non-null    int64  \n",
+      " 26  traction_scaled                  158 non-null    int64  \n",
+      " 27  arch_neutral                     158 non-null    int64  \n",
+      " 28  arch_stability                   158 non-null    int64  \n",
+      " 29  weight_lab_oz                    158 non-null    float64\n",
+      " 30  weight_lab_g                     158 non-null    int64  \n",
+      " 31  weight_brand_oz                  155 non-null    float64\n",
+      " 32  weight_brand_g                   155 non-null    float64\n",
+      " 33  drop_lab_mm                      158 non-null    float64\n",
+      " 34  drop_brand_mm                    152 non-null    float64\n",
+      " 35  strike_heel                      158 non-null    int64  \n",
+      " 36  strike_mid                       158 non-null    int64  \n",
+      " 37  strike_forefoot                  158 non-null    int64  \n",
+      "dtypes: float64(6), int64(14), str(18)\n",
+      "memory usage: 47.0 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=['strike pattern'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9e9ece2",
+   "metadata": {},
+   "source": [
+    "## Midsole softness"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "b524c2a4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "midsole softness\n",
+      "balanced    72\n",
+      "soft        54\n",
+      "-           19\n",
+      "firm        13\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"midsole softness\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "f61696ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Value Counts Kolom Asli ---\n",
+      "midsole softness\n",
+      "balanced    72\n",
+      "soft        54\n",
+      "-           19\n",
+      "firm        13\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "--- Sebaran Nilai Hasil Ordinal (Scale 1-5) ---\n",
+      "Index 0: 19 baris\n",
+      "Index 1: 13 baris\n",
+      "Index 2: 0 baris\n",
+      "Index 3: 72 baris\n",
+      "Index 4: 0 baris\n",
+      "Index 5: 54 baris\n",
+      "\n",
+      "--- Perbandingan Data (Head) ---\n",
+      "  midsole softness  midsole_softness\n",
+      "0         balanced                 3\n",
+      "1                -                 0\n",
+      "2             soft                 5\n",
+      "3         balanced                 3\n",
+      "4                -                 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "softness_scaled = {\n",
+    "    \"firm\": 1,\n",
+    "    \"balanced\": 3,\n",
+    "    \"soft\": 5,\n",
+    "    \"-\": 0,\n",
+    "    \"0\": 0\n",
+    "}\n",
+    "\n",
+    "df['midsole_softness'] = df['midsole softness'].map(softness_scaled)\n",
+    "\n",
+    "print(\"--- Value Counts Kolom Asli ---\")\n",
+    "print(df[\"midsole softness\"].value_counts())\n",
+    "\n",
+    "print(\"\\n--- Sebaran Nilai Hasil Ordinal (Scale 1-5) ---\")\n",
+    "counts = df[\"midsole_softness\"].value_counts().sort_index()\n",
+    "for i in range(6):\n",
+    "    print(f\"Index {i}: {counts.get(i, 0)} baris\")\n",
+    "\n",
+    "print(\"\\n--- Perbandingan Data (Head) ---\")\n",
+    "print(df[[\"midsole softness\", \"midsole_softness\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "c361b27d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 38 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   brand                            158 non-null    str    \n",
+      " 1   name                             158 non-null    str    \n",
+      " 2   lightweight                      152 non-null    float64\n",
+      " 3   plate                            158 non-null    str    \n",
+      " 4   toebox durability                158 non-null    str    \n",
+      " 5   heel padding durability          158 non-null    str    \n",
+      " 6   outsole durability               158 non-null    str    \n",
+      " 7   breathability                    158 non-null    str    \n",
+      " 8   width / fit                      158 non-null    str    \n",
+      " 9   toebox width                     158 non-null    str    \n",
+      " 10  stiffness                        158 non-null    str    \n",
+      " 11  torsional rigidity               158 non-null    str    \n",
+      " 12  heel counter stiffness           158 non-null    str    \n",
+      " 13  lug depth                        158 non-null    str    \n",
+      " 14  heel stack lab heel stack brand  158 non-null    str    \n",
+      " 15  forefoot lab forefoot brand      158 non-null    str    \n",
+      " 16  season                           158 non-null    str    \n",
+      " 17  removable insole                 158 non-null    int64  \n",
+      " 18  orthotic friendly                158 non-null    int64  \n",
+      " 19  waterproofing                    156 non-null    str    \n",
+      " 20  terrain_light                    158 non-null    int64  \n",
+      " 21  terrain_moderate                 158 non-null    int64  \n",
+      " 22  terrain_technical                158 non-null    int64  \n",
+      " 23  shock_absorption                 158 non-null    int64  \n",
+      " 24  energy_return                    158 non-null    int64  \n",
+      " 25  traction_scaled                  158 non-null    int64  \n",
+      " 26  arch_neutral                     158 non-null    int64  \n",
+      " 27  arch_stability                   158 non-null    int64  \n",
+      " 28  weight_lab_oz                    158 non-null    float64\n",
+      " 29  weight_lab_g                     158 non-null    int64  \n",
+      " 30  weight_brand_oz                  155 non-null    float64\n",
+      " 31  weight_brand_g                   155 non-null    float64\n",
+      " 32  drop_lab_mm                      158 non-null    float64\n",
+      " 33  drop_brand_mm                    152 non-null    float64\n",
+      " 34  strike_heel                      158 non-null    int64  \n",
+      " 35  strike_mid                       158 non-null    int64  \n",
+      " 36  strike_forefoot                  158 non-null    int64  \n",
+      " 37  midsole_softness                 158 non-null    int64  \n",
+      "dtypes: float64(6), int64(15), str(17)\n",
+      "memory usage: 47.0 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"midsole softness\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00df0f14",
+   "metadata": {},
+   "source": [
+    "## Toebox durability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "6e926181",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "toebox durability\n",
+      "good         39\n",
+      "decent       39\n",
+      "-            36\n",
+      "bad          17\n",
+      "very bad     15\n",
+      "very good    12\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['toebox durability'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "8a18a9a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 158\n",
+      "\n",
+      "Unique Values mapping check:\n",
+      "'-' di-encode menjadi 0 (Total: 36)\n",
+      "'very bad' di-encode menjadi 1 (Total: 15)\n",
+      "'bad' di-encode menjadi 2 (Total: 17)\n",
+      "'decent' di-encode menjadi 3 (Total: 39)\n",
+      "'good' di-encode menjadi 4 (Total: 39)\n",
+      "'very good' di-encode menjadi 5 (Total: 12)\n",
+      "\n",
+      "Sample Data:\n",
+      "  toebox durability  toebox_durability\n",
+      "0              good                  4\n",
+      "1                 -                  0\n",
+      "2            decent                  3\n",
+      "3            decent                  3\n",
+      "4                 -                  0\n",
+      "5                 -                  0\n",
+      "6                 -                  0\n",
+      "7              good                  4\n",
+      "8            decent                  3\n",
+      "9                 -                  0\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['toebox durability'] = df['toebox durability'].astype(str).str.lower()\n",
+    "\n",
+    "durability_map = {\n",
+    "    \"-\": 0,\n",
+    "    \"very bad\": 1,\n",
+    "    \"bad\": 2,\n",
+    "    \"decent\": 3,\n",
+    "    \"good\": 4,\n",
+    "    \"very good\": 5\n",
+    "}\n",
+    "\n",
+    "df['toebox_durability'] = df['toebox durability'].map(durability_map)\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "print(\"\\nUnique Values mapping check:\")\n",
+    "for label, value in durability_map.items():\n",
+    "    count = (df['toebox durability'] == label).sum()\n",
+    "    print(f\"'{label}' di-encode menjadi {value} (Total: {count})\")\n",
+    "\n",
+    "print(\"\\nSample Data:\")\n",
+    "print(df[[\"toebox durability\", \"toebox_durability\"]].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "96538ff1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 38 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   brand                            158 non-null    str    \n",
+      " 1   name                             158 non-null    str    \n",
+      " 2   lightweight                      152 non-null    float64\n",
+      " 3   plate                            158 non-null    str    \n",
+      " 4   heel padding durability          158 non-null    str    \n",
+      " 5   outsole durability               158 non-null    str    \n",
+      " 6   breathability                    158 non-null    str    \n",
+      " 7   width / fit                      158 non-null    str    \n",
+      " 8   toebox width                     158 non-null    str    \n",
+      " 9   stiffness                        158 non-null    str    \n",
+      " 10  torsional rigidity               158 non-null    str    \n",
+      " 11  heel counter stiffness           158 non-null    str    \n",
+      " 12  lug depth                        158 non-null    str    \n",
+      " 13  heel stack lab heel stack brand  158 non-null    str    \n",
+      " 14  forefoot lab forefoot brand      158 non-null    str    \n",
+      " 15  season                           158 non-null    str    \n",
+      " 16  removable insole                 158 non-null    int64  \n",
+      " 17  orthotic friendly                158 non-null    int64  \n",
+      " 18  waterproofing                    156 non-null    str    \n",
+      " 19  terrain_light                    158 non-null    int64  \n",
+      " 20  terrain_moderate                 158 non-null    int64  \n",
+      " 21  terrain_technical                158 non-null    int64  \n",
+      " 22  shock_absorption                 158 non-null    int64  \n",
+      " 23  energy_return                    158 non-null    int64  \n",
+      " 24  traction_scaled                  158 non-null    int64  \n",
+      " 25  arch_neutral                     158 non-null    int64  \n",
+      " 26  arch_stability                   158 non-null    int64  \n",
+      " 27  weight_lab_oz                    158 non-null    float64\n",
+      " 28  weight_lab_g                     158 non-null    int64  \n",
+      " 29  weight_brand_oz                  155 non-null    float64\n",
+      " 30  weight_brand_g                   155 non-null    float64\n",
+      " 31  drop_lab_mm                      158 non-null    float64\n",
+      " 32  drop_brand_mm                    152 non-null    float64\n",
+      " 33  strike_heel                      158 non-null    int64  \n",
+      " 34  strike_mid                       158 non-null    int64  \n",
+      " 35  strike_forefoot                  158 non-null    int64  \n",
+      " 36  midsole_softness                 158 non-null    int64  \n",
+      " 37  toebox_durability                158 non-null    int64  \n",
+      "dtypes: float64(6), int64(16), str(16)\n",
+      "memory usage: 47.0 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=['toebox durability'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "554157d1",
+   "metadata": {},
+   "source": [
+    "## Heel padding durability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "0d78f5d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "heel padding durability\n",
+      "decent    51\n",
+      "good      50\n",
+      "-         38\n",
+      "bad       19\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"heel padding durability\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "c5df3591",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Value Counts Kolom Asli ---\n",
+      "heel padding durability\n",
+      "decent    51\n",
+      "good      50\n",
+      "-         38\n",
+      "bad       19\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "--- Sebaran Nilai Hasil Ordinal (Scale 1-5) ---\n",
+      "Index 0: 38 baris\n",
+      "Index 1: 0 baris\n",
+      "Index 2: 19 baris\n",
+      "Index 3: 51 baris\n",
+      "Index 4: 50 baris\n",
+      "Index 5: 0 baris\n",
+      "\n",
+      "--- Perbandingan Data (Head) ---\n",
+      "  heel padding durability  heel_durability\n",
+      "0                    good                4\n",
+      "1                       -                0\n",
+      "2                  decent                3\n",
+      "3                    good                4\n",
+      "4                       -                0\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['heel padding durability'] = df['heel padding durability'].astype(str).str.lower()\n",
+    "\n",
+    "durability_scale_5 = {\n",
+    "    \"-\": 0,\n",
+    "    \"very bad\": 1,\n",
+    "    \"bad\": 2,\n",
+    "    \"decent\": 3,\n",
+    "    \"good\": 4,\n",
+    "    \"very good\": 5\n",
+    "}\n",
+    "\n",
+    "\n",
+    "df['heel_durability'] = df['heel padding durability'].map(durability_scale_5)\n",
+    "\n",
+    "\n",
+    "print(\"--- Value Counts Kolom Asli ---\")\n",
+    "print(df[\"heel padding durability\"].value_counts())\n",
+    "\n",
+    "print(\"\\n--- Sebaran Nilai Hasil Ordinal (Scale 1-5) ---\")\n",
+    "counts = df[\"heel_durability\"].value_counts().sort_index()\n",
+    "for i in range(6):\n",
+    "    print(f\"Index {i}: {counts.get(i, 0)} baris\")\n",
+    "\n",
+    "print(\"\\n--- Perbandingan Data (Head) ---\")\n",
+    "print(df[[\"heel padding durability\", \"heel_durability\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "5126e7a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 38 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   brand                            158 non-null    str    \n",
+      " 1   name                             158 non-null    str    \n",
+      " 2   lightweight                      152 non-null    float64\n",
+      " 3   plate                            158 non-null    str    \n",
+      " 4   outsole durability               158 non-null    str    \n",
+      " 5   breathability                    158 non-null    str    \n",
+      " 6   width / fit                      158 non-null    str    \n",
+      " 7   toebox width                     158 non-null    str    \n",
+      " 8   stiffness                        158 non-null    str    \n",
+      " 9   torsional rigidity               158 non-null    str    \n",
+      " 10  heel counter stiffness           158 non-null    str    \n",
+      " 11  lug depth                        158 non-null    str    \n",
+      " 12  heel stack lab heel stack brand  158 non-null    str    \n",
+      " 13  forefoot lab forefoot brand      158 non-null    str    \n",
+      " 14  season                           158 non-null    str    \n",
+      " 15  removable insole                 158 non-null    int64  \n",
+      " 16  orthotic friendly                158 non-null    int64  \n",
+      " 17  waterproofing                    156 non-null    str    \n",
+      " 18  terrain_light                    158 non-null    int64  \n",
+      " 19  terrain_moderate                 158 non-null    int64  \n",
+      " 20  terrain_technical                158 non-null    int64  \n",
+      " 21  shock_absorption                 158 non-null    int64  \n",
+      " 22  energy_return                    158 non-null    int64  \n",
+      " 23  traction_scaled                  158 non-null    int64  \n",
+      " 24  arch_neutral                     158 non-null    int64  \n",
+      " 25  arch_stability                   158 non-null    int64  \n",
+      " 26  weight_lab_oz                    158 non-null    float64\n",
+      " 27  weight_lab_g                     158 non-null    int64  \n",
+      " 28  weight_brand_oz                  155 non-null    float64\n",
+      " 29  weight_brand_g                   155 non-null    float64\n",
+      " 30  drop_lab_mm                      158 non-null    float64\n",
+      " 31  drop_brand_mm                    152 non-null    float64\n",
+      " 32  strike_heel                      158 non-null    int64  \n",
+      " 33  strike_mid                       158 non-null    int64  \n",
+      " 34  strike_forefoot                  158 non-null    int64  \n",
+      " 35  midsole_softness                 158 non-null    int64  \n",
+      " 36  toebox_durability                158 non-null    int64  \n",
+      " 37  heel_durability                  158 non-null    int64  \n",
+      "dtypes: float64(6), int64(17), str(15)\n",
+      "memory usage: 47.0 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop('heel padding durability', axis=1, inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "46a4046e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>brand</th>\n",
+       "      <th>name</th>\n",
+       "      <th>lightweight</th>\n",
+       "      <th>plate</th>\n",
+       "      <th>outsole durability</th>\n",
+       "      <th>breathability</th>\n",
+       "      <th>width / fit</th>\n",
+       "      <th>toebox width</th>\n",
+       "      <th>stiffness</th>\n",
+       "      <th>torsional rigidity</th>\n",
+       "      <th>...</th>\n",
+       "      <th>weight_brand_oz</th>\n",
+       "      <th>weight_brand_g</th>\n",
+       "      <th>drop_lab_mm</th>\n",
+       "      <th>drop_brand_mm</th>\n",
+       "      <th>strike_heel</th>\n",
+       "      <th>strike_mid</th>\n",
+       "      <th>strike_forefoot</th>\n",
+       "      <th>midsole_softness</th>\n",
+       "      <th>toebox_durability</th>\n",
+       "      <th>heel_durability</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>terrex agravic speed ultra</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>decent</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>medium</td>\n",
+       "      <td>narrow</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>terrex speed ultra</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>narrow</td>\n",
+       "      <td>-</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>255.0</td>\n",
+       "      <td>8.2</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>experience wild</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>good</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>wide</td>\n",
+       "      <td>wide</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>273.0</td>\n",
+       "      <td>4.3</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>experience wild 2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>good</td>\n",
+       "      <td>warm</td>\n",
+       "      <td>wide</td>\n",
+       "      <td>wide</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10.3</td>\n",
+       "      <td>293.0</td>\n",
+       "      <td>6.1</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>lone peak 5.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>rock plate</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>narrow</td>\n",
+       "      <td>-</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10.6</td>\n",
+       "      <td>301.0</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 38 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    brand                        name  lightweight       plate  \\\n",
+       "0  adidas  terrex agravic speed ultra          0.0           0   \n",
+       "1  adidas          terrex speed ultra          0.0           0   \n",
+       "2   altra             experience wild          0.0           0   \n",
+       "3   altra           experience wild 2          0.0           0   \n",
+       "4   altra               lone peak 5.0          0.0  rock plate   \n",
+       "\n",
+       "  outsole durability breathability width / fit toebox width stiffness  \\\n",
+       "0             decent      moderate      medium       narrow  moderate   \n",
+       "1                  -             -      narrow            -     stiff   \n",
+       "2               good      moderate        wide         wide  moderate   \n",
+       "3               good          warm        wide         wide  moderate   \n",
+       "4                  -             -      narrow            -     stiff   \n",
+       "\n",
+       "  torsional rigidity  ... weight_brand_oz weight_brand_g drop_lab_mm  \\\n",
+       "0              stiff  ...             9.5          270.0         0.3   \n",
+       "1           flexible  ...             9.0          255.0         8.2   \n",
+       "2              stiff  ...             9.6          273.0         4.3   \n",
+       "3           moderate  ...            10.3          293.0         6.1   \n",
+       "4           flexible  ...            10.6          301.0         0.2   \n",
+       "\n",
+       "  drop_brand_mm strike_heel  strike_mid  strike_forefoot midsole_softness  \\\n",
+       "0           8.0           0           1                1                3   \n",
+       "1           8.0           1           1                1                0   \n",
+       "2           4.0           0           1                1                5   \n",
+       "3           4.0           0           1                1                3   \n",
+       "4           0.0           0           1                1                0   \n",
+       "\n",
+       "   toebox_durability  heel_durability  \n",
+       "0                  4                4  \n",
+       "1                  0                0  \n",
+       "2                  3                3  \n",
+       "3                  3                4  \n",
+       "4                  0                0  \n",
+       "\n",
+       "[5 rows x 38 columns]"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20b755dc",
+   "metadata": {},
+   "source": [
+    "## Outsole durability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "0647abf7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "outsole durability\n",
+      "good      77\n",
+      "-         42\n",
+      "decent    38\n",
+      "bad        1\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"outsole durability\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "fbba911c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Value Counts Kolom Asli ---\n",
+      "outsole durability\n",
+      "good      77\n",
+      "-         42\n",
+      "decent    38\n",
+      "bad        1\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "--- Sebaran Nilai Hasil Ordinal (Scale 1-5) ---\n",
+      "Index 0: 42 baris\n",
+      "Index 1: 0 baris\n",
+      "Index 2: 1 baris\n",
+      "Index 3: 38 baris\n",
+      "Index 4: 77 baris\n",
+      "Index 5: 0 baris\n",
+      "\n",
+      "--- Perbandingan Data (Head) ---\n",
+      "  outsole durability  outsole_durability\n",
+      "0             decent                   3\n",
+      "1                  -                   0\n",
+      "2               good                   4\n",
+      "3               good                   4\n",
+      "4                  -                   0\n"
+     ]
+    }
+   ],
+   "source": [
+    "durability_scaled = {\n",
+    "    \"-\": 0,\n",
+    "    \"very bad\": 1,\n",
+    "    \"bad\": 2,\n",
+    "    \"decent\": 3,\n",
+    "    \"good\": 4,\n",
+    "    \"very good\": 5\n",
+    "}\n",
+    "\n",
+    "\n",
+    "df['outsole_durability'] = df['outsole durability'].map(durability_scaled)\n",
+    "print(\"--- Value Counts Kolom Asli ---\")\n",
+    "print(df[\"outsole durability\"].value_counts())\n",
+    "\n",
+    "print(\"\\n--- Sebaran Nilai Hasil Ordinal (Scale 1-5) ---\")\n",
+    "counts = df[\"outsole_durability\"].value_counts().sort_index()\n",
+    "for i in range(6):\n",
+    "    print(f\"Index {i}: {counts.get(i, 0)} baris\")\n",
+    "\n",
+    "print(\"\\n--- Perbandingan Data (Head) ---\")\n",
+    "print(df[[\"outsole durability\", \"outsole_durability\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "b9a91d05",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 38 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   brand                            158 non-null    str    \n",
+      " 1   name                             158 non-null    str    \n",
+      " 2   lightweight                      152 non-null    float64\n",
+      " 3   plate                            158 non-null    str    \n",
+      " 4   breathability                    158 non-null    str    \n",
+      " 5   width / fit                      158 non-null    str    \n",
+      " 6   toebox width                     158 non-null    str    \n",
+      " 7   stiffness                        158 non-null    str    \n",
+      " 8   torsional rigidity               158 non-null    str    \n",
+      " 9   heel counter stiffness           158 non-null    str    \n",
+      " 10  lug depth                        158 non-null    str    \n",
+      " 11  heel stack lab heel stack brand  158 non-null    str    \n",
+      " 12  forefoot lab forefoot brand      158 non-null    str    \n",
+      " 13  season                           158 non-null    str    \n",
+      " 14  removable insole                 158 non-null    int64  \n",
+      " 15  orthotic friendly                158 non-null    int64  \n",
+      " 16  waterproofing                    156 non-null    str    \n",
+      " 17  terrain_light                    158 non-null    int64  \n",
+      " 18  terrain_moderate                 158 non-null    int64  \n",
+      " 19  terrain_technical                158 non-null    int64  \n",
+      " 20  shock_absorption                 158 non-null    int64  \n",
+      " 21  energy_return                    158 non-null    int64  \n",
+      " 22  traction_scaled                  158 non-null    int64  \n",
+      " 23  arch_neutral                     158 non-null    int64  \n",
+      " 24  arch_stability                   158 non-null    int64  \n",
+      " 25  weight_lab_oz                    158 non-null    float64\n",
+      " 26  weight_lab_g                     158 non-null    int64  \n",
+      " 27  weight_brand_oz                  155 non-null    float64\n",
+      " 28  weight_brand_g                   155 non-null    float64\n",
+      " 29  drop_lab_mm                      158 non-null    float64\n",
+      " 30  drop_brand_mm                    152 non-null    float64\n",
+      " 31  strike_heel                      158 non-null    int64  \n",
+      " 32  strike_mid                       158 non-null    int64  \n",
+      " 33  strike_forefoot                  158 non-null    int64  \n",
+      " 34  midsole_softness                 158 non-null    int64  \n",
+      " 35  toebox_durability                158 non-null    int64  \n",
+      " 36  heel_durability                  158 non-null    int64  \n",
+      " 37  outsole_durability               158 non-null    int64  \n",
+      "dtypes: float64(6), int64(18), str(14)\n",
+      "memory usage: 47.0 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop('outsole durability', axis=1, inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6e10b84",
+   "metadata": {},
+   "source": [
+    "## Breathability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "c6a78523",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "breathability\n",
+      "moderate      92\n",
+      "warm          31\n",
+      "-             19\n",
+      "breathable    16\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"breathability\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "35945fd4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Value Counts Kolom Asli ---\n",
+      "breathability\n",
+      "moderate      92\n",
+      "warm          31\n",
+      "-             19\n",
+      "breathable    16\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "--- Sebaran Nilai Hasil Ordinal ---\n",
+      "Index 0: 19 baris\n",
+      "Index 1: 31 baris\n",
+      "Index 2: 0 baris\n",
+      "Index 3: 92 baris\n",
+      "Index 4: 0 baris\n",
+      "Index 5: 16 baris\n",
+      "\n",
+      "--- Perbandingan Data (Head) ---\n",
+      "  breathability  breathability_scaled\n",
+      "0      moderate                     3\n",
+      "1             -                     0\n",
+      "2      moderate                     3\n",
+      "3          warm                     1\n",
+      "4             -                     0\n"
+     ]
+    }
+   ],
+   "source": [
+    "breathability_scaled = {\n",
+    "    \"-\": 0,\n",
+    "    # \"suffocating\": 1,\n",
+    "    \"warm\": 1,\n",
+    "    \"moderate\": 3,\n",
+    "    \"good\": 4,\n",
+    "    \"breathable\": 5\n",
+    "}\n",
+    "\n",
+    "df['breathability_scaled'] = df['breathability'].map(breathability_scaled)\n",
+    "\n",
+    "print(\"--- Value Counts Kolom Asli ---\")\n",
+    "print(df[\"breathability\"].value_counts())\n",
+    "\n",
+    "print(\"\\n--- Sebaran Nilai Hasil Ordinal ---\")\n",
+    "counts = df[\"breathability_scaled\"].value_counts().sort_index()\n",
+    "for i in range(6):\n",
+    "    print(f\"Index {i}: {counts.get(i, 0)} baris\")\n",
+    "\n",
+    "print(\"\\n--- Perbandingan Data (Head) ---\")\n",
+    "print(df[[\"breathability\", \"breathability_scaled\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "f9163564",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 38 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   brand                            158 non-null    str    \n",
+      " 1   name                             158 non-null    str    \n",
+      " 2   lightweight                      152 non-null    float64\n",
+      " 3   plate                            158 non-null    str    \n",
+      " 4   width / fit                      158 non-null    str    \n",
+      " 5   toebox width                     158 non-null    str    \n",
+      " 6   stiffness                        158 non-null    str    \n",
+      " 7   torsional rigidity               158 non-null    str    \n",
+      " 8   heel counter stiffness           158 non-null    str    \n",
+      " 9   lug depth                        158 non-null    str    \n",
+      " 10  heel stack lab heel stack brand  158 non-null    str    \n",
+      " 11  forefoot lab forefoot brand      158 non-null    str    \n",
+      " 12  season                           158 non-null    str    \n",
+      " 13  removable insole                 158 non-null    int64  \n",
+      " 14  orthotic friendly                158 non-null    int64  \n",
+      " 15  waterproofing                    156 non-null    str    \n",
+      " 16  terrain_light                    158 non-null    int64  \n",
+      " 17  terrain_moderate                 158 non-null    int64  \n",
+      " 18  terrain_technical                158 non-null    int64  \n",
+      " 19  shock_absorption                 158 non-null    int64  \n",
+      " 20  energy_return                    158 non-null    int64  \n",
+      " 21  traction_scaled                  158 non-null    int64  \n",
+      " 22  arch_neutral                     158 non-null    int64  \n",
+      " 23  arch_stability                   158 non-null    int64  \n",
+      " 24  weight_lab_oz                    158 non-null    float64\n",
+      " 25  weight_lab_g                     158 non-null    int64  \n",
+      " 26  weight_brand_oz                  155 non-null    float64\n",
+      " 27  weight_brand_g                   155 non-null    float64\n",
+      " 28  drop_lab_mm                      158 non-null    float64\n",
+      " 29  drop_brand_mm                    152 non-null    float64\n",
+      " 30  strike_heel                      158 non-null    int64  \n",
+      " 31  strike_mid                       158 non-null    int64  \n",
+      " 32  strike_forefoot                  158 non-null    int64  \n",
+      " 33  midsole_softness                 158 non-null    int64  \n",
+      " 34  toebox_durability                158 non-null    int64  \n",
+      " 35  heel_durability                  158 non-null    int64  \n",
+      " 36  outsole_durability               158 non-null    int64  \n",
+      " 37  breathability_scaled             158 non-null    int64  \n",
+      "dtypes: float64(6), int64(19), str(13)\n",
+      "memory usage: 47.0 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop('breathability', axis=1, inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "838283e8",
+   "metadata": {},
+   "source": [
+    "## Plate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "eb229705",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "plate\n",
+      "0               112\n",
+      "rock plate       35\n",
+      "carbon plate     11\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"plate\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "a97958df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 158\n",
+      "NULL Value: 112\n",
+      "\n",
+      "Sample Comparison:\n",
+      "        plate  plate_rock_plate  plate_carbon_plate\n",
+      "0           0                 0                   0\n",
+      "1           0                 0                   0\n",
+      "2           0                 0                   0\n",
+      "3           0                 0                   0\n",
+      "4  rock plate                 1                   0\n",
+      "5  rock plate                 1                   0\n",
+      "6           0                 0                   0\n",
+      "7           0                 0                   0\n",
+      "8           0                 0                   0\n",
+      "9           0                 0                   0\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['plate'] = df['plate'].astype(str).str.lower()\n",
+    "base_plate = ['rock plate', 'carbon plate']\n",
+    "\n",
+    "for level in base_plate:\n",
+    "    column_name = f\"plate_{level.replace(' ', '_')}\"\n",
+    "    df[column_name] = df['plate'].str.contains(level, na=False).astype(int)\n",
+    "\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "plate_cols = [f\"plate_{l.replace(' ', '_')}\" for l in base_plate]\n",
+    "zero_vector_count = (df[plate_cols].sum(axis=1) == 0).sum()\n",
+    "print(f\"NULL Value: {zero_vector_count}\")\n",
+    "\n",
+    "print(\"\\nSample Comparison:\")\n",
+    "print(df[[\"plate\"] + plate_cols].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "ff871a52",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "plate\n",
+      "0               112\n",
+      "rock plate       35\n",
+      "carbon plate     11\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "plate_rock_plate sum: 35\n",
+      "plate_carbon_plate sum: 11\n",
+      "\n",
+      "        plate  plate_rock_plate  plate_carbon_plate\n",
+      "0           0                 0                   0\n",
+      "1           0                 0                   0\n",
+      "2           0                 0                   0\n",
+      "3           0                 0                   0\n",
+      "4  rock plate                 1                   0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"plate\"].value_counts())\n",
+    "\n",
+    "print()\n",
+    "for level in base_plate:\n",
+    "    col = f\"plate_{level.replace(' ', '_')}\"\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"plate\"] + plate_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "8173c5b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 39 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   brand                            158 non-null    str    \n",
+      " 1   name                             158 non-null    str    \n",
+      " 2   lightweight                      152 non-null    float64\n",
+      " 3   width / fit                      158 non-null    str    \n",
+      " 4   toebox width                     158 non-null    str    \n",
+      " 5   stiffness                        158 non-null    str    \n",
+      " 6   torsional rigidity               158 non-null    str    \n",
+      " 7   heel counter stiffness           158 non-null    str    \n",
+      " 8   lug depth                        158 non-null    str    \n",
+      " 9   heel stack lab heel stack brand  158 non-null    str    \n",
+      " 10  forefoot lab forefoot brand      158 non-null    str    \n",
+      " 11  season                           158 non-null    str    \n",
+      " 12  removable insole                 158 non-null    int64  \n",
+      " 13  orthotic friendly                158 non-null    int64  \n",
+      " 14  waterproofing                    156 non-null    str    \n",
+      " 15  terrain_light                    158 non-null    int64  \n",
+      " 16  terrain_moderate                 158 non-null    int64  \n",
+      " 17  terrain_technical                158 non-null    int64  \n",
+      " 18  shock_absorption                 158 non-null    int64  \n",
+      " 19  energy_return                    158 non-null    int64  \n",
+      " 20  traction_scaled                  158 non-null    int64  \n",
+      " 21  arch_neutral                     158 non-null    int64  \n",
+      " 22  arch_stability                   158 non-null    int64  \n",
+      " 23  weight_lab_oz                    158 non-null    float64\n",
+      " 24  weight_lab_g                     158 non-null    int64  \n",
+      " 25  weight_brand_oz                  155 non-null    float64\n",
+      " 26  weight_brand_g                   155 non-null    float64\n",
+      " 27  drop_lab_mm                      158 non-null    float64\n",
+      " 28  drop_brand_mm                    152 non-null    float64\n",
+      " 29  strike_heel                      158 non-null    int64  \n",
+      " 30  strike_mid                       158 non-null    int64  \n",
+      " 31  strike_forefoot                  158 non-null    int64  \n",
+      " 32  midsole_softness                 158 non-null    int64  \n",
+      " 33  toebox_durability                158 non-null    int64  \n",
+      " 34  heel_durability                  158 non-null    int64  \n",
+      " 35  outsole_durability               158 non-null    int64  \n",
+      " 36  breathability_scaled             158 non-null    int64  \n",
+      " 37  plate_rock_plate                 158 non-null    int64  \n",
+      " 38  plate_carbon_plate               158 non-null    int64  \n",
+      "dtypes: float64(6), int64(21), str(12)\n",
+      "memory usage: 48.3 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"plate\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "273e5d26",
+   "metadata": {},
+   "source": [
+    "## Width / fit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "229ae3b1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "width / fit\n",
+      "medium    92\n",
+      "narrow    47\n",
+      "wide      19\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['width / fit'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "9e4b2c22",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Value Counts Kolom Asli ---\n",
+      "width / fit\n",
+      "medium    92\n",
+      "narrow    47\n",
+      "wide      19\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "--- Sebaran Nilai Hasil Ordinal ---\n",
+      "Index 0: 0 baris\n",
+      "Index 1: 47 baris\n",
+      "Index 2: 0 baris\n",
+      "Index 3: 92 baris\n",
+      "Index 4: 0 baris\n",
+      "Index 5: 19 baris\n",
+      "\n",
+      "--- Perbandingan Data (Head) ---\n",
+      "  width / fit  width_fit\n",
+      "0      medium          3\n",
+      "1      narrow          1\n",
+      "2        wide          5\n",
+      "3        wide          5\n",
+      "4      narrow          1\n"
+     ]
+    }
+   ],
+   "source": [
+    "width_scaled = {\n",
+    "    \"narrow\": 1,\n",
+    "    \"medium\": 3,\n",
+    "    \"wide\": 5,\n",
+    "    \"-\": 0,\n",
+    "    \"0\": 0\n",
+    "}\n",
+    "\n",
+    "df['width_fit'] = df['width / fit'].map(width_scaled)\n",
+    "\n",
+    "print(\"--- Value Counts Kolom Asli ---\")\n",
+    "print(df[\"width / fit\"].value_counts())\n",
+    "\n",
+    "print(\"\\n--- Sebaran Nilai Hasil Ordinal ---\")\n",
+    "counts = df[\"width_fit\"].value_counts().sort_index()\n",
+    "for i in range(6):\n",
+    "    print(f\"Index {i}: {counts.get(i, 0)} baris\")\n",
+    "\n",
+    "print(\"\\n--- Perbandingan Data (Head) ---\")\n",
+    "print(df[[\"width / fit\", \"width_fit\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "227f8218",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 39 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   brand                            158 non-null    str    \n",
+      " 1   name                             158 non-null    str    \n",
+      " 2   lightweight                      152 non-null    float64\n",
+      " 3   toebox width                     158 non-null    str    \n",
+      " 4   stiffness                        158 non-null    str    \n",
+      " 5   torsional rigidity               158 non-null    str    \n",
+      " 6   heel counter stiffness           158 non-null    str    \n",
+      " 7   lug depth                        158 non-null    str    \n",
+      " 8   heel stack lab heel stack brand  158 non-null    str    \n",
+      " 9   forefoot lab forefoot brand      158 non-null    str    \n",
+      " 10  season                           158 non-null    str    \n",
+      " 11  removable insole                 158 non-null    int64  \n",
+      " 12  orthotic friendly                158 non-null    int64  \n",
+      " 13  waterproofing                    156 non-null    str    \n",
+      " 14  terrain_light                    158 non-null    int64  \n",
+      " 15  terrain_moderate                 158 non-null    int64  \n",
+      " 16  terrain_technical                158 non-null    int64  \n",
+      " 17  shock_absorption                 158 non-null    int64  \n",
+      " 18  energy_return                    158 non-null    int64  \n",
+      " 19  traction_scaled                  158 non-null    int64  \n",
+      " 20  arch_neutral                     158 non-null    int64  \n",
+      " 21  arch_stability                   158 non-null    int64  \n",
+      " 22  weight_lab_oz                    158 non-null    float64\n",
+      " 23  weight_lab_g                     158 non-null    int64  \n",
+      " 24  weight_brand_oz                  155 non-null    float64\n",
+      " 25  weight_brand_g                   155 non-null    float64\n",
+      " 26  drop_lab_mm                      158 non-null    float64\n",
+      " 27  drop_brand_mm                    152 non-null    float64\n",
+      " 28  strike_heel                      158 non-null    int64  \n",
+      " 29  strike_mid                       158 non-null    int64  \n",
+      " 30  strike_forefoot                  158 non-null    int64  \n",
+      " 31  midsole_softness                 158 non-null    int64  \n",
+      " 32  toebox_durability                158 non-null    int64  \n",
+      " 33  heel_durability                  158 non-null    int64  \n",
+      " 34  outsole_durability               158 non-null    int64  \n",
+      " 35  breathability_scaled             158 non-null    int64  \n",
+      " 36  plate_rock_plate                 158 non-null    int64  \n",
+      " 37  plate_carbon_plate               158 non-null    int64  \n",
+      " 38  width_fit                        158 non-null    int64  \n",
+      "dtypes: float64(6), int64(22), str(11)\n",
+      "memory usage: 48.3 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"width / fit\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f75044e",
+   "metadata": {},
+   "source": [
+    "## Toebox width"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "5534fbde",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "toebox width\n",
+      "medium    68\n",
+      "wide      38\n",
+      "-         32\n",
+      "narrow    20\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['toebox width'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "8a4afd8d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Value Counts Kolom Asli ---\n",
+      "toebox width\n",
+      "medium    68\n",
+      "wide      38\n",
+      "-         32\n",
+      "narrow    20\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "--- Sebaran Nilai Hasil Ordinal ---\n",
+      "Index 0: 32 baris\n",
+      "Index 1: 20 baris\n",
+      "Index 2: 0 baris\n",
+      "Index 3: 68 baris\n",
+      "Index 4: 0 baris\n",
+      "Index 5: 38 baris\n",
+      "\n",
+      "--- Perbandingan Data (Head) ---\n",
+      "  toebox width  toebox_width\n",
+      "0       narrow             1\n",
+      "1            -             0\n",
+      "2         wide             5\n",
+      "3         wide             5\n",
+      "4            -             0\n"
+     ]
+    }
+   ],
+   "source": [
+    "toebox_scaled = {\n",
+    "    \"narrow\": 1,\n",
+    "    \"medium\": 3,\n",
+    "    \"wide\": 5,\n",
+    "    \"-\": 0,\n",
+    "    \"0\": 0\n",
+    "}\n",
+    "\n",
+    "df['toebox_width'] = df['toebox width'].map(toebox_scaled)\n",
+    "\n",
+    "print(\"--- Value Counts Kolom Asli ---\")\n",
+    "print(df[\"toebox width\"].value_counts())\n",
+    "\n",
+    "print(\"\\n--- Sebaran Nilai Hasil Ordinal ---\")\n",
+    "counts = df[\"toebox_width\"].value_counts().sort_index()\n",
+    "for i in range(6):\n",
+    "    print(f\"Index {i}: {counts.get(i, 0)} baris\")\n",
+    "\n",
+    "print(\"\\n--- Perbandingan Data (Head) ---\")\n",
+    "print(df[[\"toebox width\", \"toebox_width\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "54c66b79",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 39 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   brand                            158 non-null    str    \n",
+      " 1   name                             158 non-null    str    \n",
+      " 2   lightweight                      152 non-null    float64\n",
+      " 3   stiffness                        158 non-null    str    \n",
+      " 4   torsional rigidity               158 non-null    str    \n",
+      " 5   heel counter stiffness           158 non-null    str    \n",
+      " 6   lug depth                        158 non-null    str    \n",
+      " 7   heel stack lab heel stack brand  158 non-null    str    \n",
+      " 8   forefoot lab forefoot brand      158 non-null    str    \n",
+      " 9   season                           158 non-null    str    \n",
+      " 10  removable insole                 158 non-null    int64  \n",
+      " 11  orthotic friendly                158 non-null    int64  \n",
+      " 12  waterproofing                    156 non-null    str    \n",
+      " 13  terrain_light                    158 non-null    int64  \n",
+      " 14  terrain_moderate                 158 non-null    int64  \n",
+      " 15  terrain_technical                158 non-null    int64  \n",
+      " 16  shock_absorption                 158 non-null    int64  \n",
+      " 17  energy_return                    158 non-null    int64  \n",
+      " 18  traction_scaled                  158 non-null    int64  \n",
+      " 19  arch_neutral                     158 non-null    int64  \n",
+      " 20  arch_stability                   158 non-null    int64  \n",
+      " 21  weight_lab_oz                    158 non-null    float64\n",
+      " 22  weight_lab_g                     158 non-null    int64  \n",
+      " 23  weight_brand_oz                  155 non-null    float64\n",
+      " 24  weight_brand_g                   155 non-null    float64\n",
+      " 25  drop_lab_mm                      158 non-null    float64\n",
+      " 26  drop_brand_mm                    152 non-null    float64\n",
+      " 27  strike_heel                      158 non-null    int64  \n",
+      " 28  strike_mid                       158 non-null    int64  \n",
+      " 29  strike_forefoot                  158 non-null    int64  \n",
+      " 30  midsole_softness                 158 non-null    int64  \n",
+      " 31  toebox_durability                158 non-null    int64  \n",
+      " 32  heel_durability                  158 non-null    int64  \n",
+      " 33  outsole_durability               158 non-null    int64  \n",
+      " 34  breathability_scaled             158 non-null    int64  \n",
+      " 35  plate_rock_plate                 158 non-null    int64  \n",
+      " 36  plate_carbon_plate               158 non-null    int64  \n",
+      " 37  width_fit                        158 non-null    int64  \n",
+      " 38  toebox_width                     158 non-null    int64  \n",
+      "dtypes: float64(6), int64(23), str(10)\n",
+      "memory usage: 48.3 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"toebox width\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a723b8bd",
+   "metadata": {},
+   "source": [
+    "## Stiffness"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "76d488f5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "stiffness\n",
+      "stiff       99\n",
+      "moderate    53\n",
+      "flexible     6\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['stiffness'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "18ae5a28",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Value Counts Kolom Asli ---\n",
+      "stiffness\n",
+      "stiff       99\n",
+      "moderate    53\n",
+      "flexible     6\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "--- Sebaran Nilai Hasil Ordinal  ---\n",
+      "Index 0: 0 baris\n",
+      "Index 1: 6 baris\n",
+      "Index 2: 0 baris\n",
+      "Index 3: 53 baris\n",
+      "Index 4: 0 baris\n",
+      "Index 5: 99 baris\n",
+      "\n",
+      "--- Perbandingan Data ---\n",
+      "  stiffness  stiffness_scaled\n",
+      "0  moderate                 3\n",
+      "1     stiff                 5\n",
+      "2  moderate                 3\n",
+      "3  moderate                 3\n",
+      "4     stiff                 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "stiffness_scaled = {\n",
+    "    \"flexible\": 1,\n",
+    "    \"moderate\": 3,\n",
+    "    \"stiff\": 5,\n",
+    "    \"-\": 0,\n",
+    "    \"0\": 0\n",
+    "}\n",
+    "\n",
+    "df['stiffness_scaled'] = df['stiffness'].map(stiffness_scaled)\n",
+    "\n",
+    "print(\"--- Value Counts Kolom Asli ---\")\n",
+    "print(df[\"stiffness\"].value_counts())\n",
+    "\n",
+    "print(\"\\n--- Sebaran Nilai Hasil Ordinal  ---\")\n",
+    "counts = df[\"stiffness_scaled\"].value_counts().sort_index()\n",
+    "for i in range(6):\n",
+    "    print(f\"Index {i}: {counts.get(i, 0)} baris\")\n",
+    "\n",
+    "print(\"\\n--- Perbandingan Data ---\")\n",
+    "print(df[[\"stiffness\", \"stiffness_scaled\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "429e0c4e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 39 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   brand                            158 non-null    str    \n",
+      " 1   name                             158 non-null    str    \n",
+      " 2   lightweight                      152 non-null    float64\n",
+      " 3   torsional rigidity               158 non-null    str    \n",
+      " 4   heel counter stiffness           158 non-null    str    \n",
+      " 5   lug depth                        158 non-null    str    \n",
+      " 6   heel stack lab heel stack brand  158 non-null    str    \n",
+      " 7   forefoot lab forefoot brand      158 non-null    str    \n",
+      " 8   season                           158 non-null    str    \n",
+      " 9   removable insole                 158 non-null    int64  \n",
+      " 10  orthotic friendly                158 non-null    int64  \n",
+      " 11  waterproofing                    156 non-null    str    \n",
+      " 12  terrain_light                    158 non-null    int64  \n",
+      " 13  terrain_moderate                 158 non-null    int64  \n",
+      " 14  terrain_technical                158 non-null    int64  \n",
+      " 15  shock_absorption                 158 non-null    int64  \n",
+      " 16  energy_return                    158 non-null    int64  \n",
+      " 17  traction_scaled                  158 non-null    int64  \n",
+      " 18  arch_neutral                     158 non-null    int64  \n",
+      " 19  arch_stability                   158 non-null    int64  \n",
+      " 20  weight_lab_oz                    158 non-null    float64\n",
+      " 21  weight_lab_g                     158 non-null    int64  \n",
+      " 22  weight_brand_oz                  155 non-null    float64\n",
+      " 23  weight_brand_g                   155 non-null    float64\n",
+      " 24  drop_lab_mm                      158 non-null    float64\n",
+      " 25  drop_brand_mm                    152 non-null    float64\n",
+      " 26  strike_heel                      158 non-null    int64  \n",
+      " 27  strike_mid                       158 non-null    int64  \n",
+      " 28  strike_forefoot                  158 non-null    int64  \n",
+      " 29  midsole_softness                 158 non-null    int64  \n",
+      " 30  toebox_durability                158 non-null    int64  \n",
+      " 31  heel_durability                  158 non-null    int64  \n",
+      " 32  outsole_durability               158 non-null    int64  \n",
+      " 33  breathability_scaled             158 non-null    int64  \n",
+      " 34  plate_rock_plate                 158 non-null    int64  \n",
+      " 35  plate_carbon_plate               158 non-null    int64  \n",
+      " 36  width_fit                        158 non-null    int64  \n",
+      " 37  toebox_width                     158 non-null    int64  \n",
+      " 38  stiffness_scaled                 158 non-null    int64  \n",
+      "dtypes: float64(6), int64(24), str(9)\n",
+      "memory usage: 48.3 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"stiffness\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "347d1f39",
+   "metadata": {},
+   "source": [
+    "## Torsional rigidity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "898bbd3d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torsional rigidity\n",
+      "stiff       93\n",
+      "moderate    37\n",
+      "flexible    22\n",
+      "-            6\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['torsional rigidity'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "94fbcfb9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Value Counts Kolom Asli ---\n",
+      "torsional rigidity\n",
+      "stiff       93\n",
+      "moderate    37\n",
+      "flexible    22\n",
+      "-            6\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "--- Sebaran Nilai Hasil Ordinal (Scale 0-5) ---\n",
+      "Index 0: 6 baris\n",
+      "Index 1: 22 baris\n",
+      "Index 2: 0 baris\n",
+      "Index 3: 37 baris\n",
+      "Index 4: 0 baris\n",
+      "Index 5: 93 baris\n",
+      "\n",
+      "--- Perbandingan Data (Head) ---\n",
+      "  torsional rigidity  torsional_rigidity\n",
+      "0              stiff                   5\n",
+      "1           flexible                   1\n",
+      "2              stiff                   5\n",
+      "3           moderate                   3\n",
+      "4           flexible                   1\n"
+     ]
+    }
+   ],
+   "source": [
+    "torsional_scaled = {\n",
+    "    \"flexible\": 1,\n",
+    "    \"moderate\": 3,\n",
+    "    \"stiff\": 5,\n",
+    "    \"-\": 0,\n",
+    "    \"0\": 0,\n",
+    "    \"nan\": 0 \n",
+    "}\n",
+    "\n",
+    "df['torsional_rigidity'] = df['torsional rigidity'].map(torsional_scaled).fillna(0).astype(int)\n",
+    "\n",
+    "print(\"--- Value Counts Kolom Asli ---\")\n",
+    "print(df[\"torsional rigidity\"].value_counts())\n",
+    "\n",
+    "print(\"\\n--- Sebaran Nilai Hasil Ordinal (Scale 0-5) ---\")\n",
+    "counts = df[\"torsional_rigidity\"].value_counts().sort_index()\n",
+    "for i in range(6):\n",
+    "    print(f\"Index {i}: {counts.get(i, 0)} baris\")\n",
+    "\n",
+    "print(\"\\n--- Perbandingan Data (Head) ---\")\n",
+    "print(df[[\"torsional rigidity\", \"torsional_rigidity\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "5087be70",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 39 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   brand                            158 non-null    str    \n",
+      " 1   name                             158 non-null    str    \n",
+      " 2   lightweight                      152 non-null    float64\n",
+      " 3   heel counter stiffness           158 non-null    str    \n",
+      " 4   lug depth                        158 non-null    str    \n",
+      " 5   heel stack lab heel stack brand  158 non-null    str    \n",
+      " 6   forefoot lab forefoot brand      158 non-null    str    \n",
+      " 7   season                           158 non-null    str    \n",
+      " 8   removable insole                 158 non-null    int64  \n",
+      " 9   orthotic friendly                158 non-null    int64  \n",
+      " 10  waterproofing                    156 non-null    str    \n",
+      " 11  terrain_light                    158 non-null    int64  \n",
+      " 12  terrain_moderate                 158 non-null    int64  \n",
+      " 13  terrain_technical                158 non-null    int64  \n",
+      " 14  shock_absorption                 158 non-null    int64  \n",
+      " 15  energy_return                    158 non-null    int64  \n",
+      " 16  traction_scaled                  158 non-null    int64  \n",
+      " 17  arch_neutral                     158 non-null    int64  \n",
+      " 18  arch_stability                   158 non-null    int64  \n",
+      " 19  weight_lab_oz                    158 non-null    float64\n",
+      " 20  weight_lab_g                     158 non-null    int64  \n",
+      " 21  weight_brand_oz                  155 non-null    float64\n",
+      " 22  weight_brand_g                   155 non-null    float64\n",
+      " 23  drop_lab_mm                      158 non-null    float64\n",
+      " 24  drop_brand_mm                    152 non-null    float64\n",
+      " 25  strike_heel                      158 non-null    int64  \n",
+      " 26  strike_mid                       158 non-null    int64  \n",
+      " 27  strike_forefoot                  158 non-null    int64  \n",
+      " 28  midsole_softness                 158 non-null    int64  \n",
+      " 29  toebox_durability                158 non-null    int64  \n",
+      " 30  heel_durability                  158 non-null    int64  \n",
+      " 31  outsole_durability               158 non-null    int64  \n",
+      " 32  breathability_scaled             158 non-null    int64  \n",
+      " 33  plate_rock_plate                 158 non-null    int64  \n",
+      " 34  plate_carbon_plate               158 non-null    int64  \n",
+      " 35  width_fit                        158 non-null    int64  \n",
+      " 36  toebox_width                     158 non-null    int64  \n",
+      " 37  stiffness_scaled                 158 non-null    int64  \n",
+      " 38  torsional_rigidity               158 non-null    int64  \n",
+      "dtypes: float64(6), int64(25), str(8)\n",
+      "memory usage: 48.3 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"torsional rigidity\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4fab1922",
+   "metadata": {},
+   "source": [
+    "## Heel counter stiffness"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "d17ba028",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "heel counter stiffness\n",
+      "moderate    55\n",
+      "stiff       49\n",
+      "flexible    46\n",
+      "-            8\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['heel counter stiffness'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "1ffb3c8c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 158\n",
+      "NULL Value (tidak cocok dengan kategori): 0\n",
+      "\n",
+      "Sample Comparison:\n",
+      "  heel counter stiffness  heel_stiff\n",
+      "0               flexible           1\n",
+      "1               flexible           1\n",
+      "2               moderate           3\n",
+      "3               flexible           1\n",
+      "4                      -           0\n",
+      "5                      -           0\n",
+      "6               flexible           1\n",
+      "7               flexible           1\n",
+      "8               flexible           1\n",
+      "9                      -           0\n"
+     ]
+    }
+   ],
+   "source": [
+    "heel_stiff_map = {\n",
+    "    'flexible': 1,\n",
+    "    'moderate': 3,\n",
+    "    'stiff': 5\n",
+    "}\n",
+    "\n",
+    "df['heel_stiff'] = df['heel counter stiffness'].map(heel_stiff_map).fillna(0).astype(int)\n",
+    "\n",
+    "# Cek hasil\n",
+    "print(f\"Rows: {len(df)}\")\n",
+    "print(f\"NULL Value (tidak cocok dengan kategori): {df['heel_stiff'].isna().sum()}\")\n",
+    "print(\"\\nSample Comparison:\")\n",
+    "print(df[['heel counter stiffness', 'heel_stiff']].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "65d93f9a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 39 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   brand                            158 non-null    str    \n",
+      " 1   name                             158 non-null    str    \n",
+      " 2   lightweight                      152 non-null    float64\n",
+      " 3   lug depth                        158 non-null    str    \n",
+      " 4   heel stack lab heel stack brand  158 non-null    str    \n",
+      " 5   forefoot lab forefoot brand      158 non-null    str    \n",
+      " 6   season                           158 non-null    str    \n",
+      " 7   removable insole                 158 non-null    int64  \n",
+      " 8   orthotic friendly                158 non-null    int64  \n",
+      " 9   waterproofing                    156 non-null    str    \n",
+      " 10  terrain_light                    158 non-null    int64  \n",
+      " 11  terrain_moderate                 158 non-null    int64  \n",
+      " 12  terrain_technical                158 non-null    int64  \n",
+      " 13  shock_absorption                 158 non-null    int64  \n",
+      " 14  energy_return                    158 non-null    int64  \n",
+      " 15  traction_scaled                  158 non-null    int64  \n",
+      " 16  arch_neutral                     158 non-null    int64  \n",
+      " 17  arch_stability                   158 non-null    int64  \n",
+      " 18  weight_lab_oz                    158 non-null    float64\n",
+      " 19  weight_lab_g                     158 non-null    int64  \n",
+      " 20  weight_brand_oz                  155 non-null    float64\n",
+      " 21  weight_brand_g                   155 non-null    float64\n",
+      " 22  drop_lab_mm                      158 non-null    float64\n",
+      " 23  drop_brand_mm                    152 non-null    float64\n",
+      " 24  strike_heel                      158 non-null    int64  \n",
+      " 25  strike_mid                       158 non-null    int64  \n",
+      " 26  strike_forefoot                  158 non-null    int64  \n",
+      " 27  midsole_softness                 158 non-null    int64  \n",
+      " 28  toebox_durability                158 non-null    int64  \n",
+      " 29  heel_durability                  158 non-null    int64  \n",
+      " 30  outsole_durability               158 non-null    int64  \n",
+      " 31  breathability_scaled             158 non-null    int64  \n",
+      " 32  plate_rock_plate                 158 non-null    int64  \n",
+      " 33  plate_carbon_plate               158 non-null    int64  \n",
+      " 34  width_fit                        158 non-null    int64  \n",
+      " 35  toebox_width                     158 non-null    int64  \n",
+      " 36  stiffness_scaled                 158 non-null    int64  \n",
+      " 37  torsional_rigidity               158 non-null    int64  \n",
+      " 38  heel_stiff                       158 non-null    int64  \n",
+      "dtypes: float64(6), int64(26), str(7)\n",
+      "memory usage: 48.3 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"heel counter stiffness\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "62efa68c",
+   "metadata": {},
+   "source": [
+    "## Lug depth"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "3c47127b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0    2.5 mm\n",
+      "1    2.6 mm\n",
+      "2    3.6 mm\n",
+      "3    3.5 mm\n",
+      "4    3.7 mm\n",
+      "Name: lug depth, dtype: str\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"lug depth\"].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "31d7c1e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Value Counts Kolom Asli  ---\n",
+      "lug depth\n",
+      "3.5 mm    15\n",
+      "3.0 mm    14\n",
+      "4.0 mm    12\n",
+      "3.4 mm    11\n",
+      "2.5 mm     7\n",
+      "2.9 mm     7\n",
+      "3.2 mm     7\n",
+      "3.6 mm     6\n",
+      "3.7 mm     6\n",
+      "4.4 mm     6\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "  lug depth  lug_dept_mm\n",
+      "0    2.5 mm          2.5\n",
+      "1    2.6 mm          2.6\n",
+      "2    3.6 mm          3.6\n",
+      "3    3.5 mm          3.5\n",
+      "4    3.7 mm          3.7\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['lug_dept_mm'] = df['lug depth'].astype(str).str.replace(' mm', '', regex=False)\n",
+    "df['lug_dept_mm'] = pd.to_numeric(df['lug_dept_mm'].replace('-', '0'), errors='coerce').fillna(0)\n",
+    "\n",
+    "print(\"--- Value Counts Kolom Asli  ---\")\n",
+    "print(df[\"lug depth\"].value_counts().head(10))\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"lug depth\", \"lug_dept_mm\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "e7b1ee60",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 39 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   brand                            158 non-null    str    \n",
+      " 1   name                             158 non-null    str    \n",
+      " 2   lightweight                      152 non-null    float64\n",
+      " 3   heel stack lab heel stack brand  158 non-null    str    \n",
+      " 4   forefoot lab forefoot brand      158 non-null    str    \n",
+      " 5   season                           158 non-null    str    \n",
+      " 6   removable insole                 158 non-null    int64  \n",
+      " 7   orthotic friendly                158 non-null    int64  \n",
+      " 8   waterproofing                    156 non-null    str    \n",
+      " 9   terrain_light                    158 non-null    int64  \n",
+      " 10  terrain_moderate                 158 non-null    int64  \n",
+      " 11  terrain_technical                158 non-null    int64  \n",
+      " 12  shock_absorption                 158 non-null    int64  \n",
+      " 13  energy_return                    158 non-null    int64  \n",
+      " 14  traction_scaled                  158 non-null    int64  \n",
+      " 15  arch_neutral                     158 non-null    int64  \n",
+      " 16  arch_stability                   158 non-null    int64  \n",
+      " 17  weight_lab_oz                    158 non-null    float64\n",
+      " 18  weight_lab_g                     158 non-null    int64  \n",
+      " 19  weight_brand_oz                  155 non-null    float64\n",
+      " 20  weight_brand_g                   155 non-null    float64\n",
+      " 21  drop_lab_mm                      158 non-null    float64\n",
+      " 22  drop_brand_mm                    152 non-null    float64\n",
+      " 23  strike_heel                      158 non-null    int64  \n",
+      " 24  strike_mid                       158 non-null    int64  \n",
+      " 25  strike_forefoot                  158 non-null    int64  \n",
+      " 26  midsole_softness                 158 non-null    int64  \n",
+      " 27  toebox_durability                158 non-null    int64  \n",
+      " 28  heel_durability                  158 non-null    int64  \n",
+      " 29  outsole_durability               158 non-null    int64  \n",
+      " 30  breathability_scaled             158 non-null    int64  \n",
+      " 31  plate_rock_plate                 158 non-null    int64  \n",
+      " 32  plate_carbon_plate               158 non-null    int64  \n",
+      " 33  width_fit                        158 non-null    int64  \n",
+      " 34  toebox_width                     158 non-null    int64  \n",
+      " 35  stiffness_scaled                 158 non-null    int64  \n",
+      " 36  torsional_rigidity               158 non-null    int64  \n",
+      " 37  heel_stiff                       158 non-null    int64  \n",
+      " 38  lug_dept_mm                      158 non-null    float64\n",
+      "dtypes: float64(7), int64(26), str(6)\n",
+      "memory usage: 48.3 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"lug depth\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "edb40b00",
+   "metadata": {},
+   "source": [
+    "## Heel stack lab Heel stack brand"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "6f8956df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0    30.6 mm 38.0 mm\n",
+      "1    32.8 mm 26.0 mm\n",
+      "2    34.5 mm 34.0 mm\n",
+      "3    32.3 mm 32.0 mm\n",
+      "4    24.5 mm 25.0 mm\n",
+      "Name: heel stack lab heel stack brand, dtype: str\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"heel stack lab heel stack brand\"].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "9123ea7c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(0)"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mask_missing = (\n",
+    "    df[\"heel stack lab heel stack brand\"].isna() |\n",
+    "    (df[\"heel stack lab heel stack brand\"].astype(str).str.strip() == \"-\")\n",
+    ")\n",
+    "mask_missing.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "dc55349b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  heel stack lab heel stack brand  heel_lab_mm  heel_brand_mm\n",
+      "0                 30.6 mm 38.0 mm         30.6           38.0\n",
+      "1                 32.8 mm 26.0 mm         32.8           26.0\n",
+      "2                 34.5 mm 34.0 mm         34.5           34.0\n",
+      "3                 32.3 mm 32.0 mm         32.3           32.0\n",
+      "4                 24.5 mm 25.0 mm         24.5           25.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "heel = df[\"heel stack lab heel stack brand\"].str.findall(r\"[\\d.]+\")\n",
+    "\n",
+    "df[[\"heel_lab_mm\", \"heel_brand_mm\"]] = (\n",
+    "    pd.DataFrame(heel.tolist(), index=df.index)\n",
+    ")\n",
+    "\n",
+    "for col in [\"heel_lab_mm\", \"heel_brand_mm\"]:\n",
+    "    df[col] = pd.to_numeric(df[col], errors=\"coerce\")\n",
+    "\n",
+    "print(df[[\"heel stack lab heel stack brand\", \"heel_lab_mm\", \"heel_brand_mm\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "2f899e58",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 40 columns):\n",
+      " #   Column                       Non-Null Count  Dtype  \n",
+      "---  ------                       --------------  -----  \n",
+      " 0   brand                        158 non-null    str    \n",
+      " 1   name                         158 non-null    str    \n",
+      " 2   lightweight                  152 non-null    float64\n",
+      " 3   forefoot lab forefoot brand  158 non-null    str    \n",
+      " 4   season                       158 non-null    str    \n",
+      " 5   removable insole             158 non-null    int64  \n",
+      " 6   orthotic friendly            158 non-null    int64  \n",
+      " 7   waterproofing                156 non-null    str    \n",
+      " 8   terrain_light                158 non-null    int64  \n",
+      " 9   terrain_moderate             158 non-null    int64  \n",
+      " 10  terrain_technical            158 non-null    int64  \n",
+      " 11  shock_absorption             158 non-null    int64  \n",
+      " 12  energy_return                158 non-null    int64  \n",
+      " 13  traction_scaled              158 non-null    int64  \n",
+      " 14  arch_neutral                 158 non-null    int64  \n",
+      " 15  arch_stability               158 non-null    int64  \n",
+      " 16  weight_lab_oz                158 non-null    float64\n",
+      " 17  weight_lab_g                 158 non-null    int64  \n",
+      " 18  weight_brand_oz              155 non-null    float64\n",
+      " 19  weight_brand_g               155 non-null    float64\n",
+      " 20  drop_lab_mm                  158 non-null    float64\n",
+      " 21  drop_brand_mm                152 non-null    float64\n",
+      " 22  strike_heel                  158 non-null    int64  \n",
+      " 23  strike_mid                   158 non-null    int64  \n",
+      " 24  strike_forefoot              158 non-null    int64  \n",
+      " 25  midsole_softness             158 non-null    int64  \n",
+      " 26  toebox_durability            158 non-null    int64  \n",
+      " 27  heel_durability              158 non-null    int64  \n",
+      " 28  outsole_durability           158 non-null    int64  \n",
+      " 29  breathability_scaled         158 non-null    int64  \n",
+      " 30  plate_rock_plate             158 non-null    int64  \n",
+      " 31  plate_carbon_plate           158 non-null    int64  \n",
+      " 32  width_fit                    158 non-null    int64  \n",
+      " 33  toebox_width                 158 non-null    int64  \n",
+      " 34  stiffness_scaled             158 non-null    int64  \n",
+      " 35  torsional_rigidity           158 non-null    int64  \n",
+      " 36  heel_stiff                   158 non-null    int64  \n",
+      " 37  lug_dept_mm                  158 non-null    float64\n",
+      " 38  heel_lab_mm                  158 non-null    float64\n",
+      " 39  heel_brand_mm                145 non-null    float64\n",
+      "dtypes: float64(9), int64(26), str(5)\n",
+      "memory usage: 49.5 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"heel stack lab heel stack brand\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69155f32",
+   "metadata": {},
+   "source": [
+    "## Forefoot lab Forefoot brand"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "586618dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0    30.3 mm 30.0 mm\n",
+      "1    24.6 mm 18.0 mm\n",
+      "2    30.2 mm 30.0 mm\n",
+      "3    26.2 mm 28.0 mm\n",
+      "4    24.3 mm 25.0 mm\n",
+      "Name: forefoot lab forefoot brand, dtype: str\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['forefoot lab forefoot brand'].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "b1323541",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(0)"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mask_missing = (\n",
+    "    df[\"forefoot lab forefoot brand\"].isna() |\n",
+    "    (df[\"forefoot lab forefoot brand\"].astype(str).str.strip() == \"-\")\n",
+    ")\n",
+    "mask_missing.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "a29bbb63",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  forefoot lab forefoot brand  forefoot_lab_mm  forefoot_brand_mm\n",
+      "0             30.3 mm 30.0 mm             30.3               30.0\n",
+      "1             24.6 mm 18.0 mm             24.6               18.0\n",
+      "2             30.2 mm 30.0 mm             30.2               30.0\n",
+      "3             26.2 mm 28.0 mm             26.2               28.0\n",
+      "4             24.3 mm 25.0 mm             24.3               25.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "forefoot = df[\"forefoot lab forefoot brand\"].str.findall(r\"[\\d.]+\")\n",
+    "\n",
+    "df[[\"forefoot_lab_mm\", \"forefoot_brand_mm\"]] = (\n",
+    "    pd.DataFrame(forefoot.tolist(), index=df.index)\n",
+    ")\n",
+    "\n",
+    "for col in [\"forefoot_lab_mm\", \"forefoot_brand_mm\"]:\n",
+    "    df[col] = pd.to_numeric(df[col], errors=\"coerce\")\n",
+    "\n",
+    "print(df[[\"forefoot lab forefoot brand\", \"forefoot_lab_mm\", \"forefoot_brand_mm\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "081b4449",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 41 columns):\n",
+      " #   Column                Non-Null Count  Dtype  \n",
+      "---  ------                --------------  -----  \n",
+      " 0   brand                 158 non-null    str    \n",
+      " 1   name                  158 non-null    str    \n",
+      " 2   lightweight           152 non-null    float64\n",
+      " 3   season                158 non-null    str    \n",
+      " 4   removable insole      158 non-null    int64  \n",
+      " 5   orthotic friendly     158 non-null    int64  \n",
+      " 6   waterproofing         156 non-null    str    \n",
+      " 7   terrain_light         158 non-null    int64  \n",
+      " 8   terrain_moderate      158 non-null    int64  \n",
+      " 9   terrain_technical     158 non-null    int64  \n",
+      " 10  shock_absorption      158 non-null    int64  \n",
+      " 11  energy_return         158 non-null    int64  \n",
+      " 12  traction_scaled       158 non-null    int64  \n",
+      " 13  arch_neutral          158 non-null    int64  \n",
+      " 14  arch_stability        158 non-null    int64  \n",
+      " 15  weight_lab_oz         158 non-null    float64\n",
+      " 16  weight_lab_g          158 non-null    int64  \n",
+      " 17  weight_brand_oz       155 non-null    float64\n",
+      " 18  weight_brand_g        155 non-null    float64\n",
+      " 19  drop_lab_mm           158 non-null    float64\n",
+      " 20  drop_brand_mm         152 non-null    float64\n",
+      " 21  strike_heel           158 non-null    int64  \n",
+      " 22  strike_mid            158 non-null    int64  \n",
+      " 23  strike_forefoot       158 non-null    int64  \n",
+      " 24  midsole_softness      158 non-null    int64  \n",
+      " 25  toebox_durability     158 non-null    int64  \n",
+      " 26  heel_durability       158 non-null    int64  \n",
+      " 27  outsole_durability    158 non-null    int64  \n",
+      " 28  breathability_scaled  158 non-null    int64  \n",
+      " 29  plate_rock_plate      158 non-null    int64  \n",
+      " 30  plate_carbon_plate    158 non-null    int64  \n",
+      " 31  width_fit             158 non-null    int64  \n",
+      " 32  toebox_width          158 non-null    int64  \n",
+      " 33  stiffness_scaled      158 non-null    int64  \n",
+      " 34  torsional_rigidity    158 non-null    int64  \n",
+      " 35  heel_stiff            158 non-null    int64  \n",
+      " 36  lug_dept_mm           158 non-null    float64\n",
+      " 37  heel_lab_mm           158 non-null    float64\n",
+      " 38  heel_brand_mm         145 non-null    float64\n",
+      " 39  forefoot_lab_mm       158 non-null    float64\n",
+      " 40  forefoot_brand_mm     143 non-null    float64\n",
+      "dtypes: float64(11), int64(26), str(4)\n",
+      "memory usage: 50.7 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"forefoot lab forefoot brand\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21494e05",
+   "metadata": {},
+   "source": [
+    "## Season"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "c3e07080",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "season\n",
+      "all seasons           107\n",
+      "-                      19\n",
+      "summer all seasons     15\n",
+      "winter                 15\n",
+      "0                       1\n",
+      "summerall seasons       1\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"season\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93dfd937",
+   "metadata": {},
+   "source": [
+    "Summer All seasons = sepatu yang dirancang secara spesifik untuk summer tapi diklaim bisa dipakai all season"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "fa7f5747",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Jumlah baris dengan '-' atau '0': 20\n",
+      "\n",
+      "--- Detail Baris (season = '-' atau '0') ---\n",
+      "       brand                    name season\n",
+      "1     adidas      terrex speed ultra      -\n",
+      "4      altra           lone peak 5.0      -\n",
+      "5      altra             lone peak 6      -\n",
+      "9      altra              mont blanc      -\n",
+      "36    brooks             cascadia 16      -\n",
+      "57      hoka                tecton x      -\n",
+      "61      hoka                   zinal      -\n",
+      "67     inov8              trailtalon      0\n",
+      "68    kailas        flythorn air 2.0      -\n",
+      "70    kailas            fuga elite 2      -\n",
+      "71    kailas               fuga ex 2      -\n",
+      "73    kailas             fuga ex boa      -\n",
+      "75    kailas              fuga pro 4      -\n",
+      "89   merrell                  nova 2      -\n",
+      "101     nike  air zoom terra kiger 6      -\n",
+      "104     nike         pegasus trail 4      -\n",
+      "124  salomon             sense pro 4      -\n",
+      "140  saucony         endorphin trail      -\n",
+      "141  saucony            peregrine 11      -\n",
+      "142  saucony            peregrine 12      -\n",
+      "\n",
+      "Frekuensi spesifik:\n",
+      "season\n",
+      "-    19\n",
+      "0     1\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check weird values\n",
+    "filter_condition = df['season'].astype(str).isin(['-', '0'])\n",
+    "rows_to_check = df[filter_condition]\n",
+    "\n",
+    "print(f\"Jumlah baris dengan '-' atau '0': {len(rows_to_check)}\")\n",
+    "print(\"\\n--- Detail Baris (season = '-' atau '0') ---\")\n",
+    "print(rows_to_check[['brand', 'name', 'season']])\n",
+    "\n",
+    "\n",
+    "print(\"\\nFrekuensi spesifik:\")\n",
+    "print(df[df['season'].astype(str).isin(['-', '0'])]['season'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "40f37790",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 158\n",
+      "NULL/Unknown Value (0 dan -): 20\n",
+      "\n",
+      "Sample Comparison (Multi-label):\n",
+      "                season  season_summer  season_winter  season_all\n",
+      "13  summer all seasons              1              0           1\n",
+      "17  summer all seasons              1              0           1\n",
+      "26  summer all seasons              1              0           1\n",
+      "28  summer all seasons              1              0           1\n",
+      "29  summer all seasons              1              0           1\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['season'] = df['season'].astype(str).str.lower()\n",
+    "base_seasons = ['summer', 'winter', 'all seasons']\n",
+    "\n",
+    "for level in base_seasons:\n",
+    "    clean_name = level.replace(' seasons', '').replace(' ', '_')\n",
+    "    column_name = f\"season_{clean_name}\"\n",
+    "    df[column_name] = df['season'].str.contains(level, na=False).astype(int)\n",
+    "\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "season_cols = [col for col in df.columns if col.startswith('season_')]\n",
+    "zero_vector_count = (df[season_cols].sum(axis=1) == 0).sum()\n",
+    "print(f\"NULL/Unknown Value (0 dan -): {zero_vector_count}\")\n",
+    "\n",
+    "print(\"\\nSample Comparison (Multi-label):\")\n",
+    "print(df[df[season_cols].sum(axis=1) > 1][['season'] + season_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "39a01a66",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "season\n",
+      "all seasons           107\n",
+      "-                      19\n",
+      "summer all seasons     15\n",
+      "winter                 15\n",
+      "0                       1\n",
+      "summerall seasons       1\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "season_summer sum: 16\n",
+      "season_winter sum: 15\n",
+      "season_all sum: 123\n",
+      "\n",
+      "        season  season_summer  season_winter  season_all\n",
+      "0  all seasons              0              0           1\n",
+      "1            -              0              0           0\n",
+      "2  all seasons              0              0           1\n",
+      "3  all seasons              0              0           1\n",
+      "4            -              0              0           0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"season\"].value_counts())\n",
+    "\n",
+    "print()\n",
+    "for col in season_cols:\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"season\"] + season_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "d8eea361",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 43 columns):\n",
+      " #   Column                Non-Null Count  Dtype  \n",
+      "---  ------                --------------  -----  \n",
+      " 0   brand                 158 non-null    str    \n",
+      " 1   name                  158 non-null    str    \n",
+      " 2   lightweight           152 non-null    float64\n",
+      " 3   removable insole      158 non-null    int64  \n",
+      " 4   orthotic friendly     158 non-null    int64  \n",
+      " 5   waterproofing         156 non-null    str    \n",
+      " 6   terrain_light         158 non-null    int64  \n",
+      " 7   terrain_moderate      158 non-null    int64  \n",
+      " 8   terrain_technical     158 non-null    int64  \n",
+      " 9   shock_absorption      158 non-null    int64  \n",
+      " 10  energy_return         158 non-null    int64  \n",
+      " 11  traction_scaled       158 non-null    int64  \n",
+      " 12  arch_neutral          158 non-null    int64  \n",
+      " 13  arch_stability        158 non-null    int64  \n",
+      " 14  weight_lab_oz         158 non-null    float64\n",
+      " 15  weight_lab_g          158 non-null    int64  \n",
+      " 16  weight_brand_oz       155 non-null    float64\n",
+      " 17  weight_brand_g        155 non-null    float64\n",
+      " 18  drop_lab_mm           158 non-null    float64\n",
+      " 19  drop_brand_mm         152 non-null    float64\n",
+      " 20  strike_heel           158 non-null    int64  \n",
+      " 21  strike_mid            158 non-null    int64  \n",
+      " 22  strike_forefoot       158 non-null    int64  \n",
+      " 23  midsole_softness      158 non-null    int64  \n",
+      " 24  toebox_durability     158 non-null    int64  \n",
+      " 25  heel_durability       158 non-null    int64  \n",
+      " 26  outsole_durability    158 non-null    int64  \n",
+      " 27  breathability_scaled  158 non-null    int64  \n",
+      " 28  plate_rock_plate      158 non-null    int64  \n",
+      " 29  plate_carbon_plate    158 non-null    int64  \n",
+      " 30  width_fit             158 non-null    int64  \n",
+      " 31  toebox_width          158 non-null    int64  \n",
+      " 32  stiffness_scaled      158 non-null    int64  \n",
+      " 33  torsional_rigidity    158 non-null    int64  \n",
+      " 34  heel_stiff            158 non-null    int64  \n",
+      " 35  lug_dept_mm           158 non-null    float64\n",
+      " 36  heel_lab_mm           158 non-null    float64\n",
+      " 37  heel_brand_mm         145 non-null    float64\n",
+      " 38  forefoot_lab_mm       158 non-null    float64\n",
+      " 39  forefoot_brand_mm     143 non-null    float64\n",
+      " 40  season_summer         158 non-null    int64  \n",
+      " 41  season_winter         158 non-null    int64  \n",
+      " 42  season_all            158 non-null    int64  \n",
+      "dtypes: float64(11), int64(29), str(3)\n",
+      "memory usage: 53.2 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"season\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "acdd890f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    brand                        name  removable insole  orthotic friendly\n",
+      "0  adidas  terrex agravic speed ultra                 1                  1\n",
+      "1  adidas          terrex speed ultra                 1                  1\n",
+      "2   altra             experience wild                 1                  1\n",
+      "3   altra           experience wild 2                 1                  1\n",
+      "4   altra               lone peak 5.0                 1                  1\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[[\"brand\", \"name\", \"removable insole\", \"orthotic friendly\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00fb8fe8",
+   "metadata": {},
+   "source": [
+    "## Removable insole"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "68f9c5b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "removable insole\n",
+      "1    147\n",
+      "0     11\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"removable insole\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "4916d5ba",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   removable insole  removable_insole\n",
+      "0                 1                 1\n",
+      "1                 1                 1\n",
+      "2                 1                 1\n",
+      "3                 1                 1\n",
+      "4                 1                 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "# rename Removable insole to removable_insole\n",
+    "df['removable_insole'] = df['removable insole'].fillna(0).astype(int)\n",
+    "print(df[['removable insole', 'removable_insole']].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "9eb46039",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 43 columns):\n",
+      " #   Column                Non-Null Count  Dtype  \n",
+      "---  ------                --------------  -----  \n",
+      " 0   brand                 158 non-null    str    \n",
+      " 1   name                  158 non-null    str    \n",
+      " 2   lightweight           152 non-null    float64\n",
+      " 3   orthotic friendly     158 non-null    int64  \n",
+      " 4   waterproofing         156 non-null    str    \n",
+      " 5   terrain_light         158 non-null    int64  \n",
+      " 6   terrain_moderate      158 non-null    int64  \n",
+      " 7   terrain_technical     158 non-null    int64  \n",
+      " 8   shock_absorption      158 non-null    int64  \n",
+      " 9   energy_return         158 non-null    int64  \n",
+      " 10  traction_scaled       158 non-null    int64  \n",
+      " 11  arch_neutral          158 non-null    int64  \n",
+      " 12  arch_stability        158 non-null    int64  \n",
+      " 13  weight_lab_oz         158 non-null    float64\n",
+      " 14  weight_lab_g          158 non-null    int64  \n",
+      " 15  weight_brand_oz       155 non-null    float64\n",
+      " 16  weight_brand_g        155 non-null    float64\n",
+      " 17  drop_lab_mm           158 non-null    float64\n",
+      " 18  drop_brand_mm         152 non-null    float64\n",
+      " 19  strike_heel           158 non-null    int64  \n",
+      " 20  strike_mid            158 non-null    int64  \n",
+      " 21  strike_forefoot       158 non-null    int64  \n",
+      " 22  midsole_softness      158 non-null    int64  \n",
+      " 23  toebox_durability     158 non-null    int64  \n",
+      " 24  heel_durability       158 non-null    int64  \n",
+      " 25  outsole_durability    158 non-null    int64  \n",
+      " 26  breathability_scaled  158 non-null    int64  \n",
+      " 27  plate_rock_plate      158 non-null    int64  \n",
+      " 28  plate_carbon_plate    158 non-null    int64  \n",
+      " 29  width_fit             158 non-null    int64  \n",
+      " 30  toebox_width          158 non-null    int64  \n",
+      " 31  stiffness_scaled      158 non-null    int64  \n",
+      " 32  torsional_rigidity    158 non-null    int64  \n",
+      " 33  heel_stiff            158 non-null    int64  \n",
+      " 34  lug_dept_mm           158 non-null    float64\n",
+      " 35  heel_lab_mm           158 non-null    float64\n",
+      " 36  heel_brand_mm         145 non-null    float64\n",
+      " 37  forefoot_lab_mm       158 non-null    float64\n",
+      " 38  forefoot_brand_mm     143 non-null    float64\n",
+      " 39  season_summer         158 non-null    int64  \n",
+      " 40  season_winter         158 non-null    int64  \n",
+      " 41  season_all            158 non-null    int64  \n",
+      " 42  removable_insole      158 non-null    int64  \n",
+      "dtypes: float64(11), int64(29), str(3)\n",
+      "memory usage: 53.2 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=['removable insole'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f145cc43",
+   "metadata": {},
+   "source": [
+    "## Orthotic friendly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "id": "d52a6df6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "orthotic friendly\n",
+      "1    147\n",
+      "0     11\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"orthotic friendly\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "8f548dc5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   orthotic friendly  orthotic_friendly\n",
+      "0                  1                  1\n",
+      "1                  1                  1\n",
+      "2                  1                  1\n",
+      "3                  1                  1\n",
+      "4                  1                  1\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Rename Orthotic friendly to orthotic_friendly\n",
+    "df['orthotic_friendly'] = df['orthotic friendly'].fillna(0).astype(int)\n",
+    "print(df[['orthotic friendly', 'orthotic_friendly']].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "id": "059a1824",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 43 columns):\n",
+      " #   Column                Non-Null Count  Dtype  \n",
+      "---  ------                --------------  -----  \n",
+      " 0   brand                 158 non-null    str    \n",
+      " 1   name                  158 non-null    str    \n",
+      " 2   lightweight           152 non-null    float64\n",
+      " 3   waterproofing         156 non-null    str    \n",
+      " 4   terrain_light         158 non-null    int64  \n",
+      " 5   terrain_moderate      158 non-null    int64  \n",
+      " 6   terrain_technical     158 non-null    int64  \n",
+      " 7   shock_absorption      158 non-null    int64  \n",
+      " 8   energy_return         158 non-null    int64  \n",
+      " 9   traction_scaled       158 non-null    int64  \n",
+      " 10  arch_neutral          158 non-null    int64  \n",
+      " 11  arch_stability        158 non-null    int64  \n",
+      " 12  weight_lab_oz         158 non-null    float64\n",
+      " 13  weight_lab_g          158 non-null    int64  \n",
+      " 14  weight_brand_oz       155 non-null    float64\n",
+      " 15  weight_brand_g        155 non-null    float64\n",
+      " 16  drop_lab_mm           158 non-null    float64\n",
+      " 17  drop_brand_mm         152 non-null    float64\n",
+      " 18  strike_heel           158 non-null    int64  \n",
+      " 19  strike_mid            158 non-null    int64  \n",
+      " 20  strike_forefoot       158 non-null    int64  \n",
+      " 21  midsole_softness      158 non-null    int64  \n",
+      " 22  toebox_durability     158 non-null    int64  \n",
+      " 23  heel_durability       158 non-null    int64  \n",
+      " 24  outsole_durability    158 non-null    int64  \n",
+      " 25  breathability_scaled  158 non-null    int64  \n",
+      " 26  plate_rock_plate      158 non-null    int64  \n",
+      " 27  plate_carbon_plate    158 non-null    int64  \n",
+      " 28  width_fit             158 non-null    int64  \n",
+      " 29  toebox_width          158 non-null    int64  \n",
+      " 30  stiffness_scaled      158 non-null    int64  \n",
+      " 31  torsional_rigidity    158 non-null    int64  \n",
+      " 32  heel_stiff            158 non-null    int64  \n",
+      " 33  lug_dept_mm           158 non-null    float64\n",
+      " 34  heel_lab_mm           158 non-null    float64\n",
+      " 35  heel_brand_mm         145 non-null    float64\n",
+      " 36  forefoot_lab_mm       158 non-null    float64\n",
+      " 37  forefoot_brand_mm     143 non-null    float64\n",
+      " 38  season_summer         158 non-null    int64  \n",
+      " 39  season_winter         158 non-null    int64  \n",
+      " 40  season_all            158 non-null    int64  \n",
+      " 41  removable_insole      158 non-null    int64  \n",
+      " 42  orthotic_friendly     158 non-null    int64  \n",
+      "dtypes: float64(11), int64(29), str(3)\n",
+      "memory usage: 53.2 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=['orthotic friendly'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "442b3a27",
+   "metadata": {},
+   "source": [
+    "## Waterproofing        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "a056f12c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "waterproofing\n",
+      "-                             137\n",
+      "waterproof                     12\n",
+      "water repellent                 5\n",
+      "waterproof water repellent      1\n",
+      "0                               1\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['waterproofing'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7494a16e",
+   "metadata": {},
+   "source": [
+    "Water repellent cuma nahan menolak air di permukaan tapi kalau terendam, kakinya tetap basah. kalau waterproof bener bener tahan air"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "d52d4d09",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 158\n",
+      "\n",
+      "Sample Comparison:\n",
+      "  waterproofing  waterproof  water_repellent\n",
+      "0             -           0                0\n",
+      "1             -           0                0\n",
+      "2             -           0                0\n",
+      "3             -           0                0\n",
+      "4             -           0                0\n",
+      "5             -           0                0\n",
+      "6             -           0                0\n",
+      "7             -           0                0\n",
+      "8             -           0                0\n",
+      "9             -           0                0\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['waterproofing'] = df['waterproofing'].astype(str).str.lower()\n",
+    "base_water = ['waterproof', 'water repellent']\n",
+    "\n",
+    "def check_not_waterproof(val):\n",
+    "    if val in ['-', '0', 'nan', 'none']:\n",
+    "        return 1\n",
+    "    return 0\n",
+    "\n",
+    "for level in base_water:\n",
+    "    column_name = level.replace(' ', '_')\n",
+    "    \n",
+    "    if level == 'not waterproof':\n",
+    "        df[column_name] = df['waterproofing'].apply(check_not_waterproof)\n",
+    "    else:\n",
+    "        df[column_name] = df['waterproofing'].str.contains(level, na=False).astype(int)\n",
+    "        df.loc[df['waterproofing'].isin(['-', '0']), column_name] = 0\n",
+    "\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "water_cols = [l.replace(' ', '_') for l in base_water]\n",
+    "\n",
+    "print(\"\\nSample Comparison:\")\n",
+    "print(df[[\"waterproofing\"] + water_cols].head(10))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "9d6e1eb8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "waterproofing\n",
+      "-                             137\n",
+      "waterproof                     12\n",
+      "water repellent                 5\n",
+      "waterproof water repellent      1\n",
+      "0                               1\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "--- Sum Per Kolom ---\n",
+      "waterproof sum: 13\n",
+      "water_repellent sum: 6\n",
+      "\n",
+      "Total Check (Harus >= 158): 19\n",
+      "\n",
+      "  waterproofing  waterproof  water_repellent\n",
+      "0             -           0                0\n",
+      "1             -           0                0\n",
+      "2             -           0                0\n",
+      "3             -           0                0\n",
+      "4             -           0                0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"waterproofing\"].value_counts())\n",
+    "\n",
+    "print(\"\\n--- Sum Per Kolom ---\")\n",
+    "for col in water_cols:\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "total_sum = df[water_cols].sum().sum()\n",
+    "print(f\"\\nTotal Check (Harus >= {len(df)}): {total_sum}\")\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"waterproofing\"] + water_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "1804482a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 44 columns):\n",
+      " #   Column                Non-Null Count  Dtype  \n",
+      "---  ------                --------------  -----  \n",
+      " 0   brand                 158 non-null    str    \n",
+      " 1   name                  158 non-null    str    \n",
+      " 2   lightweight           152 non-null    float64\n",
+      " 3   terrain_light         158 non-null    int64  \n",
+      " 4   terrain_moderate      158 non-null    int64  \n",
+      " 5   terrain_technical     158 non-null    int64  \n",
+      " 6   shock_absorption      158 non-null    int64  \n",
+      " 7   energy_return         158 non-null    int64  \n",
+      " 8   traction_scaled       158 non-null    int64  \n",
+      " 9   arch_neutral          158 non-null    int64  \n",
+      " 10  arch_stability        158 non-null    int64  \n",
+      " 11  weight_lab_oz         158 non-null    float64\n",
+      " 12  weight_lab_g          158 non-null    int64  \n",
+      " 13  weight_brand_oz       155 non-null    float64\n",
+      " 14  weight_brand_g        155 non-null    float64\n",
+      " 15  drop_lab_mm           158 non-null    float64\n",
+      " 16  drop_brand_mm         152 non-null    float64\n",
+      " 17  strike_heel           158 non-null    int64  \n",
+      " 18  strike_mid            158 non-null    int64  \n",
+      " 19  strike_forefoot       158 non-null    int64  \n",
+      " 20  midsole_softness      158 non-null    int64  \n",
+      " 21  toebox_durability     158 non-null    int64  \n",
+      " 22  heel_durability       158 non-null    int64  \n",
+      " 23  outsole_durability    158 non-null    int64  \n",
+      " 24  breathability_scaled  158 non-null    int64  \n",
+      " 25  plate_rock_plate      158 non-null    int64  \n",
+      " 26  plate_carbon_plate    158 non-null    int64  \n",
+      " 27  width_fit             158 non-null    int64  \n",
+      " 28  toebox_width          158 non-null    int64  \n",
+      " 29  stiffness_scaled      158 non-null    int64  \n",
+      " 30  torsional_rigidity    158 non-null    int64  \n",
+      " 31  heel_stiff            158 non-null    int64  \n",
+      " 32  lug_dept_mm           158 non-null    float64\n",
+      " 33  heel_lab_mm           158 non-null    float64\n",
+      " 34  heel_brand_mm         145 non-null    float64\n",
+      " 35  forefoot_lab_mm       158 non-null    float64\n",
+      " 36  forefoot_brand_mm     143 non-null    float64\n",
+      " 37  season_summer         158 non-null    int64  \n",
+      " 38  season_winter         158 non-null    int64  \n",
+      " 39  season_all            158 non-null    int64  \n",
+      " 40  removable_insole      158 non-null    int64  \n",
+      " 41  orthotic_friendly     158 non-null    int64  \n",
+      " 42  waterproof            158 non-null    int64  \n",
+      " 43  water_repellent       158 non-null    int64  \n",
+      "dtypes: float64(11), int64(31), str(2)\n",
+      "memory usage: 54.4 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"waterproofing\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a88a6c8",
+   "metadata": {},
+   "source": [
+    "# Finishing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "26dc1dc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 44 columns):\n",
+      " #   Column                Non-Null Count  Dtype  \n",
+      "---  ------                --------------  -----  \n",
+      " 0   brand                 158 non-null    str    \n",
+      " 1   name                  158 non-null    str    \n",
+      " 2   lightweight           158 non-null    int64  \n",
+      " 3   terrain_light         158 non-null    int64  \n",
+      " 4   terrain_moderate      158 non-null    int64  \n",
+      " 5   terrain_technical     158 non-null    int64  \n",
+      " 6   shock_absorption      158 non-null    int64  \n",
+      " 7   energy_return         158 non-null    int64  \n",
+      " 8   traction_scaled       158 non-null    int64  \n",
+      " 9   arch_neutral          158 non-null    int64  \n",
+      " 10  arch_stability        158 non-null    int64  \n",
+      " 11  weight_lab_oz         158 non-null    float64\n",
+      " 12  weight_lab_g          158 non-null    int64  \n",
+      " 13  weight_brand_oz       155 non-null    float64\n",
+      " 14  weight_brand_g        155 non-null    float64\n",
+      " 15  drop_lab_mm           158 non-null    float64\n",
+      " 16  drop_brand_mm         152 non-null    float64\n",
+      " 17  strike_heel           158 non-null    int64  \n",
+      " 18  strike_mid            158 non-null    int64  \n",
+      " 19  strike_forefoot       158 non-null    int64  \n",
+      " 20  midsole_softness      158 non-null    int64  \n",
+      " 21  toebox_durability     158 non-null    int64  \n",
+      " 22  heel_durability       158 non-null    int64  \n",
+      " 23  outsole_durability    158 non-null    int64  \n",
+      " 24  breathability_scaled  158 non-null    int64  \n",
+      " 25  plate_rock_plate      158 non-null    int64  \n",
+      " 26  plate_carbon_plate    158 non-null    int64  \n",
+      " 27  width_fit             158 non-null    int64  \n",
+      " 28  toebox_width          158 non-null    int64  \n",
+      " 29  stiffness_scaled      158 non-null    int64  \n",
+      " 30  torsional_rigidity    158 non-null    int64  \n",
+      " 31  heel_stiff            158 non-null    int64  \n",
+      " 32  lug_dept_mm           158 non-null    float64\n",
+      " 33  heel_lab_mm           158 non-null    float64\n",
+      " 34  heel_brand_mm         145 non-null    float64\n",
+      " 35  forefoot_lab_mm       158 non-null    float64\n",
+      " 36  forefoot_brand_mm     143 non-null    float64\n",
+      " 37  season_summer         158 non-null    int64  \n",
+      " 38  season_winter         158 non-null    int64  \n",
+      " 39  season_all            158 non-null    int64  \n",
+      " 40  removable_insole      158 non-null    int64  \n",
+      " 41  orthotic_friendly     158 non-null    int64  \n",
+      " 42  waterproof            158 non-null    int64  \n",
+      " 43  water_repellent       158 non-null    int64  \n",
+      "dtypes: float64(10), int64(32), str(2)\n",
+      "memory usage: 54.4 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# change lightweight to int\n",
+    "df['lightweight'] = df['lightweight'].fillna(0).astype(int)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "9b96e4f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 41 columns):\n",
+      " #   Column                Non-Null Count  Dtype  \n",
+      "---  ------                --------------  -----  \n",
+      " 0   brand                 158 non-null    str    \n",
+      " 1   name                  158 non-null    str    \n",
+      " 2   lightweight           158 non-null    int64  \n",
+      " 3   terrain_light         158 non-null    int64  \n",
+      " 4   terrain_moderate      158 non-null    int64  \n",
+      " 5   terrain_technical     158 non-null    int64  \n",
+      " 6   shock_absorption      158 non-null    int64  \n",
+      " 7   energy_return         158 non-null    int64  \n",
+      " 8   traction_scaled       158 non-null    int64  \n",
+      " 9   arch_neutral          158 non-null    int64  \n",
+      " 10  arch_stability        158 non-null    int64  \n",
+      " 11  weight_lab_oz         158 non-null    float64\n",
+      " 12  drop_lab_mm           158 non-null    float64\n",
+      " 13  drop_brand_mm         152 non-null    float64\n",
+      " 14  strike_heel           158 non-null    int64  \n",
+      " 15  strike_mid            158 non-null    int64  \n",
+      " 16  strike_forefoot       158 non-null    int64  \n",
+      " 17  midsole_softness      158 non-null    int64  \n",
+      " 18  toebox_durability     158 non-null    int64  \n",
+      " 19  heel_durability       158 non-null    int64  \n",
+      " 20  outsole_durability    158 non-null    int64  \n",
+      " 21  breathability_scaled  158 non-null    int64  \n",
+      " 22  plate_rock_plate      158 non-null    int64  \n",
+      " 23  plate_carbon_plate    158 non-null    int64  \n",
+      " 24  width_fit             158 non-null    int64  \n",
+      " 25  toebox_width          158 non-null    int64  \n",
+      " 26  stiffness_scaled      158 non-null    int64  \n",
+      " 27  torsional_rigidity    158 non-null    int64  \n",
+      " 28  heel_stiff            158 non-null    int64  \n",
+      " 29  lug_dept_mm           158 non-null    float64\n",
+      " 30  heel_lab_mm           158 non-null    float64\n",
+      " 31  heel_brand_mm         145 non-null    float64\n",
+      " 32  forefoot_lab_mm       158 non-null    float64\n",
+      " 33  forefoot_brand_mm     143 non-null    float64\n",
+      " 34  season_summer         158 non-null    int64  \n",
+      " 35  season_winter         158 non-null    int64  \n",
+      " 36  season_all            158 non-null    int64  \n",
+      " 37  removable_insole      158 non-null    int64  \n",
+      " 38  orthotic_friendly     158 non-null    int64  \n",
+      " 39  waterproof            158 non-null    int64  \n",
+      " 40  water_repellent       158 non-null    int64  \n",
+      "dtypes: float64(8), int64(31), str(2)\n",
+      "memory usage: 50.7 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Weight cuma pakai yg lab_oz\n",
+    "df.drop(columns=['weight_brand_oz', 'weight_lab_g', 'weight_brand_g'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "id": "40a900de",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 40 columns):\n",
+      " #   Column                Non-Null Count  Dtype  \n",
+      "---  ------                --------------  -----  \n",
+      " 0   brand                 158 non-null    str    \n",
+      " 1   name                  158 non-null    str    \n",
+      " 2   lightweight           158 non-null    int64  \n",
+      " 3   terrain_light         158 non-null    int64  \n",
+      " 4   terrain_moderate      158 non-null    int64  \n",
+      " 5   terrain_technical     158 non-null    int64  \n",
+      " 6   shock_absorption      158 non-null    int64  \n",
+      " 7   energy_return         158 non-null    int64  \n",
+      " 8   traction_scaled       158 non-null    int64  \n",
+      " 9   arch_neutral          158 non-null    int64  \n",
+      " 10  arch_stability        158 non-null    int64  \n",
+      " 11  weight_lab_oz         158 non-null    float64\n",
+      " 12  drop_lab_mm           158 non-null    float64\n",
+      " 13  strike_heel           158 non-null    int64  \n",
+      " 14  strike_mid            158 non-null    int64  \n",
+      " 15  strike_forefoot       158 non-null    int64  \n",
+      " 16  midsole_softness      158 non-null    int64  \n",
+      " 17  toebox_durability     158 non-null    int64  \n",
+      " 18  heel_durability       158 non-null    int64  \n",
+      " 19  outsole_durability    158 non-null    int64  \n",
+      " 20  breathability_scaled  158 non-null    int64  \n",
+      " 21  plate_rock_plate      158 non-null    int64  \n",
+      " 22  plate_carbon_plate    158 non-null    int64  \n",
+      " 23  width_fit             158 non-null    int64  \n",
+      " 24  toebox_width          158 non-null    int64  \n",
+      " 25  stiffness_scaled      158 non-null    int64  \n",
+      " 26  torsional_rigidity    158 non-null    int64  \n",
+      " 27  heel_stiff            158 non-null    int64  \n",
+      " 28  lug_dept_mm           158 non-null    float64\n",
+      " 29  heel_lab_mm           158 non-null    float64\n",
+      " 30  heel_brand_mm         145 non-null    float64\n",
+      " 31  forefoot_lab_mm       158 non-null    float64\n",
+      " 32  forefoot_brand_mm     143 non-null    float64\n",
+      " 33  season_summer         158 non-null    int64  \n",
+      " 34  season_winter         158 non-null    int64  \n",
+      " 35  season_all            158 non-null    int64  \n",
+      " 36  removable_insole      158 non-null    int64  \n",
+      " 37  orthotic_friendly     158 non-null    int64  \n",
+      " 38  waterproof            158 non-null    int64  \n",
+      " 39  water_repellent       158 non-null    int64  \n",
+      "dtypes: float64(7), int64(31), str(2)\n",
+      "memory usage: 49.5 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# drop cuma pakai yg lab_mm\n",
+    "df.drop(columns=['drop_brand_mm'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "id": "84034462",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 39 columns):\n",
+      " #   Column                Non-Null Count  Dtype  \n",
+      "---  ------                --------------  -----  \n",
+      " 0   brand                 158 non-null    str    \n",
+      " 1   name                  158 non-null    str    \n",
+      " 2   lightweight           158 non-null    int64  \n",
+      " 3   terrain_light         158 non-null    int64  \n",
+      " 4   terrain_moderate      158 non-null    int64  \n",
+      " 5   terrain_technical     158 non-null    int64  \n",
+      " 6   shock_absorption      158 non-null    int64  \n",
+      " 7   energy_return         158 non-null    int64  \n",
+      " 8   traction_scaled       158 non-null    int64  \n",
+      " 9   arch_neutral          158 non-null    int64  \n",
+      " 10  arch_stability        158 non-null    int64  \n",
+      " 11  weight_lab_oz         158 non-null    float64\n",
+      " 12  drop_lab_mm           158 non-null    float64\n",
+      " 13  strike_heel           158 non-null    int64  \n",
+      " 14  strike_mid            158 non-null    int64  \n",
+      " 15  strike_forefoot       158 non-null    int64  \n",
+      " 16  midsole_softness      158 non-null    int64  \n",
+      " 17  toebox_durability     158 non-null    int64  \n",
+      " 18  heel_durability       158 non-null    int64  \n",
+      " 19  outsole_durability    158 non-null    int64  \n",
+      " 20  breathability_scaled  158 non-null    int64  \n",
+      " 21  plate_rock_plate      158 non-null    int64  \n",
+      " 22  plate_carbon_plate    158 non-null    int64  \n",
+      " 23  width_fit             158 non-null    int64  \n",
+      " 24  toebox_width          158 non-null    int64  \n",
+      " 25  stiffness_scaled      158 non-null    int64  \n",
+      " 26  torsional_rigidity    158 non-null    int64  \n",
+      " 27  heel_stiff            158 non-null    int64  \n",
+      " 28  lug_dept_mm           158 non-null    float64\n",
+      " 29  heel_lab_mm           158 non-null    float64\n",
+      " 30  forefoot_lab_mm       158 non-null    float64\n",
+      " 31  forefoot_brand_mm     143 non-null    float64\n",
+      " 32  season_summer         158 non-null    int64  \n",
+      " 33  season_winter         158 non-null    int64  \n",
+      " 34  season_all            158 non-null    int64  \n",
+      " 35  removable_insole      158 non-null    int64  \n",
+      " 36  orthotic_friendly     158 non-null    int64  \n",
+      " 37  waterproof            158 non-null    int64  \n",
+      " 38  water_repellent       158 non-null    int64  \n",
+      "dtypes: float64(6), int64(31), str(2)\n",
+      "memory usage: 48.3 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# heel pakai yang heel_lab_mm\n",
+    "df.drop(columns=['heel_brand_mm'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "id": "becce231",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 38 columns):\n",
+      " #   Column                Non-Null Count  Dtype  \n",
+      "---  ------                --------------  -----  \n",
+      " 0   brand                 158 non-null    str    \n",
+      " 1   name                  158 non-null    str    \n",
+      " 2   lightweight           158 non-null    int64  \n",
+      " 3   terrain_light         158 non-null    int64  \n",
+      " 4   terrain_moderate      158 non-null    int64  \n",
+      " 5   terrain_technical     158 non-null    int64  \n",
+      " 6   shock_absorption      158 non-null    int64  \n",
+      " 7   energy_return         158 non-null    int64  \n",
+      " 8   traction_scaled       158 non-null    int64  \n",
+      " 9   arch_neutral          158 non-null    int64  \n",
+      " 10  arch_stability        158 non-null    int64  \n",
+      " 11  weight_lab_oz         158 non-null    float64\n",
+      " 12  drop_lab_mm           158 non-null    float64\n",
+      " 13  strike_heel           158 non-null    int64  \n",
+      " 14  strike_mid            158 non-null    int64  \n",
+      " 15  strike_forefoot       158 non-null    int64  \n",
+      " 16  midsole_softness      158 non-null    int64  \n",
+      " 17  toebox_durability     158 non-null    int64  \n",
+      " 18  heel_durability       158 non-null    int64  \n",
+      " 19  outsole_durability    158 non-null    int64  \n",
+      " 20  breathability_scaled  158 non-null    int64  \n",
+      " 21  plate_rock_plate      158 non-null    int64  \n",
+      " 22  plate_carbon_plate    158 non-null    int64  \n",
+      " 23  width_fit             158 non-null    int64  \n",
+      " 24  toebox_width          158 non-null    int64  \n",
+      " 25  stiffness_scaled      158 non-null    int64  \n",
+      " 26  torsional_rigidity    158 non-null    int64  \n",
+      " 27  heel_stiff            158 non-null    int64  \n",
+      " 28  lug_dept_mm           158 non-null    float64\n",
+      " 29  heel_lab_mm           158 non-null    float64\n",
+      " 30  forefoot_lab_mm       158 non-null    float64\n",
+      " 31  season_summer         158 non-null    int64  \n",
+      " 32  season_winter         158 non-null    int64  \n",
+      " 33  season_all            158 non-null    int64  \n",
+      " 34  removable_insole      158 non-null    int64  \n",
+      " 35  orthotic_friendly     158 non-null    int64  \n",
+      " 36  waterproof            158 non-null    int64  \n",
+      " 37  water_repellent       158 non-null    int64  \n",
+      "dtypes: float64(5), int64(31), str(2)\n",
+      "memory usage: 47.0 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Forefoot pakai yang forefoot_lab_mm\n",
+    "df.drop(columns=['forefoot_brand_mm'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "id": "4c936ab8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 37 columns):\n",
+      " #   Column                Non-Null Count  Dtype  \n",
+      "---  ------                --------------  -----  \n",
+      " 0   brand                 158 non-null    str    \n",
+      " 1   name                  158 non-null    str    \n",
+      " 2   lightweight           158 non-null    int64  \n",
+      " 3   terrain_light         158 non-null    int64  \n",
+      " 4   terrain_moderate      158 non-null    int64  \n",
+      " 5   terrain_technical     158 non-null    int64  \n",
+      " 6   shock_absorption      158 non-null    int64  \n",
+      " 7   energy_return         158 non-null    int64  \n",
+      " 8   traction_scaled       158 non-null    int64  \n",
+      " 9   arch_neutral          158 non-null    int64  \n",
+      " 10  arch_stability        158 non-null    int64  \n",
+      " 11  weight_lab_oz         158 non-null    float64\n",
+      " 12  drop_lab_mm           158 non-null    float64\n",
+      " 13  strike_heel           158 non-null    int64  \n",
+      " 14  strike_mid            158 non-null    int64  \n",
+      " 15  strike_forefoot       158 non-null    int64  \n",
+      " 16  midsole_softness      158 non-null    int64  \n",
+      " 17  toebox_durability     158 non-null    int64  \n",
+      " 18  heel_durability       158 non-null    int64  \n",
+      " 19  outsole_durability    158 non-null    int64  \n",
+      " 20  breathability_scaled  158 non-null    int64  \n",
+      " 21  plate_rock_plate      158 non-null    int64  \n",
+      " 22  plate_carbon_plate    158 non-null    int64  \n",
+      " 23  width_fit             158 non-null    int64  \n",
+      " 24  toebox_width          158 non-null    int64  \n",
+      " 25  stiffness_scaled      158 non-null    int64  \n",
+      " 26  torsional_rigidity    158 non-null    int64  \n",
+      " 27  heel_stiff            158 non-null    int64  \n",
+      " 28  lug_dept_mm           158 non-null    float64\n",
+      " 29  heel_lab_mm           158 non-null    float64\n",
+      " 30  forefoot_lab_mm       158 non-null    float64\n",
+      " 31  season_summer         158 non-null    int64  \n",
+      " 32  season_winter         158 non-null    int64  \n",
+      " 33  season_all            158 non-null    int64  \n",
+      " 34  removable_insole      158 non-null    int64  \n",
+      " 35  waterproof            158 non-null    int64  \n",
+      " 36  water_repellent       158 non-null    int64  \n",
+      "dtypes: float64(5), int64(30), str(2)\n",
+      "memory usage: 45.8 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Only take removable_insole feature \n",
+    "df.drop(columns='orthotic_friendly', inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "id": "5cc8f859",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv('../../data/trail_dataset.csv', index=False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/data-preparation-v3/road-shoes-preparation.ipynb b/notebooks/data-preparation-v3/road-shoes-preparation.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..cd1b9bbf056538ad717e008a4fc485dc4017cbf1
--- /dev/null
+++ b/notebooks/data-preparation-v3/road-shoes-preparation.ipynb
@@ -0,0 +1,3988 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b09a9f4d",
+   "metadata": {},
+   "source": [
+    "# Road shoes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "efbbb069",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    Brand         Name  Audience score Price                Pace Arch support  \\\n",
+      "0  Brooks     Launch 9   87\\r\\n Great!  $110  Daily runningTempo      Neutral   \n",
+      "1  Brooks   Levitate 6  90\\r\\n Superb!  $150       Daily running      Neutral   \n",
+      "2  Adidas        4DFWD  90\\r\\n Superb!  $200       Daily running      Neutral   \n",
+      "3  Adidas      4DFWD 2  90\\r\\n Superb!  $200       Daily running      Neutral   \n",
+      "4  Adidas      4DFWD 3   88\\r\\n Great!  $200       Daily running      Neutral   \n",
+      "\n",
+      "         Weight lab Weight brand  Lightweight Drop lab Drop brand  \\\n",
+      "0    7.9 oz / 225g 8.1 oz / 230g            1      9.4 mm 10.0 mm   \n",
+      "1  10.7 oz / 304g 10.9 oz / 309g            0       7.7 mm 8.0 mm   \n",
+      "2  11.9 oz / 336g 11.5 oz / 327g            0      8.9 mm 10.0 mm   \n",
+      "3  12.6 oz / 356g 12.4 oz / 352g            0     10.6 mm 11.0 mm   \n",
+      "4  12.3 oz / 348g 12.2 oz / 345g            0      9.9 mm 10.0 mm   \n",
+      "\n",
+      "     Strike pattern  ... Heel lab Heel brand Forefoot lab Forefoot brand  \\\n",
+      "0  HeelMid/forefoot  ...     32.4 mm 36.0 mm             23.0 mm 26.0 mm   \n",
+      "1      Mid/forefoot  ...     34.3 mm 32.5 mm             26.6 mm 24.5 mm   \n",
+      "2  HeelMid/forefoot  ...     33.3 mm 32.5 mm             24.4 mm 22.5 mm   \n",
+      "3              Heel  ...     31.8 mm 32.0 mm             21.2 mm 21.0 mm   \n",
+      "4  HeelMid/forefoot  ...     32.6 mm 34.0 mm             22.7 mm 24.0 mm   \n",
+      "\n",
+      "  Widths available Orthotic friendly             Season Removable insole  \\\n",
+      "0       NormalWide                 1                  -                1   \n",
+      "1           Normal                 1  SummerAll seasons                1   \n",
+      "2           Normal                 1        All seasons                1   \n",
+      "3           Normal                 1        All seasons                1   \n",
+      "4           Normal                 1        All seasons                1   \n",
+      "\n",
+      "        Ranking       Popularity Gender Terrain  \n",
+      "0  #301 Top 47%  #352 Bottom 45%    NaN     NaN  \n",
+      "1   #72 Top 20%  #255 Bottom 30%    NaN     NaN  \n",
+      "2  #104 Top 17%  #368 Bottom 42%    NaN     NaN  \n",
+      "3  #126 Top 20%  #541 Bottom 16%    NaN     NaN  \n",
+      "4  #116 Top 32%   #339 Bottom 7%    NaN     NaN  \n",
+      "\n",
+      "[5 rows x 33 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.read_csv('../../data/SONIX utilities - Road.csv')\n",
+    "print(df.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "48c076e5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1170\n",
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 1170 entries, 0 to 1169\n",
+      "Data columns (total 33 columns):\n",
+      " #   Column                       Non-Null Count  Dtype\n",
+      "---  ------                       --------------  -----\n",
+      " 0   Brand                        1170 non-null   str  \n",
+      " 1   Name                         1170 non-null   str  \n",
+      " 2   Audience score               1164 non-null   str  \n",
+      " 3   Price                        1170 non-null   str  \n",
+      " 4   Pace                         1170 non-null   str  \n",
+      " 5   Arch support                 1170 non-null   str  \n",
+      " 6   Weight lab Weight brand      1170 non-null   str  \n",
+      " 7   Lightweight                  1170 non-null   int64\n",
+      " 8   Drop lab Drop brand          1170 non-null   str  \n",
+      " 9   Strike pattern               1170 non-null   str  \n",
+      " 10  Size                         1170 non-null   str  \n",
+      " 11  Midsole softness             1170 non-null   str  \n",
+      " 12  Toebox durability            1170 non-null   str  \n",
+      " 13  Heel padding durability      1170 non-null   str  \n",
+      " 14  Outsole durability           1170 non-null   str  \n",
+      " 15  Breathability                1170 non-null   str  \n",
+      " 16  Width / fit                  1170 non-null   str  \n",
+      " 17  Toebox width                 1170 non-null   str  \n",
+      " 18  Stiffness                    1170 non-null   str  \n",
+      " 19  Torsional rigidity           1170 non-null   str  \n",
+      " 20  Heel counter stiffness       1170 non-null   str  \n",
+      " 21  Plate                        1170 non-null   str  \n",
+      " 22  Rocker                       1170 non-null   int64\n",
+      " 23  Heel lab Heel brand          1170 non-null   str  \n",
+      " 24  Forefoot lab Forefoot brand  1170 non-null   str  \n",
+      " 25  Widths available             1170 non-null   str  \n",
+      " 26  Orthotic friendly            1170 non-null   int64\n",
+      " 27  Season                       1170 non-null   str  \n",
+      " 28  Removable insole             1170 non-null   int64\n",
+      " 29  Ranking                      1170 non-null   str  \n",
+      " 30  Popularity                   1170 non-null   str  \n",
+      " 31  Gender                       10 non-null     str  \n",
+      " 32  Terrain                      16 non-null     str  \n",
+      "dtypes: int64(4), str(29)\n",
+      "memory usage: 301.8 KB\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(len(df))\n",
+    "print(df.info())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "78885e1c",
+   "metadata": {},
+   "source": [
+    "# Pre-EDA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "60412a53",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 1170 entries, 0 to 1169\n",
+      "Data columns (total 27 columns):\n",
+      " #   Column                       Non-Null Count  Dtype\n",
+      "---  ------                       --------------  -----\n",
+      " 0   Brand                        1170 non-null   str  \n",
+      " 1   Name                         1170 non-null   str  \n",
+      " 2   Pace                         1170 non-null   str  \n",
+      " 3   Arch support                 1170 non-null   str  \n",
+      " 4   Weight lab Weight brand      1170 non-null   str  \n",
+      " 5   Lightweight                  1170 non-null   int64\n",
+      " 6   Drop lab Drop brand          1170 non-null   str  \n",
+      " 7   Strike pattern               1170 non-null   str  \n",
+      " 8   Size                         1170 non-null   str  \n",
+      " 9   Midsole softness             1170 non-null   str  \n",
+      " 10  Toebox durability            1170 non-null   str  \n",
+      " 11  Heel padding durability      1170 non-null   str  \n",
+      " 12  Outsole durability           1170 non-null   str  \n",
+      " 13  Breathability                1170 non-null   str  \n",
+      " 14  Width / fit                  1170 non-null   str  \n",
+      " 15  Toebox width                 1170 non-null   str  \n",
+      " 16  Stiffness                    1170 non-null   str  \n",
+      " 17  Torsional rigidity           1170 non-null   str  \n",
+      " 18  Heel counter stiffness       1170 non-null   str  \n",
+      " 19  Plate                        1170 non-null   str  \n",
+      " 20  Rocker                       1170 non-null   int64\n",
+      " 21  Heel lab Heel brand          1170 non-null   str  \n",
+      " 22  Forefoot lab Forefoot brand  1170 non-null   str  \n",
+      " 23  Widths available             1170 non-null   str  \n",
+      " 24  Orthotic friendly            1170 non-null   int64\n",
+      " 25  Season                       1170 non-null   str  \n",
+      " 26  Removable insole             1170 non-null   int64\n",
+      "dtypes: int64(4), str(23)\n",
+      "memory usage: 246.9 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Remove unnecessary columns that we got from RunRepeat\n",
+    "df.drop(columns=['Audience score', 'Price', 'Gender', 'Terrain', 'Ranking', 'Popularity'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8d6a771e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "       brand                    name                pace arch support  \\\n",
+      "0     brooks                launch 9  daily runningtempo      neutral   \n",
+      "1     brooks              levitate 6       daily running      neutral   \n",
+      "2     adidas                   4dfwd       daily running      neutral   \n",
+      "3     adidas                 4dfwd 2       daily running      neutral   \n",
+      "4     adidas                 4dfwd 3       daily running      neutral   \n",
+      "...      ...                     ...                 ...          ...   \n",
+      "1165    nike         zoomx streakfly               tempo      neutral   \n",
+      "1166    nike         zoomx streakfly               tempo      neutral   \n",
+      "1167    nike         zoomx streakfly               tempo      neutral   \n",
+      "1168    nike         zoomx streakfly               tempo      neutral   \n",
+      "1169    nike  zoomx vaporfly next% 2         competition      neutral   \n",
+      "\n",
+      "            weight lab weight brand  lightweight drop lab drop brand  \\\n",
+      "0       7.9 oz / 225g 8.1 oz / 230g            1      9.4 mm 10.0 mm   \n",
+      "1     10.7 oz / 304g 10.9 oz / 309g            0       7.7 mm 8.0 mm   \n",
+      "2     11.9 oz / 336g 11.5 oz / 327g            0      8.9 mm 10.0 mm   \n",
+      "3     12.6 oz / 356g 12.4 oz / 352g            0     10.6 mm 11.0 mm   \n",
+      "4     12.3 oz / 348g 12.2 oz / 345g            0      9.9 mm 10.0 mm   \n",
+      "...                             ...          ...                 ...   \n",
+      "1165        6 oz / 171g 6 oz / 171g            1       6.3 mm 6.0 mm   \n",
+      "1166        6 oz / 171g 6 oz / 171g            1       6.3 mm 6.0 mm   \n",
+      "1167        6 oz / 171g 6 oz / 171g            1       6.3 mm 6.0 mm   \n",
+      "1168        6 oz / 171g 6 oz / 171g            1       6.3 mm 6.0 mm   \n",
+      "1169    6.9 oz / 196g 6.9 oz / 196g            1       7.7 mm 7.7 mm   \n",
+      "\n",
+      "        strike pattern            size midsole softness  ...  \\\n",
+      "0     heelmid/forefoot    true to size         balanced  ...   \n",
+      "1         mid/forefoot    true to size             soft  ...   \n",
+      "2     heelmid/forefoot    true to size             firm  ...   \n",
+      "3                 heel  slightly small             firm  ...   \n",
+      "4     heelmid/forefoot    true to size             firm  ...   \n",
+      "...                ...             ...              ...  ...   \n",
+      "1165      mid/forefoot    true to size             soft  ...   \n",
+      "1166      mid/forefoot    true to size             soft  ...   \n",
+      "1167      mid/forefoot    true to size             soft  ...   \n",
+      "1168      mid/forefoot    true to size             soft  ...   \n",
+      "1169      mid/forefoot  slightly small             soft  ...   \n",
+      "\n",
+      "     torsional rigidity heel counter stiffness         plate rocker  \\\n",
+      "0                 stiff               flexible             0      0   \n",
+      "1              moderate               moderate             0      0   \n",
+      "2              flexible               flexible             0      0   \n",
+      "3              flexible               moderate             0      0   \n",
+      "4              flexible               flexible             0      0   \n",
+      "...                 ...                    ...           ...    ...   \n",
+      "1165           flexible               flexible             0      1   \n",
+      "1166           flexible               flexible             0      1   \n",
+      "1167           flexible               flexible             0      1   \n",
+      "1168           flexible               flexible             0      1   \n",
+      "1169              stiff               flexible  carbon plate      1   \n",
+      "\n",
+      "     heel lab heel brand forefoot lab forefoot brand widths available  \\\n",
+      "0        32.4 mm 36.0 mm             23.0 mm 26.0 mm       normalwide   \n",
+      "1        34.3 mm 32.5 mm             26.6 mm 24.5 mm           normal   \n",
+      "2        33.3 mm 32.5 mm             24.4 mm 22.5 mm           normal   \n",
+      "3        31.8 mm 32.0 mm             21.2 mm 21.0 mm           normal   \n",
+      "4        32.6 mm 34.0 mm             22.7 mm 24.0 mm           normal   \n",
+      "...                  ...                         ...              ...   \n",
+      "1165     31.7 mm 32.0 mm             25.4 mm 26.0 mm           normal   \n",
+      "1166     31.7 mm 32.0 mm             25.4 mm 26.0 mm           normal   \n",
+      "1167     31.7 mm 32.0 mm             25.4 mm 26.0 mm           normal   \n",
+      "1168     31.7 mm 32.0 mm             25.4 mm 26.0 mm           normal   \n",
+      "1169     38.6 mm 38.6 mm             30.9 mm 30.9 mm           normal   \n",
+      "\n",
+      "     orthotic friendly             season removable insole  \n",
+      "0                    1                  -                1  \n",
+      "1                    1  summerall seasons                1  \n",
+      "2                    1        all seasons                1  \n",
+      "3                    1        all seasons                1  \n",
+      "4                    1        all seasons                1  \n",
+      "...                ...                ...              ...  \n",
+      "1165                 1  summerall seasons                1  \n",
+      "1166                 1  summerall seasons                1  \n",
+      "1167                 1  summerall seasons                1  \n",
+      "1168                 1  summerall seasons                1  \n",
+      "1169                 0  summerall seasons                0  \n",
+      "\n",
+      "[1170 rows x 27 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Naming convention: all to lowercase\n",
+    "df.columns = df.columns.str.strip().str.lower()\n",
+    "df = df.map(lambda x: x.strip().lower() if isinstance(x, str) else x)\n",
+    "\n",
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ebe6b8a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1170\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>brand</th>\n",
+       "      <th>name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>4dfwd 3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>adistar 3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>adistar 3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>adistar 3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>adistar 3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>163</th>\n",
+       "      <td>on</td>\n",
+       "      <td>cloudflyer 5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>164</th>\n",
+       "      <td>on</td>\n",
+       "      <td>cloudflyer 5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166</th>\n",
+       "      <td>on</td>\n",
+       "      <td>cloudgo</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>169</th>\n",
+       "      <td>on</td>\n",
+       "      <td>cloudmonster 2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>170</th>\n",
+       "      <td>on</td>\n",
+       "      <td>cloudmonster 2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      brand            name\n",
+       "5    adidas         4dfwd 3\n",
+       "11   adidas       adistar 3\n",
+       "12   adidas       adistar 3\n",
+       "13   adidas       adistar 3\n",
+       "14   adidas       adistar 3\n",
+       "..      ...             ...\n",
+       "163      on    cloudflyer 5\n",
+       "164      on    cloudflyer 5\n",
+       "166      on         cloudgo\n",
+       "169      on  cloudmonster 2\n",
+       "170      on  cloudmonster 2\n",
+       "\n",
+       "[100 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dup_mask = df.duplicated(subset=[\"brand\", \"name\"], keep=\"first\")\n",
+    "print(len(dup_mask))\n",
+    "df.loc[dup_mask, [\"brand\", \"name\"]].head(100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "9809b458",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Before: 1170\n",
+      "After : 433\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>brand</th>\n",
+       "      <th>name</th>\n",
+       "      <th>pace</th>\n",
+       "      <th>arch support</th>\n",
+       "      <th>weight lab weight brand</th>\n",
+       "      <th>lightweight</th>\n",
+       "      <th>drop lab drop brand</th>\n",
+       "      <th>strike pattern</th>\n",
+       "      <th>size</th>\n",
+       "      <th>midsole softness</th>\n",
+       "      <th>...</th>\n",
+       "      <th>torsional rigidity</th>\n",
+       "      <th>heel counter stiffness</th>\n",
+       "      <th>plate</th>\n",
+       "      <th>rocker</th>\n",
+       "      <th>heel lab heel brand</th>\n",
+       "      <th>forefoot lab forefoot brand</th>\n",
+       "      <th>widths available</th>\n",
+       "      <th>orthotic friendly</th>\n",
+       "      <th>season</th>\n",
+       "      <th>removable insole</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>brooks</td>\n",
+       "      <td>launch 9</td>\n",
+       "      <td>daily runningtempo</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>7.9 oz / 225g 8.1 oz / 230g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.4 mm 10.0 mm</td>\n",
+       "      <td>heelmid/forefoot</td>\n",
+       "      <td>true to size</td>\n",
+       "      <td>balanced</td>\n",
+       "      <td>...</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>32.4 mm 36.0 mm</td>\n",
+       "      <td>23.0 mm 26.0 mm</td>\n",
+       "      <td>normalwide</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>brooks</td>\n",
+       "      <td>levitate 6</td>\n",
+       "      <td>daily running</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>10.7 oz / 304g 10.9 oz / 309g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.7 mm 8.0 mm</td>\n",
+       "      <td>mid/forefoot</td>\n",
+       "      <td>true to size</td>\n",
+       "      <td>soft</td>\n",
+       "      <td>...</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>34.3 mm 32.5 mm</td>\n",
+       "      <td>26.6 mm 24.5 mm</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>summerall seasons</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>4dfwd</td>\n",
+       "      <td>daily running</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>11.9 oz / 336g 11.5 oz / 327g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.9 mm 10.0 mm</td>\n",
+       "      <td>heelmid/forefoot</td>\n",
+       "      <td>true to size</td>\n",
+       "      <td>firm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>33.3 mm 32.5 mm</td>\n",
+       "      <td>24.4 mm 22.5 mm</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>all seasons</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>4dfwd 2</td>\n",
+       "      <td>daily running</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>12.6 oz / 356g 12.4 oz / 352g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.6 mm 11.0 mm</td>\n",
+       "      <td>heel</td>\n",
+       "      <td>slightly small</td>\n",
+       "      <td>firm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>31.8 mm 32.0 mm</td>\n",
+       "      <td>21.2 mm 21.0 mm</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>all seasons</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>4dfwd 3</td>\n",
+       "      <td>daily running</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>12.3 oz / 348g 12.2 oz / 345g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.9 mm 10.0 mm</td>\n",
+       "      <td>heelmid/forefoot</td>\n",
+       "      <td>true to size</td>\n",
+       "      <td>firm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>32.6 mm 34.0 mm</td>\n",
+       "      <td>22.7 mm 24.0 mm</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>all seasons</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 27 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    brand        name                pace arch support  \\\n",
+       "0  brooks    launch 9  daily runningtempo      neutral   \n",
+       "1  brooks  levitate 6       daily running      neutral   \n",
+       "2  adidas       4dfwd       daily running      neutral   \n",
+       "3  adidas     4dfwd 2       daily running      neutral   \n",
+       "4  adidas     4dfwd 3       daily running      neutral   \n",
+       "\n",
+       "         weight lab weight brand  lightweight drop lab drop brand  \\\n",
+       "0    7.9 oz / 225g 8.1 oz / 230g            1      9.4 mm 10.0 mm   \n",
+       "1  10.7 oz / 304g 10.9 oz / 309g            0       7.7 mm 8.0 mm   \n",
+       "2  11.9 oz / 336g 11.5 oz / 327g            0      8.9 mm 10.0 mm   \n",
+       "3  12.6 oz / 356g 12.4 oz / 352g            0     10.6 mm 11.0 mm   \n",
+       "4  12.3 oz / 348g 12.2 oz / 345g            0      9.9 mm 10.0 mm   \n",
+       "\n",
+       "     strike pattern            size midsole softness  ... torsional rigidity  \\\n",
+       "0  heelmid/forefoot    true to size         balanced  ...              stiff   \n",
+       "1      mid/forefoot    true to size             soft  ...           moderate   \n",
+       "2  heelmid/forefoot    true to size             firm  ...           flexible   \n",
+       "3              heel  slightly small             firm  ...           flexible   \n",
+       "4  heelmid/forefoot    true to size             firm  ...           flexible   \n",
+       "\n",
+       "  heel counter stiffness plate rocker heel lab heel brand  \\\n",
+       "0               flexible     0      0     32.4 mm 36.0 mm   \n",
+       "1               moderate     0      0     34.3 mm 32.5 mm   \n",
+       "2               flexible     0      0     33.3 mm 32.5 mm   \n",
+       "3               moderate     0      0     31.8 mm 32.0 mm   \n",
+       "4               flexible     0      0     32.6 mm 34.0 mm   \n",
+       "\n",
+       "  forefoot lab forefoot brand widths available orthotic friendly  \\\n",
+       "0             23.0 mm 26.0 mm       normalwide                 1   \n",
+       "1             26.6 mm 24.5 mm           normal                 1   \n",
+       "2             24.4 mm 22.5 mm           normal                 1   \n",
+       "3             21.2 mm 21.0 mm           normal                 1   \n",
+       "4             22.7 mm 24.0 mm           normal                 1   \n",
+       "\n",
+       "              season removable insole  \n",
+       "0                  -                1  \n",
+       "1  summerall seasons                1  \n",
+       "2        all seasons                1  \n",
+       "3        all seasons                1  \n",
+       "4        all seasons                1  \n",
+       "\n",
+       "[5 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# sebelum hapus\n",
+    "print(\"Before:\", len(df))\n",
+    "\n",
+    "#hapus\n",
+    "df = df.drop_duplicates(subset=[\"brand\", \"name\"], keep=\"first\").reset_index(drop=True)\n",
+    "\n",
+    "print(\"After :\", len(df))\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "00aa7a9f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ditemukan 9 baris yang memiliki spesifikasi identik.\n",
+      "\n",
+      "           brand                   name                pace arch support  \\\n",
+      "40          nike             alphafly 3         competition      neutral   \n",
+      "285         nike        nike alphafly 3         competition      neutral   \n",
+      "119  new balance         foam arishi v4       daily running      neutral   \n",
+      "127  new balance   fresh foam arishi v4       daily running      neutral   \n",
+      "281       mizuno  mizuno wave horizon 7       daily running    stability   \n",
+      "406       mizuno         wave horizon 7       daily running    stability   \n",
+      "424       mizuno        wwave horizon 7       daily running    stability   \n",
+      "6         adidas     adidas adizero sl2  daily runningtempo      neutral   \n",
+      "23        adidas            adizero sl2  daily runningtempo      neutral   \n",
+      "\n",
+      "           weight lab weight brand  \n",
+      "40       7.1 oz / 201g 7 oz / 198g  \n",
+      "285      7.1 oz / 201g 7 oz / 198g  \n",
+      "119    8.5 oz / 242g 8.7 oz / 246g  \n",
+      "127    8.5 oz / 242g 8.7 oz / 246g  \n",
+      "281  11.6 oz / 329g 11.8 oz / 334g  \n",
+      "406  11.6 oz / 329g 11.8 oz / 334g  \n",
+      "424  11.6 oz / 329g 11.8 oz / 334g  \n",
+      "6      8.6 oz / 245g 8.4 oz / 238g  \n",
+      "23     8.6 oz / 245g 8.4 oz / 238g  \n"
+     ]
+    }
+   ],
+   "source": [
+    "# Searching for duplicate technical specifications\n",
+    "tech_columns = df.columns[2:].tolist()\n",
+    "duplicates = df[df.duplicated(subset=tech_columns, keep=False)]\n",
+    "\n",
+    "duplicates_sorted = duplicates.sort_values(by=tech_columns[:3])\n",
+    "\n",
+    "print(f\"Ditemukan {len(duplicates_sorted)} baris yang memiliki spesifikasi identik.\\n\")\n",
+    "print(duplicates_sorted[['brand', 'name'] + tech_columns[:3]].head(30))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6fdef3ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Before: 433\n",
+      "After : 428\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>brand</th>\n",
+       "      <th>name</th>\n",
+       "      <th>pace</th>\n",
+       "      <th>arch support</th>\n",
+       "      <th>weight lab weight brand</th>\n",
+       "      <th>lightweight</th>\n",
+       "      <th>drop lab drop brand</th>\n",
+       "      <th>strike pattern</th>\n",
+       "      <th>size</th>\n",
+       "      <th>midsole softness</th>\n",
+       "      <th>...</th>\n",
+       "      <th>torsional rigidity</th>\n",
+       "      <th>heel counter stiffness</th>\n",
+       "      <th>plate</th>\n",
+       "      <th>rocker</th>\n",
+       "      <th>heel lab heel brand</th>\n",
+       "      <th>forefoot lab forefoot brand</th>\n",
+       "      <th>widths available</th>\n",
+       "      <th>orthotic friendly</th>\n",
+       "      <th>season</th>\n",
+       "      <th>removable insole</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>brooks</td>\n",
+       "      <td>launch 9</td>\n",
+       "      <td>daily runningtempo</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>7.9 oz / 225g 8.1 oz / 230g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.4 mm 10.0 mm</td>\n",
+       "      <td>heelmid/forefoot</td>\n",
+       "      <td>true to size</td>\n",
+       "      <td>balanced</td>\n",
+       "      <td>...</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>32.4 mm 36.0 mm</td>\n",
+       "      <td>23.0 mm 26.0 mm</td>\n",
+       "      <td>normalwide</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>brooks</td>\n",
+       "      <td>levitate 6</td>\n",
+       "      <td>daily running</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>10.7 oz / 304g 10.9 oz / 309g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.7 mm 8.0 mm</td>\n",
+       "      <td>mid/forefoot</td>\n",
+       "      <td>true to size</td>\n",
+       "      <td>soft</td>\n",
+       "      <td>...</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>34.3 mm 32.5 mm</td>\n",
+       "      <td>26.6 mm 24.5 mm</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>summerall seasons</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>4dfwd</td>\n",
+       "      <td>daily running</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>11.9 oz / 336g 11.5 oz / 327g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.9 mm 10.0 mm</td>\n",
+       "      <td>heelmid/forefoot</td>\n",
+       "      <td>true to size</td>\n",
+       "      <td>firm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>33.3 mm 32.5 mm</td>\n",
+       "      <td>24.4 mm 22.5 mm</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>all seasons</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>4dfwd 2</td>\n",
+       "      <td>daily running</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>12.6 oz / 356g 12.4 oz / 352g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.6 mm 11.0 mm</td>\n",
+       "      <td>heel</td>\n",
+       "      <td>slightly small</td>\n",
+       "      <td>firm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>31.8 mm 32.0 mm</td>\n",
+       "      <td>21.2 mm 21.0 mm</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>all seasons</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>4dfwd 3</td>\n",
+       "      <td>daily running</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>12.3 oz / 348g 12.2 oz / 345g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.9 mm 10.0 mm</td>\n",
+       "      <td>heelmid/forefoot</td>\n",
+       "      <td>true to size</td>\n",
+       "      <td>firm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>32.6 mm 34.0 mm</td>\n",
+       "      <td>22.7 mm 24.0 mm</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>all seasons</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 27 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    brand        name                pace arch support  \\\n",
+       "0  brooks    launch 9  daily runningtempo      neutral   \n",
+       "1  brooks  levitate 6       daily running      neutral   \n",
+       "2  adidas       4dfwd       daily running      neutral   \n",
+       "3  adidas     4dfwd 2       daily running      neutral   \n",
+       "4  adidas     4dfwd 3       daily running      neutral   \n",
+       "\n",
+       "         weight lab weight brand  lightweight drop lab drop brand  \\\n",
+       "0    7.9 oz / 225g 8.1 oz / 230g            1      9.4 mm 10.0 mm   \n",
+       "1  10.7 oz / 304g 10.9 oz / 309g            0       7.7 mm 8.0 mm   \n",
+       "2  11.9 oz / 336g 11.5 oz / 327g            0      8.9 mm 10.0 mm   \n",
+       "3  12.6 oz / 356g 12.4 oz / 352g            0     10.6 mm 11.0 mm   \n",
+       "4  12.3 oz / 348g 12.2 oz / 345g            0      9.9 mm 10.0 mm   \n",
+       "\n",
+       "     strike pattern            size midsole softness  ... torsional rigidity  \\\n",
+       "0  heelmid/forefoot    true to size         balanced  ...              stiff   \n",
+       "1      mid/forefoot    true to size             soft  ...           moderate   \n",
+       "2  heelmid/forefoot    true to size             firm  ...           flexible   \n",
+       "3              heel  slightly small             firm  ...           flexible   \n",
+       "4  heelmid/forefoot    true to size             firm  ...           flexible   \n",
+       "\n",
+       "  heel counter stiffness plate rocker heel lab heel brand  \\\n",
+       "0               flexible     0      0     32.4 mm 36.0 mm   \n",
+       "1               moderate     0      0     34.3 mm 32.5 mm   \n",
+       "2               flexible     0      0     33.3 mm 32.5 mm   \n",
+       "3               moderate     0      0     31.8 mm 32.0 mm   \n",
+       "4               flexible     0      0     32.6 mm 34.0 mm   \n",
+       "\n",
+       "  forefoot lab forefoot brand widths available orthotic friendly  \\\n",
+       "0             23.0 mm 26.0 mm       normalwide                 1   \n",
+       "1             26.6 mm 24.5 mm           normal                 1   \n",
+       "2             24.4 mm 22.5 mm           normal                 1   \n",
+       "3             21.2 mm 21.0 mm           normal                 1   \n",
+       "4             22.7 mm 24.0 mm           normal                 1   \n",
+       "\n",
+       "              season removable insole  \n",
+       "0                  -                1  \n",
+       "1  summerall seasons                1  \n",
+       "2        all seasons                1  \n",
+       "3        all seasons                1  \n",
+       "4        all seasons                1  \n",
+       "\n",
+       "[5 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# sebelum hapus\n",
+    "print(\"Before:\", len(df))\n",
+    "\n",
+    "#hapus\n",
+    "df = df.drop_duplicates(subset=tech_columns, keep='first').copy()\n",
+    "\n",
+    "print(\"After :\", len(df))\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cf8752ab",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== HASIL VERIFIKASI ===\n",
+      "1. Total baris dengan 'brand_name' yang sama: 0\n",
+      "2. Total baris dengan spesifikasi yang sama: 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Last Verification of Duplicates\n",
+    "df['brand_name'] = df['brand'] + ' ' + df['name']\n",
+    "dup_brand_name = df[df.duplicated(subset=['brand_name'], keep=False)]\n",
+    "dup_tech = df[df.duplicated(subset=tech_columns, keep=False)]\n",
+    "\n",
+    "print(f\"=== HASIL VERIFIKASI ===\")\n",
+    "print(f\"1. Total baris dengan 'brand_name' yang sama: {len(dup_brand_name)}\")\n",
+    "print(f\"2. Total baris dengan spesifikasi yang sama: {len(dup_tech)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "fc730313",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 27 columns):\n",
+      " #   Column                       Non-Null Count  Dtype\n",
+      "---  ------                       --------------  -----\n",
+      " 0   brand                        428 non-null    str  \n",
+      " 1   name                         428 non-null    str  \n",
+      " 2   pace                         428 non-null    str  \n",
+      " 3   arch support                 428 non-null    str  \n",
+      " 4   weight lab weight brand      428 non-null    str  \n",
+      " 5   lightweight                  428 non-null    int64\n",
+      " 6   drop lab drop brand          428 non-null    str  \n",
+      " 7   strike pattern               428 non-null    str  \n",
+      " 8   size                         428 non-null    str  \n",
+      " 9   midsole softness             428 non-null    str  \n",
+      " 10  toebox durability            428 non-null    str  \n",
+      " 11  heel padding durability      428 non-null    str  \n",
+      " 12  outsole durability           428 non-null    str  \n",
+      " 13  breathability                428 non-null    str  \n",
+      " 14  width / fit                  428 non-null    str  \n",
+      " 15  toebox width                 428 non-null    str  \n",
+      " 16  stiffness                    428 non-null    str  \n",
+      " 17  torsional rigidity           428 non-null    str  \n",
+      " 18  heel counter stiffness       428 non-null    str  \n",
+      " 19  plate                        428 non-null    str  \n",
+      " 20  rocker                       428 non-null    int64\n",
+      " 21  heel lab heel brand          428 non-null    str  \n",
+      " 22  forefoot lab forefoot brand  428 non-null    str  \n",
+      " 23  widths available             428 non-null    str  \n",
+      " 24  orthotic friendly            428 non-null    int64\n",
+      " 25  season                       428 non-null    str  \n",
+      " 26  removable insole             428 non-null    int64\n",
+      "dtypes: int64(4), str(23)\n",
+      "memory usage: 93.6 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=['brand_name'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9baa75a0",
+   "metadata": {},
+   "source": [
+    "# EDA (Exploratory Data Analysis)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e468f573",
+   "metadata": {},
+   "source": [
+    "## Brand"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "caedf9a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "brand\n",
+      "asics           65\n",
+      "nike            54\n",
+      "adidas          53\n",
+      "brooks          51\n",
+      "saucony         39\n",
+      "new balance     37\n",
+      "hoka            21\n",
+      "altra           21\n",
+      "mizuno          19\n",
+      "on              16\n",
+      "under armour    10\n",
+      "salomon          7\n",
+      "skechers         6\n",
+      "reebok           6\n",
+      "puma             4\n",
+      "nobull           3\n",
+      "topo             3\n",
+      "diadora          3\n",
+      "xero             3\n",
+      "allbirds         3\n",
+      "jordan           1\n",
+      "inov8            1\n",
+      "apl              1\n",
+      "merrell          1\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['brand'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8f8b6950",
+   "metadata": {},
+   "source": [
+    "Terdapat kekhawatiran pada AI yang akan bias terhadap brand tertentu dikarenakan jumlah yang tidak seimbang. Untuk itu diperlukan pengecekan lanjutan pada hasil klasterisasi di akhir."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff64e16a",
+   "metadata": {},
+   "source": [
+    "## Running Purposes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "e1102b55",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pace\n",
+      "daily running         294\n",
+      "daily runningtempo     53\n",
+      "tempo                  31\n",
+      "competition            30\n",
+      "competitiontempo       20\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['pace'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2828887c",
+   "metadata": {},
+   "source": [
+    "Terdapat sepatu yang memiliki multi-value pace yang berantakan"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "32b00a7c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Jumlah Sepatu Per Kategori:\n",
+      "Daily: 347\n",
+      "Tempo: 104\n",
+      "Competition: 50\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib_venn import venn3 \n",
+    "\n",
+    "# encode base value\n",
+    "df['for_daily'] = df['pace'].str.contains('daily running').astype(int)\n",
+    "df['for_tempo'] = df['pace'].str.contains('tempo').astype(int)\n",
+    "df['for_competition'] = df['pace'].str.contains('competition').astype(int)\n",
+    "\n",
+    "\n",
+    "print(\"Jumlah Sepatu Per Kategori:\")\n",
+    "print(f\"Daily: {df['for_daily'].sum()}\")\n",
+    "print(f\"Tempo: {df['for_tempo'].sum()}\")\n",
+    "print(f\"Competition: {df['for_competition'].sum()}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "7ae0d328",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plotting intersection of each shoes\n",
+    "only_daily = len(df[(df['for_daily'] == 1) & (df['for_tempo'] == 0) & (df['for_competition'] == 0)])\n",
+    "only_tempo = len(df[(df['for_daily'] == 0) & (df['for_tempo'] == 1) & (df['for_competition'] == 0)])\n",
+    "only_comp  = len(df[(df['for_daily'] == 0) & (df['for_tempo'] == 0) & (df['for_competition'] == 1)])\n",
+    "\n",
+    "daily_tempo = len(df[(df['for_daily'] == 1) & (df['for_tempo'] == 1) & (df['for_competition'] == 0)])\n",
+    "daily_comp  = len(df[(df['for_daily'] == 1) & (df['for_tempo'] == 0) & (df['for_competition'] == 1)])\n",
+    "tempo_comp  = len(df[(df['for_daily'] == 0) & (df['for_tempo'] == 1) & (df['for_competition'] == 1)])\n",
+    "\n",
+    "all_three   = len(df[(df['for_daily'] == 1) & (df['for_tempo'] == 1) & (df['for_competition'] == 1)])\n",
+    "\n",
+    "plt.figure(figsize=(10, 8))\n",
+    "venn3(subsets = (only_daily, only_tempo, daily_tempo, only_comp, daily_comp, tempo_comp, all_three),\n",
+    "      set_labels = ('Daily Running', 'Tempo', 'Competition'),\n",
+    "      alpha = 0.5)\n",
+    "\n",
+    "plt.title(\"Running Purpose Intersection Analysis on Road Shoes\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ee8499f",
+   "metadata": {},
+   "source": [
+    "Kebanyakan sepatu yang ada adalah daily running, dimana daily running adalah sepatu yang dibuat untuk kebutuhan lari sehari-hari, tempo adalah sepatu untuk kebutuhan latihan serius sebelum pertandingan, dan competition adalah sepatu yang digunakan oleh pelari untuk berkompetisi. \n",
+    "\n",
+    "Feature ini merupakan feature penting yang memerlukan input user langsung \"Running Purpose\" untuk mengetahui kebutuhan lari user."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "1c33f9ef",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 30 columns):\n",
+      " #   Column                       Non-Null Count  Dtype\n",
+      "---  ------                       --------------  -----\n",
+      " 0   brand                        428 non-null    str  \n",
+      " 1   name                         428 non-null    str  \n",
+      " 2   pace                         428 non-null    str  \n",
+      " 3   arch support                 428 non-null    str  \n",
+      " 4   weight lab weight brand      428 non-null    str  \n",
+      " 5   lightweight                  428 non-null    int64\n",
+      " 6   drop lab drop brand          428 non-null    str  \n",
+      " 7   strike pattern               428 non-null    str  \n",
+      " 8   size                         428 non-null    str  \n",
+      " 9   midsole softness             428 non-null    str  \n",
+      " 10  toebox durability            428 non-null    str  \n",
+      " 11  heel padding durability      428 non-null    str  \n",
+      " 12  outsole durability           428 non-null    str  \n",
+      " 13  breathability                428 non-null    str  \n",
+      " 14  width / fit                  428 non-null    str  \n",
+      " 15  toebox width                 428 non-null    str  \n",
+      " 16  stiffness                    428 non-null    str  \n",
+      " 17  torsional rigidity           428 non-null    str  \n",
+      " 18  heel counter stiffness       428 non-null    str  \n",
+      " 19  plate                        428 non-null    str  \n",
+      " 20  rocker                       428 non-null    int64\n",
+      " 21  heel lab heel brand          428 non-null    str  \n",
+      " 22  forefoot lab forefoot brand  428 non-null    str  \n",
+      " 23  widths available             428 non-null    str  \n",
+      " 24  orthotic friendly            428 non-null    int64\n",
+      " 25  season                       428 non-null    str  \n",
+      " 26  removable insole             428 non-null    int64\n",
+      " 27  for_daily                    428 non-null    int64\n",
+      " 28  for_tempo                    428 non-null    int64\n",
+      " 29  for_competition              428 non-null    int64\n",
+      "dtypes: int64(7), str(23)\n",
+      "memory usage: 103.7 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "427236c6",
+   "metadata": {},
+   "source": [
+    "## Arch support"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "414218c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "arch support\n",
+      "neutral           366\n",
+      "stability          61\n",
+      "motion control      1\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['arch support'].value_counts())"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "6a1f943e",
+   "metadata": {},
+   "source": [
+    "Diketahui terdapat 3 jenis arch support pada dataset, yaitu\n",
+    "- neutral : sepatunya dirancang untuk tipe kaki normal hingga high arch (kecenderungan lengkungan tinggi)\n",
+    "- stability : sepatunya dirancang untuk tipe kaki normal hingga low arch (kecenderungan lengkungan rendah)\n",
+    "- motion control : khusus untuk pemilik flat feet (kaki rata) atau yang mengalami severe overpronation (kondisi kaki yang miring ke dalam dengan sangat tajam).\n",
+    "\n",
+    "Diambil data sebagai berikut dari website RunRepeat;\n",
+    "![image.png](attachment:image.png)\n",
+    "\n",
+    "Yang perlu diperhatikan adalah bahwa hanya terdapat 1 buah sepatu dengan kategori motion control sehingga ke depannya sepatu jenis ini akan dimasukkan ke dalam kategori stability terlebih dahulu. Jika di masa yang akan mendatang ditemukan semakin banyak sepatu jenis ini, maka akan dilakukan pemisahan antara stability dan motion control.\n",
+    "\n",
+    "Feature ini dipengaruhi oleh lengkungan pada kaki user sehingga diperlukan user input langsung berupa \"Arch Type\" untuk mengetahui sepatu yang cocok untuk mereka."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "eb80883e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 30 columns):\n",
+      " #   Column                       Non-Null Count  Dtype\n",
+      "---  ------                       --------------  -----\n",
+      " 0   brand                        428 non-null    str  \n",
+      " 1   name                         428 non-null    str  \n",
+      " 2   pace                         428 non-null    str  \n",
+      " 3   arch support                 428 non-null    str  \n",
+      " 4   weight lab weight brand      428 non-null    str  \n",
+      " 5   lightweight                  428 non-null    int64\n",
+      " 6   drop lab drop brand          428 non-null    str  \n",
+      " 7   strike pattern               428 non-null    str  \n",
+      " 8   size                         428 non-null    str  \n",
+      " 9   midsole softness             428 non-null    str  \n",
+      " 10  toebox durability            428 non-null    str  \n",
+      " 11  heel padding durability      428 non-null    str  \n",
+      " 12  outsole durability           428 non-null    str  \n",
+      " 13  breathability                428 non-null    str  \n",
+      " 14  width / fit                  428 non-null    str  \n",
+      " 15  toebox width                 428 non-null    str  \n",
+      " 16  stiffness                    428 non-null    str  \n",
+      " 17  torsional rigidity           428 non-null    str  \n",
+      " 18  heel counter stiffness       428 non-null    str  \n",
+      " 19  plate                        428 non-null    str  \n",
+      " 20  rocker                       428 non-null    int64\n",
+      " 21  heel lab heel brand          428 non-null    str  \n",
+      " 22  forefoot lab forefoot brand  428 non-null    str  \n",
+      " 23  widths available             428 non-null    str  \n",
+      " 24  orthotic friendly            428 non-null    int64\n",
+      " 25  season                       428 non-null    str  \n",
+      " 26  removable insole             428 non-null    int64\n",
+      " 27  for_daily                    428 non-null    int64\n",
+      " 28  for_tempo                    428 non-null    int64\n",
+      " 29  for_competition              428 non-null    int64\n",
+      "dtypes: int64(7), str(23)\n",
+      "memory usage: 103.7 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "63786960",
+   "metadata": {},
+   "source": [
+    "## Weight lab weight brand"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "984853e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "count                             428\n",
+      "unique                            393\n",
+      "top       9.7 oz / 275g 9.7 oz / 275g\n",
+      "freq                                4\n",
+      "Name: weight lab weight brand, dtype: object\n",
+      "0      7.9 oz / 225g 8.1 oz / 230g\n",
+      "1    10.7 oz / 304g 10.9 oz / 309g\n",
+      "2    11.9 oz / 336g 11.5 oz / 327g\n",
+      "3    12.6 oz / 356g 12.4 oz / 352g\n",
+      "4    12.3 oz / 348g 12.2 oz / 345g\n",
+      "Name: weight lab weight brand, dtype: str\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['weight lab weight brand'].describe())\n",
+    "print(df['weight lab weight brand'].head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d51e216",
+   "metadata": {},
+   "source": [
+    "Karena RunRepeat sendiri biasanya melakukan penimbangan berat sepatu menggunakan satuan oz, maka untuk menyamaratakan berat di semua sepatu, ke depannya akan digunakan weight lab dengan satuan oz."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "8fe6a1a2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         weight lab weight brand  weight_lab_oz\n",
+      "0    7.9 oz / 225g 8.1 oz / 230g            7.9\n",
+      "1  10.7 oz / 304g 10.9 oz / 309g           10.7\n",
+      "2  11.9 oz / 336g 11.5 oz / 327g           11.9\n",
+      "3  12.6 oz / 356g 12.4 oz / 352g           12.6\n",
+      "4  12.3 oz / 348g 12.2 oz / 345g           12.3\n"
+     ]
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import re\n",
+    "\n",
+    "# extract ounces from the 'weight lab weight brand' column \n",
+    "def extract_oz(text):\n",
+    "    if pd.isna(text): return None\n",
+    "    match = re.search(r'(\\d+\\.?\\d*)\\s*oz', str(text))\n",
+    "    return float(match.group(1)) if match else None\n",
+    "\n",
+    "# extract and typecast to float\n",
+    "df['weight_lab_oz'] = df['weight lab weight brand'].apply(extract_oz).astype(float)\n",
+    "\n",
+    "print(df[['weight lab weight brand', 'weight_lab_oz']].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "4bb7a16a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "count    428.000000\n",
+      "mean       9.359112\n",
+      "std        1.370511\n",
+      "min        4.500000\n",
+      "25%        8.600000\n",
+      "50%        9.600000\n",
+      "75%       10.300000\n",
+      "max       12.600000\n",
+      "Name: weight_lab_oz, dtype: float64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(df['weight_lab_oz'].describe())\n",
+    "\n",
+    "# Distribution Visualization\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "\n",
+    "# Subplot 1: Histogram\n",
+    "plt.subplot(1, 2, 1)\n",
+    "sns.histplot(df['weight_lab_oz'], kde=True, color='teal', bins=20)\n",
+    "plt.axvline(df['weight_lab_oz'].mean(), color='red', linestyle='--', label=f\"Mean: {df['weight_lab_oz'].mean():.2f}\")\n",
+    "plt.axvline(df['weight_lab_oz'].median(), color='green', linestyle='-', label=f\"Median: {df['weight_lab_oz'].median():.2f}\")\n",
+    "plt.title('Distribusi Berat Sepatu (Histogram & KDE)')\n",
+    "plt.xlabel('Weight (oz)')\n",
+    "plt.ylabel('Frekuensi')\n",
+    "plt.legend()\n",
+    "\n",
+    "# Subplot 2: Boxplot \n",
+    "plt.subplot(1, 2, 2)\n",
+    "sns.boxplot(y=df['weight_lab_oz'], color='lightblue')\n",
+    "plt.title('Boxplot Berat Sepatu')\n",
+    "plt.ylabel('Weight (oz)')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "517fcbf3",
+   "metadata": {},
+   "source": [
+    "Nantinya, weight akan menjadi filter yang disesuaikan oleh keinginan user. Misalnya, setelah hasil top 10 rekomendasi sepatu sudah keluar, user bisa sort sepatu tersebut berdasarkan rekomendasi dari 1-10 atau dari sepatu terberat/teringan (dengan tetap memedulikan similarity recommendation)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a48e460",
+   "metadata": {},
+   "source": [
+    "## Lightweight"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "fb583cbb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "lightweight\n",
+      "0    301\n",
+      "1    127\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['lightweight'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "cff4bbed",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Perbandingan Statistik Berat (weight_lab_oz):\n",
+      "             min   max       mean\n",
+      "lightweight                      \n",
+      "0            8.9  12.6  10.070764\n",
+      "1            4.5   8.8   7.672441\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Comparative Statistics Based on 'lightweight' Category\n",
+    "weight_stats = df.groupby('lightweight')['weight_lab_oz'].agg(['min', 'max', 'mean'])\n",
+    "\n",
+    "print(\"Perbandingan Statistik Berat (weight_lab_oz):\")\n",
+    "print(weight_stats)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee8bb9ea",
+   "metadata": {},
+   "source": [
+    "Dari sini didapatkan informasi bahwa RunRepeat mengkategorikan sepatu dengan berat <=8.8oz ke dalam jenis sepatu yang ringan (lightweight = 1) dan sisanya termasuk ke sepatu berkategori agak berat."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "aac417b8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 31 columns):\n",
+      " #   Column                       Non-Null Count  Dtype  \n",
+      "---  ------                       --------------  -----  \n",
+      " 0   brand                        428 non-null    str    \n",
+      " 1   name                         428 non-null    str    \n",
+      " 2   pace                         428 non-null    str    \n",
+      " 3   arch support                 428 non-null    str    \n",
+      " 4   weight lab weight brand      428 non-null    str    \n",
+      " 5   lightweight                  428 non-null    int64  \n",
+      " 6   drop lab drop brand          428 non-null    str    \n",
+      " 7   strike pattern               428 non-null    str    \n",
+      " 8   size                         428 non-null    str    \n",
+      " 9   midsole softness             428 non-null    str    \n",
+      " 10  toebox durability            428 non-null    str    \n",
+      " 11  heel padding durability      428 non-null    str    \n",
+      " 12  outsole durability           428 non-null    str    \n",
+      " 13  breathability                428 non-null    str    \n",
+      " 14  width / fit                  428 non-null    str    \n",
+      " 15  toebox width                 428 non-null    str    \n",
+      " 16  stiffness                    428 non-null    str    \n",
+      " 17  torsional rigidity           428 non-null    str    \n",
+      " 18  heel counter stiffness       428 non-null    str    \n",
+      " 19  plate                        428 non-null    str    \n",
+      " 20  rocker                       428 non-null    int64  \n",
+      " 21  heel lab heel brand          428 non-null    str    \n",
+      " 22  forefoot lab forefoot brand  428 non-null    str    \n",
+      " 23  widths available             428 non-null    str    \n",
+      " 24  orthotic friendly            428 non-null    int64  \n",
+      " 25  season                       428 non-null    str    \n",
+      " 26  removable insole             428 non-null    int64  \n",
+      " 27  for_daily                    428 non-null    int64  \n",
+      " 28  for_tempo                    428 non-null    int64  \n",
+      " 29  for_competition              428 non-null    int64  \n",
+      " 30  weight_lab_oz                428 non-null    float64\n",
+      "dtypes: float64(1), int64(7), str(23)\n",
+      "memory usage: 107.0 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9968dd5d",
+   "metadata": {},
+   "source": [
+    "## Drop Heel Forefoot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "cc69c025",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Heel Lab Heel Brand Statistics:\n",
+      "count                 428\n",
+      "unique                380\n",
+      "top       39.9 mm 40.0 mm\n",
+      "freq                    4\n",
+      "Name: heel lab heel brand, dtype: object\n",
+      "0    32.4 mm 36.0 mm\n",
+      "1    34.3 mm 32.5 mm\n",
+      "2    33.3 mm 32.5 mm\n",
+      "3    31.8 mm 32.0 mm\n",
+      "4    32.6 mm 34.0 mm\n",
+      "Name: heel lab heel brand, dtype: str\n",
+      "\n",
+      "Forefoot Lab Forefoot Brand Statistics:\n",
+      "count                 428\n",
+      "unique                377\n",
+      "top       22.7 mm 24.0 mm\n",
+      "freq                    3\n",
+      "Name: forefoot lab forefoot brand, dtype: object\n",
+      "0    23.0 mm 26.0 mm\n",
+      "1    26.6 mm 24.5 mm\n",
+      "2    24.4 mm 22.5 mm\n",
+      "3    21.2 mm 21.0 mm\n",
+      "4    22.7 mm 24.0 mm\n",
+      "Name: forefoot lab forefoot brand, dtype: str\n",
+      "\n",
+      "Drop Lab Drop Brand Statistics:\n",
+      "count               428\n",
+      "unique              266\n",
+      "top       9.6 mm 8.0 mm\n",
+      "freq                  7\n",
+      "Name: drop lab drop brand, dtype: object\n",
+      "0     9.4 mm 10.0 mm\n",
+      "1      7.7 mm 8.0 mm\n",
+      "2     8.9 mm 10.0 mm\n",
+      "3    10.6 mm 11.0 mm\n",
+      "4     9.9 mm 10.0 mm\n",
+      "Name: drop lab drop brand, dtype: str\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Heel Lab Heel Brand Statistics:\")\n",
+    "print(df['heel lab heel brand'].describe())\n",
+    "print(df['heel lab heel brand'].head())\n",
+    "\n",
+    "print(\"\\nForefoot Lab Forefoot Brand Statistics:\")\n",
+    "print(df['forefoot lab forefoot brand'].describe())\n",
+    "print(df['forefoot lab forefoot brand'].head())\n",
+    "\n",
+    "print(\"\\nDrop Lab Drop Brand Statistics:\")\n",
+    "print(df['drop lab drop brand'].describe())\n",
+    "print(df['drop lab drop brand'].head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7864c436",
+   "metadata": {},
+   "source": [
+    "For convention we will use lab measured value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "11fe3ce8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  heel lab heel brand forefoot lab forefoot brand drop lab drop brand  \\\n",
+      "0     32.4 mm 36.0 mm             23.0 mm 26.0 mm      9.4 mm 10.0 mm   \n",
+      "1     34.3 mm 32.5 mm             26.6 mm 24.5 mm       7.7 mm 8.0 mm   \n",
+      "2     33.3 mm 32.5 mm             24.4 mm 22.5 mm      8.9 mm 10.0 mm   \n",
+      "3     31.8 mm 32.0 mm             21.2 mm 21.0 mm     10.6 mm 11.0 mm   \n",
+      "4     32.6 mm 34.0 mm             22.7 mm 24.0 mm      9.9 mm 10.0 mm   \n",
+      "\n",
+      "   heel_lab_mm  forefoot_lab_mm  drop_lab_mm  \n",
+      "0         32.4             23.0          9.4  \n",
+      "1         34.3             26.6          7.7  \n",
+      "2         33.3             24.4          8.9  \n",
+      "3         31.8             21.2         10.6  \n",
+      "4         32.6             22.7          9.9  \n"
+     ]
+    }
+   ],
+   "source": [
+    "cols = ['heel lab heel brand', 'forefoot lab forefoot brand', 'drop lab drop brand']\n",
+    "\n",
+    "for col in cols:\n",
+    "    new_col_name = col.split(' ')[0] + '_lab_mm' \n",
+    "    df[new_col_name] = (df[col].str.split(' ')\n",
+    "                        .str[0]\n",
+    "                        .str.replace('mm', '', case=False)\n",
+    "                        .replace('-', None)\n",
+    "                        .astype(float))\n",
+    "\n",
+    "# Verify\n",
+    "new_cols = [c.split(' ')[0] + '_lab_mm' for c in cols]\n",
+    "print(df[cols + new_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "4bbebb8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   heel_lab_mm  forefoot_lab_mm  drop_lab_mm  drop_meas_mm     drop_diff\n",
+      "0         32.4             23.0          9.4           9.4  1.776357e-15\n",
+      "1         34.3             26.6          7.7           7.7  4.440892e-15\n",
+      "2         33.3             24.4          8.9           8.9  1.776357e-15\n",
+      "3         31.8             21.2         10.6          10.6 -1.776357e-15\n",
+      "4         32.6             22.7          9.9           9.9 -1.776357e-15\n",
+      "\n",
+      "5.329070518200751e-15\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Selisih drop\n",
+    "df['drop_meas_mm'] = df['heel_lab_mm'] - df['forefoot_lab_mm']\n",
+    "df['drop_diff'] = df['drop_lab_mm'] - (df['heel_lab_mm'] - df['forefoot_lab_mm'])\n",
+    "print(df[['heel_lab_mm','forefoot_lab_mm', 'drop_lab_mm', 'drop_meas_mm', 'drop_diff']].head())\n",
+    "\n",
+    "\n",
+    "for index, row in df.iterrows():\n",
+    "    if row['drop_diff'] > 0.9:\n",
+    "        print(f\"Index: {index} | Shoe: {row['name']}\")\n",
+    "        print(f\"  - Heel/Forefoot: {row['heel_lab_mm']}/{row['forefoot_lab_mm']} (Calc: {row['drop_meas_mm']:.1f}mm)\")\n",
+    "        print(f\"  - Drop Lab:      {row['drop_lab_mm']}mm\")\n",
+    "        print(f\"  - Diff:          {row['drop_diff']:.2f}mm\")\n",
+    "        print(\"-\" * 40)\n",
+    "\n",
+    "# print max drop_diff\n",
+    "print()\n",
+    "print(df['drop_diff'].max())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c81bac7",
+   "metadata": {},
+   "source": [
+    "Perbedaan antara measured drop dengan drop_lab biasanya disebabkan oleh drop yang diukur oleh lab menyertakan ketebalan insole, sementara perhitungan manual mungkin tidak konsisten atau perbedaan pada midsole yang ada. Dikarenakan perbedaan yang tidak terlalu jauh, maka ke depannya akan digunakan drop_lab_mm saja."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "9b9dce2c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 34 columns):\n",
+      " #   Column                       Non-Null Count  Dtype  \n",
+      "---  ------                       --------------  -----  \n",
+      " 0   brand                        428 non-null    str    \n",
+      " 1   name                         428 non-null    str    \n",
+      " 2   pace                         428 non-null    str    \n",
+      " 3   arch support                 428 non-null    str    \n",
+      " 4   weight lab weight brand      428 non-null    str    \n",
+      " 5   lightweight                  428 non-null    int64  \n",
+      " 6   drop lab drop brand          428 non-null    str    \n",
+      " 7   strike pattern               428 non-null    str    \n",
+      " 8   size                         428 non-null    str    \n",
+      " 9   midsole softness             428 non-null    str    \n",
+      " 10  toebox durability            428 non-null    str    \n",
+      " 11  heel padding durability      428 non-null    str    \n",
+      " 12  outsole durability           428 non-null    str    \n",
+      " 13  breathability                428 non-null    str    \n",
+      " 14  width / fit                  428 non-null    str    \n",
+      " 15  toebox width                 428 non-null    str    \n",
+      " 16  stiffness                    428 non-null    str    \n",
+      " 17  torsional rigidity           428 non-null    str    \n",
+      " 18  heel counter stiffness       428 non-null    str    \n",
+      " 19  plate                        428 non-null    str    \n",
+      " 20  rocker                       428 non-null    int64  \n",
+      " 21  heel lab heel brand          428 non-null    str    \n",
+      " 22  forefoot lab forefoot brand  428 non-null    str    \n",
+      " 23  widths available             428 non-null    str    \n",
+      " 24  orthotic friendly            428 non-null    int64  \n",
+      " 25  season                       428 non-null    str    \n",
+      " 26  removable insole             428 non-null    int64  \n",
+      " 27  for_daily                    428 non-null    int64  \n",
+      " 28  for_tempo                    428 non-null    int64  \n",
+      " 29  for_competition              428 non-null    int64  \n",
+      " 30  weight_lab_oz                428 non-null    float64\n",
+      " 31  heel_lab_mm                  428 non-null    float64\n",
+      " 32  forefoot_lab_mm              428 non-null    float64\n",
+      " 33  drop_lab_mm                  428 non-null    float64\n",
+      "dtypes: float64(4), int64(7), str(23)\n",
+      "memory usage: 117.0 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=['drop_diff', 'drop_meas_mm'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "3f25d4bc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Statistik Deskriptif Geometri Midsole:\n",
+      "       heel_lab_mm  forefoot_lab_mm  drop_lab_mm\n",
+      "count   428.000000       428.000000   428.000000\n",
+      "mean     34.672196        26.082477     8.589486\n",
+      "std       5.139009         4.691054     2.959573\n",
+      "min       7.600000         7.600000    -0.800000\n",
+      "25%      31.800000        23.075000     7.100000\n",
+      "50%      34.600000        25.850000     8.900000\n",
+      "75%      38.100000        29.200000    10.425000\n",
+      "max      50.100000        41.300000    16.100000\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Checking heel forefoot drop statistics and distributions\n",
+    "stats = df[['heel_lab_mm', 'forefoot_lab_mm', 'drop_lab_mm']].describe(percentiles=[.25, .5, .75])\n",
+    "print(\"Statistik Deskriptif Geometri Midsole:\")\n",
+    "print(stats)\n",
+    "\n",
+    "# Cheking distributions\n",
+    "plt.figure(figsize=(15, 5))\n",
+    "\n",
+    "# Plot Heel\n",
+    "plt.subplot(1, 3, 1)\n",
+    "sns.histplot(df['heel_lab_mm'], kde=True, color='skyblue')\n",
+    "plt.title('Distribusi Heel Stack')\n",
+    "\n",
+    "# Plot Forefoot\n",
+    "plt.subplot(1, 3, 2)\n",
+    "sns.histplot(df['forefoot_lab_mm'], kde=True, color='salmon')\n",
+    "plt.title('Distribusi Forefoot Stack')\n",
+    "\n",
+    "# Plot Drop\n",
+    "plt.subplot(1, 3, 3)\n",
+    "sns.histplot(df['drop_lab_mm'], kde=True, color='gold')\n",
+    "plt.title('Distribusi Drop')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Checking outliers\n",
+    "plt.figure(figsize=(15, 5))\n",
+    "\n",
+    "# Plot Heel Boxplot\n",
+    "plt.subplot(1, 3, 1)\n",
+    "sns.boxplot(y=df['heel_lab_mm'], color='lightgreen')\n",
+    "plt.title('Boxplot Heel Stack')\n",
+    "plt.ylabel('Heel Stack (mm)')\n",
+    "\n",
+    "# Plot Forefoot Boxplot\n",
+    "plt.subplot(1, 3, 2)\n",
+    "sns.boxplot(y=df['forefoot_lab_mm'], color='lightcoral')\n",
+    "plt.title('Boxplot Forefoot Stack')\n",
+    "plt.ylabel('Forefoot Stack (mm)')\n",
+    "\n",
+    "# Plot Drop Boxplot\n",
+    "plt.subplot(1, 3, 3)\n",
+    "sns.boxplot(y=df['drop_lab_mm'], color='lightgoldenrodyellow')\n",
+    "plt.title('Boxplot Drop')\n",
+    "plt.ylabel('Drop (mm)')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37ac147a",
+   "metadata": {},
+   "source": [
+    "terdapat beberapa outliers tapi untuk saat ini kita ignore dulu, mungkin nanti di preprocessing bakal ngelakuin scaling tapi untuk saat ini kita ubah ke kategorikal based on data yg ada aja."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "7fb63a73",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hasil Binning Kategori:\n",
+      "  heel_category forefoot_category drop_category\n",
+      "0           low               low        medium\n",
+      "1        medium            medium           low\n",
+      "2        medium            medium        medium\n",
+      "3           low               low          high\n",
+      "4           low               low          high\n",
+      "\n",
+      "Rentang nilai untuk heel_category:\n",
+      "                min   max\n",
+      "heel_category            \n",
+      "low             7.6  32.9\n",
+      "medium         33.0  36.8\n",
+      "high           36.9  50.1\n",
+      "\n",
+      "Rentang nilai untuk forefoot_category:\n",
+      "                    min   max\n",
+      "forefoot_category            \n",
+      "low                 7.6  24.1\n",
+      "medium             24.2  27.8\n",
+      "high               27.9  41.3\n",
+      "\n",
+      "Rentang nilai untuk drop_category:\n",
+      "               min   max\n",
+      "drop_category           \n",
+      "low           -0.8   7.9\n",
+      "medium         8.0   9.8\n",
+      "high           9.9  16.1\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Binning into categories based on tertiles\n",
+    "cols_to_bin = ['heel_lab_mm', 'forefoot_lab_mm', 'drop_lab_mm']\n",
+    "\n",
+    "for col in cols_to_bin:\n",
+    "    q1 = df[col].quantile(0.33)\n",
+    "    q2 = df[col].quantile(0.66)\n",
+    "    \n",
+    "    new_cat_col = col.replace('_lab_mm', '_category')\n",
+    "    df[new_cat_col] = pd.qcut(df[col], q=3, labels=['low', 'medium', 'high'])\n",
+    "\n",
+    "# Verify\n",
+    "print(\"Hasil Binning Kategori:\")\n",
+    "print(df[['heel_category', 'forefoot_category', 'drop_category']].head())\n",
+    "\n",
+    "# Checking ranges for each category\n",
+    "for col in ['heel_category', 'forefoot_category', 'drop_category']:\n",
+    "    print(f\"\\nRentang nilai untuk {col}:\")\n",
+    "    print(df.groupby(col)[col.replace('_category', '_lab_mm')].agg(['min', 'max']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "928fa02e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 37 columns):\n",
+      " #   Column                       Non-Null Count  Dtype   \n",
+      "---  ------                       --------------  -----   \n",
+      " 0   brand                        428 non-null    str     \n",
+      " 1   name                         428 non-null    str     \n",
+      " 2   pace                         428 non-null    str     \n",
+      " 3   arch support                 428 non-null    str     \n",
+      " 4   weight lab weight brand      428 non-null    str     \n",
+      " 5   lightweight                  428 non-null    int64   \n",
+      " 6   drop lab drop brand          428 non-null    str     \n",
+      " 7   strike pattern               428 non-null    str     \n",
+      " 8   size                         428 non-null    str     \n",
+      " 9   midsole softness             428 non-null    str     \n",
+      " 10  toebox durability            428 non-null    str     \n",
+      " 11  heel padding durability      428 non-null    str     \n",
+      " 12  outsole durability           428 non-null    str     \n",
+      " 13  breathability                428 non-null    str     \n",
+      " 14  width / fit                  428 non-null    str     \n",
+      " 15  toebox width                 428 non-null    str     \n",
+      " 16  stiffness                    428 non-null    str     \n",
+      " 17  torsional rigidity           428 non-null    str     \n",
+      " 18  heel counter stiffness       428 non-null    str     \n",
+      " 19  plate                        428 non-null    str     \n",
+      " 20  rocker                       428 non-null    int64   \n",
+      " 21  heel lab heel brand          428 non-null    str     \n",
+      " 22  forefoot lab forefoot brand  428 non-null    str     \n",
+      " 23  widths available             428 non-null    str     \n",
+      " 24  orthotic friendly            428 non-null    int64   \n",
+      " 25  season                       428 non-null    str     \n",
+      " 26  removable insole             428 non-null    int64   \n",
+      " 27  for_daily                    428 non-null    int64   \n",
+      " 28  for_tempo                    428 non-null    int64   \n",
+      " 29  for_competition              428 non-null    int64   \n",
+      " 30  weight_lab_oz                428 non-null    float64 \n",
+      " 31  heel_lab_mm                  428 non-null    float64 \n",
+      " 32  forefoot_lab_mm              428 non-null    float64 \n",
+      " 33  drop_lab_mm                  428 non-null    float64 \n",
+      " 34  heel_category                428 non-null    category\n",
+      " 35  forefoot_category            428 non-null    category\n",
+      " 36  drop_category                428 non-null    category\n",
+      "dtypes: category(3), float64(4), int64(7), str(23)\n",
+      "memory usage: 118.7 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5597014b",
+   "metadata": {},
+   "source": [
+    "## Strike pattern"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "0fa13a37",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "strike pattern\n",
+      "heelmid/forefoot     162\n",
+      "mid/forefoot         142\n",
+      "heel                 122\n",
+      "-                      1\n",
+      "heel mid/forefoot      1\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['strike pattern'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43c5f3b7",
+   "metadata": {},
+   "source": [
+    "Strike pattern merupakan fitur yang mendeskripsikan cara pelari untuk mendarat. Ada 3 jenis;\n",
+    "- heel : Pelari mendarat menggunakan tumit terlebih dahulu. Biasanya butuh sepatu dengan bantalan belakang yang tebal (heel_lab_mm) dan drop tinggi untuk mengurangi beban pada tendon Achilles.\n",
+    "- mid : Mendarat di bagian tengah kaki sehingga biasanya butuh drop yang stabil agar distribusi merata.\n",
+    "- forefoot : Pelari mendarat di bagian depan sehingga biasanya lebih suka sepatu dengan drop rendah (forefoot < heel) agar posisi kaki lebih natural."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "a7a17f62",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     strike pattern    strike_pattern  strike_heel  strike_mid  strike_fore\n",
+      "0  heelmid/forefoot  heelmid/forefoot            1           1            1\n",
+      "1      mid/forefoot      mid/forefoot            0           1            1\n",
+      "2  heelmid/forefoot  heelmid/forefoot            1           1            1\n",
+      "3              heel              heel            1           0            0\n",
+      "4  heelmid/forefoot  heelmid/forefoot            1           1            1\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['strike_pattern'] = df['strike pattern'].str.replace(' ', '').str.replace('-', 'unknown')\n",
+    "\n",
+    "# encode its base value\n",
+    "df['strike_heel'] = df['strike_pattern'].str.contains('heel').astype(int)\n",
+    "df['strike_mid'] = df['strike_pattern'].str.contains('mid').astype(int)\n",
+    "df['strike_fore'] = df['strike_pattern'].str.contains('forefoot').astype(int)\n",
+    "\n",
+    "print(df[['strike pattern', 'strike_pattern', 'strike_heel', 'strike_mid', 'strike_fore']].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "aa39f164",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "---  Strike vs Forefoot Category (%) ---\n",
+      "forefoot_category    low  medium   high\n",
+      "strike_fore                            \n",
+      "0                  43.09   39.84  17.07\n",
+      "1                  29.51   31.15  39.34\n",
+      "\n",
+      "--- Strike vs Heel Category (%) ---\n",
+      "heel_category    low  medium   high\n",
+      "strike_heel                        \n",
+      "0              49.65   27.27  23.08\n",
+      "1              25.61   36.49  37.89\n"
+     ]
+    }
+   ],
+   "source": [
+    "ct_forefoot = pd.crosstab(df['strike_fore'], df['forefoot_category'], normalize='index') * 100\n",
+    "ct_heel = pd.crosstab(df['strike_heel'], df['heel_category'], normalize='index') * 100\n",
+    "\n",
+    "print(\"---  Strike vs Forefoot Category (%) ---\")\n",
+    "print(ct_forefoot.round(2))\n",
+    "print(\"\\n--- Strike vs Heel Category (%) ---\")\n",
+    "print(ct_heel.round(2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "0d78d635",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rata-rata Nilai Asli Berdasarkan Strike Pattern:\n",
+      "                  forefoot_lab_mm  drop_lab_mm\n",
+      "strike_pattern                                \n",
+      "heel                    24.535246    11.726230\n",
+      "heelmid/forefoot        26.639877     8.988957\n",
+      "mid/forefoot            26.859155     5.423239\n",
+      "unknown                 13.700000    10.400000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Perbandingan rata-rata nilai asli \n",
+    "strike_analysis = df.groupby('strike_pattern')[['forefoot_lab_mm', 'drop_lab_mm']].mean()\n",
+    "\n",
+    "print(\"Rata-rata Nilai Asli Berdasarkan Strike Pattern:\")\n",
+    "print(strike_analysis)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ba54b77",
+   "metadata": {},
+   "source": [
+    "Diketahui semenjak era super shoes, banyak brand yang lebih suka masukin busa (foam) ke are forefoot makanya banyak forefoot yang tinggi. Hal ini dilakukan dengan harapan agar pantulan  energinya (energy return) maksimal. Seperti contohnya, sepatu forefoot striker punya forefoot yang tebal dan drop rendah agar feel sepatunya tetap empuk."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "01654410",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\caxyl\\AppData\\Local\\Temp\\ipykernel_40788\\3679098273.py:23: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  sns.barplot(x=['Heel Support', 'Midfoot Support', 'Forefoot Support'], y=base_counts, ax=axes[1, 1], palette='viridis')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x1200 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plotting Diagram\n",
+    "fig, axes = plt.subplots(2, 2, figsize=(16, 12))\n",
+    "\n",
+    "# running style distribution\n",
+    "strike_counts = df['strike_pattern'].value_counts()\n",
+    "axes[0, 0].pie(strike_counts, labels=strike_counts.index, autopct='%1.1f%%', colors=sns.color_palette('pastel'))\n",
+    "axes[0, 0].set_title('Proporsi Strike Pattern di Dataset')\n",
+    "\n",
+    "# B. Stacked Bar - Strike vs Forefoot (Korelasi Penting)\n",
+    "ct_forefoot.plot(kind='bar', stacked=True, ax=axes[0, 1], colormap='RdYlGn')\n",
+    "axes[0, 1].set_title('Hubungan Strike Pattern vs Forefoot Category')\n",
+    "axes[0, 1].set_ylabel('Persentase (%)')\n",
+    "axes[0, 1].legend(title='Forefoot Category', bbox_to_anchor=(1, 1))\n",
+    "\n",
+    "# C. Stacked Bar - Strike vs Heel Stack\n",
+    "ct_heel.plot(kind='bar', stacked=True, ax=axes[1, 0], colormap='Blues')\n",
+    "axes[1, 0].set_title('Hubungan Strike Pattern vs Heel Stack Category')\n",
+    "axes[1, 0].set_ylabel('Persentase (%)')\n",
+    "axes[1, 0].legend(title='Heel Stack Category', bbox_to_anchor=(1, 1))\n",
+    "\n",
+    "# D. Bar Chart - Base Value Count\n",
+    "base_counts = [df['strike_heel'].sum(), df['strike_mid'].sum(), df['strike_fore'].sum()]\n",
+    "sns.barplot(x=['Heel Support', 'Midfoot Support', 'Forefoot Support'], y=base_counts, ax=axes[1, 1], palette='viridis')\n",
+    "axes[1, 1].set_title('Total Sepatu Berdasarkan Support Pendaratan')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca0305a6",
+   "metadata": {},
+   "source": [
+    "Untuk menjangkau fitur ini secara eksplisit diperlukan user input mengenai strike pattern mereka, tetapi untuk pelari awam biasanya tidak mengerti mengenai hal tersebut. Maka dari itu, akan dibuat optional input untuk advanced user (pelari senior) yang berisikan opsi-opsi untuk strike pattern mereka. \n",
+    "\n",
+    "Skenario 1 - user tidak mengisi input strike pattern : \n",
+    "- masuk ke default strike pattern yang aman untuk semua pelari yaitu sepatu heel/mid/forefoot\n",
+    "- pilih kategori drop medium\n",
+    "- menggunakan heel dan forefoot medium\n",
+    "\n",
+    "Skenario 2 - user memilih salah satu strike pattern:\n",
+    "- heel striker membutuhkan proteksi lebih pada tumit mereka sehingga mappingnya akan menjadi Drop: high, Heel : high, Forefoot : low\n",
+    "- midfoot striker membutuhkan transisi yang lancar dan bantalan di tengah ke depan sehingga kita pilih Drop: medium, Heel : medium, Forefoot: high\n",
+    "- Forefoot striker membutuhkan posisi natural kaki dengan energy return yang maksimal sehingga mappingnya akan menjadi Drop: low, Heel : medium, Forefoot: high"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "a0b6c53d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 41 columns):\n",
+      " #   Column                       Non-Null Count  Dtype   \n",
+      "---  ------                       --------------  -----   \n",
+      " 0   brand                        428 non-null    str     \n",
+      " 1   name                         428 non-null    str     \n",
+      " 2   pace                         428 non-null    str     \n",
+      " 3   arch support                 428 non-null    str     \n",
+      " 4   weight lab weight brand      428 non-null    str     \n",
+      " 5   lightweight                  428 non-null    int64   \n",
+      " 6   drop lab drop brand          428 non-null    str     \n",
+      " 7   strike pattern               428 non-null    str     \n",
+      " 8   size                         428 non-null    str     \n",
+      " 9   midsole softness             428 non-null    str     \n",
+      " 10  toebox durability            428 non-null    str     \n",
+      " 11  heel padding durability      428 non-null    str     \n",
+      " 12  outsole durability           428 non-null    str     \n",
+      " 13  breathability                428 non-null    str     \n",
+      " 14  width / fit                  428 non-null    str     \n",
+      " 15  toebox width                 428 non-null    str     \n",
+      " 16  stiffness                    428 non-null    str     \n",
+      " 17  torsional rigidity           428 non-null    str     \n",
+      " 18  heel counter stiffness       428 non-null    str     \n",
+      " 19  plate                        428 non-null    str     \n",
+      " 20  rocker                       428 non-null    int64   \n",
+      " 21  heel lab heel brand          428 non-null    str     \n",
+      " 22  forefoot lab forefoot brand  428 non-null    str     \n",
+      " 23  widths available             428 non-null    str     \n",
+      " 24  orthotic friendly            428 non-null    int64   \n",
+      " 25  season                       428 non-null    str     \n",
+      " 26  removable insole             428 non-null    int64   \n",
+      " 27  for_daily                    428 non-null    int64   \n",
+      " 28  for_tempo                    428 non-null    int64   \n",
+      " 29  for_competition              428 non-null    int64   \n",
+      " 30  weight_lab_oz                428 non-null    float64 \n",
+      " 31  heel_lab_mm                  428 non-null    float64 \n",
+      " 32  forefoot_lab_mm              428 non-null    float64 \n",
+      " 33  drop_lab_mm                  428 non-null    float64 \n",
+      " 34  heel_category                428 non-null    category\n",
+      " 35  forefoot_category            428 non-null    category\n",
+      " 36  drop_category                428 non-null    category\n",
+      " 37  strike_pattern               428 non-null    str     \n",
+      " 38  strike_heel                  428 non-null    int64   \n",
+      " 39  strike_mid                   428 non-null    int64   \n",
+      " 40  strike_fore                  428 non-null    int64   \n",
+      "dtypes: category(3), float64(4), int64(10), str(24)\n",
+      "memory usage: 132.0 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cadee62c",
+   "metadata": {},
+   "source": [
+    "## Size, width / fit, width available, toebox width"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "a3dc5218",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "             size width / fit widths available toebox width\n",
+      "0    true to size      narrow       normalwide            -\n",
+      "1    true to size      narrow           normal       medium\n",
+      "2    true to size      narrow           normal            -\n",
+      "3  slightly small      narrow           normal            -\n",
+      "4    true to size      narrow           normal       medium\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[['size', 'width / fit', 'widths available', 'toebox width']].head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79104384",
+   "metadata": {},
+   "source": [
+    "Size diambil dari voting user RunRepeat dan widths available merupakan pilihan yang ditawarkan oleh brand sesuai availability di tokonya. Ini merupakan informasi yang tidak berdasar sehingga untuk ke depannya akan digunakan feature width / fit sebagai acuan ukuran sepatu saat dipakai pada ukuran standar (D untuk pria dan B untuk wanita)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "6e8a5907",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 39 columns):\n",
+      " #   Column                       Non-Null Count  Dtype   \n",
+      "---  ------                       --------------  -----   \n",
+      " 0   brand                        428 non-null    str     \n",
+      " 1   name                         428 non-null    str     \n",
+      " 2   pace                         428 non-null    str     \n",
+      " 3   arch support                 428 non-null    str     \n",
+      " 4   weight lab weight brand      428 non-null    str     \n",
+      " 5   lightweight                  428 non-null    int64   \n",
+      " 6   drop lab drop brand          428 non-null    str     \n",
+      " 7   strike pattern               428 non-null    str     \n",
+      " 8   midsole softness             428 non-null    str     \n",
+      " 9   toebox durability            428 non-null    str     \n",
+      " 10  heel padding durability      428 non-null    str     \n",
+      " 11  outsole durability           428 non-null    str     \n",
+      " 12  breathability                428 non-null    str     \n",
+      " 13  width / fit                  428 non-null    str     \n",
+      " 14  toebox width                 428 non-null    str     \n",
+      " 15  stiffness                    428 non-null    str     \n",
+      " 16  torsional rigidity           428 non-null    str     \n",
+      " 17  heel counter stiffness       428 non-null    str     \n",
+      " 18  plate                        428 non-null    str     \n",
+      " 19  rocker                       428 non-null    int64   \n",
+      " 20  heel lab heel brand          428 non-null    str     \n",
+      " 21  forefoot lab forefoot brand  428 non-null    str     \n",
+      " 22  orthotic friendly            428 non-null    int64   \n",
+      " 23  season                       428 non-null    str     \n",
+      " 24  removable insole             428 non-null    int64   \n",
+      " 25  for_daily                    428 non-null    int64   \n",
+      " 26  for_tempo                    428 non-null    int64   \n",
+      " 27  for_competition              428 non-null    int64   \n",
+      " 28  weight_lab_oz                428 non-null    float64 \n",
+      " 29  heel_lab_mm                  428 non-null    float64 \n",
+      " 30  forefoot_lab_mm              428 non-null    float64 \n",
+      " 31  drop_lab_mm                  428 non-null    float64 \n",
+      " 32  heel_category                428 non-null    category\n",
+      " 33  forefoot_category            428 non-null    category\n",
+      " 34  drop_category                428 non-null    category\n",
+      " 35  strike_pattern               428 non-null    str     \n",
+      " 36  strike_heel                  428 non-null    int64   \n",
+      " 37  strike_mid                   428 non-null    int64   \n",
+      " 38  strike_fore                  428 non-null    int64   \n",
+      "dtypes: category(3), float64(4), int64(10), str(22)\n",
+      "memory usage: 125.4 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=['size','widths available'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "2770bb83",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "width / fit\n",
+      "medium    256\n",
+      "narrow    140\n",
+      "wide       32\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['width / fit'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79e49592",
+   "metadata": {},
+   "source": [
+    "Terdapat 3 kategori sepatu:\n",
+    "- medium : standar sepatu yang diciptakan untuk mayoritas daily runner\n",
+    "- narrow : sepatu yang terasa agak sempit\n",
+    "- wide : sepatu untuk kaki yang lebar\n",
+    "\n",
+    "Feature ini membutuhkan input user langsung yang berisikan jenis ukuran kaki mereka."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "03a5fedd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Crossing Table Width / Fit vs Toebox Width (%):\n",
+      "toebox width         -    medium    narrow      wide\n",
+      "width / fit                                         \n",
+      "medium        17.96875  57.81250  10.15625  14.06250\n",
+      "narrow        42.14286  32.85714  20.71429   4.28571\n",
+      "wide           9.37500  31.25000   0.00000  59.37500\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Crossing table width / fit vs toebox width\n",
+    "ct_width_toebox = pd.crosstab(df['width / fit'], df['toebox width'], normalize='index') * 100\n",
+    "print(\"Crossing Table Width / Fit vs Toebox Width (%):\")\n",
+    "print(ct_width_toebox.round(5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5077b376",
+   "metadata": {},
+   "source": [
+    "Rendahnya korelasi antara width / fit dan toebox width menandakan bahwa width / fit tidak bisa ditentukan oleh toebox-nya saja melainkan dari overall kesuluruhan sepatu. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a8688e0a",
+   "metadata": {},
+   "source": [
+    "## Midsole softness"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "520a0116",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "midsole softness\n",
+      "soft        177\n",
+      "balanced    172\n",
+      "-            61\n",
+      "firm         18\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['midsole softness'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "2e58c6c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Heel Stack (mm) vs Midsole Softness Analysis:\n",
+      "                   min       mean   max\n",
+      "midsole softness                       \n",
+      "soft              24.6  36.490960  50.1\n",
+      "balanced          22.5  34.153488  45.7\n",
+      "-                  7.6  32.019672  42.3\n",
+      "firm              13.1  30.733333  38.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "ms_analysis = df.groupby('midsole softness')['heel_lab_mm'].agg(['min', 'mean', 'max'])\n",
+    "ms_analysis = ms_analysis.sort_values('mean', ascending=False)\n",
+    "\n",
+    "print(\"Heel Stack (mm) vs Midsole Softness Analysis:\")\n",
+    "print(ms_analysis)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "85f4dad6",
+   "metadata": {},
+   "source": [
+    "Sepatu dengan midsole berkategori soft memiliki minimum heel tertinggi di antara kategori lainnya, akan tetapi terdapat sepatu berkategori firm yang mempunyai heel setara sepatu berkategori soft. Hal ini menunjukan bahwa beberapa sepatu yang memiliki heel tinggi tidak selalu berartikan bahwa sepatu itu empuk (mempunyai midsole yang soft)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "319bde26",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 39 columns):\n",
+      " #   Column                       Non-Null Count  Dtype   \n",
+      "---  ------                       --------------  -----   \n",
+      " 0   brand                        428 non-null    str     \n",
+      " 1   name                         428 non-null    str     \n",
+      " 2   pace                         428 non-null    str     \n",
+      " 3   arch support                 428 non-null    str     \n",
+      " 4   weight lab weight brand      428 non-null    str     \n",
+      " 5   lightweight                  428 non-null    int64   \n",
+      " 6   drop lab drop brand          428 non-null    str     \n",
+      " 7   strike pattern               428 non-null    str     \n",
+      " 8   midsole softness             428 non-null    str     \n",
+      " 9   toebox durability            428 non-null    str     \n",
+      " 10  heel padding durability      428 non-null    str     \n",
+      " 11  outsole durability           428 non-null    str     \n",
+      " 12  breathability                428 non-null    str     \n",
+      " 13  width / fit                  428 non-null    str     \n",
+      " 14  toebox width                 428 non-null    str     \n",
+      " 15  stiffness                    428 non-null    str     \n",
+      " 16  torsional rigidity           428 non-null    str     \n",
+      " 17  heel counter stiffness       428 non-null    str     \n",
+      " 18  plate                        428 non-null    str     \n",
+      " 19  rocker                       428 non-null    int64   \n",
+      " 20  heel lab heel brand          428 non-null    str     \n",
+      " 21  forefoot lab forefoot brand  428 non-null    str     \n",
+      " 22  orthotic friendly            428 non-null    int64   \n",
+      " 23  season                       428 non-null    str     \n",
+      " 24  removable insole             428 non-null    int64   \n",
+      " 25  for_daily                    428 non-null    int64   \n",
+      " 26  for_tempo                    428 non-null    int64   \n",
+      " 27  for_competition              428 non-null    int64   \n",
+      " 28  weight_lab_oz                428 non-null    float64 \n",
+      " 29  heel_lab_mm                  428 non-null    float64 \n",
+      " 30  forefoot_lab_mm              428 non-null    float64 \n",
+      " 31  drop_lab_mm                  428 non-null    float64 \n",
+      " 32  heel_category                428 non-null    category\n",
+      " 33  forefoot_category            428 non-null    category\n",
+      " 34  drop_category                428 non-null    category\n",
+      " 35  strike_pattern               428 non-null    str     \n",
+      " 36  strike_heel                  428 non-null    int64   \n",
+      " 37  strike_mid                   428 non-null    int64   \n",
+      " 38  strike_fore                  428 non-null    int64   \n",
+      "dtypes: category(3), float64(4), int64(10), str(22)\n",
+      "memory usage: 125.4 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "776aa33e",
+   "metadata": {},
+   "source": [
+    "## Toebox, Heel padding, Outsole Durability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "4092c00e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  toebox durability heel padding durability outsole durability\n",
+      "0                 -                       -                  -\n",
+      "1              good                    good               good\n",
+      "2                 -                    good                  -\n",
+      "3                 -                       -                  -\n",
+      "4              good                    good               good\n",
+      "\n",
+      "Toebox Durability Value Counts:\n",
+      "toebox durability\n",
+      "decent    150\n",
+      "-         117\n",
+      "bad        88\n",
+      "good       73\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Heel Padding Durability Value Counts:\n",
+      "heel padding durability\n",
+      "good      179\n",
+      "-         122\n",
+      "decent     77\n",
+      "bad        50\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Outsole Durability Value Counts:\n",
+      "outsole durability\n",
+      "good      206\n",
+      "-         134\n",
+      "decent     67\n",
+      "bad        21\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[['toebox durability', 'heel padding durability', 'outsole durability']].head())\n",
+    "\n",
+    "print(\"\\nToebox Durability Value Counts:\")\n",
+    "print(df['toebox durability'].value_counts())\n",
+    "\n",
+    "print(\"\\nHeel Padding Durability Value Counts:\")\n",
+    "print(df['heel padding durability'].value_counts())\n",
+    "\n",
+    "print(\"\\nOutsole Durability Value Counts:\")\n",
+    "print(df['outsole durability'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b06fec21",
+   "metadata": {},
+   "source": [
+    "- Toebox durability merupakan ketahanan bagian atas (mesh) di area jari kaki terhadap gesekan atau robekan. Ini penting buat pelari yang jempolnya suka \"nembus\" ke atas atau sering lari di medan yang banyak kerikil, sehingga diperlukan korelasi antara feature ini dan rocker serta plate sepatunya.\n",
+    "\n",
+    "- Heel padding durability menandakan seberapa kuat kain dan busa di bagian tumit belakang. Perlu dilihat korelasi antara feature ini dengan heel striker.\n",
+    "\n",
+    "- Outsole durability adalah ketahanan karet bagian bawah terhadap pengikisan aspal. Ini yang menentukan seberapa cepat terjadinya kebotakan pada grip sepat."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "cf364d10",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\caxyl\\OneDrive\\Documents\\a_project\\sonix-ml\\env\\Lib\\site-packages\\seaborn\\matrix.py:202: RuntimeWarning: All-NaN slice encountered\n",
+      "  vmin = np.nanmin(calc_data)\n",
+      "c:\\Users\\caxyl\\OneDrive\\Documents\\a_project\\sonix-ml\\env\\Lib\\site-packages\\seaborn\\matrix.py:207: RuntimeWarning: All-NaN slice encountered\n",
+      "  vmax = np.nanmax(calc_data)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rata-rata Skor Durabilitas:\n",
+      "toebox durability         NaN\n",
+      "heel padding durability   NaN\n",
+      "outsole durability        NaN\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Correlation between toebox durability, heel padding durability, and outsole durability\n",
+    "durability_cols = ['toebox durability', 'heel padding durability', 'outsole durability']\n",
+    "df_durability = df[durability_cols].apply(pd.to_numeric, errors='coerce')\n",
+    "corr_matrix = df_durability.corr()\n",
+    "\n",
+    "# Visualize\n",
+    "plt.figure(figsize=(10, 8))\n",
+    "sns.heatmap(corr_matrix, annot=True, cmap='YlGnBu', fmt=\".2f\", linewidths=0.5)\n",
+    "plt.title('Korelasi Antar Fitur Durabilitas')\n",
+    "plt.show()\n",
+    "\n",
+    "print(\"Rata-rata Skor Durabilitas:\")\n",
+    "print(df_durability.mean().sort_values())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "563bf4a5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Detail Crosstab: Outsole vs Heel Padding (Normalized by Outsole)\n",
+      "heel padding durability     -   bad  decent  good\n",
+      "outsole durability                               \n",
+      "-                        91.0   4.0     1.0   3.0\n",
+      "bad                       0.0  14.0    24.0  62.0\n",
+      "decent                    0.0  21.0    28.0  51.0\n",
+      "good                      0.0  13.0    25.0  62.0\n",
+      "\n",
+      "Detail Crosstab: Outsole vs Toebox (Normalized by Outsole)\n",
+      "toebox durability      -   bad  decent  good\n",
+      "outsole durability                          \n",
+      "-                   87.0   6.0     4.0   2.0\n",
+      "bad                  0.0  29.0    33.0  38.0\n",
+      "decent               0.0  36.0    49.0  15.0\n",
+      "good                 0.0  24.0    50.0  25.0\n",
+      "\n",
+      "Detail Crosstab: Toebox vs Heel Padding (Normalized by Toebox)\n",
+      "heel padding durability     -   bad  decent  good\n",
+      "toebox durability                                \n",
+      "-                        97.0   2.0     0.0   2.0\n",
+      "bad                       6.0  23.0    22.0  50.0\n",
+      "decent                    2.0  16.0    27.0  55.0\n",
+      "good                      1.0   5.0    23.0  70.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Crossing tables between durability features\n",
+    "pairs = [\n",
+    "    ('outsole durability', 'heel padding durability'),\n",
+    "    ('outsole durability', 'toebox durability'),\n",
+    "    ('toebox durability', 'heel padding durability')\n",
+    "]\n",
+    "\n",
+    "plt.figure(figsize=(18, 5))\n",
+    "\n",
+    "for i, (feat_x, feat_y) in enumerate(pairs):\n",
+    "    ct = pd.crosstab(df[feat_x], df[feat_y], normalize='index') * 100\n",
+    "    \n",
+    "    # Plotting Heatmap\n",
+    "    plt.subplot(1, 3, i+1)\n",
+    "    sns.heatmap(ct, annot=True, fmt=\".1f\", cmap=\"YlOrRd\", cbar=False)\n",
+    "    plt.title(f'{feat_x} vs {feat_y} (%)')\n",
+    "    plt.xlabel(feat_y)\n",
+    "    plt.ylabel(feat_x)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Outsole vs Heel Padding Durability Crosstab\n",
+    "print(\"Detail Crosstab: Outsole vs Heel Padding (Normalized by Outsole)\")\n",
+    "print(pd.crosstab(df['outsole durability'], df['heel padding durability'], normalize='index').round(2) * 100)\n",
+    "\n",
+    "# Outsole vs Toebox Durability Crosstab\n",
+    "print(\"\\nDetail Crosstab: Outsole vs Toebox (Normalized by Outsole)\")\n",
+    "print(pd.crosstab(df['outsole durability'], df['toebox durability'], normalize='index').round(2) * 100)\n",
+    "\n",
+    "# Toebox vs Heel Padding Durability Crosstab\n",
+    "print(\"\\nDetail Crosstab: Toebox vs Heel Padding (Normalized by Toebox)\")\n",
+    "print(pd.crosstab(df['toebox durability'], df['heel padding durability'], normalize='index').round(2) * 100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d666f5d",
+   "metadata": {},
+   "source": [
+    "- Pada analisis mengenai outsole dan heel padding, kebanyakan sepatu memiliki heel padding yang bagus walaupun outsole sepatunya dikategorikan buruk.\n",
+    "\n",
+    "- Pada analisis mengenai outsole dan toebox, hanya terdapat 25% sepatu yang mempunyai outsole dan toebox berkategori baik, kebanyakannya hanya decent atau biasa saja. Bahkan, banyak sepatu yang memiliki sol kuat tetapi kain bagian depannya lebih cepat jebol.\n",
+    "\n",
+    "- Pada analisis mengenai toebox dan heel padding, datanya banyak yang ksosong sehingga tidak bisa diinferensikan\n",
+    "\n",
+    "\n",
+    "\n",
+    "Dikarenakan sedikitnya data yang menunjukan sepatu berkategori \"good\" di semua durability cols, diperlukan penyesuaian lebih lanjut mengenai hal ini."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "dc15a43a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Komposisi Heel Padding Durability: Heel Striker vs Others (%) ---\n",
+      "heel padding durability         -       bad    decent      good\n",
+      "strike_heel                                                    \n",
+      "0                        31.46853  10.48951  19.58042  38.46154\n",
+      "1                        27.01754  12.28070  17.19298  43.50877\n",
+      "\n",
+      "--- Komposisi Heel Padding Durability: Plate vs Others (%) ---\n",
+      "heel padding durability          -       bad    decent      good\n",
+      "plate                                                           \n",
+      "0                         29.26829  13.00813  17.88618  39.83740\n",
+      "carbon plate              22.41379   3.44828  18.96552  55.17241\n",
+      "carbon platerock plate   100.00000   0.00000   0.00000   0.00000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Checking heel padding durability with heel striker\n",
+    "ct_heel_strike = pd.crosstab(df['strike_heel'], df['heel padding durability'], normalize='index') * 100\n",
+    "print(\"--- Komposisi Heel Padding Durability: Heel Striker vs Others (%) ---\")\n",
+    "print(ct_heel_strike.round(5))\n",
+    "\n",
+    "print()\n",
+    "\n",
+    "# Cheking heel padding durability with plate\n",
+    "ct_heel_plate = pd.crosstab(df['plate'], df['heel padding durability'], normalize='index') * 100\n",
+    "print(\"--- Komposisi Heel Padding Durability: Plate vs Others (%) ---\")\n",
+    "print(ct_heel_plate.round(5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "045e4f6f",
+   "metadata": {},
+   "source": [
+    "Terdapat sekitar 43% sepatu yang memiliki heel padding berkategori \"good\" dengan kategori sepatu heel striker. Akan tetapi, semua sepatu yang memiliki carbon dan rocker plate kekurangan informasi terhadap heel padding durability. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "a1a29318",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Komposisi Outsole Durability: Heel Striker vs Others (%) ---\n",
+      "outsole durability         -      bad    decent      good\n",
+      "strike_heel                                              \n",
+      "0                   33.56643  3.49650  16.08392  46.85315\n",
+      "1                   30.17544  5.61404  15.43860  48.77193\n",
+      "--- Komposisi Outsole Durability: Midfoot Striker vs Others (%) ---\n",
+      "outsole durability         -      bad    decent      good\n",
+      "strike_mid                                               \n",
+      "0                   31.70732  5.69106  15.44715  47.15447\n",
+      "1                   31.14754  4.59016  15.73770  48.52459\n",
+      "--- Komposisi Outsole Durability: Forefoot Striker vs Others (%) ---\n",
+      "outsole durability         -      bad    decent      good\n",
+      "strike_fore                                              \n",
+      "0                   31.70732  5.69106  15.44715  47.15447\n",
+      "1                   31.14754  4.59016  15.73770  48.52459\n",
+      "--- Komposisi Outsole Durability: Plate vs Others (%) ---\n",
+      "outsole durability              -       bad    decent      good\n",
+      "plate                                                          \n",
+      "0                        32.24932   4.06504  15.17615  48.50949\n",
+      "carbon plate             24.13793  10.34483  18.96552  46.55172\n",
+      "carbon platerock plate  100.00000   0.00000   0.00000   0.00000\n",
+      "--- Komposisi Outsole Durability: Rocker vs Others (%) ---\n",
+      "outsole durability         -      bad    decent     good\n",
+      "rocker                                                  \n",
+      "0                   31.81818  3.84615  17.13287  47.2028\n",
+      "1                   30.28169  7.04225  12.67606  50.0000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Checking outsole durability with striker\n",
+    "ct_heel_strike = pd.crosstab(df['strike_heel'], df['outsole durability'], normalize='index') * 100\n",
+    "print(\"--- Komposisi Outsole Durability: Heel Striker vs Others (%) ---\")\n",
+    "print(ct_heel_strike.round(5))\n",
+    "\n",
+    "ct_heel_strike = pd.crosstab(df['strike_mid'], df['outsole durability'], normalize='index') * 100\n",
+    "print(\"--- Komposisi Outsole Durability: Midfoot Striker vs Others (%) ---\")\n",
+    "print(ct_heel_strike.round(5))\n",
+    "\n",
+    "ct_heel_strike = pd.crosstab(df['strike_fore'], df['outsole durability'], normalize='index') * 100\n",
+    "print(\"--- Komposisi Outsole Durability: Forefoot Striker vs Others (%) ---\")\n",
+    "print(ct_heel_strike.round(5))\n",
+    "\n",
+    "# Checking outsole durability with plate\n",
+    "ct_heel_plate = pd.crosstab(df['plate'], df['outsole durability'], normalize='index') * 100\n",
+    "print(\"--- Komposisi Outsole Durability: Plate vs Others (%) ---\")\n",
+    "print(ct_heel_plate.round(5))\n",
+    "\n",
+    "# Checking outsole durability with rocker\n",
+    "ct_heel_rocker = pd.crosstab(df['rocker'], df['outsole durability'], normalize='index') * 100\n",
+    "print(\"--- Komposisi Outsole Durability: Rocker vs Others (%) ---\")\n",
+    "print(ct_heel_rocker.round(5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65a086a6",
+   "metadata": {},
+   "source": [
+    "Rata rata untuk semua striker baik itu heel, midfoot, dan forefoot striker memiliki outsole durability yang tergolong baik. Adapun dapat dilihat juga bahwa durabilitas pada outsole tidak terdefinisikan dengan teknologi untuk plate ataupun rocker yang digunakan.\n",
+    "\n",
+    "Sepatu yang direkomendasikan nantinya tentunya akan disorting berdasarkan durability terbaik, tetapi akan ada trade off masing - masing, sepertinya misalnya jika user adalah heel striker maka akan dipilih heel padding durability terbaik, dsb."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "b9716e67",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 39 columns):\n",
+      " #   Column                       Non-Null Count  Dtype   \n",
+      "---  ------                       --------------  -----   \n",
+      " 0   brand                        428 non-null    str     \n",
+      " 1   name                         428 non-null    str     \n",
+      " 2   pace                         428 non-null    str     \n",
+      " 3   arch support                 428 non-null    str     \n",
+      " 4   weight lab weight brand      428 non-null    str     \n",
+      " 5   lightweight                  428 non-null    int64   \n",
+      " 6   drop lab drop brand          428 non-null    str     \n",
+      " 7   strike pattern               428 non-null    str     \n",
+      " 8   midsole softness             428 non-null    str     \n",
+      " 9   toebox durability            428 non-null    str     \n",
+      " 10  heel padding durability      428 non-null    str     \n",
+      " 11  outsole durability           428 non-null    str     \n",
+      " 12  breathability                428 non-null    str     \n",
+      " 13  width / fit                  428 non-null    str     \n",
+      " 14  toebox width                 428 non-null    str     \n",
+      " 15  stiffness                    428 non-null    str     \n",
+      " 16  torsional rigidity           428 non-null    str     \n",
+      " 17  heel counter stiffness       428 non-null    str     \n",
+      " 18  plate                        428 non-null    str     \n",
+      " 19  rocker                       428 non-null    int64   \n",
+      " 20  heel lab heel brand          428 non-null    str     \n",
+      " 21  forefoot lab forefoot brand  428 non-null    str     \n",
+      " 22  orthotic friendly            428 non-null    int64   \n",
+      " 23  season                       428 non-null    str     \n",
+      " 24  removable insole             428 non-null    int64   \n",
+      " 25  for_daily                    428 non-null    int64   \n",
+      " 26  for_tempo                    428 non-null    int64   \n",
+      " 27  for_competition              428 non-null    int64   \n",
+      " 28  weight_lab_oz                428 non-null    float64 \n",
+      " 29  heel_lab_mm                  428 non-null    float64 \n",
+      " 30  forefoot_lab_mm              428 non-null    float64 \n",
+      " 31  drop_lab_mm                  428 non-null    float64 \n",
+      " 32  heel_category                428 non-null    category\n",
+      " 33  forefoot_category            428 non-null    category\n",
+      " 34  drop_category                428 non-null    category\n",
+      " 35  strike_pattern               428 non-null    str     \n",
+      " 36  strike_heel                  428 non-null    int64   \n",
+      " 37  strike_mid                   428 non-null    int64   \n",
+      " 38  strike_fore                  428 non-null    int64   \n",
+      "dtypes: category(3), float64(4), int64(10), str(22)\n",
+      "memory usage: 125.4 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0796660",
+   "metadata": {},
+   "source": [
+    "## Breathability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "5ab1e237",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "breathability\n",
+      "moderate      208\n",
+      "breathable    109\n",
+      "-              58\n",
+      "warm           53\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['breathability'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57ff414d",
+   "metadata": {},
+   "source": [
+    "RunRepeat claims bahwa sepatu untuk summer season adalah sepatu yang memiliki breathability tertinggi dan winter season shoes adalah sepatu yang memiliki breathability low/mid. Lalu, ada beberapa feature lain yang berinteraksi dengan feature ini seperti shoes durability dan weight."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "46e27c1b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Komposisi Breathability berdasarkan Musim (%) ---\n",
+      "season             -  all seasons  summerall seasons    winter\n",
+      "breathability                                                 \n",
+      "-              100.0      0.00000                0.0   0.00000\n",
+      "breathable       0.0      0.00000              100.0   0.00000\n",
+      "moderate         0.0    100.00000                0.0   0.00000\n",
+      "warm             0.0     77.35849                0.0  22.64151\n",
+      "\n",
+      "--- Komposisi Breathability berdasarkan Toebox Durability (%) ---\n",
+      "toebox durability          -       bad    decent      good\n",
+      "breathability                                             \n",
+      "-                  100.00000   0.00000   0.00000   0.00000\n",
+      "breathable          18.34862  34.86239  31.19266  15.59633\n",
+      "moderate            13.94231  21.63462  46.63462  17.78846\n",
+      "warm                18.86792   9.43396  35.84906  35.84906\n",
+      "\n",
+      "--- Komposisi Breathability berdasarkan Outsole Durability (%) ---\n",
+      "outsole durability          -      bad    decent      good\n",
+      "breathability                                             \n",
+      "-                   100.00000  0.00000   0.00000   0.00000\n",
+      "breathable           22.93578  6.42202  21.10092  49.54128\n",
+      "moderate             18.75000  4.32692  18.75000  58.17308\n",
+      "warm                 22.64151  9.43396   9.43396  58.49057\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\caxyl\\AppData\\Local\\Temp\\ipykernel_40788\\668151946.py:18: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  sns.boxplot(x='breathability', y='weight_lab_oz', data=df, palette='Set2')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Checking breathability correlation with seasons\n",
+    "ct_breathability_seasons = pd.crosstab(df['breathability'], df['season'], normalize='index') * 100\n",
+    "print(\"--- Komposisi Breathability berdasarkan Musim (%) ---\")\n",
+    "print(ct_breathability_seasons.round(5))\n",
+    "\n",
+    "# Checking breathability correlation with toebox durability\n",
+    "ct_breathability_toebox_dur = pd.crosstab(df['breathability'], df['toebox durability'], normalize='index') * 100\n",
+    "print(\"\\n--- Komposisi Breathability berdasarkan Toebox Durability (%) ---\")\n",
+    "print(ct_breathability_toebox_dur.round(5))\n",
+    "\n",
+    "# Checking breathability correlation with outsole durability\n",
+    "ct_breathability_outsole_dur = pd.crosstab(df['breathability'], df['outsole durability'], normalize='index') * 100\n",
+    "print(\"\\n--- Komposisi Breathability berdasarkan Outsole Durability (%) ---\")\n",
+    "print(ct_breathability_outsole_dur.round(5))\n",
+    "\n",
+    "# Cheking breathability distribution with weight_lab_oz\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.boxplot(x='breathability', y='weight_lab_oz', data=df, palette='Set2')\n",
+    "plt.title('Distribusi Berat Sepatu berdasarkan Kategori Breathability')\n",
+    "plt.xlabel('Breathability')\n",
+    "plt.ylabel('Weight (oz)')\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "54ff5dcf",
+   "metadata": {},
+   "source": [
+    "Bisa dilihat bahwa:\n",
+    "- Seluruh sepatu dengan kategori breathability breathable adalah sepatu untuk summer all season. Lalu, seluruh sepatu dengan kategori breathability moderate adalah sepatu untuk all season atau daily basics. Sedangkan, sepatu dengan kategori warm adalah sepatu yang cocok untuk winter dan juga all seasons.\n",
+    "- Antara breathability dan durabilitas, tidak ada korelasi signifikan kecuali 58% sepatu berkategori warm yang memiliki good outsole durability.\n",
+    "- Sepatu yang breatahble cenderung memiliki weight yang lebih ringan dibanding sepatu lainnya dan sepatu berkategori warm cenderung menjadi yang terberat di antara kategori lainnya.\n",
+    "\n",
+    "\n",
+    "Untuk requirement dasar saat ini, dikarenakan main market target kita adalah orang orang Indonesia maka dari itu secara default akan dipasangkan untuk sepatu dengan breathability breathable-moderate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "10219d7b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 39 columns):\n",
+      " #   Column                       Non-Null Count  Dtype   \n",
+      "---  ------                       --------------  -----   \n",
+      " 0   brand                        428 non-null    str     \n",
+      " 1   name                         428 non-null    str     \n",
+      " 2   pace                         428 non-null    str     \n",
+      " 3   arch support                 428 non-null    str     \n",
+      " 4   weight lab weight brand      428 non-null    str     \n",
+      " 5   lightweight                  428 non-null    int64   \n",
+      " 6   drop lab drop brand          428 non-null    str     \n",
+      " 7   strike pattern               428 non-null    str     \n",
+      " 8   midsole softness             428 non-null    str     \n",
+      " 9   toebox durability            428 non-null    str     \n",
+      " 10  heel padding durability      428 non-null    str     \n",
+      " 11  outsole durability           428 non-null    str     \n",
+      " 12  breathability                428 non-null    str     \n",
+      " 13  width / fit                  428 non-null    str     \n",
+      " 14  toebox width                 428 non-null    str     \n",
+      " 15  stiffness                    428 non-null    str     \n",
+      " 16  torsional rigidity           428 non-null    str     \n",
+      " 17  heel counter stiffness       428 non-null    str     \n",
+      " 18  plate                        428 non-null    str     \n",
+      " 19  rocker                       428 non-null    int64   \n",
+      " 20  heel lab heel brand          428 non-null    str     \n",
+      " 21  forefoot lab forefoot brand  428 non-null    str     \n",
+      " 22  orthotic friendly            428 non-null    int64   \n",
+      " 23  season                       428 non-null    str     \n",
+      " 24  removable insole             428 non-null    int64   \n",
+      " 25  for_daily                    428 non-null    int64   \n",
+      " 26  for_tempo                    428 non-null    int64   \n",
+      " 27  for_competition              428 non-null    int64   \n",
+      " 28  weight_lab_oz                428 non-null    float64 \n",
+      " 29  heel_lab_mm                  428 non-null    float64 \n",
+      " 30  forefoot_lab_mm              428 non-null    float64 \n",
+      " 31  drop_lab_mm                  428 non-null    float64 \n",
+      " 32  heel_category                428 non-null    category\n",
+      " 33  forefoot_category            428 non-null    category\n",
+      " 34  drop_category                428 non-null    category\n",
+      " 35  strike_pattern               428 non-null    str     \n",
+      " 36  strike_heel                  428 non-null    int64   \n",
+      " 37  strike_mid                   428 non-null    int64   \n",
+      " 38  strike_fore                  428 non-null    int64   \n",
+      "dtypes: category(3), float64(4), int64(10), str(22)\n",
+      "memory usage: 125.4 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b895dcf8",
+   "metadata": {},
+   "source": [
+    "## Stiffness, torsional rigidity, and heel counter stiffness"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "a9f52e8c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  stiffness torsional rigidity heel counter stiffness\n",
+      "0     stiff              stiff               flexible\n",
+      "1     stiff           moderate               moderate\n",
+      "2     stiff           flexible               flexible\n",
+      "3     stiff           flexible               moderate\n",
+      "4  moderate           flexible               flexible\n",
+      "\n",
+      "stiffness\n",
+      "stiff       217\n",
+      "moderate    163\n",
+      "flexible     35\n",
+      "-            13\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "torsional rigidity\n",
+      "stiff       213\n",
+      "moderate    123\n",
+      "flexible     74\n",
+      "-            18\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "heel counter stiffness\n",
+      "moderate    146\n",
+      "flexible    132\n",
+      "stiff       121\n",
+      "-            29\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[['stiffness', 'torsional rigidity', 'heel counter stiffness']].head())\n",
+    "print()\n",
+    "print(df['stiffness'].value_counts())\n",
+    "print()\n",
+    "print(df['torsional rigidity'].value_counts())\n",
+    "print()\n",
+    "print(df['heel counter stiffness'].value_counts())"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "12d1d0bb",
+   "metadata": {},
+   "source": [
+    "- Stiffness menandakan kesulitan untuk menekuk sepatu dari bagian depan hingga belakang. Sepatu dengan plate tambahan atau busa yang sangat tebal akan membuat sepatunya masuk ke kategori stiff.\n",
+    "- Torsional rigidity menandakan sebara sulit sepatu tersebut dipuntir atau diputar. Torsional rigidity yang stiff menjaga kaki tetap sejajar (lurus) dan biasanya digunakan oleh orang yang memiliki kaki overpronasi.\n",
+    "- Heel counter stiffness menandakan seberapa kaku mangkuk pelindung tumit di bagian belakang. Di sini, kategori stiff mengunci tumit agar tidak goyang ke kanan atau ke kiri.\n",
+    "\n",
+    "![image.png](attachment:image.png)\n",
+    "\n",
+    "Untuk menjangkau feature ini, ditambahkan satu optional input stability need yang berisikan pilihan antara neutral atau guided. Sepatu guided adalah sepatu yang memiliki stiffness stiff sedangkan neutral lebih cocok untuk moderate shoes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "1ac4720d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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irRzbtk7uXL0o2QvQBhFfcG2YPPFXtmxZ2b17t36eOHFiOXPmTJSWVEiIXrx4IZvWr5bPGzUN9w7kn38KvKO9xmf1dOIP5qISu+rO0+B3hdrZ2cnJo4citI0Nq72kaInS4pohUzTtJWLaMz9f/W/KIAne0JZJkdI+zKSf6l18+uBu+fCzxvLT8B5y4+JZ3dD1UZ0vpECp8tG274han5TKLpsOXZNFPapIuQIZ5cb9JzJz7UmZu/G0nu/ulkYyOtnLX0dfl6Dx8Xsh+87cltJ5XMNN/E1sU07WHbgqW45eJ/EXA4il3t2O7VukVJkPpF/PLnL44H5xcXWVup83ks/q1X/jus0b/5+8eP5cPDxzSYs27aUwVRRMQZWwPrVnu7zwfyaZPfPLk0cPdK+9gh9UlnmDOsnDWzckfaZs8lGD5pI1T6Fwt7Vu3iTxLFpaPAoWJ/Fnguvi2K6t8tz/mWTLXSDUJO+BLWt1XOToHPHftP5+vvp325vKrCNue/okMM5OneZ1+Ur/Z89k2uj+0qx9D0kbgVKud27ekEcP7kmBICWG7VOllhx5Csi5U8dI/EUTYql39+LFczl18oS0aNXWOk218ZV+v6wcPRJ2+8NTPz+p8XFF/RszX/780qFzV/H0zBUFe4S48J15Yvc2HUtlyZ3/rbYREPBC95IvV+cLm+sqR6Hicu3MySjcW8TkdXFmb2CMndEz8LpQz1dNGylVmnWQVGltO7ZExMuAF3J0yxqxs08lLlTXiLe4NqJXrA/GcPHiRXFx4U6fd7Vj21+6917VT8LupaUSfWtXLpMZC/545/dD3GRvn0ryFiwsv86fKVndPSRtuvSybdM6OX3iqGTM/Oa70+/dvS379+yQngNGxMj+IvqpH1Or5k2W7HkK6p5+oXni81D+WrpQSga5ez3kMg90w9a25b9I1YYt9Tg2Zw7vlUXjBkirgRMkR/6i0XgUiCoebmmkdfV8MmnFMRm95JAUz+Ui41qVlecBL2XRlrOSIW3gWDy3H/rZrHf70VNxSxf2OD31y+WUojmdpVz3ZZysWEAs9XZuXL8mXkt+k4ZNvpJmLdrIqZPHZOLYkZIsWTKp8WmdUNdRd6d37zNQ8uYvoBN/K72WSsc2zWXm/MWSJ9/bNW4g9t2+ckEn9gJePNclsT/vMkiP9Xf9bGDj0t9/LpDKX7TVY/sd+3ujLBrRU9p8P0uXGwrNiV1b5ObFs9Ji6NQYPhJEpZtXLsi0vu2t18WX3YeKW5DSVLvWe8m6n6frhKBLpqzSst9YSZo0WYS2/eK5v6xdNFMKf1BZj/eH+Btn/zxjvOTKX0SyuOe0Tv9l1gQ99p+l996bqKSf4hisoo9jWid5+N88RC9iqbfz4MEDffNx+mAlPdXrSxcvhLqOu7uHDBoyQnLlySO+jx/Lgnk/yddfNpKlXqvFLUOGt9wTxLZbVy7I7P4drN+ZDbsN1mP9vQ0/n0divHoVoqRnKsd0cvf6lSjaY8QE1RPvlyGdrdeFGsPP+b/xkLf8Ml0y58ovnsF6jL7J+UO7ZdXUETqWSp3WST7vOUrsg9x8g/iBa8PEib+jR49KwYIF9R0bjx49kmPHwh7Et3Dh0AcEtvD399cP22mBPZwSkrWrlkmp9z8QZ5fQ7zL1e/JEvh/cR7r2HiiOaamHbWbd+w2XiSMHSbO6VSVxkiTimTuvfFi5upw7c+qN625eu1JSp04j75e37T6P+GvFnIly6+pFaTfkx1DnP/N7IvNG9dZj/VWp/3WY2zFeGfrf/CU+kHK1AnvDZHLPJVf+PSF7Nqwg8RdPJE6USA6evyMDf96nXx+5eE8KZHOS1tXy68Tf28jinErGtCojtQaueeMYgIhHsdSLJKaPpVSDbd78BaVth2/169x588nFc+fEa+nvYSb+srl76IdFoSLF5Ma1q/L7Lwuk/9BRMbbviFqqdGerETPE/+kTOb1nu6ycPlq+7Ddel99TilWqJUUqVNfPM7jnkksnDsmRrev02IDB+dy7LRsXTJHGvUdL0uSMgRufOWfKKh3HzBZ/vydybPc2WTJlpLQe/IM1+VesfBXJVbiEPH5wT/5e+Zv8MmGwtBv6oyR7w3hsLwMCZPGEwSq6kjqtusTQ0SA6LJg6Wq5fviD9xr4ep/Hg7u1y8sh+GfrjQj70BB5LvUpsZ/pY6m0UKVpMP4K+rvdZTVnyx6/yv46BMRniZyzV7vtZ+jvz5J5t4jX1e/l64IS3Tv7BHJwyZpFmw6bp6+LMvr9l7cwx0rDPWF1F48rJw9Js6LRIbzNr/iJ6m08f+8jRrWtk5eRh0mTQJEnlQFt3fMK1YeLEX9GiReXmzZu6vKd6rkqcWH5YK5bX6l9191B4Ro4cKYMHqx9Or33bs6907dVfEopb3jfk0L7dMnDkhDCXuXH9qtz0viH9enSyTlN30ChVyxWTeb+ukExZ3twjDHGf6tn3/eQ58uzpU/F74itOzi4yamBPyZAx/LHY1P+5jWu8pGLVT3RvB8R/y+dMlNMHd0mbwZPEMX3ImwL8n/rJ3BE9xS5l4F3sSZKG/ZVg7+CoE8kqQRiUS+bscvnfsH8kI265+cBPTl19aDPt9LUHUqdMYCLj5n89/VzT2svNB0+ty7g6ppSjF0O/47xYTmdxS2svu8bXs05LmiSxlMufUdrVLCCO9efIq/8Sx4g/sVT33v2lZ58Bpj5l6Z1dxN3jdQ8NJbtHDtn618ZIbSdfgUJy9DDl0+OzJEmTidN/47Jl9MgtNy78K/vW/yllPw0ca8RyZ7KFKvf56N7tULflffGs7kk/p287m5j7yuljsn+Dl3w3f60kTpwkWo8HUUP13nP+r1dn5hx55Nr507JzzVKp26abnqZKdKqHc8YskjV3fhnS/FM5sfcfKVqucrhJv18mDJIHd29JqwHj6e0Xjy2YOkYO7/1H+o6eIU5BxmhUSb/b3tekXX3b62DSiO8kT4Gi0uf76SG25ZgusLfUowf3bUqDPnp4X7LnyB2tx5GQRXcs1affQOk3YJCYWbp06SRJkiRy757t7wT12tn5zWVuFdX2kCdfPrl6hZ5c8f070zLGbaYcueX6+X9lz9o/5dPWXSO9LdX2kChxYvF99MBmuirBrnp4IX7F2OncAq+LDB655eaFM3JwwzJJmsxOHt72lh/b1bVZfsWkoZI5T0Fp1GdsmNtMbpdSkrtl1tvN5JlPZvf4Wo5vWyelP309HjfiPq4NEyf+gpZRUM/fRe/evaVrV9svkttPJEFZt9pLj9v3ftmwx9nKlt1DZv281Gba3JmT5emTJ9K+Sy9xcaOkgtmkSJlSPx4/9pGDe3dK82/Cv3vu2OH9utdC1Vq2X7yIf9QP1BU//SAn9/4jrQdNFCfXjKH29FNj9SVNlkya9RzxxrvTVSCfJWdeuXPjqs30u95XJW2Qxg7EbbtO35LcmW3LYOTKlFau3Hmsn1+69Vi87/tJxcKZrIm+NCmTScncrjJrXei9hrccuSHFO9mWkJ7ZsYL8e/2RjPvzMEm/eBpL+bwwf2JC9da7ctn2s7t65ZJkyBi5MW7Pnjmtk4gw1/foyxcvxNElg6ROl17uedt+992/eU1yFnk9FldQ7gWKSetRs2ymrZo5RtJnzCZlPm1I0i8eU9UPVKmq0Gca+vEy4Pkbk373bl6TVgMnSirKUsXbvw8Lp42VA7u2Su9R08Tlv4Zui1r1m8lH1WyH3+jTvrE0ad1FipUuF+o2XTJk0sm/k0f2SfacgYm+p36+cuHfE1L5k/+LxqNJ2KI7llI9/swuWbLkki9/Adm7Z5dUqlzFWlFBvW7U+MsIbUMlVc+dPSPlykesNC7iB8N4JQEvXrzVuqrtIZNHbrl4/KDkK1nOel1dOH5QSlULvSoH4s91oWLsD+o2k0IfBVbTsJjfp61UbNJWchR7P5LbNN76WkPcwbVhosRf9uyv75q9fPmyHlQ5abCeJgEBAbJz506bZUOjSicEL5/wKMC2xIKZqS+/9auXy8c1PwvRW2fU4D7i7OImrdp3luR2duKR03awZFXSUQk+HfHbgT07xRBDsmR1F+/rV2TO1AmSJZuHfFwz8AfovOmT9Fh+3foNs1lvwyovyZO/kLjn8IylPUdU9vQ78s8madpzuO7N9/hhYAJH3ZWuEnyBSb/u8sLfXxp27KtLm6mHksohrbVRcvy3TaXaF22kQKnAmwo+/KyRLk3lka+I5ChYVI/xd/rATp1cRPzw44pjsmVUbenxeVFZ+s8FKZnbRVpUzSsdpv5tXWbKymPSq/57cu6Gj1y67SMDvyipk4Er9lyyLrNmyCeyYvclmb7mhPg+eyEnrwS7G9M/QO4/fhZiOuJPLOXvG2D609WwSTNp1/xLWfDTTKn0cTU5efyYrPhzifTs+/ru/Ok/TpA7d25L/yEj9WtV0jNjpizikTOnPPdXY/wtkYP79sj4KbaJHsQfW36drZN4Ds6u8vypn5zY+ZdcPnVEGvcapXt5lPmkgWxfOl/csuXUY/wd/XuD3LtxVf6v80DrNhaN6CG5S3wgJavWEbuU9uKa9XU5WCWZXQpJmcYhxHTEXet+mSl5ipaWtM6u4v/sqRz+Z5NcPHlYmvcdI/dv3ZCjO7dIriIldNz06N4d2eb1iyRNbid5gjRWBY2jVNJv0fiBcuPiGfmq10gxXr20xmcpUztEeGxAxL75U0fL7q3r5dsBYyVFSnt5eP+unm6fKrUkt0uhe+wF7bVnkd7FzSZJ2KtNfan/dXspUbai/ltTrU4jWf7rT+KWKau4uGWSpQunS9r0zvJeBMcJRNyLpZ4mkHbops2aS/++vSR/gYJSsGBhWfTzfHn69KnUrhNYDaRf757i6uomnboE9paeMW2yFCpcVLJly65vUp4/d45437ghdf8vcDgJxD+bFs8Sz6KlxDG9mzx/5ifHdmyWSyePSNPe3+v5jx/eF9+H9+X+revWsZWTp7QXR2dXsU/toKfNH9pN8pYsJ6WrB96IXuaT+rJ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+      "text/plain": [
+       "<Figure size 1800x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "stiff = [\n",
+    "    ('stiffness', 'torsional rigidity'),\n",
+    "    ('stiffness', 'heel counter stiffness'),\n",
+    "    ('torsional rigidity', 'heel counter stiffness')\n",
+    "]\n",
+    "\n",
+    "plt.figure(figsize=(18, 5))\n",
+    "\n",
+    "for i, (feat_x, feat_y) in enumerate(stiff):\n",
+    "    ct = pd.crosstab(df[feat_x], df[feat_y], normalize='index') * 100\n",
+    "    plt.subplot(1, 3, i+1)\n",
+    "    sns.heatmap(ct, annot=True, fmt=\".1f\", cmap=\"Blues\", cbar=False)\n",
+    "    plt.title(f'{feat_x} vs {feat_y} (%)')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "0872440f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Correlation between Stiffness and Plate (%):\n",
+      "plate             0  carbon plate  carbon platerock plate\n",
+      "stiffness                                                \n",
+      "-          92.30769       7.69231                 0.00000\n",
+      "flexible   97.14286       2.85714                 0.00000\n",
+      "moderate   96.93252       3.06748                 0.00000\n",
+      "stiff      76.03687      23.50230                 0.46083\n",
+      "\n",
+      "Correlation between Stiffness and Midsole Softness (%):\n",
+      "midsole softness          -  balanced     firm      soft\n",
+      "stiffness                                               \n",
+      "-                 100.00000   0.00000  0.00000   0.00000\n",
+      "flexible           17.14286  42.85714  5.71429  34.28571\n",
+      "moderate            0.61350  44.17178  3.68098  51.53374\n",
+      "stiff              18.89401  39.17051  4.60829  37.32719\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Correlation details between stiffness and plates\n",
+    "ct_stiff_plate = pd.crosstab(df['stiffness'], df['plate'], normalize='index') * 100\n",
+    "print(\"Correlation between Stiffness and Plate (%):\")\n",
+    "print(ct_stiff_plate.round(5))\n",
+    "\n",
+    "# Correlation details between stiffness and midsole softness\n",
+    "ct_stiff_midsole = pd.crosstab(df['stiffness'], df['midsole softness'], normalize='index') * 100\n",
+    "print(\"\\nCorrelation between Stiffness and Midsole Softness (%):\")\n",
+    "print(ct_stiff_midsole.round(5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "857a0fe8",
+   "metadata": {},
+   "source": [
+    "Tidak ada korelasi signifikan antara stiffness dan plate ataupun midsole softness, kecuali pada 51% sepatu stiffness berkategori moderate yang memiliki midsole soft."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "ad19be4d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 39 columns):\n",
+      " #   Column                       Non-Null Count  Dtype   \n",
+      "---  ------                       --------------  -----   \n",
+      " 0   brand                        428 non-null    str     \n",
+      " 1   name                         428 non-null    str     \n",
+      " 2   pace                         428 non-null    str     \n",
+      " 3   arch support                 428 non-null    str     \n",
+      " 4   weight lab weight brand      428 non-null    str     \n",
+      " 5   lightweight                  428 non-null    int64   \n",
+      " 6   drop lab drop brand          428 non-null    str     \n",
+      " 7   strike pattern               428 non-null    str     \n",
+      " 8   midsole softness             428 non-null    str     \n",
+      " 9   toebox durability            428 non-null    str     \n",
+      " 10  heel padding durability      428 non-null    str     \n",
+      " 11  outsole durability           428 non-null    str     \n",
+      " 12  breathability                428 non-null    str     \n",
+      " 13  width / fit                  428 non-null    str     \n",
+      " 14  toebox width                 428 non-null    str     \n",
+      " 15  stiffness                    428 non-null    str     \n",
+      " 16  torsional rigidity           428 non-null    str     \n",
+      " 17  heel counter stiffness       428 non-null    str     \n",
+      " 18  plate                        428 non-null    str     \n",
+      " 19  rocker                       428 non-null    int64   \n",
+      " 20  heel lab heel brand          428 non-null    str     \n",
+      " 21  forefoot lab forefoot brand  428 non-null    str     \n",
+      " 22  orthotic friendly            428 non-null    int64   \n",
+      " 23  season                       428 non-null    str     \n",
+      " 24  removable insole             428 non-null    int64   \n",
+      " 25  for_daily                    428 non-null    int64   \n",
+      " 26  for_tempo                    428 non-null    int64   \n",
+      " 27  for_competition              428 non-null    int64   \n",
+      " 28  weight_lab_oz                428 non-null    float64 \n",
+      " 29  heel_lab_mm                  428 non-null    float64 \n",
+      " 30  forefoot_lab_mm              428 non-null    float64 \n",
+      " 31  drop_lab_mm                  428 non-null    float64 \n",
+      " 32  heel_category                428 non-null    category\n",
+      " 33  forefoot_category            428 non-null    category\n",
+      " 34  drop_category                428 non-null    category\n",
+      " 35  strike_pattern               428 non-null    str     \n",
+      " 36  strike_heel                  428 non-null    int64   \n",
+      " 37  strike_mid                   428 non-null    int64   \n",
+      " 38  strike_fore                  428 non-null    int64   \n",
+      "dtypes: category(3), float64(4), int64(10), str(22)\n",
+      "memory usage: 125.4 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5daabccd",
+   "metadata": {},
+   "source": [
+    "## Plate and rocker"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "5ae27201",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "plate\n",
+      "0                         369\n",
+      "carbon plate               58\n",
+      "carbon platerock plate      1\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "rocker\n",
+      "0    286\n",
+      "1    142\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "correlation between plate and rocker\n",
+      "rocker                         0          1\n",
+      "plate                                      \n",
+      "0                       75.33875   24.66125\n",
+      "carbon plate            13.79310   86.20690\n",
+      "carbon platerock plate   0.00000  100.00000\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['plate'].value_counts())\n",
+    "print()\n",
+    "print(df['rocker'].value_counts())\n",
+    "\n",
+    "print(\"\\ncorrelation between plate and rocker\")\n",
+    "ct_plate_rocker = pd.crosstab(df['plate'], df['rocker'], normalize='index') * 100\n",
+    "print(ct_plate_rocker.round(5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "227237f3",
+   "metadata": {},
+   "source": [
+    "Kebanyakan sepatu pada dataset adalah sepatu dtanpa tambahan plate. Akan tetapi, mayoritas sepatu yang memiliki tambahan carbon plate adalah sepatu yang bagian bawahnya punya lengkungan (rocker shoes). Sepatu dengan feature rocker adalah sepatu yang memiliki lengkungan dengan tujuan untuk mengembalikan energi kepada pengunanya. Untuk rekomendasi yang maksimal, disarankan agar setiap user yang ingin mengikuti kompetisi untuk mendapatkan sepatu dengan plate carbon dan rocker = 1."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "1ead91b1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "Index: 428 entries, 0 to 432\n",
+      "Data columns (total 39 columns):\n",
+      " #   Column                       Non-Null Count  Dtype   \n",
+      "---  ------                       --------------  -----   \n",
+      " 0   brand                        428 non-null    str     \n",
+      " 1   name                         428 non-null    str     \n",
+      " 2   pace                         428 non-null    str     \n",
+      " 3   arch support                 428 non-null    str     \n",
+      " 4   weight lab weight brand      428 non-null    str     \n",
+      " 5   lightweight                  428 non-null    int64   \n",
+      " 6   drop lab drop brand          428 non-null    str     \n",
+      " 7   strike pattern               428 non-null    str     \n",
+      " 8   midsole softness             428 non-null    str     \n",
+      " 9   toebox durability            428 non-null    str     \n",
+      " 10  heel padding durability      428 non-null    str     \n",
+      " 11  outsole durability           428 non-null    str     \n",
+      " 12  breathability                428 non-null    str     \n",
+      " 13  width / fit                  428 non-null    str     \n",
+      " 14  toebox width                 428 non-null    str     \n",
+      " 15  stiffness                    428 non-null    str     \n",
+      " 16  torsional rigidity           428 non-null    str     \n",
+      " 17  heel counter stiffness       428 non-null    str     \n",
+      " 18  plate                        428 non-null    str     \n",
+      " 19  rocker                       428 non-null    int64   \n",
+      " 20  heel lab heel brand          428 non-null    str     \n",
+      " 21  forefoot lab forefoot brand  428 non-null    str     \n",
+      " 22  orthotic friendly            428 non-null    int64   \n",
+      " 23  season                       428 non-null    str     \n",
+      " 24  removable insole             428 non-null    int64   \n",
+      " 25  for_daily                    428 non-null    int64   \n",
+      " 26  for_tempo                    428 non-null    int64   \n",
+      " 27  for_competition              428 non-null    int64   \n",
+      " 28  weight_lab_oz                428 non-null    float64 \n",
+      " 29  heel_lab_mm                  428 non-null    float64 \n",
+      " 30  forefoot_lab_mm              428 non-null    float64 \n",
+      " 31  drop_lab_mm                  428 non-null    float64 \n",
+      " 32  heel_category                428 non-null    category\n",
+      " 33  forefoot_category            428 non-null    category\n",
+      " 34  drop_category                428 non-null    category\n",
+      " 35  strike_pattern               428 non-null    str     \n",
+      " 36  strike_heel                  428 non-null    int64   \n",
+      " 37  strike_mid                   428 non-null    int64   \n",
+      " 38  strike_fore                  428 non-null    int64   \n",
+      "dtypes: category(3), float64(4), int64(10), str(22)\n",
+      "memory usage: 125.4 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8ca8cbbe",
+   "metadata": {},
+   "source": [
+    "## Orthotic friendly and removable insole"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "8f3cc5ab",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   orthotic friendly  removable insole\n",
+      "0                  1                 1\n",
+      "1                  1                 1\n",
+      "2                  1                 1\n",
+      "3                  1                 1\n",
+      "4                  1                 1\n",
+      "\n",
+      "orthotic friendly\n",
+      "1    388\n",
+      "0     40\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "removable insole\n",
+      "1    388\n",
+      "0     40\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "correlation between orthotic friendly and removable insole\n",
+      "removable insole       0      1\n",
+      "orthotic friendly              \n",
+      "0                  100.0    0.0\n",
+      "1                    0.0  100.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[['orthotic friendly', 'removable insole']].head())\n",
+    "\n",
+    "print()\n",
+    "print(df['orthotic friendly'].value_counts())\n",
+    "\n",
+    "print()\n",
+    "print(df['removable insole'].value_counts())\n",
+    "\n",
+    "print(\"\\ncorrelation between orthotic friendly and removable insole\")\n",
+    "ct_ortho_removable = pd.crosstab(df['orthotic friendly'], df['removable insole'], normalize='index') * 100\n",
+    "print(ct_ortho_removable.round(5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9bd2f116",
+   "metadata": {},
+   "source": [
+    "Sepatu orthotic friendly adalah sepatu yang dirancang khusus untuk pelari yang memiliki masalah kaki seperti plantar fasciitis, flat feet yang ekstrem, atau perbedaan panjang kaki sehingga mereka membutuhkan desain khusus terhadap insole sepatunya. Hal ini sudah sesuai karena sepatu yang orthotic friendly pasti removable insole, sehingga ke depannya hanya akan digunakan salah satu feature dari sini."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d0317b3b",
+   "metadata": {},
+   "source": [
+    "## Season"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "bb32b6f3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "season\n",
+      "all seasons          249\n",
+      "summerall seasons    109\n",
+      "-                     58\n",
+      "winter                12\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['season'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "7c5492df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Jumlah Sepatu Per Kategori Musim:\n",
+      "Summer: 109\n",
+      "Winter: 12\n",
+      "All seasons: 358\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['season'] = df['season'].replace('-', 'Unknown')\n",
+    "\n",
+    "# encode base value\n",
+    "df['is_summer'] = df['season'].str.contains('summer').astype(int)\n",
+    "df['is_winter'] = df['season'].str.contains('winter').astype(int)\n",
+    "df['is_all_season'] = df['season'].str.contains('all season').astype(int)\n",
+    "\n",
+    "print(\"Jumlah Sepatu Per Kategori Musim:\")\n",
+    "print(f\"Summer: {df['is_summer'].sum()}\")\n",
+    "print(f\"Winter: {df['is_winter'].sum()}\")\n",
+    "print(f\"All seasons: {df['is_all_season'].sum()}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "ebc3e7ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total Sepatu: 428\n",
+      "Irisan Summer & All Season: 109\n",
+      "Winter Only: 12\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Subset counts\n",
+    "'''\n",
+    "\n",
+    "- huruf A pada subset menunjukan kehadiran all seasons dalam subset tersebut; A = all seasons == 1, a = all seasons == 0\n",
+    "- huruf B pada subset menunjukan kehadiran summer dalam subset tersebut; B = summer == 1, b = summer == 0\n",
+    "- huruf C pada subset menunjukan kehadiran winter dalam subset tersebut; C = winter == 1, c = winter == 0\n",
+    "\n",
+    "'''\n",
+    "Abc = len(df[(df['is_all_season'] == 1) & (df['is_summer'] == 0) & (df['is_winter'] == 0)]) \n",
+    "aBc = len(df[(df['is_all_season'] == 0) & (df['is_summer'] == 1) & (df['is_winter'] == 0)]) \n",
+    "ABc = len(df[(df['is_all_season'] == 1) & (df['is_summer'] == 1) & (df['is_winter'] == 0)]) \n",
+    "abC = len(df[(df['is_all_season'] == 0) & (df['is_summer'] == 0) & (df['is_winter'] == 1)]) \n",
+    "AbC = len(df[(df['is_all_season'] == 1) & (df['is_summer'] == 0) & (df['is_winter'] == 1)]) \n",
+    "aBC = len(df[(df['is_all_season'] == 0) & (df['is_summer'] == 1) & (df['is_winter'] == 1)]) \n",
+    "ABC = len(df[(df['is_all_season'] == 1) & (df['is_summer'] == 1) & (df['is_winter'] == 1)]) \n",
+    "\n",
+    "# Plotting\n",
+    "plt.figure(figsize=(10, 8))\n",
+    "v = venn3(subsets = (Abc, aBc, ABc, abC, AbC, aBC, ABC), \n",
+    "          set_labels = ('All Seasons', 'Summer', 'Winter'),\n",
+    "          alpha = 0.6)\n",
+    "\n",
+    "if v.get_patch_by_id('100'): v.get_patch_by_id('100').set_color('skyblue')\n",
+    "if v.get_patch_by_id('010'): v.get_patch_by_id('010').set_color('orange')\n",
+    "if v.get_patch_by_id('001'): v.get_patch_by_id('001').set_color('lightgrey')\n",
+    "if v.get_patch_by_id('110'): v.get_patch_by_id('110').set_color('green')\n",
+    "\n",
+    "plt.title(\"Venn Diagram: Season Intersection - Project Rush\", fontsize=15)\n",
+    "plt.show()\n",
+    "\n",
+    "# Verify\n",
+    "print(f\"Total Sepatu: {len(df)}\")\n",
+    "print(f\"Irisan Summer & All Season: {ABc}\")\n",
+    "print(f\"Winter Only: {abC}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba27cda8",
+   "metadata": {},
+   "source": [
+    "- Summer shoes adalah sepatu yang berfokus pada feature breathable pada sepatu, hal ini membuat sepatu yang cocok untuk summer (karena terasa sejuk di kaki) secara otomatis nyaman dipakai di semua musim. \n",
+    "- Winter shoes adalah sepatu yang berfokus pada insulasi untuk menjaga panas sehingga membutuh breathability berkategori warm. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "7701b7ad",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Korelasi Sepatu Summer vs Kategori Breathability (%) ---\n",
+      "breathability         -  breathable  moderate      warm\n",
+      "is_summer                                              \n",
+      "0              18.18182         0.0  65.20376  16.61442\n",
+      "1               0.00000       100.0   0.00000   0.00000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Correlation between summer shoes with breathability category\n",
+    "ct_summer_breathability = pd.crosstab(df['is_summer'], df['breathability'], normalize='index') * 100\n",
+    "print(\"--- Korelasi Sepatu Summer vs Kategori Breathability (%) ---\")\n",
+    "print(ct_summer_breathability.round(5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "1cfc5944",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Korelasi Sepatu Winter vs Kategori Breathability (%) ---\n",
+      "breathability         -  breathable  moderate       warm\n",
+      "is_winter                                               \n",
+      "0              13.94231    26.20192      50.0    9.85577\n",
+      "1               0.00000     0.00000       0.0  100.00000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Correlation between winter shoes with breathability category\n",
+    "ct_winter_breathability = pd.crosstab(df['is_winter'], df['breathability'], normalize='index') * 100\n",
+    "print(\"--- Korelasi Sepatu Winter vs Kategori Breathability (%) ---\")\n",
+    "print(ct_winter_breathability.round(5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3acc8467",
+   "metadata": {},
+   "source": [
+    "Untuk requirement dasar saat ini, dikarenakan main market target kita adalah orang orang Indonesia maka dari itu secara default akan dipasangkan untuk sepatu dengan kategori summer atau all seasons."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "40c278e8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x1500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# Final Correlation Check\n",
+    "ordinal_map = {\n",
+    "    'midsole softness': {'soft': 1, 'balanced': 2, 'firm': 3},\n",
+    "    'stiffness': {'flexible': 1, 'moderate': 2, 'stiff': 3},\n",
+    "    'torsional rigidity': {'flexible': 1, 'moderate': 2, 'stiff': 3},\n",
+    "    'heel counter stiffness': {'flexible': 1, 'moderate': 2, 'stiff': 3},\n",
+    "    'toebox durability': {'bad': 1, 'decent': 2, 'good': 3},\n",
+    "    'heel padding durability': {'bad': 1, 'decent': 2, 'good': 3},\n",
+    "    'outsole durability': {'bad': 1, 'decent': 2, 'good': 3}\n",
+    "}\n",
+    "\n",
+    "df_encoded = df.copy()\n",
+    "for col, mapping in ordinal_map.items():\n",
+    "    if col in df_encoded.columns:\n",
+    "        df_encoded[col] = df_encoded[col].map(mapping)\n",
+    "\n",
+    "df_numeric = df_encoded.select_dtypes(include=[np.number])\n",
+    "corr = df_numeric.corr()\n",
+    "\n",
+    "# Plot heatmap\n",
+    "plt.figure(figsize=(20, 15))\n",
+    "mask = np.triu(np.ones_like(corr, dtype=bool))\n",
+    "\n",
+    "sns.heatmap(corr, mask=mask, annot=True, fmt=\".2f\", cmap='RdYlGn', center=0,\n",
+    "            square=True, linewidths=.5, cbar_kws={\"shrink\": .7})\n",
+    "\n",
+    "plt.title('Correlation Matrix All Features - Project Rush', fontsize=18)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6f6bc07",
+   "metadata": {},
+   "source": [
+    "# Preprocessing Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa704713",
+   "metadata": {},
+   "source": [
+    "Di tahap ini, akan dilakukan encoding (One-Hot dan Ordinal), feature selection, dan pembersihan data jika diperlukan"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/data-preparation-v3/trail-shoes-preparation.ipynb b/notebooks/data-preparation-v3/trail-shoes-preparation.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..c93f6c07f1839c58fcd33862b2f358485003c2cd
--- /dev/null
+++ b/notebooks/data-preparation-v3/trail-shoes-preparation.ipynb
@@ -0,0 +1,6583 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f5cc0f6b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand-Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Trail terrain</th>\n",
+       "      <th>Shock absorption</th>\n",
+       "      <th>Energy return</th>\n",
+       "      <th>Traction</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Heel stack lab Heel stack brand</th>\n",
+       "      <th>Forefoot lab Forefoot brand</th>\n",
+       "      <th>Widths available</th>\n",
+       "      <th>For heavy runners</th>\n",
+       "      <th>Season</th>\n",
+       "      <th>Removable insole</th>\n",
+       "      <th>Orthotic friendly</th>\n",
+       "      <th>Waterproofing</th>\n",
+       "      <th>Ranking</th>\n",
+       "      <th>Popularity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Adidas Terrex Agravic Speed Ultra</td>\n",
+       "      <td>90 Great!</td>\n",
+       "      <td>$220</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>High</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.1 oz / 259g 9.5 oz / 270g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>30.6 mm 38.0 mm</td>\n",
+       "      <td>30.3 mm 30.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#76 Top 21%</td>\n",
+       "      <td>#177 Top 47%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Adidas Terrex Speed Ultra</td>\n",
+       "      <td>90 Great!</td>\n",
+       "      <td>3559500 Rp</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.1 oz / 258g 9 oz / 255g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>32.8 mm 26.0 mm</td>\n",
+       "      <td>24.6 mm 18.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#49 Top 13%</td>\n",
+       "      <td>#298 Bottom 21%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Altra Experience Wild</td>\n",
+       "      <td>88 Great!</td>\n",
+       "      <td>2966250 Rp</td>\n",
+       "      <td>Light Moderate</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.1 oz / 285g 9.6 oz / 273g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>34.5 mm 34.0 mm</td>\n",
+       "      <td>30.2 mm 30.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#263 Top 40%</td>\n",
+       "      <td>#326 Top 49%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Altra Experience Wild 2</td>\n",
+       "      <td>84 Good!</td>\n",
+       "      <td>2966250 Rp</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Low</td>\n",
+       "      <td>High</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.4 oz / 266g 10.3 oz / 293g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>32.3 mm 32.0 mm</td>\n",
+       "      <td>26.2 mm 28.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#245 Bottom 35%</td>\n",
+       "      <td>#154 Top 41%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Altra Lone Peak 5.0</td>\n",
+       "      <td>91 Superb!</td>\n",
+       "      <td>$130</td>\n",
+       "      <td>Light Moderate</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 302g 10.6 oz / 301g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>24.5 mm 25.0 mm</td>\n",
+       "      <td>24.3 mm 25.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#68 Top 11%</td>\n",
+       "      <td>#55 Top 9%</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 36 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                          Brand-Name Audience score       Price  \\\n",
+       "0  Adidas Terrex Agravic Speed Ultra      90 Great!        $220   \n",
+       "1          Adidas Terrex Speed Ultra      90 Great!  3559500 Rp   \n",
+       "2              Altra Experience Wild      88 Great!  2966250 Rp   \n",
+       "3            Altra Experience Wild 2       84 Good!  2966250 Rp   \n",
+       "4                Altra Lone Peak 5.0     91 Superb!        $130   \n",
+       "\n",
+       "    Trail terrain Shock absorption Energy return Traction Arch support  \\\n",
+       "0           Light         Moderate          High        -      Neutral   \n",
+       "1           Light                -             -        -      Neutral   \n",
+       "2  Light Moderate         Moderate           Low        -      Neutral   \n",
+       "3           Light         Moderate           Low     High      Neutral   \n",
+       "4  Light Moderate                -             -        -      Neutral   \n",
+       "\n",
+       "         Weight lab Weight brand  Lightweight  ...  \\\n",
+       "0    9.1 oz / 259g 9.5 oz / 270g          0.0  ...   \n",
+       "1      9.1 oz / 258g 9 oz / 255g          0.0  ...   \n",
+       "2   10.1 oz / 285g 9.6 oz / 273g          0.0  ...   \n",
+       "3   9.4 oz / 266g 10.3 oz / 293g          0.0  ...   \n",
+       "4  10.7 oz / 302g 10.6 oz / 301g          0.0  ...   \n",
+       "\n",
+       "  Heel stack lab Heel stack brand Forefoot lab Forefoot brand  \\\n",
+       "0                 30.6 mm 38.0 mm             30.3 mm 30.0 mm   \n",
+       "1                 32.8 mm 26.0 mm             24.6 mm 18.0 mm   \n",
+       "2                 34.5 mm 34.0 mm             30.2 mm 30.0 mm   \n",
+       "3                 32.3 mm 32.0 mm             26.2 mm 28.0 mm   \n",
+       "4                 24.5 mm 25.0 mm             24.3 mm 25.0 mm   \n",
+       "\n",
+       "  Widths available For heavy runners       Season Removable insole  \\\n",
+       "0           Normal               0.0  All seasons                1   \n",
+       "1           Normal               0.0            -                1   \n",
+       "2           Normal               0.0  All seasons                1   \n",
+       "3           Normal               0.0  All seasons                1   \n",
+       "4           Normal               0.0            -                1   \n",
+       "\n",
+       "  Orthotic friendly Waterproofing          Ranking       Popularity  \n",
+       "0                 1             -      #76 Top 21%     #177 Top 47%  \n",
+       "1                 1             -      #49 Top 13%  #298 Bottom 21%  \n",
+       "2                 1             -     #263 Top 40%     #326 Top 49%  \n",
+       "3                 1             -  #245 Bottom 35%     #154 Top 41%  \n",
+       "4                 1             -      #68 Top 11%       #55 Top 9%  \n",
+       "\n",
+       "[5 rows x 36 columns]"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df_ori = pd.read_csv('../../data/SONIX utilities - Trail.csv')\n",
+    "df_ori.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "eda2f776",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 183 entries, 0 to 182\n",
+      "Data columns (total 36 columns):\n",
+      " #   Column                                  Non-Null Count  Dtype  \n",
+      "---  ------                                  --------------  -----  \n",
+      " 0   Brand-Name                              183 non-null    str    \n",
+      " 1   Audience score                          181 non-null    str    \n",
+      " 2   Price                                   183 non-null    str    \n",
+      " 3   Trail terrain                           183 non-null    str    \n",
+      " 4   Shock absorption                        183 non-null    str    \n",
+      " 5   Energy return                           183 non-null    str    \n",
+      " 6   Traction                                178 non-null    str    \n",
+      " 7   Arch support                            183 non-null    str    \n",
+      " 8   Weight lab Weight brand                 183 non-null    str    \n",
+      " 9   Lightweight                             176 non-null    float64\n",
+      " 10  Drop lab Drop brand                     183 non-null    str    \n",
+      " 11  Strike pattern                          183 non-null    str    \n",
+      " 12  Size                                    183 non-null    str    \n",
+      " 13  Midsole softness                        183 non-null    str    \n",
+      " 14  Difference in midsole softness in cold  183 non-null    str    \n",
+      " 15  Plate                                   183 non-null    str    \n",
+      " 16  Toebox durability                       183 non-null    str    \n",
+      " 17  Heel padding durability                 183 non-null    str    \n",
+      " 18  Outsole durability                      183 non-null    str    \n",
+      " 19  Breathability                           183 non-null    str    \n",
+      " 20  Width / fit                             183 non-null    str    \n",
+      " 21  Toebox width                            183 non-null    str    \n",
+      " 22  Stiffness                               183 non-null    str    \n",
+      " 23  Torsional rigidity                      183 non-null    str    \n",
+      " 24  Heel counter stiffness                  183 non-null    str    \n",
+      " 25  Lug depth                               183 non-null    str    \n",
+      " 26  Heel stack lab Heel stack brand         183 non-null    str    \n",
+      " 27  Forefoot lab Forefoot brand             183 non-null    str    \n",
+      " 28  Widths available                        183 non-null    str    \n",
+      " 29  For heavy runners                       179 non-null    float64\n",
+      " 30  Season                                  183 non-null    str    \n",
+      " 31  Removable insole                        183 non-null    int64  \n",
+      " 32  Orthotic friendly                       183 non-null    int64  \n",
+      " 33  Waterproofing                           175 non-null    str    \n",
+      " 34  Ranking                                 183 non-null    str    \n",
+      " 35  Popularity                              183 non-null    str    \n",
+      "dtypes: float64(2), int64(2), str(32)\n",
+      "memory usage: 51.6 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_ori.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e53b33d",
+   "metadata": {},
+   "source": [
+    "# Pre-EDA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6791d8a5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 183 entries, 0 to 182\n",
+      "Data columns (total 28 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   Brand-Name                       183 non-null    str    \n",
+      " 1   Trail terrain                    183 non-null    str    \n",
+      " 2   Shock absorption                 183 non-null    str    \n",
+      " 3   Energy return                    183 non-null    str    \n",
+      " 4   Traction                         178 non-null    str    \n",
+      " 5   Arch support                     183 non-null    str    \n",
+      " 6   Weight lab Weight brand          183 non-null    str    \n",
+      " 7   Lightweight                      176 non-null    float64\n",
+      " 8   Drop lab Drop brand              183 non-null    str    \n",
+      " 9   Strike pattern                   183 non-null    str    \n",
+      " 10  Midsole softness                 183 non-null    str    \n",
+      " 11  Plate                            183 non-null    str    \n",
+      " 12  Toebox durability                183 non-null    str    \n",
+      " 13  Heel padding durability          183 non-null    str    \n",
+      " 14  Outsole durability               183 non-null    str    \n",
+      " 15  Breathability                    183 non-null    str    \n",
+      " 16  Width / fit                      183 non-null    str    \n",
+      " 17  Toebox width                     183 non-null    str    \n",
+      " 18  Stiffness                        183 non-null    str    \n",
+      " 19  Torsional rigidity               183 non-null    str    \n",
+      " 20  Heel counter stiffness           183 non-null    str    \n",
+      " 21  Lug depth                        183 non-null    str    \n",
+      " 22  Heel stack lab Heel stack brand  183 non-null    str    \n",
+      " 23  Forefoot lab Forefoot brand      183 non-null    str    \n",
+      " 24  Season                           183 non-null    str    \n",
+      " 25  Removable insole                 183 non-null    int64  \n",
+      " 26  Orthotic friendly                183 non-null    int64  \n",
+      " 27  Waterproofing                    175 non-null    str    \n",
+      "dtypes: float64(1), int64(2), str(25)\n",
+      "memory usage: 40.2 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_ori.drop(columns=['Audience score', 'Size', 'Price', 'Widths available', 'Difference in midsole softness in cold', 'For heavy runners', 'Ranking', 'Popularity'], inplace=True)\n",
+    "df_ori.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a240381d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>brand-name</th>\n",
+       "      <th>trail terrain</th>\n",
+       "      <th>shock absorption</th>\n",
+       "      <th>energy return</th>\n",
+       "      <th>traction</th>\n",
+       "      <th>arch support</th>\n",
+       "      <th>weight lab weight brand</th>\n",
+       "      <th>lightweight</th>\n",
+       "      <th>drop lab drop brand</th>\n",
+       "      <th>strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>stiffness</th>\n",
+       "      <th>torsional rigidity</th>\n",
+       "      <th>heel counter stiffness</th>\n",
+       "      <th>lug depth</th>\n",
+       "      <th>heel stack lab heel stack brand</th>\n",
+       "      <th>forefoot lab forefoot brand</th>\n",
+       "      <th>season</th>\n",
+       "      <th>removable insole</th>\n",
+       "      <th>orthotic friendly</th>\n",
+       "      <th>waterproofing</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>adidas terrex agravic speed ultra</td>\n",
+       "      <td>light</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>high</td>\n",
+       "      <td>-</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>9.1 oz / 259g 9.5 oz / 270g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.3 mm 8.0 mm</td>\n",
+       "      <td>mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>2.5 mm</td>\n",
+       "      <td>30.6 mm 38.0 mm</td>\n",
+       "      <td>30.3 mm 30.0 mm</td>\n",
+       "      <td>all seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>adidas terrex speed ultra</td>\n",
+       "      <td>light</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>9.1 oz / 258g 9 oz / 255g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8.2 mm 8.0 mm</td>\n",
+       "      <td>heel mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>2.6 mm</td>\n",
+       "      <td>32.8 mm 26.0 mm</td>\n",
+       "      <td>24.6 mm 18.0 mm</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>altra experience wild</td>\n",
+       "      <td>light moderate</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>low</td>\n",
+       "      <td>-</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>10.1 oz / 285g 9.6 oz / 273g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.3 mm 4.0 mm</td>\n",
+       "      <td>mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>3.6 mm</td>\n",
+       "      <td>34.5 mm 34.0 mm</td>\n",
+       "      <td>30.2 mm 30.0 mm</td>\n",
+       "      <td>all seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>altra experience wild 2</td>\n",
+       "      <td>light</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>low</td>\n",
+       "      <td>high</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>9.4 oz / 266g 10.3 oz / 293g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.1 mm 4.0 mm</td>\n",
+       "      <td>mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>3.5 mm</td>\n",
+       "      <td>32.3 mm 32.0 mm</td>\n",
+       "      <td>26.2 mm 28.0 mm</td>\n",
+       "      <td>all seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>altra lone peak 5.0</td>\n",
+       "      <td>light moderate</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>10.7 oz / 302g 10.6 oz / 301g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.2 mm 0.0 mm</td>\n",
+       "      <td>mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>-</td>\n",
+       "      <td>3.7 mm</td>\n",
+       "      <td>24.5 mm 25.0 mm</td>\n",
+       "      <td>24.3 mm 25.0 mm</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 28 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                          brand-name   trail terrain shock absorption  \\\n",
+       "0  adidas terrex agravic speed ultra           light         moderate   \n",
+       "1          adidas terrex speed ultra           light                -   \n",
+       "2              altra experience wild  light moderate         moderate   \n",
+       "3            altra experience wild 2           light         moderate   \n",
+       "4                altra lone peak 5.0  light moderate                -   \n",
+       "\n",
+       "  energy return traction arch support        weight lab weight brand  \\\n",
+       "0          high        -      neutral    9.1 oz / 259g 9.5 oz / 270g   \n",
+       "1             -        -      neutral      9.1 oz / 258g 9 oz / 255g   \n",
+       "2           low        -      neutral   10.1 oz / 285g 9.6 oz / 273g   \n",
+       "3           low     high      neutral   9.4 oz / 266g 10.3 oz / 293g   \n",
+       "4             -        -      neutral  10.7 oz / 302g 10.6 oz / 301g   \n",
+       "\n",
+       "   lightweight drop lab drop brand     strike pattern  ... stiffness  \\\n",
+       "0          0.0       0.3 mm 8.0 mm       mid/forefoot  ...  moderate   \n",
+       "1          0.0       8.2 mm 8.0 mm  heel mid/forefoot  ...     stiff   \n",
+       "2          0.0       4.3 mm 4.0 mm       mid/forefoot  ...  moderate   \n",
+       "3          0.0       6.1 mm 4.0 mm       mid/forefoot  ...  moderate   \n",
+       "4          0.0       0.2 mm 0.0 mm       mid/forefoot  ...     stiff   \n",
+       "\n",
+       "  torsional rigidity heel counter stiffness lug depth  \\\n",
+       "0              stiff               flexible    2.5 mm   \n",
+       "1           flexible               flexible    2.6 mm   \n",
+       "2              stiff               moderate    3.6 mm   \n",
+       "3           moderate               flexible    3.5 mm   \n",
+       "4           flexible                      -    3.7 mm   \n",
+       "\n",
+       "  heel stack lab heel stack brand forefoot lab forefoot brand       season  \\\n",
+       "0                 30.6 mm 38.0 mm             30.3 mm 30.0 mm  all seasons   \n",
+       "1                 32.8 mm 26.0 mm             24.6 mm 18.0 mm            -   \n",
+       "2                 34.5 mm 34.0 mm             30.2 mm 30.0 mm  all seasons   \n",
+       "3                 32.3 mm 32.0 mm             26.2 mm 28.0 mm  all seasons   \n",
+       "4                 24.5 mm 25.0 mm             24.3 mm 25.0 mm            -   \n",
+       "\n",
+       "  removable insole orthotic friendly waterproofing  \n",
+       "0                1                 1             -  \n",
+       "1                1                 1             -  \n",
+       "2                1                 1             -  \n",
+       "3                1                 1             -  \n",
+       "4                1                 1             -  \n",
+       "\n",
+       "[5 rows x 28 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# convert all to lowercase\n",
+    "\n",
+    "df_ori.columns = df_ori.columns.str.strip().str.lower()\n",
+    "df_ori = df_ori.map(lambda x: x.strip().lower() if isinstance(x, str) else x)\n",
+    "\n",
+    "df_ori.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ccddc8ad",
+   "metadata": {},
+   "source": [
+    "## Separate Brand-Name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "0bebd201",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "''' \n",
+    "Running shoes for trail brand in our dataset include:\n",
+    "    Adidas\n",
+    "    Altra\n",
+    "    ASICS\n",
+    "    Brooks\n",
+    "    HOKA\n",
+    "    Icebug\n",
+    "    Inov8\n",
+    "    Kailas\n",
+    "    KEEN\n",
+    "    La Sportiva\n",
+    "    Merrell\n",
+    "    New Balance\n",
+    "    Nike\n",
+    "    NNormal\n",
+    "    On\n",
+    "    Salomon\n",
+    "    Saucony\n",
+    "    Topo\n",
+    "    Xero\n",
+    "    Scarpa\n",
+    "    The North Face\n",
+    "'''\n",
+    "\n",
+    "brands_list = [\n",
+    "    \"Adidas\", \"Altra\", \"ASICS\", \"Brooks\", \"HOKA\", \"Icebug\", \"Inov8\", \n",
+    "    \"Kailas\", \"KEEN\", \"La Sportiva\", \"Merrell\", \"New Balance\", \"Nike\", \n",
+    "    \"NNormal\", \"On\", \"Salomon\", \"Saucony\", \"Topo\", \"Xero\", \"Scarpa\", \n",
+    "    \"The North Face\"\n",
+    "]\n",
+    "brands = [b.lower() for b in brands_list]\n",
+    "brands.sort(key=len, reverse=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3a304c3d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>brand</th>\n",
+       "      <th>name</th>\n",
+       "      <th>trail terrain</th>\n",
+       "      <th>shock absorption</th>\n",
+       "      <th>energy return</th>\n",
+       "      <th>traction</th>\n",
+       "      <th>arch support</th>\n",
+       "      <th>weight lab weight brand</th>\n",
+       "      <th>lightweight</th>\n",
+       "      <th>drop lab drop brand</th>\n",
+       "      <th>...</th>\n",
+       "      <th>stiffness</th>\n",
+       "      <th>torsional rigidity</th>\n",
+       "      <th>heel counter stiffness</th>\n",
+       "      <th>lug depth</th>\n",
+       "      <th>heel stack lab heel stack brand</th>\n",
+       "      <th>forefoot lab forefoot brand</th>\n",
+       "      <th>season</th>\n",
+       "      <th>removable insole</th>\n",
+       "      <th>orthotic friendly</th>\n",
+       "      <th>waterproofing</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>terrex agravic speed ultra</td>\n",
+       "      <td>light</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>high</td>\n",
+       "      <td>-</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>9.1 oz / 259g 9.5 oz / 270g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.3 mm 8.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>2.5 mm</td>\n",
+       "      <td>30.6 mm 38.0 mm</td>\n",
+       "      <td>30.3 mm 30.0 mm</td>\n",
+       "      <td>all seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>terrex speed ultra</td>\n",
+       "      <td>light</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>9.1 oz / 258g 9 oz / 255g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8.2 mm 8.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>2.6 mm</td>\n",
+       "      <td>32.8 mm 26.0 mm</td>\n",
+       "      <td>24.6 mm 18.0 mm</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>experience wild</td>\n",
+       "      <td>light moderate</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>low</td>\n",
+       "      <td>-</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>10.1 oz / 285g 9.6 oz / 273g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.3 mm 4.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>3.6 mm</td>\n",
+       "      <td>34.5 mm 34.0 mm</td>\n",
+       "      <td>30.2 mm 30.0 mm</td>\n",
+       "      <td>all seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>experience wild 2</td>\n",
+       "      <td>light</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>low</td>\n",
+       "      <td>high</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>9.4 oz / 266g 10.3 oz / 293g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.1 mm 4.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>3.5 mm</td>\n",
+       "      <td>32.3 mm 32.0 mm</td>\n",
+       "      <td>26.2 mm 28.0 mm</td>\n",
+       "      <td>all seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>lone peak 5.0</td>\n",
+       "      <td>light moderate</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>10.7 oz / 302g 10.6 oz / 301g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.2 mm 0.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>-</td>\n",
+       "      <td>3.7 mm</td>\n",
+       "      <td>24.5 mm 25.0 mm</td>\n",
+       "      <td>24.3 mm 25.0 mm</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 29 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    brand                        name   trail terrain shock absorption  \\\n",
+       "0  adidas  terrex agravic speed ultra           light         moderate   \n",
+       "1  adidas          terrex speed ultra           light                -   \n",
+       "2   altra             experience wild  light moderate         moderate   \n",
+       "3   altra           experience wild 2           light         moderate   \n",
+       "4   altra               lone peak 5.0  light moderate                -   \n",
+       "\n",
+       "  energy return traction arch support        weight lab weight brand  \\\n",
+       "0          high        -      neutral    9.1 oz / 259g 9.5 oz / 270g   \n",
+       "1             -        -      neutral      9.1 oz / 258g 9 oz / 255g   \n",
+       "2           low        -      neutral   10.1 oz / 285g 9.6 oz / 273g   \n",
+       "3           low     high      neutral   9.4 oz / 266g 10.3 oz / 293g   \n",
+       "4             -        -      neutral  10.7 oz / 302g 10.6 oz / 301g   \n",
+       "\n",
+       "   lightweight drop lab drop brand  ... stiffness torsional rigidity  \\\n",
+       "0          0.0       0.3 mm 8.0 mm  ...  moderate              stiff   \n",
+       "1          0.0       8.2 mm 8.0 mm  ...     stiff           flexible   \n",
+       "2          0.0       4.3 mm 4.0 mm  ...  moderate              stiff   \n",
+       "3          0.0       6.1 mm 4.0 mm  ...  moderate           moderate   \n",
+       "4          0.0       0.2 mm 0.0 mm  ...     stiff           flexible   \n",
+       "\n",
+       "  heel counter stiffness lug depth heel stack lab heel stack brand  \\\n",
+       "0               flexible    2.5 mm                 30.6 mm 38.0 mm   \n",
+       "1               flexible    2.6 mm                 32.8 mm 26.0 mm   \n",
+       "2               moderate    3.6 mm                 34.5 mm 34.0 mm   \n",
+       "3               flexible    3.5 mm                 32.3 mm 32.0 mm   \n",
+       "4                      -    3.7 mm                 24.5 mm 25.0 mm   \n",
+       "\n",
+       "  forefoot lab forefoot brand       season removable insole orthotic friendly  \\\n",
+       "0             30.3 mm 30.0 mm  all seasons                1                 1   \n",
+       "1             24.6 mm 18.0 mm            -                1                 1   \n",
+       "2             30.2 mm 30.0 mm  all seasons                1                 1   \n",
+       "3             26.2 mm 28.0 mm  all seasons                1                 1   \n",
+       "4             24.3 mm 25.0 mm            -                1                 1   \n",
+       "\n",
+       "  waterproofing  \n",
+       "0             -  \n",
+       "1             -  \n",
+       "2             -  \n",
+       "3             -  \n",
+       "4             -  \n",
+       "\n",
+       "[5 rows x 29 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def split_brand_name(full_text):\n",
+    "    for brand in brands:\n",
+    "        if full_text.startswith(brand):\n",
+    "            # Sisa dari brand dijadiin name semua\n",
+    "            name = full_text[len(brand):].strip()\n",
+    "            return brand, name\n",
+    "    return \"Unknown\", full_text \n",
+    "\n",
+    "\n",
+    "df_ori[['brand', 'name']] = df_ori['brand-name'].apply(lambda x: pd.Series(split_brand_name(x)))\n",
+    "\n",
+    "# Atur urutan kolom agar brand dan name tetap ada di depan\n",
+    "cols = ['brand', 'name'] + [c for c in df_ori.columns if c not in ['brand', 'name', 'brand-name']]\n",
+    "df_ori = df_ori[cols]\n",
+    "\n",
+    "df_ori.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d7d61ef1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# df_ori.head(40)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "209d339d",
+   "metadata": {},
+   "source": [
+    "## Remove Duplicates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "498021b3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "183\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>brand</th>\n",
+       "      <th>name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>hoka</td>\n",
+       "      <td>mafate x</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66</th>\n",
+       "      <td>inov8</td>\n",
+       "      <td>trailfly</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>83</th>\n",
+       "      <td>la sportiva</td>\n",
+       "      <td>prodigio</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>84</th>\n",
+       "      <td>la sportiva</td>\n",
+       "      <td>prodigio</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>124</th>\n",
+       "      <td>nike</td>\n",
+       "      <td>terra kiger 9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>137</th>\n",
+       "      <td>on</td>\n",
+       "      <td>cloudsurfer trail 2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>141</th>\n",
+       "      <td>on</td>\n",
+       "      <td>cloudvista 2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>180</th>\n",
+       "      <td>topo</td>\n",
+       "      <td>ultraventure 4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           brand                 name\n",
+       "52          hoka             mafate x\n",
+       "66         inov8             trailfly\n",
+       "83   la sportiva             prodigio\n",
+       "84   la sportiva             prodigio\n",
+       "124         nike        terra kiger 9\n",
+       "137           on  cloudsurfer trail 2\n",
+       "141           on         cloudvista 2\n",
+       "180         topo       ultraventure 4"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dup_mask = df_ori.duplicated(subset=[\"brand\", \"name\"], keep=\"first\")\n",
+    "print(len(dup_mask))\n",
+    "df_ori.loc[dup_mask, [\"brand\", \"name\"]].head(100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "0f107030",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Before: 183\n",
+      "After : 175\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>brand</th>\n",
+       "      <th>name</th>\n",
+       "      <th>trail terrain</th>\n",
+       "      <th>shock absorption</th>\n",
+       "      <th>energy return</th>\n",
+       "      <th>traction</th>\n",
+       "      <th>arch support</th>\n",
+       "      <th>weight lab weight brand</th>\n",
+       "      <th>lightweight</th>\n",
+       "      <th>drop lab drop brand</th>\n",
+       "      <th>...</th>\n",
+       "      <th>stiffness</th>\n",
+       "      <th>torsional rigidity</th>\n",
+       "      <th>heel counter stiffness</th>\n",
+       "      <th>lug depth</th>\n",
+       "      <th>heel stack lab heel stack brand</th>\n",
+       "      <th>forefoot lab forefoot brand</th>\n",
+       "      <th>season</th>\n",
+       "      <th>removable insole</th>\n",
+       "      <th>orthotic friendly</th>\n",
+       "      <th>waterproofing</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>terrex agravic speed ultra</td>\n",
+       "      <td>light</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>high</td>\n",
+       "      <td>-</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>9.1 oz / 259g 9.5 oz / 270g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.3 mm 8.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>2.5 mm</td>\n",
+       "      <td>30.6 mm 38.0 mm</td>\n",
+       "      <td>30.3 mm 30.0 mm</td>\n",
+       "      <td>all seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>terrex speed ultra</td>\n",
+       "      <td>light</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>9.1 oz / 258g 9 oz / 255g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8.2 mm 8.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>2.6 mm</td>\n",
+       "      <td>32.8 mm 26.0 mm</td>\n",
+       "      <td>24.6 mm 18.0 mm</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>experience wild</td>\n",
+       "      <td>light moderate</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>low</td>\n",
+       "      <td>-</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>10.1 oz / 285g 9.6 oz / 273g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.3 mm 4.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>3.6 mm</td>\n",
+       "      <td>34.5 mm 34.0 mm</td>\n",
+       "      <td>30.2 mm 30.0 mm</td>\n",
+       "      <td>all seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>experience wild 2</td>\n",
+       "      <td>light</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>low</td>\n",
+       "      <td>high</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>9.4 oz / 266g 10.3 oz / 293g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.1 mm 4.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>3.5 mm</td>\n",
+       "      <td>32.3 mm 32.0 mm</td>\n",
+       "      <td>26.2 mm 28.0 mm</td>\n",
+       "      <td>all seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>lone peak 5.0</td>\n",
+       "      <td>light moderate</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>10.7 oz / 302g 10.6 oz / 301g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.2 mm 0.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>-</td>\n",
+       "      <td>3.7 mm</td>\n",
+       "      <td>24.5 mm 25.0 mm</td>\n",
+       "      <td>24.3 mm 25.0 mm</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 29 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    brand                        name   trail terrain shock absorption  \\\n",
+       "0  adidas  terrex agravic speed ultra           light         moderate   \n",
+       "1  adidas          terrex speed ultra           light                -   \n",
+       "2   altra             experience wild  light moderate         moderate   \n",
+       "3   altra           experience wild 2           light         moderate   \n",
+       "4   altra               lone peak 5.0  light moderate                -   \n",
+       "\n",
+       "  energy return traction arch support        weight lab weight brand  \\\n",
+       "0          high        -      neutral    9.1 oz / 259g 9.5 oz / 270g   \n",
+       "1             -        -      neutral      9.1 oz / 258g 9 oz / 255g   \n",
+       "2           low        -      neutral   10.1 oz / 285g 9.6 oz / 273g   \n",
+       "3           low     high      neutral   9.4 oz / 266g 10.3 oz / 293g   \n",
+       "4             -        -      neutral  10.7 oz / 302g 10.6 oz / 301g   \n",
+       "\n",
+       "   lightweight drop lab drop brand  ... stiffness torsional rigidity  \\\n",
+       "0          0.0       0.3 mm 8.0 mm  ...  moderate              stiff   \n",
+       "1          0.0       8.2 mm 8.0 mm  ...     stiff           flexible   \n",
+       "2          0.0       4.3 mm 4.0 mm  ...  moderate              stiff   \n",
+       "3          0.0       6.1 mm 4.0 mm  ...  moderate           moderate   \n",
+       "4          0.0       0.2 mm 0.0 mm  ...     stiff           flexible   \n",
+       "\n",
+       "  heel counter stiffness lug depth heel stack lab heel stack brand  \\\n",
+       "0               flexible    2.5 mm                 30.6 mm 38.0 mm   \n",
+       "1               flexible    2.6 mm                 32.8 mm 26.0 mm   \n",
+       "2               moderate    3.6 mm                 34.5 mm 34.0 mm   \n",
+       "3               flexible    3.5 mm                 32.3 mm 32.0 mm   \n",
+       "4                      -    3.7 mm                 24.5 mm 25.0 mm   \n",
+       "\n",
+       "  forefoot lab forefoot brand       season removable insole orthotic friendly  \\\n",
+       "0             30.3 mm 30.0 mm  all seasons                1                 1   \n",
+       "1             24.6 mm 18.0 mm            -                1                 1   \n",
+       "2             30.2 mm 30.0 mm  all seasons                1                 1   \n",
+       "3             26.2 mm 28.0 mm  all seasons                1                 1   \n",
+       "4             24.3 mm 25.0 mm            -                1                 1   \n",
+       "\n",
+       "  waterproofing  \n",
+       "0             -  \n",
+       "1             -  \n",
+       "2             -  \n",
+       "3             -  \n",
+       "4             -  \n",
+       "\n",
+       "[5 rows x 29 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# sebelum hapus\n",
+    "print(\"Before:\", len(df_ori))\n",
+    "\n",
+    "#hapus\n",
+    "df_ori = df_ori.drop_duplicates(subset=[\"brand\", \"name\"], keep=\"first\").reset_index(drop=True)\n",
+    "\n",
+    "print(\"After :\", len(df_ori))\n",
+    "df_ori.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "3e3d8e69",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ditemukan 0 baris yang memiliki spesifikasi identik.\n",
+      "\n",
+      "Empty DataFrame\n",
+      "Columns: [brand, name, trail terrain, shock absorption, energy return]\n",
+      "Index: []\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Searching for duplicate technical specifications\n",
+    "tech_columns = df_ori.columns[2:].tolist()\n",
+    "duplicates = df_ori[df_ori.duplicated(subset=tech_columns, keep=False)]\n",
+    "\n",
+    "duplicates_sorted = duplicates.sort_values(by=tech_columns[:3])\n",
+    "\n",
+    "print(f\"Ditemukan {len(duplicates_sorted)} baris yang memiliki spesifikasi identik.\\n\")\n",
+    "print(duplicates_sorted[['brand', 'name'] + tech_columns[:3]].head(30))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "8ec3d162",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Before: 175\n",
+      "After : 175\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>brand</th>\n",
+       "      <th>name</th>\n",
+       "      <th>trail terrain</th>\n",
+       "      <th>shock absorption</th>\n",
+       "      <th>energy return</th>\n",
+       "      <th>traction</th>\n",
+       "      <th>arch support</th>\n",
+       "      <th>weight lab weight brand</th>\n",
+       "      <th>lightweight</th>\n",
+       "      <th>drop lab drop brand</th>\n",
+       "      <th>...</th>\n",
+       "      <th>stiffness</th>\n",
+       "      <th>torsional rigidity</th>\n",
+       "      <th>heel counter stiffness</th>\n",
+       "      <th>lug depth</th>\n",
+       "      <th>heel stack lab heel stack brand</th>\n",
+       "      <th>forefoot lab forefoot brand</th>\n",
+       "      <th>season</th>\n",
+       "      <th>removable insole</th>\n",
+       "      <th>orthotic friendly</th>\n",
+       "      <th>waterproofing</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>terrex agravic speed ultra</td>\n",
+       "      <td>light</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>high</td>\n",
+       "      <td>-</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>9.1 oz / 259g 9.5 oz / 270g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.3 mm 8.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>2.5 mm</td>\n",
+       "      <td>30.6 mm 38.0 mm</td>\n",
+       "      <td>30.3 mm 30.0 mm</td>\n",
+       "      <td>all seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>terrex speed ultra</td>\n",
+       "      <td>light</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>9.1 oz / 258g 9 oz / 255g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8.2 mm 8.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>2.6 mm</td>\n",
+       "      <td>32.8 mm 26.0 mm</td>\n",
+       "      <td>24.6 mm 18.0 mm</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>experience wild</td>\n",
+       "      <td>light moderate</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>low</td>\n",
+       "      <td>-</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>10.1 oz / 285g 9.6 oz / 273g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.3 mm 4.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>3.6 mm</td>\n",
+       "      <td>34.5 mm 34.0 mm</td>\n",
+       "      <td>30.2 mm 30.0 mm</td>\n",
+       "      <td>all seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>experience wild 2</td>\n",
+       "      <td>light</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>low</td>\n",
+       "      <td>high</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>9.4 oz / 266g 10.3 oz / 293g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.1 mm 4.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>3.5 mm</td>\n",
+       "      <td>32.3 mm 32.0 mm</td>\n",
+       "      <td>26.2 mm 28.0 mm</td>\n",
+       "      <td>all seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>lone peak 5.0</td>\n",
+       "      <td>light moderate</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>10.7 oz / 302g 10.6 oz / 301g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.2 mm 0.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>-</td>\n",
+       "      <td>3.7 mm</td>\n",
+       "      <td>24.5 mm 25.0 mm</td>\n",
+       "      <td>24.3 mm 25.0 mm</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 29 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    brand                        name   trail terrain shock absorption  \\\n",
+       "0  adidas  terrex agravic speed ultra           light         moderate   \n",
+       "1  adidas          terrex speed ultra           light                -   \n",
+       "2   altra             experience wild  light moderate         moderate   \n",
+       "3   altra           experience wild 2           light         moderate   \n",
+       "4   altra               lone peak 5.0  light moderate                -   \n",
+       "\n",
+       "  energy return traction arch support        weight lab weight brand  \\\n",
+       "0          high        -      neutral    9.1 oz / 259g 9.5 oz / 270g   \n",
+       "1             -        -      neutral      9.1 oz / 258g 9 oz / 255g   \n",
+       "2           low        -      neutral   10.1 oz / 285g 9.6 oz / 273g   \n",
+       "3           low     high      neutral   9.4 oz / 266g 10.3 oz / 293g   \n",
+       "4             -        -      neutral  10.7 oz / 302g 10.6 oz / 301g   \n",
+       "\n",
+       "   lightweight drop lab drop brand  ... stiffness torsional rigidity  \\\n",
+       "0          0.0       0.3 mm 8.0 mm  ...  moderate              stiff   \n",
+       "1          0.0       8.2 mm 8.0 mm  ...     stiff           flexible   \n",
+       "2          0.0       4.3 mm 4.0 mm  ...  moderate              stiff   \n",
+       "3          0.0       6.1 mm 4.0 mm  ...  moderate           moderate   \n",
+       "4          0.0       0.2 mm 0.0 mm  ...     stiff           flexible   \n",
+       "\n",
+       "  heel counter stiffness lug depth heel stack lab heel stack brand  \\\n",
+       "0               flexible    2.5 mm                 30.6 mm 38.0 mm   \n",
+       "1               flexible    2.6 mm                 32.8 mm 26.0 mm   \n",
+       "2               moderate    3.6 mm                 34.5 mm 34.0 mm   \n",
+       "3               flexible    3.5 mm                 32.3 mm 32.0 mm   \n",
+       "4                      -    3.7 mm                 24.5 mm 25.0 mm   \n",
+       "\n",
+       "  forefoot lab forefoot brand       season removable insole orthotic friendly  \\\n",
+       "0             30.3 mm 30.0 mm  all seasons                1                 1   \n",
+       "1             24.6 mm 18.0 mm            -                1                 1   \n",
+       "2             30.2 mm 30.0 mm  all seasons                1                 1   \n",
+       "3             26.2 mm 28.0 mm  all seasons                1                 1   \n",
+       "4             24.3 mm 25.0 mm            -                1                 1   \n",
+       "\n",
+       "  waterproofing  \n",
+       "0             -  \n",
+       "1             -  \n",
+       "2             -  \n",
+       "3             -  \n",
+       "4             -  \n",
+       "\n",
+       "[5 rows x 29 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# sebelum hapus\n",
+    "print(\"Before:\", len(df_ori))\n",
+    "\n",
+    "#hapus\n",
+    "df_ori = df_ori.drop_duplicates(subset=tech_columns, keep='first').copy()\n",
+    "\n",
+    "print(\"After :\", len(df_ori))\n",
+    "df_ori.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "8f770d4b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 175 entries, 0 to 174\n",
+      "Data columns (total 29 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   brand                            175 non-null    str    \n",
+      " 1   name                             175 non-null    str    \n",
+      " 2   trail terrain                    175 non-null    str    \n",
+      " 3   shock absorption                 175 non-null    str    \n",
+      " 4   energy return                    175 non-null    str    \n",
+      " 5   traction                         170 non-null    str    \n",
+      " 6   arch support                     175 non-null    str    \n",
+      " 7   weight lab weight brand          175 non-null    str    \n",
+      " 8   lightweight                      168 non-null    float64\n",
+      " 9   drop lab drop brand              175 non-null    str    \n",
+      " 10  strike pattern                   175 non-null    str    \n",
+      " 11  midsole softness                 175 non-null    str    \n",
+      " 12  plate                            175 non-null    str    \n",
+      " 13  toebox durability                175 non-null    str    \n",
+      " 14  heel padding durability          175 non-null    str    \n",
+      " 15  outsole durability               175 non-null    str    \n",
+      " 16  breathability                    175 non-null    str    \n",
+      " 17  width / fit                      175 non-null    str    \n",
+      " 18  toebox width                     175 non-null    str    \n",
+      " 19  stiffness                        175 non-null    str    \n",
+      " 20  torsional rigidity               175 non-null    str    \n",
+      " 21  heel counter stiffness           175 non-null    str    \n",
+      " 22  lug depth                        175 non-null    str    \n",
+      " 23  heel stack lab heel stack brand  175 non-null    str    \n",
+      " 24  forefoot lab forefoot brand      175 non-null    str    \n",
+      " 25  season                           175 non-null    str    \n",
+      " 26  removable insole                 175 non-null    int64  \n",
+      " 27  orthotic friendly                175 non-null    int64  \n",
+      " 28  waterproofing                    169 non-null    str    \n",
+      "dtypes: float64(1), int64(2), str(26)\n",
+      "memory usage: 39.8 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_ori.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "983f5eed",
+   "metadata": {},
+   "source": [
+    "# EDA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1023bc2a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd2cfc21",
+   "metadata": {},
+   "source": [
+    "# Preprocessing\n",
+    "\n",
+    "In this stage we will encode, scale, and bin the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "9f966e10",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>brand</th>\n",
+       "      <th>name</th>\n",
+       "      <th>trail terrain</th>\n",
+       "      <th>shock absorption</th>\n",
+       "      <th>energy return</th>\n",
+       "      <th>traction</th>\n",
+       "      <th>arch support</th>\n",
+       "      <th>weight lab weight brand</th>\n",
+       "      <th>lightweight</th>\n",
+       "      <th>drop lab drop brand</th>\n",
+       "      <th>...</th>\n",
+       "      <th>stiffness</th>\n",
+       "      <th>torsional rigidity</th>\n",
+       "      <th>heel counter stiffness</th>\n",
+       "      <th>lug depth</th>\n",
+       "      <th>heel stack lab heel stack brand</th>\n",
+       "      <th>forefoot lab forefoot brand</th>\n",
+       "      <th>season</th>\n",
+       "      <th>removable insole</th>\n",
+       "      <th>orthotic friendly</th>\n",
+       "      <th>waterproofing</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>terrex agravic speed ultra</td>\n",
+       "      <td>light</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>high</td>\n",
+       "      <td>-</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>9.1 oz / 259g 9.5 oz / 270g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.3 mm 8.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>2.5 mm</td>\n",
+       "      <td>30.6 mm 38.0 mm</td>\n",
+       "      <td>30.3 mm 30.0 mm</td>\n",
+       "      <td>all seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>terrex speed ultra</td>\n",
+       "      <td>light</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>9.1 oz / 258g 9 oz / 255g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8.2 mm 8.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>2.6 mm</td>\n",
+       "      <td>32.8 mm 26.0 mm</td>\n",
+       "      <td>24.6 mm 18.0 mm</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>experience wild</td>\n",
+       "      <td>light moderate</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>low</td>\n",
+       "      <td>-</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>10.1 oz / 285g 9.6 oz / 273g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.3 mm 4.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>3.6 mm</td>\n",
+       "      <td>34.5 mm 34.0 mm</td>\n",
+       "      <td>30.2 mm 30.0 mm</td>\n",
+       "      <td>all seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>experience wild 2</td>\n",
+       "      <td>light</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>low</td>\n",
+       "      <td>high</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>9.4 oz / 266g 10.3 oz / 293g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.1 mm 4.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>3.5 mm</td>\n",
+       "      <td>32.3 mm 32.0 mm</td>\n",
+       "      <td>26.2 mm 28.0 mm</td>\n",
+       "      <td>all seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>lone peak 5.0</td>\n",
+       "      <td>light moderate</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>neutral</td>\n",
+       "      <td>10.7 oz / 302g 10.6 oz / 301g</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.2 mm 0.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>-</td>\n",
+       "      <td>3.7 mm</td>\n",
+       "      <td>24.5 mm 25.0 mm</td>\n",
+       "      <td>24.3 mm 25.0 mm</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 29 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    brand                        name   trail terrain shock absorption  \\\n",
+       "0  adidas  terrex agravic speed ultra           light         moderate   \n",
+       "1  adidas          terrex speed ultra           light                -   \n",
+       "2   altra             experience wild  light moderate         moderate   \n",
+       "3   altra           experience wild 2           light         moderate   \n",
+       "4   altra               lone peak 5.0  light moderate                -   \n",
+       "\n",
+       "  energy return traction arch support        weight lab weight brand  \\\n",
+       "0          high        -      neutral    9.1 oz / 259g 9.5 oz / 270g   \n",
+       "1             -        -      neutral      9.1 oz / 258g 9 oz / 255g   \n",
+       "2           low        -      neutral   10.1 oz / 285g 9.6 oz / 273g   \n",
+       "3           low     high      neutral   9.4 oz / 266g 10.3 oz / 293g   \n",
+       "4             -        -      neutral  10.7 oz / 302g 10.6 oz / 301g   \n",
+       "\n",
+       "   lightweight drop lab drop brand  ... stiffness torsional rigidity  \\\n",
+       "0          0.0       0.3 mm 8.0 mm  ...  moderate              stiff   \n",
+       "1          0.0       8.2 mm 8.0 mm  ...     stiff           flexible   \n",
+       "2          0.0       4.3 mm 4.0 mm  ...  moderate              stiff   \n",
+       "3          0.0       6.1 mm 4.0 mm  ...  moderate           moderate   \n",
+       "4          0.0       0.2 mm 0.0 mm  ...     stiff           flexible   \n",
+       "\n",
+       "  heel counter stiffness lug depth heel stack lab heel stack brand  \\\n",
+       "0               flexible    2.5 mm                 30.6 mm 38.0 mm   \n",
+       "1               flexible    2.6 mm                 32.8 mm 26.0 mm   \n",
+       "2               moderate    3.6 mm                 34.5 mm 34.0 mm   \n",
+       "3               flexible    3.5 mm                 32.3 mm 32.0 mm   \n",
+       "4                      -    3.7 mm                 24.5 mm 25.0 mm   \n",
+       "\n",
+       "  forefoot lab forefoot brand       season removable insole orthotic friendly  \\\n",
+       "0             30.3 mm 30.0 mm  all seasons                1                 1   \n",
+       "1             24.6 mm 18.0 mm            -                1                 1   \n",
+       "2             30.2 mm 30.0 mm  all seasons                1                 1   \n",
+       "3             26.2 mm 28.0 mm  all seasons                1                 1   \n",
+       "4             24.3 mm 25.0 mm            -                1                 1   \n",
+       "\n",
+       "  waterproofing  \n",
+       "0             -  \n",
+       "1             -  \n",
+       "2             -  \n",
+       "3             -  \n",
+       "4             -  \n",
+       "\n",
+       "[5 rows x 29 columns]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = df_ori.copy()\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5729690",
+   "metadata": {},
+   "source": [
+    "## Trail terrain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "351d019a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trail terrain\n",
+      "light moderate        54\n",
+      "light                 48\n",
+      "moderate technical    21\n",
+      "moderate              19\n",
+      "-                     17\n",
+      "technical             11\n",
+      "lightmoderate          3\n",
+      "moderatetechnical      2\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['trail terrain'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "1370c40c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trail terrain\n",
+      "light moderate        54\n",
+      "light                 48\n",
+      "moderate technical    21\n",
+      "moderate              19\n",
+      "technical             11\n",
+      "lightmoderate          3\n",
+      "moderatetechnical      2\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = df[df['trail terrain'] != \"-\"].reset_index(drop=True)\n",
+    "print(df['trail terrain'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "d67f4175",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows after cleaning: 158\n",
+      "\n",
+      "Unique Values in original column (before drop):\n",
+      "<StringArray>\n",
+      "[             'light',     'light moderate', 'moderate technical',\n",
+      "           'moderate',  'moderatetechnical',          'technical',\n",
+      "      'lightmoderate']\n",
+      "Length: 7, dtype: str\n",
+      "\n",
+      "Sample Comparison (Multi-value Mapping):\n",
+      "        trail terrain  terrain_light  terrain_moderate  terrain_technical\n",
+      "0               light              1                 0                  0\n",
+      "1               light              1                 0                  0\n",
+      "2      light moderate              1                 1                  0\n",
+      "3               light              1                 0                  0\n",
+      "4      light moderate              1                 1                  0\n",
+      "5  moderate technical              0                 1                  1\n",
+      "6            moderate              0                 1                  0\n",
+      "7      light moderate              1                 1                  0\n",
+      "8      light moderate              1                 1                  0\n",
+      "9      light moderate              1                 1                  0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Naming convention: all lowercase\n",
+    "df['trail terrain'] = df['trail terrain'].astype(str).str.lower()\n",
+    "base_terrains = ['light', 'moderate', 'technical']\n",
+    "\n",
+    "for terrain in base_terrains:\n",
+    "    column_name = f\"terrain_{terrain}\"\n",
+    "    df[column_name] = df['trail terrain'].str.contains(terrain).astype(int)\n",
+    "\n",
+    "print(\"Rows after cleaning:\", len(df))\n",
+    "\n",
+    "print(\"\\nUnique Values in original column (before drop):\")\n",
+    "print(df[\"trail terrain\"].unique())\n",
+    "\n",
+    "print(\"\\nSample Comparison (Multi-value Mapping):\")\n",
+    "check_cols = [\"trail terrain\"] + [f\"terrain_{t}\" for t in base_terrains]\n",
+    "print(df[check_cols].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "66775876",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trail terrain\n",
+      "light moderate        54\n",
+      "light                 48\n",
+      "moderate technical    21\n",
+      "moderate              19\n",
+      "technical             11\n",
+      "lightmoderate          3\n",
+      "moderatetechnical      2\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Sum of each terrain type:\n",
+      "terrain_light sum: 105\n",
+      "terrain_moderate sum: 99\n",
+      "terrain_technical sum: 34\n",
+      "\n",
+      "    trail terrain  terrain_light  terrain_moderate  terrain_technical\n",
+      "0           light              1                 0                  0\n",
+      "1           light              1                 0                  0\n",
+      "2  light moderate              1                 1                  0\n",
+      "3           light              1                 0                  0\n",
+      "4  light moderate              1                 1                  0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['trail terrain'].value_counts())\n",
+    "\n",
+    "print(\"\\nSum of each terrain type:\")\n",
+    "for terrain in base_terrains:\n",
+    "    col = f\"terrain_{terrain}\"\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "print()\n",
+    "print(df[['trail terrain', 'terrain_light', 'terrain_moderate', 'terrain_technical']].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "8b78d616",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 31 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   brand                            158 non-null    str    \n",
+      " 1   name                             158 non-null    str    \n",
+      " 2   shock absorption                 158 non-null    str    \n",
+      " 3   energy return                    158 non-null    str    \n",
+      " 4   traction                         153 non-null    str    \n",
+      " 5   arch support                     158 non-null    str    \n",
+      " 6   weight lab weight brand          158 non-null    str    \n",
+      " 7   lightweight                      152 non-null    float64\n",
+      " 8   drop lab drop brand              158 non-null    str    \n",
+      " 9   strike pattern                   158 non-null    str    \n",
+      " 10  midsole softness                 158 non-null    str    \n",
+      " 11  plate                            158 non-null    str    \n",
+      " 12  toebox durability                158 non-null    str    \n",
+      " 13  heel padding durability          158 non-null    str    \n",
+      " 14  outsole durability               158 non-null    str    \n",
+      " 15  breathability                    158 non-null    str    \n",
+      " 16  width / fit                      158 non-null    str    \n",
+      " 17  toebox width                     158 non-null    str    \n",
+      " 18  stiffness                        158 non-null    str    \n",
+      " 19  torsional rigidity               158 non-null    str    \n",
+      " 20  heel counter stiffness           158 non-null    str    \n",
+      " 21  lug depth                        158 non-null    str    \n",
+      " 22  heel stack lab heel stack brand  158 non-null    str    \n",
+      " 23  forefoot lab forefoot brand      158 non-null    str    \n",
+      " 24  season                           158 non-null    str    \n",
+      " 25  removable insole                 158 non-null    int64  \n",
+      " 26  orthotic friendly                158 non-null    int64  \n",
+      " 27  waterproofing                    156 non-null    str    \n",
+      " 28  terrain_light                    158 non-null    int64  \n",
+      " 29  terrain_moderate                 158 non-null    int64  \n",
+      " 30  terrain_technical                158 non-null    int64  \n",
+      "dtypes: float64(1), int64(5), str(25)\n",
+      "memory usage: 38.4 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop('trail terrain', axis=1, inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee89a11f",
+   "metadata": {},
+   "source": [
+    "## Shock absorption"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "abd73756",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "shock absorption\n",
+      "-           81\n",
+      "moderate    45\n",
+      "high        20\n",
+      "low         12\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Checking null values first\n",
+    "print(df['shock absorption'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "8ec1e95b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Value Counts Kolom Asli ---\n",
+      "shock absorption\n",
+      "-           81\n",
+      "moderate    45\n",
+      "high        20\n",
+      "low         12\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "--- Ordinal encoding ---\n",
+      "Index 0: 81 baris\n",
+      "Index 1: 12 baris\n",
+      "Index 2: 0 baris\n",
+      "Index 3: 45 baris\n",
+      "Index 4: 0 baris\n",
+      "Index 5: 20 baris\n",
+      "\n",
+      "--- Perbandingan Data (Head) ---\n",
+      "  shock absorption  shock_absorption\n",
+      "0         moderate                 3\n",
+      "1                -                 0\n",
+      "2         moderate                 3\n",
+      "3         moderate                 3\n",
+      "4                -                 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "shock_scaled = {\n",
+    "    \"-\": 0,\n",
+    "    \"low\": 1,\n",
+    "    \"moderate\": 3,\n",
+    "    \"high\": 5\n",
+    "}\n",
+    "\n",
+    "df['shock_absorption'] = df['shock absorption'].map(shock_scaled)\n",
+    "\n",
+    "print(\"--- Value Counts Kolom Asli ---\")\n",
+    "print(df[\"shock absorption\"].value_counts())\n",
+    "\n",
+    "print(\"\\n--- Ordinal encoding ---\")\n",
+    "counts = df[\"shock_absorption\"].value_counts().sort_index()\n",
+    "for i in range(6):\n",
+    "    print(f\"Index {i}: {counts.get(i, 0)} baris\")\n",
+    "\n",
+    "print(\"\\n--- Perbandingan Data (Head) ---\")\n",
+    "print(df[[\"shock absorption\", \"shock_absorption\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "857a652d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 31 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   brand                            158 non-null    str    \n",
+      " 1   name                             158 non-null    str    \n",
+      " 2   energy return                    158 non-null    str    \n",
+      " 3   traction                         153 non-null    str    \n",
+      " 4   arch support                     158 non-null    str    \n",
+      " 5   weight lab weight brand          158 non-null    str    \n",
+      " 6   lightweight                      152 non-null    float64\n",
+      " 7   drop lab drop brand              158 non-null    str    \n",
+      " 8   strike pattern                   158 non-null    str    \n",
+      " 9   midsole softness                 158 non-null    str    \n",
+      " 10  plate                            158 non-null    str    \n",
+      " 11  toebox durability                158 non-null    str    \n",
+      " 12  heel padding durability          158 non-null    str    \n",
+      " 13  outsole durability               158 non-null    str    \n",
+      " 14  breathability                    158 non-null    str    \n",
+      " 15  width / fit                      158 non-null    str    \n",
+      " 16  toebox width                     158 non-null    str    \n",
+      " 17  stiffness                        158 non-null    str    \n",
+      " 18  torsional rigidity               158 non-null    str    \n",
+      " 19  heel counter stiffness           158 non-null    str    \n",
+      " 20  lug depth                        158 non-null    str    \n",
+      " 21  heel stack lab heel stack brand  158 non-null    str    \n",
+      " 22  forefoot lab forefoot brand      158 non-null    str    \n",
+      " 23  season                           158 non-null    str    \n",
+      " 24  removable insole                 158 non-null    int64  \n",
+      " 25  orthotic friendly                158 non-null    int64  \n",
+      " 26  waterproofing                    156 non-null    str    \n",
+      " 27  terrain_light                    158 non-null    int64  \n",
+      " 28  terrain_moderate                 158 non-null    int64  \n",
+      " 29  terrain_technical                158 non-null    int64  \n",
+      " 30  shock_absorption                 158 non-null    int64  \n",
+      "dtypes: float64(1), int64(6), str(24)\n",
+      "memory usage: 38.4 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop('shock absorption', axis=1, inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0dfe6ab4",
+   "metadata": {},
+   "source": [
+    "## Energy return"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "e1583547",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "energy return\n",
+      "-           81\n",
+      "low         36\n",
+      "moderate    36\n",
+      "high         5\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"energy return\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "74393e16",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Value Counts Kolom Asli ---\n",
+      "energy return\n",
+      "-           81\n",
+      "low         36\n",
+      "moderate    36\n",
+      "high         5\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "--- Sebaran Nilai Hasil Ordinal (Scale 1-5) ---\n",
+      "Index 0: 81 baris\n",
+      "Index 1: 36 baris\n",
+      "Index 2: 0 baris\n",
+      "Index 3: 36 baris\n",
+      "Index 4: 0 baris\n",
+      "Index 5: 5 baris\n",
+      "\n",
+      "--- Perbandingan Data (Head) ---\n",
+      "  energy return  energy_return\n",
+      "0          high              5\n",
+      "1             -              0\n",
+      "2           low              1\n",
+      "3           low              1\n",
+      "4             -              0\n"
+     ]
+    }
+   ],
+   "source": [
+    "energy_scaled = {\n",
+    "    \"-\": 0,\n",
+    "    \"low\": 1,\n",
+    "    \"moderate\": 3,\n",
+    "    \"high\": 5\n",
+    "}\n",
+    "\n",
+    "df['energy_return'] = df['energy return'].map(energy_scaled)\n",
+    "\n",
+    "print(\"--- Value Counts Kolom Asli ---\")\n",
+    "print(df[\"energy return\"].value_counts())\n",
+    "\n",
+    "print(\"\\n--- Sebaran Nilai Hasil Ordinal (Scale 1-5) ---\")\n",
+    "counts = df[\"energy_return\"].value_counts().sort_index()\n",
+    "for i in range(6):\n",
+    "    print(f\"Index {i}: {counts.get(i, 0)} baris\")\n",
+    "\n",
+    "print(\"\\n--- Perbandingan Data (Head) ---\")\n",
+    "print(df[[\"energy return\", \"energy_return\"]].head())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "8b60e596",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 31 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   brand                            158 non-null    str    \n",
+      " 1   name                             158 non-null    str    \n",
+      " 2   traction                         153 non-null    str    \n",
+      " 3   arch support                     158 non-null    str    \n",
+      " 4   weight lab weight brand          158 non-null    str    \n",
+      " 5   lightweight                      152 non-null    float64\n",
+      " 6   drop lab drop brand              158 non-null    str    \n",
+      " 7   strike pattern                   158 non-null    str    \n",
+      " 8   midsole softness                 158 non-null    str    \n",
+      " 9   plate                            158 non-null    str    \n",
+      " 10  toebox durability                158 non-null    str    \n",
+      " 11  heel padding durability          158 non-null    str    \n",
+      " 12  outsole durability               158 non-null    str    \n",
+      " 13  breathability                    158 non-null    str    \n",
+      " 14  width / fit                      158 non-null    str    \n",
+      " 15  toebox width                     158 non-null    str    \n",
+      " 16  stiffness                        158 non-null    str    \n",
+      " 17  torsional rigidity               158 non-null    str    \n",
+      " 18  heel counter stiffness           158 non-null    str    \n",
+      " 19  lug depth                        158 non-null    str    \n",
+      " 20  heel stack lab heel stack brand  158 non-null    str    \n",
+      " 21  forefoot lab forefoot brand      158 non-null    str    \n",
+      " 22  season                           158 non-null    str    \n",
+      " 23  removable insole                 158 non-null    int64  \n",
+      " 24  orthotic friendly                158 non-null    int64  \n",
+      " 25  waterproofing                    156 non-null    str    \n",
+      " 26  terrain_light                    158 non-null    int64  \n",
+      " 27  terrain_moderate                 158 non-null    int64  \n",
+      " 28  terrain_technical                158 non-null    int64  \n",
+      " 29  shock_absorption                 158 non-null    int64  \n",
+      " 30  energy_return                    158 non-null    int64  \n",
+      "dtypes: float64(1), int64(7), str(23)\n",
+      "memory usage: 38.4 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop('energy return', axis=1, inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "050f4527",
+   "metadata": {},
+   "source": [
+    "## Traction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "6a938fe9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "traction\n",
+      "-           127\n",
+      "high         25\n",
+      "moderate      1\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"traction\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "7fd8d46f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Value Counts Kolom Asli ---\n",
+      "traction\n",
+      "0.0    127\n",
+      "5.0     25\n",
+      "3.0      1\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "--- Sebaran Nilai Hasil Ordinal (Scale 1-5) ---\n",
+      "Index 0: 127 baris\n",
+      "Index 1: 0 baris\n",
+      "Index 2: 0 baris\n",
+      "Index 3: 1 baris\n",
+      "Index 4: 0 baris\n",
+      "Index 5: 25 baris\n",
+      "\n",
+      "--- Perbandingan Data (Head) ---\n",
+      "   traction  traction\n",
+      "0       0.0       0.0\n",
+      "1       0.0       0.0\n",
+      "2       0.0       0.0\n",
+      "3       5.0       5.0\n",
+      "4       0.0       0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "traction_scaled = {\n",
+    "    \"-\": 0,\n",
+    "    \"low\": 1,\n",
+    "    \"moderate\": 3,\n",
+    "    \"high\": 5\n",
+    "}\n",
+    "\n",
+    "df['traction'] = df['traction'].map(traction_scaled)\n",
+    "\n",
+    "print(\"--- Value Counts Kolom Asli ---\")\n",
+    "print(df[\"traction\"].value_counts())\n",
+    "\n",
+    "print(\"\\n--- Sebaran Nilai Hasil Ordinal (Scale 1-5) ---\")\n",
+    "counts = df[\"traction\"].value_counts().sort_index()\n",
+    "for i in range(6):\n",
+    "    print(f\"Index {i}: {counts.get(i, 0)} baris\")\n",
+    "\n",
+    "print(\"\\n--- Perbandingan Data (Head) ---\")\n",
+    "print(df[[\"traction\", \"traction\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "3a83c57d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 30 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   brand                            158 non-null    str    \n",
+      " 1   name                             158 non-null    str    \n",
+      " 2   arch support                     158 non-null    str    \n",
+      " 3   weight lab weight brand          158 non-null    str    \n",
+      " 4   lightweight                      152 non-null    float64\n",
+      " 5   drop lab drop brand              158 non-null    str    \n",
+      " 6   strike pattern                   158 non-null    str    \n",
+      " 7   midsole softness                 158 non-null    str    \n",
+      " 8   plate                            158 non-null    str    \n",
+      " 9   toebox durability                158 non-null    str    \n",
+      " 10  heel padding durability          158 non-null    str    \n",
+      " 11  outsole durability               158 non-null    str    \n",
+      " 12  breathability                    158 non-null    str    \n",
+      " 13  width / fit                      158 non-null    str    \n",
+      " 14  toebox width                     158 non-null    str    \n",
+      " 15  stiffness                        158 non-null    str    \n",
+      " 16  torsional rigidity               158 non-null    str    \n",
+      " 17  heel counter stiffness           158 non-null    str    \n",
+      " 18  lug depth                        158 non-null    str    \n",
+      " 19  heel stack lab heel stack brand  158 non-null    str    \n",
+      " 20  forefoot lab forefoot brand      158 non-null    str    \n",
+      " 21  season                           158 non-null    str    \n",
+      " 22  removable insole                 158 non-null    int64  \n",
+      " 23  orthotic friendly                158 non-null    int64  \n",
+      " 24  waterproofing                    156 non-null    str    \n",
+      " 25  terrain_light                    158 non-null    int64  \n",
+      " 26  terrain_moderate                 158 non-null    int64  \n",
+      " 27  terrain_technical                158 non-null    int64  \n",
+      " 28  shock_absorption                 158 non-null    int64  \n",
+      " 29  energy_return                    158 non-null    int64  \n",
+      "dtypes: float64(1), int64(7), str(22)\n",
+      "memory usage: 37.2 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop('traction', axis=1, inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d283f13a",
+   "metadata": {},
+   "source": [
+    "## Arch support"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "1d055a43",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "arch support\n",
+      "neutral      154\n",
+      "stability      4\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"arch support\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "8ee15f86",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 158\n",
+      "NULL Value: 0\n",
+      "\n",
+      "Sample Comparison:\n",
+      "  arch support  arch_neutral  arch_stability\n",
+      "0      neutral             1               0\n",
+      "1      neutral             1               0\n",
+      "2      neutral             1               0\n",
+      "3      neutral             1               0\n",
+      "4      neutral             1               0\n",
+      "5      neutral             1               0\n",
+      "6      neutral             1               0\n",
+      "7      neutral             1               0\n",
+      "8      neutral             1               0\n",
+      "9      neutral             1               0\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['arch support'] = df['arch support'].astype(str).str.lower()\n",
+    "base_arch = ['neutral', 'stability']\n",
+    "\n",
+    "\n",
+    "for level in base_arch:\n",
+    "    column_name = f\"arch_{level}\"\n",
+    "    df[column_name] = df['arch support'].str.contains(level, na=False).astype(int)\n",
+    "\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "\n",
+    "arch_cols = [f\"arch_{l}\" for l in base_arch]\n",
+    "zero_vector_count = (df[arch_cols].sum(axis=1) == 0).sum()\n",
+    "print(f\"NULL Value: {zero_vector_count}\")\n",
+    "\n",
+    "print(\"\\nSample Comparison:\")\n",
+    "print(df[[\"arch support\"] + arch_cols].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "bddc5889",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "arch support\n",
+      "neutral      154\n",
+      "stability      4\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "arch_neutral sum: 154\n",
+      "arch_stability sum: 4\n",
+      "\n",
+      "  arch support  arch_neutral  arch_stability\n",
+      "0      neutral             1               0\n",
+      "1      neutral             1               0\n",
+      "2      neutral             1               0\n",
+      "3      neutral             1               0\n",
+      "4      neutral             1               0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"arch support\"].value_counts())\n",
+    "\n",
+    "print()\n",
+    "for level in base_arch:\n",
+    "    col = f\"arch_{level}\"\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"arch support\"] + arch_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "d8b2e073",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 31 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   brand                            158 non-null    str    \n",
+      " 1   name                             158 non-null    str    \n",
+      " 2   weight lab weight brand          158 non-null    str    \n",
+      " 3   lightweight                      152 non-null    float64\n",
+      " 4   drop lab drop brand              158 non-null    str    \n",
+      " 5   strike pattern                   158 non-null    str    \n",
+      " 6   midsole softness                 158 non-null    str    \n",
+      " 7   plate                            158 non-null    str    \n",
+      " 8   toebox durability                158 non-null    str    \n",
+      " 9   heel padding durability          158 non-null    str    \n",
+      " 10  outsole durability               158 non-null    str    \n",
+      " 11  breathability                    158 non-null    str    \n",
+      " 12  width / fit                      158 non-null    str    \n",
+      " 13  toebox width                     158 non-null    str    \n",
+      " 14  stiffness                        158 non-null    str    \n",
+      " 15  torsional rigidity               158 non-null    str    \n",
+      " 16  heel counter stiffness           158 non-null    str    \n",
+      " 17  lug depth                        158 non-null    str    \n",
+      " 18  heel stack lab heel stack brand  158 non-null    str    \n",
+      " 19  forefoot lab forefoot brand      158 non-null    str    \n",
+      " 20  season                           158 non-null    str    \n",
+      " 21  removable insole                 158 non-null    int64  \n",
+      " 22  orthotic friendly                158 non-null    int64  \n",
+      " 23  waterproofing                    156 non-null    str    \n",
+      " 24  terrain_light                    158 non-null    int64  \n",
+      " 25  terrain_moderate                 158 non-null    int64  \n",
+      " 26  terrain_technical                158 non-null    int64  \n",
+      " 27  shock_absorption                 158 non-null    int64  \n",
+      " 28  energy_return                    158 non-null    int64  \n",
+      " 29  arch_neutral                     158 non-null    int64  \n",
+      " 30  arch_stability                   158 non-null    int64  \n",
+      "dtypes: float64(1), int64(9), str(21)\n",
+      "memory usage: 38.4 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=['arch support'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eba72c42",
+   "metadata": {},
+   "source": [
+    "## Split Weight lab Weight brand"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "4027e6f8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(0)"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mask_missing = (\n",
+    "    df[\"weight lab weight brand\"].isna() |\n",
+    "    (df[\"weight lab weight brand\"].astype(str).str.strip() == \"-\")\n",
+    ")\n",
+    "mask_missing.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "dd34c04a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         weight lab weight brand  weight_lab_oz  weight_lab_g  \\\n",
+      "0    9.1 oz / 259g 9.5 oz / 270g            9.1           259   \n",
+      "1      9.1 oz / 258g 9 oz / 255g            9.1           258   \n",
+      "2   10.1 oz / 285g 9.6 oz / 273g           10.1           285   \n",
+      "3   9.4 oz / 266g 10.3 oz / 293g            9.4           266   \n",
+      "4  10.7 oz / 302g 10.6 oz / 301g           10.7           302   \n",
+      "\n",
+      "   weight_brand_oz  weight_brand_g  \n",
+      "0              9.5           270.0  \n",
+      "1              9.0           255.0  \n",
+      "2              9.6           273.0  \n",
+      "3             10.3           293.0  \n",
+      "4             10.6           301.0  \n"
+     ]
+    }
+   ],
+   "source": [
+    "weight = df[\"weight lab weight brand\"].str.findall(r\"[\\d.]+\")\n",
+    "df[[\"weight_lab_oz\", \"weight_lab_g\", \"weight_brand_oz\", \"weight_brand_g\"]] = (\n",
+    "    pd.DataFrame(weight.tolist(), index=df.index)\n",
+    ")\n",
+    "\n",
+    "for col in [\"weight_lab_oz\", \"weight_lab_g\", \"weight_brand_oz\", \"weight_brand_g\"]:\n",
+    "    df[col] = pd.to_numeric(df[col], errors=\"coerce\")\n",
+    "\n",
+    "print(df[[\"weight lab weight brand\", \"weight_lab_oz\", \"weight_lab_g\", \"weight_brand_oz\", \"weight_brand_g\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "4ad81586",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 34 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   brand                            158 non-null    str    \n",
+      " 1   name                             158 non-null    str    \n",
+      " 2   lightweight                      152 non-null    float64\n",
+      " 3   drop lab drop brand              158 non-null    str    \n",
+      " 4   strike pattern                   158 non-null    str    \n",
+      " 5   midsole softness                 158 non-null    str    \n",
+      " 6   plate                            158 non-null    str    \n",
+      " 7   toebox durability                158 non-null    str    \n",
+      " 8   heel padding durability          158 non-null    str    \n",
+      " 9   outsole durability               158 non-null    str    \n",
+      " 10  breathability                    158 non-null    str    \n",
+      " 11  width / fit                      158 non-null    str    \n",
+      " 12  toebox width                     158 non-null    str    \n",
+      " 13  stiffness                        158 non-null    str    \n",
+      " 14  torsional rigidity               158 non-null    str    \n",
+      " 15  heel counter stiffness           158 non-null    str    \n",
+      " 16  lug depth                        158 non-null    str    \n",
+      " 17  heel stack lab heel stack brand  158 non-null    str    \n",
+      " 18  forefoot lab forefoot brand      158 non-null    str    \n",
+      " 19  season                           158 non-null    str    \n",
+      " 20  removable insole                 158 non-null    int64  \n",
+      " 21  orthotic friendly                158 non-null    int64  \n",
+      " 22  waterproofing                    156 non-null    str    \n",
+      " 23  terrain_light                    158 non-null    int64  \n",
+      " 24  terrain_moderate                 158 non-null    int64  \n",
+      " 25  terrain_technical                158 non-null    int64  \n",
+      " 26  shock_absorption                 158 non-null    int64  \n",
+      " 27  energy_return                    158 non-null    int64  \n",
+      " 28  arch_neutral                     158 non-null    int64  \n",
+      " 29  arch_stability                   158 non-null    int64  \n",
+      " 30  weight_lab_oz                    158 non-null    float64\n",
+      " 31  weight_lab_g                     158 non-null    int64  \n",
+      " 32  weight_brand_oz                  155 non-null    float64\n",
+      " 33  weight_brand_g                   155 non-null    float64\n",
+      "dtypes: float64(4), int64(10), str(20)\n",
+      "memory usage: 42.1 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"weight lab weight brand\",], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f8f3114",
+   "metadata": {},
+   "source": [
+    "## Split Drop lab Drop brand"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "d96d69ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(0)"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mask_missing = (\n",
+    "    df[\"drop lab drop brand\"].isna() |\n",
+    "    (df[\"drop lab drop brand\"].astype(str).str.strip() == \"-\")\n",
+    ")\n",
+    "mask_missing.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "33f141ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  drop lab drop brand  drop_lab_mm  drop_brand_mm\n",
+      "0       0.3 mm 8.0 mm          0.3            8.0\n",
+      "1       8.2 mm 8.0 mm          8.2            8.0\n",
+      "2       4.3 mm 4.0 mm          4.3            4.0\n",
+      "3       6.1 mm 4.0 mm          6.1            4.0\n",
+      "4       0.2 mm 0.0 mm          0.2            0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "drop = df[\"drop lab drop brand\"].str.findall(r\"[\\d.]+\")\n",
+    "\n",
+    "df[[\"drop_lab_mm\", \"drop_brand_mm\"]] = (\n",
+    "    pd.DataFrame(drop.tolist(), index=df.index)\n",
+    ")\n",
+    "\n",
+    "for col in [\"drop_lab_mm\", \"drop_brand_mm\"]:\n",
+    "    df[col] = pd.to_numeric(df[col], errors=\"coerce\")\n",
+    "\n",
+    "print(df[[\"drop lab drop brand\", \"drop_lab_mm\", \"drop_brand_mm\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "1d1c4eac",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 35 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   brand                            158 non-null    str    \n",
+      " 1   name                             158 non-null    str    \n",
+      " 2   lightweight                      152 non-null    float64\n",
+      " 3   strike pattern                   158 non-null    str    \n",
+      " 4   midsole softness                 158 non-null    str    \n",
+      " 5   plate                            158 non-null    str    \n",
+      " 6   toebox durability                158 non-null    str    \n",
+      " 7   heel padding durability          158 non-null    str    \n",
+      " 8   outsole durability               158 non-null    str    \n",
+      " 9   breathability                    158 non-null    str    \n",
+      " 10  width / fit                      158 non-null    str    \n",
+      " 11  toebox width                     158 non-null    str    \n",
+      " 12  stiffness                        158 non-null    str    \n",
+      " 13  torsional rigidity               158 non-null    str    \n",
+      " 14  heel counter stiffness           158 non-null    str    \n",
+      " 15  lug depth                        158 non-null    str    \n",
+      " 16  heel stack lab heel stack brand  158 non-null    str    \n",
+      " 17  forefoot lab forefoot brand      158 non-null    str    \n",
+      " 18  season                           158 non-null    str    \n",
+      " 19  removable insole                 158 non-null    int64  \n",
+      " 20  orthotic friendly                158 non-null    int64  \n",
+      " 21  waterproofing                    156 non-null    str    \n",
+      " 22  terrain_light                    158 non-null    int64  \n",
+      " 23  terrain_moderate                 158 non-null    int64  \n",
+      " 24  terrain_technical                158 non-null    int64  \n",
+      " 25  shock_absorption                 158 non-null    int64  \n",
+      " 26  energy_return                    158 non-null    int64  \n",
+      " 27  arch_neutral                     158 non-null    int64  \n",
+      " 28  arch_stability                   158 non-null    int64  \n",
+      " 29  weight_lab_oz                    158 non-null    float64\n",
+      " 30  weight_lab_g                     158 non-null    int64  \n",
+      " 31  weight_brand_oz                  155 non-null    float64\n",
+      " 32  weight_brand_g                   155 non-null    float64\n",
+      " 33  drop_lab_mm                      158 non-null    float64\n",
+      " 34  drop_brand_mm                    152 non-null    float64\n",
+      "dtypes: float64(6), int64(10), str(19)\n",
+      "memory usage: 43.3 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"drop lab drop brand\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a60c8a59",
+   "metadata": {},
+   "source": [
+    "## Strike pattern"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "53e91a22",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "strike pattern\n",
+      "mid/forefoot         83\n",
+      "heel                 48\n",
+      "heel mid/forefoot    25\n",
+      "heelmid/forefoot      2\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"strike pattern\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "8f141a55",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 158\n",
+      "\n",
+      "Unique Values in original column:\n",
+      "<StringArray>\n",
+      "['mid/forefoot', 'heel mid/forefoot', 'heel', 'heelmid/forefoot']\n",
+      "Length: 4, dtype: str\n",
+      "\n",
+      "Sample Comparison (Multi-label Mapping):\n",
+      "      strike pattern  strike_heel  strike_mid  strike_forefoot\n",
+      "0       mid/forefoot            0           1                1\n",
+      "1  heel mid/forefoot            1           1                1\n",
+      "2       mid/forefoot            0           1                1\n",
+      "3       mid/forefoot            0           1                1\n",
+      "4       mid/forefoot            0           1                1\n",
+      "5       mid/forefoot            0           1                1\n",
+      "6       mid/forefoot            0           1                1\n",
+      "7       mid/forefoot            0           1                1\n",
+      "8       mid/forefoot            0           1                1\n",
+      "9       mid/forefoot            0           1                1\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['strike pattern'] = df['strike pattern'].astype(str).str.lower()\n",
+    "base_strikes = ['heel', 'mid', 'forefoot']\n",
+    "\n",
+    "for strike in base_strikes:\n",
+    "    column_name = f\"strike_{strike}\"\n",
+    "    df[column_name] = df['strike pattern'].str.contains(strike, na=False).astype(int)\n",
+    "\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "print(\"\\nUnique Values in original column:\")\n",
+    "print(df[\"strike pattern\"].unique())\n",
+    "\n",
+    "print(\"\\nSample Comparison (Multi-label Mapping):\")\n",
+    "strike_cols = [f\"strike_{s}\" for s in base_strikes]\n",
+    "print(df[[\"strike pattern\"] + strike_cols].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "1b1de021",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "strike pattern\n",
+      "mid/forefoot         83\n",
+      "heel                 48\n",
+      "heel mid/forefoot    25\n",
+      "heelmid/forefoot      2\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "strike_heel sum: 75\n",
+      "strike_mid sum: 110\n",
+      "strike_forefoot sum: 110\n",
+      "\n",
+      "      strike pattern  strike_heel  strike_mid  strike_forefoot\n",
+      "0       mid/forefoot            0           1                1\n",
+      "1  heel mid/forefoot            1           1                1\n",
+      "2       mid/forefoot            0           1                1\n",
+      "3       mid/forefoot            0           1                1\n",
+      "4       mid/forefoot            0           1                1\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"strike pattern\"].value_counts())\n",
+    "\n",
+    "print()\n",
+    "for strike in base_strikes:\n",
+    "    col = f\"strike_{strike}\"\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"strike pattern\"] + strike_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "767fe00e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 37 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   brand                            158 non-null    str    \n",
+      " 1   name                             158 non-null    str    \n",
+      " 2   lightweight                      152 non-null    float64\n",
+      " 3   midsole softness                 158 non-null    str    \n",
+      " 4   plate                            158 non-null    str    \n",
+      " 5   toebox durability                158 non-null    str    \n",
+      " 6   heel padding durability          158 non-null    str    \n",
+      " 7   outsole durability               158 non-null    str    \n",
+      " 8   breathability                    158 non-null    str    \n",
+      " 9   width / fit                      158 non-null    str    \n",
+      " 10  toebox width                     158 non-null    str    \n",
+      " 11  stiffness                        158 non-null    str    \n",
+      " 12  torsional rigidity               158 non-null    str    \n",
+      " 13  heel counter stiffness           158 non-null    str    \n",
+      " 14  lug depth                        158 non-null    str    \n",
+      " 15  heel stack lab heel stack brand  158 non-null    str    \n",
+      " 16  forefoot lab forefoot brand      158 non-null    str    \n",
+      " 17  season                           158 non-null    str    \n",
+      " 18  removable insole                 158 non-null    int64  \n",
+      " 19  orthotic friendly                158 non-null    int64  \n",
+      " 20  waterproofing                    156 non-null    str    \n",
+      " 21  terrain_light                    158 non-null    int64  \n",
+      " 22  terrain_moderate                 158 non-null    int64  \n",
+      " 23  terrain_technical                158 non-null    int64  \n",
+      " 24  shock_absorption                 158 non-null    int64  \n",
+      " 25  energy_return                    158 non-null    int64  \n",
+      " 26  arch_neutral                     158 non-null    int64  \n",
+      " 27  arch_stability                   158 non-null    int64  \n",
+      " 28  weight_lab_oz                    158 non-null    float64\n",
+      " 29  weight_lab_g                     158 non-null    int64  \n",
+      " 30  weight_brand_oz                  155 non-null    float64\n",
+      " 31  weight_brand_g                   155 non-null    float64\n",
+      " 32  drop_lab_mm                      158 non-null    float64\n",
+      " 33  drop_brand_mm                    152 non-null    float64\n",
+      " 34  strike_heel                      158 non-null    int64  \n",
+      " 35  strike_mid                       158 non-null    int64  \n",
+      " 36  strike_forefoot                  158 non-null    int64  \n",
+      "dtypes: float64(6), int64(13), str(18)\n",
+      "memory usage: 45.8 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=['strike pattern'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9e9ece2",
+   "metadata": {},
+   "source": [
+    "## Midsole softness"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "b524c2a4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "midsole softness\n",
+      "balanced    72\n",
+      "soft        54\n",
+      "-           19\n",
+      "firm        13\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"midsole softness\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "f61696ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Value Counts Kolom Asli ---\n",
+      "midsole softness\n",
+      "balanced    72\n",
+      "soft        54\n",
+      "-           19\n",
+      "firm        13\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "--- Sebaran Nilai Hasil Ordinal (Scale 1-5) ---\n",
+      "Index 0: 19 baris\n",
+      "Index 1: 13 baris\n",
+      "Index 2: 0 baris\n",
+      "Index 3: 72 baris\n",
+      "Index 4: 0 baris\n",
+      "Index 5: 54 baris\n",
+      "\n",
+      "--- Perbandingan Data (Head) ---\n",
+      "  midsole softness  midsole_softness\n",
+      "0         balanced                 3\n",
+      "1                -                 0\n",
+      "2             soft                 5\n",
+      "3         balanced                 3\n",
+      "4                -                 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "softness_scaled = {\n",
+    "    \"firm\": 1,\n",
+    "    \"balanced\": 3,\n",
+    "    \"soft\": 5,\n",
+    "    \"-\": 0,\n",
+    "    \"0\": 0\n",
+    "}\n",
+    "\n",
+    "df['midsole_softness'] = df['midsole softness'].map(softness_scaled)\n",
+    "\n",
+    "print(\"--- Value Counts Kolom Asli ---\")\n",
+    "print(df[\"midsole softness\"].value_counts())\n",
+    "\n",
+    "print(\"\\n--- Sebaran Nilai Hasil Ordinal (Scale 1-5) ---\")\n",
+    "counts = df[\"midsole_softness\"].value_counts().sort_index()\n",
+    "for i in range(6):\n",
+    "    print(f\"Index {i}: {counts.get(i, 0)} baris\")\n",
+    "\n",
+    "print(\"\\n--- Perbandingan Data (Head) ---\")\n",
+    "print(df[[\"midsole softness\", \"midsole_softness\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "c361b27d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 37 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   brand                            158 non-null    str    \n",
+      " 1   name                             158 non-null    str    \n",
+      " 2   lightweight                      152 non-null    float64\n",
+      " 3   plate                            158 non-null    str    \n",
+      " 4   toebox durability                158 non-null    str    \n",
+      " 5   heel padding durability          158 non-null    str    \n",
+      " 6   outsole durability               158 non-null    str    \n",
+      " 7   breathability                    158 non-null    str    \n",
+      " 8   width / fit                      158 non-null    str    \n",
+      " 9   toebox width                     158 non-null    str    \n",
+      " 10  stiffness                        158 non-null    str    \n",
+      " 11  torsional rigidity               158 non-null    str    \n",
+      " 12  heel counter stiffness           158 non-null    str    \n",
+      " 13  lug depth                        158 non-null    str    \n",
+      " 14  heel stack lab heel stack brand  158 non-null    str    \n",
+      " 15  forefoot lab forefoot brand      158 non-null    str    \n",
+      " 16  season                           158 non-null    str    \n",
+      " 17  removable insole                 158 non-null    int64  \n",
+      " 18  orthotic friendly                158 non-null    int64  \n",
+      " 19  waterproofing                    156 non-null    str    \n",
+      " 20  terrain_light                    158 non-null    int64  \n",
+      " 21  terrain_moderate                 158 non-null    int64  \n",
+      " 22  terrain_technical                158 non-null    int64  \n",
+      " 23  shock_absorption                 158 non-null    int64  \n",
+      " 24  energy_return                    158 non-null    int64  \n",
+      " 25  arch_neutral                     158 non-null    int64  \n",
+      " 26  arch_stability                   158 non-null    int64  \n",
+      " 27  weight_lab_oz                    158 non-null    float64\n",
+      " 28  weight_lab_g                     158 non-null    int64  \n",
+      " 29  weight_brand_oz                  155 non-null    float64\n",
+      " 30  weight_brand_g                   155 non-null    float64\n",
+      " 31  drop_lab_mm                      158 non-null    float64\n",
+      " 32  drop_brand_mm                    152 non-null    float64\n",
+      " 33  strike_heel                      158 non-null    int64  \n",
+      " 34  strike_mid                       158 non-null    int64  \n",
+      " 35  strike_forefoot                  158 non-null    int64  \n",
+      " 36  midsole_softness                 158 non-null    int64  \n",
+      "dtypes: float64(6), int64(14), str(17)\n",
+      "memory usage: 45.8 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"midsole softness\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00df0f14",
+   "metadata": {},
+   "source": [
+    "## Toebox durability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "6e926181",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "toebox durability\n",
+      "good         39\n",
+      "decent       39\n",
+      "-            36\n",
+      "bad          17\n",
+      "very bad     15\n",
+      "very good    12\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['toebox durability'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "8a18a9a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 158\n",
+      "\n",
+      "Unique Values mapping check:\n",
+      "'-' di-encode menjadi 0 (Total: 36)\n",
+      "'very bad' di-encode menjadi 1 (Total: 15)\n",
+      "'bad' di-encode menjadi 2 (Total: 17)\n",
+      "'decent' di-encode menjadi 3 (Total: 39)\n",
+      "'good' di-encode menjadi 4 (Total: 39)\n",
+      "'very good' di-encode menjadi 5 (Total: 12)\n",
+      "\n",
+      "Sample Data:\n",
+      "  toebox durability  toebox_durability\n",
+      "0              good                  4\n",
+      "1                 -                  0\n",
+      "2            decent                  3\n",
+      "3            decent                  3\n",
+      "4                 -                  0\n",
+      "5                 -                  0\n",
+      "6                 -                  0\n",
+      "7              good                  4\n",
+      "8            decent                  3\n",
+      "9                 -                  0\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['toebox durability'] = df['toebox durability'].astype(str).str.lower()\n",
+    "\n",
+    "durability_map = {\n",
+    "    \"-\": 0,\n",
+    "    \"very bad\": 1,\n",
+    "    \"bad\": 2,\n",
+    "    \"decent\": 3,\n",
+    "    \"good\": 4,\n",
+    "    \"very good\": 5\n",
+    "}\n",
+    "\n",
+    "df['toebox_durability'] = df['toebox durability'].map(durability_map)\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "print(\"\\nUnique Values mapping check:\")\n",
+    "for label, value in durability_map.items():\n",
+    "    count = (df['toebox durability'] == label).sum()\n",
+    "    print(f\"'{label}' di-encode menjadi {value} (Total: {count})\")\n",
+    "\n",
+    "print(\"\\nSample Data:\")\n",
+    "print(df[[\"toebox durability\", \"toebox_durability\"]].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "96538ff1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 37 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   brand                            158 non-null    str    \n",
+      " 1   name                             158 non-null    str    \n",
+      " 2   lightweight                      152 non-null    float64\n",
+      " 3   plate                            158 non-null    str    \n",
+      " 4   heel padding durability          158 non-null    str    \n",
+      " 5   outsole durability               158 non-null    str    \n",
+      " 6   breathability                    158 non-null    str    \n",
+      " 7   width / fit                      158 non-null    str    \n",
+      " 8   toebox width                     158 non-null    str    \n",
+      " 9   stiffness                        158 non-null    str    \n",
+      " 10  torsional rigidity               158 non-null    str    \n",
+      " 11  heel counter stiffness           158 non-null    str    \n",
+      " 12  lug depth                        158 non-null    str    \n",
+      " 13  heel stack lab heel stack brand  158 non-null    str    \n",
+      " 14  forefoot lab forefoot brand      158 non-null    str    \n",
+      " 15  season                           158 non-null    str    \n",
+      " 16  removable insole                 158 non-null    int64  \n",
+      " 17  orthotic friendly                158 non-null    int64  \n",
+      " 18  waterproofing                    156 non-null    str    \n",
+      " 19  terrain_light                    158 non-null    int64  \n",
+      " 20  terrain_moderate                 158 non-null    int64  \n",
+      " 21  terrain_technical                158 non-null    int64  \n",
+      " 22  shock_absorption                 158 non-null    int64  \n",
+      " 23  energy_return                    158 non-null    int64  \n",
+      " 24  arch_neutral                     158 non-null    int64  \n",
+      " 25  arch_stability                   158 non-null    int64  \n",
+      " 26  weight_lab_oz                    158 non-null    float64\n",
+      " 27  weight_lab_g                     158 non-null    int64  \n",
+      " 28  weight_brand_oz                  155 non-null    float64\n",
+      " 29  weight_brand_g                   155 non-null    float64\n",
+      " 30  drop_lab_mm                      158 non-null    float64\n",
+      " 31  drop_brand_mm                    152 non-null    float64\n",
+      " 32  strike_heel                      158 non-null    int64  \n",
+      " 33  strike_mid                       158 non-null    int64  \n",
+      " 34  strike_forefoot                  158 non-null    int64  \n",
+      " 35  midsole_softness                 158 non-null    int64  \n",
+      " 36  toebox_durability                158 non-null    int64  \n",
+      "dtypes: float64(6), int64(15), str(16)\n",
+      "memory usage: 45.8 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=['toebox durability'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "554157d1",
+   "metadata": {},
+   "source": [
+    "## Heel padding durability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "0d78f5d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "heel padding durability\n",
+      "decent    51\n",
+      "good      50\n",
+      "-         38\n",
+      "bad       19\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"heel padding durability\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "c5df3591",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Value Counts Kolom Asli ---\n",
+      "heel padding durability\n",
+      "decent    51\n",
+      "good      50\n",
+      "-         38\n",
+      "bad       19\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "--- Sebaran Nilai Hasil Ordinal (Scale 1-5) ---\n",
+      "Index 0: 38 baris\n",
+      "Index 1: 0 baris\n",
+      "Index 2: 19 baris\n",
+      "Index 3: 51 baris\n",
+      "Index 4: 50 baris\n",
+      "Index 5: 0 baris\n",
+      "\n",
+      "--- Perbandingan Data (Head) ---\n",
+      "  heel padding durability  heel_durability\n",
+      "0                    good                4\n",
+      "1                       -                0\n",
+      "2                  decent                3\n",
+      "3                    good                4\n",
+      "4                       -                0\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['heel padding durability'] = df['heel padding durability'].astype(str).str.lower()\n",
+    "\n",
+    "durability_scale_5 = {\n",
+    "    \"-\": 0,\n",
+    "    \"very bad\": 1,\n",
+    "    \"bad\": 2,\n",
+    "    \"decent\": 3,\n",
+    "    \"good\": 4,\n",
+    "    \"very good\": 5\n",
+    "}\n",
+    "\n",
+    "\n",
+    "df['heel_durability'] = df['heel padding durability'].map(durability_scale_5)\n",
+    "\n",
+    "\n",
+    "print(\"--- Value Counts Kolom Asli ---\")\n",
+    "print(df[\"heel padding durability\"].value_counts())\n",
+    "\n",
+    "print(\"\\n--- Sebaran Nilai Hasil Ordinal (Scale 1-5) ---\")\n",
+    "counts = df[\"heel_durability\"].value_counts().sort_index()\n",
+    "for i in range(6):\n",
+    "    print(f\"Index {i}: {counts.get(i, 0)} baris\")\n",
+    "\n",
+    "print(\"\\n--- Perbandingan Data (Head) ---\")\n",
+    "print(df[[\"heel padding durability\", \"heel_durability\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "5126e7a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 37 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   brand                            158 non-null    str    \n",
+      " 1   name                             158 non-null    str    \n",
+      " 2   lightweight                      152 non-null    float64\n",
+      " 3   plate                            158 non-null    str    \n",
+      " 4   outsole durability               158 non-null    str    \n",
+      " 5   breathability                    158 non-null    str    \n",
+      " 6   width / fit                      158 non-null    str    \n",
+      " 7   toebox width                     158 non-null    str    \n",
+      " 8   stiffness                        158 non-null    str    \n",
+      " 9   torsional rigidity               158 non-null    str    \n",
+      " 10  heel counter stiffness           158 non-null    str    \n",
+      " 11  lug depth                        158 non-null    str    \n",
+      " 12  heel stack lab heel stack brand  158 non-null    str    \n",
+      " 13  forefoot lab forefoot brand      158 non-null    str    \n",
+      " 14  season                           158 non-null    str    \n",
+      " 15  removable insole                 158 non-null    int64  \n",
+      " 16  orthotic friendly                158 non-null    int64  \n",
+      " 17  waterproofing                    156 non-null    str    \n",
+      " 18  terrain_light                    158 non-null    int64  \n",
+      " 19  terrain_moderate                 158 non-null    int64  \n",
+      " 20  terrain_technical                158 non-null    int64  \n",
+      " 21  shock_absorption                 158 non-null    int64  \n",
+      " 22  energy_return                    158 non-null    int64  \n",
+      " 23  arch_neutral                     158 non-null    int64  \n",
+      " 24  arch_stability                   158 non-null    int64  \n",
+      " 25  weight_lab_oz                    158 non-null    float64\n",
+      " 26  weight_lab_g                     158 non-null    int64  \n",
+      " 27  weight_brand_oz                  155 non-null    float64\n",
+      " 28  weight_brand_g                   155 non-null    float64\n",
+      " 29  drop_lab_mm                      158 non-null    float64\n",
+      " 30  drop_brand_mm                    152 non-null    float64\n",
+      " 31  strike_heel                      158 non-null    int64  \n",
+      " 32  strike_mid                       158 non-null    int64  \n",
+      " 33  strike_forefoot                  158 non-null    int64  \n",
+      " 34  midsole_softness                 158 non-null    int64  \n",
+      " 35  toebox_durability                158 non-null    int64  \n",
+      " 36  heel_durability                  158 non-null    int64  \n",
+      "dtypes: float64(6), int64(16), str(15)\n",
+      "memory usage: 45.8 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop('heel padding durability', axis=1, inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "46a4046e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>brand</th>\n",
+       "      <th>name</th>\n",
+       "      <th>lightweight</th>\n",
+       "      <th>plate</th>\n",
+       "      <th>outsole durability</th>\n",
+       "      <th>breathability</th>\n",
+       "      <th>width / fit</th>\n",
+       "      <th>toebox width</th>\n",
+       "      <th>stiffness</th>\n",
+       "      <th>torsional rigidity</th>\n",
+       "      <th>...</th>\n",
+       "      <th>weight_brand_oz</th>\n",
+       "      <th>weight_brand_g</th>\n",
+       "      <th>drop_lab_mm</th>\n",
+       "      <th>drop_brand_mm</th>\n",
+       "      <th>strike_heel</th>\n",
+       "      <th>strike_mid</th>\n",
+       "      <th>strike_forefoot</th>\n",
+       "      <th>midsole_softness</th>\n",
+       "      <th>toebox_durability</th>\n",
+       "      <th>heel_durability</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>terrex agravic speed ultra</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>decent</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>medium</td>\n",
+       "      <td>narrow</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>terrex speed ultra</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>narrow</td>\n",
+       "      <td>-</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>255.0</td>\n",
+       "      <td>8.2</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>experience wild</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>good</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>wide</td>\n",
+       "      <td>wide</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>273.0</td>\n",
+       "      <td>4.3</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>experience wild 2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>good</td>\n",
+       "      <td>warm</td>\n",
+       "      <td>wide</td>\n",
+       "      <td>wide</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>moderate</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10.3</td>\n",
+       "      <td>293.0</td>\n",
+       "      <td>6.1</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>lone peak 5.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>rock plate</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>narrow</td>\n",
+       "      <td>-</td>\n",
+       "      <td>stiff</td>\n",
+       "      <td>flexible</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10.6</td>\n",
+       "      <td>301.0</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 37 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    brand                        name  lightweight       plate  \\\n",
+       "0  adidas  terrex agravic speed ultra          0.0           0   \n",
+       "1  adidas          terrex speed ultra          0.0           0   \n",
+       "2   altra             experience wild          0.0           0   \n",
+       "3   altra           experience wild 2          0.0           0   \n",
+       "4   altra               lone peak 5.0          0.0  rock plate   \n",
+       "\n",
+       "  outsole durability breathability width / fit toebox width stiffness  \\\n",
+       "0             decent      moderate      medium       narrow  moderate   \n",
+       "1                  -             -      narrow            -     stiff   \n",
+       "2               good      moderate        wide         wide  moderate   \n",
+       "3               good          warm        wide         wide  moderate   \n",
+       "4                  -             -      narrow            -     stiff   \n",
+       "\n",
+       "  torsional rigidity  ... weight_brand_oz weight_brand_g drop_lab_mm  \\\n",
+       "0              stiff  ...             9.5          270.0         0.3   \n",
+       "1           flexible  ...             9.0          255.0         8.2   \n",
+       "2              stiff  ...             9.6          273.0         4.3   \n",
+       "3           moderate  ...            10.3          293.0         6.1   \n",
+       "4           flexible  ...            10.6          301.0         0.2   \n",
+       "\n",
+       "  drop_brand_mm strike_heel  strike_mid  strike_forefoot midsole_softness  \\\n",
+       "0           8.0           0           1                1                3   \n",
+       "1           8.0           1           1                1                0   \n",
+       "2           4.0           0           1                1                5   \n",
+       "3           4.0           0           1                1                3   \n",
+       "4           0.0           0           1                1                0   \n",
+       "\n",
+       "   toebox_durability  heel_durability  \n",
+       "0                  4                4  \n",
+       "1                  0                0  \n",
+       "2                  3                3  \n",
+       "3                  3                4  \n",
+       "4                  0                0  \n",
+       "\n",
+       "[5 rows x 37 columns]"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20b755dc",
+   "metadata": {},
+   "source": [
+    "## Outsole durability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "0647abf7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "outsole durability\n",
+      "good      77\n",
+      "-         42\n",
+      "decent    38\n",
+      "bad        1\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"outsole durability\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "fbba911c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Value Counts Kolom Asli ---\n",
+      "outsole durability\n",
+      "good      77\n",
+      "-         42\n",
+      "decent    38\n",
+      "bad        1\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "--- Sebaran Nilai Hasil Ordinal (Scale 1-5) ---\n",
+      "Index 0: 42 baris\n",
+      "Index 1: 0 baris\n",
+      "Index 2: 1 baris\n",
+      "Index 3: 38 baris\n",
+      "Index 4: 77 baris\n",
+      "Index 5: 0 baris\n",
+      "\n",
+      "--- Perbandingan Data (Head) ---\n",
+      "  outsole durability  outsole_durability\n",
+      "0             decent                   3\n",
+      "1                  -                   0\n",
+      "2               good                   4\n",
+      "3               good                   4\n",
+      "4                  -                   0\n"
+     ]
+    }
+   ],
+   "source": [
+    "durability_scaled = {\n",
+    "    \"-\": 0,\n",
+    "    \"very bad\": 1,\n",
+    "    \"bad\": 2,\n",
+    "    \"decent\": 3,\n",
+    "    \"good\": 4,\n",
+    "    \"very good\": 5\n",
+    "}\n",
+    "\n",
+    "\n",
+    "df['outsole_durability'] = df['outsole durability'].map(durability_scaled)\n",
+    "print(\"--- Value Counts Kolom Asli ---\")\n",
+    "print(df[\"outsole durability\"].value_counts())\n",
+    "\n",
+    "print(\"\\n--- Sebaran Nilai Hasil Ordinal (Scale 1-5) ---\")\n",
+    "counts = df[\"outsole_durability\"].value_counts().sort_index()\n",
+    "for i in range(6):\n",
+    "    print(f\"Index {i}: {counts.get(i, 0)} baris\")\n",
+    "\n",
+    "print(\"\\n--- Perbandingan Data (Head) ---\")\n",
+    "print(df[[\"outsole durability\", \"outsole_durability\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "b9a91d05",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 37 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   brand                            158 non-null    str    \n",
+      " 1   name                             158 non-null    str    \n",
+      " 2   lightweight                      152 non-null    float64\n",
+      " 3   plate                            158 non-null    str    \n",
+      " 4   breathability                    158 non-null    str    \n",
+      " 5   width / fit                      158 non-null    str    \n",
+      " 6   toebox width                     158 non-null    str    \n",
+      " 7   stiffness                        158 non-null    str    \n",
+      " 8   torsional rigidity               158 non-null    str    \n",
+      " 9   heel counter stiffness           158 non-null    str    \n",
+      " 10  lug depth                        158 non-null    str    \n",
+      " 11  heel stack lab heel stack brand  158 non-null    str    \n",
+      " 12  forefoot lab forefoot brand      158 non-null    str    \n",
+      " 13  season                           158 non-null    str    \n",
+      " 14  removable insole                 158 non-null    int64  \n",
+      " 15  orthotic friendly                158 non-null    int64  \n",
+      " 16  waterproofing                    156 non-null    str    \n",
+      " 17  terrain_light                    158 non-null    int64  \n",
+      " 18  terrain_moderate                 158 non-null    int64  \n",
+      " 19  terrain_technical                158 non-null    int64  \n",
+      " 20  shock_absorption                 158 non-null    int64  \n",
+      " 21  energy_return                    158 non-null    int64  \n",
+      " 22  arch_neutral                     158 non-null    int64  \n",
+      " 23  arch_stability                   158 non-null    int64  \n",
+      " 24  weight_lab_oz                    158 non-null    float64\n",
+      " 25  weight_lab_g                     158 non-null    int64  \n",
+      " 26  weight_brand_oz                  155 non-null    float64\n",
+      " 27  weight_brand_g                   155 non-null    float64\n",
+      " 28  drop_lab_mm                      158 non-null    float64\n",
+      " 29  drop_brand_mm                    152 non-null    float64\n",
+      " 30  strike_heel                      158 non-null    int64  \n",
+      " 31  strike_mid                       158 non-null    int64  \n",
+      " 32  strike_forefoot                  158 non-null    int64  \n",
+      " 33  midsole_softness                 158 non-null    int64  \n",
+      " 34  toebox_durability                158 non-null    int64  \n",
+      " 35  heel_durability                  158 non-null    int64  \n",
+      " 36  outsole_durability               158 non-null    int64  \n",
+      "dtypes: float64(6), int64(17), str(14)\n",
+      "memory usage: 45.8 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop('outsole durability', axis=1, inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6e10b84",
+   "metadata": {},
+   "source": [
+    "## Breathability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "c6a78523",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "breathability\n",
+      "moderate      92\n",
+      "warm          31\n",
+      "-             19\n",
+      "breathable    16\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"breathability\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "35945fd4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Value Counts Kolom Asli ---\n",
+      "breathability\n",
+      "3    92\n",
+      "2    31\n",
+      "0    19\n",
+      "5    16\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "--- Sebaran Nilai Hasil Ordinal (Scale 1-5) ---\n",
+      "Index 0: 19 baris\n",
+      "Index 1: 0 baris\n",
+      "Index 2: 31 baris\n",
+      "Index 3: 92 baris\n",
+      "Index 4: 0 baris\n",
+      "Index 5: 16 baris\n",
+      "\n",
+      "--- Perbandingan Data (Head) ---\n",
+      "   breathability  breathability\n",
+      "0              3              3\n",
+      "1              0              0\n",
+      "2              3              3\n",
+      "3              2              2\n",
+      "4              0              0\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['breathability'] = df['breathability'].astype(str).str.lower()\n",
+    "\n",
+    "breathability_scaled = {\n",
+    "    \"-\": 0,\n",
+    "    \"suffocating\": 1,\n",
+    "    \"warm\": 2,\n",
+    "    \"moderate\": 3,\n",
+    "    \"good\": 4,\n",
+    "    \"breathable\": 5\n",
+    "}\n",
+    "\n",
+    "df['breathability'] = df['breathability'].map(breathability_scaled)\n",
+    "\n",
+    "print(\"--- Value Counts Kolom Asli ---\")\n",
+    "print(df[\"breathability\"].value_counts())\n",
+    "\n",
+    "print(\"\\n--- Sebaran Nilai Hasil Ordinal (Scale 1-5) ---\")\n",
+    "counts = df[\"breathability\"].value_counts().sort_index()\n",
+    "for i in range(6):\n",
+    "    print(f\"Index {i}: {counts.get(i, 0)} baris\")\n",
+    "\n",
+    "print(\"\\n--- Perbandingan Data (Head) ---\")\n",
+    "print(df[[\"breathability\", \"breathability\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "f9163564",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 36 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   brand                            158 non-null    str    \n",
+      " 1   name                             158 non-null    str    \n",
+      " 2   lightweight                      152 non-null    float64\n",
+      " 3   plate                            158 non-null    str    \n",
+      " 4   width / fit                      158 non-null    str    \n",
+      " 5   toebox width                     158 non-null    str    \n",
+      " 6   stiffness                        158 non-null    str    \n",
+      " 7   torsional rigidity               158 non-null    str    \n",
+      " 8   heel counter stiffness           158 non-null    str    \n",
+      " 9   lug depth                        158 non-null    str    \n",
+      " 10  heel stack lab heel stack brand  158 non-null    str    \n",
+      " 11  forefoot lab forefoot brand      158 non-null    str    \n",
+      " 12  season                           158 non-null    str    \n",
+      " 13  removable insole                 158 non-null    int64  \n",
+      " 14  orthotic friendly                158 non-null    int64  \n",
+      " 15  waterproofing                    156 non-null    str    \n",
+      " 16  terrain_light                    158 non-null    int64  \n",
+      " 17  terrain_moderate                 158 non-null    int64  \n",
+      " 18  terrain_technical                158 non-null    int64  \n",
+      " 19  shock_absorption                 158 non-null    int64  \n",
+      " 20  energy_return                    158 non-null    int64  \n",
+      " 21  arch_neutral                     158 non-null    int64  \n",
+      " 22  arch_stability                   158 non-null    int64  \n",
+      " 23  weight_lab_oz                    158 non-null    float64\n",
+      " 24  weight_lab_g                     158 non-null    int64  \n",
+      " 25  weight_brand_oz                  155 non-null    float64\n",
+      " 26  weight_brand_g                   155 non-null    float64\n",
+      " 27  drop_lab_mm                      158 non-null    float64\n",
+      " 28  drop_brand_mm                    152 non-null    float64\n",
+      " 29  strike_heel                      158 non-null    int64  \n",
+      " 30  strike_mid                       158 non-null    int64  \n",
+      " 31  strike_forefoot                  158 non-null    int64  \n",
+      " 32  midsole_softness                 158 non-null    int64  \n",
+      " 33  toebox_durability                158 non-null    int64  \n",
+      " 34  heel_durability                  158 non-null    int64  \n",
+      " 35  outsole_durability               158 non-null    int64  \n",
+      "dtypes: float64(6), int64(17), str(13)\n",
+      "memory usage: 44.6 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop('breathability', axis=1, inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "838283e8",
+   "metadata": {},
+   "source": [
+    "## Plate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "eb229705",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "plate\n",
+      "0               112\n",
+      "rock plate       35\n",
+      "carbon plate     11\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"plate\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "a97958df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 158\n",
+      "NULL Value: 112\n",
+      "\n",
+      "Sample Comparison:\n",
+      "        plate  plate_rock_plate  plate_carbon_plate\n",
+      "0           0                 0                   0\n",
+      "1           0                 0                   0\n",
+      "2           0                 0                   0\n",
+      "3           0                 0                   0\n",
+      "4  rock plate                 1                   0\n",
+      "5  rock plate                 1                   0\n",
+      "6           0                 0                   0\n",
+      "7           0                 0                   0\n",
+      "8           0                 0                   0\n",
+      "9           0                 0                   0\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['plate'] = df['plate'].astype(str).str.lower()\n",
+    "base_plate = ['rock plate', 'carbon plate']\n",
+    "\n",
+    "for level in base_plate:\n",
+    "    column_name = f\"plate_{level.replace(' ', '_')}\"\n",
+    "    df[column_name] = df['plate'].str.contains(level, na=False).astype(int)\n",
+    "\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "plate_cols = [f\"plate_{l.replace(' ', '_')}\" for l in base_plate]\n",
+    "zero_vector_count = (df[plate_cols].sum(axis=1) == 0).sum()\n",
+    "print(f\"NULL Value: {zero_vector_count}\")\n",
+    "\n",
+    "print(\"\\nSample Comparison:\")\n",
+    "print(df[[\"plate\"] + plate_cols].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "ff871a52",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "plate\n",
+      "0               112\n",
+      "rock plate       35\n",
+      "carbon plate     11\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "plate_rock_plate sum: 35\n",
+      "plate_carbon_plate sum: 11\n",
+      "\n",
+      "        plate  plate_rock_plate  plate_carbon_plate\n",
+      "0           0                 0                   0\n",
+      "1           0                 0                   0\n",
+      "2           0                 0                   0\n",
+      "3           0                 0                   0\n",
+      "4  rock plate                 1                   0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"plate\"].value_counts())\n",
+    "\n",
+    "print()\n",
+    "for level in base_plate:\n",
+    "    col = f\"plate_{level.replace(' ', '_')}\"\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"plate\"] + plate_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "8173c5b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 37 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   brand                            158 non-null    str    \n",
+      " 1   name                             158 non-null    str    \n",
+      " 2   lightweight                      152 non-null    float64\n",
+      " 3   width / fit                      158 non-null    str    \n",
+      " 4   toebox width                     158 non-null    str    \n",
+      " 5   stiffness                        158 non-null    str    \n",
+      " 6   torsional rigidity               158 non-null    str    \n",
+      " 7   heel counter stiffness           158 non-null    str    \n",
+      " 8   lug depth                        158 non-null    str    \n",
+      " 9   heel stack lab heel stack brand  158 non-null    str    \n",
+      " 10  forefoot lab forefoot brand      158 non-null    str    \n",
+      " 11  season                           158 non-null    str    \n",
+      " 12  removable insole                 158 non-null    int64  \n",
+      " 13  orthotic friendly                158 non-null    int64  \n",
+      " 14  waterproofing                    156 non-null    str    \n",
+      " 15  terrain_light                    158 non-null    int64  \n",
+      " 16  terrain_moderate                 158 non-null    int64  \n",
+      " 17  terrain_technical                158 non-null    int64  \n",
+      " 18  shock_absorption                 158 non-null    int64  \n",
+      " 19  energy_return                    158 non-null    int64  \n",
+      " 20  arch_neutral                     158 non-null    int64  \n",
+      " 21  arch_stability                   158 non-null    int64  \n",
+      " 22  weight_lab_oz                    158 non-null    float64\n",
+      " 23  weight_lab_g                     158 non-null    int64  \n",
+      " 24  weight_brand_oz                  155 non-null    float64\n",
+      " 25  weight_brand_g                   155 non-null    float64\n",
+      " 26  drop_lab_mm                      158 non-null    float64\n",
+      " 27  drop_brand_mm                    152 non-null    float64\n",
+      " 28  strike_heel                      158 non-null    int64  \n",
+      " 29  strike_mid                       158 non-null    int64  \n",
+      " 30  strike_forefoot                  158 non-null    int64  \n",
+      " 31  midsole_softness                 158 non-null    int64  \n",
+      " 32  toebox_durability                158 non-null    int64  \n",
+      " 33  heel_durability                  158 non-null    int64  \n",
+      " 34  outsole_durability               158 non-null    int64  \n",
+      " 35  plate_rock_plate                 158 non-null    int64  \n",
+      " 36  plate_carbon_plate               158 non-null    int64  \n",
+      "dtypes: float64(6), int64(19), str(12)\n",
+      "memory usage: 45.8 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"plate\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "273e5d26",
+   "metadata": {},
+   "source": [
+    "## Width / fit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "229ae3b1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "width / fit\n",
+      "medium    92\n",
+      "narrow    47\n",
+      "wide      19\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['width / fit'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "9e4b2c22",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Value Counts Kolom Asli ---\n",
+      "width / fit\n",
+      "medium    92\n",
+      "narrow    47\n",
+      "wide      19\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "--- Sebaran Nilai Hasil Ordinal ---\n",
+      "Index 0: 0 baris\n",
+      "Index 1: 47 baris\n",
+      "Index 2: 0 baris\n",
+      "Index 3: 92 baris\n",
+      "Index 4: 0 baris\n",
+      "Index 5: 19 baris\n",
+      "\n",
+      "--- Perbandingan Data (Head) ---\n",
+      "  width / fit  width_fit\n",
+      "0      medium          3\n",
+      "1      narrow          1\n",
+      "2        wide          5\n",
+      "3        wide          5\n",
+      "4      narrow          1\n"
+     ]
+    }
+   ],
+   "source": [
+    "width_scaled = {\n",
+    "    \"narrow\": 1,\n",
+    "    \"medium\": 3,\n",
+    "    \"wide\": 5,\n",
+    "    \"-\": 0,\n",
+    "    \"0\": 0\n",
+    "}\n",
+    "\n",
+    "df['width_fit'] = df['width / fit'].map(width_scaled)\n",
+    "\n",
+    "print(\"--- Value Counts Kolom Asli ---\")\n",
+    "print(df[\"width / fit\"].value_counts())\n",
+    "\n",
+    "print(\"\\n--- Sebaran Nilai Hasil Ordinal ---\")\n",
+    "counts = df[\"width_fit\"].value_counts().sort_index()\n",
+    "for i in range(6):\n",
+    "    print(f\"Index {i}: {counts.get(i, 0)} baris\")\n",
+    "\n",
+    "print(\"\\n--- Perbandingan Data (Head) ---\")\n",
+    "print(df[[\"width / fit\", \"width_fit\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "227f8218",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 37 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   brand                            158 non-null    str    \n",
+      " 1   name                             158 non-null    str    \n",
+      " 2   lightweight                      152 non-null    float64\n",
+      " 3   toebox width                     158 non-null    str    \n",
+      " 4   stiffness                        158 non-null    str    \n",
+      " 5   torsional rigidity               158 non-null    str    \n",
+      " 6   heel counter stiffness           158 non-null    str    \n",
+      " 7   lug depth                        158 non-null    str    \n",
+      " 8   heel stack lab heel stack brand  158 non-null    str    \n",
+      " 9   forefoot lab forefoot brand      158 non-null    str    \n",
+      " 10  season                           158 non-null    str    \n",
+      " 11  removable insole                 158 non-null    int64  \n",
+      " 12  orthotic friendly                158 non-null    int64  \n",
+      " 13  waterproofing                    156 non-null    str    \n",
+      " 14  terrain_light                    158 non-null    int64  \n",
+      " 15  terrain_moderate                 158 non-null    int64  \n",
+      " 16  terrain_technical                158 non-null    int64  \n",
+      " 17  shock_absorption                 158 non-null    int64  \n",
+      " 18  energy_return                    158 non-null    int64  \n",
+      " 19  arch_neutral                     158 non-null    int64  \n",
+      " 20  arch_stability                   158 non-null    int64  \n",
+      " 21  weight_lab_oz                    158 non-null    float64\n",
+      " 22  weight_lab_g                     158 non-null    int64  \n",
+      " 23  weight_brand_oz                  155 non-null    float64\n",
+      " 24  weight_brand_g                   155 non-null    float64\n",
+      " 25  drop_lab_mm                      158 non-null    float64\n",
+      " 26  drop_brand_mm                    152 non-null    float64\n",
+      " 27  strike_heel                      158 non-null    int64  \n",
+      " 28  strike_mid                       158 non-null    int64  \n",
+      " 29  strike_forefoot                  158 non-null    int64  \n",
+      " 30  midsole_softness                 158 non-null    int64  \n",
+      " 31  toebox_durability                158 non-null    int64  \n",
+      " 32  heel_durability                  158 non-null    int64  \n",
+      " 33  outsole_durability               158 non-null    int64  \n",
+      " 34  plate_rock_plate                 158 non-null    int64  \n",
+      " 35  plate_carbon_plate               158 non-null    int64  \n",
+      " 36  width_fit                        158 non-null    int64  \n",
+      "dtypes: float64(6), int64(20), str(11)\n",
+      "memory usage: 45.8 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"width / fit\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f75044e",
+   "metadata": {},
+   "source": [
+    "## Toebox width"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "5534fbde",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "toebox width\n",
+      "medium    68\n",
+      "wide      38\n",
+      "-         32\n",
+      "narrow    20\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['toebox width'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "8a4afd8d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Value Counts Kolom Asli ---\n",
+      "toebox width\n",
+      "medium    68\n",
+      "wide      38\n",
+      "-         32\n",
+      "narrow    20\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "--- Sebaran Nilai Hasil Ordinal ---\n",
+      "Index 0: 32 baris\n",
+      "Index 1: 20 baris\n",
+      "Index 2: 0 baris\n",
+      "Index 3: 68 baris\n",
+      "Index 4: 0 baris\n",
+      "Index 5: 38 baris\n",
+      "\n",
+      "--- Perbandingan Data (Head) ---\n",
+      "  toebox width  toebox_width\n",
+      "0       narrow             1\n",
+      "1            -             0\n",
+      "2         wide             5\n",
+      "3         wide             5\n",
+      "4            -             0\n"
+     ]
+    }
+   ],
+   "source": [
+    "toebox_scaled = {\n",
+    "    \"narrow\": 1,\n",
+    "    \"medium\": 3,\n",
+    "    \"wide\": 5,\n",
+    "    \"-\": 0,\n",
+    "    \"0\": 0\n",
+    "}\n",
+    "\n",
+    "df['toebox_width'] = df['toebox width'].map(toebox_scaled)\n",
+    "\n",
+    "print(\"--- Value Counts Kolom Asli ---\")\n",
+    "print(df[\"toebox width\"].value_counts())\n",
+    "\n",
+    "print(\"\\n--- Sebaran Nilai Hasil Ordinal ---\")\n",
+    "counts = df[\"toebox_width\"].value_counts().sort_index()\n",
+    "for i in range(6):\n",
+    "    print(f\"Index {i}: {counts.get(i, 0)} baris\")\n",
+    "\n",
+    "print(\"\\n--- Perbandingan Data (Head) ---\")\n",
+    "print(df[[\"toebox width\", \"toebox_width\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "54c66b79",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 37 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   brand                            158 non-null    str    \n",
+      " 1   name                             158 non-null    str    \n",
+      " 2   lightweight                      152 non-null    float64\n",
+      " 3   stiffness                        158 non-null    str    \n",
+      " 4   torsional rigidity               158 non-null    str    \n",
+      " 5   heel counter stiffness           158 non-null    str    \n",
+      " 6   lug depth                        158 non-null    str    \n",
+      " 7   heel stack lab heel stack brand  158 non-null    str    \n",
+      " 8   forefoot lab forefoot brand      158 non-null    str    \n",
+      " 9   season                           158 non-null    str    \n",
+      " 10  removable insole                 158 non-null    int64  \n",
+      " 11  orthotic friendly                158 non-null    int64  \n",
+      " 12  waterproofing                    156 non-null    str    \n",
+      " 13  terrain_light                    158 non-null    int64  \n",
+      " 14  terrain_moderate                 158 non-null    int64  \n",
+      " 15  terrain_technical                158 non-null    int64  \n",
+      " 16  shock_absorption                 158 non-null    int64  \n",
+      " 17  energy_return                    158 non-null    int64  \n",
+      " 18  arch_neutral                     158 non-null    int64  \n",
+      " 19  arch_stability                   158 non-null    int64  \n",
+      " 20  weight_lab_oz                    158 non-null    float64\n",
+      " 21  weight_lab_g                     158 non-null    int64  \n",
+      " 22  weight_brand_oz                  155 non-null    float64\n",
+      " 23  weight_brand_g                   155 non-null    float64\n",
+      " 24  drop_lab_mm                      158 non-null    float64\n",
+      " 25  drop_brand_mm                    152 non-null    float64\n",
+      " 26  strike_heel                      158 non-null    int64  \n",
+      " 27  strike_mid                       158 non-null    int64  \n",
+      " 28  strike_forefoot                  158 non-null    int64  \n",
+      " 29  midsole_softness                 158 non-null    int64  \n",
+      " 30  toebox_durability                158 non-null    int64  \n",
+      " 31  heel_durability                  158 non-null    int64  \n",
+      " 32  outsole_durability               158 non-null    int64  \n",
+      " 33  plate_rock_plate                 158 non-null    int64  \n",
+      " 34  plate_carbon_plate               158 non-null    int64  \n",
+      " 35  width_fit                        158 non-null    int64  \n",
+      " 36  toebox_width                     158 non-null    int64  \n",
+      "dtypes: float64(6), int64(21), str(10)\n",
+      "memory usage: 45.8 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"toebox width\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a723b8bd",
+   "metadata": {},
+   "source": [
+    "## Stiffness"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "76d488f5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "stiffness\n",
+      "stiff       99\n",
+      "moderate    53\n",
+      "flexible     6\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['stiffness'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "18ae5a28",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Value Counts Kolom Asli ---\n",
+      "stiffness\n",
+      "5    99\n",
+      "3    53\n",
+      "1     6\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "--- Sebaran Nilai Hasil Ordinal  ---\n",
+      "Index 0: 0 baris\n",
+      "Index 1: 6 baris\n",
+      "Index 2: 0 baris\n",
+      "Index 3: 53 baris\n",
+      "Index 4: 0 baris\n",
+      "Index 5: 99 baris\n",
+      "\n",
+      "--- Perbandingan Data ---\n",
+      "   stiffness  stiffness\n",
+      "0          3          3\n",
+      "1          5          5\n",
+      "2          3          3\n",
+      "3          3          3\n",
+      "4          5          5\n"
+     ]
+    }
+   ],
+   "source": [
+    "stiffness_scaled = {\n",
+    "    \"flexible\": 1,\n",
+    "    \"moderate\": 3,\n",
+    "    \"stiff\": 5,\n",
+    "    \"-\": 0,\n",
+    "    \"0\": 0\n",
+    "}\n",
+    "\n",
+    "df['stiffness'] = df['stiffness'].map(stiffness_scaled)\n",
+    "\n",
+    "print(\"--- Value Counts Kolom Asli ---\")\n",
+    "print(df[\"stiffness\"].value_counts())\n",
+    "\n",
+    "print(\"\\n--- Sebaran Nilai Hasil Ordinal  ---\")\n",
+    "counts = df[\"stiffness\"].value_counts().sort_index()\n",
+    "for i in range(6):\n",
+    "    print(f\"Index {i}: {counts.get(i, 0)} baris\")\n",
+    "\n",
+    "print(\"\\n--- Perbandingan Data ---\")\n",
+    "print(df[[\"stiffness\", \"stiffness\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "429e0c4e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 36 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   brand                            158 non-null    str    \n",
+      " 1   name                             158 non-null    str    \n",
+      " 2   lightweight                      152 non-null    float64\n",
+      " 3   torsional rigidity               158 non-null    str    \n",
+      " 4   heel counter stiffness           158 non-null    str    \n",
+      " 5   lug depth                        158 non-null    str    \n",
+      " 6   heel stack lab heel stack brand  158 non-null    str    \n",
+      " 7   forefoot lab forefoot brand      158 non-null    str    \n",
+      " 8   season                           158 non-null    str    \n",
+      " 9   removable insole                 158 non-null    int64  \n",
+      " 10  orthotic friendly                158 non-null    int64  \n",
+      " 11  waterproofing                    156 non-null    str    \n",
+      " 12  terrain_light                    158 non-null    int64  \n",
+      " 13  terrain_moderate                 158 non-null    int64  \n",
+      " 14  terrain_technical                158 non-null    int64  \n",
+      " 15  shock_absorption                 158 non-null    int64  \n",
+      " 16  energy_return                    158 non-null    int64  \n",
+      " 17  arch_neutral                     158 non-null    int64  \n",
+      " 18  arch_stability                   158 non-null    int64  \n",
+      " 19  weight_lab_oz                    158 non-null    float64\n",
+      " 20  weight_lab_g                     158 non-null    int64  \n",
+      " 21  weight_brand_oz                  155 non-null    float64\n",
+      " 22  weight_brand_g                   155 non-null    float64\n",
+      " 23  drop_lab_mm                      158 non-null    float64\n",
+      " 24  drop_brand_mm                    152 non-null    float64\n",
+      " 25  strike_heel                      158 non-null    int64  \n",
+      " 26  strike_mid                       158 non-null    int64  \n",
+      " 27  strike_forefoot                  158 non-null    int64  \n",
+      " 28  midsole_softness                 158 non-null    int64  \n",
+      " 29  toebox_durability                158 non-null    int64  \n",
+      " 30  heel_durability                  158 non-null    int64  \n",
+      " 31  outsole_durability               158 non-null    int64  \n",
+      " 32  plate_rock_plate                 158 non-null    int64  \n",
+      " 33  plate_carbon_plate               158 non-null    int64  \n",
+      " 34  width_fit                        158 non-null    int64  \n",
+      " 35  toebox_width                     158 non-null    int64  \n",
+      "dtypes: float64(6), int64(21), str(9)\n",
+      "memory usage: 44.6 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"stiffness\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "347d1f39",
+   "metadata": {},
+   "source": [
+    "## Torsional rigidity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "898bbd3d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torsional rigidity\n",
+      "stiff       93\n",
+      "moderate    37\n",
+      "flexible    22\n",
+      "-            6\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['torsional rigidity'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "94fbcfb9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Value Counts Kolom Asli ---\n",
+      "torsional rigidity\n",
+      "stiff       93\n",
+      "moderate    37\n",
+      "flexible    22\n",
+      "-            6\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "--- Sebaran Nilai Hasil Ordinal (Scale 0-5) ---\n",
+      "Index 0: 6 baris\n",
+      "Index 1: 22 baris\n",
+      "Index 2: 0 baris\n",
+      "Index 3: 37 baris\n",
+      "Index 4: 0 baris\n",
+      "Index 5: 93 baris\n",
+      "\n",
+      "--- Perbandingan Data (Head) ---\n",
+      "  torsional rigidity  torsional_rigidity\n",
+      "0              stiff                   5\n",
+      "1           flexible                   1\n",
+      "2              stiff                   5\n",
+      "3           moderate                   3\n",
+      "4           flexible                   1\n"
+     ]
+    }
+   ],
+   "source": [
+    "torsional_scaled = {\n",
+    "    \"flexible\": 1,\n",
+    "    \"moderate\": 3,\n",
+    "    \"stiff\": 5,\n",
+    "    \"-\": 0,\n",
+    "    \"0\": 0,\n",
+    "    \"nan\": 0 \n",
+    "}\n",
+    "\n",
+    "df['torsional_rigidity'] = df['torsional rigidity'].map(torsional_scaled).fillna(0).astype(int)\n",
+    "\n",
+    "print(\"--- Value Counts Kolom Asli ---\")\n",
+    "print(df[\"torsional rigidity\"].value_counts())\n",
+    "\n",
+    "print(\"\\n--- Sebaran Nilai Hasil Ordinal (Scale 0-5) ---\")\n",
+    "counts = df[\"torsional_rigidity\"].value_counts().sort_index()\n",
+    "for i in range(6):\n",
+    "    print(f\"Index {i}: {counts.get(i, 0)} baris\")\n",
+    "\n",
+    "print(\"\\n--- Perbandingan Data (Head) ---\")\n",
+    "print(df[[\"torsional rigidity\", \"torsional_rigidity\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "5087be70",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 36 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   brand                            158 non-null    str    \n",
+      " 1   name                             158 non-null    str    \n",
+      " 2   lightweight                      152 non-null    float64\n",
+      " 3   heel counter stiffness           158 non-null    str    \n",
+      " 4   lug depth                        158 non-null    str    \n",
+      " 5   heel stack lab heel stack brand  158 non-null    str    \n",
+      " 6   forefoot lab forefoot brand      158 non-null    str    \n",
+      " 7   season                           158 non-null    str    \n",
+      " 8   removable insole                 158 non-null    int64  \n",
+      " 9   orthotic friendly                158 non-null    int64  \n",
+      " 10  waterproofing                    156 non-null    str    \n",
+      " 11  terrain_light                    158 non-null    int64  \n",
+      " 12  terrain_moderate                 158 non-null    int64  \n",
+      " 13  terrain_technical                158 non-null    int64  \n",
+      " 14  shock_absorption                 158 non-null    int64  \n",
+      " 15  energy_return                    158 non-null    int64  \n",
+      " 16  arch_neutral                     158 non-null    int64  \n",
+      " 17  arch_stability                   158 non-null    int64  \n",
+      " 18  weight_lab_oz                    158 non-null    float64\n",
+      " 19  weight_lab_g                     158 non-null    int64  \n",
+      " 20  weight_brand_oz                  155 non-null    float64\n",
+      " 21  weight_brand_g                   155 non-null    float64\n",
+      " 22  drop_lab_mm                      158 non-null    float64\n",
+      " 23  drop_brand_mm                    152 non-null    float64\n",
+      " 24  strike_heel                      158 non-null    int64  \n",
+      " 25  strike_mid                       158 non-null    int64  \n",
+      " 26  strike_forefoot                  158 non-null    int64  \n",
+      " 27  midsole_softness                 158 non-null    int64  \n",
+      " 28  toebox_durability                158 non-null    int64  \n",
+      " 29  heel_durability                  158 non-null    int64  \n",
+      " 30  outsole_durability               158 non-null    int64  \n",
+      " 31  plate_rock_plate                 158 non-null    int64  \n",
+      " 32  plate_carbon_plate               158 non-null    int64  \n",
+      " 33  width_fit                        158 non-null    int64  \n",
+      " 34  toebox_width                     158 non-null    int64  \n",
+      " 35  torsional_rigidity               158 non-null    int64  \n",
+      "dtypes: float64(6), int64(22), str(8)\n",
+      "memory usage: 44.6 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"torsional rigidity\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4fab1922",
+   "metadata": {},
+   "source": [
+    "## Heel counter stiffness"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "d17ba028",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "heel counter stiffness\n",
+      "moderate    55\n",
+      "stiff       49\n",
+      "flexible    46\n",
+      "-            8\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['heel counter stiffness'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "1ffb3c8c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 158\n",
+      "NULL Value (tidak cocok dengan kategori): 0\n",
+      "\n",
+      "Sample Comparison:\n",
+      "  heel counter stiffness  heel_stiff\n",
+      "0               flexible           1\n",
+      "1               flexible           1\n",
+      "2               moderate           3\n",
+      "3               flexible           1\n",
+      "4                      -           0\n",
+      "5                      -           0\n",
+      "6               flexible           1\n",
+      "7               flexible           1\n",
+      "8               flexible           1\n",
+      "9                      -           0\n"
+     ]
+    }
+   ],
+   "source": [
+    "heel_stiff_map = {\n",
+    "    'flexible': 1,\n",
+    "    'moderate': 3,\n",
+    "    'stiff': 5\n",
+    "}\n",
+    "\n",
+    "df['heel_stiff'] = df['heel counter stiffness'].map(heel_stiff_map).fillna(0).astype(int)\n",
+    "\n",
+    "# Cek hasil\n",
+    "print(f\"Rows: {len(df)}\")\n",
+    "print(f\"NULL Value (tidak cocok dengan kategori): {df['heel_stiff'].isna().sum()}\")\n",
+    "print(\"\\nSample Comparison:\")\n",
+    "print(df[['heel counter stiffness', 'heel_stiff']].head(10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "65d93f9a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 36 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   brand                            158 non-null    str    \n",
+      " 1   name                             158 non-null    str    \n",
+      " 2   lightweight                      152 non-null    float64\n",
+      " 3   lug depth                        158 non-null    str    \n",
+      " 4   heel stack lab heel stack brand  158 non-null    str    \n",
+      " 5   forefoot lab forefoot brand      158 non-null    str    \n",
+      " 6   season                           158 non-null    str    \n",
+      " 7   removable insole                 158 non-null    int64  \n",
+      " 8   orthotic friendly                158 non-null    int64  \n",
+      " 9   waterproofing                    156 non-null    str    \n",
+      " 10  terrain_light                    158 non-null    int64  \n",
+      " 11  terrain_moderate                 158 non-null    int64  \n",
+      " 12  terrain_technical                158 non-null    int64  \n",
+      " 13  shock_absorption                 158 non-null    int64  \n",
+      " 14  energy_return                    158 non-null    int64  \n",
+      " 15  arch_neutral                     158 non-null    int64  \n",
+      " 16  arch_stability                   158 non-null    int64  \n",
+      " 17  weight_lab_oz                    158 non-null    float64\n",
+      " 18  weight_lab_g                     158 non-null    int64  \n",
+      " 19  weight_brand_oz                  155 non-null    float64\n",
+      " 20  weight_brand_g                   155 non-null    float64\n",
+      " 21  drop_lab_mm                      158 non-null    float64\n",
+      " 22  drop_brand_mm                    152 non-null    float64\n",
+      " 23  strike_heel                      158 non-null    int64  \n",
+      " 24  strike_mid                       158 non-null    int64  \n",
+      " 25  strike_forefoot                  158 non-null    int64  \n",
+      " 26  midsole_softness                 158 non-null    int64  \n",
+      " 27  toebox_durability                158 non-null    int64  \n",
+      " 28  heel_durability                  158 non-null    int64  \n",
+      " 29  outsole_durability               158 non-null    int64  \n",
+      " 30  plate_rock_plate                 158 non-null    int64  \n",
+      " 31  plate_carbon_plate               158 non-null    int64  \n",
+      " 32  width_fit                        158 non-null    int64  \n",
+      " 33  toebox_width                     158 non-null    int64  \n",
+      " 34  torsional_rigidity               158 non-null    int64  \n",
+      " 35  heel_stiff                       158 non-null    int64  \n",
+      "dtypes: float64(6), int64(23), str(7)\n",
+      "memory usage: 44.6 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"heel counter stiffness\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "62efa68c",
+   "metadata": {},
+   "source": [
+    "## Lug depth"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "3c47127b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0    2.5 mm\n",
+      "1    2.6 mm\n",
+      "2    3.6 mm\n",
+      "3    3.5 mm\n",
+      "4    3.7 mm\n",
+      "Name: lug depth, dtype: str\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"lug depth\"].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "31d7c1e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Value Counts Kolom Asli  ---\n",
+      "lug depth\n",
+      "3.5 mm    15\n",
+      "3.0 mm    14\n",
+      "4.0 mm    12\n",
+      "3.4 mm    11\n",
+      "2.5 mm     7\n",
+      "2.9 mm     7\n",
+      "3.2 mm     7\n",
+      "3.6 mm     6\n",
+      "3.7 mm     6\n",
+      "4.4 mm     6\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "  lug depth  lug_dept_mm\n",
+      "0    2.5 mm          2.5\n",
+      "1    2.6 mm          2.6\n",
+      "2    3.6 mm          3.6\n",
+      "3    3.5 mm          3.5\n",
+      "4    3.7 mm          3.7\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['lug_dept_mm'] = df['lug depth'].astype(str).str.replace(' mm', '', regex=False)\n",
+    "df['lug_dept_mm'] = pd.to_numeric(df['lug_dept_mm'].replace('-', '0'), errors='coerce').fillna(0)\n",
+    "\n",
+    "print(\"--- Value Counts Kolom Asli  ---\")\n",
+    "print(df[\"lug depth\"].value_counts().head(10))\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"lug depth\", \"lug_dept_mm\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "e7b1ee60",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 36 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   brand                            158 non-null    str    \n",
+      " 1   name                             158 non-null    str    \n",
+      " 2   lightweight                      152 non-null    float64\n",
+      " 3   heel stack lab heel stack brand  158 non-null    str    \n",
+      " 4   forefoot lab forefoot brand      158 non-null    str    \n",
+      " 5   season                           158 non-null    str    \n",
+      " 6   removable insole                 158 non-null    int64  \n",
+      " 7   orthotic friendly                158 non-null    int64  \n",
+      " 8   waterproofing                    156 non-null    str    \n",
+      " 9   terrain_light                    158 non-null    int64  \n",
+      " 10  terrain_moderate                 158 non-null    int64  \n",
+      " 11  terrain_technical                158 non-null    int64  \n",
+      " 12  shock_absorption                 158 non-null    int64  \n",
+      " 13  energy_return                    158 non-null    int64  \n",
+      " 14  arch_neutral                     158 non-null    int64  \n",
+      " 15  arch_stability                   158 non-null    int64  \n",
+      " 16  weight_lab_oz                    158 non-null    float64\n",
+      " 17  weight_lab_g                     158 non-null    int64  \n",
+      " 18  weight_brand_oz                  155 non-null    float64\n",
+      " 19  weight_brand_g                   155 non-null    float64\n",
+      " 20  drop_lab_mm                      158 non-null    float64\n",
+      " 21  drop_brand_mm                    152 non-null    float64\n",
+      " 22  strike_heel                      158 non-null    int64  \n",
+      " 23  strike_mid                       158 non-null    int64  \n",
+      " 24  strike_forefoot                  158 non-null    int64  \n",
+      " 25  midsole_softness                 158 non-null    int64  \n",
+      " 26  toebox_durability                158 non-null    int64  \n",
+      " 27  heel_durability                  158 non-null    int64  \n",
+      " 28  outsole_durability               158 non-null    int64  \n",
+      " 29  plate_rock_plate                 158 non-null    int64  \n",
+      " 30  plate_carbon_plate               158 non-null    int64  \n",
+      " 31  width_fit                        158 non-null    int64  \n",
+      " 32  toebox_width                     158 non-null    int64  \n",
+      " 33  torsional_rigidity               158 non-null    int64  \n",
+      " 34  heel_stiff                       158 non-null    int64  \n",
+      " 35  lug_dept_mm                      158 non-null    float64\n",
+      "dtypes: float64(7), int64(23), str(6)\n",
+      "memory usage: 44.6 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"lug depth\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "edb40b00",
+   "metadata": {},
+   "source": [
+    "## Heel stack lab Heel stack brand"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "6f8956df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0    30.6 mm 38.0 mm\n",
+      "1    32.8 mm 26.0 mm\n",
+      "2    34.5 mm 34.0 mm\n",
+      "3    32.3 mm 32.0 mm\n",
+      "4    24.5 mm 25.0 mm\n",
+      "Name: heel stack lab heel stack brand, dtype: str\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"heel stack lab heel stack brand\"].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "9123ea7c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(0)"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mask_missing = (\n",
+    "    df[\"heel stack lab heel stack brand\"].isna() |\n",
+    "    (df[\"heel stack lab heel stack brand\"].astype(str).str.strip() == \"-\")\n",
+    ")\n",
+    "mask_missing.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "dc55349b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  heel stack lab heel stack brand  heel_lab_mm  heel_brand_mm\n",
+      "0                 30.6 mm 38.0 mm         30.6           38.0\n",
+      "1                 32.8 mm 26.0 mm         32.8           26.0\n",
+      "2                 34.5 mm 34.0 mm         34.5           34.0\n",
+      "3                 32.3 mm 32.0 mm         32.3           32.0\n",
+      "4                 24.5 mm 25.0 mm         24.5           25.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "heel = df[\"heel stack lab heel stack brand\"].str.findall(r\"[\\d.]+\")\n",
+    "\n",
+    "df[[\"heel_lab_mm\", \"heel_brand_mm\"]] = (\n",
+    "    pd.DataFrame(heel.tolist(), index=df.index)\n",
+    ")\n",
+    "\n",
+    "for col in [\"heel_lab_mm\", \"heel_brand_mm\"]:\n",
+    "    df[col] = pd.to_numeric(df[col], errors=\"coerce\")\n",
+    "\n",
+    "print(df[[\"heel stack lab heel stack brand\", \"heel_lab_mm\", \"heel_brand_mm\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "2f899e58",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 37 columns):\n",
+      " #   Column                       Non-Null Count  Dtype  \n",
+      "---  ------                       --------------  -----  \n",
+      " 0   brand                        158 non-null    str    \n",
+      " 1   name                         158 non-null    str    \n",
+      " 2   lightweight                  152 non-null    float64\n",
+      " 3   forefoot lab forefoot brand  158 non-null    str    \n",
+      " 4   season                       158 non-null    str    \n",
+      " 5   removable insole             158 non-null    int64  \n",
+      " 6   orthotic friendly            158 non-null    int64  \n",
+      " 7   waterproofing                156 non-null    str    \n",
+      " 8   terrain_light                158 non-null    int64  \n",
+      " 9   terrain_moderate             158 non-null    int64  \n",
+      " 10  terrain_technical            158 non-null    int64  \n",
+      " 11  shock_absorption             158 non-null    int64  \n",
+      " 12  energy_return                158 non-null    int64  \n",
+      " 13  arch_neutral                 158 non-null    int64  \n",
+      " 14  arch_stability               158 non-null    int64  \n",
+      " 15  weight_lab_oz                158 non-null    float64\n",
+      " 16  weight_lab_g                 158 non-null    int64  \n",
+      " 17  weight_brand_oz              155 non-null    float64\n",
+      " 18  weight_brand_g               155 non-null    float64\n",
+      " 19  drop_lab_mm                  158 non-null    float64\n",
+      " 20  drop_brand_mm                152 non-null    float64\n",
+      " 21  strike_heel                  158 non-null    int64  \n",
+      " 22  strike_mid                   158 non-null    int64  \n",
+      " 23  strike_forefoot              158 non-null    int64  \n",
+      " 24  midsole_softness             158 non-null    int64  \n",
+      " 25  toebox_durability            158 non-null    int64  \n",
+      " 26  heel_durability              158 non-null    int64  \n",
+      " 27  outsole_durability           158 non-null    int64  \n",
+      " 28  plate_rock_plate             158 non-null    int64  \n",
+      " 29  plate_carbon_plate           158 non-null    int64  \n",
+      " 30  width_fit                    158 non-null    int64  \n",
+      " 31  toebox_width                 158 non-null    int64  \n",
+      " 32  torsional_rigidity           158 non-null    int64  \n",
+      " 33  heel_stiff                   158 non-null    int64  \n",
+      " 34  lug_dept_mm                  158 non-null    float64\n",
+      " 35  heel_lab_mm                  158 non-null    float64\n",
+      " 36  heel_brand_mm                145 non-null    float64\n",
+      "dtypes: float64(9), int64(23), str(5)\n",
+      "memory usage: 45.8 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"heel stack lab heel stack brand\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69155f32",
+   "metadata": {},
+   "source": [
+    "## Forefoot lab Forefoot brand"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "586618dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0    30.3 mm 30.0 mm\n",
+      "1    24.6 mm 18.0 mm\n",
+      "2    30.2 mm 30.0 mm\n",
+      "3    26.2 mm 28.0 mm\n",
+      "4    24.3 mm 25.0 mm\n",
+      "Name: forefoot lab forefoot brand, dtype: str\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['forefoot lab forefoot brand'].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "b1323541",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(0)"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mask_missing = (\n",
+    "    df[\"forefoot lab forefoot brand\"].isna() |\n",
+    "    (df[\"forefoot lab forefoot brand\"].astype(str).str.strip() == \"-\")\n",
+    ")\n",
+    "mask_missing.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "a29bbb63",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  forefoot lab forefoot brand  forefoot_lab_mm  forefoot_brand_mm\n",
+      "0             30.3 mm 30.0 mm             30.3               30.0\n",
+      "1             24.6 mm 18.0 mm             24.6               18.0\n",
+      "2             30.2 mm 30.0 mm             30.2               30.0\n",
+      "3             26.2 mm 28.0 mm             26.2               28.0\n",
+      "4             24.3 mm 25.0 mm             24.3               25.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "forefoot = df[\"forefoot lab forefoot brand\"].str.findall(r\"[\\d.]+\")\n",
+    "\n",
+    "df[[\"forefoot_lab_mm\", \"forefoot_brand_mm\"]] = (\n",
+    "    pd.DataFrame(forefoot.tolist(), index=df.index)\n",
+    ")\n",
+    "\n",
+    "for col in [\"forefoot_lab_mm\", \"forefoot_brand_mm\"]:\n",
+    "    df[col] = pd.to_numeric(df[col], errors=\"coerce\")\n",
+    "\n",
+    "print(df[[\"forefoot lab forefoot brand\", \"forefoot_lab_mm\", \"forefoot_brand_mm\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "081b4449",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 38 columns):\n",
+      " #   Column              Non-Null Count  Dtype  \n",
+      "---  ------              --------------  -----  \n",
+      " 0   brand               158 non-null    str    \n",
+      " 1   name                158 non-null    str    \n",
+      " 2   lightweight         152 non-null    float64\n",
+      " 3   season              158 non-null    str    \n",
+      " 4   removable insole    158 non-null    int64  \n",
+      " 5   orthotic friendly   158 non-null    int64  \n",
+      " 6   waterproofing       156 non-null    str    \n",
+      " 7   terrain_light       158 non-null    int64  \n",
+      " 8   terrain_moderate    158 non-null    int64  \n",
+      " 9   terrain_technical   158 non-null    int64  \n",
+      " 10  shock_absorption    158 non-null    int64  \n",
+      " 11  energy_return       158 non-null    int64  \n",
+      " 12  arch_neutral        158 non-null    int64  \n",
+      " 13  arch_stability      158 non-null    int64  \n",
+      " 14  weight_lab_oz       158 non-null    float64\n",
+      " 15  weight_lab_g        158 non-null    int64  \n",
+      " 16  weight_brand_oz     155 non-null    float64\n",
+      " 17  weight_brand_g      155 non-null    float64\n",
+      " 18  drop_lab_mm         158 non-null    float64\n",
+      " 19  drop_brand_mm       152 non-null    float64\n",
+      " 20  strike_heel         158 non-null    int64  \n",
+      " 21  strike_mid          158 non-null    int64  \n",
+      " 22  strike_forefoot     158 non-null    int64  \n",
+      " 23  midsole_softness    158 non-null    int64  \n",
+      " 24  toebox_durability   158 non-null    int64  \n",
+      " 25  heel_durability     158 non-null    int64  \n",
+      " 26  outsole_durability  158 non-null    int64  \n",
+      " 27  plate_rock_plate    158 non-null    int64  \n",
+      " 28  plate_carbon_plate  158 non-null    int64  \n",
+      " 29  width_fit           158 non-null    int64  \n",
+      " 30  toebox_width        158 non-null    int64  \n",
+      " 31  torsional_rigidity  158 non-null    int64  \n",
+      " 32  heel_stiff          158 non-null    int64  \n",
+      " 33  lug_dept_mm         158 non-null    float64\n",
+      " 34  heel_lab_mm         158 non-null    float64\n",
+      " 35  heel_brand_mm       145 non-null    float64\n",
+      " 36  forefoot_lab_mm     158 non-null    float64\n",
+      " 37  forefoot_brand_mm   143 non-null    float64\n",
+      "dtypes: float64(11), int64(23), str(4)\n",
+      "memory usage: 47.0 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"forefoot lab forefoot brand\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21494e05",
+   "metadata": {},
+   "source": [
+    "## Season"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "c3e07080",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "season\n",
+      "all seasons           107\n",
+      "-                      19\n",
+      "summer all seasons     15\n",
+      "winter                 15\n",
+      "0                       1\n",
+      "summerall seasons       1\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"season\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93dfd937",
+   "metadata": {},
+   "source": [
+    "Summer All seasons = sepatu yang dirancang secara spesifik untuk summer tapi diklaim bisa dipakai all season"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "fa7f5747",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Jumlah baris dengan '-' atau '0': 20\n",
+      "\n",
+      "--- Detail Baris (season = '-' atau '0') ---\n",
+      "       brand                    name season\n",
+      "1     adidas      terrex speed ultra      -\n",
+      "4      altra           lone peak 5.0      -\n",
+      "5      altra             lone peak 6      -\n",
+      "9      altra              mont blanc      -\n",
+      "36    brooks             cascadia 16      -\n",
+      "57      hoka                tecton x      -\n",
+      "61      hoka                   zinal      -\n",
+      "67     inov8              trailtalon      0\n",
+      "68    kailas        flythorn air 2.0      -\n",
+      "70    kailas            fuga elite 2      -\n",
+      "71    kailas               fuga ex 2      -\n",
+      "73    kailas             fuga ex boa      -\n",
+      "75    kailas              fuga pro 4      -\n",
+      "89   merrell                  nova 2      -\n",
+      "101     nike  air zoom terra kiger 6      -\n",
+      "104     nike         pegasus trail 4      -\n",
+      "124  salomon             sense pro 4      -\n",
+      "140  saucony         endorphin trail      -\n",
+      "141  saucony            peregrine 11      -\n",
+      "142  saucony            peregrine 12      -\n",
+      "\n",
+      "Frekuensi spesifik:\n",
+      "season\n",
+      "-    19\n",
+      "0     1\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check weird values\n",
+    "filter_condition = df['season'].astype(str).isin(['-', '0'])\n",
+    "rows_to_check = df[filter_condition]\n",
+    "\n",
+    "print(f\"Jumlah baris dengan '-' atau '0': {len(rows_to_check)}\")\n",
+    "print(\"\\n--- Detail Baris (season = '-' atau '0') ---\")\n",
+    "print(rows_to_check[['brand', 'name', 'season']])\n",
+    "\n",
+    "\n",
+    "print(\"\\nFrekuensi spesifik:\")\n",
+    "print(df[df['season'].astype(str).isin(['-', '0'])]['season'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "40f37790",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 158\n",
+      "NULL/Unknown Value (0 dan -): 20\n",
+      "\n",
+      "Sample Comparison (Multi-label):\n",
+      "                season  season_summer  season_winter  season_all\n",
+      "13  summer all seasons              1              0           1\n",
+      "17  summer all seasons              1              0           1\n",
+      "26  summer all seasons              1              0           1\n",
+      "28  summer all seasons              1              0           1\n",
+      "29  summer all seasons              1              0           1\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['season'] = df['season'].astype(str).str.lower()\n",
+    "base_seasons = ['summer', 'winter', 'all seasons']\n",
+    "\n",
+    "for level in base_seasons:\n",
+    "    clean_name = level.replace(' seasons', '').replace(' ', '_')\n",
+    "    column_name = f\"season_{clean_name}\"\n",
+    "    df[column_name] = df['season'].str.contains(level, na=False).astype(int)\n",
+    "\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "season_cols = [col for col in df.columns if col.startswith('season_')]\n",
+    "zero_vector_count = (df[season_cols].sum(axis=1) == 0).sum()\n",
+    "print(f\"NULL/Unknown Value (0 dan -): {zero_vector_count}\")\n",
+    "\n",
+    "print(\"\\nSample Comparison (Multi-label):\")\n",
+    "print(df[df[season_cols].sum(axis=1) > 1][['season'] + season_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "39a01a66",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "season\n",
+      "all seasons           107\n",
+      "-                      19\n",
+      "summer all seasons     15\n",
+      "winter                 15\n",
+      "0                       1\n",
+      "summerall seasons       1\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "season_summer sum: 16\n",
+      "season_winter sum: 15\n",
+      "season_all sum: 123\n",
+      "\n",
+      "        season  season_summer  season_winter  season_all\n",
+      "0  all seasons              0              0           1\n",
+      "1            -              0              0           0\n",
+      "2  all seasons              0              0           1\n",
+      "3  all seasons              0              0           1\n",
+      "4            -              0              0           0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"season\"].value_counts())\n",
+    "\n",
+    "print()\n",
+    "for col in season_cols:\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"season\"] + season_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "d8eea361",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 40 columns):\n",
+      " #   Column              Non-Null Count  Dtype  \n",
+      "---  ------              --------------  -----  \n",
+      " 0   brand               158 non-null    str    \n",
+      " 1   name                158 non-null    str    \n",
+      " 2   lightweight         152 non-null    float64\n",
+      " 3   removable insole    158 non-null    int64  \n",
+      " 4   orthotic friendly   158 non-null    int64  \n",
+      " 5   waterproofing       156 non-null    str    \n",
+      " 6   terrain_light       158 non-null    int64  \n",
+      " 7   terrain_moderate    158 non-null    int64  \n",
+      " 8   terrain_technical   158 non-null    int64  \n",
+      " 9   shock_absorption    158 non-null    int64  \n",
+      " 10  energy_return       158 non-null    int64  \n",
+      " 11  arch_neutral        158 non-null    int64  \n",
+      " 12  arch_stability      158 non-null    int64  \n",
+      " 13  weight_lab_oz       158 non-null    float64\n",
+      " 14  weight_lab_g        158 non-null    int64  \n",
+      " 15  weight_brand_oz     155 non-null    float64\n",
+      " 16  weight_brand_g      155 non-null    float64\n",
+      " 17  drop_lab_mm         158 non-null    float64\n",
+      " 18  drop_brand_mm       152 non-null    float64\n",
+      " 19  strike_heel         158 non-null    int64  \n",
+      " 20  strike_mid          158 non-null    int64  \n",
+      " 21  strike_forefoot     158 non-null    int64  \n",
+      " 22  midsole_softness    158 non-null    int64  \n",
+      " 23  toebox_durability   158 non-null    int64  \n",
+      " 24  heel_durability     158 non-null    int64  \n",
+      " 25  outsole_durability  158 non-null    int64  \n",
+      " 26  plate_rock_plate    158 non-null    int64  \n",
+      " 27  plate_carbon_plate  158 non-null    int64  \n",
+      " 28  width_fit           158 non-null    int64  \n",
+      " 29  toebox_width        158 non-null    int64  \n",
+      " 30  torsional_rigidity  158 non-null    int64  \n",
+      " 31  heel_stiff          158 non-null    int64  \n",
+      " 32  lug_dept_mm         158 non-null    float64\n",
+      " 33  heel_lab_mm         158 non-null    float64\n",
+      " 34  heel_brand_mm       145 non-null    float64\n",
+      " 35  forefoot_lab_mm     158 non-null    float64\n",
+      " 36  forefoot_brand_mm   143 non-null    float64\n",
+      " 37  season_summer       158 non-null    int64  \n",
+      " 38  season_winter       158 non-null    int64  \n",
+      " 39  season_all          158 non-null    int64  \n",
+      "dtypes: float64(11), int64(26), str(3)\n",
+      "memory usage: 49.5 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"season\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "acdd890f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    brand                        name  removable insole  orthotic friendly\n",
+      "0  adidas  terrex agravic speed ultra                 1                  1\n",
+      "1  adidas          terrex speed ultra                 1                  1\n",
+      "2   altra             experience wild                 1                  1\n",
+      "3   altra           experience wild 2                 1                  1\n",
+      "4   altra               lone peak 5.0                 1                  1\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[[\"brand\", \"name\", \"removable insole\", \"orthotic friendly\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00fb8fe8",
+   "metadata": {},
+   "source": [
+    "## Removable insole"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "68f9c5b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "removable insole\n",
+      "1    147\n",
+      "0     11\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"removable insole\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "4916d5ba",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   removable insole  removable_insole\n",
+      "0                 1                 1\n",
+      "1                 1                 1\n",
+      "2                 1                 1\n",
+      "3                 1                 1\n",
+      "4                 1                 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "# rename Removable insole to removable_insole\n",
+    "df['removable_insole'] = df['removable insole'].fillna(0).astype(int)\n",
+    "print(df[['removable insole', 'removable_insole']].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "9eb46039",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 40 columns):\n",
+      " #   Column              Non-Null Count  Dtype  \n",
+      "---  ------              --------------  -----  \n",
+      " 0   brand               158 non-null    str    \n",
+      " 1   name                158 non-null    str    \n",
+      " 2   lightweight         152 non-null    float64\n",
+      " 3   orthotic friendly   158 non-null    int64  \n",
+      " 4   waterproofing       156 non-null    str    \n",
+      " 5   terrain_light       158 non-null    int64  \n",
+      " 6   terrain_moderate    158 non-null    int64  \n",
+      " 7   terrain_technical   158 non-null    int64  \n",
+      " 8   shock_absorption    158 non-null    int64  \n",
+      " 9   energy_return       158 non-null    int64  \n",
+      " 10  arch_neutral        158 non-null    int64  \n",
+      " 11  arch_stability      158 non-null    int64  \n",
+      " 12  weight_lab_oz       158 non-null    float64\n",
+      " 13  weight_lab_g        158 non-null    int64  \n",
+      " 14  weight_brand_oz     155 non-null    float64\n",
+      " 15  weight_brand_g      155 non-null    float64\n",
+      " 16  drop_lab_mm         158 non-null    float64\n",
+      " 17  drop_brand_mm       152 non-null    float64\n",
+      " 18  strike_heel         158 non-null    int64  \n",
+      " 19  strike_mid          158 non-null    int64  \n",
+      " 20  strike_forefoot     158 non-null    int64  \n",
+      " 21  midsole_softness    158 non-null    int64  \n",
+      " 22  toebox_durability   158 non-null    int64  \n",
+      " 23  heel_durability     158 non-null    int64  \n",
+      " 24  outsole_durability  158 non-null    int64  \n",
+      " 25  plate_rock_plate    158 non-null    int64  \n",
+      " 26  plate_carbon_plate  158 non-null    int64  \n",
+      " 27  width_fit           158 non-null    int64  \n",
+      " 28  toebox_width        158 non-null    int64  \n",
+      " 29  torsional_rigidity  158 non-null    int64  \n",
+      " 30  heel_stiff          158 non-null    int64  \n",
+      " 31  lug_dept_mm         158 non-null    float64\n",
+      " 32  heel_lab_mm         158 non-null    float64\n",
+      " 33  heel_brand_mm       145 non-null    float64\n",
+      " 34  forefoot_lab_mm     158 non-null    float64\n",
+      " 35  forefoot_brand_mm   143 non-null    float64\n",
+      " 36  season_summer       158 non-null    int64  \n",
+      " 37  season_winter       158 non-null    int64  \n",
+      " 38  season_all          158 non-null    int64  \n",
+      " 39  removable_insole    158 non-null    int64  \n",
+      "dtypes: float64(11), int64(26), str(3)\n",
+      "memory usage: 49.5 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=['removable insole'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f145cc43",
+   "metadata": {},
+   "source": [
+    "## Orthotic friendly"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "d52a6df6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "orthotic friendly\n",
+      "1    147\n",
+      "0     11\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"orthotic friendly\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "id": "8f548dc5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   orthotic friendly  orthotic_friendly\n",
+      "0                  1                  1\n",
+      "1                  1                  1\n",
+      "2                  1                  1\n",
+      "3                  1                  1\n",
+      "4                  1                  1\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Rename Orthotic friendly to orthotic_friendly\n",
+    "df['orthotic_friendly'] = df['orthotic friendly'].fillna(0).astype(int)\n",
+    "print(df[['orthotic friendly', 'orthotic_friendly']].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "059a1824",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 40 columns):\n",
+      " #   Column              Non-Null Count  Dtype  \n",
+      "---  ------              --------------  -----  \n",
+      " 0   brand               158 non-null    str    \n",
+      " 1   name                158 non-null    str    \n",
+      " 2   lightweight         152 non-null    float64\n",
+      " 3   waterproofing       156 non-null    str    \n",
+      " 4   terrain_light       158 non-null    int64  \n",
+      " 5   terrain_moderate    158 non-null    int64  \n",
+      " 6   terrain_technical   158 non-null    int64  \n",
+      " 7   shock_absorption    158 non-null    int64  \n",
+      " 8   energy_return       158 non-null    int64  \n",
+      " 9   arch_neutral        158 non-null    int64  \n",
+      " 10  arch_stability      158 non-null    int64  \n",
+      " 11  weight_lab_oz       158 non-null    float64\n",
+      " 12  weight_lab_g        158 non-null    int64  \n",
+      " 13  weight_brand_oz     155 non-null    float64\n",
+      " 14  weight_brand_g      155 non-null    float64\n",
+      " 15  drop_lab_mm         158 non-null    float64\n",
+      " 16  drop_brand_mm       152 non-null    float64\n",
+      " 17  strike_heel         158 non-null    int64  \n",
+      " 18  strike_mid          158 non-null    int64  \n",
+      " 19  strike_forefoot     158 non-null    int64  \n",
+      " 20  midsole_softness    158 non-null    int64  \n",
+      " 21  toebox_durability   158 non-null    int64  \n",
+      " 22  heel_durability     158 non-null    int64  \n",
+      " 23  outsole_durability  158 non-null    int64  \n",
+      " 24  plate_rock_plate    158 non-null    int64  \n",
+      " 25  plate_carbon_plate  158 non-null    int64  \n",
+      " 26  width_fit           158 non-null    int64  \n",
+      " 27  toebox_width        158 non-null    int64  \n",
+      " 28  torsional_rigidity  158 non-null    int64  \n",
+      " 29  heel_stiff          158 non-null    int64  \n",
+      " 30  lug_dept_mm         158 non-null    float64\n",
+      " 31  heel_lab_mm         158 non-null    float64\n",
+      " 32  heel_brand_mm       145 non-null    float64\n",
+      " 33  forefoot_lab_mm     158 non-null    float64\n",
+      " 34  forefoot_brand_mm   143 non-null    float64\n",
+      " 35  season_summer       158 non-null    int64  \n",
+      " 36  season_winter       158 non-null    int64  \n",
+      " 37  season_all          158 non-null    int64  \n",
+      " 38  removable_insole    158 non-null    int64  \n",
+      " 39  orthotic_friendly   158 non-null    int64  \n",
+      "dtypes: float64(11), int64(26), str(3)\n",
+      "memory usage: 49.5 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=['orthotic friendly'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "442b3a27",
+   "metadata": {},
+   "source": [
+    "## Waterproofing        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "id": "a056f12c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "waterproofing\n",
+      "-                             137\n",
+      "waterproof                     12\n",
+      "water repellent                 5\n",
+      "waterproof water repellent      1\n",
+      "0                               1\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['waterproofing'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7494a16e",
+   "metadata": {},
+   "source": [
+    "Water repellent cuma nahan menolak air di permukaan tapi kalau terendam, kakinya tetap basah. kalau waterproof bener bener tahan air"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "d52d4d09",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 158\n",
+      "\n",
+      "Sample Comparison:\n",
+      "  waterproofing  waterproof  water_repellent\n",
+      "0             -           0                0\n",
+      "1             -           0                0\n",
+      "2             -           0                0\n",
+      "3             -           0                0\n",
+      "4             -           0                0\n",
+      "5             -           0                0\n",
+      "6             -           0                0\n",
+      "7             -           0                0\n",
+      "8             -           0                0\n",
+      "9             -           0                0\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['waterproofing'] = df['waterproofing'].astype(str).str.lower()\n",
+    "base_water = ['waterproof', 'water repellent']\n",
+    "\n",
+    "def check_not_waterproof(val):\n",
+    "    if val in ['-', '0', 'nan', 'none']:\n",
+    "        return 1\n",
+    "    return 0\n",
+    "\n",
+    "for level in base_water:\n",
+    "    column_name = level.replace(' ', '_')\n",
+    "    \n",
+    "    if level == 'not waterproof':\n",
+    "        df[column_name] = df['waterproofing'].apply(check_not_waterproof)\n",
+    "    else:\n",
+    "        df[column_name] = df['waterproofing'].str.contains(level, na=False).astype(int)\n",
+    "        df.loc[df['waterproofing'].isin(['-', '0']), column_name] = 0\n",
+    "\n",
+    "print(\"Rows:\", len(df))\n",
+    "\n",
+    "water_cols = [l.replace(' ', '_') for l in base_water]\n",
+    "\n",
+    "print(\"\\nSample Comparison:\")\n",
+    "print(df[[\"waterproofing\"] + water_cols].head(10))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "9d6e1eb8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "waterproofing\n",
+      "-                             137\n",
+      "waterproof                     12\n",
+      "water repellent                 5\n",
+      "waterproof water repellent      1\n",
+      "0                               1\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "--- Sum Per Kolom ---\n",
+      "waterproof sum: 13\n",
+      "water_repellent sum: 6\n",
+      "\n",
+      "Total Check (Harus >= 158): 19\n",
+      "\n",
+      "  waterproofing  waterproof  water_repellent\n",
+      "0             -           0                0\n",
+      "1             -           0                0\n",
+      "2             -           0                0\n",
+      "3             -           0                0\n",
+      "4             -           0                0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"waterproofing\"].value_counts())\n",
+    "\n",
+    "print(\"\\n--- Sum Per Kolom ---\")\n",
+    "for col in water_cols:\n",
+    "    print(f\"{col} sum:\", int(df[col].sum()))\n",
+    "\n",
+    "total_sum = df[water_cols].sum().sum()\n",
+    "print(f\"\\nTotal Check (Harus >= {len(df)}): {total_sum}\")\n",
+    "\n",
+    "print()\n",
+    "print(df[[\"waterproofing\"] + water_cols].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "1804482a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 41 columns):\n",
+      " #   Column              Non-Null Count  Dtype  \n",
+      "---  ------              --------------  -----  \n",
+      " 0   brand               158 non-null    str    \n",
+      " 1   name                158 non-null    str    \n",
+      " 2   lightweight         152 non-null    float64\n",
+      " 3   terrain_light       158 non-null    int64  \n",
+      " 4   terrain_moderate    158 non-null    int64  \n",
+      " 5   terrain_technical   158 non-null    int64  \n",
+      " 6   shock_absorption    158 non-null    int64  \n",
+      " 7   energy_return       158 non-null    int64  \n",
+      " 8   arch_neutral        158 non-null    int64  \n",
+      " 9   arch_stability      158 non-null    int64  \n",
+      " 10  weight_lab_oz       158 non-null    float64\n",
+      " 11  weight_lab_g        158 non-null    int64  \n",
+      " 12  weight_brand_oz     155 non-null    float64\n",
+      " 13  weight_brand_g      155 non-null    float64\n",
+      " 14  drop_lab_mm         158 non-null    float64\n",
+      " 15  drop_brand_mm       152 non-null    float64\n",
+      " 16  strike_heel         158 non-null    int64  \n",
+      " 17  strike_mid          158 non-null    int64  \n",
+      " 18  strike_forefoot     158 non-null    int64  \n",
+      " 19  midsole_softness    158 non-null    int64  \n",
+      " 20  toebox_durability   158 non-null    int64  \n",
+      " 21  heel_durability     158 non-null    int64  \n",
+      " 22  outsole_durability  158 non-null    int64  \n",
+      " 23  plate_rock_plate    158 non-null    int64  \n",
+      " 24  plate_carbon_plate  158 non-null    int64  \n",
+      " 25  width_fit           158 non-null    int64  \n",
+      " 26  toebox_width        158 non-null    int64  \n",
+      " 27  torsional_rigidity  158 non-null    int64  \n",
+      " 28  heel_stiff          158 non-null    int64  \n",
+      " 29  lug_dept_mm         158 non-null    float64\n",
+      " 30  heel_lab_mm         158 non-null    float64\n",
+      " 31  heel_brand_mm       145 non-null    float64\n",
+      " 32  forefoot_lab_mm     158 non-null    float64\n",
+      " 33  forefoot_brand_mm   143 non-null    float64\n",
+      " 34  season_summer       158 non-null    int64  \n",
+      " 35  season_winter       158 non-null    int64  \n",
+      " 36  season_all          158 non-null    int64  \n",
+      " 37  removable_insole    158 non-null    int64  \n",
+      " 38  orthotic_friendly   158 non-null    int64  \n",
+      " 39  waterproof          158 non-null    int64  \n",
+      " 40  water_repellent     158 non-null    int64  \n",
+      "dtypes: float64(11), int64(28), str(2)\n",
+      "memory usage: 50.7 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"waterproofing\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a88a6c8",
+   "metadata": {},
+   "source": [
+    "# Finishing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "26dc1dc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 40 columns):\n",
+      " #   Column              Non-Null Count  Dtype  \n",
+      "---  ------              --------------  -----  \n",
+      " 0   brand               158 non-null    str    \n",
+      " 1   name                158 non-null    str    \n",
+      " 2   terrain_light       158 non-null    int64  \n",
+      " 3   terrain_moderate    158 non-null    int64  \n",
+      " 4   terrain_technical   158 non-null    int64  \n",
+      " 5   shock_absorption    158 non-null    int64  \n",
+      " 6   energy_return       158 non-null    int64  \n",
+      " 7   arch_neutral        158 non-null    int64  \n",
+      " 8   arch_stability      158 non-null    int64  \n",
+      " 9   weight_lab_oz       158 non-null    float64\n",
+      " 10  weight_lab_g        158 non-null    int64  \n",
+      " 11  weight_brand_oz     155 non-null    float64\n",
+      " 12  weight_brand_g      155 non-null    float64\n",
+      " 13  drop_lab_mm         158 non-null    float64\n",
+      " 14  drop_brand_mm       152 non-null    float64\n",
+      " 15  strike_heel         158 non-null    int64  \n",
+      " 16  strike_mid          158 non-null    int64  \n",
+      " 17  strike_forefoot     158 non-null    int64  \n",
+      " 18  midsole_softness    158 non-null    int64  \n",
+      " 19  toebox_durability   158 non-null    int64  \n",
+      " 20  heel_durability     158 non-null    int64  \n",
+      " 21  outsole_durability  158 non-null    int64  \n",
+      " 22  plate_rock_plate    158 non-null    int64  \n",
+      " 23  plate_carbon_plate  158 non-null    int64  \n",
+      " 24  width_fit           158 non-null    int64  \n",
+      " 25  toebox_width        158 non-null    int64  \n",
+      " 26  torsional_rigidity  158 non-null    int64  \n",
+      " 27  heel_stiff          158 non-null    int64  \n",
+      " 28  lug_dept_mm         158 non-null    float64\n",
+      " 29  heel_lab_mm         158 non-null    float64\n",
+      " 30  heel_brand_mm       145 non-null    float64\n",
+      " 31  forefoot_lab_mm     158 non-null    float64\n",
+      " 32  forefoot_brand_mm   143 non-null    float64\n",
+      " 33  season_summer       158 non-null    int64  \n",
+      " 34  season_winter       158 non-null    int64  \n",
+      " 35  season_all          158 non-null    int64  \n",
+      " 36  removable_insole    158 non-null    int64  \n",
+      " 37  orthotic_friendly   158 non-null    int64  \n",
+      " 38  waterproof          158 non-null    int64  \n",
+      " 39  water_repellent     158 non-null    int64  \n",
+      "dtypes: float64(10), int64(28), str(2)\n",
+      "memory usage: 49.5 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# change lightweight to int\n",
+    "df['lightweight'] = df['lightweight'].fillna(0).astype(int)\n",
+    "df.drop(columns=['lightweight'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "9b96e4f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 37 columns):\n",
+      " #   Column              Non-Null Count  Dtype  \n",
+      "---  ------              --------------  -----  \n",
+      " 0   brand               158 non-null    str    \n",
+      " 1   name                158 non-null    str    \n",
+      " 2   terrain_light       158 non-null    int64  \n",
+      " 3   terrain_moderate    158 non-null    int64  \n",
+      " 4   terrain_technical   158 non-null    int64  \n",
+      " 5   shock_absorption    158 non-null    int64  \n",
+      " 6   energy_return       158 non-null    int64  \n",
+      " 7   arch_neutral        158 non-null    int64  \n",
+      " 8   arch_stability      158 non-null    int64  \n",
+      " 9   weight_lab_oz       158 non-null    float64\n",
+      " 10  drop_lab_mm         158 non-null    float64\n",
+      " 11  drop_brand_mm       152 non-null    float64\n",
+      " 12  strike_heel         158 non-null    int64  \n",
+      " 13  strike_mid          158 non-null    int64  \n",
+      " 14  strike_forefoot     158 non-null    int64  \n",
+      " 15  midsole_softness    158 non-null    int64  \n",
+      " 16  toebox_durability   158 non-null    int64  \n",
+      " 17  heel_durability     158 non-null    int64  \n",
+      " 18  outsole_durability  158 non-null    int64  \n",
+      " 19  plate_rock_plate    158 non-null    int64  \n",
+      " 20  plate_carbon_plate  158 non-null    int64  \n",
+      " 21  width_fit           158 non-null    int64  \n",
+      " 22  toebox_width        158 non-null    int64  \n",
+      " 23  torsional_rigidity  158 non-null    int64  \n",
+      " 24  heel_stiff          158 non-null    int64  \n",
+      " 25  lug_dept_mm         158 non-null    float64\n",
+      " 26  heel_lab_mm         158 non-null    float64\n",
+      " 27  heel_brand_mm       145 non-null    float64\n",
+      " 28  forefoot_lab_mm     158 non-null    float64\n",
+      " 29  forefoot_brand_mm   143 non-null    float64\n",
+      " 30  season_summer       158 non-null    int64  \n",
+      " 31  season_winter       158 non-null    int64  \n",
+      " 32  season_all          158 non-null    int64  \n",
+      " 33  removable_insole    158 non-null    int64  \n",
+      " 34  orthotic_friendly   158 non-null    int64  \n",
+      " 35  waterproof          158 non-null    int64  \n",
+      " 36  water_repellent     158 non-null    int64  \n",
+      "dtypes: float64(8), int64(27), str(2)\n",
+      "memory usage: 45.8 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Weight cuma pakai yg lab_oz\n",
+    "df.drop(columns=['weight_brand_oz', 'weight_lab_g', 'weight_brand_g'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "40a900de",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 36 columns):\n",
+      " #   Column              Non-Null Count  Dtype  \n",
+      "---  ------              --------------  -----  \n",
+      " 0   brand               158 non-null    str    \n",
+      " 1   name                158 non-null    str    \n",
+      " 2   terrain_light       158 non-null    int64  \n",
+      " 3   terrain_moderate    158 non-null    int64  \n",
+      " 4   terrain_technical   158 non-null    int64  \n",
+      " 5   shock_absorption    158 non-null    int64  \n",
+      " 6   energy_return       158 non-null    int64  \n",
+      " 7   arch_neutral        158 non-null    int64  \n",
+      " 8   arch_stability      158 non-null    int64  \n",
+      " 9   weight_lab_oz       158 non-null    float64\n",
+      " 10  drop_lab_mm         158 non-null    float64\n",
+      " 11  strike_heel         158 non-null    int64  \n",
+      " 12  strike_mid          158 non-null    int64  \n",
+      " 13  strike_forefoot     158 non-null    int64  \n",
+      " 14  midsole_softness    158 non-null    int64  \n",
+      " 15  toebox_durability   158 non-null    int64  \n",
+      " 16  heel_durability     158 non-null    int64  \n",
+      " 17  outsole_durability  158 non-null    int64  \n",
+      " 18  plate_rock_plate    158 non-null    int64  \n",
+      " 19  plate_carbon_plate  158 non-null    int64  \n",
+      " 20  width_fit           158 non-null    int64  \n",
+      " 21  toebox_width        158 non-null    int64  \n",
+      " 22  torsional_rigidity  158 non-null    int64  \n",
+      " 23  heel_stiff          158 non-null    int64  \n",
+      " 24  lug_dept_mm         158 non-null    float64\n",
+      " 25  heel_lab_mm         158 non-null    float64\n",
+      " 26  heel_brand_mm       145 non-null    float64\n",
+      " 27  forefoot_lab_mm     158 non-null    float64\n",
+      " 28  forefoot_brand_mm   143 non-null    float64\n",
+      " 29  season_summer       158 non-null    int64  \n",
+      " 30  season_winter       158 non-null    int64  \n",
+      " 31  season_all          158 non-null    int64  \n",
+      " 32  removable_insole    158 non-null    int64  \n",
+      " 33  orthotic_friendly   158 non-null    int64  \n",
+      " 34  waterproof          158 non-null    int64  \n",
+      " 35  water_repellent     158 non-null    int64  \n",
+      "dtypes: float64(7), int64(27), str(2)\n",
+      "memory usage: 44.6 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# drop cuma pakai yg lab_mm\n",
+    "df.drop(columns=['drop_brand_mm'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "id": "84034462",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 35 columns):\n",
+      " #   Column              Non-Null Count  Dtype  \n",
+      "---  ------              --------------  -----  \n",
+      " 0   brand               158 non-null    str    \n",
+      " 1   name                158 non-null    str    \n",
+      " 2   terrain_light       158 non-null    int64  \n",
+      " 3   terrain_moderate    158 non-null    int64  \n",
+      " 4   terrain_technical   158 non-null    int64  \n",
+      " 5   shock_absorption    158 non-null    int64  \n",
+      " 6   energy_return       158 non-null    int64  \n",
+      " 7   arch_neutral        158 non-null    int64  \n",
+      " 8   arch_stability      158 non-null    int64  \n",
+      " 9   weight_lab_oz       158 non-null    float64\n",
+      " 10  drop_lab_mm         158 non-null    float64\n",
+      " 11  strike_heel         158 non-null    int64  \n",
+      " 12  strike_mid          158 non-null    int64  \n",
+      " 13  strike_forefoot     158 non-null    int64  \n",
+      " 14  midsole_softness    158 non-null    int64  \n",
+      " 15  toebox_durability   158 non-null    int64  \n",
+      " 16  heel_durability     158 non-null    int64  \n",
+      " 17  outsole_durability  158 non-null    int64  \n",
+      " 18  plate_rock_plate    158 non-null    int64  \n",
+      " 19  plate_carbon_plate  158 non-null    int64  \n",
+      " 20  width_fit           158 non-null    int64  \n",
+      " 21  toebox_width        158 non-null    int64  \n",
+      " 22  torsional_rigidity  158 non-null    int64  \n",
+      " 23  heel_stiff          158 non-null    int64  \n",
+      " 24  lug_dept_mm         158 non-null    float64\n",
+      " 25  heel_lab_mm         158 non-null    float64\n",
+      " 26  forefoot_lab_mm     158 non-null    float64\n",
+      " 27  forefoot_brand_mm   143 non-null    float64\n",
+      " 28  season_summer       158 non-null    int64  \n",
+      " 29  season_winter       158 non-null    int64  \n",
+      " 30  season_all          158 non-null    int64  \n",
+      " 31  removable_insole    158 non-null    int64  \n",
+      " 32  orthotic_friendly   158 non-null    int64  \n",
+      " 33  waterproof          158 non-null    int64  \n",
+      " 34  water_repellent     158 non-null    int64  \n",
+      "dtypes: float64(6), int64(27), str(2)\n",
+      "memory usage: 43.3 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# heel pakai yang heel_lab_mm\n",
+    "df.drop(columns=['heel_brand_mm'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "id": "becce231",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 34 columns):\n",
+      " #   Column              Non-Null Count  Dtype  \n",
+      "---  ------              --------------  -----  \n",
+      " 0   brand               158 non-null    str    \n",
+      " 1   name                158 non-null    str    \n",
+      " 2   terrain_light       158 non-null    int64  \n",
+      " 3   terrain_moderate    158 non-null    int64  \n",
+      " 4   terrain_technical   158 non-null    int64  \n",
+      " 5   shock_absorption    158 non-null    int64  \n",
+      " 6   energy_return       158 non-null    int64  \n",
+      " 7   arch_neutral        158 non-null    int64  \n",
+      " 8   arch_stability      158 non-null    int64  \n",
+      " 9   weight_lab_oz       158 non-null    float64\n",
+      " 10  drop_lab_mm         158 non-null    float64\n",
+      " 11  strike_heel         158 non-null    int64  \n",
+      " 12  strike_mid          158 non-null    int64  \n",
+      " 13  strike_forefoot     158 non-null    int64  \n",
+      " 14  midsole_softness    158 non-null    int64  \n",
+      " 15  toebox_durability   158 non-null    int64  \n",
+      " 16  heel_durability     158 non-null    int64  \n",
+      " 17  outsole_durability  158 non-null    int64  \n",
+      " 18  plate_rock_plate    158 non-null    int64  \n",
+      " 19  plate_carbon_plate  158 non-null    int64  \n",
+      " 20  width_fit           158 non-null    int64  \n",
+      " 21  toebox_width        158 non-null    int64  \n",
+      " 22  torsional_rigidity  158 non-null    int64  \n",
+      " 23  heel_stiff          158 non-null    int64  \n",
+      " 24  lug_dept_mm         158 non-null    float64\n",
+      " 25  heel_lab_mm         158 non-null    float64\n",
+      " 26  forefoot_lab_mm     158 non-null    float64\n",
+      " 27  season_summer       158 non-null    int64  \n",
+      " 28  season_winter       158 non-null    int64  \n",
+      " 29  season_all          158 non-null    int64  \n",
+      " 30  removable_insole    158 non-null    int64  \n",
+      " 31  orthotic_friendly   158 non-null    int64  \n",
+      " 32  waterproof          158 non-null    int64  \n",
+      " 33  water_repellent     158 non-null    int64  \n",
+      "dtypes: float64(5), int64(27), str(2)\n",
+      "memory usage: 42.1 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Forefoot pakai yang forefoot_lab_mm\n",
+    "df.drop(columns=['forefoot_brand_mm'], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "id": "4c936ab8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 33 columns):\n",
+      " #   Column              Non-Null Count  Dtype  \n",
+      "---  ------              --------------  -----  \n",
+      " 0   brand               158 non-null    str    \n",
+      " 1   name                158 non-null    str    \n",
+      " 2   terrain_light       158 non-null    int64  \n",
+      " 3   terrain_moderate    158 non-null    int64  \n",
+      " 4   terrain_technical   158 non-null    int64  \n",
+      " 5   shock_absorption    158 non-null    int64  \n",
+      " 6   energy_return       158 non-null    int64  \n",
+      " 7   arch_neutral        158 non-null    int64  \n",
+      " 8   arch_stability      158 non-null    int64  \n",
+      " 9   weight_lab_oz       158 non-null    float64\n",
+      " 10  drop_lab_mm         158 non-null    float64\n",
+      " 11  strike_heel         158 non-null    int64  \n",
+      " 12  strike_mid          158 non-null    int64  \n",
+      " 13  strike_forefoot     158 non-null    int64  \n",
+      " 14  midsole_softness    158 non-null    int64  \n",
+      " 15  toebox_durability   158 non-null    int64  \n",
+      " 16  heel_durability     158 non-null    int64  \n",
+      " 17  outsole_durability  158 non-null    int64  \n",
+      " 18  plate_rock_plate    158 non-null    int64  \n",
+      " 19  plate_carbon_plate  158 non-null    int64  \n",
+      " 20  width_fit           158 non-null    int64  \n",
+      " 21  toebox_width        158 non-null    int64  \n",
+      " 22  torsional_rigidity  158 non-null    int64  \n",
+      " 23  heel_stiff          158 non-null    int64  \n",
+      " 24  lug_dept_mm         158 non-null    float64\n",
+      " 25  heel_lab_mm         158 non-null    float64\n",
+      " 26  forefoot_lab_mm     158 non-null    float64\n",
+      " 27  season_summer       158 non-null    int64  \n",
+      " 28  season_winter       158 non-null    int64  \n",
+      " 29  season_all          158 non-null    int64  \n",
+      " 30  removable_insole    158 non-null    int64  \n",
+      " 31  waterproof          158 non-null    int64  \n",
+      " 32  water_repellent     158 non-null    int64  \n",
+      "dtypes: float64(5), int64(26), str(2)\n",
+      "memory usage: 40.9 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Only take removable_insole feature \n",
+    "df.drop(columns='orthotic_friendly', inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "id": "5cc8f859",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv('../../data/trail_dataset.csv', index=False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/data-preparation/pre-eda-road.ipynb b/notebooks/data-preparation/pre-eda-road.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..0600ece18eb936580ef591fa1c6c5796cb2dc7ef
--- /dev/null
+++ b/notebooks/data-preparation/pre-eda-road.ipynb
@@ -0,0 +1,11631 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4944982d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Pace</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Heel lab Heel brand</th>\n",
+       "      <th>Forefoot lab Forefoot brand</th>\n",
+       "      <th>Widths available</th>\n",
+       "      <th>Orthotic friendly</th>\n",
+       "      <th>Season</th>\n",
+       "      <th>Removable insole</th>\n",
+       "      <th>Ranking</th>\n",
+       "      <th>Popularity</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Terrain</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Launch 9</td>\n",
+       "      <td>87\\n Great!</td>\n",
+       "      <td>$110</td>\n",
+       "      <td>Daily runningTempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>7.9 oz / 225g 8.1 oz / 230g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.4 mm 10.0 mm</td>\n",
+       "      <td>HeelMid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>32.4 mm 36.0 mm</td>\n",
+       "      <td>23.0 mm 26.0 mm</td>\n",
+       "      <td>NormalWide</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#301 Top 47%</td>\n",
+       "      <td>#352 Bottom 45%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Levitate 6</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$150</td>\n",
+       "      <td>Daily running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 304g 10.9 oz / 309g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.7 mm 8.0 mm</td>\n",
+       "      <td>Mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>34.3 mm 32.5 mm</td>\n",
+       "      <td>26.6 mm 24.5 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>SummerAll seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#72 Top 20%</td>\n",
+       "      <td>#255 Bottom 30%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>11.9 oz / 336g 11.5 oz / 327g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.9 mm 10.0 mm</td>\n",
+       "      <td>HeelMid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>33.3 mm 32.5 mm</td>\n",
+       "      <td>24.4 mm 22.5 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#104 Top 17%</td>\n",
+       "      <td>#368 Bottom 42%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 2</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.6 oz / 356g 12.4 oz / 352g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.6 mm 11.0 mm</td>\n",
+       "      <td>Heel</td>\n",
+       "      <td>...</td>\n",
+       "      <td>31.8 mm 32.0 mm</td>\n",
+       "      <td>21.2 mm 21.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#126 Top 20%</td>\n",
+       "      <td>#541 Bottom 16%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 3</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.3 oz / 348g 12.2 oz / 345g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.9 mm 10.0 mm</td>\n",
+       "      <td>HeelMid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>32.6 mm 34.0 mm</td>\n",
+       "      <td>22.7 mm 24.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#116 Top 32%</td>\n",
+       "      <td>#339 Bottom 7%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 33 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand         Name Audience score Price                Pace Arch support  \\\n",
+       "0  Brooks     Launch 9    87\\n Great!  $110  Daily runningTempo      Neutral   \n",
+       "1  Brooks   Levitate 6   90\\n Superb!  $150       Daily running      Neutral   \n",
+       "2  Adidas        4DFWD   90\\n Superb!  $200       Daily running      Neutral   \n",
+       "3  Adidas      4DFWD 2   90\\n Superb!  $200       Daily running      Neutral   \n",
+       "4  Adidas      4DFWD 3    88\\n Great!  $200       Daily running      Neutral   \n",
+       "\n",
+       "         Weight lab Weight brand  Lightweight Drop lab Drop brand  \\\n",
+       "0    7.9 oz / 225g 8.1 oz / 230g            1      9.4 mm 10.0 mm   \n",
+       "1  10.7 oz / 304g 10.9 oz / 309g            0       7.7 mm 8.0 mm   \n",
+       "2  11.9 oz / 336g 11.5 oz / 327g            0      8.9 mm 10.0 mm   \n",
+       "3  12.6 oz / 356g 12.4 oz / 352g            0     10.6 mm 11.0 mm   \n",
+       "4  12.3 oz / 348g 12.2 oz / 345g            0      9.9 mm 10.0 mm   \n",
+       "\n",
+       "     Strike pattern  ... Heel lab Heel brand Forefoot lab Forefoot brand  \\\n",
+       "0  HeelMid/forefoot  ...     32.4 mm 36.0 mm             23.0 mm 26.0 mm   \n",
+       "1      Mid/forefoot  ...     34.3 mm 32.5 mm             26.6 mm 24.5 mm   \n",
+       "2  HeelMid/forefoot  ...     33.3 mm 32.5 mm             24.4 mm 22.5 mm   \n",
+       "3              Heel  ...     31.8 mm 32.0 mm             21.2 mm 21.0 mm   \n",
+       "4  HeelMid/forefoot  ...     32.6 mm 34.0 mm             22.7 mm 24.0 mm   \n",
+       "\n",
+       "  Widths available Orthotic friendly             Season Removable insole  \\\n",
+       "0       NormalWide                 1                  -                1   \n",
+       "1           Normal                 1  SummerAll seasons                1   \n",
+       "2           Normal                 1        All seasons                1   \n",
+       "3           Normal                 1        All seasons                1   \n",
+       "4           Normal                 1        All seasons                1   \n",
+       "\n",
+       "        Ranking       Popularity Gender Terrain  \n",
+       "0  #301 Top 47%  #352 Bottom 45%    NaN     NaN  \n",
+       "1   #72 Top 20%  #255 Bottom 30%    NaN     NaN  \n",
+       "2  #104 Top 17%  #368 Bottom 42%    NaN     NaN  \n",
+       "3  #126 Top 20%  #541 Bottom 16%    NaN     NaN  \n",
+       "4  #116 Top 32%   #339 Bottom 7%    NaN     NaN  \n",
+       "\n",
+       "[5 rows x 33 columns]"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.read_csv('../../data/SONIX utilities - Road.csv')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d066ba9",
+   "metadata": {},
+   "source": [
+    "value \"✗\" sama \"✓\" udah diubah ke 0 1 manual di sheet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "bc8a931f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 1170 entries, 0 to 1169\n",
+      "Data columns (total 33 columns):\n",
+      " #   Column                       Non-Null Count  Dtype\n",
+      "---  ------                       --------------  -----\n",
+      " 0   Brand                        1170 non-null   str  \n",
+      " 1   Name                         1170 non-null   str  \n",
+      " 2   Audience score               1164 non-null   str  \n",
+      " 3   Price                        1170 non-null   str  \n",
+      " 4   Pace                         1170 non-null   str  \n",
+      " 5   Arch support                 1170 non-null   str  \n",
+      " 6   Weight lab Weight brand      1170 non-null   str  \n",
+      " 7   Lightweight                  1170 non-null   int64\n",
+      " 8   Drop lab Drop brand          1170 non-null   str  \n",
+      " 9   Strike pattern               1170 non-null   str  \n",
+      " 10  Size                         1170 non-null   str  \n",
+      " 11  Midsole softness             1170 non-null   str  \n",
+      " 12  Toebox durability            1170 non-null   str  \n",
+      " 13  Heel padding durability      1170 non-null   str  \n",
+      " 14  Outsole durability           1170 non-null   str  \n",
+      " 15  Breathability                1170 non-null   str  \n",
+      " 16  Width / fit                  1170 non-null   str  \n",
+      " 17  Toebox width                 1170 non-null   str  \n",
+      " 18  Stiffness                    1170 non-null   str  \n",
+      " 19  Torsional rigidity           1170 non-null   str  \n",
+      " 20  Heel counter stiffness       1170 non-null   str  \n",
+      " 21  Plate                        1170 non-null   str  \n",
+      " 22  Rocker                       1170 non-null   int64\n",
+      " 23  Heel lab Heel brand          1170 non-null   str  \n",
+      " 24  Forefoot lab Forefoot brand  1170 non-null   str  \n",
+      " 25  Widths available             1170 non-null   str  \n",
+      " 26  Orthotic friendly            1170 non-null   int64\n",
+      " 27  Season                       1170 non-null   str  \n",
+      " 28  Removable insole             1170 non-null   int64\n",
+      " 29  Ranking                      1170 non-null   str  \n",
+      " 30  Popularity                   1170 non-null   str  \n",
+      " 31  Gender                       10 non-null     str  \n",
+      " 32  Terrain                      16 non-null     str  \n",
+      "dtypes: int64(4), str(29)\n",
+      "memory usage: 301.8 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c0b39803",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Brand (32 unique)\n",
+      "<StringArray>\n",
+      "[      'Brooks',       'Adidas',     'Skechers',      'Salomon',\n",
+      "         'Nike',       'NOBULL',         'Hoka',         'HOKA',\n",
+      "         'Topo',      'Diadora',      'Saucony', 'Under Armour',\n",
+      "           'On',           'on',          'On ',         'PUMA',\n",
+      "         'Puma',         'NIke',        'ASICS',        'Altra',\n",
+      "       'Reebok',  'New Balance',  'new Balance',       'Mizuno',\n",
+      "        'Nike ',       'Jordan',        'Inov8',      'SAlomon',\n",
+      "         'Xero',          'APL',     'Allbirds',      'Merrell']\n",
+      "Length: 32, dtype: str\n",
+      "\n",
+      "Pace (5 unique)\n",
+      "<StringArray>\n",
+      "['Daily runningTempo',      'Daily running',              'Tempo',\n",
+      "   'CompetitionTempo',        'Competition']\n",
+      "Length: 5, dtype: str\n",
+      "\n",
+      "Arch support (3 unique)\n",
+      "<StringArray>\n",
+      "['Neutral', 'Motion control', 'Stability']\n",
+      "Length: 3, dtype: str\n",
+      "\n",
+      "Lightweight (2 unique)\n",
+      "[1 0]\n",
+      "\n",
+      "Strike pattern (5 unique)\n",
+      "<StringArray>\n",
+      "['HeelMid/forefoot', 'Mid/forefoot', 'Heel', '-', 'Heel Mid/forefoot']\n",
+      "Length: 5, dtype: str\n",
+      "\n",
+      "Size (6 unique)\n",
+      "<StringArray>\n",
+      "[   'True to size',  'Slightly small', 'Half size small',  'Slightly large',\n",
+      "               '-', 'Half size large']\n",
+      "Length: 6, dtype: str\n",
+      "\n",
+      "Midsole softness (4 unique)\n",
+      "<StringArray>\n",
+      "['Balanced', 'Soft', 'Firm', '-']\n",
+      "Length: 4, dtype: str\n",
+      "\n",
+      "Toebox durability (4 unique)\n",
+      "<StringArray>\n",
+      "['-', 'Good', 'Decent', 'Bad']\n",
+      "Length: 4, dtype: str\n",
+      "\n",
+      "Heel padding durability (4 unique)\n",
+      "<StringArray>\n",
+      "['-', 'Good', 'Decent', 'Bad']\n",
+      "Length: 4, dtype: str\n",
+      "\n",
+      "Outsole durability (4 unique)\n",
+      "<StringArray>\n",
+      "['-', 'Good', 'Decent', 'Bad']\n",
+      "Length: 4, dtype: str\n",
+      "\n",
+      "Breathability (4 unique)\n",
+      "<StringArray>\n",
+      "['-', 'Breathable', 'Warm', 'Moderate']\n",
+      "Length: 4, dtype: str\n",
+      "\n",
+      "Width / fit (3 unique)\n",
+      "<StringArray>\n",
+      "['Narrow', 'Medium', 'Wide']\n",
+      "Length: 3, dtype: str\n",
+      "\n",
+      "Toebox width (4 unique)\n",
+      "<StringArray>\n",
+      "['-', 'Medium', 'Wide', 'Narrow']\n",
+      "Length: 4, dtype: str\n",
+      "\n",
+      "Stiffness (4 unique)\n",
+      "<StringArray>\n",
+      "['Stiff', 'Moderate', 'Flexible', '-']\n",
+      "Length: 4, dtype: str\n",
+      "\n",
+      "Torsional rigidity (4 unique)\n",
+      "<StringArray>\n",
+      "['Stiff', 'Moderate', 'Flexible', '-']\n",
+      "Length: 4, dtype: str\n",
+      "\n",
+      "Heel counter stiffness (4 unique)\n",
+      "<StringArray>\n",
+      "['Flexible', 'Moderate', 'Stiff', '-']\n",
+      "Length: 4, dtype: str\n",
+      "\n",
+      "Plate (3 unique)\n",
+      "<StringArray>\n",
+      "['0', 'Carbon plate', 'Carbon plateRock plate']\n",
+      "Length: 3, dtype: str\n",
+      "\n",
+      "Rocker (2 unique)\n",
+      "[0 1]\n",
+      "\n",
+      "Widths available (11 unique)\n",
+      "<StringArray>\n",
+      "[               'NormalWide',                    'Normal',\n",
+      "    'NarrowNormalWideX-Wide',              'NormalX-Wide',\n",
+      "          'NarrowNormalWide',          'NormalWideX-Wide',\n",
+      " 'Narrow Normal Wide X-Wide',              'NarrowNormal',\n",
+      "               'Normal Wide',        'NarrowNormalX-Wide',\n",
+      "        'Normal Wide X-Wide']\n",
+      "Length: 11, dtype: str\n",
+      "\n",
+      "Orthotic friendly (2 unique)\n",
+      "[1 0]\n",
+      "\n",
+      "Season (4 unique)\n",
+      "<StringArray>\n",
+      "['-', 'SummerAll seasons', 'All seasons', 'Winter']\n",
+      "Length: 4, dtype: str\n",
+      "\n",
+      "Removable insole (2 unique)\n",
+      "[1 0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "observed_col = [\n",
+    "    'Brand',\n",
+    "    'Pace',\n",
+    "    'Arch support',\n",
+    "    'Lightweight',\n",
+    "    'Strike pattern',\n",
+    "    'Size',\n",
+    "    'Midsole softness',\n",
+    "    'Toebox durability',\n",
+    "    'Heel padding durability',\n",
+    "    'Outsole durability',\n",
+    "    'Breathability',\n",
+    "    'Width / fit',\n",
+    "    'Toebox width',\n",
+    "    'Stiffness',\n",
+    "    'Torsional rigidity',\n",
+    "    'Heel counter stiffness',\n",
+    "    'Plate',\n",
+    "    'Rocker',\n",
+    "    'Widths available',\n",
+    "    'Orthotic friendly',\n",
+    "    'Season',\n",
+    "    'Removable insole'\n",
+    "]\n",
+    "\n",
+    "for col in observed_col:    \n",
+    "    uniques = df[col].dropna().unique()\n",
+    "    print(f\"\\n{col} ({len(uniques)} unique)\")\n",
+    "    print(uniques)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bbabcc2e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Pace</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Heel lab Heel brand</th>\n",
+       "      <th>Forefoot lab Forefoot brand</th>\n",
+       "      <th>Widths available</th>\n",
+       "      <th>Orthotic friendly</th>\n",
+       "      <th>Season</th>\n",
+       "      <th>Removable insole</th>\n",
+       "      <th>Ranking</th>\n",
+       "      <th>Popularity</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Terrain</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>0 rows × 33 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [Brand, Name, Audience score, Price, Pace, Arch support, Weight lab Weight brand, Lightweight, Drop lab Drop brand, Strike pattern, Size, Midsole softness, Toebox durability, Heel padding durability, Outsole durability, Breathability, Width / fit, Toebox width, Stiffness, Torsional rigidity, Heel counter stiffness, Plate, Rocker, Heel lab Heel brand, Forefoot lab Forefoot brand, Widths available, Orthotic friendly, Season, Removable insole, Ranking, Popularity, Gender, Terrain]\n",
+       "Index: []\n",
+       "\n",
+       "[0 rows x 33 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# check row dari filter masih kemasukan apa ngga\n",
+    "df[df[\"Pace\"]== \"Select\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89492cc7",
+   "metadata": {},
+   "source": [
+    "# Cleaning Brand"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "23002f3b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Brand (24 uniques): \n",
+      " ['Adidas', 'Allbirds', 'Altra', 'Apl', 'Asics', 'Brooks', 'Diadora', 'Hoka', 'Inov8', 'Jordan', 'Merrell', 'Mizuno', 'New Balance', 'Nike', 'Nobull', 'On', 'Puma', 'Reebok', 'Salomon', 'Saucony', 'Skechers', 'Topo', 'Under Armour', 'Xero']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Brand\n",
+       "Asics           190\n",
+       "Adidas          157\n",
+       "Nike            148\n",
+       "Brooks          130\n",
+       "Saucony          83\n",
+       "New Balance      82\n",
+       "Hoka             67\n",
+       "Mizuno           57\n",
+       "On               52\n",
+       "Altra            50\n",
+       "Puma             42\n",
+       "Under Armour     26\n",
+       "Reebok           18\n",
+       "Skechers         16\n",
+       "Salomon          10\n",
+       "Allbirds          9\n",
+       "Nobull            7\n",
+       "Xero              7\n",
+       "Diadora           6\n",
+       "Topo              5\n",
+       "Inov8             4\n",
+       "Merrell           2\n",
+       "Jordan            1\n",
+       "Apl               1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Brand\"] = (\n",
+    "    df[\"Brand\"]\n",
+    "    .astype(str)\n",
+    "    .str.strip()\n",
+    "    .str.replace(r\"\\s+\", \" \", regex=True)\n",
+    "    .str.title()\n",
+    ")\n",
+    "\n",
+    "brand_uniques = df[\"Brand\"].dropna().unique()\n",
+    "# brand_uniques.sort()\n",
+    "brand_uniques = sorted(brand_uniques)\n",
+    "print(f\"Brand ({len(brand_uniques)} uniques): \\n\",brand_uniques)\n",
+    "\n",
+    "df[\"Brand\"].value_counts().head(25)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69438526",
+   "metadata": {},
+   "source": [
+    "wow beda (cooked)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "402ada5a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total sepatu (rows): 1170\n"
+     ]
+    }
+   ],
+   "source": [
+    "total_rows = len(df)\n",
+    "print(\"Total sepatu (rows):\", total_rows)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "62f507b4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Jumlah sepatu dari seluruh brand: 1170\n",
+      "Jumlah brand unik: 24\n"
+     ]
+    }
+   ],
+   "source": [
+    "brand_counts = df[\"Brand\"].value_counts()\n",
+    "print(\"Jumlah sepatu dari seluruh brand:\", brand_counts.sum())\n",
+    "print(\"Jumlah brand unik:\", brand_counts.shape[0])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d7df55d3",
+   "metadata": {},
+   "source": [
+    "terlihat sudah sama, lalu salahnya dimana? stay tuned"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "392e4487",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Pace</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Heel lab Heel brand</th>\n",
+       "      <th>Forefoot lab Forefoot brand</th>\n",
+       "      <th>Widths available</th>\n",
+       "      <th>Orthotic friendly</th>\n",
+       "      <th>Season</th>\n",
+       "      <th>Removable insole</th>\n",
+       "      <th>Ranking</th>\n",
+       "      <th>Popularity</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Terrain</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>839</th>\n",
+       "      <td>Jordan</td>\n",
+       "      <td>React Havoc</td>\n",
+       "      <td>83\\n Good!</td>\n",
+       "      <td>$130</td>\n",
+       "      <td>Daily running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.5 oz / 268g 10.8 oz / 306g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.2 mm 9.0 mm</td>\n",
+       "      <td>Heel</td>\n",
+       "      <td>...</td>\n",
+       "      <td>32.3 mm 28.0 mm</td>\n",
+       "      <td>22.1 mm 19.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>0</td>\n",
+       "      <td>#258 Bottom 29%</td>\n",
+       "      <td>#339 Bottom 7%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 33 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Brand         Name Audience score Price           Pace Arch support  \\\n",
+       "839  Jordan  React Havoc     83\\n Good!  $130  Daily running      Neutral   \n",
+       "\n",
+       "          Weight lab Weight brand  Lightweight Drop lab Drop brand  \\\n",
+       "839  9.5 oz / 268g 10.8 oz / 306g            0      10.2 mm 9.0 mm   \n",
+       "\n",
+       "    Strike pattern  ... Heel lab Heel brand Forefoot lab Forefoot brand  \\\n",
+       "839           Heel  ...     32.3 mm 28.0 mm             22.1 mm 19.0 mm   \n",
+       "\n",
+       "    Widths available Orthotic friendly Season Removable insole  \\\n",
+       "839           Normal                 0      -                0   \n",
+       "\n",
+       "             Ranking      Popularity Gender Terrain  \n",
+       "839  #258 Bottom 29%  #339 Bottom 7%    NaN     NaN  \n",
+       "\n",
+       "[1 rows x 33 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df[\"Brand\"]== \"Jordan\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b45c770b",
+   "metadata": {},
+   "source": [
+    "jujur bingung dan sudah ingin crash out"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "8fb7fb1d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Asics           : 190\n",
+      "Adidas          : 157\n",
+      "Nike            : 148\n",
+      "Brooks          : 130\n",
+      "Saucony         : 83\n",
+      "New Balance     : 82\n",
+      "Hoka            : 67\n",
+      "Mizuno          : 57\n",
+      "On              : 52\n",
+      "Altra           : 50\n",
+      "Puma            : 42\n",
+      "Under Armour    : 26\n",
+      "Reebok          : 18\n",
+      "Skechers        : 16\n",
+      "Salomon         : 10\n",
+      "Allbirds        : 9\n",
+      "Nobull          : 7\n",
+      "Xero            : 7\n",
+      "Diadora         : 6\n",
+      "Topo            : 5\n",
+      "Inov8           : 4\n",
+      "Merrell         : 2\n",
+      "Jordan          : 1\n",
+      "Apl             : 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "brands = df[\"Brand\"].value_counts()\n",
+    "\n",
+    "for brand, count in brands.items():\n",
+    "    print(f\"{brand:<15} : {count}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "fc1ea3e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TOTAL sepatu (sum of brand counts): 1170\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"TOTAL sepatu (sum of brand counts):\", brand_counts.sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4f4e70d6",
+   "metadata": {},
+   "source": [
+    "kocak ternyata sudah benar"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "94e4aaaa",
+   "metadata": {},
+   "source": [
+    "### Asumsi : \n",
+    "Ada 24 unique brand dengan 1195 total sepatu. belum ada observasi lanjutan sih"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab3a7e31",
+   "metadata": {},
+   "source": [
+    "# Cleaning Pace"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "27408c18",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pace (5 uniques): \n",
+      " ['Competition', 'Competitiontempo', 'Daily Running', 'Daily Runningtempo', 'Tempo']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Pace\n",
+       "Daily Running         813\n",
+       "Daily Runningtempo    125\n",
+       "Competition            88\n",
+       "Tempo                  75\n",
+       "Competitiontempo       69\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Pace\"] = (\n",
+    "    df[\"Pace\"]\n",
+    "    .astype(str)\n",
+    "    .str.strip()\n",
+    "    .str.replace(r\"\\s+\", \" \", regex=True)\n",
+    "    .str.title()\n",
+    ")\n",
+    "\n",
+    "pace_uniques = df[\"Pace\"].dropna().unique()\n",
+    "# pace_uniques.sort()\n",
+    "pace_uniques = sorted(pace_uniques)\n",
+    "print(f\"Pace ({len(pace_uniques)} uniques): \\n\",pace_uniques)\n",
+    "\n",
+    "df[\"Pace\"].value_counts().head(20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4001f1bb",
+   "metadata": {},
+   "source": [
+    "### Analisis Pace\n",
+    "\n",
+    "A. Lari Daily Running \n",
+    "Tujuan: lari harian, easy run, long run \n",
+    "Karakter: cushioning empuk, stabil, tahan lama \n",
+    "Kelebihan: nyaman & aman untuk jarak jauh \n",
+    "Kekurangan: berat, kurang responsif untuk ngebut \n",
+    "\n",
+    "B. Tempo \n",
+    "Tujuan: tempo run, interval, latihan kecepatan \n",
+    "Karakter: lebih ringan, responsif, midsole lebih firm \n",
+    "Kelebihan: enak buat pace cepat tanpa lomba \n",
+    "Kekurangan: kurang nyaman untuk lari santai jauh \n",
+    "\n",
+    "C. Competition \n",
+    "Tujuan: race day, time trial \n",
+    "Karakter: sangat ringan, agresif, sering pakai plate \n",
+    "Kelebihan: paling cepat & efisien \n",
+    "Kekurangan: durability rendah, tidak cocok dipakai sering"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4879a798",
+   "metadata": {},
+   "source": [
+    "### Cleaning Code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "3595b5fb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "UNMAPPED Pace values:\n",
+      " Series([], Name: count, dtype: int64)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# mapping si nilai kegabung itu\n",
+    "\n",
+    "pace_map = {\n",
+    "    \"Competitiontempo\": \"Competition|Tempo\",\n",
+    "    \"Daily Runningtempo\": \"Daily Running|Tempo\",\n",
+    "    \"Competition\": \"Competition\",\n",
+    "    \"Daily Running\": \"Daily Running\",\n",
+    "    \"Tempo\": \"Tempo\",\n",
+    "}\n",
+    "\n",
+    "df[\"Pace_norm\"] = df[\"Pace\"].map(pace_map)\n",
+    "\n",
+    "unmapped = df[df[\"Pace_norm\"].isna()][\"Pace\"].value_counts()\n",
+    "print(\"UNMAPPED Pace values:\\n\", unmapped)\n",
+    "assert df[\"Pace_norm\"].notna().all()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "855d02a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pace - norm : (5) uniques\n",
+      " <StringArray>\n",
+      "['Daily Running|Tempo',       'Daily Running',               'Tempo',\n",
+      "   'Competition|Tempo',         'Competition']\n",
+      "Length: 5, dtype: str\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Pace_norm\n",
+       "Daily Running          813\n",
+       "Daily Running|Tempo    125\n",
+       "Competition             88\n",
+       "Tempo                   75\n",
+       "Competition|Tempo       69\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pace_norms_unique = df[\"Pace_norm\"].dropna().unique()\n",
+    "print(f\"Pace - norm : ({len(pace_norms_unique)}) uniques\\n\", pace_norms_unique)\n",
+    "\n",
+    "\n",
+    "df[\"Pace_norm\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "22a5e00c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Pace</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Widths available</th>\n",
+       "      <th>Orthotic friendly</th>\n",
+       "      <th>Season</th>\n",
+       "      <th>Removable insole</th>\n",
+       "      <th>Ranking</th>\n",
+       "      <th>Popularity</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Terrain</th>\n",
+       "      <th>Pace_norm</th>\n",
+       "      <th>Pace_lists</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Launch 9</td>\n",
+       "      <td>87\\n Great!</td>\n",
+       "      <td>$110</td>\n",
+       "      <td>Daily Runningtempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>7.9 oz / 225g 8.1 oz / 230g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.4 mm 10.0 mm</td>\n",
+       "      <td>HeelMid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NormalWide</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#301 Top 47%</td>\n",
+       "      <td>#352 Bottom 45%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Daily Running|Tempo</td>\n",
+       "      <td>[Daily Running, Tempo]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Levitate 6</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$150</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 304g 10.9 oz / 309g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.7 mm 8.0 mm</td>\n",
+       "      <td>Mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>SummerAll seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#72 Top 20%</td>\n",
+       "      <td>#255 Bottom 30%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>[Daily Running]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>11.9 oz / 336g 11.5 oz / 327g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.9 mm 10.0 mm</td>\n",
+       "      <td>HeelMid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#104 Top 17%</td>\n",
+       "      <td>#368 Bottom 42%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>[Daily Running]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 2</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.6 oz / 356g 12.4 oz / 352g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.6 mm 11.0 mm</td>\n",
+       "      <td>Heel</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#126 Top 20%</td>\n",
+       "      <td>#541 Bottom 16%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>[Daily Running]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 3</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.3 oz / 348g 12.2 oz / 345g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.9 mm 10.0 mm</td>\n",
+       "      <td>HeelMid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#116 Top 32%</td>\n",
+       "      <td>#339 Bottom 7%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>[Daily Running]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 35 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand         Name Audience score Price                Pace Arch support  \\\n",
+       "0  Brooks     Launch 9    87\\n Great!  $110  Daily Runningtempo      Neutral   \n",
+       "1  Brooks   Levitate 6   90\\n Superb!  $150       Daily Running      Neutral   \n",
+       "2  Adidas        4DFWD   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "3  Adidas      4DFWD 2   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "4  Adidas      4DFWD 3    88\\n Great!  $200       Daily Running      Neutral   \n",
+       "\n",
+       "         Weight lab Weight brand  Lightweight Drop lab Drop brand  \\\n",
+       "0    7.9 oz / 225g 8.1 oz / 230g            1      9.4 mm 10.0 mm   \n",
+       "1  10.7 oz / 304g 10.9 oz / 309g            0       7.7 mm 8.0 mm   \n",
+       "2  11.9 oz / 336g 11.5 oz / 327g            0      8.9 mm 10.0 mm   \n",
+       "3  12.6 oz / 356g 12.4 oz / 352g            0     10.6 mm 11.0 mm   \n",
+       "4  12.3 oz / 348g 12.2 oz / 345g            0      9.9 mm 10.0 mm   \n",
+       "\n",
+       "     Strike pattern  ... Widths available Orthotic friendly  \\\n",
+       "0  HeelMid/forefoot  ...       NormalWide                 1   \n",
+       "1      Mid/forefoot  ...           Normal                 1   \n",
+       "2  HeelMid/forefoot  ...           Normal                 1   \n",
+       "3              Heel  ...           Normal                 1   \n",
+       "4  HeelMid/forefoot  ...           Normal                 1   \n",
+       "\n",
+       "              Season Removable insole       Ranking       Popularity Gender  \\\n",
+       "0                  -                1  #301 Top 47%  #352 Bottom 45%    NaN   \n",
+       "1  SummerAll seasons                1   #72 Top 20%  #255 Bottom 30%    NaN   \n",
+       "2        All seasons                1  #104 Top 17%  #368 Bottom 42%    NaN   \n",
+       "3        All seasons                1  #126 Top 20%  #541 Bottom 16%    NaN   \n",
+       "4        All seasons                1  #116 Top 32%   #339 Bottom 7%    NaN   \n",
+       "\n",
+       "  Terrain            Pace_norm              Pace_lists  \n",
+       "0     NaN  Daily Running|Tempo  [Daily Running, Tempo]  \n",
+       "1     NaN        Daily Running         [Daily Running]  \n",
+       "2     NaN        Daily Running         [Daily Running]  \n",
+       "3     NaN        Daily Running         [Daily Running]  \n",
+       "4     NaN        Daily Running         [Daily Running]  \n",
+       "\n",
+       "[5 rows x 35 columns]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Pace_lists\"] = df[\"Pace_norm\"].str.split(\"|\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "16bae2d7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# df = df.drop(labels=\"Pace_list\", axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "25796d54",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Pace</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Widths available</th>\n",
+       "      <th>Orthotic friendly</th>\n",
+       "      <th>Season</th>\n",
+       "      <th>Removable insole</th>\n",
+       "      <th>Ranking</th>\n",
+       "      <th>Popularity</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Terrain</th>\n",
+       "      <th>Pace_norm</th>\n",
+       "      <th>Pace_lists</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Launch 9</td>\n",
+       "      <td>87\\n Great!</td>\n",
+       "      <td>$110</td>\n",
+       "      <td>Daily Runningtempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>7.9 oz / 225g 8.1 oz / 230g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.4 mm 10.0 mm</td>\n",
+       "      <td>HeelMid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NormalWide</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#301 Top 47%</td>\n",
+       "      <td>#352 Bottom 45%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Daily Running|Tempo</td>\n",
+       "      <td>[Daily Running, Tempo]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Levitate 6</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$150</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 304g 10.9 oz / 309g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.7 mm 8.0 mm</td>\n",
+       "      <td>Mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>SummerAll seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#72 Top 20%</td>\n",
+       "      <td>#255 Bottom 30%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>[Daily Running]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>11.9 oz / 336g 11.5 oz / 327g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.9 mm 10.0 mm</td>\n",
+       "      <td>HeelMid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#104 Top 17%</td>\n",
+       "      <td>#368 Bottom 42%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>[Daily Running]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 2</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.6 oz / 356g 12.4 oz / 352g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.6 mm 11.0 mm</td>\n",
+       "      <td>Heel</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#126 Top 20%</td>\n",
+       "      <td>#541 Bottom 16%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>[Daily Running]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 3</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.3 oz / 348g 12.2 oz / 345g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.9 mm 10.0 mm</td>\n",
+       "      <td>HeelMid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#116 Top 32%</td>\n",
+       "      <td>#339 Bottom 7%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>[Daily Running]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 35 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand         Name Audience score Price                Pace Arch support  \\\n",
+       "0  Brooks     Launch 9    87\\n Great!  $110  Daily Runningtempo      Neutral   \n",
+       "1  Brooks   Levitate 6   90\\n Superb!  $150       Daily Running      Neutral   \n",
+       "2  Adidas        4DFWD   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "3  Adidas      4DFWD 2   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "4  Adidas      4DFWD 3    88\\n Great!  $200       Daily Running      Neutral   \n",
+       "\n",
+       "         Weight lab Weight brand  Lightweight Drop lab Drop brand  \\\n",
+       "0    7.9 oz / 225g 8.1 oz / 230g            1      9.4 mm 10.0 mm   \n",
+       "1  10.7 oz / 304g 10.9 oz / 309g            0       7.7 mm 8.0 mm   \n",
+       "2  11.9 oz / 336g 11.5 oz / 327g            0      8.9 mm 10.0 mm   \n",
+       "3  12.6 oz / 356g 12.4 oz / 352g            0     10.6 mm 11.0 mm   \n",
+       "4  12.3 oz / 348g 12.2 oz / 345g            0      9.9 mm 10.0 mm   \n",
+       "\n",
+       "     Strike pattern  ... Widths available Orthotic friendly  \\\n",
+       "0  HeelMid/forefoot  ...       NormalWide                 1   \n",
+       "1      Mid/forefoot  ...           Normal                 1   \n",
+       "2  HeelMid/forefoot  ...           Normal                 1   \n",
+       "3              Heel  ...           Normal                 1   \n",
+       "4  HeelMid/forefoot  ...           Normal                 1   \n",
+       "\n",
+       "              Season Removable insole       Ranking       Popularity Gender  \\\n",
+       "0                  -                1  #301 Top 47%  #352 Bottom 45%    NaN   \n",
+       "1  SummerAll seasons                1   #72 Top 20%  #255 Bottom 30%    NaN   \n",
+       "2        All seasons                1  #104 Top 17%  #368 Bottom 42%    NaN   \n",
+       "3        All seasons                1  #126 Top 20%  #541 Bottom 16%    NaN   \n",
+       "4        All seasons                1  #116 Top 32%   #339 Bottom 7%    NaN   \n",
+       "\n",
+       "  Terrain            Pace_norm              Pace_lists  \n",
+       "0     NaN  Daily Running|Tempo  [Daily Running, Tempo]  \n",
+       "1     NaN        Daily Running         [Daily Running]  \n",
+       "2     NaN        Daily Running         [Daily Running]  \n",
+       "3     NaN        Daily Running         [Daily Running]  \n",
+       "4     NaN        Daily Running         [Daily Running]  \n",
+       "\n",
+       "[5 rows x 35 columns]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "97aed74a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# jujur ini vibe ah coding\n",
+    "pace_exploded = df[\"Pace_lists\"].explode()\n",
+    "\n",
+    "pace_ohe = (\n",
+    "    pd.crosstab(pace_exploded.index, pace_exploded)\n",
+    "    .reindex(df.index, fill_value=0)   # jaga urutan index sama df\n",
+    ")\n",
+    "\n",
+    "# rename kolom biar konsisten untuk ML pipeline\n",
+    "pace_ohe = pace_ohe.rename(columns={\n",
+    "    \"Competition\": \"pace_competition\",\n",
+    "    \"Daily Running\": \"pace_daily_running\",\n",
+    "    \"Tempo\": \"pace_tempo\"\n",
+    "})\n",
+    "\n",
+    "# gabung ke df\n",
+    "df = pd.concat([df, pace_ohe], axis=1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "7ce9dc57",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Pace</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Removable insole</th>\n",
+       "      <th>Ranking</th>\n",
+       "      <th>Popularity</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Terrain</th>\n",
+       "      <th>Pace_norm</th>\n",
+       "      <th>Pace_lists</th>\n",
+       "      <th>pace_competition</th>\n",
+       "      <th>pace_daily_running</th>\n",
+       "      <th>pace_tempo</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Launch 9</td>\n",
+       "      <td>87\\n Great!</td>\n",
+       "      <td>$110</td>\n",
+       "      <td>Daily Runningtempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>7.9 oz / 225g 8.1 oz / 230g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.4 mm 10.0 mm</td>\n",
+       "      <td>HeelMid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#301 Top 47%</td>\n",
+       "      <td>#352 Bottom 45%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Daily Running|Tempo</td>\n",
+       "      <td>[Daily Running, Tempo]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Levitate 6</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$150</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 304g 10.9 oz / 309g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.7 mm 8.0 mm</td>\n",
+       "      <td>Mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#72 Top 20%</td>\n",
+       "      <td>#255 Bottom 30%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>[Daily Running]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>11.9 oz / 336g 11.5 oz / 327g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.9 mm 10.0 mm</td>\n",
+       "      <td>HeelMid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#104 Top 17%</td>\n",
+       "      <td>#368 Bottom 42%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>[Daily Running]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 2</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.6 oz / 356g 12.4 oz / 352g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.6 mm 11.0 mm</td>\n",
+       "      <td>Heel</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#126 Top 20%</td>\n",
+       "      <td>#541 Bottom 16%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>[Daily Running]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 3</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.3 oz / 348g 12.2 oz / 345g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.9 mm 10.0 mm</td>\n",
+       "      <td>HeelMid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>#116 Top 32%</td>\n",
+       "      <td>#339 Bottom 7%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>[Daily Running]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 38 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand         Name Audience score Price                Pace Arch support  \\\n",
+       "0  Brooks     Launch 9    87\\n Great!  $110  Daily Runningtempo      Neutral   \n",
+       "1  Brooks   Levitate 6   90\\n Superb!  $150       Daily Running      Neutral   \n",
+       "2  Adidas        4DFWD   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "3  Adidas      4DFWD 2   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "4  Adidas      4DFWD 3    88\\n Great!  $200       Daily Running      Neutral   \n",
+       "\n",
+       "         Weight lab Weight brand  Lightweight Drop lab Drop brand  \\\n",
+       "0    7.9 oz / 225g 8.1 oz / 230g            1      9.4 mm 10.0 mm   \n",
+       "1  10.7 oz / 304g 10.9 oz / 309g            0       7.7 mm 8.0 mm   \n",
+       "2  11.9 oz / 336g 11.5 oz / 327g            0      8.9 mm 10.0 mm   \n",
+       "3  12.6 oz / 356g 12.4 oz / 352g            0     10.6 mm 11.0 mm   \n",
+       "4  12.3 oz / 348g 12.2 oz / 345g            0      9.9 mm 10.0 mm   \n",
+       "\n",
+       "     Strike pattern  ... Removable insole       Ranking       Popularity  \\\n",
+       "0  HeelMid/forefoot  ...                1  #301 Top 47%  #352 Bottom 45%   \n",
+       "1      Mid/forefoot  ...                1   #72 Top 20%  #255 Bottom 30%   \n",
+       "2  HeelMid/forefoot  ...                1  #104 Top 17%  #368 Bottom 42%   \n",
+       "3              Heel  ...                1  #126 Top 20%  #541 Bottom 16%   \n",
+       "4  HeelMid/forefoot  ...                1  #116 Top 32%   #339 Bottom 7%   \n",
+       "\n",
+       "  Gender Terrain            Pace_norm              Pace_lists  \\\n",
+       "0    NaN     NaN  Daily Running|Tempo  [Daily Running, Tempo]   \n",
+       "1    NaN     NaN        Daily Running         [Daily Running]   \n",
+       "2    NaN     NaN        Daily Running         [Daily Running]   \n",
+       "3    NaN     NaN        Daily Running         [Daily Running]   \n",
+       "4    NaN     NaN        Daily Running         [Daily Running]   \n",
+       "\n",
+       "  pace_competition pace_daily_running pace_tempo  \n",
+       "0                0                  1          1  \n",
+       "1                0                  1          0  \n",
+       "2                0                  1          0  \n",
+       "3                0                  1          0  \n",
+       "4                0                  1          0  \n",
+       "\n",
+       "[5 rows x 38 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "53f241a3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# make sure value-nya bener 0/1\n",
+    "for c in [\"pace_competition\", \"pace_daily_running\", \"pace_tempo\"]:\n",
+    "    assert set(df[c].unique()).issubset({0, 1})\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "7e51526a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                Pace          Pace_norm  pace_competition  pace_tempo  \\\n",
+      "24  Competitiontempo  Competition|Tempo                 1           1   \n",
+      "32  Competitiontempo  Competition|Tempo                 1           1   \n",
+      "33  Competitiontempo  Competition|Tempo                 1           1   \n",
+      "34  Competitiontempo  Competition|Tempo                 1           1   \n",
+      "38  Competitiontempo  Competition|Tempo                 1           1   \n",
+      "\n",
+      "    pace_daily_running  \n",
+      "24                   0  \n",
+      "32                   0  \n",
+      "33                   0  \n",
+      "34                   0  \n",
+      "38                   0  \n",
+      "                  Pace            Pace_norm  pace_daily_running  pace_tempo  \\\n",
+      "0   Daily Runningtempo  Daily Running|Tempo                   1           1   \n",
+      "7   Daily Runningtempo  Daily Running|Tempo                   1           1   \n",
+      "35  Daily Runningtempo  Daily Running|Tempo                   1           1   \n",
+      "36  Daily Runningtempo  Daily Running|Tempo                   1           1   \n",
+      "37  Daily Runningtempo  Daily Running|Tempo                   1           1   \n",
+      "\n",
+      "    pace_competition  \n",
+      "0                  0  \n",
+      "7                  0  \n",
+      "35                 0  \n",
+      "36                 0  \n",
+      "37                 0  \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[df[\"Pace\"]==\"Competitiontempo\"][[\"Pace\",\"Pace_norm\",\"pace_competition\",\"pace_tempo\",\"pace_daily_running\"]].head())\n",
+    "print(df[df[\"Pace\"]==\"Daily Runningtempo\"][[\"Pace\",\"Pace_norm\",\"pace_daily_running\",\"pace_tempo\",\"pace_competition\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "67cbfef0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 1170\n",
+      "competition sum: 157\n",
+      "daily sum: 938\n",
+      "tempo sum: 269\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Rows:\", len(df))\n",
+    "print(\"competition sum:\", int(df[\"pace_competition\"].sum()))\n",
+    "print(\"daily sum:\", int(df[\"pace_daily_running\"].sum()))\n",
+    "print(\"tempo sum:\", int(df[\"pace_tempo\"].sum()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc4bd121",
+   "metadata": {},
+   "source": [
+    "### Asumsi : \n",
+    "ada 2 jenis kombinasi : competion tempo sama daily tempo, setelah dipisah jadi dapet gini\n",
+    "Rows: 1195\n",
+    "competition sum: 157\n",
+    "daily sum: 963\n",
+    "tempo sum: 272"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9cc22c3c",
+   "metadata": {},
+   "source": [
+    "# Cleaning Arch Support"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "90afbf61",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Arch (3 uniques): \n",
+      " ['Motion Control', 'Neutral', 'Stability']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Arch support\n",
+       "Neutral           999\n",
+       "Stability         170\n",
+       "Motion Control      1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Arch support\"] = (\n",
+    "    df[\"Arch support\"]\n",
+    "    .astype(str)\n",
+    "    .str.strip()\n",
+    "    .str.replace(r\"\\s+\", \" \", regex=True)\n",
+    "    .str.title()\n",
+    ")\n",
+    "\n",
+    "arch_uniques = df[\"Arch support\"].dropna().unique()\n",
+    "# arch_uniques.sort()\n",
+    "arch_uniques = sorted(arch_uniques)\n",
+    "print(f\"Arch ({len(arch_uniques)} uniques): \\n\",arch_uniques)\n",
+    "\n",
+    "df[\"Arch support\"].value_counts().head(20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e9c82b15",
+   "metadata": {},
+   "source": [
+    "Neutral\n",
+    "Untuk siapa: pelari dengan gait normal / netral\n",
+    "Ciri: tidak ada koreksi khusus pada midsole\n",
+    "Kelebihan: fleksibel, natural, nyaman untuk mayoritas pelari\n",
+    "Catatan: ini adalah default dan paling umum di pasaran\n",
+    "\n",
+    "Stability\n",
+    "Untuk siapa: pelari dengan overpronation ringan–sedang\n",
+    "Ciri: ada struktur tambahan (medial support, geometry khusus)\n",
+    "Kelebihan: membantu menjaga kaki tetap stabil tanpa terlalu kaku\n",
+    "Catatan: masih nyaman untuk daily running\n",
+    "\n",
+    "Motion Control\n",
+    "Untuk siapa: overpronation berat\n",
+    "Ciri: sangat kaku dan korektif\n",
+    "Kelebihan: kontrol maksimal\n",
+    "Kekurangan: berat, kurang nyaman, sangat niche\n",
+    "Catatan: di market modern, kategori ini hampir punah"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33f99ae3",
+   "metadata": {},
+   "source": [
+    "bentar ya saya mengantuk"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cee5e7e6",
+   "metadata": {},
+   "source": [
+    "------------------------- day 2 ------------------------------------"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "e4a2846c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Arch_grouped\n",
+      "Neutral      999\n",
+      "Stability    171\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "arch_map = {\n",
+    "    \"Neutral\": \"Neutral\",\n",
+    "    \"Stability\": \"Stability\",\n",
+    "    \"Motion Control\": \"Stability\" # cuma ada 1 makanya digabung ke stability, mereka sama sama buat low arch\n",
+    "}\n",
+    "\n",
+    "df[\"Arch_grouped\"] = df[\"Arch support\"].map(arch_map)\n",
+    "\n",
+    "print(df[\"Arch_grouped\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "6026d0e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Pace</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Terrain</th>\n",
+       "      <th>Pace_norm</th>\n",
+       "      <th>Pace_lists</th>\n",
+       "      <th>pace_competition</th>\n",
+       "      <th>pace_daily_running</th>\n",
+       "      <th>pace_tempo</th>\n",
+       "      <th>Arch_grouped</th>\n",
+       "      <th>arch_neutral</th>\n",
+       "      <th>arch_stability</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Launch 9</td>\n",
+       "      <td>87\\n Great!</td>\n",
+       "      <td>$110</td>\n",
+       "      <td>Daily Runningtempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>7.9 oz / 225g 8.1 oz / 230g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.4 mm 10.0 mm</td>\n",
+       "      <td>HeelMid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Daily Running|Tempo</td>\n",
+       "      <td>[Daily Running, Tempo]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Levitate 6</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$150</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 304g 10.9 oz / 309g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.7 mm 8.0 mm</td>\n",
+       "      <td>Mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>[Daily Running]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>11.9 oz / 336g 11.5 oz / 327g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.9 mm 10.0 mm</td>\n",
+       "      <td>HeelMid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>[Daily Running]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 2</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.6 oz / 356g 12.4 oz / 352g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.6 mm 11.0 mm</td>\n",
+       "      <td>Heel</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>[Daily Running]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 3</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.3 oz / 348g 12.2 oz / 345g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.9 mm 10.0 mm</td>\n",
+       "      <td>HeelMid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>[Daily Running]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 41 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand         Name Audience score Price                Pace Arch support  \\\n",
+       "0  Brooks     Launch 9    87\\n Great!  $110  Daily Runningtempo      Neutral   \n",
+       "1  Brooks   Levitate 6   90\\n Superb!  $150       Daily Running      Neutral   \n",
+       "2  Adidas        4DFWD   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "3  Adidas      4DFWD 2   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "4  Adidas      4DFWD 3    88\\n Great!  $200       Daily Running      Neutral   \n",
+       "\n",
+       "         Weight lab Weight brand  Lightweight Drop lab Drop brand  \\\n",
+       "0    7.9 oz / 225g 8.1 oz / 230g            1      9.4 mm 10.0 mm   \n",
+       "1  10.7 oz / 304g 10.9 oz / 309g            0       7.7 mm 8.0 mm   \n",
+       "2  11.9 oz / 336g 11.5 oz / 327g            0      8.9 mm 10.0 mm   \n",
+       "3  12.6 oz / 356g 12.4 oz / 352g            0     10.6 mm 11.0 mm   \n",
+       "4  12.3 oz / 348g 12.2 oz / 345g            0      9.9 mm 10.0 mm   \n",
+       "\n",
+       "     Strike pattern  ... Gender Terrain            Pace_norm  \\\n",
+       "0  HeelMid/forefoot  ...    NaN     NaN  Daily Running|Tempo   \n",
+       "1      Mid/forefoot  ...    NaN     NaN        Daily Running   \n",
+       "2  HeelMid/forefoot  ...    NaN     NaN        Daily Running   \n",
+       "3              Heel  ...    NaN     NaN        Daily Running   \n",
+       "4  HeelMid/forefoot  ...    NaN     NaN        Daily Running   \n",
+       "\n",
+       "               Pace_lists pace_competition pace_daily_running pace_tempo  \\\n",
+       "0  [Daily Running, Tempo]                0                  1          1   \n",
+       "1         [Daily Running]                0                  1          0   \n",
+       "2         [Daily Running]                0                  1          0   \n",
+       "3         [Daily Running]                0                  1          0   \n",
+       "4         [Daily Running]                0                  1          0   \n",
+       "\n",
+       "  Arch_grouped arch_neutral arch_stability  \n",
+       "0      Neutral            1              0  \n",
+       "1      Neutral            1              0  \n",
+       "2      Neutral            1              0  \n",
+       "3      Neutral            1              0  \n",
+       "4      Neutral            1              0  \n",
+       "\n",
+       "[5 rows x 41 columns]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# vibe ah coding\n",
+    "arch_ohe = pd.get_dummies(df[\"Arch_grouped\"], prefix=\"arch\", dtype=int)\n",
+    "\n",
+    "# Rename kolom biar lowercase dan konsisten (opsional, tapi rapi)\n",
+    "arch_ohe = arch_ohe.rename(columns={\n",
+    "    \"arch_Neutral\": \"arch_neutral\",\n",
+    "    \"arch_Stability\": \"arch_stability\"\n",
+    "})\n",
+    "\n",
+    "\n",
+    "\n",
+    "df = pd.concat([df, arch_ohe], axis=1)\n",
+    "df.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "cc40c12f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# jujur arch grouped gaperlu jadi kita buang aja\n",
+    "df = df.drop(labels=\"Arch_grouped\", axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "f7996392",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Validasi One-Hot Encoding Arch Support:\n",
+      "     Arch support  arch_neutral  arch_stability\n",
+      "0         Neutral             1               0\n",
+      "1         Neutral             1               0\n",
+      "2         Neutral             1               0\n",
+      "3         Neutral             1               0\n",
+      "4         Neutral             1               0\n",
+      "5         Neutral             1               0\n",
+      "6  Motion Control             0               1\n",
+      "7         Neutral             1               0\n",
+      "8         Neutral             1               0\n",
+      "9         Neutral             1               0\n",
+      "\n",
+      "Total Neutral   : 999\n",
+      "Total Stability : 171\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Pastikan setiap sepatu punya salah satu (tidak bisa 0 dua-duanya atau 1 dua-duanya, karena ini single choice)\n",
+    "assert (df[\"arch_neutral\"] + df[\"arch_stability\"] == 1).all(), \"Error: Ada baris yang tidak punya kategori arch atau ganda!\"\n",
+    "\n",
+    "print(\"\\nValidasi One-Hot Encoding Arch Support:\")\n",
+    "print(df[[\"Arch support\", \"arch_neutral\", \"arch_stability\"]].head(10))\n",
+    "\n",
+    "# Cek total count untuk laporan\n",
+    "print(\"\\nTotal Neutral   :\", df[\"arch_neutral\"].sum())\n",
+    "print(\"Total Stability :\", df[\"arch_stability\"].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d85aa91e",
+   "metadata": {},
+   "source": [
+    "harusnya sebenernya motion diubah ke stability tuh pas preprocessing aja"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "291b7806",
+   "metadata": {},
+   "source": [
+    "# Cleaning Strike"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "462dfdeb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Strike pattern (5 uniques): \n",
+      " ['-', 'Heel', 'Heel Mid/Forefoot', 'Heelmid/Forefoot', 'Mid/Forefoot']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Strike pattern\n",
+       "Heelmid/Forefoot     539\n",
+       "Mid/Forefoot         339\n",
+       "Heel                 290\n",
+       "-                      1\n",
+       "Heel Mid/Forefoot      1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Strike pattern\"] = (\n",
+    "    df[\"Strike pattern\"]\n",
+    "    .astype(str)\n",
+    "    .str.strip()\n",
+    "    .str.replace(r\"\\s+\", \" \", regex=True)\n",
+    "    .str.title()\n",
+    ")\n",
+    "\n",
+    "strike_pattern_uniques = df[\"Strike pattern\"].dropna().unique()\n",
+    "# strike_pattern_uniques.sort()\n",
+    "strike_pattern_uniques = sorted(strike_pattern_uniques)\n",
+    "print(f\"Strike pattern ({len(strike_pattern_uniques)} uniques): \\n\",strike_pattern_uniques)\n",
+    "\n",
+    "df[\"Strike pattern\"].value_counts().head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "06e495eb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Pace</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Popularity</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Terrain</th>\n",
+       "      <th>Pace_norm</th>\n",
+       "      <th>Pace_lists</th>\n",
+       "      <th>pace_competition</th>\n",
+       "      <th>pace_daily_running</th>\n",
+       "      <th>pace_tempo</th>\n",
+       "      <th>arch_neutral</th>\n",
+       "      <th>arch_stability</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>301</th>\n",
+       "      <td>Nike</td>\n",
+       "      <td>Flex Experience Run 10</td>\n",
+       "      <td>79\\n Good!</td>\n",
+       "      <td>$65</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>7.1 oz / 201g 8 oz / 227g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10.4 mm</td>\n",
+       "      <td>-</td>\n",
+       "      <td>...</td>\n",
+       "      <td>#523 Bottom 18%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>[Daily Running]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 40 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand                    Name Audience score Price           Pace  \\\n",
+       "301  Nike  Flex Experience Run 10     79\\n Good!   $65  Daily Running   \n",
+       "\n",
+       "    Arch support    Weight lab Weight brand  Lightweight Drop lab Drop brand  \\\n",
+       "301      Neutral  7.1 oz / 201g 8 oz / 227g            1             10.4 mm   \n",
+       "\n",
+       "    Strike pattern  ...       Popularity Gender Terrain      Pace_norm  \\\n",
+       "301              -  ...  #523 Bottom 18%    NaN     NaN  Daily Running   \n",
+       "\n",
+       "          Pace_lists pace_competition pace_daily_running pace_tempo  \\\n",
+       "301  [Daily Running]                0                  1          0   \n",
+       "\n",
+       "    arch_neutral arch_stability  \n",
+       "301            1              0  \n",
+       "\n",
+       "[1 rows x 40 columns]"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df[\"Strike pattern\"] == \"-\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c09196ce",
+   "metadata": {},
+   "source": [
+    "seharusnya ga ada issue kalo kita hapus si FER 10 soalnya dia udah punya FER 12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "9c31c1ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# df[df[\"Name\"] == \"Flex Experience Run 12\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "dc940543",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Pace</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Popularity</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Terrain</th>\n",
+       "      <th>Pace_norm</th>\n",
+       "      <th>Pace_lists</th>\n",
+       "      <th>pace_competition</th>\n",
+       "      <th>pace_daily_running</th>\n",
+       "      <th>pace_tempo</th>\n",
+       "      <th>arch_neutral</th>\n",
+       "      <th>arch_stability</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1090</th>\n",
+       "      <td>Nike</td>\n",
+       "      <td>Vomero Plus</td>\n",
+       "      <td>91 Superb!</td>\n",
+       "      <td>$180</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.2 oz / 289g 10.1 oz / 285g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.6 mm 10.0 mm</td>\n",
+       "      <td>Heel Mid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>#7 Top 2%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>[Daily Running]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 40 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Brand         Name Audience score Price           Pace Arch support  \\\n",
+       "1090  Nike  Vomero Plus     91 Superb!  $180  Daily Running      Neutral   \n",
+       "\n",
+       "            Weight lab Weight brand  Lightweight Drop lab Drop brand  \\\n",
+       "1090  10.2 oz / 289g 10.1 oz / 285g            0      9.6 mm 10.0 mm   \n",
+       "\n",
+       "         Strike pattern  ... Popularity Gender Terrain      Pace_norm  \\\n",
+       "1090  Heel Mid/Forefoot  ...  #7 Top 2%    NaN     NaN  Daily Running   \n",
+       "\n",
+       "           Pace_lists pace_competition pace_daily_running pace_tempo  \\\n",
+       "1090  [Daily Running]                0                  1          0   \n",
+       "\n",
+       "     arch_neutral arch_stability  \n",
+       "1090            1              0  \n",
+       "\n",
+       "[1 rows x 40 columns]"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df[\"Strike pattern\"] == \"Heel Mid/Forefoot\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "a32a50e8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Pace</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Popularity</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Terrain</th>\n",
+       "      <th>Pace_norm</th>\n",
+       "      <th>Pace_lists</th>\n",
+       "      <th>pace_competition</th>\n",
+       "      <th>pace_daily_running</th>\n",
+       "      <th>pace_tempo</th>\n",
+       "      <th>arch_neutral</th>\n",
+       "      <th>arch_stability</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Launch 9</td>\n",
+       "      <td>87\\n Great!</td>\n",
+       "      <td>$110</td>\n",
+       "      <td>Daily Runningtempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>7.9 oz / 225g 8.1 oz / 230g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.4 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>#352 Bottom 45%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Daily Running|Tempo</td>\n",
+       "      <td>[Daily Running, Tempo]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>11.9 oz / 336g 11.5 oz / 327g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>#368 Bottom 42%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>[Daily Running]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 3</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.3 oz / 348g 12.2 oz / 345g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>#339 Bottom 7%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>[Daily Running]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 3</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.3 oz / 348g 12.2 oz / 345g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>#339 Bottom 7%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>[Daily Running]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Adidas Adizero SL2</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$130</td>\n",
+       "      <td>Daily Runningtempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>8.6 oz / 245g 8.4 oz / 238g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8.2 mm 9.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>#166 Top 46%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Daily Running|Tempo</td>\n",
+       "      <td>[Daily Running, Tempo]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1154</th>\n",
+       "      <td>Mizuno</td>\n",
+       "      <td>WWave Horizon 7</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$170</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Stability</td>\n",
+       "      <td>11.6 oz / 329g 11.8 oz / 334g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.2 mm 8.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>#294 Bottom 19%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>[Daily Running]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1155</th>\n",
+       "      <td>Reebok</td>\n",
+       "      <td>Zig Dynamica 4</td>\n",
+       "      <td>80\\n Good!</td>\n",
+       "      <td>$85</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.4 oz / 352g 12.3 oz / 350g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.5 mm 9.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>#533 Bottom 17%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>[Daily Running]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1160</th>\n",
+       "      <td>Nike</td>\n",
+       "      <td>Zoom Fly 6</td>\n",
+       "      <td>92\\n Superb!</td>\n",
+       "      <td>$170</td>\n",
+       "      <td>Competitiontempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>8.7 oz / 248g 8.6 oz / 244g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.6 mm 8.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>#26 Top 8%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Competition|Tempo</td>\n",
+       "      <td>[Competition, Tempo]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1161</th>\n",
+       "      <td>Nike</td>\n",
+       "      <td>Zoom Fly 6</td>\n",
+       "      <td>92\\n Superb!</td>\n",
+       "      <td>$170</td>\n",
+       "      <td>Competitiontempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>8.7 oz / 248g 8.6 oz / 244g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.6 mm 8.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>#26 Top 8%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Competition|Tempo</td>\n",
+       "      <td>[Competition, Tempo]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1162</th>\n",
+       "      <td>Nike</td>\n",
+       "      <td>Zoom Fly 6</td>\n",
+       "      <td>92\\n Superb!</td>\n",
+       "      <td>$170</td>\n",
+       "      <td>Competitiontempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>8.7 oz / 248g 8.6 oz / 244g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.6 mm 8.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>#26 Top 8%</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Competition|Tempo</td>\n",
+       "      <td>[Competition, Tempo]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>539 rows × 40 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Brand                Name Audience score Price                Pace  \\\n",
+       "0     Brooks            Launch 9    87\\n Great!  $110  Daily Runningtempo   \n",
+       "2     Adidas               4DFWD   90\\n Superb!  $200       Daily Running   \n",
+       "4     Adidas             4DFWD 3    88\\n Great!  $200       Daily Running   \n",
+       "5     Adidas             4DFWD 3    88\\n Great!  $200       Daily Running   \n",
+       "7     Adidas  Adidas Adizero SL2   90\\n Superb!  $130  Daily Runningtempo   \n",
+       "...      ...                 ...            ...   ...                 ...   \n",
+       "1154  Mizuno     WWave Horizon 7    88\\n Great!  $170       Daily Running   \n",
+       "1155  Reebok      Zig Dynamica 4     80\\n Good!   $85       Daily Running   \n",
+       "1160    Nike          Zoom Fly 6   92\\n Superb!  $170    Competitiontempo   \n",
+       "1161    Nike          Zoom Fly 6   92\\n Superb!  $170    Competitiontempo   \n",
+       "1162    Nike          Zoom Fly 6   92\\n Superb!  $170    Competitiontempo   \n",
+       "\n",
+       "     Arch support        Weight lab Weight brand  Lightweight  \\\n",
+       "0         Neutral    7.9 oz / 225g 8.1 oz / 230g            1   \n",
+       "2         Neutral  11.9 oz / 336g 11.5 oz / 327g            0   \n",
+       "4         Neutral  12.3 oz / 348g 12.2 oz / 345g            0   \n",
+       "5         Neutral  12.3 oz / 348g 12.2 oz / 345g            0   \n",
+       "7         Neutral    8.6 oz / 245g 8.4 oz / 238g            1   \n",
+       "...           ...                            ...          ...   \n",
+       "1154    Stability  11.6 oz / 329g 11.8 oz / 334g            0   \n",
+       "1155      Neutral  12.4 oz / 352g 12.3 oz / 350g            0   \n",
+       "1160      Neutral    8.7 oz / 248g 8.6 oz / 244g            1   \n",
+       "1161      Neutral    8.7 oz / 248g 8.6 oz / 244g            1   \n",
+       "1162      Neutral    8.7 oz / 248g 8.6 oz / 244g            1   \n",
+       "\n",
+       "     Drop lab Drop brand    Strike pattern  ...       Popularity Gender  \\\n",
+       "0         9.4 mm 10.0 mm  Heelmid/Forefoot  ...  #352 Bottom 45%    NaN   \n",
+       "2         8.9 mm 10.0 mm  Heelmid/Forefoot  ...  #368 Bottom 42%    NaN   \n",
+       "4         9.9 mm 10.0 mm  Heelmid/Forefoot  ...   #339 Bottom 7%    NaN   \n",
+       "5         9.9 mm 10.0 mm  Heelmid/Forefoot  ...   #339 Bottom 7%    NaN   \n",
+       "7          8.2 mm 9.0 mm  Heelmid/Forefoot  ...     #166 Top 46%    NaN   \n",
+       "...                  ...               ...  ...              ...    ...   \n",
+       "1154       7.2 mm 8.0 mm  Heelmid/Forefoot  ...  #294 Bottom 19%    NaN   \n",
+       "1155       8.5 mm 9.0 mm  Heelmid/Forefoot  ...  #533 Bottom 17%    NaN   \n",
+       "1160       9.6 mm 8.0 mm  Heelmid/Forefoot  ...       #26 Top 8%    NaN   \n",
+       "1161       9.6 mm 8.0 mm  Heelmid/Forefoot  ...       #26 Top 8%    NaN   \n",
+       "1162       9.6 mm 8.0 mm  Heelmid/Forefoot  ...       #26 Top 8%    NaN   \n",
+       "\n",
+       "     Terrain            Pace_norm              Pace_lists pace_competition  \\\n",
+       "0        NaN  Daily Running|Tempo  [Daily Running, Tempo]                0   \n",
+       "2        NaN        Daily Running         [Daily Running]                0   \n",
+       "4        NaN        Daily Running         [Daily Running]                0   \n",
+       "5        NaN        Daily Running         [Daily Running]                0   \n",
+       "7        NaN  Daily Running|Tempo  [Daily Running, Tempo]                0   \n",
+       "...      ...                  ...                     ...              ...   \n",
+       "1154     NaN        Daily Running         [Daily Running]                0   \n",
+       "1155     NaN        Daily Running         [Daily Running]                0   \n",
+       "1160     NaN    Competition|Tempo    [Competition, Tempo]                1   \n",
+       "1161     NaN    Competition|Tempo    [Competition, Tempo]                1   \n",
+       "1162     NaN    Competition|Tempo    [Competition, Tempo]                1   \n",
+       "\n",
+       "     pace_daily_running pace_tempo arch_neutral arch_stability  \n",
+       "0                     1          1            1              0  \n",
+       "2                     1          0            1              0  \n",
+       "4                     1          0            1              0  \n",
+       "5                     1          0            1              0  \n",
+       "7                     1          1            1              0  \n",
+       "...                 ...        ...          ...            ...  \n",
+       "1154                  1          0            0              1  \n",
+       "1155                  1          0            1              0  \n",
+       "1160                  0          1            1              0  \n",
+       "1161                  0          1            1              0  \n",
+       "1162                  0          1            1              0  \n",
+       "\n",
+       "[539 rows x 40 columns]"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df[\"Strike pattern\"] == \"Heelmid/Forefoot\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0cc507c7",
+   "metadata": {},
+   "source": [
+    "### Analisis Strike Pattern"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "2f00986c",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9c3ed8b",
+   "metadata": {},
+   "source": [
+    "A. Heel Strike (Pendaratan Tumit)\n",
+    "Artinya: Pelari mendarat dengan tumit terlebih dahulu menyentuh tanah.\n",
+    "Populasi: Sangat umum, mencakup sekitar 75-90% pelari rekreasional/pemula.\n",
+    "Kebutuhan Sepatu: Membutuhkan bantalan (cushioning) yang tebal di bagian tumit untuk meredam benturan (impact). Biasanya memiliki High Drop (8mm - 12mm).\n",
+    "\n",
+    "B. Mid/Forefoot Strike (Pendaratan Tengah/Depan)\n",
+    "Artinya: Pelari mendarat dengan bagian tengah atau depan kaki (jinjit).\n",
+    "Populasi: Lebih umum pada pelari cepat, atlet elite, atau pelari minimalis.\n",
+    "Kebutuhan Sepatu: Tidak butuh tumit tebal (karena tumit jarang menyentuh tanah keras). Biasanya butuh sepatu yang responsif dengan Low Drop (0mm - 6mm) agar pendaratan lebih natural.\n",
+    "\n",
+    "C. Heel/Mid/Forefoot (Versatile / All-Rounder)\n",
+    "Artinya: Sepatu ini didesain fleksibel untuk mengakomodasi semua gaya lari.\n",
+    "Teknologi: Biasanya menggunakan desain Rocker (lengkungan sol seperti kursi goyang) yang membuat transisi dari tumit ke ujung kaki menjadi mulus (smooth transition).\n",
+    "Cocok untuk: Pelari yang gaya larinya berubah-ubah tergantung kelelahan (misal: awal lari midfoot, pas capek jadi heel strike)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cabe5a60",
+   "metadata": {},
+   "source": [
+    "### Cleaning code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "bdbee9f5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "UNMAPPED strike values:\n",
+      " Series([], Name: count, dtype: int64)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#  ['-' 'Heel' 'Heel Mid/Forefoot' 'Heelmid/Forefoot' 'Mid/Forefoot']\n",
+    "\n",
+    "\n",
+    "strike_map = {\n",
+    "    \"Mid/Forefoot\": \"Mid|Forefoot\",\n",
+    "    \"Heelmid/Forefoot\": \"Heel|Mid|Forefoot\",\n",
+    "    \"Heel Mid/Forefoot\": \"Heel|Mid|Forefoot\",\n",
+    "    \"-\": \"-\",\n",
+    "    \"Heel\": \"Heel\",\n",
+    "}\n",
+    "\n",
+    "df[\"Strike_norm\"] = df[\"Strike pattern\"].map(strike_map)\n",
+    "\n",
+    "unmapped = df[df[\"Strike_norm\"].isna()][\"Strike pattern\"].value_counts()\n",
+    "print(\"UNMAPPED strike values:\\n\", unmapped)\n",
+    "assert df[\"Strike_norm\"].notna().all()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "6ce0b921",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Strike - norm : (4) uniques\n",
+      " <StringArray>\n",
+      "['Heel|Mid|Forefoot', 'Mid|Forefoot', 'Heel', '-']\n",
+      "Length: 4, dtype: str\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Strike_norm\n",
+       "Heel|Mid|Forefoot    540\n",
+       "Mid|Forefoot         339\n",
+       "Heel                 290\n",
+       "-                      1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "strike_norm_unique = df[\"Strike_norm\"].dropna().unique()\n",
+    "print(f\"Strike - norm : ({len(strike_norm_unique)}) uniques\\n\", strike_norm_unique)\n",
+    "\n",
+    "\n",
+    "df[\"Strike_norm\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "e31dde3b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Pace</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Terrain</th>\n",
+       "      <th>Pace_norm</th>\n",
+       "      <th>Pace_lists</th>\n",
+       "      <th>pace_competition</th>\n",
+       "      <th>pace_daily_running</th>\n",
+       "      <th>pace_tempo</th>\n",
+       "      <th>arch_neutral</th>\n",
+       "      <th>arch_stability</th>\n",
+       "      <th>Strike_norm</th>\n",
+       "      <th>Strike_lists</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Launch 9</td>\n",
+       "      <td>87\\n Great!</td>\n",
+       "      <td>$110</td>\n",
+       "      <td>Daily Runningtempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>7.9 oz / 225g 8.1 oz / 230g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.4 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Daily Running|Tempo</td>\n",
+       "      <td>[Daily Running, Tempo]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Heel|Mid|Forefoot</td>\n",
+       "      <td>[Heel, Mid, Forefoot]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Levitate 6</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$150</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 304g 10.9 oz / 309g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.7 mm 8.0 mm</td>\n",
+       "      <td>Mid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>[Daily Running]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Mid|Forefoot</td>\n",
+       "      <td>[Mid, Forefoot]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>11.9 oz / 336g 11.5 oz / 327g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>[Daily Running]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Heel|Mid|Forefoot</td>\n",
+       "      <td>[Heel, Mid, Forefoot]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 2</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.6 oz / 356g 12.4 oz / 352g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.6 mm 11.0 mm</td>\n",
+       "      <td>Heel</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>[Daily Running]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Heel</td>\n",
+       "      <td>[Heel]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 3</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.3 oz / 348g 12.2 oz / 345g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>[Daily Running]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Heel|Mid|Forefoot</td>\n",
+       "      <td>[Heel, Mid, Forefoot]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 42 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand         Name Audience score Price                Pace Arch support  \\\n",
+       "0  Brooks     Launch 9    87\\n Great!  $110  Daily Runningtempo      Neutral   \n",
+       "1  Brooks   Levitate 6   90\\n Superb!  $150       Daily Running      Neutral   \n",
+       "2  Adidas        4DFWD   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "3  Adidas      4DFWD 2   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "4  Adidas      4DFWD 3    88\\n Great!  $200       Daily Running      Neutral   \n",
+       "\n",
+       "         Weight lab Weight brand  Lightweight Drop lab Drop brand  \\\n",
+       "0    7.9 oz / 225g 8.1 oz / 230g            1      9.4 mm 10.0 mm   \n",
+       "1  10.7 oz / 304g 10.9 oz / 309g            0       7.7 mm 8.0 mm   \n",
+       "2  11.9 oz / 336g 11.5 oz / 327g            0      8.9 mm 10.0 mm   \n",
+       "3  12.6 oz / 356g 12.4 oz / 352g            0     10.6 mm 11.0 mm   \n",
+       "4  12.3 oz / 348g 12.2 oz / 345g            0      9.9 mm 10.0 mm   \n",
+       "\n",
+       "     Strike pattern  ... Terrain            Pace_norm              Pace_lists  \\\n",
+       "0  Heelmid/Forefoot  ...     NaN  Daily Running|Tempo  [Daily Running, Tempo]   \n",
+       "1      Mid/Forefoot  ...     NaN        Daily Running         [Daily Running]   \n",
+       "2  Heelmid/Forefoot  ...     NaN        Daily Running         [Daily Running]   \n",
+       "3              Heel  ...     NaN        Daily Running         [Daily Running]   \n",
+       "4  Heelmid/Forefoot  ...     NaN        Daily Running         [Daily Running]   \n",
+       "\n",
+       "  pace_competition pace_daily_running pace_tempo arch_neutral arch_stability  \\\n",
+       "0                0                  1          1            1              0   \n",
+       "1                0                  1          0            1              0   \n",
+       "2                0                  1          0            1              0   \n",
+       "3                0                  1          0            1              0   \n",
+       "4                0                  1          0            1              0   \n",
+       "\n",
+       "         Strike_norm           Strike_lists  \n",
+       "0  Heel|Mid|Forefoot  [Heel, Mid, Forefoot]  \n",
+       "1       Mid|Forefoot        [Mid, Forefoot]  \n",
+       "2  Heel|Mid|Forefoot  [Heel, Mid, Forefoot]  \n",
+       "3               Heel                 [Heel]  \n",
+       "4  Heel|Mid|Forefoot  [Heel, Mid, Forefoot]  \n",
+       "\n",
+       "[5 rows x 42 columns]"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Strike_lists\"] = df[\"Strike_norm\"].str.split(\"|\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "752457fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\n# jujur ini vibe ah coding\\npace_exploded = df[\"Pace_lists\"].explode()\\n\\npace_ohe = (\\n    pd.crosstab(pace_exploded.index, pace_exploded)\\n    .reindex(df.index, fill_value=0)   # jaga urutan index sama df\\n)\\n\\n# rename kolom biar konsisten untuk ML pipeline\\npace_ohe = pace_ohe.rename(columns={\\n    \"Competition\": \"pace_competition\",\\n    \"Daily Running\": \"pace_daily_running\",\\n    \"Tempo\": \"pace_tempo\"\\n})\\n\\n# gabung ke df\\ndf = pd.concat([df, pace_ohe], axis=1)\\n# make sure value-nya bener 0/1\\nfor c in [\"pace_competition\", \"pace_daily_running\", \"pace_tempo\"]:\\n    assert set(df[c].unique()).issubset({0, 1})\\nprint(df[df[\"Pace\"]==\"Competitiontempo\"][[\"Pace\",\"Pace_norm\",\"pace_competition\",\"pace_tempo\",\"pace_daily_running\"]].head())\\nprint(df[df[\"Pace\"]==\"Daily Runningtempo\"][[\"Pace\",\"Pace_norm\",\"pace_daily_running\",\"pace_tempo\",\"pace_competition\"]].head())\\nprint(\"Rows:\", len(df))\\nprint(\"competition sum:\", int(df[\"pace_competition\"].sum()))\\nprint(\"daily sum:\", int(df[\"pace_daily_running\"].sum()))\\nprint(\"tempo sum:\", int(df[\"pace_tempo\"].sum()))\\n'"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "'''\n",
+    "# jujur ini vibe ah coding\n",
+    "pace_exploded = df[\"Pace_lists\"].explode()\n",
+    "\n",
+    "pace_ohe = (\n",
+    "    pd.crosstab(pace_exploded.index, pace_exploded)\n",
+    "    .reindex(df.index, fill_value=0)   # jaga urutan index sama df\n",
+    ")\n",
+    "\n",
+    "# rename kolom biar konsisten untuk ML pipeline\n",
+    "pace_ohe = pace_ohe.rename(columns={\n",
+    "    \"Competition\": \"pace_competition\",\n",
+    "    \"Daily Running\": \"pace_daily_running\",\n",
+    "    \"Tempo\": \"pace_tempo\"\n",
+    "})\n",
+    "\n",
+    "# gabung ke df\n",
+    "df = pd.concat([df, pace_ohe], axis=1)\n",
+    "# make sure value-nya bener 0/1\n",
+    "for c in [\"pace_competition\", \"pace_daily_running\", \"pace_tempo\"]:\n",
+    "    assert set(df[c].unique()).issubset({0, 1})\n",
+    "print(df[df[\"Pace\"]==\"Competitiontempo\"][[\"Pace\",\"Pace_norm\",\"pace_competition\",\"pace_tempo\",\"pace_daily_running\"]].head())\n",
+    "print(df[df[\"Pace\"]==\"Daily Runningtempo\"][[\"Pace\",\"Pace_norm\",\"pace_daily_running\",\"pace_tempo\",\"pace_competition\"]].head())\n",
+    "print(\"Rows:\", len(df))\n",
+    "print(\"competition sum:\", int(df[\"pace_competition\"].sum()))\n",
+    "print(\"daily sum:\", int(df[\"pace_daily_running\"].sum()))\n",
+    "print(\"tempo sum:\", int(df[\"pace_tempo\"].sum()))\n",
+    "'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "3f553259",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "strike_exploded = df[\"Strike_lists\"].explode()\n",
+    "\n",
+    "strike_ohe = (\n",
+    "    pd.crosstab(strike_exploded.index, strike_exploded)\n",
+    "    .reindex(df.index, fill_value=0)   # jaga urutan index sama df\n",
+    ")\n",
+    "\n",
+    "strike_ohe = strike_ohe.rename(columns={\n",
+    "    \"Heel\": \"strike_heel\",\n",
+    "    \"Mid\": \"strike_mid\",\n",
+    "    \"Forefoot\": \"strike_forefoot\"\n",
+    "})\n",
+    "\n",
+    "df = pd.concat([df, strike_ohe], axis=1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "89c7f686",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for c in [\"strike_heel\", \"strike_mid\", \"strike_forefoot\"]:\n",
+    "    assert set(df[c].unique()).issubset({0, 1})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "536b9e6c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 1170\n",
+      "Heels sum: 830\n",
+      "Mid sum: 879\n",
+      "Forefoot sum: 879\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Rows:\", len(df))\n",
+    "print(\"Heels sum:\", int(df[\"strike_heel\"].sum()))\n",
+    "print(\"Mid sum:\", int(df[\"strike_mid\"].sum()))\n",
+    "print(\"Forefoot sum:\", int(df[\"strike_forefoot\"].sum()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "0ef394bc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Pace</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>pace_daily_running</th>\n",
+       "      <th>pace_tempo</th>\n",
+       "      <th>arch_neutral</th>\n",
+       "      <th>arch_stability</th>\n",
+       "      <th>Strike_norm</th>\n",
+       "      <th>Strike_lists</th>\n",
+       "      <th>-</th>\n",
+       "      <th>strike_forefoot</th>\n",
+       "      <th>strike_heel</th>\n",
+       "      <th>strike_mid</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Launch 9</td>\n",
+       "      <td>87\\n Great!</td>\n",
+       "      <td>$110</td>\n",
+       "      <td>Daily Runningtempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>7.9 oz / 225g 8.1 oz / 230g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.4 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Heel|Mid|Forefoot</td>\n",
+       "      <td>[Heel, Mid, Forefoot]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Levitate 6</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$150</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 304g 10.9 oz / 309g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.7 mm 8.0 mm</td>\n",
+       "      <td>Mid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Mid|Forefoot</td>\n",
+       "      <td>[Mid, Forefoot]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>11.9 oz / 336g 11.5 oz / 327g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Heel|Mid|Forefoot</td>\n",
+       "      <td>[Heel, Mid, Forefoot]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 2</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.6 oz / 356g 12.4 oz / 352g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.6 mm 11.0 mm</td>\n",
+       "      <td>Heel</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Heel</td>\n",
+       "      <td>[Heel]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 3</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.3 oz / 348g 12.2 oz / 345g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Heel|Mid|Forefoot</td>\n",
+       "      <td>[Heel, Mid, Forefoot]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 46 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand         Name Audience score Price                Pace Arch support  \\\n",
+       "0  Brooks     Launch 9    87\\n Great!  $110  Daily Runningtempo      Neutral   \n",
+       "1  Brooks   Levitate 6   90\\n Superb!  $150       Daily Running      Neutral   \n",
+       "2  Adidas        4DFWD   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "3  Adidas      4DFWD 2   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "4  Adidas      4DFWD 3    88\\n Great!  $200       Daily Running      Neutral   \n",
+       "\n",
+       "         Weight lab Weight brand  Lightweight Drop lab Drop brand  \\\n",
+       "0    7.9 oz / 225g 8.1 oz / 230g            1      9.4 mm 10.0 mm   \n",
+       "1  10.7 oz / 304g 10.9 oz / 309g            0       7.7 mm 8.0 mm   \n",
+       "2  11.9 oz / 336g 11.5 oz / 327g            0      8.9 mm 10.0 mm   \n",
+       "3  12.6 oz / 356g 12.4 oz / 352g            0     10.6 mm 11.0 mm   \n",
+       "4  12.3 oz / 348g 12.2 oz / 345g            0      9.9 mm 10.0 mm   \n",
+       "\n",
+       "     Strike pattern  ... pace_daily_running pace_tempo arch_neutral  \\\n",
+       "0  Heelmid/Forefoot  ...                  1          1            1   \n",
+       "1      Mid/Forefoot  ...                  1          0            1   \n",
+       "2  Heelmid/Forefoot  ...                  1          0            1   \n",
+       "3              Heel  ...                  1          0            1   \n",
+       "4  Heelmid/Forefoot  ...                  1          0            1   \n",
+       "\n",
+       "  arch_stability        Strike_norm           Strike_lists  - strike_forefoot  \\\n",
+       "0              0  Heel|Mid|Forefoot  [Heel, Mid, Forefoot]  0               1   \n",
+       "1              0       Mid|Forefoot        [Mid, Forefoot]  0               1   \n",
+       "2              0  Heel|Mid|Forefoot  [Heel, Mid, Forefoot]  0               1   \n",
+       "3              0               Heel                 [Heel]  0               0   \n",
+       "4              0  Heel|Mid|Forefoot  [Heel, Mid, Forefoot]  0               1   \n",
+       "\n",
+       "  strike_heel strike_mid  \n",
+       "0           1          1  \n",
+       "1           0          1  \n",
+       "2           1          1  \n",
+       "3           1          0  \n",
+       "4           1          1  \n",
+       "\n",
+       "[5 rows x 46 columns]"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a89f428",
+   "metadata": {},
+   "source": [
+    "# Cleaning Size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "485f039e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# df = df.drop(labels=\"fit_category\", axis=1)\n",
+    "# df = df.drop(labels=\"fit_large\", axis=1)\n",
+    "# df = df.drop(labels=\"fit_small\", axis=1)\n",
+    "# df = df.drop(labels=\"fit_true\", axis=1)\n",
+    "# df = df.drop(labels=\"fit_missing\", axis=1)\n",
+    "\n",
+    "# df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "139c3649",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Size (6 uniques): \n",
+      " ['-', 'Half Size Large', 'Half Size Small', 'Slightly Large', 'Slightly Small', 'True To Size']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Size\n",
+       "True To Size       797\n",
+       "Slightly Small     231\n",
+       "Half Size Small     59\n",
+       "-                   44\n",
+       "Slightly Large      31\n",
+       "Half Size Large      8\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Size\"] = (\n",
+    "    df[\"Size\"]\n",
+    "    .astype(str)\n",
+    "    .str.strip()\n",
+    "    .str.replace(r\"\\s+\", \" \", regex=True)\n",
+    "    .str.title()\n",
+    ")\n",
+    "\n",
+    "size_uniques = df[\"Size\"].dropna().unique()\n",
+    "# size_uniques.sort()\n",
+    "size_uniques = sorted(size_uniques)\n",
+    "print(f\"Size ({len(size_uniques)} uniques): \\n\",size_uniques)\n",
+    "\n",
+    "df[\"Size\"].value_counts().head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "fe9461ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Size</th>\n",
+       "      <th>Size_repr</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>Nike</td>\n",
+       "      <td>Air Zoom Pegasus 38 FlyEase</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>Nobull</td>\n",
+       "      <td>Allday Knit</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>Nobull</td>\n",
+       "      <td>Allday Knit</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>92</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Anthem 4</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>Diadora</td>\n",
+       "      <td>Atomo Star</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>186</th>\n",
+       "      <td>On</td>\n",
+       "      <td>Cloudsurfer Max</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>298</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Experience Flow 2</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>331</th>\n",
+       "      <td>Saucony</td>\n",
+       "      <td>Freedom 4</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>454</th>\n",
+       "      <td>Asics</td>\n",
+       "      <td>Gel Kayano Lite 2</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>474</th>\n",
+       "      <td>Asics</td>\n",
+       "      <td>Gel Nimbus Lite 3</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>556</th>\n",
+       "      <td>Asics</td>\n",
+       "      <td>GT 1000 9</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>575</th>\n",
+       "      <td>Saucony</td>\n",
+       "      <td>Hurricane 25</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>585</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Hyperion Elite 5</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>586</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Hyperion Elite 5</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>587</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Hyperion Elite 5</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>614</th>\n",
+       "      <td>Under Armour</td>\n",
+       "      <td>Infinite Pro</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>634</th>\n",
+       "      <td>Nobull</td>\n",
+       "      <td>Journey</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>635</th>\n",
+       "      <td>Nobull</td>\n",
+       "      <td>Journey</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>636</th>\n",
+       "      <td>Nobull</td>\n",
+       "      <td>Journey</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>637</th>\n",
+       "      <td>Nobull</td>\n",
+       "      <td>Journey</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>668</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Levitate 5</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>680</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Levitate Stealthfit 5</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>681</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Levitate Stealthfit 6</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>692</th>\n",
+       "      <td>Hoka</td>\n",
+       "      <td>Mach X 3</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>839</th>\n",
+       "      <td>Jordan</td>\n",
+       "      <td>React Havoc</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>866</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Ricochet 3</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>915</th>\n",
+       "      <td>Salomon</td>\n",
+       "      <td>S/Lab Phantasm 2</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>916</th>\n",
+       "      <td>Salomon</td>\n",
+       "      <td>S/Lab Spectur</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>954</th>\n",
+       "      <td>Asics</td>\n",
+       "      <td>Sonicblast</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>955</th>\n",
+       "      <td>Asics</td>\n",
+       "      <td>Sonicblast</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>956</th>\n",
+       "      <td>Salomon</td>\n",
+       "      <td>Spectur 2</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1006</th>\n",
+       "      <td>Under Armour</td>\n",
+       "      <td>Surge 4</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1095</th>\n",
+       "      <td>Nike</td>\n",
+       "      <td>Vomero Premium</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1129</th>\n",
+       "      <td>Mizuno</td>\n",
+       "      <td>Wave Rider 29</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1130</th>\n",
+       "      <td>Mizuno</td>\n",
+       "      <td>Wave Rider 29</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1131</th>\n",
+       "      <td>Mizuno</td>\n",
+       "      <td>Wave Rider 29</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1132</th>\n",
+       "      <td>Mizuno</td>\n",
+       "      <td>Wave Rider 29</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1133</th>\n",
+       "      <td>Mizuno</td>\n",
+       "      <td>Wave Rider 29</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1134</th>\n",
+       "      <td>Mizuno</td>\n",
+       "      <td>Wave Rider 29</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1135</th>\n",
+       "      <td>Mizuno</td>\n",
+       "      <td>Wave Rider 29</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1136</th>\n",
+       "      <td>Mizuno</td>\n",
+       "      <td>Wave Rider 29</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1137</th>\n",
+       "      <td>Mizuno</td>\n",
+       "      <td>Wave Rider 29</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1156</th>\n",
+       "      <td>Reebok</td>\n",
+       "      <td>Zig Dynamica 5</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1157</th>\n",
+       "      <td>Reebok</td>\n",
+       "      <td>Zig Dynamica 5</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Brand                         Name Size Size_repr\n",
+       "71            Nike  Air Zoom Pegasus 38 FlyEase    -       '-'\n",
+       "73          Nobull                  Allday Knit    -       '-'\n",
+       "74          Nobull                  Allday Knit    -       '-'\n",
+       "92          Brooks                     Anthem 4    -       '-'\n",
+       "98         Diadora                   Atomo Star    -       '-'\n",
+       "186             On              Cloudsurfer Max    -       '-'\n",
+       "298          Altra            Experience Flow 2    -       '-'\n",
+       "331        Saucony                    Freedom 4    -       '-'\n",
+       "454          Asics            Gel Kayano Lite 2    -       '-'\n",
+       "474          Asics            Gel Nimbus Lite 3    -       '-'\n",
+       "556          Asics                    GT 1000 9    -       '-'\n",
+       "575        Saucony                 Hurricane 25    -       '-'\n",
+       "585         Brooks             Hyperion Elite 5    -       '-'\n",
+       "586         Brooks             Hyperion Elite 5    -       '-'\n",
+       "587         Brooks             Hyperion Elite 5    -       '-'\n",
+       "614   Under Armour                 Infinite Pro    -       '-'\n",
+       "634         Nobull                      Journey    -       '-'\n",
+       "635         Nobull                      Journey    -       '-'\n",
+       "636         Nobull                      Journey    -       '-'\n",
+       "637         Nobull                      Journey    -       '-'\n",
+       "668         Brooks                   Levitate 5    -       '-'\n",
+       "680         Brooks        Levitate Stealthfit 5    -       '-'\n",
+       "681         Brooks        Levitate Stealthfit 6    -       '-'\n",
+       "692           Hoka                     Mach X 3    -       '-'\n",
+       "839         Jordan                  React Havoc    -       '-'\n",
+       "866         Brooks                   Ricochet 3    -       '-'\n",
+       "915        Salomon             S/Lab Phantasm 2    -       '-'\n",
+       "916        Salomon                S/Lab Spectur    -       '-'\n",
+       "954          Asics                   Sonicblast    -       '-'\n",
+       "955          Asics                   Sonicblast    -       '-'\n",
+       "956        Salomon                    Spectur 2    -       '-'\n",
+       "1006  Under Armour                      Surge 4    -       '-'\n",
+       "1095          Nike               Vomero Premium    -       '-'\n",
+       "1129        Mizuno                Wave Rider 29    -       '-'\n",
+       "1130        Mizuno                Wave Rider 29    -       '-'\n",
+       "1131        Mizuno                Wave Rider 29    -       '-'\n",
+       "1132        Mizuno                Wave Rider 29    -       '-'\n",
+       "1133        Mizuno                Wave Rider 29    -       '-'\n",
+       "1134        Mizuno                Wave Rider 29    -       '-'\n",
+       "1135        Mizuno                Wave Rider 29    -       '-'\n",
+       "1136        Mizuno                Wave Rider 29    -       '-'\n",
+       "1137        Mizuno                Wave Rider 29    -       '-'\n",
+       "1156        Reebok               Zig Dynamica 5    -       '-'\n",
+       "1157        Reebok               Zig Dynamica 5    -       '-'"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mask = df[\"Size\"].astype(str).str.strip() == \"-\"\n",
+    "df.loc[mask, [\"Brand\",\"Name\", \"Size\"]].assign(\n",
+    "    Size_repr=df.loc[mask, \"Size\"].apply(repr)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "007b5d56",
+   "metadata": {},
+   "source": [
+    "kenapaaaaaaaa............dia.........strip....doang..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "cc19d779",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "size_map = {\n",
+    "    \"Half Size Small\": \"small\",\n",
+    "    \"Slightly Small\": \"small\",\n",
+    "    \"True To Size\": \"true\",\n",
+    "    \"Slightly Large\": \"large\",\n",
+    "    \"Half Size Large\": \"large\",\n",
+    "}\n",
+    "\n",
+    "df[\"fit_category\"] = df[\"Size\"].map(size_map)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "db763764",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fit_ohe = pd.get_dummies(df[\"fit_category\"], prefix=\"fit\").astype(int)\n",
+    "\n",
+    "for col in [\"fit_missing\", \"fit_true\", \"fit_small\", \"fit_large\"]:\n",
+    "    if col not in fit_ohe.columns:\n",
+    "        fit_ohe[col] = 0\n",
+    "\n",
+    "fit_ohe[\"fit_missing\"] = df[\"fit_category\"].isna().astype(int)\n",
+    "\n",
+    "df = pd.concat([df, fit_ohe], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "a4499637",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "fit_missing     44\n",
+      "fit_true       797\n",
+      "fit_small      290\n",
+      "fit_large       39\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[[\"fit_missing\",\"fit_true\",\"fit_small\",\"fit_large\"]].sum())\n",
+    "assert (\n",
+    "    df[[\"fit_missing\",\"fit_true\",\"fit_small\",\"fit_large\"]].sum(axis=1) == 1\n",
+    ").all()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "526b6a24",
+   "metadata": {},
+   "source": [
+    "yang slightly sama half size gitu langsung dimasukin ke kolom kegedean apa kekecilan. hasilnya begitu"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "27e44d2b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Pace</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Strike_lists</th>\n",
+       "      <th>-</th>\n",
+       "      <th>strike_forefoot</th>\n",
+       "      <th>strike_heel</th>\n",
+       "      <th>strike_mid</th>\n",
+       "      <th>fit_category</th>\n",
+       "      <th>fit_large</th>\n",
+       "      <th>fit_small</th>\n",
+       "      <th>fit_true</th>\n",
+       "      <th>fit_missing</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Launch 9</td>\n",
+       "      <td>87\\n Great!</td>\n",
+       "      <td>$110</td>\n",
+       "      <td>Daily Runningtempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>7.9 oz / 225g 8.1 oz / 230g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.4 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[Heel, Mid, Forefoot]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>true</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Levitate 6</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$150</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 304g 10.9 oz / 309g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.7 mm 8.0 mm</td>\n",
+       "      <td>Mid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[Mid, Forefoot]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>true</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>11.9 oz / 336g 11.5 oz / 327g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[Heel, Mid, Forefoot]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>true</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 2</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.6 oz / 356g 12.4 oz / 352g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.6 mm 11.0 mm</td>\n",
+       "      <td>Heel</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[Heel]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>small</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 3</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.3 oz / 348g 12.2 oz / 345g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[Heel, Mid, Forefoot]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>true</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 51 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand         Name Audience score Price                Pace Arch support  \\\n",
+       "0  Brooks     Launch 9    87\\n Great!  $110  Daily Runningtempo      Neutral   \n",
+       "1  Brooks   Levitate 6   90\\n Superb!  $150       Daily Running      Neutral   \n",
+       "2  Adidas        4DFWD   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "3  Adidas      4DFWD 2   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "4  Adidas      4DFWD 3    88\\n Great!  $200       Daily Running      Neutral   \n",
+       "\n",
+       "         Weight lab Weight brand  Lightweight Drop lab Drop brand  \\\n",
+       "0    7.9 oz / 225g 8.1 oz / 230g            1      9.4 mm 10.0 mm   \n",
+       "1  10.7 oz / 304g 10.9 oz / 309g            0       7.7 mm 8.0 mm   \n",
+       "2  11.9 oz / 336g 11.5 oz / 327g            0      8.9 mm 10.0 mm   \n",
+       "3  12.6 oz / 356g 12.4 oz / 352g            0     10.6 mm 11.0 mm   \n",
+       "4  12.3 oz / 348g 12.2 oz / 345g            0      9.9 mm 10.0 mm   \n",
+       "\n",
+       "     Strike pattern  ...           Strike_lists  - strike_forefoot  \\\n",
+       "0  Heelmid/Forefoot  ...  [Heel, Mid, Forefoot]  0               1   \n",
+       "1      Mid/Forefoot  ...        [Mid, Forefoot]  0               1   \n",
+       "2  Heelmid/Forefoot  ...  [Heel, Mid, Forefoot]  0               1   \n",
+       "3              Heel  ...                 [Heel]  0               0   \n",
+       "4  Heelmid/Forefoot  ...  [Heel, Mid, Forefoot]  0               1   \n",
+       "\n",
+       "  strike_heel strike_mid fit_category fit_large fit_small fit_true fit_missing  \n",
+       "0           1          1         true         0         0        1           0  \n",
+       "1           0          1         true         0         0        1           0  \n",
+       "2           1          1         true         0         0        1           0  \n",
+       "3           1          0        small         0         1        0           0  \n",
+       "4           1          1         true         0         0        1           0  \n",
+       "\n",
+       "[5 rows x 51 columns]"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "4416dafe",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 1170 entries, 0 to 1169\n",
+      "Data columns (total 51 columns):\n",
+      " #   Column                       Non-Null Count  Dtype \n",
+      "---  ------                       --------------  ----- \n",
+      " 0   Brand                        1170 non-null   str   \n",
+      " 1   Name                         1170 non-null   str   \n",
+      " 2   Audience score               1164 non-null   str   \n",
+      " 3   Price                        1170 non-null   str   \n",
+      " 4   Pace                         1170 non-null   str   \n",
+      " 5   Arch support                 1170 non-null   str   \n",
+      " 6   Weight lab Weight brand      1170 non-null   str   \n",
+      " 7   Lightweight                  1170 non-null   int64 \n",
+      " 8   Drop lab Drop brand          1170 non-null   str   \n",
+      " 9   Strike pattern               1170 non-null   str   \n",
+      " 10  Size                         1170 non-null   str   \n",
+      " 11  Midsole softness             1170 non-null   str   \n",
+      " 12  Toebox durability            1170 non-null   str   \n",
+      " 13  Heel padding durability      1170 non-null   str   \n",
+      " 14  Outsole durability           1170 non-null   str   \n",
+      " 15  Breathability                1170 non-null   str   \n",
+      " 16  Width / fit                  1170 non-null   str   \n",
+      " 17  Toebox width                 1170 non-null   str   \n",
+      " 18  Stiffness                    1170 non-null   str   \n",
+      " 19  Torsional rigidity           1170 non-null   str   \n",
+      " 20  Heel counter stiffness       1170 non-null   str   \n",
+      " 21  Plate                        1170 non-null   str   \n",
+      " 22  Rocker                       1170 non-null   int64 \n",
+      " 23  Heel lab Heel brand          1170 non-null   str   \n",
+      " 24  Forefoot lab Forefoot brand  1170 non-null   str   \n",
+      " 25  Widths available             1170 non-null   str   \n",
+      " 26  Orthotic friendly            1170 non-null   int64 \n",
+      " 27  Season                       1170 non-null   str   \n",
+      " 28  Removable insole             1170 non-null   int64 \n",
+      " 29  Ranking                      1170 non-null   str   \n",
+      " 30  Popularity                   1170 non-null   str   \n",
+      " 31  Gender                       10 non-null     str   \n",
+      " 32  Terrain                      16 non-null     str   \n",
+      " 33  Pace_norm                    1170 non-null   str   \n",
+      " 34  Pace_lists                   1170 non-null   object\n",
+      " 35  pace_competition             1170 non-null   int64 \n",
+      " 36  pace_daily_running           1170 non-null   int64 \n",
+      " 37  pace_tempo                   1170 non-null   int64 \n",
+      " 38  arch_neutral                 1170 non-null   int64 \n",
+      " 39  arch_stability               1170 non-null   int64 \n",
+      " 40  Strike_norm                  1170 non-null   str   \n",
+      " 41  Strike_lists                 1170 non-null   object\n",
+      " 42  -                            1170 non-null   int64 \n",
+      " 43  strike_forefoot              1170 non-null   int64 \n",
+      " 44  strike_heel                  1170 non-null   int64 \n",
+      " 45  strike_mid                   1170 non-null   int64 \n",
+      " 46  fit_category                 1126 non-null   str   \n",
+      " 47  fit_large                    1170 non-null   int64 \n",
+      " 48  fit_small                    1170 non-null   int64 \n",
+      " 49  fit_true                     1170 non-null   int64 \n",
+      " 50  fit_missing                  1170 non-null   int64 \n",
+      "dtypes: int64(17), object(2), str(32)\n",
+      "memory usage: 466.3+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "dc1856f3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# df.rename(columns={\"-\": \"strike_missing\"}, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9aff0dc",
+   "metadata": {},
+   "source": [
+    "# Cleaning Midsole"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "7a77b6f6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Midsole (4 uniques): \n",
+      " ['-', 'Balanced', 'Firm', 'Soft']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Midsole softness\n",
+       "Balanced    543\n",
+       "Soft        504\n",
+       "-            72\n",
+       "Firm         51\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Midsole softness\"] = (\n",
+    "    df[\"Midsole softness\"]\n",
+    "    .astype(str)\n",
+    "    .str.strip()\n",
+    "    .str.replace(r\"\\s+\", \" \", regex=True)\n",
+    "    .str.title()\n",
+    ")\n",
+    "\n",
+    "midsole_uniques = df[\"Midsole softness\"].dropna().unique()\n",
+    "# midsole_uniques.sort()\n",
+    "midsole_uniques = sorted(midsole_uniques)\n",
+    "print(f\"Midsole ({len(midsole_uniques)} uniques): \\n\",midsole_uniques)\n",
+    "\n",
+    "df[\"Midsole softness\"].value_counts().head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "b8050c57",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Midsole softness</th>\n",
+       "      <th>midsole_repr</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Adizero Adios Pro 2.0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Adrenaline GTS 22</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>Nike</td>\n",
+       "      <td>Air Zoom Pegasus 38</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>Nike</td>\n",
+       "      <td>Air Zoom Pegasus 38 FlyEase</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>92</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Anthem 4</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1050</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Ultraboost 21</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1071</th>\n",
+       "      <td>Merrell</td>\n",
+       "      <td>Vapor Glove 6</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1072</th>\n",
+       "      <td>Merrell</td>\n",
+       "      <td>Vapor Glove 6</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1124</th>\n",
+       "      <td>Mizuno</td>\n",
+       "      <td>Wave Rider 25</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1158</th>\n",
+       "      <td>Nike</td>\n",
+       "      <td>Zoom Fly 4</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>72 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Brand                         Name Midsole softness midsole_repr\n",
+       "25     Adidas        Adizero Adios Pro 2.0                -          '-'\n",
+       "53     Brooks            Adrenaline GTS 22                -          '-'\n",
+       "70       Nike          Air Zoom Pegasus 38                -          '-'\n",
+       "71       Nike  Air Zoom Pegasus 38 FlyEase                -          '-'\n",
+       "92     Brooks                     Anthem 4                -          '-'\n",
+       "...       ...                          ...              ...          ...\n",
+       "1050   Adidas                Ultraboost 21                -          '-'\n",
+       "1071  Merrell                Vapor Glove 6                -          '-'\n",
+       "1072  Merrell                Vapor Glove 6                -          '-'\n",
+       "1124   Mizuno                Wave Rider 25                -          '-'\n",
+       "1158     Nike                   Zoom Fly 4                -          '-'\n",
+       "\n",
+       "[72 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mask = df[\"Midsole softness\"].astype(str).str.strip() == \"-\"\n",
+    "df.loc[mask, [\"Brand\",\"Name\", \"Midsole softness\"]].assign(\n",
+    "    midsole_repr=df.loc[mask, \"Midsole softness\"].apply(repr)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "9cb14667",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# midsole_map = {\n",
+    "   \n",
+    "# }\n",
+    "\n",
+    "# df[\"midsole_category\"] = df[\"Midsole softness\"].map(midsole_map)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "27c8c72b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "soft_ohe = pd.get_dummies(df[\"Midsole softness\"], prefix=\"midsole\").astype(int)\n",
+    "\n",
+    "# pastikan kolom konsisten untuk pipeline\n",
+    "for col in [\"midsole_Soft\", \"midsole_Balanced\", \"midsole_Firm\"]:\n",
+    "    if col not in soft_ohe.columns:\n",
+    "        soft_ohe[col] = 0\n",
+    "\n",
+    "# jujur enakan lowercase\n",
+    "soft_ohe = soft_ohe.rename(columns={\n",
+    "    \"midsole_Soft\": \"midsole_soft\",\n",
+    "    \"midsole_Balanced\": \"midsole_balanced\",\n",
+    "    \"midsole_Firm\": \"midsole_firm\"\n",
+    "})\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "5f2d647e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Pace</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>strike_heel</th>\n",
+       "      <th>strike_mid</th>\n",
+       "      <th>fit_category</th>\n",
+       "      <th>fit_large</th>\n",
+       "      <th>fit_small</th>\n",
+       "      <th>fit_true</th>\n",
+       "      <th>fit_missing</th>\n",
+       "      <th>midsole_soft</th>\n",
+       "      <th>midsole_balanced</th>\n",
+       "      <th>midsole_firm</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Launch 9</td>\n",
+       "      <td>87\\n Great!</td>\n",
+       "      <td>$110</td>\n",
+       "      <td>Daily Runningtempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>7.9 oz / 225g 8.1 oz / 230g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.4 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>true</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Levitate 6</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$150</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 304g 10.9 oz / 309g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.7 mm 8.0 mm</td>\n",
+       "      <td>Mid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>true</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>11.9 oz / 336g 11.5 oz / 327g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>true</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 2</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.6 oz / 356g 12.4 oz / 352g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.6 mm 11.0 mm</td>\n",
+       "      <td>Heel</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>small</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 3</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.3 oz / 348g 12.2 oz / 345g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>true</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 54 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand         Name Audience score Price                Pace Arch support  \\\n",
+       "0  Brooks     Launch 9    87\\n Great!  $110  Daily Runningtempo      Neutral   \n",
+       "1  Brooks   Levitate 6   90\\n Superb!  $150       Daily Running      Neutral   \n",
+       "2  Adidas        4DFWD   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "3  Adidas      4DFWD 2   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "4  Adidas      4DFWD 3    88\\n Great!  $200       Daily Running      Neutral   \n",
+       "\n",
+       "         Weight lab Weight brand  Lightweight Drop lab Drop brand  \\\n",
+       "0    7.9 oz / 225g 8.1 oz / 230g            1      9.4 mm 10.0 mm   \n",
+       "1  10.7 oz / 304g 10.9 oz / 309g            0       7.7 mm 8.0 mm   \n",
+       "2  11.9 oz / 336g 11.5 oz / 327g            0      8.9 mm 10.0 mm   \n",
+       "3  12.6 oz / 356g 12.4 oz / 352g            0     10.6 mm 11.0 mm   \n",
+       "4  12.3 oz / 348g 12.2 oz / 345g            0      9.9 mm 10.0 mm   \n",
+       "\n",
+       "     Strike pattern  ... strike_heel strike_mid fit_category fit_large  \\\n",
+       "0  Heelmid/Forefoot  ...           1          1         true         0   \n",
+       "1      Mid/Forefoot  ...           0          1         true         0   \n",
+       "2  Heelmid/Forefoot  ...           1          1         true         0   \n",
+       "3              Heel  ...           1          0        small         0   \n",
+       "4  Heelmid/Forefoot  ...           1          1         true         0   \n",
+       "\n",
+       "  fit_small fit_true fit_missing midsole_soft midsole_balanced midsole_firm  \n",
+       "0         0        1           0            0                1            0  \n",
+       "1         0        1           0            1                0            0  \n",
+       "2         0        1           0            0                0            1  \n",
+       "3         1        0           0            0                0            1  \n",
+       "4         0        1           0            0                0            1  \n",
+       "\n",
+       "[5 rows x 54 columns]"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.concat([df, soft_ohe[[\"midsole_soft\",\"midsole_balanced\",\"midsole_firm\"]]], axis=1)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "1bd6f66d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 1170\n",
+      "Soft: 504\n",
+      "Balanced: 543\n",
+      "Firm: 51\n",
+      "Missing midsole softness rows: 72\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Rows:\", len(df))\n",
+    "print(\"Soft:\", int(df[\"midsole_soft\"].sum()))\n",
+    "print(\"Balanced:\", int(df[\"midsole_balanced\"].sum()))\n",
+    "print(\"Firm:\", int(df[\"midsole_firm\"].sum()))\n",
+    "\n",
+    "missing_midsole = (df[[\"midsole_soft\",\"midsole_balanced\",\"midsole_firm\"]].sum(axis=1) == 0).sum()\n",
+    "print(\"Missing midsole softness rows:\", int(missing_midsole))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "341e1a94",
+   "metadata": {},
+   "source": [
+    "maaf kalo kurang berprinsip, nanti deh analisis asumsi rangkuman itunya"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "31a9f82e",
+   "metadata": {},
+   "source": [
+    "### Analisis Midsole Softness \n",
+    "A. Soft\n",
+    "Rasa: empuk, plush, compressible\n",
+    "Cocok untuk: easy run, long run, recovery\n",
+    "Kelebihan: nyaman, ramah kaki, menyerap impact\n",
+    "Kekurangan: responsivitas lebih rendah, bisa terasa “tenggelam” saat ngebut\n",
+    "\n",
+    "B. Balanced\n",
+    "Rasa: seimbang antara empuk dan firm\n",
+    "Cocok untuk: daily running serbaguna, tempo ringan\n",
+    "Kelebihan: stabil, fleksibel, paling aman untuk mayoritas pelari\n",
+    "Kekurangan: tidak se-empuk soft, tidak se-responsif firm\n",
+    "\n",
+    "C. Firm\n",
+    "Rasa: padat, minim kompresi\n",
+    "Cocok untuk: tempo run, interval, racing\n",
+    "Kelebihan: responsif, efisien energi, stabil saat pace cepat\n",
+    "Kekurangan: kurang nyaman untuk jarak jauh santai"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6e28c3a",
+   "metadata": {},
+   "source": [
+    "# Cleaning Toebox durability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "13d90252",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Toebox (4 uniques): \n",
+      " ['-', 'Bad', 'Decent', 'Good']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Toebox durability\n",
+       "Decent    486\n",
+       "Bad       263\n",
+       "Good      247\n",
+       "-         174\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Toebox durability\"] = (\n",
+    "    df[\"Toebox durability\"]\n",
+    "    .astype(str)\n",
+    "    .str.strip()\n",
+    "    .str.replace(r\"\\s+\", \" \", regex=True)\n",
+    "    .str.title()\n",
+    ")\n",
+    "\n",
+    "toebox_uniques = df[\"Toebox durability\"].dropna().unique()\n",
+    "# toebox_uniques.sort()\n",
+    "toebox_uniques = sorted(toebox_uniques)\n",
+    "print(f\"Toebox ({len(toebox_uniques)} uniques): \\n\",toebox_uniques)\n",
+    "\n",
+    "df[\"Toebox durability\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "1a7d7688",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "toe_ohe = pd.get_dummies(df[\"Toebox durability\"], prefix=\"toebox\").astype(int)\n",
+    "\n",
+    "for col in [\"toebox_Bad\", \"toebox_Decent\", \"toebox_Good\"]:\n",
+    "    if col not in toe_ohe.columns:\n",
+    "        toe_ohe[col] = 0\n",
+    "\n",
+    "toe_ohe = toe_ohe.rename(columns={\n",
+    "    \"toebox_Bad\": \"toebox_bad\",\n",
+    "    \"toebox_Decent\": \"toebox_decent\",\n",
+    "    \"toebox_Good\": \"toebox_good\"\n",
+    "})\n",
+    "\n",
+    "df = pd.concat([df, toe_ohe[[\"toebox_bad\",\"toebox_decent\",\"toebox_good\"]]], axis=1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "98e49c7a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Pace</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>fit_large</th>\n",
+       "      <th>fit_small</th>\n",
+       "      <th>fit_true</th>\n",
+       "      <th>fit_missing</th>\n",
+       "      <th>midsole_soft</th>\n",
+       "      <th>midsole_balanced</th>\n",
+       "      <th>midsole_firm</th>\n",
+       "      <th>toebox_bad</th>\n",
+       "      <th>toebox_decent</th>\n",
+       "      <th>toebox_good</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Launch 9</td>\n",
+       "      <td>87\\n Great!</td>\n",
+       "      <td>$110</td>\n",
+       "      <td>Daily Runningtempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>7.9 oz / 225g 8.1 oz / 230g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.4 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Levitate 6</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$150</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 304g 10.9 oz / 309g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.7 mm 8.0 mm</td>\n",
+       "      <td>Mid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>11.9 oz / 336g 11.5 oz / 327g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 2</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.6 oz / 356g 12.4 oz / 352g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.6 mm 11.0 mm</td>\n",
+       "      <td>Heel</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 3</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.3 oz / 348g 12.2 oz / 345g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 57 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand         Name Audience score Price                Pace Arch support  \\\n",
+       "0  Brooks     Launch 9    87\\n Great!  $110  Daily Runningtempo      Neutral   \n",
+       "1  Brooks   Levitate 6   90\\n Superb!  $150       Daily Running      Neutral   \n",
+       "2  Adidas        4DFWD   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "3  Adidas      4DFWD 2   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "4  Adidas      4DFWD 3    88\\n Great!  $200       Daily Running      Neutral   \n",
+       "\n",
+       "         Weight lab Weight brand  Lightweight Drop lab Drop brand  \\\n",
+       "0    7.9 oz / 225g 8.1 oz / 230g            1      9.4 mm 10.0 mm   \n",
+       "1  10.7 oz / 304g 10.9 oz / 309g            0       7.7 mm 8.0 mm   \n",
+       "2  11.9 oz / 336g 11.5 oz / 327g            0      8.9 mm 10.0 mm   \n",
+       "3  12.6 oz / 356g 12.4 oz / 352g            0     10.6 mm 11.0 mm   \n",
+       "4  12.3 oz / 348g 12.2 oz / 345g            0      9.9 mm 10.0 mm   \n",
+       "\n",
+       "     Strike pattern  ... fit_large fit_small fit_true fit_missing  \\\n",
+       "0  Heelmid/Forefoot  ...         0         0        1           0   \n",
+       "1      Mid/Forefoot  ...         0         0        1           0   \n",
+       "2  Heelmid/Forefoot  ...         0         0        1           0   \n",
+       "3              Heel  ...         0         1        0           0   \n",
+       "4  Heelmid/Forefoot  ...         0         0        1           0   \n",
+       "\n",
+       "  midsole_soft midsole_balanced midsole_firm toebox_bad toebox_decent  \\\n",
+       "0            0                1            0          0             0   \n",
+       "1            1                0            0          0             0   \n",
+       "2            0                0            1          0             0   \n",
+       "3            0                0            1          0             0   \n",
+       "4            0                0            1          0             0   \n",
+       "\n",
+       "  toebox_good  \n",
+       "0           0  \n",
+       "1           1  \n",
+       "2           0  \n",
+       "3           0  \n",
+       "4           1  \n",
+       "\n",
+       "[5 rows x 57 columns]"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "1e4ee12e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 1170\n",
+      "Bad: 263\n",
+      "Decent: 486\n",
+      "Good: 247\n",
+      "Missing toebox rows: 174\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Rows:\", len(df))\n",
+    "print(\"Bad:\", int(df[\"toebox_bad\"].sum()))\n",
+    "print(\"Decent:\", int(df[\"toebox_decent\"].sum()))\n",
+    "print(\"Good:\", int(df[\"toebox_good\"].sum()))\n",
+    "print(\"Missing toebox rows:\",\n",
+    "      int((df[[\"toebox_bad\",\"toebox_decent\",\"toebox_good\"]].sum(axis=1) == 0).sum()))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c40a81d",
+   "metadata": {},
+   "source": [
+    "### Analisis Toebox Durability\n",
+    "Toebox durability menggambarkan ketahanan bagian depan sepatu (area jari kaki) terhadap aus, robek, atau jebol akibat gesekan dan tekanan saat berlari. Fitur ini tidak berhubungan dengan kenyamanan, tapi umur pakai sepatu, terutama untuk pelari dengan tekanan forefoot tinggi atau mileage besar.\n",
+    "\n",
+    "A. Bad = Toebox mudah aus / cepat rusak\n",
+    "Umum pada sepatu:\n",
+    "- sangat ringan\n",
+    "- racing-oriented\n",
+    "Risiko: cepat jebol jika dipakai intens\n",
+    "Trade-off: biasanya lebih breathable & ringan\n",
+    "\n",
+    "B. Decent = Ketahanan cukup untuk pemakaian normal\n",
+    "Aman untuk:\n",
+    "- daily running\n",
+    "- latihan reguler\n",
+    "Trade-off: tidak sekuat kategori “Good”, tapi seimbang\n",
+    "\n",
+    "C. Good = Toebox kuat dan tahan lama\n",
+    "Cocok untuk:\n",
+    "- mileage tinggi\n",
+    "- forefoot striker\n",
+    "- pemakaian kasar / jangka panjang\n",
+    "Trade-off: kadang sedikit lebih berat atau kurang breathable"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db78fbae",
+   "metadata": {},
+   "source": [
+    "# Cleaning Heel padding durability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "0e7f91f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Heel padding (4 uniques): \n",
+      " ['-', 'Bad', 'Decent', 'Good']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Heel padding durability\n",
+       "Good      555\n",
+       "Decent    248\n",
+       "Bad       191\n",
+       "-         176\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Heel padding durability\"] = (\n",
+    "    df[\"Heel padding durability\"]\n",
+    "    .astype(str)\n",
+    "    .str.strip()\n",
+    "    .str.replace(r\"\\s+\", \" \", regex=True)\n",
+    "    .str.title()\n",
+    ")\n",
+    "\n",
+    "heel_padding_uniques = df[\"Heel padding durability\"].dropna().unique()\n",
+    "# heel_padding_uniques.sort()\n",
+    "heel_padding_uniques = sorted(heel_padding_uniques)\n",
+    "print(f\"Heel padding ({len(heel_padding_uniques)} uniques): \\n\",heel_padding_uniques)\n",
+    "\n",
+    "df[\"Heel padding durability\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "7c9c18c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Pace</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>fit_missing</th>\n",
+       "      <th>midsole_soft</th>\n",
+       "      <th>midsole_balanced</th>\n",
+       "      <th>midsole_firm</th>\n",
+       "      <th>toebox_bad</th>\n",
+       "      <th>toebox_decent</th>\n",
+       "      <th>toebox_good</th>\n",
+       "      <th>heelpad_bad</th>\n",
+       "      <th>heelpad_decent</th>\n",
+       "      <th>heelpad_good</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Launch 9</td>\n",
+       "      <td>87\\n Great!</td>\n",
+       "      <td>$110</td>\n",
+       "      <td>Daily Runningtempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>7.9 oz / 225g 8.1 oz / 230g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.4 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Levitate 6</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$150</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 304g 10.9 oz / 309g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.7 mm 8.0 mm</td>\n",
+       "      <td>Mid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>11.9 oz / 336g 11.5 oz / 327g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 2</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.6 oz / 356g 12.4 oz / 352g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.6 mm 11.0 mm</td>\n",
+       "      <td>Heel</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 3</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.3 oz / 348g 12.2 oz / 345g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 60 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand         Name Audience score Price                Pace Arch support  \\\n",
+       "0  Brooks     Launch 9    87\\n Great!  $110  Daily Runningtempo      Neutral   \n",
+       "1  Brooks   Levitate 6   90\\n Superb!  $150       Daily Running      Neutral   \n",
+       "2  Adidas        4DFWD   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "3  Adidas      4DFWD 2   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "4  Adidas      4DFWD 3    88\\n Great!  $200       Daily Running      Neutral   \n",
+       "\n",
+       "         Weight lab Weight brand  Lightweight Drop lab Drop brand  \\\n",
+       "0    7.9 oz / 225g 8.1 oz / 230g            1      9.4 mm 10.0 mm   \n",
+       "1  10.7 oz / 304g 10.9 oz / 309g            0       7.7 mm 8.0 mm   \n",
+       "2  11.9 oz / 336g 11.5 oz / 327g            0      8.9 mm 10.0 mm   \n",
+       "3  12.6 oz / 356g 12.4 oz / 352g            0     10.6 mm 11.0 mm   \n",
+       "4  12.3 oz / 348g 12.2 oz / 345g            0      9.9 mm 10.0 mm   \n",
+       "\n",
+       "     Strike pattern  ... fit_missing midsole_soft midsole_balanced  \\\n",
+       "0  Heelmid/Forefoot  ...           0            0                1   \n",
+       "1      Mid/Forefoot  ...           0            1                0   \n",
+       "2  Heelmid/Forefoot  ...           0            0                0   \n",
+       "3              Heel  ...           0            0                0   \n",
+       "4  Heelmid/Forefoot  ...           0            0                0   \n",
+       "\n",
+       "  midsole_firm toebox_bad toebox_decent toebox_good heelpad_bad  \\\n",
+       "0            0          0             0           0           0   \n",
+       "1            0          0             0           1           0   \n",
+       "2            1          0             0           0           0   \n",
+       "3            1          0             0           0           0   \n",
+       "4            1          0             0           1           0   \n",
+       "\n",
+       "  heelpad_decent heelpad_good  \n",
+       "0              0            0  \n",
+       "1              0            1  \n",
+       "2              0            1  \n",
+       "3              0            0  \n",
+       "4              0            1  \n",
+       "\n",
+       "[5 rows x 60 columns]"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hpd_ohe = pd.get_dummies(df[\"Heel padding durability\"], prefix=\"heel_pad\").astype(int)\n",
+    "\n",
+    "\n",
+    "for col in [\"heel_pad_Bad\", \"heel_pad_Decent\", \"heel_pad_Good\"]:\n",
+    "    if col not in hpd_ohe.columns:\n",
+    "        hpd_ohe[col] = 0\n",
+    "\n",
+    "\n",
+    "hpd_ohe = hpd_ohe.rename(columns={\n",
+    "    \"heel_pad_Bad\": \"heelpad_bad\",\n",
+    "    \"heel_pad_Decent\": \"heelpad_decent\",\n",
+    "    \"heel_pad_Good\": \"heelpad_good\"\n",
+    "})\n",
+    "\n",
+    "df = pd.concat([df, hpd_ohe[[\"heelpad_bad\", \"heelpad_decent\", \"heelpad_good\"]]], axis=1)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "0cdc7d72",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 1170\n",
+      "Bad: 191\n",
+      "Decent: 248\n",
+      "Good: 555\n",
+      "Missing heelpad durability rows: 176\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Rows:\", len(df))\n",
+    "print(\"Bad:\", int(df[\"heelpad_bad\"].sum()))\n",
+    "print(\"Decent:\", int(df[\"heelpad_decent\"].sum()))\n",
+    "print(\"Good:\", int(df[\"heelpad_good\"].sum()))\n",
+    "print(\"Missing heelpad durability rows:\",\n",
+    "      int((df[[\"heelpad_bad\",\"heelpad_decent\",\"heelpad_good\"]].sum(axis=1) == 0).sum()))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44af6117",
+   "metadata": {},
+   "source": [
+    "makin banyak missingnya bre"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7225f80",
+   "metadata": {},
+   "source": [
+    "### Analisis Heel Padding Durability\n",
+    "Heel padding durability menggambarkan ketahanan bantalan di area tumit bagian dalam sepatu terhadap aus, kempes, atau rusak akibat gesekan dan tekanan berulang saat berlari. Fitur ini berpengaruh pada kenyamanan jangka panjang, stabilitas tumit, dan umur pakai sepatu, terutama bagi pelari yang dominan heel strike.\n",
+    "\n",
+    "Bad: \n",
+    "Bantalan tumit cepat aus atau kempes\n",
+    "Berpotensi menyebabkan:\n",
+    "rasa tidak nyaman\n",
+    "gesekan berlebih di tumit\n",
+    "Umum pada sepatu:\n",
+    "ringan\n",
+    "fokus ke performa jangka pendek\n",
+    "Trade-off: bobot lebih ringan, tapi durability rendah\n",
+    "\n",
+    "Decent: \n",
+    "Ketahanan bantalan cukup untuk pemakaian normal\n",
+    "Aman untuk:\n",
+    "daily running\n",
+    "latihan reguler\n",
+    "Trade-off: tidak sekuat kategori “Good”, tapi seimbang\n",
+    "\n",
+    "Good: \n",
+    "Bantalan tumit kuat dan tahan lama\n",
+    "Cocok untuk:\n",
+    "mileage tinggi\n",
+    "pemakaian jangka panjang\n",
+    "pelari heel strike\n",
+    "Trade-off: kadang sedikit lebih berat atau kurang breathable"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00d5e6f9",
+   "metadata": {},
+   "source": [
+    "# Cleaning Outsole Durability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "cc9ba6e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Outsole (4 uniques): \n",
+      " ['-', 'Bad', 'Decent', 'Good']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Outsole durability\n",
+       "Good      631\n",
+       "Decent    253\n",
+       "-         209\n",
+       "Bad        77\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Outsole durability\"] = (\n",
+    "    df[\"Outsole durability\"]\n",
+    "    .astype(str)\n",
+    "    .str.strip()\n",
+    "    .str.replace(r\"\\s+\", \" \", regex=True)\n",
+    "    .str.title()\n",
+    ")\n",
+    "\n",
+    "outsole_uniques = df[\"Outsole durability\"].dropna().unique()\n",
+    "# outsole_uniques.sort()\n",
+    "outsole_uniques = sorted(outsole_uniques)\n",
+    "print(f\"Outsole ({len(outsole_uniques)} uniques): \\n\",outsole_uniques)\n",
+    "\n",
+    "df[\"Outsole durability\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "28bf5e69",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Pace</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>midsole_firm</th>\n",
+       "      <th>toebox_bad</th>\n",
+       "      <th>toebox_decent</th>\n",
+       "      <th>toebox_good</th>\n",
+       "      <th>heelpad_bad</th>\n",
+       "      <th>heelpad_decent</th>\n",
+       "      <th>heelpad_good</th>\n",
+       "      <th>outsole_bad</th>\n",
+       "      <th>outsole_decent</th>\n",
+       "      <th>outsole_good</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Launch 9</td>\n",
+       "      <td>87\\n Great!</td>\n",
+       "      <td>$110</td>\n",
+       "      <td>Daily Runningtempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>7.9 oz / 225g 8.1 oz / 230g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.4 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Levitate 6</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$150</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 304g 10.9 oz / 309g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.7 mm 8.0 mm</td>\n",
+       "      <td>Mid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>11.9 oz / 336g 11.5 oz / 327g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 2</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.6 oz / 356g 12.4 oz / 352g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.6 mm 11.0 mm</td>\n",
+       "      <td>Heel</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 3</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.3 oz / 348g 12.2 oz / 345g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 63 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand         Name Audience score Price                Pace Arch support  \\\n",
+       "0  Brooks     Launch 9    87\\n Great!  $110  Daily Runningtempo      Neutral   \n",
+       "1  Brooks   Levitate 6   90\\n Superb!  $150       Daily Running      Neutral   \n",
+       "2  Adidas        4DFWD   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "3  Adidas      4DFWD 2   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "4  Adidas      4DFWD 3    88\\n Great!  $200       Daily Running      Neutral   \n",
+       "\n",
+       "         Weight lab Weight brand  Lightweight Drop lab Drop brand  \\\n",
+       "0    7.9 oz / 225g 8.1 oz / 230g            1      9.4 mm 10.0 mm   \n",
+       "1  10.7 oz / 304g 10.9 oz / 309g            0       7.7 mm 8.0 mm   \n",
+       "2  11.9 oz / 336g 11.5 oz / 327g            0      8.9 mm 10.0 mm   \n",
+       "3  12.6 oz / 356g 12.4 oz / 352g            0     10.6 mm 11.0 mm   \n",
+       "4  12.3 oz / 348g 12.2 oz / 345g            0      9.9 mm 10.0 mm   \n",
+       "\n",
+       "     Strike pattern  ... midsole_firm toebox_bad toebox_decent toebox_good  \\\n",
+       "0  Heelmid/Forefoot  ...            0          0             0           0   \n",
+       "1      Mid/Forefoot  ...            0          0             0           1   \n",
+       "2  Heelmid/Forefoot  ...            1          0             0           0   \n",
+       "3              Heel  ...            1          0             0           0   \n",
+       "4  Heelmid/Forefoot  ...            1          0             0           1   \n",
+       "\n",
+       "  heelpad_bad heelpad_decent heelpad_good outsole_bad outsole_decent  \\\n",
+       "0           0              0            0           0              0   \n",
+       "1           0              0            1           0              0   \n",
+       "2           0              0            1           0              0   \n",
+       "3           0              0            0           0              0   \n",
+       "4           0              0            1           0              0   \n",
+       "\n",
+       "  outsole_good  \n",
+       "0            0  \n",
+       "1            1  \n",
+       "2            0  \n",
+       "3            0  \n",
+       "4            1  \n",
+       "\n",
+       "[5 rows x 63 columns]"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "out_ohe = pd.get_dummies(df[\"Outsole durability\"], prefix=\"outsole\").astype(int)\n",
+    "\n",
+    "for col in [\"outsole_Bad\", \"outsole_Decent\", \"outsole_Good\"]:\n",
+    "    if col not in out_ohe.columns:\n",
+    "        out_ohe[col] = 0\n",
+    "\n",
+    "out_ohe = out_ohe.rename(columns={\n",
+    "    \"outsole_Bad\": \"outsole_bad\",\n",
+    "    \"outsole_Decent\": \"outsole_decent\",\n",
+    "    \"outsole_Good\": \"outsole_good\"\n",
+    "})\n",
+    "\n",
+    "df = pd.concat([df, out_ohe[[\"outsole_bad\",\"outsole_decent\",\"outsole_good\"]]], axis=1)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "6e11f54a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "outsole_bad        77\n",
+      "outsole_decent    253\n",
+      "outsole_good      631\n",
+      "dtype: int64\n",
+      "Missing outsole rows: 209\n"
+     ]
+    }
+   ],
+   "source": [
+    "# hanya 0 / 1\n",
+    "for c in [\"outsole_bad\",\"outsole_decent\",\"outsole_good\"]:\n",
+    "    assert set(df[c].unique()).issubset({0, 1})\n",
+    "\n",
+    "# single-label atau missing\n",
+    "assert df[[\"outsole_bad\",\"outsole_decent\",\"outsole_good\"]].sum(axis=1).le(1).all()\n",
+    "\n",
+    "print(df[[\"outsole_bad\",\"outsole_decent\",\"outsole_good\"]].sum())\n",
+    "print(\"Missing outsole rows:\",\n",
+    "      int((df[[\"outsole_bad\",\"outsole_decent\",\"outsole_good\"]].sum(axis=1) == 0).sum()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "74ad3110",
+   "metadata": {},
+   "source": [
+    "### Analisis Outsole Durability\n",
+    "\n",
+    "Outsole durability menggambarkan ketahanan sol bagian bawah sepatu terhadap aus akibat kontak langsung dengan permukaan lari (aspal, beton, treadmill, track).\n",
+    "\n",
+    "Fitur ini berhubungan langsung dengan umur pakai sepatu dan efisiensi biaya, terutama bagi pelari dengan mileage tinggi atau yang sering berlari di permukaan keras.\n",
+    "\n",
+    "- Bad: \n",
+    "Outsole cepat aus\n",
+    "Grip dan perlindungan cepat menurun\n",
+    "Umum pada sepatu:\n",
+    "ringan\n",
+    "racing-oriented\n",
+    "Trade-off: bobot ringan, tapi umur pakai pendek\n",
+    "\n",
+    "- Decent: \n",
+    "Ketahanan cukup untuk pemakaian normal\n",
+    "Aman untuk:\n",
+    "daily running\n",
+    "latihan reguler\n",
+    "Trade-off: bukan yang paling awet, tapi seimbang\n",
+    "\n",
+    "- Good: \n",
+    "Outsole sangat tahan lama\n",
+    "Cocok untuk:\n",
+    "mileage tinggi\n",
+    "pemakaian jangka panjang\n",
+    "lari di permukaan kasar\n",
+    "Trade-off: kadang lebih berat atau kurang fleksibel"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0da23b5",
+   "metadata": {},
+   "source": [
+    "# Cleaning Breathability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "5953a816",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# df = df.drop(labels=\"breathable\", axis=1)\n",
+    "# df = df.drop(labels=\"moderate\", axis=1)\n",
+    "# df = df.drop(labels=\"warm\", axis=1)\n",
+    "# df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "b90e8e6c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Breathability (4 uniques): \n",
+      " ['-', 'Breathable', 'Moderate', 'Warm']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Breathability\n",
+       "Moderate      642\n",
+       "Breathable    347\n",
+       "Warm          118\n",
+       "-              63\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Breathability\"] = (\n",
+    "    df[\"Breathability\"]\n",
+    "    .astype(str)\n",
+    "    .str.strip()\n",
+    "    .str.replace(r\"\\s+\", \" \", regex=True)\n",
+    "    .str.title()\n",
+    ")\n",
+    "\n",
+    "breathability_uniques = df[\"Breathability\"].dropna().unique()\n",
+    "# breathability_uniques.sort()\n",
+    "breathability_uniques = sorted(breathability_uniques)\n",
+    "print(f\"Breathability ({len(breathability_uniques)} uniques): \\n\",breathability_uniques)\n",
+    "\n",
+    "df[\"Breathability\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "61b2650b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Pace</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>toebox_good</th>\n",
+       "      <th>heelpad_bad</th>\n",
+       "      <th>heelpad_decent</th>\n",
+       "      <th>heelpad_good</th>\n",
+       "      <th>outsole_bad</th>\n",
+       "      <th>outsole_decent</th>\n",
+       "      <th>outsole_good</th>\n",
+       "      <th>breath_breathable</th>\n",
+       "      <th>breath_moderate</th>\n",
+       "      <th>breath_warm</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Launch 9</td>\n",
+       "      <td>87\\n Great!</td>\n",
+       "      <td>$110</td>\n",
+       "      <td>Daily Runningtempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>7.9 oz / 225g 8.1 oz / 230g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.4 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Levitate 6</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$150</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 304g 10.9 oz / 309g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.7 mm 8.0 mm</td>\n",
+       "      <td>Mid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>11.9 oz / 336g 11.5 oz / 327g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 2</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.6 oz / 356g 12.4 oz / 352g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.6 mm 11.0 mm</td>\n",
+       "      <td>Heel</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 3</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.3 oz / 348g 12.2 oz / 345g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 66 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand         Name Audience score Price                Pace Arch support  \\\n",
+       "0  Brooks     Launch 9    87\\n Great!  $110  Daily Runningtempo      Neutral   \n",
+       "1  Brooks   Levitate 6   90\\n Superb!  $150       Daily Running      Neutral   \n",
+       "2  Adidas        4DFWD   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "3  Adidas      4DFWD 2   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "4  Adidas      4DFWD 3    88\\n Great!  $200       Daily Running      Neutral   \n",
+       "\n",
+       "         Weight lab Weight brand  Lightweight Drop lab Drop brand  \\\n",
+       "0    7.9 oz / 225g 8.1 oz / 230g            1      9.4 mm 10.0 mm   \n",
+       "1  10.7 oz / 304g 10.9 oz / 309g            0       7.7 mm 8.0 mm   \n",
+       "2  11.9 oz / 336g 11.5 oz / 327g            0      8.9 mm 10.0 mm   \n",
+       "3  12.6 oz / 356g 12.4 oz / 352g            0     10.6 mm 11.0 mm   \n",
+       "4  12.3 oz / 348g 12.2 oz / 345g            0      9.9 mm 10.0 mm   \n",
+       "\n",
+       "     Strike pattern  ... toebox_good heelpad_bad heelpad_decent heelpad_good  \\\n",
+       "0  Heelmid/Forefoot  ...           0           0              0            0   \n",
+       "1      Mid/Forefoot  ...           1           0              0            1   \n",
+       "2  Heelmid/Forefoot  ...           0           0              0            1   \n",
+       "3              Heel  ...           0           0              0            0   \n",
+       "4  Heelmid/Forefoot  ...           1           0              0            1   \n",
+       "\n",
+       "  outsole_bad outsole_decent outsole_good breath_breathable breath_moderate  \\\n",
+       "0           0              0            0                 0               0   \n",
+       "1           0              0            1                 1               0   \n",
+       "2           0              0            0                 0               0   \n",
+       "3           0              0            0                 0               0   \n",
+       "4           0              0            1                 0               0   \n",
+       "\n",
+       "  breath_warm  \n",
+       "0           0  \n",
+       "1           0  \n",
+       "2           1  \n",
+       "3           1  \n",
+       "4           1  \n",
+       "\n",
+       "[5 rows x 66 columns]"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "breath_ohe = pd.get_dummies(df[\"Breathability\"], prefix=\"breath\").astype(int)\n",
+    "\n",
+    "for col in [\"breath_Breathable\", \"breath_Moderate\", \"breath_Warm\"]:\n",
+    "    if col not in breath_ohe.columns:\n",
+    "        breath_ohe[col] = 0\n",
+    "\n",
+    "breath_ohe = breath_ohe.rename(columns={\n",
+    "    \"breath_Breathable\": \"breath_breathable\",\n",
+    "    \"breath_Moderate\": \"breath_moderate\",\n",
+    "    \"breath_Warm\": \"breath_warm\"\n",
+    "})\n",
+    "\n",
+    "df = pd.concat([df, breath_ohe[[\"breath_breathable\",\"breath_moderate\",\"breath_warm\"]]], axis=1)\n",
+    "df.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "06e99a17",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "breath_breathable    347\n",
+      "breath_moderate      642\n",
+      "breath_warm          118\n",
+      "dtype: int64\n",
+      "Missing breathability rows: 63\n"
+     ]
+    }
+   ],
+   "source": [
+    "# hanya 0 / 1\n",
+    "for c in [\"breath_breathable\",\"breath_moderate\",\"breath_warm\"]:\n",
+    "    assert set(df[c].unique()).issubset({0, 1})\n",
+    "\n",
+    "# single-label atau missing\n",
+    "assert df[[\"breath_breathable\",\"breath_moderate\",\"breath_warm\"]].sum(axis=1).le(1).all()\n",
+    "\n",
+    "print(df[[\"breath_breathable\",\"breath_moderate\",\"breath_warm\"]].sum())\n",
+    "print(\"Missing breathability rows:\",\n",
+    "      int((df[[\"breath_breathable\",\"breath_moderate\",\"breath_warm\"]].sum(axis=1) == 0).sum()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0d08520e",
+   "metadata": {},
+   "source": [
+    "### Analisis Breathability\n",
+    "\n",
+    "Breathability menggambarkan seberapa baik sepatu memungkinkan sirkulasi udara di bagian upper, yang berpengaruh pada suhu kaki, kenyamanan, dan manajemen kelembapan saat berlari.\n",
+    "\n",
+    "Fitur ini penting terutama untuk:\n",
+    "\n",
+    "lari jarak jauh\n",
+    "\n",
+    "cuaca panas\n",
+    "\n",
+    "pelari dengan kaki mudah berkeringat\n",
+    "\n",
+    "Breathable\n",
+    "\n",
+    "Sirkulasi udara sangat baik\n",
+    "\n",
+    "Kaki terasa lebih sejuk dan kering\n",
+    "\n",
+    "Cocok untuk:\n",
+    "\n",
+    "cuaca panas\n",
+    "\n",
+    "long run\n",
+    "\n",
+    "Trade-off: biasanya material lebih tipis → durability bisa lebih rendah\n",
+    "\n",
+    "Moderate\n",
+    "\n",
+    "Sirkulasi udara cukup / seimbang\n",
+    "\n",
+    "Aman untuk pemakaian umum\n",
+    "\n",
+    "Cocok untuk:\n",
+    "\n",
+    "daily running\n",
+    "\n",
+    "berbagai kondisi cuaca\n",
+    "\n",
+    "Trade-off: tidak seadem “Breathable”, tidak sehangat “Warm”\n",
+    "\n",
+    "Warm\n",
+    "\n",
+    "Ventilasi minim\n",
+    "\n",
+    "Menjaga kaki tetap hangat\n",
+    "\n",
+    "Cocok untuk:\n",
+    "\n",
+    "cuaca dingin\n",
+    "\n",
+    "winter running\n",
+    "\n",
+    "Trade-off: kaki bisa terasa panas di cuaca hangat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b7c347e",
+   "metadata": {},
+   "source": [
+    "# Cleaning Width / fit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "4c25ac2d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Width / fit (3 uniques): \n",
+      " ['Medium', 'Narrow', 'Wide']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Width / fit\n",
+       "Medium    786\n",
+       "Narrow    287\n",
+       "Wide       97\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Width / fit\"] = (\n",
+    "    df[\"Width / fit\"]\n",
+    "    .astype(str)\n",
+    "    .str.strip()\n",
+    "    .str.replace(r\"\\s+\", \" \", regex=True)\n",
+    "    .str.title()\n",
+    ")\n",
+    "\n",
+    "width_uniques = df[\"Width / fit\"].dropna().unique()\n",
+    "# width_uniques.sort()\n",
+    "width_uniques = sorted(width_uniques)\n",
+    "print(f\"Width / fit ({len(width_uniques)} uniques): \\n\",width_uniques)\n",
+    "\n",
+    "df[\"Width / fit\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "d33d3ef6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Pace</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>heelpad_good</th>\n",
+       "      <th>outsole_bad</th>\n",
+       "      <th>outsole_decent</th>\n",
+       "      <th>outsole_good</th>\n",
+       "      <th>breath_breathable</th>\n",
+       "      <th>breath_moderate</th>\n",
+       "      <th>breath_warm</th>\n",
+       "      <th>width_narrow</th>\n",
+       "      <th>width_medium</th>\n",
+       "      <th>width_wide</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Launch 9</td>\n",
+       "      <td>87\\n Great!</td>\n",
+       "      <td>$110</td>\n",
+       "      <td>Daily Runningtempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>7.9 oz / 225g 8.1 oz / 230g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.4 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Levitate 6</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$150</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 304g 10.9 oz / 309g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.7 mm 8.0 mm</td>\n",
+       "      <td>Mid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>11.9 oz / 336g 11.5 oz / 327g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 2</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.6 oz / 356g 12.4 oz / 352g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.6 mm 11.0 mm</td>\n",
+       "      <td>Heel</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 3</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.3 oz / 348g 12.2 oz / 345g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 69 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand         Name Audience score Price                Pace Arch support  \\\n",
+       "0  Brooks     Launch 9    87\\n Great!  $110  Daily Runningtempo      Neutral   \n",
+       "1  Brooks   Levitate 6   90\\n Superb!  $150       Daily Running      Neutral   \n",
+       "2  Adidas        4DFWD   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "3  Adidas      4DFWD 2   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "4  Adidas      4DFWD 3    88\\n Great!  $200       Daily Running      Neutral   \n",
+       "\n",
+       "         Weight lab Weight brand  Lightweight Drop lab Drop brand  \\\n",
+       "0    7.9 oz / 225g 8.1 oz / 230g            1      9.4 mm 10.0 mm   \n",
+       "1  10.7 oz / 304g 10.9 oz / 309g            0       7.7 mm 8.0 mm   \n",
+       "2  11.9 oz / 336g 11.5 oz / 327g            0      8.9 mm 10.0 mm   \n",
+       "3  12.6 oz / 356g 12.4 oz / 352g            0     10.6 mm 11.0 mm   \n",
+       "4  12.3 oz / 348g 12.2 oz / 345g            0      9.9 mm 10.0 mm   \n",
+       "\n",
+       "     Strike pattern  ... heelpad_good outsole_bad outsole_decent outsole_good  \\\n",
+       "0  Heelmid/Forefoot  ...            0           0              0            0   \n",
+       "1      Mid/Forefoot  ...            1           0              0            1   \n",
+       "2  Heelmid/Forefoot  ...            1           0              0            0   \n",
+       "3              Heel  ...            0           0              0            0   \n",
+       "4  Heelmid/Forefoot  ...            1           0              0            1   \n",
+       "\n",
+       "  breath_breathable breath_moderate breath_warm width_narrow width_medium  \\\n",
+       "0                 0               0           0            1            0   \n",
+       "1                 1               0           0            1            0   \n",
+       "2                 0               0           1            1            0   \n",
+       "3                 0               0           1            1            0   \n",
+       "4                 0               0           1            1            0   \n",
+       "\n",
+       "  width_wide  \n",
+       "0          0  \n",
+       "1          0  \n",
+       "2          0  \n",
+       "3          0  \n",
+       "4          0  \n",
+       "\n",
+       "[5 rows x 69 columns]"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "width_ohe = pd.get_dummies(df[\"Width / fit\"], prefix=\"width\").astype(int)\n",
+    "\n",
+    "for col in [\"width_Narrow\", \"width_Medium\", \"width_Wide\"]:\n",
+    "    if col not in width_ohe.columns:\n",
+    "        width_ohe[col] = 0\n",
+    "\n",
+    "width_ohe = width_ohe.rename(columns={\n",
+    "    \"width_Narrow\": \"width_narrow\",\n",
+    "    \"width_Medium\": \"width_medium\",\n",
+    "    \"width_Wide\": \"width_wide\"\n",
+    "})\n",
+    "\n",
+    "df = pd.concat([df, width_ohe[[\"width_narrow\",\"width_medium\",\"width_wide\"]]], axis=1)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "1ec7610a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "width_narrow    287\n",
+      "width_medium    786\n",
+      "width_wide       97\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# hanya 0 / 1\n",
+    "for c in [\"width_narrow\",\"width_medium\",\"width_wide\"]:\n",
+    "    assert set(df[c].unique()).issubset({0, 1})\n",
+    "\n",
+    "# HARUS tepat satu (tidak ada missing)\n",
+    "assert (df[[\"width_narrow\",\"width_medium\",\"width_wide\"]].sum(axis=1) == 1).all()\n",
+    "\n",
+    "print(df[[\"width_narrow\",\"width_medium\",\"width_wide\"]].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf698833",
+   "metadata": {},
+   "source": [
+    "### Analisis Width / Fit\n",
+    "\n",
+    "Width / fit menggambarkan lebar sepatu pada bagian forefoot (area depan kaki), yang memengaruhi kenyamanan, stabilitas, dan risiko lecet saat berlari.\n",
+    "\n",
+    "Fitur ini bukan soal ukuran panjang (EU/US), melainkan ruang horizontal untuk kaki.\n",
+    "\n",
+    "Narrow\n",
+    "\n",
+    "Sepatu terasa lebih sempit dari standar\n",
+    "\n",
+    "Cocok untuk:\n",
+    "\n",
+    "kaki ramping\n",
+    "\n",
+    "pelari yang suka fit ketat\n",
+    "\n",
+    "Risiko: tekanan di sisi kaki jika dipakai oleh kaki lebar\n",
+    "\n",
+    "Medium\n",
+    "\n",
+    "Lebar standar / normal\n",
+    "\n",
+    "Cocok untuk:\n",
+    "\n",
+    "mayoritas pelari\n",
+    "\n",
+    "Catatan: ini adalah default fit di pasaran\n",
+    "\n",
+    "Wide\n",
+    "\n",
+    "Sepatu menyediakan ruang lebih lega\n",
+    "\n",
+    "Cocok untuk:\n",
+    "\n",
+    "kaki lebar\n",
+    "\n",
+    "pelari yang sering merasa jari tertekan\n",
+    "\n",
+    "Trade-off: bisa terasa kurang “locked-in” untuk kaki sempit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "928c5523",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 1170 entries, 0 to 1169\n",
+      "Data columns (total 69 columns):\n",
+      " #   Column                       Non-Null Count  Dtype \n",
+      "---  ------                       --------------  ----- \n",
+      " 0   Brand                        1170 non-null   str   \n",
+      " 1   Name                         1170 non-null   str   \n",
+      " 2   Audience score               1164 non-null   str   \n",
+      " 3   Price                        1170 non-null   str   \n",
+      " 4   Pace                         1170 non-null   str   \n",
+      " 5   Arch support                 1170 non-null   str   \n",
+      " 6   Weight lab Weight brand      1170 non-null   str   \n",
+      " 7   Lightweight                  1170 non-null   int64 \n",
+      " 8   Drop lab Drop brand          1170 non-null   str   \n",
+      " 9   Strike pattern               1170 non-null   str   \n",
+      " 10  Size                         1170 non-null   str   \n",
+      " 11  Midsole softness             1170 non-null   str   \n",
+      " 12  Toebox durability            1170 non-null   str   \n",
+      " 13  Heel padding durability      1170 non-null   str   \n",
+      " 14  Outsole durability           1170 non-null   str   \n",
+      " 15  Breathability                1170 non-null   str   \n",
+      " 16  Width / fit                  1170 non-null   str   \n",
+      " 17  Toebox width                 1170 non-null   str   \n",
+      " 18  Stiffness                    1170 non-null   str   \n",
+      " 19  Torsional rigidity           1170 non-null   str   \n",
+      " 20  Heel counter stiffness       1170 non-null   str   \n",
+      " 21  Plate                        1170 non-null   str   \n",
+      " 22  Rocker                       1170 non-null   int64 \n",
+      " 23  Heel lab Heel brand          1170 non-null   str   \n",
+      " 24  Forefoot lab Forefoot brand  1170 non-null   str   \n",
+      " 25  Widths available             1170 non-null   str   \n",
+      " 26  Orthotic friendly            1170 non-null   int64 \n",
+      " 27  Season                       1170 non-null   str   \n",
+      " 28  Removable insole             1170 non-null   int64 \n",
+      " 29  Ranking                      1170 non-null   str   \n",
+      " 30  Popularity                   1170 non-null   str   \n",
+      " 31  Gender                       10 non-null     str   \n",
+      " 32  Terrain                      16 non-null     str   \n",
+      " 33  Pace_norm                    1170 non-null   str   \n",
+      " 34  Pace_lists                   1170 non-null   object\n",
+      " 35  pace_competition             1170 non-null   int64 \n",
+      " 36  pace_daily_running           1170 non-null   int64 \n",
+      " 37  pace_tempo                   1170 non-null   int64 \n",
+      " 38  arch_neutral                 1170 non-null   int64 \n",
+      " 39  arch_stability               1170 non-null   int64 \n",
+      " 40  Strike_norm                  1170 non-null   str   \n",
+      " 41  Strike_lists                 1170 non-null   object\n",
+      " 42  -                            1170 non-null   int64 \n",
+      " 43  strike_forefoot              1170 non-null   int64 \n",
+      " 44  strike_heel                  1170 non-null   int64 \n",
+      " 45  strike_mid                   1170 non-null   int64 \n",
+      " 46  fit_category                 1126 non-null   str   \n",
+      " 47  fit_large                    1170 non-null   int64 \n",
+      " 48  fit_small                    1170 non-null   int64 \n",
+      " 49  fit_true                     1170 non-null   int64 \n",
+      " 50  fit_missing                  1170 non-null   int64 \n",
+      " 51  midsole_soft                 1170 non-null   int64 \n",
+      " 52  midsole_balanced             1170 non-null   int64 \n",
+      " 53  midsole_firm                 1170 non-null   int64 \n",
+      " 54  toebox_bad                   1170 non-null   int64 \n",
+      " 55  toebox_decent                1170 non-null   int64 \n",
+      " 56  toebox_good                  1170 non-null   int64 \n",
+      " 57  heelpad_bad                  1170 non-null   int64 \n",
+      " 58  heelpad_decent               1170 non-null   int64 \n",
+      " 59  heelpad_good                 1170 non-null   int64 \n",
+      " 60  outsole_bad                  1170 non-null   int64 \n",
+      " 61  outsole_decent               1170 non-null   int64 \n",
+      " 62  outsole_good                 1170 non-null   int64 \n",
+      " 63  breath_breathable            1170 non-null   int64 \n",
+      " 64  breath_moderate              1170 non-null   int64 \n",
+      " 65  breath_warm                  1170 non-null   int64 \n",
+      " 66  width_narrow                 1170 non-null   int64 \n",
+      " 67  width_medium                 1170 non-null   int64 \n",
+      " 68  width_wide                   1170 non-null   int64 \n",
+      "dtypes: int64(35), object(2), str(32)\n",
+      "memory usage: 630.8+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9df086d9",
+   "metadata": {},
+   "source": [
+    "# Cleaning Toebox Width"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "d88dbf71",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Toebox width (4 uniques): \n",
+      " ['-', 'Medium', 'Narrow', 'Wide']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Toebox width\n",
+       "Medium    669\n",
+       "Wide      179\n",
+       "Narrow    170\n",
+       "-         152\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Toebox width\"] = (\n",
+    "    df[\"Toebox width\"]\n",
+    "    .astype(str)\n",
+    "    .str.strip()\n",
+    "    .str.replace(r\"\\s+\", \" \", regex=True)\n",
+    "    .str.title()\n",
+    ")\n",
+    "\n",
+    "toebox_uniques = df[\"Toebox width\"].dropna().unique()\n",
+    "# toebox_uniques.sort()\n",
+    "toebox_uniques = sorted(toebox_uniques)\n",
+    "print(f\"Toebox width ({len(toebox_uniques)} uniques): \\n\",toebox_uniques)\n",
+    "\n",
+    "df[\"Toebox width\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "8cb44aed",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Pace</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>outsole_good</th>\n",
+       "      <th>breath_breathable</th>\n",
+       "      <th>breath_moderate</th>\n",
+       "      <th>breath_warm</th>\n",
+       "      <th>width_narrow</th>\n",
+       "      <th>width_medium</th>\n",
+       "      <th>width_wide</th>\n",
+       "      <th>toeboxwidth_narrow</th>\n",
+       "      <th>toeboxwidth_medium</th>\n",
+       "      <th>toeboxwidth_wide</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Launch 9</td>\n",
+       "      <td>87\\n Great!</td>\n",
+       "      <td>$110</td>\n",
+       "      <td>Daily Runningtempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>7.9 oz / 225g 8.1 oz / 230g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.4 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Levitate 6</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$150</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 304g 10.9 oz / 309g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.7 mm 8.0 mm</td>\n",
+       "      <td>Mid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>11.9 oz / 336g 11.5 oz / 327g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 2</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.6 oz / 356g 12.4 oz / 352g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.6 mm 11.0 mm</td>\n",
+       "      <td>Heel</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 3</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.3 oz / 348g 12.2 oz / 345g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 72 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand         Name Audience score Price                Pace Arch support  \\\n",
+       "0  Brooks     Launch 9    87\\n Great!  $110  Daily Runningtempo      Neutral   \n",
+       "1  Brooks   Levitate 6   90\\n Superb!  $150       Daily Running      Neutral   \n",
+       "2  Adidas        4DFWD   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "3  Adidas      4DFWD 2   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "4  Adidas      4DFWD 3    88\\n Great!  $200       Daily Running      Neutral   \n",
+       "\n",
+       "         Weight lab Weight brand  Lightweight Drop lab Drop brand  \\\n",
+       "0    7.9 oz / 225g 8.1 oz / 230g            1      9.4 mm 10.0 mm   \n",
+       "1  10.7 oz / 304g 10.9 oz / 309g            0       7.7 mm 8.0 mm   \n",
+       "2  11.9 oz / 336g 11.5 oz / 327g            0      8.9 mm 10.0 mm   \n",
+       "3  12.6 oz / 356g 12.4 oz / 352g            0     10.6 mm 11.0 mm   \n",
+       "4  12.3 oz / 348g 12.2 oz / 345g            0      9.9 mm 10.0 mm   \n",
+       "\n",
+       "     Strike pattern  ... outsole_good breath_breathable breath_moderate  \\\n",
+       "0  Heelmid/Forefoot  ...            0                 0               0   \n",
+       "1      Mid/Forefoot  ...            1                 1               0   \n",
+       "2  Heelmid/Forefoot  ...            0                 0               0   \n",
+       "3              Heel  ...            0                 0               0   \n",
+       "4  Heelmid/Forefoot  ...            1                 0               0   \n",
+       "\n",
+       "  breath_warm width_narrow width_medium width_wide toeboxwidth_narrow  \\\n",
+       "0           0            1            0          0                  0   \n",
+       "1           0            1            0          0                  0   \n",
+       "2           1            1            0          0                  0   \n",
+       "3           1            1            0          0                  0   \n",
+       "4           1            1            0          0                  0   \n",
+       "\n",
+       "  toeboxwidth_medium toeboxwidth_wide  \n",
+       "0                  0                0  \n",
+       "1                  1                0  \n",
+       "2                  0                0  \n",
+       "3                  0                0  \n",
+       "4                  1                0  \n",
+       "\n",
+       "[5 rows x 72 columns]"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tw_ohe = pd.get_dummies(df[\"Toebox width\"], prefix=\"toeboxwidth\").astype(int)\n",
+    "\n",
+    "for col in [\"toeboxwidth_Narrow\", \"toeboxwidth_Medium\", \"toeboxwidth_Wide\"]:\n",
+    "    if col not in tw_ohe.columns:\n",
+    "        tw_ohe[col] = 0\n",
+    "\n",
+    "tw_ohe = tw_ohe.rename(columns={\n",
+    "    \"toeboxwidth_Narrow\": \"toeboxwidth_narrow\",\n",
+    "    \"toeboxwidth_Medium\": \"toeboxwidth_medium\",\n",
+    "    \"toeboxwidth_Wide\": \"toeboxwidth_wide\"\n",
+    "})\n",
+    "\n",
+    "df = pd.concat([df, tw_ohe[[\"toeboxwidth_narrow\",\"toeboxwidth_medium\",\"toeboxwidth_wide\"]]], axis=1)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "8615c63c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "toeboxwidth_narrow    170\n",
+      "toeboxwidth_medium    669\n",
+      "toeboxwidth_wide      179\n",
+      "dtype: int64\n",
+      "Missing toebox width rows: 152\n"
+     ]
+    }
+   ],
+   "source": [
+    "# hanya 0 / 1\n",
+    "for c in [\"toeboxwidth_narrow\",\"toeboxwidth_medium\",\"toeboxwidth_wide\"]:\n",
+    "    assert set(df[c].unique()).issubset({0, 1})\n",
+    "\n",
+    "# single-label atau missing\n",
+    "assert df[[\"toeboxwidth_narrow\",\"toeboxwidth_medium\",\"toeboxwidth_wide\"]].sum(axis=1).le(1).all()\n",
+    "\n",
+    "print(df[[\"toeboxwidth_narrow\",\"toeboxwidth_medium\",\"toeboxwidth_wide\"]].sum())\n",
+    "print(\n",
+    "    \"Missing toebox width rows:\",\n",
+    "    int((df[[\"toeboxwidth_narrow\",\"toeboxwidth_medium\",\"toeboxwidth_wide\"]].sum(axis=1) == 0).sum())\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a0c1581",
+   "metadata": {},
+   "source": [
+    "### Analisis Toebox Width\n",
+    "\n",
+    "Toebox width menggambarkan lebar ruang di bagian depan sepatu (area jari kaki).\n",
+    "Fitur ini memengaruhi kenyamanan jari, risiko lecet, dan rasa sempit/lega saat berlari, terutama pada jarak jauh.\n",
+    "\n",
+    "Berbeda dengan Width / fit (lebar sepatu secara umum), toebox width fokus ke area jari kaki.\n",
+    "\n",
+    "Narrow\n",
+    "\n",
+    "Ruang jari sempit\n",
+    "\n",
+    "Cocok untuk:\n",
+    "\n",
+    "kaki ramping\n",
+    "\n",
+    "pelari yang suka fit ketat\n",
+    "\n",
+    "Risiko: tekanan pada jari kaki, potensi lecet\n",
+    "\n",
+    "Medium\n",
+    "\n",
+    "Ruang jari standar\n",
+    "\n",
+    "Cocok untuk:\n",
+    "\n",
+    "mayoritas pelari\n",
+    "\n",
+    "Catatan: ini adalah default di pasaran\n",
+    "\n",
+    "Wide\n",
+    "\n",
+    "Ruang jari lebih lega\n",
+    "\n",
+    "Cocok untuk:\n",
+    "\n",
+    "kaki lebar\n",
+    "\n",
+    "pelari yang sering merasa jari tertekan\n",
+    "\n",
+    "Trade-off: bisa terasa kurang “locked-in” bagi kaki sempit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e9b17fa9",
+   "metadata": {},
+   "source": [
+    "# Cleaning Stiffness"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "f11dfa24",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Stiffness (4 uniques): \n",
+      " ['-', 'Flexible', 'Moderate', 'Stiff']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Stiffness\n",
+       "Moderate    530\n",
+       "Stiff       510\n",
+       "Flexible    114\n",
+       "-            16\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Stiffness\"] = (\n",
+    "    df[\"Stiffness\"]\n",
+    "    .astype(str)\n",
+    "    .str.strip()\n",
+    "    .str.replace(r\"\\s+\", \" \", regex=True)\n",
+    "    .str.title()\n",
+    ")\n",
+    "\n",
+    "stiffness_uniques = df[\"Stiffness\"].dropna().unique()\n",
+    "# stiffness_uniques.sort()\n",
+    "stiffness_uniques = sorted(stiffness_uniques)\n",
+    "print(f\"Stiffness ({len(stiffness_uniques)} uniques): \\n\",stiffness_uniques)\n",
+    "\n",
+    "df[\"Stiffness\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "047339b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Pace</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>breath_warm</th>\n",
+       "      <th>width_narrow</th>\n",
+       "      <th>width_medium</th>\n",
+       "      <th>width_wide</th>\n",
+       "      <th>toeboxwidth_narrow</th>\n",
+       "      <th>toeboxwidth_medium</th>\n",
+       "      <th>toeboxwidth_wide</th>\n",
+       "      <th>stiff_flexible</th>\n",
+       "      <th>stiff_moderate</th>\n",
+       "      <th>stiff_stiff</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Launch 9</td>\n",
+       "      <td>87\\n Great!</td>\n",
+       "      <td>$110</td>\n",
+       "      <td>Daily Runningtempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>7.9 oz / 225g 8.1 oz / 230g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.4 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Levitate 6</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$150</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 304g 10.9 oz / 309g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.7 mm 8.0 mm</td>\n",
+       "      <td>Mid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>11.9 oz / 336g 11.5 oz / 327g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 2</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.6 oz / 356g 12.4 oz / 352g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.6 mm 11.0 mm</td>\n",
+       "      <td>Heel</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 3</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.3 oz / 348g 12.2 oz / 345g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 75 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand         Name Audience score Price                Pace Arch support  \\\n",
+       "0  Brooks     Launch 9    87\\n Great!  $110  Daily Runningtempo      Neutral   \n",
+       "1  Brooks   Levitate 6   90\\n Superb!  $150       Daily Running      Neutral   \n",
+       "2  Adidas        4DFWD   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "3  Adidas      4DFWD 2   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "4  Adidas      4DFWD 3    88\\n Great!  $200       Daily Running      Neutral   \n",
+       "\n",
+       "         Weight lab Weight brand  Lightweight Drop lab Drop brand  \\\n",
+       "0    7.9 oz / 225g 8.1 oz / 230g            1      9.4 mm 10.0 mm   \n",
+       "1  10.7 oz / 304g 10.9 oz / 309g            0       7.7 mm 8.0 mm   \n",
+       "2  11.9 oz / 336g 11.5 oz / 327g            0      8.9 mm 10.0 mm   \n",
+       "3  12.6 oz / 356g 12.4 oz / 352g            0     10.6 mm 11.0 mm   \n",
+       "4  12.3 oz / 348g 12.2 oz / 345g            0      9.9 mm 10.0 mm   \n",
+       "\n",
+       "     Strike pattern  ... breath_warm width_narrow width_medium width_wide  \\\n",
+       "0  Heelmid/Forefoot  ...           0            1            0          0   \n",
+       "1      Mid/Forefoot  ...           0            1            0          0   \n",
+       "2  Heelmid/Forefoot  ...           1            1            0          0   \n",
+       "3              Heel  ...           1            1            0          0   \n",
+       "4  Heelmid/Forefoot  ...           1            1            0          0   \n",
+       "\n",
+       "  toeboxwidth_narrow toeboxwidth_medium toeboxwidth_wide stiff_flexible  \\\n",
+       "0                  0                  0                0              0   \n",
+       "1                  0                  1                0              0   \n",
+       "2                  0                  0                0              0   \n",
+       "3                  0                  0                0              0   \n",
+       "4                  0                  1                0              0   \n",
+       "\n",
+       "  stiff_moderate stiff_stiff  \n",
+       "0              0           1  \n",
+       "1              0           1  \n",
+       "2              0           1  \n",
+       "3              0           1  \n",
+       "4              1           0  \n",
+       "\n",
+       "[5 rows x 75 columns]"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stiff_ohe = pd.get_dummies(df[\"Stiffness\"], prefix=\"stiff\").astype(int)\n",
+    "\n",
+    "for col in [\"stiff_Flexible\", \"stiff_Moderate\", \"stiff_Stiff\"]:\n",
+    "    if col not in stiff_ohe.columns:\n",
+    "        stiff_ohe[col] = 0\n",
+    "\n",
+    "stiff_ohe = stiff_ohe.rename(columns={\n",
+    "    \"stiff_Flexible\": \"stiff_flexible\",\n",
+    "    \"stiff_Moderate\": \"stiff_moderate\",\n",
+    "    \"stiff_Stiff\": \"stiff_stiff\"\n",
+    "})\n",
+    "\n",
+    "df = pd.concat([df, stiff_ohe[[\"stiff_flexible\",\"stiff_moderate\",\"stiff_stiff\"]]], axis=1)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "3a78b017",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "stiff_flexible    114\n",
+      "stiff_moderate    530\n",
+      "stiff_stiff       510\n",
+      "dtype: int64\n",
+      "Missing stiffness rows: 16\n"
+     ]
+    }
+   ],
+   "source": [
+    "# hanya 0 / 1\n",
+    "for c in [\"stiff_flexible\",\"stiff_moderate\",\"stiff_stiff\"]:\n",
+    "    assert set(df[c].unique()).issubset({0, 1})\n",
+    "\n",
+    "# single-label atau missing\n",
+    "assert df[[\"stiff_flexible\",\"stiff_moderate\",\"stiff_stiff\"]].sum(axis=1).le(1).all()\n",
+    "\n",
+    "print(df[[\"stiff_flexible\",\"stiff_moderate\",\"stiff_stiff\"]].sum())\n",
+    "print(\n",
+    "    \"Missing stiffness rows:\",\n",
+    "    int((df[[\"stiff_flexible\",\"stiff_moderate\",\"stiff_stiff\"]].sum(axis=1) == 0).sum())\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f6bd750",
+   "metadata": {},
+   "source": [
+    "### Analisis Stiffness\n",
+    "Stiffness menggambarkan tingkat kekakuan sepatu secara keseluruhan, terutama saat sepatu ditekuk atau diberi beban.\n",
+    "Fitur ini memengaruhi fleksibilitas langkah, responsivitas, dan kenyamanan.\n",
+    "\n",
+    "Berbeda dengan torsional rigidity atau heel counter stiffness, stiffness di sini bersifat global.\n",
+    "\n",
+    "Flexible\n",
+    "\n",
+    "Sepatu mudah ditekuk\n",
+    "\n",
+    "Memberikan rasa:\n",
+    "\n",
+    "natural\n",
+    "\n",
+    "bebas\n",
+    "\n",
+    "Cocok untuk:\n",
+    "\n",
+    "easy run\n",
+    "\n",
+    "pelari yang suka feel santai\n",
+    "\n",
+    "Trade-off: stabilitas & responsivitas lebih rendah\n",
+    "\n",
+    "Moderate\n",
+    "\n",
+    "Kekakuan seimbang\n",
+    "\n",
+    "Cocok untuk:\n",
+    "\n",
+    "daily running\n",
+    "\n",
+    "penggunaan serbaguna\n",
+    "\n",
+    "Catatan: ini kategori paling aman & umum\n",
+    "\n",
+    "Stiff\n",
+    "\n",
+    "Sepatu kaku dan stabil\n",
+    "\n",
+    "Cocok untuk:\n",
+    "\n",
+    "tempo run\n",
+    "\n",
+    "sepatu dengan plate\n",
+    "\n",
+    "lari cepat\n",
+    "\n",
+    "Trade-off: kurang nyaman untuk pace santai atau jarak jauh"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "01d8345b",
+   "metadata": {},
+   "source": [
+    "# Cleaning Torsional rigidity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "03cdb82f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Torsional rigidity (4 uniques): \n",
+      " ['-', 'Flexible', 'Moderate', 'Stiff']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Torsional rigidity\n",
+       "Stiff       610\n",
+       "Moderate    368\n",
+       "Flexible    174\n",
+       "-            18\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Torsional rigidity\"] = (\n",
+    "    df[\"Torsional rigidity\"]\n",
+    "    .astype(str)\n",
+    "    .str.strip()\n",
+    "    .str.replace(r\"\\s+\", \" \", regex=True)\n",
+    "    .str.title()\n",
+    ")\n",
+    "\n",
+    "torsional_rigidity_uniques = df[\"Torsional rigidity\"].dropna().unique()\n",
+    "# torsional_rigidity_uniques.sort()\n",
+    "torsional_rigidity_uniques = sorted(torsional_rigidity_uniques)\n",
+    "print(f\"Torsional rigidity ({len(torsional_rigidity_uniques)} uniques): \\n\",torsional_rigidity_uniques)\n",
+    "\n",
+    "df[\"Torsional rigidity\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "34f7fa79",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Pace</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>width_wide</th>\n",
+       "      <th>toeboxwidth_narrow</th>\n",
+       "      <th>toeboxwidth_medium</th>\n",
+       "      <th>toeboxwidth_wide</th>\n",
+       "      <th>stiff_flexible</th>\n",
+       "      <th>stiff_moderate</th>\n",
+       "      <th>stiff_stiff</th>\n",
+       "      <th>torsion_flexible</th>\n",
+       "      <th>torsion_moderate</th>\n",
+       "      <th>torsion_stiff</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Launch 9</td>\n",
+       "      <td>87\\n Great!</td>\n",
+       "      <td>$110</td>\n",
+       "      <td>Daily Runningtempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>7.9 oz / 225g 8.1 oz / 230g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.4 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Levitate 6</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$150</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 304g 10.9 oz / 309g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.7 mm 8.0 mm</td>\n",
+       "      <td>Mid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>11.9 oz / 336g 11.5 oz / 327g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 2</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.6 oz / 356g 12.4 oz / 352g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.6 mm 11.0 mm</td>\n",
+       "      <td>Heel</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 3</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.3 oz / 348g 12.2 oz / 345g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 78 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand         Name Audience score Price                Pace Arch support  \\\n",
+       "0  Brooks     Launch 9    87\\n Great!  $110  Daily Runningtempo      Neutral   \n",
+       "1  Brooks   Levitate 6   90\\n Superb!  $150       Daily Running      Neutral   \n",
+       "2  Adidas        4DFWD   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "3  Adidas      4DFWD 2   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "4  Adidas      4DFWD 3    88\\n Great!  $200       Daily Running      Neutral   \n",
+       "\n",
+       "         Weight lab Weight brand  Lightweight Drop lab Drop brand  \\\n",
+       "0    7.9 oz / 225g 8.1 oz / 230g            1      9.4 mm 10.0 mm   \n",
+       "1  10.7 oz / 304g 10.9 oz / 309g            0       7.7 mm 8.0 mm   \n",
+       "2  11.9 oz / 336g 11.5 oz / 327g            0      8.9 mm 10.0 mm   \n",
+       "3  12.6 oz / 356g 12.4 oz / 352g            0     10.6 mm 11.0 mm   \n",
+       "4  12.3 oz / 348g 12.2 oz / 345g            0      9.9 mm 10.0 mm   \n",
+       "\n",
+       "     Strike pattern  ... width_wide toeboxwidth_narrow toeboxwidth_medium  \\\n",
+       "0  Heelmid/Forefoot  ...          0                  0                  0   \n",
+       "1      Mid/Forefoot  ...          0                  0                  1   \n",
+       "2  Heelmid/Forefoot  ...          0                  0                  0   \n",
+       "3              Heel  ...          0                  0                  0   \n",
+       "4  Heelmid/Forefoot  ...          0                  0                  1   \n",
+       "\n",
+       "  toeboxwidth_wide stiff_flexible stiff_moderate stiff_stiff torsion_flexible  \\\n",
+       "0                0              0              0           1                0   \n",
+       "1                0              0              0           1                0   \n",
+       "2                0              0              0           1                1   \n",
+       "3                0              0              0           1                1   \n",
+       "4                0              0              1           0                1   \n",
+       "\n",
+       "  torsion_moderate torsion_stiff  \n",
+       "0                0             1  \n",
+       "1                1             0  \n",
+       "2                0             0  \n",
+       "3                0             0  \n",
+       "4                0             0  \n",
+       "\n",
+       "[5 rows x 78 columns]"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tors_ohe = pd.get_dummies(df[\"Torsional rigidity\"], prefix=\"torsion\").astype(int)\n",
+    "\n",
+    "for col in [\"torsion_Flexible\", \"torsion_Moderate\", \"torsion_Stiff\"]:\n",
+    "    if col not in tors_ohe.columns:\n",
+    "        tors_ohe[col] = 0\n",
+    "\n",
+    "tors_ohe = tors_ohe.rename(columns={\n",
+    "    \"torsion_Flexible\": \"torsion_flexible\",\n",
+    "    \"torsion_Moderate\": \"torsion_moderate\",\n",
+    "    \"torsion_Stiff\": \"torsion_stiff\"\n",
+    "})\n",
+    "\n",
+    "df = pd.concat([df, tors_ohe[[\"torsion_flexible\",\"torsion_moderate\",\"torsion_stiff\"]]], axis=1)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "ef8e64fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torsion_flexible    174\n",
+      "torsion_moderate    368\n",
+      "torsion_stiff       610\n",
+      "dtype: int64\n",
+      "Missing torsional rigidity rows: 18\n"
+     ]
+    }
+   ],
+   "source": [
+    "# hanya 0 / 1\n",
+    "for c in [\"torsion_flexible\",\"torsion_moderate\",\"torsion_stiff\"]:\n",
+    "    assert set(df[c].unique()).issubset({0, 1})\n",
+    "\n",
+    "# single-label atau missing\n",
+    "assert df[[\"torsion_flexible\",\"torsion_moderate\",\"torsion_stiff\"]].sum(axis=1).le(1).all()\n",
+    "\n",
+    "print(df[[\"torsion_flexible\",\"torsion_moderate\",\"torsion_stiff\"]].sum())\n",
+    "print(\n",
+    "    \"Missing torsional rigidity rows:\",\n",
+    "    int((df[[\"torsion_flexible\",\"torsion_moderate\",\"torsion_stiff\"]].sum(axis=1) == 0).sum())\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3236dda7",
+   "metadata": {},
+   "source": [
+    "### Analisis Torsional Rigidity\n",
+    "\n",
+    "Torsional rigidity menggambarkan seberapa sulit sepatu dipuntir (twist) dari depan ke belakang.\n",
+    "Fitur ini berhubungan dengan stabilitas lateral, kontrol kaki, dan dukungan saat mendarat.\n",
+    "\n",
+    "Berbeda dengan overall stiffness (tekuk depan-belakang), torsional rigidity fokus pada puntiran samping.\n",
+    "\n",
+    "Flexible\n",
+    "\n",
+    "Sepatu mudah dipuntir\n",
+    "\n",
+    "Memberikan feel:\n",
+    "\n",
+    "natural\n",
+    "\n",
+    "bebas\n",
+    "\n",
+    "Cocok untuk:\n",
+    "\n",
+    "pelari dengan gait stabil\n",
+    "\n",
+    "easy run\n",
+    "\n",
+    "Trade-off: stabilitas lebih rendah\n",
+    "\n",
+    "Moderate\n",
+    "\n",
+    "Tingkat puntiran seimbang\n",
+    "\n",
+    "Cocok untuk:\n",
+    "\n",
+    "daily running\n",
+    "\n",
+    "mayoritas pelari\n",
+    "\n",
+    "Catatan: kategori paling aman & umum\n",
+    "\n",
+    "Stiff\n",
+    "\n",
+    "Sepatu sulit dipuntir\n",
+    "\n",
+    "Memberikan:\n",
+    "\n",
+    "stabilitas tinggi\n",
+    "\n",
+    "kontrol tambahan\n",
+    "\n",
+    "Cocok untuk:\n",
+    "\n",
+    "pelari yang butuh support\n",
+    "\n",
+    "sepatu berstruktur / plated\n",
+    "\n",
+    "Trade-off: feel lebih kaku"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d8a7b505",
+   "metadata": {},
+   "source": [
+    "# Cleaning Heel counter stiffness"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "b617da2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Heel counter stiffness (4 uniques): \n",
+      " ['-', 'Flexible', 'Moderate', 'Stiff']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Heel counter stiffness\n",
+       "Moderate    419\n",
+       "Stiff       368\n",
+       "Flexible    351\n",
+       "-            32\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Heel counter stiffness\"] = (\n",
+    "    df[\"Heel counter stiffness\"]\n",
+    "    .astype(str)\n",
+    "    .str.strip()\n",
+    "    .str.replace(r\"\\s+\", \" \", regex=True)\n",
+    "    .str.title()\n",
+    ")\n",
+    "\n",
+    "heel_counter_stiffness_uniques = df[\"Heel counter stiffness\"].dropna().unique()\n",
+    "# heel_counter_stiffness_uniques.sort()\n",
+    "heel_counter_stiffness_uniques = sorted(heel_counter_stiffness_uniques)\n",
+    "print(f\"Heel counter stiffness ({len(heel_counter_stiffness_uniques)} uniques): \\n\",heel_counter_stiffness_uniques)\n",
+    "\n",
+    "df[\"Heel counter stiffness\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "id": "f477af46",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Pace</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>toeboxwidth_wide</th>\n",
+       "      <th>stiff_flexible</th>\n",
+       "      <th>stiff_moderate</th>\n",
+       "      <th>stiff_stiff</th>\n",
+       "      <th>torsion_flexible</th>\n",
+       "      <th>torsion_moderate</th>\n",
+       "      <th>torsion_stiff</th>\n",
+       "      <th>heelcounter_flexible</th>\n",
+       "      <th>heelcounter_moderate</th>\n",
+       "      <th>heelcounter_stiff</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Launch 9</td>\n",
+       "      <td>87\\n Great!</td>\n",
+       "      <td>$110</td>\n",
+       "      <td>Daily Runningtempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>7.9 oz / 225g 8.1 oz / 230g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.4 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Levitate 6</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$150</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 304g 10.9 oz / 309g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.7 mm 8.0 mm</td>\n",
+       "      <td>Mid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>11.9 oz / 336g 11.5 oz / 327g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 2</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.6 oz / 356g 12.4 oz / 352g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.6 mm 11.0 mm</td>\n",
+       "      <td>Heel</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 3</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.3 oz / 348g 12.2 oz / 345g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 81 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand         Name Audience score Price                Pace Arch support  \\\n",
+       "0  Brooks     Launch 9    87\\n Great!  $110  Daily Runningtempo      Neutral   \n",
+       "1  Brooks   Levitate 6   90\\n Superb!  $150       Daily Running      Neutral   \n",
+       "2  Adidas        4DFWD   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "3  Adidas      4DFWD 2   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "4  Adidas      4DFWD 3    88\\n Great!  $200       Daily Running      Neutral   \n",
+       "\n",
+       "         Weight lab Weight brand  Lightweight Drop lab Drop brand  \\\n",
+       "0    7.9 oz / 225g 8.1 oz / 230g            1      9.4 mm 10.0 mm   \n",
+       "1  10.7 oz / 304g 10.9 oz / 309g            0       7.7 mm 8.0 mm   \n",
+       "2  11.9 oz / 336g 11.5 oz / 327g            0      8.9 mm 10.0 mm   \n",
+       "3  12.6 oz / 356g 12.4 oz / 352g            0     10.6 mm 11.0 mm   \n",
+       "4  12.3 oz / 348g 12.2 oz / 345g            0      9.9 mm 10.0 mm   \n",
+       "\n",
+       "     Strike pattern  ... toeboxwidth_wide stiff_flexible stiff_moderate  \\\n",
+       "0  Heelmid/Forefoot  ...                0              0              0   \n",
+       "1      Mid/Forefoot  ...                0              0              0   \n",
+       "2  Heelmid/Forefoot  ...                0              0              0   \n",
+       "3              Heel  ...                0              0              0   \n",
+       "4  Heelmid/Forefoot  ...                0              0              1   \n",
+       "\n",
+       "  stiff_stiff torsion_flexible torsion_moderate torsion_stiff  \\\n",
+       "0           1                0                0             1   \n",
+       "1           1                0                1             0   \n",
+       "2           1                1                0             0   \n",
+       "3           1                1                0             0   \n",
+       "4           0                1                0             0   \n",
+       "\n",
+       "  heelcounter_flexible heelcounter_moderate heelcounter_stiff  \n",
+       "0                    1                    0                 0  \n",
+       "1                    0                    1                 0  \n",
+       "2                    1                    0                 0  \n",
+       "3                    0                    1                 0  \n",
+       "4                    1                    0                 0  \n",
+       "\n",
+       "[5 rows x 81 columns]"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hc_ohe = pd.get_dummies(df[\"Heel counter stiffness\"], prefix=\"heelcounter\").astype(int)\n",
+    "\n",
+    "for col in [\"heelcounter_Flexible\", \"heelcounter_Moderate\", \"heelcounter_Stiff\"]:\n",
+    "    if col not in hc_ohe.columns:\n",
+    "        hc_ohe[col] = 0\n",
+    "\n",
+    "hc_ohe = hc_ohe.rename(columns={\n",
+    "    \"heelcounter_Flexible\": \"heelcounter_flexible\",\n",
+    "    \"heelcounter_Moderate\": \"heelcounter_moderate\",\n",
+    "    \"heelcounter_Stiff\": \"heelcounter_stiff\"\n",
+    "})\n",
+    "\n",
+    "df = pd.concat([df, hc_ohe[[\"heelcounter_flexible\",\"heelcounter_moderate\",\"heelcounter_stiff\"]]], axis=1)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "9b91e5e5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "heelcounter_flexible    351\n",
+      "heelcounter_moderate    419\n",
+      "heelcounter_stiff       368\n",
+      "dtype: int64\n",
+      "Missing heel counter rows: 32\n"
+     ]
+    }
+   ],
+   "source": [
+    "# hanya 0 / 1\n",
+    "for c in [\"heelcounter_flexible\",\"heelcounter_moderate\",\"heelcounter_stiff\"]:\n",
+    "    assert set(df[c].unique()).issubset({0, 1})\n",
+    "\n",
+    "# single-label atau missing\n",
+    "assert df[[\"heelcounter_flexible\",\"heelcounter_moderate\",\"heelcounter_stiff\"]].sum(axis=1).le(1).all()\n",
+    "\n",
+    "print(df[[\"heelcounter_flexible\",\"heelcounter_moderate\",\"heelcounter_stiff\"]].sum())\n",
+    "print(\n",
+    "    \"Missing heel counter rows:\",\n",
+    "    int((df[[\"heelcounter_flexible\",\"heelcounter_moderate\",\"heelcounter_stiff\"]].sum(axis=1) == 0).sum())\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0528c1ca",
+   "metadata": {},
+   "source": [
+    "### Analisis Heel Counter Stiffness\n",
+    "\n",
+    "Heel counter stiffness menggambarkan tingkat kekakuan struktur di bagian tumit belakang sepatu (heel counter), yang berfungsi menjaga tumit tetap stabil dan terkunci saat berlari.\n",
+    "\n",
+    "Fitur ini berpengaruh pada stabilitas tumit, kontrol kaki, dan rasa aman saat mendarat, terutama untuk pelari heel strike atau yang membutuhkan support tambahan.\n",
+    "\n",
+    "Flexible\n",
+    "\n",
+    "Heel counter mudah ditekan\n",
+    "\n",
+    "Memberikan rasa:\n",
+    "\n",
+    "lebih nyaman\n",
+    "\n",
+    "lebih natural\n",
+    "\n",
+    "Cocok untuk:\n",
+    "\n",
+    "pelari dengan gait stabil\n",
+    "\n",
+    "sepatu santai / fleksibel\n",
+    "\n",
+    "Trade-off: stabilitas tumit lebih rendah\n",
+    "\n",
+    "Moderate\n",
+    "\n",
+    "Kekakuan seimbang\n",
+    "\n",
+    "Cocok untuk:\n",
+    "\n",
+    "daily running\n",
+    "\n",
+    "mayoritas pelari\n",
+    "\n",
+    "Catatan: ini kategori paling aman & umum\n",
+    "\n",
+    "Stiff\n",
+    "\n",
+    "Heel counter kaku dan kokoh\n",
+    "\n",
+    "Memberikan:\n",
+    "\n",
+    "stabilitas tumit tinggi\n",
+    "\n",
+    "rasa “locked-in”\n",
+    "\n",
+    "Cocok untuk:\n",
+    "\n",
+    "pelari yang butuh support\n",
+    "\n",
+    "sepatu berstruktur / plated\n",
+    "\n",
+    "Trade-off: bisa terasa kurang nyaman bagi sebagian orang"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "515d8b22",
+   "metadata": {},
+   "source": [
+    "# Cleaning Widths available"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "3b3f7bec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Widths available (11 uniques): \n",
+      " ['Narrow Normal Wide X-Wide', 'Narrownormal', 'Narrownormalwide', 'Narrownormalwidex-Wide', 'Narrownormalx-Wide', 'Normal', 'Normal Wide', 'Normal Wide X-Wide', 'Normalwide', 'Normalwidex-Wide', 'Normalx-Wide']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Widths available\n",
+       "Normal                       537\n",
+       "Normalwide                   353\n",
+       "Normalwidex-Wide             166\n",
+       "Narrownormalwidex-Wide        68\n",
+       "Narrownormal                  19\n",
+       "Normalx-Wide                  18\n",
+       "Narrownormalwide               4\n",
+       "Normal Wide X-Wide             2\n",
+       "Narrow Normal Wide X-Wide      1\n",
+       "Normal Wide                    1\n",
+       "Narrownormalx-Wide             1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Widths available\"] = (\n",
+    "    df[\"Widths available\"]\n",
+    "    .astype(str)\n",
+    "    .str.strip()\n",
+    "    .str.replace(r\"\\s+\", \" \", regex=True)\n",
+    "    .str.title()\n",
+    ")\n",
+    "\n",
+    "widths_available_uniques = df[\"Widths available\"].dropna().unique()\n",
+    "# widths_available_uniques.sort()\n",
+    "widths_available_uniques = sorted(widths_available_uniques)\n",
+    "print(f\"Widths available ({len(widths_available_uniques)} uniques): \\n\",widths_available_uniques)\n",
+    "\n",
+    "df[\"Widths available\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "23a2a180",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "UNMAPPED Widths available values:\n",
+      " Series([], Name: count, dtype: int64)\n"
+     ]
+    }
+   ],
+   "source": [
+    "wa_map = {\n",
+    "    \"Normal\": \"Normal\",\n",
+    "    \"Normalwide\": \"Normal|Wide\",\n",
+    "    \"Normalwidex-Wide\": \"Normal|Wide|X-Wide\",\n",
+    "    \"Narrownormalwidex-Wide\": \"Narrow|Normal|Wide|X-Wide\",\n",
+    "    \"Normalx-Wide\": \"Normal|X-Wide\",\n",
+    "    \"Narrownormal\": \"Narrow|Normal\",\n",
+    "    \"Narrownormalwide\": \"Narrow|Normal|Wide\",\n",
+    "    \"Normal Wide X-Wide\": \"Normal|Wide|X-Wide\",\n",
+    "    \"Narrow Normal Wide X-Wide\": \"Narrow|Normal|Wide|X-Wide\",\n",
+    "    \"Normal Wide\": \"Normal|Wide\",\n",
+    "    \"Narrownormalx-Wide\": \"Narrow|Normal|X-Wide\",\n",
+    "}\n",
+    "\n",
+    "df[\"widthavail_norm\"] = df[\"Widths available\"].map(wa_map)\n",
+    "\n",
+    "unmapped = df[df[\"widthavail_norm\"].isna()][\"Widths available\"].value_counts()\n",
+    "print(\"UNMAPPED Widths available values:\\n\", unmapped)\n",
+    "assert df[\"widthavail_norm\"].notna().all()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "211dba05",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Widths available - norm: (8) uniques\n",
+      " <StringArray>\n",
+      "[              'Normal|Wide',                    'Normal',\n",
+      " 'Narrow|Normal|Wide|X-Wide',             'Normal|X-Wide',\n",
+      "        'Narrow|Normal|Wide',        'Normal|Wide|X-Wide',\n",
+      "             'Narrow|Normal',      'Narrow|Normal|X-Wide']\n",
+      "Length: 8, dtype: str\n",
+      "widthavail_norm\n",
+      "Normal                       537\n",
+      "Normal|Wide                  354\n",
+      "Normal|Wide|X-Wide           168\n",
+      "Narrow|Normal|Wide|X-Wide     69\n",
+      "Narrow|Normal                 19\n",
+      "Normal|X-Wide                 18\n",
+      "Narrow|Normal|Wide             4\n",
+      "Narrow|Normal|X-Wide           1\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "wa_norm_unique = df[\"widthavail_norm\"].dropna().unique()\n",
+    "print(f'Widths available - norm: ({len(wa_norm_unique)}) uniques\\n', wa_norm_unique)\n",
+    "\n",
+    "print(df[\"widthavail_norm\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "b63b2f3e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"widthavail_list\"] = df[\"widthavail_norm\"].str.split(\"|\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "17dee763",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Pace</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>torsion_stiff</th>\n",
+       "      <th>heelcounter_flexible</th>\n",
+       "      <th>heelcounter_moderate</th>\n",
+       "      <th>heelcounter_stiff</th>\n",
+       "      <th>widthavail_norm</th>\n",
+       "      <th>widthavail_list</th>\n",
+       "      <th>widthavail_narrow</th>\n",
+       "      <th>widthavail_normal</th>\n",
+       "      <th>widthavail_wide</th>\n",
+       "      <th>widthavail_xwide</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Launch 9</td>\n",
+       "      <td>87\\n Great!</td>\n",
+       "      <td>$110</td>\n",
+       "      <td>Daily Runningtempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>7.9 oz / 225g 8.1 oz / 230g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.4 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Normal|Wide</td>\n",
+       "      <td>[Normal, Wide]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Levitate 6</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$150</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 304g 10.9 oz / 309g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.7 mm 8.0 mm</td>\n",
+       "      <td>Mid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>[Normal]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>11.9 oz / 336g 11.5 oz / 327g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>[Normal]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 2</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.6 oz / 356g 12.4 oz / 352g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.6 mm 11.0 mm</td>\n",
+       "      <td>Heel</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>[Normal]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 3</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.3 oz / 348g 12.2 oz / 345g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>[Normal]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 87 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand         Name Audience score Price                Pace Arch support  \\\n",
+       "0  Brooks     Launch 9    87\\n Great!  $110  Daily Runningtempo      Neutral   \n",
+       "1  Brooks   Levitate 6   90\\n Superb!  $150       Daily Running      Neutral   \n",
+       "2  Adidas        4DFWD   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "3  Adidas      4DFWD 2   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "4  Adidas      4DFWD 3    88\\n Great!  $200       Daily Running      Neutral   \n",
+       "\n",
+       "         Weight lab Weight brand  Lightweight Drop lab Drop brand  \\\n",
+       "0    7.9 oz / 225g 8.1 oz / 230g            1      9.4 mm 10.0 mm   \n",
+       "1  10.7 oz / 304g 10.9 oz / 309g            0       7.7 mm 8.0 mm   \n",
+       "2  11.9 oz / 336g 11.5 oz / 327g            0      8.9 mm 10.0 mm   \n",
+       "3  12.6 oz / 356g 12.4 oz / 352g            0     10.6 mm 11.0 mm   \n",
+       "4  12.3 oz / 348g 12.2 oz / 345g            0      9.9 mm 10.0 mm   \n",
+       "\n",
+       "     Strike pattern  ... torsion_stiff heelcounter_flexible  \\\n",
+       "0  Heelmid/Forefoot  ...             1                    1   \n",
+       "1      Mid/Forefoot  ...             0                    0   \n",
+       "2  Heelmid/Forefoot  ...             0                    1   \n",
+       "3              Heel  ...             0                    0   \n",
+       "4  Heelmid/Forefoot  ...             0                    1   \n",
+       "\n",
+       "  heelcounter_moderate heelcounter_stiff widthavail_norm widthavail_list  \\\n",
+       "0                    0                 0     Normal|Wide  [Normal, Wide]   \n",
+       "1                    1                 0          Normal        [Normal]   \n",
+       "2                    0                 0          Normal        [Normal]   \n",
+       "3                    1                 0          Normal        [Normal]   \n",
+       "4                    0                 0          Normal        [Normal]   \n",
+       "\n",
+       "  widthavail_narrow widthavail_normal widthavail_wide widthavail_xwide  \n",
+       "0                 0                 1               1                0  \n",
+       "1                 0                 1               0                0  \n",
+       "2                 0                 1               0                0  \n",
+       "3                 0                 1               0                0  \n",
+       "4                 0                 1               0                0  \n",
+       "\n",
+       "[5 rows x 87 columns]"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wa_exploded = df[\"widthavail_list\"].explode()\n",
+    "\n",
+    "wa_ohe = (\n",
+    "    pd.crosstab(wa_exploded.index, wa_exploded)\n",
+    "    .reindex(df.index, fill_value=0)\n",
+    ")\n",
+    "\n",
+    "wa_ohe = wa_ohe.rename(columns={\n",
+    "    \"Narrow\": \"widthavail_narrow\",\n",
+    "    \"Normal\": \"widthavail_normal\",\n",
+    "    \"Wide\": \"widthavail_wide\",\n",
+    "    \"X-Wide\": \"widthavail_xwide\"\n",
+    "})\n",
+    "\n",
+    "for col in [\"widthavail_narrow\", \"widthavail_normal\", \"widthavail_wide\", \"widthavail_xwide\"]:\n",
+    "    if col not in wa_ohe.columns:\n",
+    "        wa_ohe[col] = 0\n",
+    "\n",
+    "wa_ohe = wa_ohe[[\"widthavail_narrow\",\"widthavail_normal\",\"widthavail_wide\",\"widthavail_xwide\"]]\n",
+    "\n",
+    "df = pd.concat([df, wa_ohe], axis=1)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "8ba40a97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for c in [\"widthavail_narrow\",\"widthavail_normal\",\"widthavail_wide\",\"widthavail_xwide\"]:\n",
+    "    assert set(df[c].unique()).issubset({0, 1})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "id": "0d75845f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 1170\n",
+      "narrow sum: 93\n",
+      "normal sum: 1170\n",
+      "wide sum: 595\n",
+      "xwide sum: 256\n",
+      "   Widths available widthavail_norm  widthavail_normal  widthavail_wide  \\\n",
+      "0        Normalwide     Normal|Wide                  1                1   \n",
+      "7        Normalwide     Normal|Wide                  1                1   \n",
+      "10       Normalwide     Normal|Wide                  1                1   \n",
+      "11       Normalwide     Normal|Wide                  1                1   \n",
+      "12       Normalwide     Normal|Wide                  1                1   \n",
+      "13       Normalwide     Normal|Wide                  1                1   \n",
+      "14       Normalwide     Normal|Wide                  1                1   \n",
+      "15       Normalwide     Normal|Wide                  1                1   \n",
+      "16       Normalwide     Normal|Wide                  1                1   \n",
+      "17       Normalwide     Normal|Wide                  1                1   \n",
+      "\n",
+      "    widthavail_xwide  widthavail_narrow  \n",
+      "0                  0                  0  \n",
+      "7                  0                  0  \n",
+      "10                 0                  0  \n",
+      "11                 0                  0  \n",
+      "12                 0                  0  \n",
+      "13                 0                  0  \n",
+      "14                 0                  0  \n",
+      "15                 0                  0  \n",
+      "16                 0                  0  \n",
+      "17                 0                  0  \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Rows:\", len(df))\n",
+    "print(\"narrow sum:\", int(df[\"widthavail_narrow\"].sum()))\n",
+    "print(\"normal sum:\", int(df[\"widthavail_normal\"].sum()))\n",
+    "print(\"wide sum:\", int(df[\"widthavail_wide\"].sum()))\n",
+    "print(\"xwide sum:\", int(df[\"widthavail_xwide\"].sum()))\n",
+    "\n",
+    "print(df[df[\"Widths available\"].isin([\"Normalwide\",\"Normalwidex-Wide\"])][\n",
+    "    [\"Widths available\",\"widthavail_norm\",\"widthavail_normal\",\"widthavail_wide\",\"widthavail_xwide\",\"widthavail_narrow\"]\n",
+    "].head(10))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a93dbbd",
+   "metadata": {},
+   "source": [
+    "### Analisis Widths Available\n",
+    "\n",
+    "Widths available menjelaskan opsi lebar yang tersedia untuk model sepatu tersebut (bukan “fit feel”), misalnya hanya Normal, atau tersedia juga Wide dan X-Wide.\n",
+    "\n",
+    "Berbeda dengan Width / fit yang menggambarkan rasa lebar sepatu secara umum, Widths available itu varian produk yang dijual.\n",
+    "\n",
+    "Kategori yang relevan\n",
+    "\n",
+    "Narrow: opsi lebar sempit tersedia\n",
+    "\n",
+    "Normal: opsi standar tersedia\n",
+    "\n",
+    "Wide: opsi lebar tersedia\n",
+    "\n",
+    "X-Wide: opsi ekstra lebar tersedia"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c875704",
+   "metadata": {},
+   "source": [
+    "# Cleaning Season"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "id": "cde921aa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Season (4 uniques): \n",
+      " ['-', 'All Seasons', 'Summerall Seasons', 'Winter']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Season\n",
+       "All Seasons          741\n",
+       "Summerall Seasons    347\n",
+       "-                     63\n",
+       "Winter                19\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Season\"] = (\n",
+    "    df[\"Season\"]\n",
+    "    .astype(str)\n",
+    "    .str.strip()\n",
+    "    .str.replace(r\"\\s+\", \" \", regex=True)\n",
+    "    .str.title()\n",
+    ")\n",
+    "\n",
+    "season_uniques = df[\"Season\"].dropna().unique()\n",
+    "# season_uniques.sort()\n",
+    "season_uniques = sorted(season_uniques)\n",
+    "print(f\"Season ({len(season_uniques)} uniques): \\n\",season_uniques)\n",
+    "\n",
+    "df[\"Season\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "id": "0e512dee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "season_map = {\n",
+    "    \"All Seasons\": \"All\",\n",
+    "    \"Summerall Seasons\": \"Summer|All\",\n",
+    "    \"Winter\": \"Winter\",\n",
+    "    \"-\": pd.NA,\n",
+    "}\n",
+    "\n",
+    "df[\"season_norm\"] = df[\"Season\"].map(season_map)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "id": "5652f560",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "UNMAPPED Season values (should be empty):\n",
+      " Series([], Name: count, dtype: int64)\n"
+     ]
+    }
+   ],
+   "source": [
+    "unmapped = df[df[\"season_norm\"].isna() & df[\"Season\"].ne(\"-\")][\"Season\"].value_counts()\n",
+    "print(\"UNMAPPED Season values (should be empty):\\n\", unmapped)\n",
+    "assert unmapped.empty"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "id": "5ec9108f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Season - norm: (4) uniques\n",
+      " <StringArray>\n",
+      "[nan, 'Summer|All', 'All', 'Winter']\n",
+      "Length: 4, dtype: str\n",
+      "season_norm\n",
+      "All           741\n",
+      "Summer|All    347\n",
+      "Winter         19\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "season_norm_unique = df[\"season_norm\"].unique()\n",
+    "print(f'Season - norm: ({len(season_norm_unique)}) uniques\\n', season_norm_unique)\n",
+    "print(df[\"season_norm\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "id": "1face7ad",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Pace</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>widthavail_list</th>\n",
+       "      <th>widthavail_narrow</th>\n",
+       "      <th>widthavail_normal</th>\n",
+       "      <th>widthavail_wide</th>\n",
+       "      <th>widthavail_xwide</th>\n",
+       "      <th>season_norm</th>\n",
+       "      <th>season_list</th>\n",
+       "      <th>season_all</th>\n",
+       "      <th>season_summer</th>\n",
+       "      <th>season_winter</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Launch 9</td>\n",
+       "      <td>87\\n Great!</td>\n",
+       "      <td>$110</td>\n",
+       "      <td>Daily Runningtempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>7.9 oz / 225g 8.1 oz / 230g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.4 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[Normal, Wide]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Levitate 6</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$150</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 304g 10.9 oz / 309g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.7 mm 8.0 mm</td>\n",
+       "      <td>Mid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[Normal]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Summer|All</td>\n",
+       "      <td>[Summer, All]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>11.9 oz / 336g 11.5 oz / 327g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[Normal]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>All</td>\n",
+       "      <td>[All]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 2</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.6 oz / 356g 12.4 oz / 352g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.6 mm 11.0 mm</td>\n",
+       "      <td>Heel</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[Normal]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>All</td>\n",
+       "      <td>[All]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 3</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.3 oz / 348g 12.2 oz / 345g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[Normal]</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>All</td>\n",
+       "      <td>[All]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 92 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand         Name Audience score Price                Pace Arch support  \\\n",
+       "0  Brooks     Launch 9    87\\n Great!  $110  Daily Runningtempo      Neutral   \n",
+       "1  Brooks   Levitate 6   90\\n Superb!  $150       Daily Running      Neutral   \n",
+       "2  Adidas        4DFWD   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "3  Adidas      4DFWD 2   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "4  Adidas      4DFWD 3    88\\n Great!  $200       Daily Running      Neutral   \n",
+       "\n",
+       "         Weight lab Weight brand  Lightweight Drop lab Drop brand  \\\n",
+       "0    7.9 oz / 225g 8.1 oz / 230g            1      9.4 mm 10.0 mm   \n",
+       "1  10.7 oz / 304g 10.9 oz / 309g            0       7.7 mm 8.0 mm   \n",
+       "2  11.9 oz / 336g 11.5 oz / 327g            0      8.9 mm 10.0 mm   \n",
+       "3  12.6 oz / 356g 12.4 oz / 352g            0     10.6 mm 11.0 mm   \n",
+       "4  12.3 oz / 348g 12.2 oz / 345g            0      9.9 mm 10.0 mm   \n",
+       "\n",
+       "     Strike pattern  ... widthavail_list widthavail_narrow widthavail_normal  \\\n",
+       "0  Heelmid/Forefoot  ...  [Normal, Wide]                 0                 1   \n",
+       "1      Mid/Forefoot  ...        [Normal]                 0                 1   \n",
+       "2  Heelmid/Forefoot  ...        [Normal]                 0                 1   \n",
+       "3              Heel  ...        [Normal]                 0                 1   \n",
+       "4  Heelmid/Forefoot  ...        [Normal]                 0                 1   \n",
+       "\n",
+       "  widthavail_wide widthavail_xwide season_norm    season_list season_all  \\\n",
+       "0               1                0         NaN            NaN          0   \n",
+       "1               0                0  Summer|All  [Summer, All]          1   \n",
+       "2               0                0         All          [All]          1   \n",
+       "3               0                0         All          [All]          1   \n",
+       "4               0                0         All          [All]          1   \n",
+       "\n",
+       "  season_summer season_winter  \n",
+       "0             0             0  \n",
+       "1             1             0  \n",
+       "2             0             0  \n",
+       "3             0             0  \n",
+       "4             0             0  \n",
+       "\n",
+       "[5 rows x 92 columns]"
+      ]
+     },
+     "execution_count": 113,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"season_list\"] = df[\"season_norm\"].str.split(\"|\")\n",
+    "\n",
+    "season_exploded = df[\"season_list\"].explode()\n",
+    "\n",
+    "season_ohe = (\n",
+    "    pd.crosstab(season_exploded.index, season_exploded)\n",
+    "    .reindex(df.index, fill_value=0)\n",
+    ")\n",
+    "\n",
+    "season_ohe = season_ohe.rename(columns={\n",
+    "    \"All\": \"season_all\",\n",
+    "    \"Summer\": \"season_summer\",\n",
+    "    \"Winter\": \"season_winter\"\n",
+    "})\n",
+    "\n",
+    "for col in [\"season_all\",\"season_summer\",\"season_winter\"]:\n",
+    "    if col not in season_ohe.columns:\n",
+    "        season_ohe[col] = 0\n",
+    "\n",
+    "season_ohe = season_ohe[[\"season_all\",\"season_summer\",\"season_winter\"]]\n",
+    "\n",
+    "df = pd.concat([df, season_ohe], axis=1)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a18946b6",
+   "metadata": {},
+   "source": [
+    "jujur janggal temen temen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "id": "c1515be9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 1170\n",
+      "all seasons sum: 1088\n",
+      "summer sum: 347\n",
+      "winter sum: 19\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Rows:\", len(df))\n",
+    "print(\"all seasons sum:\", int(df[\"season_all\"].sum()))\n",
+    "print(\"summer sum:\", int(df[\"season_summer\"].sum()))\n",
+    "print(\"winter sum:\", int(df[\"season_winter\"].sum()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "id": "80f95641",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "               Season season_norm  season_all  season_summer  season_winter\n",
+      "1   Summerall Seasons  Summer|All           1              1              0\n",
+      "7   Summerall Seasons  Summer|All           1              1              0\n",
+      "9   Summerall Seasons  Summer|All           1              1              0\n",
+      "10  Summerall Seasons  Summer|All           1              1              0\n",
+      "11  Summerall Seasons  Summer|All           1              1              0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[df[\"Season\"]==\"Summerall Seasons\"][\n",
+    "    [\"Season\",\"season_norm\",\"season_all\",\"season_summer\",\"season_winter\"]\n",
+    "].head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8a94b15",
+   "metadata": {},
+   "source": [
+    "# Split Weight lab Weight brand"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "id": "cf9c609f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(0)"
+      ]
+     },
+     "execution_count": 116,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Cek \"-\" dan null\n",
+    "mask_missing = (\n",
+    "    df[\"Weight lab Weight brand\"].isna() |\n",
+    "    (df[\"Weight lab Weight brand\"].astype(str).str.strip() == \"-\")\n",
+    ")\n",
+    "mask_missing.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "id": "0ff4d1f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Pace</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>widthavail_xwide</th>\n",
+       "      <th>season_norm</th>\n",
+       "      <th>season_list</th>\n",
+       "      <th>season_all</th>\n",
+       "      <th>season_summer</th>\n",
+       "      <th>season_winter</th>\n",
+       "      <th>weight_lab_oz</th>\n",
+       "      <th>weight_lab_g</th>\n",
+       "      <th>weight_brand_oz</th>\n",
+       "      <th>weight_brand_g</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Launch 9</td>\n",
+       "      <td>87\\n Great!</td>\n",
+       "      <td>$110</td>\n",
+       "      <td>Daily Runningtempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>7.9 oz / 225g 8.1 oz / 230g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.4 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.9</td>\n",
+       "      <td>225</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>230.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Levitate 6</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$150</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 304g 10.9 oz / 309g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.7 mm 8.0 mm</td>\n",
+       "      <td>Mid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Summer|All</td>\n",
+       "      <td>[Summer, All]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.7</td>\n",
+       "      <td>304</td>\n",
+       "      <td>10.9</td>\n",
+       "      <td>309.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>11.9 oz / 336g 11.5 oz / 327g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>All</td>\n",
+       "      <td>[All]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>11.9</td>\n",
+       "      <td>336</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>327.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 2</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.6 oz / 356g 12.4 oz / 352g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.6 mm 11.0 mm</td>\n",
+       "      <td>Heel</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>All</td>\n",
+       "      <td>[All]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12.6</td>\n",
+       "      <td>356</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>352.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 3</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.3 oz / 348g 12.2 oz / 345g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>All</td>\n",
+       "      <td>[All]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12.3</td>\n",
+       "      <td>348</td>\n",
+       "      <td>12.2</td>\n",
+       "      <td>345.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 96 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand         Name Audience score Price                Pace Arch support  \\\n",
+       "0  Brooks     Launch 9    87\\n Great!  $110  Daily Runningtempo      Neutral   \n",
+       "1  Brooks   Levitate 6   90\\n Superb!  $150       Daily Running      Neutral   \n",
+       "2  Adidas        4DFWD   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "3  Adidas      4DFWD 2   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "4  Adidas      4DFWD 3    88\\n Great!  $200       Daily Running      Neutral   \n",
+       "\n",
+       "         Weight lab Weight brand  Lightweight Drop lab Drop brand  \\\n",
+       "0    7.9 oz / 225g 8.1 oz / 230g            1      9.4 mm 10.0 mm   \n",
+       "1  10.7 oz / 304g 10.9 oz / 309g            0       7.7 mm 8.0 mm   \n",
+       "2  11.9 oz / 336g 11.5 oz / 327g            0      8.9 mm 10.0 mm   \n",
+       "3  12.6 oz / 356g 12.4 oz / 352g            0     10.6 mm 11.0 mm   \n",
+       "4  12.3 oz / 348g 12.2 oz / 345g            0      9.9 mm 10.0 mm   \n",
+       "\n",
+       "     Strike pattern  ... widthavail_xwide season_norm    season_list  \\\n",
+       "0  Heelmid/Forefoot  ...                0         NaN            NaN   \n",
+       "1      Mid/Forefoot  ...                0  Summer|All  [Summer, All]   \n",
+       "2  Heelmid/Forefoot  ...                0         All          [All]   \n",
+       "3              Heel  ...                0         All          [All]   \n",
+       "4  Heelmid/Forefoot  ...                0         All          [All]   \n",
+       "\n",
+       "  season_all season_summer season_winter weight_lab_oz weight_lab_g  \\\n",
+       "0          0             0             0           7.9          225   \n",
+       "1          1             1             0          10.7          304   \n",
+       "2          1             0             0          11.9          336   \n",
+       "3          1             0             0          12.6          356   \n",
+       "4          1             0             0          12.3          348   \n",
+       "\n",
+       "  weight_brand_oz weight_brand_g  \n",
+       "0             8.1          230.0  \n",
+       "1            10.9          309.0  \n",
+       "2            11.5          327.0  \n",
+       "3            12.4          352.0  \n",
+       "4            12.2          345.0  \n",
+       "\n",
+       "[5 rows x 96 columns]"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Ambil angka pakai regex\n",
+    "weight = df[\"Weight lab Weight brand\"].str.findall(r\"[\\d.]+\")\n",
+    "\n",
+    "# Ubah jadi 4 kolom\n",
+    "df[[\"weight_lab_oz\", \"weight_lab_g\", \"weight_brand_oz\", \"weight_brand_g\"]] = (\n",
+    "    pd.DataFrame(weight.tolist(), index=df.index)\n",
+    ")\n",
+    "\n",
+    "# ubah ke numeric\n",
+    "for col in [\"weight_lab_oz\", \"weight_lab_g\", \"weight_brand_oz\", \"weight_brand_g\"]:\n",
+    "    df[col] = pd.to_numeric(df[col], errors=\"coerce\")\n",
+    "\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ac8090cb",
+   "metadata": {},
+   "source": [
+    "# Split Drop lab Drop Brand"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "id": "07739c09",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(0)"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Cek \"-\" dan null\n",
+    "mask_missing = (\n",
+    "    df[\"Drop lab Drop brand\"].isna() |\n",
+    "    (df[\"Drop lab Drop brand\"].astype(str).str.strip() == \"-\")\n",
+    ")\n",
+    "mask_missing.sum()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "id": "3b2af74a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Pace</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>season_list</th>\n",
+       "      <th>season_all</th>\n",
+       "      <th>season_summer</th>\n",
+       "      <th>season_winter</th>\n",
+       "      <th>weight_lab_oz</th>\n",
+       "      <th>weight_lab_g</th>\n",
+       "      <th>weight_brand_oz</th>\n",
+       "      <th>weight_brand_g</th>\n",
+       "      <th>drop_lab_mm</th>\n",
+       "      <th>drop_brand_mm</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Launch 9</td>\n",
+       "      <td>87\\n Great!</td>\n",
+       "      <td>$110</td>\n",
+       "      <td>Daily Runningtempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>7.9 oz / 225g 8.1 oz / 230g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.4 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.9</td>\n",
+       "      <td>225</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>230.0</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Levitate 6</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$150</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 304g 10.9 oz / 309g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.7 mm 8.0 mm</td>\n",
+       "      <td>Mid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[Summer, All]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.7</td>\n",
+       "      <td>304</td>\n",
+       "      <td>10.9</td>\n",
+       "      <td>309.0</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>11.9 oz / 336g 11.5 oz / 327g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[All]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>11.9</td>\n",
+       "      <td>336</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>327.0</td>\n",
+       "      <td>8.9</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 2</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.6 oz / 356g 12.4 oz / 352g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.6 mm 11.0 mm</td>\n",
+       "      <td>Heel</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[All]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12.6</td>\n",
+       "      <td>356</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>352.0</td>\n",
+       "      <td>10.6</td>\n",
+       "      <td>11.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 3</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.3 oz / 348g 12.2 oz / 345g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>[All]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12.3</td>\n",
+       "      <td>348</td>\n",
+       "      <td>12.2</td>\n",
+       "      <td>345.0</td>\n",
+       "      <td>9.9</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 98 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand         Name Audience score Price                Pace Arch support  \\\n",
+       "0  Brooks     Launch 9    87\\n Great!  $110  Daily Runningtempo      Neutral   \n",
+       "1  Brooks   Levitate 6   90\\n Superb!  $150       Daily Running      Neutral   \n",
+       "2  Adidas        4DFWD   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "3  Adidas      4DFWD 2   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "4  Adidas      4DFWD 3    88\\n Great!  $200       Daily Running      Neutral   \n",
+       "\n",
+       "         Weight lab Weight brand  Lightweight Drop lab Drop brand  \\\n",
+       "0    7.9 oz / 225g 8.1 oz / 230g            1      9.4 mm 10.0 mm   \n",
+       "1  10.7 oz / 304g 10.9 oz / 309g            0       7.7 mm 8.0 mm   \n",
+       "2  11.9 oz / 336g 11.5 oz / 327g            0      8.9 mm 10.0 mm   \n",
+       "3  12.6 oz / 356g 12.4 oz / 352g            0     10.6 mm 11.0 mm   \n",
+       "4  12.3 oz / 348g 12.2 oz / 345g            0      9.9 mm 10.0 mm   \n",
+       "\n",
+       "     Strike pattern  ...    season_list season_all season_summer  \\\n",
+       "0  Heelmid/Forefoot  ...            NaN          0             0   \n",
+       "1      Mid/Forefoot  ...  [Summer, All]          1             1   \n",
+       "2  Heelmid/Forefoot  ...          [All]          1             0   \n",
+       "3              Heel  ...          [All]          1             0   \n",
+       "4  Heelmid/Forefoot  ...          [All]          1             0   \n",
+       "\n",
+       "  season_winter weight_lab_oz weight_lab_g weight_brand_oz weight_brand_g  \\\n",
+       "0             0           7.9          225             8.1          230.0   \n",
+       "1             0          10.7          304            10.9          309.0   \n",
+       "2             0          11.9          336            11.5          327.0   \n",
+       "3             0          12.6          356            12.4          352.0   \n",
+       "4             0          12.3          348            12.2          345.0   \n",
+       "\n",
+       "  drop_lab_mm drop_brand_mm  \n",
+       "0         9.4          10.0  \n",
+       "1         7.7           8.0  \n",
+       "2         8.9          10.0  \n",
+       "3        10.6          11.0  \n",
+       "4         9.9          10.0  \n",
+       "\n",
+       "[5 rows x 98 columns]"
+      ]
+     },
+     "execution_count": 119,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Ambil angka pakai regex\n",
+    "drop = df[\"Drop lab Drop brand\"].str.findall(r\"[\\d.]+\")\n",
+    "\n",
+    "# Ubah jadi 2 kolom\n",
+    "df[[\"drop_lab_mm\", \"drop_brand_mm\"]] = (\n",
+    "    pd.DataFrame(drop.tolist(), index=df.index)\n",
+    ")\n",
+    "\n",
+    "# ubah ke numeric\n",
+    "for col in [\"drop_lab_mm\", \"drop_brand_mm\"]:\n",
+    "    df[col] = pd.to_numeric(df[col], errors=\"coerce\")\n",
+    "\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "id": "4e164df8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# df = df.drop(labels=\"drop_brand\", axis=1)\n",
+    "# df = df.drop(labels=\"drop_lab\", axis=1)\n",
+    "# df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bda80a11",
+   "metadata": {},
+   "source": [
+    "# Split Heel lab dan Heel brand"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "id": "27cb847d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(0)"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Cek \"-\" dan null\n",
+    "mask_missing = (\n",
+    "    df[\"Heel lab Heel brand\"].isna() |\n",
+    "    (df[\"Heel lab Heel brand\"].astype(str).str.strip() == \"-\")\n",
+    ")\n",
+    "mask_missing.sum()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "id": "dacdb07a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Pace</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>season_summer</th>\n",
+       "      <th>season_winter</th>\n",
+       "      <th>weight_lab_oz</th>\n",
+       "      <th>weight_lab_g</th>\n",
+       "      <th>weight_brand_oz</th>\n",
+       "      <th>weight_brand_g</th>\n",
+       "      <th>drop_lab_mm</th>\n",
+       "      <th>drop_brand_mm</th>\n",
+       "      <th>heel_lab_mm</th>\n",
+       "      <th>heel_brand_mm</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Launch 9</td>\n",
+       "      <td>87\\n Great!</td>\n",
+       "      <td>$110</td>\n",
+       "      <td>Daily Runningtempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>7.9 oz / 225g 8.1 oz / 230g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.4 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.9</td>\n",
+       "      <td>225</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>230.0</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>32.4</td>\n",
+       "      <td>36.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Levitate 6</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$150</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 304g 10.9 oz / 309g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.7 mm 8.0 mm</td>\n",
+       "      <td>Mid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.7</td>\n",
+       "      <td>304</td>\n",
+       "      <td>10.9</td>\n",
+       "      <td>309.0</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>34.3</td>\n",
+       "      <td>32.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>11.9 oz / 336g 11.5 oz / 327g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>11.9</td>\n",
+       "      <td>336</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>327.0</td>\n",
+       "      <td>8.9</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>33.3</td>\n",
+       "      <td>32.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 2</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.6 oz / 356g 12.4 oz / 352g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.6 mm 11.0 mm</td>\n",
+       "      <td>Heel</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12.6</td>\n",
+       "      <td>356</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>352.0</td>\n",
+       "      <td>10.6</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>31.8</td>\n",
+       "      <td>32.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 3</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.3 oz / 348g 12.2 oz / 345g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12.3</td>\n",
+       "      <td>348</td>\n",
+       "      <td>12.2</td>\n",
+       "      <td>345.0</td>\n",
+       "      <td>9.9</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>32.6</td>\n",
+       "      <td>34.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 100 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand         Name Audience score Price                Pace Arch support  \\\n",
+       "0  Brooks     Launch 9    87\\n Great!  $110  Daily Runningtempo      Neutral   \n",
+       "1  Brooks   Levitate 6   90\\n Superb!  $150       Daily Running      Neutral   \n",
+       "2  Adidas        4DFWD   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "3  Adidas      4DFWD 2   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "4  Adidas      4DFWD 3    88\\n Great!  $200       Daily Running      Neutral   \n",
+       "\n",
+       "         Weight lab Weight brand  Lightweight Drop lab Drop brand  \\\n",
+       "0    7.9 oz / 225g 8.1 oz / 230g            1      9.4 mm 10.0 mm   \n",
+       "1  10.7 oz / 304g 10.9 oz / 309g            0       7.7 mm 8.0 mm   \n",
+       "2  11.9 oz / 336g 11.5 oz / 327g            0      8.9 mm 10.0 mm   \n",
+       "3  12.6 oz / 356g 12.4 oz / 352g            0     10.6 mm 11.0 mm   \n",
+       "4  12.3 oz / 348g 12.2 oz / 345g            0      9.9 mm 10.0 mm   \n",
+       "\n",
+       "     Strike pattern  ... season_summer season_winter weight_lab_oz  \\\n",
+       "0  Heelmid/Forefoot  ...             0             0           7.9   \n",
+       "1      Mid/Forefoot  ...             1             0          10.7   \n",
+       "2  Heelmid/Forefoot  ...             0             0          11.9   \n",
+       "3              Heel  ...             0             0          12.6   \n",
+       "4  Heelmid/Forefoot  ...             0             0          12.3   \n",
+       "\n",
+       "  weight_lab_g weight_brand_oz weight_brand_g drop_lab_mm drop_brand_mm  \\\n",
+       "0          225             8.1          230.0         9.4          10.0   \n",
+       "1          304            10.9          309.0         7.7           8.0   \n",
+       "2          336            11.5          327.0         8.9          10.0   \n",
+       "3          356            12.4          352.0        10.6          11.0   \n",
+       "4          348            12.2          345.0         9.9          10.0   \n",
+       "\n",
+       "  heel_lab_mm heel_brand_mm  \n",
+       "0        32.4          36.0  \n",
+       "1        34.3          32.5  \n",
+       "2        33.3          32.5  \n",
+       "3        31.8          32.0  \n",
+       "4        32.6          34.0  \n",
+       "\n",
+       "[5 rows x 100 columns]"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Ambil angka pakai regex\n",
+    "Heel = df[\"Heel lab Heel brand\"].str.findall(r\"[\\d.]+\")\n",
+    "\n",
+    "# Ubah jadi 2 kolom\n",
+    "df[[\"heel_lab_mm\", \"heel_brand_mm\"]] = (\n",
+    "    pd.DataFrame(Heel.tolist(), index=df.index)\n",
+    ")\n",
+    "\n",
+    "# ubah ke numeric\n",
+    "for col in [\"heel_lab_mm\", \"heel_brand_mm\"]:\n",
+    "    df[col] = pd.to_numeric(df[col], errors=\"coerce\")\n",
+    "\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a5facbd7",
+   "metadata": {},
+   "source": [
+    "# Split Forefoot lab dan Forefoot brand\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "id": "0fca0f24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(0)"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Cek \"-\" dan null\n",
+    "mask_missing = (\n",
+    "    df[\"Forefoot lab Forefoot brand\"].isna() |\n",
+    "    (df[\"Forefoot lab Forefoot brand\"].astype(str).str.strip() == \"-\")\n",
+    ")\n",
+    "mask_missing.sum()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "id": "dcd7f1b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Pace</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>weight_lab_oz</th>\n",
+       "      <th>weight_lab_g</th>\n",
+       "      <th>weight_brand_oz</th>\n",
+       "      <th>weight_brand_g</th>\n",
+       "      <th>drop_lab_mm</th>\n",
+       "      <th>drop_brand_mm</th>\n",
+       "      <th>heel_lab_mm</th>\n",
+       "      <th>heel_brand_mm</th>\n",
+       "      <th>forefoot_lab_mm</th>\n",
+       "      <th>forefoot_brand_mm</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Launch 9</td>\n",
+       "      <td>87\\n Great!</td>\n",
+       "      <td>$110</td>\n",
+       "      <td>Daily Runningtempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>7.9 oz / 225g 8.1 oz / 230g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.4 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7.9</td>\n",
+       "      <td>225</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>230.0</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>32.4</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>26.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Levitate 6</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$150</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 304g 10.9 oz / 309g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.7 mm 8.0 mm</td>\n",
+       "      <td>Mid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10.7</td>\n",
+       "      <td>304</td>\n",
+       "      <td>10.9</td>\n",
+       "      <td>309.0</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>34.3</td>\n",
+       "      <td>32.5</td>\n",
+       "      <td>26.6</td>\n",
+       "      <td>24.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>11.9 oz / 336g 11.5 oz / 327g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>11.9</td>\n",
+       "      <td>336</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>327.0</td>\n",
+       "      <td>8.9</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>33.3</td>\n",
+       "      <td>32.5</td>\n",
+       "      <td>24.4</td>\n",
+       "      <td>22.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 2</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.6 oz / 356g 12.4 oz / 352g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.6 mm 11.0 mm</td>\n",
+       "      <td>Heel</td>\n",
+       "      <td>...</td>\n",
+       "      <td>12.6</td>\n",
+       "      <td>356</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>352.0</td>\n",
+       "      <td>10.6</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>31.8</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>21.2</td>\n",
+       "      <td>21.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 3</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.3 oz / 348g 12.2 oz / 345g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>12.3</td>\n",
+       "      <td>348</td>\n",
+       "      <td>12.2</td>\n",
+       "      <td>345.0</td>\n",
+       "      <td>9.9</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>32.6</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>22.7</td>\n",
+       "      <td>24.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 102 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand         Name Audience score Price                Pace Arch support  \\\n",
+       "0  Brooks     Launch 9    87\\n Great!  $110  Daily Runningtempo      Neutral   \n",
+       "1  Brooks   Levitate 6   90\\n Superb!  $150       Daily Running      Neutral   \n",
+       "2  Adidas        4DFWD   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "3  Adidas      4DFWD 2   90\\n Superb!  $200       Daily Running      Neutral   \n",
+       "4  Adidas      4DFWD 3    88\\n Great!  $200       Daily Running      Neutral   \n",
+       "\n",
+       "         Weight lab Weight brand  Lightweight Drop lab Drop brand  \\\n",
+       "0    7.9 oz / 225g 8.1 oz / 230g            1      9.4 mm 10.0 mm   \n",
+       "1  10.7 oz / 304g 10.9 oz / 309g            0       7.7 mm 8.0 mm   \n",
+       "2  11.9 oz / 336g 11.5 oz / 327g            0      8.9 mm 10.0 mm   \n",
+       "3  12.6 oz / 356g 12.4 oz / 352g            0     10.6 mm 11.0 mm   \n",
+       "4  12.3 oz / 348g 12.2 oz / 345g            0      9.9 mm 10.0 mm   \n",
+       "\n",
+       "     Strike pattern  ... weight_lab_oz weight_lab_g weight_brand_oz  \\\n",
+       "0  Heelmid/Forefoot  ...           7.9          225             8.1   \n",
+       "1      Mid/Forefoot  ...          10.7          304            10.9   \n",
+       "2  Heelmid/Forefoot  ...          11.9          336            11.5   \n",
+       "3              Heel  ...          12.6          356            12.4   \n",
+       "4  Heelmid/Forefoot  ...          12.3          348            12.2   \n",
+       "\n",
+       "  weight_brand_g drop_lab_mm drop_brand_mm heel_lab_mm heel_brand_mm  \\\n",
+       "0          230.0         9.4          10.0        32.4          36.0   \n",
+       "1          309.0         7.7           8.0        34.3          32.5   \n",
+       "2          327.0         8.9          10.0        33.3          32.5   \n",
+       "3          352.0        10.6          11.0        31.8          32.0   \n",
+       "4          345.0         9.9          10.0        32.6          34.0   \n",
+       "\n",
+       "  forefoot_lab_mm forefoot_brand_mm  \n",
+       "0            23.0              26.0  \n",
+       "1            26.6              24.5  \n",
+       "2            24.4              22.5  \n",
+       "3            21.2              21.0  \n",
+       "4            22.7              24.0  \n",
+       "\n",
+       "[5 rows x 102 columns]"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Ambil angka pakai regex\n",
+    "forefoot = df[\"Forefoot lab Forefoot brand\"].str.findall(r\"[\\d.]+\")\n",
+    "\n",
+    "# Ubah jadi 2 kolom\n",
+    "df[[\"forefoot_lab_mm\", \"forefoot_brand_mm\"]] = (\n",
+    "    pd.DataFrame(forefoot.tolist(), index=df.index)\n",
+    ")\n",
+    "\n",
+    "# ubah ke numeric\n",
+    "for col in [\"forefoot_lab_mm\", \"forefoot_brand_mm\"]:\n",
+    "    df[col] = pd.to_numeric(df[col], errors=\"coerce\")\n",
+    "\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "id": "7ccecc50",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"Audience score\"] = (\n",
+    "    df[\"Audience score\"]\n",
+    "    .astype(str)\n",
+    "    .str.replace(\"\\n\", \" - \", regex=False)\n",
+    "    .str.strip()\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19dd690d",
+   "metadata": {},
+   "source": [
+    "# Remove duplicates"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4f09526",
+   "metadata": {},
+   "source": [
+    "cuma ada 433 shoes road"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "id": "7559acf1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Adistar 3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Adistar 3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Adistar 3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Adistar 3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>163</th>\n",
+       "      <td>On</td>\n",
+       "      <td>Cloudflyer 5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>164</th>\n",
+       "      <td>On</td>\n",
+       "      <td>Cloudflyer 5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>166</th>\n",
+       "      <td>On</td>\n",
+       "      <td>Cloudgo</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>169</th>\n",
+       "      <td>On</td>\n",
+       "      <td>Cloudmonster 2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>170</th>\n",
+       "      <td>On</td>\n",
+       "      <td>Cloudmonster 2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Brand            Name\n",
+       "5    Adidas         4DFWD 3\n",
+       "11   Adidas       Adistar 3\n",
+       "12   Adidas       Adistar 3\n",
+       "13   Adidas       Adistar 3\n",
+       "14   Adidas       Adistar 3\n",
+       "..      ...             ...\n",
+       "163      On    Cloudflyer 5\n",
+       "164      On    Cloudflyer 5\n",
+       "166      On         Cloudgo\n",
+       "169      On  Cloudmonster 2\n",
+       "170      On  Cloudmonster 2\n",
+       "\n",
+       "[100 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dup_mask = df.duplicated(subset=[\"Brand\", \"Name\"], keep=\"first\")\n",
+    "# print(len(dup_mask))\n",
+    "df.loc[dup_mask, [\"Brand\", \"Name\"]].head(100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "id": "4e91ab51",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Before: 1170\n",
+      "After : 434\n"
+     ]
+    }
+   ],
+   "source": [
+    "# sebelum hapus\n",
+    "print(\"Before:\", len(df))\n",
+    "\n",
+    "#hapus\n",
+    "df = df.drop_duplicates(subset=[\"Brand\", \"Name\"], keep=\"first\").reset_index(drop=True)\n",
+    "\n",
+    "print(\"After :\", len(df))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "id": "783d9c01",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [Brand, Name]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dup_mask = df.duplicated(subset=[\"Brand\", \"Name\"], keep=\"first\")\n",
+    "df.loc[dup_mask, [\"Brand\", \"Name\"]].head(20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1873def0",
+   "metadata": {},
+   "source": [
+    "udah ga ada duplicate, sisa 433 sepatu"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a094cf88",
+   "metadata": {},
+   "source": [
+    "# Done cleaned"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "id": "079b45bd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 434 entries, 0 to 433\n",
+      "Columns: 102 entries, Brand to forefoot_brand_mm\n",
+      "dtypes: float64(9), int64(55), object(4), str(34)\n",
+      "memory usage: 346.0+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "813168be",
+   "metadata": {},
+   "source": [
+    "kenapa ada int ya"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "id": "82f15329",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Pace</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>weight_lab_oz</th>\n",
+       "      <th>weight_lab_g</th>\n",
+       "      <th>weight_brand_oz</th>\n",
+       "      <th>weight_brand_g</th>\n",
+       "      <th>drop_lab_mm</th>\n",
+       "      <th>drop_brand_mm</th>\n",
+       "      <th>heel_lab_mm</th>\n",
+       "      <th>heel_brand_mm</th>\n",
+       "      <th>forefoot_lab_mm</th>\n",
+       "      <th>forefoot_brand_mm</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Launch 9</td>\n",
+       "      <td>87 -&nbsp;&nbsp;Great!</td>\n",
+       "      <td>$110</td>\n",
+       "      <td>Daily Runningtempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>7.9 oz / 225g 8.1 oz / 230g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.4 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7.9</td>\n",
+       "      <td>225</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>230.0</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>32.4</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>26.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Levitate 6</td>\n",
+       "      <td>90 -&nbsp;&nbsp;Superb!</td>\n",
+       "      <td>$150</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 304g 10.9 oz / 309g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.7 mm 8.0 mm</td>\n",
+       "      <td>Mid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10.7</td>\n",
+       "      <td>304</td>\n",
+       "      <td>10.9</td>\n",
+       "      <td>309.0</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>34.3</td>\n",
+       "      <td>32.5</td>\n",
+       "      <td>26.6</td>\n",
+       "      <td>24.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD</td>\n",
+       "      <td>90 -&nbsp;&nbsp;Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>11.9 oz / 336g 11.5 oz / 327g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>11.9</td>\n",
+       "      <td>336</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>327.0</td>\n",
+       "      <td>8.9</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>33.3</td>\n",
+       "      <td>32.5</td>\n",
+       "      <td>24.4</td>\n",
+       "      <td>22.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 2</td>\n",
+       "      <td>90 -&nbsp;&nbsp;Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.6 oz / 356g 12.4 oz / 352g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.6 mm 11.0 mm</td>\n",
+       "      <td>Heel</td>\n",
+       "      <td>...</td>\n",
+       "      <td>12.6</td>\n",
+       "      <td>356</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>352.0</td>\n",
+       "      <td>10.6</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>31.8</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>21.2</td>\n",
+       "      <td>21.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 3</td>\n",
+       "      <td>88 -&nbsp;&nbsp;Great!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.3 oz / 348g 12.2 oz / 345g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>12.3</td>\n",
+       "      <td>348</td>\n",
+       "      <td>12.2</td>\n",
+       "      <td>345.0</td>\n",
+       "      <td>9.9</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>32.6</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>22.7</td>\n",
+       "      <td>24.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 102 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand         Name Audience score Price                Pace Arch support  \\\n",
+       "0  Brooks     Launch 9   87 -  Great!  $110  Daily Runningtempo      Neutral   \n",
+       "1  Brooks   Levitate 6  90 -  Superb!  $150       Daily Running      Neutral   \n",
+       "2  Adidas        4DFWD  90 -  Superb!  $200       Daily Running      Neutral   \n",
+       "3  Adidas      4DFWD 2  90 -  Superb!  $200       Daily Running      Neutral   \n",
+       "4  Adidas      4DFWD 3   88 -  Great!  $200       Daily Running      Neutral   \n",
+       "\n",
+       "         Weight lab Weight brand  Lightweight Drop lab Drop brand  \\\n",
+       "0    7.9 oz / 225g 8.1 oz / 230g            1      9.4 mm 10.0 mm   \n",
+       "1  10.7 oz / 304g 10.9 oz / 309g            0       7.7 mm 8.0 mm   \n",
+       "2  11.9 oz / 336g 11.5 oz / 327g            0      8.9 mm 10.0 mm   \n",
+       "3  12.6 oz / 356g 12.4 oz / 352g            0     10.6 mm 11.0 mm   \n",
+       "4  12.3 oz / 348g 12.2 oz / 345g            0      9.9 mm 10.0 mm   \n",
+       "\n",
+       "     Strike pattern  ... weight_lab_oz weight_lab_g weight_brand_oz  \\\n",
+       "0  Heelmid/Forefoot  ...           7.9          225             8.1   \n",
+       "1      Mid/Forefoot  ...          10.7          304            10.9   \n",
+       "2  Heelmid/Forefoot  ...          11.9          336            11.5   \n",
+       "3              Heel  ...          12.6          356            12.4   \n",
+       "4  Heelmid/Forefoot  ...          12.3          348            12.2   \n",
+       "\n",
+       "  weight_brand_g drop_lab_mm drop_brand_mm heel_lab_mm heel_brand_mm  \\\n",
+       "0          230.0         9.4          10.0        32.4          36.0   \n",
+       "1          309.0         7.7           8.0        34.3          32.5   \n",
+       "2          327.0         8.9          10.0        33.3          32.5   \n",
+       "3          352.0        10.6          11.0        31.8          32.0   \n",
+       "4          345.0         9.9          10.0        32.6          34.0   \n",
+       "\n",
+       "  forefoot_lab_mm forefoot_brand_mm  \n",
+       "0            23.0              26.0  \n",
+       "1            26.6              24.5  \n",
+       "2            24.4              22.5  \n",
+       "3            21.2              21.0  \n",
+       "4            22.7              24.0  \n",
+       "\n",
+       "[5 rows x 102 columns]"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "id": "2832aee5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "           Weight lab Weight brand  weight_lab_oz  weight_lab_g  \\\n",
+      "0      7.9 oz / 225g 8.1 oz / 230g            7.9           225   \n",
+      "1    10.7 oz / 304g 10.9 oz / 309g           10.7           304   \n",
+      "2    11.9 oz / 336g 11.5 oz / 327g           11.9           336   \n",
+      "3    12.6 oz / 356g 12.4 oz / 352g           12.6           356   \n",
+      "4    12.3 oz / 348g 12.2 oz / 345g           12.3           348   \n",
+      "..                             ...            ...           ...   \n",
+      "429   9.8 oz / 279g 10.1 oz / 286g            9.8           279   \n",
+      "430    8.7 oz / 248g 8.6 oz / 244g            8.7           248   \n",
+      "431   10.3 oz / 291g 9.7 oz / 274g           10.3           291   \n",
+      "432        6 oz / 171g 6 oz / 171g            6.0           171   \n",
+      "433    6.9 oz / 196g 6.9 oz / 196g            6.9           196   \n",
+      "\n",
+      "     weight_brand_oz  weight_brand_g  \n",
+      "0                8.1           230.0  \n",
+      "1               10.9           309.0  \n",
+      "2               11.5           327.0  \n",
+      "3               12.4           352.0  \n",
+      "4               12.2           345.0  \n",
+      "..               ...             ...  \n",
+      "429             10.1           286.0  \n",
+      "430              8.6           244.0  \n",
+      "431              9.7           274.0  \n",
+      "432              6.0           171.0  \n",
+      "433              6.9           196.0  \n",
+      "\n",
+      "[434 rows x 5 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[['Weight lab Weight brand', 'weight_lab_oz', 'weight_lab_g', 'weight_brand_oz',\t'weight_brand_g']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "id": "9266754f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Pace</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>weight_lab_oz</th>\n",
+       "      <th>weight_lab_g</th>\n",
+       "      <th>weight_brand_oz</th>\n",
+       "      <th>weight_brand_g</th>\n",
+       "      <th>drop_lab_mm</th>\n",
+       "      <th>drop_brand_mm</th>\n",
+       "      <th>heel_lab_mm</th>\n",
+       "      <th>heel_brand_mm</th>\n",
+       "      <th>forefoot_lab_mm</th>\n",
+       "      <th>forefoot_brand_mm</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Launch 9</td>\n",
+       "      <td>87 -&nbsp;&nbsp;Great!</td>\n",
+       "      <td>$110</td>\n",
+       "      <td>Daily Runningtempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>7.9 oz / 225g 8.1 oz / 230g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.4 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7.9</td>\n",
+       "      <td>225</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>230.0</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>32.4</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>26.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Levitate 6</td>\n",
+       "      <td>90 -&nbsp;&nbsp;Superb!</td>\n",
+       "      <td>$150</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 304g 10.9 oz / 309g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.7 mm 8.0 mm</td>\n",
+       "      <td>Mid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10.7</td>\n",
+       "      <td>304</td>\n",
+       "      <td>10.9</td>\n",
+       "      <td>309.0</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>34.3</td>\n",
+       "      <td>32.5</td>\n",
+       "      <td>26.6</td>\n",
+       "      <td>24.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD</td>\n",
+       "      <td>90 -&nbsp;&nbsp;Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>11.9 oz / 336g 11.5 oz / 327g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>11.9</td>\n",
+       "      <td>336</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>327.0</td>\n",
+       "      <td>8.9</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>33.3</td>\n",
+       "      <td>32.5</td>\n",
+       "      <td>24.4</td>\n",
+       "      <td>22.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 2</td>\n",
+       "      <td>90 -&nbsp;&nbsp;Superb!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.6 oz / 356g 12.4 oz / 352g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.6 mm 11.0 mm</td>\n",
+       "      <td>Heel</td>\n",
+       "      <td>...</td>\n",
+       "      <td>12.6</td>\n",
+       "      <td>356</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>352.0</td>\n",
+       "      <td>10.6</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>31.8</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>21.2</td>\n",
+       "      <td>21.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 3</td>\n",
+       "      <td>88 -&nbsp;&nbsp;Great!</td>\n",
+       "      <td>$200</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.3 oz / 348g 12.2 oz / 345g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>12.3</td>\n",
+       "      <td>348</td>\n",
+       "      <td>12.2</td>\n",
+       "      <td>345.0</td>\n",
+       "      <td>9.9</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>32.6</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>22.7</td>\n",
+       "      <td>24.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>428</th>\n",
+       "      <td>Nike</td>\n",
+       "      <td>Zoom Fly 4</td>\n",
+       "      <td>86 -&nbsp;&nbsp;Good!</td>\n",
+       "      <td>$160</td>\n",
+       "      <td>Tempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.6 oz / 271g 8.8 oz / 249g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.1 mm 8.0 mm</td>\n",
+       "      <td>Mid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>271</td>\n",
+       "      <td>8.8</td>\n",
+       "      <td>249.0</td>\n",
+       "      <td>7.1</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>38.4</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>31.3</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>429</th>\n",
+       "      <td>Nike</td>\n",
+       "      <td>Zoom Fly 5</td>\n",
+       "      <td>80 -&nbsp;&nbsp;Good!</td>\n",
+       "      <td>$160</td>\n",
+       "      <td>Daily Runningtempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.8 oz / 279g 10.1 oz / 286g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.5 mm 8.0 mm</td>\n",
+       "      <td>Mid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>279</td>\n",
+       "      <td>10.1</td>\n",
+       "      <td>286.0</td>\n",
+       "      <td>7.5</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>36.9</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>29.4</td>\n",
+       "      <td>33.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>430</th>\n",
+       "      <td>Nike</td>\n",
+       "      <td>Zoom Fly 6</td>\n",
+       "      <td>92 -&nbsp;&nbsp;Superb!</td>\n",
+       "      <td>$170</td>\n",
+       "      <td>Competitiontempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>8.7 oz / 248g 8.6 oz / 244g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.6 mm 8.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.7</td>\n",
+       "      <td>248</td>\n",
+       "      <td>8.6</td>\n",
+       "      <td>244.0</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>39.7</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>30.1</td>\n",
+       "      <td>32.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>431</th>\n",
+       "      <td>Nike</td>\n",
+       "      <td>ZoomX Invincible Run Flyknit 2</td>\n",
+       "      <td>86 -&nbsp;&nbsp;Good!</td>\n",
+       "      <td>$180</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.3 oz / 291g 9.7 oz / 274g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12.0 mm 9.0 mm</td>\n",
+       "      <td>Heel</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10.3</td>\n",
+       "      <td>291</td>\n",
+       "      <td>9.7</td>\n",
+       "      <td>274.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>35.5</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>23.5</td>\n",
+       "      <td>28.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>432</th>\n",
+       "      <td>Nike</td>\n",
+       "      <td>ZoomX Streakfly</td>\n",
+       "      <td>87 -&nbsp;&nbsp;Great!</td>\n",
+       "      <td>$160</td>\n",
+       "      <td>Tempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>6 oz / 171g 6 oz / 171g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6.3 mm 6.0 mm</td>\n",
+       "      <td>Mid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>171</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>171.0</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>31.7</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>25.4</td>\n",
+       "      <td>26.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>433 rows × 102 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Brand                            Name Audience score Price  \\\n",
+       "0    Brooks                        Launch 9   87 -  Great!  $110   \n",
+       "1    Brooks                      Levitate 6  90 -  Superb!  $150   \n",
+       "2    Adidas                           4DFWD  90 -  Superb!  $200   \n",
+       "3    Adidas                         4DFWD 2  90 -  Superb!  $200   \n",
+       "4    Adidas                         4DFWD 3   88 -  Great!  $200   \n",
+       "..      ...                             ...            ...   ...   \n",
+       "428    Nike                      Zoom Fly 4    86 -  Good!  $160   \n",
+       "429    Nike                      Zoom Fly 5    80 -  Good!  $160   \n",
+       "430    Nike                      Zoom Fly 6  92 -  Superb!  $170   \n",
+       "431    Nike  ZoomX Invincible Run Flyknit 2    86 -  Good!  $180   \n",
+       "432    Nike                 ZoomX Streakfly   87 -  Great!  $160   \n",
+       "\n",
+       "                   Pace Arch support        Weight lab Weight brand  \\\n",
+       "0    Daily Runningtempo      Neutral    7.9 oz / 225g 8.1 oz / 230g   \n",
+       "1         Daily Running      Neutral  10.7 oz / 304g 10.9 oz / 309g   \n",
+       "2         Daily Running      Neutral  11.9 oz / 336g 11.5 oz / 327g   \n",
+       "3         Daily Running      Neutral  12.6 oz / 356g 12.4 oz / 352g   \n",
+       "4         Daily Running      Neutral  12.3 oz / 348g 12.2 oz / 345g   \n",
+       "..                  ...          ...                            ...   \n",
+       "428               Tempo      Neutral    9.6 oz / 271g 8.8 oz / 249g   \n",
+       "429  Daily Runningtempo      Neutral   9.8 oz / 279g 10.1 oz / 286g   \n",
+       "430    Competitiontempo      Neutral    8.7 oz / 248g 8.6 oz / 244g   \n",
+       "431       Daily Running      Neutral   10.3 oz / 291g 9.7 oz / 274g   \n",
+       "432               Tempo      Neutral        6 oz / 171g 6 oz / 171g   \n",
+       "\n",
+       "     Lightweight Drop lab Drop brand    Strike pattern  ... weight_lab_oz  \\\n",
+       "0              1      9.4 mm 10.0 mm  Heelmid/Forefoot  ...           7.9   \n",
+       "1              0       7.7 mm 8.0 mm      Mid/Forefoot  ...          10.7   \n",
+       "2              0      8.9 mm 10.0 mm  Heelmid/Forefoot  ...          11.9   \n",
+       "3              0     10.6 mm 11.0 mm              Heel  ...          12.6   \n",
+       "4              0      9.9 mm 10.0 mm  Heelmid/Forefoot  ...          12.3   \n",
+       "..           ...                 ...               ...  ...           ...   \n",
+       "428            0       7.1 mm 8.0 mm      Mid/Forefoot  ...           9.6   \n",
+       "429            0       7.5 mm 8.0 mm      Mid/Forefoot  ...           9.8   \n",
+       "430            1       9.6 mm 8.0 mm  Heelmid/Forefoot  ...           8.7   \n",
+       "431            0      12.0 mm 9.0 mm              Heel  ...          10.3   \n",
+       "432            1       6.3 mm 6.0 mm      Mid/Forefoot  ...           6.0   \n",
+       "\n",
+       "    weight_lab_g weight_brand_oz weight_brand_g drop_lab_mm drop_brand_mm  \\\n",
+       "0            225             8.1          230.0         9.4          10.0   \n",
+       "1            304            10.9          309.0         7.7           8.0   \n",
+       "2            336            11.5          327.0         8.9          10.0   \n",
+       "3            356            12.4          352.0        10.6          11.0   \n",
+       "4            348            12.2          345.0         9.9          10.0   \n",
+       "..           ...             ...            ...         ...           ...   \n",
+       "428          271             8.8          249.0         7.1           8.0   \n",
+       "429          279            10.1          286.0         7.5           8.0   \n",
+       "430          248             8.6          244.0         9.6           8.0   \n",
+       "431          291             9.7          274.0        12.0           9.0   \n",
+       "432          171             6.0          171.0         6.3           6.0   \n",
+       "\n",
+       "    heel_lab_mm heel_brand_mm forefoot_lab_mm forefoot_brand_mm  \n",
+       "0          32.4          36.0            23.0              26.0  \n",
+       "1          34.3          32.5            26.6              24.5  \n",
+       "2          33.3          32.5            24.4              22.5  \n",
+       "3          31.8          32.0            21.2              21.0  \n",
+       "4          32.6          34.0            22.7              24.0  \n",
+       "..          ...           ...             ...               ...  \n",
+       "428        38.4          36.0            31.3               NaN  \n",
+       "429        36.9          41.0            29.4              33.0  \n",
+       "430        39.7          40.0            30.1              32.0  \n",
+       "431        35.5          37.0            23.5              28.0  \n",
+       "432        31.7          32.0            25.4              26.0  \n",
+       "\n",
+       "[433 rows x 102 columns]"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(433)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "id": "6a9ceda0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Pace</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>Size</th>\n",
+       "      <th>Midsole softness</th>\n",
+       "      <th>...</th>\n",
+       "      <th>weight_lab_oz</th>\n",
+       "      <th>weight_lab_g</th>\n",
+       "      <th>weight_brand_oz</th>\n",
+       "      <th>weight_brand_g</th>\n",
+       "      <th>drop_lab_mm</th>\n",
+       "      <th>drop_brand_mm</th>\n",
+       "      <th>heel_lab_mm</th>\n",
+       "      <th>heel_brand_mm</th>\n",
+       "      <th>forefoot_lab_mm</th>\n",
+       "      <th>forefoot_brand_mm</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Launch 9</td>\n",
+       "      <td>Daily Runningtempo</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>7.9 oz / 225g 8.1 oz / 230g</td>\n",
+       "      <td>1</td>\n",
+       "      <td>9.4 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>True To Size</td>\n",
+       "      <td>Balanced</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7.9</td>\n",
+       "      <td>225</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>230.0</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>32.4</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>26.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Levitate 6</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 304g 10.9 oz / 309g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.7 mm 8.0 mm</td>\n",
+       "      <td>Mid/Forefoot</td>\n",
+       "      <td>True To Size</td>\n",
+       "      <td>Soft</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10.7</td>\n",
+       "      <td>304</td>\n",
+       "      <td>10.9</td>\n",
+       "      <td>309.0</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>34.3</td>\n",
+       "      <td>32.5</td>\n",
+       "      <td>26.6</td>\n",
+       "      <td>24.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>11.9 oz / 336g 11.5 oz / 327g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>True To Size</td>\n",
+       "      <td>Firm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>11.9</td>\n",
+       "      <td>336</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>327.0</td>\n",
+       "      <td>8.9</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>33.3</td>\n",
+       "      <td>32.5</td>\n",
+       "      <td>24.4</td>\n",
+       "      <td>22.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 2</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.6 oz / 356g 12.4 oz / 352g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10.6 mm 11.0 mm</td>\n",
+       "      <td>Heel</td>\n",
+       "      <td>Slightly Small</td>\n",
+       "      <td>Firm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>12.6</td>\n",
+       "      <td>356</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>352.0</td>\n",
+       "      <td>10.6</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>31.8</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>21.2</td>\n",
+       "      <td>21.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>4DFWD 3</td>\n",
+       "      <td>Daily Running</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>12.3 oz / 348g 12.2 oz / 345g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9.9 mm 10.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>True To Size</td>\n",
+       "      <td>Firm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>12.3</td>\n",
+       "      <td>348</td>\n",
+       "      <td>12.2</td>\n",
+       "      <td>345.0</td>\n",
+       "      <td>9.9</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>32.6</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>22.7</td>\n",
+       "      <td>24.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 100 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand         Name                Pace Arch support  \\\n",
+       "0  Brooks     Launch 9  Daily Runningtempo      Neutral   \n",
+       "1  Brooks   Levitate 6       Daily Running      Neutral   \n",
+       "2  Adidas        4DFWD       Daily Running      Neutral   \n",
+       "3  Adidas      4DFWD 2       Daily Running      Neutral   \n",
+       "4  Adidas      4DFWD 3       Daily Running      Neutral   \n",
+       "\n",
+       "         Weight lab Weight brand  Lightweight Drop lab Drop brand  \\\n",
+       "0    7.9 oz / 225g 8.1 oz / 230g            1      9.4 mm 10.0 mm   \n",
+       "1  10.7 oz / 304g 10.9 oz / 309g            0       7.7 mm 8.0 mm   \n",
+       "2  11.9 oz / 336g 11.5 oz / 327g            0      8.9 mm 10.0 mm   \n",
+       "3  12.6 oz / 356g 12.4 oz / 352g            0     10.6 mm 11.0 mm   \n",
+       "4  12.3 oz / 348g 12.2 oz / 345g            0      9.9 mm 10.0 mm   \n",
+       "\n",
+       "     Strike pattern            Size Midsole softness  ... weight_lab_oz  \\\n",
+       "0  Heelmid/Forefoot    True To Size         Balanced  ...           7.9   \n",
+       "1      Mid/Forefoot    True To Size             Soft  ...          10.7   \n",
+       "2  Heelmid/Forefoot    True To Size             Firm  ...          11.9   \n",
+       "3              Heel  Slightly Small             Firm  ...          12.6   \n",
+       "4  Heelmid/Forefoot    True To Size             Firm  ...          12.3   \n",
+       "\n",
+       "  weight_lab_g weight_brand_oz weight_brand_g drop_lab_mm drop_brand_mm  \\\n",
+       "0          225             8.1          230.0         9.4          10.0   \n",
+       "1          304            10.9          309.0         7.7           8.0   \n",
+       "2          336            11.5          327.0         8.9          10.0   \n",
+       "3          356            12.4          352.0        10.6          11.0   \n",
+       "4          348            12.2          345.0         9.9          10.0   \n",
+       "\n",
+       "  heel_lab_mm heel_brand_mm forefoot_lab_mm forefoot_brand_mm  \n",
+       "0        32.4          36.0            23.0              26.0  \n",
+       "1        34.3          32.5            26.6              24.5  \n",
+       "2        33.3          32.5            24.4              22.5  \n",
+       "3        31.8          32.0            21.2              21.0  \n",
+       "4        32.6          34.0            22.7              24.0  \n",
+       "\n",
+       "[5 rows x 100 columns]"
+      ]
+     },
+     "execution_count": 138,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"Audience score\", \"Price\"], inplace=True)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "id": "154d8336",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv('../../data/road_dataset.csv', index=False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/data-preparation/pre-eda-trail.ipynb b/notebooks/data-preparation/pre-eda-trail.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..30179848a666d7a4a03fbff09a8a35a6ab521185
--- /dev/null
+++ b/notebooks/data-preparation/pre-eda-trail.ipynb
@@ -0,0 +1,9767 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2ac958fd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Trail terrain</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Heel stack lab Heel stack brand</th>\n",
+       "      <th>Forefoot lab Forefoot brand</th>\n",
+       "      <th>Widths available</th>\n",
+       "      <th>For heavy runners</th>\n",
+       "      <th>Season</th>\n",
+       "      <th>Removable insole</th>\n",
+       "      <th>Orthotic friendly</th>\n",
+       "      <th>Waterproofing</th>\n",
+       "      <th>Ranking</th>\n",
+       "      <th>Popularity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Terrex Agravic Speed Ultra</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$220</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.1 oz / 259g 9.5 oz / 270g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.3 mm 8.0 mm</td>\n",
+       "      <td>Mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>30.6 mm 38.0 mm</td>\n",
+       "      <td>30.3 mm 30.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#67 Top 19%</td>\n",
+       "      <td>#163 Top 45%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Terrex Speed Ultra</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$160</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.1 oz / 258g 9 oz / 255g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.2 mm 8.0 mm</td>\n",
+       "      <td>HeelMid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>32.8 mm 26.0 mm</td>\n",
+       "      <td>24.6 mm 18.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#45 Top 13%</td>\n",
+       "      <td>#294 Bottom 20%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Experience Wild</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$145</td>\n",
+       "      <td>LightModerate</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.1 oz / 285g 9.6 oz / 273g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4.3 mm 4.0 mm</td>\n",
+       "      <td>Mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>34.5 mm 34.0 mm</td>\n",
+       "      <td>30.2 mm 30.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#251 Top 39%</td>\n",
+       "      <td>#308 Top 48%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Experience Wild 2</td>\n",
+       "      <td>79\\n Good!</td>\n",
+       "      <td>$140</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.4 oz / 266g 10.3 oz / 293g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.1 mm 4.0 mm</td>\n",
+       "      <td>Mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>32.3 mm 32.0 mm</td>\n",
+       "      <td>26.2 mm 28.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#315 Bottom 14%</td>\n",
+       "      <td>#211 Bottom 42%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Lone Peak 5.0</td>\n",
+       "      <td>91\\n Superb!</td>\n",
+       "      <td>$130</td>\n",
+       "      <td>LightModerate</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 302g 10.6 oz / 301g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.2 mm 0.0 mm</td>\n",
+       "      <td>Mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>24.5 mm 25.0 mm</td>\n",
+       "      <td>24.3 mm 25.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#62 Top 10%</td>\n",
+       "      <td>#63 Top 10%</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 33 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand                        Name Audience score Price  Trail terrain  \\\n",
+       "0  Adidas  Terrex Agravic Speed Ultra   90\\n Superb!  $220          Light   \n",
+       "1  Adidas          Terrex Speed Ultra   90\\n Superb!  $160          Light   \n",
+       "2   Altra             Experience Wild    88\\n Great!  $145  LightModerate   \n",
+       "3   Altra           Experience Wild 2     79\\n Good!  $140          Light   \n",
+       "4   Altra               Lone Peak 5.0   91\\n Superb!  $130  LightModerate   \n",
+       "\n",
+       "  Arch support        Weight lab Weight brand Lightweight Drop lab Drop brand  \\\n",
+       "0      Neutral    9.1 oz / 259g 9.5 oz / 270g           0       0.3 mm 8.0 mm   \n",
+       "1      Neutral      9.1 oz / 258g 9 oz / 255g           0       8.2 mm 8.0 mm   \n",
+       "2      Neutral   10.1 oz / 285g 9.6 oz / 273g           0       4.3 mm 4.0 mm   \n",
+       "3      Neutral   9.4 oz / 266g 10.3 oz / 293g           0       6.1 mm 4.0 mm   \n",
+       "4      Neutral  10.7 oz / 302g 10.6 oz / 301g           0       0.2 mm 0.0 mm   \n",
+       "\n",
+       "     Strike pattern  ... Heel stack lab Heel stack brand  \\\n",
+       "0      Mid/forefoot  ...                 30.6 mm 38.0 mm   \n",
+       "1  HeelMid/forefoot  ...                 32.8 mm 26.0 mm   \n",
+       "2      Mid/forefoot  ...                 34.5 mm 34.0 mm   \n",
+       "3      Mid/forefoot  ...                 32.3 mm 32.0 mm   \n",
+       "4      Mid/forefoot  ...                 24.5 mm 25.0 mm   \n",
+       "\n",
+       "  Forefoot lab Forefoot brand Widths available For heavy runners       Season  \\\n",
+       "0             30.3 mm 30.0 mm           Normal                 0  All seasons   \n",
+       "1             24.6 mm 18.0 mm           Normal                 0            -   \n",
+       "2             30.2 mm 30.0 mm           Normal                 0  All seasons   \n",
+       "3             26.2 mm 28.0 mm           Normal                 0  All seasons   \n",
+       "4             24.3 mm 25.0 mm           Normal                 0            -   \n",
+       "\n",
+       "  Removable insole Orthotic friendly Waterproofing          Ranking  \\\n",
+       "0                1                 1             -      #67 Top 19%   \n",
+       "1                1                 1             -      #45 Top 13%   \n",
+       "2                1                 1             -     #251 Top 39%   \n",
+       "3                1                 1             -  #315 Bottom 14%   \n",
+       "4                1                 1             -      #62 Top 10%   \n",
+       "\n",
+       "        Popularity  \n",
+       "0     #163 Top 45%  \n",
+       "1  #294 Bottom 20%  \n",
+       "2     #308 Top 48%  \n",
+       "3  #211 Bottom 42%  \n",
+       "4      #63 Top 10%  \n",
+       "\n",
+       "[5 rows x 33 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.read_csv('../../data/SONIX utilities - Trail.csv')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1d5187d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 498 entries, 0 to 497\n",
+      "Data columns (total 33 columns):\n",
+      " #   Column                           Non-Null Count  Dtype\n",
+      "---  ------                           --------------  -----\n",
+      " 0   Brand                            459 non-null    str  \n",
+      " 1   Name                             460 non-null    str  \n",
+      " 2   Audience score                   458 non-null    str  \n",
+      " 3   Price                            460 non-null    str  \n",
+      " 4   Trail terrain                    460 non-null    str  \n",
+      " 5   Arch support                     461 non-null    str  \n",
+      " 6   Weight lab Weight brand          461 non-null    str  \n",
+      " 7   Lightweight                      461 non-null    str  \n",
+      " 8   Drop lab Drop brand              460 non-null    str  \n",
+      " 9   Strike pattern                   460 non-null    str  \n",
+      " 10  Size                             460 non-null    str  \n",
+      " 11  Midsole softness                 460 non-null    str  \n",
+      " 12  Plate                            460 non-null    str  \n",
+      " 13  Toebox durability                460 non-null    str  \n",
+      " 14  Heel padding durability          460 non-null    str  \n",
+      " 15  Outsole durability               460 non-null    str  \n",
+      " 16  Breathability                    460 non-null    str  \n",
+      " 17  Width / fit                      460 non-null    str  \n",
+      " 18  Toebox width                     460 non-null    str  \n",
+      " 19  Stiffness                        460 non-null    str  \n",
+      " 20  Torsional rigidity               460 non-null    str  \n",
+      " 21  Heel counter stiffness           460 non-null    str  \n",
+      " 22  Lug depth                        460 non-null    str  \n",
+      " 23  Heel stack lab Heel stack brand  460 non-null    str  \n",
+      " 24  Forefoot lab Forefoot brand      460 non-null    str  \n",
+      " 25  Widths available                 460 non-null    str  \n",
+      " 26  For heavy runners                460 non-null    str  \n",
+      " 27  Season                           460 non-null    str  \n",
+      " 28  Removable insole                 460 non-null    str  \n",
+      " 29  Orthotic friendly                460 non-null    str  \n",
+      " 30  Waterproofing                    459 non-null    str  \n",
+      " 31  Ranking                          460 non-null    str  \n",
+      " 32  Popularity                       459 non-null    str  \n",
+      "dtypes: str(33)\n",
+      "memory usage: 128.5 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7e52cec2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Trail terrain</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Heel stack lab Heel stack brand</th>\n",
+       "      <th>Forefoot lab Forefoot brand</th>\n",
+       "      <th>Widths available</th>\n",
+       "      <th>For heavy runners</th>\n",
+       "      <th>Season</th>\n",
+       "      <th>Removable insole</th>\n",
+       "      <th>Orthotic friendly</th>\n",
+       "      <th>Waterproofing</th>\n",
+       "      <th>Ranking</th>\n",
+       "      <th>Popularity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Terrex Agravic Speed Ultra</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$220</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.1 oz / 259g 9.5 oz / 270g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.3 mm 8.0 mm</td>\n",
+       "      <td>Mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>30.6 mm 38.0 mm</td>\n",
+       "      <td>30.3 mm 30.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#67 Top 19%</td>\n",
+       "      <td>#163 Top 45%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Terrex Speed Ultra</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$160</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.1 oz / 258g 9 oz / 255g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.2 mm 8.0 mm</td>\n",
+       "      <td>HeelMid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>32.8 mm 26.0 mm</td>\n",
+       "      <td>24.6 mm 18.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#45 Top 13%</td>\n",
+       "      <td>#294 Bottom 20%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Experience Wild</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$145</td>\n",
+       "      <td>LightModerate</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.1 oz / 285g 9.6 oz / 273g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4.3 mm 4.0 mm</td>\n",
+       "      <td>Mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>34.5 mm 34.0 mm</td>\n",
+       "      <td>30.2 mm 30.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#251 Top 39%</td>\n",
+       "      <td>#308 Top 48%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Experience Wild 2</td>\n",
+       "      <td>79\\n Good!</td>\n",
+       "      <td>$140</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.4 oz / 266g 10.3 oz / 293g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.1 mm 4.0 mm</td>\n",
+       "      <td>Mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>32.3 mm 32.0 mm</td>\n",
+       "      <td>26.2 mm 28.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#315 Bottom 14%</td>\n",
+       "      <td>#211 Bottom 42%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Lone Peak 5.0</td>\n",
+       "      <td>91\\n Superb!</td>\n",
+       "      <td>$130</td>\n",
+       "      <td>LightModerate</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 302g 10.6 oz / 301g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.2 mm 0.0 mm</td>\n",
+       "      <td>Mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>24.5 mm 25.0 mm</td>\n",
+       "      <td>24.3 mm 25.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#62 Top 10%</td>\n",
+       "      <td>#63 Top 10%</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 33 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand                        Name Audience score Price  Trail terrain  \\\n",
+       "0  Adidas  Terrex Agravic Speed Ultra   90\\n Superb!  $220          Light   \n",
+       "1  Adidas          Terrex Speed Ultra   90\\n Superb!  $160          Light   \n",
+       "2   Altra             Experience Wild    88\\n Great!  $145  LightModerate   \n",
+       "3   Altra           Experience Wild 2     79\\n Good!  $140          Light   \n",
+       "4   Altra               Lone Peak 5.0   91\\n Superb!  $130  LightModerate   \n",
+       "\n",
+       "  Arch support        Weight lab Weight brand Lightweight Drop lab Drop brand  \\\n",
+       "0      Neutral    9.1 oz / 259g 9.5 oz / 270g           0       0.3 mm 8.0 mm   \n",
+       "1      Neutral      9.1 oz / 258g 9 oz / 255g           0       8.2 mm 8.0 mm   \n",
+       "2      Neutral   10.1 oz / 285g 9.6 oz / 273g           0       4.3 mm 4.0 mm   \n",
+       "3      Neutral   9.4 oz / 266g 10.3 oz / 293g           0       6.1 mm 4.0 mm   \n",
+       "4      Neutral  10.7 oz / 302g 10.6 oz / 301g           0       0.2 mm 0.0 mm   \n",
+       "\n",
+       "     Strike pattern  ... Heel stack lab Heel stack brand  \\\n",
+       "0      Mid/forefoot  ...                 30.6 mm 38.0 mm   \n",
+       "1  HeelMid/forefoot  ...                 32.8 mm 26.0 mm   \n",
+       "2      Mid/forefoot  ...                 34.5 mm 34.0 mm   \n",
+       "3      Mid/forefoot  ...                 32.3 mm 32.0 mm   \n",
+       "4      Mid/forefoot  ...                 24.5 mm 25.0 mm   \n",
+       "\n",
+       "  Forefoot lab Forefoot brand Widths available For heavy runners       Season  \\\n",
+       "0             30.3 mm 30.0 mm           Normal                 0  All seasons   \n",
+       "1             24.6 mm 18.0 mm           Normal                 0            -   \n",
+       "2             30.2 mm 30.0 mm           Normal                 0  All seasons   \n",
+       "3             26.2 mm 28.0 mm           Normal                 0  All seasons   \n",
+       "4             24.3 mm 25.0 mm           Normal                 0            -   \n",
+       "\n",
+       "  Removable insole Orthotic friendly Waterproofing          Ranking  \\\n",
+       "0                1                 1             -      #67 Top 19%   \n",
+       "1                1                 1             -      #45 Top 13%   \n",
+       "2                1                 1             -     #251 Top 39%   \n",
+       "3                1                 1             -  #315 Bottom 14%   \n",
+       "4                1                 1             -      #62 Top 10%   \n",
+       "\n",
+       "        Popularity  \n",
+       "0     #163 Top 45%  \n",
+       "1  #294 Bottom 20%  \n",
+       "2     #308 Top 48%  \n",
+       "3  #211 Bottom 42%  \n",
+       "4      #63 Top 10%  \n",
+       "\n",
+       "[5 rows x 33 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8778d8f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Brand (28 unique)\n",
+      "<StringArray>\n",
+      "[        'Adidas',          'Altra',          'ASICS',         'Brooks',\n",
+      "           'Hoka',           'HOKA',           'hoka',          'Inov8',\n",
+      "           'KEEN',    'La Sportiva',        'Merrell',    'new Balance',\n",
+      "    'New Balance',           'Nike',           'nike',        'NNormal',\n",
+      "             'On',             'on',        'salomon',        'Salomon',\n",
+      "        'Saucony',        'SAucony',         'Scarpa', 'The North Face',\n",
+      "           'Topo',     'Xero Shoes',         'Kailas',         'Icebug']\n",
+      "Length: 28, dtype: str\n",
+      "\n",
+      "Trail terrain (6 unique)\n",
+      "<StringArray>\n",
+      "[            'Light',     'LightModerate', 'ModerateTechnical',\n",
+      "          'Moderate',         'Technical',     'Trail terrain']\n",
+      "Length: 6, dtype: str\n",
+      "\n",
+      "Arch support (4 unique)\n",
+      "<StringArray>\n",
+      "['Neutral', 'Stability', 'Shock absorption', 'Arch support']\n",
+      "Length: 4, dtype: str\n",
+      "\n",
+      "Lightweight (4 unique)\n",
+      "<StringArray>\n",
+      "['0', '1', 'Traction', 'Lightweight']\n",
+      "Length: 4, dtype: str\n",
+      "\n",
+      "Strike pattern (4 unique)\n",
+      "<StringArray>\n",
+      "['Mid/forefoot', 'HeelMid/forefoot', 'Heel', 'Strike pattern']\n",
+      "Length: 4, dtype: str\n",
+      "\n",
+      "Size (6 unique)\n",
+      "<StringArray>\n",
+      "[ 'Slightly large',    'True to size',               '-',  'Slightly small',\n",
+      " 'Half size small',            'Size']\n",
+      "Length: 6, dtype: str\n",
+      "\n",
+      "Midsole softness (5 unique)\n",
+      "<StringArray>\n",
+      "['Balanced', '-', 'Soft', 'Firm', 'Midsole softness']\n",
+      "Length: 5, dtype: str\n",
+      "\n",
+      "Plate (5 unique)\n",
+      "<StringArray>\n",
+      "[                                     '0',\n",
+      "                             'Rock plate',\n",
+      "                           'Carbon plate',\n",
+      " 'Difference in midsole softness in cold',\n",
+      "                                  'Plate']\n",
+      "Length: 5, dtype: str\n",
+      "\n",
+      "Toebox durability (7 unique)\n",
+      "<StringArray>\n",
+      "['Good', '-', 'Decent', 'Very good', 'Bad', 'Very bad', 'Toebox durability']\n",
+      "Length: 7, dtype: str\n",
+      "\n",
+      "Heel padding durability (5 unique)\n",
+      "<StringArray>\n",
+      "['Good', '-', 'Decent', 'Bad', 'Heel padding durability']\n",
+      "Length: 5, dtype: str\n",
+      "\n",
+      "Outsole durability (5 unique)\n",
+      "<StringArray>\n",
+      "['Decent', '-', 'Good', 'Bad', 'Outsole durability']\n",
+      "Length: 5, dtype: str\n",
+      "\n",
+      "Breathability (5 unique)\n",
+      "<StringArray>\n",
+      "['Moderate', '-', 'Warm', 'Breathable', 'Breathability']\n",
+      "Length: 5, dtype: str\n",
+      "\n",
+      "Width / fit (4 unique)\n",
+      "<StringArray>\n",
+      "['Medium', 'Narrow', 'Wide', 'Width / fit']\n",
+      "Length: 4, dtype: str\n",
+      "\n",
+      "Toebox width (5 unique)\n",
+      "<StringArray>\n",
+      "['Narrow', '-', 'Wide', 'Medium', 'Toebox width']\n",
+      "Length: 5, dtype: str\n",
+      "\n",
+      "Stiffness (4 unique)\n",
+      "<StringArray>\n",
+      "['Moderate', 'Stiff', 'Flexible', 'Stiffness']\n",
+      "Length: 4, dtype: str\n",
+      "\n",
+      "Torsional rigidity (5 unique)\n",
+      "<StringArray>\n",
+      "['Stiff', 'Flexible', 'Moderate', '-', 'Torsional rigidity']\n",
+      "Length: 5, dtype: str\n",
+      "\n",
+      "Heel counter stiffness (5 unique)\n",
+      "<StringArray>\n",
+      "['Flexible', 'Moderate', '-', 'Stiff', 'Heel counter stiffness']\n",
+      "Length: 5, dtype: str\n",
+      "\n",
+      "Waterproofing (5 unique)\n",
+      "<StringArray>\n",
+      "['-', 'Waterproof', 'Water repellent', 'WaterproofWater repellent', 'Ranking']\n",
+      "Length: 5, dtype: str\n",
+      "\n",
+      "Widths available (6 unique)\n",
+      "<StringArray>\n",
+      "[          'Normal',       'NormalWide', 'NormalWideX-Wide',\n",
+      "     'NarrowNormal',             'Wide', 'Widths available']\n",
+      "Length: 6, dtype: str\n",
+      "\n",
+      "Orthotic friendly (3 unique)\n",
+      "<StringArray>\n",
+      "['1', '0', 'Orthotic friendly']\n",
+      "Length: 3, dtype: str\n",
+      "\n",
+      "For heavy runners (3 unique)\n",
+      "<StringArray>\n",
+      "['0', '1', 'For heavy runners']\n",
+      "Length: 3, dtype: str\n",
+      "\n",
+      "Season (5 unique)\n",
+      "<StringArray>\n",
+      "['All seasons', '-', 'SummerAll seasons', 'Winter', 'Season']\n",
+      "Length: 5, dtype: str\n",
+      "\n",
+      "Removable insole (3 unique)\n",
+      "<StringArray>\n",
+      "['1', '0', 'Removable insole']\n",
+      "Length: 3, dtype: str\n"
+     ]
+    }
+   ],
+   "source": [
+    "observed_col = [ \n",
+    "    'Brand',\n",
+    "    'Trail terrain',\n",
+    "    'Arch support',\n",
+    "    'Lightweight',\n",
+    "    'Strike pattern',\n",
+    "    'Size', #ini actually sama kaya observed col-nya road \n",
+    "    'Midsole softness',\n",
+    "    'Plate',\n",
+    "    'Toebox durability',\n",
+    "    'Heel padding durability',\n",
+    "    'Outsole durability',\n",
+    "    'Breathability',\n",
+    "    'Width / fit',\n",
+    "    'Toebox width',\n",
+    "    'Stiffness',\n",
+    "    'Torsional rigidity',\n",
+    "    'Heel counter stiffness',                 \n",
+    "    'Waterproofing',\n",
+    "    'Widths available', # aku mikir ini ga perlu soalnya ini available size, bukan size yang dipake user\n",
+    "    'Orthotic friendly',\n",
+    "    'For heavy runners',\n",
+    "    'Season',\n",
+    "    'Removable insole'\n",
+    "]\n",
+    "\n",
+    "for col in observed_col:    \n",
+    "    uniques = df[col].dropna().unique()\n",
+    "    print(f\"\\n{col} ({len(uniques)} unique)\")\n",
+    "    print(uniques)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7df7bd0d",
+   "metadata": {},
+   "source": [
+    "# Clear duplicate rows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "f67e8393",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Before: 498\n",
+      "After : 183\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Before:\", len(df))\n",
+    "df = df.drop_duplicates(subset=[\"Brand\", \"Name\"], keep=\"first\").reset_index(drop=True)\n",
+    "print(\"After :\", len(df))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af1f7980",
+   "metadata": {},
+   "source": [
+    "# Clearing Brand"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "f4f07a73",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Brand (21 uniques): \n",
+      " ['Adidas', 'Altra', 'Asics', 'Brooks', 'Hoka', 'Icebug', 'Inov8', 'Kailas', 'Keen', 'La Sportiva', 'Merrell', 'New Balance', 'Nike', 'Nnormal', 'On', 'Salomon', 'Saucony', 'Scarpa', 'The North Face', 'Topo', 'Xero Shoes']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Brand\n",
+       "Salomon           26\n",
+       "Hoka              21\n",
+       "Nike              20\n",
+       "Altra             18\n",
+       "New Balance       15\n",
+       "Saucony           13\n",
+       "Brooks            12\n",
+       "Asics             11\n",
+       "Merrell           11\n",
+       "Kailas            10\n",
+       "On                 5\n",
+       "Inov8              4\n",
+       "Topo               4\n",
+       "Adidas             2\n",
+       "La Sportiva        2\n",
+       "Xero Shoes         2\n",
+       "Keen               1\n",
+       "Nnormal            1\n",
+       "Scarpa             1\n",
+       "The North Face     1\n",
+       "Icebug             1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Brand\"] = (\n",
+    "    df[\"Brand\"]\n",
+    "    .astype(str)\n",
+    "    .str.strip()\n",
+    "    .str.replace(r\"\\s+\", \" \", regex=True)\n",
+    "    .str.title()\n",
+    ")\n",
+    "\n",
+    "brand_uniques = df[\"Brand\"].dropna().unique()\n",
+    "# brand_uniques.sort()\n",
+    "brand_uniques = sorted(brand_uniques)\n",
+    "print(f\"Brand ({len(brand_uniques)} uniques): \\n\",brand_uniques)\n",
+    "\n",
+    "df[\"Brand\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f915b663",
+   "metadata": {},
+   "source": [
+    "# Cleaning Terrain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "11a03b3e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trail terrain (5 uniques): \n",
+      " ['Light', 'Lightmoderate', 'Moderate', 'Moderatetechnical', 'Technical']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Trail terrain\n",
+       "Lightmoderate        66\n",
+       "Light                54\n",
+       "Moderatetechnical    27\n",
+       "Moderate             22\n",
+       "Technical            12\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Trail terrain\"] = (\n",
+    "    df[\"Trail terrain\"]\n",
+    "    .astype(str)\n",
+    "    .str.strip()\n",
+    "    .str.replace(r\"\\s+\", \" \", regex=True)\n",
+    "    .str.title()\n",
+    ")\n",
+    "\n",
+    "terrain_uniques = df[\"Trail terrain\"].dropna().unique()\n",
+    "# terrain_uniques.sort()\n",
+    "terrain_uniques = sorted(terrain_uniques)\n",
+    "print(f\"Trail terrain ({len(terrain_uniques)} uniques): \\n\",terrain_uniques)\n",
+    "\n",
+    "df[\"Trail terrain\"].value_counts().head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "68ada20b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Trail terrain</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Forefoot lab Forefoot brand</th>\n",
+       "      <th>Widths available</th>\n",
+       "      <th>For heavy runners</th>\n",
+       "      <th>Season</th>\n",
+       "      <th>Removable insole</th>\n",
+       "      <th>Orthotic friendly</th>\n",
+       "      <th>Waterproofing</th>\n",
+       "      <th>Ranking</th>\n",
+       "      <th>Popularity</th>\n",
+       "      <th>terrain_norm</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Terrex Agravic Speed Ultra</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$220</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.1 oz / 259g 9.5 oz / 270g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.3 mm 8.0 mm</td>\n",
+       "      <td>Mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>30.3 mm 30.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#67 Top 19%</td>\n",
+       "      <td>#163 Top 45%</td>\n",
+       "      <td>Light</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Terrex Speed Ultra</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$160</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.1 oz / 258g 9 oz / 255g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.2 mm 8.0 mm</td>\n",
+       "      <td>HeelMid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>24.6 mm 18.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#45 Top 13%</td>\n",
+       "      <td>#294 Bottom 20%</td>\n",
+       "      <td>Light</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Experience Wild</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$145</td>\n",
+       "      <td>Lightmoderate</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.1 oz / 285g 9.6 oz / 273g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4.3 mm 4.0 mm</td>\n",
+       "      <td>Mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>30.2 mm 30.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#251 Top 39%</td>\n",
+       "      <td>#308 Top 48%</td>\n",
+       "      <td>Light|Moderate</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Experience Wild 2</td>\n",
+       "      <td>79\\n Good!</td>\n",
+       "      <td>$140</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.4 oz / 266g 10.3 oz / 293g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.1 mm 4.0 mm</td>\n",
+       "      <td>Mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>26.2 mm 28.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#315 Bottom 14%</td>\n",
+       "      <td>#211 Bottom 42%</td>\n",
+       "      <td>Light</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Lone Peak 5.0</td>\n",
+       "      <td>91\\n Superb!</td>\n",
+       "      <td>$130</td>\n",
+       "      <td>Lightmoderate</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 302g 10.6 oz / 301g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.2 mm 0.0 mm</td>\n",
+       "      <td>Mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>24.3 mm 25.0 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#62 Top 10%</td>\n",
+       "      <td>#63 Top 10%</td>\n",
+       "      <td>Light|Moderate</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 34 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand                        Name Audience score Price  Trail terrain  \\\n",
+       "0  Adidas  Terrex Agravic Speed Ultra   90\\n Superb!  $220          Light   \n",
+       "1  Adidas          Terrex Speed Ultra   90\\n Superb!  $160          Light   \n",
+       "2   Altra             Experience Wild    88\\n Great!  $145  Lightmoderate   \n",
+       "3   Altra           Experience Wild 2     79\\n Good!  $140          Light   \n",
+       "4   Altra               Lone Peak 5.0   91\\n Superb!  $130  Lightmoderate   \n",
+       "\n",
+       "  Arch support        Weight lab Weight brand Lightweight Drop lab Drop brand  \\\n",
+       "0      Neutral    9.1 oz / 259g 9.5 oz / 270g           0       0.3 mm 8.0 mm   \n",
+       "1      Neutral      9.1 oz / 258g 9 oz / 255g           0       8.2 mm 8.0 mm   \n",
+       "2      Neutral   10.1 oz / 285g 9.6 oz / 273g           0       4.3 mm 4.0 mm   \n",
+       "3      Neutral   9.4 oz / 266g 10.3 oz / 293g           0       6.1 mm 4.0 mm   \n",
+       "4      Neutral  10.7 oz / 302g 10.6 oz / 301g           0       0.2 mm 0.0 mm   \n",
+       "\n",
+       "     Strike pattern  ... Forefoot lab Forefoot brand Widths available  \\\n",
+       "0      Mid/forefoot  ...             30.3 mm 30.0 mm           Normal   \n",
+       "1  HeelMid/forefoot  ...             24.6 mm 18.0 mm           Normal   \n",
+       "2      Mid/forefoot  ...             30.2 mm 30.0 mm           Normal   \n",
+       "3      Mid/forefoot  ...             26.2 mm 28.0 mm           Normal   \n",
+       "4      Mid/forefoot  ...             24.3 mm 25.0 mm           Normal   \n",
+       "\n",
+       "  For heavy runners       Season Removable insole Orthotic friendly  \\\n",
+       "0                 0  All seasons                1                 1   \n",
+       "1                 0            -                1                 1   \n",
+       "2                 0  All seasons                1                 1   \n",
+       "3                 0  All seasons                1                 1   \n",
+       "4                 0            -                1                 1   \n",
+       "\n",
+       "  Waterproofing          Ranking       Popularity    terrain_norm  \n",
+       "0             -      #67 Top 19%     #163 Top 45%           Light  \n",
+       "1             -      #45 Top 13%  #294 Bottom 20%           Light  \n",
+       "2             -     #251 Top 39%     #308 Top 48%  Light|Moderate  \n",
+       "3             -  #315 Bottom 14%  #211 Bottom 42%           Light  \n",
+       "4             -      #62 Top 10%      #63 Top 10%  Light|Moderate  \n",
+       "\n",
+       "[5 rows x 34 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "terrain_map = {\n",
+    "    \"Light\": \"Light\",\n",
+    "    \"Moderate\": \"Moderate\",\n",
+    "    \"Technical\": \"Technical\",\n",
+    "    \"Lightmoderate\": \"Light|Moderate\",\n",
+    "    \"Moderatetechnical\": \"Moderate|Technical\",\n",
+    "}\n",
+    "\n",
+    "df[\"terrain_norm\"] = df[\"Trail terrain\"].map(terrain_map)\n",
+    "\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "dae161d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "UNMAPPED terrain values:\n",
+      " Series([], Name: count, dtype: int64)\n"
+     ]
+    }
+   ],
+   "source": [
+    "unmapped = df[df[\"terrain_norm\"].isna()][\"Trail terrain\"].value_counts()\n",
+    "print(\"UNMAPPED terrain values:\\n\", unmapped)\n",
+    "# assert df[\"terrain_norm\"].notna().all()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "489fe0ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "terrain - norm : (5) uniques\n",
+      " <StringArray>\n",
+      "['Light', 'Light|Moderate', 'Moderate|Technical', 'Moderate', 'Technical']\n",
+      "Length: 5, dtype: str\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "terrain_norm\n",
+       "Light|Moderate        66\n",
+       "Light                 54\n",
+       "Moderate|Technical    27\n",
+       "Moderate              22\n",
+       "Technical             12\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "terrain_norms_unique = df[\"terrain_norm\"].dropna().unique()\n",
+    "print(f\"terrain - norm : ({len(terrain_norms_unique)}) uniques\\n\", terrain_norms_unique)\n",
+    "\n",
+    "\n",
+    "df[\"terrain_norm\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "8b96c34c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Trail terrain</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Widths available</th>\n",
+       "      <th>For heavy runners</th>\n",
+       "      <th>Season</th>\n",
+       "      <th>Removable insole</th>\n",
+       "      <th>Orthotic friendly</th>\n",
+       "      <th>Waterproofing</th>\n",
+       "      <th>Ranking</th>\n",
+       "      <th>Popularity</th>\n",
+       "      <th>terrain_norm</th>\n",
+       "      <th>terrain_lists</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Terrex Agravic Speed Ultra</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$220</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.1 oz / 259g 9.5 oz / 270g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.3 mm 8.0 mm</td>\n",
+       "      <td>Mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#67 Top 19%</td>\n",
+       "      <td>#163 Top 45%</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>[Light]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Terrex Speed Ultra</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$160</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.1 oz / 258g 9 oz / 255g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.2 mm 8.0 mm</td>\n",
+       "      <td>HeelMid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#45 Top 13%</td>\n",
+       "      <td>#294 Bottom 20%</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>[Light]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Experience Wild</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$145</td>\n",
+       "      <td>Lightmoderate</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.1 oz / 285g 9.6 oz / 273g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4.3 mm 4.0 mm</td>\n",
+       "      <td>Mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#251 Top 39%</td>\n",
+       "      <td>#308 Top 48%</td>\n",
+       "      <td>Light|Moderate</td>\n",
+       "      <td>[Light, Moderate]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Experience Wild 2</td>\n",
+       "      <td>79\\n Good!</td>\n",
+       "      <td>$140</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.4 oz / 266g 10.3 oz / 293g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.1 mm 4.0 mm</td>\n",
+       "      <td>Mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>All seasons</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#315 Bottom 14%</td>\n",
+       "      <td>#211 Bottom 42%</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>[Light]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Lone Peak 5.0</td>\n",
+       "      <td>91\\n Superb!</td>\n",
+       "      <td>$130</td>\n",
+       "      <td>Lightmoderate</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 302g 10.6 oz / 301g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.2 mm 0.0 mm</td>\n",
+       "      <td>Mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#62 Top 10%</td>\n",
+       "      <td>#63 Top 10%</td>\n",
+       "      <td>Light|Moderate</td>\n",
+       "      <td>[Light, Moderate]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 35 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand                        Name Audience score Price  Trail terrain  \\\n",
+       "0  Adidas  Terrex Agravic Speed Ultra   90\\n Superb!  $220          Light   \n",
+       "1  Adidas          Terrex Speed Ultra   90\\n Superb!  $160          Light   \n",
+       "2   Altra             Experience Wild    88\\n Great!  $145  Lightmoderate   \n",
+       "3   Altra           Experience Wild 2     79\\n Good!  $140          Light   \n",
+       "4   Altra               Lone Peak 5.0   91\\n Superb!  $130  Lightmoderate   \n",
+       "\n",
+       "  Arch support        Weight lab Weight brand Lightweight Drop lab Drop brand  \\\n",
+       "0      Neutral    9.1 oz / 259g 9.5 oz / 270g           0       0.3 mm 8.0 mm   \n",
+       "1      Neutral      9.1 oz / 258g 9 oz / 255g           0       8.2 mm 8.0 mm   \n",
+       "2      Neutral   10.1 oz / 285g 9.6 oz / 273g           0       4.3 mm 4.0 mm   \n",
+       "3      Neutral   9.4 oz / 266g 10.3 oz / 293g           0       6.1 mm 4.0 mm   \n",
+       "4      Neutral  10.7 oz / 302g 10.6 oz / 301g           0       0.2 mm 0.0 mm   \n",
+       "\n",
+       "     Strike pattern  ... Widths available For heavy runners       Season  \\\n",
+       "0      Mid/forefoot  ...           Normal                 0  All seasons   \n",
+       "1  HeelMid/forefoot  ...           Normal                 0            -   \n",
+       "2      Mid/forefoot  ...           Normal                 0  All seasons   \n",
+       "3      Mid/forefoot  ...           Normal                 0  All seasons   \n",
+       "4      Mid/forefoot  ...           Normal                 0            -   \n",
+       "\n",
+       "  Removable insole Orthotic friendly Waterproofing          Ranking  \\\n",
+       "0                1                 1             -      #67 Top 19%   \n",
+       "1                1                 1             -      #45 Top 13%   \n",
+       "2                1                 1             -     #251 Top 39%   \n",
+       "3                1                 1             -  #315 Bottom 14%   \n",
+       "4                1                 1             -      #62 Top 10%   \n",
+       "\n",
+       "        Popularity    terrain_norm      terrain_lists  \n",
+       "0     #163 Top 45%           Light            [Light]  \n",
+       "1  #294 Bottom 20%           Light            [Light]  \n",
+       "2     #308 Top 48%  Light|Moderate  [Light, Moderate]  \n",
+       "3  #211 Bottom 42%           Light            [Light]  \n",
+       "4      #63 Top 10%  Light|Moderate  [Light, Moderate]  \n",
+       "\n",
+       "[5 rows x 35 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"terrain_lists\"] = df[\"terrain_norm\"].str.split(\"|\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "3a790715",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "terrain_exploded = df[\"terrain_lists\"].explode()\n",
+    "\n",
+    "terrain_ohe = (\n",
+    "    pd.crosstab(terrain_exploded.index, terrain_exploded)\n",
+    "    .reindex(df.index, fill_value=0)   \n",
+    ")\n",
+    "\n",
+    "terrain_ohe = terrain_ohe.rename(columns={\n",
+    "    \"Light\": \"terrain_light\",\n",
+    "    \"Moderate\": \"terrain_moderate\",\n",
+    "    \"Technical\": \"terrain_technical\"\n",
+    "})\n",
+    "\n",
+    "# gabung ke df\n",
+    "df = pd.concat([df, terrain_ohe], axis=1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "c51729b9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Trail terrain</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Removable insole</th>\n",
+       "      <th>Orthotic friendly</th>\n",
+       "      <th>Waterproofing</th>\n",
+       "      <th>Ranking</th>\n",
+       "      <th>Popularity</th>\n",
+       "      <th>terrain_norm</th>\n",
+       "      <th>terrain_lists</th>\n",
+       "      <th>terrain_light</th>\n",
+       "      <th>terrain_moderate</th>\n",
+       "      <th>terrain_technical</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Terrex Agravic Speed Ultra</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$220</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.1 oz / 259g 9.5 oz / 270g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.3 mm 8.0 mm</td>\n",
+       "      <td>Mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#67 Top 19%</td>\n",
+       "      <td>#163 Top 45%</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>[Light]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Terrex Speed Ultra</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$160</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.1 oz / 258g 9 oz / 255g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.2 mm 8.0 mm</td>\n",
+       "      <td>HeelMid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#45 Top 13%</td>\n",
+       "      <td>#294 Bottom 20%</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>[Light]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Experience Wild</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$145</td>\n",
+       "      <td>Lightmoderate</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.1 oz / 285g 9.6 oz / 273g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4.3 mm 4.0 mm</td>\n",
+       "      <td>Mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#251 Top 39%</td>\n",
+       "      <td>#308 Top 48%</td>\n",
+       "      <td>Light|Moderate</td>\n",
+       "      <td>[Light, Moderate]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Experience Wild 2</td>\n",
+       "      <td>79\\n Good!</td>\n",
+       "      <td>$140</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.4 oz / 266g 10.3 oz / 293g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.1 mm 4.0 mm</td>\n",
+       "      <td>Mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#315 Bottom 14%</td>\n",
+       "      <td>#211 Bottom 42%</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>[Light]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Lone Peak 5.0</td>\n",
+       "      <td>91\\n Superb!</td>\n",
+       "      <td>$130</td>\n",
+       "      <td>Lightmoderate</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 302g 10.6 oz / 301g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.2 mm 0.0 mm</td>\n",
+       "      <td>Mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-</td>\n",
+       "      <td>#62 Top 10%</td>\n",
+       "      <td>#63 Top 10%</td>\n",
+       "      <td>Light|Moderate</td>\n",
+       "      <td>[Light, Moderate]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 38 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand                        Name Audience score Price  Trail terrain  \\\n",
+       "0  Adidas  Terrex Agravic Speed Ultra   90\\n Superb!  $220          Light   \n",
+       "1  Adidas          Terrex Speed Ultra   90\\n Superb!  $160          Light   \n",
+       "2   Altra             Experience Wild    88\\n Great!  $145  Lightmoderate   \n",
+       "3   Altra           Experience Wild 2     79\\n Good!  $140          Light   \n",
+       "4   Altra               Lone Peak 5.0   91\\n Superb!  $130  Lightmoderate   \n",
+       "\n",
+       "  Arch support        Weight lab Weight brand Lightweight Drop lab Drop brand  \\\n",
+       "0      Neutral    9.1 oz / 259g 9.5 oz / 270g           0       0.3 mm 8.0 mm   \n",
+       "1      Neutral      9.1 oz / 258g 9 oz / 255g           0       8.2 mm 8.0 mm   \n",
+       "2      Neutral   10.1 oz / 285g 9.6 oz / 273g           0       4.3 mm 4.0 mm   \n",
+       "3      Neutral   9.4 oz / 266g 10.3 oz / 293g           0       6.1 mm 4.0 mm   \n",
+       "4      Neutral  10.7 oz / 302g 10.6 oz / 301g           0       0.2 mm 0.0 mm   \n",
+       "\n",
+       "     Strike pattern  ... Removable insole Orthotic friendly Waterproofing  \\\n",
+       "0      Mid/forefoot  ...                1                 1             -   \n",
+       "1  HeelMid/forefoot  ...                1                 1             -   \n",
+       "2      Mid/forefoot  ...                1                 1             -   \n",
+       "3      Mid/forefoot  ...                1                 1             -   \n",
+       "4      Mid/forefoot  ...                1                 1             -   \n",
+       "\n",
+       "           Ranking       Popularity    terrain_norm      terrain_lists  \\\n",
+       "0      #67 Top 19%     #163 Top 45%           Light            [Light]   \n",
+       "1      #45 Top 13%  #294 Bottom 20%           Light            [Light]   \n",
+       "2     #251 Top 39%     #308 Top 48%  Light|Moderate  [Light, Moderate]   \n",
+       "3  #315 Bottom 14%  #211 Bottom 42%           Light            [Light]   \n",
+       "4      #62 Top 10%      #63 Top 10%  Light|Moderate  [Light, Moderate]   \n",
+       "\n",
+       "  terrain_light terrain_moderate terrain_technical  \n",
+       "0             1                0                 0  \n",
+       "1             1                0                 0  \n",
+       "2             1                1                 0  \n",
+       "3             1                0                 0  \n",
+       "4             1                1                 0  \n",
+       "\n",
+       "[5 rows x 38 columns]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "d03312ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# make sure value-nya bener 0/1\n",
+    "for c in [\"terrain_light\", \"terrain_moderate\", \"terrain_technical\"]:\n",
+    "    assert set(df[c].unique()).issubset({0, 1})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "62af3a33",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   Trail terrain terrain_norm  terrain_light  terrain_moderate  \\\n",
+      "0          Light        Light              1                 0   \n",
+      "1          Light        Light              1                 0   \n",
+      "3          Light        Light              1                 0   \n",
+      "13         Light        Light              1                 0   \n",
+      "14         Light        Light              1                 0   \n",
+      "\n",
+      "    terrain_technical  \n",
+      "0                   0  \n",
+      "1                   0  \n",
+      "3                   0  \n",
+      "13                  0  \n",
+      "14                  0  \n",
+      "   Trail terrain terrain_norm  terrain_moderate  terrain_technical  \\\n",
+      "6       Moderate     Moderate                 1                  0   \n",
+      "10      Moderate     Moderate                 1                  0   \n",
+      "48      Moderate     Moderate                 1                  0   \n",
+      "49      Moderate     Moderate                 1                  0   \n",
+      "50      Moderate     Moderate                 1                  0   \n",
+      "\n",
+      "    terrain_light  \n",
+      "6               0  \n",
+      "10              0  \n",
+      "48              0  \n",
+      "49              0  \n",
+      "50              0  \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[df[\"Trail terrain\"]==\"Light\"][[\"Trail terrain\",\"terrain_norm\",\"terrain_light\",\"terrain_moderate\",\"terrain_technical\"]].head())\n",
+    "print(df[df[\"Trail terrain\"]==\"Moderate\"][[\"Trail terrain\",\"terrain_norm\",\"terrain_moderate\",\"terrain_technical\",\"terrain_light\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "6eccf7c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 183\n",
+      "light sum: 120\n",
+      "moderate sum: 115\n",
+      "technical sum: 39\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Rows:\", len(df))\n",
+    "print(\"light sum:\", int(df[\"terrain_light\"].sum()))\n",
+    "print(\"moderate sum:\", int(df[\"terrain_moderate\"].sum()))\n",
+    "print(\"technical sum:\", int(df[\"terrain_technical\"].sum()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d1730fa8",
+   "metadata": {},
+   "source": [
+    "# Cleaning arch support"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "48fb916e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Arch (2 uniques): \n",
+      " ['Neutral', 'Stability']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Arch support\n",
+       "Neutral      177\n",
+       "Stability      4\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Arch support\"] = (\n",
+    "    df[\"Arch support\"]\n",
+    "    .astype(str)\n",
+    "    .str.strip()\n",
+    "    .str.replace(r\"\\s+\", \" \", regex=True)\n",
+    "    .str.title()\n",
+    ")\n",
+    "\n",
+    "arch_uniques = df[\"Arch support\"].dropna().unique()\n",
+    "# arch_uniques.sort()\n",
+    "arch_uniques = sorted(arch_uniques)\n",
+    "print(f\"Arch ({len(arch_uniques)} uniques): \\n\",arch_uniques)\n",
+    "\n",
+    "df[\"Arch support\"].value_counts().head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "fdc2e1c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Arch_grouped\n",
+      "Neutral      177\n",
+      "Stability      4\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "arch_map = {\n",
+    "    \"Neutral\": \"Neutral\",\n",
+    "    \"Stability\": \"Stability\",\n",
+    "}\n",
+    "\n",
+    "df[\"Arch_grouped\"] = df[\"Arch support\"].map(arch_map)\n",
+    "\n",
+    "print(df[\"Arch_grouped\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "2e5b55df",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Trail terrain</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Ranking</th>\n",
+       "      <th>Popularity</th>\n",
+       "      <th>terrain_norm</th>\n",
+       "      <th>terrain_lists</th>\n",
+       "      <th>terrain_light</th>\n",
+       "      <th>terrain_moderate</th>\n",
+       "      <th>terrain_technical</th>\n",
+       "      <th>Arch_grouped</th>\n",
+       "      <th>arch_neutral</th>\n",
+       "      <th>arch_stability</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Terrex Agravic Speed Ultra</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$220</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.1 oz / 259g 9.5 oz / 270g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.3 mm 8.0 mm</td>\n",
+       "      <td>Mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>#67 Top 19%</td>\n",
+       "      <td>#163 Top 45%</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>[Light]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Terrex Speed Ultra</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$160</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.1 oz / 258g 9 oz / 255g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.2 mm 8.0 mm</td>\n",
+       "      <td>HeelMid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>#45 Top 13%</td>\n",
+       "      <td>#294 Bottom 20%</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>[Light]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Experience Wild</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$145</td>\n",
+       "      <td>Lightmoderate</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.1 oz / 285g 9.6 oz / 273g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4.3 mm 4.0 mm</td>\n",
+       "      <td>Mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>#251 Top 39%</td>\n",
+       "      <td>#308 Top 48%</td>\n",
+       "      <td>Light|Moderate</td>\n",
+       "      <td>[Light, Moderate]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Experience Wild 2</td>\n",
+       "      <td>79\\n Good!</td>\n",
+       "      <td>$140</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.4 oz / 266g 10.3 oz / 293g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.1 mm 4.0 mm</td>\n",
+       "      <td>Mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>#315 Bottom 14%</td>\n",
+       "      <td>#211 Bottom 42%</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>[Light]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Lone Peak 5.0</td>\n",
+       "      <td>91\\n Superb!</td>\n",
+       "      <td>$130</td>\n",
+       "      <td>Lightmoderate</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 302g 10.6 oz / 301g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.2 mm 0.0 mm</td>\n",
+       "      <td>Mid/forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>#62 Top 10%</td>\n",
+       "      <td>#63 Top 10%</td>\n",
+       "      <td>Light|Moderate</td>\n",
+       "      <td>[Light, Moderate]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 41 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand                        Name Audience score Price  Trail terrain  \\\n",
+       "0  Adidas  Terrex Agravic Speed Ultra   90\\n Superb!  $220          Light   \n",
+       "1  Adidas          Terrex Speed Ultra   90\\n Superb!  $160          Light   \n",
+       "2   Altra             Experience Wild    88\\n Great!  $145  Lightmoderate   \n",
+       "3   Altra           Experience Wild 2     79\\n Good!  $140          Light   \n",
+       "4   Altra               Lone Peak 5.0   91\\n Superb!  $130  Lightmoderate   \n",
+       "\n",
+       "  Arch support        Weight lab Weight brand Lightweight Drop lab Drop brand  \\\n",
+       "0      Neutral    9.1 oz / 259g 9.5 oz / 270g           0       0.3 mm 8.0 mm   \n",
+       "1      Neutral      9.1 oz / 258g 9 oz / 255g           0       8.2 mm 8.0 mm   \n",
+       "2      Neutral   10.1 oz / 285g 9.6 oz / 273g           0       4.3 mm 4.0 mm   \n",
+       "3      Neutral   9.4 oz / 266g 10.3 oz / 293g           0       6.1 mm 4.0 mm   \n",
+       "4      Neutral  10.7 oz / 302g 10.6 oz / 301g           0       0.2 mm 0.0 mm   \n",
+       "\n",
+       "     Strike pattern  ...          Ranking       Popularity    terrain_norm  \\\n",
+       "0      Mid/forefoot  ...      #67 Top 19%     #163 Top 45%           Light   \n",
+       "1  HeelMid/forefoot  ...      #45 Top 13%  #294 Bottom 20%           Light   \n",
+       "2      Mid/forefoot  ...     #251 Top 39%     #308 Top 48%  Light|Moderate   \n",
+       "3      Mid/forefoot  ...  #315 Bottom 14%  #211 Bottom 42%           Light   \n",
+       "4      Mid/forefoot  ...      #62 Top 10%      #63 Top 10%  Light|Moderate   \n",
+       "\n",
+       "       terrain_lists terrain_light terrain_moderate terrain_technical  \\\n",
+       "0            [Light]             1                0                 0   \n",
+       "1            [Light]             1                0                 0   \n",
+       "2  [Light, Moderate]             1                1                 0   \n",
+       "3            [Light]             1                0                 0   \n",
+       "4  [Light, Moderate]             1                1                 0   \n",
+       "\n",
+       "  Arch_grouped arch_neutral arch_stability  \n",
+       "0      Neutral            1              0  \n",
+       "1      Neutral            1              0  \n",
+       "2      Neutral            1              0  \n",
+       "3      Neutral            1              0  \n",
+       "4      Neutral            1              0  \n",
+       "\n",
+       "[5 rows x 41 columns]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "arch_ohe = pd.get_dummies(df[\"Arch_grouped\"], prefix=\"arch\", dtype=int)\n",
+    "\n",
+    "arch_ohe = arch_ohe.rename(columns={\n",
+    "    \"arch_Neutral\": \"arch_neutral\",\n",
+    "    \"arch_Stability\": \"arch_stability\"\n",
+    "})\n",
+    "\n",
+    "df = pd.concat([df, arch_ohe], axis=1)\n",
+    "df.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "2998a49f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# jujur arch grouped gaperlu jadi kita buang aja\n",
+    "# df = df.drop(labels=\"Arch_grouped\", axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "715b4a89",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Validasi One-Hot Encoding Arch Support:\n",
+      "  Arch support  arch_neutral  arch_stability\n",
+      "0      Neutral             1               0\n",
+      "1      Neutral             1               0\n",
+      "2      Neutral             1               0\n",
+      "3      Neutral             1               0\n",
+      "4      Neutral             1               0\n",
+      "5      Neutral             1               0\n",
+      "6      Neutral             1               0\n",
+      "7      Neutral             1               0\n",
+      "8      Neutral             1               0\n",
+      "9      Neutral             1               0\n",
+      "\n",
+      "Total Neutral   : 177\n",
+      "Total Stability : 4\n"
+     ]
+    }
+   ],
+   "source": [
+    "# assert (df[\"arch_neutral\"] + df[\"arch_stability\"] == 1).all(), \"Error: Ada baris yang tidak punya kategori arch atau ganda!\"\n",
+    "\n",
+    "print(\"\\nValidasi One-Hot Encoding Arch Support:\")\n",
+    "print(df[[\"Arch support\", \"arch_neutral\", \"arch_stability\"]].head(10))\n",
+    "\n",
+    "# Cek total count untuk laporan\n",
+    "print(\"\\nTotal Neutral   :\", df[\"arch_neutral\"].sum())\n",
+    "print(\"Total Stability :\", df[\"arch_stability\"].sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5344af8e",
+   "metadata": {},
+   "source": [
+    "# Cleaning Strike"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "45d6d7b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Strike pattern (3 uniques): \n",
+      " ['Heel', 'Heelmid/Forefoot', 'Mid/Forefoot']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Strike pattern\n",
+       "Mid/Forefoot        96\n",
+       "Heel                50\n",
+       "Heelmid/Forefoot    35\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Strike pattern\"] = (\n",
+    "    df[\"Strike pattern\"]\n",
+    "    .astype(str)\n",
+    "    .str.strip()\n",
+    "    .str.replace(r\"\\s+\", \" \", regex=True)\n",
+    "    .str.title()\n",
+    ")\n",
+    "\n",
+    "strike_pattern_uniques = df[\"Strike pattern\"].dropna().unique()\n",
+    "# strike_pattern_uniques.sort()\n",
+    "strike_pattern_uniques = sorted(strike_pattern_uniques)\n",
+    "print(f\"Strike pattern ({len(strike_pattern_uniques)} uniques): \\n\",strike_pattern_uniques)\n",
+    "\n",
+    "df[\"Strike pattern\"].value_counts().head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "5bcb981a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "UNMAPPED strike values:\n",
+      " Series([], Name: count, dtype: int64)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#  ['-' 'Heel' 'Heel Mid/Forefoot' 'Heelmid/Forefoot' 'Mid/Forefoot']\n",
+    "\n",
+    "\n",
+    "strike_map = {\n",
+    "    \"Mid/Forefoot\": \"Mid|Forefoot\",\n",
+    "    \"Heelmid/Forefoot\": \"Heel|Mid|Forefoot\",\n",
+    "    \"Heel\": \"Heel\",\n",
+    "}\n",
+    "\n",
+    "df[\"Strike_norm\"] = df[\"Strike pattern\"].map(strike_map)\n",
+    "\n",
+    "unmapped = df[df[\"Strike_norm\"].isna()][\"Strike pattern\"].value_counts()\n",
+    "print(\"UNMAPPED strike values:\\n\", unmapped)\n",
+    "# assert df[\"Strike_norm\"].notna().all()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "c30b882d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Strike - norm : (3) uniques\n",
+      " <StringArray>\n",
+      "['Mid|Forefoot', 'Heel|Mid|Forefoot', 'Heel']\n",
+      "Length: 3, dtype: str\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Strike_norm\n",
+       "Mid|Forefoot         96\n",
+       "Heel                 50\n",
+       "Heel|Mid|Forefoot    35\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "strike_norm_unique = df[\"Strike_norm\"].dropna().unique()\n",
+    "print(f\"Strike - norm : ({len(strike_norm_unique)}) uniques\\n\", strike_norm_unique)\n",
+    "\n",
+    "\n",
+    "df[\"Strike_norm\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "ecaca627",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Trail terrain</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Weight lab Weight brand</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>...</th>\n",
+       "      <th>terrain_norm</th>\n",
+       "      <th>terrain_lists</th>\n",
+       "      <th>terrain_light</th>\n",
+       "      <th>terrain_moderate</th>\n",
+       "      <th>terrain_technical</th>\n",
+       "      <th>Arch_grouped</th>\n",
+       "      <th>arch_neutral</th>\n",
+       "      <th>arch_stability</th>\n",
+       "      <th>Strike_norm</th>\n",
+       "      <th>Strike_lists</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Terrex Agravic Speed Ultra</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$220</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.1 oz / 259g 9.5 oz / 270g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.3 mm 8.0 mm</td>\n",
+       "      <td>Mid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>[Light]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Mid|Forefoot</td>\n",
+       "      <td>[Mid, Forefoot]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Terrex Speed Ultra</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$160</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.1 oz / 258g 9 oz / 255g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.2 mm 8.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>[Light]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Heel|Mid|Forefoot</td>\n",
+       "      <td>[Heel, Mid, Forefoot]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Experience Wild</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$145</td>\n",
+       "      <td>Lightmoderate</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.1 oz / 285g 9.6 oz / 273g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4.3 mm 4.0 mm</td>\n",
+       "      <td>Mid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Light|Moderate</td>\n",
+       "      <td>[Light, Moderate]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Mid|Forefoot</td>\n",
+       "      <td>[Mid, Forefoot]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Experience Wild 2</td>\n",
+       "      <td>79\\n Good!</td>\n",
+       "      <td>$140</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>9.4 oz / 266g 10.3 oz / 293g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.1 mm 4.0 mm</td>\n",
+       "      <td>Mid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>[Light]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Mid|Forefoot</td>\n",
+       "      <td>[Mid, Forefoot]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Lone Peak 5.0</td>\n",
+       "      <td>91\\n Superb!</td>\n",
+       "      <td>$130</td>\n",
+       "      <td>Lightmoderate</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>10.7 oz / 302g 10.6 oz / 301g</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.2 mm 0.0 mm</td>\n",
+       "      <td>Mid/Forefoot</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Light|Moderate</td>\n",
+       "      <td>[Light, Moderate]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Mid|Forefoot</td>\n",
+       "      <td>[Mid, Forefoot]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 43 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand                        Name Audience score Price  Trail terrain  \\\n",
+       "0  Adidas  Terrex Agravic Speed Ultra   90\\n Superb!  $220          Light   \n",
+       "1  Adidas          Terrex Speed Ultra   90\\n Superb!  $160          Light   \n",
+       "2   Altra             Experience Wild    88\\n Great!  $145  Lightmoderate   \n",
+       "3   Altra           Experience Wild 2     79\\n Good!  $140          Light   \n",
+       "4   Altra               Lone Peak 5.0   91\\n Superb!  $130  Lightmoderate   \n",
+       "\n",
+       "  Arch support        Weight lab Weight brand Lightweight Drop lab Drop brand  \\\n",
+       "0      Neutral    9.1 oz / 259g 9.5 oz / 270g           0       0.3 mm 8.0 mm   \n",
+       "1      Neutral      9.1 oz / 258g 9 oz / 255g           0       8.2 mm 8.0 mm   \n",
+       "2      Neutral   10.1 oz / 285g 9.6 oz / 273g           0       4.3 mm 4.0 mm   \n",
+       "3      Neutral   9.4 oz / 266g 10.3 oz / 293g           0       6.1 mm 4.0 mm   \n",
+       "4      Neutral  10.7 oz / 302g 10.6 oz / 301g           0       0.2 mm 0.0 mm   \n",
+       "\n",
+       "     Strike pattern  ...    terrain_norm      terrain_lists terrain_light  \\\n",
+       "0      Mid/Forefoot  ...           Light            [Light]             1   \n",
+       "1  Heelmid/Forefoot  ...           Light            [Light]             1   \n",
+       "2      Mid/Forefoot  ...  Light|Moderate  [Light, Moderate]             1   \n",
+       "3      Mid/Forefoot  ...           Light            [Light]             1   \n",
+       "4      Mid/Forefoot  ...  Light|Moderate  [Light, Moderate]             1   \n",
+       "\n",
+       "  terrain_moderate terrain_technical Arch_grouped arch_neutral arch_stability  \\\n",
+       "0                0                 0      Neutral            1              0   \n",
+       "1                0                 0      Neutral            1              0   \n",
+       "2                1                 0      Neutral            1              0   \n",
+       "3                0                 0      Neutral            1              0   \n",
+       "4                1                 0      Neutral            1              0   \n",
+       "\n",
+       "         Strike_norm           Strike_lists  \n",
+       "0       Mid|Forefoot        [Mid, Forefoot]  \n",
+       "1  Heel|Mid|Forefoot  [Heel, Mid, Forefoot]  \n",
+       "2       Mid|Forefoot        [Mid, Forefoot]  \n",
+       "3       Mid|Forefoot        [Mid, Forefoot]  \n",
+       "4       Mid|Forefoot        [Mid, Forefoot]  \n",
+       "\n",
+       "[5 rows x 43 columns]"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Strike_lists\"] = df[\"Strike_norm\"].str.split(\"|\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "10b41050",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "strike_exploded = df[\"Strike_lists\"].explode()\n",
+    "\n",
+    "strike_ohe = (\n",
+    "    pd.crosstab(strike_exploded.index, strike_exploded)\n",
+    "    .reindex(df.index, fill_value=0)   # jaga urutan index sama df\n",
+    ")\n",
+    "\n",
+    "strike_ohe = strike_ohe.rename(columns={\n",
+    "    \"Heel\": \"strike_heel\",\n",
+    "    \"Mid\": \"strike_mid\",\n",
+    "    \"Forefoot\": \"strike_forefoot\"\n",
+    "})\n",
+    "\n",
+    "df = pd.concat([df, strike_ohe], axis=1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "c1164248",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for c in [\"strike_heel\", \"strike_mid\", \"strike_forefoot\"]:\n",
+    "    assert set(df[c].unique()).issubset({0, 1})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "8af9e800",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 183\n",
+      "Heels sum: 85\n",
+      "Mid sum: 131\n",
+      "Forefoot sum: 131\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Rows:\", len(df))\n",
+    "print(\"Heels sum:\", int(df[\"strike_heel\"].sum()))\n",
+    "print(\"Mid sum:\", int(df[\"strike_mid\"].sum()))\n",
+    "print(\"Forefoot sum:\", int(df[\"strike_forefoot\"].sum()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "ab00d7b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 183 entries, 0 to 182\n",
+      "Data columns (total 46 columns):\n",
+      " #   Column                           Non-Null Count  Dtype \n",
+      "---  ------                           --------------  ----- \n",
+      " 0   Brand                            181 non-null    str   \n",
+      " 1   Name                             182 non-null    str   \n",
+      " 2   Audience score                   179 non-null    str   \n",
+      " 3   Price                            181 non-null    str   \n",
+      " 4   Trail terrain                    181 non-null    str   \n",
+      " 5   Arch support                     181 non-null    str   \n",
+      " 6   Weight lab Weight brand          181 non-null    str   \n",
+      " 7   Lightweight                      181 non-null    str   \n",
+      " 8   Drop lab Drop brand              181 non-null    str   \n",
+      " 9   Strike pattern                   181 non-null    str   \n",
+      " 10  Size                             181 non-null    str   \n",
+      " 11  Midsole softness                 181 non-null    str   \n",
+      " 12  Plate                            180 non-null    str   \n",
+      " 13  Toebox durability                181 non-null    str   \n",
+      " 14  Heel padding durability          181 non-null    str   \n",
+      " 15  Outsole durability               181 non-null    str   \n",
+      " 16  Breathability                    181 non-null    str   \n",
+      " 17  Width / fit                      181 non-null    str   \n",
+      " 18  Toebox width                     181 non-null    str   \n",
+      " 19  Stiffness                        181 non-null    str   \n",
+      " 20  Torsional rigidity               181 non-null    str   \n",
+      " 21  Heel counter stiffness           181 non-null    str   \n",
+      " 22  Lug depth                        181 non-null    str   \n",
+      " 23  Heel stack lab Heel stack brand  181 non-null    str   \n",
+      " 24  Forefoot lab Forefoot brand      181 non-null    str   \n",
+      " 25  Widths available                 181 non-null    str   \n",
+      " 26  For heavy runners                181 non-null    str   \n",
+      " 27  Season                           181 non-null    str   \n",
+      " 28  Removable insole                 181 non-null    str   \n",
+      " 29  Orthotic friendly                181 non-null    str   \n",
+      " 30  Waterproofing                    180 non-null    str   \n",
+      " 31  Ranking                          181 non-null    str   \n",
+      " 32  Popularity                       181 non-null    str   \n",
+      " 33  terrain_norm                     181 non-null    str   \n",
+      " 34  terrain_lists                    181 non-null    object\n",
+      " 35  terrain_light                    183 non-null    int64 \n",
+      " 36  terrain_moderate                 183 non-null    int64 \n",
+      " 37  terrain_technical                183 non-null    int64 \n",
+      " 38  Arch_grouped                     181 non-null    str   \n",
+      " 39  arch_neutral                     183 non-null    int64 \n",
+      " 40  arch_stability                   183 non-null    int64 \n",
+      " 41  Strike_norm                      181 non-null    str   \n",
+      " 42  Strike_lists                     181 non-null    object\n",
+      " 43  strike_forefoot                  183 non-null    int64 \n",
+      " 44  strike_heel                      183 non-null    int64 \n",
+      " 45  strike_mid                       183 non-null    int64 \n",
+      "dtypes: int64(8), object(2), str(36)\n",
+      "memory usage: 65.9+ KB\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.head()\n",
+    "print(df.info())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "eeb2d2c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<StringArray>\n",
+      "['Neutral', 'Stability', nan]\n",
+      "Length: 3, dtype: str\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Arch_grouped\"].unique())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b9f6608a",
+   "metadata": {},
+   "source": [
+    "# Split Wight Lab Weight Brand"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "f1989222",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(2)"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mask_missing = (\n",
+    "    df[\"Weight lab Weight brand\"].isna() |\n",
+    "    (df[\"Weight lab Weight brand\"].astype(str).str.strip() == \"-\")\n",
+    ")\n",
+    "mask_missing.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "7b357341",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# weight = df[\"Weight lab Weight brand\"].fillna(\"\").str.findall(r\"[\\d.]+\")\n",
+    "\n",
+    "# # Fill missing values with empty lists\n",
+    "# weight = weight.apply(lambda x: x if isinstance(x, list) else [])\n",
+    "\n",
+    "# # Expand the lists into separate columns\n",
+    "# weight_df = pd.DataFrame(weight.tolist(), index=df.index)\n",
+    "\n",
+    "# # Handle cases where we have fewer than 4 columns\n",
+    "# while len(weight_df.columns) < 4:\n",
+    "#     weight_df[len(weight_df.columns)] = None\n",
+    "\n",
+    "# df[[\"weight_lab_oz\", \"weight_lab_g\", \"weight_brand_oz\", \"weight_brand_g\"]] = weight_df.iloc[:, :4]\n",
+    "\n",
+    "# for col in [\"weight_lab_oz\", \"weight_lab_g\", \"weight_brand_oz\", \"weight_brand_g\"]:\n",
+    "#     df[col] = pd.to_numeric(df[col], errors=\"coerce\")\n",
+    "\n",
+    "# print(df[[\"weight_lab_oz\", \"weight_lab_g\", \"weight_brand_oz\", \"weight_brand_g\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "a3ba22a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 183 entries, 0 to 182\n",
+      "Data columns (total 46 columns):\n",
+      " #   Column                           Non-Null Count  Dtype \n",
+      "---  ------                           --------------  ----- \n",
+      " 0   Brand                            181 non-null    str   \n",
+      " 1   Name                             182 non-null    str   \n",
+      " 2   Audience score                   179 non-null    str   \n",
+      " 3   Price                            181 non-null    str   \n",
+      " 4   Trail terrain                    181 non-null    str   \n",
+      " 5   Arch support                     181 non-null    str   \n",
+      " 6   Weight lab Weight brand          181 non-null    str   \n",
+      " 7   Lightweight                      181 non-null    str   \n",
+      " 8   Drop lab Drop brand              181 non-null    str   \n",
+      " 9   Strike pattern                   181 non-null    str   \n",
+      " 10  Size                             181 non-null    str   \n",
+      " 11  Midsole softness                 181 non-null    str   \n",
+      " 12  Plate                            180 non-null    str   \n",
+      " 13  Toebox durability                181 non-null    str   \n",
+      " 14  Heel padding durability          181 non-null    str   \n",
+      " 15  Outsole durability               181 non-null    str   \n",
+      " 16  Breathability                    181 non-null    str   \n",
+      " 17  Width / fit                      181 non-null    str   \n",
+      " 18  Toebox width                     181 non-null    str   \n",
+      " 19  Stiffness                        181 non-null    str   \n",
+      " 20  Torsional rigidity               181 non-null    str   \n",
+      " 21  Heel counter stiffness           181 non-null    str   \n",
+      " 22  Lug depth                        181 non-null    str   \n",
+      " 23  Heel stack lab Heel stack brand  181 non-null    str   \n",
+      " 24  Forefoot lab Forefoot brand      181 non-null    str   \n",
+      " 25  Widths available                 181 non-null    str   \n",
+      " 26  For heavy runners                181 non-null    str   \n",
+      " 27  Season                           181 non-null    str   \n",
+      " 28  Removable insole                 181 non-null    str   \n",
+      " 29  Orthotic friendly                181 non-null    str   \n",
+      " 30  Waterproofing                    180 non-null    str   \n",
+      " 31  Ranking                          181 non-null    str   \n",
+      " 32  Popularity                       181 non-null    str   \n",
+      " 33  terrain_norm                     181 non-null    str   \n",
+      " 34  terrain_lists                    181 non-null    object\n",
+      " 35  terrain_light                    183 non-null    int64 \n",
+      " 36  terrain_moderate                 183 non-null    int64 \n",
+      " 37  terrain_technical                183 non-null    int64 \n",
+      " 38  Arch_grouped                     181 non-null    str   \n",
+      " 39  arch_neutral                     183 non-null    int64 \n",
+      " 40  arch_stability                   183 non-null    int64 \n",
+      " 41  Strike_norm                      181 non-null    str   \n",
+      " 42  Strike_lists                     181 non-null    object\n",
+      " 43  strike_forefoot                  183 non-null    int64 \n",
+      " 44  strike_heel                      183 non-null    int64 \n",
+      " 45  strike_mid                       183 non-null    int64 \n",
+      "dtypes: int64(8), object(2), str(36)\n",
+      "memory usage: 65.9+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "3ef08676",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Trail terrain</th>\n",
+       "      <th>Arch support</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>Size</th>\n",
+       "      <th>...</th>\n",
+       "      <th>terrain_light</th>\n",
+       "      <th>terrain_moderate</th>\n",
+       "      <th>terrain_technical</th>\n",
+       "      <th>Arch_grouped</th>\n",
+       "      <th>arch_neutral</th>\n",
+       "      <th>arch_stability</th>\n",
+       "      <th>Strike_norm</th>\n",
+       "      <th>strike_forefoot</th>\n",
+       "      <th>strike_heel</th>\n",
+       "      <th>strike_mid</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Terrex Agravic Speed Ultra</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$220</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.3 mm 8.0 mm</td>\n",
+       "      <td>Mid/Forefoot</td>\n",
+       "      <td>Slightly large</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Mid|Forefoot</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Terrex Speed Ultra</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$160</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.2 mm 8.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>True to size</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Heel|Mid|Forefoot</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Experience Wild</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$145</td>\n",
+       "      <td>Lightmoderate</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4.3 mm 4.0 mm</td>\n",
+       "      <td>Mid/Forefoot</td>\n",
+       "      <td>True to size</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Mid|Forefoot</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Experience Wild 2</td>\n",
+       "      <td>79\\n Good!</td>\n",
+       "      <td>$140</td>\n",
+       "      <td>Light</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.1 mm 4.0 mm</td>\n",
+       "      <td>Mid/Forefoot</td>\n",
+       "      <td>-</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Mid|Forefoot</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Lone Peak 5.0</td>\n",
+       "      <td>91\\n Superb!</td>\n",
+       "      <td>$130</td>\n",
+       "      <td>Lightmoderate</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.2 mm 0.0 mm</td>\n",
+       "      <td>Mid/Forefoot</td>\n",
+       "      <td>True to size</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Mid|Forefoot</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 43 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand                        Name Audience score Price  Trail terrain  \\\n",
+       "0  Adidas  Terrex Agravic Speed Ultra   90\\n Superb!  $220          Light   \n",
+       "1  Adidas          Terrex Speed Ultra   90\\n Superb!  $160          Light   \n",
+       "2   Altra             Experience Wild    88\\n Great!  $145  Lightmoderate   \n",
+       "3   Altra           Experience Wild 2     79\\n Good!  $140          Light   \n",
+       "4   Altra               Lone Peak 5.0   91\\n Superb!  $130  Lightmoderate   \n",
+       "\n",
+       "  Arch support Lightweight Drop lab Drop brand    Strike pattern  \\\n",
+       "0      Neutral           0       0.3 mm 8.0 mm      Mid/Forefoot   \n",
+       "1      Neutral           0       8.2 mm 8.0 mm  Heelmid/Forefoot   \n",
+       "2      Neutral           0       4.3 mm 4.0 mm      Mid/Forefoot   \n",
+       "3      Neutral           0       6.1 mm 4.0 mm      Mid/Forefoot   \n",
+       "4      Neutral           0       0.2 mm 0.0 mm      Mid/Forefoot   \n",
+       "\n",
+       "             Size  ... terrain_light terrain_moderate terrain_technical  \\\n",
+       "0  Slightly large  ...             1                0                 0   \n",
+       "1    True to size  ...             1                0                 0   \n",
+       "2    True to size  ...             1                1                 0   \n",
+       "3               -  ...             1                0                 0   \n",
+       "4    True to size  ...             1                1                 0   \n",
+       "\n",
+       "  Arch_grouped arch_neutral arch_stability        Strike_norm strike_forefoot  \\\n",
+       "0      Neutral            1              0       Mid|Forefoot               1   \n",
+       "1      Neutral            1              0  Heel|Mid|Forefoot               1   \n",
+       "2      Neutral            1              0       Mid|Forefoot               1   \n",
+       "3      Neutral            1              0       Mid|Forefoot               1   \n",
+       "4      Neutral            1              0       Mid|Forefoot               1   \n",
+       "\n",
+       "  strike_heel strike_mid  \n",
+       "0           0          1  \n",
+       "1           1          1  \n",
+       "2           0          1  \n",
+       "3           0          1  \n",
+       "4           0          1  \n",
+       "\n",
+       "[5 rows x 43 columns]"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"Weight lab Weight brand\",\"terrain_lists\",\"Strike_lists\"], inplace=True)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "ca5feca3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 183 entries, 0 to 182\n",
+      "Data columns (total 43 columns):\n",
+      " #   Column                           Non-Null Count  Dtype\n",
+      "---  ------                           --------------  -----\n",
+      " 0   Brand                            181 non-null    str  \n",
+      " 1   Name                             182 non-null    str  \n",
+      " 2   Audience score                   179 non-null    str  \n",
+      " 3   Price                            181 non-null    str  \n",
+      " 4   Trail terrain                    181 non-null    str  \n",
+      " 5   Arch support                     181 non-null    str  \n",
+      " 6   Lightweight                      181 non-null    str  \n",
+      " 7   Drop lab Drop brand              181 non-null    str  \n",
+      " 8   Strike pattern                   181 non-null    str  \n",
+      " 9   Size                             181 non-null    str  \n",
+      " 10  Midsole softness                 181 non-null    str  \n",
+      " 11  Plate                            180 non-null    str  \n",
+      " 12  Toebox durability                181 non-null    str  \n",
+      " 13  Heel padding durability          181 non-null    str  \n",
+      " 14  Outsole durability               181 non-null    str  \n",
+      " 15  Breathability                    181 non-null    str  \n",
+      " 16  Width / fit                      181 non-null    str  \n",
+      " 17  Toebox width                     181 non-null    str  \n",
+      " 18  Stiffness                        181 non-null    str  \n",
+      " 19  Torsional rigidity               181 non-null    str  \n",
+      " 20  Heel counter stiffness           181 non-null    str  \n",
+      " 21  Lug depth                        181 non-null    str  \n",
+      " 22  Heel stack lab Heel stack brand  181 non-null    str  \n",
+      " 23  Forefoot lab Forefoot brand      181 non-null    str  \n",
+      " 24  Widths available                 181 non-null    str  \n",
+      " 25  For heavy runners                181 non-null    str  \n",
+      " 26  Season                           181 non-null    str  \n",
+      " 27  Removable insole                 181 non-null    str  \n",
+      " 28  Orthotic friendly                181 non-null    str  \n",
+      " 29  Waterproofing                    180 non-null    str  \n",
+      " 30  Ranking                          181 non-null    str  \n",
+      " 31  Popularity                       181 non-null    str  \n",
+      " 32  terrain_norm                     181 non-null    str  \n",
+      " 33  terrain_light                    183 non-null    int64\n",
+      " 34  terrain_moderate                 183 non-null    int64\n",
+      " 35  terrain_technical                183 non-null    int64\n",
+      " 36  Arch_grouped                     181 non-null    str  \n",
+      " 37  arch_neutral                     183 non-null    int64\n",
+      " 38  arch_stability                   183 non-null    int64\n",
+      " 39  Strike_norm                      181 non-null    str  \n",
+      " 40  strike_forefoot                  183 non-null    int64\n",
+      " 41  strike_heel                      183 non-null    int64\n",
+      " 42  strike_mid                       183 non-null    int64\n",
+      "dtypes: int64(8), str(35)\n",
+      "memory usage: 61.6 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "8316aaa1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Drop lab Drop brand</th>\n",
+       "      <th>Strike pattern</th>\n",
+       "      <th>Size</th>\n",
+       "      <th>Midsole softness</th>\n",
+       "      <th>Plate</th>\n",
+       "      <th>...</th>\n",
+       "      <th>terrain_light</th>\n",
+       "      <th>terrain_moderate</th>\n",
+       "      <th>terrain_technical</th>\n",
+       "      <th>Arch_grouped</th>\n",
+       "      <th>arch_neutral</th>\n",
+       "      <th>arch_stability</th>\n",
+       "      <th>Strike_norm</th>\n",
+       "      <th>strike_forefoot</th>\n",
+       "      <th>strike_heel</th>\n",
+       "      <th>strike_mid</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Terrex Agravic Speed Ultra</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$220</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.3 mm 8.0 mm</td>\n",
+       "      <td>Mid/Forefoot</td>\n",
+       "      <td>Slightly large</td>\n",
+       "      <td>Balanced</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Mid|Forefoot</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Terrex Speed Ultra</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$160</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.2 mm 8.0 mm</td>\n",
+       "      <td>Heelmid/Forefoot</td>\n",
+       "      <td>True to size</td>\n",
+       "      <td>-</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Heel|Mid|Forefoot</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Experience Wild</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$145</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4.3 mm 4.0 mm</td>\n",
+       "      <td>Mid/Forefoot</td>\n",
+       "      <td>True to size</td>\n",
+       "      <td>Soft</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Mid|Forefoot</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Experience Wild 2</td>\n",
+       "      <td>79\\n Good!</td>\n",
+       "      <td>$140</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.1 mm 4.0 mm</td>\n",
+       "      <td>Mid/Forefoot</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Balanced</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Mid|Forefoot</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Lone Peak 5.0</td>\n",
+       "      <td>91\\n Superb!</td>\n",
+       "      <td>$130</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.2 mm 0.0 mm</td>\n",
+       "      <td>Mid/Forefoot</td>\n",
+       "      <td>True to size</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Rock plate</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Neutral</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Mid|Forefoot</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 41 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand                        Name Audience score Price Lightweight  \\\n",
+       "0  Adidas  Terrex Agravic Speed Ultra   90\\n Superb!  $220           0   \n",
+       "1  Adidas          Terrex Speed Ultra   90\\n Superb!  $160           0   \n",
+       "2   Altra             Experience Wild    88\\n Great!  $145           0   \n",
+       "3   Altra           Experience Wild 2     79\\n Good!  $140           0   \n",
+       "4   Altra               Lone Peak 5.0   91\\n Superb!  $130           0   \n",
+       "\n",
+       "  Drop lab Drop brand    Strike pattern            Size Midsole softness  \\\n",
+       "0       0.3 mm 8.0 mm      Mid/Forefoot  Slightly large         Balanced   \n",
+       "1       8.2 mm 8.0 mm  Heelmid/Forefoot    True to size                -   \n",
+       "2       4.3 mm 4.0 mm      Mid/Forefoot    True to size             Soft   \n",
+       "3       6.1 mm 4.0 mm      Mid/Forefoot               -         Balanced   \n",
+       "4       0.2 mm 0.0 mm      Mid/Forefoot    True to size                -   \n",
+       "\n",
+       "        Plate  ... terrain_light terrain_moderate terrain_technical  \\\n",
+       "0           0  ...             1                0                 0   \n",
+       "1           0  ...             1                0                 0   \n",
+       "2           0  ...             1                1                 0   \n",
+       "3           0  ...             1                0                 0   \n",
+       "4  Rock plate  ...             1                1                 0   \n",
+       "\n",
+       "  Arch_grouped arch_neutral arch_stability        Strike_norm strike_forefoot  \\\n",
+       "0      Neutral            1              0       Mid|Forefoot               1   \n",
+       "1      Neutral            1              0  Heel|Mid|Forefoot               1   \n",
+       "2      Neutral            1              0       Mid|Forefoot               1   \n",
+       "3      Neutral            1              0       Mid|Forefoot               1   \n",
+       "4      Neutral            1              0       Mid|Forefoot               1   \n",
+       "\n",
+       "  strike_heel strike_mid  \n",
+       "0           0          1  \n",
+       "1           1          1  \n",
+       "2           0          1  \n",
+       "3           0          1  \n",
+       "4           0          1  \n",
+       "\n",
+       "[5 rows x 41 columns]"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"Trail terrain\", \"Arch support\"], inplace=True)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "32c33365",
+   "metadata": {},
+   "source": [
+    "# Split Drop Lab Drop Brand"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "32a1491d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(2)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mask_missing = (\n",
+    "    df[\"Drop lab Drop brand\"].isna() |\n",
+    "    (df[\"Drop lab Drop brand\"].astype(str).str.strip() == \"-\")\n",
+    ")\n",
+    "mask_missing.sum()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "f8d8e3bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Drop lab Drop brand  drop_lab_mm  drop_brand_mm\n",
+      "0       0.3 mm 8.0 mm          0.3            8.0\n",
+      "1       8.2 mm 8.0 mm          8.2            8.0\n",
+      "2       4.3 mm 4.0 mm          4.3            4.0\n",
+      "3       6.1 mm 4.0 mm          6.1            4.0\n",
+      "4       0.2 mm 0.0 mm          0.2            0.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "drop = df[\"Drop lab Drop brand\"].fillna(\"\").str.findall(r\"[\\d.]+\")\n",
+    "\n",
+    "# Fill missing values with empty lists\n",
+    "drop = drop.apply(lambda x: x if isinstance(x, list) else [])\n",
+    "\n",
+    "# Expand the lists into separate columns\n",
+    "drop_df = pd.DataFrame(drop.tolist(), index=df.index)\n",
+    "\n",
+    "# Handle cases where we have fewer than 2 columns\n",
+    "while len(drop_df.columns) < 2:\n",
+    "    drop_df[len(drop_df.columns)] = None\n",
+    "\n",
+    "df[[\"drop_lab_mm\", \"drop_brand_mm\"]] = drop_df.iloc[:, :2]\n",
+    "\n",
+    "for col in [\"drop_lab_mm\", \"drop_brand_mm\"]:\n",
+    "    df[col] = pd.to_numeric(df[col], errors=\"coerce\")\n",
+    "\n",
+    "print(df[[\"Drop lab Drop brand\", \"drop_lab_mm\", \"drop_brand_mm\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "e29836d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 183 entries, 0 to 182\n",
+      "Data columns (total 42 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   Brand                            181 non-null    str    \n",
+      " 1   Name                             182 non-null    str    \n",
+      " 2   Audience score                   179 non-null    str    \n",
+      " 3   Price                            181 non-null    str    \n",
+      " 4   Lightweight                      181 non-null    str    \n",
+      " 5   Strike pattern                   181 non-null    str    \n",
+      " 6   Size                             181 non-null    str    \n",
+      " 7   Midsole softness                 181 non-null    str    \n",
+      " 8   Plate                            180 non-null    str    \n",
+      " 9   Toebox durability                181 non-null    str    \n",
+      " 10  Heel padding durability          181 non-null    str    \n",
+      " 11  Outsole durability               181 non-null    str    \n",
+      " 12  Breathability                    181 non-null    str    \n",
+      " 13  Width / fit                      181 non-null    str    \n",
+      " 14  Toebox width                     181 non-null    str    \n",
+      " 15  Stiffness                        181 non-null    str    \n",
+      " 16  Torsional rigidity               181 non-null    str    \n",
+      " 17  Heel counter stiffness           181 non-null    str    \n",
+      " 18  Lug depth                        181 non-null    str    \n",
+      " 19  Heel stack lab Heel stack brand  181 non-null    str    \n",
+      " 20  Forefoot lab Forefoot brand      181 non-null    str    \n",
+      " 21  Widths available                 181 non-null    str    \n",
+      " 22  For heavy runners                181 non-null    str    \n",
+      " 23  Season                           181 non-null    str    \n",
+      " 24  Removable insole                 181 non-null    str    \n",
+      " 25  Orthotic friendly                181 non-null    str    \n",
+      " 26  Waterproofing                    180 non-null    str    \n",
+      " 27  Ranking                          181 non-null    str    \n",
+      " 28  Popularity                       181 non-null    str    \n",
+      " 29  terrain_norm                     181 non-null    str    \n",
+      " 30  terrain_light                    183 non-null    int64  \n",
+      " 31  terrain_moderate                 183 non-null    int64  \n",
+      " 32  terrain_technical                183 non-null    int64  \n",
+      " 33  Arch_grouped                     181 non-null    str    \n",
+      " 34  arch_neutral                     183 non-null    int64  \n",
+      " 35  arch_stability                   183 non-null    int64  \n",
+      " 36  Strike_norm                      181 non-null    str    \n",
+      " 37  strike_forefoot                  183 non-null    int64  \n",
+      " 38  strike_heel                      183 non-null    int64  \n",
+      " 39  strike_mid                       183 non-null    int64  \n",
+      " 40  drop_lab_mm                      181 non-null    float64\n",
+      " 41  drop_brand_mm                    174 non-null    float64\n",
+      "dtypes: float64(2), int64(8), str(32)\n",
+      "memory usage: 60.2 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"Drop lab Drop brand\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "1fe64880",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 183 entries, 0 to 182\n",
+      "Data columns (total 41 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   Brand                            181 non-null    str    \n",
+      " 1   Name                             182 non-null    str    \n",
+      " 2   Audience score                   179 non-null    str    \n",
+      " 3   Price                            181 non-null    str    \n",
+      " 4   Lightweight                      181 non-null    str    \n",
+      " 5   Size                             181 non-null    str    \n",
+      " 6   Midsole softness                 181 non-null    str    \n",
+      " 7   Plate                            180 non-null    str    \n",
+      " 8   Toebox durability                181 non-null    str    \n",
+      " 9   Heel padding durability          181 non-null    str    \n",
+      " 10  Outsole durability               181 non-null    str    \n",
+      " 11  Breathability                    181 non-null    str    \n",
+      " 12  Width / fit                      181 non-null    str    \n",
+      " 13  Toebox width                     181 non-null    str    \n",
+      " 14  Stiffness                        181 non-null    str    \n",
+      " 15  Torsional rigidity               181 non-null    str    \n",
+      " 16  Heel counter stiffness           181 non-null    str    \n",
+      " 17  Lug depth                        181 non-null    str    \n",
+      " 18  Heel stack lab Heel stack brand  181 non-null    str    \n",
+      " 19  Forefoot lab Forefoot brand      181 non-null    str    \n",
+      " 20  Widths available                 181 non-null    str    \n",
+      " 21  For heavy runners                181 non-null    str    \n",
+      " 22  Season                           181 non-null    str    \n",
+      " 23  Removable insole                 181 non-null    str    \n",
+      " 24  Orthotic friendly                181 non-null    str    \n",
+      " 25  Waterproofing                    180 non-null    str    \n",
+      " 26  Ranking                          181 non-null    str    \n",
+      " 27  Popularity                       181 non-null    str    \n",
+      " 28  terrain_norm                     181 non-null    str    \n",
+      " 29  terrain_light                    183 non-null    int64  \n",
+      " 30  terrain_moderate                 183 non-null    int64  \n",
+      " 31  terrain_technical                183 non-null    int64  \n",
+      " 32  Arch_grouped                     181 non-null    str    \n",
+      " 33  arch_neutral                     183 non-null    int64  \n",
+      " 34  arch_stability                   183 non-null    int64  \n",
+      " 35  Strike_norm                      181 non-null    str    \n",
+      " 36  strike_forefoot                  183 non-null    int64  \n",
+      " 37  strike_heel                      183 non-null    int64  \n",
+      " 38  strike_mid                       183 non-null    int64  \n",
+      " 39  drop_lab_mm                      181 non-null    float64\n",
+      " 40  drop_brand_mm                    174 non-null    float64\n",
+      "dtypes: float64(2), int64(8), str(31)\n",
+      "memory usage: 58.7 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"Strike pattern\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6125a966",
+   "metadata": {},
+   "source": [
+    "# Cleaning Midsole Softness"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "607f2da7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Midsole (4 uniques): \n",
+      " ['-', 'Balanced', 'Firm', 'Soft']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Midsole softness\n",
+       "Balanced    74\n",
+       "Soft        68\n",
+       "-           24\n",
+       "Firm        15\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Midsole softness\"] = (\n",
+    "    df[\"Midsole softness\"]\n",
+    "    .astype(str)\n",
+    "    .str.strip()\n",
+    "    .str.replace(r\"\\s+\", \" \", regex=True)\n",
+    "    .str.title()\n",
+    ")\n",
+    "\n",
+    "midsole_uniques = df[\"Midsole softness\"].dropna().unique()\n",
+    "# midsole_uniques.sort()\n",
+    "midsole_uniques = sorted(midsole_uniques)\n",
+    "print(f\"Midsole ({len(midsole_uniques)} uniques): \\n\",midsole_uniques)\n",
+    "\n",
+    "df[\"Midsole softness\"].value_counts().head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "d7c4cbc4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Midsole softness</th>\n",
+       "      <th>midsole_repr</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Terrex Speed Ultra</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Lone Peak 5.0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Lone Peak 6</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Mont Blanc</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>Brooks</td>\n",
+       "      <td>Cascadia 16</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57</th>\n",
+       "      <td>Hoka</td>\n",
+       "      <td>Zinal</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>Merrell</td>\n",
+       "      <td>Nova 2</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>78</th>\n",
+       "      <td>New Balance</td>\n",
+       "      <td>Fresh Foam Hierro v6</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>87</th>\n",
+       "      <td>New Balance</td>\n",
+       "      <td>Shando</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>89</th>\n",
+       "      <td>Nike</td>\n",
+       "      <td>Air Zoom Terra Kiger 6</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>90</th>\n",
+       "      <td>Nike</td>\n",
+       "      <td>Juniper Trail</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>Nike</td>\n",
+       "      <td>Pegasus Trail 3 GTX</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>105</th>\n",
+       "      <td>Nike</td>\n",
+       "      <td>Wildhorse 7</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>122</th>\n",
+       "      <td>Salomon</td>\n",
+       "      <td>Sense Pro 4</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>123</th>\n",
+       "      <td>Salomon</td>\n",
+       "      <td>Sense Ride 4</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>142</th>\n",
+       "      <td>Saucony</td>\n",
+       "      <td>Endorphin Trail</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>143</th>\n",
+       "      <td>Saucony</td>\n",
+       "      <td>Peregrine 11</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>144</th>\n",
+       "      <td>Saucony</td>\n",
+       "      <td>Peregrine 12</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>170</th>\n",
+       "      <td>Kailas</td>\n",
+       "      <td>Fuga EX BOA</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>174</th>\n",
+       "      <td>Kailas</td>\n",
+       "      <td>Fuga Pro 4</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175</th>\n",
+       "      <td>Kailas</td>\n",
+       "      <td>Fuga EX 2</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>176</th>\n",
+       "      <td>Kailas</td>\n",
+       "      <td>Fuga Elite 2</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>178</th>\n",
+       "      <td>Kailas</td>\n",
+       "      <td>Flythorn Air 2.0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>180</th>\n",
+       "      <td>Nike</td>\n",
+       "      <td>Pegasus Trail 4</td>\n",
+       "      <td>-</td>\n",
+       "      <td>'-'</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Brand                    Name Midsole softness midsole_repr\n",
+       "1         Adidas      Terrex Speed Ultra                -          '-'\n",
+       "4          Altra           Lone Peak 5.0                -          '-'\n",
+       "5          Altra             Lone Peak 6                -          '-'\n",
+       "9          Altra              Mont Blanc                -          '-'\n",
+       "29        Brooks             Cascadia 16                -          '-'\n",
+       "57          Hoka                   Zinal                -          '-'\n",
+       "70       Merrell                  Nova 2                -          '-'\n",
+       "78   New Balance    Fresh Foam Hierro v6                -          '-'\n",
+       "87   New Balance                  Shando                -          '-'\n",
+       "89          Nike  Air Zoom Terra Kiger 6                -          '-'\n",
+       "90          Nike           Juniper Trail                -          '-'\n",
+       "96          Nike     Pegasus Trail 3 GTX                -          '-'\n",
+       "105         Nike             Wildhorse 7                -          '-'\n",
+       "122      Salomon             Sense Pro 4                -          '-'\n",
+       "123      Salomon            Sense Ride 4                -          '-'\n",
+       "142      Saucony         Endorphin Trail                -          '-'\n",
+       "143      Saucony            Peregrine 11                -          '-'\n",
+       "144      Saucony            Peregrine 12                -          '-'\n",
+       "170       Kailas             Fuga EX BOA                -          '-'\n",
+       "174       Kailas              Fuga Pro 4                -          '-'\n",
+       "175       Kailas               Fuga EX 2                -          '-'\n",
+       "176       Kailas            Fuga Elite 2                -          '-'\n",
+       "178       Kailas        Flythorn Air 2.0                -          '-'\n",
+       "180         Nike         Pegasus Trail 4                -          '-'"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mask = df[\"Midsole softness\"].astype(str).str.strip() == \"-\"\n",
+    "df.loc[mask, [\"Brand\",\"Name\", \"Midsole softness\"]].assign(\n",
+    "    midsole_repr=df.loc[mask, \"Midsole softness\"].apply(repr)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "aa55a067",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "soft_ohe = pd.get_dummies(df[\"Midsole softness\"], prefix=\"midsole\").astype(int)\n",
+    "\n",
+    "# pastikan kolom konsisten untuk pipeline\n",
+    "for col in [\"midsole_Soft\", \"midsole_Balanced\", \"midsole_Firm\"]:\n",
+    "    if col not in soft_ohe.columns:\n",
+    "        soft_ohe[col] = 0\n",
+    "\n",
+    "# jujur enakan lowercase\n",
+    "soft_ohe = soft_ohe.rename(columns={\n",
+    "    \"midsole_Soft\": \"midsole_soft\",\n",
+    "    \"midsole_Balanced\": \"midsole_balanced\",\n",
+    "    \"midsole_Firm\": \"midsole_firm\"\n",
+    "})\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "3bec47b3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Size</th>\n",
+       "      <th>Midsole softness</th>\n",
+       "      <th>Plate</th>\n",
+       "      <th>Toebox durability</th>\n",
+       "      <th>Heel padding durability</th>\n",
+       "      <th>...</th>\n",
+       "      <th>arch_stability</th>\n",
+       "      <th>Strike_norm</th>\n",
+       "      <th>strike_forefoot</th>\n",
+       "      <th>strike_heel</th>\n",
+       "      <th>strike_mid</th>\n",
+       "      <th>drop_lab_mm</th>\n",
+       "      <th>drop_brand_mm</th>\n",
+       "      <th>midsole_soft</th>\n",
+       "      <th>midsole_balanced</th>\n",
+       "      <th>midsole_firm</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Terrex Agravic Speed Ultra</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$220</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Slightly large</td>\n",
+       "      <td>Balanced</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Good</td>\n",
+       "      <td>Good</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Mid|Forefoot</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Terrex Speed Ultra</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$160</td>\n",
+       "      <td>0</td>\n",
+       "      <td>True to size</td>\n",
+       "      <td>-</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Heel|Mid|Forefoot</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8.2</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Experience Wild</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$145</td>\n",
+       "      <td>0</td>\n",
+       "      <td>True to size</td>\n",
+       "      <td>Soft</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Decent</td>\n",
+       "      <td>Decent</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Mid|Forefoot</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4.3</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Experience Wild 2</td>\n",
+       "      <td>79\\n Good!</td>\n",
+       "      <td>$140</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Balanced</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Decent</td>\n",
+       "      <td>Good</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Mid|Forefoot</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6.1</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Lone Peak 5.0</td>\n",
+       "      <td>91\\n Superb!</td>\n",
+       "      <td>$130</td>\n",
+       "      <td>0</td>\n",
+       "      <td>True to size</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Rock plate</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Mid|Forefoot</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 44 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand                        Name Audience score Price Lightweight  \\\n",
+       "0  Adidas  Terrex Agravic Speed Ultra   90\\n Superb!  $220           0   \n",
+       "1  Adidas          Terrex Speed Ultra   90\\n Superb!  $160           0   \n",
+       "2   Altra             Experience Wild    88\\n Great!  $145           0   \n",
+       "3   Altra           Experience Wild 2     79\\n Good!  $140           0   \n",
+       "4   Altra               Lone Peak 5.0   91\\n Superb!  $130           0   \n",
+       "\n",
+       "             Size Midsole softness       Plate Toebox durability  \\\n",
+       "0  Slightly large         Balanced           0              Good   \n",
+       "1    True to size                -           0                 -   \n",
+       "2    True to size             Soft           0            Decent   \n",
+       "3               -         Balanced           0            Decent   \n",
+       "4    True to size                -  Rock plate                 -   \n",
+       "\n",
+       "  Heel padding durability  ... arch_stability        Strike_norm  \\\n",
+       "0                    Good  ...              0       Mid|Forefoot   \n",
+       "1                       -  ...              0  Heel|Mid|Forefoot   \n",
+       "2                  Decent  ...              0       Mid|Forefoot   \n",
+       "3                    Good  ...              0       Mid|Forefoot   \n",
+       "4                       -  ...              0       Mid|Forefoot   \n",
+       "\n",
+       "  strike_forefoot strike_heel strike_mid drop_lab_mm drop_brand_mm  \\\n",
+       "0               1           0          1         0.3           8.0   \n",
+       "1               1           1          1         8.2           8.0   \n",
+       "2               1           0          1         4.3           4.0   \n",
+       "3               1           0          1         6.1           4.0   \n",
+       "4               1           0          1         0.2           0.0   \n",
+       "\n",
+       "  midsole_soft midsole_balanced midsole_firm  \n",
+       "0            0                1            0  \n",
+       "1            0                0            0  \n",
+       "2            1                0            0  \n",
+       "3            0                1            0  \n",
+       "4            0                0            0  \n",
+       "\n",
+       "[5 rows x 44 columns]"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.concat([df, soft_ohe[[\"midsole_soft\",\"midsole_balanced\",\"midsole_firm\"]]], axis=1)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "0f0b4517",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 183\n",
+      "Soft: 68\n",
+      "Balanced: 74\n",
+      "Firm: 15\n",
+      "Missing midsole softness rows: 26\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Rows:\", len(df))\n",
+    "print(\"Soft:\", int(df[\"midsole_soft\"].sum()))\n",
+    "print(\"Balanced:\", int(df[\"midsole_balanced\"].sum()))\n",
+    "print(\"Firm:\", int(df[\"midsole_firm\"].sum()))\n",
+    "\n",
+    "missing_midsole = (df[[\"midsole_soft\",\"midsole_balanced\",\"midsole_firm\"]].sum(axis=1) == 0).sum()\n",
+    "print(\"Missing midsole softness rows:\", int(missing_midsole))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "0ba8bc2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Midsole softness  midsole_soft  midsole_balanced  midsole_firm\n",
+      "0         Balanced             0                 1             0\n",
+      "1                -             0                 0             0\n",
+      "2             Soft             1                 0             0\n",
+      "3         Balanced             0                 1             0\n",
+      "4                -             0                 0             0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[[\"Midsole softness\", \"midsole_soft\", \"midsole_balanced\", \"midsole_firm\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "6c9577e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 183 entries, 0 to 182\n",
+      "Data columns (total 43 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   Brand                            181 non-null    str    \n",
+      " 1   Name                             182 non-null    str    \n",
+      " 2   Audience score                   179 non-null    str    \n",
+      " 3   Price                            181 non-null    str    \n",
+      " 4   Lightweight                      181 non-null    str    \n",
+      " 5   Size                             181 non-null    str    \n",
+      " 6   Plate                            180 non-null    str    \n",
+      " 7   Toebox durability                181 non-null    str    \n",
+      " 8   Heel padding durability          181 non-null    str    \n",
+      " 9   Outsole durability               181 non-null    str    \n",
+      " 10  Breathability                    181 non-null    str    \n",
+      " 11  Width / fit                      181 non-null    str    \n",
+      " 12  Toebox width                     181 non-null    str    \n",
+      " 13  Stiffness                        181 non-null    str    \n",
+      " 14  Torsional rigidity               181 non-null    str    \n",
+      " 15  Heel counter stiffness           181 non-null    str    \n",
+      " 16  Lug depth                        181 non-null    str    \n",
+      " 17  Heel stack lab Heel stack brand  181 non-null    str    \n",
+      " 18  Forefoot lab Forefoot brand      181 non-null    str    \n",
+      " 19  Widths available                 181 non-null    str    \n",
+      " 20  For heavy runners                181 non-null    str    \n",
+      " 21  Season                           181 non-null    str    \n",
+      " 22  Removable insole                 181 non-null    str    \n",
+      " 23  Orthotic friendly                181 non-null    str    \n",
+      " 24  Waterproofing                    180 non-null    str    \n",
+      " 25  Ranking                          181 non-null    str    \n",
+      " 26  Popularity                       181 non-null    str    \n",
+      " 27  terrain_norm                     181 non-null    str    \n",
+      " 28  terrain_light                    183 non-null    int64  \n",
+      " 29  terrain_moderate                 183 non-null    int64  \n",
+      " 30  terrain_technical                183 non-null    int64  \n",
+      " 31  Arch_grouped                     181 non-null    str    \n",
+      " 32  arch_neutral                     183 non-null    int64  \n",
+      " 33  arch_stability                   183 non-null    int64  \n",
+      " 34  Strike_norm                      181 non-null    str    \n",
+      " 35  strike_forefoot                  183 non-null    int64  \n",
+      " 36  strike_heel                      183 non-null    int64  \n",
+      " 37  strike_mid                       183 non-null    int64  \n",
+      " 38  drop_lab_mm                      181 non-null    float64\n",
+      " 39  drop_brand_mm                    174 non-null    float64\n",
+      " 40  midsole_soft                     183 non-null    int64  \n",
+      " 41  midsole_balanced                 183 non-null    int64  \n",
+      " 42  midsole_firm                     183 non-null    int64  \n",
+      "dtypes: float64(2), int64(11), str(30)\n",
+      "memory usage: 61.6 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"Midsole softness\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d07928e",
+   "metadata": {},
+   "source": [
+    "# Plate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "2dae17bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<StringArray>\n",
+      "['0', 'Rock plate', 'Carbon plate', nan]\n",
+      "Length: 4, dtype: str\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Plate\"].unique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "4e624338",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# df = df.loc[:, ~df.columns.duplicated()]\n",
+    "\n",
+    "# temp_plate = (\n",
+    "#     df[\"Plate\"]\n",
+    "#     .astype(str)\n",
+    "#     .str.strip()\n",
+    "#     .str.replace(r\"\\s+\", \" \", regex=True)\n",
+    "#     .str.title()\n",
+    "# )\n",
+    "\n",
+    "# plate_ohe = pd.get_dummies(temp_plate, prefix=\"plate\").astype(int)\n",
+    "\n",
+    "# mapping = {\n",
+    "#     \"plate_Carbon Plate\": \"plate_carbon\",\n",
+    "#     \"plate_Rock Plate\": \"plate_rock\",\n",
+    "#     \"plate_0\": \"plate_none\"\n",
+    "# }\n",
+    "\n",
+    "# plate_ohe = plate_ohe.rename(columns=mapping)\n",
+    "\n",
+    "# target_cols = [\"plate_carbon\", \"plate_rock\", \"plate_none\"]\n",
+    "\n",
+    "\n",
+    "# for col in target_cols:\n",
+    "#     if col not in plate_ohe.columns:\n",
+    "#         plate_ohe[col] = 0\n",
+    "\n",
+    "\n",
+    "# df = df.drop(columns=[c for c in target_cols if c in df.columns])\n",
+    "\n",
+    "\n",
+    "# df = pd.concat([df, plate_ohe[target_cols]], axis=1)\n",
+    "\n",
+    "\n",
+    "# print(\"Sampel Plate '0':\")\n",
+    "# print(df[df[\"Plate\"] == \"0\"][[\"Plate\"] + target_cols].head(2))\n",
+    "\n",
+    "# print(\"\\nSampel Plate 'NaN' atau '-':\")\n",
+    "# mask_null = df[\"Plate\"].isna() | df[\"Plate\"].isin([\"-\", \"nan\", \"Nan\"])\n",
+    "# print(df[mask_null][[\"Plate\"] + target_cols].head(2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "26a50bd1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 183 entries, 0 to 182\n",
+      "Data columns (total 45 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   Brand                            181 non-null    str    \n",
+      " 1   Name                             182 non-null    str    \n",
+      " 2   Audience score                   179 non-null    str    \n",
+      " 3   Price                            181 non-null    str    \n",
+      " 4   Lightweight                      181 non-null    str    \n",
+      " 5   Size                             181 non-null    str    \n",
+      " 6   Toebox durability                181 non-null    str    \n",
+      " 7   Heel padding durability          181 non-null    str    \n",
+      " 8   Outsole durability               181 non-null    str    \n",
+      " 9   Breathability                    181 non-null    str    \n",
+      " 10  Width / fit                      181 non-null    str    \n",
+      " 11  Toebox width                     181 non-null    str    \n",
+      " 12  Stiffness                        181 non-null    str    \n",
+      " 13  Torsional rigidity               181 non-null    str    \n",
+      " 14  Heel counter stiffness           181 non-null    str    \n",
+      " 15  Lug depth                        181 non-null    str    \n",
+      " 16  Heel stack lab Heel stack brand  181 non-null    str    \n",
+      " 17  Forefoot lab Forefoot brand      181 non-null    str    \n",
+      " 18  Widths available                 181 non-null    str    \n",
+      " 19  For heavy runners                181 non-null    str    \n",
+      " 20  Season                           181 non-null    str    \n",
+      " 21  Removable insole                 181 non-null    str    \n",
+      " 22  Orthotic friendly                181 non-null    str    \n",
+      " 23  Waterproofing                    180 non-null    str    \n",
+      " 24  Ranking                          181 non-null    str    \n",
+      " 25  Popularity                       181 non-null    str    \n",
+      " 26  terrain_norm                     181 non-null    str    \n",
+      " 27  terrain_light                    183 non-null    int64  \n",
+      " 28  terrain_moderate                 183 non-null    int64  \n",
+      " 29  terrain_technical                183 non-null    int64  \n",
+      " 30  Arch_grouped                     181 non-null    str    \n",
+      " 31  arch_neutral                     183 non-null    int64  \n",
+      " 32  arch_stability                   183 non-null    int64  \n",
+      " 33  Strike_norm                      181 non-null    str    \n",
+      " 34  strike_forefoot                  183 non-null    int64  \n",
+      " 35  strike_heel                      183 non-null    int64  \n",
+      " 36  strike_mid                       183 non-null    int64  \n",
+      " 37  drop_lab_mm                      181 non-null    float64\n",
+      " 38  drop_brand_mm                    174 non-null    float64\n",
+      " 39  midsole_soft                     183 non-null    int64  \n",
+      " 40  midsole_balanced                 183 non-null    int64  \n",
+      " 41  midsole_firm                     183 non-null    int64  \n",
+      " 42  plate_carbon                     183 non-null    int64  \n",
+      " 43  plate_rock                       183 non-null    int64  \n",
+      " 44  plate_none                       183 non-null    int64  \n",
+      "dtypes: float64(2), int64(14), str(29)\n",
+      "memory usage: 64.5 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "7bb1e26e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 183 entries, 0 to 182\n",
+      "Data columns (total 45 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   Brand                            181 non-null    str    \n",
+      " 1   Name                             182 non-null    str    \n",
+      " 2   Audience score                   179 non-null    str    \n",
+      " 3   Price                            181 non-null    str    \n",
+      " 4   Lightweight                      181 non-null    str    \n",
+      " 5   Size                             181 non-null    str    \n",
+      " 6   Toebox durability                181 non-null    str    \n",
+      " 7   Heel padding durability          181 non-null    str    \n",
+      " 8   Outsole durability               181 non-null    str    \n",
+      " 9   Breathability                    181 non-null    str    \n",
+      " 10  Width / fit                      181 non-null    str    \n",
+      " 11  Toebox width                     181 non-null    str    \n",
+      " 12  Stiffness                        181 non-null    str    \n",
+      " 13  Torsional rigidity               181 non-null    str    \n",
+      " 14  Heel counter stiffness           181 non-null    str    \n",
+      " 15  Lug depth                        181 non-null    str    \n",
+      " 16  Heel stack lab Heel stack brand  181 non-null    str    \n",
+      " 17  Forefoot lab Forefoot brand      181 non-null    str    \n",
+      " 18  Widths available                 181 non-null    str    \n",
+      " 19  For heavy runners                181 non-null    str    \n",
+      " 20  Season                           181 non-null    str    \n",
+      " 21  Removable insole                 181 non-null    str    \n",
+      " 22  Orthotic friendly                181 non-null    str    \n",
+      " 23  Waterproofing                    180 non-null    str    \n",
+      " 24  Ranking                          181 non-null    str    \n",
+      " 25  Popularity                       181 non-null    str    \n",
+      " 26  terrain_norm                     181 non-null    str    \n",
+      " 27  terrain_light                    183 non-null    int64  \n",
+      " 28  terrain_moderate                 183 non-null    int64  \n",
+      " 29  terrain_technical                183 non-null    int64  \n",
+      " 30  Arch_grouped                     181 non-null    str    \n",
+      " 31  arch_neutral                     183 non-null    int64  \n",
+      " 32  arch_stability                   183 non-null    int64  \n",
+      " 33  Strike_norm                      181 non-null    str    \n",
+      " 34  strike_forefoot                  183 non-null    int64  \n",
+      " 35  strike_heel                      183 non-null    int64  \n",
+      " 36  strike_mid                       183 non-null    int64  \n",
+      " 37  drop_lab_mm                      181 non-null    float64\n",
+      " 38  drop_brand_mm                    174 non-null    float64\n",
+      " 39  midsole_soft                     183 non-null    int64  \n",
+      " 40  midsole_balanced                 183 non-null    int64  \n",
+      " 41  midsole_firm                     183 non-null    int64  \n",
+      " 42  plate_carbon                     183 non-null    int64  \n",
+      " 43  plate_rock                       183 non-null    int64  \n",
+      " 44  plate_none                       183 non-null    int64  \n",
+      "dtypes: float64(2), int64(14), str(29)\n",
+      "memory usage: 64.5 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# df.drop(columns=[\"Plate\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5452b19f",
+   "metadata": {},
+   "source": [
+    "# Toebox durability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "dbf23d8e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Toebox (6 uniques): \n",
+      " ['-', 'Bad', 'Decent', 'Good', 'Very Bad', 'Very Good']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Toebox durability\n",
+       "-            44\n",
+       "Good         43\n",
+       "Decent       42\n",
+       "Bad          19\n",
+       "Very Bad     18\n",
+       "Very Good    15\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Toebox durability\"] = (\n",
+    "    df[\"Toebox durability\"]\n",
+    "    .astype(str)\n",
+    "    .str.strip()\n",
+    "    .str.replace(r\"\\s+\", \" \", regex=True)\n",
+    "    .str.title()\n",
+    ")\n",
+    "\n",
+    "toebox_uniques = df[\"Toebox durability\"].dropna().unique()\n",
+    "# toebox_uniques.sort()\n",
+    "toebox_uniques = sorted(toebox_uniques)\n",
+    "print(f\"Toebox ({len(toebox_uniques)} uniques): \\n\",toebox_uniques)\n",
+    "\n",
+    "df[\"Toebox durability\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "76cc6296",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "toe_ohe = pd.get_dummies(df[\"Toebox durability\"], prefix=\"toebox\").astype(int)\n",
+    "\n",
+    "for col in [\"toebox_Bad\", \"toebox_Decent\", \"toebox_Good\"]:\n",
+    "    if col not in toe_ohe.columns:\n",
+    "        toe_ohe[col] = 0\n",
+    "\n",
+    "toe_ohe = toe_ohe.rename(columns={\n",
+    "    \"toebox_Bad\": \"toebox_bad\",\n",
+    "    \"toebox_Decent\": \"toebox_decent\",\n",
+    "    \"toebox_Good\": \"toebox_good\"\n",
+    "})\n",
+    "\n",
+    "df = pd.concat([df, toe_ohe[[\"toebox_bad\",\"toebox_decent\",\"toebox_good\"]]], axis=1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "40fc1dcb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 183\n",
+      "Bad: 19\n",
+      "Decent: 42\n",
+      "Good: 43\n",
+      "Missing toebox rows: 79\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Rows:\", len(df))\n",
+    "print(\"Bad:\", int(df[\"toebox_bad\"].sum()))\n",
+    "print(\"Decent:\", int(df[\"toebox_decent\"].sum()))\n",
+    "print(\"Good:\", int(df[\"toebox_good\"].sum()))\n",
+    "print(\"Missing toebox rows:\",\n",
+    "      int((df[[\"toebox_bad\",\"toebox_decent\",\"toebox_good\"]].sum(axis=1) == 0).sum()))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "392dbb42",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Toebox durability  toebox_decent  toebox_bad  toebox_good\n",
+      "0              Good              0           0            1\n",
+      "1                 -              0           0            0\n",
+      "2            Decent              1           0            0\n",
+      "3            Decent              1           0            0\n",
+      "4                 -              0           0            0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[[\"Toebox durability\", \"toebox_decent\", \"toebox_bad\", \"toebox_good\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "e683bba3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<StringArray>\n",
+      "['Good', '-', 'Decent', 'Very Good', 'Bad', 'Very Bad', nan]\n",
+      "Length: 7, dtype: str\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Toebox durability\"].unique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "e295ef0a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 183 entries, 0 to 182\n",
+      "Data columns (total 47 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   Brand                            181 non-null    str    \n",
+      " 1   Name                             182 non-null    str    \n",
+      " 2   Audience score                   179 non-null    str    \n",
+      " 3   Price                            181 non-null    str    \n",
+      " 4   Lightweight                      181 non-null    str    \n",
+      " 5   Size                             181 non-null    str    \n",
+      " 6   Heel padding durability          181 non-null    str    \n",
+      " 7   Outsole durability               181 non-null    str    \n",
+      " 8   Breathability                    181 non-null    str    \n",
+      " 9   Width / fit                      181 non-null    str    \n",
+      " 10  Toebox width                     181 non-null    str    \n",
+      " 11  Stiffness                        181 non-null    str    \n",
+      " 12  Torsional rigidity               181 non-null    str    \n",
+      " 13  Heel counter stiffness           181 non-null    str    \n",
+      " 14  Lug depth                        181 non-null    str    \n",
+      " 15  Heel stack lab Heel stack brand  181 non-null    str    \n",
+      " 16  Forefoot lab Forefoot brand      181 non-null    str    \n",
+      " 17  Widths available                 181 non-null    str    \n",
+      " 18  For heavy runners                181 non-null    str    \n",
+      " 19  Season                           181 non-null    str    \n",
+      " 20  Removable insole                 181 non-null    str    \n",
+      " 21  Orthotic friendly                181 non-null    str    \n",
+      " 22  Waterproofing                    180 non-null    str    \n",
+      " 23  Ranking                          181 non-null    str    \n",
+      " 24  Popularity                       181 non-null    str    \n",
+      " 25  terrain_norm                     181 non-null    str    \n",
+      " 26  terrain_light                    183 non-null    int64  \n",
+      " 27  terrain_moderate                 183 non-null    int64  \n",
+      " 28  terrain_technical                183 non-null    int64  \n",
+      " 29  Arch_grouped                     181 non-null    str    \n",
+      " 30  arch_neutral                     183 non-null    int64  \n",
+      " 31  arch_stability                   183 non-null    int64  \n",
+      " 32  Strike_norm                      181 non-null    str    \n",
+      " 33  strike_forefoot                  183 non-null    int64  \n",
+      " 34  strike_heel                      183 non-null    int64  \n",
+      " 35  strike_mid                       183 non-null    int64  \n",
+      " 36  drop_lab_mm                      181 non-null    float64\n",
+      " 37  drop_brand_mm                    174 non-null    float64\n",
+      " 38  midsole_soft                     183 non-null    int64  \n",
+      " 39  midsole_balanced                 183 non-null    int64  \n",
+      " 40  midsole_firm                     183 non-null    int64  \n",
+      " 41  plate_carbon                     183 non-null    int64  \n",
+      " 42  plate_rock                       183 non-null    int64  \n",
+      " 43  plate_none                       183 non-null    int64  \n",
+      " 44  toebox_bad                       183 non-null    int64  \n",
+      " 45  toebox_decent                    183 non-null    int64  \n",
+      " 46  toebox_good                      183 non-null    int64  \n",
+      "dtypes: float64(2), int64(17), str(28)\n",
+      "memory usage: 67.3 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"Toebox durability\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1567eabc",
+   "metadata": {},
+   "source": [
+    "# Heelpad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "1b350a6d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Heel padding (4 uniques): \n",
+      " ['-', 'Bad', 'Decent', 'Good']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Heel padding durability\n",
+       "Good      59\n",
+       "Decent    52\n",
+       "-         46\n",
+       "Bad       24\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Heel padding durability\"] = (\n",
+    "    df[\"Heel padding durability\"]\n",
+    "    .astype(str)\n",
+    "    .str.strip()\n",
+    "    .str.replace(r\"\\s+\", \" \", regex=True)\n",
+    "    .str.title()\n",
+    ")\n",
+    "\n",
+    "heel_padding_uniques = df[\"Heel padding durability\"].dropna().unique()\n",
+    "# heel_padding_uniques.sort()\n",
+    "heel_padding_uniques = sorted(heel_padding_uniques)\n",
+    "print(f\"Heel padding ({len(heel_padding_uniques)} uniques): \\n\",heel_padding_uniques)\n",
+    "\n",
+    "df[\"Heel padding durability\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "75fb7094",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Size</th>\n",
+       "      <th>Heel padding durability</th>\n",
+       "      <th>Outsole durability</th>\n",
+       "      <th>Breathability</th>\n",
+       "      <th>Width / fit</th>\n",
+       "      <th>...</th>\n",
+       "      <th>midsole_firm</th>\n",
+       "      <th>plate_carbon</th>\n",
+       "      <th>plate_rock</th>\n",
+       "      <th>plate_none</th>\n",
+       "      <th>toebox_bad</th>\n",
+       "      <th>toebox_decent</th>\n",
+       "      <th>toebox_good</th>\n",
+       "      <th>heelpad_bad</th>\n",
+       "      <th>heelpad_decent</th>\n",
+       "      <th>heelpad_good</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Terrex Agravic Speed Ultra</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$220</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Slightly large</td>\n",
+       "      <td>Good</td>\n",
+       "      <td>Decent</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Medium</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Terrex Speed Ultra</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$160</td>\n",
+       "      <td>0</td>\n",
+       "      <td>True to size</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Narrow</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Experience Wild</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$145</td>\n",
+       "      <td>0</td>\n",
+       "      <td>True to size</td>\n",
+       "      <td>Decent</td>\n",
+       "      <td>Good</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Wide</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Experience Wild 2</td>\n",
+       "      <td>79\\n Good!</td>\n",
+       "      <td>$140</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Good</td>\n",
+       "      <td>Good</td>\n",
+       "      <td>Warm</td>\n",
+       "      <td>Wide</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Lone Peak 5.0</td>\n",
+       "      <td>91\\n Superb!</td>\n",
+       "      <td>$130</td>\n",
+       "      <td>0</td>\n",
+       "      <td>True to size</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Narrow</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 50 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand                        Name Audience score Price Lightweight  \\\n",
+       "0  Adidas  Terrex Agravic Speed Ultra   90\\n Superb!  $220           0   \n",
+       "1  Adidas          Terrex Speed Ultra   90\\n Superb!  $160           0   \n",
+       "2   Altra             Experience Wild    88\\n Great!  $145           0   \n",
+       "3   Altra           Experience Wild 2     79\\n Good!  $140           0   \n",
+       "4   Altra               Lone Peak 5.0   91\\n Superb!  $130           0   \n",
+       "\n",
+       "             Size Heel padding durability Outsole durability Breathability  \\\n",
+       "0  Slightly large                    Good             Decent      Moderate   \n",
+       "1    True to size                       -                  -             -   \n",
+       "2    True to size                  Decent               Good      Moderate   \n",
+       "3               -                    Good               Good          Warm   \n",
+       "4    True to size                       -                  -             -   \n",
+       "\n",
+       "  Width / fit  ... midsole_firm plate_carbon plate_rock plate_none toebox_bad  \\\n",
+       "0      Medium  ...            0            0          0          1          0   \n",
+       "1      Narrow  ...            0            0          0          1          0   \n",
+       "2        Wide  ...            0            0          0          1          0   \n",
+       "3        Wide  ...            0            0          0          1          0   \n",
+       "4      Narrow  ...            0            0          1          0          0   \n",
+       "\n",
+       "  toebox_decent toebox_good heelpad_bad heelpad_decent heelpad_good  \n",
+       "0             0           1           0              0            1  \n",
+       "1             0           0           0              0            0  \n",
+       "2             1           0           0              1            0  \n",
+       "3             1           0           0              0            1  \n",
+       "4             0           0           0              0            0  \n",
+       "\n",
+       "[5 rows x 50 columns]"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hpd_ohe = pd.get_dummies(df[\"Heel padding durability\"], prefix=\"heel_pad\").astype(int)\n",
+    "\n",
+    "\n",
+    "for col in [\"heel_pad_Bad\", \"heel_pad_Decent\", \"heel_pad_Good\"]:\n",
+    "    if col not in hpd_ohe.columns:\n",
+    "        hpd_ohe[col] = 0\n",
+    "\n",
+    "\n",
+    "hpd_ohe = hpd_ohe.rename(columns={\n",
+    "    \"heel_pad_Bad\": \"heelpad_bad\",\n",
+    "    \"heel_pad_Decent\": \"heelpad_decent\",\n",
+    "    \"heel_pad_Good\": \"heelpad_good\"\n",
+    "})\n",
+    "\n",
+    "df = pd.concat([df, hpd_ohe[[\"heelpad_bad\", \"heelpad_decent\", \"heelpad_good\"]]], axis=1)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "15f701fb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 183\n",
+      "Bad: 24\n",
+      "Decent: 52\n",
+      "Good: 59\n",
+      "Missing heelpad durability rows: 48\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Rows:\", len(df))\n",
+    "print(\"Bad:\", int(df[\"heelpad_bad\"].sum()))\n",
+    "print(\"Decent:\", int(df[\"heelpad_decent\"].sum()))\n",
+    "print(\"Good:\", int(df[\"heelpad_good\"].sum()))\n",
+    "print(\"Missing heelpad durability rows:\",\n",
+    "      int((df[[\"heelpad_bad\",\"heelpad_decent\",\"heelpad_good\"]].sum(axis=1) == 0).sum()))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "c92cd1e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<StringArray>\n",
+      "['Good', '-', 'Decent', 'Bad', nan]\n",
+      "Length: 5, dtype: str\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Heel padding durability\"].unique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "e394e344",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Heel padding durability  heelpad_bad  heelpad_decent  heelpad_good\n",
+      "0                    Good            0               0             1\n",
+      "1                       -            0               0             0\n",
+      "2                  Decent            0               1             0\n",
+      "3                    Good            0               0             1\n",
+      "4                       -            0               0             0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[[\"Heel padding durability\", \"heelpad_bad\", \"heelpad_decent\", \"heelpad_good\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "690d9045",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 183 entries, 0 to 182\n",
+      "Data columns (total 49 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   Brand                            181 non-null    str    \n",
+      " 1   Name                             182 non-null    str    \n",
+      " 2   Audience score                   179 non-null    str    \n",
+      " 3   Price                            181 non-null    str    \n",
+      " 4   Lightweight                      181 non-null    str    \n",
+      " 5   Size                             181 non-null    str    \n",
+      " 6   Outsole durability               181 non-null    str    \n",
+      " 7   Breathability                    181 non-null    str    \n",
+      " 8   Width / fit                      181 non-null    str    \n",
+      " 9   Toebox width                     181 non-null    str    \n",
+      " 10  Stiffness                        181 non-null    str    \n",
+      " 11  Torsional rigidity               181 non-null    str    \n",
+      " 12  Heel counter stiffness           181 non-null    str    \n",
+      " 13  Lug depth                        181 non-null    str    \n",
+      " 14  Heel stack lab Heel stack brand  181 non-null    str    \n",
+      " 15  Forefoot lab Forefoot brand      181 non-null    str    \n",
+      " 16  Widths available                 181 non-null    str    \n",
+      " 17  For heavy runners                181 non-null    str    \n",
+      " 18  Season                           181 non-null    str    \n",
+      " 19  Removable insole                 181 non-null    str    \n",
+      " 20  Orthotic friendly                181 non-null    str    \n",
+      " 21  Waterproofing                    180 non-null    str    \n",
+      " 22  Ranking                          181 non-null    str    \n",
+      " 23  Popularity                       181 non-null    str    \n",
+      " 24  terrain_norm                     181 non-null    str    \n",
+      " 25  terrain_light                    183 non-null    int64  \n",
+      " 26  terrain_moderate                 183 non-null    int64  \n",
+      " 27  terrain_technical                183 non-null    int64  \n",
+      " 28  Arch_grouped                     181 non-null    str    \n",
+      " 29  arch_neutral                     183 non-null    int64  \n",
+      " 30  arch_stability                   183 non-null    int64  \n",
+      " 31  Strike_norm                      181 non-null    str    \n",
+      " 32  strike_forefoot                  183 non-null    int64  \n",
+      " 33  strike_heel                      183 non-null    int64  \n",
+      " 34  strike_mid                       183 non-null    int64  \n",
+      " 35  drop_lab_mm                      181 non-null    float64\n",
+      " 36  drop_brand_mm                    174 non-null    float64\n",
+      " 37  midsole_soft                     183 non-null    int64  \n",
+      " 38  midsole_balanced                 183 non-null    int64  \n",
+      " 39  midsole_firm                     183 non-null    int64  \n",
+      " 40  plate_carbon                     183 non-null    int64  \n",
+      " 41  plate_rock                       183 non-null    int64  \n",
+      " 42  plate_none                       183 non-null    int64  \n",
+      " 43  toebox_bad                       183 non-null    int64  \n",
+      " 44  toebox_decent                    183 non-null    int64  \n",
+      " 45  toebox_good                      183 non-null    int64  \n",
+      " 46  heelpad_bad                      183 non-null    int64  \n",
+      " 47  heelpad_decent                   183 non-null    int64  \n",
+      " 48  heelpad_good                     183 non-null    int64  \n",
+      "dtypes: float64(2), int64(20), str(27)\n",
+      "memory usage: 70.2 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"Heel padding durability\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e7bf899",
+   "metadata": {},
+   "source": [
+    "# Outsole"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "27659489",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Outsole (4 uniques): \n",
+      " ['-', 'Bad', 'Decent', 'Good']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Outsole durability\n",
+       "Good      85\n",
+       "-         52\n",
+       "Decent    43\n",
+       "Bad        1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Outsole durability\"] = (\n",
+    "    df[\"Outsole durability\"]\n",
+    "    .astype(str)\n",
+    "    .str.strip()\n",
+    "    .str.replace(r\"\\s+\", \" \", regex=True)\n",
+    "    .str.title()\n",
+    ")\n",
+    "\n",
+    "outsole_uniques = df[\"Outsole durability\"].dropna().unique()\n",
+    "# outsole_uniques.sort()\n",
+    "outsole_uniques = sorted(outsole_uniques)\n",
+    "print(f\"Outsole ({len(outsole_uniques)} uniques): \\n\",outsole_uniques)\n",
+    "\n",
+    "df[\"Outsole durability\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "d81802d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Size</th>\n",
+       "      <th>Outsole durability</th>\n",
+       "      <th>Breathability</th>\n",
+       "      <th>Width / fit</th>\n",
+       "      <th>Toebox width</th>\n",
+       "      <th>...</th>\n",
+       "      <th>plate_none</th>\n",
+       "      <th>toebox_bad</th>\n",
+       "      <th>toebox_decent</th>\n",
+       "      <th>toebox_good</th>\n",
+       "      <th>heelpad_bad</th>\n",
+       "      <th>heelpad_decent</th>\n",
+       "      <th>heelpad_good</th>\n",
+       "      <th>outsole_bad</th>\n",
+       "      <th>outsole_decent</th>\n",
+       "      <th>outsole_good</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Terrex Agravic Speed Ultra</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$220</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Slightly large</td>\n",
+       "      <td>Decent</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Medium</td>\n",
+       "      <td>Narrow</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Terrex Speed Ultra</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$160</td>\n",
+       "      <td>0</td>\n",
+       "      <td>True to size</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Narrow</td>\n",
+       "      <td>-</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Experience Wild</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$145</td>\n",
+       "      <td>0</td>\n",
+       "      <td>True to size</td>\n",
+       "      <td>Good</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Wide</td>\n",
+       "      <td>Wide</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Experience Wild 2</td>\n",
+       "      <td>79\\n Good!</td>\n",
+       "      <td>$140</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Good</td>\n",
+       "      <td>Warm</td>\n",
+       "      <td>Wide</td>\n",
+       "      <td>Wide</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Lone Peak 5.0</td>\n",
+       "      <td>91\\n Superb!</td>\n",
+       "      <td>$130</td>\n",
+       "      <td>0</td>\n",
+       "      <td>True to size</td>\n",
+       "      <td>-</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Narrow</td>\n",
+       "      <td>-</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 52 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand                        Name Audience score Price Lightweight  \\\n",
+       "0  Adidas  Terrex Agravic Speed Ultra   90\\n Superb!  $220           0   \n",
+       "1  Adidas          Terrex Speed Ultra   90\\n Superb!  $160           0   \n",
+       "2   Altra             Experience Wild    88\\n Great!  $145           0   \n",
+       "3   Altra           Experience Wild 2     79\\n Good!  $140           0   \n",
+       "4   Altra               Lone Peak 5.0   91\\n Superb!  $130           0   \n",
+       "\n",
+       "             Size Outsole durability Breathability Width / fit Toebox width  \\\n",
+       "0  Slightly large             Decent      Moderate      Medium       Narrow   \n",
+       "1    True to size                  -             -      Narrow            -   \n",
+       "2    True to size               Good      Moderate        Wide         Wide   \n",
+       "3               -               Good          Warm        Wide         Wide   \n",
+       "4    True to size                  -             -      Narrow            -   \n",
+       "\n",
+       "   ... plate_none toebox_bad toebox_decent toebox_good heelpad_bad  \\\n",
+       "0  ...          1          0             0           1           0   \n",
+       "1  ...          1          0             0           0           0   \n",
+       "2  ...          1          0             1           0           0   \n",
+       "3  ...          1          0             1           0           0   \n",
+       "4  ...          0          0             0           0           0   \n",
+       "\n",
+       "  heelpad_decent heelpad_good outsole_bad outsole_decent outsole_good  \n",
+       "0              0            1           0              1            0  \n",
+       "1              0            0           0              0            0  \n",
+       "2              1            0           0              0            1  \n",
+       "3              0            1           0              0            1  \n",
+       "4              0            0           0              0            0  \n",
+       "\n",
+       "[5 rows x 52 columns]"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "out_ohe = pd.get_dummies(df[\"Outsole durability\"], prefix=\"outsole\").astype(int)\n",
+    "\n",
+    "for col in [\"outsole_Bad\", \"outsole_Decent\", \"outsole_Good\"]:\n",
+    "    if col not in out_ohe.columns:\n",
+    "        out_ohe[col] = 0\n",
+    "\n",
+    "out_ohe = out_ohe.rename(columns={\n",
+    "    \"outsole_Bad\": \"outsole_bad\",\n",
+    "    \"outsole_Decent\": \"outsole_decent\",\n",
+    "    \"outsole_Good\": \"outsole_good\"\n",
+    "})\n",
+    "\n",
+    "df = pd.concat([df, out_ohe[[\"outsole_bad\",\"outsole_decent\",\"outsole_good\"]]], axis=1)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "17ec6fc9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "outsole_bad        1\n",
+      "outsole_decent    43\n",
+      "outsole_good      85\n",
+      "dtype: int64\n",
+      "Missing outsole rows: 54\n"
+     ]
+    }
+   ],
+   "source": [
+    "for c in [\"outsole_bad\",\"outsole_decent\",\"outsole_good\"]:\n",
+    "    assert set(df[c].unique()).issubset({0, 1})\n",
+    "\n",
+    "assert df[[\"outsole_bad\",\"outsole_decent\",\"outsole_good\"]].sum(axis=1).le(1).all()\n",
+    "\n",
+    "print(df[[\"outsole_bad\",\"outsole_decent\",\"outsole_good\"]].sum())\n",
+    "print(\"Missing outsole rows:\",\n",
+    "      int((df[[\"outsole_bad\",\"outsole_decent\",\"outsole_good\"]].sum(axis=1) == 0).sum()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "1e7e51c6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<StringArray>\n",
+      "['Decent', '-', 'Good', 'Bad', nan]\n",
+      "Length: 5, dtype: str\n",
+      "   outsole_bad  outsole_decent  outsole_good Outsole durability\n",
+      "0            0               1             0             Decent\n",
+      "1            0               0             0                  -\n",
+      "2            0               0             1               Good\n",
+      "3            0               0             1               Good\n",
+      "4            0               0             0                  -\n",
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 183 entries, 0 to 182\n",
+      "Data columns (total 51 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   Brand                            181 non-null    str    \n",
+      " 1   Name                             182 non-null    str    \n",
+      " 2   Audience score                   179 non-null    str    \n",
+      " 3   Price                            181 non-null    str    \n",
+      " 4   Lightweight                      181 non-null    str    \n",
+      " 5   Size                             181 non-null    str    \n",
+      " 6   Breathability                    181 non-null    str    \n",
+      " 7   Width / fit                      181 non-null    str    \n",
+      " 8   Toebox width                     181 non-null    str    \n",
+      " 9   Stiffness                        181 non-null    str    \n",
+      " 10  Torsional rigidity               181 non-null    str    \n",
+      " 11  Heel counter stiffness           181 non-null    str    \n",
+      " 12  Lug depth                        181 non-null    str    \n",
+      " 13  Heel stack lab Heel stack brand  181 non-null    str    \n",
+      " 14  Forefoot lab Forefoot brand      181 non-null    str    \n",
+      " 15  Widths available                 181 non-null    str    \n",
+      " 16  For heavy runners                181 non-null    str    \n",
+      " 17  Season                           181 non-null    str    \n",
+      " 18  Removable insole                 181 non-null    str    \n",
+      " 19  Orthotic friendly                181 non-null    str    \n",
+      " 20  Waterproofing                    180 non-null    str    \n",
+      " 21  Ranking                          181 non-null    str    \n",
+      " 22  Popularity                       181 non-null    str    \n",
+      " 23  terrain_norm                     181 non-null    str    \n",
+      " 24  terrain_light                    183 non-null    int64  \n",
+      " 25  terrain_moderate                 183 non-null    int64  \n",
+      " 26  terrain_technical                183 non-null    int64  \n",
+      " 27  Arch_grouped                     181 non-null    str    \n",
+      " 28  arch_neutral                     183 non-null    int64  \n",
+      " 29  arch_stability                   183 non-null    int64  \n",
+      " 30  Strike_norm                      181 non-null    str    \n",
+      " 31  strike_forefoot                  183 non-null    int64  \n",
+      " 32  strike_heel                      183 non-null    int64  \n",
+      " 33  strike_mid                       183 non-null    int64  \n",
+      " 34  drop_lab_mm                      181 non-null    float64\n",
+      " 35  drop_brand_mm                    174 non-null    float64\n",
+      " 36  midsole_soft                     183 non-null    int64  \n",
+      " 37  midsole_balanced                 183 non-null    int64  \n",
+      " 38  midsole_firm                     183 non-null    int64  \n",
+      " 39  plate_carbon                     183 non-null    int64  \n",
+      " 40  plate_rock                       183 non-null    int64  \n",
+      " 41  plate_none                       183 non-null    int64  \n",
+      " 42  toebox_bad                       183 non-null    int64  \n",
+      " 43  toebox_decent                    183 non-null    int64  \n",
+      " 44  toebox_good                      183 non-null    int64  \n",
+      " 45  heelpad_bad                      183 non-null    int64  \n",
+      " 46  heelpad_decent                   183 non-null    int64  \n",
+      " 47  heelpad_good                     183 non-null    int64  \n",
+      " 48  outsole_bad                      183 non-null    int64  \n",
+      " 49  outsole_decent                   183 non-null    int64  \n",
+      " 50  outsole_good                     183 non-null    int64  \n",
+      "dtypes: float64(2), int64(23), str(26)\n",
+      "memory usage: 73.0 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Outsole durability\"].unique())\n",
+    "\n",
+    "print(df[[\"outsole_bad\", \"outsole_decent\", \"outsole_good\", \"Outsole durability\"]].head())\n",
+    "\n",
+    "df.drop(columns=[\"Outsole durability\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c904fbb",
+   "metadata": {},
+   "source": [
+    "# Breathability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "127dae78",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<StringArray>\n",
+      "['Moderate', '-', 'Warm', 'Breathable', nan]\n",
+      "Length: 5, dtype: str\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Breathability\"].unique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "eabb5c43",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Breathability (4 uniques): \n",
+      " ['-', 'Breathable', 'Moderate', 'Warm']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Breathability\n",
+       "Moderate      102\n",
+       "Warm           39\n",
+       "-              24\n",
+       "Breathable     16\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Breathability\"] = (\n",
+    "    df[\"Breathability\"]\n",
+    "    .astype(str)\n",
+    "    .str.strip()\n",
+    "    .str.replace(r\"\\s+\", \" \", regex=True)\n",
+    "    .str.title()\n",
+    ")\n",
+    "\n",
+    "breathability_uniques = df[\"Breathability\"].dropna().unique()\n",
+    "# breathability_uniques.sort()\n",
+    "breathability_uniques = sorted(breathability_uniques)\n",
+    "print(f\"Breathability ({len(breathability_uniques)} uniques): \\n\",breathability_uniques)\n",
+    "\n",
+    "df[\"Breathability\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "9e10b644",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Size</th>\n",
+       "      <th>Breathability</th>\n",
+       "      <th>Width / fit</th>\n",
+       "      <th>Toebox width</th>\n",
+       "      <th>Stiffness</th>\n",
+       "      <th>...</th>\n",
+       "      <th>toebox_good</th>\n",
+       "      <th>heelpad_bad</th>\n",
+       "      <th>heelpad_decent</th>\n",
+       "      <th>heelpad_good</th>\n",
+       "      <th>outsole_bad</th>\n",
+       "      <th>outsole_decent</th>\n",
+       "      <th>outsole_good</th>\n",
+       "      <th>breath_breathable</th>\n",
+       "      <th>breath_moderate</th>\n",
+       "      <th>breath_warm</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Terrex Agravic Speed Ultra</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$220</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Slightly large</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Medium</td>\n",
+       "      <td>Narrow</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Terrex Speed Ultra</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$160</td>\n",
+       "      <td>0</td>\n",
+       "      <td>True to size</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Narrow</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Stiff</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Experience Wild</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$145</td>\n",
+       "      <td>0</td>\n",
+       "      <td>True to size</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Wide</td>\n",
+       "      <td>Wide</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Experience Wild 2</td>\n",
+       "      <td>79\\n Good!</td>\n",
+       "      <td>$140</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Warm</td>\n",
+       "      <td>Wide</td>\n",
+       "      <td>Wide</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Lone Peak 5.0</td>\n",
+       "      <td>91\\n Superb!</td>\n",
+       "      <td>$130</td>\n",
+       "      <td>0</td>\n",
+       "      <td>True to size</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Narrow</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Stiff</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 54 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand                        Name Audience score Price Lightweight  \\\n",
+       "0  Adidas  Terrex Agravic Speed Ultra   90\\n Superb!  $220           0   \n",
+       "1  Adidas          Terrex Speed Ultra   90\\n Superb!  $160           0   \n",
+       "2   Altra             Experience Wild    88\\n Great!  $145           0   \n",
+       "3   Altra           Experience Wild 2     79\\n Good!  $140           0   \n",
+       "4   Altra               Lone Peak 5.0   91\\n Superb!  $130           0   \n",
+       "\n",
+       "             Size Breathability Width / fit Toebox width Stiffness  ...  \\\n",
+       "0  Slightly large      Moderate      Medium       Narrow  Moderate  ...   \n",
+       "1    True to size             -      Narrow            -     Stiff  ...   \n",
+       "2    True to size      Moderate        Wide         Wide  Moderate  ...   \n",
+       "3               -          Warm        Wide         Wide  Moderate  ...   \n",
+       "4    True to size             -      Narrow            -     Stiff  ...   \n",
+       "\n",
+       "  toebox_good heelpad_bad heelpad_decent heelpad_good outsole_bad  \\\n",
+       "0           1           0              0            1           0   \n",
+       "1           0           0              0            0           0   \n",
+       "2           0           0              1            0           0   \n",
+       "3           0           0              0            1           0   \n",
+       "4           0           0              0            0           0   \n",
+       "\n",
+       "  outsole_decent outsole_good breath_breathable breath_moderate breath_warm  \n",
+       "0              1            0                 0               1           0  \n",
+       "1              0            0                 0               0           0  \n",
+       "2              0            1                 0               1           0  \n",
+       "3              0            1                 0               0           1  \n",
+       "4              0            0                 0               0           0  \n",
+       "\n",
+       "[5 rows x 54 columns]"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "breath_ohe = pd.get_dummies(df[\"Breathability\"], prefix=\"breath\").astype(int)\n",
+    "\n",
+    "for col in [\"breath_Breathable\", \"breath_Moderate\", \"breath_Warm\"]:\n",
+    "    if col not in breath_ohe.columns:\n",
+    "        breath_ohe[col] = 0\n",
+    "\n",
+    "breath_ohe = breath_ohe.rename(columns={\n",
+    "    \"breath_Breathable\": \"breath_breathable\",\n",
+    "    \"breath_Moderate\": \"breath_moderate\",\n",
+    "    \"breath_Warm\": \"breath_warm\"\n",
+    "})\n",
+    "\n",
+    "df = pd.concat([df, breath_ohe[[\"breath_breathable\",\"breath_moderate\",\"breath_warm\"]]], axis=1)\n",
+    "df.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "7c517e8f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "breath_breathable     16\n",
+      "breath_moderate      102\n",
+      "breath_warm           39\n",
+      "dtype: int64\n",
+      "Missing breathability rows: 26\n"
+     ]
+    }
+   ],
+   "source": [
+    "for c in [\"breath_breathable\",\"breath_moderate\",\"breath_warm\"]:\n",
+    "    assert set(df[c].unique()).issubset({0, 1})\n",
+    "\n",
+    "assert df[[\"breath_breathable\",\"breath_moderate\",\"breath_warm\"]].sum(axis=1).le(1).all()\n",
+    "\n",
+    "print(df[[\"breath_breathable\",\"breath_moderate\",\"breath_warm\"]].sum())\n",
+    "print(\"Missing breathability rows:\",\n",
+    "      int((df[[\"breath_breathable\",\"breath_moderate\",\"breath_warm\"]].sum(axis=1) == 0).sum()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "3fc80c24",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<StringArray>\n",
+      "['Moderate', '-', 'Warm', 'Breathable', nan]\n",
+      "Length: 5, dtype: str\n",
+      "  Breathability  breath_breathable  breath_moderate  breath_warm\n",
+      "0      Moderate                  0                1            0\n",
+      "1             -                  0                0            0\n",
+      "2      Moderate                  0                1            0\n",
+      "3          Warm                  0                0            1\n",
+      "4             -                  0                0            0\n",
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 183 entries, 0 to 182\n",
+      "Data columns (total 53 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   Brand                            181 non-null    str    \n",
+      " 1   Name                             182 non-null    str    \n",
+      " 2   Audience score                   179 non-null    str    \n",
+      " 3   Price                            181 non-null    str    \n",
+      " 4   Lightweight                      181 non-null    str    \n",
+      " 5   Size                             181 non-null    str    \n",
+      " 6   Width / fit                      181 non-null    str    \n",
+      " 7   Toebox width                     181 non-null    str    \n",
+      " 8   Stiffness                        181 non-null    str    \n",
+      " 9   Torsional rigidity               181 non-null    str    \n",
+      " 10  Heel counter stiffness           181 non-null    str    \n",
+      " 11  Lug depth                        181 non-null    str    \n",
+      " 12  Heel stack lab Heel stack brand  181 non-null    str    \n",
+      " 13  Forefoot lab Forefoot brand      181 non-null    str    \n",
+      " 14  Widths available                 181 non-null    str    \n",
+      " 15  For heavy runners                181 non-null    str    \n",
+      " 16  Season                           181 non-null    str    \n",
+      " 17  Removable insole                 181 non-null    str    \n",
+      " 18  Orthotic friendly                181 non-null    str    \n",
+      " 19  Waterproofing                    180 non-null    str    \n",
+      " 20  Ranking                          181 non-null    str    \n",
+      " 21  Popularity                       181 non-null    str    \n",
+      " 22  terrain_norm                     181 non-null    str    \n",
+      " 23  terrain_light                    183 non-null    int64  \n",
+      " 24  terrain_moderate                 183 non-null    int64  \n",
+      " 25  terrain_technical                183 non-null    int64  \n",
+      " 26  Arch_grouped                     181 non-null    str    \n",
+      " 27  arch_neutral                     183 non-null    int64  \n",
+      " 28  arch_stability                   183 non-null    int64  \n",
+      " 29  Strike_norm                      181 non-null    str    \n",
+      " 30  strike_forefoot                  183 non-null    int64  \n",
+      " 31  strike_heel                      183 non-null    int64  \n",
+      " 32  strike_mid                       183 non-null    int64  \n",
+      " 33  drop_lab_mm                      181 non-null    float64\n",
+      " 34  drop_brand_mm                    174 non-null    float64\n",
+      " 35  midsole_soft                     183 non-null    int64  \n",
+      " 36  midsole_balanced                 183 non-null    int64  \n",
+      " 37  midsole_firm                     183 non-null    int64  \n",
+      " 38  plate_carbon                     183 non-null    int64  \n",
+      " 39  plate_rock                       183 non-null    int64  \n",
+      " 40  plate_none                       183 non-null    int64  \n",
+      " 41  toebox_bad                       183 non-null    int64  \n",
+      " 42  toebox_decent                    183 non-null    int64  \n",
+      " 43  toebox_good                      183 non-null    int64  \n",
+      " 44  heelpad_bad                      183 non-null    int64  \n",
+      " 45  heelpad_decent                   183 non-null    int64  \n",
+      " 46  heelpad_good                     183 non-null    int64  \n",
+      " 47  outsole_bad                      183 non-null    int64  \n",
+      " 48  outsole_decent                   183 non-null    int64  \n",
+      " 49  outsole_good                     183 non-null    int64  \n",
+      " 50  breath_breathable                183 non-null    int64  \n",
+      " 51  breath_moderate                  183 non-null    int64  \n",
+      " 52  breath_warm                      183 non-null    int64  \n",
+      "dtypes: float64(2), int64(26), str(25)\n",
+      "memory usage: 75.9 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Breathability\"].unique())\n",
+    "\n",
+    "print(df[[\"Breathability\", \"breath_breathable\", \"breath_moderate\",\"breath_warm\"]].head())\n",
+    "\n",
+    "df.drop(columns=[\"Breathability\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "78bea9cd",
+   "metadata": {},
+   "source": [
+    "# Width / Fit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "e78e0974",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<StringArray>\n",
+      "['Medium', 'Narrow', 'Wide', nan]\n",
+      "Length: 4, dtype: str\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Width / fit\"].unique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "f8b15fc4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Width / fit (3 uniques): \n",
+      " ['Medium', 'Narrow', 'Wide']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Width / fit\n",
+       "Medium    112\n",
+       "Narrow     51\n",
+       "Wide       18\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Width / fit\"] = (\n",
+    "    df[\"Width / fit\"]\n",
+    "    .astype(str)\n",
+    "    .str.strip()\n",
+    "    .str.replace(r\"\\s+\", \" \", regex=True)\n",
+    "    .str.title()\n",
+    ")\n",
+    "\n",
+    "width_uniques = df[\"Width / fit\"].dropna().unique()\n",
+    "# width_uniques.sort()\n",
+    "width_uniques = sorted(width_uniques)\n",
+    "print(f\"Width / fit ({len(width_uniques)} uniques): \\n\",width_uniques)\n",
+    "\n",
+    "df[\"Width / fit\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "443214dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Size</th>\n",
+       "      <th>Width / fit</th>\n",
+       "      <th>Toebox width</th>\n",
+       "      <th>Stiffness</th>\n",
+       "      <th>Torsional rigidity</th>\n",
+       "      <th>...</th>\n",
+       "      <th>heelpad_good</th>\n",
+       "      <th>outsole_bad</th>\n",
+       "      <th>outsole_decent</th>\n",
+       "      <th>outsole_good</th>\n",
+       "      <th>breath_breathable</th>\n",
+       "      <th>breath_moderate</th>\n",
+       "      <th>breath_warm</th>\n",
+       "      <th>width_narrow</th>\n",
+       "      <th>width_medium</th>\n",
+       "      <th>width_wide</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Terrex Agravic Speed Ultra</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$220</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Slightly large</td>\n",
+       "      <td>Medium</td>\n",
+       "      <td>Narrow</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Stiff</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Terrex Speed Ultra</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$160</td>\n",
+       "      <td>0</td>\n",
+       "      <td>True to size</td>\n",
+       "      <td>Narrow</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Stiff</td>\n",
+       "      <td>Flexible</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Experience Wild</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$145</td>\n",
+       "      <td>0</td>\n",
+       "      <td>True to size</td>\n",
+       "      <td>Wide</td>\n",
+       "      <td>Wide</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Stiff</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Experience Wild 2</td>\n",
+       "      <td>79\\n Good!</td>\n",
+       "      <td>$140</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Wide</td>\n",
+       "      <td>Wide</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Lone Peak 5.0</td>\n",
+       "      <td>91\\n Superb!</td>\n",
+       "      <td>$130</td>\n",
+       "      <td>0</td>\n",
+       "      <td>True to size</td>\n",
+       "      <td>Narrow</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Stiff</td>\n",
+       "      <td>Flexible</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 56 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand                        Name Audience score Price Lightweight  \\\n",
+       "0  Adidas  Terrex Agravic Speed Ultra   90\\n Superb!  $220           0   \n",
+       "1  Adidas          Terrex Speed Ultra   90\\n Superb!  $160           0   \n",
+       "2   Altra             Experience Wild    88\\n Great!  $145           0   \n",
+       "3   Altra           Experience Wild 2     79\\n Good!  $140           0   \n",
+       "4   Altra               Lone Peak 5.0   91\\n Superb!  $130           0   \n",
+       "\n",
+       "             Size Width / fit Toebox width Stiffness Torsional rigidity  ...  \\\n",
+       "0  Slightly large      Medium       Narrow  Moderate              Stiff  ...   \n",
+       "1    True to size      Narrow            -     Stiff           Flexible  ...   \n",
+       "2    True to size        Wide         Wide  Moderate              Stiff  ...   \n",
+       "3               -        Wide         Wide  Moderate           Moderate  ...   \n",
+       "4    True to size      Narrow            -     Stiff           Flexible  ...   \n",
+       "\n",
+       "  heelpad_good outsole_bad outsole_decent outsole_good breath_breathable  \\\n",
+       "0            1           0              1            0                 0   \n",
+       "1            0           0              0            0                 0   \n",
+       "2            0           0              0            1                 0   \n",
+       "3            1           0              0            1                 0   \n",
+       "4            0           0              0            0                 0   \n",
+       "\n",
+       "  breath_moderate breath_warm width_narrow width_medium width_wide  \n",
+       "0               1           0            0            1          0  \n",
+       "1               0           0            1            0          0  \n",
+       "2               1           0            0            0          1  \n",
+       "3               0           1            0            0          1  \n",
+       "4               0           0            1            0          0  \n",
+       "\n",
+       "[5 rows x 56 columns]"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "width_ohe = pd.get_dummies(df[\"Width / fit\"], prefix=\"width\").astype(int)\n",
+    "\n",
+    "for col in [\"width_Narrow\", \"width_Medium\", \"width_Wide\"]:\n",
+    "    if col not in width_ohe.columns:\n",
+    "        width_ohe[col] = 0\n",
+    "\n",
+    "width_ohe = width_ohe.rename(columns={\n",
+    "    \"width_Narrow\": \"width_narrow\",\n",
+    "    \"width_Medium\": \"width_medium\",\n",
+    "    \"width_Wide\": \"width_wide\"\n",
+    "})\n",
+    "\n",
+    "df = pd.concat([df, width_ohe[[\"width_narrow\",\"width_medium\",\"width_wide\"]]], axis=1)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "67c6646d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "width_narrow     51\n",
+      "width_medium    112\n",
+      "width_wide       18\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# for c in [\"width_narrow\",\"width_medium\",\"width_wide\"]:\n",
+    "#     assert set(df[c].unique()).issubset({0, 1})\n",
+    "\n",
+    "\n",
+    "# assert (df[[\"width_narrow\",\"width_medium\",\"width_wide\"]].sum(axis=1) == 1).all()\n",
+    "\n",
+    "print(df[[\"width_narrow\",\"width_medium\",\"width_wide\"]].sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "941f04d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<StringArray>\n",
+      "['Medium', 'Narrow', 'Wide', nan]\n",
+      "Length: 4, dtype: str\n",
+      "  Width / fit  width_narrow  width_medium  width_wide\n",
+      "0      Medium             0             1           0\n",
+      "1      Narrow             1             0           0\n",
+      "2        Wide             0             0           1\n",
+      "3        Wide             0             0           1\n",
+      "4      Narrow             1             0           0\n",
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 183 entries, 0 to 182\n",
+      "Data columns (total 55 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   Brand                            181 non-null    str    \n",
+      " 1   Name                             182 non-null    str    \n",
+      " 2   Audience score                   179 non-null    str    \n",
+      " 3   Price                            181 non-null    str    \n",
+      " 4   Lightweight                      181 non-null    str    \n",
+      " 5   Size                             181 non-null    str    \n",
+      " 6   Toebox width                     181 non-null    str    \n",
+      " 7   Stiffness                        181 non-null    str    \n",
+      " 8   Torsional rigidity               181 non-null    str    \n",
+      " 9   Heel counter stiffness           181 non-null    str    \n",
+      " 10  Lug depth                        181 non-null    str    \n",
+      " 11  Heel stack lab Heel stack brand  181 non-null    str    \n",
+      " 12  Forefoot lab Forefoot brand      181 non-null    str    \n",
+      " 13  Widths available                 181 non-null    str    \n",
+      " 14  For heavy runners                181 non-null    str    \n",
+      " 15  Season                           181 non-null    str    \n",
+      " 16  Removable insole                 181 non-null    str    \n",
+      " 17  Orthotic friendly                181 non-null    str    \n",
+      " 18  Waterproofing                    180 non-null    str    \n",
+      " 19  Ranking                          181 non-null    str    \n",
+      " 20  Popularity                       181 non-null    str    \n",
+      " 21  terrain_norm                     181 non-null    str    \n",
+      " 22  terrain_light                    183 non-null    int64  \n",
+      " 23  terrain_moderate                 183 non-null    int64  \n",
+      " 24  terrain_technical                183 non-null    int64  \n",
+      " 25  Arch_grouped                     181 non-null    str    \n",
+      " 26  arch_neutral                     183 non-null    int64  \n",
+      " 27  arch_stability                   183 non-null    int64  \n",
+      " 28  Strike_norm                      181 non-null    str    \n",
+      " 29  strike_forefoot                  183 non-null    int64  \n",
+      " 30  strike_heel                      183 non-null    int64  \n",
+      " 31  strike_mid                       183 non-null    int64  \n",
+      " 32  drop_lab_mm                      181 non-null    float64\n",
+      " 33  drop_brand_mm                    174 non-null    float64\n",
+      " 34  midsole_soft                     183 non-null    int64  \n",
+      " 35  midsole_balanced                 183 non-null    int64  \n",
+      " 36  midsole_firm                     183 non-null    int64  \n",
+      " 37  plate_carbon                     183 non-null    int64  \n",
+      " 38  plate_rock                       183 non-null    int64  \n",
+      " 39  plate_none                       183 non-null    int64  \n",
+      " 40  toebox_bad                       183 non-null    int64  \n",
+      " 41  toebox_decent                    183 non-null    int64  \n",
+      " 42  toebox_good                      183 non-null    int64  \n",
+      " 43  heelpad_bad                      183 non-null    int64  \n",
+      " 44  heelpad_decent                   183 non-null    int64  \n",
+      " 45  heelpad_good                     183 non-null    int64  \n",
+      " 46  outsole_bad                      183 non-null    int64  \n",
+      " 47  outsole_decent                   183 non-null    int64  \n",
+      " 48  outsole_good                     183 non-null    int64  \n",
+      " 49  breath_breathable                183 non-null    int64  \n",
+      " 50  breath_moderate                  183 non-null    int64  \n",
+      " 51  breath_warm                      183 non-null    int64  \n",
+      " 52  width_narrow                     183 non-null    int64  \n",
+      " 53  width_medium                     183 non-null    int64  \n",
+      " 54  width_wide                       183 non-null    int64  \n",
+      "dtypes: float64(2), int64(29), str(24)\n",
+      "memory usage: 78.8 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Width / fit\"].unique())\n",
+    "\n",
+    "print(df[[\"Width / fit\", \"width_narrow\",\"width_medium\",\"width_wide\"]].head())\n",
+    "\n",
+    "df.drop(columns=[\"Width / fit\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05e199f9",
+   "metadata": {},
+   "source": [
+    "# Toebox Witdth"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "13ec5038",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<StringArray>\n",
+      "['Narrow', '-', 'Wide', 'Medium', nan]\n",
+      "Length: 5, dtype: str\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Toebox width\"].unique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "0e011a58",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Toebox width (4 uniques): \n",
+      " ['-', 'Medium', 'Narrow', 'Wide']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Toebox width\n",
+       "Medium    80\n",
+       "Wide      41\n",
+       "-         39\n",
+       "Narrow    21\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Toebox width\"] = (\n",
+    "    df[\"Toebox width\"]\n",
+    "    .astype(str)\n",
+    "    .str.strip()\n",
+    "    .str.replace(r\"\\s+\", \" \", regex=True)\n",
+    "    .str.title()\n",
+    ")\n",
+    "\n",
+    "toebox_uniques = df[\"Toebox width\"].dropna().unique()\n",
+    "# toebox_uniques.sort()\n",
+    "toebox_uniques = sorted(toebox_uniques)\n",
+    "print(f\"Toebox width ({len(toebox_uniques)} uniques): \\n\",toebox_uniques)\n",
+    "\n",
+    "df[\"Toebox width\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "id": "733f304c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Size</th>\n",
+       "      <th>Toebox width</th>\n",
+       "      <th>Stiffness</th>\n",
+       "      <th>Torsional rigidity</th>\n",
+       "      <th>Heel counter stiffness</th>\n",
+       "      <th>...</th>\n",
+       "      <th>outsole_good</th>\n",
+       "      <th>breath_breathable</th>\n",
+       "      <th>breath_moderate</th>\n",
+       "      <th>breath_warm</th>\n",
+       "      <th>width_narrow</th>\n",
+       "      <th>width_medium</th>\n",
+       "      <th>width_wide</th>\n",
+       "      <th>toeboxwidth_narrow</th>\n",
+       "      <th>toeboxwidth_medium</th>\n",
+       "      <th>toeboxwidth_wide</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Terrex Agravic Speed Ultra</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$220</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Slightly large</td>\n",
+       "      <td>Narrow</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Stiff</td>\n",
+       "      <td>Flexible</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Terrex Speed Ultra</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$160</td>\n",
+       "      <td>0</td>\n",
+       "      <td>True to size</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Stiff</td>\n",
+       "      <td>Flexible</td>\n",
+       "      <td>Flexible</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Experience Wild</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$145</td>\n",
+       "      <td>0</td>\n",
+       "      <td>True to size</td>\n",
+       "      <td>Wide</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Stiff</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Experience Wild 2</td>\n",
+       "      <td>79\\n Good!</td>\n",
+       "      <td>$140</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Wide</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Flexible</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Lone Peak 5.0</td>\n",
+       "      <td>91\\n Superb!</td>\n",
+       "      <td>$130</td>\n",
+       "      <td>0</td>\n",
+       "      <td>True to size</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Stiff</td>\n",
+       "      <td>Flexible</td>\n",
+       "      <td>-</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 58 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand                        Name Audience score Price Lightweight  \\\n",
+       "0  Adidas  Terrex Agravic Speed Ultra   90\\n Superb!  $220           0   \n",
+       "1  Adidas          Terrex Speed Ultra   90\\n Superb!  $160           0   \n",
+       "2   Altra             Experience Wild    88\\n Great!  $145           0   \n",
+       "3   Altra           Experience Wild 2     79\\n Good!  $140           0   \n",
+       "4   Altra               Lone Peak 5.0   91\\n Superb!  $130           0   \n",
+       "\n",
+       "             Size Toebox width Stiffness Torsional rigidity  \\\n",
+       "0  Slightly large       Narrow  Moderate              Stiff   \n",
+       "1    True to size            -     Stiff           Flexible   \n",
+       "2    True to size         Wide  Moderate              Stiff   \n",
+       "3               -         Wide  Moderate           Moderate   \n",
+       "4    True to size            -     Stiff           Flexible   \n",
+       "\n",
+       "  Heel counter stiffness  ... outsole_good breath_breathable breath_moderate  \\\n",
+       "0               Flexible  ...            0                 0               1   \n",
+       "1               Flexible  ...            0                 0               0   \n",
+       "2               Moderate  ...            1                 0               1   \n",
+       "3               Flexible  ...            1                 0               0   \n",
+       "4                      -  ...            0                 0               0   \n",
+       "\n",
+       "  breath_warm width_narrow width_medium width_wide toeboxwidth_narrow  \\\n",
+       "0           0            0            1          0                  1   \n",
+       "1           0            1            0          0                  0   \n",
+       "2           0            0            0          1                  0   \n",
+       "3           1            0            0          1                  0   \n",
+       "4           0            1            0          0                  0   \n",
+       "\n",
+       "  toeboxwidth_medium toeboxwidth_wide  \n",
+       "0                  0                0  \n",
+       "1                  0                0  \n",
+       "2                  0                1  \n",
+       "3                  0                1  \n",
+       "4                  0                0  \n",
+       "\n",
+       "[5 rows x 58 columns]"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tw_ohe = pd.get_dummies(df[\"Toebox width\"], prefix=\"toeboxwidth\").astype(int)\n",
+    "\n",
+    "for col in [\"toeboxwidth_Narrow\", \"toeboxwidth_Medium\", \"toeboxwidth_Wide\"]:\n",
+    "    if col not in tw_ohe.columns:\n",
+    "        tw_ohe[col] = 0\n",
+    "\n",
+    "tw_ohe = tw_ohe.rename(columns={\n",
+    "    \"toeboxwidth_Narrow\": \"toeboxwidth_narrow\",\n",
+    "    \"toeboxwidth_Medium\": \"toeboxwidth_medium\",\n",
+    "    \"toeboxwidth_Wide\": \"toeboxwidth_wide\"\n",
+    "})\n",
+    "\n",
+    "df = pd.concat([df, tw_ohe[[\"toeboxwidth_narrow\",\"toeboxwidth_medium\",\"toeboxwidth_wide\"]]], axis=1)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "ea5b559b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "toeboxwidth_narrow    21\n",
+      "toeboxwidth_medium    80\n",
+      "toeboxwidth_wide      41\n",
+      "dtype: int64\n",
+      "Missing toebox width rows: 41\n"
+     ]
+    }
+   ],
+   "source": [
+    "# hanya 0 / 1\n",
+    "for c in [\"toeboxwidth_narrow\",\"toeboxwidth_medium\",\"toeboxwidth_wide\"]:\n",
+    "    assert set(df[c].unique()).issubset({0, 1})\n",
+    "\n",
+    "# single-label atau missing\n",
+    "assert df[[\"toeboxwidth_narrow\",\"toeboxwidth_medium\",\"toeboxwidth_wide\"]].sum(axis=1).le(1).all()\n",
+    "\n",
+    "print(df[[\"toeboxwidth_narrow\",\"toeboxwidth_medium\",\"toeboxwidth_wide\"]].sum())\n",
+    "print(\n",
+    "    \"Missing toebox width rows:\",\n",
+    "    int((df[[\"toeboxwidth_narrow\",\"toeboxwidth_medium\",\"toeboxwidth_wide\"]].sum(axis=1) == 0).sum())\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "id": "a9535476",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<StringArray>\n",
+      "['Narrow', '-', 'Wide', 'Medium', nan]\n",
+      "Length: 5, dtype: str\n",
+      "  Toebox width  toeboxwidth_narrow  toeboxwidth_medium  toeboxwidth_wide\n",
+      "0       Narrow                   1                   0                 0\n",
+      "1            -                   0                   0                 0\n",
+      "2         Wide                   0                   0                 1\n",
+      "3         Wide                   0                   0                 1\n",
+      "4            -                   0                   0                 0\n",
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 183 entries, 0 to 182\n",
+      "Data columns (total 57 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   Brand                            181 non-null    str    \n",
+      " 1   Name                             182 non-null    str    \n",
+      " 2   Audience score                   179 non-null    str    \n",
+      " 3   Price                            181 non-null    str    \n",
+      " 4   Lightweight                      181 non-null    str    \n",
+      " 5   Size                             181 non-null    str    \n",
+      " 6   Stiffness                        181 non-null    str    \n",
+      " 7   Torsional rigidity               181 non-null    str    \n",
+      " 8   Heel counter stiffness           181 non-null    str    \n",
+      " 9   Lug depth                        181 non-null    str    \n",
+      " 10  Heel stack lab Heel stack brand  181 non-null    str    \n",
+      " 11  Forefoot lab Forefoot brand      181 non-null    str    \n",
+      " 12  Widths available                 181 non-null    str    \n",
+      " 13  For heavy runners                181 non-null    str    \n",
+      " 14  Season                           181 non-null    str    \n",
+      " 15  Removable insole                 181 non-null    str    \n",
+      " 16  Orthotic friendly                181 non-null    str    \n",
+      " 17  Waterproofing                    180 non-null    str    \n",
+      " 18  Ranking                          181 non-null    str    \n",
+      " 19  Popularity                       181 non-null    str    \n",
+      " 20  terrain_norm                     181 non-null    str    \n",
+      " 21  terrain_light                    183 non-null    int64  \n",
+      " 22  terrain_moderate                 183 non-null    int64  \n",
+      " 23  terrain_technical                183 non-null    int64  \n",
+      " 24  Arch_grouped                     181 non-null    str    \n",
+      " 25  arch_neutral                     183 non-null    int64  \n",
+      " 26  arch_stability                   183 non-null    int64  \n",
+      " 27  Strike_norm                      181 non-null    str    \n",
+      " 28  strike_forefoot                  183 non-null    int64  \n",
+      " 29  strike_heel                      183 non-null    int64  \n",
+      " 30  strike_mid                       183 non-null    int64  \n",
+      " 31  drop_lab_mm                      181 non-null    float64\n",
+      " 32  drop_brand_mm                    174 non-null    float64\n",
+      " 33  midsole_soft                     183 non-null    int64  \n",
+      " 34  midsole_balanced                 183 non-null    int64  \n",
+      " 35  midsole_firm                     183 non-null    int64  \n",
+      " 36  plate_carbon                     183 non-null    int64  \n",
+      " 37  plate_rock                       183 non-null    int64  \n",
+      " 38  plate_none                       183 non-null    int64  \n",
+      " 39  toebox_bad                       183 non-null    int64  \n",
+      " 40  toebox_decent                    183 non-null    int64  \n",
+      " 41  toebox_good                      183 non-null    int64  \n",
+      " 42  heelpad_bad                      183 non-null    int64  \n",
+      " 43  heelpad_decent                   183 non-null    int64  \n",
+      " 44  heelpad_good                     183 non-null    int64  \n",
+      " 45  outsole_bad                      183 non-null    int64  \n",
+      " 46  outsole_decent                   183 non-null    int64  \n",
+      " 47  outsole_good                     183 non-null    int64  \n",
+      " 48  breath_breathable                183 non-null    int64  \n",
+      " 49  breath_moderate                  183 non-null    int64  \n",
+      " 50  breath_warm                      183 non-null    int64  \n",
+      " 51  width_narrow                     183 non-null    int64  \n",
+      " 52  width_medium                     183 non-null    int64  \n",
+      " 53  width_wide                       183 non-null    int64  \n",
+      " 54  toeboxwidth_narrow               183 non-null    int64  \n",
+      " 55  toeboxwidth_medium               183 non-null    int64  \n",
+      " 56  toeboxwidth_wide                 183 non-null    int64  \n",
+      "dtypes: float64(2), int64(32), str(23)\n",
+      "memory usage: 81.6 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Toebox width\"].unique())\n",
+    "\n",
+    "print(df[[\"Toebox width\", \"toeboxwidth_narrow\",\"toeboxwidth_medium\",\"toeboxwidth_wide\"]].head())\n",
+    "\n",
+    "df.drop(columns=[\"Toebox width\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "004e80e2",
+   "metadata": {},
+   "source": [
+    "# Stiffness"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "c70cdef8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<StringArray>\n",
+      "['Moderate', 'Stiff', 'Flexible', nan]\n",
+      "Length: 4, dtype: str\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Stiffness\"].unique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "37050473",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Stiffness (3 uniques): \n",
+      " ['Flexible', 'Moderate', 'Stiff']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Stiffness\n",
+       "Stiff       110\n",
+       "Moderate     65\n",
+       "Flexible      6\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Stiffness\"] = (\n",
+    "    df[\"Stiffness\"]\n",
+    "    .astype(str)\n",
+    "    .str.strip()\n",
+    "    .str.replace(r\"\\s+\", \" \", regex=True)\n",
+    "    .str.title()\n",
+    ")\n",
+    "\n",
+    "stiffness_uniques = df[\"Stiffness\"].dropna().unique()\n",
+    "# stiffness_uniques.sort()\n",
+    "stiffness_uniques = sorted(stiffness_uniques)\n",
+    "print(f\"Stiffness ({len(stiffness_uniques)} uniques): \\n\",stiffness_uniques)\n",
+    "\n",
+    "df[\"Stiffness\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "08aa7453",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Size</th>\n",
+       "      <th>Stiffness</th>\n",
+       "      <th>Torsional rigidity</th>\n",
+       "      <th>Heel counter stiffness</th>\n",
+       "      <th>Lug depth</th>\n",
+       "      <th>...</th>\n",
+       "      <th>breath_warm</th>\n",
+       "      <th>width_narrow</th>\n",
+       "      <th>width_medium</th>\n",
+       "      <th>width_wide</th>\n",
+       "      <th>toeboxwidth_narrow</th>\n",
+       "      <th>toeboxwidth_medium</th>\n",
+       "      <th>toeboxwidth_wide</th>\n",
+       "      <th>stiff_flexible</th>\n",
+       "      <th>stiff_moderate</th>\n",
+       "      <th>stiff_stiff</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Terrex Agravic Speed Ultra</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$220</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Slightly large</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Stiff</td>\n",
+       "      <td>Flexible</td>\n",
+       "      <td>2.5 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Terrex Speed Ultra</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$160</td>\n",
+       "      <td>0</td>\n",
+       "      <td>True to size</td>\n",
+       "      <td>Stiff</td>\n",
+       "      <td>Flexible</td>\n",
+       "      <td>Flexible</td>\n",
+       "      <td>2.6 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Experience Wild</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$145</td>\n",
+       "      <td>0</td>\n",
+       "      <td>True to size</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Stiff</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>3.6 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Experience Wild 2</td>\n",
+       "      <td>79\\n Good!</td>\n",
+       "      <td>$140</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Flexible</td>\n",
+       "      <td>3.5 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Lone Peak 5.0</td>\n",
+       "      <td>91\\n Superb!</td>\n",
+       "      <td>$130</td>\n",
+       "      <td>0</td>\n",
+       "      <td>True to size</td>\n",
+       "      <td>Stiff</td>\n",
+       "      <td>Flexible</td>\n",
+       "      <td>-</td>\n",
+       "      <td>3.7 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 60 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand                        Name Audience score Price Lightweight  \\\n",
+       "0  Adidas  Terrex Agravic Speed Ultra   90\\n Superb!  $220           0   \n",
+       "1  Adidas          Terrex Speed Ultra   90\\n Superb!  $160           0   \n",
+       "2   Altra             Experience Wild    88\\n Great!  $145           0   \n",
+       "3   Altra           Experience Wild 2     79\\n Good!  $140           0   \n",
+       "4   Altra               Lone Peak 5.0   91\\n Superb!  $130           0   \n",
+       "\n",
+       "             Size Stiffness Torsional rigidity Heel counter stiffness  \\\n",
+       "0  Slightly large  Moderate              Stiff               Flexible   \n",
+       "1    True to size     Stiff           Flexible               Flexible   \n",
+       "2    True to size  Moderate              Stiff               Moderate   \n",
+       "3               -  Moderate           Moderate               Flexible   \n",
+       "4    True to size     Stiff           Flexible                      -   \n",
+       "\n",
+       "  Lug depth  ... breath_warm width_narrow width_medium width_wide  \\\n",
+       "0    2.5 mm  ...           0            0            1          0   \n",
+       "1    2.6 mm  ...           0            1            0          0   \n",
+       "2    3.6 mm  ...           0            0            0          1   \n",
+       "3    3.5 mm  ...           1            0            0          1   \n",
+       "4    3.7 mm  ...           0            1            0          0   \n",
+       "\n",
+       "  toeboxwidth_narrow toeboxwidth_medium toeboxwidth_wide stiff_flexible  \\\n",
+       "0                  1                  0                0              0   \n",
+       "1                  0                  0                0              0   \n",
+       "2                  0                  0                1              0   \n",
+       "3                  0                  0                1              0   \n",
+       "4                  0                  0                0              0   \n",
+       "\n",
+       "  stiff_moderate stiff_stiff  \n",
+       "0              1           0  \n",
+       "1              0           1  \n",
+       "2              1           0  \n",
+       "3              1           0  \n",
+       "4              0           1  \n",
+       "\n",
+       "[5 rows x 60 columns]"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stiff_ohe = pd.get_dummies(df[\"Stiffness\"], prefix=\"stiff\").astype(int)\n",
+    "\n",
+    "for col in [\"stiff_Flexible\", \"stiff_Moderate\", \"stiff_Stiff\"]:\n",
+    "    if col not in stiff_ohe.columns:\n",
+    "        stiff_ohe[col] = 0\n",
+    "\n",
+    "stiff_ohe = stiff_ohe.rename(columns={\n",
+    "    \"stiff_Flexible\": \"stiff_flexible\",\n",
+    "    \"stiff_Moderate\": \"stiff_moderate\",\n",
+    "    \"stiff_Stiff\": \"stiff_stiff\"\n",
+    "})\n",
+    "\n",
+    "df = pd.concat([df, stiff_ohe[[\"stiff_flexible\",\"stiff_moderate\",\"stiff_stiff\"]]], axis=1)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "0c014eba",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "stiff_flexible      6\n",
+      "stiff_moderate     65\n",
+      "stiff_stiff       110\n",
+      "dtype: int64\n",
+      "Missing stiffness rows: 2\n"
+     ]
+    }
+   ],
+   "source": [
+    "# hanya 0 / 1\n",
+    "for c in [\"stiff_flexible\",\"stiff_moderate\",\"stiff_stiff\"]:\n",
+    "    assert set(df[c].unique()).issubset({0, 1})\n",
+    "\n",
+    "# single-label atau missing\n",
+    "assert df[[\"stiff_flexible\",\"stiff_moderate\",\"stiff_stiff\"]].sum(axis=1).le(1).all()\n",
+    "\n",
+    "print(df[[\"stiff_flexible\",\"stiff_moderate\",\"stiff_stiff\"]].sum())\n",
+    "print(\n",
+    "    \"Missing stiffness rows:\",\n",
+    "    int((df[[\"stiff_flexible\",\"stiff_moderate\",\"stiff_stiff\"]].sum(axis=1) == 0).sum())\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "b6f0c746",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<StringArray>\n",
+      "['Moderate', 'Stiff', 'Flexible', nan]\n",
+      "Length: 4, dtype: str\n",
+      "  Stiffness  stiff_flexible  stiff_moderate  stiff_stiff\n",
+      "0  Moderate               0               1            0\n",
+      "1     Stiff               0               0            1\n",
+      "2  Moderate               0               1            0\n",
+      "3  Moderate               0               1            0\n",
+      "4     Stiff               0               0            1\n",
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 183 entries, 0 to 182\n",
+      "Data columns (total 59 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   Brand                            181 non-null    str    \n",
+      " 1   Name                             182 non-null    str    \n",
+      " 2   Audience score                   179 non-null    str    \n",
+      " 3   Price                            181 non-null    str    \n",
+      " 4   Lightweight                      181 non-null    str    \n",
+      " 5   Size                             181 non-null    str    \n",
+      " 6   Torsional rigidity               181 non-null    str    \n",
+      " 7   Heel counter stiffness           181 non-null    str    \n",
+      " 8   Lug depth                        181 non-null    str    \n",
+      " 9   Heel stack lab Heel stack brand  181 non-null    str    \n",
+      " 10  Forefoot lab Forefoot brand      181 non-null    str    \n",
+      " 11  Widths available                 181 non-null    str    \n",
+      " 12  For heavy runners                181 non-null    str    \n",
+      " 13  Season                           181 non-null    str    \n",
+      " 14  Removable insole                 181 non-null    str    \n",
+      " 15  Orthotic friendly                181 non-null    str    \n",
+      " 16  Waterproofing                    180 non-null    str    \n",
+      " 17  Ranking                          181 non-null    str    \n",
+      " 18  Popularity                       181 non-null    str    \n",
+      " 19  terrain_norm                     181 non-null    str    \n",
+      " 20  terrain_light                    183 non-null    int64  \n",
+      " 21  terrain_moderate                 183 non-null    int64  \n",
+      " 22  terrain_technical                183 non-null    int64  \n",
+      " 23  Arch_grouped                     181 non-null    str    \n",
+      " 24  arch_neutral                     183 non-null    int64  \n",
+      " 25  arch_stability                   183 non-null    int64  \n",
+      " 26  Strike_norm                      181 non-null    str    \n",
+      " 27  strike_forefoot                  183 non-null    int64  \n",
+      " 28  strike_heel                      183 non-null    int64  \n",
+      " 29  strike_mid                       183 non-null    int64  \n",
+      " 30  drop_lab_mm                      181 non-null    float64\n",
+      " 31  drop_brand_mm                    174 non-null    float64\n",
+      " 32  midsole_soft                     183 non-null    int64  \n",
+      " 33  midsole_balanced                 183 non-null    int64  \n",
+      " 34  midsole_firm                     183 non-null    int64  \n",
+      " 35  plate_carbon                     183 non-null    int64  \n",
+      " 36  plate_rock                       183 non-null    int64  \n",
+      " 37  plate_none                       183 non-null    int64  \n",
+      " 38  toebox_bad                       183 non-null    int64  \n",
+      " 39  toebox_decent                    183 non-null    int64  \n",
+      " 40  toebox_good                      183 non-null    int64  \n",
+      " 41  heelpad_bad                      183 non-null    int64  \n",
+      " 42  heelpad_decent                   183 non-null    int64  \n",
+      " 43  heelpad_good                     183 non-null    int64  \n",
+      " 44  outsole_bad                      183 non-null    int64  \n",
+      " 45  outsole_decent                   183 non-null    int64  \n",
+      " 46  outsole_good                     183 non-null    int64  \n",
+      " 47  breath_breathable                183 non-null    int64  \n",
+      " 48  breath_moderate                  183 non-null    int64  \n",
+      " 49  breath_warm                      183 non-null    int64  \n",
+      " 50  width_narrow                     183 non-null    int64  \n",
+      " 51  width_medium                     183 non-null    int64  \n",
+      " 52  width_wide                       183 non-null    int64  \n",
+      " 53  toeboxwidth_narrow               183 non-null    int64  \n",
+      " 54  toeboxwidth_medium               183 non-null    int64  \n",
+      " 55  toeboxwidth_wide                 183 non-null    int64  \n",
+      " 56  stiff_flexible                   183 non-null    int64  \n",
+      " 57  stiff_moderate                   183 non-null    int64  \n",
+      " 58  stiff_stiff                      183 non-null    int64  \n",
+      "dtypes: float64(2), int64(35), str(22)\n",
+      "memory usage: 84.5 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Stiffness\"].unique())\n",
+    "\n",
+    "print(df[[\"Stiffness\", \"stiff_flexible\",\"stiff_moderate\",\"stiff_stiff\"]].head())\n",
+    "\n",
+    "df.drop(columns=[\"Stiffness\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f1bff4bd",
+   "metadata": {},
+   "source": [
+    "# Torsional Rigidity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "id": "e39cc286",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<StringArray>\n",
+      "['Stiff', 'Flexible', 'Moderate', '-', nan]\n",
+      "Length: 5, dtype: str\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Torsional rigidity\"].unique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "id": "371b0c25",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Torsional rigidity (4 uniques): \n",
+      " ['-', 'Flexible', 'Moderate', 'Stiff']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Torsional rigidity\n",
+       "Stiff       103\n",
+       "Moderate     46\n",
+       "Flexible     26\n",
+       "-             6\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Torsional rigidity\"] = (\n",
+    "    df[\"Torsional rigidity\"]\n",
+    "    .astype(str)\n",
+    "    .str.strip()\n",
+    "    .str.replace(r\"\\s+\", \" \", regex=True)\n",
+    "    .str.title()\n",
+    ")\n",
+    "\n",
+    "torsional_rigidity_uniques = df[\"Torsional rigidity\"].dropna().unique()\n",
+    "# torsional_rigidity_uniques.sort()\n",
+    "torsional_rigidity_uniques = sorted(torsional_rigidity_uniques)\n",
+    "print(f\"Torsional rigidity ({len(torsional_rigidity_uniques)} uniques): \\n\",torsional_rigidity_uniques)\n",
+    "\n",
+    "df[\"Torsional rigidity\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "id": "383815f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Size</th>\n",
+       "      <th>Torsional rigidity</th>\n",
+       "      <th>Heel counter stiffness</th>\n",
+       "      <th>Lug depth</th>\n",
+       "      <th>Heel stack lab Heel stack brand</th>\n",
+       "      <th>...</th>\n",
+       "      <th>width_wide</th>\n",
+       "      <th>toeboxwidth_narrow</th>\n",
+       "      <th>toeboxwidth_medium</th>\n",
+       "      <th>toeboxwidth_wide</th>\n",
+       "      <th>stiff_flexible</th>\n",
+       "      <th>stiff_moderate</th>\n",
+       "      <th>stiff_stiff</th>\n",
+       "      <th>torsion_flexible</th>\n",
+       "      <th>torsion_moderate</th>\n",
+       "      <th>torsion_stiff</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Terrex Agravic Speed Ultra</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$220</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Slightly large</td>\n",
+       "      <td>Stiff</td>\n",
+       "      <td>Flexible</td>\n",
+       "      <td>2.5 mm</td>\n",
+       "      <td>30.6 mm 38.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Terrex Speed Ultra</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$160</td>\n",
+       "      <td>0</td>\n",
+       "      <td>True to size</td>\n",
+       "      <td>Flexible</td>\n",
+       "      <td>Flexible</td>\n",
+       "      <td>2.6 mm</td>\n",
+       "      <td>32.8 mm 26.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Experience Wild</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$145</td>\n",
+       "      <td>0</td>\n",
+       "      <td>True to size</td>\n",
+       "      <td>Stiff</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>3.6 mm</td>\n",
+       "      <td>34.5 mm 34.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Experience Wild 2</td>\n",
+       "      <td>79\\n Good!</td>\n",
+       "      <td>$140</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>Flexible</td>\n",
+       "      <td>3.5 mm</td>\n",
+       "      <td>32.3 mm 32.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Lone Peak 5.0</td>\n",
+       "      <td>91\\n Superb!</td>\n",
+       "      <td>$130</td>\n",
+       "      <td>0</td>\n",
+       "      <td>True to size</td>\n",
+       "      <td>Flexible</td>\n",
+       "      <td>-</td>\n",
+       "      <td>3.7 mm</td>\n",
+       "      <td>24.5 mm 25.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 62 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand                        Name Audience score Price Lightweight  \\\n",
+       "0  Adidas  Terrex Agravic Speed Ultra   90\\n Superb!  $220           0   \n",
+       "1  Adidas          Terrex Speed Ultra   90\\n Superb!  $160           0   \n",
+       "2   Altra             Experience Wild    88\\n Great!  $145           0   \n",
+       "3   Altra           Experience Wild 2     79\\n Good!  $140           0   \n",
+       "4   Altra               Lone Peak 5.0   91\\n Superb!  $130           0   \n",
+       "\n",
+       "             Size Torsional rigidity Heel counter stiffness Lug depth  \\\n",
+       "0  Slightly large              Stiff               Flexible    2.5 mm   \n",
+       "1    True to size           Flexible               Flexible    2.6 mm   \n",
+       "2    True to size              Stiff               Moderate    3.6 mm   \n",
+       "3               -           Moderate               Flexible    3.5 mm   \n",
+       "4    True to size           Flexible                      -    3.7 mm   \n",
+       "\n",
+       "  Heel stack lab Heel stack brand  ... width_wide toeboxwidth_narrow  \\\n",
+       "0                 30.6 mm 38.0 mm  ...          0                  1   \n",
+       "1                 32.8 mm 26.0 mm  ...          0                  0   \n",
+       "2                 34.5 mm 34.0 mm  ...          1                  0   \n",
+       "3                 32.3 mm 32.0 mm  ...          1                  0   \n",
+       "4                 24.5 mm 25.0 mm  ...          0                  0   \n",
+       "\n",
+       "  toeboxwidth_medium toeboxwidth_wide stiff_flexible stiff_moderate  \\\n",
+       "0                  0                0              0              1   \n",
+       "1                  0                0              0              0   \n",
+       "2                  0                1              0              1   \n",
+       "3                  0                1              0              1   \n",
+       "4                  0                0              0              0   \n",
+       "\n",
+       "  stiff_stiff torsion_flexible torsion_moderate torsion_stiff  \n",
+       "0           0                0                0             1  \n",
+       "1           1                1                0             0  \n",
+       "2           0                0                0             1  \n",
+       "3           0                0                1             0  \n",
+       "4           1                1                0             0  \n",
+       "\n",
+       "[5 rows x 62 columns]"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tors_ohe = pd.get_dummies(df[\"Torsional rigidity\"], prefix=\"torsion\").astype(int)\n",
+    "\n",
+    "for col in [\"torsion_Flexible\", \"torsion_Moderate\", \"torsion_Stiff\"]:\n",
+    "    if col not in tors_ohe.columns:\n",
+    "        tors_ohe[col] = 0\n",
+    "\n",
+    "tors_ohe = tors_ohe.rename(columns={\n",
+    "    \"torsion_Flexible\": \"torsion_flexible\",\n",
+    "    \"torsion_Moderate\": \"torsion_moderate\",\n",
+    "    \"torsion_Stiff\": \"torsion_stiff\"\n",
+    "})\n",
+    "\n",
+    "df = pd.concat([df, tors_ohe[[\"torsion_flexible\",\"torsion_moderate\",\"torsion_stiff\"]]], axis=1)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "id": "d7ae7eb4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torsion_flexible     26\n",
+      "torsion_moderate     46\n",
+      "torsion_stiff       103\n",
+      "dtype: int64\n",
+      "Missing torsional rigidity rows: 8\n"
+     ]
+    }
+   ],
+   "source": [
+    "for c in [\"torsion_flexible\",\"torsion_moderate\",\"torsion_stiff\"]:\n",
+    "    assert set(df[c].unique()).issubset({0, 1})\n",
+    "\n",
+    "assert df[[\"torsion_flexible\",\"torsion_moderate\",\"torsion_stiff\"]].sum(axis=1).le(1).all()\n",
+    "\n",
+    "print(df[[\"torsion_flexible\",\"torsion_moderate\",\"torsion_stiff\"]].sum())\n",
+    "print(\n",
+    "    \"Missing torsional rigidity rows:\",\n",
+    "    int((df[[\"torsion_flexible\",\"torsion_moderate\",\"torsion_stiff\"]].sum(axis=1) == 0).sum())\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "id": "eefda480",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<StringArray>\n",
+      "['Stiff', 'Flexible', 'Moderate', '-', nan]\n",
+      "Length: 5, dtype: str\n",
+      "  Torsional rigidity  torsion_flexible  torsion_moderate  torsion_stiff\n",
+      "0              Stiff                 0                 0              1\n",
+      "1           Flexible                 1                 0              0\n",
+      "2              Stiff                 0                 0              1\n",
+      "3           Moderate                 0                 1              0\n",
+      "4           Flexible                 1                 0              0\n",
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 183 entries, 0 to 182\n",
+      "Data columns (total 61 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   Brand                            181 non-null    str    \n",
+      " 1   Name                             182 non-null    str    \n",
+      " 2   Audience score                   179 non-null    str    \n",
+      " 3   Price                            181 non-null    str    \n",
+      " 4   Lightweight                      181 non-null    str    \n",
+      " 5   Size                             181 non-null    str    \n",
+      " 6   Heel counter stiffness           181 non-null    str    \n",
+      " 7   Lug depth                        181 non-null    str    \n",
+      " 8   Heel stack lab Heel stack brand  181 non-null    str    \n",
+      " 9   Forefoot lab Forefoot brand      181 non-null    str    \n",
+      " 10  Widths available                 181 non-null    str    \n",
+      " 11  For heavy runners                181 non-null    str    \n",
+      " 12  Season                           181 non-null    str    \n",
+      " 13  Removable insole                 181 non-null    str    \n",
+      " 14  Orthotic friendly                181 non-null    str    \n",
+      " 15  Waterproofing                    180 non-null    str    \n",
+      " 16  Ranking                          181 non-null    str    \n",
+      " 17  Popularity                       181 non-null    str    \n",
+      " 18  terrain_norm                     181 non-null    str    \n",
+      " 19  terrain_light                    183 non-null    int64  \n",
+      " 20  terrain_moderate                 183 non-null    int64  \n",
+      " 21  terrain_technical                183 non-null    int64  \n",
+      " 22  Arch_grouped                     181 non-null    str    \n",
+      " 23  arch_neutral                     183 non-null    int64  \n",
+      " 24  arch_stability                   183 non-null    int64  \n",
+      " 25  Strike_norm                      181 non-null    str    \n",
+      " 26  strike_forefoot                  183 non-null    int64  \n",
+      " 27  strike_heel                      183 non-null    int64  \n",
+      " 28  strike_mid                       183 non-null    int64  \n",
+      " 29  drop_lab_mm                      181 non-null    float64\n",
+      " 30  drop_brand_mm                    174 non-null    float64\n",
+      " 31  midsole_soft                     183 non-null    int64  \n",
+      " 32  midsole_balanced                 183 non-null    int64  \n",
+      " 33  midsole_firm                     183 non-null    int64  \n",
+      " 34  plate_carbon                     183 non-null    int64  \n",
+      " 35  plate_rock                       183 non-null    int64  \n",
+      " 36  plate_none                       183 non-null    int64  \n",
+      " 37  toebox_bad                       183 non-null    int64  \n",
+      " 38  toebox_decent                    183 non-null    int64  \n",
+      " 39  toebox_good                      183 non-null    int64  \n",
+      " 40  heelpad_bad                      183 non-null    int64  \n",
+      " 41  heelpad_decent                   183 non-null    int64  \n",
+      " 42  heelpad_good                     183 non-null    int64  \n",
+      " 43  outsole_bad                      183 non-null    int64  \n",
+      " 44  outsole_decent                   183 non-null    int64  \n",
+      " 45  outsole_good                     183 non-null    int64  \n",
+      " 46  breath_breathable                183 non-null    int64  \n",
+      " 47  breath_moderate                  183 non-null    int64  \n",
+      " 48  breath_warm                      183 non-null    int64  \n",
+      " 49  width_narrow                     183 non-null    int64  \n",
+      " 50  width_medium                     183 non-null    int64  \n",
+      " 51  width_wide                       183 non-null    int64  \n",
+      " 52  toeboxwidth_narrow               183 non-null    int64  \n",
+      " 53  toeboxwidth_medium               183 non-null    int64  \n",
+      " 54  toeboxwidth_wide                 183 non-null    int64  \n",
+      " 55  stiff_flexible                   183 non-null    int64  \n",
+      " 56  stiff_moderate                   183 non-null    int64  \n",
+      " 57  stiff_stiff                      183 non-null    int64  \n",
+      " 58  torsion_flexible                 183 non-null    int64  \n",
+      " 59  torsion_moderate                 183 non-null    int64  \n",
+      " 60  torsion_stiff                    183 non-null    int64  \n",
+      "dtypes: float64(2), int64(38), str(21)\n",
+      "memory usage: 87.3 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Torsional rigidity\"].unique())\n",
+    "\n",
+    "print(df[[\"Torsional rigidity\", \"torsion_flexible\",\"torsion_moderate\",\"torsion_stiff\"]].head())\n",
+    "\n",
+    "df.drop(columns=[\"Torsional rigidity\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b6ebe4f",
+   "metadata": {},
+   "source": [
+    "# Heel counter stiffness"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "id": "f8d673a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Heel counter stiffness (4 uniques): \n",
+      " ['-', 'Flexible', 'Moderate', 'Stiff']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Heel counter stiffness\n",
+       "Moderate    63\n",
+       "Stiff       56\n",
+       "Flexible    54\n",
+       "-            8\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Heel counter stiffness\"] = (\n",
+    "    df[\"Heel counter stiffness\"]\n",
+    "    .astype(str)\n",
+    "    .str.strip()\n",
+    "    .str.replace(r\"\\s+\", \" \", regex=True)\n",
+    "    .str.title()\n",
+    ")\n",
+    "\n",
+    "heel_counter_stiffness_uniques = df[\"Heel counter stiffness\"].dropna().unique()\n",
+    "# heel_counter_stiffness_uniques.sort()\n",
+    "heel_counter_stiffness_uniques = sorted(heel_counter_stiffness_uniques)\n",
+    "print(f\"Heel counter stiffness ({len(heel_counter_stiffness_uniques)} uniques): \\n\",heel_counter_stiffness_uniques)\n",
+    "\n",
+    "df[\"Heel counter stiffness\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "id": "1af82ce0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Size</th>\n",
+       "      <th>Heel counter stiffness</th>\n",
+       "      <th>Lug depth</th>\n",
+       "      <th>Heel stack lab Heel stack brand</th>\n",
+       "      <th>Forefoot lab Forefoot brand</th>\n",
+       "      <th>...</th>\n",
+       "      <th>toeboxwidth_wide</th>\n",
+       "      <th>stiff_flexible</th>\n",
+       "      <th>stiff_moderate</th>\n",
+       "      <th>stiff_stiff</th>\n",
+       "      <th>torsion_flexible</th>\n",
+       "      <th>torsion_moderate</th>\n",
+       "      <th>torsion_stiff</th>\n",
+       "      <th>heelcounter_flexible</th>\n",
+       "      <th>heelcounter_moderate</th>\n",
+       "      <th>heelcounter_stiff</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Terrex Agravic Speed Ultra</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$220</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Slightly large</td>\n",
+       "      <td>Flexible</td>\n",
+       "      <td>2.5 mm</td>\n",
+       "      <td>30.6 mm 38.0 mm</td>\n",
+       "      <td>30.3 mm 30.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Terrex Speed Ultra</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$160</td>\n",
+       "      <td>0</td>\n",
+       "      <td>True to size</td>\n",
+       "      <td>Flexible</td>\n",
+       "      <td>2.6 mm</td>\n",
+       "      <td>32.8 mm 26.0 mm</td>\n",
+       "      <td>24.6 mm 18.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Experience Wild</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$145</td>\n",
+       "      <td>0</td>\n",
+       "      <td>True to size</td>\n",
+       "      <td>Moderate</td>\n",
+       "      <td>3.6 mm</td>\n",
+       "      <td>34.5 mm 34.0 mm</td>\n",
+       "      <td>30.2 mm 30.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Experience Wild 2</td>\n",
+       "      <td>79\\n Good!</td>\n",
+       "      <td>$140</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Flexible</td>\n",
+       "      <td>3.5 mm</td>\n",
+       "      <td>32.3 mm 32.0 mm</td>\n",
+       "      <td>26.2 mm 28.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Lone Peak 5.0</td>\n",
+       "      <td>91\\n Superb!</td>\n",
+       "      <td>$130</td>\n",
+       "      <td>0</td>\n",
+       "      <td>True to size</td>\n",
+       "      <td>-</td>\n",
+       "      <td>3.7 mm</td>\n",
+       "      <td>24.5 mm 25.0 mm</td>\n",
+       "      <td>24.3 mm 25.0 mm</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 64 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand                        Name Audience score Price Lightweight  \\\n",
+       "0  Adidas  Terrex Agravic Speed Ultra   90\\n Superb!  $220           0   \n",
+       "1  Adidas          Terrex Speed Ultra   90\\n Superb!  $160           0   \n",
+       "2   Altra             Experience Wild    88\\n Great!  $145           0   \n",
+       "3   Altra           Experience Wild 2     79\\n Good!  $140           0   \n",
+       "4   Altra               Lone Peak 5.0   91\\n Superb!  $130           0   \n",
+       "\n",
+       "             Size Heel counter stiffness Lug depth  \\\n",
+       "0  Slightly large               Flexible    2.5 mm   \n",
+       "1    True to size               Flexible    2.6 mm   \n",
+       "2    True to size               Moderate    3.6 mm   \n",
+       "3               -               Flexible    3.5 mm   \n",
+       "4    True to size                      -    3.7 mm   \n",
+       "\n",
+       "  Heel stack lab Heel stack brand Forefoot lab Forefoot brand  ...  \\\n",
+       "0                 30.6 mm 38.0 mm             30.3 mm 30.0 mm  ...   \n",
+       "1                 32.8 mm 26.0 mm             24.6 mm 18.0 mm  ...   \n",
+       "2                 34.5 mm 34.0 mm             30.2 mm 30.0 mm  ...   \n",
+       "3                 32.3 mm 32.0 mm             26.2 mm 28.0 mm  ...   \n",
+       "4                 24.5 mm 25.0 mm             24.3 mm 25.0 mm  ...   \n",
+       "\n",
+       "  toeboxwidth_wide stiff_flexible stiff_moderate stiff_stiff torsion_flexible  \\\n",
+       "0                0              0              1           0                0   \n",
+       "1                0              0              0           1                1   \n",
+       "2                1              0              1           0                0   \n",
+       "3                1              0              1           0                0   \n",
+       "4                0              0              0           1                1   \n",
+       "\n",
+       "  torsion_moderate torsion_stiff heelcounter_flexible heelcounter_moderate  \\\n",
+       "0                0             1                    1                    0   \n",
+       "1                0             0                    1                    0   \n",
+       "2                0             1                    0                    1   \n",
+       "3                1             0                    1                    0   \n",
+       "4                0             0                    0                    0   \n",
+       "\n",
+       "   heelcounter_stiff  \n",
+       "0                  0  \n",
+       "1                  0  \n",
+       "2                  0  \n",
+       "3                  0  \n",
+       "4                  0  \n",
+       "\n",
+       "[5 rows x 64 columns]"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hc_ohe = pd.get_dummies(df[\"Heel counter stiffness\"], prefix=\"heelcounter\").astype(int)\n",
+    "\n",
+    "for col in [\"heelcounter_Flexible\", \"heelcounter_Moderate\", \"heelcounter_Stiff\"]:\n",
+    "    if col not in hc_ohe.columns:\n",
+    "        hc_ohe[col] = 0\n",
+    "\n",
+    "hc_ohe = hc_ohe.rename(columns={\n",
+    "    \"heelcounter_Flexible\": \"heelcounter_flexible\",\n",
+    "    \"heelcounter_Moderate\": \"heelcounter_moderate\",\n",
+    "    \"heelcounter_Stiff\": \"heelcounter_stiff\"\n",
+    "})\n",
+    "\n",
+    "df = pd.concat([df, hc_ohe[[\"heelcounter_flexible\",\"heelcounter_moderate\",\"heelcounter_stiff\"]]], axis=1)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "id": "b496a864",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "heelcounter_flexible    54\n",
+      "heelcounter_moderate    63\n",
+      "heelcounter_stiff       56\n",
+      "dtype: int64\n",
+      "Missing heel counter rows: 10\n"
+     ]
+    }
+   ],
+   "source": [
+    "for c in [\"heelcounter_flexible\",\"heelcounter_moderate\",\"heelcounter_stiff\"]:\n",
+    "    assert set(df[c].unique()).issubset({0, 1})\n",
+    "\n",
+    "assert df[[\"heelcounter_flexible\",\"heelcounter_moderate\",\"heelcounter_stiff\"]].sum(axis=1).le(1).all()\n",
+    "\n",
+    "print(df[[\"heelcounter_flexible\",\"heelcounter_moderate\",\"heelcounter_stiff\"]].sum())\n",
+    "print(\n",
+    "    \"Missing heel counter rows:\",\n",
+    "    int((df[[\"heelcounter_flexible\",\"heelcounter_moderate\",\"heelcounter_stiff\"]].sum(axis=1) == 0).sum())\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "id": "efeecffb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<StringArray>\n",
+      "['Flexible', 'Moderate', '-', 'Stiff', nan]\n",
+      "Length: 5, dtype: str\n",
+      "  Heel counter stiffness  heelcounter_flexible  heelcounter_moderate  \\\n",
+      "0               Flexible                     1                     0   \n",
+      "1               Flexible                     1                     0   \n",
+      "2               Moderate                     0                     1   \n",
+      "3               Flexible                     1                     0   \n",
+      "4                      -                     0                     0   \n",
+      "\n",
+      "   heelcounter_stiff  \n",
+      "0                  0  \n",
+      "1                  0  \n",
+      "2                  0  \n",
+      "3                  0  \n",
+      "4                  0  \n",
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 183 entries, 0 to 182\n",
+      "Data columns (total 63 columns):\n",
+      " #   Column                           Non-Null Count  Dtype  \n",
+      "---  ------                           --------------  -----  \n",
+      " 0   Brand                            181 non-null    str    \n",
+      " 1   Name                             182 non-null    str    \n",
+      " 2   Audience score                   179 non-null    str    \n",
+      " 3   Price                            181 non-null    str    \n",
+      " 4   Lightweight                      181 non-null    str    \n",
+      " 5   Size                             181 non-null    str    \n",
+      " 6   Lug depth                        181 non-null    str    \n",
+      " 7   Heel stack lab Heel stack brand  181 non-null    str    \n",
+      " 8   Forefoot lab Forefoot brand      181 non-null    str    \n",
+      " 9   Widths available                 181 non-null    str    \n",
+      " 10  For heavy runners                181 non-null    str    \n",
+      " 11  Season                           181 non-null    str    \n",
+      " 12  Removable insole                 181 non-null    str    \n",
+      " 13  Orthotic friendly                181 non-null    str    \n",
+      " 14  Waterproofing                    180 non-null    str    \n",
+      " 15  Ranking                          181 non-null    str    \n",
+      " 16  Popularity                       181 non-null    str    \n",
+      " 17  terrain_norm                     181 non-null    str    \n",
+      " 18  terrain_light                    183 non-null    int64  \n",
+      " 19  terrain_moderate                 183 non-null    int64  \n",
+      " 20  terrain_technical                183 non-null    int64  \n",
+      " 21  Arch_grouped                     181 non-null    str    \n",
+      " 22  arch_neutral                     183 non-null    int64  \n",
+      " 23  arch_stability                   183 non-null    int64  \n",
+      " 24  Strike_norm                      181 non-null    str    \n",
+      " 25  strike_forefoot                  183 non-null    int64  \n",
+      " 26  strike_heel                      183 non-null    int64  \n",
+      " 27  strike_mid                       183 non-null    int64  \n",
+      " 28  drop_lab_mm                      181 non-null    float64\n",
+      " 29  drop_brand_mm                    174 non-null    float64\n",
+      " 30  midsole_soft                     183 non-null    int64  \n",
+      " 31  midsole_balanced                 183 non-null    int64  \n",
+      " 32  midsole_firm                     183 non-null    int64  \n",
+      " 33  plate_carbon                     183 non-null    int64  \n",
+      " 34  plate_rock                       183 non-null    int64  \n",
+      " 35  plate_none                       183 non-null    int64  \n",
+      " 36  toebox_bad                       183 non-null    int64  \n",
+      " 37  toebox_decent                    183 non-null    int64  \n",
+      " 38  toebox_good                      183 non-null    int64  \n",
+      " 39  heelpad_bad                      183 non-null    int64  \n",
+      " 40  heelpad_decent                   183 non-null    int64  \n",
+      " 41  heelpad_good                     183 non-null    int64  \n",
+      " 42  outsole_bad                      183 non-null    int64  \n",
+      " 43  outsole_decent                   183 non-null    int64  \n",
+      " 44  outsole_good                     183 non-null    int64  \n",
+      " 45  breath_breathable                183 non-null    int64  \n",
+      " 46  breath_moderate                  183 non-null    int64  \n",
+      " 47  breath_warm                      183 non-null    int64  \n",
+      " 48  width_narrow                     183 non-null    int64  \n",
+      " 49  width_medium                     183 non-null    int64  \n",
+      " 50  width_wide                       183 non-null    int64  \n",
+      " 51  toeboxwidth_narrow               183 non-null    int64  \n",
+      " 52  toeboxwidth_medium               183 non-null    int64  \n",
+      " 53  toeboxwidth_wide                 183 non-null    int64  \n",
+      " 54  stiff_flexible                   183 non-null    int64  \n",
+      " 55  stiff_moderate                   183 non-null    int64  \n",
+      " 56  stiff_stiff                      183 non-null    int64  \n",
+      " 57  torsion_flexible                 183 non-null    int64  \n",
+      " 58  torsion_moderate                 183 non-null    int64  \n",
+      " 59  torsion_stiff                    183 non-null    int64  \n",
+      " 60  heelcounter_flexible             183 non-null    int64  \n",
+      " 61  heelcounter_moderate             183 non-null    int64  \n",
+      " 62  heelcounter_stiff                183 non-null    int64  \n",
+      "dtypes: float64(2), int64(41), str(20)\n",
+      "memory usage: 90.2 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Heel counter stiffness\"].unique())\n",
+    "\n",
+    "print(df[[\"Heel counter stiffness\", \"heelcounter_flexible\",\"heelcounter_moderate\",\"heelcounter_stiff\"]].head())\n",
+    "\n",
+    "df.drop(columns=[\"Heel counter stiffness\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65c2c307",
+   "metadata": {},
+   "source": [
+    "# split heel stack lab heel stack brand"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "id": "afd2ce1d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(2)"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mask_missing = (\n",
+    "    df[\"Heel stack lab Heel stack brand\"].isna() |\n",
+    "    (df[\"Heel stack lab Heel stack brand\"].astype(str).str.strip() == \"-\")\n",
+    ")\n",
+    "mask_missing.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "id": "c3f9b5a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Heel stack lab Heel stack brand  heel_lab_mm  heel_brand_mm\n",
+      "0                 30.6 mm 38.0 mm         30.6           38.0\n",
+      "1                 32.8 mm 26.0 mm         32.8           26.0\n",
+      "2                 34.5 mm 34.0 mm         34.5           34.0\n",
+      "3                 32.3 mm 32.0 mm         32.3           32.0\n",
+      "4                 24.5 mm 25.0 mm         24.5           25.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "Heel = df[\"Heel stack lab Heel stack brand\"].fillna(\"\").str.findall(r\"[\\d.]+\")\n",
+    "\n",
+    "Heel = Heel.apply(lambda x: x if isinstance(x, list) else [])\n",
+    "\n",
+    "heel_df = pd.DataFrame(Heel.tolist(), index=df.index)\n",
+    "\n",
+    "while len(heel_df.columns) < 2:\n",
+    "    heel_df[len(heel_df.columns)] = None\n",
+    "\n",
+    "df[[\"heel_lab_mm\", \"heel_brand_mm\"]] = heel_df.iloc[:, :2]\n",
+    "\n",
+    "for col in [\"heel_lab_mm\", \"heel_brand_mm\"]:\n",
+    "    df[col] = pd.to_numeric(df[col], errors=\"coerce\")\n",
+    "\n",
+    "print(df[[\"Heel stack lab Heel stack brand\", \"heel_lab_mm\", \"heel_brand_mm\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "id": "fae30d16",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 183 entries, 0 to 182\n",
+      "Data columns (total 64 columns):\n",
+      " #   Column                       Non-Null Count  Dtype  \n",
+      "---  ------                       --------------  -----  \n",
+      " 0   Brand                        181 non-null    str    \n",
+      " 1   Name                         182 non-null    str    \n",
+      " 2   Audience score               179 non-null    str    \n",
+      " 3   Price                        181 non-null    str    \n",
+      " 4   Lightweight                  181 non-null    str    \n",
+      " 5   Size                         181 non-null    str    \n",
+      " 6   Lug depth                    181 non-null    str    \n",
+      " 7   Forefoot lab Forefoot brand  181 non-null    str    \n",
+      " 8   Widths available             181 non-null    str    \n",
+      " 9   For heavy runners            181 non-null    str    \n",
+      " 10  Season                       181 non-null    str    \n",
+      " 11  Removable insole             181 non-null    str    \n",
+      " 12  Orthotic friendly            181 non-null    str    \n",
+      " 13  Waterproofing                180 non-null    str    \n",
+      " 14  Ranking                      181 non-null    str    \n",
+      " 15  Popularity                   181 non-null    str    \n",
+      " 16  terrain_norm                 181 non-null    str    \n",
+      " 17  terrain_light                183 non-null    int64  \n",
+      " 18  terrain_moderate             183 non-null    int64  \n",
+      " 19  terrain_technical            183 non-null    int64  \n",
+      " 20  Arch_grouped                 181 non-null    str    \n",
+      " 21  arch_neutral                 183 non-null    int64  \n",
+      " 22  arch_stability               183 non-null    int64  \n",
+      " 23  Strike_norm                  181 non-null    str    \n",
+      " 24  strike_forefoot              183 non-null    int64  \n",
+      " 25  strike_heel                  183 non-null    int64  \n",
+      " 26  strike_mid                   183 non-null    int64  \n",
+      " 27  drop_lab_mm                  181 non-null    float64\n",
+      " 28  drop_brand_mm                174 non-null    float64\n",
+      " 29  midsole_soft                 183 non-null    int64  \n",
+      " 30  midsole_balanced             183 non-null    int64  \n",
+      " 31  midsole_firm                 183 non-null    int64  \n",
+      " 32  plate_carbon                 183 non-null    int64  \n",
+      " 33  plate_rock                   183 non-null    int64  \n",
+      " 34  plate_none                   183 non-null    int64  \n",
+      " 35  toebox_bad                   183 non-null    int64  \n",
+      " 36  toebox_decent                183 non-null    int64  \n",
+      " 37  toebox_good                  183 non-null    int64  \n",
+      " 38  heelpad_bad                  183 non-null    int64  \n",
+      " 39  heelpad_decent               183 non-null    int64  \n",
+      " 40  heelpad_good                 183 non-null    int64  \n",
+      " 41  outsole_bad                  183 non-null    int64  \n",
+      " 42  outsole_decent               183 non-null    int64  \n",
+      " 43  outsole_good                 183 non-null    int64  \n",
+      " 44  breath_breathable            183 non-null    int64  \n",
+      " 45  breath_moderate              183 non-null    int64  \n",
+      " 46  breath_warm                  183 non-null    int64  \n",
+      " 47  width_narrow                 183 non-null    int64  \n",
+      " 48  width_medium                 183 non-null    int64  \n",
+      " 49  width_wide                   183 non-null    int64  \n",
+      " 50  toeboxwidth_narrow           183 non-null    int64  \n",
+      " 51  toeboxwidth_medium           183 non-null    int64  \n",
+      " 52  toeboxwidth_wide             183 non-null    int64  \n",
+      " 53  stiff_flexible               183 non-null    int64  \n",
+      " 54  stiff_moderate               183 non-null    int64  \n",
+      " 55  stiff_stiff                  183 non-null    int64  \n",
+      " 56  torsion_flexible             183 non-null    int64  \n",
+      " 57  torsion_moderate             183 non-null    int64  \n",
+      " 58  torsion_stiff                183 non-null    int64  \n",
+      " 59  heelcounter_flexible         183 non-null    int64  \n",
+      " 60  heelcounter_moderate         183 non-null    int64  \n",
+      " 61  heelcounter_stiff            183 non-null    int64  \n",
+      " 62  heel_lab_mm                  181 non-null    float64\n",
+      " 63  heel_brand_mm                166 non-null    float64\n",
+      "dtypes: float64(4), int64(41), str(19)\n",
+      "memory usage: 91.6 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"Heel stack lab Heel stack brand\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ae97aa1",
+   "metadata": {},
+   "source": [
+    "# split forefoot lab brand"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "id": "7fe9a60d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(2)"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mask_missing = (\n",
+    "    df[\"Forefoot lab Forefoot brand\"].isna() |\n",
+    "    (df[\"Forefoot lab Forefoot brand\"].astype(str).str.strip() == \"-\")\n",
+    ")\n",
+    "mask_missing.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "id": "ee250ac4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Forefoot lab Forefoot brand  forefoot_lab_mm  forefoot_brand_mm\n",
+      "0             30.3 mm 30.0 mm             30.3               30.0\n",
+      "1             24.6 mm 18.0 mm             24.6               18.0\n",
+      "2             30.2 mm 30.0 mm             30.2               30.0\n",
+      "3             26.2 mm 28.0 mm             26.2               28.0\n",
+      "4             24.3 mm 25.0 mm             24.3               25.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "forefoot = df[\"Forefoot lab Forefoot brand\"].fillna(\"\").str.findall(r\"[\\d.]+\")\n",
+    "\n",
+    "forefoot = forefoot.apply(lambda x: x if isinstance(x, list) else [])\n",
+    "\n",
+    "forefoot_df = pd.DataFrame(forefoot.tolist(), index=df.index)\n",
+    "\n",
+    "while len(forefoot_df.columns) < 2:\n",
+    "    forefoot_df[len(forefoot_df.columns)] = None\n",
+    "\n",
+    "df[[\"forefoot_lab_mm\", \"forefoot_brand_mm\"]] = forefoot_df.iloc[:, :2]\n",
+    "\n",
+    "for col in [\"forefoot_lab_mm\", \"forefoot_brand_mm\"]:\n",
+    "    df[col] = pd.to_numeric(df[col], errors=\"coerce\")\n",
+    "\n",
+    "print(df[[\"Forefoot lab Forefoot brand\", \"forefoot_lab_mm\", \"forefoot_brand_mm\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "id": "15ee0e59",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 183 entries, 0 to 182\n",
+      "Data columns (total 65 columns):\n",
+      " #   Column                Non-Null Count  Dtype  \n",
+      "---  ------                --------------  -----  \n",
+      " 0   Brand                 181 non-null    str    \n",
+      " 1   Name                  182 non-null    str    \n",
+      " 2   Audience score        179 non-null    str    \n",
+      " 3   Price                 181 non-null    str    \n",
+      " 4   Lightweight           181 non-null    str    \n",
+      " 5   Size                  181 non-null    str    \n",
+      " 6   Lug depth             181 non-null    str    \n",
+      " 7   Widths available      181 non-null    str    \n",
+      " 8   For heavy runners     181 non-null    str    \n",
+      " 9   Season                181 non-null    str    \n",
+      " 10  Removable insole      181 non-null    str    \n",
+      " 11  Orthotic friendly     181 non-null    str    \n",
+      " 12  Waterproofing         180 non-null    str    \n",
+      " 13  Ranking               181 non-null    str    \n",
+      " 14  Popularity            181 non-null    str    \n",
+      " 15  terrain_norm          181 non-null    str    \n",
+      " 16  terrain_light         183 non-null    int64  \n",
+      " 17  terrain_moderate      183 non-null    int64  \n",
+      " 18  terrain_technical     183 non-null    int64  \n",
+      " 19  Arch_grouped          181 non-null    str    \n",
+      " 20  arch_neutral          183 non-null    int64  \n",
+      " 21  arch_stability        183 non-null    int64  \n",
+      " 22  Strike_norm           181 non-null    str    \n",
+      " 23  strike_forefoot       183 non-null    int64  \n",
+      " 24  strike_heel           183 non-null    int64  \n",
+      " 25  strike_mid            183 non-null    int64  \n",
+      " 26  drop_lab_mm           181 non-null    float64\n",
+      " 27  drop_brand_mm         174 non-null    float64\n",
+      " 28  midsole_soft          183 non-null    int64  \n",
+      " 29  midsole_balanced      183 non-null    int64  \n",
+      " 30  midsole_firm          183 non-null    int64  \n",
+      " 31  plate_carbon          183 non-null    int64  \n",
+      " 32  plate_rock            183 non-null    int64  \n",
+      " 33  plate_none            183 non-null    int64  \n",
+      " 34  toebox_bad            183 non-null    int64  \n",
+      " 35  toebox_decent         183 non-null    int64  \n",
+      " 36  toebox_good           183 non-null    int64  \n",
+      " 37  heelpad_bad           183 non-null    int64  \n",
+      " 38  heelpad_decent        183 non-null    int64  \n",
+      " 39  heelpad_good          183 non-null    int64  \n",
+      " 40  outsole_bad           183 non-null    int64  \n",
+      " 41  outsole_decent        183 non-null    int64  \n",
+      " 42  outsole_good          183 non-null    int64  \n",
+      " 43  breath_breathable     183 non-null    int64  \n",
+      " 44  breath_moderate       183 non-null    int64  \n",
+      " 45  breath_warm           183 non-null    int64  \n",
+      " 46  width_narrow          183 non-null    int64  \n",
+      " 47  width_medium          183 non-null    int64  \n",
+      " 48  width_wide            183 non-null    int64  \n",
+      " 49  toeboxwidth_narrow    183 non-null    int64  \n",
+      " 50  toeboxwidth_medium    183 non-null    int64  \n",
+      " 51  toeboxwidth_wide      183 non-null    int64  \n",
+      " 52  stiff_flexible        183 non-null    int64  \n",
+      " 53  stiff_moderate        183 non-null    int64  \n",
+      " 54  stiff_stiff           183 non-null    int64  \n",
+      " 55  torsion_flexible      183 non-null    int64  \n",
+      " 56  torsion_moderate      183 non-null    int64  \n",
+      " 57  torsion_stiff         183 non-null    int64  \n",
+      " 58  heelcounter_flexible  183 non-null    int64  \n",
+      " 59  heelcounter_moderate  183 non-null    int64  \n",
+      " 60  heelcounter_stiff     183 non-null    int64  \n",
+      " 61  heel_lab_mm           181 non-null    float64\n",
+      " 62  heel_brand_mm         166 non-null    float64\n",
+      " 63  forefoot_lab_mm       181 non-null    float64\n",
+      " 64  forefoot_brand_mm     164 non-null    float64\n",
+      "dtypes: float64(6), int64(41), str(18)\n",
+      "memory usage: 93.1 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"Forefoot lab Forefoot brand\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "id": "3e2e0cea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 183 entries, 0 to 182\n",
+      "Data columns (total 65 columns):\n",
+      " #   Column                Non-Null Count  Dtype  \n",
+      "---  ------                --------------  -----  \n",
+      " 0   Brand                 181 non-null    str    \n",
+      " 1   Name                  182 non-null    str    \n",
+      " 2   Audience score        179 non-null    str    \n",
+      " 3   Price                 181 non-null    str    \n",
+      " 4   Lightweight           181 non-null    str    \n",
+      " 5   Size                  181 non-null    str    \n",
+      " 6   Lug depth             181 non-null    str    \n",
+      " 7   Widths available      181 non-null    str    \n",
+      " 8   For heavy runners     181 non-null    str    \n",
+      " 9   Season                181 non-null    str    \n",
+      " 10  Removable insole      181 non-null    str    \n",
+      " 11  Orthotic friendly     181 non-null    str    \n",
+      " 12  Waterproofing         180 non-null    str    \n",
+      " 13  Ranking               181 non-null    str    \n",
+      " 14  Popularity            181 non-null    str    \n",
+      " 15  terrain_norm          181 non-null    str    \n",
+      " 16  terrain_light         183 non-null    int64  \n",
+      " 17  terrain_moderate      183 non-null    int64  \n",
+      " 18  terrain_technical     183 non-null    int64  \n",
+      " 19  Arch_grouped          181 non-null    str    \n",
+      " 20  arch_neutral          183 non-null    int64  \n",
+      " 21  arch_stability        183 non-null    int64  \n",
+      " 22  Strike_norm           181 non-null    str    \n",
+      " 23  strike_forefoot       183 non-null    int64  \n",
+      " 24  strike_heel           183 non-null    int64  \n",
+      " 25  strike_mid            183 non-null    int64  \n",
+      " 26  drop_lab_mm           181 non-null    float64\n",
+      " 27  drop_brand_mm         174 non-null    float64\n",
+      " 28  midsole_soft          183 non-null    int64  \n",
+      " 29  midsole_balanced      183 non-null    int64  \n",
+      " 30  midsole_firm          183 non-null    int64  \n",
+      " 31  plate_carbon          183 non-null    int64  \n",
+      " 32  plate_rock            183 non-null    int64  \n",
+      " 33  plate_none            183 non-null    int64  \n",
+      " 34  toebox_bad            183 non-null    int64  \n",
+      " 35  toebox_decent         183 non-null    int64  \n",
+      " 36  toebox_good           183 non-null    int64  \n",
+      " 37  heelpad_bad           183 non-null    int64  \n",
+      " 38  heelpad_decent        183 non-null    int64  \n",
+      " 39  heelpad_good          183 non-null    int64  \n",
+      " 40  outsole_bad           183 non-null    int64  \n",
+      " 41  outsole_decent        183 non-null    int64  \n",
+      " 42  outsole_good          183 non-null    int64  \n",
+      " 43  breath_breathable     183 non-null    int64  \n",
+      " 44  breath_moderate       183 non-null    int64  \n",
+      " 45  breath_warm           183 non-null    int64  \n",
+      " 46  width_narrow          183 non-null    int64  \n",
+      " 47  width_medium          183 non-null    int64  \n",
+      " 48  width_wide            183 non-null    int64  \n",
+      " 49  toeboxwidth_narrow    183 non-null    int64  \n",
+      " 50  toeboxwidth_medium    183 non-null    int64  \n",
+      " 51  toeboxwidth_wide      183 non-null    int64  \n",
+      " 52  stiff_flexible        183 non-null    int64  \n",
+      " 53  stiff_moderate        183 non-null    int64  \n",
+      " 54  stiff_stiff           183 non-null    int64  \n",
+      " 55  torsion_flexible      183 non-null    int64  \n",
+      " 56  torsion_moderate      183 non-null    int64  \n",
+      " 57  torsion_stiff         183 non-null    int64  \n",
+      " 58  heelcounter_flexible  183 non-null    int64  \n",
+      " 59  heelcounter_moderate  183 non-null    int64  \n",
+      " 60  heelcounter_stiff     183 non-null    int64  \n",
+      " 61  heel_lab_mm           181 non-null    float64\n",
+      " 62  heel_brand_mm         166 non-null    float64\n",
+      " 63  forefoot_lab_mm       181 non-null    float64\n",
+      " 64  forefoot_brand_mm     164 non-null    float64\n",
+      "dtypes: float64(6), int64(41), str(18)\n",
+      "memory usage: 93.1 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# df.drop(columns=[\"Widths available\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6d9cce28",
+   "metadata": {},
+   "source": [
+    "# Season"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "id": "4beb740b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Season (4 uniques): \n",
+      " ['-', 'All Seasons', 'Summerall Seasons', 'Winter']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Season\n",
+       "All Seasons          122\n",
+       "-                     25\n",
+       "Winter                18\n",
+       "Summerall Seasons     16\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"Season\"] = (\n",
+    "    df[\"Season\"]\n",
+    "    .astype(str)\n",
+    "    .str.strip()\n",
+    "    .str.replace(r\"\\s+\", \" \", regex=True)\n",
+    "    .str.title()\n",
+    ")\n",
+    "\n",
+    "heel_counter_stiffness_uniques = df[\"Season\"].dropna().unique()\n",
+    "# heel_counter_stiffness_uniques.sort()\n",
+    "heel_counter_stiffness_uniques = sorted(heel_counter_stiffness_uniques)\n",
+    "print(f\"Season ({len(heel_counter_stiffness_uniques)} uniques): \\n\",heel_counter_stiffness_uniques)\n",
+    "\n",
+    "df[\"Season\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "id": "af24c27a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "season_map = {\n",
+    "    \"All Seasons\": \"All\",\n",
+    "    \"Summerall Seasons\": \"Summer|All\",\n",
+    "    \"Winter\": \"Winter\",\n",
+    "    \"-\": pd.NA,\n",
+    "}\n",
+    "\n",
+    "df[\"season_norm\"] = df[\"Season\"].map(season_map)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "id": "6e39de7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "UNMAPPED Season values (should be empty):\n",
+      " Series([], Name: count, dtype: int64)\n"
+     ]
+    }
+   ],
+   "source": [
+    "unmapped = df[df[\"season_norm\"].isna() & df[\"Season\"].ne(\"-\")][\"Season\"].value_counts()\n",
+    "print(\"UNMAPPED Season values (should be empty):\\n\", unmapped)\n",
+    "# assert unmapped.empty"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "id": "f8b3c169",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Season - norm: (4) uniques\n",
+      " <StringArray>\n",
+      "['All', nan, 'Summer|All', 'Winter']\n",
+      "Length: 4, dtype: str\n",
+      "season_norm\n",
+      "All           122\n",
+      "Winter         18\n",
+      "Summer|All     16\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "season_norm_unique = df[\"season_norm\"].unique()\n",
+    "print(f'Season - norm: ({len(season_norm_unique)}) uniques\\n', season_norm_unique)\n",
+    "print(df[\"season_norm\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "id": "fecca795",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Audience score</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Lightweight</th>\n",
+       "      <th>Size</th>\n",
+       "      <th>Lug depth</th>\n",
+       "      <th>Widths available</th>\n",
+       "      <th>For heavy runners</th>\n",
+       "      <th>Season</th>\n",
+       "      <th>...</th>\n",
+       "      <th>heelcounter_stiff</th>\n",
+       "      <th>heel_lab_mm</th>\n",
+       "      <th>heel_brand_mm</th>\n",
+       "      <th>forefoot_lab_mm</th>\n",
+       "      <th>forefoot_brand_mm</th>\n",
+       "      <th>season_norm</th>\n",
+       "      <th>season_list</th>\n",
+       "      <th>season_all</th>\n",
+       "      <th>season_summer</th>\n",
+       "      <th>season_winter</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Terrex Agravic Speed Ultra</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$220</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Slightly large</td>\n",
+       "      <td>2.5 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>All Seasons</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>30.6</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>30.3</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>All</td>\n",
+       "      <td>[All]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Adidas</td>\n",
+       "      <td>Terrex Speed Ultra</td>\n",
+       "      <td>90\\n Superb!</td>\n",
+       "      <td>$160</td>\n",
+       "      <td>0</td>\n",
+       "      <td>True to size</td>\n",
+       "      <td>2.6 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>32.8</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>24.6</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Experience Wild</td>\n",
+       "      <td>88\\n Great!</td>\n",
+       "      <td>$145</td>\n",
+       "      <td>0</td>\n",
+       "      <td>True to size</td>\n",
+       "      <td>3.6 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>All Seasons</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>34.5</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>30.2</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>All</td>\n",
+       "      <td>[All]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Experience Wild 2</td>\n",
+       "      <td>79\\n Good!</td>\n",
+       "      <td>$140</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>3.5 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>All Seasons</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>32.3</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>26.2</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>All</td>\n",
+       "      <td>[All]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Altra</td>\n",
+       "      <td>Lone Peak 5.0</td>\n",
+       "      <td>91\\n Superb!</td>\n",
+       "      <td>$130</td>\n",
+       "      <td>0</td>\n",
+       "      <td>True to size</td>\n",
+       "      <td>3.7 mm</td>\n",
+       "      <td>Normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>24.5</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>24.3</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 70 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Brand                        Name Audience score Price Lightweight  \\\n",
+       "0  Adidas  Terrex Agravic Speed Ultra   90\\n Superb!  $220           0   \n",
+       "1  Adidas          Terrex Speed Ultra   90\\n Superb!  $160           0   \n",
+       "2   Altra             Experience Wild    88\\n Great!  $145           0   \n",
+       "3   Altra           Experience Wild 2     79\\n Good!  $140           0   \n",
+       "4   Altra               Lone Peak 5.0   91\\n Superb!  $130           0   \n",
+       "\n",
+       "             Size Lug depth Widths available For heavy runners       Season  \\\n",
+       "0  Slightly large    2.5 mm           Normal                 0  All Seasons   \n",
+       "1    True to size    2.6 mm           Normal                 0            -   \n",
+       "2    True to size    3.6 mm           Normal                 0  All Seasons   \n",
+       "3               -    3.5 mm           Normal                 0  All Seasons   \n",
+       "4    True to size    3.7 mm           Normal                 0            -   \n",
+       "\n",
+       "   ... heelcounter_stiff heel_lab_mm heel_brand_mm forefoot_lab_mm  \\\n",
+       "0  ...                 0        30.6          38.0            30.3   \n",
+       "1  ...                 0        32.8          26.0            24.6   \n",
+       "2  ...                 0        34.5          34.0            30.2   \n",
+       "3  ...                 0        32.3          32.0            26.2   \n",
+       "4  ...                 0        24.5          25.0            24.3   \n",
+       "\n",
+       "  forefoot_brand_mm season_norm  season_list  season_all  season_summer  \\\n",
+       "0              30.0         All        [All]           1              0   \n",
+       "1              18.0         NaN          NaN           0              0   \n",
+       "2              30.0         All        [All]           1              0   \n",
+       "3              28.0         All        [All]           1              0   \n",
+       "4              25.0         NaN          NaN           0              0   \n",
+       "\n",
+       "  season_winter  \n",
+       "0             0  \n",
+       "1             0  \n",
+       "2             0  \n",
+       "3             0  \n",
+       "4             0  \n",
+       "\n",
+       "[5 rows x 70 columns]"
+      ]
+     },
+     "execution_count": 130,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"season_list\"] = df[\"season_norm\"].str.split(\"|\")\n",
+    "\n",
+    "season_exploded = df[\"season_list\"].explode()\n",
+    "\n",
+    "season_ohe = (\n",
+    "    pd.crosstab(season_exploded.index, season_exploded)\n",
+    "    .reindex(df.index, fill_value=0)\n",
+    ")\n",
+    "\n",
+    "season_ohe = season_ohe.rename(columns={\n",
+    "    \"All\": \"season_all\",\n",
+    "    \"Summer\": \"season_summer\",\n",
+    "    \"Winter\": \"season_winter\"\n",
+    "})\n",
+    "\n",
+    "for col in [\"season_all\",\"season_summer\",\"season_winter\"]:\n",
+    "    if col not in season_ohe.columns:\n",
+    "        season_ohe[col] = 0\n",
+    "\n",
+    "season_ohe = season_ohe[[\"season_all\",\"season_summer\",\"season_winter\"]]\n",
+    "\n",
+    "df = pd.concat([df, season_ohe], axis=1)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "id": "5cf4a935",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rows: 183\n",
+      "all seasons sum: 138\n",
+      "summer sum: 16\n",
+      "winter sum: 18\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Rows:\", len(df))\n",
+    "print(\"all seasons sum:\", int(df[\"season_all\"].sum()))\n",
+    "print(\"summer sum:\", int(df[\"season_summer\"].sum()))\n",
+    "print(\"winter sum:\", int(df[\"season_winter\"].sum()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "id": "02dc1854",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "               Season season_norm  season_all  season_summer  season_winter\n",
+      "12  Summerall Seasons  Summer|All           1              1              0\n",
+      "16  Summerall Seasons  Summer|All           1              1              0\n",
+      "23  Summerall Seasons  Summer|All           1              1              0\n",
+      "27  Summerall Seasons  Summer|All           1              1              0\n",
+      "30  Summerall Seasons  Summer|All           1              1              0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[df[\"Season\"]==\"Summerall Seasons\"][\n",
+    "    [\"Season\",\"season_norm\",\"season_all\",\"season_summer\",\"season_winter\"]\n",
+    "].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "id": "e23d6b62",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<StringArray>\n",
+      "['All Seasons', '-', 'Summerall Seasons', 'Winter', nan]\n",
+      "Length: 5, dtype: str\n",
+      "        Season season_norm  season_all  season_summer  season_winter\n",
+      "0  All Seasons         All           1              0              0\n",
+      "1            -         NaN           0              0              0\n",
+      "2  All Seasons         All           1              0              0\n",
+      "3  All Seasons         All           1              0              0\n",
+      "4            -         NaN           0              0              0\n",
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 183 entries, 0 to 182\n",
+      "Data columns (total 68 columns):\n",
+      " #   Column                Non-Null Count  Dtype  \n",
+      "---  ------                --------------  -----  \n",
+      " 0   Brand                 181 non-null    str    \n",
+      " 1   Name                  182 non-null    str    \n",
+      " 2   Audience score        179 non-null    str    \n",
+      " 3   Price                 181 non-null    str    \n",
+      " 4   Lightweight           181 non-null    str    \n",
+      " 5   Size                  181 non-null    str    \n",
+      " 6   Lug depth             181 non-null    str    \n",
+      " 7   Widths available      181 non-null    str    \n",
+      " 8   For heavy runners     181 non-null    str    \n",
+      " 9   Removable insole      181 non-null    str    \n",
+      " 10  Orthotic friendly     181 non-null    str    \n",
+      " 11  Waterproofing         180 non-null    str    \n",
+      " 12  Ranking               181 non-null    str    \n",
+      " 13  Popularity            181 non-null    str    \n",
+      " 14  terrain_norm          181 non-null    str    \n",
+      " 15  terrain_light         183 non-null    int64  \n",
+      " 16  terrain_moderate      183 non-null    int64  \n",
+      " 17  terrain_technical     183 non-null    int64  \n",
+      " 18  Arch_grouped          181 non-null    str    \n",
+      " 19  arch_neutral          183 non-null    int64  \n",
+      " 20  arch_stability        183 non-null    int64  \n",
+      " 21  Strike_norm           181 non-null    str    \n",
+      " 22  strike_forefoot       183 non-null    int64  \n",
+      " 23  strike_heel           183 non-null    int64  \n",
+      " 24  strike_mid            183 non-null    int64  \n",
+      " 25  drop_lab_mm           181 non-null    float64\n",
+      " 26  drop_brand_mm         174 non-null    float64\n",
+      " 27  midsole_soft          183 non-null    int64  \n",
+      " 28  midsole_balanced      183 non-null    int64  \n",
+      " 29  midsole_firm          183 non-null    int64  \n",
+      " 30  plate_carbon          183 non-null    int64  \n",
+      " 31  plate_rock            183 non-null    int64  \n",
+      " 32  plate_none            183 non-null    int64  \n",
+      " 33  toebox_bad            183 non-null    int64  \n",
+      " 34  toebox_decent         183 non-null    int64  \n",
+      " 35  toebox_good           183 non-null    int64  \n",
+      " 36  heelpad_bad           183 non-null    int64  \n",
+      " 37  heelpad_decent        183 non-null    int64  \n",
+      " 38  heelpad_good          183 non-null    int64  \n",
+      " 39  outsole_bad           183 non-null    int64  \n",
+      " 40  outsole_decent        183 non-null    int64  \n",
+      " 41  outsole_good          183 non-null    int64  \n",
+      " 42  breath_breathable     183 non-null    int64  \n",
+      " 43  breath_moderate       183 non-null    int64  \n",
+      " 44  breath_warm           183 non-null    int64  \n",
+      " 45  width_narrow          183 non-null    int64  \n",
+      " 46  width_medium          183 non-null    int64  \n",
+      " 47  width_wide            183 non-null    int64  \n",
+      " 48  toeboxwidth_narrow    183 non-null    int64  \n",
+      " 49  toeboxwidth_medium    183 non-null    int64  \n",
+      " 50  toeboxwidth_wide      183 non-null    int64  \n",
+      " 51  stiff_flexible        183 non-null    int64  \n",
+      " 52  stiff_moderate        183 non-null    int64  \n",
+      " 53  stiff_stiff           183 non-null    int64  \n",
+      " 54  torsion_flexible      183 non-null    int64  \n",
+      " 55  torsion_moderate      183 non-null    int64  \n",
+      " 56  torsion_stiff         183 non-null    int64  \n",
+      " 57  heelcounter_flexible  183 non-null    int64  \n",
+      " 58  heelcounter_moderate  183 non-null    int64  \n",
+      " 59  heelcounter_stiff     183 non-null    int64  \n",
+      " 60  heel_lab_mm           181 non-null    float64\n",
+      " 61  heel_brand_mm         166 non-null    float64\n",
+      " 62  forefoot_lab_mm       181 non-null    float64\n",
+      " 63  forefoot_brand_mm     164 non-null    float64\n",
+      " 64  season_list           156 non-null    object \n",
+      " 65  season_all            183 non-null    int64  \n",
+      " 66  season_summer         183 non-null    int64  \n",
+      " 67  season_winter         183 non-null    int64  \n",
+      "dtypes: float64(6), int64(44), object(1), str(17)\n",
+      "memory usage: 97.3+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Season\"].unique())\n",
+    "print(df[[\"Season\",\"season_norm\",\"season_all\",\"season_summer\",\"season_winter\"]].head())\n",
+    "\n",
+    "df.drop(columns=[\"Season\", \"season_norm\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "id": "b3ae0fbb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Removable insole For heavy runners Orthotic friendly Waterproofing\n",
+      "0                1                 0                 1             -\n",
+      "1                1                 0                 1             -\n",
+      "2                1                 0                 1             -\n",
+      "3                1                 0                 1             -\n",
+      "4                1                 0                 1             -\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[[\"Removable insole\",\"For heavy runners\",\"Orthotic friendly\",\"Waterproofing\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba48e879",
+   "metadata": {},
+   "source": [
+    "# Waterproofing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "id": "ba7c8232",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<StringArray>\n",
+      "['-', 'Waterproof', 'Water repellent', 'WaterproofWater repellent', nan]\n",
+      "Length: 5, dtype: str\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Waterproofing\"].unique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "id": "0ff1cd70",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pengecekan Hasil Waterproofing:\n",
+      "  Waterproofing  water_proof  water_repellent  water_both  water_none\n",
+      "0             -            0                0           0           1\n",
+      "1             -            0                0           0           1\n",
+      "2             -            0                0           0           1\n",
+      "3             -            0                0           0           1\n",
+      "4             -            0                0           0           1\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = df.loc[:, ~df.columns.duplicated()]\n",
+    "\n",
+    "temp_water = (\n",
+    "    df[\"Waterproofing\"]\n",
+    "    .astype(str)\n",
+    "    .str.strip()\n",
+    "    .str.replace(r\"\\s+\", \" \", regex=True)\n",
+    ")\n",
+    "\n",
+    "water_ohe = pd.get_dummies(temp_water, prefix=\"water\").astype(int)\n",
+    "\n",
+    "mapping = {\n",
+    "    \"water_Waterproof\": \"water_proof\",\n",
+    "    \"water_Water repellent\": \"water_repellent\",\n",
+    "    \"water_WaterproofWater repellent\": \"water_both\", # atau bisa dipisah nanti\n",
+    "    \"water_-\": \"water_none\"\n",
+    "}\n",
+    "\n",
+    "water_ohe = water_ohe.rename(columns=mapping)\n",
+    "\n",
+    "target_cols = [\"water_proof\", \"water_repellent\", \"water_both\", \"water_none\"]\n",
+    "\n",
+    "for col in target_cols:\n",
+    "    if col not in water_ohe.columns:\n",
+    "        water_ohe[col] = 0\n",
+    "\n",
+    "\n",
+    "df = df.drop(columns=[c for c in target_cols if c in df.columns])\n",
+    "\n",
+    "df = pd.concat([df, water_ohe[target_cols]], axis=1)\n",
+    "\n",
+    "print(\"Pengecekan Hasil Waterproofing:\")\n",
+    "sample_check = df[df[\"Waterproofing\"].isna() | (df[\"Waterproofing\"] == \"-\")].head(5)\n",
+    "print(sample_check[[\"Waterproofing\"] + target_cols])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "id": "d7d37364",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Waterproofing  water_proof  water_repellent  water_both  water_none\n",
+      "0             -            0                0           0           1\n",
+      "1             -            0                0           0           1\n",
+      "2             -            0                0           0           1\n",
+      "3             -            0                0           0           1\n",
+      "4             -            0                0           0           1\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[[\"Waterproofing\",\"water_proof\", \"water_repellent\", \"water_both\", \"water_none\"]].head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "id": "28da3118",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 183 entries, 0 to 182\n",
+      "Data columns (total 71 columns):\n",
+      " #   Column                Non-Null Count  Dtype  \n",
+      "---  ------                --------------  -----  \n",
+      " 0   Brand                 181 non-null    str    \n",
+      " 1   Name                  182 non-null    str    \n",
+      " 2   Audience score        179 non-null    str    \n",
+      " 3   Price                 181 non-null    str    \n",
+      " 4   Lightweight           181 non-null    str    \n",
+      " 5   Size                  181 non-null    str    \n",
+      " 6   Lug depth             181 non-null    str    \n",
+      " 7   Widths available      181 non-null    str    \n",
+      " 8   For heavy runners     181 non-null    str    \n",
+      " 9   Removable insole      181 non-null    str    \n",
+      " 10  Orthotic friendly     181 non-null    str    \n",
+      " 11  Ranking               181 non-null    str    \n",
+      " 12  Popularity            181 non-null    str    \n",
+      " 13  terrain_norm          181 non-null    str    \n",
+      " 14  terrain_light         183 non-null    int64  \n",
+      " 15  terrain_moderate      183 non-null    int64  \n",
+      " 16  terrain_technical     183 non-null    int64  \n",
+      " 17  Arch_grouped          181 non-null    str    \n",
+      " 18  arch_neutral          183 non-null    int64  \n",
+      " 19  arch_stability        183 non-null    int64  \n",
+      " 20  Strike_norm           181 non-null    str    \n",
+      " 21  strike_forefoot       183 non-null    int64  \n",
+      " 22  strike_heel           183 non-null    int64  \n",
+      " 23  strike_mid            183 non-null    int64  \n",
+      " 24  drop_lab_mm           181 non-null    float64\n",
+      " 25  drop_brand_mm         174 non-null    float64\n",
+      " 26  midsole_soft          183 non-null    int64  \n",
+      " 27  midsole_balanced      183 non-null    int64  \n",
+      " 28  midsole_firm          183 non-null    int64  \n",
+      " 29  plate_carbon          183 non-null    int64  \n",
+      " 30  plate_rock            183 non-null    int64  \n",
+      " 31  plate_none            183 non-null    int64  \n",
+      " 32  toebox_bad            183 non-null    int64  \n",
+      " 33  toebox_decent         183 non-null    int64  \n",
+      " 34  toebox_good           183 non-null    int64  \n",
+      " 35  heelpad_bad           183 non-null    int64  \n",
+      " 36  heelpad_decent        183 non-null    int64  \n",
+      " 37  heelpad_good          183 non-null    int64  \n",
+      " 38  outsole_bad           183 non-null    int64  \n",
+      " 39  outsole_decent        183 non-null    int64  \n",
+      " 40  outsole_good          183 non-null    int64  \n",
+      " 41  breath_breathable     183 non-null    int64  \n",
+      " 42  breath_moderate       183 non-null    int64  \n",
+      " 43  breath_warm           183 non-null    int64  \n",
+      " 44  width_narrow          183 non-null    int64  \n",
+      " 45  width_medium          183 non-null    int64  \n",
+      " 46  width_wide            183 non-null    int64  \n",
+      " 47  toeboxwidth_narrow    183 non-null    int64  \n",
+      " 48  toeboxwidth_medium    183 non-null    int64  \n",
+      " 49  toeboxwidth_wide      183 non-null    int64  \n",
+      " 50  stiff_flexible        183 non-null    int64  \n",
+      " 51  stiff_moderate        183 non-null    int64  \n",
+      " 52  stiff_stiff           183 non-null    int64  \n",
+      " 53  torsion_flexible      183 non-null    int64  \n",
+      " 54  torsion_moderate      183 non-null    int64  \n",
+      " 55  torsion_stiff         183 non-null    int64  \n",
+      " 56  heelcounter_flexible  183 non-null    int64  \n",
+      " 57  heelcounter_moderate  183 non-null    int64  \n",
+      " 58  heelcounter_stiff     183 non-null    int64  \n",
+      " 59  heel_lab_mm           181 non-null    float64\n",
+      " 60  heel_brand_mm         166 non-null    float64\n",
+      " 61  forefoot_lab_mm       181 non-null    float64\n",
+      " 62  forefoot_brand_mm     164 non-null    float64\n",
+      " 63  season_list           156 non-null    object \n",
+      " 64  season_all            183 non-null    int64  \n",
+      " 65  season_summer         183 non-null    int64  \n",
+      " 66  season_winter         183 non-null    int64  \n",
+      " 67  water_proof           183 non-null    int64  \n",
+      " 68  water_repellent       183 non-null    int64  \n",
+      " 69  water_both            183 non-null    int64  \n",
+      " 70  water_none            183 non-null    int64  \n",
+      "dtypes: float64(6), int64(48), object(1), str(16)\n",
+      "memory usage: 101.6+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"Waterproofing\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a079d462",
+   "metadata": {},
+   "source": [
+    "# remove duplicate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "id": "4b9a9ea9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "183\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>Hoka</td>\n",
+       "      <td>Mafate Speed 4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>Hoka</td>\n",
+       "      <td>Mafate Speed 4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>Hoka</td>\n",
+       "      <td>Speedgoat 6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>82</th>\n",
+       "      <td>New Balance</td>\n",
+       "      <td>Fresh Foam X Hierro v9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>Nike</td>\n",
+       "      <td>Pegasus Trail 5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>110</th>\n",
+       "      <td>On</td>\n",
+       "      <td>Cloudsurfer Trail</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115</th>\n",
+       "      <td>Salomon</td>\n",
+       "      <td>Genesis</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>117</th>\n",
+       "      <td>Salomon</td>\n",
+       "      <td>Pulsar Trail</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>125</th>\n",
+       "      <td>Salomon</td>\n",
+       "      <td>Sense Ride 5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>128</th>\n",
+       "      <td>Salomon</td>\n",
+       "      <td>Speedcross 6 GTX</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>131</th>\n",
+       "      <td>Salomon</td>\n",
+       "      <td>Thundercross</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>135</th>\n",
+       "      <td>Salomon</td>\n",
+       "      <td>Ultra Glide 2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>152</th>\n",
+       "      <td>Saucony</td>\n",
+       "      <td>Xodus Ultra 4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Brand                    Name\n",
+       "43          Hoka          Mafate Speed 4\n",
+       "44          Hoka          Mafate Speed 4\n",
+       "50          Hoka             Speedgoat 6\n",
+       "82   New Balance  Fresh Foam X Hierro v9\n",
+       "99          Nike         Pegasus Trail 5\n",
+       "110           On       Cloudsurfer Trail\n",
+       "115      Salomon                 Genesis\n",
+       "117      Salomon            Pulsar Trail\n",
+       "125      Salomon            Sense Ride 5\n",
+       "128      Salomon        Speedcross 6 GTX\n",
+       "131      Salomon            Thundercross\n",
+       "135      Salomon           Ultra Glide 2\n",
+       "152      Saucony           Xodus Ultra 4"
+      ]
+     },
+     "execution_count": 139,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dup_mask = df.duplicated(subset=[\"Brand\", \"Name\"], keep=\"first\")\n",
+    "print(len(dup_mask))\n",
+    "df.loc[dup_mask, [\"Brand\", \"Name\"]].head(100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "id": "e2ba26dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Before: 183\n",
+      "After : 170\n"
+     ]
+    }
+   ],
+   "source": [
+    "# sebelum hapus\n",
+    "print(\"Before:\", len(df))\n",
+    "\n",
+    "#hapus\n",
+    "df = df.drop_duplicates(subset=[\"Brand\", \"Name\"], keep=\"first\").reset_index(drop=True)\n",
+    "\n",
+    "print(\"After :\", len(df))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "id": "b1ff9301",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [Brand, Name]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dup_mask = df.duplicated(subset=[\"Brand\", \"Name\"], keep=\"first\")\n",
+    "df.loc[dup_mask, [\"Brand\", \"Name\"]].head(20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44f09d23",
+   "metadata": {},
+   "source": [
+    "# to csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "id": "445f650f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 170 entries, 0 to 169\n",
+      "Data columns (total 71 columns):\n",
+      " #   Column                Non-Null Count  Dtype  \n",
+      "---  ------                --------------  -----  \n",
+      " 0   Brand                 168 non-null    str    \n",
+      " 1   Name                  169 non-null    str    \n",
+      " 2   Audience score        166 non-null    str    \n",
+      " 3   Price                 168 non-null    str    \n",
+      " 4   Lightweight           168 non-null    str    \n",
+      " 5   Size                  168 non-null    str    \n",
+      " 6   Lug depth             168 non-null    str    \n",
+      " 7   Widths available      168 non-null    str    \n",
+      " 8   For heavy runners     168 non-null    str    \n",
+      " 9   Removable insole      168 non-null    str    \n",
+      " 10  Orthotic friendly     168 non-null    str    \n",
+      " 11  Ranking               168 non-null    str    \n",
+      " 12  Popularity            168 non-null    str    \n",
+      " 13  terrain_norm          168 non-null    str    \n",
+      " 14  terrain_light         170 non-null    int64  \n",
+      " 15  terrain_moderate      170 non-null    int64  \n",
+      " 16  terrain_technical     170 non-null    int64  \n",
+      " 17  Arch_grouped          168 non-null    str    \n",
+      " 18  arch_neutral          170 non-null    int64  \n",
+      " 19  arch_stability        170 non-null    int64  \n",
+      " 20  Strike_norm           168 non-null    str    \n",
+      " 21  strike_forefoot       170 non-null    int64  \n",
+      " 22  strike_heel           170 non-null    int64  \n",
+      " 23  strike_mid            170 non-null    int64  \n",
+      " 24  drop_lab_mm           168 non-null    float64\n",
+      " 25  drop_brand_mm         161 non-null    float64\n",
+      " 26  midsole_soft          170 non-null    int64  \n",
+      " 27  midsole_balanced      170 non-null    int64  \n",
+      " 28  midsole_firm          170 non-null    int64  \n",
+      " 29  plate_carbon          170 non-null    int64  \n",
+      " 30  plate_rock            170 non-null    int64  \n",
+      " 31  plate_none            170 non-null    int64  \n",
+      " 32  toebox_bad            170 non-null    int64  \n",
+      " 33  toebox_decent         170 non-null    int64  \n",
+      " 34  toebox_good           170 non-null    int64  \n",
+      " 35  heelpad_bad           170 non-null    int64  \n",
+      " 36  heelpad_decent        170 non-null    int64  \n",
+      " 37  heelpad_good          170 non-null    int64  \n",
+      " 38  outsole_bad           170 non-null    int64  \n",
+      " 39  outsole_decent        170 non-null    int64  \n",
+      " 40  outsole_good          170 non-null    int64  \n",
+      " 41  breath_breathable     170 non-null    int64  \n",
+      " 42  breath_moderate       170 non-null    int64  \n",
+      " 43  breath_warm           170 non-null    int64  \n",
+      " 44  width_narrow          170 non-null    int64  \n",
+      " 45  width_medium          170 non-null    int64  \n",
+      " 46  width_wide            170 non-null    int64  \n",
+      " 47  toeboxwidth_narrow    170 non-null    int64  \n",
+      " 48  toeboxwidth_medium    170 non-null    int64  \n",
+      " 49  toeboxwidth_wide      170 non-null    int64  \n",
+      " 50  stiff_flexible        170 non-null    int64  \n",
+      " 51  stiff_moderate        170 non-null    int64  \n",
+      " 52  stiff_stiff           170 non-null    int64  \n",
+      " 53  torsion_flexible      170 non-null    int64  \n",
+      " 54  torsion_moderate      170 non-null    int64  \n",
+      " 55  torsion_stiff         170 non-null    int64  \n",
+      " 56  heelcounter_flexible  170 non-null    int64  \n",
+      " 57  heelcounter_moderate  170 non-null    int64  \n",
+      " 58  heelcounter_stiff     170 non-null    int64  \n",
+      " 59  heel_lab_mm           168 non-null    float64\n",
+      " 60  heel_brand_mm         153 non-null    float64\n",
+      " 61  forefoot_lab_mm       168 non-null    float64\n",
+      " 62  forefoot_brand_mm     151 non-null    float64\n",
+      " 63  season_list           143 non-null    object \n",
+      " 64  season_all            170 non-null    int64  \n",
+      " 65  season_summer         170 non-null    int64  \n",
+      " 66  season_winter         170 non-null    int64  \n",
+      " 67  water_proof           170 non-null    int64  \n",
+      " 68  water_repellent       170 non-null    int64  \n",
+      " 69  water_both            170 non-null    int64  \n",
+      " 70  water_none            170 non-null    int64  \n",
+      "dtypes: float64(6), int64(48), object(1), str(16)\n",
+      "memory usage: 94.4+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "id": "67c2efb2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 170 entries, 0 to 169\n",
+      "Data columns (total 70 columns):\n",
+      " #   Column                Non-Null Count  Dtype  \n",
+      "---  ------                --------------  -----  \n",
+      " 0   Brand                 168 non-null    str    \n",
+      " 1   Name                  169 non-null    str    \n",
+      " 2   Audience score        166 non-null    str    \n",
+      " 3   Price                 168 non-null    str    \n",
+      " 4   Lightweight           168 non-null    str    \n",
+      " 5   Size                  168 non-null    str    \n",
+      " 6   Lug depth             168 non-null    str    \n",
+      " 7   Widths available      168 non-null    str    \n",
+      " 8   For heavy runners     168 non-null    str    \n",
+      " 9   Removable insole      168 non-null    str    \n",
+      " 10  Orthotic friendly     168 non-null    str    \n",
+      " 11  Ranking               168 non-null    str    \n",
+      " 12  Popularity            168 non-null    str    \n",
+      " 13  terrain_norm          168 non-null    str    \n",
+      " 14  terrain_light         170 non-null    int64  \n",
+      " 15  terrain_moderate      170 non-null    int64  \n",
+      " 16  terrain_technical     170 non-null    int64  \n",
+      " 17  Arch_grouped          168 non-null    str    \n",
+      " 18  arch_neutral          170 non-null    int64  \n",
+      " 19  arch_stability        170 non-null    int64  \n",
+      " 20  Strike_norm           168 non-null    str    \n",
+      " 21  strike_forefoot       170 non-null    int64  \n",
+      " 22  strike_heel           170 non-null    int64  \n",
+      " 23  strike_mid            170 non-null    int64  \n",
+      " 24  drop_lab_mm           168 non-null    float64\n",
+      " 25  drop_brand_mm         161 non-null    float64\n",
+      " 26  midsole_soft          170 non-null    int64  \n",
+      " 27  midsole_balanced      170 non-null    int64  \n",
+      " 28  midsole_firm          170 non-null    int64  \n",
+      " 29  plate_carbon          170 non-null    int64  \n",
+      " 30  plate_rock            170 non-null    int64  \n",
+      " 31  plate_none            170 non-null    int64  \n",
+      " 32  toebox_bad            170 non-null    int64  \n",
+      " 33  toebox_decent         170 non-null    int64  \n",
+      " 34  toebox_good           170 non-null    int64  \n",
+      " 35  heelpad_bad           170 non-null    int64  \n",
+      " 36  heelpad_decent        170 non-null    int64  \n",
+      " 37  heelpad_good          170 non-null    int64  \n",
+      " 38  outsole_bad           170 non-null    int64  \n",
+      " 39  outsole_decent        170 non-null    int64  \n",
+      " 40  outsole_good          170 non-null    int64  \n",
+      " 41  breath_breathable     170 non-null    int64  \n",
+      " 42  breath_moderate       170 non-null    int64  \n",
+      " 43  breath_warm           170 non-null    int64  \n",
+      " 44  width_narrow          170 non-null    int64  \n",
+      " 45  width_medium          170 non-null    int64  \n",
+      " 46  width_wide            170 non-null    int64  \n",
+      " 47  toeboxwidth_narrow    170 non-null    int64  \n",
+      " 48  toeboxwidth_medium    170 non-null    int64  \n",
+      " 49  toeboxwidth_wide      170 non-null    int64  \n",
+      " 50  stiff_flexible        170 non-null    int64  \n",
+      " 51  stiff_moderate        170 non-null    int64  \n",
+      " 52  stiff_stiff           170 non-null    int64  \n",
+      " 53  torsion_flexible      170 non-null    int64  \n",
+      " 54  torsion_moderate      170 non-null    int64  \n",
+      " 55  torsion_stiff         170 non-null    int64  \n",
+      " 56  heelcounter_flexible  170 non-null    int64  \n",
+      " 57  heelcounter_moderate  170 non-null    int64  \n",
+      " 58  heelcounter_stiff     170 non-null    int64  \n",
+      " 59  heel_lab_mm           168 non-null    float64\n",
+      " 60  heel_brand_mm         153 non-null    float64\n",
+      " 61  forefoot_lab_mm       168 non-null    float64\n",
+      " 62  forefoot_brand_mm     151 non-null    float64\n",
+      " 63  season_all            170 non-null    int64  \n",
+      " 64  season_summer         170 non-null    int64  \n",
+      " 65  season_winter         170 non-null    int64  \n",
+      " 66  water_proof           170 non-null    int64  \n",
+      " 67  water_repellent       170 non-null    int64  \n",
+      " 68  water_both            170 non-null    int64  \n",
+      " 69  water_none            170 non-null    int64  \n",
+      "dtypes: float64(6), int64(48), str(16)\n",
+      "memory usage: 93.1 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"season_list\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "id": "6e18b1b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 170 entries, 0 to 169\n",
+      "Data columns (total 70 columns):\n",
+      " #   Column                Non-Null Count  Dtype  \n",
+      "---  ------                --------------  -----  \n",
+      " 0   Brand                 168 non-null    str    \n",
+      " 1   Name                  169 non-null    str    \n",
+      " 2   Audience score        166 non-null    str    \n",
+      " 3   Price                 168 non-null    str    \n",
+      " 4   Lightweight           168 non-null    str    \n",
+      " 5   Size                  168 non-null    str    \n",
+      " 6   Lug depth             168 non-null    str    \n",
+      " 7   Widths available      168 non-null    str    \n",
+      " 8   For heavy runners     168 non-null    str    \n",
+      " 9   Removable insole      168 non-null    str    \n",
+      " 10  Orthotic friendly     168 non-null    str    \n",
+      " 11  Ranking               168 non-null    str    \n",
+      " 12  Popularity            168 non-null    str    \n",
+      " 13  terrain_norm          168 non-null    str    \n",
+      " 14  terrain_light         170 non-null    int64  \n",
+      " 15  terrain_moderate      170 non-null    int64  \n",
+      " 16  terrain_technical     170 non-null    int64  \n",
+      " 17  Arch_grouped          168 non-null    str    \n",
+      " 18  arch_neutral          170 non-null    int64  \n",
+      " 19  arch_stability        170 non-null    int64  \n",
+      " 20  Strike_norm           168 non-null    str    \n",
+      " 21  strike_forefoot       170 non-null    int64  \n",
+      " 22  strike_heel           170 non-null    int64  \n",
+      " 23  strike_mid            170 non-null    int64  \n",
+      " 24  drop_lab_mm           168 non-null    float64\n",
+      " 25  drop_brand_mm         161 non-null    float64\n",
+      " 26  midsole_soft          170 non-null    int64  \n",
+      " 27  midsole_balanced      170 non-null    int64  \n",
+      " 28  midsole_firm          170 non-null    int64  \n",
+      " 29  plate_carbon          170 non-null    int64  \n",
+      " 30  plate_rock            170 non-null    int64  \n",
+      " 31  plate_none            170 non-null    int64  \n",
+      " 32  toebox_bad            170 non-null    int64  \n",
+      " 33  toebox_decent         170 non-null    int64  \n",
+      " 34  toebox_good           170 non-null    int64  \n",
+      " 35  heelpad_bad           170 non-null    int64  \n",
+      " 36  heelpad_decent        170 non-null    int64  \n",
+      " 37  heelpad_good          170 non-null    int64  \n",
+      " 38  outsole_bad           170 non-null    int64  \n",
+      " 39  outsole_decent        170 non-null    int64  \n",
+      " 40  outsole_good          170 non-null    int64  \n",
+      " 41  breath_breathable     170 non-null    int64  \n",
+      " 42  breath_moderate       170 non-null    int64  \n",
+      " 43  breath_warm           170 non-null    int64  \n",
+      " 44  width_narrow          170 non-null    int64  \n",
+      " 45  width_medium          170 non-null    int64  \n",
+      " 46  width_wide            170 non-null    int64  \n",
+      " 47  toeboxwidth_narrow    170 non-null    int64  \n",
+      " 48  toeboxwidth_medium    170 non-null    int64  \n",
+      " 49  toeboxwidth_wide      170 non-null    int64  \n",
+      " 50  stiff_flexible        170 non-null    int64  \n",
+      " 51  stiff_moderate        170 non-null    int64  \n",
+      " 52  stiff_stiff           170 non-null    int64  \n",
+      " 53  torsion_flexible      170 non-null    int64  \n",
+      " 54  torsion_moderate      170 non-null    int64  \n",
+      " 55  torsion_stiff         170 non-null    int64  \n",
+      " 56  heelcounter_flexible  170 non-null    int64  \n",
+      " 57  heelcounter_moderate  170 non-null    int64  \n",
+      " 58  heelcounter_stiff     170 non-null    int64  \n",
+      " 59  heel_lab_mm           168 non-null    float64\n",
+      " 60  heel_brand_mm         153 non-null    float64\n",
+      " 61  forefoot_lab_mm       168 non-null    float64\n",
+      " 62  forefoot_brand_mm     151 non-null    float64\n",
+      " 63  season_all            170 non-null    int64  \n",
+      " 64  season_summer         170 non-null    int64  \n",
+      " 65  season_winter         170 non-null    int64  \n",
+      " 66  water_proof           170 non-null    int64  \n",
+      " 67  water_repellent       170 non-null    int64  \n",
+      " 68  water_both            170 non-null    int64  \n",
+      " 69  water_none            170 non-null    int64  \n",
+      "dtypes: float64(6), int64(48), str(16)\n",
+      "memory usage: 93.1 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# df.drop(columns=[\"weight_lab_g\", \"weight_brand_g\", \"weight_brand_oz\", \"drop_brand_mm\", \"heel_brand_mm\", \"forefoot_brand_mm\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "id": "24ed1981",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 170 entries, 0 to 169\n",
+      "Data columns (total 68 columns):\n",
+      " #   Column                Non-Null Count  Dtype  \n",
+      "---  ------                --------------  -----  \n",
+      " 0   Brand                 168 non-null    str    \n",
+      " 1   Name                  169 non-null    str    \n",
+      " 2   Lightweight           168 non-null    str    \n",
+      " 3   Size                  168 non-null    str    \n",
+      " 4   Lug depth             168 non-null    str    \n",
+      " 5   Widths available      168 non-null    str    \n",
+      " 6   For heavy runners     168 non-null    str    \n",
+      " 7   Removable insole      168 non-null    str    \n",
+      " 8   Orthotic friendly     168 non-null    str    \n",
+      " 9   Ranking               168 non-null    str    \n",
+      " 10  Popularity            168 non-null    str    \n",
+      " 11  terrain_norm          168 non-null    str    \n",
+      " 12  terrain_light         170 non-null    int64  \n",
+      " 13  terrain_moderate      170 non-null    int64  \n",
+      " 14  terrain_technical     170 non-null    int64  \n",
+      " 15  Arch_grouped          168 non-null    str    \n",
+      " 16  arch_neutral          170 non-null    int64  \n",
+      " 17  arch_stability        170 non-null    int64  \n",
+      " 18  Strike_norm           168 non-null    str    \n",
+      " 19  strike_forefoot       170 non-null    int64  \n",
+      " 20  strike_heel           170 non-null    int64  \n",
+      " 21  strike_mid            170 non-null    int64  \n",
+      " 22  drop_lab_mm           168 non-null    float64\n",
+      " 23  drop_brand_mm         161 non-null    float64\n",
+      " 24  midsole_soft          170 non-null    int64  \n",
+      " 25  midsole_balanced      170 non-null    int64  \n",
+      " 26  midsole_firm          170 non-null    int64  \n",
+      " 27  plate_carbon          170 non-null    int64  \n",
+      " 28  plate_rock            170 non-null    int64  \n",
+      " 29  plate_none            170 non-null    int64  \n",
+      " 30  toebox_bad            170 non-null    int64  \n",
+      " 31  toebox_decent         170 non-null    int64  \n",
+      " 32  toebox_good           170 non-null    int64  \n",
+      " 33  heelpad_bad           170 non-null    int64  \n",
+      " 34  heelpad_decent        170 non-null    int64  \n",
+      " 35  heelpad_good          170 non-null    int64  \n",
+      " 36  outsole_bad           170 non-null    int64  \n",
+      " 37  outsole_decent        170 non-null    int64  \n",
+      " 38  outsole_good          170 non-null    int64  \n",
+      " 39  breath_breathable     170 non-null    int64  \n",
+      " 40  breath_moderate       170 non-null    int64  \n",
+      " 41  breath_warm           170 non-null    int64  \n",
+      " 42  width_narrow          170 non-null    int64  \n",
+      " 43  width_medium          170 non-null    int64  \n",
+      " 44  width_wide            170 non-null    int64  \n",
+      " 45  toeboxwidth_narrow    170 non-null    int64  \n",
+      " 46  toeboxwidth_medium    170 non-null    int64  \n",
+      " 47  toeboxwidth_wide      170 non-null    int64  \n",
+      " 48  stiff_flexible        170 non-null    int64  \n",
+      " 49  stiff_moderate        170 non-null    int64  \n",
+      " 50  stiff_stiff           170 non-null    int64  \n",
+      " 51  torsion_flexible      170 non-null    int64  \n",
+      " 52  torsion_moderate      170 non-null    int64  \n",
+      " 53  torsion_stiff         170 non-null    int64  \n",
+      " 54  heelcounter_flexible  170 non-null    int64  \n",
+      " 55  heelcounter_moderate  170 non-null    int64  \n",
+      " 56  heelcounter_stiff     170 non-null    int64  \n",
+      " 57  heel_lab_mm           168 non-null    float64\n",
+      " 58  heel_brand_mm         153 non-null    float64\n",
+      " 59  forefoot_lab_mm       168 non-null    float64\n",
+      " 60  forefoot_brand_mm     151 non-null    float64\n",
+      " 61  season_all            170 non-null    int64  \n",
+      " 62  season_summer         170 non-null    int64  \n",
+      " 63  season_winter         170 non-null    int64  \n",
+      " 64  water_proof           170 non-null    int64  \n",
+      " 65  water_repellent       170 non-null    int64  \n",
+      " 66  water_both            170 non-null    int64  \n",
+      " 67  water_none            170 non-null    int64  \n",
+      "dtypes: float64(6), int64(48), str(14)\n",
+      "memory usage: 90.4 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.drop(columns=[\"Audience score\", \"Price\"], inplace=True)\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "id": "8d080857",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv('../../data/trail_dataset.csv', index=False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/modelling/road_ml.ipynb b/notebooks/modelling/road_ml.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..d2b2e362248fc2d346eb75f588ca9b13c0b7ce2a
--- /dev/null
+++ b/notebooks/modelling/road_ml.ipynb
@@ -0,0 +1,2415 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c0cdf0b4",
+   "metadata": {},
+   "source": [
+    "# Import Library & Configuration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "d37034b9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Libraries loaded\n",
+      "Time: 2026-02-14 11:47:50\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "os.environ['PYTHONHASHSEED'] = '0'\n",
+    "os.environ['TF_DETERMINISTIC_OPS'] = '1'\n",
+    "os.environ['TF_CUDNN_DETERMINISM'] = '1'\n",
+    "\n",
+    "import random\n",
+    "random.seed(42)\n",
+    "\n",
+    "import math\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "np.random.seed(42)\n",
+    "\n",
+    "from datetime import datetime\n",
+    "import pickle\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "\"\"\"\n",
+    "Deep Learning Framework\n",
+    "TensorFlow & Keras: Autoencoder architecture with Dense, BatchNormalization, Dropout layers\n",
+    "\"\"\"\n",
+    "import tensorflow as tf\n",
+    "tf.random.set_seed(42)\n",
+    "from tensorflow.keras import layers, Model, optimizers, callbacks\n",
+    "from tensorflow.keras.layers import Input, Dense, Dropout, BatchNormalization\n",
+    "\n",
+    "\"\"\"\n",
+    "Machine Learning & Metrics\n",
+    "- KMeans: K-means clustering for shoe recommendation groups\n",
+    "- StandardScaler/MinMaxScaler: Feature normalization for ML models\n",
+    "- Clustering Metrics: Silhouette, Davies-Bouldin, Calinski-Harabasz indices\n",
+    "- Similarity: Cosine similarity for recommendation ranking\n",
+    "\"\"\"\n",
+    "from sklearn.cluster import KMeans\n",
+    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
+    "from sklearn.metrics import (\n",
+    "    silhouette_score, davies_bouldin_score, calinski_harabasz_score,\n",
+    "    adjusted_rand_score\n",
+    ")\n",
+    "from sklearn.metrics.pairwise import cosine_similarity, euclidean_distances\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "\n",
+    "\"\"\"\n",
+    "Data Visualization\n",
+    "Matplotlib & Seaborn for statistical plots and cluster visualization\n",
+    "\"\"\"\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set_style('whitegrid')\n",
+    "plt.rcParams['figure.figsize'] = (14, 6)\n",
+    "\n",
+    "np.random.seed(42)\n",
+    "tf.random.set_seed(42)\n",
+    "\n",
+    "print('Libraries loaded')\n",
+    "print(f'Time: {datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4b97aa73",
+   "metadata": {},
+   "source": [
+    "# Load Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "ea0a9377",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded: 428 shoes × 32 columns\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>brand</th>\n",
+       "      <th>name</th>\n",
+       "      <th>lightweight</th>\n",
+       "      <th>rocker</th>\n",
+       "      <th>removable_insole</th>\n",
+       "      <th>pace_daily_running</th>\n",
+       "      <th>pace_tempo</th>\n",
+       "      <th>pace_competition</th>\n",
+       "      <th>arch_neutral</th>\n",
+       "      <th>arch_stability</th>\n",
+       "      <th>...</th>\n",
+       "      <th>stiffness_scaled</th>\n",
+       "      <th>torsional_rigidity</th>\n",
+       "      <th>heel_stiff</th>\n",
+       "      <th>plate_rock_plate</th>\n",
+       "      <th>plate_carbon_plate</th>\n",
+       "      <th>heel_lab_mm</th>\n",
+       "      <th>forefoot_lab_mm</th>\n",
+       "      <th>season_summer</th>\n",
+       "      <th>season_winter</th>\n",
+       "      <th>season_all</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>brooks</td>\n",
+       "      <td>launch 9</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>32.4</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>brooks</td>\n",
+       "      <td>levitate 6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>34.3</td>\n",
+       "      <td>26.6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>4dfwd</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>33.3</td>\n",
+       "      <td>24.4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>4dfwd 2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>31.8</td>\n",
+       "      <td>21.2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>4dfwd 3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>32.6</td>\n",
+       "      <td>22.7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 32 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    brand        name  lightweight  rocker  removable_insole  \\\n",
+       "0  brooks    launch 9            1       0                 1   \n",
+       "1  brooks  levitate 6            0       0                 1   \n",
+       "2  adidas       4dfwd            0       0                 1   \n",
+       "3  adidas     4dfwd 2            0       0                 1   \n",
+       "4  adidas     4dfwd 3            0       0                 1   \n",
+       "\n",
+       "   pace_daily_running  pace_tempo  pace_competition  arch_neutral  \\\n",
+       "0                   1           1                 0             1   \n",
+       "1                   1           0                 0             1   \n",
+       "2                   1           0                 0             1   \n",
+       "3                   1           0                 0             1   \n",
+       "4                   1           0                 0             1   \n",
+       "\n",
+       "   arch_stability  ...  stiffness_scaled  torsional_rigidity  heel_stiff  \\\n",
+       "0               0  ...                 5                   5           1   \n",
+       "1               0  ...                 5                   3           3   \n",
+       "2               0  ...                 5                   1           1   \n",
+       "3               0  ...                 5                   1           3   \n",
+       "4               0  ...                 3                   1           1   \n",
+       "\n",
+       "   plate_rock_plate  plate_carbon_plate  heel_lab_mm  forefoot_lab_mm  \\\n",
+       "0                 0                   0         32.4             23.0   \n",
+       "1                 0                   0         34.3             26.6   \n",
+       "2                 0                   0         33.3             24.4   \n",
+       "3                 0                   0         31.8             21.2   \n",
+       "4                 0                   0         32.6             22.7   \n",
+       "\n",
+       "   season_summer  season_winter  season_all  \n",
+       "0              0              0           0  \n",
+       "1              1              0           1  \n",
+       "2              0              0           1  \n",
+       "3              0              0           1  \n",
+       "4              0              0           1  \n",
+       "\n",
+       "[5 rows x 32 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "file = '../../data/road_dataset.csv'\n",
+    "\n",
+    "try:\n",
+    "    df = pd.read_csv(file)\n",
+    "    print(f'Loaded: {df.shape[0]} shoes × {df.shape[1]} columns')\n",
+    "    display(df.head())\n",
+    "except FileNotFoundError:\n",
+    "    print(f\"WARNING: '{file}' not found.\")\n",
+    "    print(\"Please upload the correct dataset file to run with actual data.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "ff806013",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 428 entries, 0 to 427\n",
+      "Data columns (total 32 columns):\n",
+      " #   Column                Non-Null Count  Dtype  \n",
+      "---  ------                --------------  -----  \n",
+      " 0   brand                 428 non-null    str    \n",
+      " 1   name                  428 non-null    str    \n",
+      " 2   lightweight           428 non-null    int64  \n",
+      " 3   rocker                428 non-null    int64  \n",
+      " 4   removable_insole      428 non-null    int64  \n",
+      " 5   pace_daily_running    428 non-null    int64  \n",
+      " 6   pace_tempo            428 non-null    int64  \n",
+      " 7   pace_competition      428 non-null    int64  \n",
+      " 8   arch_neutral          428 non-null    int64  \n",
+      " 9   arch_stability        428 non-null    int64  \n",
+      " 10  weight_lab_oz         428 non-null    float64\n",
+      " 11  drop_lab_mm           428 non-null    float64\n",
+      " 12  strike_heel           428 non-null    int64  \n",
+      " 13  strike_mid            428 non-null    int64  \n",
+      " 14  strike_forefoot       428 non-null    int64  \n",
+      " 15  midsole_softness      428 non-null    int64  \n",
+      " 16  toebox_durability     428 non-null    int64  \n",
+      " 17  heel_durability       428 non-null    int64  \n",
+      " 18  outsole_durability    428 non-null    int64  \n",
+      " 19  breathability_scaled  428 non-null    int64  \n",
+      " 20  width_fit             428 non-null    int64  \n",
+      " 21  toebox_width          428 non-null    int64  \n",
+      " 22  stiffness_scaled      428 non-null    int64  \n",
+      " 23  torsional_rigidity    428 non-null    int64  \n",
+      " 24  heel_stiff            428 non-null    int64  \n",
+      " 25  plate_rock_plate      428 non-null    int64  \n",
+      " 26  plate_carbon_plate    428 non-null    int64  \n",
+      " 27  heel_lab_mm           428 non-null    float64\n",
+      " 28  forefoot_lab_mm       428 non-null    float64\n",
+      " 29  season_summer         428 non-null    int64  \n",
+      " 30  season_winter         428 non-null    int64  \n",
+      " 31  season_all            428 non-null    int64  \n",
+      "dtypes: float64(4), int64(26), str(2)\n",
+      "memory usage: 107.1 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "74a2caea",
+   "metadata": {},
+   "source": [
+    "# Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d94fbce6",
+   "metadata": {},
+   "source": [
+    "## Feature Engineering\n",
+    "Separates numeric features into two categories for different preprocessing strategies."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "9f7f5da2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Features: 30 total\n",
+      "  Binary     : 16\n",
+      "  Continuous : 14\n"
+     ]
+    }
+   ],
+   "source": [
+    "numeric_cols = df.select_dtypes(include=[np.number]).columns.tolist()\n",
+    "\n",
+    "binary_cols = [col for col in numeric_cols if set(df[col].unique()).issubset({0, 1})]\n",
+    "continuous_cols = [col for col in numeric_cols if col not in binary_cols]\n",
+    "\n",
+    "print(f'Features: {len(numeric_cols)} total')\n",
+    "print(f'  Binary     : {len(binary_cols)}')\n",
+    "print(f'  Continuous : {len(continuous_cols)}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5fee0af0",
+   "metadata": {},
+   "source": [
+    "## Normalization\n",
+    "- Binary features: kept as-is (0-1 range)\n",
+    "- Continuous features: MinMaxScaler to [0, 1]\n",
+    "- Combined array: binary + continuous scaled features\n",
+    "This ensures neural network compatibility and distance metric compatibility."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "c04a0228",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Neural input shape: (428, 30)\n",
+      "Range: [0.000000, 1.000000]\n"
+     ]
+    }
+   ],
+   "source": [
+    "feature_cols = numeric_cols.copy()\n",
+    "X = df[feature_cols]\n",
+    "\n",
+    "X_binary = X[binary_cols].values\n",
+    "X_continuous = X[continuous_cols].values\n",
+    "\n",
+    "scaler_continuous = MinMaxScaler()\n",
+    "X_continuous_scaled = scaler_continuous.fit_transform(X_continuous)\n",
+    "\n",
+    "X_combined = np.concatenate([X_binary, X_continuous_scaled], axis=1)\n",
+    "\n",
+    "scaler_standard = StandardScaler()\n",
+    "X_standard = scaler_standard.fit_transform(X)\n",
+    "\n",
+    "print(f'Neural input shape: {X_combined.shape}')\n",
+    "print(f'Range: [{X_combined.min():.6f}, {X_combined.max():.6f}]')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05980d55",
+   "metadata": {},
+   "source": [
+    "# Auto-Encoder"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9be84505",
+   "metadata": {},
+   "source": [
+    "## Modelling\n",
+    "- Purpose: Dimensionality reduction (high-D features → 8D latent space)\n",
+    "- Architecture: Encoder [input → 32 → 16 → 8] + Decoder [8 → 16 → 32 → reconstructed]\n",
+    "- Regularization: BatchNormalization + Dropout(0.2) at each dense layer\n",
+    "- Loss: MSE (reconstruction error) | Optimizer: Adam(lr=0.001)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "8e286cb1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Autoencoder architecture:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_2\"</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1mModel: \"functional_2\"\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ input_layer_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">992</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_5           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">528</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_6           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)             │            <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">136</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_7           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)              │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_8           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)             │            <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">544</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_9           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">990</span> │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ input_layer_1 (\u001b[38;5;33mInputLayer\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m)             │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_6 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)             │           \u001b[38;5;34m992\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_5           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)             │           \u001b[38;5;34m128\u001b[0m │\n",
+       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_5 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)             │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_7 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m)             │           \u001b[38;5;34m528\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_6           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m)             │            \u001b[38;5;34m64\u001b[0m │\n",
+       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_6 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m)             │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_8 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m)              │           \u001b[38;5;34m136\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_7           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m)              │            \u001b[38;5;34m32\u001b[0m │\n",
+       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_7 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m)              │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_9 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m)             │           \u001b[38;5;34m144\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_8           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m)             │            \u001b[38;5;34m64\u001b[0m │\n",
+       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_8 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m)             │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_10 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)             │           \u001b[38;5;34m544\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_9           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)             │           \u001b[38;5;34m128\u001b[0m │\n",
+       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_9 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)             │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_11 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m)             │           \u001b[38;5;34m990\u001b[0m │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">3,750</span> (14.65 KB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m3,750\u001b[0m (14.65 KB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">3,542</span> (13.84 KB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m3,542\u001b[0m (13.84 KB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">208</span> (832.00 B)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m208\u001b[0m (832.00 B)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "input_dim = X_combined.shape[1]\n",
+    "encoding_dims = [32, 16, 8]\n",
+    "\n",
+    "input_layer = Input(shape=(input_dim,))\n",
+    "x = input_layer\n",
+    "for dim in encoding_dims:\n",
+    "    x = Dense(dim, activation='relu')(x)\n",
+    "    x = BatchNormalization()(x)\n",
+    "    x = Dropout(0.3)(x)\n",
+    "\n",
+    "latent = x\n",
+    "\n",
+    "for dim in reversed(encoding_dims[:-1]):\n",
+    "    x = Dense(dim, activation='relu')(x)\n",
+    "    x = BatchNormalization()(x)\n",
+    "    x = Dropout(0.3)(x)\n",
+    "\n",
+    "output_layer = Dense(input_dim, activation='sigmoid')(x)\n",
+    "\n",
+    "autoencoder = Model(input_layer, output_layer)\n",
+    "encoder = Model(input_layer, latent)\n",
+    "\n",
+    "autoencoder.compile(\n",
+    "    optimizer=optimizers.Adam(0.001),\n",
+    "    loss='mse',\n",
+    "    metrics=['mae']\n",
+    ")\n",
+    "\n",
+    "print('Autoencoder architecture:')\n",
+    "autoencoder.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3bb013f",
+   "metadata": {},
+   "source": [
+    "## Training\n",
+    "Training Configuration\n",
+    "- Epochs: 300 (with early stopping)\n",
+    "- Batch size: 64\n",
+    "- Validation split: 20%\n",
+    "- Early stopping: patience=20 (stop if val_loss doesn't improve)\n",
+    "- LR reduction: factor=0.5, patience=10, min_lr=1e-5\n",
+    "- Output: X_latent (8D embeddings) for KMeans clustering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "3f610379",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training done!\n",
+      "Final loss: 0.098345\n",
+      "  Val loss: 0.074054\n",
+      "Latent space: (428, 8) (8D embeddings)\n"
+     ]
+    }
+   ],
+   "source": [
+    "history = autoencoder.fit(\n",
+    "    X_combined, X_combined,\n",
+    "    epochs=300,\n",
+    "    batch_size=64,\n",
+    "    validation_split=0.25,\n",
+    "    shuffle=False,\n",
+    "    callbacks=[\n",
+    "        callbacks.EarlyStopping(monitor='val_loss', patience=20, restore_best_weights=True),\n",
+    "        callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=10, min_lr=1e-5)\n",
+    "    ],\n",
+    "    verbose=0\n",
+    ")\n",
+    "\n",
+    "print(f'Training done!')\n",
+    "print(f'Final loss: {history.history[\"loss\"][-1]:.6f}')\n",
+    "print(f'  Val loss: {history.history[\"val_loss\"][-1]:.6f}')\n",
+    "\n",
+    "X_latent = encoder.predict(X_combined, verbose=0)\n",
+    "print(f'Latent space: {X_latent.shape} (8D embeddings)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6d811ee5",
+   "metadata": {},
+   "source": [
+    "# Metrics Function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fcb64663",
+   "metadata": {},
+   "source": [
+    "## Interpretability Score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "a6f5f27d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calculate_interpretability_score(df, cluster_col, binary_cols, top_n=5, threshold=0.70):\n",
+    "    \"\"\"\n",
+    "    Calculate cluster interpretability based on top feature clarity.\n",
+    "    \n",
+    "    Strategy: Focus on strongest features (not averages) to reduce noise.\n",
+    "    Strength Definition: Distance from neutral point (0.5)\n",
+    "      - Features closer to 0 or 1 = strong patterns\n",
+    "      - Features closer to 0.5 = ambiguous patterns\n",
+    "    \n",
+    "    Args:\n",
+    "        df (pd.DataFrame): Cluster-labeled dataset\n",
+    "        cluster_col (str): Column with cluster assignments\n",
+    "        binary_cols (list): Binary feature names\n",
+    "        top_n (int): Number of top features to evaluate (default: 5)\n",
+    "        threshold (float): Unused parameter (API compatibility)\n",
+    "    \n",
+    "    Returns:\n",
+    "        dict: {'mean_interpretability': float [0, 1]}\n",
+    "              1.0 = clear feature patterns, 0.0 = no patterns\n",
+    "    \"\"\"\n",
+    "    scores = []\n",
+    "    unique_clusters = df[cluster_col].unique()\n",
+    "    \n",
+    "    for cid in unique_clusters:\n",
+    "        cdata = df[df[cluster_col] == cid]\n",
+    "        n = len(cdata)\n",
+    "        if n == 0: \n",
+    "            scores.append(0)\n",
+    "            continue\n",
+    "        \n",
+    "        feature_strength = []\n",
+    "        for col in binary_cols:\n",
+    "            if col in cdata.columns:\n",
+    "                avg = cdata[col].mean()\n",
+    "                strength = abs(avg - 0.5) * 2  # Normalize distance from neutral (0.5) to [0, 1]\n",
+    "                feature_strength.append(strength)\n",
+    "        \n",
+    "        if feature_strength:\n",
+    "            feature_strength.sort(reverse=True)\n",
+    "            top_features = feature_strength[:top_n]\n",
+    "            scores.append(np.mean(top_features))  # Average of top-N features\n",
+    "        else:\n",
+    "            scores.append(0)\n",
+    "            \n",
+    "    return {'mean_interpretability': np.mean(scores) if scores else 0}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e5489774",
+   "metadata": {},
+   "source": [
+    "## Cluster Purity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "ac03f346",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calculate_cluster_purity(df, cluster_col, binary_cols):\n",
+    "    \"\"\"\n",
+    "    Measure internal cluster homogeneity via majority class dominance.\n",
+    "    \n",
+    "    Purity Calculation: For each feature, compute max(class0_pct, class1_pct)\n",
+    "    Average across all features = cluster purity\n",
+    "    Range: [0.5, 1.0] where 1.0 = perfect homogeneity\n",
+    "    \n",
+    "    Args:\n",
+    "        df (pd.DataFrame): Cluster-labeled dataset\n",
+    "        cluster_col (str): Column with cluster assignments\n",
+    "        binary_cols (list): Binary feature names\n",
+    "    \n",
+    "    Returns:\n",
+    "        dict: {'mean_purity': float [0.5, 1.0]}\n",
+    "    \"\"\"\n",
+    "    purity_by_cluster = []\n",
+    "    unique_clusters = df[cluster_col].unique()\n",
+    "    \n",
+    "    for cid in unique_clusters:\n",
+    "        cdata = df[df[cluster_col] == cid]\n",
+    "        n = len(cdata)\n",
+    "        if n == 0: continue\n",
+    "        \n",
+    "        dominances = []\n",
+    "        for col in binary_cols:\n",
+    "            if col in cdata.columns:\n",
+    "                avg = cdata[col].mean()\n",
+    "                dominances.append(max(avg, 1 - avg))  # Majority class percentage\n",
+    "        \n",
+    "        if dominances:\n",
+    "            purity_by_cluster.append(np.mean(dominances))\n",
+    "    \n",
+    "    return {'mean_purity': np.mean(purity_by_cluster) if purity_by_cluster else 0}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c82f8d93",
+   "metadata": {},
+   "source": [
+    "## Cluster Stability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "f4b03254",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calculate_cluster_stability(X, labels, model_func, n_iter=5, seed=42):\n",
+    "    \"\"\"\n",
+    "    Bootstrap stability testing via Adjusted Rand Index (ARI).\n",
+    "    \n",
+    "    Process:\n",
+    "    1. Train model on bootstrap sample (with replacement)\n",
+    "    2. Compare original vs bootstrap clustering using ARI\n",
+    "    3. Average ARI across iterations\n",
+    "    \n",
+    "    ARI Range: [-1, 1]\n",
+    "      > 0.5: excellent stability\n",
+    "      0.2-0.5: fair stability\n",
+    "      < 0.2: poor stability\n",
+    "    \n",
+    "    Args:\n",
+    "        X (np.ndarray): Feature matrix\n",
+    "        labels (np.ndarray): Original cluster assignments\n",
+    "        model_func (callable): Returns instantiated clustering model\n",
+    "        n_iter (int): Bootstrap iterations (default: 5)\n",
+    "    \n",
+    "    Returns:\n",
+    "        dict: {'mean_ari': float [-1, 1]}\n",
+    "    \"\"\"\n",
+    "    if len(np.unique(labels)) < 2:\n",
+    "        return {'mean_ari': 0}\n",
+    "\n",
+    "    n = len(X)\n",
+    "    ari_scores = []\n",
+    "\n",
+    "    for i in range(n_iter):\n",
+    "        rng = np.random.default_rng(seed=42 + i)\n",
+    "        idx = rng.choice(n, n, replace=True)\n",
+    "        try:\n",
+    "            boot_model = model_func()\n",
+    "            boot_labels = boot_model.fit_predict(X[idx])\n",
+    "            ari = adjusted_rand_score(labels[idx], boot_labels)\n",
+    "            ari_scores.append(ari)\n",
+    "        except Exception:\n",
+    "            continue\n",
+    "\n",
+    "    m = np.mean(ari_scores) if ari_scores else 0\n",
+    "    return {'mean_ari': m}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "346b09d4",
+   "metadata": {},
+   "source": [
+    "## Comprehensive Evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "514690f8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Metrics Function Ready.\n"
+     ]
+    }
+   ],
+   "source": [
+    "def evaluate_clustering_comprehensive(X, labels, df_original, model_func, binary_cols):\n",
+    "    \"\"\"\n",
+    "    Multi-metric clustering evaluation combining geometric and business metrics.\n",
+    "    \n",
+    "    Evaluation Framework:\n",
+    "    \n",
+    "    1. GEOMETRIC METRICS (Scikit-learn):\n",
+    "       Silhouette [-1, 1]: cluster separation quality\n",
+    "       Davies-Bouldin [0, ∞): intra-cluster density (lower better)\n",
+    "       Calinski-Harabasz [0, ∞): cluster definition (higher better)\n",
+    "    \n",
+    "    2. BUSINESS METRICS:\n",
+    "       Purity: internal homogeneity\n",
+    "       Interpretability: feature pattern clarity\n",
+    "       Stability: clustering consistency\n",
+    "    \n",
+    "    3. COMPOSITE SCORING (strategic weights):\n",
+    "       Structure (40%): 40% Silhouette + 30% Davies-Bouldin + 30% Calinski-Harabasz\n",
+    "       Explainability (30%): 50% Interpretability + 50% Purity\n",
+    "       Reliability (30%): Bootstrap ARI stability\n",
+    "    \n",
+    "    Args:\n",
+    "        X (np.ndarray): Latent feature space (typically autoencoder output)\n",
+    "        labels (np.ndarray): Cluster assignments [0, K-1]\n",
+    "        df_original (pd.DataFrame): Original shoe metadata\n",
+    "        model_func (callable): KMeans factory function\n",
+    "        binary_cols (list): Binary feature column names\n",
+    "    \n",
+    "    Returns:\n",
+    "        dict: {\n",
+    "            'metrics': {silhouette, davies_bouldin, calinski_harabasz, purity, stability, interpretability},\n",
+    "            'composite_score': float [0, 1]\n",
+    "        }\n",
+    "    \"\"\"\n",
+    "    df_eval = df_original.copy()\n",
+    "    df_eval['cluster'] = labels\n",
+    "    \n",
+    "    sil = silhouette_score(X, labels)\n",
+    "    db = davies_bouldin_score(X, labels)\n",
+    "    ch = calinski_harabasz_score(X, labels)\n",
+    "    \n",
+    "    purity_res = calculate_cluster_purity(df_eval, 'cluster', binary_cols)\n",
+    "    interp_res = calculate_interpretability_score(df_eval, 'cluster', binary_cols, top_n=5)\n",
+    "    stability_res = calculate_cluster_stability(X, labels, model_func, n_iter=3)\n",
+    "    \n",
+    "    val_purity = purity_res['mean_purity']\n",
+    "    val_interp = interp_res['mean_interpretability']\n",
+    "    val_stability = stability_res['mean_ari']\n",
+    "\n",
+    "    sil_norm = (sil + 1) / 2  # Map Silhouette [-1, 1] → [0, 1]\n",
+    "    db_norm = np.exp(-0.5 * db)  # Exponential decay: DB lower is better\n",
+    "    \n",
+    "    if ch > 0:\n",
+    "        ch_log = np.log1p(ch)\n",
+    "        ch_norm = min(ch_log / 8, 1.0)  # Log scaling: assume max log(CH) ≈ 9.2\n",
+    "    else:\n",
+    "        ch_norm = 0\n",
+    "\n",
+    "    score_structure = (0.4 * sil_norm) + (0.3 * db_norm) + (0.3 * ch_norm)  # Weight: Silhouette 40%, DB 30%, CH 30%\n",
+    "    score_explain = (0.5 * val_interp) + (0.5 * val_purity)  # Weight: Interpretability 50%, Purity 50%\n",
+    "    score_reliability = max(val_stability, 0)  # Clip negative ARI to 0\n",
+    "\n",
+    "    composite = (0.40 * score_structure) + (0.30 * score_explain) + (0.30 * score_reliability)\n",
+    "\n",
+    "    return {\n",
+    "        'metrics': {\n",
+    "            'silhouette': sil, \n",
+    "            'davies_bouldin': db, \n",
+    "            'calinski_harabasz': ch,\n",
+    "            'purity': val_purity, \n",
+    "            'stability': val_stability,\n",
+    "            'interpretability': val_interp\n",
+    "        },\n",
+    "        'composite_score': composite\n",
+    "    }\n",
+    "\n",
+    "print('Metrics Function Ready.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8373ffc",
+   "metadata": {},
+   "source": [
+    "# Model Selection\n",
+    "Model Selection Pipeline: K-means Clustering (K=3 to K=9)\n",
+    "\n",
+    "For each K value:\n",
+    "  - Train KMeans model\n",
+    "  - Evaluate using comprehensive metrics\n",
+    "  - Compute composite score\n",
+    "\n",
+    "Select K with highest composite score (40% geometry, 30% explainability, 30% reliability)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "d78a6cd5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "|  K  |  Score   |   Sil.   |    DB    |     CH     |  Purity  |  Stab.   |  Interp  |\n",
+      "|-----+----------+----------+----------+------------+----------+----------+----------|\n",
+      "|  3  | 0.725575 | 0.442093 | 1.004044 | 223.989851 | 0.849337 | 0.628044 | 0.936774 |\n",
+      "|  4  | 0.860043 | 0.530613 | 0.835884 | 336.071705 | 0.863760 | 0.982536 | 0.979710 |\n",
+      "|  5  | 0.877370 | 0.555704 | 0.694095 | 438.120406 | 0.866944 | 0.997151 | 0.984283 |\n",
+      "|  6  | 0.849927 | 0.485995 | 0.751707 | 440.021792 | 0.868007 | 0.930737 | 0.985898 |\n",
+      "|  7  | 0.818026 | 0.469977 | 0.834006 | 411.950115 | 0.858330 | 0.847469 | 0.986705 |\n",
+      "|  8  | 0.783303 | 0.468941 | 0.889430 | 396.703919 | 0.870448 | 0.734619 | 0.987520 |\n",
+      "|  9  | 0.789632 | 0.466899 | 0.874739 | 399.636980 | 0.874462 | 0.749608 | 0.992294 |\n"
+     ]
+    }
+   ],
+   "source": [
+    "results = []\n",
+    "\n",
+    "print(f\"| {'K':^3} | {'Score':^8} | {'Sil.':^8} | {'DB':^8} | {'CH':^10} | {'Purity':^8} | {'Stab.':^8} | {'Interp':^8} |\")\n",
+    "print(f\"|{'-'*5}+{'-'*10}+{'-'*10}+{'-'*10}+{'-'*12}+{'-'*10}+{'-'*10}+{'-'*10}|\")\n",
+    "\n",
+    "for i in range(3, 10):\n",
+    "    model_factory = lambda: KMeans(n_clusters=i, random_state=42, n_init=20)\n",
+    "    \n",
+    "    model = model_factory()\n",
+    "    labels = model.fit_predict(X_latent)\n",
+    "\n",
+    "    metrics_res = evaluate_clustering_comprehensive(\n",
+    "        X_latent, labels, df.copy(),\n",
+    "        model_factory,\n",
+    "        binary_cols\n",
+    "    )\n",
+    "\n",
+    "    raw_metrics = metrics_res['metrics'] \n",
+    "    comp_score  = metrics_res['composite_score']\n",
+    "\n",
+    "    record = {\n",
+    "        'k': i,\n",
+    "        'model': model,\n",
+    "        'labels': labels,\n",
+    "        'composite_score': comp_score,\n",
+    "        **raw_metrics\n",
+    "    }\n",
+    "    results.append(record)\n",
+    "\n",
+    "    print(f\"| {i:^3} | {comp_score:<8.6f} | {raw_metrics['silhouette']:<6.6f} | \"\n",
+    "          f\"{raw_metrics['davies_bouldin']:<6.6f} | {raw_metrics['calinski_harabasz']:<8.6f} | \"\n",
+    "          f\"{raw_metrics['purity']:<6.6f} | {raw_metrics['stability']:<6.6f} | {raw_metrics['interpretability']:<6.6f} |\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "4bad2a8a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--------------------------------------------------------------------------------\n",
+      "SELECTED BEST K: 5\n",
+      "   Silhouette      : 0.555704\n",
+      "   Composite Score : 0.877370\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_results = pd.DataFrame(results)\n",
+    "\n",
+    "best_idx = df_results['composite_score'].idxmax()\n",
+    "best_config = df_results.loc[best_idx]\n",
+    "\n",
+    "best_model = best_config['model']\n",
+    "best_labels = best_config['labels']\n",
+    "best_k = best_config['k']\n",
+    "X_for_clustering = X_latent\n",
+    "\n",
+    "print(\"-\" * 80)\n",
+    "print(f'SELECTED BEST K: {best_k}')\n",
+    "print(f'   Silhouette      : {best_config[\"silhouette\"]:.6f}') \n",
+    "print(f'   Composite Score : {best_config[\"composite_score\"]:.6f}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c318097d",
+   "metadata": {},
+   "source": [
+    "# Generate Cluster Label"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8384d05b",
+   "metadata": {},
+   "source": [
+    "## Binning\n",
+    "Divides each continuous feature into 3 quantile bins (tertiles).\n",
+    "\n",
+    "Labels: 0 (low), 0.5 (medium), 1 (high)\n",
+    "\n",
+    "Enables interpretable cluster profiling and feature discretization."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "0f5aaff8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for col in df.select_dtypes('float64').columns.tolist():\n",
+    "    new_col_name = col + '_bin'\n",
+    "    df[new_col_name] = pd.qcut(df[col], q=3, labels=[0, 0.5, 1]).astype(int)\n",
+    "\n",
+    "non_numeric_cols = df.select_dtypes(exclude=[np.number]).columns.tolist()\n",
+    "\n",
+    "new_column_order = []\n",
+    "\n",
+    "for col in non_numeric_cols:\n",
+    "    if col in df.columns:\n",
+    "        new_column_order.append(col)\n",
+    "\n",
+    "for col in binary_cols:\n",
+    "    if col in df.columns:\n",
+    "        new_column_order.append(col)\n",
+    "\n",
+    "for col in continuous_cols:\n",
+    "    if col in df.columns:\n",
+    "        new_column_order.append(col)\n",
+    "    bin_col_name = col + '_bin'\n",
+    "    if bin_col_name in df.columns:\n",
+    "        new_column_order.append(bin_col_name)\n",
+    "\n",
+    "if 'cluster' in df.columns and 'cluster' not in new_column_order:\n",
+    "    new_column_order.append('cluster')\n",
+    "\n",
+    "df = df[new_column_order]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "3309c7d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 428 entries, 0 to 427\n",
+      "Data columns (total 36 columns):\n",
+      " #   Column                Non-Null Count  Dtype  \n",
+      "---  ------                --------------  -----  \n",
+      " 0   brand                 428 non-null    str    \n",
+      " 1   name                  428 non-null    str    \n",
+      " 2   lightweight           428 non-null    int64  \n",
+      " 3   rocker                428 non-null    int64  \n",
+      " 4   removable_insole      428 non-null    int64  \n",
+      " 5   pace_daily_running    428 non-null    int64  \n",
+      " 6   pace_tempo            428 non-null    int64  \n",
+      " 7   pace_competition      428 non-null    int64  \n",
+      " 8   arch_neutral          428 non-null    int64  \n",
+      " 9   arch_stability        428 non-null    int64  \n",
+      " 10  strike_heel           428 non-null    int64  \n",
+      " 11  strike_mid            428 non-null    int64  \n",
+      " 12  strike_forefoot       428 non-null    int64  \n",
+      " 13  plate_rock_plate      428 non-null    int64  \n",
+      " 14  plate_carbon_plate    428 non-null    int64  \n",
+      " 15  season_summer         428 non-null    int64  \n",
+      " 16  season_winter         428 non-null    int64  \n",
+      " 17  season_all            428 non-null    int64  \n",
+      " 18  weight_lab_oz         428 non-null    float64\n",
+      " 19  weight_lab_oz_bin     428 non-null    int64  \n",
+      " 20  drop_lab_mm           428 non-null    float64\n",
+      " 21  drop_lab_mm_bin       428 non-null    int64  \n",
+      " 22  midsole_softness      428 non-null    int64  \n",
+      " 23  toebox_durability     428 non-null    int64  \n",
+      " 24  heel_durability       428 non-null    int64  \n",
+      " 25  outsole_durability    428 non-null    int64  \n",
+      " 26  breathability_scaled  428 non-null    int64  \n",
+      " 27  width_fit             428 non-null    int64  \n",
+      " 28  toebox_width          428 non-null    int64  \n",
+      " 29  stiffness_scaled      428 non-null    int64  \n",
+      " 30  torsional_rigidity    428 non-null    int64  \n",
+      " 31  heel_stiff            428 non-null    int64  \n",
+      " 32  heel_lab_mm           428 non-null    float64\n",
+      " 33  heel_lab_mm_bin       428 non-null    int64  \n",
+      " 34  forefoot_lab_mm       428 non-null    float64\n",
+      " 35  forefoot_lab_mm_bin   428 non-null    int64  \n",
+      "dtypes: float64(4), int64(30), str(2)\n",
+      "memory usage: 120.5 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b8a8479",
+   "metadata": {},
+   "source": [
+    "## Cluster Summary\n",
+    "Creates interpretable profile for each cluster showing:\n",
+    "- Size (count + percentage)\n",
+    "- Continuous features (mean values)\n",
+    "- Binary features (dominant variant + prevalence)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "03ab527b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cluster Summary:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>percentage</th>\n",
+       "      <th>weight_lab_oz</th>\n",
+       "      <th>drop_lab_mm</th>\n",
+       "      <th>midsole_softness</th>\n",
+       "      <th>toebox_durability</th>\n",
+       "      <th>heel_durability</th>\n",
+       "      <th>outsole_durability</th>\n",
+       "      <th>breathability_scaled</th>\n",
+       "      <th>width_fit</th>\n",
+       "      <th>...</th>\n",
+       "      <th>lightweight</th>\n",
+       "      <th>rocker</th>\n",
+       "      <th>removable_insole</th>\n",
+       "      <th>pace_daily_running</th>\n",
+       "      <th>pace</th>\n",
+       "      <th>arch</th>\n",
+       "      <th>strike</th>\n",
+       "      <th>plate_rock_plate</th>\n",
+       "      <th>plate_carbon_plate</th>\n",
+       "      <th>season</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>29</td>\n",
+       "      <td>6.8%</td>\n",
+       "      <td>9.70</td>\n",
+       "      <td>10.46</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>2.66</td>\n",
+       "      <td>...</td>\n",
+       "      <td>no (17%)</td>\n",
+       "      <td>no (10%)</td>\n",
+       "      <td>no (28%)</td>\n",
+       "      <td>yes (100%)</td>\n",
+       "      <td>tempo (10%)</td>\n",
+       "      <td>neutral (76%)</td>\n",
+       "      <td>heel (83%)</td>\n",
+       "      <td>no (0%)</td>\n",
+       "      <td>no (0%)</td>\n",
+       "      <td>summer (0%)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>74</td>\n",
+       "      <td>17.3%</td>\n",
+       "      <td>7.47</td>\n",
+       "      <td>7.66</td>\n",
+       "      <td>3.50</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>2.65</td>\n",
+       "      <td>2.43</td>\n",
+       "      <td>4.11</td>\n",
+       "      <td>2.11</td>\n",
+       "      <td>...</td>\n",
+       "      <td>yes (92%)</td>\n",
+       "      <td>yes (70%)</td>\n",
+       "      <td>yes (80%)</td>\n",
+       "      <td>no (9%)</td>\n",
+       "      <td>competition (65%)</td>\n",
+       "      <td>neutral (99%)</td>\n",
+       "      <td>mid (86%)</td>\n",
+       "      <td>no (1%)</td>\n",
+       "      <td>yes (64%)</td>\n",
+       "      <td>all (95%)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>165</td>\n",
+       "      <td>38.6%</td>\n",
+       "      <td>9.68</td>\n",
+       "      <td>7.35</td>\n",
+       "      <td>3.87</td>\n",
+       "      <td>3.05</td>\n",
+       "      <td>3.40</td>\n",
+       "      <td>3.64</td>\n",
+       "      <td>3.07</td>\n",
+       "      <td>2.89</td>\n",
+       "      <td>...</td>\n",
+       "      <td>no (16%)</td>\n",
+       "      <td>no (30%)</td>\n",
+       "      <td>yes (99%)</td>\n",
+       "      <td>yes (96%)</td>\n",
+       "      <td>tempo (18%)</td>\n",
+       "      <td>neutral (85%)</td>\n",
+       "      <td>mid (100%)</td>\n",
+       "      <td>no (0%)</td>\n",
+       "      <td>no (5%)</td>\n",
+       "      <td>all (98%)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>78</td>\n",
+       "      <td>18.2%</td>\n",
+       "      <td>9.69</td>\n",
+       "      <td>8.20</td>\n",
+       "      <td>2.69</td>\n",
+       "      <td>0.44</td>\n",
+       "      <td>0.29</td>\n",
+       "      <td>0.15</td>\n",
+       "      <td>2.01</td>\n",
+       "      <td>1.74</td>\n",
+       "      <td>...</td>\n",
+       "      <td>no (23%)</td>\n",
+       "      <td>no (29%)</td>\n",
+       "      <td>yes (96%)</td>\n",
+       "      <td>yes (96%)</td>\n",
+       "      <td>tempo (18%)</td>\n",
+       "      <td>neutral (86%)</td>\n",
+       "      <td>mid (81%)</td>\n",
+       "      <td>no (0%)</td>\n",
+       "      <td>no (1%)</td>\n",
+       "      <td>all (60%)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>82</td>\n",
+       "      <td>19.2%</td>\n",
+       "      <td>9.98</td>\n",
+       "      <td>11.66</td>\n",
+       "      <td>3.78</td>\n",
+       "      <td>2.84</td>\n",
+       "      <td>3.26</td>\n",
+       "      <td>3.35</td>\n",
+       "      <td>3.10</td>\n",
+       "      <td>2.71</td>\n",
+       "      <td>...</td>\n",
+       "      <td>no (11%)</td>\n",
+       "      <td>no (17%)</td>\n",
+       "      <td>yes (100%)</td>\n",
+       "      <td>yes (94%)</td>\n",
+       "      <td>tempo (17%)</td>\n",
+       "      <td>neutral (77%)</td>\n",
+       "      <td>heel (100%)</td>\n",
+       "      <td>no (0%)</td>\n",
+       "      <td>no (4%)</td>\n",
+       "      <td>all (96%)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   count percentage  weight_lab_oz  drop_lab_mm  midsole_softness  \\\n",
+       "0     29       6.8%           9.70        10.46              0.03   \n",
+       "1     74      17.3%           7.47         7.66              3.50   \n",
+       "2    165      38.6%           9.68         7.35              3.87   \n",
+       "3     78      18.2%           9.69         8.20              2.69   \n",
+       "4     82      19.2%           9.98        11.66              3.78   \n",
+       "\n",
+       "   toebox_durability  heel_durability  outsole_durability  \\\n",
+       "0               0.00             0.00                0.00   \n",
+       "1               2.00             2.65                2.43   \n",
+       "2               3.05             3.40                3.64   \n",
+       "3               0.44             0.29                0.15   \n",
+       "4               2.84             3.26                3.35   \n",
+       "\n",
+       "   breathability_scaled  width_fit  ...  lightweight     rocker  \\\n",
+       "0                  0.00       2.66  ...     no (17%)   no (10%)   \n",
+       "1                  4.11       2.11  ...    yes (92%)  yes (70%)   \n",
+       "2                  3.07       2.89  ...     no (16%)   no (30%)   \n",
+       "3                  2.01       1.74  ...     no (23%)   no (29%)   \n",
+       "4                  3.10       2.71  ...     no (11%)   no (17%)   \n",
+       "\n",
+       "   removable_insole  pace_daily_running               pace           arch  \\\n",
+       "0          no (28%)          yes (100%)        tempo (10%)  neutral (76%)   \n",
+       "1         yes (80%)             no (9%)  competition (65%)  neutral (99%)   \n",
+       "2         yes (99%)           yes (96%)        tempo (18%)  neutral (85%)   \n",
+       "3         yes (96%)           yes (96%)        tempo (18%)  neutral (86%)   \n",
+       "4        yes (100%)           yes (94%)        tempo (17%)  neutral (77%)   \n",
+       "\n",
+       "        strike plate_rock_plate plate_carbon_plate       season  \n",
+       "0   heel (83%)          no (0%)            no (0%)  summer (0%)  \n",
+       "1    mid (86%)          no (1%)          yes (64%)    all (95%)  \n",
+       "2   mid (100%)          no (0%)            no (5%)    all (98%)  \n",
+       "3    mid (81%)          no (0%)            no (1%)    all (60%)  \n",
+       "4  heel (100%)          no (0%)            no (4%)    all (96%)  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['cluster'] = best_labels \n",
+    "\n",
+    "bin_groups = {}\n",
+    "for col in binary_cols:\n",
+    "    parts = col.split('_')\n",
+    "    \n",
+    "    if len(parts) > 1:\n",
+    "        prefix = '_'.join(parts[:-1])\n",
+    "    else:\n",
+    "        prefix = col\n",
+    "        \n",
+    "    bin_groups.setdefault(prefix, []).append(col)\n",
+    "\n",
+    "rows = []\n",
+    "for cid in sorted(df['cluster'].unique()):\n",
+    "    subset = df[df['cluster'] == cid]\n",
+    "    n = len(subset)\n",
+    "    \n",
+    "    row = {'count': n, 'percentage': f\"{n/len(df)*100:.1f}%\"}\n",
+    "\n",
+    "    for col in continuous_cols:\n",
+    "        row[col.lower()] = round(subset[col].mean(), 2)\n",
+    "\n",
+    "    for prefix, cols in bin_groups.items():\n",
+    "        means = subset[cols].mean()\n",
+    "        best_col = means.idxmax()\n",
+    "        best_val = means.max()\n",
+    "        \n",
+    "        if len(cols) > 1:\n",
+    "            header = prefix.lower()\n",
+    "            val_str = best_col.replace(f\"{prefix}_\", \"\").lower()\n",
+    "            row[header] = f\"{val_str} ({best_val*100:.0f}%)\"\n",
+    "            \n",
+    "        else:\n",
+    "            header = cols[0].lower()\n",
+    "            val_str = \"yes\" if best_val > 0.5 else \"no\"\n",
+    "            row[header] = f\"{val_str} ({best_val*100:.0f}%)\"\n",
+    "\n",
+    "    rows.append(row)\n",
+    "\n",
+    "df_summary = pd.DataFrame(rows, index=sorted(df['cluster'].unique()))\n",
+    "df_summary.index.name = None \n",
+    "\n",
+    "print(\"Cluster Summary:\")\n",
+    "display(df_summary)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ce21add3",
+   "metadata": {},
+   "source": [
+    "# Deep Learn Recommender"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93d616fb",
+   "metadata": {},
+   "source": [
+    "## Priority Handler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "83f48aba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_priority_val(user_input, priority_list, mapping_dicts):\n",
+    "    \"\"\"\n",
+    "    Extract feature value from user input with priority hierarchy.\n",
+    "    \n",
+    "    Strategy: Checks inputs in priority order, returns mapped value from\n",
+    "    first non-empty input, ignores lower-priority inputs if higher-priority exists.\n",
+    "    \n",
+    "    Args:\n",
+    "        user_input (dict): User preferences {'running_purpose': 'Daily', ...}\n",
+    "        priority_list (list): Input sources in priority order\n",
+    "        mapping_dicts (dict): Maps {source: {option: feature_value}}\n",
+    "    \n",
+    "    Returns:\n",
+    "        float: Feature value [0, 1] or 0.5 (neutral) if not found\n",
+    "    \"\"\"\n",
+    "    for source_key in priority_list:\n",
+    "        if source_key in user_input and user_input[source_key]:\n",
+    "            user_choice = user_input[source_key]\n",
+    "            if source_key in mapping_dicts:\n",
+    "                mapping = mapping_dicts[source_key]\n",
+    "                if user_choice in mapping:\n",
+    "                    return mapping[user_choice]\n",
+    "    return 0.5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d49d3b53",
+   "metadata": {},
+   "source": [
+    "## Input Handler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "a0a4d712",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def preprocess_user_input_with_mask(user_input, binary_cols, continuous_cols):\n",
+    "    \"\"\"\n",
+    "    Transform user preferences into feature vector with intelligent masking.\n",
+    "    \n",
+    "    Feature Construction:\n",
+    "    1. SIMPLE FEATURES (single source dependency e.g., lightweight from pace)\n",
+    "    2. PRIORITY OVERWRITE (multi-source with hierarchy e.g., strike_pattern > pace)\n",
+    "    3. MASKING (only include features derived from provided inputs)\n",
+    "    4. FALLBACK (default to 0.5 for unknowns)\n",
+    "    \n",
+    "    Returns:\n",
+    "        tuple: (full_vector_raw, valid_indices)\n",
+    "            - full_vector_raw: Feature vector [0-1] for all features\n",
+    "            - valid_indices: Positions of user-provided features\n",
+    "                (used for masked similarity calculation)\n",
+    "    \"\"\"\n",
+    "    feats = {col: 0.0 for col in binary_cols + continuous_cols}\n",
+    "    \n",
+    "    feats['lightweight'] = get_priority_val(user_input, ['pace'], \n",
+    "        {'pace': {'Easy': 0.5, 'Steady': 0.5, 'Fast': 1.0}})\n",
+    "    feats['rocker'] = get_priority_val(user_input, ['running_purpose'], \n",
+    "        {'running_purpose': {'Race': 1.0, 'Tempo': 0.5, 'Daily': 0.0}})\n",
+    "    feats['removable_insole'] = get_priority_val(user_input, ['orthotic_usage'], \n",
+    "        {'orthotic_usage': {'Yes': 1.0, 'No': 0.5}})\n",
+    "    \n",
+    "    purp = user_input.get('running_purpose', 'Daily')\n",
+    "    feats['pace_daily_running'] = 1.0 if purp == 'Daily' else (0.5 if purp == 'Tempo' else 0.0)\n",
+    "    feats['pace_tempo'] = 1.0 if purp == 'Tempo' else 0.5\n",
+    "    feats['pace_competition'] = 1.0 if purp == 'Race' else (0.5 if purp == 'Tempo' else 0.0)\n",
+    "\n",
+    "    feats['arch_neutral'] = get_priority_val(user_input, ['arch_type'], \n",
+    "        {'arch_type': {'Flat': 0.0, 'Normal': 0.8, 'High': 1.0}})\n",
+    "    feats['arch_stability'] = get_priority_val(user_input, ['arch_type'], \n",
+    "        {'arch_type': {'Flat': 1.0, 'Normal': 0.2, 'High': 0.0}})\n",
+    "    \n",
+    "    feats['drop_lab_mm'] = get_priority_val(user_input, ['pace'], \n",
+    "        {'pace': {'Easy': 1.0, 'Steady': 0.5, 'Fast': 0.0}})\n",
+    "\n",
+    "    prio_strike = ['strike_pattern', 'pace']\n",
+    "    feats['strike_heel'] = get_priority_val(user_input, prio_strike, {\n",
+    "        'strike_pattern': {'Heel': 1.0, 'Mid': 0.5, 'Forefoot': 0.0}, \n",
+    "        'pace': {'Easy': 1.0, 'Steady': 0.5, 'Fast': 0.0}})\n",
+    "    feats['strike_mid'] = get_priority_val(user_input, prio_strike, {\n",
+    "        'strike_pattern': {'Heel': 0.5, 'Mid': 1.0, 'Forefoot': 0.5}, \n",
+    "        'pace': {'Easy': 0.5, 'Steady': 1.0, 'Fast': 0.5}})\n",
+    "    feats['strike_forefoot'] = get_priority_val(user_input, prio_strike, {\n",
+    "        'strike_pattern': {'Heel': 0.0, 'Mid': 0.0, 'Forefoot': 1.0}, \n",
+    "        'pace': {'Easy': 0.0, 'Steady': 0.5, 'Fast': 1.0}})\n",
+    "\n",
+    "    prio_soft = ['cushion_preferences', 'pace']\n",
+    "    feats['midsole_softness'] = get_priority_val(user_input, prio_soft, {\n",
+    "        'cushion_preferences': {'Soft': 1.0, 'Balanced': 0.6, 'Firm': 0.2}, \n",
+    "        'pace': {'Easy': 1.0, 'Steady': 0.6, 'Fast': 0.2}})\n",
+    "\n",
+    "    prio_width = ['stability_need', 'foot_width']\n",
+    "    feats['width_fit'] = get_priority_val(user_input, prio_width, {\n",
+    "        'stability_need': {'Neutral': 0.5, 'Guided': 0.2}, \n",
+    "        'foot_width': {'Narrow': 0.2, 'Regular': 0.6, 'Wide': 1}})\n",
+    "    \n",
+    "    feats['toebox_width'] = get_priority_val(user_input, ['stability_need'], \n",
+    "        {'stability_need': {'Neutral': 0.5, 'Guided': 0.2}})\n",
+    "    \n",
+    "    prio_stiff = ['arch_type', 'pace', 'running_purpose']\n",
+    "    feats['stiffness_scaled'] = get_priority_val(user_input, prio_stiff, {\n",
+    "        'arch_type': {'Flat': 0.0, 'Normal': 0.5, 'High': 0.5}, \n",
+    "        'pace': {'Easy': 0.2, 'Steady': 0.6, 'Fast': 1.0}, \n",
+    "        'running_purpose': {'Daily': 0.2, 'Tempo': 0.6, 'Race': 1}})\n",
+    "\n",
+    "    prio_tor = ['arch_type', 'pace']\n",
+    "    feats['torsional_rigidity'] = get_priority_val(user_input, prio_tor, {\n",
+    "        'arch_type': {'Flat': 1.0, 'Normal': 0.5, 'High': 0.5}, \n",
+    "        'pace': {'Easy': 0.2, 'Steady': 0.6, 'Fast': 1.0}})\n",
+    "\n",
+    "    feats['heel_stiff'] = get_priority_val(user_input, ['arch_type'], \n",
+    "        {'arch_type': {'Flat': 1.0, 'Normal': 0.6, 'High': 0.2}})\n",
+    "\n",
+    "    prio_plate = ['pace', 'running_purpose']\n",
+    "    feats['plate_rock'] = get_priority_val(user_input, prio_plate, {\n",
+    "        'pace': {'Easy': 0.5, 'Steady': 0.5, 'Fast': 0.5}, \n",
+    "        'running_purpose': {'Daily': 0.5, 'Tempo': 0.5, 'Race': 0.5}})\n",
+    "    feats['plate_carbon'] = get_priority_val(user_input, prio_plate, {\n",
+    "        'pace': {'Easy': 0.5, 'Steady': 0.5, 'Fast': 1.0}, \n",
+    "        'running_purpose': {'Daily': 0.5, 'Tempo': 0.5, 'Race': 1.0}})\n",
+    "\n",
+    "    prio_stack = ['strike_pattern', 'pace', 'running_purpose']\n",
+    "    feats['heel_lab_mm'] = get_priority_val(user_input, prio_stack, {\n",
+    "        'strike_pattern': {'Heel': 1.0, 'Mid': 0.5, 'Forefoot': 0.0}, \n",
+    "        'pace': {'Easy': 1.0, 'Steady': 0.5, 'Fast': 0.0}, \n",
+    "        'running_purpose': {'Daily': 1.0, 'Tempo': 0.5, 'Race': 0.5}})\n",
+    "    feats['forefoot_lab_mm'] = get_priority_val(user_input, prio_stack, {\n",
+    "        'strike_pattern': {'Heel': 0.0, 'Mid': 0.5, 'Forefoot': 1.0}, \n",
+    "        'pace': {'Easy': 0.0, 'Steady': 0.5, 'Fast': 1.0}, \n",
+    "        'running_purpose': {'Daily': 1.0, 'Tempo': 0.5, 'Race': 0.5}})\n",
+    "\n",
+    "    feats['weight_lab_oz'] = 1.0 - feats['lightweight']\n",
+    "    feats['toebox_durability'] = 1.0\n",
+    "    feats['heel_durability'] = 1.0\n",
+    "    feats['outsole_durability'] = 1.0\n",
+    "    feats['breathability'] = 1.0\n",
+    "\n",
+    "    feats['season_summer'] = get_priority_val(user_input, ['season'], \n",
+    "        {'season': {'Summer': 1.0, 'Spring & Fall': 0.5, 'Winter': 0.0}})\n",
+    "    feats['season_winter'] = get_priority_val(user_input, ['season'], \n",
+    "        {'season': {'Summer': 0.0, 'Spring & Fall': 0.0, 'Winter': 1.0}})\n",
+    "    feats['season_all'] = get_priority_val(user_input, ['season'], \n",
+    "        {'season': {'Summer': 0.5, 'Spring & Fall': 1.0, 'Winter': 0.0}})\n",
+    "    \n",
+    "    provided_inputs = {k for k, v in user_input.items() if v}  # Track which inputs user provided\n",
+    "    \n",
+    "    feature_sources = {\n",
+    "        'lightweight': ['pace'], 'rocker': ['running_purpose'], 'removable_insole': ['orthotic_usage'],\n",
+    "        'pace_daily_running': ['running_purpose'], 'pace_tempo': ['running_purpose'], 'pace_competition': ['running_purpose'],\n",
+    "        'arch_neutral': ['arch_type'], 'arch_stability': ['arch_type'],\n",
+    "        'drop_lab_mm': ['pace'],\n",
+    "        'strike_heel': ['strike_pattern', 'pace'], 'strike_mid': ['strike_pattern', 'pace'], 'strike_forefoot': ['strike_pattern', 'pace'],\n",
+    "        'midsole_softness': ['cushion_preferences', 'pace'],\n",
+    "        'width_fit': ['stability_need', 'foot_width'],\n",
+    "        'toebox_width': ['stability_need'],\n",
+    "        'stiffness_scaled': ['arch_type', 'pace', 'running_purpose'],\n",
+    "        'torsional_rigidity': ['arch_type', 'pace'],\n",
+    "        'heel_stiff': ['arch_type'],\n",
+    "        'plate_rock': ['pace', 'running_purpose'], 'plate_carbon': ['pace', 'running_purpose'],\n",
+    "        'heel_lab_mm': ['strike_pattern', 'pace', 'running_purpose'], \n",
+    "        'forefoot_lab_mm': ['strike_pattern', 'pace', 'running_purpose'],\n",
+    "        'weight_lab_oz': ['pace'],\n",
+    "        'season_summer': ['season'], 'season_winter': ['season'], 'season_all': ['season'],\n",
+    "        'toebox_durability': [], 'heel_durability': [], 'outsole_durability': [], 'breathability': []\n",
+    "    }\n",
+    "    \n",
+    "    all_cols = binary_cols + continuous_cols\n",
+    "    full_vector_raw = []\n",
+    "    for col in binary_cols:\n",
+    "        full_vector_raw.append(feats.get(col, 0.0))\n",
+    "    for col in continuous_cols:\n",
+    "        full_vector_raw.append(feats.get(col, 0.5))\n",
+    "\n",
+    "    valid_indices = []\n",
+    "    for i, col in enumerate(all_cols):\n",
+    "        sources = feature_sources.get(col, [])\n",
+    "        if any(src in provided_inputs for src in sources):\n",
+    "            valid_indices.append(i)\n",
+    "            \n",
+    "    if not valid_indices:\n",
+    "        valid_indices = list(range(len(all_cols)))\n",
+    "        \n",
+    "    return full_vector_raw, valid_indices"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "faa62fd4",
+   "metadata": {},
+   "source": [
+    "## Recommendation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "9fbd93a4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def recommend_shoes_deep_masked(user_input, df_data, encoder_model, kmeans_model, binary_cols, continuous_cols, X_combined_data):\n",
+    "    \"\"\"\n",
+    "    Deep Learning Recommendation Pipeline with Masked Similarity.\n",
+    "    \n",
+    "    Pipeline:\n",
+    "    1. USER PREPROCESSING: Convert user preferences to feature vector with masking\n",
+    "    2. CLUSTER ROUTING: Encode user→latent space, select top K/3 closest clusters\n",
+    "    3. CANDIDATE RANKING: Score shoes via masked cosine similarity\n",
+    "    4. RESULT: Return top 10 ranked recommendations\n",
+    "    \n",
+    "    Masking Benefits:\n",
+    "    - Reduces noise from unanswered questions\n",
+    "    - Focuses similarity on user-provided dimensions only\n",
+    "    - Example: if user only provided 'pace', similarity computed on pace-related features\n",
+    "    \n",
+    "    Args:\n",
+    "        user_input (dict): User questionnaire responses\n",
+    "        df_data (pd.DataFrame): Shoe catalog\n",
+    "        encoder_model: Trained keras encoder\n",
+    "        kmeans_model: Trained KMeans model (K clusters)\n",
+    "        binary_cols (list): Binary feature names\n",
+    "        continuous_cols (list): Continuous feature names\n",
+    "        X_combined_data (np.ndarray): Preprocessed feature matrix (n_shoes, n_features)\n",
+    "    \n",
+    "    Returns:\n",
+    "        pd.DataFrame: Top 10 shoes with index (row number) and match_score, sorted descending\n",
+    "    \"\"\"\n",
+    "    full_vector, valid_idx = preprocess_user_input_with_mask(user_input, binary_cols, continuous_cols)\n",
+    "    full_vector = np.array([full_vector])\n",
+    "\n",
+    "    user_latent = encoder_model.predict(full_vector, verbose=0)\n",
+    "    distances = kmeans_model.transform(user_latent)[0]\n",
+    "    n_select = math.ceil(kmeans_model.n_clusters / 3)  # Select top 1/3 clusters for diversity\n",
+    "    closest_clusters = np.argsort(distances)[:n_select]\n",
+    "    \n",
+    "    print(f\"User mapped to Clusters: {closest_clusters}\")\n",
+    "    \n",
+    "    candidates = df_data[df_data['cluster'].isin(closest_clusters)].copy()\n",
+    "    if candidates.empty: \n",
+    "        return pd.DataFrame()\n",
+    "    \n",
+    "    candidate_vectors = X_combined_data[candidates.index]\n",
+    "    \n",
+    "    user_vec_masked = full_vector[:, valid_idx]  # Slice user vector to only relevant features\n",
+    "    cand_vecs_masked = candidate_vectors[:, valid_idx]  # Slice candidate vectors accordingly\n",
+    "    \n",
+    "    if np.all(user_vec_masked == 0):\n",
+    "        scores = np.zeros(len(candidates))\n",
+    "    else:\n",
+    "        scores = cosine_similarity(user_vec_masked, cand_vecs_masked)[0]  # Masked similarity calculation\n",
+    "    \n",
+    "    candidates['match_score'] = scores\n",
+    "    \n",
+    "    # Return: sorted by match_score descending, take top 10, keep only match_score (index included as row identifier)\n",
+    "    return candidates.sort_values('match_score', ascending=False).head(10)[['match_score']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c21dceba",
+   "metadata": {},
+   "source": [
+    "# Testing\n",
+    "Input options for recommendation engine test cases.\n",
+    "\n",
+    "Allows generation of random user preference combinations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ce081fc",
+   "metadata": {},
+   "source": [
+    "## Define Options"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "f4bb1851",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_options = {\n",
+    "    'running_purpose': ['Daily', 'Tempo', 'Race'],\n",
+    "    'pace': ['Easy', 'Steady', 'Fast'],\n",
+    "    'orthotic_usage': ['Yes', 'No'],\n",
+    "    'arch_type': ['Flat', 'Normal', 'High'],\n",
+    "    'strike_pattern': ['Heel', 'Mid', 'Forefoot'],\n",
+    "    'cushion_preferences': ['Soft', 'Balanced', 'Firm'],\n",
+    "    'foot_width': ['Narrow', 'Regular', 'Wide'],\n",
+    "    'stability_need': ['Neutral', 'Guided'],\n",
+    "    'season': ['Summer', 'Winter', 'Spring & Fall']\n",
+    "}\n",
+    "\n",
+    "def generate_random_user_input(num_features):\n",
+    "    \"\"\"\n",
+    "    Generate randomized user preference input for testing and validation.\n",
+    "    \n",
+    "    Purpose: Creates realistic test cases with variable input completeness.\n",
+    "    \n",
+    "    Args:\n",
+    "        num_features (int): Number of random features to include\n",
+    "    \n",
+    "    Returns:\n",
+    "        dict: User preferences with num_features random keys/values\n",
+    "              e.g., {'pace': 'Fast', 'arch_type': 'Normal', 'season': 'Summer'}\n",
+    "    \"\"\"\n",
+    "    all_keys = list(input_options.keys())\n",
+    "    selected_keys = random.sample(all_keys, k=min(num_features, len(all_keys)))\n",
+    "    \n",
+    "    user_input = {}\n",
+    "    for key in selected_keys:\n",
+    "        user_input[key] = random.choice(input_options[key])\n",
+    "        \n",
+    "    return user_input"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fc2c6a0e",
+   "metadata": {},
+   "source": [
+    "## Execution\n",
+    "Test Suite Execution\n",
+    "Runs recommendation engine on multiple test cases with varying input completeness.\n",
+    "\n",
+    "Tests: 3 features (partial), 6 features (moderate), 9 features (complete)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "3f033cef",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== RECOMMENDATION ENGINE TEST SUITE ===\n",
+      "\n",
+      "------------------------------------------------------------\n",
+      "TEST CASE #1: User providing 3 preferences\n",
+      "User Input:\n",
+      "{'cushion_preferences': 'Soft', 'strike_pattern': 'Forefoot', 'foot_width': 'Regular'}\n",
+      "User mapped to Clusters: [2 4]\n",
+      "\n",
+      "Top 10 Recommendations:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>brand</th>\n",
+       "      <th>name</th>\n",
+       "      <th>match_score</th>\n",
+       "      <th>cluster</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>via olympus 2</td>\n",
+       "      <td>0.914598</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>torin 7</td>\n",
+       "      <td>0.911951</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>paradigm 7</td>\n",
+       "      <td>0.908588</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>hoka</td>\n",
+       "      <td>gaviota 5</td>\n",
+       "      <td>0.907943</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>rivera 3</td>\n",
+       "      <td>0.907725</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>new balance</td>\n",
+       "      <td>fresh foam x kaiha road</td>\n",
+       "      <td>0.903470</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>new balance</td>\n",
+       "      <td>fresh foam x 880 v15</td>\n",
+       "      <td>0.899275</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>new balance</td>\n",
+       "      <td>fresh foam x more v6</td>\n",
+       "      <td>0.898708</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>experience flow</td>\n",
+       "      <td>0.897785</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>new balance</td>\n",
+       "      <td>fresh foam x vongo v6</td>\n",
+       "      <td>0.897727</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         brand                     name  match_score  cluster\n",
+       "0        altra            via olympus 2     0.914598        2\n",
+       "1        altra                  torin 7     0.911951        2\n",
+       "2        altra               paradigm 7     0.908588        2\n",
+       "3         hoka                gaviota 5     0.907943        2\n",
+       "4        altra                 rivera 3     0.907725        2\n",
+       "5  new balance  fresh foam x kaiha road     0.903470        2\n",
+       "6  new balance     fresh foam x 880 v15     0.899275        2\n",
+       "7  new balance     fresh foam x more v6     0.898708        2\n",
+       "8        altra          experience flow     0.897785        2\n",
+       "9  new balance    fresh foam x vongo v6     0.897727        2"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "------------------------------------------------------------\n",
+      "TEST CASE #2: User providing 6 preferences\n",
+      "User Input:\n",
+      "{'season': 'Spring & Fall', 'pace': 'Fast', 'arch_type': 'Normal', 'running_purpose': 'Race', 'strike_pattern': 'Heel', 'orthotic_usage': 'Yes'}\n",
+      "User mapped to Clusters: [1 4]\n",
+      "\n",
+      "Top 10 Recommendations:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>brand</th>\n",
+       "      <th>name</th>\n",
+       "      <th>match_score</th>\n",
+       "      <th>cluster</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>brooks</td>\n",
+       "      <td>hyperion elite 4</td>\n",
+       "      <td>0.888332</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>new balance</td>\n",
+       "      <td>fuelcell supercomp elite v3</td>\n",
+       "      <td>0.880129</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>on</td>\n",
+       "      <td>cloudboom echo 3</td>\n",
+       "      <td>0.880056</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>hoka</td>\n",
+       "      <td>cielo x1 2.0</td>\n",
+       "      <td>0.877701</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>brooks</td>\n",
+       "      <td>hyperion elite 5</td>\n",
+       "      <td>0.865486</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>adizero adios pro 3</td>\n",
+       "      <td>0.861127</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>saucony</td>\n",
+       "      <td>endorphin pro 2</td>\n",
+       "      <td>0.860182</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>new balance</td>\n",
+       "      <td>fuelcell supercomp elite v5</td>\n",
+       "      <td>0.860054</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>saucony</td>\n",
+       "      <td>endorphin pro 4</td>\n",
+       "      <td>0.859389</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>asics</td>\n",
+       "      <td>magic speed 4</td>\n",
+       "      <td>0.858192</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         brand                         name  match_score  cluster\n",
+       "0       brooks             hyperion elite 4     0.888332        1\n",
+       "1  new balance  fuelcell supercomp elite v3     0.880129        1\n",
+       "2           on             cloudboom echo 3     0.880056        1\n",
+       "3         hoka                 cielo x1 2.0     0.877701        1\n",
+       "4       brooks             hyperion elite 5     0.865486        1\n",
+       "5       adidas          adizero adios pro 3     0.861127        1\n",
+       "6      saucony              endorphin pro 2     0.860182        1\n",
+       "7  new balance  fuelcell supercomp elite v5     0.860054        1\n",
+       "8      saucony              endorphin pro 4     0.859389        1\n",
+       "9        asics                magic speed 4     0.858192        1"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "------------------------------------------------------------\n",
+      "TEST CASE #3: User providing 9 preferences\n",
+      "User Input:\n",
+      "{'running_purpose': 'Tempo', 'arch_type': 'High', 'foot_width': 'Regular', 'orthotic_usage': 'Yes', 'season': 'Winter', 'pace': 'Steady', 'strike_pattern': 'Heel', 'stability_need': 'Guided', 'cushion_preferences': 'Firm'}\n",
+      "User mapped to Clusters: [3 1]\n",
+      "\n",
+      "Top 10 Recommendations:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>brand</th>\n",
+       "      <th>name</th>\n",
+       "      <th>match_score</th>\n",
+       "      <th>cluster</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>under armour</td>\n",
+       "      <td>slipspeed mega</td>\n",
+       "      <td>0.800023</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>brooks</td>\n",
+       "      <td>launch 9</td>\n",
+       "      <td>0.788730</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>puma</td>\n",
+       "      <td>deviate nitro elite 3</td>\n",
+       "      <td>0.772177</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>asics</td>\n",
+       "      <td>megablast</td>\n",
+       "      <td>0.770719</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>adizero prime x 2 strung</td>\n",
+       "      <td>0.766143</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>brooks</td>\n",
+       "      <td>hyperion elite 4</td>\n",
+       "      <td>0.763507</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>adizero prime x3 strung</td>\n",
+       "      <td>0.762527</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>nike</td>\n",
+       "      <td>zoom fly 6</td>\n",
+       "      <td>0.761424</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>runfalcon</td>\n",
+       "      <td>0.759134</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>asics</td>\n",
+       "      <td>noosa tri 14</td>\n",
+       "      <td>0.758233</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          brand                      name  match_score  cluster\n",
+       "0  under armour            slipspeed mega     0.800023        3\n",
+       "1        brooks                  launch 9     0.788730        3\n",
+       "2          puma     deviate nitro elite 3     0.772177        1\n",
+       "3         asics                 megablast     0.770719        1\n",
+       "4        adidas  adizero prime x 2 strung     0.766143        1\n",
+       "5        brooks          hyperion elite 4     0.763507        1\n",
+       "6        adidas   adizero prime x3 strung     0.762527        1\n",
+       "7          nike                zoom fly 6     0.761424        1\n",
+       "8        adidas                 runfalcon     0.759134        3\n",
+       "9         asics              noosa tri 14     0.758233        3"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "target_counts = [3, 6, 9]\n",
+    "\n",
+    "print(\"=== RECOMMENDATION ENGINE TEST SUITE ===\")\n",
+    "\n",
+    "for i, count in enumerate(target_counts):\n",
+    "    print(f\"\\n{'-'*60}\")\n",
+    "    print(f\"TEST CASE #{i+1}: User providing {count} preferences\")\n",
+    "    \n",
+    "    random_input = generate_random_user_input(count)\n",
+    "    print(f\"User Input:\\n{random_input}\")\n",
+    "    \n",
+    "    try:\n",
+    "        recommendations = recommend_shoes_deep_masked(\n",
+    "            random_input, \n",
+    "            df, \n",
+    "            encoder, \n",
+    "            best_model, \n",
+    "            binary_cols, \n",
+    "            continuous_cols, \n",
+    "            X_combined\n",
+    "        )\n",
+    "        \n",
+    "        if not recommendations.empty:\n",
+    "            print(\"\\nTop 10 Recommendations:\")\n",
+    "            # Get brand, name, cluster from original df using index, add match_score from recommendations\n",
+    "            result_df = pd.DataFrame({\n",
+    "                'brand': df.loc[recommendations.index, 'brand'].values,\n",
+    "                'name': df.loc[recommendations.index, 'name'].values,\n",
+    "                'match_score': recommendations['match_score'].values,\n",
+    "                'cluster': df.loc[recommendations.index, 'cluster'].values\n",
+    "            })\n",
+    "            display(result_df)\n",
+    "        else:\n",
+    "            print(\"\\nNo recommendations found (cluster empty).\")\n",
+    "            \n",
+    "    except NameError:\n",
+    "        print(\"\\nERROR: Ensure model and preprocessing functions are loaded.\")\n",
+    "    except Exception as e:\n",
+    "        print(f\"\\nERROR: {e}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a509959",
+   "metadata": {},
+   "source": [
+    "# Save Artifacts\n",
+    "Saves 4 artifacts for complete model reconstruction:\n",
+    "1. shoe_encoder.keras: Trained autoencoder (feature encoding)\n",
+    "2. kmeans_model.pkl: Trained K-means clusters\n",
+    "3. shoe_metadata.pkl: Complete shoe dataset with cluster assignments\n",
+    "4. shoe_features.pkl: Preprocessed feature matrix (X_combined)\n",
+    "\n",
+    "Artifacts stored in timestamped versioned directories for traceability."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "9cb4fe88",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving models to: ../../model_artifacts/road/v_20260214_114821\n",
+      "Models saved successfully!\n"
+     ]
+    }
+   ],
+   "source": [
+    "timestamp = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n",
+    "save_dir = f\"../../model_artifacts/road/v_{timestamp}\"\n",
+    "\n",
+    "os.makedirs(save_dir, exist_ok=True)\n",
+    "print(f\"Saving models to: {save_dir}\")\n",
+    "\n",
+    "encoder.save(f'{save_dir}/shoe_encoder.keras')\n",
+    "\n",
+    "with open(f'{save_dir}/kmeans_model.pkl', 'wb') as f:\n",
+    "    pickle.dump(best_model, f)\n",
+    "\n",
+    "df.to_pickle(f'{save_dir}/shoe_metadata.pkl')\n",
+    "\n",
+    "with open(f'{save_dir}/shoe_features.pkl', 'wb') as f:\n",
+    "    pickle.dump(X_combined, f)\n",
+    "\n",
+    "print(\"Models saved successfully!\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "env (3.13.1)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/modelling/trail_ml.ipynb b/notebooks/modelling/trail_ml.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..e98afe971487fab4dc5317ac2e1b6feec18d9270
--- /dev/null
+++ b/notebooks/modelling/trail_ml.ipynb
@@ -0,0 +1,2455 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c0cdf0b4",
+   "metadata": {},
+   "source": [
+    "# Import Library & Configuration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "d37034b9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Libraries loaded\n",
+      "Time: 2026-02-14 11:47:49\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "os.environ['PYTHONHASHSEED'] = '0'\n",
+    "os.environ['TF_DETERMINISTIC_OPS'] = '1'\n",
+    "os.environ['TF_CUDNN_DETERMINISM'] = '1'\n",
+    "\n",
+    "import random\n",
+    "random.seed(42)\n",
+    "\n",
+    "import math\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "np.random.seed(42)\n",
+    "\n",
+    "from datetime import datetime\n",
+    "import pickle\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "\"\"\"\n",
+    "Deep Learning Framework\n",
+    "TensorFlow & Keras: Autoencoder architecture with Dense, BatchNormalization, Dropout layers\n",
+    "\"\"\"\n",
+    "import tensorflow as tf\n",
+    "tf.random.set_seed(42)\n",
+    "from tensorflow.keras import layers, Model, optimizers, callbacks\n",
+    "from tensorflow.keras.layers import Input, Dense, Dropout, BatchNormalization\n",
+    "\n",
+    "\"\"\"\n",
+    "Machine Learning & Metrics\n",
+    "- KMeans: K-means clustering for shoe recommendation groups\n",
+    "- StandardScaler/MinMaxScaler: Feature normalization for ML models\n",
+    "- Clustering Metrics: Silhouette, Davies-Bouldin, Calinski-Harabasz indices\n",
+    "- Similarity: Cosine similarity for recommendation ranking\n",
+    "\"\"\"\n",
+    "from sklearn.cluster import KMeans\n",
+    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
+    "from sklearn.metrics import (\n",
+    "    silhouette_score, davies_bouldin_score, calinski_harabasz_score,\n",
+    "    adjusted_rand_score\n",
+    ")\n",
+    "from sklearn.metrics.pairwise import cosine_similarity, euclidean_distances\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "\n",
+    "\"\"\"\n",
+    "Data Visualization\n",
+    "Matplotlib & Seaborn for statistical plots and cluster visualization\n",
+    "\"\"\"\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "sns.set_style('whitegrid')\n",
+    "plt.rcParams['figure.figsize'] = (14, 6)\n",
+    "\n",
+    "np.random.seed(42)\n",
+    "tf.random.set_seed(42)\n",
+    "\n",
+    "print('Libraries loaded')\n",
+    "print(f'Time: {datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9409bb62",
+   "metadata": {},
+   "source": [
+    "# Load Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "93ac2c18",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded: 158 shoes × 37 columns\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>brand</th>\n",
+       "      <th>name</th>\n",
+       "      <th>lightweight</th>\n",
+       "      <th>terrain_light</th>\n",
+       "      <th>terrain_moderate</th>\n",
+       "      <th>terrain_technical</th>\n",
+       "      <th>shock_absorption</th>\n",
+       "      <th>energy_return</th>\n",
+       "      <th>traction_scaled</th>\n",
+       "      <th>arch_neutral</th>\n",
+       "      <th>...</th>\n",
+       "      <th>heel_stiff</th>\n",
+       "      <th>lug_dept_mm</th>\n",
+       "      <th>heel_lab_mm</th>\n",
+       "      <th>forefoot_lab_mm</th>\n",
+       "      <th>season_summer</th>\n",
+       "      <th>season_winter</th>\n",
+       "      <th>season_all</th>\n",
+       "      <th>removable_insole</th>\n",
+       "      <th>waterproof</th>\n",
+       "      <th>water_repellent</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>terrex agravic speed ultra</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>30.6</td>\n",
+       "      <td>30.3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>adidas</td>\n",
+       "      <td>terrex speed ultra</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.6</td>\n",
+       "      <td>32.8</td>\n",
+       "      <td>24.6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>experience wild</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>34.5</td>\n",
+       "      <td>30.2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>experience wild 2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>32.3</td>\n",
+       "      <td>26.2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>lone peak 5.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.7</td>\n",
+       "      <td>24.5</td>\n",
+       "      <td>24.3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 37 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    brand                        name  lightweight  terrain_light  \\\n",
+       "0  adidas  terrex agravic speed ultra            0              1   \n",
+       "1  adidas          terrex speed ultra            0              1   \n",
+       "2   altra             experience wild            0              1   \n",
+       "3   altra           experience wild 2            0              1   \n",
+       "4   altra               lone peak 5.0            0              1   \n",
+       "\n",
+       "   terrain_moderate  terrain_technical  shock_absorption  energy_return  \\\n",
+       "0                 0                  0                 3              5   \n",
+       "1                 0                  0                 0              0   \n",
+       "2                 1                  0                 3              1   \n",
+       "3                 0                  0                 3              1   \n",
+       "4                 1                  0                 0              0   \n",
+       "\n",
+       "   traction_scaled  arch_neutral  ...  heel_stiff  lug_dept_mm  heel_lab_mm  \\\n",
+       "0                0             1  ...           1          2.5         30.6   \n",
+       "1                0             1  ...           1          2.6         32.8   \n",
+       "2                0             1  ...           3          3.6         34.5   \n",
+       "3                5             1  ...           1          3.5         32.3   \n",
+       "4                0             1  ...           0          3.7         24.5   \n",
+       "\n",
+       "   forefoot_lab_mm  season_summer  season_winter  season_all  \\\n",
+       "0             30.3              0              0           1   \n",
+       "1             24.6              0              0           0   \n",
+       "2             30.2              0              0           1   \n",
+       "3             26.2              0              0           1   \n",
+       "4             24.3              0              0           0   \n",
+       "\n",
+       "   removable_insole  waterproof  water_repellent  \n",
+       "0                 1           0                0  \n",
+       "1                 1           0                0  \n",
+       "2                 1           0                0  \n",
+       "3                 1           0                0  \n",
+       "4                 1           0                0  \n",
+       "\n",
+       "[5 rows x 37 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "file = '../../data/trail_dataset.csv'\n",
+    "\n",
+    "try:\n",
+    "    df = pd.read_csv(file)\n",
+    "    print(f'Loaded: {df.shape[0]} shoes × {df.shape[1]} columns')\n",
+    "    display(df.head())\n",
+    "except FileNotFoundError:\n",
+    "    print(f\"WARNING: '{file}' not found.\")\n",
+    "    print(\"Please upload the correct dataset file to run with actual data.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "5b87ce3b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 37 columns):\n",
+      " #   Column                Non-Null Count  Dtype  \n",
+      "---  ------                --------------  -----  \n",
+      " 0   brand                 158 non-null    str    \n",
+      " 1   name                  158 non-null    str    \n",
+      " 2   lightweight           158 non-null    int64  \n",
+      " 3   terrain_light         158 non-null    int64  \n",
+      " 4   terrain_moderate      158 non-null    int64  \n",
+      " 5   terrain_technical     158 non-null    int64  \n",
+      " 6   shock_absorption      158 non-null    int64  \n",
+      " 7   energy_return         158 non-null    int64  \n",
+      " 8   traction_scaled       158 non-null    int64  \n",
+      " 9   arch_neutral          158 non-null    int64  \n",
+      " 10  arch_stability        158 non-null    int64  \n",
+      " 11  weight_lab_oz         158 non-null    float64\n",
+      " 12  drop_lab_mm           158 non-null    float64\n",
+      " 13  strike_heel           158 non-null    int64  \n",
+      " 14  strike_mid            158 non-null    int64  \n",
+      " 15  strike_forefoot       158 non-null    int64  \n",
+      " 16  midsole_softness      158 non-null    int64  \n",
+      " 17  toebox_durability     158 non-null    int64  \n",
+      " 18  heel_durability       158 non-null    int64  \n",
+      " 19  outsole_durability    158 non-null    int64  \n",
+      " 20  breathability_scaled  158 non-null    int64  \n",
+      " 21  plate_rock_plate      158 non-null    int64  \n",
+      " 22  plate_carbon_plate    158 non-null    int64  \n",
+      " 23  width_fit             158 non-null    int64  \n",
+      " 24  toebox_width          158 non-null    int64  \n",
+      " 25  stiffness_scaled      158 non-null    int64  \n",
+      " 26  torsional_rigidity    158 non-null    int64  \n",
+      " 27  heel_stiff            158 non-null    int64  \n",
+      " 28  lug_dept_mm           158 non-null    float64\n",
+      " 29  heel_lab_mm           158 non-null    float64\n",
+      " 30  forefoot_lab_mm       158 non-null    float64\n",
+      " 31  season_summer         158 non-null    int64  \n",
+      " 32  season_winter         158 non-null    int64  \n",
+      " 33  season_all            158 non-null    int64  \n",
+      " 34  removable_insole      158 non-null    int64  \n",
+      " 35  waterproof            158 non-null    int64  \n",
+      " 36  water_repellent       158 non-null    int64  \n",
+      "dtypes: float64(5), int64(30), str(2)\n",
+      "memory usage: 45.8 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e66e0f9b",
+   "metadata": {},
+   "source": [
+    "# Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "099b2d8c",
+   "metadata": {},
+   "source": [
+    "## Feature Engineering\n",
+    "Separates numeric features into two categories for different preprocessing strategies."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "44420bdb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Features: 35 total\n",
+      "  Binary     : 17\n",
+      "  Continuous : 18\n"
+     ]
+    }
+   ],
+   "source": [
+    "numeric_cols = df.select_dtypes(include=[np.number]).columns.tolist()\n",
+    "\n",
+    "binary_cols = [col for col in numeric_cols if set(df[col].unique()).issubset({0, 1})]\n",
+    "continuous_cols = [col for col in numeric_cols if col not in binary_cols]\n",
+    "\n",
+    "print(f'Features: {len(numeric_cols)} total')\n",
+    "print(f'  Binary     : {len(binary_cols)}')\n",
+    "print(f'  Continuous : {len(continuous_cols)}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "71d852a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Features: 35 total\n",
+      "  Binary     : 17\n",
+      "  Continuous : 18\n"
+     ]
+    }
+   ],
+   "source": [
+    "numeric_cols = df.select_dtypes(include=[np.number]).columns.tolist()\n",
+    "\n",
+    "binary_cols = [col for col in numeric_cols if set(df[col].unique()).issubset({0, 1})]\n",
+    "continuous_cols = [col for col in numeric_cols if col not in binary_cols]\n",
+    "\n",
+    "print(f'Features: {len(numeric_cols)} total')\n",
+    "print(f'  Binary     : {len(binary_cols)}')\n",
+    "print(f'  Continuous : {len(continuous_cols)}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5890ac84",
+   "metadata": {},
+   "source": [
+    "## Normalization\n",
+    "- Binary features: kept as-is (0-1 range)\n",
+    "- Continuous features: MinMaxScaler to [0, 1]\n",
+    "- Combined array: binary + continuous scaled features\n",
+    "This ensures neural network compatibility and distance metric compatibility."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "074d2042",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Neural input shape: (158, 35)\n",
+      "Range: [0.000000, 1.000000]\n"
+     ]
+    }
+   ],
+   "source": [
+    "feature_cols = numeric_cols.copy()\n",
+    "X = df[feature_cols]\n",
+    "\n",
+    "X_binary = X[binary_cols].values\n",
+    "X_continuous = X[continuous_cols].values\n",
+    "\n",
+    "scaler_continuous = MinMaxScaler()\n",
+    "X_continuous_scaled = scaler_continuous.fit_transform(X_continuous)\n",
+    "\n",
+    "X_combined = np.concatenate([X_binary, X_continuous_scaled], axis=1)\n",
+    "\n",
+    "scaler_standard = StandardScaler()\n",
+    "X_standard = scaler_standard.fit_transform(X)\n",
+    "\n",
+    "print(f'Neural input shape: {X_combined.shape}')\n",
+    "print(f'Range: [{X_combined.min():.6f}, {X_combined.max():.6f}]')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eb77513e",
+   "metadata": {},
+   "source": [
+    "# Auto-Encoder"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38b73e33",
+   "metadata": {},
+   "source": [
+    "## Modelling\n",
+    "- Purpose: Dimensionality reduction (high-D features → 8D latent space)\n",
+    "- Architecture: Encoder [input → 32 → 16 → 8] + Decoder [8 → 16 → 32 → reconstructed]\n",
+    "- Regularization: BatchNormalization + Dropout(0.3) at each dense layer\n",
+    "- Loss: MSE (reconstruction error) | Optimizer: Adam(lr=0.001)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "ce2eb639",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Autoencoder architecture:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_4\"</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1mModel: \"functional_4\"\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ input_layer_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">35</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,152</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_10          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">528</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_11          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)             │            <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_14 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">136</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_12          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)              │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_15 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">144</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_13          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)             │            <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_16 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">544</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_14          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
+       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_14 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_17 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">35</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,155</span> │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ input_layer_2 (\u001b[38;5;33mInputLayer\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m35\u001b[0m)             │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_12 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)             │         \u001b[38;5;34m1,152\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_10          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)             │           \u001b[38;5;34m128\u001b[0m │\n",
+       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_10 (\u001b[38;5;33mDropout\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)             │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_13 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m)             │           \u001b[38;5;34m528\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_11          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m)             │            \u001b[38;5;34m64\u001b[0m │\n",
+       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_11 (\u001b[38;5;33mDropout\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m)             │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_14 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m)              │           \u001b[38;5;34m136\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_12          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m)              │            \u001b[38;5;34m32\u001b[0m │\n",
+       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_12 (\u001b[38;5;33mDropout\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m)              │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_15 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m)             │           \u001b[38;5;34m144\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_13          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m)             │            \u001b[38;5;34m64\u001b[0m │\n",
+       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_13 (\u001b[38;5;33mDropout\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m)             │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_16 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)             │           \u001b[38;5;34m544\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ batch_normalization_14          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)             │           \u001b[38;5;34m128\u001b[0m │\n",
+       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_14 (\u001b[38;5;33mDropout\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)             │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_17 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m35\u001b[0m)             │         \u001b[38;5;34m1,155\u001b[0m │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">4,075</span> (15.92 KB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m4,075\u001b[0m (15.92 KB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">3,867</span> (15.11 KB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m3,867\u001b[0m (15.11 KB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">208</span> (832.00 B)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m208\u001b[0m (832.00 B)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "input_dim = X_combined.shape[1]\n",
+    "encoding_dims = [32, 16, 8]\n",
+    "\n",
+    "input_layer = Input(shape=(input_dim,))\n",
+    "x = input_layer\n",
+    "for dim in encoding_dims:\n",
+    "    x = Dense(dim, activation='relu')(x)\n",
+    "    x = BatchNormalization()(x)\n",
+    "    x = Dropout(0.3)(x)\n",
+    "\n",
+    "latent = x\n",
+    "\n",
+    "for dim in reversed(encoding_dims[:-1]):\n",
+    "    x = Dense(dim, activation='relu')(x)\n",
+    "    x = BatchNormalization()(x)\n",
+    "    x = Dropout(0.3)(x)\n",
+    "\n",
+    "output_layer = Dense(input_dim, activation='sigmoid')(x)\n",
+    "\n",
+    "autoencoder = Model(input_layer, output_layer)\n",
+    "encoder = Model(input_layer, latent)\n",
+    "\n",
+    "autoencoder.compile(\n",
+    "    optimizer=optimizers.Adam(0.001),\n",
+    "    loss='mse',\n",
+    "    metrics=['mae']\n",
+    ")\n",
+    "\n",
+    "print('Autoencoder architecture:')\n",
+    "autoencoder.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f6aaab33",
+   "metadata": {},
+   "source": [
+    "## Training\n",
+    "Training Configuration\n",
+    "- Epochs: 300 (with early stopping)\n",
+    "- Batch size: 64\n",
+    "- Validation split: 20%\n",
+    "- Early stopping: patience=20 (stop if val_loss doesn't improve)\n",
+    "- LR reduction: factor=0.5, patience=10, min_lr=1e-5\n",
+    "- Output: X_latent (8D embeddings) for KMeans clustering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "a6fb0de5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training done!\n",
+      "Final loss: 0.078108\n",
+      "  Val loss: 0.078716\n",
+      "Latent space: (158, 8) (8D embeddings)\n"
+     ]
+    }
+   ],
+   "source": [
+    "history = autoencoder.fit(\n",
+    "    X_combined, X_combined,\n",
+    "    epochs=300,\n",
+    "    batch_size=64,\n",
+    "    validation_split=0.25,\n",
+    "    shuffle=False,\n",
+    "    callbacks=[\n",
+    "        callbacks.EarlyStopping(monitor='val_loss', patience=20, restore_best_weights=True),\n",
+    "        callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=10, min_lr=1e-5)\n",
+    "    ],\n",
+    "    verbose=0\n",
+    ")\n",
+    "\n",
+    "print(f'Training done!')\n",
+    "print(f'Final loss: {history.history[\"loss\"][-1]:.6f}')\n",
+    "print(f'  Val loss: {history.history[\"val_loss\"][-1]:.6f}')\n",
+    "\n",
+    "X_latent = encoder.predict(X_combined, verbose=0)\n",
+    "print(f'Latent space: {X_latent.shape} (8D embeddings)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70cb6321",
+   "metadata": {},
+   "source": [
+    "# Metrics Function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4789df65",
+   "metadata": {},
+   "source": [
+    "## Interpretability Score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "601dd47c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calculate_interpretability_score(df, cluster_col, binary_cols, top_n=5, threshold=0.70):\n",
+    "    \"\"\"\n",
+    "    Calculate cluster interpretability based on top feature clarity.\n",
+    "    \n",
+    "    Strategy: Focus on strongest features (not averages) to reduce noise.\n",
+    "    Strength Definition: Distance from neutral point (0.5)\n",
+    "      - Features closer to 0 or 1 = strong patterns\n",
+    "      - Features closer to 0.5 = ambiguous patterns\n",
+    "    \n",
+    "    Args:\n",
+    "        df (pd.DataFrame): Cluster-labeled dataset\n",
+    "        cluster_col (str): Column with cluster assignments\n",
+    "        binary_cols (list): Binary feature names\n",
+    "        top_n (int): Number of top features to evaluate (default: 5)\n",
+    "        threshold (float): Unused parameter (API compatibility)\n",
+    "    \n",
+    "    Returns:\n",
+    "        dict: {'mean_interpretability': float [0, 1]}\n",
+    "              1.0 = clear feature patterns, 0.0 = no patterns\n",
+    "    \"\"\"\n",
+    "    scores = []\n",
+    "    unique_clusters = df[cluster_col].unique()\n",
+    "    \n",
+    "    for cid in unique_clusters:\n",
+    "        cdata = df[df[cluster_col] == cid]\n",
+    "        n = len(cdata)\n",
+    "        if n == 0: \n",
+    "            scores.append(0)\n",
+    "            continue\n",
+    "        \n",
+    "        feature_strength = []\n",
+    "        for col in binary_cols:\n",
+    "            if col in cdata.columns:\n",
+    "                avg = cdata[col].mean()\n",
+    "                strength = abs(avg - 0.5) * 2  # Normalize distance from neutral (0.5) to [0, 1]\n",
+    "                feature_strength.append(strength)\n",
+    "        \n",
+    "        if feature_strength:\n",
+    "            feature_strength.sort(reverse=True)\n",
+    "            top_features = feature_strength[:top_n]\n",
+    "            scores.append(np.mean(top_features))  # Average of top-N features\n",
+    "        else:\n",
+    "            scores.append(0)\n",
+    "            \n",
+    "    return {'mean_interpretability': np.mean(scores) if scores else 0}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36f4699d",
+   "metadata": {},
+   "source": [
+    "## Cluster Purity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "41ebefdf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calculate_cluster_purity(df, cluster_col, binary_cols):\n",
+    "    \"\"\"\n",
+    "    Measure internal cluster homogeneity via majority class dominance.\n",
+    "    \n",
+    "    Purity Calculation: For each feature, compute max(class0_pct, class1_pct)\n",
+    "    Average across all features = cluster purity\n",
+    "    Range: [0.5, 1.0] where 1.0 = perfect homogeneity\n",
+    "    \n",
+    "    Args:\n",
+    "        df (pd.DataFrame): Cluster-labeled dataset\n",
+    "        cluster_col (str): Column with cluster assignments\n",
+    "        binary_cols (list): Binary feature names\n",
+    "    \n",
+    "    Returns:\n",
+    "        dict: {'mean_purity': float [0.5, 1.0]}\n",
+    "    \"\"\"\n",
+    "    purity_by_cluster = []\n",
+    "    unique_clusters = df[cluster_col].unique()\n",
+    "    \n",
+    "    for cid in unique_clusters:\n",
+    "        cdata = df[df[cluster_col] == cid]\n",
+    "        n = len(cdata)\n",
+    "        if n == 0: continue\n",
+    "        \n",
+    "        dominances = []\n",
+    "        for col in binary_cols:\n",
+    "            if col in cdata.columns:\n",
+    "                avg = cdata[col].mean()\n",
+    "                dominances.append(max(avg, 1 - avg))  # Majority class percentage\n",
+    "        \n",
+    "        if dominances:\n",
+    "            purity_by_cluster.append(np.mean(dominances))\n",
+    "    \n",
+    "    return {'mean_purity': np.mean(purity_by_cluster) if purity_by_cluster else 0}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d373c67f",
+   "metadata": {},
+   "source": [
+    "## Cluster Stability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "059bd68b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calculate_cluster_stability(X, labels, model_func, n_iter=5, seed=42):\n",
+    "    \"\"\"\n",
+    "    Bootstrap stability testing via Adjusted Rand Index (ARI).\n",
+    "    \n",
+    "    Process:\n",
+    "    1. Train model on bootstrap sample (with replacement)\n",
+    "    2. Compare original vs bootstrap clustering using ARI\n",
+    "    3. Average ARI across iterations\n",
+    "    \n",
+    "    ARI Range: [-1, 1]\n",
+    "      > 0.5: excellent stability\n",
+    "      0.2-0.5: fair stability\n",
+    "      < 0.2: poor stability\n",
+    "    \n",
+    "    Args:\n",
+    "        X (np.ndarray): Feature matrix\n",
+    "        labels (np.ndarray): Original cluster assignments\n",
+    "        model_func (callable): Returns instantiated clustering model\n",
+    "        n_iter (int): Bootstrap iterations (default: 5)\n",
+    "    \n",
+    "    Returns:\n",
+    "        dict: {'mean_ari': float [-1, 1]}\n",
+    "    \"\"\"\n",
+    "    if len(np.unique(labels)) < 2:\n",
+    "        return {'mean_ari': 0}\n",
+    "\n",
+    "    n = len(X)\n",
+    "    ari_scores = []\n",
+    "\n",
+    "    for i in range(n_iter):\n",
+    "        rng = np.random.default_rng(seed=42 + i)\n",
+    "        idx = rng.choice(n, n, replace=True)\n",
+    "        try:\n",
+    "            boot_model = model_func()\n",
+    "            boot_labels = boot_model.fit_predict(X[idx])\n",
+    "            ari = adjusted_rand_score(labels[idx], boot_labels)\n",
+    "            ari_scores.append(ari)\n",
+    "        except Exception:\n",
+    "            continue\n",
+    "\n",
+    "    m = np.mean(ari_scores) if ari_scores else 0\n",
+    "    return {'mean_ari': m}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6d0b1f8d",
+   "metadata": {},
+   "source": [
+    "## Comprehensive Evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "acbf4808",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimized Metrics Function Ready.\n"
+     ]
+    }
+   ],
+   "source": [
+    "def evaluate_clustering_comprehensive(X, labels, df_original, model_func, binary_cols):\n",
+    "    \"\"\"\n",
+    "    Multi-metric clustering evaluation combining geometric and business metrics.\n",
+    "    \n",
+    "    Evaluation Framework:\n",
+    "    \n",
+    "    1. GEOMETRIC METRICS (Scikit-learn):\n",
+    "       Silhouette [-1, 1]: cluster separation quality\n",
+    "       Davies-Bouldin [0, ∞): intra-cluster density (lower better)\n",
+    "       Calinski-Harabasz [0, ∞): cluster definition (higher better)\n",
+    "    \n",
+    "    2. BUSINESS METRICS:\n",
+    "       Purity: internal homogeneity\n",
+    "       Interpretability: feature pattern clarity\n",
+    "       Stability: clustering consistency\n",
+    "    \n",
+    "    3. COMPOSITE SCORING (strategic weights):\n",
+    "       Structure (40%): 40% Silhouette + 30% Davies-Bouldin + 40% Calinski-Harabasz\n",
+    "       Explainability (30%): 50% Interpretability + 50% Purity\n",
+    "       Reliability (30%): Bootstrap ARI stability\n",
+    "    \n",
+    "    Args:\n",
+    "        X (np.ndarray): Latent feature space (typically autoencoder output)\n",
+    "        labels (np.ndarray): Cluster assignments [0, K-1]\n",
+    "        df_original (pd.DataFrame): Original shoe metadata\n",
+    "        model_func (callable): KMeans factory function\n",
+    "        binary_cols (list): Binary feature column names\n",
+    "    \n",
+    "    Returns:\n",
+    "        dict: {\n",
+    "            'metrics': {silhouette, davies_bouldin, calinski_harabasz, purity, stability, interpretability},\n",
+    "            'composite_score': float [0, 1]\n",
+    "        }\n",
+    "    \"\"\"\n",
+    "    df_eval = df_original.copy()\n",
+    "    df_eval['cluster'] = labels\n",
+    "    \n",
+    "    sil = silhouette_score(X, labels)\n",
+    "    db = davies_bouldin_score(X, labels)\n",
+    "    ch = calinski_harabasz_score(X, labels)\n",
+    "    \n",
+    "    purity_res = calculate_cluster_purity(df_eval, 'cluster', binary_cols)\n",
+    "    interp_res = calculate_interpretability_score(df_eval, 'cluster', binary_cols, top_n=5)\n",
+    "    stability_res = calculate_cluster_stability(X, labels, model_func, n_iter=3)\n",
+    "    \n",
+    "    val_purity = purity_res['mean_purity']\n",
+    "    val_interp = interp_res['mean_interpretability']\n",
+    "    val_stability = stability_res['mean_ari']\n",
+    "\n",
+    "    sil_norm = (sil + 1) / 2  # Map Silhouette [-1, 1] → [0, 1]\n",
+    "    db_norm = np.exp(-0.5 * db)  # Exponential decay: DB lower is better\n",
+    "    \n",
+    "    if ch > 0:\n",
+    "        ch_log = np.log1p(ch)\n",
+    "        ch_norm = min(ch_log / 8, 1.0)  # Log scaling: assume max log(CH) ≈ 9.2\n",
+    "    else:\n",
+    "        ch_norm = 0\n",
+    "\n",
+    "    score_structure = (0.4 * sil_norm) + (0.3 * db_norm) + (0.3 * ch_norm)  # Weight: Silhouette 40%, DB 30%, CH 30%\n",
+    "    score_explain = (0.5 * val_interp) + (0.5 * val_purity)  # Weight: Interpretability 50%, Purity 50%\n",
+    "    score_reliability = max(val_stability, 0)  # Clip negative ARI to 0\n",
+    "\n",
+    "    composite = (0.40 * score_structure) + (0.30 * score_explain) + (0.30 * score_reliability)\n",
+    " \n",
+    "    return {\n",
+    "        'metrics': {\n",
+    "            'silhouette': sil, \n",
+    "            'davies_bouldin': db, \n",
+    "            'calinski_harabasz': ch,\n",
+    "            'purity': val_purity, \n",
+    "            'stability': val_stability,\n",
+    "            'interpretability': val_interp\n",
+    "        },\n",
+    "        'composite_score': composite\n",
+    "    }\n",
+    "\n",
+    "print('Optimized Metrics Function Ready.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a26ba812",
+   "metadata": {},
+   "source": [
+    "# Model Selection\n",
+    "Model Selection Pipeline: K-means Clustering (K=3 to K=9)\n",
+    "\n",
+    "For each K value:\n",
+    "  - Train KMeans model\n",
+    "  - Evaluate using comprehensive metrics\n",
+    "  - Compute composite score\n",
+    "\n",
+    "Select K with highest composite score (40% geometry, 30% explainability, 30% reliability)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "8e1f4985",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "|  K  |  Score   |   Sil.   |    DB    |     CH     |  Purity  |  Stab.   |  Interp  |\n",
+      "|-----+----------+----------+----------+------------+----------+----------+----------|\n",
+      "|  3  | 0.765033 | 0.375950 | 1.123361 | 72.019419 | 0.857093 | 0.827530 | 0.968952 |\n",
+      "|  4  | 0.823837 | 0.434731 | 0.908956 | 92.039301 | 0.879525 | 0.953780 | 0.978846 |\n",
+      "|  5  | 0.846234 | 0.466877 | 0.762381 | 119.549157 | 0.881545 | 0.983211 | 0.985616 |\n",
+      "|  6  | 0.785558 | 0.469993 | 0.784870 | 108.848327 | 0.888658 | 0.778595 | 0.996970 |\n",
+      "|  7  | 0.751643 | 0.431342 | 0.899164 | 102.980001 | 0.889365 | 0.692877 | 0.997714 |\n",
+      "|  8  | 0.783562 | 0.382712 | 0.941965 | 99.927615 | 0.887725 | 0.818811 | 1.000000 |\n",
+      "|  9  | 0.801397 | 0.380008 | 0.995231 | 98.352993 | 0.898996 | 0.880696 | 1.000000 |\n"
+     ]
+    }
+   ],
+   "source": [
+    "results = []\n",
+    "\n",
+    "print(f\"| {'K':^3} | {'Score':^8} | {'Sil.':^8} | {'DB':^8} | {'CH':^10} | {'Purity':^8} | {'Stab.':^8} | {'Interp':^8} |\")\n",
+    "print(f\"|{'-'*5}+{'-'*10}+{'-'*10}+{'-'*10}+{'-'*12}+{'-'*10}+{'-'*10}+{'-'*10}|\")\n",
+    "\n",
+    "for i in range(3, 10):\n",
+    "    model_factory = lambda: KMeans(n_clusters=i, random_state=42, n_init=20)\n",
+    "    \n",
+    "    model = model_factory()\n",
+    "    labels = model.fit_predict(X_latent)\n",
+    "\n",
+    "    metrics_res = evaluate_clustering_comprehensive(\n",
+    "        X_latent, labels, df.copy(),\n",
+    "        model_factory,\n",
+    "        binary_cols\n",
+    "    )\n",
+    "\n",
+    "    raw_metrics = metrics_res['metrics'] \n",
+    "    comp_score  = metrics_res['composite_score']\n",
+    "\n",
+    "    record = {\n",
+    "        'k': i,\n",
+    "        'model': model,\n",
+    "        'labels': labels,\n",
+    "        'composite_score': comp_score,\n",
+    "        **raw_metrics\n",
+    "    }\n",
+    "    results.append(record)\n",
+    "\n",
+    "    print(f\"| {i:^3} | {comp_score:<8.6f} | {raw_metrics['silhouette']:<6.6f} | \"\n",
+    "          f\"{raw_metrics['davies_bouldin']:<6.6f} | {raw_metrics['calinski_harabasz']:<8.6f} | \"\n",
+    "          f\"{raw_metrics['purity']:<6.6f} | {raw_metrics['stability']:<6.6f} | {raw_metrics['interpretability']:<6.6f} |\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "3aa23de1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--------------------------------------------------------------------------------\n",
+      "SELECTED BEST K: 5\n",
+      "   Silhouette      : 0.466877\n",
+      "   Composite Score : 0.846234\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_results = pd.DataFrame(results)\n",
+    "\n",
+    "best_idx = df_results['composite_score'].idxmax()\n",
+    "best_config = df_results.loc[best_idx]\n",
+    "\n",
+    "best_model = best_config['model']\n",
+    "best_labels = best_config['labels']\n",
+    "best_k = best_config['k']\n",
+    "X_for_clustering = X_latent\n",
+    "\n",
+    "print(\"-\" * 80)\n",
+    "print(f'SELECTED BEST K: {best_k}')\n",
+    "print(f'   Silhouette      : {best_config[\"silhouette\"]:.6f}') \n",
+    "print(f'   Composite Score : {best_config[\"composite_score\"]:.6f}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "84409589",
+   "metadata": {},
+   "source": [
+    "# Generate Cluster Labels"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dcedd13e",
+   "metadata": {},
+   "source": [
+    "## Binning\n",
+    "Divides each continuous feature into 3 quantile bins (tertiles).\n",
+    "\n",
+    "Labels: 0 (low), 0.5 (medium), 1 (high)\n",
+    "\n",
+    "Enables interpretable cluster profiling and feature discretization."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "ecb087b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for col in df.select_dtypes('float64').columns.tolist():\n",
+    "    new_col_name = col + '_bin'\n",
+    "    df[new_col_name] = pd.qcut(df[col], q=3, labels=[0, 0.5, 1]).astype(int)\n",
+    "\n",
+    "non_numeric_cols = df.select_dtypes(exclude=[np.number]).columns.tolist()\n",
+    "\n",
+    "new_column_order = []\n",
+    "\n",
+    "for col in non_numeric_cols:\n",
+    "    if col in df.columns:\n",
+    "        new_column_order.append(col)\n",
+    "\n",
+    "for col in binary_cols:\n",
+    "    if col in df.columns:\n",
+    "        new_column_order.append(col)\n",
+    "\n",
+    "for col in continuous_cols:\n",
+    "    if col in df.columns:\n",
+    "        new_column_order.append(col)\n",
+    "    bin_col_name = col + '_bin'\n",
+    "    if bin_col_name in df.columns:\n",
+    "        new_column_order.append(bin_col_name)\n",
+    "\n",
+    "if 'cluster' in df.columns and 'cluster' not in new_column_order:\n",
+    "    new_column_order.append('cluster')\n",
+    "\n",
+    "df = df[new_column_order]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "9e638024",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 158 entries, 0 to 157\n",
+      "Data columns (total 42 columns):\n",
+      " #   Column                Non-Null Count  Dtype  \n",
+      "---  ------                --------------  -----  \n",
+      " 0   brand                 158 non-null    str    \n",
+      " 1   name                  158 non-null    str    \n",
+      " 2   lightweight           158 non-null    int64  \n",
+      " 3   terrain_light         158 non-null    int64  \n",
+      " 4   terrain_moderate      158 non-null    int64  \n",
+      " 5   terrain_technical     158 non-null    int64  \n",
+      " 6   arch_neutral          158 non-null    int64  \n",
+      " 7   arch_stability        158 non-null    int64  \n",
+      " 8   strike_heel           158 non-null    int64  \n",
+      " 9   strike_mid            158 non-null    int64  \n",
+      " 10  strike_forefoot       158 non-null    int64  \n",
+      " 11  plate_rock_plate      158 non-null    int64  \n",
+      " 12  plate_carbon_plate    158 non-null    int64  \n",
+      " 13  season_summer         158 non-null    int64  \n",
+      " 14  season_winter         158 non-null    int64  \n",
+      " 15  season_all            158 non-null    int64  \n",
+      " 16  removable_insole      158 non-null    int64  \n",
+      " 17  waterproof            158 non-null    int64  \n",
+      " 18  water_repellent       158 non-null    int64  \n",
+      " 19  shock_absorption      158 non-null    int64  \n",
+      " 20  energy_return         158 non-null    int64  \n",
+      " 21  traction_scaled       158 non-null    int64  \n",
+      " 22  weight_lab_oz         158 non-null    float64\n",
+      " 23  weight_lab_oz_bin     158 non-null    int64  \n",
+      " 24  drop_lab_mm           158 non-null    float64\n",
+      " 25  drop_lab_mm_bin       158 non-null    int64  \n",
+      " 26  midsole_softness      158 non-null    int64  \n",
+      " 27  toebox_durability     158 non-null    int64  \n",
+      " 28  heel_durability       158 non-null    int64  \n",
+      " 29  outsole_durability    158 non-null    int64  \n",
+      " 30  breathability_scaled  158 non-null    int64  \n",
+      " 31  width_fit             158 non-null    int64  \n",
+      " 32  toebox_width          158 non-null    int64  \n",
+      " 33  stiffness_scaled      158 non-null    int64  \n",
+      " 34  torsional_rigidity    158 non-null    int64  \n",
+      " 35  heel_stiff            158 non-null    int64  \n",
+      " 36  lug_dept_mm           158 non-null    float64\n",
+      " 37  lug_dept_mm_bin       158 non-null    int64  \n",
+      " 38  heel_lab_mm           158 non-null    float64\n",
+      " 39  heel_lab_mm_bin       158 non-null    int64  \n",
+      " 40  forefoot_lab_mm       158 non-null    float64\n",
+      " 41  forefoot_lab_mm_bin   158 non-null    int64  \n",
+      "dtypes: float64(5), int64(35), str(2)\n",
+      "memory usage: 52.0 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cb72334",
+   "metadata": {},
+   "source": [
+    "## Cluster Summary\n",
+    "Creates interpretable profile for each cluster showing:\n",
+    "- Size (count + percentage)\n",
+    "- Continuous features (mean values)\n",
+    "- Binary features (dominant variant + prevalence)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "c58df965",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cluster Summary:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>percentage</th>\n",
+       "      <th>shock_absorption</th>\n",
+       "      <th>energy_return</th>\n",
+       "      <th>traction_scaled</th>\n",
+       "      <th>weight_lab_oz</th>\n",
+       "      <th>drop_lab_mm</th>\n",
+       "      <th>midsole_softness</th>\n",
+       "      <th>toebox_durability</th>\n",
+       "      <th>heel_durability</th>\n",
+       "      <th>...</th>\n",
+       "      <th>lightweight</th>\n",
+       "      <th>terrain</th>\n",
+       "      <th>arch</th>\n",
+       "      <th>strike</th>\n",
+       "      <th>plate_rock_plate</th>\n",
+       "      <th>plate_carbon_plate</th>\n",
+       "      <th>season</th>\n",
+       "      <th>removable_insole</th>\n",
+       "      <th>waterproof</th>\n",
+       "      <th>water_repellent</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>45</td>\n",
+       "      <td>28.5%</td>\n",
+       "      <td>1.84</td>\n",
+       "      <td>1.04</td>\n",
+       "      <td>0.84</td>\n",
+       "      <td>10.27</td>\n",
+       "      <td>10.88</td>\n",
+       "      <td>3.40</td>\n",
+       "      <td>2.07</td>\n",
+       "      <td>2.29</td>\n",
+       "      <td>...</td>\n",
+       "      <td>no (4%)</td>\n",
+       "      <td>light (87%)</td>\n",
+       "      <td>neutral (96%)</td>\n",
+       "      <td>heel (93%)</td>\n",
+       "      <td>no (7%)</td>\n",
+       "      <td>no (16%)</td>\n",
+       "      <td>all (93%)</td>\n",
+       "      <td>yes (100%)</td>\n",
+       "      <td>no (2%)</td>\n",
+       "      <td>no (0%)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>60</td>\n",
+       "      <td>38.0%</td>\n",
+       "      <td>2.02</td>\n",
+       "      <td>1.45</td>\n",
+       "      <td>1.33</td>\n",
+       "      <td>10.00</td>\n",
+       "      <td>5.21</td>\n",
+       "      <td>3.67</td>\n",
+       "      <td>2.97</td>\n",
+       "      <td>3.07</td>\n",
+       "      <td>...</td>\n",
+       "      <td>no (12%)</td>\n",
+       "      <td>light (93%)</td>\n",
+       "      <td>neutral (100%)</td>\n",
+       "      <td>mid (100%)</td>\n",
+       "      <td>no (20%)</td>\n",
+       "      <td>no (3%)</td>\n",
+       "      <td>all (95%)</td>\n",
+       "      <td>yes (92%)</td>\n",
+       "      <td>no (5%)</td>\n",
+       "      <td>no (5%)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>22</td>\n",
+       "      <td>13.9%</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>10.13</td>\n",
+       "      <td>5.75</td>\n",
+       "      <td>0.59</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>...</td>\n",
+       "      <td>no (9%)</td>\n",
+       "      <td>moderate (73%)</td>\n",
+       "      <td>neutral (100%)</td>\n",
+       "      <td>mid (82%)</td>\n",
+       "      <td>no (45%)</td>\n",
+       "      <td>no (5%)</td>\n",
+       "      <td>all (23%)</td>\n",
+       "      <td>yes (73%)</td>\n",
+       "      <td>no (0%)</td>\n",
+       "      <td>no (5%)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11</td>\n",
+       "      <td>7.0%</td>\n",
+       "      <td>0.45</td>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>10.90</td>\n",
+       "      <td>11.18</td>\n",
+       "      <td>2.64</td>\n",
+       "      <td>4.09</td>\n",
+       "      <td>3.09</td>\n",
+       "      <td>...</td>\n",
+       "      <td>no (0%)</td>\n",
+       "      <td>moderate (73%)</td>\n",
+       "      <td>neutral (82%)</td>\n",
+       "      <td>heel (82%)</td>\n",
+       "      <td>no (27%)</td>\n",
+       "      <td>no (0%)</td>\n",
+       "      <td>winter (91%)</td>\n",
+       "      <td>yes (100%)</td>\n",
+       "      <td>yes (82%)</td>\n",
+       "      <td>no (9%)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>20</td>\n",
+       "      <td>12.7%</td>\n",
+       "      <td>1.90</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>0.50</td>\n",
+       "      <td>10.28</td>\n",
+       "      <td>6.37</td>\n",
+       "      <td>4.20</td>\n",
+       "      <td>3.30</td>\n",
+       "      <td>3.50</td>\n",
+       "      <td>...</td>\n",
+       "      <td>no (0%)</td>\n",
+       "      <td>moderate (85%)</td>\n",
+       "      <td>neutral (100%)</td>\n",
+       "      <td>mid (100%)</td>\n",
+       "      <td>no (35%)</td>\n",
+       "      <td>no (5%)</td>\n",
+       "      <td>all (90%)</td>\n",
+       "      <td>yes (100%)</td>\n",
+       "      <td>no (0%)</td>\n",
+       "      <td>no (5%)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 30 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   count percentage  shock_absorption  energy_return  traction_scaled  \\\n",
+       "0     45      28.5%              1.84           1.04             0.84   \n",
+       "1     60      38.0%              2.02           1.45             1.33   \n",
+       "2     22      13.9%              0.00           0.00             0.00   \n",
+       "3     11       7.0%              0.45           0.27             0.00   \n",
+       "4     20      12.7%              1.90           1.60             0.50   \n",
+       "\n",
+       "   weight_lab_oz  drop_lab_mm  midsole_softness  toebox_durability  \\\n",
+       "0          10.27        10.88              3.40               2.07   \n",
+       "1          10.00         5.21              3.67               2.97   \n",
+       "2          10.13         5.75              0.59               0.00   \n",
+       "3          10.90        11.18              2.64               4.09   \n",
+       "4          10.28         6.37              4.20               3.30   \n",
+       "\n",
+       "   heel_durability  ...  lightweight         terrain            arch  \\\n",
+       "0             2.29  ...      no (4%)     light (87%)   neutral (96%)   \n",
+       "1             3.07  ...     no (12%)     light (93%)  neutral (100%)   \n",
+       "2             0.00  ...      no (9%)  moderate (73%)  neutral (100%)   \n",
+       "3             3.09  ...      no (0%)  moderate (73%)   neutral (82%)   \n",
+       "4             3.50  ...      no (0%)  moderate (85%)  neutral (100%)   \n",
+       "\n",
+       "       strike  plate_rock_plate  plate_carbon_plate        season  \\\n",
+       "0  heel (93%)           no (7%)            no (16%)     all (93%)   \n",
+       "1  mid (100%)          no (20%)             no (3%)     all (95%)   \n",
+       "2   mid (82%)          no (45%)             no (5%)     all (23%)   \n",
+       "3  heel (82%)          no (27%)             no (0%)  winter (91%)   \n",
+       "4  mid (100%)          no (35%)             no (5%)     all (90%)   \n",
+       "\n",
+       "   removable_insole  waterproof  water_repellent  \n",
+       "0        yes (100%)     no (2%)          no (0%)  \n",
+       "1         yes (92%)     no (5%)          no (5%)  \n",
+       "2         yes (73%)     no (0%)          no (5%)  \n",
+       "3        yes (100%)   yes (82%)          no (9%)  \n",
+       "4        yes (100%)     no (0%)          no (5%)  \n",
+       "\n",
+       "[5 rows x 30 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['cluster'] = best_labels \n",
+    "\n",
+    "bin_groups = {}\n",
+    "for col in binary_cols:\n",
+    "    parts = col.split('_')\n",
+    "    \n",
+    "    if len(parts) > 1:\n",
+    "        prefix = '_'.join(parts[:-1])\n",
+    "    else:\n",
+    "        prefix = col\n",
+    "        \n",
+    "    bin_groups.setdefault(prefix, []).append(col)\n",
+    "\n",
+    "rows = []\n",
+    "for cid in sorted(df['cluster'].unique()):\n",
+    "    subset = df[df['cluster'] == cid]\n",
+    "    n = len(subset)\n",
+    "    \n",
+    "    row = {'count': n, 'percentage': f\"{n/len(df)*100:.1f}%\"}\n",
+    "\n",
+    "    for col in continuous_cols:\n",
+    "        row[col.lower()] = round(subset[col].mean(), 2)\n",
+    "\n",
+    "    for prefix, cols in bin_groups.items():\n",
+    "        means = subset[cols].mean()\n",
+    "        best_col = means.idxmax()\n",
+    "        best_val = means.max()\n",
+    "        \n",
+    "        if len(cols) > 1:\n",
+    "            header = prefix.lower()\n",
+    "            val_str = best_col.replace(f\"{prefix}_\", \"\").lower()\n",
+    "            row[header] = f\"{val_str} ({best_val*100:.0f}%)\"\n",
+    "            \n",
+    "        else:\n",
+    "            header = cols[0].lower()\n",
+    "            val_str = \"yes\" if best_val > 0.5 else \"no\"\n",
+    "            row[header] = f\"{val_str} ({best_val*100:.0f}%)\"\n",
+    "\n",
+    "    rows.append(row)\n",
+    "\n",
+    "df_summary = pd.DataFrame(rows, index=sorted(df['cluster'].unique()))\n",
+    "df_summary.index.name = None \n",
+    "\n",
+    "print(\"Cluster Summary:\")\n",
+    "display(df_summary)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a9fa3aff",
+   "metadata": {},
+   "source": [
+    "# Deep Learn Recommender"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f35b760e",
+   "metadata": {},
+   "source": [
+    "## Priority Handler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "9225b9c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_priority_val(user_input, priority_list, mapping_dicts):\n",
+    "    \"\"\"\n",
+    "    Extract feature value from user input with priority hierarchy.\n",
+    "    \n",
+    "    Strategy: Checks inputs in priority order, returns mapped value from\n",
+    "    first non-empty input, ignores lower-priority inputs if higher-priority exists.\n",
+    "    \n",
+    "    Args:\n",
+    "        user_input (dict): User preferences {'running_purpose': 'Daily', ...}\n",
+    "        priority_list (list): Input sources in priority order\n",
+    "        mapping_dicts (dict): Maps {source: {option: feature_value}}\n",
+    "    \n",
+    "    Returns:\n",
+    "        float: Feature value [0, 1] or 0.5 (neutral) if not found\n",
+    "    \"\"\"\n",
+    "    for source_key in priority_list:\n",
+    "        if source_key in user_input and user_input[source_key]:\n",
+    "            user_choice = user_input[source_key]\n",
+    "            if source_key in mapping_dicts:\n",
+    "                mapping = mapping_dicts[source_key]\n",
+    "                if user_choice in mapping:\n",
+    "                    return mapping[user_choice]\n",
+    "    return 0.5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19abb5f6",
+   "metadata": {},
+   "source": [
+    "## Input Handler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "a44b15a3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def preprocess_user_input_with_mask(user_input, binary_cols, continuous_cols):\n",
+    "    \"\"\"\n",
+    "    Translates user inputs (Terrain, Pace, etc.) into 34 engineered features \n",
+    "    based on the specific logic provided.\n",
+    "    \"\"\"\n",
+    "    feats = {col: 0.0 for col in binary_cols + continuous_cols}\n",
+    "    \n",
+    "    feats['terrain_light'] = get_priority_val(user_input, ['terrain'], \n",
+    "        {'terrain': {'Light': 1.0, 'Mixed': 0.5, 'Rocky': 0.0, 'Muddy': 0.0}})\n",
+    "    feats['terrain_moderate'] = get_priority_val(user_input, ['terrain'], \n",
+    "        {'terrain': {'Light': 0.5, 'Mixed': 1.0, 'Rocky': 0.5, 'Muddy': 0.5}})\n",
+    "    feats['terrain_technical'] = get_priority_val(user_input, ['terrain'], \n",
+    "        {'terrain': {'Light': 0.0, 'Mixed': 0.5, 'Rocky': 1.0, 'Muddy': 1.0}})\n",
+    "\n",
+    "    feats['shock_absorption'] = get_priority_val(user_input, ['rock_sensitive', 'terrain'], \n",
+    "        {'rock_sensitive': {'Yes': 1.0, 'No': 0.0}, \n",
+    "         'terrain': {'Light': 0.2, 'Mixed': 0.6, 'Rocky': 1.0, 'Muddy': 0.0}})\n",
+    "\n",
+    "    feats['energy_return'] = 1.0\n",
+    "    feats['traction_scaled'] = get_priority_val(user_input, ['terrain'], \n",
+    "        {'terrain': {'Light': 0.5, 'Mixed': 1.0, 'Rocky': 0.5, 'Muddy': 1.0}})\n",
+    "\n",
+    "    feats['arch_neutral'] = get_priority_val(user_input, ['arch_type'], \n",
+    "        {'arch_type': {'Flat': 0.0, 'Normal': 0.8, 'High': 1.0}})\n",
+    "    feats['arch_stability'] = get_priority_val(user_input, ['arch_type'], \n",
+    "        {'arch_type': {'Flat': 1.0, 'Normal': 0.2, 'High': 0.0}})\n",
+    "\n",
+    "    feats['drop_lab_mm'] = get_priority_val(user_input, ['pace'], \n",
+    "        {'pace': {'Easy': 1.0, 'Steady': 0.5, 'Fast': 0.0}})\n",
+    "\n",
+    "    prio_strike = ['strike_pattern', 'pace']\n",
+    "    feats['strike_heel'] = get_priority_val(user_input, prio_strike, {\n",
+    "        'strike_pattern': {'Heel': 1.0, 'Mid': 0.5, 'Forefoot': 0.0}, \n",
+    "        'pace': {'Easy': 1.0, 'Steady': 0.5, 'Fast': 0.0}})\n",
+    "    feats['strike_mid'] = get_priority_val(user_input, prio_strike, {\n",
+    "        'strike_pattern': {'Heel': 0.5, 'Mid': 1.0, 'Forefoot': 0.5}, \n",
+    "        'pace': {'Easy': 0.5, 'Steady': 1.0, 'Fast': 0.5}})\n",
+    "    feats['strike_forefoot'] = get_priority_val(user_input, prio_strike, {\n",
+    "        'strike_pattern': {'Heel': 0.0, 'Mid': 0.0, 'Forefoot': 1.0}, \n",
+    "        'pace': {'Easy': 0.0, 'Steady': 0.5, 'Fast': 1.0}})\n",
+    "\n",
+    "    feats['midsole_softness'] = get_priority_val(user_input, ['pace'], \n",
+    "        {'pace': {'Easy': 1.0, 'Steady': 0.6, 'Fast': 0.2}})\n",
+    "\n",
+    "    feats['toebox_durability'] = 1.0\n",
+    "    feats['heel_durability'] = 1.0\n",
+    "    feats['outsole_durability'] = 1.0\n",
+    "    feats['breathability'] = 1.0\n",
+    "\n",
+    "    feats['plate_rock_plate'] = get_priority_val(user_input, ['pace', 'terrain'], \n",
+    "        {'pace': {'Easy': 0.5, 'Steady': 0.5, 'Fast': 0.5}, \n",
+    "         'terrain': {'Light': 0.5, 'Mixed': 1.0, 'Rocky': 1.0, 'Muddy': 1.0}})\n",
+    "    feats['plate_carbon_plate'] = get_priority_val(user_input, ['pace', 'terrain'], \n",
+    "        {'pace': {'Easy': 0.5, 'Steady': 0.5, 'Fast': 1.0}, \n",
+    "         'terrain': {'Light': 0.5, 'Mixed': 0.5, 'Rocky': 0.5, 'Muddy': 0.5}})\n",
+    "\n",
+    "    feats['width_fit'] = get_priority_val(user_input, ['foot_width'], \n",
+    "        {'foot_width': {'Narrow': 0.2, 'Regular': 0.6, 'Wide': 1.0}})\n",
+    "    feats['toebox_width'] = get_priority_val(user_input, ['foot_width'], \n",
+    "        {'foot_width': {'Narrow': 0.2, 'Regular': 0.6, 'Wide': 1.0}})\n",
+    "\n",
+    "    feats['stiffness_scaled'] = get_priority_val(user_input, ['pace'], \n",
+    "        {'pace': {'Easy': 0.2, 'Steady': 0.6, 'Fast': 1.0}})\n",
+    "\n",
+    "    feats['torsional_rigidity'] = get_priority_val(user_input, ['arch_type', 'pace'], \n",
+    "        {'arch_type': {'Flat': 1.0, 'Normal': 0.5, 'High': 0.5}, \n",
+    "         'pace': {'Easy': 0.2, 'Steady': 0.6, 'Fast': 1.0}})\n",
+    "\n",
+    "    feats['heel_stiff'] = get_priority_val(user_input, ['arch_type'], \n",
+    "        {'arch_type': {'Flat': 1.0, 'Normal': 0.6, 'High': 0.2}})\n",
+    "\n",
+    "    feats['lug_depth'] = get_priority_val(user_input, ['terrain'], \n",
+    "        {'terrain': {'Light': 0.5, 'Mixed': 1.0, 'Rocky': 0.5, 'Muddy': 1.0}})\n",
+    "\n",
+    "    prio_stack_h = ['strike_pattern', 'pace', 'terrain']\n",
+    "    feats['heel_lab_mm'] = get_priority_val(user_input, prio_stack_h, {\n",
+    "        'strike_pattern': {'Heel': 1.0, 'Mid': 0.5, 'Forefoot': 0.0}, \n",
+    "        'pace': {'Easy': 1.0, 'Steady': 0.5, 'Fast': 0.0}, \n",
+    "        'terrain': {'Light': 0.5, 'Mixed': 1.0, 'Rocky': 1.0, 'Muddy': 1.0}})\n",
+    "\n",
+    "    prio_stack_f = ['strike_pattern', 'pace', 'terrain']\n",
+    "    feats['forefoot_lab_mm'] = get_priority_val(user_input, prio_stack_f, {\n",
+    "        'strike_pattern': {'Heel': 0.0, 'Mid': 0.5, 'Forefoot': 1.0}, \n",
+    "        'pace': {'Easy': 0.0, 'Steady': 0.5, 'Fast': 1.0}, \n",
+    "        'terrain': {'Light': 0.5, 'Mixed': 0.5, 'Rocky': 0.5, 'Muddy': 0.5}})\n",
+    "\n",
+    "    feats['season_summer'] = get_priority_val(user_input, ['season'], \n",
+    "        {'season': {'Summer': 1.0, 'Spring & Fall': 0.5, 'Winter': 0.0}})\n",
+    "    feats['season_winter'] = get_priority_val(user_input, ['season'], \n",
+    "        {'season': {'Summer': 0.0, 'Spring & Fall': 0.0, 'Winter': 1.0}})\n",
+    "    feats['season_all'] = get_priority_val(user_input, ['season'], \n",
+    "        {'season': {'Summer': 0.5, 'Spring & Fall': 1.0, 'Winter': 0.0}})\n",
+    "\n",
+    "    feats['removable_insole'] = get_priority_val(user_input, ['orthotic_usage'], \n",
+    "        {'orthotic_usage': {'Yes': 1.0, 'No': 0.5}})\n",
+    "\n",
+    "    feats['waterproof'] = get_priority_val(user_input, ['water_resistance', 'terrain'], \n",
+    "        {'water_resistance': {'Waterproof': 1.0, 'Water Repellent': 0.5}, \n",
+    "         'terrain': {'Light': 0.5, 'Mixed': 0.5, 'Rocky': 0.5, 'Muddy': 1.0}})\n",
+    "    feats['water_repellent'] = get_priority_val(user_input, ['water_resistance', 'terrain'], \n",
+    "        {'water_resistance': {'Waterproof': 1.0, 'Water Repellent': 1.0}, \n",
+    "         'terrain': {'Light': 0.5, 'Mixed': 1.0, 'Rocky': 0.5, 'Muddy': 1.0}})\n",
+    "\n",
+    "    feats['lightweight'] = get_priority_val(user_input, ['pace'], \n",
+    "        {'pace': {'Easy': 0.5, 'Steady': 0.5, 'Fast': 1.0}})\n",
+    "\n",
+    "    provided_inputs = {k for k, v in user_input.items() if v}  # Track which inputs user provided\n",
+    "\n",
+    "    feature_sources = {\n",
+    "        'terrain_light': ['terrain'], 'terrain_moderate': ['terrain'], 'terrain_technical': ['terrain'],\n",
+    "        'shock_absorption': ['rock_sensitive', 'terrain'],\n",
+    "        'traction_scaled': ['terrain'],\n",
+    "        'arch_neutral': ['arch_type'], 'arch_stability': ['arch_type'],\n",
+    "        'drop_lab_mm': ['pace'],\n",
+    "        'strike_heel': ['strike_pattern', 'pace'], 'strike_mid': ['strike_pattern', 'pace'], 'strike_forefoot': ['strike_pattern', 'pace'],\n",
+    "        'midsole_softness': ['pace'],\n",
+    "        'plate_rock_plate': ['pace', 'terrain'], 'plate_carbon_plate': ['pace', 'terrain'],\n",
+    "        'width_fit': ['foot_width'], 'toebox_width': ['foot_width'],\n",
+    "        'stiffness_scaled': ['pace'],\n",
+    "        'torsional_rigidity': ['arch_type', 'pace'],\n",
+    "        'heel_stiff': ['arch_type'],\n",
+    "        'lug_depth': ['terrain'],\n",
+    "        'heel_lab_mm': ['strike_pattern', 'pace', 'terrain'], \n",
+    "        'forefoot_lab_mm': ['strike_pattern', 'pace', 'terrain'],\n",
+    "        'season_summer': ['season'], 'season_winter': ['season'], 'season_all': ['season'],\n",
+    "        'removable_insole': ['orthotic_usage'],\n",
+    "        'waterproof': ['water_resistance', 'terrain'], 'water_repellent': ['water_resistance', 'terrain'],\n",
+    "        'lightweight': ['pace'],\n",
+    "        'energy_return': [], 'toebox_durability': [], 'heel_durability': [], 'outsole_durability': [], 'breathability': []\n",
+    "    }\n",
+    "\n",
+    "    all_cols = binary_cols + continuous_cols\n",
+    "    full_vector_raw = []\n",
+    "    for col in binary_cols:\n",
+    "        full_vector_raw.append(feats.get(col, 0.0))\n",
+    "    for col in continuous_cols:\n",
+    "        full_vector_raw.append(feats.get(col, 0.5))\n",
+    "\n",
+    "    valid_indices = []\n",
+    "    for i, col in enumerate(all_cols):\n",
+    "        sources = feature_sources.get(col, [])\n",
+    "        if any(src in provided_inputs for src in sources):\n",
+    "            valid_indices.append(i)\n",
+    "            \n",
+    "    if not valid_indices:\n",
+    "        valid_indices = list(range(len(all_cols)))\n",
+    "        \n",
+    "    return full_vector_raw, valid_indices"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b0ec844",
+   "metadata": {},
+   "source": [
+    "## Reccomendation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "8f25c530",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def recommend_shoes_deep_masked(user_input, df_data, encoder_model, kmeans_model, binary_cols, continuous_cols, X_combined_data):\n",
+    "    \"\"\"\n",
+    "    Deep Learning Recommendation Pipeline with Masked Similarity.\n",
+    "    \n",
+    "    Pipeline:\n",
+    "    1. USER PREPROCESSING: Convert user preferences to feature vector with masking\n",
+    "    2. CLUSTER ROUTING: Encode user→latent space, select top K/3 closest clusters\n",
+    "    3. CANDIDATE RANKING: Score shoes via masked cosine similarity\n",
+    "    4. RESULT: Return top 10 ranked recommendations\n",
+    "    \n",
+    "    Masking Benefits:\n",
+    "    - Reduces noise from unanswered questions\n",
+    "    - Focuses similarity on user-provided dimensions only\n",
+    "    - Example: if user only provided 'pace', similarity computed on pace-related features\n",
+    "    \n",
+    "    Args:\n",
+    "        user_input (dict): User questionnaire responses\n",
+    "        df_data (pd.DataFrame): Shoe catalog\n",
+    "        encoder_model: Trained keras encoder\n",
+    "        kmeans_model: Trained KMeans model (K clusters)\n",
+    "        binary_cols (list): Binary feature names\n",
+    "        continuous_cols (list): Continuous feature names\n",
+    "        X_combined_data (np.ndarray): Preprocessed feature matrix (n_shoes, n_features)\n",
+    "    \n",
+    "    Returns:\n",
+    "        pd.DataFrame: Top 10 shoes with index (row number) and match_score, sorted descending\n",
+    "    \"\"\"\n",
+    "    full_vector, valid_idx = preprocess_user_input_with_mask(user_input, binary_cols, continuous_cols)\n",
+    "    full_vector = np.array([full_vector])\n",
+    "\n",
+    "    user_latent = encoder_model.predict(full_vector, verbose=0)\n",
+    "    distances = kmeans_model.transform(user_latent)[0]\n",
+    "    n_select = math.ceil(kmeans_model.n_clusters / 3)  # Select top 1/3 clusters for diversity\n",
+    "    closest_clusters = np.argsort(distances)[:n_select]\n",
+    "    \n",
+    "    print(f\"User mapped to Clusters: {closest_clusters}\")\n",
+    "    \n",
+    "    candidates = df_data[df_data['cluster'].isin(closest_clusters)].copy()\n",
+    "    if candidates.empty: \n",
+    "        return pd.DataFrame()\n",
+    "    \n",
+    "    candidate_vectors = X_combined_data[candidates.index]\n",
+    "    \n",
+    "    user_vec_masked = full_vector[:, valid_idx]  # Slice user vector to only relevant features\n",
+    "    cand_vecs_masked = candidate_vectors[:, valid_idx]  # Slice candidate vectors accordingly\n",
+    "    \n",
+    "    if np.all(user_vec_masked == 0):\n",
+    "        scores = np.zeros(len(candidates))\n",
+    "    else:\n",
+    "        scores = cosine_similarity(user_vec_masked, cand_vecs_masked)[0]  # Masked similarity calculation\n",
+    "    \n",
+    "    candidates['match_score'] = scores\n",
+    "    \n",
+    "    # Return: sorted by match_score descending, take top 10, keep only match_score (index included as row identifier)\n",
+    "    return candidates.sort_values('match_score', ascending=False).head(10)[['match_score']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d8f17b84",
+   "metadata": {},
+   "source": [
+    "# Testing\n",
+    "Input options for recommendation engine test cases.\n",
+    "\n",
+    "Allows generation of random user preference combinations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4feb81e3",
+   "metadata": {},
+   "source": [
+    "## Define Options"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "c9071f9d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_options = {\n",
+    "    'terrain': ['Light', 'Mixed', 'Rocky', 'Muddy'],\n",
+    "    'rock_sensitive': ['Yes', 'No'],\n",
+    "    'pace': ['Easy', 'Steady', 'Fast'],\n",
+    "    'orthotic_usage': ['Yes', 'No'],\n",
+    "    'arch_type': ['Flat', 'Normal', 'High'],\n",
+    "    'strike_pattern': ['Heel', 'Mid', 'Forefoot'],\n",
+    "    'foot_width': ['Narrow', 'Regular', 'Wide'],\n",
+    "    'season': ['Summer', 'Spring & Fall', 'Winter'],\n",
+    "    'water_resistance': ['Waterproof', 'Water Repellent'],\n",
+    "}\n",
+    "\n",
+    "def generate_random_user_input(num_features):\n",
+    "    \"\"\"\n",
+    "    Generate randomized user preference input for testing and validation.\n",
+    "    \n",
+    "    Purpose: Creates realistic test cases with variable input completeness.\n",
+    "    \n",
+    "    Args:\n",
+    "        num_features (int): Number of random features to include\n",
+    "    \n",
+    "    Returns:\n",
+    "        dict: User preferences with num_features random keys/values\n",
+    "              e.g., {'pace': 'Fast', 'arch_type': 'Normal', 'season': 'Summer'}\n",
+    "    \"\"\"\n",
+    "    all_keys = list(input_options.keys())\n",
+    "    selected_keys = random.sample(all_keys, k=min(num_features, len(all_keys)))\n",
+    "    \n",
+    "    user_input = {}\n",
+    "    for key in selected_keys:\n",
+    "        user_input[key] = random.choice(input_options[key])\n",
+    "        \n",
+    "    return user_input"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "080a36ee",
+   "metadata": {},
+   "source": [
+    "## Execution\n",
+    "Test Suite Execution\n",
+    "Runs recommendation engine on multiple test cases with varying input completeness.\n",
+    "\n",
+    "Tests: 3 features (partial), 6 features (moderate), 9 features (complete)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "116ec942",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== RECOMMENDATION ENGINE TEST SUITE ===\n",
+      "\n",
+      "------------------------------------------------------------\n",
+      "TEST CASE #1: User providing 3 preferences\n",
+      "User Input:\n",
+      "{'foot_width': 'Regular', 'rock_sensitive': 'No', 'strike_pattern': 'Forefoot'}\n",
+      "User mapped to Clusters: [1 4]\n",
+      "\n",
+      "Top 10 Recommendations:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>brand</th>\n",
+       "      <th>name</th>\n",
+       "      <th>match_score</th>\n",
+       "      <th>cluster</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>outroad</td>\n",
+       "      <td>0.905823</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>mont blanc carbon</td>\n",
+       "      <td>0.901664</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>outroad 2</td>\n",
+       "      <td>0.894104</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>nike</td>\n",
+       "      <td>terra kiger 9</td>\n",
+       "      <td>0.892593</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>topo</td>\n",
+       "      <td>traverse</td>\n",
+       "      <td>0.890732</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>salomon</td>\n",
+       "      <td>thundercross</td>\n",
+       "      <td>0.889245</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>inov8</td>\n",
+       "      <td>trailfly zero</td>\n",
+       "      <td>0.887240</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>lone peak 9</td>\n",
+       "      <td>0.886799</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>scarpa</td>\n",
+       "      <td>spin planet</td>\n",
+       "      <td>0.886360</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>altra</td>\n",
+       "      <td>outroad 3</td>\n",
+       "      <td>0.886279</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     brand               name  match_score  cluster\n",
+       "0    altra            outroad     0.905823        1\n",
+       "1    altra  mont blanc carbon     0.901664        4\n",
+       "2    altra          outroad 2     0.894104        1\n",
+       "3     nike      terra kiger 9     0.892593        4\n",
+       "4     topo           traverse     0.890732        1\n",
+       "5  salomon       thundercross     0.889245        4\n",
+       "6    inov8      trailfly zero     0.887240        1\n",
+       "7    altra        lone peak 9     0.886799        1\n",
+       "8   scarpa        spin planet     0.886360        1\n",
+       "9    altra          outroad 3     0.886279        1"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "------------------------------------------------------------\n",
+      "TEST CASE #2: User providing 6 preferences\n",
+      "User Input:\n",
+      "{'rock_sensitive': 'No', 'arch_type': 'High', 'season': 'Spring & Fall', 'pace': 'Fast', 'orthotic_usage': 'Yes', 'strike_pattern': 'Heel'}\n",
+      "User mapped to Clusters: [0 1]\n",
+      "\n",
+      "Top 10 Recommendations:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>brand</th>\n",
+       "      <th>name</th>\n",
+       "      <th>match_score</th>\n",
+       "      <th>cluster</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>new balance</td>\n",
+       "      <td>fuelcell supercomp trail</td>\n",
+       "      <td>0.864520</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>kailas</td>\n",
+       "      <td>fuga yao</td>\n",
+       "      <td>0.804054</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>on</td>\n",
+       "      <td>cloudvista</td>\n",
+       "      <td>0.792374</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>asics</td>\n",
+       "      <td>metafuji trail</td>\n",
+       "      <td>0.776813</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>merrell</td>\n",
+       "      <td>morphlite</td>\n",
+       "      <td>0.771201</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>nike</td>\n",
+       "      <td>ultrafly</td>\n",
+       "      <td>0.764686</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>la sportiva</td>\n",
+       "      <td>mutant</td>\n",
+       "      <td>0.763105</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>hoka</td>\n",
+       "      <td>mafate x</td>\n",
+       "      <td>0.757556</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>kailas</td>\n",
+       "      <td>fuga elite 2</td>\n",
+       "      <td>0.754621</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>new balance</td>\n",
+       "      <td>tektrel</td>\n",
+       "      <td>0.748259</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         brand                      name  match_score  cluster\n",
+       "0  new balance  fuelcell supercomp trail     0.864520        0\n",
+       "1       kailas                  fuga yao     0.804054        0\n",
+       "2           on                cloudvista     0.792374        0\n",
+       "3        asics            metafuji trail     0.776813        0\n",
+       "4      merrell                 morphlite     0.771201        0\n",
+       "5         nike                  ultrafly     0.764686        0\n",
+       "6  la sportiva                    mutant     0.763105        0\n",
+       "7         hoka                  mafate x     0.757556        0\n",
+       "8       kailas              fuga elite 2     0.754621        0\n",
+       "9  new balance                   tektrel     0.748259        1"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "------------------------------------------------------------\n",
+      "TEST CASE #3: User providing 9 preferences\n",
+      "User Input:\n",
+      "{'foot_width': 'Regular', 'water_resistance': 'Waterproof', 'strike_pattern': 'Mid', 'season': 'Winter', 'rock_sensitive': 'No', 'pace': 'Steady', 'arch_type': 'Normal', 'terrain': 'Muddy', 'orthotic_usage': 'Yes'}\n",
+      "User mapped to Clusters: [3 4]\n",
+      "\n",
+      "Top 10 Recommendations:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>brand</th>\n",
+       "      <th>name</th>\n",
+       "      <th>match_score</th>\n",
+       "      <th>cluster</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>hoka</td>\n",
+       "      <td>challenger 7 gtx</td>\n",
+       "      <td>0.831882</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>merrell</td>\n",
+       "      <td>agility peak 5 gtx</td>\n",
+       "      <td>0.785247</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>salomon</td>\n",
+       "      <td>speedcross 6 gtx</td>\n",
+       "      <td>0.773513</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>hoka</td>\n",
+       "      <td>speedgoat 5 gtx</td>\n",
+       "      <td>0.748381</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>hoka</td>\n",
+       "      <td>speedgoat 6 gtx</td>\n",
+       "      <td>0.735479</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>salomon</td>\n",
+       "      <td>thundercross</td>\n",
+       "      <td>0.726806</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>salomon</td>\n",
+       "      <td>genesis</td>\n",
+       "      <td>0.720264</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>salomon</td>\n",
+       "      <td>speedcross 6</td>\n",
+       "      <td>0.714786</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>icebug</td>\n",
+       "      <td>järv rb9x</td>\n",
+       "      <td>0.706436</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>nike</td>\n",
+       "      <td>pegasus trail 4 gtx</td>\n",
+       "      <td>0.699563</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     brand                 name  match_score  cluster\n",
+       "0     hoka     challenger 7 gtx     0.831882        3\n",
+       "1  merrell   agility peak 5 gtx     0.785247        3\n",
+       "2  salomon     speedcross 6 gtx     0.773513        3\n",
+       "3     hoka      speedgoat 5 gtx     0.748381        3\n",
+       "4     hoka      speedgoat 6 gtx     0.735479        3\n",
+       "5  salomon         thundercross     0.726806        4\n",
+       "6  salomon              genesis     0.720264        4\n",
+       "7  salomon         speedcross 6     0.714786        3\n",
+       "8   icebug            järv rb9x     0.706436        4\n",
+       "9     nike  pegasus trail 4 gtx     0.699563        3"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "target_counts = [3, 6, 9]\n",
+    "\n",
+    "print(\"=== RECOMMENDATION ENGINE TEST SUITE ===\")\n",
+    "\n",
+    "for i, count in enumerate(target_counts):\n",
+    "    print(f\"\\n{'-'*60}\")\n",
+    "    print(f\"TEST CASE #{i+1}: User providing {count} preferences\")\n",
+    "    \n",
+    "    random_input = generate_random_user_input(count)\n",
+    "    print(f\"User Input:\\n{random_input}\")\n",
+    "    \n",
+    "    try:\n",
+    "        recommendations = recommend_shoes_deep_masked(\n",
+    "            random_input, \n",
+    "            df, \n",
+    "            encoder, \n",
+    "            best_model, \n",
+    "            binary_cols, \n",
+    "            continuous_cols, \n",
+    "            X_combined\n",
+    "        )\n",
+    "        \n",
+    "        if not recommendations.empty:\n",
+    "            print(\"\\nTop 10 Recommendations:\")\n",
+    "            # Get brand, name, cluster from original df using index, add match_score from recommendations\n",
+    "            result_df = pd.DataFrame({\n",
+    "                'brand': df.loc[recommendations.index, 'brand'].values,\n",
+    "                'name': df.loc[recommendations.index, 'name'].values,\n",
+    "                'match_score': recommendations['match_score'].values,\n",
+    "                'cluster': df.loc[recommendations.index, 'cluster'].values\n",
+    "            })\n",
+    "            display(result_df)\n",
+    "        else:\n",
+    "            print(\"\\nNo recommendations found (cluster empty).\")\n",
+    "            \n",
+    "    except NameError:\n",
+    "        print(\"\\nERROR: Ensure model and preprocessing functions are loaded.\")\n",
+    "    except Exception as e:\n",
+    "        print(f\"\\nERROR: {e}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b07ca18",
+   "metadata": {},
+   "source": [
+    "# Save Artifacts\n",
+    "Saves 4 artifacts for complete model reconstruction:\n",
+    "1. shoe_encoder.keras: Trained autoencoder (feature encoding)\n",
+    "2. kmeans_model.pkl: Trained K-means clusters\n",
+    "3. shoe_metadata.pkl: Complete shoe dataset with cluster assignments\n",
+    "4. shoe_features.pkl: Preprocessed feature matrix (X_combined)\n",
+    "\n",
+    "Artifacts stored in timestamped versioned directories for traceability."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "02e2f531",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saving models to: ../../model_artifacts/trail/v_20260214_114839\n",
+      "Models saved successfully!\n"
+     ]
+    }
+   ],
+   "source": [
+    "timestamp = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n",
+    "save_dir = f\"../../model_artifacts/trail/v_{timestamp}\"\n",
+    "\n",
+    "os.makedirs(save_dir, exist_ok=True)\n",
+    "print(f\"Saving models to: {save_dir}\")\n",
+    "\n",
+    "encoder.save(f'{save_dir}/shoe_encoder.keras')\n",
+    "\n",
+    "with open(f'{save_dir}/kmeans_model.pkl', 'wb') as f:\n",
+    "    pickle.dump(best_model, f)\n",
+    "\n",
+    "df.to_pickle(f'{save_dir}/shoe_metadata.pkl')\n",
+    "\n",
+    "with open(f'{save_dir}/shoe_features.pkl', 'wb') as f:\n",
+    "    pickle.dump(X_combined, f)\n",
+    "\n",
+    "print(\"Models saved successfully!\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "env (3.13.1)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/requirements-dev.txt b/requirements-dev.txt
new file mode 100644
index 0000000000000000000000000000000000000000..cb8ee60c7116ad126dbaeb8367081b3fa02f2bbd
--- /dev/null
+++ b/requirements-dev.txt
@@ -0,0 +1,16 @@
+-r requirements.txt
+
+# --- Testing Framework ---
+pytest==8.0.2
+pytest-mock==3.12.0
+pytest-asyncio==0.23.5
+iniconfig==2.0.0
+pluggy==1.4.0
+
+# --- Load Testing ---
+locust==2.24.0
+
+# --- Code Quality ---
+flake8==7.0.0
+nbqa==1.7.1
+nbformat==5.9.2
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4c7e17b3997b819513529bdcd3246f6306404197
Binary files /dev/null and b/requirements.txt differ
diff --git a/src/__init__.py b/src/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/config.py b/src/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..0edfa74f14d4cc4f0c6ec018b1d9894eeec21d21
--- /dev/null
+++ b/src/config.py
@@ -0,0 +1,92 @@
+"""
+Sonix-ML Feature Configuration Module
+-------------------------------------
+Defines the global feature sets for Road and Trail shoe categories.
+
+This module acts as the single source of truth for feature column names,
+ensuring strict naming consistency between the Supabase database schema, 
+the Machine Learning models (Autoencoder & K-Means), and the inference API.
+"""
+
+from typing import List
+
+# --- ROAD SHOE FEATURE SET ---
+# These features focus on pavement performance, speed (plates), 
+# and biomechanical efficiency (pace/strike).
+ROAD_FEATURES: List[str] = [
+    "brand", 
+    "name", 
+    "lightweight", 
+    "rocker", 
+    "removable_insole", 
+    "pace_daily_running", 
+    "pace_tempo", 
+    "pace_competition", 
+    "arch_neutral", 
+    "arch_stability", 
+    "weight_lab_oz", 
+    "drop_lab_mm", 
+    "strike_heel", 
+    "strike_mid", 
+    "strike_forefoot", 
+    "midsole_softness", 
+    "toebox_durability", 
+    "heel_durability", 
+    "outsole_durability", 
+    "breathability_scaled", 
+    "width_fit", 
+    "toebox_width", 
+    "stiffness_scaled", 
+    "torsional_rigidity", 
+    "heel_stiff", 
+    "plate_rock_plate", 
+    "plate_carbon_plate", 
+    "heel_lab_mm", 
+    "forefoot_lab_mm", 
+    "season_summer", 
+    "season_winter", 
+    "season_all"
+]
+
+# --- TRAIL SHOE FEATURE SET ---
+# These features prioritize traction (lugs), protection (shock absorption), 
+# and environmental resistance (waterproof/terrain).
+TRAIL_FEATURES: List[str] = [
+    "brand", 
+    "name", 
+    "lightweight", 
+    "terrain_light", 
+    "terrain_moderate", 
+    "terrain_technical", 
+    "shock_absorption", 
+    "energy_return", 
+    "traction_scaled", 
+    "arch_neutral", 
+    "arch_stability", 
+    "weight_lab_oz", 
+    "drop_lab_mm", 
+    "strike_heel", 
+    "strike_mid", 
+    "strike_forefoot", 
+    "midsole_softness", 
+    "toebox_durability", 
+    "heel_durability", 
+    "outsole_durability", 
+    "breathability_scaled", 
+    "plate_rock_plate", 
+    "plate_carbon_plate", 
+    "width_fit", 
+    "toebox_width", 
+    "stiffness_scaled", 
+    "torsional_rigidity", 
+    "heel_stiff", 
+    "lug_dept_mm", 
+    "heel_lab_mm", 
+    "forefoot_lab_mm", 
+    "season_summer", 
+    "season_winter", 
+    "season_all", 
+    "removable_insole", 
+    "waterproof", 
+    "water_repellent"
+]
\ No newline at end of file
diff --git a/src/database.py b/src/database.py
new file mode 100644
index 0000000000000000000000000000000000000000..199afe73f6aa7fb353e6b214b3e6fe4859ce5a6c
--- /dev/null
+++ b/src/database.py
@@ -0,0 +1,101 @@
+"""
+Sonix-ML Database Integration Layer
+-----------------------------------
+Handles Supabase operations, data ingestion for ML training, and 
+real-time interaction persistence. Refactored for O(1) complexity 
+using guard clauses and vectorized mappings.
+"""
+
+import os
+import logging
+import pandas as pd
+from supabase import create_client, Client
+from typing import Optional, List
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+SUPABASE_URL = os.getenv("SUPABASE_URL")
+SUPABASE_KEY = os.getenv("SUPABASE_KEY")
+
+def _initialize_supabase() -> Optional[Client]:
+    """Safely initializes the Supabase client."""
+    if not SUPABASE_URL or not SUPABASE_KEY:
+        logger.error("Critical: Supabase credentials missing.")
+        return None
+    try:
+        return create_client(SUPABASE_URL, SUPABASE_KEY)
+    except Exception as e:
+        logger.error(f"Failed to initialize Supabase client: {e}")
+        return None
+
+supabase = _initialize_supabase()
+
+def fetch_and_merge_training_data() -> pd.DataFrame:
+    """
+    Retrieves interaction data (Favorites & Reviews) and merges them.
+    Utilizes vectorized pandas operations to eliminate loop complexity.
+
+    Returns:
+        pd.DataFrame: Unified dataframe containing user_id, item_id, rating.
+    """
+    empty_df = pd.DataFrame(columns=['user_id', 'item_id', 'rating'])
+    if not supabase: return empty_df
+
+    try:
+        frames = []
+        
+        # 1. Process Favorites
+        res_fav = supabase.table("favorites").select("user_id, shoe_id").execute()
+        if res_fav.data:
+            df_fav = pd.DataFrame(res_fav.data).rename(columns={'shoe_id': 'item_id'})
+            df_fav['score'] = 1.0
+            frames.append(df_fav[['user_id', 'item_id', 'score']])
+
+        # 2. Process Reviews via Vectorized Mapping
+        res_rate = supabase.table("reviews").select("user_id, shoe_id, rating").execute()
+        if res_rate.data:
+            df_rate = pd.DataFrame(res_rate.data).rename(columns={'shoe_id': 'item_id'})
+            rating_map = {5: 2.0, 4: 1.0, 3: 0.1, 2: -1.0, 1: -2.0}
+            df_rate['score'] = df_rate['rating'].map(rating_map).fillna(0.1)
+            frames.append(df_rate[['user_id', 'item_id', 'score']])
+
+        if not frames: return empty_df
+
+        # 3. Merge & Aggregate
+        df_combined = pd.concat(frames)
+        df_final = df_combined.groupby(['user_id', 'item_id'], as_index=False)['score'].sum()
+        return df_final.rename(columns={'score': 'rating'})
+
+    except Exception as e:
+        logger.error(f"Error fetching training data: {e}")
+        return empty_df
+
+def save_interaction_routed(user_id: int, shoe_id: str, action_type: str, rating: Optional[int] = None) -> None:
+    """
+    Persists real-time user interactions using UPSERT to prevent duplicates.
+    """
+    if not supabase: return
+
+    try:
+        action = action_type.lower()
+        if action == 'like':
+            data = {"user_id": user_id, "shoe_id": shoe_id}
+            supabase.table("favorites").upsert(data, on_conflict="user_id, shoe_id").execute()
+            
+        elif action == 'rate' and rating is not None:
+            data = {"user_id": user_id, "shoe_id": shoe_id, "rating": rating}
+            supabase.table("reviews").upsert(data, on_conflict="user_id, shoe_id").execute()
+            
+    except Exception as e:
+        logger.error(f"Failed to persist interaction: {e}")
+
+def fetch_shoes_by_type(shoe_type: str) -> pd.DataFrame:
+    """Retrieves raw shoe metadata directly from the database."""
+    if not supabase: return pd.DataFrame()
+    try:
+        response = supabase.table("shoes").select("*").execute()
+        return pd.DataFrame(response.data) if response.data else pd.DataFrame()
+    except Exception as e:
+        logger.error(f"Failed to fetch shoes: {e}")
+        return pd.DataFrame()
\ No newline at end of file
diff --git a/src/main.py b/src/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..0ee2a94050211ccbfd28216c550e0078757881b0
--- /dev/null
+++ b/src/main.py
@@ -0,0 +1,334 @@
+"""
+Sonix-ML Hybrid Recommender API
+-------------------------------
+Orchestrates the Content-Based (Deep Autoencoder + K-Means) and 
+Collaborative Filtering (UBCF NearestNeighbors) recommendation engines.
+
+Built with FastAPI for asynchronous, high-throughput inference operations.
+Optimized with LRU Caching for sub-millisecond response times on frequent queries.
+Includes integrated real-time latency diagnostics.
+"""
+
+import os
+import glob
+import pickle
+import logging
+import json
+import time
+import statistics
+from functools import lru_cache
+from contextlib import asynccontextmanager
+from typing import Dict, Any, Optional, List
+
+import pandas as pd
+import tensorflow as tf
+from fastapi import FastAPI, HTTPException, BackgroundTasks, Request
+from fastapi.responses import RedirectResponse, UJSONResponse
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.concurrency import run_in_threadpool
+from pydantic import BaseModel
+
+from dotenv import load_dotenv
+load_dotenv()
+
+# --- Project Imports ---
+from .recommender import road_recommender, trail_recommender, collaborative_filtering
+from .database import fetch_and_merge_training_data 
+# Removed save_interaction_routed as BE handles DB writes
+
+# --- Logging Configuration ---
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - [%(levelname)s] - %(name)s - %(message)s')
+logger = logging.getLogger("sonix_ml_api")
+
+road_artifacts: Dict[str, Any] = {}
+trail_artifacts: Dict[str, Any] = {}
+cf_engine: Optional[collaborative_filtering.UserCollaborativeRecommender] = None
+
+interaction_counter = 0 
+REFRESH_THRESHOLD = 50 
+
+# --- Diagnostics Buffer ---
+MAX_LOGS = 1000 
+LATENCY_LOGS: Dict[str, List[float]] = {
+    "road": [],
+    "trail": [],
+    "interact": [],
+    "feed": []
+}
+
+# --- Core Utility Functions ---
+
+def get_latest_model_path(base_path: str, prefix: str = 'v_') -> str:
+    search_pattern = os.path.join(base_path, f'{prefix}*')
+    folders = glob.glob(search_pattern)
+    if not folders: 
+        raise FileNotFoundError(f"Critical: No model folders found in {base_path}")
+    
+    latest_version = max(folders, key=os.path.getmtime)
+    logger.info(f"Version Control: Selected latest artifact '{os.path.basename(latest_version)}'")
+    return latest_version
+
+
+def load_cb_artifacts(base_path: str) -> Dict[str, Any]:
+    try:
+        v_path = get_latest_model_path(base_path)
+        logger.info(f"Loading from: {v_path}")
+        
+        df_meta = pd.read_pickle(os.path.join(v_path, "shoe_metadata.pkl"))
+        
+        with open(os.path.join(v_path, "shoe_features.pkl"), "rb") as f:
+            X_features = pickle.load(f)
+            
+        with open(os.path.join(v_path, "scaler.pkl"), "rb") as f:
+            scaler = pickle.load(f)
+            
+        with open(os.path.join(v_path, "kmeans_model.pkl"), "rb") as f:
+            kmeans = pickle.load(f)
+            
+        # Adhering to strict Deep Learning approach
+        encoder = tf.keras.models.load_model(os.path.join(v_path, "shoe_encoder.h5"), compile=False)
+
+        return {
+            "df_data": df_meta, 
+            "X_combined_data": X_features, 
+            "scaler": scaler,
+            "encoder_model": encoder, 
+            "kmeans_model": kmeans,
+            "binary_cols": df_meta.attrs.get('binary_cols', []),
+            "continuous_cols": df_meta.attrs.get('continuous_cols', [])
+        }
+    except Exception as e:
+        logger.critical(f"Artifact Loading Failure in {base_path}: {str(e)}")
+        raise RuntimeError(f"Failed to initialize ML engine: {str(e)}")
+
+
+async def refresh_global_cf_engine() -> None:
+    global cf_engine
+    logger.info("CT Process: Syncing global CF engine with latest database state...")
+    try:
+        interaction_df = await run_in_threadpool(fetch_and_merge_training_data)
+        cf_engine = collaborative_filtering.UserCollaborativeRecommender(
+            df_interactions=interaction_df, 
+            shoe_metadata=road_artifacts.get('df_data', pd.DataFrame())
+        )
+        logger.info("CT Success: Global community matrix has been updated.")
+    except Exception as e:
+        logger.error(f"CT Failure: Background synchronization failed: {e}")
+
+# --- Inference Cache Engines ---
+
+@lru_cache(maxsize=1024)
+def cached_road_inference(payload_str: str) -> List[str]:
+    user_input = json.loads(payload_str)
+    return road_recommender.get_recommendations(user_input=user_input, artifacts=road_artifacts)
+
+@lru_cache(maxsize=1024)
+def cached_trail_inference(payload_str: str) -> List[str]:
+    user_input = json.loads(payload_str)
+    return trail_recommender.get_recommendations(user_input=user_input, artifacts=trail_artifacts)
+
+# --- API Lifespan Management ---
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    global road_artifacts, trail_artifacts, cf_engine
+    logger.info("--- Starting Sonix-ML Hybrid Engine ---")
+    try:
+        road_artifacts = load_cb_artifacts("model_artifacts/road")
+        trail_artifacts = load_cb_artifacts("model_artifacts/trail")
+        
+        interaction_df = fetch_and_merge_training_data()
+        
+        cf_engine = collaborative_filtering.UserCollaborativeRecommender(
+            df_interactions=interaction_df, 
+            shoe_metadata=road_artifacts['df_data']
+        )
+        logger.info("--- Sonix-ML API is READY ---")
+    except Exception as e:
+        logger.critical(f"Fatal Startup Error: {e}")
+        raise e
+        
+    yield 
+    
+    road_artifacts.clear()
+    trail_artifacts.clear()
+
+# --- Application Definition ---
+
+app = FastAPI(
+    title="Sonix-ML Hybrid Recommender API",
+    version="2.2.0",
+    lifespan=lifespan,
+    default_response_class=UJSONResponse 
+)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=False,
+    allow_methods=["*"],
+    allow_headers=["*"],
+    max_age=86400, 
+)
+
+# --- Latency Tracking Middleware ---
+
+@app.middleware("http")
+async def add_process_time_header(request: Request, call_next):
+    start_time = time.time()
+    
+    response = await call_next(request)
+    
+    process_time = time.time() - start_time
+    process_ms = process_time * 1000
+    
+    response.headers["X-Process-Time"] = str(process_time)
+    print(f"[{request.url.path}] Processed in {process_ms:.2f} ms")
+    
+    path = request.url.path
+    if "/recommend/road" in path:
+        LATENCY_LOGS["road"].append(process_ms)
+        if len(LATENCY_LOGS["road"]) > MAX_LOGS: LATENCY_LOGS["road"].pop(0)
+    elif "/recommend/trail" in path:
+        LATENCY_LOGS["trail"].append(process_ms)
+        if len(LATENCY_LOGS["trail"]) > MAX_LOGS: LATENCY_LOGS["trail"].pop(0)
+    elif "/interact" in path:
+        LATENCY_LOGS["interact"].append(process_ms)
+        if len(LATENCY_LOGS["interact"]) > MAX_LOGS: LATENCY_LOGS["interact"].pop(0)
+    elif "/recommend/feed" in path:
+        LATENCY_LOGS["feed"].append(process_ms)
+        if len(LATENCY_LOGS["feed"]) > MAX_LOGS: LATENCY_LOGS["feed"].pop(0)
+        
+    return response
+
+# --- Detailed Input Schemas ---
+
+class RoadInput(BaseModel):
+    pace: Optional[str] = None
+    arch_type: Optional[str] = None
+    strike_pattern: Optional[str] = None
+    foot_width: Optional[str] = None
+    season: Optional[str] = None
+    orthotic_usage: Optional[str] = None
+    running_purpose: Optional[str] = None
+    cushion_preferences: Optional[str] = None
+    stability_need: Optional[str] = None
+
+class TrailInput(BaseModel):
+    pace: Optional[str] = None
+    arch_type: Optional[str] = None
+    strike_pattern: Optional[str] = None
+    foot_width: Optional[str] = None
+    season: Optional[str] = None
+    orthotic_usage: Optional[str] = None
+    terrain: Optional[str] = None
+    rock_sensitive: Optional[str] = None
+    water_resistance: Optional[str] = None
+
+class UserAction(BaseModel):
+    user_id: int
+    shoe_id: str
+    action_type: str 
+    value: Optional[int] = None 
+
+# --- API Endpoints ---
+
+@app.get("/", include_in_schema=False)
+async def root_redirect():
+    return RedirectResponse(url="/docs")
+
+@app.get("/health")
+async def health_check():
+    return {
+        "status": "healthy", 
+        "ct_sync_progress": f"{interaction_counter}/{REFRESH_THRESHOLD}"
+    }
+
+@app.get("/report/latency", tags=["Diagnostics"])
+async def get_latency_report():
+    """Generates a real-time statistical report of server processing times."""
+    report = {}
+    for endpoint, times in LATENCY_LOGS.items():
+        if not times:
+            report[endpoint] = "No data yet"
+            continue
+            
+        report[endpoint] = {
+            "total_requests": len(times),
+            "avg_ms": round(statistics.mean(times), 2),
+            "p50_ms": round(statistics.median(times), 2),
+            "p90_ms": round(statistics.quantiles(times, n=100)[89] if len(times) > 1 else times[0], 2),
+            "p95_ms": round(statistics.quantiles(times, n=100)[94] if len(times) > 1 else times[0], 2),
+            "p99_ms": round(statistics.quantiles(times, n=100)[98] if len(times) > 1 else times[0], 2),
+            "max_ms": round(max(times), 2)
+        }
+        
+    return {"status": "success", "internal_latency_report": report}
+
+@app.post("/recommend/road", tags=["Content-Based"], response_model=List[str])
+async def recommend_road(prefs: RoadInput):
+    if not road_artifacts: 
+        raise HTTPException(status_code=503, detail="Road engine not ready")
+        
+    try:
+        input_data = prefs.model_dump(exclude_none=True)
+        payload_str = json.dumps(input_data, sort_keys=True)
+        
+        return await run_in_threadpool(cached_road_inference, payload_str)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.post("/recommend/trail", tags=["Content-Based"], response_model=List[str])
+async def recommend_trail(prefs: TrailInput):
+    if not trail_artifacts: 
+        raise HTTPException(status_code=503, detail="Trail engine not ready")
+        
+    try:
+        input_data = prefs.model_dump(exclude_none=True)
+        payload_str = json.dumps(input_data, sort_keys=True)
+        
+        return await run_in_threadpool(cached_trail_inference, payload_str)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.post("/interact", tags=["Collaborative Filtering"], response_model=List[str])
+async def user_interaction(payload: UserAction, background_tasks: BackgroundTasks):
+    global interaction_counter
+    if not cf_engine: 
+        raise HTTPException(status_code=503, detail="CF engine not ready")
+        
+    try:
+        is_like = (payload.action_type.lower() == "like")
+        
+        recommendations = await run_in_threadpool(
+            cf_engine.get_realtime_recommendations, 
+            user_id=payload.user_id, 
+            new_item_id=payload.shoe_id, 
+            new_rating_val=payload.value,
+            is_like=is_like
+        )
+        
+        interaction_counter += 1
+        if interaction_counter >= REFRESH_THRESHOLD:
+            background_tasks.add_task(refresh_global_cf_engine)
+            interaction_counter = 0 
+
+        # Returning strictly a list of IDs per system requirements
+        return recommendations
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.get("/recommend/feed/{user_id}", tags=["Hybrid Feed"], response_model=List[str])
+async def get_feed(user_id: int):
+    if not cf_engine: 
+        raise HTTPException(status_code=503, detail="CF engine not ready")
+        
+    try:
+        feed = await run_in_threadpool(cf_engine.get_realtime_recommendations, user_id=user_id)
+        return feed
+    except Exception as e:
+        raise HTTPException(status_code=404, detail=str(e))
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run("src.main:app", host="0.0.0.0", port=7860, workers=1)
\ No newline at end of file
diff --git a/src/recommender/__init__.py b/src/recommender/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/recommender/collaborative_filtering.py b/src/recommender/collaborative_filtering.py
new file mode 100644
index 0000000000000000000000000000000000000000..b8a69777be256f8659f7d237e1cbc46b7b9f4e44
--- /dev/null
+++ b/src/recommender/collaborative_filtering.py
@@ -0,0 +1,135 @@
+import logging
+import time
+import numpy as np
+import pandas as pd
+from scipy.sparse import csr_matrix
+from sklearn.neighbors import NearestNeighbors
+from typing import List, Dict, Optional, Any, Tuple
+
+logger = logging.getLogger(__name__)
+
+class UserCollaborativeRecommender:
+    """
+    User-Based Collaborative Filtering (UBCF) Engine.
+    Refactored using Extract Method to minimize CC without altering logic.
+    """
+
+    def __init__(self, df_interactions: pd.DataFrame, shoe_metadata: pd.DataFrame):
+        self.cache: Dict[int, Tuple[List[Any], float]] = {} 
+        self.cache_ttl = 60 
+        self.shoe_metadata = shoe_metadata
+        self.live_changes: Dict[int, Dict[str, float]] = {} 
+        
+        self.user_map: Dict[int, int] = {}
+        self.item_map: Dict[str, int] = {}
+        self.user_ids: List[int] = []
+        self.item_ids: List[str] = []
+        self.sparse_matrix: Optional[csr_matrix] = None
+        self.model: Optional[NearestNeighbors] = None
+
+        if df_interactions.empty:
+            logger.warning("CF Engine: Passive Mode.")
+            return
+
+        self._build_matrix(df_interactions)
+
+    def _build_matrix(self, df_interactions: pd.DataFrame) -> None:
+        self.pivot_df = df_interactions.pivot(index='user_id', columns='item_id', values='rating').fillna(0)
+        self.user_ids = list(self.pivot_df.index)
+        self.item_ids = list(self.pivot_df.columns)
+        self.user_map = {uid: i for i, uid in enumerate(self.user_ids)}
+        self.item_map = {iid: i for i, iid in enumerate(self.item_ids)}
+        
+        self.sparse_matrix = csr_matrix(self.pivot_df.values)
+        self.model = NearestNeighbors(metric='cosine', algorithm='brute')
+        self.model.fit(self.sparse_matrix)
+        logger.info(f"CF Engine Initialized: {len(self.user_ids)} Users.")
+
+    def _convert_rating(self, rating: Optional[int], is_like_action: bool = False) -> float:
+        if is_like_action: return 1.0
+        if rating is None or rating == 0: return 0.1
+        mapping = {1: -2.0, 2: -1.0, 3: 0.1, 4: 1.0, 5: 2.0}
+        return mapping.get(rating, 0.1)
+
+    def _update_buffer_and_cache(self, user_id: int, new_item_id: Optional[str], 
+                                 new_rating_val: Optional[int], is_like: bool, current_time: float) -> Optional[List[Any]]:
+        if new_item_id and new_item_id in self.item_map:
+            score_val = self._convert_rating(new_rating_val, is_like_action=is_like)
+            self.live_changes.setdefault(user_id, {})[new_item_id] = score_val
+            self.cache.pop(user_id, None)
+
+        if new_item_id is None and user_id in self.cache:
+            result, timestamp = self.cache[user_id]
+            if current_time - timestamp < self.cache_ttl:
+                return result
+        return None
+
+    def _build_user_vector(self, user_id: int) -> np.ndarray:
+        num_items = len(self.item_ids)
+        if user_id in self.user_map:
+            user_idx = self.user_map[user_id]
+            user_vector = self.sparse_matrix[user_idx].toarray()
+        else:
+            user_vector = np.zeros((1, num_items))
+
+        if user_id in self.live_changes:
+            for item_id, rating in self.live_changes[user_id].items():
+                if item_id in self.item_map:
+                    idx = self.item_map[item_id]
+                    user_vector[0, idx] = rating
+        return user_vector
+
+    def _compute_scores(self, user_id: int, distances: np.ndarray, indices: np.ndarray) -> Dict[str, float]:
+        rec_scores: Dict[str, float] = {}
+        for i, neighbor_idx in enumerate(indices[0]):
+            if user_id in self.user_map and neighbor_idx == self.user_map[user_id]: continue
+            similarity = 1.0 - distances[0][i]
+            if similarity <= 0: continue
+
+            neighbor_vector = self.sparse_matrix[neighbor_idx].toarray()[0]
+            liked_indices = np.where(neighbor_vector > 0)[0]
+            
+            for it_idx in liked_indices:
+                score = neighbor_vector[it_idx] * similarity
+                it_id = self.item_ids[it_idx]
+                rec_scores[it_id] = rec_scores.get(it_id, 0.0) + score
+        return rec_scores
+
+    def get_realtime_recommendations(self, user_id: int, new_item_id: Optional[str] = None, 
+                                     new_rating_val: Optional[int] = None, is_like: bool = False,
+                                     n_neighbors: int = 10) -> List[Any]:
+        current_time = time.time()
+        
+        cached_result = self._update_buffer_and_cache(user_id, new_item_id, new_rating_val, is_like, current_time)
+        if cached_result is not None: return cached_result
+        if self.sparse_matrix is None or self.model is None: return []
+
+        user_vector = self._build_user_vector(user_id)
+        if np.all(user_vector == 0): return []
+
+        effective_k = min(n_neighbors + 1, len(self.user_ids))
+        try:
+            distances, indices = self.model.kneighbors(user_vector, n_neighbors=effective_k)
+        except Exception:
+            return []
+
+        rec_scores = self._compute_scores(user_id, distances, indices)
+        if not rec_scores: return []
+        
+        seen_indices = np.where(user_vector[0] != 0)[0]
+        seen_items = {self.item_ids[i] for i in seen_indices}
+
+        candidates = [
+            {'shoe_id': k, 'cf_score': v} 
+            for k, v in rec_scores.items() 
+            if k not in seen_items
+        ]
+        if not candidates: return []
+
+        candidates_df = pd.DataFrame(candidates).sort_values('cf_score', ascending=False).head(20)
+        final_results = candidates_df['shoe_id'].tolist()
+
+        if new_item_id is None:
+            self.cache[user_id] = (final_results, current_time)
+
+        return final_results
\ No newline at end of file
diff --git a/src/recommender/content_based.py b/src/recommender/content_based.py
new file mode 100644
index 0000000000000000000000000000000000000000..944b8c57701d964f78345bb6df29ae6b0ba78b29
--- /dev/null
+++ b/src/recommender/content_based.py
@@ -0,0 +1,78 @@
+"""
+Content-Based Engine Module
+---------------------------
+Core recommendation pipeline leveraging a Deep Autoencoder for dimensionality 
+reduction and K-Means for latent space routing. 
+Refactored for low cyclomatic complexity using dictionary mapping.
+"""
+
+import numpy as np
+import math
+from sklearn.metrics.pairwise import cosine_similarity
+from typing import List, Dict, Any
+
+def get_priority_val(user_input: Dict[str, Any], 
+                     priority_list: List[str], 
+                     mapping_dicts: Dict[str, Dict[str, float]]) -> float:
+    """
+    Resolves feature values heuristically based on a priority list.
+    Replaces nested if-statements with flat dictionary lookups to reduce CC.
+
+    Args:
+        user_input (Dict[str, Any]): Raw user preferences.
+        priority_list (List[str]): Keys to check in order of importance.
+        mapping_dicts (Dict[str, Dict[str, float]]): Mapping definitions.
+
+    Returns:
+        float: Quantitative feature value (defaults to 0.5).
+    """
+    for source_key in priority_list:
+        user_choice = user_input.get(source_key)
+        if user_choice:
+            val = mapping_dicts.get(source_key, {}).get(user_choice)
+            if val is not None:
+                return val
+    return 0.5
+
+def run_recommendation_pipeline(full_vector_raw: List[float], 
+                                valid_indices: List[int], 
+                                artifacts: Dict[str, Any]) -> List[str]:
+    """
+    Executes the core inference pipeline: Autoencoder -> K-Means -> Cosine Similarity.
+    
+    Args:
+        full_vector_raw (List[float]): Unmasked numerical user vector.
+        valid_indices (List[int]): Active indices for masked similarity.
+        artifacts (Dict[str, Any]): Loaded ML models and metadata.
+
+    Returns:
+        List[str]: Top recommended shoe IDs.
+    """
+    df_data = artifacts['df_data']
+    encoder_model = artifacts['encoder_model']
+    kmeans_model = artifacts['kmeans_model']
+    X_combined_data = artifacts['X_combined_data']
+
+    # 1. Latent Space Projection
+    full_vector_np = np.array([full_vector_raw])
+    user_latent = encoder_model.predict(full_vector_np, verbose=0)
+    
+    # 2. Cluster Routing
+    distances = kmeans_model.transform(user_latent)[0]
+    n_select = math.ceil(kmeans_model.n_clusters / 3) 
+    closest_clusters = np.argsort(distances)[:n_select]
+    
+    # 3. Filtering & Scoring
+    candidates = df_data[df_data['cluster'].isin(closest_clusters)].copy()
+    if candidates.empty: 
+        return []
+    
+    user_vec_masked = full_vector_np[:, valid_indices]
+    cand_vecs_masked = X_combined_data[candidates.index][:, valid_indices]
+    
+    if np.all(user_vec_masked == 0):
+        candidates['match_score'] = 0.0
+    else:
+        candidates['match_score'] = cosine_similarity(user_vec_masked, cand_vecs_masked)[0]
+    
+    return candidates.sort_values('match_score', ascending=False).head(10)['shoe_id'].tolist()
\ No newline at end of file
diff --git a/src/recommender/road_recommender.py b/src/recommender/road_recommender.py
new file mode 100644
index 0000000000000000000000000000000000000000..adc1708d229fd35c16fd7f6d3174591c760d6e94
--- /dev/null
+++ b/src/recommender/road_recommender.py
@@ -0,0 +1,107 @@
+"""
+Road Recommender Module
+-----------------------
+Translates user questionnaire responses into a numerical vector tailored for 
+road running shoe features. Refactored to use unified dictionary lookups 
+to minimize cyclomatic complexity while preserving 100% of the original logic.
+"""
+
+from .content_based import get_priority_val, run_recommendation_pipeline
+from typing import List, Dict, Any, Tuple
+
+def preprocess_road_input(user_input: Dict[str, Any], 
+                          binary_cols: List[str], 
+                          continuous_cols: List[str]) -> Tuple[List[float], List[int]]:
+    """
+    Translates road running preferences into a standardized numerical vector.
+    
+    Uses dictionary-based mapping to eliminate ternary operators and if-else 
+    branching, ensuring the lowest possible cyclomatic complexity.
+
+    Args:
+        user_input (Dict[str, Any]): Raw questionnaire data from the frontend.
+        binary_cols (List[str]): List of binary feature names.
+        continuous_cols (List[str]): List of continuous feature names.
+
+    Returns:
+        Tuple[List[float], List[int]]: 
+            - The full numerical vector (N-dimensional).
+            - A list of indices for masked similarity calculation.
+    """
+    all_cols = binary_cols + continuous_cols
+    feats = {col: 0.0 for col in all_cols}
+    
+    # 1. Heuristic Priority Mappings
+    feats['lightweight'] = get_priority_val(user_input, ['pace'], {'pace': {'Easy': 0.5, 'Steady': 0.5, 'Fast': 1.0}})
+    feats['rocker'] = get_priority_val(user_input, ['running_purpose'], {'running_purpose': {'Race': 1.0, 'Tempo': 0.5, 'Daily': 0.0}})
+    feats['removable_insole'] = get_priority_val(user_input, ['orthotic_usage'], {'orthotic_usage': {'Yes': 1.0, 'No': 0.5}})
+    
+    # 2. Unified Dictionary Lookups (Replacing If-Else Ternaries)
+    purp = user_input.get('running_purpose', 'Daily')
+    feats['pace_daily_running'] = {'Daily': 1.0, 'Tempo': 0.5}.get(purp, 0.0)
+    feats['pace_tempo'] = {'Tempo': 1.0}.get(purp, 0.5)
+    feats['pace_competition'] = {'Race': 1.0, 'Tempo': 0.5}.get(purp, 0.0)
+
+    feats['arch_neutral'] = get_priority_val(user_input, ['arch_type'], {'arch_type': {'Flat': 0.0, 'Normal': 0.8, 'High': 1.0}})
+    feats['arch_stability'] = get_priority_val(user_input, ['arch_type'], {'arch_type': {'Flat': 1.0, 'Normal': 0.2, 'High': 0.0}})
+    feats['drop_lab_mm'] = get_priority_val(user_input, ['pace'], {'pace': {'Easy': 1.0, 'Steady': 0.5, 'Fast': 0.0}})
+
+    prio_strike = ['strike_pattern', 'pace']
+    feats['strike_heel'] = get_priority_val(user_input, prio_strike, {'strike_pattern': {'Heel': 1.0, 'Mid': 0.5, 'Forefoot': 0.0}, 'pace': {'Easy': 1.0, 'Steady': 0.5, 'Fast': 0.0}})
+    feats['strike_mid'] = get_priority_val(user_input, prio_strike, {'strike_pattern': {'Heel': 0.5, 'Mid': 1.0, 'Forefoot': 0.5}, 'pace': {'Easy': 0.5, 'Steady': 1.0, 'Fast': 0.5}})
+    feats['strike_forefoot'] = get_priority_val(user_input, prio_strike, {'strike_pattern': {'Heel': 0.0, 'Mid': 0.0, 'Forefoot': 1.0}, 'pace': {'Easy': 0.0, 'Steady': 0.5, 'Fast': 1.0}})
+
+    feats['midsole_softness'] = get_priority_val(user_input, ['cushion_preferences', 'pace'], {'cushion_preferences': {'Soft': 1.0, 'Balanced': 0.6, 'Firm': 0.2}, 'pace': {'Easy': 1.0, 'Steady': 0.6, 'Fast': 0.2}})
+
+    feats['width_fit'] = get_priority_val(user_input, ['stability_need', 'foot_width'], {'stability_need': {'Neutral': 0.5, 'Guided': 0.2}, 'foot_width': {'Narrow': 0.2, 'Regular': 0.6, 'Wide': 1}})
+    feats['toebox_width'] = get_priority_val(user_input, ['stability_need'], {'stability_need': {'Neutral': 0.5, 'Guided': 0.2}})
+    
+    feats['stiffness_scaled'] = get_priority_val(user_input, ['arch_type', 'pace', 'running_purpose'], {'arch_type': {'Flat': 0.0, 'Normal': 0.5, 'High': 0.5}, 'pace': {'Easy': 0.2, 'Steady': 0.6, 'Fast': 1.0}, 'running_purpose': {'Daily': 0.2, 'Tempo': 0.6, 'Race': 1}})
+    feats['torsional_rigidity'] = get_priority_val(user_input, ['arch_type', 'pace'], {'arch_type': {'Flat': 1.0, 'Normal': 0.5, 'High': 0.5}, 'pace': {'Easy': 0.2, 'Steady': 0.6, 'Fast': 1.0}})
+    feats['heel_stiff'] = get_priority_val(user_input, ['arch_type'], {'arch_type': {'Flat': 1.0, 'Normal': 0.6, 'High': 0.2}})
+
+    feats['plate_rock_plate'] = 0.5 
+    feats['plate_carbon_plate'] = get_priority_val(user_input, ['pace', 'running_purpose'], {'pace': {'Easy': 0.5, 'Steady': 0.5, 'Fast': 1.0}, 'running_purpose': {'Daily': 0.5, 'Tempo': 0.5, 'Race': 1.0}})
+
+    feats['heel_lab_mm'] = get_priority_val(user_input, ['strike_pattern', 'pace'], {'strike_pattern': {'Heel': 1.0, 'Mid': 0.5, 'Forefoot': 0.0}, 'pace': {'Easy': 1.0, 'Steady': 0.5, 'Fast': 0.0}})
+    feats['forefoot_lab_mm'] = get_priority_val(user_input, ['strike_pattern', 'pace'], {'strike_pattern': {'Heel': 0.0, 'Mid': 0.5, 'Forefoot': 1.0}, 'pace': {'Easy': 0.0, 'Steady': 0.5, 'Fast': 1.0}})
+
+    feats['weight_lab_oz'] = 1.0 - feats.get('lightweight', 0.5)
+    
+    feats['season_summer'] = get_priority_val(user_input, ['season'], {'season': {'Summer': 1.0, 'Spring & Fall': 0.5, 'Winter': 0.0}})
+    feats['season_winter'] = get_priority_val(user_input, ['season'], {'season': {'Summer': 0.0, 'Spring & Fall': 0.0, 'Winter': 1.0}})
+    feats['season_all'] = get_priority_val(user_input, ['season'], {'season': {'Summer': 0.5, 'Spring & Fall': 1.0, 'Winter': 0.0}})
+    
+    # Static Column Assignment
+    for col in ['toebox_durability', 'heel_durability', 'outsole_durability', 'breathability_scaled']:
+        feats[col] = 1.0
+
+    # 3. Vector and Masking Setup
+    binary_set = set(binary_cols)
+    full_vector_raw = [feats.get(c, 0.0 if c in binary_set else 0.5) for c in all_cols]
+    
+    provided_inputs = {k for k, v in user_input.items() if v}
+    feature_sources = {
+        'lightweight': ['pace'], 'rocker': ['running_purpose'], 'removable_insole': ['orthotic_usage'],
+        'pace_daily_running': ['running_purpose'], 'pace_tempo': ['running_purpose'], 'pace_competition': ['running_purpose'],
+        'arch_neutral': ['arch_type'], 'arch_stability': ['arch_type'], 'drop_lab_mm': ['pace'],
+        'strike_heel': ['strike_pattern', 'pace'], 'strike_mid': ['strike_pattern', 'pace'], 'strike_forefoot': ['strike_pattern', 'pace'],
+        'midsole_softness': ['cushion_preferences', 'pace'], 'width_fit': ['stability_need', 'foot_width'],
+        'toebox_width': ['stability_need'], 'stiffness_scaled': ['arch_type', 'pace', 'running_purpose'],
+        'torsional_rigidity': ['arch_type', 'pace'], 'heel_stiff': ['arch_type'],
+        'plate_rock_plate': ['pace', 'running_purpose'], 'plate_carbon_plate': ['pace', 'running_purpose'],
+        'heel_lab_mm': ['strike_pattern', 'pace'], 'forefoot_lab_mm': ['strike_pattern', 'pace'],
+        'weight_lab_oz': ['pace'], 'season_summer': ['season'], 'season_winter': ['season'], 'season_all': ['season']
+    }
+    
+    valid_indices = [
+        i for i, col in enumerate(all_cols)
+        if not feature_sources.get(col) or not set(feature_sources[col]).isdisjoint(provided_inputs)
+    ]
+    
+    return full_vector_raw, valid_indices or list(range(len(all_cols)))
+
+def get_recommendations(user_input: Dict[str, Any], artifacts: Dict[str, Any]) -> List[str]:
+    """Wrapper entry point for road recommendation."""
+    full_vector, valid_idx = preprocess_road_input(user_input, artifacts['binary_cols'], artifacts['continuous_cols'])
+    return run_recommendation_pipeline(full_vector, valid_idx, artifacts)
\ No newline at end of file
diff --git a/src/recommender/trail_recommender.py b/src/recommender/trail_recommender.py
new file mode 100644
index 0000000000000000000000000000000000000000..476d82e9ba528d1c913b2b08a4e346b78f008f6f
--- /dev/null
+++ b/src/recommender/trail_recommender.py
@@ -0,0 +1,107 @@
+"""
+Trail Recommender Module
+------------------------
+Translates trail questionnaire responses into a numerical vector.
+Unified with the road module's dictionary-based mapping style for consistency.
+"""
+
+from .content_based import get_priority_val, run_recommendation_pipeline
+from typing import List, Dict, Any, Tuple
+
+def preprocess_trail_input(user_input: Dict[str, Any], 
+                           binary_cols: List[str], 
+                           continuous_cols: List[str]) -> Tuple[List[float], List[int]]:
+    """
+    Translates trail running preferences into a standardized numerical vector.
+    
+    Standardized to match the road recommender's look-up table style.
+
+    Args:
+        user_input (Dict[str, Any]): Raw user preferences.
+        binary_cols (List[str]): List of binary feature names.
+        continuous_cols (List[str]): List of continuous feature names.
+
+    Returns:
+        Tuple[List[float], List[int]]: 
+            - The full numerical vector.
+            - A list of indices for active features.
+    """
+    all_cols = binary_cols + continuous_cols
+    feats = {col: 0.0 for col in all_cols}
+    
+    # 1. Unified Mappings
+    feats['terrain_light'] = get_priority_val(user_input, ['terrain'], {'terrain': {'Light': 1.0, 'Mixed': 0.5, 'Rocky': 0.0, 'Muddy': 0.0}})
+    feats['terrain_moderate'] = get_priority_val(user_input, ['terrain'], {'terrain': {'Light': 0.5, 'Mixed': 1.0, 'Rocky': 0.5, 'Muddy': 0.5}})
+    feats['terrain_technical'] = get_priority_val(user_input, ['terrain'], {'terrain': {'Light': 0.0, 'Mixed': 0.5, 'Rocky': 1.0, 'Muddy': 1.0}})
+
+    feats['lug_dept_mm'] = get_priority_val(user_input, ['terrain'], {'terrain': {'Light': 0.5, 'Mixed': 1.0, 'Rocky': 0.5, 'Muddy': 1.0}})
+    feats['traction_scaled'] = get_priority_val(user_input, ['terrain'], {'terrain': {'Light': 0.5, 'Mixed': 1.0, 'Rocky': 0.5, 'Muddy': 1.0}})
+
+    feats['shock_absorption'] = get_priority_val(user_input, ['rock_sensitive', 'terrain'], {'rock_sensitive': {'Yes': 1.0, 'No': 0.0}, 'terrain': {'Light': 0.2, 'Mixed': 0.6, 'Rocky': 1.0, 'Muddy': 0.0}})
+    feats['energy_return'] = 1.0 
+    
+    feats['arch_neutral'] = get_priority_val(user_input, ['arch_type'], {'arch_type': {'Flat': 0.0, 'Normal': 0.8, 'High': 1.0}})
+    feats['arch_stability'] = get_priority_val(user_input, ['arch_type'], {'arch_type': {'Flat': 1.0, 'Normal': 0.2, 'High': 0.0}})
+    feats['drop_lab_mm'] = get_priority_val(user_input, ['pace'], {'pace': {'Easy': 1.0, 'Steady': 0.5, 'Fast': 0.0}})
+
+    prio_strike = ['strike_pattern', 'pace']
+    feats['strike_heel'] = get_priority_val(user_input, prio_strike, {'strike_pattern': {'Heel': 1.0, 'Mid': 0.5, 'Forefoot': 0.0}, 'pace': {'Easy': 1.0, 'Steady': 0.5, 'Fast': 0.0}})
+    feats['strike_mid'] = get_priority_val(user_input, prio_strike, {'strike_pattern': {'Heel': 0.5, 'Mid': 1.0, 'Forefoot': 0.5}, 'pace': {'Easy': 0.5, 'Steady': 1.0, 'Fast': 0.5}})
+    feats['strike_forefoot'] = get_priority_val(user_input, prio_strike, {'strike_pattern': {'Heel': 0.0, 'Mid': 0.0, 'Forefoot': 1.0}, 'pace': {'Easy': 0.0, 'Steady': 0.5, 'Fast': 1.0}})
+
+    feats['midsole_softness'] = get_priority_val(user_input, ['pace'], {'pace': {'Easy': 1.0, 'Steady': 0.6, 'Fast': 0.2}})
+
+    feats['plate_rock_plate'] = get_priority_val(user_input, ['pace', 'terrain'], {'pace': {'Easy': 0.5, 'Steady': 0.5, 'Fast': 0.5}, 'terrain': {'Light': 0.5, 'Mixed': 1.0, 'Rocky': 1.0, 'Muddy': 1.0}})
+    feats['plate_carbon_plate'] = get_priority_val(user_input, ['pace', 'terrain'], {'pace': {'Easy': 0.5, 'Steady': 0.5, 'Fast': 1.0}, 'terrain': {'Light': 0.5, 'Mixed': 0.5, 'Rocky': 0.5, 'Muddy': 0.5}})
+
+    feats['width_fit'] = get_priority_val(user_input, ['foot_width'], {'foot_width': {'Narrow': 0.2, 'Regular': 0.6, 'Wide': 1.0}})
+    feats['toebox_width'] = get_priority_val(user_input, ['foot_width'], {'foot_width': {'Narrow': 0.2, 'Regular': 0.6, 'Wide': 1.0}})
+
+    feats['stiffness_scaled'] = get_priority_val(user_input, ['pace'], {'pace': {'Easy': 0.2, 'Steady': 0.6, 'Fast': 1.0}})
+    feats['torsional_rigidity'] = get_priority_val(user_input, ['arch_type', 'pace'], {'arch_type': {'Flat': 1.0, 'Normal': 0.5, 'High': 0.5}, 'pace': {'Easy': 0.2, 'Steady': 0.6, 'Fast': 1.0}})
+    feats['heel_stiff'] = get_priority_val(user_input, ['arch_type'], {'arch_type': {'Flat': 1.0, 'Normal': 0.6, 'High': 0.2}})
+
+    feats['heel_lab_mm'] = get_priority_val(user_input, ['strike_pattern', 'pace', 'terrain'], {'strike_pattern': {'Heel': 1.0, 'Mid': 0.5, 'Forefoot': 0.0}, 'pace': {'Easy': 1.0, 'Steady': 0.5, 'Fast': 0.0}, 'terrain': {'Light': 0.5, 'Mixed': 1.0, 'Rocky': 1.0, 'Muddy': 1.0}})
+    feats['forefoot_lab_mm'] = get_priority_val(user_input, ['strike_pattern', 'pace', 'terrain'], {'strike_pattern': {'Heel': 0.0, 'Mid': 0.5, 'Forefoot': 1.0}, 'pace': {'Easy': 0.0, 'Steady': 0.5, 'Fast': 1.0}, 'terrain': {'Light': 0.5, 'Mixed': 0.5, 'Rocky': 0.5, 'Muddy': 0.5}})
+
+    feats['waterproof'] = get_priority_val(user_input, ['water_resistance', 'terrain'], {'water_resistance': {'Waterproof': 1.0, 'Water Repellent': 0.5}, 'terrain': {'Muddy': 1.0}})
+    feats['water_repellent'] = get_priority_val(user_input, ['water_resistance', 'terrain'], {'water_resistance': {'Waterproof': 1.0, 'Water Repellent': 1.0}, 'terrain': {'Mixed': 1.0, 'Muddy': 1.0}})
+
+    feats['lightweight'] = get_priority_val(user_input, ['pace'], {'pace': {'Easy': 0.5, 'Steady': 0.5, 'Fast': 1.0}})
+    feats['removable_insole'] = get_priority_val(user_input, ['orthotic_usage'], {'orthotic_usage': {'Yes': 1.0, 'No': 0.5}})
+
+    # Static Column Assignment
+    for col in ['toebox_durability', 'heel_durability', 'outsole_durability', 'breathability_scaled']:
+        feats[col] = 1.0
+
+    # 2. Vector and Masking Setup
+    binary_set = set(binary_cols)
+    full_vector_raw = [feats.get(c, 0.0 if c in binary_set else 0.5) for c in all_cols]
+    
+    provided_inputs = {k for k, v in user_input.items() if v}
+    feature_sources = {
+        'terrain_light': ['terrain'], 'terrain_moderate': ['terrain'], 'terrain_technical': ['terrain'],
+        'shock_absorption': ['rock_sensitive', 'terrain'], 'traction_scaled': ['terrain'],
+        'arch_neutral': ['arch_type'], 'arch_stability': ['arch_type'],
+        'drop_lab_mm': ['pace'], 'midsole_softness': ['pace'],
+        'strike_heel': ['strike_pattern', 'pace'], 'strike_mid': ['strike_pattern', 'pace'], 'strike_forefoot': ['strike_pattern', 'pace'],
+        'plate_rock_plate': ['pace', 'terrain'], 'plate_carbon_plate': ['pace', 'terrain'],
+        'width_fit': ['foot_width'], 'toebox_width': ['foot_width'],
+        'stiffness_scaled': ['pace'], 'torsional_rigidity': ['arch_type', 'pace'],
+        'heel_stiff': ['arch_type'], 'lug_dept_mm': ['terrain'],
+        'heel_lab_mm': ['strike_pattern', 'pace', 'terrain'], 'forefoot_lab_mm': ['strike_pattern', 'pace', 'terrain'],
+        'removable_insole': ['orthotic_usage'], 'lightweight': ['pace'],
+        'waterproof': ['water_resistance', 'terrain'], 'water_repellent': ['water_resistance', 'terrain']
+    }
+    
+    valid_indices = [
+        i for i, col in enumerate(all_cols)
+        if not feature_sources.get(col) or not set(feature_sources[col]).isdisjoint(provided_inputs)
+    ]
+    
+    return full_vector_raw, valid_indices or list(range(len(all_cols)))
+
+def get_recommendations(user_input: Dict[str, Any], artifacts: Dict[str, Any]) -> List[Any]:
+    """Wrapper entry point for trail recommendation."""
+    full_vector, valid_idx = preprocess_trail_input(user_input, artifacts['binary_cols'], artifacts['continuous_cols'])
+    return run_recommendation_pipeline(full_vector, valid_idx, artifacts)
\ No newline at end of file
diff --git a/src/training/__init__.py b/src/training/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/training/architecture.py b/src/training/architecture.py
new file mode 100644
index 0000000000000000000000000000000000000000..429f9c2f28eb0bed778ae87dc4d91c50334815c4
--- /dev/null
+++ b/src/training/architecture.py
@@ -0,0 +1,55 @@
+"""
+Sonix-ML Neural Architecture Module
+-----------------------------------
+Defines the Deep Autoencoder structure utilized for dimensionality reduction 
+and latent feature extraction of running shoe attributes.
+"""
+
+import tensorflow as tf
+from tensorflow.keras import layers, Model, optimizers
+from typing import Tuple, List
+
+def build_autoencoder(input_dim: int, 
+                      encoding_dims: List[int] = [32, 16, 8], 
+                      dropout_rate: float = 0.3) -> Tuple[Model, Model]:
+    """
+    Constructs a symmetrical Deep Autoencoder and a standalone Encoder model.
+
+    Args:
+        input_dim (int): The number of dynamic input features.
+        encoding_dims (List[int], optional): Neuron counts for progressive compression.
+        dropout_rate (float, optional): Regularization fraction.
+
+    Returns:
+        Tuple[Model, Model]: The full Autoencoder and the standalone Encoder.
+    """
+    # --- ENCODER ---
+    input_layer = layers.Input(shape=(input_dim,), name="feature_input")
+    x = input_layer
+    
+    for i, dim in enumerate(encoding_dims):
+        x = layers.Dense(dim, activation='relu', name=f"encoder_dense_{i}")(x)
+        x = layers.BatchNormalization(name=f"encoder_bn_{i}")(x)
+        x = layers.Dropout(dropout_rate, name=f"encoder_dropout_{i}")(x)
+
+    latent_space = x 
+
+    # --- DECODER ---
+    for i, dim in enumerate(reversed(encoding_dims[:-1])):
+        x = layers.Dense(dim, activation='relu', name=f"decoder_dense_{i}")(x)
+        x = layers.BatchNormalization(name=f"decoder_bn_{i}")(x)
+        x = layers.Dropout(dropout_rate, name=f"decoder_dropout_{i}")(x)
+
+    output_layer = layers.Dense(input_dim, activation='sigmoid', name="reconstruction_output")(x)
+
+    # --- COMPILATION ---
+    autoencoder = Model(inputs=input_layer, outputs=output_layer, name="Sonix_Autoencoder")
+    encoder = Model(inputs=input_layer, outputs=latent_space, name="Sonix_Encoder")
+
+    autoencoder.compile(
+        optimizer=optimizers.Adam(learning_rate=0.001),
+        loss='mse',
+        metrics=['mae']
+    )
+    
+    return autoencoder, encoder
\ No newline at end of file
diff --git a/src/training/training_engine.py b/src/training/training_engine.py
new file mode 100644
index 0000000000000000000000000000000000000000..c95196e0536172cf28b3b0de749a3cbbed024535
--- /dev/null
+++ b/src/training/training_engine.py
@@ -0,0 +1,129 @@
+"""
+Sonix-ML Training Engine Module
+-------------------------------
+Orchestrates the training lifecycle for recommendation models.
+Refactored into a modular pipeline class to ensure Single Responsibility 
+and minimized cyclomatic complexity.
+"""
+
+import os
+import pickle
+import logging
+import pandas as pd
+import numpy as np
+from datetime import datetime
+from sklearn.cluster import KMeans
+from sklearn.preprocessing import MinMaxScaler
+from typing import Tuple, List, Any
+
+# --- CRITICAL: LOAD ENV VARS FIRST ---
+# This must be executed before importing src.database to ensure 
+# the Supabase client initializes with valid credentials.
+from dotenv import load_dotenv
+load_dotenv() 
+
+# --- Project Imports ---
+from src.training.architecture import build_autoencoder
+from src.database import fetch_shoes_by_type
+from src.config import ROAD_FEATURES, TRAIL_FEATURES
+
+# Configure logging to display output in the terminal
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - [%(levelname)s] - %(name)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+class TrainingPipeline:
+    """Modular training pipeline isolating ingestion, training, and serialization."""
+
+    def __init__(self, shoe_type: str):
+        self.shoe_type = shoe_type
+        self.target_features = ROAD_FEATURES if shoe_type == 'road' else TRAIL_FEATURES
+        self.n_clusters = 5
+
+    def _ingest_and_scale(self) -> Tuple[np.ndarray, MinMaxScaler, pd.DataFrame, List[str]]:
+        logger.info(f"Fetching data from Supabase for category: {self.shoe_type}...")
+        df = fetch_shoes_by_type(self.shoe_type)
+        
+        if df.empty:
+            raise ValueError(f"CRITICAL: No source data retrieved for {self.shoe_type}. Check your database connection or table data.")
+
+        # Ensure target features exist in the dataframe
+        numeric_cols = [c for c in self.target_features if c in df.columns]
+        
+        # Data Cleaning: Coerce non-numeric data to NaN, then fill with 0
+        for col in numeric_cols:
+            df[col] = pd.to_numeric(df[col], errors='coerce').fillna(0)
+
+        X_raw = df[numeric_cols].values
+
+        scaler = MinMaxScaler()
+        X_scaled = scaler.fit_transform(X_raw)
+        
+        return X_scaled, scaler, df, numeric_cols
+
+    def _train_models(self, X_scaled: np.ndarray) -> Tuple[Any, KMeans]:
+        autoencoder, encoder = build_autoencoder(input_dim=X_scaled.shape[1])
+        
+        logger.info("Training Deep Autoencoder (Epochs: 50, Batch: 32)...")
+        # Reduced epochs for rapid testing; increase to 300 for production
+        autoencoder.fit(X_scaled, X_scaled, epochs=50, batch_size=32, verbose=0)
+
+        X_latent = encoder.predict(X_scaled, verbose=0)
+
+        logger.info(f"Generating clusters with K-Means (K={self.n_clusters})...")
+        kmeans = KMeans(n_clusters=self.n_clusters, random_state=42, n_init=10)
+        kmeans.fit(X_latent)
+
+        return encoder, kmeans
+
+    def _save_artifacts(self, encoder: Any, kmeans: KMeans, scaler: MinMaxScaler, 
+                        X_scaled: np.ndarray, df: pd.DataFrame, numeric_cols: List[str]) -> str:
+        ts = datetime.now().strftime("%Y%m%d_%H%M%S")
+        # Ensure path compatibility across operating systems
+        save_path = os.path.join("model_artifacts", self.shoe_type, f"v_{ts}")
+        os.makedirs(save_path, exist_ok=True)
+
+        # Save Keras Model
+        encoder.save(os.path.join(save_path, "shoe_encoder.h5"))
+        
+        # Save Pickle Artifacts
+        with open(os.path.join(save_path, "kmeans_model.pkl"), "wb") as f:
+            pickle.dump(kmeans, f)
+        with open(os.path.join(save_path, "scaler.pkl"), "wb") as f:
+            pickle.dump(scaler, f)
+        with open(os.path.join(save_path, "shoe_features.pkl"), "wb") as f:
+            pickle.dump(X_scaled, f)
+
+        # Save Metadata with Attributes
+        df_meta = df.copy()
+        df_meta['cluster'] = kmeans.labels_
+        df_meta.attrs['binary_cols'] = [c for c in numeric_cols if df[c].nunique() <= 2]
+        df_meta.attrs['continuous_cols'] = [c for c in numeric_cols if df[c].nunique() > 2]
+        df_meta.to_pickle(os.path.join(save_path, "shoe_metadata.pkl"))
+
+        return save_path
+
+    def run(self) -> None:
+        """Executes the full orchestrated training sequence."""
+        logger.info(f"STARTING PIPELINE: {self.shoe_type.upper()}")
+        try:
+            X_scaled, scaler, df, numeric_cols = self._ingest_and_scale()
+            encoder, kmeans = self._train_models(X_scaled)
+            save_path = self._save_artifacts(encoder, kmeans, scaler, X_scaled, df, numeric_cols)
+            logger.info(f"PIPELINE SUCCESS. Artifacts saved to: {save_path}")
+        except Exception as e:
+            logger.error(f"PIPELINE FAILED for {self.shoe_type}: {str(e)}")
+            raise e
+
+def run_training(shoe_type: str) -> None:
+    pipeline = TrainingPipeline(shoe_type)
+    pipeline.run()
+
+# --- ENTRY POINT ---
+if __name__ == "__main__":
+    print("--- INITIATING MANUAL TRAINING JOB ---")
+    try:
+        run_training('road')
+        run_training('trail')
+        print("--- JOB COMPLETE: Models Generated Successfully ---")
+    except Exception as e:
+        print(f"--- JOB FAILED: {e} ---")
\ No newline at end of file
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/tests/conftest.py b/tests/conftest.py
new file mode 100644
index 0000000000000000000000000000000000000000..c2cb8c2afeb1c31485f806c00b67099779e39b8b
--- /dev/null
+++ b/tests/conftest.py
@@ -0,0 +1,17 @@
+import pytest
+from unittest.mock import MagicMock
+
+@pytest.fixture(autouse=True)
+def mock_supabase(mocker):
+    """
+    Automatically mocks the Supabase client for all tests.
+    This prevents real network calls to Supabase during CI.
+    """
+    # Mock the create_client call in src.database
+    mock_client = MagicMock()
+    mocker.patch("src.database.create_client", return_value=mock_client)
+    
+    # Mock the 'supabase' variable itself in src.database
+    mocker.patch("src.database.supabase", mock_client)
+    
+    return mock_client
\ No newline at end of file
diff --git a/tests/test_data_processing.py b/tests/test_data_processing.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d41529f8b4c1faa265dc365be54a109c7e7ab7d
--- /dev/null
+++ b/tests/test_data_processing.py
@@ -0,0 +1,23 @@
+import pytest
+import pandas as pd
+from src.database import fetch_and_merge_training_data
+
+def test_rating_conversion_logic():
+    """Verify star ratings (1-5) convert to symmetric weights (-2.0 to 2.0)."""
+    # Local helper mirroring the internal database logic
+    def convert(r):
+        mapping = {5: 2.0, 4: 1.0, 3: 0.1, 2: -1.0, 1: -2.0}
+        return mapping.get(r, 0.1)
+
+    assert convert(5) == 2.0  # High preference
+    assert convert(1) == -2.0 # High dislike
+    assert convert(3) == 0.1  # Neutral
+
+def test_empty_dataframe_handling():
+    """Ensure the system doesn't crash if Supabase returns no data."""
+    from src.database import fetch_shoes_by_type
+    
+    # This test confirms that our error handling returns an empty DF instead of None
+    df = fetch_shoes_by_type("non_existent_type")
+    assert isinstance(df, pd.DataFrame)
+    assert df.empty
\ No newline at end of file
diff --git a/tests/test_recommender_logic.py b/tests/test_recommender_logic.py
new file mode 100644
index 0000000000000000000000000000000000000000..880939f15d18a56c2737ca64bdd6cf10af576b42
--- /dev/null
+++ b/tests/test_recommender_logic.py
@@ -0,0 +1,31 @@
+import pytest
+from src.recommender.content_based import get_priority_val
+
+def test_get_priority_val_mapping():
+    """Verify that user inputs map to the correct heuristic weights."""
+    user_input = {'terrain': 'Rocky'}
+    mapping = {'terrain': {'Light': 0.0, 'Mixed': 0.5, 'Rocky': 1.0}}
+    
+    # Test high priority match
+    val = get_priority_val(user_input, ['terrain'], mapping)
+    assert val == 1.0
+
+def test_get_priority_val_fallback():
+    """Ensure the function returns a neutral default (0.5) for missing inputs."""
+    user_input = {'terrain': None}
+    mapping = {'terrain': {'Light': 0.0, 'Mixed': 0.5, 'Rocky': 1.0}}
+    
+    val = get_priority_val(user_input, ['terrain'], mapping)
+    assert val == 0.5
+
+def test_get_priority_val_multi_source():
+    """Test priority weight calculation from multiple input sources."""
+    user_input = {'pace': 'Fast', 'terrain': 'Muddy'}
+    mapping = {
+        'pace': {'Fast': 1.0, 'Slow': 0.0},
+        'terrain': {'Muddy': 1.0, 'Dry': 0.0}
+    }
+    
+    # Combined logic: (1.0 + 1.0) / 2 = 1.0
+    val = get_priority_val(user_input, ['pace', 'terrain'], mapping)
+    assert val == 1.0
\ No newline at end of file